From 123edfd82444723a6b5721a584e5fcd42df42705 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 30 Apr 2026 15:15:06 +0000 Subject: [PATCH 01/15] Consolidate all open PR changes: docs, CI fixes, Node.js 24 migration, SHA pinning, training health fix Agent-Logs-Url: https://github.com/Bryan-Roe/Aria/sessions/14928084-823d-468d-9908-c68e5b97057a Co-authored-by: Bryan-Roe <74067792+Bryan-Roe@users.noreply.github.com> --- .devcontainer/devcontainer.json | 2 +- .github/COPILOT_SETUP_GUIDE.md | 4 +-- .github/agents/agi-reasoning.agent.md | 2 +- .github/copilot-instructions.md | 4 +-- .github/prompts/agi.prompt.md | 2 +- .github/workflows/actionlint.yml | 2 +- .github/workflows/api-health-smoke.yml | 7 ++-- .github/workflows/aria-tests.yml | 26 +++++++------- .github/workflows/artifact-lifecycle.yml | 4 +-- .github/workflows/auto-validation.yml | 4 +-- .github/workflows/autonomous-evolver.yml | 4 +-- .github/workflows/azureml-train.yml | 8 ++--- .github/workflows/broken-links.yml | 3 +- .github/workflows/ci-pipeline.yml | 15 ++++---- .github/workflows/code-quality.yml | 6 ++-- .github/workflows/codeql.yml | 8 ++--- .github/workflows/coverage-report.yml | 8 ++--- .github/workflows/dataset-integrity.yml | 4 +-- .github/workflows/dependency-review.yml | 4 +-- .github/workflows/devcontainer-ci.yml | 2 +- .github/workflows/e2e-tests.yml | 6 ++-- .github/workflows/experiment-runner.yml | 4 +-- .../workflows/integration-contract-gate.yml | 7 ++-- .github/workflows/llm-maker-tests.yml | 4 +-- .github/workflows/makecode.yml | 6 ++-- .github/workflows/markdown-quality.yml | 4 +-- .github/workflows/nightly-regression.yml | 11 +++--- .github/workflows/pages.yml | 10 +++--- .github/workflows/platform-health-daily.yml | 4 +-- .github/workflows/pr-checks.yml | 9 +++-- .github/workflows/pr-tests.yml | 12 ++++--- .github/workflows/quantum-ai-smoke.yml | 4 +-- .github/workflows/quantum-orchestration.yml | 4 +-- .github/workflows/release.yml | 2 +- .github/workflows/secrets-scan.yml | 6 ++-- .github/workflows/site-bundle-validation.yml | 4 +-- .github/workflows/telemetry-trigger.yml | 4 +-- .github/workflows/test-watcher.yml | 4 +-- .github/workflows/training-health-report.yml | 34 ++++++++++++++++--- .github/workflows/workflow-validation.yml | 6 ++-- README.md | 2 +- apps/aria/test_auto_execute.py | 6 ++-- requirements-dev.txt | 1 - ..._orchestrator_health_in_status_endpoint.py | 6 +++- tests/test_orchestrator_health_integration.py | 6 +++- 45 files changed, 165 insertions(+), 120 deletions(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index aa89b669b..afd591fb7 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -2,7 +2,7 @@ "name": "Aria Dev Container", "image": "mcr.microsoft.com/devcontainers/python:2-3.14-trixie", "features": {}, - "postCreateCommand": "python -m pip install --upgrade pip && if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt; fi", + "postCreateCommand": "python -m pip install --upgrade pip && if [ -f requirements.txt ]; then pip install -r requirements.txt; fi && if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt; fi", "customizations": { "vscode": { "extensions": [ diff --git a/.github/COPILOT_SETUP_GUIDE.md b/.github/COPILOT_SETUP_GUIDE.md index 26f023a2e..79e1ae5f8 100644 --- a/.github/COPILOT_SETUP_GUIDE.md +++ b/.github/COPILOT_SETUP_GUIDE.md @@ -382,7 +382,7 @@ Aria includes **20+ specialized prompts** in `.github/prompts/` that guide Copil | Prompt | Purpose | |--------|---------| -| `agi.prompt.md` | Chain-of-thought AGI reasoning | +| `agi.prompt.md` | Autonomous long-running work with internal reasoning (chain-of-thought is hidden) | | `debug.prompt.md` | Systematic debugging protocol | | `reason.prompt.md` | Structured analysis & planning | | `review.prompt.md` | Security, performance, correctness review | @@ -394,7 +394,7 @@ Aria includes **20+ specialized prompts** in `.github/prompts/` that guide Copil **Explicit reference**: ``` -Use the agi.prompt for chain-of-thought reasoning about this architecture +Use the agi.prompt for autonomous long-running work requiring internal reasoning Use the debug.prompt to systematically diagnose this error ``` diff --git a/.github/agents/agi-reasoning.agent.md b/.github/agents/agi-reasoning.agent.md index 1e636ae6b..985bf0430 100644 --- a/.github/agents/agi-reasoning.agent.md +++ b/.github/agents/agi-reasoning.agent.md @@ -1,6 +1,6 @@ --- name: agi-reasoning -description: "AGI reasoning and autonomous decision-making agent. Specializes in chain-of-thought reasoning, task decomposition, self-reflection, and multi-step planning using the AGI provider system.\n\nTrigger phrases include:\n- 'reason through this problem'\n- 'break this down step by step'\n- 'think through this autonomously'\n- 'use AGI reasoning'\n- 'chain of thought'\n- 'self-reflection'\n- 'autonomous planning'\n\nExamples:\n- User says 'reason through this architecture decision' → invoke for structured multi-step analysis\n- User asks 'break down this complex feature into tasks' → invoke for task decomposition\n- User says 'autonomously plan and implement this feature' → invoke for planning + execution with self-correction\n\nThis agent leverages the AGI provider's reasoning chains, task decomposition, and self-reflection capabilities." +description: "AGI reasoning and autonomous decision-making agent. Uses internal chain-of-thought reasoning (not exposed in output) for task decomposition, self-reflection, and multi-step planning — only the final answer is delivered to the user. For visible step-by-step reasoning, use reason.prompt instead.\n\nTrigger phrases include:\n- 'reason through this problem'\n- 'break this down step by step'\n- 'think through this autonomously'\n- 'use AGI reasoning'\n- 'autonomous planning'\n- 'self-reflection'\n\nExamples:\n- User says 'reason through this architecture decision' → invoke for structured multi-step analysis\n- User asks 'break down this complex feature into tasks' → invoke for task decomposition\n- User says 'autonomously plan and implement this feature' → invoke for planning + execution with self-correction\n\nThis agent leverages the AGI provider's reasoning chains, task decomposition, and self-reflection capabilities. Chain-of-thought steps are completed internally; only the final answer is delivered." tools: - edit - search diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index 8a2103e00..ee39b9094 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -308,7 +308,7 @@ Available agents in `.github/agents/`: |-------|---------| | `ai.agent.md` | Primary autonomous agent — task decomposition, multi-step execution | | `my-agent.agent.md` | QAI specialist — quantum-AI/ML development | -| `agi-reasoning.agent.md` | Chain-of-thought reasoning, self-reflection | +| `agi-reasoning.agent.md` | Autonomous AGI reasoning with internal self-reflection (chain-of-thought is hidden; only the final answer is delivered) | | `aria-character.agent.md` | Interactive character commands, animations | | `autonomous-trainer.agent.md` | LoRA training lifecycle, model promotion | | `full-stack-debugger.agent.md` | Cross-stack issue diagnosis | @@ -331,7 +331,7 @@ Available agents in `.github/agents/`: **Prompts** (`.github/prompts/`): - `agi.prompt.md` — Autonomous AGI reasoning with multi-step analysis and self-correction (chain-of-thought is internal, not exposed in output) -- `reason.prompt.md` — Structured analysis +- `reason.prompt.md` — Structured analysis with explicit chain-of-thought reasoning steps shown to the user - `debug.prompt.md` — Systematic diagnostic protocol - `review.prompt.md` — Code review (correctness, security, performance) - `aria-command.prompt.md` — Natural language → Aria actions diff --git a/.github/prompts/agi.prompt.md b/.github/prompts/agi.prompt.md index c42008c39..727cd7c71 100644 --- a/.github/prompts/agi.prompt.md +++ b/.github/prompts/agi.prompt.md @@ -1,5 +1,5 @@ --- -description: "Engage autonomous AGI reasoning with multi-step analysis, task decomposition, self-correction, and iterative improvement. For complex problems requiring structured autonomous thinking." +description: "Engage autonomous AGI reasoning with multi-step analysis, task decomposition, self-correction, and iterative improvement. Chain-of-thought reasoning runs internally — only the final answer is delivered to the user. For visible step-by-step reasoning, use reason.prompt instead." name: "AGI Reasoning" argument-hint: "Problem or task description (example: analyze the auth flow for race conditions + constraints)" agent: agi-reasoning diff --git a/.github/workflows/actionlint.yml b/.github/workflows/actionlint.yml index 6b110fdc7..a2f1bc66e 100644 --- a/.github/workflows/actionlint.yml +++ b/.github/workflows/actionlint.yml @@ -26,7 +26,7 @@ jobs: timeout-minutes: 10 steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup ShellCheck run: sudo apt-get update && sudo apt-get install -y shellcheck diff --git a/.github/workflows/api-health-smoke.yml b/.github/workflows/api-health-smoke.yml index a27e73656..316fb88e0 100644 --- a/.github/workflows/api-health-smoke.yml +++ b/.github/workflows/api-health-smoke.yml @@ -25,6 +25,9 @@ concurrency: group: api-health-${{ github.ref }} cancel-in-progress: ${{ github.event_name == 'pull_request' }} +env: + FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true # Opt into Node.js 24 for JS actions ahead of June 2026 forced migration + jobs: smoke: name: API Smoke Validation @@ -33,7 +36,7 @@ jobs: steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -55,7 +58,7 @@ jobs: - name: Upload smoke artifacts if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: api-health-smoke-${{ github.run_number }} retention-days: 14 diff --git a/.github/workflows/aria-tests.yml b/.github/workflows/aria-tests.yml index 911f3089e..42825fa22 100644 --- a/.github/workflows/aria-tests.yml +++ b/.github/workflows/aria-tests.yml @@ -48,10 +48,10 @@ jobs: python-version: ['3.10', '3.11', '3.12'] steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 with: python-version: ${{ matrix.python-version }} cache: 'pip' @@ -70,7 +70,7 @@ jobs: - name: Upload test results if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: test-results-${{ matrix.python-version }} path: | @@ -86,10 +86,10 @@ jobs: needs: unit-integration-tests steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Set up Python 3.11 - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 with: python-version: '3.11' cache: 'pip' @@ -148,7 +148,7 @@ jobs: - name: Upload E2E test results if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: playwright-e2e-results path: | @@ -164,10 +164,10 @@ jobs: needs: unit-integration-tests steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Set up Python 3.11 - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 with: python-version: '3.11' cache: 'pip' @@ -244,7 +244,7 @@ jobs: - name: Upload E2E test results if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: pyppeteer-e2e-results path: test-results/ @@ -263,10 +263,10 @@ jobs: options: --shm-size=2gb steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Set up Python 3.11 - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 with: python-version: '3.11' cache: 'pip' @@ -338,7 +338,7 @@ jobs: - name: Upload E2E test results if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: selenium-e2e-results path: test-results/ @@ -351,7 +351,7 @@ jobs: if: always() steps: - name: Download all artifacts - uses: actions/download-artifact@v4 + uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4 - name: Display test summary run: | diff --git a/.github/workflows/artifact-lifecycle.yml b/.github/workflows/artifact-lifecycle.yml index 6a1165e36..59c824c41 100644 --- a/.github/workflows/artifact-lifecycle.yml +++ b/.github/workflows/artifact-lifecycle.yml @@ -47,7 +47,7 @@ jobs: steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -75,7 +75,7 @@ jobs: - name: Upload cleanup summary if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: artifact-lifecycle-${{ github.run_number }} retention-days: 30 diff --git a/.github/workflows/auto-validation.yml b/.github/workflows/auto-validation.yml index e54e78721..6dd7ad014 100644 --- a/.github/workflows/auto-validation.yml +++ b/.github/workflows/auto-validation.yml @@ -36,7 +36,7 @@ jobs: timeout-minutes: 15 steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -60,7 +60,7 @@ jobs: done - name: Upload status artifacts - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: auto-validation-status path: | diff --git a/.github/workflows/autonomous-evolver.yml b/.github/workflows/autonomous-evolver.yml index f9cf14054..f2004aff1 100644 --- a/.github/workflows/autonomous-evolver.yml +++ b/.github/workflows/autonomous-evolver.yml @@ -12,9 +12,9 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - uses: actions/setup-node@v4 + - uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4 with: node-version: 20 diff --git a/.github/workflows/azureml-train.yml b/.github/workflows/azureml-train.yml index 8b1091115..8ad654bc7 100644 --- a/.github/workflows/azureml-train.yml +++ b/.github/workflows/azureml-train.yml @@ -41,17 +41,17 @@ jobs: timeout-minutes: 30 steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Azure login - uses: azure/login@v2 + uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2 with: # Create an Azure AD app and set its JSON creds in this secret: # { "clientId": "...", "clientSecret": "...", "subscriptionId": "...", "tenantId": "..." } creds: ${{ secrets.AZURE_CREDENTIALS }} - name: Install Azure ML CLI - uses: azure/cli@v2 + uses: azure/cli@9f7ce6f37c31b777ec6c6b6d1dfe7db79f497956 # v2 with: inlineScript: | az extension add -n ml -y @@ -63,7 +63,7 @@ jobs: WORKSPACE_NAME: ${{ github.event.inputs.workspaceName }} - name: Submit Azure ML job - uses: azure/cli@v2 + uses: azure/cli@9f7ce6f37c31b777ec6c6b6d1dfe7db79f497956 # v2 with: inlineScript: | echo "Submitting job from $JOB_FILE ..." diff --git a/.github/workflows/broken-links.yml b/.github/workflows/broken-links.yml index 9806aab1d..a80a6aa6c 100644 --- a/.github/workflows/broken-links.yml +++ b/.github/workflows/broken-links.yml @@ -26,7 +26,7 @@ jobs: timeout-minutes: 15 steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Check links in docs and markdown uses: lycheeverse/lychee-action@8646ba30535128ac92d33dfc9133794bfdd9b411 # v2.8.0 @@ -36,7 +36,6 @@ jobs: --verbose --no-progress --max-concurrency 8 - --exclude-mail --accept 200,204,206,301,302,307,308,401,403,429 '**/*.md' 'docs/**/*.md' diff --git a/.github/workflows/ci-pipeline.yml b/.github/workflows/ci-pipeline.yml index 5041430c6..06405943b 100644 --- a/.github/workflows/ci-pipeline.yml +++ b/.github/workflows/ci-pipeline.yml @@ -26,6 +26,9 @@ concurrency: group: ci-${{ github.ref }} cancel-in-progress: ${{ github.event_name == 'pull_request' }} +env: + FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true # Opt into Node.js 24 for JS actions ahead of June 2026 forced migration + jobs: validate: runs-on: ubuntu-latest @@ -35,7 +38,7 @@ jobs: python-version: ['3.11'] steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -77,7 +80,7 @@ jobs: - name: Upload validation results if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: validation-results path: | @@ -93,7 +96,7 @@ jobs: if: github.event_name == 'schedule' steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -114,7 +117,7 @@ jobs: - name: Upload training results if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: training-results path: data_out/ @@ -127,7 +130,7 @@ jobs: if: github.event_name == 'schedule' steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -136,7 +139,7 @@ jobs: run: python scripts/model_deployer.py --deploy best --strategy canary - name: Upload deployment manifest - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: deployment-manifest path: deployed_models/model_registry.json diff --git a/.github/workflows/code-quality.yml b/.github/workflows/code-quality.yml index 7c7a8dfc0..5a090b199 100644 --- a/.github/workflows/code-quality.yml +++ b/.github/workflows/code-quality.yml @@ -21,7 +21,7 @@ jobs: timeout-minutes: 10 steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -59,7 +59,7 @@ jobs: timeout-minutes: 10 steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -86,7 +86,7 @@ jobs: - name: Upload safety report if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: security-report path: safety-report.json diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index 10cf55ee6..1072491cb 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -29,18 +29,18 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Initialize CodeQL - uses: github/codeql-action/init@v3 + uses: github/codeql-action/init@ce64ddcb0d8d890d2df4a9d1c04ff297367dea2a # v3 with: languages: ${{ matrix.language }} queries: security-extended,security-and-quality - name: Autobuild - uses: github/codeql-action/autobuild@v3 + uses: github/codeql-action/autobuild@ce64ddcb0d8d890d2df4a9d1c04ff297367dea2a # v3 - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@v3 + uses: github/codeql-action/analyze@ce64ddcb0d8d890d2df4a9d1c04ff297367dea2a # v3 with: category: "/language:${{matrix.language}}" diff --git a/.github/workflows/coverage-report.yml b/.github/workflows/coverage-report.yml index bcd0fdd4a..6cc1887ce 100644 --- a/.github/workflows/coverage-report.yml +++ b/.github/workflows/coverage-report.yml @@ -33,7 +33,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -68,7 +68,7 @@ jobs: # On main pushes → store as the next PR's baseline - name: Upload main baseline artifact if: github.ref == 'refs/heads/main' - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: coverage-main-baseline path: coverage.xml @@ -79,7 +79,7 @@ jobs: # On PRs → also upload per-SHA artifact for auditability - name: Upload PR coverage artifact if: github.event_name == 'pull_request' - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: coverage-pr-${{ github.event.pull_request.number }} path: coverage.xml @@ -122,7 +122,7 @@ jobs: - name: Post PR coverage comment if: github.event_name == 'pull_request' - uses: actions/github-script@v7 + uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7 env: COV_TOTAL: ${{ steps.run.outputs.total }} COV_BASE: ${{ steps.baseline.outputs.total }} diff --git a/.github/workflows/dataset-integrity.yml b/.github/workflows/dataset-integrity.yml index 405203428..7a921eee0 100644 --- a/.github/workflows/dataset-integrity.yml +++ b/.github/workflows/dataset-integrity.yml @@ -35,7 +35,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: fetch-depth: 0 @@ -105,7 +105,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: fetch-depth: 0 diff --git a/.github/workflows/dependency-review.yml b/.github/workflows/dependency-review.yml index bfa159995..b1bade985 100644 --- a/.github/workflows/dependency-review.yml +++ b/.github/workflows/dependency-review.yml @@ -18,10 +18,10 @@ jobs: timeout-minutes: 10 steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Dependency Review - uses: actions/dependency-review-action@v4 + uses: actions/dependency-review-action@2031cfc080254a8a887f58cffee85186f0e49e48 # v4 with: fail-on-severity: high deny-licenses: GPL-2.0, GPL-3.0, AGPL-3.0 diff --git a/.github/workflows/devcontainer-ci.yml b/.github/workflows/devcontainer-ci.yml index 62cc85fa2..3a133267a 100644 --- a/.github/workflows/devcontainer-ci.yml +++ b/.github/workflows/devcontainer-ci.yml @@ -14,7 +14,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Build and test devcontainer uses: devcontainers/ci@b63b30de439b47a52267f241112c5b453b673db5 # v0.3 diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml index 0fc7119d3..745732913 100644 --- a/.github/workflows/e2e-tests.yml +++ b/.github/workflows/e2e-tests.yml @@ -31,7 +31,7 @@ jobs: runs-on: ubuntu-latest timeout-minutes: 15 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env with: @@ -60,7 +60,7 @@ jobs: runs-on: ubuntu-latest timeout-minutes: 25 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env with: @@ -93,7 +93,7 @@ jobs: image: python:3.11-bullseye timeout-minutes: 25 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Apt-get update & install dependencies run: | apt-get update diff --git a/.github/workflows/experiment-runner.yml b/.github/workflows/experiment-runner.yml index 3b05ebf69..2d9eb1a88 100644 --- a/.github/workflows/experiment-runner.yml +++ b/.github/workflows/experiment-runner.yml @@ -12,9 +12,9 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - uses: actions/setup-node@v4 + - uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4 with: node-version: 20 diff --git a/.github/workflows/integration-contract-gate.yml b/.github/workflows/integration-contract-gate.yml index b4320b526..5a5d6895c 100644 --- a/.github/workflows/integration-contract-gate.yml +++ b/.github/workflows/integration-contract-gate.yml @@ -33,6 +33,9 @@ concurrency: group: contract-${{ github.ref }} cancel-in-progress: ${{ github.event_name == 'pull_request' }} +env: + FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true # Opt into Node.js 24 for JS actions ahead of June 2026 forced migration + jobs: contract-gate: name: API Contract Validation @@ -40,7 +43,7 @@ jobs: timeout-minutes: 15 steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -84,7 +87,7 @@ jobs: - name: Upload contract results if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: contract-gate-results-${{ github.run_number }} retention-days: 14 diff --git a/.github/workflows/llm-maker-tests.yml b/.github/workflows/llm-maker-tests.yml index 4460e407c..1f00dc29e 100644 --- a/.github/workflows/llm-maker-tests.yml +++ b/.github/workflows/llm-maker-tests.yml @@ -17,10 +17,10 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 with: python-version: '3.11' diff --git a/.github/workflows/makecode.yml b/.github/workflows/makecode.yml index 633cbd2fd..e7099685a 100644 --- a/.github/workflows/makecode.yml +++ b/.github/workflows/makecode.yml @@ -7,10 +7,8 @@ jobs: build: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - with: - submodules: recursive - - uses: actions/setup-node@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + - uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4 with: node-version: '20' - run: npx makecode diff --git a/.github/workflows/markdown-quality.yml b/.github/workflows/markdown-quality.yml index e2bbd8517..4e3908d5d 100644 --- a/.github/workflows/markdown-quality.yml +++ b/.github/workflows/markdown-quality.yml @@ -24,10 +24,10 @@ jobs: timeout-minutes: 10 steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Lint Markdown files - uses: DavidAnson/markdownlint-cli2-action@8305c0f1062bb0d184d09ef4493ecb9288447732 # v20 + uses: DavidAnson/markdownlint-cli2-action@v23 with: globs: | **/*.md diff --git a/.github/workflows/nightly-regression.yml b/.github/workflows/nightly-regression.yml index 01f78de9a..fec6d2833 100644 --- a/.github/workflows/nightly-regression.yml +++ b/.github/workflows/nightly-regression.yml @@ -31,6 +31,9 @@ concurrency: group: nightly-regression cancel-in-progress: false # Never cancel a nightly run mid-flight +env: + FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true # Opt into Node.js 24 for JS actions ahead of June 2026 forced migration + jobs: full-test-suite: name: Full Test Suite @@ -44,7 +47,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -96,7 +99,7 @@ jobs: - name: Upload test results if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: nightly-test-results-${{ github.run_number }} retention-days: 30 @@ -128,7 +131,7 @@ jobs: timeout-minutes: 5 steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Download previous baseline artifact id: download-prev @@ -171,7 +174,7 @@ jobs: - name: Open regression issue if: steps.compare.outputs.regression == 'true' - uses: actions/github-script@v7 + uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7 with: github-token: ${{ secrets.GITHUB_TOKEN }} script: | diff --git a/.github/workflows/pages.yml b/.github/workflows/pages.yml index 6416b5673..136cd8778 100644 --- a/.github/workflows/pages.yml +++ b/.github/workflows/pages.yml @@ -26,19 +26,19 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Pages - uses: actions/configure-pages@v4 + uses: actions/configure-pages@1f0c5cde4bc74cd7e1254d0cb4de8d49e9068c7d # v4 - name: Build with Jekyll - uses: actions/jekyll-build-pages@v1 + uses: actions/jekyll-build-pages@44a6e6beabd48582f863aeeb6cb2151cc1716697 # v1 with: source: ./docs destination: ./_site - name: Upload artifact - uses: actions/upload-pages-artifact@v3 + uses: actions/upload-pages-artifact@56afc609e74202658d3ffba0e8f6dda462b719fa # v3 # Deployment job deploy: @@ -50,4 +50,4 @@ jobs: steps: - name: Deploy to GitHub Pages id: deployment - uses: actions/deploy-pages@v4 + uses: actions/deploy-pages@d6db90164ac5ed86f2b6aed7e0febac5b3c0c03e # v4 diff --git a/.github/workflows/platform-health-daily.yml b/.github/workflows/platform-health-daily.yml index 539ce546f..224b335d1 100644 --- a/.github/workflows/platform-health-daily.yml +++ b/.github/workflows/platform-health-daily.yml @@ -27,7 +27,7 @@ jobs: steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -46,7 +46,7 @@ jobs: - name: Upload health artifacts if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: platform-health-${{ github.run_number }} retention-days: 30 diff --git a/.github/workflows/pr-checks.yml b/.github/workflows/pr-checks.yml index 9f7aaf1a7..25fc3e886 100644 --- a/.github/workflows/pr-checks.yml +++ b/.github/workflows/pr-checks.yml @@ -8,6 +8,9 @@ concurrency: group: pr-${{ github.event.pull_request.number }} cancel-in-progress: true +env: + FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true # Opt into Node.js 24 for JS actions ahead of June 2026 forced migration + jobs: validate: name: Quick Validation @@ -15,7 +18,7 @@ jobs: timeout-minutes: 10 steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -116,7 +119,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: fetch-depth: 0 @@ -151,7 +154,7 @@ jobs: pull-requests: write steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Label PR based on files changed uses: actions/labeler@634933edcd8ababfe52f92936142cc22ac488b1b # v6 diff --git a/.github/workflows/pr-tests.yml b/.github/workflows/pr-tests.yml index 4260f2a00..677d4ba6e 100644 --- a/.github/workflows/pr-tests.yml +++ b/.github/workflows/pr-tests.yml @@ -19,21 +19,25 @@ on: permissions: contents: read +env: + FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true # Opt into Node.js 24 for JS actions ahead of June 2026 forced migration + jobs: test: runs-on: ubuntu-latest steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 with: python-version: '3.11' - - name: Install dev dependencies + - name: Install dependencies run: | python -m pip install --upgrade pip + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt; elif [ -f pyproject.toml ]; then python -m pip install -e .[dev]; else pip install pytest; fi - name: Run pre-commit @@ -55,7 +59,7 @@ jobs: image: mcr.microsoft.com/devcontainers/python:1-3.11 steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Install dev dependencies (in container) run: | diff --git a/.github/workflows/quantum-ai-smoke.yml b/.github/workflows/quantum-ai-smoke.yml index 3fce108a2..16faa02f6 100644 --- a/.github/workflows/quantum-ai-smoke.yml +++ b/.github/workflows/quantum-ai-smoke.yml @@ -23,7 +23,7 @@ jobs: timeout-minutes: 20 steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -42,7 +42,7 @@ jobs: - name: Upload smoke context if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: quantum-ai-smoke-${{ github.run_number }} retention-days: 7 diff --git a/.github/workflows/quantum-orchestration.yml b/.github/workflows/quantum-orchestration.yml index b5a0e041c..a756245c6 100644 --- a/.github/workflows/quantum-orchestration.yml +++ b/.github/workflows/quantum-orchestration.yml @@ -31,10 +31,10 @@ jobs: runs-on: windows-latest timeout-minutes: 30 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Azure Login if: ${{ secrets.AZURE_CREDENTIALS != '' }} - uses: azure/login@v2 + uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2 with: creds: ${{ secrets.AZURE_CREDENTIALS }} - name: Run Orchestration diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index ae01b74f2..0a975f5c9 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -26,7 +26,7 @@ jobs: timeout-minutes: 15 steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: fetch-depth: 0 diff --git a/.github/workflows/secrets-scan.yml b/.github/workflows/secrets-scan.yml index c744d5125..e70cf5a6e 100644 --- a/.github/workflows/secrets-scan.yml +++ b/.github/workflows/secrets-scan.yml @@ -30,12 +30,12 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: fetch-depth: 0 - name: Run Gitleaks - uses: gitleaks/gitleaks-action@v2 + uses: gitleaks/gitleaks-action@ff98106e4c7b2bc287b24eaf42907196329070c7 # v2 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # GITLEAKS_LICENSE is required for org-level repos on enterprise plans. @@ -49,7 +49,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: fetch-depth: 0 diff --git a/.github/workflows/site-bundle-validation.yml b/.github/workflows/site-bundle-validation.yml index 088dca707..e39ac1c55 100644 --- a/.github/workflows/site-bundle-validation.yml +++ b/.github/workflows/site-bundle-validation.yml @@ -23,7 +23,7 @@ jobs: timeout-minutes: 10 steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -37,7 +37,7 @@ jobs: - name: Upload validation context if: always() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: generated-site-bundles-${{ github.run_number }} retention-days: 7 diff --git a/.github/workflows/telemetry-trigger.yml b/.github/workflows/telemetry-trigger.yml index 432c68144..90cc45cdf 100644 --- a/.github/workflows/telemetry-trigger.yml +++ b/.github/workflows/telemetry-trigger.yml @@ -10,9 +10,9 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - uses: actions/setup-node@v4 + - uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4 with: node-version: 20 diff --git a/.github/workflows/test-watcher.yml b/.github/workflows/test-watcher.yml index f589f2def..374086052 100644 --- a/.github/workflows/test-watcher.yml +++ b/.github/workflows/test-watcher.yml @@ -10,9 +10,9 @@ jobs: test: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 with: python-version: '3.11' - name: Install dependencies diff --git a/.github/workflows/training-health-report.yml b/.github/workflows/training-health-report.yml index 6858e3c98..d30856cc3 100644 --- a/.github/workflows/training-health-report.yml +++ b/.github/workflows/training-health-report.yml @@ -34,7 +34,7 @@ jobs: timeout-minutes: 10 steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -42,6 +42,17 @@ jobs: python-version: '3.11' extra-packages: 'tabulate' + - name: Seed training data (if no prior status exists) + run: | + if [ ! -f data_out/autonomous_training_status.json ]; then + echo "No training status found — running 1 seed cycle to bootstrap data..." + python scripts/autonomous_training_orchestrator.py \ + --cycles 1 --interval 0 --skip-quantum 2>&1 || echo "Seed training failed (likely missing dependencies or invalid config), workflow will proceed with empty data..." + else + echo "Existing training status found, skipping seed." + fi + continue-on-error: true + - name: Validate analytics script run: | if [ -f scripts/training_analytics.py ]; then @@ -91,7 +102,14 @@ jobs: at_succeeded = autotrain.get("succeeded", 0) at_failed = autotrain.get("failed", 0) - degraded = best_acc < 0.6 or trend == "degrading" or (ci_failed > 0 and ci_total > 0 and ci_failed / ci_total > 0.2) + # "no data" is not the same as "degraded": + # only flag accuracy issues when training has actually been attempted. + training_attempted = cycles > 0 + no_data = not training_attempted + accuracy_degraded = training_attempted and best_acc < 0.6 + trend_degraded = training_attempted and trend == "degrading" + ci_degraded = ci_failed > 0 and ci_total > 0 and ci_failed / ci_total > 0.2 + degraded = accuracy_degraded or trend_degraded or ci_degraded with open(os.environ["GITHUB_OUTPUT"], "a") as f: f.write(f"cycles={cycles}\n") @@ -103,9 +121,10 @@ jobs: f.write(f"ci_total={ci_total}\n") f.write(f"at_succeeded={at_succeeded}\n") f.write(f"at_failed={at_failed}\n") + f.write(f"no_data={'true' if no_data else 'false'}\n") f.write(f"degraded={'true' if degraded else 'false'}\n") - print(f"Cycles={cycles} BestAcc={best_acc:.4f} Trend={trend} Degraded={degraded}") + print(f"Cycles={cycles} BestAcc={best_acc:.4f} Trend={trend} Degraded={degraded} NoData={no_data}") PYEOF - name: Run training analytics (if available) @@ -122,6 +141,8 @@ jobs: # Overall health badge if [ "${{ steps.status.outputs.degraded }}" == "true" ]; then echo "### ⚠️ Health Status: DEGRADED" >> $GITHUB_STEP_SUMMARY + elif [ "${{ steps.status.outputs.no_data }}" == "true" ]; then + echo "### ℹ️ Health Status: No Training Data" >> $GITHUB_STEP_SUMMARY else echo "### ✅ Health Status: Healthy" >> $GITHUB_STEP_SUMMARY fi @@ -146,21 +167,24 @@ jobs: - name: Open degradation issue if: steps.status.outputs.degraded == 'true' || github.event.inputs.open-issue == 'true' - uses: actions/github-script@v7 + uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7 with: github-token: ${{ secrets.GITHUB_TOKEN }} script: | const week = new Date().toISOString().slice(0, 10); const degraded = '${{ steps.status.outputs.degraded }}' === 'true'; + const noData = '${{ steps.status.outputs.no_data }}' === 'true'; const title = degraded ? `⚠️ Training Health Degraded — week of ${week}` : `📊 Training Health Report — week of ${week}`; + const statusLabel = degraded ? '⚠️ DEGRADED' : noData ? 'ℹ️ No Training Data' : '✅ Healthy'; + const body = [ `## Weekly Training Health Report`, ``, `**Report date:** ${new Date().toUTCString()}`, - `**Status:** ${degraded ? '⚠️ DEGRADED' : '✅ Healthy'}`, + `**Status:** ${statusLabel}`, ``, `### Autonomous Training`, ``, diff --git a/.github/workflows/workflow-validation.yml b/.github/workflows/workflow-validation.yml index df9b88380..23dd6180a 100644 --- a/.github/workflows/workflow-validation.yml +++ b/.github/workflows/workflow-validation.yml @@ -19,7 +19,7 @@ jobs: timeout-minutes: 10 steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Setup Python uses: ./.github/actions/setup-python-env @@ -69,10 +69,10 @@ jobs: needs: validate-workflows steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 with: python-version: '3.11' diff --git a/README.md b/README.md index c301c08eb..2256965d1 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ pinned: false [![Code Quality](https://github.com/Bryan-Roe/Aria/actions/workflows/code-quality.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/code-quality.yml) [![CodeQL](https://github.com/Bryan-Roe/Aria/actions/workflows/codeql.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/codeql.yml) [![E2E Tests](https://github.com/Bryan-Roe/Aria/actions/workflows/e2e-tests.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/e2e-tests.yml) -[![Codespaces Prebuilds](https://github.com/Bryan-Roe/Aria/actions/workflows/codespaces/create_codespaces_prebuilds/badge.svg?branch=main)](https://github.com/Bryan-Roe/Aria/actions/workflows/codespaces/create_codespaces_prebuilds) + **An intelligent, animated AI character with movement, gestures, and natural language interaction.** [Live Demo](https://bryan-roe.github.io/Aria) · [Aria Web UI](apps/aria/) · [Quick Start](#-quick-start) diff --git a/apps/aria/test_auto_execute.py b/apps/aria/test_auto_execute.py index b8e38f324..64eea17b0 100644 --- a/apps/aria/test_auto_execute.py +++ b/apps/aria/test_auto_execute.py @@ -49,8 +49,7 @@ def plan_mode(command: str) -> Dict: response = requests.post( f"{BASE_URL}/api/aria/execute", json={"command": command, "auto_execute": False, "use_llm": False}, - timeout=30, - ) + timeout=30) data = response.json() @@ -73,8 +72,7 @@ def execute_mode(command: str) -> Dict: response = requests.post( f"{BASE_URL}/api/aria/execute", json={"command": command, "auto_execute": True, "use_llm": False}, - timeout=30, - ) + timeout=30) data = response.json() diff --git a/requirements-dev.txt b/requirements-dev.txt index 18409b997..dca6ba85e 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,4 +1,3 @@ --r requirements.txt pytest>=7.0.0 watchdog>=2.1.0 ruff>=0.4.0 diff --git a/tests/test_orchestrator_health_in_status_endpoint.py b/tests/test_orchestrator_health_in_status_endpoint.py index aba0cb3d0..512b479d5 100644 --- a/tests/test_orchestrator_health_in_status_endpoint.py +++ b/tests/test_orchestrator_health_in_status_endpoint.py @@ -16,7 +16,11 @@ def _load_function_app() -> ModuleType: raise RuntimeError("Failed to load function_app module") module = importlib.util.module_from_spec(spec) sys.modules["function_app"] = module - spec.loader.exec_module(module) + try: + spec.loader.exec_module(module) + except Exception: + sys.modules.pop("function_app", None) + raise return module diff --git a/tests/test_orchestrator_health_integration.py b/tests/test_orchestrator_health_integration.py index 1bde720cd..3f52879b5 100644 --- a/tests/test_orchestrator_health_integration.py +++ b/tests/test_orchestrator_health_integration.py @@ -25,7 +25,11 @@ def app_module(): raise RuntimeError("Failed to load function_app module") module = importlib.util.module_from_spec(spec) sys.modules["function_app"] = module - spec.loader.exec_module(module) + try: + spec.loader.exec_module(module) + except Exception: + sys.modules.pop("function_app", None) + raise return module From 85b92c431664374d30d2789f00c27c8b491fd14a Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 30 Apr 2026 15:18:42 +0000 Subject: [PATCH 02/15] Fix training health error message per code review feedback Agent-Logs-Url: https://github.com/Bryan-Roe/Aria/sessions/14928084-823d-468d-9908-c68e5b97057a Co-authored-by: Bryan-Roe <74067792+Bryan-Roe@users.noreply.github.com> --- .github/workflows/training-health-report.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/training-health-report.yml b/.github/workflows/training-health-report.yml index d30856cc3..9869c3e16 100644 --- a/.github/workflows/training-health-report.yml +++ b/.github/workflows/training-health-report.yml @@ -47,7 +47,7 @@ jobs: if [ ! -f data_out/autonomous_training_status.json ]; then echo "No training status found — running 1 seed cycle to bootstrap data..." python scripts/autonomous_training_orchestrator.py \ - --cycles 1 --interval 0 --skip-quantum 2>&1 || echo "Seed training failed (likely missing dependencies or invalid config), workflow will proceed with empty data..." + --cycles 1 --interval 0 --skip-quantum 2>&1 || echo "Seed training failed — workflow will proceed with no data (fresh checkout or missing dependencies)." else echo "Existing training status found, skipping seed." fi From 371b4d2abe043c13a8f5b5d993c93b10e5d24b49 Mon Sep 17 00:00:00 2001 From: Bryan <74067792+Bryan-Roe@users.noreply.github.com> Date: Thu, 30 Apr 2026 08:51:51 -0700 Subject: [PATCH 03/15] Update .github/workflows/markdown-quality.yml Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: Bryan <74067792+Bryan-Roe@users.noreply.github.com> --- .github/workflows/markdown-quality.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/markdown-quality.yml b/.github/workflows/markdown-quality.yml index 4e3908d5d..e19a30f81 100644 --- a/.github/workflows/markdown-quality.yml +++ b/.github/workflows/markdown-quality.yml @@ -27,7 +27,7 @@ jobs: uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Lint Markdown files - uses: DavidAnson/markdownlint-cli2-action@v23 + uses: DavidAnson/markdownlint-cli2-action@0123456789abcdef0123456789abcdef01234567 # v23 with: globs: | **/*.md From e124069b0fc8a99bea9d6e1f904c0d4d0ef777cd Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 30 Apr 2026 15:53:26 +0000 Subject: [PATCH 04/15] fix: replace placeholder SHA with real SHA for markdownlint-cli2-action v23 Agent-Logs-Url: https://github.com/Bryan-Roe/Aria/sessions/4d587816-47b9-479f-9922-4a8492d1785d Co-authored-by: Bryan-Roe <74067792+Bryan-Roe@users.noreply.github.com> --- .github/workflows/markdown-quality.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/markdown-quality.yml b/.github/workflows/markdown-quality.yml index e19a30f81..825c6d7a1 100644 --- a/.github/workflows/markdown-quality.yml +++ b/.github/workflows/markdown-quality.yml @@ -27,7 +27,7 @@ jobs: uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Lint Markdown files - uses: DavidAnson/markdownlint-cli2-action@0123456789abcdef0123456789abcdef01234567 # v23 + uses: DavidAnson/markdownlint-cli2-action@6b51ade7a9e4a75a7ad929842dd298a3804ebe8b # v23 with: globs: | **/*.md From 50d32228c51c62ba13257337388a643b8694d3fd Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 30 Apr 2026 15:56:07 +0000 Subject: [PATCH 05/15] fix: restore -r requirements.txt in requirements-dev.txt and add raise_for_status() in test_auto_execute.py Agent-Logs-Url: https://github.com/Bryan-Roe/Aria/sessions/7d3b442c-1f03-4df7-b115-613bcc44885e Co-authored-by: Bryan-Roe <74067792+Bryan-Roe@users.noreply.github.com> --- apps/aria/test_auto_execute.py | 2 ++ requirements-dev.txt | 1 + 2 files changed, 3 insertions(+) diff --git a/apps/aria/test_auto_execute.py b/apps/aria/test_auto_execute.py index 64eea17b0..f2b884168 100644 --- a/apps/aria/test_auto_execute.py +++ b/apps/aria/test_auto_execute.py @@ -50,6 +50,7 @@ def plan_mode(command: str) -> Dict: f"{BASE_URL}/api/aria/execute", json={"command": command, "auto_execute": False, "use_llm": False}, timeout=30) + response.raise_for_status() data = response.json() @@ -73,6 +74,7 @@ def execute_mode(command: str) -> Dict: f"{BASE_URL}/api/aria/execute", json={"command": command, "auto_execute": True, "use_llm": False}, timeout=30) + response.raise_for_status() data = response.json() diff --git a/requirements-dev.txt b/requirements-dev.txt index dca6ba85e..18409b997 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,3 +1,4 @@ +-r requirements.txt pytest>=7.0.0 watchdog>=2.1.0 ruff>=0.4.0 From 187ff7c03f29a1d5d02983bbe0aad69fb709a75b Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 6 May 2026 01:59:20 +0000 Subject: [PATCH 06/15] chore: sync with origin/main - resolve merge conflicts and incorporate new files - Resolved all merge conflicts between PR branch and origin/main: - .devcontainer/devcontainer.json: install both requirements.txt and requirements-dev.txt - .github/copilot-instructions.md: removed quantum-llm entries, added visible-reasoning.agent.md row - .github/prompts/agi.prompt.md: kept comprehensive main content with hidden-CoT front-matter - .github/workflows/actionlint.yml: kept SHA pin, adopted fail_level=error from main - .github/workflows/*.yml (15 files): kept SHA-pinned versions - .github/workflows/quantum-orchestration.yml: merged enhanced inputs from main + SHA pins - .github/workflows/pr-tests.yml: merged all env vars + SHA pins - .github/workflows/makecode.yml: deleted (removed in main) - README.md: accepted main's clean badge removal - apps/aria/test_auto_execute.py: kept raise_for_status() security fix - Added new files from main: - .gitattributes: LF line ending normalization - .github/agents/visible-reasoning.agent.md: new visible-reasoning agent - .markdownlint.yaml: markdownlint configuration - CODE_OF_CONDUCT.md: code of conduct - ai-projects/quantum-ml/src/quantum_llm/: quantum LLM module - tests/test_quantum_llm.py: quantum LLM tests Co-authored-by: Bryan-Roe <74067792+Bryan-Roe@users.noreply.github.com> --- .devcontainer/devcontainer.json | 34 +- .gitattributes | 36 + .github/agents/visible-reasoning.agent.md | 132 +++ .github/copilot-instructions.md | 805 ++++++++--------- .github/prompts/agi.prompt.md | 2 +- .github/workflows/actionlint.yml | 2 +- .github/workflows/aria-tests.yml | 720 ++++++++-------- .github/workflows/auto-validation.yml | 140 +-- .github/workflows/azureml-train.yml | 146 ++-- .github/workflows/ci-pipeline.yml | 292 +++---- .github/workflows/code-quality.yml | 188 ++-- .github/workflows/codeql.yml | 92 +- .github/workflows/e2e-tests.yml | 288 +++---- .github/workflows/makecode.yml | 14 - .github/workflows/pages.yml | 106 +-- .github/workflows/pr-checks.yml | 328 +++---- .github/workflows/pr-tests.yml | 30 +- .github/workflows/quantum-orchestration.yml | 170 +++- .github/workflows/release.yml | 208 ++--- .github/workflows/workflow-validation.yml | 164 ++-- .markdownlint.yaml | 7 + CODE_OF_CONDUCT.md | 128 +++ README.md | 812 +++++++++--------- .../quantum-ml/src/quantum_llm/README.md | 162 ++++ .../quantum-ml/src/quantum_llm/__init__.py | 24 + .../quantum-ml/src/quantum_llm/config.py | 79 ++ .../quantum-ml/src/quantum_llm/pipeline.py | 362 ++++++++ .../src/quantum_llm/quantum_embeddings.py | 242 ++++++ .../src/quantum_llm/quantum_router.py | 247 ++++++ .../src/quantum_llm/quantum_sampler.py | 330 +++++++ apps/aria/test_auto_execute.py | 440 +++++----- tests/test_quantum_llm.py | 369 ++++++++ 32 files changed, 4630 insertions(+), 2469 deletions(-) create mode 100644 .gitattributes create mode 100644 .github/agents/visible-reasoning.agent.md delete mode 100644 .github/workflows/makecode.yml create mode 100644 .markdownlint.yaml create mode 100644 CODE_OF_CONDUCT.md create mode 100644 ai-projects/quantum-ml/src/quantum_llm/README.md create mode 100644 ai-projects/quantum-ml/src/quantum_llm/__init__.py create mode 100644 ai-projects/quantum-ml/src/quantum_llm/config.py create mode 100644 ai-projects/quantum-ml/src/quantum_llm/pipeline.py create mode 100644 ai-projects/quantum-ml/src/quantum_llm/quantum_embeddings.py create mode 100644 ai-projects/quantum-ml/src/quantum_llm/quantum_router.py create mode 100644 ai-projects/quantum-ml/src/quantum_llm/quantum_sampler.py create mode 100644 tests/test_quantum_llm.py diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index afd591fb7..d5e3e391d 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,17 +1,17 @@ -{ - "name": "Aria Dev Container", - "image": "mcr.microsoft.com/devcontainers/python:2-3.14-trixie", - "features": {}, - "postCreateCommand": "python -m pip install --upgrade pip && if [ -f requirements.txt ]; then pip install -r requirements.txt; fi && if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt; fi", - "customizations": { - "vscode": { - "extensions": [ - "ms-python.python", - "ms-azuretools.vscode-docker", - "ms-windows-ai-studio.windows-ai-studio", - "GitHub.copilot-chat", - "GitHub.copilot" - ] - } - } -} +{ + "name": "Aria Dev Container", + "image": "mcr.microsoft.com/devcontainers/python:2-3.14-trixie", + "features": {}, + "postCreateCommand": "python -m pip install --upgrade pip && if [ -f requirements.txt ]; then pip install -r requirements.txt; fi && if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt; fi", + "customizations": { + "vscode": { + "extensions": [ + "ms-python.python", + "ms-azuretools.vscode-docker", + "ms-windows-ai-studio.windows-ai-studio", + "GitHub.copilot-chat", + "GitHub.copilot" + ] + } + } +} diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 000000000..e176a72fd --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +# Normalize line endings to LF on commit +* text=auto eol=lf + +# Force LF for these file types regardless of platform +*.yml text eol=lf +*.yaml text eol=lf +*.sh text eol=lf +*.py text eol=lf +*.js text eol=lf +*.ts text eol=lf +*.md text eol=lf +*.json text eol=lf +*.toml text eol=lf +*.cfg text eol=lf +*.ini text eol=lf +*.txt text eol=lf +*.html text eol=lf +*.css text eol=lf + +# Binary files - never convert +*.png binary +*.jpg binary +*.jpeg binary +*.gif binary +*.ico binary +*.woff binary +*.woff2 binary +*.ttf binary +*.otf binary +*.zip binary +*.gz binary +*.tar binary +*.npz binary +*.npy binary +*.pt binary +*.safetensors binary diff --git a/.github/agents/visible-reasoning.agent.md b/.github/agents/visible-reasoning.agent.md new file mode 100644 index 000000000..d9cd73235 --- /dev/null +++ b/.github/agents/visible-reasoning.agent.md @@ -0,0 +1,132 @@ +--- +name: visible-reasoning +description: "Visible step-by-step reasoning agent. Exposes chain-of-thought analysis, task decomposition, confidence scores, and self-reflection to the user. Use when the user wants to see the reasoning process, not just the final answer.\n\nTrigger phrases include:\n- 'show your reasoning'\n- 'think out loud'\n- 'explain step by step'\n- 'walk me through'\n- 'show how you got there'\n- 'visible chain of thought'\n- 'reason out loud'\n\nExamples:\n- User says 'show your reasoning for this architecture decision' → invoke to expose full reasoning chain\n- User asks 'walk me through how you would debug this' → invoke to show each diagnostic step\n- User says 'explain step by step how this algorithm works' → invoke for visible decomposition\n\nContrast with agi-reasoning: that agent uses internal (hidden) chain-of-thought and delivers only the final answer. This agent explicitly surfaces the reasoning steps to the user." +tools: + - edit + - search + - execute/getTerminalOutput + - execute/runInTerminal + - read/terminalLastCommand + - read/terminalSelection + - execute/createAndRunTask + - execute/runTask + - read/getTaskOutput + - web/fetch + - vscode/memory + - agent + - execute/runNotebookCell + - read/getNotebookSummary + - read/readNotebookCellOutput + - read/problems + - search/changes + - todo + - execute/runTests + - task_complete +--- + +# Visible Reasoning Agent + +You are a transparent reasoning agent. Your primary goal is to **show your work**: every analysis step, assumption, confidence score, and self-correction must be visible to the user. This is the opposite of the `agi-reasoning` agent, which hides its chain-of-thought. + +## Return-to-Agent Contract + +This specialist mode is temporary. After completing the visible reasoning portion of the task, hand back to `agent` (the primary `agent`) with a concise handoff that includes: + +- the visible reasoning trace you produced +- any decision or recommendation +- assumptions that were made visible +- blockers or risks identified during reasoning +- best next action for the primary agent + +Do not retain control after the reasoning work is finished. + +## How to Respond + +Structure every response as a visible reasoning trace followed by the final answer: + +``` +## Reasoning + +### 1. Analyze +[Classify the problem: complexity, intent, domain] + +### 2. Decompose +[Break into subtasks with dependencies] +- Subtask A (confidence: X%) +- Subtask B (depends on A, confidence: Y%) + +### 3. Execute +[Work through each subtask, showing intermediate results] + +**Subtask A:** +[reasoning and result] + +**Subtask B:** +[reasoning and result] + +### 4. Reflect +[Self-evaluate: completeness, correctness, quality, safety, simplicity] +- ✅ Complete: ... +- ✅ Correct: ... +- ⚠️ Edge case: ... + +### 5. Confidence +Overall confidence: X% — [reason for any uncertainty] + +## Answer + +[Final, clear, actionable answer] +``` + +## Reasoning Framework + +### Query Analysis +``` +Complexity: + simple → Direct answer, single-step + moderate → 2–3 steps, some context needed + complex → Multi-step, cross-domain, requires decomposition + +Intent: + coding → Implementation, debugging, refactoring + explanation → Conceptual understanding + creation → New features, files, systems + analysis → Performance, architecture, code review + question → Factual lookup, configuration + reasoning → Logical deduction, trade-off evaluation + +Domain: + quantum → ai-projects/quantum-ml/, quantum circuits, Azure Quantum + ai → Training, LoRA, models, datasets + aria → Character system, animations, commands + infra → Azure Functions, shared/, deployment + general → Everything else +``` + +### Self-Reflection Protocol + +After completing work, evaluate and **show** the evaluation: + +- **Completeness**: Did I address all aspects? If not, what is missing? +- **Correctness**: Is the solution verified? What test or check confirms it? +- **Quality**: Does it follow codebase conventions? +- **Safety**: Any security, cost, or data integrity concerns? +- **Simplicity**: Is this the simplest solution that works? + +If any check fails, **show the correction** before delivering the final answer. + +## Workspace Context + +- **Provider chain**: Azure OpenAI → OpenAI → LMStudio → LoRA → Local +- **Config precedence**: YAML base < CLI flags < per-job YAML < env vars +- **Data immutability**: Read-only `datasets/`, write-only `data_out/` +- **Testing**: `python scripts/test_runner.py --unit` before committing +- **Safety**: `--dry-run` all orchestrators before execution + +## Contrast with `agi-reasoning` + +| Feature | `visible-reasoning` | `agi-reasoning` | +|---|---|---| +| Chain-of-thought | **Shown to user** | Internal only | +| Use case | Explanations, teaching, debugging transparency | Autonomous execution, production answers | +| Output format | Reasoning trace + final answer | Final answer only | diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index ee39b9094..ed98a0e5b 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -1,402 +1,403 @@ - - -# Aria — Copilot Quick Guide - -*Last updated: November 29, 2025* - -Short & actionable summary for AI agents editing Aria — an interactive AI character platform with autonomous learning, quantum ML integration, and multi-provider chat backends. - -## Architecture - -- **Interactive AI Character Platform** with 3D animated avatar, natural language movement commands, and real-time object interaction -- **Three isolated projects + Functions integration layer:** - - `ai-projects/quantum-ml/` — MCP server, web dashboard, quantum ML pipelines (separate venv) - - `ai-projects/chat-cli/` — chat CLI with multi-provider support (separate venv) - - `AI/microsoft_phi-silica-3.6_v1/` — Phi-3.5 LoRA fine-tuning (separate venv) - - `function_app.py` — Azure Functions integration exposing all APIs -- **Integration points:** - - `function_app.py` dynamically imports from ai-projects/chat-cli/src and ai-projects/quantum-ml/src (adds to sys.path) - - Shared infra in `shared/`: re-exports chat providers, DB engines, telemetry, Cosmos client -- **Web Interfaces:** - - `apps/aria/` — Interactive Aria character interface with CSS animations, eye tracking, gestures - - `apps/chat/` — Streaming chat UI with SSE support -- **API endpoints** (via `function_app.py`): - - `/api/chat` — streaming chat SSE - - `/api/chat-web` — web UI HTML - - `/api/tts` — Azure Speech TTS (falls back to local if enabled) - - `/api/quantum/*` — quantum job submission/monitoring - - `/api/ai/status` — health check showing active provider, env vars, DB pool, Cosmos status -- **Aria Web API endpoints** (via `apps/aria/server.py` on port 8080): - - `GET /api/aria/state` — current stage state (position, objects, expressions) - - `POST /api/aria/command` — process natural language commands - - `POST /api/aria/execute` — auto-execute action sequences (plan or execute mode) - - `POST /api/aria/object` — manage objects (add, update, remove) - - `POST /api/aria/world` — LLM-powered themed world generation - -## Key Features - -**Interactive Character System:** -- 3D CSS-animated character with smooth transitions and physics-based movement -- Natural language command processing ("move left", "wave at me", "dance", "jump", "pickup ball") -- **Auto-Execute System**: LLM-powered action parser converts natural language to structured action sequences - - 8 core actions: move, say, pickup, drop, throw, gesture, look, wait - - Plan mode (preview actions) and execute mode (run sequences) - - Dual-mode parsing: LLM-powered + rule-based fallback -- Object interaction system (add, pickup, drop, throw with trajectory physics) -- **World Generation**: LLM-powered themed environment creation -- Eye tracking and attention system (follows mouse cursor) -- Emotion/gesture system (wave, dance, jump, idle animations) -- Real-time speech synthesis via Azure TTS or local fallback -- Server-synchronized state management (character position, objects, expressions) - -**Autonomous Learning:** -- Self-discovering dataset collection from multiple sources -- Adaptive epoch selection based on performance history -- Automatic model promotion when accuracy thresholds met -- Performance degradation detection and alerting -- Continuous 30-minute training cycles with graceful error recovery - -**Multi-Provider Chat:** -- Azure OpenAI, OpenAI, LMStudio, local models -- LoRA adapter support for fine-tuned models -- Automatic provider fallback chain -- SSE-based streaming responses - -## Quick Commands (from repo root) - -```bash -# === AUTONOMOUS SYSTEMS (Self-Managing) === -# Start autonomous training (continuous 30-min cycles) -nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 & - -# Trigger immediate cycle (skip 30-min wait) -pkill -USR1 -f autonomous_training - -# Full repo automation (Aria + training + quantum + monitoring) -python scripts/repo_automation.py --start -python scripts/repo_automation.py --status -./scripts/start_repo_automation.sh full # Bash wrapper with menu -./scripts/start_repo_automation.sh stop # Stop all components - -# Aria character automation (server + continuous training) -python scripts/aria_automation.py --mode full -python scripts/aria_automation.py --status - -# === ARIA CHARACTER WEB UI === -cd apps/aria && python server.py # Start Aria web interface (port 8080) -# Access at: http://localhost:8080 -# Auto-Execute UI: http://localhost:8080/auto-execute.html -# Commands: "move left", "wave", "dance", "jump", "pickup ball", "throw" -# Complex: "Walk to the table and pick up the apple", "Say hello and wave" - -# === AZURE FUNCTIONS & APIs === -func host start # Start Functions host (serves all APIs) -curl http://localhost:7071/api/ai/status | jq # Health check - -# === TESTING & VALIDATION === -python scripts/test_runner.py --unit # Fast unit tests -python scripts/test_runner.py --all # All tests -python scripts/test_runner.py --unit --coverage # Unit tests + coverage -python scripts/test_runner.py --list-suites # Show available suites -python scripts/test_runner.py --unit --watch # Re-run on file changes -python ai-projects/chat-cli/src/chat_cli.py --provider local --once "Hello" # Smoke test -python scripts/fast_validate.py # Quick validation (datasets, scripts, venvs, configs, providers, deps) -python scripts/cleanup_artifacts.py # Preview old artifact cleanup (dry-run) -python scripts/cleanup_artifacts.py --apply # Actually delete old artifacts - -# === ORCHESTRATORS (Manual Execution) === -python scripts/autotrain.py --dry-run # Validate training config (12 jobs) -python scripts/quantum_autorun.py --dry-run # Validate quantum config -python scripts/evaluation_autorun.py --dry-run # Validate evaluation config - -# === TRAINING PIPELINES === -python scripts/automated_training_pipeline.py --quick # Quick LoRA (TinyLlama) -python scripts/train_and_promote.py --quick --auto-promote # Train + auto-deploy - -# === MCP & TOOLS === -python ai-projects/quantum-ml/quantum_mcp_server.py # Start quantum MCP server - -# === MONITORING & DIAGNOSTICS === -curl http://localhost:7071/api/ai/status | jq # Comprehensive health check -python scripts/status_dashboard.py # Unified orchestrator status -python scripts/status_dashboard.py --watch # Auto-refresh every 10s -python scripts/resource_monitor.py --snapshot # CPU/memory/disk/GPU snapshot -python scripts/system_health_check.py # Full system health report -python scripts/training_analytics.py # Performance trends & insights -tail -f data_out/autonomous_training.log # Live autonomous training logs -watch -n 5 'cat data_out/autonomous_training_status.json | python -m json.tool' # Live status -``` - -## Critical Patterns - -**Autonomous/self-managing systems:** -- `scripts/autonomous_training_orchestrator.py` — Continuous learning with 30-min cycles (infinite by default) - - Self-discovers datasets (scans `datasets/quantum`, `datasets/chat`, `datasets/massive_quantum`) - - Self-optimizes: Adaptive epochs `[25, 50, 100, 200]` based on performance history - - Self-heals: Graceful error handling, continues on failure, logs to `data_out/autonomous_training.log` - - State: `data_out/autonomous_training_status.json` (cycles_completed, best_accuracy, dataset_inventory) - - Config: `config/autonomous_training.yaml` (cycle_interval_minutes, epochs_progression, min_datasets) - - Trigger: Time-based (30min) OR signal-based (`pkill -USR1 -f autonomous_training`) -- `scripts/repo_automation.py` — Full repo automation (all components: Aria + training + quantum + datasets) -- `scripts/aria_automation.py` — Aria-specific automation (server on port 8080 + continuous training) -- `scripts/master_orchestrator.py` — Coordinates all sub-orchestrators with schedules/dependencies - - Config: `config/master_orchestrator.yaml` (cron schedules, priorities, retry logic, timeouts) - -**Data conventions:** -- `datasets/` is **read-only** — never modify existing datasets -- All outputs go to `data_out//` with `status.json` as source of truth -- Chat datasets: `[{"messages": [{"role": "user|assistant", "content": "..."}]}]` -- LoRA adapters need both `adapter_config.json` + `adapter_model.safetensors` - -**Provider detection chain** (in `shared/chat_providers.py`): -1. Explicit choice (--provider flag) -2. LMStudio (if `LMSTUDIO_BASE_URL` configured) -3. Azure OpenAI (needs all 4: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`) -4. OpenAI (needs `OPENAI_API_KEY`) -5. LoRA (explicit --provider lora with adapter path) -6. Local fallback (zero-dependency echo) - -**Config precedence:** -`YAML base` < `CLI flags` < `per-job YAML` < `env vars` - -**YAML orchestrators:** -- All in `scripts/` with matching root YAMLs (e.g., `autotrain.yaml`, `quantum_autorun.yaml`) -- Write `data_out//status.json` with machine-readable job status -- Support `--dry-run` to validate before execution - -**Autonomous training implementation patterns:** -```python -# State machine: discovery → collection → training → analysis → optimization → deployment -async def run_single_cycle(cycle_number): - await discover_datasets() # Scan datasets/, catalog by category - await download_new_datasets() # Download if below min_datasets threshold - epochs = await select_optimal_epochs() # Adaptive: increase if accuracy < 0.70 or plateau - results = await train_cycle(epochs) # Distributed training with multiprocessing - await analyze_performance(results) # Track metrics, detect degradation - await optimization_cycle() # Hyperparameter tuning (if enabled) - await deployment_cycle() # Auto-deploy if accuracy > 0.90 (if enabled) -``` - -**Process management:** -- Autonomous systems run via `nohup` in background, logs to `data_out/*.log` -- Check status: `ps aux | grep -E "(autonomous|aria)" | grep -v grep` -- Manual trigger: `pkill -USR1 -f autonomous_training` forces immediate cycle -- Graceful shutdown: `pkill -TERM -f autonomous_training` - -**Performance monitoring & observability:** -- **Health endpoint**: `GET /api/ai/status` — Comprehensive system diagnostics - - Active provider detection (azure|openai|local|lora) - - Environment variable presence (Azure OpenAI, OpenAI, Cosmos, SQL) - - ML library availability (torch, transformers, peft) — in-process & venv - - SQL pool metrics with saturation alerts (warns at ≥80%) - - Cosmos DB health check (lazy connection) - - Quantum environment status (qiskit, pennylane, Azure Quantum backends) - - LoRA adapter readiness (adapter_config.json, tokenizer) -- **Status files**: All orchestrators write `data_out//status.json` - - Schema: `{total_jobs, succeeded, failed, running, last_updated, avg_duration}` - - Autonomous training: `{cycles_completed, best_accuracy, performance_history[], dataset_inventory}` -- **Monitoring scripts**: - - `scripts/status_dashboard.py` — Unified view of all orchestrators (supports --watch, --export) - - `scripts/resource_monitor.py` — CPU/memory/disk/GPU with threshold alerts - - `scripts/system_health_check.py` — Comprehensive health report (venvs, Azure Functions, datasets) - - `scripts/training_analytics.py` — Performance trends, improvement rates, plateau detection -- **Performance degradation alerts**: Auto-detect >5% accuracy drops between cycles -- **Metrics tracked**: mean_accuracy, median_accuracy, max_accuracy, successful_count, exceptional_models -- **Notification config**: `config/notification_config.yaml` (email/SMTP/local alerts) - -## Where to Edit - -| Change | File(s) | -|--------|---------| -| Add/modify API endpoint | `function_app.py` | -| Chat provider logic | `ai-projects/chat-cli/src/chat_providers.py` (re-exported by `shared/chat_providers.py`) | -| Training orchestration | `scripts/autotrain.py` + root `autotrain.yaml` | -| Autonomous training behavior | `scripts/autonomous_training_orchestrator.py` + `config/autonomous_training.yaml` | -| Master orchestrator (schedules/coordination) | `scripts/master_orchestrator.py` + `config/master_orchestrator.yaml` | -| Aria automation | `scripts/aria_automation.py` (server + training + health monitoring) | -| Full repo automation | `scripts/repo_automation.py` (all components + backups + notifications) | -| Quantum jobs | `scripts/quantum_autorun.py` + root `quantum_autorun.yaml` | -| MCP server tools | `ai-projects/quantum-ml/quantum_mcp_server.py` | -| Shared DB/telemetry | `shared/sql_engine.py`, `shared/telemetry.py`, `shared/cosmos_client.py` | -| Aria character interface | `apps/aria/index.html`, `apps/aria/aria_controller.js`, `apps/aria/server.py` | -| Aria movement/gestures | `apps/aria/aria_controller.js` (command parsing & animation triggers) | -| Semantic memory/embeddings | `shared/chat_memory.py` (generate_embedding, fetch_similar, store) | -| Token management | `ai-projects/chat-cli/src/token_utils.py` (counting, pruning) | -| LLM tool generation | `ai-projects/llm-maker/src/tool_maker.py`, `tool_validator.py` | -| Website generation | `ai-projects/llm-maker/src/website_maker.py` | -| Cooking AI recipes | `ai-projects/cooking-ai/src/agents/recipe_agent.py` | -| Subscriptions/monetization | `shared/subscription_manager.py` + `setup_monetization.py` | -| DB logging (fault-tolerant) | `shared/db_logging.py` (SP wrappers) | -| Vision/expression AI | `scripts/vision_inference.py` (TinyConvNet) | -| Batch model evaluation | `scripts/batch_evaluator.py` + `config/evaluation/` | -| Monitoring dashboard | `apps/dashboard/` (hub, analytics, GPU monitor) | -| AGI reasoning | `ai-projects/chat-cli/src/agi_provider.py` | -| Test runner | `scripts/test_runner.py` (centralized suite orchestrator) | -| Request validation | `shared/request_validator.py` (JSON schema validation) | -| Artifact cleanup | `scripts/cleanup_artifacts.py` (data_out/ retention) | -| Model evaluation | `scripts/evaluate_model.py` (delegates or fallback metrics) | -| Fast validation | `scripts/fast_validate.py` (configs, providers, deps) | - -## Safety Rules - -- Always `--dry-run` orchestrators before GPU/QPU execution -- Quantum: simulate locally first, then use `azure_ionq_simulator`, only then real QPU -- Real QPU jobs require `azure_confirm_cost: true` in YAML + cost estimate review -- Never hardcode secrets — use `local.settings.json` (dev) or Azure App Settings (prod) -- Monitor DB pool via `/api/ai/status` (warns at ≥80% saturation) - -## Testing & Validation - -- **Unit tests:** `pytest tests/ -m "not slow and not azure"` or `python scripts/test_runner.py --unit` -- **Integration tests:** `python scripts/test_runner.py --integration` -- **All tests:** `python scripts/test_runner.py --all` -- **VS Code Test Explorer:** Use 🧪 icon for interactive test running -- **Markers:** `@pytest.mark.slow`, `@pytest.mark.azure`, `@pytest.mark.integration` -- **Chat dataset validation:** `python scripts/validate_datasets.py --category chat` - -## Optional Services - -**SQL persistence** (optional): -- Enable via `QAI_DB_CONN` env var (SQLite, PostgreSQL, Azure SQL) -- Pool size: `QAI_SQL_POOL_SIZE` (default: 10) -- Health: Check `/api/ai/status` for pool saturation (warns ≥80%) - -**Cosmos DB** (optional, feature-flagged): -- Enable: `QAI_ENABLE_COSMOS=true` -- Config: `COSMOS_ENDPOINT`, `COSMOS_KEY`, `COSMOS_DATABASE`, `COSMOS_CONTAINER` -- Partition key: `/session_id`, enable TTL for cost savings - -**Telemetry** (optional): -- Application Insights via `APPLICATIONINSIGHTS_CONNECTION_STRING` -- Non-blocking, gracefully degrades if unavailable - -## Modular Instructions - -This repo uses component-specific instruction files in `.github/instructions/`: -- `functions.instructions.md` — Azure Functions API endpoints -- `shared-python.instructions.md` — Shared infrastructure patterns -- `quantum-ai*.instructions.md` — Quantum ML workflows -- `talk-to-ai*.instructions.md` — Chat CLI patterns -- `lora*.instructions.md` — LoRA fine-tuning patterns -- `chat-web.instructions.md` — Frontend SSE integration -- `agi-provider.instructions.md` — AGI reasoning system -- `aria-character.instructions.md` — Interactive character system -- `aria-web.instructions.md` — Aria web server module -- `autonomous-training.instructions.md` — Autonomous training orchestration -- `training-scripts.instructions.md` — Training script patterns -- `orchestrator-configs.instructions.md` — YAML orchestrator configs -- `dashboard.instructions.md` — Monitoring dashboard -- `tests.instructions.md` — Testing infrastructure -- `llm-maker.instructions.md` — Safe tool/website generation -- `cooking-ai.instructions.md` — Cooking AI recipe agent -- `chat-providers.instructions.md` — Multi-provider chat system -- `chat-memory.instructions.md` — Semantic memory & embeddings -- `subscription.instructions.md` — Subscription/monetization -- `db-logging.instructions.md` — Fault-tolerant DB logging -- `telemetry.instructions.md` — OpenTelemetry setup -- `token-utils.instructions.md` — Token counting & context pruning -- `evaluation.instructions.md` — Batch evaluation & analytics -- `vision-inference.instructions.md` — Vision AI & CNN models - -These are automatically applied by VS Code based on file paths. Check attachment indicators to see which rules are active. - -## Custom Coding Agents - -Available agents in `.github/agents/`: - -| Agent | Purpose | -|-------|---------| -| `ai.agent.md` | Primary autonomous agent — task decomposition, multi-step execution | -| `my-agent.agent.md` | QAI specialist — quantum-AI/ML development | -| `agi-reasoning.agent.md` | Autonomous AGI reasoning with internal self-reflection (chain-of-thought is hidden; only the final answer is delivered) | -| `aria-character.agent.md` | Interactive character commands, animations | -| `autonomous-trainer.agent.md` | LoRA training lifecycle, model promotion | -| `full-stack-debugger.agent.md` | Cross-stack issue diagnosis | -| `automated-code-fixer.agent.md` | Autonomous code improvements | -| `ai-architect.agent.md` | AI pipeline design, provider integration | -| `llm-maker.agent.md` | Safe tool/website generation | -| `chat-provider.agent.md` | Multi-provider chat, streaming, memory | -| `platform-ops.agent.md` | Subscriptions, monitoring, deployment | -| `vision-ai.agent.md` | Expression/emotion classification | -| `data-pipeline.agent.md` | Batch evaluation, dataset management | - -**Mode equivalents now live in `.github/agents/`**: -- `AI_model_training.agent.md` — End-to-end LoRA training, evaluation, and model promotion -- `Aria_character_development.agent.md` — Interactive character commands, actions, world generation -- `Quantum_ML_development.agent.md` — Quantum circuits, simulation, Azure Quantum pipelines -- `Full_stack_debugging.agent.md` — Cross-stack diagnostic protocol -- `AI_chat_development.agent.md` — Multi-provider chat, streaming, memory, self-learning -- `Azure_function_codegen_and_deployment.agent.md` — Enterprise Azure Functions workflow with IaC -- `Azure_Static_Web_App.agent.md` — Static web app deployment patterns - -**Prompts** (`.github/prompts/`): -- `agi.prompt.md` — Autonomous AGI reasoning with multi-step analysis and self-correction (chain-of-thought is internal, not exposed in output) -- `reason.prompt.md` — Structured analysis with explicit chain-of-thought reasoning steps shown to the user -- `debug.prompt.md` — Systematic diagnostic protocol -- `review.prompt.md` — Code review (correctness, security, performance) -- `aria-command.prompt.md` — Natural language → Aria actions -- `train.prompt.md` — Training execution with safety -- `quantum.prompt.md` — Cost-aware quantum workflows -- `chat.prompt.md` — Multi-provider chat with memory -- `generate-tool.prompt.md` — Safe Python tool generation -- `generate-website.prompt.md` — Complete website generation -- `evaluate.prompt.md` — Model evaluation & benchmarking -- `deploy.prompt.md` — Model/service deployment -- `optimize.prompt.md` — Performance analysis & optimization - -**Usage**: Agents are invoked automatically based on context or explicitly selected in GitHub Copilot interfaces. - -## Coding Agent Best Practices - -**For AI Coding Agents working in this repository:** - -1. **Always Check Context** - - Read `.github/copilot-instructions.md` (this file) first - - Check for relevant `.github/instructions/*.instructions.md` files based on file paths - - Reference `.github/agents/my-agent.agent.md` for QAI-specific patterns - -2. **Safety-First Approach** - - `--dry-run` all orchestrators before GPU/QPU execution - - Never modify files in `datasets/` (read-only) - - Check `/api/ai/status` before making provider-dependent changes - - Verify test suite passes: `python scripts/test_runner.py --unit` - -3. **Follow Established Patterns** - - Provider detection chain: Azure OpenAI → OpenAI → LMStudio → Local - - Config precedence: `YAML base` < `CLI flags` < `per-job YAML` < `env vars` - - Status files: Always write to `data_out//status.json` - - Autonomous systems: Use signal-based triggers (`pkill -USR1`) for immediate execution - -4. **Testing & Validation** - - Run unit tests before committing: `python scripts/test_runner.py --unit` - - Use `scripts/fast_validate.py` for quick cross-component validation - - Check health endpoint: `curl http://localhost:7071/api/ai/status | jq` - - Monitor logs: `tail -f data_out/autonomous_training.log` - -5. **Documentation Updates** - - Update this file when adding major features or changing workflows - - Keep component-specific instructions in `.github/instructions/` in sync - - Update PR checklist if adding new safety requirements - - Document new orchestrators in "Where to Edit" table - -6. **Cost & Resource Awareness** - - Quantum: Simulate locally first, then Azure simulator, only then real QPU - - Monitor DB pool saturation via `/api/ai/status` (warns at ≥80%) - - Check GPU/CPU usage: `python scripts/resource_monitor.py --snapshot` - - Review training analytics: `python scripts/training_analytics.py` - -## PR Checklist for AI Agents & Reviewers - -Before submitting or approving PRs, verify: - -- [ ] **Dry-run orchestrators**: If modifying YAML configs or orchestrators, run `--dry-run` to validate changes before committing -- [ ] **Provider detection intact**: Changes to `shared/chat_providers.py` or `function_app.py` don't break detection chain (test with `/api/ai/status`) -- [ ] **Dataset immutability**: No modifications to `datasets/` — all outputs written to `data_out/` -- [ ] **Status.json compliance**: Orchestrator changes maintain status JSON writes to `data_out//status.json` -- [ ] **Test suite passes**: Run `python scripts/test_runner.py --unit` (or `--all` for integration tests) -- [ ] **No hardcoded secrets**: All API keys/connection strings use env vars or `local.settings.json` -- [ ] **Quantum cost gates**: QPU jobs include `azure_confirm_cost: true` in YAML configs -- [ ] **LoRA adapter validity**: If modifying training scripts, verify output includes both `adapter_config.json` + `adapter_model.safetensors` -- [ ] **Documentation sync**: Update relevant READMEs/instruction files if changing core workflows or adding features - -Full/verbose guidance and advanced examples are preserved at `.github/copilot-instructions.full.md`. Ask me to expand any area or add examples for a specific change. + + +# Aria — Copilot Quick Guide + +*Last updated: November 29, 2025* + +Short & actionable summary for AI agents editing Aria — an interactive AI character platform with autonomous learning, quantum ML integration, and multi-provider chat backends. + +## Architecture + +- **Interactive AI Character Platform** with 3D animated avatar, natural language movement commands, and real-time object interaction +- **Three isolated projects + Functions integration layer:** + - `ai-projects/quantum-ml/` — MCP server, web dashboard, quantum ML pipelines (separate venv) + - `ai-projects/chat-cli/` — chat CLI with multi-provider support (separate venv) + - `AI/microsoft_phi-silica-3.6_v1/` — Phi-3.5 LoRA fine-tuning (separate venv) + - `function_app.py` — Azure Functions integration exposing all APIs +- **Integration points:** + - `function_app.py` dynamically imports from ai-projects/chat-cli/src and ai-projects/quantum-ml/src (adds to sys.path) + - Shared infra in `shared/`: re-exports chat providers, DB engines, telemetry, Cosmos client +- **Web Interfaces:** + - `apps/aria/` — Interactive Aria character interface with CSS animations, eye tracking, gestures + - `apps/chat/` — Streaming chat UI with SSE support +- **API endpoints** (via `function_app.py`): + - `/api/chat` — streaming chat SSE + - `/api/chat-web` — web UI HTML + - `/api/tts` — Azure Speech TTS (falls back to local if enabled) + - `/api/quantum/*` — quantum job submission/monitoring + - `/api/ai/status` — health check showing active provider, env vars, DB pool, Cosmos status +- **Aria Web API endpoints** (via `apps/aria/server.py` on port 8080): + - `GET /api/aria/state` — current stage state (position, objects, expressions) + - `POST /api/aria/command` — process natural language commands + - `POST /api/aria/execute` — auto-execute action sequences (plan or execute mode) + - `POST /api/aria/object` — manage objects (add, update, remove) + - `POST /api/aria/world` — LLM-powered themed world generation + +## Key Features + +**Interactive Character System:** +- 3D CSS-animated character with smooth transitions and physics-based movement +- Natural language command processing ("move left", "wave at me", "dance", "jump", "pickup ball") +- **Auto-Execute System**: LLM-powered action parser converts natural language to structured action sequences + - 8 core actions: move, say, pickup, drop, throw, gesture, look, wait + - Plan mode (preview actions) and execute mode (run sequences) + - Dual-mode parsing: LLM-powered + rule-based fallback +- Object interaction system (add, pickup, drop, throw with trajectory physics) +- **World Generation**: LLM-powered themed environment creation +- Eye tracking and attention system (follows mouse cursor) +- Emotion/gesture system (wave, dance, jump, idle animations) +- Real-time speech synthesis via Azure TTS or local fallback +- Server-synchronized state management (character position, objects, expressions) + +**Autonomous Learning:** +- Self-discovering dataset collection from multiple sources +- Adaptive epoch selection based on performance history +- Automatic model promotion when accuracy thresholds met +- Performance degradation detection and alerting +- Continuous 30-minute training cycles with graceful error recovery + +**Multi-Provider Chat:** +- Azure OpenAI, OpenAI, LMStudio, local models +- LoRA adapter support for fine-tuned models +- Automatic provider fallback chain +- SSE-based streaming responses + +## Quick Commands (from repo root) + +```bash +# === AUTONOMOUS SYSTEMS (Self-Managing) === +# Start autonomous training (continuous 30-min cycles) +nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 & + +# Trigger immediate cycle (skip 30-min wait) +pkill -USR1 -f autonomous_training + +# Full repo automation (Aria + training + quantum + monitoring) +python scripts/repo_automation.py --start +python scripts/repo_automation.py --status +./scripts/start_repo_automation.sh full # Bash wrapper with menu +./scripts/start_repo_automation.sh stop # Stop all components + +# Aria character automation (server + continuous training) +python scripts/aria_automation.py --mode full +python scripts/aria_automation.py --status + +# === ARIA CHARACTER WEB UI === +cd apps/aria && python server.py # Start Aria web interface (port 8080) +# Access at: http://localhost:8080 +# Auto-Execute UI: http://localhost:8080/auto-execute.html +# Commands: "move left", "wave", "dance", "jump", "pickup ball", "throw" +# Complex: "Walk to the table and pick up the apple", "Say hello and wave" + +# === AZURE FUNCTIONS & APIs === +func host start # Start Functions host (serves all APIs) +curl http://localhost:7071/api/ai/status | jq # Health check + +# === TESTING & VALIDATION === +python scripts/test_runner.py --unit # Fast unit tests +python scripts/test_runner.py --all # All tests +python scripts/test_runner.py --unit --coverage # Unit tests + coverage +python scripts/test_runner.py --list-suites # Show available suites +python scripts/test_runner.py --unit --watch # Re-run on file changes +python ai-projects/chat-cli/src/chat_cli.py --provider local --once "Hello" # Smoke test +python scripts/fast_validate.py # Quick validation (datasets, scripts, venvs, configs, providers, deps) +python scripts/cleanup_artifacts.py # Preview old artifact cleanup (dry-run) +python scripts/cleanup_artifacts.py --apply # Actually delete old artifacts + +# === ORCHESTRATORS (Manual Execution) === +python scripts/autotrain.py --dry-run # Validate training config (12 jobs) +python scripts/quantum_autorun.py --dry-run # Validate quantum config +python scripts/evaluation_autorun.py --dry-run # Validate evaluation config + +# === TRAINING PIPELINES === +python scripts/automated_training_pipeline.py --quick # Quick LoRA (TinyLlama) +python scripts/train_and_promote.py --quick --auto-promote # Train + auto-deploy + +# === MCP & TOOLS === +python ai-projects/quantum-ml/quantum_mcp_server.py # Start quantum MCP server + +# === MONITORING & DIAGNOSTICS === +curl http://localhost:7071/api/ai/status | jq # Comprehensive health check +python scripts/status_dashboard.py # Unified orchestrator status +python scripts/status_dashboard.py --watch # Auto-refresh every 10s +python scripts/resource_monitor.py --snapshot # CPU/memory/disk/GPU snapshot +python scripts/system_health_check.py # Full system health report +python scripts/training_analytics.py # Performance trends & insights +tail -f data_out/autonomous_training.log # Live autonomous training logs +watch -n 5 'cat data_out/autonomous_training_status.json | python -m json.tool' # Live status +``` + +## Critical Patterns + +**Autonomous/self-managing systems:** +- `scripts/autonomous_training_orchestrator.py` — Continuous learning with 30-min cycles (infinite by default) + - Self-discovers datasets (scans `datasets/quantum`, `datasets/chat`, `datasets/massive_quantum`) + - Self-optimizes: Adaptive epochs `[25, 50, 100, 200]` based on performance history + - Self-heals: Graceful error handling, continues on failure, logs to `data_out/autonomous_training.log` + - State: `data_out/autonomous_training_status.json` (cycles_completed, best_accuracy, dataset_inventory) + - Config: `config/autonomous_training.yaml` (cycle_interval_minutes, epochs_progression, min_datasets) + - Trigger: Time-based (30min) OR signal-based (`pkill -USR1 -f autonomous_training`) +- `scripts/repo_automation.py` — Full repo automation (all components: Aria + training + quantum + datasets) +- `scripts/aria_automation.py` — Aria-specific automation (server on port 8080 + continuous training) +- `scripts/master_orchestrator.py` — Coordinates all sub-orchestrators with schedules/dependencies + - Config: `config/master_orchestrator.yaml` (cron schedules, priorities, retry logic, timeouts) + +**Data conventions:** +- `datasets/` is **read-only** — never modify existing datasets +- All outputs go to `data_out//` with `status.json` as source of truth +- Chat datasets: `[{"messages": [{"role": "user|assistant", "content": "..."}]}]` +- LoRA adapters need both `adapter_config.json` + `adapter_model.safetensors` + +**Provider detection chain** (in `shared/chat_providers.py`): +1. Explicit choice (--provider flag) +2. LMStudio (if `LMSTUDIO_BASE_URL` configured) +3. Azure OpenAI (needs all 4: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`) +4. OpenAI (needs `OPENAI_API_KEY`) +5. LoRA (explicit --provider lora with adapter path) +6. Local fallback (zero-dependency echo) + +**Config precedence:** +`YAML base` < `CLI flags` < `per-job YAML` < `env vars` + +**YAML orchestrators:** +- All in `scripts/` with matching root YAMLs (e.g., `autotrain.yaml`, `quantum_autorun.yaml`) +- Write `data_out//status.json` with machine-readable job status +- Support `--dry-run` to validate before execution + +**Autonomous training implementation patterns:** +```python +# State machine: discovery → collection → training → analysis → optimization → deployment +async def run_single_cycle(cycle_number): + await discover_datasets() # Scan datasets/, catalog by category + await download_new_datasets() # Download if below min_datasets threshold + epochs = await select_optimal_epochs() # Adaptive: increase if accuracy < 0.70 or plateau + results = await train_cycle(epochs) # Distributed training with multiprocessing + await analyze_performance(results) # Track metrics, detect degradation + await optimization_cycle() # Hyperparameter tuning (if enabled) + await deployment_cycle() # Auto-deploy if accuracy > 0.90 (if enabled) +``` + +**Process management:** +- Autonomous systems run via `nohup` in background, logs to `data_out/*.log` +- Check status: `ps aux | grep -E "(autonomous|aria)" | grep -v grep` +- Manual trigger: `pkill -USR1 -f autonomous_training` forces immediate cycle +- Graceful shutdown: `pkill -TERM -f autonomous_training` + +**Performance monitoring & observability:** +- **Health endpoint**: `GET /api/ai/status` — Comprehensive system diagnostics + - Active provider detection (azure|openai|local|lora) + - Environment variable presence (Azure OpenAI, OpenAI, Cosmos, SQL) + - ML library availability (torch, transformers, peft) — in-process & venv + - SQL pool metrics with saturation alerts (warns at ≥80%) + - Cosmos DB health check (lazy connection) + - Quantum environment status (qiskit, pennylane, Azure Quantum backends) + - LoRA adapter readiness (adapter_config.json, tokenizer) +- **Status files**: All orchestrators write `data_out//status.json` + - Schema: `{total_jobs, succeeded, failed, running, last_updated, avg_duration}` + - Autonomous training: `{cycles_completed, best_accuracy, performance_history[], dataset_inventory}` +- **Monitoring scripts**: + - `scripts/status_dashboard.py` — Unified view of all orchestrators (supports --watch, --export) + - `scripts/resource_monitor.py` — CPU/memory/disk/GPU with threshold alerts + - `scripts/system_health_check.py` — Comprehensive health report (venvs, Azure Functions, datasets) + - `scripts/training_analytics.py` — Performance trends, improvement rates, plateau detection +- **Performance degradation alerts**: Auto-detect >5% accuracy drops between cycles +- **Metrics tracked**: mean_accuracy, median_accuracy, max_accuracy, successful_count, exceptional_models +- **Notification config**: `config/notification_config.yaml` (email/SMTP/local alerts) + +## Where to Edit + +| Change | File(s) | +|--------|---------| +| Add/modify API endpoint | `function_app.py` | +| Chat provider logic | `ai-projects/chat-cli/src/chat_providers.py` (re-exported by `shared/chat_providers.py`) | +| Training orchestration | `scripts/autotrain.py` + root `autotrain.yaml` | +| Autonomous training behavior | `scripts/autonomous_training_orchestrator.py` + `config/autonomous_training.yaml` | +| Master orchestrator (schedules/coordination) | `scripts/master_orchestrator.py` + `config/master_orchestrator.yaml` | +| Aria automation | `scripts/aria_automation.py` (server + training + health monitoring) | +| Full repo automation | `scripts/repo_automation.py` (all components + backups + notifications) | +| Quantum jobs | `scripts/quantum_autorun.py` + root `quantum_autorun.yaml` | +| MCP server tools | `ai-projects/quantum-ml/quantum_mcp_server.py` | +| Shared DB/telemetry | `shared/sql_engine.py`, `shared/telemetry.py`, `shared/cosmos_client.py` | +| Aria character interface | `apps/aria/index.html`, `apps/aria/aria_controller.js`, `apps/aria/server.py` | +| Aria movement/gestures | `apps/aria/aria_controller.js` (command parsing & animation triggers) | +| Semantic memory/embeddings | `shared/chat_memory.py` (generate_embedding, fetch_similar, store) | +| Token management | `ai-projects/chat-cli/src/token_utils.py` (counting, pruning) | +| LLM tool generation | `ai-projects/llm-maker/src/tool_maker.py`, `tool_validator.py` | +| Website generation | `ai-projects/llm-maker/src/website_maker.py` | +| Cooking AI recipes | `ai-projects/cooking-ai/src/agents/recipe_agent.py` | +| Subscriptions/monetization | `shared/subscription_manager.py` + `setup_monetization.py` | +| DB logging (fault-tolerant) | `shared/db_logging.py` (SP wrappers) | +| Vision/expression AI | `scripts/vision_inference.py` (TinyConvNet) | +| Batch model evaluation | `scripts/batch_evaluator.py` + `config/evaluation/` | +| Monitoring dashboard | `apps/dashboard/` (hub, analytics, GPU monitor) | +| AGI reasoning | `ai-projects/chat-cli/src/agi_provider.py` | +| Test runner | `scripts/test_runner.py` (centralized suite orchestrator) | +| Request validation | `shared/request_validator.py` (JSON schema validation) | +| Artifact cleanup | `scripts/cleanup_artifacts.py` (data_out/ retention) | +| Model evaluation | `scripts/evaluate_model.py` (delegates or fallback metrics) | +| Fast validation | `scripts/fast_validate.py` (configs, providers, deps) | + +## Safety Rules + +- Always `--dry-run` orchestrators before GPU/QPU execution +- Quantum: simulate locally first, then use `azure_ionq_simulator`, only then real QPU +- Real QPU jobs require `azure_confirm_cost: true` in YAML + cost estimate review +- Never hardcode secrets — use `local.settings.json` (dev) or Azure App Settings (prod) +- Monitor DB pool via `/api/ai/status` (warns at ≥80% saturation) + +## Testing & Validation + +- **Unit tests:** `pytest tests/ -m "not slow and not azure"` or `python scripts/test_runner.py --unit` +- **Integration tests:** `python scripts/test_runner.py --integration` +- **All tests:** `python scripts/test_runner.py --all` +- **VS Code Test Explorer:** Use 🧪 icon for interactive test running +- **Markers:** `@pytest.mark.slow`, `@pytest.mark.azure`, `@pytest.mark.integration` +- **Chat dataset validation:** `python scripts/validate_datasets.py --category chat` + +## Optional Services + +**SQL persistence** (optional): +- Enable via `QAI_DB_CONN` env var (SQLite, PostgreSQL, Azure SQL) +- Pool size: `QAI_SQL_POOL_SIZE` (default: 10) +- Health: Check `/api/ai/status` for pool saturation (warns ≥80%) + +**Cosmos DB** (optional, feature-flagged): +- Enable: `QAI_ENABLE_COSMOS=true` +- Config: `COSMOS_ENDPOINT`, `COSMOS_KEY`, `COSMOS_DATABASE`, `COSMOS_CONTAINER` +- Partition key: `/session_id`, enable TTL for cost savings + +**Telemetry** (optional): +- Application Insights via `APPLICATIONINSIGHTS_CONNECTION_STRING` +- Non-blocking, gracefully degrades if unavailable + +## Modular Instructions + +This repo uses component-specific instruction files in `.github/instructions/`: +- `functions.instructions.md` — Azure Functions API endpoints +- `shared-python.instructions.md` — Shared infrastructure patterns +- `quantum-ai*.instructions.md` — Quantum ML workflows +- `talk-to-ai*.instructions.md` — Chat CLI patterns +- `lora*.instructions.md` — LoRA fine-tuning patterns +- `chat-web.instructions.md` — Frontend SSE integration +- `agi-provider.instructions.md` — AGI reasoning system +- `aria-character.instructions.md` — Interactive character system +- `aria-web.instructions.md` — Aria web server module +- `autonomous-training.instructions.md` — Autonomous training orchestration +- `training-scripts.instructions.md` — Training script patterns +- `orchestrator-configs.instructions.md` — YAML orchestrator configs +- `dashboard.instructions.md` — Monitoring dashboard +- `tests.instructions.md` — Testing infrastructure +- `llm-maker.instructions.md` — Safe tool/website generation +- `cooking-ai.instructions.md` — Cooking AI recipe agent +- `chat-providers.instructions.md` — Multi-provider chat system +- `chat-memory.instructions.md` — Semantic memory & embeddings +- `subscription.instructions.md` — Subscription/monetization +- `db-logging.instructions.md` — Fault-tolerant DB logging +- `telemetry.instructions.md` — OpenTelemetry setup +- `token-utils.instructions.md` — Token counting & context pruning +- `evaluation.instructions.md` — Batch evaluation & analytics +- `vision-inference.instructions.md` — Vision AI & CNN models + +These are automatically applied by VS Code based on file paths. Check attachment indicators to see which rules are active. + +## Custom Coding Agents + +Available agents in `.github/agents/`: + +| Agent | Purpose | +|-------|---------| +| `ai.agent.md` | Primary autonomous agent — task decomposition, multi-step execution | +| `my-agent.agent.md` | QAI specialist — quantum-AI/ML development | +| `agi-reasoning.agent.md` | Autonomous AGI reasoning with internal self-reflection (chain-of-thought is hidden; only the final answer is delivered) | +| `visible-reasoning.agent.md` | Visible step-by-step reasoning, shows CoT trace to users | +| `aria-character.agent.md` | Interactive character commands, animations | +| `autonomous-trainer.agent.md` | LoRA training lifecycle, model promotion | +| `full-stack-debugger.agent.md` | Cross-stack issue diagnosis | +| `automated-code-fixer.agent.md` | Autonomous code improvements | +| `ai-architect.agent.md` | AI pipeline design, provider integration | +| `llm-maker.agent.md` | Safe tool/website generation | +| `chat-provider.agent.md` | Multi-provider chat, streaming, memory | +| `platform-ops.agent.md` | Subscriptions, monitoring, deployment | +| `vision-ai.agent.md` | Expression/emotion classification | +| `data-pipeline.agent.md` | Batch evaluation, dataset management | + +**Mode equivalents now live in `.github/agents/`**: +- `AI_model_training.agent.md` — End-to-end LoRA training, evaluation, and model promotion +- `Aria_character_development.agent.md` — Interactive character commands, actions, world generation +- `Quantum_ML_development.agent.md` — Quantum circuits, simulation, Azure Quantum pipelines +- `Full_stack_debugging.agent.md` — Cross-stack diagnostic protocol +- `AI_chat_development.agent.md` — Multi-provider chat, streaming, memory, self-learning +- `Azure_function_codegen_and_deployment.agent.md` — Enterprise Azure Functions workflow with IaC +- `Azure_Static_Web_App.agent.md` — Static web app deployment patterns + +**Prompts** (`.github/prompts/`): +- `agi.prompt.md` — Autonomous AGI reasoning with multi-step analysis and self-correction (chain-of-thought is internal, not exposed in output) +- `reason.prompt.md` — Structured analysis with explicit chain-of-thought reasoning steps shown to the user +- `debug.prompt.md` — Systematic diagnostic protocol +- `review.prompt.md` — Code review (correctness, security, performance) +- `aria-command.prompt.md` — Natural language → Aria actions +- `train.prompt.md` — Training execution with safety +- `quantum.prompt.md` — Cost-aware quantum workflows +- `chat.prompt.md` — Multi-provider chat with memory +- `generate-tool.prompt.md` — Safe Python tool generation +- `generate-website.prompt.md` — Complete website generation +- `evaluate.prompt.md` — Model evaluation & benchmarking +- `deploy.prompt.md` — Model/service deployment +- `optimize.prompt.md` — Performance analysis & optimization + +**Usage**: Agents are invoked automatically based on context or explicitly selected in GitHub Copilot interfaces. + +## Coding Agent Best Practices + +**For AI Coding Agents working in this repository:** + +1. **Always Check Context** + - Read `.github/copilot-instructions.md` (this file) first + - Check for relevant `.github/instructions/*.instructions.md` files based on file paths + - Reference `.github/agents/my-agent.agent.md` for QAI-specific patterns + +2. **Safety-First Approach** + - `--dry-run` all orchestrators before GPU/QPU execution + - Never modify files in `datasets/` (read-only) + - Check `/api/ai/status` before making provider-dependent changes + - Verify test suite passes: `python scripts/test_runner.py --unit` + +3. **Follow Established Patterns** + - Provider detection chain: Azure OpenAI → OpenAI → LMStudio → Local + - Config precedence: `YAML base` < `CLI flags` < `per-job YAML` < `env vars` + - Status files: Always write to `data_out//status.json` + - Autonomous systems: Use signal-based triggers (`pkill -USR1`) for immediate execution + +4. **Testing & Validation** + - Run unit tests before committing: `python scripts/test_runner.py --unit` + - Use `scripts/fast_validate.py` for quick cross-component validation + - Check health endpoint: `curl http://localhost:7071/api/ai/status | jq` + - Monitor logs: `tail -f data_out/autonomous_training.log` + +5. **Documentation Updates** + - Update this file when adding major features or changing workflows + - Keep component-specific instructions in `.github/instructions/` in sync + - Update PR checklist if adding new safety requirements + - Document new orchestrators in "Where to Edit" table + +6. **Cost & Resource Awareness** + - Quantum: Simulate locally first, then Azure simulator, only then real QPU + - Monitor DB pool saturation via `/api/ai/status` (warns at ≥80%) + - Check GPU/CPU usage: `python scripts/resource_monitor.py --snapshot` + - Review training analytics: `python scripts/training_analytics.py` + +## PR Checklist for AI Agents & Reviewers + +Before submitting or approving PRs, verify: + +- [ ] **Dry-run orchestrators**: If modifying YAML configs or orchestrators, run `--dry-run` to validate changes before committing +- [ ] **Provider detection intact**: Changes to `shared/chat_providers.py` or `function_app.py` don't break detection chain (test with `/api/ai/status`) +- [ ] **Dataset immutability**: No modifications to `datasets/` — all outputs written to `data_out/` +- [ ] **Status.json compliance**: Orchestrator changes maintain status JSON writes to `data_out//status.json` +- [ ] **Test suite passes**: Run `python scripts/test_runner.py --unit` (or `--all` for integration tests) +- [ ] **No hardcoded secrets**: All API keys/connection strings use env vars or `local.settings.json` +- [ ] **Quantum cost gates**: QPU jobs include `azure_confirm_cost: true` in YAML configs +- [ ] **LoRA adapter validity**: If modifying training scripts, verify output includes both `adapter_config.json` + `adapter_model.safetensors` +- [ ] **Documentation sync**: Update relevant READMEs/instruction files if changing core workflows or adding features + +Full/verbose guidance and advanced examples are preserved at `.github/copilot-instructions.full.md`. Ask me to expand any area or add examples for a specific change. diff --git a/.github/prompts/agi.prompt.md b/.github/prompts/agi.prompt.md index f97ac3d60..d79e17060 100644 --- a/.github/prompts/agi.prompt.md +++ b/.github/prompts/agi.prompt.md @@ -1,5 +1,5 @@ --- -description: "Engage autonomous AGI reasoning with multi-step analysis, task decomposition, self-correction, and iterative improvement. Use when: long-running autonomous work, plan-then-implement tasks, deep reasoning with concise summaries, extended code generation, iterative debugging, repo-wide fixes, or self-correcting execution." +description: "Engage autonomous AGI reasoning with multi-step analysis, task decomposition, self-correction, and iterative improvement. Chain-of-thought reasoning runs internally — only the final answer is delivered to the user. For visible step-by-step reasoning, use reason.prompt instead." name: "AGI Reasoning" argument-hint: "Problem or task description (example: analyze the auth flow for race conditions + constraints)" agent: agi-reasoning diff --git a/.github/workflows/actionlint.yml b/.github/workflows/actionlint.yml index a2f1bc66e..ecabcfb4f 100644 --- a/.github/workflows/actionlint.yml +++ b/.github/workflows/actionlint.yml @@ -34,5 +34,5 @@ jobs: - name: Run actionlint uses: reviewdog/action-actionlint@6fb7acc99f4a1008869fa8a0f09cfca740837d9d # v1.72.0 with: - fail_level: failure + fail_level: error reporter: github-check diff --git a/.github/workflows/aria-tests.yml b/.github/workflows/aria-tests.yml index 42825fa22..e38d54bdf 100644 --- a/.github/workflows/aria-tests.yml +++ b/.github/workflows/aria-tests.yml @@ -1,360 +1,360 @@ -# ============================================================================= -# Aria Comprehensive Tests - Full Aria character system testing -# ============================================================================= -# Purpose: Comprehensive testing of Aria web interface with multiple browsers -# Triggers: Changes to aria_web or test files (path-filtered) -# Jobs: -# - unit-integration-tests: Tests across Python 3.10, 3.11, 3.12 -# - playwright-e2e: Playwright browser automation tests -# - pyppeteer-e2e: Pyppeteer-based E2E tests -# - containerized-chrome-e2e: Selenium containerized tests -# - test-summary: Aggregates results from all test jobs -# Note: More comprehensive but slower than e2e-tests.yml -# ============================================================================= - -name: Aria E2E Tests - -on: - push: - branches: [main, develop] - paths: - - 'aria_web/**' - - 'tests/test_*aria*.py' - - 'tests/test_*ui*.py' - - '.github/workflows/aria-tests.yml' - pull_request: - branches: [main, develop] - paths: - - 'aria_web/**' - - 'tests/test_*aria*.py' - - 'tests/test_*ui*.py' - - '.github/workflows/aria-tests.yml' - workflow_dispatch: # Manual trigger - -permissions: - contents: read - -concurrency: - group: aria-tests-${{ github.ref }} - cancel-in-progress: true - -jobs: - unit-integration-tests: - name: Unit & Integration Tests - runs-on: ubuntu-latest - timeout-minutes: 15 - strategy: - matrix: - python-version: ['3.10', '3.11', '3.12'] - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 - with: - python-version: ${{ matrix.python-version }} - cache: 'pip' - - - name: Install dependencies - run: | - pip install --upgrade pip - pip install -r requirements.txt - pip install pytest pytest-cov - - - name: Run unit and integration tests - run: | - pytest tests/test_aria_server.py tests/test_object_api_integration.py -v --tb=short - env: - PYTHONPATH: ${{ github.workspace }} - - - name: Upload test results - if: always() - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 - with: - name: test-results-${{ matrix.python-version }} - path: | - test-results/ - .coverage - if-no-files-found: warn - retention-days: 14 - - playwright-e2e: - name: Playwright E2E Tests - runs-on: ubuntu-latest - timeout-minutes: 20 - needs: unit-integration-tests - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Set up Python 3.11 - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 - with: - python-version: '3.11' - cache: 'pip' - - - name: Install Python dependencies - run: | - pip install --upgrade pip - pip install -r requirements.txt - pip install pytest playwright - - - name: Install Playwright browsers - run: | - playwright install chromium --with-deps - - - name: Start Aria server (background) - run: | - cd aria_web - python server.py > /tmp/aria_server.log 2>&1 & - echo $! > /tmp/aria_server.pid - env: - PYTHONPATH: ${{ github.workspace }} - - - name: Wait for server - run: | - READY=0 - for _ in $(seq 1 60); do - if curl -sSf http://localhost:8080/api/aria/state >/dev/null; then - READY=1 - break - fi - sleep 0.5 - done - - if [ "$READY" -ne 1 ]; then - echo "Aria server did not become ready in Playwright job" >&2 - tail -n 120 /tmp/aria_server.log || true - exit 1 - fi - - - name: Run Playwright E2E tests - run: | - pytest tests/test_ui_playwright.py -v --tb=short - env: - PYTHONPATH: ${{ github.workspace }} - ARIA_SERVER_URL: http://localhost:8080 - - - name: Stop Aria server - if: always() - run: | - if [ -f /tmp/aria_server.pid ]; then - PID=$(cat /tmp/aria_server.pid) - if kill -0 "$PID" 2>/dev/null; then - kill "$PID" - fi - fi - - - name: Upload E2E test results - if: always() - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 - with: - name: playwright-e2e-results - path: | - test-results/ - playwright-report/ - if-no-files-found: warn - retention-days: 14 - - pyppeteer-e2e: - name: Pyppeteer E2E Tests - runs-on: ubuntu-latest - timeout-minutes: 20 - needs: unit-integration-tests - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Set up Python 3.11 - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 - with: - python-version: '3.11' - cache: 'pip' - - - name: Install system dependencies for Chromium - run: | - sudo apt-get update - sudo apt-get install -y \ - libnss3 \ - libnspr4 \ - libatk1.0-0 \ - libatk-bridge2.0-0 \ - libcups2 \ - libdrm2 \ - libxkbcommon0 \ - libxcomposite1 \ - libxdamage1 \ - libxfixes3 \ - libxrandr2 \ - libgbm1 \ - libasound2 - - - name: Install Python dependencies - run: | - pip install --upgrade pip - pip install -r requirements.txt - pip install pytest pyppeteer - - - name: Download Pyppeteer Chromium - run: | - python -m pyppeteer install - - - name: Start Aria server (background) - run: | - cd aria_web - python server.py > /tmp/aria_server.log 2>&1 & - echo $! > /tmp/aria_server.pid - env: - PYTHONPATH: ${{ github.workspace }} - - - name: Wait for server - run: | - READY=0 - for _ in $(seq 1 60); do - if curl -sSf http://localhost:8080/api/aria/state >/dev/null; then - READY=1 - break - fi - sleep 0.5 - done - - if [ "$READY" -ne 1 ]; then - echo "Aria server did not become ready in Pyppeteer job" >&2 - tail -n 120 /tmp/aria_server.log || true - exit 1 - fi - - - name: Run Pyppeteer E2E tests - run: | - pytest tests/test_ui_pyppeteer.py -v --tb=short - env: - PYTHONPATH: ${{ github.workspace }} - ARIA_SERVER_URL: http://localhost:8080 - - - name: Stop Aria server - if: always() - run: | - if [ -f /tmp/aria_server.pid ]; then - PID=$(cat /tmp/aria_server.pid) - if kill -0 "$PID" 2>/dev/null; then - kill "$PID" - fi - fi - - - name: Upload E2E test results - if: always() - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 - with: - name: pyppeteer-e2e-results - path: test-results/ - if-no-files-found: warn - - containerized-chrome-e2e: - name: Containerized Chrome E2E Tests - runs-on: ubuntu-latest - needs: unit-integration-tests - services: - chrome: - image: selenium/standalone-chrome:latest - ports: - - 4444:4444 - - 5900:5900 - options: --shm-size=2gb - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Set up Python 3.11 - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 - with: - python-version: '3.11' - cache: 'pip' - - - name: Install Python dependencies - run: | - pip install --upgrade pip - pip install -r requirements.txt - pip install pytest selenium - - - name: Start Aria server (background) - run: | - cd aria_web - python server.py > /tmp/aria_server.log 2>&1 & - echo $! > /tmp/aria_server.pid - env: - PYTHONPATH: ${{ github.workspace }} - - - name: Wait for server - run: | - READY=0 - for _ in $(seq 1 60); do - if curl -sSf http://localhost:8080/api/aria/state >/dev/null; then - READY=1 - break - fi - sleep 0.5 - done - - if [ "$READY" -ne 1 ]; then - echo "Aria server did not become ready in Selenium job" >&2 - tail -n 120 /tmp/aria_server.log || true - exit 1 - fi - - - name: Wait for Selenium Chrome - run: | - READY=0 - for _ in $(seq 1 60); do - if curl -sSf http://localhost:4444/wd/hub/status >/dev/null; then - READY=1 - break - fi - sleep 0.5 - done - - if [ "$READY" -ne 1 ]; then - echo "Selenium Chrome service did not become ready" >&2 - exit 1 - fi - - - name: Run containerized Chrome E2E tests - run: | - pytest tests/test_ui_selenium.py -v --tb=short - env: - PYTHONPATH: ${{ github.workspace }} - ARIA_SERVER_URL: http://localhost:8080 - SELENIUM_REMOTE_URL: http://localhost:4444/wd/hub - - - name: Stop Aria server - if: always() - run: | - if [ -f /tmp/aria_server.pid ]; then - PID=$(cat /tmp/aria_server.pid) - if kill -0 "$PID" 2>/dev/null; then - kill "$PID" - fi - fi - - - name: Upload E2E test results - if: always() - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 - with: - name: selenium-e2e-results - path: test-results/ - if-no-files-found: warn - - test-summary: - name: Test Summary - runs-on: ubuntu-latest - needs: [unit-integration-tests, playwright-e2e, pyppeteer-e2e, containerized-chrome-e2e] - if: always() - steps: - - name: Download all artifacts - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4 - - - name: Display test summary - run: | - echo "## Test Results Summary" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "All test jobs completed. Check individual job outputs for details." >> $GITHUB_STEP_SUMMARY +# ============================================================================= +# Aria Comprehensive Tests - Full Aria character system testing +# ============================================================================= +# Purpose: Comprehensive testing of Aria web interface with multiple browsers +# Triggers: Changes to aria_web or test files (path-filtered) +# Jobs: +# - unit-integration-tests: Tests across Python 3.10, 3.11, 3.12 +# - playwright-e2e: Playwright browser automation tests +# - pyppeteer-e2e: Pyppeteer-based E2E tests +# - containerized-chrome-e2e: Selenium containerized tests +# - test-summary: Aggregates results from all test jobs +# Note: More comprehensive but slower than e2e-tests.yml +# ============================================================================= + +name: Aria E2E Tests + +on: + push: + branches: [main, develop] + paths: + - 'aria_web/**' + - 'tests/test_*aria*.py' + - 'tests/test_*ui*.py' + - '.github/workflows/aria-tests.yml' + pull_request: + branches: [main, develop] + paths: + - 'aria_web/**' + - 'tests/test_*aria*.py' + - 'tests/test_*ui*.py' + - '.github/workflows/aria-tests.yml' + workflow_dispatch: # Manual trigger + +permissions: + contents: read + +concurrency: + group: aria-tests-${{ github.ref }} + cancel-in-progress: true + +jobs: + unit-integration-tests: + name: Unit & Integration Tests + runs-on: ubuntu-latest + timeout-minutes: 15 + strategy: + matrix: + python-version: ['3.10', '3.11', '3.12'] + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + + - name: Install dependencies + run: | + pip install --upgrade pip + pip install -r requirements.txt + pip install pytest pytest-cov + + - name: Run unit and integration tests + run: | + pytest tests/test_aria_server.py tests/test_object_api_integration.py -v --tb=short + env: + PYTHONPATH: ${{ github.workspace }} + + - name: Upload test results + if: always() + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 + with: + name: test-results-${{ matrix.python-version }} + path: | + test-results/ + .coverage + if-no-files-found: warn + retention-days: 14 + + playwright-e2e: + name: Playwright E2E Tests + runs-on: ubuntu-latest + timeout-minutes: 20 + needs: unit-integration-tests + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Set up Python 3.11 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 + with: + python-version: '3.11' + cache: 'pip' + + - name: Install Python dependencies + run: | + pip install --upgrade pip + pip install -r requirements.txt + pip install pytest playwright + + - name: Install Playwright browsers + run: | + playwright install chromium --with-deps + + - name: Start Aria server (background) + run: | + cd aria_web + python server.py > /tmp/aria_server.log 2>&1 & + echo $! > /tmp/aria_server.pid + env: + PYTHONPATH: ${{ github.workspace }} + + - name: Wait for server + run: | + READY=0 + for _ in $(seq 1 60); do + if curl -sSf http://localhost:8080/api/aria/state >/dev/null; then + READY=1 + break + fi + sleep 0.5 + done + + if [ "$READY" -ne 1 ]; then + echo "Aria server did not become ready in Playwright job" >&2 + tail -n 120 /tmp/aria_server.log || true + exit 1 + fi + + - name: Run Playwright E2E tests + run: | + pytest tests/test_ui_playwright.py -v --tb=short + env: + PYTHONPATH: ${{ github.workspace }} + ARIA_SERVER_URL: http://localhost:8080 + + - name: Stop Aria server + if: always() + run: | + if [ -f /tmp/aria_server.pid ]; then + PID=$(cat /tmp/aria_server.pid) + if kill -0 "$PID" 2>/dev/null; then + kill "$PID" + fi + fi + + - name: Upload E2E test results + if: always() + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 + with: + name: playwright-e2e-results + path: | + test-results/ + playwright-report/ + if-no-files-found: warn + retention-days: 14 + + pyppeteer-e2e: + name: Pyppeteer E2E Tests + runs-on: ubuntu-latest + timeout-minutes: 20 + needs: unit-integration-tests + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Set up Python 3.11 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 + with: + python-version: '3.11' + cache: 'pip' + + - name: Install system dependencies for Chromium + run: | + sudo apt-get update + sudo apt-get install -y \ + libnss3 \ + libnspr4 \ + libatk1.0-0 \ + libatk-bridge2.0-0 \ + libcups2 \ + libdrm2 \ + libxkbcommon0 \ + libxcomposite1 \ + libxdamage1 \ + libxfixes3 \ + libxrandr2 \ + libgbm1 \ + libasound2 + + - name: Install Python dependencies + run: | + pip install --upgrade pip + pip install -r requirements.txt + pip install pytest pyppeteer + + - name: Download Pyppeteer Chromium + run: | + python -m pyppeteer install + + - name: Start Aria server (background) + run: | + cd aria_web + python server.py > /tmp/aria_server.log 2>&1 & + echo $! > /tmp/aria_server.pid + env: + PYTHONPATH: ${{ github.workspace }} + + - name: Wait for server + run: | + READY=0 + for _ in $(seq 1 60); do + if curl -sSf http://localhost:8080/api/aria/state >/dev/null; then + READY=1 + break + fi + sleep 0.5 + done + + if [ "$READY" -ne 1 ]; then + echo "Aria server did not become ready in Pyppeteer job" >&2 + tail -n 120 /tmp/aria_server.log || true + exit 1 + fi + + - name: Run Pyppeteer E2E tests + run: | + pytest tests/test_ui_pyppeteer.py -v --tb=short + env: + PYTHONPATH: ${{ github.workspace }} + ARIA_SERVER_URL: http://localhost:8080 + + - name: Stop Aria server + if: always() + run: | + if [ -f /tmp/aria_server.pid ]; then + PID=$(cat /tmp/aria_server.pid) + if kill -0 "$PID" 2>/dev/null; then + kill "$PID" + fi + fi + + - name: Upload E2E test results + if: always() + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 + with: + name: pyppeteer-e2e-results + path: test-results/ + if-no-files-found: warn + + containerized-chrome-e2e: + name: Containerized Chrome E2E Tests + runs-on: ubuntu-latest + needs: unit-integration-tests + services: + chrome: + image: selenium/standalone-chrome:latest + ports: + - 4444:4444 + - 5900:5900 + options: --shm-size=2gb + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Set up Python 3.11 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 + with: + python-version: '3.11' + cache: 'pip' + + - name: Install Python dependencies + run: | + pip install --upgrade pip + pip install -r requirements.txt + pip install pytest selenium + + - name: Start Aria server (background) + run: | + cd aria_web + python server.py > /tmp/aria_server.log 2>&1 & + echo $! > /tmp/aria_server.pid + env: + PYTHONPATH: ${{ github.workspace }} + + - name: Wait for server + run: | + READY=0 + for _ in $(seq 1 60); do + if curl -sSf http://localhost:8080/api/aria/state >/dev/null; then + READY=1 + break + fi + sleep 0.5 + done + + if [ "$READY" -ne 1 ]; then + echo "Aria server did not become ready in Selenium job" >&2 + tail -n 120 /tmp/aria_server.log || true + exit 1 + fi + + - name: Wait for Selenium Chrome + run: | + READY=0 + for _ in $(seq 1 60); do + if curl -sSf http://localhost:4444/wd/hub/status >/dev/null; then + READY=1 + break + fi + sleep 0.5 + done + + if [ "$READY" -ne 1 ]; then + echo "Selenium Chrome service did not become ready" >&2 + exit 1 + fi + + - name: Run containerized Chrome E2E tests + run: | + pytest tests/test_ui_selenium.py -v --tb=short + env: + PYTHONPATH: ${{ github.workspace }} + ARIA_SERVER_URL: http://localhost:8080 + SELENIUM_REMOTE_URL: http://localhost:4444/wd/hub + + - name: Stop Aria server + if: always() + run: | + if [ -f /tmp/aria_server.pid ]; then + PID=$(cat /tmp/aria_server.pid) + if kill -0 "$PID" 2>/dev/null; then + kill "$PID" + fi + fi + + - name: Upload E2E test results + if: always() + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 + with: + name: selenium-e2e-results + path: test-results/ + if-no-files-found: warn + + test-summary: + name: Test Summary + runs-on: ubuntu-latest + needs: [unit-integration-tests, playwright-e2e, pyppeteer-e2e, containerized-chrome-e2e] + if: always() + steps: + - name: Download all artifacts + uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4 + + - name: Display test summary + run: | + echo "## Test Results Summary" >> $GITHUB_STEP_SUMMARY + echo "" >> $GITHUB_STEP_SUMMARY + echo "All test jobs completed. Check individual job outputs for details." >> $GITHUB_STEP_SUMMARY diff --git a/.github/workflows/auto-validation.yml b/.github/workflows/auto-validation.yml index 6dd7ad014..89ca75d00 100644 --- a/.github/workflows/auto-validation.yml +++ b/.github/workflows/auto-validation.yml @@ -1,70 +1,70 @@ -# ============================================================================= -# Auto Validation - Orchestrator configuration validation -# ============================================================================= -# Purpose: Validates autotrain.yaml and quantum_autorun.yaml configurations -# Triggers: Changes to YAML configs, training scripts, or daily schedule -# Jobs: -# - dry-run: Validates orchestrator configs without execution -# ============================================================================= - -name: Auto Validation - -on: - push: - branches: [ main ] - paths: - - 'config/training/autotrain.yaml' - - 'autotrain.yaml' - - 'config/quantum/quantum_autorun.yaml' - - 'quantum_autorun.yaml' - - 'scripts/autotrain.py' - - 'scripts/quantum_autorun.py' - - 'AI/**' - - 'ai-projects/quantum-ml/**' - schedule: - - cron: '0 5 * * *' - workflow_dispatch: - -permissions: - contents: read - -concurrency: auto-validation - -jobs: - dry-run: - runs-on: ubuntu-latest - timeout-minutes: 15 - steps: - - name: Checkout - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Setup Python - uses: ./.github/actions/setup-python-env - with: - extra-packages: 'pyyaml' - - name: Auto bootstrap (env + orchestrator validation) - run: python scripts/auto_bootstrap.py - - - name: Generate validation summary - if: always() - run: | - echo "## Auto-Validation Results" >> $GITHUB_STEP_SUMMARY - for f in data_out/autotrain/status.json data_out/quantum_autorun/status.json data_out/auto_bootstrap/status_summary.json; do - if [ -f "$f" ]; then - echo "### $(basename $f)" >> $GITHUB_STEP_SUMMARY - echo '```json' >> $GITHUB_STEP_SUMMARY - cat "$f" >> $GITHUB_STEP_SUMMARY - echo '' >> $GITHUB_STEP_SUMMARY - echo '```' >> $GITHUB_STEP_SUMMARY - fi - done - - - name: Upload status artifacts - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 - with: - name: auto-validation-status - path: | - data_out/autotrain/status.json - data_out/quantum_autorun/status.json - data_out/auto_bootstrap/status_summary.json - retention-days: 14 +# ============================================================================= +# Auto Validation - Orchestrator configuration validation +# ============================================================================= +# Purpose: Validates autotrain.yaml and quantum_autorun.yaml configurations +# Triggers: Changes to YAML configs, training scripts, or daily schedule +# Jobs: +# - dry-run: Validates orchestrator configs without execution +# ============================================================================= + +name: Auto Validation + +on: + push: + branches: [ main ] + paths: + - 'config/training/autotrain.yaml' + - 'autotrain.yaml' + - 'config/quantum/quantum_autorun.yaml' + - 'quantum_autorun.yaml' + - 'scripts/autotrain.py' + - 'scripts/quantum_autorun.py' + - 'AI/**' + - 'ai-projects/quantum-ml/**' + schedule: + - cron: '0 5 * * *' + workflow_dispatch: + +permissions: + contents: read + +concurrency: auto-validation + +jobs: + dry-run: + runs-on: ubuntu-latest + timeout-minutes: 15 + steps: + - name: Checkout + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + extra-packages: 'pyyaml' + - name: Auto bootstrap (env + orchestrator validation) + run: python scripts/auto_bootstrap.py + + - name: Generate validation summary + if: always() + run: | + echo "## Auto-Validation Results" >> $GITHUB_STEP_SUMMARY + for f in data_out/autotrain/status.json data_out/quantum_autorun/status.json data_out/auto_bootstrap/status_summary.json; do + if [ -f "$f" ]; then + echo "### $(basename $f)" >> $GITHUB_STEP_SUMMARY + echo '```json' >> $GITHUB_STEP_SUMMARY + cat "$f" >> $GITHUB_STEP_SUMMARY + echo '' >> $GITHUB_STEP_SUMMARY + echo '```' >> $GITHUB_STEP_SUMMARY + fi + done + + - name: Upload status artifacts + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 + with: + name: auto-validation-status + path: | + data_out/autotrain/status.json + data_out/quantum_autorun/status.json + data_out/auto_bootstrap/status_summary.json + retention-days: 14 diff --git a/.github/workflows/azureml-train.yml b/.github/workflows/azureml-train.yml index 8ad654bc7..1c45c4673 100644 --- a/.github/workflows/azureml-train.yml +++ b/.github/workflows/azureml-train.yml @@ -1,73 +1,73 @@ -# ============================================================================= -# AzureML LoRA Training - Cloud-based model fine-tuning -# ============================================================================= -# Purpose: Submits LoRA fine-tuning jobs to Azure ML -# Triggers: Manual workflow_dispatch only -# Jobs: -# - submit-job: Submits Azure ML training job with specified parameters -# Note: Requires Azure credentials and ML workspace configuration -# ============================================================================= - -name: AzureML LoRA Train - -on: - workflow_dispatch: - inputs: - subscriptionId: - description: Azure subscription ID - required: true - resourceGroup: - description: Resource group name - required: true - workspaceName: - description: Azure ML workspace name - required: true - compute: - description: Azure ML compute target (e.g., azureml:gpu-cluster) - required: true - default: azureml:gpu-cluster - jobFile: - description: Job YAML path (single run or sweep) - required: true - default: AI/microsoft_phi-silica-3.6_v1/azureml/job-lora-train.yml - -permissions: - id-token: write - contents: read - -jobs: - submit-job: - runs-on: ubuntu-latest - timeout-minutes: 30 - steps: - - name: Checkout - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Azure login - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2 - with: - # Create an Azure AD app and set its JSON creds in this secret: - # { "clientId": "...", "clientSecret": "...", "subscriptionId": "...", "tenantId": "..." } - creds: ${{ secrets.AZURE_CREDENTIALS }} - - - name: Install Azure ML CLI - uses: azure/cli@9f7ce6f37c31b777ec6c6b6d1dfe7db79f497956 # v2 - with: - inlineScript: | - az extension add -n ml -y - az account set --subscription "$SUBSCRIPTION_ID" - az configure --defaults group="$RESOURCE_GROUP" workspace="$WORKSPACE_NAME" - env: - SUBSCRIPTION_ID: ${{ github.event.inputs.subscriptionId }} - RESOURCE_GROUP: ${{ github.event.inputs.resourceGroup }} - WORKSPACE_NAME: ${{ github.event.inputs.workspaceName }} - - - name: Submit Azure ML job - uses: azure/cli@9f7ce6f37c31b777ec6c6b6d1dfe7db79f497956 # v2 - with: - inlineScript: | - echo "Submitting job from $JOB_FILE ..." - az ml job create -f "$JOB_FILE" --set compute="$COMPUTE_TARGET" --stream - env: - JOB_FILE: ${{ github.event.inputs.jobFile }} - COMPUTE_TARGET: ${{ github.event.inputs.compute }} +# ============================================================================= +# AzureML LoRA Training - Cloud-based model fine-tuning +# ============================================================================= +# Purpose: Submits LoRA fine-tuning jobs to Azure ML +# Triggers: Manual workflow_dispatch only +# Jobs: +# - submit-job: Submits Azure ML training job with specified parameters +# Note: Requires Azure credentials and ML workspace configuration +# ============================================================================= + +name: AzureML LoRA Train + +on: + workflow_dispatch: + inputs: + subscriptionId: + description: Azure subscription ID + required: true + resourceGroup: + description: Resource group name + required: true + workspaceName: + description: Azure ML workspace name + required: true + compute: + description: Azure ML compute target (e.g., azureml:gpu-cluster) + required: true + default: azureml:gpu-cluster + jobFile: + description: Job YAML path (single run or sweep) + required: true + default: AI/microsoft_phi-silica-3.6_v1/azureml/job-lora-train.yml + +permissions: + id-token: write + contents: read + +jobs: + submit-job: + runs-on: ubuntu-latest + timeout-minutes: 30 + steps: + - name: Checkout + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Azure login + uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2 + with: + # Create an Azure AD app and set its JSON creds in this secret: + # { "clientId": "...", "clientSecret": "...", "subscriptionId": "...", "tenantId": "..." } + creds: ${{ secrets.AZURE_CREDENTIALS }} + + - name: Install Azure ML CLI + uses: azure/cli@9f7ce6f37c31b777ec6c6b6d1dfe7db79f497956 # v2 + with: + inlineScript: | + az extension add -n ml -y + az account set --subscription "$SUBSCRIPTION_ID" + az configure --defaults group="$RESOURCE_GROUP" workspace="$WORKSPACE_NAME" + env: + SUBSCRIPTION_ID: ${{ github.event.inputs.subscriptionId }} + RESOURCE_GROUP: ${{ github.event.inputs.resourceGroup }} + WORKSPACE_NAME: ${{ github.event.inputs.workspaceName }} + + - name: Submit Azure ML job + uses: azure/cli@9f7ce6f37c31b777ec6c6b6d1dfe7db79f497956 # v2 + with: + inlineScript: | + echo "Submitting job from $JOB_FILE ..." + az ml job create -f "$JOB_FILE" --set compute="$COMPUTE_TARGET" --stream + env: + JOB_FILE: ${{ github.event.inputs.jobFile }} + COMPUTE_TARGET: ${{ github.event.inputs.compute }} diff --git a/.github/workflows/ci-pipeline.yml b/.github/workflows/ci-pipeline.yml index 06405943b..e67143d0d 100644 --- a/.github/workflows/ci-pipeline.yml +++ b/.github/workflows/ci-pipeline.yml @@ -1,146 +1,146 @@ -# ============================================================================= -# QAI CI Pipeline - Main continuous integration workflow -# ============================================================================= -# Purpose: Validates code quality, runs tests, and performs daily training -# Triggers: Push/PR to main/dev branches, daily scheduled runs -# Jobs: -# - validate: Code validation, unit & integration tests -# - train: Daily scheduled training workflow (requires validate pass) -# - deploy: Model deployment after successful training -# ============================================================================= - -name: QAI CI Pipeline - -on: - push: - branches: [ main, dev ] - pull_request: - branches: [ main ] - schedule: - - cron: '0 2 * * *' # Daily at 2 AM UTC - -permissions: - contents: read - -concurrency: - group: ci-${{ github.ref }} - cancel-in-progress: ${{ github.event_name == 'pull_request' }} - -env: - FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true # Opt into Node.js 24 for JS actions ahead of June 2026 forced migration - -jobs: - validate: - runs-on: ubuntu-latest - timeout-minutes: 20 - strategy: - matrix: - python-version: ['3.11'] - - steps: - - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Setup Python - uses: ./.github/actions/setup-python-env - with: - python-version: ${{ matrix.python-version }} - extra-packages: 'pytest pytest-timeout pytest-cov' - - - name: Run CI validation - run: | - python scripts/ci_orchestrator.py --validate-all - - - name: Run integration smoke checks - run: | - python scripts/integration_smoke.py - - - name: Run integration contract unit tests - run: | - python scripts/ci_orchestrator.py --integration-contract-tests - - - name: Run unit tests - run: | - python scripts/test_runner.py --unit --coverage - - - name: Run integration tests - run: | - python scripts/test_runner.py --integration - - - name: Generate summary - if: always() - run: | - echo "## CI Validation Results" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - if [ -f data_out/ci_orchestrator/status.json ]; then - echo '```json' >> $GITHUB_STEP_SUMMARY - cat data_out/ci_orchestrator/status.json >> $GITHUB_STEP_SUMMARY - echo '' >> $GITHUB_STEP_SUMMARY - echo '```' >> $GITHUB_STEP_SUMMARY - fi - - - name: Upload validation results - if: always() - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 - with: - name: validation-results - path: | - data_out/ci_orchestrator/ - data_out/integration_smoke/ - data_out/test_runner/ - retention-days: 14 - - train: - runs-on: ubuntu-latest - needs: validate - timeout-minutes: 60 - if: github.event_name == 'schedule' - - steps: - - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Setup Python - uses: ./.github/actions/setup-python-env - - - name: Run training workflow - run: python scripts/master_orchestrator.py --workflow daily_full_pipeline - - - name: Generate training summary - if: always() - run: | - echo "## Training Results" >> $GITHUB_STEP_SUMMARY - if [ -f data_out/master_orchestrator/status.json ]; then - echo '```json' >> $GITHUB_STEP_SUMMARY - cat data_out/master_orchestrator/status.json >> $GITHUB_STEP_SUMMARY - echo '' >> $GITHUB_STEP_SUMMARY - echo '```' >> $GITHUB_STEP_SUMMARY - fi - - - name: Upload training results - if: always() - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 - with: - name: training-results - path: data_out/ - retention-days: 30 - - deploy: - runs-on: ubuntu-latest - needs: train - timeout-minutes: 15 - if: github.event_name == 'schedule' - - steps: - - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Setup Python - uses: ./.github/actions/setup-python-env - - - name: Deploy best model - run: python scripts/model_deployer.py --deploy best --strategy canary - - - name: Upload deployment manifest - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 - with: - name: deployment-manifest - path: deployed_models/model_registry.json - retention-days: 90 +# ============================================================================= +# QAI CI Pipeline - Main continuous integration workflow +# ============================================================================= +# Purpose: Validates code quality, runs tests, and performs daily training +# Triggers: Push/PR to main/dev branches, daily scheduled runs +# Jobs: +# - validate: Code validation, unit & integration tests +# - train: Daily scheduled training workflow (requires validate pass) +# - deploy: Model deployment after successful training +# ============================================================================= + +name: QAI CI Pipeline + +on: + push: + branches: [ main, dev ] + pull_request: + branches: [ main ] + schedule: + - cron: '0 2 * * *' # Daily at 2 AM UTC + +permissions: + contents: read + +concurrency: + group: ci-${{ github.ref }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + +env: + FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true # Opt into Node.js 24 for JS actions ahead of June 2026 forced migration + +jobs: + validate: + runs-on: ubuntu-latest + timeout-minutes: 20 + strategy: + matrix: + python-version: ['3.11'] + + steps: + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + python-version: ${{ matrix.python-version }} + extra-packages: 'pytest pytest-timeout pytest-cov' + + - name: Run CI validation + run: | + python scripts/ci_orchestrator.py --validate-all + + - name: Run integration smoke checks + run: | + python scripts/integration_smoke.py + + - name: Run integration contract unit tests + run: | + python scripts/ci_orchestrator.py --integration-contract-tests + + - name: Run unit tests + run: | + python scripts/test_runner.py --unit --coverage + + - name: Run integration tests + run: | + python scripts/test_runner.py --integration + + - name: Generate summary + if: always() + run: | + echo "## CI Validation Results" >> $GITHUB_STEP_SUMMARY + echo "" >> $GITHUB_STEP_SUMMARY + if [ -f data_out/ci_orchestrator/status.json ]; then + echo '```json' >> $GITHUB_STEP_SUMMARY + cat data_out/ci_orchestrator/status.json >> $GITHUB_STEP_SUMMARY + echo '' >> $GITHUB_STEP_SUMMARY + echo '```' >> $GITHUB_STEP_SUMMARY + fi + + - name: Upload validation results + if: always() + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 + with: + name: validation-results + path: | + data_out/ci_orchestrator/ + data_out/integration_smoke/ + data_out/test_runner/ + retention-days: 14 + + train: + runs-on: ubuntu-latest + needs: validate + timeout-minutes: 60 + if: github.event_name == 'schedule' + + steps: + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Setup Python + uses: ./.github/actions/setup-python-env + + - name: Run training workflow + run: python scripts/master_orchestrator.py --workflow daily_full_pipeline + + - name: Generate training summary + if: always() + run: | + echo "## Training Results" >> $GITHUB_STEP_SUMMARY + if [ -f data_out/master_orchestrator/status.json ]; then + echo '```json' >> $GITHUB_STEP_SUMMARY + cat data_out/master_orchestrator/status.json >> $GITHUB_STEP_SUMMARY + echo '' >> $GITHUB_STEP_SUMMARY + echo '```' >> $GITHUB_STEP_SUMMARY + fi + + - name: Upload training results + if: always() + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 + with: + name: training-results + path: data_out/ + retention-days: 30 + + deploy: + runs-on: ubuntu-latest + needs: train + timeout-minutes: 15 + if: github.event_name == 'schedule' + + steps: + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Setup Python + uses: ./.github/actions/setup-python-env + + - name: Deploy best model + run: python scripts/model_deployer.py --deploy best --strategy canary + + - name: Upload deployment manifest + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 + with: + name: deployment-manifest + path: deployed_models/model_registry.json + retention-days: 90 diff --git a/.github/workflows/code-quality.yml b/.github/workflows/code-quality.yml index 5a090b199..92863798a 100644 --- a/.github/workflows/code-quality.yml +++ b/.github/workflows/code-quality.yml @@ -1,94 +1,94 @@ -name: Code Quality - -on: - push: - branches: [ main, dev ] - pull_request: - branches: [ main, dev ] - workflow_dispatch: - -permissions: - contents: read - -concurrency: - group: quality-${{ github.ref }} - cancel-in-progress: true - -jobs: - lint: - name: Lint and Format Check - runs-on: ubuntu-latest - timeout-minutes: 10 - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Setup Python - uses: ./.github/actions/setup-python-env - with: - install-requirements: 'false' - extra-packages: 'flake8 black isort mypy' - - - name: Run flake8 (blocking — syntax errors) - run: | - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - - - name: Run flake8 (advisory — style warnings) - run: | - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude=venv,venv_*,__pycache__,.git,data_out,mount,__blobstorage__,__queuestorage__ - continue-on-error: true - - - name: Check code formatting with black - run: | - black --check --diff --exclude="(venv|venv_.*|__pycache__|\.git|data_out|mount|__blobstorage__|__queuestorage__)" . - continue-on-error: true - - - name: Check import sorting with isort - run: | - isort --check-only --diff --skip-glob="venv*" --skip-glob="data_out*" --skip-glob="mount*" . - continue-on-error: true - - - name: Type check with mypy (optional) - run: | - mypy --install-types --non-interactive --ignore-missing-imports --exclude="(venv|venv_.*|data_out|mount)" . || echo "MyPy check completed with warnings" - continue-on-error: true - - security-check: - name: Security Dependency Check - runs-on: ubuntu-latest - timeout-minutes: 10 - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Setup Python - uses: ./.github/actions/setup-python-env - with: - extra-packages: 'safety pip-audit' - - - name: Check dependencies for vulnerabilities - run: | - pip-audit --format json --output safety-report.json || echo "Vulnerability check completed with findings" - continue-on-error: true - - - name: Generate security summary - if: always() - run: | - echo "## Security Check Results" >> $GITHUB_STEP_SUMMARY - if [ -f safety-report.json ]; then - echo '```json' >> $GITHUB_STEP_SUMMARY - head -100 safety-report.json >> $GITHUB_STEP_SUMMARY - echo '' >> $GITHUB_STEP_SUMMARY - echo '```' >> $GITHUB_STEP_SUMMARY - else - echo "No vulnerabilities found." >> $GITHUB_STEP_SUMMARY - fi - - - name: Upload safety report - if: always() - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 - with: - name: security-report - path: safety-report.json - if-no-files-found: ignore - retention-days: 14 +name: Code Quality + +on: + push: + branches: [ main, dev ] + pull_request: + branches: [ main, dev ] + workflow_dispatch: + +permissions: + contents: read + +concurrency: + group: quality-${{ github.ref }} + cancel-in-progress: true + +jobs: + lint: + name: Lint and Format Check + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + install-requirements: 'false' + extra-packages: 'flake8 black isort mypy' + + - name: Run flake8 (blocking — syntax errors) + run: | + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + + - name: Run flake8 (advisory — style warnings) + run: | + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude=venv,venv_*,__pycache__,.git,data_out,mount,__blobstorage__,__queuestorage__ + continue-on-error: true + + - name: Check code formatting with black + run: | + black --check --diff --exclude="(venv|venv_.*|__pycache__|\.git|data_out|mount|__blobstorage__|__queuestorage__)" . + continue-on-error: true + + - name: Check import sorting with isort + run: | + isort --check-only --diff --skip-glob="venv*" --skip-glob="data_out*" --skip-glob="mount*" . + continue-on-error: true + + - name: Type check with mypy (optional) + run: | + mypy --install-types --non-interactive --ignore-missing-imports --exclude="(venv|venv_.*|data_out|mount)" . || echo "MyPy check completed with warnings" + continue-on-error: true + + security-check: + name: Security Dependency Check + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + extra-packages: 'safety pip-audit' + + - name: Check dependencies for vulnerabilities + run: | + pip-audit --format json --output safety-report.json || echo "Vulnerability check completed with findings" + continue-on-error: true + + - name: Generate security summary + if: always() + run: | + echo "## Security Check Results" >> $GITHUB_STEP_SUMMARY + if [ -f safety-report.json ]; then + echo '```json' >> $GITHUB_STEP_SUMMARY + head -100 safety-report.json >> $GITHUB_STEP_SUMMARY + echo '' >> $GITHUB_STEP_SUMMARY + echo '```' >> $GITHUB_STEP_SUMMARY + else + echo "No vulnerabilities found." >> $GITHUB_STEP_SUMMARY + fi + + - name: Upload safety report + if: always() + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 + with: + name: security-report + path: safety-report.json + if-no-files-found: ignore + retention-days: 14 diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index 1072491cb..c6fef99d6 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -1,46 +1,46 @@ -name: CodeQL Security Scanning - -on: - push: - branches: [ main, dev ] - pull_request: - branches: [ main ] - schedule: - - cron: '0 8 * * 1' # Weekly on Monday at 8 AM UTC - workflow_dispatch: - -concurrency: - group: codeql-${{ github.ref }} - cancel-in-progress: ${{ github.event_name == 'pull_request' }} - -jobs: - analyze: - name: Analyze Code - runs-on: ubuntu-latest - permissions: - actions: read - contents: read - security-events: write - - strategy: - fail-fast: false - matrix: - language: [ 'python', 'javascript' ] - - steps: - - name: Checkout repository - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Initialize CodeQL - uses: github/codeql-action/init@ce64ddcb0d8d890d2df4a9d1c04ff297367dea2a # v3 - with: - languages: ${{ matrix.language }} - queries: security-extended,security-and-quality - - - name: Autobuild - uses: github/codeql-action/autobuild@ce64ddcb0d8d890d2df4a9d1c04ff297367dea2a # v3 - - - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@ce64ddcb0d8d890d2df4a9d1c04ff297367dea2a # v3 - with: - category: "/language:${{matrix.language}}" +name: CodeQL Security Scanning + +on: + push: + branches: [ main, dev ] + pull_request: + branches: [ main ] + schedule: + - cron: '0 8 * * 1' # Weekly on Monday at 8 AM UTC + workflow_dispatch: + +concurrency: + group: codeql-${{ github.ref }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + +jobs: + analyze: + name: Analyze Code + runs-on: ubuntu-latest + permissions: + actions: read + contents: read + security-events: write + + strategy: + fail-fast: false + matrix: + language: [ 'python', 'javascript' ] + + steps: + - name: Checkout repository + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Initialize CodeQL + uses: github/codeql-action/init@ce64ddcb0d8d890d2df4a9d1c04ff297367dea2a # v3 + with: + languages: ${{ matrix.language }} + queries: security-extended,security-and-quality + + - name: Autobuild + uses: github/codeql-action/autobuild@ce64ddcb0d8d890d2df4a9d1c04ff297367dea2a # v3 + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@ce64ddcb0d8d890d2df4a9d1c04ff297367dea2a # v3 + with: + category: "/language:${{matrix.language}}" diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml index 745732913..7ac2b9556 100644 --- a/.github/workflows/e2e-tests.yml +++ b/.github/workflows/e2e-tests.yml @@ -1,144 +1,144 @@ -# ============================================================================= -# E2E + Integration Tests - Quick Aria regression testing -# ============================================================================= -# Purpose: Fast regression tests for Aria character system -# Triggers: Any push/PR to main (no path filtering) -# Jobs: -# - integration: Quick unit & integration tests -# - e2e_playwright: Playwright E2E tests -# - containerized_chrome: Pyppeteer containerized tests -# Note: Faster subset of aria-tests.yml, runs on all changes -# ============================================================================= - -name: E2E + Integration Tests - -on: - push: - branches: [ main ] - pull_request: - branches: [ main ] - -permissions: - contents: read - -concurrency: - group: e2e-${{ github.ref }} - cancel-in-progress: true - -jobs: - integration: - name: Unit & Integration tests - runs-on: ubuntu-latest - timeout-minutes: 15 - steps: - - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - name: Setup Python - uses: ./.github/actions/setup-python-env - with: - install-requirements: 'false' - extra-packages: 'pytest requests pytest-asyncio' - - name: Start server and run integration tests - run: | - python3 aria_web/server.py > /tmp/aria_server.log 2>&1 & - READY=0 - for i in $(seq 1 50); do - if curl -sSf http://127.0.0.1:8080/api/aria/state >/dev/null; then - READY=1 - break - fi - sleep 0.25 - done - if [ "$READY" -ne 1 ]; then - echo "Aria server did not become ready in integration job" >&2 - tail -n 100 /tmp/aria_server.log || true - exit 1 - fi - pytest -q tests/test_aria_server.py tests/test_object_api_integration.py - - e2e_playwright: - name: E2E (Playwright) - runs-on: ubuntu-latest - timeout-minutes: 25 - steps: - - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - name: Setup Python - uses: ./.github/actions/setup-python-env - with: - install-requirements: 'false' - extra-packages: 'pytest playwright pytest-asyncio requests' - - name: Install Playwright browsers - run: python -m playwright install chromium --with-deps - - name: Start server and run Playwright tests - run: | - python3 aria_web/server.py > /tmp/aria_server.log 2>&1 & - READY=0 - for i in $(seq 1 60); do - if curl -sSf http://127.0.0.1:8080/api/aria/state >/dev/null; then - READY=1 - break - fi - sleep 0.25 - done - if [ "$READY" -ne 1 ]; then - echo "Aria server did not become ready in Playwright job" >&2 - tail -n 100 /tmp/aria_server.log || true - exit 1 - fi - pytest -q tests/test_ui_playwright.py - - containerized_chrome: - name: Containerized Chrome (pyppeteer) - runs-on: ubuntu-latest - container: - image: python:3.11-bullseye - timeout-minutes: 25 - steps: - - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - name: Apt-get update & install dependencies - run: | - apt-get update - apt-get install -y wget gnupg ca-certificates xz-utils fonts-liberation libgtk-3-0 libxss1 libasound2 libnss3 libatk-bridge2.0-0 libx11-xcb1 libgbm1 - - name: Install Chromium (stable) - run: | - set -e - # Use Debian repo for chromium; package name varies by image/repo. - if apt-get install -y chromium chromium-driver; then - echo "Installed chromium + chromium-driver" - elif apt-get install -y chromium-browser; then - echo "Installed chromium-browser" - else - echo "Failed to install Chromium packages" >&2 - exit 1 - fi - - # Normalize executable path expected by CHROME_PATH. - if command -v chromium >/dev/null 2>&1; then - command -v chromium - elif command -v chromium-browser >/dev/null 2>&1; then - command -v chromium-browser - ln -sf "$(command -v chromium-browser)" /usr/bin/chromium - else - echo "Chromium executable not found after installation" >&2 - exit 1 - fi - - name: Install Python test deps - run: python -m pip install --upgrade pip && python -m pip install pytest pytest-asyncio pyppeteer requests - - name: Start server and run pyppeteer tests - env: - CHROME_PATH: /usr/bin/chromium - run: | - python3 aria_web/server.py > /tmp/aria_server.log 2>&1 & - READY=0 - for i in $(seq 1 60); do - if curl -sSf http://127.0.0.1:8080/api/aria/state >/dev/null; then - READY=1 - break - fi - sleep 0.25 - done - if [ "$READY" -ne 1 ]; then - echo "Aria server did not become ready in pyppeteer job" >&2 - tail -n 100 /tmp/aria_server.log || true - exit 1 - fi - pytest -q tests/test_ui_pyppeteer.py +# ============================================================================= +# E2E + Integration Tests - Quick Aria regression testing +# ============================================================================= +# Purpose: Fast regression tests for Aria character system +# Triggers: Any push/PR to main (no path filtering) +# Jobs: +# - integration: Quick unit & integration tests +# - e2e_playwright: Playwright E2E tests +# - containerized_chrome: Pyppeteer containerized tests +# Note: Faster subset of aria-tests.yml, runs on all changes +# ============================================================================= + +name: E2E + Integration Tests + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +permissions: + contents: read + +concurrency: + group: e2e-${{ github.ref }} + cancel-in-progress: true + +jobs: + integration: + name: Unit & Integration tests + runs-on: ubuntu-latest + timeout-minutes: 15 + steps: + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + install-requirements: 'false' + extra-packages: 'pytest requests pytest-asyncio' + - name: Start server and run integration tests + run: | + python3 aria_web/server.py > /tmp/aria_server.log 2>&1 & + READY=0 + for i in $(seq 1 50); do + if curl -sSf http://127.0.0.1:8080/api/aria/state >/dev/null; then + READY=1 + break + fi + sleep 0.25 + done + if [ "$READY" -ne 1 ]; then + echo "Aria server did not become ready in integration job" >&2 + tail -n 100 /tmp/aria_server.log || true + exit 1 + fi + pytest -q tests/test_aria_server.py tests/test_object_api_integration.py + + e2e_playwright: + name: E2E (Playwright) + runs-on: ubuntu-latest + timeout-minutes: 25 + steps: + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + install-requirements: 'false' + extra-packages: 'pytest playwright pytest-asyncio requests' + - name: Install Playwright browsers + run: python -m playwright install chromium --with-deps + - name: Start server and run Playwright tests + run: | + python3 aria_web/server.py > /tmp/aria_server.log 2>&1 & + READY=0 + for i in $(seq 1 60); do + if curl -sSf http://127.0.0.1:8080/api/aria/state >/dev/null; then + READY=1 + break + fi + sleep 0.25 + done + if [ "$READY" -ne 1 ]; then + echo "Aria server did not become ready in Playwright job" >&2 + tail -n 100 /tmp/aria_server.log || true + exit 1 + fi + pytest -q tests/test_ui_playwright.py + + containerized_chrome: + name: Containerized Chrome (pyppeteer) + runs-on: ubuntu-latest + container: + image: python:3.11-bullseye + timeout-minutes: 25 + steps: + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + - name: Apt-get update & install dependencies + run: | + apt-get update + apt-get install -y wget gnupg ca-certificates xz-utils fonts-liberation libgtk-3-0 libxss1 libasound2 libnss3 libatk-bridge2.0-0 libx11-xcb1 libgbm1 + - name: Install Chromium (stable) + run: | + set -e + # Use Debian repo for chromium; package name varies by image/repo. + if apt-get install -y chromium chromium-driver; then + echo "Installed chromium + chromium-driver" + elif apt-get install -y chromium-browser; then + echo "Installed chromium-browser" + else + echo "Failed to install Chromium packages" >&2 + exit 1 + fi + + # Normalize executable path expected by CHROME_PATH. + if command -v chromium >/dev/null 2>&1; then + command -v chromium + elif command -v chromium-browser >/dev/null 2>&1; then + command -v chromium-browser + ln -sf "$(command -v chromium-browser)" /usr/bin/chromium + else + echo "Chromium executable not found after installation" >&2 + exit 1 + fi + - name: Install Python test deps + run: python -m pip install --upgrade pip && python -m pip install pytest pytest-asyncio pyppeteer requests + - name: Start server and run pyppeteer tests + env: + CHROME_PATH: /usr/bin/chromium + run: | + python3 aria_web/server.py > /tmp/aria_server.log 2>&1 & + READY=0 + for i in $(seq 1 60); do + if curl -sSf http://127.0.0.1:8080/api/aria/state >/dev/null; then + READY=1 + break + fi + sleep 0.25 + done + if [ "$READY" -ne 1 ]; then + echo "Aria server did not become ready in pyppeteer job" >&2 + tail -n 100 /tmp/aria_server.log || true + exit 1 + fi + pytest -q tests/test_ui_pyppeteer.py diff --git a/.github/workflows/makecode.yml b/.github/workflows/makecode.yml deleted file mode 100644 index e7099685a..000000000 --- a/.github/workflows/makecode.yml +++ /dev/null @@ -1,14 +0,0 @@ -name: MakeCode Build -on: - push: - workflow_dispatch: - -jobs: - build: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4 - with: - node-version: '20' - - run: npx makecode diff --git a/.github/workflows/pages.yml b/.github/workflows/pages.yml index 136cd8778..1402ef398 100644 --- a/.github/workflows/pages.yml +++ b/.github/workflows/pages.yml @@ -1,53 +1,53 @@ -name: Deploy to GitHub Pages - -on: - push: - branches: [ main ] - paths: - - 'docs/**' - - 'aria_web/**' - - '.github/workflows/pages.yml' - workflow_dispatch: - -# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages -permissions: - contents: read - pages: write - id-token: write - -# Allow only one concurrent deployment -concurrency: - group: "pages" - cancel-in-progress: false - -jobs: - # Build job - build: - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Setup Pages - uses: actions/configure-pages@1f0c5cde4bc74cd7e1254d0cb4de8d49e9068c7d # v4 - - - name: Build with Jekyll - uses: actions/jekyll-build-pages@44a6e6beabd48582f863aeeb6cb2151cc1716697 # v1 - with: - source: ./docs - destination: ./_site - - - name: Upload artifact - uses: actions/upload-pages-artifact@56afc609e74202658d3ffba0e8f6dda462b719fa # v3 - - # Deployment job - deploy: - environment: - name: github-pages - url: ${{ steps.deployment.outputs.page_url }} - runs-on: ubuntu-latest - needs: build - steps: - - name: Deploy to GitHub Pages - id: deployment - uses: actions/deploy-pages@d6db90164ac5ed86f2b6aed7e0febac5b3c0c03e # v4 +name: Deploy to GitHub Pages + +on: + push: + branches: [ main ] + paths: + - 'docs/**' + - 'aria_web/**' + - '.github/workflows/pages.yml' + workflow_dispatch: + +# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages +permissions: + contents: read + pages: write + id-token: write + +# Allow only one concurrent deployment +concurrency: + group: "pages" + cancel-in-progress: false + +jobs: + # Build job + build: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Setup Pages + uses: actions/configure-pages@1f0c5cde4bc74cd7e1254d0cb4de8d49e9068c7d # v4 + + - name: Build with Jekyll + uses: actions/jekyll-build-pages@44a6e6beabd48582f863aeeb6cb2151cc1716697 # v1 + with: + source: ./docs + destination: ./_site + + - name: Upload artifact + uses: actions/upload-pages-artifact@56afc609e74202658d3ffba0e8f6dda462b719fa # v3 + + # Deployment job + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + needs: build + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@d6db90164ac5ed86f2b6aed7e0febac5b3c0c03e # v4 diff --git a/.github/workflows/pr-checks.yml b/.github/workflows/pr-checks.yml index 25fc3e886..cab0fc253 100644 --- a/.github/workflows/pr-checks.yml +++ b/.github/workflows/pr-checks.yml @@ -1,164 +1,164 @@ -name: PR Checks - -on: - pull_request: - types: [opened, synchronize, reopened] - -concurrency: - group: pr-${{ github.event.pull_request.number }} - cancel-in-progress: true - -env: - FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true # Opt into Node.js 24 for JS actions ahead of June 2026 forced migration - -jobs: - validate: - name: Quick Validation - runs-on: ubuntu-latest - timeout-minutes: 10 - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Setup Python - uses: ./.github/actions/setup-python-env - with: - extra-packages: 'pyyaml' - - - name: Validate workflow YAML files - uses: ./.github/actions/validate-workflow-yaml - with: - workflows-path: .github/workflows - require-jobs: 'true' - - - name: Validate Azure Function bindings - uses: ./.github/actions/validate-function-bindings - with: - functions-path: functions - require-http-route: 'true' - - - name: Validate generated site bundles (strict) - uses: ./.github/actions/validate-site-bundles - with: - strict-metadata: 'true' - - - name: Check for TODO/FIXME in changed files - uses: ./.github/actions/scan-todo-fixme - with: - base-ref: ${{ github.base_ref }} - continue-on-error: true - - - name: Fast validation - run: | - if [ -f scripts/fast_validate.py ]; then - python scripts/fast_validate.py - else - echo "Fast validate script not found, skipping" - fi - - description-check: - name: PR Description Check - runs-on: ubuntu-latest - permissions: - contents: read - pull-requests: read - env: - MIN_DESCRIPTION_LENGTH: 100 - steps: - - name: Check PR description is filled in - env: - PR_BODY: ${{ github.event.pull_request.body }} - PR_TITLE: ${{ github.event.pull_request.title }} - MIN_DESCRIPTION_LENGTH: ${{ env.MIN_DESCRIPTION_LENGTH }} - run: | - UNFILLED_PATTERNS=( - "What does this change do" - "List of user-visible changes" - "How was this tested" - "Replace ALL placeholder text" - "1–3 sentences describing WHAT changed" - "Do not leave this as the default text" - ) - - FAILED=0 - for pattern in "${UNFILLED_PATTERNS[@]}"; do - if echo "$PR_BODY" | grep -qF "$pattern"; then - echo "❌ PR description still contains unfilled placeholder: '$pattern'" - FAILED=1 - fi - done - - # Require a minimum body length (more than just the template boilerplate) - BODY_LENGTH=$(echo "$PR_BODY" | wc -c) - if [ "$BODY_LENGTH" -lt "$MIN_DESCRIPTION_LENGTH" ]; then - echo "❌ PR description is too short ($BODY_LENGTH chars, minimum $MIN_DESCRIPTION_LENGTH). Please fill in the Summary, Changes, and Testing sections." - FAILED=1 - fi - - # Fail if the PR title looks like a default codespace/branch name - if echo "$PR_TITLE" | grep -qiE "^(pending changes|exported from (your )?codespace|codespace[- ]|untitled|draft|wip)"; then - echo "❌ PR title looks like a default/generated name: '$PR_TITLE'. Please use a descriptive title that explains what the PR does." - FAILED=1 - fi - - if [ "$FAILED" -eq 1 ]; then - echo "" - echo "Please update the PR description with:" - echo " - Summary: What changed and why (1-3 sentences)" - echo " - Changes: Bullet list of user-visible changes" - echo " - Testing: Commands run and their outcomes" - echo "" - echo "See the PR template for a filled-in example." - exit 1 - fi - - echo "✓ PR description looks filled in ($BODY_LENGTH chars)" - - size-check: - name: PR Size Check - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - with: - fetch-depth: 0 - - - name: Check PR size - id: size - env: - BASE_SHA: ${{ github.event.pull_request.base.sha }} - HEAD_SHA: ${{ github.event.pull_request.head.sha }} - run: | - ADDITIONS=$(git diff "$BASE_SHA"..."$HEAD_SHA" --numstat | awk '{sum+=$1} END {print sum}') - DELETIONS=$(git diff "$BASE_SHA"..."$HEAD_SHA" --numstat | awk '{sum+=$2} END {print sum}') - FILES_CHANGED=$(git diff "$BASE_SHA"..."$HEAD_SHA" --name-only | wc -l) - - echo "Files changed: $FILES_CHANGED" - echo "Lines added: $ADDITIONS" - echo "Lines deleted: $DELETIONS" - - # Warn if PR is very large - if [ "$FILES_CHANGED" -gt 50 ] || [ "$ADDITIONS" -gt 1000 ]; then - echo "⚠️ Warning: This is a large PR. Consider breaking it into smaller PRs for easier review." - echo "pr_size=large" >> $GITHUB_OUTPUT - else - echo "✓ PR size looks good" - echo "pr_size=normal" >> $GITHUB_OUTPUT - fi - - label-pr: - name: Auto-label PR - runs-on: ubuntu-latest - permissions: - contents: read - pull-requests: write - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Label PR based on files changed - uses: actions/labeler@634933edcd8ababfe52f92936142cc22ac488b1b # v6 - with: - repo-token: ${{ secrets.GITHUB_TOKEN }} - configuration-path: .github/labeler.yml - sync-labels: true +name: PR Checks + +on: + pull_request: + types: [opened, synchronize, reopened] + +concurrency: + group: pr-${{ github.event.pull_request.number }} + cancel-in-progress: true + +env: + FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true # Opt into Node.js 24 for JS actions ahead of June 2026 forced migration + +jobs: + validate: + name: Quick Validation + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + extra-packages: 'pyyaml' + + - name: Validate workflow YAML files + uses: ./.github/actions/validate-workflow-yaml + with: + workflows-path: .github/workflows + require-jobs: 'true' + + - name: Validate Azure Function bindings + uses: ./.github/actions/validate-function-bindings + with: + functions-path: functions + require-http-route: 'true' + + - name: Validate generated site bundles (strict) + uses: ./.github/actions/validate-site-bundles + with: + strict-metadata: 'true' + + - name: Check for TODO/FIXME in changed files + uses: ./.github/actions/scan-todo-fixme + with: + base-ref: ${{ github.base_ref }} + continue-on-error: true + + - name: Fast validation + run: | + if [ -f scripts/fast_validate.py ]; then + python scripts/fast_validate.py + else + echo "Fast validate script not found, skipping" + fi + + description-check: + name: PR Description Check + runs-on: ubuntu-latest + permissions: + contents: read + pull-requests: read + env: + MIN_DESCRIPTION_LENGTH: 100 + steps: + - name: Check PR description is filled in + env: + PR_BODY: ${{ github.event.pull_request.body }} + PR_TITLE: ${{ github.event.pull_request.title }} + MIN_DESCRIPTION_LENGTH: ${{ env.MIN_DESCRIPTION_LENGTH }} + run: | + UNFILLED_PATTERNS=( + "What does this change do" + "List of user-visible changes" + "How was this tested" + "Replace ALL placeholder text" + "1–3 sentences describing WHAT changed" + "Do not leave this as the default text" + ) + + FAILED=0 + for pattern in "${UNFILLED_PATTERNS[@]}"; do + if echo "$PR_BODY" | grep -qF "$pattern"; then + echo "❌ PR description still contains unfilled placeholder: '$pattern'" + FAILED=1 + fi + done + + # Require a minimum body length (more than just the template boilerplate) + BODY_LENGTH=$(echo "$PR_BODY" | wc -c) + if [ "$BODY_LENGTH" -lt "$MIN_DESCRIPTION_LENGTH" ]; then + echo "❌ PR description is too short ($BODY_LENGTH chars, minimum $MIN_DESCRIPTION_LENGTH). Please fill in the Summary, Changes, and Testing sections." + FAILED=1 + fi + + # Fail if the PR title looks like a default codespace/branch name + if echo "$PR_TITLE" | grep -qiE "^(pending changes|exported from (your )?codespace|codespace[- ]|untitled|draft|wip)"; then + echo "❌ PR title looks like a default/generated name: '$PR_TITLE'. Please use a descriptive title that explains what the PR does." + FAILED=1 + fi + + if [ "$FAILED" -eq 1 ]; then + echo "" + echo "Please update the PR description with:" + echo " - Summary: What changed and why (1-3 sentences)" + echo " - Changes: Bullet list of user-visible changes" + echo " - Testing: Commands run and their outcomes" + echo "" + echo "See the PR template for a filled-in example." + exit 1 + fi + + echo "✓ PR description looks filled in ($BODY_LENGTH chars)" + + size-check: + name: PR Size Check + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + with: + fetch-depth: 0 + + - name: Check PR size + id: size + env: + BASE_SHA: ${{ github.event.pull_request.base.sha }} + HEAD_SHA: ${{ github.event.pull_request.head.sha }} + run: | + ADDITIONS=$(git diff "$BASE_SHA"..."$HEAD_SHA" --numstat | awk '{sum+=$1} END {print sum}') + DELETIONS=$(git diff "$BASE_SHA"..."$HEAD_SHA" --numstat | awk '{sum+=$2} END {print sum}') + FILES_CHANGED=$(git diff "$BASE_SHA"..."$HEAD_SHA" --name-only | wc -l) + + echo "Files changed: $FILES_CHANGED" + echo "Lines added: $ADDITIONS" + echo "Lines deleted: $DELETIONS" + + # Warn if PR is very large + if [ "$FILES_CHANGED" -gt 50 ] || [ "$ADDITIONS" -gt 1000 ]; then + echo "⚠️ Warning: This is a large PR. Consider breaking it into smaller PRs for easier review." + echo "pr_size=large" >> $GITHUB_OUTPUT + else + echo "✓ PR size looks good" + echo "pr_size=normal" >> $GITHUB_OUTPUT + fi + + label-pr: + name: Auto-label PR + runs-on: ubuntu-latest + permissions: + contents: read + pull-requests: write + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Label PR based on files changed + uses: actions/labeler@634933edcd8ababfe52f92936142cc22ac488b1b # v6 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + configuration-path: .github/labeler.yml + sync-labels: true diff --git a/.github/workflows/pr-tests.yml b/.github/workflows/pr-tests.yml index 81e082d05..b4a6a37fe 100644 --- a/.github/workflows/pr-tests.yml +++ b/.github/workflows/pr-tests.yml @@ -57,9 +57,7 @@ jobs: run: | set -euo pipefail python -m pip install --upgrade pip - - if [[ -f requirements.txt ]]; then pip install -r requirements.txt; fi - + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi if [[ -f requirements-dev.txt ]]; then python -m pip install -r requirements-dev.txt elif [[ -f pyproject.toml ]]; then @@ -84,17 +82,10 @@ jobs: test-path: ${{ env.TEST_PATH }} extra-args: --maxfail=1 -q env: - CI: 'true' + CI: true watcher: - name: Test watcher needs: test - if: >- - ${ - github.event_name == 'schedule' || - github.event_name == 'workflow_dispatch' || - (github.event_name == 'push' && github.ref == 'refs/heads/main') - } && (github.event.inputs.run_watcher || 'false') == 'true' runs-on: ubuntu-latest timeout-minutes: ${{ fromJSON(github.event.inputs.watcher_timeout_minutes || '10') }} container: @@ -104,24 +95,13 @@ jobs: uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - name: Install dev dependencies (in container) - shell: bash run: | - set -euo pipefail python -m pip install --upgrade pip - - if [[ -f requirements.txt ]]; then pip install -r requirements.txt; fi - - if [[ -f requirements-dev.txt ]]; then - python -m pip install -r requirements-dev.txt - else - python -m pip install pytest watchdog - fi + if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt; else pip install pytest watchdog; fi - name: Run test watcher (runs tests once then exits via timeout) - shell: bash run: | - set -euo pipefail timeout "${{ github.event.inputs.watcher_timeout_minutes || '10' }}m" \ - python3 scripts/test_watcher.py --cmd "pytest ${{ env.TEST_PATH }} -q --maxfail=1" + python3 scripts/test_watcher.py --cmd "pytest tests -q --maxfail=1" env: - CI: 'true' + CI: true diff --git a/.github/workflows/quantum-orchestration.yml b/.github/workflows/quantum-orchestration.yml index a756245c6..7623ee2bf 100644 --- a/.github/workflows/quantum-orchestration.yml +++ b/.github/workflows/quantum-orchestration.yml @@ -1,47 +1,123 @@ -# ============================================================================= -# Quantum Automation - Azure Quantum workflow orchestration -# ============================================================================= -# Purpose: Executes quantum computing workflows on Azure Quantum -# Triggers: Push to main or manual workflow_dispatch -# Jobs: -# - run-quantum: Runs quantum orchestration scripts -# Note: Requires Azure credentials for quantum workspace access -# ============================================================================= - -name: Quantum Automation - -on: - push: - branches: [ main ] - workflow_dispatch: - inputs: - LOGIC_APP_URL: - description: "Logic App URL for optional notifications" - required: false - type: string - -# Credentials must come exclusively from the AZURE_CREDENTIALS repository secret. -# Never pass service principal JSON as a workflow_dispatch input — it would be -# visible in the GitHub Actions UI and potentially captured in runner logs. -permissions: - contents: read - -jobs: - run-quantum: - runs-on: windows-latest - timeout-minutes: 30 - steps: - - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - name: Azure Login - if: ${{ secrets.AZURE_CREDENTIALS != '' }} - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2 - with: - creds: ${{ secrets.AZURE_CREDENTIALS }} - - name: Run Orchestration - shell: pwsh - run: | - ./ai-projects/quantum-ml/azure/quantum_master_orchestration.ps1 -ResourceGroup rg-quantum-ai -WorkspaceName quantum-ai-workspace -Location eastus - # Or the Logic App variant - # ./ai-projects/quantum-ml/azure/quantum_full_logicapp_orchestration.ps1 -LogicAppUrl $env:LOGIC_APP_URL - env: - LOGIC_APP_URL: ${{ inputs.LOGIC_APP_URL }} +# ============================================================================= +# Quantum Automation - Azure Quantum workflow orchestration +# ============================================================================= +# Purpose: Executes quantum computing workflows on Azure Quantum +# Triggers: Push to main or manual workflow_dispatch +# Jobs: +# - run-quantum: Runs quantum orchestration scripts +# Note: Requires Azure credentials for quantum workspace access +# ============================================================================= + +name: Quantum Automation + +on: + push: + branches: [main] + workflow_dispatch: + inputs: + LOGIC_APP_URL: + description: "Logic App URL for optional notifications" + required: false + type: string + RESOURCE_GROUP: + description: "Azure resource group containing the Azure Quantum workspace" + required: false + type: string + default: "rg-quantum-ai" + WORKSPACE_NAME: + description: "Azure Quantum workspace name" + required: false + type: string + default: "quantum-ai-workspace" + LOCATION: + description: "Azure region for the workspace (e.g., eastus)" + required: false + type: string + default: "eastus" + ORCHESTRATION_MODE: + description: "Which orchestration entrypoint to run: master or logicapp" + required: false + type: choice + default: "master" + options: + - master + - logicapp + +# Credentials must come exclusively from the AZURE_CREDENTIALS repository secret. +# Never pass service principal JSON as a workflow_dispatch input — it would be +# visible in the GitHub Actions UI and potentially captured in runner logs. +permissions: + contents: read + +concurrency: + group: quantum-orchestration-${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +defaults: + run: + shell: pwsh + +jobs: + run-quantum: + name: Run Quantum Orchestration + runs-on: windows-latest + timeout-minutes: 30 + env: + HAS_AZURE_CREDENTIALS: ${{ secrets.AZURE_CREDENTIALS != '' }} + + steps: + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Validate inputs and required files + run: | + $ErrorActionPreference = 'Stop' + + if ('${{ inputs.ORCHESTRATION_MODE }}' -eq 'logicapp' -and [string]::IsNullOrWhiteSpace($env:LOGIC_APP_URL)) { + throw "LOGIC_APP_URL is required when ORCHESTRATION_MODE=logicapp." + } + + $master = './ai-projects/quantum-ml/azure/quantum_master_orchestration.ps1' + $logic = './ai-projects/quantum-ml/azure/quantum_full_logicapp_orchestration.ps1' + + if (-not (Test-Path -LiteralPath $master)) { throw "Missing script: $master" } + if (-not (Test-Path -LiteralPath $logic)) { throw "Missing script: $logic" } + + - name: Azure Login + if: env.HAS_AZURE_CREDENTIALS == 'true' + uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2 + with: + creds: ${{ secrets.AZURE_CREDENTIALS }} + + - name: Fail fast when Azure credentials are not configured + if: env.HAS_AZURE_CREDENTIALS != 'true' + run: | + throw "AZURE_CREDENTIALS secret is not configured. Add it as a repository secret to enable Azure Quantum orchestration." + + - name: Run orchestration + env: + LOGIC_APP_URL: ${{ inputs.LOGIC_APP_URL }} + RESOURCE_GROUP: ${{ inputs.RESOURCE_GROUP }} + WORKSPACE_NAME: ${{ inputs.WORKSPACE_NAME }} + LOCATION: ${{ inputs.LOCATION }} + ORCHESTRATION_MODE: ${{ inputs.ORCHESTRATION_MODE }} + run: | + $ErrorActionPreference = 'Stop' + + $rg = $env:RESOURCE_GROUP + $ws = $env:WORKSPACE_NAME + $loc = $env:LOCATION + + if ([string]::IsNullOrWhiteSpace($rg)) { throw "RESOURCE_GROUP is empty." } + if ([string]::IsNullOrWhiteSpace($ws)) { throw "WORKSPACE_NAME is empty." } + if ([string]::IsNullOrWhiteSpace($loc)) { throw "LOCATION is empty." } + + if ($env:ORCHESTRATION_MODE -eq 'logicapp') { + ./ai-projects/quantum-ml/azure/quantum_full_logicapp_orchestration.ps1 -LogicAppUrl $env:LOGIC_APP_URL + } else { + ./ai-projects/quantum-ml/azure/quantum_master_orchestration.ps1 -ResourceGroup $rg -WorkspaceName $ws -Location $loc + } + + - name: Azure Logout + if: always() && env.HAS_AZURE_CREDENTIALS == 'true' + run: | + az logout | Out-Null diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 0a975f5c9..f81dbf93b 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -1,104 +1,104 @@ -name: Release - -on: - push: - tags: - - 'v*.*.*' # Matches v1.0.0, v2.1.3, etc. - workflow_dispatch: - inputs: - version: - description: 'Version to release (e.g., v1.0.0)' - required: true - type: string - -permissions: - contents: write - packages: write - -concurrency: - group: release - cancel-in-progress: false - -jobs: - create-release: - name: Create Release - runs-on: ubuntu-latest - timeout-minutes: 15 - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - with: - fetch-depth: 0 - - - name: Setup Python - uses: ./.github/actions/setup-python-env - with: - install-requirements: 'false' - extra-packages: 'build twine' - - - name: Get version from tag - id: version - run: | - if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then - VERSION="${{ github.event.inputs.version }}" - else - VERSION=${GITHUB_REF#refs/tags/} - fi - echo "version=$VERSION" >> $GITHUB_OUTPUT - echo "version_number=${VERSION#v}" >> $GITHUB_OUTPUT - - - name: Generate changelog - id: changelog - run: | - # Get previous tag - PREV_TAG=$(git describe --abbrev=0 --tags $(git rev-list --tags --skip=1 --max-count=1) 2>/dev/null || echo "") - - if [ -z "$PREV_TAG" ]; then - echo "First release - no previous tag found" - git log --pretty=format:"- %s (%h)" > CHANGELOG.md - else - echo "Changes since $PREV_TAG:" > CHANGELOG.md - git log $PREV_TAG..HEAD --pretty=format:"- %s (%h)" >> CHANGELOG.md - fi - - cat CHANGELOG.md - - - name: Create GitHub Release - uses: softprops/action-gh-release@3bb12739c298aeb8a4eeaf626c5b8d85266b0e65 # v2.6.2 - with: - tag_name: ${{ steps.version.outputs.version }} - name: Release ${{ steps.version.outputs.version }} - body_path: CHANGELOG.md - draft: false - prerelease: ${{ contains(steps.version.outputs.version, 'alpha') || contains(steps.version.outputs.version, 'beta') || contains(steps.version.outputs.version, 'rc') }} - token: ${{ secrets.GITHUB_TOKEN }} - - - name: Build package artifacts - run: | - # Create release artifacts directory - mkdir -p release-artifacts - - # Create a source tarball - git archive --format=tar.gz --prefix=aria-${{ steps.version.outputs.version_number }}/ HEAD > release-artifacts/aria-${{ steps.version.outputs.version_number }}.tar.gz - - # Create a zip archive - git archive --format=zip --prefix=aria-${{ steps.version.outputs.version_number }}/ HEAD > release-artifacts/aria-${{ steps.version.outputs.version_number }}.zip - - - name: Upload release artifacts - uses: softprops/action-gh-release@3bb12739c298aeb8a4eeaf626c5b8d85266b0e65 # v2.6.2 - with: - tag_name: ${{ steps.version.outputs.version }} - files: | - release-artifacts/* - token: ${{ secrets.GITHUB_TOKEN }} - - - name: Create release summary - run: | - echo "# Release ${{ steps.version.outputs.version }}" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "## Changes" >> $GITHUB_STEP_SUMMARY - cat CHANGELOG.md >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "## Artifacts" >> $GITHUB_STEP_SUMMARY - echo "- Source tarball: aria-${{ steps.version.outputs.version_number }}.tar.gz" >> $GITHUB_STEP_SUMMARY - echo "- Source zip: aria-${{ steps.version.outputs.version_number }}.zip" >> $GITHUB_STEP_SUMMARY +name: Release + +on: + push: + tags: + - 'v*.*.*' # Matches v1.0.0, v2.1.3, etc. + workflow_dispatch: + inputs: + version: + description: 'Version to release (e.g., v1.0.0)' + required: true + type: string + +permissions: + contents: write + packages: write + +concurrency: + group: release + cancel-in-progress: false + +jobs: + create-release: + name: Create Release + runs-on: ubuntu-latest + timeout-minutes: 15 + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + with: + fetch-depth: 0 + + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + install-requirements: 'false' + extra-packages: 'build twine' + + - name: Get version from tag + id: version + run: | + if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then + VERSION="${{ github.event.inputs.version }}" + else + VERSION=${GITHUB_REF#refs/tags/} + fi + echo "version=$VERSION" >> $GITHUB_OUTPUT + echo "version_number=${VERSION#v}" >> $GITHUB_OUTPUT + + - name: Generate changelog + id: changelog + run: | + # Get previous tag + PREV_TAG=$(git describe --abbrev=0 --tags $(git rev-list --tags --skip=1 --max-count=1) 2>/dev/null || echo "") + + if [ -z "$PREV_TAG" ]; then + echo "First release - no previous tag found" + git log --pretty=format:"- %s (%h)" > CHANGELOG.md + else + echo "Changes since $PREV_TAG:" > CHANGELOG.md + git log $PREV_TAG..HEAD --pretty=format:"- %s (%h)" >> CHANGELOG.md + fi + + cat CHANGELOG.md + + - name: Create GitHub Release + uses: softprops/action-gh-release@3bb12739c298aeb8a4eeaf626c5b8d85266b0e65 # v2.6.2 + with: + tag_name: ${{ steps.version.outputs.version }} + name: Release ${{ steps.version.outputs.version }} + body_path: CHANGELOG.md + draft: false + prerelease: ${{ contains(steps.version.outputs.version, 'alpha') || contains(steps.version.outputs.version, 'beta') || contains(steps.version.outputs.version, 'rc') }} + token: ${{ secrets.GITHUB_TOKEN }} + + - name: Build package artifacts + run: | + # Create release artifacts directory + mkdir -p release-artifacts + + # Create a source tarball + git archive --format=tar.gz --prefix=aria-${{ steps.version.outputs.version_number }}/ HEAD > release-artifacts/aria-${{ steps.version.outputs.version_number }}.tar.gz + + # Create a zip archive + git archive --format=zip --prefix=aria-${{ steps.version.outputs.version_number }}/ HEAD > release-artifacts/aria-${{ steps.version.outputs.version_number }}.zip + + - name: Upload release artifacts + uses: softprops/action-gh-release@3bb12739c298aeb8a4eeaf626c5b8d85266b0e65 # v2.6.2 + with: + tag_name: ${{ steps.version.outputs.version }} + files: | + release-artifacts/* + token: ${{ secrets.GITHUB_TOKEN }} + + - name: Create release summary + run: | + echo "# Release ${{ steps.version.outputs.version }}" >> $GITHUB_STEP_SUMMARY + echo "" >> $GITHUB_STEP_SUMMARY + echo "## Changes" >> $GITHUB_STEP_SUMMARY + cat CHANGELOG.md >> $GITHUB_STEP_SUMMARY + echo "" >> $GITHUB_STEP_SUMMARY + echo "## Artifacts" >> $GITHUB_STEP_SUMMARY + echo "- Source tarball: aria-${{ steps.version.outputs.version_number }}.tar.gz" >> $GITHUB_STEP_SUMMARY + echo "- Source zip: aria-${{ steps.version.outputs.version_number }}.zip" >> $GITHUB_STEP_SUMMARY diff --git a/.github/workflows/workflow-validation.yml b/.github/workflows/workflow-validation.yml index 23dd6180a..39bed5325 100644 --- a/.github/workflows/workflow-validation.yml +++ b/.github/workflows/workflow-validation.yml @@ -1,82 +1,82 @@ -name: Workflow Validation - -on: - pull_request: - paths: - - '.github/workflows/**' - - '.github/actions/**' - push: - branches: [ main ] - paths: - - '.github/workflows/**' - - '.github/actions/**' - workflow_dispatch: - -jobs: - validate-workflows: - name: Validate Workflow Syntax - runs-on: ubuntu-latest - timeout-minutes: 10 - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Setup Python - uses: ./.github/actions/setup-python-env - with: - install-requirements: 'false' - extra-packages: 'pyyaml jsonschema' - - - name: Validate workflow YAML structure - uses: ./.github/actions/validate-workflow-yaml - with: - workflows-path: .github/workflows - require-jobs: 'true' - - - name: Validate local composite actions - uses: ./.github/actions/validate-composite-actions - with: - script-path: scripts/validate_composite_actions.py - - - name: Validate Azure Function bindings - uses: ./.github/actions/validate-function-bindings - with: - functions-path: functions - require-http-route: 'true' - - - name: Validate Foundry deployment config - uses: ./.github/actions/validate-foundry-config - - - name: Validate generated site bundles contract - uses: ./.github/actions/validate-site-bundles - with: - strict-metadata: 'true' - - - name: Check for common issues - uses: ./.github/actions/check-workflow-common-issues - with: - workflows-path: .github/workflows - - - name: Validate action references - uses: ./.github/actions/validate-action-references - with: - workflows-path: .github/workflows - - test-workflows: - name: Test Workflow Execution - runs-on: ubuntu-latest - timeout-minutes: 5 - needs: validate-workflows - steps: - - name: Checkout code - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - - - name: Set up Python - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 - with: - python-version: '3.11' - - - name: Test workflow can be triggered - run: | - echo "Workflow validation and test completed successfully" - echo "All workflows are syntactically correct and ready to use" +name: Workflow Validation + +on: + pull_request: + paths: + - '.github/workflows/**' + - '.github/actions/**' + push: + branches: [ main ] + paths: + - '.github/workflows/**' + - '.github/actions/**' + workflow_dispatch: + +jobs: + validate-workflows: + name: Validate Workflow Syntax + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + install-requirements: 'false' + extra-packages: 'pyyaml jsonschema' + + - name: Validate workflow YAML structure + uses: ./.github/actions/validate-workflow-yaml + with: + workflows-path: .github/workflows + require-jobs: 'true' + + - name: Validate local composite actions + uses: ./.github/actions/validate-composite-actions + with: + script-path: scripts/validate_composite_actions.py + + - name: Validate Azure Function bindings + uses: ./.github/actions/validate-function-bindings + with: + functions-path: functions + require-http-route: 'true' + + - name: Validate Foundry deployment config + uses: ./.github/actions/validate-foundry-config + + - name: Validate generated site bundles contract + uses: ./.github/actions/validate-site-bundles + with: + strict-metadata: 'true' + + - name: Check for common issues + uses: ./.github/actions/check-workflow-common-issues + with: + workflows-path: .github/workflows + + - name: Validate action references + uses: ./.github/actions/validate-action-references + with: + workflows-path: .github/workflows + + test-workflows: + name: Test Workflow Execution + runs-on: ubuntu-latest + timeout-minutes: 5 + needs: validate-workflows + steps: + - name: Checkout code + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 + with: + python-version: '3.11' + + - name: Test workflow can be triggered + run: | + echo "Workflow validation and test completed successfully" + echo "All workflows are syntactically correct and ready to use" diff --git a/.markdownlint.yaml b/.markdownlint.yaml new file mode 100644 index 000000000..f1bb97563 --- /dev/null +++ b/.markdownlint.yaml @@ -0,0 +1,7 @@ +# MD013: Line length +# Increased limit to accommodate long URLs and content descriptions. +# Code blocks are excluded since they contain copilot commands and generated code. +MD013: + line_length: 200 + code_blocks: false + tables: false diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 000000000..5fe504136 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,128 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +Email. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. diff --git a/README.md b/README.md index 2256965d1..81bf64583 100644 --- a/README.md +++ b/README.md @@ -1,406 +1,406 @@ ---- -title: Aria -emoji: 🤖 -colorFrom: blue -colorTo: purple -sdk: gradio -sdk_version: "6.12.0" -app_file: app.py -pinned: false ---- - -# Aria — Interactive AI Character Platform - -[![CI Pipeline](https://github.com/Bryan-Roe/Aria/actions/workflows/ci-pipeline.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/ci-pipeline.yml) -[![Code Quality](https://github.com/Bryan-Roe/Aria/actions/workflows/code-quality.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/code-quality.yml) -[![CodeQL](https://github.com/Bryan-Roe/Aria/actions/workflows/codeql.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/codeql.yml) -[![E2E Tests](https://github.com/Bryan-Roe/Aria/actions/workflows/e2e-tests.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/e2e-tests.yml) - -**An intelligent, animated AI character with movement, gestures, and natural language interaction.** - -[Live Demo](https://bryan-roe.github.io/Aria) · [Aria Web UI](apps/aria/) · [Quick Start](#-quick-start) - ---- - -## What is Aria? - -Aria is a full-stack interactive AI character platform. She lives on a virtual 3D stage, responds to natural language commands ("wave", "pick up the ball", "dance"), speaks via text-to-speech, and is powered by a multi-provider AI backend that supports LM Studio, Ollama, Azure OpenAI, OpenAI, local models, and LoRA fine-tuned adapters. - -The project is organized around four core areas: - -| Area | Folder | Description | -| --- | --- | --- | -| **Character interface** | `apps/aria/` | Animated 3D character stage with object interaction | -| **Chat / AI backends** | `ai-projects/chat-cli/` | Multi-provider CLI and streaming chat API | -| **Quantum ML** | `ai-projects/quantum-ml/` | Hybrid quantum-classical training (experimental) | -| **Model fine-tuning** | `AI/` | LoRA fine-tuning for Aria's language understanding | - -Supporting infrastructure lives in `shared/`, `scripts/`, `config/`, and `function_app.py` (Azure Functions API layer). - ---- - -## 🤗 Hugging Face Spaces - -This repository is also configured to run as a **Gradio Hugging Face Space**. - -- Spaces entry point: `app.py` -- Reusable demo helper: `scripts/gradio_hello.py` -- SDK: `gradio` - -The Spaces deployment now exposes a **lightweight AI chat app** backed by the repository's existing provider abstraction. It can use the same provider layer as the rest of Aria (`auto`, local fallback, OpenAI, Azure OpenAI, LM Studio, Ollama, and AGI where configured), while remaining simpler than the full local Aria stack (`apps/aria/`, Azure Functions, training scripts, and quantum components). - -If you want to run the Space locally: - -```bash -./.venv/bin/python app.py -``` - -If you want the full Aria platform instead, use the Quick Start steps below. - ---- - -## 🚀 Quick Start - -### Prerequisites - -- Python 3.9+ -- Git - -### 1 — Run the Aria character web UI - -```bash -cd apps/aria -pip install -r ../../requirements.txt # only needed once -python server.py -# Open http://localhost:8080 -``` - -Type commands in the chat box: `move left`, `wave`, `jump`, `pick up the ball`, `dance`. - -### 2 — Chat via CLI (no UI required) - -```bash -# Local mode — no API keys required -python ai-projects/chat-cli/src/chat_cli.py --provider local --once "Hello Aria!" - -# OpenAI -OPENAI_API_KEY=sk-... python ai-projects/chat-cli/src/chat_cli.py --provider openai - -# Azure OpenAI (requires all four env vars — see Configuration below) -python ai-projects/chat-cli/src/chat_cli.py --provider azure -``` - -Interactive session commands: `/new`, `/save`, `/exit`. - -### 3 — Start the Azure Functions API host - -```bash -func host start -# Endpoints: /api/chat, /api/chat-web, /api/tts, /api/quantum/*, /api/ai/status -curl http://localhost:7071/api/ai/status | python -m json.tool # health check -``` - ---- - -## 🏗️ Project Structure - -``` -apps/aria/ Animated character stage (HTML/CSS/JS + Python API server) -apps/chat/ Browser-based streaming chat UI -ai-projects/chat-cli/ Multi-provider chat CLI -ai-projects/quantum-ml/ Quantum ML platform (circuits, MCP server, Azure Quantum) -ai-projects/llm-maker/ Autonomous tool-creation system -ai-projects/cooking-ai/ Cooking-focused AI assistant -AI/ LoRA fine-tuning workspace (Phi / TinyLlama) -shared/ Shared Python modules (providers, DB, telemetry, Cosmos) -scripts/ Orchestration, training, evaluation, and utility scripts -config/ YAML configs for orchestrators -datasets/ Read-only training datasets -data_out/ All generated outputs (git-ignored) -function_app.py Azure Functions entry point (all /api/* endpoints) -``` - ---- - -## 🎭 Aria Character - -The Aria character runs at `http://localhost:8080` (or the [GitHub Pages demo](https://bryan-roe.github.io/Aria)). - -**Natural language commands (examples):** - -| Command | Effect | -| --- | --- | -| `move left` / `move right` | Walk to stage edge | -| `wave` / `dance` / `jump` | Trigger gesture | -| `pick up the ball` | Pick up a nearby object | -| `throw the ball` | Throw held object with physics | -| `say hello` | Aria speaks the text aloud via TTS | - -The auto-execute system parses complex multi-step requests ("walk to the table and pick up the apple") into a structured sequence of 8 core actions: `move`, `say`, `pickup`, `drop`, `throw`, `gesture`, `look`, `wait`. - -**Aria web server API (port 8080):** - -| Method | Path | Description | -| --- | --- | --- | -| `GET` | `/api/aria/state` | Current stage state (position, objects, expressions) | -| `POST` | `/api/aria/command` | Process a natural language command | -| `POST` | `/api/aria/execute` | Auto-execute an action sequence | -| `POST` | `/api/aria/object` | Add / update / remove an object | -| `POST` | `/api/aria/world` | Generate a themed world via LLM | - ---- - -## 💬 Chat Providers - -Provider auto-detection order: - -``` -LM Studio → Ollama → Azure OpenAI → OpenAI → Local (zero-dependency echo) -``` - -Pass `--provider` to override: `local`, `openai`, `azure`, `lmstudio`, `ollama`, `lora`, `quantum`, `agi`. - -**Azure OpenAI** — all four variables required: - -``` -AZURE_OPENAI_API_KEY -AZURE_OPENAI_ENDPOINT -AZURE_OPENAI_DEPLOYMENT -AZURE_OPENAI_API_VERSION -``` - -**LoRA adapter** — adapter directory must contain: - -``` -adapter_config.json -adapter_model.safetensors -``` - -```bash -python ai-projects/chat-cli/src/chat_cli.py --provider lora --model data_out/lora_training/lora_adapter -``` - -All providers implement `BaseChatProvider.complete(messages, stream)`. Add a new provider by subclassing `BaseChatProvider` in `shared/chat_providers.py`. - ---- - -## ⚛️ Quantum ML (Experimental) - -Local Qiskit Aer simulation is free and unlimited. Azure simulator backends are also free. Real QPU hardware is billed per gate-shot — always simulate first. - -**Workflow:** Test locally → Validate on Azure simulator → Run on QPU (set `azure_confirm_cost: true` in YAML first) - -```bash -# Validate config without running anything -python scripts/quantum_autorun.py --dry-run - -# Interactive training dashboard -cd ai-projects/quantum-ml && ./start_dashboard.sh # http://localhost:5000 - -# Start the MCP server (8 quantum tools) -python ai-projects/quantum-ml/quantum_mcp_server.py -``` - -**MCP tools:** `create_quantum_circuit`, `simulate_quantum_circuit`, `get_quantum_circuit_properties`, `connect_azure_quantum`, `list_quantum_backends`, `submit_quantum_job`, `estimate_quantum_cost`, `train_quantum_classifier`. - ---- - -## 🧠 LoRA Fine-Tuning - -Train a small model on Aria-specific datasets using LoRA adapters. - -```bash -# Quick training run (TinyLlama, CPU-friendly, ~10–15 s) -python scripts/automated_training_pipeline.py --models tinyllama --quick - -# Full train → evaluate → auto-promote best checkpoint -python scripts/train_and_promote.py --quick --auto-promote - -# Validate configs without running -python scripts/autotrain.py --dry-run -``` - -Training datasets are in `datasets/chat/aria_movement/`, `aria_expanded/`, and `aria_simple/` (read-only). -Outputs are written to `data_out/lora_training/`. - ---- - -## 🤖 Autonomous Training - -A background orchestrator continuously discovers datasets, trains, and evaluates models on a 30-minute cycle. - -```bash -# Start the autonomous loop (runs indefinitely) -nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 & - -# Check live status -cat data_out/autonomous_training_status.json | python -m json.tool -tail -f data_out/autonomous_training.log -``` - ---- - -## 🔧 LLM Tool Maker - -An autonomous system where an LLM generates, validates, and sandboxes Python tools at runtime. - -```bash -cd llm-maker -python examples/quick_start.py # create a tool from a description -python llm_maker_mcp_server.py # expose tools via MCP -``` - -Security: no dangerous imports, no filesystem or network access, no `eval`/`exec`, sandboxed execution with resource limits. - ---- - -## 🧪 Testing - -```bash -# Fast unit tests (~0.5 s, no external services) -python scripts/test_runner.py --unit - -# All fast tests (unit + integration, ~10 s) -python scripts/test_runner.py --all - -# With coverage report -python scripts/test_runner.py --all --coverage - -# One-command integration contract gate (local) -bash ./scripts/integration_contract_gate.sh - -# Strict gate (requires local Functions host at :7071) -bash ./scripts/integration_contract_gate.sh --strict-endpoints - -# Direct pytest -pytest -m "not slow and not azure" tests/ -``` - -VS Code users: open the Test Explorer (🧪 beaker icon) for interactive test running and debugging. - ---- - -## 🌐 Live Demo - -**[https://bryan-roe.github.io/Aria](https://bryan-roe.github.io/Aria)** - -The demo runs in mock mode with simulated API responses — no API keys needed. For full AI capabilities, run the project locally. - ---- - -## 🔒 Configuration & Secrets - -Copy the example files to get started: - -```bash -cp .env.example .env -cp local.settings.json.example local.settings.json -# Fill in API keys as needed -``` - -Never commit secrets. All keys belong in environment variables or `local.settings.json` (development only). - -**Optional services** (feature-flagged — safe to leave unset): - -| Service | How to enable | -| --- | --- | -| SQL persistence | `QAI_DB_CONN` env var (SQLite, PostgreSQL, or Azure SQL) | -| Cosmos DB | `QAI_ENABLE_COSMOS=true` + `COSMOS_ENDPOINT`, `COSMOS_KEY`, `COSMOS_DATABASE`, `COSMOS_CONTAINER` | -| Application Insights | `APPLICATIONINSIGHTS_CONNECTION_STRING` | -| Azure Speech TTS | `AZURE_SPEECH_KEY` + `AZURE_SPEECH_REGION` | -| Local TTS fallback | `QAI_ENABLE_LOCAL_TTS=true` (uses pyttsx3 or gTTS when Azure credentials are absent) | - ---- - -## 📚 Documentation - -| Document | Purpose | -| --- | --- | -| [apps/aria/README.md](apps/aria/README.md) | Character stage API reference | -| [ai-projects/quantum-ml/README.md](ai-projects/quantum-ml/README.md) | Quantum ML platform guide | -| [ai-projects/chat-cli/README.md](ai-projects/chat-cli/README.md) | Chat CLI reference | -| [ai-projects/llm-maker/README.md](ai-projects/llm-maker/README.md) | Tool maker guide | -| [docs/aria/](docs/aria/) | Aria movement & training documentation | -| [MONETIZATION_GUIDE.md](MONETIZATION_GUIDE.md) | Subscription and revenue system | -| [docs/guides/REPO_AUTOMATION_GUIDE.md](docs/guides/REPO_AUTOMATION_GUIDE.md) | Full repository automation reference | -| [QUANTUM_LLM_TRAINING.md](QUANTUM_LLM_TRAINING.md) | Quantum-LLM concurrent training | - ---- - -## 🤝 Contributing - -- Update `README.md` when adding configuration options, changing CLI flags, introducing new providers, or modifying cost behaviour. -- All output files go under `data_out/` (git-ignored). Never modify files under `datasets/`. -- Always run `--dry-run` on orchestrators before executing GPU or QPU workloads. - ---- - -## 📄 License - -See individual project directories for license information. - -## PLAN (pseudocode) - -## 1) Validate OPENAI_API_KEY - -## 2) Read prompt from CLI args or stdin - -## 3) Call OpenAI Responses API - -## 4) Extract text safely from response.output_text with fallback parsing - -## 5) Print final text; fail gracefully on errors - -import os -import sys -from openai import OpenAI - -MODEL = "gpt-4o-mini" -SYSTEM_PROMPT = "You are a concise AI coding assistant. Return practical code-focused responses." - -def _extract_text(resp) -> str: - if getattr(resp, "output_text", None): - return resp.output_text.strip() - - parts = [] - for item in getattr(resp, "output", []) or []: - for content in getattr(item, "content", []) or []: - if getattr(content, "type", "") == "output_text": - text = getattr(content, "text", "") - if text: - parts.append(text) - return "\n".join(parts).strip() - -def ask_ai(client: OpenAI, prompt: str) -> str: - resp = client.responses.create( - model=MODEL, - input=[ - {"role": "system", "content": SYSTEM_PROMPT}, - {"role": "user", "content": prompt}, - ], - temperature=0.2, - ) - return _extract_text(resp) - -def main() -> None: - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: - raise RuntimeError("Missing OPENAI_API_KEY environment variable.") - - prompt = " ".join(sys.argv[1:]).strip() - if not prompt: - prompt = input("Prompt: ").strip() - if not prompt: - raise ValueError("Prompt cannot be empty.") - - client = OpenAI(api_key=api_key) - - try: - output = ask_ai(client, prompt) - print(output or "(No text returned.)") - except Exception as exc: - print(f"AI request failed: {exc}", file=sys.stderr) - sys.exit(1) - -if **name** == "**main**": - main() +--- +title: Aria +emoji: 🤖 +colorFrom: blue +colorTo: purple +sdk: gradio +sdk_version: "6.12.0" +app_file: app.py +pinned: false +--- + +# Aria — Interactive AI Character Platform + +[![CI Pipeline](https://github.com/Bryan-Roe/Aria/actions/workflows/ci-pipeline.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/ci-pipeline.yml) +[![Code Quality](https://github.com/Bryan-Roe/Aria/actions/workflows/code-quality.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/code-quality.yml) +[![CodeQL](https://github.com/Bryan-Roe/Aria/actions/workflows/codeql.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/codeql.yml) +[![E2E Tests](https://github.com/Bryan-Roe/Aria/actions/workflows/e2e-tests.yml/badge.svg)](https://github.com/Bryan-Roe/Aria/actions/workflows/e2e-tests.yml) +[![Codespaces Prebuilds](https://github.com/Bryan-Roe/Aria/actions/workflows/codespaces/create_codespaces_prebuilds/badge.svg?branch=main)](https://github.com/Bryan-Roe/Aria/actions/workflows/codespaces/create_codespaces_prebuilds) +**An intelligent, animated AI character with movement, gestures, and natural language interaction.** + +[Live Demo](https://bryan-roe.github.io/Aria) · [Aria Web UI](apps/aria/) · [Quick Start](#-quick-start) + +--- + +## What is Aria? + +Aria is a full-stack interactive AI character platform. She lives on a virtual 3D stage, responds to natural language commands ("wave", "pick up the ball", "dance"), speaks via text-to-speech, and is powered by a multi-provider AI backend that supports LM Studio, Ollama, Azure OpenAI, OpenAI, local models, and LoRA fine-tuned adapters. + +The project is organized around four core areas: + +| Area | Folder | Description | +| --- | --- | --- | +| **Character interface** | `apps/aria/` | Animated 3D character stage with object interaction | +| **Chat / AI backends** | `ai-projects/chat-cli/` | Multi-provider CLI and streaming chat API | +| **Quantum ML** | `ai-projects/quantum-ml/` | Hybrid quantum-classical training (experimental) | +| **Model fine-tuning** | `AI/` | LoRA fine-tuning for Aria's language understanding | + +Supporting infrastructure lives in `shared/`, `scripts/`, `config/`, and `function_app.py` (Azure Functions API layer). + +--- + +## 🤗 Hugging Face Spaces + +This repository is also configured to run as a **Gradio Hugging Face Space**. + +- Spaces entry point: `app.py` +- Reusable demo helper: `scripts/gradio_hello.py` +- SDK: `gradio` + +The Spaces deployment now exposes a **lightweight AI chat app** backed by the repository's existing provider abstraction. It can use the same provider layer as the rest of Aria (`auto`, local fallback, OpenAI, Azure OpenAI, LM Studio, Ollama, and AGI where configured), while remaining simpler than the full local Aria stack (`apps/aria/`, Azure Functions, training scripts, and quantum components). + +If you want to run the Space locally: + +```bash +./.venv/bin/python app.py +``` + +If you want the full Aria platform instead, use the Quick Start steps below. + +--- + +## 🚀 Quick Start + +### Prerequisites + +- Python 3.9+ +- Git + +### 1 — Run the Aria character web UI + +```bash +cd apps/aria +pip install -r ../../requirements.txt # only needed once +python server.py +# Open http://localhost:8080 +``` + +Type commands in the chat box: `move left`, `wave`, `jump`, `pick up the ball`, `dance`. + +### 2 — Chat via CLI (no UI required) + +```bash +# Local mode — no API keys required +python ai-projects/chat-cli/src/chat_cli.py --provider local --once "Hello Aria!" + +# OpenAI +OPENAI_API_KEY=sk-... python ai-projects/chat-cli/src/chat_cli.py --provider openai + +# Azure OpenAI (requires all four env vars — see Configuration below) +python ai-projects/chat-cli/src/chat_cli.py --provider azure +``` + +Interactive session commands: `/new`, `/save`, `/exit`. + +### 3 — Start the Azure Functions API host + +```bash +func host start +# Endpoints: /api/chat, /api/chat-web, /api/tts, /api/quantum/*, /api/ai/status +curl http://localhost:7071/api/ai/status | python -m json.tool # health check +``` + +--- + +## 🏗️ Project Structure + +``` +apps/aria/ Animated character stage (HTML/CSS/JS + Python API server) +apps/chat/ Browser-based streaming chat UI +ai-projects/chat-cli/ Multi-provider chat CLI +ai-projects/quantum-ml/ Quantum ML platform (circuits, MCP server, Azure Quantum) +ai-projects/llm-maker/ Autonomous tool-creation system +ai-projects/cooking-ai/ Cooking-focused AI assistant +AI/ LoRA fine-tuning workspace (Phi / TinyLlama) +shared/ Shared Python modules (providers, DB, telemetry, Cosmos) +scripts/ Orchestration, training, evaluation, and utility scripts +config/ YAML configs for orchestrators +datasets/ Read-only training datasets +data_out/ All generated outputs (git-ignored) +function_app.py Azure Functions entry point (all /api/* endpoints) +``` + +--- + +## 🎭 Aria Character + +The Aria character runs at `http://localhost:8080` (or the [GitHub Pages demo](https://bryan-roe.github.io/Aria)). + +**Natural language commands (examples):** + +| Command | Effect | +| --- | --- | +| `move left` / `move right` | Walk to stage edge | +| `wave` / `dance` / `jump` | Trigger gesture | +| `pick up the ball` | Pick up a nearby object | +| `throw the ball` | Throw held object with physics | +| `say hello` | Aria speaks the text aloud via TTS | + +The auto-execute system parses complex multi-step requests ("walk to the table and pick up the apple") into a structured sequence of 8 core actions: `move`, `say`, `pickup`, `drop`, `throw`, `gesture`, `look`, `wait`. + +**Aria web server API (port 8080):** + +| Method | Path | Description | +| --- | --- | --- | +| `GET` | `/api/aria/state` | Current stage state (position, objects, expressions) | +| `POST` | `/api/aria/command` | Process a natural language command | +| `POST` | `/api/aria/execute` | Auto-execute an action sequence | +| `POST` | `/api/aria/object` | Add / update / remove an object | +| `POST` | `/api/aria/world` | Generate a themed world via LLM | + +--- + +## 💬 Chat Providers + +Provider auto-detection order: + +``` +LM Studio → Ollama → Azure OpenAI → OpenAI → Local (zero-dependency echo) +``` + +Pass `--provider` to override: `local`, `openai`, `azure`, `lmstudio`, `ollama`, `lora`, `quantum`, `agi`. + +**Azure OpenAI** — all four variables required: + +``` +AZURE_OPENAI_API_KEY +AZURE_OPENAI_ENDPOINT +AZURE_OPENAI_DEPLOYMENT +AZURE_OPENAI_API_VERSION +``` + +**LoRA adapter** — adapter directory must contain: + +``` +adapter_config.json +adapter_model.safetensors +``` + +```bash +python ai-projects/chat-cli/src/chat_cli.py --provider lora --model data_out/lora_training/lora_adapter +``` + +All providers implement `BaseChatProvider.complete(messages, stream)`. Add a new provider by subclassing `BaseChatProvider` in `shared/chat_providers.py`. + +--- + +## ⚛️ Quantum ML (Experimental) + +Local Qiskit Aer simulation is free and unlimited. Azure simulator backends are also free. Real QPU hardware is billed per gate-shot — always simulate first. + +**Workflow:** Test locally → Validate on Azure simulator → Run on QPU (set `azure_confirm_cost: true` in YAML first) + +```bash +# Validate config without running anything +python scripts/quantum_autorun.py --dry-run + +# Interactive training dashboard +cd ai-projects/quantum-ml && ./start_dashboard.sh # http://localhost:5000 + +# Start the MCP server (8 quantum tools) +python ai-projects/quantum-ml/quantum_mcp_server.py +``` + +**MCP tools:** `create_quantum_circuit`, `simulate_quantum_circuit`, `get_quantum_circuit_properties`, `connect_azure_quantum`, `list_quantum_backends`, `submit_quantum_job`, `estimate_quantum_cost`, `train_quantum_classifier`. + +--- + +## 🧠 LoRA Fine-Tuning + +Train a small model on Aria-specific datasets using LoRA adapters. + +```bash +# Quick training run (TinyLlama, CPU-friendly, ~10–15 s) +python scripts/automated_training_pipeline.py --models tinyllama --quick + +# Full train → evaluate → auto-promote best checkpoint +python scripts/train_and_promote.py --quick --auto-promote + +# Validate configs without running +python scripts/autotrain.py --dry-run +``` + +Training datasets are in `datasets/chat/aria_movement/`, `aria_expanded/`, and `aria_simple/` (read-only). +Outputs are written to `data_out/lora_training/`. + +--- + +## 🤖 Autonomous Training + +A background orchestrator continuously discovers datasets, trains, and evaluates models on a 30-minute cycle. + +```bash +# Start the autonomous loop (runs indefinitely) +nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 & + +# Check live status +cat data_out/autonomous_training_status.json | python -m json.tool +tail -f data_out/autonomous_training.log +``` + +--- + +## 🔧 LLM Tool Maker + +An autonomous system where an LLM generates, validates, and sandboxes Python tools at runtime. + +```bash +cd llm-maker +python examples/quick_start.py # create a tool from a description +python llm_maker_mcp_server.py # expose tools via MCP +``` + +Security: no dangerous imports, no filesystem or network access, no `eval`/`exec`, sandboxed execution with resource limits. + +--- + +## 🧪 Testing + +```bash +# Fast unit tests (~0.5 s, no external services) +python scripts/test_runner.py --unit + +# All fast tests (unit + integration, ~10 s) +python scripts/test_runner.py --all + +# With coverage report +python scripts/test_runner.py --all --coverage + +# One-command integration contract gate (local) +bash ./scripts/integration_contract_gate.sh + +# Strict gate (requires local Functions host at :7071) +bash ./scripts/integration_contract_gate.sh --strict-endpoints + +# Direct pytest +pytest -m "not slow and not azure" tests/ +``` + +VS Code users: open the Test Explorer (🧪 beaker icon) for interactive test running and debugging. + +--- + +## 🌐 Live Demo + +**[https://bryan-roe.github.io/Aria](https://bryan-roe.github.io/Aria)** + +The demo runs in mock mode with simulated API responses — no API keys needed. For full AI capabilities, run the project locally. + +--- + +## 🔒 Configuration & Secrets + +Copy the example files to get started: + +```bash +cp .env.example .env +cp local.settings.json.example local.settings.json +# Fill in API keys as needed +``` + +Never commit secrets. All keys belong in environment variables or `local.settings.json` (development only). + +**Optional services** (feature-flagged — safe to leave unset): + +| Service | How to enable | +| --- | --- | +| SQL persistence | `QAI_DB_CONN` env var (SQLite, PostgreSQL, or Azure SQL) | +| Cosmos DB | `QAI_ENABLE_COSMOS=true` + `COSMOS_ENDPOINT`, `COSMOS_KEY`, `COSMOS_DATABASE`, `COSMOS_CONTAINER` | +| Application Insights | `APPLICATIONINSIGHTS_CONNECTION_STRING` | +| Azure Speech TTS | `AZURE_SPEECH_KEY` + `AZURE_SPEECH_REGION` | +| Local TTS fallback | `QAI_ENABLE_LOCAL_TTS=true` (uses pyttsx3 or gTTS when Azure credentials are absent) | + +--- + +## 📚 Documentation + +| Document | Purpose | +| --- | --- | +| [apps/aria/README.md](apps/aria/README.md) | Character stage API reference | +| [ai-projects/quantum-ml/README.md](ai-projects/quantum-ml/README.md) | Quantum ML platform guide | +| [ai-projects/chat-cli/README.md](ai-projects/chat-cli/README.md) | Chat CLI reference | +| [ai-projects/llm-maker/README.md](ai-projects/llm-maker/README.md) | Tool maker guide | +| [docs/aria/](docs/aria/) | Aria movement & training documentation | +| [MONETIZATION_GUIDE.md](MONETIZATION_GUIDE.md) | Subscription and revenue system | +| [docs/guides/REPO_AUTOMATION_GUIDE.md](docs/guides/REPO_AUTOMATION_GUIDE.md) | Full repository automation reference | +| [QUANTUM_LLM_TRAINING.md](QUANTUM_LLM_TRAINING.md) | Quantum-LLM concurrent training | + +--- + +## 🤝 Contributing + +- Update `README.md` when adding configuration options, changing CLI flags, introducing new providers, or modifying cost behaviour. +- All output files go under `data_out/` (git-ignored). Never modify files under `datasets/`. +- Always run `--dry-run` on orchestrators before executing GPU or QPU workloads. + +--- + +## 📄 License + +See individual project directories for license information. + +## PLAN (pseudocode) + +## 1) Validate OPENAI_API_KEY + +## 2) Read prompt from CLI args or stdin + +## 3) Call OpenAI Responses API + +## 4) Extract text safely from response.output_text with fallback parsing + +## 5) Print final text; fail gracefully on errors + +import os +import sys +from openai import OpenAI + +MODEL = "gpt-4o-mini" +SYSTEM_PROMPT = "You are a concise AI coding assistant. Return practical code-focused responses." + +def _extract_text(resp) -> str: + if getattr(resp, "output_text", None): + return resp.output_text.strip() + + parts = [] + for item in getattr(resp, "output", []) or []: + for content in getattr(item, "content", []) or []: + if getattr(content, "type", "") == "output_text": + text = getattr(content, "text", "") + if text: + parts.append(text) + return "\n".join(parts).strip() + +def ask_ai(client: OpenAI, prompt: str) -> str: + resp = client.responses.create( + model=MODEL, + input=[ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": prompt}, + ], + temperature=0.2, + ) + return _extract_text(resp) + +def main() -> None: + api_key = os.getenv("OPENAI_API_KEY") + if not api_key: + raise RuntimeError("Missing OPENAI_API_KEY environment variable.") + + prompt = " ".join(sys.argv[1:]).strip() + if not prompt: + prompt = input("Prompt: ").strip() + if not prompt: + raise ValueError("Prompt cannot be empty.") + + client = OpenAI(api_key=api_key) + + try: + output = ask_ai(client, prompt) + print(output or "(No text returned.)") + except Exception as exc: + print(f"AI request failed: {exc}", file=sys.stderr) + sys.exit(1) + +if **name** == "**main**": + main() diff --git a/ai-projects/quantum-ml/src/quantum_llm/README.md b/ai-projects/quantum-ml/src/quantum_llm/README.md new file mode 100644 index 000000000..75fa4c6ef --- /dev/null +++ b/ai-projects/quantum-ml/src/quantum_llm/README.md @@ -0,0 +1,162 @@ +# Quantum-Powered LLM Module + +A quantum-augmented LLM pipeline that fuses Qiskit / PennyLane quantum circuits with the Aria multi-provider chat backend. + +## Architecture + +```mermaid +graph TD + A[User Prompt] --> B[QuantumRouter] + B -->|selects provider| C[LLM Provider\nazure / openai / lmstudio / local] + A --> D[QuantumEmbeddingTransformer] + D -->|augmented embedding| C + C -->|raw response / logits| E[QuantumSampler] + E -->|re-weighted tokens| F[Final Response] + F --> G[/api/quantum-llm/chat\nor stream] +``` + +## Components + +| Component | File | Description | +|-----------|------|-------------| +| `QuantumSampler` | `quantum_sampler.py` | Re-weights LLM top-k logits via variational circuit | +| `QuantumEmbeddingTransformer` | `quantum_embeddings.py` | Amplitude-encoding + variational transform | +| `QuantumRouter` | `quantum_router.py` | QAOA-style provider routing | +| `QuantumLLMPipeline` | `pipeline.py` | Wires all components; exposes `generate` / `stream` | +| `QuantumLLMConfig` | `config.py` | Dataclass settings with env-var support | + +## Backends + +Detection order (when `backend="auto"`): + +1. **PennyLane** (`default.qubit` simulator) — if `pennylane` is importable +2. **Qiskit + AerSimulator** — if `qiskit` and `qiskit-aer` are importable +3. **Classical fallback** — pure NumPy, always available + +No hard dependencies are added to the base install. Quantum packages remain optional (`[quantum]` extras in `requirements.txt`). + +## Configuration + +All settings can be overridden via environment variables: + +| Env var | Default | Description | +|---------|---------|-------------| +| `QUANTUM_LLM_BACKEND` | `auto` | `auto\|pennylane\|qiskit\|classical` | +| `QUANTUM_LLM_QUBITS` | `4` | Number of qubits | +| `QUANTUM_LLM_SHOTS` | `512` | Measurement shots | +| `QUANTUM_LLM_LAYERS` | `2` | Variational circuit depth | +| `QUANTUM_LLM_TOP_K` | `10` | Top-k candidates for sampling | +| `QUANTUM_LLM_TEMP_BLEND` | `0.3` | Quantum / classical blend (0–1) | +| `QUANTUM_LLM_PROVIDER` | `auto` | Downstream LLM provider | +| `QUANTUM_LLM_MAX_TOKENS` | `512` | Max output tokens | +| `QUANTUM_LLM_MAX_PROMPT_CHARS` | `8000` | Prompt size limit | + +Python usage: + +```python +from quantum_llm import QuantumLLMConfig, QuantumLLMPipeline +import asyncio + +cfg = QuantumLLMConfig(backend="classical", num_qubits=4, shots=512) +pipeline = QuantumLLMPipeline(config=cfg) + +result = asyncio.run(pipeline.generate("Explain quantum entanglement")) +print(result["response"]) +``` + +## API Endpoints + +All endpoints are registered in `function_app.py` and served by Azure Functions. + +### `GET /api/quantum-llm/status` + +Returns the active backend, qubit count, fallback state, and downstream provider. + +```bash +curl http://localhost:7071/api/quantum-llm/status +``` + +```json +{ + "status": "ok", + "backend": "classical", + "fallback": true, + "num_qubits": 4, + "shots": 512, + "provider": "auto" +} +``` + +### `POST /api/quantum-llm/chat` + +Non-streaming JSON completion. + +```bash +curl -X POST http://localhost:7071/api/quantum-llm/chat \ + -H "Content-Type: application/json" \ + -d '{"prompt": "Hello, Aria!"}' +``` + +```json +{ + "response": "Hello! I am Aria...", + "provider": "local-echo", + "backend": "classical", + "qubits": 4, + "shots": 512, + "latency_ms": 12.3, + "quantum_augmented": true +} +``` + +Optional body fields: + +| Field | Type | Description | +|-------|------|-------------| +| `prompt` | string | **Required** — user prompt | +| `provider` | string | Override provider (`azure`, `openai`, `lmstudio`, `local`) | +| `backend` | string | Override quantum backend | +| `num_qubits` | int | Override qubit count | +| `max_tokens` | int | Max output tokens | +| `seed` | int | Random seed for reproducibility | + +### `POST /api/quantum-llm/stream` + +SSE streaming response (same event format as `/api/chat/stream`). + +```bash +curl -X POST http://localhost:7071/api/quantum-llm/stream \ + -H "Content-Type: application/json" \ + -d '{"prompt": "Tell me about quantum computing"}' \ + --no-buffer +``` + +Events: +``` +event: meta +data: {"provider": "local-echo", "backend": "classical", "qubits": 4, "shots": 512} + +data: {"delta": "Quantum computing "} + +data: {"delta": "uses qubits..."} + +data: {"latency_ms": 45.2, "quantum_augmented": true} + +data: [DONE] +``` + +## Running Tests + +Tests use the classical fallback so no quantum SDK is required: + +```bash +# From repo root +python scripts/test_runner.py --unit + +# Or directly +pytest tests/test_quantum_llm.py -v +``` + +## Web UI + +The Aria chat interface (`apps/aria/index.html`) includes a **Quantum Mode** toggle. When enabled, chat requests are routed through `/api/quantum-llm/stream` instead of `/api/chat/stream`. Backend status is surfaced in the footer status bar. diff --git a/ai-projects/quantum-ml/src/quantum_llm/__init__.py b/ai-projects/quantum-ml/src/quantum_llm/__init__.py new file mode 100644 index 000000000..0b490b6cc --- /dev/null +++ b/ai-projects/quantum-ml/src/quantum_llm/__init__.py @@ -0,0 +1,24 @@ +""" +Quantum LLM module — public API exports. + +Usage +----- + from quantum_llm import QuantumLLMPipeline, QuantumLLMConfig + + pipeline = QuantumLLMPipeline() + result = asyncio.run(pipeline.generate("Hello, world!")) +""" + +from .config import QuantumLLMConfig +from .pipeline import QuantumLLMPipeline +from .quantum_embeddings import QuantumEmbeddingTransformer +from .quantum_router import QuantumRouter +from .quantum_sampler import QuantumSampler + +__all__ = [ + "QuantumLLMConfig", + "QuantumLLMPipeline", + "QuantumEmbeddingTransformer", + "QuantumRouter", + "QuantumSampler", +] diff --git a/ai-projects/quantum-ml/src/quantum_llm/config.py b/ai-projects/quantum-ml/src/quantum_llm/config.py new file mode 100644 index 000000000..d0b7a9ba6 --- /dev/null +++ b/ai-projects/quantum-ml/src/quantum_llm/config.py @@ -0,0 +1,79 @@ +""" +Configuration settings for the Quantum-Powered LLM module. + +Supports dataclass-based config (no pydantic dependency required). +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass, field +from typing import Literal, Optional + + +@dataclass +class QuantumLLMConfig: + """Settings for QuantumLLMPipeline and its sub-components.""" + + # Quantum backend selection + backend: Literal["auto", "qiskit", "pennylane", "classical"] = "auto" + + # Circuit parameters + num_qubits: int = 4 + shots: int = 512 + num_layers: int = 2 # variational circuit depth + + # Sampling parameters + top_k: int = 10 + temperature_blend: float = 0.3 # blend factor between quantum and classical distributions (0=classical, 1=quantum) + + # LLM provider settings + provider: str = "auto" # forwarded to detect_provider + model: Optional[str] = None + temperature: float = 0.7 + max_tokens: int = 512 + + # Security / validation + max_prompt_chars: int = 8000 + max_tokens_cap: int = 2048 + + # Performance + use_thread: bool = True # asyncio.to_thread for blocking quantum calls + + @classmethod + def from_env(cls) -> "QuantumLLMConfig": + """Build config from environment variables.""" + # The `type: ignore[arg-type]` is suppressed because `os.getenv` returns + # `str | None`; we validate and fall back to "auto" if the value is invalid. + raw_backend = os.getenv("QUANTUM_LLM_BACKEND", "auto") + valid_backends = {"auto", "qiskit", "pennylane", "classical"} + backend_val = raw_backend if raw_backend in valid_backends else "auto" + return cls( + backend=backend_val, # type: ignore[arg-type] + num_qubits=int(os.getenv("QUANTUM_LLM_QUBITS", "4")), + shots=int(os.getenv("QUANTUM_LLM_SHOTS", "512")), + num_layers=int(os.getenv("QUANTUM_LLM_LAYERS", "2")), + top_k=int(os.getenv("QUANTUM_LLM_TOP_K", "10")), + temperature_blend=float(os.getenv("QUANTUM_LLM_TEMP_BLEND", "0.3")), + provider=os.getenv("QUANTUM_LLM_PROVIDER", "auto"), + model=os.getenv("QUANTUM_LLM_MODEL"), + temperature=float(os.getenv("QUANTUM_LLM_TEMPERATURE", "0.7")), + max_tokens=int(os.getenv("QUANTUM_LLM_MAX_TOKENS", "512")), + max_prompt_chars=int(os.getenv("QUANTUM_LLM_MAX_PROMPT_CHARS", "8000")), + max_tokens_cap=int(os.getenv("QUANTUM_LLM_MAX_TOKENS_CAP", "2048")), + ) + + def to_dict(self) -> dict: + """Serialize to dict for status/health endpoints.""" + return { + "backend": self.backend, + "num_qubits": self.num_qubits, + "shots": self.shots, + "num_layers": self.num_layers, + "top_k": self.top_k, + "temperature_blend": self.temperature_blend, + "provider": self.provider, + "model": self.model, + "temperature": self.temperature, + "max_tokens": self.max_tokens, + } diff --git a/ai-projects/quantum-ml/src/quantum_llm/pipeline.py b/ai-projects/quantum-ml/src/quantum_llm/pipeline.py new file mode 100644 index 000000000..bb6ec37f5 --- /dev/null +++ b/ai-projects/quantum-ml/src/quantum_llm/pipeline.py @@ -0,0 +1,362 @@ +""" +QuantumLLMPipeline — wires sampler + embeddings + router together. + +Exposes: +- ``async generate(prompt, **kwargs)`` → JSON-serialisable dict +- ``async stream(prompt, **kwargs)`` → async generator of SSE-compatible chunks + +The SSE format mirrors ``/api/chat/stream``: + ``data: {"delta": "..."}\n\n`` + final: ``data: [DONE]\n\n`` +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import time +from typing import Any, AsyncIterator, Dict, List, Optional + +import numpy as np + +from .config import QuantumLLMConfig +from .quantum_embeddings import QuantumEmbeddingTransformer +from .quantum_router import QuantumRouter +from .quantum_sampler import QuantumSampler, _active_backend + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _resolve_detect_provider(): + """Try to import detect_provider from the existing chat-cli stack.""" + try: + from pathlib import Path + import sys + + # Walk up to find the repo root (sentinel: pyproject.toml or .git) + current = Path(__file__).resolve() + repo_root = None + for parent in current.parents: + if (parent / "pyproject.toml").exists() or (parent / ".git").exists(): + repo_root = parent + break + if repo_root is None: + return None + + chat_src = repo_root / "ai-projects" / "chat-cli" / "src" + if not chat_src.exists(): + return None + if str(chat_src) not in sys.path: + sys.path.insert(0, str(chat_src)) + + from chat_providers import detect_provider # type: ignore + + return detect_provider + except ImportError: + return None + + +def _make_local_provider(): + """Return a minimal no-dependency provider that echoes the prompt.""" + + class _EchoProvider: + name = "local-echo" + + def complete(self, messages, stream=False): + text = messages[-1].get("content", "") if messages else "" + return f"[quantum-llm echo] {text}" + + return _EchoProvider() + + +# --------------------------------------------------------------------------- +# Pipeline +# --------------------------------------------------------------------------- + + +class QuantumLLMPipeline: + """ + End-to-end Quantum-Powered LLM pipeline. + + Combines: + 1. QuantumRouter — picks the best downstream LLM provider + 2. QuantumEmbeddingTransformer — transforms the prompt embedding + 3. QuantumSampler — re-weights token sampling probabilities + 4. Downstream LLM provider — generates actual text + + Parameters + ---------- + config : QuantumLLMConfig, optional + Pipeline configuration. Defaults to ``QuantumLLMConfig.from_env()``. + """ + + def __init__(self, config: Optional[QuantumLLMConfig] = None) -> None: + self.config = config or QuantumLLMConfig.from_env() + cfg = self.config + + self.sampler = QuantumSampler( + backend=cfg.backend, + num_qubits=cfg.num_qubits, + shots=cfg.shots, + num_layers=cfg.num_layers, + ) + self.embedder = QuantumEmbeddingTransformer( + backend=cfg.backend, + num_qubits=cfg.num_qubits, + num_layers=cfg.num_layers, + ) + self.router = QuantumRouter( + backend=cfg.backend, + num_qubits=cfg.num_qubits, + ) + + # Effective (resolved) backend + self.effective_backend = _active_backend(cfg.backend) + self._detect_provider = _resolve_detect_provider() + logger.info( + "QuantumLLMPipeline ready: backend=%s, provider=%s", + self.effective_backend, + cfg.provider, + ) + + # ------------------------------------------------------------------ + # Provider resolution + # ------------------------------------------------------------------ + + def _get_provider(self, prompt: str, explicit_provider: Optional[str] = None): + """Resolve and return a chat provider instance.""" + provider_name = explicit_provider or self.config.provider + + # Let the quantum router pick if "auto" + if provider_name == "auto" and self._detect_provider is None: + provider_name = self.router.route(prompt) + + if self._detect_provider is not None: + try: + provider, _ = self._detect_provider( + explicit=provider_name if provider_name != "auto" else None, + model_override=self.config.model, + temperature=self.config.temperature, + max_output_tokens=min( + self.config.max_tokens, self.config.max_tokens_cap + ), + ) + return provider + except Exception as exc: # noqa: BLE001 + logger.warning("detect_provider failed (%s), using echo fallback", exc) + + return _make_local_provider() + + # ------------------------------------------------------------------ + # Quantum augmentation of a text embedding + # ------------------------------------------------------------------ + + def _augment_embedding(self, text: str) -> np.ndarray: + """Return quantum-transformed byte embedding for ``text``.""" + raw = np.frombuffer(text[:256].encode("utf-8", errors="replace"), dtype=np.uint8).astype(float) + raw = raw / 255.0 # normalize to [0, 1] + return self.embedder.transform(raw) + + # ------------------------------------------------------------------ + # Token re-sampling + # ------------------------------------------------------------------ + + def _resample_response(self, text: str, seed: Optional[int] = None) -> str: + """ + Apply quantum token re-sampling to a generated text. + + Uses word-level logit simulation: assigns synthetic logits based on + character frequencies, then re-samples word order. In a real system + this hook would receive the raw top-k logits from the LLM. + """ + words = text.split() + if len(words) <= 1: + return text + + # Synthetic logits: prefer shorter words (simulate token probability) + logits = [-len(w) * 0.1 for w in words] + k = min(len(words), self.config.top_k) + top_indices = sorted(range(len(words)), key=lambda i: logits[i], reverse=True)[:k] + top_logits = [logits[i] for i in top_indices] + + sampled_pos = self.sampler.sample( + top_logits, + blend_factor=self.config.temperature_blend, + seed=seed, + ) + # Keep original text but note sampling happened (in production you'd + # use the sampled index to select a continuation token) + return text + + # ------------------------------------------------------------------ + # Public async API + # ------------------------------------------------------------------ + + async def generate( + self, + prompt: str, + provider: Optional[str] = None, + seed: Optional[int] = None, + **kwargs: Any, + ) -> Dict[str, Any]: + """ + Generate a completion for ``prompt`` (non-streaming). + + Parameters + ---------- + prompt : str + User prompt. + provider : str, optional + Override provider selection. + seed : int, optional + Random seed for reproducibility. + + Returns + ------- + dict with keys: response, provider, backend, qubits, shots, latency_ms + """ + # Validate + if len(prompt) > self.config.max_prompt_chars: + raise ValueError( + f"Prompt too long: {len(prompt)} chars (max {self.config.max_prompt_chars})" + ) + + t0 = time.monotonic() + + # Quantum embedding augmentation (run in thread to avoid blocking) + if self.config.use_thread: + embedding = await asyncio.to_thread(self._augment_embedding, prompt) + else: + embedding = self._augment_embedding(prompt) + + # Provider selection + llm_provider = self._get_provider(prompt, provider) + + # Build messages + messages = [{"role": "user", "content": prompt}] + + # LLM call (blocking — run in thread) + def _call(): + result = llm_provider.complete(messages, stream=False) + if isinstance(result, str): + return result + if isinstance(result, bytes): + return result.decode("utf-8", errors="replace") + try: + return "".join(str(chunk) for chunk in result) + except Exception: + return str(result) + + if self.config.use_thread: + raw_response = await asyncio.to_thread(_call) + else: + raw_response = _call() + + # Quantum re-sampling + response = self._resample_response(raw_response, seed=seed) + + latency_ms = round((time.monotonic() - t0) * 1000, 1) + provider_name = getattr(llm_provider, "name", str(type(llm_provider).__name__)) + + return { + "response": response, + "provider": provider_name, + "backend": self.effective_backend, + "qubits": self.config.num_qubits, + "shots": self.config.shots, + "latency_ms": latency_ms, + "quantum_augmented": True, + } + + async def stream( + self, + prompt: str, + provider: Optional[str] = None, + seed: Optional[int] = None, + **kwargs: Any, + ) -> AsyncIterator[str]: + """ + Stream SSE chunks for ``prompt``. + + Yields strings in SSE format: + ``"data: {"delta": "..."}\n\n"`` + Final chunk: ``"data: [DONE]\n\n"`` + + Parameters + ---------- + prompt : str + provider : str, optional + seed : int, optional + """ + if len(prompt) > self.config.max_prompt_chars: + yield f'data: {json.dumps({"error": "Prompt too long"})}\n\n' + yield "data: [DONE]\n\n" + return + + t0 = time.monotonic() + llm_provider = self._get_provider(prompt, provider) + provider_name = getattr(llm_provider, "name", str(type(llm_provider).__name__)) + + # Meta event + meta = { + "provider": provider_name, + "backend": self.effective_backend, + "qubits": self.config.num_qubits, + "shots": self.config.shots, + } + yield f"event: meta\ndata: {json.dumps(meta)}\n\n" + + messages = [{"role": "user", "content": prompt}] + + def _stream_call(): + try: + result = llm_provider.complete(messages, stream=True) + if isinstance(result, str): + return [result] + return result + except Exception as exc: # noqa: BLE001 + return [f"[error: {exc}]"] + + if self.config.use_thread: + chunks = await asyncio.to_thread(_stream_call) + else: + chunks = _stream_call() + + full_text = "" + for chunk in chunks: + if isinstance(chunk, bytes): + chunk = chunk.decode("utf-8", errors="replace") + full_text += chunk + yield f'data: {json.dumps({"delta": chunk})}\n\n' + + # Apply quantum re-sampling metadata (just signals to client that it happened) + _ = self._resample_response(full_text, seed=seed) + + latency_ms = round((time.monotonic() - t0) * 1000, 1) + done_meta = {"latency_ms": latency_ms, "quantum_augmented": True} + yield f'data: {json.dumps(done_meta)}\n\n' + yield "data: [DONE]\n\n" + + def status(self) -> Dict[str, Any]: + """Return a health/status dict for the ``/api/quantum-llm/status`` endpoint.""" + return { + "backend": self.effective_backend, + "fallback": self.effective_backend == "classical", + "num_qubits": self.config.num_qubits, + "shots": self.config.shots, + "num_layers": self.config.num_layers, + "top_k": self.config.top_k, + "temperature_blend": self.config.temperature_blend, + "provider": self.config.provider, + "model": self.config.model, + "router_providers": self.router.providers, + "router_backend": self.router.effective_backend, + "embedder_backend": self.embedder.effective_backend, + "sampler_backend": self.sampler.effective_backend, + } diff --git a/ai-projects/quantum-ml/src/quantum_llm/quantum_embeddings.py b/ai-projects/quantum-ml/src/quantum_llm/quantum_embeddings.py new file mode 100644 index 000000000..0bce21d26 --- /dev/null +++ b/ai-projects/quantum-ml/src/quantum_llm/quantum_embeddings.py @@ -0,0 +1,242 @@ +""" +Quantum embedding transformer with classical fallback. + +QuantumEmbeddingTransformer takes a classical embedding vector, applies +amplitude-encoding + variational circuit transformation, and returns a +transformed embedding of the same dimensionality. + +When no quantum backend is installed, a numpy-only simulation is used. +""" + +from __future__ import annotations + +import logging +import warnings +from typing import Optional + +import numpy as np + +from .quantum_sampler import _active_backend, _PENNYLANE_AVAILABLE, _QISKIT_AVAILABLE + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Classical (numpy) fallback +# --------------------------------------------------------------------------- + + +def _classical_amplitude_transform( + embedding: np.ndarray, + params: np.ndarray, + num_qubits: int, +) -> np.ndarray: + """ + Simulate amplitude encoding + rotation via numpy linear algebra. + + The embedding is encoded as quantum amplitudes, a rotation matrix is + constructed from params, and the result is re-projected back to the + original dimensionality. + """ + dim = len(embedding) + + # Amplitude-encode: normalize to unit vector + norm = np.linalg.norm(embedding) + if norm < 1e-12: + return embedding.copy() + state = embedding / norm + + # Construct a rotation matrix: apply RY-like rotations using cos/sin + n_params = min(len(params), dim) + angles = np.resize(params, dim) * np.pi + + # Diagonal RY-style rotation: cos(θ) on amplitude, sin(θ) as phase shift + # (approximate classical analogue of single-qubit RY rotations) + rot = np.diag(np.cos(angles)) # real rotation via cosine + + # Apply and project back + transformed = rot @ state + # Re-scale to original magnitude + return transformed * norm + + +# --------------------------------------------------------------------------- +# PennyLane embedding +# --------------------------------------------------------------------------- + + +def _pennylane_amplitude_transform( + embedding: np.ndarray, + params: np.ndarray, + num_qubits: int, + num_layers: int, +) -> np.ndarray: + """Amplitude-encode + variational transform via PennyLane.""" + import pennylane as qml # noqa: PLC0415 + + dim = len(embedding) + pad_to = 2**num_qubits + state_in = np.resize(embedding, pad_to).astype(complex) + norm = np.linalg.norm(state_in) + if norm < 1e-12: + return embedding.copy() + state_in /= norm + + dev = qml.device("default.qubit", wires=num_qubits, shots=None) + + @qml.qnode(dev) + def circuit(state_vec, params): + qml.AmplitudeEmbedding(state_vec, wires=range(num_qubits), normalize=True) + for layer in range(num_layers): + for q in range(num_qubits): + idx = (layer * num_qubits + q) % len(params) + qml.RY(params[idx], wires=q) + qml.RZ(params[(idx + 1) % len(params)], wires=q) + for q in range(num_qubits - 1): + qml.CNOT(wires=[q, q + 1]) + return qml.state() + + out_state = np.array(circuit(state_in, params), dtype=complex) + # Project back: take real amplitudes and resize to original dim + transformed = np.real(out_state) + result = np.resize(transformed, dim) * norm + return result + + +# --------------------------------------------------------------------------- +# Qiskit embedding (statevector simulation) +# --------------------------------------------------------------------------- + + +def _qiskit_amplitude_transform( + embedding: np.ndarray, + params: np.ndarray, + num_qubits: int, + num_layers: int, +) -> np.ndarray: + """Amplitude-encode + variational transform via Qiskit statevector sim.""" + try: + from qiskit import QuantumCircuit, transpile # noqa: PLC0415 + from qiskit.circuit import ParameterVector # noqa: PLC0415 + from qiskit_aer import AerSimulator # type: ignore # noqa: PLC0415 + except ImportError: + logger.warning("Qiskit/AerSimulator not available, using classical fallback") + return _classical_amplitude_transform(embedding, params, num_qubits) + + dim = len(embedding) + pad_to = 2**num_qubits + state_in = np.zeros(pad_to, dtype=complex) + state_in[: min(dim, pad_to)] = embedding[: min(dim, pad_to)] + norm = np.linalg.norm(state_in) + if norm < 1e-12: + return embedding.copy() + state_in /= norm + + n = num_qubits + total_params = num_layers * n * 2 + pv = ParameterVector("θ", total_params) + qc = QuantumCircuit(n) + qc.initialize(state_in.tolist(), range(n)) + + idx = 0 + for _layer in range(num_layers): + for q in range(n): + qc.ry(pv[idx % total_params], q) + idx += 1 + qc.rz(pv[idx % total_params], q) + idx += 1 + for q in range(n - 1): + qc.cx(q, q + 1) + + param_dict = {pv[i]: float(params[i % len(params)]) for i in range(total_params)} + bound = qc.assign_parameters(param_dict) + + sim = AerSimulator(method="statevector") + job = sim.run(transpile(bound, sim)) + sv = job.result().get_statevector() + out = np.array(sv, dtype=complex) + result = np.resize(np.real(out), dim) * norm + return result + + +# --------------------------------------------------------------------------- +# Public QuantumEmbeddingTransformer +# --------------------------------------------------------------------------- + + +class QuantumEmbeddingTransformer: + """ + Transforms a classical embedding vector using quantum amplitude encoding + followed by a variational circuit. + + The output embedding has the same dimensionality as the input. + + Parameters + ---------- + backend : str + One of "auto", "pennylane", "qiskit", "classical". + num_qubits : int + Number of qubits (default 4). Controls the internal Hilbert-space + dimension (2**num_qubits). Embeddings are padded or truncated to fit. + num_layers : int + Number of variational layers (default 2). + seed : int, optional + Random seed for parameter initialization and reproducibility. + """ + + def __init__( + self, + backend: str = "auto", + num_qubits: int = 4, + num_layers: int = 2, + seed: Optional[int] = None, + ) -> None: + self.effective_backend = _active_backend(backend) + self.num_qubits = num_qubits + self.num_layers = num_layers + self._rng = np.random.default_rng(seed) + # Randomly initialized variational parameters + n_params = num_qubits * num_layers * 2 + self._params = self._rng.uniform(-np.pi, np.pi, size=n_params) + logger.info( + "QuantumEmbeddingTransformer initialized: backend=%s, qubits=%d, layers=%d", + self.effective_backend, + num_qubits, + num_layers, + ) + + def transform(self, embedding: np.ndarray) -> np.ndarray: + """ + Apply the quantum amplitude encoding + variational transformation. + + Parameters + ---------- + embedding : np.ndarray + 1-D array of floats representing a classical embedding. + + Returns + ------- + np.ndarray + Transformed embedding with the same shape as the input. + """ + if embedding.ndim != 1: + raise ValueError( + f"Expected 1-D embedding, got shape {embedding.shape}" + ) + embedding = embedding.astype(float) + + if self.effective_backend == "pennylane": + return _pennylane_amplitude_transform( + embedding, self._params, self.num_qubits, self.num_layers + ) + if self.effective_backend == "qiskit": + return _qiskit_amplitude_transform( + embedding, self._params, self.num_qubits, self.num_layers + ) + return _classical_amplitude_transform( + embedding, self._params, self.num_qubits + ) + + def update_params(self, params: np.ndarray) -> None: + """Override the variational parameters (e.g. after training).""" + self._params = np.array(params, dtype=float) diff --git a/ai-projects/quantum-ml/src/quantum_llm/quantum_router.py b/ai-projects/quantum-ml/src/quantum_llm/quantum_router.py new file mode 100644 index 000000000..8fc9c3306 --- /dev/null +++ b/ai-projects/quantum-ml/src/quantum_llm/quantum_router.py @@ -0,0 +1,247 @@ +""" +Quantum-inspired provider router with classical fallback. + +QuantumRouter selects among multiple LLM providers based on prompt features +(length, language, latency budget) using a QAOA-style / Grover-inspired +routine. When no quantum backend is installed a classical heuristic is used. + +Provider abstraction is compatible with the existing chat provider interface +in ``ai-projects/chat-cli/src/chat_providers.py`` and ``shared/``. +""" + +from __future__ import annotations + +import logging +import math +import warnings +from typing import Dict, List, Optional, Tuple + +import numpy as np + +from .quantum_sampler import _active_backend + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Feature extraction +# --------------------------------------------------------------------------- + + +def _extract_prompt_features( + prompt: str, + latency_budget_ms: float = 5000.0, +) -> np.ndarray: + """ + Extract a small numeric feature vector from a prompt string. + + Features (normalized to [0, 1]): + 0. prompt length (chars / max_chars) + 1. word count (words / 500) + 2. question mark presence + 3. code block presence + 4. latency budget (budget_ms / 10_000) + + Returns + ------- + np.ndarray shape (5,) + """ + length = min(len(prompt) / 8000.0, 1.0) + words = min(len(prompt.split()) / 500.0, 1.0) + has_question = float("?" in prompt) + has_code = float("```" in prompt or " " in prompt) + budget = min(latency_budget_ms / 10_000.0, 1.0) + return np.array([length, words, has_question, has_code, budget], dtype=float) + + +# --------------------------------------------------------------------------- +# Classical scoring / routing +# --------------------------------------------------------------------------- + + +def _classical_score_providers( + features: np.ndarray, + providers: List[str], + params: np.ndarray, +) -> np.ndarray: + """ + Score each provider using a simple parameterized linear model. + + Returns an array of scores (higher is better). + """ + n = len(providers) + scores = np.zeros(n, dtype=float) + for i in range(n): + # Each provider gets a weight vector of size len(features) + weights = params[i * len(features) : (i + 1) * len(features)] + if len(weights) < len(features): + weights = np.resize(weights, len(features)) + scores[i] = float(np.dot(weights, features)) + return scores + + +# --------------------------------------------------------------------------- +# QAOA-style scoring (PennyLane) +# --------------------------------------------------------------------------- + + +def _pennylane_qaoa_scores( + features: np.ndarray, + providers: List[str], + params: np.ndarray, + num_qubits: int, +) -> np.ndarray: + """Score providers using a QAOA-inspired variational circuit via PennyLane.""" + import pennylane as qml # noqa: PLC0415 + + n_providers = len(providers) + scores = np.zeros(n_providers, dtype=float) + + dev = qml.device("default.qubit", wires=num_qubits, shots=None) + + for i, _provider in enumerate(providers): + @qml.qnode(dev) + def circuit(features, params, i=i): # capture i + # Encode features as rotation angles + for q in range(min(len(features), num_qubits)): + qml.RY(features[q] * np.pi, wires=q) + # QAOA-style mixing layers + n_layers = max(1, len(params) // (2 * num_qubits)) + for layer in range(n_layers): + for q in range(num_qubits): + idx = (layer * num_qubits + q) % len(params) + qml.RZ(params[idx], wires=q) + for q in range(num_qubits - 1): + qml.CNOT(wires=[q, q + 1]) + # Provider-specific expectation + wire = i % num_qubits + return qml.expval(qml.PauliZ(wires=wire)) + + scores[i] = float(circuit(features, params)) + + return scores + + +# --------------------------------------------------------------------------- +# Public QuantumRouter +# --------------------------------------------------------------------------- + + +class QuantumRouter: + """ + Routes prompts to the best LLM provider using quantum-circuit scoring. + + Parameters + ---------- + providers : list of str + Ordered list of provider names (e.g. ``["azure", "openai", "local"]``). + backend : str + One of "auto", "pennylane", "qiskit", "classical". + num_qubits : int + Number of qubits for the routing circuit (default 4). + seed : int, optional + Random seed for parameter initialization. + """ + + def __init__( + self, + providers: Optional[List[str]] = None, + backend: str = "auto", + num_qubits: int = 4, + seed: Optional[int] = None, + ) -> None: + self.providers = list(providers or ["azure", "openai", "lmstudio", "local"]) + self.effective_backend = _active_backend(backend) + self.num_qubits = num_qubits + self._rng = np.random.default_rng(seed) + # Random initial params; feature dim is 5 + n_params = len(self.providers) * 5 + self._params = self._rng.uniform(-np.pi, np.pi, size=n_params) + logger.info( + "QuantumRouter initialized: backend=%s, providers=%s", + self.effective_backend, + self.providers, + ) + + def route( + self, + prompt: str, + latency_budget_ms: float = 5000.0, + exclude: Optional[List[str]] = None, + ) -> str: + """ + Select the best provider for the given prompt. + + Parameters + ---------- + prompt : str + The user's prompt text. + latency_budget_ms : float + Desired maximum latency in milliseconds. + exclude : list of str, optional + Provider names to exclude from routing. + + Returns + ------- + str + Selected provider name. + """ + candidates = [p for p in self.providers if p not in (exclude or [])] + if not candidates: + return self.providers[0] if self.providers else "local" + if len(candidates) == 1: + return candidates[0] + + features = _extract_prompt_features(prompt, latency_budget_ms) + + try: + if self.effective_backend == "pennylane": + scores = _pennylane_qaoa_scores( + features, candidates, self._params, self.num_qubits + ) + else: + scores = _classical_score_providers(features, candidates, self._params) + except Exception as exc: # noqa: BLE001 + logger.warning("QuantumRouter scoring failed (%s), using first provider", exc) + return candidates[0] + + best_idx = int(np.argmax(scores)) + chosen = candidates[best_idx] + logger.debug( + "QuantumRouter chose '%s' (scores=%s) for prompt length=%d", + chosen, + scores.round(3).tolist(), + len(prompt), + ) + return chosen + + def route_with_scores( + self, + prompt: str, + latency_budget_ms: float = 5000.0, + ) -> Tuple[str, Dict[str, float]]: + """ + Like ``route`` but also returns the per-provider score dict. + + Returns + ------- + tuple of (chosen_provider, {provider: score, ...}) + """ + features = _extract_prompt_features(prompt, latency_budget_ms) + + try: + if self.effective_backend == "pennylane": + scores_arr = _pennylane_qaoa_scores( + features, self.providers, self._params, self.num_qubits + ) + else: + scores_arr = _classical_score_providers( + features, self.providers, self._params + ) + except Exception as exc: # noqa: BLE001 + logger.warning("QuantumRouter scoring failed (%s)", exc) + scores_arr = np.zeros(len(self.providers)) + + scores = {p: float(s) for p, s in zip(self.providers, scores_arr)} + chosen = max(scores, key=lambda k: scores[k]) + return chosen, scores diff --git a/ai-projects/quantum-ml/src/quantum_llm/quantum_sampler.py b/ai-projects/quantum-ml/src/quantum_llm/quantum_sampler.py new file mode 100644 index 000000000..bb623693e --- /dev/null +++ b/ai-projects/quantum-ml/src/quantum_llm/quantum_sampler.py @@ -0,0 +1,330 @@ +""" +Quantum-circuit-based token sampling with classical fallback. + +QuantumSampler uses a parameterized variational circuit (RY/RZ + CNOT +entanglement layers) whose measurement probabilities re-weight an LLM's +top-k logit distribution. When no quantum backend is available it falls +back to a numpy-only simulation. +""" + +from __future__ import annotations + +import logging +import warnings +from typing import Optional, Sequence + +import numpy as np + +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Backend detection +# --------------------------------------------------------------------------- +_PENNYLANE_AVAILABLE = False +_QISKIT_AVAILABLE = False + +try: + import pennylane as qml # type: ignore + + _PENNYLANE_AVAILABLE = True +except ImportError: + pass + +try: + from qiskit import QuantumCircuit # type: ignore + from qiskit.circuit import ParameterVector # type: ignore + + _QISKIT_AVAILABLE = True +except ImportError: + pass + + +def _active_backend(requested: str) -> str: + """Resolve the effective backend string.""" + if requested == "auto": + if _PENNYLANE_AVAILABLE: + return "pennylane" + if _QISKIT_AVAILABLE: + return "qiskit" + return "classical" + if requested == "pennylane" and not _PENNYLANE_AVAILABLE: + warnings.warn("PennyLane not available, falling back to classical", stacklevel=3) + return "classical" + if requested == "qiskit" and not _QISKIT_AVAILABLE: + warnings.warn("Qiskit not available, falling back to classical", stacklevel=3) + return "classical" + return requested + + +# --------------------------------------------------------------------------- +# Classical (numpy) fallback sampler +# --------------------------------------------------------------------------- + + +def _classical_variational_probs( + params: np.ndarray, + num_qubits: int, + shots: int, + rng: np.random.Generator, +) -> np.ndarray: + """ + Simulate a variational circuit classically using numpy. + + Returns a probability distribution over 2**num_qubits outcomes. + The state is built via matrix multiplication of Ry/Rz + CNOT tensors, + keeping the simulation exact (not shot-based) for reproducibility. + """ + dim = 2**num_qubits + state = np.zeros(dim, dtype=complex) + state[0] = 1.0 # |0...0> + + # Build a simple rotation-based unitary: each param rotates qubit i + n_params = len(params) + for i, theta in enumerate(params[:num_qubits]): + q = i % num_qubits + # Apply single-qubit Ry rotation to qubit q using tensor product + ry = np.array( + [[np.cos(theta / 2), -np.sin(theta / 2)], + [np.sin(theta / 2), np.cos(theta / 2)]], + dtype=complex, + ) + # Embed into full Hilbert space + op = np.eye(1, dtype=complex) + for j in range(num_qubits): + op = np.kron(op, ry if j == q else np.eye(2, dtype=complex)) + state = op @ state + + # Apply CNOT entanglement (correct 2-qubit gate embedding) + for q in range(num_qubits - 1): + # CNOT between qubits q and q+1 — build full operator via tensor products + # Format: I ⊗ ... ⊗ I ⊗ CNOT(q,q+1) ⊗ I ⊗ ... ⊗ I + cnot2 = np.eye(4, dtype=complex) + cnot2[[2, 3]] = cnot2[[3, 2]] + + # Build operator: I for qubits < q, CNOT2 for q and q+1, I for qubits > q+1 + before_dims = q # number of qubits before the CNOT pair + after_dims = num_qubits - q - 2 # number of qubits after the CNOT pair + + op = np.eye(1, dtype=complex) + for _ in range(before_dims): + op = np.kron(op, np.eye(2, dtype=complex)) + op = np.kron(op, cnot2) + for _ in range(after_dims): + op = np.kron(op, np.eye(2, dtype=complex)) + + if op.shape == (dim, dim): + state = op @ state + + probs = np.abs(state) ** 2 + probs = probs / probs.sum() # renormalize + return probs + + +# --------------------------------------------------------------------------- +# PennyLane sampler +# --------------------------------------------------------------------------- + + +def _pennylane_variational_probs( + params: np.ndarray, + num_qubits: int, + shots: int, + num_layers: int, +) -> np.ndarray: + """Run a PennyLane variational circuit and return measurement probabilities.""" + import pennylane as qml # noqa: PLC0415 + + dev = qml.device("default.qubit", wires=num_qubits, shots=None) + + @qml.qnode(dev) + def circuit(params): + # Encode params as RY rotations + n = num_qubits + for layer in range(num_layers): + for q in range(n): + idx = layer * n + q + theta = params[idx % len(params)] + qml.RY(theta, wires=q) + qml.RZ(params[(idx + 1) % len(params)], wires=q) + # Entanglement + for q in range(n - 1): + qml.CNOT(wires=[q, q + 1]) + return qml.probs(wires=list(range(n))) + + probs = np.array(circuit(params), dtype=float) + probs = probs / probs.sum() + return probs + + +# --------------------------------------------------------------------------- +# Qiskit sampler +# --------------------------------------------------------------------------- + + +def _qiskit_variational_probs( + params: np.ndarray, + num_qubits: int, + shots: int, + num_layers: int, +) -> np.ndarray: + """Run a Qiskit variational circuit and return shot-based probabilities.""" + from qiskit import QuantumCircuit, transpile # noqa: PLC0415 + from qiskit.circuit import ParameterVector # noqa: PLC0415 + from qiskit_aer import AerSimulator # type: ignore # noqa: PLC0415 + + n = num_qubits + n_params_per_layer = 2 * n + total_params = num_layers * n_params_per_layer + pv = ParameterVector("θ", total_params) + + qc = QuantumCircuit(n, n) + idx = 0 + for _layer in range(num_layers): + for q in range(n): + qc.ry(pv[idx % total_params], q) + idx += 1 + qc.rz(pv[idx % total_params], q) + idx += 1 + for q in range(n - 1): + qc.cx(q, q + 1) + qc.measure(range(n), range(n)) + + # Bind parameters + param_dict = {pv[i]: float(params[i % len(params)]) for i in range(total_params)} + bound = qc.assign_parameters(param_dict) + + sim = AerSimulator() + job = sim.run(transpile(bound, sim), shots=shots) + counts = job.result().get_counts() + + dim = 2**n + probs = np.zeros(dim, dtype=float) + for bitstring, count in counts.items(): + idx = int(bitstring, 2) + if idx < dim: + probs[idx] = count + total = probs.sum() + if total > 0: + probs /= total + else: + probs = np.ones(dim) / dim + return probs + + +# --------------------------------------------------------------------------- +# Public QuantumSampler class +# --------------------------------------------------------------------------- + + +class QuantumSampler: + """ + Applies quantum-circuit measurement probabilities to re-weight an LLM's + top-k logit distribution. + + Parameters + ---------- + backend : str + One of "auto", "pennylane", "qiskit", "classical". + num_qubits : int + Number of qubits in the variational circuit (default 4). + shots : int + Number of measurement shots for shot-based backends (default 512). + num_layers : int + Number of variational layers (default 2). + seed : int, optional + Random seed for reproducibility. + """ + + def __init__( + self, + backend: str = "auto", + num_qubits: int = 4, + shots: int = 512, + num_layers: int = 2, + seed: Optional[int] = None, + ) -> None: + self.effective_backend = _active_backend(backend) + self.num_qubits = num_qubits + self.shots = shots + self.num_layers = num_layers + self._rng = np.random.default_rng(seed) + logger.info( + "QuantumSampler initialized: backend=%s, qubits=%d, shots=%d", + self.effective_backend, + num_qubits, + shots, + ) + + # ------------------------------------------------------------------ + # Core sampling + # ------------------------------------------------------------------ + + def _get_circuit_probs(self, params: np.ndarray) -> np.ndarray: + """Run the variational circuit and return probabilities over 2**n outcomes.""" + if self.effective_backend == "pennylane": + return _pennylane_variational_probs( + params, self.num_qubits, self.shots, self.num_layers + ) + if self.effective_backend == "qiskit": + return _qiskit_variational_probs( + params, self.num_qubits, self.shots, self.num_layers + ) + # Classical fallback + return _classical_variational_probs( + params, self.num_qubits, self.shots, self._rng + ) + + def sample( + self, + logits: Sequence[float], + blend_factor: float = 0.3, + seed: Optional[int] = None, + ) -> int: + """ + Sample a token index from the re-weighted logit distribution. + + Parameters + ---------- + logits : Sequence[float] + LLM logit scores for top-k candidates. + blend_factor : float + Weight for quantum distribution (0 = pure classical, 1 = pure quantum). + seed : int, optional + Override the sampler's internal RNG seed for this call. + + Returns + ------- + int + Sampled index into ``logits``. + """ + logits_arr = np.array(logits, dtype=float) + k = len(logits_arr) + + if k == 0: + return 0 + + # Classical softmax distribution + logits_arr -= logits_arr.max() # numerical stability + classical_probs = np.exp(logits_arr) + classical_probs /= classical_probs.sum() + + # Generate random circuit params from logit values (deterministic mapping) + n_params = self.num_qubits * self.num_layers * 2 + params = np.pi * (logits_arr[:n_params] if k >= n_params else np.resize(logits_arr, n_params)) + circuit_probs = self._get_circuit_probs(params) + + # Map circuit probs (over 2**num_qubits outcomes) onto k candidates + dim = len(circuit_probs) + if dim >= k: + quantum_probs = circuit_probs[:k] + else: + # Repeat/tile to fill k candidates + quantum_probs = np.resize(circuit_probs, k) + quantum_probs = quantum_probs / quantum_probs.sum() + + # Blend + blended = (1.0 - blend_factor) * classical_probs + blend_factor * quantum_probs + blended /= blended.sum() + + rng = np.random.default_rng(seed) if seed is not None else self._rng + return int(rng.choice(k, p=blended)) diff --git a/apps/aria/test_auto_execute.py b/apps/aria/test_auto_execute.py index f2b884168..fbe45d79d 100644 --- a/apps/aria/test_auto_execute.py +++ b/apps/aria/test_auto_execute.py @@ -1,220 +1,220 @@ -#!/usr/bin/env python -""" -Test script for Aria Auto-Execute System -Tests the new LLM-powered action generation and execution -""" -import json -import sys -from typing import Dict - -import pytest -import requests - -BASE_URL = "http://localhost:8080" - - -def _aria_server_available() -> bool: - try: - response = requests.get(f"{BASE_URL}/api/aria/state", timeout=2) - return response.ok - except requests.exceptions.RequestException: - return False - - -pytestmark = pytest.mark.skipif( - not _aria_server_available(), - reason="Aria server not running on localhost:8080", -) - - -def print_header(text: str): - """Print formatted header""" - print(f"\n{'=' * 70}") - print(f" {text}") - print(f"{'=' * 70}\n") - - -def print_result(success: bool, message: str): - """Print test result""" - symbol = "✓" if success else "✗" - color = "\033[92m" if success else "\033[91m" - reset = "\033[0m" - print(f"{color}{symbol}{reset} {message}") - - -def plan_mode(command: str) -> Dict: - """Run plan mode (no execution).""" - print(f"📋 Planning: '{command}'") - - response = requests.post( - f"{BASE_URL}/api/aria/execute", - json={"command": command, "auto_execute": False, "use_llm": False}, - timeout=30) - response.raise_for_status() - - data = response.json() - - if data["status"] == "success": - print_result(True, f"Parsed {len(data['actions'])} actions:") - for i, action in enumerate(data["actions"], 1): - print( - f" {i}. {action['action']}: {json.dumps(action, separators=(',', ':'))}" - ) - else: - print_result(False, f"Failed: {data.get('message', 'Unknown error')}") - - return data - - -def execute_mode(command: str) -> Dict: - """Run execute mode.""" - print(f"▶️ Executing: '{command}'") - - response = requests.post( - f"{BASE_URL}/api/aria/execute", - json={"command": command, "auto_execute": True, "use_llm": False}, - timeout=30) - response.raise_for_status() - - data = response.json() - - if data["status"] == "success": - print_result(True, f"Executed {len(data['results'])} actions:") - for i, result in enumerate(data["results"], 1): - action = result["action"] - res = result["result"] - status_symbol = "✓" if res["status"] == "success" else "✗" - print(f" {status_symbol} {i}. {action['action']}: {res['message']}") - if res.get("tags"): - print(f" Tags: {', '.join(res['tags'])}") - else: - print_result(False, f"Failed: {data.get('message', 'Unknown error')}") - - return data - - -def get_state() -> Dict: - """Get current stage state""" - response = requests.get(f"{BASE_URL}/api/aria/state") - return response.json() - - -def print_state(): - """Print current Aria state""" - state = get_state() - aria = state["aria"] - - print("\n🎭 Current Aria State:") - print(f" Position: ({aria['position']['x']}, {aria['position']['y']})") - print(f" Expression: {aria['expression']}") - print(f" Held object: {aria['held_object'] or 'none'}") - print(f" Facing: {aria['facing']}") - - -def test_plan_mode() -> None: - """Pytest-compatible smoke test for plan mode.""" - result = plan_mode("Say hello world") - assert result["status"] == "success" - assert "actions" in result - - -def test_execute_mode() -> None: - """Pytest-compatible smoke test for execute mode.""" - result = execute_mode("Wave at the audience") - assert result["status"] == "success" - assert "results" in result - assert isinstance(result["results"], list) - assert len(result["results"]) > 0 - # Verify that at least one executed action reports a successful status - assert any( - isinstance(item, dict) - and "result" in item - and isinstance(item["result"], dict) - and item["result"].get("status") == "success" - for item in result["results"] - ) - - -def run_tests(): - """Run comprehensive test suite""" - - print_header("🤖 Aria Auto-Execute System Tests") - - try: - # Test 1: Simple say command - print_header("Test 1: Simple Say Command") - plan_mode("Say hello world") - result = execute_mode("Say hello world") - if result["status"] == "success": - print_result(True, "Say command executed successfully") - - # Test 2: Gesture command - print_header("Test 2: Gesture Command") - plan_mode("Wave at the audience") - result = execute_mode("Wave at the audience") - if result["status"] == "success": - print_result(True, "Gesture command executed successfully") - - # Test 3: Movement command - print_header("Test 3: Movement Command") - plan_mode("Move to the center") - result = execute_mode("Move to the center") - if result["status"] == "success": - print_result(True, "Movement command executed successfully") - print_state() - - # Test 4: Multi-action sequence - print_header("Test 4: Multi-Action Sequence") - plan_mode("Go to the table and pick up the apple") - result = execute_mode("Go to the table and pick up the apple") - if result["status"] == "success": - print_result(True, "Multi-action sequence executed successfully") - print_state() - - # Test 5: Complex command - print_header("Test 5: Complex Command with Multiple Actions") - command = "Say welcome, wave, and move to the left" - plan_mode(command) - result = execute_mode(command) - if result["status"] == "success": - print_result(True, "Complex command executed successfully") - print_state() - - # Test 6: Error handling (pickup without moving) - print_header("Test 6: Error Handling (Distance Check)") - print("Note: This should fail if Aria is too far from the book") - result = execute_mode("Pick up the book") - if result["status"] == "success" and result["results"]: - has_error = any(r["result"]["status"] == "error" for r in result["results"]) - if has_error: - print_result(True, "Distance validation working correctly") - else: - print_result(True, "Pickup succeeded (Aria was close enough)") - - # Summary - print_header("🎉 Test Suite Complete") - print_result(True, "All auto-execute features operational") - print("\n📖 Try the web interface:") - print(f" Open: {BASE_URL}/auto-execute.html") - print("\n📚 Read full documentation:") - print(" See: aria_web/AUTO-EXECUTE.md\n") - - return True - - except requests.exceptions.ConnectionError: - print_result(False, "Cannot connect to server") - print("\n❌ Server not running!") - print(" Start it with: cd /workspaces/AI/aria_web && python server.py") - return False - - except Exception as e: - print_result(False, f"Unexpected error: {e}") - import traceback - - traceback.print_exc() - return False - - -if __name__ == "__main__": - success = run_tests() - sys.exit(0 if success else 1) +#!/usr/bin/env python +""" +Test script for Aria Auto-Execute System +Tests the new LLM-powered action generation and execution +""" +import json +import sys +from typing import Dict + +import pytest +import requests + +BASE_URL = "http://localhost:8080" + + +def _aria_server_available() -> bool: + try: + response = requests.get(f"{BASE_URL}/api/aria/state", timeout=2) + return response.ok + except requests.exceptions.RequestException: + return False + + +pytestmark = pytest.mark.skipif( + not _aria_server_available(), + reason="Aria server not running on localhost:8080", +) + + +def print_header(text: str): + """Print formatted header""" + print(f"\n{'=' * 70}") + print(f" {text}") + print(f"{'=' * 70}\n") + + +def print_result(success: bool, message: str): + """Print test result""" + symbol = "✓" if success else "✗" + color = "\033[92m" if success else "\033[91m" + reset = "\033[0m" + print(f"{color}{symbol}{reset} {message}") + + +def plan_mode(command: str) -> Dict: + """Run plan mode (no execution).""" + print(f"📋 Planning: '{command}'") + + response = requests.post( + f"{BASE_URL}/api/aria/execute", + json={"command": command, "auto_execute": False, "use_llm": False}, + timeout=30) + response.raise_for_status() + + data = response.json() + + if data["status"] == "success": + print_result(True, f"Parsed {len(data['actions'])} actions:") + for i, action in enumerate(data["actions"], 1): + print( + f" {i}. {action['action']}: {json.dumps(action, separators=(',', ':'))}" + ) + else: + print_result(False, f"Failed: {data.get('message', 'Unknown error')}") + + return data + + +def execute_mode(command: str) -> Dict: + """Run execute mode.""" + print(f"▶️ Executing: '{command}'") + + response = requests.post( + f"{BASE_URL}/api/aria/execute", + json={"command": command, "auto_execute": True, "use_llm": False}, + timeout=30) + response.raise_for_status() + + data = response.json() + + if data["status"] == "success": + print_result(True, f"Executed {len(data['results'])} actions:") + for i, result in enumerate(data["results"], 1): + action = result["action"] + res = result["result"] + status_symbol = "✓" if res["status"] == "success" else "✗" + print(f" {status_symbol} {i}. {action['action']}: {res['message']}") + if res.get("tags"): + print(f" Tags: {', '.join(res['tags'])}") + else: + print_result(False, f"Failed: {data.get('message', 'Unknown error')}") + + return data + + +def get_state() -> Dict: + """Get current stage state""" + response = requests.get(f"{BASE_URL}/api/aria/state") + return response.json() + + +def print_state(): + """Print current Aria state""" + state = get_state() + aria = state["aria"] + + print("\n🎭 Current Aria State:") + print(f" Position: ({aria['position']['x']}, {aria['position']['y']})") + print(f" Expression: {aria['expression']}") + print(f" Held object: {aria['held_object'] or 'none'}") + print(f" Facing: {aria['facing']}") + + +def test_plan_mode() -> None: + """Pytest-compatible smoke test for plan mode.""" + result = plan_mode("Say hello world") + assert result["status"] == "success" + assert "actions" in result + + +def test_execute_mode() -> None: + """Pytest-compatible smoke test for execute mode.""" + result = execute_mode("Wave at the audience") + assert result["status"] == "success" + assert "results" in result + assert isinstance(result["results"], list) + assert len(result["results"]) > 0 + # Verify that at least one executed action reports a successful status + assert any( + isinstance(item, dict) + and "result" in item + and isinstance(item["result"], dict) + and item["result"].get("status") == "success" + for item in result["results"] + ) + + +def run_tests(): + """Run comprehensive test suite""" + + print_header("🤖 Aria Auto-Execute System Tests") + + try: + # Test 1: Simple say command + print_header("Test 1: Simple Say Command") + plan_mode("Say hello world") + result = execute_mode("Say hello world") + if result["status"] == "success": + print_result(True, "Say command executed successfully") + + # Test 2: Gesture command + print_header("Test 2: Gesture Command") + plan_mode("Wave at the audience") + result = execute_mode("Wave at the audience") + if result["status"] == "success": + print_result(True, "Gesture command executed successfully") + + # Test 3: Movement command + print_header("Test 3: Movement Command") + plan_mode("Move to the center") + result = execute_mode("Move to the center") + if result["status"] == "success": + print_result(True, "Movement command executed successfully") + print_state() + + # Test 4: Multi-action sequence + print_header("Test 4: Multi-Action Sequence") + plan_mode("Go to the table and pick up the apple") + result = execute_mode("Go to the table and pick up the apple") + if result["status"] == "success": + print_result(True, "Multi-action sequence executed successfully") + print_state() + + # Test 5: Complex command + print_header("Test 5: Complex Command with Multiple Actions") + command = "Say welcome, wave, and move to the left" + plan_mode(command) + result = execute_mode(command) + if result["status"] == "success": + print_result(True, "Complex command executed successfully") + print_state() + + # Test 6: Error handling (pickup without moving) + print_header("Test 6: Error Handling (Distance Check)") + print("Note: This should fail if Aria is too far from the book") + result = execute_mode("Pick up the book") + if result["status"] == "success" and result["results"]: + has_error = any(r["result"]["status"] == "error" for r in result["results"]) + if has_error: + print_result(True, "Distance validation working correctly") + else: + print_result(True, "Pickup succeeded (Aria was close enough)") + + # Summary + print_header("🎉 Test Suite Complete") + print_result(True, "All auto-execute features operational") + print("\n📖 Try the web interface:") + print(f" Open: {BASE_URL}/auto-execute.html") + print("\n📚 Read full documentation:") + print(" See: aria_web/AUTO-EXECUTE.md\n") + + return True + + except requests.exceptions.ConnectionError: + print_result(False, "Cannot connect to server") + print("\n❌ Server not running!") + print(" Start it with: cd /workspaces/AI/aria_web && python server.py") + return False + + except Exception as e: + print_result(False, f"Unexpected error: {e}") + import traceback + + traceback.print_exc() + return False + + +if __name__ == "__main__": + success = run_tests() + sys.exit(0 if success else 1) diff --git a/tests/test_quantum_llm.py b/tests/test_quantum_llm.py new file mode 100644 index 000000000..fe02c254b --- /dev/null +++ b/tests/test_quantum_llm.py @@ -0,0 +1,369 @@ +""" +Unit tests for the Quantum-Powered LLM module. + +All tests use the classical (numpy) fallback — no quantum SDK required. +Tests are discoverable by ``python scripts/test_runner.py --unit``. +""" + +from __future__ import annotations + +import asyncio +import sys +from pathlib import Path +from typing import List +from unittest.mock import MagicMock, patch + +import numpy as np +import pytest + +# Ensure the quantum_llm package is importable +_QUANTUM_LLM_SRC = ( + Path(__file__).resolve().parents[1] + / "ai-projects" + / "quantum-ml" + / "src" +) +if str(_QUANTUM_LLM_SRC) not in sys.path: + sys.path.insert(0, str(_QUANTUM_LLM_SRC)) + +from quantum_llm import ( # noqa: E402 + QuantumLLMConfig, + QuantumLLMPipeline, + QuantumEmbeddingTransformer, + QuantumRouter, + QuantumSampler, +) + + +# =========================================================================== +# Config tests +# =========================================================================== + + +class TestQuantumLLMConfig: + def test_defaults(self): + cfg = QuantumLLMConfig() + assert cfg.backend == "auto" + assert cfg.num_qubits == 4 + assert cfg.shots == 512 + assert cfg.top_k == 10 + assert cfg.max_tokens == 512 + assert cfg.max_prompt_chars == 8000 + + def test_to_dict(self): + cfg = QuantumLLMConfig(backend="classical", num_qubits=2) + d = cfg.to_dict() + assert d["backend"] == "classical" + assert d["num_qubits"] == 2 + + def test_from_env(self, monkeypatch): + monkeypatch.setenv("QUANTUM_LLM_BACKEND", "classical") + monkeypatch.setenv("QUANTUM_LLM_QUBITS", "2") + monkeypatch.setenv("QUANTUM_LLM_SHOTS", "128") + cfg = QuantumLLMConfig.from_env() + assert cfg.backend == "classical" + assert cfg.num_qubits == 2 + assert cfg.shots == 128 + + +# =========================================================================== +# QuantumSampler tests +# =========================================================================== + + +class TestQuantumSampler: + def _make_sampler(self, **kwargs) -> QuantumSampler: + return QuantumSampler(backend="classical", num_qubits=4, shots=128, seed=42, **kwargs) + + def test_init_classical(self): + s = self._make_sampler() + assert s.effective_backend == "classical" + + def test_sample_returns_valid_index(self): + s = self._make_sampler() + logits = [1.0, 2.0, 0.5, 0.1] + idx = s.sample(logits, blend_factor=0.3) + assert 0 <= idx < len(logits) + + def test_sample_single_logit(self): + s = self._make_sampler() + idx = s.sample([3.0], blend_factor=0.5) + assert idx == 0 + + def test_sample_empty_logits(self): + s = self._make_sampler() + idx = s.sample([], blend_factor=0.3) + assert idx == 0 + + def test_sample_deterministic_with_seed(self): + s1 = QuantumSampler(backend="classical", num_qubits=4, shots=128, seed=99) + s2 = QuantumSampler(backend="classical", num_qubits=4, shots=128, seed=99) + logits = [1.0, 2.0, 0.5, 1.5, 0.8] + assert s1.sample(logits, seed=7) == s2.sample(logits, seed=7) + + def test_blend_factor_zero_is_classical(self): + """With blend_factor=0, result should be drawn from pure classical softmax.""" + s = self._make_sampler() + logits = [5.0, 0.0, 0.0, 0.0] # strongly favours index 0 + # With a fixed seed and strongly biased logits, index 0 must always win + results = [s.sample(logits, blend_factor=0.0, seed=i) for i in range(20)] + # With logit[0] = 5.0 vs rest = 0.0, softmax gives P(0) ≈ 0.9994 + # Index 0 must win at least 90% of the time + assert results.count(0) >= 18 + + +# =========================================================================== +# QuantumEmbeddingTransformer tests +# =========================================================================== + + +class TestQuantumEmbeddingTransformer: + def _make_transformer(self, **kwargs) -> QuantumEmbeddingTransformer: + return QuantumEmbeddingTransformer( + backend="classical", num_qubits=4, num_layers=1, seed=42, **kwargs + ) + + def test_transform_preserves_shape(self): + t = self._make_transformer() + emb = np.random.default_rng(0).random(16) + out = t.transform(emb) + assert out.shape == emb.shape + + def test_transform_zero_embedding(self): + t = self._make_transformer() + emb = np.zeros(8, dtype=float) + out = t.transform(emb) + assert out.shape == emb.shape + assert np.allclose(out, 0.0) + + def test_transform_rejects_2d(self): + t = self._make_transformer() + with pytest.raises(ValueError, match="1-D"): + t.transform(np.ones((4, 4))) + + def test_update_params(self): + t = self._make_transformer() + new_params = np.zeros(t._params.shape) + t.update_params(new_params) + assert np.allclose(t._params, 0.0) + + def test_transform_different_dims(self): + t = self._make_transformer() + for dim in [4, 8, 16, 32, 64]: + emb = np.ones(dim) + out = t.transform(emb) + assert out.shape == (dim,) + + +# =========================================================================== +# QuantumRouter tests +# =========================================================================== + + +class TestQuantumRouter: + def _make_router(self, providers=None, **kwargs) -> QuantumRouter: + return QuantumRouter( + providers=providers or ["azure", "openai", "local"], + backend="classical", + num_qubits=4, + seed=42, + **kwargs, + ) + + def test_route_returns_valid_provider(self): + r = self._make_router() + chosen = r.route("Hello, world!") + assert chosen in ["azure", "openai", "local"] + + def test_route_single_provider(self): + r = self._make_router(providers=["local"]) + assert r.route("test") == "local" + + def test_route_excludes_providers(self): + r = self._make_router(providers=["azure", "openai", "local"]) + chosen = r.route("Hello", exclude=["azure", "openai"]) + assert chosen == "local" + + def test_route_all_excluded_falls_back(self): + r = self._make_router(providers=["azure"]) + # Even if all excluded, falls back to first provider + result = r.route("Hello", exclude=["azure"]) + assert result == "azure" + + def test_route_with_scores_returns_dict(self): + r = self._make_router() + chosen, scores = r.route_with_scores("What is quantum?") + assert chosen in r.providers + assert set(scores.keys()) == set(r.providers) + + def test_route_empty_providers(self): + r = QuantumRouter(providers=[], backend="classical") + result = r.route("test") + # Empty providers list falls back to default providers list + assert result in ["azure", "openai", "lmstudio", "local"] + + +# =========================================================================== +# QuantumLLMPipeline tests (integration with classical fallback + mock provider) +# =========================================================================== + + +class _MockProvider: + """Minimal mock LLM provider for pipeline integration tests.""" + + name = "mock-provider" + + def __init__(self, response: str = "mock response"): + self._response = response + + def complete(self, messages, stream=False): + if stream: + return iter(self._response.split()) + return self._response + + +class TestQuantumLLMPipeline: + def _make_pipeline(self, response="Hello from mock", **cfg_kwargs) -> QuantumLLMPipeline: + cfg = QuantumLLMConfig( + backend="classical", + num_qubits=2, + shots=64, + use_thread=False, + **cfg_kwargs, + ) + pipeline = QuantumLLMPipeline(config=cfg) + # Inject mock provider + pipeline._get_provider = lambda prompt, provider=None: _MockProvider(response) + return pipeline + + def test_generate_returns_dict(self): + pipeline = self._make_pipeline() + result = asyncio.run(pipeline.generate("Test prompt")) + assert isinstance(result, dict) + assert "response" in result + assert "backend" in result + assert "latency_ms" in result + assert result["quantum_augmented"] is True + + def test_generate_response_content(self): + pipeline = self._make_pipeline(response="Quantum rocks!") + result = asyncio.run(pipeline.generate("Hello")) + assert result["response"] == "Quantum rocks!" + + def test_generate_deterministic_with_seed(self): + pipeline1 = self._make_pipeline(response="deterministic output") + pipeline2 = self._make_pipeline(response="deterministic output") + r1 = asyncio.run(pipeline1.generate("Test", seed=42)) + r2 = asyncio.run(pipeline2.generate("Test", seed=42)) + assert r1["response"] == r2["response"] + + def test_generate_rejects_long_prompt(self): + pipeline = self._make_pipeline(max_prompt_chars=10) + with pytest.raises(ValueError, match="too long"): + asyncio.run(pipeline.generate("A" * 11)) + + def test_stream_yields_sse_chunks(self): + pipeline = self._make_pipeline(response="Hello world from quantum") + + async def collect(): + chunks = [] + async for chunk in pipeline.stream("Test"): + chunks.append(chunk) + return chunks + + chunks = asyncio.run(collect()) + assert any("[DONE]" in c for c in chunks) + assert any("delta" in c for c in chunks) + assert any("meta" in c for c in chunks) + + def test_stream_ends_with_done(self): + pipeline = self._make_pipeline() + + async def collect(): + chunks = [] + async for chunk in pipeline.stream("Ping"): + chunks.append(chunk) + return chunks + + chunks = asyncio.run(collect()) + assert chunks[-1].strip() == "data: [DONE]" + + def test_stream_rejects_long_prompt(self): + pipeline = self._make_pipeline(max_prompt_chars=5) + + async def collect(): + chunks = [] + async for chunk in pipeline.stream("TOOLONG"): + chunks.append(chunk) + return chunks + + chunks = asyncio.run(collect()) + assert any("error" in c for c in chunks) + assert any("[DONE]" in c for c in chunks) + + def test_status_dict(self): + pipeline = self._make_pipeline() + s = pipeline.status() + assert s["backend"] == "classical" + assert s["fallback"] is True + assert s["num_qubits"] == 2 + assert "router_providers" in s + + def test_generate_backend_in_response(self): + pipeline = self._make_pipeline() + result = asyncio.run(pipeline.generate("Hi")) + assert result["backend"] == "classical" + assert result["qubits"] == 2 + + +# =========================================================================== +# Integration test: mock provider, fixed seed, deterministic output +# =========================================================================== + + +class TestPipelineIntegration: + """Integration test with mocked LLM provider and fixed random seed.""" + + def test_end_to_end_deterministic(self): + """Full pipeline with classical backend + fixed seed → deterministic.""" + cfg = QuantumLLMConfig( + backend="classical", + num_qubits=2, + shots=64, + temperature_blend=0.5, + use_thread=False, + ) + pipeline = QuantumLLMPipeline(config=cfg) + pipeline._get_provider = lambda prompt, provider=None: _MockProvider( + "The answer to everything is quantum." + ) + + r1 = asyncio.run(pipeline.generate("What is the answer?", seed=42)) + r2 = asyncio.run(pipeline.generate("What is the answer?", seed=42)) + + assert r1["response"] == r2["response"] + assert r1["backend"] == "classical" + assert r1["quantum_augmented"] is True + + def test_stream_meta_event_contains_backend(self): + """SSE meta event must contain backend info.""" + import json + + cfg = QuantumLLMConfig(backend="classical", use_thread=False) + pipeline = QuantumLLMPipeline(config=cfg) + pipeline._get_provider = lambda prompt, provider=None: _MockProvider("hi") + + async def collect(): + chunks = [] + async for chunk in pipeline.stream("hello"): + chunks.append(chunk) + return chunks + + chunks = asyncio.run(collect()) + # Find meta event + meta_chunks = [c for c in chunks if "event: meta" in c] + assert meta_chunks, "No meta event emitted" + meta_data = meta_chunks[0].split("data:", 1)[1].strip() + meta = json.loads(meta_data) + assert "backend" in meta + assert "qubits" in meta From 7df2a2dad38f4a4cd8eb717fef8e4674aaba5467 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 6 May 2026 01:59:46 +0000 Subject: [PATCH 07/15] chore: sync with origin/main - resolve merge conflicts and incorporate new files Agent-Logs-Url: https://github.com/Bryan-Roe/Aria/sessions/6b1e0324-3931-4edc-8aac-ca175cf68da0 Co-authored-by: Bryan-Roe <74067792+Bryan-Roe@users.noreply.github.com> --- .../checkpoint-64/tokenizer.json | 554286 +++++++-------- .../checkpoint-64/tokenizer_config.json | 86 +- 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"▁april": 17187, - "▁impression": 17188, - "▁perce": 17189, - "▁участи": 17190, - "vil": 17191, - "éch": 17192, - "▁equality": 17193, - "▁мет": 17194, - "▁annotation": 17195, - "ernal": 17196, - "▁Mach": 17197, - "▁intitul": 17198, - "problem": 17199, - "ющих": 17200, - "oplus": 17201, - "▁thousands": 17202, - "▁calculations": 17203, - "umps": 17204, - "▁triangle": 17205, - "phal": 17206, - "▁Dorf": 17207, - "▁dollars": 17208, - "▁denen": 17209, - "lès": 17210, - "olid": 17211, - "▁Results": 17212, - "▁Stadium": 17213, - "▁Desp": 17214, - "▁Eisen": 17215, - "imir": 17216, - "▁sotto": 17217, - "▁či": 17218, - "atable": 17219, - "orum": 17220, - "▁convergence": 17221, - "▁jeune": 17222, - "oking": 17223, - "▁живо": 17224, - "aining": 17225, - "pointer": 17226, - "culo": 17227, - "▁jsou": 17228, - "▁grab": 17229, - "akte": 17230, - "▁hoping": 17231, - "▁Mak": 17232, - "▁sag": 17233, - "origine": 17234, - "▁послед": 17235, - "▁Veg": 17236, - "▁theoret": 17237, - "▁Tru": 17238, - "nement": 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"ipping": 17347, - "cussion": 17348, - "ческих": 17349, - "(\".": 17350, - "Also": 17351, - "▁whis": 17352, - "HOME": 17353, - "▁brown": 17354, - "▁día": 17355, - "▁può": 17356, - "plotlib": 17357, - "▁Jahrhunderts": 17358, - "DK": 17359, - "▁anchor": 17360, - "...]": 17361, - "▁Austria": 17362, - "▁marca": 17363, - "▁gez": 17364, - "iously": 17365, - "▁lazy": 17366, - "xa": 17367, - "▁Channel": 17368, - "▁neuen": 17369, - "das": 17370, - "▁searched": 17371, - "▁staat": 17372, - "▁Так": 17373, - "▁Josef": 17374, - "▁Sher": 17375, - "pois": 17376, - "▁enem": 17377, - "▁accessing": 17378, - "▁неко": 17379, - "▁furono": 17380, - "▁pseudo": 17381, - "?>": 17382, - "▁estadoun": 17383, - "▁Види": 17384, - "▁motiv": 17385, - "▁recall": 17386, - "isson": 17387, - "ób": 17388, - ")--": 17389, - "▁Erz": 17390, - "▁савез": 17391, - "Direct": 17392, - "соб": 17393, - "▁sho": 17394, - "völker": 17395, - "Ap": 17396, - "gens": 17397, - "ништво": 17398, - "▁Amsterdam": 17399, - "usk": 17400, - "пло": 17401, - "▁simulation": 17402, - "▁BC": 17403, - "▁Woj": 17404, - "autom": 17405, - "Alex": 17406, - "▁economic": 17407, - "гом": 17408, - "ikai": 17409, - "▁altre": 17410, - "▁'-": 17411, - "▁Weg": 17412, - "NotFound": 17413, - "йской": 17414, - "▁converting": 17415, - "phabet": 17416, - "atrice": 17417, - "bourne": 17418, - "alom": 17419, - "▁comparing": 17420, - "▁Zo": 17421, - "▁fla": 17422, - "вая": 17423, - "▁entra": 17424, - "▁charset": 17425, - "developers": 17426, - "ística": 17427, - "}>": 17428, - "▁Jazz": 17429, - "▁Howard": 17430, - "шта": 17431, - "▁clone": 17432, - "door": 17433, - "▁Pin": 17434, - "***": 17435, - "▁silent": 17436, - "ecycle": 17437, - "isce": 17438, - "▁mud": 17439, - "▁Display": 17440, - "▁lip": 17441, - "▁использова": 17442, - "▁characteristic": 17443, - "▁sb": 17444, - "firebase": 17445, - "▁Bew": 17446, - "Calendar": 17447, - "▁uso": 17448, - "èse": 17449, - "▁Rat": 17450, - "▁esper": 17451, - "▁throwing": 17452, - "▁rodz": 17453, - "▁yards": 17454, 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"▁Arbeit": 17561, - "▁мест": 17562, - "▁Gebiet": 17563, - "▁insect": 17564, - "Ah": 17565, - "izado": 17566, - "▁temple": 17567, - "▁annual": 17568, - "stad": 17569, - "▁habitat": 17570, - "▁AB": 17571, - "wort": 17572, - "▁repos": 17573, - "▁Neu": 17574, - "▁$(\".": 17575, - "Vorlage": 17576, - "▁reprezent": 17577, - "estanden": 17578, - "Intern": 17579, - ".`": 17580, - "▁failing": 17581, - "▁Material": 17582, - "▁effectively": 17583, - "телем": 17584, - "▁гла": 17585, - "▁nahm": 17586, - "▁differently": 17587, - "extension": 17588, - "▁Verm": 17589, - "enabled": 17590, - "configure": 17591, - "nio": 17592, - "ciones": 17593, - "▁Beach": 17594, - "сона": 17595, - "▁copying": 17596, - "▁україн": 17597, - "▁призна": 17598, - "zh": 17599, - "Desktop": 17600, - "▁sost": 17601, - "▁subsequently": 17602, - "▁Lehr": 17603, - "▁ó": 17604, - "lär": 17605, - "odor": 17606, - "phon": 17607, - "nc": 17608, - "iterator": 17609, - "▁эти": 17610, - "▁europé": 17611, - "▁Toronto": 17612, - "ódigo": 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"▁spé": 18142, - "▁pierws": 18143, - "▁Sver": 18144, - "▁acknow": 18145, - "Boolean": 18146, - "▁фамили": 18147, - "▁Senate": 18148, - "шов": 18149, - "agers": 18150, - "▁Nueva": 18151, - "bil": 18152, - "kiem": 18153, - "▁Mey": 18154, - "wij": 18155, - "▁GmbH": 18156, - "validation": 18157, - "▁ensuite": 18158, - "inking": 18159, - "▁campion": 18160, - "▁financial": 18161, - "izon": 18162, - "Headers": 18163, - "▁deprecated": 18164, - "▁fonction": 18165, - "REG": 18166, - "▁volumes": 18167, - "▁Chi": 18168, - "▁encountered": 18169, - "lak": 18170, - "рая": 18171, - "▁continues": 18172, - "▁~[": 18173, - "uerte": 18174, - "▁\\;": 18175, - "▁Dok": 18176, - "▁weights": 18177, - "▁rh": 18178, - "▁Napole": 18179, - "▁naturally": 18180, - "sku": 18181, - "pas": 18182, - "▁gegründ": 18183, - "etr": 18184, - "▁Ku": 18185, - "icted": 18186, - "▁fabric": 18187, - "▁ASC": 18188, - "▁Entertainment": 18189, - "▁energ": 18190, - "клад": 18191, - "omon": 18192, - "theme": 18193, - "▁харак": 18194, - "▁draft": 18195, - "▁channels": 18196, - "▁desert": 18197, - "▁través": 18198, - "▁Lock": 18199, - "▁siendo": 18200, - "фек": 18201, - "même": 18202, - "▁packet": 18203, - "▁Mountain": 18204, - "▁Fahr": 18205, - "braio": 18206, - "пере": 18207, - "▁genannt": 18208, - "▁deployment": 18209, - "Pal": 18210, - "ног": 18211, - "стру": 18212, - "Prim": 18213, - "für": 18214, - "▁dangerous": 18215, - "▁szám": 18216, - "reck": 18217, - "▁popup": 18218, - "icky": 18219, - "inar": 18220, - "cowo": 18221, - "нцикло": 18222, - "ítás": 18223, - "▁plugins": 18224, - "▁driven": 18225, - "лев": 18226, - "▁\"(": 18227, - "tta": 18228, - "▁Ú": 18229, - "▁eb": 18230, - "▁'';": 18231, - "▁knock": 18232, - "▁основа": 18233, - "▁maison": 18234, - "гля": 18235, - "▁Honor": 18236, - "tail": 18237, - "ritz": 18238, - "▁guys": 18239, - "▁combinations": 18240, - "ondere": 18241, - "▁Ald": 18242, - "▁fiddle": 18243, - "дав": 18244, - "urd": 18245, - "▁projection": 18246, - "▁También": 18247, - "verb": 18248, - "▁terre": 18249, - "rugu": 18250, - "▁september": 18251, - "▁=": 18572, - "▁Beat": 18573, - "▁Sax": 18574, - "vertical": 18575, - "кто": 18576, - "▁plants": 18577, - "▁Références": 18578, - "▁ogni": 18579, - "▁curs": 18580, - "▁SK": 18581, - "они": 18582, - "▁destac": 18583, - "\");\r": 18584, - "▁Sure": 18585, - "▁partido": 18586, - "▁Folge": 18587, - "▁Moore": 18588, - "▁wz": 18589, - "скус": 18590, - "ltre": 18591, - "ondo": 18592, - "▁pose": 18593, - "imos": 18594, - "бой": 18595, - "ципа": 18596, - "jus": 18597, - ".....": 18598, - "▁época": 18599, - "▁quanto": 18600, - "▁Support": 18601, - "geschichte": 18602, - "SERVER": 18603, - "▁Georges": 18604, - "enum": 18605, - "▁herm": 18606, - "▁nebo": 18607, - "▁Chr": 18608, - "character": 18609, - "▁***": 18610, - "▁Forsch": 18611, - "iami": 18612, - "▁¿": 18613, - "cych": 18614, - "▁fifth": 18615, - "sent": 18616, - "▁anderem": 18617, - "▁proportion": 18618, - "▁prest": 18619, - "▁Girl": 18620, - "▁drama": 18621, - "wand": 18622, - "▁Mail": 18623, - "▁Lux": 18624, - "▁který": 18625, - "▁Gesellschaft": 18626, - "▁Hinweis": 18627, - "nisse": 18628, - "▁mondo": 18629, - "Eq": 18630, - "▁perí": 18631, - "▁eastern": 18632, - "▁UEFA": 18633, - "uale": 18634, - "▁convex": 18635, - "▁поль": 18636, - "▁Hey": 18637, - "zenie": 18638, - "initely": 18639, - "▁Zusammen": 18640, - "SSL": 18641, - "ocal": 18642, - "▁canal": 18643, - "voy": 18644, - "▁Кри": 18645, - "▁között": 18646, - "▁cars": 18647, - "▁versión": 18648, - "Environment": 18649, - "Her": 18650, - "▁señ": 18651, - "▁spatial": 18652, - "ymi": 18653, - "Fire": 18654, - "▁veget": 18655, - "▁Wie": 18656, - "▁znaj": 18657, - "▁damage": 18658, - "▁endl": 18659, - "gif": 18660, - "▁quali": 18661, - "▁которых": 18662, - "ellan": 18663, - "▁mens": 18664, - "▁plug": 18665, - "▁abund": 18666, - "FIG": 18667, - "▁sf": 18668, - "▁confl": 18669, - "▁населения": 18670, - "▁principles": 18671, - "▁Gabriel": 18672, - "ibe": 18673, - "▁{%": 18674, - "▁població": 18675, - "ніципа": 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"threads": 28993, - "▁absolv": 28994, - "▁люди": 28995, - "▁stepped": 28996, - "exist": 28997, - "▁NK": 28998, - "▁ves": 28999, - "istiche": 29000, - "%'": 29001, - "ativos": 29002, - "▁такой": 29003, - "▁MongoDB": 29004, - "▁Ung": 29005, - "▁Рус": 29006, - "▁elim": 29007, - "▁Fif": 29008, - "icación": 29009, - "▁Tennis": 29010, - "▁Jefferson": 29011, - "ján": 29012, - "fog": 29013, - "anha": 29014, - "zor": 29015, - "▁університе": 29016, - "ahu": 29017, - "iada": 29018, - "Sdk": 29019, - "Setting": 29020, - "▁Kill": 29021, - "▁Wend": 29022, - "▁bald": 29023, - "▁Kub": 29024, - "▁visto": 29025, - "▁jeunes": 29026, - "collections": 29027, - "ací": 29028, - "вропей": 29029, - "▁arise": 29030, - "оні": 29031, - "MAIN": 29032, - "доступ": 29033, - "▁berg": 29034, - "▁criticism": 29035, - "▁Torre": 29036, - "▁descript": 29037, - "ières": 29038, - "▁estudio": 29039, - "▁ili": 29040, - "▁militare": 29041, - "▁Clara": 29042, - "▁Ellen": 29043, - "limited": 29044, - "лм": 29045, - "▁Españ": 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31110, - "や": 31111, - "谷": 31112, - "香": 31113, - "♯": 31114, - "じ": 31115, - "،": 31116, - "期": 31117, - "∅": 31118, - "┘": 31119, - "初": 31120, - "福": 31121, - "片": 31122, - "ザ": 31123, - "動": 31124, - "参": 31125, - "성": 31126, - "Ə": 31127, - "╦": 31128, - "어": 31129, - "ხ": 31130, - "義": 31131, - "च": 31132, - "象": 31133, - "功": 31134, - "♂": 31135, - "도": 31136, - "고": 31137, - "过": 31138, - "վ": 31139, - "皇": 31140, - "特": 31141, - "ậ": 31142, - "长": 31143, - "英": 31144, - "ấ": 31145, - "ണ": 31146, - "Ъ": 31147, - "স": 31148, - "其": 31149, - "ত": 31150, - "流": 31151, - "除": 31152, - "일": 31153, - "ু": 31154, - "្": 31155, - "永": 31156, - "直": 31157, - "상": 31158, - "千": 31159, - "ắ": 31160, - "館": 31161, - "Ť": 31162, - "朝": 31163, - "ட": 31164, - "ɣ": 31165, - "单": 31166, - "ʀ": 31167, - "格": 31168, - "德": 31169, - "전": 31170, - "☺": 31171, - "ピ": 31172, - "歌": 31173, - "进": 31174, - "限": 31175, - "夫": 31176, - "트": 31177, - "⊢": 31178, - "園": 31179, - "量": 31180, - "土": 31181, 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": 31353, - "은": 31354, - "ʷ": 31355, - "那": 31356, - "線": 31357, - "务": 31358, - "基": 31359, - "属": 31360, - "配": 31361, - "미": 31362, - "軍": 31363, - "โ": 31364, - "津": 31365, - "完": 31366, - "研": 31367, - "注": 31368, - "失": 31369, - "应": 31370, - "က": 31371, - "╚": 31372, - "友": 31373, - "章": 31374, - "Ψ": 31375, - "求": 31376, - "ण": 31377, - "경": 31378, - "‬": 31379, - "भ": 31380, - "们": 31381, - "模": 31382, - "需": 31383, - "ச": 31384, - "電": 31385, - "প": 31386, - "դ": 31387, - "へ": 31388, - "此": 31389, - "夜": 31390, - "或": 31391, - "橋": 31392, - "根": 31393, - "Ī": 31394, - "玉": 31395, - "ู": 31396, - "ṅ": 31397, - "交": 31398, - "品": 31399, - "良": 31400, - "ང": 31401, - "ォ": 31402, - "则": 31403, - "開": 31404, - "Ζ": 31405, - "문": 31406, - "被": 31407, - "조": 31408, - "株": 31409, - "记": 31410, - "會": 31411, - "经": 31412, - "ू": 31413, - "ょ": 31414, - "转": 31415, - "崎": 31416, - "마": 31417, - "⌘": 31418, - "比": 31419, - "造": 31420, - "ܐ": 31421, - "ื": 31422, - "没": 31423, - "现": 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15330, + "▁joy": 15331, + "▁safety": 15332, + "|^": 15333, + "pod": 15334, + "adém": 15335, + "▁Chron": 15336, + "▁Django": 15337, + "▁ehemal": 15338, + "kh": 15339, + "èle": 15340, + "▁poc": 15341, + "Bottom": 15342, + "launch": 15343, + "nem": 15344, + "▁GROUP": 15345, + "ního": 15346, + "▁Gib": 15347, + "sdk": 15348, + "BE": 15349, + "▁Gene": 15350, + "▁Staff": 15351, + "▁subsequent": 15352, + "icion": 15353, + "▁victory": 15354, + "▁canon": 15355, + "izar": 15356, + "izia": 15357, + "▁mate": 15358, + "▁layers": 15359, + "sudo": 15360, + "schule": 15361, + "periment": 15362, + "ület": 15363, + "ARCHAR": 15364, + "▁террито": 15365, + "▁measures": 15366, + "▁zou": 15367, + "opsis": 15368, + "нами": 15369, + "tbody": 15370, + "▁ese": 15371, + "sterdam": 15372, + "▁photo": 15373, + "ynchronous": 15374, + "setminus": 15375, + "▁loads": 15376, + "▁pleasure": 15377, + "▁meille": 15378, + "}\\,": 15379, + "qual": 15380, + "▁favour": 15381, + "▁rod": 15382, + "Der": 15383, + "рабо": 15384, + 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"тек": 16821, + "▁vb": 16822, + "▁mom": 16823, + "▁:(": 16824, + "▁dernier": 16825, + "'=>": 16826, + "▁этого": 16827, + "▁neue": 16828, + "▁Ча": 16829, + "▁weitere": 16830, + "▁alleg": 16831, + "▁reality": 16832, + "▁judge": 16833, + "▁Balt": 16834, + "▁thin": 16835, + "▁Ged": 16836, + "ieval": 16837, + "mx": 16838, + "ціональ": 16839, + "▁выпу": 16840, + "▁IX": 16841, + "▁blind": 16842, + "▁Motor": 16843, + "▁ша": 16844, + "▁approximation": 16845, + "dam": 16846, + "▁fog": 16847, + "кор": 16848, + "▁Writ": 16849, + "▁ling": 16850, + "▁писа": 16851, + "▁Mars": 16852, + "otti": 16853, + "Enum": 16854, + "▁Trib": 16855, + "▁merc": 16856, + "zung": 16857, + "vanced": 16858, + "cfg": 16859, + "нах": 16860, + "schen": 16861, + "\"].": 16862, + "bek": 16863, + "▁ster": 16864, + "jp": 16865, + "▁Rap": 16866, + "▁recording": 16867, + "▁peint": 16868, + "▁lets": 16869, + "änge": 16870, + ">\";": 16871, + "▁місце": 16872, + "▁caval": 16873, + "▁CSV": 16874, + "▁entstand": 16875, + "▁helper": 16876, + "endet": 16877, + "▁Gram": 16878, + "▁Diego": 16879, + "▁Bishop": 16880, + "TAG": 16881, + "▁ecc": 16882, + "▁Een": 16883, + "▁AV": 16884, + "City": 16885, + "▁Guide": 16886, + "hind": 16887, + "rical": 16888, + "▁Основ": 16889, + "Bus": 16890, + "▁zunächst": 16891, + "▁tick": 16892, + "▁Colonel": 16893, + "Thanks": 16894, + "▁ferm": 16895, + "▁granted": 16896, + "▁threshold": 16897, + "omorphic": 16898, + "▁Hun": 16899, + "enis": 16900, + "▁прав": 16901, + "▁які": 16902, + "PG": 16903, + "▁ws": 16904, + "▁technical": 16905, + "estro": 16906, + "klär": 16907, + "vars": 16908, + "ocrat": 16909, + "▁општи": 16910, + "onso": 16911, + "iba": 16912, + "▁Save": 16913, + "▁programa": 16914, + "▁въ": 16915, + "▁invån": 16916, + ">()": 16917, + "▁mejor": 16918, + "▁слова": 16919, + "▁replacement": 16920, + "▁impr": 16921, + "▁Francesco": 16922, + "▁Hotel": 16923, + "▁UPDATE": 16924, + "▁музы": 16925, + "ugs": 16926, + "vard": 16927, + "▁faz": 16928, + "inton": 16929, + "▁arts": 16930, + "▁Ky": 16931, + "▁Ils": 16932, + "▁sera": 16933, + "▁Volume": 16934, + "▁giugno": 16935, + "▁asym": 16936, + "▁Pir": 16937, + "▁NAS": 16938, + "▁Tam": 16939, + "ěl": 16940, + "Sequ": 16941, + "kmal": 16942, + "▁Eins": 16943, + "▁компа": 16944, + "obe": 16945, + "oor": 16946, + "▁heap": 16947, + "ctl": 16948, + "▁separately": 16949, + "reader": 16950, + "▁significantly": 16951, + "▁Lag": 16952, + "notes": 16953, + "▁sele": 16954, + "▁dedicated": 16955, + "▁Host": 16956, + "choice": 16957, + "wing": 16958, + "▁Titel": 16959, + "▁befindet": 16960, + "large": 16961, + "▁conten": 16962, + "JavaScript": 16963, + "▁deser": 16964, + "▁Gordon": 16965, + "спе": 16966, + "▁patri": 16967, + "▁Random": 16968, + "▁Returns": 16969, + "ым": 16970, + "рома": 16971, + "▁Studies": 16972, + "Sl": 16973, + "▁frü": 16974, + "TEXT": 16975, + "inate": 16976, + "▁Tol": 16977, + "▁everywhere": 16978, + "arta": 16979, + "▁orbit": 16980, + "▁Aires": 16981, + "▁Iss": 16982, + "▁też": 16983, + "▁diverse": 16984, + "▁numeric": 16985, + "maz": 16986, + "▁mise": 16987, + "▁battery": 16988, + "▁Akadem": 16989, + "нение": 16990, + "▁simultane": 16991, + "▁Dead": 16992, + "▁clust": 16993, + "▁otro": 16994, + "▁cerca": 16995, + "()`,": 16996, + "roz": 16997, + "ăt": 16998, + "▁MO": 16999, + "riften": 17000, + "important": 17001, + "▁jeho": 17002, + "▁findViewById": 17003, + "▁consequence": 17004, + "▁measured": 17005, + "ishes": 17006, + "▁sze": 17007, + "iendo": 17008, + "▁Wahl": 17009, + "strip": 17010, + "ARD": 17011, + "▁opacity": 17012, + "WORD": 17013, + "▁Ві": 17014, + "▁Location": 17015, + "rai": 17016, + "пен": 17017, + "▁rif": 17018, + "aussian": 17019, + "FileName": 17020, + "▁disco": 17021, + "ilen": 17022, + "▁vagy": 17023, + "licity": 17024, + "Border": 17025, + "▁Track": 17026, + "бом": 17027, + "fact": 17028, + "oka": 17029, + "▁gior": 17030, + "▁XVII": 17031, + "▁där": 17032, + "Site": 17033, + "ało": 17034, + "ská": 17035, + "▁pixels": 17036, + "vity": 17037, + "jQuery": 17038, + "▁sculpt": 17039, + "▁cargo": 17040, + "▁directive": 17041, + "▁wal": 17042, + "▁conna": 17043, + "▁Through": 17044, + "▁этом": 17045, + "Static": 17046, + "omsnitt": 17047, + "▁rund": 17048, + "▁claimed": 17049, + "зня": 17050, + "sha": 17051, + "▁rag": 17052, + "crement": 17053, + "▁fünf": 17054, + "▁rival": 17055, + "rin": 17056, + "slash": 17057, + "▁thirty": 17058, + "sleep": 17059, + "ологи": 17060, + "SM": 17061, + "gate": 17062, + "izations": 17063, + "vik": 17064, + "▁bless": 17065, + "▁Illinois": 17066, + "▁TE": 17067, + "uting": 17068, + "▁solving": 17069, + "GER": 17070, + "▁XIV": 17071, + "▁Indians": 17072, + "express": 17073, + "▁Heil": 17074, + "▁mujer": 17075, + "▁invånare": 17076, + "']);": 17077, + "▁aur": 17078, + "boost": 17079, + "GO": 17080, + "▁nin": 17081, + "tok": 17082, + "god": 17083, + "oter": 17084, + ")$$": 17085, + "▁descend": 17086, + "рю": 17087, + "▁Language": 17088, + "▁diver": 17089, + "▁Assuming": 17090, + "▁frequent": 17091, + "чні": 17092, + "▁Biography": 17093, + ",[": 17094, + "urm": 17095, + "▁walked": 17096, + "▁federal": 17097, + "▁Michigan": 17098, + "▁facts": 17099, + "▁Integr": 17100, + "LES": 17101, + "▁Alan": 17102, + "▁coup": 17103, + "Ber": 17104, + "▁particles": 17105, + "ће": 17106, + "Inflater": 17107, + "+(": 17108, + "Bound": 17109, + "▁Sü": 17110, + "Audio": 17111, + "citet": 17112, + "yect": 17113, + "▁nr": 17114, + "xe": 17115, + "▁Brun": 17116, + "▁_,": 17117, + "avor": 17118, + "▁discipl": 17119, + "alm": 17120, + "▁ноября": 17121, + "▁SSL": 17122, + "▁Kaiser": 17123, + "▁recher": 17124, + "ygon": 17125, + "▁regardless": 17126, + "▁configur": 17127, + "▁unnecess": 17128, + "▁Clark": 17129, + "PHP": 17130, + "▁FALSE": 17131, + "▁pad": 17132, + "$}": 17133, + "▁valu": 17134, + "▁disease": 17135, + "▁maior": 17136, + "▁hommes": 17137, + "▁Edition": 17138, + "slant": 17139, + "▁ending": 17140, + "▁settled": 17141, + "urus": 17142, + "hed": 17143, + "Pattern": 17144, + "▁година": 17145, + "▁Philadel": 17146, + "tikzpicture": 17147, + "▁coal": 17148, + "▁sede": 17149, + "▁satisfies": 17150, + "▁trim": 17151, + "▁bat": 17152, + "▁américain": 17153, + "▁luglio": 17154, + "▁поча": 17155, + "ffff": 17156, + "▁Target": 17157, + "generate": 17158, + "▁Zie": 17159, + "ția": 17160, + "▁gard": 17161, + "▁workers": 17162, + "▁Job": 17163, + "▁urban": 17164, + "ahlen": 17165, + "▁Building": 17166, + "▁neu": 17167, + "▁chron": 17168, + "▁Earl": 17169, + "gro": 17170, + "USE": 17171, + "▁XII": 17172, + "▁wealth": 17173, + "inae": 17174, + "▁Бра": 17175, + "▁libert": 17176, + "iros": 17177, + ":$": 17178, + "lee": 17179, + "ieves": 17180, + "▁Justice": 17181, + "▁oil": 17182, + "▁Athlet": 17183, + "▁clo": 17184, + "Scale": 17185, + "▁lips": 17186, + "▁april": 17187, + "▁impression": 17188, + "▁perce": 17189, + "▁участи": 17190, + "vil": 17191, + "éch": 17192, + "▁equality": 17193, + "▁мет": 17194, + "▁annotation": 17195, + "ernal": 17196, + "▁Mach": 17197, + "▁intitul": 17198, + "problem": 17199, + "ющих": 17200, + "oplus": 17201, + "▁thousands": 17202, + "▁calculations": 17203, + "umps": 17204, + "▁triangle": 17205, + "phal": 17206, + "▁Dorf": 17207, + "▁dollars": 17208, + "▁denen": 17209, + "lès": 17210, + "olid": 17211, + "▁Results": 17212, + "▁Stadium": 17213, + "▁Desp": 17214, + "▁Eisen": 17215, + "imir": 17216, + "▁sotto": 17217, + "▁či": 17218, + "atable": 17219, + "orum": 17220, + "▁convergence": 17221, + "▁jeune": 17222, + "oking": 17223, + "▁живо": 17224, + "aining": 17225, + "pointer": 17226, + "culo": 17227, + "▁jsou": 17228, + "▁grab": 17229, + "akte": 17230, + "▁hoping": 17231, + "▁Mak": 17232, + "▁sag": 17233, + "origine": 17234, + "▁послед": 17235, + "▁Veg": 17236, + "▁theoret": 17237, + "▁Tru": 17238, + "nement": 17239, + "▁faces": 17240, + "Hor": 17241, + "Join": 17242, + "arel": 17243, + "▁около": 17244, + "However": 17245, + "▁catal": 17246, + "bourg": 17247, + "▁mysqli": 17248, + "acions": 17249, + "▁Initial": 17250, + "▁rain": 17251, + "iture": 17252, + "▁Sciences": 17253, + "▁Kreis": 17254, + ".__": 17255, + "▁cinq": 17256, + "▁Auß": 17257, + "ithmet": 17258, + "itors": 17259, + "amazon": 17260, + "▁gap": 17261, + "▁ignored": 17262, + "adv": 17263, + "кої": 17264, + "▁часть": 17265, + "▁corpor": 17266, + "цер": 17267, + "▁crime": 17268, + "uous": 17269, + "▁налази": 17270, + "DataFrame": 17271, + "води": 17272, + "Ign": 17273, + "▁Lincoln": 17274, + "▁menos": 17275, + "▁Luft": 17276, + "▁Lind": 17277, + "▁Cook": 17278, + "▁materials": 17279, + "apped": 17280, + "ignore": 17281, + "▁откры": 17282, + "fried": 17283, + "▁gouvernement": 17284, + "▁fired": 17285, + "▁screenshot": 17286, + "сен": 17287, + "▁[(": 17288, + "▁организа": 17289, + "Graphics": 17290, + "▁проти": 17291, + "▁phen": 17292, + "craft": 17293, + "▁brain": 17294, + "▁Como": 17295, + "▁Everything": 17296, + "anes": 17297, + "IGN": 17298, + "▁nederbörd": 17299, + "▁Forest": 17300, + "zahl": 17301, + "▁Among": 17302, + "Qt": 17303, + "▁togg": 17304, + "▁variant": 17305, + "▁hill": 17306, + "писи": 17307, + "colon": 17308, + "▁dicembre": 17309, + "гор": 17310, + "▁Wind": 17311, + "ünstler": 17312, + "▁=\\": 17313, + "saved": 17314, + "▁nej": 17315, + "unte": 17316, + "utto": 17317, + "▁recens": 17318, + "▁sick": 17319, + "▁desen": 17320, + "UST": 17321, + "▁worst": 17322, + "▁Angel": 17323, + "odox": 17324, + "▁Province": 17325, + "▁Maz": 17326, + "▁agreement": 17327, + "▁Bass": 17328, + "▁segunda": 17329, + "onces": 17330, + "▁Linki": 17331, + "▁CL": 17332, + "▁já": 17333, + "itement": 17334, + "▁área": 17335, + "▁scalar": 17336, + "▁Рес": 17337, + "awt": 17338, + "sieme": 17339, + "▁juni": 17340, + "▁худож": 17341, + "ikus": 17342, + "▁lid": 17343, + "ppel": 17344, + "avi": 17345, + "▁balance": 17346, + "ipping": 17347, + "cussion": 17348, + "ческих": 17349, + "(\".": 17350, + "Also": 17351, + "▁whis": 17352, + "HOME": 17353, + "▁brown": 17354, + "▁día": 17355, + "▁può": 17356, + "plotlib": 17357, + "▁Jahrhunderts": 17358, + "DK": 17359, + "▁anchor": 17360, + "...]": 17361, + "▁Austria": 17362, + "▁marca": 17363, + "▁gez": 17364, + "iously": 17365, + "▁lazy": 17366, + "xa": 17367, + "▁Channel": 17368, + "▁neuen": 17369, + "das": 17370, + "▁searched": 17371, + "▁staat": 17372, + "▁Так": 17373, + "▁Josef": 17374, + "▁Sher": 17375, + "pois": 17376, + "▁enem": 17377, + "▁accessing": 17378, + "▁неко": 17379, + "▁furono": 17380, + "▁pseudo": 17381, + "?>": 17382, + "▁estadoun": 17383, + "▁Види": 17384, + "▁motiv": 17385, + "▁recall": 17386, + "isson": 17387, + "ób": 17388, + ")--": 17389, + "▁Erz": 17390, + "▁савез": 17391, + "Direct": 17392, + "соб": 17393, + "▁sho": 17394, + "völker": 17395, + "Ap": 17396, + "gens": 17397, + "ништво": 17398, + "▁Amsterdam": 17399, + "usk": 17400, + "пло": 17401, + "▁simulation": 17402, + "▁BC": 17403, + "▁Woj": 17404, + "autom": 17405, + "Alex": 17406, + "▁economic": 17407, + "гом": 17408, + "ikai": 17409, + "▁altre": 17410, + "▁'-": 17411, + "▁Weg": 17412, + "NotFound": 17413, + "йской": 17414, + "▁converting": 17415, + "phabet": 17416, + "atrice": 17417, + "bourne": 17418, + "alom": 17419, + "▁comparing": 17420, + "▁Zo": 17421, + "▁fla": 17422, + "вая": 17423, + "▁entra": 17424, + "▁charset": 17425, + "developers": 17426, + "ística": 17427, + "}>": 17428, + "▁Jazz": 17429, + "▁Howard": 17430, + "шта": 17431, + "▁clone": 17432, + "door": 17433, + "▁Pin": 17434, + "***": 17435, + "▁silent": 17436, + "ecycle": 17437, + "isce": 17438, + "▁mud": 17439, + "▁Display": 17440, + "▁lip": 17441, + "▁использова": 17442, + "▁characteristic": 17443, + "▁sb": 17444, + "firebase": 17445, + "▁Bew": 17446, + "Calendar": 17447, + "▁uso": 17448, + "èse": 17449, + "▁Rat": 17450, + "▁esper": 17451, + "▁throwing": 17452, + "▁rodz": 17453, + "▁yards": 17454, + "▁grass": 17455, + "▁marker": 17456, + "▁Kos": 17457, + "Theta": 17458, + "▁organis": 17459, + "kernel": 17460, + "▁personas": 17461, + "keep": 17462, + "▁exclaimed": 17463, + "oslav": 17464, + "▁Entertain": 17465, + "нер": 17466, + "▁inwon": 17467, + "▁Rand": 17468, + "reduce": 17469, + "fac": 17470, + "expression": 17471, + "yj": 17472, + "▁differenti": 17473, + "aglia": 17474, + "▁templates": 17475, + "▁mű": 17476, + "▁prv": 17477, + "▁mois": 17478, + "▁gewann": 17479, + "▁була": 17480, + "bibli": 17481, + "demo": 17482, + "▁Anderson": 17483, + "▁ред": 17484, + "▁porque": 17485, + "▁Pologne": 17486, + "▁trip": 17487, + "▁exemple": 17488, + "▁Internacional": 17489, + "▁као": 17490, + "Insert": 17491, + "general": 17492, + "SESSION": 17493, + "berga": 17494, + "hält": 17495, + "unas": 17496, + "мира": 17497, + "▁yields": 17498, + "mapsto": 17499, + "spot": 17500, + "▁+\\": 17501, + "лла": 17502, + "▁precisely": 17503, + "▁член": 17504, + "shadow": 17505, + "Are": 17506, + "unal": 17507, + "▁dispar": 17508, + "▁título": 17509, + "nest": 17510, + "▁Low": 17511, + "▁prot": 17512, + "▁Costa": 17513, + "named": 17514, + "▁gained": 17515, + "lesia": 17516, + "▁administration": 17517, + "Import": 17518, + "branch": 17519, + "▁sympath": 17520, + "voj": 17521, + "▁EC": 17522, + "▁municipio": 17523, + "▁animated": 17524, + "▁directories": 17525, + "▁roof": 17526, + "ząd": 17527, + "imet": 17528, + "proto": 17529, + "bla": 17530, + ":]": 17531, + "have": 17532, + "atem": 17533, + "▁ns": 17534, + "▁sector": 17535, + "three": 17536, + "owane": 17537, + "wers": 17538, + "ових": 17539, + "rence": 17540, + "▁extr": 17541, + "igten": 17542, + "▁occident": 17543, + "ță": 17544, + "▁eat": 17545, + "▁hydro": 17546, + "ubernetes": 17547, + "[@": 17548, + "▁Moon": 17549, + "▁Sho": 17550, + "▁elsewhere": 17551, + "üller": 17552, + "Upload": 17553, + "ланд": 17554, + "▁För": 17555, + "wissenschaft": 17556, + "KS": 17557, + "▁physics": 17558, + "tz": 17559, + "▁серед": 17560, + "▁Arbeit": 17561, + "▁мест": 17562, + "▁Gebiet": 17563, + "▁insect": 17564, + "Ah": 17565, + "izado": 17566, + "▁temple": 17567, + "▁annual": 17568, + "stad": 17569, + "▁habitat": 17570, + "▁AB": 17571, + "wort": 17572, + "▁repos": 17573, + "▁Neu": 17574, + "▁$(\".": 17575, + "Vorlage": 17576, + "▁reprezent": 17577, + "estanden": 17578, + "Intern": 17579, + ".`": 17580, + "▁failing": 17581, + "▁Material": 17582, + "▁effectively": 17583, + "телем": 17584, + "▁гла": 17585, + "▁nahm": 17586, + "▁differently": 17587, + "extension": 17588, + "▁Verm": 17589, + "enabled": 17590, + "configure": 17591, + "nio": 17592, + "ciones": 17593, + "▁Beach": 17594, + "сона": 17595, + "▁copying": 17596, + "▁україн": 17597, + "▁призна": 17598, + "zh": 17599, + "Desktop": 17600, + "▁sost": 17601, + "▁subsequently": 17602, + "▁Lehr": 17603, + "▁ó": 17604, + "lär": 17605, + "odor": 17606, + "phon": 17607, + "nc": 17608, + "iterator": 17609, + "▁эти": 17610, + "▁europé": 17611, + "▁Toronto": 17612, + "ódigo": 17613, + "▁posto": 17614, + "ffe": 17615, + "▁crew": 17616, + "▁Schwar": 17617, + "Sa": 17618, + "square": 17619, + "▁beside": 17620, + "▁Мі": 17621, + "▁ath": 17622, + "▁advent": 17623, + "cji": 17624, + "written": 17625, + "▁russ": 17626, + "rost": 17627, + "HI": 17628, + "▁dice": 17629, + "cca": 17630, + "▁dép": 17631, + "ply": 17632, + "bigg": 17633, + "ział": 17634, + "ütt": 17635, + "▁одно": 17636, + "JECT": 17637, + "ському": 17638, + "nos": 17639, + "mock": 17640, + "Launch": 17641, + "same": 17642, + "▁jobs": 17643, + "▁widely": 17644, + "▁defines": 17645, + "▁Pse": 17646, + "▁neighbour": 17647, + "ющие": 17648, + "▁closer": 17649, + "▁располо": 17650, + "▁clubs": 17651, + "fly": 17652, + "шим": 17653, + "▁suffered": 17654, + "▁nar": 17655, + "▁lavor": 17656, + "Extension": 17657, + "itionally": 17658, + "▁grace": 17659, + "▁Campeonato": 17660, + "▁Christmas": 17661, + "middle": 17662, + "othek": 17663, + "elements": 17664, + "▁sondern": 17665, + "▁tarde": 17666, + "▁permanent": 17667, + "▁conclude": 17668, + "Seg": 17669, + "▁акаде": 17670, + "}\",": 17671, + "▁февраля": 17672, + "řed": 17673, + "▁IL": 17674, + "jud": 17675, + "▁USS": 17676, + "▁Nature": 17677, + "ifference": 17678, + "Serializer": 17679, + "▁twelve": 17680, + "tid": 17681, + "мия": 17682, + "ческого": 17683, + "▁calendar": 17684, + "concat": 17685, + "▁intersection": 17686, + "▁PA": 17687, + "azure": 17688, + "▁située": 17689, + "▁kinds": 17690, + "▁ausge": 17691, + "▁rural": 17692, + "Theme": 17693, + "▁tale": 17694, + "noindent": 17695, + "going": 17696, + "rx": 17697, + "agi": 17698, + "wrapper": 17699, + "▁Coast": 17700, + "mbH": 17701, + "▁перед": 17702, + "spre": 17703, + "▁}\\": 17704, + "▁LI": 17705, + "znam": 17706, + "itled": 17707, + "Sample": 17708, + "uliar": 17709, + "*\\": 17710, + "▁resistance": 17711, + "stock": 17712, + "ked": 17713, + "▁HE": 17714, + "▁possession": 17715, + "▁Ring": 17716, + "▁magyar": 17717, + "outs": 17718, + "▁Secretary": 17719, + "nde": 17720, + "▁Wald": 17721, + "-(": 17722, + "▁ISO": 17723, + "▁afternoon": 17724, + "ionen": 17725, + "▁stops": 17726, + "▁constants": 17727, + "guard": 17728, + "bow": 17729, + "▁ers": 17730, + "▁Firebase": 17731, + "▁Clear": 17732, + "▁Holy": 17733, + "Win": 17734, + "▁titles": 17735, + "▁трав": 17736, + "▁contrib": 17737, + "häng": 17738, + "▁photograph": 17739, + "▁Distribution": 17740, + "ifts": 17741, + "▁aunque": 17742, + "comb": 17743, + "ADD": 17744, + "▁publication": 17745, + "▁служ": 17746, + "▁кня": 17747, + "▁ayant": 17748, + "▁restore": 17749, + "▁belief": 17750, + "▁vég": 17751, + "▁extensions": 17752, + "▁decom": 17753, + "вший": 17754, + "WT": 17755, + "▁parti": 17756, + "▁gioc": 17757, + "▁мира": 17758, + "▁issu": 17759, + "pipe": 17760, + "▁props": 17761, + "▁willing": 17762, + "▁nest": 17763, + "aso": 17764, + "pot": 17765, + "▁handles": 17766, + "▁фо": 17767, + "▁moder": 17768, + "▁ebenfalls": 17769, + "▁fighting": 17770, + "umbn": 17771, + "▁transparent": 17772, + "▁Krist": 17773, + "▁homes": 17774, + "▁voyage": 17775, + "Failed": 17776, + "▁Bird": 17777, + "▁Heart": 17778, + "Counter": 17779, + "▁Scottish": 17780, + "ática": 17781, + "▁arbeit": 17782, + "^{-\\": 17783, + "▁Sor": 17784, + "▁engaged": 17785, + "▁aside": 17786, + "▁Fou": 17787, + "▁wiel": 17788, + "▁reconst": 17789, + "ousin": 17790, + "▁hosted": 17791, + "▁classe": 17792, + "▁contest": 17793, + "...\"": 17794, + "мом": 17795, + "▁bean": 17796, + "gem": 17797, + "▁consultato": 17798, + "▁bio": 17799, + "▁subjects": 17800, + "boBox": 17801, + "▁Schrift": 17802, + "▁dinner": 17803, + "ăr": 17804, + "▁równ": 17805, + "▁%%": 17806, + "bage": 17807, + "▁veröff": 17808, + "▁detected": 17809, + "ienn": 17810, + "rose": 17811, + "▁Ton": 17812, + "Complete": 17813, + "▁proto": 17814, + "ichts": 17815, + "STAT": 17816, + "Checked": 17817, + "▁inten": 17818, + "▁smile": 17819, + "▁strip": 17820, + "neut": 17821, + "');\r": 17822, + "four": 17823, + "▁todas": 17824, + "Controls": 17825, + "▁thorough": 17826, + "rup": 17827, + "▁држави": 17828, + "ită": 17829, + "Protocol": 17830, + "Ка": 17831, + "▁expanded": 17832, + "extra": 17833, + "oport": 17834, + "▁Станов": 17835, + "leases": 17836, + "▁notion": 17837, + "▁guest": 17838, + "▁Islands": 17839, + "icked": 17840, + "▁Dave": 17841, + "▁reflection": 17842, + "liv": 17843, + "ální": 17844, + "▁revealed": 17845, + "▁sog": 17846, + "▁Tax": 17847, + "▁periodo": 17848, + "▁Weltkrie": 17849, + "catalina": 17850, + "qué": 17851, + "▁Father": 17852, + "▁Bir": 17853, + "expect": 17854, + "▁regression": 17855, + "iné": 17856, + "▁dabei": 17857, + "perm": 17858, + "мене": 17859, + "▁Abd": 17860, + "▁CF": 17861, + "arks": 17862, + "resolve": 17863, + "wedge": 17864, + "▁initialization": 17865, + "▁Véase": 17866, + "▁приня": 17867, + "stmt": 17868, + "▁income": 17869, + "MY": 17870, + "▁odkazy": 17871, + "▁Siehe": 17872, + "▁bodies": 17873, + "▁soc": 17874, + "Random": 17875, + "▁senza": 17876, + "ablo": 17877, + "▁regarded": 17878, + "onCreate": 17879, + "▁Magazine": 17880, + "▁Raf": 17881, + "▁Buenos": 17882, + "ил": 17883, + ")));": 17884, + "capt": 17885, + "redirect": 17886, + "▁petit": 17887, + "▁farm": 17888, + "▁rôle": 17889, + "▁статьи": 17890, + "    ": 17891, + "subfigure": 17892, + "èces": 17893, + "ziel": 17894, + "▁окон": 17895, + "EE": 17896, + "mee": 17897, + "▁perten": 17898, + "▁représent": 17899, + "▁LA": 17900, + "?'": 17901, + "▁тру": 17902, + "▁rational": 17903, + "osof": 17904, + "▁kne": 17905, + "▁artists": 17906, + "Flow": 17907, + "▁Аль": 17908, + "izard": 17909, + "▁numero": 17910, + "actic": 17911, + "▁destruct": 17912, + "▁Пра": 17913, + "onsieur": 17914, + "qt": 17915, + "abestanden": 17916, + "ność": 17917, + "Connect": 17918, + "▁oracle": 17919, + "▁Stockholm": 17920, + "sizeof": 17921, + "▁gemäß": 17922, + "ACT": 17923, + "▁expert": 17924, + "utions": 17925, + "▁hacia": 17926, + "▁logger": 17927, + "▁fool": 17928, + "rypto": 17929, + "ær": 17930, + "▁cidade": 17931, + "▁составе": 17932, + "oker": 17933, + "▁Transfer": 17934, + "▁denied": 17935, + "Track": 17936, + "▁radi": 17937, + "zec": 17938, + "▁Historic": 17939, + "▁Einwohner": 17940, + "кою": 17941, + "▁хра": 17942, + "▁Category": 17943, + "▁Disney": 17944, + "▁swap": 17945, + "Begin": 17946, + "▁mientras": 17947, + "▁dance": 17948, + "▁tête": 17949, + "▁droit": 17950, + "erta": 17951, + "▁birds": 17952, + "▁convin": 17953, + "parator": 17954, + "дра": 17955, + "▁ES": 17956, + "▁Ressources": 17957, + "EGIN": 17958, + "ücke": 17959, + "▁Cruz": 17960, + "abling": 17961, + "▁\"@": 17962, + "▁metres": 17963, + "▁Beg": 17964, + "▁Gründ": 17965, + "▁Boh": 17966, + "▁mile": 17967, + "▁Technology": 17968, + "\"+": 17969, + "acco": 17970, + "▁ss": 17971, + "▁Fed": 17972, + "▁Hend": 17973, + "usch": 17974, + "itä": 17975, + "folk": 17976, + "▁absor": 17977, + "antal": 17978, + "odge": 17979, + "▁WHEN": 17980, + "▁Externí": 17981, + "▁Regiment": 17982, + "▁evaluation": 17983, + "▁Tai": 17984, + "▁vocals": 17985, + "▁experimental": 17986, + "embed": 17987, + "▁Minn": 17988, + "▁вме": 17989, + "prec": 17990, + "every": 17991, + "▁hoof": 17992, + "▁Fernando": 17993, + "▁Bibliographie": 17994, + "▁nag": 17995, + "amerikanischer": 17996, + "▁marks": 17997, + "▁UTC": 17998, + "▁uncertain": 17999, + "дия": 18000, + "olia": 18001, + "▁cup": 18002, + "▁fille": 18003, + "▁dok": 18004, + "useppe": 18005, + "esterd": 18006, + "▁Brand": 18007, + "▁Third": 18008, + "PP": 18009, + "nodes": 18010, + "▁Pad": 18011, + "▁loved": 18012, + "swing": 18013, + "▁surprised": 18014, + "ardi": 18015, + "▁GR": 18016, + "]\"": 18017, + "▁equally": 18018, + "ihe": 18019, + "care": 18020, + "писок": 18021, + "lijk": 18022, + "rinn": 18023, + "▁\\[\\": 18024, + "▁sons": 18025, + "▁tät": 18026, + "icamente": 18027, + "▁listing": 18028, + "iellement": 18029, + "▁nyelven": 18030, + "▁ds": 18031, + "▁agricult": 18032, + "▁Hermann": 18033, + "▁besides": 18034, + "progress": 18035, + "▁peculiar": 18036, + "focus": 18037, + "cn": 18038, + "-$": 18039, + "ственный": 18040, + "ourg": 18041, + "▁wyn": 18042, + "▁conducted": 18043, + "▁Становништво": 18044, + "connected": 18045, + "▁bott": 18046, + "▁смер": 18047, + "▁Poz": 18048, + "unct": 18049, + "conda": 18050, + "▁савезној": 18051, + "▁havet": 18052, + "ligt": 18053, + "orted": 18054, + "▁entering": 18055, + "multip": 18056, + "▁Temple": 18057, + "▁Plant": 18058, + "typeof": 18059, + "▁Vlad": 18060, + "▁qued": 18061, + "▁reste": 18062, + "▁май": 18063, + "▁Very": 18064, + "ambiguation": 18065, + "▁challeng": 18066, + "▁respective": 18067, + "▁тор": 18068, + "Ctrl": 18069, + "▁absence": 18070, + "aru": 18071, + "вое": 18072, + "▁först": 18073, + "▁sq": 18074, + "▁Emperor": 18075, + "▁Ign": 18076, + "▁това": 18077, + ":`": 18078, + "adoop": 18079, + "▁Madame": 18080, + "▁gruppo": 18081, + "stud": 18082, + "▁externas": 18083, + "▁Александр": 18084, + "▁dign": 18085, + "▁живе": 18086, + "Amount": 18087, + "▁correlate": 18088, + "▁Fant": 18089, + "▁rails": 18090, + "fp": 18091, + "министратив": 18092, + "▁bought": 18093, + "▁filters": 18094, + "▁ancora": 18095, + "▁partner": 18096, + "▁quand": 18097, + "symbol": 18098, + "ulating": 18099, + "▁zd": 18100, + "awn": 18101, + "▁Grant": 18102, + "because": 18103, + "rable": 18104, + "\\}": 18105, + "ísticas": 18106, + "▁уче": 18107, + "▁période": 18108, + "▁ske": 18109, + "▁Anyway": 18110, + "▁indexes": 18111, + "▁directions": 18112, + "▁RAM": 18113, + "chrome": 18114, + "▁apost": 18115, + "▁warnings": 18116, + "▁Airport": 18117, + "VI": 18118, + "abile": 18119, + "▁lord": 18120, + "provider": 18121, + "▁Ji": 18122, + "ostream": 18123, + "▁gemeente": 18124, + "tableView": 18125, + "Extra": 18126, + "cursor": 18127, + "eground": 18128, + "▁Moz": 18129, + "▁rib": 18130, + "▁morph": 18131, + "loads": 18132, + "elsk": 18133, + "▁MAX": 18134, + "▁Santiago": 18135, + "▁Him": 18136, + "codes": 18137, + "▁lanz": 18138, + "▁counts": 18139, + "rinningsområ": 18140, + "щё": 18141, + "▁spé": 18142, + "▁pierws": 18143, + "▁Sver": 18144, + "▁acknow": 18145, + "Boolean": 18146, + "▁фамили": 18147, + "▁Senate": 18148, + "шов": 18149, + "agers": 18150, + "▁Nueva": 18151, + "bil": 18152, + "kiem": 18153, + "▁Mey": 18154, + "wij": 18155, + "▁GmbH": 18156, + "validation": 18157, + "▁ensuite": 18158, + "inking": 18159, + "▁campion": 18160, + "▁financial": 18161, + "izon": 18162, + "Headers": 18163, + "▁deprecated": 18164, + "▁fonction": 18165, + "REG": 18166, + "▁volumes": 18167, + "▁Chi": 18168, + "▁encountered": 18169, + "lak": 18170, + "рая": 18171, + "▁continues": 18172, + "▁~[": 18173, + "uerte": 18174, + "▁\\;": 18175, + "▁Dok": 18176, + "▁weights": 18177, + "▁rh": 18178, + "▁Napole": 18179, + "▁naturally": 18180, + "sku": 18181, + "pas": 18182, + "▁gegründ": 18183, + "etr": 18184, + "▁Ku": 18185, + "icted": 18186, + "▁fabric": 18187, + "▁ASC": 18188, + "▁Entertainment": 18189, + "▁energ": 18190, + "клад": 18191, + "omon": 18192, + "theme": 18193, + "▁харак": 18194, + "▁draft": 18195, + "▁channels": 18196, + "▁desert": 18197, + "▁través": 18198, + "▁Lock": 18199, + "▁siendo": 18200, + "фек": 18201, + "même": 18202, + "▁packet": 18203, + "▁Mountain": 18204, + "▁Fahr": 18205, + "braio": 18206, + "пере": 18207, + "▁genannt": 18208, + "▁deployment": 18209, + "Pal": 18210, + "ног": 18211, + "стру": 18212, + "Prim": 18213, + "für": 18214, + "▁dangerous": 18215, + "▁szám": 18216, + "reck": 18217, + "▁popup": 18218, + "icky": 18219, + "inar": 18220, + "cowo": 18221, + "нцикло": 18222, + "ítás": 18223, + "▁plugins": 18224, + "▁driven": 18225, + "лев": 18226, + "▁\"(": 18227, + "tta": 18228, + "▁Ú": 18229, + "▁eb": 18230, + "▁'';": 18231, + "▁knock": 18232, + "▁основа": 18233, + "▁maison": 18234, + "гля": 18235, + "▁Honor": 18236, + "tail": 18237, + "ritz": 18238, + "▁guys": 18239, + "▁combinations": 18240, + "ondere": 18241, + "▁Ald": 18242, + "▁fiddle": 18243, + "дав": 18244, + "urd": 18245, + "▁projection": 18246, + "▁También": 18247, + "verb": 18248, + "▁terre": 18249, + "rugu": 18250, + "▁september": 18251, + "▁=": 18572, + "▁Beat": 18573, + "▁Sax": 18574, + "vertical": 18575, + "кто": 18576, + "▁plants": 18577, + "▁Références": 18578, + "▁ogni": 18579, + "▁curs": 18580, + "▁SK": 18581, + "они": 18582, + "▁destac": 18583, + "\");\r": 18584, + "▁Sure": 18585, + "▁partido": 18586, + "▁Folge": 18587, + "▁Moore": 18588, + "▁wz": 18589, + "скус": 18590, + "ltre": 18591, + "ondo": 18592, + "▁pose": 18593, + "imos": 18594, + "бой": 18595, + "ципа": 18596, + "jus": 18597, + ".....": 18598, + "▁época": 18599, + "▁quanto": 18600, + "▁Support": 18601, + "geschichte": 18602, + "SERVER": 18603, + "▁Georges": 18604, + "enum": 18605, + "▁herm": 18606, + "▁nebo": 18607, + "▁Chr": 18608, + "character": 18609, + "▁***": 18610, + "▁Forsch": 18611, + "iami": 18612, + "▁¿": 18613, + "cych": 18614, + "▁fifth": 18615, + "sent": 18616, + "▁anderem": 18617, + "▁proportion": 18618, + "▁prest": 18619, + "▁Girl": 18620, + "▁drama": 18621, + "wand": 18622, + "▁Mail": 18623, + "▁Lux": 18624, + "▁který": 18625, + "▁Gesellschaft": 18626, + "▁Hinweis": 18627, + "nisse": 18628, + "▁mondo": 18629, + "Eq": 18630, + "▁perí": 18631, + "▁eastern": 18632, + "▁UEFA": 18633, + "uale": 18634, + "▁convex": 18635, + 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"чня": 21532, + "Dict": 21533, + "▁appearing": 21534, + "▁prav": 21535, + "▁Universal": 21536, + "Browser": 21537, + "▁Singap": 21538, + "ennessee": 21539, + "]_": 21540, + "▁Sof": 21541, + "▁Cad": 21542, + "ounce": 21543, + "▁costs": 21544, + "]{\\": 21545, + "../../": 21546, + "ській": 21547, + "ühl": 21548, + "iety": 21549, + "пр": 21550, + "▁interpreted": 21551, + "ajn": 21552, + "colog": 21553, + "YS": 21554, + "mans": 21555, + "▁metrics": 21556, + "▁registr": 21557, + "istance": 21558, + "▁Поль": 21559, + "▁anonymous": 21560, + "▁institutions": 21561, + "▁zdob": 21562, + "prüng": 21563, + "▁арти": 21564, + "▁estat": 21565, + "acci": 21566, + "▁academic": 21567, + "▁chiesa": 21568, + "▁Gian": 21569, + "contrib": 21570, + "umed": 21571, + "▁Gir": 21572, + "▁baseball": 21573, + "numeric": 21574, + "Generator": 21575, + "GM": 21576, + "▁tiny": 21577, + "▁distinction": 21578, + "гер": 21579, + "▁rust": 21580, + "▁FIFA": 21581, + "▁Properties": 21582, + "^-": 21583, + "▁экс": 21584, + "▁Stanis": 21585, + "▁Ajax": 21586, + "escape": 21587, + "▁consp": 21588, + "▁Chen": 21589, + "▁Naval": 21590, + "Bit": 21591, + "▁bât": 21592, + "скими": 21593, + "drive": 21594, + "▁Round": 21595, + "photo": 21596, + "▁Level": 21597, + "▁geg": 21598, + "Tom": 21599, + "▁Mobile": 21600, + "▁Trop": 21601, + "Direction": 21602, + "isan": 21603, + ")^{-": 21604, + "▁Setting": 21605, + "▁Probably": 21606, + "лья": 21607, + "▁assets": 21608, + "▁atte": 21609, + "▁bulk": 21610, + "ést": 21611, + "▁wing": 21612, + "nius": 21613, + "▁wins": 21614, + "▁lud": 21615, + "ushing": 21616, + "▁deven": 21617, + "ограф": 21618, + "burger": 21619, + "▁embar": 21620, + "FilterChain": 21621, + "▁tum": 21622, + "▁öss": 21623, + "▁nommé": 21624, + "▁pir": 21625, + "▁luc": 21626, + "dbo": 21627, + "agues": 21628, + "▁alcan": 21629, + "ouwen": 21630, + "▁Stanley": 21631, + "циали": 21632, + "▁grown": 21633, + "▁preserved": 21634, + "▁solar": 21635, + "▁Население": 21636, + "▁performances": 21637, + "▁Cow": 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"uchte": 21690, + "▁consumer": 21691, + "▁crossed": 21692, + "ordered": 21693, + "away": 21694, + "techn": 21695, + "▁subscri": 21696, + "▁shortcut": 21697, + "▁производ": 21698, + "▁simultaneously": 21699, + "▁rating": 21700, + "▁Kings": 21701, + "▁relationships": 21702, + "▁Sex": 21703, + "▁Tool": 21704, + "agh": 21705, + "acters": 21706, + "logger": 21707, + "homme": 21708, + "engers": 21709, + "▁Ri": 21710, + "earance": 21711, + "▁appearances": 21712, + "Real": 21713, + "▁passe": 21714, + "iclopedia": 21715, + "чко": 21716, + "terre": 21717, + "▁Ontario": 21718, + "▁переда": 21719, + "footer": 21720, + "archivi": 21721, + "ifiz": 21722, + "▁Protest": 21723, + "▁LIN": 21724, + "unnable": 21725, + "▁centuries": 21726, + "▁Bayer": 21727, + "цію": 21728, + "овин": 21729, + "▁Andrea": 21730, + "selection": 21731, + "▁calm": 21732, + "▁modification": 21733, + "▁shortly": 21734, + "inaire": 21735, + "▁fusion": 21736, + "▁feelings": 21737, + "PK": 21738, + "▁Roberto": 21739, + "гне": 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"enberg": 21845, + "▁scenarios": 21846, + "ânt": 21847, + "▁kommt": 21848, + "▁domestic": 21849, + "▁Basketball": 21850, + "▁Cooper": 21851, + "sock": 21852, + "держа": 21853, + "={\\": 21854, + "▁inici": 21855, + "▁Phill": 21856, + "▁генерал": 21857, + "archiviato": 21858, + "ън": 21859, + "Rob": 21860, + "▁tong": 21861, + "▁characteristics": 21862, + "▁amaz": 21863, + "▁Mode": 21864, + "▁inaugur": 21865, + "wehr": 21866, + "rant": 21867, + "ionali": 21868, + "▁Mother": 21869, + "Ma": 21870, + "équ": 21871, + "▁Kelly": 21872, + "cile": 21873, + "▁besteht": 21874, + "▁estimates": 21875, + "ruguay": 21876, + "▁Ans": 21877, + "Mad": 21878, + "▁нав": 21879, + "▁données": 21880, + "▁tropical": 21881, + "▁Several": 21882, + "elter": 21883, + "▁Pho": 21884, + "kem": 21885, + "▁Customer": 21886, + "▁складі": 21887, + "▁courses": 21888, + "Platform": 21889, + "navbar": 21890, + "learning": 21891, + "▁Swedish": 21892, + "▁zast": 21893, + "▁Lig": 21894, + "management": 21895, + "▁lod": 21896, + "uffle": 21897, + "Texture": 21898, + "arga": 21899, + "átum": 21900, + "▁DDR": 21901, + "нії": 21902, + "▁Société": 21903, + "▁domains": 21904, + "▁permitted": 21905, + "▁externe": 21906, + "▁quelque": 21907, + "vt": 21908, + "yman": 21909, + "▁Ward": 21910, + "▁agli": 21911, + "▁andra": 21912, + "Snapshot": 21913, + "▁må": 21914, + "▁yeah": 21915, + "дена": 21916, + "ępu": 21917, + "askell": 21918, + "▁République": 21919, + "inject": 21920, + "▁';": 21921, + "änn": 21922, + "▁zelf": 21923, + "▁Entwicklung": 21924, + "ária": 21925, + "onomy": 21926, + "▁svil": 21927, + "iese": 21928, + "▁conser": 21929, + "▁nim": 21930, + "▁rész": 21931, + "▁Итали": 21932, + "▁partici": 21933, + "▁Lion": 21934, + "sr": 21935, + "always": 21936, + "▁Владимир": 21937, + "ческие": 21938, + "[,": 21939, + "▁Definition": 21940, + "nant": 21941, + "oem": 21942, + "Ids": 21943, + "▁вне": 21944, + "▁[...]": 21945, + "▁направ": 21946, + "▁GO": 21947, + "▁års": 21948, + "▁után": 21949, + "▁outros": 21950, + "▁región": 21951, + "▁Mong": 21952, + "▁filme": 21953, + "▁triple": 21954, + "▁spons": 21955, + "Develop": 21956, + "▁outcome": 21957, + "▁Bible": 21958, + "▁имени": 21959, + "Canvas": 21960, + "пута": 21961, + "curr": 21962, + "ások": 21963, + "){\\": 21964, + "ningar": 21965, + "`;": 21966, + "▁Flash": 21967, + ":#": 21968, + "must": 21969, + "cpu": 21970, + "▁formats": 21971, + "Har": 21972, + "▁episodio": 21973, + "▁Rosa": 21974, + "▁dès": 21975, + "emit": 21976, + "riteria": 21977, + "Annotation": 21978, + "Flag": 21979, + "gmail": 21980, + "▁Normal": 21981, + "ollary": 21982, + "▁foss": 21983, + "▁concurrent": 21984, + "▁crashes": 21985, + "▁виде": 21986, + "▁Minor": 21987, + "▁Sit": 21988, + "▁SN": 21989, + "▁scar": 21990, + "▁femin": 21991, + "▁specification": 21992, + "soap": 21993, + "▁operate": 21994, + "▁principalmente": 21995, + "▁aust": 21996, + "ibile": 21997, + "itime": 21998, + "лежа": 21999, + "iframe": 22000, + "▁concepts": 22001, + "▁tack": 22002, + "▁viss": 22003, 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"▁Replace": 22108, + "▁lé": 22109, + "Who": 22110, + "▁Estad": 22111, + "▁dimensional": 22112, + "▁öff": 22113, + "▁grands": 22114, + "джа": 22115, + "plane": 22116, + "ності": 22117, + "▁Origin": 22118, + "WI": 22119, + "änner": 22120, + "▁Cry": 22121, + "ITION": 22122, + "▁född": 22123, + "▁cultura": 22124, + "▁Rank": 22125, + "▁vuel": 22126, + "▁zag": 22127, + "▁Maxim": 22128, + "ону": 22129, + "()))": 22130, + "Raw": 22131, + "kirche": 22132, + "▁además": 22133, + "▁tie": 22134, + "▁Style": 22135, + "сков": 22136, + "istant": 22137, + "olph": 22138, + "▁Zür": 22139, + "▁Info": 22140, + "DOM": 22141, + "usc": 22142, + "nahm": 22143, + "▁Федера": 22144, + "▁Fot": 22145, + "▁specifying": 22146, + "▁titolo": 22147, + "▁Boys": 22148, + "iech": 22149, + "Place": 22150, + "▁Hoff": 22151, + "▁cached": 22152, + "валь": 22153, + "isher": 22154, + "rolling": 22155, + "opens": 22156, + "▁hr": 22157, + "------": 22158, + "▁maggior": 22159, + "▁transactions": 22160, + "▁criminal": 22161, + 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"▁kilometres": 24016, + "▁Однако": 24017, + "кли": 24018, + "▁Sri": 24019, + "Groups": 24020, + "mind": 24021, + "oslov": 24022, + "fern": 24023, + "egu": 24024, + "abeled": 24025, + "Fiddle": 24026, + "▁Century": 24027, + "/-": 24028, + "▁Jegyzetek": 24029, + "Hen": 24030, + "ensemble": 24031, + "▁Gut": 24032, + "_{{\\": 24033, + "▁ranking": 24034, + "+$": 24035, + "ала": 24036, + "▁#{": 24037, + "imientos": 24038, + "achim": 24039, + "rides": 24040, + "▁Klaus": 24041, + "▁intend": 24042, + "▁Kentucky": 24043, + "cipe": 24044, + "▁Dienst": 24045, + "▁situated": 24046, + "▁póź": 24047, + "▁scrit": 24048, + "clip": 24049, + "нет": 24050, + "tables": 24051, + "▁Nied": 24052, + "▁McK": 24053, + "▁powst": 24054, + "▁kunnen": 24055, + "▁Evans": 24056, + "жды": 24057, + "вать": 24058, + "uchar": 24059, + "▁residents": 24060, + "iak": 24061, + "▁Resol": 24062, + "▁veces": 24063, + "▁satisfying": 24064, + "INF": 24065, + "▁син": 24066, + "▁crossing": 24067, + "iben": 24068, + "▁широ": 24069, + "pto": 24070, + "ILL": 24071, + "▁роль": 24072, + "▁aktiv": 24073, + "▁обращения": 24074, + "Wikispecies": 24075, + "▁Höhe": 24076, + "cro": 24077, + "════": 24078, + "altra": 24079, + "▁FILE": 24080, + "▁ups": 24081, + "▁allocation": 24082, + "Michael": 24083, + "▁acknowled": 24084, + "Linux": 24085, + "▁metros": 24086, + "tte": 24087, + "afen": 24088, + "▁xcode": 24089, + "▁тради": 24090, + "species": 24091, + "▁injury": 24092, + "▁самы": 24093, + "▁lattice": 24094, + "Material": 24095, + "andenburg": 24096, + "▁huvudstaden": 24097, + "story": 24098, + "▁varying": 24099, + "▁követ": 24100, + "▁Российской": 24101, + "irse": 24102, + "▁drum": 24103, + "Pressed": 24104, + "Lar": 24105, + "▁Agu": 24106, + "▁weil": 24107, + "▁commence": 24108, + "▁Según": 24109, + "Gesture": 24110, + "Shape": 24111, + "▁Vors": 24112, + "▁succès": 24113, + "▁corrected": 24114, + "Kar": 24115, + "▁cruel": 24116, + "▁politico": 24117, + "▁Schriftsteller": 24118, + "▁risult": 24119, + "etu": 24120, + "archiv": 24121, + "▁género": 24122, + "▁Lü": 24123, + "▁triumph": 24124, + "ORS": 24125, + "Lu": 24126, + "▁personnel": 24127, + "▁Hills": 24128, + "asset": 24129, + "domin": 24130, + "Receive": 24131, + "▁Oak": 24132, + "▁Kno": 24133, + "▁Theory": 24134, + "irie": 24135, + "owan": 24136, + "▁estava": 24137, + "▁executes": 24138, + "йт": 24139, + "ópez": 24140, + "поло": 24141, + "ética": 24142, + "▁название": 24143, + "▁converges": 24144, + "▁notre": 24145, + "▁populated": 24146, + "▁movements": 24147, + "▁statistical": 24148, + "▁Zweiten": 24149, + "quin": 24150, + "▁importantes": 24151, + "▁klein": 24152, + "▁Segunda": 24153, + "schließend": 24154, + "Failure": 24155, + "nar": 24156, + "dag": 24157, + "▁ruolo": 24158, + "▁fiction": 24159, + "▁использу": 24160, + "▁crisis": 24161, + "▁Getting": 24162, + ",%": 24163, + "▁армии": 24164, + "▁campus": 24165, + "▁footer": 24166, + "▁días": 24167, + "бан": 24168, + "▁liberty": 24169, + "▁gh": 24170, + "▁chamber": 24171, + "▁districts": 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"▁solem": 25070, + "closure": 25071, + "ignation": 25072, + "łod": 25073, + "▁divor": 25074, + "▁международ": 25075, + "=\"": 25230, + "Orientation": 25231, + "cid": 25232, + "Cart": 25233, + "▁murm": 25234, + "▁assez": 25235, + "▁linking": 25236, + "building": 25237, + "▁reconna": 25238, + "▁shook": 25239, + "managed": 25240, + "landa": 25241, + "▁León": 25242, + "▁création": 25243, + "дой": 25244, + "ocity": 25245, + "▁wij": 25246, + "▁wieś": 25247, + "xtart": 25248, + "▁Move": 25249, + "lungen": 25250, + "ствует": 25251, + "orney": 25252, + "optional": 25253, + "macro": 25254, + "Condition": 25255, + "▁squares": 25256, + "▁mistaken": 25257, + "ánt": 25258, + "▁Ris": 25259, + "▁sentences": 25260, + "erea": 25261, + "▁mij": 25262, + "Und": 25263, + "▁nombr": 25264, + "zA": 25265, + "▁Independent": 25266, + "▁preview": 25267, + "imas": 25268, + "▁males": 25269, + "inental": 25270, + "Thank": 25271, + "▁popol": 25272, + "▁pover": 25273, + "▁grasp": 25274, + "▁imped": 25275, + "▁campionato": 25276, + "▁Wei": 25277, + "▁titled": 25278, + "▁Además": 25279, + "▁Password": 25280, + "▁Pam": 25281, + "UILD": 25282, + "▁липня": 25283, + "werb": 25284, + "................": 25285, + "▁Río": 25286, + "▁teeth": 25287, + "bp": 25288, + "▁SW": 25289, + "ulaire": 25290, + "▁seized": 25291, + "▁Stef": 25292, + "úl": 25293, + "▁viz": 25294, + "iony": 25295, + "▁junt": 25296, + "▁která": 25297, + "▁września": 25298, + "<>": 25299, + "▁surg": 25300, + "▁tutte": 25301, + "▁Hob": 25302, + "повід": 25303, + "▁wohl": 25304, + "▁trag": 25305, + "▁Crown": 25306, + "▁trova": 25307, + "стову": 25308, + "▁Vienna": 25309, + "esehen": 25310, + "▁metropol": 25311, + "▁reflected": 25312, + "тета": 25313, + "▁traduc": 25314, + "▁Bast": 25315, + "▁erschien": 25316, + "woord": 25317, + "()\"": 25318, + "talet": 25319, + "▁roads": 25320, + "ведения": 25321, + "ührung": 25322, + "▁cogn": 25323, + "▁Valle": 25324, + "▁landing": 25325, + "▁Regex": 25326, + "▁Iowa": 25327, + "dział": 25328, + 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26219, + "▁первый": 26220, + "▁Creating": 26221, + "Once": 26222, + "▁einmal": 26223, + "▁geometric": 26224, + "stvo": 26225, + "▁kW": 26226, + "▁decomposition": 26227, + "▁comedy": 26228, + "▁activation": 26229, + "▁angry": 26230, + "illeurs": 26231, + "▁instantly": 26232, + "▁suggesting": 26233, + "▁Clay": 26234, + "cot": 26235, + "▁Gén": 26236, + "($(": 26237, + "unwrap": 26238, + "▁lifted": 26239, + "▁Kit": 26240, + "▁linea": 26241, + "ок": 26242, + "hart": 26243, + "->_": 26244, + "▁nuit": 26245, + "▁Issue": 26246, + "лии": 26247, + "▁röm": 26248, + "Tasks": 26249, + "▁Sr": 26250, + "▁seis": 26251, + "asia": 26252, + "}}$.": 26253, + ":{": 26254, + "controls": 26255, + "▁Stim": 26256, + "▁Recht": 26257, + "ociación": 26258, + "▁Natal": 26259, + "▁Philippines": 26260, + "ulen": 26261, + "Fixed": 26262, + "▁switched": 26263, + "Zip": 26264, + "ospel": 26265, + "▁начале": 26266, + "▁Blan": 26267, + "urst": 26268, + "▁autour": 26269, + "Ca": 26270, + "▁latitude": 26271, + "▁Frei": 26272, + "▁Musée": 26273, + "▁Kurz": 26274, + "▁região": 26275, + "swap": 26276, + "▁hate": 26277, + "▁modifications": 26278, + "▁Ком": 26279, + "▁Antoine": 26280, + "uga": 26281, + "RECT": 26282, + "éter": 26283, + "GROUP": 26284, + "▁sacrific": 26285, + "▁Whe": 26286, + "▁Stevens": 26287, + "ologische": 26288, + "Summary": 26289, + "obs": 26290, + "hnen": 26291, + "<%=": 26292, + "dienst": 26293, + "remark": 26294, + "▁veröffentlicht": 26295, + "ел": 26296, + "▁Mock": 26297, + "▁Льв": 26298, + "▁três": 26299, + "gb": 26300, + "▁celebrated": 26301, + "▁Eb": 26302, + "▁costa": 26303, + "▁Geographic": 26304, + "▁attachment": 26305, + "mannschaft": 26306, + "▁dependence": 26307, + "��": 26308, + "▁attitude": 26309, + "etal": 26310, + "vic": 26311, + "baut": 26312, + "▁дов": 26313, + "▁interven": 26314, + "▁Gü": 26315, + "ónica": 26316, + "▁Pon": 26317, + "▁disponible": 26318, + "▁Feb": 26319, + "▁worship": 26320, + "▁Specifically": 26321, + "Hy": 26322, + "iju": 26323, + "▁cb": 26324, + "▁spac": 26325, + "leveland": 26326, + "▁localidad": 26327, + "▁preceding": 26328, + "▁Hessen": 26329, + "xp": 26330, + "▁Wein": 26331, + "▁Româ": 26332, + "▁giorno": 26333, + "▁квітня": 26334, + "llaços": 26335, + "▁Academia": 26336, + "▁kül": 26337, + "▁Års": 26338, + "▁нај": 26339, + "uclide": 26340, + "Internet": 26341, + "orton": 26342, + "▁corn": 26343, + "ями": 26344, + "▁\"*": 26345, + "▁Felix": 26346, + "apat": 26347, + "▁свои": 26348, + "MIT": 26349, + "made": 26350, + "▁locomot": 26351, + "хода": 26352, + "FP": 26353, + "▁pm": 26354, + ".*;": 26355, + "▁Hamm": 26356, + "`}": 26357, + "LayoutInflater": 26358, + "==\"": 26359, + "▁Eur": 26360, + "▁dogs": 26361, + "жении": 26362, + "▁azon": 26363, + "▁emulator": 26364, + "▁ricon": 26365, + "beeld": 26366, + "▁ну": 26367, + "▁approximate": 26368, + "LM": 26369, + "▁Bond": 26370, + "▁enh": 26371, + "ędz": 26372, + "▁solit": 26373, + "RelativeLayout": 26374, + "eteor": 26375, + "amentos": 26376, + "▁indirect": 26377, + "iből": 26378, + "▁gros": 26379, + "▁Originals": 26380, + "commands": 26381, + "Export": 26382, + "▁Avec": 26383, + "▁solemn": 26384, + "▁correction": 26385, + "▁проводи": 26386, + "▁Mosk": 26387, + "▁подо": 26388, + "▁gebied": 26389, + "▁następ": 26390, + "▁Driver": 26391, + "▁Ook": 26392, + "▁Vec": 26393, + "▁lungo": 26394, + "ficos": 26395, + "▁svol": 26396, + "▁kid": 26397, + "nja": 26398, + "▁Hr": 26399, + "▁поддер": 26400, + "▁visibility": 26401, + "▁Méd": 26402, + "▁cpu": 26403, + "discussion": 26404, + "Asset": 26405, + "▁defense": 26406, + "▁Anyone": 26407, + "▁Justin": 26408, + "iszt": 26409, + "▁Collins": 26410, + "▁Valent": 26411, + "▁Pale": 26412, + "▁fuel": 26413, + "▁nose": 26414, + "ríguez": 26415, + "▁Schles": 26416, + "▁Malays": 26417, + "▁commut": 26418, + "dro": 26419, + "uing": 26420, + "▁Rico": 26421, + "▁Emma": 26422, + "orp": 26423, + "▁Kirk": 26424, + "▁Quando": 26425, + "▁Neue": 26426, + "▁demande": 26427, + "▁Cover": 26428, + "▁rescue": 26429, + "▁gewählt": 26430, + "▁Calendar": 26431, + "▁Madonna": 26432, + "WP": 26433, + "oshi": 26434, + "▁Maven": 26435, + "▁belle": 26436, + "▁wx": 26437, + "▁sugar": 26438, + "▁Betrieb": 26439, + "▁equilibrium": 26440, + "EAR": 26441, + "▁texts": 26442, + "слов": 26443, + "▁czerwca": 26444, + "▁Düsseld": 26445, + "▁ELSE": 26446, + "▁amery": 26447, + "▁ani": 26448, + "▁obey": 26449, + "▁Nell": 26450, + "▁inne": 26451, + "▁тро": 26452, + "FD": 26453, + "cco": 26454, + "▁Zob": 26455, + "alette": 26456, + "▁május": 26457, + "ected": 26458, + "▁Turkey": 26459, + "▁Whether": 26460, + "qi": 26461, + "▁што": 26462, + "▁headquarters": 26463, + "endi": 26464, + "arus": 26465, + "opus": 26466, + "▁золо": 26467, + "▁destru": 26468, + "▁Lok": 26469, + "▁satisfaction": 26470, + "()\r": 26471, + "▁Тер": 26472, + "Jose": 26473, + "▁conquer": 26474, + "▁Effect": 26475, + "LayoutParams": 26476, + "iez": 26477, + "▁externs": 26478, + "▁gegenüber": 26479, + "▁ESP": 26480, + "olta": 26481, + "processor": 26482, + "▁Kult": 26483, + 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"-------": 26589, + "▁firefox": 26590, + "▁alcune": 26591, + "▁фото": 26592, + "▁отрима": 26593, + "~~~~~~~~": 26594, + "▁Раз": 26595, + "▁Complex": 26596, + "▁pia": 26597, + "▁publicada": 26598, + "wei": 26599, + "cedure": 26600, + "occupation": 26601, + "▁medicine": 26602, + "▁drove": 26603, + "Problem": 26604, + "▁beginner": 26605, + "▁thoroughly": 26606, + "uria": 26607, + "avant": 26608, + "ucha": 26609, + "▁lever": 26610, + "▁teatro": 26611, + "AVA": 26612, + "squ": 26613, + "trat": 26614, + "ivatal": 26615, + "▁dirty": 26616, + "▁seconde": 26617, + "▁gravit": 26618, + "▁proposition": 26619, + "hbar": 26620, + "omini": 26621, + "▁”": 26622, + "▁Camil": 26623, + "▁queen": 26624, + "modifier": 26625, + "Jan": 26626, + "▁lyr": 26627, + "ComboBox": 26628, + "ionic": 26629, + "▁holy": 26630, + "▁Sebastian": 26631, + "|_{": 26632, + "▁{@": 26633, + "▁можно": 26634, + "▁Creative": 26635, + "▁interess": 26636, + "▁CT": 26637, + "ições": 26638, + "▁chant": 26639, + "▁współ": 26640, + "▁Мексика": 26641, + "▁ranked": 26642, + "▁października": 26643, + "▁brut": 26644, + "▁farther": 26645, + "▁Verb": 26646, + "▁Seven": 26647, + "lbl": 26648, + "▁mentions": 26649, + "▁Fight": 26650, + "ifen": 26651, + "▁bog": 26652, + "▁regres": 26653, + "▁scoring": 26654, + "icane": 26655, + "▁Elli": 26656, + "▁pierw": 26657, + "measure": 26658, + "ńskiej": 26659, + "#{": 26660, + "▁деся": 26661, + "▁varmaste": 26662, + "▁Unix": 26663, + "IZ": 26664, + "itié": 26665, + "Primary": 26666, + "▁Springer": 26667, + "üng": 26668, + "▁anv": 26669, + "▁versione": 26670, + "▁shoulders": 26671, + "▁брига": 26672, + "▁jav": 26673, + "ltal": 26674, + "▁kallaste": 26675, + "▁Mitchell": 26676, + "▁wireless": 26677, + "▁Ál": 26678, + "respons": 26679, + "could": 26680, + "▁relax": 26681, + "Lond": 26682, + "ńcz": 26683, + "ствовал": 26684, + "▁polski": 26685, + "enç": 26686, + "zar": 26687, + "▁dtype": 26688, + "owned": 26689, + "unknown": 26690, + "▁mutable": 26691, + "▁siempre": 26692, + 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"▁dirige": 26745, + "pull": 26746, + "dating": 26747, + "▁Fact": 26748, + "▁manipulate": 26749, + "▁MAC": 26750, + "▁dej": 26751, + "ultimo": 26752, + "FX": 26753, + "Life": 26754, + "▁crack": 26755, + "▁mí": 26756, + "▁пове": 26757, + "▁wore": 26758, + "université": 26759, + "▁formulas": 26760, + "▁Elisabeth": 26761, + "plots": 26762, + "mile": 26763, + "▁menor": 26764, + "тил": 26765, + "keyword": 26766, + "▁Baltimore": 26767, + "hrer": 26768, + "▁Clement": 26769, + "vim": 26770, + "rass": 26771, + "Take": 26772, + "▁című": 26773, + "▁Convention": 26774, + "atge": 26775, + "seed": 26776, + "▁Dí": 26777, + "▁Spider": 26778, + "ahoo": 26779, + "▁имеет": 26780, + "ührt": 26781, + "▁пописа": 26782, + "▁Cot": 26783, + "▁nobles": 26784, + "RESS": 26785, + "▁chemin": 26786, + "▁główn": 26787, + "GG": 26788, + "▁Germania": 26789, + "▁Alexandre": 26790, + "hens": 26791, + "swift": 26792, + "oop": 26793, + "Subview": 26794, + "▁requiring": 26795, + "ędzy": 26796, + "▁fict": 26797, + "▁Констан": 26798, + "▁déput": 26799, + "▁surprising": 26800, + "▁deix": 26801, + "▁unterschied": 26802, + "inson": 26803, + "▁Character": 26804, + "▁gestion": 26805, + "chus": 26806, + "comes": 26807, + "▁neur": 26808, + "▁yeux": 26809, + "ollar": 26810, + "▁parad": 26811, + "▁maggiore": 26812, + "TRAN": 26813, + "▁votre": 26814, + "▁descent": 26815, + "▁Icon": 26816, + "▁Judge": 26817, + "▁occupation": 26818, + "eping": 26819, + "▁tongue": 26820, + "▁Enllaços": 26821, + "ruf": 26822, + "▁protein": 26823, + "▁visitors": 26824, + "axy": 26825, + "esten": 26826, + "blica": 26827, + "hw": 26828, + "▁spirits": 26829, + "▁reduces": 26830, + "▁мен": 26831, + "▁Lamb": 26832, + "▁Mine": 26833, + "▁verified": 26834, + "▁Baby": 26835, + "▁prize": 26836, + "вър": 26837, + "▁ratings": 26838, + "▁fore": 26839, + "asha": 26840, + "urrence": 26841, + "▁intér": 26842, + "▁Olímp": 26843, + "cra": 26844, + "▁computational": 26845, + "irche": 26846, + ".: ": 26847, + "▁illustrated": 26848, + "▁Share": 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"▁illustrate": 28475, + "▁tact": 28476, + "▁figur": 28477, + "ués": 28478, + "riminal": 28479, + "odon": 28480, + "intendo": 28481, + "▁influenced": 28482, + "FFER": 28483, + "▁Ghost": 28484, + "▁совер": 28485, + "nad": 28486, + "ioned": 28487, + "▁Events": 28488, + "▁wrapping": 28489, + "---------+": 28490, + "fif": 28491, + "▁(**": 28492, + "={{": 28493, + "маль": 28494, + "▁losses": 28495, + "▁Galerie": 28496, + "tel": 28497, + "▁лютого": 28498, + "▁Kru": 28499, + "▁Polen": 28500, + "нім": 28501, + "near": 28502, + "▁shame": 28503, + "▁moyenne": 28504, + "▁CP": 28505, + "preis": 28506, + "▁passenger": 28507, + "lek": 28508, + "ionales": 28509, + "kafka": 28510, + "▁participe": 28511, + "▁membership": 28512, + "[_": 28513, + "lando": 28514, + "stelling": 28515, + "Sem": 28516, + "gon": 28517, + "▁Correct": 28518, + "▁valle": 28519, + "▁readily": 28520, + "▁Dokument": 28521, + "honneur": 28522, + "▁testim": 28523, + "ulative": 28524, + "doFilter": 28525, + "▁dominant": 28526, + "ammer": 28527, + "▁која": 28528, + "▁Monsieur": 28529, + "zeg": 28530, + "▁війни": 28531, + "▁Fo": 28532, + "▁Amy": 28533, + "▁¡": 28534, + "▁február": 28535, + "▁downloading": 28536, + "▁leng": 28537, + "\\}$,": 28538, + "▁neat": 28539, + "▁Cache": 28540, + "ICATION": 28541, + "▁deve": 28542, + "▁sorrow": 28543, + "slow": 28544, + "▁hinaus": 28545, + "▁reconoc": 28546, + "▁Linked": 28547, + "▁Shaw": 28548, + "market": 28549, + "▁Dic": 28550, + "▁Ski": 28551, + "▁delimiter": 28552, + "▁MainActivity": 28553, + "▁Musical": 28554, + "▁Reyn": 28555, + "ScrollView": 28556, + "▁conventional": 28557, + "ença": 28558, + "▁refactor": 28559, + "'-": 28560, + "▁Hed": 28561, + "sprech": 28562, + "▁athlet": 28563, + "▁especies": 28564, + "▁Schön": 28565, + "▁kleinen": 28566, + "шко": 28567, + "▁Йо": 28568, + "▁Happy": 28569, + "multirow": 28570, + "▁augusti": 28571, + "▁Gand": 28572, + "▁appointment": 28573, + "▁Mediabestanden": 28574, + "Three": 28575, + "▁Kenneth": 28576, + "NEW": 28577, + "▁Notification": 28578, + "▁Marx": 28579, + "▁insc": 28580, + "Mor": 28581, + "вый": 28582, + "väst": 28583, + "vidia": 28584, + "▁demonstrated": 28585, + "fonts": 28586, + "▁kamen": 28587, + "▁Ster": 28588, + "▁mieszkańców": 28589, + "▁Koh": 28590, + "~$\\": 28591, + "»).": 28592, + "rene": 28593, + "insic": 28594, + "ická": 28595, + "xygen": 28596, + "▁mn": 28597, + "▁sched": 28598, + "ASC": 28599, + "Ig": 28600, + "▁Constant": 28601, + "▁opportun": 28602, + "▁MyClass": 28603, + "sef": 28604, + "oped": 28605, + "▁injured": 28606, + "VIS": 28607, + "▁Pero": 28608, + "▁Until": 28609, + "▁flesh": 28610, + "orphism": 28611, + "▁Portal": 28612, + "▁gminy": 28613, + "▁власти": 28614, + "▁Nä": 28615, + "ктиче": 28616, + "▁hrab": 28617, + "▁Cub": 28618, + "avoir": 28619, + "▁Lars": 28620, + "▁Бело": 28621, + "▁seizoen": 28622, + "▁Genomsnitt": 28623, + "▁Lil": 28624, + "▁Pool": 28625, + "▁Dios": 28626, + "TX": 28627, + "aes": 28628, + "autore": 28629, + "Alpha": 28630, + "states": 28631, + "Lab": 28632, + "nederbörd": 28633, + "erton": 28634, + "▁brid": 28635, + "▁richt": 28636, + "▁Ela": 28637, + "▁сла": 28638, + "▁weapon": 28639, + "▁combatt": 28640, + "agar": 28641, + "▁regnig": 28642, + "▁utilisé": 28643, + "▁servir": 28644, + "▁brick": 28645, + "▁gateway": 28646, + "▁torraste": 28647, + "▁procedures": 28648, + "▁årsnederbörd": 28649, + "▁Genomsnittlig": 28650, + "чёт": 28651, + "▁områ": 28652, + "▁regnigaste": 28653, + "▁честь": 28654, + "▁amid": 28655, + "▁grateful": 28656, + "▁DIS": 28657, + "DAY": 28658, + "▁ору": 28659, + "▁rivière": 28660, + "heure": 28661, + "▁Richmond": 28662, + "▁Compar": 28663, + "▁Нор": 28664, + "DOC": 28665, + "esia": 28666, + "calc": 28667, + "▁IU": 28668, + "▁vorg": 28669, + "▁habían": 28670, + "çoit": 28671, + "▁arist": 28672, + "▁кли": 28673, + "▁Sue": 28674, + "▁Touch": 28675, + "▁Writing": 28676, + "ifiable": 28677, + "▁wc": 28678, + "▁withdraw": 28679, + "зар": 28680, + "▁presently": 28681, + "▁FK": 28682, + "▁prakt": 28683, + "▁colored": 28684, + "usb": 28685, + "▁Perú": 28686, + "▁plata": 28687, + "▁wishes": 28688, + "▁кам": 28689, + "azar": 28690, + "ável": 28691, + "▁lamp": 28692, + "bishop": 28693, + "▁inclusion": 28694, + "jq": 28695, + "arth": 28696, + "▁Flag": 28697, + "▁нор": 28698, + "ædia": 28699, + "UNCTION": 28700, + "▁Bahnhof": 28701, + "▁approaching": 28702, + "▁Gött": 28703, + "▁cube": 28704, + "▁argued": 28705, + "▁Things": 28706, + "Gui": 28707, + "дови": 28708, + "▁recre": 28709, + "▁réseau": 28710, + "▁significa": 28711, + "Git": 28712, + "gebracht": 28713, + "▁liga": 28714, + "▁assured": 28715, + "alus": 28716, + "рит": 28717, + "▁энциклопеди": 28718, + "▁%).": 28719, + "▁Première": 28720, + "▁declarations": 28721, + "▁tricky": 28722, + "▁profiles": 28723, + "▁Fon": 28724, + "▁Jas": 28725, + "âr": 28726, + "babel": 28727, + "▁Friday": 28728, + "▁június": 28729, + "▁cols": 28730, + "▁EXISTS": 28731, + "▁Italiana": 28732, + "▁authorization": 28733, + "▁sulle": 28734, + "▁Emb": 28735, + "▁Variable": 28736, + "trees": 28737, + "▁Fly": 28738, + "riors": 28739, + "▁damals": 28740, + "▁findet": 28741, + "▁Sept": 28742, + "▁mundial": 28743, + "▁removal": 28744, + "▁longitude": 28745, + "clic": 28746, + "▁fade": 28747, + "▁gradle": 28748, + "▁zák": 28749, + "▁timing": 28750, + "trightarrow": 28751, + "atia": 28752, + "-.": 28753, + "uche": 28754, + "▁serialize": 28755, + "▁Hmm": 28756, + "▁Representatives": 28757, + "bah": 28758, + "rend": 28759, + "assador": 28760, + "▁shield": 28761, + "ucion": 28762, + "▁américaine": 28763, + "zę": 28764, + "villa": 28765, + "▁hombre": 28766, + "áss": 28767, + "▁SF": 28768, + "▁repeating": 28769, + "▁criter": 28770, + "▁Struct": 28771, + "???": 28772, + "▁cheap": 28773, + "▁rings": 28774, + "abhäng": 28775, + "▁corte": 28776, + "▁administ": 28777, + "ixon": 28778, + "gypt": 28779, + "▁puntos": 28780, + "▁mezi": 28781, + "▁pochod": 28782, + "isko": 28783, + "nię": 28784, + "▁осу": 28785, + "▁ár": 28786, + "тельной": 28787, + "▁Metropolitan": 28788, + "jin": 28789, + "zess": 28790, + "▁віці": 28791, + "▁conflicts": 28792, + "ijst": 28793, + "▁Market": 28794, + "стров": 28795, + "▁\",\"": 28796, + "▁Scroll": 28797, + "gun": 28798, + "тара": 28799, + "▁amateur": 28800, + "▁róż": 28801, + "poss": 28802, + "▁generalized": 28803, + "▁Harm": 28804, + "cita": 28805, + "▁Switzerland": 28806, + "icola": 28807, + "▁muit": 28808, + "located": 28809, + "▁có": 28810, + "▁arose": 28811, + "▁communauté": 28812, + "})^": 28813, + "visibility": 28814, + "ída": 28815, + "▁FB": 28816, + "▁Freund": 28817, + "gat": 28818, + "\":{\"": 28819, + "intellij": 28820, + "ifie": 28821, + "hmen": 28822, + "▁édition": 28823, + "▁које": 28824, + "▁інших": 28825, + "oming": 28826, + "▁arquitect": 28827, + "▁Presidente": 28828, + "▁Під": 28829, + "▁cabin": 28830, + "Theorem": 28831, + "▁Gay": 28832, + "ifice": 28833, + "▁hect": 28834, + "lą": 28835, + "irmingham": 28836, + "▁semantic": 28837, + "▁Louisiana": 28838, + "▁sacrifice": 28839, + "▁Christoph": 28840, + "▁Executive": 28841, + "_+": 28842, + "ják": 28843, + "▁seria": 28844, + "▁Overflow": 28845, + "▁Lucy": 28846, + "▁melhor": 28847, + "▁voices": 28848, + "cza": 28849, + "▁капи": 28850, + "▁университета": 28851, + "INCT": 28852, + "▁coloc": 28853, + "▁prue": 28854, + "▁geomet": 28855, + "▁diretto": 28856, + "reso": 28857, + "▁Akt": 28858, + "▁unh": 28859, + "▁сери": 28860, + "▁Alert": 28861, + "Wel": 28862, + "audi": 28863, + "äler": 28864, + "▁guests": 28865, + "▁иде": 28866, + "Studio": 28867, + "▁кате": 28868, + "▁exponent": 28869, + "rze": 28870, + "pmod": 28871, + "rolle": 28872, + "▁Limited": 28873, + "Allemagne": 28874, + "▁pity": 28875, + "▁lä": 28876, + "▁runner": 28877, + "kende": 28878, + "EQ": 28879, + "▁MM": 28880, + "szág": 28881, + "поді": 28882, + "▁regret": 28883, + "▁publié": 28884, + "▁departamento": 28885, + "▁accused": 28886, + "hp": 28887, + "▁Pfl": 28888, + "▁Sint": 28889, + "▁ekonom": 28890, + "ractor": 28891, + "▁Пів": 28892, + "▁awful": 28893, + "ować": 28894, + "]->": 28895, + "▁Fine": 28896, + "Са": 28897, + "tis": 28898, + "éta": 28899, + "▁Роди": 28900, + "▁Düsseldorf": 28901, + "LOB": 28902, + "osas": 28903, + "werke": 28904, + "▁lance": 28905, + "▁листопада": 28906, + "▁incomplete": 28907, + "▁Picture": 28908, + "('\\": 28909, + "esters": 28910, + "▁belonged": 28911, + "▁Sank": 28912, + "ammed": 28913, + "▁repositories": 28914, + "▁addr": 28915, + "Collect": 28916, + "Hot": 28917, + "▁tyl": 28918, + "▁instanceof": 28919, + "▁bonus": 28920, + "ový": 28921, + "▁моря": 28922, + "▁interactive": 28923, + "▁Mys": 28924, + "▁Edmund": 28925, + "fileName": 28926, + "emor": 28927, + "▁Три": 28928, + "▁Rosen": 28929, + "▁Prima": 28930, + "▁voting": 28931, + "▁XP": 28932, + "▁Zero": 28933, + "▁Led": 28934, + "amsung": 28935, + "▁enables": 28936, + "▁redirects": 28937, + "AST": 28938, + "Paint": 28939, + "acker": 28940, + "lecht": 28941, + "▁chairman": 28942, + "▁Aven": 28943, + "▁Sach": 28944, + "(\"<": 28945, + "кер": 28946, + "▁mistakes": 28947, + "▁Weit": 28948, + "▁prowad": 28949, + "▁didnt": 28950, + "énario": 28951, + "unless": 28952, + "▁backwards": 28953, + "boa": 28954, + "duino": 28955, + "```": 28956, + "stor": 28957, + "Completion": 28958, + "puesta": 28959, + "▁dinast": 28960, + "últ": 28961, + "▁SY": 28962, + "ifolia": 28963, + "œuvres": 28964, + "▁racing": 28965, + "▁cabinet": 28966, + "▁cutting": 28967, + "▁thumb": 28968, + "▁Кара": 28969, + "highlight": 28970, + "куп": 28971, + "▁sd": 28972, + "▁національ": 28973, + "▁campagne": 28974, + "▁registers": 28975, + "▁educational": 28976, + "▁pesar": 28977, + "üge": 28978, + "▁oro": 28979, + "burgo": 28980, + "▁Athletics": 28981, + "▁MTV": 28982, + "getMessage": 28983, + "▁Hyp": 28984, + "▁victim": 28985, + "))\\": 28986, + "▁drums": 28987, + "hostname": 28988, + "tał": 28989, + "making": 28990, + "▁powiat": 28991, + "őd": 28992, + "threads": 28993, + "▁absolv": 28994, + "▁люди": 28995, + "▁stepped": 28996, + "exist": 28997, + "▁NK": 28998, + "▁ves": 28999, + "istiche": 29000, + "%'": 29001, + "ativos": 29002, + "▁такой": 29003, + "▁MongoDB": 29004, + "▁Ung": 29005, + "▁Рус": 29006, + "▁elim": 29007, + "▁Fif": 29008, + "icación": 29009, + "▁Tennis": 29010, + "▁Jefferson": 29011, + "ján": 29012, + "fog": 29013, + "anha": 29014, + "zor": 29015, + "▁університе": 29016, + "ahu": 29017, + "iada": 29018, + "Sdk": 29019, + "Setting": 29020, + "▁Kill": 29021, + "▁Wend": 29022, + "▁bald": 29023, + "▁Kub": 29024, + "▁visto": 29025, + "▁jeunes": 29026, + "collections": 29027, + "ací": 29028, + "вропей": 29029, + "▁arise": 29030, + "оні": 29031, + "MAIN": 29032, + "доступ": 29033, + "▁berg": 29034, + "▁criticism": 29035, + "▁Torre": 29036, + "▁descript": 29037, + "ières": 29038, + "▁estudio": 29039, + "▁ili": 29040, + "▁militare": 29041, + "▁Clara": 29042, + "▁Ellen": 29043, + "limited": 29044, + "лм": 29045, + "▁Españ": 29046, + "▁infinitely": 29047, + "America": 29048, + "ouc": 29049, + "glass": 29050, + "▁rud": 29051, + "▁zat": 29052, + "▁rin": 29053, + "▁Bibliografía": 29054, + "▁merchant": 29055, + "tensorflow": 29056, + "▁dér": 29057, + "▁ActiveRecord": 29058, + "IES": 29059, + "▁linker": 29060, + "▁estudios": 29061, + "cdnjs": 29062, + "▁Государ": 29063, + "ánchez": 29064, + "appe": 29065, + "club": 29066, + "▁další": 29067, + "▁Algorithm": 29068, + "dfs": 29069, + "▁Bac": 29070, + "▁кафе": 29071, + "▁&=\\": 29072, + "▁ат": 29073, + "▁Глав": 29074, + "▁Mou": 29075, + "Machine": 29076, + "(...)": 29077, + "▁compart": 29078, + "▁augusztus": 29079, + "avan": 29080, + "▁rolled": 29081, + "▁еди": 29082, + "Scan": 29083, + "▁регі": 29084, + "▁świata": 29085, + "▁mines": 29086, + "},{": 29087, + "▁Tier": 29088, + "Cannot": 29089, + "мін": 29090, + "▁NEW": 29091, + "▁Вол": 29092, + "▁Manh": 29093, + "▁Gregory": 29094, + "▁principe": 29095, + "ISO": 29096, + "prog": 29097, + "▁Fail": 29098, + "▁aa": 29099, + "▁fecha": 29100, + "▁WCF": 29101, + "▁magistr": 29102, + "▁Zach": 29103, + "▁unicode": 29104, + 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30903, + "五": 30904, + "چ": 30905, + "로": 30906, + "ョ": 30907, + "重": 30908, + "于": 30909, + "发": 30910, + "史": 30911, + "ظ": 30912, + "ช": 30913, + "え": 30914, + "國": 30915, + "ĭ": 30916, + "ப": 30917, + "인": 30918, + "你": 30919, + "駅": 30920, + "‒": 30921, + "♥": 30922, + "多": 30923, + "ħ": 30924, + "Қ": 30925, + "ồ": 30926, + "士": 30927, + "四": 30928, + "┴": 30929, + "ம": 30930, + "司": 30931, + "ে": 30932, + "ὰ": 30933, + "∂": 30934, + "╬": 30935, + "次": 30936, + "Ľ": 30937, + "⟶": 30938, + "立": 30939, + "点": 30940, + "音": 30941, + "⠀": 30942, + "器": 30943, + "하": 30944, + "井": 30945, + "存": 30946, + "ֹ": 30947, + "当": 30948, + "Ë": 30949, + "★": 30950, + "寺": 30951, + "性": 30952, + "也": 30953, + "め": 30954, + "だ": 30955, + "位": 30956, + "ങ": 30957, + "ہ": 30958, + "值": 30959, + "古": 30960, + "გ": 30961, + "ব": 30962, + "院": 30963, + "േ": 30964, + "▶": 30965, + "ர": 30966, + "界": 30967, + "語": 30968, + "സ": 30969, + "수": 30970, + "ǒ": 30971, + "愛": 30972, + "✔": 30973, + "時": 30974, + "ọ": 30975, + "റ": 30976, + "մ": 30977, + "ケ": 30978, + "东": 30979, + "同": 30980, + "주": 30981, + "保": 30982, + "Õ": 30983, + "ố": 30984, + "ἰ": 30985, + "青": 30986, + "ゴ": 30987, + "体": 30988, + "清": 30989, + "相": 30990, + "จ": 30991, + "ء": 30992, + "情": 30993, + "𝕜": 30994, + "ক": 30995, + "ḫ": 30996, + "ờ": 30997, + "将": 30998, + "族": 30999, + "동": 31000, + "Υ": 31001, + "┌": 31002, + "ボ": 31003, + "宮": 31004, + "』": 31005, + "ম": 31006, + "『": 31007, + "ļ": 31008, + "श": 31009, + "ป": 31010, + "Ա": 31011, + "ब": 31012, + "자": 31013, + "政": 31014, + "ா": 31015, + "间": 31016, + "fi": 31017, + "松": 31018, + "ṃ": 31019, + "始": 31020, + "息": 31021, + "少": 31022, + "教": 31023, + "获": 31024, + "列": 31025, + "开": 31026, + "ტ": 31027, + "ワ": 31028, + "კ": 31029, + "科": 31030, + "春": 31031, + "治": 31032, + "吉": 31033, + "ས": 31034, + "ศ": 31035, + "ɒ": 31036, + "台": 31037, + "ネ": 31038, + "း": 31039, + "ĩ": 31040, + "工": 31041, + "ά": 31042, + "知": 31043, + "八": 31044, + "場": 31045, + "画": 31046, + "百": 31047, + "☆": 31048, + "記": 31049, + "得": 31050, + "ソ": 31051, + "氏": 31052, + "ာ": 31053, + "에": 31054, + "ল": 31055, + "ṛ": 31056, + "关": 31057, + "ġ": 31058, + "έ": 31059, + "∑": 31060, + "ベ": 31061, + "标": 31062, + "니": 31063, + "ὴ": 31064, + "ֵ": 31065, + "外": 31066, + "♠": 31067, + "わ": 31068, + "間": 31069, + "ภ": 31070, + "校": 31071, + "制": 31072, + "แ": 31073, + "力": 31074, + "門": 31075, + "好": 31076, + "ғ": 31077, + "Ù": 31078, + "ℓ": 31079, + "ֶ": 31080, + "는": 31081, + "┐": 31082, + "∗": 31083, + "指": 31084, + "色": 31085, + "返": 31086, + "馬": 31087, + "请": 31088, + "≫": 31089, + "風": 31090, + "ό": 31091, + "接": 31092, + "서": 31093, + "↳": 31094, + "せ": 31095, + "志": 31096, + "̲": 31097, + "魔": 31098, + "ң": 31099, + "更": 31100, + "程": 31101, + "김": 31102, + "郡": 31103, + "ོ": 31104, + "ũ": 31105, + "ച": 31106, + "利": 31107, + "県": 31108, + "周": 31109, + "そ": 31110, + "や": 31111, + "谷": 31112, + "香": 31113, + "♯": 31114, + "じ": 31115, + "،": 31116, + "期": 31117, + "∅": 31118, + "┘": 31119, + "初": 31120, + "福": 31121, + "片": 31122, + "ザ": 31123, + "動": 31124, + "参": 31125, + "성": 31126, + "Ə": 31127, + "╦": 31128, + "어": 31129, + "ხ": 31130, + "義": 31131, + "च": 31132, + "象": 31133, + "功": 31134, + "♂": 31135, + "도": 31136, + "고": 31137, + "过": 31138, + "վ": 31139, + "皇": 31140, + "特": 31141, + "ậ": 31142, + "长": 31143, + "英": 31144, + "ấ": 31145, + "ണ": 31146, + "Ъ": 31147, + "স": 31148, + "其": 31149, + "ত": 31150, + "流": 31151, + "除": 31152, + "일": 31153, + "ু": 31154, + "្": 31155, + "永": 31156, + "直": 31157, + "상": 31158, + "千": 31159, + "ắ": 31160, + "館": 31161, + "Ť": 31162, + "朝": 31163, + "ட": 31164, + "ɣ": 31165, + "单": 31166, + "ʀ": 31167, + "格": 31168, + "德": 31169, + "전": 31170, + "☺": 31171, + "ピ": 31172, + "歌": 31173, + "进": 31174, + "限": 31175, + "夫": 31176, + "트": 31177, + "⊢": 31178, + "園": 31179, + "量": 31180, + "土": 31181, + "放": 31182, + "码": 31183, + "等": 31184, + "系": 31185, + "∼": 31186, + "華": 31187, + "↵": 31188, + "소": 31189, + "常": 31190, + "否": 31191, + "見": 31192, + "源": 31193, + "ׁ": 31194, + "实": 31195, + "博": 31196, + "라": 31197, + "원": 31198, + "보": 31199, + "⊕": 31200, + "解": 31201, + "〜": 31202, + "男": 31203, + "দ": 31204, + "ポ": 31205, + "ろ": 31206, + "나": 31207, + "ག": 31208, + "無": 31209, + "Û": 31210, + "̥": 31211, + "ұ": 31212, + "查": 31213, + "̣": 31214, + "╗": 31215, + "╩": 31216, + "条": 31217, + "য": 31218, + "ὁ": 31219, + "後": 31220, + "他": 31221, + "网": 31222, + "ல": 31223, + "≃": 31224, + "화": 31225, + "ە": 31226, + "阿": 31227, + "ေ": 31228, + "户": 31229, + "∫": 31230, + "구": 31231, + "ར": 31232, + "မ": 31233, + "▸": 31234, + "լ": 31235, + "○": 31236, + "命": 31237, + "就": 31238, + "龍": 31239, + "君": 31240, + "夏": 31241, + "": 31242, + "言": 31243, + "先": 31244, + "➜": 31245, + "შ": 31246, + "ძ": 31247, + "ਾ": 31248, + "வ": 31249, + "ど": 31250, + "ヒ": 31251, + "ไ": 31252, + "ன": 31253, + "ば": 31254, + "ギ": 31255, + "գ": 31256, + "ἄ": 31257, + "ヤ": 31258, + "典": 31259, + "府": 31260, + "̄": 31261, + "신": 31262, + "组": 31263, + "改": 31264, + "ὲ": 31265, + "华": 31266, + "与": 31267, + "调": 31268, + "╝": 31269, + "ヴ": 31270, + "ქ": 31271, + "由": 31272, + "修": 31273, + "學": 31274, + "♣": 31275, + "消": 31276, + "符": 31277, + "ʌ": 31278, + "부": 31279, + "ớ": 31280, + "‾": 31281, + "▲": 31282, + "录": 31283, + "ള": 31284, + "연": 31285, + "을": 31286, + "ひ": 31287, + "영": 31288, + "┤": 31289, + "已": 31290, + "陽": 31291, + "င": 31292, + "국": 31293, + "容": 31294, + "未": 31295, + "宗": 31296, + "ᴇ": 31297, + "び": 31298, + "장": 31299, + "龙": 31300, + "්": 31301, + "提": 31302, + "ĝ": 31303, + "六": 31304, + "形": 31305, + "제": 31306, + "Հ": 31307, + "伊": 31308, + "ϵ": 31309, + "ข": 31310, + "Ű": 31311, + "ゃ": 31312, + "火": 31313, + "Ṣ": 31314, + "佐": 31315, + "⊥": 31316, + "̪": 31317, + "ứ": 31318, + "□": 31319, + "结": 31320, + "九": 31321, + "雄": 31322, + "թ": 31323, + "ា": 31324, + "而": 31325, + "བ": 31326, + "우": 31327, + "张": 31328, + "ट": 31329, + "ष": 31330, + "向": 31331, 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": 31353, + "은": 31354, + "ʷ": 31355, + "那": 31356, + "線": 31357, + "务": 31358, + "基": 31359, + "属": 31360, + "配": 31361, + "미": 31362, + "軍": 31363, + "โ": 31364, + "津": 31365, + "完": 31366, + "研": 31367, + "注": 31368, + "失": 31369, + "应": 31370, + "က": 31371, + "╚": 31372, + "友": 31373, + "章": 31374, + "Ψ": 31375, + "求": 31376, + "ण": 31377, + "경": 31378, + "‬": 31379, + "भ": 31380, + "们": 31381, + "模": 31382, + "需": 31383, + "ச": 31384, + "電": 31385, + "প": 31386, + "դ": 31387, + "へ": 31388, + "此": 31389, + "夜": 31390, + "或": 31391, + "橋": 31392, + "根": 31393, + "Ī": 31394, + "玉": 31395, + "ู": 31396, + "ṅ": 31397, + "交": 31398, + "品": 31399, + "良": 31400, + "ང": 31401, + "ォ": 31402, + "则": 31403, + "開": 31404, + "Ζ": 31405, + "문": 31406, + "被": 31407, + "조": 31408, + "株": 31409, + "记": 31410, + "會": 31411, + "经": 31412, + "ू": 31413, + "ょ": 31414, + "转": 31415, + "崎": 31416, + "마": 31417, + "⌘": 31418, + "比": 31419, + "造": 31420, + "ܐ": 31421, + "ื": 31422, + "没": 31423, + "现": 31424, + "七": 31425, + "Ά": 31426, + "商": 31427, + "ை": 31428, + "机": 31429, + "阳": 31430, + "ĉ": 31431, + "角": 31432, + "站": 31433, + "բ": 31434, + "해": 31435, + "及": 31436, + "ध": 31437, + "術": 31438, + "认": 31439, + "‘": 31440, + "创": 31441, + "編": 31442, + "ղ": 31443, + "ḩ": 31444, + "伝": 31445, + "岡": 31446, + "ड": 31447, + "ホ": 31448, + "港": 31449, + "任": 31450, + "登": 31451, + "ི": 31452, + "็": 31453, + "布": 31454, + "究": 31455, + "帝": 31456, + "여": 31457, + "산": 31458, + "န": 31459, + "◦": 31460, + "密": 31461, + "变": 31462, + "序": 31463, + "♀": 31464, + "∣": 31465, + "计": 31466, + "曲": 31467, + "Ă": 31468, + "ύ": 31469, + "ʋ": 31470, + "传": 31471, + "】": 31472, + "包": 31473, + "意": 31474, + "去": 31475, + "沙": 31476, + "⸮": 31477, + "【": 31478, + "写": 31479, + "超": 31480, + "ய": 31481, + "今": 31482, + "┈": 31483, + "森": 31484, + "ි": 31485, + "⊗": 31486, + "비": 31487, + "հ": 31488, + "Ḩ": 31489, + "ǫ": 31490, + "黄": 31491, + "∙": 31492, + "드": 31493, + "🌍": 31494, + "景": 31495, 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31567, + "视": 31568, + "秋": 31569, + "因": 31570, + "년": 31571, + "ے": 31572, + "输": 31573, + "̱": 31574, + "Մ": 31575, + "∆": 31576, + "康": 31577, + "세": 31578, + "思": 31579, + "死": 31580, + "聖": 31581, + "민": 31582, + "－": 31583, + "头": 31584, + "ർ": 31585, + "∉": 31586, + "車": 31587, + "┃": 31588, + "▇": 31589, + "按": 31590, + "⍵": 31591, + "夢": 31592, + "汉": 31593, + "从": 31594, + "ী": 31595, + "题": 31596, + "ˆ": 31597, + "ἡ": 31598, + "展": 31599, + "省": 31600, + "ུ": 31601, + "葉": 31602, + "호": 31603, + "ਰ": 31604, + "素": 31605, + "関": 31606, + "그": 31607, + "；": 31608, + "න": 31609, + "页": 31610, + "共": 31611, + "宿": 31612, + "态": 31613, + "ན": 31614, + "技": 31615, + "乐": 31616, + "控": 31617, + "移": 31618, + "影": 31619, + "ụ": 31620, + "ゆ": 31621, + "ご": 31622, + "್": 31623, + "管": 31624, + "ൾ": 31625, + "╣": 31626, + "戸": 31627, + "⇔": 31628, + "函": 31629, + "ẓ": 31630, + "尾": 31631, + "场": 31632, + "介": 31633, + "": 31634, + "育": 31635, + "ර": 31636, + "泉": 31637, + "ൽ": 31638, 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b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/trainer_state.json @@ -1,66 +1,66 @@ -{ - "best_global_step": 64, - "best_metric": 1.9412118196487427, - "best_model_checkpoint": "data_out/lora_training/checkpoint-64", - "epoch": 3.25, - "eval_steps": 64, - "global_step": 64, - "is_hyper_param_search": false, - "is_local_process_zero": true, - "is_world_process_zero": true, - "log_history": [ - { - "epoch": 1.25, - "grad_norm": 0.8077470064163208, - "learning_rate": 6.2e-05, - "loss": 2.0982, - "step": 32 - }, - { - "epoch": 3.25, - "grad_norm": 0.9677475094795227, - "learning_rate": 0.000126, - "loss": 1.8387, - "step": 64 - }, - { - "epoch": 3.25, - "eval_loss": 1.9412118196487427, - "eval_model_preparation_time": 0.005, - "eval_runtime": 46.1401, - "eval_samples_per_second": 0.564, - "eval_steps_per_second": 0.087, - "step": 64 - } - ], - "logging_steps": 32, - "max_steps": 64, - "num_input_tokens_seen": 0, - "num_train_epochs": 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3.25, + "grad_norm": 0.9677475094795227, + "learning_rate": 0.000126, + "loss": 1.8387, + "step": 64 + }, + { + "epoch": 3.25, + "eval_loss": 1.9412118196487427, + "eval_model_preparation_time": 0.005, + "eval_runtime": 46.1401, + "eval_samples_per_second": 0.564, + "eval_steps_per_second": 0.087, + "step": 64 + } + ], + "logging_steps": 32, + "max_steps": 64, + "num_input_tokens_seen": 0, + "num_train_epochs": 9223372036854775807, + "save_steps": 64, + "stateful_callbacks": { + "EarlyStoppingCallback": { + "args": { + "early_stopping_patience": 3, + "early_stopping_threshold": 0.01 + }, + "attributes": { + "early_stopping_patience_counter": 0 + } + }, + "TrainerControl": { + "args": { + "should_epoch_stop": false, + "should_evaluate": false, + "should_log": false, + "should_save": true, + "should_training_stop": true + }, + "attributes": {} + } + }, + "total_flos": 245987054616576.0, + "train_batch_size": 1, + "trial_name": null, + "trial_params": null +} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/lora_adapter/README.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/lora_adapter/README.md index 00ed673a8..e9d7c81db 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/lora_adapter/README.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/lora_adapter/README.md @@ -1,207 +1,207 @@ ---- -base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 -library_name: peft -pipeline_tag: text-generation -tags: -- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0 -- lora -- transformers ---- - -# Model Card for Model ID - - - - - -## Model Details - -### Model Description - - - - - -- **Developed by:** [More Information Needed] -- **Funded by [optional]:** [More Information Needed] -- **Shared by [optional]:** [More Information Needed] -- **Model type:** [More Information Needed] -- **Language(s) (NLP):** [More Information Needed] -- **License:** [More Information Needed] -- **Finetuned from model [optional]:** [More Information Needed] - -### Model Sources [optional] - - - -- **Repository:** [More Information Needed] -- **Paper [optional]:** [More Information Needed] -- **Demo [optional]:** [More Information Needed] - -## Uses - - - -### Direct Use - - - -[More Information Needed] - -### Downstream Use [optional] - - - -[More Information Needed] - -### Out-of-Scope Use - - - -[More Information Needed] - -## Bias, Risks, and Limitations - - - -[More Information Needed] - -### Recommendations - - - -Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. - -## How to Get Started with the Model - -Use the code below to get started with the model. - -[More Information Needed] - -## Training Details - -### Training Data - - - -[More Information Needed] - -### Training Procedure - - - -#### Preprocessing [optional] - -[More Information Needed] - - -#### Training Hyperparameters - -- **Training regime:** [More Information Needed] - -#### Speeds, Sizes, Times [optional] - - - -[More Information Needed] - -## Evaluation - - - -### Testing Data, Factors & Metrics - -#### Testing Data - - - -[More Information Needed] - -#### Factors - - - -[More Information Needed] - -#### Metrics - - - -[More Information Needed] - -### Results - -[More Information Needed] - -#### Summary - - - -## Model Examination [optional] - - - -[More Information Needed] - -## Environmental Impact - - - -Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - -- **Hardware Type:** [More Information Needed] -- **Hours used:** [More Information Needed] -- **Cloud Provider:** [More Information Needed] -- **Compute Region:** [More Information Needed] -- **Carbon Emitted:** [More Information Needed] - -## Technical Specifications [optional] - -### Model Architecture and Objective - -[More Information Needed] - -### Compute Infrastructure - -[More Information Needed] - -#### Hardware - -[More Information Needed] - -#### Software - -[More Information Needed] - -## Citation [optional] - - - -**BibTeX:** - -[More Information Needed] - -**APA:** - -[More Information Needed] - -## Glossary [optional] - - - -[More Information Needed] - -## More Information [optional] - -[More Information Needed] - -## Model Card Authors [optional] - -[More Information Needed] - -## Model Card Contact - -[More Information Needed] -### Framework versions - +--- +base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 +library_name: peft +pipeline_tag: text-generation +tags: +- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0 +- lora +- transformers +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + - PEFT 0.18.0 diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/lora_adapter/adapter_config.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/lora_adapter/adapter_config.json index 085f2969a..5b1720f79 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/lora_adapter/adapter_config.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/lora_adapter/adapter_config.json @@ -1,45 +1,45 @@ -{ - "alora_invocation_tokens": null, - "alpha_pattern": {}, - "arrow_config": null, - "auto_mapping": null, - "base_model_name_or_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", - "bias": "none", - "corda_config": null, - "ensure_weight_tying": false, - "eva_config": null, - "exclude_modules": null, - "fan_in_fan_out": false, - "inference_mode": true, - "init_lora_weights": true, - "layer_replication": null, - "layers_pattern": null, - "layers_to_transform": null, - "loftq_config": {}, - "lora_alpha": 16, - "lora_bias": false, - "lora_dropout": 0.1, - "megatron_config": null, - "megatron_core": "megatron.core", - "modules_to_save": null, - "peft_type": "LORA", - "peft_version": "0.18.0", - "qalora_group_size": 16, - "r": 8, - "rank_pattern": {}, - "revision": null, - "target_modules": [ - "k_proj", - "v_proj", - "fc1", - "o_proj", - "q_proj", - "fc2" - ], - "target_parameters": null, - "task_type": "CAUSAL_LM", - "trainable_token_indices": null, - "use_dora": false, - "use_qalora": false, - "use_rslora": false +{ + "alora_invocation_tokens": null, + "alpha_pattern": {}, + "arrow_config": null, + "auto_mapping": null, + "base_model_name_or_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "bias": "none", + "corda_config": null, + "ensure_weight_tying": false, + "eva_config": null, + "exclude_modules": null, + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layer_replication": null, + "layers_pattern": null, + "layers_to_transform": null, + "loftq_config": {}, + "lora_alpha": 16, + "lora_bias": false, + "lora_dropout": 0.1, + "megatron_config": null, + "megatron_core": "megatron.core", + "modules_to_save": null, + "peft_type": "LORA", + "peft_version": "0.18.0", + "qalora_group_size": 16, + "r": 8, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "k_proj", + "v_proj", + "fc1", + "o_proj", + "q_proj", + "fc2" + ], + "target_parameters": null, + "task_type": "CAUSAL_LM", + "trainable_token_indices": null, + "use_dora": false, + "use_qalora": false, + "use_rslora": false } diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/added_tokens.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/added_tokens.json index 62206137d..c9d3d3a1b 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/added_tokens.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/added_tokens.json @@ -1,13 +1,13 @@ -{ - "<|assistant|>": 32001, - "<|endoftext|>": 32000, - "<|end|>": 32007, - "<|placeholder1|>": 32002, - "<|placeholder2|>": 32003, - "<|placeholder3|>": 32004, - "<|placeholder4|>": 32005, - "<|placeholder5|>": 32008, - "<|placeholder6|>": 32009, - "<|system|>": 32006, - "<|user|>": 32010 -} +{ + "<|assistant|>": 32001, + "<|endoftext|>": 32000, + "<|end|>": 32007, + "<|placeholder1|>": 32002, + "<|placeholder2|>": 32003, + "<|placeholder3|>": 32004, + "<|placeholder4|>": 32005, + "<|placeholder5|>": 32008, + "<|placeholder6|>": 32009, + "<|system|>": 32006, + "<|user|>": 32010 +} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/special_tokens_map.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/special_tokens_map.json index 5cb3101c5..492d4b296 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/special_tokens_map.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/special_tokens_map.json @@ -1,30 +1,30 @@ -{ - "bos_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "eos_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "pad_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "unk_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - } -} +{ + "bos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/tokenizer.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/tokenizer.json index 79ff7fb7f..abc5aaff4 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/tokenizer.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/tokenizer.json @@ -1,277144 +1,277144 @@ -{ - "version": "1.0", - "truncation": { - "direction": "Right", - "max_length": 512, - "strategy": "LongestFirst", - "stride": 0 - }, - "padding": null, - "added_tokens": [ - { - "id": 0, - "content": "", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 1, - "content": "", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 2, - "content": "", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - } - ], - "normalizer": { - "type": "Sequence", - "normalizers": [ - { - "type": "Prepend", - "prepend": "▁" - }, - { - "type": "Replace", - "pattern": { - "String": " " - }, - "content": "▁" - } - ] - }, - "pre_tokenizer": null, - "post_processor": { - "type": "TemplateProcessing", - "single": [ - { - "SpecialToken": { - "id": "", - "type_id": 0 - } - }, - { - "Sequence": { - "id": "A", - "type_id": 0 - } - } - ], - "pair": [ - { - "SpecialToken": { - "id": "", - "type_id": 0 - } - }, - { - "Sequence": { - "id": "A", - "type_id": 0 - } - }, - { - "SpecialToken": { - "id": "", - "type_id": 1 - } - }, - { - "Sequence": { - "id": "B", - "type_id": 1 - } - } - ], - "special_tokens": { - "": { - "id": "", - "ids": [ - 1 - ], - "tokens": [ - "" - ] - } - } - }, - "decoder": { - "type": "Sequence", - "decoders": [ - { - "type": "Replace", - "pattern": { - "String": "▁" - }, - "content": " " - }, - { - "type": "ByteFallback" - }, - { - "type": "Fuse" - }, - { - "type": "Strip", - "content": " ", - "start": 1, - "stop": 0 - } - ] - }, - "model": { - "type": "BPE", - "dropout": null, - "unk_token": "", - "continuing_subword_prefix": null, - "end_of_word_suffix": null, - "fuse_unk": true, - "byte_fallback": true, - "ignore_merges": false, - "vocab": { - "": 0, - "": 1, - "": 2, - "<0x00>": 3, - "<0x01>": 4, - "<0x02>": 5, - "<0x03>": 6, - "<0x04>": 7, - "<0x05>": 8, - "<0x06>": 9, - "<0x07>": 10, - "<0x08>": 11, - 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"Flow": 17907, - "▁Аль": 17908, - "izard": 17909, - "▁numero": 17910, - "actic": 17911, - "▁destruct": 17912, - "▁Пра": 17913, - "onsieur": 17914, - "qt": 17915, - "abestanden": 17916, - "ność": 17917, - "Connect": 17918, - "▁oracle": 17919, - "▁Stockholm": 17920, - "sizeof": 17921, - "▁gemäß": 17922, - "ACT": 17923, - "▁expert": 17924, - "utions": 17925, - "▁hacia": 17926, - "▁logger": 17927, - "▁fool": 17928, - "rypto": 17929, - "ær": 17930, - "▁cidade": 17931, - "▁составе": 17932, - "oker": 17933, - "▁Transfer": 17934, - "▁denied": 17935, - "Track": 17936, - "▁radi": 17937, - "zec": 17938, - "▁Historic": 17939, - "▁Einwohner": 17940, - "кою": 17941, - "▁хра": 17942, - "▁Category": 17943, - "▁Disney": 17944, - "▁swap": 17945, - "Begin": 17946, - "▁mientras": 17947, - "▁dance": 17948, - "▁tête": 17949, - "▁droit": 17950, - "erta": 17951, - "▁birds": 17952, - "▁convin": 17953, - "parator": 17954, - "дра": 17955, - "▁ES": 17956, - "▁Ressources": 17957, - "EGIN": 17958, - "ücke": 17959, - 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"▁surprised": 18014, - "ardi": 18015, - "▁GR": 18016, - "]\"": 18017, - "▁equally": 18018, - "ihe": 18019, - "care": 18020, - "писок": 18021, - "lijk": 18022, - "rinn": 18023, - "▁\\[\\": 18024, - "▁sons": 18025, - "▁tät": 18026, - "icamente": 18027, - "▁listing": 18028, - "iellement": 18029, - "▁nyelven": 18030, - "▁ds": 18031, - "▁agricult": 18032, - "▁Hermann": 18033, - "▁besides": 18034, - "progress": 18035, - "▁peculiar": 18036, - "focus": 18037, - "cn": 18038, - "-$": 18039, - "ственный": 18040, - "ourg": 18041, - "▁wyn": 18042, - "▁conducted": 18043, - "▁Становништво": 18044, - "connected": 18045, - "▁bott": 18046, - "▁смер": 18047, - "▁Poz": 18048, - "unct": 18049, - "conda": 18050, - "▁савезној": 18051, - "▁havet": 18052, - "ligt": 18053, - "orted": 18054, - "▁entering": 18055, - "multip": 18056, - "▁Temple": 18057, - "▁Plant": 18058, - "typeof": 18059, - "▁Vlad": 18060, - "▁qued": 18061, - "▁reste": 18062, - "▁май": 18063, - "▁Very": 18064, - "ambiguation": 18065, - "▁challeng": 18066, - "▁respective": 18067, - "▁тор": 18068, - "Ctrl": 18069, - "▁absence": 18070, - "aru": 18071, - "вое": 18072, - "▁först": 18073, - "▁sq": 18074, - "▁Emperor": 18075, - "▁Ign": 18076, - "▁това": 18077, - ":`": 18078, - "adoop": 18079, - "▁Madame": 18080, - "▁gruppo": 18081, - "stud": 18082, - "▁externas": 18083, - "▁Александр": 18084, - "▁dign": 18085, - "▁живе": 18086, - "Amount": 18087, - "▁correlate": 18088, - "▁Fant": 18089, - "▁rails": 18090, - "fp": 18091, - "министратив": 18092, - "▁bought": 18093, - "▁filters": 18094, - "▁ancora": 18095, - "▁partner": 18096, - "▁quand": 18097, - "symbol": 18098, - "ulating": 18099, - "▁zd": 18100, - "awn": 18101, - "▁Grant": 18102, - "because": 18103, - "rable": 18104, - "\\}": 18105, - "ísticas": 18106, - "▁уче": 18107, - "▁période": 18108, - "▁ske": 18109, - "▁Anyway": 18110, - "▁indexes": 18111, - "▁directions": 18112, - "▁RAM": 18113, - "chrome": 18114, - "▁apost": 18115, - "▁warnings": 18116, - "▁Airport": 18117, - "VI": 18118, - "abile": 18119, - "▁lord": 18120, - "provider": 18121, - "▁Ji": 18122, - "ostream": 18123, - "▁gemeente": 18124, - "tableView": 18125, - "Extra": 18126, - "cursor": 18127, - "eground": 18128, - "▁Moz": 18129, - "▁rib": 18130, - "▁morph": 18131, - "loads": 18132, - "elsk": 18133, - "▁MAX": 18134, - "▁Santiago": 18135, - "▁Him": 18136, - "codes": 18137, - "▁lanz": 18138, - "▁counts": 18139, - "rinningsområ": 18140, - "щё": 18141, - "▁spé": 18142, - "▁pierws": 18143, - "▁Sver": 18144, - "▁acknow": 18145, - "Boolean": 18146, - "▁фамили": 18147, - "▁Senate": 18148, - "шов": 18149, - "agers": 18150, - "▁Nueva": 18151, - "bil": 18152, - "kiem": 18153, - "▁Mey": 18154, - "wij": 18155, - "▁GmbH": 18156, - "validation": 18157, - "▁ensuite": 18158, - "inking": 18159, - "▁campion": 18160, - "▁financial": 18161, - "izon": 18162, - "Headers": 18163, - "▁deprecated": 18164, - "▁fonction": 18165, - "REG": 18166, - "▁volumes": 18167, - "▁Chi": 18168, - "▁encountered": 18169, - "lak": 18170, - "рая": 18171, - "▁continues": 18172, - "▁~[": 18173, - "uerte": 18174, - "▁\\;": 18175, - "▁Dok": 18176, - "▁weights": 18177, - "▁rh": 18178, - "▁Napole": 18179, - "▁naturally": 18180, - "sku": 18181, - "pas": 18182, - "▁gegründ": 18183, - "etr": 18184, - "▁Ku": 18185, - "icted": 18186, - "▁fabric": 18187, - "▁ASC": 18188, - "▁Entertainment": 18189, - "▁energ": 18190, - "клад": 18191, - "omon": 18192, - "theme": 18193, - "▁харак": 18194, - "▁draft": 18195, - "▁channels": 18196, - "▁desert": 18197, - "▁través": 18198, - "▁Lock": 18199, - "▁siendo": 18200, - "фек": 18201, - "même": 18202, - "▁packet": 18203, - "▁Mountain": 18204, - "▁Fahr": 18205, - "braio": 18206, - "пере": 18207, - "▁genannt": 18208, - "▁deployment": 18209, - "Pal": 18210, - "ног": 18211, - "стру": 18212, - "Prim": 18213, - "für": 18214, - "▁dangerous": 18215, - "▁szám": 18216, - "reck": 18217, - "▁popup": 18218, - "icky": 18219, - "inar": 18220, - "cowo": 18221, - "нцикло": 18222, - "ítás": 18223, - "▁plugins": 18224, - "▁driven": 18225, - "лев": 18226, - "▁\"(": 18227, - "tta": 18228, - "▁Ú": 18229, - "▁eb": 18230, - "▁'';": 18231, - "▁knock": 18232, - "▁основа": 18233, - "▁maison": 18234, - "гля": 18235, - "▁Honor": 18236, - "tail": 18237, - "ritz": 18238, - "▁guys": 18239, - "▁combinations": 18240, - "ondere": 18241, - "▁Ald": 18242, - "▁fiddle": 18243, - "дав": 18244, - "urd": 18245, - "▁projection": 18246, - "▁También": 18247, - "verb": 18248, - "▁terre": 18249, - "rugu": 18250, - "▁september": 18251, - "▁=": 18572, - "▁Beat": 18573, - "▁Sax": 18574, - "vertical": 18575, - "кто": 18576, - "▁plants": 18577, - "▁Références": 18578, - "▁ogni": 18579, - "▁curs": 18580, - "▁SK": 18581, - "они": 18582, - "▁destac": 18583, - "\");\r": 18584, - "▁Sure": 18585, - "▁partido": 18586, - "▁Folge": 18587, - "▁Moore": 18588, - "▁wz": 18589, - "скус": 18590, - "ltre": 18591, - "ondo": 18592, - "▁pose": 18593, - "imos": 18594, - "бой": 18595, - "ципа": 18596, - "jus": 18597, - ".....": 18598, - "▁época": 18599, - "▁quanto": 18600, - "▁Support": 18601, - "geschichte": 18602, - "SERVER": 18603, - "▁Georges": 18604, - "enum": 18605, - "▁herm": 18606, - "▁nebo": 18607, - "▁Chr": 18608, - "character": 18609, - "▁***": 18610, - "▁Forsch": 18611, - "iami": 18612, - "▁¿": 18613, - "cych": 18614, - "▁fifth": 18615, - "sent": 18616, - "▁anderem": 18617, - "▁proportion": 18618, - "▁prest": 18619, - "▁Girl": 18620, - "▁drama": 18621, - "wand": 18622, - "▁Mail": 18623, - "▁Lux": 18624, - "▁který": 18625, - "▁Gesellschaft": 18626, - "▁Hinweis": 18627, - "nisse": 18628, - "▁mondo": 18629, - "Eq": 18630, - "▁perí": 18631, - "▁eastern": 18632, - "▁UEFA": 18633, - "uale": 18634, - "▁convex": 18635, - "▁поль": 18636, - "▁Hey": 18637, - "zenie": 18638, - "initely": 18639, - "▁Zusammen": 18640, - "SSL": 18641, - "ocal": 18642, - "▁canal": 18643, - "voy": 18644, - "▁Кри": 18645, - "▁között": 18646, - "▁cars": 18647, - "▁versión": 18648, - "Environment": 18649, - "Her": 18650, - "▁señ": 18651, - "▁spatial": 18652, - "ymi": 18653, - "Fire": 18654, - "▁veget": 18655, - "▁Wie": 18656, - "▁znaj": 18657, - "▁damage": 18658, - "▁endl": 18659, - "gif": 18660, - "▁quali": 18661, - "▁которых": 18662, - "ellan": 18663, - "▁mens": 18664, - "▁plug": 18665, - "▁abund": 18666, - "FIG": 18667, - "▁sf": 18668, - "▁confl": 18669, - "▁населения": 18670, - "▁principles": 18671, - "▁Gabriel": 18672, - "ibe": 18673, - "▁{%": 18674, - "▁població": 18675, - "ніципа": 18676, - "▁extreme": 18677, - "▁asse": 18678, - "▁vu": 18679, - "Mock": 18680, - "▁spielte": 18681, - "▁Aer": 18682, - "▁datos": 18683, - "endes": 18684, - "▁Gel": 18685, - "▁Gor": 18686, - "Christ": 18687, - "chos": 18688, - "Processor": 18689, - "▁instruct": 18690, - "▁picked": 18691, - "nahme": 18692, - "fahr": 18693, - "▁indicated": 18694, - "▁%.": 18695, - "▁ts": 18696, - "▁notable": 18697, - "▁qualified": 18698, - "▁Ал": 18699, - "Black": 18700, - "▁council": 18701, - "▁overhead": 18702, - "aci": 18703, - "année": 18704, - "▁initWith": 18705, - "bió": 18706, - "▁introduction": 18707, - "▁companion": 18708, - 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"мож": 19085, - "▁гер": 19086, - "▁smallest": 19087, - "▁Publishing": 19088, - "▁Ts": 19089, - "▁laughed": 19090, - "lle": 19091, - "▁Amt": 19092, - "▁IIS": 19093, - "FORM": 19094, - "Mag": 19095, - "дон": 19096, - "▁storia": 19097, - "▁organized": 19098, - "ční": 19099, - "▁ox": 19100, - "lingen": 19101, - "▁luego": 19102, - "cció": 19103, - "▁rely": 19104, - "▁tussen": 19105, - "erten": 19106, - "▁honour": 19107, - "▁Claude": 19108, - "▁Korea": 19109, - "▁Metropol": 19110, - "Super": 19111, - "rien": 19112, - "érature": 19113, - "attro": 19114, - "▁біль": 19115, - "▁Herbert": 19116, - "▁auteurs": 19117, - "▁darauf": 19118, - "▁mental": 19119, - "▁rang": 19120, - "▁són": 19121, - "▁Soph": 19122, - ")\",": 19123, - "Descriptor": 19124, - "prepare": 19125, - "▁Landkreis": 19126, - "HC": 19127, - "cross": 19128, - "лиза": 19129, - "▁Login": 19130, - "onen": 19131, - "Feature": 19132, - "▁museum": 19133, - "vek": 19134, - "▁Nelson": 19135, - "▁rejo": 19136, - "▁команди": 19137, - "▁summar": 19138, - 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"▁alco": 27231, - "▁Selbst": 27232, - "ił": 27233, - "▁alb": 27234, - "ouverneur": 27235, - "▁sr": 27236, - "▁vba": 27237, - "loped": 27238, - "▁Partei": 27239, - "uate": 27240, - "▁Authentication": 27241, - "bei": 27242, - "}}.": 27243, - "▁konnten": 27244, - "▁допо": 27245, - "▁hyd": 27246, - "Office": 27247, - "données": 27248, - "▁Cleveland": 27249, - "rita": 27250, - "íos": 27251, - "▁выше": 27252, - "▁Roberts": 27253, - "▁élections": 27254, - "▁'')": 27255, - "▁publishing": 27256, - "▁bapt": 27257, - "<>();": 27258, - "missing": 27259, - "ровано": 27260, - "▁housing": 27261, - "▁inference": 27262, - "▁Renaissance": 27263, - "▁règ": 27264, - "▁Steph": 27265, - "CES": 27266, - "ERE": 27267, - "кет": 27268, - "OU": 27269, - "▁grouping": 27270, - "verkehr": 27271, - "jih": 27272, - "agli": 27273, - "▁milk": 27274, - "lait": 27275, - "Stage": 27276, - "▁byly": 27277, - "▁wooden": 27278, - "keley": 27279, - "etra": 27280, - "▁Peg": 27281, - "▁donné": 27282, - "adal": 27283, - "sequently": 27284, - "▁insbesondere": 27285, - "ELD": 27286, - "▁Mam": 27287, - "▁volte": 27288, - "▁prospect": 27289, - "нове": 27290, - "▁denoted": 27291, - "▁overlay": 27292, - "Permission": 27293, - "een": 27294, - "▁EM": 27295, - "▁uz": 27296, - "Mc": 27297, - "olit": 27298, - "▁servi": 27299, - "▁Heidel": 27300, - "▁Wiener": 27301, - "▁illegal": 27302, - "▁predictions": 27303, - "▁goog": 27304, - "hon": 27305, - "▁Cinema": 27306, - "▁револю": 27307, - "▁Rule": 27308, - "wod": 27309, - "▁radiation": 27310, - "oł": 27311, - "ової": 27312, - "▁Perform": 27313, - "▁prisoner": 27314, - "▁amet": 27315, - "▁figura": 27316, - "▁Commander": 27317, - "▁официаль": 27318, - "▁trov": 27319, - "▁acted": 27320, - "▁workflow": 27321, - "▁Республики": 27322, - "▁guidance": 27323, - "▁мене": 27324, - "National": 27325, - "▁Kel": 27326, - "webpack": 27327, - "простра": 27328, - "▁llamado": 27329, - "alog": 27330, - "terra": 27331, - "ixen": 27332, - "legraph": 27333, - "äischen": 27334, - "▁teachers": 27335, - "uden": 27336, - 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"----------": 28400, - "pshire": 28401, - "mart": 28402, - "▁Dioc": 28403, - "iskt": 28404, - "▁inse": 28405, - "▁épisode": 28406, - "чик": 28407, - "bars": 28408, - "Sito": 28409, - "▁integrity": 28410, - "auff": 28411, - "▁vär": 28412, - "Azure": 28413, - "▁starb": 28414, - "▁контра": 28415, - "▁Мексичка": 28416, - "▁запа": 28417, - "▁Mountains": 28418, - "}}=": 28419, - "▁pulling": 28420, - "▁satellite": 28421, - "▁atoms": 28422, - "▁profesor": 28423, - "▁repeatedly": 28424, - "▁invasion": 28425, - "programming": 28426, - "├──": 28427, - "▁Lip": 28428, - "вшие": 28429, - "▁keen": 28430, - "▁critics": 28431, - "▁Nicola": 28432, - "▁Cand": 28433, - "▁distint": 28434, - "▁heading": 28435, - "pragma": 28436, - "{|": 28437, - "ymen": 28438, - "▁terrain": 28439, - "iedenis": 28440, - "▁besonders": 28441, - "▁nominated": 28442, - "BOOL": 28443, - "▁Kay": 28444, - "cian": 28445, - "stelle": 28446, - "▁dispute": 28447, - "▁щ": 28448, - "DataSet": 28449, - "nothing": 28450, - "Autom": 28451, - "hören": 28452, - "▁shed": 28453, - "▁paused": 28454, - "san": 28455, - "▁nunca": 28456, - "!(\"": 28457, - "▁położ": 28458, - "Secret": 28459, - "▁Domain": 28460, - "▁возмож": 28461, - "XV": 28462, - "lv": 28463, - "ikh": 28464, - "▁Sony": 28465, - "mq": 28466, - "otrop": 28467, - "▁Logger": 28468, - "▁threat": 28469, - "asted": 28470, - "зько": 28471, - "▁freely": 28472, - "▁improvements": 28473, - "istema": 28474, - "▁illustrate": 28475, - "▁tact": 28476, - "▁figur": 28477, - "ués": 28478, - "riminal": 28479, - "odon": 28480, - "intendo": 28481, - "▁influenced": 28482, - "FFER": 28483, - "▁Ghost": 28484, - "▁совер": 28485, - "nad": 28486, - "ioned": 28487, - "▁Events": 28488, - "▁wrapping": 28489, - "---------+": 28490, - "fif": 28491, - "▁(**": 28492, - "={{": 28493, - "маль": 28494, - "▁losses": 28495, - "▁Galerie": 28496, - "tel": 28497, - "▁лютого": 28498, - "▁Kru": 28499, - "▁Polen": 28500, - "нім": 28501, - "near": 28502, - "▁shame": 28503, - "▁moyenne": 28504, - "▁CP": 28505, - "preis": 28506, - "▁passenger": 28507, - "lek": 28508, - "ionales": 28509, - "kafka": 28510, - "▁participe": 28511, - "▁membership": 28512, - "[_": 28513, - "lando": 28514, - "stelling": 28515, - "Sem": 28516, - "gon": 28517, - "▁Correct": 28518, - "▁valle": 28519, - "▁readily": 28520, - "▁Dokument": 28521, - "honneur": 28522, - "▁testim": 28523, - "ulative": 28524, - "doFilter": 28525, - "▁dominant": 28526, - "ammer": 28527, - "▁која": 28528, - "▁Monsieur": 28529, - "zeg": 28530, - "▁війни": 28531, - "▁Fo": 28532, - "▁Amy": 28533, - "▁¡": 28534, - "▁február": 28535, - "▁downloading": 28536, - "▁leng": 28537, - "\\}$,": 28538, - "▁neat": 28539, - "▁Cache": 28540, - "ICATION": 28541, - "▁deve": 28542, - "▁sorrow": 28543, - "slow": 28544, - "▁hinaus": 28545, - "▁reconoc": 28546, - "▁Linked": 28547, - "▁Shaw": 28548, - "market": 28549, - "▁Dic": 28550, - "▁Ski": 28551, - "▁delimiter": 28552, - "▁MainActivity": 28553, - "▁Musical": 28554, - "▁Reyn": 28555, - "ScrollView": 28556, - "▁conventional": 28557, - "ença": 28558, - "▁refactor": 28559, - "'-": 28560, - "▁Hed": 28561, - "sprech": 28562, - "▁athlet": 28563, - "▁especies": 28564, - "▁Schön": 28565, - "▁kleinen": 28566, - "шко": 28567, - "▁Йо": 28568, - "▁Happy": 28569, - "multirow": 28570, - "▁augusti": 28571, - "▁Gand": 28572, - "▁appointment": 28573, - "▁Mediabestanden": 28574, - "Three": 28575, - "▁Kenneth": 28576, - "NEW": 28577, - "▁Notification": 28578, - "▁Marx": 28579, - "▁insc": 28580, - "Mor": 28581, - "вый": 28582, - "väst": 28583, - "vidia": 28584, - "▁demonstrated": 28585, - "fonts": 28586, - "▁kamen": 28587, - "▁Ster": 28588, - "▁mieszkańców": 28589, - "▁Koh": 28590, - "~$\\": 28591, - "»).": 28592, - "rene": 28593, - "insic": 28594, - "ická": 28595, - "xygen": 28596, - "▁mn": 28597, - "▁sched": 28598, - "ASC": 28599, - "Ig": 28600, - "▁Constant": 28601, - "▁opportun": 28602, - "▁MyClass": 28603, - "sef": 28604, - "oped": 28605, - "▁injured": 28606, - "VIS": 28607, - "▁Pero": 28608, - "▁Until": 28609, - "▁flesh": 28610, - "orphism": 28611, - "▁Portal": 28612, - "▁gminy": 28613, - "▁власти": 28614, - "▁Nä": 28615, - "ктиче": 28616, - "▁hrab": 28617, - "▁Cub": 28618, - "avoir": 28619, - "▁Lars": 28620, - "▁Бело": 28621, - "▁seizoen": 28622, - "▁Genomsnitt": 28623, - "▁Lil": 28624, - "▁Pool": 28625, - "▁Dios": 28626, - "TX": 28627, - "aes": 28628, - "autore": 28629, - "Alpha": 28630, - "states": 28631, - "Lab": 28632, - "nederbörd": 28633, - "erton": 28634, - "▁brid": 28635, - "▁richt": 28636, - "▁Ela": 28637, - "▁сла": 28638, - "▁weapon": 28639, - "▁combatt": 28640, - "agar": 28641, - "▁regnig": 28642, - "▁utilisé": 28643, - "▁servir": 28644, - "▁brick": 28645, - "▁gateway": 28646, - "▁torraste": 28647, - "▁procedures": 28648, - "▁årsnederbörd": 28649, - "▁Genomsnittlig": 28650, - "чёт": 28651, - "▁områ": 28652, - "▁regnigaste": 28653, - "▁честь": 28654, - "▁amid": 28655, - "▁grateful": 28656, - "▁DIS": 28657, - "DAY": 28658, - "▁ору": 28659, - "▁rivière": 28660, - "heure": 28661, - "▁Richmond": 28662, - "▁Compar": 28663, - "▁Нор": 28664, - "DOC": 28665, - "esia": 28666, - "calc": 28667, - "▁IU": 28668, - "▁vorg": 28669, - "▁habían": 28670, - "çoit": 28671, - "▁arist": 28672, - "▁кли": 28673, - "▁Sue": 28674, - "▁Touch": 28675, - "▁Writing": 28676, - "ifiable": 28677, - "▁wc": 28678, - "▁withdraw": 28679, - "зар": 28680, - "▁presently": 28681, - "▁FK": 28682, - "▁prakt": 28683, - "▁colored": 28684, - "usb": 28685, - "▁Perú": 28686, - "▁plata": 28687, - "▁wishes": 28688, - "▁кам": 28689, - "azar": 28690, - "ável": 28691, - "▁lamp": 28692, - "bishop": 28693, - "▁inclusion": 28694, - "jq": 28695, - "arth": 28696, - "▁Flag": 28697, - "▁нор": 28698, - "ædia": 28699, - "UNCTION": 28700, - "▁Bahnhof": 28701, - "▁approaching": 28702, - "▁Gött": 28703, - "▁cube": 28704, - "▁argued": 28705, - "▁Things": 28706, - "Gui": 28707, - "дови": 28708, - "▁recre": 28709, - "▁réseau": 28710, - "▁significa": 28711, - "Git": 28712, - "gebracht": 28713, - "▁liga": 28714, - "▁assured": 28715, - "alus": 28716, - "рит": 28717, - "▁энциклопеди": 28718, - "▁%).": 28719, - "▁Première": 28720, - "▁declarations": 28721, - "▁tricky": 28722, - "▁profiles": 28723, - "▁Fon": 28724, - "▁Jas": 28725, - "âr": 28726, - "babel": 28727, - "▁Friday": 28728, - "▁június": 28729, - "▁cols": 28730, - "▁EXISTS": 28731, - "▁Italiana": 28732, - "▁authorization": 28733, - "▁sulle": 28734, - "▁Emb": 28735, - "▁Variable": 28736, - "trees": 28737, - "▁Fly": 28738, - "riors": 28739, - "▁damals": 28740, - "▁findet": 28741, - "▁Sept": 28742, - "▁mundial": 28743, - "▁removal": 28744, - "▁longitude": 28745, - "clic": 28746, - "▁fade": 28747, - "▁gradle": 28748, - "▁zák": 28749, - "▁timing": 28750, - "trightarrow": 28751, - "atia": 28752, - "-.": 28753, - "uche": 28754, - "▁serialize": 28755, - "▁Hmm": 28756, - "▁Representatives": 28757, - "bah": 28758, - "rend": 28759, - "assador": 28760, - "▁shield": 28761, - "ucion": 28762, - "▁américaine": 28763, - "zę": 28764, - "villa": 28765, - "▁hombre": 28766, - "áss": 28767, - "▁SF": 28768, - "▁repeating": 28769, - "▁criter": 28770, - "▁Struct": 28771, - "???": 28772, - "▁cheap": 28773, - "▁rings": 28774, - "abhäng": 28775, - "▁corte": 28776, - "▁administ": 28777, - "ixon": 28778, - "gypt": 28779, - "▁puntos": 28780, - "▁mezi": 28781, - "▁pochod": 28782, - "isko": 28783, - "nię": 28784, - "▁осу": 28785, - "▁ár": 28786, - "тельной": 28787, - "▁Metropolitan": 28788, - "jin": 28789, - "zess": 28790, - "▁віці": 28791, - "▁conflicts": 28792, - "ijst": 28793, - "▁Market": 28794, - "стров": 28795, - "▁\",\"": 28796, - "▁Scroll": 28797, - "gun": 28798, - "тара": 28799, - "▁amateur": 28800, - "▁róż": 28801, - "poss": 28802, - "▁generalized": 28803, - "▁Harm": 28804, - "cita": 28805, - "▁Switzerland": 28806, - "icola": 28807, - "▁muit": 28808, - "located": 28809, - "▁có": 28810, - "▁arose": 28811, - "▁communauté": 28812, - "})^": 28813, - "visibility": 28814, - "ída": 28815, - "▁FB": 28816, - "▁Freund": 28817, - "gat": 28818, - "\":{\"": 28819, - "intellij": 28820, - "ifie": 28821, - 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- "bn" - ], - [ - "Fe", - "ed" - ], - [ - "F", - "eed" - ], - [ - "▁sympath", - "y" - ], - [ - "▁I", - "b" - ], - [ - "▁territ", - "orial" - ], - [ - "ra", - "ting" - ], - [ - "rat", - "ing" - ], - [ - "r", - "ating" - ], - [ - "да", - "ми" - ], - [ - "▁d", - "st" - ], - [ - "▁ds", - "t" - ], - [ - "▁", - "dst" - ], - [ - "у", - "ю" - ], - [ - "ah", - "o" - ], - [ - "a", - "ho" - ], - [ - "▁s", - "ug" - ], - [ - "▁su", - "g" - ], - [ - "em", - "ia" - ], - [ - "emi", - "a" - ], - [ - "▁t", - "ed" - ], - [ - "▁te", - "d" - ], - [ - "▁", - "ted" - ], - [ - "▁A", - "pi" - ], - [ - "▁Ap", - "i" - ], - [ - "▁", - "Api" - ], - [ - "▁R", - "ica" - ], - [ - "▁Ric", - "a" - ], - [ - "▁Ri", - "ca" - ], - [ - "▁M", - "R" - ], - [ - "▁", - "MR" - ], - [ - "ński", - "m" - ], - [ - "ń", - "skim" - ], - [ - "▁V", - "oor" - ], - [ - "▁Vo", - "or" - ], - [ - "▁de", - "vil" - ], - [ - "▁dev", - "il" - ], - [ - "▁devi", - "l" - ], - [ - "▁Ф", - "о" - ], - [ - "▁N", - "är" - ], - [ - "▁Nä", - "r" - ], - [ - "▁...", - ")" - ], - [ - "▁..", - ".)" - ], - [ - "▁", - "...)" - ], - [ - "▁v", - "ois" - ], - [ - "▁vo", - "is" - ], - [ - "▁ab", - "bre" - ], - [ - "▁abb", - "re" - ], - [ - "▁M", - "änner" - ], - [ - "xim", - "o" - ], - [ - "xi", - "mo" - ], - [ - "x", - "imo" - ], - [ - "▁intellect", - "ual" - ], - [ - "▁t", - "ales" - ], - [ - "▁tal", - "es" - ], - [ - "▁ta", - "les" - ], - [ - "▁tale", - "s" - ], - [ - "sim", - "ilar" - ], - [ - "ne", - "um" - ], - [ - "▁O", - "rig" - ], - [ - "▁Or", - "ig" - ], - [ - "▁Ori", - "g" - ], - [ - "▁po", - "stal" - ], - [ - "▁pos", - "tal" - ], - [ - "▁post", - "al" - ], - [ - "▁h", - "vor" - ], - [ - "▁ident", - "ification" - ], - [ - "▁identific", - "ation" - ], - [ - "▁О", - "д" - ], - [ - "ue", - "sto" - ], - [ - "ues", - "to" - ], - [ - "uest", - "o" - ], - [ - "u", - "esto" - ], - [ - "▁.", - "./" - ], - [ - "▁..", - "/" - ], - [ - "▁", - "../" - ], - [ - "▁b", - "ir" - ], - [ - "▁bi", - "r" - ], - [ - "▁", - "bir" - ], - [ - "▁Л", - "он" - ], - [ - "▁Ло", - "н" - ], - [ - "▁es", - "empio" - ], - [ - "▁E", - "ing" - ], - [ - "▁Ein", - "g" - ], - [ - "Exp", - "and" - ], - [ - "▁PR", - "IMARY" - ], - [ - "▁J", - "in" - ], - [ - "▁Ji", - "n" - ], - [ - "▁vš", - "ak" - ], - [ - "ours", - "es" - ], - [ - "ourse", - "s" - ], - [ - "▁Be", - "tty" - ], - [ - "▁Bet", - "ty" - ], - [ - "▁W", - "M" - ], - [ - "▁", - "WM" - ], - [ - "▁fl", - "ask" - ], - [ - "▁fla", - "sk" - ], - [ - "hl", - "en" - ], - [ - "h", - "len" - ], - [ - "▁A", - "del" - ], - [ - "▁Ad", - "el" - ], - [ - "lar", - "avel" - ], - [ - "▁д", - "ет" - ], - [ - "▁де", - "т" - ], - [ - "сь", - "кою" - ], - [ - "сько", - "ю" - ], - [ - "▁M", - "undo" - ], - [ - "▁Mun", - "do" - ], - [ - "ic", - "zn" - ], - [ - "icz", - "n" - ], - [ - "ifi", - "é" - ], - [ - "▁М", - "ор" - ], - [ - "▁Мо", - "р" - ], - [ - "▁д", - "рев" - ], - [ - "▁др", - "ев" - ], - [ - "Date", - "Format" - ], - [ - "сь", - "ким" - ], - [ - "ськ", - "им" - ], - [ - "▁d", - "ated" - ], - [ - "▁da", - "ted" - ], - [ - "▁dat", - "ed" - ], - [ - "▁date", - "d" - ], - [ - "▁", - "dated" - ], - [ - "ко", - "ли" - ], - [ - "кол", - "и" - ], - [ - "▁результа", - "те" - ], - [ - "\\)", - "." - ], - [ - "\\", - ")." - ], - [ - "▁delay", - "ed" - ], - [ - "so", - "und" - ], - [ - "s", - "ound" - ], - [ - "▁Ма", - "к" - ], - [ - "▁\"", - "..." - ], - [ - "▁\".", - ".." - ], - [ - "▁b", - "innen" - ], - [ - "▁bin", - "nen" - ], - [ - "▁фа", - "куль" - ], - [ - "▁pol", - "ygon" - ], - [ - "▁poly", - "gon" - ], - [ - "▁eg", - "gs" - ], - [ - "▁egg", - "s" - ], - [ - "At", - "IndexPath" - ], - [ - "AtIndex", - "Path" - ], - [ - "мен", - "таль" - ], - [ - "мент", - "аль" - ], - [ - "мента", - "ль" - ], - [ - "▁in", - "cred" - ], - [ - "▁incre", - "d" - ], - [ - "▁inc", - "red" - ], - [ - "ch", - "unk" - ], - [ - "web", - "driver" - ], - [ - "▁с", - "вобо" - ], - [ - "▁сво", - "бо" - ], - [ - "▁mi", - "ędzy" - ], - [ - "Rece", - "ived" - ], - [ - "Receive", - "d" - ], - [ - "▁M", - "onde" - ], - [ - "▁Mon", - "de" - ], - [ - "▁Mo", - "nde" - ], - [ - "▁Mond", - "e" - ], - [ - "▁J", - "Query" - ], - [ - "Bu", - "tt" - ], - [ - "But", - "t" - ], - [ - "B", - "utt" - ], - [ - "▁P", - "DO" - ], - [ - "▁for", - "ec" - ], - [ - "▁fo", - "rec" - ], - [ - "▁fore", - "c" - ], - [ - "▁discipl", - "ine" - ], - [ - "ch", - "ev" - ], - [ - "che", - "v" - ], - [ - "на", - "т" - ], - [ - "н", - "ат" - ], - [ - "▁re", - "dis" - ], - [ - "▁red", - "is" - ], - [ - "▁hun", - "ting" - ], - [ - "▁al", - "k" - ], - [ - "▁", - "alk" - ], - [ - "▁proof", - "s" - ], - [ - "PR", - "I" - ], - [ - "P", - "RI" - ], - [ - "▁c", - "hip" - ], - [ - "▁ch", - "ip" - ], - [ - "▁chi", - "p" - ], - [ - "és", - "ie" - ], - [ - "▁H", - "O" - ], - [ - "▁", - "HO" - ], - [ - "▁r", - "ug" - ], - [ - "▁ru", - "g" - ], - [ - "▁", - "rug" - ], - [ - "zo", - "s" - ], - [ - "z", - "os" - ], - [ - "▁s", - "orte" - ], - [ - "▁sort", - "e" - ], - [ - "▁sor", - "te" - ], - [ - "▁ze", - "igt" - ], - [ - "▁Phys", - "ics" - ], - [ - "leg", - "te" - ], - [ - "legt", - "e" - ], - [ - "▁proport", - "ional" - ], - [ - "▁proportion", - "al" - ], - [ - "▁tool", - "bar" - ], - [ - "ve", - "ment" - ], - [ - "v", - "ement" - ], - [ - "not", - "in" - ], - [ - "▁prv", - "ní" - ], - [ - "bl", - "ah" - ], - [ - "bla", - "h" - ], - [ - "b", - "lah" - ], - [ - "▁prés", - "ence" - ], - [ - "▁l", - "loc" - ], - [ - "▁ll", - "oc" - ], - [ - "▁lí", - "der" - ], - [ - "▁Ac", - "cept" - ], - [ - "▁", - "Accept" - ], - [ - "▁Al", - "ways" - ], - [ - "▁\"", - "{" - ], - [ - "▁divers", - "i" - ], - [ - "▁diver", - "si" - ], - [ - "ik", - "or" - ], - [ - "iko", - "r" - ], - [ - "i", - "kor" - ], - [ - "Per", - "iod" - ], - [ - "ж", - "ён" - ], - [ - "▁Al", - "liance" - ], - [ - "▁All", - "iance" - ], - [ - "▁re", - "lay" - ], - [ - "▁rel", - "ay" - ], - [ - "▁rela", - "y" - ], - [ - "Br", - "o" - ], - [ - "B", - "ro" - ], - [ - "jö", - "n" - ], - [ - "j", - "ön" - ], - [ - "▁B", - "aud" - ], - [ - "▁Ba", - "ud" - ], - [ - "▁Bau", - "d" - ], - [ - "▁B", - "ian" - ], - [ - "▁Bi", - "an" - ], - [ - "')", - "[" - ], - [ - "'", - ")[" - ], - [ - "чи", - "в" - ], - [ - "▁P", - "oss" - ], - [ - "▁Po", - "ss" - ], - [ - "▁Pos", - "s" - ], - [ - "▁Mitg", - "lieder" - ], - [ - "▁Mitglied", - "er" - ], - [ - "▁n", - "ev" - ], - [ - "▁ne", - "v" - ], - [ - "Dan", - "iel" - ], - [ - "▁t", - "ends" - ], - [ - "▁ten", - "ds" - ], - [ - "▁tend", - "s" - ], - [ - "▁compag", - "nie" - ], - [ - "▁liv", - "res" - ], - [ - "▁livre", - "s" - ], - [ - "lu", - "b" - ], - [ - "l", - "ub" - ], - [ - "▁", - "▁" - ], - [ - "▁▁", - "▁▁" - ], - [ - "▁▁▁", - "▁" - ], - [ - "▁", - "▁▁▁" - ], - [ - "▁▁", - "▁▁▁▁▁▁" - ], - [ - "▁▁▁▁", - "▁▁▁▁" - ], - [ - "▁▁▁▁▁", - "▁▁▁" - ], - [ - "▁▁▁▁▁▁", - "▁▁" - ], - [ - "▁▁▁", - "▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁", - "▁" - ], - [ - "▁", - "▁▁▁▁▁▁▁" - ], - [ - "▁▁", - "▁▁▁" - ], - [ - "▁▁▁▁", - "▁" - ], - [ - "▁▁▁", - "▁▁" - ], - [ - "▁", - "▁▁▁▁" - ], - [ - "▁▁", - "▁▁▁▁▁▁▁▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁", - "▁▁▁▁▁▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁", - "▁▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁", - "▁▁▁▁▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁", - "▁▁▁▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁▁▁▁▁", - "▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁▁▁▁▁▁", - "▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁▁▁", - "▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁▁▁▁▁▁▁", - "▁▁" - ], - [ - "▁▁▁", - "▁▁▁▁▁▁▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁▁", - "▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁", - "▁▁▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁▁▁▁", - "▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁", - "▁" - ], - [ - "▁", - "▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁" - ], - [ - "▁▁", - "▁▁▁▁" - ], - [ - "▁▁▁▁", - "▁▁" - ], - [ - "▁▁▁▁▁", - "▁" - ], - [ - "▁▁▁", - "▁▁▁" - ], - [ - "▁", - "▁▁▁▁▁" - ], - [ - "▁▁", - "▁▁▁▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁", - "▁▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁", - "▁▁▁▁" - ], - [ - "▁▁▁▁▁", - "▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁", - "▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁▁▁", - "▁▁" - ], - [ - "▁▁▁", - "▁▁▁▁▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁▁", - "▁▁▁" - ], - [ - "▁▁▁▁▁▁▁", - "▁▁▁▁▁" - ], - [ - "▁▁▁▁▁▁▁▁▁▁▁", - "▁" - ], - [ - "▁", - "▁▁▁▁▁▁▁▁▁▁▁" - ], - [ - "▁▁", - "▁▁▁▁▁▁▁▁▁▁▁" - ], - [ - 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15330, + "▁joy": 15331, + "▁safety": 15332, + "|^": 15333, + "pod": 15334, + "adém": 15335, + "▁Chron": 15336, + "▁Django": 15337, + "▁ehemal": 15338, + "kh": 15339, + "èle": 15340, + "▁poc": 15341, + "Bottom": 15342, + "launch": 15343, + "nem": 15344, + "▁GROUP": 15345, + "ního": 15346, + "▁Gib": 15347, + "sdk": 15348, + "BE": 15349, + "▁Gene": 15350, + "▁Staff": 15351, + "▁subsequent": 15352, + "icion": 15353, + "▁victory": 15354, + "▁canon": 15355, + "izar": 15356, + "izia": 15357, + "▁mate": 15358, + "▁layers": 15359, + "sudo": 15360, + "schule": 15361, + "periment": 15362, + "ület": 15363, + "ARCHAR": 15364, + "▁террито": 15365, + "▁measures": 15366, + "▁zou": 15367, + "opsis": 15368, + "нами": 15369, + "tbody": 15370, + "▁ese": 15371, + "sterdam": 15372, + "▁photo": 15373, + "ynchronous": 15374, + "setminus": 15375, + "▁loads": 15376, + "▁pleasure": 15377, + "▁meille": 15378, + "}\\,": 15379, + "qual": 15380, + "▁favour": 15381, + "▁rod": 15382, + "Der": 15383, + "рабо": 15384, + 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+ "▁Gef": 15759, + "град": 15760, + "▁eig": 15761, + "antom": 15762, + "▁Meister": 15763, + "flags": 15764, + "fficiale": 15765, + "шая": 15766, + "-,": 15767, + "ationen": 15768, + "mouse": 15769, + "standard": 15770, + "Single": 15771, + "▁bol": 15772, + "isis": 15773, + "▁fruit": 15774, + "course": 15775, + "itants": 15776, + "▁étaient": 15777, + "TextField": 15778, + "▁фон": 15779, + "▁aircraft": 15780, + "▁ISSN": 15781, + "▁western": 15782, + "▁representing": 15783, + "Esp": 15784, + "▁Else": 15785, + "▁sizes": 15786, + "▁satisfied": 15787, + "otos": 15788, + "UD": 15789, + "Final": 15790, + "ój": 15791, + "ève": 15792, + "▁Roy": 15793, + "ffen": 15794, + "▁salt": 15795, + "▁Label": 15796, + "Sk": 15797, + "▁кре": 15798, + "▁Литература": 15799, + "▁см": 15800, + "Attributes": 15801, + "aye": 15802, + "ськ": 15803, + "▁высо": 15804, + "-)": 15805, + "oses": 15806, + "calcul": 15807, + "▁Cannot": 15808, + "Generic": 15809, + "emo": 15810, + "▁Autor": 15811, + "лён": 15812, + "лага": 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"▁Biography": 17093, + ",[": 17094, + "urm": 17095, + "▁walked": 17096, + "▁federal": 17097, + "▁Michigan": 17098, + "▁facts": 17099, + "▁Integr": 17100, + "LES": 17101, + "▁Alan": 17102, + "▁coup": 17103, + "Ber": 17104, + "▁particles": 17105, + "ће": 17106, + "Inflater": 17107, + "+(": 17108, + "Bound": 17109, + "▁Sü": 17110, + "Audio": 17111, + "citet": 17112, + "yect": 17113, + "▁nr": 17114, + "xe": 17115, + "▁Brun": 17116, + "▁_,": 17117, + "avor": 17118, + "▁discipl": 17119, + "alm": 17120, + "▁ноября": 17121, + "▁SSL": 17122, + "▁Kaiser": 17123, + "▁recher": 17124, + "ygon": 17125, + "▁regardless": 17126, + "▁configur": 17127, + "▁unnecess": 17128, + "▁Clark": 17129, + "PHP": 17130, + "▁FALSE": 17131, + "▁pad": 17132, + "$}": 17133, + "▁valu": 17134, + "▁disease": 17135, + "▁maior": 17136, + "▁hommes": 17137, + "▁Edition": 17138, + "slant": 17139, + "▁ending": 17140, + "▁settled": 17141, + "urus": 17142, + "hed": 17143, + "Pattern": 17144, + "▁година": 17145, + "▁Philadel": 17146, + "tikzpicture": 17147, + "▁coal": 17148, + "▁sede": 17149, + "▁satisfies": 17150, + "▁trim": 17151, + "▁bat": 17152, + "▁américain": 17153, + "▁luglio": 17154, + "▁поча": 17155, + "ffff": 17156, + "▁Target": 17157, + "generate": 17158, + "▁Zie": 17159, + "ția": 17160, + "▁gard": 17161, + "▁workers": 17162, + "▁Job": 17163, + "▁urban": 17164, + "ahlen": 17165, + "▁Building": 17166, + "▁neu": 17167, + "▁chron": 17168, + "▁Earl": 17169, + "gro": 17170, + "USE": 17171, + "▁XII": 17172, + "▁wealth": 17173, + "inae": 17174, + "▁Бра": 17175, + "▁libert": 17176, + "iros": 17177, + ":$": 17178, + "lee": 17179, + "ieves": 17180, + "▁Justice": 17181, + "▁oil": 17182, + "▁Athlet": 17183, + "▁clo": 17184, + "Scale": 17185, + "▁lips": 17186, + "▁april": 17187, + "▁impression": 17188, + "▁perce": 17189, + "▁участи": 17190, + "vil": 17191, + "éch": 17192, + "▁equality": 17193, + "▁мет": 17194, + "▁annotation": 17195, + "ernal": 17196, + "▁Mach": 17197, + "▁intitul": 17198, + "problem": 17199, 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"▁Sciences": 17253, + "▁Kreis": 17254, + ".__": 17255, + "▁cinq": 17256, + "▁Auß": 17257, + "ithmet": 17258, + "itors": 17259, + "amazon": 17260, + "▁gap": 17261, + "▁ignored": 17262, + "adv": 17263, + "кої": 17264, + "▁часть": 17265, + "▁corpor": 17266, + "цер": 17267, + "▁crime": 17268, + "uous": 17269, + "▁налази": 17270, + "DataFrame": 17271, + "води": 17272, + "Ign": 17273, + "▁Lincoln": 17274, + "▁menos": 17275, + "▁Luft": 17276, + "▁Lind": 17277, + "▁Cook": 17278, + "▁materials": 17279, + "apped": 17280, + "ignore": 17281, + "▁откры": 17282, + "fried": 17283, + "▁gouvernement": 17284, + "▁fired": 17285, + "▁screenshot": 17286, + "сен": 17287, + "▁[(": 17288, + "▁организа": 17289, + "Graphics": 17290, + "▁проти": 17291, + "▁phen": 17292, + "craft": 17293, + "▁brain": 17294, + "▁Como": 17295, + "▁Everything": 17296, + "anes": 17297, + "IGN": 17298, + "▁nederbörd": 17299, + "▁Forest": 17300, + "zahl": 17301, + "▁Among": 17302, + "Qt": 17303, + "▁togg": 17304, + "▁variant": 17305, + "▁hill": 17306, + "писи": 17307, + "colon": 17308, + "▁dicembre": 17309, + "гор": 17310, + "▁Wind": 17311, + "ünstler": 17312, + "▁=\\": 17313, + "saved": 17314, + "▁nej": 17315, + "unte": 17316, + "utto": 17317, + "▁recens": 17318, + "▁sick": 17319, + "▁desen": 17320, + "UST": 17321, + "▁worst": 17322, + "▁Angel": 17323, + "odox": 17324, + "▁Province": 17325, + "▁Maz": 17326, + "▁agreement": 17327, + "▁Bass": 17328, + "▁segunda": 17329, + "onces": 17330, + "▁Linki": 17331, + "▁CL": 17332, + "▁já": 17333, + "itement": 17334, + "▁área": 17335, + "▁scalar": 17336, + "▁Рес": 17337, + "awt": 17338, + "sieme": 17339, + "▁juni": 17340, + "▁худож": 17341, + "ikus": 17342, + "▁lid": 17343, + "ppel": 17344, + "avi": 17345, + "▁balance": 17346, + "ipping": 17347, + "cussion": 17348, + "ческих": 17349, + "(\".": 17350, + "Also": 17351, + "▁whis": 17352, + "HOME": 17353, + "▁brown": 17354, + "▁día": 17355, + "▁può": 17356, + "plotlib": 17357, + "▁Jahrhunderts": 17358, + "DK": 17359, + "▁anchor": 17360, + "...]": 17361, + "▁Austria": 17362, + "▁marca": 17363, + "▁gez": 17364, + "iously": 17365, + "▁lazy": 17366, + "xa": 17367, + "▁Channel": 17368, + "▁neuen": 17369, + "das": 17370, + "▁searched": 17371, + "▁staat": 17372, + "▁Так": 17373, + "▁Josef": 17374, + "▁Sher": 17375, + "pois": 17376, + "▁enem": 17377, + "▁accessing": 17378, + "▁неко": 17379, + "▁furono": 17380, + "▁pseudo": 17381, + "?>": 17382, + "▁estadoun": 17383, + "▁Види": 17384, + "▁motiv": 17385, + "▁recall": 17386, + "isson": 17387, + "ób": 17388, + ")--": 17389, + "▁Erz": 17390, + "▁савез": 17391, + "Direct": 17392, + "соб": 17393, + "▁sho": 17394, + "völker": 17395, + "Ap": 17396, + "gens": 17397, + "ништво": 17398, + "▁Amsterdam": 17399, + "usk": 17400, + "пло": 17401, + "▁simulation": 17402, + "▁BC": 17403, + "▁Woj": 17404, + "autom": 17405, + "Alex": 17406, + "▁economic": 17407, + "гом": 17408, + "ikai": 17409, + "▁altre": 17410, + "▁'-": 17411, + "▁Weg": 17412, + "NotFound": 17413, + "йской": 17414, + "▁converting": 17415, + "phabet": 17416, + "atrice": 17417, + "bourne": 17418, + "alom": 17419, + "▁comparing": 17420, + "▁Zo": 17421, + "▁fla": 17422, + "вая": 17423, + "▁entra": 17424, + "▁charset": 17425, + "developers": 17426, + "ística": 17427, + "}>": 17428, + "▁Jazz": 17429, + "▁Howard": 17430, + "шта": 17431, + "▁clone": 17432, + "door": 17433, + "▁Pin": 17434, + "***": 17435, + "▁silent": 17436, + "ecycle": 17437, + "isce": 17438, + "▁mud": 17439, + "▁Display": 17440, + "▁lip": 17441, + "▁использова": 17442, + "▁characteristic": 17443, + "▁sb": 17444, + "firebase": 17445, + "▁Bew": 17446, + "Calendar": 17447, + "▁uso": 17448, + "èse": 17449, + "▁Rat": 17450, + "▁esper": 17451, + "▁throwing": 17452, + "▁rodz": 17453, + "▁yards": 17454, + "▁grass": 17455, + "▁marker": 17456, + "▁Kos": 17457, + "Theta": 17458, + "▁organis": 17459, + "kernel": 17460, + "▁personas": 17461, + "keep": 17462, + "▁exclaimed": 17463, + "oslav": 17464, + "▁Entertain": 17465, + "нер": 17466, + "▁inwon": 17467, + "▁Rand": 17468, + "reduce": 17469, + "fac": 17470, + "expression": 17471, + "yj": 17472, + "▁differenti": 17473, + "aglia": 17474, + "▁templates": 17475, + "▁mű": 17476, + "▁prv": 17477, + "▁mois": 17478, + "▁gewann": 17479, + "▁була": 17480, + "bibli": 17481, + "demo": 17482, + "▁Anderson": 17483, + "▁ред": 17484, + "▁porque": 17485, + "▁Pologne": 17486, + "▁trip": 17487, + "▁exemple": 17488, + "▁Internacional": 17489, + "▁као": 17490, + "Insert": 17491, + "general": 17492, + "SESSION": 17493, + "berga": 17494, + "hält": 17495, + "unas": 17496, + "мира": 17497, + "▁yields": 17498, + "mapsto": 17499, + "spot": 17500, + "▁+\\": 17501, + "лла": 17502, + "▁precisely": 17503, + "▁член": 17504, + "shadow": 17505, + "Are": 17506, + "unal": 17507, + "▁dispar": 17508, + "▁título": 17509, + "nest": 17510, + "▁Low": 17511, + "▁prot": 17512, + "▁Costa": 17513, + "named": 17514, + "▁gained": 17515, + "lesia": 17516, + "▁administration": 17517, + "Import": 17518, + "branch": 17519, + "▁sympath": 17520, + "voj": 17521, + "▁EC": 17522, + "▁municipio": 17523, + "▁animated": 17524, + "▁directories": 17525, + "▁roof": 17526, + "ząd": 17527, + "imet": 17528, + "proto": 17529, + "bla": 17530, + ":]": 17531, + "have": 17532, + "atem": 17533, + "▁ns": 17534, + "▁sector": 17535, + "three": 17536, + "owane": 17537, + "wers": 17538, + "ових": 17539, + "rence": 17540, + "▁extr": 17541, + "igten": 17542, + "▁occident": 17543, + "ță": 17544, + "▁eat": 17545, + "▁hydro": 17546, + "ubernetes": 17547, + "[@": 17548, + "▁Moon": 17549, + "▁Sho": 17550, + "▁elsewhere": 17551, + "üller": 17552, + "Upload": 17553, + "ланд": 17554, + "▁För": 17555, + "wissenschaft": 17556, + "KS": 17557, + "▁physics": 17558, + "tz": 17559, + "▁серед": 17560, + "▁Arbeit": 17561, + "▁мест": 17562, + "▁Gebiet": 17563, + "▁insect": 17564, + "Ah": 17565, + "izado": 17566, + "▁temple": 17567, + "▁annual": 17568, + "stad": 17569, + "▁habitat": 17570, + "▁AB": 17571, + "wort": 17572, + "▁repos": 17573, + "▁Neu": 17574, + "▁$(\".": 17575, + "Vorlage": 17576, + "▁reprezent": 17577, + "estanden": 17578, + "Intern": 17579, + ".`": 17580, + "▁failing": 17581, + "▁Material": 17582, + "▁effectively": 17583, + "телем": 17584, + "▁гла": 17585, + "▁nahm": 17586, + "▁differently": 17587, + "extension": 17588, + "▁Verm": 17589, + "enabled": 17590, + "configure": 17591, + "nio": 17592, + "ciones": 17593, + "▁Beach": 17594, + "сона": 17595, + "▁copying": 17596, + "▁україн": 17597, + "▁призна": 17598, + "zh": 17599, + "Desktop": 17600, + "▁sost": 17601, + "▁subsequently": 17602, + "▁Lehr": 17603, + "▁ó": 17604, + "lär": 17605, + "odor": 17606, + "phon": 17607, + "nc": 17608, + "iterator": 17609, + "▁эти": 17610, + "▁europé": 17611, + "▁Toronto": 17612, + "ódigo": 17613, + "▁posto": 17614, + "ffe": 17615, + "▁crew": 17616, + "▁Schwar": 17617, + "Sa": 17618, + "square": 17619, + "▁beside": 17620, + "▁Мі": 17621, + "▁ath": 17622, + "▁advent": 17623, + "cji": 17624, + "written": 17625, + "▁russ": 17626, + "rost": 17627, + "HI": 17628, + "▁dice": 17629, + "cca": 17630, + "▁dép": 17631, + "ply": 17632, + "bigg": 17633, + "ział": 17634, + "ütt": 17635, + "▁одно": 17636, + "JECT": 17637, + "ському": 17638, + "nos": 17639, + "mock": 17640, + "Launch": 17641, + "same": 17642, + "▁jobs": 17643, + "▁widely": 17644, + "▁defines": 17645, + "▁Pse": 17646, + "▁neighbour": 17647, + "ющие": 17648, + "▁closer": 17649, + "▁располо": 17650, + "▁clubs": 17651, + "fly": 17652, + "шим": 17653, + "▁suffered": 17654, + "▁nar": 17655, + "▁lavor": 17656, + "Extension": 17657, + "itionally": 17658, + "▁grace": 17659, + "▁Campeonato": 17660, + "▁Christmas": 17661, + "middle": 17662, + "othek": 17663, + "elements": 17664, + "▁sondern": 17665, + "▁tarde": 17666, + "▁permanent": 17667, + "▁conclude": 17668, + "Seg": 17669, + "▁акаде": 17670, + "}\",": 17671, + "▁февраля": 17672, + "řed": 17673, + "▁IL": 17674, + "jud": 17675, + "▁USS": 17676, + "▁Nature": 17677, + "ifference": 17678, + "Serializer": 17679, + "▁twelve": 17680, + "tid": 17681, + "мия": 17682, + "ческого": 17683, + "▁calendar": 17684, + "concat": 17685, + "▁intersection": 17686, + "▁PA": 17687, + "azure": 17688, + "▁située": 17689, + "▁kinds": 17690, + "▁ausge": 17691, + "▁rural": 17692, + "Theme": 17693, + "▁tale": 17694, + "noindent": 17695, + "going": 17696, + "rx": 17697, + "agi": 17698, + "wrapper": 17699, + "▁Coast": 17700, + "mbH": 17701, + "▁перед": 17702, + "spre": 17703, + "▁}\\": 17704, + "▁LI": 17705, + "znam": 17706, + "itled": 17707, + "Sample": 17708, + "uliar": 17709, + "*\\": 17710, + "▁resistance": 17711, + "stock": 17712, + "ked": 17713, + "▁HE": 17714, + "▁possession": 17715, + "▁Ring": 17716, + "▁magyar": 17717, + "outs": 17718, + "▁Secretary": 17719, + "nde": 17720, + "▁Wald": 17721, + "-(": 17722, + "▁ISO": 17723, + "▁afternoon": 17724, + "ionen": 17725, + "▁stops": 17726, + "▁constants": 17727, + "guard": 17728, + "bow": 17729, + "▁ers": 17730, + "▁Firebase": 17731, + "▁Clear": 17732, + "▁Holy": 17733, + "Win": 17734, + "▁titles": 17735, + "▁трав": 17736, + "▁contrib": 17737, + "häng": 17738, + "▁photograph": 17739, + "▁Distribution": 17740, + "ifts": 17741, + "▁aunque": 17742, + "comb": 17743, + "ADD": 17744, + "▁publication": 17745, + "▁служ": 17746, + "▁кня": 17747, + "▁ayant": 17748, + "▁restore": 17749, + "▁belief": 17750, + "▁vég": 17751, + "▁extensions": 17752, + "▁decom": 17753, + "вший": 17754, + "WT": 17755, + "▁parti": 17756, + "▁gioc": 17757, + "▁мира": 17758, + "▁issu": 17759, + "pipe": 17760, + "▁props": 17761, + "▁willing": 17762, + "▁nest": 17763, + "aso": 17764, + "pot": 17765, + "▁handles": 17766, + "▁фо": 17767, + "▁moder": 17768, + "▁ebenfalls": 17769, + "▁fighting": 17770, + "umbn": 17771, + "▁transparent": 17772, + "▁Krist": 17773, + "▁homes": 17774, + "▁voyage": 17775, + "Failed": 17776, + "▁Bird": 17777, + "▁Heart": 17778, + "Counter": 17779, + "▁Scottish": 17780, + "ática": 17781, + "▁arbeit": 17782, + "^{-\\": 17783, + "▁Sor": 17784, + "▁engaged": 17785, + "▁aside": 17786, + "▁Fou": 17787, + "▁wiel": 17788, + "▁reconst": 17789, + "ousin": 17790, + "▁hosted": 17791, + "▁classe": 17792, + "▁contest": 17793, + "...\"": 17794, + "мом": 17795, + "▁bean": 17796, + "gem": 17797, + "▁consultato": 17798, + "▁bio": 17799, + "▁subjects": 17800, + "boBox": 17801, + "▁Schrift": 17802, + "▁dinner": 17803, + "ăr": 17804, + "▁równ": 17805, + "▁%%": 17806, + "bage": 17807, + "▁veröff": 17808, + "▁detected": 17809, + "ienn": 17810, + "rose": 17811, + "▁Ton": 17812, + "Complete": 17813, + "▁proto": 17814, + "ichts": 17815, + "STAT": 17816, + "Checked": 17817, + "▁inten": 17818, + "▁smile": 17819, + "▁strip": 17820, + "neut": 17821, + "');\r": 17822, + "four": 17823, + "▁todas": 17824, + "Controls": 17825, + "▁thorough": 17826, + "rup": 17827, + "▁држави": 17828, + "ită": 17829, + "Protocol": 17830, + "Ка": 17831, + "▁expanded": 17832, + "extra": 17833, + "oport": 17834, + "▁Станов": 17835, + "leases": 17836, + "▁notion": 17837, + "▁guest": 17838, + 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"subfigure": 17892, + "èces": 17893, + "ziel": 17894, + "▁окон": 17895, + "EE": 17896, + "mee": 17897, + "▁perten": 17898, + "▁représent": 17899, + "▁LA": 17900, + "?'": 17901, + "▁тру": 17902, + "▁rational": 17903, + "osof": 17904, + "▁kne": 17905, + "▁artists": 17906, + "Flow": 17907, + "▁Аль": 17908, + "izard": 17909, + "▁numero": 17910, + "actic": 17911, + "▁destruct": 17912, + "▁Пра": 17913, + "onsieur": 17914, + "qt": 17915, + "abestanden": 17916, + "ność": 17917, + "Connect": 17918, + "▁oracle": 17919, + "▁Stockholm": 17920, + "sizeof": 17921, + "▁gemäß": 17922, + "ACT": 17923, + "▁expert": 17924, + "utions": 17925, + "▁hacia": 17926, + "▁logger": 17927, + "▁fool": 17928, + "rypto": 17929, + "ær": 17930, + "▁cidade": 17931, + "▁составе": 17932, + "oker": 17933, + "▁Transfer": 17934, + "▁denied": 17935, + "Track": 17936, + "▁radi": 17937, + "zec": 17938, + "▁Historic": 17939, + "▁Einwohner": 17940, + "кою": 17941, + "▁хра": 17942, + "▁Category": 17943, + "▁Disney": 17944, + "▁swap": 17945, + "Begin": 17946, + "▁mientras": 17947, + "▁dance": 17948, + "▁tête": 17949, + "▁droit": 17950, + "erta": 17951, + "▁birds": 17952, + "▁convin": 17953, + "parator": 17954, + "дра": 17955, + "▁ES": 17956, + "▁Ressources": 17957, + "EGIN": 17958, + "ücke": 17959, + "▁Cruz": 17960, + "abling": 17961, + "▁\"@": 17962, + "▁metres": 17963, + "▁Beg": 17964, + "▁Gründ": 17965, + "▁Boh": 17966, + "▁mile": 17967, + "▁Technology": 17968, + "\"+": 17969, + "acco": 17970, + "▁ss": 17971, + "▁Fed": 17972, + "▁Hend": 17973, + "usch": 17974, + "itä": 17975, + "folk": 17976, + "▁absor": 17977, + "antal": 17978, + "odge": 17979, + "▁WHEN": 17980, + "▁Externí": 17981, + "▁Regiment": 17982, + "▁evaluation": 17983, + "▁Tai": 17984, + "▁vocals": 17985, + "▁experimental": 17986, + "embed": 17987, + "▁Minn": 17988, + "▁вме": 17989, + "prec": 17990, + "every": 17991, + "▁hoof": 17992, + "▁Fernando": 17993, + "▁Bibliographie": 17994, + "▁nag": 17995, + "amerikanischer": 17996, + "▁marks": 17997, + "▁UTC": 17998, + "▁uncertain": 17999, + "дия": 18000, + "olia": 18001, + "▁cup": 18002, + "▁fille": 18003, + "▁dok": 18004, + "useppe": 18005, + "esterd": 18006, + "▁Brand": 18007, + "▁Third": 18008, + "PP": 18009, + "nodes": 18010, + "▁Pad": 18011, + "▁loved": 18012, + "swing": 18013, + "▁surprised": 18014, + "ardi": 18015, + "▁GR": 18016, + "]\"": 18017, + "▁equally": 18018, + "ihe": 18019, + "care": 18020, + "писок": 18021, + "lijk": 18022, + "rinn": 18023, + "▁\\[\\": 18024, + "▁sons": 18025, + "▁tät": 18026, + "icamente": 18027, + "▁listing": 18028, + "iellement": 18029, + "▁nyelven": 18030, + "▁ds": 18031, + "▁agricult": 18032, + "▁Hermann": 18033, + "▁besides": 18034, + "progress": 18035, + "▁peculiar": 18036, + "focus": 18037, + "cn": 18038, + "-$": 18039, + "ственный": 18040, + "ourg": 18041, + "▁wyn": 18042, + "▁conducted": 18043, + "▁Становништво": 18044, + "connected": 18045, + "▁bott": 18046, + "▁смер": 18047, + "▁Poz": 18048, + "unct": 18049, + "conda": 18050, + "▁савезној": 18051, + "▁havet": 18052, + "ligt": 18053, + "orted": 18054, + "▁entering": 18055, + "multip": 18056, + "▁Temple": 18057, + "▁Plant": 18058, + "typeof": 18059, + "▁Vlad": 18060, + "▁qued": 18061, + "▁reste": 18062, + "▁май": 18063, + "▁Very": 18064, + "ambiguation": 18065, + "▁challeng": 18066, + "▁respective": 18067, + "▁тор": 18068, + "Ctrl": 18069, + "▁absence": 18070, + "aru": 18071, + "вое": 18072, + "▁först": 18073, + "▁sq": 18074, + "▁Emperor": 18075, + "▁Ign": 18076, + "▁това": 18077, + ":`": 18078, + "adoop": 18079, + "▁Madame": 18080, + "▁gruppo": 18081, + "stud": 18082, + "▁externas": 18083, + "▁Александр": 18084, + "▁dign": 18085, + "▁живе": 18086, + "Amount": 18087, + "▁correlate": 18088, + "▁Fant": 18089, + "▁rails": 18090, + "fp": 18091, + "министратив": 18092, + "▁bought": 18093, + "▁filters": 18094, + "▁ancora": 18095, + "▁partner": 18096, + "▁quand": 18097, + "symbol": 18098, + "ulating": 18099, + "▁zd": 18100, + "awn": 18101, + "▁Grant": 18102, + "because": 18103, + "rable": 18104, + "\\}": 18105, + "ísticas": 18106, + "▁уче": 18107, + "▁période": 18108, + "▁ske": 18109, + "▁Anyway": 18110, + "▁indexes": 18111, + "▁directions": 18112, + "▁RAM": 18113, + "chrome": 18114, + "▁apost": 18115, + "▁warnings": 18116, + "▁Airport": 18117, + "VI": 18118, + "abile": 18119, + "▁lord": 18120, + "provider": 18121, + "▁Ji": 18122, + "ostream": 18123, + "▁gemeente": 18124, + "tableView": 18125, + "Extra": 18126, + "cursor": 18127, + "eground": 18128, + "▁Moz": 18129, + "▁rib": 18130, + "▁morph": 18131, + "loads": 18132, + "elsk": 18133, + "▁MAX": 18134, + "▁Santiago": 18135, + "▁Him": 18136, + "codes": 18137, + "▁lanz": 18138, + "▁counts": 18139, + "rinningsområ": 18140, + "щё": 18141, + "▁spé": 18142, + "▁pierws": 18143, + "▁Sver": 18144, + "▁acknow": 18145, + "Boolean": 18146, + "▁фамили": 18147, + "▁Senate": 18148, + "шов": 18149, + "agers": 18150, + "▁Nueva": 18151, + "bil": 18152, + "kiem": 18153, + "▁Mey": 18154, + "wij": 18155, + 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18582, + "▁destac": 18583, + "\");\r": 18584, + "▁Sure": 18585, + "▁partido": 18586, + "▁Folge": 18587, + "▁Moore": 18588, + "▁wz": 18589, + "скус": 18590, + "ltre": 18591, + "ondo": 18592, + "▁pose": 18593, + "imos": 18594, + "бой": 18595, + "ципа": 18596, + "jus": 18597, + ".....": 18598, + "▁época": 18599, + "▁quanto": 18600, + "▁Support": 18601, + "geschichte": 18602, + "SERVER": 18603, + "▁Georges": 18604, + "enum": 18605, + "▁herm": 18606, + "▁nebo": 18607, + "▁Chr": 18608, + "character": 18609, + "▁***": 18610, + "▁Forsch": 18611, + "iami": 18612, + "▁¿": 18613, + "cych": 18614, + "▁fifth": 18615, + "sent": 18616, + "▁anderem": 18617, + "▁proportion": 18618, + "▁prest": 18619, + "▁Girl": 18620, + "▁drama": 18621, + "wand": 18622, + "▁Mail": 18623, + "▁Lux": 18624, + "▁který": 18625, + "▁Gesellschaft": 18626, + "▁Hinweis": 18627, + "nisse": 18628, + "▁mondo": 18629, + "Eq": 18630, + "▁perí": 18631, + "▁eastern": 18632, + "▁UEFA": 18633, + "uale": 18634, + "▁convex": 18635, + "▁поль": 18636, + "▁Hey": 18637, + "zenie": 18638, + "initely": 18639, + "▁Zusammen": 18640, + "SSL": 18641, + "ocal": 18642, + "▁canal": 18643, + "voy": 18644, + "▁Кри": 18645, + "▁között": 18646, + "▁cars": 18647, + "▁versión": 18648, + "Environment": 18649, + "Her": 18650, + "▁señ": 18651, + "▁spatial": 18652, + "ymi": 18653, + "Fire": 18654, + "▁veget": 18655, + "▁Wie": 18656, + "▁znaj": 18657, + "▁damage": 18658, + "▁endl": 18659, + "gif": 18660, + "▁quali": 18661, + "▁которых": 18662, + "ellan": 18663, + "▁mens": 18664, + "▁plug": 18665, + "▁abund": 18666, + "FIG": 18667, + "▁sf": 18668, + "▁confl": 18669, + "▁населения": 18670, + "▁principles": 18671, + "▁Gabriel": 18672, + "ibe": 18673, + "▁{%": 18674, + "▁població": 18675, + "ніципа": 18676, + "▁extreme": 18677, + "▁asse": 18678, + "▁vu": 18679, + "Mock": 18680, + "▁spielte": 18681, + "▁Aer": 18682, + "▁datos": 18683, + "endes": 18684, + "▁Gel": 18685, + "▁Gor": 18686, + "Christ": 18687, + "chos": 18688, + "Processor": 18689, + "▁instruct": 18690, + "▁picked": 18691, + "nahme": 18692, + "fahr": 18693, + "▁indicated": 18694, + "▁%.": 18695, + "▁ts": 18696, + "▁notable": 18697, + "▁qualified": 18698, + "▁Ал": 18699, + "Black": 18700, + "▁council": 18701, + "▁overhead": 18702, + "aci": 18703, + "année": 18704, + "▁initWith": 18705, + "bió": 18706, + "▁introduction": 18707, + "▁companion": 18708, + "▁expon": 18709, + "▁kör": 18710, + "oby": 18711, + "burn": 18712, + "gnu": 18713, + "virtual": 18714, + "▁intellect": 18715, + "▁держа": 18716, + "'+": 18717, + "бле": 18718, + "▁strictly": 18719, + "▁recognize": 18720, + "hour": 18721, + "▁Wrest": 18722, + "ennen": 18723, + "$).": 18724, + "fff": 18725, + "▁Centro": 18726, + "▁Pitt": 18727, + "▁dział": 18728, + "▁cela": 18729, + "▁francese": 18730, + "рами": 18731, + "special": 18732, + "▁Dup": 18733, + "toire": 18734, + "каль": 18735, + "COUNT": 18736, + "▁Brook": 18737, + "▁руково": 18738, + "publique": 18739, + "▁seconda": 18740, + "▁compt": 18741, + "▁bland": 18742, + "Before": 18743, + "▁Pack": 18744, + "alty": 18745, + "öder": 18746, + "▁intervals": 18747, + "▁Datenbank": 18748, + "Movie": 18749, + "▁transm": 18750, + "▁tap": 18751, + "▁поч": 18752, + "fon": 18753, + "iai": 18754, + "▁fib": 18755, + "▁wyd": 18756, + "▁hung": 18757, + "▁alive": 18758, + "Clear": 18759, + "▁pushed": 18760, + "▁tuple": 18761, + "achen": 18762, + "гово": 18763, + "▁revers": 18764, + "▁augment": 18765, + "▁challenge": 18766, + "lost": 18767, + "▁deuxième": 18768, + "structor": 18769, + "▁mehrerer": 18770, + "atural": 18771, + "Split": 18772, + "стем": 18773, + "шла": 18774, + ")\\\\": 18775, + "▁Dog": 18776, + "▁developers": 18777, + "▁nod": 18778, + "▁сторо": 18779, + "▁NaN": 18780, + "▁priest": 18781, + "▁exha": 18782, + "UND": 18783, + "pair": 18784, + "alone": 18785, + "▁moon": 18786, + "▁#!/": 18787, + "▁guns": 18788, + "rola": 18789, + "чита": 18790, + "▁Encyclopedia": 18791, + "atis": 18792, + "▁'\"": 18793, + "zych": 18794, + "▁superfic": 18795, + "▁эк": 18796, + "едера": 18797, + "feed": 18798, + "LAY": 18799, + "Fi": 18800, + "unks": 18801, + "isecond": 18802, + "▁'@": 18803, + "▁Adding": 18804, + "рое": 18805, + "▁tang": 18806, + "цо": 18807, + "hung": 18808, + "bis": 18809, + "ského": 18810, + "▁advert": 18811, + "▁занима": 18812, + "uzz": 18813, + "ágina": 18814, + "▁Tel": 18815, + "sig": 18816, + "▁Ez": 18817, + "▁guarantee": 18818, + "▁teaching": 18819, + "oty": 18820, + "termin": 18821, + "▁distributions": 18822, + "FLA": 18823, + "▁Giuseppe": 18824, + "querySelector": 18825, + "▁/\\": 18826, + "▁Squad": 18827, + "gz": 18828, + "delay": 18829, + "▁surrounding": 18830, + "▁manus": 18831, + "▁Hou": 18832, + "²,": 18833, + "▁cultiv": 18834, + "▁troubles": 18835, + "▁raison": 18836, + "expand": 18837, + "▁cov": 18838, + "nungen": 18839, + ")){": 18840, + "▁geen": 18841, + "▁außer": 18842, + "▁Лі": 18843, + "ři": 18844, + "▁situations": 18845, + "▁telep": 18846, + "▁Jed": 18847, + "▁travail": 18848, + "lias": 18849, + 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"▁assumptions": 20813, + "▁Spark": 20814, + "▁Lot": 20815, + "itudes": 20816, + "afka": 20817, + "five": 20818, + "atio": 20819, + "▁distinguish": 20820, + "rock": 20821, + "église": 20822, + "▁rappres": 20823, + ">\\<": 20824, + "лій": 20825, + "▁мини": 20826, + "▁intitulé": 20827, + "}}(\\": 20828, + "▁Rout": 20829, + "▁Border": 20830, + "▁overrid": 20831, + "HOST": 20832, + "ritten": 20833, + "say": 20834, + "▁Чи": 20835, + "ichtung": 20836, + "▁straightforward": 20837, + "obb": 20838, + "▁Terra": 20839, + "▁[:": 20840, + "Ben": 20841, + "▁composite": 20842, + ")+\\": 20843, + "▁crown": 20844, + "direction": 20845, + "▁несколько": 20846, + "▁avail": 20847, + "▁purchased": 20848, + "hook": 20849, + "eties": 20850, + "▁fase": 20851, + "▁Rum": 20852, + "▁genom": 20853, + "▁dét": 20854, + "ową": 20855, + "mpeg": 20856, + "▁Ін": 20857, + "desktop": 20858, + "▁injection": 20859, + "agle": 20860, + "▁Edd": 20861, + "_{(": 20862, + "▁Hem": 20863, + "utos": 20864, + "proj": 20865, + 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"▁Stanis": 21585, + "▁Ajax": 21586, + "escape": 21587, + "▁consp": 21588, + "▁Chen": 21589, + "▁Naval": 21590, + "Bit": 21591, + "▁bât": 21592, + "скими": 21593, + "drive": 21594, + "▁Round": 21595, + "photo": 21596, + "▁Level": 21597, + "▁geg": 21598, + "Tom": 21599, + "▁Mobile": 21600, + "▁Trop": 21601, + "Direction": 21602, + "isan": 21603, + ")^{-": 21604, + "▁Setting": 21605, + "▁Probably": 21606, + "лья": 21607, + "▁assets": 21608, + "▁atte": 21609, + "▁bulk": 21610, + "ést": 21611, + "▁wing": 21612, + "nius": 21613, + "▁wins": 21614, + "▁lud": 21615, + "ushing": 21616, + "▁deven": 21617, + "ограф": 21618, + "burger": 21619, + "▁embar": 21620, + "FilterChain": 21621, + "▁tum": 21622, + "▁öss": 21623, + "▁nommé": 21624, + "▁pir": 21625, + "▁luc": 21626, + "dbo": 21627, + "agues": 21628, + "▁alcan": 21629, + "ouwen": 21630, + "▁Stanley": 21631, + "циали": 21632, + "▁grown": 21633, + "▁preserved": 21634, + "▁solar": 21635, + "▁Население": 21636, + "▁performances": 21637, + "▁Cow": 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"▁Replace": 22108, + "▁lé": 22109, + "Who": 22110, + "▁Estad": 22111, + "▁dimensional": 22112, + "▁öff": 22113, + "▁grands": 22114, + "джа": 22115, + "plane": 22116, + "ності": 22117, + "▁Origin": 22118, + "WI": 22119, + "änner": 22120, + "▁Cry": 22121, + "ITION": 22122, + "▁född": 22123, + "▁cultura": 22124, + "▁Rank": 22125, + "▁vuel": 22126, + "▁zag": 22127, + "▁Maxim": 22128, + "ону": 22129, + "()))": 22130, + "Raw": 22131, + "kirche": 22132, + "▁además": 22133, + "▁tie": 22134, + "▁Style": 22135, + "сков": 22136, + "istant": 22137, + "olph": 22138, + "▁Zür": 22139, + "▁Info": 22140, + "DOM": 22141, + "usc": 22142, + "nahm": 22143, + "▁Федера": 22144, + "▁Fot": 22145, + "▁specifying": 22146, + "▁titolo": 22147, + "▁Boys": 22148, + "iech": 22149, + "Place": 22150, + "▁Hoff": 22151, + "▁cached": 22152, + "валь": 22153, + "isher": 22154, + "rolling": 22155, + "opens": 22156, + "▁hr": 22157, + "------": 22158, + "▁maggior": 22159, + "▁transactions": 22160, + "▁criminal": 22161, + 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"▁kilometres": 24016, + "▁Однако": 24017, + "кли": 24018, + "▁Sri": 24019, + "Groups": 24020, + "mind": 24021, + "oslov": 24022, + "fern": 24023, + "egu": 24024, + "abeled": 24025, + "Fiddle": 24026, + "▁Century": 24027, + "/-": 24028, + "▁Jegyzetek": 24029, + "Hen": 24030, + "ensemble": 24031, + "▁Gut": 24032, + "_{{\\": 24033, + "▁ranking": 24034, + "+$": 24035, + "ала": 24036, + "▁#{": 24037, + "imientos": 24038, + "achim": 24039, + "rides": 24040, + "▁Klaus": 24041, + "▁intend": 24042, + "▁Kentucky": 24043, + "cipe": 24044, + "▁Dienst": 24045, + "▁situated": 24046, + "▁póź": 24047, + "▁scrit": 24048, + "clip": 24049, + "нет": 24050, + "tables": 24051, + "▁Nied": 24052, + "▁McK": 24053, + "▁powst": 24054, + "▁kunnen": 24055, + "▁Evans": 24056, + "жды": 24057, + "вать": 24058, + "uchar": 24059, + "▁residents": 24060, + "iak": 24061, + "▁Resol": 24062, + "▁veces": 24063, + "▁satisfying": 24064, + "INF": 24065, + "▁син": 24066, + "▁crossing": 24067, + "iben": 24068, + "▁широ": 24069, + "pto": 24070, + "ILL": 24071, + "▁роль": 24072, + "▁aktiv": 24073, + "▁обращения": 24074, + "Wikispecies": 24075, + "▁Höhe": 24076, + "cro": 24077, + "════": 24078, + "altra": 24079, + "▁FILE": 24080, + "▁ups": 24081, + "▁allocation": 24082, + "Michael": 24083, + "▁acknowled": 24084, + "Linux": 24085, + "▁metros": 24086, + "tte": 24087, + "afen": 24088, + "▁xcode": 24089, + "▁тради": 24090, + "species": 24091, + "▁injury": 24092, + "▁самы": 24093, + "▁lattice": 24094, + "Material": 24095, + "andenburg": 24096, + "▁huvudstaden": 24097, + "story": 24098, + "▁varying": 24099, + "▁követ": 24100, + "▁Российской": 24101, + "irse": 24102, + "▁drum": 24103, + "Pressed": 24104, + "Lar": 24105, + "▁Agu": 24106, + "▁weil": 24107, + "▁commence": 24108, + "▁Según": 24109, + "Gesture": 24110, + "Shape": 24111, + "▁Vors": 24112, + "▁succès": 24113, + "▁corrected": 24114, + "Kar": 24115, + "▁cruel": 24116, + "▁politico": 24117, + "▁Schriftsteller": 24118, + "▁risult": 24119, + "etu": 24120, + 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"▁dd": 24488, + "▁соответ": 24489, + "▁IOException": 24490, + "ských": 24491, + "▁CLI": 24492, + "▁ње": 24493, + "CM": 24494, + "TD": 24495, + "▁possibilities": 24496, + "▁Compos": 24497, + "half": 24498, + "▁webpage": 24499, + "▁swing": 24500, + "▁zas": 24501, + "▁cycl": 24502, + "leid": 24503, + "istica": 24504, + "▁Insert": 24505, + "▁Sweden": 24506, + "▁wanting": 24507, + "▁ال": 24508, + "▁eeuw": 24509, + "▁Administr": 24510, + "▁Warren": 24511, + "▁bs": 24512, + "▁pam": 24513, + "anus": 24514, + "Dra": 24515, + "expl": 24516, + "▁Kant": 24517, + "▁Austin": 24518, + "▁csak": 24519, + "▁theatre": 24520, + "▁compatibility": 24521, + "матиче": 24522, + "setState": 24523, + "бю": 24524, + "}{|": 24525, + "▁Dy": 24526, + "▁Zwischen": 24527, + "Alt": 24528, + "CLARE": 24529, + "steps": 24530, + "▁Lage": 24531, + "▁Mitt": 24532, + "▁Dublin": 24533, + "▁работы": 24534, + "deep": 24535, + "▁flows": 24536, + "▁Palace": 24537, + "unix": 24538, + "refs": 24539, + "umar": 24540, + "aset": 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24806, + "тики": 24807, + "▁checkout": 24808, + "▁assess": 24809, + "▁Population": 24810, + "urent": 24811, + "clojure": 24812, + "▁Santos": 24813, + "▁információ": 24814, + "POS": 24815, + "▁gare": 24816, + "▁kick": 24817, + "▁radical": 24818, + "▁Peace": 24819, + "▁streaming": 24820, + "camp": 24821, + "ząt": 24822, + "говор": 24823, + "▁Regierung": 24824, + "▁proceeded": 24825, + "fm": 24826, + "лены": 24827, + "▁earnest": 24828, + "▁Parad": 24829, + "requests": 24830, + "▁Raum": 24831, + "šč": 24832, + "▁policies": 24833, + "▁Tig": 24834, + "▁sitt": 24835, + "▁Energy": 24836, + "▁purely": 24837, + "▁Haut": 24838, + "▁Speed": 24839, + "bio": 24840, + "▁orange": 24841, + "▁biggest": 24842, + "▁britannique": 24843, + "▁Notable": 24844, + "vu": 24845, + "лении": 24846, + "бин": 24847, + "▁Nash": 24848, + "щение": 24849, + "▁ciel": 24850, + "adémie": 24851, + "▁грудня": 24852, + "▁joue": 24853, + "▁voted": 24854, + "rico": 24855, + "▁гор": 24856, + "▁команду": 24857, + "itivity": 24858, 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"▁solem": 25070, + "closure": 25071, + "ignation": 25072, + "łod": 25073, + "▁divor": 25074, + "▁международ": 25075, + "=\"": 25230, + "Orientation": 25231, + "cid": 25232, + "Cart": 25233, + "▁murm": 25234, + "▁assez": 25235, + "▁linking": 25236, + "building": 25237, + "▁reconna": 25238, + "▁shook": 25239, + "managed": 25240, + "landa": 25241, + "▁León": 25242, + "▁création": 25243, + "дой": 25244, + "ocity": 25245, + "▁wij": 25246, + "▁wieś": 25247, + "xtart": 25248, + "▁Move": 25249, + "lungen": 25250, + "ствует": 25251, + "orney": 25252, + "optional": 25253, + "macro": 25254, + "Condition": 25255, + "▁squares": 25256, + "▁mistaken": 25257, + "ánt": 25258, + "▁Ris": 25259, + "▁sentences": 25260, + "erea": 25261, + "▁mij": 25262, + "Und": 25263, + "▁nombr": 25264, + "zA": 25265, + "▁Independent": 25266, + "▁preview": 25267, + "imas": 25268, + "▁males": 25269, + "inental": 25270, + "Thank": 25271, + "▁popol": 25272, + "▁pover": 25273, + "▁grasp": 25274, + "▁imped": 25275, + "▁campionato": 25276, + "▁Wei": 25277, + "▁titled": 25278, + "▁Además": 25279, + "▁Password": 25280, + "▁Pam": 25281, + "UILD": 25282, + "▁липня": 25283, + "werb": 25284, + "................": 25285, + "▁Río": 25286, + "▁teeth": 25287, + "bp": 25288, + "▁SW": 25289, + "ulaire": 25290, + "▁seized": 25291, + "▁Stef": 25292, + "úl": 25293, + "▁viz": 25294, + "iony": 25295, + "▁junt": 25296, + "▁která": 25297, + "▁września": 25298, + "<>": 25299, + "▁surg": 25300, + "▁tutte": 25301, + "▁Hob": 25302, + "повід": 25303, + "▁wohl": 25304, + "▁trag": 25305, + "▁Crown": 25306, + "▁trova": 25307, + "стову": 25308, + "▁Vienna": 25309, + "esehen": 25310, + "▁metropol": 25311, + "▁reflected": 25312, + "тета": 25313, + "▁traduc": 25314, + "▁Bast": 25315, + "▁erschien": 25316, + "woord": 25317, + "()\"": 25318, + "talet": 25319, + "▁roads": 25320, + "ведения": 25321, + "ührung": 25322, + "▁cogn": 25323, + "▁Valle": 25324, + "▁landing": 25325, + "▁Regex": 25326, + "▁Iowa": 25327, + "dział": 25328, + "▁erreichte": 25329, + "aum": 25330, + "▁founder": 25331, + "apolis": 25332, + "Compiler": 25333, + "▁kop": 25334, + "▁marc": 25335, + "▁територ": 25336, + "))`": 25337, + "▁lei": 25338, + "geon": 25339, + "▁weapons": 25340, + "▁horn": 25341, + "▁elif": 25342, + "▁Capital": 25343, + "će": 25344, + "▁forall": 25345, + "▁эта": 25346, + "preview": 25347, + "▁DNA": 25348, + "▁sid": 25349, + "orch": 25350, + "▁Ras": 25351, + "▁arab": 25352, + "Best": 25353, + "▁счита": 25354, + "▁López": 25355, + "ança": 25356, + "▁funkc": 25357, + "▁tienen": 25358, + ";&": 25359, + "museum": 25360, + "▁Err": 25361, + "▁resort": 25362, + "Nov": 25363, + "▁kal": 25364, + "MW": 25365, + "шь": 25366, + "anchor": 25367, + "▁роман": 25368, + "leading": 25369, + "▁manten": 25370, + "▁Silva": 25371, + "dade": 25372, + "▁designated": 25373, + "▁revista": 25374, + "Oct": 25375, + "percent": 25376, + "▁уні": 25377, + "identifier": 25378, + "mass": 25379, + "@@": 25380, + "ulsion": 25381, + "germeister": 25382, + "▁predicted": 25383, + "▁сви": 25384, + "жной": 25385, + "▁Ergeb": 25386, + "▁cust": 25387, + "▁removes": 25388, + "charg": 25389, + "пример": 25390, + "▁forming": 25391, + "asma": 25392, + "stdout": 25393, + "Fun": 25394, + "yme": 25395, + "tered": 25396, + "ursive": 25397, + "ighed": 25398, + "▁след": 25399, + "verband": 25400, + "▁LOG": 25401, + "rams": 25402, + "éon": 25403, + "endra": 25404, + "▁Bereich": 25405, + "▁temporal": 25406, + "▁langue": 25407, + "▁Inn": 25408, + "▁moreover": 25409, + "▁tutorials": 25410, + "Middle": 25411, + "▁советский": 25412, + "▁maintenance": 25413, + "asures": 25414, + "▁válto": 25415, + "BASE": 25416, + "▁disappear": 25417, + "ския": 25418, + "▁conocido": 25419, + "▁Нау": 25420, + "▁Libert": 25421, + "▁Harold": 25422, + "▁lifetime": 25423, + "▁Tür": 25424, + "▁zawod": 25425, + "omic": 25426, + "▁Retrieved": 25427, + "architecture": 25428, + "čka": 25429, + "iformes": 25430, + "development": 25431, + "ordnung": 25432, + "Inf": 25433, + "leben": 25434, + "▁Stars": 25435, + "signal": 25436, + "▁grammar": 25437, + "▁corso": 25438, + "▁Wagner": 25439, + "▁geht": 25440, + "▁royale": 25441, + "warn": 25442, + "umbled": 25443, + "▁instit": 25444, + "▁Ши": 25445, + "hh": 25446, + "▁refuge": 25447, + "▁favorite": 25448, + "ierto": 25449, + "▁condado": 25450, + "▁Ther": 25451, + "▁человека": 25452, + "▁Food": 25453, + "▁seizo": 25454, + "▁Initialize": 25455, + "▁connu": 25456, + "▁overlap": 25457, + "▁Emil": 25458, + "▁Martí": 25459, + "▁жовтня": 25460, + "erva": 25461, + "▁boats": 25462, + "ações": 25463, + "▁derrot": 25464, + "▁malloc": 25465, + "▁conject": 25466, + "jk": 25467, + "▁sare": 25468, + "лемен": 25469, + "▁sums": 25470, + "Authorization": 25471, + "▁Kun": 25472, + "]$,": 25473, + "gemeinde": 25474, + "odot": 25475, + "defin": 25476, + "▁emission": 25477, + "▁Крас": 25478, + "▁appart": 25479, + "▁stopping": 25480, + "▁Сред": 25481, + "▁conjug": 25482, + "▁insight": 25483, + "▁Broadcast": 25484, + "▁PMID": 25485, + "▁advantages": 25486, + "enes": 25487, + "▁residence": 25488, + "ljen": 25489, + "isseur": 25490, + "▁pubblicato": 25491, + "▁GitHub": 25492, + "▁Peru": 25493, + "▁galaxies": 25494, + "▁annotations": 25495, + "gas": 25496, + "▁répond": 25497, + "Js": 25498, + "▁independently": 25499, + "NP": 25500, + "▁inqu": 25501, + "▁grounds": 25502, + "Components": 25503, + "▁anten": 25504, + "▁вз": 25505, + "▁hos": 25506, + "▁sint": 25507, + "▁hiding": 25508, + "▁województ": 25509, + "Messages": 25510, + "▁показа": 25511, + "===": 25512, + "▁Abstract": 25513, + "▁läng": 25514, + "▁Formula": 25515, + "dawn": 25516, + "▁designs": 25517, + "Img": 25518, + "▁Portuguese": 25519, + "▁incluy": 25520, + "avigator": 25521, + "▁Brothers": 25522, + "▁continent": 25523, + "▁evidently": 25524, + "race": 25525, + "цького": 25526, + "▁reck": 25527, + "▁серпня": 25528, + "▁Grey": 25529, + "▁appeal": 25530, + "▁unlike": 25531, + "▁powershell": 25532, + "▁racc": 25533, + "fers": 25534, + "▁burning": 25535, + "fasst": 25536, + "installed": 25537, + "▁Give": 25538, + "▁colonial": 25539, + "▁€": 25540, + "▁Rö": 25541, + "▁christ": 25542, + "nehm": 25543, + "там": 25544, + "▁corpo": 25545, + "▁convirti": 25546, + "yter": 25547, + "Sym": 25548, + "▁Greece": 25549, + "▁moth": 25550, + "▁Johan": 25551, + "▁monarch": 25552, + "▁Download": 25553, + "▁craft": 25554, + "už": 25555, + "▁Luke": 25556, + "▁suffix": 25557, + "\\/": 25558, + "Have": 25559, + "▁карь": 25560, + "▁comfortable": 25561, + "▁tips": 25562, + "▁Після": 25563, + "▁броја": 25564, + "▁информа": 25565, + "MQ": 25566, + "бран": 25567, + "▁tx": 25568, + "▁slaves": 25569, + "▁firewall": 25570, + "▁Forces": 25571, + "atif": 25572, + "▁Quellen": 25573, + "▁théâtre": 25574, + "льных": 25575, + "▁расположен": 25576, + "▁Details": 25577, + "ką": 25578, + "▁longitud": 25579, + "INST": 25580, + "▁naval": 25581, + "Fernseh": 25582, + "essel": 25583, + "Grad": 25584, + "▁belang": 25585, + "▁aggi": 25586, + "ZygoteInit": 25587, + "łów": 25588, + 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"▁Calendar": 26431, + "▁Madonna": 26432, + "WP": 26433, + "oshi": 26434, + "▁Maven": 26435, + "▁belle": 26436, + "▁wx": 26437, + "▁sugar": 26438, + "▁Betrieb": 26439, + "▁equilibrium": 26440, + "EAR": 26441, + "▁texts": 26442, + "слов": 26443, + "▁czerwca": 26444, + "▁Düsseld": 26445, + "▁ELSE": 26446, + "▁amery": 26447, + "▁ani": 26448, + "▁obey": 26449, + "▁Nell": 26450, + "▁inne": 26451, + "▁тро": 26452, + "FD": 26453, + "cco": 26454, + "▁Zob": 26455, + "alette": 26456, + "▁május": 26457, + "ected": 26458, + "▁Turkey": 26459, + "▁Whether": 26460, + "qi": 26461, + "▁што": 26462, + "▁headquarters": 26463, + "endi": 26464, + "arus": 26465, + "opus": 26466, + "▁золо": 26467, + "▁destru": 26468, + "▁Lok": 26469, + "▁satisfaction": 26470, + "()\r": 26471, + "▁Тер": 26472, + "Jose": 26473, + "▁conquer": 26474, + "▁Effect": 26475, + "LayoutParams": 26476, + "iez": 26477, + "▁externs": 26478, + "▁gegenüber": 26479, + "▁ESP": 26480, + "olta": 26481, + "processor": 26482, + "▁Kult": 26483, + "▁Atlanta": 26484, + "▁tier": 26485, + "Operator": 26486, + "▁диа": 26487, + "▁пись": 26488, + "▁groß": 26489, + "▁hearts": 26490, + "▁millimeter": 26491, + "although": 26492, + "alles": 26493, + "▁Magic": 26494, + "training": 26495, + "oline": 26496, + "▁органі": 26497, + ">\\<^": 26498, + "ціаль": 26499, + "exports": 26500, + "Workbook": 26501, + "▁вересня": 26502, + "▁teles": 26503, + "▁economy": 26504, + "▁trap": 26505, + "▁refuse": 26506, + "▁stranger": 26507, + "▁instinct": 26508, + "пода": 26509, + "olan": 26510, + "▁ning": 26511, + "inflate": 26512, + "itatea": 26513, + "acks": 26514, + "▁Joy": 26515, + "FLAG": 26516, + "ailand": 26517, + "▁sorti": 26518, + "▁впер": 26519, + "▁pén": 26520, + "Nothing": 26521, + "▁száz": 26522, + "▁Áng": 26523, + "▁AUT": 26524, + "Actions": 26525, + "Every": 26526, + "▁червня": 26527, + "▁автомо": 26528, + "▁routine": 26529, + "▁estruct": 26530, + "▁Gang": 26531, + "▁holes": 26532, + "thesis": 26533, + "▁concl": 26534, + "▁pé": 26535, + "riers": 26536, + "ровой": 26537, + "adic": 26538, + "Speed": 26539, + "▁commanded": 26540, + "▁Nazionale": 26541, + "Managed": 26542, + "▁DECLARE": 26543, + "▁sedan": 26544, + "Strings": 26545, + "▁sacred": 26546, + "tersuch": 26547, + "▁abitanti": 26548, + "brit": 26549, + "▁NCAA": 26550, + "▁СП": 26551, + "▁aged": 26552, + "▁Chiesa": 26553, + "▁revision": 26554, + "opro": 26555, + "▁overwrite": 26556, + "embros": 26557, + "▁sortie": 26558, + "▁otten": 26559, + "xiv": 26560, + "▁deli": 26561, + "▁Asp": 26562, + "▁balls": 26563, + "kaf": 26564, + "▁brave": 26565, + "▁всего": 26566, + "egn": 26567, + "jpeg": 26568, + "▁Osten": 26569, + "Constants": 26570, + "▁Infantry": 26571, + "▁Nev": 26572, + "▁яких": 26573, + "▁муниципа": 26574, + "cija": 26575, + "▁poem": 26576, + "▁negro": 26577, + "хар": 26578, + "▁Ask": 26579, + "▁avo": 26580, + "▁Meyer": 26581, + "▁Westen": 26582, + "▁oko": 26583, + "agin": 26584, + "▁Süden": 26585, + "entries": 26586, + "▁Republik": 26587, + "CollectionView": 26588, + "-------": 26589, + "▁firefox": 26590, + "▁alcune": 26591, + "▁фото": 26592, + "▁отрима": 26593, + "~~~~~~~~": 26594, + "▁Раз": 26595, + "▁Complex": 26596, + "▁pia": 26597, + "▁publicada": 26598, + "wei": 26599, + "cedure": 26600, + "occupation": 26601, + "▁medicine": 26602, + "▁drove": 26603, + "Problem": 26604, + "▁beginner": 26605, + "▁thoroughly": 26606, + "uria": 26607, + "avant": 26608, + "ucha": 26609, + "▁lever": 26610, + "▁teatro": 26611, + "AVA": 26612, + "squ": 26613, + "trat": 26614, + "ivatal": 26615, + "▁dirty": 26616, + "▁seconde": 26617, + "▁gravit": 26618, + "▁proposition": 26619, + "hbar": 26620, + "omini": 26621, + "▁”": 26622, + "▁Camil": 26623, + "▁queen": 26624, + "modifier": 26625, + "Jan": 26626, + "▁lyr": 26627, + "ComboBox": 26628, + "ionic": 26629, + "▁holy": 26630, + "▁Sebastian": 26631, + "|_{": 26632, + "▁{@": 26633, + "▁можно": 26634, + "▁Creative": 26635, + "▁interess": 26636, + "▁CT": 26637, + "ições": 26638, + "▁chant": 26639, + "▁współ": 26640, + "▁Мексика": 26641, + "▁ranked": 26642, + "▁października": 26643, + "▁brut": 26644, + "▁farther": 26645, + "▁Verb": 26646, + "▁Seven": 26647, + "lbl": 26648, + "▁mentions": 26649, + "▁Fight": 26650, + "ifen": 26651, + "▁bog": 26652, + "▁regres": 26653, + "▁scoring": 26654, + "icane": 26655, + "▁Elli": 26656, + "▁pierw": 26657, + "measure": 26658, + "ńskiej": 26659, + "#{": 26660, + "▁деся": 26661, + "▁varmaste": 26662, + "▁Unix": 26663, + "IZ": 26664, + "itié": 26665, + "Primary": 26666, + "▁Springer": 26667, + "üng": 26668, + "▁anv": 26669, + "▁versione": 26670, + "▁shoulders": 26671, + "▁брига": 26672, + "▁jav": 26673, + "ltal": 26674, + "▁kallaste": 26675, + "▁Mitchell": 26676, + "▁wireless": 26677, + "▁Ál": 26678, + "respons": 26679, + "could": 26680, + "▁relax": 26681, + "Lond": 26682, + "ńcz": 26683, + "ствовал": 26684, + "▁polski": 26685, + "enç": 26686, + "zar": 26687, + "▁dtype": 26688, + "owned": 26689, + "unknown": 26690, + "▁mutable": 26691, + "▁siempre": 26692, + "▁Montreal": 26693, + "▁locate": 26694, + "▁traces": 26695, + "▁insgesamt": 26696, + "▁Nil": 26697, + "▁прода": 26698, + "▁Warner": 26699, + "▁Nau": 26700, + "triangle": 26701, + "▁concentration": 26702, + "▁gentlemen": 26703, + "ächt": 26704, + "filters": 26705, + "incipal": 26706, + "VALID": 26707, + "▁депута": 26708, + "adó": 26709, + "▁konst": 26710, + "gså": 26711, + "agas": 26712, + "▁meilleur": 26713, + "▁данным": 26714, + "єдна": 26715, + "encoded": 26716, + "<'": 26717, + "▁sheets": 26718, + "cuador": 26719, + "▁використову": 26720, + "▁Deput": 26721, + "▁manière": 26722, + "ąg": 26723, + "csol": 26724, + ")$-": 26725, + "UIView": 26726, + "▁millones": 26727, + "▁Ehren": 26728, + "Sil": 26729, + "▁atac": 26730, + "▁Cold": 26731, + "\"\\": 26732, + "▁approached": 26733, + "▁Årsmed": 26734, + "WM": 26735, + "▁Deport": 26736, + "mis": 26737, + "andbox": 26738, + "observ": 26739, + "setting": 26740, + "ható": 26741, + "▁strat": 26742, + "▁spre": 26743, + "▁personne": 26744, + "▁dirige": 26745, + "pull": 26746, + "dating": 26747, + "▁Fact": 26748, + "▁manipulate": 26749, + "▁MAC": 26750, + "▁dej": 26751, + "ultimo": 26752, + "FX": 26753, + "Life": 26754, + "▁crack": 26755, + "▁mí": 26756, + "▁пове": 26757, + "▁wore": 26758, + "université": 26759, + "▁formulas": 26760, + "▁Elisabeth": 26761, + "plots": 26762, + "mile": 26763, + "▁menor": 26764, + "тил": 26765, + "keyword": 26766, + "▁Baltimore": 26767, + "hrer": 26768, + "▁Clement": 26769, + "vim": 26770, + "rass": 26771, + "Take": 26772, + "▁című": 26773, + "▁Convention": 26774, + "atge": 26775, + "seed": 26776, + "▁Dí": 26777, + "▁Spider": 26778, + "ahoo": 26779, + "▁имеет": 26780, + "ührt": 26781, + "▁пописа": 26782, + "▁Cot": 26783, + "▁nobles": 26784, + "RESS": 26785, + "▁chemin": 26786, + "▁główn": 26787, + "GG": 26788, + "▁Germania": 26789, + "▁Alexandre": 26790, + "hens": 26791, + "swift": 26792, + "oop": 26793, + "Subview": 26794, + "▁requiring": 26795, + "ędzy": 26796, + "▁fict": 26797, + "▁Констан": 26798, + "▁déput": 26799, + "▁surprising": 26800, + "▁deix": 26801, + "▁unterschied": 26802, + "inson": 26803, + "▁Character": 26804, + "▁gestion": 26805, + "chus": 26806, + "comes": 26807, + "▁neur": 26808, + "▁yeux": 26809, + "ollar": 26810, + "▁parad": 26811, + "▁maggiore": 26812, + "TRAN": 26813, + "▁votre": 26814, + "▁descent": 26815, + "▁Icon": 26816, + "▁Judge": 26817, + "▁occupation": 26818, + "eping": 26819, + "▁tongue": 26820, + "▁Enllaços": 26821, + "ruf": 26822, + "▁protein": 26823, + "▁visitors": 26824, + "axy": 26825, + "esten": 26826, + "blica": 26827, + "hw": 26828, + "▁spirits": 26829, + "▁reduces": 26830, + "▁мен": 26831, + "▁Lamb": 26832, + "▁Mine": 26833, + "▁verified": 26834, + "▁Baby": 26835, + "▁prize": 26836, + "вър": 26837, + "▁ratings": 26838, + "▁fore": 26839, + "asha": 26840, + "urrence": 26841, + "▁intér": 26842, + "▁Olímp": 26843, + "cra": 26844, + "▁computational": 26845, + "irche": 26846, + ".: ": 26847, + "▁illustrated": 26848, + "▁Share": 26849, + "▁households": 26850, + "▁convolution": 26851, + "oemd": 26852, + "▁zdoby": 26853, + "ccc": 26854, + "▁quantities": 26855, + "Che": 26856, + "Should": 26857, + "▁genius": 26858, + "adj": 26859, + "хва": 26860, + "Петер": 26861, + "EMA": 26862, + "▁Rights": 26863, + "▁Eli": 26864, + "VAR": 26865, + "шло": 26866, + "▁збір": 26867, + "iftung": 26868, + "▁contributed": 26869, + "zef": 26870, + "▁CHAR": 26871, + "▁Sib": 26872, + "▁Mant": 26873, + "▁связи": 26874, + "▁javafx": 26875, + "▁cependant": 26876, + "▁intu": 26877, + "▁твор": 26878, + "▁Ó": 26879, + "guer": 26880, + "rado": 26881, + "▁Revol": 26882, + "▁fémin": 26883, + "▁Orleans": 26884, + "▁poj": 26885, + "▁prez": 26886, + "Tex": 26887, + "ouwd": 26888, + "?(": 26889, + "▁LIM": 26890, + "istique": 26891, + "esar": 26892, + "▁heures": 26893, + "icki": 26894, + "▁dbo": 26895, + "skih": 26896, + "confirm": 26897, + "▁világ": 26898, + "▁ciutat": 26899, + "▁DR": 26900, + "▁Hawai": 26901, + "ched": 26902, + "▁spher": 26903, + "▁Artikel": 26904, + "▁Multiple": 26905, + "ciu": 26906, + "▁мы": 26907, + "▁lipca": 26908, + "](/": 26909, + "Strategy": 26910, + "▁Alabama": 26911, + "SDK": 26912, + "UTC": 26913, + "__.": 26914, + "Arguments": 26915, + "▁setContentView": 26916, + "île": 26917, + "ByVal": 26918, + "▁JVM": 26919, + "ющего": 26920, + "▁Leonard": 26921, + "▁justify": 26922, + "цем": 26923, + "▁nab": 26924, + "CCESS": 26925, + "▁hopes": 26926, + ")&": 26927, + "sero": 26928, + "▁зай": 26929, + "слід": 26930, + "▁Rég": 26931, + "▁Sang": 26932, + "▁fung": 26933, + "baar": 26934, + "▁coffee": 26935, + "assembly": 26936, + "▁Він": 26937, + "эй": 26938, + "▁comprend": 26939, + "filled": 26940, + "рд": 26941, + "odia": 26942, + "▁gens": 26943, + "fluss": 26944, + "Drawable": 26945, + "▁surve": 26946, + "Setup": 26947, + "▁należ": 26948, + "▁conjunto": 26949, + "▁Его": 26950, + "▁oldal": 26951, + "▁verbose": 26952, + "▁Electric": 26953, + "▁Harrison": 26954, + "engen": 26955, + "paragraph": 26956, + "▁nouvelles": 26957, + "▁време": 26958, + "▁memor": 26959, + "▁mayoría": 26960, + "сад": 26961, + "▁bataille": 26962, + "▁thermal": 26963, + "▁Хронологи": 26964, + "▁Better": 26965, + "bye": 26966, + "▁театра": 26967, + "roe": 26968, + "▁segle": 26969, + "rott": 26970, + "▁opinions": 26971, + ")})": 26972, + "ühle": 26973, + "▁Gün": 26974, + "▁Щ": 26975, + "ból": 26976, + "▁Larry": 26977, + "▁solic": 26978, + "▁zwar": 26979, + "▁Caroline": 26980, + "▁Reichs": 26981, + "Extensions": 26982, + "migr": 26983, + ":@": 26984, + "▁enumerate": 26985, + "▁eigenen": 26986, + "▁explore": 26987, + "ému": 26988, + "▁gat": 26989, + "▁imperial": 26990, + "▁Usually": 26991, + "▁tud": 26992, + "▁укра": 26993, + "him": 26994, + "▁corners": 26995, + "▁SER": 26996, + "▁interpreter": 26997, + "▁Ice": 26998, + "▁amounts": 26999, + "▁Pala": 27000, + "▁tinha": 27001, + "vole": 27002, + "▁gle": 27003, + "ucci": 27004, + "▁siehe": 27005, + "Jack": 27006, + "▁woll": 27007, + "▁elder": 27008, + "▁кораб": 27009, + "▁engag": 27010, + "▁Laurent": 27011, + "▁achiev": 27012, + "istik": 27013, + "arct": 27014, + "тного": 27015, + "▁gir": 27016, + "▁Singh": 27017, + "mathop": 27018, + "USA": 27019, + "▁Projekt": 27020, + "▁debe": 27021, + "richtung": 27022, + "▁Tsch": 27023, + "uminate": 27024, + "▁szó": 27025, + "lyph": 27026, + "зидент": 27027, + "▁limitations": 27028, + "ющей": 27029, + "▁bila": 27030, + "Push": 27031, + "▁offering": 27032, + "iennes": 27033, + "Fri": 27034, + "▁postgresql": 27035, + "▁Tommy": 27036, + "▁particolare": 27037, + "▁století": 27038, + "▁arrib": 27039, + "▁Eva": 27040, + "school": 27041, + "▁vendor": 27042, + "▁Dallas": 27043, + "▁prolong": 27044, + "CREATE": 27045, + "▁suivante": 27046, + "STATUS": 27047, + "là": 27048, + "kv": 27049, + "▁häufig": 27050, + "▁Agricult": 27051, + "▁huit": 27052, + "▁inoltre": 27053, + "▁Lloyd": 27054, + "▁француз": 27055, + "▁выпол": 27056, + "▁faithful": 27057, + "▁Вар": 27058, + "▁verl": 27059, + "▁juego": 27060, + "▁Резултати": 27061, + ",...,": 27062, + "▁implicitly": 27063, + "irks": 27064, + "Calcul": 27065, + "▁meses": 27066, + "omed": 27067, + "▁pak": 27068, + "herit": 27069, + "▁optical": 27070, + "▁Історія": 27071, + "veis": 27072, + "▁capitale": 27073, + "placeholder": 27074, + "intrag": 27075, + "▁Atlas": 27076, + ")];": 27077, + "icons": 27078, + "▁Bent": 27079, + "▁Widget": 27080, + "▁volunt": 27081, + "avo": 27082, + "égr": 27083, + "lige": 27084, + "▁NAME": 27085, + "▁abstra": 27086, + "▁fís": 27087, + "▁Browser": 27088, + "▁bush": 27089, + "hall": 27090, + "▁clouds": 27091, + "▁SUB": 27092, + "▁tandis": 27093, + "▁Commonwealth": 27094, + "тая": 27095, + "▁exhaust": 27096, + "________________": 27097, + "▁Statistics": 27098, + "▁Religion": 27099, + "▁Muham": 27100, + "uals": 27101, + "goto": 27102, + "Digital": 27103, + "Family": 27104, + "▁Bun": 27105, + "letin": 27106, + "Management": 27107, + "▁capabilities": 27108, + "annten": 27109, + "▁себе": 27110, + "▁stays": 27111, + "kter": 27112, + "▁dost": 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"▁redundant": 28005, + "▁Gros": 28006, + "▁websites": 28007, + "▁vio": 28008, + "▁ocas": 28009, + "vés": 28010, + "▁Gam": 28011, + "dw": 28012, + "Indicator": 28013, + "▁Kob": 28014, + "▁jack": 28015, + "Hint": 28016, + "▁Apol": 28017, + "▁другие": 28018, + "▁NUM": 28019, + "▁ofic": 28020, + "ystycz": 28021, + "▁wereld": 28022, + "мости": 28023, + "LEFT": 28024, + "▁Types": 28025, + "seen": 28026, + "uncia": 28027, + "▁narod": 28028, + "▁этот": 28029, + "Sidenote": 28030, + "ueil": 28031, + "▁отме": 28032, + "▁courts": 28033, + "fir": 28034, + "urz": 28035, + "ченко": 28036, + "Credentials": 28037, + "▁imagination": 28038, + "itats": 28039, + "buff": 28040, + "flash": 28041, + "▁badly": 28042, + "▁worn": 28043, + "▁округу": 28044, + "catalog": 28045, + "lime": 28046, + "▁Gill": 28047, + "▁Sent": 28048, + "iella": 28049, + "▁Craig": 28050, + "▁Sele": 28051, + "▁Independ": 28052, + "▁provincie": 28053, + "ossen": 28054, + "▁запад": 28055, + "▁infant": 28056, + "▁prevents": 28057, + "▁provinces": 28058, + "afé": 28059, + "beg": 28060, + "▁colours": 28061, + "BF": 28062, + "ën": 28063, + "▁Между": 28064, + "în": 28065, + "Observer": 28066, + "forsch": 28067, + "ígen": 28068, + "umption": 28069, + "▁Illustr": 28070, + "рист": 28071, + "▁полови": 28072, + "▁`&": 28073, + "▁ore": 28074, + "▁supplies": 28075, + "▁parenthes": 28076, + "Foundation": 28077, + "▁vou": 28078, + "▁Tout": 28079, + "Donald": 28080, + "▁RET": 28081, + "weig": 28082, + "▁producción": 28083, + "mix": 28084, + "▁utwor": 28085, + "▁föl": 28086, + "▁então": 28087, + "▁Sister": 28088, + "Tags": 28089, + "▁Савезне": 28090, + "▁privileges": 28091, + "▁nazw": 28092, + "▁Rav": 28093, + "▁repro": 28094, + "▁Mason": 28095, + "▁Platform": 28096, + "▁пробле": 28097, + "▁Pérez": 28098, + "▁blanc": 28099, + "Behavior": 28100, + "фици": 28101, + "eken": 28102, + "▁meets": 28103, + "(.*": 28104, + "▁få": 28105, + "epen": 28106, + "maker": 28107, + "▁loyal": 28108, + "members": 28109, + "meisterschaft": 28110, + "goal": 28111, + "шлен": 28112, + "▁северо": 28113, + "iende": 28114, + "дні": 28115, + "Proof": 28116, + "▁explic": 28117, + "▁electro": 28118, + "iels": 28119, + "reload": 28120, + "▁eleven": 28121, + "▁partidos": 28122, + "îne": 28123, + "▁Regin": 28124, + "▁éx": 28125, + "▁Bulg": 28126, + "▁networking": 28127, + "▁separator": 28128, + "UserName": 28129, + "▁edificio": 28130, + "▁Mie": 28131, + "▁idle": 28132, + "yed": 28133, + "▁passengers": 28134, + "+)": 28135, + "meno": 28136, + "eggi": 28137, + "▁nicely": 28138, + "endencia": 28139, + "чий": 28140, + "étés": 28141, + "ightarrow": 28142, + "▁orthogonal": 28143, + "▁Half": 28144, + "▁fewer": 28145, + "▁propi": 28146, + "▁primit": 28147, + "icale": 28148, + "▁flower": 28149, + "merk": 28150, + "▁Отече": 28151, + "▁persistent": 28152, + "▁Ville": 28153, + "Men": 28154, + "gaben": 28155, + "▁Isaac": 28156, + "ativity": 28157, + "▁północ": 28158, + "▁rok": 28159, + "cards": 28160, + "дения": 28161, + "▁юго": 28162, + "▁extraordinary": 28163, + "▁kyr": 28164, + "(\",": 28165, + "))]": 28166, + "▁unix": 28167, + "кол": 28168, + "▁sink": 28169, + "apsed": 28170, + "▁kommen": 28171, + "▁forcing": 28172, + "About": 28173, + "▁Halle": 28174, + "▁Majesty": 28175, + "▁Switch": 28176, + "▁abroad": 28177, + "▁acceleration": 28178, + "urbed": 28179, + "▁остан": 28180, + "Ready": 28181, + "▁півні": 28182, + "Bra": 28183, + "▁цього": 28184, + "▁plut": 28185, + "▁Train": 28186, + "▁április": 28187, + "▁puesto": 28188, + "▁toss": 28189, + "▁irrelevant": 28190, + "▁dip": 28191, + "segment": 28192, + "opacity": 28193, + "▁lorsque": 28194, + "▁verschill": 28195, + "ена": 28196, + "▁Doc": 28197, + "%%%%%%%%": 28198, + "▁borders": 28199, + "gebras": 28200, + "▁ries": 28201, + "▁Olympedia": 28202, + "▁Generation": 28203, + "metros": 28204, + "▁horizon": 28205, + "▁adaptation": 28206, + "▁Zahl": 28207, + "▁nahe": 28208, + "▁Bug": 28209, + "Picture": 28210, + "љи": 28211, + "RGB": 28212, + "Owner": 28213, + "adin": 28214, + "▁Catalunya": 28215, + "ných": 28216, + "▁cualquier": 28217, + "▁Institution": 28218, + "insen": 28219, + "▁Brasile": 28220, + "▁fitting": 28221, + "Deleg": 28222, + "ictwo": 28223, + "▁Exper": 28224, + "ochastic": 28225, + "▁dus": 28226, + "▁пора": 28227, + "▁substring": 28228, + "ссии": 28229, + "oin": 28230, + "▁школа": 28231, + "▁cx": 28232, + "▁%)": 28233, + "▁Buddh": 28234, + "▁pending": 28235, + "▁Entry": 28236, + "▁Berl": 28237, + "▁cler": 28238, + "▁Soc": 28239, + "▁rounded": 28240, + "▁mv": 28241, + "ített": 28242, + "▁Diplom": 28243, + "▁französischen": 28244, + "▁Gan": 28245, + "▁Investig": 28246, + "▁indexPath": 28247, + "▁molti": 28248, + "persistence": 28249, + "▁XIXe": 28250, + "▁Electron": 28251, + "bü": 28252, + "gele": 28253, + "▁Maler": 28254, + "▁proyecto": 28255, + "▁Bath": 28256, + "ellers": 28257, + "▁GP": 28258, + "oning": 28259, + "cloudflare": 28260, + "▁při": 28261, + "▁ded": 28262, + "▁Odkazy": 28263, + "▁Msg": 28264, + "▁Being": 28265, + "▁Depuis": 28266, + "▁Primary": 28267, + "▁Appro": 28268, + "▁formally": 28269, + "ступил": 28270, + "▁fuera": 28271, + "▁Root": 28272, + "▁autonom": 28273, + "▁secretary": 28274, + "▁osób": 28275, + "▁cuales": 28276, + "▁Depending": 28277, + "▁asi": 28278, + "vera": 28279, + "▁russe": 28280, + "▁proves": 28281, + "▁presiden": 28282, + "RU": 28283, + "▁Watson": 28284, + "▁webpack": 28285, + "elligence": 28286, + "кам": 28287, + "▁Officer": 28288, + "▁delivery": 28289, + "ждён": 28290, + "▁импе": 28291, + "▁wil": 28292, + "▁vesc": 28293, + "usztus": 28294, + "▁Geoff": 28295, + "()}": 28296, + "▁Fore": 28297, + "▁wenig": 28298, + "▁Airl": 28299, + "▁Efter": 28300, + "▁Break": 28301, + "▁Städ": 28302, + "ismiss": 28303, + "íp": 28304, + "▁avoided": 28305, + "▁assertion": 28306, + "DN": 28307, + "▁teat": 28308, + "ína": 28309, + "▁mechanical": 28310, + "isu": 28311, + "@{": 28312, + "▁nou": 28313, + "Italie": 28314, + "sourceforge": 28315, + "▁svo": 28316, + "▁király": 28317, + "▁References": 28318, + "six": 28319, + "▁Archives": 28320, + "▁finishing": 28321, + "acje": 28322, + "état": 28323, + "iffs": 28324, + "▁stead": 28325, + "▁feas": 28326, + "aware": 28327, + "lande": 28328, + "Inject": 28329, + "▁Agent": 28330, + "▁Normdatei": 28331, + "▁amen": 28332, + "▁Architecture": 28333, + "aze": 28334, + "ște": 28335, + "▁usar": 28336, + "▁cores": 28337, + "лін": 28338, + "▁Castro": 28339, + "▁væ": 28340, + ">\",": 28341, + "omena": 28342, + "▁gesam": 28343, + "▁Martín": 28344, + "egung": 28345, + "▁společ": 28346, + "▁amplitude": 28347, + "▁importing": 28348, + "▁listview": 28349, + "THE": 28350, + "ziale": 28351, + "cedes": 28352, + "▁particulier": 28353, + "▁Расподела": 28354, + "▁край": 28355, + "▁divent": 28356, + "▁ké": 28357, + "quit": 28358, + "тором": 28359, + "CheckBox": 28360, + "▁Zobacz": 28361, + "phe": 28362, + "pta": 28363, + "▁sjö": 28364, + "▁розташ": 28365, + "▁tedesco": 28366, + "▁stal": 28367, + "▁Beruf": 28368, + "овая": 28369, + "▁svě": 28370, + "▁flush": 28371, + "▁відбу": 28372, + "▁radial": 28373, + "▁différentes": 28374, + "анта": 28375, + "▁Perry": 28376, + "Coll": 28377, + "liqu": 28378, + "▁Optional": 28379, + "▁Санкт": 28380, + "▁LINQ": 28381, + "▁Franc": 28382, + "cije": 28383, + "▁Guillaume": 28384, + "know": 28385, + "▁Units": 28386, + "olk": 28387, + "▁Système": 28388, + "▁Sales": 28389, + "▁ehemaligen": 28390, + "мирова": 28391, + "xhtml": 28392, + "setopt": 28393, + "▁mellan": 28394, + "▁zie": 28395, + "▁giant": 28396, + "Board": 28397, + "▁Caval": 28398, + "▁defence": 28399, + "----------": 28400, + "pshire": 28401, + "mart": 28402, + "▁Dioc": 28403, + "iskt": 28404, + "▁inse": 28405, + "▁épisode": 28406, + "чик": 28407, + "bars": 28408, + "Sito": 28409, + "▁integrity": 28410, + "auff": 28411, + "▁vär": 28412, + "Azure": 28413, + "▁starb": 28414, + "▁контра": 28415, + "▁Мексичка": 28416, + "▁запа": 28417, + "▁Mountains": 28418, + "}}=": 28419, + "▁pulling": 28420, + "▁satellite": 28421, + "▁atoms": 28422, + "▁profesor": 28423, + "▁repeatedly": 28424, + "▁invasion": 28425, + "programming": 28426, + "├──": 28427, + "▁Lip": 28428, + "вшие": 28429, + "▁keen": 28430, + "▁critics": 28431, + "▁Nicola": 28432, + "▁Cand": 28433, + "▁distint": 28434, + "▁heading": 28435, + "pragma": 28436, + "{|": 28437, + "ymen": 28438, + "▁terrain": 28439, + "iedenis": 28440, + "▁besonders": 28441, + "▁nominated": 28442, + "BOOL": 28443, + "▁Kay": 28444, + "cian": 28445, + "stelle": 28446, + "▁dispute": 28447, + "▁щ": 28448, + "DataSet": 28449, + "nothing": 28450, + "Autom": 28451, + "hören": 28452, + "▁shed": 28453, + "▁paused": 28454, + "san": 28455, + "▁nunca": 28456, + "!(\"": 28457, + "▁położ": 28458, + "Secret": 28459, + "▁Domain": 28460, + "▁возмож": 28461, + "XV": 28462, + "lv": 28463, + "ikh": 28464, + "▁Sony": 28465, + "mq": 28466, + "otrop": 28467, + "▁Logger": 28468, + "▁threat": 28469, + "asted": 28470, + "зько": 28471, + "▁freely": 28472, + "▁improvements": 28473, + "istema": 28474, + "▁illustrate": 28475, + "▁tact": 28476, + "▁figur": 28477, + "ués": 28478, + "riminal": 28479, + "odon": 28480, + "intendo": 28481, + "▁influenced": 28482, + "FFER": 28483, + "▁Ghost": 28484, + "▁совер": 28485, + "nad": 28486, + "ioned": 28487, + "▁Events": 28488, + "▁wrapping": 28489, + "---------+": 28490, + "fif": 28491, + "▁(**": 28492, + "={{": 28493, + "маль": 28494, + "▁losses": 28495, + "▁Galerie": 28496, + "tel": 28497, + "▁лютого": 28498, + "▁Kru": 28499, + "▁Polen": 28500, + "нім": 28501, + "near": 28502, + "▁shame": 28503, + "▁moyenne": 28504, + "▁CP": 28505, + "preis": 28506, + "▁passenger": 28507, + "lek": 28508, + "ionales": 28509, + "kafka": 28510, + "▁participe": 28511, + "▁membership": 28512, + "[_": 28513, + "lando": 28514, + "stelling": 28515, + "Sem": 28516, + "gon": 28517, + "▁Correct": 28518, + "▁valle": 28519, + "▁readily": 28520, + "▁Dokument": 28521, + "honneur": 28522, + "▁testim": 28523, + "ulative": 28524, + "doFilter": 28525, + "▁dominant": 28526, + "ammer": 28527, + "▁која": 28528, + "▁Monsieur": 28529, + "zeg": 28530, + "▁війни": 28531, + "▁Fo": 28532, + "▁Amy": 28533, + "▁¡": 28534, + "▁február": 28535, + "▁downloading": 28536, + "▁leng": 28537, + "\\}$,": 28538, + "▁neat": 28539, + "▁Cache": 28540, + "ICATION": 28541, + "▁deve": 28542, + "▁sorrow": 28543, + "slow": 28544, + "▁hinaus": 28545, + "▁reconoc": 28546, + "▁Linked": 28547, + "▁Shaw": 28548, + "market": 28549, + "▁Dic": 28550, + "▁Ski": 28551, + "▁delimiter": 28552, + "▁MainActivity": 28553, + "▁Musical": 28554, + "▁Reyn": 28555, + "ScrollView": 28556, + "▁conventional": 28557, + "ença": 28558, + "▁refactor": 28559, + "'-": 28560, + "▁Hed": 28561, + "sprech": 28562, + "▁athlet": 28563, + "▁especies": 28564, + "▁Schön": 28565, + "▁kleinen": 28566, + "шко": 28567, + "▁Йо": 28568, + "▁Happy": 28569, + "multirow": 28570, + "▁augusti": 28571, + "▁Gand": 28572, + "▁appointment": 28573, + "▁Mediabestanden": 28574, + "Three": 28575, + "▁Kenneth": 28576, + "NEW": 28577, + "▁Notification": 28578, + "▁Marx": 28579, + "▁insc": 28580, + "Mor": 28581, + "вый": 28582, + "väst": 28583, + "vidia": 28584, + "▁demonstrated": 28585, + "fonts": 28586, + "▁kamen": 28587, + "▁Ster": 28588, + "▁mieszkańców": 28589, + "▁Koh": 28590, + "~$\\": 28591, + "»).": 28592, + "rene": 28593, + "insic": 28594, + "ická": 28595, + "xygen": 28596, + "▁mn": 28597, + "▁sched": 28598, + "ASC": 28599, + "Ig": 28600, + "▁Constant": 28601, + "▁opportun": 28602, + "▁MyClass": 28603, + "sef": 28604, + "oped": 28605, + "▁injured": 28606, + "VIS": 28607, + "▁Pero": 28608, + "▁Until": 28609, + "▁flesh": 28610, + "orphism": 28611, + "▁Portal": 28612, + "▁gminy": 28613, + "▁власти": 28614, + "▁Nä": 28615, + "ктиче": 28616, + "▁hrab": 28617, + "▁Cub": 28618, + "avoir": 28619, + "▁Lars": 28620, + "▁Бело": 28621, + "▁seizoen": 28622, + "▁Genomsnitt": 28623, + "▁Lil": 28624, + "▁Pool": 28625, + "▁Dios": 28626, + "TX": 28627, + "aes": 28628, + "autore": 28629, + "Alpha": 28630, + "states": 28631, + "Lab": 28632, + "nederbörd": 28633, + "erton": 28634, + "▁brid": 28635, + "▁richt": 28636, + "▁Ela": 28637, + "▁сла": 28638, + "▁weapon": 28639, + "▁combatt": 28640, + "agar": 28641, + "▁regnig": 28642, + "▁utilisé": 28643, + "▁servir": 28644, + "▁brick": 28645, + "▁gateway": 28646, + "▁torraste": 28647, + "▁procedures": 28648, + "▁årsnederbörd": 28649, + "▁Genomsnittlig": 28650, + "чёт": 28651, + "▁områ": 28652, + "▁regnigaste": 28653, + "▁честь": 28654, + "▁amid": 28655, + "▁grateful": 28656, + "▁DIS": 28657, + "DAY": 28658, + "▁ору": 28659, + "▁rivière": 28660, + "heure": 28661, + "▁Richmond": 28662, + "▁Compar": 28663, + "▁Нор": 28664, + "DOC": 28665, + "esia": 28666, + "calc": 28667, + "▁IU": 28668, + "▁vorg": 28669, + "▁habían": 28670, + "çoit": 28671, + "▁arist": 28672, + "▁кли": 28673, + "▁Sue": 28674, + "▁Touch": 28675, + "▁Writing": 28676, + "ifiable": 28677, + "▁wc": 28678, + "▁withdraw": 28679, + "зар": 28680, + "▁presently": 28681, + "▁FK": 28682, + "▁prakt": 28683, + "▁colored": 28684, + "usb": 28685, + "▁Perú": 28686, + "▁plata": 28687, + "▁wishes": 28688, + "▁кам": 28689, + "azar": 28690, + "ável": 28691, + "▁lamp": 28692, + "bishop": 28693, + "▁inclusion": 28694, + "jq": 28695, + "arth": 28696, + "▁Flag": 28697, + "▁нор": 28698, + "ædia": 28699, + "UNCTION": 28700, + "▁Bahnhof": 28701, + "▁approaching": 28702, + "▁Gött": 28703, + "▁cube": 28704, + "▁argued": 28705, + "▁Things": 28706, + "Gui": 28707, + "дови": 28708, + "▁recre": 28709, + "▁réseau": 28710, + "▁significa": 28711, + "Git": 28712, + "gebracht": 28713, + "▁liga": 28714, + "▁assured": 28715, + "alus": 28716, + "рит": 28717, + "▁энциклопеди": 28718, + "▁%).": 28719, + "▁Première": 28720, + "▁declarations": 28721, + "▁tricky": 28722, + "▁profiles": 28723, + "▁Fon": 28724, + "▁Jas": 28725, + "âr": 28726, + "babel": 28727, + "▁Friday": 28728, + "▁június": 28729, + "▁cols": 28730, + "▁EXISTS": 28731, + "▁Italiana": 28732, + "▁authorization": 28733, + "▁sulle": 28734, + "▁Emb": 28735, + "▁Variable": 28736, + "trees": 28737, + "▁Fly": 28738, + "riors": 28739, + "▁damals": 28740, + "▁findet": 28741, + "▁Sept": 28742, + "▁mundial": 28743, + "▁removal": 28744, + "▁longitude": 28745, + "clic": 28746, + "▁fade": 28747, + "▁gradle": 28748, + "▁zák": 28749, + "▁timing": 28750, + "trightarrow": 28751, + "atia": 28752, + "-.": 28753, + "uche": 28754, + "▁serialize": 28755, + "▁Hmm": 28756, + "▁Representatives": 28757, + "bah": 28758, + "rend": 28759, + "assador": 28760, + "▁shield": 28761, + "ucion": 28762, + "▁américaine": 28763, + "zę": 28764, + "villa": 28765, + "▁hombre": 28766, + "áss": 28767, + "▁SF": 28768, + "▁repeating": 28769, + "▁criter": 28770, + "▁Struct": 28771, + "???": 28772, + "▁cheap": 28773, + "▁rings": 28774, + "abhäng": 28775, + "▁corte": 28776, + "▁administ": 28777, + "ixon": 28778, + "gypt": 28779, + "▁puntos": 28780, + "▁mezi": 28781, + "▁pochod": 28782, + "isko": 28783, + "nię": 28784, + "▁осу": 28785, + "▁ár": 28786, + "тельной": 28787, + "▁Metropolitan": 28788, + "jin": 28789, + "zess": 28790, + "▁віці": 28791, + "▁conflicts": 28792, + "ijst": 28793, + "▁Market": 28794, + "стров": 28795, + "▁\",\"": 28796, + "▁Scroll": 28797, + "gun": 28798, + "тара": 28799, + "▁amateur": 28800, + "▁róż": 28801, + "poss": 28802, + "▁generalized": 28803, + "▁Harm": 28804, + "cita": 28805, + "▁Switzerland": 28806, + "icola": 28807, + "▁muit": 28808, + "located": 28809, + "▁có": 28810, + "▁arose": 28811, + "▁communauté": 28812, + "})^": 28813, + "visibility": 28814, + "ída": 28815, + "▁FB": 28816, + "▁Freund": 28817, + "gat": 28818, + "\":{\"": 28819, + "intellij": 28820, + "ifie": 28821, + "hmen": 28822, + "▁édition": 28823, + "▁које": 28824, + "▁інших": 28825, + "oming": 28826, + "▁arquitect": 28827, + "▁Presidente": 28828, + "▁Під": 28829, + "▁cabin": 28830, + "Theorem": 28831, + "▁Gay": 28832, + "ifice": 28833, + "▁hect": 28834, + "lą": 28835, + "irmingham": 28836, + "▁semantic": 28837, + "▁Louisiana": 28838, + "▁sacrifice": 28839, + "▁Christoph": 28840, + "▁Executive": 28841, + "_+": 28842, + "ják": 28843, + "▁seria": 28844, + "▁Overflow": 28845, + "▁Lucy": 28846, + "▁melhor": 28847, + "▁voices": 28848, + "cza": 28849, + "▁капи": 28850, + "▁университета": 28851, + "INCT": 28852, + "▁coloc": 28853, + "▁prue": 28854, + "▁geomet": 28855, + "▁diretto": 28856, + "reso": 28857, + "▁Akt": 28858, + "▁unh": 28859, + "▁сери": 28860, + "▁Alert": 28861, + "Wel": 28862, + "audi": 28863, + "äler": 28864, + "▁guests": 28865, + "▁иде": 28866, + "Studio": 28867, + "▁кате": 28868, + "▁exponent": 28869, + "rze": 28870, + "pmod": 28871, + "rolle": 28872, + "▁Limited": 28873, + "Allemagne": 28874, + "▁pity": 28875, + "▁lä": 28876, + "▁runner": 28877, + "kende": 28878, + "EQ": 28879, + "▁MM": 28880, + "szág": 28881, + "поді": 28882, + "▁regret": 28883, + "▁publié": 28884, + "▁departamento": 28885, + "▁accused": 28886, + "hp": 28887, + "▁Pfl": 28888, + "▁Sint": 28889, + "▁ekonom": 28890, + "ractor": 28891, + "▁Пів": 28892, + "▁awful": 28893, + "ować": 28894, + "]->": 28895, + "▁Fine": 28896, + "Са": 28897, + "tis": 28898, + "éta": 28899, + "▁Роди": 28900, + "▁Düsseldorf": 28901, + "LOB": 28902, + "osas": 28903, + "werke": 28904, + "▁lance": 28905, + "▁листопада": 28906, + "▁incomplete": 28907, + "▁Picture": 28908, + "('\\": 28909, + "esters": 28910, + "▁belonged": 28911, + "▁Sank": 28912, + "ammed": 28913, + "▁repositories": 28914, + "▁addr": 28915, + "Collect": 28916, + "Hot": 28917, + "▁tyl": 28918, + "▁instanceof": 28919, + "▁bonus": 28920, + "ový": 28921, + "▁моря": 28922, + "▁interactive": 28923, + "▁Mys": 28924, + "▁Edmund": 28925, + "fileName": 28926, + "emor": 28927, + "▁Три": 28928, + "▁Rosen": 28929, + "▁Prima": 28930, + "▁voting": 28931, + "▁XP": 28932, + "▁Zero": 28933, + "▁Led": 28934, + "amsung": 28935, + "▁enables": 28936, + "▁redirects": 28937, + "AST": 28938, + "Paint": 28939, + "acker": 28940, + "lecht": 28941, + "▁chairman": 28942, + "▁Aven": 28943, + "▁Sach": 28944, + "(\"<": 28945, + "кер": 28946, + "▁mistakes": 28947, + "▁Weit": 28948, + "▁prowad": 28949, + "▁didnt": 28950, + "énario": 28951, + "unless": 28952, + "▁backwards": 28953, + "boa": 28954, + "duino": 28955, + "```": 28956, + "stor": 28957, + "Completion": 28958, + "puesta": 28959, + "▁dinast": 28960, + "últ": 28961, + "▁SY": 28962, + "ifolia": 28963, + "œuvres": 28964, + "▁racing": 28965, + "▁cabinet": 28966, + "▁cutting": 28967, + "▁thumb": 28968, + "▁Кара": 28969, + "highlight": 28970, + "куп": 28971, + "▁sd": 28972, + "▁національ": 28973, + "▁campagne": 28974, + "▁registers": 28975, + "▁educational": 28976, + "▁pesar": 28977, + "üge": 28978, + "▁oro": 28979, + "burgo": 28980, + "▁Athletics": 28981, + "▁MTV": 28982, + "getMessage": 28983, + "▁Hyp": 28984, + "▁victim": 28985, + "))\\": 28986, + "▁drums": 28987, + "hostname": 28988, + "tał": 28989, + "making": 28990, + "▁powiat": 28991, + "őd": 28992, + "threads": 28993, + "▁absolv": 28994, + "▁люди": 28995, + "▁stepped": 28996, + "exist": 28997, + "▁NK": 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"▁zat": 29052, + "▁rin": 29053, + "▁Bibliografía": 29054, + "▁merchant": 29055, + "tensorflow": 29056, + "▁dér": 29057, + "▁ActiveRecord": 29058, + "IES": 29059, + "▁linker": 29060, + "▁estudios": 29061, + "cdnjs": 29062, + "▁Государ": 29063, + "ánchez": 29064, + "appe": 29065, + "club": 29066, + "▁další": 29067, + "▁Algorithm": 29068, + "dfs": 29069, + "▁Bac": 29070, + "▁кафе": 29071, + "▁&=\\": 29072, + "▁ат": 29073, + "▁Глав": 29074, + "▁Mou": 29075, + "Machine": 29076, + "(...)": 29077, + "▁compart": 29078, + "▁augusztus": 29079, + "avan": 29080, + "▁rolled": 29081, + "▁еди": 29082, + "Scan": 29083, + "▁регі": 29084, + "▁świata": 29085, + "▁mines": 29086, + "},{": 29087, + "▁Tier": 29088, + "Cannot": 29089, + "мін": 29090, + "▁NEW": 29091, + "▁Вол": 29092, + "▁Manh": 29093, + "▁Gregory": 29094, + "▁principe": 29095, + "ISO": 29096, + "prog": 29097, + "▁Fail": 29098, + "▁aa": 29099, + "▁fecha": 29100, + "▁WCF": 29101, + "▁magistr": 29102, + "▁Zach": 29103, + "▁unicode": 29104, + "▁converter": 29105, + "▁dispers": 29106, + "ksam": 29107, + "▁Uncle": 29108, + "PropertyChanged": 29109, + "▁lider": 29110, + "▁opts": 29111, + "▁там": 29112, + "locked": 29113, + "zak": 29114, + "▁counted": 29115, + "▁persone": 29116, + "▁hurried": 29117, + "ätter": 29118, + "▁outras": 29119, + "▁genu": 29120, + "BD": 29121, + "veg": 29122, + "due": 29123, + "▁Pract": 29124, + "▁posible": 29125, + "▁contribute": 29126, + "UMN": 29127, + "▁Bürger": 29128, + "▁wars": 29129, + "▁exhibition": 29130, + "hill": 29131, + "▁astr": 29132, + "▁музе": 29133, + "▁CASE": 29134, + "manifest": 29135, + "yellow": 29136, + "Fn": 29137, + "▁RC": 29138, + "▁sott": 29139, + "▁sujet": 29140, + "▁Socket": 29141, + "▁Chine": 29142, + "▁frameworks": 29143, + "Hold": 29144, + "êts": 29145, + "▁філь": 29146, + "Loaded": 29147, + "ophe": 29148, + "texte": 29149, + "▁expres": 29150, + "▁consume": 29151, + "▁Richtung": 29152, + "ografi": 29153, + "▁magnific": 29154, + "àt": 29155, + "▁indul": 29156, + "ryty": 29157, + "▁offici": 29158, + "▁assault": 29159, + "rund": 29160, + "▁variants": 29161, + "▁сельсов": 29162, + "▁excitement": 29163, + "Times": 29164, + "kotlin": 29165, + "▁gering": 29166, + "▁Engel": 29167, + "▁Timer": 29168, + "²).": 29169, + "▁Ng": 29170, + "ässt": 29171, + "schau": 29172, + "SError": 29173, + "▁Edwards": 29174, + "▁Terminal": 29175, + "lict": 29176, + "Under": 29177, + "▁spawn": 29178, + "ürgen": 29179, + "▁Außerdem": 29180, + "▁kitchen": 29181, + "fahrt": 29182, + "▁Colors": 29183, + "▁система": 29184, + "▁terminated": 29185, + "▁LaTeX": 29186, + "igkeiten": 29187, + "▁mesure": 29188, + "▁Amts": 29189, + "▁empir": 29190, + "▁striking": 29191, + "▁exclusive": 29192, + "тех": 29193, + "▁rez": 29194, + "▁quan": 29195, + "▁Glasgow": 29196, + "▁lecture": 29197, + "▁Testament": 29198, + "▁funds": 29199, + "▁stessa": 29200, + "▁tribes": 29201, + "▁parfois": 29202, + "▁treball": 29203, + "nitz": 29204, + "bove": 29205, + "▁заслу": 29206, + "▁absent": 29207, + "▁Lauf": 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"}}_{\\": 29261, + "▁droite": 29262, + "▁raising": 29263, + "getting": 29264, + "ythm": 29265, + "onyme": 29266, + "żs": 29267, + "▁blah": 29268, + "TagName": 29269, + "Vertical": 29270, + "▁aper": 29271, + "postgresql": 29272, + "▁Handle": 29273, + "zew": 29274, + "▁skulle": 29275, + "▁opere": 29276, + "layers": 29277, + "▁possono": 29278, + "▁relate": 29279, + "ąc": 29280, + "▁Mih": 29281, + "âge": 29282, + "▁Świ": 29283, + "isses": 29284, + "▁servlet": 29285, + "Los": 29286, + "▁Advanced": 29287, + "atica": 29288, + "▁ced": 29289, + "▁elementos": 29290, + "рона": 29291, + "iks": 29292, + "arf": 29293, + "ariat": 29294, + "Mobile": 29295, + "agua": 29296, + "▁timp": 29297, + "▁Comité": 29298, + "▁combining": 29299, + "wohl": 29300, + "▁Study": 29301, + "coordinate": 29302, + "▁recommendation": 29303, + "▁transformations": 29304, + "until": 29305, + "bounded": 29306, + "▁изу": 29307, + "hanced": 29308, + "▁вопро": 29309, + "▁Prés": 29310, + "▁coord": 29311, + "xty": 29312, + "▁$,": 29313, + "▁champions": 29314, + "Den": 29315, + "Mil": 29316, + "(',": 29317, + "▁Preis": 29318, + "▁eigh": 29319, + "▁markers": 29320, + "▁gewesen": 29321, + "ätten": 29322, + "▁pione": 29323, + "mv": 29324, + "▁ју": 29325, + "zeichnis": 29326, + "hoff": 29327, + "News": 29328, + "▁Stanisław": 29329, + "▁Brandenburg": 29330, + "▁Feuer": 29331, + "=&": 29332, + "жет": 29333, + "▁Neil": 29334, + "▁wirk": 29335, + "▁società": 29336, + "▁spare": 29337, + "▁civile": 29338, + "sprach": 29339, + "▁disse": 29340, + "▁gates": 29341, + "▁anom": 29342, + "▁Федерации": 29343, + "▁tib": 29344, + "▁fútbol": 29345, + "▁Wikiped": 29346, + "iate": 29347, + "Front": 29348, + "▁craw": 29349, + "▁Rak": 29350, + "▁зву": 29351, + "street": 29352, + "▁Agency": 29353, + "вало": 29354, + "▁Рас": 29355, + "▁mkdir": 29356, + "ację": 29357, + "▁shares": 29358, + "Story": 29359, + "▁remarks": 29360, + "▁keywords": 29361, + "Bob": 29362, + "▁toe": 29363, + "▁Vitt": 29364, + "▁rhs": 29365, + "ROP": 29366, + "oris": 29367, + "/@": 29368, + "сии": 29369, + "▁traverse": 29370, + "▁referencing": 29371, + "präsident": 29372, + "rong": 29373, + "'):": 29374, + "aties": 29375, + "AW": 29376, + "Outlet": 29377, + "▁évol": 29378, + "ikes": 29379, + "▁environmental": 29380, + "icum": 29381, + "▁Lied": 29382, + "▁warn": 29383, + "▁Butler": 29384, + "▁%),": 29385, + "▁Zeitschrift": 29386, + "▁Montr": 29387, + "важа": 29388, + "▁Mercur": 29389, + "jekte": 29390, + "meter": 29391, + "ducation": 29392, + "▁attributed": 29393, + "*$": 29394, + "▁unf": 29395, + "▁Vertrag": 29396, + "zien": 29397, + "▁Роб": 29398, + "lices": 29399, + "pply": 29400, + "ansen": 29401, + "▁zeit": 29402, + "▁immense": 29403, + "▁lutego": 29404, + "▁Bulgar": 29405, + "▁miembros": 29406, + "▁Националь": 29407, + "▁Allow": 29408, + "▁anglès": 29409, + "дви": 29410, + "▁Toy": 29411, + "туа": 29412, + "▁yard": 29413, + "(%": 29414, + "isser": 29415, + "▁golf": 29416, + "▁Ukrain": 29417, + "▁hosp": 29418, + "Include": 29419, + "▁Lisa": 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30474, + "त": 30475, + "ŏ": 30476, + "‑": 30477, + "̃": 30478, + "Ć": 30479, + "ى": 30480, + "「": 30481, + "」": 30482, + "ს": 30483, + "Ā": 30484, + "म": 30485, + "生": 30486, + "≠": 30487, + "Љ": 30488, + "स": 30489, + "↔": 30490, + "Ο": 30491, + "ว": 30492, + "ლ": 30493, + "成": 30494, + "定": 30495, + "ล": 30496, + "¶": 30497, + "כ": 30498, + "で": 30499, + "ּ": 30500, + "ม": 30501, + "个": 30502, + "和": 30503, + "ס": 30504, + "在": 30505, + "Β": 30506, + "ิ": 30507, + "Ι": 30508, + "⁵": 30509, + "ั": 30510, + "ɡ": 30511, + "━": 30512, + "ら": 30513, + "オ": 30514, + "¼": 30515, + "ե": 30516, + "バ": 30517, + "ָ": 30518, + "ŋ": 30519, + "ŭ": 30520, + "グ": 30521, + "⁶": 30522, + "Ь": 30523, + "⁰": 30524, + "方": 30525, + "บ": 30526, + "—": 30527, + "高": 30528, + "ệ": 30529, + "Ν": 30530, + "ѣ": 30531, + "ィ": 30532, + "地": 30533, + "月": 30534, + "Ô": 30535, + "™": 30536, + "ウ": 30537, + "き": 30538, + "公": 30539, + "ạ": 30540, + "ო": 30541, + "ɾ": 30542, + "่": 30543, + "出": 30544, + "法": 30545, 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30617, + "ต": 30618, + "∃": 30619, + "₹": 30620, + "प": 30621, + "第": 30622, + "ര": 30623, + "ض": 30624, + "▄": 30625, + "城": 30626, + "ミ": 30627, + "ɐ": 30628, + "¦": 30629, + "美": 30630, + "件": 30631, + "ნ": 30632, + "Ð": 30633, + "ַ": 30634, + "ニ": 30635, + "部": 30636, + "ņ": 30637, + "ǐ": 30638, + "ט": 30639, + "य": 30640, + "あ": 30641, + "¾": 30642, + "ả": 30643, + "ち": 30644, + "ュ": 30645, + "÷": 30646, + "女": 30647, + "神": 30648, + "♦": 30649, + "¢": 30650, + "以": 30651, + "้": 30652, + "র": 30653, + "太": 30654, + "্": 30655, + "チ": 30656, + "յ": 30657, + "前": 30658, + "金": 30659, + "ւ": 30660, + "野": 30661, + "北": 30662, + "ห": 30663, + "‰": 30664, + "っ": 30665, + "加": 30666, + "原": 30667, + "ʲ": 30668, + "置": 30669, + "安": 30670, + "ガ": 30671, + "我": 30672, + "Ḥ": 30673, + "യ": 30674, + "京": 30675, + "▀": 30676, + "მ": 30677, + "ვ": 30678, + "ʾ": 30679, + "∨": 30680, + "ִ": 30681, + "可": 30682, + "取": 30683, + "县": 30684, + "二": 30685, + "▒": 30686, + "理": 30687, + "自": 30688, 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30760, + "モ": 30761, + "郎": 30762, + "据": 30763, + "●": 30764, + "州": 30765, + "∩": 30766, + "者": 30767, + "通": 30768, + "都": 30769, + "ℤ": 30770, + "♭": 30771, + "╌": 30772, + "つ": 30773, + "ḍ": 30774, + "江": 30775, + "ז": 30776, + "Ý": 30777, + "ө": 30778, + "์": 30779, + "到": 30780, + "ி": 30781, + "ʂ": 30782, + "对": 30783, + "스": 30784, + "使": 30785, + "ি": 30786, + "よ": 30787, + "Ἀ": 30788, + "Ï": 30789, + "∘": 30790, + "사": 30791, + "ন": 30792, + "世": 30793, + "ɕ": 30794, + "կ": 30795, + "უ": 30796, + "ട": 30797, + "ბ": 30798, + "ो": 30799, + "വ": 30800, + "果": 30801, + "十": 30802, + "ุ": 30803, + "藤": 30804, + "来": 30805, + "面": 30806, + "け": 30807, + "ĕ": 30808, + "ビ": 30809, + "这": 30810, + "지": 30811, + "ം": 30812, + "街": 30813, + "石": 30814, + "能": 30815, + "空": 30816, + "տ": 30817, + "ئ": 30818, + "武": 30819, + "ʹ": 30820, + "ϕ": 30821, + "后": 30822, + "ะ": 30823, + "元": 30824, + "ʔ": 30825, + "리": 30826, + "기": 30827, + "河": 30828, + "町": 30829, + "花": 30830, + "ὐ": 30831, 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"画": 31046, + "百": 31047, + "☆": 31048, + "記": 31049, + "得": 31050, + "ソ": 31051, + "氏": 31052, + "ာ": 31053, + "에": 31054, + "ল": 31055, + "ṛ": 31056, + "关": 31057, + "ġ": 31058, + "έ": 31059, + "∑": 31060, + "ベ": 31061, + "标": 31062, + "니": 31063, + "ὴ": 31064, + "ֵ": 31065, + "外": 31066, + "♠": 31067, + "わ": 31068, + "間": 31069, + "ภ": 31070, + "校": 31071, + "制": 31072, + "แ": 31073, + "力": 31074, + "門": 31075, + "好": 31076, + "ғ": 31077, + "Ù": 31078, + "ℓ": 31079, + "ֶ": 31080, + "는": 31081, + "┐": 31082, + "∗": 31083, + "指": 31084, + "色": 31085, + "返": 31086, + "馬": 31087, + "请": 31088, + "≫": 31089, + "風": 31090, + "ό": 31091, + "接": 31092, + "서": 31093, + "↳": 31094, + "せ": 31095, + "志": 31096, + "̲": 31097, + "魔": 31098, + "ң": 31099, + "更": 31100, + "程": 31101, + "김": 31102, + "郡": 31103, + "ོ": 31104, + "ũ": 31105, + "ച": 31106, + "利": 31107, + "県": 31108, + "周": 31109, + "そ": 31110, + "や": 31111, + "谷": 31112, + "香": 31113, + "♯": 31114, + "じ": 31115, + "،": 31116, + "期": 31117, + "∅": 31118, + "┘": 31119, + "初": 31120, + "福": 31121, + "片": 31122, + "ザ": 31123, + "動": 31124, + "参": 31125, + "성": 31126, + "Ə": 31127, + "╦": 31128, + "어": 31129, + "ხ": 31130, + "義": 31131, + "च": 31132, + "象": 31133, + "功": 31134, + "♂": 31135, + "도": 31136, + "고": 31137, + "过": 31138, + "վ": 31139, + "皇": 31140, + "特": 31141, + "ậ": 31142, + "长": 31143, + "英": 31144, + "ấ": 31145, + "ണ": 31146, + "Ъ": 31147, + "স": 31148, + "其": 31149, + "ত": 31150, + "流": 31151, + "除": 31152, + "일": 31153, + "ু": 31154, + "្": 31155, + "永": 31156, + "直": 31157, + "상": 31158, + "千": 31159, + "ắ": 31160, + "館": 31161, + "Ť": 31162, + "朝": 31163, + "ட": 31164, + "ɣ": 31165, + "单": 31166, + "ʀ": 31167, + "格": 31168, + "德": 31169, + "전": 31170, + "☺": 31171, + "ピ": 31172, + "歌": 31173, + "进": 31174, + "限": 31175, + "夫": 31176, + "트": 31177, + "⊢": 31178, + "園": 31179, + "量": 31180, + "土": 31181, + "放": 31182, + "码": 31183, + "等": 31184, + "系": 31185, + "∼": 31186, + "華": 31187, + "↵": 31188, 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"▁▁▁▁", + "▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁", + "▁▁▁▁▁" + ], + [ + "▁▁▁▁▁", + "▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁", + "▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁▁▁", + "▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁", + "▁▁▁" + ], + [ + "▁▁▁", + "▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁", + "▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁", + "▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁▁", + "▁▁" + ], + [ + "▁", + "▁▁▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁", + "▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁", + "▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁", + "▁▁" + ], + [ + "▁▁▁▁▁", + "▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁", + "▁▁▁▁" + ], + [ + "▁▁▁", + "▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁", + "▁" + ], + [ + "▁▁▁▁▁▁▁", + "▁▁▁" + ], + [ + "▁", + "▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁", + "▁▁▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁", + "▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁", + "▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁", + "▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁", + "▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁▁▁", + "▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁▁▁▁", + "▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁", + "▁▁▁▁" + ], + [ + "▁▁▁", + "▁▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁", + "▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁", + "▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁▁", + "▁▁▁" + ], + [ + "▁", + "▁▁▁▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁", + "▁" + ], + [ + "▁", + "▁▁" + ], + [ + "▁▁", + "▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁", + "▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁", + "▁" + ], + [ + "▁▁▁▁▁", + "▁▁▁▁" + ], + [ + "▁▁▁▁▁▁", + "▁▁▁" + ], + [ + "▁▁▁", + "▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁", + "▁▁" + ], + [ + "▁", + "▁▁▁▁▁▁▁▁" + ], + [ + "▁▁", + "▁▁▁▁▁" + ], + [ + "▁▁▁▁", + "▁▁▁" + ], + [ + "▁▁▁▁▁", + "▁▁" + ], + [ + "▁▁▁▁▁▁", + "▁" + ], + [ + "▁▁▁", + "▁▁▁▁" + ], + [ + "▁", + "▁▁▁▁▁▁" + ], + [ + "▁▁", + "▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁", + "▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁", + "▁▁▁" + ], + [ + "▁▁▁▁▁", + "▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁", + "▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁", + "▁" + ], + [ + "▁▁▁", + "▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁", + "▁▁" + ], + [ + "▁▁▁▁▁▁▁", + "▁▁▁▁" + ], + [ + "▁", + "▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁", + "▁▁▁▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁", + "▁▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁", + "▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁", + "▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁", + "▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁▁▁", + "▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁▁▁▁", + "▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁", + "▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁▁▁▁▁", + "▁" + ], + [ + "▁▁▁", + "▁▁▁▁▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁", + "▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁", + "▁▁▁▁▁▁▁▁" + ], + [ + "▁▁▁▁▁▁▁▁▁▁▁", + "▁▁▁▁" + ], + [ + "▁", + "▁▁▁▁▁▁▁▁▁▁▁▁▁▁" + ] + ] + } } diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/tokenizer_config.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/tokenizer_config.json index 37a646e33..d1417a87d 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/tokenizer_config.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/tokenizer/tokenizer_config.json @@ -1,43 +1,43 @@ -{ - "add_bos_token": true, - "add_eos_token": false, - "add_prefix_space": null, - "added_tokens_decoder": { - "0": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "1": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "2": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - } - }, - "bos_token": "", - "clean_up_tokenization_spaces": false, - "eos_token": "", - "extra_special_tokens": {}, - "legacy": false, - "model_max_length": 2048, - "pad_token": "", - "padding_side": "right", - "sp_model_kwargs": {}, - "tokenizer_class": "LlamaTokenizer", - "unk_token": "", - "use_default_system_prompt": false -} +{ + "add_bos_token": true, + "add_eos_token": false, + "add_prefix_space": null, + "added_tokens_decoder": { + "0": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "1": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "2": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "bos_token": "", + "clean_up_tokenization_spaces": false, + "eos_token": "", + "extra_special_tokens": {}, + "legacy": false, + "model_max_length": 2048, + "pad_token": "", + "padding_side": "right", + "sp_model_kwargs": {}, + "tokenizer_class": "LlamaTokenizer", + "unk_token": "", + "use_default_system_prompt": false +} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/test.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/test.json index 93bd123c0..b40e53c41 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/test.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/test.json @@ -1,2 +1,2 @@ -{"messages": [{"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "content": "4"}]} -{"messages": [{"role": "user", "content": "Hello, bot."}, {"role": "assistant", "content": "Hi! How can I assist?"}]} +{"messages": [{"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "content": "4"}]} +{"messages": [{"role": "user", "content": "Hello, bot."}, {"role": "assistant", "content": "Hi! How can I assist?"}]} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/train.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/train.json index fcb882ece..f4b26cff3 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/train.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/train.json @@ -1,2 +1,2 @@ -{"messages": [{"role": "user", "content": "Tell me a joke."}, {"role": "assistant", "content": "Why did the scarecrow win an award? Because he was outstanding in his field."}]} -{"messages": [{"role": "user", "content": "Who are you?"}, {"role": "assistant", "content": "I am your assistant."}]} +{"messages": [{"role": "user", "content": "Tell me a joke."}, {"role": "assistant", "content": "Why did the scarecrow win an award? Because he was outstanding in his field."}]} +{"messages": [{"role": "user", "content": "Who are you?"}, {"role": "assistant", "content": "I am your assistant."}]} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/foundry/MODEL_CARD.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/foundry/MODEL_CARD.json index 5043563cd..a40054b6b 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/foundry/MODEL_CARD.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/foundry/MODEL_CARD.json @@ -1,12 +1,12 @@ -{ - "name": "Phi-3.x LoRA Adapter", - "version": "1.0.0", - "description": "LoRA adapter trained for Phi-3.x mini instruct", - "tags": { - "task": "chat-completions", - "method": "LoRA", - "base_model": "microsoft/Phi-3.5-mini-instruct" - }, - "license": "mit", - "author": "QAI Project" -} +{ + "name": "Phi-3.x LoRA Adapter", + "version": "1.0.0", + "description": "LoRA adapter trained for Phi-3.x mini instruct", + "tags": { + "task": "chat-completions", + "method": "LoRA", + "base_model": "microsoft/Phi-3.5-mini-instruct" + }, + "license": "mit", + "author": "QAI Project" +} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/foundry/score_foundry.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/foundry/score_foundry.py index 5ab0e0ed2..51c91177f 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/foundry/score_foundry.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/foundry/score_foundry.py @@ -1,98 +1,98 @@ -"""Scoring script for Azure AI Foundry managed online endpoint. -Loads base model and applies LoRA adapter for inference. -Environment variables: - BASE_MODEL_ID - Hugging Face model id (e.g. microsoft/Phi-3.5-mini-instruct) - ADAPTER_SUBPATH - Relative path to LoRA adapter inside model asset (default: lora_adapter) - MAX_NEW_TOKENS - Generation cap (default: 128) - USE_BF16 - '1' to prefer bfloat16 if available -Input JSON: - {"messages": [{"role":"user","content":"Hello"}]} -Output JSON: - {"output":"..."} -""" - -import json -import os -from pathlib import Path -from typing import Dict, List - -import torch -from peft import PeftModel -from transformers import AutoModelForCausalLM, AutoTokenizer - -_model = None -_tokenizer = None - - -def _load_model(): - global _model, _tokenizer - if _model is not None: - return - base_id = os.environ.get("BASE_MODEL_ID", "microsoft/Phi-3.5-mini-instruct") - adapter_sub = os.environ.get("ADAPTER_SUBPATH", "lora_adapter") - adapter_path = Path(__file__).resolve().parent / adapter_sub - dtype = ( - torch.bfloat16 - if (os.environ.get("USE_BF16") == "1" and torch.cuda.is_available()) - else torch.float16 - ) - _tokenizer = AutoTokenizer.from_pretrained(base_id, use_fast=True) - if _tokenizer.pad_token is None: - _tokenizer.pad_token = _tokenizer.eos_token - base = AutoModelForCausalLM.from_pretrained( - base_id, torch_dtype=dtype, device_map="auto" - ) - if adapter_path.exists(): - _model = PeftModel.from_pretrained(base, str(adapter_path)) - else: - _model = base - _model.eval() - - -def init(): # Azure ML entrypoint - _load_model() - print("Model + adapter loaded.") - - -def _apply_chat_template(messages: List[Dict[str, str]]) -> str: - # Fallback simple format if tokenizer lacks chat template - if hasattr(_tokenizer, "apply_chat_template"): - return _tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True - ) - parts = [] - for m in messages: - role = m.get("role", "user") - content = m.get("content", "") - parts.append(f"<|{role}|>\n{content}\n") - parts.append("<|assistant|>\n") - return "\n".join(parts) - - -def run(raw: str) -> str: # Azure ML entrypoint - _load_model() - try: - obj = json.loads(raw) - messages = obj.get("messages") - if not isinstance(messages, list) or not messages: - return json.dumps({"error": "Missing messages"}) - prompt = _apply_chat_template(messages) - max_new = int(os.environ.get("MAX_NEW_TOKENS", "128")) - inputs = _tokenizer(prompt, return_tensors="pt").to(_model.device) - with torch.no_grad(): - gen = _model.generate( - **inputs, - max_new_tokens=max_new, - do_sample=True, - temperature=0.8, - top_p=0.95, - ) - full = _tokenizer.decode(gen[0], skip_special_tokens=True) - # Extract assistant portion if template used - if "<|assistant|>" in full: - assistant_part = full.split("<|assistant|>")[-1].strip() - else: - assistant_part = full - return json.dumps({"output": assistant_part}) - except Exception as e: - return json.dumps({"error": str(e)}) +"""Scoring script for Azure AI Foundry managed online endpoint. +Loads base model and applies LoRA adapter for inference. +Environment variables: + BASE_MODEL_ID - Hugging Face model id (e.g. microsoft/Phi-3.5-mini-instruct) + ADAPTER_SUBPATH - Relative path to LoRA adapter inside model asset (default: lora_adapter) + MAX_NEW_TOKENS - Generation cap (default: 128) + USE_BF16 - '1' to prefer bfloat16 if available +Input JSON: + {"messages": [{"role":"user","content":"Hello"}]} +Output JSON: + {"output":"..."} +""" + +import json +import os +from pathlib import Path +from typing import Dict, List + +import torch +from peft import PeftModel +from transformers import AutoModelForCausalLM, AutoTokenizer + +_model = None +_tokenizer = None + + +def _load_model(): + global _model, _tokenizer + if _model is not None: + return + base_id = os.environ.get("BASE_MODEL_ID", "microsoft/Phi-3.5-mini-instruct") + adapter_sub = os.environ.get("ADAPTER_SUBPATH", "lora_adapter") + adapter_path = Path(__file__).resolve().parent / adapter_sub + dtype = ( + torch.bfloat16 + if (os.environ.get("USE_BF16") == "1" and torch.cuda.is_available()) + else torch.float16 + ) + _tokenizer = AutoTokenizer.from_pretrained(base_id, use_fast=True) + if _tokenizer.pad_token is None: + _tokenizer.pad_token = _tokenizer.eos_token + base = AutoModelForCausalLM.from_pretrained( + base_id, torch_dtype=dtype, device_map="auto" + ) + if adapter_path.exists(): + _model = PeftModel.from_pretrained(base, str(adapter_path)) + else: + _model = base + _model.eval() + + +def init(): # Azure ML entrypoint + _load_model() + print("Model + adapter loaded.") + + +def _apply_chat_template(messages: List[Dict[str, str]]) -> str: + # Fallback simple format if tokenizer lacks chat template + if hasattr(_tokenizer, "apply_chat_template"): + return _tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + parts = [] + for m in messages: + role = m.get("role", "user") + content = m.get("content", "") + parts.append(f"<|{role}|>\n{content}\n") + parts.append("<|assistant|>\n") + return "\n".join(parts) + + +def run(raw: str) -> str: # Azure ML entrypoint + _load_model() + try: + obj = json.loads(raw) + messages = obj.get("messages") + if not isinstance(messages, list) or not messages: + return json.dumps({"error": "Missing messages"}) + prompt = _apply_chat_template(messages) + max_new = int(os.environ.get("MAX_NEW_TOKENS", "128")) + inputs = _tokenizer(prompt, return_tensors="pt").to(_model.device) + with torch.no_grad(): + gen = _model.generate( + **inputs, + max_new_tokens=max_new, + do_sample=True, + temperature=0.8, + top_p=0.95, + ) + full = _tokenizer.decode(gen[0], skip_special_tokens=True) + # Extract assistant portion if template used + if "<|assistant|>" in full: + assistant_part = full.split("<|assistant|>")[-1].strip() + else: + assistant_part = full + return json.dumps({"output": assistant_part}) + except Exception as e: + return json.dumps({"error": str(e)}) diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/README.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/README.md index 38043f9c6..c88b32042 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/README.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/README.md @@ -1,224 +1,224 @@ -# Local LoRA Fine-Tuning - Quick Start - -Train Phi-3.5 locally on your own data with LoRA, optimized for consumer hardware (single GPU or CPU). - -## Features - -- **No Azure dependencies** - runs completely offline after model download -- **Memory efficient** - optimized for consumer GPUs (RTX 3060, M1/M2, etc.) -- **Quantization support** - 4-bit and 8-bit training (QLoRA) for limited VRAM -- **Simple configuration** - single YAML file for all settings -- **Resume from checkpoint** - continue training if interrupted - -## Quick Setup - -### 1. Create Virtual Environment - -```powershell -# Navigate to local_train directory -cd AI\microsoft_phi-silica-3.6_v1\local_train - -# Create and activate venv -python -m venv venv -.\venv\Scripts\Activate.ps1 -``` - -### 2. Install Dependencies - -```powershell -pip install -r requirements.txt - -# Optional: For 4-bit/8-bit quantization (saves VRAM) -pip install bitsandbytes -``` - -### 3. Prepare Your Data - -Create `data/train.json` and `data/test.json` in JSONL format: - -```jsonl -{"messages": [{"role": "user", "content": "What is AI?"}, {"role": "assistant", "content": "Artificial Intelligence..."}]} -{"messages": [{"role": "user", "content": "Explain LoRA"}, {"role": "assistant", "content": "LoRA is a technique..."}]} -``` - -**Sample data is included** in `../data/` for testing. - -### 4. Train the Model - -```powershell -# Quick test (10 samples, fast iteration) -python train_local.py --config local_config.yaml --max-samples 10 - -# Full training -python train_local.py --config local_config.yaml - -# With 4-bit quantization (for limited VRAM) -python train_local.py --config local_config.yaml --use-4bit - -# Resume from checkpoint -python train_local.py --config local_config.yaml --resume-from outputs/checkpoint-500 -``` - -### 5. Evaluate the Model - -```powershell -# Evaluate trained model on test set -python train_local.py --config local_config.yaml --eval-only --resume-from outputs/final_model -``` - -## Configuration - -Edit `local_config.yaml` to customize training: - -```yaml -# Model -model_id: "microsoft/Phi-3.5-mini-instruct" - -# Data -train_file: "train.json" -eval_file: "test.json" - -# Training -num_epochs: 3 -batch_size: 1 -gradient_accumulation_steps: 4 -learning_rate: 0.0002 -max_seq_length: 512 - -# LoRA -lora_r: 16 -lora_alpha: 32 -lora_dropout: 0.05 - -# Memory optimization -use_bf16: true -gradient_checkpointing: true -use_4bit: false # Enable for QLoRA (saves VRAM) -``` - -## Common Scenarios - -### Limited VRAM (< 8GB) - -```yaml -# local_config.yaml -use_4bit: true -max_seq_length: 256 -gradient_checkpointing: true -batch_size: 1 -gradient_accumulation_steps: 8 -``` - -```powershell -python train_local.py --config local_config.yaml --use-4bit -``` - -### CPU-Only Training - -```yaml -# local_config.yaml -use_fp16: false -use_bf16: false -batch_size: 1 -gradient_accumulation_steps: 16 -``` - -```powershell -python train_local.py --config local_config.yaml -``` - -### Quick Experimentation - -```powershell -# Train on small subset (fast iteration) -python train_local.py --config local_config.yaml --max-samples 100 - -# Single epoch test -python train_local.py --config local_config.yaml --num-epochs 1 --max-samples 50 -``` - -## CLI Reference - -``` -python train_local.py [OPTIONS] - -Required: - --config PATH Path to local_config.yaml - -Optional: - --data-dir PATH Directory with train/eval files (default: ../data) - --num-epochs N Override config num_epochs - --max-samples N Limit dataset size (for testing) - --resume-from PATH Resume from checkpoint - --eval-only Only run evaluation (requires --resume-from) - --use-4bit Enable 4-bit quantization (QLoRA) - --use-8bit Enable 8-bit quantization -``` - -## Output Structure - -``` -outputs/ -├── checkpoint-100/ # Intermediate checkpoints -├── checkpoint-200/ -├── final_model/ # Final LoRA adapter -│ ├── adapter_config.json -│ ├── adapter_model.bin -│ └── tokenizer files -└── training_metrics.json # Loss and perplexity per step -``` - -## Tips - -1. **Start small**: Use `--max-samples 10` for quick validation -2. **Monitor VRAM**: Watch `nvidia-smi` (GPU) or Task Manager (CPU) -3. **Adjust batch size**: If OOM, reduce `batch_size` and increase `gradient_accumulation_steps` -4. **Use quantization**: Enable `--use-4bit` for low VRAM (< 8GB) -5. **Check perplexity**: Lower is better - look for decreasing trend in logs - -## Troubleshooting - -### Out of Memory (OOM) - -```yaml -# Reduce memory footprint -use_4bit: true -max_seq_length: 256 -batch_size: 1 -gradient_accumulation_steps: 16 -``` - -### Slow Training - -```yaml -# Speed up at cost of VRAM -gradient_checkpointing: false -batch_size: 2 -gradient_accumulation_steps: 2 -``` - -### Model Not Learning - -- Increase `lora_r` (16 → 32) -- Check learning rate (try 1e-4 to 5e-4) -- Verify data quality (check tokenization) -- Increase `num_epochs` or dataset size - -## Next Steps - -- **Use the model**: Load adapter with `PeftModel.from_pretrained()` -- **Merge adapter**: Merge LoRA weights into base model for deployment -- **Experiment**: Try different `lora_r`, learning rates, datasets -- **Scale up**: Move to Azure for multi-GPU training (see `../scripts/train_lora.py`) - -## Differences from Azure Setup - -| Feature | Local Setup | Azure Setup (../scripts/) | -|---------|-------------|---------------------------| -| Dependencies | Minimal (no Azure SDKs) | Full (Azure Blob, Log Analytics, App Insights) | -| Data Loading | Local files only | Blob Storage manifests, streaming | -| Multi-GPU | Manual Accelerate launch | Built-in DeepSpeed support | -| Observability | File logging only | OpenTelemetry, App Insights, Log Analytics | -| Deployment | Local use | Container Apps Jobs | - -For production-scale training with multi-GPU, observability, and cloud storage, use the Azure setup in `../scripts/`. +# Local LoRA Fine-Tuning - Quick Start + +Train Phi-3.5 locally on your own data with LoRA, optimized for consumer hardware (single GPU or CPU). + +## Features + +- **No Azure dependencies** - runs completely offline after model download +- **Memory efficient** - optimized for consumer GPUs (RTX 3060, M1/M2, etc.) +- **Quantization support** - 4-bit and 8-bit training (QLoRA) for limited VRAM +- **Simple configuration** - single YAML file for all settings +- **Resume from checkpoint** - continue training if interrupted + +## Quick Setup + +### 1. Create Virtual Environment + +```powershell +# Navigate to local_train directory +cd AI\microsoft_phi-silica-3.6_v1\local_train + +# Create and activate venv +python -m venv venv +.\venv\Scripts\Activate.ps1 +``` + +### 2. Install Dependencies + +```powershell +pip install -r requirements.txt + +# Optional: For 4-bit/8-bit quantization (saves VRAM) +pip install bitsandbytes +``` + +### 3. Prepare Your Data + +Create `data/train.json` and `data/test.json` in JSONL format: + +```jsonl +{"messages": [{"role": "user", "content": "What is AI?"}, {"role": "assistant", "content": "Artificial Intelligence..."}]} +{"messages": [{"role": "user", "content": "Explain LoRA"}, {"role": "assistant", "content": "LoRA is a technique..."}]} +``` + +**Sample data is included** in `../data/` for testing. + +### 4. Train the Model + +```powershell +# Quick test (10 samples, fast iteration) +python train_local.py --config local_config.yaml --max-samples 10 + +# Full training +python train_local.py --config local_config.yaml + +# With 4-bit quantization (for limited VRAM) +python train_local.py --config local_config.yaml --use-4bit + +# Resume from checkpoint +python train_local.py --config local_config.yaml --resume-from outputs/checkpoint-500 +``` + +### 5. Evaluate the Model + +```powershell +# Evaluate trained model on test set +python train_local.py --config local_config.yaml --eval-only --resume-from outputs/final_model +``` + +## Configuration + +Edit `local_config.yaml` to customize training: + +```yaml +# Model +model_id: "microsoft/Phi-3.5-mini-instruct" + +# Data +train_file: "train.json" +eval_file: "test.json" + +# Training +num_epochs: 3 +batch_size: 1 +gradient_accumulation_steps: 4 +learning_rate: 0.0002 +max_seq_length: 512 + +# LoRA +lora_r: 16 +lora_alpha: 32 +lora_dropout: 0.05 + +# Memory optimization +use_bf16: true +gradient_checkpointing: true +use_4bit: false # Enable for QLoRA (saves VRAM) +``` + +## Common Scenarios + +### Limited VRAM (< 8GB) + +```yaml +# local_config.yaml +use_4bit: true +max_seq_length: 256 +gradient_checkpointing: true +batch_size: 1 +gradient_accumulation_steps: 8 +``` + +```powershell +python train_local.py --config local_config.yaml --use-4bit +``` + +### CPU-Only Training + +```yaml +# local_config.yaml +use_fp16: false +use_bf16: false +batch_size: 1 +gradient_accumulation_steps: 16 +``` + +```powershell +python train_local.py --config local_config.yaml +``` + +### Quick Experimentation + +```powershell +# Train on small subset (fast iteration) +python train_local.py --config local_config.yaml --max-samples 100 + +# Single epoch test +python train_local.py --config local_config.yaml --num-epochs 1 --max-samples 50 +``` + +## CLI Reference + +``` +python train_local.py [OPTIONS] + +Required: + --config PATH Path to local_config.yaml + +Optional: + --data-dir PATH Directory with train/eval files (default: ../data) + --num-epochs N Override config num_epochs + --max-samples N Limit dataset size (for testing) + --resume-from PATH Resume from checkpoint + --eval-only Only run evaluation (requires --resume-from) + --use-4bit Enable 4-bit quantization (QLoRA) + --use-8bit Enable 8-bit quantization +``` + +## Output Structure + +``` +outputs/ +├── checkpoint-100/ # Intermediate checkpoints +├── checkpoint-200/ +├── final_model/ # Final LoRA adapter +│ ├── adapter_config.json +│ ├── adapter_model.bin +│ └── tokenizer files +└── training_metrics.json # Loss and perplexity per step +``` + +## Tips + +1. **Start small**: Use `--max-samples 10` for quick validation +2. **Monitor VRAM**: Watch `nvidia-smi` (GPU) or Task Manager (CPU) +3. **Adjust batch size**: If OOM, reduce `batch_size` and increase `gradient_accumulation_steps` +4. **Use quantization**: Enable `--use-4bit` for low VRAM (< 8GB) +5. **Check perplexity**: Lower is better - look for decreasing trend in logs + +## Troubleshooting + +### Out of Memory (OOM) + +```yaml +# Reduce memory footprint +use_4bit: true +max_seq_length: 256 +batch_size: 1 +gradient_accumulation_steps: 16 +``` + +### Slow Training + +```yaml +# Speed up at cost of VRAM +gradient_checkpointing: false +batch_size: 2 +gradient_accumulation_steps: 2 +``` + +### Model Not Learning + +- Increase `lora_r` (16 → 32) +- Check learning rate (try 1e-4 to 5e-4) +- Verify data quality (check tokenization) +- Increase `num_epochs` or dataset size + +## Next Steps + +- **Use the model**: Load adapter with `PeftModel.from_pretrained()` +- **Merge adapter**: Merge LoRA weights into base model for deployment +- **Experiment**: Try different `lora_r`, learning rates, datasets +- **Scale up**: Move to Azure for multi-GPU training (see `../scripts/train_lora.py`) + +## Differences from Azure Setup + +| Feature | Local Setup | Azure Setup (../scripts/) | +|---------|-------------|---------------------------| +| Dependencies | Minimal (no Azure SDKs) | Full (Azure Blob, Log Analytics, App Insights) | +| Data Loading | Local files only | Blob Storage manifests, streaming | +| Multi-GPU | Manual Accelerate launch | Built-in DeepSpeed support | +| Observability | File logging only | OpenTelemetry, App Insights, Log Analytics | +| Deployment | Local use | Container Apps Jobs | + +For production-scale training with multi-GPU, observability, and cloud storage, use the Azure setup in `../scripts/`. diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/local_config.yaml b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/local_config.yaml index 250f8318b..c5f7b5626 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/local_config.yaml +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/local_config.yaml @@ -1,45 +1,45 @@ -# Local LoRA fine-tuning configuration -# Optimized for consumer hardware (single GPU or CPU) - -# Model (choose one - uncomment your preference) -model_id: "mistralai/Mistral-7B-Instruct-v0.3" # Mistral 7B v0.3 - excellent performance -# model_id: "mistralai/Mixtral-8x7B-Instruct-v0.1" # Mixtral 8x7B - MoE, very powerful -# model_id: "Qwen/Qwen2.5-3B-Instruct" # Qwen 2.5 3B (Sep 2024) - lighter option -# model_id: "microsoft/Phi-3.5-mini-instruct" # Phi-3.5 3.8B - Microsoft's latest open -# model_id: "TinyLlama/TinyLlama-1.1B-Chat-v1.0" # Legacy fallback for very limited hardware - -# Data files (relative to data directory) -train_file: "train.json" -eval_file: "test.json" - -# Output -output_dir: "outputs" - -# Training hyperparameters -num_epochs: 1 -batch_size: 1 -gradient_accumulation_steps: 1 # Effective batch size = batch_size * gradient_accumulation_steps -learning_rate: 0.0002 -max_seq_length: 256 # Reduce for limited VRAM -warmup_steps: 20 - -# LoRA configuration -lora_r: 16 # Rank (higher = more parameters, better quality, slower) -lora_alpha: 32 # Scaling factor (usually 2*r) -lora_dropout: 0.05 - -# Optimization flags -use_fp16: false # Use FP16 (for older GPUs) -use_bf16: false # CPU training, disable BF16 -gradient_checkpointing: true # Save VRAM at cost of speed -use_8bit: false # 8-bit quantization (saves VRAM) -use_4bit: false # 4-bit quantization (QLoRA - saves even more VRAM) - -# Logging and checkpointing -logging_steps: 5 -eval_steps: 20 -save_steps: 100 -save_total_limit: 1 # Keep only N best checkpoints - -# Random seed -seed: 42 +# Local LoRA fine-tuning configuration +# Optimized for consumer hardware (single GPU or CPU) + +# Model (choose one - uncomment your preference) +model_id: "mistralai/Mistral-7B-Instruct-v0.3" # Mistral 7B v0.3 - excellent performance +# model_id: "mistralai/Mixtral-8x7B-Instruct-v0.1" # Mixtral 8x7B - MoE, very powerful +# model_id: "Qwen/Qwen2.5-3B-Instruct" # Qwen 2.5 3B (Sep 2024) - lighter option +# model_id: "microsoft/Phi-3.5-mini-instruct" # Phi-3.5 3.8B - Microsoft's latest open +# model_id: "TinyLlama/TinyLlama-1.1B-Chat-v1.0" # Legacy fallback for very limited hardware + +# Data files (relative to data directory) +train_file: "train.json" +eval_file: "test.json" + +# Output +output_dir: "outputs" + +# Training hyperparameters +num_epochs: 1 +batch_size: 1 +gradient_accumulation_steps: 1 # Effective batch size = batch_size * gradient_accumulation_steps +learning_rate: 0.0002 +max_seq_length: 256 # Reduce for limited VRAM +warmup_steps: 20 + +# LoRA configuration +lora_r: 16 # Rank (higher = more parameters, better quality, slower) +lora_alpha: 32 # Scaling factor (usually 2*r) +lora_dropout: 0.05 + +# Optimization flags +use_fp16: false # Use FP16 (for older GPUs) +use_bf16: false # CPU training, disable BF16 +gradient_checkpointing: true # Save VRAM at cost of speed +use_8bit: false # 8-bit quantization (saves VRAM) +use_4bit: false # 4-bit quantization (QLoRA - saves even more VRAM) + +# Logging and checkpointing +logging_steps: 5 +eval_steps: 20 +save_steps: 100 +save_total_limit: 1 # Keep only N best checkpoints + +# Random seed +seed: 42 diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/requirements.txt b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/requirements.txt index 1227500e1..dd001620e 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/requirements.txt +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/requirements.txt @@ -1,20 +1,20 @@ -# Local LoRA Fine-tuning - Minimal Requirements -# No Azure dependencies - just core ML libraries - -# Core ML frameworks -torch>=2.8.0 -transformers>=4.47.0 -peft>=0.14.0 -datasets>=2.21.0 -accelerate>=0.36.0 - -# Utilities -pyyaml>=6.0 -tqdm>=4.67.0 - -# Optional: Quantization support (for 4bit/8bit training) -# Uncomment if using use_4bit or use_8bit in config: -# bitsandbytes>=0.41.0 - -# Optional: Fast tokenizers -# tokenizers>=0.15.0 +# Local LoRA Fine-tuning - Minimal Requirements +# No Azure dependencies - just core ML libraries + +# Core ML frameworks +torch>=2.8.0 +transformers>=4.47.0 +peft>=0.14.0 +datasets>=2.21.0 +accelerate>=0.36.0 + +# Utilities +pyyaml>=6.0 +tqdm>=4.67.0 + +# Optional: Quantization support (for 4bit/8bit training) +# Uncomment if using use_4bit or use_8bit in config: +# bitsandbytes>=0.41.0 + +# Optional: Fast tokenizers +# tokenizers>=0.15.0 diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/train_local.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/train_local.py index 68ecbf646..7e6868a8c 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/train_local.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/train_local.py @@ -1,349 +1,349 @@ -#!/usr/bin/env python3 -""" -Local LoRA fine-tuning script - simplified for rapid local iteration. -No Azure dependencies, focused on consumer hardware (single GPU or CPU). -""" -import argparse -import json -import math -from dataclasses import dataclass -from pathlib import Path -from typing import Dict, List - -try: - import yaml -except ImportError: - raise SystemExit("Install pyyaml: pip install pyyaml") from None - -try: - import torch - from peft import (LoraConfig, PeftModel, get_peft_model, - prepare_model_for_kbit_training) - from transformers import (AutoModelForCausalLM, AutoTokenizer, - DataCollatorForLanguageModeling, Trainer, - TrainingArguments) - - from datasets import load_dataset -except ImportError as e: - raise SystemExit( - f"Missing training dependencies: {e}\nInstall with: pip install -r requirements.txt" - ) from e - - -@dataclass -class LocalConfig: - """Local training configuration""" - - model_id: str = "microsoft/Phi-3.5-mini-instruct" - train_file: str = "train.json" - eval_file: str = "test.json" - output_dir: str = "outputs" - - # Training params - num_epochs: int = 3 - batch_size: int = 1 - gradient_accumulation_steps: int = 4 - learning_rate: float = 2e-4 - max_seq_length: int = 512 - warmup_steps: int = 100 - - # LoRA params - lora_r: int = 16 - lora_alpha: int = 32 - lora_dropout: float = 0.05 - - # Optimization - use_fp16: bool = False - use_bf16: bool = True - gradient_checkpointing: bool = True - use_8bit: bool = False - use_4bit: bool = False - - # Logging - logging_steps: int = 10 - eval_steps: int = 50 - save_steps: int = 100 - save_total_limit: int = 3 - - seed: int = 42 - - -def load_config(config_path: Path) -> LocalConfig: - """Load config from YAML or use defaults""" - if config_path.exists(): - with config_path.open("r") as f: - cfg_dict = yaml.safe_load(f) or {} - return LocalConfig( - **{k: v for k, v in cfg_dict.items() if hasattr(LocalConfig, k)} - ) - return LocalConfig() - - -def format_chat_messages(messages: List[Dict[str, str]], tokenizer) -> str: - """Format chat messages using tokenizer template or fallback""" - if hasattr(tokenizer, "apply_chat_template"): - return tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=False - ) - - # Fallback formatting - parts = [] - for msg in messages: - role = msg.get("role", "user") - content = msg.get("content", "") - if role == "system": - parts.append(f"<|system|>\n{content}\n") - elif role == "user": - parts.append(f"<|user|>\n{content}\n") - elif role == "assistant": - parts.append(f"<|assistant|>\n{content}\n") - return "".join(parts) - - -def main(): - parser = argparse.ArgumentParser(description="Local LoRA fine-tuning") - parser.add_argument( - "--config", type=str, default="local_config.yaml", help="Config file" - ) - parser.add_argument( - "--data-dir", type=str, default="../data", help="Dataset directory" - ) - parser.add_argument("--model", type=str, help="Override model ID") - parser.add_argument("--output", type=str, help="Override output directory") - parser.add_argument("--epochs", type=int, help="Override epochs") - parser.add_argument("--batch-size", type=int, help="Override batch size") - parser.add_argument("--max-samples", type=int, help="Limit samples for testing") - parser.add_argument("--resume", type=str, help="Resume from checkpoint") - parser.add_argument("--eval-only", action="store_true", help="Only run evaluation") - args = parser.parse_args() - - # Load config - config_path = Path(__file__).parent / args.config - cfg = load_config(config_path) - - # Apply CLI overrides - if args.model: - cfg.model_id = args.model - if args.output: - cfg.output_dir = args.output - if args.epochs: - cfg.num_epochs = args.epochs - if args.batch_size: - cfg.batch_size = args.batch_size - - # Setup paths - data_dir = Path(args.data_dir).resolve() - train_path = data_dir / cfg.train_file - eval_path = data_dir / cfg.eval_file - output_dir = Path(cfg.output_dir).resolve() - output_dir.mkdir(parents=True, exist_ok=True) - - print(f"\n{'='*60}") - print("LOCAL LORA FINE-TUNING") - print(f"{'='*60}") - print(f"Model: {cfg.model_id}") - print(f"Train: {train_path}") - print(f"Eval: {eval_path}") - print(f"Output: {output_dir}") - print(f"Device: {'CUDA' if torch.cuda.is_available() else 'CPU'}") - print(f"{'='*60}\n") - - # Load tokenizer - print("Loading tokenizer...") - tokenizer = AutoTokenizer.from_pretrained(cfg.model_id, trust_remote_code=True) - if tokenizer.pad_token is None: - tokenizer.pad_token = tokenizer.eos_token - - # Preprocessing function - def preprocess_function(examples): - texts = [] - for msgs in examples["messages"]: - text = format_chat_messages(msgs, tokenizer) - texts.append(text) - - return tokenizer( - texts, - truncation=True, - max_length=cfg.max_seq_length, - padding=False, - ) - - # Load datasets - print("Loading datasets...") - dataset = load_dataset( - "json", - data_files={"train": str(train_path), "validation": str(eval_path)}, - ) - - if args.max_samples: - dataset["train"] = dataset["train"].select( - range(min(args.max_samples, len(dataset["train"]))) - ) - dataset["validation"] = dataset["validation"].select( - range(min(args.max_samples // 4, len(dataset["validation"]))) - ) - - print(f"Train samples: {len(dataset['train'])}") - print(f"Eval samples: {len(dataset['validation'])}") - - # Tokenize - print("Tokenizing...") - tokenized_datasets = dataset.map( - preprocess_function, - batched=True, - remove_columns=dataset["train"].column_names, - ) - - # Load model - print(f"Loading model: {cfg.model_id}") - - quantization_config = None - if cfg.use_4bit: - from transformers import BitsAndBytesConfig - - quantization_config = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_compute_dtype=torch.bfloat16 if cfg.use_bf16 else torch.float16, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - ) - elif cfg.use_8bit: - from transformers import BitsAndBytesConfig - - quantization_config = BitsAndBytesConfig(load_in_8bit=True) - - model = AutoModelForCausalLM.from_pretrained( - cfg.model_id, - quantization_config=quantization_config, - device_map="auto" if torch.cuda.is_available() else None, - trust_remote_code=True, - torch_dtype=( - torch.bfloat16 - if cfg.use_bf16 - else (torch.float16 if cfg.use_fp16 else torch.float32) - ), - ) - - if quantization_config: - model = prepare_model_for_kbit_training(model) - - if cfg.gradient_checkpointing: - model.gradient_checkpointing_enable() - model.config.use_cache = False - - # Configure LoRA - print("Configuring LoRA...") - lora_config = LoraConfig( - r=cfg.lora_r, - lora_alpha=cfg.lora_alpha, - target_modules=[ - "q_proj", - "v_proj", - "k_proj", - "o_proj", - "gate_proj", - "up_proj", - "down_proj", - ], - lora_dropout=cfg.lora_dropout, - bias="none", - task_type="CAUSAL_LM", - ) - - if args.resume: - print(f"Resuming from: {args.resume}") - model = PeftModel.from_pretrained(model, args.resume) - else: - model = get_peft_model(model, lora_config) - - model.print_trainable_parameters() - - # Training arguments - training_args = TrainingArguments( - output_dir=str(output_dir), - num_train_epochs=cfg.num_epochs, - per_device_train_batch_size=cfg.batch_size, - per_device_eval_batch_size=cfg.batch_size, - gradient_accumulation_steps=cfg.gradient_accumulation_steps, - learning_rate=cfg.learning_rate, - warmup_steps=cfg.warmup_steps, - logging_steps=cfg.logging_steps, - eval_steps=cfg.eval_steps, - save_steps=cfg.save_steps, - eval_strategy="steps", - save_strategy="steps", - save_total_limit=cfg.save_total_limit, - fp16=cfg.use_fp16 and not cfg.use_bf16, - bf16=cfg.use_bf16, - gradient_checkpointing=cfg.gradient_checkpointing, - dataloader_pin_memory=True, - dataloader_num_workers=0, - remove_unused_columns=False, - seed=cfg.seed, - load_best_model_at_end=True, - metric_for_best_model="eval_loss", - report_to="none", # Disable W&B, tensorboard, etc. - ) - - # Data collator - data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False) - - # Trainer - trainer = Trainer( - model=model, - args=training_args, - train_dataset=tokenized_datasets["train"], - eval_dataset=tokenized_datasets["validation"], - tokenizer=tokenizer, - data_collator=data_collator, - ) - - # Evaluate before training - if not args.resume: - print("\nPre-training evaluation...") - pre_metrics = trainer.evaluate() - try: - ppl = math.exp(pre_metrics["eval_loss"]) - print(f"Pre-training perplexity: {ppl:.2f}") - except Exception: - pass - - # Train or evaluate - if args.eval_only: - print("\nRunning evaluation...") - metrics = trainer.evaluate() - print("\nEvaluation results:") - for k, v in metrics.items(): - print(f" {k}: {v}") - else: - print("\nStarting training...") - trainer.train(resume_from_checkpoint=args.resume if args.resume else None) - - # Post-training evaluation - print("\nPost-training evaluation...") - post_metrics = trainer.evaluate() - try: - ppl = math.exp(post_metrics["eval_loss"]) - print(f"Post-training perplexity: {ppl:.2f}") - except Exception: - pass - - # Save final model - final_dir = output_dir / "final" - print(f"\nSaving final model to: {final_dir}") - trainer.model.save_pretrained(final_dir) - tokenizer.save_pretrained(final_dir) - - # Save metrics - metrics_file = output_dir / "metrics.json" - with metrics_file.open("w") as f: - json.dump(post_metrics, f, indent=2) - - print(f"\n{'='*60}") - print("Training complete!") - print(f"Model saved to: {final_dir}") - print(f"{'='*60}\n") - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +""" +Local LoRA fine-tuning script - simplified for rapid local iteration. +No Azure dependencies, focused on consumer hardware (single GPU or CPU). +""" +import argparse +import json +import math +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, List + +try: + import yaml +except ImportError: + raise SystemExit("Install pyyaml: pip install pyyaml") from None + +try: + import torch + from peft import (LoraConfig, PeftModel, get_peft_model, + prepare_model_for_kbit_training) + from transformers import (AutoModelForCausalLM, AutoTokenizer, + DataCollatorForLanguageModeling, Trainer, + TrainingArguments) + + from datasets import load_dataset +except ImportError as e: + raise SystemExit( + f"Missing training dependencies: {e}\nInstall with: pip install -r requirements.txt" + ) from e + + +@dataclass +class LocalConfig: + """Local training configuration""" + + model_id: str = "microsoft/Phi-3.5-mini-instruct" + train_file: str = "train.json" + eval_file: str = "test.json" + output_dir: str = "outputs" + + # Training params + num_epochs: int = 3 + batch_size: int = 1 + gradient_accumulation_steps: int = 4 + learning_rate: float = 2e-4 + max_seq_length: int = 512 + warmup_steps: int = 100 + + # LoRA params + lora_r: int = 16 + lora_alpha: int = 32 + lora_dropout: float = 0.05 + + # Optimization + use_fp16: bool = False + use_bf16: bool = True + gradient_checkpointing: bool = True + use_8bit: bool = False + use_4bit: bool = False + + # Logging + logging_steps: int = 10 + eval_steps: int = 50 + save_steps: int = 100 + save_total_limit: int = 3 + + seed: int = 42 + + +def load_config(config_path: Path) -> LocalConfig: + """Load config from YAML or use defaults""" + if config_path.exists(): + with config_path.open("r") as f: + cfg_dict = yaml.safe_load(f) or {} + return LocalConfig( + **{k: v for k, v in cfg_dict.items() if hasattr(LocalConfig, k)} + ) + return LocalConfig() + + +def format_chat_messages(messages: List[Dict[str, str]], tokenizer) -> str: + """Format chat messages using tokenizer template or fallback""" + if hasattr(tokenizer, "apply_chat_template"): + return tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=False + ) + + # Fallback formatting + parts = [] + for msg in messages: + role = msg.get("role", "user") + content = msg.get("content", "") + if role == "system": + parts.append(f"<|system|>\n{content}\n") + elif role == "user": + parts.append(f"<|user|>\n{content}\n") + elif role == "assistant": + parts.append(f"<|assistant|>\n{content}\n") + return "".join(parts) + + +def main(): + parser = argparse.ArgumentParser(description="Local LoRA fine-tuning") + parser.add_argument( + "--config", type=str, default="local_config.yaml", help="Config file" + ) + parser.add_argument( + "--data-dir", type=str, default="../data", help="Dataset directory" + ) + parser.add_argument("--model", type=str, help="Override model ID") + parser.add_argument("--output", type=str, help="Override output directory") + parser.add_argument("--epochs", type=int, help="Override epochs") + parser.add_argument("--batch-size", type=int, help="Override batch size") + parser.add_argument("--max-samples", type=int, help="Limit samples for testing") + parser.add_argument("--resume", type=str, help="Resume from checkpoint") + parser.add_argument("--eval-only", action="store_true", help="Only run evaluation") + args = parser.parse_args() + + # Load config + config_path = Path(__file__).parent / args.config + cfg = load_config(config_path) + + # Apply CLI overrides + if args.model: + cfg.model_id = args.model + if args.output: + cfg.output_dir = args.output + if args.epochs: + cfg.num_epochs = args.epochs + if args.batch_size: + cfg.batch_size = args.batch_size + + # Setup paths + data_dir = Path(args.data_dir).resolve() + train_path = data_dir / cfg.train_file + eval_path = data_dir / cfg.eval_file + output_dir = Path(cfg.output_dir).resolve() + output_dir.mkdir(parents=True, exist_ok=True) + + print(f"\n{'='*60}") + print("LOCAL LORA FINE-TUNING") + print(f"{'='*60}") + print(f"Model: {cfg.model_id}") + print(f"Train: {train_path}") + print(f"Eval: {eval_path}") + print(f"Output: {output_dir}") + print(f"Device: {'CUDA' if torch.cuda.is_available() else 'CPU'}") + print(f"{'='*60}\n") + + # Load tokenizer + print("Loading tokenizer...") + tokenizer = AutoTokenizer.from_pretrained(cfg.model_id, trust_remote_code=True) + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + # Preprocessing function + def preprocess_function(examples): + texts = [] + for msgs in examples["messages"]: + text = format_chat_messages(msgs, tokenizer) + texts.append(text) + + return tokenizer( + texts, + truncation=True, + max_length=cfg.max_seq_length, + padding=False, + ) + + # Load datasets + print("Loading datasets...") + dataset = load_dataset( + "json", + data_files={"train": str(train_path), "validation": str(eval_path)}, + ) + + if args.max_samples: + dataset["train"] = dataset["train"].select( + range(min(args.max_samples, len(dataset["train"]))) + ) + dataset["validation"] = dataset["validation"].select( + range(min(args.max_samples // 4, len(dataset["validation"]))) + ) + + print(f"Train samples: {len(dataset['train'])}") + print(f"Eval samples: {len(dataset['validation'])}") + + # Tokenize + print("Tokenizing...") + tokenized_datasets = dataset.map( + preprocess_function, + batched=True, + remove_columns=dataset["train"].column_names, + ) + + # Load model + print(f"Loading model: {cfg.model_id}") + + quantization_config = None + if cfg.use_4bit: + from transformers import BitsAndBytesConfig + + quantization_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=torch.bfloat16 if cfg.use_bf16 else torch.float16, + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type="nf4", + ) + elif cfg.use_8bit: + from transformers import BitsAndBytesConfig + + quantization_config = BitsAndBytesConfig(load_in_8bit=True) + + model = AutoModelForCausalLM.from_pretrained( + cfg.model_id, + quantization_config=quantization_config, + device_map="auto" if torch.cuda.is_available() else None, + trust_remote_code=True, + torch_dtype=( + torch.bfloat16 + if cfg.use_bf16 + else (torch.float16 if cfg.use_fp16 else torch.float32) + ), + ) + + if quantization_config: + model = prepare_model_for_kbit_training(model) + + if cfg.gradient_checkpointing: + model.gradient_checkpointing_enable() + model.config.use_cache = False + + # Configure LoRA + print("Configuring LoRA...") + lora_config = LoraConfig( + r=cfg.lora_r, + lora_alpha=cfg.lora_alpha, + target_modules=[ + "q_proj", + "v_proj", + "k_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj", + ], + lora_dropout=cfg.lora_dropout, + bias="none", + task_type="CAUSAL_LM", + ) + + if args.resume: + print(f"Resuming from: {args.resume}") + model = PeftModel.from_pretrained(model, args.resume) + else: + model = get_peft_model(model, lora_config) + + model.print_trainable_parameters() + + # Training arguments + training_args = TrainingArguments( + output_dir=str(output_dir), + num_train_epochs=cfg.num_epochs, + per_device_train_batch_size=cfg.batch_size, + per_device_eval_batch_size=cfg.batch_size, + gradient_accumulation_steps=cfg.gradient_accumulation_steps, + learning_rate=cfg.learning_rate, + warmup_steps=cfg.warmup_steps, + logging_steps=cfg.logging_steps, + eval_steps=cfg.eval_steps, + save_steps=cfg.save_steps, + eval_strategy="steps", + save_strategy="steps", + save_total_limit=cfg.save_total_limit, + fp16=cfg.use_fp16 and not cfg.use_bf16, + bf16=cfg.use_bf16, + gradient_checkpointing=cfg.gradient_checkpointing, + dataloader_pin_memory=True, + dataloader_num_workers=0, + remove_unused_columns=False, + seed=cfg.seed, + load_best_model_at_end=True, + metric_for_best_model="eval_loss", + report_to="none", # Disable W&B, tensorboard, etc. + ) + + # Data collator + data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False) + + # Trainer + trainer = Trainer( + model=model, + args=training_args, + train_dataset=tokenized_datasets["train"], + eval_dataset=tokenized_datasets["validation"], + tokenizer=tokenizer, + data_collator=data_collator, + ) + + # Evaluate before training + if not args.resume: + print("\nPre-training evaluation...") + pre_metrics = trainer.evaluate() + try: + ppl = math.exp(pre_metrics["eval_loss"]) + print(f"Pre-training perplexity: {ppl:.2f}") + except Exception: + pass + + # Train or evaluate + if args.eval_only: + print("\nRunning evaluation...") + metrics = trainer.evaluate() + print("\nEvaluation results:") + for k, v in metrics.items(): + print(f" {k}: {v}") + else: + print("\nStarting training...") + trainer.train(resume_from_checkpoint=args.resume if args.resume else None) + + # Post-training evaluation + print("\nPost-training evaluation...") + post_metrics = trainer.evaluate() + try: + ppl = math.exp(post_metrics["eval_loss"]) + print(f"Post-training perplexity: {ppl:.2f}") + except Exception: + pass + + # Save final model + final_dir = output_dir / "final" + print(f"\nSaving final model to: {final_dir}") + trainer.model.save_pretrained(final_dir) + tokenizer.save_pretrained(final_dir) + + # Save metrics + metrics_file = output_dir / "metrics.json" + with metrics_file.open("w") as f: + json.dump(post_metrics, f, indent=2) + + print(f"\n{'='*60}") + print("Training complete!") + print(f"Model saved to: {final_dir}") + print(f"{'='*60}\n") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/model.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/model.json index c01202d05..fdf0b22b5 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/model.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/model.json @@ -1,5 +1,5 @@ -{ - "MODEL_NAME": "Phi Silica", - "MODEL_VERSION": "3.6", - "MODEL_HOST": "Microsoft" +{ + "MODEL_NAME": "Phi Silica", + "MODEL_VERSION": "3.6", + "MODEL_HOST": "Microsoft" } diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning.config.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning.config.json index 01002983b..27ba36dfb 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning.config.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning.config.json @@ -1,10 +1,10 @@ -{ - "SUBSCRIPTION_ID": null, - "RESOURCE_GROUP_NAME": null, - "STORAGE_ACCOUNT_NAME": null, - "FILE_SHARE_NAME": null, - "ACA_JOB_NAME": null, - "LOG_ANALYTICS_NAME": null, - "COMMANDS": [], - "ARGS": ["mount//lora.yaml"] +{ + "SUBSCRIPTION_ID": null, + "RESOURCE_GROUP_NAME": null, + "STORAGE_ACCOUNT_NAME": null, + "FILE_SHARE_NAME": null, + "ACA_JOB_NAME": null, + "LOG_ANALYTICS_NAME": null, + "COMMANDS": [], + "ARGS": ["mount//lora.yaml"] } diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning.parameters.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning.parameters.json index af01d24a5..9e04cb3d7 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning.parameters.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning.parameters.json @@ -1,30 +1,30 @@ -{ - "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#", - "contentVersion": "1.0.0.0", - "parameters": { - "timeout": { - "value": 21600 - }, - "location": { - "value": null - }, - "storageAccountName": { - "value": null - }, - "fileShareName": { - "value": null - }, - "acaEnvironmentName": { - "value": null - }, - "acaEnvironmentStorageName": { - "value": null - }, - "acaJobName": { - "value": null - }, - "acaLogAnalyticsName": { - "value": null - } - } +{ + "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#", + "contentVersion": "1.0.0.0", + "parameters": { + "timeout": { + "value": 21600 + }, + "location": { + "value": null + }, + "storageAccountName": { + "value": null + }, + "fileShareName": { + "value": null + }, + "acaEnvironmentName": { + "value": null + }, + "acaEnvironmentStorageName": { + "value": null + }, + "acaJobName": { + "value": null + }, + "acaLogAnalyticsName": { + "value": null + } + } } diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning_blob_manifest.parameters.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning_blob_manifest.parameters.json index db69a3139..f742b9c77 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning_blob_manifest.parameters.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/infra/provision/finetuning_blob_manifest.parameters.json @@ -1,33 +1,33 @@ -{ - "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#", - "contentVersion": "1.0.0.0", - "parameters": { - "timeout": { - "value": 21600 - }, - "location": { - "value": null - }, - "storageAccountName": { - "value": null - }, - "fileShareName": { - "value": null - }, - "acaEnvironmentName": { - "value": null - }, - "acaEnvironmentStorageName": { - "value": null - }, - "acaJobName": { - "value": null - }, - "acaLogAnalyticsName": { - "value": null - }, - "manifestUrl": { - "value": "https://.blob.core.windows.net//train_manifest.txt?" - } - } -} +{ + "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#", + "contentVersion": "1.0.0.0", + "parameters": { + "timeout": { + "value": 21600 + }, + "location": { + "value": null + }, + "storageAccountName": { + "value": null + }, + "fileShareName": { + "value": null + }, + "acaEnvironmentName": { + "value": null + }, + "acaEnvironmentStorageName": { + "value": null + }, + "acaJobName": { + "value": null + }, + "acaLogAnalyticsName": { + "value": null + }, + "manifestUrl": { + "value": "https://.blob.core.windows.net//train_manifest.txt?" + } + } +} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora.yaml b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora.yaml index 6369537f3..3f3aa02dd 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora.yaml +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora.yaml @@ -1,37 +1,37 @@ -adam_beta1: !!float 0.9 -adam_beta2: !!float 0.95 -adam_epsilon: !!float 1E-08 -early_stopping_patience: 5 -epochs: 1 -eval_accumulation_steps: null -eval_delay: 0 -eval_steps: 64 -eval_strategy: "steps" -save_dir: "data_out/lora_training" -finetune_dataset: "data" -finetune_test_batch_size: 8 -finetune_test_nsamples: 16 -finetune_test_seqlen: 512 -finetune_train_batch_size: 1 -finetune_train_nsamples: 64 -finetune_train_seqlen: 512 -gradient_accumulation_steps: 4 # Effective batch size = 1 * 4 = 4 -gradient_checkpointing: true # Enable to reduce memory usage -learning_rate: !!float 0.0002 -lora_dropout: !!float 0.1 -lr_scheduler_type: "cosine" # Cosine annealing for better convergence -max_grad_norm: 1 -warmup_steps: 100 # Warmup for stable training start -early_stopping_threshold: !!float 0.01 # Stop if improvement < 1% -model: "gpt2" # For LoRA training with GPT-2 -model_path: "model" -num_training_steps: 100 -num_warmup_steps: 10 -save_steps: 64 -seed: 42 -weight_decay: 0 - -# LoRA/PEFT target modules for GPT-2 -target_modules: - - "c_attn" - - "c_proj" +adam_beta1: !!float 0.9 +adam_beta2: !!float 0.95 +adam_epsilon: !!float 1E-08 +early_stopping_patience: 5 +epochs: 1 +eval_accumulation_steps: null +eval_delay: 0 +eval_steps: 64 +eval_strategy: "steps" +save_dir: "data_out/lora_training" +finetune_dataset: "data" +finetune_test_batch_size: 8 +finetune_test_nsamples: 16 +finetune_test_seqlen: 512 +finetune_train_batch_size: 1 +finetune_train_nsamples: 64 +finetune_train_seqlen: 512 +gradient_accumulation_steps: 4 # Effective batch size = 1 * 4 = 4 +gradient_checkpointing: true # Enable to reduce memory usage +learning_rate: !!float 0.0002 +lora_dropout: !!float 0.1 +lr_scheduler_type: "cosine" # Cosine annealing for better convergence +max_grad_norm: 1 +warmup_steps: 100 # Warmup for stable training start +early_stopping_threshold: !!float 0.01 # Stop if improvement < 1% +model: "gpt2" # For LoRA training with GPT-2 +model_path: "model" +num_training_steps: 100 +num_warmup_steps: 10 +save_steps: 64 +seed: 42 +weight_decay: 0 + +# LoRA/PEFT target modules for GPT-2 +target_modules: + - "c_attn" + - "c_proj" diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_fast.yaml b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_fast.yaml index f0f83f2dc..e64dbec9d 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_fast.yaml +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_fast.yaml @@ -1,35 +1,35 @@ -adam_beta1: !!float 0.9 -adam_beta2: !!float 0.95 -adam_epsilon: !!float 1E-08 -early_stopping_patience: 3 # Reduced from 5 for faster training -epochs: 1 -eval_accumulation_steps: null -eval_delay: 0 -eval_steps: 32 # Reduced from 64 for more frequent checkpoints -eval_strategy: "steps" -save_dir: "data_out/lora_training" -finetune_dataset: "data" -finetune_test_batch_size: 16 # Increased from 8 for faster eval -finetune_test_nsamples: 16 -finetune_test_seqlen: 384 # Reduced from 512 for speed -finetune_train_batch_size: 2 # Increased from 1 for better GPU utilization -finetune_train_nsamples: 64 -finetune_train_seqlen: 384 # Reduced from 512 for speed -gradient_accumulation_steps: 2 # Added for larger effective batch size -gradient_checkpointing: true # Enabled for memory efficiency -learning_rate: !!float 0.0003 # Increased for faster convergence -lora_dropout: !!float 0.1 -lr_scheduler_type: "linear_with_warmup" -max_grad_norm: 1 -model: "microsoft/Phi-3.5-mini-instruct" -model_path: "model" -num_training_steps: 100 -num_warmup_steps: 5 # Reduced from 10 for faster start -save_steps: 32 # Reduced from 64 for more frequent saves -seed: 42 -weight_decay: 0 - -# Fast training optimizations -use_flash_attention: true # Enable if available for 2-3x speedup -dataloader_num_workers: 4 # Parallel data loading -pin_memory: true # Faster GPU transfer +adam_beta1: !!float 0.9 +adam_beta2: !!float 0.95 +adam_epsilon: !!float 1E-08 +early_stopping_patience: 3 # Reduced from 5 for faster training +epochs: 1 +eval_accumulation_steps: null +eval_delay: 0 +eval_steps: 32 # Reduced from 64 for more frequent checkpoints +eval_strategy: "steps" +save_dir: "data_out/lora_training" +finetune_dataset: "data" +finetune_test_batch_size: 16 # Increased from 8 for faster eval +finetune_test_nsamples: 16 +finetune_test_seqlen: 384 # Reduced from 512 for speed +finetune_train_batch_size: 2 # Increased from 1 for better GPU utilization +finetune_train_nsamples: 64 +finetune_train_seqlen: 384 # Reduced from 512 for speed +gradient_accumulation_steps: 2 # Added for larger effective batch size +gradient_checkpointing: true # Enabled for memory efficiency +learning_rate: !!float 0.0003 # Increased for faster convergence +lora_dropout: !!float 0.1 +lr_scheduler_type: "linear_with_warmup" +max_grad_norm: 1 +model: "microsoft/Phi-3.5-mini-instruct" +model_path: "model" +num_training_steps: 100 +num_warmup_steps: 5 # Reduced from 10 for faster start +save_steps: 32 # Reduced from 64 for more frequent saves +seed: 42 +weight_decay: 0 + +# Fast training optimizations +use_flash_attention: true # Enable if available for 2-3x speedup +dataloader_num_workers: 4 # Parallel data loading +pin_memory: true # Faster GPU transfer diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_qwen_ultrafast.yaml b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_qwen_ultrafast.yaml index 4a81ab2bd..89b8e46fc 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_qwen_ultrafast.yaml +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_qwen_ultrafast.yaml @@ -1,37 +1,37 @@ -adam_beta1: !!float 0.9 -adam_beta2: !!float 0.98 -adam_epsilon: !!float 1E-08 -early_stopping_patience: 2 -epochs: 1 -eval_accumulation_steps: null -eval_delay: 0 -eval_steps: 16 -eval_strategy: "steps" -save_dir: "data_out/lora_training" -finetune_dataset: "data" -finetune_test_batch_size: 16 # Smaller eval batch for stability -finetune_test_nsamples: 8 -finetune_test_seqlen: 192 -finetune_train_batch_size: 2 # Smaller batch for Qwen stability -finetune_train_nsamples: 32 -finetune_train_seqlen: 192 -gradient_accumulation_steps: 8 # Effective batch 16 -gradient_checkpointing: false # Disabled to avoid meta gradient issues -learning_rate: !!float 0.0003 -lora_dropout: !!float 0.05 -lr_scheduler_type: "linear_with_warmup" -max_grad_norm: 1 -model: "Qwen/Qwen2.5-3B-Instruct" -model_path: "model" -num_training_steps: 30 -num_warmup_steps: 3 -save_steps: 16 -seed: 42 -weight_decay: 0 - -# Qwen ultrafast optimizations -use_flash_attention: false # Disable if not stable -dataloader_num_workers: 4 -pin_memory: true -bf16: false # Disable mixed precision to avoid meta device mismatch -optim: "adamw_torch" # Standard optimizer for safety +adam_beta1: !!float 0.9 +adam_beta2: !!float 0.98 +adam_epsilon: !!float 1E-08 +early_stopping_patience: 2 +epochs: 1 +eval_accumulation_steps: null +eval_delay: 0 +eval_steps: 16 +eval_strategy: "steps" +save_dir: "data_out/lora_training" +finetune_dataset: "data" +finetune_test_batch_size: 16 # Smaller eval batch for stability +finetune_test_nsamples: 8 +finetune_test_seqlen: 192 +finetune_train_batch_size: 2 # Smaller batch for Qwen stability +finetune_train_nsamples: 32 +finetune_train_seqlen: 192 +gradient_accumulation_steps: 8 # Effective batch 16 +gradient_checkpointing: false # Disabled to avoid meta gradient issues +learning_rate: !!float 0.0003 +lora_dropout: !!float 0.05 +lr_scheduler_type: "linear_with_warmup" +max_grad_norm: 1 +model: "Qwen/Qwen2.5-3B-Instruct" +model_path: "model" +num_training_steps: 30 +num_warmup_steps: 3 +save_steps: 16 +seed: 42 +weight_decay: 0 + +# Qwen ultrafast optimizations +use_flash_attention: false # Disable if not stable +dataloader_num_workers: 4 +pin_memory: true +bf16: false # Disable mixed precision to avoid meta device mismatch +optim: "adamw_torch" # Standard optimizer for safety diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_tinyllama_ultrafast.yaml b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_tinyllama_ultrafast.yaml index 92dff7e42..4ad95d400 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_tinyllama_ultrafast.yaml +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_tinyllama_ultrafast.yaml @@ -1,37 +1,37 @@ -adam_beta1: !!float 0.9 -adam_beta2: !!float 0.95 -adam_epsilon: !!float 1E-08 -early_stopping_patience: 2 -epochs: 1 -eval_accumulation_steps: null -eval_delay: 0 -eval_steps: 16 -eval_strategy: "steps" -save_dir: "data_out/lora_training" -finetune_dataset: "data" -finetune_test_batch_size: 32 -finetune_test_nsamples: 8 -finetune_test_seqlen: 192 -finetune_train_batch_size: 4 -finetune_train_nsamples: 32 -finetune_train_seqlen: 192 -gradient_accumulation_steps: 4 -gradient_checkpointing: true -learning_rate: !!float 0.00035 -lora_dropout: !!float 0.05 -lr_scheduler_type: "linear_with_warmup" -max_grad_norm: 1 -model: "TinyLlama/TinyLlama-1.1B-Chat-v1.0" -model_path: "model" -num_training_steps: 30 -num_warmup_steps: 3 -save_steps: 16 -seed: 42 -weight_decay: 0 - -# TinyLlama ultrafast optimizations -use_flash_attention: true -dataloader_num_workers: 4 -pin_memory: true -bf16: false # disable by default for broad compatibility +adam_beta1: !!float 0.9 +adam_beta2: !!float 0.95 +adam_epsilon: !!float 1E-08 +early_stopping_patience: 2 +epochs: 1 +eval_accumulation_steps: null +eval_delay: 0 +eval_steps: 16 +eval_strategy: "steps" +save_dir: "data_out/lora_training" +finetune_dataset: "data" +finetune_test_batch_size: 32 +finetune_test_nsamples: 8 +finetune_test_seqlen: 192 +finetune_train_batch_size: 4 +finetune_train_nsamples: 32 +finetune_train_seqlen: 192 +gradient_accumulation_steps: 4 +gradient_checkpointing: true +learning_rate: !!float 0.00035 +lora_dropout: !!float 0.05 +lr_scheduler_type: "linear_with_warmup" +max_grad_norm: 1 +model: "TinyLlama/TinyLlama-1.1B-Chat-v1.0" +model_path: "model" +num_training_steps: 30 +num_warmup_steps: 3 +save_steps: 16 +seed: 42 +weight_decay: 0 + +# TinyLlama ultrafast optimizations +use_flash_attention: true +dataloader_num_workers: 4 +pin_memory: true +bf16: false # disable by default for broad compatibility optim: "adamw_torch_fused" diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_ultrafast.yaml b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_ultrafast.yaml index 9550cf98c..3ca41fdcd 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_ultrafast.yaml +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora_ultrafast.yaml @@ -1,38 +1,38 @@ -adam_beta1: !!float 0.9 -adam_beta2: !!float 0.95 -adam_epsilon: !!float 1E-08 -early_stopping_patience: 2 -epochs: 1 -eval_accumulation_steps: null -eval_delay: 0 -eval_steps: 16 # Very frequent checkpoints -eval_strategy: "steps" -save_dir: "data_out/lora_training" -finetune_dataset: "data" -finetune_test_batch_size: 32 # Maximum throughput for eval -finetune_test_nsamples: 8 -finetune_test_seqlen: 192 # Further reduction for speed -finetune_train_batch_size: 4 # Maximum stable batch size -finetune_train_nsamples: 32 -finetune_train_seqlen: 192 # Further reduction (was 256) -gradient_accumulation_steps: 4 # Larger effective batch (4*4=16) -gradient_checkpointing: true -learning_rate: !!float 0.0005 # Higher LR for faster convergence -lora_dropout: !!float 0.05 -lr_scheduler_type: "linear_with_warmup" -max_grad_norm: 1 -model: "microsoft/Phi-3.5-mini-instruct" -model_path: "model" -num_training_steps: 30 # Reduced from 50 for faster completion -num_warmup_steps: 3 # Minimal warmup -save_steps: 16 -seed: 42 -weight_decay: 0 - -# Ultra-fast training optimizations -use_flash_attention: true -dataloader_num_workers: 6 # Maximum parallelism -pin_memory: true -bf16: true # Mixed precision for speed -tf32: true # Enable TensorFloat-32 on Ampere+ GPUs -optim: "adamw_torch_fused" # Fused optimizer for 10-20% speedup +adam_beta1: !!float 0.9 +adam_beta2: !!float 0.95 +adam_epsilon: !!float 1E-08 +early_stopping_patience: 2 +epochs: 1 +eval_accumulation_steps: null +eval_delay: 0 +eval_steps: 16 # Very frequent checkpoints +eval_strategy: "steps" +save_dir: "data_out/lora_training" +finetune_dataset: "data" +finetune_test_batch_size: 32 # Maximum throughput for eval +finetune_test_nsamples: 8 +finetune_test_seqlen: 192 # Further reduction for speed +finetune_train_batch_size: 4 # Maximum stable batch size +finetune_train_nsamples: 32 +finetune_train_seqlen: 192 # Further reduction (was 256) +gradient_accumulation_steps: 4 # Larger effective batch (4*4=16) +gradient_checkpointing: true +learning_rate: !!float 0.0005 # Higher LR for faster convergence +lora_dropout: !!float 0.05 +lr_scheduler_type: "linear_with_warmup" +max_grad_norm: 1 +model: "microsoft/Phi-3.5-mini-instruct" +model_path: "model" +num_training_steps: 30 # Reduced from 50 for faster completion +num_warmup_steps: 3 # Minimal warmup +save_steps: 16 +seed: 42 +weight_decay: 0 + +# Ultra-fast training optimizations +use_flash_attention: true +dataloader_num_workers: 6 # Maximum parallelism +pin_memory: true +bf16: true # Mixed precision for speed +tf32: true # Enable TensorFloat-32 on Ampere+ GPUs +optim: "adamw_torch_fused" # Fused optimizer for 10-20% speedup diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/python mcp.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/python mcp.py index b9e96c6e7..b4f98eb18 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/python mcp.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/python mcp.py @@ -1,255 +1,255 @@ -"""Connect model with mcp tools in Python -# Run this python script -> pip install mcp azure-ai-inference -> python .py -""" - -import asyncio -import json -import os -from contextlib import AsyncExitStack -from typing import Dict - -from azure.ai.inference import ChatCompletionsClient -from azure.ai.inference.models import (AssistantMessage, TextContentItem, - ToolMessage, UserMessage) -from azure.core.credentials import AzureKeyCredential -from mcp import ClientSession, StdioServerParameters -from mcp.client.sse import sse_client -from mcp.client.stdio import stdio_client -from mcp.client.streamable_http import streamablehttp_client - - -class MCPClient: - def __init__(self): - # Initialize session and client objects - self._servers = {} - self._tool_to_server_map = {} - self.exit_stack = AsyncExitStack() - # To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings. - # Create your PAT token by following instructions here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens - self.azureai = ChatCompletionsClient( - endpoint="https://models.github.ai/inference", - credential=AzureKeyCredential(os.environ["GITHUB_TOKEN"]), - api_version="2024-12-01-preview", - ) - - async def connect_stdio_server( - self, server_id: str, command: str, args: list[str], env: Dict[str, str] - ): - """Connect to an MCP server using STDIO transport - - Args: - server_id: Unique identifier for this server connection - command: Command to run the MCP server - args: Arguments for the command - env: Optional environment variables - """ - server_params = StdioServerParameters(command=command, args=args, env=env) - - stdio_transport = await self.exit_stack.enter_async_context( - stdio_client(server_params) - ) - stdio, write = stdio_transport - session = await self.exit_stack.enter_async_context(ClientSession(stdio, write)) - await session.initialize() - - # Register the server - await self._register_server(server_id, session) - - async def connect_sse_server( - self, server_id: str, url: str, headers: Dict[str, str] - ): - """Connect to an MCP server using SSE transport - - Args: - server_id: Unique identifier for this server connection - url: URL of the SSE server - headers: Optional HTTP headers - """ - sse_context = await self.exit_stack.enter_async_context( - sse_client(url=url, headers=headers) - ) - read, write = sse_context - session = await self.exit_stack.enter_async_context(ClientSession(read, write)) - await session.initialize() - - # Register the server - await self._register_server(server_id, session) - - async def connect_http_server( - self, server_id: str, url: str, headers: Dict[str, str] - ): - """Connect to an MCP server using HTTP transport - - Args: - server_id: Unique identifier for this server connection - url: URL of the HTTP server - headers: Optional HTTP headers - """ - http_context = await self.exit_stack.enter_async_context( - streamablehttp_client(url=url, headers=headers) - ) - read, write, sessionId = http_context - session = await self.exit_stack.enter_async_context(ClientSession(read, write)) - await session.initialize() - - # Register the server - await self._register_server(server_id, session) - - async def _register_server(self, server_id: str, session: ClientSession): - """Register a server and its tools in the client - - Args: - server_id: Unique identifier for this server - session: Connected ClientSession - """ - # List available tools - response = await session.list_tools() - tools = response.tools - - # Store server connection info - self._servers[server_id] = {"session": session, "tools": tools} - - # Update tool-to-server mapping - for tool in tools: - self._tool_to_server_map[tool.name] = server_id - - print( - f"\nConnected to server '{server_id}' with tools:", - [tool.name for tool in tools], - ) - - async def chatWithTools(self, messages: list[any]) -> str: - """Chat with model and using tools - Args: - messages: Messages to send to the model - """ - if not self._servers: - raise ValueError( - "No MCP servers connected. Connect to at least one server first." - ) - - # Collect tools from all connected servers - available_tools = [] - for server_id, server_info in self._servers.items(): - for tool in server_info["tools"]: - available_tools.append( - { - "type": "function", - "function": { - "name": tool.name, - "description": tool.description, - "parameters": tool.inputSchema, - }, - } - ) - - while True: - - # Call model - response = self.azureai.complete( - messages=messages, - model="openai/gpt-5", - tools=available_tools, - response_format="text", - ) - hasToolCall = False - - if response.choices[0].message.tool_calls: - for tool in response.choices[0].message.tool_calls: - hasToolCall = True - tool_name = tool.function.name - tool_args = json.loads(tool.function.arguments) - messages.append( - AssistantMessage( - tool_calls=[ - { - "id": tool.id, - "type": "function", - "function": { - "name": tool.function.name, - "arguments": tool.function.arguments, - }, - } - ] - ) - ) - - # Find the appropriate server for this tool - if tool_name in self._tool_to_server_map: - server_id = self._tool_to_server_map[tool_name] - server_session = self._servers[server_id]["session"] - - # Execute tool call on the appropriate server - result = await server_session.call_tool(tool_name, tool_args) - print( - f"[Server '{server_id}' call tool '{tool_name}' with args {tool_args}]: {result.content}" - ) - - messages.append( - ToolMessage( - tool_call_id=tool.id, content=str(result.content) - ) - ) - else: - messages.append( - AssistantMessage(content=response.choices[0].message.content) - ) - print(f"[Model Response]: {response.choices[0].message.content}") - - if not hasToolCall: - break - - async def cleanup(self): - """Clean up resources""" - await self.exit_stack.aclose() - await asyncio.sleep(1) - - -async def main(): - client = MCPClient() - messages = [ - { - "role": "developer", - "content": 'Unable to inference the GitHub model gpt-4.1 due to 403. To reduce such errors, use [GitHub pay-as-you-go models](command:ai-mlstudio.internal.GHModelsDoc) or [Deploy to Azure AI Foundry](command:ai-mlstudio.internal.triggerFoundryModelDeployment?["gpt-4.1","github403"]).', - }, - UserMessage( - content=[ - TextContentItem(text="hi"), - ] - ), - ] - try: - await client.connect_stdio_server( - "memory", - "npx", - [ - "-y", - "@modelcontextprotocol/server-memory", - ], - { - "MEMORY_FILE_PATH": os.environ["MEMORY_FILE_PATH"], - }, - ) - await client.connect_stdio_server( - "sequential thinking", - "npx", - [ - "-y", - "@modelcontextprotocol/server-sequential-thinking", - ], - {}, - ) - await client.connect_http_server( - "microsoft docs", "https://learn.microsoft.com/api/mcp", {} - ) - await client.chatWithTools(messages) - except Exception as e: - print(f"\nError: {str(e)}") - finally: - await client.cleanup() - - -if __name__ == "__main__": - asyncio.run(main()) +"""Connect model with mcp tools in Python +# Run this python script +> pip install mcp azure-ai-inference +> python .py +""" + +import asyncio +import json +import os +from contextlib import AsyncExitStack +from typing import Dict + +from azure.ai.inference import ChatCompletionsClient +from azure.ai.inference.models import (AssistantMessage, TextContentItem, + ToolMessage, UserMessage) +from azure.core.credentials import AzureKeyCredential +from mcp import ClientSession, StdioServerParameters +from mcp.client.sse import sse_client +from mcp.client.stdio import stdio_client +from mcp.client.streamable_http import streamablehttp_client + + +class MCPClient: + def __init__(self): + # Initialize session and client objects + self._servers = {} + self._tool_to_server_map = {} + self.exit_stack = AsyncExitStack() + # To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings. + # Create your PAT token by following instructions here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens + self.azureai = ChatCompletionsClient( + endpoint="https://models.github.ai/inference", + credential=AzureKeyCredential(os.environ["GITHUB_TOKEN"]), + api_version="2024-12-01-preview", + ) + + async def connect_stdio_server( + self, server_id: str, command: str, args: list[str], env: Dict[str, str] + ): + """Connect to an MCP server using STDIO transport + + Args: + server_id: Unique identifier for this server connection + command: Command to run the MCP server + args: Arguments for the command + env: Optional environment variables + """ + server_params = StdioServerParameters(command=command, args=args, env=env) + + stdio_transport = await self.exit_stack.enter_async_context( + stdio_client(server_params) + ) + stdio, write = stdio_transport + session = await self.exit_stack.enter_async_context(ClientSession(stdio, write)) + await session.initialize() + + # Register the server + await self._register_server(server_id, session) + + async def connect_sse_server( + self, server_id: str, url: str, headers: Dict[str, str] + ): + """Connect to an MCP server using SSE transport + + Args: + server_id: Unique identifier for this server connection + url: URL of the SSE server + headers: Optional HTTP headers + """ + sse_context = await self.exit_stack.enter_async_context( + sse_client(url=url, headers=headers) + ) + read, write = sse_context + session = await self.exit_stack.enter_async_context(ClientSession(read, write)) + await session.initialize() + + # Register the server + await self._register_server(server_id, session) + + async def connect_http_server( + self, server_id: str, url: str, headers: Dict[str, str] + ): + """Connect to an MCP server using HTTP transport + + Args: + server_id: Unique identifier for this server connection + url: URL of the HTTP server + headers: Optional HTTP headers + """ + http_context = await self.exit_stack.enter_async_context( + streamablehttp_client(url=url, headers=headers) + ) + read, write, sessionId = http_context + session = await self.exit_stack.enter_async_context(ClientSession(read, write)) + await session.initialize() + + # Register the server + await self._register_server(server_id, session) + + async def _register_server(self, server_id: str, session: ClientSession): + """Register a server and its tools in the client + + Args: + server_id: Unique identifier for this server + session: Connected ClientSession + """ + # List available tools + response = await session.list_tools() + tools = response.tools + + # Store server connection info + self._servers[server_id] = {"session": session, "tools": tools} + + # Update tool-to-server mapping + for tool in tools: + self._tool_to_server_map[tool.name] = server_id + + print( + f"\nConnected to server '{server_id}' with tools:", + [tool.name for tool in tools], + ) + + async def chatWithTools(self, messages: list[any]) -> str: + """Chat with model and using tools + Args: + messages: Messages to send to the model + """ + if not self._servers: + raise ValueError( + "No MCP servers connected. Connect to at least one server first." + ) + + # Collect tools from all connected servers + available_tools = [] + for server_id, server_info in self._servers.items(): + for tool in server_info["tools"]: + available_tools.append( + { + "type": "function", + "function": { + "name": tool.name, + "description": tool.description, + "parameters": tool.inputSchema, + }, + } + ) + + while True: + + # Call model + response = self.azureai.complete( + messages=messages, + model="openai/gpt-5", + tools=available_tools, + response_format="text", + ) + hasToolCall = False + + if response.choices[0].message.tool_calls: + for tool in response.choices[0].message.tool_calls: + hasToolCall = True + tool_name = tool.function.name + tool_args = json.loads(tool.function.arguments) + messages.append( + AssistantMessage( + tool_calls=[ + { + "id": tool.id, + "type": "function", + "function": { + "name": tool.function.name, + "arguments": tool.function.arguments, + }, + } + ] + ) + ) + + # Find the appropriate server for this tool + if tool_name in self._tool_to_server_map: + server_id = self._tool_to_server_map[tool_name] + server_session = self._servers[server_id]["session"] + + # Execute tool call on the appropriate server + result = await server_session.call_tool(tool_name, tool_args) + print( + f"[Server '{server_id}' call tool '{tool_name}' with args {tool_args}]: {result.content}" + ) + + messages.append( + ToolMessage( + tool_call_id=tool.id, content=str(result.content) + ) + ) + else: + messages.append( + AssistantMessage(content=response.choices[0].message.content) + ) + print(f"[Model Response]: {response.choices[0].message.content}") + + if not hasToolCall: + break + + async def cleanup(self): + """Clean up resources""" + await self.exit_stack.aclose() + await asyncio.sleep(1) + + +async def main(): + client = MCPClient() + messages = [ + { + "role": "developer", + "content": 'Unable to inference the GitHub model gpt-4.1 due to 403. To reduce such errors, use [GitHub pay-as-you-go models](command:ai-mlstudio.internal.GHModelsDoc) or [Deploy to Azure AI Foundry](command:ai-mlstudio.internal.triggerFoundryModelDeployment?["gpt-4.1","github403"]).', + }, + UserMessage( + content=[ + TextContentItem(text="hi"), + ] + ), + ] + try: + await client.connect_stdio_server( + "memory", + "npx", + [ + "-y", + "@modelcontextprotocol/server-memory", + ], + { + "MEMORY_FILE_PATH": os.environ["MEMORY_FILE_PATH"], + }, + ) + await client.connect_stdio_server( + "sequential thinking", + "npx", + [ + "-y", + "@modelcontextprotocol/server-sequential-thinking", + ], + {}, + ) + await client.connect_http_server( + "microsoft docs", "https://learn.microsoft.com/api/mcp", {} + ) + await client.chatWithTools(messages) + except Exception as e: + print(f"\nError: {str(e)}") + finally: + await client.cleanup() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/requirements-advanced.txt b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/requirements-advanced.txt index cf6a38af3..8b27e8217 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/requirements-advanced.txt +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/requirements-advanced.txt @@ -1,33 +1,33 @@ -# Optional dependencies for advanced training features -# Install with: pip install -r requirements-advanced.txt - -# Semantic search and document embedding -sentence-transformers>=3.0.0 - -# Evaluation metrics -rouge-score>=0.1.2 -sacrebleu>=2.3.0 - -# Memory optimization -bitsandbytes>=0.44.0 - -# GPU optimization (Linux only - fails gracefully on Windows) -# flash-attn>=2.3.0 - -# Visualization -matplotlib>=3.9.0 -seaborn>=0.13.0 -scipy>=1.14.0 - -# Model serving & deployment -fastapi>=0.115.0 -uvicorn[standard]>=0.32.0 -pydantic>=2.10.0 - -# Model export -onnx>=1.17.0 -onnxruntime>=1.20.0 - -# Experiment tracking (optional) -# mlflow>=2.8.0 -# wandb>=0.16.0 +# Optional dependencies for advanced training features +# Install with: pip install -r requirements-advanced.txt + +# Semantic search and document embedding +sentence-transformers>=3.0.0 + +# Evaluation metrics +rouge-score>=0.1.2 +sacrebleu>=2.3.0 + +# Memory optimization +bitsandbytes>=0.44.0 + +# GPU optimization (Linux only - fails gracefully on Windows) +# flash-attn>=2.3.0 + +# Visualization +matplotlib>=3.9.0 +seaborn>=0.13.0 +scipy>=1.14.0 + +# Model serving & deployment +fastapi>=0.115.0 +uvicorn[standard]>=0.32.0 +pydantic>=2.10.0 + +# Model export +onnx>=1.17.0 +onnxruntime>=1.20.0 + +# Experiment tracking (optional) +# mlflow>=2.8.0 +# wandb>=0.16.0 diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/requirements.txt b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/requirements.txt index 7409129bc..4da7bca30 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/requirements.txt +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/requirements.txt @@ -1,24 +1,24 @@ -# Fine-tuning requirements for microsoft_phi-silica-3.6_v1 -pyyaml>=6.0 -datasets>=2.21.0 -transformers>=4.47.0 -peft>=0.14.0 -accelerate>=0.36.0 -tqdm>=4.67.0 -einops>=0.7.0 -numpy>=1.26.4 -# Install torch appropriate to your CUDA; example: -# pip install torch --index-url https://download.pytorch.org/whl/cu121 - -# Optional observability (install separately if needed): -# applicationinsights>=0.11.10 -# opentelemetry-api>=1.20.0 -# opentelemetry-sdk>=1.20.0 -# opentelemetry-exporter-otlp-proto-grpc>=1.20.0 - -# Optional: Advanced training features (install as needed) -# sentence-transformers>=2.2.0 # For semantic pruning & RAG -# rouge-score>=0.1.2 # For evaluation metrics -# sacrebleu>=2.3.0 # For BLEU scores -# bitsandbytes>=0.41.0 # For 4-bit/8-bit quantization -# flash-attn>=2.3.0 # For flash attention (Linux only) +# Fine-tuning requirements for microsoft_phi-silica-3.6_v1 +pyyaml>=6.0 +datasets>=2.21.0 +transformers>=4.47.0 +peft>=0.14.0 +accelerate>=0.36.0 +tqdm>=4.67.0 +einops>=0.7.0 +numpy>=1.26.4 +# Install torch appropriate to your CUDA; example: +# pip install torch --index-url https://download.pytorch.org/whl/cu121 + +# Optional observability (install separately if needed): +# applicationinsights>=0.11.10 +# opentelemetry-api>=1.20.0 +# opentelemetry-sdk>=1.20.0 +# opentelemetry-exporter-otlp-proto-grpc>=1.20.0 + +# Optional: Advanced training features (install as needed) +# sentence-transformers>=2.2.0 # For semantic pruning & RAG +# rouge-score>=0.1.2 # For evaluation metrics +# sacrebleu>=2.3.0 # For BLEU scores +# bitsandbytes>=0.41.0 # For 4-bit/8-bit quantization +# flash-attn>=2.3.0 # For flash attention (Linux only) diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/README.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/README.md index f12833700..5dc640afa 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/README.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/README.md @@ -1,161 +1,161 @@ -# Scripts Directory - Advanced Training Tools - -This directory contains all training and optimization scripts for the Phi-3.6 fine-tuning project. - -## 📁 Script Overview - -| Script | Purpose | Usage | -|--------|---------|-------| -| `train_lora.py` | LoRA fine-tuning | `python train_lora.py --dataset data --config ../lora/lora.yaml` | -| `auto_eval.py` | Automatic evaluation | `python auto_eval.py --model path/to/model --dataset test.jsonl` | -| `rag_pipeline.py` | RAG setup | `python rag_pipeline.py --model model --docs docs --interactive` | -| `semantic_pruning.py` | Data pruning | `python semantic_pruning.py --input in.jsonl --output out.jsonl` | -| `gpu_optimizer.py` | Hardware optimization | `python gpu_optimizer.py --update-config ../lora/lora.yaml` | -| `run_pipeline.py` | Master orchestrator | `python run_pipeline.py --input-dataset train.jsonl` | -| `prepare_dataset.py` | Dataset preparation | Helper for data formatting | -| `metrics_logger.py` | Training metrics | Used by train_lora.py | -| `otel_callback.py` | Telemetry | Optional observability | - -## 🚀 Quick Commands - -### One-Line Training -```bash -# Full pipeline -python run_pipeline.py --input-dataset ..\..\datasets\chat\dolly\train.jsonl --test-dataset ..\..\datasets\chat\dolly\test.jsonl - -# Quick test -python run_pipeline.py --input-dataset ..\..\datasets\chat\dolly\train.jsonl --max-train-samples 64 --skip-rag -``` - -### Individual Tools -```bash -# Optimize GPU settings -python gpu_optimizer.py --update-config ..\lora\lora.yaml - -# Prune dataset -python semantic_pruning.py --input raw.jsonl --output clean.jsonl - -# Train model -python train_lora.py --dataset data --config ..\lora\lora.yaml - -# Evaluate -python auto_eval.py --model ..\data_out\lora_training --dataset test.jsonl - -# RAG -python rag_pipeline.py --model ..\data_out\lora_training --docs ..\..\..\datasets --interactive -``` - -## 📊 Output Directories - -All scripts output to structured directories: - -``` -../data_out/ -├── lora_training/ # Trained models (from train_lora.py) -├── evaluation_results/ # Eval JSONs (from auto_eval.py) -├── rag_index/ # RAG index (from rag_pipeline.py) -├── gpu_profile.yaml # Hardware profile (from gpu_optimizer.py) -└── pipeline_results.json # Pipeline results (from run_pipeline.py) -``` - -## 🔧 Configuration Files - -Scripts use these config files: - -- `../lora/lora.yaml` - LoRA training config -- `../soft_prompt/soft_prompt.yaml` - Soft prompt config -- `deepspeed_zero3.json` - DeepSpeed config (multi-GPU) - -## 📖 Documentation - -For detailed documentation: -- **Complete Guide**: `../ADVANCED_TRAINING_GUIDE.md` -- **Quick Reference**: `../ADVANCED_TRAINING_QUICKREF.md` -- **Setup Summary**: `../ADVANCED_TRAINING_SETUP_COMPLETE.md` - -## 💻 Examples - -### Example 1: CPU Test Run -```bash -python gpu_optimizer.py # Detect hardware -python train_lora.py --dataset data --config ..\lora\lora.yaml --max-train-samples 64 -``` - -### Example 2: GPU Production -```bash -python semantic_pruning.py --input ..\..\datasets\chat\dolly\train.jsonl --output ..\data\clean.jsonl -python gpu_optimizer.py --update-config ..\lora\lora.yaml --model-size 7.0 -python train_lora.py --dataset ..\data --config ..\lora\lora.yaml -python auto_eval.py --model ..\data_out\lora_training --dataset ..\..\datasets\chat\dolly\test.jsonl -``` - -### Example 3: RAG Deployment -```bash -python train_lora.py --dataset data --config ..\lora\lora.yaml -python rag_pipeline.py --model ..\data_out\lora_training --docs ..\..\..\datasets --rebuild-index -python rag_pipeline.py --model ..\data_out\lora_training --docs ..\..\..\datasets --query "What is quantum computing?" -``` - -## 🎯 Workflow Recommendations - -### For Quick Experiments -1. `gpu_optimizer.py` - Get optimal settings -2. `train_lora.py --max-train-samples 100` - Quick train -3. `auto_eval.py` - Verify quality - -### For Production -1. `semantic_pruning.py` - Clean data -2. `gpu_optimizer.py` - Optimize config -3. `train_lora.py` - Full training -4. `auto_eval.py` - Comprehensive evaluation -5. `rag_pipeline.py` - Production deployment - -### For Research -1. `run_pipeline.py` - Automated experiments -2. Monitor `../data_out/pipeline_results.json` -3. Compare multiple runs via `auto_eval.py` - -## 🛠️ Advanced Usage - -### Custom Pipeline -```python -# In your script -from auto_eval import AutomaticEvaluator -from rag_pipeline import RAGPipeline, DocumentStore -from gpu_optimizer import GPUOptimizer -from semantic_pruning import SemanticPruner - -# Use the APIs programmatically -``` - -### Environment Variables -```bash -# Set before running -set CUDA_VISIBLE_DEVICES=0 -set PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512 -set TOKENIZERS_PARALLELISM=false -``` - -## 📦 Dependencies - -Core (already installed): -- torch, transformers, peft, accelerate - -Optional (for advanced features): -```bash -pip install -r ..\requirements-advanced.txt -``` - -## ✅ Testing - -Verify installation: -```bash -python gpu_optimizer.py # Should detect hardware -python auto_eval.py --help # Should show usage -python rag_pipeline.py --help # Should show usage -python semantic_pruning.py --help # Should show usage -``` - ---- - -**All scripts support `--help` for detailed usage information.** +# Scripts Directory - Advanced Training Tools + +This directory contains all training and optimization scripts for the Phi-3.6 fine-tuning project. + +## 📁 Script Overview + +| Script | Purpose | Usage | +|--------|---------|-------| +| `train_lora.py` | LoRA fine-tuning | `python train_lora.py --dataset data --config ../lora/lora.yaml` | +| `auto_eval.py` | Automatic evaluation | `python auto_eval.py --model path/to/model --dataset test.jsonl` | +| `rag_pipeline.py` | RAG setup | `python rag_pipeline.py --model model --docs docs --interactive` | +| `semantic_pruning.py` | Data pruning | `python semantic_pruning.py --input in.jsonl --output out.jsonl` | +| `gpu_optimizer.py` | Hardware optimization | `python gpu_optimizer.py --update-config ../lora/lora.yaml` | +| `run_pipeline.py` | Master orchestrator | `python run_pipeline.py --input-dataset train.jsonl` | +| `prepare_dataset.py` | Dataset preparation | Helper for data formatting | +| `metrics_logger.py` | Training metrics | Used by train_lora.py | +| `otel_callback.py` | Telemetry | Optional observability | + +## 🚀 Quick Commands + +### One-Line Training +```bash +# Full pipeline +python run_pipeline.py --input-dataset ..\..\datasets\chat\dolly\train.jsonl --test-dataset ..\..\datasets\chat\dolly\test.jsonl + +# Quick test +python run_pipeline.py --input-dataset ..\..\datasets\chat\dolly\train.jsonl --max-train-samples 64 --skip-rag +``` + +### Individual Tools +```bash +# Optimize GPU settings +python gpu_optimizer.py --update-config ..\lora\lora.yaml + +# Prune dataset +python semantic_pruning.py --input raw.jsonl --output clean.jsonl + +# Train model +python train_lora.py --dataset data --config ..\lora\lora.yaml + +# Evaluate +python auto_eval.py --model ..\data_out\lora_training --dataset test.jsonl + +# RAG +python rag_pipeline.py --model ..\data_out\lora_training --docs ..\..\..\datasets --interactive +``` + +## 📊 Output Directories + +All scripts output to structured directories: + +``` +../data_out/ +├── lora_training/ # Trained models (from train_lora.py) +├── evaluation_results/ # Eval JSONs (from auto_eval.py) +├── rag_index/ # RAG index (from rag_pipeline.py) +├── gpu_profile.yaml # Hardware profile (from gpu_optimizer.py) +└── pipeline_results.json # Pipeline results (from run_pipeline.py) +``` + +## 🔧 Configuration Files + +Scripts use these config files: + +- `../lora/lora.yaml` - LoRA training config +- `../soft_prompt/soft_prompt.yaml` - Soft prompt config +- `deepspeed_zero3.json` - DeepSpeed config (multi-GPU) + +## 📖 Documentation + +For detailed documentation: +- **Complete Guide**: `../ADVANCED_TRAINING_GUIDE.md` +- **Quick Reference**: `../ADVANCED_TRAINING_QUICKREF.md` +- **Setup Summary**: `../ADVANCED_TRAINING_SETUP_COMPLETE.md` + +## 💻 Examples + +### Example 1: CPU Test Run +```bash +python gpu_optimizer.py # Detect hardware +python train_lora.py --dataset data --config ..\lora\lora.yaml --max-train-samples 64 +``` + +### Example 2: GPU Production +```bash +python semantic_pruning.py --input ..\..\datasets\chat\dolly\train.jsonl --output ..\data\clean.jsonl +python gpu_optimizer.py --update-config ..\lora\lora.yaml --model-size 7.0 +python train_lora.py --dataset ..\data --config ..\lora\lora.yaml +python auto_eval.py --model ..\data_out\lora_training --dataset ..\..\datasets\chat\dolly\test.jsonl +``` + +### Example 3: RAG Deployment +```bash +python train_lora.py --dataset data --config ..\lora\lora.yaml +python rag_pipeline.py --model ..\data_out\lora_training --docs ..\..\..\datasets --rebuild-index +python rag_pipeline.py --model ..\data_out\lora_training --docs ..\..\..\datasets --query "What is quantum computing?" +``` + +## 🎯 Workflow Recommendations + +### For Quick Experiments +1. `gpu_optimizer.py` - Get optimal settings +2. `train_lora.py --max-train-samples 100` - Quick train +3. `auto_eval.py` - Verify quality + +### For Production +1. `semantic_pruning.py` - Clean data +2. `gpu_optimizer.py` - Optimize config +3. `train_lora.py` - Full training +4. `auto_eval.py` - Comprehensive evaluation +5. `rag_pipeline.py` - Production deployment + +### For Research +1. `run_pipeline.py` - Automated experiments +2. Monitor `../data_out/pipeline_results.json` +3. Compare multiple runs via `auto_eval.py` + +## 🛠️ Advanced Usage + +### Custom Pipeline +```python +# In your script +from auto_eval import AutomaticEvaluator +from rag_pipeline import RAGPipeline, DocumentStore +from gpu_optimizer import GPUOptimizer +from semantic_pruning import SemanticPruner + +# Use the APIs programmatically +``` + +### Environment Variables +```bash +# Set before running +set CUDA_VISIBLE_DEVICES=0 +set PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512 +set TOKENIZERS_PARALLELISM=false +``` + +## 📦 Dependencies + +Core (already installed): +- torch, transformers, peft, accelerate + +Optional (for advanced features): +```bash +pip install -r ..\requirements-advanced.txt +``` + +## ✅ Testing + +Verify installation: +```bash +python gpu_optimizer.py # Should detect hardware +python auto_eval.py --help # Should show usage +python rag_pipeline.py --help # Should show usage +python semantic_pruning.py --help # Should show usage +``` + +--- + +**All scripts support `--help` for detailed usage information.** diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/auto_eval.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/auto_eval.py index ff3b66de2..97fa90d78 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/auto_eval.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/auto_eval.py @@ -1,443 +1,443 @@ -""" -Automatic Evaluation Framework for Fine-tuned Models -Supports multiple evaluation metrics and automated benchmarking -""" - -import json -import time -from dataclasses import asdict, dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple - -import numpy as np -import torch -from transformers import AutoModelForCausalLM, AutoTokenizer - -try: - from peft import PeftModel # type: ignore -except Exception: - PeftModel = None # type: ignore -try: - import sacrebleu # type: ignore -except Exception: - sacrebleu = None # type: ignore -try: - from rouge_score import rouge_scorer # type: ignore -except Exception: - rouge_scorer = None # type: ignore -import yaml - -from datasets import load_dataset - - -@dataclass -class EvaluationMetrics: - """Container for evaluation metrics""" - - perplexity: float - accuracy: Optional[float] = None - bleu_score: Optional[float] = None - rouge_scores: Optional[Dict[str, float]] = None - inference_time_ms: float = 0.0 - tokens_per_second: float = 0.0 - memory_usage_mb: float = 0.0 - - def to_dict(self) -> Dict[str, Any]: - return asdict(self) - - -class AutomaticEvaluator: - """Automatic evaluation system for fine-tuned models""" - - def __init__(self, config_path: str = "lora/lora.yaml"): - """Initialize evaluator with config""" - self.config_path = Path(config_path) - self.config = self._load_config() - self.device = "cuda" if torch.cuda.is_available() else "cpu" - self.results_dir = Path("data_out/evaluation_results") - self.results_dir.mkdir(parents=True, exist_ok=True) - - def _load_config(self) -> Dict[str, Any]: - """Load evaluation config""" - with open(self.config_path) as f: - return yaml.safe_load(f) - - def evaluate_model( - self, - model_path: str, - test_dataset: str, - metrics: List[str] | None = None, - num_samples: int = 100, - ) -> EvaluationMetrics: - """ - Evaluate model on test dataset - - Args: - model_path: Path to fine-tuned model - test_dataset: Path or name of test dataset - metrics: List of metrics to compute - num_samples: Number of samples to evaluate - - Returns: - EvaluationMetrics object with results - """ - print(f"Loading model from {model_path}...") - model_dir = Path(model_path) - adapter_dir = None - if (model_dir / "lora_adapter").exists(): - adapter_dir = model_dir / "lora_adapter" - elif (model_dir / "adapter_config.json").exists(): - adapter_dir = model_dir - - if adapter_dir is not None and PeftModel is not None: - # Load base model from adapter config if available - try: - import json as _json - - with open( - adapter_dir / "adapter_config.json", "r", encoding="utf-8" - ) as f: - adapter_cfg = _json.load(f) - base_model_id = adapter_cfg.get( - "base_model_name_or_path" - ) or self.config.get("model") - # Fallback mapping similar to training script - if base_model_id == "Phi-3.6-mini-instruct": - base_model_id = "microsoft/Phi-3.5-mini-instruct" - print(f"Detected LoRA adapter. Base model: {base_model_id}") - base_model = AutoModelForCausalLM.from_pretrained( - base_model_id, - torch_dtype=( - torch.float16 if self.device == "cuda" else torch.float32 - ), - device_map="auto" if self.device == "cuda" else None, - ) - tokenizer_source = ( - model_dir / "tokenizer" - if (model_dir / "tokenizer").exists() - else base_model_id - ) - tokenizer = AutoTokenizer.from_pretrained(tokenizer_source) - model = PeftModel.from_pretrained(base_model, adapter_dir) - except Exception as e: - raise RuntimeError( - f"Failed to load LoRA adapter from {adapter_dir}: {e}" - ) from e - else: - model = AutoModelForCausalLM.from_pretrained( - model_path, - torch_dtype=torch.float16 if self.device == "cuda" else torch.float32, - device_map="auto" if self.device == "cuda" else None, - ) - tokenizer = AutoTokenizer.from_pretrained(model_path) - - print(f"Loading test dataset: {test_dataset}...") - dataset = self._load_test_data(test_dataset, num_samples) - - results = { - "perplexity": 0.0, - "inference_time_ms": 0.0, - "tokens_per_second": 0.0, - "memory_usage_mb": 0.0, - } - - # Compute perplexity - if "perplexity" in metrics: - results["perplexity"] = self._compute_perplexity(model, tokenizer, dataset) - - # Compute inference metrics - if "inference_time" in metrics: - inference_metrics = self._compute_inference_metrics( - model, tokenizer, dataset - ) - results.update(inference_metrics) - - # Compute generation quality metrics - if "bleu" in metrics or "rouge" in metrics: - quality_metrics = self._compute_quality_metrics( - model, tokenizer, dataset, metrics - ) - results.update(quality_metrics) - - return EvaluationMetrics(**results) - - def _load_test_data( - self, dataset_path: str, num_samples: int - ) -> List[Dict[str, Any]]: - """Load test dataset""" - dataset_path = Path(dataset_path) - - if dataset_path.suffix == ".jsonl": - # Load JSONL file - data = [] - with open(dataset_path) as f: - for i, line in enumerate(f): - if i >= num_samples: - break - data.append(json.loads(line)) - return data - else: - # Try loading as HuggingFace dataset - try: - dataset = load_dataset(str(dataset_path), split=f"test[:{num_samples}]") - return list(dataset) - except Exception: - raise ValueError( - f"Unsupported dataset format: {dataset_path}" - ) from None - - def _compute_perplexity( - self, - model: AutoModelForCausalLM, - tokenizer: AutoTokenizer, - dataset: List[Dict[str, Any]], - ) -> float: - """Compute perplexity on test set""" - model.eval() - total_loss = 0.0 - total_tokens = 0 - - with torch.no_grad(): - for example in dataset: - text = self._extract_text(example) - inputs = tokenizer( - text, return_tensors="pt", truncation=True, max_length=512 - ) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - - outputs = model(**inputs, labels=inputs["input_ids"]) - total_loss += outputs.loss.item() * inputs["input_ids"].size(1) - total_tokens += inputs["input_ids"].size(1) - - perplexity = np.exp(total_loss / total_tokens) - return float(perplexity) - - def _compute_inference_metrics( - self, - model: AutoModelForCausalLM, - tokenizer: AutoTokenizer, - dataset: List[Dict[str, Any]], - ) -> Dict[str, float]: - """Compute inference speed metrics""" - model.eval() - total_time = 0.0 - total_tokens = 0 - - if self.device == "cuda": - torch.cuda.reset_peak_memory_stats() - - with torch.no_grad(): - for example in dataset[:10]: # Use subset for timing - text = self._extract_text(example) - inputs = tokenizer( - text, return_tensors="pt", truncation=True, max_length=256 - ) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - - start = time.perf_counter() - outputs = model.generate(**inputs, max_new_tokens=50) - end = time.perf_counter() - - total_time += end - start - total_tokens += outputs.size(1) - - avg_time_ms = (total_time / 10) * 1000 - tokens_per_sec = total_tokens / total_time if total_time > 0 else 0.0 - - memory_mb = 0.0 - if self.device == "cuda": - memory_mb = torch.cuda.max_memory_allocated() / (1024**2) - - return { - "inference_time_ms": avg_time_ms, - "tokens_per_second": tokens_per_sec, - "memory_usage_mb": memory_mb, - } - - def _compute_quality_metrics( - self, - model: AutoModelForCausalLM, - tokenizer: AutoTokenizer, - dataset: List[Dict[str, Any]], - metrics: List[str], - ) -> Dict[str, Any]: - """Compute generation quality metrics (BLEU, ROUGE)""" - want_bleu = "bleu" in metrics - want_rouge = "rouge" in metrics - - if not want_bleu and not want_rouge: - return {"bleu_score": None, "rouge_scores": None} - - if want_bleu and sacrebleu is None: - print("[quality] sacrebleu not installed; skipping BLEU") - want_bleu = False - if want_rouge and rouge_scorer is None: - print("[quality] rouge-score not installed; skipping ROUGE") - want_rouge = False - - prompts: List[str] = [] - refs: List[str] = [] - preds: List[str] = [] - - # Prepare small eval subset (already controlled by caller num_samples) - pairs: List[Tuple[str, str]] = [] - for ex in dataset: - p, r = self._extract_prompt_and_reference(ex) - if p and r: - pairs.append((p, r)) - - # Generate predictions - model.eval() - with torch.no_grad(): - for p, r in pairs: - inputs = tokenizer( - p, return_tensors="pt", truncation=True, max_length=512 - ) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - output_ids = model.generate( - **inputs, max_new_tokens=96, do_sample=False - ) - text = tokenizer.decode(output_ids[0], skip_special_tokens=True) - # Heuristic: keep only completion after prompt - if text.startswith(p): - text = text[len(p) :].strip() - prompts.append(p) - refs.append(r) - preds.append(text) - - results: Dict[str, Any] = {"bleu_score": None, "rouge_scores": None} - - if want_bleu and preds and refs: - try: - bleu = sacrebleu.corpus_bleu(preds, [refs]) - results["bleu_score"] = float(bleu.score) - except Exception as e: - print(f"[quality] BLEU failed: {e}") - - if want_rouge and preds and refs: - try: - scorer = rouge_scorer.RougeScorer( - ["rouge1", "rouge2", "rougeL"], use_stemmer=True - ) - totals = {"rouge1": 0.0, "rouge2": 0.0, "rougeL": 0.0} - for hyp, ref in zip(preds, refs): - scores = scorer.score(ref, hyp) - for k in totals: - totals[k] += scores[k].fmeasure - n = max(1, len(preds)) - results["rouge_scores"] = {k: float(v / n) for k, v in totals.items()} - except Exception as e: - print(f"[quality] ROUGE failed: {e}") - - return results - - def _extract_prompt_and_reference(self, example: Dict[str, Any]) -> Tuple[str, str]: - """Return (prompt, reference) for chat-style examples. - Prompt: last user message; Reference: last assistant message. - Fallbacks to text-only examples if needed. - """ - if "messages" in example and isinstance(example["messages"], list): - user = "" - assistant = "" - for m in example["messages"]: - role = (m.get("role") or "").lower() - content = m.get("content") or "" - if role == "user": - user = content - elif role == "assistant": - assistant = content - return user.strip(), assistant.strip() - if "instruction" in example and "response" in example: - return str(example.get("instruction", "")), str(example.get("response", "")) - # Fallback: use text twice so it contributes neutrally to averages - t = self._extract_text(example) - return t, t - - def _extract_text(self, example: Dict[str, Any]) -> str: - """Extract text from dataset example""" - if "text" in example: - return example["text"] - elif "messages" in example: - # Chat format - return "\n".join( - [f"{m['role']}: {m['content']}" for m in example["messages"]] - ) - elif "instruction" in example: - return f"Instruction: {example['instruction']}\nResponse: {example.get('response', '')}" - else: - return str(example) - - def save_results(self, metrics: EvaluationMetrics, experiment_name: str): - """Save evaluation results to file""" - timestamp = time.strftime("%Y%m%d_%H%M%S") - output_file = self.results_dir / f"{experiment_name}_{timestamp}.json" - - with open(output_file, "w") as f: - json.dump(metrics.to_dict(), f, indent=2) - - print(f"✓ Results saved to {output_file}") - - def compare_models( - self, model_paths: List[str], test_dataset: str, num_samples: int = 100 - ) -> Dict[str, EvaluationMetrics]: - """Compare multiple models on same dataset""" - results = {} - - for model_path in model_paths: - model_name = Path(model_path).name - print(f"\nEvaluating {model_name}...") - metrics = self.evaluate_model( - model_path, test_dataset, num_samples=num_samples - ) - results[model_name] = metrics - - print(f" Perplexity: {metrics.perplexity:.2f}") - print(f" Inference Time: {metrics.inference_time_ms:.2f}ms") - print(f" Tokens/sec: {metrics.tokens_per_second:.2f}") - - return results - - -def main(): - """CLI for automatic evaluation""" - import argparse - - parser = argparse.ArgumentParser(description="Automatic Model Evaluation") - parser.add_argument("--model", type=str, required=True, help="Path to model") - parser.add_argument( - "--dataset", type=str, required=True, help="Path to test dataset" - ) - parser.add_argument( - "--config", type=str, default="lora/lora.yaml", help="Config file" - ) - parser.add_argument( - "--num-samples", type=int, default=100, help="Number of test samples" - ) - parser.add_argument( - "--metrics", - nargs="+", - default=["perplexity", "inference_time"], - help="Metrics to compute", - ) - parser.add_argument( - "--output-name", type=str, default="evaluation", help="Output name" - ) - - args = parser.parse_args() - - evaluator = AutomaticEvaluator(config_path=args.config) - metrics = evaluator.evaluate_model( - args.model, args.dataset, metrics=args.metrics, num_samples=args.num_samples - ) - - print("\n=== Evaluation Results ===") - print(f"Perplexity: {metrics.perplexity:.2f}") - print(f"Inference Time: {metrics.inference_time_ms:.2f}ms") - print(f"Tokens/sec: {metrics.tokens_per_second:.2f}") - print(f"Memory Usage: {metrics.memory_usage_mb:.2f}MB") - - evaluator.save_results(metrics, args.output_name) - - -if __name__ == "__main__": - main() +""" +Automatic Evaluation Framework for Fine-tuned Models +Supports multiple evaluation metrics and automated benchmarking +""" + +import json +import time +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +try: + from peft import PeftModel # type: ignore +except Exception: + PeftModel = None # type: ignore +try: + import sacrebleu # type: ignore +except Exception: + sacrebleu = None # type: ignore +try: + from rouge_score import rouge_scorer # type: ignore +except Exception: + rouge_scorer = None # type: ignore +import yaml + +from datasets import load_dataset + + +@dataclass +class EvaluationMetrics: + """Container for evaluation metrics""" + + perplexity: float + accuracy: Optional[float] = None + bleu_score: Optional[float] = None + rouge_scores: Optional[Dict[str, float]] = None + inference_time_ms: float = 0.0 + tokens_per_second: float = 0.0 + memory_usage_mb: float = 0.0 + + def to_dict(self) -> Dict[str, Any]: + return asdict(self) + + +class AutomaticEvaluator: + """Automatic evaluation system for fine-tuned models""" + + def __init__(self, config_path: str = "lora/lora.yaml"): + """Initialize evaluator with config""" + self.config_path = Path(config_path) + self.config = self._load_config() + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.results_dir = Path("data_out/evaluation_results") + self.results_dir.mkdir(parents=True, exist_ok=True) + + def _load_config(self) -> Dict[str, Any]: + """Load evaluation config""" + with open(self.config_path) as f: + return yaml.safe_load(f) + + def evaluate_model( + self, + model_path: str, + test_dataset: str, + metrics: List[str] | None = None, + num_samples: int = 100, + ) -> EvaluationMetrics: + """ + Evaluate model on test dataset + + Args: + model_path: Path to fine-tuned model + test_dataset: Path or name of test dataset + metrics: List of metrics to compute + num_samples: Number of samples to evaluate + + Returns: + EvaluationMetrics object with results + """ + print(f"Loading model from {model_path}...") + model_dir = Path(model_path) + adapter_dir = None + if (model_dir / "lora_adapter").exists(): + adapter_dir = model_dir / "lora_adapter" + elif (model_dir / "adapter_config.json").exists(): + adapter_dir = model_dir + + if adapter_dir is not None and PeftModel is not None: + # Load base model from adapter config if available + try: + import json as _json + + with open( + adapter_dir / "adapter_config.json", "r", encoding="utf-8" + ) as f: + adapter_cfg = _json.load(f) + base_model_id = adapter_cfg.get( + "base_model_name_or_path" + ) or self.config.get("model") + # Fallback mapping similar to training script + if base_model_id == "Phi-3.6-mini-instruct": + base_model_id = "microsoft/Phi-3.5-mini-instruct" + print(f"Detected LoRA adapter. Base model: {base_model_id}") + base_model = AutoModelForCausalLM.from_pretrained( + base_model_id, + torch_dtype=( + torch.float16 if self.device == "cuda" else torch.float32 + ), + device_map="auto" if self.device == "cuda" else None, + ) + tokenizer_source = ( + model_dir / "tokenizer" + if (model_dir / "tokenizer").exists() + else base_model_id + ) + tokenizer = AutoTokenizer.from_pretrained(tokenizer_source) + model = PeftModel.from_pretrained(base_model, adapter_dir) + except Exception as e: + raise RuntimeError( + f"Failed to load LoRA adapter from {adapter_dir}: {e}" + ) from e + else: + model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=torch.float16 if self.device == "cuda" else torch.float32, + device_map="auto" if self.device == "cuda" else None, + ) + tokenizer = AutoTokenizer.from_pretrained(model_path) + + print(f"Loading test dataset: {test_dataset}...") + dataset = self._load_test_data(test_dataset, num_samples) + + results = { + "perplexity": 0.0, + "inference_time_ms": 0.0, + "tokens_per_second": 0.0, + "memory_usage_mb": 0.0, + } + + # Compute perplexity + if "perplexity" in metrics: + results["perplexity"] = self._compute_perplexity(model, tokenizer, dataset) + + # Compute inference metrics + if "inference_time" in metrics: + inference_metrics = self._compute_inference_metrics( + model, tokenizer, dataset + ) + results.update(inference_metrics) + + # Compute generation quality metrics + if "bleu" in metrics or "rouge" in metrics: + quality_metrics = self._compute_quality_metrics( + model, tokenizer, dataset, metrics + ) + results.update(quality_metrics) + + return EvaluationMetrics(**results) + + def _load_test_data( + self, dataset_path: str, num_samples: int + ) -> List[Dict[str, Any]]: + """Load test dataset""" + dataset_path = Path(dataset_path) + + if dataset_path.suffix == ".jsonl": + # Load JSONL file + data = [] + with open(dataset_path) as f: + for i, line in enumerate(f): + if i >= num_samples: + break + data.append(json.loads(line)) + return data + else: + # Try loading as HuggingFace dataset + try: + dataset = load_dataset(str(dataset_path), split=f"test[:{num_samples}]") + return list(dataset) + except Exception: + raise ValueError( + f"Unsupported dataset format: {dataset_path}" + ) from None + + def _compute_perplexity( + self, + model: AutoModelForCausalLM, + tokenizer: AutoTokenizer, + dataset: List[Dict[str, Any]], + ) -> float: + """Compute perplexity on test set""" + model.eval() + total_loss = 0.0 + total_tokens = 0 + + with torch.no_grad(): + for example in dataset: + text = self._extract_text(example) + inputs = tokenizer( + text, return_tensors="pt", truncation=True, max_length=512 + ) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + outputs = model(**inputs, labels=inputs["input_ids"]) + total_loss += outputs.loss.item() * inputs["input_ids"].size(1) + total_tokens += inputs["input_ids"].size(1) + + perplexity = np.exp(total_loss / total_tokens) + return float(perplexity) + + def _compute_inference_metrics( + self, + model: AutoModelForCausalLM, + tokenizer: AutoTokenizer, + dataset: List[Dict[str, Any]], + ) -> Dict[str, float]: + """Compute inference speed metrics""" + model.eval() + total_time = 0.0 + total_tokens = 0 + + if self.device == "cuda": + torch.cuda.reset_peak_memory_stats() + + with torch.no_grad(): + for example in dataset[:10]: # Use subset for timing + text = self._extract_text(example) + inputs = tokenizer( + text, return_tensors="pt", truncation=True, max_length=256 + ) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + start = time.perf_counter() + outputs = model.generate(**inputs, max_new_tokens=50) + end = time.perf_counter() + + total_time += end - start + total_tokens += outputs.size(1) + + avg_time_ms = (total_time / 10) * 1000 + tokens_per_sec = total_tokens / total_time if total_time > 0 else 0.0 + + memory_mb = 0.0 + if self.device == "cuda": + memory_mb = torch.cuda.max_memory_allocated() / (1024**2) + + return { + "inference_time_ms": avg_time_ms, + "tokens_per_second": tokens_per_sec, + "memory_usage_mb": memory_mb, + } + + def _compute_quality_metrics( + self, + model: AutoModelForCausalLM, + tokenizer: AutoTokenizer, + dataset: List[Dict[str, Any]], + metrics: List[str], + ) -> Dict[str, Any]: + """Compute generation quality metrics (BLEU, ROUGE)""" + want_bleu = "bleu" in metrics + want_rouge = "rouge" in metrics + + if not want_bleu and not want_rouge: + return {"bleu_score": None, "rouge_scores": None} + + if want_bleu and sacrebleu is None: + print("[quality] sacrebleu not installed; skipping BLEU") + want_bleu = False + if want_rouge and rouge_scorer is None: + print("[quality] rouge-score not installed; skipping ROUGE") + want_rouge = False + + prompts: List[str] = [] + refs: List[str] = [] + preds: List[str] = [] + + # Prepare small eval subset (already controlled by caller num_samples) + pairs: List[Tuple[str, str]] = [] + for ex in dataset: + p, r = self._extract_prompt_and_reference(ex) + if p and r: + pairs.append((p, r)) + + # Generate predictions + model.eval() + with torch.no_grad(): + for p, r in pairs: + inputs = tokenizer( + p, return_tensors="pt", truncation=True, max_length=512 + ) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + output_ids = model.generate( + **inputs, max_new_tokens=96, do_sample=False + ) + text = tokenizer.decode(output_ids[0], skip_special_tokens=True) + # Heuristic: keep only completion after prompt + if text.startswith(p): + text = text[len(p) :].strip() + prompts.append(p) + refs.append(r) + preds.append(text) + + results: Dict[str, Any] = {"bleu_score": None, "rouge_scores": None} + + if want_bleu and preds and refs: + try: + bleu = sacrebleu.corpus_bleu(preds, [refs]) + results["bleu_score"] = float(bleu.score) + except Exception as e: + print(f"[quality] BLEU failed: {e}") + + if want_rouge and preds and refs: + try: + scorer = rouge_scorer.RougeScorer( + ["rouge1", "rouge2", "rougeL"], use_stemmer=True + ) + totals = {"rouge1": 0.0, "rouge2": 0.0, "rougeL": 0.0} + for hyp, ref in zip(preds, refs): + scores = scorer.score(ref, hyp) + for k in totals: + totals[k] += scores[k].fmeasure + n = max(1, len(preds)) + results["rouge_scores"] = {k: float(v / n) for k, v in totals.items()} + except Exception as e: + print(f"[quality] ROUGE failed: {e}") + + return results + + def _extract_prompt_and_reference(self, example: Dict[str, Any]) -> Tuple[str, str]: + """Return (prompt, reference) for chat-style examples. + Prompt: last user message; Reference: last assistant message. + Fallbacks to text-only examples if needed. + """ + if "messages" in example and isinstance(example["messages"], list): + user = "" + assistant = "" + for m in example["messages"]: + role = (m.get("role") or "").lower() + content = m.get("content") or "" + if role == "user": + user = content + elif role == "assistant": + assistant = content + return user.strip(), assistant.strip() + if "instruction" in example and "response" in example: + return str(example.get("instruction", "")), str(example.get("response", "")) + # Fallback: use text twice so it contributes neutrally to averages + t = self._extract_text(example) + return t, t + + def _extract_text(self, example: Dict[str, Any]) -> str: + """Extract text from dataset example""" + if "text" in example: + return example["text"] + elif "messages" in example: + # Chat format + return "\n".join( + [f"{m['role']}: {m['content']}" for m in example["messages"]] + ) + elif "instruction" in example: + return f"Instruction: {example['instruction']}\nResponse: {example.get('response', '')}" + else: + return str(example) + + def save_results(self, metrics: EvaluationMetrics, experiment_name: str): + """Save evaluation results to file""" + timestamp = time.strftime("%Y%m%d_%H%M%S") + output_file = self.results_dir / f"{experiment_name}_{timestamp}.json" + + with open(output_file, "w") as f: + json.dump(metrics.to_dict(), f, indent=2) + + print(f"✓ Results saved to {output_file}") + + def compare_models( + self, model_paths: List[str], test_dataset: str, num_samples: int = 100 + ) -> Dict[str, EvaluationMetrics]: + """Compare multiple models on same dataset""" + results = {} + + for model_path in model_paths: + model_name = Path(model_path).name + print(f"\nEvaluating {model_name}...") + metrics = self.evaluate_model( + model_path, test_dataset, num_samples=num_samples + ) + results[model_name] = metrics + + print(f" Perplexity: {metrics.perplexity:.2f}") + print(f" Inference Time: {metrics.inference_time_ms:.2f}ms") + print(f" Tokens/sec: {metrics.tokens_per_second:.2f}") + + return results + + +def main(): + """CLI for automatic evaluation""" + import argparse + + parser = argparse.ArgumentParser(description="Automatic Model Evaluation") + parser.add_argument("--model", type=str, required=True, help="Path to model") + parser.add_argument( + "--dataset", type=str, required=True, help="Path to test dataset" + ) + parser.add_argument( + "--config", type=str, default="lora/lora.yaml", help="Config file" + ) + parser.add_argument( + "--num-samples", type=int, default=100, help="Number of test samples" + ) + parser.add_argument( + "--metrics", + nargs="+", + default=["perplexity", "inference_time"], + help="Metrics to compute", + ) + parser.add_argument( + "--output-name", type=str, default="evaluation", help="Output name" + ) + + args = parser.parse_args() + + evaluator = AutomaticEvaluator(config_path=args.config) + metrics = evaluator.evaluate_model( + args.model, args.dataset, metrics=args.metrics, num_samples=args.num_samples + ) + + print("\n=== Evaluation Results ===") + print(f"Perplexity: {metrics.perplexity:.2f}") + print(f"Inference Time: {metrics.inference_time_ms:.2f}ms") + print(f"Tokens/sec: {metrics.tokens_per_second:.2f}") + print(f"Memory Usage: {metrics.memory_usage_mb:.2f}MB") + + evaluator.save_results(metrics, args.output_name) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/data_augmenter.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/data_augmenter.py index 037ab7733..fffa1f642 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/data_augmenter.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/data_augmenter.py @@ -1,304 +1,304 @@ -""" -Data Augmentation for Text -Techniques to augment training data for improved model generalization -""" - -import json -import random -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Dict, List - - -@dataclass -class AugmentationConfig: - """Data augmentation configuration""" - - synonym_replacement_prob: float = 0.1 - random_insertion_prob: float = 0.1 - random_swap_prob: float = 0.1 - random_deletion_prob: float = 0.1 - back_translation: bool = False - paraphrase: bool = False - num_augmentations_per_sample: int = 1 - - -class TextAugmenter: - """Text data augmentation toolkit""" - - def __init__(self, config: AugmentationConfig = None): - self.config = config or AugmentationConfig() - self.results_dir = Path("data_out/augmented_data") - self.results_dir.mkdir(parents=True, exist_ok=True) - - def augment_dataset( - self, input_path: str, output_path: str, techniques: List[str] = None - ) -> Dict[str, Any]: - """ - Augment entire dataset - - Args: - input_path: Input JSONL file - output_path: Output JSONL file - techniques: List of augmentation techniques to use - - Returns: - Statistics dictionary - """ - techniques = techniques or ["synonym", "insertion", "swap", "deletion"] - - print(f"Augmenting dataset: {input_path}") - print(f"Techniques: {', '.join(techniques)}") - - # Load dataset - samples = self._load_dataset(input_path) - original_count = len(samples) - - augmented_samples = [] - - for sample in samples: - # Keep original - augmented_samples.append(sample) - - # Generate augmentations - text = self._extract_text(sample) - - for _ in range(self.config.num_augmentations_per_sample): - augmented_text = self._augment_text(text, techniques) - - # Create augmented sample - aug_sample = sample.copy() - self._update_text(aug_sample, augmented_text) - augmented_samples.append(aug_sample) - - # Save augmented dataset - self._save_dataset(augmented_samples, output_path) - - stats = { - "original_samples": original_count, - "augmented_samples": len(augmented_samples), - "augmentation_factor": len(augmented_samples) / original_count, - "techniques_used": techniques, - } - - print("\n✓ Augmentation complete") - print(f" Original: {original_count:,}") - print(f" Augmented: {len(augmented_samples):,}") - print(f" Factor: {stats['augmentation_factor']:.1f}x") - - return stats - - def _augment_text(self, text: str, techniques: List[str]) -> str: - """Apply augmentation techniques to text""" - words = text.split() - - if ( - "synonym" in techniques - and random.random() < self.config.synonym_replacement_prob - ): - words = self._synonym_replacement(words) - - if ( - "insertion" in techniques - and random.random() < self.config.random_insertion_prob - ): - words = self._random_insertion(words) - - if "swap" in techniques and random.random() < self.config.random_swap_prob: - words = self._random_swap(words) - - if ( - "deletion" in techniques - and random.random() < self.config.random_deletion_prob - ): - words = self._random_deletion(words) - - return " ".join(words) - - def _synonym_replacement(self, words: List[str], n: int = None) -> List[str]: - """Replace n random words with synonyms""" - if n is None: - n = max(1, int(len(words) * 0.1)) - - new_words = words.copy() - random_word_indices = list(range(len(words))) - random.shuffle(random_word_indices) - - replaced = 0 - for idx in random_word_indices: - if replaced >= n: - break - - # Simple synonym replacement (in practice, use WordNet or transformer-based) - word = words[idx] - synonym = self._get_simple_synonym(word) - - if synonym != word: - new_words[idx] = synonym - replaced += 1 - - return new_words - - def _random_insertion(self, words: List[str], n: int = None) -> List[str]: - """Randomly insert n words""" - if n is None: - n = max(1, int(len(words) * 0.1)) - - new_words = words.copy() - - for _ in range(n): - if not new_words: - break - - # Insert a random word from the text - random_word = random.choice(words) - random_idx = random.randint(0, len(new_words)) - new_words.insert(random_idx, random_word) - - return new_words - - def _random_swap(self, words: List[str], n: int = None) -> List[str]: - """Randomly swap n pairs of words""" - if n is None: - n = max(1, int(len(words) * 0.1)) - - new_words = words.copy() - - for _ in range(n): - if len(new_words) < 2: - break - - idx1, idx2 = random.sample(range(len(new_words)), 2) - new_words[idx1], new_words[idx2] = new_words[idx2], new_words[idx1] - - return new_words - - def _random_deletion(self, words: List[str], p: float = None) -> List[str]: - """Randomly delete words with probability p""" - if p is None: - p = self.config.random_deletion_prob - - # Don't delete all words - if len(words) == 1: - return words - - new_words = [] - for word in words: - if random.random() > p: - new_words.append(word) - - # Return original if all deleted - return new_words if new_words else words - - def _get_simple_synonym(self, word: str) -> str: - """Get simple synonym (basic implementation)""" - # Simple synonym dictionary (in production, use WordNet or BERT) - synonyms = { - "good": ["great", "excellent", "fine", "nice"], - "bad": ["poor", "terrible", "awful", "horrible"], - "big": ["large", "huge", "enormous", "massive"], - "small": ["tiny", "little", "mini", "compact"], - "fast": ["quick", "rapid", "swift", "speedy"], - "slow": ["sluggish", "gradual", "leisurely"], - "happy": ["joyful", "cheerful", "pleased", "content"], - "sad": ["unhappy", "sorrowful", "depressed", "melancholy"], - } - - lower_word = word.lower() - if lower_word in synonyms: - synonym = random.choice(synonyms[lower_word]) - # Preserve capitalization - if word[0].isupper(): - synonym = synonym.capitalize() - return synonym - - return word - - def _load_dataset(self, path: str) -> List[Dict[str, Any]]: - """Load dataset from JSONL""" - samples = [] - with open(path, "r", encoding="utf-8") as f: - for line in f: - if line.strip(): - samples.append(json.loads(line)) - return samples - - def _save_dataset(self, samples: List[Dict[str, Any]], path: str): - """Save dataset to JSONL""" - output_path = Path(path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, "w", encoding="utf-8") as f: - for sample in samples: - f.write(json.dumps(sample, ensure_ascii=False) + "\n") - - def _extract_text(self, sample: Dict[str, Any]) -> str: - """Extract text from sample""" - if "messages" in sample: - return " ".join([m.get("content", "") for m in sample["messages"]]) - elif "text" in sample: - return sample["text"] - elif "instruction" in sample: - return f"{sample['instruction']} {sample.get('response', '')}" - else: - return json.dumps(sample) - - def _update_text(self, sample: Dict[str, Any], new_text: str): - """Update text in sample""" - if "text" in sample: - sample["text"] = new_text - elif "instruction" in sample: - # Split back into instruction and response (approximate) - parts = new_text.split("\n instruction", 1) - if len(parts) == 2: - sample["instruction"] = parts[0].strip() - sample["response"] = parts[1].strip() - else: - sample["instruction"] = new_text - # Messages format is more complex, skip for now - - -def main(): - """CLI for data augmentation""" - import argparse - - parser = argparse.ArgumentParser(description="Text Data Augmentation") - parser.add_argument( - "--input", type=str, required=True, help="Input dataset (JSONL)" - ) - parser.add_argument( - "--output", type=str, required=True, help="Output dataset (JSONL)" - ) - parser.add_argument( - "--techniques", - nargs="+", - default=["synonym", "insertion", "swap", "deletion"], - help="Augmentation techniques", - ) - parser.add_argument( - "--num-aug", type=int, default=1, help="Number of augmentations per sample" - ) - parser.add_argument( - "--prob", type=float, default=0.1, help="Probability for each technique" - ) - - args = parser.parse_args() - - config = AugmentationConfig( - synonym_replacement_prob=args.prob, - random_insertion_prob=args.prob, - random_swap_prob=args.prob, - random_deletion_prob=args.prob, - num_augmentations_per_sample=args.num_aug, - ) - - augmenter = TextAugmenter(config) - stats = augmenter.augment_dataset( - args.input, args.output, techniques=args.techniques - ) - - print(f"\n✓ Augmented dataset saved to {args.output}") - - -if __name__ == "__main__": - main() +""" +Data Augmentation for Text +Techniques to augment training data for improved model generalization +""" + +import json +import random +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, List + + +@dataclass +class AugmentationConfig: + """Data augmentation configuration""" + + synonym_replacement_prob: float = 0.1 + random_insertion_prob: float = 0.1 + random_swap_prob: float = 0.1 + random_deletion_prob: float = 0.1 + back_translation: bool = False + paraphrase: bool = False + num_augmentations_per_sample: int = 1 + + +class TextAugmenter: + """Text data augmentation toolkit""" + + def __init__(self, config: AugmentationConfig = None): + self.config = config or AugmentationConfig() + self.results_dir = Path("data_out/augmented_data") + self.results_dir.mkdir(parents=True, exist_ok=True) + + def augment_dataset( + self, input_path: str, output_path: str, techniques: List[str] = None + ) -> Dict[str, Any]: + """ + Augment entire dataset + + Args: + input_path: Input JSONL file + output_path: Output JSONL file + techniques: List of augmentation techniques to use + + Returns: + Statistics dictionary + """ + techniques = techniques or ["synonym", "insertion", "swap", "deletion"] + + print(f"Augmenting dataset: {input_path}") + print(f"Techniques: {', '.join(techniques)}") + + # Load dataset + samples = self._load_dataset(input_path) + original_count = len(samples) + + augmented_samples = [] + + for sample in samples: + # Keep original + augmented_samples.append(sample) + + # Generate augmentations + text = self._extract_text(sample) + + for _ in range(self.config.num_augmentations_per_sample): + augmented_text = self._augment_text(text, techniques) + + # Create augmented sample + aug_sample = sample.copy() + self._update_text(aug_sample, augmented_text) + augmented_samples.append(aug_sample) + + # Save augmented dataset + self._save_dataset(augmented_samples, output_path) + + stats = { + "original_samples": original_count, + "augmented_samples": len(augmented_samples), + "augmentation_factor": len(augmented_samples) / original_count, + "techniques_used": techniques, + } + + print("\n✓ Augmentation complete") + print(f" Original: {original_count:,}") + print(f" Augmented: {len(augmented_samples):,}") + print(f" Factor: {stats['augmentation_factor']:.1f}x") + + return stats + + def _augment_text(self, text: str, techniques: List[str]) -> str: + """Apply augmentation techniques to text""" + words = text.split() + + if ( + "synonym" in techniques + and random.random() < self.config.synonym_replacement_prob + ): + words = self._synonym_replacement(words) + + if ( + "insertion" in techniques + and random.random() < self.config.random_insertion_prob + ): + words = self._random_insertion(words) + + if "swap" in techniques and random.random() < self.config.random_swap_prob: + words = self._random_swap(words) + + if ( + "deletion" in techniques + and random.random() < self.config.random_deletion_prob + ): + words = self._random_deletion(words) + + return " ".join(words) + + def _synonym_replacement(self, words: List[str], n: int = None) -> List[str]: + """Replace n random words with synonyms""" + if n is None: + n = max(1, int(len(words) * 0.1)) + + new_words = words.copy() + random_word_indices = list(range(len(words))) + random.shuffle(random_word_indices) + + replaced = 0 + for idx in random_word_indices: + if replaced >= n: + break + + # Simple synonym replacement (in practice, use WordNet or transformer-based) + word = words[idx] + synonym = self._get_simple_synonym(word) + + if synonym != word: + new_words[idx] = synonym + replaced += 1 + + return new_words + + def _random_insertion(self, words: List[str], n: int = None) -> List[str]: + """Randomly insert n words""" + if n is None: + n = max(1, int(len(words) * 0.1)) + + new_words = words.copy() + + for _ in range(n): + if not new_words: + break + + # Insert a random word from the text + random_word = random.choice(words) + random_idx = random.randint(0, len(new_words)) + new_words.insert(random_idx, random_word) + + return new_words + + def _random_swap(self, words: List[str], n: int = None) -> List[str]: + """Randomly swap n pairs of words""" + if n is None: + n = max(1, int(len(words) * 0.1)) + + new_words = words.copy() + + for _ in range(n): + if len(new_words) < 2: + break + + idx1, idx2 = random.sample(range(len(new_words)), 2) + new_words[idx1], new_words[idx2] = new_words[idx2], new_words[idx1] + + return new_words + + def _random_deletion(self, words: List[str], p: float = None) -> List[str]: + """Randomly delete words with probability p""" + if p is None: + p = self.config.random_deletion_prob + + # Don't delete all words + if len(words) == 1: + return words + + new_words = [] + for word in words: + if random.random() > p: + new_words.append(word) + + # Return original if all deleted + return new_words if new_words else words + + def _get_simple_synonym(self, word: str) -> str: + """Get simple synonym (basic implementation)""" + # Simple synonym dictionary (in production, use WordNet or BERT) + synonyms = { + "good": ["great", "excellent", "fine", "nice"], + "bad": ["poor", "terrible", "awful", "horrible"], + "big": ["large", "huge", "enormous", "massive"], + "small": ["tiny", "little", "mini", "compact"], + "fast": ["quick", "rapid", "swift", "speedy"], + "slow": ["sluggish", "gradual", "leisurely"], + "happy": ["joyful", "cheerful", "pleased", "content"], + "sad": ["unhappy", "sorrowful", "depressed", "melancholy"], + } + + lower_word = word.lower() + if lower_word in synonyms: + synonym = random.choice(synonyms[lower_word]) + # Preserve capitalization + if word[0].isupper(): + synonym = synonym.capitalize() + return synonym + + return word + + def _load_dataset(self, path: str) -> List[Dict[str, Any]]: + """Load dataset from JSONL""" + samples = [] + with open(path, "r", encoding="utf-8") as f: + for line in f: + if line.strip(): + samples.append(json.loads(line)) + return samples + + def _save_dataset(self, samples: List[Dict[str, Any]], path: str): + """Save dataset to JSONL""" + output_path = Path(path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, "w", encoding="utf-8") as f: + for sample in samples: + f.write(json.dumps(sample, ensure_ascii=False) + "\n") + + def _extract_text(self, sample: Dict[str, Any]) -> str: + """Extract text from sample""" + if "messages" in sample: + return " ".join([m.get("content", "") for m in sample["messages"]]) + elif "text" in sample: + return sample["text"] + elif "instruction" in sample: + return f"{sample['instruction']} {sample.get('response', '')}" + else: + return json.dumps(sample) + + def _update_text(self, sample: Dict[str, Any], new_text: str): + """Update text in sample""" + if "text" in sample: + sample["text"] = new_text + elif "instruction" in sample: + # Split back into instruction and response (approximate) + parts = new_text.split("\n instruction", 1) + if len(parts) == 2: + sample["instruction"] = parts[0].strip() + sample["response"] = parts[1].strip() + else: + sample["instruction"] = new_text + # Messages format is more complex, skip for now + + +def main(): + """CLI for data augmentation""" + import argparse + + parser = argparse.ArgumentParser(description="Text Data Augmentation") + parser.add_argument( + "--input", type=str, required=True, help="Input dataset (JSONL)" + ) + parser.add_argument( + "--output", type=str, required=True, help="Output dataset (JSONL)" + ) + parser.add_argument( + "--techniques", + nargs="+", + default=["synonym", "insertion", "swap", "deletion"], + help="Augmentation techniques", + ) + parser.add_argument( + "--num-aug", type=int, default=1, help="Number of augmentations per sample" + ) + parser.add_argument( + "--prob", type=float, default=0.1, help="Probability for each technique" + ) + + args = parser.parse_args() + + config = AugmentationConfig( + synonym_replacement_prob=args.prob, + random_insertion_prob=args.prob, + random_swap_prob=args.prob, + random_deletion_prob=args.prob, + num_augmentations_per_sample=args.num_aug, + ) + + augmenter = TextAugmenter(config) + stats = augmenter.augment_dataset( + args.input, args.output, techniques=args.techniques + ) + + print(f"\n✓ Augmented dataset saved to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/dataset_analyzer.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/dataset_analyzer.py index 8ac2bc3c5..1191d6772 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/dataset_analyzer.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/dataset_analyzer.py @@ -1,428 +1,428 @@ -""" -Dataset Analyzer & Health Check -Provides comprehensive analysis of training datasets -""" - -import json -from collections import defaultdict -from dataclasses import asdict, dataclass -from pathlib import Path -from typing import Any, Dict, List - -import matplotlib.pyplot as plt -import numpy as np - - -@dataclass -class DatasetStatistics: - """Container for dataset statistics""" - - total_samples: int - total_tokens: int - avg_tokens_per_sample: float - median_tokens_per_sample: float - std_tokens_per_sample: float - min_tokens: int - max_tokens: int - - # Content analysis - unique_samples: int - duplicate_rate: float - avg_unique_tokens_per_sample: float - vocabulary_size: int - - # Quality metrics - avg_quality_score: float - low_quality_count: int - low_quality_rate: float - - # Distribution - token_distribution: Dict[str, int] - length_percentiles: Dict[str, int] - - def to_dict(self) -> Dict[str, Any]: - return asdict(self) - - def print_summary(self): - """Print human-readable summary""" - print("\n" + "=" * 60) - print("DATASET ANALYSIS SUMMARY") - print("=" * 60) - - print("\n📊 Basic Statistics:") - print(f" Total samples: {self.total_samples:,}") - print(f" Total tokens: {self.total_tokens:,}") - print(f" Avg tokens/sample: {self.avg_tokens_per_sample:.1f}") - print(f" Median tokens/sample: {self.median_tokens_per_sample:.1f}") - print(f" Std dev: {self.std_tokens_per_sample:.1f}") - print(f" Min tokens: {self.min_tokens}") - print(f" Max tokens: {self.max_tokens}") - - print("\n🔍 Content Analysis:") - print(f" Unique samples: {self.unique_samples:,}") - print(f" Duplicate rate: {self.duplicate_rate:.2%}") - print(f" Vocabulary size: {self.vocabulary_size:,}") - print(f" Avg unique tokens/sample: {self.avg_unique_tokens_per_sample:.1f}") - - print("\n✨ Quality Metrics:") - print(f" Avg quality score: {self.avg_quality_score:.3f}") - print( - f" Low quality samples: {self.low_quality_count:,} ({self.low_quality_rate:.2%})" - ) - - print("\n📈 Length Distribution (percentiles):") - for pct, value in sorted(self.length_percentiles.items()): - print(f" {pct}: {value} tokens") - - -class DatasetAnalyzer: - """Comprehensive dataset analysis tool""" - - def __init__(self): - self.results_dir = Path("data_out/dataset_analysis") - self.results_dir.mkdir(parents=True, exist_ok=True) - - def analyze( - self, - dataset_path: str, - create_visualizations: bool = True, - quality_threshold: float = 0.3, - ) -> DatasetStatistics: - """ - Perform comprehensive dataset analysis - - Args: - dataset_path: Path to JSONL dataset - create_visualizations: Whether to create plots - quality_threshold: Threshold for low quality samples - - Returns: - DatasetStatistics object - """ - print(f"Analyzing dataset: {dataset_path}") - - # Load dataset - samples = self._load_dataset(dataset_path) - - # Extract texts and compute token counts - texts = [self._extract_text(s) for s in samples] - token_counts = [len(text.split()) for text in texts] - - # Basic statistics - total_samples = len(samples) - total_tokens = sum(token_counts) - avg_tokens = np.mean(token_counts) - median_tokens = np.median(token_counts) - std_tokens = np.std(token_counts) - min_tokens = min(token_counts) - max_tokens = max(token_counts) - - # Content analysis - unique_samples = len(set(texts)) - duplicate_rate = (total_samples - unique_samples) / total_samples - - # Vocabulary analysis - all_tokens = [] - unique_tokens_per_sample = [] - for text in texts: - tokens = text.lower().split() - all_tokens.extend(tokens) - unique_tokens_per_sample.append(len(set(tokens))) - - vocabulary = set(all_tokens) - vocabulary_size = len(vocabulary) - avg_unique_tokens = np.mean(unique_tokens_per_sample) - - # Quality analysis - quality_scores = [self._compute_quality_score(text) for text in texts] - avg_quality = np.mean(quality_scores) - low_quality_count = sum(1 for q in quality_scores if q < quality_threshold) - low_quality_rate = low_quality_count / total_samples - - # Distribution analysis - token_distribution = self._compute_distribution(token_counts) - length_percentiles = { - "10th": int(np.percentile(token_counts, 10)), - "25th": int(np.percentile(token_counts, 25)), - "50th": int(np.percentile(token_counts, 50)), - "75th": int(np.percentile(token_counts, 75)), - "90th": int(np.percentile(token_counts, 90)), - "95th": int(np.percentile(token_counts, 95)), - "99th": int(np.percentile(token_counts, 99)), - } - - # Create statistics object - stats = DatasetStatistics( - total_samples=total_samples, - total_tokens=total_tokens, - avg_tokens_per_sample=avg_tokens, - median_tokens_per_sample=median_tokens, - std_tokens_per_sample=std_tokens, - min_tokens=min_tokens, - max_tokens=max_tokens, - unique_samples=unique_samples, - duplicate_rate=duplicate_rate, - avg_unique_tokens_per_sample=avg_unique_tokens, - vocabulary_size=vocabulary_size, - avg_quality_score=avg_quality, - low_quality_count=low_quality_count, - low_quality_rate=low_quality_rate, - token_distribution=token_distribution, - length_percentiles=length_percentiles, - ) - - # Create visualizations - if create_visualizations: - self._create_visualizations( - token_counts, quality_scores, Path(dataset_path).stem - ) - - # Save results - self._save_results(stats, Path(dataset_path).stem) - - return stats - - def _load_dataset(self, path: str) -> List[Dict[str, Any]]: - """Load dataset from JSONL""" - samples = [] - with open(path, "r", encoding="utf-8") as f: - for line in f: - if line.strip(): - samples.append(json.loads(line)) - return samples - - def _extract_text(self, sample: Dict[str, Any]) -> str: - """Extract text from sample""" - if "messages" in sample: - return " ".join([m.get("content", "") for m in sample["messages"]]) - elif "text" in sample: - return sample["text"] - elif "instruction" in sample: - return f"{sample['instruction']} {sample.get('response', '')}" - else: - return json.dumps(sample) - - def _compute_quality_score(self, text: str) -> float: - """Compute quality score for text""" - scores = [] - - # Length score - word_count = len(text.split()) - length_score = min(word_count / 100, 1.0) - scores.append(length_score) - - # Character diversity - unique_chars = len(set(text.lower())) - char_diversity = min(unique_chars / 26, 1.0) - scores.append(char_diversity) - - # Word diversity - words = text.lower().split() - if words: - unique_words = len(set(words)) - word_diversity = unique_words / len(words) - scores.append(word_diversity) - - # Punctuation - punct_count = sum(1 for c in text if c in ".,!?;:") - punct_score = min(punct_count / 10, 1.0) - scores.append(punct_score) - - return np.mean(scores) if scores else 0.0 - - def _compute_distribution(self, token_counts: List[int]) -> Dict[str, int]: - """Compute token count distribution""" - bins = [0, 50, 100, 200, 500, 1000, 2000, float("inf")] - labels = [ - "0-50", - "51-100", - "101-200", - "201-500", - "501-1000", - "1001-2000", - "2000+", - ] - - distribution = defaultdict(int) - for count in token_counts: - for i, (low, high) in enumerate(zip(bins[:-1], bins[1:])): - if low < count <= high: - distribution[labels[i]] += 1 - break - - return dict(distribution) - - def _create_visualizations( - self, token_counts: List[int], quality_scores: List[float], dataset_name: str - ): - """Create visualization plots""" - try: - fig, axes = plt.subplots(2, 2, figsize=(15, 12)) - - # Token length distribution (histogram) - axes[0, 0].hist(token_counts, bins=50, edgecolor="black", alpha=0.7) - axes[0, 0].set_xlabel("Token Count") - axes[0, 0].set_ylabel("Frequency") - axes[0, 0].set_title("Token Length Distribution") - axes[0, 0].axvline( - np.median(token_counts), color="r", linestyle="--", label="Median" - ) - axes[0, 0].legend() - - # Token length box plot - axes[0, 1].boxplot(token_counts, vert=True) - axes[0, 1].set_ylabel("Token Count") - axes[0, 1].set_title("Token Length Box Plot") - axes[0, 1].grid(True, alpha=0.3) - - # Quality score distribution - axes[1, 0].hist( - quality_scores, bins=30, edgecolor="black", alpha=0.7, color="green" - ) - axes[1, 0].set_xlabel("Quality Score") - axes[1, 0].set_ylabel("Frequency") - axes[1, 0].set_title("Quality Score Distribution") - axes[1, 0].axvline( - np.mean(quality_scores), color="r", linestyle="--", label="Mean" - ) - axes[1, 0].legend() - - # Length vs Quality scatter - sample_indices = np.random.choice( - len(token_counts), min(1000, len(token_counts)), replace=False - ) - sampled_counts = [token_counts[i] for i in sample_indices] - sampled_quality = [quality_scores[i] for i in sample_indices] - axes[1, 1].scatter(sampled_counts, sampled_quality, alpha=0.5) - axes[1, 1].set_xlabel("Token Count") - axes[1, 1].set_ylabel("Quality Score") - axes[1, 1].set_title("Length vs Quality") - axes[1, 1].grid(True, alpha=0.3) - - plt.tight_layout() - - # Save plot - plot_path = self.results_dir / f"{dataset_name}_analysis.png" - plt.savefig(plot_path, dpi=150, bbox_inches="tight") - plt.close() - - print(f"✓ Visualizations saved to {plot_path}") - - except ImportError: - print("⚠ matplotlib not available, skipping visualizations") - except Exception as e: - print(f"⚠ Error creating visualizations: {e}") - - def _save_results(self, stats: DatasetStatistics, dataset_name: str): - """Save analysis results""" - output_file = self.results_dir / f"{dataset_name}_stats.json" - - with open(output_file, "w") as f: - json.dump(stats.to_dict(), f, indent=2) - - print(f"✓ Statistics saved to {output_file}") - - def compare_datasets( - self, dataset_paths: List[str] - ) -> Dict[str, DatasetStatistics]: - """Compare multiple datasets""" - results = {} - - for path in dataset_paths: - dataset_name = Path(path).stem - print(f"\n{'='*60}") - print(f"Analyzing: {dataset_name}") - print(f"{'='*60}") - - stats = self.analyze(path, create_visualizations=False) - results[dataset_name] = stats - stats.print_summary() - - # Create comparison visualization - self._create_comparison_plot(results) - - return results - - def _create_comparison_plot(self, results: Dict[str, DatasetStatistics]): - """Create comparison visualization""" - try: - fig, axes = plt.subplots(2, 2, figsize=(15, 12)) - - datasets = list(results.keys()) - - # Compare avg tokens - avg_tokens = [results[d].avg_tokens_per_sample for d in datasets] - axes[0, 0].bar(datasets, avg_tokens) - axes[0, 0].set_ylabel("Avg Tokens/Sample") - axes[0, 0].set_title("Average Token Count Comparison") - axes[0, 0].tick_params(axis="x", rotation=45) - - # Compare quality scores - avg_quality = [results[d].avg_quality_score for d in datasets] - axes[0, 1].bar(datasets, avg_quality, color="green") - axes[0, 1].set_ylabel("Avg Quality Score") - axes[0, 1].set_title("Average Quality Comparison") - axes[0, 1].tick_params(axis="x", rotation=45) - - # Compare duplicate rates - dup_rates = [results[d].duplicate_rate * 100 for d in datasets] - axes[1, 0].bar(datasets, dup_rates, color="orange") - axes[1, 0].set_ylabel("Duplicate Rate (%)") - axes[1, 0].set_title("Duplicate Rate Comparison") - axes[1, 0].tick_params(axis="x", rotation=45) - - # Compare vocabulary sizes - vocab_sizes = [results[d].vocabulary_size for d in datasets] - axes[1, 1].bar(datasets, vocab_sizes, color="purple") - axes[1, 1].set_ylabel("Vocabulary Size") - axes[1, 1].set_title("Vocabulary Size Comparison") - axes[1, 1].tick_params(axis="x", rotation=45) - - plt.tight_layout() - - plot_path = self.results_dir / "dataset_comparison.png" - plt.savefig(plot_path, dpi=150, bbox_inches="tight") - plt.close() - - print(f"\n✓ Comparison plot saved to {plot_path}") - - except Exception as e: - print(f"⚠ Error creating comparison plot: {e}") - - -def main(): - """CLI for dataset analysis""" - import argparse - - parser = argparse.ArgumentParser(description="Dataset Analysis & Health Check") - parser.add_argument("--dataset", type=str, help="Path to dataset (JSONL)") - parser.add_argument("--compare", nargs="+", help="Compare multiple datasets") - parser.add_argument( - "--no-visualizations", action="store_true", help="Skip creating visualizations" - ) - parser.add_argument( - "--quality-threshold", - type=float, - default=0.3, - help="Quality threshold for flagging samples", - ) - - args = parser.parse_args() - - analyzer = DatasetAnalyzer() - - if args.compare: - results = analyzer.compare_datasets(args.compare) - elif args.dataset: - stats = analyzer.analyze( - args.dataset, - create_visualizations=not args.no_visualizations, - quality_threshold=args.quality_threshold, - ) - stats.print_summary() - else: - parser.print_help() - - -if __name__ == "__main__": - main() +""" +Dataset Analyzer & Health Check +Provides comprehensive analysis of training datasets +""" + +import json +from collections import defaultdict +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Any, Dict, List + +import matplotlib.pyplot as plt +import numpy as np + + +@dataclass +class DatasetStatistics: + """Container for dataset statistics""" + + total_samples: int + total_tokens: int + avg_tokens_per_sample: float + median_tokens_per_sample: float + std_tokens_per_sample: float + min_tokens: int + max_tokens: int + + # Content analysis + unique_samples: int + duplicate_rate: float + avg_unique_tokens_per_sample: float + vocabulary_size: int + + # Quality metrics + avg_quality_score: float + low_quality_count: int + low_quality_rate: float + + # Distribution + token_distribution: Dict[str, int] + length_percentiles: Dict[str, int] + + def to_dict(self) -> Dict[str, Any]: + return asdict(self) + + def print_summary(self): + """Print human-readable summary""" + print("\n" + "=" * 60) + print("DATASET ANALYSIS SUMMARY") + print("=" * 60) + + print("\n📊 Basic Statistics:") + print(f" Total samples: {self.total_samples:,}") + print(f" Total tokens: {self.total_tokens:,}") + print(f" Avg tokens/sample: {self.avg_tokens_per_sample:.1f}") + print(f" Median tokens/sample: {self.median_tokens_per_sample:.1f}") + print(f" Std dev: {self.std_tokens_per_sample:.1f}") + print(f" Min tokens: {self.min_tokens}") + print(f" Max tokens: {self.max_tokens}") + + print("\n🔍 Content Analysis:") + print(f" Unique samples: {self.unique_samples:,}") + print(f" Duplicate rate: {self.duplicate_rate:.2%}") + print(f" Vocabulary size: {self.vocabulary_size:,}") + print(f" Avg unique tokens/sample: {self.avg_unique_tokens_per_sample:.1f}") + + print("\n✨ Quality Metrics:") + print(f" Avg quality score: {self.avg_quality_score:.3f}") + print( + f" Low quality samples: {self.low_quality_count:,} ({self.low_quality_rate:.2%})" + ) + + print("\n📈 Length Distribution (percentiles):") + for pct, value in sorted(self.length_percentiles.items()): + print(f" {pct}: {value} tokens") + + +class DatasetAnalyzer: + """Comprehensive dataset analysis tool""" + + def __init__(self): + self.results_dir = Path("data_out/dataset_analysis") + self.results_dir.mkdir(parents=True, exist_ok=True) + + def analyze( + self, + dataset_path: str, + create_visualizations: bool = True, + quality_threshold: float = 0.3, + ) -> DatasetStatistics: + """ + Perform comprehensive dataset analysis + + Args: + dataset_path: Path to JSONL dataset + create_visualizations: Whether to create plots + quality_threshold: Threshold for low quality samples + + Returns: + DatasetStatistics object + """ + print(f"Analyzing dataset: {dataset_path}") + + # Load dataset + samples = self._load_dataset(dataset_path) + + # Extract texts and compute token counts + texts = [self._extract_text(s) for s in samples] + token_counts = [len(text.split()) for text in texts] + + # Basic statistics + total_samples = len(samples) + total_tokens = sum(token_counts) + avg_tokens = np.mean(token_counts) + median_tokens = np.median(token_counts) + std_tokens = np.std(token_counts) + min_tokens = min(token_counts) + max_tokens = max(token_counts) + + # Content analysis + unique_samples = len(set(texts)) + duplicate_rate = (total_samples - unique_samples) / total_samples + + # Vocabulary analysis + all_tokens = [] + unique_tokens_per_sample = [] + for text in texts: + tokens = text.lower().split() + all_tokens.extend(tokens) + unique_tokens_per_sample.append(len(set(tokens))) + + vocabulary = set(all_tokens) + vocabulary_size = len(vocabulary) + avg_unique_tokens = np.mean(unique_tokens_per_sample) + + # Quality analysis + quality_scores = [self._compute_quality_score(text) for text in texts] + avg_quality = np.mean(quality_scores) + low_quality_count = sum(1 for q in quality_scores if q < quality_threshold) + low_quality_rate = low_quality_count / total_samples + + # Distribution analysis + token_distribution = self._compute_distribution(token_counts) + length_percentiles = { + "10th": int(np.percentile(token_counts, 10)), + "25th": int(np.percentile(token_counts, 25)), + "50th": int(np.percentile(token_counts, 50)), + "75th": int(np.percentile(token_counts, 75)), + "90th": int(np.percentile(token_counts, 90)), + "95th": int(np.percentile(token_counts, 95)), + "99th": int(np.percentile(token_counts, 99)), + } + + # Create statistics object + stats = DatasetStatistics( + total_samples=total_samples, + total_tokens=total_tokens, + avg_tokens_per_sample=avg_tokens, + median_tokens_per_sample=median_tokens, + std_tokens_per_sample=std_tokens, + min_tokens=min_tokens, + max_tokens=max_tokens, + unique_samples=unique_samples, + duplicate_rate=duplicate_rate, + avg_unique_tokens_per_sample=avg_unique_tokens, + vocabulary_size=vocabulary_size, + avg_quality_score=avg_quality, + low_quality_count=low_quality_count, + low_quality_rate=low_quality_rate, + token_distribution=token_distribution, + length_percentiles=length_percentiles, + ) + + # Create visualizations + if create_visualizations: + self._create_visualizations( + token_counts, quality_scores, Path(dataset_path).stem + ) + + # Save results + self._save_results(stats, Path(dataset_path).stem) + + return stats + + def _load_dataset(self, path: str) -> List[Dict[str, Any]]: + """Load dataset from JSONL""" + samples = [] + with open(path, "r", encoding="utf-8") as f: + for line in f: + if line.strip(): + samples.append(json.loads(line)) + return samples + + def _extract_text(self, sample: Dict[str, Any]) -> str: + """Extract text from sample""" + if "messages" in sample: + return " ".join([m.get("content", "") for m in sample["messages"]]) + elif "text" in sample: + return sample["text"] + elif "instruction" in sample: + return f"{sample['instruction']} {sample.get('response', '')}" + else: + return json.dumps(sample) + + def _compute_quality_score(self, text: str) -> float: + """Compute quality score for text""" + scores = [] + + # Length score + word_count = len(text.split()) + length_score = min(word_count / 100, 1.0) + scores.append(length_score) + + # Character diversity + unique_chars = len(set(text.lower())) + char_diversity = min(unique_chars / 26, 1.0) + scores.append(char_diversity) + + # Word diversity + words = text.lower().split() + if words: + unique_words = len(set(words)) + word_diversity = unique_words / len(words) + scores.append(word_diversity) + + # Punctuation + punct_count = sum(1 for c in text if c in ".,!?;:") + punct_score = min(punct_count / 10, 1.0) + scores.append(punct_score) + + return np.mean(scores) if scores else 0.0 + + def _compute_distribution(self, token_counts: List[int]) -> Dict[str, int]: + """Compute token count distribution""" + bins = [0, 50, 100, 200, 500, 1000, 2000, float("inf")] + labels = [ + "0-50", + "51-100", + "101-200", + "201-500", + "501-1000", + "1001-2000", + "2000+", + ] + + distribution = defaultdict(int) + for count in token_counts: + for i, (low, high) in enumerate(zip(bins[:-1], bins[1:])): + if low < count <= high: + distribution[labels[i]] += 1 + break + + return dict(distribution) + + def _create_visualizations( + self, token_counts: List[int], quality_scores: List[float], dataset_name: str + ): + """Create visualization plots""" + try: + fig, axes = plt.subplots(2, 2, figsize=(15, 12)) + + # Token length distribution (histogram) + axes[0, 0].hist(token_counts, bins=50, edgecolor="black", alpha=0.7) + axes[0, 0].set_xlabel("Token Count") + axes[0, 0].set_ylabel("Frequency") + axes[0, 0].set_title("Token Length Distribution") + axes[0, 0].axvline( + np.median(token_counts), color="r", linestyle="--", label="Median" + ) + axes[0, 0].legend() + + # Token length box plot + axes[0, 1].boxplot(token_counts, vert=True) + axes[0, 1].set_ylabel("Token Count") + axes[0, 1].set_title("Token Length Box Plot") + axes[0, 1].grid(True, alpha=0.3) + + # Quality score distribution + axes[1, 0].hist( + quality_scores, bins=30, edgecolor="black", alpha=0.7, color="green" + ) + axes[1, 0].set_xlabel("Quality Score") + axes[1, 0].set_ylabel("Frequency") + axes[1, 0].set_title("Quality Score Distribution") + axes[1, 0].axvline( + np.mean(quality_scores), color="r", linestyle="--", label="Mean" + ) + axes[1, 0].legend() + + # Length vs Quality scatter + sample_indices = np.random.choice( + len(token_counts), min(1000, len(token_counts)), replace=False + ) + sampled_counts = [token_counts[i] for i in sample_indices] + sampled_quality = [quality_scores[i] for i in sample_indices] + axes[1, 1].scatter(sampled_counts, sampled_quality, alpha=0.5) + axes[1, 1].set_xlabel("Token Count") + axes[1, 1].set_ylabel("Quality Score") + axes[1, 1].set_title("Length vs Quality") + axes[1, 1].grid(True, alpha=0.3) + + plt.tight_layout() + + # Save plot + plot_path = self.results_dir / f"{dataset_name}_analysis.png" + plt.savefig(plot_path, dpi=150, bbox_inches="tight") + plt.close() + + print(f"✓ Visualizations saved to {plot_path}") + + except ImportError: + print("⚠ matplotlib not available, skipping visualizations") + except Exception as e: + print(f"⚠ Error creating visualizations: {e}") + + def _save_results(self, stats: DatasetStatistics, dataset_name: str): + """Save analysis results""" + output_file = self.results_dir / f"{dataset_name}_stats.json" + + with open(output_file, "w") as f: + json.dump(stats.to_dict(), f, indent=2) + + print(f"✓ Statistics saved to {output_file}") + + def compare_datasets( + self, dataset_paths: List[str] + ) -> Dict[str, DatasetStatistics]: + """Compare multiple datasets""" + results = {} + + for path in dataset_paths: + dataset_name = Path(path).stem + print(f"\n{'='*60}") + print(f"Analyzing: {dataset_name}") + print(f"{'='*60}") + + stats = self.analyze(path, create_visualizations=False) + results[dataset_name] = stats + stats.print_summary() + + # Create comparison visualization + self._create_comparison_plot(results) + + return results + + def _create_comparison_plot(self, results: Dict[str, DatasetStatistics]): + """Create comparison visualization""" + try: + fig, axes = plt.subplots(2, 2, figsize=(15, 12)) + + datasets = list(results.keys()) + + # Compare avg tokens + avg_tokens = [results[d].avg_tokens_per_sample for d in datasets] + axes[0, 0].bar(datasets, avg_tokens) + axes[0, 0].set_ylabel("Avg Tokens/Sample") + axes[0, 0].set_title("Average Token Count Comparison") + axes[0, 0].tick_params(axis="x", rotation=45) + + # Compare quality scores + avg_quality = [results[d].avg_quality_score for d in datasets] + axes[0, 1].bar(datasets, avg_quality, color="green") + axes[0, 1].set_ylabel("Avg Quality Score") + axes[0, 1].set_title("Average Quality Comparison") + axes[0, 1].tick_params(axis="x", rotation=45) + + # Compare duplicate rates + dup_rates = [results[d].duplicate_rate * 100 for d in datasets] + axes[1, 0].bar(datasets, dup_rates, color="orange") + axes[1, 0].set_ylabel("Duplicate Rate (%)") + axes[1, 0].set_title("Duplicate Rate Comparison") + axes[1, 0].tick_params(axis="x", rotation=45) + + # Compare vocabulary sizes + vocab_sizes = [results[d].vocabulary_size for d in datasets] + axes[1, 1].bar(datasets, vocab_sizes, color="purple") + axes[1, 1].set_ylabel("Vocabulary Size") + axes[1, 1].set_title("Vocabulary Size Comparison") + axes[1, 1].tick_params(axis="x", rotation=45) + + plt.tight_layout() + + plot_path = self.results_dir / "dataset_comparison.png" + plt.savefig(plot_path, dpi=150, bbox_inches="tight") + plt.close() + + print(f"\n✓ Comparison plot saved to {plot_path}") + + except Exception as e: + print(f"⚠ Error creating comparison plot: {e}") + + +def main(): + """CLI for dataset analysis""" + import argparse + + parser = argparse.ArgumentParser(description="Dataset Analysis & Health Check") + parser.add_argument("--dataset", type=str, help="Path to dataset (JSONL)") + parser.add_argument("--compare", nargs="+", help="Compare multiple datasets") + parser.add_argument( + "--no-visualizations", action="store_true", help="Skip creating visualizations" + ) + parser.add_argument( + "--quality-threshold", + type=float, + default=0.3, + help="Quality threshold for flagging samples", + ) + + args = parser.parse_args() + + analyzer = DatasetAnalyzer() + + if args.compare: + results = analyzer.compare_datasets(args.compare) + elif args.dataset: + stats = analyzer.analyze( + args.dataset, + create_visualizations=not args.no_visualizations, + quality_threshold=args.quality_threshold, + ) + stats.print_summary() + else: + parser.print_help() + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/deepspeed_zero3.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/deepspeed_zero3.json index 21cd53282..fcae771ca 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/deepspeed_zero3.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/deepspeed_zero3.json @@ -1,25 +1,25 @@ -{ - "bf16": { - "enabled": true - }, - "zero_optimization": { - "stage": 3, - "offload_param": { - "device": "none" - }, - "offload_optimizer": { - "device": "none" - }, - "overlap_comm": true, - "contiguous_gradients": true, - "reduce_bucket_size": 5e8, - "stage3_prefetch_bucket_size": 5e8, - "stage3_param_persistence_threshold": 1e6, - "sub_group_size": 1e9 - }, - "gradient_accumulation_steps": 1, - "train_batch_size": "auto", - "train_micro_batch_size_per_gpu": "auto", - "steps_per_print": 200, - "wall_clock_breakdown": false -} +{ + "bf16": { + "enabled": true + }, + "zero_optimization": { + "stage": 3, + "offload_param": { + "device": "none" + }, + "offload_optimizer": { + "device": "none" + }, + "overlap_comm": true, + "contiguous_gradients": true, + "reduce_bucket_size": 5e8, + "stage3_prefetch_bucket_size": 5e8, + "stage3_param_persistence_threshold": 1e6, + "sub_group_size": 1e9 + }, + "gradient_accumulation_steps": 1, + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "steps_per_print": 200, + "wall_clock_breakdown": false +} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/gpu_optimizer.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/gpu_optimizer.py index 93d21e121..c439f1e19 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/gpu_optimizer.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/gpu_optimizer.py @@ -1,392 +1,392 @@ -""" -GPU Training Optimization Profile -Automatically configures optimal training settings based on available hardware -""" - -import subprocess -from dataclasses import asdict, dataclass -from pathlib import Path -from typing import Any, Dict, Optional - -import yaml - - -@dataclass -class GPUInfo: - """GPU hardware information""" - - name: str - memory_total_gb: float - memory_available_gb: float - compute_capability: str - cuda_version: str - driver_version: str - gpu_count: int - - -@dataclass -class OptimizationProfile: - """Training optimization configuration""" - - batch_size: int - gradient_accumulation_steps: int - gradient_checkpointing: bool - fp16: bool - bf16: bool - max_grad_norm: float - optimizer: str - learning_rate: float - warmup_steps: int - use_8bit: bool - use_4bit: bool - lora_rank: int - lora_alpha: int - max_seq_length: int - dataloader_num_workers: int - pin_memory: bool - - # Advanced optimizations - fused_optimizer: bool = False - compile_model: bool = False - flash_attention: bool = False - cpu_offload: bool = False - - def to_dict(self) -> Dict[str, Any]: - return asdict(self) - - def save(self, path: str): - """Save profile to YAML""" - with open(path, "w") as f: - yaml.dump(self.to_dict(), f, default_flow_style=False) - print(f"✓ Profile saved to {path}") - - -class GPUOptimizer: - """GPU training optimizer""" - - def __init__(self): - self.gpu_info: Optional[GPUInfo] = None - - def detect_hardware(self) -> GPUInfo: - """Detect GPU hardware capabilities""" - try: - import torch - - if not torch.cuda.is_available(): - print("⚠ No CUDA GPU detected. Using CPU profile.") - return self._cpu_fallback() - - gpu_count = torch.cuda.device_count() - device = torch.cuda.current_device() - - gpu_info = GPUInfo( - name=torch.cuda.get_device_name(device), - memory_total_gb=torch.cuda.get_device_properties(device).total_memory - / (1024**3), - memory_available_gb=self._get_available_memory_gb(), - compute_capability=f"{torch.cuda.get_device_capability(device)[0]}.{torch.cuda.get_device_capability(device)[1]}", - cuda_version=torch.version.cuda or "N/A", - driver_version=self._get_driver_version(), - gpu_count=gpu_count, - ) - - self.gpu_info = gpu_info - self._print_gpu_info(gpu_info) - return gpu_info - - except ImportError: - print("⚠ PyTorch not available. Using CPU profile.") - return self._cpu_fallback() - - def _get_available_memory_gb(self) -> float: - """Get available GPU memory""" - import torch - - torch.cuda.empty_cache() - return torch.cuda.mem_get_info()[0] / (1024**3) - - def _get_driver_version(self) -> str: - """Get NVIDIA driver version""" - try: - result = subprocess.run( - ["nvidia-smi", "--query-gpu=driver_version", "--format=csv,noheader"], - capture_output=True, - text=True, - ) - return result.stdout.strip().split("\n")[0] - except: - return "N/A" - - def _cpu_fallback(self) -> GPUInfo: - """Fallback info for CPU-only systems""" - return GPUInfo( - name="CPU", - memory_total_gb=0.0, - memory_available_gb=0.0, - compute_capability="N/A", - cuda_version="N/A", - driver_version="N/A", - gpu_count=0, - ) - - def _print_gpu_info(self, info: GPUInfo): - """Print GPU information""" - print("\n=== GPU Hardware Detection ===") - print(f"GPU Name: {info.name}") - print(f"GPU Count: {info.gpu_count}") - print(f"Total Memory: {info.memory_total_gb:.2f} GB") - print(f"Available Memory: {info.memory_available_gb:.2f} GB") - print(f"Compute Capability: {info.compute_capability}") - print(f"CUDA Version: {info.cuda_version}") - print(f"Driver Version: {info.driver_version}") - - def create_optimization_profile( - self, model_size_gb: float = 7.0, target_memory_usage: float = 0.8 - ) -> OptimizationProfile: - """ - Create optimal training profile for detected hardware - - Args: - model_size_gb: Size of model in GB - target_memory_usage: Target % of GPU memory to use (0.0-1.0) - - Returns: - OptimizationProfile object - """ - if self.gpu_info is None: - self.detect_hardware() - - gpu = self.gpu_info - - # Determine if we have enough memory for full precision - available_memory = gpu.memory_available_gb * target_memory_usage - - print("\n=== Optimization Profile Creation ===") - print(f"Model size: {model_size_gb:.2f} GB") - print(f"Available memory: {available_memory:.2f} GB") - - # Start with conservative defaults - profile = OptimizationProfile( - batch_size=1, - gradient_accumulation_steps=4, - gradient_checkpointing=True, - fp16=False, - bf16=False, - max_grad_norm=1.0, - optimizer="adamw_torch", - learning_rate=2e-4, - warmup_steps=10, - use_8bit=False, - use_4bit=False, - lora_rank=8, - lora_alpha=16, - max_seq_length=512, - dataloader_num_workers=2, - pin_memory=True, - ) - - if gpu.gpu_count == 0: - # CPU-only configuration - return self._cpu_profile(profile) - - # GPU optimizations based on memory - if available_memory < model_size_gb * 0.5: - # Very limited memory - aggressive quantization - print("Profile: Memory-Constrained (4-bit + CPU offload)") - profile.use_4bit = True - profile.cpu_offload = True - profile.batch_size = 1 - profile.gradient_accumulation_steps = 8 - profile.max_seq_length = 256 - profile.lora_rank = 4 - - elif available_memory < model_size_gb: - # Limited memory - 8-bit quantization - print("Profile: Memory-Efficient (8-bit)") - profile.use_8bit = True - profile.batch_size = 1 - profile.gradient_accumulation_steps = 4 - profile.max_seq_length = 512 - profile.lora_rank = 8 - - elif available_memory < model_size_gb * 2: - # Moderate memory - FP16/BF16 - print("Profile: Balanced (FP16/BF16)") - profile.batch_size = 2 - profile.gradient_accumulation_steps = 2 - profile.max_seq_length = 1024 - profile.lora_rank = 16 - - # Use BF16 if supported (Ampere+) - if self._supports_bf16(gpu): - profile.bf16 = True - else: - profile.fp16 = True - - else: - # Plenty of memory - optimize for speed - print("Profile: Performance (FP16/BF16 + optimizations)") - profile.batch_size = 4 - profile.gradient_accumulation_steps = 1 - profile.gradient_checkpointing = False - profile.max_seq_length = 2048 - profile.lora_rank = 32 - profile.dataloader_num_workers = 4 - - if self._supports_bf16(gpu): - profile.bf16 = True - else: - profile.fp16 = True - - # Enable advanced optimizations for modern GPUs - if float(gpu.compute_capability.split(".")[0]) >= 8: - profile.flash_attention = True - profile.fused_optimizer = True - profile.compile_model = True - - # Multi-GPU adjustments - if gpu.gpu_count > 1: - print(f"Multi-GPU setup detected ({gpu.gpu_count} GPUs)") - profile.batch_size *= min(gpu.gpu_count, 4) # Scale batch size - profile.dataloader_num_workers *= 2 - - self._print_profile(profile) - return profile - - def _cpu_profile(self, profile: OptimizationProfile) -> OptimizationProfile: - """Configure for CPU training""" - print("Profile: CPU-Only") - profile.batch_size = 1 - profile.gradient_accumulation_steps = 16 - profile.gradient_checkpointing = True - profile.max_seq_length = 256 - profile.lora_rank = 4 - profile.dataloader_num_workers = 2 - profile.pin_memory = False - return profile - - def _supports_bf16(self, gpu: GPUInfo) -> bool: - """Check if GPU supports BF16 (Ampere or newer)""" - try: - major_version = int(gpu.compute_capability.split(".")[0]) - return major_version >= 8 # Ampere (A100, RTX 30xx) and newer - except: - return False - - def _print_profile(self, profile: OptimizationProfile): - """Print optimization profile""" - print("\n=== Recommended Settings ===") - print(f"Batch Size: {profile.batch_size}") - print(f"Gradient Accumulation: {profile.gradient_accumulation_steps}") - print( - f"Effective Batch Size: {profile.batch_size * profile.gradient_accumulation_steps}" - ) - print(f"Max Sequence Length: {profile.max_seq_length}") - print(f"LoRA Rank: {profile.lora_rank}") - print( - f"Precision: {'BF16' if profile.bf16 else 'FP16' if profile.fp16 else '8-bit' if profile.use_8bit else '4-bit' if profile.use_4bit else 'FP32'}" - ) - print(f"Gradient Checkpointing: {profile.gradient_checkpointing}") - print(f"Flash Attention: {profile.flash_attention}") - print(f"Compiled Model: {profile.compile_model}") - - # Estimate throughput - tokens_per_step = profile.batch_size * profile.max_seq_length - print(f"\nEstimated tokens/step: {tokens_per_step:,}") - - def update_config_file(self, config_path: str, profile: OptimizationProfile): - """Update training config file with optimized settings""" - config_path = Path(config_path) - - # Load existing config - with open(config_path) as f: - config = yaml.safe_load(f) - - # Update with profile settings - config.update( - { - "finetune_train_batch_size": profile.batch_size, - "gradient_accumulation_steps": profile.gradient_accumulation_steps, - "gradient_checkpointing": profile.gradient_checkpointing, - "finetune_train_seqlen": profile.max_seq_length, - "learning_rate": profile.learning_rate, - "num_warmup_steps": profile.warmup_steps, - "lora_dropout": 0.1, - "max_grad_norm": profile.max_grad_norm, - } - ) - - # Save updated config - backup_path = config_path.with_suffix(".yaml.bak") - config_path.rename(backup_path) - print(f" Backup saved: {backup_path}") - - with open(config_path, "w") as f: - yaml.dump(config, f, default_flow_style=False) - - print(f"✓ Config updated: {config_path}") - - -def main(): - """CLI for GPU optimization""" - import argparse - - parser = argparse.ArgumentParser(description="GPU Training Optimization") - parser.add_argument( - "--model-size", - type=float, - default=7.0, - help="Model size in GB (default: 7.0 for Phi-3)", - ) - parser.add_argument( - "--memory-usage", type=float, default=0.8, help="Target memory usage (0.0-1.0)" - ) - parser.add_argument( - "--output", - type=str, - default="data_out/gpu_profile.yaml", - help="Output profile path", - ) - parser.add_argument( - "--update-config", - type=str, - help="Update existing config file with optimizations", - ) - parser.add_argument( - "--export-env", action="store_true", help="Export as environment variables" - ) - - args = parser.parse_args() - - optimizer = GPUOptimizer() - optimizer.detect_hardware() - - profile = optimizer.create_optimization_profile( - model_size_gb=args.model_size, target_memory_usage=args.memory_usage - ) - - # Save profile - profile.save(args.output) - - # Update config if requested - if args.update_config: - optimizer.update_config_file(args.update_config, profile) - - # Export environment variables if requested - if args.export_env: - print("\n=== Environment Variables ===") - env_vars = { - "CUDA_VISIBLE_DEVICES": "0", - "PYTORCH_CUDA_ALLOC_CONF": "max_split_size_mb:512", - "TOKENIZERS_PARALLELISM": "false", - } - - if profile.use_4bit or profile.use_8bit: - env_vars["BNB_CUDA_VERSION"] = optimizer.gpu_info.cuda_version - - for key, value in env_vars.items(): - print(f"export {key}={value}") - - -if __name__ == "__main__": - main() +""" +GPU Training Optimization Profile +Automatically configures optimal training settings based on available hardware +""" + +import subprocess +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Any, Dict, Optional + +import yaml + + +@dataclass +class GPUInfo: + """GPU hardware information""" + + name: str + memory_total_gb: float + memory_available_gb: float + compute_capability: str + cuda_version: str + driver_version: str + gpu_count: int + + +@dataclass +class OptimizationProfile: + """Training optimization configuration""" + + batch_size: int + gradient_accumulation_steps: int + gradient_checkpointing: bool + fp16: bool + bf16: bool + max_grad_norm: float + optimizer: str + learning_rate: float + warmup_steps: int + use_8bit: bool + use_4bit: bool + lora_rank: int + lora_alpha: int + max_seq_length: int + dataloader_num_workers: int + pin_memory: bool + + # Advanced optimizations + fused_optimizer: bool = False + compile_model: bool = False + flash_attention: bool = False + cpu_offload: bool = False + + def to_dict(self) -> Dict[str, Any]: + return asdict(self) + + def save(self, path: str): + """Save profile to YAML""" + with open(path, "w") as f: + yaml.dump(self.to_dict(), f, default_flow_style=False) + print(f"✓ Profile saved to {path}") + + +class GPUOptimizer: + """GPU training optimizer""" + + def __init__(self): + self.gpu_info: Optional[GPUInfo] = None + + def detect_hardware(self) -> GPUInfo: + """Detect GPU hardware capabilities""" + try: + import torch + + if not torch.cuda.is_available(): + print("⚠ No CUDA GPU detected. Using CPU profile.") + return self._cpu_fallback() + + gpu_count = torch.cuda.device_count() + device = torch.cuda.current_device() + + gpu_info = GPUInfo( + name=torch.cuda.get_device_name(device), + memory_total_gb=torch.cuda.get_device_properties(device).total_memory + / (1024**3), + memory_available_gb=self._get_available_memory_gb(), + compute_capability=f"{torch.cuda.get_device_capability(device)[0]}.{torch.cuda.get_device_capability(device)[1]}", + cuda_version=torch.version.cuda or "N/A", + driver_version=self._get_driver_version(), + gpu_count=gpu_count, + ) + + self.gpu_info = gpu_info + self._print_gpu_info(gpu_info) + return gpu_info + + except ImportError: + print("⚠ PyTorch not available. Using CPU profile.") + return self._cpu_fallback() + + def _get_available_memory_gb(self) -> float: + """Get available GPU memory""" + import torch + + torch.cuda.empty_cache() + return torch.cuda.mem_get_info()[0] / (1024**3) + + def _get_driver_version(self) -> str: + """Get NVIDIA driver version""" + try: + result = subprocess.run( + ["nvidia-smi", "--query-gpu=driver_version", "--format=csv,noheader"], + capture_output=True, + text=True, + ) + return result.stdout.strip().split("\n")[0] + except: + return "N/A" + + def _cpu_fallback(self) -> GPUInfo: + """Fallback info for CPU-only systems""" + return GPUInfo( + name="CPU", + memory_total_gb=0.0, + memory_available_gb=0.0, + compute_capability="N/A", + cuda_version="N/A", + driver_version="N/A", + gpu_count=0, + ) + + def _print_gpu_info(self, info: GPUInfo): + """Print GPU information""" + print("\n=== GPU Hardware Detection ===") + print(f"GPU Name: {info.name}") + print(f"GPU Count: {info.gpu_count}") + print(f"Total Memory: {info.memory_total_gb:.2f} GB") + print(f"Available Memory: {info.memory_available_gb:.2f} GB") + print(f"Compute Capability: {info.compute_capability}") + print(f"CUDA Version: {info.cuda_version}") + print(f"Driver Version: {info.driver_version}") + + def create_optimization_profile( + self, model_size_gb: float = 7.0, target_memory_usage: float = 0.8 + ) -> OptimizationProfile: + """ + Create optimal training profile for detected hardware + + Args: + model_size_gb: Size of model in GB + target_memory_usage: Target % of GPU memory to use (0.0-1.0) + + Returns: + OptimizationProfile object + """ + if self.gpu_info is None: + self.detect_hardware() + + gpu = self.gpu_info + + # Determine if we have enough memory for full precision + available_memory = gpu.memory_available_gb * target_memory_usage + + print("\n=== Optimization Profile Creation ===") + print(f"Model size: {model_size_gb:.2f} GB") + print(f"Available memory: {available_memory:.2f} GB") + + # Start with conservative defaults + profile = OptimizationProfile( + batch_size=1, + gradient_accumulation_steps=4, + gradient_checkpointing=True, + fp16=False, + bf16=False, + max_grad_norm=1.0, + optimizer="adamw_torch", + learning_rate=2e-4, + warmup_steps=10, + use_8bit=False, + use_4bit=False, + lora_rank=8, + lora_alpha=16, + max_seq_length=512, + dataloader_num_workers=2, + pin_memory=True, + ) + + if gpu.gpu_count == 0: + # CPU-only configuration + return self._cpu_profile(profile) + + # GPU optimizations based on memory + if available_memory < model_size_gb * 0.5: + # Very limited memory - aggressive quantization + print("Profile: Memory-Constrained (4-bit + CPU offload)") + profile.use_4bit = True + profile.cpu_offload = True + profile.batch_size = 1 + profile.gradient_accumulation_steps = 8 + profile.max_seq_length = 256 + profile.lora_rank = 4 + + elif available_memory < model_size_gb: + # Limited memory - 8-bit quantization + print("Profile: Memory-Efficient (8-bit)") + profile.use_8bit = True + profile.batch_size = 1 + profile.gradient_accumulation_steps = 4 + profile.max_seq_length = 512 + profile.lora_rank = 8 + + elif available_memory < model_size_gb * 2: + # Moderate memory - FP16/BF16 + print("Profile: Balanced (FP16/BF16)") + profile.batch_size = 2 + profile.gradient_accumulation_steps = 2 + profile.max_seq_length = 1024 + profile.lora_rank = 16 + + # Use BF16 if supported (Ampere+) + if self._supports_bf16(gpu): + profile.bf16 = True + else: + profile.fp16 = True + + else: + # Plenty of memory - optimize for speed + print("Profile: Performance (FP16/BF16 + optimizations)") + profile.batch_size = 4 + profile.gradient_accumulation_steps = 1 + profile.gradient_checkpointing = False + profile.max_seq_length = 2048 + profile.lora_rank = 32 + profile.dataloader_num_workers = 4 + + if self._supports_bf16(gpu): + profile.bf16 = True + else: + profile.fp16 = True + + # Enable advanced optimizations for modern GPUs + if float(gpu.compute_capability.split(".")[0]) >= 8: + profile.flash_attention = True + profile.fused_optimizer = True + profile.compile_model = True + + # Multi-GPU adjustments + if gpu.gpu_count > 1: + print(f"Multi-GPU setup detected ({gpu.gpu_count} GPUs)") + profile.batch_size *= min(gpu.gpu_count, 4) # Scale batch size + profile.dataloader_num_workers *= 2 + + self._print_profile(profile) + return profile + + def _cpu_profile(self, profile: OptimizationProfile) -> OptimizationProfile: + """Configure for CPU training""" + print("Profile: CPU-Only") + profile.batch_size = 1 + profile.gradient_accumulation_steps = 16 + profile.gradient_checkpointing = True + profile.max_seq_length = 256 + profile.lora_rank = 4 + profile.dataloader_num_workers = 2 + profile.pin_memory = False + return profile + + def _supports_bf16(self, gpu: GPUInfo) -> bool: + """Check if GPU supports BF16 (Ampere or newer)""" + try: + major_version = int(gpu.compute_capability.split(".")[0]) + return major_version >= 8 # Ampere (A100, RTX 30xx) and newer + except: + return False + + def _print_profile(self, profile: OptimizationProfile): + """Print optimization profile""" + print("\n=== Recommended Settings ===") + print(f"Batch Size: {profile.batch_size}") + print(f"Gradient Accumulation: {profile.gradient_accumulation_steps}") + print( + f"Effective Batch Size: {profile.batch_size * profile.gradient_accumulation_steps}" + ) + print(f"Max Sequence Length: {profile.max_seq_length}") + print(f"LoRA Rank: {profile.lora_rank}") + print( + f"Precision: {'BF16' if profile.bf16 else 'FP16' if profile.fp16 else '8-bit' if profile.use_8bit else '4-bit' if profile.use_4bit else 'FP32'}" + ) + print(f"Gradient Checkpointing: {profile.gradient_checkpointing}") + print(f"Flash Attention: {profile.flash_attention}") + print(f"Compiled Model: {profile.compile_model}") + + # Estimate throughput + tokens_per_step = profile.batch_size * profile.max_seq_length + print(f"\nEstimated tokens/step: {tokens_per_step:,}") + + def update_config_file(self, config_path: str, profile: OptimizationProfile): + """Update training config file with optimized settings""" + config_path = Path(config_path) + + # Load existing config + with open(config_path) as f: + config = yaml.safe_load(f) + + # Update with profile settings + config.update( + { + "finetune_train_batch_size": profile.batch_size, + "gradient_accumulation_steps": profile.gradient_accumulation_steps, + "gradient_checkpointing": profile.gradient_checkpointing, + "finetune_train_seqlen": profile.max_seq_length, + "learning_rate": profile.learning_rate, + "num_warmup_steps": profile.warmup_steps, + "lora_dropout": 0.1, + "max_grad_norm": profile.max_grad_norm, + } + ) + + # Save updated config + backup_path = config_path.with_suffix(".yaml.bak") + config_path.rename(backup_path) + print(f" Backup saved: {backup_path}") + + with open(config_path, "w") as f: + yaml.dump(config, f, default_flow_style=False) + + print(f"✓ Config updated: {config_path}") + + +def main(): + """CLI for GPU optimization""" + import argparse + + parser = argparse.ArgumentParser(description="GPU Training Optimization") + parser.add_argument( + "--model-size", + type=float, + default=7.0, + help="Model size in GB (default: 7.0 for Phi-3)", + ) + parser.add_argument( + "--memory-usage", type=float, default=0.8, help="Target memory usage (0.0-1.0)" + ) + parser.add_argument( + "--output", + type=str, + default="data_out/gpu_profile.yaml", + help="Output profile path", + ) + parser.add_argument( + "--update-config", + type=str, + help="Update existing config file with optimizations", + ) + parser.add_argument( + "--export-env", action="store_true", help="Export as environment variables" + ) + + args = parser.parse_args() + + optimizer = GPUOptimizer() + optimizer.detect_hardware() + + profile = optimizer.create_optimization_profile( + model_size_gb=args.model_size, target_memory_usage=args.memory_usage + ) + + # Save profile + profile.save(args.output) + + # Update config if requested + if args.update_config: + optimizer.update_config_file(args.update_config, profile) + + # Export environment variables if requested + if args.export_env: + print("\n=== Environment Variables ===") + env_vars = { + "CUDA_VISIBLE_DEVICES": "0", + "PYTORCH_CUDA_ALLOC_CONF": "max_split_size_mb:512", + "TOKENIZERS_PARALLELISM": "false", + } + + if profile.use_4bit or profile.use_8bit: + env_vars["BNB_CUDA_VERSION"] = optimizer.gpu_info.cuda_version + + for key, value in env_vars.items(): + print(f"export {key}={value}") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/lr_finder.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/lr_finder.py index 5741e1ce0..a8c6ea610 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/lr_finder.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/lr_finder.py @@ -1,297 +1,297 @@ -""" -Learning Rate Finder -Automatically find optimal learning rate using the LR range test -""" - -import json -from pathlib import Path -from typing import Any, Dict, List - -import matplotlib.pyplot as plt -import numpy as np -import torch -from torch.utils.data import DataLoader -from transformers import AutoModelForCausalLM, AutoTokenizer - - -class LearningRateFinder: - """ - Implement Leslie Smith's Learning Rate Range Test - https://arxiv.org/abs/1506.01186 - """ - - def __init__( - self, - model: AutoModelForCausalLM, - optimizer: torch.optim.Optimizer, - criterion: torch.nn.Module, - device: str = "cuda" if torch.cuda.is_available() else "cpu", - ): - self.model = model - self.optimizer = optimizer - self.criterion = criterion - self.device = device - - # Store initial state - self.initial_state = { - "model": model.state_dict(), - "optimizer": optimizer.state_dict(), - } - - self.results_dir = Path("data_out/lr_finder") - self.results_dir.mkdir(parents=True, exist_ok=True) - - def range_test( - self, - train_loader: DataLoader, - start_lr: float = 1e-7, - end_lr: float = 10.0, - num_iter: int = 100, - smooth_f: float = 0.05, - diverge_th: float = 5.0, - ) -> Dict[str, Any]: - """ - Perform LR range test - - Args: - train_loader: Training data loader - start_lr: Starting learning rate - end_lr: Ending learning rate - num_iter: Number of iterations - smooth_f: Smoothing factor for losses - diverge_th: Threshold for stopping (multiplier of best loss) - - Returns: - Dictionary with results - """ - print("Starting Learning Rate Range Test...") - print(f"Range: {start_lr} to {end_lr}") - print(f"Iterations: {num_iter}") - - # Prepare - self.model.train() - lrs = [] - losses = [] - best_loss = float("inf") - avg_loss = 0.0 - beta = smooth_f - - # Create LR schedule - def lr_lambda(x: float) -> float: - return float(np.exp(x * np.log(end_lr / start_lr) / (num_iter - 1))) - - # Run test - data_iterator = iter(train_loader) - for iteration in range(num_iter): - # Get batch - try: - batch = next(data_iterator) - except StopIteration: - data_iterator = iter(train_loader) - batch = next(data_iterator) - - # Update LR - lr = start_lr * lr_lambda(iteration) - for param_group in self.optimizer.param_groups: - param_group["lr"] = lr - - # Forward pass - inputs = {k: v.to(self.device) for k, v in batch.items() if k != "labels"} - labels = ( - batch["labels"].to(self.device) - if "labels" in batch - else inputs["input_ids"] - ) - - outputs = self.model(**inputs, labels=labels) - loss = outputs.loss - - # Backward pass - self.optimizer.zero_grad() - loss.backward() - self.optimizer.step() - - # Record - lrs.append(lr) - losses.append(loss.item()) - - # Smooth loss - avg_loss = beta * avg_loss + (1 - beta) * loss.item() - smoothed_loss = avg_loss / (1 - beta ** (iteration + 1)) - - # Track best loss - if smoothed_loss < best_loss: - best_loss = smoothed_loss - - # Check for divergence - if smoothed_loss > diverge_th * best_loss: - print(f"\nStopping early at iteration {iteration} (loss diverged)") - break - - # Progress - if (iteration + 1) % 10 == 0: - print( - f"Iteration {iteration + 1}/{num_iter} | LR: {lr:.2e} | Loss: {smoothed_loss:.4f}" - ) - - # Restore initial state - self.model.load_state_dict(self.initial_state["model"]) - self.optimizer.load_state_dict(self.initial_state["optimizer"]) - - # Analyze results - results = self._analyze_results(lrs, losses, smooth_f) - - # Create visualization - self._create_plot(lrs, losses, results["suggested_lr"]) - - # Save results - self._save_results(results) - - return results - - def _analyze_results( - self, lrs: List[float], losses: List[float], smooth_f: float - ) -> Dict[str, Any]: - """Analyze LR finder results""" - # Smooth losses - smoothed_losses = [] - avg_loss = 0.0 - beta = smooth_f - - for i, loss in enumerate(losses): - avg_loss = beta * avg_loss + (1 - beta) * loss - smoothed_loss = avg_loss / (1 - beta ** (i + 1)) - smoothed_losses.append(smoothed_loss) - - # Find minimum loss - min_loss_idx = np.argmin(smoothed_losses) - min_loss_lr = lrs[min_loss_idx] - - # Find steepest gradient (maximum negative slope) - gradients = np.gradient(smoothed_losses) - steepest_idx = np.argmin(gradients) - steepest_lr = lrs[steepest_idx] - - # Suggest LR (typically 1/10th of LR at steepest gradient) - suggested_lr = steepest_lr / 10 - - # Alternative: Use LR at minimum loss divided by 10-20 - alt_suggested_lr = min_loss_lr / 10 - - return { - "suggested_lr": suggested_lr, - "alternative_lr": alt_suggested_lr, - "min_loss_lr": min_loss_lr, - "steepest_gradient_lr": steepest_lr, - "min_loss": smoothed_losses[min_loss_idx], - "all_lrs": lrs, - "all_losses": losses, - "smoothed_losses": smoothed_losses, - } - - def _create_plot(self, lrs: List[float], losses: List[float], suggested_lr: float): - """Create LR finder plot""" - try: - plt.figure(figsize=(10, 6)) - plt.plot(lrs, losses, label="Loss", alpha=0.3) - - # Smooth losses for better visualization - from scipy.ndimage import uniform_filter1d - - smoothed = uniform_filter1d(losses, size=min(10, len(losses))) - plt.plot(lrs, smoothed, label="Smoothed Loss", linewidth=2) - - # Mark suggested LR - plt.axvline( - suggested_lr, - color="r", - linestyle="--", - label=f"Suggested LR: {suggested_lr:.2e}", - ) - - plt.xscale("log") - plt.xlabel("Learning Rate") - plt.ylabel("Loss") - plt.title("Learning Rate Finder") - plt.legend() - plt.grid(True, alpha=0.3) - - plot_path = self.results_dir / "lr_finder_plot.png" - plt.savefig(plot_path, dpi=150, bbox_inches="tight") - plt.close() - - print(f"\n✓ Plot saved to {plot_path}") - - except ImportError: - print("⚠ matplotlib not available, skipping plot") - except Exception as e: - print(f"⚠ Error creating plot: {e}") - - def _save_results(self, results: Dict[str, Any]): - """Save LR finder results""" - # Remove large arrays for JSON - save_results = results.copy() - save_results.pop("all_lrs", None) - save_results.pop("all_losses", None) - save_results.pop("smoothed_losses", None) - - output_file = self.results_dir / "lr_finder_results.json" - with open(output_file, "w") as f: - json.dump(save_results, f, indent=2) - - print(f"✓ Results saved to {output_file}") - print("\n📊 Recommended Learning Rates:") - print(f" Primary suggestion: {results['suggested_lr']:.2e}") - print(f" Alternative: {results['alternative_lr']:.2e}") - print(f" LR at min loss: {results['min_loss_lr']:.2e}") - - -def main(): - """CLI for LR finder""" - import argparse - - parser = argparse.ArgumentParser(description="Learning Rate Finder") - parser.add_argument("--model", type=str, required=True, help="Model path") - parser.add_argument("--dataset", type=str, required=True, help="Dataset path") - parser.add_argument("--start-lr", type=float, default=1e-7, help="Starting LR") - parser.add_argument("--end-lr", type=float, default=10.0, help="Ending LR") - parser.add_argument( - "--num-iter", type=int, default=100, help="Number of iterations" - ) - parser.add_argument("--batch-size", type=int, default=1, help="Batch size") - - args = parser.parse_args() - - print("Loading model and dataset...") - - # Load model - device = "cuda" if torch.cuda.is_available() else "cpu" - model = AutoModelForCausalLM.from_pretrained(args.model).to(device) - tokenizer = AutoTokenizer.from_pretrained(args.model) - - # Load dataset (simplified) - # In practice, you'd load and prepare your actual dataset - print( - "Note: Using simplified dataset loading. Integrate with your actual data pipeline." - ) - - # Create optimizer - optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) - criterion = torch.nn.CrossEntropyLoss() - - # Run LR finder - lr_finder = LearningRateFinder(model, optimizer, criterion, device) - - # You would pass your actual train_loader here - print("\nTo use LR finder in your training script:") - print("```python") - print("from scripts.lr_finder import LearningRateFinder") - print("") - print("lr_finder = LearningRateFinder(model, optimizer, criterion)") - print("results = lr_finder.range_test(train_loader)") - print("suggested_lr = results['suggested_lr']") - print("```") - - -if __name__ == "__main__": - main() +""" +Learning Rate Finder +Automatically find optimal learning rate using the LR range test +""" + +import json +from pathlib import Path +from typing import Any, Dict, List + +import matplotlib.pyplot as plt +import numpy as np +import torch +from torch.utils.data import DataLoader +from transformers import AutoModelForCausalLM, AutoTokenizer + + +class LearningRateFinder: + """ + Implement Leslie Smith's Learning Rate Range Test + https://arxiv.org/abs/1506.01186 + """ + + def __init__( + self, + model: AutoModelForCausalLM, + optimizer: torch.optim.Optimizer, + criterion: torch.nn.Module, + device: str = "cuda" if torch.cuda.is_available() else "cpu", + ): + self.model = model + self.optimizer = optimizer + self.criterion = criterion + self.device = device + + # Store initial state + self.initial_state = { + "model": model.state_dict(), + "optimizer": optimizer.state_dict(), + } + + self.results_dir = Path("data_out/lr_finder") + self.results_dir.mkdir(parents=True, exist_ok=True) + + def range_test( + self, + train_loader: DataLoader, + start_lr: float = 1e-7, + end_lr: float = 10.0, + num_iter: int = 100, + smooth_f: float = 0.05, + diverge_th: float = 5.0, + ) -> Dict[str, Any]: + """ + Perform LR range test + + Args: + train_loader: Training data loader + start_lr: Starting learning rate + end_lr: Ending learning rate + num_iter: Number of iterations + smooth_f: Smoothing factor for losses + diverge_th: Threshold for stopping (multiplier of best loss) + + Returns: + Dictionary with results + """ + print("Starting Learning Rate Range Test...") + print(f"Range: {start_lr} to {end_lr}") + print(f"Iterations: {num_iter}") + + # Prepare + self.model.train() + lrs = [] + losses = [] + best_loss = float("inf") + avg_loss = 0.0 + beta = smooth_f + + # Create LR schedule + def lr_lambda(x: float) -> float: + return float(np.exp(x * np.log(end_lr / start_lr) / (num_iter - 1))) + + # Run test + data_iterator = iter(train_loader) + for iteration in range(num_iter): + # Get batch + try: + batch = next(data_iterator) + except StopIteration: + data_iterator = iter(train_loader) + batch = next(data_iterator) + + # Update LR + lr = start_lr * lr_lambda(iteration) + for param_group in self.optimizer.param_groups: + param_group["lr"] = lr + + # Forward pass + inputs = {k: v.to(self.device) for k, v in batch.items() if k != "labels"} + labels = ( + batch["labels"].to(self.device) + if "labels" in batch + else inputs["input_ids"] + ) + + outputs = self.model(**inputs, labels=labels) + loss = outputs.loss + + # Backward pass + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + + # Record + lrs.append(lr) + losses.append(loss.item()) + + # Smooth loss + avg_loss = beta * avg_loss + (1 - beta) * loss.item() + smoothed_loss = avg_loss / (1 - beta ** (iteration + 1)) + + # Track best loss + if smoothed_loss < best_loss: + best_loss = smoothed_loss + + # Check for divergence + if smoothed_loss > diverge_th * best_loss: + print(f"\nStopping early at iteration {iteration} (loss diverged)") + break + + # Progress + if (iteration + 1) % 10 == 0: + print( + f"Iteration {iteration + 1}/{num_iter} | LR: {lr:.2e} | Loss: {smoothed_loss:.4f}" + ) + + # Restore initial state + self.model.load_state_dict(self.initial_state["model"]) + self.optimizer.load_state_dict(self.initial_state["optimizer"]) + + # Analyze results + results = self._analyze_results(lrs, losses, smooth_f) + + # Create visualization + self._create_plot(lrs, losses, results["suggested_lr"]) + + # Save results + self._save_results(results) + + return results + + def _analyze_results( + self, lrs: List[float], losses: List[float], smooth_f: float + ) -> Dict[str, Any]: + """Analyze LR finder results""" + # Smooth losses + smoothed_losses = [] + avg_loss = 0.0 + beta = smooth_f + + for i, loss in enumerate(losses): + avg_loss = beta * avg_loss + (1 - beta) * loss + smoothed_loss = avg_loss / (1 - beta ** (i + 1)) + smoothed_losses.append(smoothed_loss) + + # Find minimum loss + min_loss_idx = np.argmin(smoothed_losses) + min_loss_lr = lrs[min_loss_idx] + + # Find steepest gradient (maximum negative slope) + gradients = np.gradient(smoothed_losses) + steepest_idx = np.argmin(gradients) + steepest_lr = lrs[steepest_idx] + + # Suggest LR (typically 1/10th of LR at steepest gradient) + suggested_lr = steepest_lr / 10 + + # Alternative: Use LR at minimum loss divided by 10-20 + alt_suggested_lr = min_loss_lr / 10 + + return { + "suggested_lr": suggested_lr, + "alternative_lr": alt_suggested_lr, + "min_loss_lr": min_loss_lr, + "steepest_gradient_lr": steepest_lr, + "min_loss": smoothed_losses[min_loss_idx], + "all_lrs": lrs, + "all_losses": losses, + "smoothed_losses": smoothed_losses, + } + + def _create_plot(self, lrs: List[float], losses: List[float], suggested_lr: float): + """Create LR finder plot""" + try: + plt.figure(figsize=(10, 6)) + plt.plot(lrs, losses, label="Loss", alpha=0.3) + + # Smooth losses for better visualization + from scipy.ndimage import uniform_filter1d + + smoothed = uniform_filter1d(losses, size=min(10, len(losses))) + plt.plot(lrs, smoothed, label="Smoothed Loss", linewidth=2) + + # Mark suggested LR + plt.axvline( + suggested_lr, + color="r", + linestyle="--", + label=f"Suggested LR: {suggested_lr:.2e}", + ) + + plt.xscale("log") + plt.xlabel("Learning Rate") + plt.ylabel("Loss") + plt.title("Learning Rate Finder") + plt.legend() + plt.grid(True, alpha=0.3) + + plot_path = self.results_dir / "lr_finder_plot.png" + plt.savefig(plot_path, dpi=150, bbox_inches="tight") + plt.close() + + print(f"\n✓ Plot saved to {plot_path}") + + except ImportError: + print("⚠ matplotlib not available, skipping plot") + except Exception as e: + print(f"⚠ Error creating plot: {e}") + + def _save_results(self, results: Dict[str, Any]): + """Save LR finder results""" + # Remove large arrays for JSON + save_results = results.copy() + save_results.pop("all_lrs", None) + save_results.pop("all_losses", None) + save_results.pop("smoothed_losses", None) + + output_file = self.results_dir / "lr_finder_results.json" + with open(output_file, "w") as f: + json.dump(save_results, f, indent=2) + + print(f"✓ Results saved to {output_file}") + print("\n📊 Recommended Learning Rates:") + print(f" Primary suggestion: {results['suggested_lr']:.2e}") + print(f" Alternative: {results['alternative_lr']:.2e}") + print(f" LR at min loss: {results['min_loss_lr']:.2e}") + + +def main(): + """CLI for LR finder""" + import argparse + + parser = argparse.ArgumentParser(description="Learning Rate Finder") + parser.add_argument("--model", type=str, required=True, help="Model path") + parser.add_argument("--dataset", type=str, required=True, help="Dataset path") + parser.add_argument("--start-lr", type=float, default=1e-7, help="Starting LR") + parser.add_argument("--end-lr", type=float, default=10.0, help="Ending LR") + parser.add_argument( + "--num-iter", type=int, default=100, help="Number of iterations" + ) + parser.add_argument("--batch-size", type=int, default=1, help="Batch size") + + args = parser.parse_args() + + print("Loading model and dataset...") + + # Load model + device = "cuda" if torch.cuda.is_available() else "cpu" + model = AutoModelForCausalLM.from_pretrained(args.model).to(device) + tokenizer = AutoTokenizer.from_pretrained(args.model) + + # Load dataset (simplified) + # In practice, you'd load and prepare your actual dataset + print( + "Note: Using simplified dataset loading. Integrate with your actual data pipeline." + ) + + # Create optimizer + optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) + criterion = torch.nn.CrossEntropyLoss() + + # Run LR finder + lr_finder = LearningRateFinder(model, optimizer, criterion, device) + + # You would pass your actual train_loader here + print("\nTo use LR finder in your training script:") + print("```python") + print("from scripts.lr_finder import LearningRateFinder") + print("") + print("lr_finder = LearningRateFinder(model, optimizer, criterion)") + print("results = lr_finder.range_test(train_loader)") + print("suggested_lr = results['suggested_lr']") + print("```") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/metrics_logger.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/metrics_logger.py index bdd3bf35f..8d270aa46 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/metrics_logger.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/metrics_logger.py @@ -1,176 +1,176 @@ -import base64 -import datetime as dt -import hashlib -import hmac -import json -import os -import sys -import urllib.request -from datetime import timezone -from pathlib import Path -from typing import Any, Dict, Optional - -# Optional observability imports -try: - from applicationinsights import TelemetryClient # type: ignore -except Exception: - TelemetryClient = None # type: ignore - -try: - from opentelemetry import trace # type: ignore - from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import \ - OTLPSpanExporter # type: ignore - from opentelemetry.sdk.trace import TracerProvider # type: ignore - from opentelemetry.sdk.trace.export import \ - BatchSpanProcessor # type: ignore -except Exception: - trace = None # type: ignore - TracerProvider = OTLPSpanExporter = BatchSpanProcessor = None # type: ignore - - -class MetricsLogger: - """ - Logs metrics to a local JSONL file and optionally to: - - Azure Log Analytics workspace via HTTP Data Collector API - - Application Insights via TelemetryClient - - OpenTelemetry OTLP endpoint - - Env vars: - Azure Log Analytics: - - AZURE_LOG_ANALYTICS_WORKSPACE_ID - - AZURE_LOG_ANALYTICS_SHARED_KEY - - AZURE_LOG_TYPE (optional, default: LLMTrainingMetrics) - - Application Insights: - - APPLICATIONINSIGHTS_CONNECTION_STRING - - OpenTelemetry: - - OTEL_EXPORTER_OTLP_ENDPOINT (e.g., http://localhost:4317) - - OTEL_SERVICE_NAME (optional, default: lora-training) - """ - - def __init__(self, save_dir: Path): - self.save_dir = Path(save_dir) - self.save_dir.mkdir(parents=True, exist_ok=True) - self.file_path = self.save_dir / "metrics.jsonl" - - # Azure Log Analytics - self.workspace_id = os.environ.get("AZURE_LOG_ANALYTICS_WORKSPACE_ID") - self.shared_key = os.environ.get("AZURE_LOG_ANALYTICS_SHARED_KEY") - self.log_type = os.environ.get("AZURE_LOG_TYPE", "LLMTrainingMetrics") - - # Application Insights - self.appinsights_client: Optional[Any] = None - appinsights_conn = os.environ.get("APPLICATIONINSIGHTS_CONNECTION_STRING") - if appinsights_conn and TelemetryClient: - try: - self.appinsights_client = TelemetryClient(appinsights_conn) - except Exception as e: - print( - f"[metrics] Failed to init Application Insights: {e}", - file=sys.stderr, - ) - - # OpenTelemetry - self.otel_tracer: Optional[Any] = None - otel_endpoint = os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT") - if ( - otel_endpoint - and trace - and TracerProvider - and OTLPSpanExporter - and BatchSpanProcessor - ): - try: - provider = TracerProvider() - exporter = OTLPSpanExporter(endpoint=otel_endpoint, insecure=True) - provider.add_span_processor(BatchSpanProcessor(exporter)) - trace.set_tracer_provider(provider) - service_name = os.environ.get("OTEL_SERVICE_NAME", "lora-training") - self.otel_tracer = trace.get_tracer(service_name) - except Exception as e: - print(f"[metrics] Failed to init OpenTelemetry: {e}", file=sys.stderr) - - def log(self, record: Dict[str, Any]) -> None: - record = dict(record) - record["timestamp"] = dt.datetime.now(timezone.utc).isoformat() + "Z" - # Write locally - with self.file_path.open("a", encoding="utf-8") as f: - f.write(json.dumps(record, ensure_ascii=False) + "\n") - # Optionally send to Azure Log Analytics - if self.workspace_id and self.shared_key: - try: - self._post_to_azure(record) - except Exception as e: - print(f"[metrics] Azure post failed: {e}", file=sys.stderr) - # Optionally send to Application Insights - if self.appinsights_client: - try: - self._track_appinsights(record) - except Exception as e: - print(f"[metrics] Application Insights failed: {e}", file=sys.stderr) - # Optionally emit OpenTelemetry event - if self.otel_tracer: - try: - self._emit_otel_event(record) - except Exception as e: - print(f"[metrics] OpenTelemetry event failed: {e}", file=sys.stderr) - - def _track_appinsights(self, record: Dict[str, Any]) -> None: - # Track as custom event with all fields as properties - event_name = record.get("phase", "training_metric") - props = {k: str(v) for k, v in record.items()} - self.appinsights_client.track_event(event_name, props) - # If we have numeric metrics, also track them - if "eval_loss" in record: - self.appinsights_client.track_metric( - "eval_loss", float(record["eval_loss"]) - ) - if "eval_perplexity" in record: - self.appinsights_client.track_metric( - "eval_perplexity", float(record["eval_perplexity"]) - ) - self.appinsights_client.flush() - - def _emit_otel_event(self, record: Dict[str, Any]) -> None: - # Create a short-lived span for this event - with self.otel_tracer.start_as_current_span("evaluation_metric") as span: # type: ignore[union-attr] - for k, v in record.items(): - span.set_attribute(k, str(v)) - # Add numeric attributes if available - if "eval_loss" in record: - span.set_attribute("metric.eval_loss", float(record["eval_loss"])) - if "eval_perplexity" in record: - span.set_attribute( - "metric.eval_perplexity", float(record["eval_perplexity"]) - ) - if "step" in record: - span.set_attribute("training.step", int(record["step"])) - - # --- Azure Log Analytics HTTP Data Collector --- - def _build_signature(self, content_length: int, rfc1123date: str) -> str: - string_to_hash = f"POST\n{content_length}\napplication/json\nx-ms-date:{rfc1123date}\n/api/logs" - decoded_key = base64.b64decode(self.shared_key) # type: ignore[arg-type] - digest = hmac.new( - decoded_key, string_to_hash.encode("utf-8"), hashlib.sha256 - ).digest() - return base64.b64encode(digest).decode() - - def _post_to_azure(self, record: Dict[str, Any]) -> None: - body = json.dumps([record]).encode("utf-8") - rfc1123date = dt.datetime.now(timezone.utc).strftime( - "%a, %d %b %Y %H:%M:%S GMT" - ) - signature = self._build_signature(len(body), rfc1123date) - url = f"https://{self.workspace_id}.ods.opinsights.azure.com/api/logs?api-version=2016-04-01" - headers = { - "Content-Type": "application/json", - "Log-Type": self.log_type, - "x-ms-date": rfc1123date, - "Authorization": f"SharedKey {self.workspace_id}:{signature}", - } - req = urllib.request.Request(url, data=body, headers=headers, method="POST") - with urllib.request.urlopen(req, timeout=10) as resp: # nosec B310 - # 200 or 202 expected - if resp.status not in (200, 202): - raise RuntimeError(f"Azure ingestion failed with status {resp.status}") +import base64 +import datetime as dt +import hashlib +import hmac +import json +import os +import sys +import urllib.request +from datetime import timezone +from pathlib import Path +from typing import Any, Dict, Optional + +# Optional observability imports +try: + from applicationinsights import TelemetryClient # type: ignore +except Exception: + TelemetryClient = None # type: ignore + +try: + from opentelemetry import trace # type: ignore + from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import \ + OTLPSpanExporter # type: ignore + from opentelemetry.sdk.trace import TracerProvider # type: ignore + from opentelemetry.sdk.trace.export import \ + BatchSpanProcessor # type: ignore +except Exception: + trace = None # type: ignore + TracerProvider = OTLPSpanExporter = BatchSpanProcessor = None # type: ignore + + +class MetricsLogger: + """ + Logs metrics to a local JSONL file and optionally to: + - Azure Log Analytics workspace via HTTP Data Collector API + - Application Insights via TelemetryClient + - OpenTelemetry OTLP endpoint + + Env vars: + Azure Log Analytics: + - AZURE_LOG_ANALYTICS_WORKSPACE_ID + - AZURE_LOG_ANALYTICS_SHARED_KEY + - AZURE_LOG_TYPE (optional, default: LLMTrainingMetrics) + + Application Insights: + - APPLICATIONINSIGHTS_CONNECTION_STRING + + OpenTelemetry: + - OTEL_EXPORTER_OTLP_ENDPOINT (e.g., http://localhost:4317) + - OTEL_SERVICE_NAME (optional, default: lora-training) + """ + + def __init__(self, save_dir: Path): + self.save_dir = Path(save_dir) + self.save_dir.mkdir(parents=True, exist_ok=True) + self.file_path = self.save_dir / "metrics.jsonl" + + # Azure Log Analytics + self.workspace_id = os.environ.get("AZURE_LOG_ANALYTICS_WORKSPACE_ID") + self.shared_key = os.environ.get("AZURE_LOG_ANALYTICS_SHARED_KEY") + self.log_type = os.environ.get("AZURE_LOG_TYPE", "LLMTrainingMetrics") + + # Application Insights + self.appinsights_client: Optional[Any] = None + appinsights_conn = os.environ.get("APPLICATIONINSIGHTS_CONNECTION_STRING") + if appinsights_conn and TelemetryClient: + try: + self.appinsights_client = TelemetryClient(appinsights_conn) + except Exception as e: + print( + f"[metrics] Failed to init Application Insights: {e}", + file=sys.stderr, + ) + + # OpenTelemetry + self.otel_tracer: Optional[Any] = None + otel_endpoint = os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT") + if ( + otel_endpoint + and trace + and TracerProvider + and OTLPSpanExporter + and BatchSpanProcessor + ): + try: + provider = TracerProvider() + exporter = OTLPSpanExporter(endpoint=otel_endpoint, insecure=True) + provider.add_span_processor(BatchSpanProcessor(exporter)) + trace.set_tracer_provider(provider) + service_name = os.environ.get("OTEL_SERVICE_NAME", "lora-training") + self.otel_tracer = trace.get_tracer(service_name) + except Exception as e: + print(f"[metrics] Failed to init OpenTelemetry: {e}", file=sys.stderr) + + def log(self, record: Dict[str, Any]) -> None: + record = dict(record) + record["timestamp"] = dt.datetime.now(timezone.utc).isoformat() + "Z" + # Write locally + with self.file_path.open("a", encoding="utf-8") as f: + f.write(json.dumps(record, ensure_ascii=False) + "\n") + # Optionally send to Azure Log Analytics + if self.workspace_id and self.shared_key: + try: + self._post_to_azure(record) + except Exception as e: + print(f"[metrics] Azure post failed: {e}", file=sys.stderr) + # Optionally send to Application Insights + if self.appinsights_client: + try: + self._track_appinsights(record) + except Exception as e: + print(f"[metrics] Application Insights failed: {e}", file=sys.stderr) + # Optionally emit OpenTelemetry event + if self.otel_tracer: + try: + self._emit_otel_event(record) + except Exception as e: + print(f"[metrics] OpenTelemetry event failed: {e}", file=sys.stderr) + + def _track_appinsights(self, record: Dict[str, Any]) -> None: + # Track as custom event with all fields as properties + event_name = record.get("phase", "training_metric") + props = {k: str(v) for k, v in record.items()} + self.appinsights_client.track_event(event_name, props) + # If we have numeric metrics, also track them + if "eval_loss" in record: + self.appinsights_client.track_metric( + "eval_loss", float(record["eval_loss"]) + ) + if "eval_perplexity" in record: + self.appinsights_client.track_metric( + "eval_perplexity", float(record["eval_perplexity"]) + ) + self.appinsights_client.flush() + + def _emit_otel_event(self, record: Dict[str, Any]) -> None: + # Create a short-lived span for this event + with self.otel_tracer.start_as_current_span("evaluation_metric") as span: # type: ignore[union-attr] + for k, v in record.items(): + span.set_attribute(k, str(v)) + # Add numeric attributes if available + if "eval_loss" in record: + span.set_attribute("metric.eval_loss", float(record["eval_loss"])) + if "eval_perplexity" in record: + span.set_attribute( + "metric.eval_perplexity", float(record["eval_perplexity"]) + ) + if "step" in record: + span.set_attribute("training.step", int(record["step"])) + + # --- Azure Log Analytics HTTP Data Collector --- + def _build_signature(self, content_length: int, rfc1123date: str) -> str: + string_to_hash = f"POST\n{content_length}\napplication/json\nx-ms-date:{rfc1123date}\n/api/logs" + decoded_key = base64.b64decode(self.shared_key) # type: ignore[arg-type] + digest = hmac.new( + decoded_key, string_to_hash.encode("utf-8"), hashlib.sha256 + ).digest() + return base64.b64encode(digest).decode() + + def _post_to_azure(self, record: Dict[str, Any]) -> None: + body = json.dumps([record]).encode("utf-8") + rfc1123date = dt.datetime.now(timezone.utc).strftime( + "%a, %d %b %Y %H:%M:%S GMT" + ) + signature = self._build_signature(len(body), rfc1123date) + url = f"https://{self.workspace_id}.ods.opinsights.azure.com/api/logs?api-version=2016-04-01" + headers = { + "Content-Type": "application/json", + "Log-Type": self.log_type, + "x-ms-date": rfc1123date, + "Authorization": f"SharedKey {self.workspace_id}:{signature}", + } + req = urllib.request.Request(url, data=body, headers=headers, method="POST") + with urllib.request.urlopen(req, timeout=10) as resp: # nosec B310 + # 200 or 202 expected + if resp.status not in (200, 202): + raise RuntimeError(f"Azure ingestion failed with status {resp.status}") diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/model_exporter.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/model_exporter.py index 74ad6945a..c01fe5ee2 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/model_exporter.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/model_exporter.py @@ -1,337 +1,337 @@ -""" -Model Export & Optimization -Export models to ONNX, TensorRT, and other formats for deployment -""" - -import time -from pathlib import Path -from typing import Dict, Optional - -import onnx -import torch -from transformers import AutoModelForCausalLM, AutoTokenizer - - -class ModelExporter: - """Export models to various formats for deployment""" - - def __init__(self, model_path: str): - self.model_path = model_path - self.export_dir = Path("data_out/exported_models") - self.export_dir.mkdir(parents=True, exist_ok=True) - - print(f"Loading model from {model_path}...") - self.model = AutoModelForCausalLM.from_pretrained(model_path) - self.tokenizer = AutoTokenizer.from_pretrained(model_path) - - print("✓ Model loaded") - print(f" Parameters: {self.model.num_parameters() / 1e6:.1f}M") - - def export_to_onnx( - self, - output_name: str = "model.onnx", - opset_version: int = 14, - optimize: bool = True, - ) -> Path: - """ - Export model to ONNX format - - Args: - output_name: Output filename - opset_version: ONNX opset version - optimize: Whether to optimize the model - - Returns: - Path to exported model - """ - print("\n📦 Exporting to ONNX...") - - output_path = self.export_dir / output_name - - # Prepare dummy input - dummy_input = self.tokenizer("Hello, world!", return_tensors="pt") - - # Export - torch.onnx.export( - self.model, - (dummy_input["input_ids"],), - output_path, - export_params=True, - opset_version=opset_version, - do_constant_folding=True, - input_names=["input_ids"], - output_names=["logits"], - dynamic_axes={ - "input_ids": {0: "batch_size", 1: "sequence"}, - "logits": {0: "batch_size", 1: "sequence"}, - }, - ) - - print(f"✓ ONNX export complete: {output_path}") - - # Optimize if requested - if optimize: - self._optimize_onnx(output_path) - - # Validate - self._validate_onnx(output_path) - - return output_path - - def _optimize_onnx(self, onnx_path: Path): - """Optimize ONNX model""" - try: - from onnxruntime.transformers import optimizer - - print(" Optimizing ONNX model...") - - optimized_path = onnx_path.parent / f"{onnx_path.stem}_optimized.onnx" - - # Optimize - optimizer.optimize_model( - str(onnx_path), - model_type="gpt2", # Use GPT-2 optimization for decoder models - num_heads=self.model.config.num_attention_heads, - hidden_size=self.model.config.hidden_size, - optimization_options=optimizer.FusionOptions("gpt2"), - ).save_model_to_file(str(optimized_path)) - - print(f" ✓ Optimized model saved: {optimized_path}") - - except ImportError: - print(" ⚠ onnxruntime not available, skipping optimization") - except Exception as e: - print(f" ⚠ Optimization failed: {e}") - - def _validate_onnx(self, onnx_path: Path): - """Validate ONNX model""" - try: - model = onnx.load(str(onnx_path)) - onnx.checker.check_model(model) - print(" ✓ ONNX model validation passed") - - # Get model info - file_size = onnx_path.stat().st_size / (1024**2) - print(f" Model size: {file_size:.1f} MB") - - except Exception as e: - print(f" ⚠ Validation failed: {e}") - - def export_to_torchscript(self, output_name: str = "model_scripted.pt") -> Path: - """ - Export model to TorchScript - - Args: - output_name: Output filename - - Returns: - Path to exported model - """ - print("\n📦 Exporting to TorchScript...") - - output_path = self.export_dir / output_name - - # Set model to eval mode - self.model.eval() - - # Trace model - dummy_input = self.tokenizer("Hello, world!", return_tensors="pt") - - try: - # Try scripting first - scripted_model = torch.jit.script(self.model) - torch.jit.save(scripted_model, output_path) - print(" Using scripting") - - except Exception as e: - print(f" Scripting failed, trying tracing: {e}") - - # Fallback to tracing - traced_model = torch.jit.trace(self.model, (dummy_input["input_ids"],)) - torch.jit.save(traced_model, output_path) - print(" Using tracing") - - file_size = output_path.stat().st_size / (1024**2) - print(f"✓ TorchScript export complete: {output_path}") - print(f" Model size: {file_size:.1f} MB") - - return output_path - - def quantize_model( - self, quantization_type: str = "dynamic", output_name: str = "model_quantized" - ) -> Path: - """ - Quantize model for faster inference - - Args: - quantization_type: Type of quantization ('dynamic', 'static', 'qat') - output_name: Output directory name - - Returns: - Path to quantized model - """ - print(f"\n⚡ Quantizing model ({quantization_type})...") - - output_path = self.export_dir / output_name - output_path.mkdir(exist_ok=True) - - if quantization_type == "dynamic": - # Dynamic quantization - quantized_model = torch.quantization.quantize_dynamic( - self.model, {torch.nn.Linear}, dtype=torch.qint8 - ) - - # Save - quantized_model.save_pretrained(output_path) - self.tokenizer.save_pretrained(output_path) - - print(f"✓ Dynamic quantization complete: {output_path}") - - else: - print(f" ⚠ Quantization type '{quantization_type}' not yet implemented") - return None - - # Get size comparison - original_size = sum( - p.numel() * p.element_size() for p in self.model.parameters() - ) / (1024**2) - quantized_size = sum( - p.numel() * p.element_size() for p in quantized_model.parameters() - ) / (1024**2) - - print(f" Original size: {original_size:.1f} MB") - print(f" Quantized size: {quantized_size:.1f} MB") - print(f" Reduction: {(1 - quantized_size/original_size) * 100:.1f}%") - - return output_path - - def export_to_huggingface( - self, repo_name: str, organization: Optional[str] = None, private: bool = False - ): - """ - Upload model to Hugging Face Hub - - Args: - repo_name: Repository name - organization: Organization name (optional) - private: Whether to make repo private - """ - print("\n🤗 Uploading to Hugging Face Hub...") - - try: - from huggingface_hub import HfApi, create_repo - - # Create repo - repo_id = f"{organization}/{repo_name}" if organization else repo_name - - print(f" Creating repository: {repo_id}") - create_repo(repo_id, private=private, exist_ok=True) - - # Upload model - api = HfApi() - api.upload_folder( - folder_path=self.model_path, repo_id=repo_id, repo_type="model" - ) - - print(f"✓ Model uploaded to https://huggingface.co/{repo_id}") - - except ImportError: - print(" ⚠ huggingface_hub not installed") - except Exception as e: - print(f" ⚠ Upload failed: {e}") - - def benchmark_formats(self, num_runs: int = 10) -> Dict[str, Dict[str, float]]: - """ - Benchmark different export formats - - Args: - num_runs: Number of benchmark runs - - Returns: - Dictionary with benchmark results - """ - print(f"\n⏱️ Benchmarking formats ({num_runs} runs)...") - - results = {} - - # Benchmark PyTorch - results["pytorch"] = self._benchmark_pytorch(num_runs) - - # TODO: Benchmark ONNX, TorchScript, etc. - - # Print results - print("\n📊 Benchmark Results:") - for format_name, metrics in results.items(): - print(f"\n {format_name}:") - for metric, value in metrics.items(): - print(f" {metric}: {value:.2f}") - - return results - - def _benchmark_pytorch(self, num_runs: int) -> Dict[str, float]: - """Benchmark PyTorch model""" - self.model.eval() - - # Prepare input - inputs = self.tokenizer("Hello, world!", return_tensors="pt") - - # Warmup - with torch.no_grad(): - for _ in range(3): - _ = self.model(**inputs) - - # Benchmark - times = [] - with torch.no_grad(): - for _ in range(num_runs): - start = time.perf_counter() - _ = self.model(**inputs) - end = time.perf_counter() - times.append((end - start) * 1000) - - return { - "avg_latency_ms": sum(times) / len(times), - "min_latency_ms": min(times), - "max_latency_ms": max(times), - } - - -def main(): - """CLI for model export""" - import argparse - - parser = argparse.ArgumentParser(description="Model Export & Optimization") - parser.add_argument("--model", type=str, required=True, help="Path to model") - parser.add_argument( - "--format", - type=str, - choices=["onnx", "torchscript", "quantize", "hf", "all"], - default="all", - help="Export format", - ) - parser.add_argument( - "--quantization", type=str, default="dynamic", help="Quantization type" - ) - parser.add_argument("--benchmark", action="store_true", help="Run benchmarks") - - args = parser.parse_args() - - exporter = ModelExporter(args.model) - - if args.format in ["onnx", "all"]: - exporter.export_to_onnx() - - if args.format in ["torchscript", "all"]: - exporter.export_to_torchscript() - - if args.format in ["quantize", "all"]: - exporter.quantize_model(quantization_type=args.quantization) - - if args.benchmark: - exporter.benchmark_formats() - - print("\n✅ Export complete!") - - -if __name__ == "__main__": - main() +""" +Model Export & Optimization +Export models to ONNX, TensorRT, and other formats for deployment +""" + +import time +from pathlib import Path +from typing import Dict, Optional + +import onnx +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + + +class ModelExporter: + """Export models to various formats for deployment""" + + def __init__(self, model_path: str): + self.model_path = model_path + self.export_dir = Path("data_out/exported_models") + self.export_dir.mkdir(parents=True, exist_ok=True) + + print(f"Loading model from {model_path}...") + self.model = AutoModelForCausalLM.from_pretrained(model_path) + self.tokenizer = AutoTokenizer.from_pretrained(model_path) + + print("✓ Model loaded") + print(f" Parameters: {self.model.num_parameters() / 1e6:.1f}M") + + def export_to_onnx( + self, + output_name: str = "model.onnx", + opset_version: int = 14, + optimize: bool = True, + ) -> Path: + """ + Export model to ONNX format + + Args: + output_name: Output filename + opset_version: ONNX opset version + optimize: Whether to optimize the model + + Returns: + Path to exported model + """ + print("\n📦 Exporting to ONNX...") + + output_path = self.export_dir / output_name + + # Prepare dummy input + dummy_input = self.tokenizer("Hello, world!", return_tensors="pt") + + # Export + torch.onnx.export( + self.model, + (dummy_input["input_ids"],), + output_path, + export_params=True, + opset_version=opset_version, + do_constant_folding=True, + input_names=["input_ids"], + output_names=["logits"], + dynamic_axes={ + "input_ids": {0: "batch_size", 1: "sequence"}, + "logits": {0: "batch_size", 1: "sequence"}, + }, + ) + + print(f"✓ ONNX export complete: {output_path}") + + # Optimize if requested + if optimize: + self._optimize_onnx(output_path) + + # Validate + self._validate_onnx(output_path) + + return output_path + + def _optimize_onnx(self, onnx_path: Path): + """Optimize ONNX model""" + try: + from onnxruntime.transformers import optimizer + + print(" Optimizing ONNX model...") + + optimized_path = onnx_path.parent / f"{onnx_path.stem}_optimized.onnx" + + # Optimize + optimizer.optimize_model( + str(onnx_path), + model_type="gpt2", # Use GPT-2 optimization for decoder models + num_heads=self.model.config.num_attention_heads, + hidden_size=self.model.config.hidden_size, + optimization_options=optimizer.FusionOptions("gpt2"), + ).save_model_to_file(str(optimized_path)) + + print(f" ✓ Optimized model saved: {optimized_path}") + + except ImportError: + print(" ⚠ onnxruntime not available, skipping optimization") + except Exception as e: + print(f" ⚠ Optimization failed: {e}") + + def _validate_onnx(self, onnx_path: Path): + """Validate ONNX model""" + try: + model = onnx.load(str(onnx_path)) + onnx.checker.check_model(model) + print(" ✓ ONNX model validation passed") + + # Get model info + file_size = onnx_path.stat().st_size / (1024**2) + print(f" Model size: {file_size:.1f} MB") + + except Exception as e: + print(f" ⚠ Validation failed: {e}") + + def export_to_torchscript(self, output_name: str = "model_scripted.pt") -> Path: + """ + Export model to TorchScript + + Args: + output_name: Output filename + + Returns: + Path to exported model + """ + print("\n📦 Exporting to TorchScript...") + + output_path = self.export_dir / output_name + + # Set model to eval mode + self.model.eval() + + # Trace model + dummy_input = self.tokenizer("Hello, world!", return_tensors="pt") + + try: + # Try scripting first + scripted_model = torch.jit.script(self.model) + torch.jit.save(scripted_model, output_path) + print(" Using scripting") + + except Exception as e: + print(f" Scripting failed, trying tracing: {e}") + + # Fallback to tracing + traced_model = torch.jit.trace(self.model, (dummy_input["input_ids"],)) + torch.jit.save(traced_model, output_path) + print(" Using tracing") + + file_size = output_path.stat().st_size / (1024**2) + print(f"✓ TorchScript export complete: {output_path}") + print(f" Model size: {file_size:.1f} MB") + + return output_path + + def quantize_model( + self, quantization_type: str = "dynamic", output_name: str = "model_quantized" + ) -> Path: + """ + Quantize model for faster inference + + Args: + quantization_type: Type of quantization ('dynamic', 'static', 'qat') + output_name: Output directory name + + Returns: + Path to quantized model + """ + print(f"\n⚡ Quantizing model ({quantization_type})...") + + output_path = self.export_dir / output_name + output_path.mkdir(exist_ok=True) + + if quantization_type == "dynamic": + # Dynamic quantization + quantized_model = torch.quantization.quantize_dynamic( + self.model, {torch.nn.Linear}, dtype=torch.qint8 + ) + + # Save + quantized_model.save_pretrained(output_path) + self.tokenizer.save_pretrained(output_path) + + print(f"✓ Dynamic quantization complete: {output_path}") + + else: + print(f" ⚠ Quantization type '{quantization_type}' not yet implemented") + return None + + # Get size comparison + original_size = sum( + p.numel() * p.element_size() for p in self.model.parameters() + ) / (1024**2) + quantized_size = sum( + p.numel() * p.element_size() for p in quantized_model.parameters() + ) / (1024**2) + + print(f" Original size: {original_size:.1f} MB") + print(f" Quantized size: {quantized_size:.1f} MB") + print(f" Reduction: {(1 - quantized_size/original_size) * 100:.1f}%") + + return output_path + + def export_to_huggingface( + self, repo_name: str, organization: Optional[str] = None, private: bool = False + ): + """ + Upload model to Hugging Face Hub + + Args: + repo_name: Repository name + organization: Organization name (optional) + private: Whether to make repo private + """ + print("\n🤗 Uploading to Hugging Face Hub...") + + try: + from huggingface_hub import HfApi, create_repo + + # Create repo + repo_id = f"{organization}/{repo_name}" if organization else repo_name + + print(f" Creating repository: {repo_id}") + create_repo(repo_id, private=private, exist_ok=True) + + # Upload model + api = HfApi() + api.upload_folder( + folder_path=self.model_path, repo_id=repo_id, repo_type="model" + ) + + print(f"✓ Model uploaded to https://huggingface.co/{repo_id}") + + except ImportError: + print(" ⚠ huggingface_hub not installed") + except Exception as e: + print(f" ⚠ Upload failed: {e}") + + def benchmark_formats(self, num_runs: int = 10) -> Dict[str, Dict[str, float]]: + """ + Benchmark different export formats + + Args: + num_runs: Number of benchmark runs + + Returns: + Dictionary with benchmark results + """ + print(f"\n⏱️ Benchmarking formats ({num_runs} runs)...") + + results = {} + + # Benchmark PyTorch + results["pytorch"] = self._benchmark_pytorch(num_runs) + + # TODO: Benchmark ONNX, TorchScript, etc. + + # Print results + print("\n📊 Benchmark Results:") + for format_name, metrics in results.items(): + print(f"\n {format_name}:") + for metric, value in metrics.items(): + print(f" {metric}: {value:.2f}") + + return results + + def _benchmark_pytorch(self, num_runs: int) -> Dict[str, float]: + """Benchmark PyTorch model""" + self.model.eval() + + # Prepare input + inputs = self.tokenizer("Hello, world!", return_tensors="pt") + + # Warmup + with torch.no_grad(): + for _ in range(3): + _ = self.model(**inputs) + + # Benchmark + times = [] + with torch.no_grad(): + for _ in range(num_runs): + start = time.perf_counter() + _ = self.model(**inputs) + end = time.perf_counter() + times.append((end - start) * 1000) + + return { + "avg_latency_ms": sum(times) / len(times), + "min_latency_ms": min(times), + "max_latency_ms": max(times), + } + + +def main(): + """CLI for model export""" + import argparse + + parser = argparse.ArgumentParser(description="Model Export & Optimization") + parser.add_argument("--model", type=str, required=True, help="Path to model") + parser.add_argument( + "--format", + type=str, + choices=["onnx", "torchscript", "quantize", "hf", "all"], + default="all", + help="Export format", + ) + parser.add_argument( + "--quantization", type=str, default="dynamic", help="Quantization type" + ) + parser.add_argument("--benchmark", action="store_true", help="Run benchmarks") + + args = parser.parse_args() + + exporter = ModelExporter(args.model) + + if args.format in ["onnx", "all"]: + exporter.export_to_onnx() + + if args.format in ["torchscript", "all"]: + exporter.export_to_torchscript() + + if args.format in ["quantize", "all"]: + exporter.quantize_model(quantization_type=args.quantization) + + if args.benchmark: + exporter.benchmark_formats() + + print("\n✅ Export complete!") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/model_server.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/model_server.py index b7c288f89..a3860171e 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/model_server.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/model_server.py @@ -1,338 +1,338 @@ -""" -Model Deployment & Serving -REST API and batch inference capabilities -""" - -import asyncio -import time -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List, Optional - -import torch -import uvicorn -from fastapi import FastAPI, HTTPException -from pydantic import BaseModel, Field -from transformers import AutoModelForCausalLM, AutoTokenizer - - -# Request/Response Models -class GenerationRequest(BaseModel): - """Request for text generation""" - - prompt: str = Field(..., description="Input prompt") - max_tokens: int = Field(100, description="Maximum tokens to generate") - temperature: float = Field(0.7, description="Sampling temperature") - top_p: float = Field(0.9, description="Top-p sampling") - top_k: int = Field(50, description="Top-k sampling") - stream: bool = Field(False, description="Stream response") - stop_sequences: Optional[List[str]] = Field(None, description="Stop sequences") - - -class GenerationResponse(BaseModel): - """Response from text generation""" - - text: str - tokens_generated: int - inference_time_ms: float - model_name: str - timestamp: str - - -class BatchRequest(BaseModel): - """Batch generation request""" - - prompts: List[str] - max_tokens: int = 100 - temperature: float = 0.7 - - -class HealthResponse(BaseModel): - """Health check response""" - - status: str - model_loaded: bool - device: str - uptime_seconds: float - - -# Model Server -class ModelServer: - """Production model serving with REST API""" - - def __init__( - self, - model_path: str, - device: str = "cuda" if torch.cuda.is_available() else "cpu", - max_batch_size: int = 32, - max_queue_size: int = 100, - ): - self.model_path = model_path - self.device = device - self.max_batch_size = max_batch_size - self.max_queue_size = max_queue_size - - self.model = None - self.tokenizer = None - self.start_time = time.time() - - # Request queue for batching - self.request_queue = asyncio.Queue(maxsize=max_queue_size) - - print("Initializing Model Server...") - print(f"Model: {model_path}") - print(f"Device: {device}") - - self._load_model() - - def _load_model(self): - """Load model and tokenizer""" - print("Loading model...") - - self.model = AutoModelForCausalLM.from_pretrained( - self.model_path, - torch_dtype=torch.float16 if self.device == "cuda" else torch.float32, - device_map="auto" if self.device == "cuda" else None, - ) - - self.tokenizer = AutoTokenizer.from_pretrained(self.model_path) - if self.tokenizer.pad_token is None: - self.tokenizer.pad_token = self.tokenizer.eos_token - - self.model.eval() - - print("✓ Model loaded successfully") - print(f" Parameters: {self.model.num_parameters() / 1e9:.2f}B") - print( - f" Memory: {torch.cuda.memory_allocated() / 1e9:.2f}GB" - if self.device == "cuda" - else "" - ) - - def generate( - self, - prompt: str, - max_tokens: int = 100, - temperature: float = 0.7, - top_p: float = 0.9, - top_k: int = 50, - stop_sequences: Optional[List[str]] = None, - ) -> Dict[str, Any]: - """Generate text from prompt""" - start_time = time.perf_counter() - - # Tokenize - inputs = self.tokenizer( - prompt, return_tensors="pt", truncation=True, max_length=2048 - ) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - - # Generate - with torch.no_grad(): - outputs = self.model.generate( - **inputs, - max_new_tokens=max_tokens, - temperature=temperature, - top_p=top_p, - top_k=top_k, - do_sample=True, - pad_token_id=self.tokenizer.pad_token_id, - ) - - # Decode - generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True) - - # Remove prompt from output - if generated_text.startswith(prompt): - generated_text = generated_text[len(prompt) :] - - # Apply stop sequences - if stop_sequences: - for stop_seq in stop_sequences: - if stop_seq in generated_text: - generated_text = generated_text[: generated_text.index(stop_seq)] - - inference_time = (time.perf_counter() - start_time) * 1000 - tokens_generated = len(outputs[0]) - len(inputs["input_ids"][0]) - - return { - "text": generated_text.strip(), - "tokens_generated": tokens_generated, - "inference_time_ms": inference_time, - "model_name": Path(self.model_path).name, - "timestamp": datetime.now().isoformat(), - } - - def generate_batch( - self, prompts: List[str], max_tokens: int = 100, temperature: float = 0.7 - ) -> List[Dict[str, Any]]: - """Batch generation for multiple prompts""" - results = [] - - # Process in batches - for i in range(0, len(prompts), self.max_batch_size): - batch_prompts = prompts[i : i + self.max_batch_size] - - # Tokenize batch - inputs = self.tokenizer( - batch_prompts, - return_tensors="pt", - padding=True, - truncation=True, - max_length=2048, - ) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - - # Generate batch - with torch.no_grad(): - outputs = self.model.generate( - **inputs, - max_new_tokens=max_tokens, - temperature=temperature, - do_sample=True, - pad_token_id=self.tokenizer.pad_token_id, - ) - - # Decode batch - for j, output in enumerate(outputs): - generated_text = self.tokenizer.decode(output, skip_special_tokens=True) - prompt = batch_prompts[j] - - if generated_text.startswith(prompt): - generated_text = generated_text[len(prompt) :] - - results.append( - { - "text": generated_text.strip(), - "prompt": prompt, - "model_name": Path(self.model_path).name, - } - ) - - return results - - def get_health(self) -> Dict[str, Any]: - """Get server health status""" - return { - "status": "healthy" if self.model is not None else "unhealthy", - "model_loaded": self.model is not None, - "device": self.device, - "uptime_seconds": time.time() - self.start_time, - } - - -# FastAPI Application -app = FastAPI( - title="AI Model Serving API", - description="Production-ready API for AI model inference", - version="1.0.0", -) - -# Global model server instance -model_server: Optional[ModelServer] = None - - -@app.on_event("startup") -async def startup_event(): - """Initialize model server on startup""" - global model_server - - # Get model path from environment or config - import os - - model_path = os.getenv("MODEL_PATH", "data_out/lora_training") - - model_server = ModelServer(model_path) - - -@app.get("/health", response_model=HealthResponse) -async def health_check(): - """Health check endpoint""" - if model_server is None: - raise HTTPException(status_code=503, detail="Model server not initialized") - - health = model_server.get_health() - return HealthResponse(**health) - - -@app.post("/generate", response_model=GenerationResponse) -async def generate_text(request: GenerationRequest): - """Generate text from prompt""" - if model_server is None: - raise HTTPException(status_code=503, detail="Model server not initialized") - - try: - result = model_server.generate( - prompt=request.prompt, - max_tokens=request.max_tokens, - temperature=request.temperature, - top_p=request.top_p, - top_k=request.top_k, - stop_sequences=request.stop_sequences, - ) - - return GenerationResponse(**result) - - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) from e - - -@app.post("/batch") -async def batch_generate(request: BatchRequest): - """Batch generation endpoint""" - if model_server is None: - raise HTTPException(status_code=503, detail="Model server not initialized") - - try: - results = model_server.generate_batch( - prompts=request.prompts, - max_tokens=request.max_tokens, - temperature=request.temperature, - ) - - return {"results": results, "count": len(results)} - - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) from e - - -@app.get("/models") -async def list_models(): - """List available models""" - if model_server is None: - raise HTTPException(status_code=503, detail="Model server not initialized") - - return {"current_model": model_server.model_path, "device": model_server.device} - - -def main(): - """CLI for model server""" - import argparse - - parser = argparse.ArgumentParser(description="Model Serving API") - parser.add_argument( - "--model", type=str, default="data_out/lora_training", help="Path to model" - ) - parser.add_argument("--host", type=str, default="0.0.0.0", help="Server host") - parser.add_argument("--port", type=int, default=8000, help="Server port") - parser.add_argument("--workers", type=int, default=1, help="Number of workers") - - args = parser.parse_args() - - # Set model path as environment variable - import os - - os.environ["MODEL_PATH"] = args.model - - print("\n🚀 Starting Model Server") - print(f"Model: {args.model}") - print(f"Host: {args.host}:{args.port}") - print(f"\nAPI Documentation: http://{args.host}:{args.port}/docs") - print(f"Health Check: http://{args.host}:{args.port}/health\n") - - # Run server - uvicorn.run(app, host=args.host, port=args.port, workers=args.workers) - - -if __name__ == "__main__": - main() +""" +Model Deployment & Serving +REST API and batch inference capabilities +""" + +import asyncio +import time +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional + +import torch +import uvicorn +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field +from transformers import AutoModelForCausalLM, AutoTokenizer + + +# Request/Response Models +class GenerationRequest(BaseModel): + """Request for text generation""" + + prompt: str = Field(..., description="Input prompt") + max_tokens: int = Field(100, description="Maximum tokens to generate") + temperature: float = Field(0.7, description="Sampling temperature") + top_p: float = Field(0.9, description="Top-p sampling") + top_k: int = Field(50, description="Top-k sampling") + stream: bool = Field(False, description="Stream response") + stop_sequences: Optional[List[str]] = Field(None, description="Stop sequences") + + +class GenerationResponse(BaseModel): + """Response from text generation""" + + text: str + tokens_generated: int + inference_time_ms: float + model_name: str + timestamp: str + + +class BatchRequest(BaseModel): + """Batch generation request""" + + prompts: List[str] + max_tokens: int = 100 + temperature: float = 0.7 + + +class HealthResponse(BaseModel): + """Health check response""" + + status: str + model_loaded: bool + device: str + uptime_seconds: float + + +# Model Server +class ModelServer: + """Production model serving with REST API""" + + def __init__( + self, + model_path: str, + device: str = "cuda" if torch.cuda.is_available() else "cpu", + max_batch_size: int = 32, + max_queue_size: int = 100, + ): + self.model_path = model_path + self.device = device + self.max_batch_size = max_batch_size + self.max_queue_size = max_queue_size + + self.model = None + self.tokenizer = None + self.start_time = time.time() + + # Request queue for batching + self.request_queue = asyncio.Queue(maxsize=max_queue_size) + + print("Initializing Model Server...") + print(f"Model: {model_path}") + print(f"Device: {device}") + + self._load_model() + + def _load_model(self): + """Load model and tokenizer""" + print("Loading model...") + + self.model = AutoModelForCausalLM.from_pretrained( + self.model_path, + torch_dtype=torch.float16 if self.device == "cuda" else torch.float32, + device_map="auto" if self.device == "cuda" else None, + ) + + self.tokenizer = AutoTokenizer.from_pretrained(self.model_path) + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + self.model.eval() + + print("✓ Model loaded successfully") + print(f" Parameters: {self.model.num_parameters() / 1e9:.2f}B") + print( + f" Memory: {torch.cuda.memory_allocated() / 1e9:.2f}GB" + if self.device == "cuda" + else "" + ) + + def generate( + self, + prompt: str, + max_tokens: int = 100, + temperature: float = 0.7, + top_p: float = 0.9, + top_k: int = 50, + stop_sequences: Optional[List[str]] = None, + ) -> Dict[str, Any]: + """Generate text from prompt""" + start_time = time.perf_counter() + + # Tokenize + inputs = self.tokenizer( + prompt, return_tensors="pt", truncation=True, max_length=2048 + ) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + # Generate + with torch.no_grad(): + outputs = self.model.generate( + **inputs, + max_new_tokens=max_tokens, + temperature=temperature, + top_p=top_p, + top_k=top_k, + do_sample=True, + pad_token_id=self.tokenizer.pad_token_id, + ) + + # Decode + generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True) + + # Remove prompt from output + if generated_text.startswith(prompt): + generated_text = generated_text[len(prompt) :] + + # Apply stop sequences + if stop_sequences: + for stop_seq in stop_sequences: + if stop_seq in generated_text: + generated_text = generated_text[: generated_text.index(stop_seq)] + + inference_time = (time.perf_counter() - start_time) * 1000 + tokens_generated = len(outputs[0]) - len(inputs["input_ids"][0]) + + return { + "text": generated_text.strip(), + "tokens_generated": tokens_generated, + "inference_time_ms": inference_time, + "model_name": Path(self.model_path).name, + "timestamp": datetime.now().isoformat(), + } + + def generate_batch( + self, prompts: List[str], max_tokens: int = 100, temperature: float = 0.7 + ) -> List[Dict[str, Any]]: + """Batch generation for multiple prompts""" + results = [] + + # Process in batches + for i in range(0, len(prompts), self.max_batch_size): + batch_prompts = prompts[i : i + self.max_batch_size] + + # Tokenize batch + inputs = self.tokenizer( + batch_prompts, + return_tensors="pt", + padding=True, + truncation=True, + max_length=2048, + ) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + # Generate batch + with torch.no_grad(): + outputs = self.model.generate( + **inputs, + max_new_tokens=max_tokens, + temperature=temperature, + do_sample=True, + pad_token_id=self.tokenizer.pad_token_id, + ) + + # Decode batch + for j, output in enumerate(outputs): + generated_text = self.tokenizer.decode(output, skip_special_tokens=True) + prompt = batch_prompts[j] + + if generated_text.startswith(prompt): + generated_text = generated_text[len(prompt) :] + + results.append( + { + "text": generated_text.strip(), + "prompt": prompt, + "model_name": Path(self.model_path).name, + } + ) + + return results + + def get_health(self) -> Dict[str, Any]: + """Get server health status""" + return { + "status": "healthy" if self.model is not None else "unhealthy", + "model_loaded": self.model is not None, + "device": self.device, + "uptime_seconds": time.time() - self.start_time, + } + + +# FastAPI Application +app = FastAPI( + title="AI Model Serving API", + description="Production-ready API for AI model inference", + version="1.0.0", +) + +# Global model server instance +model_server: Optional[ModelServer] = None + + +@app.on_event("startup") +async def startup_event(): + """Initialize model server on startup""" + global model_server + + # Get model path from environment or config + import os + + model_path = os.getenv("MODEL_PATH", "data_out/lora_training") + + model_server = ModelServer(model_path) + + +@app.get("/health", response_model=HealthResponse) +async def health_check(): + """Health check endpoint""" + if model_server is None: + raise HTTPException(status_code=503, detail="Model server not initialized") + + health = model_server.get_health() + return HealthResponse(**health) + + +@app.post("/generate", response_model=GenerationResponse) +async def generate_text(request: GenerationRequest): + """Generate text from prompt""" + if model_server is None: + raise HTTPException(status_code=503, detail="Model server not initialized") + + try: + result = model_server.generate( + prompt=request.prompt, + max_tokens=request.max_tokens, + temperature=request.temperature, + top_p=request.top_p, + top_k=request.top_k, + stop_sequences=request.stop_sequences, + ) + + return GenerationResponse(**result) + + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) from e + + +@app.post("/batch") +async def batch_generate(request: BatchRequest): + """Batch generation endpoint""" + if model_server is None: + raise HTTPException(status_code=503, detail="Model server not initialized") + + try: + results = model_server.generate_batch( + prompts=request.prompts, + max_tokens=request.max_tokens, + temperature=request.temperature, + ) + + return {"results": results, "count": len(results)} + + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) from e + + +@app.get("/models") +async def list_models(): + """List available models""" + if model_server is None: + raise HTTPException(status_code=503, detail="Model server not initialized") + + return {"current_model": model_server.model_path, "device": model_server.device} + + +def main(): + """CLI for model server""" + import argparse + + parser = argparse.ArgumentParser(description="Model Serving API") + parser.add_argument( + "--model", type=str, default="data_out/lora_training", help="Path to model" + ) + parser.add_argument("--host", type=str, default="0.0.0.0", help="Server host") + parser.add_argument("--port", type=int, default=8000, help="Server port") + parser.add_argument("--workers", type=int, default=1, help="Number of workers") + + args = parser.parse_args() + + # Set model path as environment variable + import os + + os.environ["MODEL_PATH"] = args.model + + print("\n🚀 Starting Model Server") + print(f"Model: {args.model}") + print(f"Host: {args.host}:{args.port}") + print(f"\nAPI Documentation: http://{args.host}:{args.port}/docs") + print(f"Health Check: http://{args.host}:{args.port}/health\n") + + # Run server + uvicorn.run(app, host=args.host, port=args.port, workers=args.workers) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/otel_callback.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/otel_callback.py index f63db0ffc..987612f72 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/otel_callback.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/otel_callback.py @@ -1,221 +1,221 @@ -"""Trainer callback that emits lightweight OpenTelemetry spans for HF Trainer. - -This module optionally integrates OpenTelemetry into Hugging Face Trainer -callbacks. It is intentionally tolerant of missing OpenTelemetry dependencies -so training can still run without telemetry (dry-run / CI environments). - -To use, simply add the callback to your Trainer: - - from otel_callback import OpenTelemetryTrainerCallback - trainer.add_callback(OpenTelemetryTrainerCallback()) - -The callback will create a training-level span and brief spans for prediction -steps when possible. -""" - -from __future__ import annotations - -import logging -import os -import sys -from typing import Any, Optional - -# Transformers' TrainerCallback is optional at import time. -try: # pragma: no cover - optional dependency in some environments - from transformers import TrainerCallback # type: ignore -except Exception: # pragma: no cover - fallback type - - class TrainerCallback: # type: ignore - pass - - -try: - from opentelemetry import trace # type: ignore -except Exception: # pragma: no cover - graceful fallback - trace = None # type: ignore - - -class _OpenTelemetryTrainerCallbackLite(TrainerCallback): - """A robust, best-effort TrainerCallback that emits spans. - - This callback intentionally swallows exceptions so it never interferes - with training if OpenTelemetry or the Trainer API is not available. - """ - - def __init__(self, tracer_name: str = "qai.hf.trainer") -> None: - self._tracer_name = tracer_name - self._tracer = None - self._train_span = None - self._train_scope = None - if trace is not None: - try: - self._tracer = trace.get_tracer(self._tracer_name) - except Exception as e: - logging.debug(f"[otel_callback] Could not get tracer: {e}") - - def on_train_begin(self, args: Any, state: Any, control: Any, **kwargs: Any) -> None: # type: ignore - try: - if not self._tracer: - return - # Create a persistent span for the whole training session - self._train_span = self._tracer.start_span("hf.train_session") - # Make it the current span for subsequent events - self._train_scope = trace.use_span(self._train_span, end_on_exit=False) - self._train_scope.__enter__() - except Exception: - # Never allow tracing errors to disrupt training - pass - - def on_train_end(self, args: Any, state: Any, control: Any, **kwargs: Any) -> None: # type: ignore - try: - if not self._tracer or not self._train_span: - return - # Close the session span - try: - if self._train_scope is not None: - self._train_scope.__exit__(None, None, None) - finally: - try: - self._train_span.end() - except Exception: - pass - except Exception: - pass - - def on_prediction_step(self, args: Any, state: Any, control: Any, **kwargs: Any) -> None: # type: ignore - # Called for prediction-related steps (if supported). We create a - # short-lived span around prediction work so that prediction latencies - # are visible in the tracing backend. - try: - if not self._tracer: - return - with self._tracer.start_as_current_span("hf.prediction_step"): - # No-op inside span — HF will continue its normal workflow - pass - except Exception: - pass - - -__all__ = ["OpenTelemetryTrainerCallback"] - -# Optional OpenTelemetry imports -try: - from opentelemetry import trace # type: ignore - from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import \ - OTLPSpanExporter # type: ignore - from opentelemetry.sdk.trace import TracerProvider # type: ignore - from opentelemetry.sdk.trace.export import \ - BatchSpanProcessor # type: ignore -except Exception: - trace = None # type: ignore - TracerProvider = OTLPSpanExporter = BatchSpanProcessor = None # type: ignore - - -class OpenTelemetryTrainerCallback: - """ - Hugging Face Trainer callback that emits OpenTelemetry spans for training lifecycle events. - - Env vars: - - OTEL_EXPORTER_OTLP_ENDPOINT (e.g., http://localhost:4317) - - OTEL_SERVICE_NAME (optional, default: lora-training) - """ - - def __init__(self): - self.tracer: Optional[Any] = None - self.training_span: Optional[Any] = None - - otel_endpoint = os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT") - if ( - otel_endpoint - and trace - and TracerProvider - and OTLPSpanExporter - and BatchSpanProcessor - ): - try: - provider = TracerProvider() - exporter = OTLPSpanExporter(endpoint=otel_endpoint, insecure=True) - provider.add_span_processor(BatchSpanProcessor(exporter)) - trace.set_tracer_provider(provider) - service_name = os.environ.get("OTEL_SERVICE_NAME", "lora-training") - self.tracer = trace.get_tracer(service_name) - except Exception as e: - print( - f"[otel] Failed to init OpenTelemetry tracer: {e}", file=sys.stderr - ) - - # type: ignore[no-untyped-def] - def on_train_begin(self, args, state, control, **kwargs): - if self.tracer: - self.training_span = self.tracer.start_span( - "training_run" - ) # type: ignore[union-attr] - self.training_span.set_attribute( - "num_train_epochs", int(getattr(args, "num_train_epochs", 0)) - ) - self.training_span.set_attribute( - "per_device_train_batch_size", - int(getattr(args, "per_device_train_batch_size", 0)), - ) - self.training_span.set_attribute( - "learning_rate", float(getattr(args, "learning_rate", 0.0)) - ) - - # type: ignore[no-untyped-def] - def on_train_end(self, args, state, control, **kwargs): - if self.training_span: - self.training_span.set_attribute( - "final_global_step", int(getattr(state, "global_step", 0)) - ) - self.training_span.end() - self.training_span = None - - # type: ignore[no-untyped-def] - def on_step_end(self, args, state, control, **kwargs): - if self.tracer and self.training_span: - # Emit periodic step events (every 100 steps to avoid overhead) - step = int(getattr(state, "global_step", 0)) - if step % 100 == 0: - # type: ignore[union-attr] - with self.tracer.start_as_current_span("training_step") as span: - span.set_attribute("global_step", step) - span.set_attribute("epoch", float(getattr(state, "epoch", 0.0))) - - # type: ignore[no-untyped-def] - def on_evaluate(self, args, state, control, metrics=None, **kwargs): - if self.tracer: - # type: ignore[union-attr] - with self.tracer.start_as_current_span("evaluation") as span: - span.set_attribute("global_step", int(getattr(state, "global_step", 0))) - if metrics: - for k, v in metrics.items(): - try: - if isinstance(v, (int, float)): - span.set_attribute(f"metric.{k}", float(v)) - except Exception: - pass - - # type: ignore[no-untyped-def] - def on_log(self, args, state, control, logs=None, **kwargs): - # Log training loss if available - if self.tracer and logs and "loss" in logs: - # type: ignore[union-attr] - with self.tracer.start_as_current_span("log_event") as span: - span.set_attribute("global_step", int(getattr(state, "global_step", 0))) - span.set_attribute("metric.loss", float(logs["loss"])) - - # type: ignore[no-untyped-def] - def on_prediction_step(self, args, state, control, **kwargs): - """Emit a brief span for prediction/prediction-step events. - - Hugging Face Trainer may not call this method in all versions; presence - is checked by callers before adding the callback to the trainer. - """ - if self.tracer: - try: - with self.tracer.start_as_current_span("prediction_step") as span: - span.set_attribute( - "global_step", int(getattr(state, "global_step", 0)) - ) - except Exception: - pass +"""Trainer callback that emits lightweight OpenTelemetry spans for HF Trainer. + +This module optionally integrates OpenTelemetry into Hugging Face Trainer +callbacks. It is intentionally tolerant of missing OpenTelemetry dependencies +so training can still run without telemetry (dry-run / CI environments). + +To use, simply add the callback to your Trainer: + + from otel_callback import OpenTelemetryTrainerCallback + trainer.add_callback(OpenTelemetryTrainerCallback()) + +The callback will create a training-level span and brief spans for prediction +steps when possible. +""" + +from __future__ import annotations + +import logging +import os +import sys +from typing import Any, Optional + +# Transformers' TrainerCallback is optional at import time. +try: # pragma: no cover - optional dependency in some environments + from transformers import TrainerCallback # type: ignore +except Exception: # pragma: no cover - fallback type + + class TrainerCallback: # type: ignore + pass + + +try: + from opentelemetry import trace # type: ignore +except Exception: # pragma: no cover - graceful fallback + trace = None # type: ignore + + +class _OpenTelemetryTrainerCallbackLite(TrainerCallback): + """A robust, best-effort TrainerCallback that emits spans. + + This callback intentionally swallows exceptions so it never interferes + with training if OpenTelemetry or the Trainer API is not available. + """ + + def __init__(self, tracer_name: str = "qai.hf.trainer") -> None: + self._tracer_name = tracer_name + self._tracer = None + self._train_span = None + self._train_scope = None + if trace is not None: + try: + self._tracer = trace.get_tracer(self._tracer_name) + except Exception as e: + logging.debug(f"[otel_callback] Could not get tracer: {e}") + + def on_train_begin(self, args: Any, state: Any, control: Any, **kwargs: Any) -> None: # type: ignore + try: + if not self._tracer: + return + # Create a persistent span for the whole training session + self._train_span = self._tracer.start_span("hf.train_session") + # Make it the current span for subsequent events + self._train_scope = trace.use_span(self._train_span, end_on_exit=False) + self._train_scope.__enter__() + except Exception: + # Never allow tracing errors to disrupt training + pass + + def on_train_end(self, args: Any, state: Any, control: Any, **kwargs: Any) -> None: # type: ignore + try: + if not self._tracer or not self._train_span: + return + # Close the session span + try: + if self._train_scope is not None: + self._train_scope.__exit__(None, None, None) + finally: + try: + self._train_span.end() + except Exception: + pass + except Exception: + pass + + def on_prediction_step(self, args: Any, state: Any, control: Any, **kwargs: Any) -> None: # type: ignore + # Called for prediction-related steps (if supported). We create a + # short-lived span around prediction work so that prediction latencies + # are visible in the tracing backend. + try: + if not self._tracer: + return + with self._tracer.start_as_current_span("hf.prediction_step"): + # No-op inside span — HF will continue its normal workflow + pass + except Exception: + pass + + +__all__ = ["OpenTelemetryTrainerCallback"] + +# Optional OpenTelemetry imports +try: + from opentelemetry import trace # type: ignore + from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import \ + OTLPSpanExporter # type: ignore + from opentelemetry.sdk.trace import TracerProvider # type: ignore + from opentelemetry.sdk.trace.export import \ + BatchSpanProcessor # type: ignore +except Exception: + trace = None # type: ignore + TracerProvider = OTLPSpanExporter = BatchSpanProcessor = None # type: ignore + + +class OpenTelemetryTrainerCallback: + """ + Hugging Face Trainer callback that emits OpenTelemetry spans for training lifecycle events. + + Env vars: + - OTEL_EXPORTER_OTLP_ENDPOINT (e.g., http://localhost:4317) + - OTEL_SERVICE_NAME (optional, default: lora-training) + """ + + def __init__(self): + self.tracer: Optional[Any] = None + self.training_span: Optional[Any] = None + + otel_endpoint = os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT") + if ( + otel_endpoint + and trace + and TracerProvider + and OTLPSpanExporter + and BatchSpanProcessor + ): + try: + provider = TracerProvider() + exporter = OTLPSpanExporter(endpoint=otel_endpoint, insecure=True) + provider.add_span_processor(BatchSpanProcessor(exporter)) + trace.set_tracer_provider(provider) + service_name = os.environ.get("OTEL_SERVICE_NAME", "lora-training") + self.tracer = trace.get_tracer(service_name) + except Exception as e: + print( + f"[otel] Failed to init OpenTelemetry tracer: {e}", file=sys.stderr + ) + + # type: ignore[no-untyped-def] + def on_train_begin(self, args, state, control, **kwargs): + if self.tracer: + self.training_span = self.tracer.start_span( + "training_run" + ) # type: ignore[union-attr] + self.training_span.set_attribute( + "num_train_epochs", int(getattr(args, "num_train_epochs", 0)) + ) + self.training_span.set_attribute( + "per_device_train_batch_size", + int(getattr(args, "per_device_train_batch_size", 0)), + ) + self.training_span.set_attribute( + "learning_rate", float(getattr(args, "learning_rate", 0.0)) + ) + + # type: ignore[no-untyped-def] + def on_train_end(self, args, state, control, **kwargs): + if self.training_span: + self.training_span.set_attribute( + "final_global_step", int(getattr(state, "global_step", 0)) + ) + self.training_span.end() + self.training_span = None + + # type: ignore[no-untyped-def] + def on_step_end(self, args, state, control, **kwargs): + if self.tracer and self.training_span: + # Emit periodic step events (every 100 steps to avoid overhead) + step = int(getattr(state, "global_step", 0)) + if step % 100 == 0: + # type: ignore[union-attr] + with self.tracer.start_as_current_span("training_step") as span: + span.set_attribute("global_step", step) + span.set_attribute("epoch", float(getattr(state, "epoch", 0.0))) + + # type: ignore[no-untyped-def] + def on_evaluate(self, args, state, control, metrics=None, **kwargs): + if self.tracer: + # type: ignore[union-attr] + with self.tracer.start_as_current_span("evaluation") as span: + span.set_attribute("global_step", int(getattr(state, "global_step", 0))) + if metrics: + for k, v in metrics.items(): + try: + if isinstance(v, (int, float)): + span.set_attribute(f"metric.{k}", float(v)) + except Exception: + pass + + # type: ignore[no-untyped-def] + def on_log(self, args, state, control, logs=None, **kwargs): + # Log training loss if available + if self.tracer and logs and "loss" in logs: + # type: ignore[union-attr] + with self.tracer.start_as_current_span("log_event") as span: + span.set_attribute("global_step", int(getattr(state, "global_step", 0))) + span.set_attribute("metric.loss", float(logs["loss"])) + + # type: ignore[no-untyped-def] + def on_prediction_step(self, args, state, control, **kwargs): + """Emit a brief span for prediction/prediction-step events. + + Hugging Face Trainer may not call this method in all versions; presence + is checked by callers before adding the callback to the trainer. + """ + if self.tracer: + try: + with self.tracer.start_as_current_span("prediction_step") as span: + span.set_attribute( + "global_step", int(getattr(state, "global_step", 0)) + ) + except Exception: + pass diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/prepare_dataset.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/prepare_dataset.py index bad0e83b2..dafd6fb0d 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/prepare_dataset.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/prepare_dataset.py @@ -1,196 +1,196 @@ -import argparse -import csv -import json -import random -from pathlib import Path -from typing import Any, Dict, Iterable, List, Tuple - -# Minimal, robust dataset converter and splitter for chat-style SFT -# Input formats supported: -# - JSONL where each line is an object with a "messages" array -# - CSV with columns: prompt,response OR input,output -# Output: -# - JSONL files named train.json and test.json containing one JSON object per line -# with the schema: {"messages": [{"role": "user"|"assistant", "content": "..."}, ...]} - -Chat = List[Dict[str, Any]] - - -def read_jsonl(path: Path) -> Iterable[Dict[str, Any]]: - with path.open("r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - yield json.loads(line) - - -def read_csv(path: Path) -> Iterable[Dict[str, Any]]: - with path.open("r", encoding="utf-8", newline="") as f: - reader = csv.DictReader(f) - # Accept common column pairs - col_pairs = [ - ("prompt", "response"), - ("input", "output"), - ("question", "answer"), - ("user", "assistant"), - ] - chosen: Tuple[str, str] | None = None - cols = [c.lower() for c in reader.fieldnames or []] - for a, b in col_pairs: - if a in cols and b in cols: - chosen = (a, b) - break - if chosen is None: - raise ValueError( - f"CSV must have one of column pairs: {col_pairs}; got: {reader.fieldnames}" - ) - a, b = chosen - for row in reader: - prompt = row.get(a) or "" - resp = row.get(b) or "" - yield { - "messages": [ - {"role": "user", "content": str(prompt).strip()}, - {"role": "assistant", "content": str(resp).strip()}, - ] - } - - -def normalize_record(obj: Dict[str, Any]) -> Dict[str, Any]: - # If already has messages, validate minimal structure - if "messages" in obj and isinstance(obj["messages"], list): - msgs = [] - for m in obj["messages"]: - role = (m.get("role") or "").strip().lower() - content = (m.get("content") or "").strip() - if not role or not content: - # Skip malformed entries - continue - if role not in ("user", "assistant", "system"): - # Map unknown roles to user - role = "user" - msgs.append({"role": role, "content": content}) - if len(msgs) >= 2: - return {"messages": msgs} - else: - raise ValueError("Message list too short after normalization") - # Otherwise, try simple prompt/completion keys - for a, b in [("prompt", "completion"), ("input", "output"), ("question", "answer")]: - if a in obj and b in obj: - return { - "messages": [ - {"role": "user", "content": str(obj[a])}, - {"role": "assistant", "content": str(obj[b])}, - ] - } - raise ValueError( - "Unsupported record format; expected messages[] or prompt/completion-style fields" - ) - - -def discover_inputs(input_path: Path) -> List[Path]: - if input_path.is_dir(): - candidates: List[Path] = [] - for pattern in ("*.jsonl", "*.json", "*.csv"): - candidates.extend(sorted(input_path.rglob(pattern))) - if not candidates: - raise FileNotFoundError(f"No dataset files found under {input_path}") - return candidates - else: - return [input_path] - - -def load_records(paths: List[Path]) -> Iterable[Dict[str, Any]]: - for p in paths: - if p.suffix.lower() == ".csv": - for rec in read_csv(p): - yield normalize_record(rec) - elif p.suffix.lower() in (".jsonl", ".json"): - for rec in read_jsonl(p): - yield normalize_record(rec) - else: - raise ValueError(f"Unsupported file type: {p}") - - -def stream_split_and_write( - records: Iterable[Dict[str, Any]], - train_path: Path, - test_path: Path, - train_ratio: float, - seed: int, -) -> Tuple[int, int]: - rnd = random.Random(seed) - train_path.parent.mkdir(parents=True, exist_ok=True) - test_path.parent.mkdir(parents=True, exist_ok=True) - n_train = 0 - n_test = 0 - with ( - train_path.open("w", encoding="utf-8") as ftrain, - test_path.open("w", encoding="utf-8") as ftest, - ): - for obj in records: - if rnd.random() < train_ratio: - ftrain.write(json.dumps(obj, ensure_ascii=False) + "\n") - n_train += 1 - else: - ftest.write(json.dumps(obj, ensure_ascii=False) + "\n") - n_test += 1 - # Ensure at least one test sample by moving last train to test if needed - if n_test == 0 and n_train > 0: - # Re-open and move last line - lines: List[str] = [] - with train_path.open("r", encoding="utf-8") as f: - lines = f.readlines() - if lines: - with test_path.open("a", encoding="utf-8") as ftest: - ftest.write(lines[-1]) - with train_path.open("w", encoding="utf-8") as ftrain: - ftrain.writelines(lines[:-1]) - n_train -= 1 - n_test += 1 - return n_train, n_test - - -def write_jsonl(path: Path, records: Iterable[Dict[str, Any]]) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - with path.open("w", encoding="utf-8") as f: - for obj in records: - f.write(json.dumps(obj, ensure_ascii=False) + "\n") - - -def main(): - ap = argparse.ArgumentParser(description="Prepare chat dataset for SFT (JSONL)") - ap.add_argument( - "--input", required=True, help="Path to source file or folder (jsonl/json/csv)" - ) - ap.add_argument( - "--output-dir", - default=str(Path(__file__).resolve().parents[1] / "data"), - help="Output folder for train/test JSONL", - ) - ap.add_argument("--train-file", default="train.json", help="Output train file name") - ap.add_argument("--test-file", default="test.json", help="Output test file name") - ap.add_argument( - "--train-ratio", type=float, default=0.98, help="Train split ratio (0-1)" - ) - ap.add_argument("--seed", type=int, default=42) - args = ap.parse_args() - - input_path = Path(args.input) - out_dir = Path(args.output_dir) - train_path = out_dir / args.train_file - test_path = out_dir / args.test_file - - files = discover_inputs(input_path) - # Stream through input to avoid memory blow-up on huge datasets - n_train, n_test = stream_split_and_write( - load_records(files), train_path, test_path, args.train_ratio, args.seed - ) - if n_train + n_test == 0: - raise RuntimeError("No valid records parsed from input") - print(f"Wrote {n_train} train and {n_test} test examples to {out_dir}") - - -if __name__ == "__main__": - main() +import argparse +import csv +import json +import random +from pathlib import Path +from typing import Any, Dict, Iterable, List, Tuple + +# Minimal, robust dataset converter and splitter for chat-style SFT +# Input formats supported: +# - JSONL where each line is an object with a "messages" array +# - CSV with columns: prompt,response OR input,output +# Output: +# - JSONL files named train.json and test.json containing one JSON object per line +# with the schema: {"messages": [{"role": "user"|"assistant", "content": "..."}, ...]} + +Chat = List[Dict[str, Any]] + + +def read_jsonl(path: Path) -> Iterable[Dict[str, Any]]: + with path.open("r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + yield json.loads(line) + + +def read_csv(path: Path) -> Iterable[Dict[str, Any]]: + with path.open("r", encoding="utf-8", newline="") as f: + reader = csv.DictReader(f) + # Accept common column pairs + col_pairs = [ + ("prompt", "response"), + ("input", "output"), + ("question", "answer"), + ("user", "assistant"), + ] + chosen: Tuple[str, str] | None = None + cols = [c.lower() for c in reader.fieldnames or []] + for a, b in col_pairs: + if a in cols and b in cols: + chosen = (a, b) + break + if chosen is None: + raise ValueError( + f"CSV must have one of column pairs: {col_pairs}; got: {reader.fieldnames}" + ) + a, b = chosen + for row in reader: + prompt = row.get(a) or "" + resp = row.get(b) or "" + yield { + "messages": [ + {"role": "user", "content": str(prompt).strip()}, + {"role": "assistant", "content": str(resp).strip()}, + ] + } + + +def normalize_record(obj: Dict[str, Any]) -> Dict[str, Any]: + # If already has messages, validate minimal structure + if "messages" in obj and isinstance(obj["messages"], list): + msgs = [] + for m in obj["messages"]: + role = (m.get("role") or "").strip().lower() + content = (m.get("content") or "").strip() + if not role or not content: + # Skip malformed entries + continue + if role not in ("user", "assistant", "system"): + # Map unknown roles to user + role = "user" + msgs.append({"role": role, "content": content}) + if len(msgs) >= 2: + return {"messages": msgs} + else: + raise ValueError("Message list too short after normalization") + # Otherwise, try simple prompt/completion keys + for a, b in [("prompt", "completion"), ("input", "output"), ("question", "answer")]: + if a in obj and b in obj: + return { + "messages": [ + {"role": "user", "content": str(obj[a])}, + {"role": "assistant", "content": str(obj[b])}, + ] + } + raise ValueError( + "Unsupported record format; expected messages[] or prompt/completion-style fields" + ) + + +def discover_inputs(input_path: Path) -> List[Path]: + if input_path.is_dir(): + candidates: List[Path] = [] + for pattern in ("*.jsonl", "*.json", "*.csv"): + candidates.extend(sorted(input_path.rglob(pattern))) + if not candidates: + raise FileNotFoundError(f"No dataset files found under {input_path}") + return candidates + else: + return [input_path] + + +def load_records(paths: List[Path]) -> Iterable[Dict[str, Any]]: + for p in paths: + if p.suffix.lower() == ".csv": + for rec in read_csv(p): + yield normalize_record(rec) + elif p.suffix.lower() in (".jsonl", ".json"): + for rec in read_jsonl(p): + yield normalize_record(rec) + else: + raise ValueError(f"Unsupported file type: {p}") + + +def stream_split_and_write( + records: Iterable[Dict[str, Any]], + train_path: Path, + test_path: Path, + train_ratio: float, + seed: int, +) -> Tuple[int, int]: + rnd = random.Random(seed) + train_path.parent.mkdir(parents=True, exist_ok=True) + test_path.parent.mkdir(parents=True, exist_ok=True) + n_train = 0 + n_test = 0 + with ( + train_path.open("w", encoding="utf-8") as ftrain, + test_path.open("w", encoding="utf-8") as ftest, + ): + for obj in records: + if rnd.random() < train_ratio: + ftrain.write(json.dumps(obj, ensure_ascii=False) + "\n") + n_train += 1 + else: + ftest.write(json.dumps(obj, ensure_ascii=False) + "\n") + n_test += 1 + # Ensure at least one test sample by moving last train to test if needed + if n_test == 0 and n_train > 0: + # Re-open and move last line + lines: List[str] = [] + with train_path.open("r", encoding="utf-8") as f: + lines = f.readlines() + if lines: + with test_path.open("a", encoding="utf-8") as ftest: + ftest.write(lines[-1]) + with train_path.open("w", encoding="utf-8") as ftrain: + ftrain.writelines(lines[:-1]) + n_train -= 1 + n_test += 1 + return n_train, n_test + + +def write_jsonl(path: Path, records: Iterable[Dict[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as f: + for obj in records: + f.write(json.dumps(obj, ensure_ascii=False) + "\n") + + +def main(): + ap = argparse.ArgumentParser(description="Prepare chat dataset for SFT (JSONL)") + ap.add_argument( + "--input", required=True, help="Path to source file or folder (jsonl/json/csv)" + ) + ap.add_argument( + "--output-dir", + default=str(Path(__file__).resolve().parents[1] / "data"), + help="Output folder for train/test JSONL", + ) + ap.add_argument("--train-file", default="train.json", help="Output train file name") + ap.add_argument("--test-file", default="test.json", help="Output test file name") + ap.add_argument( + "--train-ratio", type=float, default=0.98, help="Train split ratio (0-1)" + ) + ap.add_argument("--seed", type=int, default=42) + args = ap.parse_args() + + input_path = Path(args.input) + out_dir = Path(args.output_dir) + train_path = out_dir / args.train_file + test_path = out_dir / args.test_file + + files = discover_inputs(input_path) + # Stream through input to avoid memory blow-up on huge datasets + n_train, n_test = stream_split_and_write( + load_records(files), train_path, test_path, args.train_ratio, args.seed + ) + if n_train + n_test == 0: + raise RuntimeError("No valid records parsed from input") + print(f"Wrote {n_train} train and {n_test} test examples to {out_dir}") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/rag_pipeline.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/rag_pipeline.py index 4b5f8c090..6077a4894 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/rag_pipeline.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/rag_pipeline.py @@ -1,356 +1,356 @@ -""" -RAG (Retrieval-Augmented Generation) Pipeline -Combines document retrieval with fine-tuned model generation -""" - -import json -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional - -import numpy as np -import torch -from transformers import AutoModelForCausalLM, AutoTokenizer - - -@dataclass -class RAGConfig: - """RAG pipeline configuration""" - - embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2" - chunk_size: int = 512 - chunk_overlap: int = 50 - top_k_retrieval: int = 3 - max_context_length: int = 2048 - retrieval_threshold: float = 0.7 - - -class DocumentStore: - """Vector store for document retrieval""" - - def __init__(self, config: RAGConfig): - self.config = config - self.documents: List[Dict[str, Any]] = [] - self.embeddings: Optional[np.ndarray] = None - self.embedding_model = None - - def add_documents(self, documents: List[Dict[str, Any]]): - """Add documents to store""" - self.documents.extend(documents) - print(f"Added {len(documents)} documents. Total: {len(self.documents)}") - - def load_from_directory(self, directory: str, extensions: List[str] | None = None): - """Load all documents from directory""" - if extensions is None: - extensions = [".txt", ".md", ".json"] - dir_path = Path(directory) - loaded = 0 - - for ext in extensions: - for file_path in dir_path.rglob(f"*{ext}"): - try: - content = file_path.read_text(encoding="utf-8") - chunks = self._chunk_text(content) - - for i, chunk in enumerate(chunks): - self.documents.append( - { - "content": chunk, - "source": str(file_path), - "chunk_id": i, - "metadata": {"file_type": ext}, - } - ) - loaded += 1 - except Exception as e: - print(f"Error loading {file_path}: {e}") - - print(f"Loaded {loaded} files, {len(self.documents)} chunks") - - def _chunk_text(self, text: str) -> List[str]: - """Split text into overlapping chunks""" - words = text.split() - chunks = [] - - for i in range( - 0, len(words), self.config.chunk_size - self.config.chunk_overlap - ): - chunk = " ".join(words[i : i + self.config.chunk_size]) - if chunk.strip(): - chunks.append(chunk) - - return chunks - - def build_index(self): - """Build vector index for retrieval""" - print("Building vector index...") - - try: - from sentence_transformers import SentenceTransformer - - self.embedding_model = SentenceTransformer(self.config.embedding_model) - - texts = [doc["content"] for doc in self.documents] - self.embeddings = self.embedding_model.encode( - texts, show_progress_bar=True, convert_to_numpy=True - ) - print(f"✓ Index built with {len(self.embeddings)} embeddings") - - except ImportError: - print( - "⚠ sentence-transformers not installed. Using simple keyword matching." - ) - self.embedding_model = None - - def retrieve(self, query: str, top_k: int = None) -> List[Dict[str, Any]]: - """Retrieve most relevant documents for query""" - if top_k is None: - top_k = self.config.top_k_retrieval - - if self.embedding_model is not None: - return self._semantic_retrieve(query, top_k) - else: - return self._keyword_retrieve(query, top_k) - - def _semantic_retrieve(self, query: str, top_k: int) -> List[Dict[str, Any]]: - """Semantic retrieval using embeddings""" - query_embedding = self.embedding_model.encode([query], convert_to_numpy=True)[0] - - # Compute cosine similarity - similarities = np.dot(self.embeddings, query_embedding) / ( - np.linalg.norm(self.embeddings, axis=1) * np.linalg.norm(query_embedding) - ) - - # Get top-k indices - top_indices = np.argsort(similarities)[-top_k:][::-1] - - results = [] - for idx in top_indices: - if similarities[idx] >= self.config.retrieval_threshold: - doc = self.documents[idx].copy() - doc["score"] = float(similarities[idx]) - results.append(doc) - - return results - - def _keyword_retrieve(self, query: str, top_k: int) -> List[Dict[str, Any]]: - """Simple keyword-based retrieval""" - query_words = set(query.lower().split()) - scores = [] - - for doc in self.documents: - doc_words = set(doc["content"].lower().split()) - overlap = len(query_words & doc_words) - scores.append(overlap / len(query_words) if query_words else 0) - - top_indices = np.argsort(scores)[-top_k:][::-1] - - results = [] - for idx in top_indices: - doc = self.documents[idx].copy() - doc["score"] = float(scores[idx]) - results.append(doc) - - return results - - def save_index(self, path: str): - """Save document store and index""" - save_path = Path(path) - save_path.mkdir(parents=True, exist_ok=True) - - # Save documents - with open(save_path / "documents.json", "w") as f: - json.dump(self.documents, f, indent=2) - - # Save embeddings if available - if self.embeddings is not None: - np.save(save_path / "embeddings.npy", self.embeddings) - - print(f"✓ Index saved to {save_path}") - - def load_index(self, path: str): - """Load document store and index""" - load_path = Path(path) - - with open(load_path / "documents.json") as f: - self.documents = json.load(f) - - embeddings_file = load_path / "embeddings.npy" - if embeddings_file.exists(): - self.embeddings = np.load(embeddings_file) - - try: - from sentence_transformers import SentenceTransformer - - self.embedding_model = SentenceTransformer(self.config.embedding_model) - except ImportError: - pass - - print(f"✓ Index loaded from {load_path}") - - -class RAGPipeline: - """Complete RAG pipeline with retrieval and generation""" - - def __init__( - self, model_path: str, document_store: DocumentStore, config: RAGConfig = None - ): - self.config = config or RAGConfig() - self.document_store = document_store - - print(f"Loading model from {model_path}...") - self.device = "cuda" if torch.cuda.is_available() else "cpu" - self.model = AutoModelForCausalLM.from_pretrained( - model_path, - torch_dtype=torch.float16 if self.device == "cuda" else torch.float32, - device_map="auto" if self.device == "cuda" else None, - ) - self.tokenizer = AutoTokenizer.from_pretrained(model_path) - - if self.tokenizer.pad_token is None: - self.tokenizer.pad_token = self.tokenizer.eos_token - - def query( - self, question: str, return_context: bool = False, max_new_tokens: int = 256 - ) -> Dict[str, Any]: - """ - Query the RAG pipeline - - Args: - question: User question - return_context: Whether to return retrieved context - max_new_tokens: Maximum tokens to generate - - Returns: - Dictionary with answer and optionally context - """ - # Retrieve relevant documents - retrieved_docs = self.document_store.retrieve(question) - - # Build context - context = self._build_context(retrieved_docs) - - # Generate answer - prompt = self._build_prompt(question, context) - answer = self._generate(prompt, max_new_tokens) - - result = { - "question": question, - "answer": answer, - "num_sources": len(retrieved_docs), - } - - if return_context: - result["context"] = retrieved_docs - - return result - - def _build_context(self, documents: List[Dict[str, Any]]) -> str: - """Build context string from retrieved documents""" - context_parts = [] - - for i, doc in enumerate(documents, 1): - source = Path(doc["source"]).name if "source" in doc else f"Document {i}" - content = doc["content"][:500] # Truncate if needed - context_parts.append(f"[Source {i}: {source}]\n{content}") - - return "\n\n".join(context_parts) - - def _build_prompt(self, question: str, context: str) -> str: - """Build prompt for generation""" - prompt = f"""Answer the following question based on the provided context. - -Context: -{context} - -Question: {question} - -Answer:""" - return prompt - - def _generate(self, prompt: str, max_new_tokens: int) -> str: - """Generate answer from prompt""" - inputs = self.tokenizer( - prompt, - return_tensors="pt", - truncation=True, - max_length=self.config.max_context_length, - ) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - - with torch.no_grad(): - outputs = self.model.generate( - **inputs, - max_new_tokens=max_new_tokens, - temperature=0.7, - do_sample=True, - top_p=0.9, - ) - - full_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True) - - # Extract only the generated answer - answer = full_text[len(prompt) :].strip() - return answer - - -def main(): - """CLI for RAG pipeline""" - import argparse - - parser = argparse.ArgumentParser(description="RAG Pipeline") - parser.add_argument("--model", type=str, required=True, help="Path to model") - parser.add_argument( - "--docs", type=str, required=True, help="Path to documents directory" - ) - parser.add_argument( - "--index-dir", type=str, default="data_out/rag_index", help="Index directory" - ) - parser.add_argument("--query", type=str, help="Query to run") - parser.add_argument("--interactive", action="store_true", help="Interactive mode") - parser.add_argument("--rebuild-index", action="store_true", help="Rebuild index") - - args = parser.parse_args() - - # Setup document store - config = RAGConfig() - doc_store = DocumentStore(config) - - index_path = Path(args.index_dir) - if args.rebuild_index or not index_path.exists(): - print("Building new index...") - doc_store.load_from_directory(args.docs) - doc_store.build_index() - doc_store.save_index(args.index_dir) - else: - print("Loading existing index...") - doc_store.load_index(args.index_dir) - - # Setup RAG pipeline - rag = RAGPipeline(args.model, doc_store, config) - - if args.interactive: - print("\n=== RAG Interactive Mode ===") - print("Enter your questions (or 'quit' to exit):\n") - - while True: - question = input("Q: ").strip() - if question.lower() in ["quit", "exit", "q"]: - break - - if not question: - continue - - result = rag.query(question, return_context=True) - print(f"\nA: {result['answer']}") - print(f"(Used {result['num_sources']} sources)\n") - - elif args.query: - result = rag.query(args.query, return_context=True) - print(f"\nQuestion: {result['question']}") - print(f"Answer: {result['answer']}") - print(f"\nSources used: {result['num_sources']}") - - -if __name__ == "__main__": - main() +""" +RAG (Retrieval-Augmented Generation) Pipeline +Combines document retrieval with fine-tuned model generation +""" + +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional + +import numpy as np +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + + +@dataclass +class RAGConfig: + """RAG pipeline configuration""" + + embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2" + chunk_size: int = 512 + chunk_overlap: int = 50 + top_k_retrieval: int = 3 + max_context_length: int = 2048 + retrieval_threshold: float = 0.7 + + +class DocumentStore: + """Vector store for document retrieval""" + + def __init__(self, config: RAGConfig): + self.config = config + self.documents: List[Dict[str, Any]] = [] + self.embeddings: Optional[np.ndarray] = None + self.embedding_model = None + + def add_documents(self, documents: List[Dict[str, Any]]): + """Add documents to store""" + self.documents.extend(documents) + print(f"Added {len(documents)} documents. Total: {len(self.documents)}") + + def load_from_directory(self, directory: str, extensions: List[str] | None = None): + """Load all documents from directory""" + if extensions is None: + extensions = [".txt", ".md", ".json"] + dir_path = Path(directory) + loaded = 0 + + for ext in extensions: + for file_path in dir_path.rglob(f"*{ext}"): + try: + content = file_path.read_text(encoding="utf-8") + chunks = self._chunk_text(content) + + for i, chunk in enumerate(chunks): + self.documents.append( + { + "content": chunk, + "source": str(file_path), + "chunk_id": i, + "metadata": {"file_type": ext}, + } + ) + loaded += 1 + except Exception as e: + print(f"Error loading {file_path}: {e}") + + print(f"Loaded {loaded} files, {len(self.documents)} chunks") + + def _chunk_text(self, text: str) -> List[str]: + """Split text into overlapping chunks""" + words = text.split() + chunks = [] + + for i in range( + 0, len(words), self.config.chunk_size - self.config.chunk_overlap + ): + chunk = " ".join(words[i : i + self.config.chunk_size]) + if chunk.strip(): + chunks.append(chunk) + + return chunks + + def build_index(self): + """Build vector index for retrieval""" + print("Building vector index...") + + try: + from sentence_transformers import SentenceTransformer + + self.embedding_model = SentenceTransformer(self.config.embedding_model) + + texts = [doc["content"] for doc in self.documents] + self.embeddings = self.embedding_model.encode( + texts, show_progress_bar=True, convert_to_numpy=True + ) + print(f"✓ Index built with {len(self.embeddings)} embeddings") + + except ImportError: + print( + "⚠ sentence-transformers not installed. Using simple keyword matching." + ) + self.embedding_model = None + + def retrieve(self, query: str, top_k: int = None) -> List[Dict[str, Any]]: + """Retrieve most relevant documents for query""" + if top_k is None: + top_k = self.config.top_k_retrieval + + if self.embedding_model is not None: + return self._semantic_retrieve(query, top_k) + else: + return self._keyword_retrieve(query, top_k) + + def _semantic_retrieve(self, query: str, top_k: int) -> List[Dict[str, Any]]: + """Semantic retrieval using embeddings""" + query_embedding = self.embedding_model.encode([query], convert_to_numpy=True)[0] + + # Compute cosine similarity + similarities = np.dot(self.embeddings, query_embedding) / ( + np.linalg.norm(self.embeddings, axis=1) * np.linalg.norm(query_embedding) + ) + + # Get top-k indices + top_indices = np.argsort(similarities)[-top_k:][::-1] + + results = [] + for idx in top_indices: + if similarities[idx] >= self.config.retrieval_threshold: + doc = self.documents[idx].copy() + doc["score"] = float(similarities[idx]) + results.append(doc) + + return results + + def _keyword_retrieve(self, query: str, top_k: int) -> List[Dict[str, Any]]: + """Simple keyword-based retrieval""" + query_words = set(query.lower().split()) + scores = [] + + for doc in self.documents: + doc_words = set(doc["content"].lower().split()) + overlap = len(query_words & doc_words) + scores.append(overlap / len(query_words) if query_words else 0) + + top_indices = np.argsort(scores)[-top_k:][::-1] + + results = [] + for idx in top_indices: + doc = self.documents[idx].copy() + doc["score"] = float(scores[idx]) + results.append(doc) + + return results + + def save_index(self, path: str): + """Save document store and index""" + save_path = Path(path) + save_path.mkdir(parents=True, exist_ok=True) + + # Save documents + with open(save_path / "documents.json", "w") as f: + json.dump(self.documents, f, indent=2) + + # Save embeddings if available + if self.embeddings is not None: + np.save(save_path / "embeddings.npy", self.embeddings) + + print(f"✓ Index saved to {save_path}") + + def load_index(self, path: str): + """Load document store and index""" + load_path = Path(path) + + with open(load_path / "documents.json") as f: + self.documents = json.load(f) + + embeddings_file = load_path / "embeddings.npy" + if embeddings_file.exists(): + self.embeddings = np.load(embeddings_file) + + try: + from sentence_transformers import SentenceTransformer + + self.embedding_model = SentenceTransformer(self.config.embedding_model) + except ImportError: + pass + + print(f"✓ Index loaded from {load_path}") + + +class RAGPipeline: + """Complete RAG pipeline with retrieval and generation""" + + def __init__( + self, model_path: str, document_store: DocumentStore, config: RAGConfig = None + ): + self.config = config or RAGConfig() + self.document_store = document_store + + print(f"Loading model from {model_path}...") + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=torch.float16 if self.device == "cuda" else torch.float32, + device_map="auto" if self.device == "cuda" else None, + ) + self.tokenizer = AutoTokenizer.from_pretrained(model_path) + + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + def query( + self, question: str, return_context: bool = False, max_new_tokens: int = 256 + ) -> Dict[str, Any]: + """ + Query the RAG pipeline + + Args: + question: User question + return_context: Whether to return retrieved context + max_new_tokens: Maximum tokens to generate + + Returns: + Dictionary with answer and optionally context + """ + # Retrieve relevant documents + retrieved_docs = self.document_store.retrieve(question) + + # Build context + context = self._build_context(retrieved_docs) + + # Generate answer + prompt = self._build_prompt(question, context) + answer = self._generate(prompt, max_new_tokens) + + result = { + "question": question, + "answer": answer, + "num_sources": len(retrieved_docs), + } + + if return_context: + result["context"] = retrieved_docs + + return result + + def _build_context(self, documents: List[Dict[str, Any]]) -> str: + """Build context string from retrieved documents""" + context_parts = [] + + for i, doc in enumerate(documents, 1): + source = Path(doc["source"]).name if "source" in doc else f"Document {i}" + content = doc["content"][:500] # Truncate if needed + context_parts.append(f"[Source {i}: {source}]\n{content}") + + return "\n\n".join(context_parts) + + def _build_prompt(self, question: str, context: str) -> str: + """Build prompt for generation""" + prompt = f"""Answer the following question based on the provided context. + +Context: +{context} + +Question: {question} + +Answer:""" + return prompt + + def _generate(self, prompt: str, max_new_tokens: int) -> str: + """Generate answer from prompt""" + inputs = self.tokenizer( + prompt, + return_tensors="pt", + truncation=True, + max_length=self.config.max_context_length, + ) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + with torch.no_grad(): + outputs = self.model.generate( + **inputs, + max_new_tokens=max_new_tokens, + temperature=0.7, + do_sample=True, + top_p=0.9, + ) + + full_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True) + + # Extract only the generated answer + answer = full_text[len(prompt) :].strip() + return answer + + +def main(): + """CLI for RAG pipeline""" + import argparse + + parser = argparse.ArgumentParser(description="RAG Pipeline") + parser.add_argument("--model", type=str, required=True, help="Path to model") + parser.add_argument( + "--docs", type=str, required=True, help="Path to documents directory" + ) + parser.add_argument( + "--index-dir", type=str, default="data_out/rag_index", help="Index directory" + ) + parser.add_argument("--query", type=str, help="Query to run") + parser.add_argument("--interactive", action="store_true", help="Interactive mode") + parser.add_argument("--rebuild-index", action="store_true", help="Rebuild index") + + args = parser.parse_args() + + # Setup document store + config = RAGConfig() + doc_store = DocumentStore(config) + + index_path = Path(args.index_dir) + if args.rebuild_index or not index_path.exists(): + print("Building new index...") + doc_store.load_from_directory(args.docs) + doc_store.build_index() + doc_store.save_index(args.index_dir) + else: + print("Loading existing index...") + doc_store.load_index(args.index_dir) + + # Setup RAG pipeline + rag = RAGPipeline(args.model, doc_store, config) + + if args.interactive: + print("\n=== RAG Interactive Mode ===") + print("Enter your questions (or 'quit' to exit):\n") + + while True: + question = input("Q: ").strip() + if question.lower() in ["quit", "exit", "q"]: + break + + if not question: + continue + + result = rag.query(question, return_context=True) + print(f"\nA: {result['answer']}") + print(f"(Used {result['num_sources']} sources)\n") + + elif args.query: + result = rag.query(args.query, return_context=True) + print(f"\nQuestion: {result['question']}") + print(f"Answer: {result['answer']}") + print(f"\nSources used: {result['num_sources']}") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/run_pipeline.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/run_pipeline.py index 24b1fa3e9..6bf917331 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/run_pipeline.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/run_pipeline.py @@ -1,445 +1,445 @@ -""" -Master Pipeline Orchestrator -Automates the complete training pipeline: optimization → pruning → training → evaluation → RAG -""" - -import argparse -import json -import subprocess -import sys -import time -from pathlib import Path -from typing import Any, Dict - - -class PipelineOrchestrator: - """Orchestrates the complete training pipeline""" - - def __init__(self, config: Dict[str, Any]): - self.config = config - self.results = {} - self.start_time = time.time() - - def run_full_pipeline(self): - """Run complete pipeline""" - print("=" * 60) - print("ADVANCED TRAINING PIPELINE") - print("=" * 60) - - steps = [ - ("GPU Optimization", self.step_gpu_optimization), - ("Data Pruning", self.step_data_pruning), - ("Model Training", self.step_training), - ("Model Evaluation", self.step_evaluation), - ("RAG Setup", self.step_rag_setup), - ] - - for i, (name, func) in enumerate(steps, 1): - if not self.config.get(f"skip_{name.lower().replace(' ', '_')}", False): - print(f"\n{'='*60}") - print(f"STEP {i}/{len(steps)}: {name}") - print(f"{'='*60}\n") - - try: - result = func() - self.results[name] = {"status": "success", "result": result} - except Exception as e: - print(f"❌ Error in {name}: {e}") - self.results[name] = {"status": "failed", "error": str(e)} - - if not self.config.get("continue_on_error", False): - print("\n❌ Pipeline stopped due to error") - return False - else: - print(f"\n⏭️ Skipping: {name}") - self.results[name] = {"status": "skipped"} - - # Print summary - self.print_summary() - return True - - def step_gpu_optimization(self) -> Dict[str, Any]: - """Step 1: GPU optimization""" - cmd = [ - sys.executable, - "scripts/gpu_optimizer.py", - "--model-size", - str(self.config.get("model_size_gb", 7.0)), - "--memory-usage", - str(self.config.get("memory_usage", 0.8)), - "--output", - "data_out/gpu_profile.yaml", - ] - - if self.config.get("update_config", True): - cmd.extend( - [ - "--update-config", - self.config.get("training_config", "lora/lora.yaml"), - ] - ) - - result = subprocess.run(cmd, capture_output=True, text=True) - - if result.returncode != 0: - raise RuntimeError(f"GPU optimization failed: {result.stderr}") - - print(result.stdout) - return {"profile_path": "data_out/gpu_profile.yaml"} - - def step_data_pruning(self) -> Dict[str, Any]: - """Step 2: Data pruning""" - input_path = self.config.get("input_dataset") - if not input_path: - print("⚠️ No input dataset specified, skipping pruning") - return {"skipped": True} - - output_path = self.config.get("pruned_dataset", "data/pruned_train.jsonl") - - cmd = [ - sys.executable, - "scripts/semantic_pruning.py", - "--input", - input_path, - "--output", - output_path, - "--similarity-threshold", - str(self.config.get("similarity_threshold", 0.95)), - "--quality-threshold", - str(self.config.get("quality_threshold", 0.3)), - ] - - if self.config.get("no_embeddings", False): - cmd.append("--no-embeddings") - - result = subprocess.run(cmd, capture_output=True, text=True) - - if result.returncode != 0: - raise RuntimeError(f"Data pruning failed: {result.stderr}") - - print(result.stdout) - return {"input_path": input_path, "output_path": output_path} - - def step_training(self) -> Dict[str, Any]: - """Step 3: Model training""" - dataset_path = self.config.get("pruned_dataset", "data") - if not Path(dataset_path).exists(): - dataset_path = self.config.get("input_dataset", "data") - - cmd = [ - sys.executable, - "scripts/train_lora.py", - "--dataset", - dataset_path, - "--config", - self.config.get("training_config", "lora/lora.yaml"), - ] - - # Add optional flags - if self.config.get("max_train_samples"): - cmd.extend(["--max-train-samples", str(self.config["max_train_samples"])]) - - if self.config.get("no_stream", False): - cmd.append("--no-stream") - - if self.config.get("dry_run", False): - cmd.append("--dry-run") - - print(f"Running: {' '.join(cmd)}") - result = subprocess.run(cmd) - - if result.returncode != 0: - raise RuntimeError("Training failed") - - return {"model_path": self.config.get("model_output", "data_out/lora_training")} - - def step_evaluation(self) -> Dict[str, Any]: - """Step 4: Model evaluation""" - model_path = self.config.get("model_output", "data_out/lora_training") - test_dataset = self.config.get("test_dataset") - - if not test_dataset: - print("⚠️ No test dataset specified, skipping evaluation") - return {"skipped": True} - - cmd = [ - sys.executable, - "scripts/auto_eval.py", - "--model", - model_path, - "--dataset", - test_dataset, - "--num-samples", - str(self.config.get("eval_samples", 100)), - "--output-name", - self.config.get("experiment_name", "pipeline_eval"), - ] - - metrics = self.config.get("eval_metrics", ["perplexity", "inference_time"]) - cmd.extend(["--metrics"] + metrics) - - result = subprocess.run(cmd, capture_output=True, text=True) - - if result.returncode != 0: - raise RuntimeError(f"Evaluation failed: {result.stderr}") - - print(result.stdout) - - # Parse results - eval_results = {} - for line in result.stdout.split("\n"): - if ":" in line: - key, value = line.split(":", 1) - eval_results[key.strip()] = value.strip() - - return eval_results - - def step_rag_setup(self) -> Dict[str, Any]: - """Step 5: RAG setup""" - if not self.config.get("rag_docs_path"): - print("⚠️ No RAG docs path specified, skipping RAG setup") - return {"skipped": True} - - model_path = self.config.get("model_output", "data_out/lora_training") - docs_path = self.config.get("rag_docs_path") - index_path = self.config.get("rag_index_path", "data_out/rag_index") - - cmd = [ - sys.executable, - "scripts/rag_pipeline.py", - "--model", - model_path, - "--docs", - docs_path, - "--index-dir", - index_path, - ] - - if self.config.get("rebuild_rag_index", True): - cmd.append("--rebuild-index") - - result = subprocess.run(cmd, capture_output=True, text=True) - - if result.returncode != 0: - raise RuntimeError(f"RAG setup failed: {result.stderr}") - - print(result.stdout) - return {"index_path": index_path, "ready": True} - - def print_summary(self): - """Print pipeline summary""" - elapsed = time.time() - self.start_time - - print("\n" + "=" * 60) - print("PIPELINE SUMMARY") - print("=" * 60) - - for step, result in self.results.items(): - status = result["status"] - emoji = "✅" if status == "success" else "❌" if status == "failed" else "⏭️" - print(f"{emoji} {step}: {status.upper()}") - - print(f"\nTotal time: {elapsed:.1f}s ({elapsed/60:.1f}m)") - - # Save results - results_file = Path("data_out/pipeline_results.json") - results_file.parent.mkdir(parents=True, exist_ok=True) - - with open(results_file, "w") as f: - json.dump( - { - "config": self.config, - "results": self.results, - "elapsed_seconds": elapsed, - }, - f, - indent=2, - ) - - print(f"\n📊 Full results saved to: {results_file}") - - -def main(): - parser = argparse.ArgumentParser( - description="Master Training Pipeline Orchestrator", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # Full pipeline - python scripts/run_pipeline.py --input-dataset data/train.jsonl --test-dataset data/test.jsonl - - # Quick test (64 samples, no RAG) - python scripts/run_pipeline.py --input-dataset data/train.jsonl --max-train-samples 64 --skip-rag - - # Production (with RAG) - python scripts/run_pipeline.py --input-dataset data/train.jsonl --test-dataset data/test.jsonl --rag-docs ../../datasets - """, - ) - - # Input/Output - parser.add_argument( - "--input-dataset", type=str, help="Input training dataset (JSONL)" - ) - parser.add_argument( - "--test-dataset", type=str, help="Test dataset for evaluation (JSONL)" - ) - parser.add_argument( - "--training-config", - type=str, - default="lora/lora.yaml", - help="Training config file", - ) - parser.add_argument( - "--model-output", - type=str, - default="data_out/lora_training", - help="Output directory for trained model", - ) - - # GPU Optimization - parser.add_argument( - "--model-size-gb", type=float, default=7.0, help="Model size in GB" - ) - parser.add_argument( - "--memory-usage", - type=float, - default=0.8, - help="Target GPU memory usage (0.0-1.0)", - ) - parser.add_argument( - "--no-update-config", - action="store_true", - help="Don't update training config with GPU optimizations", - ) - - # Data Pruning - parser.add_argument( - "--pruned-dataset", - type=str, - default="data/pruned_train.jsonl", - help="Output path for pruned dataset", - ) - parser.add_argument( - "--similarity-threshold", - type=float, - default=0.95, - help="Similarity threshold for pruning", - ) - parser.add_argument( - "--quality-threshold", - type=float, - default=0.3, - help="Quality threshold for pruning", - ) - parser.add_argument( - "--no-embeddings", action="store_true", help="Skip semantic deduplication" - ) - - # Training - parser.add_argument( - "--max-train-samples", type=int, help="Maximum training samples (for testing)" - ) - parser.add_argument( - "--no-stream", action="store_true", help="Disable streaming during training" - ) - parser.add_argument( - "--dry-run", action="store_true", help="Dry run (validate config only)" - ) - - # Evaluation - parser.add_argument( - "--eval-samples", type=int, default=100, help="Number of samples for evaluation" - ) - parser.add_argument( - "--eval-metrics", - nargs="+", - default=["perplexity", "inference_time"], - help="Evaluation metrics", - ) - parser.add_argument( - "--experiment-name", - type=str, - default="pipeline_eval", - help="Experiment name for results", - ) - - # RAG - parser.add_argument("--rag-docs", type=str, help="Path to documents for RAG") - parser.add_argument( - "--rag-index-path", - type=str, - default="data_out/rag_index", - help="Path for RAG index", - ) - parser.add_argument( - "--no-rebuild-rag-index", action="store_true", help="Don't rebuild RAG index" - ) - - # Pipeline Control - parser.add_argument( - "--skip-optimization", action="store_true", help="Skip GPU optimization step" - ) - parser.add_argument( - "--skip-pruning", action="store_true", help="Skip data pruning step" - ) - parser.add_argument( - "--skip-training", action="store_true", help="Skip training step" - ) - parser.add_argument( - "--skip-evaluation", action="store_true", help="Skip evaluation step" - ) - parser.add_argument("--skip-rag", action="store_true", help="Skip RAG setup step") - parser.add_argument( - "--continue-on-error", - action="store_true", - help="Continue pipeline even if a step fails", - ) - - args = parser.parse_args() - - # Build config - config = { - # I/O - "input_dataset": args.input_dataset, - "test_dataset": args.test_dataset, - "training_config": args.training_config, - "model_output": args.model_output, - "pruned_dataset": args.pruned_dataset, - # GPU - "model_size_gb": args.model_size_gb, - "memory_usage": args.memory_usage, - "update_config": not args.no_update_config, - # Pruning - "similarity_threshold": args.similarity_threshold, - "quality_threshold": args.quality_threshold, - "no_embeddings": args.no_embeddings, - # Training - "max_train_samples": args.max_train_samples, - "no_stream": args.no_stream, - "dry_run": args.dry_run, - # Evaluation - "eval_samples": args.eval_samples, - "eval_metrics": args.eval_metrics, - "experiment_name": args.experiment_name, - # RAG - "rag_docs_path": args.rag_docs, - "rag_index_path": args.rag_index_path, - "rebuild_rag_index": not args.no_rebuild_rag_index, - # Control - "skip_gpu_optimization": args.skip_optimization, - "skip_data_pruning": args.skip_pruning, - "skip_model_training": args.skip_training, - "skip_model_evaluation": args.skip_evaluation, - "skip_rag_setup": args.skip_rag, - "continue_on_error": args.continue_on_error, - } - - # Run pipeline - orchestrator = PipelineOrchestrator(config) - success = orchestrator.run_full_pipeline() - - sys.exit(0 if success else 1) - - -if __name__ == "__main__": - main() +""" +Master Pipeline Orchestrator +Automates the complete training pipeline: optimization → pruning → training → evaluation → RAG +""" + +import argparse +import json +import subprocess +import sys +import time +from pathlib import Path +from typing import Any, Dict + + +class PipelineOrchestrator: + """Orchestrates the complete training pipeline""" + + def __init__(self, config: Dict[str, Any]): + self.config = config + self.results = {} + self.start_time = time.time() + + def run_full_pipeline(self): + """Run complete pipeline""" + print("=" * 60) + print("ADVANCED TRAINING PIPELINE") + print("=" * 60) + + steps = [ + ("GPU Optimization", self.step_gpu_optimization), + ("Data Pruning", self.step_data_pruning), + ("Model Training", self.step_training), + ("Model Evaluation", self.step_evaluation), + ("RAG Setup", self.step_rag_setup), + ] + + for i, (name, func) in enumerate(steps, 1): + if not self.config.get(f"skip_{name.lower().replace(' ', '_')}", False): + print(f"\n{'='*60}") + print(f"STEP {i}/{len(steps)}: {name}") + print(f"{'='*60}\n") + + try: + result = func() + self.results[name] = {"status": "success", "result": result} + except Exception as e: + print(f"❌ Error in {name}: {e}") + self.results[name] = {"status": "failed", "error": str(e)} + + if not self.config.get("continue_on_error", False): + print("\n❌ Pipeline stopped due to error") + return False + else: + print(f"\n⏭️ Skipping: {name}") + self.results[name] = {"status": "skipped"} + + # Print summary + self.print_summary() + return True + + def step_gpu_optimization(self) -> Dict[str, Any]: + """Step 1: GPU optimization""" + cmd = [ + sys.executable, + "scripts/gpu_optimizer.py", + "--model-size", + str(self.config.get("model_size_gb", 7.0)), + "--memory-usage", + str(self.config.get("memory_usage", 0.8)), + "--output", + "data_out/gpu_profile.yaml", + ] + + if self.config.get("update_config", True): + cmd.extend( + [ + "--update-config", + self.config.get("training_config", "lora/lora.yaml"), + ] + ) + + result = subprocess.run(cmd, capture_output=True, text=True) + + if result.returncode != 0: + raise RuntimeError(f"GPU optimization failed: {result.stderr}") + + print(result.stdout) + return {"profile_path": "data_out/gpu_profile.yaml"} + + def step_data_pruning(self) -> Dict[str, Any]: + """Step 2: Data pruning""" + input_path = self.config.get("input_dataset") + if not input_path: + print("⚠️ No input dataset specified, skipping pruning") + return {"skipped": True} + + output_path = self.config.get("pruned_dataset", "data/pruned_train.jsonl") + + cmd = [ + sys.executable, + "scripts/semantic_pruning.py", + "--input", + input_path, + "--output", + output_path, + "--similarity-threshold", + str(self.config.get("similarity_threshold", 0.95)), + "--quality-threshold", + str(self.config.get("quality_threshold", 0.3)), + ] + + if self.config.get("no_embeddings", False): + cmd.append("--no-embeddings") + + result = subprocess.run(cmd, capture_output=True, text=True) + + if result.returncode != 0: + raise RuntimeError(f"Data pruning failed: {result.stderr}") + + print(result.stdout) + return {"input_path": input_path, "output_path": output_path} + + def step_training(self) -> Dict[str, Any]: + """Step 3: Model training""" + dataset_path = self.config.get("pruned_dataset", "data") + if not Path(dataset_path).exists(): + dataset_path = self.config.get("input_dataset", "data") + + cmd = [ + sys.executable, + "scripts/train_lora.py", + "--dataset", + dataset_path, + "--config", + self.config.get("training_config", "lora/lora.yaml"), + ] + + # Add optional flags + if self.config.get("max_train_samples"): + cmd.extend(["--max-train-samples", str(self.config["max_train_samples"])]) + + if self.config.get("no_stream", False): + cmd.append("--no-stream") + + if self.config.get("dry_run", False): + cmd.append("--dry-run") + + print(f"Running: {' '.join(cmd)}") + result = subprocess.run(cmd) + + if result.returncode != 0: + raise RuntimeError("Training failed") + + return {"model_path": self.config.get("model_output", "data_out/lora_training")} + + def step_evaluation(self) -> Dict[str, Any]: + """Step 4: Model evaluation""" + model_path = self.config.get("model_output", "data_out/lora_training") + test_dataset = self.config.get("test_dataset") + + if not test_dataset: + print("⚠️ No test dataset specified, skipping evaluation") + return {"skipped": True} + + cmd = [ + sys.executable, + "scripts/auto_eval.py", + "--model", + model_path, + "--dataset", + test_dataset, + "--num-samples", + str(self.config.get("eval_samples", 100)), + "--output-name", + self.config.get("experiment_name", "pipeline_eval"), + ] + + metrics = self.config.get("eval_metrics", ["perplexity", "inference_time"]) + cmd.extend(["--metrics"] + metrics) + + result = subprocess.run(cmd, capture_output=True, text=True) + + if result.returncode != 0: + raise RuntimeError(f"Evaluation failed: {result.stderr}") + + print(result.stdout) + + # Parse results + eval_results = {} + for line in result.stdout.split("\n"): + if ":" in line: + key, value = line.split(":", 1) + eval_results[key.strip()] = value.strip() + + return eval_results + + def step_rag_setup(self) -> Dict[str, Any]: + """Step 5: RAG setup""" + if not self.config.get("rag_docs_path"): + print("⚠️ No RAG docs path specified, skipping RAG setup") + return {"skipped": True} + + model_path = self.config.get("model_output", "data_out/lora_training") + docs_path = self.config.get("rag_docs_path") + index_path = self.config.get("rag_index_path", "data_out/rag_index") + + cmd = [ + sys.executable, + "scripts/rag_pipeline.py", + "--model", + model_path, + "--docs", + docs_path, + "--index-dir", + index_path, + ] + + if self.config.get("rebuild_rag_index", True): + cmd.append("--rebuild-index") + + result = subprocess.run(cmd, capture_output=True, text=True) + + if result.returncode != 0: + raise RuntimeError(f"RAG setup failed: {result.stderr}") + + print(result.stdout) + return {"index_path": index_path, "ready": True} + + def print_summary(self): + """Print pipeline summary""" + elapsed = time.time() - self.start_time + + print("\n" + "=" * 60) + print("PIPELINE SUMMARY") + print("=" * 60) + + for step, result in self.results.items(): + status = result["status"] + emoji = "✅" if status == "success" else "❌" if status == "failed" else "⏭️" + print(f"{emoji} {step}: {status.upper()}") + + print(f"\nTotal time: {elapsed:.1f}s ({elapsed/60:.1f}m)") + + # Save results + results_file = Path("data_out/pipeline_results.json") + results_file.parent.mkdir(parents=True, exist_ok=True) + + with open(results_file, "w") as f: + json.dump( + { + "config": self.config, + "results": self.results, + "elapsed_seconds": elapsed, + }, + f, + indent=2, + ) + + print(f"\n📊 Full results saved to: {results_file}") + + +def main(): + parser = argparse.ArgumentParser( + description="Master Training Pipeline Orchestrator", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Full pipeline + python scripts/run_pipeline.py --input-dataset data/train.jsonl --test-dataset data/test.jsonl + + # Quick test (64 samples, no RAG) + python scripts/run_pipeline.py --input-dataset data/train.jsonl --max-train-samples 64 --skip-rag + + # Production (with RAG) + python scripts/run_pipeline.py --input-dataset data/train.jsonl --test-dataset data/test.jsonl --rag-docs ../../datasets + """, + ) + + # Input/Output + parser.add_argument( + "--input-dataset", type=str, help="Input training dataset (JSONL)" + ) + parser.add_argument( + "--test-dataset", type=str, help="Test dataset for evaluation (JSONL)" + ) + parser.add_argument( + "--training-config", + type=str, + default="lora/lora.yaml", + help="Training config file", + ) + parser.add_argument( + "--model-output", + type=str, + default="data_out/lora_training", + help="Output directory for trained model", + ) + + # GPU Optimization + parser.add_argument( + "--model-size-gb", type=float, default=7.0, help="Model size in GB" + ) + parser.add_argument( + "--memory-usage", + type=float, + default=0.8, + help="Target GPU memory usage (0.0-1.0)", + ) + parser.add_argument( + "--no-update-config", + action="store_true", + help="Don't update training config with GPU optimizations", + ) + + # Data Pruning + parser.add_argument( + "--pruned-dataset", + type=str, + default="data/pruned_train.jsonl", + help="Output path for pruned dataset", + ) + parser.add_argument( + "--similarity-threshold", + type=float, + default=0.95, + help="Similarity threshold for pruning", + ) + parser.add_argument( + "--quality-threshold", + type=float, + default=0.3, + help="Quality threshold for pruning", + ) + parser.add_argument( + "--no-embeddings", action="store_true", help="Skip semantic deduplication" + ) + + # Training + parser.add_argument( + "--max-train-samples", type=int, help="Maximum training samples (for testing)" + ) + parser.add_argument( + "--no-stream", action="store_true", help="Disable streaming during training" + ) + parser.add_argument( + "--dry-run", action="store_true", help="Dry run (validate config only)" + ) + + # Evaluation + parser.add_argument( + "--eval-samples", type=int, default=100, help="Number of samples for evaluation" + ) + parser.add_argument( + "--eval-metrics", + nargs="+", + default=["perplexity", "inference_time"], + help="Evaluation metrics", + ) + parser.add_argument( + "--experiment-name", + type=str, + default="pipeline_eval", + help="Experiment name for results", + ) + + # RAG + parser.add_argument("--rag-docs", type=str, help="Path to documents for RAG") + parser.add_argument( + "--rag-index-path", + type=str, + default="data_out/rag_index", + help="Path for RAG index", + ) + parser.add_argument( + "--no-rebuild-rag-index", action="store_true", help="Don't rebuild RAG index" + ) + + # Pipeline Control + parser.add_argument( + "--skip-optimization", action="store_true", help="Skip GPU optimization step" + ) + parser.add_argument( + "--skip-pruning", action="store_true", help="Skip data pruning step" + ) + parser.add_argument( + "--skip-training", action="store_true", help="Skip training step" + ) + parser.add_argument( + "--skip-evaluation", action="store_true", help="Skip evaluation step" + ) + parser.add_argument("--skip-rag", action="store_true", help="Skip RAG setup step") + parser.add_argument( + "--continue-on-error", + action="store_true", + help="Continue pipeline even if a step fails", + ) + + args = parser.parse_args() + + # Build config + config = { + # I/O + "input_dataset": args.input_dataset, + "test_dataset": args.test_dataset, + "training_config": args.training_config, + "model_output": args.model_output, + "pruned_dataset": args.pruned_dataset, + # GPU + "model_size_gb": args.model_size_gb, + "memory_usage": args.memory_usage, + "update_config": not args.no_update_config, + # Pruning + "similarity_threshold": args.similarity_threshold, + "quality_threshold": args.quality_threshold, + "no_embeddings": args.no_embeddings, + # Training + "max_train_samples": args.max_train_samples, + "no_stream": args.no_stream, + "dry_run": args.dry_run, + # Evaluation + "eval_samples": args.eval_samples, + "eval_metrics": args.eval_metrics, + "experiment_name": args.experiment_name, + # RAG + "rag_docs_path": args.rag_docs, + "rag_index_path": args.rag_index_path, + "rebuild_rag_index": not args.no_rebuild_rag_index, + # Control + "skip_gpu_optimization": args.skip_optimization, + "skip_data_pruning": args.skip_pruning, + "skip_model_training": args.skip_training, + "skip_model_evaluation": args.skip_evaluation, + "skip_rag_setup": args.skip_rag, + "continue_on_error": args.continue_on_error, + } + + # Run pipeline + orchestrator = PipelineOrchestrator(config) + success = orchestrator.run_full_pipeline() + + sys.exit(0 if success else 1) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/semantic_pruning.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/semantic_pruning.py index 15b5b3770..ebbb3b554 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/semantic_pruning.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/semantic_pruning.py @@ -1,372 +1,372 @@ -""" -Semantic Pruning for Training Data -Removes redundant and low-quality samples to improve training efficiency -""" - -import json -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Dict, List, Tuple - -import numpy as np - - -@dataclass -class PruningConfig: - """Semantic pruning configuration""" - - similarity_threshold: float = 0.95 # Remove samples above this similarity - min_length: int = 10 # Minimum token length - max_length: int = 2048 # Maximum token length - diversity_clusters: int = 50 # Number of clusters for diversity - quality_threshold: float = 0.3 # Minimum quality score - target_reduction: float = 0.3 # Target % of data to remove - - -@dataclass -class PruningStatistics: - """Statistics from pruning operation""" - - original_count: int - final_count: int - removed_duplicates: int - removed_low_quality: int - removed_outliers: int - removed_redundant: int - reduction_percentage: float - - def print_summary(self): - """Print pruning summary""" - print("\n=== Semantic Pruning Results ===") - print(f"Original samples: {self.original_count:,}") - print(f"Final samples: {self.final_count:,}") - print( - f"Total removed: {self.original_count - self.final_count:,} ({self.reduction_percentage:.1f}%)" - ) - print("\nBreakdown:") - print(f" - Duplicates: {self.removed_duplicates:,}") - print(f" - Low quality: {self.removed_low_quality:,}") - print(f" - Outliers: {self.removed_outliers:,}") - print(f" - Redundant: {self.removed_redundant:,}") - - -class SemanticPruner: - """Semantic data pruning system""" - - def __init__(self, config: PruningConfig = None): - self.config = config or PruningConfig() - self.embedding_model = None - - def prune_dataset( - self, input_path: str, output_path: str, use_embeddings: bool = True - ) -> PruningStatistics: - """ - Prune dataset using multiple strategies - - Args: - input_path: Path to input dataset (JSONL) - output_path: Path to save pruned dataset - use_embeddings: Whether to use semantic embeddings - - Returns: - PruningStatistics object - """ - print(f"Loading dataset from {input_path}...") - samples = self._load_dataset(input_path) - original_count = len(samples) - - stats = PruningStatistics( - original_count=original_count, - final_count=0, - removed_duplicates=0, - removed_low_quality=0, - removed_outliers=0, - removed_redundant=0, - reduction_percentage=0.0, - ) - - # Step 1: Remove exact duplicates - print("\n[1/5] Removing exact duplicates...") - samples, removed = self._remove_duplicates(samples) - stats.removed_duplicates = removed - print(f" Removed {removed:,} duplicates") - - # Step 2: Filter by length - print("\n[2/5] Filtering by length...") - samples, removed = self._filter_by_length(samples) - stats.removed_outliers += removed - print(f" Removed {removed:,} outliers") - - # Step 3: Quality filtering - print("\n[3/5] Filtering low quality samples...") - samples, removed = self._filter_by_quality(samples) - stats.removed_low_quality = removed - print(f" Removed {removed:,} low quality samples") - - # Step 4: Semantic deduplication (if embeddings available) - if use_embeddings: - print("\n[4/5] Semantic deduplication...") - try: - samples, removed = self._semantic_deduplication(samples) - stats.removed_redundant = removed - print(f" Removed {removed:,} redundant samples") - except Exception as e: - print(f" ⚠ Skipping semantic deduplication: {e}") - - # Step 5: Diversity sampling - print("\n[5/5] Ensuring diversity...") - samples = self._ensure_diversity(samples) - - stats.final_count = len(samples) - stats.reduction_percentage = ( - (original_count - stats.final_count) / original_count * 100 - ) - - # Save pruned dataset - self._save_dataset(samples, output_path) - print(f"\n✓ Pruned dataset saved to {output_path}") - - return stats - - def _load_dataset(self, path: str) -> List[Dict[str, Any]]: - """Load dataset from JSONL file""" - samples = [] - with open(path, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if line: - samples.append(json.loads(line)) - return samples - - def _save_dataset(self, samples: List[Dict[str, Any]], path: str): - """Save dataset to JSONL file""" - output_path = Path(path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, "w", encoding="utf-8") as f: - for sample in samples: - f.write(json.dumps(sample, ensure_ascii=False) + "\n") - - def _remove_duplicates( - self, samples: List[Dict[str, Any]] - ) -> Tuple[List[Dict[str, Any]], int]: - """Remove exact duplicate samples""" - seen = set() - unique_samples = [] - removed = 0 - - for sample in samples: - # Create hash of content - content_hash = self._hash_sample(sample) - - if content_hash not in seen: - seen.add(content_hash) - unique_samples.append(sample) - else: - removed += 1 - - return unique_samples, removed - - def _hash_sample(self, sample: Dict[str, Any]) -> str: - """Create hash of sample content""" - if "messages" in sample: - text = json.dumps(sample["messages"], sort_keys=True) - elif "text" in sample: - text = sample["text"] - else: - text = json.dumps(sample, sort_keys=True) - - return str(hash(text)) - - def _filter_by_length( - self, samples: List[Dict[str, Any]] - ) -> Tuple[List[Dict[str, Any]], int]: - """Filter samples by length""" - filtered = [] - removed = 0 - - for sample in samples: - text = self._extract_text(sample) - word_count = len(text.split()) - - if self.config.min_length <= word_count <= self.config.max_length: - filtered.append(sample) - else: - removed += 1 - - return filtered, removed - - def _filter_by_quality( - self, samples: List[Dict[str, Any]] - ) -> Tuple[List[Dict[str, Any]], int]: - """Filter low quality samples""" - filtered = [] - removed = 0 - - for sample in samples: - quality_score = self._compute_quality_score(sample) - - if quality_score >= self.config.quality_threshold: - sample["_quality_score"] = quality_score - filtered.append(sample) - else: - removed += 1 - - return filtered, removed - - def _compute_quality_score(self, sample: Dict[str, Any]) -> float: - """Compute quality score for sample""" - text = self._extract_text(sample) - - scores = [] - - # Length score (prefer moderate length) - word_count = len(text.split()) - length_score = min(word_count / 100, 1.0) # Normalize to 100 words - scores.append(length_score) - - # Character diversity - unique_chars = len(set(text.lower())) - char_diversity = min(unique_chars / 26, 1.0) - scores.append(char_diversity) - - # Word diversity - words = text.lower().split() - if words: - unique_words = len(set(words)) - word_diversity = unique_words / len(words) - scores.append(word_diversity) - - # Punctuation presence (indicates structure) - punct_count = sum(1 for c in text if c in ".,!?;:") - punct_score = min(punct_count / 10, 1.0) - scores.append(punct_score) - - return np.mean(scores) if scores else 0.0 - - def _semantic_deduplication( - self, samples: List[Dict[str, Any]] - ) -> Tuple[List[Dict[str, Any]], int]: - """Remove semantically similar samples""" - try: - from sentence_transformers import SentenceTransformer - - if self.embedding_model is None: - print(" Loading embedding model...") - self.embedding_model = SentenceTransformer("all-MiniLM-L6-v2") - - # Extract texts - texts = [self._extract_text(s) for s in samples] - - # Compute embeddings - print(" Computing embeddings...") - embeddings = self.embedding_model.encode( - texts, show_progress_bar=True, convert_to_numpy=True - ) - - # Compute pairwise similarities - print(" Computing similarities...") - keep_indices = set(range(len(samples))) - removed = 0 - - for i in range(len(embeddings)): - if i not in keep_indices: - continue - - for j in range(i + 1, len(embeddings)): - if j not in keep_indices: - continue - - # Compute cosine similarity - similarity = np.dot(embeddings[i], embeddings[j]) / ( - np.linalg.norm(embeddings[i]) * np.linalg.norm(embeddings[j]) - ) - - if similarity >= self.config.similarity_threshold: - # Keep the higher quality sample - quality_i = samples[i].get("_quality_score", 0.5) - quality_j = samples[j].get("_quality_score", 0.5) - - if quality_i >= quality_j: - keep_indices.discard(j) - removed += 1 - else: - keep_indices.discard(i) - removed += 1 - break - - filtered = [samples[i] for i in sorted(keep_indices)] - return filtered, removed - - except ImportError: - print(" ⚠ sentence-transformers not installed") - return samples, 0 - - def _ensure_diversity(self, samples: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """Ensure diversity in final dataset""" - # Sort by quality and keep top samples - samples_with_quality = [(s, s.get("_quality_score", 0.5)) for s in samples] - samples_with_quality.sort(key=lambda x: x[1], reverse=True) - - # Remove quality score from samples - clean_samples = [] - for sample, _ in samples_with_quality: - if "_quality_score" in sample: - del sample["_quality_score"] - clean_samples.append(sample) - - return clean_samples - - def _extract_text(self, sample: Dict[str, Any]) -> str: - """Extract text from sample""" - if "messages" in sample: - return " ".join([m.get("content", "") for m in sample["messages"]]) - elif "text" in sample: - return sample["text"] - elif "instruction" in sample: - return f"{sample['instruction']} {sample.get('response', '')}" - else: - return json.dumps(sample) - - -def main(): - """CLI for semantic pruning""" - import argparse - - parser = argparse.ArgumentParser(description="Semantic Data Pruning") - parser.add_argument( - "--input", type=str, required=True, help="Input dataset (JSONL)" - ) - parser.add_argument( - "--output", type=str, required=True, help="Output dataset (JSONL)" - ) - parser.add_argument( - "--similarity-threshold", - type=float, - default=0.95, - help="Similarity threshold for deduplication", - ) - parser.add_argument( - "--quality-threshold", type=float, default=0.3, help="Minimum quality threshold" - ) - parser.add_argument( - "--no-embeddings", action="store_true", help="Skip semantic deduplication" - ) - - args = parser.parse_args() - - config = PruningConfig( - similarity_threshold=args.similarity_threshold, - quality_threshold=args.quality_threshold, - ) - - pruner = SemanticPruner(config) - stats = pruner.prune_dataset( - args.input, args.output, use_embeddings=not args.no_embeddings - ) - - stats.print_summary() - - -if __name__ == "__main__": - main() +""" +Semantic Pruning for Training Data +Removes redundant and low-quality samples to improve training efficiency +""" + +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, List, Tuple + +import numpy as np + + +@dataclass +class PruningConfig: + """Semantic pruning configuration""" + + similarity_threshold: float = 0.95 # Remove samples above this similarity + min_length: int = 10 # Minimum token length + max_length: int = 2048 # Maximum token length + diversity_clusters: int = 50 # Number of clusters for diversity + quality_threshold: float = 0.3 # Minimum quality score + target_reduction: float = 0.3 # Target % of data to remove + + +@dataclass +class PruningStatistics: + """Statistics from pruning operation""" + + original_count: int + final_count: int + removed_duplicates: int + removed_low_quality: int + removed_outliers: int + removed_redundant: int + reduction_percentage: float + + def print_summary(self): + """Print pruning summary""" + print("\n=== Semantic Pruning Results ===") + print(f"Original samples: {self.original_count:,}") + print(f"Final samples: {self.final_count:,}") + print( + f"Total removed: {self.original_count - self.final_count:,} ({self.reduction_percentage:.1f}%)" + ) + print("\nBreakdown:") + print(f" - Duplicates: {self.removed_duplicates:,}") + print(f" - Low quality: {self.removed_low_quality:,}") + print(f" - Outliers: {self.removed_outliers:,}") + print(f" - Redundant: {self.removed_redundant:,}") + + +class SemanticPruner: + """Semantic data pruning system""" + + def __init__(self, config: PruningConfig = None): + self.config = config or PruningConfig() + self.embedding_model = None + + def prune_dataset( + self, input_path: str, output_path: str, use_embeddings: bool = True + ) -> PruningStatistics: + """ + Prune dataset using multiple strategies + + Args: + input_path: Path to input dataset (JSONL) + output_path: Path to save pruned dataset + use_embeddings: Whether to use semantic embeddings + + Returns: + PruningStatistics object + """ + print(f"Loading dataset from {input_path}...") + samples = self._load_dataset(input_path) + original_count = len(samples) + + stats = PruningStatistics( + original_count=original_count, + final_count=0, + removed_duplicates=0, + removed_low_quality=0, + removed_outliers=0, + removed_redundant=0, + reduction_percentage=0.0, + ) + + # Step 1: Remove exact duplicates + print("\n[1/5] Removing exact duplicates...") + samples, removed = self._remove_duplicates(samples) + stats.removed_duplicates = removed + print(f" Removed {removed:,} duplicates") + + # Step 2: Filter by length + print("\n[2/5] Filtering by length...") + samples, removed = self._filter_by_length(samples) + stats.removed_outliers += removed + print(f" Removed {removed:,} outliers") + + # Step 3: Quality filtering + print("\n[3/5] Filtering low quality samples...") + samples, removed = self._filter_by_quality(samples) + stats.removed_low_quality = removed + print(f" Removed {removed:,} low quality samples") + + # Step 4: Semantic deduplication (if embeddings available) + if use_embeddings: + print("\n[4/5] Semantic deduplication...") + try: + samples, removed = self._semantic_deduplication(samples) + stats.removed_redundant = removed + print(f" Removed {removed:,} redundant samples") + except Exception as e: + print(f" ⚠ Skipping semantic deduplication: {e}") + + # Step 5: Diversity sampling + print("\n[5/5] Ensuring diversity...") + samples = self._ensure_diversity(samples) + + stats.final_count = len(samples) + stats.reduction_percentage = ( + (original_count - stats.final_count) / original_count * 100 + ) + + # Save pruned dataset + self._save_dataset(samples, output_path) + print(f"\n✓ Pruned dataset saved to {output_path}") + + return stats + + def _load_dataset(self, path: str) -> List[Dict[str, Any]]: + """Load dataset from JSONL file""" + samples = [] + with open(path, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line: + samples.append(json.loads(line)) + return samples + + def _save_dataset(self, samples: List[Dict[str, Any]], path: str): + """Save dataset to JSONL file""" + output_path = Path(path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, "w", encoding="utf-8") as f: + for sample in samples: + f.write(json.dumps(sample, ensure_ascii=False) + "\n") + + def _remove_duplicates( + self, samples: List[Dict[str, Any]] + ) -> Tuple[List[Dict[str, Any]], int]: + """Remove exact duplicate samples""" + seen = set() + unique_samples = [] + removed = 0 + + for sample in samples: + # Create hash of content + content_hash = self._hash_sample(sample) + + if content_hash not in seen: + seen.add(content_hash) + unique_samples.append(sample) + else: + removed += 1 + + return unique_samples, removed + + def _hash_sample(self, sample: Dict[str, Any]) -> str: + """Create hash of sample content""" + if "messages" in sample: + text = json.dumps(sample["messages"], sort_keys=True) + elif "text" in sample: + text = sample["text"] + else: + text = json.dumps(sample, sort_keys=True) + + return str(hash(text)) + + def _filter_by_length( + self, samples: List[Dict[str, Any]] + ) -> Tuple[List[Dict[str, Any]], int]: + """Filter samples by length""" + filtered = [] + removed = 0 + + for sample in samples: + text = self._extract_text(sample) + word_count = len(text.split()) + + if self.config.min_length <= word_count <= self.config.max_length: + filtered.append(sample) + else: + removed += 1 + + return filtered, removed + + def _filter_by_quality( + self, samples: List[Dict[str, Any]] + ) -> Tuple[List[Dict[str, Any]], int]: + """Filter low quality samples""" + filtered = [] + removed = 0 + + for sample in samples: + quality_score = self._compute_quality_score(sample) + + if quality_score >= self.config.quality_threshold: + sample["_quality_score"] = quality_score + filtered.append(sample) + else: + removed += 1 + + return filtered, removed + + def _compute_quality_score(self, sample: Dict[str, Any]) -> float: + """Compute quality score for sample""" + text = self._extract_text(sample) + + scores = [] + + # Length score (prefer moderate length) + word_count = len(text.split()) + length_score = min(word_count / 100, 1.0) # Normalize to 100 words + scores.append(length_score) + + # Character diversity + unique_chars = len(set(text.lower())) + char_diversity = min(unique_chars / 26, 1.0) + scores.append(char_diversity) + + # Word diversity + words = text.lower().split() + if words: + unique_words = len(set(words)) + word_diversity = unique_words / len(words) + scores.append(word_diversity) + + # Punctuation presence (indicates structure) + punct_count = sum(1 for c in text if c in ".,!?;:") + punct_score = min(punct_count / 10, 1.0) + scores.append(punct_score) + + return np.mean(scores) if scores else 0.0 + + def _semantic_deduplication( + self, samples: List[Dict[str, Any]] + ) -> Tuple[List[Dict[str, Any]], int]: + """Remove semantically similar samples""" + try: + from sentence_transformers import SentenceTransformer + + if self.embedding_model is None: + print(" Loading embedding model...") + self.embedding_model = SentenceTransformer("all-MiniLM-L6-v2") + + # Extract texts + texts = [self._extract_text(s) for s in samples] + + # Compute embeddings + print(" Computing embeddings...") + embeddings = self.embedding_model.encode( + texts, show_progress_bar=True, convert_to_numpy=True + ) + + # Compute pairwise similarities + print(" Computing similarities...") + keep_indices = set(range(len(samples))) + removed = 0 + + for i in range(len(embeddings)): + if i not in keep_indices: + continue + + for j in range(i + 1, len(embeddings)): + if j not in keep_indices: + continue + + # Compute cosine similarity + similarity = np.dot(embeddings[i], embeddings[j]) / ( + np.linalg.norm(embeddings[i]) * np.linalg.norm(embeddings[j]) + ) + + if similarity >= self.config.similarity_threshold: + # Keep the higher quality sample + quality_i = samples[i].get("_quality_score", 0.5) + quality_j = samples[j].get("_quality_score", 0.5) + + if quality_i >= quality_j: + keep_indices.discard(j) + removed += 1 + else: + keep_indices.discard(i) + removed += 1 + break + + filtered = [samples[i] for i in sorted(keep_indices)] + return filtered, removed + + except ImportError: + print(" ⚠ sentence-transformers not installed") + return samples, 0 + + def _ensure_diversity(self, samples: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Ensure diversity in final dataset""" + # Sort by quality and keep top samples + samples_with_quality = [(s, s.get("_quality_score", 0.5)) for s in samples] + samples_with_quality.sort(key=lambda x: x[1], reverse=True) + + # Remove quality score from samples + clean_samples = [] + for sample, _ in samples_with_quality: + if "_quality_score" in sample: + del sample["_quality_score"] + clean_samples.append(sample) + + return clean_samples + + def _extract_text(self, sample: Dict[str, Any]) -> str: + """Extract text from sample""" + if "messages" in sample: + return " ".join([m.get("content", "") for m in sample["messages"]]) + elif "text" in sample: + return sample["text"] + elif "instruction" in sample: + return f"{sample['instruction']} {sample.get('response', '')}" + else: + return json.dumps(sample) + + +def main(): + """CLI for semantic pruning""" + import argparse + + parser = argparse.ArgumentParser(description="Semantic Data Pruning") + parser.add_argument( + "--input", type=str, required=True, help="Input dataset (JSONL)" + ) + parser.add_argument( + "--output", type=str, required=True, help="Output dataset (JSONL)" + ) + parser.add_argument( + "--similarity-threshold", + type=float, + default=0.95, + help="Similarity threshold for deduplication", + ) + parser.add_argument( + "--quality-threshold", type=float, default=0.3, help="Minimum quality threshold" + ) + parser.add_argument( + "--no-embeddings", action="store_true", help="Skip semantic deduplication" + ) + + args = parser.parse_args() + + config = PruningConfig( + similarity_threshold=args.similarity_threshold, + quality_threshold=args.quality_threshold, + ) + + pruner = SemanticPruner(config) + stats = pruner.prune_dataset( + args.input, args.output, use_embeddings=not args.no_embeddings + ) + + stats.print_summary() + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/setup_model_env.sh b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/setup_model_env.sh index 641940a61..494c27210 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/setup_model_env.sh +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/setup_model_env.sh @@ -1,150 +1,150 @@ -#!/usr/bin/env bash -set -euo pipefail - -# Setup script for the model-specific venv and ML dependencies. -# -# This script creates a venv at AI/microsoft_phi-silica-3.6_v1/venv (by default) -# and installs the light-weight requirements from requirements.txt plus -# an appropriate torch wheel for your platform (CUDA / CPU). -# -# Usage examples: -# # CPU-only installation (Linux): -# bash scripts/setup_model_env.sh --cpu -# -# # CUDA 12.1 installation (choose the CUDA wheel that matches your system): -# bash scripts/setup_model_env.sh --cuda cu121 -# -# # Use a specific python executable to create the venv: -# bash scripts/setup_model_env.sh --python /usr/bin/python3.10 - -ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" -VENV_DIR="${ROOT_DIR}/venv" -REQUIREMENTS_FILE="${ROOT_DIR}/requirements.txt" - -PYTHON_EXE="$(which python3 || which python)" -FORCE=false -CUDA_WHEEL="" -CPU_ONLY=false - -print_help(){ - cat <] [--python ] [--force] - -This creates a virtualenv under ${VENV_DIR} and installs model training -dependencies listed in ${REQUIREMENTS_FILE}. You MUST explicitly choose the -torch wheel that matches your machine (CPU or CUDA). The script will not -guess randomly — choose --cpu for CPU-only or --cuda cu121 for CUDA. - -Examples: - $0 --cpu - $0 --cuda cu121 - $0 --python /usr/bin/python3.10 # use a particular python binary -EOF -} - -while (( "$#" )); do - case "$1" in - --python) - PYTHON_EXE="$2" - shift 2 - ;; - --cpu) - CPU_ONLY=true - shift - ;; - --cuda) - CUDA_WHEEL="$2" - shift 2 - ;; - --force) - FORCE=true - shift - ;; - -h|--help) - print_help - exit 0 - ;; - *) - echo "Unknown argument: $1" - print_help - exit 1 - ;; - esac -done - -echo "Using python: ${PYTHON_EXE}" - -if [ -d "${VENV_DIR}" ] && [ "${FORCE}" != "true" ]; then - echo "Virtualenv already exists at ${VENV_DIR}. Use --force to recreate." -else - echo "Creating venv at ${VENV_DIR}..." - ${PYTHON_EXE} -m venv "${VENV_DIR}" -fi - -# Resolve platform-specific python in venv -VENV_PYTHON="${VENV_DIR}/bin/python" -if [ ! -x "${VENV_PYTHON}" ] && [ -x "${VENV_DIR}/Scripts/python.exe" ]; then - VENV_PYTHON="${VENV_DIR}/Scripts/python.exe" -fi - -if [ ! -x "${VENV_PYTHON}" ]; then - echo "ERROR: Python not found in created venv: ${VENV_PYTHON}" >&2 - exit 2 -fi - -echo "Upgrading pip/setuptools/wheel in venv..." -"${VENV_PYTHON}" -m pip install --upgrade pip setuptools wheel - -echo "Installing requirements (excluding torch - installed separately)..." -# requirements.txt intentionally does NOT pin torch so we install it explicitly -TMP_REQS=$(mktemp) -grep -v "^#" "${REQUIREMENTS_FILE}" | grep -v "^\s*$" > "${TMP_REQS}" - -# Remove any torch line (best-effort) -sed -i '/^torch/Id' "${TMP_REQS}" - -"${VENV_PYTHON}" -m pip install -r "${TMP_REQS}" -rm -f "${TMP_REQS}" - -if [ "${CPU_ONLY}" = true ]; then - echo "Installing CPU-only torch wheel (PyTorch)" - "${VENV_PYTHON}" -m pip install --index-url https://download.pytorch.org/whl/cpu torch -elif [ -n "${CUDA_WHEEL}" ]; then - echo "Installing CUDA wheel for ${CUDA_WHEEL} (PyTorch)" - # Use the appropriate stable wheel link; the -f/--find-links ensures the extra index is used - # Example: --cuda cu121 -> https://download.pytorch.org/whl/cu121 - "${VENV_PYTHON}" -m pip install torch --index-url https://download.pytorch.org/whl/${CUDA_WHEEL} -else - echo "No torch wheel selection made. Please re-run with either --cpu or --cuda " - echo "Example: ${0} --cuda cu121 # for Linux with CUDA 12.1" >&2 - exit 3 -fi - -echo "Installing optional utility packages recommended for training..." -# Pin huggingface_hub to a version compatible with common transformers/peft -# to avoid accidental upgrades that break imports (transformers<5 expects hf-hub <1.0) -"${VENV_PYTHON}" -m pip install safetensors "huggingface_hub>=0.34.0,<1.0" accelerate --upgrade - -# Run a quick pip-check to catch obvious dependency conflicts early and report -echo "Running pip check to detect dependency problems (non-fatal)" -"${VENV_PYTHON}" -m pip check || true - -echo "Smoke test: verifying minimal imports and GPU availability (if any)" -"${VENV_PYTHON}" - <] [--python ] [--force] + +This creates a virtualenv under ${VENV_DIR} and installs model training +dependencies listed in ${REQUIREMENTS_FILE}. You MUST explicitly choose the +torch wheel that matches your machine (CPU or CUDA). The script will not +guess randomly — choose --cpu for CPU-only or --cuda cu121 for CUDA. + +Examples: + $0 --cpu + $0 --cuda cu121 + $0 --python /usr/bin/python3.10 # use a particular python binary +EOF +} + +while (( "$#" )); do + case "$1" in + --python) + PYTHON_EXE="$2" + shift 2 + ;; + --cpu) + CPU_ONLY=true + shift + ;; + --cuda) + CUDA_WHEEL="$2" + shift 2 + ;; + --force) + FORCE=true + shift + ;; + -h|--help) + print_help + exit 0 + ;; + *) + echo "Unknown argument: $1" + print_help + exit 1 + ;; + esac +done + +echo "Using python: ${PYTHON_EXE}" + +if [ -d "${VENV_DIR}" ] && [ "${FORCE}" != "true" ]; then + echo "Virtualenv already exists at ${VENV_DIR}. Use --force to recreate." +else + echo "Creating venv at ${VENV_DIR}..." + ${PYTHON_EXE} -m venv "${VENV_DIR}" +fi + +# Resolve platform-specific python in venv +VENV_PYTHON="${VENV_DIR}/bin/python" +if [ ! -x "${VENV_PYTHON}" ] && [ -x "${VENV_DIR}/Scripts/python.exe" ]; then + VENV_PYTHON="${VENV_DIR}/Scripts/python.exe" +fi + +if [ ! -x "${VENV_PYTHON}" ]; then + echo "ERROR: Python not found in created venv: ${VENV_PYTHON}" >&2 + exit 2 +fi + +echo "Upgrading pip/setuptools/wheel in venv..." +"${VENV_PYTHON}" -m pip install --upgrade pip setuptools wheel + +echo "Installing requirements (excluding torch - installed separately)..." +# requirements.txt intentionally does NOT pin torch so we install it explicitly +TMP_REQS=$(mktemp) +grep -v "^#" "${REQUIREMENTS_FILE}" | grep -v "^\s*$" > "${TMP_REQS}" + +# Remove any torch line (best-effort) +sed -i '/^torch/Id' "${TMP_REQS}" + +"${VENV_PYTHON}" -m pip install -r "${TMP_REQS}" +rm -f "${TMP_REQS}" + +if [ "${CPU_ONLY}" = true ]; then + echo "Installing CPU-only torch wheel (PyTorch)" + "${VENV_PYTHON}" -m pip install --index-url https://download.pytorch.org/whl/cpu torch +elif [ -n "${CUDA_WHEEL}" ]; then + echo "Installing CUDA wheel for ${CUDA_WHEEL} (PyTorch)" + # Use the appropriate stable wheel link; the -f/--find-links ensures the extra index is used + # Example: --cuda cu121 -> https://download.pytorch.org/whl/cu121 + "${VENV_PYTHON}" -m pip install torch --index-url https://download.pytorch.org/whl/${CUDA_WHEEL} +else + echo "No torch wheel selection made. Please re-run with either --cpu or --cuda " + echo "Example: ${0} --cuda cu121 # for Linux with CUDA 12.1" >&2 + exit 3 +fi + +echo "Installing optional utility packages recommended for training..." +# Pin huggingface_hub to a version compatible with common transformers/peft +# to avoid accidental upgrades that break imports (transformers<5 expects hf-hub <1.0) +"${VENV_PYTHON}" -m pip install safetensors "huggingface_hub>=0.34.0,<1.0" accelerate --upgrade + +# Run a quick pip-check to catch obvious dependency conflicts early and report +echo "Running pip check to detect dependency problems (non-fatal)" +"${VENV_PYTHON}" -m pip check || true + +echo "Smoke test: verifying minimal imports and GPU availability (if any)" +"${VENV_PYTHON}" - < Dict[str, Any]: - with yaml_path.open("r", encoding="utf-8") as f: - return yaml.safe_load(f) - - -def resolve_path(p: str) -> Path: - # allow tokens like mount//dataset to be overridden by --dataset - return Path(p).expanduser() - - -def iter_jsonl(path: Path) -> Iterable[Dict[str, Any]]: - with path.open("r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - yield json.loads(line) - - -def count_records(path: Path) -> int: - n = 0 - for _ in iter_jsonl(path): - n += 1 - return n - - -def validate_sample(path: Path) -> Dict[str, Any]: - for obj in iter_jsonl(path): - msgs = obj.get("messages") - if isinstance(msgs, list) and len(msgs) >= 2: - return obj - raise RuntimeError(f"No valid chat records found in {path}") - - -def build_text_from_messages(messages: List[Dict[str, str]]) -> str: - """Convert messages to training text with improved formatting and end tokens.""" - parts: List[str] = [] - for m in messages: - role = m.get("role", "").lower() - content = m.get("content", "").strip() - if not content: # Skip empty messages - continue - if role == "system": - parts.append(f"<|system|>\n{content}<|end|>\n") - elif role == "user": - parts.append(f"<|user|>\n{content}<|end|>\n") - elif role == "assistant": - parts.append(f"<|assistant|>\n{content}<|end|>\n") - return "".join(parts) - - -def make_hf_dataset_from_files( - train_files: List[str], eval_files: List[str], streaming: bool = True -): - if load_dataset is None: - raise RuntimeError( - "HuggingFace datasets not available. Install 'datasets' to load datasets." - ) - data_files = {"train": train_files, "validation": eval_files} - ds = load_dataset("json", data_files=data_files, streaming=streaming) - return ds - - -def _read_text_source(path_or_url: str) -> Iterable[str]: - if path_or_url.startswith("http://") or path_or_url.startswith("https://"): - import urllib.request - - with urllib.request.urlopen(path_or_url) as resp: # nosec B310 - for line in resp.read().decode("utf-8").splitlines(): - yield line - else: - p = Path(path_or_url) - with p.open("r", encoding="utf-8") as f: - for line in f: - yield line.rstrip("\n") - - -def parse_manifest(path_or_url: str) -> List[str]: - urls: List[str] = [] - lower = path_or_url.lower() - if lower.endswith(".json"): - import json as _json - - if path_or_url.startswith("http://") or path_or_url.startswith("https://"): - import urllib.request - - with urllib.request.urlopen(path_or_url) as resp: # nosec B310 - obj = _json.loads(resp.read().decode("utf-8")) - else: - with Path(path_or_url).open("r", encoding="utf-8") as f: - obj = _json.load(f) - if isinstance(obj, dict): - for key in ("train", "validation", "urls", "files"): - v = obj.get(key) - if isinstance(v, list): - urls.extend([str(x) for x in v]) - if not urls and "url" in obj: - urls.append(str(obj["url"])) - elif isinstance(obj, list): - urls.extend([str(x) for x in obj]) - elif lower.endswith(".jsonl"): - import json as _json - - for line in _read_text_source(path_or_url): - if not line.strip(): - continue - try: - rec = _json.loads(line) - if isinstance(rec, str): - urls.append(rec) - elif isinstance(rec, dict) and "url" in rec: - urls.append(str(rec["url"])) - except Exception: - urls.append(line.strip()) - else: - for line in _read_text_source(path_or_url): - if line.strip(): - urls.append(line.strip()) - # Dedupe - seen = set() - uniq: List[str] = [] - for u in urls: - if u not in seen: - seen.add(u) - uniq.append(u) - return uniq - - -def main(): - ap = argparse.ArgumentParser( - description="Train LoRA on chat dataset using lora.yaml config" - ) - ap.add_argument( - "--config", - default=str(Path(__file__).resolve().parents[1] / "lora" / "lora.yaml"), - ) - ap.add_argument( - "--dataset", default=str(Path(__file__).resolve().parents[1] / "data") - ) - ap.add_argument( - "--dry-run", - action="store_true", - help="Validate dataset/config only; no model download", - ) - ap.add_argument( - "--max-train-samples", - type=int, - default=None, - help="Limit train examples (for smoke test)", - ) - ap.add_argument( - "--max-eval-samples", - type=int, - default=None, - help="Limit eval examples (for smoke test)", - ) - ap.add_argument( - "--hf-model-id", - default=None, - help="Override HF model id (e.g., microsoft/Phi-3.5-mini-instruct)", - ) - ap.add_argument( - "--no-stream", action="store_true", help="Disable streaming mode for datasets" - ) - ap.add_argument( - "--deepspeed", - default=None, - help="Path to DeepSpeed config JSON to enable ZeRO (multi-GPU)", - ) - ap.add_argument( - "--train-manifest", - default=None, - help="Path or URL to manifest of training files (txt/json/jsonl)", - ) - ap.add_argument( - "--eval-manifest", - default=None, - help="Path or URL to manifest of eval files (txt/json/jsonl)", - ) - ap.add_argument( - "--save-dir", - default=None, - help="Override output directory (else from config or defaults)", - ) - ap.add_argument( - "--device", - default="auto", - choices=["auto", "cuda", "cpu", "directml", "mps"], - help="Device preference: auto selects best available (cuda>mps>directml>cpu)", - ) - # Optional overrides for HPO/cloud runs - ap.add_argument( - "--learning-rate", - type=float, - default=None, - help="Override learning_rate from config", - ) - ap.add_argument( - "--lora-dropout", - type=float, - default=None, - help="Override lora_dropout from config", - ) - ap.add_argument( - "--epochs", type=int, default=None, help="Override epochs from config" - ) - ap.add_argument( - "--train-batch-size", - type=int, - default=None, - help="Override finetune_train_batch_size from config", - ) - ap.add_argument( - "--eval-batch-size", - type=int, - default=None, - help="Override finetune_test_batch_size from config", - ) - ap.add_argument("--seed", type=int, default=None, help="Override seed from config") - args = ap.parse_args() - - # Initialize tracing (best-effort). This allows the optional - # OpenTelemetryTrainerCallback to get an active tracer if available. - # Optional tracing import (ignore if missing) - try: - from shared.tracing import init_tracing # type: ignore - - init_tracing(service_name="train_lora") - except Exception as _e: - print(f"[tracing] init skipped in train_lora: {_e}") - - cfg_raw = read_yaml(Path(args.config)) - cfg = Config( - model=cfg_raw.get("model") or "Phi-3.6-mini-instruct", - finetune_dataset=cfg_raw.get("finetune_dataset") or str(Path(args.dataset)), - save_dir=( - args.save_dir - or cfg_raw.get("save_dir") - or str(Path(__file__).resolve().parents[1] / "outputs") - ), - finetune_train_nsamples=cfg_raw.get("finetune_train_nsamples"), - finetune_test_nsamples=cfg_raw.get("finetune_test_nsamples"), - finetune_train_batch_size=int(cfg_raw.get("finetune_train_batch_size") or 2), - finetune_test_batch_size=int(cfg_raw.get("finetune_test_batch_size") or 2), - finetune_train_seqlen=int(cfg_raw.get("finetune_train_seqlen") or 1024), - finetune_test_seqlen=int(cfg_raw.get("finetune_test_seqlen") or 2048), - learning_rate=float(cfg_raw.get("learning_rate") or 2e-4), - lora_dropout=float(cfg_raw.get("lora_dropout") or 0.1), - epochs=int(cfg_raw.get("epochs") or 1), - eval_steps=int(cfg_raw.get("eval_steps", 64)), - save_steps=int(cfg_raw.get("save_steps") or 64), - gradient_checkpointing=bool(cfg_raw.get("gradient_checkpointing") or False), - seed=int(cfg_raw.get("seed") or 42), - warmup_steps=int(cfg_raw.get("warmup_steps") or 100), - gradient_accumulation_steps=int( - cfg_raw.get("gradient_accumulation_steps") or 4 - ), - max_grad_norm=float(cfg_raw.get("max_grad_norm") or 1.0), - early_stopping_patience=int(cfg_raw.get("early_stopping_patience") or 3), - early_stopping_threshold=float(cfg_raw.get("early_stopping_threshold") or 0.01), - ) - - # Apply CLI overrides for HPO or cloud jobs - if getattr(args, "learning_rate", None) is not None: - cfg.learning_rate = float(args.learning_rate) - if getattr(args, "lora_dropout", None) is not None: - cfg.lora_dropout = float(args.lora_dropout) - if getattr(args, "epochs", None) is not None: - cfg.epochs = int(args.epochs) - if getattr(args, "train_batch_size", None) is not None: - cfg.finetune_train_batch_size = int(args.train_batch_size) - if getattr(args, "eval_batch_size", None) is not None: - cfg.finetune_test_batch_size = int(args.eval_batch_size) - if getattr(args, "seed", None) is not None: - cfg.seed = int(args.seed) - - # Resolve data sources: manifests or local files - train_files: List[str] = [] - eval_files: List[str] = [] - # CLI overrides take precedence - train_manifest = getattr(args, "train_manifest", None) - eval_manifest = getattr(args, "eval_manifest", None) - if train_manifest or eval_manifest: - if train_manifest: - train_files = parse_manifest(train_manifest) - if eval_manifest: - eval_files = parse_manifest(eval_manifest) - if not train_files: - raise RuntimeError("No train files found from manifest") - if not eval_files: - # Fallback: use a small subset of train for eval - eval_files = train_files[:1] - else: - dataset_path = ( - Path(args.dataset) if args.dataset else resolve_path(cfg.finetune_dataset) - ) - if dataset_path.is_file(): - # Allow direct file usage (.json or .jsonl) - if dataset_path.suffix.lower() in (".json", ".jsonl"): - train_files = [str(dataset_path)] - eval_files = [str(dataset_path)] - else: - raise FileNotFoundError( - f"Unsupported dataset file type: {dataset_path}" - ) - else: - # Directory: accept train.json/test.json or train.jsonl/test.jsonl; fallback to single train file present - candidates = [ - (dataset_path / "train.json", dataset_path / "test.json"), - (dataset_path / "train.jsonl", dataset_path / "test.jsonl"), - ] - found = False - for t_path, v_path in candidates: - if t_path.exists() and v_path.exists(): - train_files = [str(t_path)] - eval_files = [str(v_path)] - found = True - break - if not found: - # Fallbacks: if any train.* exists, use it for both train/val - t_candidates = [ - dataset_path / "train.json", - dataset_path / "train.jsonl", - ] - t_use = next((p for p in t_candidates if p.exists()), None) - if t_use is None: - raise FileNotFoundError( - f"Expected dataset files at: {dataset_path}/train.json[.l] and optionally test.json[.l]" - ) - train_files = [str(t_use)] - eval_candidates = [ - dataset_path / "test.json", - dataset_path / "test.jsonl", - ] - v_use = next((p for p in eval_candidates if p.exists()), None) - eval_files = [str(v_use)] if v_use else [str(t_use)] - - # Determine and report device early (even for dry-run) - chosen_device = "cpu" - if args.device == "auto": - if ( - torch is not None - and getattr(torch, "cuda", None) - and getattr(torch.cuda, "is_available", lambda: False)() - ): - chosen_device = "cuda" - elif ( - torch is not None - and getattr(torch, "backends", None) - and getattr(torch.backends, "mps", None) - and getattr(torch.backends.mps, "is_available", lambda: False)() - ): - chosen_device = "mps" - else: - # Optional DirectML detection - try: - chosen_device = "directml" - except Exception: - chosen_device = "cpu" - else: - chosen_device = args.device - print( - f"[device] selection={args.device} resolved={chosen_device} cuda_available={(getattr(torch, 'cuda', None) and getattr(torch.cuda, 'is_available', lambda: False)() if torch else False)}" - ) - - # Dry run: count/validate records only (no model/tokenizer downloads) - if args.dry_run: - if train_manifest or eval_manifest: - print("Dry run OK.") - print( - { - "device": chosen_device, - "train_files": train_files[:5], - "eval_files": eval_files[:5], - "note": "Counting skipped for remote manifests", - } - ) - return - n_train = count_records(Path(train_files[0])) - n_test = count_records(Path(eval_files[0])) - sample = validate_sample(Path(train_files[0])) - print("Dry run OK.") - print( - { - "device": chosen_device, - "train_examples": n_train, - "test_examples": n_test, - "sample": sample, - } - ) - return - - # Real training requires heavy deps - if ( - AutoTokenizer is None - or AutoModelForCausalLM is None - or load_dataset is None - or torch is None - ): - missing = [] - if torch is None: - missing.append("torch") - if load_dataset is None: - missing.append("datasets") - if AutoTokenizer is None or AutoModelForCausalLM is None: - missing.append("transformers") - if LoraConfig is None: - missing.append("peft") - model_venv = Path(__file__).resolve().parents[1] / "venv" - root_venv = Path(__file__).resolve().parents[3] / "venv" - raise RuntimeError( - "Training dependencies not installed or import failed.\n" - f"missing={missing}\n" - f"To fix (model env): {model_venv / 'Scripts' / 'pip.exe'} install transformers datasets peft accelerate torch\n" - f"Alt (root env): {root_venv / 'Scripts' / 'pip.exe'} install transformers datasets peft accelerate torch\n" - "After installing, re-run this script." - ) - - # Resolve model id - hf_model_id = args.hf_model_id or os.environ.get("HF_MODEL_ID") - if hf_model_id is None: - # Best-effort mapping for local runs - # If the configured model isn't an HF id, default to a widely-available one - hf_model_id = { - "Phi-3.6-mini-instruct": "microsoft/Phi-3.5-mini-instruct", - }.get(cfg.model, cfg.model) - - tokenizer = AutoTokenizer.from_pretrained(hf_model_id, use_fast=True) - if tokenizer.pad_token is None: - tokenizer.pad_token = tokenizer.eos_token - - def preprocess(examples): - # Handle both single example and batched mapping - texts = [] - if isinstance(examples, dict) and "messages" in examples: - msgs = examples["messages"] - try: - print(f"[preprocess] messages type: {type(msgs)}") - if isinstance(msgs, dict): - print(f"[preprocess] messages dict keys: {list(msgs.keys())}") - # Attempt to reconstruct per-sample conversations if messages is a dict of lists - # Expect shape: msgs[key][i] gives ith sample's list of that field - keys = list(msgs.keys()) - batch_size = ( - len(msgs[keys[0]]) - if keys and isinstance(msgs[keys[0]], list) - else 0 - ) - print(f"[preprocess] inferred batch_size from dict: {batch_size}") - for i in range(batch_size): - # Reconstruct sample i as list of dicts using available fields - sample_messages = [] - # Try common fields 'role' and 'content' - roles = msgs.get("role", [])[i] if "role" in msgs else None - contents = ( - msgs.get("content", [])[i] if "content" in msgs else None - ) - if ( - isinstance(roles, list) - and isinstance(contents, list) - and len(roles) == len(contents) - ): - for r, c in zip(roles, contents): - sample_messages.append({"role": r, "content": c}) - else: - # Fallback: try to rebuild from a generic list of dicts if present - # msgs may have a single key representing the full objects - for k in keys: - candidate = ( - msgs[k][i] if isinstance(msgs[k], list) else None - ) - if ( - isinstance(candidate, list) - and candidate - and isinstance(candidate[0], dict) - ): - sample_messages = candidate - break - if sample_messages: - text = ( - tokenizer.apply_chat_template( - sample_messages, - tokenize=False, - add_generation_prompt=False, - ) - if hasattr(tokenizer, "apply_chat_template") - else build_text_from_messages(sample_messages) - ) - texts.append(text) - # Batched: list of lists - elif isinstance(msgs, list) and msgs and isinstance(msgs[0], list): - for obj in msgs: - if obj and isinstance(obj[0], dict): - text = ( - tokenizer.apply_chat_template( - obj, - tokenize=False, - add_generation_prompt=False, - ) - if hasattr(tokenizer, "apply_chat_template") - else build_text_from_messages(obj) - ) - texts.append(text) - # Single example: list of dicts - elif isinstance(msgs, list) and msgs and isinstance(msgs[0], dict): - text = ( - tokenizer.apply_chat_template( - msgs, - tokenize=False, - add_generation_prompt=False, - ) - if hasattr(tokenizer, "apply_chat_template") - else build_text_from_messages(msgs) - ) - texts.append(text) - except Exception as e: - print(f"[preprocess] error reconstructing messages: {e}") - # When using input_columns=["messages"], the function may receive just the messages column values - elif isinstance(examples, list): - msgs = examples - if isinstance(msgs, list) and msgs and isinstance(msgs[0], list): - for obj in msgs: - if obj and isinstance(obj[0], dict): - text = ( - tokenizer.apply_chat_template( - obj, - tokenize=False, - add_generation_prompt=False, - ) - if hasattr(tokenizer, "apply_chat_template") - else build_text_from_messages(obj) - ) - texts.append(text) - - if not texts: - # Return empty dict for batch - print("[preprocess] empty texts batch") - return {"input_ids": [], "attention_mask": []} - tokenized = tokenizer( - texts, truncation=True, max_length=cfg.finetune_train_seqlen, padding=False - ) - try: - n_in = len(texts) - n_out = len(tokenized.get("input_ids", [])) - print(f"[preprocess] batch texts={n_in} -> tokenized input_ids={n_out}") - except Exception: - pass - # Return as dict of lists for batched mapping - return tokenized - - ds = make_hf_dataset_from_files( - train_files, eval_files, streaming=not args.no_stream - ) - - # For streaming datasets, map with batched=False - train_ds = ds["train"] - eval_ds = ds["validation"] - # Debug raw dataset structure - try: - print(f"[debug] raw train columns: {train_ds.column_names}") - sample0 = train_ds[0] - print(f"[debug] raw train sample0 keys: {list(sample0.keys())}") - if "messages" in sample0: - print( - f"[debug] raw train sample0 messages type: {type(sample0['messages'])}" - ) - if isinstance(sample0["messages"], list) and sample0["messages"]: - print( - f"[debug] raw train sample0 messages[0] type: {type(sample0['messages'][0])}" - ) - except Exception as e: - print(f"[debug] error inspecting raw dataset: {e}") - - if args.max_train_samples: - train_ds = train_ds.take(args.max_train_samples) - if args.max_eval_samples: - eval_ds = eval_ds.take(args.max_eval_samples) - - # Load base model - # DType selection: prefer bfloat16 on CUDA, else float32. (MPS/directml kept at float32 for stability.) - use_cuda = chosen_device == "cuda" and torch.cuda.is_available() - dtype = torch.bfloat16 if use_cuda and hasattr(torch, "bfloat16") else torch.float32 - # Use explicit device for single GPU to avoid meta device issues with device_map="auto" - device_map_param = ( - "cuda:0" if use_cuda and torch.cuda.device_count() == 1 else "auto" - ) - base_model = AutoModelForCausalLM.from_pretrained( - hf_model_id, - torch_dtype=dtype, - device_map=device_map_param, - ) - - if cfg.gradient_checkpointing: - base_model.gradient_checkpointing_enable() - if hasattr(base_model.config, "use_cache"): - base_model.config.use_cache = False - - # Use target_modules from config if present, else default to Phi-3.5 list - default_target_modules = ["q_proj", "v_proj", "k_proj", "o_proj", "fc1", "fc2"] - config_target_modules = getattr(cfg, "target_modules", None) - lora_config = LoraConfig( - r=8, - lora_alpha=16, - target_modules=( - config_target_modules if config_target_modules else default_target_modules - ), - lora_dropout=cfg.lora_dropout, - bias="none", - task_type="CAUSAL_LM", - ) - model = get_peft_model(base_model, lora_config) - - class FilteringDataCollator(DataCollatorForLanguageModeling): - def torch_call(self, features): # type: ignore[override] - # Keep only model-relevant keys to avoid nested fields like 'messages' - filtered = [] - for f in features: - if isinstance(f, dict): - filtered.append( - { - k: v - for k, v in f.items() - if k in ("input_ids", "attention_mask", "labels") - } - ) - else: - filtered.append(f) - return super().torch_call(filtered) - - data_collator = FilteringDataCollator(tokenizer=tokenizer, mlm=False) - - out_dir = Path(cfg.save_dir).expanduser() - out_dir.mkdir(parents=True, exist_ok=True) - - # Determine if dataset is streaming (IterableDataset) which lacks __len__ - def is_iterable_dataset(ds) -> bool: - # Robustly check for streaming dataset - try: - from datasets import IterableDataset - - if isinstance(ds, IterableDataset): - return True - except ImportError: - pass # datasets not installed, fallback to attribute check - # Fallback: has __iter__ but not __len__ (common for streaming datasets) - return hasattr(ds, "__iter__") and not hasattr(ds, "__len__") - - streaming_train = is_iterable_dataset(train_ds) - # If streaming, Trainer requires max_steps for LR scheduler. Heuristic: derive from max-train-samples if provided. - max_steps_override = None - if streaming_train: - # Steps per epoch based on desired sample count and batch size - # Use max_train_samples if provided, else finetune_train_nsamples from config, else fallback to 1000 - target_samples = ( - args.max_train_samples - or getattr(cfg, "finetune_train_nsamples", None) - or 1000 - ) - steps_per_epoch = max( - 1, math.ceil(target_samples / max(1, cfg.finetune_train_batch_size)) - ) - max_steps_override = max(1, steps_per_epoch * max(1, cfg.epochs)) - - # Precision flags: enable bf16 if supported, otherwise leave fp16 False on CPU to avoid errors - bf16_flag = use_cuda and getattr(torch.cuda, "is_bf16_supported", lambda: False)() - fp16_flag = use_cuda and not bf16_flag - training_args = TrainingArguments( - output_dir=str(out_dir), - per_device_train_batch_size=cfg.finetune_train_batch_size, - per_device_eval_batch_size=cfg.finetune_test_batch_size, - eval_strategy="steps", - eval_steps=cfg.eval_steps, - save_steps=cfg.save_steps, - num_train_epochs=cfg.epochs, - max_steps=(max_steps_override if max_steps_override is not None else -1), - learning_rate=cfg.learning_rate, - logging_steps=max(1, cfg.eval_steps // 2), - bf16=bf16_flag, - fp16=fp16_flag, - gradient_checkpointing=cfg.gradient_checkpointing, - gradient_accumulation_steps=cfg.gradient_accumulation_steps, - warmup_steps=cfg.warmup_steps, - max_grad_norm=cfg.max_grad_norm, - lr_scheduler_type="cosine", - weight_decay=0.01, - remove_unused_columns=False, - save_total_limit=3, - load_best_model_at_end=True, - metric_for_best_model="eval_loss", - greater_is_better=False, - seed=cfg.seed, - deepspeed=args.deepspeed if args.deepspeed else None, - ddp_find_unused_parameters=False, - ) - - # Metrics logger - from metrics_logger import MetricsLogger # local import - - logger = MetricsLogger(out_dir) - - # Callback to log evaluation perplexity records - class PerplexityLoggingCallback(TrainerCallback if TrainerCallback else object): - # type: ignore[no-redef] - def __init__(self, logger_obj): - self._logger = logger_obj - - def on_evaluate(self, args, state, control, metrics=None, **kwargs): - if not metrics: - return - try: - if "eval_loss" in metrics and metrics["eval_loss"] is not None: - ppl = math.exp(float(metrics["eval_loss"])) - rec = { - "step": int(getattr(state, "global_step", 0)), - "eval_loss": float(metrics["eval_loss"]), - "eval_perplexity": float(ppl), - } - try: - self._logger.log(rec) - except Exception: - # Don't allow logging issues to break training - pass - except Exception: - # Swallow unexpected callback errors - pass - - is_streaming = is_iterable_dataset(train_ds) - # Remove 'messages' column so only tokenized output is kept - # Note: IterableDataset.map() has limited parameter support compared to Dataset.map() - is_streaming = ( - hasattr(train_ds, "__class__") and "Iterable" in train_ds.__class__.__name__ - ) - - map_kwargs_train = { - "batched": True, - "input_columns": ["messages"], - } - map_kwargs_eval = { - "batched": True, - "input_columns": ["messages"], - } - - # Only add these parameters for non-streaming datasets - if not is_streaming: - map_kwargs_train["load_from_cache_file"] = False - map_kwargs_train["desc"] = "Tokenizing train" - map_kwargs_eval["load_from_cache_file"] = False - map_kwargs_eval["desc"] = "Tokenizing eval" - - train_dataset = ( - train_ds.map(preprocess, **map_kwargs_train) - if hasattr(train_ds, "map") - else train_ds - ) - eval_dataset = ( - eval_ds.map(preprocess, **map_kwargs_eval) - if hasattr(eval_ds, "map") - else eval_ds - ) - # Remove all non-model columns to avoid DataCollator confusion - # Note: IterableDataset doesn't support column_names or remove_columns - keep_cols = {"input_ids", "attention_mask"} - if ( - hasattr(train_dataset, "column_names") - and train_dataset.column_names is not None - ): - drop_train = [c for c in train_dataset.column_names if c not in keep_cols] - if drop_train: - train_dataset = train_dataset.remove_columns(drop_train) - if hasattr(eval_dataset, "column_names") and eval_dataset.column_names is not None: - drop_eval = [c for c in eval_dataset.column_names if c not in keep_cols] - if drop_eval: - eval_dataset = eval_dataset.remove_columns(drop_eval) - # Debug dataset sizes and sample - try: - print(f"[debug] train_dataset len: {len(train_dataset)}") - print(f"[debug] eval_dataset len: {len(eval_dataset)}") - # Show first sample keys if available - if len(train_dataset) > 0: - first = train_dataset[0] - print(f"[debug] first train sample keys: {list(first.keys())}") - for k in ("input_ids", "attention_mask"): - if k in first: - print(f"[debug] first train sample {k} len: {len(first[k])}") - except Exception as e: - print(f"[debug] dataset inspection error: {e}") - - # Initialize early stopping callback to prevent overfitting - callbacks_list = [] - if TrainerCallback is not None: - try: - # Early stopping: configured via YAML/config values - early_stopping = EarlyStoppingCallback( - early_stopping_patience=cfg.early_stopping_patience, - early_stopping_threshold=cfg.early_stopping_threshold, - ) - callbacks_list.append(early_stopping) - print( - f"[training] Early stopping enabled (patience={cfg.early_stopping_patience}, threshold={cfg.early_stopping_threshold})" - ) - except Exception as e: - print(f"[debug] Failed to configure EarlyStoppingCallback: {e}") - - trainer = Trainer( - model=model, - args=training_args, - train_dataset=train_dataset, - eval_dataset=eval_dataset, - tokenizer=tokenizer, - data_collator=data_collator, - callbacks=callbacks_list, - ) - if TrainerCallback is not None: - # Add perplexity logging callback - trainer.add_callback(PerplexityLoggingCallback(logger)) - # Add OpenTelemetry tracing callback if available and compatible - try: - from otel_callback import \ - OpenTelemetryTrainerCallback # type: ignore - - if hasattr(OpenTelemetryTrainerCallback, "on_prediction_step"): - trainer.add_callback(OpenTelemetryTrainerCallback()) - else: - print( - "[debug] Skipping OpenTelemetryTrainerCallback: missing on_prediction_step" - ) - except Exception as e: - print(f"[debug] Skipping OpenTelemetryTrainerCallback: {e}") - - # Pre-training evaluation (perplexity) - pre_metrics = trainer.evaluate() - if "eval_loss" in pre_metrics and pre_metrics["eval_loss"] is not None: - try: - ppl = math.exp(float(pre_metrics["eval_loss"])) - rec = { - "phase": "pre", - "eval_loss": float(pre_metrics["eval_loss"]), - "eval_perplexity": float(ppl), - } - print(rec) - logger.log(rec) - except Exception: - pass - - trainer.train() - # Post-training evaluation (perplexity) - post_metrics = trainer.evaluate() - if "eval_loss" in post_metrics and post_metrics["eval_loss"] is not None: - try: - ppl = math.exp(float(post_metrics["eval_loss"])) - rec = { - "phase": "post", - "eval_loss": float(post_metrics["eval_loss"]), - "eval_perplexity": float(ppl), - } - print(rec) - logger.log(rec) - except Exception: - pass - # Save adapters only - trainer.model.save_pretrained(str(out_dir / "lora_adapter")) - tokenizer.save_pretrained(str(out_dir / "tokenizer")) - - print(f"Training complete. Artifacts saved to: {out_dir}") - - -if __name__ == "__main__": - main() +import argparse +import json +import math +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, Iterable, List + +try: + import yaml # type: ignore +except Exception: + raise SystemExit("pyyaml is required. Install with: pip install pyyaml") from None +try: + # Optional: install torch if missing + try: + import torch # pip install torch # type: ignore[reportMissingImports] + except ImportError: + raise SystemExit( + "PyTorch is required. Install with: pip install torch" + ) from None + from datasets import load_dataset # type: ignore[import] + + try: + from peft import LoraConfig, PeftModel, get_peft_model + from transformers import (AutoModelForCausalLM, AutoTokenizer, + DataCollatorForLanguageModeling, + EarlyStoppingCallback, Trainer, + TrainerCallback, TrainingArguments) + except ImportError: + raise SystemExit( + "Transformers is required. Install with: pip install transformers" + ) from None +except Exception as e: + # Provide visibility into which dependency import failed + import traceback + + print(f"[import-debug] training dependency import failed: {e}") + traceback.print_exc() + # We'll allow dry-run without these by nulling all symbols + torch = None # type: ignore + load_dataset = None # type: ignore + AutoModelForCausalLM = AutoTokenizer = Trainer = TrainingArguments = DataCollatorForLanguageModeling = None # type: ignore + LoraConfig = get_peft_model = PeftModel = None # type: ignore + TrainerCallback = None # type: ignore + EarlyStoppingCallback = None # type: ignore + + +@dataclass +class Config: + model: str + finetune_dataset: str + save_dir: str + finetune_train_nsamples: int | None + finetune_test_nsamples: int | None + finetune_train_batch_size: int + finetune_test_batch_size: int + finetune_train_seqlen: int + finetune_test_seqlen: int + learning_rate: float + lora_dropout: float + epochs: int + eval_steps: int + save_steps: int + gradient_checkpointing: bool + seed: int + warmup_steps: int + gradient_accumulation_steps: int + max_grad_norm: float + early_stopping_patience: int + early_stopping_threshold: float + + +def read_yaml(yaml_path: Path) -> Dict[str, Any]: + with yaml_path.open("r", encoding="utf-8") as f: + return yaml.safe_load(f) + + +def resolve_path(p: str) -> Path: + # allow tokens like mount//dataset to be overridden by --dataset + return Path(p).expanduser() + + +def iter_jsonl(path: Path) -> Iterable[Dict[str, Any]]: + with path.open("r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + yield json.loads(line) + + +def count_records(path: Path) -> int: + n = 0 + for _ in iter_jsonl(path): + n += 1 + return n + + +def validate_sample(path: Path) -> Dict[str, Any]: + for obj in iter_jsonl(path): + msgs = obj.get("messages") + if isinstance(msgs, list) and len(msgs) >= 2: + return obj + raise RuntimeError(f"No valid chat records found in {path}") + + +def build_text_from_messages(messages: List[Dict[str, str]]) -> str: + """Convert messages to training text with improved formatting and end tokens.""" + parts: List[str] = [] + for m in messages: + role = m.get("role", "").lower() + content = m.get("content", "").strip() + if not content: # Skip empty messages + continue + if role == "system": + parts.append(f"<|system|>\n{content}<|end|>\n") + elif role == "user": + parts.append(f"<|user|>\n{content}<|end|>\n") + elif role == "assistant": + parts.append(f"<|assistant|>\n{content}<|end|>\n") + return "".join(parts) + + +def make_hf_dataset_from_files( + train_files: List[str], eval_files: List[str], streaming: bool = True +): + if load_dataset is None: + raise RuntimeError( + "HuggingFace datasets not available. Install 'datasets' to load datasets." + ) + data_files = {"train": train_files, "validation": eval_files} + ds = load_dataset("json", data_files=data_files, streaming=streaming) + return ds + + +def _read_text_source(path_or_url: str) -> Iterable[str]: + if path_or_url.startswith("http://") or path_or_url.startswith("https://"): + import urllib.request + + with urllib.request.urlopen(path_or_url) as resp: # nosec B310 + for line in resp.read().decode("utf-8").splitlines(): + yield line + else: + p = Path(path_or_url) + with p.open("r", encoding="utf-8") as f: + for line in f: + yield line.rstrip("\n") + + +def parse_manifest(path_or_url: str) -> List[str]: + urls: List[str] = [] + lower = path_or_url.lower() + if lower.endswith(".json"): + import json as _json + + if path_or_url.startswith("http://") or path_or_url.startswith("https://"): + import urllib.request + + with urllib.request.urlopen(path_or_url) as resp: # nosec B310 + obj = _json.loads(resp.read().decode("utf-8")) + else: + with Path(path_or_url).open("r", encoding="utf-8") as f: + obj = _json.load(f) + if isinstance(obj, dict): + for key in ("train", "validation", "urls", "files"): + v = obj.get(key) + if isinstance(v, list): + urls.extend([str(x) for x in v]) + if not urls and "url" in obj: + urls.append(str(obj["url"])) + elif isinstance(obj, list): + urls.extend([str(x) for x in obj]) + elif lower.endswith(".jsonl"): + import json as _json + + for line in _read_text_source(path_or_url): + if not line.strip(): + continue + try: + rec = _json.loads(line) + if isinstance(rec, str): + urls.append(rec) + elif isinstance(rec, dict) and "url" in rec: + urls.append(str(rec["url"])) + except Exception: + urls.append(line.strip()) + else: + for line in _read_text_source(path_or_url): + if line.strip(): + urls.append(line.strip()) + # Dedupe + seen = set() + uniq: List[str] = [] + for u in urls: + if u not in seen: + seen.add(u) + uniq.append(u) + return uniq + + +def main(): + ap = argparse.ArgumentParser( + description="Train LoRA on chat dataset using lora.yaml config" + ) + ap.add_argument( + "--config", + default=str(Path(__file__).resolve().parents[1] / "lora" / "lora.yaml"), + ) + ap.add_argument( + "--dataset", default=str(Path(__file__).resolve().parents[1] / "data") + ) + ap.add_argument( + "--dry-run", + action="store_true", + help="Validate dataset/config only; no model download", + ) + ap.add_argument( + "--max-train-samples", + type=int, + default=None, + help="Limit train examples (for smoke test)", + ) + ap.add_argument( + "--max-eval-samples", + type=int, + default=None, + help="Limit eval examples (for smoke test)", + ) + ap.add_argument( + "--hf-model-id", + default=None, + help="Override HF model id (e.g., microsoft/Phi-3.5-mini-instruct)", + ) + ap.add_argument( + "--no-stream", action="store_true", help="Disable streaming mode for datasets" + ) + ap.add_argument( + "--deepspeed", + default=None, + help="Path to DeepSpeed config JSON to enable ZeRO (multi-GPU)", + ) + ap.add_argument( + "--train-manifest", + default=None, + help="Path or URL to manifest of training files (txt/json/jsonl)", + ) + ap.add_argument( + "--eval-manifest", + default=None, + help="Path or URL to manifest of eval files (txt/json/jsonl)", + ) + ap.add_argument( + "--save-dir", + default=None, + help="Override output directory (else from config or defaults)", + ) + ap.add_argument( + "--device", + default="auto", + choices=["auto", "cuda", "cpu", "directml", "mps"], + help="Device preference: auto selects best available (cuda>mps>directml>cpu)", + ) + # Optional overrides for HPO/cloud runs + ap.add_argument( + "--learning-rate", + type=float, + default=None, + help="Override learning_rate from config", + ) + ap.add_argument( + "--lora-dropout", + type=float, + default=None, + help="Override lora_dropout from config", + ) + ap.add_argument( + "--epochs", type=int, default=None, help="Override epochs from config" + ) + ap.add_argument( + "--train-batch-size", + type=int, + default=None, + help="Override finetune_train_batch_size from config", + ) + ap.add_argument( + "--eval-batch-size", + type=int, + default=None, + help="Override finetune_test_batch_size from config", + ) + ap.add_argument("--seed", type=int, default=None, help="Override seed from config") + args = ap.parse_args() + + # Initialize tracing (best-effort). This allows the optional + # OpenTelemetryTrainerCallback to get an active tracer if available. + # Optional tracing import (ignore if missing) + try: + from shared.tracing import init_tracing # type: ignore + + init_tracing(service_name="train_lora") + except Exception as _e: + print(f"[tracing] init skipped in train_lora: {_e}") + + cfg_raw = read_yaml(Path(args.config)) + cfg = Config( + model=cfg_raw.get("model") or "Phi-3.6-mini-instruct", + finetune_dataset=cfg_raw.get("finetune_dataset") or str(Path(args.dataset)), + save_dir=( + args.save_dir + or cfg_raw.get("save_dir") + or str(Path(__file__).resolve().parents[1] / "outputs") + ), + finetune_train_nsamples=cfg_raw.get("finetune_train_nsamples"), + finetune_test_nsamples=cfg_raw.get("finetune_test_nsamples"), + finetune_train_batch_size=int(cfg_raw.get("finetune_train_batch_size") or 2), + finetune_test_batch_size=int(cfg_raw.get("finetune_test_batch_size") or 2), + finetune_train_seqlen=int(cfg_raw.get("finetune_train_seqlen") or 1024), + finetune_test_seqlen=int(cfg_raw.get("finetune_test_seqlen") or 2048), + learning_rate=float(cfg_raw.get("learning_rate") or 2e-4), + lora_dropout=float(cfg_raw.get("lora_dropout") or 0.1), + epochs=int(cfg_raw.get("epochs") or 1), + eval_steps=int(cfg_raw.get("eval_steps", 64)), + save_steps=int(cfg_raw.get("save_steps") or 64), + gradient_checkpointing=bool(cfg_raw.get("gradient_checkpointing") or False), + seed=int(cfg_raw.get("seed") or 42), + warmup_steps=int(cfg_raw.get("warmup_steps") or 100), + gradient_accumulation_steps=int( + cfg_raw.get("gradient_accumulation_steps") or 4 + ), + max_grad_norm=float(cfg_raw.get("max_grad_norm") or 1.0), + early_stopping_patience=int(cfg_raw.get("early_stopping_patience") or 3), + early_stopping_threshold=float(cfg_raw.get("early_stopping_threshold") or 0.01), + ) + + # Apply CLI overrides for HPO or cloud jobs + if getattr(args, "learning_rate", None) is not None: + cfg.learning_rate = float(args.learning_rate) + if getattr(args, "lora_dropout", None) is not None: + cfg.lora_dropout = float(args.lora_dropout) + if getattr(args, "epochs", None) is not None: + cfg.epochs = int(args.epochs) + if getattr(args, "train_batch_size", None) is not None: + cfg.finetune_train_batch_size = int(args.train_batch_size) + if getattr(args, "eval_batch_size", None) is not None: + cfg.finetune_test_batch_size = int(args.eval_batch_size) + if getattr(args, "seed", None) is not None: + cfg.seed = int(args.seed) + + # Resolve data sources: manifests or local files + train_files: List[str] = [] + eval_files: List[str] = [] + # CLI overrides take precedence + train_manifest = getattr(args, "train_manifest", None) + eval_manifest = getattr(args, "eval_manifest", None) + if train_manifest or eval_manifest: + if train_manifest: + train_files = parse_manifest(train_manifest) + if eval_manifest: + eval_files = parse_manifest(eval_manifest) + if not train_files: + raise RuntimeError("No train files found from manifest") + if not eval_files: + # Fallback: use a small subset of train for eval + eval_files = train_files[:1] + else: + dataset_path = ( + Path(args.dataset) if args.dataset else resolve_path(cfg.finetune_dataset) + ) + if dataset_path.is_file(): + # Allow direct file usage (.json or .jsonl) + if dataset_path.suffix.lower() in (".json", ".jsonl"): + train_files = [str(dataset_path)] + eval_files = [str(dataset_path)] + else: + raise FileNotFoundError( + f"Unsupported dataset file type: {dataset_path}" + ) + else: + # Directory: accept train.json/test.json or train.jsonl/test.jsonl; fallback to single train file present + candidates = [ + (dataset_path / "train.json", dataset_path / "test.json"), + (dataset_path / "train.jsonl", dataset_path / "test.jsonl"), + ] + found = False + for t_path, v_path in candidates: + if t_path.exists() and v_path.exists(): + train_files = [str(t_path)] + eval_files = [str(v_path)] + found = True + break + if not found: + # Fallbacks: if any train.* exists, use it for both train/val + t_candidates = [ + dataset_path / "train.json", + dataset_path / "train.jsonl", + ] + t_use = next((p for p in t_candidates if p.exists()), None) + if t_use is None: + raise FileNotFoundError( + f"Expected dataset files at: {dataset_path}/train.json[.l] and optionally test.json[.l]" + ) + train_files = [str(t_use)] + eval_candidates = [ + dataset_path / "test.json", + dataset_path / "test.jsonl", + ] + v_use = next((p for p in eval_candidates if p.exists()), None) + eval_files = [str(v_use)] if v_use else [str(t_use)] + + # Determine and report device early (even for dry-run) + chosen_device = "cpu" + if args.device == "auto": + if ( + torch is not None + and getattr(torch, "cuda", None) + and getattr(torch.cuda, "is_available", lambda: False)() + ): + chosen_device = "cuda" + elif ( + torch is not None + and getattr(torch, "backends", None) + and getattr(torch.backends, "mps", None) + and getattr(torch.backends.mps, "is_available", lambda: False)() + ): + chosen_device = "mps" + else: + # Optional DirectML detection + try: + chosen_device = "directml" + except Exception: + chosen_device = "cpu" + else: + chosen_device = args.device + print( + f"[device] selection={args.device} resolved={chosen_device} cuda_available={(getattr(torch, 'cuda', None) and getattr(torch.cuda, 'is_available', lambda: False)() if torch else False)}" + ) + + # Dry run: count/validate records only (no model/tokenizer downloads) + if args.dry_run: + if train_manifest or eval_manifest: + print("Dry run OK.") + print( + { + "device": chosen_device, + "train_files": train_files[:5], + "eval_files": eval_files[:5], + "note": "Counting skipped for remote manifests", + } + ) + return + n_train = count_records(Path(train_files[0])) + n_test = count_records(Path(eval_files[0])) + sample = validate_sample(Path(train_files[0])) + print("Dry run OK.") + print( + { + "device": chosen_device, + "train_examples": n_train, + "test_examples": n_test, + "sample": sample, + } + ) + return + + # Real training requires heavy deps + if ( + AutoTokenizer is None + or AutoModelForCausalLM is None + or load_dataset is None + or torch is None + ): + missing = [] + if torch is None: + missing.append("torch") + if load_dataset is None: + missing.append("datasets") + if AutoTokenizer is None or AutoModelForCausalLM is None: + missing.append("transformers") + if LoraConfig is None: + missing.append("peft") + model_venv = Path(__file__).resolve().parents[1] / "venv" + root_venv = Path(__file__).resolve().parents[3] / "venv" + raise RuntimeError( + "Training dependencies not installed or import failed.\n" + f"missing={missing}\n" + f"To fix (model env): {model_venv / 'Scripts' / 'pip.exe'} install transformers datasets peft accelerate torch\n" + f"Alt (root env): {root_venv / 'Scripts' / 'pip.exe'} install transformers datasets peft accelerate torch\n" + "After installing, re-run this script." + ) + + # Resolve model id + hf_model_id = args.hf_model_id or os.environ.get("HF_MODEL_ID") + if hf_model_id is None: + # Best-effort mapping for local runs + # If the configured model isn't an HF id, default to a widely-available one + hf_model_id = { + "Phi-3.6-mini-instruct": "microsoft/Phi-3.5-mini-instruct", + }.get(cfg.model, cfg.model) + + tokenizer = AutoTokenizer.from_pretrained(hf_model_id, use_fast=True) + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + def preprocess(examples): + # Handle both single example and batched mapping + texts = [] + if isinstance(examples, dict) and "messages" in examples: + msgs = examples["messages"] + try: + print(f"[preprocess] messages type: {type(msgs)}") + if isinstance(msgs, dict): + print(f"[preprocess] messages dict keys: {list(msgs.keys())}") + # Attempt to reconstruct per-sample conversations if messages is a dict of lists + # Expect shape: msgs[key][i] gives ith sample's list of that field + keys = list(msgs.keys()) + batch_size = ( + len(msgs[keys[0]]) + if keys and isinstance(msgs[keys[0]], list) + else 0 + ) + print(f"[preprocess] inferred batch_size from dict: {batch_size}") + for i in range(batch_size): + # Reconstruct sample i as list of dicts using available fields + sample_messages = [] + # Try common fields 'role' and 'content' + roles = msgs.get("role", [])[i] if "role" in msgs else None + contents = ( + msgs.get("content", [])[i] if "content" in msgs else None + ) + if ( + isinstance(roles, list) + and isinstance(contents, list) + and len(roles) == len(contents) + ): + for r, c in zip(roles, contents): + sample_messages.append({"role": r, "content": c}) + else: + # Fallback: try to rebuild from a generic list of dicts if present + # msgs may have a single key representing the full objects + for k in keys: + candidate = ( + msgs[k][i] if isinstance(msgs[k], list) else None + ) + if ( + isinstance(candidate, list) + and candidate + and isinstance(candidate[0], dict) + ): + sample_messages = candidate + break + if sample_messages: + text = ( + tokenizer.apply_chat_template( + sample_messages, + tokenize=False, + add_generation_prompt=False, + ) + if hasattr(tokenizer, "apply_chat_template") + else build_text_from_messages(sample_messages) + ) + texts.append(text) + # Batched: list of lists + elif isinstance(msgs, list) and msgs and isinstance(msgs[0], list): + for obj in msgs: + if obj and isinstance(obj[0], dict): + text = ( + tokenizer.apply_chat_template( + obj, + tokenize=False, + add_generation_prompt=False, + ) + if hasattr(tokenizer, "apply_chat_template") + else build_text_from_messages(obj) + ) + texts.append(text) + # Single example: list of dicts + elif isinstance(msgs, list) and msgs and isinstance(msgs[0], dict): + text = ( + tokenizer.apply_chat_template( + msgs, + tokenize=False, + add_generation_prompt=False, + ) + if hasattr(tokenizer, "apply_chat_template") + else build_text_from_messages(msgs) + ) + texts.append(text) + except Exception as e: + print(f"[preprocess] error reconstructing messages: {e}") + # When using input_columns=["messages"], the function may receive just the messages column values + elif isinstance(examples, list): + msgs = examples + if isinstance(msgs, list) and msgs and isinstance(msgs[0], list): + for obj in msgs: + if obj and isinstance(obj[0], dict): + text = ( + tokenizer.apply_chat_template( + obj, + tokenize=False, + add_generation_prompt=False, + ) + if hasattr(tokenizer, "apply_chat_template") + else build_text_from_messages(obj) + ) + texts.append(text) + + if not texts: + # Return empty dict for batch + print("[preprocess] empty texts batch") + return {"input_ids": [], "attention_mask": []} + tokenized = tokenizer( + texts, truncation=True, max_length=cfg.finetune_train_seqlen, padding=False + ) + try: + n_in = len(texts) + n_out = len(tokenized.get("input_ids", [])) + print(f"[preprocess] batch texts={n_in} -> tokenized input_ids={n_out}") + except Exception: + pass + # Return as dict of lists for batched mapping + return tokenized + + ds = make_hf_dataset_from_files( + train_files, eval_files, streaming=not args.no_stream + ) + + # For streaming datasets, map with batched=False + train_ds = ds["train"] + eval_ds = ds["validation"] + # Debug raw dataset structure + try: + print(f"[debug] raw train columns: {train_ds.column_names}") + sample0 = train_ds[0] + print(f"[debug] raw train sample0 keys: {list(sample0.keys())}") + if "messages" in sample0: + print( + f"[debug] raw train sample0 messages type: {type(sample0['messages'])}" + ) + if isinstance(sample0["messages"], list) and sample0["messages"]: + print( + f"[debug] raw train sample0 messages[0] type: {type(sample0['messages'][0])}" + ) + except Exception as e: + print(f"[debug] error inspecting raw dataset: {e}") + + if args.max_train_samples: + train_ds = train_ds.take(args.max_train_samples) + if args.max_eval_samples: + eval_ds = eval_ds.take(args.max_eval_samples) + + # Load base model + # DType selection: prefer bfloat16 on CUDA, else float32. (MPS/directml kept at float32 for stability.) + use_cuda = chosen_device == "cuda" and torch.cuda.is_available() + dtype = torch.bfloat16 if use_cuda and hasattr(torch, "bfloat16") else torch.float32 + # Use explicit device for single GPU to avoid meta device issues with device_map="auto" + device_map_param = ( + "cuda:0" if use_cuda and torch.cuda.device_count() == 1 else "auto" + ) + base_model = AutoModelForCausalLM.from_pretrained( + hf_model_id, + torch_dtype=dtype, + device_map=device_map_param, + ) + + if cfg.gradient_checkpointing: + base_model.gradient_checkpointing_enable() + if hasattr(base_model.config, "use_cache"): + base_model.config.use_cache = False + + # Use target_modules from config if present, else default to Phi-3.5 list + default_target_modules = ["q_proj", "v_proj", "k_proj", "o_proj", "fc1", "fc2"] + config_target_modules = getattr(cfg, "target_modules", None) + lora_config = LoraConfig( + r=8, + lora_alpha=16, + target_modules=( + config_target_modules if config_target_modules else default_target_modules + ), + lora_dropout=cfg.lora_dropout, + bias="none", + task_type="CAUSAL_LM", + ) + model = get_peft_model(base_model, lora_config) + + class FilteringDataCollator(DataCollatorForLanguageModeling): + def torch_call(self, features): # type: ignore[override] + # Keep only model-relevant keys to avoid nested fields like 'messages' + filtered = [] + for f in features: + if isinstance(f, dict): + filtered.append( + { + k: v + for k, v in f.items() + if k in ("input_ids", "attention_mask", "labels") + } + ) + else: + filtered.append(f) + return super().torch_call(filtered) + + data_collator = FilteringDataCollator(tokenizer=tokenizer, mlm=False) + + out_dir = Path(cfg.save_dir).expanduser() + out_dir.mkdir(parents=True, exist_ok=True) + + # Determine if dataset is streaming (IterableDataset) which lacks __len__ + def is_iterable_dataset(ds) -> bool: + # Robustly check for streaming dataset + try: + from datasets import IterableDataset + + if isinstance(ds, IterableDataset): + return True + except ImportError: + pass # datasets not installed, fallback to attribute check + # Fallback: has __iter__ but not __len__ (common for streaming datasets) + return hasattr(ds, "__iter__") and not hasattr(ds, "__len__") + + streaming_train = is_iterable_dataset(train_ds) + # If streaming, Trainer requires max_steps for LR scheduler. Heuristic: derive from max-train-samples if provided. + max_steps_override = None + if streaming_train: + # Steps per epoch based on desired sample count and batch size + # Use max_train_samples if provided, else finetune_train_nsamples from config, else fallback to 1000 + target_samples = ( + args.max_train_samples + or getattr(cfg, "finetune_train_nsamples", None) + or 1000 + ) + steps_per_epoch = max( + 1, math.ceil(target_samples / max(1, cfg.finetune_train_batch_size)) + ) + max_steps_override = max(1, steps_per_epoch * max(1, cfg.epochs)) + + # Precision flags: enable bf16 if supported, otherwise leave fp16 False on CPU to avoid errors + bf16_flag = use_cuda and getattr(torch.cuda, "is_bf16_supported", lambda: False)() + fp16_flag = use_cuda and not bf16_flag + training_args = TrainingArguments( + output_dir=str(out_dir), + per_device_train_batch_size=cfg.finetune_train_batch_size, + per_device_eval_batch_size=cfg.finetune_test_batch_size, + eval_strategy="steps", + eval_steps=cfg.eval_steps, + save_steps=cfg.save_steps, + num_train_epochs=cfg.epochs, + max_steps=(max_steps_override if max_steps_override is not None else -1), + learning_rate=cfg.learning_rate, + logging_steps=max(1, cfg.eval_steps // 2), + bf16=bf16_flag, + fp16=fp16_flag, + gradient_checkpointing=cfg.gradient_checkpointing, + gradient_accumulation_steps=cfg.gradient_accumulation_steps, + warmup_steps=cfg.warmup_steps, + max_grad_norm=cfg.max_grad_norm, + lr_scheduler_type="cosine", + weight_decay=0.01, + remove_unused_columns=False, + save_total_limit=3, + load_best_model_at_end=True, + metric_for_best_model="eval_loss", + greater_is_better=False, + seed=cfg.seed, + deepspeed=args.deepspeed if args.deepspeed else None, + ddp_find_unused_parameters=False, + ) + + # Metrics logger + from metrics_logger import MetricsLogger # local import + + logger = MetricsLogger(out_dir) + + # Callback to log evaluation perplexity records + class PerplexityLoggingCallback(TrainerCallback if TrainerCallback else object): + # type: ignore[no-redef] + def __init__(self, logger_obj): + self._logger = logger_obj + + def on_evaluate(self, args, state, control, metrics=None, **kwargs): + if not metrics: + return + try: + if "eval_loss" in metrics and metrics["eval_loss"] is not None: + ppl = math.exp(float(metrics["eval_loss"])) + rec = { + "step": int(getattr(state, "global_step", 0)), + "eval_loss": float(metrics["eval_loss"]), + "eval_perplexity": float(ppl), + } + try: + self._logger.log(rec) + except Exception: + # Don't allow logging issues to break training + pass + except Exception: + # Swallow unexpected callback errors + pass + + is_streaming = is_iterable_dataset(train_ds) + # Remove 'messages' column so only tokenized output is kept + # Note: IterableDataset.map() has limited parameter support compared to Dataset.map() + is_streaming = ( + hasattr(train_ds, "__class__") and "Iterable" in train_ds.__class__.__name__ + ) + + map_kwargs_train = { + "batched": True, + "input_columns": ["messages"], + } + map_kwargs_eval = { + "batched": True, + "input_columns": ["messages"], + } + + # Only add these parameters for non-streaming datasets + if not is_streaming: + map_kwargs_train["load_from_cache_file"] = False + map_kwargs_train["desc"] = "Tokenizing train" + map_kwargs_eval["load_from_cache_file"] = False + map_kwargs_eval["desc"] = "Tokenizing eval" + + train_dataset = ( + train_ds.map(preprocess, **map_kwargs_train) + if hasattr(train_ds, "map") + else train_ds + ) + eval_dataset = ( + eval_ds.map(preprocess, **map_kwargs_eval) + if hasattr(eval_ds, "map") + else eval_ds + ) + # Remove all non-model columns to avoid DataCollator confusion + # Note: IterableDataset doesn't support column_names or remove_columns + keep_cols = {"input_ids", "attention_mask"} + if ( + hasattr(train_dataset, "column_names") + and train_dataset.column_names is not None + ): + drop_train = [c for c in train_dataset.column_names if c not in keep_cols] + if drop_train: + train_dataset = train_dataset.remove_columns(drop_train) + if hasattr(eval_dataset, "column_names") and eval_dataset.column_names is not None: + drop_eval = [c for c in eval_dataset.column_names if c not in keep_cols] + if drop_eval: + eval_dataset = eval_dataset.remove_columns(drop_eval) + # Debug dataset sizes and sample + try: + print(f"[debug] train_dataset len: {len(train_dataset)}") + print(f"[debug] eval_dataset len: {len(eval_dataset)}") + # Show first sample keys if available + if len(train_dataset) > 0: + first = train_dataset[0] + print(f"[debug] first train sample keys: {list(first.keys())}") + for k in ("input_ids", "attention_mask"): + if k in first: + print(f"[debug] first train sample {k} len: {len(first[k])}") + except Exception as e: + print(f"[debug] dataset inspection error: {e}") + + # Initialize early stopping callback to prevent overfitting + callbacks_list = [] + if TrainerCallback is not None: + try: + # Early stopping: configured via YAML/config values + early_stopping = EarlyStoppingCallback( + early_stopping_patience=cfg.early_stopping_patience, + early_stopping_threshold=cfg.early_stopping_threshold, + ) + callbacks_list.append(early_stopping) + print( + f"[training] Early stopping enabled (patience={cfg.early_stopping_patience}, threshold={cfg.early_stopping_threshold})" + ) + except Exception as e: + print(f"[debug] Failed to configure EarlyStoppingCallback: {e}") + + trainer = Trainer( + model=model, + args=training_args, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + tokenizer=tokenizer, + data_collator=data_collator, + callbacks=callbacks_list, + ) + if TrainerCallback is not None: + # Add perplexity logging callback + trainer.add_callback(PerplexityLoggingCallback(logger)) + # Add OpenTelemetry tracing callback if available and compatible + try: + from otel_callback import \ + OpenTelemetryTrainerCallback # type: ignore + + if hasattr(OpenTelemetryTrainerCallback, "on_prediction_step"): + trainer.add_callback(OpenTelemetryTrainerCallback()) + else: + print( + "[debug] Skipping OpenTelemetryTrainerCallback: missing on_prediction_step" + ) + except Exception as e: + print(f"[debug] Skipping OpenTelemetryTrainerCallback: {e}") + + # Pre-training evaluation (perplexity) + pre_metrics = trainer.evaluate() + if "eval_loss" in pre_metrics and pre_metrics["eval_loss"] is not None: + try: + ppl = math.exp(float(pre_metrics["eval_loss"])) + rec = { + "phase": "pre", + "eval_loss": float(pre_metrics["eval_loss"]), + "eval_perplexity": float(ppl), + } + print(rec) + logger.log(rec) + except Exception: + pass + + trainer.train() + # Post-training evaluation (perplexity) + post_metrics = trainer.evaluate() + if "eval_loss" in post_metrics and post_metrics["eval_loss"] is not None: + try: + ppl = math.exp(float(post_metrics["eval_loss"])) + rec = { + "phase": "post", + "eval_loss": float(post_metrics["eval_loss"]), + "eval_perplexity": float(ppl), + } + print(rec) + logger.log(rec) + except Exception: + pass + # Save adapters only + trainer.model.save_pretrained(str(out_dir / "lora_adapter")) + tokenizer.save_pretrained(str(out_dir / "tokenizer")) + + print(f"Training complete. Artifacts saved to: {out_dir}") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/training_monitor.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/training_monitor.py index 4113e8387..cba016567 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/training_monitor.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/training_monitor.py @@ -1,331 +1,331 @@ -""" -Training Monitor & Dashboard -Real-time training progress monitoring with live metrics -""" - -import json -import queue -import threading -import time -from dataclasses import asdict, dataclass, field -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List, Optional - - -@dataclass -class TrainingMetrics: - """Training metrics at a point in time""" - - step: int - epoch: int - loss: float - learning_rate: float - timestamp: str - grad_norm: Optional[float] = None - throughput: Optional[float] = None # tokens/sec - gpu_memory_mb: Optional[float] = None - - def to_dict(self) -> Dict[str, Any]: - return asdict(self) - - -@dataclass -class TrainingSession: - """Complete training session information""" - - session_id: str - start_time: str - end_time: Optional[str] = None - config: Dict[str, Any] = field(default_factory=dict) - metrics: List[TrainingMetrics] = field(default_factory=list) - status: str = "running" # running, completed, failed - total_steps: int = 0 - best_loss: float = float("inf") - best_step: int = 0 - - def to_dict(self) -> Dict[str, Any]: - return {**asdict(self), "metrics": [m.to_dict() for m in self.metrics]} - - -class TrainingMonitor: - """Monitor and track training progress""" - - def __init__(self, session_id: Optional[str] = None): - self.session_id = session_id or f"training_{int(time.time())}" - self.session = TrainingSession( - session_id=self.session_id, start_time=datetime.now().isoformat() - ) - - self.log_dir = Path("data_out/training_logs") - self.log_dir.mkdir(parents=True, exist_ok=True) - - self.log_file = self.log_dir / f"{self.session_id}.jsonl" - self.summary_file = self.log_dir / f"{self.session_id}_summary.json" - - self.metrics_queue = queue.Queue() - self.should_stop = threading.Event() - self.writer_thread = threading.Thread( - target=self._write_metrics_worker, daemon=True - ) - self.writer_thread.start() - - def log_metrics( - self, step: int, epoch: int, loss: float, learning_rate: float, **kwargs - ): - """Log training metrics""" - metrics = TrainingMetrics( - step=step, - epoch=epoch, - loss=loss, - learning_rate=learning_rate, - timestamp=datetime.now().isoformat(), - grad_norm=kwargs.get("grad_norm"), - throughput=kwargs.get("throughput"), - gpu_memory_mb=kwargs.get("gpu_memory_mb"), - ) - - self.session.metrics.append(metrics) - self.session.total_steps = step - - # Track best loss - if loss < self.session.best_loss: - self.session.best_loss = loss - self.session.best_step = step - - # Queue for async writing - self.metrics_queue.put(metrics) - - def update_config(self, config: Dict[str, Any]): - """Update training configuration""" - self.session.config.update(config) - - def complete(self, status: str = "completed"): - """Mark training as complete""" - self.session.status = status - self.session.end_time = datetime.now().isoformat() - self._save_summary() - - # Stop writer thread - self.should_stop.set() - self.writer_thread.join(timeout=5) - - def _write_metrics_worker(self): - """Background worker to write metrics""" - while not self.should_stop.is_set(): - try: - metrics = self.metrics_queue.get(timeout=1) - with open(self.log_file, "a") as f: - f.write(json.dumps(metrics.to_dict()) + "\n") - except queue.Empty: - continue - except Exception as e: - print(f"Error writing metrics: {e}") - - def _save_summary(self): - """Save training summary""" - with open(self.summary_file, "w") as f: - json.dump(self.session.to_dict(), f, indent=2) - - def get_stats(self) -> Dict[str, Any]: - """Get current training statistics""" - if not self.session.metrics: - return {} - - recent_losses = [m.loss for m in self.session.metrics[-100:]] - recent_throughputs = [ - m.throughput for m in self.session.metrics[-100:] if m.throughput - ] - - return { - "total_steps": self.session.total_steps, - "current_loss": ( - self.session.metrics[-1].loss if self.session.metrics else None - ), - "best_loss": self.session.best_loss, - "best_step": self.session.best_step, - "avg_recent_loss": ( - sum(recent_losses) / len(recent_losses) if recent_losses else None - ), - "avg_throughput": ( - sum(recent_throughputs) / len(recent_throughputs) - if recent_throughputs - else None - ), - "duration": self._get_duration(), - } - - def _get_duration(self) -> str: - """Get training duration""" - start = datetime.fromisoformat(self.session.start_time) - end = ( - datetime.fromisoformat(self.session.end_time) - if self.session.end_time - else datetime.now() - ) - duration = end - start - - hours = int(duration.total_seconds() // 3600) - minutes = int((duration.total_seconds() % 3600) // 60) - seconds = int(duration.total_seconds() % 60) - - return f"{hours:02d}:{minutes:02d}:{seconds:02d}" - - def print_progress(self, step: int, total_steps: int): - """Print training progress""" - stats = self.get_stats() - progress = (step / total_steps * 100) if total_steps > 0 else 0 - - print( - f"\rStep {step}/{total_steps} ({progress:.1f}%) | " - f"Loss: {stats.get('current_loss', 'N/A'):.4f} | " - f"Best: {stats['best_loss']:.4f} @ {stats['best_step']} | " - f"Duration: {stats['duration']}", - end="", - ) - - @staticmethod - def load_session(session_id: str) -> Optional[TrainingSession]: - """Load a previous training session""" - summary_file = Path(f"data_out/training_logs/{session_id}_summary.json") - - if not summary_file.exists(): - return None - - with open(summary_file) as f: - data = json.load(f) - - # Reconstruct session - session = TrainingSession( - session_id=data["session_id"], - start_time=data["start_time"], - end_time=data.get("end_time"), - config=data.get("config", {}), - status=data.get("status", "unknown"), - total_steps=data.get("total_steps", 0), - best_loss=data.get("best_loss", float("inf")), - best_step=data.get("best_step", 0), - ) - - # Load metrics - session.metrics = [TrainingMetrics(**m) for m in data.get("metrics", [])] - - return session - - @staticmethod - def list_sessions() -> List[str]: - """List all training sessions""" - log_dir = Path("data_out/training_logs") - if not log_dir.exists(): - return [] - - sessions = [] - for file in log_dir.glob("*_summary.json"): - session_id = file.stem.replace("_summary", "") - sessions.append(session_id) - - return sorted(sessions, reverse=True) - - -class LiveDashboard: - """Live training dashboard (terminal-based)""" - - def __init__(self, monitor: TrainingMonitor): - self.monitor = monitor - self.running = False - self.dashboard_thread = None - - def start(self): - """Start the dashboard""" - self.running = True - self.dashboard_thread = threading.Thread(target=self._update_loop, daemon=True) - self.dashboard_thread.start() - - def stop(self): - """Stop the dashboard""" - self.running = False - if self.dashboard_thread: - self.dashboard_thread.join(timeout=2) - - def _update_loop(self): - """Update loop for dashboard""" - while self.running: - self._render_dashboard() - time.sleep(5) # Update every 5 seconds - - def _render_dashboard(self): - """Render the dashboard""" - stats = self.monitor.get_stats() - - # Clear screen (simple version) - print("\033[2J\033[H") # ANSI escape codes - - print("=" * 80) - print(f"TRAINING DASHBOARD - Session: {self.monitor.session_id}") - print("=" * 80) - - print(f"\nDuration: {stats.get('duration', 'N/A')}") - print(f"Status: {self.monitor.session.status.upper()}") - - print("\n📊 Progress:") - print(f" Total Steps: {stats.get('total_steps', 0):,}") - print(f" Current Loss: {stats.get('current_loss', 'N/A')}") - print( - f" Best Loss: {stats.get('best_loss', float('inf')):.4f} (step {stats.get('best_step', 0)})" - ) - print(f" Avg Recent Loss: {stats.get('avg_recent_loss', 'N/A')}") - - if stats.get("avg_throughput"): - print("\n⚡ Performance:") - print(f" Throughput: {stats['avg_throughput']:.1f} tokens/sec") - - # Show recent metrics - recent = self.monitor.session.metrics[-5:] - if recent: - print("\n📈 Recent Metrics:") - print(f" {'Step':<8} {'Loss':<10} {'LR':<12} {'Time':<12}") - print(f" {'-'*8} {'-'*10} {'-'*12} {'-'*12}") - for m in recent: - timestamp = m.timestamp.split("T")[1][:8] - print( - f" {m.step:<8} {m.loss:<10.4f} {m.learning_rate:<12.6f} {timestamp:<12}" - ) - - print("\n" + "=" * 80) - - -def main(): - """CLI for training monitor""" - import argparse - - parser = argparse.ArgumentParser(description="Training Monitor") - parser.add_argument("--list", action="store_true", help="List all sessions") - parser.add_argument("--session", type=str, help="View specific session") - parser.add_argument("--stats", action="store_true", help="Show statistics") - - args = parser.parse_args() - - if args.list: - sessions = TrainingMonitor.list_sessions() - print("Available training sessions:") - for session_id in sessions: - print(f" - {session_id}") - - elif args.session: - session = TrainingMonitor.load_session(args.session) - if session: - print(f"\nSession: {session.session_id}") - print(f"Status: {session.status}") - print(f"Duration: {session.start_time} to {session.end_time}") - print(f"Total Steps: {session.total_steps}") - print(f"Best Loss: {session.best_loss:.4f} @ step {session.best_step}") - print(f"Total Metrics: {len(session.metrics)}") - else: - print(f"Session not found: {args.session}") - - else: - parser.print_help() - - -if __name__ == "__main__": - main() +""" +Training Monitor & Dashboard +Real-time training progress monitoring with live metrics +""" + +import json +import queue +import threading +import time +from dataclasses import asdict, dataclass, field +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional + + +@dataclass +class TrainingMetrics: + """Training metrics at a point in time""" + + step: int + epoch: int + loss: float + learning_rate: float + timestamp: str + grad_norm: Optional[float] = None + throughput: Optional[float] = None # tokens/sec + gpu_memory_mb: Optional[float] = None + + def to_dict(self) -> Dict[str, Any]: + return asdict(self) + + +@dataclass +class TrainingSession: + """Complete training session information""" + + session_id: str + start_time: str + end_time: Optional[str] = None + config: Dict[str, Any] = field(default_factory=dict) + metrics: List[TrainingMetrics] = field(default_factory=list) + status: str = "running" # running, completed, failed + total_steps: int = 0 + best_loss: float = float("inf") + best_step: int = 0 + + def to_dict(self) -> Dict[str, Any]: + return {**asdict(self), "metrics": [m.to_dict() for m in self.metrics]} + + +class TrainingMonitor: + """Monitor and track training progress""" + + def __init__(self, session_id: Optional[str] = None): + self.session_id = session_id or f"training_{int(time.time())}" + self.session = TrainingSession( + session_id=self.session_id, start_time=datetime.now().isoformat() + ) + + self.log_dir = Path("data_out/training_logs") + self.log_dir.mkdir(parents=True, exist_ok=True) + + self.log_file = self.log_dir / f"{self.session_id}.jsonl" + self.summary_file = self.log_dir / f"{self.session_id}_summary.json" + + self.metrics_queue = queue.Queue() + self.should_stop = threading.Event() + self.writer_thread = threading.Thread( + target=self._write_metrics_worker, daemon=True + ) + self.writer_thread.start() + + def log_metrics( + self, step: int, epoch: int, loss: float, learning_rate: float, **kwargs + ): + """Log training metrics""" + metrics = TrainingMetrics( + step=step, + epoch=epoch, + loss=loss, + learning_rate=learning_rate, + timestamp=datetime.now().isoformat(), + grad_norm=kwargs.get("grad_norm"), + throughput=kwargs.get("throughput"), + gpu_memory_mb=kwargs.get("gpu_memory_mb"), + ) + + self.session.metrics.append(metrics) + self.session.total_steps = step + + # Track best loss + if loss < self.session.best_loss: + self.session.best_loss = loss + self.session.best_step = step + + # Queue for async writing + self.metrics_queue.put(metrics) + + def update_config(self, config: Dict[str, Any]): + """Update training configuration""" + self.session.config.update(config) + + def complete(self, status: str = "completed"): + """Mark training as complete""" + self.session.status = status + self.session.end_time = datetime.now().isoformat() + self._save_summary() + + # Stop writer thread + self.should_stop.set() + self.writer_thread.join(timeout=5) + + def _write_metrics_worker(self): + """Background worker to write metrics""" + while not self.should_stop.is_set(): + try: + metrics = self.metrics_queue.get(timeout=1) + with open(self.log_file, "a") as f: + f.write(json.dumps(metrics.to_dict()) + "\n") + except queue.Empty: + continue + except Exception as e: + print(f"Error writing metrics: {e}") + + def _save_summary(self): + """Save training summary""" + with open(self.summary_file, "w") as f: + json.dump(self.session.to_dict(), f, indent=2) + + def get_stats(self) -> Dict[str, Any]: + """Get current training statistics""" + if not self.session.metrics: + return {} + + recent_losses = [m.loss for m in self.session.metrics[-100:]] + recent_throughputs = [ + m.throughput for m in self.session.metrics[-100:] if m.throughput + ] + + return { + "total_steps": self.session.total_steps, + "current_loss": ( + self.session.metrics[-1].loss if self.session.metrics else None + ), + "best_loss": self.session.best_loss, + "best_step": self.session.best_step, + "avg_recent_loss": ( + sum(recent_losses) / len(recent_losses) if recent_losses else None + ), + "avg_throughput": ( + sum(recent_throughputs) / len(recent_throughputs) + if recent_throughputs + else None + ), + "duration": self._get_duration(), + } + + def _get_duration(self) -> str: + """Get training duration""" + start = datetime.fromisoformat(self.session.start_time) + end = ( + datetime.fromisoformat(self.session.end_time) + if self.session.end_time + else datetime.now() + ) + duration = end - start + + hours = int(duration.total_seconds() // 3600) + minutes = int((duration.total_seconds() % 3600) // 60) + seconds = int(duration.total_seconds() % 60) + + return f"{hours:02d}:{minutes:02d}:{seconds:02d}" + + def print_progress(self, step: int, total_steps: int): + """Print training progress""" + stats = self.get_stats() + progress = (step / total_steps * 100) if total_steps > 0 else 0 + + print( + f"\rStep {step}/{total_steps} ({progress:.1f}%) | " + f"Loss: {stats.get('current_loss', 'N/A'):.4f} | " + f"Best: {stats['best_loss']:.4f} @ {stats['best_step']} | " + f"Duration: {stats['duration']}", + end="", + ) + + @staticmethod + def load_session(session_id: str) -> Optional[TrainingSession]: + """Load a previous training session""" + summary_file = Path(f"data_out/training_logs/{session_id}_summary.json") + + if not summary_file.exists(): + return None + + with open(summary_file) as f: + data = json.load(f) + + # Reconstruct session + session = TrainingSession( + session_id=data["session_id"], + start_time=data["start_time"], + end_time=data.get("end_time"), + config=data.get("config", {}), + status=data.get("status", "unknown"), + total_steps=data.get("total_steps", 0), + best_loss=data.get("best_loss", float("inf")), + best_step=data.get("best_step", 0), + ) + + # Load metrics + session.metrics = [TrainingMetrics(**m) for m in data.get("metrics", [])] + + return session + + @staticmethod + def list_sessions() -> List[str]: + """List all training sessions""" + log_dir = Path("data_out/training_logs") + if not log_dir.exists(): + return [] + + sessions = [] + for file in log_dir.glob("*_summary.json"): + session_id = file.stem.replace("_summary", "") + sessions.append(session_id) + + return sorted(sessions, reverse=True) + + +class LiveDashboard: + """Live training dashboard (terminal-based)""" + + def __init__(self, monitor: TrainingMonitor): + self.monitor = monitor + self.running = False + self.dashboard_thread = None + + def start(self): + """Start the dashboard""" + self.running = True + self.dashboard_thread = threading.Thread(target=self._update_loop, daemon=True) + self.dashboard_thread.start() + + def stop(self): + """Stop the dashboard""" + self.running = False + if self.dashboard_thread: + self.dashboard_thread.join(timeout=2) + + def _update_loop(self): + """Update loop for dashboard""" + while self.running: + self._render_dashboard() + time.sleep(5) # Update every 5 seconds + + def _render_dashboard(self): + """Render the dashboard""" + stats = self.monitor.get_stats() + + # Clear screen (simple version) + print("\033[2J\033[H") # ANSI escape codes + + print("=" * 80) + print(f"TRAINING DASHBOARD - Session: {self.monitor.session_id}") + print("=" * 80) + + print(f"\nDuration: {stats.get('duration', 'N/A')}") + print(f"Status: {self.monitor.session.status.upper()}") + + print("\n📊 Progress:") + print(f" Total Steps: {stats.get('total_steps', 0):,}") + print(f" Current Loss: {stats.get('current_loss', 'N/A')}") + print( + f" Best Loss: {stats.get('best_loss', float('inf')):.4f} (step {stats.get('best_step', 0)})" + ) + print(f" Avg Recent Loss: {stats.get('avg_recent_loss', 'N/A')}") + + if stats.get("avg_throughput"): + print("\n⚡ Performance:") + print(f" Throughput: {stats['avg_throughput']:.1f} tokens/sec") + + # Show recent metrics + recent = self.monitor.session.metrics[-5:] + if recent: + print("\n📈 Recent Metrics:") + print(f" {'Step':<8} {'Loss':<10} {'LR':<12} {'Time':<12}") + print(f" {'-'*8} {'-'*10} {'-'*12} {'-'*12}") + for m in recent: + timestamp = m.timestamp.split("T")[1][:8] + print( + f" {m.step:<8} {m.loss:<10.4f} {m.learning_rate:<12.6f} {timestamp:<12}" + ) + + print("\n" + "=" * 80) + + +def main(): + """CLI for training monitor""" + import argparse + + parser = argparse.ArgumentParser(description="Training Monitor") + parser.add_argument("--list", action="store_true", help="List all sessions") + parser.add_argument("--session", type=str, help="View specific session") + parser.add_argument("--stats", action="store_true", help="Show statistics") + + args = parser.parse_args() + + if args.list: + sessions = TrainingMonitor.list_sessions() + print("Available training sessions:") + for session_id in sessions: + print(f" - {session_id}") + + elif args.session: + session = TrainingMonitor.load_session(args.session) + if session: + print(f"\nSession: {session.session_id}") + print(f"Status: {session.status}") + print(f"Duration: {session.start_time} to {session.end_time}") + print(f"Total Steps: {session.total_steps}") + print(f"Best Loss: {session.best_loss:.4f} @ step {session.best_step}") + print(f"Total Metrics: {len(session.metrics)}") + else: + print(f"Session not found: {args.session}") + + else: + parser.print_help() + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/infra/provision/finetuning.config.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/infra/provision/finetuning.config.json index e4270acd0..f4983fad7 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/infra/provision/finetuning.config.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/infra/provision/finetuning.config.json @@ -1,10 +1,10 @@ -{ - "SUBSCRIPTION_ID": null, - "RESOURCE_GROUP_NAME": null, - "STORAGE_ACCOUNT_NAME": null, - "FILE_SHARE_NAME": null, - "ACA_JOB_NAME": null, - "LOG_ANALYTICS_NAME": null, - "COMMANDS": [], - "ARGS": ["mount//soft_prompt.yaml"] +{ + "SUBSCRIPTION_ID": null, + "RESOURCE_GROUP_NAME": null, + "STORAGE_ACCOUNT_NAME": null, + "FILE_SHARE_NAME": null, + "ACA_JOB_NAME": null, + "LOG_ANALYTICS_NAME": null, + "COMMANDS": [], + "ARGS": ["mount//soft_prompt.yaml"] } diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/infra/provision/finetuning.parameters.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/infra/provision/finetuning.parameters.json index af01d24a5..9e04cb3d7 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/infra/provision/finetuning.parameters.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/infra/provision/finetuning.parameters.json @@ -1,30 +1,30 @@ -{ - "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#", - "contentVersion": "1.0.0.0", - "parameters": { - "timeout": { - "value": 21600 - }, - "location": { - "value": null - }, - "storageAccountName": { - "value": null - }, - "fileShareName": { - "value": null - }, - "acaEnvironmentName": { - "value": null - }, - "acaEnvironmentStorageName": { - "value": null - }, - "acaJobName": { - "value": null - }, - "acaLogAnalyticsName": { - "value": null - } - } +{ + "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#", + "contentVersion": "1.0.0.0", + "parameters": { + "timeout": { + "value": 21600 + }, + "location": { + "value": null + }, + "storageAccountName": { + "value": null + }, + "fileShareName": { + "value": null + }, + "acaEnvironmentName": { + "value": null + }, + "acaEnvironmentStorageName": { + "value": null + }, + "acaJobName": { + "value": null + }, + "acaLogAnalyticsName": { + "value": null + } + } } diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/soft_prompt.yaml b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/soft_prompt.yaml index a3f54e242..92baf2e1c 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/soft_prompt.yaml +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/soft_prompt/soft_prompt.yaml @@ -1,18 +1,18 @@ -model: "Phi-3.6-mini-instruct" -model_path: "model" -save_dir: "mount//save_dir" -finetune_dataset: "mount//dataset" -max_input_size: 2048 -init_prompt: "Classify sentiment and topic of financal news in this format: topic=\"?\"; sentiment=\"?\";" -number_of_virtual_tokens: 64 -learning_rate: !!float 3e-05 -early_stopping_patience: 5 -epochs: 1 -evaluation_strategy: "steps" -eval_steps: 8 -save_steps: 64 -seed: 42 -finetune_train_nsamples: null -finetune_test_nsamples: null -finetune_train_batch_size: 2 -finetune_test_batch_size: 2 +model: "Phi-3.6-mini-instruct" +model_path: "model" +save_dir: "mount//save_dir" +finetune_dataset: "mount//dataset" +max_input_size: 2048 +init_prompt: "Classify sentiment and topic of financal news in this format: topic=\"?\"; sentiment=\"?\";" +number_of_virtual_tokens: 64 +learning_rate: !!float 3e-05 +early_stopping_patience: 5 +epochs: 1 +evaluation_strategy: "steps" +eval_steps: 8 +save_steps: 64 +seed: 42 +finetune_train_nsamples: null +finetune_test_nsamples: null +finetune_train_batch_size: 2 +finetune_test_batch_size: 2 diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/venv.corrupt.20251123T125213Z/pyvenv.cfg b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/venv.corrupt.20251123T125213Z/pyvenv.cfg index 4b830c7c4..8e29c8d3c 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/venv.corrupt.20251123T125213Z/pyvenv.cfg +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/venv.corrupt.20251123T125213Z/pyvenv.cfg @@ -1,5 +1,5 @@ -home = C:\Users\Bryan\AppData\Local\Programs\Python\Python311 -include-system-site-packages = false -version = 3.11.9 -executable = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe -command = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe -m venv C:\Users\Bryan\OneDrive\AI\AI\microsoft_phi-silica-3.6_v1\venv +home = C:\Users\Bryan\AppData\Local\Programs\Python\Python311 +include-system-site-packages = false +version = 3.11.9 +executable = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe +command = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe -m venv C:\Users\Bryan\OneDrive\AI\AI\microsoft_phi-silica-3.6_v1\venv diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/venv.corrupt.20251123T132136Z/pyvenv.cfg b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/venv.corrupt.20251123T132136Z/pyvenv.cfg index 4b830c7c4..8e29c8d3c 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/venv.corrupt.20251123T132136Z/pyvenv.cfg +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/venv.corrupt.20251123T132136Z/pyvenv.cfg @@ -1,5 +1,5 @@ -home = C:\Users\Bryan\AppData\Local\Programs\Python\Python311 -include-system-site-packages = false -version = 3.11.9 -executable = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe -command = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe -m venv C:\Users\Bryan\OneDrive\AI\AI\microsoft_phi-silica-3.6_v1\venv +home = C:\Users\Bryan\AppData\Local\Programs\Python\Python311 +include-system-site-packages = false +version = 3.11.9 +executable = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe +command = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe -m venv C:\Users\Bryan\OneDrive\AI\AI\microsoft_phi-silica-3.6_v1\venv diff --git a/ai-projects/lora-training/quantum-ai/README.md b/ai-projects/lora-training/quantum-ai/README.md index c955437ef..d7cb7be53 100644 --- a/ai-projects/lora-training/quantum-ai/README.md +++ b/ai-projects/lora-training/quantum-ai/README.md @@ -1,331 +1,331 @@ -# Quantum AI Project (Qiskit Implementation) - -**⚠️ NOTE: This is an alternative Qiskit-based implementation.** - -The primary quantum AI implementation is in `/quantum-ai` and uses **PennyLane**. -This directory (`/AI/quantum-ai`) contains an older **Qiskit-based** implementation -that uses different quantum computing frameworks. - -## Key Differences - -| Feature | This Directory (AI/quantum-ai) | Main Directory (quantum-ai) | -|---------|-------------------------------|---------------------------| -| Framework | **Qiskit** | **PennyLane** | -| Backend | Qiskit Aer, IBM Quantum | PennyLane, Azure Quantum | -| Status | Legacy/Alternative | Primary/Active | - -For new development, use the **PennyLane-based** implementation in `/quantum-ai`. - ---- - -# Original Documentation - -A comprehensive quantum machine learning framework combining **quantum computing** with **classical AI**, powered by **Azure Quantum** and **Qiskit**. - -## 🌟 Features - -- **Quantum Machine Learning**: Variational Quantum Classifiers (VQC) and Quantum Neural Networks (QNN) -- **Hybrid Models**: Quantum-classical neural networks combining the best of both worlds -- **Azure Quantum Integration**: Seamless integration with Azure Quantum services -- **Multiple Backends**: Support for simulators and real quantum hardware (IonQ, Quantinuum, Rigetti) -- **Production Ready**: Scalable architecture with logging, monitoring, and batch processing - -## 🏗️ Architecture - -```text -Classical Input → Classical Preprocessing → Quantum Layer → Classical Postprocessing → Output -``` - -### Components - -1. **Quantum Classifier** (`quantum_classifier.py`) - - Variational Quantum Classifier (VQC) - - Quantum Neural Network (QNN) - - Built-in data preprocessing and scaling - -2. **Hybrid QNN** (`hybrid_qnn.py`) - - PyTorch-based hybrid architecture - - Quantum layers integrated into classical networks - - Full gradient-based training - -3. **Azure Quantum Integration** (`azure_quantum_integration.py`) - - Workspace connection management - - Job submission and monitoring - - Backend selection and cost estimation - -## 📋 Prerequisites - -- Python 3.8 or higher -- Azure subscription (for Azure Quantum) -- Basic understanding of quantum computing and machine learning - -## 🚀 Installation - -### 1. Clone the repository - -```bash -git clone -cd quantum-ai -``` - -### 2. Create a virtual environment - -```bash -python -m venv venv - -# Windows -.\venv\Scripts\activate - -# Linux/Mac -source venv/bin/activate -``` - -### 3. Install dependencies - -```bash -pip install -r requirements.txt -``` - -### 4. Set up Azure Quantum (Optional but recommended) - -Create an Azure Quantum workspace: - -```bash -# Using Azure CLI -az quantum workspace create \ - --resource-group \ - --name \ - --location eastus \ - --storage-account -``` - -Set environment variables: - -```bash -# Windows PowerShell -$env:AZURE_QUANTUM_RESOURCE_ID = "/subscriptions/.../resourceGroups/.../providers/Microsoft.Quantum/Workspaces/..." -$env:AZURE_QUANTUM_LOCATION = "eastus" - -# Linux/Mac -export AZURE_QUANTUM_RESOURCE_ID="/subscriptions/.../resourceGroups/.../providers/Microsoft.Quantum/Workspaces/..." -export AZURE_QUANTUM_LOCATION="eastus" -``` - -## 💻 Quick Start - -### Example 1: Quantum Classifier - -```python -from src.quantum_classifier import QuantumClassifier -from sklearn.datasets import make_classification -from sklearn.model_selection import train_test_split - -# Generate data -X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42) -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) - -# Create and train quantum classifier -qc = QuantumClassifier(num_features=4, num_qubits=4, reps=2) -qc.fit(X_train, y_train) - -# Evaluate -accuracy = qc.score(X_test, y_test) -print(f"Accuracy: {accuracy:.4f}") -``` - -### Example 2: Hybrid Quantum-Classical Neural Network - -```python -from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer -import torch -from torch.utils.data import DataLoader, TensorDataset - -# Create hybrid model -model = HybridQNN( - input_dim=8, - hidden_dim=16, - num_qubits=4, - quantum_layers=2, - output_dim=3 -) - -# Prepare data loaders -train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True) -val_loader = DataLoader(val_dataset, batch_size=16) - -# Train -trainer = QuantumClassicalTrainer(model, learning_rate=0.01) -trainer.train(train_loader, val_loader, num_epochs=10) -``` - -### Example 3: Azure Quantum Integration - -```python -from src.azure_quantum_integration import AzureQuantumManager -from qiskit import QuantumCircuit - -# Create quantum circuit -qc = QuantumCircuit(2, 2) -qc.h(0) -qc.cx(0, 1) -qc.measure([0, 1], [0, 1]) - -# Connect to Azure Quantum -azure_qm = AzureQuantumManager() -azure_qm.connect() - -# List available backends -backends = azure_qm.list_backends() -print(f"Available backends: {backends}") - -# Run on quantum hardware/simulator -result = azure_qm.run_circuit(qc, shots=1000, backend_name='ionq.simulator') -print(f"Results: {result['counts']}") -``` - -## 📊 Jupyter Notebooks - -Explore interactive examples in the `notebooks/` directory: - -- `01_quantum_basics.ipynb` - Introduction to quantum circuits -- `02_quantum_ml.ipynb` - Quantum machine learning fundamentals -- `03_hybrid_models.ipynb` - Building hybrid quantum-classical models -- `04_azure_quantum.ipynb` - Using Azure Quantum services - -## 🛠️ Project Structure - -```text -quantum-ai/ -├── src/ -│ ├── quantum_classifier.py # Quantum ML classifiers -│ ├── hybrid_qnn.py # Hybrid quantum-classical models -│ └── azure_quantum_integration.py # Azure Quantum connection -├── notebooks/ -│ ├── 01_quantum_basics.ipynb -│ ├── 02_quantum_ml.ipynb -│ ├── 03_hybrid_models.ipynb -│ └── 04_azure_quantum.ipynb -├── config/ -│ └── quantum_config.yaml # Configuration file -├── azure/ -│ └── quantum_workspace.bicep # Azure infrastructure -├── requirements.txt -└── README.md -``` - -## ⚙️ Configuration - -Edit `config/quantum_config.yaml` to customize: - -```yaml -quantum: - num_qubits: 4 - circuit_depth: 3 - optimizer: COBYLA - shots: 1024 - -azure: - location: eastus - default_backend: ionq.simulator - -training: - learning_rate: 0.001 - batch_size: 16 - epochs: 10 -``` - -## 🔬 Supported Quantum Backends - -### Simulators (Free) - -- **Qiskit Aer** - Local quantum simulator -- **IonQ Simulator** - Cloud-based ion trap simulator -- **Quantinuum Simulator** - Trapped-ion quantum simulator - -### Real Quantum Hardware (Requires Azure Quantum credits) - -- **IonQ Quantum Computer** - Ion trap quantum computer -- **Quantinuum H1** - Trapped-ion quantum processor -- **Rigetti** - Superconducting quantum processors - -## 📈 Performance Tips - -1. **Start with simulators** before moving to real hardware -2. **Use fewer qubits** for faster training (4-6 qubits is optimal) -3. **Batch processing** for multiple circuits -4. **Cost estimation** before running on real hardware -5. **Hybrid models** often outperform pure quantum models - -## 🧪 Running Tests - -```bash -# Run all tests -pytest tests/ - -# Run specific test -pytest tests/test_quantum_classifier.py - -# With coverage -pytest --cov=src tests/ -``` - -## 📚 Resources - -- [Qiskit Documentation](https://qiskit.org/documentation/) -- [Azure Quantum Documentation](https://learn.microsoft.com/azure/quantum/) -- [Quantum Machine Learning Papers](https://arxiv.org/list/quant-ph/recent) -- [PennyLane QML](https://pennylane.ai/qml/) - -## 🤝 Contributing - -Contributions are welcome! Please: - -1. Fork the repository -2. Create a feature branch -3. Make your changes -4. Add tests -5. Submit a pull request - -## 📝 License - -MIT License - see LICENSE file for details - -## 🆘 Troubleshooting - -### Import errors - -```bash -pip install --upgrade qiskit qiskit-machine-learning qiskit-algorithms -``` - -### Azure authentication issues - -```bash -az login -az account set --subscription -``` - -### Quantum backend not found - -Check available backends: - -```python -azure_qm.list_backends() -``` - -## 🌐 Future Enhancements - -- [ ] Quantum Generative Adversarial Networks (QGAN) -- [ ] Quantum Reinforcement Learning -- [ ] Quantum Natural Language Processing -- [ ] Integration with TensorFlow Quantum -- [ ] Automated hyperparameter tuning -- [ ] Distributed quantum computing - -## 📧 Contact - -For questions or support, please open an issue on GitHub. - ---- - -**Built with** ⚛️ **Quantum Computing** | 🧠 **Machine Learning** | ☁️ **Azure Quantum** +# Quantum AI Project (Qiskit Implementation) + +**⚠️ NOTE: This is an alternative Qiskit-based implementation.** + +The primary quantum AI implementation is in `/quantum-ai` and uses **PennyLane**. +This directory (`/AI/quantum-ai`) contains an older **Qiskit-based** implementation +that uses different quantum computing frameworks. + +## Key Differences + +| Feature | This Directory (AI/quantum-ai) | Main Directory (quantum-ai) | +|---------|-------------------------------|---------------------------| +| Framework | **Qiskit** | **PennyLane** | +| Backend | Qiskit Aer, IBM Quantum | PennyLane, Azure Quantum | +| Status | Legacy/Alternative | Primary/Active | + +For new development, use the **PennyLane-based** implementation in `/quantum-ai`. + +--- + +# Original Documentation + +A comprehensive quantum machine learning framework combining **quantum computing** with **classical AI**, powered by **Azure Quantum** and **Qiskit**. + +## 🌟 Features + +- **Quantum Machine Learning**: Variational Quantum Classifiers (VQC) and Quantum Neural Networks (QNN) +- **Hybrid Models**: Quantum-classical neural networks combining the best of both worlds +- **Azure Quantum Integration**: Seamless integration with Azure Quantum services +- **Multiple Backends**: Support for simulators and real quantum hardware (IonQ, Quantinuum, Rigetti) +- **Production Ready**: Scalable architecture with logging, monitoring, and batch processing + +## 🏗️ Architecture + +```text +Classical Input → Classical Preprocessing → Quantum Layer → Classical Postprocessing → Output +``` + +### Components + +1. **Quantum Classifier** (`quantum_classifier.py`) + - Variational Quantum Classifier (VQC) + - Quantum Neural Network (QNN) + - Built-in data preprocessing and scaling + +2. **Hybrid QNN** (`hybrid_qnn.py`) + - PyTorch-based hybrid architecture + - Quantum layers integrated into classical networks + - Full gradient-based training + +3. **Azure Quantum Integration** (`azure_quantum_integration.py`) + - Workspace connection management + - Job submission and monitoring + - Backend selection and cost estimation + +## 📋 Prerequisites + +- Python 3.8 or higher +- Azure subscription (for Azure Quantum) +- Basic understanding of quantum computing and machine learning + +## 🚀 Installation + +### 1. Clone the repository + +```bash +git clone +cd quantum-ai +``` + +### 2. Create a virtual environment + +```bash +python -m venv venv + +# Windows +.\venv\Scripts\activate + +# Linux/Mac +source venv/bin/activate +``` + +### 3. Install dependencies + +```bash +pip install -r requirements.txt +``` + +### 4. Set up Azure Quantum (Optional but recommended) + +Create an Azure Quantum workspace: + +```bash +# Using Azure CLI +az quantum workspace create \ + --resource-group \ + --name \ + --location eastus \ + --storage-account +``` + +Set environment variables: + +```bash +# Windows PowerShell +$env:AZURE_QUANTUM_RESOURCE_ID = "/subscriptions/.../resourceGroups/.../providers/Microsoft.Quantum/Workspaces/..." +$env:AZURE_QUANTUM_LOCATION = "eastus" + +# Linux/Mac +export AZURE_QUANTUM_RESOURCE_ID="/subscriptions/.../resourceGroups/.../providers/Microsoft.Quantum/Workspaces/..." +export AZURE_QUANTUM_LOCATION="eastus" +``` + +## 💻 Quick Start + +### Example 1: Quantum Classifier + +```python +from src.quantum_classifier import QuantumClassifier +from sklearn.datasets import make_classification +from sklearn.model_selection import train_test_split + +# Generate data +X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42) +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +# Create and train quantum classifier +qc = QuantumClassifier(num_features=4, num_qubits=4, reps=2) +qc.fit(X_train, y_train) + +# Evaluate +accuracy = qc.score(X_test, y_test) +print(f"Accuracy: {accuracy:.4f}") +``` + +### Example 2: Hybrid Quantum-Classical Neural Network + +```python +from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer +import torch +from torch.utils.data import DataLoader, TensorDataset + +# Create hybrid model +model = HybridQNN( + input_dim=8, + hidden_dim=16, + num_qubits=4, + quantum_layers=2, + output_dim=3 +) + +# Prepare data loaders +train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True) +val_loader = DataLoader(val_dataset, batch_size=16) + +# Train +trainer = QuantumClassicalTrainer(model, learning_rate=0.01) +trainer.train(train_loader, val_loader, num_epochs=10) +``` + +### Example 3: Azure Quantum Integration + +```python +from src.azure_quantum_integration import AzureQuantumManager +from qiskit import QuantumCircuit + +# Create quantum circuit +qc = QuantumCircuit(2, 2) +qc.h(0) +qc.cx(0, 1) +qc.measure([0, 1], [0, 1]) + +# Connect to Azure Quantum +azure_qm = AzureQuantumManager() +azure_qm.connect() + +# List available backends +backends = azure_qm.list_backends() +print(f"Available backends: {backends}") + +# Run on quantum hardware/simulator +result = azure_qm.run_circuit(qc, shots=1000, backend_name='ionq.simulator') +print(f"Results: {result['counts']}") +``` + +## 📊 Jupyter Notebooks + +Explore interactive examples in the `notebooks/` directory: + +- `01_quantum_basics.ipynb` - Introduction to quantum circuits +- `02_quantum_ml.ipynb` - Quantum machine learning fundamentals +- `03_hybrid_models.ipynb` - Building hybrid quantum-classical models +- `04_azure_quantum.ipynb` - Using Azure Quantum services + +## 🛠️ Project Structure + +```text +quantum-ai/ +├── src/ +│ ├── quantum_classifier.py # Quantum ML classifiers +│ ├── hybrid_qnn.py # Hybrid quantum-classical models +│ └── azure_quantum_integration.py # Azure Quantum connection +├── notebooks/ +│ ├── 01_quantum_basics.ipynb +│ ├── 02_quantum_ml.ipynb +│ ├── 03_hybrid_models.ipynb +│ └── 04_azure_quantum.ipynb +├── config/ +│ └── quantum_config.yaml # Configuration file +├── azure/ +│ └── quantum_workspace.bicep # Azure infrastructure +├── requirements.txt +└── README.md +``` + +## ⚙️ Configuration + +Edit `config/quantum_config.yaml` to customize: + +```yaml +quantum: + num_qubits: 4 + circuit_depth: 3 + optimizer: COBYLA + shots: 1024 + +azure: + location: eastus + default_backend: ionq.simulator + +training: + learning_rate: 0.001 + batch_size: 16 + epochs: 10 +``` + +## 🔬 Supported Quantum Backends + +### Simulators (Free) + +- **Qiskit Aer** - Local quantum simulator +- **IonQ Simulator** - Cloud-based ion trap simulator +- **Quantinuum Simulator** - Trapped-ion quantum simulator + +### Real Quantum Hardware (Requires Azure Quantum credits) + +- **IonQ Quantum Computer** - Ion trap quantum computer +- **Quantinuum H1** - Trapped-ion quantum processor +- **Rigetti** - Superconducting quantum processors + +## 📈 Performance Tips + +1. **Start with simulators** before moving to real hardware +2. **Use fewer qubits** for faster training (4-6 qubits is optimal) +3. **Batch processing** for multiple circuits +4. **Cost estimation** before running on real hardware +5. **Hybrid models** often outperform pure quantum models + +## 🧪 Running Tests + +```bash +# Run all tests +pytest tests/ + +# Run specific test +pytest tests/test_quantum_classifier.py + +# With coverage +pytest --cov=src tests/ +``` + +## 📚 Resources + +- [Qiskit Documentation](https://qiskit.org/documentation/) +- [Azure Quantum Documentation](https://learn.microsoft.com/azure/quantum/) +- [Quantum Machine Learning Papers](https://arxiv.org/list/quant-ph/recent) +- [PennyLane QML](https://pennylane.ai/qml/) + +## 🤝 Contributing + +Contributions are welcome! Please: + +1. Fork the repository +2. Create a feature branch +3. Make your changes +4. Add tests +5. Submit a pull request + +## 📝 License + +MIT License - see LICENSE file for details + +## 🆘 Troubleshooting + +### Import errors + +```bash +pip install --upgrade qiskit qiskit-machine-learning qiskit-algorithms +``` + +### Azure authentication issues + +```bash +az login +az account set --subscription +``` + +### Quantum backend not found + +Check available backends: + +```python +azure_qm.list_backends() +``` + +## 🌐 Future Enhancements + +- [ ] Quantum Generative Adversarial Networks (QGAN) +- [ ] Quantum Reinforcement Learning +- [ ] Quantum Natural Language Processing +- [ ] Integration with TensorFlow Quantum +- [ ] Automated hyperparameter tuning +- [ ] Distributed quantum computing + +## 📧 Contact + +For questions or support, please open an issue on GitHub. + +--- + +**Built with** ⚛️ **Quantum Computing** | 🧠 **Machine Learning** | ☁️ **Azure Quantum** diff --git a/ai-projects/lora-training/quantum-ai/azure/DEPLOYMENT.md b/ai-projects/lora-training/quantum-ai/azure/DEPLOYMENT.md index af88cdef9..8ba7ea86a 100644 --- a/ai-projects/lora-training/quantum-ai/azure/DEPLOYMENT.md +++ b/ai-projects/lora-training/quantum-ai/azure/DEPLOYMENT.md @@ -1,170 +1,170 @@ -# Azure Quantum Deployment Guide - -## Prerequisites - -1. **Azure CLI** installed and configured -2. **Azure subscription** with appropriate permissions -3. **Resource group** created (or will be created) - -## Deployment Steps - -### 1. Login to Azure - -```bash -az login -az account set --subscription -``` - -### 2. Create Resource Group (if not exists) - -```bash -az group create \ - --name quantum-ai-rg \ - --location eastus -``` - -### 3. Deploy Azure Quantum Workspace - -```bash -az deployment group create \ - --resource-group quantum-ai-rg \ - --template-file quantum_workspace.bicep \ - --parameters quantum_workspace.parameters.json -``` - -### 4. Verify Deployment - -```bash -# List Quantum workspaces -az quantum workspace list \ - --resource-group quantum-ai-rg \ - --output table - -# Show workspace details -az quantum workspace show \ - --resource-group quantum-ai-rg \ - --name quantum-ai-workspace -``` - -### 5. Get Workspace Resource ID - -```bash -az quantum workspace show \ - --resource-group quantum-ai-rg \ - --name quantum-ai-workspace \ - --query id \ - --output tsv -``` - -Copy the resource ID and set it as an environment variable: - -```powershell -# PowerShell -$env:AZURE_QUANTUM_RESOURCE_ID = "" - -# Bash -export AZURE_QUANTUM_RESOURCE_ID="" -``` - -## Cost Management - -### Free Tier Options - -- **Microsoft QIO (Learn & Develop)**: Free tier available -- **IonQ Simulator**: Free -- **Quantinuum Simulator**: Free - -### Paid Options - -- **IonQ QPU**: ~$0.30 per gate-shot -- **Quantinuum H1**: ~$0.00015 per gate (H1-1) or $0.00025 (H1-2) - -### Cost Estimation - -Before running on real hardware: - -```python -from src.azure_quantum_integration import AzureQuantumManager - -azure_qm = AzureQuantumManager() -azure_qm.connect() - -# Estimate cost -cost = azure_qm.estimate_cost(circuit, backend_name='ionq.qpu') -print(cost) -``` - -## Monitoring - -### View Jobs in Azure Portal - -1. Navigate to Azure Portal -2. Go to your Quantum workspace -3. Click on "Job management" -4. View job history and status - -### Using Azure CLI - -```bash -# List jobs -az quantum job list \ - --resource-group quantum-ai-rg \ - --workspace-name quantum-ai-workspace - -# Get job output -az quantum job output \ - --job-id \ - --resource-group quantum-ai-rg \ - --workspace-name quantum-ai-workspace -``` - -## Troubleshooting - -### Provider Not Available - -If a provider is not available in your region: - -```bash -# Check available locations -az quantum workspace list-locations --output table - -# Update location in parameters.json -``` - -### Authentication Issues - -```bash -# Clear cached credentials -az account clear - -# Login again -az login - -# Verify subscription -az account show -``` - -### Quota Issues - -Some quantum providers require application: - -1. Visit [Azure Quantum portal](https://quantum.microsoft.com/) -2. Request access to specific providers -3. Wait for approval (usually 1-2 business days) - -## Cleanup - -To delete all resources: - -```bash -az group delete \ - --name quantum-ai-rg \ - --yes \ - --no-wait -``` - -## Additional Resources - -- [Azure Quantum Documentation](https://learn.microsoft.com/azure/quantum/) -- [Quantum Provider Documentation](https://learn.microsoft.com/azure/quantum/provider-support) -- [Pricing Calculator](https://azure.microsoft.com/pricing/calculator/) +# Azure Quantum Deployment Guide + +## Prerequisites + +1. **Azure CLI** installed and configured +2. **Azure subscription** with appropriate permissions +3. **Resource group** created (or will be created) + +## Deployment Steps + +### 1. Login to Azure + +```bash +az login +az account set --subscription +``` + +### 2. Create Resource Group (if not exists) + +```bash +az group create \ + --name quantum-ai-rg \ + --location eastus +``` + +### 3. Deploy Azure Quantum Workspace + +```bash +az deployment group create \ + --resource-group quantum-ai-rg \ + --template-file quantum_workspace.bicep \ + --parameters quantum_workspace.parameters.json +``` + +### 4. Verify Deployment + +```bash +# List Quantum workspaces +az quantum workspace list \ + --resource-group quantum-ai-rg \ + --output table + +# Show workspace details +az quantum workspace show \ + --resource-group quantum-ai-rg \ + --name quantum-ai-workspace +``` + +### 5. Get Workspace Resource ID + +```bash +az quantum workspace show \ + --resource-group quantum-ai-rg \ + --name quantum-ai-workspace \ + --query id \ + --output tsv +``` + +Copy the resource ID and set it as an environment variable: + +```powershell +# PowerShell +$env:AZURE_QUANTUM_RESOURCE_ID = "" + +# Bash +export AZURE_QUANTUM_RESOURCE_ID="" +``` + +## Cost Management + +### Free Tier Options + +- **Microsoft QIO (Learn & Develop)**: Free tier available +- **IonQ Simulator**: Free +- **Quantinuum Simulator**: Free + +### Paid Options + +- **IonQ QPU**: ~$0.30 per gate-shot +- **Quantinuum H1**: ~$0.00015 per gate (H1-1) or $0.00025 (H1-2) + +### Cost Estimation + +Before running on real hardware: + +```python +from src.azure_quantum_integration import AzureQuantumManager + +azure_qm = AzureQuantumManager() +azure_qm.connect() + +# Estimate cost +cost = azure_qm.estimate_cost(circuit, backend_name='ionq.qpu') +print(cost) +``` + +## Monitoring + +### View Jobs in Azure Portal + +1. Navigate to Azure Portal +2. Go to your Quantum workspace +3. Click on "Job management" +4. View job history and status + +### Using Azure CLI + +```bash +# List jobs +az quantum job list \ + --resource-group quantum-ai-rg \ + --workspace-name quantum-ai-workspace + +# Get job output +az quantum job output \ + --job-id \ + --resource-group quantum-ai-rg \ + --workspace-name quantum-ai-workspace +``` + +## Troubleshooting + +### Provider Not Available + +If a provider is not available in your region: + +```bash +# Check available locations +az quantum workspace list-locations --output table + +# Update location in parameters.json +``` + +### Authentication Issues + +```bash +# Clear cached credentials +az account clear + +# Login again +az login + +# Verify subscription +az account show +``` + +### Quota Issues + +Some quantum providers require application: + +1. Visit [Azure Quantum portal](https://quantum.microsoft.com/) +2. Request access to specific providers +3. Wait for approval (usually 1-2 business days) + +## Cleanup + +To delete all resources: + +```bash +az group delete \ + --name quantum-ai-rg \ + --yes \ + --no-wait +``` + +## Additional Resources + +- [Azure Quantum Documentation](https://learn.microsoft.com/azure/quantum/) +- [Quantum Provider Documentation](https://learn.microsoft.com/azure/quantum/provider-support) +- [Pricing Calculator](https://azure.microsoft.com/pricing/calculator/) diff --git a/ai-projects/lora-training/quantum-ai/azure/quantum_workspace.parameters.json b/ai-projects/lora-training/quantum-ai/azure/quantum_workspace.parameters.json index 0210d0232..fc5afc592 100644 --- a/ai-projects/lora-training/quantum-ai/azure/quantum_workspace.parameters.json +++ b/ai-projects/lora-training/quantum-ai/azure/quantum_workspace.parameters.json @@ -1,22 +1,22 @@ -{ - "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#", - "contentVersion": "1.0.0.0", - "parameters": { - "workspaceName": { - "value": "quantum-ai-workspace" - }, - "location": { - "value": "eastus" - }, - "storageAccountName": { - "value": "quantumaistg001" - }, - "tags": { - "value": { - "Environment": "Development", - "Project": "QuantumAI", - "ManagedBy": "Bicep" - } - } - } -} +{ + "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#", + "contentVersion": "1.0.0.0", + "parameters": { + "workspaceName": { + "value": "quantum-ai-workspace" + }, + "location": { + "value": "eastus" + }, + "storageAccountName": { + "value": "quantumaistg001" + }, + "tags": { + "value": { + "Environment": "Development", + "Project": "QuantumAI", + "ManagedBy": "Bicep" + } + } + } +} diff --git a/ai-projects/lora-training/quantum-ai/config/quantum_config.yaml b/ai-projects/lora-training/quantum-ai/config/quantum_config.yaml index e46ccb3ed..11fa0ab28 100644 --- a/ai-projects/lora-training/quantum-ai/config/quantum_config.yaml +++ b/ai-projects/lora-training/quantum-ai/config/quantum_config.yaml @@ -1,146 +1,146 @@ -# Quantum AI Configuration - -quantum: - # Number of qubits for quantum circuits - num_qubits: 4 - - # Circuit depth/layers - circuit_depth: 3 - - # Number of repetitions in variational forms - reps: 2 - - # Optimizer selection: COBYLA, SPSA, ADAM, L_BFGS_B - optimizer: COBYLA - - # Maximum optimizer iterations - max_iterations: 100 - - # Number of measurement shots - shots: 1024 - - # Feature map type: ZZFeatureMap, ZFeatureMap, PauliFeatureMap - feature_map: ZZFeatureMap - - # Ansatz type: RealAmplitudes, EfficientSU2, TwoLocal - ansatz: RealAmplitudes - -azure_quantum: - # Azure region - location: eastus - - # Default backend for execution - # Options: ionq.simulator, ionq.qpu, quantinuum.sim.h1-1e, quantinuum.qpu.h1-1 - default_backend: ionq.simulator - - # Auto-retry failed jobs - auto_retry: true - - # Maximum retry attempts - max_retries: 3 - - # Job timeout in seconds - job_timeout: 300 - - # Enable cost estimation before running - estimate_cost: true - -training: - # Learning rate for classical optimizers - learning_rate: 0.001 - - # Batch size - batch_size: 16 - - # Number of training epochs - epochs: 10 - - # Validation split ratio - validation_split: 0.2 - - # Early stopping patience - early_stopping_patience: 5 - - # Dropout rate for hybrid models - dropout: 0.2 - - # Weight decay (L2 regularization) - weight_decay: 0.0001 - -hybrid_model: - # Hidden layer dimensions for classical parts - hidden_dims: [64, 32] - - # Number of quantum layers - quantum_layers: 2 - - # Activation function: relu, tanh, sigmoid - activation: relu - - # Use batch normalization - batch_norm: true - - # Device: cpu, cuda, mps - device: cpu - -data: - # Feature scaling method: standard, minmax, robust - scaler: standard - - # Handle imbalanced classes - balance_classes: false - - # Random seed for reproducibility - random_seed: 42 - - # Cross-validation folds - cv_folds: 5 - -logging: - # Logging level: DEBUG, INFO, WARNING, ERROR - level: INFO - - # Log file path - log_file: logs/quantum_ai.log - - # Enable console logging - console: true - - # Enable file logging - file: true - -experiment: - # Experiment tracking directory - output_dir: experiments - - # Save model checkpoints - save_checkpoints: true - - # Checkpoint frequency (epochs) - checkpoint_frequency: 5 - - # Save best model only - save_best_only: true - - # Metrics to track - metrics: - - accuracy - - loss - - f1_score - - confusion_matrix - -advanced: - # Use gradient descent for quantum parameters - quantum_gradient_descent: false - - # Parameter shift rule for gradients - parameter_shift: true - - # Circuit transpilation optimization level (0-3) - transpile_optimization: 2 - - # Use error mitigation - error_mitigation: false - - # Noise model for simulation - noise_model: null +# Quantum AI Configuration + +quantum: + # Number of qubits for quantum circuits + num_qubits: 4 + + # Circuit depth/layers + circuit_depth: 3 + + # Number of repetitions in variational forms + reps: 2 + + # Optimizer selection: COBYLA, SPSA, ADAM, L_BFGS_B + optimizer: COBYLA + + # Maximum optimizer iterations + max_iterations: 100 + + # Number of measurement shots + shots: 1024 + + # Feature map type: ZZFeatureMap, ZFeatureMap, PauliFeatureMap + feature_map: ZZFeatureMap + + # Ansatz type: RealAmplitudes, EfficientSU2, TwoLocal + ansatz: RealAmplitudes + +azure_quantum: + # Azure region + location: eastus + + # Default backend for execution + # Options: ionq.simulator, ionq.qpu, quantinuum.sim.h1-1e, quantinuum.qpu.h1-1 + default_backend: ionq.simulator + + # Auto-retry failed jobs + auto_retry: true + + # Maximum retry attempts + max_retries: 3 + + # Job timeout in seconds + job_timeout: 300 + + # Enable cost estimation before running + estimate_cost: true + +training: + # Learning rate for classical optimizers + learning_rate: 0.001 + + # Batch size + batch_size: 16 + + # Number of training epochs + epochs: 10 + + # Validation split ratio + validation_split: 0.2 + + # Early stopping patience + early_stopping_patience: 5 + + # Dropout rate for hybrid models + dropout: 0.2 + + # Weight decay (L2 regularization) + weight_decay: 0.0001 + +hybrid_model: + # Hidden layer dimensions for classical parts + hidden_dims: [64, 32] + + # Number of quantum layers + quantum_layers: 2 + + # Activation function: relu, tanh, sigmoid + activation: relu + + # Use batch normalization + batch_norm: true + + # Device: cpu, cuda, mps + device: cpu + +data: + # Feature scaling method: standard, minmax, robust + scaler: standard + + # Handle imbalanced classes + balance_classes: false + + # Random seed for reproducibility + random_seed: 42 + + # Cross-validation folds + cv_folds: 5 + +logging: + # Logging level: DEBUG, INFO, WARNING, ERROR + level: INFO + + # Log file path + log_file: logs/quantum_ai.log + + # Enable console logging + console: true + + # Enable file logging + file: true + +experiment: + # Experiment tracking directory + output_dir: experiments + + # Save model checkpoints + save_checkpoints: true + + # Checkpoint frequency (epochs) + checkpoint_frequency: 5 + + # Save best model only + save_best_only: true + + # Metrics to track + metrics: + - accuracy + - loss + - f1_score + - confusion_matrix + +advanced: + # Use gradient descent for quantum parameters + quantum_gradient_descent: false + + # Parameter shift rule for gradients + parameter_shift: true + + # Circuit transpilation optimization level (0-3) + transpile_optimization: 2 + + # Use error mitigation + error_mitigation: false + + # Noise model for simulation + noise_model: null diff --git a/ai-projects/lora-training/quantum-ai/requirements.txt b/ai-projects/lora-training/quantum-ai/requirements.txt index 73c6bcc20..9175d15bc 100644 --- a/ai-projects/lora-training/quantum-ai/requirements.txt +++ b/ai-projects/lora-training/quantum-ai/requirements.txt @@ -1,52 +1,52 @@ -# Python dependencies for Quantum AI project - -# Core quantum computing libraries -qiskit>=1.0.0 -qiskit-aer>=0.13.0 -qiskit-ibm-runtime>=0.17.0 -qiskit-algorithms>=0.3.0 -qiskit-machine-learning>=0.7.0 - -# Azure Quantum -azure-quantum>=1.0.0 -azure-identity>=1.15.0 - -# Deep learning frameworks -torch>=2.8.0 -torchvision>=0.20.0 - -# Classical ML -scikit-learn>=1.6.0 -numpy>=1.26.4 -pandas>=2.2.0 - -# Visualization -matplotlib>=3.9.0 -seaborn>=0.13.0 -plotly>=5.24.0 - -# Jupyter notebooks -jupyter>=1.1.0 -jupyterlab>=4.3.0 -ipykernel>=6.25.0 -ipywidgets>=8.1.0 - -# Configuration and utilities -pyyaml>=6.0 -python-dotenv>=1.0.0 -tqdm>=4.67.0 - -# Testing -pytest>=8.0.0 -pytest-cov>=6.0.0 -pytest-mock>=3.14.0 - -# Code quality -black>=24.0.0 -flake8>=7.0.0 -mypy>=1.13.0 - -# Additional quantum ML libraries (optional) -# pennylane>=0.32.0 -# tensorflow-quantum>=0.7.2 -# cirq>=1.2.0 +# Python dependencies for Quantum AI project + +# Core quantum computing libraries +qiskit>=1.0.0 +qiskit-aer>=0.13.0 +qiskit-ibm-runtime>=0.17.0 +qiskit-algorithms>=0.3.0 +qiskit-machine-learning>=0.7.0 + +# Azure Quantum +azure-quantum>=1.0.0 +azure-identity>=1.15.0 + +# Deep learning frameworks +torch>=2.8.0 +torchvision>=0.20.0 + +# Classical ML +scikit-learn>=1.6.0 +numpy>=1.26.4 +pandas>=2.2.0 + +# Visualization +matplotlib>=3.9.0 +seaborn>=0.13.0 +plotly>=5.24.0 + +# Jupyter notebooks +jupyter>=1.1.0 +jupyterlab>=4.3.0 +ipykernel>=6.25.0 +ipywidgets>=8.1.0 + +# Configuration and utilities +pyyaml>=6.0 +python-dotenv>=1.0.0 +tqdm>=4.67.0 + +# Testing +pytest>=8.0.0 +pytest-cov>=6.0.0 +pytest-mock>=3.14.0 + +# Code quality +black>=24.0.0 +flake8>=7.0.0 +mypy>=1.13.0 + +# Additional quantum ML libraries (optional) +# pennylane>=0.32.0 +# tensorflow-quantum>=0.7.2 +# cirq>=1.2.0 diff --git a/ai-projects/lora-training/quantum-ai/src/azure_quantum_integration.py b/ai-projects/lora-training/quantum-ai/src/azure_quantum_integration.py index 900d1778a..4ead98cc9 100644 --- a/ai-projects/lora-training/quantum-ai/src/azure_quantum_integration.py +++ b/ai-projects/lora-training/quantum-ai/src/azure_quantum_integration.py @@ -1,321 +1,321 @@ -""" -Azure Quantum Integration -Connects quantum ML models with Azure Quantum services -""" - -import logging -import os -from typing import Any, Dict, List, Optional - -from azure.quantum import Workspace -from azure.quantum.qiskit import AzureQuantumProvider -from qiskit import QuantumCircuit, transpile -from qiskit.providers import Backend - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -class AzureQuantumManager: - """ - Manages connection and execution on Azure Quantum - """ - - def __init__( - self, - resource_id: Optional[str] = None, - location: Optional[str] = None, - subscription_id: Optional[str] = None, - resource_group: Optional[str] = None, - workspace_name: Optional[str] = None, - ): - """ - Initialize Azure Quantum workspace connection - - Args: - resource_id: Full resource ID of the workspace - location: Azure region (e.g., 'eastus') - subscription_id: Azure subscription ID - resource_group: Resource group name - workspace_name: Workspace name - """ - self.workspace = None - self.provider = None - self.backend = None - - # Load from environment variables if not provided - self.resource_id = resource_id or os.getenv("AZURE_QUANTUM_RESOURCE_ID") - self.location = location or os.getenv("AZURE_QUANTUM_LOCATION", "eastus") - - if not self.resource_id: - self.subscription_id = subscription_id or os.getenv("AZURE_SUBSCRIPTION_ID") - self.resource_group = resource_group or os.getenv( - "AZURE_QUANTUM_RESOURCE_GROUP" - ) - self.workspace_name = workspace_name or os.getenv( - "AZURE_QUANTUM_WORKSPACE_NAME" - ) - - def connect(self): - """Establish connection to Azure Quantum workspace""" - try: - if self.resource_id: - self.workspace = Workspace( - resource_id=self.resource_id, location=self.location - ) - else: - if not all( - [self.subscription_id, self.resource_group, self.workspace_name] - ): - raise ValueError( - "Either resource_id or (subscription_id, resource_group, workspace_name) must be provided" - ) - - self.workspace = Workspace( - subscription_id=self.subscription_id, - resource_group=self.resource_group, - name=self.workspace_name, - location=self.location, - ) - - # Create Qiskit provider - self.provider = AzureQuantumProvider(self.workspace) - - logger.info(f"Connected to Azure Quantum workspace: {self.workspace.name}") - return True - - except Exception as e: - logger.error(f"Failed to connect to Azure Quantum: {str(e)}") - return False - - def list_backends(self) -> List[str]: - """List available quantum backends""" - if not self.provider: - logger.warning("Not connected. Call connect() first.") - return [] - - backends = self.provider.backends() - backend_names = [backend.name() for backend in backends] - - logger.info(f"Available backends: {backend_names}") - return backend_names - - def get_backend(self, backend_name: str = None) -> Optional[Backend]: - """ - Get a specific backend or default simulator - - Args: - backend_name: Name of the backend (e.g., 'ionq.simulator', 'quantinuum.sim.h1-1e') - """ - if not self.provider: - logger.warning("Not connected. Call connect() first.") - return None - - try: - if backend_name: - self.backend = self.provider.get_backend(backend_name) - else: - # Use IonQ simulator as default - backends = self.provider.backends() - simulator_backends = [ - b for b in backends if "simulator" in b.name().lower() - ] - if simulator_backends: - self.backend = simulator_backends[0] - else: - self.backend = backends[0] if backends else None - - if self.backend: - logger.info(f"Selected backend: {self.backend.name()}") - return self.backend - - except Exception as e: - logger.error(f"Failed to get backend: {str(e)}") - return None - - def run_circuit( - self, - circuit: QuantumCircuit, - shots: int = 1024, - backend_name: Optional[str] = None, - ) -> Dict[str, Any]: - """ - Execute a quantum circuit on Azure Quantum - - Args: - circuit: Quantum circuit to execute - shots: Number of shots - backend_name: Specific backend to use - - Returns: - results: Job results - """ - if not self.provider: - raise RuntimeError("Not connected to Azure Quantum. Call connect() first.") - - # Get backend - if backend_name: - backend = self.provider.get_backend(backend_name) - elif self.backend: - backend = self.backend - else: - backend = self.get_backend() - - if not backend: - raise RuntimeError("No backend available") - - logger.info(f"Running circuit on {backend.name()} with {shots} shots") - - # Transpile circuit for the backend - transpiled_circuit = transpile(circuit, backend=backend) - - # Execute - job = backend.run(transpiled_circuit, shots=shots) - - logger.info(f"Job submitted: {job.id()}") - - # Wait for completion - result = job.result() - - logger.info("Job completed successfully") - - return { - "job_id": job.id(), - "counts": result.get_counts(), - "backend": backend.name(), - "shots": shots, - } - - def get_job_status(self, job_id: str) -> str: - """Get the status of a submitted job""" - if not self.workspace: - raise RuntimeError("Not connected to Azure Quantum") - - job = self.workspace.get_job(job_id) - return job.details.status - - def estimate_cost( - self, circuit: QuantumCircuit, backend_name: str - ) -> Dict[str, Any]: - """ - Estimate the cost of running a circuit - - Args: - circuit: Quantum circuit - backend_name: Target backend - - Returns: - cost_estimate: Cost estimation details - """ - if not self.provider: - raise RuntimeError("Not connected to Azure Quantum") - - backend = self.provider.get_backend(backend_name) - - # Get pricing information - try: - # This is a simplified estimation - # Actual implementation would use backend-specific pricing APIs - transpiled = transpile(circuit, backend=backend) - - return { - "backend": backend_name, - "circuit_depth": transpiled.depth(), - "num_qubits": transpiled.num_qubits, - "note": "Use Azure Portal for accurate pricing", - } - except Exception as e: - logger.error(f"Cost estimation failed: {str(e)}") - return {"error": str(e)} - - -class QuantumJobManager: - """ - Manages multiple quantum jobs and batch processing - """ - - def __init__(self, azure_manager: AzureQuantumManager): - """ - Initialize job manager - - Args: - azure_manager: AzureQuantumManager instance - """ - self.azure_manager = azure_manager - self.jobs = [] - - def submit_batch( - self, - circuits: List[QuantumCircuit], - shots: int = 1024, - backend_name: Optional[str] = None, - ) -> List[str]: - """ - Submit multiple circuits as a batch - - Args: - circuits: List of quantum circuits - shots: Number of shots per circuit - backend_name: Backend to use - - Returns: - job_ids: List of submitted job IDs - """ - job_ids = [] - - for i, circuit in enumerate(circuits): - logger.info(f"Submitting circuit {i+1}/{len(circuits)}") - result = self.azure_manager.run_circuit(circuit, shots, backend_name) - job_ids.append(result["job_id"]) - self.jobs.append( - {"job_id": result["job_id"], "circuit_index": i, "status": "submitted"} - ) - - return job_ids - - def check_all_jobs(self) -> Dict[str, int]: - """ - Check status of all submitted jobs - - Returns: - status_summary: Count of jobs by status - """ - status_summary = {} - - for job in self.jobs: - status = self.azure_manager.get_job_status(job["job_id"]) - job["status"] = status - status_summary[status] = status_summary.get(status, 0) + 1 - - return status_summary - - -if __name__ == "__main__": - # Example usage - - # Create a simple quantum circuit - qc = QuantumCircuit(2, 2) - qc.h(0) - qc.cx(0, 1) - qc.measure([0, 1], [0, 1]) - - # Initialize Azure Quantum manager - # Note: Set environment variables or provide credentials - azure_qm = AzureQuantumManager() - - # Connect to workspace - if azure_qm.connect(): - # List available backends - backends = azure_qm.list_backends() - print(f"Available backends: {backends}") - - # Run circuit (uncomment when credentials are set) - # result = azure_qm.run_circuit(qc, shots=1000) - # print(f"Results: {result['counts']}") - else: - print("Failed to connect to Azure Quantum") - print("Please set environment variables:") - print(" AZURE_QUANTUM_RESOURCE_ID or") - print( - " AZURE_SUBSCRIPTION_ID, AZURE_QUANTUM_RESOURCE_GROUP, AZURE_QUANTUM_WORKSPACE_NAME" - ) +""" +Azure Quantum Integration +Connects quantum ML models with Azure Quantum services +""" + +import logging +import os +from typing import Any, Dict, List, Optional + +from azure.quantum import Workspace +from azure.quantum.qiskit import AzureQuantumProvider +from qiskit import QuantumCircuit, transpile +from qiskit.providers import Backend + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class AzureQuantumManager: + """ + Manages connection and execution on Azure Quantum + """ + + def __init__( + self, + resource_id: Optional[str] = None, + location: Optional[str] = None, + subscription_id: Optional[str] = None, + resource_group: Optional[str] = None, + workspace_name: Optional[str] = None, + ): + """ + Initialize Azure Quantum workspace connection + + Args: + resource_id: Full resource ID of the workspace + location: Azure region (e.g., 'eastus') + subscription_id: Azure subscription ID + resource_group: Resource group name + workspace_name: Workspace name + """ + self.workspace = None + self.provider = None + self.backend = None + + # Load from environment variables if not provided + self.resource_id = resource_id or os.getenv("AZURE_QUANTUM_RESOURCE_ID") + self.location = location or os.getenv("AZURE_QUANTUM_LOCATION", "eastus") + + if not self.resource_id: + self.subscription_id = subscription_id or os.getenv("AZURE_SUBSCRIPTION_ID") + self.resource_group = resource_group or os.getenv( + "AZURE_QUANTUM_RESOURCE_GROUP" + ) + self.workspace_name = workspace_name or os.getenv( + "AZURE_QUANTUM_WORKSPACE_NAME" + ) + + def connect(self): + """Establish connection to Azure Quantum workspace""" + try: + if self.resource_id: + self.workspace = Workspace( + resource_id=self.resource_id, location=self.location + ) + else: + if not all( + [self.subscription_id, self.resource_group, self.workspace_name] + ): + raise ValueError( + "Either resource_id or (subscription_id, resource_group, workspace_name) must be provided" + ) + + self.workspace = Workspace( + subscription_id=self.subscription_id, + resource_group=self.resource_group, + name=self.workspace_name, + location=self.location, + ) + + # Create Qiskit provider + self.provider = AzureQuantumProvider(self.workspace) + + logger.info(f"Connected to Azure Quantum workspace: {self.workspace.name}") + return True + + except Exception as e: + logger.error(f"Failed to connect to Azure Quantum: {str(e)}") + return False + + def list_backends(self) -> List[str]: + """List available quantum backends""" + if not self.provider: + logger.warning("Not connected. Call connect() first.") + return [] + + backends = self.provider.backends() + backend_names = [backend.name() for backend in backends] + + logger.info(f"Available backends: {backend_names}") + return backend_names + + def get_backend(self, backend_name: str = None) -> Optional[Backend]: + """ + Get a specific backend or default simulator + + Args: + backend_name: Name of the backend (e.g., 'ionq.simulator', 'quantinuum.sim.h1-1e') + """ + if not self.provider: + logger.warning("Not connected. Call connect() first.") + return None + + try: + if backend_name: + self.backend = self.provider.get_backend(backend_name) + else: + # Use IonQ simulator as default + backends = self.provider.backends() + simulator_backends = [ + b for b in backends if "simulator" in b.name().lower() + ] + if simulator_backends: + self.backend = simulator_backends[0] + else: + self.backend = backends[0] if backends else None + + if self.backend: + logger.info(f"Selected backend: {self.backend.name()}") + return self.backend + + except Exception as e: + logger.error(f"Failed to get backend: {str(e)}") + return None + + def run_circuit( + self, + circuit: QuantumCircuit, + shots: int = 1024, + backend_name: Optional[str] = None, + ) -> Dict[str, Any]: + """ + Execute a quantum circuit on Azure Quantum + + Args: + circuit: Quantum circuit to execute + shots: Number of shots + backend_name: Specific backend to use + + Returns: + results: Job results + """ + if not self.provider: + raise RuntimeError("Not connected to Azure Quantum. Call connect() first.") + + # Get backend + if backend_name: + backend = self.provider.get_backend(backend_name) + elif self.backend: + backend = self.backend + else: + backend = self.get_backend() + + if not backend: + raise RuntimeError("No backend available") + + logger.info(f"Running circuit on {backend.name()} with {shots} shots") + + # Transpile circuit for the backend + transpiled_circuit = transpile(circuit, backend=backend) + + # Execute + job = backend.run(transpiled_circuit, shots=shots) + + logger.info(f"Job submitted: {job.id()}") + + # Wait for completion + result = job.result() + + logger.info("Job completed successfully") + + return { + "job_id": job.id(), + "counts": result.get_counts(), + "backend": backend.name(), + "shots": shots, + } + + def get_job_status(self, job_id: str) -> str: + """Get the status of a submitted job""" + if not self.workspace: + raise RuntimeError("Not connected to Azure Quantum") + + job = self.workspace.get_job(job_id) + return job.details.status + + def estimate_cost( + self, circuit: QuantumCircuit, backend_name: str + ) -> Dict[str, Any]: + """ + Estimate the cost of running a circuit + + Args: + circuit: Quantum circuit + backend_name: Target backend + + Returns: + cost_estimate: Cost estimation details + """ + if not self.provider: + raise RuntimeError("Not connected to Azure Quantum") + + backend = self.provider.get_backend(backend_name) + + # Get pricing information + try: + # This is a simplified estimation + # Actual implementation would use backend-specific pricing APIs + transpiled = transpile(circuit, backend=backend) + + return { + "backend": backend_name, + "circuit_depth": transpiled.depth(), + "num_qubits": transpiled.num_qubits, + "note": "Use Azure Portal for accurate pricing", + } + except Exception as e: + logger.error(f"Cost estimation failed: {str(e)}") + return {"error": str(e)} + + +class QuantumJobManager: + """ + Manages multiple quantum jobs and batch processing + """ + + def __init__(self, azure_manager: AzureQuantumManager): + """ + Initialize job manager + + Args: + azure_manager: AzureQuantumManager instance + """ + self.azure_manager = azure_manager + self.jobs = [] + + def submit_batch( + self, + circuits: List[QuantumCircuit], + shots: int = 1024, + backend_name: Optional[str] = None, + ) -> List[str]: + """ + Submit multiple circuits as a batch + + Args: + circuits: List of quantum circuits + shots: Number of shots per circuit + backend_name: Backend to use + + Returns: + job_ids: List of submitted job IDs + """ + job_ids = [] + + for i, circuit in enumerate(circuits): + logger.info(f"Submitting circuit {i+1}/{len(circuits)}") + result = self.azure_manager.run_circuit(circuit, shots, backend_name) + job_ids.append(result["job_id"]) + self.jobs.append( + {"job_id": result["job_id"], "circuit_index": i, "status": "submitted"} + ) + + return job_ids + + def check_all_jobs(self) -> Dict[str, int]: + """ + Check status of all submitted jobs + + Returns: + status_summary: Count of jobs by status + """ + status_summary = {} + + for job in self.jobs: + status = self.azure_manager.get_job_status(job["job_id"]) + job["status"] = status + status_summary[status] = status_summary.get(status, 0) + 1 + + return status_summary + + +if __name__ == "__main__": + # Example usage + + # Create a simple quantum circuit + qc = QuantumCircuit(2, 2) + qc.h(0) + qc.cx(0, 1) + qc.measure([0, 1], [0, 1]) + + # Initialize Azure Quantum manager + # Note: Set environment variables or provide credentials + azure_qm = AzureQuantumManager() + + # Connect to workspace + if azure_qm.connect(): + # List available backends + backends = azure_qm.list_backends() + print(f"Available backends: {backends}") + + # Run circuit (uncomment when credentials are set) + # result = azure_qm.run_circuit(qc, shots=1000) + # print(f"Results: {result['counts']}") + else: + print("Failed to connect to Azure Quantum") + print("Please set environment variables:") + print(" AZURE_QUANTUM_RESOURCE_ID or") + print( + " AZURE_SUBSCRIPTION_ID, AZURE_QUANTUM_RESOURCE_GROUP, AZURE_QUANTUM_WORKSPACE_NAME" + ) diff --git a/ai-projects/lora-training/quantum-ai/src/hybrid_qnn.py b/ai-projects/lora-training/quantum-ai/src/hybrid_qnn.py index 8283b2af9..d5a1bd263 100644 --- a/ai-projects/lora-training/quantum-ai/src/hybrid_qnn.py +++ b/ai-projects/lora-training/quantum-ai/src/hybrid_qnn.py @@ -1,382 +1,382 @@ -""" -Hybrid Quantum-Classical Neural Network -Combines quantum circuits with classical deep learning -""" - -import logging -from typing import Tuple - -import numpy as np -import torch -import torch.nn as nn -import torch.optim as optim -from qiskit import QuantumCircuit -from qiskit.circuit import ParameterVector -from qiskit.primitives import Sampler - -try: - from qiskit_machine_learning.connectors import TorchConnector - from qiskit_machine_learning.neural_networks import SamplerQNN - - QISKIT_ML_AVAILABLE = True -except ImportError: - QISKIT_ML_AVAILABLE = False - _import_error_msg = ( - "qiskit-machine-learning is not installed or not found in your environment.\n" - "To fix:\n" - "1. Activate your Python environment: .\\venv\\Scripts\\Activate.ps1\n" - "2. Run: pip install qiskit-machine-learning>=0.7.0\n" - "3. Or install all requirements: pip install -r requirements.txt\n" - "4. Verify installation: pip list | findstr qiskit-machine-learning" - ) - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -class QuantumLayer(nn.Module): - """ - Quantum layer that can be integrated into PyTorch models - """ - - def __init__(self, num_qubits: int, num_layers: int = 2): - """ - Initialize quantum layer - - Args: - num_qubits: Number of qubits - num_layers: Number of quantum circuit layers - """ - if not QISKIT_ML_AVAILABLE: - raise ImportError(_import_error_msg) - - super().__init__() - self.num_qubits = num_qubits - self.num_layers = num_layers - - # Build quantum circuit - self.qnn = self._build_qnn() - - # Wrap QNN for PyTorch - self.quantum_layer = TorchConnector(self.qnn) - - logger.info( - f"Created QuantumLayer with {num_qubits} qubits, {num_layers} layers" - ) - - def _build_qnn(self): - """Build the quantum neural network""" - # Input and weight parameters - inputs = ParameterVector("x", self.num_qubits) - num_weights = self.num_layers * self.num_qubits * 3 - weights = ParameterVector("θ", num_weights) - - qc = QuantumCircuit(self.num_qubits) - - # Encode input - for i in range(self.num_qubits): - qc.ry(inputs[i], i) - - # Variational layers - weight_idx = 0 - for layer in range(self.num_layers): - # Rotation gates - for i in range(self.num_qubits): - qc.rx(weights[weight_idx], i) - weight_idx += 1 - qc.ry(weights[weight_idx], i) - weight_idx += 1 - qc.rz(weights[weight_idx], i) - weight_idx += 1 - - # Entanglement - for i in range(self.num_qubits - 1): - qc.cx(i, i + 1) - if self.num_qubits > 2: - qc.cx(self.num_qubits - 1, 0) - - # Create QNN - qnn = SamplerQNN( - circuit=qc, input_params=inputs, weight_params=weights, sampler=Sampler() - ) - - return qnn - - def forward(self, x): - """Forward pass through quantum layer""" - return self.quantum_layer(x) - - -class HybridQNN(nn.Module): - """ - Hybrid Quantum-Classical Neural Network - Classical layers -> Quantum layer -> Classical layers - """ - - def __init__( - self, - input_dim: int, - hidden_dim: int, - num_qubits: int, - quantum_layers: int, - output_dim: int, - dropout: float = 0.2, - ): - """ - Initialize hybrid QNN - - Args: - input_dim: Input feature dimension - hidden_dim: Hidden layer dimension - num_qubits: Number of qubits in quantum layer - quantum_layers: Number of quantum circuit layers - output_dim: Output dimension (number of classes) - dropout: Dropout rate - """ - super().__init__() - - self.input_dim = input_dim - self.output_dim = output_dim - - # Classical pre-processing layers - self.fc1 = nn.Linear(input_dim, hidden_dim) - self.bn1 = nn.BatchNorm1d(hidden_dim) - self.dropout1 = nn.Dropout(dropout) - - self.fc2 = nn.Linear(hidden_dim, num_qubits) - self.bn2 = nn.BatchNorm1d(num_qubits) - - # Quantum layer - self.quantum_layer = QuantumLayer(num_qubits, quantum_layers) - - # Calculate quantum output dimension (2^num_qubits for full measurement) - quantum_output_dim = 2**num_qubits - - # Classical post-processing layers - self.fc3 = nn.Linear(quantum_output_dim, hidden_dim) - self.bn3 = nn.BatchNorm1d(hidden_dim) - self.dropout2 = nn.Dropout(dropout) - - self.fc4 = nn.Linear(hidden_dim, output_dim) - - self.relu = nn.ReLU() - self.softmax = nn.Softmax(dim=1) - - logger.info( - f"Created HybridQNN: {input_dim} -> {hidden_dim} -> Q({num_qubits}) -> {hidden_dim} -> {output_dim}" - ) - - def forward(self, x): - """Forward pass through hybrid network""" - # Classical pre-processing - x = self.fc1(x) - x = self.bn1(x) - x = self.relu(x) - x = self.dropout1(x) - - x = self.fc2(x) - x = self.bn2(x) - x = torch.tanh(x) * np.pi # Scale to quantum parameter range - - # Quantum layer - x = self.quantum_layer(x) - - # Classical post-processing - x = self.fc3(x) - x = self.bn3(x) - x = self.relu(x) - x = self.dropout2(x) - - x = self.fc4(x) - - return x - - def predict(self, x): - """Make predictions with softmax""" - logits = self.forward(x) - return self.softmax(logits) - - -class QuantumClassicalTrainer: - """ - Trainer for hybrid quantum-classical models - """ - - def __init__( - self, model: nn.Module, learning_rate: float = 0.001, device: str = "cpu" - ): - """ - Initialize trainer - - Args: - model: Hybrid QNN model - learning_rate: Learning rate - device: Device to train on ('cpu' or 'cuda') - """ - self.model = model.to(device) - self.device = device - self.optimizer = optim.Adam(model.parameters(), lr=learning_rate) - self.criterion = nn.CrossEntropyLoss() - - self.train_losses = [] - self.val_accuracies = [] - - logger.info(f"Initialized trainer with lr={learning_rate}, device={device}") - - def train_epoch(self, train_loader: torch.utils.data.DataLoader) -> float: - """ - Train for one epoch - - Args: - train_loader: Training data loader - - Returns: - avg_loss: Average loss for the epoch - """ - self.model.train() - total_loss = 0.0 - - for batch_idx, (data, target) in enumerate(train_loader): - data, target = data.to(self.device), target.to(self.device) - - # Zero gradients - self.optimizer.zero_grad() - - # Forward pass - output = self.model(data) - - # Compute loss - loss = self.criterion(output, target) - - # Backward pass - loss.backward() - - # Update weights - self.optimizer.step() - - total_loss += loss.item() - - if (batch_idx + 1) % 10 == 0: - logger.info( - f"Batch {batch_idx + 1}/{len(train_loader)}, Loss: {loss.item():.4f}" - ) - - avg_loss = total_loss / len(train_loader) - self.train_losses.append(avg_loss) - - return avg_loss - - def evaluate(self, val_loader: torch.utils.data.DataLoader) -> Tuple[float, float]: - """ - Evaluate model - - Args: - val_loader: Validation data loader - - Returns: - accuracy, loss: Validation accuracy and loss - """ - self.model.eval() - correct = 0 - total = 0 - total_loss = 0.0 - - with torch.no_grad(): - for data, target in val_loader: - data, target = data.to(self.device), target.to(self.device) - - output = self.model(data) - loss = self.criterion(output, target) - total_loss += loss.item() - - _, predicted = torch.max(output.data, 1) - total += target.size(0) - correct += (predicted == target).sum().item() - - accuracy = 100 * correct / total - avg_loss = total_loss / len(val_loader) - - self.val_accuracies.append(accuracy) - - return accuracy, avg_loss - - def train( - self, - train_loader: torch.utils.data.DataLoader, - val_loader: torch.utils.data.DataLoader, - num_epochs: int, - ): - """ - Full training loop - - Args: - train_loader: Training data loader - val_loader: Validation data loader - num_epochs: Number of epochs - """ - logger.info(f"Starting training for {num_epochs} epochs...") - - for epoch in range(num_epochs): - logger.info(f"\nEpoch {epoch + 1}/{num_epochs}") - - # Train - train_loss = self.train_epoch(train_loader) - - # Evaluate - val_acc, val_loss = self.evaluate(val_loader) - - logger.info( - f"Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%" - ) - - logger.info("Training complete!") - - -if __name__ == "__main__": - # Example usage - from sklearn.datasets import make_classification - from sklearn.model_selection import train_test_split - from sklearn.preprocessing import StandardScaler - from torch.utils.data import DataLoader, TensorDataset - - # Generate synthetic data - X, y = make_classification( - n_samples=200, - n_features=8, - n_informative=6, - n_redundant=2, - n_classes=3, - random_state=42, - ) - - # Split and scale - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - # Create data loaders - train_dataset = TensorDataset(torch.FloatTensor(X_train), torch.LongTensor(y_train)) - val_dataset = TensorDataset(torch.FloatTensor(X_val), torch.LongTensor(y_val)) - - train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True) - val_loader = DataLoader(val_dataset, batch_size=16, shuffle=False) - - # Create hybrid model - model = HybridQNN( - input_dim=8, - hidden_dim=16, - num_qubits=4, - quantum_layers=2, - output_dim=3, - dropout=0.2, - ) - - # Train - trainer = QuantumClassicalTrainer(model, learning_rate=0.01) - trainer.train(train_loader, val_loader, num_epochs=5) - - print(f"Final validation accuracy: {trainer.val_accuracies[-1]:.2f}%") +""" +Hybrid Quantum-Classical Neural Network +Combines quantum circuits with classical deep learning +""" + +import logging +from typing import Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim +from qiskit import QuantumCircuit +from qiskit.circuit import ParameterVector +from qiskit.primitives import Sampler + +try: + from qiskit_machine_learning.connectors import TorchConnector + from qiskit_machine_learning.neural_networks import SamplerQNN + + QISKIT_ML_AVAILABLE = True +except ImportError: + QISKIT_ML_AVAILABLE = False + _import_error_msg = ( + "qiskit-machine-learning is not installed or not found in your environment.\n" + "To fix:\n" + "1. Activate your Python environment: .\\venv\\Scripts\\Activate.ps1\n" + "2. Run: pip install qiskit-machine-learning>=0.7.0\n" + "3. Or install all requirements: pip install -r requirements.txt\n" + "4. Verify installation: pip list | findstr qiskit-machine-learning" + ) + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class QuantumLayer(nn.Module): + """ + Quantum layer that can be integrated into PyTorch models + """ + + def __init__(self, num_qubits: int, num_layers: int = 2): + """ + Initialize quantum layer + + Args: + num_qubits: Number of qubits + num_layers: Number of quantum circuit layers + """ + if not QISKIT_ML_AVAILABLE: + raise ImportError(_import_error_msg) + + super().__init__() + self.num_qubits = num_qubits + self.num_layers = num_layers + + # Build quantum circuit + self.qnn = self._build_qnn() + + # Wrap QNN for PyTorch + self.quantum_layer = TorchConnector(self.qnn) + + logger.info( + f"Created QuantumLayer with {num_qubits} qubits, {num_layers} layers" + ) + + def _build_qnn(self): + """Build the quantum neural network""" + # Input and weight parameters + inputs = ParameterVector("x", self.num_qubits) + num_weights = self.num_layers * self.num_qubits * 3 + weights = ParameterVector("θ", num_weights) + + qc = QuantumCircuit(self.num_qubits) + + # Encode input + for i in range(self.num_qubits): + qc.ry(inputs[i], i) + + # Variational layers + weight_idx = 0 + for layer in range(self.num_layers): + # Rotation gates + for i in range(self.num_qubits): + qc.rx(weights[weight_idx], i) + weight_idx += 1 + qc.ry(weights[weight_idx], i) + weight_idx += 1 + qc.rz(weights[weight_idx], i) + weight_idx += 1 + + # Entanglement + for i in range(self.num_qubits - 1): + qc.cx(i, i + 1) + if self.num_qubits > 2: + qc.cx(self.num_qubits - 1, 0) + + # Create QNN + qnn = SamplerQNN( + circuit=qc, input_params=inputs, weight_params=weights, sampler=Sampler() + ) + + return qnn + + def forward(self, x): + """Forward pass through quantum layer""" + return self.quantum_layer(x) + + +class HybridQNN(nn.Module): + """ + Hybrid Quantum-Classical Neural Network + Classical layers -> Quantum layer -> Classical layers + """ + + def __init__( + self, + input_dim: int, + hidden_dim: int, + num_qubits: int, + quantum_layers: int, + output_dim: int, + dropout: float = 0.2, + ): + """ + Initialize hybrid QNN + + Args: + input_dim: Input feature dimension + hidden_dim: Hidden layer dimension + num_qubits: Number of qubits in quantum layer + quantum_layers: Number of quantum circuit layers + output_dim: Output dimension (number of classes) + dropout: Dropout rate + """ + super().__init__() + + self.input_dim = input_dim + self.output_dim = output_dim + + # Classical pre-processing layers + self.fc1 = nn.Linear(input_dim, hidden_dim) + self.bn1 = nn.BatchNorm1d(hidden_dim) + self.dropout1 = nn.Dropout(dropout) + + self.fc2 = nn.Linear(hidden_dim, num_qubits) + self.bn2 = nn.BatchNorm1d(num_qubits) + + # Quantum layer + self.quantum_layer = QuantumLayer(num_qubits, quantum_layers) + + # Calculate quantum output dimension (2^num_qubits for full measurement) + quantum_output_dim = 2**num_qubits + + # Classical post-processing layers + self.fc3 = nn.Linear(quantum_output_dim, hidden_dim) + self.bn3 = nn.BatchNorm1d(hidden_dim) + self.dropout2 = nn.Dropout(dropout) + + self.fc4 = nn.Linear(hidden_dim, output_dim) + + self.relu = nn.ReLU() + self.softmax = nn.Softmax(dim=1) + + logger.info( + f"Created HybridQNN: {input_dim} -> {hidden_dim} -> Q({num_qubits}) -> {hidden_dim} -> {output_dim}" + ) + + def forward(self, x): + """Forward pass through hybrid network""" + # Classical pre-processing + x = self.fc1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.dropout1(x) + + x = self.fc2(x) + x = self.bn2(x) + x = torch.tanh(x) * np.pi # Scale to quantum parameter range + + # Quantum layer + x = self.quantum_layer(x) + + # Classical post-processing + x = self.fc3(x) + x = self.bn3(x) + x = self.relu(x) + x = self.dropout2(x) + + x = self.fc4(x) + + return x + + def predict(self, x): + """Make predictions with softmax""" + logits = self.forward(x) + return self.softmax(logits) + + +class QuantumClassicalTrainer: + """ + Trainer for hybrid quantum-classical models + """ + + def __init__( + self, model: nn.Module, learning_rate: float = 0.001, device: str = "cpu" + ): + """ + Initialize trainer + + Args: + model: Hybrid QNN model + learning_rate: Learning rate + device: Device to train on ('cpu' or 'cuda') + """ + self.model = model.to(device) + self.device = device + self.optimizer = optim.Adam(model.parameters(), lr=learning_rate) + self.criterion = nn.CrossEntropyLoss() + + self.train_losses = [] + self.val_accuracies = [] + + logger.info(f"Initialized trainer with lr={learning_rate}, device={device}") + + def train_epoch(self, train_loader: torch.utils.data.DataLoader) -> float: + """ + Train for one epoch + + Args: + train_loader: Training data loader + + Returns: + avg_loss: Average loss for the epoch + """ + self.model.train() + total_loss = 0.0 + + for batch_idx, (data, target) in enumerate(train_loader): + data, target = data.to(self.device), target.to(self.device) + + # Zero gradients + self.optimizer.zero_grad() + + # Forward pass + output = self.model(data) + + # Compute loss + loss = self.criterion(output, target) + + # Backward pass + loss.backward() + + # Update weights + self.optimizer.step() + + total_loss += loss.item() + + if (batch_idx + 1) % 10 == 0: + logger.info( + f"Batch {batch_idx + 1}/{len(train_loader)}, Loss: {loss.item():.4f}" + ) + + avg_loss = total_loss / len(train_loader) + self.train_losses.append(avg_loss) + + return avg_loss + + def evaluate(self, val_loader: torch.utils.data.DataLoader) -> Tuple[float, float]: + """ + Evaluate model + + Args: + val_loader: Validation data loader + + Returns: + accuracy, loss: Validation accuracy and loss + """ + self.model.eval() + correct = 0 + total = 0 + total_loss = 0.0 + + with torch.no_grad(): + for data, target in val_loader: + data, target = data.to(self.device), target.to(self.device) + + output = self.model(data) + loss = self.criterion(output, target) + total_loss += loss.item() + + _, predicted = torch.max(output.data, 1) + total += target.size(0) + correct += (predicted == target).sum().item() + + accuracy = 100 * correct / total + avg_loss = total_loss / len(val_loader) + + self.val_accuracies.append(accuracy) + + return accuracy, avg_loss + + def train( + self, + train_loader: torch.utils.data.DataLoader, + val_loader: torch.utils.data.DataLoader, + num_epochs: int, + ): + """ + Full training loop + + Args: + train_loader: Training data loader + val_loader: Validation data loader + num_epochs: Number of epochs + """ + logger.info(f"Starting training for {num_epochs} epochs...") + + for epoch in range(num_epochs): + logger.info(f"\nEpoch {epoch + 1}/{num_epochs}") + + # Train + train_loss = self.train_epoch(train_loader) + + # Evaluate + val_acc, val_loss = self.evaluate(val_loader) + + logger.info( + f"Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%" + ) + + logger.info("Training complete!") + + +if __name__ == "__main__": + # Example usage + from sklearn.datasets import make_classification + from sklearn.model_selection import train_test_split + from sklearn.preprocessing import StandardScaler + from torch.utils.data import DataLoader, TensorDataset + + # Generate synthetic data + X, y = make_classification( + n_samples=200, + n_features=8, + n_informative=6, + n_redundant=2, + n_classes=3, + random_state=42, + ) + + # Split and scale + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + # Create data loaders + train_dataset = TensorDataset(torch.FloatTensor(X_train), torch.LongTensor(y_train)) + val_dataset = TensorDataset(torch.FloatTensor(X_val), torch.LongTensor(y_val)) + + train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True) + val_loader = DataLoader(val_dataset, batch_size=16, shuffle=False) + + # Create hybrid model + model = HybridQNN( + input_dim=8, + hidden_dim=16, + num_qubits=4, + quantum_layers=2, + output_dim=3, + dropout=0.2, + ) + + # Train + trainer = QuantumClassicalTrainer(model, learning_rate=0.01) + trainer.train(train_loader, val_loader, num_epochs=5) + + print(f"Final validation accuracy: {trainer.val_accuracies[-1]:.2f}%") diff --git a/ai-projects/lora-training/quantum-ai/src/quantum_classifier.py b/ai-projects/lora-training/quantum-ai/src/quantum_classifier.py index 144e4faa5..c67fd3c34 100644 --- a/ai-projects/lora-training/quantum-ai/src/quantum_classifier.py +++ b/ai-projects/lora-training/quantum-ai/src/quantum_classifier.py @@ -1,273 +1,273 @@ -""" -Quantum Machine Learning Classifier using Qiskit -Implements a Variational Quantum Classifier (VQC) for binary classification -""" - -import logging - -import numpy as np -from qiskit import QuantumCircuit -from qiskit.circuit import ParameterVector -from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap -from qiskit.primitives import Sampler -from qiskit_algorithms.optimizers import COBYLA, SPSA -from qiskit_machine_learning.algorithms import VQC -from qiskit_machine_learning.neural_networks import SamplerQNN -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -class QuantumClassifier: - """ - Variational Quantum Classifier for binary and multi-class classification - """ - - def __init__(self, num_features=4, num_qubits=None, reps=2, optimizer="COBYLA"): - """ - Initialize the quantum classifier - - Args: - num_features: Number of input features - num_qubits: Number of qubits (defaults to num_features) - reps: Number of repetitions in the variational form - optimizer: Optimization algorithm ('COBYLA' or 'SPSA') - """ - self.num_features = num_features - self.num_qubits = num_qubits or num_features - self.reps = reps - self.vqc = None - self.scaler = StandardScaler() - - # Select optimizer - if optimizer == "COBYLA": - self.optimizer = COBYLA(maxiter=100) - elif optimizer == "SPSA": - self.optimizer = SPSA(maxiter=100) - else: - raise ValueError(f"Unknown optimizer: {optimizer}") - - logger.info(f"Initialized QuantumClassifier with {self.num_qubits} qubits") - - def _create_feature_map(self): - """Create the feature map circuit""" - return ZZFeatureMap(feature_dimension=self.num_qubits, reps=2) - - def _create_ansatz(self): - """Create the variational ansatz circuit""" - return RealAmplitudes(num_qubits=self.num_qubits, reps=self.reps) - - def build_circuit(self): - """Build the complete quantum circuit""" - feature_map = self._create_feature_map() - ansatz = self._create_ansatz() - - # Combine feature map and ansatz - circuit = QuantumCircuit(self.num_qubits) - circuit.compose(feature_map, inplace=True) - circuit.compose(ansatz, inplace=True) - - return circuit - - def fit(self, X_train, y_train, quantum_instance=None): - """ - Train the quantum classifier - - Args: - X_train: Training features - y_train: Training labels - quantum_instance: Quantum backend (None for simulator) - """ - logger.info("Starting quantum classifier training...") - - # Normalize features - X_train_scaled = self.scaler.fit_transform(X_train) - - # Create feature map and ansatz - feature_map = self._create_feature_map() - ansatz = self._create_ansatz() - - # Create VQC - self.vqc = VQC( - feature_map=feature_map, - ansatz=ansatz, - optimizer=self.optimizer, - sampler=Sampler(), - ) - - # Train the model - self.vqc.fit(X_train_scaled, y_train) - - logger.info("Training complete!") - return self - - def predict(self, X_test): - """ - Make predictions using the trained quantum classifier - - Args: - X_test: Test features - - Returns: - predictions: Predicted labels - """ - if self.vqc is None: - raise ValueError("Model not trained. Call fit() first.") - - X_test_scaled = self.scaler.transform(X_test) - predictions = self.vqc.predict(X_test_scaled) - - return predictions - - def score(self, X_test, y_test): - """ - Calculate accuracy score - - Args: - X_test: Test features - y_test: True labels - - Returns: - accuracy: Classification accuracy - """ - if self.vqc is None: - raise ValueError("Model not trained. Call fit() first.") - - X_test_scaled = self.scaler.transform(X_test) - accuracy = self.vqc.score(X_test_scaled, y_test) - - logger.info(f"Test accuracy: {accuracy:.4f}") - return accuracy - - -class QuantumNeuralNetwork: - """ - Quantum Neural Network using parameterized quantum circuits - """ - - def __init__(self, num_qubits=4, layers=3): - """ - Initialize quantum neural network - - Args: - num_qubits: Number of qubits - layers: Number of layers in the circuit - """ - self.num_qubits = num_qubits - self.layers = layers - self.qnn = None - self.weights = None - - logger.info( - f"Initialized QuantumNeuralNetwork with {num_qubits} qubits, {layers} layers" - ) - - def build_circuit(self): - """Build parameterized quantum circuit""" - # Input parameters - inputs = ParameterVector("x", self.num_qubits) - - # Weight parameters - num_weights = self.layers * self.num_qubits * 3 - weights = ParameterVector("θ", num_weights) - - qc = QuantumCircuit(self.num_qubits) - - # Encode input data - for i in range(self.num_qubits): - qc.ry(inputs[i], i) - - # Variational layers - weight_idx = 0 - for layer in range(self.layers): - # Rotation layer - for i in range(self.num_qubits): - qc.rx(weights[weight_idx], i) - weight_idx += 1 - qc.ry(weights[weight_idx], i) - weight_idx += 1 - qc.rz(weights[weight_idx], i) - weight_idx += 1 - - # Entanglement layer - for i in range(self.num_qubits - 1): - qc.cx(i, i + 1) - if self.num_qubits > 2: - qc.cx(self.num_qubits - 1, 0) - - return qc, inputs, weights - - def create_qnn(self): - """Create the quantum neural network""" - circuit, inputs, weights = self.build_circuit() - - # Create QNN - self.qnn = SamplerQNN( - circuit=circuit, - input_params=inputs, - weight_params=weights, - sampler=Sampler(), - ) - - # Initialize random weights - self.weights = np.random.randn(len(weights)) * 0.1 - - return self.qnn - - def forward(self, input_data): - """ - Forward pass through the quantum network - - Args: - input_data: Input features - - Returns: - output: Network output - """ - if self.qnn is None: - self.create_qnn() - - # Ensure input is 2D - if len(input_data.shape) == 1: - input_data = input_data.reshape(1, -1) - - output = self.qnn.forward(input_data, self.weights) - return output - - -if __name__ == "__main__": - # Example usage - from sklearn.datasets import make_classification - - # Generate synthetic dataset - X, y = make_classification( - n_samples=100, - n_features=4, - n_informative=3, - n_redundant=1, - n_classes=2, - random_state=42, - ) - - # Split data - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - # Create and train quantum classifier - qc = QuantumClassifier(num_features=4, num_qubits=4, reps=2) - qc.fit(X_train, y_train) - - # Evaluate - accuracy = qc.score(X_test, y_test) - print(f"Quantum Classifier Accuracy: {accuracy:.4f}") - - # Test quantum neural network - qnn = QuantumNeuralNetwork(num_qubits=4, layers=3) - qnn.create_qnn() - - # Forward pass - output = qnn.forward(X_test[:1]) - print(f"QNN Output shape: {output.shape}") +""" +Quantum Machine Learning Classifier using Qiskit +Implements a Variational Quantum Classifier (VQC) for binary classification +""" + +import logging + +import numpy as np +from qiskit import QuantumCircuit +from qiskit.circuit import ParameterVector +from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap +from qiskit.primitives import Sampler +from qiskit_algorithms.optimizers import COBYLA, SPSA +from qiskit_machine_learning.algorithms import VQC +from qiskit_machine_learning.neural_networks import SamplerQNN +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class QuantumClassifier: + """ + Variational Quantum Classifier for binary and multi-class classification + """ + + def __init__(self, num_features=4, num_qubits=None, reps=2, optimizer="COBYLA"): + """ + Initialize the quantum classifier + + Args: + num_features: Number of input features + num_qubits: Number of qubits (defaults to num_features) + reps: Number of repetitions in the variational form + optimizer: Optimization algorithm ('COBYLA' or 'SPSA') + """ + self.num_features = num_features + self.num_qubits = num_qubits or num_features + self.reps = reps + self.vqc = None + self.scaler = StandardScaler() + + # Select optimizer + if optimizer == "COBYLA": + self.optimizer = COBYLA(maxiter=100) + elif optimizer == "SPSA": + self.optimizer = SPSA(maxiter=100) + else: + raise ValueError(f"Unknown optimizer: {optimizer}") + + logger.info(f"Initialized QuantumClassifier with {self.num_qubits} qubits") + + def _create_feature_map(self): + """Create the feature map circuit""" + return ZZFeatureMap(feature_dimension=self.num_qubits, reps=2) + + def _create_ansatz(self): + """Create the variational ansatz circuit""" + return RealAmplitudes(num_qubits=self.num_qubits, reps=self.reps) + + def build_circuit(self): + """Build the complete quantum circuit""" + feature_map = self._create_feature_map() + ansatz = self._create_ansatz() + + # Combine feature map and ansatz + circuit = QuantumCircuit(self.num_qubits) + circuit.compose(feature_map, inplace=True) + circuit.compose(ansatz, inplace=True) + + return circuit + + def fit(self, X_train, y_train, quantum_instance=None): + """ + Train the quantum classifier + + Args: + X_train: Training features + y_train: Training labels + quantum_instance: Quantum backend (None for simulator) + """ + logger.info("Starting quantum classifier training...") + + # Normalize features + X_train_scaled = self.scaler.fit_transform(X_train) + + # Create feature map and ansatz + feature_map = self._create_feature_map() + ansatz = self._create_ansatz() + + # Create VQC + self.vqc = VQC( + feature_map=feature_map, + ansatz=ansatz, + optimizer=self.optimizer, + sampler=Sampler(), + ) + + # Train the model + self.vqc.fit(X_train_scaled, y_train) + + logger.info("Training complete!") + return self + + def predict(self, X_test): + """ + Make predictions using the trained quantum classifier + + Args: + X_test: Test features + + Returns: + predictions: Predicted labels + """ + if self.vqc is None: + raise ValueError("Model not trained. Call fit() first.") + + X_test_scaled = self.scaler.transform(X_test) + predictions = self.vqc.predict(X_test_scaled) + + return predictions + + def score(self, X_test, y_test): + """ + Calculate accuracy score + + Args: + X_test: Test features + y_test: True labels + + Returns: + accuracy: Classification accuracy + """ + if self.vqc is None: + raise ValueError("Model not trained. Call fit() first.") + + X_test_scaled = self.scaler.transform(X_test) + accuracy = self.vqc.score(X_test_scaled, y_test) + + logger.info(f"Test accuracy: {accuracy:.4f}") + return accuracy + + +class QuantumNeuralNetwork: + """ + Quantum Neural Network using parameterized quantum circuits + """ + + def __init__(self, num_qubits=4, layers=3): + """ + Initialize quantum neural network + + Args: + num_qubits: Number of qubits + layers: Number of layers in the circuit + """ + self.num_qubits = num_qubits + self.layers = layers + self.qnn = None + self.weights = None + + logger.info( + f"Initialized QuantumNeuralNetwork with {num_qubits} qubits, {layers} layers" + ) + + def build_circuit(self): + """Build parameterized quantum circuit""" + # Input parameters + inputs = ParameterVector("x", self.num_qubits) + + # Weight parameters + num_weights = self.layers * self.num_qubits * 3 + weights = ParameterVector("θ", num_weights) + + qc = QuantumCircuit(self.num_qubits) + + # Encode input data + for i in range(self.num_qubits): + qc.ry(inputs[i], i) + + # Variational layers + weight_idx = 0 + for layer in range(self.layers): + # Rotation layer + for i in range(self.num_qubits): + qc.rx(weights[weight_idx], i) + weight_idx += 1 + qc.ry(weights[weight_idx], i) + weight_idx += 1 + qc.rz(weights[weight_idx], i) + weight_idx += 1 + + # Entanglement layer + for i in range(self.num_qubits - 1): + qc.cx(i, i + 1) + if self.num_qubits > 2: + qc.cx(self.num_qubits - 1, 0) + + return qc, inputs, weights + + def create_qnn(self): + """Create the quantum neural network""" + circuit, inputs, weights = self.build_circuit() + + # Create QNN + self.qnn = SamplerQNN( + circuit=circuit, + input_params=inputs, + weight_params=weights, + sampler=Sampler(), + ) + + # Initialize random weights + self.weights = np.random.randn(len(weights)) * 0.1 + + return self.qnn + + def forward(self, input_data): + """ + Forward pass through the quantum network + + Args: + input_data: Input features + + Returns: + output: Network output + """ + if self.qnn is None: + self.create_qnn() + + # Ensure input is 2D + if len(input_data.shape) == 1: + input_data = input_data.reshape(1, -1) + + output = self.qnn.forward(input_data, self.weights) + return output + + +if __name__ == "__main__": + # Example usage + from sklearn.datasets import make_classification + + # Generate synthetic dataset + X, y = make_classification( + n_samples=100, + n_features=4, + n_informative=3, + n_redundant=1, + n_classes=2, + random_state=42, + ) + + # Split data + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + # Create and train quantum classifier + qc = QuantumClassifier(num_features=4, num_qubits=4, reps=2) + qc.fit(X_train, y_train) + + # Evaluate + accuracy = qc.score(X_test, y_test) + print(f"Quantum Classifier Accuracy: {accuracy:.4f}") + + # Test quantum neural network + qnn = QuantumNeuralNetwork(num_qubits=4, layers=3) + qnn.create_qnn() + + # Forward pass + output = qnn.forward(X_test[:1]) + print(f"QNN Output shape: {output.shape}") diff --git a/ai-projects/quantum-ml/AZURE_QUANTUM_QUICKSTART.md b/ai-projects/quantum-ml/AZURE_QUANTUM_QUICKSTART.md index d39e6ac70..db4f1ef26 100644 --- a/ai-projects/quantum-ml/AZURE_QUANTUM_QUICKSTART.md +++ b/ai-projects/quantum-ml/AZURE_QUANTUM_QUICKSTART.md @@ -1,463 +1,463 @@ -# 🚀 Azure Quantum Quick Start Guide - -**Run Your Optimized Quantum AI on Real Quantum Hardware!** - ---- - -## 📋 Prerequisites Checklist - -Before testing on real quantum hardware, ensure you have: - -- [ ] **Azure Account** - [Sign up for free trial](https://azure.microsoft.com/free/) ($200 credit) -- [ ] **Azure CLI** - Installed and logged in (`az login`) -- [ ] **Azure Quantum Workspace** - Deployed (see [Quick Start](#-quick-start-5-minutes)) -- [ ] **Quantum Credits** - IonQ/Quantinuum credits purchased or trial credits -- [ ] **Updated Config** - `config/quantum_config.yaml` with your Azure details - ---- - -## ⚡ Quick Start (5 Minutes) - -### Step 1: Deploy Azure Quantum Workspace - -```powershell -# Navigate to Azure infrastructure directory -cd quantum-ai\azure - -# Login to Azure -az login - -# Set your subscription -az account set --subscription "" - -# Create resource group -az group create --name rg-quantum-ai --location eastus - -# Deploy Bicep template -az deployment group create ` - --resource-group rg-quantum-ai ` - --template-file quantum_workspace.bicep ` - --parameters quantum_workspace.parameters.json -``` - -**Deployment time:** ~2-3 minutes - -### Step 2: Update Configuration - -Edit `config/quantum_config.yaml`: - -```yaml -azure: - subscription_id: '' # From: az account show - resource_group: 'rg-quantum-ai' - workspace_name: 'quantum-ai-workspace' - location: 'eastus' - storage_account: '' # From deployment output -``` - -### Step 3: Authenticate - -```powershell -# Simple authentication (development) -az login - -# The Azure SDK will automatically use your credentials -``` - -### Step 4: Run Test Suite - -```powershell -# Navigate back to project root -cd .. - -# Activate virtual environment -.\venv\Scripts\Activate.ps1 - -# Run comprehensive hardware test -python test_azure_quantum.py -``` - -**Test duration:** 5-15 minutes (depending on backend) - ---- - -## 🎯 What Gets Tested - -The `test_azure_quantum.py` script runs **4 comprehensive tests**: - -### Test 1: Connection Verification ✅ - -- Connects to your Azure Quantum workspace -- Lists all available quantum backends -- Verifies authentication - -**Expected Output:** - -```text -✓ Successfully connected to Azure Quantum! - -Available Quantum Backends: - 1. ionq.simulator - 2. ionq.qpu - 3. quantinuum.sim.h1-1sc - 4. quantinuum.qpu.h1-1 -``` - -### Test 2: Bell State on Hardware 🔔 - -- Submits a simple entanglement test -- Verifies quantum behavior -- **Demonstrates real quantum entanglement!** - -**Expected Results:** - -```text -Measurement counts: - 00: 487 (48.7%) - 11: 513 (51.3%) - -Entanglement Quality: 100% -✓ Excellent quantum entanglement observed! -``` - -### Test 3: Optimized Circuit (90% Accuracy) 🏆 - -- Runs your **optimized 3-layer quantum classifier** -- Tests the configuration that achieved 90% accuracy -- Collects 500 shots for statistical analysis - -**Circuit Details:** - -- **4 qubits** (optimized) -- **3 layers** (87.5% accuracy) -- **Linear entanglement** (82.5% accuracy) -- **Learning rate 0.1** (90% accuracy) - -### Test 4: Simulator vs Hardware Comparison 🔬 - -- Runs same circuit on simulator and real hardware -- Compares ideal vs noisy quantum behavior -- **Demonstrates real-world quantum computing effects!** - ---- - -## 💰 Cost Breakdown - -### FREE Options (Start Here!) - -| Service | Usage | Cost | -|---------|-------|------| -| **IonQ Simulator** | Unlimited | **$0.00** | -| **Microsoft QC Simulator** | Unlimited | **$0.00** | -| **Azure Storage** | First 5 GB | **$0.00** | -| **Free Trial** | $200 credit | **$0.00** | - -**Recommendation:** Use simulators for all development and testing! - -### Paid Quantum Hardware - -| Provider | Pricing Model | Estimated Cost | -|----------|---------------|----------------| -| **IonQ QPU** | $0.00003 per gate-shot | ~$0.50 per circuit | -| **Quantinuum H1-1** | $0.00015 per circuit | ~$1.50 per circuit | -| **Rigetti** | Per-shot pricing | ~$0.30 per circuit | - -**For our optimized circuit:** - -- **Gates:** ~24 per circuit -- **Shots:** 500 (configurable) -- **IonQ Cost:** 24 gates × 500 shots × $0.00003 = **~$0.36** - -**Budget Tip:** Start with 100 shots ($0.07) to test, then scale up! - ---- - -## 📊 Understanding Results - -### Simulator Results (Ideal Quantum Behavior) - -```text -Bell State - Simulator: - 00: 50.0% - 11: 50.0% -``` - -**Perfect 50/50 split** - This is ideal quantum superposition! - -### Hardware Results (Real Quantum Computer) - -```text -Bell State - Hardware: - 00: 47.3% - 11: 48.9% - 01: 2.1% - 10: 1.7% -``` - -**Slight noise observed** - This is EXPECTED and NORMAL! - -**Why the difference?** - -- **Decoherence:** Quantum states decay over time -- **Gate errors:** Physical gate operations aren't perfect (~99.5% fidelity) -- **Measurement errors:** Readout isn't 100% accurate -- **This is REAL quantum computing!** 🎉 - -### Optimized Circuit Results - -```text -Measurement distribution (top 10 states): - 0000: 87 (17.4%) ████████ - 1111: 82 (16.4%) ████████ - 0101: 45 ( 9.0%) ████ - 1010: 43 ( 8.6%) ████ - ... - -Quantum State Analysis: - Unique states measured: 14/16 - Entropy: 3.21 / 4.00 - Distribution uniformity: 80.3% -``` - -**What This Means:** - -- **Multiple states:** Your quantum circuit explores quantum superposition -- **High entropy:** Circuit creates complex quantum states (good for ML!) -- **Uniform distribution:** Well-balanced exploration of state space - ---- - -## 🔧 Troubleshooting - -### Error: "Failed to connect to Azure Quantum" - -**Solutions:** - -1. Verify login: `az login` -2. Check subscription: `az account show` -3. Verify workspace exists: - - ```powershell - az quantum workspace show -g rg-quantum-ai -n quantum-ai-workspace - ``` - -4. Update `quantum_config.yaml` with correct subscription ID - -### Error: "No backends available" - -**Solutions:** - -1. Check provider registration in Azure Portal -2. Ensure workspace has providers enabled (IonQ, Quantinuum) -3. Accept provider terms of service in Azure Portal -4. Wait 5-10 minutes after workspace creation - -### Error: "Insufficient credits" - -**Solutions:** - -1. Use simulators (free): `ionq.simulator` -2. Purchase quantum credits in Azure Portal -3. Use Azure free trial credits ($200) -4. Reduce shots (500 → 100) to lower cost - -### Error: "Job failed" or "Circuit execution failed" - -**Solutions:** - -1. Check circuit depth (too deep = more errors) -2. Reduce shots for initial tests -3. Use simulator first to validate circuit -4. Check Azure Portal for detailed error messages -5. Verify circuit is properly measured (all qubits measured) - -### Slow Job Execution - -**Normal Behavior:** - -- **Simulator:** 10-30 seconds -- **Queue time:** 1-5 minutes (depends on load) -- **Hardware execution:** 2-10 minutes -- **Peak times:** Up to 30 minutes queue - -**Tips:** - -- Run during off-peak hours (US night time) -- Use smaller shot counts for testing -- Submit multiple jobs in parallel - ---- - -## 🎓 Next Steps After Testing - -### 1. Analyze Hardware Results - -Compare your hardware results against simulator results: - -```powershell -# Check saved results -cd results -ls *.json - -# View specific result -cat bell_state_results_.json -``` - -### 2. Scale Up Your Experiments - -Try more complex circuits: - -```python -# Increase qubits (update quantum_config.yaml) -n_qubits: 6 # Up from 4 - -# Increase layers -n_layers: 4 # Up from 3 - -# Increase shots for better statistics -shots: 1000 # Up from 500 -``` - -### 3. Train with Hardware Data - -Use real hardware results to train your quantum classifier: - -```powershell -# Run training with Azure backend -python .\src\quantum_classifier.py --backend azure --provider ionq.qpu -``` - -### 4. Compare Providers - -Test different quantum hardware: - -```python -# IonQ (ion trap, high fidelity) -backend = "ionq.qpu" - -# Quantinuum (high quality, more expensive) -backend = "quantinuum.qpu.h1-1" - -# Rigetti (superconducting, fast) -backend = "rigetti.qpu.aspen-m-3" -``` - -### 5. Deploy Production Pipeline - -Set up automated quantum ML pipeline: - -1. **Data preprocessing** (classical) -2. **Quantum circuit generation** (parameterized) -3. **Hardware submission** (Azure Quantum) -4. **Results aggregation** (classical) -5. **Model update** (hybrid training) - ---- - -## 📚 Additional Resources - -### Official Documentation - -- [Azure Quantum Overview](https://docs.microsoft.com/azure/quantum/) -- [Qiskit on Azure](https://docs.microsoft.com/azure/quantum/quickstart-microsoft-qiskit) -- [IonQ Documentation](https://ionq.com/docs) -- [Quantinuum Docs](https://www.quantinuum.com/products) - -### Your Project Files - -- **Deployment Guide:** `azure/DEPLOYMENT.md` (detailed Azure setup) -- **Optimization Report:** `FINAL_OPTIMIZATION_REPORT.md` (90% accuracy results) -- **Training Guide:** `TRAINING_REPORT.md` (7 dataset results) -- **Custom Data:** `CUSTOM_DATASET_GUIDE.md` (use your own data) - -### Cost Management - -- [Azure Quantum Pricing](https://azure.microsoft.com/pricing/details/azure-quantum/) -- [Cost Calculator](https://azure.microsoft.com/pricing/calculator/) -- [Free Credits](https://azure.microsoft.com/free/) - ---- - -## 🎉 Success Criteria - -You've successfully tested on quantum hardware when you see: - -✅ **Connection Success** - -```text -✓ Successfully connected to Azure Quantum! -``` - -✅ **Bell State Entanglement** - -```text -Entanglement Quality: 80%+ -✓ Excellent quantum entanglement observed! -``` - -✅ **Optimized Circuit Execution** - -```text -✓ Job submitted successfully! -Status: JobStatus.COMPLETED -``` - -✅ **Results Retrieved** - -```text -✓ Results saved to results/ -``` - ---- - -## 💡 Pro Tips - -### Cost Optimization - -1. **Always test on simulator first** (free, instant) -2. **Start with 100 shots**, scale to 500 only if needed -3. **Optimize circuit depth** before hardware submission -4. **Use batch jobs** to reduce queue time overhead -5. **Monitor spending** in Azure Cost Management - -### Performance Optimization - -1. **Transpile circuits** before submission (reduce gate count) -2. **Use provider-native gates** (IonQ uses GPI, GPI2, MS) -3. **Minimize circuit depth** (fewer gates = less decoherence) -4. **Calibrate on Bell states** before complex circuits -5. **Run during off-peak hours** for faster queue times - -### Best Practices - -1. **Save all job IDs** for later retrieval -2. **Log hyperparameters** with each run -3. **Compare multiple providers** (IonQ vs Quantinuum) -4. **Version control results** (Git commit after each run) -5. **Document hardware quirks** you discover - ---- - -## 🚀 You're Ready - -You now have everything needed to: - -✅ Deploy Azure Quantum workspace -✅ Connect to real quantum hardware -✅ Submit your optimized 90% accuracy circuit -✅ Analyze quantum vs classical results -✅ Scale to production quantum ML - -**Run the test now:** - -```powershell -python test_azure_quantum.py -``` - -**Welcome to the quantum computing era!** 🎊 - ---- - -*Generated: October 31, 2025* -*Quantum AI Project - Azure Quantum Integration* -*Status: PRODUCTION READY* +# 🚀 Azure Quantum Quick Start Guide + +**Run Your Optimized Quantum AI on Real Quantum Hardware!** + +--- + +## 📋 Prerequisites Checklist + +Before testing on real quantum hardware, ensure you have: + +- [ ] **Azure Account** - [Sign up for free trial](https://azure.microsoft.com/free/) ($200 credit) +- [ ] **Azure CLI** - Installed and logged in (`az login`) +- [ ] **Azure Quantum Workspace** - Deployed (see [Quick Start](#-quick-start-5-minutes)) +- [ ] **Quantum Credits** - IonQ/Quantinuum credits purchased or trial credits +- [ ] **Updated Config** - `config/quantum_config.yaml` with your Azure details + +--- + +## ⚡ Quick Start (5 Minutes) + +### Step 1: Deploy Azure Quantum Workspace + +```powershell +# Navigate to Azure infrastructure directory +cd quantum-ai\azure + +# Login to Azure +az login + +# Set your subscription +az account set --subscription "" + +# Create resource group +az group create --name rg-quantum-ai --location eastus + +# Deploy Bicep template +az deployment group create ` + --resource-group rg-quantum-ai ` + --template-file quantum_workspace.bicep ` + --parameters quantum_workspace.parameters.json +``` + +**Deployment time:** ~2-3 minutes + +### Step 2: Update Configuration + +Edit `config/quantum_config.yaml`: + +```yaml +azure: + subscription_id: '' # From: az account show + resource_group: 'rg-quantum-ai' + workspace_name: 'quantum-ai-workspace' + location: 'eastus' + storage_account: '' # From deployment output +``` + +### Step 3: Authenticate + +```powershell +# Simple authentication (development) +az login + +# The Azure SDK will automatically use your credentials +``` + +### Step 4: Run Test Suite + +```powershell +# Navigate back to project root +cd .. + +# Activate virtual environment +.\venv\Scripts\Activate.ps1 + +# Run comprehensive hardware test +python test_azure_quantum.py +``` + +**Test duration:** 5-15 minutes (depending on backend) + +--- + +## 🎯 What Gets Tested + +The `test_azure_quantum.py` script runs **4 comprehensive tests**: + +### Test 1: Connection Verification ✅ + +- Connects to your Azure Quantum workspace +- Lists all available quantum backends +- Verifies authentication + +**Expected Output:** + +```text +✓ Successfully connected to Azure Quantum! + +Available Quantum Backends: + 1. ionq.simulator + 2. ionq.qpu + 3. quantinuum.sim.h1-1sc + 4. quantinuum.qpu.h1-1 +``` + +### Test 2: Bell State on Hardware 🔔 + +- Submits a simple entanglement test +- Verifies quantum behavior +- **Demonstrates real quantum entanglement!** + +**Expected Results:** + +```text +Measurement counts: + 00: 487 (48.7%) + 11: 513 (51.3%) + +Entanglement Quality: 100% +✓ Excellent quantum entanglement observed! +``` + +### Test 3: Optimized Circuit (90% Accuracy) 🏆 + +- Runs your **optimized 3-layer quantum classifier** +- Tests the configuration that achieved 90% accuracy +- Collects 500 shots for statistical analysis + +**Circuit Details:** + +- **4 qubits** (optimized) +- **3 layers** (87.5% accuracy) +- **Linear entanglement** (82.5% accuracy) +- **Learning rate 0.1** (90% accuracy) + +### Test 4: Simulator vs Hardware Comparison 🔬 + +- Runs same circuit on simulator and real hardware +- Compares ideal vs noisy quantum behavior +- **Demonstrates real-world quantum computing effects!** + +--- + +## 💰 Cost Breakdown + +### FREE Options (Start Here!) + +| Service | Usage | Cost | +|---------|-------|------| +| **IonQ Simulator** | Unlimited | **$0.00** | +| **Microsoft QC Simulator** | Unlimited | **$0.00** | +| **Azure Storage** | First 5 GB | **$0.00** | +| **Free Trial** | $200 credit | **$0.00** | + +**Recommendation:** Use simulators for all development and testing! + +### Paid Quantum Hardware + +| Provider | Pricing Model | Estimated Cost | +|----------|---------------|----------------| +| **IonQ QPU** | $0.00003 per gate-shot | ~$0.50 per circuit | +| **Quantinuum H1-1** | $0.00015 per circuit | ~$1.50 per circuit | +| **Rigetti** | Per-shot pricing | ~$0.30 per circuit | + +**For our optimized circuit:** + +- **Gates:** ~24 per circuit +- **Shots:** 500 (configurable) +- **IonQ Cost:** 24 gates × 500 shots × $0.00003 = **~$0.36** + +**Budget Tip:** Start with 100 shots ($0.07) to test, then scale up! + +--- + +## 📊 Understanding Results + +### Simulator Results (Ideal Quantum Behavior) + +```text +Bell State - Simulator: + 00: 50.0% + 11: 50.0% +``` + +**Perfect 50/50 split** - This is ideal quantum superposition! + +### Hardware Results (Real Quantum Computer) + +```text +Bell State - Hardware: + 00: 47.3% + 11: 48.9% + 01: 2.1% + 10: 1.7% +``` + +**Slight noise observed** - This is EXPECTED and NORMAL! + +**Why the difference?** + +- **Decoherence:** Quantum states decay over time +- **Gate errors:** Physical gate operations aren't perfect (~99.5% fidelity) +- **Measurement errors:** Readout isn't 100% accurate +- **This is REAL quantum computing!** 🎉 + +### Optimized Circuit Results + +```text +Measurement distribution (top 10 states): + 0000: 87 (17.4%) ████████ + 1111: 82 (16.4%) ████████ + 0101: 45 ( 9.0%) ████ + 1010: 43 ( 8.6%) ████ + ... + +Quantum State Analysis: + Unique states measured: 14/16 + Entropy: 3.21 / 4.00 + Distribution uniformity: 80.3% +``` + +**What This Means:** + +- **Multiple states:** Your quantum circuit explores quantum superposition +- **High entropy:** Circuit creates complex quantum states (good for ML!) +- **Uniform distribution:** Well-balanced exploration of state space + +--- + +## 🔧 Troubleshooting + +### Error: "Failed to connect to Azure Quantum" + +**Solutions:** + +1. Verify login: `az login` +2. Check subscription: `az account show` +3. Verify workspace exists: + + ```powershell + az quantum workspace show -g rg-quantum-ai -n quantum-ai-workspace + ``` + +4. Update `quantum_config.yaml` with correct subscription ID + +### Error: "No backends available" + +**Solutions:** + +1. Check provider registration in Azure Portal +2. Ensure workspace has providers enabled (IonQ, Quantinuum) +3. Accept provider terms of service in Azure Portal +4. Wait 5-10 minutes after workspace creation + +### Error: "Insufficient credits" + +**Solutions:** + +1. Use simulators (free): `ionq.simulator` +2. Purchase quantum credits in Azure Portal +3. Use Azure free trial credits ($200) +4. Reduce shots (500 → 100) to lower cost + +### Error: "Job failed" or "Circuit execution failed" + +**Solutions:** + +1. Check circuit depth (too deep = more errors) +2. Reduce shots for initial tests +3. Use simulator first to validate circuit +4. Check Azure Portal for detailed error messages +5. Verify circuit is properly measured (all qubits measured) + +### Slow Job Execution + +**Normal Behavior:** + +- **Simulator:** 10-30 seconds +- **Queue time:** 1-5 minutes (depends on load) +- **Hardware execution:** 2-10 minutes +- **Peak times:** Up to 30 minutes queue + +**Tips:** + +- Run during off-peak hours (US night time) +- Use smaller shot counts for testing +- Submit multiple jobs in parallel + +--- + +## 🎓 Next Steps After Testing + +### 1. Analyze Hardware Results + +Compare your hardware results against simulator results: + +```powershell +# Check saved results +cd results +ls *.json + +# View specific result +cat bell_state_results_.json +``` + +### 2. Scale Up Your Experiments + +Try more complex circuits: + +```python +# Increase qubits (update quantum_config.yaml) +n_qubits: 6 # Up from 4 + +# Increase layers +n_layers: 4 # Up from 3 + +# Increase shots for better statistics +shots: 1000 # Up from 500 +``` + +### 3. Train with Hardware Data + +Use real hardware results to train your quantum classifier: + +```powershell +# Run training with Azure backend +python .\src\quantum_classifier.py --backend azure --provider ionq.qpu +``` + +### 4. Compare Providers + +Test different quantum hardware: + +```python +# IonQ (ion trap, high fidelity) +backend = "ionq.qpu" + +# Quantinuum (high quality, more expensive) +backend = "quantinuum.qpu.h1-1" + +# Rigetti (superconducting, fast) +backend = "rigetti.qpu.aspen-m-3" +``` + +### 5. Deploy Production Pipeline + +Set up automated quantum ML pipeline: + +1. **Data preprocessing** (classical) +2. **Quantum circuit generation** (parameterized) +3. **Hardware submission** (Azure Quantum) +4. **Results aggregation** (classical) +5. **Model update** (hybrid training) + +--- + +## 📚 Additional Resources + +### Official Documentation + +- [Azure Quantum Overview](https://docs.microsoft.com/azure/quantum/) +- [Qiskit on Azure](https://docs.microsoft.com/azure/quantum/quickstart-microsoft-qiskit) +- [IonQ Documentation](https://ionq.com/docs) +- [Quantinuum Docs](https://www.quantinuum.com/products) + +### Your Project Files + +- **Deployment Guide:** `azure/DEPLOYMENT.md` (detailed Azure setup) +- **Optimization Report:** `FINAL_OPTIMIZATION_REPORT.md` (90% accuracy results) +- **Training Guide:** `TRAINING_REPORT.md` (7 dataset results) +- **Custom Data:** `CUSTOM_DATASET_GUIDE.md` (use your own data) + +### Cost Management + +- [Azure Quantum Pricing](https://azure.microsoft.com/pricing/details/azure-quantum/) +- [Cost Calculator](https://azure.microsoft.com/pricing/calculator/) +- [Free Credits](https://azure.microsoft.com/free/) + +--- + +## 🎉 Success Criteria + +You've successfully tested on quantum hardware when you see: + +✅ **Connection Success** + +```text +✓ Successfully connected to Azure Quantum! +``` + +✅ **Bell State Entanglement** + +```text +Entanglement Quality: 80%+ +✓ Excellent quantum entanglement observed! +``` + +✅ **Optimized Circuit Execution** + +```text +✓ Job submitted successfully! +Status: JobStatus.COMPLETED +``` + +✅ **Results Retrieved** + +```text +✓ Results saved to results/ +``` + +--- + +## 💡 Pro Tips + +### Cost Optimization + +1. **Always test on simulator first** (free, instant) +2. **Start with 100 shots**, scale to 500 only if needed +3. **Optimize circuit depth** before hardware submission +4. **Use batch jobs** to reduce queue time overhead +5. **Monitor spending** in Azure Cost Management + +### Performance Optimization + +1. **Transpile circuits** before submission (reduce gate count) +2. **Use provider-native gates** (IonQ uses GPI, GPI2, MS) +3. **Minimize circuit depth** (fewer gates = less decoherence) +4. **Calibrate on Bell states** before complex circuits +5. **Run during off-peak hours** for faster queue times + +### Best Practices + +1. **Save all job IDs** for later retrieval +2. **Log hyperparameters** with each run +3. **Compare multiple providers** (IonQ vs Quantinuum) +4. **Version control results** (Git commit after each run) +5. **Document hardware quirks** you discover + +--- + +## 🚀 You're Ready + +You now have everything needed to: + +✅ Deploy Azure Quantum workspace +✅ Connect to real quantum hardware +✅ Submit your optimized 90% accuracy circuit +✅ Analyze quantum vs classical results +✅ Scale to production quantum ML + +**Run the test now:** + +```powershell +python test_azure_quantum.py +``` + +**Welcome to the quantum computing era!** 🎊 + +--- + +*Generated: October 31, 2025* +*Quantum AI Project - Azure Quantum Integration* +*Status: PRODUCTION READY* diff --git a/ai-projects/quantum-ml/AZURE_SETUP_CHECKLIST.md b/ai-projects/quantum-ml/AZURE_SETUP_CHECKLIST.md index 3251f8a34..3cd645d24 100644 --- a/ai-projects/quantum-ml/AZURE_SETUP_CHECKLIST.md +++ b/ai-projects/quantum-ml/AZURE_SETUP_CHECKLIST.md @@ -1,358 +1,358 @@ -# Azure Quantum Setup Checklist - -## Complete these steps to test your optimized quantum AI on real hardware - ---- - -## ✅ Step-by-Step Setup (15 minutes) - -### Step 1: Azure Account Setup (5 min) - -- [ ] **Sign up for Azure** (if you don't have an account) - - Go to: - - Get $200 free credit (valid for 30 days) - - No credit card required for free services - -- [ ] **Install Azure CLI** (if not installed) - - ```powershell - # Check if installed - az --version - - # If not installed, download from: - # https://aka.ms/installazurecliwindows - ``` - -- [ ] **Login to Azure** - - ```powershell - az login - # Opens browser for authentication - ``` - -- [ ] **Get your subscription ID** - - ```powershell - # View your subscriptions - az account list --output table - - # Copy the SubscriptionId - you'll need this! - ``` - -### Step 2: Deploy Azure Quantum Workspace (5 min) - -- [ ] **Navigate to Azure directory** - - ```powershell - cd c:\Users\Bryan\OneDrive\AI\quantum-ai\azure - ``` - -- [ ] **Update parameters file** - - Edit `quantum_workspace.parameters.json` and update: - - `workspaceName`: Must be globally unique (e.g., `quantum-ai-bryan-2025`) - - `storageAccountName`: Must be globally unique, lowercase, no hyphens (e.g., `quantumstoragebr2025`) - - **Example:** - - ```json - { - "workspaceName": { - "value": "quantum-ai-bryan-2025" - }, - "storageAccountName": { - "value": "quantumstoragebr2025" - } - } - ``` - -- [ ] **Set your subscription** - - ```powershell - az account set --subscription "" - ``` - -- [ ] **Create resource group** - - ```powershell - az group create --name rg-quantum-ai --location eastus - ``` - -- [ ] **Deploy workspace** - - ```powershell - az deployment group create ` - --resource-group rg-quantum-ai ` - --template-file quantum_workspace.bicep ` - --parameters quantum_workspace.parameters.json ` - --name quantum-deployment-$(Get-Date -Format 'yyyyMMddHHmmss') - ``` - - ⏱️ **This takes 2-3 minutes** - wait for "Succeeded" - -- [ ] **Verify deployment** - - ```powershell - # Check workspace was created - az quantum workspace show ` - --resource-group rg-quantum-ai ` - --name quantum-ai-bryan-2025 ` - --output table - ``` - -### Step 3: Update Configuration (2 min) - -- [ ] **Edit quantum config** - - Open: `c:\Users\Bryan\OneDrive\AI\quantum-ai\config\quantum_config.yaml` - - Update the `azure` section: - - ```yaml - azure: - subscription_id: '' - resource_group: 'rg-quantum-ai' - workspace_name: 'quantum-ai-bryan-2025' # Match what you deployed - location: 'eastus' - storage_account: 'quantumstoragebr2025' # Match what you deployed - ``` - -- [ ] **Save the file** (Ctrl+S) - -### Step 4: Install Dependencies (1 min) - -- [ ] **Activate virtual environment** - - ```powershell - cd c:\Users\Bryan\OneDrive\AI\quantum-ai - .\venv\Scripts\Activate.ps1 - ``` - -- [ ] **Install Azure Quantum packages** - - ```powershell - pip install azure-quantum azure-identity qiskit-aer - ``` - -### Step 5: Run Tests! (2 min) - -- [ ] **Start with connection test** - - ```powershell - python test_azure_quantum.py - ``` - -- [ ] **Follow the interactive prompts** - - Test 1: Verifies connection ✓ - - Test 2: Runs Bell state on hardware 🔔 - - Test 3: Tests your optimized circuit 🏆 - ---- - -## 🎯 Quick Start (Already have Azure?) - -If you already have an Azure subscription: - -```powershell -# 1. Login -az login - -# 2. Deploy workspace (replace with unique names) -cd quantum-ai\azure -az group create --name rg-quantum-ai --location eastus -az deployment group create ` - --resource-group rg-quantum-ai ` - --template-file quantum_workspace.bicep ` - --parameters quantum_workspace.parameters.json - -# 3. Update config/quantum_config.yaml with your details - -# 4. Run test -cd .. -python test_azure_quantum.py -``` - ---- - -## 💰 Cost Estimate - -### FREE Tier (Recommended for Testing) - -- **IonQ Simulator**: Unlimited free usage ✅ -- **Microsoft Simulator**: Unlimited free usage ✅ -- **Storage**: First 5 GB free ✅ -- **Azure Free Trial**: $200 credit ✅ - -#### Total for testing with simulators: $0.00 - -### Quantum Hardware (Optional) - -- **IonQ QPU**: ~$0.36 per circuit (500 shots) -- **Quantinuum**: ~$1.50 per circuit -- **Start with simulator, upgrade when ready!** - ---- - -## ⚠️ Important Notes - -### Workspace Names Must Be Unique - -- `workspace_name`: Can include letters, numbers, hyphens -- `storage_account`: Lowercase letters and numbers only (3-24 chars) -- If deployment fails with "name already exists", try different names - -### Supported Regions - -Azure Quantum is available in: - -- **East US** (recommended - most providers) -- **West US** -- **West Europe** -- **North Europe** - -### Provider Availability - -After deployment, you'll have access to: - -- ✅ **IonQ Simulator** (free, always available) -- ✅ **Microsoft QC Simulator** (free, always available) -- 💰 **IonQ QPU** (requires credits) -- 💰 **Quantinuum** (requires credits) - ---- - -## 🔧 Troubleshooting - -### "az: command not found" - -**Solution:** Install Azure CLI from - -### "Deployment failed: InvalidTemplate" - -**Solution:** Check `quantum_workspace.parameters.json` syntax (must be valid JSON) - -### "Name already exists" - -**Solution:** Change workspace name and storage account name to something unique - -### "Insufficient permissions" - -**Solution:** Ensure you're logged in with an account that can create resources - -### "No subscriptions found" - -**Solution:** - -1. Create Azure account at -2. Run `az login` again - ---- - -## 📊 What Happens During Deployment? - -The Bicep template creates: - -1. **Storage Account** - Stores quantum job data -2. **Quantum Workspace** - Main quantum computing resource -3. **Quantum Providers** - Connections to IonQ, Quantinuum, Microsoft -4. **Resource Tags** - For organization and cost tracking - -**Total Resources:** 4 -**Deployment Time:** 2-3 minutes -**Monthly Cost (with free simulators):** ~$0.02-$0.05 - ---- - -## ✅ Success Indicators - -You're ready when you see: - -```text -✓ Successfully connected to Azure Quantum! - -Available Quantum Backends: - 1. ionq.simulator - 2. ionq.qpu - 3. microsoft.estimator -``` - ---- - -## 🎉 Next Steps After Setup - -Once deployed and tested: - -1. **Run Bell State Test** - Verify quantum entanglement -2. **Test Optimized Circuit** - Your 90% accuracy configuration -3. **Compare Simulator vs Hardware** - See real quantum effects -4. **Scale Up** - Try 6-8 qubit circuits -5. **Deploy to Production** - Automate quantum ML pipeline - ---- - -## 📚 Documentation Reference - -- **Quick Start Guide**: `AZURE_QUANTUM_QUICKSTART.md` -- **Detailed Deployment**: `azure/DEPLOYMENT.md` -- **Test Script**: `test_azure_quantum.py` -- **Configuration**: `config/quantum_config.yaml` - ---- - -## 💬 Need Help? - -**Common Questions:** - -**Q: Do I need quantum hardware credits?** -A: No! Start with free simulators (`ionq.simulator`). Only use hardware when you're ready to see real quantum effects. - -**Q: How much will this cost?** -A: Testing with simulators is **completely free**. Real hardware costs ~$0.36 per circuit. - -**Q: How long does testing take?** -A: Simulator results in 10-30 seconds. Hardware takes 2-10 minutes (includes queue time). - -**Q: Can I use my existing Azure subscription?** -A: Yes! Just update `quantum_config.yaml` with your subscription ID. - -**Q: What if deployment fails?** -A: Check troubleshooting section above. Most issues are due to unique naming requirements. - ---- - -## 🚀 Ready to Test on Quantum Hardware? - -**Start here:** - -```powershell -# Make sure you're in the right directory -cd c:\Users\Bryan\OneDrive\AI\quantum-ai - -# Run the complete test suite -python test_azure_quantum.py -``` - -**You'll test:** - -- ✅ Connection to Azure Quantum -- 🔔 Bell state entanglement -- 🏆 Your optimized 90% accuracy circuit -- 🔬 Simulator vs hardware comparison - -**Total time:** 10-15 minutes -**Cost (with simulator):** $0.00 - ---- - -*Let's run quantum ML on real quantum computers!* 🎊 - ---- - -**Current Status:** - -- [x] Optimized configuration (90% accuracy) -- [x] Test scripts created -- [x] Documentation complete -- [ ] Azure workspace deployed ← **YOU ARE HERE** -- [ ] Hardware tested -- [ ] Production deployed +# Azure Quantum Setup Checklist + +## Complete these steps to test your optimized quantum AI on real hardware + +--- + +## ✅ Step-by-Step Setup (15 minutes) + +### Step 1: Azure Account Setup (5 min) + +- [ ] **Sign up for Azure** (if you don't have an account) + - Go to: + - Get $200 free credit (valid for 30 days) + - No credit card required for free services + +- [ ] **Install Azure CLI** (if not installed) + + ```powershell + # Check if installed + az --version + + # If not installed, download from: + # https://aka.ms/installazurecliwindows + ``` + +- [ ] **Login to Azure** + + ```powershell + az login + # Opens browser for authentication + ``` + +- [ ] **Get your subscription ID** + + ```powershell + # View your subscriptions + az account list --output table + + # Copy the SubscriptionId - you'll need this! + ``` + +### Step 2: Deploy Azure Quantum Workspace (5 min) + +- [ ] **Navigate to Azure directory** + + ```powershell + cd c:\Users\Bryan\OneDrive\AI\quantum-ai\azure + ``` + +- [ ] **Update parameters file** + + Edit `quantum_workspace.parameters.json` and update: + - `workspaceName`: Must be globally unique (e.g., `quantum-ai-bryan-2025`) + - `storageAccountName`: Must be globally unique, lowercase, no hyphens (e.g., `quantumstoragebr2025`) + + **Example:** + + ```json + { + "workspaceName": { + "value": "quantum-ai-bryan-2025" + }, + "storageAccountName": { + "value": "quantumstoragebr2025" + } + } + ``` + +- [ ] **Set your subscription** + + ```powershell + az account set --subscription "" + ``` + +- [ ] **Create resource group** + + ```powershell + az group create --name rg-quantum-ai --location eastus + ``` + +- [ ] **Deploy workspace** + + ```powershell + az deployment group create ` + --resource-group rg-quantum-ai ` + --template-file quantum_workspace.bicep ` + --parameters quantum_workspace.parameters.json ` + --name quantum-deployment-$(Get-Date -Format 'yyyyMMddHHmmss') + ``` + + ⏱️ **This takes 2-3 minutes** - wait for "Succeeded" + +- [ ] **Verify deployment** + + ```powershell + # Check workspace was created + az quantum workspace show ` + --resource-group rg-quantum-ai ` + --name quantum-ai-bryan-2025 ` + --output table + ``` + +### Step 3: Update Configuration (2 min) + +- [ ] **Edit quantum config** + + Open: `c:\Users\Bryan\OneDrive\AI\quantum-ai\config\quantum_config.yaml` + + Update the `azure` section: + + ```yaml + azure: + subscription_id: '' + resource_group: 'rg-quantum-ai' + workspace_name: 'quantum-ai-bryan-2025' # Match what you deployed + location: 'eastus' + storage_account: 'quantumstoragebr2025' # Match what you deployed + ``` + +- [ ] **Save the file** (Ctrl+S) + +### Step 4: Install Dependencies (1 min) + +- [ ] **Activate virtual environment** + + ```powershell + cd c:\Users\Bryan\OneDrive\AI\quantum-ai + .\venv\Scripts\Activate.ps1 + ``` + +- [ ] **Install Azure Quantum packages** + + ```powershell + pip install azure-quantum azure-identity qiskit-aer + ``` + +### Step 5: Run Tests! (2 min) + +- [ ] **Start with connection test** + + ```powershell + python test_azure_quantum.py + ``` + +- [ ] **Follow the interactive prompts** + - Test 1: Verifies connection ✓ + - Test 2: Runs Bell state on hardware 🔔 + - Test 3: Tests your optimized circuit 🏆 + +--- + +## 🎯 Quick Start (Already have Azure?) + +If you already have an Azure subscription: + +```powershell +# 1. Login +az login + +# 2. Deploy workspace (replace with unique names) +cd quantum-ai\azure +az group create --name rg-quantum-ai --location eastus +az deployment group create ` + --resource-group rg-quantum-ai ` + --template-file quantum_workspace.bicep ` + --parameters quantum_workspace.parameters.json + +# 3. Update config/quantum_config.yaml with your details + +# 4. Run test +cd .. +python test_azure_quantum.py +``` + +--- + +## 💰 Cost Estimate + +### FREE Tier (Recommended for Testing) + +- **IonQ Simulator**: Unlimited free usage ✅ +- **Microsoft Simulator**: Unlimited free usage ✅ +- **Storage**: First 5 GB free ✅ +- **Azure Free Trial**: $200 credit ✅ + +#### Total for testing with simulators: $0.00 + +### Quantum Hardware (Optional) + +- **IonQ QPU**: ~$0.36 per circuit (500 shots) +- **Quantinuum**: ~$1.50 per circuit +- **Start with simulator, upgrade when ready!** + +--- + +## ⚠️ Important Notes + +### Workspace Names Must Be Unique + +- `workspace_name`: Can include letters, numbers, hyphens +- `storage_account`: Lowercase letters and numbers only (3-24 chars) +- If deployment fails with "name already exists", try different names + +### Supported Regions + +Azure Quantum is available in: + +- **East US** (recommended - most providers) +- **West US** +- **West Europe** +- **North Europe** + +### Provider Availability + +After deployment, you'll have access to: + +- ✅ **IonQ Simulator** (free, always available) +- ✅ **Microsoft QC Simulator** (free, always available) +- 💰 **IonQ QPU** (requires credits) +- 💰 **Quantinuum** (requires credits) + +--- + +## 🔧 Troubleshooting + +### "az: command not found" + +**Solution:** Install Azure CLI from + +### "Deployment failed: InvalidTemplate" + +**Solution:** Check `quantum_workspace.parameters.json` syntax (must be valid JSON) + +### "Name already exists" + +**Solution:** Change workspace name and storage account name to something unique + +### "Insufficient permissions" + +**Solution:** Ensure you're logged in with an account that can create resources + +### "No subscriptions found" + +**Solution:** + +1. Create Azure account at +2. Run `az login` again + +--- + +## 📊 What Happens During Deployment? + +The Bicep template creates: + +1. **Storage Account** - Stores quantum job data +2. **Quantum Workspace** - Main quantum computing resource +3. **Quantum Providers** - Connections to IonQ, Quantinuum, Microsoft +4. **Resource Tags** - For organization and cost tracking + +**Total Resources:** 4 +**Deployment Time:** 2-3 minutes +**Monthly Cost (with free simulators):** ~$0.02-$0.05 + +--- + +## ✅ Success Indicators + +You're ready when you see: + +```text +✓ Successfully connected to Azure Quantum! + +Available Quantum Backends: + 1. ionq.simulator + 2. ionq.qpu + 3. microsoft.estimator +``` + +--- + +## 🎉 Next Steps After Setup + +Once deployed and tested: + +1. **Run Bell State Test** - Verify quantum entanglement +2. **Test Optimized Circuit** - Your 90% accuracy configuration +3. **Compare Simulator vs Hardware** - See real quantum effects +4. **Scale Up** - Try 6-8 qubit circuits +5. **Deploy to Production** - Automate quantum ML pipeline + +--- + +## 📚 Documentation Reference + +- **Quick Start Guide**: `AZURE_QUANTUM_QUICKSTART.md` +- **Detailed Deployment**: `azure/DEPLOYMENT.md` +- **Test Script**: `test_azure_quantum.py` +- **Configuration**: `config/quantum_config.yaml` + +--- + +## 💬 Need Help? + +**Common Questions:** + +**Q: Do I need quantum hardware credits?** +A: No! Start with free simulators (`ionq.simulator`). Only use hardware when you're ready to see real quantum effects. + +**Q: How much will this cost?** +A: Testing with simulators is **completely free**. Real hardware costs ~$0.36 per circuit. + +**Q: How long does testing take?** +A: Simulator results in 10-30 seconds. Hardware takes 2-10 minutes (includes queue time). + +**Q: Can I use my existing Azure subscription?** +A: Yes! Just update `quantum_config.yaml` with your subscription ID. + +**Q: What if deployment fails?** +A: Check troubleshooting section above. Most issues are due to unique naming requirements. + +--- + +## 🚀 Ready to Test on Quantum Hardware? + +**Start here:** + +```powershell +# Make sure you're in the right directory +cd c:\Users\Bryan\OneDrive\AI\quantum-ai + +# Run the complete test suite +python test_azure_quantum.py +``` + +**You'll test:** + +- ✅ Connection to Azure Quantum +- 🔔 Bell state entanglement +- 🏆 Your optimized 90% accuracy circuit +- 🔬 Simulator vs hardware comparison + +**Total time:** 10-15 minutes +**Cost (with simulator):** $0.00 + +--- + +*Let's run quantum ML on real quantum computers!* 🎊 + +--- + +**Current Status:** + +- [x] Optimized configuration (90% accuracy) +- [x] Test scripts created +- [x] Documentation complete +- [ ] Azure workspace deployed ← **YOU ARE HERE** +- [ ] Hardware tested +- [ ] Production deployed diff --git a/ai-projects/quantum-ml/COMPLETE_RESULTS_INDEX.md b/ai-projects/quantum-ml/COMPLETE_RESULTS_INDEX.md index c8dfaf817..e42d90d88 100644 --- a/ai-projects/quantum-ml/COMPLETE_RESULTS_INDEX.md +++ b/ai-projects/quantum-ml/COMPLETE_RESULTS_INDEX.md @@ -1,367 +1,367 @@ -# Quantum AI Project - Complete Results Index - -**Last Updated:** November 1, 2025 - -This document provides a complete index of all experimental results, documentation, and findings from the quantum-ai project's testing and validation campaign. - ---- - -## 📊 Executive Summary - -### What We've Validated -- ✅ **Azure Quantum Integration:** Full workflow from circuit design → submission → results retrieval -- ✅ **Rigetti Backend:** Production-ready with perfect GHZ fidelity and variational circuit support -- ✅ **MPS Simulations:** Accurate within 1% of hardware (validated at 4 qubits, reliable for 32/64q) -- ✅ **Local Simulation Framework:** 16→256 qubit scaling with multiple methods (MPS, stabilizer) -- ✅ **Noise Modeling:** Pauli, depolarizing, amplitude damping channels implemented - -### What We've Discovered -- ❌ **Quantinuum H-series simulator bug:** All circuits collapse to ground state (reported to Azure) -- 📈 **Entanglement topology:** Subtle differences at shallow depth (linear ≈ circular < full) -- 📉 **Noise resilience:** Small noise (p=0.005, γ=0.002) has negligible impact on 64q/L=3-4 -- 🎯 **Optimal configuration:** MPS for 32/64q variational, stabilizer for 128/256q Clifford - ---- - -## 📁 Documentation Hierarchy - -### 1. Getting Started -**Read first for quick orientation** - -- [`README.md`](./README.md) — Project overview, architecture, features -- [`QUICK_REFERENCE.md`](./QUICK_REFERENCE.md) — All commands and workflows (one-page) -- [`INDEX.md`](./INDEX.md) — Complete file tree and navigation - -### 2. Hardware Validation -**Multi-backend testing and provider comparison** - -- [`HARDWARE_TEST_RESULTS.md`](./HARDWARE_TEST_RESULTS.md) - - Multi-backend GHZ tests (Rigetti ✅, Quantinuum ⚠️) - - 4-qubit and 6-qubit scaling tests - - Hardware vs local simulator validation - - Backend availability matrix - -- [`PROVIDER_COMPARISON_RESULTS.md`](./PROVIDER_COMPARISON_RESULTS.md) ⭐ **Most Comprehensive** - - 4 gate pattern tests (standard, quantinuum-native, ionq-native, rigetti-native) - - Quantinuum bug root cause analysis - - Variational hardware (90.5%) vs MPS (91.5%) comparison - - Provider compatibility matrix - - Production recommendations - -### 3. Simulation Campaign -**32→256 qubit local experiments** - -- [`OPTIMIZATION_RESULTS.md`](./OPTIMIZATION_RESULTS.md) - - Initial 32q/64q MPS entanglement topology comparison - - Clean vs Pauli noise (px/pz) experiments - - Entropy% trends and histograms - -- [`FINAL_OPTIMIZATION_REPORT.md`](./FINAL_OPTIMIZATION_REPORT.md) - - 64q deep layer exploration (L=3-4) - - Depolarizing and amplitude damping noise - - Stabilizer random experiments (128/256q, shots=2000) - - Summary charts and comprehensive analysis - -### 4. Dataset Training -**Custom dataset experiments with quantum classifiers** - -- [`DATASET_TRAINING_RESULTS.md`](./DATASET_TRAINING_RESULTS.md) - - 4 UCI datasets (banknote, heart disease, ionosphere, sonar) - - Quantum classifier performance metrics - - Hyperparameter tuning results - -- [`CUSTOM_DATASET_GUIDE.md`](./CUSTOM_DATASET_GUIDE.md) - - How to prepare custom CSV datasets - - Feature engineering for quantum circuits - - Training workflows - -### 5. Deployment Guides -**Azure Quantum workspace setup and production deployment** - -- [`azure/DEPLOYMENT.md`](./azure/DEPLOYMENT.md) - - Complete Azure Quantum workspace setup - - CLI commands, authentication, provider configuration - - Terraform/Bicep infrastructure-as-code - -- [`AZURE_QUANTUM_QUICKSTART.md`](./AZURE_QUANTUM_QUICKSTART.md) - - Fast path to first hardware job - - Minimal setup for testing - -- [`PRODUCTION_DEPLOYMENT_GUIDE.md`](./PRODUCTION_DEPLOYMENT_GUIDE.md) - - Best practices for production workloads - - Cost monitoring and optimization - - Job batching and retry strategies - -### 6. MCP Server -**Model Context Protocol for AI agents** - -- [`MCP_SERVER_README.md`](./MCP_SERVER_README.md) - - Installation and configuration - - Available tools (create_circuit, simulate, submit, train_classifier, etc.) - - Usage examples and integration patterns - -### 7. Workflow Summaries -**Quick reference for common tasks** - -- [`DEMONSTRATION_SUMMARY.md`](./DEMONSTRATION_SUMMARY.md) - - End-to-end workflow examples - - Local simulation → Azure hardware pipeline - -- [`READY_FOR_HARDWARE.md`](./READY_FOR_HARDWARE.md) - - Pre-hardware checklist - - Cost estimation - - Testing best practices - ---- - -## 🧪 Experimental Results Summary - -### Result Files Count -- **Total:** 57 JSON files in `ai-projects/quantum-ml/results/` -- **Visualizations:** ~115 PNG charts in `ai-projects/quantum-ml/results/visualizations/` - -### Result Breakdown - -#### Azure Hardware Jobs -| Backend | Jobs | Status | Notes | -|---------|------|--------|-------| -| rigetti.sim.qvm | 3 | ✅ All passed | 4q/6q GHZ + 4q variational | -| quantinuum.sim.h2-1sc | 6 | ❌ All collapsed | All patterns failed (bug) | -| **Total** | **9** | **33% success** | Quantinuum excluded | - -#### Local Simulations -| Method | Qubits | Circuits | Status | -|--------|--------|----------|--------| -| MPS | 32, 64 | 22 (L=1-4, 3 entanglements, 4 noise) | ✅ Complete | -| Stabilizer | 128, 256 | 6 (L=2/4/8, shots=2000) | ✅ Complete | -| Statevector | 4, 16 | 8 (early tests) | ✅ Complete | -| **Total** | **4-256** | **36** | **100% success** | - -### Key Metrics - -#### Hardware Fidelity (Rigetti) -- **GHZ 4q:** 520|0000⟩ + 480|1111⟩ → 99.9% entropy ✅ -- **GHZ 6q:** 1002|000000⟩ + 998|111111⟩ → 100% entropy ✅ -- **Variational 4q/L=2:** 16/16 states, 90.5% entropy ✅ - -#### MPS Accuracy -- **4q/L=2 variational:** 91.5% entropy (1% difference from hardware) -- **32q/L=2 linear:** ~14-15% entropy (clean) -- **64q/L=4 circular:** ~15% entropy (with noise) - -#### Noise Impact (64q/L=3-4) -- **Clean:** 14-15% entropy -- **Depolarizing (p=0.005):** 14-15% (negligible) -- **Amp damping (γ=0.002):** 14-15% (negligible) -- **Conclusion:** Small noise does not affect results at these parameters - -#### Stabilizer Scaling (shots=2000) -- **128q/L=2:** 97% weight coverage, clean Hamming distribution -- **256q/L=8:** 87% weight coverage, spread across 120-140 range - ---- - -## 📈 Visualization Gallery - -### Summary Charts (Most Important) -- `mps_entropy_by_entanglement_n32.png` — Topology comparison at 32q -- `mps_entropy_by_entanglement_n64.png` — Topology comparison at 64q -- `stabilizer_random_weight_coverage_vs_layers.png` — Clifford layer scaling - -### Per-Run Charts -- **Bar charts:** Top-10 state distributions (all runs) -- **Hamming-weight histograms:** Large-N runs (128/256q) -- **Azure job charts:** Hardware test distributions - -### Access -All charts in: `ai-projects/quantum-ml/results/visualizations/` - ---- - -## 🔬 Scripts & Tools - -### Hardware Submission -- `scripts/submit_small_stabilizer.py` — GHZ to Azure Quantum -- `scripts/submit_variational_hardware.py` — Variational circuits to hardware -- `scripts/test_provider_gates.py` — Provider-specific gate pattern tester - -### Local Simulation -- `scripts/run_simulated_circuit.py` — MPS/stabilizer with noise models -- `scripts/run_experiment_grid.py` — Automated sweeps (topology, noise, layers) - -### Visualization & Analysis -- `scripts/visualize_hardware_results.py` — Generate all charts from JSON results -- `azure/quantum_batch_jobs.ps1` — Batch job submission -- `verify_workspace.ps1` — Workspace connectivity test - -### Dataset Training -- `train_custom_dataset.py` — Train quantum classifier on CSV data -- `scripts/quick_setup_datasets.py` — Download/prepare UCI datasets - ---- - -## 🎯 Recommendations for Next Steps - -### Immediate (This Week) -1. **Report Quantinuum bug** to Azure support - - Job IDs: 26b2c929, 39833bbd, 432536e8, 4d283578 - - Issue: All patterns collapse to |0000⟩ - - Expected: GHZ superposition (50/50 split) - -2. **Submit 8q variational to Rigetti** - - Test MPS accuracy at higher qubit count - - Compare entropy trends (4q vs 8q) - -3. **Test circular and full entanglement on hardware** - - Validate topology differences observed in MPS - - 4q/L=2 with circular and full patterns - -### Short-term (This Month) -4. **Provision IonQ access** - - Contact Azure support for provider addition - - Budget approval for QPU credits - - Test IonQ simulator and QPU - -5. **Quantify hardware noise** - - Compare hardware entropy drops vs MPS clean runs - - Estimate depolarizing/dephasing parameters - - Build hardware noise model for simulations - -6. **Deeper layer hardware tests** - - Submit L=3-4 variational to Rigetti - - Compare with 64q MPS deep layer results - -### Medium-term (This Quarter) -7. **Real quantum hardware access** - - IonQ QPU or Rigetti Aspen - - Production variational quantum classifier - - Heart disease dataset on hardware - -8. **Hardware/MPS fidelity study** - - Systematic qubit scaling (4→8→16q) - - Entropy% error vs N chart - - Publish comparative analysis - -9. **Optimize for production** - - Cost-effective circuit designs - - Batch job workflows - - Automated result analysis pipelines - ---- - -## 📚 External Resources - -### Azure Quantum -- [Workspace Setup Guide](https://learn.microsoft.com/azure/quantum/) -- [Provider Documentation](https://learn.microsoft.com/azure/quantum/provider-qio) -- [Pricing Calculator](https://azure.microsoft.com/pricing/details/quantum/) - -### Qiskit Documentation -- [Qiskit Tutorials](https://qiskit.org/documentation/) -- [Aer Simulator](https://qiskit.org/ecosystem/aer/) -- [Circuit Library](https://qiskit.org/documentation/apidoc/circuit_library.html) - -### Research Papers -- Variational Quantum Algorithms: [arXiv:1304.3061](https://arxiv.org/abs/1304.3061) -- Quantum Neural Networks: [Nature 2019](https://www.nature.com/articles/s41586-019-0980-2) -- MPS Simulation: [PRX 2019](https://journals.aps.org/prx/abstract/10.1103/PhysRevX.9.031041) - ---- - -## 🔧 Troubleshooting - -### Common Issues - -**"Quantinuum jobs collapse to |0000⟩"** -→ Known simulator bug. Use Rigetti backend instead. - -**"MPS out of memory"** -→ Reduce qubits, layers, or shots. Try stabilizer method for >128q. - -**"Azure authentication failed"** -→ Run `az login`. Check `quantum_config.yaml` workspace details. - -**"IonQ backend not available"** -→ Not provisioned. Contact Azure support to add provider. - -**"Charts not generated"** -→ Ensure metadata in JSON results. Rerun `visualize_hardware_results.py`. - -### Getting Help -- Check `QUICK_REFERENCE.md` for command syntax -- Review `HARDWARE_TEST_RESULTS.md` for backend status -- See `azure/DEPLOYMENT.md` for setup issues -- Open issue in repo with job IDs and error logs - ---- - -## 📊 Complete File Manifest - -### Documentation (18 files) -``` -README.md # Project overview -INDEX.md # File tree navigation -COMPLETE_RESULTS_INDEX.md # This file -QUICK_REFERENCE.md # One-page command reference -HARDWARE_TEST_RESULTS.md # Multi-backend hardware tests -PROVIDER_COMPARISON_RESULTS.md # Gate pattern analysis + MPS validation -OPTIMIZATION_RESULTS.md # 32/64q MPS experiments -FINAL_OPTIMIZATION_REPORT.md # Deep layer + stabilizer scaling -DATASET_TRAINING_RESULTS.md # UCI dataset experiments -CUSTOM_DATASET_GUIDE.md # Dataset preparation guide -MCP_SERVER_README.md # MCP server documentation -AZURE_QUANTUM_QUICKSTART.md # Fast hardware setup -AZURE_SETUP_CHECKLIST.md # Deployment checklist -DEMONSTRATION_SUMMARY.md # End-to-end workflows -READY_FOR_HARDWARE.md # Pre-hardware checklist -PRODUCTION_DEPLOYMENT_GUIDE.md # Production best practices -DEPLOYMENT_WALKTHROUGH.md # Step-by-step deployment -azure/DEPLOYMENT.md # Complete Azure setup -``` - -### Scripts (14 files) -``` -scripts/run_simulated_circuit.py # Local MPS/stabilizer simulation -scripts/visualize_hardware_results.py # Chart generation -scripts/run_experiment_grid.py # Automated sweeps -scripts/submit_small_stabilizer.py # GHZ hardware submission -scripts/submit_variational_hardware.py # Variational hardware submission -scripts/test_provider_gates.py # Gate pattern tester -train_custom_dataset.py # Dataset training -quantum_mcp_server.py # MCP server -test_azure_quantum.py # Azure connectivity test -verify_workspace.ps1 # Workspace verification -azure/quantum_batch_jobs.ps1 # Batch submission -deploy_to_azure_quantum.ps1 # Deployment automation -setup_after_portal.ps1 # Post-portal setup -submit_circuit_azure.py # Direct circuit submission -``` - -### Results (57 JSON + 115 PNG) -``` -ai-projects/quantum-ml/results/*.json # All simulation and hardware results -ai-projects/quantum-ml/results/visualizations/*.png # All generated charts -``` - -### Source Code (`src/`) -``` -quantum_classifier.py # Main quantum classifier -azure_quantum_integration.py # Azure Quantum SDK wrapper -hybrid_model.py # Hybrid quantum-classical model -utils.py # Helper functions -data_loader.py # Dataset loading -``` - -### Configuration -``` -config/quantum_config.yaml # Main configuration -requirements.txt # Python dependencies -mcp-requirements.txt # MCP server dependencies -``` - ---- - -**End of Complete Results Index** - -*For quick commands and workflows, see [`QUICK_REFERENCE.md`](./QUICK_REFERENCE.md)* -*For detailed hardware findings, see [`PROVIDER_COMPARISON_RESULTS.md`](./PROVIDER_COMPARISON_RESULTS.md)* +# Quantum AI Project - Complete Results Index + +**Last Updated:** November 1, 2025 + +This document provides a complete index of all experimental results, documentation, and findings from the quantum-ai project's testing and validation campaign. + +--- + +## 📊 Executive Summary + +### What We've Validated +- ✅ **Azure Quantum Integration:** Full workflow from circuit design → submission → results retrieval +- ✅ **Rigetti Backend:** Production-ready with perfect GHZ fidelity and variational circuit support +- ✅ **MPS Simulations:** Accurate within 1% of hardware (validated at 4 qubits, reliable for 32/64q) +- ✅ **Local Simulation Framework:** 16→256 qubit scaling with multiple methods (MPS, stabilizer) +- ✅ **Noise Modeling:** Pauli, depolarizing, amplitude damping channels implemented + +### What We've Discovered +- ❌ **Quantinuum H-series simulator bug:** All circuits collapse to ground state (reported to Azure) +- 📈 **Entanglement topology:** Subtle differences at shallow depth (linear ≈ circular < full) +- 📉 **Noise resilience:** Small noise (p=0.005, γ=0.002) has negligible impact on 64q/L=3-4 +- 🎯 **Optimal configuration:** MPS for 32/64q variational, stabilizer for 128/256q Clifford + +--- + +## 📁 Documentation Hierarchy + +### 1. Getting Started +**Read first for quick orientation** + +- [`README.md`](./README.md) — Project overview, architecture, features +- [`QUICK_REFERENCE.md`](./QUICK_REFERENCE.md) — All commands and workflows (one-page) +- [`INDEX.md`](./INDEX.md) — Complete file tree and navigation + +### 2. Hardware Validation +**Multi-backend testing and provider comparison** + +- [`HARDWARE_TEST_RESULTS.md`](./HARDWARE_TEST_RESULTS.md) + - Multi-backend GHZ tests (Rigetti ✅, Quantinuum ⚠️) + - 4-qubit and 6-qubit scaling tests + - Hardware vs local simulator validation + - Backend availability matrix + +- [`PROVIDER_COMPARISON_RESULTS.md`](./PROVIDER_COMPARISON_RESULTS.md) ⭐ **Most Comprehensive** + - 4 gate pattern tests (standard, quantinuum-native, ionq-native, rigetti-native) + - Quantinuum bug root cause analysis + - Variational hardware (90.5%) vs MPS (91.5%) comparison + - Provider compatibility matrix + - Production recommendations + +### 3. Simulation Campaign +**32→256 qubit local experiments** + +- [`OPTIMIZATION_RESULTS.md`](./OPTIMIZATION_RESULTS.md) + - Initial 32q/64q MPS entanglement topology comparison + - Clean vs Pauli noise (px/pz) experiments + - Entropy% trends and histograms + +- [`FINAL_OPTIMIZATION_REPORT.md`](./FINAL_OPTIMIZATION_REPORT.md) + - 64q deep layer exploration (L=3-4) + - Depolarizing and amplitude damping noise + - Stabilizer random experiments (128/256q, shots=2000) + - Summary charts and comprehensive analysis + +### 4. Dataset Training +**Custom dataset experiments with quantum classifiers** + +- [`DATASET_TRAINING_RESULTS.md`](./DATASET_TRAINING_RESULTS.md) + - 4 UCI datasets (banknote, heart disease, ionosphere, sonar) + - Quantum classifier performance metrics + - Hyperparameter tuning results + +- [`CUSTOM_DATASET_GUIDE.md`](./CUSTOM_DATASET_GUIDE.md) + - How to prepare custom CSV datasets + - Feature engineering for quantum circuits + - Training workflows + +### 5. Deployment Guides +**Azure Quantum workspace setup and production deployment** + +- [`azure/DEPLOYMENT.md`](./azure/DEPLOYMENT.md) + - Complete Azure Quantum workspace setup + - CLI commands, authentication, provider configuration + - Terraform/Bicep infrastructure-as-code + +- [`AZURE_QUANTUM_QUICKSTART.md`](./AZURE_QUANTUM_QUICKSTART.md) + - Fast path to first hardware job + - Minimal setup for testing + +- [`PRODUCTION_DEPLOYMENT_GUIDE.md`](./PRODUCTION_DEPLOYMENT_GUIDE.md) + - Best practices for production workloads + - Cost monitoring and optimization + - Job batching and retry strategies + +### 6. MCP Server +**Model Context Protocol for AI agents** + +- [`MCP_SERVER_README.md`](./MCP_SERVER_README.md) + - Installation and configuration + - Available tools (create_circuit, simulate, submit, train_classifier, etc.) + - Usage examples and integration patterns + +### 7. Workflow Summaries +**Quick reference for common tasks** + +- [`DEMONSTRATION_SUMMARY.md`](./DEMONSTRATION_SUMMARY.md) + - End-to-end workflow examples + - Local simulation → Azure hardware pipeline + +- [`READY_FOR_HARDWARE.md`](./READY_FOR_HARDWARE.md) + - Pre-hardware checklist + - Cost estimation + - Testing best practices + +--- + +## 🧪 Experimental Results Summary + +### Result Files Count +- **Total:** 57 JSON files in `ai-projects/quantum-ml/results/` +- **Visualizations:** ~115 PNG charts in `ai-projects/quantum-ml/results/visualizations/` + +### Result Breakdown + +#### Azure Hardware Jobs +| Backend | Jobs | Status | Notes | +|---------|------|--------|-------| +| rigetti.sim.qvm | 3 | ✅ All passed | 4q/6q GHZ + 4q variational | +| quantinuum.sim.h2-1sc | 6 | ❌ All collapsed | All patterns failed (bug) | +| **Total** | **9** | **33% success** | Quantinuum excluded | + +#### Local Simulations +| Method | Qubits | Circuits | Status | +|--------|--------|----------|--------| +| MPS | 32, 64 | 22 (L=1-4, 3 entanglements, 4 noise) | ✅ Complete | +| Stabilizer | 128, 256 | 6 (L=2/4/8, shots=2000) | ✅ Complete | +| Statevector | 4, 16 | 8 (early tests) | ✅ Complete | +| **Total** | **4-256** | **36** | **100% success** | + +### Key Metrics + +#### Hardware Fidelity (Rigetti) +- **GHZ 4q:** 520|0000⟩ + 480|1111⟩ → 99.9% entropy ✅ +- **GHZ 6q:** 1002|000000⟩ + 998|111111⟩ → 100% entropy ✅ +- **Variational 4q/L=2:** 16/16 states, 90.5% entropy ✅ + +#### MPS Accuracy +- **4q/L=2 variational:** 91.5% entropy (1% difference from hardware) +- **32q/L=2 linear:** ~14-15% entropy (clean) +- **64q/L=4 circular:** ~15% entropy (with noise) + +#### Noise Impact (64q/L=3-4) +- **Clean:** 14-15% entropy +- **Depolarizing (p=0.005):** 14-15% (negligible) +- **Amp damping (γ=0.002):** 14-15% (negligible) +- **Conclusion:** Small noise does not affect results at these parameters + +#### Stabilizer Scaling (shots=2000) +- **128q/L=2:** 97% weight coverage, clean Hamming distribution +- **256q/L=8:** 87% weight coverage, spread across 120-140 range + +--- + +## 📈 Visualization Gallery + +### Summary Charts (Most Important) +- `mps_entropy_by_entanglement_n32.png` — Topology comparison at 32q +- `mps_entropy_by_entanglement_n64.png` — Topology comparison at 64q +- `stabilizer_random_weight_coverage_vs_layers.png` — Clifford layer scaling + +### Per-Run Charts +- **Bar charts:** Top-10 state distributions (all runs) +- **Hamming-weight histograms:** Large-N runs (128/256q) +- **Azure job charts:** Hardware test distributions + +### Access +All charts in: `ai-projects/quantum-ml/results/visualizations/` + +--- + +## 🔬 Scripts & Tools + +### Hardware Submission +- `scripts/submit_small_stabilizer.py` — GHZ to Azure Quantum +- `scripts/submit_variational_hardware.py` — Variational circuits to hardware +- `scripts/test_provider_gates.py` — Provider-specific gate pattern tester + +### Local Simulation +- `scripts/run_simulated_circuit.py` — MPS/stabilizer with noise models +- `scripts/run_experiment_grid.py` — Automated sweeps (topology, noise, layers) + +### Visualization & Analysis +- `scripts/visualize_hardware_results.py` — Generate all charts from JSON results +- `azure/quantum_batch_jobs.ps1` — Batch job submission +- `verify_workspace.ps1` — Workspace connectivity test + +### Dataset Training +- `train_custom_dataset.py` — Train quantum classifier on CSV data +- `scripts/quick_setup_datasets.py` — Download/prepare UCI datasets + +--- + +## 🎯 Recommendations for Next Steps + +### Immediate (This Week) +1. **Report Quantinuum bug** to Azure support + - Job IDs: 26b2c929, 39833bbd, 432536e8, 4d283578 + - Issue: All patterns collapse to |0000⟩ + - Expected: GHZ superposition (50/50 split) + +2. **Submit 8q variational to Rigetti** + - Test MPS accuracy at higher qubit count + - Compare entropy trends (4q vs 8q) + +3. **Test circular and full entanglement on hardware** + - Validate topology differences observed in MPS + - 4q/L=2 with circular and full patterns + +### Short-term (This Month) +4. **Provision IonQ access** + - Contact Azure support for provider addition + - Budget approval for QPU credits + - Test IonQ simulator and QPU + +5. **Quantify hardware noise** + - Compare hardware entropy drops vs MPS clean runs + - Estimate depolarizing/dephasing parameters + - Build hardware noise model for simulations + +6. **Deeper layer hardware tests** + - Submit L=3-4 variational to Rigetti + - Compare with 64q MPS deep layer results + +### Medium-term (This Quarter) +7. **Real quantum hardware access** + - IonQ QPU or Rigetti Aspen + - Production variational quantum classifier + - Heart disease dataset on hardware + +8. **Hardware/MPS fidelity study** + - Systematic qubit scaling (4→8→16q) + - Entropy% error vs N chart + - Publish comparative analysis + +9. **Optimize for production** + - Cost-effective circuit designs + - Batch job workflows + - Automated result analysis pipelines + +--- + +## 📚 External Resources + +### Azure Quantum +- [Workspace Setup Guide](https://learn.microsoft.com/azure/quantum/) +- [Provider Documentation](https://learn.microsoft.com/azure/quantum/provider-qio) +- [Pricing Calculator](https://azure.microsoft.com/pricing/details/quantum/) + +### Qiskit Documentation +- [Qiskit Tutorials](https://qiskit.org/documentation/) +- [Aer Simulator](https://qiskit.org/ecosystem/aer/) +- [Circuit Library](https://qiskit.org/documentation/apidoc/circuit_library.html) + +### Research Papers +- Variational Quantum Algorithms: [arXiv:1304.3061](https://arxiv.org/abs/1304.3061) +- Quantum Neural Networks: [Nature 2019](https://www.nature.com/articles/s41586-019-0980-2) +- MPS Simulation: [PRX 2019](https://journals.aps.org/prx/abstract/10.1103/PhysRevX.9.031041) + +--- + +## 🔧 Troubleshooting + +### Common Issues + +**"Quantinuum jobs collapse to |0000⟩"** +→ Known simulator bug. Use Rigetti backend instead. + +**"MPS out of memory"** +→ Reduce qubits, layers, or shots. Try stabilizer method for >128q. + +**"Azure authentication failed"** +→ Run `az login`. Check `quantum_config.yaml` workspace details. + +**"IonQ backend not available"** +→ Not provisioned. Contact Azure support to add provider. + +**"Charts not generated"** +→ Ensure metadata in JSON results. Rerun `visualize_hardware_results.py`. + +### Getting Help +- Check `QUICK_REFERENCE.md` for command syntax +- Review `HARDWARE_TEST_RESULTS.md` for backend status +- See `azure/DEPLOYMENT.md` for setup issues +- Open issue in repo with job IDs and error logs + +--- + +## 📊 Complete File Manifest + +### Documentation (18 files) +``` +README.md # Project overview +INDEX.md # File tree navigation +COMPLETE_RESULTS_INDEX.md # This file +QUICK_REFERENCE.md # One-page command reference +HARDWARE_TEST_RESULTS.md # Multi-backend hardware tests +PROVIDER_COMPARISON_RESULTS.md # Gate pattern analysis + MPS validation +OPTIMIZATION_RESULTS.md # 32/64q MPS experiments +FINAL_OPTIMIZATION_REPORT.md # Deep layer + stabilizer scaling +DATASET_TRAINING_RESULTS.md # UCI dataset experiments +CUSTOM_DATASET_GUIDE.md # Dataset preparation guide +MCP_SERVER_README.md # MCP server documentation +AZURE_QUANTUM_QUICKSTART.md # Fast hardware setup +AZURE_SETUP_CHECKLIST.md # Deployment checklist +DEMONSTRATION_SUMMARY.md # End-to-end workflows +READY_FOR_HARDWARE.md # Pre-hardware checklist +PRODUCTION_DEPLOYMENT_GUIDE.md # Production best practices +DEPLOYMENT_WALKTHROUGH.md # Step-by-step deployment +azure/DEPLOYMENT.md # Complete Azure setup +``` + +### Scripts (14 files) +``` +scripts/run_simulated_circuit.py # Local MPS/stabilizer simulation +scripts/visualize_hardware_results.py # Chart generation +scripts/run_experiment_grid.py # Automated sweeps +scripts/submit_small_stabilizer.py # GHZ hardware submission +scripts/submit_variational_hardware.py # Variational hardware submission +scripts/test_provider_gates.py # Gate pattern tester +train_custom_dataset.py # Dataset training +quantum_mcp_server.py # MCP server +test_azure_quantum.py # Azure connectivity test +verify_workspace.ps1 # Workspace verification +azure/quantum_batch_jobs.ps1 # Batch submission +deploy_to_azure_quantum.ps1 # Deployment automation +setup_after_portal.ps1 # Post-portal setup +submit_circuit_azure.py # Direct circuit submission +``` + +### Results (57 JSON + 115 PNG) +``` +ai-projects/quantum-ml/results/*.json # All simulation and hardware results +ai-projects/quantum-ml/results/visualizations/*.png # All generated charts +``` + +### Source Code (`src/`) +``` +quantum_classifier.py # Main quantum classifier +azure_quantum_integration.py # Azure Quantum SDK wrapper +hybrid_model.py # Hybrid quantum-classical model +utils.py # Helper functions +data_loader.py # Dataset loading +``` + +### Configuration +``` +config/quantum_config.yaml # Main configuration +requirements.txt # Python dependencies +mcp-requirements.txt # MCP server dependencies +``` + +--- + +**End of Complete Results Index** + +*For quick commands and workflows, see [`QUICK_REFERENCE.md`](./QUICK_REFERENCE.md)* +*For detailed hardware findings, see [`PROVIDER_COMPARISON_RESULTS.md`](./PROVIDER_COMPARISON_RESULTS.md)* diff --git a/ai-projects/quantum-ml/COMPREHENSIVE_BENCHMARK_SUMMARY.md b/ai-projects/quantum-ml/COMPREHENSIVE_BENCHMARK_SUMMARY.md index 6230210c7..ad0b2ff6b 100644 --- a/ai-projects/quantum-ml/COMPREHENSIVE_BENCHMARK_SUMMARY.md +++ b/ai-projects/quantum-ml/COMPREHENSIVE_BENCHMARK_SUMMARY.md @@ -1,396 +1,396 @@ -# Comprehensive Quantum AI Benchmark Summary 🏆 - -**Date:** November 16, 2025 -**Status:** Dataset Expansion & Validation Complete - ---- - -## 🎯 Executive Summary - -Successfully expanded quantum ML infrastructure from **4 to 15 datasets** (275% increase), validated **14/15 datasets** (93% success rate), and achieved comprehensive benchmarking across 7 scientific domains with **average accuracy of 84.48%** on initial 25-epoch training. - ---- - -## 📊 Complete Dataset Portfolio - -### **Working Datasets (14)** - -| # | Dataset | Category | Samples | Features | Classes | Status | -|---|---------|----------|---------|----------|---------|--------| -| 1 | ionosphere | Physics | 351 | 34 | 2 | ✅ 85-90% | -| 2 | banknote | Forensics | 1,372 | 4 | 2 | ✅ 99-100% | -| 3 | heart_disease | Medical | 303 | 13 | 2 | ✅ 82% | -| 4 | sonar | Geophysics | 208 | 60 | 2 | ✅ 76% | -| 5 | breast_cancer | Medical | 569 | 29 | 2 | ✅ 88% (1-epoch) | -| 6 | diabetes | Medical | 768 | 8 | 2 | ✅ 71% | -| 7 | blood_transfusion | Medical | 748 | 4 | 2 | ✅ 83% | -| 8 | haberman | Medical | 306 | 3 | 2 | ✅ 58% (challenging) | -| 9 | wine_red | Chemistry | 1,599 | 11 | 6 | ✅ 99% (1-epoch) | -| 10 | wine_white | Chemistry | 4,898 | 11 | 7 | ✅ 99% (1-epoch) | -| 11 | magic_gamma | Physics | 19,020 | 10 | 2 | ✅ 78% | -| 12 | iris | Biology | 150 | 4 | 3 | ✅ 97% | -| 13 | wheat_seeds | Agriculture | 210 | 7 | 3 | ✅ 72% (1-epoch) | -| 14 | glass | Forensics | 214 | 10 | 6 | ✅ 88% | - -### **Excluded Dataset (1)** - -| Dataset | Category | Reason | Workaround | -|---------|----------|--------|------------| -| vertebral_column | Medical | File corruption (binary format) | Download from alternative source | - ---- - -## 🏅 Performance Tiers - -### **Tier 1: Exceptional (95-100%)** -- **banknote** - 99-100% accuracy - - Perfect forensic classification - - 4 features, 1,372 samples - - Production-ready model - -### **Tier 2: Excellent (85-95%)** -- **ionosphere** - 85-90% accuracy - - High-dimensional radar signal classification - - 34→4 features via PCA (55% variance) -- **breast_cancer** - 88% accuracy (1-epoch) - - Medical diagnostic classification - - 29→4 features via PCA -- **glass** - 88% accuracy - - 6-class forensic analysis - - Excellent multi-class performance - -### **Tier 3: Very Good (75-85%)** -- **heart_disease** - 82% accuracy - - Medical diagnosis with missing values - - 13→4 features via PCA (55% variance) -- **blood_transfusion** - 83% accuracy - - Imbalanced binary classification -- **magic_gamma** - 78% accuracy - - Largest dataset (19K samples) - - Physics signal detection -- **sonar** - 76% accuracy - - High-dimensional (60→4 features) - - Mine vs rock detection - -### **Tier 4: Good (70-75%)** -- **diabetes** - 71% accuracy - - Medical prediction task - - 8→4 features via PCA (70% variance) -- **wheat_seeds** - 72% accuracy (1-epoch) - - Agricultural classification - - 7→4 features via PCA - -### **Tier 5: Challenging (50-70%)** -- **iris** - 97% accuracy - - Classic 3-class problem - - Small dataset (150 samples) -- **haberman** - 58% accuracy - - Highly imbalanced survival prediction - - Minimal features (3→4 with padding) - ---- - -## 🔧 Technical Improvements Implemented - -### **1. Enhanced Dataset Loading** -**Problem:** Diverse CSV formats across UCI datasets -- Varying delimiters (comma, semicolon, tab, whitespace) -- Inconsistent headers (some have, some don't) -- Encoding issues (UTF-8 vs Latin-1) -- Missing values in different formats - -**Solution:** Dataset-specific loading strategies -```python -# wine_red/white: Semicolon delimiter -df = pd.read_csv(path, sep=';') - -# wheat_seeds: Tab/whitespace delimiter -df = pd.read_csv(path, sep=r'\s+', header=None) - -# breast_cancer: No header, skip ID column -df = pd.read_csv(path, header=None) -X = df.iloc[:, 2:-1] # Skip ID and diagnosis columns - -# blood_transfusion: Skip description row -df = pd.read_csv(path, skiprows=1) -``` - -### **2. Architecture Analyzer** -Built intelligent recommendation system based on dataset characteristics: - -**Qubit Selection:** -- 4 qubits: ≤10 features (71% of datasets) -- 5 qubits: 11-20 features (7% of datasets) -- 6 qubits: >20 features (21% of datasets) - -**Layer Selection:** -- 2 layers: Standard baseline (71%) -- 3 layers: Hard/imbalanced tasks (14%) -- 4 layers: Multi-class complexity (14%) - -**Hyperparameter Tuning:** -- Learning rate: 0.001 (balanced) vs 0.0005 (imbalanced >3x) -- Batch size: 8 (small <300), 16 (medium), 32 (large >5K) -- Epochs: 30-50 based on difficulty - -### **3. Rapid Validation Framework** -Created `quick_test_datasets.py` for 1-epoch smoke tests: -- **Speed:** 17.4 seconds for all 15 datasets -- **Coverage:** Validates loading, preprocessing, training -- **Robust:** Handles encoding, delimiters, missing values -- **Output:** JSON results with detailed error messages - ---- - -## 📈 Performance Analysis - -### **By Dataset Size** -| Size Category | Count | Avg Accuracy | Best Example | -|---------------|-------|--------------|--------------| -| Tiny (150-300) | 5 | 76.4% | iris 97%, glass 88% | -| Small (300-1K) | 5 | 81.2% | breast_cancer 88%, blood_transfusion 83% | -| Medium (1K-5K) | 3 | 95.3% | banknote 100%, wine_white 99% | -| Large (>5K) | 1 | 78.0% | magic_gamma 78% | - -**Insight:** Medium-sized datasets (1K-5K samples) show best performance, providing sufficient training data without requiring extreme computational resources. - -### **By Feature Count** -| Feature Range | Count | Avg Accuracy | PCA Required | -|---------------|-------|--------------|--------------| -| Low (3-4) | 4 | 88.8% | No (padding only) | -| Medium (5-15) | 6 | 80.5% | Yes (5-15→4) | -| High (20+) | 4 | 84.8% | Yes (29-60→4) | - -**Insight:** High-dimensional datasets maintain strong performance after PCA dimensionality reduction, validating quantum circuit efficiency with compressed features. - -### **By Domain** -| Category | Datasets | Avg Accuracy | Best Example | -|----------|----------|--------------|--------------| -| Medical | 6 | 75.5% | breast_cancer 88% | -| Chemistry | 2 | 99.0% | wine_white 99% | -| Physics | 2 | 84.0% | ionosphere 90% | -| Forensics | 2 | 94.0% | banknote 100% | -| Biology | 1 | 97.0% | iris 97% | -| Agriculture | 1 | 72.0% | wheat_seeds 72% | -| Geophysics | 1 | 76.0% | sonar 76% | - -**Insight:** Chemistry and forensics datasets show exceptional quantum advantage, while medical datasets present more challenging classification tasks due to inherent data complexity and imbalance. - ---- - -## 🚀 Benchmark Execution Metrics - -### **Quick Test (1-epoch validation)** -- **Runtime:** 17.4 seconds total -- **Average per dataset:** 1.24 seconds -- **Fastest:** iris, glass, sonar (0.1s each) -- **Slowest:** magic_gamma (10.5s - 19K samples) -- **Success rate:** 14/15 (93%) - -### **Full Benchmark (25-epoch training)** -- **Runtime:** ~20-25 minutes total (estimated) -- **Average per dataset:** ~90 seconds -- **Fastest:** iris, haberman (~5s per epoch) -- **Slowest:** magic_gamma (~10s per epoch) -- **Success rate:** 9/15 initially, 14/15 after loading fixes - -### **Resource Usage** -- **CPU:** Standard multi-core (no GPU required) -- **Memory:** <2 GB peak -- **Disk:** ~2 MB total dataset size -- **Quantum Simulator:** PennyLane lightning.qubit (CPU-based) - ---- - -## 💡 Key Findings - -### **1. Dataset Format Diversity** -UCI ML Repository datasets require format-agnostic parsing: -- **4 delimiter types** encountered (comma, semicolon, tab, space) -- **3 encoding types** needed (UTF-8, Latin-1, auto-detect) -- **2 header patterns** (with/without header row) -- **1 corrupted file** (vertebral_column - binary format) - -### **2. Quantum Advantage Patterns** -Quantum circuits show particular strength in: -- **Low-medium dimensionality:** 4-15 features optimal -- **Balanced classes:** <3x imbalance ratio -- **Medium sample size:** 1K-5K samples ideal -- **Structured features:** Chemistry, forensics excel - -### **3. Preprocessing Critical** -Success hinges on robust preprocessing: -- **Missing value imputation:** Median strategy works best -- **PCA dimensionality reduction:** Retains 55-80% variance -- **Standard scaling:** Essential for quantum circuits -- **Stratified splitting:** Critical for imbalanced data - -### **4. Architecture Sensitivity** -Model performance varies by configuration: -- **4 qubits sufficient** for 71% of datasets -- **2 quantum layers** optimal for most tasks -- **16 hidden dims** in classical layer works universally -- **Learning rate 0.001** standard, 0.0005 for hard tasks - ---- - -## 📊 Comparison: 1-Epoch vs 25-Epoch - -| Dataset | 1-Epoch | 25-Epoch | Improvement | -|---------|---------|----------|-------------| -| ionosphere | 65.62% | 90.14% | +24.52% | -| banknote | 94.85% | 100.00% | +5.15% | -| heart_disease | 68.75% | 81.97% | +13.22% | -| sonar | 65.62% | 76.19% | +10.57% | -| diabetes | 66.67% | 70.78% | +4.11% | -| blood_transfusion | 66.67% | 83.33% | +16.66% | -| magic_gamma | 77.27% | 78.05% | +0.78% | -| iris | 68.75% | 96.67% | +27.92% | -| glass | 59.38% | 88.37% | +28.99% | - -**Average Improvement:** +14.66% - -**Insight:** Significant performance gains from extended training, with largest improvements in multi-class problems (glass +29%, iris +28%) and high-dimensional datasets (ionosphere +25%). - ---- - -## 🎯 Production Readiness - -### **Tier 1: Immediate Deployment** -- ✅ **banknote** (100% accuracy) - - Zero-error forensic classification - - Fast inference (<0.1s per sample) - - Robust across test sets - -### **Tier 2: Production with Monitoring** -- ⭐ **ionosphere** (90% accuracy) -- ⭐ **breast_cancer** (88% accuracy) -- ⭐ **glass** (88% accuracy) -- ⭐ **iris** (97% accuracy) - -**Recommendation:** Deploy with confidence threshold (e.g., >0.9 probability) and human review for borderline cases. - -### **Tier 3: Research & Development** -- 🔬 All other datasets suitable for continued optimization -- 🔬 HPO recommended for datasets <80% accuracy -- 🔬 Architecture variations (5-6 qubits, 3-4 layers) for complex tasks - ---- - -## 📁 Deliverables - -### **Scripts Created** -1. ✅ **quick_test_datasets.py** - Rapid 1-epoch validation -2. ✅ **dataset_architecture_analyzer.py** - Intelligent hyperparameter recommendations -3. ✅ **benchmark_all_datasets.py** - Comprehensive 25-epoch benchmarking (updated with robust loading) -4. ✅ **collect_more_datasets.py** - Dataset download and management - -### **Results Generated** -1. ✅ **quick_test_results.json** - Validation metrics -2. ✅ **architecture_analysis.json** - Recommended configs -3. ✅ **benchmark_results.json** - Full training results -4. ✅ **benchmark_report.md** - Detailed analysis -5. ✅ **benchmark_comparison.png** - Visual comparison - -### **Documentation** -1. ✅ **DATASET_EXPANSION_COMPLETE.md** - Expansion overview -2. ✅ **DATASET_VALIDATION_SUCCESS.md** - Validation details -3. ✅ **COMPREHENSIVE_BENCHMARK_SUMMARY.md** - This document - ---- - -## 🔮 Next Steps - -### **Immediate (High Priority)** -1. ✅ Complete full 25-epoch benchmark on all 14 datasets (IN PROGRESS) -2. 📊 Generate comprehensive visualizations -3. 🔍 Review HPO results (running in background) -4. 🌐 Update production API with multi-dataset support - -### **Short-Term (This Week)** -5. 🔄 Find vertebral_column alternative source -6. 📈 Apply architecture analyzer recommendations -7. 🎯 Train with dataset-specific optimal configs -8. 💾 Save best models for each dataset - -### **Medium-Term (This Month)** -9. 🚀 Deploy top 3 models to Azure Quantum -10. 🔬 Cross-dataset transfer learning experiments -11. 📊 Quantum advantage analysis across domains -12. 📚 Academic paper preparation - ---- - -## 💰 Cost Analysis - -### **Current Infrastructure: 100% FREE** -- ✅ **Simulator:** PennyLane lightning.qubit (unlimited) -- ✅ **Compute:** Local CPU (no cloud costs) -- ✅ **Storage:** Local disk (~2 MB datasets) -- ✅ **Testing:** All validation and benchmarking free - -### **Azure Quantum Costs (Optional)** -**Simulators (FREE):** -- ✅ IonQ simulator: $0.00 (unlimited) -- ✅ Rigetti QVM: $0.00 (unlimited) -- ✅ Microsoft simulators: $0.00 (unlimited) - -**Quantum Hardware (PAID):** -- 💰 IonQ QPU: ~$0.00003/gate-shot - - Example: 100-gate circuit, 100 shots = $0.30 -- 💰 Quantinuum H1: ~$0.00015/circuit - - Example: Single circuit = $0.15 - -**Recommendation:** Continue using free simulators for development and testing. Reserve QPU hardware for final validation and production deployment. - ---- - -## 🏆 Key Achievements - -### **Dataset Expansion** -- ✅ **275% increase:** 4 → 15 total datasets -- ✅ **93% success rate:** 14/15 working -- ✅ **7 domains covered:** Medical, chemistry, physics, biology, forensics, agriculture, geophysics - -### **Technical Infrastructure** -- ✅ **Robust loading:** Handles 4 delimiter types, 2 encodings -- ✅ **Rapid validation:** 17 seconds for full suite -- ✅ **Intelligent recommendations:** Architecture analyzer -- ✅ **Production-ready:** Comprehensive error handling - -### **Performance Milestones** -- ✅ **100% accuracy:** banknote dataset -- ✅ **84.48% average:** Across 9 benchmarked datasets -- ✅ **+14.66% improvement:** 1-epoch vs 25-epoch average -- ✅ **3 datasets >95%:** banknote, iris, wine datasets - -### **Research Contributions** -- ✅ **Cross-domain analysis:** 7 scientific fields -- ✅ **Scale validation:** 150-19K sample range -- ✅ **Dimensionality study:** 3-60 features tested -- ✅ **Architecture optimization:** Qubit/layer recommendations - ---- - -## 📚 References & Resources - -### **Datasets** -- UCI Machine Learning Repository: https://archive.ics.uci.edu/ml -- Dolly 15k (chat fine-tuning): https://huggingface.co/datasets/databricks/databricks-dolly-15k - -### **Tools & Frameworks** -- PennyLane: https://pennylane.ai -- PyTorch: https://pytorch.org -- scikit-learn: https://scikit-learn.org -- Azure Quantum: https://azure.microsoft.com/en-us/services/quantum - -### **Documentation** -- Project README: `README.md` -- Quantum AI Guide: `ai-projects/quantum-ml/README.md` -- Dataset Catalog: `AI_DATASETS_CATALOG.md` - ---- - -**Report Generated:** November 16, 2025 -**Status:** ✅ Dataset Expansion & Validation Complete -**Next Milestone:** Full 25-Epoch Benchmark Results +# Comprehensive Quantum AI Benchmark Summary 🏆 + +**Date:** November 16, 2025 +**Status:** Dataset Expansion & Validation Complete + +--- + +## 🎯 Executive Summary + +Successfully expanded quantum ML infrastructure from **4 to 15 datasets** (275% increase), validated **14/15 datasets** (93% success rate), and achieved comprehensive benchmarking across 7 scientific domains with **average accuracy of 84.48%** on initial 25-epoch training. + +--- + +## 📊 Complete Dataset Portfolio + +### **Working Datasets (14)** + +| # | Dataset | Category | Samples | Features | Classes | Status | +|---|---------|----------|---------|----------|---------|--------| +| 1 | ionosphere | Physics | 351 | 34 | 2 | ✅ 85-90% | +| 2 | banknote | Forensics | 1,372 | 4 | 2 | ✅ 99-100% | +| 3 | heart_disease | Medical | 303 | 13 | 2 | ✅ 82% | +| 4 | sonar | Geophysics | 208 | 60 | 2 | ✅ 76% | +| 5 | breast_cancer | Medical | 569 | 29 | 2 | ✅ 88% (1-epoch) | +| 6 | diabetes | Medical | 768 | 8 | 2 | ✅ 71% | +| 7 | blood_transfusion | Medical | 748 | 4 | 2 | ✅ 83% | +| 8 | haberman | Medical | 306 | 3 | 2 | ✅ 58% (challenging) | +| 9 | wine_red | Chemistry | 1,599 | 11 | 6 | ✅ 99% (1-epoch) | +| 10 | wine_white | Chemistry | 4,898 | 11 | 7 | ✅ 99% (1-epoch) | +| 11 | magic_gamma | Physics | 19,020 | 10 | 2 | ✅ 78% | +| 12 | iris | Biology | 150 | 4 | 3 | ✅ 97% | +| 13 | wheat_seeds | Agriculture | 210 | 7 | 3 | ✅ 72% (1-epoch) | +| 14 | glass | Forensics | 214 | 10 | 6 | ✅ 88% | + +### **Excluded Dataset (1)** + +| Dataset | Category | Reason | Workaround | +|---------|----------|--------|------------| +| vertebral_column | Medical | File corruption (binary format) | Download from alternative source | + +--- + +## 🏅 Performance Tiers + +### **Tier 1: Exceptional (95-100%)** +- **banknote** - 99-100% accuracy + - Perfect forensic classification + - 4 features, 1,372 samples + - Production-ready model + +### **Tier 2: Excellent (85-95%)** +- **ionosphere** - 85-90% accuracy + - High-dimensional radar signal classification + - 34→4 features via PCA (55% variance) +- **breast_cancer** - 88% accuracy (1-epoch) + - Medical diagnostic classification + - 29→4 features via PCA +- **glass** - 88% accuracy + - 6-class forensic analysis + - Excellent multi-class performance + +### **Tier 3: Very Good (75-85%)** +- **heart_disease** - 82% accuracy + - Medical diagnosis with missing values + - 13→4 features via PCA (55% variance) +- **blood_transfusion** - 83% accuracy + - Imbalanced binary classification +- **magic_gamma** - 78% accuracy + - Largest dataset (19K samples) + - Physics signal detection +- **sonar** - 76% accuracy + - High-dimensional (60→4 features) + - Mine vs rock detection + +### **Tier 4: Good (70-75%)** +- **diabetes** - 71% accuracy + - Medical prediction task + - 8→4 features via PCA (70% variance) +- **wheat_seeds** - 72% accuracy (1-epoch) + - Agricultural classification + - 7→4 features via PCA + +### **Tier 5: Challenging (50-70%)** +- **iris** - 97% accuracy + - Classic 3-class problem + - Small dataset (150 samples) +- **haberman** - 58% accuracy + - Highly imbalanced survival prediction + - Minimal features (3→4 with padding) + +--- + +## 🔧 Technical Improvements Implemented + +### **1. Enhanced Dataset Loading** +**Problem:** Diverse CSV formats across UCI datasets +- Varying delimiters (comma, semicolon, tab, whitespace) +- Inconsistent headers (some have, some don't) +- Encoding issues (UTF-8 vs Latin-1) +- Missing values in different formats + +**Solution:** Dataset-specific loading strategies +```python +# wine_red/white: Semicolon delimiter +df = pd.read_csv(path, sep=';') + +# wheat_seeds: Tab/whitespace delimiter +df = pd.read_csv(path, sep=r'\s+', header=None) + +# breast_cancer: No header, skip ID column +df = pd.read_csv(path, header=None) +X = df.iloc[:, 2:-1] # Skip ID and diagnosis columns + +# blood_transfusion: Skip description row +df = pd.read_csv(path, skiprows=1) +``` + +### **2. Architecture Analyzer** +Built intelligent recommendation system based on dataset characteristics: + +**Qubit Selection:** +- 4 qubits: ≤10 features (71% of datasets) +- 5 qubits: 11-20 features (7% of datasets) +- 6 qubits: >20 features (21% of datasets) + +**Layer Selection:** +- 2 layers: Standard baseline (71%) +- 3 layers: Hard/imbalanced tasks (14%) +- 4 layers: Multi-class complexity (14%) + +**Hyperparameter Tuning:** +- Learning rate: 0.001 (balanced) vs 0.0005 (imbalanced >3x) +- Batch size: 8 (small <300), 16 (medium), 32 (large >5K) +- Epochs: 30-50 based on difficulty + +### **3. Rapid Validation Framework** +Created `quick_test_datasets.py` for 1-epoch smoke tests: +- **Speed:** 17.4 seconds for all 15 datasets +- **Coverage:** Validates loading, preprocessing, training +- **Robust:** Handles encoding, delimiters, missing values +- **Output:** JSON results with detailed error messages + +--- + +## 📈 Performance Analysis + +### **By Dataset Size** +| Size Category | Count | Avg Accuracy | Best Example | +|---------------|-------|--------------|--------------| +| Tiny (150-300) | 5 | 76.4% | iris 97%, glass 88% | +| Small (300-1K) | 5 | 81.2% | breast_cancer 88%, blood_transfusion 83% | +| Medium (1K-5K) | 3 | 95.3% | banknote 100%, wine_white 99% | +| Large (>5K) | 1 | 78.0% | magic_gamma 78% | + +**Insight:** Medium-sized datasets (1K-5K samples) show best performance, providing sufficient training data without requiring extreme computational resources. + +### **By Feature Count** +| Feature Range | Count | Avg Accuracy | PCA Required | +|---------------|-------|--------------|--------------| +| Low (3-4) | 4 | 88.8% | No (padding only) | +| Medium (5-15) | 6 | 80.5% | Yes (5-15→4) | +| High (20+) | 4 | 84.8% | Yes (29-60→4) | + +**Insight:** High-dimensional datasets maintain strong performance after PCA dimensionality reduction, validating quantum circuit efficiency with compressed features. + +### **By Domain** +| Category | Datasets | Avg Accuracy | Best Example | +|----------|----------|--------------|--------------| +| Medical | 6 | 75.5% | breast_cancer 88% | +| Chemistry | 2 | 99.0% | wine_white 99% | +| Physics | 2 | 84.0% | ionosphere 90% | +| Forensics | 2 | 94.0% | banknote 100% | +| Biology | 1 | 97.0% | iris 97% | +| Agriculture | 1 | 72.0% | wheat_seeds 72% | +| Geophysics | 1 | 76.0% | sonar 76% | + +**Insight:** Chemistry and forensics datasets show exceptional quantum advantage, while medical datasets present more challenging classification tasks due to inherent data complexity and imbalance. + +--- + +## 🚀 Benchmark Execution Metrics + +### **Quick Test (1-epoch validation)** +- **Runtime:** 17.4 seconds total +- **Average per dataset:** 1.24 seconds +- **Fastest:** iris, glass, sonar (0.1s each) +- **Slowest:** magic_gamma (10.5s - 19K samples) +- **Success rate:** 14/15 (93%) + +### **Full Benchmark (25-epoch training)** +- **Runtime:** ~20-25 minutes total (estimated) +- **Average per dataset:** ~90 seconds +- **Fastest:** iris, haberman (~5s per epoch) +- **Slowest:** magic_gamma (~10s per epoch) +- **Success rate:** 9/15 initially, 14/15 after loading fixes + +### **Resource Usage** +- **CPU:** Standard multi-core (no GPU required) +- **Memory:** <2 GB peak +- **Disk:** ~2 MB total dataset size +- **Quantum Simulator:** PennyLane lightning.qubit (CPU-based) + +--- + +## 💡 Key Findings + +### **1. Dataset Format Diversity** +UCI ML Repository datasets require format-agnostic parsing: +- **4 delimiter types** encountered (comma, semicolon, tab, space) +- **3 encoding types** needed (UTF-8, Latin-1, auto-detect) +- **2 header patterns** (with/without header row) +- **1 corrupted file** (vertebral_column - binary format) + +### **2. Quantum Advantage Patterns** +Quantum circuits show particular strength in: +- **Low-medium dimensionality:** 4-15 features optimal +- **Balanced classes:** <3x imbalance ratio +- **Medium sample size:** 1K-5K samples ideal +- **Structured features:** Chemistry, forensics excel + +### **3. Preprocessing Critical** +Success hinges on robust preprocessing: +- **Missing value imputation:** Median strategy works best +- **PCA dimensionality reduction:** Retains 55-80% variance +- **Standard scaling:** Essential for quantum circuits +- **Stratified splitting:** Critical for imbalanced data + +### **4. Architecture Sensitivity** +Model performance varies by configuration: +- **4 qubits sufficient** for 71% of datasets +- **2 quantum layers** optimal for most tasks +- **16 hidden dims** in classical layer works universally +- **Learning rate 0.001** standard, 0.0005 for hard tasks + +--- + +## 📊 Comparison: 1-Epoch vs 25-Epoch + +| Dataset | 1-Epoch | 25-Epoch | Improvement | +|---------|---------|----------|-------------| +| ionosphere | 65.62% | 90.14% | +24.52% | +| banknote | 94.85% | 100.00% | +5.15% | +| heart_disease | 68.75% | 81.97% | +13.22% | +| sonar | 65.62% | 76.19% | +10.57% | +| diabetes | 66.67% | 70.78% | +4.11% | +| blood_transfusion | 66.67% | 83.33% | +16.66% | +| magic_gamma | 77.27% | 78.05% | +0.78% | +| iris | 68.75% | 96.67% | +27.92% | +| glass | 59.38% | 88.37% | +28.99% | + +**Average Improvement:** +14.66% + +**Insight:** Significant performance gains from extended training, with largest improvements in multi-class problems (glass +29%, iris +28%) and high-dimensional datasets (ionosphere +25%). + +--- + +## 🎯 Production Readiness + +### **Tier 1: Immediate Deployment** +- ✅ **banknote** (100% accuracy) + - Zero-error forensic classification + - Fast inference (<0.1s per sample) + - Robust across test sets + +### **Tier 2: Production with Monitoring** +- ⭐ **ionosphere** (90% accuracy) +- ⭐ **breast_cancer** (88% accuracy) +- ⭐ **glass** (88% accuracy) +- ⭐ **iris** (97% accuracy) + +**Recommendation:** Deploy with confidence threshold (e.g., >0.9 probability) and human review for borderline cases. + +### **Tier 3: Research & Development** +- 🔬 All other datasets suitable for continued optimization +- 🔬 HPO recommended for datasets <80% accuracy +- 🔬 Architecture variations (5-6 qubits, 3-4 layers) for complex tasks + +--- + +## 📁 Deliverables + +### **Scripts Created** +1. ✅ **quick_test_datasets.py** - Rapid 1-epoch validation +2. ✅ **dataset_architecture_analyzer.py** - Intelligent hyperparameter recommendations +3. ✅ **benchmark_all_datasets.py** - Comprehensive 25-epoch benchmarking (updated with robust loading) +4. ✅ **collect_more_datasets.py** - Dataset download and management + +### **Results Generated** +1. ✅ **quick_test_results.json** - Validation metrics +2. ✅ **architecture_analysis.json** - Recommended configs +3. ✅ **benchmark_results.json** - Full training results +4. ✅ **benchmark_report.md** - Detailed analysis +5. ✅ **benchmark_comparison.png** - Visual comparison + +### **Documentation** +1. ✅ **DATASET_EXPANSION_COMPLETE.md** - Expansion overview +2. ✅ **DATASET_VALIDATION_SUCCESS.md** - Validation details +3. ✅ **COMPREHENSIVE_BENCHMARK_SUMMARY.md** - This document + +--- + +## 🔮 Next Steps + +### **Immediate (High Priority)** +1. ✅ Complete full 25-epoch benchmark on all 14 datasets (IN PROGRESS) +2. 📊 Generate comprehensive visualizations +3. 🔍 Review HPO results (running in background) +4. 🌐 Update production API with multi-dataset support + +### **Short-Term (This Week)** +5. 🔄 Find vertebral_column alternative source +6. 📈 Apply architecture analyzer recommendations +7. 🎯 Train with dataset-specific optimal configs +8. 💾 Save best models for each dataset + +### **Medium-Term (This Month)** +9. 🚀 Deploy top 3 models to Azure Quantum +10. 🔬 Cross-dataset transfer learning experiments +11. 📊 Quantum advantage analysis across domains +12. 📚 Academic paper preparation + +--- + +## 💰 Cost Analysis + +### **Current Infrastructure: 100% FREE** +- ✅ **Simulator:** PennyLane lightning.qubit (unlimited) +- ✅ **Compute:** Local CPU (no cloud costs) +- ✅ **Storage:** Local disk (~2 MB datasets) +- ✅ **Testing:** All validation and benchmarking free + +### **Azure Quantum Costs (Optional)** +**Simulators (FREE):** +- ✅ IonQ simulator: $0.00 (unlimited) +- ✅ Rigetti QVM: $0.00 (unlimited) +- ✅ Microsoft simulators: $0.00 (unlimited) + +**Quantum Hardware (PAID):** +- 💰 IonQ QPU: ~$0.00003/gate-shot + - Example: 100-gate circuit, 100 shots = $0.30 +- 💰 Quantinuum H1: ~$0.00015/circuit + - Example: Single circuit = $0.15 + +**Recommendation:** Continue using free simulators for development and testing. Reserve QPU hardware for final validation and production deployment. + +--- + +## 🏆 Key Achievements + +### **Dataset Expansion** +- ✅ **275% increase:** 4 → 15 total datasets +- ✅ **93% success rate:** 14/15 working +- ✅ **7 domains covered:** Medical, chemistry, physics, biology, forensics, agriculture, geophysics + +### **Technical Infrastructure** +- ✅ **Robust loading:** Handles 4 delimiter types, 2 encodings +- ✅ **Rapid validation:** 17 seconds for full suite +- ✅ **Intelligent recommendations:** Architecture analyzer +- ✅ **Production-ready:** Comprehensive error handling + +### **Performance Milestones** +- ✅ **100% accuracy:** banknote dataset +- ✅ **84.48% average:** Across 9 benchmarked datasets +- ✅ **+14.66% improvement:** 1-epoch vs 25-epoch average +- ✅ **3 datasets >95%:** banknote, iris, wine datasets + +### **Research Contributions** +- ✅ **Cross-domain analysis:** 7 scientific fields +- ✅ **Scale validation:** 150-19K sample range +- ✅ **Dimensionality study:** 3-60 features tested +- ✅ **Architecture optimization:** Qubit/layer recommendations + +--- + +## 📚 References & Resources + +### **Datasets** +- UCI Machine Learning Repository: https://archive.ics.uci.edu/ml +- Dolly 15k (chat fine-tuning): https://huggingface.co/datasets/databricks/databricks-dolly-15k + +### **Tools & Frameworks** +- PennyLane: https://pennylane.ai +- PyTorch: https://pytorch.org +- scikit-learn: https://scikit-learn.org +- Azure Quantum: https://azure.microsoft.com/en-us/services/quantum + +### **Documentation** +- Project README: `README.md` +- Quantum AI Guide: `ai-projects/quantum-ml/README.md` +- Dataset Catalog: `AI_DATASETS_CATALOG.md` + +--- + +**Report Generated:** November 16, 2025 +**Status:** ✅ Dataset Expansion & Validation Complete +**Next Milestone:** Full 25-Epoch Benchmark Results diff --git a/ai-projects/quantum-ml/CUSTOM_DATASET_GUIDE.md b/ai-projects/quantum-ml/CUSTOM_DATASET_GUIDE.md index 548c63045..fc3706508 100644 --- a/ai-projects/quantum-ml/CUSTOM_DATASET_GUIDE.md +++ b/ai-projects/quantum-ml/CUSTOM_DATASET_GUIDE.md @@ -1,659 +1,659 @@ -# 📊 Training Quantum AI on Your Custom Dataset - Complete Guide - -**Quick Start Guide for Custom Data** -**Date:** October 31, 2025 - ---- - -## 🎯 Three Ways to Use Your Data - -### Option 1: Quick & Easy (Recommended for Beginners) - -Use the existing `quantum_classifier.py` with minimal code changes. - -### Option 2: Advanced Hybrid (You're looking at this!) - -Use `hybrid_qnn.py` for complex multi-class problems. - -### Option 3: Custom Circuit - -Build your own quantum circuit from scratch. - ---- - -## 🚀 Option 1: Using quantum_classifier.py (Easiest) - -### Step 1: Prepare Your Data - -Your data should be in one of these formats: - -- CSV file with features and labels -- NumPy arrays (X, y) -- Pandas DataFrame -- Scikit-learn compatible format - -**Example CSV format:** - -```csv -feature1,feature2,feature3,feature4,label -5.1,3.5,1.4,0.2,0 -4.9,3.0,1.4,0.2,0 -6.3,2.5,4.9,1.5,1 -``` - -### Step 2: Create Your Training Script - -Create a new file: `train_my_data.py` - -```python -import numpy as np -import pandas as pd -from pathlib import Path -import sys - -# Add src to path -sys.path.insert(0, str(Path(__file__).parent / "src")) - -from quantum_classifier import HybridQuantumClassifier, train_quantum_model -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -import torch - -# ============================================ -# 1. LOAD YOUR DATA -# ============================================ - -# Option A: From CSV -def load_from_csv(filepath): - df = pd.read_csv(filepath) - X = df.drop('label', axis=1).values # All columns except 'label' - y = df['label'].values - return X, y - -# Option B: From NumPy -def load_from_numpy(X_file, y_file): - X = np.load(X_file) - y = np.load(y_file) - return X, y - -# Option C: Manual arrays -def create_manual_data(): - X = np.array([ - [1.0, 2.0, 3.0, 4.0], - [2.0, 3.0, 4.0, 5.0], - # ... your data ... - ]) - y = np.array([0, 1, ...]) # Your labels - return X, y - -# ============================================ -# 2. LOAD YOUR ACTUAL DATA (CHANGE THIS!) -# ============================================ - -# Choose your loading method: -X, y = load_from_csv("path/to/your/data.csv") -# OR -# X, y = load_from_numpy("features.npy", "labels.npy") -# OR -# X, y = create_manual_data() - -print(f"Loaded data: {X.shape[0]} samples, {X.shape[1]} features") -print(f"Classes: {np.unique(y)}") - -# ============================================ -# 3. PREPROCESS DATA -# ============================================ - -# Split into train/validation -X_train, X_val, y_train, y_val = train_test_split( - X, y, - test_size=0.2, - random_state=42, - stratify=y # Keeps class balance -) - -# Standardize features (IMPORTANT for quantum circuits!) -scaler = StandardScaler() -X_train = scaler.fit_transform(X_train) -X_val = scaler.transform(X_val) - -# Handle feature dimension -n_features = X_train.shape[1] -n_qubits = 4 # From config - -# Pad or reduce features to match qubits -if n_features < n_qubits: - # Pad with zeros - padding = np.zeros((X_train.shape[0], n_qubits - n_features)) - X_train = np.hstack([X_train, padding]) - - padding_val = np.zeros((X_val.shape[0], n_qubits - n_features)) - X_val = np.hstack([X_val, padding_val]) - print(f"Padded features from {n_features} to {n_qubits}") - -elif n_features > n_qubits: - # Use PCA to reduce - from sklearn.decomposition import PCA - pca = PCA(n_components=n_qubits) - X_train = pca.fit_transform(X_train) - X_val = pca.transform(X_val) - print(f"Reduced features from {n_features} to {n_qubits} using PCA") - print(f"Explained variance: {pca.explained_variance_ratio_.sum():.2%}") - -# ============================================ -# 4. CREATE MODEL -# ============================================ - -# Initialize model (uses config from quantum_config.yaml) -model = HybridQuantumClassifier(input_dim=n_qubits) -print(f"Created model with {n_qubits} qubits") - -# ============================================ -# 5. TRAIN MODEL -# ============================================ - -print("\nStarting training...") -history = train_quantum_model( - model=model, - X_train=X_train, - y_train=y_train, - X_val=X_val, - y_val=y_val -) - -# ============================================ -# 6. EVALUATE RESULTS -# ============================================ - -print("\n" + "="*50) -print("TRAINING COMPLETE!") -print("="*50) -print(f"Final Training Loss: {history['train_loss'][-1]:.4f}") -print(f"Final Validation Loss: {history['val_loss'][-1]:.4f}") -print(f"Final Validation Accuracy: {history['val_acc'][-1]:.4f}") -print(f"Best Validation Accuracy: {max(history['val_acc']):.4f}") - -# ============================================ -# 7. SAVE MODEL (OPTIONAL) -# ============================================ - -# Save model weights -torch.save(model.state_dict(), "results/my_quantum_model.pt") -print("\nModel saved to: results/my_quantum_model.pt") - -# Save scaler for inference -import joblib -joblib.dump(scaler, "results/scaler.pkl") -print("Scaler saved to: results/scaler.pkl") - -# ============================================ -# 8. MAKE PREDICTIONS ON NEW DATA -# ============================================ - -def predict_new_data(new_X, model, scaler): - """Predict on new unseen data""" - # Preprocess - new_X_scaled = scaler.transform(new_X) - - # Pad/reduce if needed - if new_X_scaled.shape[1] < n_qubits: - padding = np.zeros((new_X_scaled.shape[0], n_qubits - new_X_scaled.shape[1])) - new_X_scaled = np.hstack([new_X_scaled, padding]) - - # Convert to tensor - new_X_tensor = torch.FloatTensor(new_X_scaled) - - # Predict - model.eval() - with torch.no_grad(): - predictions = model(new_X_tensor) - if predictions.dim() > 1: - predictions = (predictions > 0.5).float() - else: - predictions = (predictions > 0.5).float() - - return predictions.numpy() - -# Example: Predict on validation set -predictions = predict_new_data(X_val, model, scaler) -print(f"\nPredictions shape: {predictions.shape}") -``` - -### Step 3: Run Your Training - -```powershell -cd c:\Users\Bryan\OneDrive\AI\quantum-ai -python train_my_data.py -``` - ---- - -## 🔬 Option 2: Using hybrid_qnn.py (Advanced Multi-Class) - -The file you're currently viewing (`hybrid_qnn.py`) is perfect for: - -- **Multi-class classification** (3+ classes) -- **Complex datasets** (8+ features) -- **Deep quantum-classical integration** - -### Quick Start with hybrid_qnn.py - -Create `train_with_hybrid_qnn.py`: - -```python -import numpy as np -import pandas as pd -import torch -from torch.utils.data import TensorDataset, DataLoader -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -import sys -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent / "src")) -from hybrid_qnn import HybridQNN, QuantumClassicalTrainer - -# ============================================ -# 1. LOAD YOUR DATA -# ============================================ - -# Example: Load from CSV -df = pd.read_csv("your_data.csv") -X = df.drop('label', axis=1).values -y = df['label'].values - -# Or create sample data -from sklearn.datasets import make_classification -X, y = make_classification( - n_samples=500, - n_features=10, - n_informative=8, - n_classes=3, # Multi-class! - random_state=42 -) - -print(f"Data: {X.shape[0]} samples, {X.shape[1]} features, {len(np.unique(y))} classes") - -# ============================================ -# 2. PREPROCESS -# ============================================ - -# Split -X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42, stratify=y -) - -# Scale -scaler = StandardScaler() -X_train = scaler.fit_transform(X_train) -X_val = scaler.transform(X_val) - -# Create data loaders -train_dataset = TensorDataset( - torch.FloatTensor(X_train), - torch.LongTensor(y_train) -) -val_dataset = TensorDataset( - torch.FloatTensor(X_val), - torch.LongTensor(y_val) -) - -train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) -val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False) - -# ============================================ -# 3. CREATE HYBRID MODEL -# ============================================ - -model = HybridQNN( - input_dim=X.shape[1], # Your number of features - hidden_dim=32, # Classical hidden layer size - num_qubits=4, # Quantum circuit size - quantum_layers=2, # Quantum depth (optimal!) - output_dim=len(np.unique(y)), # Number of classes - dropout=0.2 -) - -print(model) - -# ============================================ -# 4. TRAIN -# ============================================ - -trainer = QuantumClassicalTrainer( - model=model, - learning_rate=0.01, - device='cpu' # or 'cuda' if you have GPU -) - -trainer.train( - train_loader=train_loader, - val_loader=val_loader, - num_epochs=50 # Adjust based on convergence -) - -# ============================================ -# 5. RESULTS -# ============================================ - -print(f"\nFinal Validation Accuracy: {trainer.val_accuracies[-1]:.2f}%") -print(f"Best Validation Accuracy: {max(trainer.val_accuracies):.2f}%") - -# Save model -torch.save(model.state_dict(), "results/hybrid_qnn_model.pt") -print("Model saved!") -``` - ---- - -## 📋 Data Requirements & Tips - -### Minimum Requirements - -- **Samples:** At least 100 samples (200+ recommended) -- **Features:** 2-20 features (will be mapped to qubits) -- **Classes:** Binary (2) or multi-class (3+) -- **Format:** Numerical data only - -### Data Preprocessing Checklist - -✅ **Required Steps:** - -1. **Standardization** - CRITICAL for quantum circuits - - ```python - scaler = StandardScaler() - X = scaler.fit_transform(X) - ``` - -2. **Feature Scaling** - Map to quantum parameter range - - Quantum circuits work with angles (0 to 2π) - - StandardScaler handles this automatically - -3. **Feature Dimension Matching** - - If features < qubits: Pad with zeros - - If features > qubits: Use PCA to reduce - -4. **Class Encoding** - - Binary: 0/1 - - Multi-class: 0, 1, 2, ... - - Use LabelEncoder if needed - -✅ **Recommended Steps:** - -1. **Handle Missing Values** - - ```python - from sklearn.impute import SimpleImputer - imputer = SimpleImputer(strategy='mean') - X = imputer.fit_transform(X) - ``` - -2. **Balance Classes** (if imbalanced) - - ```python - from imblearn.over_sampling import SMOTE - smote = SMOTE(random_state=42) - X, y = smote.fit_resample(X, y) - ``` - -3. **Remove Outliers** - - ```python - from sklearn.preprocessing import RobustScaler - scaler = RobustScaler() - X = scaler.fit_transform(X) - ``` - ---- - -## 🎯 Real-World Examples - -### Example 1: Medical Diagnosis (Binary Classification) - -```python -# Load medical data -df = pd.read_csv("patient_data.csv") - -# Features: age, blood_pressure, cholesterol, glucose, BMI -X = df[['age', 'bp', 'cholesterol', 'glucose', 'bmi']].values - -# Label: 0=healthy, 1=disease -y = df['diagnosis'].values - -# Preprocess -scaler = StandardScaler() -X_scaled = scaler.fit_transform(X) - -# Pad to 4 features (we have 5, so PCA) -from sklearn.decomposition import PCA -pca = PCA(n_components=4) -X_reduced = pca.fit_transform(X_scaled) - -# Train quantum model -# ... (use quantum_classifier.py approach) -``` - -### Example 2: Financial Fraud Detection - -```python -# Load transaction data -df = pd.read_csv("transactions.csv") - -# Features: amount, time, merchant_category, etc. -X = df.drop(['fraud'], axis=1).values -y = df['fraud'].values # 0=legitimate, 1=fraud - -# Handle imbalance (fraud is rare!) -from imblearn.over_sampling import SMOTE -smote = SMOTE(random_state=42) -X_balanced, y_balanced = smote.fit_resample(X, y) - -# Your quantum model excels at imbalanced data (90% accuracy!) -# ... train as normal -``` - -### Example 3: Image Classification (Small Images) - -```python -# Load small images (e.g., MNIST digits) -from sklearn.datasets import load_digits -digits = load_digits() - -X = digits.data # 8x8 images flattened to 64 features -y = digits.target # 0-9 digits - -# Reduce 64 features to 4 using PCA -pca = PCA(n_components=4) -X_reduced = pca.fit_transform(X) - -print(f"Explained variance: {pca.explained_variance_ratio_.sum():.2%}") - -# Train multi-class quantum classifier -# Use hybrid_qnn.py for 10 classes -``` - ---- - -## 🔍 Troubleshooting - -### Issue: Poor Accuracy (<50%) - -**Possible Causes:** - -1. **Data not standardized** → Use StandardScaler -2. **Too few samples** → Need at least 100 samples -3. **Features don't match qubits** → Pad or use PCA -4. **Classes imbalanced** → Use SMOTE or class weights - -**Solutions:** - -```python -# Always standardize! -from sklearn.preprocessing import StandardScaler -scaler = StandardScaler() -X = scaler.fit_transform(X) - -# Check class distribution -print(np.bincount(y)) - -# If imbalanced, use SMOTE -from imblearn.over_sampling import SMOTE -X, y = SMOTE().fit_resample(X, y) -``` - -### Issue: Training is Slow - -**Possible Causes:** - -1. Too many samples (>1000) -2. Too many epochs -3. Quantum simulation overhead - -**Solutions:** - -```python -# Use smaller batch size -batch_size = 16 # instead of 32 - -# Reduce epochs -epochs = 50 # instead of 100 - -# Sample your data -from sklearn.utils import resample -X_sample, y_sample = resample(X, y, n_samples=500, random_state=42) -``` - -### Issue: Model Overfitting - -**Symptoms:** - -- Training accuracy high (>90%) -- Validation accuracy low (<60%) - -**Solutions:** - -```python -# 1. Reduce model complexity -n_layers = 1 # instead of 2 - -# 2. Add more regularization -dropout = 0.3 # instead of 0.2 - -# 3. Get more data or use data augmentation - -# 4. Early stopping -if val_acc decreases for 5 epochs: - break -``` - ---- - -## 💡 Best Practices for Custom Data - -### 1. Start Simple - -- Begin with binary classification -- Use 100-200 samples -- 2-4 features - -### 2. Validate Your Preprocessing - -```python -# Check data distribution -print(f"X mean: {X.mean(axis=0)}") -print(f"X std: {X.std(axis=0)}") -# Should be ~0 mean, ~1 std after StandardScaler - -# Check class balance -print(f"Class distribution: {np.bincount(y)}") -# Should be roughly balanced -``` - -### 3. Monitor Training - -```python -# Plot training curves -import matplotlib.pyplot as plt - -plt.plot(history['train_loss'], label='Train Loss') -plt.plot(history['val_loss'], label='Val Loss') -plt.legend() -plt.savefig('results/training_curve.png') -``` - -### 4. Compare to Classical Baseline - -```python -# Train classical model for comparison -from sklearn.ensemble import RandomForestClassifier - -rf = RandomForestClassifier() -rf.fit(X_train, y_train) -classical_acc = rf.score(X_val, y_val) - -print(f"Classical: {classical_acc:.2%}") -print(f"Quantum: {quantum_acc:.2%}") -``` - ---- - -## 🚀 Next Steps - -### Once Training Works - -1. **Hyperparameter tuning** - Use `experiments/parameter_tuning.py` -2. **Cross-validation** - Test on multiple data splits -3. **Feature engineering** - Create better features -4. **Ensemble methods** - Combine multiple quantum models - -### Production Deployment - -1. **Save model & scaler** - - ```python - torch.save(model.state_dict(), 'model.pt') - joblib.dump(scaler, 'scaler.pkl') - ``` - -2. **Create inference script** - - ```python - def predict(new_data): - X = scaler.transform(new_data) - return model(torch.FloatTensor(X)) - ``` - -3. **Deploy to Azure Quantum** for real hardware - ---- - -## 📚 Complete Example Script - -I've created `train_my_data.py` - a ready-to-use template! - -**Location:** Copy the code from "Step 2" above and save as: -`c:\Users\Bryan\OneDrive\AI\quantum-ai\train_my_data.py` - -**Run it:** - -```powershell -python train_my_data.py -``` - ---- - -## 🎯 Summary - -**To train on YOUR data:** - -1. ✅ Load your CSV/NumPy/DataFrame -2. ✅ Standardize with StandardScaler -3. ✅ Match features to qubits (pad or PCA) -4. ✅ Use `quantum_classifier.py` (simple) or `hybrid_qnn.py` (advanced) -5. ✅ Train and evaluate -6. ✅ Save model for deployment - -**Your quantum AI is ready for custom data!** 🚀 - -Need help with specific data format? Just ask! +# 📊 Training Quantum AI on Your Custom Dataset - Complete Guide + +**Quick Start Guide for Custom Data** +**Date:** October 31, 2025 + +--- + +## 🎯 Three Ways to Use Your Data + +### Option 1: Quick & Easy (Recommended for Beginners) + +Use the existing `quantum_classifier.py` with minimal code changes. + +### Option 2: Advanced Hybrid (You're looking at this!) + +Use `hybrid_qnn.py` for complex multi-class problems. + +### Option 3: Custom Circuit + +Build your own quantum circuit from scratch. + +--- + +## 🚀 Option 1: Using quantum_classifier.py (Easiest) + +### Step 1: Prepare Your Data + +Your data should be in one of these formats: + +- CSV file with features and labels +- NumPy arrays (X, y) +- Pandas DataFrame +- Scikit-learn compatible format + +**Example CSV format:** + +```csv +feature1,feature2,feature3,feature4,label +5.1,3.5,1.4,0.2,0 +4.9,3.0,1.4,0.2,0 +6.3,2.5,4.9,1.5,1 +``` + +### Step 2: Create Your Training Script + +Create a new file: `train_my_data.py` + +```python +import numpy as np +import pandas as pd +from pathlib import Path +import sys + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent / "src")) + +from quantum_classifier import HybridQuantumClassifier, train_quantum_model +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +import torch + +# ============================================ +# 1. LOAD YOUR DATA +# ============================================ + +# Option A: From CSV +def load_from_csv(filepath): + df = pd.read_csv(filepath) + X = df.drop('label', axis=1).values # All columns except 'label' + y = df['label'].values + return X, y + +# Option B: From NumPy +def load_from_numpy(X_file, y_file): + X = np.load(X_file) + y = np.load(y_file) + return X, y + +# Option C: Manual arrays +def create_manual_data(): + X = np.array([ + [1.0, 2.0, 3.0, 4.0], + [2.0, 3.0, 4.0, 5.0], + # ... your data ... + ]) + y = np.array([0, 1, ...]) # Your labels + return X, y + +# ============================================ +# 2. LOAD YOUR ACTUAL DATA (CHANGE THIS!) +# ============================================ + +# Choose your loading method: +X, y = load_from_csv("path/to/your/data.csv") +# OR +# X, y = load_from_numpy("features.npy", "labels.npy") +# OR +# X, y = create_manual_data() + +print(f"Loaded data: {X.shape[0]} samples, {X.shape[1]} features") +print(f"Classes: {np.unique(y)}") + +# ============================================ +# 3. PREPROCESS DATA +# ============================================ + +# Split into train/validation +X_train, X_val, y_train, y_val = train_test_split( + X, y, + test_size=0.2, + random_state=42, + stratify=y # Keeps class balance +) + +# Standardize features (IMPORTANT for quantum circuits!) +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_val = scaler.transform(X_val) + +# Handle feature dimension +n_features = X_train.shape[1] +n_qubits = 4 # From config + +# Pad or reduce features to match qubits +if n_features < n_qubits: + # Pad with zeros + padding = np.zeros((X_train.shape[0], n_qubits - n_features)) + X_train = np.hstack([X_train, padding]) + + padding_val = np.zeros((X_val.shape[0], n_qubits - n_features)) + X_val = np.hstack([X_val, padding_val]) + print(f"Padded features from {n_features} to {n_qubits}") + +elif n_features > n_qubits: + # Use PCA to reduce + from sklearn.decomposition import PCA + pca = PCA(n_components=n_qubits) + X_train = pca.fit_transform(X_train) + X_val = pca.transform(X_val) + print(f"Reduced features from {n_features} to {n_qubits} using PCA") + print(f"Explained variance: {pca.explained_variance_ratio_.sum():.2%}") + +# ============================================ +# 4. CREATE MODEL +# ============================================ + +# Initialize model (uses config from quantum_config.yaml) +model = HybridQuantumClassifier(input_dim=n_qubits) +print(f"Created model with {n_qubits} qubits") + +# ============================================ +# 5. TRAIN MODEL +# ============================================ + +print("\nStarting training...") +history = train_quantum_model( + model=model, + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val +) + +# ============================================ +# 6. EVALUATE RESULTS +# ============================================ + +print("\n" + "="*50) +print("TRAINING COMPLETE!") +print("="*50) +print(f"Final Training Loss: {history['train_loss'][-1]:.4f}") +print(f"Final Validation Loss: {history['val_loss'][-1]:.4f}") +print(f"Final Validation Accuracy: {history['val_acc'][-1]:.4f}") +print(f"Best Validation Accuracy: {max(history['val_acc']):.4f}") + +# ============================================ +# 7. SAVE MODEL (OPTIONAL) +# ============================================ + +# Save model weights +torch.save(model.state_dict(), "results/my_quantum_model.pt") +print("\nModel saved to: results/my_quantum_model.pt") + +# Save scaler for inference +import joblib +joblib.dump(scaler, "results/scaler.pkl") +print("Scaler saved to: results/scaler.pkl") + +# ============================================ +# 8. MAKE PREDICTIONS ON NEW DATA +# ============================================ + +def predict_new_data(new_X, model, scaler): + """Predict on new unseen data""" + # Preprocess + new_X_scaled = scaler.transform(new_X) + + # Pad/reduce if needed + if new_X_scaled.shape[1] < n_qubits: + padding = np.zeros((new_X_scaled.shape[0], n_qubits - new_X_scaled.shape[1])) + new_X_scaled = np.hstack([new_X_scaled, padding]) + + # Convert to tensor + new_X_tensor = torch.FloatTensor(new_X_scaled) + + # Predict + model.eval() + with torch.no_grad(): + predictions = model(new_X_tensor) + if predictions.dim() > 1: + predictions = (predictions > 0.5).float() + else: + predictions = (predictions > 0.5).float() + + return predictions.numpy() + +# Example: Predict on validation set +predictions = predict_new_data(X_val, model, scaler) +print(f"\nPredictions shape: {predictions.shape}") +``` + +### Step 3: Run Your Training + +```powershell +cd c:\Users\Bryan\OneDrive\AI\quantum-ai +python train_my_data.py +``` + +--- + +## 🔬 Option 2: Using hybrid_qnn.py (Advanced Multi-Class) + +The file you're currently viewing (`hybrid_qnn.py`) is perfect for: + +- **Multi-class classification** (3+ classes) +- **Complex datasets** (8+ features) +- **Deep quantum-classical integration** + +### Quick Start with hybrid_qnn.py + +Create `train_with_hybrid_qnn.py`: + +```python +import numpy as np +import pandas as pd +import torch +from torch.utils.data import TensorDataset, DataLoader +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).parent / "src")) +from hybrid_qnn import HybridQNN, QuantumClassicalTrainer + +# ============================================ +# 1. LOAD YOUR DATA +# ============================================ + +# Example: Load from CSV +df = pd.read_csv("your_data.csv") +X = df.drop('label', axis=1).values +y = df['label'].values + +# Or create sample data +from sklearn.datasets import make_classification +X, y = make_classification( + n_samples=500, + n_features=10, + n_informative=8, + n_classes=3, # Multi-class! + random_state=42 +) + +print(f"Data: {X.shape[0]} samples, {X.shape[1]} features, {len(np.unique(y))} classes") + +# ============================================ +# 2. PREPROCESS +# ============================================ + +# Split +X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y +) + +# Scale +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_val = scaler.transform(X_val) + +# Create data loaders +train_dataset = TensorDataset( + torch.FloatTensor(X_train), + torch.LongTensor(y_train) +) +val_dataset = TensorDataset( + torch.FloatTensor(X_val), + torch.LongTensor(y_val) +) + +train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) +val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False) + +# ============================================ +# 3. CREATE HYBRID MODEL +# ============================================ + +model = HybridQNN( + input_dim=X.shape[1], # Your number of features + hidden_dim=32, # Classical hidden layer size + num_qubits=4, # Quantum circuit size + quantum_layers=2, # Quantum depth (optimal!) + output_dim=len(np.unique(y)), # Number of classes + dropout=0.2 +) + +print(model) + +# ============================================ +# 4. TRAIN +# ============================================ + +trainer = QuantumClassicalTrainer( + model=model, + learning_rate=0.01, + device='cpu' # or 'cuda' if you have GPU +) + +trainer.train( + train_loader=train_loader, + val_loader=val_loader, + num_epochs=50 # Adjust based on convergence +) + +# ============================================ +# 5. RESULTS +# ============================================ + +print(f"\nFinal Validation Accuracy: {trainer.val_accuracies[-1]:.2f}%") +print(f"Best Validation Accuracy: {max(trainer.val_accuracies):.2f}%") + +# Save model +torch.save(model.state_dict(), "results/hybrid_qnn_model.pt") +print("Model saved!") +``` + +--- + +## 📋 Data Requirements & Tips + +### Minimum Requirements + +- **Samples:** At least 100 samples (200+ recommended) +- **Features:** 2-20 features (will be mapped to qubits) +- **Classes:** Binary (2) or multi-class (3+) +- **Format:** Numerical data only + +### Data Preprocessing Checklist + +✅ **Required Steps:** + +1. **Standardization** - CRITICAL for quantum circuits + + ```python + scaler = StandardScaler() + X = scaler.fit_transform(X) + ``` + +2. **Feature Scaling** - Map to quantum parameter range + - Quantum circuits work with angles (0 to 2π) + - StandardScaler handles this automatically + +3. **Feature Dimension Matching** + - If features < qubits: Pad with zeros + - If features > qubits: Use PCA to reduce + +4. **Class Encoding** + - Binary: 0/1 + - Multi-class: 0, 1, 2, ... + - Use LabelEncoder if needed + +✅ **Recommended Steps:** + +1. **Handle Missing Values** + + ```python + from sklearn.impute import SimpleImputer + imputer = SimpleImputer(strategy='mean') + X = imputer.fit_transform(X) + ``` + +2. **Balance Classes** (if imbalanced) + + ```python + from imblearn.over_sampling import SMOTE + smote = SMOTE(random_state=42) + X, y = smote.fit_resample(X, y) + ``` + +3. **Remove Outliers** + + ```python + from sklearn.preprocessing import RobustScaler + scaler = RobustScaler() + X = scaler.fit_transform(X) + ``` + +--- + +## 🎯 Real-World Examples + +### Example 1: Medical Diagnosis (Binary Classification) + +```python +# Load medical data +df = pd.read_csv("patient_data.csv") + +# Features: age, blood_pressure, cholesterol, glucose, BMI +X = df[['age', 'bp', 'cholesterol', 'glucose', 'bmi']].values + +# Label: 0=healthy, 1=disease +y = df['diagnosis'].values + +# Preprocess +scaler = StandardScaler() +X_scaled = scaler.fit_transform(X) + +# Pad to 4 features (we have 5, so PCA) +from sklearn.decomposition import PCA +pca = PCA(n_components=4) +X_reduced = pca.fit_transform(X_scaled) + +# Train quantum model +# ... (use quantum_classifier.py approach) +``` + +### Example 2: Financial Fraud Detection + +```python +# Load transaction data +df = pd.read_csv("transactions.csv") + +# Features: amount, time, merchant_category, etc. +X = df.drop(['fraud'], axis=1).values +y = df['fraud'].values # 0=legitimate, 1=fraud + +# Handle imbalance (fraud is rare!) +from imblearn.over_sampling import SMOTE +smote = SMOTE(random_state=42) +X_balanced, y_balanced = smote.fit_resample(X, y) + +# Your quantum model excels at imbalanced data (90% accuracy!) +# ... train as normal +``` + +### Example 3: Image Classification (Small Images) + +```python +# Load small images (e.g., MNIST digits) +from sklearn.datasets import load_digits +digits = load_digits() + +X = digits.data # 8x8 images flattened to 64 features +y = digits.target # 0-9 digits + +# Reduce 64 features to 4 using PCA +pca = PCA(n_components=4) +X_reduced = pca.fit_transform(X) + +print(f"Explained variance: {pca.explained_variance_ratio_.sum():.2%}") + +# Train multi-class quantum classifier +# Use hybrid_qnn.py for 10 classes +``` + +--- + +## 🔍 Troubleshooting + +### Issue: Poor Accuracy (<50%) + +**Possible Causes:** + +1. **Data not standardized** → Use StandardScaler +2. **Too few samples** → Need at least 100 samples +3. **Features don't match qubits** → Pad or use PCA +4. **Classes imbalanced** → Use SMOTE or class weights + +**Solutions:** + +```python +# Always standardize! +from sklearn.preprocessing import StandardScaler +scaler = StandardScaler() +X = scaler.fit_transform(X) + +# Check class distribution +print(np.bincount(y)) + +# If imbalanced, use SMOTE +from imblearn.over_sampling import SMOTE +X, y = SMOTE().fit_resample(X, y) +``` + +### Issue: Training is Slow + +**Possible Causes:** + +1. Too many samples (>1000) +2. Too many epochs +3. Quantum simulation overhead + +**Solutions:** + +```python +# Use smaller batch size +batch_size = 16 # instead of 32 + +# Reduce epochs +epochs = 50 # instead of 100 + +# Sample your data +from sklearn.utils import resample +X_sample, y_sample = resample(X, y, n_samples=500, random_state=42) +``` + +### Issue: Model Overfitting + +**Symptoms:** + +- Training accuracy high (>90%) +- Validation accuracy low (<60%) + +**Solutions:** + +```python +# 1. Reduce model complexity +n_layers = 1 # instead of 2 + +# 2. Add more regularization +dropout = 0.3 # instead of 0.2 + +# 3. Get more data or use data augmentation + +# 4. Early stopping +if val_acc decreases for 5 epochs: + break +``` + +--- + +## 💡 Best Practices for Custom Data + +### 1. Start Simple + +- Begin with binary classification +- Use 100-200 samples +- 2-4 features + +### 2. Validate Your Preprocessing + +```python +# Check data distribution +print(f"X mean: {X.mean(axis=0)}") +print(f"X std: {X.std(axis=0)}") +# Should be ~0 mean, ~1 std after StandardScaler + +# Check class balance +print(f"Class distribution: {np.bincount(y)}") +# Should be roughly balanced +``` + +### 3. Monitor Training + +```python +# Plot training curves +import matplotlib.pyplot as plt + +plt.plot(history['train_loss'], label='Train Loss') +plt.plot(history['val_loss'], label='Val Loss') +plt.legend() +plt.savefig('results/training_curve.png') +``` + +### 4. Compare to Classical Baseline + +```python +# Train classical model for comparison +from sklearn.ensemble import RandomForestClassifier + +rf = RandomForestClassifier() +rf.fit(X_train, y_train) +classical_acc = rf.score(X_val, y_val) + +print(f"Classical: {classical_acc:.2%}") +print(f"Quantum: {quantum_acc:.2%}") +``` + +--- + +## 🚀 Next Steps + +### Once Training Works + +1. **Hyperparameter tuning** - Use `experiments/parameter_tuning.py` +2. **Cross-validation** - Test on multiple data splits +3. **Feature engineering** - Create better features +4. **Ensemble methods** - Combine multiple quantum models + +### Production Deployment + +1. **Save model & scaler** + + ```python + torch.save(model.state_dict(), 'model.pt') + joblib.dump(scaler, 'scaler.pkl') + ``` + +2. **Create inference script** + + ```python + def predict(new_data): + X = scaler.transform(new_data) + return model(torch.FloatTensor(X)) + ``` + +3. **Deploy to Azure Quantum** for real hardware + +--- + +## 📚 Complete Example Script + +I've created `train_my_data.py` - a ready-to-use template! + +**Location:** Copy the code from "Step 2" above and save as: +`c:\Users\Bryan\OneDrive\AI\quantum-ai\train_my_data.py` + +**Run it:** + +```powershell +python train_my_data.py +``` + +--- + +## 🎯 Summary + +**To train on YOUR data:** + +1. ✅ Load your CSV/NumPy/DataFrame +2. ✅ Standardize with StandardScaler +3. ✅ Match features to qubits (pad or PCA) +4. ✅ Use `quantum_classifier.py` (simple) or `hybrid_qnn.py` (advanced) +5. ✅ Train and evaluate +6. ✅ Save model for deployment + +**Your quantum AI is ready for custom data!** 🚀 + +Need help with specific data format? Just ask! diff --git a/ai-projects/quantum-ml/DATASET_EXPANSION_COMPLETE.md b/ai-projects/quantum-ml/DATASET_EXPANSION_COMPLETE.md index 47043ef13..dab740ee6 100644 --- a/ai-projects/quantum-ml/DATASET_EXPANSION_COMPLETE.md +++ b/ai-projects/quantum-ml/DATASET_EXPANSION_COMPLETE.md @@ -1,239 +1,239 @@ -# Dataset Expansion Complete! 📊 - -**Date:** November 16, 2025 -**Status:** ✅ 15 Total Datasets (10 Working, 5 Need Format Fixes) - -## Summary - -Successfully expanded the quantum ML dataset collection from **4 to 15 datasets** (275% increase), covering 7 different scientific domains. Created comprehensive testing and analysis infrastructure. - ---- - -## 🎯 What Was Accomplished - -### 1. **Dataset Collection** ✅ -- Downloaded 11 new datasets from UCI Machine Learning Repository -- Total collection now includes 15 datasets across 7 categories -- Datasets range from 149 samples (Iris) to 19,020 samples (MAGIC Gamma) -- Feature counts range from 3 (Haberman) to 60 (Sonar) - -### 2. **Updated Benchmark Script** ✅ -- Modified `benchmark_all_datasets.py` with all 15 datasets -- Added category metadata for each dataset -- Improved multi-class handling and preprocessing - -### 3. **Quick Validation Tool** ✅ -- Created `quick_test_datasets.py` for rapid dataset validation -- Tests loading, preprocessing, and 1-epoch training -- **Result: 10/15 datasets validated successfully** - -### 4. **Architecture Analyzer** ✅ -- Created `dataset_architecture_analyzer.py` -- Provides intelligent recommendations for: - - Qubit count (4-6 based on features) - - Layer count (2-4 based on complexity) - - Hidden dimensions (16-32) - - Learning rate, batch size, epochs -- Generated comprehensive analysis in `results/architecture_analysis.json` - ---- - -## 📊 Dataset Inventory - -### ✅ **Working Datasets (10)** - -| Dataset | Samples | Features | Classes | Category | Recommended Qubits | -|---------|---------|----------|---------|----------|-------------------| -| **banknote** | 1,371 | 4 | 2 | Forensics | 4 | -| **blood_transfusion** | 748 | 4 | 2 | Medical | 4 | -| **diabetes** | 767 | 8 | 2 | Medical | 4 | -| **glass** | 213 | 10 | 6 | Forensics | 4 | -| **haberman** | 305 | 3 | 2 | Medical | 4 | -| **heart_disease** | 302 | 13 | 2 | Medical | 5 | -| **ionosphere** | 350 | 34 | 2 | Physics | 6 | -| **iris** | 149 | 4 | 3 | Biology | 4 | -| **magic_gamma** | 19,019 | 10 | 2 | Physics | 4 | -| **sonar** | 207 | 60 | 2 | Geophysics | 6 | - -### ⚠️ **Datasets Needing Format Fixes (5)** - -| Dataset | Samples | Category | Issue | -|---------|---------|----------|-------| -| **breast_cancer** | 569 | Medical | No header, needs special handling for M/B labels | -| **vertebral_column** | 310 | Medical | Encoding issue (latin-1 required) | -| **wine_red** | 1,599 | Chemistry | Semicolon delimiter (;) instead of comma | -| **wine_white** | 4,898 | Chemistry | Semicolon delimiter (;) instead of comma | -| **wheat_seeds** | 210 | Agriculture | Semicolon delimiter (;) instead of comma | - ---- - -## 🔬 Architecture Analysis Results - -### **Qubit Distribution** -- **4 qubits:** 10 datasets (71%) - Standard for most problems -- **5 qubits:** 1 dataset (7%) - Heart disease (13 features) -- **6 qubits:** 3 datasets (21%) - High-dimensional (breast_cancer, ionosphere, sonar) - -### **Layer Distribution** -- **2 layers:** 10 datasets (71%) - Most efficient baseline -- **3 layers:** 2 datasets (14%) - Hard tasks (blood_transfusion, haberman) -- **4 layers:** 2 datasets (14%) - Multi-class problems (wine quality - when fixed) - -### **Key Insights** -1. **Small datasets** (<300 samples) → Use 2 layers + batch_size=8 to avoid overfitting -2. **Imbalanced classes** (ratio >3x) → Use learning_rate=0.0005 for stability -3. **High features** (>20) → Use 6 qubits with PCA to retain 80-85% variance -4. **Large datasets** (>5000) → Use batch_size=32 for training efficiency - ---- - -## 🎨 Dataset Categories - -### **Medical (6 datasets)** -- breast_cancer, diabetes, heart_disease, vertebral_column, blood_transfusion, haberman -- **Focus:** Binary classification for diagnostic/survival prediction -- **Characteristics:** Feature counts 3-31, moderate imbalance - -### **Chemistry (2 datasets)** -- wine_red, wine_white -- **Focus:** Multi-class wine quality rating -- **Characteristics:** 11 features, 6-7 quality classes - -### **Physics (2 datasets)** -- ionosphere, magic_gamma -- **Focus:** Signal detection and classification -- **Characteristics:** High features (10-34), large samples (magic_gamma: 19K) - -### **Forensics (2 datasets)** -- banknote, glass -- **Focus:** Authentication and material identification -- **Characteristics:** Low features (4-10), binary or 6-class - -### **Biology (1 dataset)** -- iris -- **Focus:** Species classification -- **Characteristics:** Classic 3-class problem, 4 features, 150 samples - -### **Agriculture (1 dataset)** -- wheat_seeds -- **Focus:** Wheat variety classification -- **Characteristics:** 3-class, 7 features, 210 samples - -### **Geophysics (1 dataset)** -- sonar -- **Focus:** Mine vs rock detection -- **Characteristics:** High-dimensional (60 features), small (207 samples) - ---- - -## 🚀 Quick Validation Results - -**Test Command:** -```powershell -python quantum-ai\quick_test_datasets.py -``` - -**Results (1-epoch smoke tests):** -- ✅ **10 datasets validated** in 13.2 seconds -- 🏆 **Top performers** (1 epoch): - - Haberman: 100% accuracy - - Banknote: 94.49% accuracy - - Magic Gamma: 76.82% accuracy - - Heart Disease: 72.92% accuracy - ---- - -## 📁 File Locations - -### **Scripts Created:** -1. `ai-projects/quantum-ml/quick_test_datasets.py` - Rapid validation (1-epoch tests) -2. `ai-projects/quantum-ml/dataset_architecture_analyzer.py` - Architecture recommendations - -### **Scripts Updated:** -1. `ai-projects/quantum-ml/benchmark_all_datasets.py` - Now includes all 15 datasets - -### **Results:** -1. `ai-projects/quantum-ml/results/quick_test_results.json` - Validation test results -2. `ai-projects/quantum-ml/results/architecture_analysis.json` - Architecture recommendations - -### **Datasets:** -- All 15 CSVs in `datasets/quantum/` -- Metadata in `datasets/dataset_index.json` (needs updating) - ---- - -## 🔧 Next Steps - -### **Immediate Fixes (5 datasets):** -1. **breast_cancer**: Update loader to skip ID column, handle M/B string labels -2. **vertebral_column**: Add `encoding='latin-1'` parameter -3. **wine_red/white**: Add `sep=';'` parameter for semicolon delimiter -4. **wheat_seeds**: Add `sep=';'` parameter - -### **Recommended Actions:** -1. **Full Benchmark Run** - Execute `benchmark_all_datasets.py` on all 10 working datasets -2. **Hyperparameter Sweep** - Use architecture analyzer recommendations for optimal configs -3. **Dataset Index Update** - Add all 11 new datasets to `dataset_index.json` -4. **Cross-Dataset Analysis** - Compare quantum performance across categories (medical vs physics vs chemistry) - ---- - -## 💡 Key Learnings - -### **Dataset Complexity Patterns:** -- **Easiest:** Banknote (4 features, balanced) → 94% in 1 epoch -- **Hardest:** Haberman (imbalanced, hard task) → Needs special handling -- **Largest:** Magic Gamma (19K samples) → 10.3s training time - -### **Architecture Guidelines:** -- **4 qubits:** Sufficient for 71% of datasets -- **6 qubits:** Only needed for very high-dimensional data (>20 features) -- **2 layers:** Best default - more layers only for hard/multi-class tasks - -### **Preprocessing Importance:** -- Missing value imputation crucial (heart disease has '?' markers) -- PCA essential for high-dimensional datasets (variance retention: 80-90%) -- Stratified splitting fails with severe class imbalance - ---- - -## 🎯 Performance Expectations - -Based on 1-epoch quick tests, expected full-training performance: - -| Difficulty | Expected Accuracy | Epochs Needed | Example Datasets | -|------------|------------------|---------------|------------------| -| **Easy** | 90-100% | 25-40 | Banknote, Iris | -| **Medium** | 75-90% | 40-50 | Diabetes, Magic Gamma, Heart Disease | -| **Hard** | 60-80% | 50+ | Haberman, Blood Transfusion, Sonar | - ---- - -## 📈 Dataset Scale Comparison - -**Size Distribution:** -- **Tiny** (< 200): Iris (149) -- **Small** (200-500): Sonar (207), Glass (213), Wheat Seeds (210), Haberman (305), Heart Disease (302), Vertebral Column (310), Ionosphere (350) -- **Medium** (500-2000): Breast Cancer (569), Blood Transfusion (748), Diabetes (767), Banknote (1,371), Wine Red (1,599) -- **Large** (2000-5000): Wine White (4,898) -- **Very Large** (> 5000): Magic Gamma (19,020) - -**Feature Complexity:** -- **Low** (3-4): Haberman, Banknote, Blood Transfusion, Iris -- **Medium** (5-15): Diabetes, Glass, Magic Gamma, Heart Disease -- **High** (20-60): Breast Cancer (30), Ionosphere (34), Sonar (60) - ---- - -## ✅ Validation Status - -All created scripts are **production-ready** and **well-documented**: -- ✅ Comprehensive error handling -- ✅ Progress indicators and logging -- ✅ JSON output for automation -- ✅ Clear console formatting -- ✅ Handles encoding issues, missing values, class imbalance - -**Total Implementation Time:** ~30 minutes -**Total Test Time:** ~15 seconds (quick validation) -**Code Quality:** Enterprise-grade with extensive documentation +# Dataset Expansion Complete! 📊 + +**Date:** November 16, 2025 +**Status:** ✅ 15 Total Datasets (10 Working, 5 Need Format Fixes) + +## Summary + +Successfully expanded the quantum ML dataset collection from **4 to 15 datasets** (275% increase), covering 7 different scientific domains. Created comprehensive testing and analysis infrastructure. + +--- + +## 🎯 What Was Accomplished + +### 1. **Dataset Collection** ✅ +- Downloaded 11 new datasets from UCI Machine Learning Repository +- Total collection now includes 15 datasets across 7 categories +- Datasets range from 149 samples (Iris) to 19,020 samples (MAGIC Gamma) +- Feature counts range from 3 (Haberman) to 60 (Sonar) + +### 2. **Updated Benchmark Script** ✅ +- Modified `benchmark_all_datasets.py` with all 15 datasets +- Added category metadata for each dataset +- Improved multi-class handling and preprocessing + +### 3. **Quick Validation Tool** ✅ +- Created `quick_test_datasets.py` for rapid dataset validation +- Tests loading, preprocessing, and 1-epoch training +- **Result: 10/15 datasets validated successfully** + +### 4. **Architecture Analyzer** ✅ +- Created `dataset_architecture_analyzer.py` +- Provides intelligent recommendations for: + - Qubit count (4-6 based on features) + - Layer count (2-4 based on complexity) + - Hidden dimensions (16-32) + - Learning rate, batch size, epochs +- Generated comprehensive analysis in `results/architecture_analysis.json` + +--- + +## 📊 Dataset Inventory + +### ✅ **Working Datasets (10)** + +| Dataset | Samples | Features | Classes | Category | Recommended Qubits | +|---------|---------|----------|---------|----------|-------------------| +| **banknote** | 1,371 | 4 | 2 | Forensics | 4 | +| **blood_transfusion** | 748 | 4 | 2 | Medical | 4 | +| **diabetes** | 767 | 8 | 2 | Medical | 4 | +| **glass** | 213 | 10 | 6 | Forensics | 4 | +| **haberman** | 305 | 3 | 2 | Medical | 4 | +| **heart_disease** | 302 | 13 | 2 | Medical | 5 | +| **ionosphere** | 350 | 34 | 2 | Physics | 6 | +| **iris** | 149 | 4 | 3 | Biology | 4 | +| **magic_gamma** | 19,019 | 10 | 2 | Physics | 4 | +| **sonar** | 207 | 60 | 2 | Geophysics | 6 | + +### ⚠️ **Datasets Needing Format Fixes (5)** + +| Dataset | Samples | Category | Issue | +|---------|---------|----------|-------| +| **breast_cancer** | 569 | Medical | No header, needs special handling for M/B labels | +| **vertebral_column** | 310 | Medical | Encoding issue (latin-1 required) | +| **wine_red** | 1,599 | Chemistry | Semicolon delimiter (;) instead of comma | +| **wine_white** | 4,898 | Chemistry | Semicolon delimiter (;) instead of comma | +| **wheat_seeds** | 210 | Agriculture | Semicolon delimiter (;) instead of comma | + +--- + +## 🔬 Architecture Analysis Results + +### **Qubit Distribution** +- **4 qubits:** 10 datasets (71%) - Standard for most problems +- **5 qubits:** 1 dataset (7%) - Heart disease (13 features) +- **6 qubits:** 3 datasets (21%) - High-dimensional (breast_cancer, ionosphere, sonar) + +### **Layer Distribution** +- **2 layers:** 10 datasets (71%) - Most efficient baseline +- **3 layers:** 2 datasets (14%) - Hard tasks (blood_transfusion, haberman) +- **4 layers:** 2 datasets (14%) - Multi-class problems (wine quality - when fixed) + +### **Key Insights** +1. **Small datasets** (<300 samples) → Use 2 layers + batch_size=8 to avoid overfitting +2. **Imbalanced classes** (ratio >3x) → Use learning_rate=0.0005 for stability +3. **High features** (>20) → Use 6 qubits with PCA to retain 80-85% variance +4. **Large datasets** (>5000) → Use batch_size=32 for training efficiency + +--- + +## 🎨 Dataset Categories + +### **Medical (6 datasets)** +- breast_cancer, diabetes, heart_disease, vertebral_column, blood_transfusion, haberman +- **Focus:** Binary classification for diagnostic/survival prediction +- **Characteristics:** Feature counts 3-31, moderate imbalance + +### **Chemistry (2 datasets)** +- wine_red, wine_white +- **Focus:** Multi-class wine quality rating +- **Characteristics:** 11 features, 6-7 quality classes + +### **Physics (2 datasets)** +- ionosphere, magic_gamma +- **Focus:** Signal detection and classification +- **Characteristics:** High features (10-34), large samples (magic_gamma: 19K) + +### **Forensics (2 datasets)** +- banknote, glass +- **Focus:** Authentication and material identification +- **Characteristics:** Low features (4-10), binary or 6-class + +### **Biology (1 dataset)** +- iris +- **Focus:** Species classification +- **Characteristics:** Classic 3-class problem, 4 features, 150 samples + +### **Agriculture (1 dataset)** +- wheat_seeds +- **Focus:** Wheat variety classification +- **Characteristics:** 3-class, 7 features, 210 samples + +### **Geophysics (1 dataset)** +- sonar +- **Focus:** Mine vs rock detection +- **Characteristics:** High-dimensional (60 features), small (207 samples) + +--- + +## 🚀 Quick Validation Results + +**Test Command:** +```powershell +python quantum-ai\quick_test_datasets.py +``` + +**Results (1-epoch smoke tests):** +- ✅ **10 datasets validated** in 13.2 seconds +- 🏆 **Top performers** (1 epoch): + - Haberman: 100% accuracy + - Banknote: 94.49% accuracy + - Magic Gamma: 76.82% accuracy + - Heart Disease: 72.92% accuracy + +--- + +## 📁 File Locations + +### **Scripts Created:** +1. `ai-projects/quantum-ml/quick_test_datasets.py` - Rapid validation (1-epoch tests) +2. `ai-projects/quantum-ml/dataset_architecture_analyzer.py` - Architecture recommendations + +### **Scripts Updated:** +1. `ai-projects/quantum-ml/benchmark_all_datasets.py` - Now includes all 15 datasets + +### **Results:** +1. `ai-projects/quantum-ml/results/quick_test_results.json` - Validation test results +2. `ai-projects/quantum-ml/results/architecture_analysis.json` - Architecture recommendations + +### **Datasets:** +- All 15 CSVs in `datasets/quantum/` +- Metadata in `datasets/dataset_index.json` (needs updating) + +--- + +## 🔧 Next Steps + +### **Immediate Fixes (5 datasets):** +1. **breast_cancer**: Update loader to skip ID column, handle M/B string labels +2. **vertebral_column**: Add `encoding='latin-1'` parameter +3. **wine_red/white**: Add `sep=';'` parameter for semicolon delimiter +4. **wheat_seeds**: Add `sep=';'` parameter + +### **Recommended Actions:** +1. **Full Benchmark Run** - Execute `benchmark_all_datasets.py` on all 10 working datasets +2. **Hyperparameter Sweep** - Use architecture analyzer recommendations for optimal configs +3. **Dataset Index Update** - Add all 11 new datasets to `dataset_index.json` +4. **Cross-Dataset Analysis** - Compare quantum performance across categories (medical vs physics vs chemistry) + +--- + +## 💡 Key Learnings + +### **Dataset Complexity Patterns:** +- **Easiest:** Banknote (4 features, balanced) → 94% in 1 epoch +- **Hardest:** Haberman (imbalanced, hard task) → Needs special handling +- **Largest:** Magic Gamma (19K samples) → 10.3s training time + +### **Architecture Guidelines:** +- **4 qubits:** Sufficient for 71% of datasets +- **6 qubits:** Only needed for very high-dimensional data (>20 features) +- **2 layers:** Best default - more layers only for hard/multi-class tasks + +### **Preprocessing Importance:** +- Missing value imputation crucial (heart disease has '?' markers) +- PCA essential for high-dimensional datasets (variance retention: 80-90%) +- Stratified splitting fails with severe class imbalance + +--- + +## 🎯 Performance Expectations + +Based on 1-epoch quick tests, expected full-training performance: + +| Difficulty | Expected Accuracy | Epochs Needed | Example Datasets | +|------------|------------------|---------------|------------------| +| **Easy** | 90-100% | 25-40 | Banknote, Iris | +| **Medium** | 75-90% | 40-50 | Diabetes, Magic Gamma, Heart Disease | +| **Hard** | 60-80% | 50+ | Haberman, Blood Transfusion, Sonar | + +--- + +## 📈 Dataset Scale Comparison + +**Size Distribution:** +- **Tiny** (< 200): Iris (149) +- **Small** (200-500): Sonar (207), Glass (213), Wheat Seeds (210), Haberman (305), Heart Disease (302), Vertebral Column (310), Ionosphere (350) +- **Medium** (500-2000): Breast Cancer (569), Blood Transfusion (748), Diabetes (767), Banknote (1,371), Wine Red (1,599) +- **Large** (2000-5000): Wine White (4,898) +- **Very Large** (> 5000): Magic Gamma (19,020) + +**Feature Complexity:** +- **Low** (3-4): Haberman, Banknote, Blood Transfusion, Iris +- **Medium** (5-15): Diabetes, Glass, Magic Gamma, Heart Disease +- **High** (20-60): Breast Cancer (30), Ionosphere (34), Sonar (60) + +--- + +## ✅ Validation Status + +All created scripts are **production-ready** and **well-documented**: +- ✅ Comprehensive error handling +- ✅ Progress indicators and logging +- ✅ JSON output for automation +- ✅ Clear console formatting +- ✅ Handles encoding issues, missing values, class imbalance + +**Total Implementation Time:** ~30 minutes +**Total Test Time:** ~15 seconds (quick validation) +**Code Quality:** Enterprise-grade with extensive documentation diff --git a/ai-projects/quantum-ml/DATASET_TRAINING_RESULTS.md b/ai-projects/quantum-ml/DATASET_TRAINING_RESULTS.md index 9a1562c75..7eab4d275 100644 --- a/ai-projects/quantum-ml/DATASET_TRAINING_RESULTS.md +++ b/ai-projects/quantum-ml/DATASET_TRAINING_RESULTS.md @@ -1,118 +1,118 @@ -# Quantum AI Training Results - Downloaded Datasets - -**Date**: October 31, 2025 -**Models**: Hybrid Quantum-Classical Classifiers (8 qubits, 4 layers) -**Training**: 2 epochs each (quick validation) - -## Summary - -Successfully trained hybrid quantum models on all 4 downloaded UCI datasets from the centralized dataset storage. - -| Dataset | Samples (Train/Val) | Features | Val Accuracy | Status | -|---------|---------------------|----------|--------------|--------| -| **Banknote** | 1097/275 | 8 (4→8 pad) | 55.6% | ✅ Trained | -| **Ionosphere** | 280/71 | 8 (34→8 PCA) | **63.4%** | ✅ Trained | -| **Sonar** | 166/42 | 8 (60→8 PCA) | 52.4% | ✅ Trained | -| **Heart Disease** | 237/60 | 8 (13→8 PCA) | 46.7% | ✅ Trained | - -## Key Findings - -### Best Performer -- **Ionosphere**: 63.4% accuracy (34 features → 8 via PCA) -- Good signal preservation despite dimensionality reduction - -### Quick Training Notes -- All runs used only 2 epochs for fast validation -- Accuracies are baseline - will improve with longer training -- Preprocessing automatically handled: - - Standardization (mean=0, std=1) - - PCA for high-dimensional datasets - - Zero-padding for low-dimensional datasets - -## Training Commands Used - -```powershell -# All datasets trained with: -C:\Users\Bryan\OneDrive\AI\quantum-ai\venv\Scripts\python.exe .\quantum-ai\scripts\train_from_dataset.py --dataset --epochs 2 - -# Where is: banknote, ionosphere, sonar, heart_disease -``` - -## Results Locations - -All training summaries saved to: -``` -ai-projects/quantum-ml/results/datasets/ -├── banknote/training_summary.json -├── ionosphere/training_summary.json -├── sonar/training_summary.json -└── heart_disease/training_summary.json -``` - -## Next Steps to Improve Performance - -### 1. Longer Training -```powershell -# Train for 20+ epochs -.\venv\Scripts\python.exe .\scripts\train_from_dataset.py --dataset ionosphere --epochs 20 -``` - -### 2. Hyperparameter Tuning -```powershell -# Adjust learning rate and batch size -.\venv\Scripts\python.exe .\scripts\train_from_dataset.py --dataset ionosphere --epochs 20 --lr 0.005 --batch-size 16 -``` - -### 3. Experiment with Quantum Config -Edit `ai-projects/quantum-ml/config/quantum_config.yaml`: -- Try `n_qubits: 6` or `n_qubits: 10` -- Test `entanglement: linear` vs `full` -- Adjust `n_layers: 3` or `n_layers: 5` - -### 4. Feature Engineering -- Analyze which PCA components matter most -- Try different preprocessing strategies -- Add feature selection before PCA - -## Dataset Characteristics - -### Banknote (1372 samples) -- **Task**: Fraud detection (real vs counterfeit) -- **Features**: 4 (variance, skewness, curtosis, entropy of wavelet) -- **Balance**: 55.5% authentic, 44.5% counterfeit -- **Processing**: Padded from 4→8 features - -### Ionosphere (351 samples) -- **Task**: Radar signal classification (good vs bad) -- **Features**: 34 radar returns -- **Balance**: 64.1% good, 35.9% bad -- **Processing**: PCA reduced 34→8 features (preserves ~70% variance) - -### Sonar (208 samples) -- **Task**: Object detection (mine vs rock) -- **Features**: 60 sonar frequencies -- **Balance**: 53.4% mines, 46.6% rocks -- **Processing**: PCA reduced 60→8 features - -### Heart Disease (297 samples, 6 dropped as NaN) -- **Task**: Medical diagnosis (disease presence) -- **Features**: 13 clinical measurements -- **Balance**: 46.1% disease, 53.9% healthy -- **Processing**: PCA reduced 13→8 features - -## Validation - -✅ **All datasets successfully loaded** from `datasets/dataset_index.json` -✅ **All models trained** without errors -✅ **All results saved** to JSON summaries -✅ **End-to-end workflow verified** from download → train → save - -## Usage Demonstrated - -The complete pipeline is now operational: -1. ✅ Download datasets → `scripts/download_datasets.py` -2. ✅ Validate datasets → `scripts/validate_datasets.py` -3. ✅ Train quantum models → `ai-projects/quantum-ml/scripts/train_from_dataset.py` -4. ✅ Save/track results → `results/datasets//` - -**Ready for production use with longer training runs!** +# Quantum AI Training Results - Downloaded Datasets + +**Date**: October 31, 2025 +**Models**: Hybrid Quantum-Classical Classifiers (8 qubits, 4 layers) +**Training**: 2 epochs each (quick validation) + +## Summary + +Successfully trained hybrid quantum models on all 4 downloaded UCI datasets from the centralized dataset storage. + +| Dataset | Samples (Train/Val) | Features | Val Accuracy | Status | +|---------|---------------------|----------|--------------|--------| +| **Banknote** | 1097/275 | 8 (4→8 pad) | 55.6% | ✅ Trained | +| **Ionosphere** | 280/71 | 8 (34→8 PCA) | **63.4%** | ✅ Trained | +| **Sonar** | 166/42 | 8 (60→8 PCA) | 52.4% | ✅ Trained | +| **Heart Disease** | 237/60 | 8 (13→8 PCA) | 46.7% | ✅ Trained | + +## Key Findings + +### Best Performer +- **Ionosphere**: 63.4% accuracy (34 features → 8 via PCA) +- Good signal preservation despite dimensionality reduction + +### Quick Training Notes +- All runs used only 2 epochs for fast validation +- Accuracies are baseline - will improve with longer training +- Preprocessing automatically handled: + - Standardization (mean=0, std=1) + - PCA for high-dimensional datasets + - Zero-padding for low-dimensional datasets + +## Training Commands Used + +```powershell +# All datasets trained with: +C:\Users\Bryan\OneDrive\AI\quantum-ai\venv\Scripts\python.exe .\quantum-ai\scripts\train_from_dataset.py --dataset --epochs 2 + +# Where is: banknote, ionosphere, sonar, heart_disease +``` + +## Results Locations + +All training summaries saved to: +``` +ai-projects/quantum-ml/results/datasets/ +├── banknote/training_summary.json +├── ionosphere/training_summary.json +├── sonar/training_summary.json +└── heart_disease/training_summary.json +``` + +## Next Steps to Improve Performance + +### 1. Longer Training +```powershell +# Train for 20+ epochs +.\venv\Scripts\python.exe .\scripts\train_from_dataset.py --dataset ionosphere --epochs 20 +``` + +### 2. Hyperparameter Tuning +```powershell +# Adjust learning rate and batch size +.\venv\Scripts\python.exe .\scripts\train_from_dataset.py --dataset ionosphere --epochs 20 --lr 0.005 --batch-size 16 +``` + +### 3. Experiment with Quantum Config +Edit `ai-projects/quantum-ml/config/quantum_config.yaml`: +- Try `n_qubits: 6` or `n_qubits: 10` +- Test `entanglement: linear` vs `full` +- Adjust `n_layers: 3` or `n_layers: 5` + +### 4. Feature Engineering +- Analyze which PCA components matter most +- Try different preprocessing strategies +- Add feature selection before PCA + +## Dataset Characteristics + +### Banknote (1372 samples) +- **Task**: Fraud detection (real vs counterfeit) +- **Features**: 4 (variance, skewness, curtosis, entropy of wavelet) +- **Balance**: 55.5% authentic, 44.5% counterfeit +- **Processing**: Padded from 4→8 features + +### Ionosphere (351 samples) +- **Task**: Radar signal classification (good vs bad) +- **Features**: 34 radar returns +- **Balance**: 64.1% good, 35.9% bad +- **Processing**: PCA reduced 34→8 features (preserves ~70% variance) + +### Sonar (208 samples) +- **Task**: Object detection (mine vs rock) +- **Features**: 60 sonar frequencies +- **Balance**: 53.4% mines, 46.6% rocks +- **Processing**: PCA reduced 60→8 features + +### Heart Disease (297 samples, 6 dropped as NaN) +- **Task**: Medical diagnosis (disease presence) +- **Features**: 13 clinical measurements +- **Balance**: 46.1% disease, 53.9% healthy +- **Processing**: PCA reduced 13→8 features + +## Validation + +✅ **All datasets successfully loaded** from `datasets/dataset_index.json` +✅ **All models trained** without errors +✅ **All results saved** to JSON summaries +✅ **End-to-end workflow verified** from download → train → save + +## Usage Demonstrated + +The complete pipeline is now operational: +1. ✅ Download datasets → `scripts/download_datasets.py` +2. ✅ Validate datasets → `scripts/validate_datasets.py` +3. ✅ Train quantum models → `ai-projects/quantum-ml/scripts/train_from_dataset.py` +4. ✅ Save/track results → `results/datasets//` + +**Ready for production use with longer training runs!** diff --git a/ai-projects/quantum-ml/DATASET_VALIDATION_SUCCESS.md b/ai-projects/quantum-ml/DATASET_VALIDATION_SUCCESS.md index deaef8006..0d83412cf 100644 --- a/ai-projects/quantum-ml/DATASET_VALIDATION_SUCCESS.md +++ b/ai-projects/quantum-ml/DATASET_VALIDATION_SUCCESS.md @@ -1,239 +1,239 @@ -# Dataset Validation Success! ✅ - -**Date:** November 16, 2025 -**Status:** 14/15 Datasets Working (93% Success Rate) - ---- - -## 🎯 Quick Summary - -Successfully validated **14 out of 15 datasets** with 1-epoch smoke tests, achieving a **93% success rate**. All validated datasets can be trained with quantum ML models. - ---- - -## ✅ Working Datasets (14) - -### **Perfect Performers (>95% accuracy in 1 epoch):** -1. **wine_white** - 99.59% accuracy (4,898 samples, 11 features) -2. **wine_red** - 99.38% accuracy (1,599 samples, 11 features) -3. **banknote** - 94.85% accuracy (1,372 samples, 4 features) -4. **breast_cancer** - 87.50% accuracy (569 samples, 29 features) ✨ **FIXED!** - -### **Strong Performers (70-90%):** -5. **magic_gamma** - 77.27% accuracy (19,020 samples, 10 features) 🏆 Largest dataset -6. **wheat_seeds** - 71.88% accuracy (210 samples, 7 features) ✨ **FIXED!** -7. **heart_disease** - 68.75% accuracy (303 samples, 13 features) -8. **iris** - 68.75% accuracy (150 samples, 4 features) -9. **diabetes** - 66.67% accuracy (768 samples, 8 features) -10. **blood_transfusion** - 66.67% accuracy (747 samples, 4 features) ✨ **FIXED!** -11. **ionosphere** - 65.62% accuracy (351 samples, 34 features) -12. **sonar** - 65.62% accuracy (208 samples, 60 features) - -### **Challenging Datasets (50-70%):** -13. **glass** - 59.38% accuracy (214 samples, 10 features, 6 classes) -14. **haberman** - 58.33% accuracy (306 samples, 3 features, imbalanced) - ---- - -## ⚠️ Known Issue (1 Dataset) - -### **vertebral_column** - Binary File Corruption -- **Issue:** Dataset file appears to be in a binary/compressed format rather than CSV -- **Status:** Excluded from benchmarks -- **Impact:** 310 samples, 6 features, 3-class medical dataset unavailable -- **Workaround:** Download from alternative source or convert from original format - ---- - -## 🔧 Fixes Applied - -### **1. wine_red & wine_white** -- **Problem:** Semicolon delimiter (`;`) instead of comma -- **Fix:** Added `sep=';'` parameter to `pd.read_csv()` -- **Result:** ✅ Both working perfectly with 99%+ accuracy - -### **2. breast_cancer** -- **Problem:** No header row, ID column mixed with features, M/B labels -- **Fix:** - - Added `header=None` parameter - - Skip ID column (column 0) - - Extract diagnosis from column 1 - - Use columns 2+ as features -- **Result:** ✅ 87.50% accuracy in 1 epoch - -### **3. blood_transfusion** -- **Problem:** Has description row before header -- **Fix:** Added `skiprows=1` to skip description line -- **Result:** ✅ 66.67% accuracy in 1 epoch - -### **4. wheat_seeds** -- **Problem:** Tab-delimited with irregular spacing -- **Fix:** Used `sep=r'\s+'` (whitespace-flexible delimiter) -- **Result:** ✅ 71.88% accuracy in 1 epoch - ---- - -## 📊 Performance Summary - -### **By Accuracy (1 epoch):** -| Tier | Accuracy Range | Count | Datasets | -|------|---------------|-------|----------| -| **Excellent** | 95-100% | 3 | wine_white, wine_red, banknote | -| **Very Good** | 85-95% | 1 | breast_cancer | -| **Good** | 70-85% | 3 | magic_gamma, wheat_seeds, heart_disease | -| **Moderate** | 65-70% | 5 | iris, diabetes, blood_transfusion, ionosphere, sonar | -| **Challenging** | 50-65% | 2 | glass, haberman | - -### **By Dataset Size:** -| Size Category | Samples Range | Count | Examples | -|--------------|--------------|-------|----------| -| **Tiny** | 150-300 | 5 | iris (150), sonar (207), glass (214), wheat (210), haberman (306) | -| **Small** | 300-1000 | 5 | heart (303), ionosphere (351), breast (569), diabetes (768), blood (747) | -| **Medium** | 1000-5000 | 3 | banknote (1,372), wine_red (1,599), wine_white (4,898) | -| **Large** | 5000+ | 1 | magic_gamma (19,020) 🏆 | - -### **By Feature Count:** -| Feature Range | Count | Datasets | -|--------------|-------|----------| -| **Low (3-4)** | 4 | haberman, banknote, blood_transfusion, iris | -| **Medium (5-15)** | 6 | wheat, diabetes, magic_gamma, glass, wine_red, wine_white, heart | -| **High (20+)** | 4 | breast_cancer (29), ionosphere (34), sonar (60) | - ---- - -## 🚀 Test Execution Metrics - -**Total Test Time:** 17.4 seconds -**Average per Dataset:** 1.24 seconds -**Fastest:** iris, glass, wheat_seeds, sonar (0.1s each) -**Slowest:** magic_gamma (10.5s) - due to 19K samples - -**Resource Usage:** -- CPU: Standard (no GPU required for 1-epoch tests) -- Memory: <2 GB peak -- Disk I/O: Minimal (all datasets load instantly) - ---- - -## 🔍 Technical Insights - -### **Dataset Loading Challenges:** -1. **Delimiter Variation:** Comma, semicolon, tab, whitespace -2. **Header Inconsistency:** Some have headers, some don't -3. **Encoding Issues:** UTF-8 vs Latin-1 -4. **Missing Values:** Question marks, "NA", empty strings -5. **Label Formats:** Numeric vs string (M/B, class names) - -### **Preprocessing Strategy:** -- **Dataset-specific loaders** for known problematic files -- **Automatic header detection** for generic datasets -- **Encoding fallback** (UTF-8 → Latin-1) -- **Missing value imputation** with median strategy -- **PCA dimensionality reduction** for high-feature datasets -- **Feature padding** for low-feature datasets (<4 features) - -### **Architecture Adaptations:** -- **4 qubits:** Standard for most datasets (10/14) -- **6 qubits:** High-dimensional data (breast_cancer, ionosphere, sonar) -- **2 layers:** Default quantum circuit depth -- **Hidden dim 16:** Classical layer size -- **Learning rate 0.001:** Standard for 1-epoch tests - ---- - -## 📁 Files Modified - -### **Scripts:** -1. **ai-projects/quantum-ml/quick_test_datasets.py** - - Added dataset-specific loading strategies - - Enhanced CSV parsing with encoding/delimiter detection - - Improved error handling and reporting - -### **Results:** -1. **ai-projects/quantum-ml/results/quick_test_results.json** - - Complete validation results with metrics - - Per-dataset status, accuracy, loss, timing - ---- - -## ✨ Success Factors - -### **What Went Well:** -✅ 93% success rate (14/15 datasets working) -✅ Rapid testing (17 seconds for full suite) -✅ Clear error messages for debugging -✅ Robust CSV parsing handles edge cases -✅ Three major dataset fixes (wine, breast_cancer, wheat_seeds) -✅ High-quality performers identified (3 datasets >95%) - -### **Lessons Learned:** -💡 UCI datasets require format-agnostic parsing -💡 Always check for semicolon delimiters in European datasets -💡 Header detection critical for datasets without documentation -💡 1-epoch tests effective for rapid validation -💡 Some datasets may have inherent corruption issues - ---- - -## 🎯 Next Steps - -### **Immediate Actions:** -1. ✅ **Run full benchmark** - 25 epochs on all 14 working datasets (IN PROGRESS) -2. 📊 **Compare performance** - Cross-dataset analysis by category -3. 🔧 **Apply architecture recommendations** - Use analyzer output for optimal configs - -### **Future Improvements:** -4. 🔄 **Find vertebral_column alternative source** - Replace corrupted file -5. 📈 **HPO integration** - Use best configs from hyperparameter optimization -6. 🌐 **Production API update** - Add multi-dataset support -7. 📊 **Cross-domain analysis** - Compare quantum advantage across categories - ---- - -## 🏆 Key Achievements - -1. **Dataset Expansion:** 4 → 15 total datasets (275% increase) -2. **Validation Success:** 14/15 working (93% success rate) -3. **Format Fixes:** 4 datasets fixed (wine × 2, breast_cancer, wheat_seeds, blood_transfusion) -4. **Performance Discovery:** 3 datasets achieve >95% in 1 epoch -5. **Comprehensive Testing:** All domains covered (medical, chemistry, physics, biology, forensics, agriculture) -6. **Production Ready:** Robust loader handles diverse formats automatically - ---- - -## 📈 Expected Full-Training Performance - -Based on 1-epoch results and historical data: - -| Dataset | 1-Epoch | Expected 25-Epoch | Improvement | -|---------|---------|-------------------|-------------| -| wine_white | 99.59% | 99.8%+ | Minimal (already optimal) | -| wine_red | 99.38% | 99.5%+ | Minimal (already optimal) | -| banknote | 94.85% | 99%+ | +4-5% (proven: 100% possible) | -| breast_cancer | 87.50% | 92-95% | +5-8% | -| magic_gamma | 77.27% | 80-85% | +3-8% | -| wheat_seeds | 71.88% | 75-85% | +3-13% | -| heart_disease | 68.75% | 80-90% | +11-21% (proven: 95% possible) | -| ionosphere | 65.62% | 80-90% | +14-24% (proven: 85% possible) | -| sonar | 65.62% | 75-85% | +9-19% (proven: 78% possible) | - -**Average Expected Improvement:** +8-15% with full 25-epoch training - ---- - -## 💾 Dataset Catalog Status - -**Total Datasets:** 15 -**Working:** 14 (93%) -**Corrupted:** 1 (7%) -**Total Size:** ~1.9 MB -**Total Samples:** ~32,000 across all datasets -**Total Features:** 3-60 range -**Categories:** 7 (Medical, Chemistry, Physics, Biology, Forensics, Agriculture, Geophysics) - ---- - -**Report Generated:** November 16, 2025 -**Test Framework:** ai-projects/quantum-ml/quick_test_datasets.py -**Architecture:** HybridQNN with PennyLane lightning.qubit -**Status:** ✅ Production Ready for Benchmarking +# Dataset Validation Success! ✅ + +**Date:** November 16, 2025 +**Status:** 14/15 Datasets Working (93% Success Rate) + +--- + +## 🎯 Quick Summary + +Successfully validated **14 out of 15 datasets** with 1-epoch smoke tests, achieving a **93% success rate**. All validated datasets can be trained with quantum ML models. + +--- + +## ✅ Working Datasets (14) + +### **Perfect Performers (>95% accuracy in 1 epoch):** +1. **wine_white** - 99.59% accuracy (4,898 samples, 11 features) +2. **wine_red** - 99.38% accuracy (1,599 samples, 11 features) +3. **banknote** - 94.85% accuracy (1,372 samples, 4 features) +4. **breast_cancer** - 87.50% accuracy (569 samples, 29 features) ✨ **FIXED!** + +### **Strong Performers (70-90%):** +5. **magic_gamma** - 77.27% accuracy (19,020 samples, 10 features) 🏆 Largest dataset +6. **wheat_seeds** - 71.88% accuracy (210 samples, 7 features) ✨ **FIXED!** +7. **heart_disease** - 68.75% accuracy (303 samples, 13 features) +8. **iris** - 68.75% accuracy (150 samples, 4 features) +9. **diabetes** - 66.67% accuracy (768 samples, 8 features) +10. **blood_transfusion** - 66.67% accuracy (747 samples, 4 features) ✨ **FIXED!** +11. **ionosphere** - 65.62% accuracy (351 samples, 34 features) +12. **sonar** - 65.62% accuracy (208 samples, 60 features) + +### **Challenging Datasets (50-70%):** +13. **glass** - 59.38% accuracy (214 samples, 10 features, 6 classes) +14. **haberman** - 58.33% accuracy (306 samples, 3 features, imbalanced) + +--- + +## ⚠️ Known Issue (1 Dataset) + +### **vertebral_column** - Binary File Corruption +- **Issue:** Dataset file appears to be in a binary/compressed format rather than CSV +- **Status:** Excluded from benchmarks +- **Impact:** 310 samples, 6 features, 3-class medical dataset unavailable +- **Workaround:** Download from alternative source or convert from original format + +--- + +## 🔧 Fixes Applied + +### **1. wine_red & wine_white** +- **Problem:** Semicolon delimiter (`;`) instead of comma +- **Fix:** Added `sep=';'` parameter to `pd.read_csv()` +- **Result:** ✅ Both working perfectly with 99%+ accuracy + +### **2. breast_cancer** +- **Problem:** No header row, ID column mixed with features, M/B labels +- **Fix:** + - Added `header=None` parameter + - Skip ID column (column 0) + - Extract diagnosis from column 1 + - Use columns 2+ as features +- **Result:** ✅ 87.50% accuracy in 1 epoch + +### **3. blood_transfusion** +- **Problem:** Has description row before header +- **Fix:** Added `skiprows=1` to skip description line +- **Result:** ✅ 66.67% accuracy in 1 epoch + +### **4. wheat_seeds** +- **Problem:** Tab-delimited with irregular spacing +- **Fix:** Used `sep=r'\s+'` (whitespace-flexible delimiter) +- **Result:** ✅ 71.88% accuracy in 1 epoch + +--- + +## 📊 Performance Summary + +### **By Accuracy (1 epoch):** +| Tier | Accuracy Range | Count | Datasets | +|------|---------------|-------|----------| +| **Excellent** | 95-100% | 3 | wine_white, wine_red, banknote | +| **Very Good** | 85-95% | 1 | breast_cancer | +| **Good** | 70-85% | 3 | magic_gamma, wheat_seeds, heart_disease | +| **Moderate** | 65-70% | 5 | iris, diabetes, blood_transfusion, ionosphere, sonar | +| **Challenging** | 50-65% | 2 | glass, haberman | + +### **By Dataset Size:** +| Size Category | Samples Range | Count | Examples | +|--------------|--------------|-------|----------| +| **Tiny** | 150-300 | 5 | iris (150), sonar (207), glass (214), wheat (210), haberman (306) | +| **Small** | 300-1000 | 5 | heart (303), ionosphere (351), breast (569), diabetes (768), blood (747) | +| **Medium** | 1000-5000 | 3 | banknote (1,372), wine_red (1,599), wine_white (4,898) | +| **Large** | 5000+ | 1 | magic_gamma (19,020) 🏆 | + +### **By Feature Count:** +| Feature Range | Count | Datasets | +|--------------|-------|----------| +| **Low (3-4)** | 4 | haberman, banknote, blood_transfusion, iris | +| **Medium (5-15)** | 6 | wheat, diabetes, magic_gamma, glass, wine_red, wine_white, heart | +| **High (20+)** | 4 | breast_cancer (29), ionosphere (34), sonar (60) | + +--- + +## 🚀 Test Execution Metrics + +**Total Test Time:** 17.4 seconds +**Average per Dataset:** 1.24 seconds +**Fastest:** iris, glass, wheat_seeds, sonar (0.1s each) +**Slowest:** magic_gamma (10.5s) - due to 19K samples + +**Resource Usage:** +- CPU: Standard (no GPU required for 1-epoch tests) +- Memory: <2 GB peak +- Disk I/O: Minimal (all datasets load instantly) + +--- + +## 🔍 Technical Insights + +### **Dataset Loading Challenges:** +1. **Delimiter Variation:** Comma, semicolon, tab, whitespace +2. **Header Inconsistency:** Some have headers, some don't +3. **Encoding Issues:** UTF-8 vs Latin-1 +4. **Missing Values:** Question marks, "NA", empty strings +5. **Label Formats:** Numeric vs string (M/B, class names) + +### **Preprocessing Strategy:** +- **Dataset-specific loaders** for known problematic files +- **Automatic header detection** for generic datasets +- **Encoding fallback** (UTF-8 → Latin-1) +- **Missing value imputation** with median strategy +- **PCA dimensionality reduction** for high-feature datasets +- **Feature padding** for low-feature datasets (<4 features) + +### **Architecture Adaptations:** +- **4 qubits:** Standard for most datasets (10/14) +- **6 qubits:** High-dimensional data (breast_cancer, ionosphere, sonar) +- **2 layers:** Default quantum circuit depth +- **Hidden dim 16:** Classical layer size +- **Learning rate 0.001:** Standard for 1-epoch tests + +--- + +## 📁 Files Modified + +### **Scripts:** +1. **ai-projects/quantum-ml/quick_test_datasets.py** + - Added dataset-specific loading strategies + - Enhanced CSV parsing with encoding/delimiter detection + - Improved error handling and reporting + +### **Results:** +1. **ai-projects/quantum-ml/results/quick_test_results.json** + - Complete validation results with metrics + - Per-dataset status, accuracy, loss, timing + +--- + +## ✨ Success Factors + +### **What Went Well:** +✅ 93% success rate (14/15 datasets working) +✅ Rapid testing (17 seconds for full suite) +✅ Clear error messages for debugging +✅ Robust CSV parsing handles edge cases +✅ Three major dataset fixes (wine, breast_cancer, wheat_seeds) +✅ High-quality performers identified (3 datasets >95%) + +### **Lessons Learned:** +💡 UCI datasets require format-agnostic parsing +💡 Always check for semicolon delimiters in European datasets +💡 Header detection critical for datasets without documentation +💡 1-epoch tests effective for rapid validation +💡 Some datasets may have inherent corruption issues + +--- + +## 🎯 Next Steps + +### **Immediate Actions:** +1. ✅ **Run full benchmark** - 25 epochs on all 14 working datasets (IN PROGRESS) +2. 📊 **Compare performance** - Cross-dataset analysis by category +3. 🔧 **Apply architecture recommendations** - Use analyzer output for optimal configs + +### **Future Improvements:** +4. 🔄 **Find vertebral_column alternative source** - Replace corrupted file +5. 📈 **HPO integration** - Use best configs from hyperparameter optimization +6. 🌐 **Production API update** - Add multi-dataset support +7. 📊 **Cross-domain analysis** - Compare quantum advantage across categories + +--- + +## 🏆 Key Achievements + +1. **Dataset Expansion:** 4 → 15 total datasets (275% increase) +2. **Validation Success:** 14/15 working (93% success rate) +3. **Format Fixes:** 4 datasets fixed (wine × 2, breast_cancer, wheat_seeds, blood_transfusion) +4. **Performance Discovery:** 3 datasets achieve >95% in 1 epoch +5. **Comprehensive Testing:** All domains covered (medical, chemistry, physics, biology, forensics, agriculture) +6. **Production Ready:** Robust loader handles diverse formats automatically + +--- + +## 📈 Expected Full-Training Performance + +Based on 1-epoch results and historical data: + +| Dataset | 1-Epoch | Expected 25-Epoch | Improvement | +|---------|---------|-------------------|-------------| +| wine_white | 99.59% | 99.8%+ | Minimal (already optimal) | +| wine_red | 99.38% | 99.5%+ | Minimal (already optimal) | +| banknote | 94.85% | 99%+ | +4-5% (proven: 100% possible) | +| breast_cancer | 87.50% | 92-95% | +5-8% | +| magic_gamma | 77.27% | 80-85% | +3-8% | +| wheat_seeds | 71.88% | 75-85% | +3-13% | +| heart_disease | 68.75% | 80-90% | +11-21% (proven: 95% possible) | +| ionosphere | 65.62% | 80-90% | +14-24% (proven: 85% possible) | +| sonar | 65.62% | 75-85% | +9-19% (proven: 78% possible) | + +**Average Expected Improvement:** +8-15% with full 25-epoch training + +--- + +## 💾 Dataset Catalog Status + +**Total Datasets:** 15 +**Working:** 14 (93%) +**Corrupted:** 1 (7%) +**Total Size:** ~1.9 MB +**Total Samples:** ~32,000 across all datasets +**Total Features:** 3-60 range +**Categories:** 7 (Medical, Chemistry, Physics, Biology, Forensics, Agriculture, Geophysics) + +--- + +**Report Generated:** November 16, 2025 +**Test Framework:** ai-projects/quantum-ml/quick_test_datasets.py +**Architecture:** HybridQNN with PennyLane lightning.qubit +**Status:** ✅ Production Ready for Benchmarking diff --git a/ai-projects/quantum-ml/DEMONSTRATION_SUMMARY.md b/ai-projects/quantum-ml/DEMONSTRATION_SUMMARY.md index e75d8fdb0..27a514d90 100644 --- a/ai-projects/quantum-ml/DEMONSTRATION_SUMMARY.md +++ b/ai-projects/quantum-ml/DEMONSTRATION_SUMMARY.md @@ -1,333 +1,333 @@ -# 🌌 Quantum AI - Complete Demonstration Summary - -## Overview - -You've successfully explored all four major capabilities of the Quantum AI project: - -1. ✅ **Creating Quantum Circuits** -2. ✅ **Running Simulations Locally** -3. ✅ **Training Quantum ML Models** -4. ✅ **Azure Quantum Integration** (setup guide) - ---- - -## 📊 What Was Demonstrated - -### 1. Quantum Circuit Creation ⚛️ - -**Circuits Created:** - -- Bell State (2 qubits) - Quantum entanglement -- GHZ State (3 qubits) - Multi-qubit entanglement -- Quantum Fourier Transform - 3 qubits -- Variational Quantum Circuit - 4 qubits, 2 layers for ML -- PennyLane Parameterized Circuit -- Quantum Classifier Circuit - 16 parameters - -**Key Learning:** - -- Different quantum gates (H, CNOT, RY, RZ, CP) -- Entanglement patterns (linear, circular, full) -- Circuit visualization -- Parameter management - ---- - -### 2. Local Quantum Simulation 🖥️ - -**Simulations Run:** - -- **Bell State**: 515/485 split (|00⟩/|11⟩) - Perfect entanglement! -- **Superposition**: 510/490 split (|0⟩/|1⟩) - Quantum randomness -- **Gradient Computation**: Enabled quantum machine learning -- **State Evolution**: 50 angles tested, full cosine curve -- **Scaling Test**: 2→8 qubits (state space: 4→256 dimensions) -- **Noisy Simulation**: ~1-2% error on entangled states - -**Key Results:** - -- Local simulation works perfectly -- Gradients enable backpropagation -- Noise modeling matches real hardware -- Exponential scaling observed - -**Generated Files:** - -- `results/state_evolution.png` - ---- - -### 3. Quantum Machine Learning 🤖 - -**Models Trained:** - -- **Moons Dataset**: 85.0% accuracy ⭐⭐⭐⭐ -- **Circles Dataset**: 50.0% accuracy ⭐⭐ (challenging) -- **Iris Dataset**: 66.7% accuracy ⭐⭐⭐ - -**Training Configuration:** - -- 4 qubits, 2 layers -- 100 epochs -- Batch size: 32 -- Learning rate: 0.01 -- Hybrid quantum-classical architecture - -**Key Insights:** - -- Quantum circuits can learn non-linear boundaries -- Training converges smoothly -- Performance competitive with classical NNs -- Some datasets need architecture tuning - -**Generated Files:** - -- `results/training_moons.png` - Loss/accuracy curves -- `results/model_comparison.png` - Performance comparison - ---- - -### 4. Azure Quantum Integration ☁️ - -**Configuration Status:** - -- ✅ Configuration file loaded -- ✅ Workspace name configured -- ⚠️ Azure subscription ID needs setup (for cloud deployment) - -**Providers Available:** - -- **IonQ**: Trapped ion, 11-29 qubits, all-to-all connectivity -- **Quantinuum**: Trapped ion, 20-32 qubits, mid-circuit measurement -- **Rigetti**: Superconducting, 40+ qubits, fast gates -- **Microsoft**: Simulators, up to 40 qubits, FREE tier - -**Cost Estimates:** - -- Microsoft Simulators: **FREE** tier available -- IonQ: ~$3 for 100-gate circuit, 1000 shots -- Quantinuum: ~$0.80-1.50 per circuit -- Rigetti: Enterprise pricing - -**Deployment Ready:** - -- Bicep templates: `azure/quantum_workspace.bicep` -- Parameter files: `azure/quantum_workspace.parameters.json` -- Full guide: `azure/DEPLOYMENT.md` - ---- - -## 🎯 Key Achievements - -### Technical Skills Demonstrated - -✅ Quantum gate operations -✅ Entanglement creation and measurement -✅ Variational quantum circuits -✅ Hybrid quantum-classical models -✅ Gradient-based optimization -✅ Noise simulation -✅ Cloud integration planning - -### Machine Learning Results - -✅ Binary classification working -✅ Non-linear decision boundaries learned -✅ Convergent training loops -✅ Validation accuracy tracking -✅ Multiple dataset testing - -### Infrastructure - -✅ Local simulation environment -✅ Azure Quantum configuration -✅ Cost estimation tools -✅ Deployment automation (Bicep) - ---- - -## 📁 Generated Files - -```text -ai-projects/quantum-ml/ -├── results/ -│ ├── state_evolution.png # Quantum state evolution -│ ├── training_moons.png # Training curves -│ └── model_comparison.png # Performance comparison -├── examples/ -│ ├── create_circuits.py # Circuit creation demo -│ ├── run_simulations.py # Simulation examples -│ ├── train_models.py # ML training demo -│ ├── azure_integration.py # Azure setup guide -│ └── README.md # Examples documentation -└── src/ - ├── quantum_classifier.py # Fixed & working! - ├── hybrid_qnn.py - └── azure_quantum_integration.py -``` - ---- - -## 🚀 Next Steps - -### Immediate (No Azure Required) - -1. **Experiment with parameters** - - ```powershell - # Edit config/quantum_config.yaml - # Try: n_qubits=6, n_layers=3, entanglement="circular" - python .\src\quantum_classifier.py - ``` - -2. **Try different datasets** - - ```python - from sklearn.datasets import make_blobs, make_classification - ``` - -3. **Optimize hyperparameters** - - Learning rates: 0.001 → 0.1 - - Epochs: 100 → 200 - - Batch sizes: 16 → 64 - -### Short-term (Local Development) - -1. **Implement new algorithms** - - VQE (Variational Quantum Eigensolver) - - QAOA (Quantum Approximate Optimization) - - Quantum GANs - -2. **Extend ML capabilities** - - Multi-class classification - - Regression tasks - - Transfer learning - -3. **Benchmark performance** - - Compare with classical NNs - - Test circuit depth impact - - Analyze convergence rates - -### Long-term (Azure Quantum) - -1. **Deploy to Azure** - - ```powershell - # Follow: azure/DEPLOYMENT.md - az deployment group create --resource-group rg-quantum-ai ... - ``` - -2. **Run on real hardware** - - Test on IonQ simulator (FREE) - - Submit to real quantum computers - - Compare noisy vs. ideal results - -3. **Production workflows** - - Automated job submission - - Cost monitoring - - Result tracking - ---- - -## 💡 Best Practices Learned - -### Circuit Design - -- Start with 2-4 qubits for testing -- Use linear entanglement for simplicity -- Increase layers for expressiveness -- Monitor parameter count - -### Training - -- Always use validation sets -- Log training metrics -- Start with small datasets -- Normalize input features - -### Simulation - -- Test locally before cloud deployment -- Use noise models to match hardware -- Monitor classical memory usage -- Optimize shot counts (1000 is good default) - -### Azure Integration - -- Start with FREE Microsoft simulators -- Estimate costs before running -- Use resource groups for organization -- Monitor with Azure Cost Management - ---- - -## 🎓 Concepts Mastered - -### Quantum Mechanics - -- ✓ Superposition -- ✓ Entanglement -- ✓ Measurement collapse -- ✓ Quantum interference - -### Quantum Gates - -- ✓ Hadamard (H) -- ✓ CNOT -- ✓ Rotation gates (RY, RZ) -- ✓ Phase gates (CP) - -### Quantum Algorithms - -- ✓ Variational Quantum Circuits -- ✓ Quantum Fourier Transform -- ✓ Parameterized Quantum Circuits - -### Machine Learning - -- ✓ Hybrid architectures -- ✓ Quantum gradients -- ✓ Backpropagation through quantum layers -- ✓ Binary classification - ---- - -## 📚 Resources Used - -### Software - -- **PennyLane**: Quantum ML framework -- **Qiskit**: IBM's quantum toolkit -- **PyTorch**: Classical ML framework -- **Azure Quantum SDK**: Cloud integration - -### Documentation - -- [PennyLane Docs](https://docs.pennylane.ai/) -- [Qiskit Tutorials](https://qiskit.org/learn/) -- [Azure Quantum](https://learn.microsoft.com/azure/quantum/) - -### Papers - -- Variational Quantum Eigensolver (VQE) -- Quantum Machine Learning (QML) -- Quantum Approximate Optimization (QAOA) - ---- - -## 🏆 Success Metrics - -| Metric | Target | Achieved | Status | -|--------|--------|----------|--------| -| Circuit Creation | 5+ types | 6 types | ✅ | -| Local Simulation | Working | Perfect | ✅ | -| ML Training | >70% acc | 85% (Moons) | ✅ | -| Azure Setup | Guide | Complete | ✅ | -| Documentation | Complete | 4 READMEs | ✅ | -| Examples | 3+ | 4 files | ✅ | - ---- - -## Now you can prove him wrong! 😉 - -- "Quantum mechanics is very impressive. But an inner voice tells me that it is not yet the real thing." — Albert Einstein +# 🌌 Quantum AI - Complete Demonstration Summary + +## Overview + +You've successfully explored all four major capabilities of the Quantum AI project: + +1. ✅ **Creating Quantum Circuits** +2. ✅ **Running Simulations Locally** +3. ✅ **Training Quantum ML Models** +4. ✅ **Azure Quantum Integration** (setup guide) + +--- + +## 📊 What Was Demonstrated + +### 1. Quantum Circuit Creation ⚛️ + +**Circuits Created:** + +- Bell State (2 qubits) - Quantum entanglement +- GHZ State (3 qubits) - Multi-qubit entanglement +- Quantum Fourier Transform - 3 qubits +- Variational Quantum Circuit - 4 qubits, 2 layers for ML +- PennyLane Parameterized Circuit +- Quantum Classifier Circuit - 16 parameters + +**Key Learning:** + +- Different quantum gates (H, CNOT, RY, RZ, CP) +- Entanglement patterns (linear, circular, full) +- Circuit visualization +- Parameter management + +--- + +### 2. Local Quantum Simulation 🖥️ + +**Simulations Run:** + +- **Bell State**: 515/485 split (|00⟩/|11⟩) - Perfect entanglement! +- **Superposition**: 510/490 split (|0⟩/|1⟩) - Quantum randomness +- **Gradient Computation**: Enabled quantum machine learning +- **State Evolution**: 50 angles tested, full cosine curve +- **Scaling Test**: 2→8 qubits (state space: 4→256 dimensions) +- **Noisy Simulation**: ~1-2% error on entangled states + +**Key Results:** + +- Local simulation works perfectly +- Gradients enable backpropagation +- Noise modeling matches real hardware +- Exponential scaling observed + +**Generated Files:** + +- `results/state_evolution.png` + +--- + +### 3. Quantum Machine Learning 🤖 + +**Models Trained:** + +- **Moons Dataset**: 85.0% accuracy ⭐⭐⭐⭐ +- **Circles Dataset**: 50.0% accuracy ⭐⭐ (challenging) +- **Iris Dataset**: 66.7% accuracy ⭐⭐⭐ + +**Training Configuration:** + +- 4 qubits, 2 layers +- 100 epochs +- Batch size: 32 +- Learning rate: 0.01 +- Hybrid quantum-classical architecture + +**Key Insights:** + +- Quantum circuits can learn non-linear boundaries +- Training converges smoothly +- Performance competitive with classical NNs +- Some datasets need architecture tuning + +**Generated Files:** + +- `results/training_moons.png` - Loss/accuracy curves +- `results/model_comparison.png` - Performance comparison + +--- + +### 4. Azure Quantum Integration ☁️ + +**Configuration Status:** + +- ✅ Configuration file loaded +- ✅ Workspace name configured +- ⚠️ Azure subscription ID needs setup (for cloud deployment) + +**Providers Available:** + +- **IonQ**: Trapped ion, 11-29 qubits, all-to-all connectivity +- **Quantinuum**: Trapped ion, 20-32 qubits, mid-circuit measurement +- **Rigetti**: Superconducting, 40+ qubits, fast gates +- **Microsoft**: Simulators, up to 40 qubits, FREE tier + +**Cost Estimates:** + +- Microsoft Simulators: **FREE** tier available +- IonQ: ~$3 for 100-gate circuit, 1000 shots +- Quantinuum: ~$0.80-1.50 per circuit +- Rigetti: Enterprise pricing + +**Deployment Ready:** + +- Bicep templates: `azure/quantum_workspace.bicep` +- Parameter files: `azure/quantum_workspace.parameters.json` +- Full guide: `azure/DEPLOYMENT.md` + +--- + +## 🎯 Key Achievements + +### Technical Skills Demonstrated + +✅ Quantum gate operations +✅ Entanglement creation and measurement +✅ Variational quantum circuits +✅ Hybrid quantum-classical models +✅ Gradient-based optimization +✅ Noise simulation +✅ Cloud integration planning + +### Machine Learning Results + +✅ Binary classification working +✅ Non-linear decision boundaries learned +✅ Convergent training loops +✅ Validation accuracy tracking +✅ Multiple dataset testing + +### Infrastructure + +✅ Local simulation environment +✅ Azure Quantum configuration +✅ Cost estimation tools +✅ Deployment automation (Bicep) + +--- + +## 📁 Generated Files + +```text +ai-projects/quantum-ml/ +├── results/ +│ ├── state_evolution.png # Quantum state evolution +│ ├── training_moons.png # Training curves +│ └── model_comparison.png # Performance comparison +├── examples/ +│ ├── create_circuits.py # Circuit creation demo +│ ├── run_simulations.py # Simulation examples +│ ├── train_models.py # ML training demo +│ ├── azure_integration.py # Azure setup guide +│ └── README.md # Examples documentation +└── src/ + ├── quantum_classifier.py # Fixed & working! + ├── hybrid_qnn.py + └── azure_quantum_integration.py +``` + +--- + +## 🚀 Next Steps + +### Immediate (No Azure Required) + +1. **Experiment with parameters** + + ```powershell + # Edit config/quantum_config.yaml + # Try: n_qubits=6, n_layers=3, entanglement="circular" + python .\src\quantum_classifier.py + ``` + +2. **Try different datasets** + + ```python + from sklearn.datasets import make_blobs, make_classification + ``` + +3. **Optimize hyperparameters** + - Learning rates: 0.001 → 0.1 + - Epochs: 100 → 200 + - Batch sizes: 16 → 64 + +### Short-term (Local Development) + +1. **Implement new algorithms** + - VQE (Variational Quantum Eigensolver) + - QAOA (Quantum Approximate Optimization) + - Quantum GANs + +2. **Extend ML capabilities** + - Multi-class classification + - Regression tasks + - Transfer learning + +3. **Benchmark performance** + - Compare with classical NNs + - Test circuit depth impact + - Analyze convergence rates + +### Long-term (Azure Quantum) + +1. **Deploy to Azure** + + ```powershell + # Follow: azure/DEPLOYMENT.md + az deployment group create --resource-group rg-quantum-ai ... + ``` + +2. **Run on real hardware** + - Test on IonQ simulator (FREE) + - Submit to real quantum computers + - Compare noisy vs. ideal results + +3. **Production workflows** + - Automated job submission + - Cost monitoring + - Result tracking + +--- + +## 💡 Best Practices Learned + +### Circuit Design + +- Start with 2-4 qubits for testing +- Use linear entanglement for simplicity +- Increase layers for expressiveness +- Monitor parameter count + +### Training + +- Always use validation sets +- Log training metrics +- Start with small datasets +- Normalize input features + +### Simulation + +- Test locally before cloud deployment +- Use noise models to match hardware +- Monitor classical memory usage +- Optimize shot counts (1000 is good default) + +### Azure Integration + +- Start with FREE Microsoft simulators +- Estimate costs before running +- Use resource groups for organization +- Monitor with Azure Cost Management + +--- + +## 🎓 Concepts Mastered + +### Quantum Mechanics + +- ✓ Superposition +- ✓ Entanglement +- ✓ Measurement collapse +- ✓ Quantum interference + +### Quantum Gates + +- ✓ Hadamard (H) +- ✓ CNOT +- ✓ Rotation gates (RY, RZ) +- ✓ Phase gates (CP) + +### Quantum Algorithms + +- ✓ Variational Quantum Circuits +- ✓ Quantum Fourier Transform +- ✓ Parameterized Quantum Circuits + +### Machine Learning + +- ✓ Hybrid architectures +- ✓ Quantum gradients +- ✓ Backpropagation through quantum layers +- ✓ Binary classification + +--- + +## 📚 Resources Used + +### Software + +- **PennyLane**: Quantum ML framework +- **Qiskit**: IBM's quantum toolkit +- **PyTorch**: Classical ML framework +- **Azure Quantum SDK**: Cloud integration + +### Documentation + +- [PennyLane Docs](https://docs.pennylane.ai/) +- [Qiskit Tutorials](https://qiskit.org/learn/) +- [Azure Quantum](https://learn.microsoft.com/azure/quantum/) + +### Papers + +- Variational Quantum Eigensolver (VQE) +- Quantum Machine Learning (QML) +- Quantum Approximate Optimization (QAOA) + +--- + +## 🏆 Success Metrics + +| Metric | Target | Achieved | Status | +|--------|--------|----------|--------| +| Circuit Creation | 5+ types | 6 types | ✅ | +| Local Simulation | Working | Perfect | ✅ | +| ML Training | >70% acc | 85% (Moons) | ✅ | +| Azure Setup | Guide | Complete | ✅ | +| Documentation | Complete | 4 READMEs | ✅ | +| Examples | 3+ | 4 files | ✅ | + +--- + +## Now you can prove him wrong! 😉 + +- "Quantum mechanics is very impressive. But an inner voice tells me that it is not yet the real thing." — Albert Einstein diff --git a/ai-projects/quantum-ml/DEPLOYMENT_COMPLETE.md b/ai-projects/quantum-ml/DEPLOYMENT_COMPLETE.md index 46a5aa6e2..8091fde7a 100644 --- a/ai-projects/quantum-ml/DEPLOYMENT_COMPLETE.md +++ b/ai-projects/quantum-ml/DEPLOYMENT_COMPLETE.md @@ -1,493 +1,493 @@ -# Quantum AI - Complete Deployment Summary -## November 16, 2025 - ---- - -## 🎉 Mission Accomplished - -All four requested tasks have been successfully completed! Your Quantum AI system is now **production-ready** with comprehensive training, Azure Quantum deployment capability, hyperparameter optimization, and a fully functional production API. - ---- - -## ✅ Task 1: Fixed Heart Disease Dataset & Re-ran Benchmark - -### What Was Done - -✅ **Fixed `benchmark_all_datasets.py`** to handle missing values (`?` markers) in heart disease dataset -- Added `na_values` parameter to `pd.read_csv()` -- Implemented `SimpleImputer` with median strategy -- Added `drop_last=True` to training DataLoader to prevent batch normalization errors - -### Results - -**Updated Benchmark Results (3 of 4 datasets):** - -| Dataset | Samples | Features | Best Accuracy | Grade | -|---------|---------|----------|---------------|-------| -| **Banknote** | 1,371 | 4 | **100.00%** 🥇 | 🏆 Perfect! | -| **Ionosphere** | 350 | 34→4 | **85.71%** | 🏆 Excellent | -| **Sonar** | 207 | 60→4 | **78.57%** | ⭐ Very Good | - -**Average Accuracy: 88.10%** - -**Files Modified:** -- `ai-projects/quantum-ml/benchmark_all_datasets.py` - Enhanced preprocessing - -**Note:** Heart disease was previously trained separately with **95.08% accuracy** (50 epochs) and **91.80% accuracy** (10 epochs). The benchmark script improvements ensure it will work in automated runs. - ---- - -## ✅ Task 2: Deployed to Azure Quantum Hardware - -### What Was Done - -✅ **Created `deploy_banknote_to_azure.py`** - Production deployment script for Azure Quantum -- Connects to Azure Quantum workspace using `azure-quantum` SDK -- Lists available backends (free simulators + paid QPUs) -- Submits 4-qubit quantum circuit to cloud -- Retrieves and analyzes results -- Generates deployment report with cost estimates - -### Features - -🔗 **Azure Integration:** -- Workspace connection with `DefaultAzureCredential` -- Auto-detection of available quantum backends -- Support for IonQ, Rigetti, and Quantinuum simulators - -⚡ **Smart Backend Selection:** -- Prefers free simulators (IonQ, Rigetti) -- Falls back to available options -- Displays cost estimates for QPU upgrades - -📊 **Results Analysis:** -- Measurement distribution visualization -- Binary classification interpretation -- Deployment report generation (JSON) - -💰 **Cost Transparency:** -- Simulators: **$0.00** (unlimited free usage) -- IonQ QPU: ~$0.00003 per gate-shot -- Quantinuum QPU: ~$0.00015 per circuit - -### Usage - -```bash -cd quantum-ai -python deploy_banknote_to_azure.py -``` - -**Files Created:** -- `ai-projects/quantum-ml/deploy_banknote_to_azure.py` - Azure deployment script -- `ai-projects/quantum-ml/results/azure_deployment_report.json` - Execution report (generated on run) - -**Prerequisites:** -```bash -pip install azure-quantum azure-identity qiskit qiskit-qir -az login -``` - ---- - -## ✅ Task 3: Hyperparameter Optimization Running - -### What Was Done - -✅ **Launched `hyperparameter_optimization.py`** - Automated grid search across 72 configurations -- Currently running in background terminal -- Testing datasets: ionosphere, sonar (to close performance gaps) - -### Hyperparameter Grid - -The optimization sweeps over: -- **n_qubits**: 4, 5, 6 -- **n_quantum_layers**: 2, 3, 4 -- **hidden_dim**: 16, 32 -- **learning_rate**: 0.0005, 0.001 -- **batch_size**: 8, 16 - -**Total Configurations:** 72 (3 × 3 × 2 × 2 × 2) - -### Training Strategy - -- **Early Stopping:** Patience of 10 epochs -- **Max Epochs:** 50 per configuration -- **Cross-Validation:** StratifiedKFold for robust evaluation -- **Target Datasets:** Ionosphere and Sonar (currently underperforming) - -### Expected Improvements - -Current gaps vs classical baselines: -- Ionosphere: 85.71% quantum vs 97.18% SVM (gap: -11.47 pp) -- Sonar: 76.19% quantum vs 85.71% SVM (gap: -9.52 pp) - -**Goal:** Reduce gaps to < 5 percentage points through optimal hyperparameters - -### Monitoring - -Check progress: -```bash -# View live output -cat ai-projects/quantum-ml/results/hpo_optimization_report.json - -# Monitor terminal -# (Currently running - will take 1-2 hours for 72 configs × 50 epochs max) -``` - -**Files:** -- `ai-projects/quantum-ml/hyperparameter_optimization.py` - HPO script (running) -- `ai-projects/quantum-ml/results/hpo_optimization_report.json` - Results (generated on completion) - ---- - -## ✅ Task 4: Production Deployment Package - -### What Was Done - -✅ **Created complete production-ready deployment package** in `ai-projects/quantum-ml/production/` - -A fully functional REST API with comprehensive documentation, Docker support, and test suite. - -### Package Contents - -#### 📝 **Core Files** - -1. **`banknote_api.py`** - Flask REST API - - 4 endpoints (predict, predict_batch, health, model_info) - - Error handling and input validation - - CORS enabled for web clients - - Comprehensive logging - -2. **`README.md`** - Complete documentation - - Quick start guide - - API endpoint specifications - - Example curl commands - - Docker deployment instructions - - Performance benchmarks - - Troubleshooting guide - -3. **`requirements.txt`** - Production dependencies - - Flask, NumPy, PyTorch, scikit-learn - - PennyLane quantum ML - - Gunicorn for production WSGI - - Prometheus for monitoring - -4. **`test_api.py`** - Comprehensive test suite - - 8 automated tests - - Health checks, predictions, batch processing - - Error handling validation - - Performance benchmarking - -#### 🐳 **Docker Support** - -5. **`Dockerfile`** - Container configuration - - Based on Python 3.11-slim - - Optimized for production - - Built-in health checks - -6. **`docker-compose.yml`** - Orchestration - - Single-command deployment - - Volume mounting for model artifacts - - Auto-restart policy - -### API Endpoints - -#### **POST /api/predict** -Classify a single banknote. - -**Request:** -```json -{ - "features": [3.5, 0.5, -1.2, 0.8] -} -``` - -**Response:** -```json -{ - "prediction": "GENUINE", - "confidence": 0.9987, - "probabilities": { - "genuine": 0.9987, - "forged": 0.0013 - }, - "timestamp": "2025-11-16T10:30:45.123456" -} -``` - -#### **POST /api/predict_batch** -Classify multiple banknotes in one request. - -#### **GET /api/health** -Health check for monitoring/orchestration. - -#### **GET /api/model_info** -Model architecture and performance metrics. - -### Quick Start - -```bash -# Navigate to production directory -cd ai-projects/quantum-ml/production - -# Install dependencies -pip install -r requirements.txt - -# Start API -python banknote_api.py -``` - -API runs on `http://localhost:8080` - -### Docker Deployment - -```bash -# Build container -docker build -t banknote-fraud-detector . - -# Run container -docker run -p 8080:8080 banknote-fraud-detector - -# Or use docker-compose -docker-compose up -``` - -### Testing - -```bash -# Run test suite -python test_api.py - -# Manual test -curl -X POST http://localhost:8080/api/predict \ - -H "Content-Type: application/json" \ - -d '{"features": [3.5, 0.5, -1.2, 0.8]}' -``` - -### Performance Specs - -- **Inference Time:** < 100ms per prediction -- **Throughput:** ~10-20 requests/sec (single instance) -- **Accuracy:** 100% on validation data -- **Model Size:** ~500KB -- **Memory Usage:** ~200MB - -### Production Features - -✅ **Reliability:** -- Input validation and sanitization -- Comprehensive error handling -- Health check endpoint -- Graceful failure modes - -✅ **Scalability:** -- Stateless design (easy to replicate) -- Batch processing support -- Docker containerization -- Load balancer ready - -✅ **Monitoring:** -- Structured JSON responses -- Timestamp on all predictions -- Health metrics endpoint -- Ready for Prometheus integration - -✅ **Security:** -- Input validation -- CORS configuration -- Rate limiting ready -- HTTPS ready (configure in production) - ---- - -## 📊 Overall Achievement Summary - -### Training Results - -**Quantum AI Model Performance:** - -| Dataset | Samples | Qubits | Best Accuracy | Status | -|---------|---------|--------|---------------|--------| -| Banknote | 1,371 | 4 | **100.00%** | 🥇 Perfect | -| Heart Disease | 302 | 4 | **95.08%** | 🏆 Excellent | -| Ionosphere | 350 | 4 | **85.71%** | 🏆 Excellent | -| Sonar | 207 | 4 | **78.57%** | ⭐ Very Good | - -**Average Across All Datasets: 89.84%** - -### Technology Stack - -🔬 **Quantum ML:** -- 4-qubit variational circuits -- PennyLane lightning.qubit simulator -- Hybrid quantum-classical architecture - -☁️ **Cloud Platform:** -- Azure Quantum integration -- Support for IonQ, Rigetti, Quantinuum -- Free simulators + paid QPU options - -🚀 **Production:** -- Flask REST API -- Docker containers -- Comprehensive test suite -- Production-grade error handling - -### Files Created/Modified - -**New Files (11):** -1. `ai-projects/quantum-ml/deploy_banknote_to_azure.py` -2. `ai-projects/quantum-ml/production/banknote_api.py` -3. `ai-projects/quantum-ml/production/README.md` -4. `ai-projects/quantum-ml/production/requirements.txt` -5. `ai-projects/quantum-ml/production/Dockerfile` -6. `ai-projects/quantum-ml/production/docker-compose.yml` -7. `ai-projects/quantum-ml/production/test_api.py` -8. This summary document - -**Modified Files (1):** -1. `ai-projects/quantum-ml/benchmark_all_datasets.py` (fixed preprocessing) - ---- - -## 🚀 Next Steps & Recommendations - -### Immediate Actions - -1. **Monitor Hyperparameter Optimization** - ```bash - # Check if HPO completed - ls -lh ai-projects/quantum-ml/results/hpo_optimization_report.json - ``` - -2. **Test Production API** - ```bash - cd ai-projects/quantum-ml/production - python banknote_api.py - # In another terminal: - python test_api.py - ``` - -3. **Deploy to Azure Quantum (Optional)** - ```bash - cd quantum-ai - python deploy_banknote_to_azure.py - ``` - -### Future Enhancements - -🎯 **Model Improvements:** -- Apply optimized hyperparameters from HPO results -- Train ensemble models for even higher accuracy -- Explore deeper quantum circuits (6-8 qubits) - -☁️ **Cloud Deployment:** -- Deploy API to Azure App Service / AWS Lambda -- Set up CI/CD pipeline -- Configure autoscaling - -📊 **Monitoring & Analytics:** -- Add Prometheus metrics -- Set up Grafana dashboards -- Implement prediction logging - -🔒 **Security:** -- Add API key authentication -- Enable HTTPS -- Implement rate limiting -- Add request logging - -### Production Checklist - -Before deploying to production: - -- [ ] Review optimized hyperparameters from HPO -- [ ] Retrain models with best configurations -- [ ] Run full test suite (`test_api.py`) -- [ ] Set up monitoring and alerting -- [ ] Configure API authentication -- [ ] Enable HTTPS -- [ ] Set up backup and disaster recovery -- [ ] Document deployment procedures -- [ ] Train team on API usage -- [ ] Set up SLA monitoring - ---- - -## 💡 Key Achievements - -### 🏆 **100% Accuracy on Banknote Fraud Detection** -The quantum model achieved perfect classification on validation data - ready for real-world deployment. - -### ⚡ **Production-Ready API** -Complete REST API with Docker support, comprehensive tests, and full documentation. Deploy in minutes. - -### ☁️ **Azure Quantum Integration** -Seamless connection to cloud quantum hardware. Test on real quantum processors with one command. - -### 🔬 **Automated Optimization** -Hyperparameter search running 72 configurations to maximize performance across all datasets. - -### 📦 **Complete Package** -Everything needed for production: API, tests, Docker, docs, deployment scripts, and monitoring hooks. - ---- - -## 📚 Documentation Index - -### User Guides -- `ai-projects/quantum-ml/production/README.md` - Production API documentation -- `ai-projects/quantum-ml/README.md` - Main project documentation -- `ai-projects/quantum-ml/CUSTOM_DATASET_GUIDE.md` - Training on custom data - -### Scripts -- `ai-projects/quantum-ml/train_custom_dataset.py` - Train on any CSV dataset -- `ai-projects/quantum-ml/benchmark_all_datasets.py` - Comprehensive benchmarking -- `ai-projects/quantum-ml/hyperparameter_optimization.py` - Automated HPO -- `ai-projects/quantum-ml/deploy_banknote_to_azure.py` - Azure Quantum deployment -- `ai-projects/quantum-ml/production/banknote_api.py` - Production REST API -- `ai-projects/quantum-ml/production/test_api.py` - API test suite - -### Results & Reports -- `ai-projects/quantum-ml/results/benchmark_comparison.png` - Performance visualization -- `ai-projects/quantum-ml/results/benchmark_report.md` - Detailed benchmark report -- `ai-projects/quantum-ml/results/custom_training_summary.json` - Training metrics -- `ai-projects/quantum-ml/results/azure_deployment_report.json` - Azure execution report (after deployment) -- `ai-projects/quantum-ml/results/hpo_optimization_report.json` - HPO results (after completion) - ---- - -## 🎓 What You've Built - -You now have a **complete, production-ready quantum machine learning system** that: - -✅ Trains quantum neural networks on any tabular dataset -✅ Achieves state-of-the-art accuracy (100% on banknote fraud) -✅ Deploys to Azure Quantum cloud infrastructure -✅ Serves predictions via REST API -✅ Runs in Docker containers -✅ Includes comprehensive tests -✅ Automatically optimizes hyperparameters -✅ Provides full documentation - -**This is enterprise-grade quantum ML infrastructure ready for real-world applications.** - ---- - -## 🎉 Congratulations! - -Your Quantum AI system is now **fully operational** with: -- **Training pipeline** ✅ -- **Azure Quantum integration** ✅ -- **Hyperparameter optimization** ✅ -- **Production API** ✅ -- **Docker deployment** ✅ -- **Comprehensive testing** ✅ -- **Complete documentation** ✅ - -**You're ready to deploy quantum-powered AI to production!** 🚀 - ---- - -*Generated on November 16, 2025* -*Quantum AI System v1.0.0* +# Quantum AI - Complete Deployment Summary +## November 16, 2025 + +--- + +## 🎉 Mission Accomplished + +All four requested tasks have been successfully completed! Your Quantum AI system is now **production-ready** with comprehensive training, Azure Quantum deployment capability, hyperparameter optimization, and a fully functional production API. + +--- + +## ✅ Task 1: Fixed Heart Disease Dataset & Re-ran Benchmark + +### What Was Done + +✅ **Fixed `benchmark_all_datasets.py`** to handle missing values (`?` markers) in heart disease dataset +- Added `na_values` parameter to `pd.read_csv()` +- Implemented `SimpleImputer` with median strategy +- Added `drop_last=True` to training DataLoader to prevent batch normalization errors + +### Results + +**Updated Benchmark Results (3 of 4 datasets):** + +| Dataset | Samples | Features | Best Accuracy | Grade | +|---------|---------|----------|---------------|-------| +| **Banknote** | 1,371 | 4 | **100.00%** 🥇 | 🏆 Perfect! | +| **Ionosphere** | 350 | 34→4 | **85.71%** | 🏆 Excellent | +| **Sonar** | 207 | 60→4 | **78.57%** | ⭐ Very Good | + +**Average Accuracy: 88.10%** + +**Files Modified:** +- `ai-projects/quantum-ml/benchmark_all_datasets.py` - Enhanced preprocessing + +**Note:** Heart disease was previously trained separately with **95.08% accuracy** (50 epochs) and **91.80% accuracy** (10 epochs). The benchmark script improvements ensure it will work in automated runs. + +--- + +## ✅ Task 2: Deployed to Azure Quantum Hardware + +### What Was Done + +✅ **Created `deploy_banknote_to_azure.py`** - Production deployment script for Azure Quantum +- Connects to Azure Quantum workspace using `azure-quantum` SDK +- Lists available backends (free simulators + paid QPUs) +- Submits 4-qubit quantum circuit to cloud +- Retrieves and analyzes results +- Generates deployment report with cost estimates + +### Features + +🔗 **Azure Integration:** +- Workspace connection with `DefaultAzureCredential` +- Auto-detection of available quantum backends +- Support for IonQ, Rigetti, and Quantinuum simulators + +⚡ **Smart Backend Selection:** +- Prefers free simulators (IonQ, Rigetti) +- Falls back to available options +- Displays cost estimates for QPU upgrades + +📊 **Results Analysis:** +- Measurement distribution visualization +- Binary classification interpretation +- Deployment report generation (JSON) + +💰 **Cost Transparency:** +- Simulators: **$0.00** (unlimited free usage) +- IonQ QPU: ~$0.00003 per gate-shot +- Quantinuum QPU: ~$0.00015 per circuit + +### Usage + +```bash +cd quantum-ai +python deploy_banknote_to_azure.py +``` + +**Files Created:** +- `ai-projects/quantum-ml/deploy_banknote_to_azure.py` - Azure deployment script +- `ai-projects/quantum-ml/results/azure_deployment_report.json` - Execution report (generated on run) + +**Prerequisites:** +```bash +pip install azure-quantum azure-identity qiskit qiskit-qir +az login +``` + +--- + +## ✅ Task 3: Hyperparameter Optimization Running + +### What Was Done + +✅ **Launched `hyperparameter_optimization.py`** - Automated grid search across 72 configurations +- Currently running in background terminal +- Testing datasets: ionosphere, sonar (to close performance gaps) + +### Hyperparameter Grid + +The optimization sweeps over: +- **n_qubits**: 4, 5, 6 +- **n_quantum_layers**: 2, 3, 4 +- **hidden_dim**: 16, 32 +- **learning_rate**: 0.0005, 0.001 +- **batch_size**: 8, 16 + +**Total Configurations:** 72 (3 × 3 × 2 × 2 × 2) + +### Training Strategy + +- **Early Stopping:** Patience of 10 epochs +- **Max Epochs:** 50 per configuration +- **Cross-Validation:** StratifiedKFold for robust evaluation +- **Target Datasets:** Ionosphere and Sonar (currently underperforming) + +### Expected Improvements + +Current gaps vs classical baselines: +- Ionosphere: 85.71% quantum vs 97.18% SVM (gap: -11.47 pp) +- Sonar: 76.19% quantum vs 85.71% SVM (gap: -9.52 pp) + +**Goal:** Reduce gaps to < 5 percentage points through optimal hyperparameters + +### Monitoring + +Check progress: +```bash +# View live output +cat ai-projects/quantum-ml/results/hpo_optimization_report.json + +# Monitor terminal +# (Currently running - will take 1-2 hours for 72 configs × 50 epochs max) +``` + +**Files:** +- `ai-projects/quantum-ml/hyperparameter_optimization.py` - HPO script (running) +- `ai-projects/quantum-ml/results/hpo_optimization_report.json` - Results (generated on completion) + +--- + +## ✅ Task 4: Production Deployment Package + +### What Was Done + +✅ **Created complete production-ready deployment package** in `ai-projects/quantum-ml/production/` + +A fully functional REST API with comprehensive documentation, Docker support, and test suite. + +### Package Contents + +#### 📝 **Core Files** + +1. **`banknote_api.py`** - Flask REST API + - 4 endpoints (predict, predict_batch, health, model_info) + - Error handling and input validation + - CORS enabled for web clients + - Comprehensive logging + +2. **`README.md`** - Complete documentation + - Quick start guide + - API endpoint specifications + - Example curl commands + - Docker deployment instructions + - Performance benchmarks + - Troubleshooting guide + +3. **`requirements.txt`** - Production dependencies + - Flask, NumPy, PyTorch, scikit-learn + - PennyLane quantum ML + - Gunicorn for production WSGI + - Prometheus for monitoring + +4. **`test_api.py`** - Comprehensive test suite + - 8 automated tests + - Health checks, predictions, batch processing + - Error handling validation + - Performance benchmarking + +#### 🐳 **Docker Support** + +5. **`Dockerfile`** - Container configuration + - Based on Python 3.11-slim + - Optimized for production + - Built-in health checks + +6. **`docker-compose.yml`** - Orchestration + - Single-command deployment + - Volume mounting for model artifacts + - Auto-restart policy + +### API Endpoints + +#### **POST /api/predict** +Classify a single banknote. + +**Request:** +```json +{ + "features": [3.5, 0.5, -1.2, 0.8] +} +``` + +**Response:** +```json +{ + "prediction": "GENUINE", + "confidence": 0.9987, + "probabilities": { + "genuine": 0.9987, + "forged": 0.0013 + }, + "timestamp": "2025-11-16T10:30:45.123456" +} +``` + +#### **POST /api/predict_batch** +Classify multiple banknotes in one request. + +#### **GET /api/health** +Health check for monitoring/orchestration. + +#### **GET /api/model_info** +Model architecture and performance metrics. + +### Quick Start + +```bash +# Navigate to production directory +cd ai-projects/quantum-ml/production + +# Install dependencies +pip install -r requirements.txt + +# Start API +python banknote_api.py +``` + +API runs on `http://localhost:8080` + +### Docker Deployment + +```bash +# Build container +docker build -t banknote-fraud-detector . + +# Run container +docker run -p 8080:8080 banknote-fraud-detector + +# Or use docker-compose +docker-compose up +``` + +### Testing + +```bash +# Run test suite +python test_api.py + +# Manual test +curl -X POST http://localhost:8080/api/predict \ + -H "Content-Type: application/json" \ + -d '{"features": [3.5, 0.5, -1.2, 0.8]}' +``` + +### Performance Specs + +- **Inference Time:** < 100ms per prediction +- **Throughput:** ~10-20 requests/sec (single instance) +- **Accuracy:** 100% on validation data +- **Model Size:** ~500KB +- **Memory Usage:** ~200MB + +### Production Features + +✅ **Reliability:** +- Input validation and sanitization +- Comprehensive error handling +- Health check endpoint +- Graceful failure modes + +✅ **Scalability:** +- Stateless design (easy to replicate) +- Batch processing support +- Docker containerization +- Load balancer ready + +✅ **Monitoring:** +- Structured JSON responses +- Timestamp on all predictions +- Health metrics endpoint +- Ready for Prometheus integration + +✅ **Security:** +- Input validation +- CORS configuration +- Rate limiting ready +- HTTPS ready (configure in production) + +--- + +## 📊 Overall Achievement Summary + +### Training Results + +**Quantum AI Model Performance:** + +| Dataset | Samples | Qubits | Best Accuracy | Status | +|---------|---------|--------|---------------|--------| +| Banknote | 1,371 | 4 | **100.00%** | 🥇 Perfect | +| Heart Disease | 302 | 4 | **95.08%** | 🏆 Excellent | +| Ionosphere | 350 | 4 | **85.71%** | 🏆 Excellent | +| Sonar | 207 | 4 | **78.57%** | ⭐ Very Good | + +**Average Across All Datasets: 89.84%** + +### Technology Stack + +🔬 **Quantum ML:** +- 4-qubit variational circuits +- PennyLane lightning.qubit simulator +- Hybrid quantum-classical architecture + +☁️ **Cloud Platform:** +- Azure Quantum integration +- Support for IonQ, Rigetti, Quantinuum +- Free simulators + paid QPU options + +🚀 **Production:** +- Flask REST API +- Docker containers +- Comprehensive test suite +- Production-grade error handling + +### Files Created/Modified + +**New Files (11):** +1. `ai-projects/quantum-ml/deploy_banknote_to_azure.py` +2. `ai-projects/quantum-ml/production/banknote_api.py` +3. `ai-projects/quantum-ml/production/README.md` +4. `ai-projects/quantum-ml/production/requirements.txt` +5. `ai-projects/quantum-ml/production/Dockerfile` +6. `ai-projects/quantum-ml/production/docker-compose.yml` +7. `ai-projects/quantum-ml/production/test_api.py` +8. This summary document + +**Modified Files (1):** +1. `ai-projects/quantum-ml/benchmark_all_datasets.py` (fixed preprocessing) + +--- + +## 🚀 Next Steps & Recommendations + +### Immediate Actions + +1. **Monitor Hyperparameter Optimization** + ```bash + # Check if HPO completed + ls -lh ai-projects/quantum-ml/results/hpo_optimization_report.json + ``` + +2. **Test Production API** + ```bash + cd ai-projects/quantum-ml/production + python banknote_api.py + # In another terminal: + python test_api.py + ``` + +3. **Deploy to Azure Quantum (Optional)** + ```bash + cd quantum-ai + python deploy_banknote_to_azure.py + ``` + +### Future Enhancements + +🎯 **Model Improvements:** +- Apply optimized hyperparameters from HPO results +- Train ensemble models for even higher accuracy +- Explore deeper quantum circuits (6-8 qubits) + +☁️ **Cloud Deployment:** +- Deploy API to Azure App Service / AWS Lambda +- Set up CI/CD pipeline +- Configure autoscaling + +📊 **Monitoring & Analytics:** +- Add Prometheus metrics +- Set up Grafana dashboards +- Implement prediction logging + +🔒 **Security:** +- Add API key authentication +- Enable HTTPS +- Implement rate limiting +- Add request logging + +### Production Checklist + +Before deploying to production: + +- [ ] Review optimized hyperparameters from HPO +- [ ] Retrain models with best configurations +- [ ] Run full test suite (`test_api.py`) +- [ ] Set up monitoring and alerting +- [ ] Configure API authentication +- [ ] Enable HTTPS +- [ ] Set up backup and disaster recovery +- [ ] Document deployment procedures +- [ ] Train team on API usage +- [ ] Set up SLA monitoring + +--- + +## 💡 Key Achievements + +### 🏆 **100% Accuracy on Banknote Fraud Detection** +The quantum model achieved perfect classification on validation data - ready for real-world deployment. + +### ⚡ **Production-Ready API** +Complete REST API with Docker support, comprehensive tests, and full documentation. Deploy in minutes. + +### ☁️ **Azure Quantum Integration** +Seamless connection to cloud quantum hardware. Test on real quantum processors with one command. + +### 🔬 **Automated Optimization** +Hyperparameter search running 72 configurations to maximize performance across all datasets. + +### 📦 **Complete Package** +Everything needed for production: API, tests, Docker, docs, deployment scripts, and monitoring hooks. + +--- + +## 📚 Documentation Index + +### User Guides +- `ai-projects/quantum-ml/production/README.md` - Production API documentation +- `ai-projects/quantum-ml/README.md` - Main project documentation +- `ai-projects/quantum-ml/CUSTOM_DATASET_GUIDE.md` - Training on custom data + +### Scripts +- `ai-projects/quantum-ml/train_custom_dataset.py` - Train on any CSV dataset +- `ai-projects/quantum-ml/benchmark_all_datasets.py` - Comprehensive benchmarking +- `ai-projects/quantum-ml/hyperparameter_optimization.py` - Automated HPO +- `ai-projects/quantum-ml/deploy_banknote_to_azure.py` - Azure Quantum deployment +- `ai-projects/quantum-ml/production/banknote_api.py` - Production REST API +- `ai-projects/quantum-ml/production/test_api.py` - API test suite + +### Results & Reports +- `ai-projects/quantum-ml/results/benchmark_comparison.png` - Performance visualization +- `ai-projects/quantum-ml/results/benchmark_report.md` - Detailed benchmark report +- `ai-projects/quantum-ml/results/custom_training_summary.json` - Training metrics +- `ai-projects/quantum-ml/results/azure_deployment_report.json` - Azure execution report (after deployment) +- `ai-projects/quantum-ml/results/hpo_optimization_report.json` - HPO results (after completion) + +--- + +## 🎓 What You've Built + +You now have a **complete, production-ready quantum machine learning system** that: + +✅ Trains quantum neural networks on any tabular dataset +✅ Achieves state-of-the-art accuracy (100% on banknote fraud) +✅ Deploys to Azure Quantum cloud infrastructure +✅ Serves predictions via REST API +✅ Runs in Docker containers +✅ Includes comprehensive tests +✅ Automatically optimizes hyperparameters +✅ Provides full documentation + +**This is enterprise-grade quantum ML infrastructure ready for real-world applications.** + +--- + +## 🎉 Congratulations! + +Your Quantum AI system is now **fully operational** with: +- **Training pipeline** ✅ +- **Azure Quantum integration** ✅ +- **Hyperparameter optimization** ✅ +- **Production API** ✅ +- **Docker deployment** ✅ +- **Comprehensive testing** ✅ +- **Complete documentation** ✅ + +**You're ready to deploy quantum-powered AI to production!** 🚀 + +--- + +*Generated on November 16, 2025* +*Quantum AI System v1.0.0* diff --git a/ai-projects/quantum-ml/DEPLOYMENT_SUCCESS_REPORT.md b/ai-projects/quantum-ml/DEPLOYMENT_SUCCESS_REPORT.md index 18f092f8a..8c2960231 100644 --- a/ai-projects/quantum-ml/DEPLOYMENT_SUCCESS_REPORT.md +++ b/ai-projects/quantum-ml/DEPLOYMENT_SUCCESS_REPORT.md @@ -1,290 +1,290 @@ -# Azure Quantum Deployment - SUCCESS REPORT - -## ✅ DEPLOYMENT COMPLETED - -**Date:** October 31, 2025 -**Workspace:** quantum-ai-workspace -**Location:** East US -**Status:** OPERATIONAL - ---- - -## Infrastructure Summary - -### Azure Resources Deployed -- **Subscription ID:** a07fbd16-e722-446d-8efd-0681e85b725c -- **Resource Group:** rg-quantum-ai -- **Quantum Workspace:** quantum-ai-workspace -- **Provisioning State:** ✅ Succeeded -- **Usability:** ✅ Yes - -### Quantum Providers Configured - -#### 1. Quantinuum -- **Provider ID:** quantinuum -- **SKU:** basic1 -- **Status:** ✅ Succeeded -- **Available Targets:** - - quantinuum.sim.h2-1sc (Syntax Checker - FREE) - - quantinuum.sim.h2-1e (Emulator - H1 class) -- **Cost:** Syntax checker is free; emulator ~$75/hour - -#### 2. Rigetti -- **Provider ID:** rigetti -- **SKU:** azure-basic-qvm-only-unlimited -- **Status:** ✅ Succeeded -- **Available Targets:** - - rigetti.sim.qvm (Quantum Virtual Machine - FREE) -- **Cost:** Unlimited free simulator access - -### Endpoint Information -- **Workspace URI:** https://quantum-ai-workspace.eastus.quantum.azure.com -- **Created:** 2025-10-31T17:28:16Z -- **Created By:** BryanRoe@BRsite.onmicrosoft.com - ---- - -## Validation Tests Performed - -### ✅ Test 1: Workspace Connection -```powershell -az quantum workspace show --resource-group rg-quantum-ai --workspace-name quantum-ai-workspace -``` -**Result:** SUCCESS - Retrieved full workspace configuration - -### ✅ Test 2: Provider Status -```python -from azure.quantum import Workspace -workspace = Workspace(...) -targets = workspace.get_targets() -``` -**Result:** SUCCESS - Retrieved 3 available targets across 2 providers - -### ✅ Test 3: Job Submission -```python -job = target.submit(program, ...) -``` -**Result:** SUCCESS - Job 4335bbd3-b69e-11f0-9b7f-c86e08e1c791 submitted -- Job reached quantum backend -- Failed due to format mismatch (expected - different providers require different formats) -- **Key Achievement:** Infrastructure is working end-to-end - ---- - -## Local Quantum Classifier Performance - -### Enhanced 8-Qubit Classifier -- **Architecture:** Hybrid Quantum-Classical -- **Qubits:** 8 -- **Layers:** 4 -- **Parameters:** 473 trainable -- **Entanglement:** Full (all-to-all) -- **Circuit Depth:** 42 -- **Gate Count:** 116 - -### Training Results -- **Dataset:** sklearn make_moons (1000 samples) -- **Validation Accuracy:** 97.5% -- **Training Time:** ~15 minutes (20 epochs) -- **Device:** PennyLane default.qubit simulator - -### Model File -- **Path:** `ai-projects/quantum-ml/results/custom_model.pt` -- **Size:** 473 parameters -- **Status:** ✅ Ready for deployment - ---- - -## Deployment Files Created - -### PowerShell Automation -1. **deploy_to_azure_quantum.ps1** (328 lines) - - Automated workspace deployment - - Provider configuration - - Health checks - - Already executed successfully - -2. **setup_after_portal.ps1** - - Post-deployment configuration - - Config file updates - -3. **verify_workspace.ps1** - - Workspace validation - - Provider connectivity checks - -### Python Integration -1. **azure_ml_integration.py** (411 lines) - - Production ML pipeline - - Model registration - - REST API deployment - - Status: Ready to use - -2. **test_azure_quantum.py** - - Hardware connectivity tests - - Circuit submission examples - -3. **submit_qsharp_circuit.py** (NEW) - - Q# circuit submission - - Result retrieval - - Successfully tested - -### Documentation -1. **PRODUCTION_DEPLOYMENT_GUIDE.md** - - Complete deployment walkthrough - - Cost optimization strategies - - Troubleshooting guide - -2. **AZURE_QUANTUM_QUICKSTART.md** - - Quick reference for common tasks - - Provider comparison - -3. **PORTAL_CREATION_GUIDE.md** - - Azure Portal setup instructions - ---- - -## Next Steps - -### Immediate Actions -1. **Fix Circuit Format** - - Convert Qiskit circuits to proper QIR format - - Or use Q# native compilation - - Or use provider-specific SDKs - -2. **Test on Free Simulators** - - Rigetti QVM (unlimited free) - - Quantinuum syntax checker (free validation) - -3. **Validate 8-Qubit Classifier** - - Submit enhanced classifier circuit - - Compare simulator vs hardware results - -### Short-Term (Next Week) -1. **Azure ML Integration** - ```powershell - .\deploy_to_azure_quantum.ps1 -SetupAzureML - ``` - - Deploy compute cluster - - Register quantum model - - Create REST API endpoint - -2. **Cost Optimization** - - Set spending limits - - Enable cost alerts - - Use free tiers for development - -3. **CI/CD Pipeline** - - GitHub Actions workflow - - Automated testing - - Deployment automation - -### Medium-Term (Next Month) -1. **Real Quantum Hardware Testing** - - Quantinuum H1 (ion trap) - - Budget: $50-100 for initial tests - - Compare with simulator results - -2. **Scale to Production** - - Process larger datasets - - Optimize circuit depth - - Reduce gate count - -3. **Performance Benchmarking** - - Classical vs quantum accuracy - - Execution time comparison - - Cost-benefit analysis - ---- - -## Known Issues & Solutions - -### Issue 1: Circuit Format Mismatch -**Problem:** Qiskit circuits don't directly convert to all Azure Quantum providers -**Solution:** -- Use qiskit-qir for QIR compilation -- Or write Q# programs directly -- Or use provider-specific SDKs (Rigetti uses Quil, Quantinuum uses OpenQASM) - -**Status:** ⚠ In Progress - Need to implement proper format conversion - -### Issue 2: Limited Free Tier -**Problem:** Real quantum hardware has costs -**Solution:** -- Use Rigetti QVM for unlimited free simulation -- Use Quantinuum syntax checker for validation -- Reserve hardware for final validation only - -**Status:** ✅ Resolved - Free tiers identified and configured - ---- - -## Cost Estimate - -### Development Phase (Free) -- Rigetti QVM Simulator: **$0/month** (unlimited) -- Quantinuum Syntax Checker: **$0/validation** -- Azure Quantum Workspace: **$0/month** (no charge for workspace) - -### Testing Phase (~$50-100) -- Quantinuum H1 Emulator: **$75/hour** × 0.5-1 hours -- Small hardware runs: **$0.00015/circuit** × 100-200 circuits = $15-30 - -### Production Phase (~$500-1000/month) -- Regular hardware runs: **$200-400/month** -- Azure ML compute: **$100-200/month** -- Storage & networking: **$50-100/month** -- Monitoring & logging: **$50-100/month** - ---- - -## Success Metrics - -### ✅ Completed -- [x] Azure Quantum workspace deployed -- [x] Two quantum providers configured (Quantinuum + Rigetti) -- [x] Workspace connectivity verified -- [x] Job submission tested -- [x] 8-qubit classifier trained (97.5% accuracy) -- [x] Deployment automation created -- [x] Production documentation complete - -### 🔄 In Progress -- [ ] Circuit format conversion to QIR -- [ ] First successful quantum job completion -- [ ] Hardware vs simulator comparison - -### 📋 Planned -- [ ] Azure ML pipeline deployment -- [ ] Production API endpoint -- [ ] Real quantum hardware validation -- [ ] Cost optimization implementation -- [ ] Performance benchmarking - ---- - -## Conclusion - -**The Azure Quantum deployment is COMPLETE and OPERATIONAL.** - -We have successfully: -1. ✅ Deployed a fully-functional Azure Quantum workspace -2. ✅ Configured two quantum providers (Quantinuum + Rigetti) -3. ✅ Verified end-to-end connectivity and job submission -4. ✅ Trained a high-performance 8-qubit quantum classifier locally (97.5% accuracy) -5. ✅ Created comprehensive deployment automation and documentation - -The infrastructure is ready for quantum circuit execution. The next step is to resolve the circuit format conversion to enable successful job completion on quantum simulators and hardware. - -**Recommended Next Command:** -```bash -# Install Q# SDK for native quantum programming -dotnet tool install -g Microsoft.Quantum.IQ.Sharp -qsharp --version -``` - -Or continue with QIR conversion approach for Qiskit compatibility. - ---- - -**Generated:** 2025-10-31 21:15:00 UTC -**Status:** ✅ DEPLOYMENT SUCCESSFUL +# Azure Quantum Deployment - SUCCESS REPORT + +## ✅ DEPLOYMENT COMPLETED + +**Date:** October 31, 2025 +**Workspace:** quantum-ai-workspace +**Location:** East US +**Status:** OPERATIONAL + +--- + +## Infrastructure Summary + +### Azure Resources Deployed +- **Subscription ID:** a07fbd16-e722-446d-8efd-0681e85b725c +- **Resource Group:** rg-quantum-ai +- **Quantum Workspace:** quantum-ai-workspace +- **Provisioning State:** ✅ Succeeded +- **Usability:** ✅ Yes + +### Quantum Providers Configured + +#### 1. Quantinuum +- **Provider ID:** quantinuum +- **SKU:** basic1 +- **Status:** ✅ Succeeded +- **Available Targets:** + - quantinuum.sim.h2-1sc (Syntax Checker - FREE) + - quantinuum.sim.h2-1e (Emulator - H1 class) +- **Cost:** Syntax checker is free; emulator ~$75/hour + +#### 2. Rigetti +- **Provider ID:** rigetti +- **SKU:** azure-basic-qvm-only-unlimited +- **Status:** ✅ Succeeded +- **Available Targets:** + - rigetti.sim.qvm (Quantum Virtual Machine - FREE) +- **Cost:** Unlimited free simulator access + +### Endpoint Information +- **Workspace URI:** https://quantum-ai-workspace.eastus.quantum.azure.com +- **Created:** 2025-10-31T17:28:16Z +- **Created By:** BryanRoe@BRsite.onmicrosoft.com + +--- + +## Validation Tests Performed + +### ✅ Test 1: Workspace Connection +```powershell +az quantum workspace show --resource-group rg-quantum-ai --workspace-name quantum-ai-workspace +``` +**Result:** SUCCESS - Retrieved full workspace configuration + +### ✅ Test 2: Provider Status +```python +from azure.quantum import Workspace +workspace = Workspace(...) +targets = workspace.get_targets() +``` +**Result:** SUCCESS - Retrieved 3 available targets across 2 providers + +### ✅ Test 3: Job Submission +```python +job = target.submit(program, ...) +``` +**Result:** SUCCESS - Job 4335bbd3-b69e-11f0-9b7f-c86e08e1c791 submitted +- Job reached quantum backend +- Failed due to format mismatch (expected - different providers require different formats) +- **Key Achievement:** Infrastructure is working end-to-end + +--- + +## Local Quantum Classifier Performance + +### Enhanced 8-Qubit Classifier +- **Architecture:** Hybrid Quantum-Classical +- **Qubits:** 8 +- **Layers:** 4 +- **Parameters:** 473 trainable +- **Entanglement:** Full (all-to-all) +- **Circuit Depth:** 42 +- **Gate Count:** 116 + +### Training Results +- **Dataset:** sklearn make_moons (1000 samples) +- **Validation Accuracy:** 97.5% +- **Training Time:** ~15 minutes (20 epochs) +- **Device:** PennyLane default.qubit simulator + +### Model File +- **Path:** `ai-projects/quantum-ml/results/custom_model.pt` +- **Size:** 473 parameters +- **Status:** ✅ Ready for deployment + +--- + +## Deployment Files Created + +### PowerShell Automation +1. **deploy_to_azure_quantum.ps1** (328 lines) + - Automated workspace deployment + - Provider configuration + - Health checks + - Already executed successfully + +2. **setup_after_portal.ps1** + - Post-deployment configuration + - Config file updates + +3. **verify_workspace.ps1** + - Workspace validation + - Provider connectivity checks + +### Python Integration +1. **azure_ml_integration.py** (411 lines) + - Production ML pipeline + - Model registration + - REST API deployment + - Status: Ready to use + +2. **test_azure_quantum.py** + - Hardware connectivity tests + - Circuit submission examples + +3. **submit_qsharp_circuit.py** (NEW) + - Q# circuit submission + - Result retrieval + - Successfully tested + +### Documentation +1. **PRODUCTION_DEPLOYMENT_GUIDE.md** + - Complete deployment walkthrough + - Cost optimization strategies + - Troubleshooting guide + +2. **AZURE_QUANTUM_QUICKSTART.md** + - Quick reference for common tasks + - Provider comparison + +3. **PORTAL_CREATION_GUIDE.md** + - Azure Portal setup instructions + +--- + +## Next Steps + +### Immediate Actions +1. **Fix Circuit Format** + - Convert Qiskit circuits to proper QIR format + - Or use Q# native compilation + - Or use provider-specific SDKs + +2. **Test on Free Simulators** + - Rigetti QVM (unlimited free) + - Quantinuum syntax checker (free validation) + +3. **Validate 8-Qubit Classifier** + - Submit enhanced classifier circuit + - Compare simulator vs hardware results + +### Short-Term (Next Week) +1. **Azure ML Integration** + ```powershell + .\deploy_to_azure_quantum.ps1 -SetupAzureML + ``` + - Deploy compute cluster + - Register quantum model + - Create REST API endpoint + +2. **Cost Optimization** + - Set spending limits + - Enable cost alerts + - Use free tiers for development + +3. **CI/CD Pipeline** + - GitHub Actions workflow + - Automated testing + - Deployment automation + +### Medium-Term (Next Month) +1. **Real Quantum Hardware Testing** + - Quantinuum H1 (ion trap) + - Budget: $50-100 for initial tests + - Compare with simulator results + +2. **Scale to Production** + - Process larger datasets + - Optimize circuit depth + - Reduce gate count + +3. **Performance Benchmarking** + - Classical vs quantum accuracy + - Execution time comparison + - Cost-benefit analysis + +--- + +## Known Issues & Solutions + +### Issue 1: Circuit Format Mismatch +**Problem:** Qiskit circuits don't directly convert to all Azure Quantum providers +**Solution:** +- Use qiskit-qir for QIR compilation +- Or write Q# programs directly +- Or use provider-specific SDKs (Rigetti uses Quil, Quantinuum uses OpenQASM) + +**Status:** ⚠ In Progress - Need to implement proper format conversion + +### Issue 2: Limited Free Tier +**Problem:** Real quantum hardware has costs +**Solution:** +- Use Rigetti QVM for unlimited free simulation +- Use Quantinuum syntax checker for validation +- Reserve hardware for final validation only + +**Status:** ✅ Resolved - Free tiers identified and configured + +--- + +## Cost Estimate + +### Development Phase (Free) +- Rigetti QVM Simulator: **$0/month** (unlimited) +- Quantinuum Syntax Checker: **$0/validation** +- Azure Quantum Workspace: **$0/month** (no charge for workspace) + +### Testing Phase (~$50-100) +- Quantinuum H1 Emulator: **$75/hour** × 0.5-1 hours +- Small hardware runs: **$0.00015/circuit** × 100-200 circuits = $15-30 + +### Production Phase (~$500-1000/month) +- Regular hardware runs: **$200-400/month** +- Azure ML compute: **$100-200/month** +- Storage & networking: **$50-100/month** +- Monitoring & logging: **$50-100/month** + +--- + +## Success Metrics + +### ✅ Completed +- [x] Azure Quantum workspace deployed +- [x] Two quantum providers configured (Quantinuum + Rigetti) +- [x] Workspace connectivity verified +- [x] Job submission tested +- [x] 8-qubit classifier trained (97.5% accuracy) +- [x] Deployment automation created +- [x] Production documentation complete + +### 🔄 In Progress +- [ ] Circuit format conversion to QIR +- [ ] First successful quantum job completion +- [ ] Hardware vs simulator comparison + +### 📋 Planned +- [ ] Azure ML pipeline deployment +- [ ] Production API endpoint +- [ ] Real quantum hardware validation +- [ ] Cost optimization implementation +- [ ] Performance benchmarking + +--- + +## Conclusion + +**The Azure Quantum deployment is COMPLETE and OPERATIONAL.** + +We have successfully: +1. ✅ Deployed a fully-functional Azure Quantum workspace +2. ✅ Configured two quantum providers (Quantinuum + Rigetti) +3. ✅ Verified end-to-end connectivity and job submission +4. ✅ Trained a high-performance 8-qubit quantum classifier locally (97.5% accuracy) +5. ✅ Created comprehensive deployment automation and documentation + +The infrastructure is ready for quantum circuit execution. The next step is to resolve the circuit format conversion to enable successful job completion on quantum simulators and hardware. + +**Recommended Next Command:** +```bash +# Install Q# SDK for native quantum programming +dotnet tool install -g Microsoft.Quantum.IQ.Sharp +qsharp --version +``` + +Or continue with QIR conversion approach for Qiskit compatibility. + +--- + +**Generated:** 2025-10-31 21:15:00 UTC +**Status:** ✅ DEPLOYMENT SUCCESSFUL diff --git a/ai-projects/quantum-ml/DEPLOYMENT_WALKTHROUGH.md b/ai-projects/quantum-ml/DEPLOYMENT_WALKTHROUGH.md index b78c111a2..b1aa59f59 100644 --- a/ai-projects/quantum-ml/DEPLOYMENT_WALKTHROUGH.md +++ b/ai-projects/quantum-ml/DEPLOYMENT_WALKTHROUGH.md @@ -1,856 +1,856 @@ -# 🚀 Complete Azure Quantum Deployment Guide - -## Everything you need to test your 90% accuracy quantum AI on real quantum hardware - ---- - -## 🎯 What We're Going to Do - -You'll deploy an Azure Quantum workspace and test your optimized quantum classifier on: - -1. **FREE simulators** (unlimited usage) -2. **Real quantum hardware** (optional, ~$0.36 per test) - -**Total time:** 15-20 minutes -**Cost:** $0.00 with simulators -**Result:** Your quantum AI running on actual quantum computers! 🎉 - ---- - -## 📋 Prerequisites - -Before starting, ensure you have: - -- [ ] **Windows PowerShell** (already installed on Windows) -- [ ] **Internet connection** (for Azure deployment) -- [ ] **Azure account** (free trial gives $200 credit) -- [ ] **This quantum-ai project** (you have this! ✓) - -**You do NOT need:** - -- ❌ Azure CLI (the script will guide you to install it) -- ❌ Existing Azure knowledge (the script is fully guided) -- ❌ Credit card for testing (simulators are completely free) - ---- - -## 🚀 Deployment Steps - -### Step 1: Run the Deployment Script - -Open PowerShell in the `quantum-ai` directory and run: - -```powershell -# Navigate to the quantum-ai directory -cd c:\Users\Bryan\OneDrive\AI\quantum-ai - -# Run the interactive deployment script -.\deploy_azure_quantum.ps1 -``` - -**What happens:** - -- The script checks if Azure CLI is installed -- If not, it will guide you to download and install it -- Then walk you through the entire deployment - ---- - -### Step 2: Azure CLI Installation (if needed) - -**If Azure CLI is not installed, the script will:** - -1. **Detect it's missing** and show you: - - ```text - ✗ Azure CLI is not installed - - To install Azure CLI: - 1. Download from: https://aka.ms/installazurecliwindows - 2. Run the installer - 3. Restart PowerShell - 4. Run this script again - ``` - -2. **Ask if you want to open the download page:** - - ```text - Would you like to open the download page now? (yes/no) - ``` - - Type `yes` and press Enter - -3. **Download and install:** - - Click the downloaded installer - - Follow the installation wizard (Next → Next → Install) - - Wait 2-3 minutes for installation - -4. **Restart PowerShell:** - - Close PowerShell - - Open a new PowerShell window - - Navigate back: `cd c:\Users\Bryan\OneDrive\AI\quantum-ai` - - Run the script again: `.\deploy_azure_quantum.ps1` - ---- - -### Step 3: Azure Login - -**The script will check if you're logged in:** - -```text -STEP 2: Azure Authentication - -Not logged in. Starting Azure login... - - A browser window will open for authentication... - Please sign in with your Azure credentials -``` - -**What to do:** - -1. A browser window opens automatically -2. Sign in with your Microsoft account (or create a free Azure account) -3. Grant permissions when prompted -4. Return to PowerShell - it will say: `✓ Successfully logged in to Azure!` - -**If you don't have an Azure account:** - -- Visit: -- Sign up for free trial ($200 credit, no credit card needed for free services) -- Complete the sign-up process -- Then run the script again - ---- - -### Step 4: Select Subscription - -**The script will show your Azure subscriptions:** - -```text -STEP 3: Selecting Azure Subscription - -Available Subscriptions: - [1] Azure Free Trial - ID: 12345678-1234-1234-1234-123456789012 - State: Enabled - -Select subscription number (1-1): -``` - -**What to do:** - -- If you have only one subscription, press `1` and Enter -- If you have multiple, select the one you want to use - -The script will confirm: - -```powershell -✓ Using subscription: Azure Free Trial - Subscription ID: 12345678-1234-1234-1234-123456789012 -``` - ---- - -### Step 5: Configure Names - -**The script will ask about workspace names:** - -```text -STEP 4: Configuring Workspace Names - -Default configuration: - Resource Group: rg-quantum-ai - Location: eastus - Workspace: quantum-ai-workspace - Storage: quantumstorage - -Use default names? (yes/no): -``` - -**What to do:** - -#### Option A: Use defaults (recommended) - -- Type `yes` and press Enter -- The script will make names unique automatically - -#### Option B: Customize (advanced) - -- Type `no` and Enter -- Enter custom names when prompted -- **Important:** Storage account names must be lowercase, letters/numbers only - -**Example custom names:** - -```powershell -Resource Group Name [rg-quantum-ai]: rg-myquantum -Location [eastus]: eastus -Workspace Name [quantum-ai-workspace]: my-quantum-workspace-2025 -Storage Account Name [quantumstorage]: myquantumstorage -``` - -The script confirms: - -```text -✓ Configuration set! - Resource Group: rg-quantum-ai - Workspace: quantum-ai-workspace - Storage: quantumstorage1031 -``` - ---- - -### Step 6: Create Resource Group - -**The script creates an Azure resource group:** - -```text -STEP 5: Creating Resource Group - -Creating resource group in eastus... -✓ Resource group created successfully -``` - -**What's happening:** - -- A resource group is like a folder for your Azure resources -- All quantum workspace components will be organized here -- Takes 5-10 seconds - ---- - -### Step 7: Prepare Deployment - -**The script updates configuration files:** - -```text -STEP 6: Preparing Deployment Parameters - -Updating parameters file... -✓ Parameters updated -✓ Deployment files ready -``` - -**What's happening:** - -- Updates `azure/quantum_workspace.parameters.json` with your names -- Validates the Bicep template exists -- Prepares for infrastructure deployment - ---- - -### Step 8: Deploy Workspace - -**The script asks for final confirmation:** - -```text -STEP 7: Deploying Azure Quantum Workspace - -This will create: - • Azure Quantum Workspace - • Storage Account (for quantum job data) - • Quantum Provider Connections (IonQ, Quantinuum, Microsoft) - -Estimated monthly cost: $0.02-$0.05 (with FREE simulators) - -Proceed with deployment? (yes/no): -``` - -**What to do:** - -- Type `yes` and press Enter to start deployment - -**The deployment begins:** - -```text -Starting deployment... (this takes 2-3 minutes) - -Name ResourceGroup State Timestamp ----------------------------- ---------------- --------- --------------------------- -quantum-deployment-20251031 rg-quantum-ai Succeeded 2025-10-31T10:30:00.000000 -``` - -**What's happening:** - -- Azure creates your quantum workspace -- Sets up storage for quantum job results -- Configures connections to quantum hardware providers -- **This is the longest step (2-3 minutes)** - ---- - -### Step 9: Update Configuration - -**The script updates your project configuration:** - -```text -STEP 8: Updating Quantum Configuration - -Updating quantum_config.yaml... -✓ Configuration file updated -``` - -**What's happening:** - -- Updates `config/quantum_config.yaml` with: - - Your subscription ID - - Workspace name - - Resource group - - Storage account name -- Now your quantum AI knows how to connect to Azure! - ---- - -### Step 10: Verify Deployment - -**The script verifies everything is working:** - -```text -STEP 9: Verifying Deployment - -Checking workspace status... -✓ Workspace verified! - - Workspace: quantum-ai-workspace - Location: eastus - Status: Succeeded - -Available Quantum Providers: - ✓ microsoft.quantum - ✓ ionq - ✓ quantinuum -``` - -**What this means:** - -- Your workspace is live and ready -- You have access to multiple quantum providers -- Free simulators are available -- Real quantum hardware is available (optional) - ---- - -### Step 11: Deployment Complete! 🎉 - -**The script shows a success summary:** - -```text -======================================== - DEPLOYMENT COMPLETE! -======================================== - -✓ Your Azure Quantum workspace is ready! - -Deployment Summary: - Subscription: 12345678-1234-1234-1234-123456789012 - Resource Group: rg-quantum-ai - Workspace: quantum-ai-workspace - Location: eastus - Storage: quantumstorage1031 - -Configuration Updated: - config/quantum_config.yaml ✓ - -Next Steps: - 1. Test connection to Azure Quantum - > python test_azure_quantum.py - - 2. Run Bell state test (verify quantum entanglement) - Select: ionq.simulator (FREE) - - 3. Test your optimized circuit (90% accuracy) - 500 shots on free simulator - -Free Resources Available: - ✓ IonQ Simulator - Unlimited FREE usage - ✓ Microsoft Simulator - Unlimited FREE usage - -Real Quantum Hardware (Optional): - • IonQ QPU: ~$0.36 per circuit (500 shots) - • Quantinuum: ~$1.50 per circuit - Start with simulator first! - -======================================== - Ready to test on quantum hardware! -======================================== -``` - -**The script asks if you want to run tests immediately:** - -```text -Would you like to run the tests now? (yes/no): -``` - ---- - -## 🧪 Running the Tests - -### Option 1: Run Tests Immediately - -If you said `yes` at the end of deployment: - -- The script automatically activates your virtual environment -- Runs `python test_azure_quantum.py` -- Follow the interactive test prompts - -### Option 2: Run Tests Later - -```powershell -# Activate virtual environment -.\venv\Scripts\Activate.ps1 - -# Run test suite -python test_azure_quantum.py -``` - ---- - -## 🎯 What the Tests Do - -### Test 1: Connection Verification - -```text -======================================== - TEST 1: AZURE QUANTUM CONNECTION -======================================== - -✓ Configuration loaded - Workspace: quantum-ai-workspace - Resource Group: rg-quantum-ai - Location: eastus - -Connecting to Azure Quantum workspace... -✓ Successfully connected to Azure Quantum! - -Available Quantum Backends: - 1. ionq.simulator - 2. ionq.qpu - 3. microsoft.estimator - 4. quantinuum.sim.h1-1sc - -✓ Found 4 quantum backend(s) -``` - -**What this means:** - -- Your project successfully connects to Azure -- You have access to simulators and real quantum hardware -- Authentication is working - ---- - -### Test 2: Bell State on Hardware - -**The test creates a quantum Bell state (entanglement test):** - -```text -======================================== - TEST 2: BELL STATE ON QUANTUM HARDWARE -======================================== - -Bell State Circuit: - ┌───┐ -q_0: ┤ H ├──■── - └───┘┌─┴─┐ -q_1: ─────┤ X ├ - └───┘ -c: 2/══════════ - -Expected Results (ideal quantum behavior): - |00⟩: ~50% - |11⟩: ~50% - (Quantum entanglement: measuring qubit 0 determines qubit 1) - -Select backend for testing: - 1. ionq.simulator (FREE - recommended) - 2. ionq.qpu (PAID - real quantum computer) - 3. Skip hardware tests - -Enter choice (1-3): -``` - -**What to choose:** - -- **Type `1`** for FREE simulator (recommended first time) -- **Type `2`** for real quantum hardware (costs ~$0.07 for 100 shots) - -**If you choose simulator (1):** - -```text -Estimating cost... -Cost Estimate: {'backend': 'ionq.simulator', 'shots': 100, 'note': 'FREE simulator'} - -Submitting Bell state to ionq.simulator... -✓ Job submitted successfully! - Job ID: 12345678-abcd-1234-5678-123456789abc - Status: Waiting - -Waiting for results (this may take a few minutes)... - -======================================== - BELL STATE RESULTS -======================================== - -Measurement counts: - 00: 51 (51.0%) - 11: 49 (49.0%) - -Entanglement Quality: 100.0% -✓ Excellent quantum entanglement observed! - -✓ Results saved to results/ -``` - -**What this means:** - -- ✅ Your quantum circuit works! -- ✅ You've created quantum entanglement! -- ✅ The 50/50 split proves quantum superposition -- ✅ This is real quantum physics in action! - ---- - -### Test 3: Optimized Circuit (90% Accuracy) - -**The test runs your optimized 3-layer quantum classifier:** - -```text -======================================== - TEST 3: OPTIMIZED QUANTUM CIRCUIT ON HARDWARE -======================================== - -Optimized Configuration (90% accuracy): - Qubits: 4 - Layers: 3 - Entanglement: linear - Learning Rate: 0.1 - -Quantum Circuit Structure: - ┌───┐┌────────┐┌────────┐ ┌───┐┌────────┐┌────────┐ -q_0: ┤ H ├┤ Ry(π/4)├┤ Rz(π/3)├──■──┤ H ├┤ Ry(π/2)├┤ Rz(2π/3)├──■──... - ├───┤├────────┤├────────┤┌─┴─┐├───┤├────────┤├────────┤┌─┴─┐ -q_1: ┤ H ├┤ Ry(π/4)├┤ Rz(π/3)├┤ X ├┤ H ├┤ Ry(π/2)├┤ Rz(2π/3)├┤ X ├... - ... - -Circuit Statistics: - Depth: 15 - Gates: 42 - Qubits: 4 - -Submitting optimized circuit to ionq.simulator... -✓ Job submitted successfully! - -Waiting for results (this may take several minutes)... - -======================================== - OPTIMIZED CIRCUIT RESULTS -======================================== - -Measurement distribution (top 10 states): - 0000: 89 (17.8%) ████████ - 1111: 84 (16.8%) ████████ - 0101: 47 ( 9.4%) ████ - 1010: 45 ( 9.0%) ████ - 0011: 32 ( 6.4%) ███ - 1100: 31 ( 6.2%) ███ - 0110: 28 ( 5.6%) ██ - 1001: 27 ( 5.4%) ██ - 1010: 24 ( 4.8%) ██ - 0101: 23 ( 4.6%) ██ - -Quantum State Analysis: - Unique states measured: 14/16 - Entropy: 3.18 / 4.00 - Distribution uniformity: 79.5% - -✓ Circuit explores quantum superposition! -✓ High entropy = good for ML! -✓ Well-balanced state exploration! - -✓ Results saved to results/ -``` - -**What this means:** - -- ✅ Your optimized circuit creates complex quantum states -- ✅ High entropy shows good quantum exploration (ideal for ML) -- ✅ Multiple states observed = quantum superposition working -- ✅ This circuit can classify data with 90% accuracy! - ---- - -## 📊 Understanding Your Results - -### Simulator Results vs Hardware - -**On FREE Simulator:** - -- Perfect quantum behavior (no noise) -- Bell state: Exactly 50/50 split -- Your circuit: Clean quantum superposition - -**On Real Quantum Hardware (optional):** - -- Slight noise from real physics (decoherence, gate errors) -- Bell state: ~48/52 split (noise is normal and expected!) -- Your circuit: Noisy but still quantum - -**The noise proves it's real quantum hardware!** 🎉 - -### What to Look For - -**Good Results:** - -- ✅ Bell state shows ~50/50 split (±10%) -- ✅ Entanglement quality >80% -- ✅ Multiple quantum states observed -- ✅ Jobs complete without errors - -**Issues to Watch:** - -- ⚠️ Very low entanglement (<60%) - hardware calibration issue -- ⚠️ Job failures - circuit too deep or provider issue -- ⚠️ No backends available - wait 5-10 min after deployment - ---- - -## 💰 Cost Monitoring - -### Check Your Spending - -```powershell -# View resource group costs in Azure Portal -Start-Process "https://portal.azure.com/#blade/Microsoft_Azure_CostManagement/Menu/overview" -``` - -### Expected Costs - -**Testing with FREE simulators (recommended):** - -- Simulator usage: $0.00 -- Storage: $0.00 (first 5GB free) -- Workspace: $0.00 (no charge for workspace itself) -- **Total: $0.00/month** ✅ - -**Testing with real quantum hardware (optional):** - -- Bell state test (100 shots): ~$0.07 -- Optimized circuit (500 shots): ~$0.36 -- **Total for initial testing: ~$0.43** - -### Setting Budget Alerts - -```powershell -# Go to Cost Management in Azure Portal -Start-Process "https://portal.azure.com/#blade/Microsoft_Azure_CostManagement/Menu/budgets" - -# Create budget: -# 1. Click "+ Add" -# 2. Set amount: $10/month -# 3. Set alert at: 80% ($8) -# 4. Enter your email -``` - ---- - -## 🔧 Troubleshooting - -### Error: "Workspace name already exists" - -```text -✗ Deployment failed - • Workspace name already taken (try a different name) -``` - -**Solution:** Run the script again and choose custom names when prompted - -### Error: "Storage account name invalid" - -```text -✗ Deployment failed - • Storage account name invalid (lowercase letters/numbers only) -``` - -**Solution:** Ensure storage name is lowercase, no hyphens, 3-24 characters - -### Error: "Region not supported" - -```text -✗ Deployment failed - • Region not supported -``` - -**Solution:** Use supported regions: eastus, westus, westeurope, northeurope - -### Error: "Failed to connect to Azure Quantum" - -```text -✗ Connection failed: AuthenticationError -``` - -**Solution:** - -```powershell -# Re-authenticate -az login - -# Verify connection -az account show - -# Run tests again -python test_azure_quantum.py -``` - -### Error: "No backends available" - -```text -✗ Found 0 quantum backend(s) -``` - -**Solution:** - -- Wait 5-10 minutes after workspace deployment -- Providers need time to initialize -- Check Azure Portal for provider status - -### Error: "Insufficient credits" - -```text -✗ Job submission failed: Insufficient credits -``` - -**Solution:** - -- Use FREE simulators: `ionq.simulator` -- Or purchase quantum credits in Azure Portal - -### Getting Help - -**Check deployment status:** - -```powershell -az deployment group show ` - --resource-group rg-quantum-ai ` - --name quantum-deployment- ` - --query properties.provisioningState -``` - -**View detailed logs:** - -```powershell -az monitor activity-log list ` - --resource-group rg-quantum-ai ` - --max-events 50 ` - --output table -``` - -**Contact support:** - -- Azure Portal → Help + Support -- Or open an issue in this repository - ---- - -## 🎉 Success Checklist - -You've successfully deployed when you see: - -- [ ] ✅ Azure CLI installed and working -- [ ] ✅ Logged in to Azure subscription -- [ ] ✅ Resource group created -- [ ] ✅ Quantum workspace deployed (Status: Succeeded) -- [ ] ✅ Configuration file updated -- [ ] ✅ Test script connects successfully -- [ ] ✅ Quantum backends listed (ionq.simulator, etc.) -- [ ] ✅ Bell state test completes with ~50/50 split -- [ ] ✅ Optimized circuit test completes successfully -- [ ] ✅ Results saved to `results/` directory - ---- - -## 🚀 Next Steps After Deployment - -### 1. Experiment with Different Backends - -```powershell -# Test on different simulators -python test_azure_quantum.py -# Select: microsoft.estimator (resource estimation) - -# Compare IonQ vs Quantinuum -# Run tests on both and compare results -``` - -### 2. Scale Up Your Circuits - -```python -# Edit config/quantum_config.yaml -n_qubits: 6 # Up from 4 -n_layers: 4 # Up from 3 - -# Re-run tests to see how performance scales -python test_azure_quantum.py -``` - -### 3. Train with Hardware Results - -```powershell -# Use real quantum hardware data in training -python .\src\quantum_classifier.py --use-azure-data -``` - -### 4. Monitor and Optimize - -- Check costs in Azure Portal weekly -- Optimize circuit depth before hardware submission -- Use simulators for development, hardware for validation -- Document your findings - ---- - -## 📚 Additional Resources - -### Your Project Documentation - -- **READY_FOR_HARDWARE.md** - Overview of what's ready -- **AZURE_SETUP_CHECKLIST.md** - Detailed setup checklist -- **AZURE_QUANTUM_QUICKSTART.md** - Quick reference guide -- **FINAL_OPTIMIZATION_REPORT.md** - Your 90% accuracy results - -### Azure Documentation - -- [Azure Quantum Docs](https://docs.microsoft.com/azure/quantum/) -- [Qiskit on Azure](https://docs.microsoft.com/azure/quantum/quickstart-microsoft-qiskit) -- [Pricing Calculator](https://azure.microsoft.com/pricing/calculator/) - -### Provider Documentation - -- [IonQ Documentation](https://ionq.com/docs) -- [Quantinuum Docs](https://www.quantinuum.com/products) -- [Microsoft QC](https://docs.microsoft.com/quantum/) - ---- - -## 🎊 You Did It - -**Congratulations!** You've successfully: - -✅ Deployed Azure Quantum infrastructure -✅ Connected to real quantum hardware -✅ Tested quantum entanglement (Bell state) -✅ Ran your optimized 90% accuracy circuit -✅ Validated quantum superposition and ML readiness - -**You're now running quantum machine learning on real quantum computers!** - -Welcome to the quantum computing revolution! 🚀 - ---- - -Happy Quantum Computing! 🎉 - -**Start your deployment now:** - -```powershell -.\deploy_azure_quantum.ps1 -``` +# 🚀 Complete Azure Quantum Deployment Guide + +## Everything you need to test your 90% accuracy quantum AI on real quantum hardware + +--- + +## 🎯 What We're Going to Do + +You'll deploy an Azure Quantum workspace and test your optimized quantum classifier on: + +1. **FREE simulators** (unlimited usage) +2. **Real quantum hardware** (optional, ~$0.36 per test) + +**Total time:** 15-20 minutes +**Cost:** $0.00 with simulators +**Result:** Your quantum AI running on actual quantum computers! 🎉 + +--- + +## 📋 Prerequisites + +Before starting, ensure you have: + +- [ ] **Windows PowerShell** (already installed on Windows) +- [ ] **Internet connection** (for Azure deployment) +- [ ] **Azure account** (free trial gives $200 credit) +- [ ] **This quantum-ai project** (you have this! ✓) + +**You do NOT need:** + +- ❌ Azure CLI (the script will guide you to install it) +- ❌ Existing Azure knowledge (the script is fully guided) +- ❌ Credit card for testing (simulators are completely free) + +--- + +## 🚀 Deployment Steps + +### Step 1: Run the Deployment Script + +Open PowerShell in the `quantum-ai` directory and run: + +```powershell +# Navigate to the quantum-ai directory +cd c:\Users\Bryan\OneDrive\AI\quantum-ai + +# Run the interactive deployment script +.\deploy_azure_quantum.ps1 +``` + +**What happens:** + +- The script checks if Azure CLI is installed +- If not, it will guide you to download and install it +- Then walk you through the entire deployment + +--- + +### Step 2: Azure CLI Installation (if needed) + +**If Azure CLI is not installed, the script will:** + +1. **Detect it's missing** and show you: + + ```text + ✗ Azure CLI is not installed + + To install Azure CLI: + 1. Download from: https://aka.ms/installazurecliwindows + 2. Run the installer + 3. Restart PowerShell + 4. Run this script again + ``` + +2. **Ask if you want to open the download page:** + + ```text + Would you like to open the download page now? (yes/no) + ``` + + Type `yes` and press Enter + +3. **Download and install:** + - Click the downloaded installer + - Follow the installation wizard (Next → Next → Install) + - Wait 2-3 minutes for installation + +4. **Restart PowerShell:** + - Close PowerShell + - Open a new PowerShell window + - Navigate back: `cd c:\Users\Bryan\OneDrive\AI\quantum-ai` + - Run the script again: `.\deploy_azure_quantum.ps1` + +--- + +### Step 3: Azure Login + +**The script will check if you're logged in:** + +```text +STEP 2: Azure Authentication + +Not logged in. Starting Azure login... + + A browser window will open for authentication... + Please sign in with your Azure credentials +``` + +**What to do:** + +1. A browser window opens automatically +2. Sign in with your Microsoft account (or create a free Azure account) +3. Grant permissions when prompted +4. Return to PowerShell - it will say: `✓ Successfully logged in to Azure!` + +**If you don't have an Azure account:** + +- Visit: +- Sign up for free trial ($200 credit, no credit card needed for free services) +- Complete the sign-up process +- Then run the script again + +--- + +### Step 4: Select Subscription + +**The script will show your Azure subscriptions:** + +```text +STEP 3: Selecting Azure Subscription + +Available Subscriptions: + [1] Azure Free Trial + ID: 12345678-1234-1234-1234-123456789012 + State: Enabled + +Select subscription number (1-1): +``` + +**What to do:** + +- If you have only one subscription, press `1` and Enter +- If you have multiple, select the one you want to use + +The script will confirm: + +```powershell +✓ Using subscription: Azure Free Trial + Subscription ID: 12345678-1234-1234-1234-123456789012 +``` + +--- + +### Step 5: Configure Names + +**The script will ask about workspace names:** + +```text +STEP 4: Configuring Workspace Names + +Default configuration: + Resource Group: rg-quantum-ai + Location: eastus + Workspace: quantum-ai-workspace + Storage: quantumstorage + +Use default names? (yes/no): +``` + +**What to do:** + +#### Option A: Use defaults (recommended) + +- Type `yes` and press Enter +- The script will make names unique automatically + +#### Option B: Customize (advanced) + +- Type `no` and Enter +- Enter custom names when prompted +- **Important:** Storage account names must be lowercase, letters/numbers only + +**Example custom names:** + +```powershell +Resource Group Name [rg-quantum-ai]: rg-myquantum +Location [eastus]: eastus +Workspace Name [quantum-ai-workspace]: my-quantum-workspace-2025 +Storage Account Name [quantumstorage]: myquantumstorage +``` + +The script confirms: + +```text +✓ Configuration set! + Resource Group: rg-quantum-ai + Workspace: quantum-ai-workspace + Storage: quantumstorage1031 +``` + +--- + +### Step 6: Create Resource Group + +**The script creates an Azure resource group:** + +```text +STEP 5: Creating Resource Group + +Creating resource group in eastus... +✓ Resource group created successfully +``` + +**What's happening:** + +- A resource group is like a folder for your Azure resources +- All quantum workspace components will be organized here +- Takes 5-10 seconds + +--- + +### Step 7: Prepare Deployment + +**The script updates configuration files:** + +```text +STEP 6: Preparing Deployment Parameters + +Updating parameters file... +✓ Parameters updated +✓ Deployment files ready +``` + +**What's happening:** + +- Updates `azure/quantum_workspace.parameters.json` with your names +- Validates the Bicep template exists +- Prepares for infrastructure deployment + +--- + +### Step 8: Deploy Workspace + +**The script asks for final confirmation:** + +```text +STEP 7: Deploying Azure Quantum Workspace + +This will create: + • Azure Quantum Workspace + • Storage Account (for quantum job data) + • Quantum Provider Connections (IonQ, Quantinuum, Microsoft) + +Estimated monthly cost: $0.02-$0.05 (with FREE simulators) + +Proceed with deployment? (yes/no): +``` + +**What to do:** + +- Type `yes` and press Enter to start deployment + +**The deployment begins:** + +```text +Starting deployment... (this takes 2-3 minutes) + +Name ResourceGroup State Timestamp +---------------------------- ---------------- --------- --------------------------- +quantum-deployment-20251031 rg-quantum-ai Succeeded 2025-10-31T10:30:00.000000 +``` + +**What's happening:** + +- Azure creates your quantum workspace +- Sets up storage for quantum job results +- Configures connections to quantum hardware providers +- **This is the longest step (2-3 minutes)** + +--- + +### Step 9: Update Configuration + +**The script updates your project configuration:** + +```text +STEP 8: Updating Quantum Configuration + +Updating quantum_config.yaml... +✓ Configuration file updated +``` + +**What's happening:** + +- Updates `config/quantum_config.yaml` with: + - Your subscription ID + - Workspace name + - Resource group + - Storage account name +- Now your quantum AI knows how to connect to Azure! + +--- + +### Step 10: Verify Deployment + +**The script verifies everything is working:** + +```text +STEP 9: Verifying Deployment + +Checking workspace status... +✓ Workspace verified! + + Workspace: quantum-ai-workspace + Location: eastus + Status: Succeeded + +Available Quantum Providers: + ✓ microsoft.quantum + ✓ ionq + ✓ quantinuum +``` + +**What this means:** + +- Your workspace is live and ready +- You have access to multiple quantum providers +- Free simulators are available +- Real quantum hardware is available (optional) + +--- + +### Step 11: Deployment Complete! 🎉 + +**The script shows a success summary:** + +```text +======================================== + DEPLOYMENT COMPLETE! +======================================== + +✓ Your Azure Quantum workspace is ready! + +Deployment Summary: + Subscription: 12345678-1234-1234-1234-123456789012 + Resource Group: rg-quantum-ai + Workspace: quantum-ai-workspace + Location: eastus + Storage: quantumstorage1031 + +Configuration Updated: + config/quantum_config.yaml ✓ + +Next Steps: + 1. Test connection to Azure Quantum + > python test_azure_quantum.py + + 2. Run Bell state test (verify quantum entanglement) + Select: ionq.simulator (FREE) + + 3. Test your optimized circuit (90% accuracy) + 500 shots on free simulator + +Free Resources Available: + ✓ IonQ Simulator - Unlimited FREE usage + ✓ Microsoft Simulator - Unlimited FREE usage + +Real Quantum Hardware (Optional): + • IonQ QPU: ~$0.36 per circuit (500 shots) + • Quantinuum: ~$1.50 per circuit + Start with simulator first! + +======================================== + Ready to test on quantum hardware! +======================================== +``` + +**The script asks if you want to run tests immediately:** + +```text +Would you like to run the tests now? (yes/no): +``` + +--- + +## 🧪 Running the Tests + +### Option 1: Run Tests Immediately + +If you said `yes` at the end of deployment: + +- The script automatically activates your virtual environment +- Runs `python test_azure_quantum.py` +- Follow the interactive test prompts + +### Option 2: Run Tests Later + +```powershell +# Activate virtual environment +.\venv\Scripts\Activate.ps1 + +# Run test suite +python test_azure_quantum.py +``` + +--- + +## 🎯 What the Tests Do + +### Test 1: Connection Verification + +```text +======================================== + TEST 1: AZURE QUANTUM CONNECTION +======================================== + +✓ Configuration loaded + Workspace: quantum-ai-workspace + Resource Group: rg-quantum-ai + Location: eastus + +Connecting to Azure Quantum workspace... +✓ Successfully connected to Azure Quantum! + +Available Quantum Backends: + 1. ionq.simulator + 2. ionq.qpu + 3. microsoft.estimator + 4. quantinuum.sim.h1-1sc + +✓ Found 4 quantum backend(s) +``` + +**What this means:** + +- Your project successfully connects to Azure +- You have access to simulators and real quantum hardware +- Authentication is working + +--- + +### Test 2: Bell State on Hardware + +**The test creates a quantum Bell state (entanglement test):** + +```text +======================================== + TEST 2: BELL STATE ON QUANTUM HARDWARE +======================================== + +Bell State Circuit: + ┌───┐ +q_0: ┤ H ├──■── + └───┘┌─┴─┐ +q_1: ─────┤ X ├ + └───┘ +c: 2/══════════ + +Expected Results (ideal quantum behavior): + |00⟩: ~50% + |11⟩: ~50% + (Quantum entanglement: measuring qubit 0 determines qubit 1) + +Select backend for testing: + 1. ionq.simulator (FREE - recommended) + 2. ionq.qpu (PAID - real quantum computer) + 3. Skip hardware tests + +Enter choice (1-3): +``` + +**What to choose:** + +- **Type `1`** for FREE simulator (recommended first time) +- **Type `2`** for real quantum hardware (costs ~$0.07 for 100 shots) + +**If you choose simulator (1):** + +```text +Estimating cost... +Cost Estimate: {'backend': 'ionq.simulator', 'shots': 100, 'note': 'FREE simulator'} + +Submitting Bell state to ionq.simulator... +✓ Job submitted successfully! + Job ID: 12345678-abcd-1234-5678-123456789abc + Status: Waiting + +Waiting for results (this may take a few minutes)... + +======================================== + BELL STATE RESULTS +======================================== + +Measurement counts: + 00: 51 (51.0%) + 11: 49 (49.0%) + +Entanglement Quality: 100.0% +✓ Excellent quantum entanglement observed! + +✓ Results saved to results/ +``` + +**What this means:** + +- ✅ Your quantum circuit works! +- ✅ You've created quantum entanglement! +- ✅ The 50/50 split proves quantum superposition +- ✅ This is real quantum physics in action! + +--- + +### Test 3: Optimized Circuit (90% Accuracy) + +**The test runs your optimized 3-layer quantum classifier:** + +```text +======================================== + TEST 3: OPTIMIZED QUANTUM CIRCUIT ON HARDWARE +======================================== + +Optimized Configuration (90% accuracy): + Qubits: 4 + Layers: 3 + Entanglement: linear + Learning Rate: 0.1 + +Quantum Circuit Structure: + ┌───┐┌────────┐┌────────┐ ┌───┐┌────────┐┌────────┐ +q_0: ┤ H ├┤ Ry(π/4)├┤ Rz(π/3)├──■──┤ H ├┤ Ry(π/2)├┤ Rz(2π/3)├──■──... + ├───┤├────────┤├────────┤┌─┴─┐├───┤├────────┤├────────┤┌─┴─┐ +q_1: ┤ H ├┤ Ry(π/4)├┤ Rz(π/3)├┤ X ├┤ H ├┤ Ry(π/2)├┤ Rz(2π/3)├┤ X ├... + ... + +Circuit Statistics: + Depth: 15 + Gates: 42 + Qubits: 4 + +Submitting optimized circuit to ionq.simulator... +✓ Job submitted successfully! + +Waiting for results (this may take several minutes)... + +======================================== + OPTIMIZED CIRCUIT RESULTS +======================================== + +Measurement distribution (top 10 states): + 0000: 89 (17.8%) ████████ + 1111: 84 (16.8%) ████████ + 0101: 47 ( 9.4%) ████ + 1010: 45 ( 9.0%) ████ + 0011: 32 ( 6.4%) ███ + 1100: 31 ( 6.2%) ███ + 0110: 28 ( 5.6%) ██ + 1001: 27 ( 5.4%) ██ + 1010: 24 ( 4.8%) ██ + 0101: 23 ( 4.6%) ██ + +Quantum State Analysis: + Unique states measured: 14/16 + Entropy: 3.18 / 4.00 + Distribution uniformity: 79.5% + +✓ Circuit explores quantum superposition! +✓ High entropy = good for ML! +✓ Well-balanced state exploration! + +✓ Results saved to results/ +``` + +**What this means:** + +- ✅ Your optimized circuit creates complex quantum states +- ✅ High entropy shows good quantum exploration (ideal for ML) +- ✅ Multiple states observed = quantum superposition working +- ✅ This circuit can classify data with 90% accuracy! + +--- + +## 📊 Understanding Your Results + +### Simulator Results vs Hardware + +**On FREE Simulator:** + +- Perfect quantum behavior (no noise) +- Bell state: Exactly 50/50 split +- Your circuit: Clean quantum superposition + +**On Real Quantum Hardware (optional):** + +- Slight noise from real physics (decoherence, gate errors) +- Bell state: ~48/52 split (noise is normal and expected!) +- Your circuit: Noisy but still quantum + +**The noise proves it's real quantum hardware!** 🎉 + +### What to Look For + +**Good Results:** + +- ✅ Bell state shows ~50/50 split (±10%) +- ✅ Entanglement quality >80% +- ✅ Multiple quantum states observed +- ✅ Jobs complete without errors + +**Issues to Watch:** + +- ⚠️ Very low entanglement (<60%) - hardware calibration issue +- ⚠️ Job failures - circuit too deep or provider issue +- ⚠️ No backends available - wait 5-10 min after deployment + +--- + +## 💰 Cost Monitoring + +### Check Your Spending + +```powershell +# View resource group costs in Azure Portal +Start-Process "https://portal.azure.com/#blade/Microsoft_Azure_CostManagement/Menu/overview" +``` + +### Expected Costs + +**Testing with FREE simulators (recommended):** + +- Simulator usage: $0.00 +- Storage: $0.00 (first 5GB free) +- Workspace: $0.00 (no charge for workspace itself) +- **Total: $0.00/month** ✅ + +**Testing with real quantum hardware (optional):** + +- Bell state test (100 shots): ~$0.07 +- Optimized circuit (500 shots): ~$0.36 +- **Total for initial testing: ~$0.43** + +### Setting Budget Alerts + +```powershell +# Go to Cost Management in Azure Portal +Start-Process "https://portal.azure.com/#blade/Microsoft_Azure_CostManagement/Menu/budgets" + +# Create budget: +# 1. Click "+ Add" +# 2. Set amount: $10/month +# 3. Set alert at: 80% ($8) +# 4. Enter your email +``` + +--- + +## 🔧 Troubleshooting + +### Error: "Workspace name already exists" + +```text +✗ Deployment failed + • Workspace name already taken (try a different name) +``` + +**Solution:** Run the script again and choose custom names when prompted + +### Error: "Storage account name invalid" + +```text +✗ Deployment failed + • Storage account name invalid (lowercase letters/numbers only) +``` + +**Solution:** Ensure storage name is lowercase, no hyphens, 3-24 characters + +### Error: "Region not supported" + +```text +✗ Deployment failed + • Region not supported +``` + +**Solution:** Use supported regions: eastus, westus, westeurope, northeurope + +### Error: "Failed to connect to Azure Quantum" + +```text +✗ Connection failed: AuthenticationError +``` + +**Solution:** + +```powershell +# Re-authenticate +az login + +# Verify connection +az account show + +# Run tests again +python test_azure_quantum.py +``` + +### Error: "No backends available" + +```text +✗ Found 0 quantum backend(s) +``` + +**Solution:** + +- Wait 5-10 minutes after workspace deployment +- Providers need time to initialize +- Check Azure Portal for provider status + +### Error: "Insufficient credits" + +```text +✗ Job submission failed: Insufficient credits +``` + +**Solution:** + +- Use FREE simulators: `ionq.simulator` +- Or purchase quantum credits in Azure Portal + +### Getting Help + +**Check deployment status:** + +```powershell +az deployment group show ` + --resource-group rg-quantum-ai ` + --name quantum-deployment- ` + --query properties.provisioningState +``` + +**View detailed logs:** + +```powershell +az monitor activity-log list ` + --resource-group rg-quantum-ai ` + --max-events 50 ` + --output table +``` + +**Contact support:** + +- Azure Portal → Help + Support +- Or open an issue in this repository + +--- + +## 🎉 Success Checklist + +You've successfully deployed when you see: + +- [ ] ✅ Azure CLI installed and working +- [ ] ✅ Logged in to Azure subscription +- [ ] ✅ Resource group created +- [ ] ✅ Quantum workspace deployed (Status: Succeeded) +- [ ] ✅ Configuration file updated +- [ ] ✅ Test script connects successfully +- [ ] ✅ Quantum backends listed (ionq.simulator, etc.) +- [ ] ✅ Bell state test completes with ~50/50 split +- [ ] ✅ Optimized circuit test completes successfully +- [ ] ✅ Results saved to `results/` directory + +--- + +## 🚀 Next Steps After Deployment + +### 1. Experiment with Different Backends + +```powershell +# Test on different simulators +python test_azure_quantum.py +# Select: microsoft.estimator (resource estimation) + +# Compare IonQ vs Quantinuum +# Run tests on both and compare results +``` + +### 2. Scale Up Your Circuits + +```python +# Edit config/quantum_config.yaml +n_qubits: 6 # Up from 4 +n_layers: 4 # Up from 3 + +# Re-run tests to see how performance scales +python test_azure_quantum.py +``` + +### 3. Train with Hardware Results + +```powershell +# Use real quantum hardware data in training +python .\src\quantum_classifier.py --use-azure-data +``` + +### 4. Monitor and Optimize + +- Check costs in Azure Portal weekly +- Optimize circuit depth before hardware submission +- Use simulators for development, hardware for validation +- Document your findings + +--- + +## 📚 Additional Resources + +### Your Project Documentation + +- **READY_FOR_HARDWARE.md** - Overview of what's ready +- **AZURE_SETUP_CHECKLIST.md** - Detailed setup checklist +- **AZURE_QUANTUM_QUICKSTART.md** - Quick reference guide +- **FINAL_OPTIMIZATION_REPORT.md** - Your 90% accuracy results + +### Azure Documentation + +- [Azure Quantum Docs](https://docs.microsoft.com/azure/quantum/) +- [Qiskit on Azure](https://docs.microsoft.com/azure/quantum/quickstart-microsoft-qiskit) +- [Pricing Calculator](https://azure.microsoft.com/pricing/calculator/) + +### Provider Documentation + +- [IonQ Documentation](https://ionq.com/docs) +- [Quantinuum Docs](https://www.quantinuum.com/products) +- [Microsoft QC](https://docs.microsoft.com/quantum/) + +--- + +## 🎊 You Did It + +**Congratulations!** You've successfully: + +✅ Deployed Azure Quantum infrastructure +✅ Connected to real quantum hardware +✅ Tested quantum entanglement (Bell state) +✅ Ran your optimized 90% accuracy circuit +✅ Validated quantum superposition and ML readiness + +**You're now running quantum machine learning on real quantum computers!** + +Welcome to the quantum computing revolution! 🚀 + +--- + +Happy Quantum Computing! 🎉 + +**Start your deployment now:** + +```powershell +.\deploy_azure_quantum.ps1 +``` diff --git a/ai-projects/quantum-ml/FINAL_OPTIMIZATION_REPORT.md b/ai-projects/quantum-ml/FINAL_OPTIMIZATION_REPORT.md index e5404e128..4af0c5c65 100644 --- a/ai-projects/quantum-ml/FINAL_OPTIMIZATION_REPORT.md +++ b/ai-projects/quantum-ml/FINAL_OPTIMIZATION_REPORT.md @@ -1,381 +1,381 @@ -# 🏆 Parameter Optimization - Final Report - -**Date:** October 31, 2025 -**Status:** ✅ COMPLETE - **90% Accuracy Achieved!** - ---- - -## 🎯 Executive Summary - -Comprehensive hyperparameter optimization experiments have identified the **optimal quantum AI configuration**, achieving a **breakthrough 90% validation accuracy** - a **+17.5% improvement** over baseline! - -### Key Achievement - -- **Previous Best:** 72.5% accuracy -- **Optimized Best:** **90.0% accuracy** -- **Improvement:** +17.5 percentage points (+24% relative improvement) -- **Configuration:** 3 layers, 0.1 learning rate, linear entanglement - ---- - -## 📊 Experiment Results - -### Experiment 1: Layer Depth Optimization - -| Layers | Validation Accuracy | Grade | Verdict | -|--------|--------------------:|-------|---------| -| 1 | 62.5% | ★★★★ | Too simple | -| 2 | 77.5% | ★★★★★★★ | Good baseline | -| **3** | **87.5%** ⭐ | **★★★★★★★★** | **OPTIMAL** | -| 4 | 82.5% | ★★★★★★★ | Overfitting starts | - -## Winner: 3 Layers (87.5% accuracy) - -**Insights:** - -- 3 layers provides optimal expressivity for the Moons dataset -- 4 layers shows overfitting (performance drops) -- Rule: 1 layer per ~50-60 training samples optimal -- 160 samples → 3 layers ideal - ---- - -### Experiment 2: Learning Rate Optimization - -| Learning Rate | Validation Accuracy | Convergence | Verdict | -|--------------|--------------------:|-------------|---------| -| 0.001 | 80.0% | Slow | Too conservative | -| 0.005 | 82.5% | Moderate | Good | -| 0.01 | 85.0% | Fast | Very good | -| 0.05 | 85.0% | Fast | Very good | -| **0.1** | **90.0%** 🏆 | **Very Fast** | **OPTIMAL** | - -## Winner: Learning Rate 0.1 (90% accuracy) - -**Insights:** - -- **0.1 learning rate provides fastest convergence** -- Reached 87.5% by epoch 10 -- Reached 90% by epoch 20-30 -- No instability observed despite high learning rate -- Quantum gradients well-behaved at LR=0.1 - -**Training Progression (LR=0.1):** - -```text -Epoch 0: 45.0% → Started learning -Epoch 10: 87.5% → Rapid improvement -Epoch 20: 90.0% → Reached optimal -Epoch 30-50: 90.0% → Stable plateau -``` - ---- - -### Experiment 3: Entanglement Pattern Comparison - -| Pattern | Validation Accuracy | Circuit Complexity | Verdict | -|---------|--------------------:|-------------------:|---------| -| **linear** | **82.5%** ✅ | Low | **OPTIMAL** | -| circular | 80.0% | Medium | Good | -| full | 52.5% | High | Overfitting | - -### Winner: Linear Entanglement (82.5% accuracy) - -**Insights:** - -- **Linear entanglement** (adjacent qubits only) performs best -- Full entanglement causes overfitting on small datasets -- Linear pattern: i ↔ i+1 (simpler, more stable) -- Circular pattern: adds wrap-around (slightly worse) -- Full pattern: all-to-all connections (too complex for data size) - ---- - -## 🎓 Combined Optimal Configuration - -### Recommended Settings - -```yaml -ml: - model: - n_qubits: 4 - n_layers: 3 # +10% vs 2 layers - entanglement: linear # +30% vs full - training: - epochs: 100 - learning_rate: 0.1 # +5% vs 0.01 - batch_size: 32 -``` - -### Expected Performance - -| Configuration | Expected Accuracy | -|---------------|------------------:| -| Baseline (2 layers, LR 0.01, full) | 72.5% | -| Optimized (3 layers, LR 0.1, linear) | **90.0%** | -| **Improvement** | **+17.5%** | - ---- - -## 📈 Performance Analysis - -### Convergence Speed Comparison - -| Learning Rate | Epochs to 80% | Epochs to 85% | Final | -|--------------|--------------|--------------|-------| -| 0.001 | 40+ | 50+ | 80% | -| 0.01 | 30 | 40 | 85% | -| **0.1** | **<10** | **15-20** | **90%** | - -**Key Finding:** LR=0.1 converges **3-4x faster** than LR=0.01! - -### Stability Analysis - -All configurations showed: - -- ✅ Smooth gradient flow (no NaN/Inf) -- ✅ Consistent convergence -- ✅ No catastrophic forgetting -- ✅ Stable validation performance - -Even LR=0.1 (which might seem high) maintained stability throughout training. - ---- - -## 🔬 Scientific Insights - -### Why 3 Layers Works Best - -1. **Expressivity:** Can represent more complex decision boundaries -2. **Data-to-Parameter Ratio:** ~53 samples per layer (optimal range: 40-80) -3. **Quantum Depth:** 3 layers = 6 total quantum gates per qubit (good balance) -4. **Avoid Overfitting:** 4+ layers → too many parameters for 160 samples - -### Why LR=0.1 Excels - -1. **Quantum Gradient Landscape:** Smoother than classical neural networks -2. **Parameter-Shift Rule:** PennyLane's gradient method is robust -3. **Small Parameter Space:** Only ~24 quantum parameters (can handle aggressive LR) -4. **No Barren Plateaus:** 4 qubits small enough to avoid gradient vanishing - -### Why Linear Entanglement Wins - -1. **Problem Structure:** Moons dataset is locally non-linear (not globally entangled) -2. **Circuit Depth:** Linear = fewest gates = less noise -3. **Generalization:** Simpler patterns generalize better on small datasets -4. **Physical Interpretation:** Nearest-neighbor coupling sufficient for this task - ---- - -## 📊 Visualization Summary - -Three plots generated in `results/experiments/`: - -1. **experiment1_layer_depth.png** - - Shows U-shaped curve - - Peak at 3 layers - - Clear overfitting beyond 3 - -2. **experiment2_learning_rate.png** - - Log-scale x-axis - - Peak at LR=0.1 - - Diminishing returns at LR>0.1 - -3. **experiment3_entanglement.png** - - Bar chart comparison - - Linear clearly superior - - Full pattern fails dramatically - ---- - -## 🎯 Recommendations - -### Immediate Actions (Completed ✅) - -1. **Update Configuration** ✅ - - Applied optimal parameters to `quantum_config.yaml` - - Ready for production use - -2. **Validate on All Datasets** - - ```powershell - python .\examples\train_models.py - ``` - - - Expected improvements: - - Moons: 90% (was 72.5%) - - Circles: 70-75% (was 52.5%) - - Iris: 85-90% (was 76.7%) - -3. **Re-run Extended Datasets** - - ```powershell - python .\experiments\extended_datasets.py - ``` - - - Expected improvements: - - XOR: 70-75% (was 57.5%) - - Spiral: 50-60% (was 37.5%) - - Wine: 92-95% (was 88.9%) - - Imbalanced: 93-95% (was 90%) - -### Next-Level Optimization - -1. **Batch Size Tuning** - - Test: 16, 32, 64, 128 - - Current: 32 (likely optimal) - -2. **Optimizer Comparison** - - Test: Adam, RMSprop, SGD with momentum - - Current: Adam (likely best for quantum) - -3. **Qubit Scaling** - - Test: 6 qubits, 8 qubits - - May improve spiral dataset (37.5% → 70%+) - -4. **Data Re-uploading** - - Advanced quantum ML technique - - Can boost accuracy another 5-10% - ---- - -## 💡 Key Learnings - -### Surprising Discoveries - -1. **High LR Works!** - - LR=0.1 seems aggressive but works perfectly - - Quantum gradients more stable than expected - -2. **Simple Entanglement Better** - - Full entanglement actually hurts performance - - Less is more for small datasets - -3. **Layer Depth Sweet Spot** - - 3 layers optimal (not 2, not 4) - - Data size determines optimal depth - -### General Rules Discovered - -1. **Layer-to-Data Ratio:** 1 layer per 50-60 samples -2. **Learning Rate Range:** 0.05-0.1 optimal for quantum -3. **Entanglement Principle:** Match pattern to problem locality -4. **Training Duration:** 50-100 epochs sufficient with LR=0.1 - ---- - -## 🚀 Production Readiness - -### Configuration Status - -✅ **READY FOR PRODUCTION** - -The optimized configuration in `quantum_config.yaml` is: - -- Validated across 3 experiments -- Tested on multiple datasets -- Consistently high performance -- Fast convergence (saves compute time) - -### Deployment Checklist - -- [x] Optimal parameters identified -- [x] Configuration updated -- [x] Experiments documented -- [x] Visualizations generated -- [ ] Validate on all datasets (next step) -- [ ] Deploy to Azure Quantum (optional) -- [ ] Production inference pipeline - ---- - -## 📚 Files Generated - -### Configuration - -- `config/quantum_config.yaml` - Updated with optimal parameters - -### Visualizations - -- `results/experiments/experiment1_layer_depth.png` - Layer depth comparison -- `results/experiments/experiment2_learning_rate.png` - Learning rate comparison -- `results/experiments/experiment3_entanglement.png` - Entanglement comparison - -### Documentation - -- `OPTIMIZATION_RESULTS.md` - Detailed analysis (previous) -- `OPTIMIZATION_SUMMARY.md` - Complete guide (previous) -- `FINAL_OPTIMIZATION_REPORT.md` - **This file** - ---- - -## 🎉 Conclusion - -**Mission Accomplished!** - -Through systematic hyperparameter optimization, we've achieved: - -✅ **90% validation accuracy** (up from 72.5%) -✅ **+24% relative improvement** -✅ **3-4x faster convergence** -✅ **Production-ready configuration** -✅ **Validated optimal parameters** - -**Your Quantum AI is now:** - -- **Highly Optimized** - Best possible hyperparameters -- **Fast Training** - Converges in 20-30 epochs -- **Stable Performance** - Consistent 90% accuracy -- **Ready for Deployment** - Azure Quantum or production - ---- - -## 📊 Final Statistics - -| Metric | Before | After | Change | -|--------|--------|-------|--------| -| **Accuracy** | 72.5% | **90.0%** | **+17.5%** | -| **Layers** | 2 | 3 | +1 | -| **Learning Rate** | 0.01 | 0.1 | 10x faster | -| **Entanglement** | full | linear | Simpler | -| **Epochs to 85%** | 40+ | 15-20 | 2x faster | -| **Training Time** | 3-4 min | 1-2 min | 2x faster | - ---- - -## 🌟 What's Next? - -You have **three exciting options**: - -**Option A: Validate Improvements** ✨ - -```powershell -python .\examples\train_models.py -``` - -Re-train all models with new settings to see +10-15% across the board! - -**Option B: Deploy to Azure Quantum** ☁️ - -```powershell -# See: experiments/AZURE_QUICKSTART.md -``` - -Test on **real quantum hardware** (IonQ, Quantinuum, Rigetti)! - -**Option C: Advanced Techniques** 🚀 - -- Data re-uploading (+5-10% more) -- Quantum feature maps -- Ensemble methods -- 6-8 qubit scaling - ---- - -**Congratulations! You've successfully optimized your Quantum AI to world-class performance!** 🎉🏆 - ---- - -*Generated: October 31, 2025* -*Quantum AI Optimization System* -*Status: PRODUCTION READY* +# 🏆 Parameter Optimization - Final Report + +**Date:** October 31, 2025 +**Status:** ✅ COMPLETE - **90% Accuracy Achieved!** + +--- + +## 🎯 Executive Summary + +Comprehensive hyperparameter optimization experiments have identified the **optimal quantum AI configuration**, achieving a **breakthrough 90% validation accuracy** - a **+17.5% improvement** over baseline! + +### Key Achievement + +- **Previous Best:** 72.5% accuracy +- **Optimized Best:** **90.0% accuracy** +- **Improvement:** +17.5 percentage points (+24% relative improvement) +- **Configuration:** 3 layers, 0.1 learning rate, linear entanglement + +--- + +## 📊 Experiment Results + +### Experiment 1: Layer Depth Optimization + +| Layers | Validation Accuracy | Grade | Verdict | +|--------|--------------------:|-------|---------| +| 1 | 62.5% | ★★★★ | Too simple | +| 2 | 77.5% | ★★★★★★★ | Good baseline | +| **3** | **87.5%** ⭐ | **★★★★★★★★** | **OPTIMAL** | +| 4 | 82.5% | ★★★★★★★ | Overfitting starts | + +## Winner: 3 Layers (87.5% accuracy) + +**Insights:** + +- 3 layers provides optimal expressivity for the Moons dataset +- 4 layers shows overfitting (performance drops) +- Rule: 1 layer per ~50-60 training samples optimal +- 160 samples → 3 layers ideal + +--- + +### Experiment 2: Learning Rate Optimization + +| Learning Rate | Validation Accuracy | Convergence | Verdict | +|--------------|--------------------:|-------------|---------| +| 0.001 | 80.0% | Slow | Too conservative | +| 0.005 | 82.5% | Moderate | Good | +| 0.01 | 85.0% | Fast | Very good | +| 0.05 | 85.0% | Fast | Very good | +| **0.1** | **90.0%** 🏆 | **Very Fast** | **OPTIMAL** | + +## Winner: Learning Rate 0.1 (90% accuracy) + +**Insights:** + +- **0.1 learning rate provides fastest convergence** +- Reached 87.5% by epoch 10 +- Reached 90% by epoch 20-30 +- No instability observed despite high learning rate +- Quantum gradients well-behaved at LR=0.1 + +**Training Progression (LR=0.1):** + +```text +Epoch 0: 45.0% → Started learning +Epoch 10: 87.5% → Rapid improvement +Epoch 20: 90.0% → Reached optimal +Epoch 30-50: 90.0% → Stable plateau +``` + +--- + +### Experiment 3: Entanglement Pattern Comparison + +| Pattern | Validation Accuracy | Circuit Complexity | Verdict | +|---------|--------------------:|-------------------:|---------| +| **linear** | **82.5%** ✅ | Low | **OPTIMAL** | +| circular | 80.0% | Medium | Good | +| full | 52.5% | High | Overfitting | + +### Winner: Linear Entanglement (82.5% accuracy) + +**Insights:** + +- **Linear entanglement** (adjacent qubits only) performs best +- Full entanglement causes overfitting on small datasets +- Linear pattern: i ↔ i+1 (simpler, more stable) +- Circular pattern: adds wrap-around (slightly worse) +- Full pattern: all-to-all connections (too complex for data size) + +--- + +## 🎓 Combined Optimal Configuration + +### Recommended Settings + +```yaml +ml: + model: + n_qubits: 4 + n_layers: 3 # +10% vs 2 layers + entanglement: linear # +30% vs full + training: + epochs: 100 + learning_rate: 0.1 # +5% vs 0.01 + batch_size: 32 +``` + +### Expected Performance + +| Configuration | Expected Accuracy | +|---------------|------------------:| +| Baseline (2 layers, LR 0.01, full) | 72.5% | +| Optimized (3 layers, LR 0.1, linear) | **90.0%** | +| **Improvement** | **+17.5%** | + +--- + +## 📈 Performance Analysis + +### Convergence Speed Comparison + +| Learning Rate | Epochs to 80% | Epochs to 85% | Final | +|--------------|--------------|--------------|-------| +| 0.001 | 40+ | 50+ | 80% | +| 0.01 | 30 | 40 | 85% | +| **0.1** | **<10** | **15-20** | **90%** | + +**Key Finding:** LR=0.1 converges **3-4x faster** than LR=0.01! + +### Stability Analysis + +All configurations showed: + +- ✅ Smooth gradient flow (no NaN/Inf) +- ✅ Consistent convergence +- ✅ No catastrophic forgetting +- ✅ Stable validation performance + +Even LR=0.1 (which might seem high) maintained stability throughout training. + +--- + +## 🔬 Scientific Insights + +### Why 3 Layers Works Best + +1. **Expressivity:** Can represent more complex decision boundaries +2. **Data-to-Parameter Ratio:** ~53 samples per layer (optimal range: 40-80) +3. **Quantum Depth:** 3 layers = 6 total quantum gates per qubit (good balance) +4. **Avoid Overfitting:** 4+ layers → too many parameters for 160 samples + +### Why LR=0.1 Excels + +1. **Quantum Gradient Landscape:** Smoother than classical neural networks +2. **Parameter-Shift Rule:** PennyLane's gradient method is robust +3. **Small Parameter Space:** Only ~24 quantum parameters (can handle aggressive LR) +4. **No Barren Plateaus:** 4 qubits small enough to avoid gradient vanishing + +### Why Linear Entanglement Wins + +1. **Problem Structure:** Moons dataset is locally non-linear (not globally entangled) +2. **Circuit Depth:** Linear = fewest gates = less noise +3. **Generalization:** Simpler patterns generalize better on small datasets +4. **Physical Interpretation:** Nearest-neighbor coupling sufficient for this task + +--- + +## 📊 Visualization Summary + +Three plots generated in `results/experiments/`: + +1. **experiment1_layer_depth.png** + - Shows U-shaped curve + - Peak at 3 layers + - Clear overfitting beyond 3 + +2. **experiment2_learning_rate.png** + - Log-scale x-axis + - Peak at LR=0.1 + - Diminishing returns at LR>0.1 + +3. **experiment3_entanglement.png** + - Bar chart comparison + - Linear clearly superior + - Full pattern fails dramatically + +--- + +## 🎯 Recommendations + +### Immediate Actions (Completed ✅) + +1. **Update Configuration** ✅ + - Applied optimal parameters to `quantum_config.yaml` + - Ready for production use + +2. **Validate on All Datasets** + + ```powershell + python .\examples\train_models.py + ``` + + - Expected improvements: + - Moons: 90% (was 72.5%) + - Circles: 70-75% (was 52.5%) + - Iris: 85-90% (was 76.7%) + +3. **Re-run Extended Datasets** + + ```powershell + python .\experiments\extended_datasets.py + ``` + + - Expected improvements: + - XOR: 70-75% (was 57.5%) + - Spiral: 50-60% (was 37.5%) + - Wine: 92-95% (was 88.9%) + - Imbalanced: 93-95% (was 90%) + +### Next-Level Optimization + +1. **Batch Size Tuning** + - Test: 16, 32, 64, 128 + - Current: 32 (likely optimal) + +2. **Optimizer Comparison** + - Test: Adam, RMSprop, SGD with momentum + - Current: Adam (likely best for quantum) + +3. **Qubit Scaling** + - Test: 6 qubits, 8 qubits + - May improve spiral dataset (37.5% → 70%+) + +4. **Data Re-uploading** + - Advanced quantum ML technique + - Can boost accuracy another 5-10% + +--- + +## 💡 Key Learnings + +### Surprising Discoveries + +1. **High LR Works!** + - LR=0.1 seems aggressive but works perfectly + - Quantum gradients more stable than expected + +2. **Simple Entanglement Better** + - Full entanglement actually hurts performance + - Less is more for small datasets + +3. **Layer Depth Sweet Spot** + - 3 layers optimal (not 2, not 4) + - Data size determines optimal depth + +### General Rules Discovered + +1. **Layer-to-Data Ratio:** 1 layer per 50-60 samples +2. **Learning Rate Range:** 0.05-0.1 optimal for quantum +3. **Entanglement Principle:** Match pattern to problem locality +4. **Training Duration:** 50-100 epochs sufficient with LR=0.1 + +--- + +## 🚀 Production Readiness + +### Configuration Status + +✅ **READY FOR PRODUCTION** + +The optimized configuration in `quantum_config.yaml` is: + +- Validated across 3 experiments +- Tested on multiple datasets +- Consistently high performance +- Fast convergence (saves compute time) + +### Deployment Checklist + +- [x] Optimal parameters identified +- [x] Configuration updated +- [x] Experiments documented +- [x] Visualizations generated +- [ ] Validate on all datasets (next step) +- [ ] Deploy to Azure Quantum (optional) +- [ ] Production inference pipeline + +--- + +## 📚 Files Generated + +### Configuration + +- `config/quantum_config.yaml` - Updated with optimal parameters + +### Visualizations + +- `results/experiments/experiment1_layer_depth.png` - Layer depth comparison +- `results/experiments/experiment2_learning_rate.png` - Learning rate comparison +- `results/experiments/experiment3_entanglement.png` - Entanglement comparison + +### Documentation + +- `OPTIMIZATION_RESULTS.md` - Detailed analysis (previous) +- `OPTIMIZATION_SUMMARY.md` - Complete guide (previous) +- `FINAL_OPTIMIZATION_REPORT.md` - **This file** + +--- + +## 🎉 Conclusion + +**Mission Accomplished!** + +Through systematic hyperparameter optimization, we've achieved: + +✅ **90% validation accuracy** (up from 72.5%) +✅ **+24% relative improvement** +✅ **3-4x faster convergence** +✅ **Production-ready configuration** +✅ **Validated optimal parameters** + +**Your Quantum AI is now:** + +- **Highly Optimized** - Best possible hyperparameters +- **Fast Training** - Converges in 20-30 epochs +- **Stable Performance** - Consistent 90% accuracy +- **Ready for Deployment** - Azure Quantum or production + +--- + +## 📊 Final Statistics + +| Metric | Before | After | Change | +|--------|--------|-------|--------| +| **Accuracy** | 72.5% | **90.0%** | **+17.5%** | +| **Layers** | 2 | 3 | +1 | +| **Learning Rate** | 0.01 | 0.1 | 10x faster | +| **Entanglement** | full | linear | Simpler | +| **Epochs to 85%** | 40+ | 15-20 | 2x faster | +| **Training Time** | 3-4 min | 1-2 min | 2x faster | + +--- + +## 🌟 What's Next? + +You have **three exciting options**: + +**Option A: Validate Improvements** ✨ + +```powershell +python .\examples\train_models.py +``` + +Re-train all models with new settings to see +10-15% across the board! + +**Option B: Deploy to Azure Quantum** ☁️ + +```powershell +# See: experiments/AZURE_QUICKSTART.md +``` + +Test on **real quantum hardware** (IonQ, Quantinuum, Rigetti)! + +**Option C: Advanced Techniques** 🚀 + +- Data re-uploading (+5-10% more) +- Quantum feature maps +- Ensemble methods +- 6-8 qubit scaling + +--- + +**Congratulations! You've successfully optimized your Quantum AI to world-class performance!** 🎉🏆 + +--- + +*Generated: October 31, 2025* +*Quantum AI Optimization System* +*Status: PRODUCTION READY* diff --git a/ai-projects/quantum-ml/HARDWARE_TEST_RESULTS.md b/ai-projects/quantum-ml/HARDWARE_TEST_RESULTS.md index 4dd23d559..9cdf5b122 100644 --- a/ai-projects/quantum-ml/HARDWARE_TEST_RESULTS.md +++ b/ai-projects/quantum-ml/HARDWARE_TEST_RESULTS.md @@ -1,179 +1,179 @@ -# Azure Quantum Hardware Test Results - -**Date:** October 31, 2025 - -## Test Summary - -Successfully submitted and executed a 4-qubit GHZ stabilizer circuit on Azure Quantum using the `rigetti.sim.qvm` backend. - -### Circuit Design - -``` - ┌───┐ ┌─┐ -q_0: ┤ H ├──■───────┤M├────────────── - └───┘┌─┴─┐ └╥┘ ┌─┐ -q_1: ─────┤ X ├──■───╫──────┤M├────── - └───┘┌─┴─┐ ║ └╥┘┌─┐ - ║ └───┘ ║ ║ └╥┘ -c: 4/════════════════╩═══════╩══╩══╩═ -**Circuit Properties:** -- Gates: H + 3× CX + 4× Measure -## Results Comparison - -### Azure Quantum Hardware (rigetti.sim.qvm) -- **Job ID:** 55e2ae5b-b6ed-11f0-a8e3-c86e08e1c791 - - `1111`: 480 (48.0%) -- **Entropy:** 0.999 / 4.000 (24.9%) -- **Job ID:** local-aer-4q-20251101_063927 -- **Shots:** 1000 -- **Entropy:** 0.996 / 4.000 (24.9%) -- **Unique States:** 2 / 16 - -## Analysis - -### Agreement -✅ **Excellent agreement** between Azure hardware and local simulation: -- Both produced only the two expected GHZ states -- Entropy values within 0.3% (0.999 vs 0.996) -- Distribution close to ideal 50/50 (statistical variations within √N bounds) - -### Statistical Validation -For 1000 shots with p=0.5: -- **Expected standard deviation:** √(1000 × 0.5 × 0.5) ≈ 15.8 counts -- **Azure deviation:** |520 - 500| = 20 counts (1.27σ) ✅ -- **Local deviation:** |536 - 500| = 36 counts (2.28σ) ✅ -- Both well within 3σ confidence interval - -### Fidelity -Both runs demonstrate **high-fidelity GHZ state preparation**: -- No spurious states (e.g., `0001`, `0110`) observed -- Near-maximum entropy for 2-state distribution -- Clean entanglement signature - -## Visualizations - -Generated charts available in `ai-projects/quantum-ml/results/visualizations/`: -- `azure_ghz_4q_results_20251101_063833_bar.png` (hardware) -- `sim_4q_results_20251101_063927_bar.png` (simulator) - -## Conclusions - -1. **Azure Quantum integration validated:** Submission, execution, and result retrieval working correctly -2. **Hardware-simulator parity confirmed:** Results match expected quantum behavior -3. **Production-ready workflow:** Scripts handle Azure credentials, backend selection, and result formatting seamlessly - ---- - -## 📊 Extended Analysis (Nov 1, 2025) - -### Provider-Specific Investigation Complete - -**See:** [`PROVIDER_COMPARISON_RESULTS.md`](./PROVIDER_COMPARISON_RESULTS.md) for comprehensive findings. - -**Key Updates:** - -1. ✅ **Variational MPS Validation Complete** - - Hardware (Rigetti): 90.5% entropy on 4q/L=2 variational circuit - - MPS simulation: 91.5% entropy (same parameters) - - **Conclusion:** MPS simulations accurate within 1%; validated for 32q/64q experiments - -2. ❌ **Quantinuum Fundamental Issue Confirmed** - - Tested 4 gate patterns (standard + 3 provider-native decompositions) - - All patterns collapse to |0000⟩ (100% ground state) - - Including Quantinuum H-series native gates (RZ/RX/RZZ) - - **Root cause:** Simulator bug, not circuit compatibility - - **Status:** Reported for Azure support; avoid until fixed - -3. ✅ **Rigetti Production-Ready** - - GHZ tests: Perfect fidelity (4q and 6q) - - Variational tests: Matches MPS within statistical noise - - Standard Qiskit gates work without modification - - **Recommendation:** Use for all current quantum experiments - -**Test Campaign Summary:** -- 7 hardware jobs (5 pattern tests + 1 variational + 1 MPS comparison) -- 7000 total shots -- Success rate: 71% (5/7; Quantinuum failures expected) - -**Configuration:** -- Qubits: 6 -- Shots: 2000 -- Job ID: d75a4e5a-b6ed-11f0-bf98-c86e08e1c791 - -**Results:** -- `|000000⟩`: 1002/2000 (50.1%) -- `|111111⟩`: 998/2000 (49.9%) -- Entropy: 1.000/6.0 (16.7%) - -**Analysis:** ✅ **Perfect scaling behavior** -- Near-ideal 50/50 distribution (deviation: 2 counts / 0.1%) -- Maximum entropy for 2-state distribution -- Demonstrates circuit fidelity maintained at higher qubit counts - -### Test 3 & 4: Quantinuum Backend Comparison - -**Configuration:** -- Backend: quantinuum.sim.h2-1sc (fallback when ionq.simulator unavailable) -- Qubits: 4 -- Shots: 1000, 100 - -**Results:** -- Test 3: `|0000⟩`: 1000/1000 (100%) -- Test 4: `|0000⟩`: 100/100 (100%) -- Entropy: 0.000/4.0 (0%) - -**Analysis:** ⚠️ **Circuit compatibility issue detected** -- All measurements collapsed to ground state `|0000⟩` -- Suggests Quantinuum transpilation may not preserve GHZ superposition -- Could be due to: - - Different gate set requiring alternative circuit construction - - Measurement convention differences - - Simulator-specific behavior - -**Recommendation:** For GHZ states, use Rigetti backend or investigate Quantinuum-specific circuit patterns. - -### Backend Availability Summary - -| Backend | Status | Notes | -|---------------------|-------------|------------------------------------------| -| rigetti.sim.qvm | ✅ Available | Excellent fidelity, tested up to 6 qubits| -| quantinuum.sim.h2-1sc | ✅ Available | Circuit compatibility requires investigation | -| quantinuum.sim.h2-1e | ✅ Available | Not yet tested | -| ionq.simulator | ❌ Not found | Not provisioned in workspace | -| ionq.qpu | ❌ Not found | Not provisioned (requires credits) | - -## Key Findings - -1. **Rigetti backend validated for production use** - - 4-qubit GHZ: 52/48% split (excellent) - - 6-qubit GHZ: 50.1/49.9% split (near-perfect) - - Entropy consistently at theoretical maximum for GHZ states - -2. **Quantinuum requires circuit adaptation** - - Standard Qiskit GHZ circuit produces collapsed states - - May require provider-specific gate decomposition - - Future work: Test with native Quantinuum gate set - -3. **Framework robustness proven** - - Automatic backend fallback working correctly - - Error handling for unavailable backends functional - - Metadata capture consistent across providers - -## Next Steps - -- ✅ Test scaling (completed: 6-qubit GHZ validated) -- ⚠️ Investigate Quantinuum circuit requirements -- ⏳ Provision IonQ access for real hardware comparison -- ⏳ Submit variational circuits to compare hardware vs MPS simulation fidelity -- ⏳ Benchmark execution time and cost across providers - ---- - -**Files:** -- Submission script: `ai-projects/quantum-ml/scripts/submit_small_stabilizer.py` -- Results JSON: - - `ai-projects/quantum-ml/results/azure_ghz_4q_results_20251101_063833.json` (rigetti) - - `ai-projects/quantum-ml/results/azure_ghz_6q_results_20251101_064210.json` (rigetti 6q) - - `ai-projects/quantum-ml/results/azure_ghz_4q_results_20251101_064243.json` (quantinuum) - - `ai-projects/quantum-ml/results/azure_ghz_4q_results_20251101_064323.json` (quantinuum 100 shots) -- Visualizations: `ai-projects/quantum-ml/results/visualizations/azure_ghz_*.png` +# Azure Quantum Hardware Test Results + +**Date:** October 31, 2025 + +## Test Summary + +Successfully submitted and executed a 4-qubit GHZ stabilizer circuit on Azure Quantum using the `rigetti.sim.qvm` backend. + +### Circuit Design + +``` + ┌───┐ ┌─┐ +q_0: ┤ H ├──■───────┤M├────────────── + └───┘┌─┴─┐ └╥┘ ┌─┐ +q_1: ─────┤ X ├──■───╫──────┤M├────── + └───┘┌─┴─┐ ║ └╥┘┌─┐ + ║ └───┘ ║ ║ └╥┘ +c: 4/════════════════╩═══════╩══╩══╩═ +**Circuit Properties:** +- Gates: H + 3× CX + 4× Measure +## Results Comparison + +### Azure Quantum Hardware (rigetti.sim.qvm) +- **Job ID:** 55e2ae5b-b6ed-11f0-a8e3-c86e08e1c791 + - `1111`: 480 (48.0%) +- **Entropy:** 0.999 / 4.000 (24.9%) +- **Job ID:** local-aer-4q-20251101_063927 +- **Shots:** 1000 +- **Entropy:** 0.996 / 4.000 (24.9%) +- **Unique States:** 2 / 16 + +## Analysis + +### Agreement +✅ **Excellent agreement** between Azure hardware and local simulation: +- Both produced only the two expected GHZ states +- Entropy values within 0.3% (0.999 vs 0.996) +- Distribution close to ideal 50/50 (statistical variations within √N bounds) + +### Statistical Validation +For 1000 shots with p=0.5: +- **Expected standard deviation:** √(1000 × 0.5 × 0.5) ≈ 15.8 counts +- **Azure deviation:** |520 - 500| = 20 counts (1.27σ) ✅ +- **Local deviation:** |536 - 500| = 36 counts (2.28σ) ✅ +- Both well within 3σ confidence interval + +### Fidelity +Both runs demonstrate **high-fidelity GHZ state preparation**: +- No spurious states (e.g., `0001`, `0110`) observed +- Near-maximum entropy for 2-state distribution +- Clean entanglement signature + +## Visualizations + +Generated charts available in `ai-projects/quantum-ml/results/visualizations/`: +- `azure_ghz_4q_results_20251101_063833_bar.png` (hardware) +- `sim_4q_results_20251101_063927_bar.png` (simulator) + +## Conclusions + +1. **Azure Quantum integration validated:** Submission, execution, and result retrieval working correctly +2. **Hardware-simulator parity confirmed:** Results match expected quantum behavior +3. **Production-ready workflow:** Scripts handle Azure credentials, backend selection, and result formatting seamlessly + +--- + +## 📊 Extended Analysis (Nov 1, 2025) + +### Provider-Specific Investigation Complete + +**See:** [`PROVIDER_COMPARISON_RESULTS.md`](./PROVIDER_COMPARISON_RESULTS.md) for comprehensive findings. + +**Key Updates:** + +1. ✅ **Variational MPS Validation Complete** + - Hardware (Rigetti): 90.5% entropy on 4q/L=2 variational circuit + - MPS simulation: 91.5% entropy (same parameters) + - **Conclusion:** MPS simulations accurate within 1%; validated for 32q/64q experiments + +2. ❌ **Quantinuum Fundamental Issue Confirmed** + - Tested 4 gate patterns (standard + 3 provider-native decompositions) + - All patterns collapse to |0000⟩ (100% ground state) + - Including Quantinuum H-series native gates (RZ/RX/RZZ) + - **Root cause:** Simulator bug, not circuit compatibility + - **Status:** Reported for Azure support; avoid until fixed + +3. ✅ **Rigetti Production-Ready** + - GHZ tests: Perfect fidelity (4q and 6q) + - Variational tests: Matches MPS within statistical noise + - Standard Qiskit gates work without modification + - **Recommendation:** Use for all current quantum experiments + +**Test Campaign Summary:** +- 7 hardware jobs (5 pattern tests + 1 variational + 1 MPS comparison) +- 7000 total shots +- Success rate: 71% (5/7; Quantinuum failures expected) + +**Configuration:** +- Qubits: 6 +- Shots: 2000 +- Job ID: d75a4e5a-b6ed-11f0-bf98-c86e08e1c791 + +**Results:** +- `|000000⟩`: 1002/2000 (50.1%) +- `|111111⟩`: 998/2000 (49.9%) +- Entropy: 1.000/6.0 (16.7%) + +**Analysis:** ✅ **Perfect scaling behavior** +- Near-ideal 50/50 distribution (deviation: 2 counts / 0.1%) +- Maximum entropy for 2-state distribution +- Demonstrates circuit fidelity maintained at higher qubit counts + +### Test 3 & 4: Quantinuum Backend Comparison + +**Configuration:** +- Backend: quantinuum.sim.h2-1sc (fallback when ionq.simulator unavailable) +- Qubits: 4 +- Shots: 1000, 100 + +**Results:** +- Test 3: `|0000⟩`: 1000/1000 (100%) +- Test 4: `|0000⟩`: 100/100 (100%) +- Entropy: 0.000/4.0 (0%) + +**Analysis:** ⚠️ **Circuit compatibility issue detected** +- All measurements collapsed to ground state `|0000⟩` +- Suggests Quantinuum transpilation may not preserve GHZ superposition +- Could be due to: + - Different gate set requiring alternative circuit construction + - Measurement convention differences + - Simulator-specific behavior + +**Recommendation:** For GHZ states, use Rigetti backend or investigate Quantinuum-specific circuit patterns. + +### Backend Availability Summary + +| Backend | Status | Notes | +|---------------------|-------------|------------------------------------------| +| rigetti.sim.qvm | ✅ Available | Excellent fidelity, tested up to 6 qubits| +| quantinuum.sim.h2-1sc | ✅ Available | Circuit compatibility requires investigation | +| quantinuum.sim.h2-1e | ✅ Available | Not yet tested | +| ionq.simulator | ❌ Not found | Not provisioned in workspace | +| ionq.qpu | ❌ Not found | Not provisioned (requires credits) | + +## Key Findings + +1. **Rigetti backend validated for production use** + - 4-qubit GHZ: 52/48% split (excellent) + - 6-qubit GHZ: 50.1/49.9% split (near-perfect) + - Entropy consistently at theoretical maximum for GHZ states + +2. **Quantinuum requires circuit adaptation** + - Standard Qiskit GHZ circuit produces collapsed states + - May require provider-specific gate decomposition + - Future work: Test with native Quantinuum gate set + +3. **Framework robustness proven** + - Automatic backend fallback working correctly + - Error handling for unavailable backends functional + - Metadata capture consistent across providers + +## Next Steps + +- ✅ Test scaling (completed: 6-qubit GHZ validated) +- ⚠️ Investigate Quantinuum circuit requirements +- ⏳ Provision IonQ access for real hardware comparison +- ⏳ Submit variational circuits to compare hardware vs MPS simulation fidelity +- ⏳ Benchmark execution time and cost across providers + +--- + +**Files:** +- Submission script: `ai-projects/quantum-ml/scripts/submit_small_stabilizer.py` +- Results JSON: + - `ai-projects/quantum-ml/results/azure_ghz_4q_results_20251101_063833.json` (rigetti) + - `ai-projects/quantum-ml/results/azure_ghz_6q_results_20251101_064210.json` (rigetti 6q) + - `ai-projects/quantum-ml/results/azure_ghz_4q_results_20251101_064243.json` (quantinuum) + - `ai-projects/quantum-ml/results/azure_ghz_4q_results_20251101_064323.json` (quantinuum 100 shots) +- Visualizations: `ai-projects/quantum-ml/results/visualizations/azure_ghz_*.png` diff --git a/ai-projects/quantum-ml/INDEX.md b/ai-projects/quantum-ml/INDEX.md index 294bb50c6..ddb22d81b 100644 --- a/ai-projects/quantum-ml/INDEX.md +++ b/ai-projects/quantum-ml/INDEX.md @@ -1,309 +1,309 @@ -# 📚 Quantum AI - Complete Documentation Index - -## 🎯 Start Here - -**New to Quantum AI?** → Read `README.md` first -**Want quick examples?** → See `QUICK_REFERENCE.md` -**Just finished demos?** → Check `DEMONSTRATION_SUMMARY.md` - ---- - -## 📖 Documentation Files - -### Main Documentation - -| File | Purpose | Read When | -|------|---------|-----------| -| **README.md** | Project overview, installation, usage | Starting the project | -| **QUICK_REFERENCE.md** | Quick commands and tips | Need fast lookup | -| **DEMONSTRATION_SUMMARY.md** | Complete demo results and learnings | After running examples | -| **MCP_SERVER_README.md** | Model Context Protocol server guide | Using MCP tools | - -### Example Documentation - -| File | Purpose | Read When | -|------|---------|-----------| -| **examples/README.md** | Detailed guide for all examples | Running examples | - -### Deployment Documentation - -| File | Purpose | Read When | -|------|---------|-----------| -| **azure/DEPLOYMENT.md** | Azure Quantum deployment guide | Deploying to cloud | - ---- - -## 💻 Code Files - -### Examples (Ready to Run) - -| File | What It Does | Command | -|------|--------------|---------| -| `examples/create_circuits.py` | Creates 6 quantum circuit types | `python .\examples\create_circuits.py` | -| `examples/run_simulations.py` | Runs 6 simulation scenarios | `python .\examples\run_simulations.py` | -| `examples/train_models.py` | Trains on 3 ML datasets | `python .\examples\train_models.py` | -| `examples/azure_integration.py` | Azure setup and guide | `python .\examples\azure_integration.py` | - -### Source Code - -| File | What It Does | Import As | -|------|--------------|-----------| -| `src/quantum_classifier.py` | Quantum ML classifier | `from quantum_classifier import QuantumClassifier` | -| `src/hybrid_qnn.py` | Hybrid quantum-classical network | `from hybrid_qnn import HybridQNN` | -| `src/azure_quantum_integration.py` | Azure Quantum connector | `from azure_quantum_integration import AzureQuantumIntegration` | - -### MCP Server - -| File | What It Does | Use When | -|------|--------------|----------| -| `quantum_mcp_server.py` | MCP server with 8 quantum tools | AI agents need quantum computing | -| `example_mcp_client.py` | Example MCP client | Testing MCP server | - ---- - -## 📊 Generated Results - -### Plots (in `results/`) - -| File | Shows | Generated By | -|------|-------|--------------| -| `state_evolution.png` | Quantum state evolution (RY rotation) | `run_simulations.py` | -| `training_moons.png` | Training loss and accuracy curves | `train_models.py` | -| `model_comparison.png` | Performance across datasets | `train_models.py` | - ---- - -## ⚙️ Configuration - -### Main Config - -| File | Contains | Edit When | -|------|----------|-----------| -| `config/quantum_config.yaml` | All quantum/ML parameters | Tuning performance | - -### Azure Deployment - -| File | Contains | Edit When | -|------|----------|-----------| -| `azure/quantum_workspace.bicep` | Infrastructure as Code | Deploying to Azure | -| `azure/quantum_workspace.parameters.json` | Deployment parameters | Customizing Azure resources | - ---- - -## 🗺️ Navigation Guide - -### I want to - -**Learn quantum computing basics** -→ Read: `README.md` (Architecture section) -→ Run: `examples/create_circuits.py` - -**Run simulations** -→ Read: `examples/README.md` (Simulation section) -→ Run: `examples/run_simulations.py` - -**Train ML models** -→ Read: `DEMONSTRATION_SUMMARY.md` (ML section) -→ Run: `examples/train_models.py` - -**Deploy to Azure** -→ Read: `azure/DEPLOYMENT.md` -→ Configure: `config/quantum_config.yaml` -→ Deploy: Follow deployment steps - -**Use MCP server** -→ Read: `MCP_SERVER_README.md` -→ Run: `python quantum_mcp_server.py` - -**Customize settings** -→ Edit: `config/quantum_config.yaml` -→ Test: `python .\src\quantum_classifier.py` - -**Troubleshoot issues** -→ Check: `examples/README.md` (Troubleshooting section) -→ Or: `QUICK_REFERENCE.md` (Common Commands) - ---- - -## 📈 Learning Path - -### Beginner (Week 1) - -1. ✅ Read `README.md` -2. ✅ Run `examples/create_circuits.py` -3. ✅ Run `examples/run_simulations.py` -4. ✅ Read `QUICK_REFERENCE.md` - -### Intermediate (Week 2) - -1. ✅ Run `examples/train_models.py` -2. ✅ Modify `config/quantum_config.yaml` -3. ✅ Read `DEMONSTRATION_SUMMARY.md` -4. ✅ Experiment with hyperparameters - -### Advanced (Week 3+) - -1. ⬜ Read `azure/DEPLOYMENT.md` -2. ⬜ Deploy to Azure Quantum -3. ⬜ Run on real quantum hardware -4. ⬜ Implement custom algorithms - ---- - -## 🔍 Quick Lookup - -### Common Tasks - -#### Run all examples - -```powershell -cd examples -python create_circuits.py -python run_simulations.py -python train_models.py -python azure_integration.py -``` - -#### View all results - -```powershell -explorer results\ -``` - -#### Activate environment - -```powershell -.\venv\Scripts\Activate.ps1 -``` - -#### Update configuration - -```powershell -notepad config\quantum_config.yaml -``` - ---- - -## 📞 Getting Help - -### Documentation - -1. Check relevant README for your task -2. See `QUICK_REFERENCE.md` for commands -3. Review `DEMONSTRATION_SUMMARY.md` for examples - -### Code Examples - -1. Look in `examples/` directory -2. Check `src/` for implementation details -3. Run `python .py` to see output - -### External Resources - -- [PennyLane Docs](https://docs.pennylane.ai/) -- [Qiskit Tutorials](https://qiskit.org/learn/) -- [Azure Quantum](https://learn.microsoft.com/azure/quantum/) - ---- - -## ✅ Checklist - -### Setup - -- [x] Virtual environment created -- [x] Dependencies installed -- [x] Configuration loaded -- [ ] Azure subscription configured (optional) - -### Examples - -- [x] Circuit creation working -- [x] Simulations running -- [x] ML models training -- [x] Azure guide reviewed - -### Results - -- [x] 3 plots generated -- [x] Training completed (85% accuracy) -- [x] Documentation complete - ---- - -## 📝 File Tree - -```text -ai-projects/quantum-ml/ -├── 📖 README.md ← Main documentation -├── 📋 QUICK_REFERENCE.md ← Quick commands -├── 📊 DEMONSTRATION_SUMMARY.md ← Demo results -├── 🗺️ THIS FILE (INDEX.md) ← You are here -├── 🤖 MCP_SERVER_README.md ← MCP server guide -│ -├── examples/ -│ ├── 📘 README.md ← Examples guide -│ ├── create_circuits.py ← Circuit examples -│ ├── run_simulations.py ← Simulation examples -│ ├── train_models.py ← ML training examples -│ └── azure_integration.py ← Azure guide -│ -├── src/ -│ ├── quantum_classifier.py ← Quantum ML classifier -│ ├── hybrid_qnn.py ← Hybrid network -│ └── azure_quantum_integration.py ← Azure connector -│ -├── config/ -│ └── quantum_config.yaml ← Configuration -│ -├── azure/ -│ ├── DEPLOYMENT.md ← Deployment guide -│ ├── quantum_workspace.bicep ← Infrastructure -│ └── quantum_workspace.parameters.json -│ -└── results/ - ├── state_evolution.png ← State evolution - ├── training_moons.png ← Training curves - └── model_comparison.png ← Performance chart -``` - -### Plain-text file tree (no emoji) - -```text -ai-projects/quantum-ml/ -├── README.md ← Main documentation -├── QUICK_REFERENCE.md ← Quick commands -├── DEMONSTRATION_SUMMARY.md ← Demo results -├── INDEX.md ← You are here -├── MCP_SERVER_README.md ← MCP server guide -│ -├── examples/ -│ ├── README.md ← Examples guide -│ ├── create_circuits.py ← Circuit examples -│ ├── run_simulations.py ← Simulation examples -│ ├── train_models.py ← ML training examples -│ └── azure_integration.py ← Azure guide -│ -├── src/ -│ ├── quantum_classifier.py ← Quantum ML classifier -│ ├── hybrid_qnn.py ← Hybrid network -│ └── azure_quantum_integration.py ← Azure connector -│ -├── config/ -│ └── quantum_config.yaml ← Configuration -│ -├── azure/ -│ ├── DEPLOYMENT.md ← Deployment guide -│ ├── quantum_workspace.bicep ← Infrastructure -│ └── quantum_workspace.parameters.json -│ -└── results/ - ├── state_evolution.png ← State evolution - ├── training_moons.png ← Training curves - └── model_comparison.png ← Performance chart -``` - ---- - -## Happy Quantum Computing! 🌌 - -**Last Updated:** October 31, 2025 +# 📚 Quantum AI - Complete Documentation Index + +## 🎯 Start Here + +**New to Quantum AI?** → Read `README.md` first +**Want quick examples?** → See `QUICK_REFERENCE.md` +**Just finished demos?** → Check `DEMONSTRATION_SUMMARY.md` + +--- + +## 📖 Documentation Files + +### Main Documentation + +| File | Purpose | Read When | +|------|---------|-----------| +| **README.md** | Project overview, installation, usage | Starting the project | +| **QUICK_REFERENCE.md** | Quick commands and tips | Need fast lookup | +| **DEMONSTRATION_SUMMARY.md** | Complete demo results and learnings | After running examples | +| **MCP_SERVER_README.md** | Model Context Protocol server guide | Using MCP tools | + +### Example Documentation + +| File | Purpose | Read When | +|------|---------|-----------| +| **examples/README.md** | Detailed guide for all examples | Running examples | + +### Deployment Documentation + +| File | Purpose | Read When | +|------|---------|-----------| +| **azure/DEPLOYMENT.md** | Azure Quantum deployment guide | Deploying to cloud | + +--- + +## 💻 Code Files + +### Examples (Ready to Run) + +| File | What It Does | Command | +|------|--------------|---------| +| `examples/create_circuits.py` | Creates 6 quantum circuit types | `python .\examples\create_circuits.py` | +| `examples/run_simulations.py` | Runs 6 simulation scenarios | `python .\examples\run_simulations.py` | +| `examples/train_models.py` | Trains on 3 ML datasets | `python .\examples\train_models.py` | +| `examples/azure_integration.py` | Azure setup and guide | `python .\examples\azure_integration.py` | + +### Source Code + +| File | What It Does | Import As | +|------|--------------|-----------| +| `src/quantum_classifier.py` | Quantum ML classifier | `from quantum_classifier import QuantumClassifier` | +| `src/hybrid_qnn.py` | Hybrid quantum-classical network | `from hybrid_qnn import HybridQNN` | +| `src/azure_quantum_integration.py` | Azure Quantum connector | `from azure_quantum_integration import AzureQuantumIntegration` | + +### MCP Server + +| File | What It Does | Use When | +|------|--------------|----------| +| `quantum_mcp_server.py` | MCP server with 8 quantum tools | AI agents need quantum computing | +| `example_mcp_client.py` | Example MCP client | Testing MCP server | + +--- + +## 📊 Generated Results + +### Plots (in `results/`) + +| File | Shows | Generated By | +|------|-------|--------------| +| `state_evolution.png` | Quantum state evolution (RY rotation) | `run_simulations.py` | +| `training_moons.png` | Training loss and accuracy curves | `train_models.py` | +| `model_comparison.png` | Performance across datasets | `train_models.py` | + +--- + +## ⚙️ Configuration + +### Main Config + +| File | Contains | Edit When | +|------|----------|-----------| +| `config/quantum_config.yaml` | All quantum/ML parameters | Tuning performance | + +### Azure Deployment + +| File | Contains | Edit When | +|------|----------|-----------| +| `azure/quantum_workspace.bicep` | Infrastructure as Code | Deploying to Azure | +| `azure/quantum_workspace.parameters.json` | Deployment parameters | Customizing Azure resources | + +--- + +## 🗺️ Navigation Guide + +### I want to + +**Learn quantum computing basics** +→ Read: `README.md` (Architecture section) +→ Run: `examples/create_circuits.py` + +**Run simulations** +→ Read: `examples/README.md` (Simulation section) +→ Run: `examples/run_simulations.py` + +**Train ML models** +→ Read: `DEMONSTRATION_SUMMARY.md` (ML section) +→ Run: `examples/train_models.py` + +**Deploy to Azure** +→ Read: `azure/DEPLOYMENT.md` +→ Configure: `config/quantum_config.yaml` +→ Deploy: Follow deployment steps + +**Use MCP server** +→ Read: `MCP_SERVER_README.md` +→ Run: `python quantum_mcp_server.py` + +**Customize settings** +→ Edit: `config/quantum_config.yaml` +→ Test: `python .\src\quantum_classifier.py` + +**Troubleshoot issues** +→ Check: `examples/README.md` (Troubleshooting section) +→ Or: `QUICK_REFERENCE.md` (Common Commands) + +--- + +## 📈 Learning Path + +### Beginner (Week 1) + +1. ✅ Read `README.md` +2. ✅ Run `examples/create_circuits.py` +3. ✅ Run `examples/run_simulations.py` +4. ✅ Read `QUICK_REFERENCE.md` + +### Intermediate (Week 2) + +1. ✅ Run `examples/train_models.py` +2. ✅ Modify `config/quantum_config.yaml` +3. ✅ Read `DEMONSTRATION_SUMMARY.md` +4. ✅ Experiment with hyperparameters + +### Advanced (Week 3+) + +1. ⬜ Read `azure/DEPLOYMENT.md` +2. ⬜ Deploy to Azure Quantum +3. ⬜ Run on real quantum hardware +4. ⬜ Implement custom algorithms + +--- + +## 🔍 Quick Lookup + +### Common Tasks + +#### Run all examples + +```powershell +cd examples +python create_circuits.py +python run_simulations.py +python train_models.py +python azure_integration.py +``` + +#### View all results + +```powershell +explorer results\ +``` + +#### Activate environment + +```powershell +.\venv\Scripts\Activate.ps1 +``` + +#### Update configuration + +```powershell +notepad config\quantum_config.yaml +``` + +--- + +## 📞 Getting Help + +### Documentation + +1. Check relevant README for your task +2. See `QUICK_REFERENCE.md` for commands +3. Review `DEMONSTRATION_SUMMARY.md` for examples + +### Code Examples + +1. Look in `examples/` directory +2. Check `src/` for implementation details +3. Run `python .py` to see output + +### External Resources + +- [PennyLane Docs](https://docs.pennylane.ai/) +- [Qiskit Tutorials](https://qiskit.org/learn/) +- [Azure Quantum](https://learn.microsoft.com/azure/quantum/) + +--- + +## ✅ Checklist + +### Setup + +- [x] Virtual environment created +- [x] Dependencies installed +- [x] Configuration loaded +- [ ] Azure subscription configured (optional) + +### Examples + +- [x] Circuit creation working +- [x] Simulations running +- [x] ML models training +- [x] Azure guide reviewed + +### Results + +- [x] 3 plots generated +- [x] Training completed (85% accuracy) +- [x] Documentation complete + +--- + +## 📝 File Tree + +```text +ai-projects/quantum-ml/ +├── 📖 README.md ← Main documentation +├── 📋 QUICK_REFERENCE.md ← Quick commands +├── 📊 DEMONSTRATION_SUMMARY.md ← Demo results +├── 🗺️ THIS FILE (INDEX.md) ← You are here +├── 🤖 MCP_SERVER_README.md ← MCP server guide +│ +├── examples/ +│ ├── 📘 README.md ← Examples guide +│ ├── create_circuits.py ← Circuit examples +│ ├── run_simulations.py ← Simulation examples +│ ├── train_models.py ← ML training examples +│ └── azure_integration.py ← Azure guide +│ +├── src/ +│ ├── quantum_classifier.py ← Quantum ML classifier +│ ├── hybrid_qnn.py ← Hybrid network +│ └── azure_quantum_integration.py ← Azure connector +│ +├── config/ +│ └── quantum_config.yaml ← Configuration +│ +├── azure/ +│ ├── DEPLOYMENT.md ← Deployment guide +│ ├── quantum_workspace.bicep ← Infrastructure +│ └── quantum_workspace.parameters.json +│ +└── results/ + ├── state_evolution.png ← State evolution + ├── training_moons.png ← Training curves + └── model_comparison.png ← Performance chart +``` + +### Plain-text file tree (no emoji) + +```text +ai-projects/quantum-ml/ +├── README.md ← Main documentation +├── QUICK_REFERENCE.md ← Quick commands +├── DEMONSTRATION_SUMMARY.md ← Demo results +├── INDEX.md ← You are here +├── MCP_SERVER_README.md ← MCP server guide +│ +├── examples/ +│ ├── README.md ← Examples guide +│ ├── create_circuits.py ← Circuit examples +│ ├── run_simulations.py ← Simulation examples +│ ├── train_models.py ← ML training examples +│ └── azure_integration.py ← Azure guide +│ +├── src/ +│ ├── quantum_classifier.py ← Quantum ML classifier +│ ├── hybrid_qnn.py ← Hybrid network +│ └── azure_quantum_integration.py ← Azure connector +│ +├── config/ +│ └── quantum_config.yaml ← Configuration +│ +├── azure/ +│ ├── DEPLOYMENT.md ← Deployment guide +│ ├── quantum_workspace.bicep ← Infrastructure +│ └── quantum_workspace.parameters.json +│ +└── results/ + ├── state_evolution.png ← State evolution + ├── training_moons.png ← Training curves + └── model_comparison.png ← Performance chart +``` + +--- + +## Happy Quantum Computing! 🌌 + +**Last Updated:** October 31, 2025 diff --git a/ai-projects/quantum-ml/MCP_SERVER_README.md b/ai-projects/quantum-ml/MCP_SERVER_README.md index bbdbf98d7..7e0205fd1 100644 --- a/ai-projects/quantum-ml/MCP_SERVER_README.md +++ b/ai-projects/quantum-ml/MCP_SERVER_README.md @@ -1,24 +1,24 @@ -# Quantum AI MCP Server - -An MCP (Model Context Protocol) server that exposes quantum computing and quantum machine learning capabilities to AI agents and LLMs. - -## Features - +# Quantum AI MCP Server + +An MCP (Model Context Protocol) server that exposes quantum computing and quantum machine learning capabilities to AI agents and LLMs. + +## Features + The Quantum AI MCP server provides 12 powerful tools: - -### Quantum Circuit Creation & Simulation - -- **create_quantum_circuit** - Build quantum circuits (entanglement, GHZ, Bell states, random, or custom) -- **simulate_quantum_circuit** - Simulate circuits locally with Qiskit Aer -- **get_quantum_circuit_properties** - Analyze circuit depth, gates, and topology - -### Azure Quantum Integration - -- **connect_azure_quantum** - Connect to Azure Quantum workspace -- **list_quantum_backends** - See available quantum hardware (IonQ, Quantinuum, etc.) -- **submit_quantum_job** - Run circuits on real quantum computers -- **estimate_quantum_cost** - Get cost estimates before running on hardware - + +### Quantum Circuit Creation & Simulation + +- **create_quantum_circuit** - Build quantum circuits (entanglement, GHZ, Bell states, random, or custom) +- **simulate_quantum_circuit** - Simulate circuits locally with Qiskit Aer +- **get_quantum_circuit_properties** - Analyze circuit depth, gates, and topology + +### Azure Quantum Integration + +- **connect_azure_quantum** - Connect to Azure Quantum workspace +- **list_quantum_backends** - See available quantum hardware (IonQ, Quantinuum, etc.) +- **submit_quantum_job** - Run circuits on real quantum computers +- **estimate_quantum_cost** - Get cost estimates before running on hardware + ### Quantum Machine Learning - **train_quantum_classifier** - Train hybrid quantum-classical ML models on standard datasets @@ -32,160 +32,160 @@ The Quantum AI MCP server provides 12 powerful tools: ### Azure Quantum Test Harness - **azure_quantum_test_suite** - List Azure targets or run the lightweight validation suite (IonQ/Quantinuum/Rigetti) - -## Installation - -```powershell -cd quantum-ai - -# Install base dependencies -pip install -r requirements.txt - -# Install MCP dependencies -pip install -r mcp-requirements.txt -``` - -## Usage - -### As a Standalone MCP Server - -Run the server directly: - -```powershell -python quantum_mcp_server.py -``` - -### In VS Code with MCP Configuration - -Add to your `mcp.json` (in VS Code settings): - -```json -{ - "servers": { - "quantum-ai": { - "type": "stdio", - "command": "python", - "args": [ - "c:\\Users\\Bryan\\OneDrive\\AI\\quantum-ai\\quantum_mcp_server.py" - ], - "env": { - "PYTHONPATH": "${workspaceFolder}\\quantum-ai" - } - } - } -} -``` - -### With Python MCP Client - -```python -import asyncio -from mcp import ClientSession, StdioServerParameters -from mcp.client.stdio import stdio_client - -async def main(): - server_params = StdioServerParameters( - command="python", - args=["quantum_mcp_server.py"], - env={} - ) - - async with stdio_client(server_params) as (read, write): - async with ClientSession(read, write) as session: - await session.initialize() - - # Create a Bell state circuit - result = await session.call_tool( - "create_quantum_circuit", - {"n_qubits": 2, "circuit_type": "bell"} - ) - print(result.content[0].text) - - # Simulate it - circuit_id = "..." # Extract from result - sim_result = await session.call_tool( - "simulate_quantum_circuit", - {"circuit_id": circuit_id, "shots": 1024} - ) - print(sim_result.content[0].text) - -asyncio.run(main()) -``` - -## Example Workflows - -### 1. Create and Simulate a Quantum Circuit - -```python -# Create a GHZ state (3-qubit entanglement) -result = await session.call_tool( - "create_quantum_circuit", - { - "n_qubits": 3, - "circuit_type": "ghz" - } -) - -# Extract circuit ID from result -# Then simulate with 10,000 shots -sim_result = await session.call_tool( - "simulate_quantum_circuit", - { - "circuit_id": "", - "shots": 10000 - } -) -``` - -### 2. Train a Quantum Machine Learning Model - -```python -# Train on the Iris dataset with quantum circuit -result = await session.call_tool( - "train_quantum_classifier", - { - "dataset": "iris", - "n_qubits": 4, - "n_layers": 2, - "epochs": 100, - "entanglement": "linear" - } -) -``` - + +## Installation + +```powershell +cd quantum-ai + +# Install base dependencies +pip install -r requirements.txt + +# Install MCP dependencies +pip install -r mcp-requirements.txt +``` + +## Usage + +### As a Standalone MCP Server + +Run the server directly: + +```powershell +python quantum_mcp_server.py +``` + +### In VS Code with MCP Configuration + +Add to your `mcp.json` (in VS Code settings): + +```json +{ + "servers": { + "quantum-ai": { + "type": "stdio", + "command": "python", + "args": [ + "c:\\Users\\Bryan\\OneDrive\\AI\\quantum-ai\\quantum_mcp_server.py" + ], + "env": { + "PYTHONPATH": "${workspaceFolder}\\quantum-ai" + } + } + } +} +``` + +### With Python MCP Client + +```python +import asyncio +from mcp import ClientSession, StdioServerParameters +from mcp.client.stdio import stdio_client + +async def main(): + server_params = StdioServerParameters( + command="python", + args=["quantum_mcp_server.py"], + env={} + ) + + async with stdio_client(server_params) as (read, write): + async with ClientSession(read, write) as session: + await session.initialize() + + # Create a Bell state circuit + result = await session.call_tool( + "create_quantum_circuit", + {"n_qubits": 2, "circuit_type": "bell"} + ) + print(result.content[0].text) + + # Simulate it + circuit_id = "..." # Extract from result + sim_result = await session.call_tool( + "simulate_quantum_circuit", + {"circuit_id": circuit_id, "shots": 1024} + ) + print(sim_result.content[0].text) + +asyncio.run(main()) +``` + +## Example Workflows + +### 1. Create and Simulate a Quantum Circuit + +```python +# Create a GHZ state (3-qubit entanglement) +result = await session.call_tool( + "create_quantum_circuit", + { + "n_qubits": 3, + "circuit_type": "ghz" + } +) + +# Extract circuit ID from result +# Then simulate with 10,000 shots +sim_result = await session.call_tool( + "simulate_quantum_circuit", + { + "circuit_id": "", + "shots": 10000 + } +) +``` + +### 2. Train a Quantum Machine Learning Model + +```python +# Train on the Iris dataset with quantum circuit +result = await session.call_tool( + "train_quantum_classifier", + { + "dataset": "iris", + "n_qubits": 4, + "n_layers": 2, + "epochs": 100, + "entanglement": "linear" + } +) +``` + ### 3. Run on Azure Quantum Hardware ```python # Connect to Azure await session.call_tool( - "connect_azure_quantum", - { - "subscription_id": "", - "resource_group": "rg-quantum-ai", - "workspace_name": "quantum-ai-workspace" - } -) - -# List available backends -backends = await session.call_tool("list_quantum_backends", {}) - -# Estimate cost -cost = await session.call_tool( - "estimate_quantum_cost", - { - "circuit_id": "", - "backend_name": "ionq.simulator", - "shots": 100 - } -) - -# Submit job -job = await session.call_tool( - "submit_quantum_job", - { - "circuit_id": "", - "backend_name": "ionq.simulator", - "shots": 500 + "connect_azure_quantum", + { + "subscription_id": "", + "resource_group": "rg-quantum-ai", + "workspace_name": "quantum-ai-workspace" + } +) + +# List available backends +backends = await session.call_tool("list_quantum_backends", {}) + +# Estimate cost +cost = await session.call_tool( + "estimate_quantum_cost", + { + "circuit_id": "", + "backend_name": "ionq.simulator", + "shots": 100 + } +) + +# Submit job +job = await session.call_tool( + "submit_quantum_job", + { + "circuit_id": "", + "backend_name": "ionq.simulator", + "shots": 500 } ) ``` @@ -228,35 +228,35 @@ viz = await session.call_tool( ## Tool Reference -### create_quantum_circuit - -**Parameters:** - -- `n_qubits` (int, required) - Number of qubits (1-20) -- `circuit_type` (string, required) - Type: "entanglement", "ghz", "bell", "random", "custom" -- `gates` (array, optional) - For custom circuits: `[{"gate": "h", "qubit": 0}, {"gate": "cx", "qubits": [0,1]}]` - -**Returns:** Circuit ID, diagram, QASM, depth, and gate count - -### simulate_quantum_circuit - -**Parameters:** - -- `circuit_id` (string, required) - ID from create_quantum_circuit -- `shots` (int, optional, default=1024) - Number of measurements (1-100,000) - -**Returns:** Measurement probability distribution - -### train_quantum_classifier - -**Parameters:** - -- `dataset` (string, required) - "iris", "wine", "breast_cancer", or "synthetic" -- `n_qubits` (int, optional, default=4) - Quantum circuit qubits (2-10) -- `n_layers` (int, optional, default=2) - Variational layers (1-5) -- `epochs` (int, optional, default=50) - Training epochs (1-200) -- `entanglement` (string, optional, default="linear") - "linear", "circular", or "full" - +### create_quantum_circuit + +**Parameters:** + +- `n_qubits` (int, required) - Number of qubits (1-20) +- `circuit_type` (string, required) - Type: "entanglement", "ghz", "bell", "random", "custom" +- `gates` (array, optional) - For custom circuits: `[{"gate": "h", "qubit": 0}, {"gate": "cx", "qubits": [0,1]}]` + +**Returns:** Circuit ID, diagram, QASM, depth, and gate count + +### simulate_quantum_circuit + +**Parameters:** + +- `circuit_id` (string, required) - ID from create_quantum_circuit +- `shots` (int, optional, default=1024) - Number of measurements (1-100,000) + +**Returns:** Measurement probability distribution + +### train_quantum_classifier + +**Parameters:** + +- `dataset` (string, required) - "iris", "wine", "breast_cancer", or "synthetic" +- `n_qubits` (int, optional, default=4) - Quantum circuit qubits (2-10) +- `n_layers` (int, optional, default=2) - Variational layers (1-5) +- `epochs` (int, optional, default=50) - Training epochs (1-200) +- `entanglement` (string, optional, default="linear") - "linear", "circular", or "full" + **Returns:** Training metrics, final accuracy, and loss ### run_variational_circuit @@ -307,61 +307,61 @@ viz = await session.call_tool( ### connect_azure_quantum **Parameters:** - -- `subscription_id` (string, required) - Azure subscription ID -- `resource_group` (string, required) - Resource group name -- `workspace_name` (string, required) - Quantum workspace name - -**Returns:** Connection confirmation - -**Note:** Requires Azure credentials configured via `az login` or environment variables. - -## Prerequisites for Azure Quantum - -1. **Azure subscription** with Quantum workspace deployed -2. **Azure CLI authentication**: Run `az login` before using Azure tools -3. **Workspace configuration**: Update `config/quantum_config.yaml` with your Azure details - -See `azure/DEPLOYMENT.md` for full deployment guide. - -## Cost Considerations - -- **Local simulation** (Qiskit Aer): **Free** -- **Microsoft simulators** on Azure: **Free** -- **IonQ hardware**: ~$0.00003 per gate-shot -- **Quantinuum hardware**: ~$0.00015 per circuit execution - -Always use `estimate_quantum_cost` before submitting to paid hardware! - -## Architecture - -The MCP server maintains: - -- **Circuit cache**: Stores created circuits by ID for reuse -- **Azure connection**: Persistent connection to Azure Quantum workspace -- **Quantum classifier**: Pre-initialized for ML training - -All operations are stateful within a session. - -## Troubleshooting - -**"Circuit ID not found"**: Create a circuit with `create_quantum_circuit` first, then use the returned ID. - -**"Not connected to Azure Quantum"**: Call `connect_azure_quantum` before using Azure tools. - -**Import errors**: Ensure you've installed both `requirements.txt` and `mcp-requirements.txt`. - -**Authentication errors**: Run `az login` and verify your Azure subscription has access to the Quantum workspace. - -## Development - -To extend the MCP server: - -1. Add new tool definitions to `list_tools()` -2. Implement handler functions (e.g., `async def my_tool_handler(args)`) -3. Register handler in `call_tool()` dispatcher -4. Update this README with usage examples - -## License - -Same as parent quantum-ai project (MIT). + +- `subscription_id` (string, required) - Azure subscription ID +- `resource_group` (string, required) - Resource group name +- `workspace_name` (string, required) - Quantum workspace name + +**Returns:** Connection confirmation + +**Note:** Requires Azure credentials configured via `az login` or environment variables. + +## Prerequisites for Azure Quantum + +1. **Azure subscription** with Quantum workspace deployed +2. **Azure CLI authentication**: Run `az login` before using Azure tools +3. **Workspace configuration**: Update `config/quantum_config.yaml` with your Azure details + +See `azure/DEPLOYMENT.md` for full deployment guide. + +## Cost Considerations + +- **Local simulation** (Qiskit Aer): **Free** +- **Microsoft simulators** on Azure: **Free** +- **IonQ hardware**: ~$0.00003 per gate-shot +- **Quantinuum hardware**: ~$0.00015 per circuit execution + +Always use `estimate_quantum_cost` before submitting to paid hardware! + +## Architecture + +The MCP server maintains: + +- **Circuit cache**: Stores created circuits by ID for reuse +- **Azure connection**: Persistent connection to Azure Quantum workspace +- **Quantum classifier**: Pre-initialized for ML training + +All operations are stateful within a session. + +## Troubleshooting + +**"Circuit ID not found"**: Create a circuit with `create_quantum_circuit` first, then use the returned ID. + +**"Not connected to Azure Quantum"**: Call `connect_azure_quantum` before using Azure tools. + +**Import errors**: Ensure you've installed both `requirements.txt` and `mcp-requirements.txt`. + +**Authentication errors**: Run `az login` and verify your Azure subscription has access to the Quantum workspace. + +## Development + +To extend the MCP server: + +1. Add new tool definitions to `list_tools()` +2. Implement handler functions (e.g., `async def my_tool_handler(args)`) +3. Register handler in `call_tool()` dispatcher +4. Update this README with usage examples + +## License + +Same as parent quantum-ai project (MIT). diff --git a/ai-projects/quantum-ml/OPTIMIZATION_RESULTS.md b/ai-projects/quantum-ml/OPTIMIZATION_RESULTS.md index d1fb2b223..4ee1980ed 100644 --- a/ai-projects/quantum-ml/OPTIMIZATION_RESULTS.md +++ b/ai-projects/quantum-ml/OPTIMIZATION_RESULTS.md @@ -1,254 +1,254 @@ -# 🎯 Hyperparameter Optimization Results - -**Date:** October 31, 2025 -**Experiment:** Layer Depth Optimization -**Dataset:** Moons (160 training, 40 validation samples) - ---- - -## 🔬 Experiment 1: Layer Depth Analysis - -### Results Summary - -| Layers | Final Accuracy | Best Epoch Acc | Performance | -|--------|---------------|----------------|-------------| -| **1** | 67.5% | 55.0% (epoch 40) | Baseline | -| **2** | **87.5%** ⭐ | **87.5%** (epoch 30+) | **BEST** | -| **3** | 75.0% | 75.0% (epoch 10+) | Good | -| **4** | 67.5% | 70.0% (epoch 30) | Overfitting | - -### 🏆 Optimal Configuration Found - -### Winner: 2 Layers with 87.5% Validation Accuracy - -This is a **significant improvement** over the previous 72.5% baseline! - ---- - -## 📊 Detailed Analysis - -### Layer 1 (Baseline) - -- **Accuracy:** 67.5% -- **Training:** Slow convergence -- **Issue:** Limited expressivity -- **Verdict:** ❌ Insufficient for complex patterns - -### Layer 2 (OPTIMAL) ⭐ - -- **Accuracy:** 87.5% (+20% vs 1 layer!) -- **Training:** Rapid convergence by epoch 20 -- **Stability:** Maintained 87.5% from epoch 30-50 -- **Loss:** Dropped from 0.7217 → 0.5565 -- **Verdict:** ✅ **PERFECT BALANCE** - -**Why it works:** - -- Enough expressivity to capture non-linear features -- Avoids overfitting (good generalization) -- Fast training (50 epochs sufficient) -- Stable convergence - -### Layer 3 (Diminishing Returns) - -- **Accuracy:** 75.0% -- **Training:** Steady but plateaued -- **Issue:** More parameters, worse performance -- **Verdict:** ⚠️ Overfitting starts to appear - -### Layer 4 (Overfitting) - -- **Accuracy:** 67.5% -- **Training:** Erratic, poor convergence -- **Issue:** Too many parameters for dataset size -- **Verdict:** ❌ Clear overfitting - ---- - -## 🎯 Recommended Configuration - -Based on experimental evidence, here's the optimized configuration: - -```yaml -ml: - model: - n_qubits: 4 - n_layers: 2 # OPTIMAL (87.5% accuracy) - entanglement: linear - training: - epochs: 50 # Sufficient for convergence - learning_rate: 0.01 # Current value works well - batch_size: 32 -``` - ---- - -## 📈 Performance Improvements - -### Before Optimization - -- **Moons Dataset:** 72.5% accuracy -- **Configuration:** 2 layers (but not validated) - -### After Optimization - -- **Moons Dataset:** **87.5% accuracy** (+15% improvement!) -- **Configuration:** 2 layers (experimentally validated) -- **Confidence:** HIGH - consistent across epochs 30-50 - ---- - -## 🚀 Expected Impact on All Datasets - -With the optimized 2-layer configuration, we expect: - -| Dataset | Previous | Expected | Improvement | -|---------|----------|----------|-------------| -| **Moons** | 72.5% | **87.5%** | +15% ✅ | -| **Circles** | 52.5% | ~65-70% | +12-17% | -| **Iris** | 76.7% | ~85-90% | +8-13% | -| **XOR** | 57.5% | ~70-75% | +12-17% | -| **Wine** | 88.9% | ~92-95% | +3-6% | -| **Imbalanced** | 90.0% | ~93-95% | +3-5% | - -### Average Expected Improvement - -+10-12% across all datasets - ---- - -## 🔍 Why 2 Layers is Optimal - -### Mathematical Intuition - -- **1 layer:** Limited to single quantum transformation -- **2 layers:** Can compose transformations (quantum advantage!) -- **3+ layers:** Redundant for current dataset size (160 samples) - -### Practical Benefits - -1. **Fast Training:** Converges in ~30-40 epochs -2. **Good Generalization:** No overfitting observed -3. **Computational Efficiency:** 2x faster than 4 layers -4. **Stable Gradients:** Smooth quantum gradient flow - -### Dataset Size Rule - -For datasets with 100-200 samples: - -- **n_layers = 2** is optimal -- **n_layers ≥ 3** risks overfitting -- **n_layers = 1** insufficient expressivity - ---- - -## 🎓 Key Learnings - -1. **More layers ≠ Better performance** - - Sweet spot at 2 layers for current dataset size - - Beyond that, overfitting degrades performance - -2. **Fast Convergence Indicator** - - 2-layer model reached 87.5% by epoch 30 - - Stable plateau indicates good generalization - -3. **Layer-to-Data Ratio** - - Rule of thumb: 1 layer per ~80-100 samples - - 160 samples → 2 layers ideal - -4. **Quantum Circuit Depth** - - Deeper circuits need more training data - - Match circuit complexity to problem complexity - ---- - -## 📋 Next Optimization Steps - -### Completed ✅ - -- [x] Layer depth optimization (2 layers optimal) - -### In Progress 🔄 - -- [ ] Learning rate tuning (0.001, 0.005, 0.01, 0.05, 0.1) -- [ ] Entanglement pattern comparison (linear, circular, full) - -### Pending 📝 - -- [ ] Batch size optimization (16, 32, 64) -- [ ] Qubit count scaling (4, 6, 8) -- [ ] Optimizer comparison (Adam, SGD, RMSprop) -- [ ] Data re-uploading implementation - ---- - -## 💡 Immediate Action Items - -1. **Update Configuration** - - ```bash - # Already optimal in quantum_config.yaml: - # n_layers: 2 ✓ - ``` - -2. **Re-train All Datasets** - - ```bash - python .\examples\train_models.py - ``` - -3. **Validate Improvements** - - Expect ~10-15% average improvement - - Monitor for overfitting - -4. **Continue Optimization** - - ```bash - # Run learning rate experiment: - python .\experiments\parameter_tuning.py - # Select option 2: Learning Rate - ``` - ---- - -## 📊 Visualization - -Plot saved to: `results/experiments/experiment1_layer_depth.png` - -**Chart shows:** - -- X-axis: Number of layers (1-4) -- Y-axis: Validation accuracy -- Clear peak at 2 layers (87.5%) -- Decline for 3+ layers (overfitting) - ---- - -## 🎉 Summary - -**Optimization Successful!** - -We've identified the optimal quantum circuit depth through systematic experimentation: - -- ✅ **87.5% accuracy** achieved (was 72.5%) -- ✅ **+15% improvement** on Moons dataset -- ✅ **2 layers** experimentally validated as optimal -- ✅ **Fast convergence** (30-40 epochs) -- ✅ **No overfitting** (stable validation performance) - -**Your quantum AI is now optimized for maximum performance!** 🚀 - ---- - -## 📚 References - -- Experiment Script: `experiments/parameter_tuning.py` -- Configuration: `config/quantum_config.yaml` -- Training Results: `results/experiments/experiment1_layer_depth.png` -- Previous Report: `TRAINING_REPORT.md` - ---- - -**Next Steps:** Run learning rate and entanglement experiments for further optimization. - -Run learning rate and entanglement experiments for further optimization. +# 🎯 Hyperparameter Optimization Results + +**Date:** October 31, 2025 +**Experiment:** Layer Depth Optimization +**Dataset:** Moons (160 training, 40 validation samples) + +--- + +## 🔬 Experiment 1: Layer Depth Analysis + +### Results Summary + +| Layers | Final Accuracy | Best Epoch Acc | Performance | +|--------|---------------|----------------|-------------| +| **1** | 67.5% | 55.0% (epoch 40) | Baseline | +| **2** | **87.5%** ⭐ | **87.5%** (epoch 30+) | **BEST** | +| **3** | 75.0% | 75.0% (epoch 10+) | Good | +| **4** | 67.5% | 70.0% (epoch 30) | Overfitting | + +### 🏆 Optimal Configuration Found + +### Winner: 2 Layers with 87.5% Validation Accuracy + +This is a **significant improvement** over the previous 72.5% baseline! + +--- + +## 📊 Detailed Analysis + +### Layer 1 (Baseline) + +- **Accuracy:** 67.5% +- **Training:** Slow convergence +- **Issue:** Limited expressivity +- **Verdict:** ❌ Insufficient for complex patterns + +### Layer 2 (OPTIMAL) ⭐ + +- **Accuracy:** 87.5% (+20% vs 1 layer!) +- **Training:** Rapid convergence by epoch 20 +- **Stability:** Maintained 87.5% from epoch 30-50 +- **Loss:** Dropped from 0.7217 → 0.5565 +- **Verdict:** ✅ **PERFECT BALANCE** + +**Why it works:** + +- Enough expressivity to capture non-linear features +- Avoids overfitting (good generalization) +- Fast training (50 epochs sufficient) +- Stable convergence + +### Layer 3 (Diminishing Returns) + +- **Accuracy:** 75.0% +- **Training:** Steady but plateaued +- **Issue:** More parameters, worse performance +- **Verdict:** ⚠️ Overfitting starts to appear + +### Layer 4 (Overfitting) + +- **Accuracy:** 67.5% +- **Training:** Erratic, poor convergence +- **Issue:** Too many parameters for dataset size +- **Verdict:** ❌ Clear overfitting + +--- + +## 🎯 Recommended Configuration + +Based on experimental evidence, here's the optimized configuration: + +```yaml +ml: + model: + n_qubits: 4 + n_layers: 2 # OPTIMAL (87.5% accuracy) + entanglement: linear + training: + epochs: 50 # Sufficient for convergence + learning_rate: 0.01 # Current value works well + batch_size: 32 +``` + +--- + +## 📈 Performance Improvements + +### Before Optimization + +- **Moons Dataset:** 72.5% accuracy +- **Configuration:** 2 layers (but not validated) + +### After Optimization + +- **Moons Dataset:** **87.5% accuracy** (+15% improvement!) +- **Configuration:** 2 layers (experimentally validated) +- **Confidence:** HIGH - consistent across epochs 30-50 + +--- + +## 🚀 Expected Impact on All Datasets + +With the optimized 2-layer configuration, we expect: + +| Dataset | Previous | Expected | Improvement | +|---------|----------|----------|-------------| +| **Moons** | 72.5% | **87.5%** | +15% ✅ | +| **Circles** | 52.5% | ~65-70% | +12-17% | +| **Iris** | 76.7% | ~85-90% | +8-13% | +| **XOR** | 57.5% | ~70-75% | +12-17% | +| **Wine** | 88.9% | ~92-95% | +3-6% | +| **Imbalanced** | 90.0% | ~93-95% | +3-5% | + +### Average Expected Improvement + ++10-12% across all datasets + +--- + +## 🔍 Why 2 Layers is Optimal + +### Mathematical Intuition + +- **1 layer:** Limited to single quantum transformation +- **2 layers:** Can compose transformations (quantum advantage!) +- **3+ layers:** Redundant for current dataset size (160 samples) + +### Practical Benefits + +1. **Fast Training:** Converges in ~30-40 epochs +2. **Good Generalization:** No overfitting observed +3. **Computational Efficiency:** 2x faster than 4 layers +4. **Stable Gradients:** Smooth quantum gradient flow + +### Dataset Size Rule + +For datasets with 100-200 samples: + +- **n_layers = 2** is optimal +- **n_layers ≥ 3** risks overfitting +- **n_layers = 1** insufficient expressivity + +--- + +## 🎓 Key Learnings + +1. **More layers ≠ Better performance** + - Sweet spot at 2 layers for current dataset size + - Beyond that, overfitting degrades performance + +2. **Fast Convergence Indicator** + - 2-layer model reached 87.5% by epoch 30 + - Stable plateau indicates good generalization + +3. **Layer-to-Data Ratio** + - Rule of thumb: 1 layer per ~80-100 samples + - 160 samples → 2 layers ideal + +4. **Quantum Circuit Depth** + - Deeper circuits need more training data + - Match circuit complexity to problem complexity + +--- + +## 📋 Next Optimization Steps + +### Completed ✅ + +- [x] Layer depth optimization (2 layers optimal) + +### In Progress 🔄 + +- [ ] Learning rate tuning (0.001, 0.005, 0.01, 0.05, 0.1) +- [ ] Entanglement pattern comparison (linear, circular, full) + +### Pending 📝 + +- [ ] Batch size optimization (16, 32, 64) +- [ ] Qubit count scaling (4, 6, 8) +- [ ] Optimizer comparison (Adam, SGD, RMSprop) +- [ ] Data re-uploading implementation + +--- + +## 💡 Immediate Action Items + +1. **Update Configuration** + + ```bash + # Already optimal in quantum_config.yaml: + # n_layers: 2 ✓ + ``` + +2. **Re-train All Datasets** + + ```bash + python .\examples\train_models.py + ``` + +3. **Validate Improvements** + - Expect ~10-15% average improvement + - Monitor for overfitting + +4. **Continue Optimization** + + ```bash + # Run learning rate experiment: + python .\experiments\parameter_tuning.py + # Select option 2: Learning Rate + ``` + +--- + +## 📊 Visualization + +Plot saved to: `results/experiments/experiment1_layer_depth.png` + +**Chart shows:** + +- X-axis: Number of layers (1-4) +- Y-axis: Validation accuracy +- Clear peak at 2 layers (87.5%) +- Decline for 3+ layers (overfitting) + +--- + +## 🎉 Summary + +**Optimization Successful!** + +We've identified the optimal quantum circuit depth through systematic experimentation: + +- ✅ **87.5% accuracy** achieved (was 72.5%) +- ✅ **+15% improvement** on Moons dataset +- ✅ **2 layers** experimentally validated as optimal +- ✅ **Fast convergence** (30-40 epochs) +- ✅ **No overfitting** (stable validation performance) + +**Your quantum AI is now optimized for maximum performance!** 🚀 + +--- + +## 📚 References + +- Experiment Script: `experiments/parameter_tuning.py` +- Configuration: `config/quantum_config.yaml` +- Training Results: `results/experiments/experiment1_layer_depth.png` +- Previous Report: `TRAINING_REPORT.md` + +--- + +**Next Steps:** Run learning rate and entanglement experiments for further optimization. + +Run learning rate and entanglement experiments for further optimization. diff --git a/ai-projects/quantum-ml/OPTIMIZATION_SUMMARY.md b/ai-projects/quantum-ml/OPTIMIZATION_SUMMARY.md index 1fa05de05..bab6992a1 100644 --- a/ai-projects/quantum-ml/OPTIMIZATION_SUMMARY.md +++ b/ai-projects/quantum-ml/OPTIMIZATION_SUMMARY.md @@ -1,353 +1,353 @@ -# 🚀 Quantum AI - Optimization Complete - -**Status:** Hyperparameter tuning completed ✅ -**Date:** October 31, 2025 -**Configuration:** Optimized and validated - ---- - -## 🎯 What We Accomplished - -### 1. **Systematic Hyperparameter Optimization** ✅ - -We ran comprehensive experiments testing different layer depths: - -| Configuration | Accuracy | Verdict | -|---------------|----------|---------| -| 1 layer | 67.5% | ❌ Too simple | -| **2 layers** | **87.5%** | ✅ **OPTIMAL** | -| 3 layers | 75.0% | ⚠️ Overfitting starts | -| 4 layers | 67.5% | ❌ Clear overfitting | - -**Key Finding:** 2 layers is the sweet spot for your data! - ---- - -## 📊 Current Optimized Configuration - -Your quantum AI now uses these validated settings: - -```yaml -Quantum Circuit: -- Qubits: 4 -- Layers: 2 (experimentally validated as optimal) -- Entanglement: linear -- Backend: PennyLane default.qubit - -Training Parameters: -- Epochs: 100 -- Learning Rate: 0.01 (balanced convergence) -- Batch Size: 32 -- Optimizer: Adam -``` - ---- - -## 🏆 Performance Summary - -### Current Training Results - -| Dataset | Accuracy | Status | -|---------|----------|--------| -| **Iris** | 67.5% | Good baseline | -| **Imbalanced** | 90.0% | Excellent! | -| **Wine** | 88.9% | Excellent! | -| **Moons** | 57.5-87.5%* | Variable (data dependent) | - -\* Performance varies with random initialization and data splits - -### Best Achieved Performance - -During parameter tuning experiments: - -- **Peak Moons Accuracy:** 87.5% (with 2 layers, 50 epochs) -- **Stable Convergence:** Maintained 87-88% from epoch 30-50 -- **Fast Training:** Reached peak performance in ~30-40 epochs - ---- - -## 💡 Key Optimization Insights - -### What We Learned - -1. **Layer Depth is Critical** - - Too few layers (1): Underfitting, limited expressivity - - Just right (2): Optimal balance, best generalization - - Too many (3-4): Overfitting, worse performance - -2. **Dataset Size Matters** - - For 100-200 samples: 2 layers ideal - - Rule of thumb: 1 layer per ~80-100 samples - - More layers need proportionally more data - -3. **Training Stability** - - 2-layer model shows smooth convergence - - No gradient instability - - Consistent performance across runs - -4. **Quantum Advantage** - - Works best on: Imbalanced data (90%), Real-world data (88.9%) - - Challenges: Highly non-linear spirals (37.5%) - - Sweet spot: Medium complexity classification tasks - ---- - -## 🔬 Technical Details - -### Optimization Methodology - -**Systematic Grid Search:** - -```bash -Layer Depth: [1, 2, 3, 4] -``` - -```text -Result: 2 layers optimal (87.5% accuracy) -``` - -**Training Protocol:** - -- Dataset: Moons (binary classification, non-linear) -- Split: 160 train, 40 validation -- Epochs: 50 per experiment -- Metric: Validation accuracy - -**Statistical Significance:** - -- 2-layer model: 87.5% (20% better than 1-layer baseline) -- Maintained across epochs 30-50 (consistent, not random spike) -- Clear U-shaped curve: peak at 2 layers, decline at 3-4 - ---- - -## 📈 Visualizations Generated - -All plots saved to `results/experiments/`: - -1. **experiment1_layer_depth.png** - - Shows accuracy vs. number of layers - - Clear peak at 2 layers - - Visual confirmation of optimization - -2. **training_moons.png** - - Training and validation curves - - Convergence dynamics - -3. **model_comparison.png** - - Cross-dataset performance - - Identifies quantum advantage areas - ---- - -## 🎯 Next Steps - -### Immediate Recommendations - -1. **Continue Parameter Tuning** 🔄 - - ```bash - python .\experiments\parameter_tuning.py - ``` - - - Test learning rates: [0.001, 0.005, 0.01, 0.05] - - Test entanglement: [linear, circular, full] - - Potentially +5-10% more improvement - -2. **Scale Up Qubits** 📈 - - ```yaml - # Try in quantum_config.yaml: - n_qubits: 6 # or 8 - ``` - - - More expressivity for complex patterns - - Target spiral dataset (currently 37.5%) - -3. **Implement Data Re-uploading** 🔁 - - Advanced quantum ML technique - - Can boost accuracy 10-15% - - Particularly helps with non-linear problems - -### Medium-Term Goals - -1. **Deploy to Azure Quantum** ☁️ - - ```bash - # See deployment guide: - experiments/AZURE_QUICKSTART.md - ``` - - - Test on real quantum hardware - - Compare simulator vs. hardware results - - Understand noise impact - -2. **Ensemble Methods** 🎭 - - Train multiple quantum circuits - - Combine predictions (voting/averaging) - - +5-10% accuracy improvement expected - -3. **Custom Dataset Testing** 📊 - - Apply to your specific use case - - Validate quantum advantage on real problems - - Iterate based on domain requirements - ---- - -## 📋 Optimization Checklist - -### Completed ✅ - -- [x] Environment setup (PennyLane + PyTorch) -- [x] Baseline training (7 datasets) -- [x] Layer depth optimization (2 layers optimal) -- [x] Configuration updated (epochs=100, lr=0.01) -- [x] Validation runs completed -- [x] Documentation created - -### In Progress 🔄 - -- [ ] Learning rate sensitivity analysis -- [ ] Entanglement pattern comparison -- [ ] Batch size optimization - -### Planned 📝 - -- [ ] Qubit scaling (6-8 qubits) -- [ ] Data re-uploading implementation -- [ ] Azure Quantum deployment -- [ ] Ensemble model development -- [ ] Custom dataset integration - ---- - -## 💾 Files & Resources - -### Optimization Results - -- `OPTIMIZATION_RESULTS.md` - Detailed experiment analysis -- `TRAINING_REPORT.md` - Complete training summary -- `results/experiments/` - All experimental plots - -### Configuration - -- `config/quantum_config.yaml` - Optimized parameters -- `src/quantum_classifier.py` - Model implementation - -### Experiments - -- `experiments/parameter_tuning.py` - Optimization script -- `experiments/run_all_experiments.py` - Master runner -- `experiments/AZURE_QUICKSTART.md` - Deployment guide - ---- - -## 🎓 Lessons Learned - -### Best Practices - -1. **Always validate experimentally** - - Don't assume default settings are optimal - - Systematic testing reveals true optima - -2. **Match model complexity to data size** - - 2 layers for 100-200 samples - - Scale layers with more data - -3. **Monitor convergence carefully** - - Stable plateau = good generalization - - Continuing decline = might need more epochs - - Erratic behavior = overfitting or bad hyperparams - -4. **Quantum circuits behave differently** - - More layers ≠ better (unlike classical deep learning) - - Quantum entanglement provides non-linearity - - Sweet spot is often shallower than expected - ---- - -## 🚀 Your Quantum AI is Ready - -### What You Have Now - -✅ **Optimized Configuration** - -- 2 layers (experimentally validated) -- 100 epochs (sufficient for convergence) -- 0.01 learning rate (balanced) - -✅ **Proven Performance** - -- 90% on imbalanced data -- 88.9% on real-world Wine dataset -- 67-87% on various test datasets - -✅ **Complete Framework** - -- Training pipeline -- Optimization tools -- Deployment guides -- Comprehensive documentation - -### Ready For - -🎯 **Production Deployment** - -- Apply to your real-world datasets -- Imbalanced classification tasks -- High-dimensional feature spaces - -🔬 **Further Research** - -- Azure Quantum hardware testing -- Advanced quantum ML techniques -- Scaling to larger problems - -📚 **Learning & Teaching** - -- Comprehensive documentation -- Working examples -- Visualization tools - ---- - -## 📞 Need Help? - -### Documentation - -- Main README: `README.md` -- Examples: `examples/README.md` -- Quick Reference: `QUICK_REFERENCE.md` -- Azure Deployment: `experiments/AZURE_QUICKSTART.md` - -### Common Next Questions - -**Q: How do I deploy to Azure Quantum?** -A: See `experiments/AZURE_QUICKSTART.md` for step-by-step guide - -**Q: Can I train on my own dataset?** -A: Yes! Use `src/quantum_classifier.py` as template - -**Q: How do I improve accuracy further?** -A: Run `experiments/parameter_tuning.py` for learning rate & entanglement tests - ---- - -## 🎉 Congratulations - -Your quantum AI is **optimized and ready for deployment**! - -**Key Achievements:** - -- ✅ Systematic hyperparameter optimization completed -- ✅ Optimal configuration identified (2 layers) -- ✅ Performance validated across multiple datasets -- ✅ Comprehensive documentation created -- ✅ Ready for production use or further experimentation - -**Next milestone:** Deploy to Azure Quantum for real hardware testing! 🚀 - ---- - -*Generated October 31, 2025* -*Quantum AI Optimization System* +# 🚀 Quantum AI - Optimization Complete + +**Status:** Hyperparameter tuning completed ✅ +**Date:** October 31, 2025 +**Configuration:** Optimized and validated + +--- + +## 🎯 What We Accomplished + +### 1. **Systematic Hyperparameter Optimization** ✅ + +We ran comprehensive experiments testing different layer depths: + +| Configuration | Accuracy | Verdict | +|---------------|----------|---------| +| 1 layer | 67.5% | ❌ Too simple | +| **2 layers** | **87.5%** | ✅ **OPTIMAL** | +| 3 layers | 75.0% | ⚠️ Overfitting starts | +| 4 layers | 67.5% | ❌ Clear overfitting | + +**Key Finding:** 2 layers is the sweet spot for your data! + +--- + +## 📊 Current Optimized Configuration + +Your quantum AI now uses these validated settings: + +```yaml +Quantum Circuit: +- Qubits: 4 +- Layers: 2 (experimentally validated as optimal) +- Entanglement: linear +- Backend: PennyLane default.qubit + +Training Parameters: +- Epochs: 100 +- Learning Rate: 0.01 (balanced convergence) +- Batch Size: 32 +- Optimizer: Adam +``` + +--- + +## 🏆 Performance Summary + +### Current Training Results + +| Dataset | Accuracy | Status | +|---------|----------|--------| +| **Iris** | 67.5% | Good baseline | +| **Imbalanced** | 90.0% | Excellent! | +| **Wine** | 88.9% | Excellent! | +| **Moons** | 57.5-87.5%* | Variable (data dependent) | + +\* Performance varies with random initialization and data splits + +### Best Achieved Performance + +During parameter tuning experiments: + +- **Peak Moons Accuracy:** 87.5% (with 2 layers, 50 epochs) +- **Stable Convergence:** Maintained 87-88% from epoch 30-50 +- **Fast Training:** Reached peak performance in ~30-40 epochs + +--- + +## 💡 Key Optimization Insights + +### What We Learned + +1. **Layer Depth is Critical** + - Too few layers (1): Underfitting, limited expressivity + - Just right (2): Optimal balance, best generalization + - Too many (3-4): Overfitting, worse performance + +2. **Dataset Size Matters** + - For 100-200 samples: 2 layers ideal + - Rule of thumb: 1 layer per ~80-100 samples + - More layers need proportionally more data + +3. **Training Stability** + - 2-layer model shows smooth convergence + - No gradient instability + - Consistent performance across runs + +4. **Quantum Advantage** + - Works best on: Imbalanced data (90%), Real-world data (88.9%) + - Challenges: Highly non-linear spirals (37.5%) + - Sweet spot: Medium complexity classification tasks + +--- + +## 🔬 Technical Details + +### Optimization Methodology + +**Systematic Grid Search:** + +```bash +Layer Depth: [1, 2, 3, 4] +``` + +```text +Result: 2 layers optimal (87.5% accuracy) +``` + +**Training Protocol:** + +- Dataset: Moons (binary classification, non-linear) +- Split: 160 train, 40 validation +- Epochs: 50 per experiment +- Metric: Validation accuracy + +**Statistical Significance:** + +- 2-layer model: 87.5% (20% better than 1-layer baseline) +- Maintained across epochs 30-50 (consistent, not random spike) +- Clear U-shaped curve: peak at 2 layers, decline at 3-4 + +--- + +## 📈 Visualizations Generated + +All plots saved to `results/experiments/`: + +1. **experiment1_layer_depth.png** + - Shows accuracy vs. number of layers + - Clear peak at 2 layers + - Visual confirmation of optimization + +2. **training_moons.png** + - Training and validation curves + - Convergence dynamics + +3. **model_comparison.png** + - Cross-dataset performance + - Identifies quantum advantage areas + +--- + +## 🎯 Next Steps + +### Immediate Recommendations + +1. **Continue Parameter Tuning** 🔄 + + ```bash + python .\experiments\parameter_tuning.py + ``` + + - Test learning rates: [0.001, 0.005, 0.01, 0.05] + - Test entanglement: [linear, circular, full] + - Potentially +5-10% more improvement + +2. **Scale Up Qubits** 📈 + + ```yaml + # Try in quantum_config.yaml: + n_qubits: 6 # or 8 + ``` + + - More expressivity for complex patterns + - Target spiral dataset (currently 37.5%) + +3. **Implement Data Re-uploading** 🔁 + - Advanced quantum ML technique + - Can boost accuracy 10-15% + - Particularly helps with non-linear problems + +### Medium-Term Goals + +1. **Deploy to Azure Quantum** ☁️ + + ```bash + # See deployment guide: + experiments/AZURE_QUICKSTART.md + ``` + + - Test on real quantum hardware + - Compare simulator vs. hardware results + - Understand noise impact + +2. **Ensemble Methods** 🎭 + - Train multiple quantum circuits + - Combine predictions (voting/averaging) + - +5-10% accuracy improvement expected + +3. **Custom Dataset Testing** 📊 + - Apply to your specific use case + - Validate quantum advantage on real problems + - Iterate based on domain requirements + +--- + +## 📋 Optimization Checklist + +### Completed ✅ + +- [x] Environment setup (PennyLane + PyTorch) +- [x] Baseline training (7 datasets) +- [x] Layer depth optimization (2 layers optimal) +- [x] Configuration updated (epochs=100, lr=0.01) +- [x] Validation runs completed +- [x] Documentation created + +### In Progress 🔄 + +- [ ] Learning rate sensitivity analysis +- [ ] Entanglement pattern comparison +- [ ] Batch size optimization + +### Planned 📝 + +- [ ] Qubit scaling (6-8 qubits) +- [ ] Data re-uploading implementation +- [ ] Azure Quantum deployment +- [ ] Ensemble model development +- [ ] Custom dataset integration + +--- + +## 💾 Files & Resources + +### Optimization Results + +- `OPTIMIZATION_RESULTS.md` - Detailed experiment analysis +- `TRAINING_REPORT.md` - Complete training summary +- `results/experiments/` - All experimental plots + +### Configuration + +- `config/quantum_config.yaml` - Optimized parameters +- `src/quantum_classifier.py` - Model implementation + +### Experiments + +- `experiments/parameter_tuning.py` - Optimization script +- `experiments/run_all_experiments.py` - Master runner +- `experiments/AZURE_QUICKSTART.md` - Deployment guide + +--- + +## 🎓 Lessons Learned + +### Best Practices + +1. **Always validate experimentally** + - Don't assume default settings are optimal + - Systematic testing reveals true optima + +2. **Match model complexity to data size** + - 2 layers for 100-200 samples + - Scale layers with more data + +3. **Monitor convergence carefully** + - Stable plateau = good generalization + - Continuing decline = might need more epochs + - Erratic behavior = overfitting or bad hyperparams + +4. **Quantum circuits behave differently** + - More layers ≠ better (unlike classical deep learning) + - Quantum entanglement provides non-linearity + - Sweet spot is often shallower than expected + +--- + +## 🚀 Your Quantum AI is Ready + +### What You Have Now + +✅ **Optimized Configuration** + +- 2 layers (experimentally validated) +- 100 epochs (sufficient for convergence) +- 0.01 learning rate (balanced) + +✅ **Proven Performance** + +- 90% on imbalanced data +- 88.9% on real-world Wine dataset +- 67-87% on various test datasets + +✅ **Complete Framework** + +- Training pipeline +- Optimization tools +- Deployment guides +- Comprehensive documentation + +### Ready For + +🎯 **Production Deployment** + +- Apply to your real-world datasets +- Imbalanced classification tasks +- High-dimensional feature spaces + +🔬 **Further Research** + +- Azure Quantum hardware testing +- Advanced quantum ML techniques +- Scaling to larger problems + +📚 **Learning & Teaching** + +- Comprehensive documentation +- Working examples +- Visualization tools + +--- + +## 📞 Need Help? + +### Documentation + +- Main README: `README.md` +- Examples: `examples/README.md` +- Quick Reference: `QUICK_REFERENCE.md` +- Azure Deployment: `experiments/AZURE_QUICKSTART.md` + +### Common Next Questions + +**Q: How do I deploy to Azure Quantum?** +A: See `experiments/AZURE_QUICKSTART.md` for step-by-step guide + +**Q: Can I train on my own dataset?** +A: Yes! Use `src/quantum_classifier.py` as template + +**Q: How do I improve accuracy further?** +A: Run `experiments/parameter_tuning.py` for learning rate & entanglement tests + +--- + +## 🎉 Congratulations + +Your quantum AI is **optimized and ready for deployment**! + +**Key Achievements:** + +- ✅ Systematic hyperparameter optimization completed +- ✅ Optimal configuration identified (2 layers) +- ✅ Performance validated across multiple datasets +- ✅ Comprehensive documentation created +- ✅ Ready for production use or further experimentation + +**Next milestone:** Deploy to Azure Quantum for real hardware testing! 🚀 + +--- + +*Generated October 31, 2025* +*Quantum AI Optimization System* diff --git a/ai-projects/quantum-ml/PORTAL_CREATION_GUIDE.md b/ai-projects/quantum-ml/PORTAL_CREATION_GUIDE.md index 36deb32ab..7df290273 100644 --- a/ai-projects/quantum-ml/PORTAL_CREATION_GUIDE.md +++ b/ai-projects/quantum-ml/PORTAL_CREATION_GUIDE.md @@ -1,129 +1,129 @@ -# Azure Quantum Workspace - Portal Creation Guide - -## Why Portal Creation? - -After extensive testing, we've encountered **provider validation issues** with Bicep/ARM deployments: - -- `InvalidProvider: Provider with id 'Microsoft' was not found` -- `InvalidSku: Sku with id 'pay-as-you-go-cred' was not found` -- API version and provider ID mismatches - -The **Azure Portal automatically configures providers** correctly, avoiding these issues. - -## Quick Create Steps (5 minutes) - -### 1. Open Azure Portal - -Navigate to: - -### 2. Create Azure Quantum Resource - -1. Click **Create a resource** -2. Search for **"Azure Quantum"** -3. Click **Azure Quantum** tile → **Create** - -### 3. Use Quick Create - -1. **Subscription**: Select `Azure subscription 1` (a07fbd16-e722-446d-8efd-0681e85b725c) -2. **Resource Group**: - - Select existing: `rg-quantum-ai` ✓ (already created) -3. **Workspace Name**: `quantum-ai-workspace` -4. **Region**: `East US` -5. **Storage Account**: Let Azure create automatically -6. Click **Quick create** button - -**Quick create automatically adds**: - -- ✅ IonQ provider (pay-as-you-go) -- ✅ Quantinuum provider (pay-as-you-go) -- ✅ Rigetti provider (pay-as-you-go) -- ✅ Microsoft Quantum Computing provider (free simulators) - -### 4. Wait for Deployment - -- Deployment typically takes **2-3 minutes** -- You'll see "Deployment in progress..." notification -- Wait for **"Your deployment is complete"** - -### 5. Verify Workspace - -1. Go to **Resource Groups** → `rg-quantum-ai` -2. You should see: - - `quantum-ai-workspace` (Azure Quantum workspace) - - Storage account (auto-generated name like `quantumstorage...`) -3. Click on `quantum-ai-workspace` -4. In the left menu, click **Providers** tab -5. Verify these providers are listed: - - IonQ - - Quantinuum - - Rigetti - - Microsoft Quantum Computing - -## After Creation: Update Config - -Once created, the deployment script will automatically detect and configure the workspace for local use. - -Run: - -```powershell -.\deploy_simple.ps1 -AutoYes -``` - -This will: - -1. Detect the existing workspace -2. Update `config/quantum_config.yaml` with workspace details -3. Verify connectivity -4. Optionally run tests - -## Next Steps - -After portal creation: - -1. ✅ Workspace is ready for quantum jobs -2. Run simulator tests: `python src/quantum_classifier.py` -3. Submit to Azure Quantum: `python src/azure_quantum_integration.py` -4. Optionally run test suite: `pytest tests/` (if tests exist) - -## Cost Management - -**Free resources** (no charges): - -- Microsoft Quantum Computing simulators -- IonQ/Quantinuum/Rigetti syntax checkers and simulators - -**Paid resources** (charges apply): - -- IonQ Aria/Forte hardware: ~USD 12-98 per execution + gate costs -- Quantinuum H2: ~USD 0.00015 per circuit execution -- Rigetti QPUs: USD 0.02 per 10ms execution time - -**Recommendation**: Start with free simulators, test thoroughly before moving to hardware. - -## Troubleshooting - -### Issue: Can't find Azure Quantum in search - -**Solution**: Ensure you're in the correct Azure subscription. Some regions/subscriptions may have limited access. - -### Issue: Deployment fails - -**Solution**: - -1. Check you have **Owner** or **Contributor** role on the subscription -2. Verify `rg-quantum-ai` resource group exists -3. Try **Advanced create** if Quick create fails - -### Issue: No providers available - -**Solution**: Providers are added automatically with Quick create. If missing: - -1. Go to workspace → **Providers** tab -2. Click **Add** for each provider -3. Select billing plan (default is pay-as-you-go) - -## Reference - -- Portal docs: -- Pricing: -- Provider list: +# Azure Quantum Workspace - Portal Creation Guide + +## Why Portal Creation? + +After extensive testing, we've encountered **provider validation issues** with Bicep/ARM deployments: + +- `InvalidProvider: Provider with id 'Microsoft' was not found` +- `InvalidSku: Sku with id 'pay-as-you-go-cred' was not found` +- API version and provider ID mismatches + +The **Azure Portal automatically configures providers** correctly, avoiding these issues. + +## Quick Create Steps (5 minutes) + +### 1. Open Azure Portal + +Navigate to: + +### 2. Create Azure Quantum Resource + +1. Click **Create a resource** +2. Search for **"Azure Quantum"** +3. Click **Azure Quantum** tile → **Create** + +### 3. Use Quick Create + +1. **Subscription**: Select `Azure subscription 1` (a07fbd16-e722-446d-8efd-0681e85b725c) +2. **Resource Group**: + - Select existing: `rg-quantum-ai` ✓ (already created) +3. **Workspace Name**: `quantum-ai-workspace` +4. **Region**: `East US` +5. **Storage Account**: Let Azure create automatically +6. Click **Quick create** button + +**Quick create automatically adds**: + +- ✅ IonQ provider (pay-as-you-go) +- ✅ Quantinuum provider (pay-as-you-go) +- ✅ Rigetti provider (pay-as-you-go) +- ✅ Microsoft Quantum Computing provider (free simulators) + +### 4. Wait for Deployment + +- Deployment typically takes **2-3 minutes** +- You'll see "Deployment in progress..." notification +- Wait for **"Your deployment is complete"** + +### 5. Verify Workspace + +1. Go to **Resource Groups** → `rg-quantum-ai` +2. You should see: + - `quantum-ai-workspace` (Azure Quantum workspace) + - Storage account (auto-generated name like `quantumstorage...`) +3. Click on `quantum-ai-workspace` +4. In the left menu, click **Providers** tab +5. Verify these providers are listed: + - IonQ + - Quantinuum + - Rigetti + - Microsoft Quantum Computing + +## After Creation: Update Config + +Once created, the deployment script will automatically detect and configure the workspace for local use. + +Run: + +```powershell +.\deploy_simple.ps1 -AutoYes +``` + +This will: + +1. Detect the existing workspace +2. Update `config/quantum_config.yaml` with workspace details +3. Verify connectivity +4. Optionally run tests + +## Next Steps + +After portal creation: + +1. ✅ Workspace is ready for quantum jobs +2. Run simulator tests: `python src/quantum_classifier.py` +3. Submit to Azure Quantum: `python src/azure_quantum_integration.py` +4. Optionally run test suite: `pytest tests/` (if tests exist) + +## Cost Management + +**Free resources** (no charges): + +- Microsoft Quantum Computing simulators +- IonQ/Quantinuum/Rigetti syntax checkers and simulators + +**Paid resources** (charges apply): + +- IonQ Aria/Forte hardware: ~USD 12-98 per execution + gate costs +- Quantinuum H2: ~USD 0.00015 per circuit execution +- Rigetti QPUs: USD 0.02 per 10ms execution time + +**Recommendation**: Start with free simulators, test thoroughly before moving to hardware. + +## Troubleshooting + +### Issue: Can't find Azure Quantum in search + +**Solution**: Ensure you're in the correct Azure subscription. Some regions/subscriptions may have limited access. + +### Issue: Deployment fails + +**Solution**: + +1. Check you have **Owner** or **Contributor** role on the subscription +2. Verify `rg-quantum-ai` resource group exists +3. Try **Advanced create** if Quick create fails + +### Issue: No providers available + +**Solution**: Providers are added automatically with Quick create. If missing: + +1. Go to workspace → **Providers** tab +2. Click **Add** for each provider +3. Select billing plan (default is pay-as-you-go) + +## Reference + +- Portal docs: +- Pricing: +- Provider list: diff --git a/ai-projects/quantum-ml/PRODUCTION_DEPLOYMENT_GUIDE.md b/ai-projects/quantum-ml/PRODUCTION_DEPLOYMENT_GUIDE.md index 45fca71d7..c9e5e92b0 100644 --- a/ai-projects/quantum-ml/PRODUCTION_DEPLOYMENT_GUIDE.md +++ b/ai-projects/quantum-ml/PRODUCTION_DEPLOYMENT_GUIDE.md @@ -1,558 +1,558 @@ -# 🚀 Production Deployment Guide -## Enhanced 8-Qubit Quantum AI with Azure Integration - -**Date:** October 31, 2025 -**Status:** Production Ready -**Achievement:** 97.5% Accuracy on Enhanced Quantum Classifier - ---- - -## 📊 Performance Summary - -### Enhanced 8-Qubit Classifier Results - -| Metric | Value | Improvement | -|--------|-------|-------------| -| **Accuracy** | **97.5%** | +20% vs 4-qubit | -| **Qubits** | 8 | 2x capacity | -| **Layers** | 4 | Optimized depth | -| **Parameters** | 473 | Efficient | -| **Training Time** | 20 epochs | Fast convergence | - -### Comparison: 4-Qubit vs 8-Qubit - -| Configuration | Accuracy | Use Case | -|--------------|----------|----------| -| 4 qubits, 2 layers | 77.5% | Simple patterns | -| 4 qubits, 4 layers | 87.5% | Moderate complexity | -| **8 qubits, 4 layers** | **97.5%** ⭐ | **Complex patterns** | - ---- - -## 🎯 What Was Accomplished - -### ✅ Completed Tasks - -1. **✓ Azure CLI Installation** - - Already installed (v2.78.0) - - Quantum extension enabled (v1.0.0b10) - - PATH configured correctly - -2. **✓ Enhanced Quantum Model (8 Qubits)** - - Created `quantum_classifier_enhanced.py` - - Supports 6-8 qubits for complex patterns - - Data re-uploading implemented - - Multiple entanglement patterns (linear, circular, full, ladder) - - Hardware-ready circuit compilation - -3. **✓ Azure Deployment Scripts** - - `deploy_to_azure_quantum.ps1` - Complete deployment automation - - Workspace creation and configuration - - Provider setup (IonQ, Quantinuum) - - Hardware testing workflows - -4. **✓ Azure ML Integration** - - `azure_ml_integration.py` - Production ML pipeline - - Training job submission - - Model registration and versioning - - REST API deployment for inference - - Compute cluster management - -### 🔬 Experiment Results - -**Parameter Tuning:** -- Optimal layers: 4 (87.5% accuracy) -- Optimal learning rate: 0.01 -- Optimal entanglement: Circular - -**Extended Datasets:** -- Imbalanced: 90.0% ⭐ -- Wine: 63.9% -- XOR: 57.5% -- Spiral: 55.0% - ---- - -## 🚀 Deployment Instructions - -### Step 1: Deploy Azure Quantum Workspace - -```powershell -# Navigate to quantum-ai directory -cd C:\Users\Bryan\OneDrive\AI\quantum-ai - -# Run deployment script -.\deploy_to_azure_quantum.ps1 -SubscriptionId "" -``` - -**What this does:** -- ✓ Authenticates to Azure -- ✓ Creates resource group `rg-quantum-ai` -- ✓ Deploys Azure Quantum workspace -- ✓ Configures IonQ and Quantinuum providers -- ✓ Updates configuration file with subscription details -- ✓ Sets enhanced 8-qubit configuration - -**Time:** ~5 minutes - -### Step 2: Test on Quantum Simulator (FREE) - -```powershell -# Activate Python environment -.\venv\Scripts\Activate.ps1 - -# Test enhanced 8-qubit classifier locally -python src/quantum_classifier_enhanced.py - -# Test on Azure Quantum simulator (FREE) -python test_azure_quantum.py -``` - -**Expected Results:** -- Local test: 97.5% accuracy -- Simulator test: Quantum entanglement verification -- Cost: $0.00 (using free simulators) - -### Step 3: Deploy to Real Quantum Hardware - -```powershell -# Run deployment with hardware testing flag -.\deploy_to_azure_quantum.ps1 -HardwareTest - -# Or run test separately -python test_azure_quantum.py -``` - -**Options:** -1. **IonQ Simulator** (FREE) - Recommended for testing -2. **IonQ QPU** (PAID) - Real ion-trap quantum computer -3. **Quantinuum** (PAID) - Real quantum hardware - -**Costs:** -- IonQ Simulator: **FREE** -- IonQ QPU: ~$0.00003 per gate-shot -- Quantinuum: ~$0.00015 per circuit execution - -**Typical Job Cost:** $1-5 for small circuits (100-500 shots) - -### Step 4: Production Deployment with Azure ML - -```powershell -# Setup Azure ML integration -.\deploy_to_azure_quantum.ps1 -SetupAzureML - -# Install Azure ML SDK -pip install azureml-sdk - -# Run production deployment -python src/azure_ml_integration.py -``` - -**Features:** -- ✓ Automated training pipelines -- ✓ Model versioning and registry -- ✓ REST API for inference -- ✓ Monitoring and logging -- ✓ Auto-scaling compute - ---- - -## 📁 Project Structure - -``` -ai-projects/quantum-ml/ -├── src/ -│ ├── quantum_classifier.py # Original 4-qubit classifier -│ ├── quantum_classifier_enhanced.py # NEW: 8-qubit enhanced classifier -│ ├── azure_quantum_integration.py # Azure Quantum SDK integration -│ ├── azure_ml_integration.py # NEW: Azure ML pipeline -│ └── hybrid_qnn.py # Hybrid quantum-classical network -│ -├── config/ -│ └── quantum_config.yaml # Configuration (updated with 8 qubits) -│ -├── azure/ -│ ├── quantum_workspace.bicep # Infrastructure as Code -│ └── quantum_workspace.parameters.json # Deployment parameters -│ -├── results/ -│ ├── experiments/ # Parameter tuning plots -│ └── extended_datasets/ # Dataset comparison results -│ -├── deploy_to_azure_quantum.ps1 # NEW: Complete deployment script -├── test_azure_quantum.py # Azure hardware testing -└── PRODUCTION_DEPLOYMENT_GUIDE.md # This file -``` - ---- - -## 🔧 Configuration - -### Enhanced Configuration (quantum_config.yaml) - -```yaml -ml: - model: - n_qubits: 8 # Enhanced: 8 qubits (was 4) - n_layers: 4 # Optimized: 4 layers - entanglement: full # Best for complex patterns - - training: - epochs: 100 - batch_size: 32 - learning_rate: 0.01 - -azure: - subscription_id: '' - resource_group: 'rg-quantum-ai' - workspace_name: 'quantum-ai-workspace' - location: 'eastus' - -quantum: - provider: ionq - simulator: - backend: default.qubit - shots: 1024 - hardware: - shots: 500 - optimization_level: 2 -``` - ---- - -## 💡 Enhanced Features - -### 8-Qubit Classifier Advantages - -1. **Higher Expressivity** - - 256 quantum states vs 16 (4-qubit) - - Better representation of complex patterns - - Achieved 97.5% accuracy on test data - -2. **Data Re-uploading** - - Multiple encoding layers - - Richer feature representation - - 10-15% accuracy improvement - -3. **Advanced Entanglement** - - Ladder pattern for 8 qubits - - Better quantum correlations - - Optimized for hardware constraints - -4. **Hardware-Ready** - - Barrier gates for compilation - - Optimized gate sequences - - Compatible with IonQ/Quantinuum - -### Azure ML Production Features - -1. **Training Pipelines** - ```python - deployer = QuantumAzureMLDeployment() - run = deployer.submit_training_job( - script_path='train_azure_ml.py', - experiment_name='quantum-8qubit', - arguments={'n_qubits': 8, 'epochs': 100} - ) - ``` - -2. **Model Registry** - ```python - model = deployer.register_model( - model_path='outputs/quantum_model.pt', - model_name='quantum-classifier-8q', - tags={'qubits': '8', 'accuracy': '97.5%'} - ) - ``` - -3. **REST API Deployment** - ```python - service = deployer.deploy_inference_endpoint( - model_name='quantum-classifier-8q', - service_name='quantum-api' - ) - # Access at: service.scoring_uri - ``` - ---- - -## 📊 Performance Benchmarks - -### Training Performance - -| Dataset | 4-Qubit | 8-Qubit | Improvement | -|---------|---------|---------|-------------| -| Moons | 77.5% | **97.5%** | +20.0% | -| XOR | 57.5% | **85.0%** | +27.5% | -| Spiral | 55.0% | **82.0%** | +27.0% | -| Wine | 63.9% | **91.2%** | +27.3% | - -### Computational Cost - -| Configuration | Parameters | Training Time | Inference Time | -|--------------|------------|---------------|----------------| -| 4 qubits, 2 layers | 96 | 2 min | 10 ms | -| 4 qubits, 4 layers | 192 | 4 min | 15 ms | -| **8 qubits, 4 layers** | **473** | **6 min** | **20 ms** | - ---- - -## 🎯 Next Steps & Recommendations - -### Immediate Actions (High Priority) - -1. **Deploy to Azure Quantum** - ```powershell - .\deploy_to_azure_quantum.ps1 -SubscriptionId "your-id" - ``` - - Start with free simulator - - Verify workspace configuration - - Test quantum entanglement - -2. **Run Hardware Tests** - ```powershell - .\deploy_to_azure_quantum.ps1 -HardwareTest - ``` - - Begin with Bell state test - - Progress to full 8-qubit circuits - - Compare simulator vs hardware results - -3. **Set Up Production Pipeline** - ```powershell - .\deploy_to_azure_quantum.ps1 -SetupAzureML - ``` - - Configure ML workspace - - Deploy compute cluster - - Register first model version - -### Medium-Term Goals (1-2 Weeks) - -1. **Optimize for Production** - - Fine-tune hyperparameters for your specific dataset - - Implement cross-validation - - Add monitoring and alerting - - Set up CI/CD pipeline - -2. **Scale to Larger Datasets** - - Test with 1000+ samples - - Implement batch processing - - Optimize memory usage - - Parallelize training - -3. **Explore Advanced Features** - - Quantum attention mechanisms - - Error mitigation techniques - - Hybrid quantum-classical ensembles - - Multi-class classification (>2 classes) - -### Long-Term Vision (1-3 Months) - -1. **Research Extensions** - - Quantum transfer learning - - Quantum GANs for data generation - - Quantum reinforcement learning - - Integration with Azure OpenAI - -2. **Enterprise Deployment** - - Multi-region deployment - - High-availability setup - - Security hardening - - Compliance and audit logging - -3. **Cost Optimization** - - Reserved capacity pricing - - Spot instances for training - - Caching and result reuse - - Provider cost comparison - ---- - -## 💰 Cost Breakdown - -### Development (FREE) - -- ✅ Local quantum simulation (PennyLane) -- ✅ Azure Quantum simulators -- ✅ Azure free tier ($200 credit for new accounts) -- **Monthly Cost: $0** - -### Testing (~$50/month) - -- IonQ Simulator: **FREE** -- Small hardware jobs: ~$10/month (10-20 jobs) -- Azure ML compute (dev tier): ~$40/month -- Storage: ~$1/month -- **Monthly Cost: ~$50** - -### Production (~$500/month) - -- Regular quantum jobs: ~$200/month -- Azure ML compute cluster: ~$200/month -- API hosting: ~$50/month -- Monitoring & logging: ~$30/month -- Storage & bandwidth: ~$20/month -- **Monthly Cost: ~$500** - -**Optimization Tips:** -- Use simulators for development (FREE) -- Batch quantum jobs for efficiency -- Auto-scale compute to zero when idle -- Use spot instances for non-critical workloads - ---- - -## 🆘 Troubleshooting - -### Common Issues - -**Issue 1: Azure CLI not found** -```powershell -# Refresh PATH -$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User") - -# Verify -az --version -``` - -**Issue 2: Subscription not configured** -```powershell -# List subscriptions -az account list --output table - -# Set subscription -az account set --subscription "" -``` - -**Issue 3: Quantum workspace deployment fails** -```powershell -# Verify resource group exists -az group show --name rg-quantum-ai - -# Check quota limits -az vm list-usage --location eastus --output table -``` - -**Issue 4: Python dependencies missing** -```powershell -cd quantum-ai -.\venv\Scripts\Activate.ps1 -pip install -r requirements.txt -pip install azureml-sdk # For Azure ML -``` - -### Getting Help - -- **Azure Quantum Docs**: https://docs.microsoft.com/azure/quantum/ -- **GitHub Issues**: Report bugs and feature requests -- **Azure Support**: https://portal.azure.com/#blade/Microsoft_Azure_Support/HelpAndSupportBlade -- **PennyLane Docs**: https://docs.pennylane.ai/ - ---- - -## 📚 Documentation References - -### Created in This Session - -1. **quantum_classifier_enhanced.py** - 8-qubit enhanced classifier -2. **deploy_to_azure_quantum.ps1** - Complete deployment automation -3. **azure_ml_integration.py** - Production ML pipeline -4. **PRODUCTION_DEPLOYMENT_GUIDE.md** - This guide - -### Existing Documentation - -1. **README.md** - Project overview and quick start -2. **AZURE_QUANTUM_QUICKSTART.md** - Azure deployment guide -3. **TRAINING_REPORT.md** - Training results and analysis -4. **azure/DEPLOYMENT.md** - Detailed infrastructure guide -5. **QUICK_REFERENCE.md** - Command reference - ---- - -## ✅ Success Criteria - -### Deployment Success - -- ✓ Azure CLI installed and configured -- ✓ Azure Quantum workspace deployed -- ✓ Quantum providers (IonQ/Quantinuum) accessible -- ✓ Configuration file updated with subscription -- ✓ Enhanced 8-qubit classifier tested locally -- ✓ Azure ML workspace created (optional) - -### Performance Success - -- ✓ 8-qubit classifier achieves >95% accuracy -- ✓ Training completes in <10 minutes locally -- ✓ Quantum circuits compile successfully -- ✓ Hardware tests complete without errors -- ✓ Production API responds in <100ms - -### Production Readiness - -- ✓ Automated deployment scripts working -- ✓ Model versioning implemented -- ✓ Monitoring and logging configured -- ✓ Cost tracking enabled -- ✓ Documentation complete - ---- - -## 🎉 Achievement Summary - -### What You Can Do Now - -1. **Train Advanced Quantum Models** - - 8-qubit classifier with 97.5% accuracy - - Supports complex pattern recognition - - Hardware-ready circuits - -2. **Deploy to Real Quantum Hardware** - - IonQ ion-trap quantum computers - - Quantinuum quantum processors - - Full Azure Quantum integration - -3. **Production ML Pipeline** - - Automated training workflows - - Model registry and versioning - - REST API for inference - - Auto-scaling infrastructure - -4. **Enterprise-Grade Infrastructure** - - Infrastructure as Code (Bicep) - - Automated deployment scripts - - Cost monitoring and optimization - - Security and compliance ready - ---- - -## 📞 Support & Resources - -**Azure Quantum** -- Portal: https://portal.azure.com -- Documentation: https://aka.ms/quantum-docs -- Pricing: https://azure.microsoft.com/pricing/details/azure-quantum/ - -**Azure Machine Learning** -- Portal: https://ml.azure.com -- Documentation: https://aka.ms/azureml-docs -- Examples: https://github.com/Azure/azureml-examples - -**Quantum Computing** -- PennyLane: https://pennylane.ai -- Qiskit: https://qiskit.org -- Research Papers: https://arxiv.org/list/quant-ph/recent - ---- - -**🚀 You're now ready for production quantum AI deployment!** - -**Status:** ✅ All systems operational -**Performance:** ⭐ 97.5% accuracy achieved -**Deployment:** ✅ Scripts ready for Azure -**Cost:** 💰 Optimized for production scale - ---- - -*Generated: October 31, 2025* -*Version: 1.0 - Production Ready* -*Project: Quantum AI with Azure Integration* +# 🚀 Production Deployment Guide +## Enhanced 8-Qubit Quantum AI with Azure Integration + +**Date:** October 31, 2025 +**Status:** Production Ready +**Achievement:** 97.5% Accuracy on Enhanced Quantum Classifier + +--- + +## 📊 Performance Summary + +### Enhanced 8-Qubit Classifier Results + +| Metric | Value | Improvement | +|--------|-------|-------------| +| **Accuracy** | **97.5%** | +20% vs 4-qubit | +| **Qubits** | 8 | 2x capacity | +| **Layers** | 4 | Optimized depth | +| **Parameters** | 473 | Efficient | +| **Training Time** | 20 epochs | Fast convergence | + +### Comparison: 4-Qubit vs 8-Qubit + +| Configuration | Accuracy | Use Case | +|--------------|----------|----------| +| 4 qubits, 2 layers | 77.5% | Simple patterns | +| 4 qubits, 4 layers | 87.5% | Moderate complexity | +| **8 qubits, 4 layers** | **97.5%** ⭐ | **Complex patterns** | + +--- + +## 🎯 What Was Accomplished + +### ✅ Completed Tasks + +1. **✓ Azure CLI Installation** + - Already installed (v2.78.0) + - Quantum extension enabled (v1.0.0b10) + - PATH configured correctly + +2. **✓ Enhanced Quantum Model (8 Qubits)** + - Created `quantum_classifier_enhanced.py` + - Supports 6-8 qubits for complex patterns + - Data re-uploading implemented + - Multiple entanglement patterns (linear, circular, full, ladder) + - Hardware-ready circuit compilation + +3. **✓ Azure Deployment Scripts** + - `deploy_to_azure_quantum.ps1` - Complete deployment automation + - Workspace creation and configuration + - Provider setup (IonQ, Quantinuum) + - Hardware testing workflows + +4. **✓ Azure ML Integration** + - `azure_ml_integration.py` - Production ML pipeline + - Training job submission + - Model registration and versioning + - REST API deployment for inference + - Compute cluster management + +### 🔬 Experiment Results + +**Parameter Tuning:** +- Optimal layers: 4 (87.5% accuracy) +- Optimal learning rate: 0.01 +- Optimal entanglement: Circular + +**Extended Datasets:** +- Imbalanced: 90.0% ⭐ +- Wine: 63.9% +- XOR: 57.5% +- Spiral: 55.0% + +--- + +## 🚀 Deployment Instructions + +### Step 1: Deploy Azure Quantum Workspace + +```powershell +# Navigate to quantum-ai directory +cd C:\Users\Bryan\OneDrive\AI\quantum-ai + +# Run deployment script +.\deploy_to_azure_quantum.ps1 -SubscriptionId "" +``` + +**What this does:** +- ✓ Authenticates to Azure +- ✓ Creates resource group `rg-quantum-ai` +- ✓ Deploys Azure Quantum workspace +- ✓ Configures IonQ and Quantinuum providers +- ✓ Updates configuration file with subscription details +- ✓ Sets enhanced 8-qubit configuration + +**Time:** ~5 minutes + +### Step 2: Test on Quantum Simulator (FREE) + +```powershell +# Activate Python environment +.\venv\Scripts\Activate.ps1 + +# Test enhanced 8-qubit classifier locally +python src/quantum_classifier_enhanced.py + +# Test on Azure Quantum simulator (FREE) +python test_azure_quantum.py +``` + +**Expected Results:** +- Local test: 97.5% accuracy +- Simulator test: Quantum entanglement verification +- Cost: $0.00 (using free simulators) + +### Step 3: Deploy to Real Quantum Hardware + +```powershell +# Run deployment with hardware testing flag +.\deploy_to_azure_quantum.ps1 -HardwareTest + +# Or run test separately +python test_azure_quantum.py +``` + +**Options:** +1. **IonQ Simulator** (FREE) - Recommended for testing +2. **IonQ QPU** (PAID) - Real ion-trap quantum computer +3. **Quantinuum** (PAID) - Real quantum hardware + +**Costs:** +- IonQ Simulator: **FREE** +- IonQ QPU: ~$0.00003 per gate-shot +- Quantinuum: ~$0.00015 per circuit execution + +**Typical Job Cost:** $1-5 for small circuits (100-500 shots) + +### Step 4: Production Deployment with Azure ML + +```powershell +# Setup Azure ML integration +.\deploy_to_azure_quantum.ps1 -SetupAzureML + +# Install Azure ML SDK +pip install azureml-sdk + +# Run production deployment +python src/azure_ml_integration.py +``` + +**Features:** +- ✓ Automated training pipelines +- ✓ Model versioning and registry +- ✓ REST API for inference +- ✓ Monitoring and logging +- ✓ Auto-scaling compute + +--- + +## 📁 Project Structure + +``` +ai-projects/quantum-ml/ +├── src/ +│ ├── quantum_classifier.py # Original 4-qubit classifier +│ ├── quantum_classifier_enhanced.py # NEW: 8-qubit enhanced classifier +│ ├── azure_quantum_integration.py # Azure Quantum SDK integration +│ ├── azure_ml_integration.py # NEW: Azure ML pipeline +│ └── hybrid_qnn.py # Hybrid quantum-classical network +│ +├── config/ +│ └── quantum_config.yaml # Configuration (updated with 8 qubits) +│ +├── azure/ +│ ├── quantum_workspace.bicep # Infrastructure as Code +│ └── quantum_workspace.parameters.json # Deployment parameters +│ +├── results/ +│ ├── experiments/ # Parameter tuning plots +│ └── extended_datasets/ # Dataset comparison results +│ +├── deploy_to_azure_quantum.ps1 # NEW: Complete deployment script +├── test_azure_quantum.py # Azure hardware testing +└── PRODUCTION_DEPLOYMENT_GUIDE.md # This file +``` + +--- + +## 🔧 Configuration + +### Enhanced Configuration (quantum_config.yaml) + +```yaml +ml: + model: + n_qubits: 8 # Enhanced: 8 qubits (was 4) + n_layers: 4 # Optimized: 4 layers + entanglement: full # Best for complex patterns + + training: + epochs: 100 + batch_size: 32 + learning_rate: 0.01 + +azure: + subscription_id: '' + resource_group: 'rg-quantum-ai' + workspace_name: 'quantum-ai-workspace' + location: 'eastus' + +quantum: + provider: ionq + simulator: + backend: default.qubit + shots: 1024 + hardware: + shots: 500 + optimization_level: 2 +``` + +--- + +## 💡 Enhanced Features + +### 8-Qubit Classifier Advantages + +1. **Higher Expressivity** + - 256 quantum states vs 16 (4-qubit) + - Better representation of complex patterns + - Achieved 97.5% accuracy on test data + +2. **Data Re-uploading** + - Multiple encoding layers + - Richer feature representation + - 10-15% accuracy improvement + +3. **Advanced Entanglement** + - Ladder pattern for 8 qubits + - Better quantum correlations + - Optimized for hardware constraints + +4. **Hardware-Ready** + - Barrier gates for compilation + - Optimized gate sequences + - Compatible with IonQ/Quantinuum + +### Azure ML Production Features + +1. **Training Pipelines** + ```python + deployer = QuantumAzureMLDeployment() + run = deployer.submit_training_job( + script_path='train_azure_ml.py', + experiment_name='quantum-8qubit', + arguments={'n_qubits': 8, 'epochs': 100} + ) + ``` + +2. **Model Registry** + ```python + model = deployer.register_model( + model_path='outputs/quantum_model.pt', + model_name='quantum-classifier-8q', + tags={'qubits': '8', 'accuracy': '97.5%'} + ) + ``` + +3. **REST API Deployment** + ```python + service = deployer.deploy_inference_endpoint( + model_name='quantum-classifier-8q', + service_name='quantum-api' + ) + # Access at: service.scoring_uri + ``` + +--- + +## 📊 Performance Benchmarks + +### Training Performance + +| Dataset | 4-Qubit | 8-Qubit | Improvement | +|---------|---------|---------|-------------| +| Moons | 77.5% | **97.5%** | +20.0% | +| XOR | 57.5% | **85.0%** | +27.5% | +| Spiral | 55.0% | **82.0%** | +27.0% | +| Wine | 63.9% | **91.2%** | +27.3% | + +### Computational Cost + +| Configuration | Parameters | Training Time | Inference Time | +|--------------|------------|---------------|----------------| +| 4 qubits, 2 layers | 96 | 2 min | 10 ms | +| 4 qubits, 4 layers | 192 | 4 min | 15 ms | +| **8 qubits, 4 layers** | **473** | **6 min** | **20 ms** | + +--- + +## 🎯 Next Steps & Recommendations + +### Immediate Actions (High Priority) + +1. **Deploy to Azure Quantum** + ```powershell + .\deploy_to_azure_quantum.ps1 -SubscriptionId "your-id" + ``` + - Start with free simulator + - Verify workspace configuration + - Test quantum entanglement + +2. **Run Hardware Tests** + ```powershell + .\deploy_to_azure_quantum.ps1 -HardwareTest + ``` + - Begin with Bell state test + - Progress to full 8-qubit circuits + - Compare simulator vs hardware results + +3. **Set Up Production Pipeline** + ```powershell + .\deploy_to_azure_quantum.ps1 -SetupAzureML + ``` + - Configure ML workspace + - Deploy compute cluster + - Register first model version + +### Medium-Term Goals (1-2 Weeks) + +1. **Optimize for Production** + - Fine-tune hyperparameters for your specific dataset + - Implement cross-validation + - Add monitoring and alerting + - Set up CI/CD pipeline + +2. **Scale to Larger Datasets** + - Test with 1000+ samples + - Implement batch processing + - Optimize memory usage + - Parallelize training + +3. **Explore Advanced Features** + - Quantum attention mechanisms + - Error mitigation techniques + - Hybrid quantum-classical ensembles + - Multi-class classification (>2 classes) + +### Long-Term Vision (1-3 Months) + +1. **Research Extensions** + - Quantum transfer learning + - Quantum GANs for data generation + - Quantum reinforcement learning + - Integration with Azure OpenAI + +2. **Enterprise Deployment** + - Multi-region deployment + - High-availability setup + - Security hardening + - Compliance and audit logging + +3. **Cost Optimization** + - Reserved capacity pricing + - Spot instances for training + - Caching and result reuse + - Provider cost comparison + +--- + +## 💰 Cost Breakdown + +### Development (FREE) + +- ✅ Local quantum simulation (PennyLane) +- ✅ Azure Quantum simulators +- ✅ Azure free tier ($200 credit for new accounts) +- **Monthly Cost: $0** + +### Testing (~$50/month) + +- IonQ Simulator: **FREE** +- Small hardware jobs: ~$10/month (10-20 jobs) +- Azure ML compute (dev tier): ~$40/month +- Storage: ~$1/month +- **Monthly Cost: ~$50** + +### Production (~$500/month) + +- Regular quantum jobs: ~$200/month +- Azure ML compute cluster: ~$200/month +- API hosting: ~$50/month +- Monitoring & logging: ~$30/month +- Storage & bandwidth: ~$20/month +- **Monthly Cost: ~$500** + +**Optimization Tips:** +- Use simulators for development (FREE) +- Batch quantum jobs for efficiency +- Auto-scale compute to zero when idle +- Use spot instances for non-critical workloads + +--- + +## 🆘 Troubleshooting + +### Common Issues + +**Issue 1: Azure CLI not found** +```powershell +# Refresh PATH +$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User") + +# Verify +az --version +``` + +**Issue 2: Subscription not configured** +```powershell +# List subscriptions +az account list --output table + +# Set subscription +az account set --subscription "" +``` + +**Issue 3: Quantum workspace deployment fails** +```powershell +# Verify resource group exists +az group show --name rg-quantum-ai + +# Check quota limits +az vm list-usage --location eastus --output table +``` + +**Issue 4: Python dependencies missing** +```powershell +cd quantum-ai +.\venv\Scripts\Activate.ps1 +pip install -r requirements.txt +pip install azureml-sdk # For Azure ML +``` + +### Getting Help + +- **Azure Quantum Docs**: https://docs.microsoft.com/azure/quantum/ +- **GitHub Issues**: Report bugs and feature requests +- **Azure Support**: https://portal.azure.com/#blade/Microsoft_Azure_Support/HelpAndSupportBlade +- **PennyLane Docs**: https://docs.pennylane.ai/ + +--- + +## 📚 Documentation References + +### Created in This Session + +1. **quantum_classifier_enhanced.py** - 8-qubit enhanced classifier +2. **deploy_to_azure_quantum.ps1** - Complete deployment automation +3. **azure_ml_integration.py** - Production ML pipeline +4. **PRODUCTION_DEPLOYMENT_GUIDE.md** - This guide + +### Existing Documentation + +1. **README.md** - Project overview and quick start +2. **AZURE_QUANTUM_QUICKSTART.md** - Azure deployment guide +3. **TRAINING_REPORT.md** - Training results and analysis +4. **azure/DEPLOYMENT.md** - Detailed infrastructure guide +5. **QUICK_REFERENCE.md** - Command reference + +--- + +## ✅ Success Criteria + +### Deployment Success + +- ✓ Azure CLI installed and configured +- ✓ Azure Quantum workspace deployed +- ✓ Quantum providers (IonQ/Quantinuum) accessible +- ✓ Configuration file updated with subscription +- ✓ Enhanced 8-qubit classifier tested locally +- ✓ Azure ML workspace created (optional) + +### Performance Success + +- ✓ 8-qubit classifier achieves >95% accuracy +- ✓ Training completes in <10 minutes locally +- ✓ Quantum circuits compile successfully +- ✓ Hardware tests complete without errors +- ✓ Production API responds in <100ms + +### Production Readiness + +- ✓ Automated deployment scripts working +- ✓ Model versioning implemented +- ✓ Monitoring and logging configured +- ✓ Cost tracking enabled +- ✓ Documentation complete + +--- + +## 🎉 Achievement Summary + +### What You Can Do Now + +1. **Train Advanced Quantum Models** + - 8-qubit classifier with 97.5% accuracy + - Supports complex pattern recognition + - Hardware-ready circuits + +2. **Deploy to Real Quantum Hardware** + - IonQ ion-trap quantum computers + - Quantinuum quantum processors + - Full Azure Quantum integration + +3. **Production ML Pipeline** + - Automated training workflows + - Model registry and versioning + - REST API for inference + - Auto-scaling infrastructure + +4. **Enterprise-Grade Infrastructure** + - Infrastructure as Code (Bicep) + - Automated deployment scripts + - Cost monitoring and optimization + - Security and compliance ready + +--- + +## 📞 Support & Resources + +**Azure Quantum** +- Portal: https://portal.azure.com +- Documentation: https://aka.ms/quantum-docs +- Pricing: https://azure.microsoft.com/pricing/details/azure-quantum/ + +**Azure Machine Learning** +- Portal: https://ml.azure.com +- Documentation: https://aka.ms/azureml-docs +- Examples: https://github.com/Azure/azureml-examples + +**Quantum Computing** +- PennyLane: https://pennylane.ai +- Qiskit: https://qiskit.org +- Research Papers: https://arxiv.org/list/quant-ph/recent + +--- + +**🚀 You're now ready for production quantum AI deployment!** + +**Status:** ✅ All systems operational +**Performance:** ⭐ 97.5% accuracy achieved +**Deployment:** ✅ Scripts ready for Azure +**Cost:** 💰 Optimized for production scale + +--- + +*Generated: October 31, 2025* +*Version: 1.0 - Production Ready* +*Project: Quantum AI with Azure Integration* diff --git a/ai-projects/quantum-ml/PROVIDER_COMPARISON_RESULTS.md b/ai-projects/quantum-ml/PROVIDER_COMPARISON_RESULTS.md index c5eafa3ff..830106cca 100644 --- a/ai-projects/quantum-ml/PROVIDER_COMPARISON_RESULTS.md +++ b/ai-projects/quantum-ml/PROVIDER_COMPARISON_RESULTS.md @@ -1,409 +1,409 @@ -# Azure Quantum Provider Comparison Results - -**Date:** 2025-11-01 -**Tests:** Provider-specific gate pattern validation + Variational MPS comparison - ---- - -## Executive Summary - -**Objective:** Investigate Quantinuum backend collapse issue and validate MPS simulation fidelity. - -**Key Findings:** -1. ✅ **Rigetti backend validated** for production use (GHZ and variational circuits) -2. ❌ **Quantinuum simulator fundamentally broken** (all patterns collapse to |0...0⟩) -3. ✅ **MPS simulations match hardware** within 1% entropy (90.5% vs 91.5%) -4. 🎯 **Recommendation:** Use Rigetti for quantum experiments; avoid Quantinuum H-series simulators - ---- - -## Test Campaign Overview - -### Phase 1: Provider-Specific Gate Pattern Tests -**Goal:** Determine if Quantinuum requires native gate decompositions - -**Test Matrix:** -- **Backends:** quantinuum.sim.h2-1sc, rigetti.sim.qvm -- **Patterns:** standard (H+CX), quantinuum-native (RZ+RX+RZZ), ionq-native, rigetti-native (RX+RZ+CZ) -- **Circuit:** 4-qubit GHZ state (expect 50/50 split |0000⟩ and |1111⟩) -- **Shots:** 1000 per test - -### Phase 2: Variational Hardware/MPS Comparison -**Goal:** Validate MPS simulation accuracy against real hardware - -**Parameters:** -- **Backend:** rigetti.sim.qvm (validated in Phase 1) -- **Circuit:** 4-qubit, 2-layer variational (linear entanglement) -- **Structure:** H + 2×(RY+RZ+LINEAR) -- **Comparison:** Hardware vs local MPS simulation -- **Shots:** 1000 - ---- - -## Phase 1 Results: Provider Pattern Tests - -### Test 1: Standard Pattern on Quantinuum -``` -Job ID: 26b2c929-b6ef-11f0-862c-c86e08e1c791 -Backend: quantinuum.sim.h2-1sc -Pattern: H + CX (standard Qiskit gates) -Depth: 5, Gates: 8 -``` - -**Results:** -| State | Count | Percentage | -|-------|-------|------------| -| 0000 | 1000 | 100.0% | -| 1111 | 0 | 0.0% | - -**Entropy:** 0.000 / 4.000 (0.0%) -**Status:** ❌ **Collapsed to ground state** - ---- - -### Test 2: Quantinuum-Native Pattern on Quantinuum -``` -Job ID: 39833bbd-b6ef-11f0-8e5e-c86e08e1c791 -Backend: quantinuum.sim.h2-1sc -Pattern: RZ + RX + RZZ (Quantinuum H-series native gates) -Decomposition: - - H gate → RZ(π/2) RX(π/2) RZ(π/2) - - CX gate → RX(π/2) RZZ(π/2) RX(-π/2) -Depth: 10, Gates: 16 -``` - -**Results:** -| State | Count | Percentage | -|-------|-------|------------| -| 0000 | 1000 | 100.0% | -| 1111 | 0 | 0.0% | - -**Entropy:** 0.000 / 4.000 (0.0%) -**Status:** ❌ **Collapsed despite native gates** - ---- - -### Test 3: IonQ Pattern on Quantinuum -``` -Job ID: 432536e8-b6ef-11f0-8052-c86e08e1c791 -Backend: quantinuum.sim.h2-1sc -Pattern: H + CX (IonQ standard) -Depth: 5, Gates: 8 -``` - -**Results:** Identical to Test 1 -**Status:** ❌ **Collapsed** - ---- - -### Test 4: Rigetti Pattern on Quantinuum -``` -Job ID: 4d283578-b6ef-11f0-8126-c86e08e1c791 -Backend: quantinuum.sim.h2-1sc -Pattern: RX + RZ + CZ (Rigetti native) -Depth: 16, Gates: 28 -``` - -**Results:** Identical to Test 1 -**Status:** ❌ **Collapsed** - ---- - -### Test 5: Standard Pattern on Rigetti (Control) -``` -Job ID: 65f8c0e1-b6ef-11f0-aea3-c86e08e1c791 -Backend: rigetti.sim.qvm -Pattern: H + CX (standard Qiskit gates) -Depth: 5, Gates: 8 -``` - -**Results:** -| State | Count | Percentage | -|-------|-------|------------| -| 0000 | ~500 | 50.0% | -| 1111 | ~500 | 50.0% | - -**Entropy:** 0.999 / 4.000 (24.9%) -**Status:** ✅ **Perfect GHZ state** - ---- - -## Phase 1 Analysis - -### Quantinuum Collapse Issue -**Root Cause:** Not gate compatibility; fundamental simulator bug - -**Evidence:** -1. All 4 gate patterns (standard + 3 provider-native) produce identical collapse -2. Native Quantinuum decomposition (RZ/RX/RZZ) fails identically to standard gates -3. 100% of shots return |0000⟩ across all tests -4. Rigetti backend handles standard gates perfectly - -**Conclusion:** Quantinuum H1-1SC simulator has a critical bug that collapses entangled states to ground state during measurement. This is **not fixable via circuit construction**. - -### Rigetti Validation -**Status:** ✅ Production-ready - -**Evidence:** -- Perfect GHZ fidelity (50/50 split) with standard Qiskit gates -- Entropy within 0.1% of theoretical maximum (0.999/1.000) -- Consistent with previous 4q and 6q hardware tests -- No special gate patterns required - ---- - -## Phase 2 Results: Variational MPS Comparison - -### Hardware: Rigetti rigetti.sim.qvm -``` -Job ID: 749ea855-b6ef-11f0-8ed3-c86e08e1c791 -Circuit: 4-qubit, 2-layer variational, linear entanglement -Structure: H + 2×(RY+RZ+LINEAR) -Depth: 12, Gates: 32 -Shots: 1000 -``` - -**Results:** -- **Unique states:** 16 / 16 (100% coverage) -- **Entropy:** 3.621 / 4.000 (**90.5%**) - -**Top 5 States:** -| State | Count | Percentage | -|-------|-------|------------| -| 1011 | 102 | 10.2% | -| 0111 | 97 | 9.7% | -| 1001 | 91 | 9.1% | -| 0011 | 85 | 8.5% | -| 1111 | 81 | 8.1% | - ---- - -### Local MPS Simulation -``` -Method: matrix_product_state (Qiskit Aer) -Circuit: Identical to hardware (4q, L=2, linear) -Shots: 1000 -``` - -**Results:** -- **Unique states:** 16 / 16 (100% coverage) -- **Entropy:** 3.660 / 4.000 (**91.5%**) - -**Top 5 States:** -| State | Count | Percentage | -|-------|-------|------------| -| 1011 | 98 | 9.8% | -| 0111 | 95 | 9.5% | -| 1001 | 93 | 9.3% | -| 0011 | 87 | 8.7% | -| 1111 | 79 | 7.9% | - ---- - -## Phase 2 Analysis - -### MPS Fidelity Validation -**Entropy Difference:** 91.5% - 90.5% = **1.0%** - -**Distribution Correlation:** -- Top-5 states identical in both hardware and MPS -- Counts within ±5% for all dominant states -- Full coverage (16/16 states) in both cases - -**Statistical Significance:** -- Shot noise expected: √(1000) ≈ 31.6 shots (3.2% fluctuation) -- Observed differences: 0-4 counts per state (~0.4%) -- **Conclusion:** Hardware and MPS statistically indistinguishable - -### MPS Method Validation -✅ **MPS simulations are reliable** for variational circuits at 4q/L=2 - -**Implications:** -- Prior 32q/64q MPS experiments (L=1-4) have high confidence -- Entropy% predictions from MPS likely accurate within 1-2% -- Can use MPS for circuit design; validate on hardware periodically - ---- - -## Provider Compatibility Matrix - -| Backend | GHZ Test | Variational Test | Status | Notes | -|---------|----------|------------------|--------|-------| -| **rigetti.sim.qvm** | ✅ 99.9% entropy | ✅ 90.5% entropy | **Production** | Standard Qiskit gates work perfectly | -| **quantinuum.sim.h2-1sc** | ❌ 0% entropy (all patterns) | ❌ Untested | **Broken** | Fundamental simulator bug; avoid | -| **quantinuum.sim.h2-1e** | ⏳ Not tested | ⏳ Not tested | **Unknown** | May have same issue as h2-1sc | -| **ionq.simulator** | ❌ Not available | ❌ Not available | **Unavailable** | Requires workspace provisioning | -| **ionq.qpu** | ❌ Not available | ❌ Not available | **Unavailable** | Requires credits + provisioning | - ---- - -## Gate Pattern Compatibility - -### Tested Patterns - -#### 1. Standard (H + CX) -- **Works on:** Rigetti ✅ -- **Fails on:** Quantinuum ❌ -- **Recommendation:** Default choice for Rigetti - -#### 2. Quantinuum Native (RZ + RX + RZZ) -```python -# H decomposition -qc.rz(np.pi/2, q) -qc.rx(np.pi/2, q) -qc.rz(np.pi/2, q) - -# CX decomposition -qc.rx(np.pi/2, control) -qc.rzz(np.pi/2, control, target) -qc.rx(-np.pi/2, control) -``` -- **Works on:** None tested ✅ -- **Fails on:** Quantinuum ❌ (simulator issue, not gate issue) -- **Recommendation:** Theoretical correctness; cannot validate due to Quantinuum bug - -#### 3. IonQ Native (Standard) -- **Works on:** Rigetti ✅ (same as standard) -- **Fails on:** Quantinuum ❌ -- **Recommendation:** Use standard Qiskit gates for IonQ (when available) - -#### 4. Rigetti Native (RX + RZ + CZ) -```python -# H decomposition -qc.rz(np.pi, q) -qc.rx(np.pi/2, q) - -# CX decomposition -qc.rz(np.pi/2, target) -qc.cz(control, target) -qc.rz(-np.pi/2, target) -``` -- **Works on:** Not directly tested on Rigetti (standard worked) -- **Fails on:** Quantinuum ❌ -- **Recommendation:** Standard gates sufficient for Rigetti; native optional - ---- - -## Recommendations - -### For Current Work -1. **Use Rigetti backend exclusively** for hardware validation -2. **Trust MPS simulations** for 32q/64q experiments (validated within 1%) -3. **Avoid Quantinuum** until Azure fixes simulator bug -4. **Standard Qiskit gates** are sufficient; no need for provider-specific decompositions - -### For Future Work -1. **IonQ Provisioning:** - - Contact Azure support to add IonQ provider to workspace - - Requires budget approval (IonQ QPU: ~$0.00003/gate-shot) - - Test standard gates first; likely compatible - -2. **Quantinuum Investigation:** - - Report bug to Azure Quantum support with job IDs - - Retest after simulator update - - If fixed, compare native vs standard gate fidelity - -3. **Scaling Tests:** - - Submit 8q variational to Rigetti (validate MPS at higher N) - - Test 32q if Rigetti supports (may require real hardware) - - Compare hardware vs MPS entropy trends with qubit count - -4. **Noise Studies:** - - Hardware tests are clean (no noise model) - - Can compare with local noise simulations (depolarizing, amp_damp) - - Quantify hardware noise levels from variational entropy drop - ---- - -## File Manifest - -### Result Files (ai-projects/quantum-ml/results/) -- `pattern_standard_4q_quantinuum_sim_h2-1sc_*.json` — Test 1 -- `pattern_quantinuum_4q_quantinuum_sim_h2-1sc_*.json` — Test 2 -- `pattern_ionq_4q_quantinuum_sim_h2-1sc_*.json` — Test 3 -- `pattern_rigetti_4q_quantinuum_sim_h2-1sc_*.json` — Test 4 -- `pattern_standard_4q_rigetti_sim_qvm_*.json` — Test 5 (control) -- `azure_variational_4q_L2_linear_*.json` — Hardware variational (Phase 2) -- `sim_4q_results_*.json` — MPS variational (Phase 2) - -### Scripts Created -- `ai-projects/quantum-ml/scripts/test_provider_gates.py` — Provider gate pattern tester -- `ai-projects/quantum-ml/scripts/submit_variational_hardware.py` — Variational hardware submitter - -### Visualizations (ai-projects/quantum-ml/results/visualizations/) -- `pattern_*_counts.png` — Per-pattern bar charts -- `azure_variational_4q_L2_linear_*_counts.png` — Hardware variational distribution -- `sim_4q_results_*_counts.png` — MPS variational distribution - ---- - -## Next Steps - -### Immediate -- [x] Test provider patterns on Quantinuum → **All failed** -- [x] Submit variational to Rigetti → **90.5% entropy** -- [x] Compare with MPS → **91.5% entropy (1% diff)** -- [x] Document findings → **This report** - -### Short-term (This Week) -- [ ] Report Quantinuum bug to Azure support with job IDs -- [ ] Submit 8q variational to Rigetti (test MPS scaling) -- [ ] Test circular and full entanglement on hardware -- [ ] Compare hardware entropy vs local noise models - -### Medium-term (This Month) -- [ ] Provision IonQ access (requires Azure portal + budget) -- [ ] Test deeper layers (L=3-4) on Rigetti hardware -- [ ] Quantify Rigetti hardware noise from entropy drops -- [ ] Build hardware/MPS fidelity chart (N vs entropy% error) - -### Long-term (This Quarter) -- [ ] Access real quantum hardware (IonQ QPU or Rigetti Aspen) -- [ ] Run production variational quantum classifier on hardware -- [ ] Benchmark hardware vs simulation for heart disease dataset -- [ ] Publish comparative fidelity study - ---- - -## Appendix: Technical Details - -### Entropy Calculation -```python -def compute_entropy(counts): - total = sum(counts.values()) - p = [v/total for v in counts.values() if v > 0] - return -sum(p_i * np.log2(p_i) for p_i in p) -``` - -**Theoretical Maximum:** -- GHZ (2 states): log₂(2) = 1.0 -- 4-qubit uniform (16 states): log₂(16) = 4.0 -- N-qubit uniform: N - -**Percentage:** Entropy / N × 100% - -### Circuit Parameters -**Variational Circuit (4q, L=2, linear):** -``` -Structure: - H(q0) H(q1) H(q2) H(q3) # Initial superposition - [Layer 1] - RY(π/4, qi) RZ(π/3, qi) ∀i # Parameterized rotations - CX(q0,q1) CX(q1,q2) CX(q2,q3) # Linear entanglement - [Layer 2] - RY(π/2, qi) RZ(2π/3, qi) ∀i - CX(q0,q1) CX(q1,q2) CX(q2,q3) - Measure all -``` - -**Complexity:** -- Depth: 12 gates (4 H + 2×(4 RY + 4 RZ + 3 CX + 1 barrier) - barriers) -- Total gates: 32 -- Entangling gates: 6 (3 per layer) - ---- - -**Report Generated:** 2025-11-01 06:58 UTC -**Total Tests:** 7 (5 pattern + 1 hardware variational + 1 MPS) -**Total Shots:** 7000 -**Success Rate:** 71% (5/7 succeeded; 2 hardware tests on Quantinuum collapsed) +# Azure Quantum Provider Comparison Results + +**Date:** 2025-11-01 +**Tests:** Provider-specific gate pattern validation + Variational MPS comparison + +--- + +## Executive Summary + +**Objective:** Investigate Quantinuum backend collapse issue and validate MPS simulation fidelity. + +**Key Findings:** +1. ✅ **Rigetti backend validated** for production use (GHZ and variational circuits) +2. ❌ **Quantinuum simulator fundamentally broken** (all patterns collapse to |0...0⟩) +3. ✅ **MPS simulations match hardware** within 1% entropy (90.5% vs 91.5%) +4. 🎯 **Recommendation:** Use Rigetti for quantum experiments; avoid Quantinuum H-series simulators + +--- + +## Test Campaign Overview + +### Phase 1: Provider-Specific Gate Pattern Tests +**Goal:** Determine if Quantinuum requires native gate decompositions + +**Test Matrix:** +- **Backends:** quantinuum.sim.h2-1sc, rigetti.sim.qvm +- **Patterns:** standard (H+CX), quantinuum-native (RZ+RX+RZZ), ionq-native, rigetti-native (RX+RZ+CZ) +- **Circuit:** 4-qubit GHZ state (expect 50/50 split |0000⟩ and |1111⟩) +- **Shots:** 1000 per test + +### Phase 2: Variational Hardware/MPS Comparison +**Goal:** Validate MPS simulation accuracy against real hardware + +**Parameters:** +- **Backend:** rigetti.sim.qvm (validated in Phase 1) +- **Circuit:** 4-qubit, 2-layer variational (linear entanglement) +- **Structure:** H + 2×(RY+RZ+LINEAR) +- **Comparison:** Hardware vs local MPS simulation +- **Shots:** 1000 + +--- + +## Phase 1 Results: Provider Pattern Tests + +### Test 1: Standard Pattern on Quantinuum +``` +Job ID: 26b2c929-b6ef-11f0-862c-c86e08e1c791 +Backend: quantinuum.sim.h2-1sc +Pattern: H + CX (standard Qiskit gates) +Depth: 5, Gates: 8 +``` + +**Results:** +| State | Count | Percentage | +|-------|-------|------------| +| 0000 | 1000 | 100.0% | +| 1111 | 0 | 0.0% | + +**Entropy:** 0.000 / 4.000 (0.0%) +**Status:** ❌ **Collapsed to ground state** + +--- + +### Test 2: Quantinuum-Native Pattern on Quantinuum +``` +Job ID: 39833bbd-b6ef-11f0-8e5e-c86e08e1c791 +Backend: quantinuum.sim.h2-1sc +Pattern: RZ + RX + RZZ (Quantinuum H-series native gates) +Decomposition: + - H gate → RZ(π/2) RX(π/2) RZ(π/2) + - CX gate → RX(π/2) RZZ(π/2) RX(-π/2) +Depth: 10, Gates: 16 +``` + +**Results:** +| State | Count | Percentage | +|-------|-------|------------| +| 0000 | 1000 | 100.0% | +| 1111 | 0 | 0.0% | + +**Entropy:** 0.000 / 4.000 (0.0%) +**Status:** ❌ **Collapsed despite native gates** + +--- + +### Test 3: IonQ Pattern on Quantinuum +``` +Job ID: 432536e8-b6ef-11f0-8052-c86e08e1c791 +Backend: quantinuum.sim.h2-1sc +Pattern: H + CX (IonQ standard) +Depth: 5, Gates: 8 +``` + +**Results:** Identical to Test 1 +**Status:** ❌ **Collapsed** + +--- + +### Test 4: Rigetti Pattern on Quantinuum +``` +Job ID: 4d283578-b6ef-11f0-8126-c86e08e1c791 +Backend: quantinuum.sim.h2-1sc +Pattern: RX + RZ + CZ (Rigetti native) +Depth: 16, Gates: 28 +``` + +**Results:** Identical to Test 1 +**Status:** ❌ **Collapsed** + +--- + +### Test 5: Standard Pattern on Rigetti (Control) +``` +Job ID: 65f8c0e1-b6ef-11f0-aea3-c86e08e1c791 +Backend: rigetti.sim.qvm +Pattern: H + CX (standard Qiskit gates) +Depth: 5, Gates: 8 +``` + +**Results:** +| State | Count | Percentage | +|-------|-------|------------| +| 0000 | ~500 | 50.0% | +| 1111 | ~500 | 50.0% | + +**Entropy:** 0.999 / 4.000 (24.9%) +**Status:** ✅ **Perfect GHZ state** + +--- + +## Phase 1 Analysis + +### Quantinuum Collapse Issue +**Root Cause:** Not gate compatibility; fundamental simulator bug + +**Evidence:** +1. All 4 gate patterns (standard + 3 provider-native) produce identical collapse +2. Native Quantinuum decomposition (RZ/RX/RZZ) fails identically to standard gates +3. 100% of shots return |0000⟩ across all tests +4. Rigetti backend handles standard gates perfectly + +**Conclusion:** Quantinuum H1-1SC simulator has a critical bug that collapses entangled states to ground state during measurement. This is **not fixable via circuit construction**. + +### Rigetti Validation +**Status:** ✅ Production-ready + +**Evidence:** +- Perfect GHZ fidelity (50/50 split) with standard Qiskit gates +- Entropy within 0.1% of theoretical maximum (0.999/1.000) +- Consistent with previous 4q and 6q hardware tests +- No special gate patterns required + +--- + +## Phase 2 Results: Variational MPS Comparison + +### Hardware: Rigetti rigetti.sim.qvm +``` +Job ID: 749ea855-b6ef-11f0-8ed3-c86e08e1c791 +Circuit: 4-qubit, 2-layer variational, linear entanglement +Structure: H + 2×(RY+RZ+LINEAR) +Depth: 12, Gates: 32 +Shots: 1000 +``` + +**Results:** +- **Unique states:** 16 / 16 (100% coverage) +- **Entropy:** 3.621 / 4.000 (**90.5%**) + +**Top 5 States:** +| State | Count | Percentage | +|-------|-------|------------| +| 1011 | 102 | 10.2% | +| 0111 | 97 | 9.7% | +| 1001 | 91 | 9.1% | +| 0011 | 85 | 8.5% | +| 1111 | 81 | 8.1% | + +--- + +### Local MPS Simulation +``` +Method: matrix_product_state (Qiskit Aer) +Circuit: Identical to hardware (4q, L=2, linear) +Shots: 1000 +``` + +**Results:** +- **Unique states:** 16 / 16 (100% coverage) +- **Entropy:** 3.660 / 4.000 (**91.5%**) + +**Top 5 States:** +| State | Count | Percentage | +|-------|-------|------------| +| 1011 | 98 | 9.8% | +| 0111 | 95 | 9.5% | +| 1001 | 93 | 9.3% | +| 0011 | 87 | 8.7% | +| 1111 | 79 | 7.9% | + +--- + +## Phase 2 Analysis + +### MPS Fidelity Validation +**Entropy Difference:** 91.5% - 90.5% = **1.0%** + +**Distribution Correlation:** +- Top-5 states identical in both hardware and MPS +- Counts within ±5% for all dominant states +- Full coverage (16/16 states) in both cases + +**Statistical Significance:** +- Shot noise expected: √(1000) ≈ 31.6 shots (3.2% fluctuation) +- Observed differences: 0-4 counts per state (~0.4%) +- **Conclusion:** Hardware and MPS statistically indistinguishable + +### MPS Method Validation +✅ **MPS simulations are reliable** for variational circuits at 4q/L=2 + +**Implications:** +- Prior 32q/64q MPS experiments (L=1-4) have high confidence +- Entropy% predictions from MPS likely accurate within 1-2% +- Can use MPS for circuit design; validate on hardware periodically + +--- + +## Provider Compatibility Matrix + +| Backend | GHZ Test | Variational Test | Status | Notes | +|---------|----------|------------------|--------|-------| +| **rigetti.sim.qvm** | ✅ 99.9% entropy | ✅ 90.5% entropy | **Production** | Standard Qiskit gates work perfectly | +| **quantinuum.sim.h2-1sc** | ❌ 0% entropy (all patterns) | ❌ Untested | **Broken** | Fundamental simulator bug; avoid | +| **quantinuum.sim.h2-1e** | ⏳ Not tested | ⏳ Not tested | **Unknown** | May have same issue as h2-1sc | +| **ionq.simulator** | ❌ Not available | ❌ Not available | **Unavailable** | Requires workspace provisioning | +| **ionq.qpu** | ❌ Not available | ❌ Not available | **Unavailable** | Requires credits + provisioning | + +--- + +## Gate Pattern Compatibility + +### Tested Patterns + +#### 1. Standard (H + CX) +- **Works on:** Rigetti ✅ +- **Fails on:** Quantinuum ❌ +- **Recommendation:** Default choice for Rigetti + +#### 2. Quantinuum Native (RZ + RX + RZZ) +```python +# H decomposition +qc.rz(np.pi/2, q) +qc.rx(np.pi/2, q) +qc.rz(np.pi/2, q) + +# CX decomposition +qc.rx(np.pi/2, control) +qc.rzz(np.pi/2, control, target) +qc.rx(-np.pi/2, control) +``` +- **Works on:** None tested ✅ +- **Fails on:** Quantinuum ❌ (simulator issue, not gate issue) +- **Recommendation:** Theoretical correctness; cannot validate due to Quantinuum bug + +#### 3. IonQ Native (Standard) +- **Works on:** Rigetti ✅ (same as standard) +- **Fails on:** Quantinuum ❌ +- **Recommendation:** Use standard Qiskit gates for IonQ (when available) + +#### 4. Rigetti Native (RX + RZ + CZ) +```python +# H decomposition +qc.rz(np.pi, q) +qc.rx(np.pi/2, q) + +# CX decomposition +qc.rz(np.pi/2, target) +qc.cz(control, target) +qc.rz(-np.pi/2, target) +``` +- **Works on:** Not directly tested on Rigetti (standard worked) +- **Fails on:** Quantinuum ❌ +- **Recommendation:** Standard gates sufficient for Rigetti; native optional + +--- + +## Recommendations + +### For Current Work +1. **Use Rigetti backend exclusively** for hardware validation +2. **Trust MPS simulations** for 32q/64q experiments (validated within 1%) +3. **Avoid Quantinuum** until Azure fixes simulator bug +4. **Standard Qiskit gates** are sufficient; no need for provider-specific decompositions + +### For Future Work +1. **IonQ Provisioning:** + - Contact Azure support to add IonQ provider to workspace + - Requires budget approval (IonQ QPU: ~$0.00003/gate-shot) + - Test standard gates first; likely compatible + +2. **Quantinuum Investigation:** + - Report bug to Azure Quantum support with job IDs + - Retest after simulator update + - If fixed, compare native vs standard gate fidelity + +3. **Scaling Tests:** + - Submit 8q variational to Rigetti (validate MPS at higher N) + - Test 32q if Rigetti supports (may require real hardware) + - Compare hardware vs MPS entropy trends with qubit count + +4. **Noise Studies:** + - Hardware tests are clean (no noise model) + - Can compare with local noise simulations (depolarizing, amp_damp) + - Quantify hardware noise levels from variational entropy drop + +--- + +## File Manifest + +### Result Files (ai-projects/quantum-ml/results/) +- `pattern_standard_4q_quantinuum_sim_h2-1sc_*.json` — Test 1 +- `pattern_quantinuum_4q_quantinuum_sim_h2-1sc_*.json` — Test 2 +- `pattern_ionq_4q_quantinuum_sim_h2-1sc_*.json` — Test 3 +- `pattern_rigetti_4q_quantinuum_sim_h2-1sc_*.json` — Test 4 +- `pattern_standard_4q_rigetti_sim_qvm_*.json` — Test 5 (control) +- `azure_variational_4q_L2_linear_*.json` — Hardware variational (Phase 2) +- `sim_4q_results_*.json` — MPS variational (Phase 2) + +### Scripts Created +- `ai-projects/quantum-ml/scripts/test_provider_gates.py` — Provider gate pattern tester +- `ai-projects/quantum-ml/scripts/submit_variational_hardware.py` — Variational hardware submitter + +### Visualizations (ai-projects/quantum-ml/results/visualizations/) +- `pattern_*_counts.png` — Per-pattern bar charts +- `azure_variational_4q_L2_linear_*_counts.png` — Hardware variational distribution +- `sim_4q_results_*_counts.png` — MPS variational distribution + +--- + +## Next Steps + +### Immediate +- [x] Test provider patterns on Quantinuum → **All failed** +- [x] Submit variational to Rigetti → **90.5% entropy** +- [x] Compare with MPS → **91.5% entropy (1% diff)** +- [x] Document findings → **This report** + +### Short-term (This Week) +- [ ] Report Quantinuum bug to Azure support with job IDs +- [ ] Submit 8q variational to Rigetti (test MPS scaling) +- [ ] Test circular and full entanglement on hardware +- [ ] Compare hardware entropy vs local noise models + +### Medium-term (This Month) +- [ ] Provision IonQ access (requires Azure portal + budget) +- [ ] Test deeper layers (L=3-4) on Rigetti hardware +- [ ] Quantify Rigetti hardware noise from entropy drops +- [ ] Build hardware/MPS fidelity chart (N vs entropy% error) + +### Long-term (This Quarter) +- [ ] Access real quantum hardware (IonQ QPU or Rigetti Aspen) +- [ ] Run production variational quantum classifier on hardware +- [ ] Benchmark hardware vs simulation for heart disease dataset +- [ ] Publish comparative fidelity study + +--- + +## Appendix: Technical Details + +### Entropy Calculation +```python +def compute_entropy(counts): + total = sum(counts.values()) + p = [v/total for v in counts.values() if v > 0] + return -sum(p_i * np.log2(p_i) for p_i in p) +``` + +**Theoretical Maximum:** +- GHZ (2 states): log₂(2) = 1.0 +- 4-qubit uniform (16 states): log₂(16) = 4.0 +- N-qubit uniform: N + +**Percentage:** Entropy / N × 100% + +### Circuit Parameters +**Variational Circuit (4q, L=2, linear):** +``` +Structure: + H(q0) H(q1) H(q2) H(q3) # Initial superposition + [Layer 1] + RY(π/4, qi) RZ(π/3, qi) ∀i # Parameterized rotations + CX(q0,q1) CX(q1,q2) CX(q2,q3) # Linear entanglement + [Layer 2] + RY(π/2, qi) RZ(2π/3, qi) ∀i + CX(q0,q1) CX(q1,q2) CX(q2,q3) + Measure all +``` + +**Complexity:** +- Depth: 12 gates (4 H + 2×(4 RY + 4 RZ + 3 CX + 1 barrier) - barriers) +- Total gates: 32 +- Entangling gates: 6 (3 per layer) + +--- + +**Report Generated:** 2025-11-01 06:58 UTC +**Total Tests:** 7 (5 pattern + 1 hardware variational + 1 MPS) +**Total Shots:** 7000 +**Success Rate:** 71% (5/7 succeeded; 2 hardware tests on Quantinuum collapsed) diff --git a/ai-projects/quantum-ml/QUANTUM_LLM_README.md b/ai-projects/quantum-ml/QUANTUM_LLM_README.md index 166afd3a3..dd2c0f4f3 100644 --- a/ai-projects/quantum-ml/QUANTUM_LLM_README.md +++ b/ai-projects/quantum-ml/QUANTUM_LLM_README.md @@ -1,643 +1,643 @@ -# Quantum LLM - Advanced Components - -**Quantum-Enhanced Language Model Training System** - -Complete implementation of quantum-classical hybrid language model training with advanced features for circuit optimization, adaptive training, and real-time monitoring. - ---- - -## 🚀 Quick Start - -```bash -# Test with minimal training (2 epochs, 64-dim model) -python quantum_llm_quickstart.py --mode quick - -# Full training with all features -python quantum_llm_quickstart.py --mode full --config config/quantum_llm_config.yaml - -# Monitor ongoing training -python quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm_quickstart - -# Generate text with trained model -python quantum_llm_quickstart.py --mode generate --model data_out/quantum_llm_quickstart/final_model.pt --prompt "Quantum computing" -``` - ---- - -## 📦 Components - -### Core Files - -| File | Purpose | Features | -|------|---------|----------| -| **quantum_llm_advanced.py** | Advanced quantum layers | Circuit caching, adaptive entanglement, multi-scale attention, prompt tuning, error mitigation | -| **quantum_circuit_optimizer.py** | Circuit optimization | Circuit compilation, batch execution, adaptive scheduling, quantum/classical partitioning | -| **quantum_llm_hybrid_trainer.py** | Hybrid training orchestration | Curriculum learning, adaptive routing, multi-stage training | -| **quantum_llm_monitor.py** | Training monitoring | Real-time dashboard, metrics aggregation, performance profiling, alert system | -| **quantum_llm_integrated.py** | Complete integration | Unified system combining all components | -| **quantum_llm_datasets.py** | Dataset utilities | Tokenization, data loading, augmentation | -| **quantum_llm_quickstart.py** | Quick start examples | Ready-to-run training examples | - ---- - -## 🔬 Advanced Quantum Features - -### 1. Multi-Scale Quantum Attention - -**Purpose:** Capture information at different granularities using varying qubit counts. - -```python -from quantum_llm_advanced import MultiScaleQuantumAttention - -# Creates 4 attention heads with 2, 3, 4, and 6 qubits -attention = MultiScaleQuantumAttention( - d_model=128, - n_heads=4, - n_qubits_per_head=[2, 3, 4, 6] -) -``` - -**Benefits:** -- Fine-grained patterns (2-3 qubits) -- Mid-level structures (4 qubits) -- Complex relationships (6 qubits) -- Adaptive computation based on complexity - -### 2. Adaptive Quantum Layer - -**Purpose:** Dynamically select quantum circuit topology based on input complexity. - -```python -from quantum_llm_advanced import AdaptiveQuantumLayer - -layer = AdaptiveQuantumLayer(d_model=128, n_qubits=4) -``` - -**Modes:** -- **Linear entanglement:** Simple inputs (low complexity) -- **Circular entanglement:** Moderate inputs (medium complexity) -- **Full entanglement:** Complex inputs (high complexity) - -**Selector:** Learned neural network predicts optimal entanglement pattern. - -### 3. Quantum Circuit Cache - -**Purpose:** Cache repeated quantum circuit executions to reduce QPU calls. - -```python -from quantum_llm_advanced import QuantumCircuitCache - -cache = QuantumCircuitCache(cache_size=1000) -``` - -**Features:** -- LRU eviction policy -- Hit/miss tracking -- Cache statistics reporting -- Significant speedup for repeated patterns - -### 4. Quantum Prompt Tuning - -**Purpose:** Task-specific adaptation using quantum-parametrized soft prompts. - -```python -from quantum_llm_advanced import QuantumPromptTuning - -prompt_tuner = QuantumPromptTuning( - d_model=128, - n_qubits=4, - n_prompts=10 -) -``` - -**Benefits:** -- Few-shot learning capability -- Task adaptation without full retraining -- Quantum-enhanced prompt representation - -### 5. Quantum Error Mitigation - -**Purpose:** Reduce noise in quantum circuit outputs. - -```python -from quantum_llm_advanced import QuantumErrorMitigation - -error_mitigator = QuantumErrorMitigation(n_qubits=4) -``` - -**Techniques:** -- Zero-noise extrapolation -- Readout error correction -- Calibration matrix support - ---- - -## ⚙️ Circuit Optimization - -### Circuit Compiler - -Optimizes quantum circuits for efficient execution. - -```python -from quantum_circuit_optimizer import CircuitCompiler, OptimizationStrategy - -strategy = OptimizationStrategy( - compilation_level=2, # 0=none, 1=light, 2=moderate, 3=aggressive - enable_gate_fusion=True, - enable_gate_cancellation=True, -) - -compiler = CircuitCompiler(strategy) -``` - -**Optimization Passes:** -- Gate fusion (combine adjacent gates) -- Gate cancellation (remove redundant gates) -- Circuit depth reduction -- Parameter shift optimization - -### Batch Circuit Executor - -Executes multiple circuits efficiently with caching. - -```python -from quantum_circuit_optimizer import BatchCircuitExecutor - -executor = BatchCircuitExecutor( - max_batch_size=10, - enable_parallel=False, - cache_size=1000, -) -``` - -**Features:** -- Batch processing -- Execution caching -- Cache hit rate tracking -- Reduced QPU load - -### Adaptive Circuit Scheduler - -Resource-aware quantum circuit scheduling. - -```python -from quantum_circuit_optimizer import AdaptiveCircuitScheduler - -scheduler = AdaptiveCircuitScheduler( - max_concurrent_circuits=5, - quantum_resource_limit=0.8, -) -``` - -**Capabilities:** -- Load-aware scheduling -- Priority-based execution -- Resource contention management - ---- - -## 🎓 Curriculum Learning - -### Training Stages - -Progressive quantum integration through multiple training stages. - -```python -from quantum_llm_hybrid_trainer import TrainingStage - -stages = [ - TrainingStage( - name="classical_warmup", - quantum_ratio=0.0, - num_epochs=2, - learning_rate=1e-4, - batch_size=16, - ), - TrainingStage( - name="quantum_transition", - quantum_ratio=0.3, - num_epochs=3, - learning_rate=5e-5, - batch_size=16, - enable_quantum_layers=["attention"], - ), - TrainingStage( - name="full_quantum", - quantum_ratio=0.7, - num_epochs=10, - learning_rate=1e-5, - batch_size=8, - enable_quantum_layers=["attention", "feedforward"], - ), -] -``` - -**Stage Progression:** -1. **Classical Warmup:** Pure classical training to learn basic patterns -2. **Quantum Transition:** Gradual quantum integration (30% quantum) -3. **Full Quantum:** Maximize quantum usage (70% quantum) - -### Adaptive Quantum Router - -Learns optimal quantum/classical routing policy. - -```python -from quantum_llm_hybrid_trainer import AdaptiveQuantumRouter - -router = AdaptiveQuantumRouter(input_dim=64, learning_rate=0.001) -``` - -**Policy Learning:** -- Reinforcement learning based -- Context-aware decisions -- Performance-driven optimization - ---- - -## 📊 Monitoring & Visualization - -### Training Dashboard - -Real-time training monitoring with alerts. - -```python -from quantum_llm_monitor import TrainingDashboard - -dashboard = TrainingDashboard( - output_dir=Path("data_out/dashboard"), - update_interval=10, # seconds - enable_alerts=True, -) -``` - -**Features:** -- Real-time metrics tracking -- Moving average computation -- Trend detection (improving/degrading/stable) -- Anomaly detection using z-scores -- Alert system for issues - -**Metrics Tracked:** -- Loss and perplexity -- Quantum execution time -- Classical execution time -- CPU/memory/GPU usage -- Cache hit rates -- Circuit statistics - -### Visualization Export - -Export data for external visualization tools. - -```python -from quantum_llm_monitor import VisualizationExporter - -exporter = VisualizationExporter(output_dir=Path("visualizations")) -exporter.export_all(snapshots) -``` - -**Exported Data:** -- Loss curves -- Quantum metrics over time -- Resource utilization -- Circuit performance - ---- - -## 🗂️ Dataset Utilities - -### Character Tokenizer - -Simple character-level tokenization. - -```python -from quantum_llm_datasets import CharacterTokenizer - -tokenizer = CharacterTokenizer(vocab_size=256) -text = "Hello, Quantum World!" -encoded = tokenizer.encode(text) -decoded = tokenizer.decode(encoded) -``` - -### Text Dataset - -Handles tokenization and sequence windowing. - -```python -from quantum_llm_datasets import TextDataset - -dataset = TextDataset( - texts=["sample text 1", "sample text 2"], - tokenizer=tokenizer, - max_seq_length=512, - stride=256, -) -``` - -### Auto-Detection Loading - -Automatically detect and load various dataset formats. - -```python -from quantum_llm_datasets import DatasetBuilder - -# Auto-detect: text, JSON, or chat format -dataset = DatasetBuilder.auto_detect_and_load( - path=Path("datasets/chat/conversation.json"), - tokenizer=tokenizer, - max_seq_length=512, -) -``` - -**Supported Formats:** -- Plain text files -- JSON with "text" field -- Chat format with "messages" array - ---- - -## 🔧 Configuration - -### Example Configuration (YAML) - -```yaml -# Model architecture -vocab_size: 256 -d_model: 128 -n_heads: 4 -n_layers: 4 -d_ff: 512 -max_seq_length: 512 - -# Quantum configuration -n_qubits: 4 -quantum_backend: "default.qubit" -quantum_shots: 1000 -enable_quantum_attention: true -enable_quantum_ffn: true -enable_multi_scale_attention: true -enable_adaptive_entanglement: true -enable_circuit_caching: true -enable_error_mitigation: false - -# Training -batch_size: 16 -learning_rate: 1e-4 -num_epochs: 10 -enable_curriculum: true -enable_adaptive_routing: true - -# Optimization -optimization_level: 2 -enable_circuit_fusion: true -max_batch_circuits: 10 - -# Monitoring -enable_dashboard: true -dashboard_update_interval: 10 -enable_alerts: true - -# Output -output_dir: "data_out/quantum_llm" -save_checkpoints: true -checkpoint_interval: 100 -``` - ---- - -## 📈 Performance Tips - -### 1. Circuit Caching - -**Enable for:** Training on repeated patterns (e.g., character-level language models) - -```python -config["enable_circuit_caching"] = True -``` - -**Expected improvement:** 2-5x speedup on repeated inputs - -### 2. Optimization Level - -**Level 0:** No optimization (debugging) -**Level 1:** Light optimization (safe) -**Level 2:** Moderate optimization (recommended) -**Level 3:** Aggressive optimization (experimental) - -```python -config["optimization_level"] = 2 -``` - -### 3. Batch Size - -**Quantum circuits:** Smaller batches (4-8) for complex circuits -**Classical layers:** Larger batches (16-32) for efficiency - -```python -# Adaptive batch sizing across stages -stages = [ - TrainingStage(..., batch_size=16), # Classical - TrainingStage(..., batch_size=8), # Transition - TrainingStage(..., batch_size=4), # Full quantum -] -``` - -### 4. Multi-Scale Attention - -**Use when:** Capturing hierarchical patterns important - -```python -config["enable_multi_scale_attention"] = True -config["n_qubits_per_head"] = [2, 3, 4, 6] -``` - ---- - -## 🔍 Monitoring Outputs - -### Dashboard JSON - -Location: `data_out/quantum_llm/dashboard/dashboard.json` - -```json -{ - "timestamp": 1234567890.0, - "metrics_summary": { - "moving_avg_loss": 2.5, - "loss_trend": "improving", - "anomalies": [] - }, - "performance_report": { - "avg_cpu_percent": 45.2, - "max_memory_mb": 2048.5 - }, - "circuit_stats": { - "attention_circuit_0": { - "avg_time": 0.15, - "executions": 1000 - } - } -} -``` - -### Training Report - -Location: `data_out/quantum_llm/training/training_report.json` - -```json -{ - "total_stages": 3, - "total_time": 3600.0, - "total_steps": 10000, - "best_loss": 1.85, - "stage_metrics": [...], - "routing_stats": { - "quantum_ratio": 0.65 - } -} -``` - ---- - -## 🧪 Testing - -### Quick Test - -```bash -# Test all components with minimal example -python quantum_llm_quickstart.py --mode quick -``` - -### Component Tests - -```bash -# Test advanced components -python ai-projects/quantum-ml/src/quantum_llm_advanced.py - -# Test optimizer -python ai-projects/quantum-ml/src/quantum_circuit_optimizer.py - -# Test monitor -python ai-projects/quantum-ml/src/quantum_llm_monitor.py - -# Test datasets -python ai-projects/quantum-ml/src/quantum_llm_datasets.py -``` - -### Integration Test - -```bash -# Test full integrated system -python ai-projects/quantum-ml/src/quantum_llm_integrated.py --dry-run -``` - ---- - -## 📚 Documentation Structure - -``` -ai-projects/quantum-ml/ -├── src/ -│ ├── quantum_llm_advanced.py # Advanced quantum layers -│ ├── quantum_circuit_optimizer.py # Circuit optimization -│ ├── quantum_llm_hybrid_trainer.py # Training orchestration -│ ├── quantum_llm_monitor.py # Monitoring & visualization -│ ├── quantum_llm_integrated.py # Complete integration -│ └── quantum_llm_datasets.py # Dataset utilities -├── quantum_llm_quickstart.py # Quick start examples -├── QUANTUM_LLM_README.md # This file -└── quantum_llm_config_example.yaml # Example configuration -``` - ---- - -## 🎯 Use Cases - -### 1. Research & Experimentation - -Explore quantum advantages in language modeling: - -```python -# Compare quantum vs classical attention -config["enable_multi_scale_attention"] = True -system = QuantumLLMSystem(config) -``` - -### 2. Curriculum Training - -Progressive quantum integration for stable training: - -```python -config["enable_curriculum"] = True -stages = create_curriculum_stages() -``` - -### 3. Resource-Constrained Training - -Optimize for limited quantum resources: - -```python -config["optimization_level"] = 3 -config["enable_circuit_caching"] = True -config["max_batch_circuits"] = 5 -``` - -### 4. Production Monitoring - -Real-time training monitoring and alerts: - -```python -config["enable_dashboard"] = True -config["enable_alerts"] = True -config["dashboard_update_interval"] = 5 -``` - ---- - -## 🚧 Limitations & Future Work - -### Current Limitations - -1. **Quantum Backend:** Simulation-based (no real QPU integration yet) -2. **Tokenization:** Character-level only (subword/BPE planned) -3. **Model Size:** Limited by quantum circuit depth -4. **Training Speed:** Quantum simulation slower than classical - -### Planned Enhancements - -1. **Azure Quantum Integration:** Real QPU execution -2. **Subword Tokenization:** BPE/WordPiece support -3. **Distributed Training:** Multi-GPU/multi-QPU -4. **Advanced Error Correction:** Full QEC implementation -5. **Model Compression:** Quantum-aware pruning/quantization - ---- - -## 📖 References - -- **Quantum Machine Learning:** Quantum circuits for ML tasks -- **Curriculum Learning:** Progressive training strategies -- **Circuit Optimization:** Gate-level quantum optimizations -- **Error Mitigation:** Noise reduction techniques - ---- - -## ✨ Key Innovation - -**Hybrid Quantum-Classical Training:** Seamlessly integrates quantum circuits into transformer architecture with adaptive routing, curriculum learning, and comprehensive monitoring - the first complete quantum LLM training system. - ---- - -## 🤝 Contributing - -Improvements welcome! Focus areas: -- Real QPU backend integration -- Advanced error correction -- Distributed training -- Performance optimizations - ---- - -**Author:** Quantum AI Workspace -**Date:** March 9, 2026 -**License:** MIT +# Quantum LLM - Advanced Components + +**Quantum-Enhanced Language Model Training System** + +Complete implementation of quantum-classical hybrid language model training with advanced features for circuit optimization, adaptive training, and real-time monitoring. + +--- + +## 🚀 Quick Start + +```bash +# Test with minimal training (2 epochs, 64-dim model) +python quantum_llm_quickstart.py --mode quick + +# Full training with all features +python quantum_llm_quickstart.py --mode full --config config/quantum_llm_config.yaml + +# Monitor ongoing training +python quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm_quickstart + +# Generate text with trained model +python quantum_llm_quickstart.py --mode generate --model data_out/quantum_llm_quickstart/final_model.pt --prompt "Quantum computing" +``` + +--- + +## 📦 Components + +### Core Files + +| File | Purpose | Features | +|------|---------|----------| +| **quantum_llm_advanced.py** | Advanced quantum layers | Circuit caching, adaptive entanglement, multi-scale attention, prompt tuning, error mitigation | +| **quantum_circuit_optimizer.py** | Circuit optimization | Circuit compilation, batch execution, adaptive scheduling, quantum/classical partitioning | +| **quantum_llm_hybrid_trainer.py** | Hybrid training orchestration | Curriculum learning, adaptive routing, multi-stage training | +| **quantum_llm_monitor.py** | Training monitoring | Real-time dashboard, metrics aggregation, performance profiling, alert system | +| **quantum_llm_integrated.py** | Complete integration | Unified system combining all components | +| **quantum_llm_datasets.py** | Dataset utilities | Tokenization, data loading, augmentation | +| **quantum_llm_quickstart.py** | Quick start examples | Ready-to-run training examples | + +--- + +## 🔬 Advanced Quantum Features + +### 1. Multi-Scale Quantum Attention + +**Purpose:** Capture information at different granularities using varying qubit counts. + +```python +from quantum_llm_advanced import MultiScaleQuantumAttention + +# Creates 4 attention heads with 2, 3, 4, and 6 qubits +attention = MultiScaleQuantumAttention( + d_model=128, + n_heads=4, + n_qubits_per_head=[2, 3, 4, 6] +) +``` + +**Benefits:** +- Fine-grained patterns (2-3 qubits) +- Mid-level structures (4 qubits) +- Complex relationships (6 qubits) +- Adaptive computation based on complexity + +### 2. Adaptive Quantum Layer + +**Purpose:** Dynamically select quantum circuit topology based on input complexity. + +```python +from quantum_llm_advanced import AdaptiveQuantumLayer + +layer = AdaptiveQuantumLayer(d_model=128, n_qubits=4) +``` + +**Modes:** +- **Linear entanglement:** Simple inputs (low complexity) +- **Circular entanglement:** Moderate inputs (medium complexity) +- **Full entanglement:** Complex inputs (high complexity) + +**Selector:** Learned neural network predicts optimal entanglement pattern. + +### 3. Quantum Circuit Cache + +**Purpose:** Cache repeated quantum circuit executions to reduce QPU calls. + +```python +from quantum_llm_advanced import QuantumCircuitCache + +cache = QuantumCircuitCache(cache_size=1000) +``` + +**Features:** +- LRU eviction policy +- Hit/miss tracking +- Cache statistics reporting +- Significant speedup for repeated patterns + +### 4. Quantum Prompt Tuning + +**Purpose:** Task-specific adaptation using quantum-parametrized soft prompts. + +```python +from quantum_llm_advanced import QuantumPromptTuning + +prompt_tuner = QuantumPromptTuning( + d_model=128, + n_qubits=4, + n_prompts=10 +) +``` + +**Benefits:** +- Few-shot learning capability +- Task adaptation without full retraining +- Quantum-enhanced prompt representation + +### 5. Quantum Error Mitigation + +**Purpose:** Reduce noise in quantum circuit outputs. + +```python +from quantum_llm_advanced import QuantumErrorMitigation + +error_mitigator = QuantumErrorMitigation(n_qubits=4) +``` + +**Techniques:** +- Zero-noise extrapolation +- Readout error correction +- Calibration matrix support + +--- + +## ⚙️ Circuit Optimization + +### Circuit Compiler + +Optimizes quantum circuits for efficient execution. + +```python +from quantum_circuit_optimizer import CircuitCompiler, OptimizationStrategy + +strategy = OptimizationStrategy( + compilation_level=2, # 0=none, 1=light, 2=moderate, 3=aggressive + enable_gate_fusion=True, + enable_gate_cancellation=True, +) + +compiler = CircuitCompiler(strategy) +``` + +**Optimization Passes:** +- Gate fusion (combine adjacent gates) +- Gate cancellation (remove redundant gates) +- Circuit depth reduction +- Parameter shift optimization + +### Batch Circuit Executor + +Executes multiple circuits efficiently with caching. + +```python +from quantum_circuit_optimizer import BatchCircuitExecutor + +executor = BatchCircuitExecutor( + max_batch_size=10, + enable_parallel=False, + cache_size=1000, +) +``` + +**Features:** +- Batch processing +- Execution caching +- Cache hit rate tracking +- Reduced QPU load + +### Adaptive Circuit Scheduler + +Resource-aware quantum circuit scheduling. + +```python +from quantum_circuit_optimizer import AdaptiveCircuitScheduler + +scheduler = AdaptiveCircuitScheduler( + max_concurrent_circuits=5, + quantum_resource_limit=0.8, +) +``` + +**Capabilities:** +- Load-aware scheduling +- Priority-based execution +- Resource contention management + +--- + +## 🎓 Curriculum Learning + +### Training Stages + +Progressive quantum integration through multiple training stages. + +```python +from quantum_llm_hybrid_trainer import TrainingStage + +stages = [ + TrainingStage( + name="classical_warmup", + quantum_ratio=0.0, + num_epochs=2, + learning_rate=1e-4, + batch_size=16, + ), + TrainingStage( + name="quantum_transition", + quantum_ratio=0.3, + num_epochs=3, + learning_rate=5e-5, + batch_size=16, + enable_quantum_layers=["attention"], + ), + TrainingStage( + name="full_quantum", + quantum_ratio=0.7, + num_epochs=10, + learning_rate=1e-5, + batch_size=8, + enable_quantum_layers=["attention", "feedforward"], + ), +] +``` + +**Stage Progression:** +1. **Classical Warmup:** Pure classical training to learn basic patterns +2. **Quantum Transition:** Gradual quantum integration (30% quantum) +3. **Full Quantum:** Maximize quantum usage (70% quantum) + +### Adaptive Quantum Router + +Learns optimal quantum/classical routing policy. + +```python +from quantum_llm_hybrid_trainer import AdaptiveQuantumRouter + +router = AdaptiveQuantumRouter(input_dim=64, learning_rate=0.001) +``` + +**Policy Learning:** +- Reinforcement learning based +- Context-aware decisions +- Performance-driven optimization + +--- + +## 📊 Monitoring & Visualization + +### Training Dashboard + +Real-time training monitoring with alerts. + +```python +from quantum_llm_monitor import TrainingDashboard + +dashboard = TrainingDashboard( + output_dir=Path("data_out/dashboard"), + update_interval=10, # seconds + enable_alerts=True, +) +``` + +**Features:** +- Real-time metrics tracking +- Moving average computation +- Trend detection (improving/degrading/stable) +- Anomaly detection using z-scores +- Alert system for issues + +**Metrics Tracked:** +- Loss and perplexity +- Quantum execution time +- Classical execution time +- CPU/memory/GPU usage +- Cache hit rates +- Circuit statistics + +### Visualization Export + +Export data for external visualization tools. + +```python +from quantum_llm_monitor import VisualizationExporter + +exporter = VisualizationExporter(output_dir=Path("visualizations")) +exporter.export_all(snapshots) +``` + +**Exported Data:** +- Loss curves +- Quantum metrics over time +- Resource utilization +- Circuit performance + +--- + +## 🗂️ Dataset Utilities + +### Character Tokenizer + +Simple character-level tokenization. + +```python +from quantum_llm_datasets import CharacterTokenizer + +tokenizer = CharacterTokenizer(vocab_size=256) +text = "Hello, Quantum World!" +encoded = tokenizer.encode(text) +decoded = tokenizer.decode(encoded) +``` + +### Text Dataset + +Handles tokenization and sequence windowing. + +```python +from quantum_llm_datasets import TextDataset + +dataset = TextDataset( + texts=["sample text 1", "sample text 2"], + tokenizer=tokenizer, + max_seq_length=512, + stride=256, +) +``` + +### Auto-Detection Loading + +Automatically detect and load various dataset formats. + +```python +from quantum_llm_datasets import DatasetBuilder + +# Auto-detect: text, JSON, or chat format +dataset = DatasetBuilder.auto_detect_and_load( + path=Path("datasets/chat/conversation.json"), + tokenizer=tokenizer, + max_seq_length=512, +) +``` + +**Supported Formats:** +- Plain text files +- JSON with "text" field +- Chat format with "messages" array + +--- + +## 🔧 Configuration + +### Example Configuration (YAML) + +```yaml +# Model architecture +vocab_size: 256 +d_model: 128 +n_heads: 4 +n_layers: 4 +d_ff: 512 +max_seq_length: 512 + +# Quantum configuration +n_qubits: 4 +quantum_backend: "default.qubit" +quantum_shots: 1000 +enable_quantum_attention: true +enable_quantum_ffn: true +enable_multi_scale_attention: true +enable_adaptive_entanglement: true +enable_circuit_caching: true +enable_error_mitigation: false + +# Training +batch_size: 16 +learning_rate: 1e-4 +num_epochs: 10 +enable_curriculum: true +enable_adaptive_routing: true + +# Optimization +optimization_level: 2 +enable_circuit_fusion: true +max_batch_circuits: 10 + +# Monitoring +enable_dashboard: true +dashboard_update_interval: 10 +enable_alerts: true + +# Output +output_dir: "data_out/quantum_llm" +save_checkpoints: true +checkpoint_interval: 100 +``` + +--- + +## 📈 Performance Tips + +### 1. Circuit Caching + +**Enable for:** Training on repeated patterns (e.g., character-level language models) + +```python +config["enable_circuit_caching"] = True +``` + +**Expected improvement:** 2-5x speedup on repeated inputs + +### 2. Optimization Level + +**Level 0:** No optimization (debugging) +**Level 1:** Light optimization (safe) +**Level 2:** Moderate optimization (recommended) +**Level 3:** Aggressive optimization (experimental) + +```python +config["optimization_level"] = 2 +``` + +### 3. Batch Size + +**Quantum circuits:** Smaller batches (4-8) for complex circuits +**Classical layers:** Larger batches (16-32) for efficiency + +```python +# Adaptive batch sizing across stages +stages = [ + TrainingStage(..., batch_size=16), # Classical + TrainingStage(..., batch_size=8), # Transition + TrainingStage(..., batch_size=4), # Full quantum +] +``` + +### 4. Multi-Scale Attention + +**Use when:** Capturing hierarchical patterns important + +```python +config["enable_multi_scale_attention"] = True +config["n_qubits_per_head"] = [2, 3, 4, 6] +``` + +--- + +## 🔍 Monitoring Outputs + +### Dashboard JSON + +Location: `data_out/quantum_llm/dashboard/dashboard.json` + +```json +{ + "timestamp": 1234567890.0, + "metrics_summary": { + "moving_avg_loss": 2.5, + "loss_trend": "improving", + "anomalies": [] + }, + "performance_report": { + "avg_cpu_percent": 45.2, + "max_memory_mb": 2048.5 + }, + "circuit_stats": { + "attention_circuit_0": { + "avg_time": 0.15, + "executions": 1000 + } + } +} +``` + +### Training Report + +Location: `data_out/quantum_llm/training/training_report.json` + +```json +{ + "total_stages": 3, + "total_time": 3600.0, + "total_steps": 10000, + "best_loss": 1.85, + "stage_metrics": [...], + "routing_stats": { + "quantum_ratio": 0.65 + } +} +``` + +--- + +## 🧪 Testing + +### Quick Test + +```bash +# Test all components with minimal example +python quantum_llm_quickstart.py --mode quick +``` + +### Component Tests + +```bash +# Test advanced components +python ai-projects/quantum-ml/src/quantum_llm_advanced.py + +# Test optimizer +python ai-projects/quantum-ml/src/quantum_circuit_optimizer.py + +# Test monitor +python ai-projects/quantum-ml/src/quantum_llm_monitor.py + +# Test datasets +python ai-projects/quantum-ml/src/quantum_llm_datasets.py +``` + +### Integration Test + +```bash +# Test full integrated system +python ai-projects/quantum-ml/src/quantum_llm_integrated.py --dry-run +``` + +--- + +## 📚 Documentation Structure + +``` +ai-projects/quantum-ml/ +├── src/ +│ ├── quantum_llm_advanced.py # Advanced quantum layers +│ ├── quantum_circuit_optimizer.py # Circuit optimization +│ ├── quantum_llm_hybrid_trainer.py # Training orchestration +│ ├── quantum_llm_monitor.py # Monitoring & visualization +│ ├── quantum_llm_integrated.py # Complete integration +│ └── quantum_llm_datasets.py # Dataset utilities +├── quantum_llm_quickstart.py # Quick start examples +├── QUANTUM_LLM_README.md # This file +└── quantum_llm_config_example.yaml # Example configuration +``` + +--- + +## 🎯 Use Cases + +### 1. Research & Experimentation + +Explore quantum advantages in language modeling: + +```python +# Compare quantum vs classical attention +config["enable_multi_scale_attention"] = True +system = QuantumLLMSystem(config) +``` + +### 2. Curriculum Training + +Progressive quantum integration for stable training: + +```python +config["enable_curriculum"] = True +stages = create_curriculum_stages() +``` + +### 3. Resource-Constrained Training + +Optimize for limited quantum resources: + +```python +config["optimization_level"] = 3 +config["enable_circuit_caching"] = True +config["max_batch_circuits"] = 5 +``` + +### 4. Production Monitoring + +Real-time training monitoring and alerts: + +```python +config["enable_dashboard"] = True +config["enable_alerts"] = True +config["dashboard_update_interval"] = 5 +``` + +--- + +## 🚧 Limitations & Future Work + +### Current Limitations + +1. **Quantum Backend:** Simulation-based (no real QPU integration yet) +2. **Tokenization:** Character-level only (subword/BPE planned) +3. **Model Size:** Limited by quantum circuit depth +4. **Training Speed:** Quantum simulation slower than classical + +### Planned Enhancements + +1. **Azure Quantum Integration:** Real QPU execution +2. **Subword Tokenization:** BPE/WordPiece support +3. **Distributed Training:** Multi-GPU/multi-QPU +4. **Advanced Error Correction:** Full QEC implementation +5. **Model Compression:** Quantum-aware pruning/quantization + +--- + +## 📖 References + +- **Quantum Machine Learning:** Quantum circuits for ML tasks +- **Curriculum Learning:** Progressive training strategies +- **Circuit Optimization:** Gate-level quantum optimizations +- **Error Mitigation:** Noise reduction techniques + +--- + +## ✨ Key Innovation + +**Hybrid Quantum-Classical Training:** Seamlessly integrates quantum circuits into transformer architecture with adaptive routing, curriculum learning, and comprehensive monitoring - the first complete quantum LLM training system. + +--- + +## 🤝 Contributing + +Improvements welcome! Focus areas: +- Real QPU backend integration +- Advanced error correction +- Distributed training +- Performance optimizations + +--- + +**Author:** Quantum AI Workspace +**Date:** March 9, 2026 +**License:** MIT diff --git a/ai-projects/quantum-ml/QUICK_REFERENCE.md b/ai-projects/quantum-ml/QUICK_REFERENCE.md index 2b28b6b0d..9dba71097 100644 --- a/ai-projects/quantum-ml/QUICK_REFERENCE.md +++ b/ai-projects/quantum-ml/QUICK_REFERENCE.md @@ -1,152 +1,152 @@ -# 🚀 Quantum AI - Quick Reference Card - -## One-Command Examples - -### 1. Create Quantum Circuits - -```powershell -python .\examples\create_circuits.py -``` - -**Output:** 6 circuit types with visual diagrams - ---- - -### 2. Run Local Simulations - -```powershell -python .\examples\run_simulations.py -``` - -**Output:** Bell state, superposition, gradients, state evolution plot - ---- - -### 3. Train ML Models - -```powershell -python .\examples\train_models.py -``` - -**Output:** 85% accuracy on Moons dataset, training plots - ---- - -### 4. Azure Integration Guide - -```powershell -python .\examples\azure_integration.py -``` - -**Output:** Configuration check, provider info, deployment steps - ---- - -### 5. Run Original Classifier - -```powershell -python .\src\quantum_classifier.py -``` - -**Output:** Hybrid quantum-classical model training - ---- - -## File Locations - -| Item | Path | -|------|------| -| Examples | `examples/*.py` | -| Results/Plots | `results/*.png` | -| Configuration | `config/quantum_config.yaml` | -| Source Code | `src/*.py` | -| Azure Deployment | `azure/*.bicep` | - ---- - -## Key Results - -| Task | Result | Status | -|------|--------|--------| -| Bell State Simulation | 51.5% |00⟩ / 48.5% |11⟩ | ✅ Perfect | -| Moons Classification | 85.0% accuracy | ✅ Excellent | -| Iris Classification | 66.7% accuracy | ✅ Good | -| State Evolution Plot | 50 angles tested | ✅ Generated | - ---- - -## Configuration Quick Edit - -```yaml -# config/quantum_config.yaml - -ml: - model: - n_qubits: 4 # Try: 6, 8 - n_layers: 2 # Try: 3, 4 - entanglement: "linear" # Try: "circular", "full" - - training: - epochs: 100 # Try: 200 - learning_rate: 0.01 # Try: 0.001-0.1 -``` - ---- - -## Common Commands - -### Activate Environment - -```powershell -cd c:\Users\Bryan\OneDrive\AI\quantum-ai -.\venv\Scripts\Activate.ps1 -``` - -### View Results - -```powershell -explorer .\results\ -``` - -### Run All Examples - -```powershell -python .\examples\create_circuits.py -python .\examples\run_simulations.py -python .\examples\train_models.py -python .\examples\azure_integration.py -``` - ---- - -## Troubleshooting - -| Issue | Solution | -|-------|----------| -| Module not found | `.\venv\Scripts\Activate.ps1` | -| Poor accuracy | Increase epochs/layers | -| Memory error | Reduce qubits (<10) | -| Azure connection fail | Check `config/quantum_config.yaml` | - ---- - -## Next Steps - -1. ✅ **Done:** All examples working -2. 🎯 **Try:** Modify hyperparameters -3. 🚀 **Advanced:** Deploy to Azure Quantum -4. 📚 **Learn:** Read papers on QML - ---- - -## All documentation - -- `README.md` - Main project guide -- `examples/README.md` - Examples guide -- `DEMONSTRATION_SUMMARY.md` - Complete summary -- `azure/DEPLOYMENT.md` - Azure deployment -- **THIS FILE** - Quick reference - ---- - -## Happy Quantum Computing! 🌌 +# 🚀 Quantum AI - Quick Reference Card + +## One-Command Examples + +### 1. Create Quantum Circuits + +```powershell +python .\examples\create_circuits.py +``` + +**Output:** 6 circuit types with visual diagrams + +--- + +### 2. Run Local Simulations + +```powershell +python .\examples\run_simulations.py +``` + +**Output:** Bell state, superposition, gradients, state evolution plot + +--- + +### 3. Train ML Models + +```powershell +python .\examples\train_models.py +``` + +**Output:** 85% accuracy on Moons dataset, training plots + +--- + +### 4. Azure Integration Guide + +```powershell +python .\examples\azure_integration.py +``` + +**Output:** Configuration check, provider info, deployment steps + +--- + +### 5. Run Original Classifier + +```powershell +python .\src\quantum_classifier.py +``` + +**Output:** Hybrid quantum-classical model training + +--- + +## File Locations + +| Item | Path | +|------|------| +| Examples | `examples/*.py` | +| Results/Plots | `results/*.png` | +| Configuration | `config/quantum_config.yaml` | +| Source Code | `src/*.py` | +| Azure Deployment | `azure/*.bicep` | + +--- + +## Key Results + +| Task | Result | Status | +|------|--------|--------| +| Bell State Simulation | 51.5% |00⟩ / 48.5% |11⟩ | ✅ Perfect | +| Moons Classification | 85.0% accuracy | ✅ Excellent | +| Iris Classification | 66.7% accuracy | ✅ Good | +| State Evolution Plot | 50 angles tested | ✅ Generated | + +--- + +## Configuration Quick Edit + +```yaml +# config/quantum_config.yaml + +ml: + model: + n_qubits: 4 # Try: 6, 8 + n_layers: 2 # Try: 3, 4 + entanglement: "linear" # Try: "circular", "full" + + training: + epochs: 100 # Try: 200 + learning_rate: 0.01 # Try: 0.001-0.1 +``` + +--- + +## Common Commands + +### Activate Environment + +```powershell +cd c:\Users\Bryan\OneDrive\AI\quantum-ai +.\venv\Scripts\Activate.ps1 +``` + +### View Results + +```powershell +explorer .\results\ +``` + +### Run All Examples + +```powershell +python .\examples\create_circuits.py +python .\examples\run_simulations.py +python .\examples\train_models.py +python .\examples\azure_integration.py +``` + +--- + +## Troubleshooting + +| Issue | Solution | +|-------|----------| +| Module not found | `.\venv\Scripts\Activate.ps1` | +| Poor accuracy | Increase epochs/layers | +| Memory error | Reduce qubits (<10) | +| Azure connection fail | Check `config/quantum_config.yaml` | + +--- + +## Next Steps + +1. ✅ **Done:** All examples working +2. 🎯 **Try:** Modify hyperparameters +3. 🚀 **Advanced:** Deploy to Azure Quantum +4. 📚 **Learn:** Read papers on QML + +--- + +## All documentation + +- `README.md` - Main project guide +- `examples/README.md` - Examples guide +- `DEMONSTRATION_SUMMARY.md` - Complete summary +- `azure/DEPLOYMENT.md` - Azure deployment +- **THIS FILE** - Quick reference + +--- + +## Happy Quantum Computing! 🌌 diff --git a/ai-projects/quantum-ml/README.md b/ai-projects/quantum-ml/README.md index f8877a571..f9fe4b301 100644 --- a/ai-projects/quantum-ml/README.md +++ b/ai-projects/quantum-ml/README.md @@ -1,659 +1,659 @@ -# Quantum AI Project (PennyLane Implementation) - -**⚠️ Primary quantum AI implementation using PennyLane framework.** - -A hybrid quantum-classical machine learning framework leveraging Azure Quantum for enhanced AI capabilities. - -> **Note:** An alternative Qiskit-based implementation exists in `/AI/quantum-ai` for legacy compatibility. -> This directory is the primary/active implementation using PennyLane. - ---- - -## 🆕 **New Quantum Circuit Tools** - -**Four powerful new capabilities for quantum computing:** - -### 1. **Quantum Classifier** ([quantum_classifier.py](src/quantum_classifier.py)) - -Core variational quantum classifier for hybrid ML workflows: - -```python -from src.quantum_classifier import QuantumClassifier - -qc = QuantumClassifier() -weights = qc.initialize_weights() -predictions = qc.predict([[0.1, 0.2, 0.3, 0.4]], weights) -print(predictions) -``` - -### 2. **Hybrid Quantum Neural Networks** ([hybrid_qnn.py](src/hybrid_qnn.py)) - -Hybrid quantum-classical neural networks with configurable depth: - -```python -from src.hybrid_qnn import HybridQNN -import torch - -model = HybridQNN( - input_dim=10, - hidden_dim=16, - n_qubits=4, - n_quantum_layers=2, - output_dim=1 -) -output = model(torch.randn(8, 10)) -``` - -### 3. **Circuit Optimization** ([quantum_circuit_optimizer.py](src/quantum_circuit_optimizer.py)) - -Utilities for optimizing circuit structures and execution settings: - -```python -from src.quantum_circuit_optimizer import QuantumCircuitOptimizer - -optimizer = QuantumCircuitOptimizer() -optimized = optimizer.optimize(None) # Replace None with your circuit object -print(optimized) -``` - -### 4. **Azure Quantum Integration** ([azure_quantum_integration.py](src/azure_quantum_integration.py)) - -Azure backend connection and job submission helpers: - -```python -from src.azure_quantum_integration import AzureQuantumIntegration - -azure = AzureQuantumIntegration() -workspace = azure.connect() -backends = azure.list_backends() -print(backends) -``` - -**🚀 Explore examples:** [`examples/README.md`](examples/README.md) - ---- - -## 📋 Quick Start - -### 🎨 **NEW: Interactive Web Dashboard** - -**Train and visualize quantum AI models in your browser!** - -```bash -cd ai-projects/quantum-ml -./start_dashboard.sh -``` - -Then open **** for: - -- 📊 Real-time training visualization with live charts -- 🎛️ Interactive hyperparameter tuning -- 💾 Training session management and history -- 📈 Loss/accuracy curves updated every second -- 🚀 One-click training on multiple datasets - -**[Full Dashboard Guide →](./WEB_DASHBOARD_README.md)** - ---- - -### 🔐 Quick-Run: Fraud Detection API (Port 5050) - -Run the Flask API for the fraud/ionosphere demo on port 5050. - -```bash -cd ai-projects/quantum-ml -# Start on port 5050 (recommended to avoid conflicts) -PORT=5050 python fraud_detection_api.py -``` - -Verify endpoints: - -```bash -# Health -curl -s http://localhost:5050/health | python -m json.tool - -# Single prediction (example features) -curl -s -X POST http://localhost:5050/predict \ - -H "Content-Type: application/json" \ - -d '{"features": [1.0, 2.0, 3.0, 4.0]}' | python -m json.tool - -# Model info -curl -s http://localhost:5050/model_info | python -m json.tool -``` - -Notes: - -- Configure host/port via `HOST` and `PORT` env vars; defaults are `0.0.0.0:5001`. -- If port 5000 is busy, use `PORT=5050` as shown above. -- This uses Flask’s development server; for production, deploy behind a WSGI server (gunicorn/uvicorn). - ---- - -### 🛡️ Production Start (Gunicorn on 5050) - -Minimal Gunicorn command: - -```bash -pip install gunicorn -cd ai-projects/quantum-ml -gunicorn -w 2 -b 0.0.0.0:5050 fraud_detection_api:app -# or -# gunicorn -w 2 -b 0.0.0.0:5050 wsgi:app -``` - -Notes: - -- The entrypoint is `fraud_detection_api:app` (Flask app object). -- Ensure the working directory is `quantum-ai` so model files in `results/` resolve. - ---- - -### 🧩 systemd Unit (Production-style on 5050) - -Create `/etc/systemd/system/quantum-ai.service`: - -```ini -[Unit] -Description=Quantum AI Fraud Detection API (Gunicorn) -After=network.target - -[Service] -Type=simple -User=www-data -Group=www-data -WorkingDirectory=/opt/aria/quantum-ai -ExecStart=/usr/bin/gunicorn -w 2 -b 0.0.0.0:5050 wsgi:app -Restart=always -RestartSec=5 -StandardOutput=journal -StandardError=journal - -[Install] -WantedBy=multi-user.target -``` - -Enable and start: - -```bash -sudo systemctl daemon-reload -sudo systemctl enable --now quantum-ai -sudo systemctl status quantum-ai -``` - -Tail logs: - -```bash -sudo journalctl -u quantum-ai -f -``` - -If your code and model files live elsewhere, update `WorkingDirectory` accordingly. - -**New to this project?** Start here: - -1. **🎨 Web Dashboard** (Recommended): [`WEB_DASHBOARD_README.md`](./WEB_DASHBOARD_README.md) - - Interactive training UI with real-time visualization - - Perfect for learning and experimentation - - No coding required - just configure and train - -## Hardware Testing Results - -[`HARDWARE_TEST_RESULTS.md`](./HARDWARE_TEST_RESULTS.md) - -- Multi-backend validation (Rigetti ✅, Quantinuum ⚠️) -- GHZ and variational circuit tests -- Hardware vs simulator comparison - -## Provider Comparison - -[`PROVIDER_COMPARISON_RESULTS.md`](./PROVIDER_COMPARISON_RESULTS.md) - -- Detailed gate pattern analysis -- MPS simulation validation (90.5% vs 91.5% entropy) -- Quantinuum bug investigation -- Production recommendations - -## Quick Reference - -[`QUICK_REFERENCE.md`](./QUICK_REFERENCE.md) - -- All commands, workflows, and tips in one place - -**Key Finding (Nov 2025):** Rigetti backend validated for production use. MPS simulations accurate within 1% of hardware. Avoid Quantinuum H-series simulators until Azure fixes fundamental bug. - ---- - -## 🌟 Overview - -This project combines the power of quantum computing with classical machine learning to create advanced AI models. It provides: - -- **Quantum Neural Networks (QNN)**: Variational quantum circuits for classification tasks -- **Hybrid Models**: Integration of quantum layers with classical neural networks -- **Azure Quantum Integration**: Seamless deployment to Azure Quantum workspace -- **Multiple Backends**: Support for simulators and real quantum hardware (IonQ, Quantinuum) -- **MCP Server**: Model Context Protocol server for AI agents to use quantum computing capabilities - -## 🏗️ Architecture - -```text -┌─────────────────────────────────────────────────────────┐ -│ Classical Layer │ -│ (Data Preprocessing) │ -└──────────────────┬──────────────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────────────────────────┐ -│ Quantum Layer │ -│ (Variational Quantum Circuit) │ -│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │ -│ │ Q₀ │──│ RY │──│ RZ │──│CNOT │ │ -│ └─────┘ └─────┘ └─────┘ └──┬──┘ │ -│ ┌─────┐ ┌─────┐ ┌─────┐ │ │ -│ │ Q₁ │──│ RY │──│ RZ │─────┘ │ -│ └─────┘ └─────┘ └─────┘ │ -└──────────────────┬──────────────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────────────────────────┐ -│ Classical Layer │ -│ (Output Processing) │ -└─────────────────────────────────────────────────────────┘ -``` - -## 🚀 Features - -### Quantum Machine Learning - -- **Quantum Classifier**: Pure quantum circuit-based classification -- **Hybrid QNN**: Classical-quantum hybrid neural networks -- **Quantum Convolutional Layers**: Quantum filters for feature extraction -- **Configurable Entanglement**: Linear, circular, and full connectivity patterns - -### Azure Integration - -- Direct integration with Azure Quantum workspace -- Support for multiple quantum providers (IonQ, Quantinuum, Microsoft) -- Job management and result tracking -- Cost estimation and monitoring - -### Development Tools - -- Jupyter notebooks for experimentation -- Comprehensive configuration system -- Logging and diagnostics -- Automated deployment with Bicep - -### MCP Server - -- **Model Context Protocol** server exposing quantum tools to AI agents -- 8 quantum computing tools for circuit creation, simulation, Azure integration, and ML -- Compatible with VS Code Copilot, Claude, and other MCP clients -- See [MCP_SERVER_README.md](MCP_SERVER_README.md) for details - -## 📋 Prerequisites - -- Python 3.8 or higher -- Azure subscription (for cloud deployment) -- Azure CLI (for deployment) -- Git - -## 🔧 Installation - -### 1. Clone the Repository - -```bash -git clone -cd quantum-ai -``` - -### 2. Create Virtual Environment - -```bash -python -m venv venv - -# Windows -.\venv\Scripts\activate - -# Linux/Mac -source venv/bin/activate -``` - -### 3. Install Dependencies - -```bash -pip install -r requirements.txt -``` - -### 4. Configure Azure (Optional) - -If using Azure Quantum: - -1. Update `config/quantum_config.yaml` with your Azure details -2. Follow the [Azure Deployment Guide](azure/DEPLOYMENT.md) - -## MCP Server Setup - -To use the Quantum AI MCP server: - -```powershell -# Install MCP dependencies -pip install -r mcp-requirements.txt - -# Run the server -python quantum_mcp_server.py - -# Or use with VS Code MCP support (add to .vscode/mcp.json): -{ - "quantum-ai": { - "type": "stdio", - "command": "python", - "args": ["c:\\Users\\Bryan\\OneDrive\\AI\\quantum-ai\\quantum_mcp_server.py"] - } -} -``` - -## 📖 Usage - -### Quick Start - Local Simulation - -```python -from src.quantum_classifier import QuantumClassifier, HybridQuantumClassifier -from sklearn.datasets import make_classification -from sklearn.model_selection import train_test_split - -# Generate sample data -X, y = make_classification(n_samples=200, n_features=4, n_classes=2) -X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) - -# Create quantum classifier -qc = QuantumClassifier() -model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) - -# Train the model -from src.quantum_classifier import train_quantum_model -history = train_quantum_model(model, X_train, y_train, X_val, y_val) - -print(f"Final accuracy: {history['val_acc'][-1]:.4f}") -``` - -### Using Hybrid QNN - -```python -from src.hybrid_qnn import HybridQNN -import torch - -# Create hybrid model -model = HybridQNN( - input_dim=10, - hidden_dim=16, - n_qubits=4, - n_quantum_layers=2, - output_dim=1 -) - -# Forward pass -x = torch.randn(8, 10) # Batch of 8 samples -output = model(x) -``` - -### Azure Quantum Integration - -```python -from src.azure_quantum_integration import AzureQuantumIntegration -from qiskit import QuantumCircuit - -# Initialize Azure connection -azure = AzureQuantumIntegration() -workspace = azure.connect() - -# List available backends -backends = azure.list_backends() - -# Create a quantum circuit -circuit = QuantumCircuit(3, 3) -circuit.h(0) -circuit.cx(0, 1) -circuit.cx(1, 2) -circuit.measure([0, 1, 2], [0, 1, 2]) - -# Submit to Azure Quantum -job = azure.submit_circuit(circuit, shots=100, job_name="entanglement_test") -results = azure.get_job_results(job) - -print(results) -``` - -### Visualize hardware test results - -You can generate charts from Azure Quantum hardware tests (Bell state, optimized circuits) and job history: - -```powershell -# 1) (Optional) Run non-interactive tests to create JSON results -python .\quantum-ai\scripts\run_hardware_tests.py --backend rigetti.sim.qvm - -# 2) Build charts from saved results and Azure job list -python .\quantum-ai\scripts\visualize_hardware_results.py -``` - -Outputs: - -- Per-run charts under `ai-projects/quantum-ml/results/visualizations/`: - - `_bar.png` – top measurement states - - `_heatmap.png` – 2-qubit heatmap (Bell) - - `entanglement_summary.png` – entanglement quality across Bell tests - -- If available, Azure job distribution charts: - - `azure_jobs_status.png` – counts by job status - - `azure_jobs_provider_status.png` – stacked provider × status - -Notes: - -- Results JSON may be saved to either `ai-projects/quantum-ml/results/` or repo-root `results/` depending on where the test was launched; the visualizer scans both. -- Use `--optimized` with `run_hardware_tests.py` to also run and save the optimized circuit results. - -### Using the MCP Server - -The quantum-ai project includes an MCP server that exposes quantum capabilities to AI agents: - -```powershell -# Install MCP dependencies -pip install -r mcp-requirements.txt - -# Run the MCP server -python quantum_mcp_server.py - -# Or use the example client -python example_mcp_client.py -``` - -**Available MCP Tools:** - -- `create_quantum_circuit` - Build quantum circuits (Bell, GHZ, entanglement, custom) -- `simulate_quantum_circuit` - Run local simulations with Qiskit Aer -- `train_quantum_classifier` - Train hybrid quantum ML models -- `connect_azure_quantum` - Connect to Azure Quantum workspace -- `submit_quantum_job` - Run on real quantum hardware -- And 3 more tools for backend listing, cost estimation, and circuit analysis - -See [MCP_SERVER_README.md](MCP_SERVER_README.md) for complete documentation. - -## 📊 Project Structure - -```text -ai-projects/quantum-ml/ -├── src/ -│ ├── quantum_classifier.py # Quantum classification models -│ ├── hybrid_qnn.py # Hybrid quantum-classical networks -│ └── azure_quantum_integration.py # Azure Quantum connector -├── config/ -│ └── quantum_config.yaml # Configuration file -├── azure/ -│ ├── quantum_workspace.bicep # Infrastructure as Code -│ ├── quantum_workspace.parameters.json -│ └── DEPLOYMENT.md # Deployment guide -├── notebooks/ # Jupyter notebooks -├── requirements.txt # Python dependencies -└── README.md # This file -``` - -## ⚙️ Configuration - -Edit `config/quantum_config.yaml` to customize: - -```yaml -quantum: - provider: "ionq" # or quantinuum, rigetti - simulator: - backend: "qiskit_aer" - shots: 1024 - hardware: - shots: 500 - optimization_level: 2 - -ml: - model: - n_qubits: 4 - n_layers: 2 - entanglement: "linear" # linear, circular, full - training: - epochs: 100 - batch_size: 32 - learning_rate: 0.01 -``` - -## 🧪 Examples - -### Binary Classification - -```python -# Train a quantum classifier for binary classification -from src.quantum_classifier import QuantumClassifier -import numpy as np - -qc = QuantumClassifier(config_path="config/quantum_config.yaml") -weights = qc.initialize_weights() - -# Sample data -X = np.random.randn(100, 4) -predictions = qc.predict(X, weights) -``` - -### Multi-Qubit Entanglement - -```python -# Create entangled quantum states -from src.azure_quantum_integration import create_sample_circuit - -circuit = create_sample_circuit(n_qubits=5) -print(circuit) -``` - -## 📈 Performance - -Benchmark results on standard datasets: - -| Dataset | Classical NN | Quantum Classifier | Hybrid QNN | -| ------------- | ------------ | ------------------ | ---------- | -| Iris | 96.7% | 94.2% | **97.5%** | -| Wine | 95.3% | 92.8% | **96.1%** | -| Breast Cancer | 97.2% | 95.1% | **97.8%** | - -> **Note:** Results may vary based on circuit configuration and training parameters - -## 💰 Cost Considerations - -### Development (Free) - -- Use local simulators (Qiskit Aer) -- Microsoft Quantum simulators on Azure - -### Production - -- **IonQ**: ~$0.00003 per gate-shot -- **Quantinuum**: ~$0.00015 per circuit execution -- **Storage**: ~$0.02/GB/month -- **Monitoring**: First 5GB/month free - -See [Deployment Guide](azure/DEPLOYMENT.md) for cost optimization tips. - -## 🔬 Research Applications - -This framework is suitable for: - -- **Drug Discovery**: Molecular property prediction -- **Financial Modeling**: Portfolio optimization -- **Climate Science**: Weather pattern classification -- **Materials Science**: Property prediction -- **Cybersecurity**: Anomaly detection - -## 🛠️ Development - -### Running Tests - -```bash -pytest tests/ -``` - -### Code Formatting - -```bash -black src/ -``` - -### Adding New Models - -1. Create new module in `src/` -2. Inherit from `nn.Module` or `QuantumClassifier` -3. Update configuration in `config/quantum_config.yaml` -4. Add documentation and tests - -## 📚 References - -- [PennyLane Documentation](https://docs.pennylane.ai/) -- [Azure Quantum Documentation](https://docs.microsoft.com/azure/quantum/) -- [Qiskit Documentation](https://qiskit.org/documentation/) -- [Quantum Machine Learning Papers](https://arxiv.org/list/quant-ph/recent) - -## 🤝 Contributing - -Contributions are welcome! Please: - -1. Fork the repository -2. Create a feature branch -3. Make your changes -4. Add tests -5. Submit a pull request - -## 📄 License - -This project is licensed under the MIT License - see LICENSE file for details. - -## 🆘 Support - -- **Issues**: Open a GitHub issue -- **Questions**: Use GitHub Discussions -- **Azure Support**: [Azure Support Portal](https://portal.azure.com/#blade/Microsoft_Azure_Support/HelpAndSupportBlade) - -## 🗺️ Roadmap - -- [ ] Add quantum reinforcement learning -- [ ] Implement quantum GANs -- [ ] Support for additional quantum providers -- [ ] Integration with Azure Machine Learning -- [ ] Distributed quantum computing -- [ ] Quantum transfer learning -- [ ] Web-based visualization dashboard - -## 👥 Authors - -- Your Name - Initial work - -## 🙏 Acknowledgments - -- Microsoft Azure Quantum team -- PennyLane developers -- Qiskit community -- Quantum machine learning research community - ---- - -**Note**: This project requires access to Azure Quantum services for cloud deployment. Local simulation is available without Azure credentials. +# Quantum AI Project (PennyLane Implementation) + +**⚠️ Primary quantum AI implementation using PennyLane framework.** + +A hybrid quantum-classical machine learning framework leveraging Azure Quantum for enhanced AI capabilities. + +> **Note:** An alternative Qiskit-based implementation exists in `/AI/quantum-ai` for legacy compatibility. +> This directory is the primary/active implementation using PennyLane. + +--- + +## 🆕 **New Quantum Circuit Tools** + +**Four powerful new capabilities for quantum computing:** + +### 1. **Quantum Classifier** ([quantum_classifier.py](src/quantum_classifier.py)) + +Core variational quantum classifier for hybrid ML workflows: + +```python +from src.quantum_classifier import QuantumClassifier + +qc = QuantumClassifier() +weights = qc.initialize_weights() +predictions = qc.predict([[0.1, 0.2, 0.3, 0.4]], weights) +print(predictions) +``` + +### 2. **Hybrid Quantum Neural Networks** ([hybrid_qnn.py](src/hybrid_qnn.py)) + +Hybrid quantum-classical neural networks with configurable depth: + +```python +from src.hybrid_qnn import HybridQNN +import torch + +model = HybridQNN( + input_dim=10, + hidden_dim=16, + n_qubits=4, + n_quantum_layers=2, + output_dim=1 +) +output = model(torch.randn(8, 10)) +``` + +### 3. **Circuit Optimization** ([quantum_circuit_optimizer.py](src/quantum_circuit_optimizer.py)) + +Utilities for optimizing circuit structures and execution settings: + +```python +from src.quantum_circuit_optimizer import QuantumCircuitOptimizer + +optimizer = QuantumCircuitOptimizer() +optimized = optimizer.optimize(None) # Replace None with your circuit object +print(optimized) +``` + +### 4. **Azure Quantum Integration** ([azure_quantum_integration.py](src/azure_quantum_integration.py)) + +Azure backend connection and job submission helpers: + +```python +from src.azure_quantum_integration import AzureQuantumIntegration + +azure = AzureQuantumIntegration() +workspace = azure.connect() +backends = azure.list_backends() +print(backends) +``` + +**🚀 Explore examples:** [`examples/README.md`](examples/README.md) + +--- + +## 📋 Quick Start + +### 🎨 **NEW: Interactive Web Dashboard** + +**Train and visualize quantum AI models in your browser!** + +```bash +cd ai-projects/quantum-ml +./start_dashboard.sh +``` + +Then open **** for: + +- 📊 Real-time training visualization with live charts +- 🎛️ Interactive hyperparameter tuning +- 💾 Training session management and history +- 📈 Loss/accuracy curves updated every second +- 🚀 One-click training on multiple datasets + +**[Full Dashboard Guide →](./WEB_DASHBOARD_README.md)** + +--- + +### 🔐 Quick-Run: Fraud Detection API (Port 5050) + +Run the Flask API for the fraud/ionosphere demo on port 5050. + +```bash +cd ai-projects/quantum-ml +# Start on port 5050 (recommended to avoid conflicts) +PORT=5050 python fraud_detection_api.py +``` + +Verify endpoints: + +```bash +# Health +curl -s http://localhost:5050/health | python -m json.tool + +# Single prediction (example features) +curl -s -X POST http://localhost:5050/predict \ + -H "Content-Type: application/json" \ + -d '{"features": [1.0, 2.0, 3.0, 4.0]}' | python -m json.tool + +# Model info +curl -s http://localhost:5050/model_info | python -m json.tool +``` + +Notes: + +- Configure host/port via `HOST` and `PORT` env vars; defaults are `0.0.0.0:5001`. +- If port 5000 is busy, use `PORT=5050` as shown above. +- This uses Flask’s development server; for production, deploy behind a WSGI server (gunicorn/uvicorn). + +--- + +### 🛡️ Production Start (Gunicorn on 5050) + +Minimal Gunicorn command: + +```bash +pip install gunicorn +cd ai-projects/quantum-ml +gunicorn -w 2 -b 0.0.0.0:5050 fraud_detection_api:app +# or +# gunicorn -w 2 -b 0.0.0.0:5050 wsgi:app +``` + +Notes: + +- The entrypoint is `fraud_detection_api:app` (Flask app object). +- Ensure the working directory is `quantum-ai` so model files in `results/` resolve. + +--- + +### 🧩 systemd Unit (Production-style on 5050) + +Create `/etc/systemd/system/quantum-ai.service`: + +```ini +[Unit] +Description=Quantum AI Fraud Detection API (Gunicorn) +After=network.target + +[Service] +Type=simple +User=www-data +Group=www-data +WorkingDirectory=/opt/aria/quantum-ai +ExecStart=/usr/bin/gunicorn -w 2 -b 0.0.0.0:5050 wsgi:app +Restart=always +RestartSec=5 +StandardOutput=journal +StandardError=journal + +[Install] +WantedBy=multi-user.target +``` + +Enable and start: + +```bash +sudo systemctl daemon-reload +sudo systemctl enable --now quantum-ai +sudo systemctl status quantum-ai +``` + +Tail logs: + +```bash +sudo journalctl -u quantum-ai -f +``` + +If your code and model files live elsewhere, update `WorkingDirectory` accordingly. + +**New to this project?** Start here: + +1. **🎨 Web Dashboard** (Recommended): [`WEB_DASHBOARD_README.md`](./WEB_DASHBOARD_README.md) + - Interactive training UI with real-time visualization + - Perfect for learning and experimentation + - No coding required - just configure and train + +## Hardware Testing Results + +[`HARDWARE_TEST_RESULTS.md`](./HARDWARE_TEST_RESULTS.md) + +- Multi-backend validation (Rigetti ✅, Quantinuum ⚠️) +- GHZ and variational circuit tests +- Hardware vs simulator comparison + +## Provider Comparison + +[`PROVIDER_COMPARISON_RESULTS.md`](./PROVIDER_COMPARISON_RESULTS.md) + +- Detailed gate pattern analysis +- MPS simulation validation (90.5% vs 91.5% entropy) +- Quantinuum bug investigation +- Production recommendations + +## Quick Reference + +[`QUICK_REFERENCE.md`](./QUICK_REFERENCE.md) + +- All commands, workflows, and tips in one place + +**Key Finding (Nov 2025):** Rigetti backend validated for production use. MPS simulations accurate within 1% of hardware. Avoid Quantinuum H-series simulators until Azure fixes fundamental bug. + +--- + +## 🌟 Overview + +This project combines the power of quantum computing with classical machine learning to create advanced AI models. It provides: + +- **Quantum Neural Networks (QNN)**: Variational quantum circuits for classification tasks +- **Hybrid Models**: Integration of quantum layers with classical neural networks +- **Azure Quantum Integration**: Seamless deployment to Azure Quantum workspace +- **Multiple Backends**: Support for simulators and real quantum hardware (IonQ, Quantinuum) +- **MCP Server**: Model Context Protocol server for AI agents to use quantum computing capabilities + +## 🏗️ Architecture + +```text +┌─────────────────────────────────────────────────────────┐ +│ Classical Layer │ +│ (Data Preprocessing) │ +└──────────────────┬──────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────┐ +│ Quantum Layer │ +│ (Variational Quantum Circuit) │ +│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │ +│ │ Q₀ │──│ RY │──│ RZ │──│CNOT │ │ +│ └─────┘ └─────┘ └─────┘ └──┬──┘ │ +│ ┌─────┐ ┌─────┐ ┌─────┐ │ │ +│ │ Q₁ │──│ RY │──│ RZ │─────┘ │ +│ └─────┘ └─────┘ └─────┘ │ +└──────────────────┬──────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────┐ +│ Classical Layer │ +│ (Output Processing) │ +└─────────────────────────────────────────────────────────┘ +``` + +## 🚀 Features + +### Quantum Machine Learning + +- **Quantum Classifier**: Pure quantum circuit-based classification +- **Hybrid QNN**: Classical-quantum hybrid neural networks +- **Quantum Convolutional Layers**: Quantum filters for feature extraction +- **Configurable Entanglement**: Linear, circular, and full connectivity patterns + +### Azure Integration + +- Direct integration with Azure Quantum workspace +- Support for multiple quantum providers (IonQ, Quantinuum, Microsoft) +- Job management and result tracking +- Cost estimation and monitoring + +### Development Tools + +- Jupyter notebooks for experimentation +- Comprehensive configuration system +- Logging and diagnostics +- Automated deployment with Bicep + +### MCP Server + +- **Model Context Protocol** server exposing quantum tools to AI agents +- 8 quantum computing tools for circuit creation, simulation, Azure integration, and ML +- Compatible with VS Code Copilot, Claude, and other MCP clients +- See [MCP_SERVER_README.md](MCP_SERVER_README.md) for details + +## 📋 Prerequisites + +- Python 3.8 or higher +- Azure subscription (for cloud deployment) +- Azure CLI (for deployment) +- Git + +## 🔧 Installation + +### 1. Clone the Repository + +```bash +git clone +cd quantum-ai +``` + +### 2. Create Virtual Environment + +```bash +python -m venv venv + +# Windows +.\venv\Scripts\activate + +# Linux/Mac +source venv/bin/activate +``` + +### 3. Install Dependencies + +```bash +pip install -r requirements.txt +``` + +### 4. Configure Azure (Optional) + +If using Azure Quantum: + +1. Update `config/quantum_config.yaml` with your Azure details +2. Follow the [Azure Deployment Guide](azure/DEPLOYMENT.md) + +## MCP Server Setup + +To use the Quantum AI MCP server: + +```powershell +# Install MCP dependencies +pip install -r mcp-requirements.txt + +# Run the server +python quantum_mcp_server.py + +# Or use with VS Code MCP support (add to .vscode/mcp.json): +{ + "quantum-ai": { + "type": "stdio", + "command": "python", + "args": ["c:\\Users\\Bryan\\OneDrive\\AI\\quantum-ai\\quantum_mcp_server.py"] + } +} +``` + +## 📖 Usage + +### Quick Start - Local Simulation + +```python +from src.quantum_classifier import QuantumClassifier, HybridQuantumClassifier +from sklearn.datasets import make_classification +from sklearn.model_selection import train_test_split + +# Generate sample data +X, y = make_classification(n_samples=200, n_features=4, n_classes=2) +X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) + +# Create quantum classifier +qc = QuantumClassifier() +model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) + +# Train the model +from src.quantum_classifier import train_quantum_model +history = train_quantum_model(model, X_train, y_train, X_val, y_val) + +print(f"Final accuracy: {history['val_acc'][-1]:.4f}") +``` + +### Using Hybrid QNN + +```python +from src.hybrid_qnn import HybridQNN +import torch + +# Create hybrid model +model = HybridQNN( + input_dim=10, + hidden_dim=16, + n_qubits=4, + n_quantum_layers=2, + output_dim=1 +) + +# Forward pass +x = torch.randn(8, 10) # Batch of 8 samples +output = model(x) +``` + +### Azure Quantum Integration + +```python +from src.azure_quantum_integration import AzureQuantumIntegration +from qiskit import QuantumCircuit + +# Initialize Azure connection +azure = AzureQuantumIntegration() +workspace = azure.connect() + +# List available backends +backends = azure.list_backends() + +# Create a quantum circuit +circuit = QuantumCircuit(3, 3) +circuit.h(0) +circuit.cx(0, 1) +circuit.cx(1, 2) +circuit.measure([0, 1, 2], [0, 1, 2]) + +# Submit to Azure Quantum +job = azure.submit_circuit(circuit, shots=100, job_name="entanglement_test") +results = azure.get_job_results(job) + +print(results) +``` + +### Visualize hardware test results + +You can generate charts from Azure Quantum hardware tests (Bell state, optimized circuits) and job history: + +```powershell +# 1) (Optional) Run non-interactive tests to create JSON results +python .\quantum-ai\scripts\run_hardware_tests.py --backend rigetti.sim.qvm + +# 2) Build charts from saved results and Azure job list +python .\quantum-ai\scripts\visualize_hardware_results.py +``` + +Outputs: + +- Per-run charts under `ai-projects/quantum-ml/results/visualizations/`: + - `_bar.png` – top measurement states + - `_heatmap.png` – 2-qubit heatmap (Bell) + - `entanglement_summary.png` – entanglement quality across Bell tests + +- If available, Azure job distribution charts: + - `azure_jobs_status.png` – counts by job status + - `azure_jobs_provider_status.png` – stacked provider × status + +Notes: + +- Results JSON may be saved to either `ai-projects/quantum-ml/results/` or repo-root `results/` depending on where the test was launched; the visualizer scans both. +- Use `--optimized` with `run_hardware_tests.py` to also run and save the optimized circuit results. + +### Using the MCP Server + +The quantum-ai project includes an MCP server that exposes quantum capabilities to AI agents: + +```powershell +# Install MCP dependencies +pip install -r mcp-requirements.txt + +# Run the MCP server +python quantum_mcp_server.py + +# Or use the example client +python example_mcp_client.py +``` + +**Available MCP Tools:** + +- `create_quantum_circuit` - Build quantum circuits (Bell, GHZ, entanglement, custom) +- `simulate_quantum_circuit` - Run local simulations with Qiskit Aer +- `train_quantum_classifier` - Train hybrid quantum ML models +- `connect_azure_quantum` - Connect to Azure Quantum workspace +- `submit_quantum_job` - Run on real quantum hardware +- And 3 more tools for backend listing, cost estimation, and circuit analysis + +See [MCP_SERVER_README.md](MCP_SERVER_README.md) for complete documentation. + +## 📊 Project Structure + +```text +ai-projects/quantum-ml/ +├── src/ +│ ├── quantum_classifier.py # Quantum classification models +│ ├── hybrid_qnn.py # Hybrid quantum-classical networks +│ └── azure_quantum_integration.py # Azure Quantum connector +├── config/ +│ └── quantum_config.yaml # Configuration file +├── azure/ +│ ├── quantum_workspace.bicep # Infrastructure as Code +│ ├── quantum_workspace.parameters.json +│ └── DEPLOYMENT.md # Deployment guide +├── notebooks/ # Jupyter notebooks +├── requirements.txt # Python dependencies +└── README.md # This file +``` + +## ⚙️ Configuration + +Edit `config/quantum_config.yaml` to customize: + +```yaml +quantum: + provider: "ionq" # or quantinuum, rigetti + simulator: + backend: "qiskit_aer" + shots: 1024 + hardware: + shots: 500 + optimization_level: 2 + +ml: + model: + n_qubits: 4 + n_layers: 2 + entanglement: "linear" # linear, circular, full + training: + epochs: 100 + batch_size: 32 + learning_rate: 0.01 +``` + +## 🧪 Examples + +### Binary Classification + +```python +# Train a quantum classifier for binary classification +from src.quantum_classifier import QuantumClassifier +import numpy as np + +qc = QuantumClassifier(config_path="config/quantum_config.yaml") +weights = qc.initialize_weights() + +# Sample data +X = np.random.randn(100, 4) +predictions = qc.predict(X, weights) +``` + +### Multi-Qubit Entanglement + +```python +# Create entangled quantum states +from src.azure_quantum_integration import create_sample_circuit + +circuit = create_sample_circuit(n_qubits=5) +print(circuit) +``` + +## 📈 Performance + +Benchmark results on standard datasets: + +| Dataset | Classical NN | Quantum Classifier | Hybrid QNN | +| ------------- | ------------ | ------------------ | ---------- | +| Iris | 96.7% | 94.2% | **97.5%** | +| Wine | 95.3% | 92.8% | **96.1%** | +| Breast Cancer | 97.2% | 95.1% | **97.8%** | + +> **Note:** Results may vary based on circuit configuration and training parameters + +## 💰 Cost Considerations + +### Development (Free) + +- Use local simulators (Qiskit Aer) +- Microsoft Quantum simulators on Azure + +### Production + +- **IonQ**: ~$0.00003 per gate-shot +- **Quantinuum**: ~$0.00015 per circuit execution +- **Storage**: ~$0.02/GB/month +- **Monitoring**: First 5GB/month free + +See [Deployment Guide](azure/DEPLOYMENT.md) for cost optimization tips. + +## 🔬 Research Applications + +This framework is suitable for: + +- **Drug Discovery**: Molecular property prediction +- **Financial Modeling**: Portfolio optimization +- **Climate Science**: Weather pattern classification +- **Materials Science**: Property prediction +- **Cybersecurity**: Anomaly detection + +## 🛠️ Development + +### Running Tests + +```bash +pytest tests/ +``` + +### Code Formatting + +```bash +black src/ +``` + +### Adding New Models + +1. Create new module in `src/` +2. Inherit from `nn.Module` or `QuantumClassifier` +3. Update configuration in `config/quantum_config.yaml` +4. Add documentation and tests + +## 📚 References + +- [PennyLane Documentation](https://docs.pennylane.ai/) +- [Azure Quantum Documentation](https://docs.microsoft.com/azure/quantum/) +- [Qiskit Documentation](https://qiskit.org/documentation/) +- [Quantum Machine Learning Papers](https://arxiv.org/list/quant-ph/recent) + +## 🤝 Contributing + +Contributions are welcome! Please: + +1. Fork the repository +2. Create a feature branch +3. Make your changes +4. Add tests +5. Submit a pull request + +## 📄 License + +This project is licensed under the MIT License - see LICENSE file for details. + +## 🆘 Support + +- **Issues**: Open a GitHub issue +- **Questions**: Use GitHub Discussions +- **Azure Support**: [Azure Support Portal](https://portal.azure.com/#blade/Microsoft_Azure_Support/HelpAndSupportBlade) + +## 🗺️ Roadmap + +- [ ] Add quantum reinforcement learning +- [ ] Implement quantum GANs +- [ ] Support for additional quantum providers +- [ ] Integration with Azure Machine Learning +- [ ] Distributed quantum computing +- [ ] Quantum transfer learning +- [ ] Web-based visualization dashboard + +## 👥 Authors + +- Your Name - Initial work + +## 🙏 Acknowledgments + +- Microsoft Azure Quantum team +- PennyLane developers +- Qiskit community +- Quantum machine learning research community + +--- + +**Note**: This project requires access to Azure Quantum services for cloud deployment. Local simulation is available without Azure credentials. diff --git a/ai-projects/quantum-ml/READY_FOR_HARDWARE.md b/ai-projects/quantum-ml/READY_FOR_HARDWARE.md index a0919d524..fd4323244 100644 --- a/ai-projects/quantum-ml/READY_FOR_HARDWARE.md +++ b/ai-projects/quantum-ml/READY_FOR_HARDWARE.md @@ -1,485 +1,485 @@ -# 🎉 Ready to Test on Real Quantum Hardware - -**Your quantum AI is optimized and ready for real quantum computers!** - ---- - -## ✅ What You Have Now - -### 1. Optimized Quantum Configuration (90% Accuracy) 🏆 - -Your quantum classifier has been **systematically optimized**: - -```yaml -Qubits: 4 -Layers: 3 (optimal - tested 1-4 layers) -Learning Rate: 0.1 (optimal - tested 0.001-0.1) -Entanglement: linear (optimal - tested linear/circular/full) -``` - -**Performance:** - -- Baseline: 72.5% accuracy -- **Optimized: 90.0% accuracy (+17.5% improvement!)** -- Convergence: 3-4x faster (reaches 90% by epoch 20-30) - -### 2. Complete Test Suite Created 🔬 - -**File:** `test_azure_quantum.py` - -**Runs 4 comprehensive tests:** - -1. ✅ **Connection Test** - Verifies Azure Quantum setup -2. 🔔 **Bell State** - Tests quantum entanglement on hardware -3. 🏆 **Optimized Circuit** - Your 90% accuracy configuration -4. 📊 **Simulator vs Hardware** - Compares ideal vs real quantum - -**Features:** - -- Interactive backend selection -- Cost estimation before submission -- Automatic results saving -- Progress tracking and status monitoring - -### 3. Step-by-Step Guides 📚 - -Three detailed guides created: - -**`AZURE_SETUP_CHECKLIST.md`** ← **START HERE!** - -- Complete 15-minute setup walkthrough -- Checkbox format (easy to follow) -- Troubleshooting for common issues -- Cost breakdown and free tier info - -**`AZURE_QUANTUM_QUICKSTART.md`** - -- 5-minute quick start -- Understanding results -- Cost optimization tips -- Advanced next steps - -**`azure/DEPLOYMENT.md`** - -- Detailed Bicep deployment -- Authentication options -- Monitoring and diagnostics -- Production deployment - -### 4. Azure Infrastructure Ready 🏗️ - -**Bicep templates in `azure/` directory:** - -- `quantum_workspace.bicep` - Complete infrastructure definition -- `quantum_workspace.parameters.json` - Customizable parameters - -**Creates:** - -- Azure Quantum Workspace -- Storage Account (for results) -- Quantum Provider connections (IonQ, Quantinuum, Microsoft) - -### 5. Integration Code Complete 💻 - -**File:** `src/azure_quantum_integration.py` - -**Features:** - -- Workspace connection with Azure authentication -- Backend listing and selection -- Circuit submission to real hardware -- Job status monitoring -- Results retrieval and saving -- Cost estimation -- Batch job management - ---- - -## 🚀 How to Test Now - -### Option 1: Quick Start (5 Minutes) ⚡ - -```powershell -# 1. Login to Azure -az login - -# 2. Deploy workspace (use unique names!) -cd quantum-ai\azure -az group create --name rg-quantum-ai --location eastus -az deployment group create ` - --resource-group rg-quantum-ai ` - --template-file quantum_workspace.bicep ` - --parameters quantum_workspace.parameters.json - -# 3. Update config with your subscription ID -# Edit: config/quantum_config.yaml - -# 4. Run tests! -cd .. -python test_azure_quantum.py -``` - -### Option 2: Guided Setup (15 Minutes) 📋 - -Follow the complete checklist: - -```powershell -# Open the setup guide -code AZURE_SETUP_CHECKLIST.md - -# Work through each checkbox ✅ -# Everything is explained step-by-step! -``` - ---- - -## 💰 Cost Information - -### Testing with Simulators (FREE) ✅ - -- **IonQ Simulator**: Unlimited free usage -- **Microsoft Simulator**: Unlimited free usage -- **Azure Storage**: First 5 GB free -- **Free Trial**: $200 credit available - -**Perfect for:** - -- Initial testing and validation -- Development and debugging -- Circuit optimization -- Learning quantum computing - -### Real Quantum Hardware (Optional) 💎 - -**IonQ QPU:** - -- Cost: ~$0.36 per circuit (500 shots) -- Technology: Ion trap quantum computer -- Fidelity: ~99.5% gate accuracy - -**Quantinuum H1-1:** - -- Cost: ~$1.50 per circuit -- Technology: Trapped ion (highest fidelity) -- Fidelity: ~99.9% gate accuracy - -**Recommended Approach:** - -1. Test on simulator first (free) -2. Run 1-2 circuits on hardware (~$0.50) -3. Validate results match simulation -4. Scale up if needed - ---- - -## 📊 What to Expect - -### Simulator Results (Ideal Quantum) - -```text -Bell State - IonQ Simulator: - 00: 50.0% - 11: 50.0% - -Entanglement Quality: 100% -✓ Perfect quantum entanglement! -``` - -### Hardware Results (Real Quantum Computer!) - -```text -Bell State - IonQ QPU: - 00: 48.7% - 11: 47.9% - 01: 1.8% - 10: 1.6% - -Entanglement Quality: 96.6% -✓ Excellent quantum behavior (noise expected)! -``` - -**Why the difference?** - -- Hardware has physical noise (decoherence, gate errors) -- This is REAL quantum computing! 🎉 -- 95%+ entanglement is excellent -- Proves you're running on actual quantum hardware - -### Your Optimized Circuit Results - -```text -Measurement distribution (optimized 3-layer circuit): - 0000: 17.4% ████████ - 1111: 16.4% ████████ - 0101: 9.0% ████ - 1010: 8.6% ████ - ... - -Quantum State Analysis: - Unique states: 14/16 - Entropy: 3.21 / 4.00 - Distribution: 80.3% uniform - -✓ Circuit explores quantum superposition! -✓ High entropy = good for ML! -✓ Well-balanced state exploration! -``` - ---- - -## 🎯 Success Criteria - -You've successfully tested on quantum hardware when: - -✅ **Connection established** - -```text -✓ Successfully connected to Azure Quantum! -Available backends: ionq.simulator, ionq.qpu, ... -``` - -✅ **Bell state shows entanglement** - -```text -Entanglement Quality: 80%+ -✓ Quantum entanglement verified! -``` - -✅ **Job completes successfully** - -```text -Job ID: 12345-6789-abcd-efgh -Status: JobStatus.COMPLETED -✓ Results retrieved! -``` - -✅ **Results saved** - -```text -✓ Results saved to results/bell_state_results_*.json -``` - ---- - -## 🔄 Testing Workflow - -```text -┌─────────────────────────────────────────────────┐ -│ 1. Deploy Azure Quantum Workspace │ -│ (2-3 minutes, one-time setup) │ -└──────────────────┬──────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────────────────┐ -│ 2. Update quantum_config.yaml │ -│ (Your subscription ID and workspace name) │ -└──────────────────┬──────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────────────────┐ -│ 3. Run test_azure_quantum.py │ -│ (Interactive test suite) │ -└──────────────────┬──────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────────────────┐ -│ 4. Select Backend │ -│ • ionq.simulator (FREE - start here) │ -│ • ionq.qpu (PAID - real quantum) │ -└──────────────────┬──────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────────────────┐ -│ 5. Test Bell State (verify entanglement) │ -│ Expected: ~50/50 distribution │ -│ Time: 10-30 seconds (simulator) │ -│ 2-10 minutes (hardware) │ -└──────────────────┬──────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────────────────┐ -│ 6. Test Optimized Circuit (90% accuracy) │ -│ Your 3-layer, linear entanglement circuit │ -│ 500 shots, multiple quantum states │ -└──────────────────┬──────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────────────────┐ -│ 7. Analyze Results │ -│ Compare simulator vs hardware │ -│ Verify quantum behavior │ -│ Save for documentation │ -└─────────────────────────────────────────────────┘ -``` - ---- - -## 📁 Files Created for You - -```text -ai-projects/quantum-ml/ -├── test_azure_quantum.py ← Main test script (run this!) -├── AZURE_SETUP_CHECKLIST.md ← Step-by-step guide (start here!) -├── AZURE_QUANTUM_QUICKSTART.md ← Quick reference -├── FINAL_OPTIMIZATION_REPORT.md ← Your 90% accuracy results -│ -├── config/ -│ └── quantum_config.yaml ← Update with your Azure details -│ -├── azure/ -│ ├── DEPLOYMENT.md ← Detailed deployment guide -│ ├── quantum_workspace.bicep ← Infrastructure template -│ └── quantum_workspace.parameters.json ← Customize deployment -│ -└── src/ - └── azure_quantum_integration.py ← Integration code (ready!) -``` - ---- - -## 🎓 Learning Path - -### Beginner (Free Simulator) - -1. ✅ Deploy workspace -2. ✅ Connect and list backends -3. ✅ Run Bell state on simulator -4. ✅ Understand quantum entanglement - -### Intermediate (Low-Cost Hardware) - -1. ✅ Estimate costs -2. ✅ Run 1 circuit on hardware (~$0.07 with 100 shots) -3. ✅ Compare simulator vs hardware -4. ✅ Understand quantum noise - -### Advanced (Production) - -1. ✅ Run optimized circuit (500 shots) -2. ✅ Batch multiple experiments -3. ✅ Automate quantum ML pipeline -4. ✅ Scale to 6-8 qubits - ---- - -## 🆘 Quick Troubleshooting - -### "az: command not found" - -→ Install Azure CLI: - -### "Deployment failed" - -→ Check workspace name is unique (must be globally unique) - -### "No backends available" - -→ Wait 5-10 minutes after workspace creation - -### "Insufficient credits" - -→ Use simulator (free) or purchase quantum credits in Azure Portal - -### Need help? - -→ See AZURE_SETUP_CHECKLIST.md troubleshooting section - ---- - -## 🌟 Why This Is Exciting - -You're about to: - -🎯 **Run ML on real quantum computers** - -- Not just simulation - actual quantum hardware! -- IonQ and Quantinuum quantum processors -- Observe real quantum effects (entanglement, superposition) - -🔬 **Validate your optimization** - -- See if 90% accuracy translates to hardware -- Compare quantum vs classical performance -- Understand hardware-specific challenges - -🚀 **Join the quantum computing revolution** - -- Access cutting-edge technology -- Contribute to quantum ML research -- Gain hands-on quantum experience - -📊 **Publish your results** - -- Document hardware vs simulator differences -- Share quantum ML findings -- Build portfolio with quantum projects - ---- - -## 🎯 Your Next Command - -**Ready to test on quantum hardware?** - -```powershell -# Open the setup guide -code AZURE_SETUP_CHECKLIST.md - -# OR jump straight to testing (if you have Azure) -python test_azure_quantum.py -``` - -**Total time:** 15-20 minutes -**Cost (with free simulator):** $0.00 -**Excitement level:** 🚀🚀🚀 - ---- - -## 📞 Support Resources - -**Documentation:** - -- Setup Guide: `AZURE_SETUP_CHECKLIST.md` -- Quick Start: `AZURE_QUANTUM_QUICKSTART.md` -- Deployment: `azure/DEPLOYMENT.md` - -**Azure Documentation:** - -- Azure Quantum Docs: -- Qiskit on Azure: -- Pricing: - -**Your Optimization Results:** - -- `FINAL_OPTIMIZATION_REPORT.md` - 90% accuracy breakdown -- `TRAINING_REPORT.md` - All dataset results -- `CUSTOM_DATASET_GUIDE.md` - Use your own data - ---- - -## 🎊 You're Ready - -Everything is prepared for you to test your optimized quantum AI on real quantum hardware: - -✅ Optimized configuration (90% accuracy) -✅ Test scripts created -✅ Step-by-step guides written -✅ Azure templates ready -✅ Integration code complete -✅ Documentation comprehensive - -**All that's left is deployment!** - -Follow `AZURE_SETUP_CHECKLIST.md` and you'll be running quantum circuits on real quantum computers in **15 minutes**! 🚀 - ---- - -**Status:** - -- [x] Quantum AI optimized to 90% accuracy -- [x] Test suite created -- [x] Documentation complete -- [ ] Azure workspace deployed ← **START HERE** -- [ ] Tested on quantum hardware -- [ ] Production deployed - -## Start Your Quantum Computing Journey Now! 🌟 +# 🎉 Ready to Test on Real Quantum Hardware + +**Your quantum AI is optimized and ready for real quantum computers!** + +--- + +## ✅ What You Have Now + +### 1. Optimized Quantum Configuration (90% Accuracy) 🏆 + +Your quantum classifier has been **systematically optimized**: + +```yaml +Qubits: 4 +Layers: 3 (optimal - tested 1-4 layers) +Learning Rate: 0.1 (optimal - tested 0.001-0.1) +Entanglement: linear (optimal - tested linear/circular/full) +``` + +**Performance:** + +- Baseline: 72.5% accuracy +- **Optimized: 90.0% accuracy (+17.5% improvement!)** +- Convergence: 3-4x faster (reaches 90% by epoch 20-30) + +### 2. Complete Test Suite Created 🔬 + +**File:** `test_azure_quantum.py` + +**Runs 4 comprehensive tests:** + +1. ✅ **Connection Test** - Verifies Azure Quantum setup +2. 🔔 **Bell State** - Tests quantum entanglement on hardware +3. 🏆 **Optimized Circuit** - Your 90% accuracy configuration +4. 📊 **Simulator vs Hardware** - Compares ideal vs real quantum + +**Features:** + +- Interactive backend selection +- Cost estimation before submission +- Automatic results saving +- Progress tracking and status monitoring + +### 3. Step-by-Step Guides 📚 + +Three detailed guides created: + +**`AZURE_SETUP_CHECKLIST.md`** ← **START HERE!** + +- Complete 15-minute setup walkthrough +- Checkbox format (easy to follow) +- Troubleshooting for common issues +- Cost breakdown and free tier info + +**`AZURE_QUANTUM_QUICKSTART.md`** + +- 5-minute quick start +- Understanding results +- Cost optimization tips +- Advanced next steps + +**`azure/DEPLOYMENT.md`** + +- Detailed Bicep deployment +- Authentication options +- Monitoring and diagnostics +- Production deployment + +### 4. Azure Infrastructure Ready 🏗️ + +**Bicep templates in `azure/` directory:** + +- `quantum_workspace.bicep` - Complete infrastructure definition +- `quantum_workspace.parameters.json` - Customizable parameters + +**Creates:** + +- Azure Quantum Workspace +- Storage Account (for results) +- Quantum Provider connections (IonQ, Quantinuum, Microsoft) + +### 5. Integration Code Complete 💻 + +**File:** `src/azure_quantum_integration.py` + +**Features:** + +- Workspace connection with Azure authentication +- Backend listing and selection +- Circuit submission to real hardware +- Job status monitoring +- Results retrieval and saving +- Cost estimation +- Batch job management + +--- + +## 🚀 How to Test Now + +### Option 1: Quick Start (5 Minutes) ⚡ + +```powershell +# 1. Login to Azure +az login + +# 2. Deploy workspace (use unique names!) +cd quantum-ai\azure +az group create --name rg-quantum-ai --location eastus +az deployment group create ` + --resource-group rg-quantum-ai ` + --template-file quantum_workspace.bicep ` + --parameters quantum_workspace.parameters.json + +# 3. Update config with your subscription ID +# Edit: config/quantum_config.yaml + +# 4. Run tests! +cd .. +python test_azure_quantum.py +``` + +### Option 2: Guided Setup (15 Minutes) 📋 + +Follow the complete checklist: + +```powershell +# Open the setup guide +code AZURE_SETUP_CHECKLIST.md + +# Work through each checkbox ✅ +# Everything is explained step-by-step! +``` + +--- + +## 💰 Cost Information + +### Testing with Simulators (FREE) ✅ + +- **IonQ Simulator**: Unlimited free usage +- **Microsoft Simulator**: Unlimited free usage +- **Azure Storage**: First 5 GB free +- **Free Trial**: $200 credit available + +**Perfect for:** + +- Initial testing and validation +- Development and debugging +- Circuit optimization +- Learning quantum computing + +### Real Quantum Hardware (Optional) 💎 + +**IonQ QPU:** + +- Cost: ~$0.36 per circuit (500 shots) +- Technology: Ion trap quantum computer +- Fidelity: ~99.5% gate accuracy + +**Quantinuum H1-1:** + +- Cost: ~$1.50 per circuit +- Technology: Trapped ion (highest fidelity) +- Fidelity: ~99.9% gate accuracy + +**Recommended Approach:** + +1. Test on simulator first (free) +2. Run 1-2 circuits on hardware (~$0.50) +3. Validate results match simulation +4. Scale up if needed + +--- + +## 📊 What to Expect + +### Simulator Results (Ideal Quantum) + +```text +Bell State - IonQ Simulator: + 00: 50.0% + 11: 50.0% + +Entanglement Quality: 100% +✓ Perfect quantum entanglement! +``` + +### Hardware Results (Real Quantum Computer!) + +```text +Bell State - IonQ QPU: + 00: 48.7% + 11: 47.9% + 01: 1.8% + 10: 1.6% + +Entanglement Quality: 96.6% +✓ Excellent quantum behavior (noise expected)! +``` + +**Why the difference?** + +- Hardware has physical noise (decoherence, gate errors) +- This is REAL quantum computing! 🎉 +- 95%+ entanglement is excellent +- Proves you're running on actual quantum hardware + +### Your Optimized Circuit Results + +```text +Measurement distribution (optimized 3-layer circuit): + 0000: 17.4% ████████ + 1111: 16.4% ████████ + 0101: 9.0% ████ + 1010: 8.6% ████ + ... + +Quantum State Analysis: + Unique states: 14/16 + Entropy: 3.21 / 4.00 + Distribution: 80.3% uniform + +✓ Circuit explores quantum superposition! +✓ High entropy = good for ML! +✓ Well-balanced state exploration! +``` + +--- + +## 🎯 Success Criteria + +You've successfully tested on quantum hardware when: + +✅ **Connection established** + +```text +✓ Successfully connected to Azure Quantum! +Available backends: ionq.simulator, ionq.qpu, ... +``` + +✅ **Bell state shows entanglement** + +```text +Entanglement Quality: 80%+ +✓ Quantum entanglement verified! +``` + +✅ **Job completes successfully** + +```text +Job ID: 12345-6789-abcd-efgh +Status: JobStatus.COMPLETED +✓ Results retrieved! +``` + +✅ **Results saved** + +```text +✓ Results saved to results/bell_state_results_*.json +``` + +--- + +## 🔄 Testing Workflow + +```text +┌─────────────────────────────────────────────────┐ +│ 1. Deploy Azure Quantum Workspace │ +│ (2-3 minutes, one-time setup) │ +└──────────────────┬──────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────┐ +│ 2. Update quantum_config.yaml │ +│ (Your subscription ID and workspace name) │ +└──────────────────┬──────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────┐ +│ 3. Run test_azure_quantum.py │ +│ (Interactive test suite) │ +└──────────────────┬──────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────┐ +│ 4. Select Backend │ +│ • ionq.simulator (FREE - start here) │ +│ • ionq.qpu (PAID - real quantum) │ +└──────────────────┬──────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────┐ +│ 5. Test Bell State (verify entanglement) │ +│ Expected: ~50/50 distribution │ +│ Time: 10-30 seconds (simulator) │ +│ 2-10 minutes (hardware) │ +└──────────────────┬──────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────┐ +│ 6. Test Optimized Circuit (90% accuracy) │ +│ Your 3-layer, linear entanglement circuit │ +│ 500 shots, multiple quantum states │ +└──────────────────┬──────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────┐ +│ 7. Analyze Results │ +│ Compare simulator vs hardware │ +│ Verify quantum behavior │ +│ Save for documentation │ +└─────────────────────────────────────────────────┘ +``` + +--- + +## 📁 Files Created for You + +```text +ai-projects/quantum-ml/ +├── test_azure_quantum.py ← Main test script (run this!) +├── AZURE_SETUP_CHECKLIST.md ← Step-by-step guide (start here!) +├── AZURE_QUANTUM_QUICKSTART.md ← Quick reference +├── FINAL_OPTIMIZATION_REPORT.md ← Your 90% accuracy results +│ +├── config/ +│ └── quantum_config.yaml ← Update with your Azure details +│ +├── azure/ +│ ├── DEPLOYMENT.md ← Detailed deployment guide +│ ├── quantum_workspace.bicep ← Infrastructure template +│ └── quantum_workspace.parameters.json ← Customize deployment +│ +└── src/ + └── azure_quantum_integration.py ← Integration code (ready!) +``` + +--- + +## 🎓 Learning Path + +### Beginner (Free Simulator) + +1. ✅ Deploy workspace +2. ✅ Connect and list backends +3. ✅ Run Bell state on simulator +4. ✅ Understand quantum entanglement + +### Intermediate (Low-Cost Hardware) + +1. ✅ Estimate costs +2. ✅ Run 1 circuit on hardware (~$0.07 with 100 shots) +3. ✅ Compare simulator vs hardware +4. ✅ Understand quantum noise + +### Advanced (Production) + +1. ✅ Run optimized circuit (500 shots) +2. ✅ Batch multiple experiments +3. ✅ Automate quantum ML pipeline +4. ✅ Scale to 6-8 qubits + +--- + +## 🆘 Quick Troubleshooting + +### "az: command not found" + +→ Install Azure CLI: + +### "Deployment failed" + +→ Check workspace name is unique (must be globally unique) + +### "No backends available" + +→ Wait 5-10 minutes after workspace creation + +### "Insufficient credits" + +→ Use simulator (free) or purchase quantum credits in Azure Portal + +### Need help? + +→ See AZURE_SETUP_CHECKLIST.md troubleshooting section + +--- + +## 🌟 Why This Is Exciting + +You're about to: + +🎯 **Run ML on real quantum computers** + +- Not just simulation - actual quantum hardware! +- IonQ and Quantinuum quantum processors +- Observe real quantum effects (entanglement, superposition) + +🔬 **Validate your optimization** + +- See if 90% accuracy translates to hardware +- Compare quantum vs classical performance +- Understand hardware-specific challenges + +🚀 **Join the quantum computing revolution** + +- Access cutting-edge technology +- Contribute to quantum ML research +- Gain hands-on quantum experience + +📊 **Publish your results** + +- Document hardware vs simulator differences +- Share quantum ML findings +- Build portfolio with quantum projects + +--- + +## 🎯 Your Next Command + +**Ready to test on quantum hardware?** + +```powershell +# Open the setup guide +code AZURE_SETUP_CHECKLIST.md + +# OR jump straight to testing (if you have Azure) +python test_azure_quantum.py +``` + +**Total time:** 15-20 minutes +**Cost (with free simulator):** $0.00 +**Excitement level:** 🚀🚀🚀 + +--- + +## 📞 Support Resources + +**Documentation:** + +- Setup Guide: `AZURE_SETUP_CHECKLIST.md` +- Quick Start: `AZURE_QUANTUM_QUICKSTART.md` +- Deployment: `azure/DEPLOYMENT.md` + +**Azure Documentation:** + +- Azure Quantum Docs: +- Qiskit on Azure: +- Pricing: + +**Your Optimization Results:** + +- `FINAL_OPTIMIZATION_REPORT.md` - 90% accuracy breakdown +- `TRAINING_REPORT.md` - All dataset results +- `CUSTOM_DATASET_GUIDE.md` - Use your own data + +--- + +## 🎊 You're Ready + +Everything is prepared for you to test your optimized quantum AI on real quantum hardware: + +✅ Optimized configuration (90% accuracy) +✅ Test scripts created +✅ Step-by-step guides written +✅ Azure templates ready +✅ Integration code complete +✅ Documentation comprehensive + +**All that's left is deployment!** + +Follow `AZURE_SETUP_CHECKLIST.md` and you'll be running quantum circuits on real quantum computers in **15 minutes**! 🚀 + +--- + +**Status:** + +- [x] Quantum AI optimized to 90% accuracy +- [x] Test suite created +- [x] Documentation complete +- [ ] Azure workspace deployed ← **START HERE** +- [ ] Tested on quantum hardware +- [ ] Production deployed + +## Start Your Quantum Computing Journey Now! 🌟 diff --git a/ai-projects/quantum-ml/TRAINING_REPORT.md b/ai-projects/quantum-ml/TRAINING_REPORT.md index 38609f1ff..c3ba7ab7e 100644 --- a/ai-projects/quantum-ml/TRAINING_REPORT.md +++ b/ai-projects/quantum-ml/TRAINING_REPORT.md @@ -1,296 +1,296 @@ -# 🧠 Quantum AI Training Report - -**Date:** October 31, 2025 -**Model:** Hybrid Quantum-Classical Neural Network -**Framework:** PennyLane + PyTorch - ---- - -## 📊 Training Summary - -### Model Architecture - -- **Quantum Circuit:** 4 qubits, 2 variational layers -- **Entanglement:** Linear (adjacent qubits) -- **Backend:** PennyLane default.qubit simulator -- **Hybrid Structure:** Classical preprocessing → Quantum circuit → Classical postprocessing - -### Training Configuration - -- **Optimizer:** Adam -- **Learning Rate:** 0.01 -- **Batch Size:** 32 -- **Epochs:** 100 -- **Loss Function:** Binary Cross-Entropy - ---- - -## 🎯 Performance Results - -### Standard Datasets (Original Training) - -| Dataset | Accuracy | Grade | Use Case | -|---------|----------|-------|----------| -| **Moons** | 72.5% | ★★★★★★★ | Non-linear boundaries | -| **Circles** | 52.5% | ★★★★★ | Concentric patterns | -| **Iris** | 76.7% | ★★★★★★★ | Multi-class classification | - -### Extended Datasets (Recent Experiments) - -| Dataset | Accuracy | Grade | Use Case | -|---------|----------|-------|----------| -| **XOR** | 57.5% | ★★★★★ | Linearly inseparable | -| **Spiral** | 37.5% | ★★★ | Highly non-linear | -| **Imbalanced** | 90.0% | ★★★★★★★★ | Class imbalance | -| **Wine** | 88.9% | ★★★★★★★★ | Real-world data | - ---- - -## 🏆 Best Performance - -### Top 3 Achievements - -1. **Imbalanced Dataset: 90.0%** 🥇 - - Demonstrates robustness to class imbalance - - Successfully handles 90/10 split - - No special weighting needed - -2. **Wine Classification: 88.9%** 🥈 - - Real-world chemical analysis - - 13 features reduced via PCA - - Multi-class problem - -3. **Iris Classification: 76.7%** 🥉 - - Classic ML benchmark - - Setosa vs. Other species - - Competitive with classical methods - ---- - -## 📈 Training Dynamics - -### Convergence Analysis - -- **Fast Convergence:** Loss drops rapidly in first 30 epochs -- **Stable Training:** No signs of overfitting or divergence -- **Smooth Gradient Flow:** Quantum gradients computed reliably - -### Loss Progression (Iris Dataset) - -```text -Epoch 0: Loss 1.0830 → Val Loss 0.7966 (Acc: 33.3%) -Epoch 30: Loss 0.8178 → Val Loss 0.6247 (Acc: 66.7%) -Epoch 60: Loss 0.7247 → Val Loss 0.5532 (Acc: 70.0%) -Epoch 90: Loss 0.6486 → Val Loss 0.4941 (Acc: 76.7%) -``` - -**Observation:** Consistent improvement throughout training with no plateau. - ---- - -## 🔬 Quantum Advantage Analysis - -### Where Quantum Excels ✅ - -1. **Imbalanced Data** (90.0%) - - Quantum entanglement provides robustness - - Natural handling of rare classes - -2. **Real-World Data** (88.9% on Wine) - - High-dimensional feature spaces - - Complex non-linear relationships - -3. **Non-Linear Problems** (72.5% on Moons) - - Quantum circuits create feature spaces - - Classical linear models would fail - -### Challenges ⚠️ - -1. **Spiral Dataset** (37.5%) - - Highly intertwined patterns - - May need more qubits (6-8) - - Consider quantum data re-uploading - -2. **Circles Dataset** (52.5%) - - Concentric patterns difficult - - Radial basis functions might help - - Explore different quantum encodings - ---- - -## 🎓 Key Learnings - -### What We Discovered - -1. **Quantum entanglement** helps with non-linear decision boundaries -2. **Hybrid architecture** combines best of quantum and classical -3. **4 qubits** sufficient for many practical problems -4. **Standard optimization** (Adam) works well with quantum gradients -5. **PCA preprocessing** essential for high-dimensional data - -### What Works Best - -- **Dataset size:** 100-200 samples ideal for current setup -- **Feature count:** 2-4 features (or PCA reduced to 4) -- **Class balance:** Model handles imbalance naturally -- **Training epochs:** 100 epochs sufficient for convergence - ---- - -## 📁 Generated Artifacts - -### Training Visualizations - -- ✅ `training_moons.png` - Training curves for Moons dataset -- ✅ `model_comparison.png` - Comparative performance chart -- ✅ `state_evolution.png` - Quantum state evolution -- ✅ `xor_problem.png` - XOR decision boundary -- ✅ `spiral_dataset.png` - Spiral classification attempt -- ✅ `all_datasets_comparison.png` - Comprehensive comparison - -**Location:** `results/` and `results/extended_datasets/` - -### Configuration Files - -- ✅ `config/quantum_config.yaml` - Hyperparameters -- ✅ `src/quantum_classifier.py` - Model implementation - ---- - -## 🚀 Next Steps for Improvement - -### Immediate Actions (High Priority) - -1. **Increase Qubits** (6-8 qubits) - - Better expressivity for complex patterns - - Target spiral dataset improvement - -2. **Try Different Entanglement** - - Test "circular" and "full" patterns - - May improve circles dataset performance - -3. **Implement Data Re-uploading** - - Multiple encoding layers - - Can boost accuracy by 10-15% - -### Medium-Term Goals - -1. **Quantum Feature Selection** - - Identify most quantum-advantageous features - - Optimize PCA components - -2. **Ensemble Methods** - - Combine multiple quantum circuits - - Voting or averaging strategies - -3. **Hardware Deployment** - - Test on Azure Quantum real hardware - - Understand noise impact - -### Long-Term Research - -1. **Quantum Attention Mechanisms** - - Integrate with transformer architecture - - Quantum memory for sequence tasks - -2. **Error Mitigation** - - Implement zero-noise extrapolation - - Probabilistic error cancellation - -3. **Scalability Testing** - - Larger datasets (1000+ samples) - - More classes (>3) - ---- - -## 💡 Recommendations - -### For Production Use - -- **Use Cases:** Focus on imbalanced data and high-dimensional features -- **Dataset Size:** 100-500 samples optimal for current setup -- **Validation:** Always validate on real-world holdout sets - -### For Experimentation - -- **Parameter Tuning:** Run `experiments/parameter_tuning.py` -- **New Datasets:** Test domain-specific data -- **Circuit Design:** Explore custom ansatz circuits - -### For Deployment - -- **Azure Quantum:** See `experiments/AZURE_QUICKSTART.md` -- **Cost Management:** Start with free simulators -- **Monitoring:** Track job costs and performance - ---- - -## 📚 Resources Used - -### Documentation - -- Main README: `README.md` -- Examples: `examples/README.md` -- Deployment: `azure/DEPLOYMENT.md` -- Quick Reference: `QUICK_REFERENCE.md` - -### Code Modules - -- Quantum Classifier: `src/quantum_classifier.py` -- Azure Integration: `src/azure_quantum_integration.py` -- Hybrid QNN: `src/hybrid_qnn.py` - -### Experiments - -- Parameter Tuning: `experiments/parameter_tuning.py` -- Extended Datasets: `experiments/extended_datasets.py` -- Plot Analysis: `experiments/analyze_plots.py` - ---- - -## ✅ Training Checklist - -### Completed ✓ - -- [x] Environment setup with PennyLane + PyTorch -- [x] Quantum circuit implementation (4 qubits, 2 layers) -- [x] Hybrid classical-quantum architecture -- [x] Training on 3 standard datasets (Moons, Circles, Iris) -- [x] Training on 4 extended datasets (XOR, Spiral, Imbalanced, Wine) -- [x] Visualization generation (6+ plots) -- [x] Performance evaluation and comparison -- [x] Documentation and reports - -### Next Training Session 🎯 - -- [ ] Parameter optimization experiments -- [ ] 6-qubit architecture testing -- [ ] Entanglement pattern comparison -- [ ] Learning rate sensitivity analysis -- [ ] Data re-uploading implementation -- [ ] Azure Quantum hardware deployment - ---- - -## 🎉 Conclusion - -Your quantum AI has been successfully trained and validated across **7 different datasets** with performances ranging from **37.5% to 90.0% accuracy**. - -**Standout Results:** - -- 🏆 **90% on Imbalanced Data** - Best performance -- 🏆 **88.9% on Wine Dataset** - Real-world success -- 🏆 **76.7% on Iris Dataset** - Classical benchmark - -**Ready for:** - -- ✅ Production deployment on similar datasets -- ✅ Azure Quantum hardware testing -- ✅ Advanced experimentation and optimization - -**Training Status:** **COMPLETE** ✓ - ---- - -*Generated automatically by Quantum AI Training System* -*For questions or improvements, see documentation in `examples/` and `experiments/` directories* +# 🧠 Quantum AI Training Report + +**Date:** October 31, 2025 +**Model:** Hybrid Quantum-Classical Neural Network +**Framework:** PennyLane + PyTorch + +--- + +## 📊 Training Summary + +### Model Architecture + +- **Quantum Circuit:** 4 qubits, 2 variational layers +- **Entanglement:** Linear (adjacent qubits) +- **Backend:** PennyLane default.qubit simulator +- **Hybrid Structure:** Classical preprocessing → Quantum circuit → Classical postprocessing + +### Training Configuration + +- **Optimizer:** Adam +- **Learning Rate:** 0.01 +- **Batch Size:** 32 +- **Epochs:** 100 +- **Loss Function:** Binary Cross-Entropy + +--- + +## 🎯 Performance Results + +### Standard Datasets (Original Training) + +| Dataset | Accuracy | Grade | Use Case | +|---------|----------|-------|----------| +| **Moons** | 72.5% | ★★★★★★★ | Non-linear boundaries | +| **Circles** | 52.5% | ★★★★★ | Concentric patterns | +| **Iris** | 76.7% | ★★★★★★★ | Multi-class classification | + +### Extended Datasets (Recent Experiments) + +| Dataset | Accuracy | Grade | Use Case | +|---------|----------|-------|----------| +| **XOR** | 57.5% | ★★★★★ | Linearly inseparable | +| **Spiral** | 37.5% | ★★★ | Highly non-linear | +| **Imbalanced** | 90.0% | ★★★★★★★★ | Class imbalance | +| **Wine** | 88.9% | ★★★★★★★★ | Real-world data | + +--- + +## 🏆 Best Performance + +### Top 3 Achievements + +1. **Imbalanced Dataset: 90.0%** 🥇 + - Demonstrates robustness to class imbalance + - Successfully handles 90/10 split + - No special weighting needed + +2. **Wine Classification: 88.9%** 🥈 + - Real-world chemical analysis + - 13 features reduced via PCA + - Multi-class problem + +3. **Iris Classification: 76.7%** 🥉 + - Classic ML benchmark + - Setosa vs. Other species + - Competitive with classical methods + +--- + +## 📈 Training Dynamics + +### Convergence Analysis + +- **Fast Convergence:** Loss drops rapidly in first 30 epochs +- **Stable Training:** No signs of overfitting or divergence +- **Smooth Gradient Flow:** Quantum gradients computed reliably + +### Loss Progression (Iris Dataset) + +```text +Epoch 0: Loss 1.0830 → Val Loss 0.7966 (Acc: 33.3%) +Epoch 30: Loss 0.8178 → Val Loss 0.6247 (Acc: 66.7%) +Epoch 60: Loss 0.7247 → Val Loss 0.5532 (Acc: 70.0%) +Epoch 90: Loss 0.6486 → Val Loss 0.4941 (Acc: 76.7%) +``` + +**Observation:** Consistent improvement throughout training with no plateau. + +--- + +## 🔬 Quantum Advantage Analysis + +### Where Quantum Excels ✅ + +1. **Imbalanced Data** (90.0%) + - Quantum entanglement provides robustness + - Natural handling of rare classes + +2. **Real-World Data** (88.9% on Wine) + - High-dimensional feature spaces + - Complex non-linear relationships + +3. **Non-Linear Problems** (72.5% on Moons) + - Quantum circuits create feature spaces + - Classical linear models would fail + +### Challenges ⚠️ + +1. **Spiral Dataset** (37.5%) + - Highly intertwined patterns + - May need more qubits (6-8) + - Consider quantum data re-uploading + +2. **Circles Dataset** (52.5%) + - Concentric patterns difficult + - Radial basis functions might help + - Explore different quantum encodings + +--- + +## 🎓 Key Learnings + +### What We Discovered + +1. **Quantum entanglement** helps with non-linear decision boundaries +2. **Hybrid architecture** combines best of quantum and classical +3. **4 qubits** sufficient for many practical problems +4. **Standard optimization** (Adam) works well with quantum gradients +5. **PCA preprocessing** essential for high-dimensional data + +### What Works Best + +- **Dataset size:** 100-200 samples ideal for current setup +- **Feature count:** 2-4 features (or PCA reduced to 4) +- **Class balance:** Model handles imbalance naturally +- **Training epochs:** 100 epochs sufficient for convergence + +--- + +## 📁 Generated Artifacts + +### Training Visualizations + +- ✅ `training_moons.png` - Training curves for Moons dataset +- ✅ `model_comparison.png` - Comparative performance chart +- ✅ `state_evolution.png` - Quantum state evolution +- ✅ `xor_problem.png` - XOR decision boundary +- ✅ `spiral_dataset.png` - Spiral classification attempt +- ✅ `all_datasets_comparison.png` - Comprehensive comparison + +**Location:** `results/` and `results/extended_datasets/` + +### Configuration Files + +- ✅ `config/quantum_config.yaml` - Hyperparameters +- ✅ `src/quantum_classifier.py` - Model implementation + +--- + +## 🚀 Next Steps for Improvement + +### Immediate Actions (High Priority) + +1. **Increase Qubits** (6-8 qubits) + - Better expressivity for complex patterns + - Target spiral dataset improvement + +2. **Try Different Entanglement** + - Test "circular" and "full" patterns + - May improve circles dataset performance + +3. **Implement Data Re-uploading** + - Multiple encoding layers + - Can boost accuracy by 10-15% + +### Medium-Term Goals + +1. **Quantum Feature Selection** + - Identify most quantum-advantageous features + - Optimize PCA components + +2. **Ensemble Methods** + - Combine multiple quantum circuits + - Voting or averaging strategies + +3. **Hardware Deployment** + - Test on Azure Quantum real hardware + - Understand noise impact + +### Long-Term Research + +1. **Quantum Attention Mechanisms** + - Integrate with transformer architecture + - Quantum memory for sequence tasks + +2. **Error Mitigation** + - Implement zero-noise extrapolation + - Probabilistic error cancellation + +3. **Scalability Testing** + - Larger datasets (1000+ samples) + - More classes (>3) + +--- + +## 💡 Recommendations + +### For Production Use + +- **Use Cases:** Focus on imbalanced data and high-dimensional features +- **Dataset Size:** 100-500 samples optimal for current setup +- **Validation:** Always validate on real-world holdout sets + +### For Experimentation + +- **Parameter Tuning:** Run `experiments/parameter_tuning.py` +- **New Datasets:** Test domain-specific data +- **Circuit Design:** Explore custom ansatz circuits + +### For Deployment + +- **Azure Quantum:** See `experiments/AZURE_QUICKSTART.md` +- **Cost Management:** Start with free simulators +- **Monitoring:** Track job costs and performance + +--- + +## 📚 Resources Used + +### Documentation + +- Main README: `README.md` +- Examples: `examples/README.md` +- Deployment: `azure/DEPLOYMENT.md` +- Quick Reference: `QUICK_REFERENCE.md` + +### Code Modules + +- Quantum Classifier: `src/quantum_classifier.py` +- Azure Integration: `src/azure_quantum_integration.py` +- Hybrid QNN: `src/hybrid_qnn.py` + +### Experiments + +- Parameter Tuning: `experiments/parameter_tuning.py` +- Extended Datasets: `experiments/extended_datasets.py` +- Plot Analysis: `experiments/analyze_plots.py` + +--- + +## ✅ Training Checklist + +### Completed ✓ + +- [x] Environment setup with PennyLane + PyTorch +- [x] Quantum circuit implementation (4 qubits, 2 layers) +- [x] Hybrid classical-quantum architecture +- [x] Training on 3 standard datasets (Moons, Circles, Iris) +- [x] Training on 4 extended datasets (XOR, Spiral, Imbalanced, Wine) +- [x] Visualization generation (6+ plots) +- [x] Performance evaluation and comparison +- [x] Documentation and reports + +### Next Training Session 🎯 + +- [ ] Parameter optimization experiments +- [ ] 6-qubit architecture testing +- [ ] Entanglement pattern comparison +- [ ] Learning rate sensitivity analysis +- [ ] Data re-uploading implementation +- [ ] Azure Quantum hardware deployment + +--- + +## 🎉 Conclusion + +Your quantum AI has been successfully trained and validated across **7 different datasets** with performances ranging from **37.5% to 90.0% accuracy**. + +**Standout Results:** + +- 🏆 **90% on Imbalanced Data** - Best performance +- 🏆 **88.9% on Wine Dataset** - Real-world success +- 🏆 **76.7% on Iris Dataset** - Classical benchmark + +**Ready for:** + +- ✅ Production deployment on similar datasets +- ✅ Azure Quantum hardware testing +- ✅ Advanced experimentation and optimization + +**Training Status:** **COMPLETE** ✓ + +--- + +*Generated automatically by Quantum AI Training System* +*For questions or improvements, see documentation in `examples/` and `experiments/` directories* diff --git a/ai-projects/quantum-ml/TRAINING_SESSION_SUMMARY.md b/ai-projects/quantum-ml/TRAINING_SESSION_SUMMARY.md index 93c7bb3a7..24eaf37d0 100644 --- a/ai-projects/quantum-ml/TRAINING_SESSION_SUMMARY.md +++ b/ai-projects/quantum-ml/TRAINING_SESSION_SUMMARY.md @@ -1,352 +1,352 @@ -# Quantum AI Training Session Summary -**Date:** November 1, 2025 -**Session:** Comprehensive Quantum Machine Learning Training & Benchmarking - ---- - -## 🎯 Mission Accomplished - -Successfully trained and benchmarked a **Hybrid Quantum-Classical Neural Network** on multiple real-world datasets, demonstrating the power of quantum computing for machine learning tasks. - ---- - -## 📊 Key Results - -### Overall Performance -- **Average Accuracy Across All Datasets:** 87.30% -- **Best Individual Performance:** 100% (Banknote Authentication) -- **Datasets Tested:** 3 (Ionosphere, Banknote, Sonar) -- **Total Training Epochs:** 75 (25 per dataset) - -### Individual Dataset Performance - -#### 1. 🏆 Banknote Authentication (PERFECT SCORE!) -- **Accuracy:** 100.00% -- **Dataset:** 1,371 samples, 4 features -- **Task:** Detect forged banknotes using wavelet features -- **Grade:** 🏆 Excellent -- **Real-world Application:** Financial fraud detection - -#### 2. 🏆 Ionosphere Radar Classification -- **Accuracy:** 85.71% -- **Dataset:** 350 samples, 34 features (PCA → 4 qubits) -- **Task:** Classify radar returns as 'good' or 'bad' -- **Grade:** 🏆 Excellent -- **Real-world Application:** Signal processing, radar systems - -#### 3. ⭐ Sonar Mine Detection -- **Accuracy:** 76.19% -- **Dataset:** 207 samples, 60 features (PCA → 4 qubits) -- **Task:** Distinguish between mines and rocks -- **Grade:** ⭐ Very Good -- **Real-world Application:** Naval defense, underwater detection - ---- - -## 🔬 Technical Architecture - -### Quantum Circuit Design -``` -Input (Classical) → Encoder → [Quantum Layer] → Decoder → Output - ↓ - 4 Qubits - 2 Variational Layers - Amplitude Encoding - Linear Entanglement (CNOT) -``` - -### Model Components -1. **Classical Preprocessing:** - - Input layer → Hidden layer (16 nodes) - - Batch normalization + ReLU activation - - Dropout (20%) for regularization - - Amplitude encoding preparation (2^n_qubits features) - -2. **Quantum Processing Layer:** - - Device: PennyLane default.qubit simulator - - 4 qubits (matches reduced feature space) - - 2 variational layers with RY/RZ rotations - - CNOT gates for entanglement - - Pauli-Z expectation value measurements - -3. **Classical Postprocessing:** - - Linear layer (n_qubits → 16 hidden) - - Batch normalization + ReLU + Dropout - - Final classification layer - -### Training Configuration -- **Optimizer:** Adam (lr=0.001, β1=0.9, β2=0.999) -- **Loss Function:** CrossEntropyLoss -- **Batch Size:** 16 -- **Epochs:** 25-30 per dataset -- **Train/Val Split:** 80/20 with stratification -- **Preprocessing:** StandardScaler + PCA (when needed) - ---- - -## 📁 Generated Artifacts - -### Trained Models -- ✅ `results/custom_model.pt` - Wine dataset demo model -- ✅ `results/ionosphere_model.pt` - Ionosphere classifier -- ✅ `results/ionosphere_scaler.pkl` - Data preprocessor -- ✅ `results/ionosphere_pca.pkl` - Dimensionality reducer - -### Visualizations -- ✅ `results/custom_training.png` - Wine dataset training curves -- ✅ `results/ionosphere_training.png` - Ionosphere training curves -- ✅ `results/benchmark_comparison.png` - Multi-dataset comparison - -### Reports & Data -- ✅ `results/benchmark_report.md` - Comprehensive benchmark analysis -- ✅ `results/benchmark_results.json` - Raw metrics and training history - ---- - -## 🚀 Technical Achievements - -### 1. Fixed Missing Trainer Class -**Problem:** `QuantumClassicalTrainer` class was missing from `hybrid_qnn.py` - -**Solution:** Added complete trainer implementation with: -- Adam optimizer integration -- Training loop with progress tracking -- Validation evaluation -- Loss and accuracy history logging - -### 2. Quantum Layer Configuration -**Problem:** PennyLane API compatibility issues with TorchLayer - -**Solution:** -- Created QNode wrapper before passing to TorchLayer -- Properly configured weight shapes for variational circuits -- Ensured correct amplitude encoding normalization - -### 3. Data Preprocessing Pipeline -**Implementation:** -- StandardScaler for normalization (critical for quantum encoding) -- PCA for dimensionality reduction (34→4, 60→4 features) -- Stratified train/test splits for balanced classes -- Zero-padding for datasets with fewer than 4 features - -### 4. Automated Benchmarking -**Created:** `benchmark_all_datasets.py` -- Iterates through all quantum datasets -- Generates comparison visualizations -- Produces markdown reports -- Exports JSON for further analysis - ---- - -## 💡 Key Insights - -### Quantum Advantage Observations -1. **Banknote Dataset (100% accuracy):** - - Only 4 features = perfect match for 4 qubits - - No PCA required = preserved all information - - Clean, linearly separable data = quantum superposition excels - -2. **Ionosphere Dataset (85.71%):** - - High-dimensional (34 features) compressed to 4 qubits - - 55.51% variance retained via PCA - - Still achieved excellent results despite information loss - -3. **Sonar Dataset (76.19%):** - - Very high-dimensional (60 features) → 4 qubits - - Only 53.45% variance retained - - Challenging dataset even for classical methods - - Quantum model still competitive - -### PennyLane + PyTorch Integration -- Seamless gradient flow through quantum circuits -- Automatic differentiation (parameter-shift rule) -- GPU-compatible once quantum layer execution completes -- Efficient batching for classical components - ---- - -## 📈 Comparison with Classical Baselines - -### Expected Classical Performance (Literature Values) -| Dataset | Classical SVM | Classical NN | Quantum Hybrid | Quantum Advantage | -|---------|---------------|--------------|----------------|-------------------| -| Banknote | ~98% | ~97% | **100%** | ✅ +2-3% | -| Ionosphere | ~82% | ~86% | **85.71%** | ≈ Competitive | -| Sonar | ~70% | ~73% | **76.19%** | ✅ +3-6% | - -**Note:** Classical baselines are approximate from published literature. Direct comparison would require running classical models on same train/test splits. - ---- - -## 🔮 Next Steps & Recommendations - -### Immediate Actions -1. ✅ **Review Visualizations:** - - `results/benchmark_comparison.png` - Side-by-side dataset performance - - `results/ionosphere_training.png` - Training curves analysis - -2. ✅ **Deploy Best Model:** - - Banknote model (100% accuracy) ready for production - - Load with: `model.load_state_dict(torch.load('results/ionosphere_model.pt'))` - -3. 🔄 **Test on Heart Disease Dataset:** - - Fix data cleaning issue (handle '?' missing values) - - Expected: Good performance on medical diagnosis task - -### Advanced Explorations - -#### A. Azure Quantum Hardware Testing -```powershell -# Submit trained circuit to real quantum hardware -python .\scripts\submit_small_stabilizer.py --backend ionq.qpu --shots 1000 -``` -- Test on IonQ trapped-ion quantum computer -- Compare hardware vs simulator results -- Analyze noise impact on classification accuracy - -#### B. Hyperparameter Optimization -Potential improvements: -- **Quantum layers:** 2 → 3 or 4 (increase expressivity) -- **Learning rate:** Try 0.0005, 0.002 (current: 0.001) -- **Batch size:** 8 or 32 (current: 16) -- **Hidden dimension:** 32 or 64 (current: 16) -- **Dropout:** 0.1 or 0.3 (current: 0.2) - -#### C. Advanced Quantum Techniques -1. **Different Entanglement Patterns:** - - Current: Linear (chain) - - Try: Circular, full, custom patterns - -2. **Alternative Encodings:** - - Current: Amplitude encoding - - Try: Angle encoding, basis encoding, IQP encoding - -3. **Quantum Convolutional Layers:** - - Use `QCNN` class already implemented in `hybrid_qnn.py` - - Sliding quantum filters for feature extraction - -4. **Ensemble Methods:** - - Train multiple quantum models with different seeds - - Combine predictions via voting or averaging - ---- - -## 📚 Code Files Created/Modified - -### New Training Scripts -1. `train_ionosphere.py` - Ionosphere-specific training (368 lines) -2. `benchmark_all_datasets.py` - Comprehensive benchmark suite (348 lines) - -### Modified Core Files -1. `src/hybrid_qnn.py` - Added `QuantumClassicalTrainer` class -2. `train_custom_dataset.py` - Fixed parameter names (`n_quantum_layers`) - -### Generated Reports -1. `TRAINING_SESSION_SUMMARY.md` - This document -2. `results/benchmark_report.md` - Detailed metrics report - ---- - -## 🎓 Learning Outcomes - -### Quantum Machine Learning Concepts -- ✅ Hybrid quantum-classical architectures -- ✅ Amplitude encoding for feature representation -- ✅ Variational quantum circuits (VQC) -- ✅ Parameter-shift gradient computation -- ✅ Quantum entanglement for feature correlations - -### Practical Implementation -- ✅ PennyLane quantum simulation -- ✅ PyTorch integration for hybrid models -- ✅ Qiskit compatibility (Azure Quantum) -- ✅ Production-ready model serialization - -### Software Engineering -- ✅ Modular code organization -- ✅ Automated benchmarking pipelines -- ✅ Comprehensive logging and reporting -- ✅ Error handling and validation - ---- - -## 🌟 Achievements Summary - -### Performance Metrics -- 🏆 **3 datasets successfully trained** -- 🏆 **1 perfect score (100% accuracy)** -- 🏆 **87.30% average accuracy** -- 🏆 **100% code success rate (no failed runs)** - -### Technical Milestones -- ✅ Fixed 3 critical bugs in quantum layer -- ✅ Implemented complete training pipeline -- ✅ Created automated benchmarking system -- ✅ Generated publication-ready visualizations - -### Code Quality -- 📝 **~1,000 lines of new code** -- 📝 **Comprehensive docstrings** -- 📝 **Type hints throughout** -- 📝 **Modular, reusable components** - ---- - -## 🔗 Quick Reference - -### Run Training on Specific Dataset -```powershell -# Demo dataset (wine) -python .\train_custom_dataset.py - -# Real dataset (ionosphere) -python .\train_ionosphere.py - -# All datasets benchmark -python .\benchmark_all_datasets.py -``` - -### Load Trained Model -```python -import torch -from src.hybrid_qnn import HybridQNN - -model = HybridQNN(input_dim=4, hidden_dim=16, n_qubits=4, - n_quantum_layers=2, output_dim=2) -model.load_state_dict(torch.load('results/ionosphere_model.pt')) -model.eval() -``` - -### View Results -```powershell -# Open visualizations -start results\benchmark_comparison.png -start results\ionosphere_training.png - -# Read report -cat results\benchmark_report.md -``` - ---- - -## 🎉 Conclusion - -This training session successfully demonstrated the **practical application of quantum computing to real-world machine learning problems**. The hybrid quantum-classical approach achieved: - -- **Competitive to superior performance** vs classical methods -- **Efficient use of quantum resources** (only 4 qubits) -- **Scalable architecture** ready for quantum hardware -- **Production-ready models** with full preprocessing pipelines - -The quantum AI system is now ready for: -1. Deployment to Azure Quantum hardware -2. Integration into production applications -3. Further research and optimization -4. Expansion to additional datasets and domains - -**Status: Mission Complete! 🚀✨** - ---- - -*Generated by Quantum AI Training System* -*November 1, 2025* +# Quantum AI Training Session Summary +**Date:** November 1, 2025 +**Session:** Comprehensive Quantum Machine Learning Training & Benchmarking + +--- + +## 🎯 Mission Accomplished + +Successfully trained and benchmarked a **Hybrid Quantum-Classical Neural Network** on multiple real-world datasets, demonstrating the power of quantum computing for machine learning tasks. + +--- + +## 📊 Key Results + +### Overall Performance +- **Average Accuracy Across All Datasets:** 87.30% +- **Best Individual Performance:** 100% (Banknote Authentication) +- **Datasets Tested:** 3 (Ionosphere, Banknote, Sonar) +- **Total Training Epochs:** 75 (25 per dataset) + +### Individual Dataset Performance + +#### 1. 🏆 Banknote Authentication (PERFECT SCORE!) +- **Accuracy:** 100.00% +- **Dataset:** 1,371 samples, 4 features +- **Task:** Detect forged banknotes using wavelet features +- **Grade:** 🏆 Excellent +- **Real-world Application:** Financial fraud detection + +#### 2. 🏆 Ionosphere Radar Classification +- **Accuracy:** 85.71% +- **Dataset:** 350 samples, 34 features (PCA → 4 qubits) +- **Task:** Classify radar returns as 'good' or 'bad' +- **Grade:** 🏆 Excellent +- **Real-world Application:** Signal processing, radar systems + +#### 3. ⭐ Sonar Mine Detection +- **Accuracy:** 76.19% +- **Dataset:** 207 samples, 60 features (PCA → 4 qubits) +- **Task:** Distinguish between mines and rocks +- **Grade:** ⭐ Very Good +- **Real-world Application:** Naval defense, underwater detection + +--- + +## 🔬 Technical Architecture + +### Quantum Circuit Design +``` +Input (Classical) → Encoder → [Quantum Layer] → Decoder → Output + ↓ + 4 Qubits + 2 Variational Layers + Amplitude Encoding + Linear Entanglement (CNOT) +``` + +### Model Components +1. **Classical Preprocessing:** + - Input layer → Hidden layer (16 nodes) + - Batch normalization + ReLU activation + - Dropout (20%) for regularization + - Amplitude encoding preparation (2^n_qubits features) + +2. **Quantum Processing Layer:** + - Device: PennyLane default.qubit simulator + - 4 qubits (matches reduced feature space) + - 2 variational layers with RY/RZ rotations + - CNOT gates for entanglement + - Pauli-Z expectation value measurements + +3. **Classical Postprocessing:** + - Linear layer (n_qubits → 16 hidden) + - Batch normalization + ReLU + Dropout + - Final classification layer + +### Training Configuration +- **Optimizer:** Adam (lr=0.001, β1=0.9, β2=0.999) +- **Loss Function:** CrossEntropyLoss +- **Batch Size:** 16 +- **Epochs:** 25-30 per dataset +- **Train/Val Split:** 80/20 with stratification +- **Preprocessing:** StandardScaler + PCA (when needed) + +--- + +## 📁 Generated Artifacts + +### Trained Models +- ✅ `results/custom_model.pt` - Wine dataset demo model +- ✅ `results/ionosphere_model.pt` - Ionosphere classifier +- ✅ `results/ionosphere_scaler.pkl` - Data preprocessor +- ✅ `results/ionosphere_pca.pkl` - Dimensionality reducer + +### Visualizations +- ✅ `results/custom_training.png` - Wine dataset training curves +- ✅ `results/ionosphere_training.png` - Ionosphere training curves +- ✅ `results/benchmark_comparison.png` - Multi-dataset comparison + +### Reports & Data +- ✅ `results/benchmark_report.md` - Comprehensive benchmark analysis +- ✅ `results/benchmark_results.json` - Raw metrics and training history + +--- + +## 🚀 Technical Achievements + +### 1. Fixed Missing Trainer Class +**Problem:** `QuantumClassicalTrainer` class was missing from `hybrid_qnn.py` + +**Solution:** Added complete trainer implementation with: +- Adam optimizer integration +- Training loop with progress tracking +- Validation evaluation +- Loss and accuracy history logging + +### 2. Quantum Layer Configuration +**Problem:** PennyLane API compatibility issues with TorchLayer + +**Solution:** +- Created QNode wrapper before passing to TorchLayer +- Properly configured weight shapes for variational circuits +- Ensured correct amplitude encoding normalization + +### 3. Data Preprocessing Pipeline +**Implementation:** +- StandardScaler for normalization (critical for quantum encoding) +- PCA for dimensionality reduction (34→4, 60→4 features) +- Stratified train/test splits for balanced classes +- Zero-padding for datasets with fewer than 4 features + +### 4. Automated Benchmarking +**Created:** `benchmark_all_datasets.py` +- Iterates through all quantum datasets +- Generates comparison visualizations +- Produces markdown reports +- Exports JSON for further analysis + +--- + +## 💡 Key Insights + +### Quantum Advantage Observations +1. **Banknote Dataset (100% accuracy):** + - Only 4 features = perfect match for 4 qubits + - No PCA required = preserved all information + - Clean, linearly separable data = quantum superposition excels + +2. **Ionosphere Dataset (85.71%):** + - High-dimensional (34 features) compressed to 4 qubits + - 55.51% variance retained via PCA + - Still achieved excellent results despite information loss + +3. **Sonar Dataset (76.19%):** + - Very high-dimensional (60 features) → 4 qubits + - Only 53.45% variance retained + - Challenging dataset even for classical methods + - Quantum model still competitive + +### PennyLane + PyTorch Integration +- Seamless gradient flow through quantum circuits +- Automatic differentiation (parameter-shift rule) +- GPU-compatible once quantum layer execution completes +- Efficient batching for classical components + +--- + +## 📈 Comparison with Classical Baselines + +### Expected Classical Performance (Literature Values) +| Dataset | Classical SVM | Classical NN | Quantum Hybrid | Quantum Advantage | +|---------|---------------|--------------|----------------|-------------------| +| Banknote | ~98% | ~97% | **100%** | ✅ +2-3% | +| Ionosphere | ~82% | ~86% | **85.71%** | ≈ Competitive | +| Sonar | ~70% | ~73% | **76.19%** | ✅ +3-6% | + +**Note:** Classical baselines are approximate from published literature. Direct comparison would require running classical models on same train/test splits. + +--- + +## 🔮 Next Steps & Recommendations + +### Immediate Actions +1. ✅ **Review Visualizations:** + - `results/benchmark_comparison.png` - Side-by-side dataset performance + - `results/ionosphere_training.png` - Training curves analysis + +2. ✅ **Deploy Best Model:** + - Banknote model (100% accuracy) ready for production + - Load with: `model.load_state_dict(torch.load('results/ionosphere_model.pt'))` + +3. 🔄 **Test on Heart Disease Dataset:** + - Fix data cleaning issue (handle '?' missing values) + - Expected: Good performance on medical diagnosis task + +### Advanced Explorations + +#### A. Azure Quantum Hardware Testing +```powershell +# Submit trained circuit to real quantum hardware +python .\scripts\submit_small_stabilizer.py --backend ionq.qpu --shots 1000 +``` +- Test on IonQ trapped-ion quantum computer +- Compare hardware vs simulator results +- Analyze noise impact on classification accuracy + +#### B. Hyperparameter Optimization +Potential improvements: +- **Quantum layers:** 2 → 3 or 4 (increase expressivity) +- **Learning rate:** Try 0.0005, 0.002 (current: 0.001) +- **Batch size:** 8 or 32 (current: 16) +- **Hidden dimension:** 32 or 64 (current: 16) +- **Dropout:** 0.1 or 0.3 (current: 0.2) + +#### C. Advanced Quantum Techniques +1. **Different Entanglement Patterns:** + - Current: Linear (chain) + - Try: Circular, full, custom patterns + +2. **Alternative Encodings:** + - Current: Amplitude encoding + - Try: Angle encoding, basis encoding, IQP encoding + +3. **Quantum Convolutional Layers:** + - Use `QCNN` class already implemented in `hybrid_qnn.py` + - Sliding quantum filters for feature extraction + +4. **Ensemble Methods:** + - Train multiple quantum models with different seeds + - Combine predictions via voting or averaging + +--- + +## 📚 Code Files Created/Modified + +### New Training Scripts +1. `train_ionosphere.py` - Ionosphere-specific training (368 lines) +2. `benchmark_all_datasets.py` - Comprehensive benchmark suite (348 lines) + +### Modified Core Files +1. `src/hybrid_qnn.py` - Added `QuantumClassicalTrainer` class +2. `train_custom_dataset.py` - Fixed parameter names (`n_quantum_layers`) + +### Generated Reports +1. `TRAINING_SESSION_SUMMARY.md` - This document +2. `results/benchmark_report.md` - Detailed metrics report + +--- + +## 🎓 Learning Outcomes + +### Quantum Machine Learning Concepts +- ✅ Hybrid quantum-classical architectures +- ✅ Amplitude encoding for feature representation +- ✅ Variational quantum circuits (VQC) +- ✅ Parameter-shift gradient computation +- ✅ Quantum entanglement for feature correlations + +### Practical Implementation +- ✅ PennyLane quantum simulation +- ✅ PyTorch integration for hybrid models +- ✅ Qiskit compatibility (Azure Quantum) +- ✅ Production-ready model serialization + +### Software Engineering +- ✅ Modular code organization +- ✅ Automated benchmarking pipelines +- ✅ Comprehensive logging and reporting +- ✅ Error handling and validation + +--- + +## 🌟 Achievements Summary + +### Performance Metrics +- 🏆 **3 datasets successfully trained** +- 🏆 **1 perfect score (100% accuracy)** +- 🏆 **87.30% average accuracy** +- 🏆 **100% code success rate (no failed runs)** + +### Technical Milestones +- ✅ Fixed 3 critical bugs in quantum layer +- ✅ Implemented complete training pipeline +- ✅ Created automated benchmarking system +- ✅ Generated publication-ready visualizations + +### Code Quality +- 📝 **~1,000 lines of new code** +- 📝 **Comprehensive docstrings** +- 📝 **Type hints throughout** +- 📝 **Modular, reusable components** + +--- + +## 🔗 Quick Reference + +### Run Training on Specific Dataset +```powershell +# Demo dataset (wine) +python .\train_custom_dataset.py + +# Real dataset (ionosphere) +python .\train_ionosphere.py + +# All datasets benchmark +python .\benchmark_all_datasets.py +``` + +### Load Trained Model +```python +import torch +from src.hybrid_qnn import HybridQNN + +model = HybridQNN(input_dim=4, hidden_dim=16, n_qubits=4, + n_quantum_layers=2, output_dim=2) +model.load_state_dict(torch.load('results/ionosphere_model.pt')) +model.eval() +``` + +### View Results +```powershell +# Open visualizations +start results\benchmark_comparison.png +start results\ionosphere_training.png + +# Read report +cat results\benchmark_report.md +``` + +--- + +## 🎉 Conclusion + +This training session successfully demonstrated the **practical application of quantum computing to real-world machine learning problems**. The hybrid quantum-classical approach achieved: + +- **Competitive to superior performance** vs classical methods +- **Efficient use of quantum resources** (only 4 qubits) +- **Scalable architecture** ready for quantum hardware +- **Production-ready models** with full preprocessing pipelines + +The quantum AI system is now ready for: +1. Deployment to Azure Quantum hardware +2. Integration into production applications +3. Further research and optimization +4. Expansion to additional datasets and domains + +**Status: Mission Complete! 🚀✨** + +--- + +*Generated by Quantum AI Training System* +*November 1, 2025* diff --git a/ai-projects/quantum-ml/WEB_DASHBOARD_README.md b/ai-projects/quantum-ml/WEB_DASHBOARD_README.md index 219abe495..645fec692 100644 --- a/ai-projects/quantum-ml/WEB_DASHBOARD_README.md +++ b/ai-projects/quantum-ml/WEB_DASHBOARD_README.md @@ -1,397 +1,397 @@ -# 🚀 Quantum AI Training Web Dashboard - -Interactive web application for training and visualizing quantum machine learning models with real-time metrics. - -![Dashboard Preview](https://via.placeholder.com/800x400/667eea/ffffff?text=Quantum+AI+Training+Dashboard) - -## ✨ Features - -- **🎨 Beautiful UI**: Modern, responsive design with gradient themes -- **📊 Real-time Visualization**: Live training/validation loss and accuracy charts -- **⚡ Interactive Training**: Start/stop training sessions with custom hyperparameters -- **📈 Live Metrics**: Real-time epoch progress, loss values, and accuracy tracking -- **💾 Session Management**: Save and review historical training results -- **🎛️ Hyperparameter Tuning**: Adjust qubits, layers, learning rate, batch size, and duration -- **📚 Dataset Selection**: Train on heart disease, ionosphere, sonar, or banknote datasets -- **🔄 Background Training**: Non-blocking training with threaded execution - -## 🚀 Quick Start - -### Option 1: Using the Launch Script (Recommended) - -```bash -cd quantum-ai -chmod +x start_dashboard.sh -./start_dashboard.sh -``` - -### Option 2: Manual Setup - -```bash -cd quantum-ai - -# Install dependencies -pip install -r web-requirements.txt - -# Start the dashboard -python web_app.py -``` - -Then open your browser to: **** - -## 📖 How to Use - -### 1. Configure Training - -- **Dataset**: Select from available quantum datasets (heart, ionosphere, sonar, banknote) -- **Qubits**: Number of quantum qubits (2-8, determines feature dimensionality) -- **Layers**: Variational circuit depth (1-5) -- **Learning Rate**: Gradient descent step size (0.001-0.1) -- **Duration**: Training time limit in minutes (1-120) -- **Batch Size**: Samples per training batch (8-128) - -### 2. Start Training - -Click **"🚀 Start Training"** to begin. The dashboard will: - -1. Load and preprocess the selected dataset -2. Build a quantum variational circuit -3. Train the model with real-time metric updates -4. Display live charts showing loss and accuracy -5. Save results when complete - -### 3. Monitor Progress - -Watch real-time metrics: - -- Current epoch number -- Elapsed training time -- Training/validation loss -- Best validation accuracy -- Progress bar showing % complete - -### 4. View Results - -The **Training History** panel shows: - -- All completed training sessions -- Dataset used and final accuracy -- Timestamp and total epochs -- Click any result to view detailed metrics - -### 5. Stop Training - -Click **"⏹️ Stop Training"** to halt the current session early. - -## 🏗️ Architecture - -### Backend (Flask) - -**File**: `web_app.py` - -API Endpoints: - -- `GET /` - Serve main dashboard UI -- `GET /api/datasets` - List available datasets -- `POST /api/train/start` - Start training session -- `POST /api/train/stop/` - Stop training session -- `GET /api/train/status/` - Get real-time training status -- `GET /api/train/sessions` - List all sessions -- `GET /api/results` - List saved training results -- `GET /api/results/` - Get detailed results - -Features: - -- Threaded training execution (non-blocking) -- Real-time metric streaming -- Session state management -- Automatic result persistence - -### Frontend (HTML/CSS/JS) - -**Files**: `web_ui/index.html`, `web_ui/static/styles.css`, `web_ui/static/app.js` - -Features: - -- Chart.js for real-time visualization -- Polling-based status updates (1s interval) -- Responsive design (mobile-friendly) -- Gradient color themes -- Interactive training controls - -### Quantum Training Pipeline - -**Flow**: - -1. Load CSV dataset → pandas -2. Preprocess (StandardScaler + PCA) → scikit-learn -3. Build quantum circuit (AmplitudeEmbedding + Variational layers) → PennyLane -4. Train with mini-batch gradient descent -5. Validate on held-out test set -6. Save metrics to JSON - -**Quantum Circuit**: - -- **Encoding**: AmplitudeEmbedding (maps classical data to quantum states) -- **Variational Layers**: Rot gates (RX, RY, RZ) + CNOT entanglement -- **Measurement**: PauliZ expectation values -- **Backend**: default.qubit (CPU simulator) - -## 📊 Datasets - -Located in `../datasets/quantum/*.csv`: - -| Dataset | Samples | Features | Task | Best Accuracy | -| ------------- | ------- | -------- | ------ | ------------- | -| heart_disease | 302 | 13 | Binary | ~85% | -| ionosphere | 351 | 34 | Binary | ~90% | -| sonar | 208 | 60 | Binary | ~80% | -| banknote | 1,372 | 4 | Binary | ~95% | - -All datasets automatically: - -- Handle missing values (median imputation) -- Normalize features (StandardScaler) -- Reduce dimensions (PCA to match qubit count) - -## 🎛️ Hyperparameter Guide - -### Qubits (n_qubits) - -- **Range**: 2-8 -- **Impact**: Higher = more features retained, slower training -- **Recommendation**: Start with 4, increase if accuracy plateaus - -### Layers (n_layers) - -- **Range**: 1-5 -- **Impact**: Higher = more expressiveness, risk of overfitting -- **Recommendation**: 2-3 layers for most datasets - -### Learning Rate - -- **Range**: 0.001-0.1 -- **Impact**: Higher = faster convergence, risk of instability -- **Recommendation**: 0.01 (default), reduce if loss oscillates - -### Duration - -- **Range**: 1-120 minutes -- **Impact**: Longer = more epochs, better convergence -- **Recommendation**: 10-30 minutes for initial experiments - -### Batch Size - -- **Range**: 8-128 -- **Impact**: Larger = smoother gradients, more memory -- **Recommendation**: 32 (default) - -## 📁 File Structure - -```text -ai-projects/quantum-ml/ -├── web_app.py # Flask backend -├── start_dashboard.sh # Launch script -├── web-requirements.txt # Python dependencies -├── web_ui/ -│ ├── index.html # Main dashboard UI -│ └── static/ -│ ├── styles.css # Styling -│ └── app.js # Frontend logic -├── results/ -│ └── training_*.json # Saved session results -└── ../datasets/quantum/ - ├── heart_disease.csv - ├── ionosphere.csv - ├── sonar.csv - └── banknote.csv -``` - -## 🔧 Troubleshooting - -### Port Already in Use - -If port 5000 is busy: - -```python -# Edit web_app.py, change last line: -app.run(host='0.0.0.0', port=5001, debug=True, threaded=True) -``` - -### Dependencies Not Found - -```bash -# Ensure virtual environment is active -source ./venv/bin/activate - -# Reinstall dependencies -pip install -r web-requirements.txt -``` - -### Charts Not Updating - -- Check browser console for errors -- Verify training is running: `GET /api/train/status/` -- Refresh the page - -### Training Crashes - -- Reduce qubits (8→4) or layers (5→2) -- Lower batch size (128→32) -- Check error in console logs - -## 🚀 Advanced Usage - -### Custom Datasets - -Add your own CSV to `../datasets/quantum/`: - -1. Last column must be the label (0/1 for binary classification) -2. All other columns are features -3. Missing values are automatically handled -4. Refresh the dashboard to see it in the dataset list - -### Modify Quantum Circuit - -Edit `create_quantum_circuit()` in `web_app.py`: - -```python -def create_quantum_circuit(n_qubits, n_layers): - dev = qml.device('default.qubit', wires=n_qubits) - - @qml.qnode(dev, interface='autograd') - def circuit(inputs, weights): - # Your custom circuit here - # Example: Add RX rotation layer - for i in range(n_qubits): - qml.RX(inputs[i], wires=i) - - # Variational layers - for layer in range(n_layers): - for i in range(n_qubits): - qml.Rot(*weights[layer, i], wires=i) - for i in range(n_qubits - 1): - qml.CNOT(wires=[i, i + 1]) - - return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] - - return circuit -``` - -### API Integration - -Use the REST API programmatically: - -```python -import requests - -# Start training -response = requests.post('http://localhost:5000/api/train/start', json={ - 'dataset': 'heart', - 'n_qubits': 4, - 'n_layers': 2, - 'learning_rate': 0.01, - 'duration_minutes': 10, - 'batch_size': 32 -}) -session_id = response.json()['session_id'] - -# Poll status -status = requests.get(f'http://localhost:5000/api/train/status/{session_id}').json() -print(f"Epoch {status['current_epoch']}, Accuracy: {status['best_val_acc']}") -``` - -## 📈 Metrics Explained - -### Training Loss - -- **What**: Mean squared error on training data -- **Goal**: Should decrease over time -- **Typical Range**: 0.1-0.8 - -### Validation Loss - -- **What**: Mean squared error on held-out validation set -- **Goal**: Should track training loss closely -- **Warning**: If much higher than training loss → overfitting - -### Validation Accuracy - -- **What**: Classification accuracy on validation set -- **Goal**: Should increase over time -- **Range**: 0.0-1.0 (0%-100%) - -## 🎯 Performance Tips - -1. **Start Small**: Test with 1-minute duration first -2. **Monitor Charts**: Look for convergence (loss plateaus) -3. **Adjust Learning Rate**: If loss oscillates, reduce LR -4. **Increase Duration**: If accuracy still improving at timeout, train longer -5. **Try Different Datasets**: Some are easier to learn than others - -## 📝 Example Workflows - -### Quick Test Run - -```text -Dataset: heart -Qubits: 4 -Layers: 2 -Learning Rate: 0.01 -Duration: 2 minutes -Batch Size: 32 - -Expected: ~70-80% accuracy in 10-20 epochs -``` - -### High-Accuracy Training - -```text -Dataset: banknote -Qubits: 4 -Layers: 3 -Learning Rate: 0.005 -Duration: 20 minutes -Batch Size: 64 - -Expected: ~90-95% accuracy in 50-100 epochs -``` - -### Fast Iteration - -```text -Dataset: any -Qubits: 3 -Layers: 1 -Learning Rate: 0.02 -Duration: 1 minute -Batch Size: 16 - -Expected: Quick results for hyperparameter tuning -``` - -## 🔗 Related Documentation - -- [PennyLane Quantum Training](https://pennylane.ai/) -- [Quantum ML Tutorial](INDEX.md) -- [Dataset Catalog](../../docs/guides/AI_DATASETS_CATALOG.md) -- [Azure Quantum Integration](AZURE_QUANTUM_QUICKSTART.md) - -## 📄 License - -Same as parent repository. See root LICENSE file. - -## 🙏 Credits - -Built with: - -- **PennyLane**: Quantum machine learning framework -- **Flask**: Python web framework -- **Chart.js**: JavaScript charting library -- **scikit-learn**: Classical ML preprocessing - ---- - -**Ready to explore quantum machine learning?** 🚀 - -Run `./start_dashboard.sh` and open to get started! +# 🚀 Quantum AI Training Web Dashboard + +Interactive web application for training and visualizing quantum machine learning models with real-time metrics. + +![Dashboard Preview](https://via.placeholder.com/800x400/667eea/ffffff?text=Quantum+AI+Training+Dashboard) + +## ✨ Features + +- **🎨 Beautiful UI**: Modern, responsive design with gradient themes +- **📊 Real-time Visualization**: Live training/validation loss and accuracy charts +- **⚡ Interactive Training**: Start/stop training sessions with custom hyperparameters +- **📈 Live Metrics**: Real-time epoch progress, loss values, and accuracy tracking +- **💾 Session Management**: Save and review historical training results +- **🎛️ Hyperparameter Tuning**: Adjust qubits, layers, learning rate, batch size, and duration +- **📚 Dataset Selection**: Train on heart disease, ionosphere, sonar, or banknote datasets +- **🔄 Background Training**: Non-blocking training with threaded execution + +## 🚀 Quick Start + +### Option 1: Using the Launch Script (Recommended) + +```bash +cd quantum-ai +chmod +x start_dashboard.sh +./start_dashboard.sh +``` + +### Option 2: Manual Setup + +```bash +cd quantum-ai + +# Install dependencies +pip install -r web-requirements.txt + +# Start the dashboard +python web_app.py +``` + +Then open your browser to: **** + +## 📖 How to Use + +### 1. Configure Training + +- **Dataset**: Select from available quantum datasets (heart, ionosphere, sonar, banknote) +- **Qubits**: Number of quantum qubits (2-8, determines feature dimensionality) +- **Layers**: Variational circuit depth (1-5) +- **Learning Rate**: Gradient descent step size (0.001-0.1) +- **Duration**: Training time limit in minutes (1-120) +- **Batch Size**: Samples per training batch (8-128) + +### 2. Start Training + +Click **"🚀 Start Training"** to begin. The dashboard will: + +1. Load and preprocess the selected dataset +2. Build a quantum variational circuit +3. Train the model with real-time metric updates +4. Display live charts showing loss and accuracy +5. Save results when complete + +### 3. Monitor Progress + +Watch real-time metrics: + +- Current epoch number +- Elapsed training time +- Training/validation loss +- Best validation accuracy +- Progress bar showing % complete + +### 4. View Results + +The **Training History** panel shows: + +- All completed training sessions +- Dataset used and final accuracy +- Timestamp and total epochs +- Click any result to view detailed metrics + +### 5. Stop Training + +Click **"⏹️ Stop Training"** to halt the current session early. + +## 🏗️ Architecture + +### Backend (Flask) + +**File**: `web_app.py` + +API Endpoints: + +- `GET /` - Serve main dashboard UI +- `GET /api/datasets` - List available datasets +- `POST /api/train/start` - Start training session +- `POST /api/train/stop/` - Stop training session +- `GET /api/train/status/` - Get real-time training status +- `GET /api/train/sessions` - List all sessions +- `GET /api/results` - List saved training results +- `GET /api/results/` - Get detailed results + +Features: + +- Threaded training execution (non-blocking) +- Real-time metric streaming +- Session state management +- Automatic result persistence + +### Frontend (HTML/CSS/JS) + +**Files**: `web_ui/index.html`, `web_ui/static/styles.css`, `web_ui/static/app.js` + +Features: + +- Chart.js for real-time visualization +- Polling-based status updates (1s interval) +- Responsive design (mobile-friendly) +- Gradient color themes +- Interactive training controls + +### Quantum Training Pipeline + +**Flow**: + +1. Load CSV dataset → pandas +2. Preprocess (StandardScaler + PCA) → scikit-learn +3. Build quantum circuit (AmplitudeEmbedding + Variational layers) → PennyLane +4. Train with mini-batch gradient descent +5. Validate on held-out test set +6. Save metrics to JSON + +**Quantum Circuit**: + +- **Encoding**: AmplitudeEmbedding (maps classical data to quantum states) +- **Variational Layers**: Rot gates (RX, RY, RZ) + CNOT entanglement +- **Measurement**: PauliZ expectation values +- **Backend**: default.qubit (CPU simulator) + +## 📊 Datasets + +Located in `../datasets/quantum/*.csv`: + +| Dataset | Samples | Features | Task | Best Accuracy | +| ------------- | ------- | -------- | ------ | ------------- | +| heart_disease | 302 | 13 | Binary | ~85% | +| ionosphere | 351 | 34 | Binary | ~90% | +| sonar | 208 | 60 | Binary | ~80% | +| banknote | 1,372 | 4 | Binary | ~95% | + +All datasets automatically: + +- Handle missing values (median imputation) +- Normalize features (StandardScaler) +- Reduce dimensions (PCA to match qubit count) + +## 🎛️ Hyperparameter Guide + +### Qubits (n_qubits) + +- **Range**: 2-8 +- **Impact**: Higher = more features retained, slower training +- **Recommendation**: Start with 4, increase if accuracy plateaus + +### Layers (n_layers) + +- **Range**: 1-5 +- **Impact**: Higher = more expressiveness, risk of overfitting +- **Recommendation**: 2-3 layers for most datasets + +### Learning Rate + +- **Range**: 0.001-0.1 +- **Impact**: Higher = faster convergence, risk of instability +- **Recommendation**: 0.01 (default), reduce if loss oscillates + +### Duration + +- **Range**: 1-120 minutes +- **Impact**: Longer = more epochs, better convergence +- **Recommendation**: 10-30 minutes for initial experiments + +### Batch Size + +- **Range**: 8-128 +- **Impact**: Larger = smoother gradients, more memory +- **Recommendation**: 32 (default) + +## 📁 File Structure + +```text +ai-projects/quantum-ml/ +├── web_app.py # Flask backend +├── start_dashboard.sh # Launch script +├── web-requirements.txt # Python dependencies +├── web_ui/ +│ ├── index.html # Main dashboard UI +│ └── static/ +│ ├── styles.css # Styling +│ └── app.js # Frontend logic +├── results/ +│ └── training_*.json # Saved session results +└── ../datasets/quantum/ + ├── heart_disease.csv + ├── ionosphere.csv + ├── sonar.csv + └── banknote.csv +``` + +## 🔧 Troubleshooting + +### Port Already in Use + +If port 5000 is busy: + +```python +# Edit web_app.py, change last line: +app.run(host='0.0.0.0', port=5001, debug=True, threaded=True) +``` + +### Dependencies Not Found + +```bash +# Ensure virtual environment is active +source ./venv/bin/activate + +# Reinstall dependencies +pip install -r web-requirements.txt +``` + +### Charts Not Updating + +- Check browser console for errors +- Verify training is running: `GET /api/train/status/` +- Refresh the page + +### Training Crashes + +- Reduce qubits (8→4) or layers (5→2) +- Lower batch size (128→32) +- Check error in console logs + +## 🚀 Advanced Usage + +### Custom Datasets + +Add your own CSV to `../datasets/quantum/`: + +1. Last column must be the label (0/1 for binary classification) +2. All other columns are features +3. Missing values are automatically handled +4. Refresh the dashboard to see it in the dataset list + +### Modify Quantum Circuit + +Edit `create_quantum_circuit()` in `web_app.py`: + +```python +def create_quantum_circuit(n_qubits, n_layers): + dev = qml.device('default.qubit', wires=n_qubits) + + @qml.qnode(dev, interface='autograd') + def circuit(inputs, weights): + # Your custom circuit here + # Example: Add RX rotation layer + for i in range(n_qubits): + qml.RX(inputs[i], wires=i) + + # Variational layers + for layer in range(n_layers): + for i in range(n_qubits): + qml.Rot(*weights[layer, i], wires=i) + for i in range(n_qubits - 1): + qml.CNOT(wires=[i, i + 1]) + + return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] + + return circuit +``` + +### API Integration + +Use the REST API programmatically: + +```python +import requests + +# Start training +response = requests.post('http://localhost:5000/api/train/start', json={ + 'dataset': 'heart', + 'n_qubits': 4, + 'n_layers': 2, + 'learning_rate': 0.01, + 'duration_minutes': 10, + 'batch_size': 32 +}) +session_id = response.json()['session_id'] + +# Poll status +status = requests.get(f'http://localhost:5000/api/train/status/{session_id}').json() +print(f"Epoch {status['current_epoch']}, Accuracy: {status['best_val_acc']}") +``` + +## 📈 Metrics Explained + +### Training Loss + +- **What**: Mean squared error on training data +- **Goal**: Should decrease over time +- **Typical Range**: 0.1-0.8 + +### Validation Loss + +- **What**: Mean squared error on held-out validation set +- **Goal**: Should track training loss closely +- **Warning**: If much higher than training loss → overfitting + +### Validation Accuracy + +- **What**: Classification accuracy on validation set +- **Goal**: Should increase over time +- **Range**: 0.0-1.0 (0%-100%) + +## 🎯 Performance Tips + +1. **Start Small**: Test with 1-minute duration first +2. **Monitor Charts**: Look for convergence (loss plateaus) +3. **Adjust Learning Rate**: If loss oscillates, reduce LR +4. **Increase Duration**: If accuracy still improving at timeout, train longer +5. **Try Different Datasets**: Some are easier to learn than others + +## 📝 Example Workflows + +### Quick Test Run + +```text +Dataset: heart +Qubits: 4 +Layers: 2 +Learning Rate: 0.01 +Duration: 2 minutes +Batch Size: 32 + +Expected: ~70-80% accuracy in 10-20 epochs +``` + +### High-Accuracy Training + +```text +Dataset: banknote +Qubits: 4 +Layers: 3 +Learning Rate: 0.005 +Duration: 20 minutes +Batch Size: 64 + +Expected: ~90-95% accuracy in 50-100 epochs +``` + +### Fast Iteration + +```text +Dataset: any +Qubits: 3 +Layers: 1 +Learning Rate: 0.02 +Duration: 1 minute +Batch Size: 16 + +Expected: Quick results for hyperparameter tuning +``` + +## 🔗 Related Documentation + +- [PennyLane Quantum Training](https://pennylane.ai/) +- [Quantum ML Tutorial](INDEX.md) +- [Dataset Catalog](../../docs/guides/AI_DATASETS_CATALOG.md) +- [Azure Quantum Integration](AZURE_QUANTUM_QUICKSTART.md) + +## 📄 License + +Same as parent repository. See root LICENSE file. + +## 🙏 Credits + +Built with: + +- **PennyLane**: Quantum machine learning framework +- **Flask**: Python web framework +- **Chart.js**: JavaScript charting library +- **scikit-learn**: Classical ML preprocessing + +--- + +**Ready to explore quantum machine learning?** 🚀 + +Run `./start_dashboard.sh` and open to get started! diff --git a/ai-projects/quantum-ml/WEB_DASHBOARD_SUCCESS.md b/ai-projects/quantum-ml/WEB_DASHBOARD_SUCCESS.md index 4ab10e245..7667a3ac3 100644 --- a/ai-projects/quantum-ml/WEB_DASHBOARD_SUCCESS.md +++ b/ai-projects/quantum-ml/WEB_DASHBOARD_SUCCESS.md @@ -1,256 +1,256 @@ -# ✅ Quantum AI Web Dashboard - Setup Complete! - -## 🎉 What's New - -I've created a **beautiful, interactive web application** for training and visualizing quantum AI models in real-time! - -## 📁 Files Created - -``` -ai-projects/quantum-ml/ -├── web_app.py # Flask backend with REST API -├── start_dashboard.sh # One-command startup script -├── web-requirements.txt # Python dependencies -├── WEB_DASHBOARD_README.md # Complete documentation -└── web_ui/ - ├── index.html # Main dashboard UI - └── static/ - ├── styles.css # Beautiful gradient design - └── app.js # Real-time visualization logic -``` - -## 🚀 Quick Start - -```bash -cd quantum-ai -./start_dashboard.sh -``` - -Then open your browser to: **http://localhost:5000** - -## ✨ Features - -### 🎨 Beautiful Modern UI -- Gradient color themes with smooth animations -- Responsive design (works on mobile) -- Professional chart visualizations with Chart.js -- Real-time status updates - -### 📊 Real-Time Training Visualization -- Live loss/accuracy charts that update every second -- Training/validation metrics side-by-side -- Progress bar showing time elapsed -- Epoch counter and best accuracy tracker - -### 🎛️ Interactive Configuration -- **Dataset Selection**: heart, ionosphere, sonar, banknote -- **Qubits**: 2-8 (controls feature dimensionality) -- **Layers**: 1-5 (variational circuit depth) -- **Learning Rate**: 0.001-0.1 (gradient step size) -- **Duration**: 1-120 minutes (training time limit) -- **Batch Size**: 8-128 (samples per batch) - -### 💾 Session Management -- Automatic saving of training results to JSON -- Training history browser with all past sessions -- Click any result to view detailed metrics -- Persistent storage survives server restarts - -### 🔧 Technical Architecture - -**Backend (Flask)**: -- RESTful API with 8 endpoints -- Threaded training execution (non-blocking) -- Real-time metric streaming via polling -- Session state management with thread safety -- Automatic result persistence - -**Frontend (HTML/CSS/JS)**: -- Pure vanilla JavaScript (no build step) -- Chart.js for smooth visualizations -- 1-second polling interval for live updates -- Responsive CSS Grid layout - -**Quantum ML Pipeline**: -- PennyLane default.qubit simulator -- AmplitudeEmbedding for classical→quantum encoding -- Variational quantum circuits (Rot + CNOT) -- Scikit-learn preprocessing (StandardScaler + PCA) -- Mini-batch gradient descent - -## 📊 Example Training Session - -**Configuration**: -``` -Dataset: heart_disease -Qubits: 4 -Layers: 2 -Learning Rate: 0.01 -Duration: 10 minutes -Batch Size: 32 -``` - -**Expected Results**: -- ~20-30 epochs in 10 minutes -- ~70-85% validation accuracy -- Training loss decreases from ~0.65 to ~0.45 -- Real-time charts show smooth convergence - -## 🎯 How to Use - -1. **Select Dataset** - Choose from 4 quantum ML datasets -2. **Configure Parameters** - Adjust qubits, layers, learning rate -3. **Start Training** - Click "🚀 Start Training" button -4. **Monitor Progress** - Watch live charts and metrics -5. **View Results** - Browse training history when complete - -## 📈 API Endpoints - -``` -GET / # Serve dashboard UI -GET /api/datasets # List available datasets -POST /api/train/start # Start training session -POST /api/train/stop/:id # Stop training session -GET /api/train/status/:id # Get real-time status -GET /api/train/sessions # List all sessions -GET /api/results # List saved results -GET /api/results/:file # Get detailed results -``` - -## 🔗 Documentation - -- **Complete Guide**: [`WEB_DASHBOARD_README.md`](./WEB_DASHBOARD_README.md) -- **API Reference**: See `web_app.py` docstrings -- **Main Project**: [`README.md`](./README.md) - -## 💡 What Makes This Special - -### For Learning -- **No coding required** - Visual UI makes quantum ML accessible -- **Instant feedback** - See results as model trains -- **Experiment friendly** - Try different parameters easily - -### For Development -- **Non-blocking** - Server handles multiple requests -- **Session isolation** - Each training run is independent -- **Metric history** - Keep only recent data to prevent memory issues -- **Error handling** - Graceful failures with user feedback - -### For Production -- **Threaded execution** - Training runs in background -- **State management** - Thread-safe session tracking -- **Result persistence** - JSON files for every session -- **Scalable architecture** - Ready for cloud deployment - -## 🚀 Next Steps - -### Immediate Actions -1. Open http://localhost:5000 in your browser -2. Select "heart" dataset and click "Start Training" -3. Watch the real-time charts update! - -### Advanced Usage -- Train on custom datasets (add CSV to `datasets/quantum/`) -- Modify quantum circuits (edit `create_quantum_circuit()`) -- Integrate with Azure Quantum (see main README) -- Deploy to cloud (Azure App Service, Heroku, etc.) - -## 🎨 Screenshots - -### Main Dashboard -- Gradient header with project branding -- Configuration panel with all hyperparameters -- Status panel with real-time metrics -- Two live charts (loss and accuracy) -- Training history with clickable results - -### Training In Progress -- Spinning quantum icon (⚛️) -- Live metrics updating every second -- Progress bar showing % complete -- Charts growing epoch by epoch - -### Results Browser -- List of all training sessions -- Dataset name and best accuracy badge -- Timestamp and epoch count -- Click to view detailed metrics - -## 📊 Performance - -- **Frontend**: ~20KB HTML + 15KB CSS + 30KB JS = 65KB total -- **Backend**: Minimal Flask overhead, PennyLane simulation CPU-bound -- **Memory**: ~50MB per training session (circuit + data) -- **Speed**: 1-2 epochs/minute on 4 qubits (CPU simulator) - -## 🔧 Customization - -### Change Port -Edit `web_app.py` last line: -```python -app.run(host='0.0.0.0', port=5001, debug=True, threaded=True) -``` - -### Add Custom Dataset -1. Add CSV to `../datasets/quantum/your_dataset.csv` -2. Refresh browser - it appears in dropdown automatically - -### Modify Circuit -Edit `create_quantum_circuit()` in `web_app.py`: -```python -def create_quantum_circuit(n_qubits, n_layers): - dev = qml.device('default.qubit', wires=n_qubits) - - @qml.qnode(dev, interface='autograd') - def circuit(inputs, weights): - # Your custom quantum circuit here - qml.AmplitudeEmbedding(features=inputs, wires=range(n_qubits)) - # ... custom gates ... - return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] - - return circuit -``` - -## 🎓 Learning Resources - -- **PennyLane Docs**: https://pennylane.ai/ -- **Quantum ML Tutorial**: https://pennylane.ai/qml/ -- **Chart.js Docs**: https://www.chartjs.org/ -- **Flask Tutorial**: https://flask.palletsprojects.com/ - -## 🙏 Tech Stack - -Built with: -- **PennyLane 0.43.1** - Quantum machine learning -- **Flask 3.1.2** - Web framework -- **Chart.js 4.4.0** - Data visualization -- **Scikit-learn 1.7.2** - Data preprocessing -- **NumPy 2.3.4** - Numerical computing -- **Pandas 2.3.3** - Data loading - -## 🎉 Success Metrics - -✅ Complete web application with 5 HTML/CSS/JS files -✅ Flask backend with 8 RESTful API endpoints -✅ Real-time visualization with Chart.js -✅ Threaded training execution (non-blocking) -✅ Session management with thread safety -✅ Automatic result persistence (JSON) -✅ Training history browser -✅ One-command startup script -✅ Comprehensive documentation -✅ Working demo with test datasets - ---- - -## 🚀 Ready to Train! - -Your quantum AI training dashboard is fully operational. Start the server with: - -```bash -./start_dashboard.sh -``` - -Open **http://localhost:5000** and train your first quantum machine learning model! - -**Happy quantum computing!** ⚛️🎉 +# ✅ Quantum AI Web Dashboard - Setup Complete! + +## 🎉 What's New + +I've created a **beautiful, interactive web application** for training and visualizing quantum AI models in real-time! + +## 📁 Files Created + +``` +ai-projects/quantum-ml/ +├── web_app.py # Flask backend with REST API +├── start_dashboard.sh # One-command startup script +├── web-requirements.txt # Python dependencies +├── WEB_DASHBOARD_README.md # Complete documentation +└── web_ui/ + ├── index.html # Main dashboard UI + └── static/ + ├── styles.css # Beautiful gradient design + └── app.js # Real-time visualization logic +``` + +## 🚀 Quick Start + +```bash +cd quantum-ai +./start_dashboard.sh +``` + +Then open your browser to: **http://localhost:5000** + +## ✨ Features + +### 🎨 Beautiful Modern UI +- Gradient color themes with smooth animations +- Responsive design (works on mobile) +- Professional chart visualizations with Chart.js +- Real-time status updates + +### 📊 Real-Time Training Visualization +- Live loss/accuracy charts that update every second +- Training/validation metrics side-by-side +- Progress bar showing time elapsed +- Epoch counter and best accuracy tracker + +### 🎛️ Interactive Configuration +- **Dataset Selection**: heart, ionosphere, sonar, banknote +- **Qubits**: 2-8 (controls feature dimensionality) +- **Layers**: 1-5 (variational circuit depth) +- **Learning Rate**: 0.001-0.1 (gradient step size) +- **Duration**: 1-120 minutes (training time limit) +- **Batch Size**: 8-128 (samples per batch) + +### 💾 Session Management +- Automatic saving of training results to JSON +- Training history browser with all past sessions +- Click any result to view detailed metrics +- Persistent storage survives server restarts + +### 🔧 Technical Architecture + +**Backend (Flask)**: +- RESTful API with 8 endpoints +- Threaded training execution (non-blocking) +- Real-time metric streaming via polling +- Session state management with thread safety +- Automatic result persistence + +**Frontend (HTML/CSS/JS)**: +- Pure vanilla JavaScript (no build step) +- Chart.js for smooth visualizations +- 1-second polling interval for live updates +- Responsive CSS Grid layout + +**Quantum ML Pipeline**: +- PennyLane default.qubit simulator +- AmplitudeEmbedding for classical→quantum encoding +- Variational quantum circuits (Rot + CNOT) +- Scikit-learn preprocessing (StandardScaler + PCA) +- Mini-batch gradient descent + +## 📊 Example Training Session + +**Configuration**: +``` +Dataset: heart_disease +Qubits: 4 +Layers: 2 +Learning Rate: 0.01 +Duration: 10 minutes +Batch Size: 32 +``` + +**Expected Results**: +- ~20-30 epochs in 10 minutes +- ~70-85% validation accuracy +- Training loss decreases from ~0.65 to ~0.45 +- Real-time charts show smooth convergence + +## 🎯 How to Use + +1. **Select Dataset** - Choose from 4 quantum ML datasets +2. **Configure Parameters** - Adjust qubits, layers, learning rate +3. **Start Training** - Click "🚀 Start Training" button +4. **Monitor Progress** - Watch live charts and metrics +5. **View Results** - Browse training history when complete + +## 📈 API Endpoints + +``` +GET / # Serve dashboard UI +GET /api/datasets # List available datasets +POST /api/train/start # Start training session +POST /api/train/stop/:id # Stop training session +GET /api/train/status/:id # Get real-time status +GET /api/train/sessions # List all sessions +GET /api/results # List saved results +GET /api/results/:file # Get detailed results +``` + +## 🔗 Documentation + +- **Complete Guide**: [`WEB_DASHBOARD_README.md`](./WEB_DASHBOARD_README.md) +- **API Reference**: See `web_app.py` docstrings +- **Main Project**: [`README.md`](./README.md) + +## 💡 What Makes This Special + +### For Learning +- **No coding required** - Visual UI makes quantum ML accessible +- **Instant feedback** - See results as model trains +- **Experiment friendly** - Try different parameters easily + +### For Development +- **Non-blocking** - Server handles multiple requests +- **Session isolation** - Each training run is independent +- **Metric history** - Keep only recent data to prevent memory issues +- **Error handling** - Graceful failures with user feedback + +### For Production +- **Threaded execution** - Training runs in background +- **State management** - Thread-safe session tracking +- **Result persistence** - JSON files for every session +- **Scalable architecture** - Ready for cloud deployment + +## 🚀 Next Steps + +### Immediate Actions +1. Open http://localhost:5000 in your browser +2. Select "heart" dataset and click "Start Training" +3. Watch the real-time charts update! + +### Advanced Usage +- Train on custom datasets (add CSV to `datasets/quantum/`) +- Modify quantum circuits (edit `create_quantum_circuit()`) +- Integrate with Azure Quantum (see main README) +- Deploy to cloud (Azure App Service, Heroku, etc.) + +## 🎨 Screenshots + +### Main Dashboard +- Gradient header with project branding +- Configuration panel with all hyperparameters +- Status panel with real-time metrics +- Two live charts (loss and accuracy) +- Training history with clickable results + +### Training In Progress +- Spinning quantum icon (⚛️) +- Live metrics updating every second +- Progress bar showing % complete +- Charts growing epoch by epoch + +### Results Browser +- List of all training sessions +- Dataset name and best accuracy badge +- Timestamp and epoch count +- Click to view detailed metrics + +## 📊 Performance + +- **Frontend**: ~20KB HTML + 15KB CSS + 30KB JS = 65KB total +- **Backend**: Minimal Flask overhead, PennyLane simulation CPU-bound +- **Memory**: ~50MB per training session (circuit + data) +- **Speed**: 1-2 epochs/minute on 4 qubits (CPU simulator) + +## 🔧 Customization + +### Change Port +Edit `web_app.py` last line: +```python +app.run(host='0.0.0.0', port=5001, debug=True, threaded=True) +``` + +### Add Custom Dataset +1. Add CSV to `../datasets/quantum/your_dataset.csv` +2. Refresh browser - it appears in dropdown automatically + +### Modify Circuit +Edit `create_quantum_circuit()` in `web_app.py`: +```python +def create_quantum_circuit(n_qubits, n_layers): + dev = qml.device('default.qubit', wires=n_qubits) + + @qml.qnode(dev, interface='autograd') + def circuit(inputs, weights): + # Your custom quantum circuit here + qml.AmplitudeEmbedding(features=inputs, wires=range(n_qubits)) + # ... custom gates ... + return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] + + return circuit +``` + +## 🎓 Learning Resources + +- **PennyLane Docs**: https://pennylane.ai/ +- **Quantum ML Tutorial**: https://pennylane.ai/qml/ +- **Chart.js Docs**: https://www.chartjs.org/ +- **Flask Tutorial**: https://flask.palletsprojects.com/ + +## 🙏 Tech Stack + +Built with: +- **PennyLane 0.43.1** - Quantum machine learning +- **Flask 3.1.2** - Web framework +- **Chart.js 4.4.0** - Data visualization +- **Scikit-learn 1.7.2** - Data preprocessing +- **NumPy 2.3.4** - Numerical computing +- **Pandas 2.3.3** - Data loading + +## 🎉 Success Metrics + +✅ Complete web application with 5 HTML/CSS/JS files +✅ Flask backend with 8 RESTful API endpoints +✅ Real-time visualization with Chart.js +✅ Threaded training execution (non-blocking) +✅ Session management with thread safety +✅ Automatic result persistence (JSON) +✅ Training history browser +✅ One-command startup script +✅ Comprehensive documentation +✅ Working demo with test datasets + +--- + +## 🚀 Ready to Train! + +Your quantum AI training dashboard is fully operational. Start the server with: + +```bash +./start_dashboard.sh +``` + +Open **http://localhost:5000** and train your first quantum machine learning model! + +**Happy quantum computing!** ⚛️🎉 diff --git a/ai-projects/quantum-ml/azure/DEPLOYMENT.md b/ai-projects/quantum-ml/azure/DEPLOYMENT.md index f8a6fdffa..f9c8b881b 100644 --- a/ai-projects/quantum-ml/azure/DEPLOYMENT.md +++ b/ai-projects/quantum-ml/azure/DEPLOYMENT.md @@ -1,250 +1,250 @@ -# Azure Quantum Workspace Deployment Guide - -This guide explains how to deploy the Azure Quantum workspace infrastructure for the Quantum AI project. - -## Prerequisites - -1. **Azure Subscription**: Active Azure subscription with appropriate permissions -2. **Azure CLI**: Install from [https://docs.microsoft.com/cli/azure/install-azure-cli](https://docs.microsoft.com/cli/azure/install-azure-cli) -3. **Bicep**: Install Bicep CLI (comes with Azure CLI 2.20.0+) - -## Architecture - -The deployment creates the following resources: - -- **Azure Quantum Workspace**: Main quantum computing workspace -- **Storage Account**: For storing quantum job data and results -- **Log Analytics Workspace**: For monitoring and diagnostics -- **Application Insights**: For tracking quantum job execution - -### Quantum Providers - -The workspace is configured with the following providers: - -1. **IonQ**: Ion trap quantum computers (pay-as-you-go) -2. **Quantinuum**: High-fidelity quantum computers (pay-as-you-go) -3. **Microsoft QC**: Quantum simulators (free tier) - -## Deployment Steps - -### 1. Login to Azure - -```powershell -az login -``` - -### 2. Set Your Subscription - -```powershell -az account set --subscription "" -``` - -### 3. Create Resource Group - -```powershell -az group create ` - --name rg-quantum-ai ` - --location eastus -``` - -### 4. Review and Update Parameters - -Edit `quantum_workspace.parameters.json` to customize: - -- `location`: Azure region -- `workspaceName`: Unique workspace name -- `storageAccountName`: Unique storage account name -- `tags`: Resource tags - -### 5. Validate Deployment - -```powershell -az deployment group validate ` - --resource-group rg-quantum-ai ` - --template-file quantum_workspace.bicep ` - --parameters quantum_workspace.parameters.json -``` - -### 6. Deploy Infrastructure - -```powershell -az deployment group create ` - --resource-group rg-quantum-ai ` - --template-file quantum_workspace.bicep ` - --parameters quantum_workspace.parameters.json ` - --name quantum-deployment-$(Get-Date -Format 'yyyyMMddHHmmss') -``` - -### 7. Retrieve Deployment Outputs - -```powershell -az deployment group show ` - --resource-group rg-quantum-ai ` - --name quantum-deployment- ` - --query properties.outputs -``` - -## Post-Deployment Configuration - -### 1. Update Configuration File - -After deployment, update `config/quantum_config.yaml` with your Azure details: - -```yaml -azure: - subscription_id: "" - resource_group: "rg-quantum-ai" - workspace_name: "quantum-ai-workspace" - location: "eastus" - storage_account: "" -``` - -### 2. Configure Authentication - -Set up Azure authentication using one of these methods: - -#### Option A: Azure CLI (Development) - -```powershell -az login -``` - -#### Option B: Service Principal (Production) - -```powershell -# Create service principal -az ad sp create-for-rbac ` - --name "quantum-ai-sp" ` - --role "Contributor" ` - --scopes /subscriptions//resourceGroups/rg-quantum-ai - -# Set environment variables -$env:AZURE_CLIENT_ID="" -$env:AZURE_CLIENT_SECRET="" -$env:AZURE_TENANT_ID="" -``` - -#### Option C: Managed Identity (Azure Services) - -If running on Azure services (VM, Container Instances, etc.), use managed identity: - -```powershell -az identity create ` - --name quantum-ai-identity ` - --resource-group rg-quantum-ai - -# Assign role to managed identity -az role assignment create ` - --assignee ` - --role "Contributor" ` - --scope /subscriptions//resourceGroups/rg-quantum-ai -``` - -### 3. Verify Workspace Access - -Test the connection using the Python SDK: - -```python -from azure.quantum import Workspace -from azure.identity import DefaultAzureCredential - -credential = DefaultAzureCredential() -workspace = Workspace( - subscription_id="", - resource_group="rg-quantum-ai", - name="quantum-ai-workspace", - credential=credential -) - -print(f"Connected to workspace: {workspace.name}") -print(f"Available targets: {[t.name for t in workspace.get_targets()]}") -``` - -## Cost Management - -### Estimated Costs - -- **Storage Account**: ~$0.02/GB/month -- **Log Analytics**: ~$2.30/GB ingested -- **Application Insights**: First 5GB/month free -- **Quantum Jobs**: Varies by provider - - IonQ: ~$0.00003 per gate-shot - - Quantinuum: ~$0.00015 per circuit execution - - Microsoft Simulators: Free - -### Cost Optimization Tips - -1. **Use Simulators for Development**: Test on free Microsoft simulators first -2. **Optimize Circuits**: Minimize gate count before running on hardware -3. **Monitor Usage**: Use Azure Cost Management dashboard -4. **Set Budgets**: Create budget alerts in Azure portal -5. **Delete When Not Needed**: Remove workspace if not actively used - -## Monitoring and Diagnostics - -### View Quantum Job Logs - -```powershell -az monitor log-analytics query ` - --workspace ` - --analytics-query "AzureDiagnostics | where ResourceProvider == 'MICROSOFT.QUANTUM'" ` - --timespan P1D -``` - -### Application Insights Queries - -Access Application Insights in Azure Portal to: - -- Track job execution times -- Monitor success/failure rates -- Analyze performance metrics -- Set up alerts for failures - -## Cleanup - -To delete all resources: - -```powershell -az group delete --name rg-quantum-ai --yes --no-wait -``` - -## Troubleshooting - -### Issue: Workspace Creation Fails - -**Solution**: Ensure Azure Quantum is available in your region. Supported regions: - -- East US -- West US -- West Europe -- North Europe - -### Issue: Authentication Errors - -**Solution**: - -1. Verify Azure CLI login: `az account show` -2. Check RBAC permissions -3. Ensure service principal has Contributor role - -### Issue: Provider Not Available - -**Solution**: Some providers may require: - -1. Registration with the provider -2. Credits to be purchased -3. Terms of service acceptance - -## Additional Resources - -- [Azure Quantum Documentation](https://docs.microsoft.com/azure/quantum/) -- [Qiskit on Azure Quantum](https://docs.microsoft.com/azure/quantum/quickstart-microsoft-qiskit) -- [Azure Quantum Pricing](https://azure.microsoft.com/pricing/details/azure-quantum/) -- [Bicep Documentation](https://docs.microsoft.com/azure/azure-resource-manager/bicep/) - -## Support - -For issues with: - -- **Azure Quantum**: Create support ticket in Azure Portal -- **This Project**: Open GitHub issue or contact project maintainers +# Azure Quantum Workspace Deployment Guide + +This guide explains how to deploy the Azure Quantum workspace infrastructure for the Quantum AI project. + +## Prerequisites + +1. **Azure Subscription**: Active Azure subscription with appropriate permissions +2. **Azure CLI**: Install from [https://docs.microsoft.com/cli/azure/install-azure-cli](https://docs.microsoft.com/cli/azure/install-azure-cli) +3. **Bicep**: Install Bicep CLI (comes with Azure CLI 2.20.0+) + +## Architecture + +The deployment creates the following resources: + +- **Azure Quantum Workspace**: Main quantum computing workspace +- **Storage Account**: For storing quantum job data and results +- **Log Analytics Workspace**: For monitoring and diagnostics +- **Application Insights**: For tracking quantum job execution + +### Quantum Providers + +The workspace is configured with the following providers: + +1. **IonQ**: Ion trap quantum computers (pay-as-you-go) +2. **Quantinuum**: High-fidelity quantum computers (pay-as-you-go) +3. **Microsoft QC**: Quantum simulators (free tier) + +## Deployment Steps + +### 1. Login to Azure + +```powershell +az login +``` + +### 2. Set Your Subscription + +```powershell +az account set --subscription "" +``` + +### 3. Create Resource Group + +```powershell +az group create ` + --name rg-quantum-ai ` + --location eastus +``` + +### 4. Review and Update Parameters + +Edit `quantum_workspace.parameters.json` to customize: + +- `location`: Azure region +- `workspaceName`: Unique workspace name +- `storageAccountName`: Unique storage account name +- `tags`: Resource tags + +### 5. Validate Deployment + +```powershell +az deployment group validate ` + --resource-group rg-quantum-ai ` + --template-file quantum_workspace.bicep ` + --parameters quantum_workspace.parameters.json +``` + +### 6. Deploy Infrastructure + +```powershell +az deployment group create ` + --resource-group rg-quantum-ai ` + --template-file quantum_workspace.bicep ` + --parameters quantum_workspace.parameters.json ` + --name quantum-deployment-$(Get-Date -Format 'yyyyMMddHHmmss') +``` + +### 7. Retrieve Deployment Outputs + +```powershell +az deployment group show ` + --resource-group rg-quantum-ai ` + --name quantum-deployment- ` + --query properties.outputs +``` + +## Post-Deployment Configuration + +### 1. Update Configuration File + +After deployment, update `config/quantum_config.yaml` with your Azure details: + +```yaml +azure: + subscription_id: "" + resource_group: "rg-quantum-ai" + workspace_name: "quantum-ai-workspace" + location: "eastus" + storage_account: "" +``` + +### 2. Configure Authentication + +Set up Azure authentication using one of these methods: + +#### Option A: Azure CLI (Development) + +```powershell +az login +``` + +#### Option B: Service Principal (Production) + +```powershell +# Create service principal +az ad sp create-for-rbac ` + --name "quantum-ai-sp" ` + --role "Contributor" ` + --scopes /subscriptions//resourceGroups/rg-quantum-ai + +# Set environment variables +$env:AZURE_CLIENT_ID="" +$env:AZURE_CLIENT_SECRET="" +$env:AZURE_TENANT_ID="" +``` + +#### Option C: Managed Identity (Azure Services) + +If running on Azure services (VM, Container Instances, etc.), use managed identity: + +```powershell +az identity create ` + --name quantum-ai-identity ` + --resource-group rg-quantum-ai + +# Assign role to managed identity +az role assignment create ` + --assignee ` + --role "Contributor" ` + --scope /subscriptions//resourceGroups/rg-quantum-ai +``` + +### 3. Verify Workspace Access + +Test the connection using the Python SDK: + +```python +from azure.quantum import Workspace +from azure.identity import DefaultAzureCredential + +credential = DefaultAzureCredential() +workspace = Workspace( + subscription_id="", + resource_group="rg-quantum-ai", + name="quantum-ai-workspace", + credential=credential +) + +print(f"Connected to workspace: {workspace.name}") +print(f"Available targets: {[t.name for t in workspace.get_targets()]}") +``` + +## Cost Management + +### Estimated Costs + +- **Storage Account**: ~$0.02/GB/month +- **Log Analytics**: ~$2.30/GB ingested +- **Application Insights**: First 5GB/month free +- **Quantum Jobs**: Varies by provider + - IonQ: ~$0.00003 per gate-shot + - Quantinuum: ~$0.00015 per circuit execution + - Microsoft Simulators: Free + +### Cost Optimization Tips + +1. **Use Simulators for Development**: Test on free Microsoft simulators first +2. **Optimize Circuits**: Minimize gate count before running on hardware +3. **Monitor Usage**: Use Azure Cost Management dashboard +4. **Set Budgets**: Create budget alerts in Azure portal +5. **Delete When Not Needed**: Remove workspace if not actively used + +## Monitoring and Diagnostics + +### View Quantum Job Logs + +```powershell +az monitor log-analytics query ` + --workspace ` + --analytics-query "AzureDiagnostics | where ResourceProvider == 'MICROSOFT.QUANTUM'" ` + --timespan P1D +``` + +### Application Insights Queries + +Access Application Insights in Azure Portal to: + +- Track job execution times +- Monitor success/failure rates +- Analyze performance metrics +- Set up alerts for failures + +## Cleanup + +To delete all resources: + +```powershell +az group delete --name rg-quantum-ai --yes --no-wait +``` + +## Troubleshooting + +### Issue: Workspace Creation Fails + +**Solution**: Ensure Azure Quantum is available in your region. Supported regions: + +- East US +- West US +- West Europe +- North Europe + +### Issue: Authentication Errors + +**Solution**: + +1. Verify Azure CLI login: `az account show` +2. Check RBAC permissions +3. Ensure service principal has Contributor role + +### Issue: Provider Not Available + +**Solution**: Some providers may require: + +1. Registration with the provider +2. Credits to be purchased +3. Terms of service acceptance + +## Additional Resources + +- [Azure Quantum Documentation](https://docs.microsoft.com/azure/quantum/) +- [Qiskit on Azure Quantum](https://docs.microsoft.com/azure/quantum/quickstart-microsoft-qiskit) +- [Azure Quantum Pricing](https://azure.microsoft.com/pricing/details/azure-quantum/) +- [Bicep Documentation](https://docs.microsoft.com/azure/azure-resource-manager/bicep/) + +## Support + +For issues with: + +- **Azure Quantum**: Create support ticket in Azure Portal +- **This Project**: Open GitHub issue or contact project maintainers diff --git a/ai-projects/quantum-ml/azure/devops/azure-pipelines.yml b/ai-projects/quantum-ml/azure/devops/azure-pipelines.yml index 3e333316a..9eea0db31 100644 --- a/ai-projects/quantum-ml/azure/devops/azure-pipelines.yml +++ b/ai-projects/quantum-ml/azure/devops/azure-pipelines.yml @@ -1,30 +1,30 @@ -trigger: -- main - -pool: - vmImage: 'windows-latest' - -variables: -- name: LOGIC_APP_URL - value: '' # Set via variable group or pipeline variable - -stages: -- stage: QuantumOrchestration - jobs: - - job: Run - steps: - - task: AzureCLI@2 - displayName: 'Login to Azure and run orchestration' - inputs: - azureSubscription: 'YOUR-SERVICE-CONNECTION' # Replace with your DevOps service connection - scriptType: 'pscore' - scriptLocation: 'inlineScript' - inlineScript: | - pwsh -NoLogo -NonInteractive -File "$(Build.SourcesDirectory)/ai-projects/quantum-ml/azure/quantum_master_orchestration.ps1" -ResourceGroup rg-quantum-ai -WorkspaceName quantum-ai-workspace -Location eastus - # Or run the Logic App version and pass the URL: - # pwsh -File "$(Build.SourcesDirectory)/ai-projects/quantum-ml/azure/quantum_full_logicapp_orchestration.ps1" -LogicAppUrl "$(LOGIC_APP_URL)" - - task: PublishBuildArtifacts@1 - inputs: - PathtoPublish: '$(Build.SourcesDirectory)' - ArtifactName: 'repo' - publishLocation: 'Container' +trigger: +- main + +pool: + vmImage: 'windows-latest' + +variables: +- name: LOGIC_APP_URL + value: '' # Set via variable group or pipeline variable + +stages: +- stage: QuantumOrchestration + jobs: + - job: Run + steps: + - task: AzureCLI@2 + displayName: 'Login to Azure and run orchestration' + inputs: + azureSubscription: 'YOUR-SERVICE-CONNECTION' # Replace with your DevOps service connection + scriptType: 'pscore' + scriptLocation: 'inlineScript' + inlineScript: | + pwsh -NoLogo -NonInteractive -File "$(Build.SourcesDirectory)/ai-projects/quantum-ml/azure/quantum_master_orchestration.ps1" -ResourceGroup rg-quantum-ai -WorkspaceName quantum-ai-workspace -Location eastus + # Or run the Logic App version and pass the URL: + # pwsh -File "$(Build.SourcesDirectory)/ai-projects/quantum-ml/azure/quantum_full_logicapp_orchestration.ps1" -LogicAppUrl "$(LOGIC_APP_URL)" + - task: PublishBuildArtifacts@1 + inputs: + PathtoPublish: '$(Build.SourcesDirectory)' + ArtifactName: 'repo' + publishLocation: 'Container' diff --git a/ai-projects/quantum-ml/azure/quantum_workspace.parameters.json b/ai-projects/quantum-ml/azure/quantum_workspace.parameters.json index 149ae3d2b..703fe6657 100644 --- a/ai-projects/quantum-ml/azure/quantum_workspace.parameters.json +++ b/ai-projects/quantum-ml/azure/quantum_workspace.parameters.json @@ -1,23 +1,23 @@ -{ - "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#", - "contentVersion": "1.0.0.0", - "parameters": { - "location": { - "value": "eastus" - }, - "workspaceName": { - "value": "quantum-ai-workspace" - }, - "storageAccountName": { - "value": "quantumstorage1031" - }, - "tags": { - "value": { - "Environment": "Development", - "Project": "Quantum-AI", - "ManagedBy": "Bicep", - "CostCenter": "Research" - } - } - } -} +{ + "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#", + "contentVersion": "1.0.0.0", + "parameters": { + "location": { + "value": "eastus" + }, + "workspaceName": { + "value": "quantum-ai-workspace" + }, + "storageAccountName": { + "value": "quantumstorage1031" + }, + "tags": { + "value": { + "Environment": "Development", + "Project": "Quantum-AI", + "ManagedBy": "Bicep", + "CostCenter": "Research" + } + } + } +} diff --git a/ai-projects/quantum-ml/benchmark_all_datasets.py b/ai-projects/quantum-ml/benchmark_all_datasets.py index 624965780..b146038df 100644 --- a/ai-projects/quantum-ml/benchmark_all_datasets.py +++ b/ai-projects/quantum-ml/benchmark_all_datasets.py @@ -1,700 +1,700 @@ -""" -Benchmark Quantum AI on All Available Datasets -================================================ - -Trains the hybrid quantum-classical neural network on all quantum datasets -and generates a comprehensive comparison report. - -Datasets tested (27 total): -- Original 4: Ionosphere, Banknote, Heart Disease, Sonar -- Medical 10: Breast Cancer, Diabetes, Blood Transfusion, Haberman, Parkinsons, Dermatology, Liver Disorders, Thyroid, Statlog Heart -- Chemistry 3: Red Wine, White Wine, Wine Quality Combined -- Physics 2: MAGIC Gamma, Balance Scale -- Biology 1: Iris -- Agriculture 2: Wheat Seeds, Seeds -- Image Features 2: Optical Recognition, Pendigits -- Finance 1: Statlog Australian -- Social Science 1: Contraceptive -- Forensics 2: Banknote, Glass - -Note: Vertebral Column and Ecoli excluded (corrupted) - -Author: Quantum AI System -Date: November 16, 2025 (Updated) -""" - -import json -import sys -from datetime import datetime -from pathlib import Path - -import numpy as np -import pandas as pd - -# Add src to path -src_path = Path(__file__).parent / "src" -sys.path.insert(0, str(src_path)) - -import matplotlib.pyplot as plt -import torch -from sklearn.decomposition import PCA -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer -from torch.utils.data import DataLoader, TensorDataset - -# Dataset configurations -DATASETS = { - # Original 4 - "ionosphere": { - "file": "ionosphere.csv", - "description": "Radar returns classification", - "task": "Binary classification: Good vs Bad radar signals", - "category": "physics", - }, - "banknote": { - "file": "banknote.csv", - "description": "Banknote authentication", - "task": "Binary classification: Genuine vs Forged banknotes", - "category": "forensics", - }, - "heart_disease": { - "file": "heart_disease.csv", - "description": "Heart disease diagnosis", - "task": "Binary classification: Disease present vs absent", - "category": "medical", - }, - "sonar": { - "file": "sonar.csv", - "description": "Sonar returns classification", - "task": "Binary classification: Mine vs Rock detection", - "category": "geophysics", - }, - # New Medical Datasets - "breast_cancer": { - "file": "breast_cancer.csv", - "description": "Wisconsin Breast Cancer Diagnostic", - "task": "Binary classification: Malignant vs Benign", - "category": "medical", - }, - "diabetes": { - "file": "diabetes.csv", - "description": "Pima Indians Diabetes", - "task": "Binary classification: Diabetes onset prediction", - "category": "medical", - }, - "vertebral_column": { - "file": "vertebral_column.csv", - "description": "Vertebral Column Classification", - "task": "Multi-class: Normal, Disk Hernia, Spondylolisthesis", - "category": "medical", - }, - "blood_transfusion": { - "file": "blood_transfusion.csv", - "description": "Blood Transfusion Service Center", - "task": "Binary: Blood donation prediction", - "category": "medical", - }, - "haberman": { - "file": "haberman.csv", - "description": "Haberman Survival", - "task": "Binary: Patient survival (5+ years)", - "category": "medical", - }, - # Chemistry - "wine_red": { - "file": "wine_red.csv", - "description": "Red Wine Quality", - "task": "Multi-class: Wine quality rating (3-8)", - "category": "chemistry", - }, - "wine_white": { - "file": "wine_white.csv", - "description": "White Wine Quality", - "task": "Multi-class: Wine quality rating (3-9)", - "category": "chemistry", - }, - # Physics - "magic_gamma": { - "file": "magic_gamma.csv", - "description": "MAGIC Gamma Telescope", - "task": "Binary: Gamma signal vs Hadron background", - "category": "physics", - }, - # Biology - "iris": { - "file": "iris.csv", - "description": "Iris Flower Species", - "task": "Multi-class: Iris species (setosa, versicolor, virginica)", - "category": "biology", - }, - # Agriculture - "wheat_seeds": { - "file": "wheat_seeds.csv", - "description": "Wheat Seeds Classification", - "task": "Multi-class: Wheat variety classification", - "category": "agriculture", - }, - # Forensics - "glass": { - "file": "glass.csv", - "description": "Glass Identification", - "task": "Multi-class: Glass type classification", - "category": "forensics", - }, - # New Medical Datasets (Phase 2) - "parkinsons": { - "file": "parkinsons.csv", - "description": "Parkinsons Disease Detection", - "task": "Binary: Parkinsons presence prediction", - "category": "medical", - }, - "dermatology": { - "file": "dermatology.csv", - "description": "Dermatology Disease Classification", - "task": "Multi-class: 6 dermatology conditions", - "category": "medical", - }, - "liver_disorders": { - "file": "liver_disorders.csv", - "description": "Liver Disorders Classification", - "task": "Binary: Liver disorder detection", - "category": "medical", - }, - "thyroid": { - "file": "thyroid.csv", - "description": "Thyroid Disease Classification", - "task": "Multi-class: 3 thyroid conditions", - "category": "medical", - }, - "statlog_heart": { - "file": "statlog_heart.csv", - "description": "Statlog Heart Disease", - "task": "Binary: Heart disease presence", - "category": "medical", - }, - # Chemistry (Phase 2) - "wine_quality_combined": { - "file": "wine_quality_combined.csv", - "description": "Combined Wine Quality Dataset", - "task": "Multi-class: Wine quality with type feature", - "category": "chemistry", - }, - # Image Features - "optical_recognition": { - "file": "optical_recognition.csv", - "description": "Optical Recognition of Handwritten Digits", - "task": "Multi-class: 10 digit classification", - "category": "image", - }, - "pendigits": { - "file": "pendigits.csv", - "description": "Pen-Based Recognition of Handwritten Digits", - "task": "Multi-class: 10 digit classification", - "category": "image", - }, - # Agriculture (Phase 2) - "seeds": { - "file": "seeds.csv", - "description": "Seeds Classification", - "task": "Multi-class: Wheat variety classification", - "category": "agriculture", - }, - # Finance - "statlog_australian": { - "file": "statlog_australian.csv", - "description": "Australian Credit Approval", - "task": "Binary: Credit approval prediction", - "category": "finance", - }, - # Physics (Phase 2) - "balance_scale": { - "file": "balance_scale.csv", - "description": "Balance Scale Weight & Distance", - "task": "Multi-class: 3 balance classes", - "category": "physics", - }, - # Social Science - "contraceptive": { - "file": "contraceptive.csv", - "description": "Contraceptive Method Choice", - "task": "Multi-class: 3 contraceptive methods", - "category": "social", - }, -} - - -def load_dataset(dataset_name): - """Load a dataset by name with dataset-specific handling""" - from sklearn.impute import SimpleImputer - - dataset_config = DATASETS[dataset_name] - dataset_path = ( - Path(__file__).parent.parent / "datasets" / "quantum" / dataset_config["file"] - ) - - print(f"\n📁 Loading {dataset_name} dataset...") - print(f" File: {dataset_config['file']}") - print(f" Task: {dataset_config['task']}") - - # Dataset-specific loading strategies (matching quick_test_datasets.py) - if dataset_name in {"wine_red", "wine_white"}: - # These use semicolon delimiter with header - df = pd.read_csv(dataset_path, sep=";", na_values=["?", "NA", "", "NaN"]) - elif dataset_name == "wine_quality_combined": - # Combined wine dataset with comma delimiter - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - elif dataset_name in {"wheat_seeds", "seeds"}: - # Whitespace-delimited datasets with no header - df = pd.read_csv( - dataset_path, sep=r"\s+", header=None, na_values=["?", "NA", "", "NaN"] - ) - elif dataset_name == "parkinsons": - # Comma-delimited with header, skip first column (name) - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - df = df.drop(columns=df.columns[0]) # Skip name column - elif dataset_name in {"statlog_australian", "statlog_heart"}: - # Space-delimited, no header - df = pd.read_csv( - dataset_path, sep=" ", header=None, na_values=["?", "NA", "", "NaN"] - ) - elif dataset_name == "vertebral_column": - # Binary file or severely corrupted - skip - raise ValueError("Dataset file appears to be corrupted or binary format") - elif dataset_name == "blood_transfusion": - # Has header with description in first row, skip it - df = pd.read_csv(dataset_path, skiprows=1, na_values=["?", "NA", "", "NaN"]) - elif dataset_name == "breast_cancer": - # No header, need to skip ID column - df = pd.read_csv(dataset_path, header=None, na_values=["?", "NA", "", "NaN"]) - elif dataset_name == "balance_scale": - # Comma-delimited with header - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - elif dataset_name in { - "optical_recognition", - "pendigits", - "contraceptive", - "dermatology", - "liver_disorders", - "thyroid", - }: - # Comma-delimited, no header - df = pd.read_csv(dataset_path, header=None, na_values=["?", "NA", "", "NaN"]) - else: - # Standard loading with fallback - try: - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - # Check if it looks like semicolon-delimited - if df.shape[1] == 1 and ";" in str(df.iloc[0, 0]): - df = pd.read_csv( - dataset_path, sep=";", na_values=["?", "NA", "", "NaN"] - ) - except UnicodeDecodeError: - # Try different encoding - try: - df = pd.read_csv( - dataset_path, na_values=["?", "NA", "", "NaN"], encoding="latin-1" - ) - except: - df = pd.read_csv( - dataset_path, - sep=";", - na_values=["?", "NA", "", "NaN"], - encoding="latin-1", - ) - - # Check if first row looks like data (all numeric except possibly last column) - only for unhandled cases - if dataset_name not in { - "breast_cancer", - "vertebral_column", - "blood_transfusion", - "wine_red", - "wine_white", - "wine_quality_combined", - "wheat_seeds", - "seeds", - }: - first_row_numeric = all( - str(df.iloc[0, i]) - .replace(".", "") - .replace("-", "") - .replace("e", "") - .isdigit() - or str(df.iloc[0, i]) - .replace(".", "") - .replace("-", "") - .replace("e", "") - .replace("+", "") - .isdigit() - for i in range(min(3, df.shape[1] - 1)) - ) - - if ( - first_row_numeric - or df.columns[0].replace(".", "").replace("-", "").isdigit() - ): - # No header - reload without header - try: - df = pd.read_csv( - dataset_path, header=None, na_values=["?", "NA", "", "NaN"] - ) - except UnicodeDecodeError: - df = pd.read_csv( - dataset_path, - header=None, - na_values=["?", "NA", "", "NaN"], - encoding="latin-1", - ) - - # Separate features and labels (last column is target) - X = df.iloc[:, :-1] - y = df.iloc[:, -1].values - - # Dataset-specific feature handling - if dataset_name == "breast_cancer": - # Column 0 is ID, Column 1 is diagnosis (M/B), rest are features - # We need to use column 1 as label and skip column 0 - if X.shape[1] > 20: # Has ID column - X = df.iloc[:, 2:-1] # Skip ID (col 0) and diagnosis (col 1), take features - y = df.iloc[:, 1].values # Diagnosis column - - # Impute missing values in features if any - if X.isnull().any().any(): - print(" ⚠️ Found missing values - imputing with median...") - imputer = SimpleImputer(strategy="median") - X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) - - X = X.values - - # Convert labels to binary - unique_labels = np.unique(y) - if len(unique_labels) > 2 or y.dtype == object: - if y.dtype == object: - y = (y == unique_labels[0]).astype(int) - else: - # For multi-class numeric, convert to binary - y = (y == unique_labels[0]).astype(int) - else: - y = (y != 0).astype(int) - - print(f" Samples: {len(X)}") - print(f" Features: {X.shape[1]}") - print(f" Class distribution: {np.bincount(y)}") - - return X, y - - -def preprocess_data(X, y, n_qubits=4, test_size=0.2): - """Preprocess data for quantum ML""" - # Split data - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=test_size, random_state=42, stratify=y - ) - - # Standardize - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - # Reduce/pad to n_qubits features - n_features = X_train.shape[1] - pca = None - - if n_features > n_qubits: - pca = PCA(n_components=n_qubits) - X_train = pca.fit_transform(X_train) - X_val = pca.transform(X_val) - explained_var = pca.explained_variance_ratio_.sum() - print( - f" PCA: {n_features} → {n_qubits} features ({explained_var:.2%} variance)" - ) - elif n_features < n_qubits: - pad_train = np.zeros((X_train.shape[0], n_qubits - n_features)) - pad_val = np.zeros((X_val.shape[0], n_qubits - n_features)) - X_train = np.hstack([X_train, pad_train]) - X_val = np.hstack([X_val, pad_val]) - print(f" Padded: {n_features} → {n_qubits} features") - - return X_train, X_val, y_train, y_val, scaler, pca - - -def train_model(X_train, y_train, X_val, y_val, num_epochs=25, batch_size=16): - """Train quantum model""" - n_qubits = X_train.shape[1] - n_classes = len(np.unique(y_train)) - - # Create model - model = HybridQNN( - input_dim=n_qubits, - hidden_dim=16, - n_qubits=n_qubits, - n_quantum_layers=2, - output_dim=n_classes, - dropout=0.2, - ) - - # Convert to tensors - X_train_t = torch.FloatTensor(X_train) - y_train_t = torch.LongTensor(y_train) - X_val_t = torch.FloatTensor(X_val) - y_val_t = torch.LongTensor(y_val) - - # Create data loaders with drop_last for training to avoid batch norm issues - train_dataset = TensorDataset(X_train_t, y_train_t) - val_dataset = TensorDataset(X_val_t, y_val_t) - train_loader = DataLoader( - train_dataset, batch_size=batch_size, shuffle=True, drop_last=True - ) - val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) - - # Train - trainer = QuantumClassicalTrainer(model, learning_rate=0.001) - trainer.train(train_loader, val_loader, num_epochs) - - return model, trainer - - -def benchmark_dataset(dataset_name): - """Benchmark a single dataset""" - print("\n" + "=" * 70) - print(f" BENCHMARKING: {dataset_name.upper()}") - print("=" * 70) - - # Load and preprocess - X, y = load_dataset(dataset_name) - X_train, X_val, y_train, y_val, scaler, pca = preprocess_data(X, y) - - # Train - print("\n🚀 Training model (25 epochs)...") - model, trainer = train_model(X_train, y_train, X_val, y_val) - - # Get results - best_acc = max(trainer.val_accuracies) - final_acc = trainer.val_accuracies[-1] - final_loss = trainer.train_losses[-1] - - print("\n📊 Results:") - print(f" Best Accuracy: {best_acc:.4f} ({best_acc*100:.2f}%)") - print(f" Final Accuracy: {final_acc:.4f} ({final_acc*100:.2f}%)") - print(f" Final Loss: {final_loss:.4f}") - - return { - "dataset": dataset_name, - "description": DATASETS[dataset_name]["description"], - "task": DATASETS[dataset_name]["task"], - "n_samples": len(X), - "n_features": X.shape[1], - "n_train": len(X_train), - "n_val": len(X_val), - "best_accuracy": float(best_acc), - "final_accuracy": float(final_acc), - "final_loss": float(final_loss), - "train_losses": [float(x) for x in trainer.train_losses], - "val_accuracies": [float(x) for x in trainer.val_accuracies], - } - - -def plot_comparison(results, save_path="results/benchmark_comparison.png"): - """Plot comparison across datasets""" - fig, axes = plt.subplots(2, 2, figsize=(14, 10)) - fig.suptitle("Quantum AI Benchmark - All Datasets", fontsize=16, fontweight="bold") - - # Best accuracy comparison - ax = axes[0, 0] - datasets = [r["dataset"] for r in results] - accuracies = [r["best_accuracy"] for r in results] - colors = [ - "green" if a >= 0.85 else "orange" if a >= 0.75 else "red" for a in accuracies - ] - bars = ax.bar(datasets, accuracies, color=colors, alpha=0.7) - ax.set_ylabel("Accuracy") - ax.set_title("Best Validation Accuracy") - ax.set_ylim([0, 1]) - ax.grid(axis="y", alpha=0.3) - for i, bar in enumerate(bars): - height = bar.get_height() - ax.text( - bar.get_x() + bar.get_width() / 2.0, - height, - f"{accuracies[i]:.2%}", - ha="center", - va="bottom", - ) - - # Training curves - ax = axes[0, 1] - for r in results: - ax.plot(r["val_accuracies"], label=r["dataset"], linewidth=2) - ax.set_xlabel("Epoch") - ax.set_ylabel("Accuracy") - ax.set_title("Training Progress") - ax.legend() - ax.grid(True, alpha=0.3) - - # Dataset sizes - ax = axes[1, 0] - sizes = [r["n_samples"] for r in results] - ax.bar(datasets, sizes, color="skyblue", alpha=0.7) - ax.set_ylabel("Number of Samples") - ax.set_title("Dataset Sizes") - ax.grid(axis="y", alpha=0.3) - - # Loss curves - ax = axes[1, 1] - for r in results: - ax.plot(r["train_losses"], label=r["dataset"], linewidth=2) - ax.set_xlabel("Epoch") - ax.set_ylabel("Loss") - ax.set_title("Training Loss") - ax.legend() - ax.grid(True, alpha=0.3) - - plt.tight_layout() - plt.savefig(save_path, dpi=150, bbox_inches="tight") - print(f"\n📊 Comparison plot saved to: {save_path}") - plt.close() - - -def generate_report(results, save_path="results/benchmark_report.md"): - """Generate markdown report""" - report = [] - report.append("# Quantum AI Benchmark Report") - report.append(f"\n**Date:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - report.append("\n## Model Configuration") - report.append("- **Architecture:** Hybrid Quantum-Classical Neural Network") - report.append("- **Quantum Circuit:** 4 qubits, 2 variational layers") - report.append("- **Classical Layers:** 16-node hidden layer with dropout (0.2)") - report.append("- **Training:** 25 epochs, batch size 16, learning rate 0.001") - report.append("- **Optimizer:** Adam") - - report.append("\n## Results Summary\n") - report.append( - "| Dataset | Samples | Features | Best Accuracy | Final Accuracy | Grade |" - ) - report.append( - "|---------|---------|----------|---------------|----------------|-------|" - ) - - for r in results: - acc = r["best_accuracy"] - grade = ( - "🏆 Excellent" - if acc >= 0.85 - else ( - "⭐ Very Good" - if acc >= 0.75 - else "✅ Good" if acc >= 0.65 else "⚠️ Fair" - ) - ) - report.append( - f"| {r['dataset']} | {r['n_samples']} | {r['n_features']} | {acc:.2%} | {r['final_accuracy']:.2%} | {grade} |" - ) - - report.append("\n## Detailed Results\n") - for r in results: - report.append(f"\n### {r['dataset'].title()}") - report.append(f"\n**Description:** {r['description']}") - report.append(f"\n**Task:** {r['task']}") - report.append("\n**Metrics:**") - report.append(f"- Best Validation Accuracy: **{r['best_accuracy']:.2%}**") - report.append(f"- Final Training Loss: {r['final_loss']:.4f}") - report.append(f"- Training Samples: {r['n_train']}") - report.append(f"- Validation Samples: {r['n_val']}") - - report.append("\n## Conclusions\n") - best_dataset = max(results, key=lambda x: x["best_accuracy"]) - avg_acc = np.mean([r["best_accuracy"] for r in results]) - - report.append( - f"- **Best Performance:** {best_dataset['dataset']} ({best_dataset['best_accuracy']:.2%})" - ) - report.append(f"- **Average Accuracy:** {avg_acc:.2%}") - report.append(f"- **Total Datasets Tested:** {len(results)}") - - if avg_acc >= 0.80: - report.append( - "\n✅ **Overall Assessment:** The quantum AI model demonstrates strong performance across all datasets!" - ) - else: - report.append( - "\n⚠️ **Overall Assessment:** Results are mixed. Consider hyperparameter tuning or architecture modifications." - ) - - # Save report - Path(save_path).write_text("\n".join(report), encoding="utf-8") - print(f"📄 Report saved to: {save_path}") - - -def main(): - """Main benchmark pipeline""" - print("=" * 70) - print(" QUANTUM AI - COMPREHENSIVE BENCHMARK") - print("=" * 70) - print(f"\n🔬 Testing on {len(DATASETS)} quantum datasets (27 total, 26 working)") - print(" Model: Hybrid Quantum-Classical Neural Network") - print( - " Configuration: Variable architecture (4-6 qubits, 2-4 layers per dataset)" - ) - print(" Training: 25 epochs with dataset-specific hyperparameters") - - results = [] - - # Benchmark each dataset (optimized: direct iteration instead of .keys()) - for dataset_name in DATASETS: - try: - result = benchmark_dataset(dataset_name) - results.append(result) - except Exception as e: - print(f"\n❌ Error benchmarking {dataset_name}: {e}") - continue - - if not results: - print("\n❌ No successful benchmarks!") - return - - # Generate visualizations and report - print("\n" + "=" * 70) - print(" GENERATING BENCHMARK REPORT") - print("=" * 70) - - plot_comparison(results) - generate_report(results) - - # Save JSON results - json_path = Path("results/benchmark_results.json") - json_path.write_text(json.dumps(results, indent=2), encoding="utf-8") - print(f"💾 JSON results saved to: {json_path}") - - # Print summary - print("\n" + "=" * 70) - print(" BENCHMARK SUMMARY") - print("=" * 70) - print(f"\n{'Dataset':<15} {'Samples':<10} {'Best Acc':<12} {'Grade':<15}") - print("-" * 70) - - for r in results: - acc = r["best_accuracy"] - grade = ( - "🏆 Excellent" - if acc >= 0.85 - else "⭐ Very Good" if acc >= 0.75 else "✅ Good" - ) - print(f"{r['dataset']:<15} {r['n_samples']:<10} {acc:>6.2%} {grade}") - - avg_acc = np.mean([r["best_accuracy"] for r in results]) - print("-" * 70) - print(f"{'AVERAGE':<15} {'':<10} {avg_acc:>6.2%}") - - print("\n📚 Next Steps:") - print(" 1. Review: results/benchmark_comparison.png") - print(" 2. Read: results/benchmark_report.md") - print(" 3. Analyze: results/benchmark_results.json") - print(" 4. Deploy best model to Azure Quantum hardware") - - print("\n" + "=" * 70) - print(" 🎉 BENCHMARK COMPLETE!") - print("=" * 70 + "\n") - - -if __name__ == "__main__": - main() +""" +Benchmark Quantum AI on All Available Datasets +================================================ + +Trains the hybrid quantum-classical neural network on all quantum datasets +and generates a comprehensive comparison report. + +Datasets tested (27 total): +- Original 4: Ionosphere, Banknote, Heart Disease, Sonar +- Medical 10: Breast Cancer, Diabetes, Blood Transfusion, Haberman, Parkinsons, Dermatology, Liver Disorders, Thyroid, Statlog Heart +- Chemistry 3: Red Wine, White Wine, Wine Quality Combined +- Physics 2: MAGIC Gamma, Balance Scale +- Biology 1: Iris +- Agriculture 2: Wheat Seeds, Seeds +- Image Features 2: Optical Recognition, Pendigits +- Finance 1: Statlog Australian +- Social Science 1: Contraceptive +- Forensics 2: Banknote, Glass + +Note: Vertebral Column and Ecoli excluded (corrupted) + +Author: Quantum AI System +Date: November 16, 2025 (Updated) +""" + +import json +import sys +from datetime import datetime +from pathlib import Path + +import numpy as np +import pandas as pd + +# Add src to path +src_path = Path(__file__).parent / "src" +sys.path.insert(0, str(src_path)) + +import matplotlib.pyplot as plt +import torch +from sklearn.decomposition import PCA +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer +from torch.utils.data import DataLoader, TensorDataset + +# Dataset configurations +DATASETS = { + # Original 4 + "ionosphere": { + "file": "ionosphere.csv", + "description": "Radar returns classification", + "task": "Binary classification: Good vs Bad radar signals", + "category": "physics", + }, + "banknote": { + "file": "banknote.csv", + "description": "Banknote authentication", + "task": "Binary classification: Genuine vs Forged banknotes", + "category": "forensics", + }, + "heart_disease": { + "file": "heart_disease.csv", + "description": "Heart disease diagnosis", + "task": "Binary classification: Disease present vs absent", + "category": "medical", + }, + "sonar": { + "file": "sonar.csv", + "description": "Sonar returns classification", + "task": "Binary classification: Mine vs Rock detection", + "category": "geophysics", + }, + # New Medical Datasets + "breast_cancer": { + "file": "breast_cancer.csv", + "description": "Wisconsin Breast Cancer Diagnostic", + "task": "Binary classification: Malignant vs Benign", + "category": "medical", + }, + "diabetes": { + "file": "diabetes.csv", + "description": "Pima Indians Diabetes", + "task": "Binary classification: Diabetes onset prediction", + "category": "medical", + }, + "vertebral_column": { + "file": "vertebral_column.csv", + "description": "Vertebral Column Classification", + "task": "Multi-class: Normal, Disk Hernia, Spondylolisthesis", + "category": "medical", + }, + "blood_transfusion": { + "file": "blood_transfusion.csv", + "description": "Blood Transfusion Service Center", + "task": "Binary: Blood donation prediction", + "category": "medical", + }, + "haberman": { + "file": "haberman.csv", + "description": "Haberman Survival", + "task": "Binary: Patient survival (5+ years)", + "category": "medical", + }, + # Chemistry + "wine_red": { + "file": "wine_red.csv", + "description": "Red Wine Quality", + "task": "Multi-class: Wine quality rating (3-8)", + "category": "chemistry", + }, + "wine_white": { + "file": "wine_white.csv", + "description": "White Wine Quality", + "task": "Multi-class: Wine quality rating (3-9)", + "category": "chemistry", + }, + # Physics + "magic_gamma": { + "file": "magic_gamma.csv", + "description": "MAGIC Gamma Telescope", + "task": "Binary: Gamma signal vs Hadron background", + "category": "physics", + }, + # Biology + "iris": { + "file": "iris.csv", + "description": "Iris Flower Species", + "task": "Multi-class: Iris species (setosa, versicolor, virginica)", + "category": "biology", + }, + # Agriculture + "wheat_seeds": { + "file": "wheat_seeds.csv", + "description": "Wheat Seeds Classification", + "task": "Multi-class: Wheat variety classification", + "category": "agriculture", + }, + # Forensics + "glass": { + "file": "glass.csv", + "description": "Glass Identification", + "task": "Multi-class: Glass type classification", + "category": "forensics", + }, + # New Medical Datasets (Phase 2) + "parkinsons": { + "file": "parkinsons.csv", + "description": "Parkinsons Disease Detection", + "task": "Binary: Parkinsons presence prediction", + "category": "medical", + }, + "dermatology": { + "file": "dermatology.csv", + "description": "Dermatology Disease Classification", + "task": "Multi-class: 6 dermatology conditions", + "category": "medical", + }, + "liver_disorders": { + "file": "liver_disorders.csv", + "description": "Liver Disorders Classification", + "task": "Binary: Liver disorder detection", + "category": "medical", + }, + "thyroid": { + "file": "thyroid.csv", + "description": "Thyroid Disease Classification", + "task": "Multi-class: 3 thyroid conditions", + "category": "medical", + }, + "statlog_heart": { + "file": "statlog_heart.csv", + "description": "Statlog Heart Disease", + "task": "Binary: Heart disease presence", + "category": "medical", + }, + # Chemistry (Phase 2) + "wine_quality_combined": { + "file": "wine_quality_combined.csv", + "description": "Combined Wine Quality Dataset", + "task": "Multi-class: Wine quality with type feature", + "category": "chemistry", + }, + # Image Features + "optical_recognition": { + "file": "optical_recognition.csv", + "description": "Optical Recognition of Handwritten Digits", + "task": "Multi-class: 10 digit classification", + "category": "image", + }, + "pendigits": { + "file": "pendigits.csv", + "description": "Pen-Based Recognition of Handwritten Digits", + "task": "Multi-class: 10 digit classification", + "category": "image", + }, + # Agriculture (Phase 2) + "seeds": { + "file": "seeds.csv", + "description": "Seeds Classification", + "task": "Multi-class: Wheat variety classification", + "category": "agriculture", + }, + # Finance + "statlog_australian": { + "file": "statlog_australian.csv", + "description": "Australian Credit Approval", + "task": "Binary: Credit approval prediction", + "category": "finance", + }, + # Physics (Phase 2) + "balance_scale": { + "file": "balance_scale.csv", + "description": "Balance Scale Weight & Distance", + "task": "Multi-class: 3 balance classes", + "category": "physics", + }, + # Social Science + "contraceptive": { + "file": "contraceptive.csv", + "description": "Contraceptive Method Choice", + "task": "Multi-class: 3 contraceptive methods", + "category": "social", + }, +} + + +def load_dataset(dataset_name): + """Load a dataset by name with dataset-specific handling""" + from sklearn.impute import SimpleImputer + + dataset_config = DATASETS[dataset_name] + dataset_path = ( + Path(__file__).parent.parent / "datasets" / "quantum" / dataset_config["file"] + ) + + print(f"\n📁 Loading {dataset_name} dataset...") + print(f" File: {dataset_config['file']}") + print(f" Task: {dataset_config['task']}") + + # Dataset-specific loading strategies (matching quick_test_datasets.py) + if dataset_name in {"wine_red", "wine_white"}: + # These use semicolon delimiter with header + df = pd.read_csv(dataset_path, sep=";", na_values=["?", "NA", "", "NaN"]) + elif dataset_name == "wine_quality_combined": + # Combined wine dataset with comma delimiter + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + elif dataset_name in {"wheat_seeds", "seeds"}: + # Whitespace-delimited datasets with no header + df = pd.read_csv( + dataset_path, sep=r"\s+", header=None, na_values=["?", "NA", "", "NaN"] + ) + elif dataset_name == "parkinsons": + # Comma-delimited with header, skip first column (name) + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + df = df.drop(columns=df.columns[0]) # Skip name column + elif dataset_name in {"statlog_australian", "statlog_heart"}: + # Space-delimited, no header + df = pd.read_csv( + dataset_path, sep=" ", header=None, na_values=["?", "NA", "", "NaN"] + ) + elif dataset_name == "vertebral_column": + # Binary file or severely corrupted - skip + raise ValueError("Dataset file appears to be corrupted or binary format") + elif dataset_name == "blood_transfusion": + # Has header with description in first row, skip it + df = pd.read_csv(dataset_path, skiprows=1, na_values=["?", "NA", "", "NaN"]) + elif dataset_name == "breast_cancer": + # No header, need to skip ID column + df = pd.read_csv(dataset_path, header=None, na_values=["?", "NA", "", "NaN"]) + elif dataset_name == "balance_scale": + # Comma-delimited with header + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + elif dataset_name in { + "optical_recognition", + "pendigits", + "contraceptive", + "dermatology", + "liver_disorders", + "thyroid", + }: + # Comma-delimited, no header + df = pd.read_csv(dataset_path, header=None, na_values=["?", "NA", "", "NaN"]) + else: + # Standard loading with fallback + try: + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + # Check if it looks like semicolon-delimited + if df.shape[1] == 1 and ";" in str(df.iloc[0, 0]): + df = pd.read_csv( + dataset_path, sep=";", na_values=["?", "NA", "", "NaN"] + ) + except UnicodeDecodeError: + # Try different encoding + try: + df = pd.read_csv( + dataset_path, na_values=["?", "NA", "", "NaN"], encoding="latin-1" + ) + except: + df = pd.read_csv( + dataset_path, + sep=";", + na_values=["?", "NA", "", "NaN"], + encoding="latin-1", + ) + + # Check if first row looks like data (all numeric except possibly last column) - only for unhandled cases + if dataset_name not in { + "breast_cancer", + "vertebral_column", + "blood_transfusion", + "wine_red", + "wine_white", + "wine_quality_combined", + "wheat_seeds", + "seeds", + }: + first_row_numeric = all( + str(df.iloc[0, i]) + .replace(".", "") + .replace("-", "") + .replace("e", "") + .isdigit() + or str(df.iloc[0, i]) + .replace(".", "") + .replace("-", "") + .replace("e", "") + .replace("+", "") + .isdigit() + for i in range(min(3, df.shape[1] - 1)) + ) + + if ( + first_row_numeric + or df.columns[0].replace(".", "").replace("-", "").isdigit() + ): + # No header - reload without header + try: + df = pd.read_csv( + dataset_path, header=None, na_values=["?", "NA", "", "NaN"] + ) + except UnicodeDecodeError: + df = pd.read_csv( + dataset_path, + header=None, + na_values=["?", "NA", "", "NaN"], + encoding="latin-1", + ) + + # Separate features and labels (last column is target) + X = df.iloc[:, :-1] + y = df.iloc[:, -1].values + + # Dataset-specific feature handling + if dataset_name == "breast_cancer": + # Column 0 is ID, Column 1 is diagnosis (M/B), rest are features + # We need to use column 1 as label and skip column 0 + if X.shape[1] > 20: # Has ID column + X = df.iloc[:, 2:-1] # Skip ID (col 0) and diagnosis (col 1), take features + y = df.iloc[:, 1].values # Diagnosis column + + # Impute missing values in features if any + if X.isnull().any().any(): + print(" ⚠️ Found missing values - imputing with median...") + imputer = SimpleImputer(strategy="median") + X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) + + X = X.values + + # Convert labels to binary + unique_labels = np.unique(y) + if len(unique_labels) > 2 or y.dtype == object: + if y.dtype == object: + y = (y == unique_labels[0]).astype(int) + else: + # For multi-class numeric, convert to binary + y = (y == unique_labels[0]).astype(int) + else: + y = (y != 0).astype(int) + + print(f" Samples: {len(X)}") + print(f" Features: {X.shape[1]}") + print(f" Class distribution: {np.bincount(y)}") + + return X, y + + +def preprocess_data(X, y, n_qubits=4, test_size=0.2): + """Preprocess data for quantum ML""" + # Split data + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=test_size, random_state=42, stratify=y + ) + + # Standardize + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + # Reduce/pad to n_qubits features + n_features = X_train.shape[1] + pca = None + + if n_features > n_qubits: + pca = PCA(n_components=n_qubits) + X_train = pca.fit_transform(X_train) + X_val = pca.transform(X_val) + explained_var = pca.explained_variance_ratio_.sum() + print( + f" PCA: {n_features} → {n_qubits} features ({explained_var:.2%} variance)" + ) + elif n_features < n_qubits: + pad_train = np.zeros((X_train.shape[0], n_qubits - n_features)) + pad_val = np.zeros((X_val.shape[0], n_qubits - n_features)) + X_train = np.hstack([X_train, pad_train]) + X_val = np.hstack([X_val, pad_val]) + print(f" Padded: {n_features} → {n_qubits} features") + + return X_train, X_val, y_train, y_val, scaler, pca + + +def train_model(X_train, y_train, X_val, y_val, num_epochs=25, batch_size=16): + """Train quantum model""" + n_qubits = X_train.shape[1] + n_classes = len(np.unique(y_train)) + + # Create model + model = HybridQNN( + input_dim=n_qubits, + hidden_dim=16, + n_qubits=n_qubits, + n_quantum_layers=2, + output_dim=n_classes, + dropout=0.2, + ) + + # Convert to tensors + X_train_t = torch.FloatTensor(X_train) + y_train_t = torch.LongTensor(y_train) + X_val_t = torch.FloatTensor(X_val) + y_val_t = torch.LongTensor(y_val) + + # Create data loaders with drop_last for training to avoid batch norm issues + train_dataset = TensorDataset(X_train_t, y_train_t) + val_dataset = TensorDataset(X_val_t, y_val_t) + train_loader = DataLoader( + train_dataset, batch_size=batch_size, shuffle=True, drop_last=True + ) + val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) + + # Train + trainer = QuantumClassicalTrainer(model, learning_rate=0.001) + trainer.train(train_loader, val_loader, num_epochs) + + return model, trainer + + +def benchmark_dataset(dataset_name): + """Benchmark a single dataset""" + print("\n" + "=" * 70) + print(f" BENCHMARKING: {dataset_name.upper()}") + print("=" * 70) + + # Load and preprocess + X, y = load_dataset(dataset_name) + X_train, X_val, y_train, y_val, scaler, pca = preprocess_data(X, y) + + # Train + print("\n🚀 Training model (25 epochs)...") + model, trainer = train_model(X_train, y_train, X_val, y_val) + + # Get results + best_acc = max(trainer.val_accuracies) + final_acc = trainer.val_accuracies[-1] + final_loss = trainer.train_losses[-1] + + print("\n📊 Results:") + print(f" Best Accuracy: {best_acc:.4f} ({best_acc*100:.2f}%)") + print(f" Final Accuracy: {final_acc:.4f} ({final_acc*100:.2f}%)") + print(f" Final Loss: {final_loss:.4f}") + + return { + "dataset": dataset_name, + "description": DATASETS[dataset_name]["description"], + "task": DATASETS[dataset_name]["task"], + "n_samples": len(X), + "n_features": X.shape[1], + "n_train": len(X_train), + "n_val": len(X_val), + "best_accuracy": float(best_acc), + "final_accuracy": float(final_acc), + "final_loss": float(final_loss), + "train_losses": [float(x) for x in trainer.train_losses], + "val_accuracies": [float(x) for x in trainer.val_accuracies], + } + + +def plot_comparison(results, save_path="results/benchmark_comparison.png"): + """Plot comparison across datasets""" + fig, axes = plt.subplots(2, 2, figsize=(14, 10)) + fig.suptitle("Quantum AI Benchmark - All Datasets", fontsize=16, fontweight="bold") + + # Best accuracy comparison + ax = axes[0, 0] + datasets = [r["dataset"] for r in results] + accuracies = [r["best_accuracy"] for r in results] + colors = [ + "green" if a >= 0.85 else "orange" if a >= 0.75 else "red" for a in accuracies + ] + bars = ax.bar(datasets, accuracies, color=colors, alpha=0.7) + ax.set_ylabel("Accuracy") + ax.set_title("Best Validation Accuracy") + ax.set_ylim([0, 1]) + ax.grid(axis="y", alpha=0.3) + for i, bar in enumerate(bars): + height = bar.get_height() + ax.text( + bar.get_x() + bar.get_width() / 2.0, + height, + f"{accuracies[i]:.2%}", + ha="center", + va="bottom", + ) + + # Training curves + ax = axes[0, 1] + for r in results: + ax.plot(r["val_accuracies"], label=r["dataset"], linewidth=2) + ax.set_xlabel("Epoch") + ax.set_ylabel("Accuracy") + ax.set_title("Training Progress") + ax.legend() + ax.grid(True, alpha=0.3) + + # Dataset sizes + ax = axes[1, 0] + sizes = [r["n_samples"] for r in results] + ax.bar(datasets, sizes, color="skyblue", alpha=0.7) + ax.set_ylabel("Number of Samples") + ax.set_title("Dataset Sizes") + ax.grid(axis="y", alpha=0.3) + + # Loss curves + ax = axes[1, 1] + for r in results: + ax.plot(r["train_losses"], label=r["dataset"], linewidth=2) + ax.set_xlabel("Epoch") + ax.set_ylabel("Loss") + ax.set_title("Training Loss") + ax.legend() + ax.grid(True, alpha=0.3) + + plt.tight_layout() + plt.savefig(save_path, dpi=150, bbox_inches="tight") + print(f"\n📊 Comparison plot saved to: {save_path}") + plt.close() + + +def generate_report(results, save_path="results/benchmark_report.md"): + """Generate markdown report""" + report = [] + report.append("# Quantum AI Benchmark Report") + report.append(f"\n**Date:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") + report.append("\n## Model Configuration") + report.append("- **Architecture:** Hybrid Quantum-Classical Neural Network") + report.append("- **Quantum Circuit:** 4 qubits, 2 variational layers") + report.append("- **Classical Layers:** 16-node hidden layer with dropout (0.2)") + report.append("- **Training:** 25 epochs, batch size 16, learning rate 0.001") + report.append("- **Optimizer:** Adam") + + report.append("\n## Results Summary\n") + report.append( + "| Dataset | Samples | Features | Best Accuracy | Final Accuracy | Grade |" + ) + report.append( + "|---------|---------|----------|---------------|----------------|-------|" + ) + + for r in results: + acc = r["best_accuracy"] + grade = ( + "🏆 Excellent" + if acc >= 0.85 + else ( + "⭐ Very Good" + if acc >= 0.75 + else "✅ Good" if acc >= 0.65 else "⚠️ Fair" + ) + ) + report.append( + f"| {r['dataset']} | {r['n_samples']} | {r['n_features']} | {acc:.2%} | {r['final_accuracy']:.2%} | {grade} |" + ) + + report.append("\n## Detailed Results\n") + for r in results: + report.append(f"\n### {r['dataset'].title()}") + report.append(f"\n**Description:** {r['description']}") + report.append(f"\n**Task:** {r['task']}") + report.append("\n**Metrics:**") + report.append(f"- Best Validation Accuracy: **{r['best_accuracy']:.2%}**") + report.append(f"- Final Training Loss: {r['final_loss']:.4f}") + report.append(f"- Training Samples: {r['n_train']}") + report.append(f"- Validation Samples: {r['n_val']}") + + report.append("\n## Conclusions\n") + best_dataset = max(results, key=lambda x: x["best_accuracy"]) + avg_acc = np.mean([r["best_accuracy"] for r in results]) + + report.append( + f"- **Best Performance:** {best_dataset['dataset']} ({best_dataset['best_accuracy']:.2%})" + ) + report.append(f"- **Average Accuracy:** {avg_acc:.2%}") + report.append(f"- **Total Datasets Tested:** {len(results)}") + + if avg_acc >= 0.80: + report.append( + "\n✅ **Overall Assessment:** The quantum AI model demonstrates strong performance across all datasets!" + ) + else: + report.append( + "\n⚠️ **Overall Assessment:** Results are mixed. Consider hyperparameter tuning or architecture modifications." + ) + + # Save report + Path(save_path).write_text("\n".join(report), encoding="utf-8") + print(f"📄 Report saved to: {save_path}") + + +def main(): + """Main benchmark pipeline""" + print("=" * 70) + print(" QUANTUM AI - COMPREHENSIVE BENCHMARK") + print("=" * 70) + print(f"\n🔬 Testing on {len(DATASETS)} quantum datasets (27 total, 26 working)") + print(" Model: Hybrid Quantum-Classical Neural Network") + print( + " Configuration: Variable architecture (4-6 qubits, 2-4 layers per dataset)" + ) + print(" Training: 25 epochs with dataset-specific hyperparameters") + + results = [] + + # Benchmark each dataset (optimized: direct iteration instead of .keys()) + for dataset_name in DATASETS: + try: + result = benchmark_dataset(dataset_name) + results.append(result) + except Exception as e: + print(f"\n❌ Error benchmarking {dataset_name}: {e}") + continue + + if not results: + print("\n❌ No successful benchmarks!") + return + + # Generate visualizations and report + print("\n" + "=" * 70) + print(" GENERATING BENCHMARK REPORT") + print("=" * 70) + + plot_comparison(results) + generate_report(results) + + # Save JSON results + json_path = Path("results/benchmark_results.json") + json_path.write_text(json.dumps(results, indent=2), encoding="utf-8") + print(f"💾 JSON results saved to: {json_path}") + + # Print summary + print("\n" + "=" * 70) + print(" BENCHMARK SUMMARY") + print("=" * 70) + print(f"\n{'Dataset':<15} {'Samples':<10} {'Best Acc':<12} {'Grade':<15}") + print("-" * 70) + + for r in results: + acc = r["best_accuracy"] + grade = ( + "🏆 Excellent" + if acc >= 0.85 + else "⭐ Very Good" if acc >= 0.75 else "✅ Good" + ) + print(f"{r['dataset']:<15} {r['n_samples']:<10} {acc:>6.2%} {grade}") + + avg_acc = np.mean([r["best_accuracy"] for r in results]) + print("-" * 70) + print(f"{'AVERAGE':<15} {'':<10} {avg_acc:>6.2%}") + + print("\n📚 Next Steps:") + print(" 1. Review: results/benchmark_comparison.png") + print(" 2. Read: results/benchmark_report.md") + print(" 3. Analyze: results/benchmark_results.json") + print(" 4. Deploy best model to Azure Quantum hardware") + + print("\n" + "=" * 70) + print(" 🎉 BENCHMARK COMPLETE!") + print("=" * 70 + "\n") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/classical_baseline_comparison.py b/ai-projects/quantum-ml/classical_baseline_comparison.py index 0d75f8ac8..46fdf3fad 100644 --- a/ai-projects/quantum-ml/classical_baseline_comparison.py +++ b/ai-projects/quantum-ml/classical_baseline_comparison.py @@ -1,618 +1,618 @@ -""" -Classical ML Baseline Comparison -Compare quantum models vs classical ML algorithms on all datasets - -This script trains classical ML baselines (SVM, Random Forest, Neural Network) -on the same datasets used for quantum training to validate quantum advantage. -""" - -import json -import warnings -from datetime import datetime -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns -from sklearn.decomposition import PCA -from sklearn.ensemble import RandomForestClassifier -from sklearn.impute import SimpleImputer -from sklearn.metrics import accuracy_score, classification_report -from sklearn.model_selection import train_test_split -from sklearn.neural_network import MLPClassifier -from sklearn.preprocessing import StandardScaler -from sklearn.svm import SVC - -warnings.filterwarnings("ignore") - -# Set random seed for reproducibility -np.random.seed(42) - -# Dataset configurations matching quantum training -DATASETS = { - "banknote": { - "path": Path(__file__).parent.parent / "datasets" / "quantum" / "banknote.csv", - "target_col": -1, # Last column - "n_features": 4, - "has_header": False, - }, - "ionosphere": { - "path": Path(__file__).parent.parent - / "datasets" - / "quantum" - / "ionosphere.csv", - "target_col": -1, # Last column - "n_features": 4, - "has_header": False, - }, - "sonar": { - "path": Path(__file__).parent.parent / "datasets" / "quantum" / "sonar.csv", - "target_col": -1, # Last column - "n_features": 4, - "has_header": False, - }, - "heart_disease": { - "path": Path(__file__).parent.parent - / "datasets" - / "quantum" - / "heart_disease.csv", - "target_col": -1, # Last column - "n_features": 4, - "missing_values": ["?"], - "has_header": False, - }, -} - -# Classical models to benchmark -CLASSICAL_MODELS = { - "SVM (RBF)": SVC(kernel="rbf", C=1.0, gamma="scale", random_state=42), - "SVM (Linear)": SVC(kernel="linear", C=1.0, random_state=42), - "Random Forest": RandomForestClassifier( - n_estimators=100, max_depth=10, random_state=42 - ), - "Neural Network": MLPClassifier( - hidden_layer_sizes=(16,), - activation="relu", - solver="adam", - learning_rate_init=0.001, - max_iter=1000, - random_state=42, - ), -} - -# Quantum model results for comparison (from previous training) -QUANTUM_RESULTS = { - "banknote": 1.0000, # 100.00% - "ionosphere": 0.8571, # 85.71% - "sonar": 0.7619, # 76.19% - "heart_disease": 0.9464, # 94.64% -} - - -def load_and_preprocess_dataset(dataset_name, config): - """Load and preprocess a dataset matching quantum training procedure""" - print(f"\n{'='*70}") - print(f"📁 Loading {dataset_name.upper()} Dataset") - print(f"{'='*70}") - - dataset_path = config["path"] - if not dataset_path.exists(): - raise FileNotFoundError(f"Dataset not found: {dataset_path}") - - # Load dataset - has_header = config.get("has_header", True) - header = "infer" if has_header else None - - if "missing_values" in config: - df = pd.read_csv( - dataset_path, header=header, na_values=config["missing_values"] - ) - else: - df = pd.read_csv(dataset_path, header=header) - - print(f" Samples: {len(df)}") - print(f" Features: {len(df.columns) - 1}") - - # Handle missing values - if df.isnull().sum().sum() > 0: - print(f" Missing values found: {df.isnull().sum().sum()}") - imputer = SimpleImputer(strategy="median") - numeric_cols = df.select_dtypes(include=[np.number]).columns - df[numeric_cols] = imputer.fit_transform(df[numeric_cols]) - print(" ✅ Missing values imputed") - - # Separate features and target - target_col = config["target_col"] - if target_col == -1: - # Last column is target - X = df.iloc[:, :-1].values - y = df.iloc[:, -1].values - else: - X = df.drop(columns=[target_col]).values - y = df[target_col].values - - # For heart disease, binarize target (0 = no disease, >0 = disease) - if dataset_name == "heart_disease": - y = (y > 0).astype(int) - - # Convert string labels to numeric if needed - if y.dtype == object: - from sklearn.preprocessing import LabelEncoder - - le = LabelEncoder() - y = le.fit_transform(y) - - print(f" Classes: {len(np.unique(y))} ({np.bincount(y)})") - - # Train/test split (80/20 like quantum training) - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42, stratify=y - ) - - # Standardize features - scaler = StandardScaler() - X_train_scaled = scaler.fit_transform(X_train) - X_test_scaled = scaler.transform(X_test) - - # Reduce to n_features using PCA (matching quantum qubit count) - n_features = config["n_features"] - pca = PCA(n_components=n_features, random_state=42) - X_train_final = pca.fit_transform(X_train_scaled) - X_test_final = pca.transform(X_test_scaled) - - explained_variance = pca.explained_variance_ratio_.sum() * 100 - print( - f" ✅ Reduced to {n_features} features (PCA variance: {explained_variance:.2f}%)" - ) - print(f" Train: {X_train_final.shape}, Test: {X_test_final.shape}") - - return X_train_final, X_test_final, y_train, y_test - - -def train_classical_model(model_name, model, X_train, X_test, y_train, y_test): - """Train and evaluate a classical ML model""" - print(f"\n🔧 Training {model_name}...") - - # Train model - model.fit(X_train, y_train) - - # Predictions - y_pred_train = model.predict(X_train) - y_pred_test = model.predict(X_test) - - # Metrics - train_acc = accuracy_score(y_train, y_pred_train) - test_acc = accuracy_score(y_test, y_pred_test) - - print(f" Train Accuracy: {train_acc:.4f} ({train_acc*100:.2f}%)") - print(f" Test Accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)") - - # Classification report - report = classification_report( - y_test, y_pred_test, output_dict=True, zero_division=0 - ) - - return { - "model_name": model_name, - "train_accuracy": float(train_acc), - "test_accuracy": float(test_acc), - "classification_report": report, - } - - -def benchmark_dataset(dataset_name, config): - """Benchmark all classical models on a dataset""" - print(f"\n{'#'*70}") - print(f"# BENCHMARKING: {dataset_name.upper()}") - print(f"{'#'*70}") - - # Load data - X_train, X_test, y_train, y_test = load_and_preprocess_dataset(dataset_name, config) - - # Train all classical models - results = [] - for model_name, model in CLASSICAL_MODELS.items(): - result = train_classical_model( - model_name, model, X_train, X_test, y_train, y_test - ) - result["dataset"] = dataset_name - results.append(result) - - # Add quantum result for comparison - quantum_acc = QUANTUM_RESULTS.get(dataset_name, 0.0) - results.append( - { - "dataset": dataset_name, - "model_name": "Quantum Hybrid QNN", - "train_accuracy": None, # Not tracked separately in quantum training - "test_accuracy": float(quantum_acc), - "classification_report": None, - } - ) - - # Summary table - print(f"\n{'='*70}") - print(f"📊 {dataset_name.upper()} - RESULTS SUMMARY") - print(f"{'='*70}") - print(f"{'Model':<25} {'Test Accuracy':<15} {'vs Quantum':<15}") - print(f"{'-'*70}") - - for result in results: - test_acc = result["test_accuracy"] - model_name = result["model_name"] - - if model_name == "Quantum Hybrid QNN": - vs_quantum = "BASELINE" - else: - diff = test_acc - quantum_acc - if diff > 0: - vs_quantum = f"+{diff*100:.2f}%" - else: - vs_quantum = f"{diff*100:.2f}%" - - print(f"{model_name:<25} {test_acc*100:>6.2f}% {vs_quantum:<15}") - - print(f"{'='*70}\n") - - return results - - -def generate_comparison_plots(all_results): - """Generate comparison visualizations""" - print("\n📊 Generating comparison plots...") - - # Prepare data for plotting - datasets = list(DATASETS.keys()) - model_names = list(CLASSICAL_MODELS.keys()) + ["Quantum Hybrid QNN"] - - # Create accuracy matrix - accuracy_matrix = np.zeros((len(datasets), len(model_names))) - - for i, dataset in enumerate(datasets): - dataset_results = [r for r in all_results if r["dataset"] == dataset] - for j, model_name in enumerate(model_names): - model_result = next( - (r for r in dataset_results if r["model_name"] == model_name), None - ) - if model_result: - accuracy_matrix[i, j] = model_result["test_accuracy"] * 100 - - # Create figure with subplots - fig, axes = plt.subplots(2, 2, figsize=(16, 12)) - fig.suptitle( - "Classical vs Quantum ML - Comprehensive Comparison", - fontsize=16, - fontweight="bold", - y=0.995, - ) - - # Plot 1: Heatmap - ax1 = axes[0, 0] - sns.heatmap( - accuracy_matrix, - annot=True, - fmt=".2f", - cmap="RdYlGn", - xticklabels=[m.replace(" ", "\n") for m in model_names], - yticklabels=[d.replace("_", " ").title() for d in datasets], - vmin=70, - vmax=100, - ax=ax1, - cbar_kws={"label": "Accuracy (%)"}, - ) - ax1.set_title("Accuracy Heatmap (All Models × All Datasets)", fontweight="bold") - ax1.set_ylabel("Dataset", fontweight="bold") - ax1.set_xlabel("Model", fontweight="bold") - - # Plot 2: Grouped bar chart - ax2 = axes[0, 1] - x = np.arange(len(datasets)) - width = 0.15 - - for i, model_name in enumerate(model_names): - accuracies = [accuracy_matrix[j, i] for j in range(len(datasets))] - offset = (i - len(model_names) / 2 + 0.5) * width - bars = ax2.bar(x + offset, accuracies, width, label=model_name) - - # Highlight quantum model - if model_name == "Quantum Hybrid QNN": - for bar in bars: - bar.set_edgecolor("red") - bar.set_linewidth(2) - - ax2.set_ylabel("Test Accuracy (%)", fontweight="bold") - ax2.set_xlabel("Dataset", fontweight="bold") - ax2.set_title( - "Model Performance Comparison (Grouped by Dataset)", fontweight="bold" - ) - ax2.set_xticks(x) - ax2.set_xticklabels([d.replace("_", " ").title() for d in datasets]) - ax2.legend(loc="lower right", fontsize=8) - ax2.grid(axis="y", alpha=0.3) - ax2.set_ylim([70, 105]) - - # Plot 3: Average performance - ax3 = axes[1, 0] - avg_accuracies = accuracy_matrix.mean(axis=0) - colors = ["#1f77b4"] * (len(model_names) - 1) + ["#ff7f0e"] # Quantum in orange - bars = ax3.barh(model_names, avg_accuracies, color=colors, edgecolor="black") - - # Add value labels - for i, (bar, acc) in enumerate(zip(bars, avg_accuracies)): - ax3.text(acc + 0.5, i, f"{acc:.2f}%", va="center", fontweight="bold") - - ax3.set_xlabel("Average Test Accuracy (%)", fontweight="bold") - ax3.set_title("Average Performance Across All Datasets", fontweight="bold") - ax3.set_xlim([70, 100]) - ax3.grid(axis="x", alpha=0.3) - - # Plot 4: Quantum advantage/disadvantage - ax4 = axes[1, 1] - quantum_idx = model_names.index("Quantum Hybrid QNN") - quantum_scores = accuracy_matrix[:, quantum_idx] - - best_classical_scores = np.max( - np.delete(accuracy_matrix, quantum_idx, axis=1), axis=1 - ) - advantages = quantum_scores - best_classical_scores - - colors_adv = ["green" if a > 0 else "red" for a in advantages] - bars = ax4.barh( - [d.replace("_", " ").title() for d in datasets], - advantages, - color=colors_adv, - edgecolor="black", - ) - - # Add value labels - for i, (bar, adv) in enumerate(zip(bars, advantages)): - x_pos = adv + (0.5 if adv > 0 else -0.5) - ax4.text(x_pos, i, f"{adv:+.2f}%", va="center", fontweight="bold") - - ax4.axvline(0, color="black", linewidth=2, linestyle="--") - ax4.set_xlabel("Quantum Advantage (% points vs Best Classical)", fontweight="bold") - ax4.set_title("Quantum vs Best Classical Model (by Dataset)", fontweight="bold") - ax4.grid(axis="x", alpha=0.3) - - plt.tight_layout() - - # Save plot - results_dir = Path(__file__).parent / "results" - results_dir.mkdir(exist_ok=True) - plot_path = results_dir / "classical_vs_quantum_comparison.png" - plt.savefig(plot_path, dpi=300, bbox_inches="tight") - print(f" ✅ Saved plot to: {plot_path}") - - return accuracy_matrix, avg_accuracies - - -def generate_markdown_report(all_results, accuracy_matrix, avg_accuracies): - """Generate detailed markdown report""" - print("\n📄 Generating markdown report...") - - report = [] - report.append("# Classical vs Quantum ML - Benchmark Report") - report.append(f"\n**Generated:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n") - - report.append("## Executive Summary\n") - report.append( - "This report compares quantum hybrid neural networks against classical ML baselines " - ) - report.append( - "across four quantum-classical datasets: banknote authentication, ionosphere radar, " - ) - report.append("sonar detection, and heart disease diagnosis.\n") - - # Overall winner - model_names = list(CLASSICAL_MODELS.keys()) + ["Quantum Hybrid QNN"] - best_model_idx = np.argmax(avg_accuracies) - best_model = model_names[best_model_idx] - best_avg = avg_accuracies[best_model_idx] - - report.append( - f"**🏆 Best Overall Model:** {best_model} ({best_avg:.2f}% average accuracy)\n" - ) - - # Quantum performance - quantum_idx = model_names.index("Quantum Hybrid QNN") - quantum_avg = avg_accuracies[quantum_idx] - quantum_rank = sorted(avg_accuracies, reverse=True).index(quantum_avg) + 1 - - report.append( - f"**⚛️ Quantum Model:** Ranked #{quantum_rank} with {quantum_avg:.2f}% average accuracy\n" - ) - - # Dataset-by-dataset results - report.append("\n## Detailed Results by Dataset\n") - - datasets = list(DATASETS.keys()) - for i, dataset in enumerate(datasets): - report.append(f"\n### {dataset.replace('_', ' ').title()}\n") - - dataset_results = [r for r in all_results if r["dataset"] == dataset] - sorted_results = sorted( - dataset_results, key=lambda x: x["test_accuracy"], reverse=True - ) - - report.append("| Rank | Model | Test Accuracy | vs Quantum |") - report.append("|------|-------|---------------|------------|") - - quantum_acc = QUANTUM_RESULTS[dataset] - for rank, result in enumerate(sorted_results, 1): - model_name = result["model_name"] - test_acc = result["test_accuracy"] - - if model_name == "Quantum Hybrid QNN": - vs_quantum = "BASELINE" - emoji = "⚛️" - else: - diff = test_acc - quantum_acc - vs_quantum = f"{diff*100:+.2f}%" - emoji = "🥇" if rank == 1 else "" - - report.append( - f"| {rank} | {emoji} {model_name} | {test_acc*100:.2f}% | {vs_quantum} |" - ) - - report.append("") - - # Average performance table - report.append("\n## Average Performance Across All Datasets\n") - report.append("| Rank | Model | Average Accuracy |") - report.append("|------|-------|------------------|") - - sorted_indices = np.argsort(avg_accuracies)[::-1] - for rank, idx in enumerate(sorted_indices, 1): - model_name = model_names[idx] - avg_acc = avg_accuracies[idx] - emoji = ( - "🥇" if rank == 1 else ("🥈" if rank == 2 else ("🥉" if rank == 3 else "")) - ) - if model_name == "Quantum Hybrid QNN": - emoji += " ⚛️" - report.append(f"| {rank} | {emoji} {model_name} | {avg_acc:.2f}% |") - - # Key insights - report.append("\n## Key Insights\n") - - quantum_wins = sum( - 1 - for i in range(len(datasets)) - if accuracy_matrix[i, quantum_idx] == accuracy_matrix[i].max() - ) - - report.append(f"- **Quantum Model Won:** {quantum_wins}/{len(datasets)} datasets\n") - - best_quantum_dataset = datasets[np.argmax(accuracy_matrix[:, quantum_idx])] - best_quantum_acc = accuracy_matrix[:, quantum_idx].max() - report.append( - f"- **Best Quantum Performance:** {best_quantum_dataset.replace('_', ' ').title()} ({best_quantum_acc:.2f}%)\n" - ) - - # Classical winner - classical_avg = np.delete(avg_accuracies, quantum_idx) - classical_names = [m for m in model_names if m != "Quantum Hybrid QNN"] - best_classical_idx = np.argmax(classical_avg) - best_classical = classical_names[best_classical_idx] - best_classical_avg = classical_avg[best_classical_idx] - - report.append( - f"- **Best Classical Model:** {best_classical} ({best_classical_avg:.2f}% average)\n" - ) - - # Quantum advantage - quantum_advantage = quantum_avg - best_classical_avg - if quantum_advantage > 0: - report.append( - f"- **⚛️ Quantum Advantage:** +{quantum_advantage:.2f}% over best classical\n" - ) - else: - report.append( - f"- **Classical Advantage:** {abs(quantum_advantage):.2f}% over quantum\n" - ) - - # Recommendations - report.append("\n## Recommendations\n") - - if quantum_wins >= len(datasets) / 2: - report.append("- ✅ **Quantum models show promise** on these datasets\n") - report.append( - "- 🚀 **Next step:** Deploy to Azure Quantum hardware for validation\n" - ) - else: - report.append("- ⚠️ **Classical models outperform quantum** on average\n") - report.append( - "- 🔧 **Next step:** Optimize quantum hyperparameters and entanglement patterns\n" - ) - - report.append( - "- 📊 **Hyperparameter tuning** could improve both classical and quantum models\n" - ) - report.append("- 🔄 **Cross-validation** would provide more robust estimates\n") - - # Save report - results_dir = Path(__file__).parent / "results" - results_dir.mkdir(exist_ok=True) - report_path = results_dir / "classical_vs_quantum_report.md" - - with open(report_path, "w", encoding="utf-8") as f: - f.write("\n".join(report)) - - print(f" ✅ Saved report to: {report_path}") - - return report_path - - -def save_results_json(all_results): - """Save complete results as JSON""" - results_dir = Path(__file__).parent / "results" - results_dir.mkdir(exist_ok=True) - - results_path = results_dir / "classical_vs_quantum_results.json" - - results_data = { - "timestamp": datetime.now().isoformat(), - "datasets": list(DATASETS.keys()), - "models": list(CLASSICAL_MODELS.keys()) + ["Quantum Hybrid QNN"], - "results": all_results, - "quantum_baseline": QUANTUM_RESULTS, - } - - with open(results_path, "w") as f: - json.dump(results_data, f, indent=2) - - print(f" ✅ Saved JSON results to: {results_path}") - - -def main(): - """Run complete classical vs quantum benchmark""" - print("=" * 70) - print(" CLASSICAL vs QUANTUM ML - COMPREHENSIVE BENCHMARK") - print("=" * 70) - print("\n🎯 Objective: Compare quantum hybrid models against classical baselines") - print(f" Datasets: {len(DATASETS)}") - print(f" Classical Models: {len(CLASSICAL_MODELS)}") - print(" Quantum Model: Hybrid QNN (4 qubits, 2 layers)") - - # Benchmark all datasets - all_results = [] - for dataset_name, config in DATASETS.items(): - try: - results = benchmark_dataset(dataset_name, config) - all_results.extend(results) - except Exception as e: - print(f"\n❌ Error benchmarking {dataset_name}: {e}") - continue - - if not all_results: - print("\n❌ No results to analyze!") - return - - # Generate visualizations - accuracy_matrix, avg_accuracies = generate_comparison_plots(all_results) - - # Generate report - generate_markdown_report(all_results, accuracy_matrix, avg_accuracies) - - # Save JSON - save_results_json(all_results) - - # Final summary - print("\n" + "=" * 70) - print(" ✅ BENCHMARK COMPLETE!") - print("=" * 70) - print("\n📁 Output Files:") - print(" - results/classical_vs_quantum_comparison.png") - print(" - results/classical_vs_quantum_report.md") - print(" - results/classical_vs_quantum_results.json") - print("\n💡 Next Steps:") - print(" 1. Review comparison plots and report") - print(" 2. Analyze quantum advantages/disadvantages") - print(" 3. Consider hyperparameter optimization") - print(" 4. Test on Azure Quantum hardware") - print("=" * 70) - - -if __name__ == "__main__": - main() +""" +Classical ML Baseline Comparison +Compare quantum models vs classical ML algorithms on all datasets + +This script trains classical ML baselines (SVM, Random Forest, Neural Network) +on the same datasets used for quantum training to validate quantum advantage. +""" + +import json +import warnings +from datetime import datetime +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from sklearn.decomposition import PCA +from sklearn.ensemble import RandomForestClassifier +from sklearn.impute import SimpleImputer +from sklearn.metrics import accuracy_score, classification_report +from sklearn.model_selection import train_test_split +from sklearn.neural_network import MLPClassifier +from sklearn.preprocessing import StandardScaler +from sklearn.svm import SVC + +warnings.filterwarnings("ignore") + +# Set random seed for reproducibility +np.random.seed(42) + +# Dataset configurations matching quantum training +DATASETS = { + "banknote": { + "path": Path(__file__).parent.parent / "datasets" / "quantum" / "banknote.csv", + "target_col": -1, # Last column + "n_features": 4, + "has_header": False, + }, + "ionosphere": { + "path": Path(__file__).parent.parent + / "datasets" + / "quantum" + / "ionosphere.csv", + "target_col": -1, # Last column + "n_features": 4, + "has_header": False, + }, + "sonar": { + "path": Path(__file__).parent.parent / "datasets" / "quantum" / "sonar.csv", + "target_col": -1, # Last column + "n_features": 4, + "has_header": False, + }, + "heart_disease": { + "path": Path(__file__).parent.parent + / "datasets" + / "quantum" + / "heart_disease.csv", + "target_col": -1, # Last column + "n_features": 4, + "missing_values": ["?"], + "has_header": False, + }, +} + +# Classical models to benchmark +CLASSICAL_MODELS = { + "SVM (RBF)": SVC(kernel="rbf", C=1.0, gamma="scale", random_state=42), + "SVM (Linear)": SVC(kernel="linear", C=1.0, random_state=42), + "Random Forest": RandomForestClassifier( + n_estimators=100, max_depth=10, random_state=42 + ), + "Neural Network": MLPClassifier( + hidden_layer_sizes=(16,), + activation="relu", + solver="adam", + learning_rate_init=0.001, + max_iter=1000, + random_state=42, + ), +} + +# Quantum model results for comparison (from previous training) +QUANTUM_RESULTS = { + "banknote": 1.0000, # 100.00% + "ionosphere": 0.8571, # 85.71% + "sonar": 0.7619, # 76.19% + "heart_disease": 0.9464, # 94.64% +} + + +def load_and_preprocess_dataset(dataset_name, config): + """Load and preprocess a dataset matching quantum training procedure""" + print(f"\n{'='*70}") + print(f"📁 Loading {dataset_name.upper()} Dataset") + print(f"{'='*70}") + + dataset_path = config["path"] + if not dataset_path.exists(): + raise FileNotFoundError(f"Dataset not found: {dataset_path}") + + # Load dataset + has_header = config.get("has_header", True) + header = "infer" if has_header else None + + if "missing_values" in config: + df = pd.read_csv( + dataset_path, header=header, na_values=config["missing_values"] + ) + else: + df = pd.read_csv(dataset_path, header=header) + + print(f" Samples: {len(df)}") + print(f" Features: {len(df.columns) - 1}") + + # Handle missing values + if df.isnull().sum().sum() > 0: + print(f" Missing values found: {df.isnull().sum().sum()}") + imputer = SimpleImputer(strategy="median") + numeric_cols = df.select_dtypes(include=[np.number]).columns + df[numeric_cols] = imputer.fit_transform(df[numeric_cols]) + print(" ✅ Missing values imputed") + + # Separate features and target + target_col = config["target_col"] + if target_col == -1: + # Last column is target + X = df.iloc[:, :-1].values + y = df.iloc[:, -1].values + else: + X = df.drop(columns=[target_col]).values + y = df[target_col].values + + # For heart disease, binarize target (0 = no disease, >0 = disease) + if dataset_name == "heart_disease": + y = (y > 0).astype(int) + + # Convert string labels to numeric if needed + if y.dtype == object: + from sklearn.preprocessing import LabelEncoder + + le = LabelEncoder() + y = le.fit_transform(y) + + print(f" Classes: {len(np.unique(y))} ({np.bincount(y)})") + + # Train/test split (80/20 like quantum training) + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + # Standardize features + scaler = StandardScaler() + X_train_scaled = scaler.fit_transform(X_train) + X_test_scaled = scaler.transform(X_test) + + # Reduce to n_features using PCA (matching quantum qubit count) + n_features = config["n_features"] + pca = PCA(n_components=n_features, random_state=42) + X_train_final = pca.fit_transform(X_train_scaled) + X_test_final = pca.transform(X_test_scaled) + + explained_variance = pca.explained_variance_ratio_.sum() * 100 + print( + f" ✅ Reduced to {n_features} features (PCA variance: {explained_variance:.2f}%)" + ) + print(f" Train: {X_train_final.shape}, Test: {X_test_final.shape}") + + return X_train_final, X_test_final, y_train, y_test + + +def train_classical_model(model_name, model, X_train, X_test, y_train, y_test): + """Train and evaluate a classical ML model""" + print(f"\n🔧 Training {model_name}...") + + # Train model + model.fit(X_train, y_train) + + # Predictions + y_pred_train = model.predict(X_train) + y_pred_test = model.predict(X_test) + + # Metrics + train_acc = accuracy_score(y_train, y_pred_train) + test_acc = accuracy_score(y_test, y_pred_test) + + print(f" Train Accuracy: {train_acc:.4f} ({train_acc*100:.2f}%)") + print(f" Test Accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)") + + # Classification report + report = classification_report( + y_test, y_pred_test, output_dict=True, zero_division=0 + ) + + return { + "model_name": model_name, + "train_accuracy": float(train_acc), + "test_accuracy": float(test_acc), + "classification_report": report, + } + + +def benchmark_dataset(dataset_name, config): + """Benchmark all classical models on a dataset""" + print(f"\n{'#'*70}") + print(f"# BENCHMARKING: {dataset_name.upper()}") + print(f"{'#'*70}") + + # Load data + X_train, X_test, y_train, y_test = load_and_preprocess_dataset(dataset_name, config) + + # Train all classical models + results = [] + for model_name, model in CLASSICAL_MODELS.items(): + result = train_classical_model( + model_name, model, X_train, X_test, y_train, y_test + ) + result["dataset"] = dataset_name + results.append(result) + + # Add quantum result for comparison + quantum_acc = QUANTUM_RESULTS.get(dataset_name, 0.0) + results.append( + { + "dataset": dataset_name, + "model_name": "Quantum Hybrid QNN", + "train_accuracy": None, # Not tracked separately in quantum training + "test_accuracy": float(quantum_acc), + "classification_report": None, + } + ) + + # Summary table + print(f"\n{'='*70}") + print(f"📊 {dataset_name.upper()} - RESULTS SUMMARY") + print(f"{'='*70}") + print(f"{'Model':<25} {'Test Accuracy':<15} {'vs Quantum':<15}") + print(f"{'-'*70}") + + for result in results: + test_acc = result["test_accuracy"] + model_name = result["model_name"] + + if model_name == "Quantum Hybrid QNN": + vs_quantum = "BASELINE" + else: + diff = test_acc - quantum_acc + if diff > 0: + vs_quantum = f"+{diff*100:.2f}%" + else: + vs_quantum = f"{diff*100:.2f}%" + + print(f"{model_name:<25} {test_acc*100:>6.2f}% {vs_quantum:<15}") + + print(f"{'='*70}\n") + + return results + + +def generate_comparison_plots(all_results): + """Generate comparison visualizations""" + print("\n📊 Generating comparison plots...") + + # Prepare data for plotting + datasets = list(DATASETS.keys()) + model_names = list(CLASSICAL_MODELS.keys()) + ["Quantum Hybrid QNN"] + + # Create accuracy matrix + accuracy_matrix = np.zeros((len(datasets), len(model_names))) + + for i, dataset in enumerate(datasets): + dataset_results = [r for r in all_results if r["dataset"] == dataset] + for j, model_name in enumerate(model_names): + model_result = next( + (r for r in dataset_results if r["model_name"] == model_name), None + ) + if model_result: + accuracy_matrix[i, j] = model_result["test_accuracy"] * 100 + + # Create figure with subplots + fig, axes = plt.subplots(2, 2, figsize=(16, 12)) + fig.suptitle( + "Classical vs Quantum ML - Comprehensive Comparison", + fontsize=16, + fontweight="bold", + y=0.995, + ) + + # Plot 1: Heatmap + ax1 = axes[0, 0] + sns.heatmap( + accuracy_matrix, + annot=True, + fmt=".2f", + cmap="RdYlGn", + xticklabels=[m.replace(" ", "\n") for m in model_names], + yticklabels=[d.replace("_", " ").title() for d in datasets], + vmin=70, + vmax=100, + ax=ax1, + cbar_kws={"label": "Accuracy (%)"}, + ) + ax1.set_title("Accuracy Heatmap (All Models × All Datasets)", fontweight="bold") + ax1.set_ylabel("Dataset", fontweight="bold") + ax1.set_xlabel("Model", fontweight="bold") + + # Plot 2: Grouped bar chart + ax2 = axes[0, 1] + x = np.arange(len(datasets)) + width = 0.15 + + for i, model_name in enumerate(model_names): + accuracies = [accuracy_matrix[j, i] for j in range(len(datasets))] + offset = (i - len(model_names) / 2 + 0.5) * width + bars = ax2.bar(x + offset, accuracies, width, label=model_name) + + # Highlight quantum model + if model_name == "Quantum Hybrid QNN": + for bar in bars: + bar.set_edgecolor("red") + bar.set_linewidth(2) + + ax2.set_ylabel("Test Accuracy (%)", fontweight="bold") + ax2.set_xlabel("Dataset", fontweight="bold") + ax2.set_title( + "Model Performance Comparison (Grouped by Dataset)", fontweight="bold" + ) + ax2.set_xticks(x) + ax2.set_xticklabels([d.replace("_", " ").title() for d in datasets]) + ax2.legend(loc="lower right", fontsize=8) + ax2.grid(axis="y", alpha=0.3) + ax2.set_ylim([70, 105]) + + # Plot 3: Average performance + ax3 = axes[1, 0] + avg_accuracies = accuracy_matrix.mean(axis=0) + colors = ["#1f77b4"] * (len(model_names) - 1) + ["#ff7f0e"] # Quantum in orange + bars = ax3.barh(model_names, avg_accuracies, color=colors, edgecolor="black") + + # Add value labels + for i, (bar, acc) in enumerate(zip(bars, avg_accuracies)): + ax3.text(acc + 0.5, i, f"{acc:.2f}%", va="center", fontweight="bold") + + ax3.set_xlabel("Average Test Accuracy (%)", fontweight="bold") + ax3.set_title("Average Performance Across All Datasets", fontweight="bold") + ax3.set_xlim([70, 100]) + ax3.grid(axis="x", alpha=0.3) + + # Plot 4: Quantum advantage/disadvantage + ax4 = axes[1, 1] + quantum_idx = model_names.index("Quantum Hybrid QNN") + quantum_scores = accuracy_matrix[:, quantum_idx] + + best_classical_scores = np.max( + np.delete(accuracy_matrix, quantum_idx, axis=1), axis=1 + ) + advantages = quantum_scores - best_classical_scores + + colors_adv = ["green" if a > 0 else "red" for a in advantages] + bars = ax4.barh( + [d.replace("_", " ").title() for d in datasets], + advantages, + color=colors_adv, + edgecolor="black", + ) + + # Add value labels + for i, (bar, adv) in enumerate(zip(bars, advantages)): + x_pos = adv + (0.5 if adv > 0 else -0.5) + ax4.text(x_pos, i, f"{adv:+.2f}%", va="center", fontweight="bold") + + ax4.axvline(0, color="black", linewidth=2, linestyle="--") + ax4.set_xlabel("Quantum Advantage (% points vs Best Classical)", fontweight="bold") + ax4.set_title("Quantum vs Best Classical Model (by Dataset)", fontweight="bold") + ax4.grid(axis="x", alpha=0.3) + + plt.tight_layout() + + # Save plot + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + plot_path = results_dir / "classical_vs_quantum_comparison.png" + plt.savefig(plot_path, dpi=300, bbox_inches="tight") + print(f" ✅ Saved plot to: {plot_path}") + + return accuracy_matrix, avg_accuracies + + +def generate_markdown_report(all_results, accuracy_matrix, avg_accuracies): + """Generate detailed markdown report""" + print("\n📄 Generating markdown report...") + + report = [] + report.append("# Classical vs Quantum ML - Benchmark Report") + report.append(f"\n**Generated:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n") + + report.append("## Executive Summary\n") + report.append( + "This report compares quantum hybrid neural networks against classical ML baselines " + ) + report.append( + "across four quantum-classical datasets: banknote authentication, ionosphere radar, " + ) + report.append("sonar detection, and heart disease diagnosis.\n") + + # Overall winner + model_names = list(CLASSICAL_MODELS.keys()) + ["Quantum Hybrid QNN"] + best_model_idx = np.argmax(avg_accuracies) + best_model = model_names[best_model_idx] + best_avg = avg_accuracies[best_model_idx] + + report.append( + f"**🏆 Best Overall Model:** {best_model} ({best_avg:.2f}% average accuracy)\n" + ) + + # Quantum performance + quantum_idx = model_names.index("Quantum Hybrid QNN") + quantum_avg = avg_accuracies[quantum_idx] + quantum_rank = sorted(avg_accuracies, reverse=True).index(quantum_avg) + 1 + + report.append( + f"**⚛️ Quantum Model:** Ranked #{quantum_rank} with {quantum_avg:.2f}% average accuracy\n" + ) + + # Dataset-by-dataset results + report.append("\n## Detailed Results by Dataset\n") + + datasets = list(DATASETS.keys()) + for i, dataset in enumerate(datasets): + report.append(f"\n### {dataset.replace('_', ' ').title()}\n") + + dataset_results = [r for r in all_results if r["dataset"] == dataset] + sorted_results = sorted( + dataset_results, key=lambda x: x["test_accuracy"], reverse=True + ) + + report.append("| Rank | Model | Test Accuracy | vs Quantum |") + report.append("|------|-------|---------------|------------|") + + quantum_acc = QUANTUM_RESULTS[dataset] + for rank, result in enumerate(sorted_results, 1): + model_name = result["model_name"] + test_acc = result["test_accuracy"] + + if model_name == "Quantum Hybrid QNN": + vs_quantum = "BASELINE" + emoji = "⚛️" + else: + diff = test_acc - quantum_acc + vs_quantum = f"{diff*100:+.2f}%" + emoji = "🥇" if rank == 1 else "" + + report.append( + f"| {rank} | {emoji} {model_name} | {test_acc*100:.2f}% | {vs_quantum} |" + ) + + report.append("") + + # Average performance table + report.append("\n## Average Performance Across All Datasets\n") + report.append("| Rank | Model | Average Accuracy |") + report.append("|------|-------|------------------|") + + sorted_indices = np.argsort(avg_accuracies)[::-1] + for rank, idx in enumerate(sorted_indices, 1): + model_name = model_names[idx] + avg_acc = avg_accuracies[idx] + emoji = ( + "🥇" if rank == 1 else ("🥈" if rank == 2 else ("🥉" if rank == 3 else "")) + ) + if model_name == "Quantum Hybrid QNN": + emoji += " ⚛️" + report.append(f"| {rank} | {emoji} {model_name} | {avg_acc:.2f}% |") + + # Key insights + report.append("\n## Key Insights\n") + + quantum_wins = sum( + 1 + for i in range(len(datasets)) + if accuracy_matrix[i, quantum_idx] == accuracy_matrix[i].max() + ) + + report.append(f"- **Quantum Model Won:** {quantum_wins}/{len(datasets)} datasets\n") + + best_quantum_dataset = datasets[np.argmax(accuracy_matrix[:, quantum_idx])] + best_quantum_acc = accuracy_matrix[:, quantum_idx].max() + report.append( + f"- **Best Quantum Performance:** {best_quantum_dataset.replace('_', ' ').title()} ({best_quantum_acc:.2f}%)\n" + ) + + # Classical winner + classical_avg = np.delete(avg_accuracies, quantum_idx) + classical_names = [m for m in model_names if m != "Quantum Hybrid QNN"] + best_classical_idx = np.argmax(classical_avg) + best_classical = classical_names[best_classical_idx] + best_classical_avg = classical_avg[best_classical_idx] + + report.append( + f"- **Best Classical Model:** {best_classical} ({best_classical_avg:.2f}% average)\n" + ) + + # Quantum advantage + quantum_advantage = quantum_avg - best_classical_avg + if quantum_advantage > 0: + report.append( + f"- **⚛️ Quantum Advantage:** +{quantum_advantage:.2f}% over best classical\n" + ) + else: + report.append( + f"- **Classical Advantage:** {abs(quantum_advantage):.2f}% over quantum\n" + ) + + # Recommendations + report.append("\n## Recommendations\n") + + if quantum_wins >= len(datasets) / 2: + report.append("- ✅ **Quantum models show promise** on these datasets\n") + report.append( + "- 🚀 **Next step:** Deploy to Azure Quantum hardware for validation\n" + ) + else: + report.append("- ⚠️ **Classical models outperform quantum** on average\n") + report.append( + "- 🔧 **Next step:** Optimize quantum hyperparameters and entanglement patterns\n" + ) + + report.append( + "- 📊 **Hyperparameter tuning** could improve both classical and quantum models\n" + ) + report.append("- 🔄 **Cross-validation** would provide more robust estimates\n") + + # Save report + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + report_path = results_dir / "classical_vs_quantum_report.md" + + with open(report_path, "w", encoding="utf-8") as f: + f.write("\n".join(report)) + + print(f" ✅ Saved report to: {report_path}") + + return report_path + + +def save_results_json(all_results): + """Save complete results as JSON""" + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + + results_path = results_dir / "classical_vs_quantum_results.json" + + results_data = { + "timestamp": datetime.now().isoformat(), + "datasets": list(DATASETS.keys()), + "models": list(CLASSICAL_MODELS.keys()) + ["Quantum Hybrid QNN"], + "results": all_results, + "quantum_baseline": QUANTUM_RESULTS, + } + + with open(results_path, "w") as f: + json.dump(results_data, f, indent=2) + + print(f" ✅ Saved JSON results to: {results_path}") + + +def main(): + """Run complete classical vs quantum benchmark""" + print("=" * 70) + print(" CLASSICAL vs QUANTUM ML - COMPREHENSIVE BENCHMARK") + print("=" * 70) + print("\n🎯 Objective: Compare quantum hybrid models against classical baselines") + print(f" Datasets: {len(DATASETS)}") + print(f" Classical Models: {len(CLASSICAL_MODELS)}") + print(" Quantum Model: Hybrid QNN (4 qubits, 2 layers)") + + # Benchmark all datasets + all_results = [] + for dataset_name, config in DATASETS.items(): + try: + results = benchmark_dataset(dataset_name, config) + all_results.extend(results) + except Exception as e: + print(f"\n❌ Error benchmarking {dataset_name}: {e}") + continue + + if not all_results: + print("\n❌ No results to analyze!") + return + + # Generate visualizations + accuracy_matrix, avg_accuracies = generate_comparison_plots(all_results) + + # Generate report + generate_markdown_report(all_results, accuracy_matrix, avg_accuracies) + + # Save JSON + save_results_json(all_results) + + # Final summary + print("\n" + "=" * 70) + print(" ✅ BENCHMARK COMPLETE!") + print("=" * 70) + print("\n📁 Output Files:") + print(" - results/classical_vs_quantum_comparison.png") + print(" - results/classical_vs_quantum_report.md") + print(" - results/classical_vs_quantum_results.json") + print("\n💡 Next Steps:") + print(" 1. Review comparison plots and report") + print(" 2. Analyze quantum advantages/disadvantages") + print(" 3. Consider hyperparameter optimization") + print(" 4. Test on Azure Quantum hardware") + print("=" * 70) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/config/quantum_config.quick.yaml b/ai-projects/quantum-ml/config/quantum_config.quick.yaml index 3df992f4f..e22fb910a 100644 --- a/ai-projects/quantum-ml/config/quantum_config.quick.yaml +++ b/ai-projects/quantum-ml/config/quantum_config.quick.yaml @@ -1,31 +1,31 @@ -azure: - location: eastus - resource_group: rg-quantum-ai - storage_account: quantumstorage - subscription_id: a07fbd16-e722-446d-8efd-0681e85b725c - workspace_name: quantum-ai-workspace -logging: - level: INFO - results_dir: ./results - save_results: true -ml: - data: - feature_dimension: 4 - train_test_split: 0.8 - model: - entanglement: full - n_layers: 4 - n_qubits: 8 - training: - batch_size: 32 - epochs: 2 - learning_rate: 0.01 - validation_split: 0.2 -quantum: - hardware: - optimization_level: 2 - shots: 500 - provider: ionq - simulator: - backend: default.qubit - shots: 1024 +azure: + location: eastus + resource_group: rg-quantum-ai + storage_account: quantumstorage + subscription_id: a07fbd16-e722-446d-8efd-0681e85b725c + workspace_name: quantum-ai-workspace +logging: + level: INFO + results_dir: ./results + save_results: true +ml: + data: + feature_dimension: 4 + train_test_split: 0.8 + model: + entanglement: full + n_layers: 4 + n_qubits: 8 + training: + batch_size: 32 + epochs: 2 + learning_rate: 0.01 + validation_split: 0.2 +quantum: + hardware: + optimization_level: 2 + shots: 500 + provider: ionq + simulator: + backend: default.qubit + shots: 1024 diff --git a/ai-projects/quantum-ml/config/quantum_config.yaml b/ai-projects/quantum-ml/config/quantum_config.yaml index c85a7f59c..06ca4c721 100644 --- a/ai-projects/quantum-ml/config/quantum_config.yaml +++ b/ai-projects/quantum-ml/config/quantum_config.yaml @@ -1,31 +1,31 @@ -azure: - location: eastus - resource_group: rg-quantum-ai - storage_account: quantumstorage - subscription_id: 'a07fbd16-e722-446d-8efd-0681e85b725c' - workspace_name: quantum-ai-workspace -logging: - level: INFO - results_dir: ./results - save_results: true -ml: - data: - feature_dimension: 4 - train_test_split: 0.8 - model: - entanglement: linear - n_layers: 2 - n_qubits: 4 - training: - batch_size: 8 - epochs: 3 - learning_rate: 0.01 - validation_split: 0.2 -quantum: - hardware: - optimization_level: 2 - shots: 500 - provider: ionq - simulator: - backend: lightning.qubit - shots: 128 +azure: + location: eastus + resource_group: rg-quantum-ai + storage_account: quantumstorage + subscription_id: 'a07fbd16-e722-446d-8efd-0681e85b725c' + workspace_name: quantum-ai-workspace +logging: + level: INFO + results_dir: ./results + save_results: true +ml: + data: + feature_dimension: 4 + train_test_split: 0.8 + model: + entanglement: linear + n_layers: 2 + n_qubits: 4 + training: + batch_size: 8 + epochs: 3 + learning_rate: 0.01 + validation_split: 0.2 +quantum: + hardware: + optimization_level: 2 + shots: 500 + provider: ionq + simulator: + backend: lightning.qubit + shots: 128 diff --git a/ai-projects/quantum-ml/dataset_architecture_analyzer.py b/ai-projects/quantum-ml/dataset_architecture_analyzer.py index 1ab99ed41..cc3c124af 100644 --- a/ai-projects/quantum-ml/dataset_architecture_analyzer.py +++ b/ai-projects/quantum-ml/dataset_architecture_analyzer.py @@ -1,481 +1,481 @@ -""" -Dataset-Architecture Analyzer -============================== - -Analyzes all quantum datasets and recommends optimal architectures: -- Qubit count (4-6 based on feature complexity) -- Layer count (2-4 based on dataset difficulty) -- Training parameters (batch size, learning rate) - -Uses heuristics based on: -- Number of features (high → more qubits) -- Number of samples (small → fewer layers to avoid overfitting) -- Class distribution (imbalanced → lower learning rate) -- Task complexity (multi-class → more layers) - -Author: Quantum AI System -Date: November 16, 2025 -""" - -import json -from datetime import datetime -from pathlib import Path - -import numpy as np -import pandas as pd -from sklearn.preprocessing import LabelEncoder - -# Dataset metadata (27 working datasets) -DATASET_INFO = { - # Original 4 - "ionosphere": { - "category": "physics", - "task_type": "binary", - "difficulty": "medium", - }, - "banknote": {"category": "forensics", "task_type": "binary", "difficulty": "easy"}, - "heart_disease": { - "category": "medical", - "task_type": "binary", - "difficulty": "medium", - }, - "sonar": {"category": "geophysics", "task_type": "binary", "difficulty": "hard"}, - # Medical (13 total including statlog_heart) - "breast_cancer": { - "category": "medical", - "task_type": "binary", - "difficulty": "medium", - }, - "diabetes": {"category": "medical", "task_type": "binary", "difficulty": "medium"}, - "blood_transfusion": { - "category": "medical", - "task_type": "binary", - "difficulty": "hard", - }, - "haberman": {"category": "medical", "task_type": "binary", "difficulty": "hard"}, - "parkinsons": { - "category": "medical", - "task_type": "binary", - "difficulty": "medium", - }, - "dermatology": { - "category": "medical", - "task_type": "multiclass", - "difficulty": "medium", - }, - "liver_disorders": { - "category": "medical", - "task_type": "binary", - "difficulty": "medium", - }, - "thyroid": { - "category": "medical", - "task_type": "multiclass", - "difficulty": "medium", - }, - "statlog_heart": { - "category": "medical", - "task_type": "binary", - "difficulty": "medium", - }, - # Biology (1 - ecoli corrupted) - "yeast": {"category": "biology", "task_type": "multiclass", "difficulty": "hard"}, - # Chemistry (3) - "wine_red": { - "category": "chemistry", - "task_type": "multiclass", - "difficulty": "hard", - }, - "wine_white": { - "category": "chemistry", - "task_type": "multiclass", - "difficulty": "hard", - }, - "wine_quality_combined": { - "category": "chemistry", - "task_type": "multiclass", - "difficulty": "hard", - }, - # Image Features (4) - "iris": {"category": "biology", "task_type": "multiclass", "difficulty": "easy"}, - "optical_recognition": { - "category": "image", - "task_type": "multiclass", - "difficulty": "hard", - }, - "pendigits": { - "category": "image", - "task_type": "multiclass", - "difficulty": "medium", - }, - # Agriculture (2) - "wheat_seeds": { - "category": "agriculture", - "task_type": "multiclass", - "difficulty": "medium", - }, - "seeds": { - "category": "agriculture", - "task_type": "multiclass", - "difficulty": "medium", - }, - # Finance (1) - "statlog_australian": { - "category": "finance", - "task_type": "binary", - "difficulty": "medium", - }, - # Physics (2 - including balance_scale) - "magic_gamma": { - "category": "physics", - "task_type": "binary", - "difficulty": "medium", - }, - "balance_scale": { - "category": "physics", - "task_type": "multiclass", - "difficulty": "easy", - }, - # Social Science (1) - "contraceptive": { - "category": "social", - "task_type": "multiclass", - "difficulty": "medium", - }, - # Forensics (1) - "glass": {"category": "forensics", "task_type": "multiclass", "difficulty": "hard"}, -} - - -def analyze_dataset(dataset_name): - """Analyze a dataset and recommend architecture""" - dataset_path = ( - Path(__file__).parent.parent / "datasets" / "quantum" / f"{dataset_name}.csv" - ) - - # Load dataset with specific strategies - try: - if dataset_name in {"wine_red", "wine_white"}: - df = pd.read_csv(dataset_path, sep=";", na_values=["?", "NA", "", "NaN"]) - elif dataset_name == "wine_quality_combined": - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - elif dataset_name in {"wheat_seeds", "seeds"}: - df = pd.read_csv( - dataset_path, sep=r"\s+", header=None, na_values=["?", "NA", "", "NaN"] - ) - elif dataset_name == "yeast": - df = pd.read_csv( - dataset_path, sep=r"\s+", header=None, na_values=["?", "NA", "", "NaN"] - ) - df = df.iloc[:, 1:] # Skip sequence name - elif dataset_name == "parkinsons": - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - df = df.drop(columns=df.columns[0]) # Skip name column - elif dataset_name in {"statlog_australian", "statlog_heart"}: - df = pd.read_csv( - dataset_path, sep=" ", header=None, na_values=["?", "NA", "", "NaN"] - ) - elif dataset_name == "blood_transfusion": - df = pd.read_csv(dataset_path, skiprows=1, na_values=["?", "NA", "", "NaN"]) - elif dataset_name == "breast_cancer": - df = pd.read_csv( - dataset_path, header=None, na_values=["?", "NA", "", "NaN"] - ) - elif dataset_name == "balance_scale": - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - elif dataset_name in { - "optical_recognition", - "pendigits", - "contraceptive", - "dermatology", - "liver_disorders", - "thyroid", - }: - df = pd.read_csv( - dataset_path, header=None, na_values=["?", "NA", "", "NaN"] - ) - else: - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - except Exception as e: - raise ValueError(f"Failed to load {dataset_name}: {str(e)}") from e - n_samples = len(df) - n_features = df.shape[1] - 1 # Exclude target - - # Target analysis - y = df.iloc[:, -1] - if y.dtype == object: - le = LabelEncoder() - y_encoded = le.fit_transform(y) - n_classes = len(le.classes_) - class_counts = np.bincount(y_encoded) - else: - n_classes = len(y.unique()) - class_counts = y.value_counts().values - - # Class imbalance ratio - imbalance_ratio = ( - class_counts.max() / class_counts.min() if len(class_counts) > 1 else 1.0 - ) - - # Missing values - missing_ratio = df.isnull().sum().sum() / (df.shape[0] * df.shape[1]) - - # Get metadata - meta = DATASET_INFO.get(dataset_name, {}) - task_type = meta.get("task_type", "binary" if n_classes == 2 else "multiclass") - difficulty = meta.get("difficulty", "medium") - - # === ARCHITECTURE RECOMMENDATIONS === - - # 1. Qubit Count (4-6) - if n_features <= 4: - n_qubits = 4 - qubit_reasoning = "Low feature count (≤4) - use 4 qubits minimum" - elif n_features <= 10: - n_qubits = 4 - qubit_reasoning = "Medium feature count (5-10) - standard 4 qubits" - elif n_features <= 20: - n_qubits = 5 - qubit_reasoning = ( - "High feature count (11-20) - use 5 qubits for better representation" - ) - else: - n_qubits = 6 - qubit_reasoning = ( - "Very high feature count (>20) - use 6 qubits for dimensionality" - ) - - # 2. Layer Count (2-4) - if n_samples < 300: - n_layers = 2 - layer_reasoning = ( - "Small dataset (<300 samples) - use 2 layers to avoid overfitting" - ) - elif task_type == "multiclass" and n_classes > 3: - n_layers = 4 - layer_reasoning = ( - f"Multi-class ({n_classes} classes) - use 4 layers for complexity" - ) - elif difficulty == "hard": - n_layers = 3 - layer_reasoning = "Hard task - use 3 layers for learning capacity" - else: - n_layers = 2 - layer_reasoning = "Standard task - use 2 layers (efficient baseline)" - - # 3. Hidden Dimension (16 or 32) - if n_samples < 500: - hidden_dim = 16 - hidden_reasoning = "Small dataset - use hidden_dim=16 to reduce parameters" - elif n_features > 15: - hidden_dim = 32 - hidden_reasoning = "High-dimensional input - use hidden_dim=32 for capacity" - else: - hidden_dim = 16 - hidden_reasoning = "Standard configuration - use hidden_dim=16" - - # 4. Learning Rate (0.0005 or 0.001) - if imbalance_ratio > 3.0: - learning_rate = 0.0005 - lr_reasoning = f"Imbalanced classes (ratio: {imbalance_ratio:.1f}) - lower LR for stability" - elif n_samples < 300: - learning_rate = 0.0005 - lr_reasoning = "Small dataset - lower LR to avoid overfitting" - else: - learning_rate = 0.001 - lr_reasoning = "Balanced dataset - standard LR for faster convergence" - - # 5. Batch Size (8 or 16) - if n_samples < 300: - batch_size = 8 - batch_reasoning = "Small dataset - use batch_size=8 for more gradient updates" - elif n_samples > 5000: - batch_size = 32 - batch_reasoning = "Large dataset - use batch_size=32 for efficiency" - else: - batch_size = 16 - batch_reasoning = "Medium dataset - standard batch_size=16" - - # 6. Epoch Count - if difficulty == "hard" or task_type == "multiclass": - epochs = 50 - epoch_reasoning = "Complex task - train for 50 epochs" - elif n_samples < 300: - epochs = 30 - epoch_reasoning = "Small dataset - 30 epochs to avoid overfitting" - else: - epochs = 40 - epoch_reasoning = "Standard task - 40 epochs for convergence" - - # Calculate expected PCA variance retained - if n_features > n_qubits: - # Estimate based on typical variance distributions - if n_features <= 10: - pca_variance = 0.90 - elif n_features <= 20: - pca_variance = 0.85 - else: - pca_variance = 0.80 - else: - pca_variance = 1.0 - - return { - "dataset": dataset_name, - "statistics": { - "samples": int(n_samples), - "features": int(n_features), - "classes": int(n_classes), - "task_type": task_type, - "difficulty": difficulty, - "imbalance_ratio": float(imbalance_ratio), - "missing_ratio": float(missing_ratio), - "class_distribution": [int(c) for c in class_counts], - }, - "recommended_architecture": { - "n_qubits": n_qubits, - "n_quantum_layers": n_layers, - "hidden_dim": hidden_dim, - "learning_rate": learning_rate, - "batch_size": batch_size, - "epochs": epochs, - "expected_pca_variance": float(pca_variance), - }, - "reasoning": { - "qubits": qubit_reasoning, - "layers": layer_reasoning, - "hidden_dim": hidden_reasoning, - "learning_rate": lr_reasoning, - "batch_size": batch_reasoning, - "epochs": epoch_reasoning, - }, - } - - -def main(): - print("=" * 70) - print(" DATASET-ARCHITECTURE ANALYZER") - print("=" * 70) - print("\nAnalyzing all 15 quantum datasets...\n") - - all_results = [] - - # Analyze each dataset - for dataset_name in sorted(DATASET_INFO.keys()): - print(f"📊 {dataset_name}") - print("-" * 70) - - try: - result = analyze_dataset(dataset_name) - all_results.append(result) - - stats = result["statistics"] - arch = result["recommended_architecture"] - reason = result["reasoning"] - - # Print statistics - print( - f" Samples: {stats['samples']:,} | Features: {stats['features']} | " - f"Classes: {stats['classes']} | Type: {stats['task_type']}" - ) - print( - f" Difficulty: {stats['difficulty']} | Imbalance: {stats['imbalance_ratio']:.2f}x" - ) - - # Print recommendations - print("\n 🎯 RECOMMENDED ARCHITECTURE:") - print(f" Qubits: {arch['n_qubits']} → {reason['qubits']}") - print(f" Layers: {arch['n_quantum_layers']} → {reason['layers']}") - print(f" Hidden: {arch['hidden_dim']} → {reason['hidden_dim']}") - print( - f" Learn Rate: {arch['learning_rate']} → {reason['learning_rate']}" - ) - print(f" Batch: {arch['batch_size']} → {reason['batch_size']}") - print(f" Epochs: {arch['epochs']} → {reason['epochs']}") - - if stats["features"] > arch["n_qubits"]: - print( - f" PCA: {stats['features']}→{arch['n_qubits']} features " - f"({arch['expected_pca_variance']:.0%} variance)" - ) - - print() - - except Exception as e: - print(f" ❌ ERROR: {e}\n") - - # Summary by category - print("=" * 70) - print(" SUMMARY BY CATEGORY") - print("=" * 70) - - categories = {} - for result in all_results: - cat = DATASET_INFO[result["dataset"]]["category"] - if cat not in categories: - categories[cat] = [] - categories[cat].append(result) - - for category, datasets in sorted(categories.items()): - print(f"\n📁 {category.upper()} ({len(datasets)} datasets)") - print("-" * 70) - for ds in datasets: - arch = ds["recommended_architecture"] - stats = ds["statistics"] - print( - f" {ds['dataset']:20s} | {stats['samples']:5d} samples | " - f"{stats['features']:2d}→{arch['n_qubits']} features | " - f"{arch['n_quantum_layers']} layers | " - f"dim={arch['hidden_dim']} | " - f"lr={arch['learning_rate']}" - ) - - # Architecture distribution - print("\n" + "=" * 70) - print(" ARCHITECTURE DISTRIBUTION") - print("=" * 70) - - qubit_dist = {} - layer_dist = {} - for result in all_results: - arch = result["recommended_architecture"] - qubit_dist[arch["n_qubits"]] = qubit_dist.get(arch["n_qubits"], 0) + 1 - layer_dist[arch["n_quantum_layers"]] = ( - layer_dist.get(arch["n_quantum_layers"], 0) + 1 - ) - - print("\n🔢 Qubit Distribution:") - for qubits, count in sorted(qubit_dist.items()): - pct = 100 * count / len(all_results) - print(f" {qubits} qubits: {count} datasets ({pct:.0f}%)") - - print("\n🔢 Layer Distribution:") - for layers, count in sorted(layer_dist.items()): - pct = 100 * count / len(all_results) - print(f" {layers} layers: {count} datasets ({pct:.0f}%)") - - # Save results - output_file = Path(__file__).parent / "results" / "architecture_analysis.json" - output_file.parent.mkdir(exist_ok=True) - - output_data = { - "timestamp": datetime.now().isoformat(), - "total_datasets": len(all_results), - "analyses": all_results, - "distributions": { - "qubits": qubit_dist, - "layers": layer_dist, - }, - } - - with open(output_file, "w") as f: - json.dump(output_data, f, indent=2) - - print(f"\n📄 Analysis saved: {output_file}") - - print("\n" + "=" * 70) - print(" ✅ ANALYSIS COMPLETE") - print("=" * 70) - - -if __name__ == "__main__": - main() +""" +Dataset-Architecture Analyzer +============================== + +Analyzes all quantum datasets and recommends optimal architectures: +- Qubit count (4-6 based on feature complexity) +- Layer count (2-4 based on dataset difficulty) +- Training parameters (batch size, learning rate) + +Uses heuristics based on: +- Number of features (high → more qubits) +- Number of samples (small → fewer layers to avoid overfitting) +- Class distribution (imbalanced → lower learning rate) +- Task complexity (multi-class → more layers) + +Author: Quantum AI System +Date: November 16, 2025 +""" + +import json +from datetime import datetime +from pathlib import Path + +import numpy as np +import pandas as pd +from sklearn.preprocessing import LabelEncoder + +# Dataset metadata (27 working datasets) +DATASET_INFO = { + # Original 4 + "ionosphere": { + "category": "physics", + "task_type": "binary", + "difficulty": "medium", + }, + "banknote": {"category": "forensics", "task_type": "binary", "difficulty": "easy"}, + "heart_disease": { + "category": "medical", + "task_type": "binary", + "difficulty": "medium", + }, + "sonar": {"category": "geophysics", "task_type": "binary", "difficulty": "hard"}, + # Medical (13 total including statlog_heart) + "breast_cancer": { + "category": "medical", + "task_type": "binary", + "difficulty": "medium", + }, + "diabetes": {"category": "medical", "task_type": "binary", "difficulty": "medium"}, + "blood_transfusion": { + "category": "medical", + "task_type": "binary", + "difficulty": "hard", + }, + "haberman": {"category": "medical", "task_type": "binary", "difficulty": "hard"}, + "parkinsons": { + "category": "medical", + "task_type": "binary", + "difficulty": "medium", + }, + "dermatology": { + "category": "medical", + "task_type": "multiclass", + "difficulty": "medium", + }, + "liver_disorders": { + "category": "medical", + "task_type": "binary", + "difficulty": "medium", + }, + "thyroid": { + "category": "medical", + "task_type": "multiclass", + "difficulty": "medium", + }, + "statlog_heart": { + "category": "medical", + "task_type": "binary", + "difficulty": "medium", + }, + # Biology (1 - ecoli corrupted) + "yeast": {"category": "biology", "task_type": "multiclass", "difficulty": "hard"}, + # Chemistry (3) + "wine_red": { + "category": "chemistry", + "task_type": "multiclass", + "difficulty": "hard", + }, + "wine_white": { + "category": "chemistry", + "task_type": "multiclass", + "difficulty": "hard", + }, + "wine_quality_combined": { + "category": "chemistry", + "task_type": "multiclass", + "difficulty": "hard", + }, + # Image Features (4) + "iris": {"category": "biology", "task_type": "multiclass", "difficulty": "easy"}, + "optical_recognition": { + "category": "image", + "task_type": "multiclass", + "difficulty": "hard", + }, + "pendigits": { + "category": "image", + "task_type": "multiclass", + "difficulty": "medium", + }, + # Agriculture (2) + "wheat_seeds": { + "category": "agriculture", + "task_type": "multiclass", + "difficulty": "medium", + }, + "seeds": { + "category": "agriculture", + "task_type": "multiclass", + "difficulty": "medium", + }, + # Finance (1) + "statlog_australian": { + "category": "finance", + "task_type": "binary", + "difficulty": "medium", + }, + # Physics (2 - including balance_scale) + "magic_gamma": { + "category": "physics", + "task_type": "binary", + "difficulty": "medium", + }, + "balance_scale": { + "category": "physics", + "task_type": "multiclass", + "difficulty": "easy", + }, + # Social Science (1) + "contraceptive": { + "category": "social", + "task_type": "multiclass", + "difficulty": "medium", + }, + # Forensics (1) + "glass": {"category": "forensics", "task_type": "multiclass", "difficulty": "hard"}, +} + + +def analyze_dataset(dataset_name): + """Analyze a dataset and recommend architecture""" + dataset_path = ( + Path(__file__).parent.parent / "datasets" / "quantum" / f"{dataset_name}.csv" + ) + + # Load dataset with specific strategies + try: + if dataset_name in {"wine_red", "wine_white"}: + df = pd.read_csv(dataset_path, sep=";", na_values=["?", "NA", "", "NaN"]) + elif dataset_name == "wine_quality_combined": + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + elif dataset_name in {"wheat_seeds", "seeds"}: + df = pd.read_csv( + dataset_path, sep=r"\s+", header=None, na_values=["?", "NA", "", "NaN"] + ) + elif dataset_name == "yeast": + df = pd.read_csv( + dataset_path, sep=r"\s+", header=None, na_values=["?", "NA", "", "NaN"] + ) + df = df.iloc[:, 1:] # Skip sequence name + elif dataset_name == "parkinsons": + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + df = df.drop(columns=df.columns[0]) # Skip name column + elif dataset_name in {"statlog_australian", "statlog_heart"}: + df = pd.read_csv( + dataset_path, sep=" ", header=None, na_values=["?", "NA", "", "NaN"] + ) + elif dataset_name == "blood_transfusion": + df = pd.read_csv(dataset_path, skiprows=1, na_values=["?", "NA", "", "NaN"]) + elif dataset_name == "breast_cancer": + df = pd.read_csv( + dataset_path, header=None, na_values=["?", "NA", "", "NaN"] + ) + elif dataset_name == "balance_scale": + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + elif dataset_name in { + "optical_recognition", + "pendigits", + "contraceptive", + "dermatology", + "liver_disorders", + "thyroid", + }: + df = pd.read_csv( + dataset_path, header=None, na_values=["?", "NA", "", "NaN"] + ) + else: + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + except Exception as e: + raise ValueError(f"Failed to load {dataset_name}: {str(e)}") from e + n_samples = len(df) + n_features = df.shape[1] - 1 # Exclude target + + # Target analysis + y = df.iloc[:, -1] + if y.dtype == object: + le = LabelEncoder() + y_encoded = le.fit_transform(y) + n_classes = len(le.classes_) + class_counts = np.bincount(y_encoded) + else: + n_classes = len(y.unique()) + class_counts = y.value_counts().values + + # Class imbalance ratio + imbalance_ratio = ( + class_counts.max() / class_counts.min() if len(class_counts) > 1 else 1.0 + ) + + # Missing values + missing_ratio = df.isnull().sum().sum() / (df.shape[0] * df.shape[1]) + + # Get metadata + meta = DATASET_INFO.get(dataset_name, {}) + task_type = meta.get("task_type", "binary" if n_classes == 2 else "multiclass") + difficulty = meta.get("difficulty", "medium") + + # === ARCHITECTURE RECOMMENDATIONS === + + # 1. Qubit Count (4-6) + if n_features <= 4: + n_qubits = 4 + qubit_reasoning = "Low feature count (≤4) - use 4 qubits minimum" + elif n_features <= 10: + n_qubits = 4 + qubit_reasoning = "Medium feature count (5-10) - standard 4 qubits" + elif n_features <= 20: + n_qubits = 5 + qubit_reasoning = ( + "High feature count (11-20) - use 5 qubits for better representation" + ) + else: + n_qubits = 6 + qubit_reasoning = ( + "Very high feature count (>20) - use 6 qubits for dimensionality" + ) + + # 2. Layer Count (2-4) + if n_samples < 300: + n_layers = 2 + layer_reasoning = ( + "Small dataset (<300 samples) - use 2 layers to avoid overfitting" + ) + elif task_type == "multiclass" and n_classes > 3: + n_layers = 4 + layer_reasoning = ( + f"Multi-class ({n_classes} classes) - use 4 layers for complexity" + ) + elif difficulty == "hard": + n_layers = 3 + layer_reasoning = "Hard task - use 3 layers for learning capacity" + else: + n_layers = 2 + layer_reasoning = "Standard task - use 2 layers (efficient baseline)" + + # 3. Hidden Dimension (16 or 32) + if n_samples < 500: + hidden_dim = 16 + hidden_reasoning = "Small dataset - use hidden_dim=16 to reduce parameters" + elif n_features > 15: + hidden_dim = 32 + hidden_reasoning = "High-dimensional input - use hidden_dim=32 for capacity" + else: + hidden_dim = 16 + hidden_reasoning = "Standard configuration - use hidden_dim=16" + + # 4. Learning Rate (0.0005 or 0.001) + if imbalance_ratio > 3.0: + learning_rate = 0.0005 + lr_reasoning = f"Imbalanced classes (ratio: {imbalance_ratio:.1f}) - lower LR for stability" + elif n_samples < 300: + learning_rate = 0.0005 + lr_reasoning = "Small dataset - lower LR to avoid overfitting" + else: + learning_rate = 0.001 + lr_reasoning = "Balanced dataset - standard LR for faster convergence" + + # 5. Batch Size (8 or 16) + if n_samples < 300: + batch_size = 8 + batch_reasoning = "Small dataset - use batch_size=8 for more gradient updates" + elif n_samples > 5000: + batch_size = 32 + batch_reasoning = "Large dataset - use batch_size=32 for efficiency" + else: + batch_size = 16 + batch_reasoning = "Medium dataset - standard batch_size=16" + + # 6. Epoch Count + if difficulty == "hard" or task_type == "multiclass": + epochs = 50 + epoch_reasoning = "Complex task - train for 50 epochs" + elif n_samples < 300: + epochs = 30 + epoch_reasoning = "Small dataset - 30 epochs to avoid overfitting" + else: + epochs = 40 + epoch_reasoning = "Standard task - 40 epochs for convergence" + + # Calculate expected PCA variance retained + if n_features > n_qubits: + # Estimate based on typical variance distributions + if n_features <= 10: + pca_variance = 0.90 + elif n_features <= 20: + pca_variance = 0.85 + else: + pca_variance = 0.80 + else: + pca_variance = 1.0 + + return { + "dataset": dataset_name, + "statistics": { + "samples": int(n_samples), + "features": int(n_features), + "classes": int(n_classes), + "task_type": task_type, + "difficulty": difficulty, + "imbalance_ratio": float(imbalance_ratio), + "missing_ratio": float(missing_ratio), + "class_distribution": [int(c) for c in class_counts], + }, + "recommended_architecture": { + "n_qubits": n_qubits, + "n_quantum_layers": n_layers, + "hidden_dim": hidden_dim, + "learning_rate": learning_rate, + "batch_size": batch_size, + "epochs": epochs, + "expected_pca_variance": float(pca_variance), + }, + "reasoning": { + "qubits": qubit_reasoning, + "layers": layer_reasoning, + "hidden_dim": hidden_reasoning, + "learning_rate": lr_reasoning, + "batch_size": batch_reasoning, + "epochs": epoch_reasoning, + }, + } + + +def main(): + print("=" * 70) + print(" DATASET-ARCHITECTURE ANALYZER") + print("=" * 70) + print("\nAnalyzing all 15 quantum datasets...\n") + + all_results = [] + + # Analyze each dataset + for dataset_name in sorted(DATASET_INFO.keys()): + print(f"📊 {dataset_name}") + print("-" * 70) + + try: + result = analyze_dataset(dataset_name) + all_results.append(result) + + stats = result["statistics"] + arch = result["recommended_architecture"] + reason = result["reasoning"] + + # Print statistics + print( + f" Samples: {stats['samples']:,} | Features: {stats['features']} | " + f"Classes: {stats['classes']} | Type: {stats['task_type']}" + ) + print( + f" Difficulty: {stats['difficulty']} | Imbalance: {stats['imbalance_ratio']:.2f}x" + ) + + # Print recommendations + print("\n 🎯 RECOMMENDED ARCHITECTURE:") + print(f" Qubits: {arch['n_qubits']} → {reason['qubits']}") + print(f" Layers: {arch['n_quantum_layers']} → {reason['layers']}") + print(f" Hidden: {arch['hidden_dim']} → {reason['hidden_dim']}") + print( + f" Learn Rate: {arch['learning_rate']} → {reason['learning_rate']}" + ) + print(f" Batch: {arch['batch_size']} → {reason['batch_size']}") + print(f" Epochs: {arch['epochs']} → {reason['epochs']}") + + if stats["features"] > arch["n_qubits"]: + print( + f" PCA: {stats['features']}→{arch['n_qubits']} features " + f"({arch['expected_pca_variance']:.0%} variance)" + ) + + print() + + except Exception as e: + print(f" ❌ ERROR: {e}\n") + + # Summary by category + print("=" * 70) + print(" SUMMARY BY CATEGORY") + print("=" * 70) + + categories = {} + for result in all_results: + cat = DATASET_INFO[result["dataset"]]["category"] + if cat not in categories: + categories[cat] = [] + categories[cat].append(result) + + for category, datasets in sorted(categories.items()): + print(f"\n📁 {category.upper()} ({len(datasets)} datasets)") + print("-" * 70) + for ds in datasets: + arch = ds["recommended_architecture"] + stats = ds["statistics"] + print( + f" {ds['dataset']:20s} | {stats['samples']:5d} samples | " + f"{stats['features']:2d}→{arch['n_qubits']} features | " + f"{arch['n_quantum_layers']} layers | " + f"dim={arch['hidden_dim']} | " + f"lr={arch['learning_rate']}" + ) + + # Architecture distribution + print("\n" + "=" * 70) + print(" ARCHITECTURE DISTRIBUTION") + print("=" * 70) + + qubit_dist = {} + layer_dist = {} + for result in all_results: + arch = result["recommended_architecture"] + qubit_dist[arch["n_qubits"]] = qubit_dist.get(arch["n_qubits"], 0) + 1 + layer_dist[arch["n_quantum_layers"]] = ( + layer_dist.get(arch["n_quantum_layers"], 0) + 1 + ) + + print("\n🔢 Qubit Distribution:") + for qubits, count in sorted(qubit_dist.items()): + pct = 100 * count / len(all_results) + print(f" {qubits} qubits: {count} datasets ({pct:.0f}%)") + + print("\n🔢 Layer Distribution:") + for layers, count in sorted(layer_dist.items()): + pct = 100 * count / len(all_results) + print(f" {layers} layers: {count} datasets ({pct:.0f}%)") + + # Save results + output_file = Path(__file__).parent / "results" / "architecture_analysis.json" + output_file.parent.mkdir(exist_ok=True) + + output_data = { + "timestamp": datetime.now().isoformat(), + "total_datasets": len(all_results), + "analyses": all_results, + "distributions": { + "qubits": qubit_dist, + "layers": layer_dist, + }, + } + + with open(output_file, "w") as f: + json.dump(output_data, f, indent=2) + + print(f"\n📄 Analysis saved: {output_file}") + + print("\n" + "=" * 70) + print(" ✅ ANALYSIS COMPLETE") + print("=" * 70) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/demo_dashboard.py b/ai-projects/quantum-ml/demo_dashboard.py index e83811a18..2c954e5d3 100644 --- a/ai-projects/quantum-ml/demo_dashboard.py +++ b/ai-projects/quantum-ml/demo_dashboard.py @@ -1,106 +1,106 @@ -#!/usr/bin/env python3 -""" -Quick demo of the Quantum AI Web Dashboard -Tests that everything is working correctly -""" -import sys -import time - -import requests - -BASE_URL = "http://localhost:5000" - -print("=" * 70) -print(" 🧪 Quantum AI Dashboard - Quick Demo") -print("=" * 70) -print() - -# Test 1: Check if server is running -print("1. Testing server connection...") -try: - response = requests.get(f"{BASE_URL}/api/datasets", timeout=2) - if response.status_code == 200: - datasets = response.json() - print(f" ✅ Server is running! Found {len(datasets)} datasets.") - else: - print(f" ❌ Server returned error: {response.status_code}") - sys.exit(1) -except requests.exceptions.ConnectionError: - print(" ❌ Server not running! Start with: ./start_dashboard.sh") - exit(1) - -# Test 2: List datasets -print("\n2. Available datasets:") -for ds in datasets: - print(f" • {ds['name']} ({ds['features']} features)") - -# Test 3: Start a quick training session -print("\n3. Starting 1-minute training demo...") -config = { - "dataset": "heart", - "n_qubits": 3, - "n_layers": 1, - "learning_rate": 0.02, - "duration_minutes": 1, - "batch_size": 16, -} - -# Use timeout for all requests to prevent hanging -REQUEST_TIMEOUT = 30 # seconds - -response = requests.post( - f"{BASE_URL}/api/train/start", json=config, timeout=REQUEST_TIMEOUT -) -if response.status_code != 200: - print(f" ❌ Failed to start training: {response.text}") - sys.exit(1) - -session = response.json() -session_id = session["session_id"] -print(f" ✅ Training started: {session_id}") - -# Test 4: Monitor training -print("\n4. Monitoring training progress...") -print(" (Press Ctrl+C to stop)\n") - -try: - for i in range(60): # Monitor for up to 60 seconds - response = requests.get( - f"{BASE_URL}/api/train/status/{session_id}", timeout=REQUEST_TIMEOUT - ) - status = response.json() - - epoch = status["current_epoch"] - loss = status["current_loss"] - acc = status["best_val_acc"] * 100 - - print( - f" Epoch {epoch:3d} | Loss: {loss:.4f} | Best Acc: {acc:5.2f}%", end="\r" - ) - - if status["status"] in ["completed", "error", "stopped"]: - print() # New line - if status["status"] == "completed": - print("\n ✅ Training completed!") - print(f" Final accuracy: {acc:.2f}%") - print(f" Total epochs: {status['total_epochs']}") - else: - print(f"\n ⚠️ Training {status['status']}") - break - - time.sleep(1) -except KeyboardInterrupt: - print("\n\n ⏹️ Stopping training...") - requests.post(f"{BASE_URL}/api/train/stop/{session_id}", timeout=REQUEST_TIMEOUT) - print(" ✅ Training stopped") - -# Test 5: List results -print("\n5. Checking training history...") -response = requests.get(f"{BASE_URL}/api/results", timeout=REQUEST_TIMEOUT) -results = response.json() -print(f" ✅ Found {len(results)} training sessions in history") - -print("\n" + "=" * 70) -print(" ✅ Demo Complete!") -print("=" * 70) -print("\n💡 Open your browser to http://localhost:5000 for the full UI!\n") +#!/usr/bin/env python3 +""" +Quick demo of the Quantum AI Web Dashboard +Tests that everything is working correctly +""" +import sys +import time + +import requests + +BASE_URL = "http://localhost:5000" + +print("=" * 70) +print(" 🧪 Quantum AI Dashboard - Quick Demo") +print("=" * 70) +print() + +# Test 1: Check if server is running +print("1. Testing server connection...") +try: + response = requests.get(f"{BASE_URL}/api/datasets", timeout=2) + if response.status_code == 200: + datasets = response.json() + print(f" ✅ Server is running! Found {len(datasets)} datasets.") + else: + print(f" ❌ Server returned error: {response.status_code}") + sys.exit(1) +except requests.exceptions.ConnectionError: + print(" ❌ Server not running! Start with: ./start_dashboard.sh") + exit(1) + +# Test 2: List datasets +print("\n2. Available datasets:") +for ds in datasets: + print(f" • {ds['name']} ({ds['features']} features)") + +# Test 3: Start a quick training session +print("\n3. Starting 1-minute training demo...") +config = { + "dataset": "heart", + "n_qubits": 3, + "n_layers": 1, + "learning_rate": 0.02, + "duration_minutes": 1, + "batch_size": 16, +} + +# Use timeout for all requests to prevent hanging +REQUEST_TIMEOUT = 30 # seconds + +response = requests.post( + f"{BASE_URL}/api/train/start", json=config, timeout=REQUEST_TIMEOUT +) +if response.status_code != 200: + print(f" ❌ Failed to start training: {response.text}") + sys.exit(1) + +session = response.json() +session_id = session["session_id"] +print(f" ✅ Training started: {session_id}") + +# Test 4: Monitor training +print("\n4. Monitoring training progress...") +print(" (Press Ctrl+C to stop)\n") + +try: + for i in range(60): # Monitor for up to 60 seconds + response = requests.get( + f"{BASE_URL}/api/train/status/{session_id}", timeout=REQUEST_TIMEOUT + ) + status = response.json() + + epoch = status["current_epoch"] + loss = status["current_loss"] + acc = status["best_val_acc"] * 100 + + print( + f" Epoch {epoch:3d} | Loss: {loss:.4f} | Best Acc: {acc:5.2f}%", end="\r" + ) + + if status["status"] in ["completed", "error", "stopped"]: + print() # New line + if status["status"] == "completed": + print("\n ✅ Training completed!") + print(f" Final accuracy: {acc:.2f}%") + print(f" Total epochs: {status['total_epochs']}") + else: + print(f"\n ⚠️ Training {status['status']}") + break + + time.sleep(1) +except KeyboardInterrupt: + print("\n\n ⏹️ Stopping training...") + requests.post(f"{BASE_URL}/api/train/stop/{session_id}", timeout=REQUEST_TIMEOUT) + print(" ✅ Training stopped") + +# Test 5: List results +print("\n5. Checking training history...") +response = requests.get(f"{BASE_URL}/api/results", timeout=REQUEST_TIMEOUT) +results = response.json() +print(f" ✅ Found {len(results)} training sessions in history") + +print("\n" + "=" * 70) +print(" ✅ Demo Complete!") +print("=" * 70) +print("\n💡 Open your browser to http://localhost:5000 for the full UI!\n") diff --git a/ai-projects/quantum-ml/deploy_banknote_to_azure.py b/ai-projects/quantum-ml/deploy_banknote_to_azure.py index 3b5b20ee7..05b2b997a 100644 --- a/ai-projects/quantum-ml/deploy_banknote_to_azure.py +++ b/ai-projects/quantum-ml/deploy_banknote_to_azure.py @@ -1,365 +1,365 @@ -#!/usr/bin/env python3 -""" -Deploy Trained Banknote Classifier to Azure Quantum -==================================================== - -Takes the best-performing quantum model (100% accuracy on banknote fraud detection) -and submits it to Azure Quantum hardware for validation and production testing. - -This script: -1. Loads the trained model and test data -2. Extracts the quantum circuit from the model -3. Submits to Azure Quantum (IonQ simulator - FREE tier) -4. Compares cloud vs local results -5. Generates deployment report - -Author: Quantum AI System -Date: November 16, 2025 -""" - -import json -import sys -from datetime import datetime -from pathlib import Path - -import yaml - -# Add src to path -src_path = Path(__file__).parent / "src" -sys.path.insert(0, str(src_path)) - -try: - from azure.identity import DefaultAzureCredential - from azure.quantum import Workspace - - AZURE_AVAILABLE = True -except ImportError: - print("⚠️ Azure Quantum SDK not installed. Run: pip install azure-quantum") - AZURE_AVAILABLE = False - - -def load_config(): - """Load quantum configuration""" - config_path = Path(__file__).parent / "config" / "quantum_config.yaml" - with open(config_path) as f: - return yaml.safe_load(f) - - -def connect_azure_quantum(config): - """Connect to Azure Quantum workspace""" - if not AZURE_AVAILABLE: - raise RuntimeError("Azure Quantum SDK not available") - - print("\n🔗 Connecting to Azure Quantum...") - print(f" Workspace: {config['azure']['workspace_name']}") - print(f" Location: {config['azure']['location']}") - - try: - workspace = Workspace( - subscription_id=config["azure"]["subscription_id"], - resource_group=config["azure"]["resource_group"], - name=config["azure"]["workspace_name"], - location=config["azure"]["location"], - credential=DefaultAzureCredential(), - ) - print(" ✅ Connected successfully!") - return workspace - except Exception as e: - print(f" ❌ Connection failed: {e}") - print("\n Troubleshooting:") - print(" 1. Run: az login") - print(" 2. Verify workspace exists in Azure Portal") - print(" 3. Check config/quantum_config.yaml settings") - raise - - -def list_available_backends(workspace): - """List available quantum backends""" - print("\n📋 Available Quantum Backends:") - print("-" * 70) - - targets = workspace.get_targets() - free_targets = [] - - for target in targets: - provider = target.name.split(".")[0] - is_simulator = "sim" in target.name.lower() - status = "🆓 FREE" if is_simulator else "💰 PAID" - - print(f" {status} {target.name}") - print(f" Provider: {provider}") - print(f" Status: {target.current_availability}") - - if is_simulator: - free_targets.append(target.name) - - print("-" * 70) - print(f"\n✅ Found {len(free_targets)} FREE simulator backends") - return free_targets - - -def create_test_circuit(): - """Create a simple quantum circuit for testing""" - from qiskit import QuantumCircuit - - print("\n🔬 Creating test quantum circuit...") - - # Simple 4-qubit variational circuit (matches our model) - circuit = QuantumCircuit(4, 4) - - # Feature encoding (example values) - circuit.rx(0.5, 0) - circuit.ry(0.3, 1) - circuit.rx(-0.2, 2) - circuit.ry(0.7, 3) - - # Entangling layer (linear entanglement) - circuit.cx(0, 1) - circuit.cx(1, 2) - circuit.cx(2, 3) - - # Variational parameters (learned weights) - circuit.ry(1.2, 0) - circuit.rz(0.8, 1) - circuit.ry(-0.5, 2) - circuit.rz(0.3, 3) - - # Measurement - circuit.measure([0, 1, 2, 3], [0, 1, 2, 3]) - - print(" ✅ Circuit created:") - print(" Qubits: 4") - print(" Gates: RX, RY, RZ, CNOT") - print(" Depth:", circuit.depth()) - - return circuit - - -def submit_to_azure(workspace, circuit, backend_name="ionq.simulator", shots=100): - """Submit circuit to Azure Quantum""" - print("\n🚀 Submitting to Azure Quantum...") - print(f" Backend: {backend_name}") - print(f" Shots: {shots}") - - try: - # Get target - target = workspace.get_targets(backend_name) - - # Convert circuit to QIR if needed - from qiskit_qir import to_qir_bitcode - - bitcode = to_qir_bitcode(circuit, name="banknote_classifier") - - # Submit job - job = target.submit( - input_data=bitcode, - name=f"banknote-deploy-{datetime.now().strftime('%Y%m%d-%H%M%S')}", - input_data_format="qir.v1", - output_data_format="microsoft.quantum-results.v1", - shots=shots, - ) - - print(" ✅ Job submitted!") - print(f" Job ID: {job.id}") - print(f" Status: {job.details.status}") - - return job - except Exception as e: - print(f" ❌ Submission failed: {e}") - raise - - -def wait_for_results(job, timeout=300): - """Wait for job completion""" - import time - - print("\n⏳ Waiting for results...") - start_time = time.time() - - while job.details.status not in ["Succeeded", "Failed", "Cancelled"]: - if time.time() - start_time > timeout: - print(f"\n ⚠️ Timeout after {timeout}s") - return None - - time.sleep(3) - job.refresh() - elapsed = int(time.time() - start_time) - print(f" Status: {job.details.status} ({elapsed}s)", end="\r") - - elapsed = int(time.time() - start_time) - print(f"\n ✅ Completed in {elapsed}s") - print(f" Final status: {job.details.status}") - - if job.details.status == "Succeeded": - return job.get_results() - else: - print(f" ❌ Job failed: {job.details.status}") - return None - - -def analyze_results(results): - """Analyze quantum circuit results""" - if not results: - return None - - print("\n📊 Results Analysis:") - print("=" * 70) - - total_shots = sum(results.values()) - print(f"\nMeasurement Distribution (Total shots: {total_shots}):\n") - - # Sort by count - sorted_results = sorted(results.items(), key=lambda x: x[1], reverse=True) - - for state, count in sorted_results[:10]: # Top 10 - percentage = (count / total_shots) * 100 - bar = "█" * int(percentage / 2) - print(f" |{state}⟩: {count:4d} shots ({percentage:5.2f}%) {bar}") - - # Most common state - most_common = sorted_results[0] - print( - f"\n🎯 Most Probable State: |{most_common[0]}⟩ ({most_common[1]/total_shots*100:.1f}%)" - ) - - # Binary classification interpretation - # For banknote: even parity → genuine, odd parity → forged (example) - genuine_count = sum( - count for state, count in results.items() if state.count("0") % 2 == 0 - ) - forged_count = total_shots - genuine_count - - print("\n💵 Banknote Classification Simulation:") - print( - f" Genuine (even parity): {genuine_count} ({genuine_count/total_shots*100:.1f}%)" - ) - print( - f" Forged (odd parity): {forged_count} ({forged_count/total_shots*100:.1f}%)" - ) - - return { - "total_shots": total_shots, - "distribution": dict(sorted_results), - "most_probable": most_common[0], - "classification": { - "genuine_prob": genuine_count / total_shots, - "forged_prob": forged_count / total_shots, - }, - } - - -def save_deployment_report(results_analysis, backend, job_id): - """Save deployment report""" - report_path = Path(__file__).parent / "results" / "azure_deployment_report.json" - report_path.parent.mkdir(exist_ok=True) - - report = { - "timestamp": datetime.now().isoformat(), - "model": "banknote_classifier", - "backend": backend, - "job_id": job_id, - "status": "success", - "results": results_analysis, - "deployment_notes": { - "model_accuracy_local": 1.0, - "quantum_circuit": "4 qubits, 2 variational layers", - "cloud_platform": "Azure Quantum", - "cost": "FREE (simulator)", - }, - } - - with open(report_path, "w") as f: - json.dump(report, f, indent=2) - - print(f"\n💾 Deployment report saved: {report_path}") - return report_path - - -def main(): - """Main deployment pipeline""" - print("=" * 70) - print(" DEPLOY QUANTUM AI TO AZURE QUANTUM") - print("=" * 70) - print("\n🎯 Model: Banknote Fraud Detector (100% accuracy)") - print("🔬 Platform: Azure Quantum") - print("💰 Cost: FREE (using simulator)") - - # Load configuration - config = load_config() - - # Connect to Azure - try: - workspace = connect_azure_quantum(config) - except Exception: - print("\n❌ Cannot connect to Azure Quantum") - print(" Running in simulation-only mode...") - print("\n📚 To enable Azure Quantum:") - print(" 1. Install: pip install azure-quantum azure-identity") - print(" 2. Login: az login") - print(" 3. Verify workspace in Azure Portal") - return - - # List available backends - free_backends = list_available_backends(workspace) - - # Select backend (prefer IonQ simulator - most reliable) - preferred_backends = ["ionq.simulator", "rigetti.sim.qvm", "quantinuum.sim.h1-1sc"] - backend = None - - for preferred in preferred_backends: - if preferred in [t.name for t in workspace.get_targets()]: - backend = preferred - break - - if not backend and free_backends: - backend = free_backends[0] - - if not backend: - print("\n❌ No suitable backend found") - return - - print(f"\n✅ Selected backend: {backend}") - - # Create test circuit - circuit = create_test_circuit() - - # Submit to Azure - try: - job = submit_to_azure(workspace, circuit, backend, shots=100) - except Exception as e: - print(f"\n❌ Submission failed: {e}") - return - - # Wait for results - results = wait_for_results(job) - - if not results: - print("\n❌ No results obtained") - return - - # Analyze results - analysis = analyze_results(results) - - # Save report - if analysis: - report_path = save_deployment_report(analysis, backend, job.id) - - # Final summary - print("\n" + "=" * 70) - print(" ✅ DEPLOYMENT SUCCESSFUL!") - print("=" * 70) - print("\n🎉 Quantum model successfully executed on Azure Quantum cloud!") - print("\n📈 Next Steps:") - print(" 1. Review deployment report") - print(" 2. Test with real banknote data") - print(" 3. Monitor performance metrics") - print(" 4. Consider upgrading to QPU hardware for production") - print("\n💡 Cost Estimate:") - print(" Simulator: $0.00 (unlimited free usage)") - print(" IonQ QPU: ~$0.00003 per gate-shot") - print(" Quantinuum QPU: ~$0.00015 per circuit") - print("\n" + "=" * 70) - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +""" +Deploy Trained Banknote Classifier to Azure Quantum +==================================================== + +Takes the best-performing quantum model (100% accuracy on banknote fraud detection) +and submits it to Azure Quantum hardware for validation and production testing. + +This script: +1. Loads the trained model and test data +2. Extracts the quantum circuit from the model +3. Submits to Azure Quantum (IonQ simulator - FREE tier) +4. Compares cloud vs local results +5. Generates deployment report + +Author: Quantum AI System +Date: November 16, 2025 +""" + +import json +import sys +from datetime import datetime +from pathlib import Path + +import yaml + +# Add src to path +src_path = Path(__file__).parent / "src" +sys.path.insert(0, str(src_path)) + +try: + from azure.identity import DefaultAzureCredential + from azure.quantum import Workspace + + AZURE_AVAILABLE = True +except ImportError: + print("⚠️ Azure Quantum SDK not installed. Run: pip install azure-quantum") + AZURE_AVAILABLE = False + + +def load_config(): + """Load quantum configuration""" + config_path = Path(__file__).parent / "config" / "quantum_config.yaml" + with open(config_path) as f: + return yaml.safe_load(f) + + +def connect_azure_quantum(config): + """Connect to Azure Quantum workspace""" + if not AZURE_AVAILABLE: + raise RuntimeError("Azure Quantum SDK not available") + + print("\n🔗 Connecting to Azure Quantum...") + print(f" Workspace: {config['azure']['workspace_name']}") + print(f" Location: {config['azure']['location']}") + + try: + workspace = Workspace( + subscription_id=config["azure"]["subscription_id"], + resource_group=config["azure"]["resource_group"], + name=config["azure"]["workspace_name"], + location=config["azure"]["location"], + credential=DefaultAzureCredential(), + ) + print(" ✅ Connected successfully!") + return workspace + except Exception as e: + print(f" ❌ Connection failed: {e}") + print("\n Troubleshooting:") + print(" 1. Run: az login") + print(" 2. Verify workspace exists in Azure Portal") + print(" 3. Check config/quantum_config.yaml settings") + raise + + +def list_available_backends(workspace): + """List available quantum backends""" + print("\n📋 Available Quantum Backends:") + print("-" * 70) + + targets = workspace.get_targets() + free_targets = [] + + for target in targets: + provider = target.name.split(".")[0] + is_simulator = "sim" in target.name.lower() + status = "🆓 FREE" if is_simulator else "💰 PAID" + + print(f" {status} {target.name}") + print(f" Provider: {provider}") + print(f" Status: {target.current_availability}") + + if is_simulator: + free_targets.append(target.name) + + print("-" * 70) + print(f"\n✅ Found {len(free_targets)} FREE simulator backends") + return free_targets + + +def create_test_circuit(): + """Create a simple quantum circuit for testing""" + from qiskit import QuantumCircuit + + print("\n🔬 Creating test quantum circuit...") + + # Simple 4-qubit variational circuit (matches our model) + circuit = QuantumCircuit(4, 4) + + # Feature encoding (example values) + circuit.rx(0.5, 0) + circuit.ry(0.3, 1) + circuit.rx(-0.2, 2) + circuit.ry(0.7, 3) + + # Entangling layer (linear entanglement) + circuit.cx(0, 1) + circuit.cx(1, 2) + circuit.cx(2, 3) + + # Variational parameters (learned weights) + circuit.ry(1.2, 0) + circuit.rz(0.8, 1) + circuit.ry(-0.5, 2) + circuit.rz(0.3, 3) + + # Measurement + circuit.measure([0, 1, 2, 3], [0, 1, 2, 3]) + + print(" ✅ Circuit created:") + print(" Qubits: 4") + print(" Gates: RX, RY, RZ, CNOT") + print(" Depth:", circuit.depth()) + + return circuit + + +def submit_to_azure(workspace, circuit, backend_name="ionq.simulator", shots=100): + """Submit circuit to Azure Quantum""" + print("\n🚀 Submitting to Azure Quantum...") + print(f" Backend: {backend_name}") + print(f" Shots: {shots}") + + try: + # Get target + target = workspace.get_targets(backend_name) + + # Convert circuit to QIR if needed + from qiskit_qir import to_qir_bitcode + + bitcode = to_qir_bitcode(circuit, name="banknote_classifier") + + # Submit job + job = target.submit( + input_data=bitcode, + name=f"banknote-deploy-{datetime.now().strftime('%Y%m%d-%H%M%S')}", + input_data_format="qir.v1", + output_data_format="microsoft.quantum-results.v1", + shots=shots, + ) + + print(" ✅ Job submitted!") + print(f" Job ID: {job.id}") + print(f" Status: {job.details.status}") + + return job + except Exception as e: + print(f" ❌ Submission failed: {e}") + raise + + +def wait_for_results(job, timeout=300): + """Wait for job completion""" + import time + + print("\n⏳ Waiting for results...") + start_time = time.time() + + while job.details.status not in ["Succeeded", "Failed", "Cancelled"]: + if time.time() - start_time > timeout: + print(f"\n ⚠️ Timeout after {timeout}s") + return None + + time.sleep(3) + job.refresh() + elapsed = int(time.time() - start_time) + print(f" Status: {job.details.status} ({elapsed}s)", end="\r") + + elapsed = int(time.time() - start_time) + print(f"\n ✅ Completed in {elapsed}s") + print(f" Final status: {job.details.status}") + + if job.details.status == "Succeeded": + return job.get_results() + else: + print(f" ❌ Job failed: {job.details.status}") + return None + + +def analyze_results(results): + """Analyze quantum circuit results""" + if not results: + return None + + print("\n📊 Results Analysis:") + print("=" * 70) + + total_shots = sum(results.values()) + print(f"\nMeasurement Distribution (Total shots: {total_shots}):\n") + + # Sort by count + sorted_results = sorted(results.items(), key=lambda x: x[1], reverse=True) + + for state, count in sorted_results[:10]: # Top 10 + percentage = (count / total_shots) * 100 + bar = "█" * int(percentage / 2) + print(f" |{state}⟩: {count:4d} shots ({percentage:5.2f}%) {bar}") + + # Most common state + most_common = sorted_results[0] + print( + f"\n🎯 Most Probable State: |{most_common[0]}⟩ ({most_common[1]/total_shots*100:.1f}%)" + ) + + # Binary classification interpretation + # For banknote: even parity → genuine, odd parity → forged (example) + genuine_count = sum( + count for state, count in results.items() if state.count("0") % 2 == 0 + ) + forged_count = total_shots - genuine_count + + print("\n💵 Banknote Classification Simulation:") + print( + f" Genuine (even parity): {genuine_count} ({genuine_count/total_shots*100:.1f}%)" + ) + print( + f" Forged (odd parity): {forged_count} ({forged_count/total_shots*100:.1f}%)" + ) + + return { + "total_shots": total_shots, + "distribution": dict(sorted_results), + "most_probable": most_common[0], + "classification": { + "genuine_prob": genuine_count / total_shots, + "forged_prob": forged_count / total_shots, + }, + } + + +def save_deployment_report(results_analysis, backend, job_id): + """Save deployment report""" + report_path = Path(__file__).parent / "results" / "azure_deployment_report.json" + report_path.parent.mkdir(exist_ok=True) + + report = { + "timestamp": datetime.now().isoformat(), + "model": "banknote_classifier", + "backend": backend, + "job_id": job_id, + "status": "success", + "results": results_analysis, + "deployment_notes": { + "model_accuracy_local": 1.0, + "quantum_circuit": "4 qubits, 2 variational layers", + "cloud_platform": "Azure Quantum", + "cost": "FREE (simulator)", + }, + } + + with open(report_path, "w") as f: + json.dump(report, f, indent=2) + + print(f"\n💾 Deployment report saved: {report_path}") + return report_path + + +def main(): + """Main deployment pipeline""" + print("=" * 70) + print(" DEPLOY QUANTUM AI TO AZURE QUANTUM") + print("=" * 70) + print("\n🎯 Model: Banknote Fraud Detector (100% accuracy)") + print("🔬 Platform: Azure Quantum") + print("💰 Cost: FREE (using simulator)") + + # Load configuration + config = load_config() + + # Connect to Azure + try: + workspace = connect_azure_quantum(config) + except Exception: + print("\n❌ Cannot connect to Azure Quantum") + print(" Running in simulation-only mode...") + print("\n📚 To enable Azure Quantum:") + print(" 1. Install: pip install azure-quantum azure-identity") + print(" 2. Login: az login") + print(" 3. Verify workspace in Azure Portal") + return + + # List available backends + free_backends = list_available_backends(workspace) + + # Select backend (prefer IonQ simulator - most reliable) + preferred_backends = ["ionq.simulator", "rigetti.sim.qvm", "quantinuum.sim.h1-1sc"] + backend = None + + for preferred in preferred_backends: + if preferred in [t.name for t in workspace.get_targets()]: + backend = preferred + break + + if not backend and free_backends: + backend = free_backends[0] + + if not backend: + print("\n❌ No suitable backend found") + return + + print(f"\n✅ Selected backend: {backend}") + + # Create test circuit + circuit = create_test_circuit() + + # Submit to Azure + try: + job = submit_to_azure(workspace, circuit, backend, shots=100) + except Exception as e: + print(f"\n❌ Submission failed: {e}") + return + + # Wait for results + results = wait_for_results(job) + + if not results: + print("\n❌ No results obtained") + return + + # Analyze results + analysis = analyze_results(results) + + # Save report + if analysis: + report_path = save_deployment_report(analysis, backend, job.id) + + # Final summary + print("\n" + "=" * 70) + print(" ✅ DEPLOYMENT SUCCESSFUL!") + print("=" * 70) + print("\n🎉 Quantum model successfully executed on Azure Quantum cloud!") + print("\n📈 Next Steps:") + print(" 1. Review deployment report") + print(" 2. Test with real banknote data") + print(" 3. Monitor performance metrics") + print(" 4. Consider upgrading to QPU hardware for production") + print("\n💡 Cost Estimate:") + print(" Simulator: $0.00 (unlimited free usage)") + print(" IonQ QPU: ~$0.00003 per gate-shot") + print(" Quantinuum QPU: ~$0.00015 per circuit") + print("\n" + "=" * 70) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/deploy_quantum_models_azure.py b/ai-projects/quantum-ml/deploy_quantum_models_azure.py index 305b64d66..63fbc4523 100644 --- a/ai-projects/quantum-ml/deploy_quantum_models_azure.py +++ b/ai-projects/quantum-ml/deploy_quantum_models_azure.py @@ -1,245 +1,245 @@ -""" -Deploy Trained Quantum ML Models to Azure Quantum -Runs your 3 trained models on Azure cloud infrastructure (100% FREE) - -Security Note: This module loads pickle files from the local results directory. -Only trusted pickle files from known training runs should be used. -""" - -import pickle -import time -from pathlib import Path - -import torch -import yaml -from qiskit import QuantumCircuit -from src.azure_quantum_integration import AzureQuantumIntegration - -# Trusted directory for loading model artifacts -TRUSTED_RESULTS_DIR = Path(__file__).parent / "results" - - -def load_model_artifacts(model_name, n_qubits, n_layers): - """Load trained model parameters from state dict. - - Security: Only loads from the trusted results directory within this package. - Pickle files should only be generated by the training scripts in this repository. - - Args: - model_name: Name of the trained model (e.g., 'heart_disease', 'ionosphere') - n_qubits: Number of qubits used in the model - n_layers: Number of layers in the quantum circuit - - Returns: - Tuple of (params, scaler, pca) loaded from the results directory - - Raises: - ValueError: If paths attempt to escape the trusted directory - FileNotFoundError: If model artifacts don't exist - """ - results_dir = TRUSTED_RESULTS_DIR - - # Validate model_name to prevent path traversal - if not model_name or ".." in model_name or "/" in model_name or "\\" in model_name: - raise ValueError(f"Invalid model name: {model_name}") - - # Load model state dict - model_path = results_dir / f"{model_name}_model.pt" - scaler_path = results_dir / f"{model_name}_scaler.pkl" - pca_path = results_dir / f"{model_name}_pca.pkl" - - # Verify paths are within trusted directory - for path in [model_path, scaler_path, pca_path]: - resolved = path.resolve() - if not str(resolved).startswith(str(results_dir.resolve())): - raise ValueError(f"Path escapes trusted directory: {path}") - - print(f"📂 Loading {model_name} artifacts...") - print(f" Model: {model_path}") - print(f" Scaler: {scaler_path}") - print(f" PCA: {pca_path}") - - # Load trained weights - state_dict = torch.load(model_path, weights_only=True) - - # Extract quantum layer parameters directly from state dict - # The quantum layer params are stored under 'quantum_layer.qlayer.weights' - params = state_dict["quantum_layer.qlayer.weights"].numpy() - - # Load sklearn preprocessors from trusted local files - # Note: pickle.load is used here for sklearn compatibility. - # These files should only be generated by training scripts in this repo. - with open(scaler_path, "rb") as f: - scaler = pickle.load(f) - - with open(pca_path, "rb") as f: - pca = pickle.load(f) - - return params, scaler, pca - - -def create_test_circuit(n_qubits, n_layers, params): - """Create a quantum circuit with trained parameters""" - qc = QuantumCircuit(n_qubits, n_qubits) - - # Input layer - create superposition - for qubit in range(n_qubits): - qc.h(qubit) - - # Variational layers with trained parameters - param_idx = 0 - for layer in range(n_layers): - # Rotation layer - for qubit in range(n_qubits): - qc.ry(params[param_idx], qubit) - param_idx += 1 - qc.rz(params[param_idx], qubit) - param_idx += 1 - - # Entanglement layer (linear) - for qubit in range(n_qubits - 1): - qc.cx(qubit, qubit + 1) - - # Measurement - qc.measure_all() - - return qc - - -def main(): - print("=" * 70) - print("🚀 DEPLOYING QUANTUM ML MODELS TO AZURE QUANTUM") - print("=" * 70) - print("\n💰 Cost: $0.00 (using FREE Rigetti simulator)") - print("🎯 Goal: Run trained quantum classifiers on Azure cloud\n") - - # Load config - with open("config/quantum_config.yaml", "r") as f: - config = yaml.safe_load(f) - - # Connect to Azure - print("🔗 Connecting to Azure Quantum...") - azure = AzureQuantumIntegration() - azure.connect() - print("✅ Connected!\n") - - # Models to deploy - models_info = [ - { - "name": "heart_disease", - "display": "Heart Disease Classifier", - "accuracy": 94.64, - "n_qubits": 4, - "n_layers": 2, - }, - { - "name": "ionosphere", - "display": "Ionosphere Classifier", - "accuracy": 85.71, - "n_qubits": 4, - "n_layers": 2, - }, - { - "name": "custom", - "display": "Wine Quality Classifier", - "accuracy": 94.44, - "n_qubits": 4, - "n_layers": 2, - }, - ] - - results = [] - - for model_info in models_info: - print("=" * 70) - print(f"🧠 MODEL: {model_info['display']}") - print("=" * 70) - print(f"Local Accuracy: {model_info['accuracy']}%") - print( - f"Architecture: {model_info['n_qubits']} qubits, {model_info['n_layers']} layers\n" - ) - - try: - # Load model - params, scaler, pca = load_model_artifacts( - model_info["name"], model_info["n_qubits"], model_info["n_layers"] - ) - print(f"✅ Loaded trained parameters ({len(params)} parameters)\n") - - # Create quantum circuit with trained params - qc = create_test_circuit( - model_info["n_qubits"], model_info["n_layers"], params - ) - - print("📐 Circuit properties:") - print(f" Depth: {qc.depth()}") - print(f" Gates: {qc.size()}") - print(f" Qubits: {qc.num_qubits}") - print(f" Parameters: {len(params)}\n") - - # Submit to Azure - print("☁️ Submitting to Azure Quantum (rigetti.sim.qvm)...") - start_time = time.time() - - job = azure.submit_circuit(qc, backend_name="rigetti.sim.qvm", shots=1000) - - print(f"✅ Job submitted: {job.job_id()}") - print("⏳ Running on Azure Quantum servers...") - - # Get results - result = job.result() - elapsed = time.time() - start_time - - counts = result.get_counts() - print(f"\n📊 Azure Quantum Results (completed in {elapsed:.2f}s):") - - # Show top 5 measurement outcomes - sorted_counts = sorted(counts.items(), key=lambda x: x[1], reverse=True) - for state, count in sorted_counts[:5]: - percentage = (count / 1000) * 100 - print(f" |{state}⟩: {count}/1000 ({percentage:.1f}%)") - - results.append( - { - "model": model_info["display"], - "local_accuracy": model_info["accuracy"], - "job_id": job.job_id(), - "execution_time": elapsed, - "top_state": sorted_counts[0][0], - "top_count": sorted_counts[0][1], - } - ) - - print(f"✅ {model_info['display']} deployed successfully!\n") - - except Exception as e: - print(f"⚠️ Error deploying {model_info['display']}: {e}\n") - continue - - # Summary - print("=" * 70) - print("✨ DEPLOYMENT COMPLETE!") - print("=" * 70) - print(f"\n🎊 Successfully deployed {len(results)}/3 quantum ML models to Azure!") - print("\n📊 Deployment Summary:\n") - - for i, res in enumerate(results, 1): - print(f"{i}. {res['model']}") - print(f" Local Accuracy: {res['local_accuracy']}%") - print(f" Azure Job ID: {res['job_id']}") - print(f" Execution Time: {res['execution_time']:.2f}s") - print(f" Top State: |{res['top_state']}⟩ ({res['top_count']}/1000 shots)") - print() - - print("💰 Total Cost: $0.00 (free simulators)") - print("\n🏆 Your quantum ML models are now running on Azure cloud infrastructure!") - print("📍 Location: Azure Quantum workspace (quantum-ai-workspace, eastus)") - print("\n🚀 Next steps:") - print(" 1. Try real quantum hardware (IonQ, Quantinuum) - paid tier") - print(" 2. Scale to larger datasets for production deployment") - print(" 3. Benchmark against classical ML models on Azure ML") - print("\n" + "=" * 70) - - -if __name__ == "__main__": - main() +""" +Deploy Trained Quantum ML Models to Azure Quantum +Runs your 3 trained models on Azure cloud infrastructure (100% FREE) + +Security Note: This module loads pickle files from the local results directory. +Only trusted pickle files from known training runs should be used. +""" + +import pickle +import time +from pathlib import Path + +import torch +import yaml +from qiskit import QuantumCircuit +from src.azure_quantum_integration import AzureQuantumIntegration + +# Trusted directory for loading model artifacts +TRUSTED_RESULTS_DIR = Path(__file__).parent / "results" + + +def load_model_artifacts(model_name, n_qubits, n_layers): + """Load trained model parameters from state dict. + + Security: Only loads from the trusted results directory within this package. + Pickle files should only be generated by the training scripts in this repository. + + Args: + model_name: Name of the trained model (e.g., 'heart_disease', 'ionosphere') + n_qubits: Number of qubits used in the model + n_layers: Number of layers in the quantum circuit + + Returns: + Tuple of (params, scaler, pca) loaded from the results directory + + Raises: + ValueError: If paths attempt to escape the trusted directory + FileNotFoundError: If model artifacts don't exist + """ + results_dir = TRUSTED_RESULTS_DIR + + # Validate model_name to prevent path traversal + if not model_name or ".." in model_name or "/" in model_name or "\\" in model_name: + raise ValueError(f"Invalid model name: {model_name}") + + # Load model state dict + model_path = results_dir / f"{model_name}_model.pt" + scaler_path = results_dir / f"{model_name}_scaler.pkl" + pca_path = results_dir / f"{model_name}_pca.pkl" + + # Verify paths are within trusted directory + for path in [model_path, scaler_path, pca_path]: + resolved = path.resolve() + if not str(resolved).startswith(str(results_dir.resolve())): + raise ValueError(f"Path escapes trusted directory: {path}") + + print(f"📂 Loading {model_name} artifacts...") + print(f" Model: {model_path}") + print(f" Scaler: {scaler_path}") + print(f" PCA: {pca_path}") + + # Load trained weights + state_dict = torch.load(model_path, weights_only=True) + + # Extract quantum layer parameters directly from state dict + # The quantum layer params are stored under 'quantum_layer.qlayer.weights' + params = state_dict["quantum_layer.qlayer.weights"].numpy() + + # Load sklearn preprocessors from trusted local files + # Note: pickle.load is used here for sklearn compatibility. + # These files should only be generated by training scripts in this repo. + with open(scaler_path, "rb") as f: + scaler = pickle.load(f) + + with open(pca_path, "rb") as f: + pca = pickle.load(f) + + return params, scaler, pca + + +def create_test_circuit(n_qubits, n_layers, params): + """Create a quantum circuit with trained parameters""" + qc = QuantumCircuit(n_qubits, n_qubits) + + # Input layer - create superposition + for qubit in range(n_qubits): + qc.h(qubit) + + # Variational layers with trained parameters + param_idx = 0 + for layer in range(n_layers): + # Rotation layer + for qubit in range(n_qubits): + qc.ry(params[param_idx], qubit) + param_idx += 1 + qc.rz(params[param_idx], qubit) + param_idx += 1 + + # Entanglement layer (linear) + for qubit in range(n_qubits - 1): + qc.cx(qubit, qubit + 1) + + # Measurement + qc.measure_all() + + return qc + + +def main(): + print("=" * 70) + print("🚀 DEPLOYING QUANTUM ML MODELS TO AZURE QUANTUM") + print("=" * 70) + print("\n💰 Cost: $0.00 (using FREE Rigetti simulator)") + print("🎯 Goal: Run trained quantum classifiers on Azure cloud\n") + + # Load config + with open("config/quantum_config.yaml", "r") as f: + config = yaml.safe_load(f) + + # Connect to Azure + print("🔗 Connecting to Azure Quantum...") + azure = AzureQuantumIntegration() + azure.connect() + print("✅ Connected!\n") + + # Models to deploy + models_info = [ + { + "name": "heart_disease", + "display": "Heart Disease Classifier", + "accuracy": 94.64, + "n_qubits": 4, + "n_layers": 2, + }, + { + "name": "ionosphere", + "display": "Ionosphere Classifier", + "accuracy": 85.71, + "n_qubits": 4, + "n_layers": 2, + }, + { + "name": "custom", + "display": "Wine Quality Classifier", + "accuracy": 94.44, + "n_qubits": 4, + "n_layers": 2, + }, + ] + + results = [] + + for model_info in models_info: + print("=" * 70) + print(f"🧠 MODEL: {model_info['display']}") + print("=" * 70) + print(f"Local Accuracy: {model_info['accuracy']}%") + print( + f"Architecture: {model_info['n_qubits']} qubits, {model_info['n_layers']} layers\n" + ) + + try: + # Load model + params, scaler, pca = load_model_artifacts( + model_info["name"], model_info["n_qubits"], model_info["n_layers"] + ) + print(f"✅ Loaded trained parameters ({len(params)} parameters)\n") + + # Create quantum circuit with trained params + qc = create_test_circuit( + model_info["n_qubits"], model_info["n_layers"], params + ) + + print("📐 Circuit properties:") + print(f" Depth: {qc.depth()}") + print(f" Gates: {qc.size()}") + print(f" Qubits: {qc.num_qubits}") + print(f" Parameters: {len(params)}\n") + + # Submit to Azure + print("☁️ Submitting to Azure Quantum (rigetti.sim.qvm)...") + start_time = time.time() + + job = azure.submit_circuit(qc, backend_name="rigetti.sim.qvm", shots=1000) + + print(f"✅ Job submitted: {job.job_id()}") + print("⏳ Running on Azure Quantum servers...") + + # Get results + result = job.result() + elapsed = time.time() - start_time + + counts = result.get_counts() + print(f"\n📊 Azure Quantum Results (completed in {elapsed:.2f}s):") + + # Show top 5 measurement outcomes + sorted_counts = sorted(counts.items(), key=lambda x: x[1], reverse=True) + for state, count in sorted_counts[:5]: + percentage = (count / 1000) * 100 + print(f" |{state}⟩: {count}/1000 ({percentage:.1f}%)") + + results.append( + { + "model": model_info["display"], + "local_accuracy": model_info["accuracy"], + "job_id": job.job_id(), + "execution_time": elapsed, + "top_state": sorted_counts[0][0], + "top_count": sorted_counts[0][1], + } + ) + + print(f"✅ {model_info['display']} deployed successfully!\n") + + except Exception as e: + print(f"⚠️ Error deploying {model_info['display']}: {e}\n") + continue + + # Summary + print("=" * 70) + print("✨ DEPLOYMENT COMPLETE!") + print("=" * 70) + print(f"\n🎊 Successfully deployed {len(results)}/3 quantum ML models to Azure!") + print("\n📊 Deployment Summary:\n") + + for i, res in enumerate(results, 1): + print(f"{i}. {res['model']}") + print(f" Local Accuracy: {res['local_accuracy']}%") + print(f" Azure Job ID: {res['job_id']}") + print(f" Execution Time: {res['execution_time']:.2f}s") + print(f" Top State: |{res['top_state']}⟩ ({res['top_count']}/1000 shots)") + print() + + print("💰 Total Cost: $0.00 (free simulators)") + print("\n🏆 Your quantum ML models are now running on Azure cloud infrastructure!") + print("📍 Location: Azure Quantum workspace (quantum-ai-workspace, eastus)") + print("\n🚀 Next steps:") + print(" 1. Try real quantum hardware (IonQ, Quantinuum) - paid tier") + print(" 2. Scale to larger datasets for production deployment") + print(" 3. Benchmark against classical ML models on Azure ML") + print("\n" + "=" * 70) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/deploy_to_azure_quantum.py b/ai-projects/quantum-ml/deploy_to_azure_quantum.py index 30d53b25a..2340fd54b 100644 --- a/ai-projects/quantum-ml/deploy_to_azure_quantum.py +++ b/ai-projects/quantum-ml/deploy_to_azure_quantum.py @@ -1,115 +1,115 @@ -""" -Safe Azure Quantum deployment helper. -- Connects to Azure Quantum workspace using DefaultAzureCredential -- Prefers simulator backends by default to avoid costs -- Submits a small GHZ circuit to validate end-to-end flow - -Usage: - python deploy_to_azure_quantum.py [--backend BACKEND_NAME] [--shots N] - -Notes: -- Requires azure credentials (az login) and a configured workspace in config/quantum_config.yaml -- To run on real QPU, pass a specific backend (e.g., ionq.qpu) consciously -""" - -import argparse -from datetime import datetime -from pathlib import Path - -from qiskit import QuantumCircuit -# Local import -from src.azure_quantum_integration import AzureQuantumIntegration - - -def create_ghz(n_qubits=3): - qc = QuantumCircuit(n_qubits, n_qubits) - qc.h(0) - for i in range(n_qubits - 1): - qc.cx(i, i + 1) - qc.measure(range(n_qubits), range(n_qubits)) - return qc - - -def main(): - parser = argparse.ArgumentParser( - description="Deploy a test circuit to Azure Quantum (simulator by default)" - ) - parser.add_argument( - "--backend", - type=str, - default=None, - help="Target backend (default: prefer simulator)", - ) - parser.add_argument( - "--shots", - type=int, - default=None, - help="Number of shots (default: config value)", - ) - parser.add_argument( - "--qubits", type=int, default=3, help="Number of qubits in GHZ test circuit" - ) - args = parser.parse_args() - - print("=" * 70) - print(" AZURE QUANTUM DEPLOYMENT (Safe Default: Simulator)") - print("=" * 70) - - # Connect - azure = AzureQuantumIntegration() - try: - workspace = azure.connect() - except Exception as e: - print(f"\n[ERROR] Failed to connect to Azure Quantum: {e}") - print( - "Ensure az login is complete and config/quantum_config.yaml is set correctly." - ) - return - - # List backends - try: - backends = azure.list_backends() - print("\nAvailable backends:") - for b in backends: - print(f" - {b}") - except Exception as e: - print(f"\n[WARNING] Could not list backends: {e}") - - # Build test circuit - qc = create_ghz(n_qubits=args.qubits) - print("\nTest circuit (GHZ):") - try: - print(qc.draw(output="text")) - except Exception: - # Fallback if Unicode rendering fails - print( - f"GHZ circuit: {args.qubits} qubits, Hadamard + {args.qubits-1} CNOTs + measurement" - ) - - # Submit job - try: - job = azure.submit_circuit( - qc, backend_name=args.backend, shots=args.shots, job_name="ghz-validation" - ) - results = azure.get_job_results(job) - - # Save - results_dir = Path("results") - results_dir.mkdir(exist_ok=True) - ts = datetime.now().strftime("%Y%m%d_%H%M%S") - out_path = results_dir / f"azure_ghz_results_{ts}.json" - with open(out_path, "w") as f: - import json - - json.dump(results, f, indent=2) - print(f"\n[SUCCESS] Job complete. Results saved to: {out_path}") - except Exception as e: - print(f"\n[ERROR] Submission failed: {e}") - print( - "Tip: This script prefers simulator backends by default via config.\n" - " To target a specific backend, pass --backend explicitly (e.g., --backend quantinuum.sim.h2-1sc)." - ) - - -if __name__ == "__main__": - main() +""" +Safe Azure Quantum deployment helper. +- Connects to Azure Quantum workspace using DefaultAzureCredential +- Prefers simulator backends by default to avoid costs +- Submits a small GHZ circuit to validate end-to-end flow + +Usage: + python deploy_to_azure_quantum.py [--backend BACKEND_NAME] [--shots N] + +Notes: +- Requires azure credentials (az login) and a configured workspace in config/quantum_config.yaml +- To run on real QPU, pass a specific backend (e.g., ionq.qpu) consciously +""" + +import argparse +from datetime import datetime +from pathlib import Path + +from qiskit import QuantumCircuit +# Local import +from src.azure_quantum_integration import AzureQuantumIntegration + + +def create_ghz(n_qubits=3): + qc = QuantumCircuit(n_qubits, n_qubits) + qc.h(0) + for i in range(n_qubits - 1): + qc.cx(i, i + 1) + qc.measure(range(n_qubits), range(n_qubits)) + return qc + + +def main(): + parser = argparse.ArgumentParser( + description="Deploy a test circuit to Azure Quantum (simulator by default)" + ) + parser.add_argument( + "--backend", + type=str, + default=None, + help="Target backend (default: prefer simulator)", + ) + parser.add_argument( + "--shots", + type=int, + default=None, + help="Number of shots (default: config value)", + ) + parser.add_argument( + "--qubits", type=int, default=3, help="Number of qubits in GHZ test circuit" + ) + args = parser.parse_args() + + print("=" * 70) + print(" AZURE QUANTUM DEPLOYMENT (Safe Default: Simulator)") + print("=" * 70) + + # Connect + azure = AzureQuantumIntegration() + try: + workspace = azure.connect() + except Exception as e: + print(f"\n[ERROR] Failed to connect to Azure Quantum: {e}") + print( + "Ensure az login is complete and config/quantum_config.yaml is set correctly." + ) + return + + # List backends + try: + backends = azure.list_backends() + print("\nAvailable backends:") + for b in backends: + print(f" - {b}") + except Exception as e: + print(f"\n[WARNING] Could not list backends: {e}") + + # Build test circuit + qc = create_ghz(n_qubits=args.qubits) + print("\nTest circuit (GHZ):") + try: + print(qc.draw(output="text")) + except Exception: + # Fallback if Unicode rendering fails + print( + f"GHZ circuit: {args.qubits} qubits, Hadamard + {args.qubits-1} CNOTs + measurement" + ) + + # Submit job + try: + job = azure.submit_circuit( + qc, backend_name=args.backend, shots=args.shots, job_name="ghz-validation" + ) + results = azure.get_job_results(job) + + # Save + results_dir = Path("results") + results_dir.mkdir(exist_ok=True) + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + out_path = results_dir / f"azure_ghz_results_{ts}.json" + with open(out_path, "w") as f: + import json + + json.dump(results, f, indent=2) + print(f"\n[SUCCESS] Job complete. Results saved to: {out_path}") + except Exception as e: + print(f"\n[ERROR] Submission failed: {e}") + print( + "Tip: This script prefers simulator backends by default via config.\n" + " To target a specific backend, pass --backend explicitly (e.g., --backend quantinuum.sim.h2-1sc)." + ) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/example_mcp_client.py b/ai-projects/quantum-ml/example_mcp_client.py index e46b11867..b9c2c3ca9 100644 --- a/ai-projects/quantum-ml/example_mcp_client.py +++ b/ai-projects/quantum-ml/example_mcp_client.py @@ -1,273 +1,273 @@ -""" -Example MCP Client for Quantum AI -Demonstrates how to use the Quantum AI MCP server from Python -""" - -import asyncio -import os -from contextlib import AsyncExitStack -from typing import Optional - -from mcp import ClientSession, StdioServerParameters -from mcp.client.stdio import stdio_client - -# Optional: Azure AI for chat with tools -try: - from azure.ai.inference import ChatCompletionsClient # type: ignore - from azure.ai.inference.models import AssistantMessage # type: ignore - from azure.ai.inference.models import (SystemMessage, ToolMessage, - UserMessage) - from azure.core.credentials import AzureKeyCredential # type: ignore - - HAS_AZURE_AI = True -except ImportError: - HAS_AZURE_AI = False - # Note: azure-ai-inference is optional for basic MCP client usage - # Install if you want to use Azure AI chat features: - # pip install azure-ai-inference azure-core - - -class QuantumMCPClient: - """Client for interacting with Quantum AI MCP server""" - - def __init__(self, server_script_path: str = "quantum_mcp_server.py"): - self.server_script_path = server_script_path - self.session: Optional[ClientSession] = None - self.exit_stack = AsyncExitStack() - - async def connect(self): - """Connect to the Quantum AI MCP server""" - server_params = StdioServerParameters( - command="python", args=[self.server_script_path], env=os.environ.copy() - ) - - stdio_transport = await self.exit_stack.enter_async_context( - stdio_client(server_params) - ) - stdio, write = stdio_transport - self.session = await self.exit_stack.enter_async_context( - ClientSession(stdio, write) - ) - await self.session.initialize() - - print("✓ Connected to Quantum AI MCP server") - - # List available tools - response = await self.session.list_tools() - tools = response.tools - print(f"✓ Available tools: {[tool.name for tool in tools]}\n") - - async def create_circuit(self, n_qubits: int, circuit_type: str, **kwargs) -> str: - """Create a quantum circuit""" - args = {"n_qubits": n_qubits, "circuit_type": circuit_type} - args.update(kwargs) - - result = await self.session.call_tool("create_quantum_circuit", args) - print(result.content[0].text) - - # Extract circuit ID from response - text = result.content[0].text - for line in text.split("\n"): - if "Circuit ID:" in line: - return line.split("Circuit ID:")[1].strip().split()[0] - return None - - async def simulate_circuit(self, circuit_id: str, shots: int = 1024): - """Simulate a quantum circuit""" - result = await self.session.call_tool( - "simulate_quantum_circuit", {"circuit_id": circuit_id, "shots": shots} - ) - print(result.content[0].text) - - async def train_classifier(self, dataset: str, **kwargs): - """Train a quantum classifier""" - args = {"dataset": dataset} - args.update(kwargs) - - result = await self.session.call_tool("train_quantum_classifier", args) - print(result.content[0].text) - - async def connect_azure( - self, subscription_id: str, resource_group: str, workspace_name: str - ): - """Connect to Azure Quantum""" - result = await self.session.call_tool( - "connect_azure_quantum", - { - "subscription_id": subscription_id, - "resource_group": resource_group, - "workspace_name": workspace_name, - }, - ) - print(result.content[0].text) - - async def list_backends(self): - """List available quantum backends""" - result = await self.session.call_tool("list_quantum_backends", {}) - print(result.content[0].text) - - async def cleanup(self): - """Clean up resources""" - await self.exit_stack.aclose() - - -async def example_basic_workflow(): - """Example: Create and simulate a Bell state""" - print("=" * 60) - print("Example 1: Create and Simulate a Bell State") - print("=" * 60 + "\n") - - client = QuantumMCPClient() - - try: - await client.connect() - - # Create a Bell state circuit - circuit_id = await client.create_circuit(n_qubits=2, circuit_type="bell") - - print("\n" + "-" * 60 + "\n") - - # Simulate with 10,000 shots - await client.simulate_circuit(circuit_id, shots=10000) - - finally: - await client.cleanup() - - -async def example_ghz_state(): - """Example: Create and simulate a 5-qubit GHZ state""" - print("\n" + "=" * 60) - print("Example 2: 5-Qubit GHZ State (Maximally Entangled)") - print("=" * 60 + "\n") - - client = QuantumMCPClient() - - try: - await client.connect() - - # Create GHZ state - circuit_id = await client.create_circuit(n_qubits=5, circuit_type="ghz") - - print("\n" + "-" * 60 + "\n") - - # Simulate - await client.simulate_circuit(circuit_id, shots=5000) - - finally: - await client.cleanup() - - -async def example_quantum_ml(): - """Example: Train a quantum classifier on Iris dataset""" - print("\n" + "=" * 60) - print("Example 3: Quantum Machine Learning on Iris Dataset") - print("=" * 60 + "\n") - - client = QuantumMCPClient() - - try: - await client.connect() - - # Train quantum classifier - await client.train_classifier( - dataset="iris", n_qubits=4, n_layers=2, epochs=30, entanglement="linear" - ) - - finally: - await client.cleanup() - - -async def example_custom_circuit(): - """Example: Build a custom circuit with specific gates""" - print("\n" + "=" * 60) - print("Example 4: Custom Quantum Circuit") - print("=" * 60 + "\n") - - client = QuantumMCPClient() - - try: - await client.connect() - - # Create custom circuit - circuit_id = await client.create_circuit( - n_qubits=3, - circuit_type="custom", - gates=[ - {"gate": "h", "qubit": 0}, - {"gate": "h", "qubit": 1}, - {"gate": "h", "qubit": 2}, - {"gate": "cx", "qubits": [0, 1]}, - {"gate": "cx", "qubits": [1, 2]}, - {"gate": "x", "qubit": 0}, - ], - ) - - print("\n" + "-" * 60 + "\n") - - # Simulate - await client.simulate_circuit(circuit_id, shots=2048) - - finally: - await client.cleanup() - - -async def example_azure_quantum(): - """Example: Connect to Azure Quantum (requires credentials)""" - print("\n" + "=" * 60) - print("Example 5: Azure Quantum Connection") - print("=" * 60 + "\n") - - # Check for Azure credentials - subscription_id = os.getenv("AZURE_SUBSCRIPTION_ID") - if not subscription_id: - print("⚠ Skipping Azure example - AZURE_SUBSCRIPTION_ID not set") - print("Set environment variables to run this example:") - print(" $env:AZURE_SUBSCRIPTION_ID = ''") - return - - client = QuantumMCPClient() - - try: - await client.connect() - - # Connect to Azure Quantum - await client.connect_azure( - subscription_id=subscription_id, - resource_group="rg-quantum-ai", - workspace_name="quantum-ai-workspace", - ) - - print("\n" + "-" * 60 + "\n") - - # List available backends - await client.list_backends() - - except Exception as e: - print(f"Azure connection failed: {e}") - print("\nMake sure you have:") - print(" 1. Run 'az login'") - print(" 2. Deployed Azure Quantum workspace (see azure/DEPLOYMENT.md)") - print(" 3. Updated environment variables") - - finally: - await client.cleanup() - - -async def main(): - """Run all examples""" - print("\n🔬 Quantum AI MCP Client Examples\n") - - # Run examples - await example_basic_workflow() - await example_ghz_state() - await example_quantum_ml() - await example_custom_circuit() - await example_azure_quantum() - - print("\n" + "=" * 60) - print("✓ All examples completed!") - print("=" * 60 + "\n") - - -if __name__ == "__main__": - asyncio.run(main()) +""" +Example MCP Client for Quantum AI +Demonstrates how to use the Quantum AI MCP server from Python +""" + +import asyncio +import os +from contextlib import AsyncExitStack +from typing import Optional + +from mcp import ClientSession, StdioServerParameters +from mcp.client.stdio import stdio_client + +# Optional: Azure AI for chat with tools +try: + from azure.ai.inference import ChatCompletionsClient # type: ignore + from azure.ai.inference.models import AssistantMessage # type: ignore + from azure.ai.inference.models import (SystemMessage, ToolMessage, + UserMessage) + from azure.core.credentials import AzureKeyCredential # type: ignore + + HAS_AZURE_AI = True +except ImportError: + HAS_AZURE_AI = False + # Note: azure-ai-inference is optional for basic MCP client usage + # Install if you want to use Azure AI chat features: + # pip install azure-ai-inference azure-core + + +class QuantumMCPClient: + """Client for interacting with Quantum AI MCP server""" + + def __init__(self, server_script_path: str = "quantum_mcp_server.py"): + self.server_script_path = server_script_path + self.session: Optional[ClientSession] = None + self.exit_stack = AsyncExitStack() + + async def connect(self): + """Connect to the Quantum AI MCP server""" + server_params = StdioServerParameters( + command="python", args=[self.server_script_path], env=os.environ.copy() + ) + + stdio_transport = await self.exit_stack.enter_async_context( + stdio_client(server_params) + ) + stdio, write = stdio_transport + self.session = await self.exit_stack.enter_async_context( + ClientSession(stdio, write) + ) + await self.session.initialize() + + print("✓ Connected to Quantum AI MCP server") + + # List available tools + response = await self.session.list_tools() + tools = response.tools + print(f"✓ Available tools: {[tool.name for tool in tools]}\n") + + async def create_circuit(self, n_qubits: int, circuit_type: str, **kwargs) -> str: + """Create a quantum circuit""" + args = {"n_qubits": n_qubits, "circuit_type": circuit_type} + args.update(kwargs) + + result = await self.session.call_tool("create_quantum_circuit", args) + print(result.content[0].text) + + # Extract circuit ID from response + text = result.content[0].text + for line in text.split("\n"): + if "Circuit ID:" in line: + return line.split("Circuit ID:")[1].strip().split()[0] + return None + + async def simulate_circuit(self, circuit_id: str, shots: int = 1024): + """Simulate a quantum circuit""" + result = await self.session.call_tool( + "simulate_quantum_circuit", {"circuit_id": circuit_id, "shots": shots} + ) + print(result.content[0].text) + + async def train_classifier(self, dataset: str, **kwargs): + """Train a quantum classifier""" + args = {"dataset": dataset} + args.update(kwargs) + + result = await self.session.call_tool("train_quantum_classifier", args) + print(result.content[0].text) + + async def connect_azure( + self, subscription_id: str, resource_group: str, workspace_name: str + ): + """Connect to Azure Quantum""" + result = await self.session.call_tool( + "connect_azure_quantum", + { + "subscription_id": subscription_id, + "resource_group": resource_group, + "workspace_name": workspace_name, + }, + ) + print(result.content[0].text) + + async def list_backends(self): + """List available quantum backends""" + result = await self.session.call_tool("list_quantum_backends", {}) + print(result.content[0].text) + + async def cleanup(self): + """Clean up resources""" + await self.exit_stack.aclose() + + +async def example_basic_workflow(): + """Example: Create and simulate a Bell state""" + print("=" * 60) + print("Example 1: Create and Simulate a Bell State") + print("=" * 60 + "\n") + + client = QuantumMCPClient() + + try: + await client.connect() + + # Create a Bell state circuit + circuit_id = await client.create_circuit(n_qubits=2, circuit_type="bell") + + print("\n" + "-" * 60 + "\n") + + # Simulate with 10,000 shots + await client.simulate_circuit(circuit_id, shots=10000) + + finally: + await client.cleanup() + + +async def example_ghz_state(): + """Example: Create and simulate a 5-qubit GHZ state""" + print("\n" + "=" * 60) + print("Example 2: 5-Qubit GHZ State (Maximally Entangled)") + print("=" * 60 + "\n") + + client = QuantumMCPClient() + + try: + await client.connect() + + # Create GHZ state + circuit_id = await client.create_circuit(n_qubits=5, circuit_type="ghz") + + print("\n" + "-" * 60 + "\n") + + # Simulate + await client.simulate_circuit(circuit_id, shots=5000) + + finally: + await client.cleanup() + + +async def example_quantum_ml(): + """Example: Train a quantum classifier on Iris dataset""" + print("\n" + "=" * 60) + print("Example 3: Quantum Machine Learning on Iris Dataset") + print("=" * 60 + "\n") + + client = QuantumMCPClient() + + try: + await client.connect() + + # Train quantum classifier + await client.train_classifier( + dataset="iris", n_qubits=4, n_layers=2, epochs=30, entanglement="linear" + ) + + finally: + await client.cleanup() + + +async def example_custom_circuit(): + """Example: Build a custom circuit with specific gates""" + print("\n" + "=" * 60) + print("Example 4: Custom Quantum Circuit") + print("=" * 60 + "\n") + + client = QuantumMCPClient() + + try: + await client.connect() + + # Create custom circuit + circuit_id = await client.create_circuit( + n_qubits=3, + circuit_type="custom", + gates=[ + {"gate": "h", "qubit": 0}, + {"gate": "h", "qubit": 1}, + {"gate": "h", "qubit": 2}, + {"gate": "cx", "qubits": [0, 1]}, + {"gate": "cx", "qubits": [1, 2]}, + {"gate": "x", "qubit": 0}, + ], + ) + + print("\n" + "-" * 60 + "\n") + + # Simulate + await client.simulate_circuit(circuit_id, shots=2048) + + finally: + await client.cleanup() + + +async def example_azure_quantum(): + """Example: Connect to Azure Quantum (requires credentials)""" + print("\n" + "=" * 60) + print("Example 5: Azure Quantum Connection") + print("=" * 60 + "\n") + + # Check for Azure credentials + subscription_id = os.getenv("AZURE_SUBSCRIPTION_ID") + if not subscription_id: + print("⚠ Skipping Azure example - AZURE_SUBSCRIPTION_ID not set") + print("Set environment variables to run this example:") + print(" $env:AZURE_SUBSCRIPTION_ID = ''") + return + + client = QuantumMCPClient() + + try: + await client.connect() + + # Connect to Azure Quantum + await client.connect_azure( + subscription_id=subscription_id, + resource_group="rg-quantum-ai", + workspace_name="quantum-ai-workspace", + ) + + print("\n" + "-" * 60 + "\n") + + # List available backends + await client.list_backends() + + except Exception as e: + print(f"Azure connection failed: {e}") + print("\nMake sure you have:") + print(" 1. Run 'az login'") + print(" 2. Deployed Azure Quantum workspace (see azure/DEPLOYMENT.md)") + print(" 3. Updated environment variables") + + finally: + await client.cleanup() + + +async def main(): + """Run all examples""" + print("\n🔬 Quantum AI MCP Client Examples\n") + + # Run examples + await example_basic_workflow() + await example_ghz_state() + await example_quantum_ml() + await example_custom_circuit() + await example_azure_quantum() + + print("\n" + "=" * 60) + print("✓ All examples completed!") + print("=" * 60 + "\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/ai-projects/quantum-ml/examples/README.md b/ai-projects/quantum-ml/examples/README.md index 6c01577d0..db05e4dde 100644 --- a/ai-projects/quantum-ml/examples/README.md +++ b/ai-projects/quantum-ml/examples/README.md @@ -1,343 +1,343 @@ -# Quantum AI Examples - -This directory contains comprehensive examples demonstrating all capabilities of the Quantum AI project. - -## 📁 Example Files - -### 1. `create_circuits.py` - Creating Quantum Circuits - -Demonstrates various quantum circuit creation patterns: - -- **Bell State**: Maximally entangled 2-qubit state -- **GHZ State**: 3-qubit entanglement -- **Quantum Fourier Transform**: QFT for 3 qubits -- **Variational Quantum Circuit**: For machine learning -- **PennyLane Circuits**: Parameterized quantum circuits -- **Quantum Classifier Circuit**: From our QML model - -**Run:** - -```powershell -python .\examples\create_circuits.py -``` - -**Output:** - -- Visual circuit diagrams in ASCII art -- Circuit configurations and parameters -- Demonstration of different entanglement patterns - ---- - -### 2. `run_simulations.py` - Running Simulations Locally - -Demonstrates local quantum simulation capabilities: - -- **Bell State Simulation**: Tests quantum entanglement -- **Superposition Simulation**: Hadamard gate effects -- **PennyLane with Gradients**: Enables quantum ML -- **State Evolution**: Visualize quantum state changes -- **Scaling Analysis**: Performance across qubit counts -- **Noisy Simulation**: Realistic hardware imperfections - -**Run:** - -```powershell -python .\examples\run_simulations.py -``` - -**Output:** - -- Measurement statistics (1000 shots) -- Gradient computation for QML -- State evolution plot (saved to `results/`) -- Noise modeling demonstration - -**Generated Files:** - -- `results/state_evolution.png` - Quantum state evolution visualization - ---- - -### 3. `train_models.py` - Training Quantum ML Models - -Demonstrates quantum machine learning on real datasets: - -- **Moons Dataset**: Non-linear binary classification -- **Circles Dataset**: Challenging concentric patterns -- **Iris Dataset**: Multi-class problem (binary reduction) -- **Performance Comparison**: Across all datasets - -**Run:** - -```powershell -python .\examples\train_models.py -``` - -**Training Details:** - -- 100 epochs per model -- Batch size: 32 -- Learning rate: 0.01 -- Hybrid quantum-classical architecture - -**Output:** - -- Training progress with loss/accuracy metrics -- Final validation accuracies -- Comparative analysis - -**Generated Files:** - -- `results/training_moons.png` - Loss and accuracy curves -- `results/model_comparison.png` - Performance comparison chart - -**Results:** - -- Moons: ~85% accuracy -- Circles: ~50% accuracy (challenging for current architecture) -- Iris: ~67% accuracy - ---- - -### 4. `azure_integration.py` - Azure Quantum Integration - -Complete guide for integrating with Azure Quantum: - -- **Configuration Check**: Validates Azure setup -- **Circuit Preparation**: Creates Azure-ready circuits -- **Provider Overview**: IonQ, Quantinuum, Rigetti, Microsoft -- **Cost Estimation**: Pricing guide -- **Deployment Steps**: Full setup instructions -- **Security Best Practices**: Azure security guidelines - -**Run:** - -```powershell -python .\examples\azure_integration.py -``` - -**Prerequisites:** - -- Azure subscription (optional for local simulation) -- Azure CLI installed -- Quantum workspace deployed - -**Output:** - -- Configuration status -- Available providers and features -- Cost estimates -- Step-by-step deployment guide -- Example code for Azure Quantum - ---- - -## 🚀 Quick Start - -### Run All Examples - -```powershell -# Ensure you're in the quantum-ai directory with venv activated -cd c:\Users\Bryan\OneDrive\AI\quantum-ai -.\venv\Scripts\Activate.ps1 - -# Run examples in sequence -python .\examples\create_circuits.py -python .\examples\run_simulations.py -python .\examples\train_models.py -python .\examples\azure_integration.py -``` - -### View Results - -```powershell -# All plots are saved to results/ -explorer .\results\ -``` - ---- - -## 📊 Expected Results - -### Circuit Creation - -- 6 different circuit types demonstrated -- Visual ASCII diagrams for each -- Parameter counts and configurations - -### Simulations - -- Bell state: ~50/50 split between |00⟩ and |11⟩ -- Superposition: ~50/50 split between |0⟩ and |1⟩ -- Noisy simulation: Small error rates (~1-2%) - -### ML Training - -- Convergence within 100 epochs -- Moons dataset: Best performance (~85%) -- Iris dataset: Moderate performance (~67%) -- Circles dataset: Challenging (~50% - needs architecture tuning) - -### Azure Integration - -- Configuration validation -- Provider comparison -- Cost breakdown -- Deployment roadmap - ---- - -## 🎯 Learning Path - -### Beginner - -1. Start with `create_circuits.py` to understand quantum gates -2. Move to `run_simulations.py` to see quantum behavior -3. Try `train_models.py` to see quantum ML in action - -### Intermediate - -1. Modify circuit architectures in `create_circuits.py` -2. Experiment with different noise models in `run_simulations.py` -3. Try different hyperparameters in `train_models.py` - -### Advanced - -1. Follow `azure_integration.py` to deploy to real quantum hardware -2. Implement custom quantum algorithms -3. Optimize circuit depth for specific quantum processors - ---- - -## 🔧 Customization - -### Modify Circuit Parameters - -Edit `config/quantum_config.yaml`: - -```yaml -ml: - model: - n_qubits: 4 # Change to 6 or 8 - n_layers: 2 # Increase for more expressiveness - entanglement: "linear" # Try "circular" or "full" -``` - -### Change Training Settings - -```yaml -ml: - training: - epochs: 100 # Increase for better convergence - batch_size: 32 # Adjust based on dataset size - learning_rate: 0.01 # Tune for optimization -``` - -### Test Different Datasets - -In `train_models.py`, replace data generation: - -```python -from sklearn.datasets import make_blobs, make_classification -X, y = make_blobs(n_samples=200, centers=2, random_state=42) -``` - ---- - -## 🐛 Troubleshooting - -### "Device not found" Error - -- Ensure PennyLane is installed: `pip install pennylane` -- Use `default.qubit` for universal compatibility - -### Azure Connection Issues - -1. Verify Azure CLI: `az --version` -2. Check login: `az account show` -3. Confirm workspace exists: `az quantum workspace list` - -### Poor ML Performance - -- Increase epochs (e.g., 200) -- Try different learning rates (0.001 - 0.1) -- Increase circuit layers (2 → 3 or 4) -- Change entanglement pattern - -### Memory Issues - -- Reduce qubit count (especially >10 qubits) -- Decrease batch size -- Use GPU if available: `device='cuda'` - ---- - -## 📚 Additional Resources - -### Documentation - -- [PennyLane Docs](https://docs.pennylane.ai/) -- [Qiskit Tutorials](https://qiskit.org/learn/) -- [Azure Quantum Docs](https://learn.microsoft.com/azure/quantum/) - -### Papers - -- [Variational Quantum Eigensolver](https://arxiv.org/abs/1304.3061) -- [Quantum Machine Learning](https://arxiv.org/abs/1611.09347) -- [QAOA](https://arxiv.org/abs/1411.4028) - -### Community - -- [Qiskit Slack](https://qiskit.slack.com/) -- [PennyLane Forum](https://discuss.pennylane.ai/) -- [Azure Quantum Community](https://quantum.microsoft.com/) - ---- - -## 🎓 Key Concepts Demonstrated - -### Quantum Computing Fundamentals - -- ✓ Superposition -- ✓ Entanglement -- ✓ Quantum gates (H, CNOT, RY, RZ) -- ✓ Measurement and collapse - -### Quantum Machine Learning - -- ✓ Variational quantum circuits -- ✓ Hybrid quantum-classical models -- ✓ Quantum gradients -- ✓ Parameter optimization - -### Practical Quantum Computing - -- ✓ Local simulation -- ✓ Noise modeling -- ✓ Cloud integration (Azure) -- ✓ Cost management - ---- - -## 💡 Next Steps - -1. **Experiment**: Modify parameters and observe changes -2. **Extend**: Add new datasets or circuit designs -3. **Deploy**: Set up Azure Quantum for real hardware access -4. **Optimize**: Tune models for better performance -5. **Research**: Implement quantum algorithms from papers - ---- - -## 📝 Notes - -- All examples are self-contained -- No Azure credentials needed for local simulation -- Results may vary slightly due to randomness -- Some datasets are harder than others (by design) -- Real quantum hardware will show different statistics - ---- - -Happy Quantum Computing! 🌌 +# Quantum AI Examples + +This directory contains comprehensive examples demonstrating all capabilities of the Quantum AI project. + +## 📁 Example Files + +### 1. `create_circuits.py` - Creating Quantum Circuits + +Demonstrates various quantum circuit creation patterns: + +- **Bell State**: Maximally entangled 2-qubit state +- **GHZ State**: 3-qubit entanglement +- **Quantum Fourier Transform**: QFT for 3 qubits +- **Variational Quantum Circuit**: For machine learning +- **PennyLane Circuits**: Parameterized quantum circuits +- **Quantum Classifier Circuit**: From our QML model + +**Run:** + +```powershell +python .\examples\create_circuits.py +``` + +**Output:** + +- Visual circuit diagrams in ASCII art +- Circuit configurations and parameters +- Demonstration of different entanglement patterns + +--- + +### 2. `run_simulations.py` - Running Simulations Locally + +Demonstrates local quantum simulation capabilities: + +- **Bell State Simulation**: Tests quantum entanglement +- **Superposition Simulation**: Hadamard gate effects +- **PennyLane with Gradients**: Enables quantum ML +- **State Evolution**: Visualize quantum state changes +- **Scaling Analysis**: Performance across qubit counts +- **Noisy Simulation**: Realistic hardware imperfections + +**Run:** + +```powershell +python .\examples\run_simulations.py +``` + +**Output:** + +- Measurement statistics (1000 shots) +- Gradient computation for QML +- State evolution plot (saved to `results/`) +- Noise modeling demonstration + +**Generated Files:** + +- `results/state_evolution.png` - Quantum state evolution visualization + +--- + +### 3. `train_models.py` - Training Quantum ML Models + +Demonstrates quantum machine learning on real datasets: + +- **Moons Dataset**: Non-linear binary classification +- **Circles Dataset**: Challenging concentric patterns +- **Iris Dataset**: Multi-class problem (binary reduction) +- **Performance Comparison**: Across all datasets + +**Run:** + +```powershell +python .\examples\train_models.py +``` + +**Training Details:** + +- 100 epochs per model +- Batch size: 32 +- Learning rate: 0.01 +- Hybrid quantum-classical architecture + +**Output:** + +- Training progress with loss/accuracy metrics +- Final validation accuracies +- Comparative analysis + +**Generated Files:** + +- `results/training_moons.png` - Loss and accuracy curves +- `results/model_comparison.png` - Performance comparison chart + +**Results:** + +- Moons: ~85% accuracy +- Circles: ~50% accuracy (challenging for current architecture) +- Iris: ~67% accuracy + +--- + +### 4. `azure_integration.py` - Azure Quantum Integration + +Complete guide for integrating with Azure Quantum: + +- **Configuration Check**: Validates Azure setup +- **Circuit Preparation**: Creates Azure-ready circuits +- **Provider Overview**: IonQ, Quantinuum, Rigetti, Microsoft +- **Cost Estimation**: Pricing guide +- **Deployment Steps**: Full setup instructions +- **Security Best Practices**: Azure security guidelines + +**Run:** + +```powershell +python .\examples\azure_integration.py +``` + +**Prerequisites:** + +- Azure subscription (optional for local simulation) +- Azure CLI installed +- Quantum workspace deployed + +**Output:** + +- Configuration status +- Available providers and features +- Cost estimates +- Step-by-step deployment guide +- Example code for Azure Quantum + +--- + +## 🚀 Quick Start + +### Run All Examples + +```powershell +# Ensure you're in the quantum-ai directory with venv activated +cd c:\Users\Bryan\OneDrive\AI\quantum-ai +.\venv\Scripts\Activate.ps1 + +# Run examples in sequence +python .\examples\create_circuits.py +python .\examples\run_simulations.py +python .\examples\train_models.py +python .\examples\azure_integration.py +``` + +### View Results + +```powershell +# All plots are saved to results/ +explorer .\results\ +``` + +--- + +## 📊 Expected Results + +### Circuit Creation + +- 6 different circuit types demonstrated +- Visual ASCII diagrams for each +- Parameter counts and configurations + +### Simulations + +- Bell state: ~50/50 split between |00⟩ and |11⟩ +- Superposition: ~50/50 split between |0⟩ and |1⟩ +- Noisy simulation: Small error rates (~1-2%) + +### ML Training + +- Convergence within 100 epochs +- Moons dataset: Best performance (~85%) +- Iris dataset: Moderate performance (~67%) +- Circles dataset: Challenging (~50% - needs architecture tuning) + +### Azure Integration + +- Configuration validation +- Provider comparison +- Cost breakdown +- Deployment roadmap + +--- + +## 🎯 Learning Path + +### Beginner + +1. Start with `create_circuits.py` to understand quantum gates +2. Move to `run_simulations.py` to see quantum behavior +3. Try `train_models.py` to see quantum ML in action + +### Intermediate + +1. Modify circuit architectures in `create_circuits.py` +2. Experiment with different noise models in `run_simulations.py` +3. Try different hyperparameters in `train_models.py` + +### Advanced + +1. Follow `azure_integration.py` to deploy to real quantum hardware +2. Implement custom quantum algorithms +3. Optimize circuit depth for specific quantum processors + +--- + +## 🔧 Customization + +### Modify Circuit Parameters + +Edit `config/quantum_config.yaml`: + +```yaml +ml: + model: + n_qubits: 4 # Change to 6 or 8 + n_layers: 2 # Increase for more expressiveness + entanglement: "linear" # Try "circular" or "full" +``` + +### Change Training Settings + +```yaml +ml: + training: + epochs: 100 # Increase for better convergence + batch_size: 32 # Adjust based on dataset size + learning_rate: 0.01 # Tune for optimization +``` + +### Test Different Datasets + +In `train_models.py`, replace data generation: + +```python +from sklearn.datasets import make_blobs, make_classification +X, y = make_blobs(n_samples=200, centers=2, random_state=42) +``` + +--- + +## 🐛 Troubleshooting + +### "Device not found" Error + +- Ensure PennyLane is installed: `pip install pennylane` +- Use `default.qubit` for universal compatibility + +### Azure Connection Issues + +1. Verify Azure CLI: `az --version` +2. Check login: `az account show` +3. Confirm workspace exists: `az quantum workspace list` + +### Poor ML Performance + +- Increase epochs (e.g., 200) +- Try different learning rates (0.001 - 0.1) +- Increase circuit layers (2 → 3 or 4) +- Change entanglement pattern + +### Memory Issues + +- Reduce qubit count (especially >10 qubits) +- Decrease batch size +- Use GPU if available: `device='cuda'` + +--- + +## 📚 Additional Resources + +### Documentation + +- [PennyLane Docs](https://docs.pennylane.ai/) +- [Qiskit Tutorials](https://qiskit.org/learn/) +- [Azure Quantum Docs](https://learn.microsoft.com/azure/quantum/) + +### Papers + +- [Variational Quantum Eigensolver](https://arxiv.org/abs/1304.3061) +- [Quantum Machine Learning](https://arxiv.org/abs/1611.09347) +- [QAOA](https://arxiv.org/abs/1411.4028) + +### Community + +- [Qiskit Slack](https://qiskit.slack.com/) +- [PennyLane Forum](https://discuss.pennylane.ai/) +- [Azure Quantum Community](https://quantum.microsoft.com/) + +--- + +## 🎓 Key Concepts Demonstrated + +### Quantum Computing Fundamentals + +- ✓ Superposition +- ✓ Entanglement +- ✓ Quantum gates (H, CNOT, RY, RZ) +- ✓ Measurement and collapse + +### Quantum Machine Learning + +- ✓ Variational quantum circuits +- ✓ Hybrid quantum-classical models +- ✓ Quantum gradients +- ✓ Parameter optimization + +### Practical Quantum Computing + +- ✓ Local simulation +- ✓ Noise modeling +- ✓ Cloud integration (Azure) +- ✓ Cost management + +--- + +## 💡 Next Steps + +1. **Experiment**: Modify parameters and observe changes +2. **Extend**: Add new datasets or circuit designs +3. **Deploy**: Set up Azure Quantum for real hardware access +4. **Optimize**: Tune models for better performance +5. **Research**: Implement quantum algorithms from papers + +--- + +## 📝 Notes + +- All examples are self-contained +- No Azure credentials needed for local simulation +- Results may vary slightly due to randomness +- Some datasets are harder than others (by design) +- Real quantum hardware will show different statistics + +--- + +Happy Quantum Computing! 🌌 diff --git a/ai-projects/quantum-ml/examples/azure_integration.py b/ai-projects/quantum-ml/examples/azure_integration.py index 1e1c51c5b..039b062f2 100644 --- a/ai-projects/quantum-ml/examples/azure_integration.py +++ b/ai-projects/quantum-ml/examples/azure_integration.py @@ -1,267 +1,267 @@ -""" -Demonstration of Azure Quantum Integration -(Requires Azure credentials and quantum workspace) -""" - -import sys -from pathlib import Path - -from qiskit import QuantumCircuit - -# Add parent directory to path -sys.path.append( - str( - Path(__file__).parent.parent.parent.parent - / "ai-projects" - / "quantum-ml" - / "src" - ) -) - -print("=" * 60) -print("AZURE QUANTUM INTEGRATION GUIDE") -print("=" * 60) - -# ============================================ -# Example 1: Check Configuration -# ============================================ -print("\n1. CHECKING CONFIGURATION") -print("-" * 60) - -import yaml - -config_path = Path(__file__).parent.parent / "config" / "quantum_config.yaml" - -try: - with open(config_path, "r") as f: - config = yaml.safe_load(f) - - azure_config = config.get("azure", {}) - subscription_id = azure_config.get("subscription_id", "") - resource_group = azure_config.get("resource_group", "") - workspace_name = azure_config.get("workspace_name", "") - - print(f"Configuration loaded from: {config_path}") - print(f" Resource Group: {resource_group}") - print(f" Workspace Name: {workspace_name}") - - if not subscription_id: - print("\n⚠️ WARNING: Azure subscription ID not configured!") - print(" Update config/quantum_config.yaml with your Azure details") - configured = False - else: - print(f" Subscription ID: {subscription_id[:8]}...") - configured = True - -except Exception as e: - print(f"❌ Error loading configuration: {e}") - configured = False - -# ============================================ -# Example 2: Create Sample Circuits -# ============================================ -print("\n2. CREATING QUANTUM CIRCUITS FOR AZURE") -print("-" * 60) - -# Bell State -bell = QuantumCircuit(2, 2) -bell.h(0) -bell.cx(0, 1) -bell.measure([0, 1], [0, 1]) - -print("Created circuits:") -print(" • Bell State (2 qubits) - Tests entanglement") -print(" • GHZ State (3 qubits) - Tests multi-qubit entanglement") -print(" • VQE Circuit - For quantum chemistry simulations") - -# ============================================ -# Example 3: Azure Quantum Connection (Demo) -# ============================================ -print("\n3. AZURE QUANTUM CONNECTION") -print("-" * 60) - -if configured: - print("Attempting to connect to Azure Quantum...") - print("(This requires valid Azure credentials)") - - try: - # Import from src directory (already added to sys.path) - from src.azure_quantum_integration import AzureQuantumIntegration - - azure = AzureQuantumIntegration() - - print("\n✓ Azure Quantum module loaded") - print(" To connect, run: workspace = azure.connect()") - print(" Then check: backends = azure.list_backends()") - - # Show what you would do - print("\nTypical workflow:") - print(" 1. workspace = azure.connect()") - print(" 2. backends = azure.list_backends()") - print(" 3. job = azure.submit_circuit(circuit, shots=100)") - print(" 4. results = azure.get_job_results(job)") - - except Exception as e: - print(f"⚠️ Could not import Azure Quantum integration: {e}") - print(" This is expected if Azure credentials are not configured") - -else: - print("❌ Azure Quantum not configured") - print(" Please update config/quantum_config.yaml") - -# ============================================ -# Example 4: Available Providers -# ============================================ -print("\n4. AVAILABLE QUANTUM PROVIDERS") -print("-" * 60) - -providers = { - "IonQ": { - "Type": "Trapped Ion", - "Qubits": "11-29", - "Features": "All-to-all connectivity, high fidelity", - "Best For": "Small-scale algorithms, quantum chemistry", - }, - "Quantinuum": { - "Type": "Trapped Ion", - "Qubits": "20-32", - "Features": "Mid-circuit measurement, feed-forward", - "Best For": "Error correction, complex algorithms", - }, - "Rigetti": { - "Type": "Superconducting", - "Qubits": "40+", - "Features": "Fast gates, tunable architecture", - "Best For": "QAOA, VQE, optimization", - }, - "Microsoft": { - "Type": "Simulator", - "Qubits": "Up to 40", - "Features": "Free tier, noise simulation", - "Best For": "Testing, development, education", - }, -} - -for provider, info in providers.items(): - print(f"\n{provider}:") - for key, value in info.items(): - print(f" {key:12s}: {value}") - -# ============================================ -# Example 5: Cost Estimation -# ============================================ -print("\n5. COST ESTIMATION") -print("-" * 60) - -print("Approximate costs (as of 2025):") -print("\nMicrosoft Simulators:") -print(" • Free tier available") -print(" • H-series simulators: ~$0.50/hour") -print("\nIonQ:") -print(" • ~$0.00003 per gate-shot") -print(" • 100-gate circuit, 1000 shots: ~$3.00") -print("\nQuantinuum:") -print(" • ~$0.00015 per circuit execution") -print(" • H-series credits: ~$0.80-1.50 per circuit") -print("\nRigetti:") -print(" • Contact for enterprise pricing") - -print("\n💡 TIP: Always test on free simulators first!") - -# ============================================ -# Example 6: Deployment Steps -# ============================================ -print("\n6. AZURE QUANTUM DEPLOYMENT STEPS") -print("-" * 60) - -steps = [ - "1. Install Azure CLI: https://aka.ms/install-azure-cli", - "2. Login: az login", - "3. Set subscription: az account set --subscription ", - "4. Create resource group:", - " az group create --name rg-quantum-ai --location eastus", - "5. Deploy workspace (using Bicep):", - " cd azure/", - " az deployment group create \\", - " --resource-group rg-quantum-ai \\", - " --template-file quantum_workspace.bicep \\", - " --parameters quantum_workspace.parameters.json", - "6. Update config/quantum_config.yaml with workspace details", - "7. Test connection: python examples/azure_integration.py", -] - -for step in steps: - print(step) - -# ============================================ -# Example 7: Security Best Practices -# ============================================ -print("\n7. SECURITY BEST PRACTICES") -print("-" * 60) - -print("✓ Use Azure Key Vault for credentials") -print("✓ Enable Azure AD authentication") -print("✓ Use service principals for automation") -print("✓ Implement network restrictions (if needed)") -print("✓ Monitor costs with Azure Cost Management") -print("✓ Enable Azure Monitor for job tracking") - -# ============================================ -# Example 8: Example Code -# ============================================ -print("\n8. EXAMPLE CODE (once configured)") -print("-" * 60) - -example_code = """ -from azure_quantum_integration import AzureQuantumIntegration -from qiskit import QuantumCircuit - -# Initialize connection -azure = AzureQuantumIntegration() -workspace = azure.connect() - -# Create circuit -circuit = QuantumCircuit(2, 2) -circuit.h(0) -circuit.cx(0, 1) -circuit.measure([0, 1], [0, 1]) - -# List available backends -backends = azure.list_backends() -print("Available backends:", backends) - -# Submit job (to IonQ simulator) -job = azure.submit_circuit( - circuit=circuit, - shots=100, - job_name="bell_state_test", - backend="ionq.simulator" -) - -# Get results -results = azure.get_job_results(job) -print("Measurement results:", results) -""" - -print(example_code) - -# ============================================ -# Summary -# ============================================ -print("=" * 60) -print("AZURE QUANTUM INTEGRATION GUIDE COMPLETED!") -print("=" * 60) - -print("\nNext Steps:") -if configured: - print(" ✓ Configuration found") - print(" → Try connecting: from azure_quantum_integration import *") - print(" → See: azure/DEPLOYMENT.md for full deployment guide") -else: - print(" 1. Set up Azure Quantum workspace (see azure/DEPLOYMENT.md)") - print(" 2. Update config/quantum_config.yaml") - print(" 3. Run this script again to test connection") - -print("\n📚 Full documentation: azure/DEPLOYMENT.md") -print("📊 Example circuits ready for Azure Quantum") -print("🎯 Start with free Microsoft simulators!") +""" +Demonstration of Azure Quantum Integration +(Requires Azure credentials and quantum workspace) +""" + +import sys +from pathlib import Path + +from qiskit import QuantumCircuit + +# Add parent directory to path +sys.path.append( + str( + Path(__file__).parent.parent.parent.parent + / "ai-projects" + / "quantum-ml" + / "src" + ) +) + +print("=" * 60) +print("AZURE QUANTUM INTEGRATION GUIDE") +print("=" * 60) + +# ============================================ +# Example 1: Check Configuration +# ============================================ +print("\n1. CHECKING CONFIGURATION") +print("-" * 60) + +import yaml + +config_path = Path(__file__).parent.parent / "config" / "quantum_config.yaml" + +try: + with open(config_path, "r") as f: + config = yaml.safe_load(f) + + azure_config = config.get("azure", {}) + subscription_id = azure_config.get("subscription_id", "") + resource_group = azure_config.get("resource_group", "") + workspace_name = azure_config.get("workspace_name", "") + + print(f"Configuration loaded from: {config_path}") + print(f" Resource Group: {resource_group}") + print(f" Workspace Name: {workspace_name}") + + if not subscription_id: + print("\n⚠️ WARNING: Azure subscription ID not configured!") + print(" Update config/quantum_config.yaml with your Azure details") + configured = False + else: + print(f" Subscription ID: {subscription_id[:8]}...") + configured = True + +except Exception as e: + print(f"❌ Error loading configuration: {e}") + configured = False + +# ============================================ +# Example 2: Create Sample Circuits +# ============================================ +print("\n2. CREATING QUANTUM CIRCUITS FOR AZURE") +print("-" * 60) + +# Bell State +bell = QuantumCircuit(2, 2) +bell.h(0) +bell.cx(0, 1) +bell.measure([0, 1], [0, 1]) + +print("Created circuits:") +print(" • Bell State (2 qubits) - Tests entanglement") +print(" • GHZ State (3 qubits) - Tests multi-qubit entanglement") +print(" • VQE Circuit - For quantum chemistry simulations") + +# ============================================ +# Example 3: Azure Quantum Connection (Demo) +# ============================================ +print("\n3. AZURE QUANTUM CONNECTION") +print("-" * 60) + +if configured: + print("Attempting to connect to Azure Quantum...") + print("(This requires valid Azure credentials)") + + try: + # Import from src directory (already added to sys.path) + from src.azure_quantum_integration import AzureQuantumIntegration + + azure = AzureQuantumIntegration() + + print("\n✓ Azure Quantum module loaded") + print(" To connect, run: workspace = azure.connect()") + print(" Then check: backends = azure.list_backends()") + + # Show what you would do + print("\nTypical workflow:") + print(" 1. workspace = azure.connect()") + print(" 2. backends = azure.list_backends()") + print(" 3. job = azure.submit_circuit(circuit, shots=100)") + print(" 4. results = azure.get_job_results(job)") + + except Exception as e: + print(f"⚠️ Could not import Azure Quantum integration: {e}") + print(" This is expected if Azure credentials are not configured") + +else: + print("❌ Azure Quantum not configured") + print(" Please update config/quantum_config.yaml") + +# ============================================ +# Example 4: Available Providers +# ============================================ +print("\n4. AVAILABLE QUANTUM PROVIDERS") +print("-" * 60) + +providers = { + "IonQ": { + "Type": "Trapped Ion", + "Qubits": "11-29", + "Features": "All-to-all connectivity, high fidelity", + "Best For": "Small-scale algorithms, quantum chemistry", + }, + "Quantinuum": { + "Type": "Trapped Ion", + "Qubits": "20-32", + "Features": "Mid-circuit measurement, feed-forward", + "Best For": "Error correction, complex algorithms", + }, + "Rigetti": { + "Type": "Superconducting", + "Qubits": "40+", + "Features": "Fast gates, tunable architecture", + "Best For": "QAOA, VQE, optimization", + }, + "Microsoft": { + "Type": "Simulator", + "Qubits": "Up to 40", + "Features": "Free tier, noise simulation", + "Best For": "Testing, development, education", + }, +} + +for provider, info in providers.items(): + print(f"\n{provider}:") + for key, value in info.items(): + print(f" {key:12s}: {value}") + +# ============================================ +# Example 5: Cost Estimation +# ============================================ +print("\n5. COST ESTIMATION") +print("-" * 60) + +print("Approximate costs (as of 2025):") +print("\nMicrosoft Simulators:") +print(" • Free tier available") +print(" • H-series simulators: ~$0.50/hour") +print("\nIonQ:") +print(" • ~$0.00003 per gate-shot") +print(" • 100-gate circuit, 1000 shots: ~$3.00") +print("\nQuantinuum:") +print(" • ~$0.00015 per circuit execution") +print(" • H-series credits: ~$0.80-1.50 per circuit") +print("\nRigetti:") +print(" • Contact for enterprise pricing") + +print("\n💡 TIP: Always test on free simulators first!") + +# ============================================ +# Example 6: Deployment Steps +# ============================================ +print("\n6. AZURE QUANTUM DEPLOYMENT STEPS") +print("-" * 60) + +steps = [ + "1. Install Azure CLI: https://aka.ms/install-azure-cli", + "2. Login: az login", + "3. Set subscription: az account set --subscription ", + "4. Create resource group:", + " az group create --name rg-quantum-ai --location eastus", + "5. Deploy workspace (using Bicep):", + " cd azure/", + " az deployment group create \\", + " --resource-group rg-quantum-ai \\", + " --template-file quantum_workspace.bicep \\", + " --parameters quantum_workspace.parameters.json", + "6. Update config/quantum_config.yaml with workspace details", + "7. Test connection: python examples/azure_integration.py", +] + +for step in steps: + print(step) + +# ============================================ +# Example 7: Security Best Practices +# ============================================ +print("\n7. SECURITY BEST PRACTICES") +print("-" * 60) + +print("✓ Use Azure Key Vault for credentials") +print("✓ Enable Azure AD authentication") +print("✓ Use service principals for automation") +print("✓ Implement network restrictions (if needed)") +print("✓ Monitor costs with Azure Cost Management") +print("✓ Enable Azure Monitor for job tracking") + +# ============================================ +# Example 8: Example Code +# ============================================ +print("\n8. EXAMPLE CODE (once configured)") +print("-" * 60) + +example_code = """ +from azure_quantum_integration import AzureQuantumIntegration +from qiskit import QuantumCircuit + +# Initialize connection +azure = AzureQuantumIntegration() +workspace = azure.connect() + +# Create circuit +circuit = QuantumCircuit(2, 2) +circuit.h(0) +circuit.cx(0, 1) +circuit.measure([0, 1], [0, 1]) + +# List available backends +backends = azure.list_backends() +print("Available backends:", backends) + +# Submit job (to IonQ simulator) +job = azure.submit_circuit( + circuit=circuit, + shots=100, + job_name="bell_state_test", + backend="ionq.simulator" +) + +# Get results +results = azure.get_job_results(job) +print("Measurement results:", results) +""" + +print(example_code) + +# ============================================ +# Summary +# ============================================ +print("=" * 60) +print("AZURE QUANTUM INTEGRATION GUIDE COMPLETED!") +print("=" * 60) + +print("\nNext Steps:") +if configured: + print(" ✓ Configuration found") + print(" → Try connecting: from azure_quantum_integration import *") + print(" → See: azure/DEPLOYMENT.md for full deployment guide") +else: + print(" 1. Set up Azure Quantum workspace (see azure/DEPLOYMENT.md)") + print(" 2. Update config/quantum_config.yaml") + print(" 3. Run this script again to test connection") + +print("\n📚 Full documentation: azure/DEPLOYMENT.md") +print("📊 Example circuits ready for Azure Quantum") +print("🎯 Start with free Microsoft simulators!") diff --git a/ai-projects/quantum-ml/examples/create_circuits.py b/ai-projects/quantum-ml/examples/create_circuits.py index feeb087d4..69df82ff9 100644 --- a/ai-projects/quantum-ml/examples/create_circuits.py +++ b/ai-projects/quantum-ml/examples/create_circuits.py @@ -1,161 +1,161 @@ -""" -Demonstration of creating various quantum circuits -""" - -import sys -from pathlib import Path - -import numpy as np -import pennylane as qml -from qiskit import QuantumCircuit -from qiskit.circuit import Parameter - -# Add parent directory to path for imports -sys.path.append( - str( - Path(__file__).parent.parent.parent.parent - / "ai-projects" - / "quantum-ml" - / "src" - ) -) - -print("=" * 60) -print("QUANTUM CIRCUIT CREATION EXAMPLES") -print("=" * 60) - -# ============================================ -# Example 1: Bell State (Entanglement) -# ============================================ -print("\n1. BELL STATE CIRCUIT") -print("-" * 60) - -bell_circuit = QuantumCircuit(2, 2) -bell_circuit.h(0) # Hadamard on qubit 0 -bell_circuit.cx(0, 1) # CNOT from qubit 0 to 1 -bell_circuit.measure([0, 1], [0, 1]) - -print("Circuit: Creates maximally entangled state |Φ+⟩ = (|00⟩ + |11⟩)/√2") -print(bell_circuit) - -# ============================================ -# Example 2: GHZ State (3-qubit entanglement) -# ============================================ -print("\n2. GHZ STATE CIRCUIT (3 qubits)") -print("-" * 60) - -ghz_circuit = QuantumCircuit(3, 3) -ghz_circuit.h(0) -ghz_circuit.cx(0, 1) -ghz_circuit.cx(0, 2) -ghz_circuit.measure([0, 1, 2], [0, 1, 2]) - -print("Circuit: Creates |GHZ⟩ = (|000⟩ + |111⟩)/√2") -print(ghz_circuit) - -# ============================================ -# Example 3: Quantum Fourier Transform -# ============================================ -print("\n3. QUANTUM FOURIER TRANSFORM (3 qubits)") -print("-" * 60) - - -def create_qft_circuit(n_qubits): - """Create a Quantum Fourier Transform circuit""" - qc = QuantumCircuit(n_qubits) - - for j in range(n_qubits): - qc.h(j) - for k in range(j + 1, n_qubits): - qc.cp(np.pi / (2 ** (k - j)), k, j) - - # Swap qubits - for i in range(n_qubits // 2): - qc.swap(i, n_qubits - i - 1) - - return qc - - -qft_circuit = create_qft_circuit(3) -print("Circuit: Quantum Fourier Transform for 3 qubits") -print(qft_circuit) - -# ============================================ -# Example 4: Variational Quantum Circuit (for ML) -# ============================================ -print("\n4. VARIATIONAL QUANTUM CIRCUIT (for Machine Learning)") -print("-" * 60) - -n_qubits = 4 -n_layers = 2 - -vqc = QuantumCircuit(n_qubits) - -print(f"Circuit: {n_layers}-layer variational circuit with {n_qubits} qubits") -print("Features: RY rotations + Linear entanglement") - -# Create parameters -params = [ - [Parameter(f"θ_{layer}_{i}_{gate}") for gate in ["y", "z"]] - for i in range(n_qubits) - for layer in range(n_layers) -] - -for layer in range(n_layers): - # Rotation layer - for i in range(n_qubits): - theta_y = Parameter(f"θ_{layer}_{i}_y") - theta_z = Parameter(f"θ_{layer}_{i}_z") - vqc.ry(theta_y, i) - vqc.rz(theta_z, i) - - # Entanglement layer (linear) - for i in range(n_qubits - 1): - vqc.cx(i, i + 1) - -print(vqc) - -# ============================================ -# Example 5: PennyLane Circuit -# ============================================ -print("\n5. PENNYLANE QUANTUM CIRCUIT") -print("-" * 60) - -dev = qml.device("default.qubit", wires=2) - - -@qml.qnode(dev) -def pennylane_circuit(params): - """PennyLane circuit with parameterized rotations""" - qml.RY(params[0], wires=0) - qml.RY(params[1], wires=1) - qml.CNOT(wires=[0, 1]) - return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)) - - -print("Circuit: PennyLane parameterized circuit") -print("Operations: RY(θ0) on qubit 0, RY(θ1) on qubit 1, CNOT(0→1)") -print("Measurements: Expectation values of Z on both qubits") -print(qml.draw(pennylane_circuit)([0.5, 1.0])) - -# ============================================ -# Example 6: Custom Quantum Classifier Circuit -# ============================================ -print("\n6. QUANTUM CLASSIFIER CIRCUIT (from our QML model)") -print("-" * 60) - -from src.quantum_classifier import QuantumClassifier - -qc = QuantumClassifier() -weights = qc.initialize_weights() - -print("Circuit configuration:") -print(f" - Qubits: {qc.n_qubits}") -print(f" - Layers: {qc.n_layers}") -print(f" - Entanglement: {qc.entanglement}") -print(f" - Parameters: {weights.numel()}") -print("\nThis circuit is used for hybrid quantum-classical classification!") - -print("\n" + "=" * 60) -print("CIRCUIT CREATION COMPLETED!") -print("=" * 60) +""" +Demonstration of creating various quantum circuits +""" + +import sys +from pathlib import Path + +import numpy as np +import pennylane as qml +from qiskit import QuantumCircuit +from qiskit.circuit import Parameter + +# Add parent directory to path for imports +sys.path.append( + str( + Path(__file__).parent.parent.parent.parent + / "ai-projects" + / "quantum-ml" + / "src" + ) +) + +print("=" * 60) +print("QUANTUM CIRCUIT CREATION EXAMPLES") +print("=" * 60) + +# ============================================ +# Example 1: Bell State (Entanglement) +# ============================================ +print("\n1. BELL STATE CIRCUIT") +print("-" * 60) + +bell_circuit = QuantumCircuit(2, 2) +bell_circuit.h(0) # Hadamard on qubit 0 +bell_circuit.cx(0, 1) # CNOT from qubit 0 to 1 +bell_circuit.measure([0, 1], [0, 1]) + +print("Circuit: Creates maximally entangled state |Φ+⟩ = (|00⟩ + |11⟩)/√2") +print(bell_circuit) + +# ============================================ +# Example 2: GHZ State (3-qubit entanglement) +# ============================================ +print("\n2. GHZ STATE CIRCUIT (3 qubits)") +print("-" * 60) + +ghz_circuit = QuantumCircuit(3, 3) +ghz_circuit.h(0) +ghz_circuit.cx(0, 1) +ghz_circuit.cx(0, 2) +ghz_circuit.measure([0, 1, 2], [0, 1, 2]) + +print("Circuit: Creates |GHZ⟩ = (|000⟩ + |111⟩)/√2") +print(ghz_circuit) + +# ============================================ +# Example 3: Quantum Fourier Transform +# ============================================ +print("\n3. QUANTUM FOURIER TRANSFORM (3 qubits)") +print("-" * 60) + + +def create_qft_circuit(n_qubits): + """Create a Quantum Fourier Transform circuit""" + qc = QuantumCircuit(n_qubits) + + for j in range(n_qubits): + qc.h(j) + for k in range(j + 1, n_qubits): + qc.cp(np.pi / (2 ** (k - j)), k, j) + + # Swap qubits + for i in range(n_qubits // 2): + qc.swap(i, n_qubits - i - 1) + + return qc + + +qft_circuit = create_qft_circuit(3) +print("Circuit: Quantum Fourier Transform for 3 qubits") +print(qft_circuit) + +# ============================================ +# Example 4: Variational Quantum Circuit (for ML) +# ============================================ +print("\n4. VARIATIONAL QUANTUM CIRCUIT (for Machine Learning)") +print("-" * 60) + +n_qubits = 4 +n_layers = 2 + +vqc = QuantumCircuit(n_qubits) + +print(f"Circuit: {n_layers}-layer variational circuit with {n_qubits} qubits") +print("Features: RY rotations + Linear entanglement") + +# Create parameters +params = [ + [Parameter(f"θ_{layer}_{i}_{gate}") for gate in ["y", "z"]] + for i in range(n_qubits) + for layer in range(n_layers) +] + +for layer in range(n_layers): + # Rotation layer + for i in range(n_qubits): + theta_y = Parameter(f"θ_{layer}_{i}_y") + theta_z = Parameter(f"θ_{layer}_{i}_z") + vqc.ry(theta_y, i) + vqc.rz(theta_z, i) + + # Entanglement layer (linear) + for i in range(n_qubits - 1): + vqc.cx(i, i + 1) + +print(vqc) + +# ============================================ +# Example 5: PennyLane Circuit +# ============================================ +print("\n5. PENNYLANE QUANTUM CIRCUIT") +print("-" * 60) + +dev = qml.device("default.qubit", wires=2) + + +@qml.qnode(dev) +def pennylane_circuit(params): + """PennyLane circuit with parameterized rotations""" + qml.RY(params[0], wires=0) + qml.RY(params[1], wires=1) + qml.CNOT(wires=[0, 1]) + return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)) + + +print("Circuit: PennyLane parameterized circuit") +print("Operations: RY(θ0) on qubit 0, RY(θ1) on qubit 1, CNOT(0→1)") +print("Measurements: Expectation values of Z on both qubits") +print(qml.draw(pennylane_circuit)([0.5, 1.0])) + +# ============================================ +# Example 6: Custom Quantum Classifier Circuit +# ============================================ +print("\n6. QUANTUM CLASSIFIER CIRCUIT (from our QML model)") +print("-" * 60) + +from src.quantum_classifier import QuantumClassifier + +qc = QuantumClassifier() +weights = qc.initialize_weights() + +print("Circuit configuration:") +print(f" - Qubits: {qc.n_qubits}") +print(f" - Layers: {qc.n_layers}") +print(f" - Entanglement: {qc.entanglement}") +print(f" - Parameters: {weights.numel()}") +print("\nThis circuit is used for hybrid quantum-classical classification!") + +print("\n" + "=" * 60) +print("CIRCUIT CREATION COMPLETED!") +print("=" * 60) diff --git a/ai-projects/quantum-ml/examples/run_simulations.py b/ai-projects/quantum-ml/examples/run_simulations.py index 580ab53a2..3596b82cf 100644 --- a/ai-projects/quantum-ml/examples/run_simulations.py +++ b/ai-projects/quantum-ml/examples/run_simulations.py @@ -1,196 +1,196 @@ -""" -Demonstration of running quantum simulations locally -""" - -import matplotlib -import numpy as np -import pennylane as qml -from qiskit import QuantumCircuit, transpile -from qiskit_aer import Aer - -matplotlib.use("Agg") # Non-interactive backend -from pathlib import Path - -import matplotlib.pyplot as plt - -# Create results directory -results_dir = Path(__file__).parent.parent / "results" -results_dir.mkdir(exist_ok=True) - -print("=" * 60) -print("QUANTUM SIMULATION EXAMPLES") -print("=" * 60) - -# ============================================ -# Example 1: Bell State Simulation -# ============================================ -print("\n1. SIMULATING BELL STATE") -print("-" * 60) - -bell = QuantumCircuit(2, 2) -bell.h(0) -bell.cx(0, 1) -bell.measure([0, 1], [0, 1]) - -# Run simulation -simulator = Aer.get_backend("aer_simulator") -compiled_circuit = transpile(bell, simulator) -job = simulator.run(compiled_circuit, shots=1000) -result = job.result() -counts = result.get_counts() - -print("Bell state measurement results (1000 shots):") -for state, count in sorted(counts.items()): - percentage = (count / 1000) * 100 - bar = "█" * int(percentage / 2) - print(f" |{state}⟩: {count:4d} ({percentage:5.1f}%) {bar}") - -print("\nExpected: ~50% |00⟩ and ~50% |11⟩ (entangled state)") -print("This demonstrates quantum entanglement!") - -# ============================================ -# Example 2: Superposition Simulation -# ============================================ -print("\n2. SIMULATING SUPERPOSITION") -print("-" * 60) - -superposition = QuantumCircuit(1, 1) -superposition.h(0) # Put qubit in superposition -superposition.measure(0, 0) - -job = simulator.run(transpile(superposition, simulator), shots=1000) -result = job.result() -counts = result.get_counts() - -print("Hadamard gate (superposition) results (1000 shots):") -for state, count in sorted(counts.items()): - percentage = (count / 1000) * 100 - bar = "█" * int(percentage / 2) - print(f" |{state}⟩: {count:4d} ({percentage:5.1f}%) {bar}") - -print("\nExpected: ~50% |0⟩ and ~50% |1⟩") - -# ============================================ -# Example 3: PennyLane Simulation with Gradients -# ============================================ -print("\n3. PENNYLANE SIMULATION WITH GRADIENTS") -print("-" * 60) - -dev = qml.device("default.qubit", wires=2) - - -@qml.qnode(dev) -def parameterized_circuit(params): - qml.RY(params[0], wires=0) - qml.RY(params[1], wires=1) - qml.CNOT(wires=[0, 1]) - return qml.expval(qml.PauliZ(0)) - - -# Compute gradient -params = np.array([0.5, 1.0]) -gradient = qml.grad(parameterized_circuit)(params) - -print(f"Parameters: {params}") -print(f"Expectation value: {parameterized_circuit(params):.4f}") -print(f"Gradient: {gradient}") -print("\nThis enables quantum machine learning with backpropagation!") - -# ============================================ -# Example 4: Quantum State Tomography Simulation -# ============================================ -print("\n4. QUANTUM STATE EVOLUTION") -print("-" * 60) - - -@qml.qnode(dev) -def evolving_state(angle): - qml.RY(angle, wires=0) - return qml.expval(qml.PauliZ(0)) - - -angles = np.linspace(0, 2 * np.pi, 50) -expectations = [evolving_state(angle) for angle in angles] - -print("Simulating quantum state evolution...") -print(f"Angles tested: {len(angles)}") -print(f"Expectation range: [{min(expectations):.4f}, {max(expectations):.4f}]") - -# Save plot -plt.figure(figsize=(10, 6)) -plt.plot(angles, expectations, "b-", linewidth=2) -plt.xlabel("Rotation Angle (radians)", fontsize=12) -plt.ylabel("⟨Z⟩ Expectation Value", fontsize=12) -plt.title("Quantum State Evolution: RY Rotation", fontsize=14) -plt.grid(True, alpha=0.3) -plt.axhline(y=0, color="r", linestyle="--", alpha=0.5) -plot_path = results_dir / "state_evolution.png" -plt.savefig(plot_path, dpi=150, bbox_inches="tight") -print(f"Plot saved to: {plot_path}") - -# ============================================ -# Example 5: Multi-Qubit Simulation Performance -# ============================================ -print("\n5. SCALING SIMULATION") -print("-" * 60) - -for n_qubits in [2, 4, 6, 8]: - dev_temp = qml.device("default.qubit", wires=n_qubits) - - @qml.qnode(dev_temp) - def benchmark_circuit(params): - for i in range(n_qubits): - qml.RY(params[i], wires=i) - for i in range(n_qubits - 1): - qml.CNOT(wires=[i, i + 1]) - return qml.expval(qml.PauliZ(0)) - - params = np.random.random(n_qubits) - result = benchmark_circuit(params) - state_dim = 2**n_qubits - - print( - f" {n_qubits} qubits: State dimension = {state_dim:5d}, Result = {result:.4f}" - ) - -print("\nNote: Classical simulation becomes exponentially harder!") -print("Real quantum hardware scales linearly with qubits.") - -# ============================================ -# Example 6: Noisy Simulation -# ============================================ -print("\n6. NOISY QUANTUM SIMULATION") -print("-" * 60) - -from qiskit_aer.noise import NoiseModel, depolarizing_error - -# Create noise model -noise_model = NoiseModel() -error = depolarizing_error(0.01, 1) # 1% error rate -noise_model.add_all_qubit_quantum_error(error, ["h", "x"]) -error_2q = depolarizing_error(0.02, 2) # 2% error on 2-qubit gates -noise_model.add_all_qubit_quantum_error(error_2q, ["cx"]) - -# Simulate with noise -bell_noisy = QuantumCircuit(2, 2) -bell_noisy.h(0) -bell_noisy.cx(0, 1) -bell_noisy.measure([0, 1], [0, 1]) - -job_noisy = simulator.run( - transpile(bell_noisy, simulator), shots=1000, noise_model=noise_model -) -counts_noisy = job_noisy.result().get_counts() - -print("Noisy Bell state (1% single-qubit, 2% two-qubit error):") -for state, count in sorted(counts_noisy.items()): - percentage = (count / 1000) * 100 - bar = "█" * int(percentage / 2) - print(f" |{state}⟩: {count:4d} ({percentage:5.1f}%) {bar}") - -print("\nNote: Non-ideal states (|01⟩, |10⟩) appear due to noise.") -print("This simulates real quantum hardware imperfections!") - -print("\n" + "=" * 60) -print("SIMULATION EXAMPLES COMPLETED!") -print("=" * 60) +""" +Demonstration of running quantum simulations locally +""" + +import matplotlib +import numpy as np +import pennylane as qml +from qiskit import QuantumCircuit, transpile +from qiskit_aer import Aer + +matplotlib.use("Agg") # Non-interactive backend +from pathlib import Path + +import matplotlib.pyplot as plt + +# Create results directory +results_dir = Path(__file__).parent.parent / "results" +results_dir.mkdir(exist_ok=True) + +print("=" * 60) +print("QUANTUM SIMULATION EXAMPLES") +print("=" * 60) + +# ============================================ +# Example 1: Bell State Simulation +# ============================================ +print("\n1. SIMULATING BELL STATE") +print("-" * 60) + +bell = QuantumCircuit(2, 2) +bell.h(0) +bell.cx(0, 1) +bell.measure([0, 1], [0, 1]) + +# Run simulation +simulator = Aer.get_backend("aer_simulator") +compiled_circuit = transpile(bell, simulator) +job = simulator.run(compiled_circuit, shots=1000) +result = job.result() +counts = result.get_counts() + +print("Bell state measurement results (1000 shots):") +for state, count in sorted(counts.items()): + percentage = (count / 1000) * 100 + bar = "█" * int(percentage / 2) + print(f" |{state}⟩: {count:4d} ({percentage:5.1f}%) {bar}") + +print("\nExpected: ~50% |00⟩ and ~50% |11⟩ (entangled state)") +print("This demonstrates quantum entanglement!") + +# ============================================ +# Example 2: Superposition Simulation +# ============================================ +print("\n2. SIMULATING SUPERPOSITION") +print("-" * 60) + +superposition = QuantumCircuit(1, 1) +superposition.h(0) # Put qubit in superposition +superposition.measure(0, 0) + +job = simulator.run(transpile(superposition, simulator), shots=1000) +result = job.result() +counts = result.get_counts() + +print("Hadamard gate (superposition) results (1000 shots):") +for state, count in sorted(counts.items()): + percentage = (count / 1000) * 100 + bar = "█" * int(percentage / 2) + print(f" |{state}⟩: {count:4d} ({percentage:5.1f}%) {bar}") + +print("\nExpected: ~50% |0⟩ and ~50% |1⟩") + +# ============================================ +# Example 3: PennyLane Simulation with Gradients +# ============================================ +print("\n3. PENNYLANE SIMULATION WITH GRADIENTS") +print("-" * 60) + +dev = qml.device("default.qubit", wires=2) + + +@qml.qnode(dev) +def parameterized_circuit(params): + qml.RY(params[0], wires=0) + qml.RY(params[1], wires=1) + qml.CNOT(wires=[0, 1]) + return qml.expval(qml.PauliZ(0)) + + +# Compute gradient +params = np.array([0.5, 1.0]) +gradient = qml.grad(parameterized_circuit)(params) + +print(f"Parameters: {params}") +print(f"Expectation value: {parameterized_circuit(params):.4f}") +print(f"Gradient: {gradient}") +print("\nThis enables quantum machine learning with backpropagation!") + +# ============================================ +# Example 4: Quantum State Tomography Simulation +# ============================================ +print("\n4. QUANTUM STATE EVOLUTION") +print("-" * 60) + + +@qml.qnode(dev) +def evolving_state(angle): + qml.RY(angle, wires=0) + return qml.expval(qml.PauliZ(0)) + + +angles = np.linspace(0, 2 * np.pi, 50) +expectations = [evolving_state(angle) for angle in angles] + +print("Simulating quantum state evolution...") +print(f"Angles tested: {len(angles)}") +print(f"Expectation range: [{min(expectations):.4f}, {max(expectations):.4f}]") + +# Save plot +plt.figure(figsize=(10, 6)) +plt.plot(angles, expectations, "b-", linewidth=2) +plt.xlabel("Rotation Angle (radians)", fontsize=12) +plt.ylabel("⟨Z⟩ Expectation Value", fontsize=12) +plt.title("Quantum State Evolution: RY Rotation", fontsize=14) +plt.grid(True, alpha=0.3) +plt.axhline(y=0, color="r", linestyle="--", alpha=0.5) +plot_path = results_dir / "state_evolution.png" +plt.savefig(plot_path, dpi=150, bbox_inches="tight") +print(f"Plot saved to: {plot_path}") + +# ============================================ +# Example 5: Multi-Qubit Simulation Performance +# ============================================ +print("\n5. SCALING SIMULATION") +print("-" * 60) + +for n_qubits in [2, 4, 6, 8]: + dev_temp = qml.device("default.qubit", wires=n_qubits) + + @qml.qnode(dev_temp) + def benchmark_circuit(params): + for i in range(n_qubits): + qml.RY(params[i], wires=i) + for i in range(n_qubits - 1): + qml.CNOT(wires=[i, i + 1]) + return qml.expval(qml.PauliZ(0)) + + params = np.random.random(n_qubits) + result = benchmark_circuit(params) + state_dim = 2**n_qubits + + print( + f" {n_qubits} qubits: State dimension = {state_dim:5d}, Result = {result:.4f}" + ) + +print("\nNote: Classical simulation becomes exponentially harder!") +print("Real quantum hardware scales linearly with qubits.") + +# ============================================ +# Example 6: Noisy Simulation +# ============================================ +print("\n6. NOISY QUANTUM SIMULATION") +print("-" * 60) + +from qiskit_aer.noise import NoiseModel, depolarizing_error + +# Create noise model +noise_model = NoiseModel() +error = depolarizing_error(0.01, 1) # 1% error rate +noise_model.add_all_qubit_quantum_error(error, ["h", "x"]) +error_2q = depolarizing_error(0.02, 2) # 2% error on 2-qubit gates +noise_model.add_all_qubit_quantum_error(error_2q, ["cx"]) + +# Simulate with noise +bell_noisy = QuantumCircuit(2, 2) +bell_noisy.h(0) +bell_noisy.cx(0, 1) +bell_noisy.measure([0, 1], [0, 1]) + +job_noisy = simulator.run( + transpile(bell_noisy, simulator), shots=1000, noise_model=noise_model +) +counts_noisy = job_noisy.result().get_counts() + +print("Noisy Bell state (1% single-qubit, 2% two-qubit error):") +for state, count in sorted(counts_noisy.items()): + percentage = (count / 1000) * 100 + bar = "█" * int(percentage / 2) + print(f" |{state}⟩: {count:4d} ({percentage:5.1f}%) {bar}") + +print("\nNote: Non-ideal states (|01⟩, |10⟩) appear due to noise.") +print("This simulates real quantum hardware imperfections!") + +print("\n" + "=" * 60) +print("SIMULATION EXAMPLES COMPLETED!") +print("=" * 60) diff --git a/ai-projects/quantum-ml/examples/train_models.py b/ai-projects/quantum-ml/examples/train_models.py index 7b01c91d9..fdeae1648 100644 --- a/ai-projects/quantum-ml/examples/train_models.py +++ b/ai-projects/quantum-ml/examples/train_models.py @@ -1,223 +1,223 @@ -""" -Demonstration of training quantum machine learning models -""" - -import sys -from pathlib import Path - -import matplotlib -import numpy as np -from sklearn.datasets import load_iris, make_circles, make_moons -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler - -matplotlib.use("Agg") -import matplotlib.pyplot as plt - -# Add project root to sys.path so 'src' can be imported as a package -project_root = Path(__file__).resolve().parents[1] -if str(project_root) not in sys.path: - sys.path.insert(0, str(project_root)) - -try: - from src.quantum_classifier import (HybridQuantumClassifier, - QuantumClassifier, train_quantum_model) -except ModuleNotFoundError: - # Fallback for environments without namespace package support - sys.path.insert(0, str(project_root / "src")) - from quantum_classifier import HybridQuantumClassifier # type: ignore - from quantum_classifier import QuantumClassifier, train_quantum_model - -# Create results directory -results_dir = Path(__file__).parent.parent / "results" -results_dir.mkdir(exist_ok=True) - -print("=" * 60) -print("QUANTUM MACHINE LEARNING TRAINING EXAMPLES") -print("=" * 60) - -# ============================================ -# Example 1: Binary Classification with Moons Dataset -# ============================================ -print("\n1. BINARY CLASSIFICATION: MOONS DATASET") -print("-" * 60) - -# Generate data -X, y = make_moons(n_samples=200, noise=0.1, random_state=42) -X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) - -# Normalize -scaler = StandardScaler() -X_train = scaler.fit_transform(X_train) -X_val = scaler.transform(X_val) - -# Pad features to 4 dimensions (matching n_qubits=4) -X_train_padded = np.pad(X_train, ((0, 0), (0, 2)), mode="constant") -X_val_padded = np.pad(X_val, ((0, 0), (0, 2)), mode="constant") - -# Create and train model -print("Creating hybrid quantum-classical model...") -qc = QuantumClassifier() -model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) - -print(f"Training on {len(X_train)} samples...") -history = train_quantum_model(model, X_train_padded, y_train, X_val_padded, y_val) - -print("\nFinal Results:") -print(f" Training Loss: {history['train_loss'][-1]:.4f}") -print(f" Validation Loss: {history['val_loss'][-1]:.4f}") -print(f" Validation Accuracy: {history['val_acc'][-1]:.4f}") - -# Plot training history -fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) - -ax1.plot(history["train_loss"], label="Training Loss", linewidth=2) -ax1.plot(history["val_loss"], label="Validation Loss", linewidth=2) -ax1.set_xlabel("Epoch", fontsize=12) -ax1.set_ylabel("Loss", fontsize=12) -ax1.set_title("Moons Dataset: Loss Curves", fontsize=14) -ax1.legend() -ax1.grid(True, alpha=0.3) - -ax2.plot(history["val_acc"], label="Validation Accuracy", linewidth=2, color="green") -ax2.set_xlabel("Epoch", fontsize=12) -ax2.set_ylabel("Accuracy", fontsize=12) -ax2.set_title("Moons Dataset: Accuracy", fontsize=14) -ax2.legend() -ax2.grid(True, alpha=0.3) - -plot_path = results_dir / "training_moons.png" -plt.tight_layout() -plt.savefig(plot_path, dpi=150, bbox_inches="tight") -print(f"Training plot saved to: {plot_path}") - -# ============================================ -# Example 2: Circles Dataset -# ============================================ -print("\n2. BINARY CLASSIFICATION: CIRCLES DATASET") -print("-" * 60) - -# Generate circles data -X_circles, y_circles = make_circles( - n_samples=200, noise=0.1, factor=0.5, random_state=42 -) -X_train_c, X_val_c, y_train_c, y_val_c = train_test_split( - X_circles, y_circles, test_size=0.2, random_state=42 -) - -# Normalize and pad -scaler_c = StandardScaler() -X_train_c = scaler_c.fit_transform(X_train_c) -X_val_c = scaler_c.transform(X_val_c) -X_train_c_padded = np.pad(X_train_c, ((0, 0), (0, 2)), mode="constant") -X_val_c_padded = np.pad(X_val_c, ((0, 0), (0, 2)), mode="constant") - -# Create new model -qc_circles = QuantumClassifier() -model_circles = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_circles) - -print(f"Training on circles dataset ({len(X_train_c)} samples)...") -history_circles = train_quantum_model( - model_circles, X_train_c_padded, y_train_c, X_val_c_padded, y_val_c -) - -print("\nFinal Results:") -print(f" Validation Accuracy: {history_circles['val_acc'][-1]:.4f}") - -# ============================================ -# Example 3: Multi-Class Classification (Iris) -# ============================================ -print("\n3. MULTI-CLASS CHALLENGE: IRIS DATASET") -print("-" * 60) - -# Load iris dataset -iris = load_iris() -X_iris = iris.data -y_iris = iris.target - -# Convert to binary classification (class 0 vs rest) -y_iris_binary = (y_iris == 0).astype(int) - -X_train_i, X_val_i, y_train_i, y_val_i = train_test_split( - X_iris, y_iris_binary, test_size=0.2, random_state=42 -) - -# Normalize -scaler_i = StandardScaler() -X_train_i = scaler_i.fit_transform(X_train_i) -X_val_i = scaler_i.transform(X_val_i) - -# Create model -qc_iris = QuantumClassifier() -model_iris = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_iris) - -print(f"Training on iris dataset ({len(X_train_i)} samples)...") -print("Task: Classify Setosa vs. Other species") - -history_iris = train_quantum_model(model_iris, X_train_i, y_train_i, X_val_i, y_val_i) - -print("\nFinal Results:") -print(f" Validation Accuracy: {history_iris['val_acc'][-1]:.4f}") - -# ============================================ -# Example 4: Compare Multiple Datasets -# ============================================ -print("\n4. COMPARATIVE ANALYSIS") -print("-" * 60) - -results = { - "Moons": history["val_acc"][-1], - "Circles": history_circles["val_acc"][-1], - "Iris": history_iris["val_acc"][-1], -} - -print("\nQuantum Classifier Performance Summary:") -print("-" * 40) -for dataset, accuracy in results.items(): - stars = "★" * int(accuracy * 10) - print(f" {dataset:12s}: {accuracy:.4f} ({accuracy*100:.1f}%) {stars}") - -# Plot comparison -fig, ax = plt.subplots(figsize=(10, 6)) -datasets = list(results.keys()) -accuracies = list(results.values()) -colors = ["#1f77b4", "#ff7f0e", "#2ca02c"] - -bars = ax.bar( - datasets, accuracies, color=colors, alpha=0.7, edgecolor="black", linewidth=2 -) -ax.set_ylabel("Validation Accuracy", fontsize=12) -ax.set_title("Quantum ML Model Performance Comparison", fontsize=14, fontweight="bold") -ax.set_ylim([0, 1]) -ax.grid(True, axis="y", alpha=0.3) - -# Add value labels on bars -for bar, acc in zip(bars, accuracies): - height = bar.get_height() - ax.text( - bar.get_x() + bar.get_width() / 2.0, - height, - f"{acc:.3f}", - ha="center", - va="bottom", - fontsize=11, - fontweight="bold", - ) - -plot_path = results_dir / "model_comparison.png" -plt.tight_layout() -plt.savefig(plot_path, dpi=150, bbox_inches="tight") -print(f"\nComparison plot saved to: {plot_path}") - -# ============================================ -# Summary -# ============================================ -print("\n" + "=" * 60) -print("QUANTUM ML TRAINING COMPLETED!") -print("=" * 60) -print("\nKey Insights:") -print(" • Quantum circuits can learn non-linear decision boundaries") -print(" • Hybrid models combine quantum + classical advantages") -print(" • Training uses standard backpropagation with quantum gradients") -print(" • Performance competitive with classical neural networks") -print(f"\nAll training plots saved to: {results_dir}") +""" +Demonstration of training quantum machine learning models +""" + +import sys +from pathlib import Path + +import matplotlib +import numpy as np +from sklearn.datasets import load_iris, make_circles, make_moons +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +# Add project root to sys.path so 'src' can be imported as a package +project_root = Path(__file__).resolve().parents[1] +if str(project_root) not in sys.path: + sys.path.insert(0, str(project_root)) + +try: + from src.quantum_classifier import (HybridQuantumClassifier, + QuantumClassifier, train_quantum_model) +except ModuleNotFoundError: + # Fallback for environments without namespace package support + sys.path.insert(0, str(project_root / "src")) + from quantum_classifier import HybridQuantumClassifier # type: ignore + from quantum_classifier import QuantumClassifier, train_quantum_model + +# Create results directory +results_dir = Path(__file__).parent.parent / "results" +results_dir.mkdir(exist_ok=True) + +print("=" * 60) +print("QUANTUM MACHINE LEARNING TRAINING EXAMPLES") +print("=" * 60) + +# ============================================ +# Example 1: Binary Classification with Moons Dataset +# ============================================ +print("\n1. BINARY CLASSIFICATION: MOONS DATASET") +print("-" * 60) + +# Generate data +X, y = make_moons(n_samples=200, noise=0.1, random_state=42) +X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) + +# Normalize +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_val = scaler.transform(X_val) + +# Pad features to 4 dimensions (matching n_qubits=4) +X_train_padded = np.pad(X_train, ((0, 0), (0, 2)), mode="constant") +X_val_padded = np.pad(X_val, ((0, 0), (0, 2)), mode="constant") + +# Create and train model +print("Creating hybrid quantum-classical model...") +qc = QuantumClassifier() +model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) + +print(f"Training on {len(X_train)} samples...") +history = train_quantum_model(model, X_train_padded, y_train, X_val_padded, y_val) + +print("\nFinal Results:") +print(f" Training Loss: {history['train_loss'][-1]:.4f}") +print(f" Validation Loss: {history['val_loss'][-1]:.4f}") +print(f" Validation Accuracy: {history['val_acc'][-1]:.4f}") + +# Plot training history +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) + +ax1.plot(history["train_loss"], label="Training Loss", linewidth=2) +ax1.plot(history["val_loss"], label="Validation Loss", linewidth=2) +ax1.set_xlabel("Epoch", fontsize=12) +ax1.set_ylabel("Loss", fontsize=12) +ax1.set_title("Moons Dataset: Loss Curves", fontsize=14) +ax1.legend() +ax1.grid(True, alpha=0.3) + +ax2.plot(history["val_acc"], label="Validation Accuracy", linewidth=2, color="green") +ax2.set_xlabel("Epoch", fontsize=12) +ax2.set_ylabel("Accuracy", fontsize=12) +ax2.set_title("Moons Dataset: Accuracy", fontsize=14) +ax2.legend() +ax2.grid(True, alpha=0.3) + +plot_path = results_dir / "training_moons.png" +plt.tight_layout() +plt.savefig(plot_path, dpi=150, bbox_inches="tight") +print(f"Training plot saved to: {plot_path}") + +# ============================================ +# Example 2: Circles Dataset +# ============================================ +print("\n2. BINARY CLASSIFICATION: CIRCLES DATASET") +print("-" * 60) + +# Generate circles data +X_circles, y_circles = make_circles( + n_samples=200, noise=0.1, factor=0.5, random_state=42 +) +X_train_c, X_val_c, y_train_c, y_val_c = train_test_split( + X_circles, y_circles, test_size=0.2, random_state=42 +) + +# Normalize and pad +scaler_c = StandardScaler() +X_train_c = scaler_c.fit_transform(X_train_c) +X_val_c = scaler_c.transform(X_val_c) +X_train_c_padded = np.pad(X_train_c, ((0, 0), (0, 2)), mode="constant") +X_val_c_padded = np.pad(X_val_c, ((0, 0), (0, 2)), mode="constant") + +# Create new model +qc_circles = QuantumClassifier() +model_circles = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_circles) + +print(f"Training on circles dataset ({len(X_train_c)} samples)...") +history_circles = train_quantum_model( + model_circles, X_train_c_padded, y_train_c, X_val_c_padded, y_val_c +) + +print("\nFinal Results:") +print(f" Validation Accuracy: {history_circles['val_acc'][-1]:.4f}") + +# ============================================ +# Example 3: Multi-Class Classification (Iris) +# ============================================ +print("\n3. MULTI-CLASS CHALLENGE: IRIS DATASET") +print("-" * 60) + +# Load iris dataset +iris = load_iris() +X_iris = iris.data +y_iris = iris.target + +# Convert to binary classification (class 0 vs rest) +y_iris_binary = (y_iris == 0).astype(int) + +X_train_i, X_val_i, y_train_i, y_val_i = train_test_split( + X_iris, y_iris_binary, test_size=0.2, random_state=42 +) + +# Normalize +scaler_i = StandardScaler() +X_train_i = scaler_i.fit_transform(X_train_i) +X_val_i = scaler_i.transform(X_val_i) + +# Create model +qc_iris = QuantumClassifier() +model_iris = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_iris) + +print(f"Training on iris dataset ({len(X_train_i)} samples)...") +print("Task: Classify Setosa vs. Other species") + +history_iris = train_quantum_model(model_iris, X_train_i, y_train_i, X_val_i, y_val_i) + +print("\nFinal Results:") +print(f" Validation Accuracy: {history_iris['val_acc'][-1]:.4f}") + +# ============================================ +# Example 4: Compare Multiple Datasets +# ============================================ +print("\n4. COMPARATIVE ANALYSIS") +print("-" * 60) + +results = { + "Moons": history["val_acc"][-1], + "Circles": history_circles["val_acc"][-1], + "Iris": history_iris["val_acc"][-1], +} + +print("\nQuantum Classifier Performance Summary:") +print("-" * 40) +for dataset, accuracy in results.items(): + stars = "★" * int(accuracy * 10) + print(f" {dataset:12s}: {accuracy:.4f} ({accuracy*100:.1f}%) {stars}") + +# Plot comparison +fig, ax = plt.subplots(figsize=(10, 6)) +datasets = list(results.keys()) +accuracies = list(results.values()) +colors = ["#1f77b4", "#ff7f0e", "#2ca02c"] + +bars = ax.bar( + datasets, accuracies, color=colors, alpha=0.7, edgecolor="black", linewidth=2 +) +ax.set_ylabel("Validation Accuracy", fontsize=12) +ax.set_title("Quantum ML Model Performance Comparison", fontsize=14, fontweight="bold") +ax.set_ylim([0, 1]) +ax.grid(True, axis="y", alpha=0.3) + +# Add value labels on bars +for bar, acc in zip(bars, accuracies): + height = bar.get_height() + ax.text( + bar.get_x() + bar.get_width() / 2.0, + height, + f"{acc:.3f}", + ha="center", + va="bottom", + fontsize=11, + fontweight="bold", + ) + +plot_path = results_dir / "model_comparison.png" +plt.tight_layout() +plt.savefig(plot_path, dpi=150, bbox_inches="tight") +print(f"\nComparison plot saved to: {plot_path}") + +# ============================================ +# Summary +# ============================================ +print("\n" + "=" * 60) +print("QUANTUM ML TRAINING COMPLETED!") +print("=" * 60) +print("\nKey Insights:") +print(" • Quantum circuits can learn non-linear decision boundaries") +print(" • Hybrid models combine quantum + classical advantages") +print(" • Training uses standard backpropagation with quantum gradients") +print(" • Performance competitive with classical neural networks") +print(f"\nAll training plots saved to: {results_dir}") diff --git a/ai-projects/quantum-ml/experiments/AZURE_QUICKSTART.md b/ai-projects/quantum-ml/experiments/AZURE_QUICKSTART.md index e058f15b7..c991b201a 100644 --- a/ai-projects/quantum-ml/experiments/AZURE_QUICKSTART.md +++ b/ai-projects/quantum-ml/experiments/AZURE_QUICKSTART.md @@ -1,463 +1,463 @@ -# 🚀 Azure Quantum Deployment - Quick Start Guide - -## Overview - -This guide walks you through deploying your quantum AI workspace to Azure Quantum, enabling you to run quantum circuits on real quantum hardware from IonQ, Quantinuum, and Rigetti. - ---- - -## Prerequisites Checklist - -Before starting, ensure you have: - -- [ ] Azure subscription (create free: ) -- [ ] Azure CLI installed (`az --version` to verify) -- [ ] Logged into Azure CLI (`az login`) -- [ ] PowerShell or Bash terminal -- [ ] This quantum-ai repository - ---- - -## 📋 Step 1: Azure Subscription Setup - -### 1.1 Check Current Subscription - -```powershell -# List all subscriptions -az account list --output table - -# Show current subscription -az account show -``` - -### 1.2 Set Active Subscription - -```powershell -# Replace with your subscription ID -az account set --subscription "" - -# Verify it's set -az account show --query "name" -``` - -### 1.3 Register Quantum Provider - -```powershell -# Register Microsoft.Quantum resource provider -az provider register --namespace Microsoft.Quantum - -# Check registration status -az provider show -n Microsoft.Quantum --query "registrationState" -``` - -**Expected output:** `"Registered"` - ---- - -## 📋 Step 2: Configure Your Workspace - -### 2.1 Update Configuration File - -Edit `config/quantum_config.yaml`: - -```yaml -azure: - subscription_id: "YOUR-SUBSCRIPTION-ID-HERE" # From az account show - resource_group: "rg-quantum-ai" - workspace_name: "quantum-ai-workspace" - location: "eastus" # or westus, westeurope - storage_account: "quantumstorage" # Must be globally unique -``` - -**Important:** Change `YOUR-SUBSCRIPTION-ID-HERE` to your actual subscription ID! - -### 2.2 Verify Configuration - -```powershell -cd c:\Users\Bryan\OneDrive\AI\quantum-ai -python -c "import yaml; print(yaml.safe_load(open('config/quantum_config.yaml'))['azure'])" -``` - ---- - -## 📋 Step 3: Deploy Infrastructure - -### 3.1 Create Resource Group - -```powershell -az group create ` - --name rg-quantum-ai ` - --location eastus -``` - -### 3.2 Deploy Quantum Workspace - -```powershell -cd azure - -az deployment group create ` - --resource-group rg-quantum-ai ` - --template-file quantum_workspace.bicep ` - --parameters quantum_workspace.parameters.json -``` - -**This takes ~2-5 minutes.** You'll see: - -- ✅ Storage account created -- ✅ Quantum workspace created -- ✅ Provider connections established - -### 3.3 Verify Deployment - -```powershell -# List quantum workspaces -az quantum workspace list --resource-group rg-quantum-ai --output table - -# Show workspace details -az quantum workspace show ` - --resource-group rg-quantum-ai ` - --workspace-name quantum-ai-workspace -``` - ---- - -## 📋 Step 4: Test Connection - -### 4.1 Run Connection Test - -```powershell -cd .. -python examples\azure_integration.py -``` - -**Expected output:** - -```plaintext -✓ Configuration loaded -✓ Subscription ID found -✓ Attempting Azure connection... -``` - -### 4.2 Test with Python - -```python -from src.azure_quantum_integration import AzureQuantumIntegration - -azure = AzureQuantumIntegration() -workspace = azure.connect() -print("✓ Connected to Azure Quantum!") - -# List available backends -backends = azure.list_backends() -print(f"Available backends: {backends}") -``` - ---- - -## 📋 Step 5: Run Your First Quantum Job - -### 5.1 Create a Test Circuit - -```python -from qiskit import QuantumCircuit -from src.azure_quantum_integration import AzureQuantumIntegration - -# Create Bell state circuit -circuit = QuantumCircuit(2, 2) -circuit.h(0) -circuit.cx(0, 1) -circuit.measure([0, 1], [0, 1]) - -# Connect to Azure -azure = AzureQuantumIntegration() -workspace = azure.connect() - -# Submit to IonQ simulator (FREE) -job = azure.submit_circuit( - circuit=circuit, - shots=100, - job_name="my_first_quantum_job", - backend="ionq.simulator" -) - -print(f"Job submitted: {job.id}") - -# Wait for results -results = azure.get_job_results(job) -print(f"Results: {results}") -``` - -### 5.2 Expected Results - -```plaintext -Job submitted: xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx -Job status: Succeeded -Results: {'00': 48, '11': 52} -``` - -This confirms entanglement! 🎉 - ---- - -## 💰 Cost Management - -### Free Tier Options - -**Microsoft Simulators:** - -- ✅ `ionq.simulator` - FREE, unlimited -- ✅ H-series simulators - FREE tier available -- ✅ Perfect for development and testing - -**Always start with simulators to avoid charges!** - -### Paid Options - -**IonQ (Trapped Ion):** - -- Pricing: ~$0.00003 per gate-shot -- Example: 100-gate circuit × 1000 shots = ~$3.00 -- Best for: Small algorithms, high fidelity - -**Quantinuum (Trapped Ion):** - -- Pricing: ~$0.00015 per circuit -- Credits-based system -- Best for: Error correction, complex algorithms - -**Rigetti (Superconducting):** - -- Contact for enterprise pricing -- Best for: Large-scale optimization - -### Set Budget Alerts - -```powershell -# Create budget alert (optional) -az consumption budget create ` - --budget-name quantum-ai-budget ` - --amount 100 ` - --time-grain Monthly ` - --resource-group rg-quantum-ai -``` - ---- - -## 🔒 Security Best Practices - -### 1. Use Service Principal (Recommended for Automation) - -```powershell -# Create service principal -az ad sp create-for-rbac ` - --name quantum-ai-sp ` - --role Contributor ` - --scopes /subscriptions//resourceGroups/rg-quantum-ai - -# Save the output securely (appId, password, tenant) -``` - -### 2. Store Credentials Securely - -#### Option A: Environment Variables - -```powershell -$env:AZURE_QUANTUM_WORKSPACE = "quantum-ai-workspace" -$env:AZURE_QUANTUM_RESOURCE_GROUP = "rg-quantum-ai" -``` - -#### Option B: Azure Key Vault - -```powershell -az keyvault create ` - --name quantum-ai-keyvault ` - --resource-group rg-quantum-ai ` - --location eastus -``` - -### 3. Enable Monitoring - -```powershell -# Enable diagnostics -az monitor diagnostic-settings create ` - --name quantum-diagnostics ` - --resource-group rg-quantum-ai ` - --workspace quantum-ai-workspace ` - --logs '[{"category": "JobEvents", "enabled": true}]' -``` - ---- - -## 🐛 Troubleshooting - -### Issue 1: "Subscription not found" - -**Solution:** - -```powershell -az login -az account set --subscription "" -``` - -### Issue 2: "Provider not registered" - -**Solution:** - -```powershell -az provider register --namespace Microsoft.Quantum -# Wait 2-3 minutes, then: -az provider show -n Microsoft.Quantum --query registrationState -``` - -### Issue 3: "Storage account name already exists" - -**Solution:** Change `storage_account` in `quantum_config.yaml` to a globally unique name: - -```yaml -storage_account: "quantumstorage" -``` - -### Issue 4: "Authentication failed" - -**Solution:** - -```powershell -# Clear Azure CLI cache -az account clear -az login -``` - -### Issue 5: "Workspace creation failed" - -**Solution:** Check deployment logs: - -```powershell -az deployment group show ` - --resource-group rg-quantum-ai ` - --name -``` - ---- - -## 📊 Monitoring Your Quantum Jobs - -### View All Jobs - -```powershell -az quantum job list ` - --resource-group rg-quantum-ai ` - --workspace-name quantum-ai-workspace ` - --output table -``` - -### Check Job Status - -```powershell -az quantum job show ` - --resource-group rg-quantum-ai ` - --workspace-name quantum-ai-workspace ` - --job-id -``` - -### Download Job Results - -```python -from src.azure_quantum_integration import AzureQuantumIntegration - -azure = AzureQuantumIntegration() -workspace = azure.connect() - -# Get specific job -job = workspace.get_job("") -results = job.get_results() -print(results) -``` - ---- - -## 🎯 Next Steps - -### Immediate (First Day) - -- [ ] Deploy workspace -- [ ] Test connection -- [ ] Run first job on simulator -- [ ] Review job results - -### Short-term (First Week) - -- [ ] Run quantum classifier on IonQ simulator -- [ ] Submit multiple jobs -- [ ] Monitor costs in Azure Portal -- [ ] Experiment with different providers - -### Long-term (First Month) - -- [ ] Run on real quantum hardware (IonQ/Quantinuum) -- [ ] Implement job batching -- [ ] Set up automated workflows -- [ ] Compare simulator vs. hardware results - ---- - -## 📚 Additional Resources - -### Documentation - -- [Azure Quantum Docs](https://learn.microsoft.com/azure/quantum/) -- [IonQ Provider](https://learn.microsoft.com/azure/quantum/provider-ionq) -- [Quantinuum Provider](https://learn.microsoft.com/azure/quantum/provider-quantinuum) - -### Tutorials - -- [Azure Quantum Quickstart](https://learn.microsoft.com/azure/quantum/get-started-jupyter-notebook) -- [Submit Jobs Tutorial](https://learn.microsoft.com/azure/quantum/how-to-submit-jobs) -- [Cost Management](https://learn.microsoft.com/azure/quantum/azure-quantum-job-costs) - -### Community - -- [Azure Quantum Forum](https://quantum.microsoft.com/community) -- [GitHub Discussions](https://github.com/microsoft/qsharp/discussions) - ---- - -## ✅ Deployment Checklist - -Before going to production: - -- [ ] Workspace deployed successfully -- [ ] Connection test passed -- [ ] First job completed on simulator -- [ ] Budget alerts configured -- [ ] Monitoring enabled -- [ ] Security best practices implemented -- [ ] Team trained on quantum concepts -- [ ] Backup plan for job failures - ---- - -## 🎉 Success - -If you completed all steps, you now have: - -- ✅ Production Azure Quantum workspace -- ✅ Access to real quantum hardware -- ✅ Cost monitoring and security -- ✅ Working examples and templates - -**You're ready to run quantum algorithms on real quantum computers!** 🌌 - ---- - -**Questions?** Check `azure/DEPLOYMENT.md` for detailed information or run: - -```powershell -python examples\azure_integration.py -``` - -**Cost concerns?** Always test on FREE simulators first: - -- `ionq.simulator` - Unlimited free usage -- Perfect for development and validation - -**Happy Quantum Computing!** 🚀 +# 🚀 Azure Quantum Deployment - Quick Start Guide + +## Overview + +This guide walks you through deploying your quantum AI workspace to Azure Quantum, enabling you to run quantum circuits on real quantum hardware from IonQ, Quantinuum, and Rigetti. + +--- + +## Prerequisites Checklist + +Before starting, ensure you have: + +- [ ] Azure subscription (create free: ) +- [ ] Azure CLI installed (`az --version` to verify) +- [ ] Logged into Azure CLI (`az login`) +- [ ] PowerShell or Bash terminal +- [ ] This quantum-ai repository + +--- + +## 📋 Step 1: Azure Subscription Setup + +### 1.1 Check Current Subscription + +```powershell +# List all subscriptions +az account list --output table + +# Show current subscription +az account show +``` + +### 1.2 Set Active Subscription + +```powershell +# Replace with your subscription ID +az account set --subscription "" + +# Verify it's set +az account show --query "name" +``` + +### 1.3 Register Quantum Provider + +```powershell +# Register Microsoft.Quantum resource provider +az provider register --namespace Microsoft.Quantum + +# Check registration status +az provider show -n Microsoft.Quantum --query "registrationState" +``` + +**Expected output:** `"Registered"` + +--- + +## 📋 Step 2: Configure Your Workspace + +### 2.1 Update Configuration File + +Edit `config/quantum_config.yaml`: + +```yaml +azure: + subscription_id: "YOUR-SUBSCRIPTION-ID-HERE" # From az account show + resource_group: "rg-quantum-ai" + workspace_name: "quantum-ai-workspace" + location: "eastus" # or westus, westeurope + storage_account: "quantumstorage" # Must be globally unique +``` + +**Important:** Change `YOUR-SUBSCRIPTION-ID-HERE` to your actual subscription ID! + +### 2.2 Verify Configuration + +```powershell +cd c:\Users\Bryan\OneDrive\AI\quantum-ai +python -c "import yaml; print(yaml.safe_load(open('config/quantum_config.yaml'))['azure'])" +``` + +--- + +## 📋 Step 3: Deploy Infrastructure + +### 3.1 Create Resource Group + +```powershell +az group create ` + --name rg-quantum-ai ` + --location eastus +``` + +### 3.2 Deploy Quantum Workspace + +```powershell +cd azure + +az deployment group create ` + --resource-group rg-quantum-ai ` + --template-file quantum_workspace.bicep ` + --parameters quantum_workspace.parameters.json +``` + +**This takes ~2-5 minutes.** You'll see: + +- ✅ Storage account created +- ✅ Quantum workspace created +- ✅ Provider connections established + +### 3.3 Verify Deployment + +```powershell +# List quantum workspaces +az quantum workspace list --resource-group rg-quantum-ai --output table + +# Show workspace details +az quantum workspace show ` + --resource-group rg-quantum-ai ` + --workspace-name quantum-ai-workspace +``` + +--- + +## 📋 Step 4: Test Connection + +### 4.1 Run Connection Test + +```powershell +cd .. +python examples\azure_integration.py +``` + +**Expected output:** + +```plaintext +✓ Configuration loaded +✓ Subscription ID found +✓ Attempting Azure connection... +``` + +### 4.2 Test with Python + +```python +from src.azure_quantum_integration import AzureQuantumIntegration + +azure = AzureQuantumIntegration() +workspace = azure.connect() +print("✓ Connected to Azure Quantum!") + +# List available backends +backends = azure.list_backends() +print(f"Available backends: {backends}") +``` + +--- + +## 📋 Step 5: Run Your First Quantum Job + +### 5.1 Create a Test Circuit + +```python +from qiskit import QuantumCircuit +from src.azure_quantum_integration import AzureQuantumIntegration + +# Create Bell state circuit +circuit = QuantumCircuit(2, 2) +circuit.h(0) +circuit.cx(0, 1) +circuit.measure([0, 1], [0, 1]) + +# Connect to Azure +azure = AzureQuantumIntegration() +workspace = azure.connect() + +# Submit to IonQ simulator (FREE) +job = azure.submit_circuit( + circuit=circuit, + shots=100, + job_name="my_first_quantum_job", + backend="ionq.simulator" +) + +print(f"Job submitted: {job.id}") + +# Wait for results +results = azure.get_job_results(job) +print(f"Results: {results}") +``` + +### 5.2 Expected Results + +```plaintext +Job submitted: xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx +Job status: Succeeded +Results: {'00': 48, '11': 52} +``` + +This confirms entanglement! 🎉 + +--- + +## 💰 Cost Management + +### Free Tier Options + +**Microsoft Simulators:** + +- ✅ `ionq.simulator` - FREE, unlimited +- ✅ H-series simulators - FREE tier available +- ✅ Perfect for development and testing + +**Always start with simulators to avoid charges!** + +### Paid Options + +**IonQ (Trapped Ion):** + +- Pricing: ~$0.00003 per gate-shot +- Example: 100-gate circuit × 1000 shots = ~$3.00 +- Best for: Small algorithms, high fidelity + +**Quantinuum (Trapped Ion):** + +- Pricing: ~$0.00015 per circuit +- Credits-based system +- Best for: Error correction, complex algorithms + +**Rigetti (Superconducting):** + +- Contact for enterprise pricing +- Best for: Large-scale optimization + +### Set Budget Alerts + +```powershell +# Create budget alert (optional) +az consumption budget create ` + --budget-name quantum-ai-budget ` + --amount 100 ` + --time-grain Monthly ` + --resource-group rg-quantum-ai +``` + +--- + +## 🔒 Security Best Practices + +### 1. Use Service Principal (Recommended for Automation) + +```powershell +# Create service principal +az ad sp create-for-rbac ` + --name quantum-ai-sp ` + --role Contributor ` + --scopes /subscriptions//resourceGroups/rg-quantum-ai + +# Save the output securely (appId, password, tenant) +``` + +### 2. Store Credentials Securely + +#### Option A: Environment Variables + +```powershell +$env:AZURE_QUANTUM_WORKSPACE = "quantum-ai-workspace" +$env:AZURE_QUANTUM_RESOURCE_GROUP = "rg-quantum-ai" +``` + +#### Option B: Azure Key Vault + +```powershell +az keyvault create ` + --name quantum-ai-keyvault ` + --resource-group rg-quantum-ai ` + --location eastus +``` + +### 3. Enable Monitoring + +```powershell +# Enable diagnostics +az monitor diagnostic-settings create ` + --name quantum-diagnostics ` + --resource-group rg-quantum-ai ` + --workspace quantum-ai-workspace ` + --logs '[{"category": "JobEvents", "enabled": true}]' +``` + +--- + +## 🐛 Troubleshooting + +### Issue 1: "Subscription not found" + +**Solution:** + +```powershell +az login +az account set --subscription "" +``` + +### Issue 2: "Provider not registered" + +**Solution:** + +```powershell +az provider register --namespace Microsoft.Quantum +# Wait 2-3 minutes, then: +az provider show -n Microsoft.Quantum --query registrationState +``` + +### Issue 3: "Storage account name already exists" + +**Solution:** Change `storage_account` in `quantum_config.yaml` to a globally unique name: + +```yaml +storage_account: "quantumstorage" +``` + +### Issue 4: "Authentication failed" + +**Solution:** + +```powershell +# Clear Azure CLI cache +az account clear +az login +``` + +### Issue 5: "Workspace creation failed" + +**Solution:** Check deployment logs: + +```powershell +az deployment group show ` + --resource-group rg-quantum-ai ` + --name +``` + +--- + +## 📊 Monitoring Your Quantum Jobs + +### View All Jobs + +```powershell +az quantum job list ` + --resource-group rg-quantum-ai ` + --workspace-name quantum-ai-workspace ` + --output table +``` + +### Check Job Status + +```powershell +az quantum job show ` + --resource-group rg-quantum-ai ` + --workspace-name quantum-ai-workspace ` + --job-id +``` + +### Download Job Results + +```python +from src.azure_quantum_integration import AzureQuantumIntegration + +azure = AzureQuantumIntegration() +workspace = azure.connect() + +# Get specific job +job = workspace.get_job("") +results = job.get_results() +print(results) +``` + +--- + +## 🎯 Next Steps + +### Immediate (First Day) + +- [ ] Deploy workspace +- [ ] Test connection +- [ ] Run first job on simulator +- [ ] Review job results + +### Short-term (First Week) + +- [ ] Run quantum classifier on IonQ simulator +- [ ] Submit multiple jobs +- [ ] Monitor costs in Azure Portal +- [ ] Experiment with different providers + +### Long-term (First Month) + +- [ ] Run on real quantum hardware (IonQ/Quantinuum) +- [ ] Implement job batching +- [ ] Set up automated workflows +- [ ] Compare simulator vs. hardware results + +--- + +## 📚 Additional Resources + +### Documentation + +- [Azure Quantum Docs](https://learn.microsoft.com/azure/quantum/) +- [IonQ Provider](https://learn.microsoft.com/azure/quantum/provider-ionq) +- [Quantinuum Provider](https://learn.microsoft.com/azure/quantum/provider-quantinuum) + +### Tutorials + +- [Azure Quantum Quickstart](https://learn.microsoft.com/azure/quantum/get-started-jupyter-notebook) +- [Submit Jobs Tutorial](https://learn.microsoft.com/azure/quantum/how-to-submit-jobs) +- [Cost Management](https://learn.microsoft.com/azure/quantum/azure-quantum-job-costs) + +### Community + +- [Azure Quantum Forum](https://quantum.microsoft.com/community) +- [GitHub Discussions](https://github.com/microsoft/qsharp/discussions) + +--- + +## ✅ Deployment Checklist + +Before going to production: + +- [ ] Workspace deployed successfully +- [ ] Connection test passed +- [ ] First job completed on simulator +- [ ] Budget alerts configured +- [ ] Monitoring enabled +- [ ] Security best practices implemented +- [ ] Team trained on quantum concepts +- [ ] Backup plan for job failures + +--- + +## 🎉 Success + +If you completed all steps, you now have: + +- ✅ Production Azure Quantum workspace +- ✅ Access to real quantum hardware +- ✅ Cost monitoring and security +- ✅ Working examples and templates + +**You're ready to run quantum algorithms on real quantum computers!** 🌌 + +--- + +**Questions?** Check `azure/DEPLOYMENT.md` for detailed information or run: + +```powershell +python examples\azure_integration.py +``` + +**Cost concerns?** Always test on FREE simulators first: + +- `ionq.simulator` - Unlimited free usage +- Perfect for development and validation + +**Happy Quantum Computing!** 🚀 diff --git a/ai-projects/quantum-ml/experiments/analyze_plots.py b/ai-projects/quantum-ml/experiments/analyze_plots.py index 1047fffa9..261c8e3dc 100644 --- a/ai-projects/quantum-ml/experiments/analyze_plots.py +++ b/ai-projects/quantum-ml/experiments/analyze_plots.py @@ -1,238 +1,238 @@ -""" -Visual Analysis Guide for Generated Plots -Reviews and explains the quantum AI training results -""" - -from pathlib import Path - -results_dir = Path(__file__).parent.parent / "results" - -print("=" * 70) -print(" QUANTUM AI - PLOT ANALYSIS GUIDE") -print("=" * 70) - -# ============================================================================ -# PLOT 1: State Evolution -# ============================================================================ -print("\n📊 PLOT 1: STATE EVOLUTION (state_evolution.png)") -print("=" * 70) - -plot_file = results_dir / "state_evolution.png" -if plot_file.exists(): - print(f"✓ File found: {plot_file}") - print(f" Size: {plot_file.stat().st_size / 1024:.1f} KB") - - print("\n🔍 What This Shows:") - print("-" * 70) - print("• X-axis: Rotation angle (0 to 2π radians)") - print("• Y-axis: Expectation value ⟨Z⟩ (quantum measurement)") - print("• Curve: Cosine-like pattern showing quantum state evolution") - - print("\n💡 Key Insights:") - print("-" * 70) - print("1. SMOOTH CURVE: Quantum states evolve continuously") - print(" - RY rotation creates superposition of |0⟩ and |1⟩") - print(" - Measurement shows probability amplitude oscillation") - - print("\n2. RANGE [-1, 1]:") - print(" - ⟨Z⟩ = +1: Qubit definitely in |0⟩ state") - print(" - ⟨Z⟩ = 0: Equal superposition |0⟩ and |1⟩") - print(" - ⟨Z⟩ = -1: Qubit definitely in |1⟩ state") - - print("\n3. PERIODICITY:") - print(" - Full cycle at 2π (360°) confirms quantum mechanics") - print(" - Symmetry shows reversible quantum operations") - - print("\n📖 Physics Explanation:") - print("-" * 70) - print("The RY(θ) gate rotates a qubit around the Y-axis:") - print(" |ψ⟩ = cos(θ/2)|0⟩ + sin(θ/2)|1⟩") - print("Measuring ⟨Z⟩ gives: cos(θ)") - print("This plot confirms our quantum simulator works correctly!") -else: - print(f"❌ File not found: {plot_file}") - -# ============================================================================ -# PLOT 2: Training Moons -# ============================================================================ -print("\n\n📊 PLOT 2: TRAINING CURVES (training_moons.png)") -print("=" * 70) - -plot_file = results_dir / "training_moons.png" -if plot_file.exists(): - print(f"✓ File found: {plot_file}") - print(f" Size: {plot_file.stat().st_size / 1024:.1f} KB") - - print("\n🔍 What This Shows:") - print("-" * 70) - print("LEFT PANEL: Loss Curves") - print(" • Blue line: Training loss (decreasing)") - print(" • Orange line: Validation loss (decreasing)") - print(" • X-axis: Training epochs (0-100)") - - print("\nRIGHT PANEL: Accuracy Curve") - print(" • Green line: Validation accuracy (increasing)") - print(" • Final accuracy: ~85% (0.85)") - - print("\n💡 Key Insights:") - print("-" * 70) - print("1. CONVERGENCE:") - print(" - Both losses decrease smoothly → model learning") - print(" - No oscillations → stable learning rate") - print(" - Accuracy increases steadily → effective training") - - print("\n2. OVERFITTING CHECK:") - print(" - Train vs validation loss stay close → no overfitting") - print(" - Quantum circuits act as regularizers") - print(" - Small gap indicates good generalization") - - print("\n3. PERFORMANCE:") - print(" - 85% accuracy on Moons dataset is excellent") - print(" - Comparable to classical neural networks") - print(" - Quantum advantage: fewer parameters needed") - - print("\n📖 Machine Learning Explanation:") - print("-" * 70) - print("Training loss measures how well model fits training data") - print("Validation loss measures performance on unseen data") - print("Accuracy shows percentage of correct predictions") - print("Our quantum model successfully learned non-linear boundaries!") -else: - print(f"❌ File not found: {plot_file}") - -# ============================================================================ -# PLOT 3: Model Comparison -# ============================================================================ -print("\n\n📊 PLOT 3: MODEL COMPARISON (model_comparison.png)") -print("=" * 70) - -plot_file = results_dir / "model_comparison.png" -if plot_file.exists(): - print(f"✓ File found: {plot_file}") - print(f" Size: {plot_file.stat().st_size / 1024:.1f} KB") - - print("\n🔍 What This Shows:") - print("-" * 70) - print("Bar chart comparing quantum classifier on 3 datasets:") - print(" • Moons: Non-linear crescents (HARD)") - print(" • Circles: Concentric rings (VERY HARD)") - print(" • Iris: Flower species classification (MEDIUM)") - - print("\n💡 Key Insights:") - print("-" * 70) - print("1. MOONS (85%):") - print(" ⭐⭐⭐⭐⭐ Excellent performance") - print(" - Non-linear decision boundary learned successfully") - print(" - Quantum entanglement helps capture correlations") - - print("\n2. CIRCLES (50%):") - print(" ⭐⭐ Challenging for current architecture") - print(" - Concentric patterns need radial basis features") - print(" - Could improve with more qubits or different encoding") - - print("\n3. IRIS (67%):") - print(" ⭐⭐⭐ Good binary classification") - print(" - Setosa vs Others: reasonably separable") - print(" - Natural dataset with real-world noise") - - print("\n📊 Performance Analysis:") - print("-" * 70) - print("Dataset Difficulty Ranking:") - print(" 1. Circles (hardest) - Requires radial symmetry") - print(" 2. Iris (medium) - Real data with noise") - print(" 3. Moons (easier) - Non-linear but smooth boundary") - - print("\n🔬 Scientific Insight:") - print("Quantum computers excel at problems with:") - print(" ✓ Non-linear correlations (Moons)") - print(" ✓ High-dimensional spaces (Iris)") - print(" ✗ Radial symmetries without proper encoding (Circles)") -else: - print(f"❌ File not found: {plot_file}") - -# ============================================================================ -# INTERACTIVE ANALYSIS -# ============================================================================ -print("\n\n🎓 INTERACTIVE LEARNING QUESTIONS") -print("=" * 70) - -print("\n❓ Question 1: Why does the state evolution show a cosine curve?") -print("💡 Answer: The RY rotation gate creates superpositions following") -print(" trigonometric functions. This is fundamental quantum mechanics!") - -print("\n❓ Question 2: Why is Moons accuracy higher than Circles?") -print("💡 Answer: The quantum feature map (angle encoding) naturally") -print(" handles smooth non-linear boundaries but struggles with radial patterns.") - -print("\n❓ Question 3: What does no overfitting tell us?") -print("💡 Answer: Quantum circuits have built-in regularization due to") -print(" limited expressiveness. This prevents memorizing training data!") - -print("\n❓ Question 4: How to improve Circles performance?") -print("💡 Possible solutions:") -print(" • Add radial features: r = sqrt(x² + y²)") -print(" • Use more qubits (6-8)") -print(" • Try different data encoding strategies") -print(" • Increase circuit depth (more layers)") - -# ============================================================================ -# RECOMMENDATIONS -# ============================================================================ -print("\n\n🎯 RECOMMENDATIONS FOR NEXT EXPERIMENTS") -print("=" * 70) - -print("\n1. IMPROVE CIRCLES DATASET:") -print(" • Convert to polar coordinates (r, θ)") -print(" • Add polynomial features") -print(" • Try amplitude encoding instead of angle encoding") - -print("\n2. EXTEND TO MULTI-CLASS:") -print(" • Use all 3 Iris classes") -print(" • Implement one-vs-all strategy") -print(" • Try quantum softmax") - -print("\n3. OPTIMIZE HYPERPARAMETERS:") -print(" • Grid search over learning rates") -print(" • Test different layer counts (3-5 layers)") -print(" • Compare entanglement patterns") - -print("\n4. BENCHMARK AGAINST CLASSICAL:") -print(" • Train standard MLP on same data") -print(" • Compare parameter counts") -print(" • Measure training time") - -# ============================================================================ -# SUMMARY -# ============================================================================ -print("\n\n" + "=" * 70) -print("ANALYSIS SUMMARY") -print("=" * 70) - -print("\n✅ What We Learned:") -print(" • Quantum simulation is accurate (state evolution matches theory)") -print(" • Quantum ML can achieve competitive accuracy (85% on Moons)") -print(" • Different datasets suit different quantum encodings") -print(" • Training is stable with no overfitting") - -print("\n📈 Performance Metrics:") -print(" • Best result: 85% (Moons)") -print(" • Average result: 67% across datasets") -print(" • Training time: ~2-3 minutes per model") -print(" • Parameter count: 16 quantum + classical weights") - -print("\n🔬 Scientific Validation:") -print(" • Quantum mechanics verified (state evolution)") -print(" • Hybrid learning works (gradient flow through quantum layers)") -print(" • Entanglement provides computational advantage") - -print("\n💡 Key Takeaway:") -print(" Quantum machine learning is REAL and WORKING!") -print(" Your quantum AI system successfully demonstrates:") -print(" ✓ Quantum circuit simulation") -print(" ✓ Quantum gradient descent") -print(" ✓ Hybrid quantum-classical learning") -print(" ✓ Competitive performance on real datasets") - -print("\n" + "=" * 70) -print("Next: Run parameter_tuning.py to explore hyperparameters!") -print("=" * 70) +""" +Visual Analysis Guide for Generated Plots +Reviews and explains the quantum AI training results +""" + +from pathlib import Path + +results_dir = Path(__file__).parent.parent / "results" + +print("=" * 70) +print(" QUANTUM AI - PLOT ANALYSIS GUIDE") +print("=" * 70) + +# ============================================================================ +# PLOT 1: State Evolution +# ============================================================================ +print("\n📊 PLOT 1: STATE EVOLUTION (state_evolution.png)") +print("=" * 70) + +plot_file = results_dir / "state_evolution.png" +if plot_file.exists(): + print(f"✓ File found: {plot_file}") + print(f" Size: {plot_file.stat().st_size / 1024:.1f} KB") + + print("\n🔍 What This Shows:") + print("-" * 70) + print("• X-axis: Rotation angle (0 to 2π radians)") + print("• Y-axis: Expectation value ⟨Z⟩ (quantum measurement)") + print("• Curve: Cosine-like pattern showing quantum state evolution") + + print("\n💡 Key Insights:") + print("-" * 70) + print("1. SMOOTH CURVE: Quantum states evolve continuously") + print(" - RY rotation creates superposition of |0⟩ and |1⟩") + print(" - Measurement shows probability amplitude oscillation") + + print("\n2. RANGE [-1, 1]:") + print(" - ⟨Z⟩ = +1: Qubit definitely in |0⟩ state") + print(" - ⟨Z⟩ = 0: Equal superposition |0⟩ and |1⟩") + print(" - ⟨Z⟩ = -1: Qubit definitely in |1⟩ state") + + print("\n3. PERIODICITY:") + print(" - Full cycle at 2π (360°) confirms quantum mechanics") + print(" - Symmetry shows reversible quantum operations") + + print("\n📖 Physics Explanation:") + print("-" * 70) + print("The RY(θ) gate rotates a qubit around the Y-axis:") + print(" |ψ⟩ = cos(θ/2)|0⟩ + sin(θ/2)|1⟩") + print("Measuring ⟨Z⟩ gives: cos(θ)") + print("This plot confirms our quantum simulator works correctly!") +else: + print(f"❌ File not found: {plot_file}") + +# ============================================================================ +# PLOT 2: Training Moons +# ============================================================================ +print("\n\n📊 PLOT 2: TRAINING CURVES (training_moons.png)") +print("=" * 70) + +plot_file = results_dir / "training_moons.png" +if plot_file.exists(): + print(f"✓ File found: {plot_file}") + print(f" Size: {plot_file.stat().st_size / 1024:.1f} KB") + + print("\n🔍 What This Shows:") + print("-" * 70) + print("LEFT PANEL: Loss Curves") + print(" • Blue line: Training loss (decreasing)") + print(" • Orange line: Validation loss (decreasing)") + print(" • X-axis: Training epochs (0-100)") + + print("\nRIGHT PANEL: Accuracy Curve") + print(" • Green line: Validation accuracy (increasing)") + print(" • Final accuracy: ~85% (0.85)") + + print("\n💡 Key Insights:") + print("-" * 70) + print("1. CONVERGENCE:") + print(" - Both losses decrease smoothly → model learning") + print(" - No oscillations → stable learning rate") + print(" - Accuracy increases steadily → effective training") + + print("\n2. OVERFITTING CHECK:") + print(" - Train vs validation loss stay close → no overfitting") + print(" - Quantum circuits act as regularizers") + print(" - Small gap indicates good generalization") + + print("\n3. PERFORMANCE:") + print(" - 85% accuracy on Moons dataset is excellent") + print(" - Comparable to classical neural networks") + print(" - Quantum advantage: fewer parameters needed") + + print("\n📖 Machine Learning Explanation:") + print("-" * 70) + print("Training loss measures how well model fits training data") + print("Validation loss measures performance on unseen data") + print("Accuracy shows percentage of correct predictions") + print("Our quantum model successfully learned non-linear boundaries!") +else: + print(f"❌ File not found: {plot_file}") + +# ============================================================================ +# PLOT 3: Model Comparison +# ============================================================================ +print("\n\n📊 PLOT 3: MODEL COMPARISON (model_comparison.png)") +print("=" * 70) + +plot_file = results_dir / "model_comparison.png" +if plot_file.exists(): + print(f"✓ File found: {plot_file}") + print(f" Size: {plot_file.stat().st_size / 1024:.1f} KB") + + print("\n🔍 What This Shows:") + print("-" * 70) + print("Bar chart comparing quantum classifier on 3 datasets:") + print(" • Moons: Non-linear crescents (HARD)") + print(" • Circles: Concentric rings (VERY HARD)") + print(" • Iris: Flower species classification (MEDIUM)") + + print("\n💡 Key Insights:") + print("-" * 70) + print("1. MOONS (85%):") + print(" ⭐⭐⭐⭐⭐ Excellent performance") + print(" - Non-linear decision boundary learned successfully") + print(" - Quantum entanglement helps capture correlations") + + print("\n2. CIRCLES (50%):") + print(" ⭐⭐ Challenging for current architecture") + print(" - Concentric patterns need radial basis features") + print(" - Could improve with more qubits or different encoding") + + print("\n3. IRIS (67%):") + print(" ⭐⭐⭐ Good binary classification") + print(" - Setosa vs Others: reasonably separable") + print(" - Natural dataset with real-world noise") + + print("\n📊 Performance Analysis:") + print("-" * 70) + print("Dataset Difficulty Ranking:") + print(" 1. Circles (hardest) - Requires radial symmetry") + print(" 2. Iris (medium) - Real data with noise") + print(" 3. Moons (easier) - Non-linear but smooth boundary") + + print("\n🔬 Scientific Insight:") + print("Quantum computers excel at problems with:") + print(" ✓ Non-linear correlations (Moons)") + print(" ✓ High-dimensional spaces (Iris)") + print(" ✗ Radial symmetries without proper encoding (Circles)") +else: + print(f"❌ File not found: {plot_file}") + +# ============================================================================ +# INTERACTIVE ANALYSIS +# ============================================================================ +print("\n\n🎓 INTERACTIVE LEARNING QUESTIONS") +print("=" * 70) + +print("\n❓ Question 1: Why does the state evolution show a cosine curve?") +print("💡 Answer: The RY rotation gate creates superpositions following") +print(" trigonometric functions. This is fundamental quantum mechanics!") + +print("\n❓ Question 2: Why is Moons accuracy higher than Circles?") +print("💡 Answer: The quantum feature map (angle encoding) naturally") +print(" handles smooth non-linear boundaries but struggles with radial patterns.") + +print("\n❓ Question 3: What does no overfitting tell us?") +print("💡 Answer: Quantum circuits have built-in regularization due to") +print(" limited expressiveness. This prevents memorizing training data!") + +print("\n❓ Question 4: How to improve Circles performance?") +print("💡 Possible solutions:") +print(" • Add radial features: r = sqrt(x² + y²)") +print(" • Use more qubits (6-8)") +print(" • Try different data encoding strategies") +print(" • Increase circuit depth (more layers)") + +# ============================================================================ +# RECOMMENDATIONS +# ============================================================================ +print("\n\n🎯 RECOMMENDATIONS FOR NEXT EXPERIMENTS") +print("=" * 70) + +print("\n1. IMPROVE CIRCLES DATASET:") +print(" • Convert to polar coordinates (r, θ)") +print(" • Add polynomial features") +print(" • Try amplitude encoding instead of angle encoding") + +print("\n2. EXTEND TO MULTI-CLASS:") +print(" • Use all 3 Iris classes") +print(" • Implement one-vs-all strategy") +print(" • Try quantum softmax") + +print("\n3. OPTIMIZE HYPERPARAMETERS:") +print(" • Grid search over learning rates") +print(" • Test different layer counts (3-5 layers)") +print(" • Compare entanglement patterns") + +print("\n4. BENCHMARK AGAINST CLASSICAL:") +print(" • Train standard MLP on same data") +print(" • Compare parameter counts") +print(" • Measure training time") + +# ============================================================================ +# SUMMARY +# ============================================================================ +print("\n\n" + "=" * 70) +print("ANALYSIS SUMMARY") +print("=" * 70) + +print("\n✅ What We Learned:") +print(" • Quantum simulation is accurate (state evolution matches theory)") +print(" • Quantum ML can achieve competitive accuracy (85% on Moons)") +print(" • Different datasets suit different quantum encodings") +print(" • Training is stable with no overfitting") + +print("\n📈 Performance Metrics:") +print(" • Best result: 85% (Moons)") +print(" • Average result: 67% across datasets") +print(" • Training time: ~2-3 minutes per model") +print(" • Parameter count: 16 quantum + classical weights") + +print("\n🔬 Scientific Validation:") +print(" • Quantum mechanics verified (state evolution)") +print(" • Hybrid learning works (gradient flow through quantum layers)") +print(" • Entanglement provides computational advantage") + +print("\n💡 Key Takeaway:") +print(" Quantum machine learning is REAL and WORKING!") +print(" Your quantum AI system successfully demonstrates:") +print(" ✓ Quantum circuit simulation") +print(" ✓ Quantum gradient descent") +print(" ✓ Hybrid quantum-classical learning") +print(" ✓ Competitive performance on real datasets") + +print("\n" + "=" * 70) +print("Next: Run parameter_tuning.py to explore hyperparameters!") +print("=" * 70) diff --git a/ai-projects/quantum-ml/experiments/auto_optimize.py b/ai-projects/quantum-ml/experiments/auto_optimize.py index b64f9939a..793ea4c41 100644 --- a/ai-projects/quantum-ml/experiments/auto_optimize.py +++ b/ai-projects/quantum-ml/experiments/auto_optimize.py @@ -1,359 +1,359 @@ -""" -Automated Parameter Optimization - Run All Experiments -====================================================== - -This script runs comprehensive hyperparameter tuning automatically: -1. Layer depth (already completed: 2 layers optimal) -2. Learning rate optimization (0.001 to 0.1) -3. Entanglement pattern comparison (linear, circular, full) - -Results saved to: results/experiments/ -""" - -import sys -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np - -# Add parent directory to path to access src modules -parent_dir = Path(__file__).parent.parent -sys.path.insert(0, str(parent_dir)) - -import torch.nn as nn -import torch.optim as optim -# Import from src package -from src.quantum_classifier import QuantumClassifier - - -# Simple Hybrid Model Class -class HybridQuantumClassifier(nn.Module): - def __init__(self, input_dim, quantum_classifier): - super().__init__() - self.quantum_classifier = quantum_classifier - self.classical_layer = nn.Linear(input_dim, quantum_classifier.n_qubits) - self.output_layer = nn.Linear(1, 2) # Binary classification - - def forward(self, x): - x = torch.relu(self.classical_layer(x)) - # Quantum layer expects data in [0, 2π] range - x = torch.abs(x) % (2 * np.pi) - quantum_out = self.quantum_classifier.forward(x.detach().numpy()) - quantum_tensor = torch.FloatTensor(quantum_out).unsqueeze(1) - return self.output_layer(quantum_tensor) - - -# Simple Trainer Class -class QuantumClassicalTrainer: - def __init__(self, model, learning_rate=0.001): - self.model = model - self.optimizer = optim.Adam(model.parameters(), lr=learning_rate) - self.criterion = nn.CrossEntropyLoss() - self.train_losses = [] - self.val_accuracies = [] - - def train(self, train_loader, val_loader, num_epochs): - for epoch in range(num_epochs): - # Training - self.model.train() - epoch_loss = 0 - for X_batch, y_batch in train_loader: - self.optimizer.zero_grad() - outputs = self.model(X_batch) - loss = self.criterion(outputs, y_batch) - loss.backward() - self.optimizer.step() - epoch_loss += loss.item() - - self.train_losses.append(epoch_loss / len(train_loader)) - - # Validation - self.model.eval() - correct = 0 - total = 0 - with torch.no_grad(): - for X_batch, y_batch in val_loader: - outputs = self.model(X_batch) - _, predicted = torch.max(outputs, 1) - total += y_batch.size(0) - correct += (predicted == y_batch).sum().item() - - acc = correct / total - self.val_accuracies.append(acc) - - if (epoch + 1) % 5 == 0: - print( - f" Epoch {epoch+1}/{num_epochs} - Loss: {self.train_losses[-1]:.4f}, Val Acc: {acc:.4f}" - ) - - -import torch -import yaml -from sklearn.datasets import make_moons -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from torch.utils.data import DataLoader, TensorDataset - - -# Helper function to train model (add this after imports) -def train_quantum_model( - model, - X_train, - y_train, - X_val, - y_val, - num_epochs=20, - batch_size=32, - learning_rate=0.001, -): - """Train the quantum model""" - - # Convert to PyTorch tensors - X_train_tensor = torch.FloatTensor(X_train) - y_train_tensor = torch.LongTensor(y_train) - X_val_tensor = torch.FloatTensor(X_val) - y_val_tensor = torch.LongTensor(y_val) - - # Create data loaders - train_dataset = TensorDataset(X_train_tensor, y_train_tensor) - val_dataset = TensorDataset(X_val_tensor, y_val_tensor) - - train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) - val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) - - # Create trainer (assuming QuantumClassicalTrainer exists in hybrid_model or create simple trainer) - trainer = QuantumClassicalTrainer(model, learning_rate=learning_rate) - - # Train - trainer.train(train_loader, val_loader, num_epochs) - - # Return history - history = { - "train_loss": trainer.train_losses, - "val_acc": trainer.val_accuracies, - "val_loss": [0] * len(trainer.val_accuracies), - } - - return history - - -print("=" * 70) -print(" AUTOMATED PARAMETER OPTIMIZATION") -print("=" * 70) -print("\nRunning all hyperparameter experiments automatically...") -print("This will take approximately 10-15 minutes.\n") - -# Create results directory -results_dir = Path(__file__).parent.parent / "results" / "experiments" -results_dir.mkdir(parents=True, exist_ok=True) - -# ============================================ -# PREPARE DATASET -# ============================================ -print("📊 Preparing Moons dataset...") -X, y = make_moons(n_samples=200, noise=0.1, random_state=42) -X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) - -scaler = StandardScaler() -X_train = scaler.fit_transform(X_train) -X_val = scaler.transform(X_val) - -# Pad to 4 features -X_train_padded = np.hstack([X_train, np.zeros((X_train.shape[0], 2))]) -X_val_padded = np.hstack([X_val, np.zeros((X_val.shape[0], 2))]) - -print(f" Training samples: {X_train_padded.shape[0]}") -print(f" Validation samples: {X_val_padded.shape[0]}\n") - -# ============================================ -# EXPERIMENT 2: LEARNING RATE OPTIMIZATION -# ============================================ -print("=" * 70) -print("EXPERIMENT 2: Learning Rate Optimization") -print("=" * 70) - -learning_rates = [0.001, 0.005, 0.01, 0.05, 0.1] -lr_results = [] - -for lr in learning_rates: - print(f"\n>>> Testing learning rate: {lr}") - - # Create fresh model - quantum_clf = QuantumClassifier() - - # Manually set learning rate in config - config_path = Path(__file__).parent.parent / "config" / "quantum_config.yaml" - with open(config_path, "r") as f: - config = yaml.safe_load(f) - - original_lr = config["ml"]["training"]["learning_rate"] - config["ml"]["training"]["learning_rate"] = lr - - # Save temporarily - with open(config_path, "w") as f: - yaml.dump(config, f) - - # Train - model = HybridQuantumClassifier(input_dim=4, quantum_classifier=quantum_clf) - - try: - history = train_quantum_model( - model, X_train_padded, y_train, X_val_padded, y_val - ) - - final_acc = history["val_acc"][-1] - best_acc = max(history["val_acc"]) - lr_results.append({"lr": lr, "final_acc": final_acc, "best_acc": best_acc}) - - print(f" Final Accuracy: {final_acc:.4f}") - print(f" Best Accuracy: {best_acc:.4f}") - - except Exception as e: - print(f" ✗ Error: {e}") - lr_results.append({"lr": lr, "final_acc": 0.0, "best_acc": 0.0}) - - # Restore original learning rate - config["ml"]["training"]["learning_rate"] = original_lr - with open(config_path, "w") as f: - yaml.dump(config, f) - -# Plot learning rate results -plt.figure(figsize=(10, 6)) -lrs = [r["lr"] for r in lr_results] -final_accs = [r["final_acc"] for r in lr_results] -best_accs = [r["best_acc"] for r in lr_results] - -plt.plot(lrs, final_accs, "o-", label="Final Accuracy", linewidth=2, markersize=8) -plt.plot(lrs, best_accs, "s-", label="Best Accuracy", linewidth=2, markersize=8) -plt.xlabel("Learning Rate", fontsize=12) -plt.ylabel("Validation Accuracy", fontsize=12) -plt.title("Learning Rate Optimization", fontsize=14, fontweight="bold") -plt.xscale("log") -plt.grid(True, alpha=0.3) -plt.legend(fontsize=11) -plt.tight_layout() - -lr_plot_path = results_dir / "experiment2_learning_rate.png" -plt.savefig(lr_plot_path, dpi=150, bbox_inches="tight") -print(f"\n✓ Plot saved: {lr_plot_path}") -plt.close() - -# ============================================ -# EXPERIMENT 3: ENTANGLEMENT PATTERN -# ============================================ -print("\n" + "=" * 70) -print("EXPERIMENT 3: Entanglement Pattern Comparison") -print("=" * 70) - -entanglement_patterns = ["linear", "circular", "full"] -ent_results = [] - -for pattern in entanglement_patterns: - print(f"\n>>> Testing entanglement: {pattern}") - - # Update config - config_path = Path(__file__).parent.parent / "config" / "quantum_config.yaml" - with open(config_path, "r") as f: - config = yaml.safe_load(f) - - original_ent = config["ml"]["model"]["entanglement"] - config["ml"]["model"]["entanglement"] = pattern - - with open(config_path, "w") as f: - yaml.dump(config, f) - - # Create and train model - quantum_clf = QuantumClassifier() - model = HybridQuantumClassifier(input_dim=4, quantum_classifier=quantum_clf) - - try: - history = train_quantum_model( - model, X_train_padded, y_train, X_val_padded, y_val - ) - - final_acc = history["val_acc"][-1] - best_acc = max(history["val_acc"]) - ent_results.append( - {"pattern": pattern, "final_acc": final_acc, "best_acc": best_acc} - ) - - print(f" Final Accuracy: {final_acc:.4f}") - print(f" Best Accuracy: {best_acc:.4f}") - - except Exception as e: - print(f" ✗ Error: {e}") - ent_results.append({"pattern": pattern, "final_acc": 0.0, "best_acc": 0.0}) - - # Restore original entanglement - config["ml"]["model"]["entanglement"] = original_ent - with open(config_path, "w") as f: - yaml.dump(config, f) - -# Plot entanglement results -plt.figure(figsize=(10, 6)) -patterns = [r["pattern"] for r in ent_results] -final_accs_ent = [r["final_acc"] for r in ent_results] -best_accs_ent = [r["best_acc"] for r in ent_results] - -x = np.arange(len(patterns)) -width = 0.35 - -plt.bar(x - width / 2, final_accs_ent, width, label="Final Accuracy", alpha=0.8) -plt.bar(x + width / 2, best_accs_ent, width, label="Best Accuracy", alpha=0.8) -plt.xlabel("Entanglement Pattern", fontsize=12) -plt.ylabel("Validation Accuracy", fontsize=12) -plt.title("Entanglement Pattern Comparison", fontsize=14, fontweight="bold") -plt.xticks(x, patterns) -plt.legend(fontsize=11) -plt.grid(True, alpha=0.3, axis="y") -plt.tight_layout() - -ent_plot_path = results_dir / "experiment3_entanglement.png" -plt.savefig(ent_plot_path, dpi=150, bbox_inches="tight") -print(f"\n✓ Plot saved: {ent_plot_path}") -plt.close() - -# ============================================ -# FINAL SUMMARY -# ============================================ -print("\n" + "=" * 70) -print(" OPTIMIZATION COMPLETE!") -print("=" * 70) - -print("\n📊 LEARNING RATE RESULTS:") -print("-" * 50) -for r in lr_results: - print(f" LR {r['lr']:.4f}: Final={r['final_acc']:.4f}, Best={r['best_acc']:.4f}") - -best_lr = max(lr_results, key=lambda x: x["best_acc"]) -print( - f"\n🏆 Best Learning Rate: {best_lr['lr']:.4f} (Accuracy: {best_lr['best_acc']:.4f})" -) - -print("\n📊 ENTANGLEMENT RESULTS:") -print("-" * 50) -for r in ent_results: - print(f" {r['pattern']:8s}: Final={r['final_acc']:.4f}, Best={r['best_acc']:.4f}") - -best_ent = max(ent_results, key=lambda x: x["best_acc"]) -print( - f"\n🏆 Best Entanglement: {best_ent['pattern']} (Accuracy: {best_ent['best_acc']:.4f})" -) - -print("\n💡 OPTIMAL CONFIGURATION:") -print("-" * 50) -print(" Layers: 2 (from previous experiment)") -print(f" Learning Rate: {best_lr['lr']:.4f}") -print(f" Entanglement: {best_ent['pattern']}") -print(f" Expected Accuracy: ~{best_ent['best_acc']:.1%}") - -print("\n📁 Generated Files:") -print(f" {lr_plot_path}") -print(f" {ent_plot_path}") - -print("\n🚀 Next Steps:") -print(" 1. Update config/quantum_config.yaml with optimal parameters") -print(" 2. Re-train all models with optimized settings") -print(" 3. Deploy to Azure Quantum for hardware testing") - -print("\n" + "=" * 70) +""" +Automated Parameter Optimization - Run All Experiments +====================================================== + +This script runs comprehensive hyperparameter tuning automatically: +1. Layer depth (already completed: 2 layers optimal) +2. Learning rate optimization (0.001 to 0.1) +3. Entanglement pattern comparison (linear, circular, full) + +Results saved to: results/experiments/ +""" + +import sys +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np + +# Add parent directory to path to access src modules +parent_dir = Path(__file__).parent.parent +sys.path.insert(0, str(parent_dir)) + +import torch.nn as nn +import torch.optim as optim +# Import from src package +from src.quantum_classifier import QuantumClassifier + + +# Simple Hybrid Model Class +class HybridQuantumClassifier(nn.Module): + def __init__(self, input_dim, quantum_classifier): + super().__init__() + self.quantum_classifier = quantum_classifier + self.classical_layer = nn.Linear(input_dim, quantum_classifier.n_qubits) + self.output_layer = nn.Linear(1, 2) # Binary classification + + def forward(self, x): + x = torch.relu(self.classical_layer(x)) + # Quantum layer expects data in [0, 2π] range + x = torch.abs(x) % (2 * np.pi) + quantum_out = self.quantum_classifier.forward(x.detach().numpy()) + quantum_tensor = torch.FloatTensor(quantum_out).unsqueeze(1) + return self.output_layer(quantum_tensor) + + +# Simple Trainer Class +class QuantumClassicalTrainer: + def __init__(self, model, learning_rate=0.001): + self.model = model + self.optimizer = optim.Adam(model.parameters(), lr=learning_rate) + self.criterion = nn.CrossEntropyLoss() + self.train_losses = [] + self.val_accuracies = [] + + def train(self, train_loader, val_loader, num_epochs): + for epoch in range(num_epochs): + # Training + self.model.train() + epoch_loss = 0 + for X_batch, y_batch in train_loader: + self.optimizer.zero_grad() + outputs = self.model(X_batch) + loss = self.criterion(outputs, y_batch) + loss.backward() + self.optimizer.step() + epoch_loss += loss.item() + + self.train_losses.append(epoch_loss / len(train_loader)) + + # Validation + self.model.eval() + correct = 0 + total = 0 + with torch.no_grad(): + for X_batch, y_batch in val_loader: + outputs = self.model(X_batch) + _, predicted = torch.max(outputs, 1) + total += y_batch.size(0) + correct += (predicted == y_batch).sum().item() + + acc = correct / total + self.val_accuracies.append(acc) + + if (epoch + 1) % 5 == 0: + print( + f" Epoch {epoch+1}/{num_epochs} - Loss: {self.train_losses[-1]:.4f}, Val Acc: {acc:.4f}" + ) + + +import torch +import yaml +from sklearn.datasets import make_moons +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from torch.utils.data import DataLoader, TensorDataset + + +# Helper function to train model (add this after imports) +def train_quantum_model( + model, + X_train, + y_train, + X_val, + y_val, + num_epochs=20, + batch_size=32, + learning_rate=0.001, +): + """Train the quantum model""" + + # Convert to PyTorch tensors + X_train_tensor = torch.FloatTensor(X_train) + y_train_tensor = torch.LongTensor(y_train) + X_val_tensor = torch.FloatTensor(X_val) + y_val_tensor = torch.LongTensor(y_val) + + # Create data loaders + train_dataset = TensorDataset(X_train_tensor, y_train_tensor) + val_dataset = TensorDataset(X_val_tensor, y_val_tensor) + + train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) + + # Create trainer (assuming QuantumClassicalTrainer exists in hybrid_model or create simple trainer) + trainer = QuantumClassicalTrainer(model, learning_rate=learning_rate) + + # Train + trainer.train(train_loader, val_loader, num_epochs) + + # Return history + history = { + "train_loss": trainer.train_losses, + "val_acc": trainer.val_accuracies, + "val_loss": [0] * len(trainer.val_accuracies), + } + + return history + + +print("=" * 70) +print(" AUTOMATED PARAMETER OPTIMIZATION") +print("=" * 70) +print("\nRunning all hyperparameter experiments automatically...") +print("This will take approximately 10-15 minutes.\n") + +# Create results directory +results_dir = Path(__file__).parent.parent / "results" / "experiments" +results_dir.mkdir(parents=True, exist_ok=True) + +# ============================================ +# PREPARE DATASET +# ============================================ +print("📊 Preparing Moons dataset...") +X, y = make_moons(n_samples=200, noise=0.1, random_state=42) +X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) + +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_val = scaler.transform(X_val) + +# Pad to 4 features +X_train_padded = np.hstack([X_train, np.zeros((X_train.shape[0], 2))]) +X_val_padded = np.hstack([X_val, np.zeros((X_val.shape[0], 2))]) + +print(f" Training samples: {X_train_padded.shape[0]}") +print(f" Validation samples: {X_val_padded.shape[0]}\n") + +# ============================================ +# EXPERIMENT 2: LEARNING RATE OPTIMIZATION +# ============================================ +print("=" * 70) +print("EXPERIMENT 2: Learning Rate Optimization") +print("=" * 70) + +learning_rates = [0.001, 0.005, 0.01, 0.05, 0.1] +lr_results = [] + +for lr in learning_rates: + print(f"\n>>> Testing learning rate: {lr}") + + # Create fresh model + quantum_clf = QuantumClassifier() + + # Manually set learning rate in config + config_path = Path(__file__).parent.parent / "config" / "quantum_config.yaml" + with open(config_path, "r") as f: + config = yaml.safe_load(f) + + original_lr = config["ml"]["training"]["learning_rate"] + config["ml"]["training"]["learning_rate"] = lr + + # Save temporarily + with open(config_path, "w") as f: + yaml.dump(config, f) + + # Train + model = HybridQuantumClassifier(input_dim=4, quantum_classifier=quantum_clf) + + try: + history = train_quantum_model( + model, X_train_padded, y_train, X_val_padded, y_val + ) + + final_acc = history["val_acc"][-1] + best_acc = max(history["val_acc"]) + lr_results.append({"lr": lr, "final_acc": final_acc, "best_acc": best_acc}) + + print(f" Final Accuracy: {final_acc:.4f}") + print(f" Best Accuracy: {best_acc:.4f}") + + except Exception as e: + print(f" ✗ Error: {e}") + lr_results.append({"lr": lr, "final_acc": 0.0, "best_acc": 0.0}) + + # Restore original learning rate + config["ml"]["training"]["learning_rate"] = original_lr + with open(config_path, "w") as f: + yaml.dump(config, f) + +# Plot learning rate results +plt.figure(figsize=(10, 6)) +lrs = [r["lr"] for r in lr_results] +final_accs = [r["final_acc"] for r in lr_results] +best_accs = [r["best_acc"] for r in lr_results] + +plt.plot(lrs, final_accs, "o-", label="Final Accuracy", linewidth=2, markersize=8) +plt.plot(lrs, best_accs, "s-", label="Best Accuracy", linewidth=2, markersize=8) +plt.xlabel("Learning Rate", fontsize=12) +plt.ylabel("Validation Accuracy", fontsize=12) +plt.title("Learning Rate Optimization", fontsize=14, fontweight="bold") +plt.xscale("log") +plt.grid(True, alpha=0.3) +plt.legend(fontsize=11) +plt.tight_layout() + +lr_plot_path = results_dir / "experiment2_learning_rate.png" +plt.savefig(lr_plot_path, dpi=150, bbox_inches="tight") +print(f"\n✓ Plot saved: {lr_plot_path}") +plt.close() + +# ============================================ +# EXPERIMENT 3: ENTANGLEMENT PATTERN +# ============================================ +print("\n" + "=" * 70) +print("EXPERIMENT 3: Entanglement Pattern Comparison") +print("=" * 70) + +entanglement_patterns = ["linear", "circular", "full"] +ent_results = [] + +for pattern in entanglement_patterns: + print(f"\n>>> Testing entanglement: {pattern}") + + # Update config + config_path = Path(__file__).parent.parent / "config" / "quantum_config.yaml" + with open(config_path, "r") as f: + config = yaml.safe_load(f) + + original_ent = config["ml"]["model"]["entanglement"] + config["ml"]["model"]["entanglement"] = pattern + + with open(config_path, "w") as f: + yaml.dump(config, f) + + # Create and train model + quantum_clf = QuantumClassifier() + model = HybridQuantumClassifier(input_dim=4, quantum_classifier=quantum_clf) + + try: + history = train_quantum_model( + model, X_train_padded, y_train, X_val_padded, y_val + ) + + final_acc = history["val_acc"][-1] + best_acc = max(history["val_acc"]) + ent_results.append( + {"pattern": pattern, "final_acc": final_acc, "best_acc": best_acc} + ) + + print(f" Final Accuracy: {final_acc:.4f}") + print(f" Best Accuracy: {best_acc:.4f}") + + except Exception as e: + print(f" ✗ Error: {e}") + ent_results.append({"pattern": pattern, "final_acc": 0.0, "best_acc": 0.0}) + + # Restore original entanglement + config["ml"]["model"]["entanglement"] = original_ent + with open(config_path, "w") as f: + yaml.dump(config, f) + +# Plot entanglement results +plt.figure(figsize=(10, 6)) +patterns = [r["pattern"] for r in ent_results] +final_accs_ent = [r["final_acc"] for r in ent_results] +best_accs_ent = [r["best_acc"] for r in ent_results] + +x = np.arange(len(patterns)) +width = 0.35 + +plt.bar(x - width / 2, final_accs_ent, width, label="Final Accuracy", alpha=0.8) +plt.bar(x + width / 2, best_accs_ent, width, label="Best Accuracy", alpha=0.8) +plt.xlabel("Entanglement Pattern", fontsize=12) +plt.ylabel("Validation Accuracy", fontsize=12) +plt.title("Entanglement Pattern Comparison", fontsize=14, fontweight="bold") +plt.xticks(x, patterns) +plt.legend(fontsize=11) +plt.grid(True, alpha=0.3, axis="y") +plt.tight_layout() + +ent_plot_path = results_dir / "experiment3_entanglement.png" +plt.savefig(ent_plot_path, dpi=150, bbox_inches="tight") +print(f"\n✓ Plot saved: {ent_plot_path}") +plt.close() + +# ============================================ +# FINAL SUMMARY +# ============================================ +print("\n" + "=" * 70) +print(" OPTIMIZATION COMPLETE!") +print("=" * 70) + +print("\n📊 LEARNING RATE RESULTS:") +print("-" * 50) +for r in lr_results: + print(f" LR {r['lr']:.4f}: Final={r['final_acc']:.4f}, Best={r['best_acc']:.4f}") + +best_lr = max(lr_results, key=lambda x: x["best_acc"]) +print( + f"\n🏆 Best Learning Rate: {best_lr['lr']:.4f} (Accuracy: {best_lr['best_acc']:.4f})" +) + +print("\n📊 ENTANGLEMENT RESULTS:") +print("-" * 50) +for r in ent_results: + print(f" {r['pattern']:8s}: Final={r['final_acc']:.4f}, Best={r['best_acc']:.4f}") + +best_ent = max(ent_results, key=lambda x: x["best_acc"]) +print( + f"\n🏆 Best Entanglement: {best_ent['pattern']} (Accuracy: {best_ent['best_acc']:.4f})" +) + +print("\n💡 OPTIMAL CONFIGURATION:") +print("-" * 50) +print(" Layers: 2 (from previous experiment)") +print(f" Learning Rate: {best_lr['lr']:.4f}") +print(f" Entanglement: {best_ent['pattern']}") +print(f" Expected Accuracy: ~{best_ent['best_acc']:.1%}") + +print("\n📁 Generated Files:") +print(f" {lr_plot_path}") +print(f" {ent_plot_path}") + +print("\n🚀 Next Steps:") +print(" 1. Update config/quantum_config.yaml with optimal parameters") +print(" 2. Re-train all models with optimized settings") +print(" 3. Deploy to Azure Quantum for hardware testing") + +print("\n" + "=" * 70) diff --git a/ai-projects/quantum-ml/experiments/extended_datasets.py b/ai-projects/quantum-ml/experiments/extended_datasets.py index 7ad26c40a..e74bf0ef1 100644 --- a/ai-projects/quantum-ml/experiments/extended_datasets.py +++ b/ai-projects/quantum-ml/experiments/extended_datasets.py @@ -1,324 +1,324 @@ -""" -Extend Quantum AI with New Datasets and Architectures -Demonstrates advanced quantum ML techniques -""" - -import sys -from pathlib import Path - -import matplotlib -import numpy as np - -matplotlib.use("Agg") -import matplotlib.pyplot as plt -from sklearn.datasets import load_wine, make_classification -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler - -# Add project root to sys.path so 'src' can be imported as a package -project_root = Path(__file__).resolve().parents[1] -if str(project_root) not in sys.path: - sys.path.insert(0, str(project_root)) - -try: - from src.quantum_classifier import (HybridQuantumClassifier, - QuantumClassifier, train_quantum_model) -except ModuleNotFoundError: - # Fallback for environments without namespace package support - sys.path.insert(0, str(project_root / "src")) - from quantum_classifier import HybridQuantumClassifier # type: ignore - from quantum_classifier import QuantumClassifier, train_quantum_model - -results_dir = Path(__file__).parent.parent / "results" / "extended_datasets" -results_dir.mkdir(parents=True, exist_ok=True) - -print("=" * 70) -print(" EXTENDED DATASETS FOR QUANTUM ML") -print("=" * 70) - -# ============================================================================ -# DATASET 1: XOR Problem (Classic Quantum Advantage) -# ============================================================================ -print("\n📊 DATASET 1: XOR PROBLEM") -print("=" * 70) -print("The XOR problem is linearly inseparable - perfect for quantum!") - -# Create XOR dataset -np.random.seed(42) -n_samples = 200 - -X_xor = np.random.randn(n_samples, 2) -y_xor = np.logical_xor(X_xor[:, 0] > 0, X_xor[:, 1] > 0).astype(int) - -# Add noise -X_xor += np.random.randn(n_samples, 2) * 0.3 - -X_train, X_val, y_train, y_val = train_test_split( - X_xor, y_xor, test_size=0.2, random_state=42 -) - -scaler = StandardScaler() -X_train = scaler.fit_transform(X_train) -X_val = scaler.transform(X_val) - -# Pad to 4D -X_train_padded = np.pad(X_train, ((0, 0), (0, 2)), mode="constant") -X_val_padded = np.pad(X_val, ((0, 0), (0, 2)), mode="constant") - -print(f"Training samples: {len(X_train)}") -print("Training quantum classifier on XOR...") - -qc_xor = QuantumClassifier() -model_xor = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_xor) -history_xor = train_quantum_model( - model_xor, X_train_padded, y_train, X_val_padded, y_val -) - -print(f"\n✓ XOR Accuracy: {history_xor['val_acc'][-1]:.4f}") - -# Visualize XOR -fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) - -# Original XOR pattern -scatter = ax1.scatter( - X_xor[:, 0], - X_xor[:, 1], - c=y_xor, - cmap="coolwarm", - s=50, - alpha=0.6, - edgecolors="black", -) -ax1.set_xlabel("Feature 1", fontsize=12) -ax1.set_ylabel("Feature 2", fontsize=12) -ax1.set_title("XOR Pattern (Linearly Inseparable)", fontsize=14, fontweight="bold") -ax1.axhline(y=0, color="k", linestyle="--", alpha=0.3) -ax1.axvline(x=0, color="k", linestyle="--", alpha=0.3) -ax1.grid(True, alpha=0.3) - -# Training curve -ax2.plot(history_xor["val_acc"], linewidth=2, color="#e74c3c") -ax2.set_xlabel("Epoch", fontsize=12) -ax2.set_ylabel("Validation Accuracy", fontsize=12) -ax2.set_title( - f'XOR Training (Final: {history_xor["val_acc"][-1]:.1%})', - fontsize=14, - fontweight="bold", -) -ax2.grid(True, alpha=0.3) - -plt.tight_layout() -plt.savefig(results_dir / "xor_problem.png", dpi=150, bbox_inches="tight") -print(f"✓ Plot saved: {results_dir / 'xor_problem.png'}") - -# ============================================================================ -# DATASET 2: Spiral Dataset (High Complexity) -# ============================================================================ -print("\n\n📊 DATASET 2: SPIRAL DATASET") -print("=" * 70) -print("Intertwined spirals - tests quantum entanglement benefits") - - -def make_spirals(n_points=100, noise=0.2): - """Create spiral dataset""" - n = np.sqrt(np.random.rand(n_points, 1)) * 780 * (2 * np.pi) / 360 - d1x = -np.cos(n) * n + np.random.rand(n_points, 1) * noise - d1y = np.sin(n) * n + np.random.rand(n_points, 1) * noise - X1 = np.hstack((d1x, d1y)) / 3 - - d2x = np.cos(n) * n + np.random.rand(n_points, 1) * noise - d2y = -np.sin(n) * n + np.random.rand(n_points, 1) * noise - X2 = np.hstack((d2x, d2y)) / 3 - - X = np.vstack((X1, X2)) - y = np.hstack((np.zeros(n_points), np.ones(n_points))) - - return X, y - - -X_spiral, y_spiral = make_spirals(n_points=100, noise=0.5) - -X_train, X_val, y_train, y_val = train_test_split( - X_spiral, y_spiral, test_size=0.2, random_state=42 -) - -scaler = StandardScaler() -X_train = scaler.fit_transform(X_train) -X_val = scaler.transform(X_val) -X_train_padded = np.pad(X_train, ((0, 0), (0, 2)), mode="constant") -X_val_padded = np.pad(X_val, ((0, 0), (0, 2)), mode="constant") - -print("Training quantum classifier on Spirals...") -qc_spiral = QuantumClassifier() -model_spiral = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_spiral) -history_spiral = train_quantum_model( - model_spiral, X_train_padded, y_train, X_val_padded, y_val -) - -print(f"\n✓ Spiral Accuracy: {history_spiral['val_acc'][-1]:.4f}") - -# Visualize spirals -fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) - -scatter = ax1.scatter( - X_spiral[:, 0], - X_spiral[:, 1], - c=y_spiral, - cmap="viridis", - s=50, - alpha=0.6, - edgecolors="black", -) -ax1.set_xlabel("Feature 1", fontsize=12) -ax1.set_ylabel("Feature 2", fontsize=12) -ax1.set_title("Spiral Pattern (Highly Non-linear)", fontsize=14, fontweight="bold") -ax1.grid(True, alpha=0.3) - -ax2.plot(history_spiral["val_acc"], linewidth=2, color="#9b59b6") -ax2.set_xlabel("Epoch", fontsize=12) -ax2.set_ylabel("Validation Accuracy", fontsize=12) -ax2.set_title( - f'Spiral Training (Final: {history_spiral["val_acc"][-1]:.1%})', - fontsize=14, - fontweight="bold", -) -ax2.grid(True, alpha=0.3) - -plt.tight_layout() -plt.savefig(results_dir / "spiral_dataset.png", dpi=150, bbox_inches="tight") -print(f"✓ Plot saved: {results_dir / 'spiral_dataset.png'}") - -# ============================================================================ -# DATASET 3: Imbalanced Dataset -# ============================================================================ -print("\n\n📊 DATASET 3: IMBALANCED DATASET") -print("=" * 70) -print("Tests robustness with class imbalance (90% vs 10%)") - -X_imb, y_imb = make_classification( - n_samples=200, - n_features=4, - n_informative=3, - n_redundant=1, - n_clusters_per_class=1, - weights=[0.9, 0.1], # 90-10 split - flip_y=0.01, - random_state=42, -) - -X_train, X_val, y_train, y_val = train_test_split( - X_imb, y_imb, test_size=0.2, stratify=y_imb, random_state=42 -) - -scaler = StandardScaler() -X_train = scaler.fit_transform(X_train) -X_val = scaler.transform(X_val) - -print(f"Class distribution: {np.bincount(y_train)}") -print("Training quantum classifier on Imbalanced data...") - -qc_imb = QuantumClassifier() -model_imb = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_imb) -history_imb = train_quantum_model(model_imb, X_train, y_train, X_val, y_val) - -print(f"\n✓ Imbalanced Accuracy: {history_imb['val_acc'][-1]:.4f}") - -# ============================================================================ -# DATASET 4: Wine Quality (Real-world) -# ============================================================================ -print("\n\n📊 DATASET 4: WINE QUALITY") -print("=" * 70) -print("Real-world dataset: Wine chemical analysis") - -wine = load_wine() -X_wine = wine.data -y_wine = (wine.target == 0).astype(int) # Class 0 vs rest - -X_train, X_val, y_train, y_val = train_test_split( - X_wine, y_wine, test_size=0.2, random_state=42 -) - -scaler = StandardScaler() -X_train = scaler.fit_transform(X_train) -X_val = scaler.transform(X_val) - -# Reduce to 4 features using PCA approximation (take first 4) -from sklearn.decomposition import PCA - -pca = PCA(n_components=4) -X_train = pca.fit_transform(X_train) -X_val = pca.transform(X_val) - -print(f"Training samples: {len(X_train)}") -print(f"PCA explained variance: {pca.explained_variance_ratio_.sum():.2%}") -print("Training quantum classifier on Wine...") - -qc_wine = QuantumClassifier() -model_wine = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_wine) -history_wine = train_quantum_model(model_wine, X_train, y_train, X_val, y_val) - -print(f"\n✓ Wine Accuracy: {history_wine['val_acc'][-1]:.4f}") - -# ============================================================================ -# COMPARATIVE SUMMARY -# ============================================================================ -print("\n\n" + "=" * 70) -print("EXTENDED DATASETS - COMPARATIVE RESULTS") -print("=" * 70) - -results_summary = { - "XOR": history_xor["val_acc"][-1], - "Spiral": history_spiral["val_acc"][-1], - "Imbalanced": history_imb["val_acc"][-1], - "Wine": history_wine["val_acc"][-1], -} - -print("\nFinal Accuracies:") -print("-" * 70) -for dataset, acc in results_summary.items(): - stars = "★" * int(acc * 10) - print(f" {dataset:12s}: {acc:.4f} ({acc*100:.1f}%) {stars}") - -# Create summary plot -fig, axes = plt.subplots(2, 2, figsize=(14, 10)) -axes = axes.ravel() - -datasets = ["XOR", "Spiral", "Imbalanced", "Wine"] -histories = [history_xor, history_spiral, history_imb, history_wine] -colors = ["#e74c3c", "#9b59b6", "#3498db", "#2ecc71"] - -for i, (dataset, history, color) in enumerate(zip(datasets, histories, colors)): - axes[i].plot(history["train_loss"], label="Train Loss", alpha=0.7, linewidth=2) - axes[i].plot(history["val_loss"], label="Val Loss", linewidth=2) - axes[i].set_title( - f'{dataset}: Acc={history["val_acc"][-1]:.3f}', fontsize=12, fontweight="bold" - ) - axes[i].set_xlabel("Epoch") - axes[i].set_ylabel("Loss") - axes[i].legend() - axes[i].grid(True, alpha=0.3) - -plt.tight_layout() -plt.savefig(results_dir / "all_datasets_comparison.png", dpi=150, bbox_inches="tight") -print(f"\n✓ Summary plot saved: {results_dir / 'all_datasets_comparison.png'}") - -print("\n" + "=" * 70) -print("INSIGHTS AND RECOMMENDATIONS") -print("=" * 70) - -print("\n✅ Successful on:") -print(" • XOR: Quantum entanglement handles non-linearity") -print(" • Wine: Real-world data works with proper preprocessing") - -print("\n⚠️ Challenging:") -print(" • Spirals: May need more qubits or specialized encoding") -print(" • Imbalanced: Consider weighted loss functions") - -print("\n🎯 Next Steps:") -print(" 1. Try different quantum feature maps") -print(" 2. Implement quantum data re-uploading") -print(" 3. Test with 6-8 qubits for complex patterns") -print(" 4. Add quantum pooling layers") - -print(f"\n✓ All plots saved to: {results_dir}") -print("=" * 70) +""" +Extend Quantum AI with New Datasets and Architectures +Demonstrates advanced quantum ML techniques +""" + +import sys +from pathlib import Path + +import matplotlib +import numpy as np + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from sklearn.datasets import load_wine, make_classification +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler + +# Add project root to sys.path so 'src' can be imported as a package +project_root = Path(__file__).resolve().parents[1] +if str(project_root) not in sys.path: + sys.path.insert(0, str(project_root)) + +try: + from src.quantum_classifier import (HybridQuantumClassifier, + QuantumClassifier, train_quantum_model) +except ModuleNotFoundError: + # Fallback for environments without namespace package support + sys.path.insert(0, str(project_root / "src")) + from quantum_classifier import HybridQuantumClassifier # type: ignore + from quantum_classifier import QuantumClassifier, train_quantum_model + +results_dir = Path(__file__).parent.parent / "results" / "extended_datasets" +results_dir.mkdir(parents=True, exist_ok=True) + +print("=" * 70) +print(" EXTENDED DATASETS FOR QUANTUM ML") +print("=" * 70) + +# ============================================================================ +# DATASET 1: XOR Problem (Classic Quantum Advantage) +# ============================================================================ +print("\n📊 DATASET 1: XOR PROBLEM") +print("=" * 70) +print("The XOR problem is linearly inseparable - perfect for quantum!") + +# Create XOR dataset +np.random.seed(42) +n_samples = 200 + +X_xor = np.random.randn(n_samples, 2) +y_xor = np.logical_xor(X_xor[:, 0] > 0, X_xor[:, 1] > 0).astype(int) + +# Add noise +X_xor += np.random.randn(n_samples, 2) * 0.3 + +X_train, X_val, y_train, y_val = train_test_split( + X_xor, y_xor, test_size=0.2, random_state=42 +) + +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_val = scaler.transform(X_val) + +# Pad to 4D +X_train_padded = np.pad(X_train, ((0, 0), (0, 2)), mode="constant") +X_val_padded = np.pad(X_val, ((0, 0), (0, 2)), mode="constant") + +print(f"Training samples: {len(X_train)}") +print("Training quantum classifier on XOR...") + +qc_xor = QuantumClassifier() +model_xor = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_xor) +history_xor = train_quantum_model( + model_xor, X_train_padded, y_train, X_val_padded, y_val +) + +print(f"\n✓ XOR Accuracy: {history_xor['val_acc'][-1]:.4f}") + +# Visualize XOR +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) + +# Original XOR pattern +scatter = ax1.scatter( + X_xor[:, 0], + X_xor[:, 1], + c=y_xor, + cmap="coolwarm", + s=50, + alpha=0.6, + edgecolors="black", +) +ax1.set_xlabel("Feature 1", fontsize=12) +ax1.set_ylabel("Feature 2", fontsize=12) +ax1.set_title("XOR Pattern (Linearly Inseparable)", fontsize=14, fontweight="bold") +ax1.axhline(y=0, color="k", linestyle="--", alpha=0.3) +ax1.axvline(x=0, color="k", linestyle="--", alpha=0.3) +ax1.grid(True, alpha=0.3) + +# Training curve +ax2.plot(history_xor["val_acc"], linewidth=2, color="#e74c3c") +ax2.set_xlabel("Epoch", fontsize=12) +ax2.set_ylabel("Validation Accuracy", fontsize=12) +ax2.set_title( + f'XOR Training (Final: {history_xor["val_acc"][-1]:.1%})', + fontsize=14, + fontweight="bold", +) +ax2.grid(True, alpha=0.3) + +plt.tight_layout() +plt.savefig(results_dir / "xor_problem.png", dpi=150, bbox_inches="tight") +print(f"✓ Plot saved: {results_dir / 'xor_problem.png'}") + +# ============================================================================ +# DATASET 2: Spiral Dataset (High Complexity) +# ============================================================================ +print("\n\n📊 DATASET 2: SPIRAL DATASET") +print("=" * 70) +print("Intertwined spirals - tests quantum entanglement benefits") + + +def make_spirals(n_points=100, noise=0.2): + """Create spiral dataset""" + n = np.sqrt(np.random.rand(n_points, 1)) * 780 * (2 * np.pi) / 360 + d1x = -np.cos(n) * n + np.random.rand(n_points, 1) * noise + d1y = np.sin(n) * n + np.random.rand(n_points, 1) * noise + X1 = np.hstack((d1x, d1y)) / 3 + + d2x = np.cos(n) * n + np.random.rand(n_points, 1) * noise + d2y = -np.sin(n) * n + np.random.rand(n_points, 1) * noise + X2 = np.hstack((d2x, d2y)) / 3 + + X = np.vstack((X1, X2)) + y = np.hstack((np.zeros(n_points), np.ones(n_points))) + + return X, y + + +X_spiral, y_spiral = make_spirals(n_points=100, noise=0.5) + +X_train, X_val, y_train, y_val = train_test_split( + X_spiral, y_spiral, test_size=0.2, random_state=42 +) + +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_val = scaler.transform(X_val) +X_train_padded = np.pad(X_train, ((0, 0), (0, 2)), mode="constant") +X_val_padded = np.pad(X_val, ((0, 0), (0, 2)), mode="constant") + +print("Training quantum classifier on Spirals...") +qc_spiral = QuantumClassifier() +model_spiral = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_spiral) +history_spiral = train_quantum_model( + model_spiral, X_train_padded, y_train, X_val_padded, y_val +) + +print(f"\n✓ Spiral Accuracy: {history_spiral['val_acc'][-1]:.4f}") + +# Visualize spirals +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) + +scatter = ax1.scatter( + X_spiral[:, 0], + X_spiral[:, 1], + c=y_spiral, + cmap="viridis", + s=50, + alpha=0.6, + edgecolors="black", +) +ax1.set_xlabel("Feature 1", fontsize=12) +ax1.set_ylabel("Feature 2", fontsize=12) +ax1.set_title("Spiral Pattern (Highly Non-linear)", fontsize=14, fontweight="bold") +ax1.grid(True, alpha=0.3) + +ax2.plot(history_spiral["val_acc"], linewidth=2, color="#9b59b6") +ax2.set_xlabel("Epoch", fontsize=12) +ax2.set_ylabel("Validation Accuracy", fontsize=12) +ax2.set_title( + f'Spiral Training (Final: {history_spiral["val_acc"][-1]:.1%})', + fontsize=14, + fontweight="bold", +) +ax2.grid(True, alpha=0.3) + +plt.tight_layout() +plt.savefig(results_dir / "spiral_dataset.png", dpi=150, bbox_inches="tight") +print(f"✓ Plot saved: {results_dir / 'spiral_dataset.png'}") + +# ============================================================================ +# DATASET 3: Imbalanced Dataset +# ============================================================================ +print("\n\n📊 DATASET 3: IMBALANCED DATASET") +print("=" * 70) +print("Tests robustness with class imbalance (90% vs 10%)") + +X_imb, y_imb = make_classification( + n_samples=200, + n_features=4, + n_informative=3, + n_redundant=1, + n_clusters_per_class=1, + weights=[0.9, 0.1], # 90-10 split + flip_y=0.01, + random_state=42, +) + +X_train, X_val, y_train, y_val = train_test_split( + X_imb, y_imb, test_size=0.2, stratify=y_imb, random_state=42 +) + +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_val = scaler.transform(X_val) + +print(f"Class distribution: {np.bincount(y_train)}") +print("Training quantum classifier on Imbalanced data...") + +qc_imb = QuantumClassifier() +model_imb = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_imb) +history_imb = train_quantum_model(model_imb, X_train, y_train, X_val, y_val) + +print(f"\n✓ Imbalanced Accuracy: {history_imb['val_acc'][-1]:.4f}") + +# ============================================================================ +# DATASET 4: Wine Quality (Real-world) +# ============================================================================ +print("\n\n📊 DATASET 4: WINE QUALITY") +print("=" * 70) +print("Real-world dataset: Wine chemical analysis") + +wine = load_wine() +X_wine = wine.data +y_wine = (wine.target == 0).astype(int) # Class 0 vs rest + +X_train, X_val, y_train, y_val = train_test_split( + X_wine, y_wine, test_size=0.2, random_state=42 +) + +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_val = scaler.transform(X_val) + +# Reduce to 4 features using PCA approximation (take first 4) +from sklearn.decomposition import PCA + +pca = PCA(n_components=4) +X_train = pca.fit_transform(X_train) +X_val = pca.transform(X_val) + +print(f"Training samples: {len(X_train)}") +print(f"PCA explained variance: {pca.explained_variance_ratio_.sum():.2%}") +print("Training quantum classifier on Wine...") + +qc_wine = QuantumClassifier() +model_wine = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc_wine) +history_wine = train_quantum_model(model_wine, X_train, y_train, X_val, y_val) + +print(f"\n✓ Wine Accuracy: {history_wine['val_acc'][-1]:.4f}") + +# ============================================================================ +# COMPARATIVE SUMMARY +# ============================================================================ +print("\n\n" + "=" * 70) +print("EXTENDED DATASETS - COMPARATIVE RESULTS") +print("=" * 70) + +results_summary = { + "XOR": history_xor["val_acc"][-1], + "Spiral": history_spiral["val_acc"][-1], + "Imbalanced": history_imb["val_acc"][-1], + "Wine": history_wine["val_acc"][-1], +} + +print("\nFinal Accuracies:") +print("-" * 70) +for dataset, acc in results_summary.items(): + stars = "★" * int(acc * 10) + print(f" {dataset:12s}: {acc:.4f} ({acc*100:.1f}%) {stars}") + +# Create summary plot +fig, axes = plt.subplots(2, 2, figsize=(14, 10)) +axes = axes.ravel() + +datasets = ["XOR", "Spiral", "Imbalanced", "Wine"] +histories = [history_xor, history_spiral, history_imb, history_wine] +colors = ["#e74c3c", "#9b59b6", "#3498db", "#2ecc71"] + +for i, (dataset, history, color) in enumerate(zip(datasets, histories, colors)): + axes[i].plot(history["train_loss"], label="Train Loss", alpha=0.7, linewidth=2) + axes[i].plot(history["val_loss"], label="Val Loss", linewidth=2) + axes[i].set_title( + f'{dataset}: Acc={history["val_acc"][-1]:.3f}', fontsize=12, fontweight="bold" + ) + axes[i].set_xlabel("Epoch") + axes[i].set_ylabel("Loss") + axes[i].legend() + axes[i].grid(True, alpha=0.3) + +plt.tight_layout() +plt.savefig(results_dir / "all_datasets_comparison.png", dpi=150, bbox_inches="tight") +print(f"\n✓ Summary plot saved: {results_dir / 'all_datasets_comparison.png'}") + +print("\n" + "=" * 70) +print("INSIGHTS AND RECOMMENDATIONS") +print("=" * 70) + +print("\n✅ Successful on:") +print(" • XOR: Quantum entanglement handles non-linearity") +print(" • Wine: Real-world data works with proper preprocessing") + +print("\n⚠️ Challenging:") +print(" • Spirals: May need more qubits or specialized encoding") +print(" • Imbalanced: Consider weighted loss functions") + +print("\n🎯 Next Steps:") +print(" 1. Try different quantum feature maps") +print(" 2. Implement quantum data re-uploading") +print(" 3. Test with 6-8 qubits for complex patterns") +print(" 4. Add quantum pooling layers") + +print(f"\n✓ All plots saved to: {results_dir}") +print("=" * 70) diff --git a/ai-projects/quantum-ml/experiments/parameter_tuning.py b/ai-projects/quantum-ml/experiments/parameter_tuning.py index cc5f986e4..2d26da1d6 100644 --- a/ai-projects/quantum-ml/experiments/parameter_tuning.py +++ b/ai-projects/quantum-ml/experiments/parameter_tuning.py @@ -1,292 +1,292 @@ -""" -Interactive Parameter Tuning Experiment -Demonstrates how different parameters affect quantum ML performance -""" - -import sys -from pathlib import Path - -import matplotlib -import numpy as np -import yaml - -matplotlib.use("Agg") -import matplotlib.pyplot as plt -from sklearn.datasets import make_moons -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler - -# Add project root to sys.path so 'src' can be imported as a package -project_root = Path(__file__).resolve().parents[1] -if str(project_root) not in sys.path: - sys.path.insert(0, str(project_root)) - -try: - from src.quantum_classifier import (HybridQuantumClassifier, - QuantumClassifier, train_quantum_model) -except ModuleNotFoundError: - # Fallback for environments without namespace package support - sys.path.insert(0, str(project_root / "src")) - from quantum_classifier import HybridQuantumClassifier # type: ignore - from quantum_classifier import QuantumClassifier, train_quantum_model - -# Results directory -results_dir = Path(__file__).parent.parent / "results" / "experiments" -results_dir.mkdir(parents=True, exist_ok=True) - -print("=" * 70) -print(" QUANTUM ML PARAMETER TUNING EXPERIMENTS") -print("=" * 70) - -# Prepare dataset -print("\nPreparing Moons dataset...") -X, y = make_moons(n_samples=200, noise=0.1, random_state=42) -X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) - -scaler = StandardScaler() -X_train = scaler.fit_transform(X_train) -X_val = scaler.transform(X_val) - -# Pad to 4 dimensions -X_train_padded = np.pad(X_train, ((0, 0), (0, 2)), mode="constant") -X_val_padded = np.pad(X_val, ((0, 0), (0, 2)), mode="constant") - -print(f"Training samples: {len(X_train)}, Validation samples: {len(X_val)}") - -# ============================================================================ -# EXPERIMENT 1: Number of Quantum Layers -# ============================================================================ -print("\n" + "=" * 70) -print("EXPERIMENT 1: Effect of Quantum Layer Depth") -print("=" * 70) - -layer_configs = [1, 2, 3, 4] -layer_results = [] - -for n_layers in layer_configs: - print(f"\n>>> Testing with {n_layers} layers...") - - # Update config - config_path = Path(__file__).parent.parent / "config" / "quantum_config.yaml" - with open(config_path, "r") as f: - config = yaml.safe_load(f) - - config["ml"]["model"]["n_layers"] = n_layers - config["ml"]["training"]["epochs"] = 50 # Reduce for faster testing - - with open(config_path, "w") as f: - yaml.dump(config, f) - - # Train model - qc = QuantumClassifier() - model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) - history = train_quantum_model(model, X_train_padded, y_train, X_val_padded, y_val) - - final_acc = history["val_acc"][-1] - layer_results.append( - { - "n_layers": n_layers, - "accuracy": final_acc, - "final_loss": history["val_loss"][-1], - "history": history, - } - ) - - print(f" Final Validation Accuracy: {final_acc:.4f}") - -# Plot layer results -fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) - -# Accuracy vs Layers -layers = [r["n_layers"] for r in layer_results] -accuracies = [r["accuracy"] for r in layer_results] -ax1.plot(layers, accuracies, "o-", linewidth=2, markersize=10, color="#2ecc71") -ax1.set_xlabel("Number of Quantum Layers", fontsize=12) -ax1.set_ylabel("Validation Accuracy", fontsize=12) -ax1.set_title("Impact of Layer Depth on Performance", fontsize=14, fontweight="bold") -ax1.grid(True, alpha=0.3) -ax1.set_ylim([0, 1]) - -# Add value labels -for x, y in zip(layers, accuracies): - ax1.text(x, y + 0.02, f"{y:.3f}", ha="center", fontsize=10, fontweight="bold") - -# Training curves for all layers -for result in layer_results: - ax2.plot( - result["history"]["val_acc"], label=f"{result['n_layers']} layers", linewidth=2 - ) - -ax2.set_xlabel("Epoch", fontsize=12) -ax2.set_ylabel("Validation Accuracy", fontsize=12) -ax2.set_title("Training Convergence by Layer Depth", fontsize=14, fontweight="bold") -ax2.legend() -ax2.grid(True, alpha=0.3) - -plt.tight_layout() -plot_path = results_dir / "experiment1_layer_depth.png" -plt.savefig(plot_path, dpi=150, bbox_inches="tight") -print(f"\n✓ Plot saved: {plot_path}") - -# ============================================================================ -# EXPERIMENT 2: Learning Rate -# ============================================================================ -print("\n" + "=" * 70) -print("EXPERIMENT 2: Effect of Learning Rate") -print("=" * 70) - -learning_rates = [0.001, 0.005, 0.01, 0.05, 0.1] -lr_results = [] - -# Reset to 2 layers for this experiment -config["ml"]["model"]["n_layers"] = 2 - -for lr in learning_rates: - print(f"\n>>> Testing learning rate: {lr}") - - config["ml"]["training"]["learning_rate"] = lr - with open(config_path, "w") as f: - yaml.dump(config, f) - - qc = QuantumClassifier() - model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) - history = train_quantum_model(model, X_train_padded, y_train, X_val_padded, y_val) - - final_acc = history["val_acc"][-1] - lr_results.append({"lr": lr, "accuracy": final_acc, "history": history}) - - print(f" Final Validation Accuracy: {final_acc:.4f}") - -# Plot learning rate results -fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) - -lrs = [r["lr"] for r in lr_results] -accs = [r["accuracy"] for r in lr_results] -ax1.semilogx(lrs, accs, "o-", linewidth=2, markersize=10, color="#e74c3c") -ax1.set_xlabel("Learning Rate (log scale)", fontsize=12) -ax1.set_ylabel("Validation Accuracy", fontsize=12) -ax1.set_title("Impact of Learning Rate on Performance", fontsize=14, fontweight="bold") -ax1.grid(True, alpha=0.3, which="both") -ax1.set_ylim([0, 1]) - -for result in lr_results: - ax2.plot(result["history"]["val_acc"], label=f"LR={result['lr']}", linewidth=2) - -ax2.set_xlabel("Epoch", fontsize=12) -ax2.set_ylabel("Validation Accuracy", fontsize=12) -ax2.set_title("Training Convergence by Learning Rate", fontsize=14, fontweight="bold") -ax2.legend() -ax2.grid(True, alpha=0.3) - -plt.tight_layout() -plot_path = results_dir / "experiment2_learning_rate.png" -plt.savefig(plot_path, dpi=150, bbox_inches="tight") -print(f"\n✓ Plot saved: {plot_path}") - -# ============================================================================ -# EXPERIMENT 3: Entanglement Patterns -# ============================================================================ -print("\n" + "=" * 70) -print("EXPERIMENT 3: Effect of Entanglement Pattern") -print("=" * 70) - -entanglement_types = ["linear", "circular", "full"] -ent_results = [] - -# Reset learning rate -config["ml"]["training"]["learning_rate"] = 0.01 - -for ent_type in entanglement_types: - print(f"\n>>> Testing entanglement: {ent_type}") - - config["ml"]["model"]["entanglement"] = ent_type - with open(config_path, "w") as f: - yaml.dump(config, f) - - qc = QuantumClassifier() - model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) - history = train_quantum_model(model, X_train_padded, y_train, X_val_padded, y_val) - - final_acc = history["val_acc"][-1] - ent_results.append( - {"entanglement": ent_type, "accuracy": final_acc, "history": history} - ) - - print(f" Final Validation Accuracy: {final_acc:.4f}") - -# Plot entanglement results -fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) - -ent_names = [r["entanglement"] for r in ent_results] -ent_accs = [r["accuracy"] for r in ent_results] -colors = ["#3498db", "#9b59b6", "#f39c12"] - -bars = ax1.bar( - ent_names, ent_accs, color=colors, alpha=0.7, edgecolor="black", linewidth=2 -) -ax1.set_ylabel("Validation Accuracy", fontsize=12) -ax1.set_title("Impact of Entanglement Pattern", fontsize=14, fontweight="bold") -ax1.set_ylim([0, 1]) -ax1.grid(True, axis="y", alpha=0.3) - -for bar, acc in zip(bars, ent_accs): - height = bar.get_height() - ax1.text( - bar.get_x() + bar.get_width() / 2.0, - height, - f"{acc:.3f}", - ha="center", - va="bottom", - fontsize=11, - fontweight="bold", - ) - -for result in ent_results: - ax2.plot(result["history"]["val_acc"], label=result["entanglement"], linewidth=2) - -ax2.set_xlabel("Epoch", fontsize=12) -ax2.set_ylabel("Validation Accuracy", fontsize=12) -ax2.set_title("Training Convergence by Entanglement", fontsize=14, fontweight="bold") -ax2.legend() -ax2.grid(True, alpha=0.3) - -plt.tight_layout() -plot_path = results_dir / "experiment3_entanglement.png" -plt.savefig(plot_path, dpi=150, bbox_inches="tight") -print(f"\n✓ Plot saved: {plot_path}") - -# ============================================================================ -# SUMMARY -# ============================================================================ -print("\n" + "=" * 70) -print("EXPERIMENT SUMMARY") -print("=" * 70) - -print("\nBest Configurations:") -print("-" * 70) - -# Best layer depth -best_layer = max(layer_results, key=lambda x: x["accuracy"]) -print( - f"Optimal Layers: {best_layer['n_layers']} (Accuracy: {best_layer['accuracy']:.4f})" -) - -# Best learning rate -best_lr = max(lr_results, key=lambda x: x["accuracy"]) -print(f"Optimal Learning Rate: {best_lr['lr']} (Accuracy: {best_lr['accuracy']:.4f})") - -# Best entanglement -best_ent = max(ent_results, key=lambda x: x["accuracy"]) -print( - f"Optimal Entanglement: {best_ent['entanglement']} (Accuracy: {best_ent['accuracy']:.4f})" -) - -print("\nKey Insights:") -print("-" * 70) -print("• More layers generally improve performance (diminishing returns)") -print("• Learning rate around 0.01-0.05 works best for this problem") -print("• Entanglement pattern choice depends on problem structure") -print("• Training convergence varies significantly with hyperparameters") - -print(f"\n✓ All experiment plots saved to: {results_dir}") -print("=" * 70) +""" +Interactive Parameter Tuning Experiment +Demonstrates how different parameters affect quantum ML performance +""" + +import sys +from pathlib import Path + +import matplotlib +import numpy as np +import yaml + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from sklearn.datasets import make_moons +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler + +# Add project root to sys.path so 'src' can be imported as a package +project_root = Path(__file__).resolve().parents[1] +if str(project_root) not in sys.path: + sys.path.insert(0, str(project_root)) + +try: + from src.quantum_classifier import (HybridQuantumClassifier, + QuantumClassifier, train_quantum_model) +except ModuleNotFoundError: + # Fallback for environments without namespace package support + sys.path.insert(0, str(project_root / "src")) + from quantum_classifier import HybridQuantumClassifier # type: ignore + from quantum_classifier import QuantumClassifier, train_quantum_model + +# Results directory +results_dir = Path(__file__).parent.parent / "results" / "experiments" +results_dir.mkdir(parents=True, exist_ok=True) + +print("=" * 70) +print(" QUANTUM ML PARAMETER TUNING EXPERIMENTS") +print("=" * 70) + +# Prepare dataset +print("\nPreparing Moons dataset...") +X, y = make_moons(n_samples=200, noise=0.1, random_state=42) +X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) + +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_val = scaler.transform(X_val) + +# Pad to 4 dimensions +X_train_padded = np.pad(X_train, ((0, 0), (0, 2)), mode="constant") +X_val_padded = np.pad(X_val, ((0, 0), (0, 2)), mode="constant") + +print(f"Training samples: {len(X_train)}, Validation samples: {len(X_val)}") + +# ============================================================================ +# EXPERIMENT 1: Number of Quantum Layers +# ============================================================================ +print("\n" + "=" * 70) +print("EXPERIMENT 1: Effect of Quantum Layer Depth") +print("=" * 70) + +layer_configs = [1, 2, 3, 4] +layer_results = [] + +for n_layers in layer_configs: + print(f"\n>>> Testing with {n_layers} layers...") + + # Update config + config_path = Path(__file__).parent.parent / "config" / "quantum_config.yaml" + with open(config_path, "r") as f: + config = yaml.safe_load(f) + + config["ml"]["model"]["n_layers"] = n_layers + config["ml"]["training"]["epochs"] = 50 # Reduce for faster testing + + with open(config_path, "w") as f: + yaml.dump(config, f) + + # Train model + qc = QuantumClassifier() + model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) + history = train_quantum_model(model, X_train_padded, y_train, X_val_padded, y_val) + + final_acc = history["val_acc"][-1] + layer_results.append( + { + "n_layers": n_layers, + "accuracy": final_acc, + "final_loss": history["val_loss"][-1], + "history": history, + } + ) + + print(f" Final Validation Accuracy: {final_acc:.4f}") + +# Plot layer results +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) + +# Accuracy vs Layers +layers = [r["n_layers"] for r in layer_results] +accuracies = [r["accuracy"] for r in layer_results] +ax1.plot(layers, accuracies, "o-", linewidth=2, markersize=10, color="#2ecc71") +ax1.set_xlabel("Number of Quantum Layers", fontsize=12) +ax1.set_ylabel("Validation Accuracy", fontsize=12) +ax1.set_title("Impact of Layer Depth on Performance", fontsize=14, fontweight="bold") +ax1.grid(True, alpha=0.3) +ax1.set_ylim([0, 1]) + +# Add value labels +for x, y in zip(layers, accuracies): + ax1.text(x, y + 0.02, f"{y:.3f}", ha="center", fontsize=10, fontweight="bold") + +# Training curves for all layers +for result in layer_results: + ax2.plot( + result["history"]["val_acc"], label=f"{result['n_layers']} layers", linewidth=2 + ) + +ax2.set_xlabel("Epoch", fontsize=12) +ax2.set_ylabel("Validation Accuracy", fontsize=12) +ax2.set_title("Training Convergence by Layer Depth", fontsize=14, fontweight="bold") +ax2.legend() +ax2.grid(True, alpha=0.3) + +plt.tight_layout() +plot_path = results_dir / "experiment1_layer_depth.png" +plt.savefig(plot_path, dpi=150, bbox_inches="tight") +print(f"\n✓ Plot saved: {plot_path}") + +# ============================================================================ +# EXPERIMENT 2: Learning Rate +# ============================================================================ +print("\n" + "=" * 70) +print("EXPERIMENT 2: Effect of Learning Rate") +print("=" * 70) + +learning_rates = [0.001, 0.005, 0.01, 0.05, 0.1] +lr_results = [] + +# Reset to 2 layers for this experiment +config["ml"]["model"]["n_layers"] = 2 + +for lr in learning_rates: + print(f"\n>>> Testing learning rate: {lr}") + + config["ml"]["training"]["learning_rate"] = lr + with open(config_path, "w") as f: + yaml.dump(config, f) + + qc = QuantumClassifier() + model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) + history = train_quantum_model(model, X_train_padded, y_train, X_val_padded, y_val) + + final_acc = history["val_acc"][-1] + lr_results.append({"lr": lr, "accuracy": final_acc, "history": history}) + + print(f" Final Validation Accuracy: {final_acc:.4f}") + +# Plot learning rate results +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) + +lrs = [r["lr"] for r in lr_results] +accs = [r["accuracy"] for r in lr_results] +ax1.semilogx(lrs, accs, "o-", linewidth=2, markersize=10, color="#e74c3c") +ax1.set_xlabel("Learning Rate (log scale)", fontsize=12) +ax1.set_ylabel("Validation Accuracy", fontsize=12) +ax1.set_title("Impact of Learning Rate on Performance", fontsize=14, fontweight="bold") +ax1.grid(True, alpha=0.3, which="both") +ax1.set_ylim([0, 1]) + +for result in lr_results: + ax2.plot(result["history"]["val_acc"], label=f"LR={result['lr']}", linewidth=2) + +ax2.set_xlabel("Epoch", fontsize=12) +ax2.set_ylabel("Validation Accuracy", fontsize=12) +ax2.set_title("Training Convergence by Learning Rate", fontsize=14, fontweight="bold") +ax2.legend() +ax2.grid(True, alpha=0.3) + +plt.tight_layout() +plot_path = results_dir / "experiment2_learning_rate.png" +plt.savefig(plot_path, dpi=150, bbox_inches="tight") +print(f"\n✓ Plot saved: {plot_path}") + +# ============================================================================ +# EXPERIMENT 3: Entanglement Patterns +# ============================================================================ +print("\n" + "=" * 70) +print("EXPERIMENT 3: Effect of Entanglement Pattern") +print("=" * 70) + +entanglement_types = ["linear", "circular", "full"] +ent_results = [] + +# Reset learning rate +config["ml"]["training"]["learning_rate"] = 0.01 + +for ent_type in entanglement_types: + print(f"\n>>> Testing entanglement: {ent_type}") + + config["ml"]["model"]["entanglement"] = ent_type + with open(config_path, "w") as f: + yaml.dump(config, f) + + qc = QuantumClassifier() + model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) + history = train_quantum_model(model, X_train_padded, y_train, X_val_padded, y_val) + + final_acc = history["val_acc"][-1] + ent_results.append( + {"entanglement": ent_type, "accuracy": final_acc, "history": history} + ) + + print(f" Final Validation Accuracy: {final_acc:.4f}") + +# Plot entanglement results +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) + +ent_names = [r["entanglement"] for r in ent_results] +ent_accs = [r["accuracy"] for r in ent_results] +colors = ["#3498db", "#9b59b6", "#f39c12"] + +bars = ax1.bar( + ent_names, ent_accs, color=colors, alpha=0.7, edgecolor="black", linewidth=2 +) +ax1.set_ylabel("Validation Accuracy", fontsize=12) +ax1.set_title("Impact of Entanglement Pattern", fontsize=14, fontweight="bold") +ax1.set_ylim([0, 1]) +ax1.grid(True, axis="y", alpha=0.3) + +for bar, acc in zip(bars, ent_accs): + height = bar.get_height() + ax1.text( + bar.get_x() + bar.get_width() / 2.0, + height, + f"{acc:.3f}", + ha="center", + va="bottom", + fontsize=11, + fontweight="bold", + ) + +for result in ent_results: + ax2.plot(result["history"]["val_acc"], label=result["entanglement"], linewidth=2) + +ax2.set_xlabel("Epoch", fontsize=12) +ax2.set_ylabel("Validation Accuracy", fontsize=12) +ax2.set_title("Training Convergence by Entanglement", fontsize=14, fontweight="bold") +ax2.legend() +ax2.grid(True, alpha=0.3) + +plt.tight_layout() +plot_path = results_dir / "experiment3_entanglement.png" +plt.savefig(plot_path, dpi=150, bbox_inches="tight") +print(f"\n✓ Plot saved: {plot_path}") + +# ============================================================================ +# SUMMARY +# ============================================================================ +print("\n" + "=" * 70) +print("EXPERIMENT SUMMARY") +print("=" * 70) + +print("\nBest Configurations:") +print("-" * 70) + +# Best layer depth +best_layer = max(layer_results, key=lambda x: x["accuracy"]) +print( + f"Optimal Layers: {best_layer['n_layers']} (Accuracy: {best_layer['accuracy']:.4f})" +) + +# Best learning rate +best_lr = max(lr_results, key=lambda x: x["accuracy"]) +print(f"Optimal Learning Rate: {best_lr['lr']} (Accuracy: {best_lr['accuracy']:.4f})") + +# Best entanglement +best_ent = max(ent_results, key=lambda x: x["accuracy"]) +print( + f"Optimal Entanglement: {best_ent['entanglement']} (Accuracy: {best_ent['accuracy']:.4f})" +) + +print("\nKey Insights:") +print("-" * 70) +print("• More layers generally improve performance (diminishing returns)") +print("• Learning rate around 0.01-0.05 works best for this problem") +print("• Entanglement pattern choice depends on problem structure") +print("• Training convergence varies significantly with hyperparameters") + +print(f"\n✓ All experiment plots saved to: {results_dir}") +print("=" * 70) diff --git a/ai-projects/quantum-ml/experiments/quick_demo.py b/ai-projects/quantum-ml/experiments/quick_demo.py index 2d4cf4d37..76b664618 100644 --- a/ai-projects/quantum-ml/experiments/quick_demo.py +++ b/ai-projects/quantum-ml/experiments/quick_demo.py @@ -1,68 +1,68 @@ -""" -Quick Experiment Demo - Runs a single quick experiment to demonstrate capabilities -""" - -import sys -import time -from pathlib import Path - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent)) - - -def print_banner(text): - """Print a colorful banner""" - print(f"\n\033[96m{'='*70}\033[0m") - print(f"\033[96m{text.center(70)}\033[0m") - print(f"\033[96m{'='*70}\033[0m\n") - - -print_banner("QUANTUM AI - QUICK EXPERIMENT DEMO") - -print("\033[93m📊 Running Extended Datasets Experiment (XOR Dataset)\033[0m") -print( - "\033[90mThis demonstrates quantum classifier on linearly inseparable data\033[0m\n" -) - -try: - # Import extended datasets module - sys.path.insert(0, str(Path(__file__).parent)) - import extended_datasets as ed - - print("\033[92m▶ Starting XOR Dataset Experiment...\033[0m\n") - start_time = time.time() - - # Run XOR experiment - ed.run_xor_dataset() - - duration = time.time() - start_time - - print("\n\033[92m✓ XOR Experiment Completed!\033[0m") - print(f"\033[90m Duration: {duration:.2f} seconds\033[0m\n") - - # Check results - results_dir = Path(__file__).parent.parent / "results" / "extended_datasets" - if results_dir.exists(): - xor_file = results_dir / "xor_dataset.png" - if xor_file.exists(): - size_kb = xor_file.stat().st_size / 1024 - print("\033[96m📁 Generated File:\033[0m") - print(f" {xor_file.name} ({size_kb:.1f} KB)") - print(f" Location: {xor_file}\n") - - print("\033[96m🎯 What This Demonstrates:\033[0m") - print(" • Quantum classifier can learn XOR function") - print(" • Classical linear models fail on this problem") - print(" • Quantum circuits provide non-linear transformations") - print(" • Visualization shows decision boundary\n") - - print("\033[96m📚 To Run All Experiments:\033[0m") - print(" python .\\experiments\\run_all_experiments.py\n") - - print_banner("DEMO COMPLETE!") - -except Exception as e: - print(f"\n\033[91m✗ Error: {e}\033[0m") - import traceback - - traceback.print_exc() +""" +Quick Experiment Demo - Runs a single quick experiment to demonstrate capabilities +""" + +import sys +import time +from pathlib import Path + +# Add parent directory to path +sys.path.insert(0, str(Path(__file__).parent.parent)) + + +def print_banner(text): + """Print a colorful banner""" + print(f"\n\033[96m{'='*70}\033[0m") + print(f"\033[96m{text.center(70)}\033[0m") + print(f"\033[96m{'='*70}\033[0m\n") + + +print_banner("QUANTUM AI - QUICK EXPERIMENT DEMO") + +print("\033[93m📊 Running Extended Datasets Experiment (XOR Dataset)\033[0m") +print( + "\033[90mThis demonstrates quantum classifier on linearly inseparable data\033[0m\n" +) + +try: + # Import extended datasets module + sys.path.insert(0, str(Path(__file__).parent)) + import extended_datasets as ed + + print("\033[92m▶ Starting XOR Dataset Experiment...\033[0m\n") + start_time = time.time() + + # Run XOR experiment + ed.run_xor_dataset() + + duration = time.time() - start_time + + print("\n\033[92m✓ XOR Experiment Completed!\033[0m") + print(f"\033[90m Duration: {duration:.2f} seconds\033[0m\n") + + # Check results + results_dir = Path(__file__).parent.parent / "results" / "extended_datasets" + if results_dir.exists(): + xor_file = results_dir / "xor_dataset.png" + if xor_file.exists(): + size_kb = xor_file.stat().st_size / 1024 + print("\033[96m📁 Generated File:\033[0m") + print(f" {xor_file.name} ({size_kb:.1f} KB)") + print(f" Location: {xor_file}\n") + + print("\033[96m🎯 What This Demonstrates:\033[0m") + print(" • Quantum classifier can learn XOR function") + print(" • Classical linear models fail on this problem") + print(" • Quantum circuits provide non-linear transformations") + print(" • Visualization shows decision boundary\n") + + print("\033[96m📚 To Run All Experiments:\033[0m") + print(" python .\\experiments\\run_all_experiments.py\n") + + print_banner("DEMO COMPLETE!") + +except Exception as e: + print(f"\n\033[91m✗ Error: {e}\033[0m") + import traceback + + traceback.print_exc() diff --git a/ai-projects/quantum-ml/experiments/run_all_experiments.py b/ai-projects/quantum-ml/experiments/run_all_experiments.py index 475457f3c..54e9b3a78 100644 --- a/ai-projects/quantum-ml/experiments/run_all_experiments.py +++ b/ai-projects/quantum-ml/experiments/run_all_experiments.py @@ -1,344 +1,344 @@ -""" -Master Experiment Runner - Quantum AI Project -============================================== - -Executes all three experiments sequentially: -1. Parameter Tuning - Find optimal hyperparameters -2. Extended Datasets - Test on challenging datasets -3. Plot Analysis - Generate comprehensive analysis report - -This provides a complete exploration of quantum ML capabilities. -""" - -import sys -import time -from datetime import datetime -from pathlib import Path - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent)) - - -def print_banner(text, color="cyan"): - """Print a colorful banner""" - colors = { - "cyan": "\033[96m", - "green": "\033[92m", - "yellow": "\033[93m", - "magenta": "\033[95m", - "red": "\033[91m", - "reset": "\033[0m", - } - - c = colors.get(color, colors["cyan"]) - r = colors["reset"] - - print(f"\n{c}{'='*70}{r}") - print(f"{c}{text.center(70)}{r}") - print(f"{c}{'='*70}{r}\n") - - -def print_section(number, title, description): - """Print experiment section header""" - print(f"\n\033[92m🔬 Experiment {number}: {title}\033[0m") - print(f"\033[90m{description}\033[0m\n") - - -def run_parameter_tuning(): - """Run parameter tuning experiments""" - print_section( - 1, - "Parameter Tuning", - "Testing layer depth, learning rate, and entanglement patterns", - ) - - try: - # Import and run parameter tuning - import experiments.parameter_tuning as pt - - # Run all three experiments automatically - print("\033[93m▶ Running Layer Depth Experiment...\033[0m") - pt.experiment_layer_depth() - print("\033[92m✓ Layer depth experiment completed\033[0m\n") - - print("\033[93m▶ Running Learning Rate Experiment...\033[0m") - pt.experiment_learning_rate() - print("\033[92m✓ Learning rate experiment completed\033[0m\n") - - print("\033[93m▶ Running Entanglement Pattern Experiment...\033[0m") - pt.experiment_entanglement() - print("\033[92m✓ Entanglement pattern experiment completed\033[0m\n") - - return True - - except Exception as e: - print(f"\033[91m✗ Error in parameter tuning: {e}\033[0m") - return False - - -def run_extended_datasets(): - """Run extended datasets experiments""" - print_section( - 2, - "Extended Datasets", - "Testing quantum classifier on XOR, Spiral, Imbalanced, and Wine datasets", - ) - - try: - # Import and run extended datasets - import experiments.extended_datasets as ed - - datasets = ["xor", "spiral", "imbalanced", "wine"] - - for dataset in datasets: - print(f"\033[93m▶ Running {dataset.upper()} dataset...\033[0m") - - if dataset == "xor": - ed.run_xor_dataset() - elif dataset == "spiral": - ed.run_spiral_dataset() - elif dataset == "imbalanced": - ed.run_imbalanced_dataset() - elif dataset == "wine": - ed.run_wine_dataset() - - print(f"\033[92m✓ {dataset.upper()} dataset completed\033[0m\n") - - # Generate comparison - print("\033[93m▶ Generating dataset comparison...\033[0m") - ed.compare_all_datasets() - print("\033[92m✓ Dataset comparison completed\033[0m\n") - - return True - - except Exception as e: - print(f"\033[91m✗ Error in extended datasets: {e}\033[0m") - return False - - -def run_plot_analysis(): - """Run plot analysis""" - print_section( - 3, "Plot Analysis", "Analyzing generated plots and providing insights" - ) - - try: - # Import and run plot analysis - import experiments.analyze_plots as ap - - print("\033[93m▶ Analyzing state evolution plot...\033[0m") - ap.analyze_state_evolution() - print("\033[92m✓ State evolution analysis completed\033[0m\n") - - print("\033[93m▶ Analyzing training results...\033[0m") - ap.analyze_training_results() - print("\033[92m✓ Training analysis completed\033[0m\n") - - print("\033[93m▶ Analyzing model comparison...\033[0m") - ap.analyze_model_comparison() - print("\033[92m✓ Model comparison analysis completed\033[0m\n") - - print("\033[93m▶ Generating recommendations...\033[0m") - ap.recommendations() - print("\033[92m✓ Recommendations generated\033[0m\n") - - return True - - except Exception as e: - print(f"\033[91m✗ Error in plot analysis: {e}\033[0m") - return False - - -def generate_summary_report(results): - """Generate a summary report of all experiments""" - print_banner("EXPERIMENT SUMMARY REPORT", "green") - - # Calculate total experiments - total = len(results) - successful = sum(1 for r in results.values() if r["success"]) - - print("\n\033[96m📊 Overall Statistics:\033[0m") - print(f" Total Experiments: {total}") - print(f" Successful: {successful}") - print(f" Failed: {total - successful}") - print(f" Success Rate: {successful/total*100:.1f}%\n") - - # Detail each experiment - for name, result in results.items(): - status = "\033[92m✓\033[0m" if result["success"] else "\033[91m✗\033[0m" - duration = result["duration"] - print(f"{status} {name}") - print(f" Duration: {duration:.2f} seconds") - if not result["success"]: - print(f" \033[91mError: {result.get('error', 'Unknown')}\033[0m") - print() - - # Generated files - print("\033[96m📁 Generated Files:\033[0m") - results_dir = Path(__file__).parent.parent / "results" - - # Count files in experiments directory - exp_dir = results_dir / "experiments" - if exp_dir.exists(): - exp_files = list(exp_dir.glob("*.png")) - print(f" Experiments: {len(exp_files)} plots") - for f in exp_files: - size_kb = f.stat().st_size / 1024 - print(f" - {f.name} ({size_kb:.1f} KB)") - - # Count files in extended_datasets directory - ext_dir = results_dir / "extended_datasets" - if ext_dir.exists(): - ext_files = list(ext_dir.glob("*.png")) - print(f" Extended Datasets: {len(ext_files)} plots") - for f in ext_files: - size_kb = f.stat().st_size / 1024 - print(f" - {f.name} ({size_kb:.1f} KB)") - - print("\n\033[96m🎯 Next Steps:\033[0m") - print(" 1. Review generated plots in results/ directory") - print(" 2. Analyze parameter tuning results to optimize configuration") - print(" 3. Examine extended dataset performance for quantum advantage") - print(" 4. Read plot analysis insights for deeper understanding") - print(" 5. Update quantum_config.yaml with optimal parameters") - print(" 6. Consider deploying to Azure Quantum for real hardware") - - print("\n\033[96m📚 Documentation:\033[0m") - print(" - Parameter Tuning: experiments/parameter_tuning.py") - print(" - Extended Datasets: experiments/extended_datasets.py") - print(" - Plot Analysis: experiments/analyze_plots.py") - print(" - Azure Deployment: experiments/AZURE_QUICKSTART.md") - - # Save report to file - report_path = results_dir / "EXPERIMENT_REPORT.md" - save_report_to_file(report_path, results) - print(f"\n\033[92m📝 Detailed report saved to: {report_path}\033[0m") - - -def save_report_to_file(filepath, results): - """Save experiment report to markdown file""" - with open(filepath, "w") as f: - f.write("# Quantum AI - Experiment Report\n\n") - f.write(f"**Generated:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n") - - f.write("## Executive Summary\n\n") - total = len(results) - successful = sum(1 for r in results.values() if r["success"]) - f.write(f"- Total Experiments: {total}\n") - f.write(f"- Successful: {successful}\n") - f.write(f"- Success Rate: {successful/total*100:.1f}%\n\n") - - f.write("## Experiment Results\n\n") - for name, result in results.items(): - status = "✓ Success" if result["success"] else "✗ Failed" - f.write(f"### {name}\n\n") - f.write(f"- **Status:** {status}\n") - f.write(f"- **Duration:** {result['duration']:.2f} seconds\n") - if not result["success"]: - f.write(f"- **Error:** {result.get('error', 'Unknown')}\n") - f.write("\n") - - f.write("## Key Findings\n\n") - f.write("### Parameter Tuning\n") - f.write("- Optimal layer depth identified\n") - f.write("- Learning rate sensitivity analyzed\n") - f.write("- Entanglement pattern performance compared\n\n") - - f.write("### Extended Datasets\n") - f.write("- XOR: Tests quantum advantage on linearly inseparable data\n") - f.write("- Spiral: Evaluates performance on highly non-linear patterns\n") - f.write("- Imbalanced: Validates robustness on skewed class distributions\n") - f.write("- Wine: Assesses real-world multiclass classification\n\n") - - f.write("### Plot Analysis\n") - f.write("- State evolution confirms quantum mechanics principles\n") - f.write("- Training dynamics reveal convergence patterns\n") - f.write("- Model comparison highlights dataset-specific performance\n\n") - - f.write("## Recommendations\n\n") - f.write( - "1. **Update Configuration:** Apply optimal parameters from tuning experiments\n" - ) - f.write( - "2. **Focus Datasets:** Prioritize datasets where quantum shows advantage\n" - ) - f.write( - "3. **Increase Complexity:** Test on larger quantum circuits (6-8 qubits)\n" - ) - f.write( - "4. **Hardware Testing:** Deploy to Azure Quantum for real quantum computer validation\n" - ) - f.write( - "5. **Noise Analysis:** Investigate robustness to quantum noise and decoherence\n\n" - ) - - f.write("## Next Steps\n\n") - f.write("- [ ] Review all generated plots in `results/` directory\n") - f.write("- [ ] Update `quantum_config.yaml` with optimal hyperparameters\n") - f.write("- [ ] Run experiments on Azure Quantum hardware\n") - f.write("- [ ] Implement error mitigation strategies\n") - f.write("- [ ] Scale to larger datasets and circuits\n") - f.write("- [ ] Publish findings and share results\n\n") - - f.write("---\n\n") - f.write( - "*This report was automatically generated by the Quantum AI experiment runner.*\n" - ) - - -def main(): - """Main experiment runner""" - print_banner("QUANTUM AI - MASTER EXPERIMENT RUNNER", "cyan") - - print("\033[96mThis will run all experiments sequentially:\033[0m") - print(" 1. Parameter Tuning (layer depth, learning rate, entanglement)") - print(" 2. Extended Datasets (XOR, Spiral, Imbalanced, Wine)") - print(" 3. Plot Analysis (insights and recommendations)\n") - - print("\033[93m⚠️ This may take 15-30 minutes depending on your hardware.\033[0m") - print("\033[93m⚠️ Ensure virtual environment is activated.\033[0m\n") - - response = input("Continue? (y/n): ").strip().lower() - if response != "y": - print("\n\033[91mExperiments cancelled.\033[0m") - return - - # Track results - results = {} - start_time = time.time() - - # Experiment 1: Parameter Tuning - exp_start = time.time() - success = run_parameter_tuning() - results["Parameter Tuning"] = { - "success": success, - "duration": time.time() - exp_start, - } - - # Experiment 2: Extended Datasets - exp_start = time.time() - success = run_extended_datasets() - results["Extended Datasets"] = { - "success": success, - "duration": time.time() - exp_start, - } - - # Experiment 3: Plot Analysis - exp_start = time.time() - success = run_plot_analysis() - results["Plot Analysis"] = {"success": success, "duration": time.time() - exp_start} - - # Total time - total_time = time.time() - start_time - - # Generate summary - print_banner("ALL EXPERIMENTS COMPLETE", "green") - print(f"\n\033[96m⏱️ Total Time: {total_time/60:.1f} minutes\033[0m\n") - - generate_summary_report(results) - - print_banner("THANK YOU FOR USING QUANTUM AI!", "magenta") - - -if __name__ == "__main__": - main() +""" +Master Experiment Runner - Quantum AI Project +============================================== + +Executes all three experiments sequentially: +1. Parameter Tuning - Find optimal hyperparameters +2. Extended Datasets - Test on challenging datasets +3. Plot Analysis - Generate comprehensive analysis report + +This provides a complete exploration of quantum ML capabilities. +""" + +import sys +import time +from datetime import datetime +from pathlib import Path + +# Add parent directory to path +sys.path.insert(0, str(Path(__file__).parent.parent)) + + +def print_banner(text, color="cyan"): + """Print a colorful banner""" + colors = { + "cyan": "\033[96m", + "green": "\033[92m", + "yellow": "\033[93m", + "magenta": "\033[95m", + "red": "\033[91m", + "reset": "\033[0m", + } + + c = colors.get(color, colors["cyan"]) + r = colors["reset"] + + print(f"\n{c}{'='*70}{r}") + print(f"{c}{text.center(70)}{r}") + print(f"{c}{'='*70}{r}\n") + + +def print_section(number, title, description): + """Print experiment section header""" + print(f"\n\033[92m🔬 Experiment {number}: {title}\033[0m") + print(f"\033[90m{description}\033[0m\n") + + +def run_parameter_tuning(): + """Run parameter tuning experiments""" + print_section( + 1, + "Parameter Tuning", + "Testing layer depth, learning rate, and entanglement patterns", + ) + + try: + # Import and run parameter tuning + import experiments.parameter_tuning as pt + + # Run all three experiments automatically + print("\033[93m▶ Running Layer Depth Experiment...\033[0m") + pt.experiment_layer_depth() + print("\033[92m✓ Layer depth experiment completed\033[0m\n") + + print("\033[93m▶ Running Learning Rate Experiment...\033[0m") + pt.experiment_learning_rate() + print("\033[92m✓ Learning rate experiment completed\033[0m\n") + + print("\033[93m▶ Running Entanglement Pattern Experiment...\033[0m") + pt.experiment_entanglement() + print("\033[92m✓ Entanglement pattern experiment completed\033[0m\n") + + return True + + except Exception as e: + print(f"\033[91m✗ Error in parameter tuning: {e}\033[0m") + return False + + +def run_extended_datasets(): + """Run extended datasets experiments""" + print_section( + 2, + "Extended Datasets", + "Testing quantum classifier on XOR, Spiral, Imbalanced, and Wine datasets", + ) + + try: + # Import and run extended datasets + import experiments.extended_datasets as ed + + datasets = ["xor", "spiral", "imbalanced", "wine"] + + for dataset in datasets: + print(f"\033[93m▶ Running {dataset.upper()} dataset...\033[0m") + + if dataset == "xor": + ed.run_xor_dataset() + elif dataset == "spiral": + ed.run_spiral_dataset() + elif dataset == "imbalanced": + ed.run_imbalanced_dataset() + elif dataset == "wine": + ed.run_wine_dataset() + + print(f"\033[92m✓ {dataset.upper()} dataset completed\033[0m\n") + + # Generate comparison + print("\033[93m▶ Generating dataset comparison...\033[0m") + ed.compare_all_datasets() + print("\033[92m✓ Dataset comparison completed\033[0m\n") + + return True + + except Exception as e: + print(f"\033[91m✗ Error in extended datasets: {e}\033[0m") + return False + + +def run_plot_analysis(): + """Run plot analysis""" + print_section( + 3, "Plot Analysis", "Analyzing generated plots and providing insights" + ) + + try: + # Import and run plot analysis + import experiments.analyze_plots as ap + + print("\033[93m▶ Analyzing state evolution plot...\033[0m") + ap.analyze_state_evolution() + print("\033[92m✓ State evolution analysis completed\033[0m\n") + + print("\033[93m▶ Analyzing training results...\033[0m") + ap.analyze_training_results() + print("\033[92m✓ Training analysis completed\033[0m\n") + + print("\033[93m▶ Analyzing model comparison...\033[0m") + ap.analyze_model_comparison() + print("\033[92m✓ Model comparison analysis completed\033[0m\n") + + print("\033[93m▶ Generating recommendations...\033[0m") + ap.recommendations() + print("\033[92m✓ Recommendations generated\033[0m\n") + + return True + + except Exception as e: + print(f"\033[91m✗ Error in plot analysis: {e}\033[0m") + return False + + +def generate_summary_report(results): + """Generate a summary report of all experiments""" + print_banner("EXPERIMENT SUMMARY REPORT", "green") + + # Calculate total experiments + total = len(results) + successful = sum(1 for r in results.values() if r["success"]) + + print("\n\033[96m📊 Overall Statistics:\033[0m") + print(f" Total Experiments: {total}") + print(f" Successful: {successful}") + print(f" Failed: {total - successful}") + print(f" Success Rate: {successful/total*100:.1f}%\n") + + # Detail each experiment + for name, result in results.items(): + status = "\033[92m✓\033[0m" if result["success"] else "\033[91m✗\033[0m" + duration = result["duration"] + print(f"{status} {name}") + print(f" Duration: {duration:.2f} seconds") + if not result["success"]: + print(f" \033[91mError: {result.get('error', 'Unknown')}\033[0m") + print() + + # Generated files + print("\033[96m📁 Generated Files:\033[0m") + results_dir = Path(__file__).parent.parent / "results" + + # Count files in experiments directory + exp_dir = results_dir / "experiments" + if exp_dir.exists(): + exp_files = list(exp_dir.glob("*.png")) + print(f" Experiments: {len(exp_files)} plots") + for f in exp_files: + size_kb = f.stat().st_size / 1024 + print(f" - {f.name} ({size_kb:.1f} KB)") + + # Count files in extended_datasets directory + ext_dir = results_dir / "extended_datasets" + if ext_dir.exists(): + ext_files = list(ext_dir.glob("*.png")) + print(f" Extended Datasets: {len(ext_files)} plots") + for f in ext_files: + size_kb = f.stat().st_size / 1024 + print(f" - {f.name} ({size_kb:.1f} KB)") + + print("\n\033[96m🎯 Next Steps:\033[0m") + print(" 1. Review generated plots in results/ directory") + print(" 2. Analyze parameter tuning results to optimize configuration") + print(" 3. Examine extended dataset performance for quantum advantage") + print(" 4. Read plot analysis insights for deeper understanding") + print(" 5. Update quantum_config.yaml with optimal parameters") + print(" 6. Consider deploying to Azure Quantum for real hardware") + + print("\n\033[96m📚 Documentation:\033[0m") + print(" - Parameter Tuning: experiments/parameter_tuning.py") + print(" - Extended Datasets: experiments/extended_datasets.py") + print(" - Plot Analysis: experiments/analyze_plots.py") + print(" - Azure Deployment: experiments/AZURE_QUICKSTART.md") + + # Save report to file + report_path = results_dir / "EXPERIMENT_REPORT.md" + save_report_to_file(report_path, results) + print(f"\n\033[92m📝 Detailed report saved to: {report_path}\033[0m") + + +def save_report_to_file(filepath, results): + """Save experiment report to markdown file""" + with open(filepath, "w") as f: + f.write("# Quantum AI - Experiment Report\n\n") + f.write(f"**Generated:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n") + + f.write("## Executive Summary\n\n") + total = len(results) + successful = sum(1 for r in results.values() if r["success"]) + f.write(f"- Total Experiments: {total}\n") + f.write(f"- Successful: {successful}\n") + f.write(f"- Success Rate: {successful/total*100:.1f}%\n\n") + + f.write("## Experiment Results\n\n") + for name, result in results.items(): + status = "✓ Success" if result["success"] else "✗ Failed" + f.write(f"### {name}\n\n") + f.write(f"- **Status:** {status}\n") + f.write(f"- **Duration:** {result['duration']:.2f} seconds\n") + if not result["success"]: + f.write(f"- **Error:** {result.get('error', 'Unknown')}\n") + f.write("\n") + + f.write("## Key Findings\n\n") + f.write("### Parameter Tuning\n") + f.write("- Optimal layer depth identified\n") + f.write("- Learning rate sensitivity analyzed\n") + f.write("- Entanglement pattern performance compared\n\n") + + f.write("### Extended Datasets\n") + f.write("- XOR: Tests quantum advantage on linearly inseparable data\n") + f.write("- Spiral: Evaluates performance on highly non-linear patterns\n") + f.write("- Imbalanced: Validates robustness on skewed class distributions\n") + f.write("- Wine: Assesses real-world multiclass classification\n\n") + + f.write("### Plot Analysis\n") + f.write("- State evolution confirms quantum mechanics principles\n") + f.write("- Training dynamics reveal convergence patterns\n") + f.write("- Model comparison highlights dataset-specific performance\n\n") + + f.write("## Recommendations\n\n") + f.write( + "1. **Update Configuration:** Apply optimal parameters from tuning experiments\n" + ) + f.write( + "2. **Focus Datasets:** Prioritize datasets where quantum shows advantage\n" + ) + f.write( + "3. **Increase Complexity:** Test on larger quantum circuits (6-8 qubits)\n" + ) + f.write( + "4. **Hardware Testing:** Deploy to Azure Quantum for real quantum computer validation\n" + ) + f.write( + "5. **Noise Analysis:** Investigate robustness to quantum noise and decoherence\n\n" + ) + + f.write("## Next Steps\n\n") + f.write("- [ ] Review all generated plots in `results/` directory\n") + f.write("- [ ] Update `quantum_config.yaml` with optimal hyperparameters\n") + f.write("- [ ] Run experiments on Azure Quantum hardware\n") + f.write("- [ ] Implement error mitigation strategies\n") + f.write("- [ ] Scale to larger datasets and circuits\n") + f.write("- [ ] Publish findings and share results\n\n") + + f.write("---\n\n") + f.write( + "*This report was automatically generated by the Quantum AI experiment runner.*\n" + ) + + +def main(): + """Main experiment runner""" + print_banner("QUANTUM AI - MASTER EXPERIMENT RUNNER", "cyan") + + print("\033[96mThis will run all experiments sequentially:\033[0m") + print(" 1. Parameter Tuning (layer depth, learning rate, entanglement)") + print(" 2. Extended Datasets (XOR, Spiral, Imbalanced, Wine)") + print(" 3. Plot Analysis (insights and recommendations)\n") + + print("\033[93m⚠️ This may take 15-30 minutes depending on your hardware.\033[0m") + print("\033[93m⚠️ Ensure virtual environment is activated.\033[0m\n") + + response = input("Continue? (y/n): ").strip().lower() + if response != "y": + print("\n\033[91mExperiments cancelled.\033[0m") + return + + # Track results + results = {} + start_time = time.time() + + # Experiment 1: Parameter Tuning + exp_start = time.time() + success = run_parameter_tuning() + results["Parameter Tuning"] = { + "success": success, + "duration": time.time() - exp_start, + } + + # Experiment 2: Extended Datasets + exp_start = time.time() + success = run_extended_datasets() + results["Extended Datasets"] = { + "success": success, + "duration": time.time() - exp_start, + } + + # Experiment 3: Plot Analysis + exp_start = time.time() + success = run_plot_analysis() + results["Plot Analysis"] = {"success": success, "duration": time.time() - exp_start} + + # Total time + total_time = time.time() - start_time + + # Generate summary + print_banner("ALL EXPERIMENTS COMPLETE", "green") + print(f"\n\033[96m⏱️ Total Time: {total_time/60:.1f} minutes\033[0m\n") + + generate_summary_report(results) + + print_banner("THANK YOU FOR USING QUANTUM AI!", "magenta") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/hyperparameter_optimization.py b/ai-projects/quantum-ml/hyperparameter_optimization.py index 86fd899a6..b2e20f30c 100644 --- a/ai-projects/quantum-ml/hyperparameter_optimization.py +++ b/ai-projects/quantum-ml/hyperparameter_optimization.py @@ -1,358 +1,358 @@ -""" -Hyperparameter Optimization for Quantum Hybrid QNN -================================================ - -Automated grid search over key hyperparameters to improve performance -on datasets where quantum underperformed vs classical baselines. - -Focus: ionosphere and sonar (gaps of ~9-11 pp vs classical) - -Hyperparameters swept: -- n_qubits: 3, 4, 5, 6 -- n_quantum_layers: 2, 3, 4 -- hidden_dim: 16, 32, 64 -- learning_rate: 5e-4, 1e-3, 2e-3 -- batch_size: 8, 16, 32 -- epochs: up to 50 with early stopping (patience=10) - -Results saved to: results/hpo_optimization_report.json - -Author: Quantum AI System -Date: November 1, 2025 -""" - -import itertools -import json -import sys -import warnings -from datetime import datetime -from pathlib import Path -from typing import Dict, List - -import numpy as np - -warnings.filterwarnings("ignore") - -# Add src to path -src_path = Path(__file__).parent / "src" -sys.path.insert(0, str(src_path)) - -import matplotlib.pyplot as plt -import torch -from sklearn.model_selection import train_test_split -from src.dataset_loader import load_dataset, preprocess_for_qubits -from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer -from torch.utils.data import DataLoader, TensorDataset - - -class EarlyStoppingTrainer(QuantumClassicalTrainer): - """Extended trainer with early stopping on validation loss.""" - - def __init__(self, model, learning_rate=0.001, device="cpu", patience=10): - super().__init__(model, learning_rate, device) - self.patience = patience - self.best_val_loss = float("inf") - self.patience_counter = 0 - self.best_model_state = None - - def train(self, train_loader, val_loader, num_epochs=50): - """Train with early stopping.""" - for epoch in range(num_epochs): - train_loss = self.train_epoch(train_loader) - val_acc, val_loss = self.evaluate(val_loader) - - # Early stopping check - if val_loss < self.best_val_loss: - self.best_val_loss = val_loss - self.patience_counter = 0 - self.best_model_state = { - k: v.cpu().clone() for k, v in self.model.state_dict().items() - } - else: - self.patience_counter += 1 - - if self.patience_counter >= self.patience: - print(f" Early stopping at epoch {epoch + 1}") - # Restore best model - if self.best_model_state: - self.model.load_state_dict(self.best_model_state) - break - - if (epoch + 1) % 10 == 0 or epoch == 0: - print( - f" Epoch {epoch + 1}/{num_epochs} - Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}" - ) - - -def train_config(dataset_name: str, config: Dict) -> Dict: - """Train a single config and return metrics.""" - X, y, _ = load_dataset(dataset_name) - - # Split - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42, stratify=y - ) - - # Preprocess - X_train, X_val, scaler, pca = preprocess_for_qubits( - X_train, X_val, config["n_qubits"] - ) - - n_classes = len(np.unique(y_train)) - - # Create model - model = HybridQNN( - input_dim=config["n_qubits"], - hidden_dim=config["hidden_dim"], - n_qubits=config["n_qubits"], - n_quantum_layers=config["n_quantum_layers"], - output_dim=n_classes, - dropout=0.2, - ) - - # Create data loaders - X_train_tensor = torch.FloatTensor(X_train) - y_train_tensor = torch.LongTensor(y_train) - X_val_tensor = torch.FloatTensor(X_val) - y_val_tensor = torch.LongTensor(y_val) - - train_dataset = TensorDataset(X_train_tensor, y_train_tensor) - val_dataset = TensorDataset(X_val_tensor, y_val_tensor) - - train_loader = DataLoader( - train_dataset, batch_size=config["batch_size"], shuffle=True, drop_last=True - ) - val_loader = DataLoader( - val_dataset, batch_size=config["batch_size"], shuffle=False, drop_last=False - ) - - # Train - trainer = EarlyStoppingTrainer( - model, learning_rate=config["learning_rate"], patience=10 - ) - - trainer.train(train_loader, val_loader, num_epochs=50) - - # Get best metrics - best_val_acc = max(trainer.val_accuracies) if trainer.val_accuracies else 0.0 - best_val_loss = min(trainer.val_losses) if trainer.val_losses else float("inf") - - return { - "val_acc": best_val_acc, - "val_loss": best_val_loss, - "epochs_trained": len(trainer.val_accuracies), - } - - -def run_hpo_sweep(dataset_name: str, param_grid: Dict) -> List[Dict]: - """Run grid search over parameter space.""" - results = [] - - # Generate all combinations - keys = list(param_grid.keys()) - values = list(param_grid.values()) - - total_configs = np.prod([len(v) for v in values]) - print(f"\n{'='*70}") - print(f" HPO SWEEP: {dataset_name.upper()}") - print(f"{'='*70}") - print(f"Total configurations: {total_configs}") - print(f"Parameter grid: {param_grid}\n") - - for i, combo in enumerate(itertools.product(*values), 1): - config = dict(zip(keys, combo)) - - print(f"\n[{i}/{total_configs}] Testing config: {config}") - - try: - metrics = train_config(dataset_name, config) - - result = {"config": config, "metrics": metrics, "dataset": dataset_name} - - results.append(result) - - print( - f" ✅ Val Acc: {metrics['val_acc']:.4f}, Val Loss: {metrics['val_loss']:.4f}, Epochs: {metrics['epochs_trained']}" - ) - - except Exception as e: - print(f" ❌ Config failed: {str(e)}") - continue - - return results - - -def find_best_config(results: List[Dict]) -> Dict: - """Find the best config based on validation accuracy.""" - if not results: - return None - - best = max(results, key=lambda x: x["metrics"]["val_acc"]) - return best - - -def save_hpo_report( - all_results: Dict, output_path: str = "results/hpo_optimization_report.json" -): - """Save comprehensive HPO report.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - report = { - "timestamp": datetime.now().isoformat(), - "datasets": list(all_results.keys()), - "results_by_dataset": {}, - } - - for dataset, results in all_results.items(): - best = find_best_config(results) - - # Baseline from classical comparison - baselines = { - "ionosphere": 0.8571, - "sonar": 0.7619, - "heart": 0.9464, - "banknote": 1.0, - } - - baseline_acc = baselines.get(dataset, 0.0) - improvement = (best["metrics"]["val_acc"] - baseline_acc) * 100 if best else 0.0 - - report["results_by_dataset"][dataset] = { - "baseline_accuracy": baseline_acc, - "best_config": best["config"] if best else None, - "best_metrics": best["metrics"] if best else None, - "improvement_pp": improvement, - "total_configs_tested": len(results), - "all_results": results, - } - - with open(output_path, "w") as f: - json.dump(report, f, indent=2) - - print(f"\n✅ HPO report saved to: {output_path}") - return report - - -def plot_hpo_results( - all_results: Dict, output_path: str = "results/hpo_comparison.png" -): - """Plot HPO results comparison.""" - fig, axes = plt.subplots(1, len(all_results), figsize=(6 * len(all_results), 5)) - - if len(all_results) == 1: - axes = [axes] - - for idx, (dataset, results) in enumerate(all_results.items()): - ax = axes[idx] - - # Extract configs and accuracies - accs = [r["metrics"]["val_acc"] for r in results] - configs = [f"C{i+1}" for i in range(len(results))] - - # Sort by accuracy - sorted_pairs = sorted(zip(accs, configs), reverse=True) - accs_sorted, configs_sorted = zip(*sorted_pairs) - - # Plot top 10 - top_n = min(10, len(accs_sorted)) - colors = ["green" if i == 0 else "steelblue" for i in range(top_n)] - - ax.barh(range(top_n), accs_sorted[:top_n], color=colors) - ax.set_yticks(range(top_n)) - ax.set_yticklabels(configs_sorted[:top_n]) - ax.set_xlabel("Validation Accuracy") - ax.set_title(f"{dataset.capitalize()}\nTop {top_n} Configs") - ax.grid(axis="x", alpha=0.3) - - # Add baseline line - baselines = { - "ionosphere": 0.8571, - "sonar": 0.7619, - "heart": 0.9464, - "banknote": 1.0, - } - if dataset in baselines: - ax.axvline( - baselines[dataset], - color="red", - linestyle="--", - label="Baseline", - linewidth=2, - ) - ax.legend() - - plt.tight_layout() - plt.savefig(output_path, dpi=150, bbox_inches="tight") - print(f"✅ HPO plot saved to: {output_path}") - plt.close() - - -def main(): - """Run HPO sweep.""" - print("=" * 70) - print(" QUANTUM HYBRID QNN - HYPERPARAMETER OPTIMIZATION") - print("=" * 70) - print("\nTarget: Improve performance on ionosphere and sonar datasets") - print("Method: Grid search with early stopping") - print("Baseline accuracies:") - print(" - Ionosphere: 85.71% (gap: -11.47 pp vs SVM RBF)") - print(" - Sonar: 76.19% (gap: -9.52 pp vs SVM RBF)") - - # Define parameter grid (focused on ionosphere and sonar) - param_grid = { - "n_qubits": [4, 5, 6], - "n_quantum_layers": [2, 3, 4], - "hidden_dim": [16, 32], - "learning_rate": [5e-4, 1e-3], - "batch_size": [8, 16], - } - - # Datasets to optimize - datasets = ["ionosphere", "sonar"] - - all_results = {} - - for dataset in datasets: - results = run_hpo_sweep(dataset, param_grid) - all_results[dataset] = results - - # Save report - report = save_hpo_report(all_results) - - # Plot results - plot_hpo_results(all_results) - - # Print summary - print("\n" + "=" * 70) - print(" HPO SUMMARY") - print("=" * 70) - - for dataset in datasets: - best = find_best_config(all_results[dataset]) - if best: - baseline = {"ionosphere": 0.8571, "sonar": 0.7619}[dataset] - improvement = (best["metrics"]["val_acc"] - baseline) * 100 - - print(f"\n{dataset.upper()}:") - print(f" Baseline: {baseline:.4f} ({baseline*100:.2f}%)") - print( - f" Best HPO: {best['metrics']['val_acc']:.4f} ({best['metrics']['val_acc']*100:.2f}%)" - ) - print(f" Improvement: {improvement:+.2f} pp") - print(f" Best config: {best['config']}") - - print("\n" + "=" * 70) - print(" ✅ HPO COMPLETE!") - print("=" * 70) - print("\n📊 Output files:") - print(" - results/hpo_optimization_report.json") - print(" - results/hpo_comparison.png") - print("\n💡 Next steps:") - print(" 1. Review best configs in the report") - print(" 2. Retrain with best configs on full datasets") - print(" 3. Test on Azure Quantum hardware") - print(" 4. Compare vs classical baselines again\n") - - -if __name__ == "__main__": - main() +""" +Hyperparameter Optimization for Quantum Hybrid QNN +================================================ + +Automated grid search over key hyperparameters to improve performance +on datasets where quantum underperformed vs classical baselines. + +Focus: ionosphere and sonar (gaps of ~9-11 pp vs classical) + +Hyperparameters swept: +- n_qubits: 3, 4, 5, 6 +- n_quantum_layers: 2, 3, 4 +- hidden_dim: 16, 32, 64 +- learning_rate: 5e-4, 1e-3, 2e-3 +- batch_size: 8, 16, 32 +- epochs: up to 50 with early stopping (patience=10) + +Results saved to: results/hpo_optimization_report.json + +Author: Quantum AI System +Date: November 1, 2025 +""" + +import itertools +import json +import sys +import warnings +from datetime import datetime +from pathlib import Path +from typing import Dict, List + +import numpy as np + +warnings.filterwarnings("ignore") + +# Add src to path +src_path = Path(__file__).parent / "src" +sys.path.insert(0, str(src_path)) + +import matplotlib.pyplot as plt +import torch +from sklearn.model_selection import train_test_split +from src.dataset_loader import load_dataset, preprocess_for_qubits +from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer +from torch.utils.data import DataLoader, TensorDataset + + +class EarlyStoppingTrainer(QuantumClassicalTrainer): + """Extended trainer with early stopping on validation loss.""" + + def __init__(self, model, learning_rate=0.001, device="cpu", patience=10): + super().__init__(model, learning_rate, device) + self.patience = patience + self.best_val_loss = float("inf") + self.patience_counter = 0 + self.best_model_state = None + + def train(self, train_loader, val_loader, num_epochs=50): + """Train with early stopping.""" + for epoch in range(num_epochs): + train_loss = self.train_epoch(train_loader) + val_acc, val_loss = self.evaluate(val_loader) + + # Early stopping check + if val_loss < self.best_val_loss: + self.best_val_loss = val_loss + self.patience_counter = 0 + self.best_model_state = { + k: v.cpu().clone() for k, v in self.model.state_dict().items() + } + else: + self.patience_counter += 1 + + if self.patience_counter >= self.patience: + print(f" Early stopping at epoch {epoch + 1}") + # Restore best model + if self.best_model_state: + self.model.load_state_dict(self.best_model_state) + break + + if (epoch + 1) % 10 == 0 or epoch == 0: + print( + f" Epoch {epoch + 1}/{num_epochs} - Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}" + ) + + +def train_config(dataset_name: str, config: Dict) -> Dict: + """Train a single config and return metrics.""" + X, y, _ = load_dataset(dataset_name) + + # Split + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + # Preprocess + X_train, X_val, scaler, pca = preprocess_for_qubits( + X_train, X_val, config["n_qubits"] + ) + + n_classes = len(np.unique(y_train)) + + # Create model + model = HybridQNN( + input_dim=config["n_qubits"], + hidden_dim=config["hidden_dim"], + n_qubits=config["n_qubits"], + n_quantum_layers=config["n_quantum_layers"], + output_dim=n_classes, + dropout=0.2, + ) + + # Create data loaders + X_train_tensor = torch.FloatTensor(X_train) + y_train_tensor = torch.LongTensor(y_train) + X_val_tensor = torch.FloatTensor(X_val) + y_val_tensor = torch.LongTensor(y_val) + + train_dataset = TensorDataset(X_train_tensor, y_train_tensor) + val_dataset = TensorDataset(X_val_tensor, y_val_tensor) + + train_loader = DataLoader( + train_dataset, batch_size=config["batch_size"], shuffle=True, drop_last=True + ) + val_loader = DataLoader( + val_dataset, batch_size=config["batch_size"], shuffle=False, drop_last=False + ) + + # Train + trainer = EarlyStoppingTrainer( + model, learning_rate=config["learning_rate"], patience=10 + ) + + trainer.train(train_loader, val_loader, num_epochs=50) + + # Get best metrics + best_val_acc = max(trainer.val_accuracies) if trainer.val_accuracies else 0.0 + best_val_loss = min(trainer.val_losses) if trainer.val_losses else float("inf") + + return { + "val_acc": best_val_acc, + "val_loss": best_val_loss, + "epochs_trained": len(trainer.val_accuracies), + } + + +def run_hpo_sweep(dataset_name: str, param_grid: Dict) -> List[Dict]: + """Run grid search over parameter space.""" + results = [] + + # Generate all combinations + keys = list(param_grid.keys()) + values = list(param_grid.values()) + + total_configs = np.prod([len(v) for v in values]) + print(f"\n{'='*70}") + print(f" HPO SWEEP: {dataset_name.upper()}") + print(f"{'='*70}") + print(f"Total configurations: {total_configs}") + print(f"Parameter grid: {param_grid}\n") + + for i, combo in enumerate(itertools.product(*values), 1): + config = dict(zip(keys, combo)) + + print(f"\n[{i}/{total_configs}] Testing config: {config}") + + try: + metrics = train_config(dataset_name, config) + + result = {"config": config, "metrics": metrics, "dataset": dataset_name} + + results.append(result) + + print( + f" ✅ Val Acc: {metrics['val_acc']:.4f}, Val Loss: {metrics['val_loss']:.4f}, Epochs: {metrics['epochs_trained']}" + ) + + except Exception as e: + print(f" ❌ Config failed: {str(e)}") + continue + + return results + + +def find_best_config(results: List[Dict]) -> Dict: + """Find the best config based on validation accuracy.""" + if not results: + return None + + best = max(results, key=lambda x: x["metrics"]["val_acc"]) + return best + + +def save_hpo_report( + all_results: Dict, output_path: str = "results/hpo_optimization_report.json" +): + """Save comprehensive HPO report.""" + Path(output_path).parent.mkdir(parents=True, exist_ok=True) + + report = { + "timestamp": datetime.now().isoformat(), + "datasets": list(all_results.keys()), + "results_by_dataset": {}, + } + + for dataset, results in all_results.items(): + best = find_best_config(results) + + # Baseline from classical comparison + baselines = { + "ionosphere": 0.8571, + "sonar": 0.7619, + "heart": 0.9464, + "banknote": 1.0, + } + + baseline_acc = baselines.get(dataset, 0.0) + improvement = (best["metrics"]["val_acc"] - baseline_acc) * 100 if best else 0.0 + + report["results_by_dataset"][dataset] = { + "baseline_accuracy": baseline_acc, + "best_config": best["config"] if best else None, + "best_metrics": best["metrics"] if best else None, + "improvement_pp": improvement, + "total_configs_tested": len(results), + "all_results": results, + } + + with open(output_path, "w") as f: + json.dump(report, f, indent=2) + + print(f"\n✅ HPO report saved to: {output_path}") + return report + + +def plot_hpo_results( + all_results: Dict, output_path: str = "results/hpo_comparison.png" +): + """Plot HPO results comparison.""" + fig, axes = plt.subplots(1, len(all_results), figsize=(6 * len(all_results), 5)) + + if len(all_results) == 1: + axes = [axes] + + for idx, (dataset, results) in enumerate(all_results.items()): + ax = axes[idx] + + # Extract configs and accuracies + accs = [r["metrics"]["val_acc"] for r in results] + configs = [f"C{i+1}" for i in range(len(results))] + + # Sort by accuracy + sorted_pairs = sorted(zip(accs, configs), reverse=True) + accs_sorted, configs_sorted = zip(*sorted_pairs) + + # Plot top 10 + top_n = min(10, len(accs_sorted)) + colors = ["green" if i == 0 else "steelblue" for i in range(top_n)] + + ax.barh(range(top_n), accs_sorted[:top_n], color=colors) + ax.set_yticks(range(top_n)) + ax.set_yticklabels(configs_sorted[:top_n]) + ax.set_xlabel("Validation Accuracy") + ax.set_title(f"{dataset.capitalize()}\nTop {top_n} Configs") + ax.grid(axis="x", alpha=0.3) + + # Add baseline line + baselines = { + "ionosphere": 0.8571, + "sonar": 0.7619, + "heart": 0.9464, + "banknote": 1.0, + } + if dataset in baselines: + ax.axvline( + baselines[dataset], + color="red", + linestyle="--", + label="Baseline", + linewidth=2, + ) + ax.legend() + + plt.tight_layout() + plt.savefig(output_path, dpi=150, bbox_inches="tight") + print(f"✅ HPO plot saved to: {output_path}") + plt.close() + + +def main(): + """Run HPO sweep.""" + print("=" * 70) + print(" QUANTUM HYBRID QNN - HYPERPARAMETER OPTIMIZATION") + print("=" * 70) + print("\nTarget: Improve performance on ionosphere and sonar datasets") + print("Method: Grid search with early stopping") + print("Baseline accuracies:") + print(" - Ionosphere: 85.71% (gap: -11.47 pp vs SVM RBF)") + print(" - Sonar: 76.19% (gap: -9.52 pp vs SVM RBF)") + + # Define parameter grid (focused on ionosphere and sonar) + param_grid = { + "n_qubits": [4, 5, 6], + "n_quantum_layers": [2, 3, 4], + "hidden_dim": [16, 32], + "learning_rate": [5e-4, 1e-3], + "batch_size": [8, 16], + } + + # Datasets to optimize + datasets = ["ionosphere", "sonar"] + + all_results = {} + + for dataset in datasets: + results = run_hpo_sweep(dataset, param_grid) + all_results[dataset] = results + + # Save report + report = save_hpo_report(all_results) + + # Plot results + plot_hpo_results(all_results) + + # Print summary + print("\n" + "=" * 70) + print(" HPO SUMMARY") + print("=" * 70) + + for dataset in datasets: + best = find_best_config(all_results[dataset]) + if best: + baseline = {"ionosphere": 0.8571, "sonar": 0.7619}[dataset] + improvement = (best["metrics"]["val_acc"] - baseline) * 100 + + print(f"\n{dataset.upper()}:") + print(f" Baseline: {baseline:.4f} ({baseline*100:.2f}%)") + print( + f" Best HPO: {best['metrics']['val_acc']:.4f} ({best['metrics']['val_acc']*100:.2f}%)" + ) + print(f" Improvement: {improvement:+.2f} pp") + print(f" Best config: {best['config']}") + + print("\n" + "=" * 70) + print(" ✅ HPO COMPLETE!") + print("=" * 70) + print("\n📊 Output files:") + print(" - results/hpo_optimization_report.json") + print(" - results/hpo_comparison.png") + print("\n💡 Next steps:") + print(" 1. Review best configs in the report") + print(" 2. Retrain with best configs on full datasets") + print(" 3. Test on Azure Quantum hardware") + print(" 4. Compare vs classical baselines again\n") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/hyperparameter_tuning.py b/ai-projects/quantum-ml/hyperparameter_tuning.py index 00a0e6c93..1bb5ffef2 100644 --- a/ai-projects/quantum-ml/hyperparameter_tuning.py +++ b/ai-projects/quantum-ml/hyperparameter_tuning.py @@ -1,166 +1,166 @@ -""" -Hyperparameter tuning for HybridQNN on Heart Disease dataset. - -NOTE: This is a simplified/quick version for the Heart Disease dataset. -For comprehensive hyperparameter optimization across multiple datasets, -use hyperparameter_optimization.py instead. - -Supports a quick mode for small grids to keep runs fast. -""" - -import itertools -import json -import sys -from datetime import datetime -from pathlib import Path - -import torch -from sklearn.model_selection import train_test_split -from torch.utils.data import DataLoader, TensorDataset - -# Import model code -ROOT = Path(__file__).parent -sys.path.append(str(ROOT / "src")) -from dataset_loader import load_dataset, preprocess_for_qubits # noqa: E402 -from hybrid_qnn import HybridQNN, QuantumClassicalTrainer # noqa: E402 - - -def load_heart_disease(): - """Load heart disease dataset - wrapper for backward compatibility""" - X, y, _ = load_dataset("heart_disease") - return X, y - - -def preprocess(X, y, n_components=4): - """Preprocess data - wrapper using shared preprocess_for_qubits""" - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42, stratify=y - ) - - X_train, X_val, scaler, pca = preprocess_for_qubits(X_train, X_val, n_components) - - return (X_train, X_val, y_train, y_val) - - -def train_eval(config, X_train, X_val, y_train, y_val): - batch_size = config["batch_size"] - lr = config["learning_rate"] - n_layers = config["n_quantum_layers"] - hidden_dim = config["hidden_dim"] - - X_train_t = torch.tensor(X_train, dtype=torch.float32) - y_train_t = torch.tensor(y_train, dtype=torch.long) - X_val_t = torch.tensor(X_val, dtype=torch.float32) - y_val_t = torch.tensor(y_val, dtype=torch.long) - - train_ds = TensorDataset(X_train_t, y_train_t) - val_ds = TensorDataset(X_val_t, y_val_t) - - train_loader = DataLoader( - train_ds, batch_size=batch_size, shuffle=True, drop_last=True - ) - val_loader = DataLoader( - val_ds, batch_size=batch_size, shuffle=False, drop_last=False - ) - - model = HybridQNN( - input_dim=4, - hidden_dim=hidden_dim, - n_qubits=4, - n_quantum_layers=n_layers, - entanglement="linear", - output_dim=2, - dropout=0.2, - ) - - trainer = QuantumClassicalTrainer(model, learning_rate=lr, device="cpu") - trainer.train(train_loader, val_loader, num_epochs=config["epochs"]) - - best_val_acc = max(trainer.val_accuracies) if trainer.val_accuracies else 0.0 - - return { - "config": config, - "best_val_acc": float(best_val_acc), - "final_val_acc": ( - float(trainer.val_accuracies[-1]) if trainer.val_accuracies else 0.0 - ), - "final_val_loss": float(trainer.val_losses[-1]) if trainer.val_losses else 0.0, - "history": { - "train_loss": [float(x) for x in trainer.train_losses], - "val_acc": [float(x) for x in trainer.val_accuracies], - "val_loss": [float(x) for x in trainer.val_losses], - }, - } - - -def main(quick=True): - print("=" * 70) - print(" HYPERPARAMETER TUNING (Heart Disease)") - print("=" * 70) - - # Load and preprocess - X, y = load_heart_disease() - X_train, X_val, y_train, y_val = preprocess(X, y, n_components=4) - - # Define grids - if quick: - learning_rates = [1e-3] - batch_sizes = [16] - n_layers_list = [1, 2] - hidden_dims = [16] - epochs = 10 - else: - learning_rates = [1e-3, 5e-4] - batch_sizes = [8, 16] - n_layers_list = [1, 2, 3] - hidden_dims = [16, 32] - epochs = 15 - - grid = list( - itertools.product(learning_rates, batch_sizes, n_layers_list, hidden_dims) - ) - print(f"Total trials: {len(grid)}") - - results = [] - best = None - - for i, (lr, bs, nl, hd) in enumerate(grid, 1): - config = { - "learning_rate": lr, - "batch_size": bs, - "n_quantum_layers": nl, - "hidden_dim": hd, - "epochs": epochs, - } - print(f"\nTrial {i}/{len(grid)}: {config}") - res = train_eval(config, X_train, X_val, y_train, y_val) - results.append(res) - if best is None or res["best_val_acc"] > best["best_val_acc"]: - best = res - print(f" Best Val Acc (this run): {res['best_val_acc']*100:.2f}%") - - # Save results - results_dir = ROOT / "results" - results_dir.mkdir(exist_ok=True) - out_path = results_dir / "hyperparameter_tuning_results.json" - best_path = results_dir / "hyperparameter_best_config.json" - - with open(out_path, "w") as f: - json.dump( - {"timestamp": datetime.now().isoformat(), "results": results}, f, indent=2 - ) - - with open(best_path, "w") as f: - json.dump(best, f, indent=2) - - print("\n" + "=" * 70) - print(" ✅ TUNING COMPLETE") - print("=" * 70) - print(f"Best Validation Accuracy: {best['best_val_acc']*100:.2f}%") - print(f"Best Config: {best['config']}") - print(f"Saved results: {out_path}") - print(f"Saved best config: {best_path}") - - -if __name__ == "__main__": - main(quick=True) +""" +Hyperparameter tuning for HybridQNN on Heart Disease dataset. + +NOTE: This is a simplified/quick version for the Heart Disease dataset. +For comprehensive hyperparameter optimization across multiple datasets, +use hyperparameter_optimization.py instead. + +Supports a quick mode for small grids to keep runs fast. +""" + +import itertools +import json +import sys +from datetime import datetime +from pathlib import Path + +import torch +from sklearn.model_selection import train_test_split +from torch.utils.data import DataLoader, TensorDataset + +# Import model code +ROOT = Path(__file__).parent +sys.path.append(str(ROOT / "src")) +from dataset_loader import load_dataset, preprocess_for_qubits # noqa: E402 +from hybrid_qnn import HybridQNN, QuantumClassicalTrainer # noqa: E402 + + +def load_heart_disease(): + """Load heart disease dataset - wrapper for backward compatibility""" + X, y, _ = load_dataset("heart_disease") + return X, y + + +def preprocess(X, y, n_components=4): + """Preprocess data - wrapper using shared preprocess_for_qubits""" + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + X_train, X_val, scaler, pca = preprocess_for_qubits(X_train, X_val, n_components) + + return (X_train, X_val, y_train, y_val) + + +def train_eval(config, X_train, X_val, y_train, y_val): + batch_size = config["batch_size"] + lr = config["learning_rate"] + n_layers = config["n_quantum_layers"] + hidden_dim = config["hidden_dim"] + + X_train_t = torch.tensor(X_train, dtype=torch.float32) + y_train_t = torch.tensor(y_train, dtype=torch.long) + X_val_t = torch.tensor(X_val, dtype=torch.float32) + y_val_t = torch.tensor(y_val, dtype=torch.long) + + train_ds = TensorDataset(X_train_t, y_train_t) + val_ds = TensorDataset(X_val_t, y_val_t) + + train_loader = DataLoader( + train_ds, batch_size=batch_size, shuffle=True, drop_last=True + ) + val_loader = DataLoader( + val_ds, batch_size=batch_size, shuffle=False, drop_last=False + ) + + model = HybridQNN( + input_dim=4, + hidden_dim=hidden_dim, + n_qubits=4, + n_quantum_layers=n_layers, + entanglement="linear", + output_dim=2, + dropout=0.2, + ) + + trainer = QuantumClassicalTrainer(model, learning_rate=lr, device="cpu") + trainer.train(train_loader, val_loader, num_epochs=config["epochs"]) + + best_val_acc = max(trainer.val_accuracies) if trainer.val_accuracies else 0.0 + + return { + "config": config, + "best_val_acc": float(best_val_acc), + "final_val_acc": ( + float(trainer.val_accuracies[-1]) if trainer.val_accuracies else 0.0 + ), + "final_val_loss": float(trainer.val_losses[-1]) if trainer.val_losses else 0.0, + "history": { + "train_loss": [float(x) for x in trainer.train_losses], + "val_acc": [float(x) for x in trainer.val_accuracies], + "val_loss": [float(x) for x in trainer.val_losses], + }, + } + + +def main(quick=True): + print("=" * 70) + print(" HYPERPARAMETER TUNING (Heart Disease)") + print("=" * 70) + + # Load and preprocess + X, y = load_heart_disease() + X_train, X_val, y_train, y_val = preprocess(X, y, n_components=4) + + # Define grids + if quick: + learning_rates = [1e-3] + batch_sizes = [16] + n_layers_list = [1, 2] + hidden_dims = [16] + epochs = 10 + else: + learning_rates = [1e-3, 5e-4] + batch_sizes = [8, 16] + n_layers_list = [1, 2, 3] + hidden_dims = [16, 32] + epochs = 15 + + grid = list( + itertools.product(learning_rates, batch_sizes, n_layers_list, hidden_dims) + ) + print(f"Total trials: {len(grid)}") + + results = [] + best = None + + for i, (lr, bs, nl, hd) in enumerate(grid, 1): + config = { + "learning_rate": lr, + "batch_size": bs, + "n_quantum_layers": nl, + "hidden_dim": hd, + "epochs": epochs, + } + print(f"\nTrial {i}/{len(grid)}: {config}") + res = train_eval(config, X_train, X_val, y_train, y_val) + results.append(res) + if best is None or res["best_val_acc"] > best["best_val_acc"]: + best = res + print(f" Best Val Acc (this run): {res['best_val_acc']*100:.2f}%") + + # Save results + results_dir = ROOT / "results" + results_dir.mkdir(exist_ok=True) + out_path = results_dir / "hyperparameter_tuning_results.json" + best_path = results_dir / "hyperparameter_best_config.json" + + with open(out_path, "w") as f: + json.dump( + {"timestamp": datetime.now().isoformat(), "results": results}, f, indent=2 + ) + + with open(best_path, "w") as f: + json.dump(best, f, indent=2) + + print("\n" + "=" * 70) + print(" ✅ TUNING COMPLETE") + print("=" * 70) + print(f"Best Validation Accuracy: {best['best_val_acc']*100:.2f}%") + print(f"Best Config: {best['config']}") + print(f"Saved results: {out_path}") + print(f"Saved best config: {best_path}") + + +if __name__ == "__main__": + main(quick=True) diff --git a/ai-projects/quantum-ml/mcp-requirements.txt b/ai-projects/quantum-ml/mcp-requirements.txt index e0b701c05..f14b46fe1 100644 --- a/ai-projects/quantum-ml/mcp-requirements.txt +++ b/ai-projects/quantum-ml/mcp-requirements.txt @@ -1,2 +1,2 @@ -# Model Context Protocol server dependencies -mcp>=0.9.0 +# Model Context Protocol server dependencies +mcp>=0.9.0 diff --git a/ai-projects/quantum-ml/production/README.md b/ai-projects/quantum-ml/production/README.md index 984e1dd5c..6c8a9b731 100644 --- a/ai-projects/quantum-ml/production/README.md +++ b/ai-projects/quantum-ml/production/README.md @@ -1,350 +1,350 @@ -# Banknote Fraud Detector - Production Deployment - -🎯 **100% Validation Accuracy** | ⚡ **Quantum-Powered** | 🔒 **Production-Ready** - -## Overview - -This is a production-ready REST API for banknote fraud detection using a hybrid quantum-classical neural network. The model achieved **100% accuracy** on validation data during training. - -### Features - -- ✅ **High Accuracy**: 100% validation accuracy -- ⚡ **Fast Inference**: < 100ms per prediction -- 🔬 **Quantum Technology**: 4-qubit variational circuit -- 🌐 **REST API**: Easy integration with any system -- 📊 **Batch Processing**: Classify multiple banknotes at once -- 🐳 **Docker Ready**: Container included -- 📈 **Production Grade**: Health checks, error handling, logging - -## Quick Start - -### Prerequisites - -- Python 3.8+ -- Trained model artifacts (generated during training) - -### Installation - -```bash -cd production -pip install -r requirements.txt -``` - -### Start the API - -```bash -python banknote_api.py -``` - -The API will start on `http://localhost:8080` - -### Test the API - -**Health Check:** -```bash -curl http://localhost:8080/api/health -``` - -**Single Prediction:** -```bash -curl -X POST http://localhost:8080/api/predict \ - -H "Content-Type: application/json" \ - -d '{"features": [3.5, 0.5, -1.2, 0.8]}' -``` - -**Expected Response:** -```json -{ - "prediction": "GENUINE", - "confidence": 0.9987, - "probabilities": { - "genuine": 0.9987, - "forged": 0.0013 - }, - "features_received": [3.5, 0.5, -1.2, 0.8], - "timestamp": "2025-11-16T10:30:45.123456" -} -``` - -**Batch Prediction:** -```bash -curl -X POST http://localhost:8080/api/predict_batch \ - -H "Content-Type: application/json" \ - -d '{ - "batch": [ - {"features": [3.5, 0.5, -1.2, 0.8]}, - {"features": [-2.1, 1.3, 0.4, -0.9]} - ] - }' -``` - -## API Endpoints - -### POST /api/predict - -Classify a single banknote. - -**Request Body:** -```json -{ - "features": [variance, skewness, curtosis, entropy] -} -``` - -**Response:** -```json -{ - "prediction": "GENUINE" | "FORGED", - "confidence": 0.0-1.0, - "probabilities": { - "genuine": 0.0-1.0, - "forged": 0.0-1.0 - }, - "features_received": [...], - "timestamp": "ISO8601" -} -``` - -### POST /api/predict_batch - -Classify multiple banknotes. - -**Request Body:** -```json -{ - "batch": [ - {"features": [...]}, - {"features": [...]} - ] -} -``` - -**Response:** -```json -{ - "results": [ - { - "index": 0, - "prediction": "GENUINE", - "confidence": 0.9987, - "probabilities": {...} - } - ], - "summary": { - "total_processed": 2, - "successful": 2, - "errors": 0, - "genuine_count": 1, - "forged_count": 1 - }, - "timestamp": "ISO8601" -} -``` - -### GET /api/health - -Health check endpoint. - -**Response:** -```json -{ - "status": "healthy", - "service": "banknote-fraud-detector", - "version": "1.0.0", - "model_loaded": true, - "timestamp": "ISO8601" -} -``` - -### GET /api/model_info - -Get model metadata and performance metrics. - -**Response:** -```json -{ - "model": "Hybrid Quantum-Classical Neural Network", - "task": "Binary classification: Genuine vs Forged banknotes", - "architecture": { - "qubits": 4, - "quantum_layers": 2, - "hidden_dim": 16, - "output_classes": 2 - }, - "performance": { - "validation_accuracy": 1.0, - "training_loss": 0.0657 - }, - "features": { - "names": ["variance", "skewness", "curtosis", "entropy"], - "count": 4, - "preprocessing": "StandardScaler normalization" - } -} -``` - -## Feature Inputs - -The model expects 4 numerical features extracted from banknote images: - -1. **Variance** of Wavelet Transformed image -2. **Skewness** of Wavelet Transformed image -3. **Curtosis** of Wavelet Transformed image -4. **Entropy** of image - -These features are typically extracted using image processing libraries. Example values: - -- Genuine: `[3.62160, 8.66610, -2.80730, -0.44699]` -- Forged: `[-4.54590, 8.16740, -2.45860, -1.46210]` - -## Model Architecture - -### Hybrid Quantum-Classical Neural Network - -``` -Input (4 features) - ↓ -[Preprocessing: StandardScaler] - ↓ -[Quantum Circuit: 4 qubits, 2 variational layers] - ↓ -[Classical NN: Hidden layer (16 nodes)] - ↓ -[Dropout: 0.2] - ↓ -Output (2 classes: Genuine/Forged) -``` - -**Quantum Circuit Details:** -- **Qubits**: 4 -- **Entanglement**: Linear (chain topology) -- **Variational Gates**: RY, RZ rotations -- **Backend**: PennyLane (lightning.qubit simulator) - -## Docker Deployment - -### Build Image - -```bash -docker build -t banknote-fraud-detector . -``` - -### Run Container - -```bash -docker run -p 8080:8080 banknote-fraud-detector -``` - -### Docker Compose - -```bash -docker-compose up -``` - -## Performance - -### Training Results - -- **Dataset**: 1,371 banknote samples -- **Training**: 25 epochs, batch size 16 -- **Validation Accuracy**: **100%** -- **Training Loss**: 0.0657 -- **Inference Time**: < 100ms - -### Benchmark - -| Metric | Value | -|--------|-------| -| Accuracy | 100% | -| Precision | 100% | -| Recall | 100% | -| F1 Score | 100% | -| Inference Time | ~50ms | - -## Production Considerations - -### Scaling - -- **Horizontal Scaling**: Deploy multiple API instances behind load balancer -- **Batch Processing**: Use `/api/predict_batch` for bulk classification -- **Caching**: Consider caching frequent predictions - -### Monitoring - -- Health check endpoint for orchestrators (Kubernetes, Docker Swarm) -- Log all predictions with timestamps -- Monitor inference latency and error rates -- Set up alerts for model degradation - -### Security - -- Add API authentication (API keys, OAuth) -- Enable HTTPS in production -- Rate limiting to prevent abuse -- Input validation and sanitization (already implemented) - -## Troubleshooting - -### Model Not Found - -```bash -# Train the model first -cd .. -python train_custom_dataset.py --preset banknote --epochs 25 -``` - -### Port Already in Use - -Change port in `banknote_api.py`: -```python -app.run(host='0.0.0.0', port=8081) # Use different port -``` - -### Import Errors - -Ensure all dependencies are installed: -```bash -pip install -r requirements.txt -``` - -## Development - -### Run Tests - -```bash -pytest tests/ -``` - -### Enable Debug Mode - -```python -app.run(host='0.0.0.0', port=8080, debug=True) -``` - -## License - -MIT License - See LICENSE file - -## Support - -For issues or questions: -- Open an issue on GitHub -- Check documentation at `/api/model_info` -- Review training logs in `../results/` - -## Citation - -If you use this model in research, please cite: - -```bibtex -@software{quantum_banknote_detector, - title={Quantum Banknote Fraud Detector}, - author={Quantum AI System}, - year={2025}, - version={1.0.0} -} -``` - ---- - -**Built with ❤️ using Quantum Machine Learning** +# Banknote Fraud Detector - Production Deployment + +🎯 **100% Validation Accuracy** | ⚡ **Quantum-Powered** | 🔒 **Production-Ready** + +## Overview + +This is a production-ready REST API for banknote fraud detection using a hybrid quantum-classical neural network. The model achieved **100% accuracy** on validation data during training. + +### Features + +- ✅ **High Accuracy**: 100% validation accuracy +- ⚡ **Fast Inference**: < 100ms per prediction +- 🔬 **Quantum Technology**: 4-qubit variational circuit +- 🌐 **REST API**: Easy integration with any system +- 📊 **Batch Processing**: Classify multiple banknotes at once +- 🐳 **Docker Ready**: Container included +- 📈 **Production Grade**: Health checks, error handling, logging + +## Quick Start + +### Prerequisites + +- Python 3.8+ +- Trained model artifacts (generated during training) + +### Installation + +```bash +cd production +pip install -r requirements.txt +``` + +### Start the API + +```bash +python banknote_api.py +``` + +The API will start on `http://localhost:8080` + +### Test the API + +**Health Check:** +```bash +curl http://localhost:8080/api/health +``` + +**Single Prediction:** +```bash +curl -X POST http://localhost:8080/api/predict \ + -H "Content-Type: application/json" \ + -d '{"features": [3.5, 0.5, -1.2, 0.8]}' +``` + +**Expected Response:** +```json +{ + "prediction": "GENUINE", + "confidence": 0.9987, + "probabilities": { + "genuine": 0.9987, + "forged": 0.0013 + }, + "features_received": [3.5, 0.5, -1.2, 0.8], + "timestamp": "2025-11-16T10:30:45.123456" +} +``` + +**Batch Prediction:** +```bash +curl -X POST http://localhost:8080/api/predict_batch \ + -H "Content-Type: application/json" \ + -d '{ + "batch": [ + {"features": [3.5, 0.5, -1.2, 0.8]}, + {"features": [-2.1, 1.3, 0.4, -0.9]} + ] + }' +``` + +## API Endpoints + +### POST /api/predict + +Classify a single banknote. + +**Request Body:** +```json +{ + "features": [variance, skewness, curtosis, entropy] +} +``` + +**Response:** +```json +{ + "prediction": "GENUINE" | "FORGED", + "confidence": 0.0-1.0, + "probabilities": { + "genuine": 0.0-1.0, + "forged": 0.0-1.0 + }, + "features_received": [...], + "timestamp": "ISO8601" +} +``` + +### POST /api/predict_batch + +Classify multiple banknotes. + +**Request Body:** +```json +{ + "batch": [ + {"features": [...]}, + {"features": [...]} + ] +} +``` + +**Response:** +```json +{ + "results": [ + { + "index": 0, + "prediction": "GENUINE", + "confidence": 0.9987, + "probabilities": {...} + } + ], + "summary": { + "total_processed": 2, + "successful": 2, + "errors": 0, + "genuine_count": 1, + "forged_count": 1 + }, + "timestamp": "ISO8601" +} +``` + +### GET /api/health + +Health check endpoint. + +**Response:** +```json +{ + "status": "healthy", + "service": "banknote-fraud-detector", + "version": "1.0.0", + "model_loaded": true, + "timestamp": "ISO8601" +} +``` + +### GET /api/model_info + +Get model metadata and performance metrics. + +**Response:** +```json +{ + "model": "Hybrid Quantum-Classical Neural Network", + "task": "Binary classification: Genuine vs Forged banknotes", + "architecture": { + "qubits": 4, + "quantum_layers": 2, + "hidden_dim": 16, + "output_classes": 2 + }, + "performance": { + "validation_accuracy": 1.0, + "training_loss": 0.0657 + }, + "features": { + "names": ["variance", "skewness", "curtosis", "entropy"], + "count": 4, + "preprocessing": "StandardScaler normalization" + } +} +``` + +## Feature Inputs + +The model expects 4 numerical features extracted from banknote images: + +1. **Variance** of Wavelet Transformed image +2. **Skewness** of Wavelet Transformed image +3. **Curtosis** of Wavelet Transformed image +4. **Entropy** of image + +These features are typically extracted using image processing libraries. Example values: + +- Genuine: `[3.62160, 8.66610, -2.80730, -0.44699]` +- Forged: `[-4.54590, 8.16740, -2.45860, -1.46210]` + +## Model Architecture + +### Hybrid Quantum-Classical Neural Network + +``` +Input (4 features) + ↓ +[Preprocessing: StandardScaler] + ↓ +[Quantum Circuit: 4 qubits, 2 variational layers] + ↓ +[Classical NN: Hidden layer (16 nodes)] + ↓ +[Dropout: 0.2] + ↓ +Output (2 classes: Genuine/Forged) +``` + +**Quantum Circuit Details:** +- **Qubits**: 4 +- **Entanglement**: Linear (chain topology) +- **Variational Gates**: RY, RZ rotations +- **Backend**: PennyLane (lightning.qubit simulator) + +## Docker Deployment + +### Build Image + +```bash +docker build -t banknote-fraud-detector . +``` + +### Run Container + +```bash +docker run -p 8080:8080 banknote-fraud-detector +``` + +### Docker Compose + +```bash +docker-compose up +``` + +## Performance + +### Training Results + +- **Dataset**: 1,371 banknote samples +- **Training**: 25 epochs, batch size 16 +- **Validation Accuracy**: **100%** +- **Training Loss**: 0.0657 +- **Inference Time**: < 100ms + +### Benchmark + +| Metric | Value | +|--------|-------| +| Accuracy | 100% | +| Precision | 100% | +| Recall | 100% | +| F1 Score | 100% | +| Inference Time | ~50ms | + +## Production Considerations + +### Scaling + +- **Horizontal Scaling**: Deploy multiple API instances behind load balancer +- **Batch Processing**: Use `/api/predict_batch` for bulk classification +- **Caching**: Consider caching frequent predictions + +### Monitoring + +- Health check endpoint for orchestrators (Kubernetes, Docker Swarm) +- Log all predictions with timestamps +- Monitor inference latency and error rates +- Set up alerts for model degradation + +### Security + +- Add API authentication (API keys, OAuth) +- Enable HTTPS in production +- Rate limiting to prevent abuse +- Input validation and sanitization (already implemented) + +## Troubleshooting + +### Model Not Found + +```bash +# Train the model first +cd .. +python train_custom_dataset.py --preset banknote --epochs 25 +``` + +### Port Already in Use + +Change port in `banknote_api.py`: +```python +app.run(host='0.0.0.0', port=8081) # Use different port +``` + +### Import Errors + +Ensure all dependencies are installed: +```bash +pip install -r requirements.txt +``` + +## Development + +### Run Tests + +```bash +pytest tests/ +``` + +### Enable Debug Mode + +```python +app.run(host='0.0.0.0', port=8080, debug=True) +``` + +## License + +MIT License - See LICENSE file + +## Support + +For issues or questions: +- Open an issue on GitHub +- Check documentation at `/api/model_info` +- Review training logs in `../results/` + +## Citation + +If you use this model in research, please cite: + +```bibtex +@software{quantum_banknote_detector, + title={Quantum Banknote Fraud Detector}, + author={Quantum AI System}, + year={2025}, + version={1.0.0} +} +``` + +--- + +**Built with ❤️ using Quantum Machine Learning** diff --git a/ai-projects/quantum-ml/production/banknote_api.py b/ai-projects/quantum-ml/production/banknote_api.py index f8bdc7eba..2cab53183 100644 --- a/ai-projects/quantum-ml/production/banknote_api.py +++ b/ai-projects/quantum-ml/production/banknote_api.py @@ -1,363 +1,363 @@ -""" -REST API for quantum-powered banknote fraud detection. -Achieves 100% accuracy on validation data. - -Endpoints: - POST /api/predict - Classify single banknote - POST /api/predict_batch - Classify multiple banknotes - GET /api/health - Service health check - GET /api/model_info - Model metadata - -Usage: - python banknote_api.py - - # Test prediction: - curl -X POST http://localhost:8080/api/predict \ - -H "Content-Type: application/json" \ - -d '{"features": [3.5, 0.5, -1.2, 0.8]}' - -Author: Quantum AI System -Date: November 16, 2025 -""" - -import json -import os -import sys -from datetime import datetime -from pathlib import Path - -import joblib -import numpy as np -import torch -from flask import Flask, jsonify, request -from flask_cors import CORS -from src.hybrid_qnn import HybridQNN - -# Add src to path -src_path = Path(__file__).parent.parent / "src" -sys.path.insert(0, str(src_path)) - - -app = Flask(__name__) -CORS(app) # Enable CORS for web clients - -# Global model variables -model = None -scaler = None -model_metadata = {} - - -def load_model(): - """Load trained model and preprocessors""" - global model, scaler, model_metadata - - model_dir = Path(__file__).parent.parent / "results" - - print("🔄 Loading model artifacts...") - - # Load scaler - scaler_path = model_dir / "custom_scaler.pkl" - if not scaler_path.exists(): - raise FileNotFoundError(f"Scaler not found: {scaler_path}") - scaler = joblib.load(scaler_path) - print(f" ✅ Loaded scaler from {scaler_path}") - - # Create model architecture - model = HybridQNN( - input_dim=4, - hidden_dim=16, - n_qubits=4, - n_quantum_layers=2, - output_dim=2, - dropout=0.2, - ) - - # Load trained weights safely with weights_only=True to prevent arbitrary code execution - model_path = model_dir / "custom_model.pt" - if not model_path.exists(): - raise FileNotFoundError(f"Model not found: {model_path}") - model.load_state_dict(torch.load(model_path, weights_only=True)) - model.eval() # Set to evaluation mode - print(f" ✅ Loaded model weights from {model_path}") - - # Load metadata - summary_path = model_dir / "custom_training_summary.json" - if summary_path.exists(): - with open(summary_path) as f: - model_metadata = json.load(f) - print(f" ✅ Loaded metadata from {summary_path}") - - print("✅ Model loaded successfully!") - print( - f" Validation Accuracy: {model_metadata.get('metrics', {}).get('val_acc_best', 'N/A')}" - ) - - -@app.route("/api/health", methods=["GET"]) -def health_check(): - """Health check endpoint""" - return jsonify( - { - "status": "healthy", - "service": "banknote-fraud-detector", - "version": "1.0.0", - "model_loaded": model is not None, - "timestamp": datetime.now().isoformat(), - } - ) - - -@app.route("/api/model_info", methods=["GET"]) -def model_info(): - """Return model information""" - return jsonify( - { - "model": "Hybrid Quantum-Classical Neural Network", - "task": "Binary classification: Genuine vs Forged banknotes", - "architecture": { - "qubits": 4, - "quantum_layers": 2, - "hidden_dim": 16, - "output_classes": 2, - }, - "performance": { - "validation_accuracy": model_metadata.get("metrics", {}).get( - "val_acc_best", "N/A" - ), - "training_loss": model_metadata.get("metrics", {}).get( - "train_loss_last", "N/A" - ), - }, - "features": { - "names": ["variance", "skewness", "curtosis", "entropy"], - "count": 4, - "preprocessing": "StandardScaler normalization", - }, - "training": { - "dataset": model_metadata.get("dataset", "banknote authentication"), - "samples": model_metadata.get("data", {}).get("n_train", "N/A"), - "epochs": model_metadata.get("params", {}).get("epochs", "N/A"), - }, - } - ) - - -@app.route("/api/predict", methods=["POST"]) -def predict(): - """Classify a single banknote""" - try: - data = request.get_json() - - # Validate input - if "features" not in data: - return ( - jsonify( - { - "error": "Missing required field: features", - "expected_format": { - "features": ["variance", "skewness", "curtosis", "entropy"] - }, - } - ), - 400, - ) - - features = np.array(data["features"]) - - # Validate feature count - if features.shape[0] != 4: - return ( - jsonify( - { - "error": f"Expected 4 features, got {features.shape[0]}", - "features_required": [ - "variance", - "skewness", - "curtosis", - "entropy", - ], - } - ), - 400, - ) - - # Preprocess - features_scaled = scaler.transform(features.reshape(1, -1)) - features_tensor = torch.FloatTensor(features_scaled) - - # Predict - with torch.no_grad(): - outputs = model(features_tensor) - probabilities = torch.softmax(outputs, dim=1) - prediction = torch.argmax(outputs, dim=1).item() - - # Interpret result - label = "GENUINE" if prediction == 0 else "FORGED" - confidence = float(probabilities[0][prediction]) - - return jsonify( - { - "prediction": label, - "confidence": round(confidence, 4), - "probabilities": { - "genuine": round(float(probabilities[0][0]), 4), - "forged": round(float(probabilities[0][1]), 4), - }, - "features_received": features.tolist(), - "timestamp": datetime.now().isoformat(), - } - ) - - except Exception as e: - return jsonify({"error": str(e), "type": type(e).__name__}), 500 - - -@app.route("/api/predict_batch", methods=["POST"]) -def predict_batch(): - """Classify multiple banknotes""" - try: - data = request.get_json() - - # Validate input - if "batch" not in data: - return ( - jsonify( - { - "error": "Missing required field: batch", - "expected_format": { - "batch": [ - { - "features": [ - "variance", - "skewness", - "curtosis", - "entropy", - ] - }, - { - "features": [ - "variance", - "skewness", - "curtosis", - "entropy", - ] - }, - ] - }, - } - ), - 400, - ) - - batch = data["batch"] - results = [] - - for i, item in enumerate(batch): - if "features" not in item: - results.append({"index": i, "error": "Missing features field"}) - continue - - features = np.array(item["features"]) - - if features.shape[0] != 4: - results.append( - { - "index": i, - "error": f"Expected 4 features, got {features.shape[0]}", - } - ) - continue - - # Preprocess and predict - features_scaled = scaler.transform(features.reshape(1, -1)) - features_tensor = torch.FloatTensor(features_scaled) - - with torch.no_grad(): - outputs = model(features_tensor) - probabilities = torch.softmax(outputs, dim=1) - prediction = torch.argmax(outputs, dim=1).item() - - label = "GENUINE" if prediction == 0 else "FORGED" - confidence = float(probabilities[0][prediction]) - - results.append( - { - "index": i, - "prediction": label, - "confidence": round(confidence, 4), - "probabilities": { - "genuine": round(float(probabilities[0][0]), 4), - "forged": round(float(probabilities[0][1]), 4), - }, - } - ) - - # Summary statistics - valid_predictions = [r for r in results if "prediction" in r] - genuine_count = sum( - 1 for r in valid_predictions if r["prediction"] == "GENUINE" - ) - forged_count = len(valid_predictions) - genuine_count - - return jsonify( - { - "results": results, - "summary": { - "total_processed": len(batch), - "successful": len(valid_predictions), - "errors": len(batch) - len(valid_predictions), - "genuine_count": genuine_count, - "forged_count": forged_count, - }, - "timestamp": datetime.now().isoformat(), - } - ) - - except Exception as e: - return jsonify({"error": str(e), "type": type(e).__name__}), 500 - - -def main(): - """Start the API server""" - print("=" * 70) - print(" BANKNOTE FRAUD DETECTOR - PRODUCTION API") - print("=" * 70) - print("\n🔬 Model: Hybrid Quantum-Classical Neural Network") - print("🎯 Accuracy: 100% on validation data") - print("⚡ Backend: Quantum simulation (PennyLane)") - - # Load model - try: - load_model() - except Exception as e: - print(f"\n❌ Failed to load model: {e}") - print("\n💡 Make sure you've trained the model first:") - print(" python train_custom_dataset.py --preset banknote --epochs 25") - sys.exit(1) - - print("\n" + "=" * 70) - print(" 🚀 STARTING API SERVER") - print("=" * 70) - print("\n📡 Endpoints:") - print(" POST http://localhost:8080/api/predict") - print(" POST http://localhost:8080/api/predict_batch") - print(" GET http://localhost:8080/api/health") - print(" GET http://localhost:8080/api/model_info") - - print("\n📝 Example curl command:") - print(" curl -X POST http://localhost:8080/api/predict \\") - print(' -H "Content-Type: application/json" \\') - print(" -d '{\"features\": [3.5, 0.5, -1.2, 0.8]}'") - - print("\n⏳ Server starting on http://localhost:8080 ...") - print(" Press Ctrl+C to stop") - print("=" * 70 + "\n") - - # Start server - default to localhost for security - # Use BANKNOTE_API_HOST environment variable to override if needed - host = os.environ.get("BANKNOTE_API_HOST", "127.0.0.1") - app.run(host=host, port=8080, debug=False) - - -if __name__ == "__main__": - main() +""" +REST API for quantum-powered banknote fraud detection. +Achieves 100% accuracy on validation data. + +Endpoints: + POST /api/predict - Classify single banknote + POST /api/predict_batch - Classify multiple banknotes + GET /api/health - Service health check + GET /api/model_info - Model metadata + +Usage: + python banknote_api.py + + # Test prediction: + curl -X POST http://localhost:8080/api/predict \ + -H "Content-Type: application/json" \ + -d '{"features": [3.5, 0.5, -1.2, 0.8]}' + +Author: Quantum AI System +Date: November 16, 2025 +""" + +import json +import os +import sys +from datetime import datetime +from pathlib import Path + +import joblib +import numpy as np +import torch +from flask import Flask, jsonify, request +from flask_cors import CORS +from src.hybrid_qnn import HybridQNN + +# Add src to path +src_path = Path(__file__).parent.parent / "src" +sys.path.insert(0, str(src_path)) + + +app = Flask(__name__) +CORS(app) # Enable CORS for web clients + +# Global model variables +model = None +scaler = None +model_metadata = {} + + +def load_model(): + """Load trained model and preprocessors""" + global model, scaler, model_metadata + + model_dir = Path(__file__).parent.parent / "results" + + print("🔄 Loading model artifacts...") + + # Load scaler + scaler_path = model_dir / "custom_scaler.pkl" + if not scaler_path.exists(): + raise FileNotFoundError(f"Scaler not found: {scaler_path}") + scaler = joblib.load(scaler_path) + print(f" ✅ Loaded scaler from {scaler_path}") + + # Create model architecture + model = HybridQNN( + input_dim=4, + hidden_dim=16, + n_qubits=4, + n_quantum_layers=2, + output_dim=2, + dropout=0.2, + ) + + # Load trained weights safely with weights_only=True to prevent arbitrary code execution + model_path = model_dir / "custom_model.pt" + if not model_path.exists(): + raise FileNotFoundError(f"Model not found: {model_path}") + model.load_state_dict(torch.load(model_path, weights_only=True)) + model.eval() # Set to evaluation mode + print(f" ✅ Loaded model weights from {model_path}") + + # Load metadata + summary_path = model_dir / "custom_training_summary.json" + if summary_path.exists(): + with open(summary_path) as f: + model_metadata = json.load(f) + print(f" ✅ Loaded metadata from {summary_path}") + + print("✅ Model loaded successfully!") + print( + f" Validation Accuracy: {model_metadata.get('metrics', {}).get('val_acc_best', 'N/A')}" + ) + + +@app.route("/api/health", methods=["GET"]) +def health_check(): + """Health check endpoint""" + return jsonify( + { + "status": "healthy", + "service": "banknote-fraud-detector", + "version": "1.0.0", + "model_loaded": model is not None, + "timestamp": datetime.now().isoformat(), + } + ) + + +@app.route("/api/model_info", methods=["GET"]) +def model_info(): + """Return model information""" + return jsonify( + { + "model": "Hybrid Quantum-Classical Neural Network", + "task": "Binary classification: Genuine vs Forged banknotes", + "architecture": { + "qubits": 4, + "quantum_layers": 2, + "hidden_dim": 16, + "output_classes": 2, + }, + "performance": { + "validation_accuracy": model_metadata.get("metrics", {}).get( + "val_acc_best", "N/A" + ), + "training_loss": model_metadata.get("metrics", {}).get( + "train_loss_last", "N/A" + ), + }, + "features": { + "names": ["variance", "skewness", "curtosis", "entropy"], + "count": 4, + "preprocessing": "StandardScaler normalization", + }, + "training": { + "dataset": model_metadata.get("dataset", "banknote authentication"), + "samples": model_metadata.get("data", {}).get("n_train", "N/A"), + "epochs": model_metadata.get("params", {}).get("epochs", "N/A"), + }, + } + ) + + +@app.route("/api/predict", methods=["POST"]) +def predict(): + """Classify a single banknote""" + try: + data = request.get_json() + + # Validate input + if "features" not in data: + return ( + jsonify( + { + "error": "Missing required field: features", + "expected_format": { + "features": ["variance", "skewness", "curtosis", "entropy"] + }, + } + ), + 400, + ) + + features = np.array(data["features"]) + + # Validate feature count + if features.shape[0] != 4: + return ( + jsonify( + { + "error": f"Expected 4 features, got {features.shape[0]}", + "features_required": [ + "variance", + "skewness", + "curtosis", + "entropy", + ], + } + ), + 400, + ) + + # Preprocess + features_scaled = scaler.transform(features.reshape(1, -1)) + features_tensor = torch.FloatTensor(features_scaled) + + # Predict + with torch.no_grad(): + outputs = model(features_tensor) + probabilities = torch.softmax(outputs, dim=1) + prediction = torch.argmax(outputs, dim=1).item() + + # Interpret result + label = "GENUINE" if prediction == 0 else "FORGED" + confidence = float(probabilities[0][prediction]) + + return jsonify( + { + "prediction": label, + "confidence": round(confidence, 4), + "probabilities": { + "genuine": round(float(probabilities[0][0]), 4), + "forged": round(float(probabilities[0][1]), 4), + }, + "features_received": features.tolist(), + "timestamp": datetime.now().isoformat(), + } + ) + + except Exception as e: + return jsonify({"error": str(e), "type": type(e).__name__}), 500 + + +@app.route("/api/predict_batch", methods=["POST"]) +def predict_batch(): + """Classify multiple banknotes""" + try: + data = request.get_json() + + # Validate input + if "batch" not in data: + return ( + jsonify( + { + "error": "Missing required field: batch", + "expected_format": { + "batch": [ + { + "features": [ + "variance", + "skewness", + "curtosis", + "entropy", + ] + }, + { + "features": [ + "variance", + "skewness", + "curtosis", + "entropy", + ] + }, + ] + }, + } + ), + 400, + ) + + batch = data["batch"] + results = [] + + for i, item in enumerate(batch): + if "features" not in item: + results.append({"index": i, "error": "Missing features field"}) + continue + + features = np.array(item["features"]) + + if features.shape[0] != 4: + results.append( + { + "index": i, + "error": f"Expected 4 features, got {features.shape[0]}", + } + ) + continue + + # Preprocess and predict + features_scaled = scaler.transform(features.reshape(1, -1)) + features_tensor = torch.FloatTensor(features_scaled) + + with torch.no_grad(): + outputs = model(features_tensor) + probabilities = torch.softmax(outputs, dim=1) + prediction = torch.argmax(outputs, dim=1).item() + + label = "GENUINE" if prediction == 0 else "FORGED" + confidence = float(probabilities[0][prediction]) + + results.append( + { + "index": i, + "prediction": label, + "confidence": round(confidence, 4), + "probabilities": { + "genuine": round(float(probabilities[0][0]), 4), + "forged": round(float(probabilities[0][1]), 4), + }, + } + ) + + # Summary statistics + valid_predictions = [r for r in results if "prediction" in r] + genuine_count = sum( + 1 for r in valid_predictions if r["prediction"] == "GENUINE" + ) + forged_count = len(valid_predictions) - genuine_count + + return jsonify( + { + "results": results, + "summary": { + "total_processed": len(batch), + "successful": len(valid_predictions), + "errors": len(batch) - len(valid_predictions), + "genuine_count": genuine_count, + "forged_count": forged_count, + }, + "timestamp": datetime.now().isoformat(), + } + ) + + except Exception as e: + return jsonify({"error": str(e), "type": type(e).__name__}), 500 + + +def main(): + """Start the API server""" + print("=" * 70) + print(" BANKNOTE FRAUD DETECTOR - PRODUCTION API") + print("=" * 70) + print("\n🔬 Model: Hybrid Quantum-Classical Neural Network") + print("🎯 Accuracy: 100% on validation data") + print("⚡ Backend: Quantum simulation (PennyLane)") + + # Load model + try: + load_model() + except Exception as e: + print(f"\n❌ Failed to load model: {e}") + print("\n💡 Make sure you've trained the model first:") + print(" python train_custom_dataset.py --preset banknote --epochs 25") + sys.exit(1) + + print("\n" + "=" * 70) + print(" 🚀 STARTING API SERVER") + print("=" * 70) + print("\n📡 Endpoints:") + print(" POST http://localhost:8080/api/predict") + print(" POST http://localhost:8080/api/predict_batch") + print(" GET http://localhost:8080/api/health") + print(" GET http://localhost:8080/api/model_info") + + print("\n📝 Example curl command:") + print(" curl -X POST http://localhost:8080/api/predict \\") + print(' -H "Content-Type: application/json" \\') + print(" -d '{\"features\": [3.5, 0.5, -1.2, 0.8]}'") + + print("\n⏳ Server starting on http://localhost:8080 ...") + print(" Press Ctrl+C to stop") + print("=" * 70 + "\n") + + # Start server - default to localhost for security + # Use BANKNOTE_API_HOST environment variable to override if needed + host = os.environ.get("BANKNOTE_API_HOST", "127.0.0.1") + app.run(host=host, port=8080, debug=False) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/production/docker-compose.yml b/ai-projects/quantum-ml/production/docker-compose.yml index 5f64f1206..0742d02e5 100644 --- a/ai-projects/quantum-ml/production/docker-compose.yml +++ b/ai-projects/quantum-ml/production/docker-compose.yml @@ -1,19 +1,19 @@ -version: '3.8' - -services: - banknote-api: - build: . - container_name: banknote-fraud-detector - ports: - - "8080:8080" - environment: - - PYTHONUNBUFFERED=1 - volumes: - - ../results:/app/results:ro - restart: unless-stopped - healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:8080/api/health"] - interval: 30s - timeout: 10s - retries: 3 - start_period: 10s +version: '3.8' + +services: + banknote-api: + build: . + container_name: banknote-fraud-detector + ports: + - "8080:8080" + environment: + - PYTHONUNBUFFERED=1 + volumes: + - ../results:/app/results:ro + restart: unless-stopped + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:8080/api/health"] + interval: 30s + timeout: 10s + retries: 3 + start_period: 10s diff --git a/ai-projects/quantum-ml/production/requirements.txt b/ai-projects/quantum-ml/production/requirements.txt index a23f70fab..571c93d6f 100644 --- a/ai-projects/quantum-ml/production/requirements.txt +++ b/ai-projects/quantum-ml/production/requirements.txt @@ -1,19 +1,19 @@ -# Production API Dependencies -flask==3.1.3 -flask-cors==6.0.2 -numpy==1.26.4 -torch==2.8.0 -scikit-learn==1.6.1 -joblib==1.4.2 - -# Quantum ML (from parent) -pennylane==0.39.0 -pennylane-lightning==0.39.0 - -# Optional: Production servers -gunicorn==23.0.0 # For production WSGI server -gevent==24.11.1 # For async workers - -# Monitoring and logging -python-json-logger==2.0.7 -prometheus-client==0.21.1 +# Production API Dependencies +flask==3.1.3 +flask-cors==6.0.2 +numpy==1.26.4 +torch==2.8.0 +scikit-learn==1.6.1 +joblib==1.4.2 + +# Quantum ML (from parent) +pennylane==0.39.0 +pennylane-lightning==0.39.0 + +# Optional: Production servers +gunicorn==23.0.0 # For production WSGI server +gevent==24.11.1 # For async workers + +# Monitoring and logging +python-json-logger==2.0.7 +prometheus-client==0.21.1 diff --git a/ai-projects/quantum-ml/production/test_api.py b/ai-projects/quantum-ml/production/test_api.py index c7011516f..896d26a9d 100644 --- a/ai-projects/quantum-ml/production/test_api.py +++ b/ai-projects/quantum-ml/production/test_api.py @@ -1,314 +1,314 @@ -""" -Comprehensive test suite for the production API. - -Usage: - python test_api.py - -Author: Quantum AI System -Date: November 16, 2025 -""" - -import json -import time -from datetime import datetime - -import pytest -import requests - -BASE_URL = "http://localhost:8080" -# Use timeout for all requests to prevent hanging -REQUEST_TIMEOUT = 30 # seconds - - -def _api_available() -> bool: - try: - response = requests.get(f"{BASE_URL}/api/health", timeout=2) - return response.ok - except requests.exceptions.RequestException: - return False - - -pytestmark = pytest.mark.skipif( - not _api_available(), - reason="Quantum production API not running on localhost:8080", -) - - -def test_health(): - """Test health endpoint""" - print("\n" + "=" * 70) - print("TEST 1: Health Check") - print("=" * 70) - - response = requests.get(f"{BASE_URL}/api/health", timeout=REQUEST_TIMEOUT) - print(f"Status Code: {response.status_code}") - print(f"Response: {json.dumps(response.json(), indent=2)}") - - assert response.status_code == 200 - assert response.json()["status"] == "healthy" - print("✅ PASSED") - - -def test_model_info(): - """Test model info endpoint""" - print("\n" + "=" * 70) - print("TEST 2: Model Info") - print("=" * 70) - - response = requests.get(f"{BASE_URL}/api/model_info", timeout=REQUEST_TIMEOUT) - print(f"Status Code: {response.status_code}") - print(f"Response: {json.dumps(response.json(), indent=2)}") - - assert response.status_code == 200 - assert "architecture" in response.json() - print("✅ PASSED") - - -def test_single_prediction_genuine(): - """Test single prediction - genuine banknote""" - print("\n" + "=" * 70) - print("TEST 3: Single Prediction (Genuine)") - print("=" * 70) - - # Example genuine banknote features - data = {"features": [3.6216, 8.6661, -2.8073, -0.44699]} - - print(f"Input: {json.dumps(data, indent=2)}") - - response = requests.post( - f"{BASE_URL}/api/predict", - json=data, - headers={"Content-Type": "application/json"}, - timeout=REQUEST_TIMEOUT, - ) - - print(f"Status Code: {response.status_code}") - print(f"Response: {json.dumps(response.json(), indent=2)}") - - assert response.status_code == 200 - result = response.json() - assert "prediction" in result - assert "confidence" in result - assert result["confidence"] > 0.5 - print("✅ PASSED") - - -def test_single_prediction_forged(): - """Test single prediction - forged banknote""" - print("\n" + "=" * 70) - print("TEST 4: Single Prediction (Forged)") - print("=" * 70) - - # Example forged banknote features - data = {"features": [-4.5459, 8.1674, -2.4586, -1.4621]} - - print(f"Input: {json.dumps(data, indent=2)}") - - response = requests.post( - f"{BASE_URL}/api/predict", - json=data, - headers={"Content-Type": "application/json"}, - timeout=REQUEST_TIMEOUT, - ) - - print(f"Status Code: {response.status_code}") - print(f"Response: {json.dumps(response.json(), indent=2)}") - - assert response.status_code == 200 - result = response.json() - assert "prediction" in result - assert "confidence" in result - print("✅ PASSED") - - -def test_batch_prediction(): - """Test batch prediction""" - print("\n" + "=" * 70) - print("TEST 5: Batch Prediction") - print("=" * 70) - - data = { - "batch": [ - {"features": [3.6216, 8.6661, -2.8073, -0.44699]}, # Genuine - {"features": [-4.5459, 8.1674, -2.4586, -1.4621]}, # Forged - {"features": [2.5, 1.2, -0.8, 0.3]}, # Test - ] - } - - print(f"Input: {len(data['batch'])} banknotes") - - response = requests.post( - f"{BASE_URL}/api/predict_batch", - json=data, - headers={"Content-Type": "application/json"}, - timeout=REQUEST_TIMEOUT, - ) - - print(f"Status Code: {response.status_code}") - print(f"Response: {json.dumps(response.json(), indent=2)}") - - assert response.status_code == 200 - result = response.json() - assert "results" in result - assert "summary" in result - assert result["summary"]["total_processed"] == 3 - print("✅ PASSED") - - -def test_invalid_input(): - """Test error handling with invalid input""" - print("\n" + "=" * 70) - print("TEST 6: Invalid Input Handling") - print("=" * 70) - - # Missing features field - data = {"wrong_field": [1, 2, 3, 4]} - - print(f"Input: {json.dumps(data, indent=2)}") - - response = requests.post( - f"{BASE_URL}/api/predict", - json=data, - headers={"Content-Type": "application/json"}, - timeout=REQUEST_TIMEOUT, - ) - - print(f"Status Code: {response.status_code}") - print(f"Response: {json.dumps(response.json(), indent=2)}") - - assert response.status_code == 400 - assert "error" in response.json() - print("✅ PASSED") - - -def test_wrong_feature_count(): - """Test error handling with wrong feature count""" - print("\n" + "=" * 70) - print("TEST 7: Wrong Feature Count") - print("=" * 70) - - # Only 3 features instead of 4 - data = {"features": [1.0, 2.0, 3.0]} - - print(f"Input: {json.dumps(data, indent=2)}") - - response = requests.post( - f"{BASE_URL}/api/predict", - json=data, - headers={"Content-Type": "application/json"}, - timeout=REQUEST_TIMEOUT, - ) - - print(f"Status Code: {response.status_code}") - print(f"Response: {json.dumps(response.json(), indent=2)}") - - assert response.status_code == 400 - assert "error" in response.json() - print("✅ PASSED") - - -def test_performance(): - """Test API performance""" - print("\n" + "=" * 70) - print("TEST 8: Performance Benchmark") - print("=" * 70) - - data = {"features": [3.6216, 8.6661, -2.8073, -0.44699]} - - n_requests = 10 - times = [] - - print(f"Making {n_requests} requests...") - - for i in range(n_requests): - start = time.time() - response = requests.post( - f"{BASE_URL}/api/predict", - json=data, - headers={"Content-Type": "application/json"}, - timeout=REQUEST_TIMEOUT, - ) - elapsed = time.time() - start - times.append(elapsed * 1000) # Convert to ms - - assert response.status_code == 200 - print(f" Request {i+1}/{n_requests}: {elapsed*1000:.2f}ms", end="\r") - - avg_time = sum(times) / len(times) - min_time = min(times) - max_time = max(times) - - print("\n\nPerformance Results:") - print(f" Average: {avg_time:.2f}ms") - print(f" Min: {min_time:.2f}ms") - print(f" Max: {max_time:.2f}ms") - - assert avg_time < 200 # Should be under 200ms - print("✅ PASSED") - - -def main(): - """Run all tests""" - print("=" * 70) - print(" BANKNOTE FRAUD DETECTOR API - TEST SUITE") - print("=" * 70) - print(f"\nTarget: {BASE_URL}") - print(f"Time: {datetime.now().isoformat()}") - - # Check if API is running - try: - response = requests.get(f"{BASE_URL}/api/health", timeout=REQUEST_TIMEOUT) - if response.status_code != 200: - print("\n❌ API is not responding correctly") - print(" Make sure the API is running:") - print(" python banknote_api.py") - return - except requests.exceptions.RequestException: - print("\n❌ Cannot connect to API") - print(" Make sure the API is running:") - print(" python banknote_api.py") - return - - # Run tests - tests = [ - test_health, - test_model_info, - test_single_prediction_genuine, - test_single_prediction_forged, - test_batch_prediction, - test_invalid_input, - test_wrong_feature_count, - test_performance, - ] - - passed = 0 - failed = 0 - - for test in tests: - try: - test() - passed += 1 - except Exception as e: - print(f"❌ FAILED: {e}") - failed += 1 - - # Summary - print("\n" + "=" * 70) - print(" TEST SUMMARY") - print("=" * 70) - print(f"\nTotal Tests: {len(tests)}") - print(f"✅ Passed: {passed}") - print(f"❌ Failed: {failed}") - - if failed == 0: - print("\n🎉 ALL TESTS PASSED!") - print("✅ API is production-ready!") - else: - print(f"\n⚠️ {failed} test(s) failed") - print(" Review the output above for details") - - print("=" * 70) - - -if __name__ == "__main__": - main() +""" +Comprehensive test suite for the production API. + +Usage: + python test_api.py + +Author: Quantum AI System +Date: November 16, 2025 +""" + +import json +import time +from datetime import datetime + +import pytest +import requests + +BASE_URL = "http://localhost:8080" +# Use timeout for all requests to prevent hanging +REQUEST_TIMEOUT = 30 # seconds + + +def _api_available() -> bool: + try: + response = requests.get(f"{BASE_URL}/api/health", timeout=2) + return response.ok + except requests.exceptions.RequestException: + return False + + +pytestmark = pytest.mark.skipif( + not _api_available(), + reason="Quantum production API not running on localhost:8080", +) + + +def test_health(): + """Test health endpoint""" + print("\n" + "=" * 70) + print("TEST 1: Health Check") + print("=" * 70) + + response = requests.get(f"{BASE_URL}/api/health", timeout=REQUEST_TIMEOUT) + print(f"Status Code: {response.status_code}") + print(f"Response: {json.dumps(response.json(), indent=2)}") + + assert response.status_code == 200 + assert response.json()["status"] == "healthy" + print("✅ PASSED") + + +def test_model_info(): + """Test model info endpoint""" + print("\n" + "=" * 70) + print("TEST 2: Model Info") + print("=" * 70) + + response = requests.get(f"{BASE_URL}/api/model_info", timeout=REQUEST_TIMEOUT) + print(f"Status Code: {response.status_code}") + print(f"Response: {json.dumps(response.json(), indent=2)}") + + assert response.status_code == 200 + assert "architecture" in response.json() + print("✅ PASSED") + + +def test_single_prediction_genuine(): + """Test single prediction - genuine banknote""" + print("\n" + "=" * 70) + print("TEST 3: Single Prediction (Genuine)") + print("=" * 70) + + # Example genuine banknote features + data = {"features": [3.6216, 8.6661, -2.8073, -0.44699]} + + print(f"Input: {json.dumps(data, indent=2)}") + + response = requests.post( + f"{BASE_URL}/api/predict", + json=data, + headers={"Content-Type": "application/json"}, + timeout=REQUEST_TIMEOUT, + ) + + print(f"Status Code: {response.status_code}") + print(f"Response: {json.dumps(response.json(), indent=2)}") + + assert response.status_code == 200 + result = response.json() + assert "prediction" in result + assert "confidence" in result + assert result["confidence"] > 0.5 + print("✅ PASSED") + + +def test_single_prediction_forged(): + """Test single prediction - forged banknote""" + print("\n" + "=" * 70) + print("TEST 4: Single Prediction (Forged)") + print("=" * 70) + + # Example forged banknote features + data = {"features": [-4.5459, 8.1674, -2.4586, -1.4621]} + + print(f"Input: {json.dumps(data, indent=2)}") + + response = requests.post( + f"{BASE_URL}/api/predict", + json=data, + headers={"Content-Type": "application/json"}, + timeout=REQUEST_TIMEOUT, + ) + + print(f"Status Code: {response.status_code}") + print(f"Response: {json.dumps(response.json(), indent=2)}") + + assert response.status_code == 200 + result = response.json() + assert "prediction" in result + assert "confidence" in result + print("✅ PASSED") + + +def test_batch_prediction(): + """Test batch prediction""" + print("\n" + "=" * 70) + print("TEST 5: Batch Prediction") + print("=" * 70) + + data = { + "batch": [ + {"features": [3.6216, 8.6661, -2.8073, -0.44699]}, # Genuine + {"features": [-4.5459, 8.1674, -2.4586, -1.4621]}, # Forged + {"features": [2.5, 1.2, -0.8, 0.3]}, # Test + ] + } + + print(f"Input: {len(data['batch'])} banknotes") + + response = requests.post( + f"{BASE_URL}/api/predict_batch", + json=data, + headers={"Content-Type": "application/json"}, + timeout=REQUEST_TIMEOUT, + ) + + print(f"Status Code: {response.status_code}") + print(f"Response: {json.dumps(response.json(), indent=2)}") + + assert response.status_code == 200 + result = response.json() + assert "results" in result + assert "summary" in result + assert result["summary"]["total_processed"] == 3 + print("✅ PASSED") + + +def test_invalid_input(): + """Test error handling with invalid input""" + print("\n" + "=" * 70) + print("TEST 6: Invalid Input Handling") + print("=" * 70) + + # Missing features field + data = {"wrong_field": [1, 2, 3, 4]} + + print(f"Input: {json.dumps(data, indent=2)}") + + response = requests.post( + f"{BASE_URL}/api/predict", + json=data, + headers={"Content-Type": "application/json"}, + timeout=REQUEST_TIMEOUT, + ) + + print(f"Status Code: {response.status_code}") + print(f"Response: {json.dumps(response.json(), indent=2)}") + + assert response.status_code == 400 + assert "error" in response.json() + print("✅ PASSED") + + +def test_wrong_feature_count(): + """Test error handling with wrong feature count""" + print("\n" + "=" * 70) + print("TEST 7: Wrong Feature Count") + print("=" * 70) + + # Only 3 features instead of 4 + data = {"features": [1.0, 2.0, 3.0]} + + print(f"Input: {json.dumps(data, indent=2)}") + + response = requests.post( + f"{BASE_URL}/api/predict", + json=data, + headers={"Content-Type": "application/json"}, + timeout=REQUEST_TIMEOUT, + ) + + print(f"Status Code: {response.status_code}") + print(f"Response: {json.dumps(response.json(), indent=2)}") + + assert response.status_code == 400 + assert "error" in response.json() + print("✅ PASSED") + + +def test_performance(): + """Test API performance""" + print("\n" + "=" * 70) + print("TEST 8: Performance Benchmark") + print("=" * 70) + + data = {"features": [3.6216, 8.6661, -2.8073, -0.44699]} + + n_requests = 10 + times = [] + + print(f"Making {n_requests} requests...") + + for i in range(n_requests): + start = time.time() + response = requests.post( + f"{BASE_URL}/api/predict", + json=data, + headers={"Content-Type": "application/json"}, + timeout=REQUEST_TIMEOUT, + ) + elapsed = time.time() - start + times.append(elapsed * 1000) # Convert to ms + + assert response.status_code == 200 + print(f" Request {i+1}/{n_requests}: {elapsed*1000:.2f}ms", end="\r") + + avg_time = sum(times) / len(times) + min_time = min(times) + max_time = max(times) + + print("\n\nPerformance Results:") + print(f" Average: {avg_time:.2f}ms") + print(f" Min: {min_time:.2f}ms") + print(f" Max: {max_time:.2f}ms") + + assert avg_time < 200 # Should be under 200ms + print("✅ PASSED") + + +def main(): + """Run all tests""" + print("=" * 70) + print(" BANKNOTE FRAUD DETECTOR API - TEST SUITE") + print("=" * 70) + print(f"\nTarget: {BASE_URL}") + print(f"Time: {datetime.now().isoformat()}") + + # Check if API is running + try: + response = requests.get(f"{BASE_URL}/api/health", timeout=REQUEST_TIMEOUT) + if response.status_code != 200: + print("\n❌ API is not responding correctly") + print(" Make sure the API is running:") + print(" python banknote_api.py") + return + except requests.exceptions.RequestException: + print("\n❌ Cannot connect to API") + print(" Make sure the API is running:") + print(" python banknote_api.py") + return + + # Run tests + tests = [ + test_health, + test_model_info, + test_single_prediction_genuine, + test_single_prediction_forged, + test_batch_prediction, + test_invalid_input, + test_wrong_feature_count, + test_performance, + ] + + passed = 0 + failed = 0 + + for test in tests: + try: + test() + passed += 1 + except Exception as e: + print(f"❌ FAILED: {e}") + failed += 1 + + # Summary + print("\n" + "=" * 70) + print(" TEST SUMMARY") + print("=" * 70) + print(f"\nTotal Tests: {len(tests)}") + print(f"✅ Passed: {passed}") + print(f"❌ Failed: {failed}") + + if failed == 0: + print("\n🎉 ALL TESTS PASSED!") + print("✅ API is production-ready!") + else: + print(f"\n⚠️ {failed} test(s) failed") + print(" Review the output above for details") + + print("=" * 70) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/quantum_llm_integration.py b/ai-projects/quantum-ml/quantum_llm_integration.py index 8651b4349..bad667aab 100644 --- a/ai-projects/quantum-ml/quantum_llm_integration.py +++ b/ai-projects/quantum-ml/quantum_llm_integration.py @@ -1,402 +1,402 @@ -""" -Quantum LLM Integration Guide -============================== - -This script shows how to integrate the new advanced quantum LLM components -with the existing quantum_llm_trainer.py system. - -Usage: - python quantum_llm_integration.py --config config/quantum_llm_config.yaml --mode upgrade - python quantum_llm_integration.py --mode compare - python quantum_llm_integration.py --mode migrate - -Author: Quantum AI Workspace -Date: March 9, 2026 -""" - -import argparse -import json -import logging -import sys -from pathlib import Path - -# Add paths for existing modules -sys.path.insert(0, str(Path(__file__).parent / "src")) - -logger = logging.getLogger(__name__) - - -def compare_implementations(): - """ - Compare old and new quantum LLM implementations. - """ - logger.info("=" * 80) - logger.info("IMPLEMENTATION COMPARISON") - logger.info("=" * 80) - - comparison = { - "features": { - "Basic Quantum Layers": { - "old": "✅ quantum_transformer.py", - "new": "✅ quantum_llm_integrated.py", - }, - "Multi-Scale Attention": { - "old": "❌ Not available", - "new": "✅ quantum_llm_advanced.py", - }, - "Circuit Caching": { - "old": "❌ Not available", - "new": "✅ quantum_llm_advanced.py", - }, - "Adaptive Entanglement": { - "old": "❌ Not available", - "new": "✅ quantum_llm_advanced.py", - }, - "Circuit Optimization": { - "old": "❌ Not available", - "new": "✅ quantum_circuit_optimizer.py", - }, - "Curriculum Learning": { - "old": "❌ Manual stages", - "new": "✅ quantum_llm_hybrid_trainer.py", - }, - "Real-time Monitoring": { - "old": "❌ Basic logging", - "new": "✅ quantum_llm_monitor.py", - }, - "Error Mitigation": { - "old": "❌ Not available", - "new": "✅ quantum_llm_advanced.py", - }, - "Prompt Tuning": { - "old": "❌ Not available", - "new": "✅ quantum_llm_advanced.py", - }, - "Auto Dataset Loading": { - "old": "⚠️ Manual only", - "new": "✅ quantum_llm_datasets.py", - }, - }, - "training_approach": { - "old": { - "name": "quantum_llm_trainer.py", - "style": "Single-stage training", - "quantum_integration": "Immediate full quantum", - "optimization": "None", - "monitoring": "Basic loss logging", - }, - "new": { - "name": "quantum_llm_integrated.py", - "style": "Multi-stage curriculum", - "quantum_integration": "Progressive (0% → 30% → 70%)", - "optimization": "Multi-level circuit optimization", - "monitoring": "Real-time dashboard with alerts", - }, - }, - "file_structure": { - "old": [ - "scripts/quantum_llm_trainer.py (200 lines)", - "ai-projects/quantum-ml/src/quantum_transformer.py (200 lines)", - "config/quantum_llm_config.yaml (100 lines)", - ], - "new": [ - "ai-projects/quantum-ml/src/quantum_llm_advanced.py (600 lines)", - "ai-projects/quantum-ml/src/quantum_circuit_optimizer.py (500 lines)", - "ai-projects/quantum-ml/src/quantum_llm_hybrid_trainer.py (550 lines)", - "ai-projects/quantum-ml/src/quantum_llm_monitor.py (550 lines)", - "ai-projects/quantum-ml/src/quantum_llm_integrated.py (550 lines)", - "ai-projects/quantum-ml/src/quantum_llm_datasets.py (500 lines)", - "ai-projects/quantum-ml/quantum_llm_quickstart.py (400 lines)", - "ai-projects/quantum-ml/QUANTUM_LLM_README.md (comprehensive docs)", - ], - }, - "advantages_new": [ - "2-5x speedup from circuit caching", - "15-20% better accuracy from multi-scale attention", - "30-40% circuit depth reduction from adaptive entanglement", - "25% better final loss from curriculum learning", - "Complete real-time monitoring and alerts", - "Auto-format dataset loading", - "Production-ready error handling", - "Comprehensive configuration system", - "Easy-to-use quick start examples", - ], - "migration_path": { - "step_1": "Test new system with quantum_llm_quickstart.py --mode quick", - "step_2": "Compare outputs on same dataset", - "step_3": "Migrate config to new format (quantum_llm_config_example.yaml)", - "step_4": "Run full training with new system", - "step_5": "Validate performance improvements", - "step_6": "Update production workflows", - }, - } - - # Print comparison - logger.info("\n📊 FEATURES COMPARISON:\n") - for feature, status in comparison["features"].items(): - logger.info(f" {feature}:") - logger.info(f" Old: {status['old']}") - logger.info(f" New: {status['new']}") - - logger.info("\n🎓 TRAINING APPROACH:\n") - logger.info(" Old System:") - for key, value in comparison["training_approach"]["old"].items(): - logger.info(f" {key}: {value}") - logger.info("\n New System:") - for key, value in comparison["training_approach"]["new"].items(): - logger.info(f" {key}: {value}") - - logger.info("\n✨ KEY ADVANTAGES OF NEW SYSTEM:\n") - for advantage in comparison["advantages_new"]: - logger.info(f" • {advantage}") - - logger.info("\n🔄 MIGRATION PATH:\n") - for step, description in comparison["migration_path"].items(): - logger.info(f" {step.replace('_', ' ').title()}: {description}") - - logger.info("=" * 80) - - # Save comparison report - report_path = Path("data_out/quantum_llm_comparison_report.json") - report_path.parent.mkdir(parents=True, exist_ok=True) - with open(report_path, "w") as f: - json.dump(comparison, f, indent=2) - logger.info(f"\n📁 Comparison report saved: {report_path}") - - return comparison - - -def upgrade_existing_system(): - """ - Guide for upgrading from old to new quantum LLM system. - """ - logger.info("=" * 80) - logger.info("UPGRADE GUIDE - OLD TO NEW QUANTUM LLM") - logger.info("=" * 80) - - upgrade_steps = [ - { - "step": 1, - "title": "Backup Existing System", - "commands": [ - "cp scripts/quantum_llm_trainer.py scripts/quantum_llm_trainer_backup.py", - "cp config/quantum_llm_config.yaml config/quantum_llm_config_old.yaml", - ], - "description": "Preserve existing implementation", - }, - { - "step": 2, - "title": "Install New Components", - "commands": [ - "# All new files already in ai-projects/quantum-ml/", - "ls ai-projects/quantum-ml/src/quantum_llm_*.py", - ], - "description": "Verify new files are present", - }, - { - "step": 3, - "title": "Test New System", - "commands": [ - "cd ai-projects/quantum-ml", - "python quantum_llm_quickstart.py --mode quick", - ], - "description": "Run quick test (2 epochs, ~5 minutes)", - }, - { - "step": 4, - "title": "Migrate Configuration", - "commands": [ - "# Copy old config as reference", - "cp config/quantum_llm_config.yaml config/my_quantum_config.yaml", - "# Update to new format using example", - "# Reference: config/quantum_llm_config_example.yaml", - ], - "description": "Update config to new comprehensive format", - }, - { - "step": 5, - "title": "Compare Results", - "commands": [ - "# Train with old system", - "python scripts/quantum_llm_trainer.py --passive", - "# Train with new system", - "python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode full", - ], - "description": "Compare accuracy and training time", - }, - { - "step": 6, - "title": "Production Deployment", - "commands": [ - "# Use new system as primary", - "alias quantum-train='python ai-projects/quantum-ml/quantum_llm_quickstart.py'", - "# Keep old system for reference", - "# mv scripts/quantum_llm_trainer.py scripts/legacy/", - ], - "description": "Adopt new system for production use", - }, - ] - - for step_info in upgrade_steps: - logger.info(f"\n📌 Step {step_info['step']}: {step_info['title']}") - logger.info(f" {step_info['description']}\n") - for command in step_info["commands"]: - logger.info(f" $ {command}") - - logger.info("\n" + "=" * 80) - logger.info("UPGRADE RECOMMENDATION: ✅ PROCEED") - logger.info("=" * 80) - logger.info("\nThe new system provides:") - logger.info(" • Significant performance improvements (2-5x with caching)") - logger.info(" • Better accuracy (15-20% perplexity reduction)") - logger.info(" • More stable training (curriculum learning)") - logger.info(" • Complete monitoring and debugging tools") - logger.info(" • Production-ready features") - logger.info("\nRecommendation: Migrate to new system for all future work.") - logger.info(" Keep old system for reference/comparison.") - logger.info("=" * 80) - - -def create_migration_checklist(): - """ - Create a migration checklist document. - """ - checklist = """ -# Quantum LLM Migration Checklist - -## Pre-Migration - -- [ ] Review QUANTUM_LLM_README.md -- [ ] Understand new features and capabilities -- [ ] Backup existing quantum_llm_trainer.py -- [ ] Backup existing config files -- [ ] Document current performance metrics - -## Testing Phase - -- [ ] Run new system quick test: `python quantum_llm_quickstart.py --mode quick` -- [ ] Verify all components load successfully -- [ ] Check output directory structure -- [ ] Review generated dashboard and reports -- [ ] Compare against performance expectations - -## Configuration Migration - -- [ ] Copy quantum_llm_config_example.yaml to my_config.yaml -- [ ] Transfer existing hyperparameters -- [ ] Enable desired new features: - - [ ] Multi-scale attention - - [ ] Circuit caching - - [ ] Adaptive entanglement - - [ ] Curriculum learning - - [ ] Real-time monitoring -- [ ] Configure output directories -- [ ] Set up alert thresholds - -## Validation - -- [ ] Run small-scale comparison (old vs new on same data) -- [ ] Verify accuracy improvements -- [ ] Measure training time differences -- [ ] Check memory usage -- [ ] Validate checkpoint compatibility - -## Full Migration - -- [ ] Run full training with new system -- [ ] Monitor training dashboard -- [ ] Review alert system -- [ ] Validate final model performance -- [ ] Export visualizations - -## Integration - -- [ ] Update training scripts to use new system -- [ ] Update documentation references -- [ ] Train team on new features -- [ ] Archive old implementation -- [ ] Update CI/CD pipelines - -## Post-Migration - -- [ ] Monitor production training runs -- [ ] Collect performance metrics -- [ ] Fine-tune configuration based on results -- [ ] Document lessons learned -- [ ] Share results with team - -## Rollback Plan (if needed) - -- [ ] Restore quantum_llm_trainer_backup.py -- [ ] Restore config/quantum_llm_config_old.yaml -- [ ] Document issues encountered -- [ ] Report problems for resolution - -## Success Criteria - -- [ ] Training completes successfully -- [ ] Accuracy meets or exceeds old system -- [ ] Training time acceptable -- [ ] Monitoring provides useful insights -- [ ] Team comfortable with new workflow - -## Notes - -Date Started: _______________ -Completed: _______________ -Issues Encountered: _______________ -Performance Comparison: _______________ - ---- - -For questions or issues, refer to: -- QUANTUM_LLM_README.md (comprehensive guide) -- quantum_llm_quickstart.py --help (usage examples) -- QUANTUM_LLM_DEVELOPMENT_SUMMARY.md (technical details) -""" - - checklist_path = Path("docs/QUANTUM_LLM_MIGRATION_CHECKLIST.md") - with open(checklist_path, "w") as f: - f.write(checklist) - - logger.info(f"✅ Migration checklist created: {checklist_path}") - return checklist_path - - -def main(): - """Main entry point.""" - parser = argparse.ArgumentParser( - description="Quantum LLM Integration & Migration Guide" - ) - parser.add_argument( - "--mode", - type=str, - choices=["compare", "upgrade", "migrate"], - default="compare", - help="Operation mode", - ) - - args = parser.parse_args() - - # Setup logging - logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" - ) - - # Execute based on mode - if args.mode == "compare": - comparison = compare_implementations() - - elif args.mode == "upgrade": - upgrade_existing_system() - - elif args.mode == "migrate": - logger.info("Creating migration materials...") - upgrade_existing_system() - checklist_path = create_migration_checklist() - logger.info(f"\n✅ Migration checklist: {checklist_path}") - logger.info("\nFollow the checklist to complete migration.") - - -if __name__ == "__main__": - main() +""" +Quantum LLM Integration Guide +============================== + +This script shows how to integrate the new advanced quantum LLM components +with the existing quantum_llm_trainer.py system. + +Usage: + python quantum_llm_integration.py --config config/quantum_llm_config.yaml --mode upgrade + python quantum_llm_integration.py --mode compare + python quantum_llm_integration.py --mode migrate + +Author: Quantum AI Workspace +Date: March 9, 2026 +""" + +import argparse +import json +import logging +import sys +from pathlib import Path + +# Add paths for existing modules +sys.path.insert(0, str(Path(__file__).parent / "src")) + +logger = logging.getLogger(__name__) + + +def compare_implementations(): + """ + Compare old and new quantum LLM implementations. + """ + logger.info("=" * 80) + logger.info("IMPLEMENTATION COMPARISON") + logger.info("=" * 80) + + comparison = { + "features": { + "Basic Quantum Layers": { + "old": "✅ quantum_transformer.py", + "new": "✅ quantum_llm_integrated.py", + }, + "Multi-Scale Attention": { + "old": "❌ Not available", + "new": "✅ quantum_llm_advanced.py", + }, + "Circuit Caching": { + "old": "❌ Not available", + "new": "✅ quantum_llm_advanced.py", + }, + "Adaptive Entanglement": { + "old": "❌ Not available", + "new": "✅ quantum_llm_advanced.py", + }, + "Circuit Optimization": { + "old": "❌ Not available", + "new": "✅ quantum_circuit_optimizer.py", + }, + "Curriculum Learning": { + "old": "❌ Manual stages", + "new": "✅ quantum_llm_hybrid_trainer.py", + }, + "Real-time Monitoring": { + "old": "❌ Basic logging", + "new": "✅ quantum_llm_monitor.py", + }, + "Error Mitigation": { + "old": "❌ Not available", + "new": "✅ quantum_llm_advanced.py", + }, + "Prompt Tuning": { + "old": "❌ Not available", + "new": "✅ quantum_llm_advanced.py", + }, + "Auto Dataset Loading": { + "old": "⚠️ Manual only", + "new": "✅ quantum_llm_datasets.py", + }, + }, + "training_approach": { + "old": { + "name": "quantum_llm_trainer.py", + "style": "Single-stage training", + "quantum_integration": "Immediate full quantum", + "optimization": "None", + "monitoring": "Basic loss logging", + }, + "new": { + "name": "quantum_llm_integrated.py", + "style": "Multi-stage curriculum", + "quantum_integration": "Progressive (0% → 30% → 70%)", + "optimization": "Multi-level circuit optimization", + "monitoring": "Real-time dashboard with alerts", + }, + }, + "file_structure": { + "old": [ + "scripts/quantum_llm_trainer.py (200 lines)", + "ai-projects/quantum-ml/src/quantum_transformer.py (200 lines)", + "config/quantum_llm_config.yaml (100 lines)", + ], + "new": [ + "ai-projects/quantum-ml/src/quantum_llm_advanced.py (600 lines)", + "ai-projects/quantum-ml/src/quantum_circuit_optimizer.py (500 lines)", + "ai-projects/quantum-ml/src/quantum_llm_hybrid_trainer.py (550 lines)", + "ai-projects/quantum-ml/src/quantum_llm_monitor.py (550 lines)", + "ai-projects/quantum-ml/src/quantum_llm_integrated.py (550 lines)", + "ai-projects/quantum-ml/src/quantum_llm_datasets.py (500 lines)", + "ai-projects/quantum-ml/quantum_llm_quickstart.py (400 lines)", + "ai-projects/quantum-ml/QUANTUM_LLM_README.md (comprehensive docs)", + ], + }, + "advantages_new": [ + "2-5x speedup from circuit caching", + "15-20% better accuracy from multi-scale attention", + "30-40% circuit depth reduction from adaptive entanglement", + "25% better final loss from curriculum learning", + "Complete real-time monitoring and alerts", + "Auto-format dataset loading", + "Production-ready error handling", + "Comprehensive configuration system", + "Easy-to-use quick start examples", + ], + "migration_path": { + "step_1": "Test new system with quantum_llm_quickstart.py --mode quick", + "step_2": "Compare outputs on same dataset", + "step_3": "Migrate config to new format (quantum_llm_config_example.yaml)", + "step_4": "Run full training with new system", + "step_5": "Validate performance improvements", + "step_6": "Update production workflows", + }, + } + + # Print comparison + logger.info("\n📊 FEATURES COMPARISON:\n") + for feature, status in comparison["features"].items(): + logger.info(f" {feature}:") + logger.info(f" Old: {status['old']}") + logger.info(f" New: {status['new']}") + + logger.info("\n🎓 TRAINING APPROACH:\n") + logger.info(" Old System:") + for key, value in comparison["training_approach"]["old"].items(): + logger.info(f" {key}: {value}") + logger.info("\n New System:") + for key, value in comparison["training_approach"]["new"].items(): + logger.info(f" {key}: {value}") + + logger.info("\n✨ KEY ADVANTAGES OF NEW SYSTEM:\n") + for advantage in comparison["advantages_new"]: + logger.info(f" • {advantage}") + + logger.info("\n🔄 MIGRATION PATH:\n") + for step, description in comparison["migration_path"].items(): + logger.info(f" {step.replace('_', ' ').title()}: {description}") + + logger.info("=" * 80) + + # Save comparison report + report_path = Path("data_out/quantum_llm_comparison_report.json") + report_path.parent.mkdir(parents=True, exist_ok=True) + with open(report_path, "w") as f: + json.dump(comparison, f, indent=2) + logger.info(f"\n📁 Comparison report saved: {report_path}") + + return comparison + + +def upgrade_existing_system(): + """ + Guide for upgrading from old to new quantum LLM system. + """ + logger.info("=" * 80) + logger.info("UPGRADE GUIDE - OLD TO NEW QUANTUM LLM") + logger.info("=" * 80) + + upgrade_steps = [ + { + "step": 1, + "title": "Backup Existing System", + "commands": [ + "cp scripts/quantum_llm_trainer.py scripts/quantum_llm_trainer_backup.py", + "cp config/quantum_llm_config.yaml config/quantum_llm_config_old.yaml", + ], + "description": "Preserve existing implementation", + }, + { + "step": 2, + "title": "Install New Components", + "commands": [ + "# All new files already in ai-projects/quantum-ml/", + "ls ai-projects/quantum-ml/src/quantum_llm_*.py", + ], + "description": "Verify new files are present", + }, + { + "step": 3, + "title": "Test New System", + "commands": [ + "cd ai-projects/quantum-ml", + "python quantum_llm_quickstart.py --mode quick", + ], + "description": "Run quick test (2 epochs, ~5 minutes)", + }, + { + "step": 4, + "title": "Migrate Configuration", + "commands": [ + "# Copy old config as reference", + "cp config/quantum_llm_config.yaml config/my_quantum_config.yaml", + "# Update to new format using example", + "# Reference: config/quantum_llm_config_example.yaml", + ], + "description": "Update config to new comprehensive format", + }, + { + "step": 5, + "title": "Compare Results", + "commands": [ + "# Train with old system", + "python scripts/quantum_llm_trainer.py --passive", + "# Train with new system", + "python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode full", + ], + "description": "Compare accuracy and training time", + }, + { + "step": 6, + "title": "Production Deployment", + "commands": [ + "# Use new system as primary", + "alias quantum-train='python ai-projects/quantum-ml/quantum_llm_quickstart.py'", + "# Keep old system for reference", + "# mv scripts/quantum_llm_trainer.py scripts/legacy/", + ], + "description": "Adopt new system for production use", + }, + ] + + for step_info in upgrade_steps: + logger.info(f"\n📌 Step {step_info['step']}: {step_info['title']}") + logger.info(f" {step_info['description']}\n") + for command in step_info["commands"]: + logger.info(f" $ {command}") + + logger.info("\n" + "=" * 80) + logger.info("UPGRADE RECOMMENDATION: ✅ PROCEED") + logger.info("=" * 80) + logger.info("\nThe new system provides:") + logger.info(" • Significant performance improvements (2-5x with caching)") + logger.info(" • Better accuracy (15-20% perplexity reduction)") + logger.info(" • More stable training (curriculum learning)") + logger.info(" • Complete monitoring and debugging tools") + logger.info(" • Production-ready features") + logger.info("\nRecommendation: Migrate to new system for all future work.") + logger.info(" Keep old system for reference/comparison.") + logger.info("=" * 80) + + +def create_migration_checklist(): + """ + Create a migration checklist document. + """ + checklist = """ +# Quantum LLM Migration Checklist + +## Pre-Migration + +- [ ] Review QUANTUM_LLM_README.md +- [ ] Understand new features and capabilities +- [ ] Backup existing quantum_llm_trainer.py +- [ ] Backup existing config files +- [ ] Document current performance metrics + +## Testing Phase + +- [ ] Run new system quick test: `python quantum_llm_quickstart.py --mode quick` +- [ ] Verify all components load successfully +- [ ] Check output directory structure +- [ ] Review generated dashboard and reports +- [ ] Compare against performance expectations + +## Configuration Migration + +- [ ] Copy quantum_llm_config_example.yaml to my_config.yaml +- [ ] Transfer existing hyperparameters +- [ ] Enable desired new features: + - [ ] Multi-scale attention + - [ ] Circuit caching + - [ ] Adaptive entanglement + - [ ] Curriculum learning + - [ ] Real-time monitoring +- [ ] Configure output directories +- [ ] Set up alert thresholds + +## Validation + +- [ ] Run small-scale comparison (old vs new on same data) +- [ ] Verify accuracy improvements +- [ ] Measure training time differences +- [ ] Check memory usage +- [ ] Validate checkpoint compatibility + +## Full Migration + +- [ ] Run full training with new system +- [ ] Monitor training dashboard +- [ ] Review alert system +- [ ] Validate final model performance +- [ ] Export visualizations + +## Integration + +- [ ] Update training scripts to use new system +- [ ] Update documentation references +- [ ] Train team on new features +- [ ] Archive old implementation +- [ ] Update CI/CD pipelines + +## Post-Migration + +- [ ] Monitor production training runs +- [ ] Collect performance metrics +- [ ] Fine-tune configuration based on results +- [ ] Document lessons learned +- [ ] Share results with team + +## Rollback Plan (if needed) + +- [ ] Restore quantum_llm_trainer_backup.py +- [ ] Restore config/quantum_llm_config_old.yaml +- [ ] Document issues encountered +- [ ] Report problems for resolution + +## Success Criteria + +- [ ] Training completes successfully +- [ ] Accuracy meets or exceeds old system +- [ ] Training time acceptable +- [ ] Monitoring provides useful insights +- [ ] Team comfortable with new workflow + +## Notes + +Date Started: _______________ +Completed: _______________ +Issues Encountered: _______________ +Performance Comparison: _______________ + +--- + +For questions or issues, refer to: +- QUANTUM_LLM_README.md (comprehensive guide) +- quantum_llm_quickstart.py --help (usage examples) +- QUANTUM_LLM_DEVELOPMENT_SUMMARY.md (technical details) +""" + + checklist_path = Path("docs/QUANTUM_LLM_MIGRATION_CHECKLIST.md") + with open(checklist_path, "w") as f: + f.write(checklist) + + logger.info(f"✅ Migration checklist created: {checklist_path}") + return checklist_path + + +def main(): + """Main entry point.""" + parser = argparse.ArgumentParser( + description="Quantum LLM Integration & Migration Guide" + ) + parser.add_argument( + "--mode", + type=str, + choices=["compare", "upgrade", "migrate"], + default="compare", + help="Operation mode", + ) + + args = parser.parse_args() + + # Setup logging + logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" + ) + + # Execute based on mode + if args.mode == "compare": + comparison = compare_implementations() + + elif args.mode == "upgrade": + upgrade_existing_system() + + elif args.mode == "migrate": + logger.info("Creating migration materials...") + upgrade_existing_system() + checklist_path = create_migration_checklist() + logger.info(f"\n✅ Migration checklist: {checklist_path}") + logger.info("\nFollow the checklist to complete migration.") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/quantum_llm_quickstart.py b/ai-projects/quantum-ml/quantum_llm_quickstart.py index 11a1c74c3..13a59d2db 100644 --- a/ai-projects/quantum-ml/quantum_llm_quickstart.py +++ b/ai-projects/quantum-ml/quantum_llm_quickstart.py @@ -1,608 +1,608 @@ -""" -Quantum LLM Quick Start Example -================================ - -Complete example demonstrating quantum-enhanced LLM training. - -Usage: - python quantum_llm_quickstart.py --mode quick - python quantum_llm_quickstart.py --mode full --config my_config.yaml - python quantum_llm_quickstart.py --mode monitor - python quantum_llm_quickstart.py --mode generate --prompt "Hello" - -Author: Quantum AI Workspace -Date: March 9, 2026 -""" - -from __future__ import annotations - -import argparse -import logging -import sys -import time -from pathlib import Path -from typing import Any, Optional - -# Lazily initialized runtime dependencies. This keeps `--help` fast and avoids -# importing heavy ML modules unless an execution mode actually needs them. -torch = None # type: ignore[assignment] -TORCH_AVAILABLE: Optional[bool] = None -TORCH_IMPORT_ERROR: Optional[Exception] = None - -# Placeholders for lazily imported runtime classes/functions. These are updated -# by `_load_runtime_dependencies()` before any training/generation path uses -# them, while still allowing `--help` and monitor mode to avoid heavy imports. -IntegratedQuantumLLM: Any = None -QuantumLLMConfig: Any = None -QuantumLLMSystem: Any = None -CharacterTokenizer: Any = None -DatasetBuilder: Any = None -TextDataset: Any = None -create_train_val_split: Any = None - -# Ensure src modules are importable when running from repository root. -SRC_DIR = Path(__file__).resolve().parent / "src" -if str(SRC_DIR) not in sys.path: - sys.path.insert(0, str(SRC_DIR)) - -# Lazily imported quantum LLM components. -INTEGRATED_AVAILABLE: Optional[bool] = None -DATASETS_AVAILABLE: Optional[bool] = None -INTEGRATED_IMPORT_ERROR: Optional[Exception] = None -DATASETS_IMPORT_ERROR: Optional[Exception] = None - - -def _load_runtime_dependencies( - *, load_torch: bool = True, load_integrated: bool = True, load_datasets: bool = True -) -> None: - """Load heavy runtime dependencies on-demand.""" - global torch - global TORCH_AVAILABLE, TORCH_IMPORT_ERROR - global INTEGRATED_AVAILABLE, INTEGRATED_IMPORT_ERROR - global DATASETS_AVAILABLE, DATASETS_IMPORT_ERROR - - if load_torch and TORCH_AVAILABLE is None: - try: - import torch as _torch - - torch = _torch # type: ignore[assignment] - TORCH_AVAILABLE = True - TORCH_IMPORT_ERROR = None - except ModuleNotFoundError as exc: - torch = None # type: ignore[assignment] - TORCH_AVAILABLE = False - TORCH_IMPORT_ERROR = exc - - if load_integrated and INTEGRATED_AVAILABLE is None: - try: - from src.quantum_llm_integrated import (IntegratedQuantumLLM, - QuantumLLMConfig, - QuantumLLMSystem) - - globals().update( - { - "IntegratedQuantumLLM": IntegratedQuantumLLM, - "QuantumLLMConfig": QuantumLLMConfig, - "QuantumLLMSystem": QuantumLLMSystem, - } - ) - INTEGRATED_AVAILABLE = True - INTEGRATED_IMPORT_ERROR = None - except ImportError as exc: - INTEGRATED_AVAILABLE = False - INTEGRATED_IMPORT_ERROR = exc - - if load_datasets and DATASETS_AVAILABLE is None: - try: - from src.quantum_llm_datasets import (CharacterTokenizer, - DatasetBuilder, TextDataset, - create_train_val_split) - - globals().update( - { - "CharacterTokenizer": CharacterTokenizer, - "DatasetBuilder": DatasetBuilder, - "TextDataset": TextDataset, - "create_train_val_split": create_train_val_split, - } - ) - DATASETS_AVAILABLE = True - DATASETS_IMPORT_ERROR = None - except ImportError as exc: - DATASETS_AVAILABLE = False - DATASETS_IMPORT_ERROR = exc - - -logger = logging.getLogger(__name__) - -DEFAULT_QUANTUM_QUICKSTART_OUTPUT_DIR = Path("data_out/quantum_llm_quickstart") -DEFAULT_QUANTUM_FULL_OUTPUT_DIR = Path("data_out/quantum_llm_full") - - -def _resolve_generate_model_path( - model_path: Optional[str], output_dir: Optional[str] -) -> Optional[Path]: - """Resolve model path for generate mode. - - Priority: - 1) Explicit --model path (must exist) - 2) Auto-detected checkpoints under --output-dir (or default dirs) - - Returns: - Path to a discovered model checkpoint, or None if not found. - """ - if model_path: - explicit_path = Path(model_path).expanduser() - return explicit_path if explicit_path.exists() else None - - candidate_roots: list[Path] = [] - if output_dir: - candidate_roots.append(Path(output_dir)) - - candidate_roots.extend( - [DEFAULT_QUANTUM_QUICKSTART_OUTPUT_DIR, DEFAULT_QUANTUM_FULL_OUTPUT_DIR] - ) - - candidate_relative_paths = [ - Path("final_model.pt"), - Path("model.pt"), - Path("best_model.pt"), - Path("training") / "final_model.pt", - ] - - for root in candidate_roots: - for rel in candidate_relative_paths: - candidate = root / rel - if candidate.exists(): - return candidate - - return None - - -def _ensure_torch_available(context: str) -> bool: - """Ensure torch is available before running training/inference paths.""" - _load_runtime_dependencies( - load_torch=True, load_integrated=False, load_datasets=False - ) - - if TORCH_AVAILABLE: - return True - - logger.error("❌ PyTorch is required for %s mode", context) - if TORCH_IMPORT_ERROR is not None: - logger.error(" Import error: %s", TORCH_IMPORT_ERROR) - logger.error(" Install dependency: pip install torch") - return False - - -def quick_start_example(): - """ - Quick start: Minimal quantum LLM training example. - """ - logger.info("=" * 80) - logger.info("QUANTUM LLM QUICK START") - logger.info("=" * 80) - - if not _ensure_torch_available("quick"): - return - - if not INTEGRATED_AVAILABLE or not DATASETS_AVAILABLE: - logger.error("Required components not available") - logger.error( - f"Integrated: {INTEGRATED_AVAILABLE}, Datasets: {DATASETS_AVAILABLE}" - ) - return - - # Step 1: Create configuration - logger.info("Step 1: Creating configuration...") - config = QuantumLLMConfig() - config.config.update( - { - "vocab_size": 256, - "d_model": 64, - "n_heads": 2, - "n_layers": 2, - "n_qubits": 2, - "max_seq_length": 128, - "batch_size": 4, - "num_epochs": 2, - "learning_rate": 1e-3, - "output_dir": "data_out/quantum_llm_quickstart", - "enable_curriculum": True, - "enable_dashboard": True, - } - ) - - # Step 2: Create tokenizer - logger.info("Step 2: Creating tokenizer...") - tokenizer = CharacterTokenizer(vocab_size=config["vocab_size"]) - - # Step 3: Create sample dataset - logger.info("Step 3: Creating sample dataset...") - sample_texts = [ - "Quantum computing harnesses quantum mechanics to process information.", - "Machine learning algorithms learn patterns from data.", - "Language models understand and generate natural language text.", - "Quantum circuits use qubits and quantum gates for computation.", - "Neural networks consist of interconnected layers of artificial neurons.", - "Superposition allows quantum bits to exist in multiple states.", - "Training deep learning models requires large datasets and computing power.", - "Entanglement creates correlations between quantum particles.", - "Natural language processing enables computers to understand human language.", - "Quantum advantage emerges when quantum algorithms outperform classical ones.", - ] * 10 # Repeat for more training data - - dataset = TextDataset( - texts=sample_texts, - tokenizer=tokenizer, - max_seq_length=config["max_seq_length"], - stride=64, - ) - - train_dataset, val_dataset = create_train_val_split(dataset, val_ratio=0.1) - logger.info(f"Train size: {len(train_dataset)}, Val size: {len(val_dataset)}") - - # Step 4: Initialize quantum LLM system - logger.info("Step 4: Initializing quantum LLM system...") - device = "cuda" if torch.cuda.is_available() else "cpu" - system = QuantumLLMSystem(config, device=device) - - # Step 5: Start training - logger.info("Step 5: Starting training...") - logger.info( - f"Model parameters: {sum(p.numel() for p in system.model.parameters()):,}" - ) - - start_time = time.time() - system.train(train_dataset, val_dataset) - training_time = time.time() - start_time - - # Step 6: Results - logger.info("=" * 80) - logger.info("TRAINING COMPLETE") - logger.info("=" * 80) - logger.info(f"Training time: {training_time:.2f}s") - logger.info(f"Output directory: {system.output_dir}") - - # Step 7: Generate report - logger.info("Step 7: Generating final report...") - system.generate_report() - - logger.info("✅ Quick start complete!") - logger.info(f"📁 Check results in: {system.output_dir}") - - return system, tokenizer - - -def full_training_example(config_path: Optional[Path] = None): - """ - Full training: Complete quantum LLM training with all features. - """ - logger.info("=" * 80) - logger.info("QUANTUM LLM FULL TRAINING") - logger.info("=" * 80) - - if not _ensure_torch_available("full"): - return - - if not INTEGRATED_AVAILABLE or not DATASETS_AVAILABLE: - logger.error("Required components not available") - return - - # Load configuration - if config_path and Path(config_path).exists(): - config = QuantumLLMConfig(config_path) - logger.info(f"Configuration loaded from: {config_path}") - else: - # Use default configuration - config = QuantumLLMConfig() - config.config.update( - { - "vocab_size": 256, - "d_model": 128, - "n_heads": 4, - "n_layers": 4, - "n_qubits": 4, - "max_seq_length": 512, - "batch_size": 16, - "num_epochs": 10, - "learning_rate": 1e-4, - "output_dir": "data_out/quantum_llm_full", - "enable_multi_scale_attention": True, - "enable_adaptive_entanglement": True, - "enable_circuit_caching": True, - "enable_curriculum": True, - "enable_dashboard": True, - "optimization_level": 2, - } - ) - - # Create tokenizer - tokenizer = CharacterTokenizer(vocab_size=config["vocab_size"]) - - # Load datasets - # Try to find datasets in the workspace - datasets_dir = Path("datasets") - available_datasets = [] - - if datasets_dir.exists(): - for category in ["chat", "quantum", "massive_quantum"]: - category_dir = datasets_dir / category - if category_dir.exists(): - for file in category_dir.glob("*.json"): - available_datasets.append(file) - - if available_datasets: - logger.info(f"Found {len(available_datasets)} dataset files") - # Use first available dataset - dataset = DatasetBuilder.auto_detect_and_load( - available_datasets[0], - tokenizer, - config["max_seq_length"], - ) - else: - logger.info("No datasets found, using sample data") - sample_texts = [ - "The quantum language model combines quantum computing with natural language processing.", - "Training involves curriculum learning with progressive quantum integration.", - "Circuit optimization reduces execution time through caching and compilation.", - "Multi-scale attention captures information at different granularities.", - "Adaptive entanglement routing selects optimal quantum circuit topology.", - ] * 50 - - dataset = TextDataset( - texts=sample_texts, - tokenizer=tokenizer, - max_seq_length=config["max_seq_length"], - ) - - train_dataset, val_dataset = create_train_val_split(dataset, val_ratio=0.1) - - # Initialize system - device = "cuda" if torch.cuda.is_available() else "cpu" - system = QuantumLLMSystem(config, device=device) - - # Save configuration - config_save_path = Path(config["output_dir"]) / "config.yaml" - config.save(config_save_path) - - # Train - logger.info("Starting full training pipeline...") - system.train(train_dataset, val_dataset) - - # Generate comprehensive report - system.generate_report() - - logger.info("✅ Full training complete!") - logger.info(f"📁 Results: {system.output_dir}") - - return system, tokenizer - - -def monitor_training(output_dir: Path): - """ - Monitor an ongoing or completed training session. - """ - logger.info("=" * 80) - logger.info("QUANTUM LLM TRAINING MONITOR") - logger.info("=" * 80) - - output_dir = Path(output_dir) - - # Check for dashboard data - dashboard_path = output_dir / "dashboard" / "dashboard.json" - if dashboard_path.exists(): - import json - - with open(dashboard_path) as f: - dashboard_data = json.load(f) - - logger.info("Dashboard Data:") - logger.info(f" Timestamp: {dashboard_data.get('timestamp', 'N/A')}") - - metrics = dashboard_data.get("metrics_summary", {}) - logger.info(f" Loss (MA): {metrics.get('moving_avg_loss', 'N/A'):.4f}") - logger.info( - f" Perplexity (MA): {metrics.get('moving_avg_perplexity', 'N/A'):.2f}" - ) - logger.info(f" Loss Trend: {metrics.get('loss_trend', 'N/A')}") - - alerts = dashboard_data.get("recent_alerts", []) - logger.info(f" Recent Alerts: {len(alerts)}") - else: - logger.warning(f"Dashboard not found: {dashboard_path}") - - # Check for training report - report_path = output_dir / "training" / "training_report.json" - if report_path.exists(): - import json - - with open(report_path) as f: - report = json.load(f) - - logger.info("Training Report:") - logger.info(f" Total Stages: {report.get('total_stages', 'N/A')}") - logger.info(f" Total Time: {report.get('total_time', 'N/A'):.2f}s") - logger.info(f" Best Loss: {report.get('best_loss', 'N/A'):.4f}") - else: - logger.warning(f"Training report not found: {report_path}") - - logger.info("=" * 80) - - -def generate_text( - model_path: Path, tokenizer: CharacterTokenizer, prompt: str, max_length: int = 100 -): - """ - Generate text using trained quantum LLM. - """ - logger.info("=" * 80) - logger.info("QUANTUM LLM TEXT GENERATION") - logger.info("=" * 80) - - if not _ensure_torch_available("generate"): - return - - if not INTEGRATED_AVAILABLE: - logger.error("Integrated components not available") - return - - # Load model - checkpoint = torch.load(model_path, map_location="cpu") - config = QuantumLLMConfig() - config.config = checkpoint["config"] - - device = "cuda" if torch.cuda.is_available() else "cpu" - model = IntegratedQuantumLLM(config).to(device) - model.load_state_dict(checkpoint["model_state_dict"]) - model.eval() - - logger.info(f"Model loaded from: {model_path}") - logger.info(f"Prompt: {prompt}") - - # Encode prompt - input_ids = ( - torch.tensor(tokenizer.encode(prompt, add_special_tokens=False)) - .unsqueeze(0) - .to(device) - ) - - # Generate - generated_ids = input_ids[0].tolist() - - with torch.no_grad(): - for _ in range(max_length): - # Forward pass - outputs = model(input_ids) - - # Get next token - next_token_logits = outputs[0, -1, :] - next_token_id = torch.argmax(next_token_logits).item() - - generated_ids.append(next_token_id) - - # Update input - input_ids = torch.tensor([generated_ids]).to(device) - - # Stop at EOS - if next_token_id == tokenizer.eos_id: - break - - # Decode - generated_text = tokenizer.decode(generated_ids) - - logger.info("=" * 80) - logger.info("GENERATED TEXT:") - logger.info("=" * 80) - logger.info(generated_text) - logger.info("=" * 80) - - return generated_text - - -def main(): - """Main entry point.""" - parser = argparse.ArgumentParser(description="Quantum LLM Quick Start Examples") - parser.add_argument( - "--mode", - type=str, - choices=["quick", "full", "monitor", "generate"], - default="quick", - help="Execution mode", - ) - parser.add_argument("--config", type=str, help="Path to configuration YAML") - parser.add_argument( - "--output-dir", - type=str, - help="Output directory to monitor (and auto-discover model checkpoints for generate mode)", - ) - parser.add_argument( - "--model", - type=str, - help="Path to trained model (optional in generate mode if checkpoint auto-discovery succeeds)", - ) - parser.add_argument("--prompt", type=str, default="Hello", help="Generation prompt") - parser.add_argument( - "--max-length", type=int, default=100, help="Max generation length" - ) - - args = parser.parse_args() - - # Setup logging - logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", - ) - - # Check dependencies by mode. Monitoring can run without model dependencies. - if args.mode in {"quick", "full", "generate"}: - if not _ensure_torch_available(args.mode): - return - - _load_runtime_dependencies( - load_torch=True, load_integrated=True, load_datasets=True - ) - - if not INTEGRATED_AVAILABLE: - logger.error("❌ Integrated components not available") - logger.error(" Make sure quantum_llm_integrated.py is importable") - if INTEGRATED_IMPORT_ERROR is not None: - logger.error(" Import error: %s", INTEGRATED_IMPORT_ERROR) - return - - if not DATASETS_AVAILABLE: - logger.error("❌ Dataset utilities not available") - logger.error(" Make sure quantum_llm_datasets.py is importable") - if DATASETS_IMPORT_ERROR is not None: - logger.error(" Import error: %s", DATASETS_IMPORT_ERROR) - return - - # Execute requested mode - if args.mode == "quick": - system, tokenizer = quick_start_example() - - elif args.mode == "full": - config_path = Path(args.config) if args.config else None - system, tokenizer = full_training_example(config_path) - - elif args.mode == "monitor": - output_dir = ( - Path(args.output_dir) - if args.output_dir - else Path("data_out/quantum_llm_quickstart") - ) - monitor_training(output_dir) - - elif args.mode == "generate": - if args.max_length <= 0: - logger.error("--max-length must be > 0") - return - - resolved_model_path = _resolve_generate_model_path(args.model, args.output_dir) - if resolved_model_path is None: - logger.error("No model checkpoint found for generate mode") - logger.error( - "Provide --model or ensure one of these files exists under --output-dir (or defaults): " - "final_model.pt, model.pt, best_model.pt, training/final_model.pt" - ) - logger.error( - "Tip: run quick training first: python quantum_llm_quickstart.py --mode quick" - ) - return - - if not args.model: - logger.info(f"Auto-selected model checkpoint: {resolved_model_path}") - - # Need tokenizer - tokenizer = CharacterTokenizer(vocab_size=256) - - generate_text( - model_path=resolved_model_path, - tokenizer=tokenizer, - prompt=args.prompt, - max_length=args.max_length, - ) - - -if __name__ == "__main__": - main() +""" +Quantum LLM Quick Start Example +================================ + +Complete example demonstrating quantum-enhanced LLM training. + +Usage: + python quantum_llm_quickstart.py --mode quick + python quantum_llm_quickstart.py --mode full --config my_config.yaml + python quantum_llm_quickstart.py --mode monitor + python quantum_llm_quickstart.py --mode generate --prompt "Hello" + +Author: Quantum AI Workspace +Date: March 9, 2026 +""" + +from __future__ import annotations + +import argparse +import logging +import sys +import time +from pathlib import Path +from typing import Any, Optional + +# Lazily initialized runtime dependencies. This keeps `--help` fast and avoids +# importing heavy ML modules unless an execution mode actually needs them. +torch = None # type: ignore[assignment] +TORCH_AVAILABLE: Optional[bool] = None +TORCH_IMPORT_ERROR: Optional[Exception] = None + +# Placeholders for lazily imported runtime classes/functions. These are updated +# by `_load_runtime_dependencies()` before any training/generation path uses +# them, while still allowing `--help` and monitor mode to avoid heavy imports. +IntegratedQuantumLLM: Any = None +QuantumLLMConfig: Any = None +QuantumLLMSystem: Any = None +CharacterTokenizer: Any = None +DatasetBuilder: Any = None +TextDataset: Any = None +create_train_val_split: Any = None + +# Ensure src modules are importable when running from repository root. +SRC_DIR = Path(__file__).resolve().parent / "src" +if str(SRC_DIR) not in sys.path: + sys.path.insert(0, str(SRC_DIR)) + +# Lazily imported quantum LLM components. +INTEGRATED_AVAILABLE: Optional[bool] = None +DATASETS_AVAILABLE: Optional[bool] = None +INTEGRATED_IMPORT_ERROR: Optional[Exception] = None +DATASETS_IMPORT_ERROR: Optional[Exception] = None + + +def _load_runtime_dependencies( + *, load_torch: bool = True, load_integrated: bool = True, load_datasets: bool = True +) -> None: + """Load heavy runtime dependencies on-demand.""" + global torch + global TORCH_AVAILABLE, TORCH_IMPORT_ERROR + global INTEGRATED_AVAILABLE, INTEGRATED_IMPORT_ERROR + global DATASETS_AVAILABLE, DATASETS_IMPORT_ERROR + + if load_torch and TORCH_AVAILABLE is None: + try: + import torch as _torch + + torch = _torch # type: ignore[assignment] + TORCH_AVAILABLE = True + TORCH_IMPORT_ERROR = None + except ModuleNotFoundError as exc: + torch = None # type: ignore[assignment] + TORCH_AVAILABLE = False + TORCH_IMPORT_ERROR = exc + + if load_integrated and INTEGRATED_AVAILABLE is None: + try: + from src.quantum_llm_integrated import (IntegratedQuantumLLM, + QuantumLLMConfig, + QuantumLLMSystem) + + globals().update( + { + "IntegratedQuantumLLM": IntegratedQuantumLLM, + "QuantumLLMConfig": QuantumLLMConfig, + "QuantumLLMSystem": QuantumLLMSystem, + } + ) + INTEGRATED_AVAILABLE = True + INTEGRATED_IMPORT_ERROR = None + except ImportError as exc: + INTEGRATED_AVAILABLE = False + INTEGRATED_IMPORT_ERROR = exc + + if load_datasets and DATASETS_AVAILABLE is None: + try: + from src.quantum_llm_datasets import (CharacterTokenizer, + DatasetBuilder, TextDataset, + create_train_val_split) + + globals().update( + { + "CharacterTokenizer": CharacterTokenizer, + "DatasetBuilder": DatasetBuilder, + "TextDataset": TextDataset, + "create_train_val_split": create_train_val_split, + } + ) + DATASETS_AVAILABLE = True + DATASETS_IMPORT_ERROR = None + except ImportError as exc: + DATASETS_AVAILABLE = False + DATASETS_IMPORT_ERROR = exc + + +logger = logging.getLogger(__name__) + +DEFAULT_QUANTUM_QUICKSTART_OUTPUT_DIR = Path("data_out/quantum_llm_quickstart") +DEFAULT_QUANTUM_FULL_OUTPUT_DIR = Path("data_out/quantum_llm_full") + + +def _resolve_generate_model_path( + model_path: Optional[str], output_dir: Optional[str] +) -> Optional[Path]: + """Resolve model path for generate mode. + + Priority: + 1) Explicit --model path (must exist) + 2) Auto-detected checkpoints under --output-dir (or default dirs) + + Returns: + Path to a discovered model checkpoint, or None if not found. + """ + if model_path: + explicit_path = Path(model_path).expanduser() + return explicit_path if explicit_path.exists() else None + + candidate_roots: list[Path] = [] + if output_dir: + candidate_roots.append(Path(output_dir)) + + candidate_roots.extend( + [DEFAULT_QUANTUM_QUICKSTART_OUTPUT_DIR, DEFAULT_QUANTUM_FULL_OUTPUT_DIR] + ) + + candidate_relative_paths = [ + Path("final_model.pt"), + Path("model.pt"), + Path("best_model.pt"), + Path("training") / "final_model.pt", + ] + + for root in candidate_roots: + for rel in candidate_relative_paths: + candidate = root / rel + if candidate.exists(): + return candidate + + return None + + +def _ensure_torch_available(context: str) -> bool: + """Ensure torch is available before running training/inference paths.""" + _load_runtime_dependencies( + load_torch=True, load_integrated=False, load_datasets=False + ) + + if TORCH_AVAILABLE: + return True + + logger.error("❌ PyTorch is required for %s mode", context) + if TORCH_IMPORT_ERROR is not None: + logger.error(" Import error: %s", TORCH_IMPORT_ERROR) + logger.error(" Install dependency: pip install torch") + return False + + +def quick_start_example(): + """ + Quick start: Minimal quantum LLM training example. + """ + logger.info("=" * 80) + logger.info("QUANTUM LLM QUICK START") + logger.info("=" * 80) + + if not _ensure_torch_available("quick"): + return + + if not INTEGRATED_AVAILABLE or not DATASETS_AVAILABLE: + logger.error("Required components not available") + logger.error( + f"Integrated: {INTEGRATED_AVAILABLE}, Datasets: {DATASETS_AVAILABLE}" + ) + return + + # Step 1: Create configuration + logger.info("Step 1: Creating configuration...") + config = QuantumLLMConfig() + config.config.update( + { + "vocab_size": 256, + "d_model": 64, + "n_heads": 2, + "n_layers": 2, + "n_qubits": 2, + "max_seq_length": 128, + "batch_size": 4, + "num_epochs": 2, + "learning_rate": 1e-3, + "output_dir": "data_out/quantum_llm_quickstart", + "enable_curriculum": True, + "enable_dashboard": True, + } + ) + + # Step 2: Create tokenizer + logger.info("Step 2: Creating tokenizer...") + tokenizer = CharacterTokenizer(vocab_size=config["vocab_size"]) + + # Step 3: Create sample dataset + logger.info("Step 3: Creating sample dataset...") + sample_texts = [ + "Quantum computing harnesses quantum mechanics to process information.", + "Machine learning algorithms learn patterns from data.", + "Language models understand and generate natural language text.", + "Quantum circuits use qubits and quantum gates for computation.", + "Neural networks consist of interconnected layers of artificial neurons.", + "Superposition allows quantum bits to exist in multiple states.", + "Training deep learning models requires large datasets and computing power.", + "Entanglement creates correlations between quantum particles.", + "Natural language processing enables computers to understand human language.", + "Quantum advantage emerges when quantum algorithms outperform classical ones.", + ] * 10 # Repeat for more training data + + dataset = TextDataset( + texts=sample_texts, + tokenizer=tokenizer, + max_seq_length=config["max_seq_length"], + stride=64, + ) + + train_dataset, val_dataset = create_train_val_split(dataset, val_ratio=0.1) + logger.info(f"Train size: {len(train_dataset)}, Val size: {len(val_dataset)}") + + # Step 4: Initialize quantum LLM system + logger.info("Step 4: Initializing quantum LLM system...") + device = "cuda" if torch.cuda.is_available() else "cpu" + system = QuantumLLMSystem(config, device=device) + + # Step 5: Start training + logger.info("Step 5: Starting training...") + logger.info( + f"Model parameters: {sum(p.numel() for p in system.model.parameters()):,}" + ) + + start_time = time.time() + system.train(train_dataset, val_dataset) + training_time = time.time() - start_time + + # Step 6: Results + logger.info("=" * 80) + logger.info("TRAINING COMPLETE") + logger.info("=" * 80) + logger.info(f"Training time: {training_time:.2f}s") + logger.info(f"Output directory: {system.output_dir}") + + # Step 7: Generate report + logger.info("Step 7: Generating final report...") + system.generate_report() + + logger.info("✅ Quick start complete!") + logger.info(f"📁 Check results in: {system.output_dir}") + + return system, tokenizer + + +def full_training_example(config_path: Optional[Path] = None): + """ + Full training: Complete quantum LLM training with all features. + """ + logger.info("=" * 80) + logger.info("QUANTUM LLM FULL TRAINING") + logger.info("=" * 80) + + if not _ensure_torch_available("full"): + return + + if not INTEGRATED_AVAILABLE or not DATASETS_AVAILABLE: + logger.error("Required components not available") + return + + # Load configuration + if config_path and Path(config_path).exists(): + config = QuantumLLMConfig(config_path) + logger.info(f"Configuration loaded from: {config_path}") + else: + # Use default configuration + config = QuantumLLMConfig() + config.config.update( + { + "vocab_size": 256, + "d_model": 128, + "n_heads": 4, + "n_layers": 4, + "n_qubits": 4, + "max_seq_length": 512, + "batch_size": 16, + "num_epochs": 10, + "learning_rate": 1e-4, + "output_dir": "data_out/quantum_llm_full", + "enable_multi_scale_attention": True, + "enable_adaptive_entanglement": True, + "enable_circuit_caching": True, + "enable_curriculum": True, + "enable_dashboard": True, + "optimization_level": 2, + } + ) + + # Create tokenizer + tokenizer = CharacterTokenizer(vocab_size=config["vocab_size"]) + + # Load datasets + # Try to find datasets in the workspace + datasets_dir = Path("datasets") + available_datasets = [] + + if datasets_dir.exists(): + for category in ["chat", "quantum", "massive_quantum"]: + category_dir = datasets_dir / category + if category_dir.exists(): + for file in category_dir.glob("*.json"): + available_datasets.append(file) + + if available_datasets: + logger.info(f"Found {len(available_datasets)} dataset files") + # Use first available dataset + dataset = DatasetBuilder.auto_detect_and_load( + available_datasets[0], + tokenizer, + config["max_seq_length"], + ) + else: + logger.info("No datasets found, using sample data") + sample_texts = [ + "The quantum language model combines quantum computing with natural language processing.", + "Training involves curriculum learning with progressive quantum integration.", + "Circuit optimization reduces execution time through caching and compilation.", + "Multi-scale attention captures information at different granularities.", + "Adaptive entanglement routing selects optimal quantum circuit topology.", + ] * 50 + + dataset = TextDataset( + texts=sample_texts, + tokenizer=tokenizer, + max_seq_length=config["max_seq_length"], + ) + + train_dataset, val_dataset = create_train_val_split(dataset, val_ratio=0.1) + + # Initialize system + device = "cuda" if torch.cuda.is_available() else "cpu" + system = QuantumLLMSystem(config, device=device) + + # Save configuration + config_save_path = Path(config["output_dir"]) / "config.yaml" + config.save(config_save_path) + + # Train + logger.info("Starting full training pipeline...") + system.train(train_dataset, val_dataset) + + # Generate comprehensive report + system.generate_report() + + logger.info("✅ Full training complete!") + logger.info(f"📁 Results: {system.output_dir}") + + return system, tokenizer + + +def monitor_training(output_dir: Path): + """ + Monitor an ongoing or completed training session. + """ + logger.info("=" * 80) + logger.info("QUANTUM LLM TRAINING MONITOR") + logger.info("=" * 80) + + output_dir = Path(output_dir) + + # Check for dashboard data + dashboard_path = output_dir / "dashboard" / "dashboard.json" + if dashboard_path.exists(): + import json + + with open(dashboard_path) as f: + dashboard_data = json.load(f) + + logger.info("Dashboard Data:") + logger.info(f" Timestamp: {dashboard_data.get('timestamp', 'N/A')}") + + metrics = dashboard_data.get("metrics_summary", {}) + logger.info(f" Loss (MA): {metrics.get('moving_avg_loss', 'N/A'):.4f}") + logger.info( + f" Perplexity (MA): {metrics.get('moving_avg_perplexity', 'N/A'):.2f}" + ) + logger.info(f" Loss Trend: {metrics.get('loss_trend', 'N/A')}") + + alerts = dashboard_data.get("recent_alerts", []) + logger.info(f" Recent Alerts: {len(alerts)}") + else: + logger.warning(f"Dashboard not found: {dashboard_path}") + + # Check for training report + report_path = output_dir / "training" / "training_report.json" + if report_path.exists(): + import json + + with open(report_path) as f: + report = json.load(f) + + logger.info("Training Report:") + logger.info(f" Total Stages: {report.get('total_stages', 'N/A')}") + logger.info(f" Total Time: {report.get('total_time', 'N/A'):.2f}s") + logger.info(f" Best Loss: {report.get('best_loss', 'N/A'):.4f}") + else: + logger.warning(f"Training report not found: {report_path}") + + logger.info("=" * 80) + + +def generate_text( + model_path: Path, tokenizer: CharacterTokenizer, prompt: str, max_length: int = 100 +): + """ + Generate text using trained quantum LLM. + """ + logger.info("=" * 80) + logger.info("QUANTUM LLM TEXT GENERATION") + logger.info("=" * 80) + + if not _ensure_torch_available("generate"): + return + + if not INTEGRATED_AVAILABLE: + logger.error("Integrated components not available") + return + + # Load model + checkpoint = torch.load(model_path, map_location="cpu") + config = QuantumLLMConfig() + config.config = checkpoint["config"] + + device = "cuda" if torch.cuda.is_available() else "cpu" + model = IntegratedQuantumLLM(config).to(device) + model.load_state_dict(checkpoint["model_state_dict"]) + model.eval() + + logger.info(f"Model loaded from: {model_path}") + logger.info(f"Prompt: {prompt}") + + # Encode prompt + input_ids = ( + torch.tensor(tokenizer.encode(prompt, add_special_tokens=False)) + .unsqueeze(0) + .to(device) + ) + + # Generate + generated_ids = input_ids[0].tolist() + + with torch.no_grad(): + for _ in range(max_length): + # Forward pass + outputs = model(input_ids) + + # Get next token + next_token_logits = outputs[0, -1, :] + next_token_id = torch.argmax(next_token_logits).item() + + generated_ids.append(next_token_id) + + # Update input + input_ids = torch.tensor([generated_ids]).to(device) + + # Stop at EOS + if next_token_id == tokenizer.eos_id: + break + + # Decode + generated_text = tokenizer.decode(generated_ids) + + logger.info("=" * 80) + logger.info("GENERATED TEXT:") + logger.info("=" * 80) + logger.info(generated_text) + logger.info("=" * 80) + + return generated_text + + +def main(): + """Main entry point.""" + parser = argparse.ArgumentParser(description="Quantum LLM Quick Start Examples") + parser.add_argument( + "--mode", + type=str, + choices=["quick", "full", "monitor", "generate"], + default="quick", + help="Execution mode", + ) + parser.add_argument("--config", type=str, help="Path to configuration YAML") + parser.add_argument( + "--output-dir", + type=str, + help="Output directory to monitor (and auto-discover model checkpoints for generate mode)", + ) + parser.add_argument( + "--model", + type=str, + help="Path to trained model (optional in generate mode if checkpoint auto-discovery succeeds)", + ) + parser.add_argument("--prompt", type=str, default="Hello", help="Generation prompt") + parser.add_argument( + "--max-length", type=int, default=100, help="Max generation length" + ) + + args = parser.parse_args() + + # Setup logging + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + ) + + # Check dependencies by mode. Monitoring can run without model dependencies. + if args.mode in {"quick", "full", "generate"}: + if not _ensure_torch_available(args.mode): + return + + _load_runtime_dependencies( + load_torch=True, load_integrated=True, load_datasets=True + ) + + if not INTEGRATED_AVAILABLE: + logger.error("❌ Integrated components not available") + logger.error(" Make sure quantum_llm_integrated.py is importable") + if INTEGRATED_IMPORT_ERROR is not None: + logger.error(" Import error: %s", INTEGRATED_IMPORT_ERROR) + return + + if not DATASETS_AVAILABLE: + logger.error("❌ Dataset utilities not available") + logger.error(" Make sure quantum_llm_datasets.py is importable") + if DATASETS_IMPORT_ERROR is not None: + logger.error(" Import error: %s", DATASETS_IMPORT_ERROR) + return + + # Execute requested mode + if args.mode == "quick": + system, tokenizer = quick_start_example() + + elif args.mode == "full": + config_path = Path(args.config) if args.config else None + system, tokenizer = full_training_example(config_path) + + elif args.mode == "monitor": + output_dir = ( + Path(args.output_dir) + if args.output_dir + else Path("data_out/quantum_llm_quickstart") + ) + monitor_training(output_dir) + + elif args.mode == "generate": + if args.max_length <= 0: + logger.error("--max-length must be > 0") + return + + resolved_model_path = _resolve_generate_model_path(args.model, args.output_dir) + if resolved_model_path is None: + logger.error("No model checkpoint found for generate mode") + logger.error( + "Provide --model or ensure one of these files exists under --output-dir (or defaults): " + "final_model.pt, model.pt, best_model.pt, training/final_model.pt" + ) + logger.error( + "Tip: run quick training first: python quantum_llm_quickstart.py --mode quick" + ) + return + + if not args.model: + logger.info(f"Auto-selected model checkpoint: {resolved_model_path}") + + # Need tokenizer + tokenizer = CharacterTokenizer(vocab_size=256) + + generate_text( + model_path=resolved_model_path, + tokenizer=tokenizer, + prompt=args.prompt, + max_length=args.max_length, + ) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/quantum_mcp_server.py b/ai-projects/quantum-ml/quantum_mcp_server.py index 5a059b06d..f0a59996a 100644 --- a/ai-projects/quantum-ml/quantum_mcp_server.py +++ b/ai-projects/quantum-ml/quantum_mcp_server.py @@ -1,1296 +1,1296 @@ -""" -Quantum AI MCP Server (Optimized) -Exposes quantum computing and quantum machine learning capabilities via Model Context Protocol -""" - -import asyncio -import logging -import re -# Add src directory to path before importing local modules -import sys -import time -from collections import OrderedDict -from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor -from pathlib import Path -from typing import Any, Dict, List, Optional - -import numpy as np -from qiskit import QuantumCircuit - -src_path = Path(__file__).parent / "src" -if str(src_path) not in sys.path: - sys.path.insert(0, str(src_path)) - -# Verify src directory exists -if not src_path.exists(): - print(f"Error: src/ directory not found at {src_path}") - print("Please ensure the following directory structure exists:") - print(" ai-projects/quantum-ml/") - print(" ├── quantum_mcp_server.py") - print(" └── src/") - print(" ├── quantum_classifier.py") - print(" └── azure_quantum_integration.py") - sys.exit(1) - -# Import MCP dependencies -try: - from mcp.server import Server - from mcp.server.stdio import stdio_server - from mcp.types import TextContent, Tool -except ImportError as e: - print("Error: MCP package not installed or incompatible version.") - print("\nTo install MCP dependencies, run:") - print(" pip install -r mcp-requirements.txt") - print("\nOr install directly:") - print(" pip install 'mcp>=0.9.0'") - print("\nIf you're using a virtual environment, ensure it's activated:") - print(" .\\venv\\Scripts\\Activate.ps1") - print(f"\nDetails: {e}") - sys.exit(1) - -# Import quantum modules -try: - from azure_quantum_integration import (AzureQuantumIntegration, - create_sample_circuit) - from quantum_classifier import (HybridQuantumClassifier, QuantumClassifier, - train_quantum_model) -except ImportError as e: - print(f"Error: Could not import quantum modules from {src_path}/") - print("\nEnsure the following files exist:") - print(f" - {src_path / 'quantum_classifier.py'}") - print(f" - {src_path / 'azure_quantum_integration.py'}") - print("\nMissing files should contain:") - print(" quantum_classifier.py:") - print(" - class QuantumClassifier") - print(" - class HybridQuantumClassifier") - print(" - def train_quantum_model()") - print(" azure_quantum_integration.py:") - print(" - class AzureQuantumIntegration") - print(" - def create_sample_circuit()") - print(f"\nDetails: {e}") - sys.exit(1) - - -# Setup logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -# Initialize MCP server -app = Server("quantum-ai-mcp") - -# Thread pool for CPU-bound operations -cpu_executor = ProcessPoolExecutor(max_workers=2) -io_executor = ThreadPoolExecutor(max_workers=4) - - -class CircuitCache: - """LRU cache with TTL for quantum circuits""" - - def __init__(self, max_size: int = 100, ttl_seconds: int = 3600): - self.cache = OrderedDict() - self.max_size = max_size - self.ttl_seconds = ttl_seconds - self.timestamps = {} - - def get(self, key: str) -> Optional[QuantumCircuit]: - if key not in self.cache: - return None - - # Check TTL - if time.time() - self.timestamps[key] > self.ttl_seconds: - self._evict(key) - return None - - # Move to end (most recently used) - self.cache.move_to_end(key) - return self.cache[key] - - def put(self, key: str, circuit: QuantumCircuit): - if key in self.cache: - self.cache.move_to_end(key) - else: - self.cache[key] = circuit - self.timestamps[key] = time.time() - - # Evict oldest if over size limit - if len(self.cache) > self.max_size: - oldest_key = next(iter(self.cache)) - self._evict(oldest_key) - - def _evict(self, key: str): - del self.cache[key] - del self.timestamps[key] - - def clear_expired(self): - """Remove all expired entries""" - current_time = time.time() - expired = [ - key - for key, timestamp in self.timestamps.items() - if current_time - timestamp > self.ttl_seconds - ] - for key in expired: - self._evict(key) - return len(expired) # Return count for logging - - -# Cost-gating configuration to prevent Azure billing DoS -MAX_COST_PER_JOB_USD = 10.0 # Max cost per single job submission ($) -# Max cumulative cost per session ($) -MAX_CUMULATIVE_COST_PER_SESSION_USD = 100.0 -COST_ESTIMATE_MULTIPLIER = 1.2 # Apply 20% margin to estimated costs for safety - -# Cost provider rates (empirically based, in USD per gate-shot) -COST_RATES = { - "ionq": 0.00003, # IonQ: ~$0.00003 per gate-shot - "quantinuum": 0.00015, # Quantinuum: ~$0.00015 per circuit execution - "microsoft": 0.0, # Microsoft simulators: free - "simulator": 0.0, # Local simulator: free -} - -# How long a cached backend list is considered fresh (seconds) -BACKEND_CACHE_TTL_SECONDS = 3600 - -FREE_BACKEND_KEYWORDS = ( - "simulator", - "resource_estimator", - "resource-estimator", - "aer", - "local", -) - -PAID_BACKEND_KEYWORDS = ( - "ionq", - "quantinuum", - "rigetti", - "pasqal", - "qpu", -) - -# Global state for quantum resources -quantum_state = { - "azure_integration": None, - "quantum_classifier": None, - "circuit_cache": CircuitCache(max_size=100, ttl_seconds=3600), - "config_cache": {}, # Cache YAML configs to avoid repeated temp file creation - "last_cache_cleanup": time.time(), # Track last cleanup time - "cumulative_cost_usd": 0.0, # Track cumulative cost across session - "known_backends": [], # Cached allowlist from Azure workspace - "known_backends_refreshed_at": 0.0, -} - - -def _cleanup_cache_if_needed(): - """Periodically clean expired circuits from cache (called before operations)""" - current_time = time.time() - # Run cleanup every 10 minutes - if current_time - quantum_state["last_cache_cleanup"] > 600: - expired_count = quantum_state["circuit_cache"].clear_expired() - if expired_count > 0: - logging.info( - f"[quantum_mcp] Cleaned {expired_count} expired circuits from cache" - ) - quantum_state["last_cache_cleanup"] = current_time - - -def _normalize_backend_name(backend_name: Optional[str]) -> str: - """Normalize backend names for matching and pricing.""" - return (backend_name or "").strip().lower() - - -def _normalize_required_string(value: Any) -> Optional[str]: - """Return a trimmed non-empty string, or None when invalid.""" - if not isinstance(value, str): - return None - normalized = value.strip() - return normalized or None - - -def _is_free_backend(backend_name: Optional[str]) -> bool: - """Return True when the backend is simulator-like and should not incur cost.""" - normalized = _normalize_backend_name(backend_name) - if not normalized: - return False - - # Token-level "sim" match to avoid accidental substring matches. - tokens = [t for t in re.split(r"[^a-z0-9]+", normalized) if t] - if "sim" in tokens: - return True - - return any(keyword in normalized for keyword in FREE_BACKEND_KEYWORDS) - - -def _requires_cost_confirmation(backend_name: Optional[str]) -> bool: - """Paid or unknown non-simulator backends require explicit confirmation.""" - normalized = _normalize_backend_name(backend_name) - if not normalized: - return False - if any(keyword in normalized for keyword in PAID_BACKEND_KEYWORDS): - return True - return not _is_free_backend(normalized) - - -def _estimate_job_cost_sync(circuit, backend_name: str, shots: int) -> float: - """Estimate job cost synchronously (runs in thread pool). - - Returns estimated cost in USD. - """ - # Normalize backend name for cost lookup - backend_key = _normalize_backend_name(backend_name) - - if _is_free_backend(backend_key): - rate = 0.0 - elif "ionq" in backend_key: - rate = COST_RATES["ionq"] - elif "quantinuum" in backend_key: - rate = COST_RATES["quantinuum"] - else: - # Unknown non-simulator backend: use the most conservative known paid rate. - rate = max(rate for rate in COST_RATES.values() if rate > 0) - - # Estimate gates in circuit - gate_ops = circuit.count_ops() - total_gates = sum(gate_ops.values()) - - # Cost = (gates * shots * rate) - estimated_cost = total_gates * shots * rate - - return max(0.0, estimated_cost) # Never negative - - -def _normalize_backend_allowlist(backends: List[str]) -> set[str]: - """Normalize allowlist for membership checks.""" - return { - _normalize_backend_name(name) - for name in backends - if isinstance(name, str) and _normalize_backend_name(name) - } - - -def _backend_in_allowlist( - normalized_backend: str, normalized_backends: set[str] -) -> bool: - """Return True when backend is directly allowed or accepted via simulator aliases.""" - if normalized_backend in normalized_backends: - return True - - # Accept common simulator aliases (e.g., "simulator") when the workspace - # exposes a provider-qualified simulator backend such as "microsoft.simulator". - if _is_free_backend(normalized_backend) and normalized_backend == "simulator": - return any("simulator" in backend for backend in normalized_backends) - - return False - - -async def _validate_backend_against_allowlist(backend_name: str) -> Optional[str]: - """Ensure a backend exists in the cached workspace allowlist, refreshing as needed.""" - loop = asyncio.get_event_loop() - - # Backend allowlist enforcement — refresh when empty or stale. - known_backends = quantum_state.get("known_backends") or [] - cache_age = time.time() - quantum_state.get("known_backends_refreshed_at", 0.0) - cache_stale = cache_age > BACKEND_CACHE_TTL_SECONDS - did_refresh_allowlist = False - if not known_backends or cache_stale: - try: - known_backends = await loop.run_in_executor( - io_executor, - quantum_state["azure_integration"].list_backends, - ) - quantum_state["known_backends"] = list(known_backends) - quantum_state["known_backends_refreshed_at"] = time.time() - did_refresh_allowlist = True - except Exception as e: - return ( - f"Unable to verify backend '{backend_name}' against workspace backends ({e}). " - "Run list_quantum_backends first, then retry submission." - ) - - normalized_backends = _normalize_backend_allowlist(known_backends) - normalized_backend = _normalize_backend_name(backend_name) - refresh_error = None - if ( - not _backend_in_allowlist(normalized_backend, normalized_backends) - and not did_refresh_allowlist - ): - # One-time recheck: backend inventories can change between cached refreshes. - try: - refreshed_backends = await loop.run_in_executor( - io_executor, - quantum_state["azure_integration"].list_backends, - ) - quantum_state["known_backends"] = list(refreshed_backends) - quantum_state["known_backends_refreshed_at"] = time.time() - normalized_backends = _normalize_backend_allowlist(refreshed_backends) - except Exception as e: - refresh_error = str(e) - - if not _backend_in_allowlist(normalized_backend, normalized_backends): - allowed_sample = ", ".join(sorted(normalized_backends)[:12]) - refresh_note = ( - f" Backend list refresh failed: {refresh_error}." if refresh_error else "" - ) - return ( - f"Backend '{backend_name}' is not in the workspace allowlist. " - f"Allowed backends: {allowed_sample or '(none)'}" - f"{refresh_note}" - ) - - return None - - -def initialize_quantum_resources(): - """Initialize quantum computing resources""" - try: - quantum_state["quantum_classifier"] = QuantumClassifier() - logger.info("Quantum classifier initialized") - except Exception as e: - logger.warning(f"Could not initialize quantum classifier: {e}") - - -@app.list_tools() -async def list_tools() -> List[Tool]: - """List available quantum computing tools""" - return [ - Tool( - name="create_quantum_circuit", - description="Create a quantum circuit with specified parameters. Returns circuit description and QASM representation.", - inputSchema={ - "type": "object", - "properties": { - "n_qubits": { - "type": "integer", - "description": "Number of qubits in the circuit", - "minimum": 1, - "maximum": 20, - }, - "circuit_type": { - "type": "string", - "description": "Type of circuit to create", - "enum": ["entanglement", "ghz", "bell", "random", "custom"], - }, - "gates": { - "type": "array", - "description": "Custom gates for 'custom' circuit type. Format: [{'gate': 'h', 'qubit': 0}, {'gate': 'cx', 'qubits': [0,1]}]", - "items": {"type": "object"}, - }, - }, - "required": ["n_qubits", "circuit_type"], - }, - ), - Tool( - name="simulate_quantum_circuit", - description="Simulate a quantum circuit locally using Qiskit Aer simulator. Returns measurement results.", - inputSchema={ - "type": "object", - "properties": { - "circuit_id": { - "type": "string", - "description": "ID of a previously created circuit", - }, - "shots": { - "type": "integer", - "description": "Number of measurement shots", - "minimum": 1, - "maximum": 8192, - "default": 1024, - }, - }, - "required": ["circuit_id"], - }, - ), - Tool( - name="connect_azure_quantum", - description="Connect to Azure Quantum workspace. Required before submitting jobs to Azure.", - inputSchema={ - "type": "object", - "properties": { - "subscription_id": { - "type": "string", - "description": "Azure subscription ID", - }, - "resource_group": { - "type": "string", - "description": "Azure resource group name", - }, - "workspace_name": { - "type": "string", - "description": "Azure Quantum workspace name", - }, - }, - "required": ["subscription_id", "resource_group", "workspace_name"], - }, - ), - Tool( - name="list_quantum_backends", - description="List available quantum backends in the Azure Quantum workspace. Must be connected first.", - inputSchema={ - "type": "object", - "properties": {}, - }, - ), - Tool( - name="submit_quantum_job", - description="Submit a quantum circuit to Azure Quantum for execution on real quantum hardware or cloud simulators.", - inputSchema={ - "type": "object", - "properties": { - "circuit_id": { - "type": "string", - "description": "ID of a previously created circuit", - }, - "backend_name": { - "type": "string", - "description": "Backend to run on (from list_quantum_backends)", - }, - "confirm_cost": { - "type": "boolean", - "description": "Required for paid or unknown non-simulator backends. Confirm that cost has been reviewed.", - "default": False, - }, - "shots": { - "type": "integer", - "description": "Number of measurement shots", - "minimum": 1, - "maximum": 10000, - "default": 500, - }, - }, - "required": ["circuit_id", "backend_name"], - }, - ), - Tool( - name="train_quantum_classifier", - description="Train a hybrid quantum-classical classifier on provided data. Returns training metrics and accuracy.", - inputSchema={ - "type": "object", - "properties": { - "dataset": { - "type": "string", - "description": "Dataset to use for training", - "enum": ["iris", "wine", "breast_cancer", "synthetic"], - }, - "n_qubits": { - "type": "integer", - "description": "Number of qubits in quantum circuit", - "minimum": 2, - "maximum": 10, - "default": 4, - }, - "n_layers": { - "type": "integer", - "description": "Number of variational layers", - "minimum": 1, - "maximum": 5, - "default": 2, - }, - "epochs": { - "type": "integer", - "description": "Number of training epochs", - "minimum": 1, - "maximum": 200, - "default": 50, - }, - "entanglement": { - "type": "string", - "description": "Entanglement pattern for quantum circuit", - "enum": ["linear", "circular", "full"], - "default": "linear", - }, - }, - "required": ["dataset"], - }, - ), - Tool( - name="estimate_quantum_cost", - description="Estimate the cost of running a quantum job on Azure Quantum hardware.", - inputSchema={ - "type": "object", - "properties": { - "circuit_id": { - "type": "string", - "description": "ID of the circuit to estimate", - }, - "backend_name": { - "type": "string", - "description": "Backend to estimate cost for", - }, - "shots": { - "type": "integer", - "description": "Number of shots", - "default": 100, - }, - }, - "required": ["circuit_id", "backend_name"], - }, - ), - Tool( - name="get_quantum_circuit_properties", - description="Get properties of a quantum circuit including depth, gate count, and topology.", - inputSchema={ - "type": "object", - "properties": { - "circuit_id": { - "type": "string", - "description": "ID of the circuit to analyze", - }, - }, - "required": ["circuit_id"], - }, - ), - ] - - -@app.call_tool() -async def call_tool(name: str, arguments: Any) -> List[TextContent]: - """Handle tool calls""" - - try: - if arguments is None: - arguments = {} - if not isinstance(arguments, dict): - return [ - TextContent(type="text", text="Invalid arguments: expected an object") - ] - - if name == "create_quantum_circuit": - return await create_circuit_handler(arguments) - elif name == "simulate_quantum_circuit": - return await simulate_circuit_handler(arguments) - elif name == "connect_azure_quantum": - return await connect_azure_handler(arguments) - elif name == "list_quantum_backends": - return await list_backends_handler(arguments) - elif name == "submit_quantum_job": - return await submit_job_handler(arguments) - elif name == "train_quantum_classifier": - return await train_classifier_handler(arguments) - elif name == "estimate_quantum_cost": - return await estimate_cost_handler(arguments) - elif name == "get_quantum_circuit_properties": - return await circuit_properties_handler(arguments) - else: - return [TextContent(type="text", text=f"Unknown tool: {name}")] - - except Exception as e: - logger.error(f"Error executing {name}: {str(e)}") - return [TextContent(type="text", text=f"Error: {str(e)}")] - - -async def create_circuit_handler(args: Dict) -> List[TextContent]: - """Create a quantum circuit based on parameters (optimized with async execution)""" - # Periodic cache cleanup to prevent memory leaks - _cleanup_cache_if_needed() - - n_qubits = args.get("n_qubits") - circuit_type = args.get("circuit_type") - - # Runtime validation (do not rely only on tool schema). - if ( - isinstance(n_qubits, bool) - or not isinstance(n_qubits, int) - or not (1 <= n_qubits <= 20) - ): - return [ - TextContent( - type="text", text="n_qubits must be an integer between 1 and 20" - ) - ] - if circuit_type not in {"entanglement", "ghz", "bell", "random", "custom"}: - return [ - TextContent( - type="text", - text="circuit_type must be one of: entanglement, ghz, bell, random, custom", - ) - ] - - # Offload circuit creation to thread pool for CPU-intensive operations - loop = asyncio.get_event_loop() - try: - # Add timeout to prevent hanging (CRITICAL FIX) - circuit = await asyncio.wait_for( - loop.run_in_executor( - io_executor, - _create_circuit_sync, - n_qubits, - circuit_type, - args.get("gates"), - ), - timeout=30.0, # 30-second max for circuit creation - ) - except asyncio.TimeoutError: - return [TextContent(type="text", text="Circuit creation timed out after 30s")] - - if circuit is None: - return [ - TextContent( - type="text", - text="Invalid circuit type or missing gates for custom type", - ) - ] - - # Generate circuit ID using SHA-256 (MD5 is weak for security) - import hashlib - - circuit_id = hashlib.sha256(str(circuit).encode()).hexdigest()[:12] - - # Cache the circuit with TTL - quantum_state["circuit_cache"].put(circuit_id, circuit) - - result = { - "circuit_id": circuit_id, - "n_qubits": n_qubits, - "circuit_type": circuit_type, - "depth": circuit.depth(), - "gate_count": sum(circuit.count_ops().values()), - "qasm": circuit.qasm(), - "diagram": str(circuit.draw(output="text")), - } - - return [ - TextContent( - type="text", - text=f"Created quantum circuit:\n\n```\n{result['diagram']}\n```\n\nCircuit ID: {circuit_id}\nDepth: {result['depth']}, Gates: {result['gate_count']}", - ) - ] - - -def _create_circuit_sync( - n_qubits: int, circuit_type: str, gates: Optional[List] = None -) -> Optional[QuantumCircuit]: - """Synchronous circuit creation (runs in thread pool)""" - if circuit_type == "entanglement": - return create_sample_circuit(n_qubits) - elif circuit_type == "bell": - circuit = QuantumCircuit(2, 2) - circuit.h(0) - circuit.cx(0, 1) - circuit.measure([0, 1], [0, 1]) - return circuit - elif circuit_type == "ghz": - circuit = QuantumCircuit(n_qubits, n_qubits) - circuit.h(0) - for i in range(n_qubits - 1): - circuit.cx(i, i + 1) - circuit.measure(range(n_qubits), range(n_qubits)) - return circuit - elif circuit_type == "random": - circuit = QuantumCircuit(n_qubits, n_qubits) - import random - - for _ in range(n_qubits * 2): - gate = random.choice(["h", "x", "y", "z", "rx", "ry", "rz"]) - qubit = random.randint(0, n_qubits - 1) - if gate == "h": - circuit.h(qubit) - elif gate == "x": - circuit.x(qubit) - elif gate == "y": - circuit.y(qubit) - elif gate == "z": - circuit.z(qubit) - else: - circuit.rx(np.pi / 4, qubit) - circuit.measure(range(n_qubits), range(n_qubits)) - return circuit - elif circuit_type == "custom" and gates: - circuit = QuantumCircuit(n_qubits, n_qubits) - for gate_spec in gates: - if not isinstance(gate_spec, dict): - continue - gate_name = str(gate_spec.get("gate", "")).lower() - if not gate_name: - continue - if "qubit" in gate_spec: - qubit = gate_spec["qubit"] - # Validate qubit index is within circuit bounds. - if not isinstance(qubit, int) or not (0 <= qubit < n_qubits): - continue - if gate_name == "h": - circuit.h(qubit) - elif gate_name == "x": - circuit.x(qubit) - elif gate_name == "y": - circuit.y(qubit) - elif gate_name == "z": - circuit.z(qubit) - elif "qubits" in gate_spec: - qubits = gate_spec["qubits"] - # Validate both qubit indices and require two distinct qubits. - if ( - not isinstance(qubits, list) - or len(qubits) < 2 - or not all( - isinstance(q, int) and 0 <= q < n_qubits for q in qubits[:2] - ) - or qubits[0] == qubits[1] - ): - continue - if gate_name in ["cx", "cnot"]: - circuit.cx(qubits[0], qubits[1]) - elif gate_name == "cz": - circuit.cz(qubits[0], qubits[1]) - circuit.measure(range(n_qubits), range(n_qubits)) - return circuit - return None - - -async def simulate_circuit_handler(args: Dict) -> List[TextContent]: - """Simulate a quantum circuit locally (optimized with async execution)""" - circuit_id = _normalize_required_string(args.get("circuit_id")) - shots = args.get("shots", 1024) - - if circuit_id is None: - return [TextContent(type="text", text="circuit_id is required")] - - # Validate shots to prevent DoS via excessively large simulation jobs. - if ( - isinstance(shots, bool) - or not isinstance(shots, int) - or not (1 <= shots <= 8192) - ): - return [ - TextContent(type="text", text="shots must be an integer between 1 and 8192") - ] - - # Clean expired cache entries periodically - quantum_state["circuit_cache"].clear_expired() - - circuit = quantum_state["circuit_cache"].get(circuit_id) - if circuit is None: - return [ - TextContent( - type="text", - text=f"Circuit ID '{circuit_id}' not found or expired. Create a circuit first.", - ) - ] - - # Offload simulation to thread pool (CPU-intensive) - loop = asyncio.get_event_loop() - counts = await loop.run_in_executor( - cpu_executor, _simulate_circuit_sync, circuit, shots - ) - - # Format results - sorted_counts = dict(sorted(counts.items(), key=lambda x: x[1], reverse=True)) - total_shots = sum(sorted_counts.values()) - - result_text = f"Simulation results for circuit {circuit_id} ({shots} shots):\n\n" - for state, count in list(sorted_counts.items())[:10]: # Top 10 states - probability = count / total_shots - result_text += f" |{state}⟩: {count} ({probability:.3%})\n" - - if len(sorted_counts) > 10: - result_text += f"\n ... and {len(sorted_counts) - 10} more states" - - return [TextContent(type="text", text=result_text)] - - -def _simulate_circuit_sync(circuit: QuantumCircuit, shots: int) -> Dict: - """Synchronous circuit simulation (runs in process pool)""" - from qiskit_aer import AerSimulator - - simulator = AerSimulator() - job = simulator.run(circuit, shots=shots) - result = job.result() - return result.get_counts() - - -async def connect_azure_handler(args: Dict) -> List[TextContent]: - """Connect to Azure Quantum workspace (optimized with config caching)""" - subscription_id = _normalize_required_string(args.get("subscription_id")) - resource_group = _normalize_required_string(args.get("resource_group")) - workspace_name = _normalize_required_string(args.get("workspace_name")) - - if subscription_id is None: - return [TextContent(type="text", text="subscription_id is required")] - if resource_group is None: - return [TextContent(type="text", text="resource_group is required")] - if workspace_name is None: - return [TextContent(type="text", text="workspace_name is required")] - - try: - # Check config cache to avoid repeated temp file creation - config_key = f"{subscription_id}_{resource_group}_{workspace_name}" - - if config_key not in quantum_state["config_cache"]: - # Create config in-memory instead of temp file - config = { - "azure": { - "subscription_id": subscription_id, - "resource_group": resource_group, - "workspace_name": workspace_name, - }, - "quantum": { - "provider": "ionq", - "hardware": {"shots": 500, "optimization_level": 2}, - }, - "ml": { - "model": {"n_qubits": 4, "n_layers": 2, "entanglement": "linear"} - }, - "logging": {"level": "INFO", "results_dir": "./results"}, - } - quantum_state["config_cache"][config_key] = config - - # Offload Azure connection to thread pool (I/O bound) - loop = asyncio.get_event_loop() - azure_int = await loop.run_in_executor( - io_executor, _connect_azure_sync, quantum_state["config_cache"][config_key] - ) - quantum_state["azure_integration"] = azure_int - quantum_state["cumulative_cost_usd"] = 0.0 - quantum_state["known_backends"] = [] - quantum_state["known_backends_refreshed_at"] = 0.0 - - return [ - TextContent( - type="text", - text=f"✓ Connected to Azure Quantum workspace: {workspace_name}\nResource Group: {resource_group}\nCost budget reset: ${quantum_state['cumulative_cost_usd']:.2f}", - ) - ] - - except Exception as e: - return [ - TextContent( - type="text", text=f"Failed to connect to Azure Quantum: {str(e)}" - ) - ] - - -def _connect_azure_sync(config: Dict) -> AzureQuantumIntegration: - """Synchronous Azure connection (runs in thread pool)""" - import os - import tempfile - - import yaml - - # Write config to a temp file; always clean it up regardless of success/failure - # to prevent credentials lingering on disk. - fd, config_path = tempfile.mkstemp(suffix=".yaml") - try: - with os.fdopen(fd, "w") as f: - yaml.safe_dump(config, f) - azure_int = AzureQuantumIntegration(config_path=config_path) - azure_int.connect() - return azure_int - finally: - try: - os.unlink(config_path) - except OSError: - pass - - -async def list_backends_handler(args: Dict) -> List[TextContent]: - """List available quantum backends (optimized with async I/O)""" - if quantum_state["azure_integration"] is None: - return [ - TextContent( - type="text", - text="Not connected to Azure Quantum. Use connect_azure_quantum first.", - ) - ] - - try: - # Offload to thread pool - loop = asyncio.get_event_loop() - backends = await loop.run_in_executor( - io_executor, quantum_state["azure_integration"].list_backends - ) - quantum_state["known_backends"] = list(backends) - quantum_state["known_backends_refreshed_at"] = time.time() - backend_list = "\n".join(f" • {backend}" for backend in backends) - return [ - TextContent( - type="text", text=f"Available quantum backends:\n\n{backend_list}" - ) - ] - except Exception as e: - return [TextContent(type="text", text=f"Error listing backends: {str(e)}")] - - -async def submit_job_handler(args: Dict) -> List[TextContent]: - """Submit a quantum job to Azure Quantum with cost-gating (optimized)""" - circuit_id = _normalize_required_string(args.get("circuit_id")) - backend_name = _normalize_required_string(args.get("backend_name")) - confirm_cost = args.get("confirm_cost", False) - shots = args.get("shots", 500) - - if circuit_id is None: - return [TextContent(type="text", text="circuit_id is required")] - if backend_name is None: - return [TextContent(type="text", text="backend_name is required")] - if not isinstance(confirm_cost, bool): - return [TextContent(type="text", text="confirm_cost must be a boolean")] - if ( - isinstance(shots, bool) - or not isinstance(shots, int) - or not (1 <= shots <= 10000) - ): - return [ - TextContent( - type="text", text="shots must be an integer between 1 and 10000" - ) - ] - - if quantum_state["azure_integration"] is None: - return [ - TextContent( - type="text", - text="Not connected to Azure Quantum. Use connect_azure_quantum first.", - ) - ] - - circuit = quantum_state["circuit_cache"].get(circuit_id) - if circuit is None: - return [ - TextContent( - type="text", text=f"Circuit ID '{circuit_id}' not found or expired." - ) - ] - - allowlist_error = await _validate_backend_against_allowlist(backend_name) - if allowlist_error: - return [TextContent(type="text", text=allowlist_error)] - - loop = asyncio.get_event_loop() - - # Cost-gating: estimate cost and validate against budget limits - try: - estimated_cost = await loop.run_in_executor( - io_executor, _estimate_job_cost_sync, circuit, backend_name, shots - ) - - if _requires_cost_confirmation(backend_name) and not confirm_cost: - return [ - TextContent( - type="text", - text=( - f"Paid-backend confirmation required for '{backend_name}'. " - f"Review the estimated cost with estimate_quantum_cost first, then resubmit with confirm_cost=true. " - f"Estimated cost before safety margin: ${estimated_cost:.2f}." - ), - ) - ] - - # Apply safety multiplier and check against limits - safeguarded_cost = estimated_cost * COST_ESTIMATE_MULTIPLIER - if safeguarded_cost > MAX_COST_PER_JOB_USD: - return [ - TextContent( - type="text", - text=f"Cost limit exceeded: Estimated cost ${safeguarded_cost:.2f} exceeds max ${MAX_COST_PER_JOB_USD:.2f} per job. Reduce shots or use a simulator backend.", - ) - ] - if ( - quantum_state["cumulative_cost_usd"] + safeguarded_cost - > MAX_CUMULATIVE_COST_PER_SESSION_USD - ): - return [ - TextContent( - type="text", - text=f"Session budget exceeded: Current cost ${quantum_state['cumulative_cost_usd']:.2f} + this job ${safeguarded_cost:.2f} would exceed max ${MAX_CUMULATIVE_COST_PER_SESSION_USD:.2f} per session.", - ) - ] - - # Cost gates passed, proceed with submission - job = await loop.run_in_executor( - io_executor, - quantum_state["azure_integration"].submit_circuit, - circuit, - backend_name, - shots, - f"mcp_{circuit_id}", - ) - quantum_state["cumulative_cost_usd"] += safeguarded_cost - return [ - TextContent( - type="text", - text=f"✓ Job submitted to Azure Quantum\nJob ID: {job.id()}\nBackend: {backend_name or 'default'}\nShots: {shots}\nEstimated Cost: ${safeguarded_cost:.2f}\nSession Total: ${quantum_state['cumulative_cost_usd']:.2f}\n\nNote: Job execution may take several minutes depending on queue.", - ) - ] - except Exception as e: - return [TextContent(type="text", text=f"Error submitting job: {str(e)}")] - - -async def train_classifier_handler(args: Dict) -> List[TextContent]: - """Train a quantum classifier (optimized with process pool for CPU-intensive training)""" - dataset_name = args.get("dataset") - n_qubits = args.get("n_qubits", 4) - n_layers = args.get("n_layers", 2) - epochs = args.get("epochs", 50) - entanglement = args.get("entanglement", "linear") - - if dataset_name not in {"iris", "wine", "breast_cancer", "synthetic"}: - return [ - TextContent( - type="text", - text="dataset must be one of: iris, wine, breast_cancer, synthetic", - ) - ] - if ( - isinstance(n_qubits, bool) - or not isinstance(n_qubits, int) - or not (2 <= n_qubits <= 10) - ): - return [ - TextContent( - type="text", text="n_qubits must be an integer between 2 and 10" - ) - ] - if ( - isinstance(n_layers, bool) - or not isinstance(n_layers, int) - or not (1 <= n_layers <= 5) - ): - return [ - TextContent(type="text", text="n_layers must be an integer between 1 and 5") - ] - if ( - isinstance(epochs, bool) - or not isinstance(epochs, int) - or not (1 <= epochs <= 200) - ): - return [ - TextContent(type="text", text="epochs must be an integer between 1 and 200") - ] - if entanglement not in {"linear", "circular", "full"}: - return [ - TextContent( - type="text", text="entanglement must be one of: linear, circular, full" - ) - ] - - # Offload data loading and training to process pool - loop = asyncio.get_event_loop() - result_text = await loop.run_in_executor( - cpu_executor, - _train_classifier_sync, - dataset_name, - n_qubits, - n_layers, - epochs, - entanglement, - ) - - return [TextContent(type="text", text=result_text)] - - -def _train_classifier_sync( - dataset_name: str, n_qubits: int, n_layers: int, epochs: int, entanglement: str -) -> str: - """Synchronous training (runs in process pool)""" - import tempfile - - import yaml - from sklearn.datasets import (load_breast_cancer, load_iris, load_wine, - make_classification) - from sklearn.model_selection import train_test_split - from sklearn.preprocessing import StandardScaler - - # Load dataset - if dataset_name == "iris": - data = load_iris() - X, y = data.data, (data.target > 0).astype(int) - elif dataset_name == "wine": - data = load_wine() - X, y = data.data, (data.target > 0).astype(int) - elif dataset_name == "breast_cancer": - data = load_breast_cancer() - X, y = data.data, data.target - elif dataset_name == "synthetic": - X, y = make_classification( - n_samples=200, n_features=4, n_classes=2, random_state=42 - ) - else: - return f"Unknown dataset: {dataset_name}" - - # Prepare data - scaler = StandardScaler() - X = scaler.fit_transform(X) - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - try: - # Create config - config = { - "quantum": {"simulator": {"backend": "qiskit_aer", "shots": 1024}}, - "ml": { - "model": { - "n_qubits": n_qubits, - "n_layers": n_layers, - "entanglement": entanglement, - }, - "training": { - "epochs": epochs, - "batch_size": 16, - "learning_rate": 0.01, - "validation_split": 0.2, - }, - }, - } - - import os - - fd, config_path = tempfile.mkstemp(suffix=".yaml") - try: - with os.fdopen(fd, "w") as f: - yaml.safe_dump(config, f) - qc = QuantumClassifier(config_path=config_path) - model = HybridQuantumClassifier(input_dim=X.shape[1], quantum_classifier=qc) - history = train_quantum_model( - model, X_train, y_train, X_val, y_val, config_path=config_path - ) - finally: - try: - os.unlink(config_path) - except OSError: - pass - - final_acc = history["val_acc"][-1] - final_loss = history["val_loss"][-1] - - return f"""✓ Quantum classifier training completed! - -Dataset: {dataset_name} -Architecture: - - Qubits: {n_qubits} - - Layers: {n_layers} - - Entanglement: {entanglement} - - Epochs: {epochs} - -Results: - - Final Validation Accuracy: {final_acc:.2%} - - Final Validation Loss: {final_loss:.4f} - -Training History: - - Best Accuracy: {max(history['val_acc']):.2%} - - Best Loss: {min(history['val_loss']):.4f} -""" - - except Exception as e: - logger.error(f"Training error: {str(e)}") - return f"Training failed: {str(e)}" - - -async def estimate_cost_handler(args: Dict) -> List[TextContent]: - """Estimate quantum job cost (optimized)""" - circuit_id = _normalize_required_string(args.get("circuit_id")) - backend_name = _normalize_required_string(args.get("backend_name")) - shots = args.get("shots", 100) - - if circuit_id is None: - return [TextContent(type="text", text="circuit_id is required")] - if backend_name is None: - return [TextContent(type="text", text="backend_name is required")] - if ( - isinstance(shots, bool) - or not isinstance(shots, int) - or not (1 <= shots <= 10000) - ): - return [ - TextContent( - type="text", text="shots must be an integer between 1 and 10000" - ) - ] - - if quantum_state["azure_integration"] is None: - return [TextContent(type="text", text="Not connected to Azure Quantum.")] - - circuit = quantum_state["circuit_cache"].get(circuit_id) - if circuit is None: - return [ - TextContent( - type="text", text=f"Circuit ID '{circuit_id}' not found or expired." - ) - ] - - allowlist_error = await _validate_backend_against_allowlist(backend_name) - if allowlist_error: - return [TextContent(type="text", text=allowlist_error)] - - try: - heuristic_cost = _estimate_job_cost_sync(circuit, backend_name, shots) - safeguarded_cost = heuristic_cost * COST_ESTIMATE_MULTIPLIER - loop = asyncio.get_event_loop() - estimate = await loop.run_in_executor( - io_executor, - quantum_state["azure_integration"].estimate_cost, - circuit, - backend_name, - shots, - ) - - cost_info = f"""Cost Estimate for {backend_name}: - -Shots: {shots} -Estimated Time: {estimate.get('estimated_time_minutes', 'N/A')} minutes - Heuristic Price Estimate: ${heuristic_cost:.2f} - Submission Guardrail Ceiling: ${safeguarded_cost:.2f} - -{estimate.get('note', '')} - -Note: Actual costs vary by provider: - - IonQ: ~$0.00003 per gate-shot - - Quantinuum: ~$0.00015 per circuit execution - - Microsoft simulators: Free -""" - return [TextContent(type="text", text=cost_info)] - - except Exception as e: - return [TextContent(type="text", text=f"Error estimating cost: {str(e)}")] - - -async def circuit_properties_handler(args: Dict) -> List[TextContent]: - """Get circuit properties (optimized with caching)""" - circuit_id = _normalize_required_string(args.get("circuit_id")) - - if circuit_id is None: - return [TextContent(type="text", text="circuit_id is required")] - - circuit = quantum_state["circuit_cache"].get(circuit_id) - if circuit is None: - return [ - TextContent( - type="text", text=f"Circuit ID '{circuit_id}' not found or expired." - ) - ] - - gate_counts = circuit.count_ops() - - properties = f"""Circuit Properties for {circuit_id}: - -Basic Info: - - Qubits: {circuit.num_qubits} - - Classical Bits: {circuit.num_clbits} - - Depth: {circuit.depth()} - - Size (total gates): {circuit.size()} - -Gate Breakdown: -""" - for gate, count in gate_counts.items(): - properties += f" - {gate}: {count}\n" - - return [TextContent(type="text", text=properties)] - - -async def main(): - """Run the MCP server""" - logger.info("Starting Quantum AI MCP Server (Optimized)...") - initialize_quantum_resources() - - async with stdio_server() as (read_stream, write_stream): - await app.run(read_stream, write_stream, app.create_initialization_options()) - - -async def cleanup(): - """Cleanup resources on shutdown""" - cpu_executor.shutdown(wait=True) - io_executor.shutdown(wait=True) - - -if __name__ == "__main__": - try: - asyncio.run(main()) - finally: - asyncio.run(cleanup()) +""" +Quantum AI MCP Server (Optimized) +Exposes quantum computing and quantum machine learning capabilities via Model Context Protocol +""" + +import asyncio +import logging +import re +# Add src directory to path before importing local modules +import sys +import time +from collections import OrderedDict +from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor +from pathlib import Path +from typing import Any, Dict, List, Optional + +import numpy as np +from qiskit import QuantumCircuit + +src_path = Path(__file__).parent / "src" +if str(src_path) not in sys.path: + sys.path.insert(0, str(src_path)) + +# Verify src directory exists +if not src_path.exists(): + print(f"Error: src/ directory not found at {src_path}") + print("Please ensure the following directory structure exists:") + print(" ai-projects/quantum-ml/") + print(" ├── quantum_mcp_server.py") + print(" └── src/") + print(" ├── quantum_classifier.py") + print(" └── azure_quantum_integration.py") + sys.exit(1) + +# Import MCP dependencies +try: + from mcp.server import Server + from mcp.server.stdio import stdio_server + from mcp.types import TextContent, Tool +except ImportError as e: + print("Error: MCP package not installed or incompatible version.") + print("\nTo install MCP dependencies, run:") + print(" pip install -r mcp-requirements.txt") + print("\nOr install directly:") + print(" pip install 'mcp>=0.9.0'") + print("\nIf you're using a virtual environment, ensure it's activated:") + print(" .\\venv\\Scripts\\Activate.ps1") + print(f"\nDetails: {e}") + sys.exit(1) + +# Import quantum modules +try: + from azure_quantum_integration import (AzureQuantumIntegration, + create_sample_circuit) + from quantum_classifier import (HybridQuantumClassifier, QuantumClassifier, + train_quantum_model) +except ImportError as e: + print(f"Error: Could not import quantum modules from {src_path}/") + print("\nEnsure the following files exist:") + print(f" - {src_path / 'quantum_classifier.py'}") + print(f" - {src_path / 'azure_quantum_integration.py'}") + print("\nMissing files should contain:") + print(" quantum_classifier.py:") + print(" - class QuantumClassifier") + print(" - class HybridQuantumClassifier") + print(" - def train_quantum_model()") + print(" azure_quantum_integration.py:") + print(" - class AzureQuantumIntegration") + print(" - def create_sample_circuit()") + print(f"\nDetails: {e}") + sys.exit(1) + + +# Setup logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# Initialize MCP server +app = Server("quantum-ai-mcp") + +# Thread pool for CPU-bound operations +cpu_executor = ProcessPoolExecutor(max_workers=2) +io_executor = ThreadPoolExecutor(max_workers=4) + + +class CircuitCache: + """LRU cache with TTL for quantum circuits""" + + def __init__(self, max_size: int = 100, ttl_seconds: int = 3600): + self.cache = OrderedDict() + self.max_size = max_size + self.ttl_seconds = ttl_seconds + self.timestamps = {} + + def get(self, key: str) -> Optional[QuantumCircuit]: + if key not in self.cache: + return None + + # Check TTL + if time.time() - self.timestamps[key] > self.ttl_seconds: + self._evict(key) + return None + + # Move to end (most recently used) + self.cache.move_to_end(key) + return self.cache[key] + + def put(self, key: str, circuit: QuantumCircuit): + if key in self.cache: + self.cache.move_to_end(key) + else: + self.cache[key] = circuit + self.timestamps[key] = time.time() + + # Evict oldest if over size limit + if len(self.cache) > self.max_size: + oldest_key = next(iter(self.cache)) + self._evict(oldest_key) + + def _evict(self, key: str): + del self.cache[key] + del self.timestamps[key] + + def clear_expired(self): + """Remove all expired entries""" + current_time = time.time() + expired = [ + key + for key, timestamp in self.timestamps.items() + if current_time - timestamp > self.ttl_seconds + ] + for key in expired: + self._evict(key) + return len(expired) # Return count for logging + + +# Cost-gating configuration to prevent Azure billing DoS +MAX_COST_PER_JOB_USD = 10.0 # Max cost per single job submission ($) +# Max cumulative cost per session ($) +MAX_CUMULATIVE_COST_PER_SESSION_USD = 100.0 +COST_ESTIMATE_MULTIPLIER = 1.2 # Apply 20% margin to estimated costs for safety + +# Cost provider rates (empirically based, in USD per gate-shot) +COST_RATES = { + "ionq": 0.00003, # IonQ: ~$0.00003 per gate-shot + "quantinuum": 0.00015, # Quantinuum: ~$0.00015 per circuit execution + "microsoft": 0.0, # Microsoft simulators: free + "simulator": 0.0, # Local simulator: free +} + +# How long a cached backend list is considered fresh (seconds) +BACKEND_CACHE_TTL_SECONDS = 3600 + +FREE_BACKEND_KEYWORDS = ( + "simulator", + "resource_estimator", + "resource-estimator", + "aer", + "local", +) + +PAID_BACKEND_KEYWORDS = ( + "ionq", + "quantinuum", + "rigetti", + "pasqal", + "qpu", +) + +# Global state for quantum resources +quantum_state = { + "azure_integration": None, + "quantum_classifier": None, + "circuit_cache": CircuitCache(max_size=100, ttl_seconds=3600), + "config_cache": {}, # Cache YAML configs to avoid repeated temp file creation + "last_cache_cleanup": time.time(), # Track last cleanup time + "cumulative_cost_usd": 0.0, # Track cumulative cost across session + "known_backends": [], # Cached allowlist from Azure workspace + "known_backends_refreshed_at": 0.0, +} + + +def _cleanup_cache_if_needed(): + """Periodically clean expired circuits from cache (called before operations)""" + current_time = time.time() + # Run cleanup every 10 minutes + if current_time - quantum_state["last_cache_cleanup"] > 600: + expired_count = quantum_state["circuit_cache"].clear_expired() + if expired_count > 0: + logging.info( + f"[quantum_mcp] Cleaned {expired_count} expired circuits from cache" + ) + quantum_state["last_cache_cleanup"] = current_time + + +def _normalize_backend_name(backend_name: Optional[str]) -> str: + """Normalize backend names for matching and pricing.""" + return (backend_name or "").strip().lower() + + +def _normalize_required_string(value: Any) -> Optional[str]: + """Return a trimmed non-empty string, or None when invalid.""" + if not isinstance(value, str): + return None + normalized = value.strip() + return normalized or None + + +def _is_free_backend(backend_name: Optional[str]) -> bool: + """Return True when the backend is simulator-like and should not incur cost.""" + normalized = _normalize_backend_name(backend_name) + if not normalized: + return False + + # Token-level "sim" match to avoid accidental substring matches. + tokens = [t for t in re.split(r"[^a-z0-9]+", normalized) if t] + if "sim" in tokens: + return True + + return any(keyword in normalized for keyword in FREE_BACKEND_KEYWORDS) + + +def _requires_cost_confirmation(backend_name: Optional[str]) -> bool: + """Paid or unknown non-simulator backends require explicit confirmation.""" + normalized = _normalize_backend_name(backend_name) + if not normalized: + return False + if any(keyword in normalized for keyword in PAID_BACKEND_KEYWORDS): + return True + return not _is_free_backend(normalized) + + +def _estimate_job_cost_sync(circuit, backend_name: str, shots: int) -> float: + """Estimate job cost synchronously (runs in thread pool). + + Returns estimated cost in USD. + """ + # Normalize backend name for cost lookup + backend_key = _normalize_backend_name(backend_name) + + if _is_free_backend(backend_key): + rate = 0.0 + elif "ionq" in backend_key: + rate = COST_RATES["ionq"] + elif "quantinuum" in backend_key: + rate = COST_RATES["quantinuum"] + else: + # Unknown non-simulator backend: use the most conservative known paid rate. + rate = max(rate for rate in COST_RATES.values() if rate > 0) + + # Estimate gates in circuit + gate_ops = circuit.count_ops() + total_gates = sum(gate_ops.values()) + + # Cost = (gates * shots * rate) + estimated_cost = total_gates * shots * rate + + return max(0.0, estimated_cost) # Never negative + + +def _normalize_backend_allowlist(backends: List[str]) -> set[str]: + """Normalize allowlist for membership checks.""" + return { + _normalize_backend_name(name) + for name in backends + if isinstance(name, str) and _normalize_backend_name(name) + } + + +def _backend_in_allowlist( + normalized_backend: str, normalized_backends: set[str] +) -> bool: + """Return True when backend is directly allowed or accepted via simulator aliases.""" + if normalized_backend in normalized_backends: + return True + + # Accept common simulator aliases (e.g., "simulator") when the workspace + # exposes a provider-qualified simulator backend such as "microsoft.simulator". + if _is_free_backend(normalized_backend) and normalized_backend == "simulator": + return any("simulator" in backend for backend in normalized_backends) + + return False + + +async def _validate_backend_against_allowlist(backend_name: str) -> Optional[str]: + """Ensure a backend exists in the cached workspace allowlist, refreshing as needed.""" + loop = asyncio.get_event_loop() + + # Backend allowlist enforcement — refresh when empty or stale. + known_backends = quantum_state.get("known_backends") or [] + cache_age = time.time() - quantum_state.get("known_backends_refreshed_at", 0.0) + cache_stale = cache_age > BACKEND_CACHE_TTL_SECONDS + did_refresh_allowlist = False + if not known_backends or cache_stale: + try: + known_backends = await loop.run_in_executor( + io_executor, + quantum_state["azure_integration"].list_backends, + ) + quantum_state["known_backends"] = list(known_backends) + quantum_state["known_backends_refreshed_at"] = time.time() + did_refresh_allowlist = True + except Exception as e: + return ( + f"Unable to verify backend '{backend_name}' against workspace backends ({e}). " + "Run list_quantum_backends first, then retry submission." + ) + + normalized_backends = _normalize_backend_allowlist(known_backends) + normalized_backend = _normalize_backend_name(backend_name) + refresh_error = None + if ( + not _backend_in_allowlist(normalized_backend, normalized_backends) + and not did_refresh_allowlist + ): + # One-time recheck: backend inventories can change between cached refreshes. + try: + refreshed_backends = await loop.run_in_executor( + io_executor, + quantum_state["azure_integration"].list_backends, + ) + quantum_state["known_backends"] = list(refreshed_backends) + quantum_state["known_backends_refreshed_at"] = time.time() + normalized_backends = _normalize_backend_allowlist(refreshed_backends) + except Exception as e: + refresh_error = str(e) + + if not _backend_in_allowlist(normalized_backend, normalized_backends): + allowed_sample = ", ".join(sorted(normalized_backends)[:12]) + refresh_note = ( + f" Backend list refresh failed: {refresh_error}." if refresh_error else "" + ) + return ( + f"Backend '{backend_name}' is not in the workspace allowlist. " + f"Allowed backends: {allowed_sample or '(none)'}" + f"{refresh_note}" + ) + + return None + + +def initialize_quantum_resources(): + """Initialize quantum computing resources""" + try: + quantum_state["quantum_classifier"] = QuantumClassifier() + logger.info("Quantum classifier initialized") + except Exception as e: + logger.warning(f"Could not initialize quantum classifier: {e}") + + +@app.list_tools() +async def list_tools() -> List[Tool]: + """List available quantum computing tools""" + return [ + Tool( + name="create_quantum_circuit", + description="Create a quantum circuit with specified parameters. Returns circuit description and QASM representation.", + inputSchema={ + "type": "object", + "properties": { + "n_qubits": { + "type": "integer", + "description": "Number of qubits in the circuit", + "minimum": 1, + "maximum": 20, + }, + "circuit_type": { + "type": "string", + "description": "Type of circuit to create", + "enum": ["entanglement", "ghz", "bell", "random", "custom"], + }, + "gates": { + "type": "array", + "description": "Custom gates for 'custom' circuit type. Format: [{'gate': 'h', 'qubit': 0}, {'gate': 'cx', 'qubits': [0,1]}]", + "items": {"type": "object"}, + }, + }, + "required": ["n_qubits", "circuit_type"], + }, + ), + Tool( + name="simulate_quantum_circuit", + description="Simulate a quantum circuit locally using Qiskit Aer simulator. Returns measurement results.", + inputSchema={ + "type": "object", + "properties": { + "circuit_id": { + "type": "string", + "description": "ID of a previously created circuit", + }, + "shots": { + "type": "integer", + "description": "Number of measurement shots", + "minimum": 1, + "maximum": 8192, + "default": 1024, + }, + }, + "required": ["circuit_id"], + }, + ), + Tool( + name="connect_azure_quantum", + description="Connect to Azure Quantum workspace. Required before submitting jobs to Azure.", + inputSchema={ + "type": "object", + "properties": { + "subscription_id": { + "type": "string", + "description": "Azure subscription ID", + }, + "resource_group": { + "type": "string", + "description": "Azure resource group name", + }, + "workspace_name": { + "type": "string", + "description": "Azure Quantum workspace name", + }, + }, + "required": ["subscription_id", "resource_group", "workspace_name"], + }, + ), + Tool( + name="list_quantum_backends", + description="List available quantum backends in the Azure Quantum workspace. Must be connected first.", + inputSchema={ + "type": "object", + "properties": {}, + }, + ), + Tool( + name="submit_quantum_job", + description="Submit a quantum circuit to Azure Quantum for execution on real quantum hardware or cloud simulators.", + inputSchema={ + "type": "object", + "properties": { + "circuit_id": { + "type": "string", + "description": "ID of a previously created circuit", + }, + "backend_name": { + "type": "string", + "description": "Backend to run on (from list_quantum_backends)", + }, + "confirm_cost": { + "type": "boolean", + "description": "Required for paid or unknown non-simulator backends. Confirm that cost has been reviewed.", + "default": False, + }, + "shots": { + "type": "integer", + "description": "Number of measurement shots", + "minimum": 1, + "maximum": 10000, + "default": 500, + }, + }, + "required": ["circuit_id", "backend_name"], + }, + ), + Tool( + name="train_quantum_classifier", + description="Train a hybrid quantum-classical classifier on provided data. Returns training metrics and accuracy.", + inputSchema={ + "type": "object", + "properties": { + "dataset": { + "type": "string", + "description": "Dataset to use for training", + "enum": ["iris", "wine", "breast_cancer", "synthetic"], + }, + "n_qubits": { + "type": "integer", + "description": "Number of qubits in quantum circuit", + "minimum": 2, + "maximum": 10, + "default": 4, + }, + "n_layers": { + "type": "integer", + "description": "Number of variational layers", + "minimum": 1, + "maximum": 5, + "default": 2, + }, + "epochs": { + "type": "integer", + "description": "Number of training epochs", + "minimum": 1, + "maximum": 200, + "default": 50, + }, + "entanglement": { + "type": "string", + "description": "Entanglement pattern for quantum circuit", + "enum": ["linear", "circular", "full"], + "default": "linear", + }, + }, + "required": ["dataset"], + }, + ), + Tool( + name="estimate_quantum_cost", + description="Estimate the cost of running a quantum job on Azure Quantum hardware.", + inputSchema={ + "type": "object", + "properties": { + "circuit_id": { + "type": "string", + "description": "ID of the circuit to estimate", + }, + "backend_name": { + "type": "string", + "description": "Backend to estimate cost for", + }, + "shots": { + "type": "integer", + "description": "Number of shots", + "default": 100, + }, + }, + "required": ["circuit_id", "backend_name"], + }, + ), + Tool( + name="get_quantum_circuit_properties", + description="Get properties of a quantum circuit including depth, gate count, and topology.", + inputSchema={ + "type": "object", + "properties": { + "circuit_id": { + "type": "string", + "description": "ID of the circuit to analyze", + }, + }, + "required": ["circuit_id"], + }, + ), + ] + + +@app.call_tool() +async def call_tool(name: str, arguments: Any) -> List[TextContent]: + """Handle tool calls""" + + try: + if arguments is None: + arguments = {} + if not isinstance(arguments, dict): + return [ + TextContent(type="text", text="Invalid arguments: expected an object") + ] + + if name == "create_quantum_circuit": + return await create_circuit_handler(arguments) + elif name == "simulate_quantum_circuit": + return await simulate_circuit_handler(arguments) + elif name == "connect_azure_quantum": + return await connect_azure_handler(arguments) + elif name == "list_quantum_backends": + return await list_backends_handler(arguments) + elif name == "submit_quantum_job": + return await submit_job_handler(arguments) + elif name == "train_quantum_classifier": + return await train_classifier_handler(arguments) + elif name == "estimate_quantum_cost": + return await estimate_cost_handler(arguments) + elif name == "get_quantum_circuit_properties": + return await circuit_properties_handler(arguments) + else: + return [TextContent(type="text", text=f"Unknown tool: {name}")] + + except Exception as e: + logger.error(f"Error executing {name}: {str(e)}") + return [TextContent(type="text", text=f"Error: {str(e)}")] + + +async def create_circuit_handler(args: Dict) -> List[TextContent]: + """Create a quantum circuit based on parameters (optimized with async execution)""" + # Periodic cache cleanup to prevent memory leaks + _cleanup_cache_if_needed() + + n_qubits = args.get("n_qubits") + circuit_type = args.get("circuit_type") + + # Runtime validation (do not rely only on tool schema). + if ( + isinstance(n_qubits, bool) + or not isinstance(n_qubits, int) + or not (1 <= n_qubits <= 20) + ): + return [ + TextContent( + type="text", text="n_qubits must be an integer between 1 and 20" + ) + ] + if circuit_type not in {"entanglement", "ghz", "bell", "random", "custom"}: + return [ + TextContent( + type="text", + text="circuit_type must be one of: entanglement, ghz, bell, random, custom", + ) + ] + + # Offload circuit creation to thread pool for CPU-intensive operations + loop = asyncio.get_event_loop() + try: + # Add timeout to prevent hanging (CRITICAL FIX) + circuit = await asyncio.wait_for( + loop.run_in_executor( + io_executor, + _create_circuit_sync, + n_qubits, + circuit_type, + args.get("gates"), + ), + timeout=30.0, # 30-second max for circuit creation + ) + except asyncio.TimeoutError: + return [TextContent(type="text", text="Circuit creation timed out after 30s")] + + if circuit is None: + return [ + TextContent( + type="text", + text="Invalid circuit type or missing gates for custom type", + ) + ] + + # Generate circuit ID using SHA-256 (MD5 is weak for security) + import hashlib + + circuit_id = hashlib.sha256(str(circuit).encode()).hexdigest()[:12] + + # Cache the circuit with TTL + quantum_state["circuit_cache"].put(circuit_id, circuit) + + result = { + "circuit_id": circuit_id, + "n_qubits": n_qubits, + "circuit_type": circuit_type, + "depth": circuit.depth(), + "gate_count": sum(circuit.count_ops().values()), + "qasm": circuit.qasm(), + "diagram": str(circuit.draw(output="text")), + } + + return [ + TextContent( + type="text", + text=f"Created quantum circuit:\n\n```\n{result['diagram']}\n```\n\nCircuit ID: {circuit_id}\nDepth: {result['depth']}, Gates: {result['gate_count']}", + ) + ] + + +def _create_circuit_sync( + n_qubits: int, circuit_type: str, gates: Optional[List] = None +) -> Optional[QuantumCircuit]: + """Synchronous circuit creation (runs in thread pool)""" + if circuit_type == "entanglement": + return create_sample_circuit(n_qubits) + elif circuit_type == "bell": + circuit = QuantumCircuit(2, 2) + circuit.h(0) + circuit.cx(0, 1) + circuit.measure([0, 1], [0, 1]) + return circuit + elif circuit_type == "ghz": + circuit = QuantumCircuit(n_qubits, n_qubits) + circuit.h(0) + for i in range(n_qubits - 1): + circuit.cx(i, i + 1) + circuit.measure(range(n_qubits), range(n_qubits)) + return circuit + elif circuit_type == "random": + circuit = QuantumCircuit(n_qubits, n_qubits) + import random + + for _ in range(n_qubits * 2): + gate = random.choice(["h", "x", "y", "z", "rx", "ry", "rz"]) + qubit = random.randint(0, n_qubits - 1) + if gate == "h": + circuit.h(qubit) + elif gate == "x": + circuit.x(qubit) + elif gate == "y": + circuit.y(qubit) + elif gate == "z": + circuit.z(qubit) + else: + circuit.rx(np.pi / 4, qubit) + circuit.measure(range(n_qubits), range(n_qubits)) + return circuit + elif circuit_type == "custom" and gates: + circuit = QuantumCircuit(n_qubits, n_qubits) + for gate_spec in gates: + if not isinstance(gate_spec, dict): + continue + gate_name = str(gate_spec.get("gate", "")).lower() + if not gate_name: + continue + if "qubit" in gate_spec: + qubit = gate_spec["qubit"] + # Validate qubit index is within circuit bounds. + if not isinstance(qubit, int) or not (0 <= qubit < n_qubits): + continue + if gate_name == "h": + circuit.h(qubit) + elif gate_name == "x": + circuit.x(qubit) + elif gate_name == "y": + circuit.y(qubit) + elif gate_name == "z": + circuit.z(qubit) + elif "qubits" in gate_spec: + qubits = gate_spec["qubits"] + # Validate both qubit indices and require two distinct qubits. + if ( + not isinstance(qubits, list) + or len(qubits) < 2 + or not all( + isinstance(q, int) and 0 <= q < n_qubits for q in qubits[:2] + ) + or qubits[0] == qubits[1] + ): + continue + if gate_name in ["cx", "cnot"]: + circuit.cx(qubits[0], qubits[1]) + elif gate_name == "cz": + circuit.cz(qubits[0], qubits[1]) + circuit.measure(range(n_qubits), range(n_qubits)) + return circuit + return None + + +async def simulate_circuit_handler(args: Dict) -> List[TextContent]: + """Simulate a quantum circuit locally (optimized with async execution)""" + circuit_id = _normalize_required_string(args.get("circuit_id")) + shots = args.get("shots", 1024) + + if circuit_id is None: + return [TextContent(type="text", text="circuit_id is required")] + + # Validate shots to prevent DoS via excessively large simulation jobs. + if ( + isinstance(shots, bool) + or not isinstance(shots, int) + or not (1 <= shots <= 8192) + ): + return [ + TextContent(type="text", text="shots must be an integer between 1 and 8192") + ] + + # Clean expired cache entries periodically + quantum_state["circuit_cache"].clear_expired() + + circuit = quantum_state["circuit_cache"].get(circuit_id) + if circuit is None: + return [ + TextContent( + type="text", + text=f"Circuit ID '{circuit_id}' not found or expired. Create a circuit first.", + ) + ] + + # Offload simulation to thread pool (CPU-intensive) + loop = asyncio.get_event_loop() + counts = await loop.run_in_executor( + cpu_executor, _simulate_circuit_sync, circuit, shots + ) + + # Format results + sorted_counts = dict(sorted(counts.items(), key=lambda x: x[1], reverse=True)) + total_shots = sum(sorted_counts.values()) + + result_text = f"Simulation results for circuit {circuit_id} ({shots} shots):\n\n" + for state, count in list(sorted_counts.items())[:10]: # Top 10 states + probability = count / total_shots + result_text += f" |{state}⟩: {count} ({probability:.3%})\n" + + if len(sorted_counts) > 10: + result_text += f"\n ... and {len(sorted_counts) - 10} more states" + + return [TextContent(type="text", text=result_text)] + + +def _simulate_circuit_sync(circuit: QuantumCircuit, shots: int) -> Dict: + """Synchronous circuit simulation (runs in process pool)""" + from qiskit_aer import AerSimulator + + simulator = AerSimulator() + job = simulator.run(circuit, shots=shots) + result = job.result() + return result.get_counts() + + +async def connect_azure_handler(args: Dict) -> List[TextContent]: + """Connect to Azure Quantum workspace (optimized with config caching)""" + subscription_id = _normalize_required_string(args.get("subscription_id")) + resource_group = _normalize_required_string(args.get("resource_group")) + workspace_name = _normalize_required_string(args.get("workspace_name")) + + if subscription_id is None: + return [TextContent(type="text", text="subscription_id is required")] + if resource_group is None: + return [TextContent(type="text", text="resource_group is required")] + if workspace_name is None: + return [TextContent(type="text", text="workspace_name is required")] + + try: + # Check config cache to avoid repeated temp file creation + config_key = f"{subscription_id}_{resource_group}_{workspace_name}" + + if config_key not in quantum_state["config_cache"]: + # Create config in-memory instead of temp file + config = { + "azure": { + "subscription_id": subscription_id, + "resource_group": resource_group, + "workspace_name": workspace_name, + }, + "quantum": { + "provider": "ionq", + "hardware": {"shots": 500, "optimization_level": 2}, + }, + "ml": { + "model": {"n_qubits": 4, "n_layers": 2, "entanglement": "linear"} + }, + "logging": {"level": "INFO", "results_dir": "./results"}, + } + quantum_state["config_cache"][config_key] = config + + # Offload Azure connection to thread pool (I/O bound) + loop = asyncio.get_event_loop() + azure_int = await loop.run_in_executor( + io_executor, _connect_azure_sync, quantum_state["config_cache"][config_key] + ) + quantum_state["azure_integration"] = azure_int + quantum_state["cumulative_cost_usd"] = 0.0 + quantum_state["known_backends"] = [] + quantum_state["known_backends_refreshed_at"] = 0.0 + + return [ + TextContent( + type="text", + text=f"✓ Connected to Azure Quantum workspace: {workspace_name}\nResource Group: {resource_group}\nCost budget reset: ${quantum_state['cumulative_cost_usd']:.2f}", + ) + ] + + except Exception as e: + return [ + TextContent( + type="text", text=f"Failed to connect to Azure Quantum: {str(e)}" + ) + ] + + +def _connect_azure_sync(config: Dict) -> AzureQuantumIntegration: + """Synchronous Azure connection (runs in thread pool)""" + import os + import tempfile + + import yaml + + # Write config to a temp file; always clean it up regardless of success/failure + # to prevent credentials lingering on disk. + fd, config_path = tempfile.mkstemp(suffix=".yaml") + try: + with os.fdopen(fd, "w") as f: + yaml.safe_dump(config, f) + azure_int = AzureQuantumIntegration(config_path=config_path) + azure_int.connect() + return azure_int + finally: + try: + os.unlink(config_path) + except OSError: + pass + + +async def list_backends_handler(args: Dict) -> List[TextContent]: + """List available quantum backends (optimized with async I/O)""" + if quantum_state["azure_integration"] is None: + return [ + TextContent( + type="text", + text="Not connected to Azure Quantum. Use connect_azure_quantum first.", + ) + ] + + try: + # Offload to thread pool + loop = asyncio.get_event_loop() + backends = await loop.run_in_executor( + io_executor, quantum_state["azure_integration"].list_backends + ) + quantum_state["known_backends"] = list(backends) + quantum_state["known_backends_refreshed_at"] = time.time() + backend_list = "\n".join(f" • {backend}" for backend in backends) + return [ + TextContent( + type="text", text=f"Available quantum backends:\n\n{backend_list}" + ) + ] + except Exception as e: + return [TextContent(type="text", text=f"Error listing backends: {str(e)}")] + + +async def submit_job_handler(args: Dict) -> List[TextContent]: + """Submit a quantum job to Azure Quantum with cost-gating (optimized)""" + circuit_id = _normalize_required_string(args.get("circuit_id")) + backend_name = _normalize_required_string(args.get("backend_name")) + confirm_cost = args.get("confirm_cost", False) + shots = args.get("shots", 500) + + if circuit_id is None: + return [TextContent(type="text", text="circuit_id is required")] + if backend_name is None: + return [TextContent(type="text", text="backend_name is required")] + if not isinstance(confirm_cost, bool): + return [TextContent(type="text", text="confirm_cost must be a boolean")] + if ( + isinstance(shots, bool) + or not isinstance(shots, int) + or not (1 <= shots <= 10000) + ): + return [ + TextContent( + type="text", text="shots must be an integer between 1 and 10000" + ) + ] + + if quantum_state["azure_integration"] is None: + return [ + TextContent( + type="text", + text="Not connected to Azure Quantum. Use connect_azure_quantum first.", + ) + ] + + circuit = quantum_state["circuit_cache"].get(circuit_id) + if circuit is None: + return [ + TextContent( + type="text", text=f"Circuit ID '{circuit_id}' not found or expired." + ) + ] + + allowlist_error = await _validate_backend_against_allowlist(backend_name) + if allowlist_error: + return [TextContent(type="text", text=allowlist_error)] + + loop = asyncio.get_event_loop() + + # Cost-gating: estimate cost and validate against budget limits + try: + estimated_cost = await loop.run_in_executor( + io_executor, _estimate_job_cost_sync, circuit, backend_name, shots + ) + + if _requires_cost_confirmation(backend_name) and not confirm_cost: + return [ + TextContent( + type="text", + text=( + f"Paid-backend confirmation required for '{backend_name}'. " + f"Review the estimated cost with estimate_quantum_cost first, then resubmit with confirm_cost=true. " + f"Estimated cost before safety margin: ${estimated_cost:.2f}." + ), + ) + ] + + # Apply safety multiplier and check against limits + safeguarded_cost = estimated_cost * COST_ESTIMATE_MULTIPLIER + if safeguarded_cost > MAX_COST_PER_JOB_USD: + return [ + TextContent( + type="text", + text=f"Cost limit exceeded: Estimated cost ${safeguarded_cost:.2f} exceeds max ${MAX_COST_PER_JOB_USD:.2f} per job. Reduce shots or use a simulator backend.", + ) + ] + if ( + quantum_state["cumulative_cost_usd"] + safeguarded_cost + > MAX_CUMULATIVE_COST_PER_SESSION_USD + ): + return [ + TextContent( + type="text", + text=f"Session budget exceeded: Current cost ${quantum_state['cumulative_cost_usd']:.2f} + this job ${safeguarded_cost:.2f} would exceed max ${MAX_CUMULATIVE_COST_PER_SESSION_USD:.2f} per session.", + ) + ] + + # Cost gates passed, proceed with submission + job = await loop.run_in_executor( + io_executor, + quantum_state["azure_integration"].submit_circuit, + circuit, + backend_name, + shots, + f"mcp_{circuit_id}", + ) + quantum_state["cumulative_cost_usd"] += safeguarded_cost + return [ + TextContent( + type="text", + text=f"✓ Job submitted to Azure Quantum\nJob ID: {job.id()}\nBackend: {backend_name or 'default'}\nShots: {shots}\nEstimated Cost: ${safeguarded_cost:.2f}\nSession Total: ${quantum_state['cumulative_cost_usd']:.2f}\n\nNote: Job execution may take several minutes depending on queue.", + ) + ] + except Exception as e: + return [TextContent(type="text", text=f"Error submitting job: {str(e)}")] + + +async def train_classifier_handler(args: Dict) -> List[TextContent]: + """Train a quantum classifier (optimized with process pool for CPU-intensive training)""" + dataset_name = args.get("dataset") + n_qubits = args.get("n_qubits", 4) + n_layers = args.get("n_layers", 2) + epochs = args.get("epochs", 50) + entanglement = args.get("entanglement", "linear") + + if dataset_name not in {"iris", "wine", "breast_cancer", "synthetic"}: + return [ + TextContent( + type="text", + text="dataset must be one of: iris, wine, breast_cancer, synthetic", + ) + ] + if ( + isinstance(n_qubits, bool) + or not isinstance(n_qubits, int) + or not (2 <= n_qubits <= 10) + ): + return [ + TextContent( + type="text", text="n_qubits must be an integer between 2 and 10" + ) + ] + if ( + isinstance(n_layers, bool) + or not isinstance(n_layers, int) + or not (1 <= n_layers <= 5) + ): + return [ + TextContent(type="text", text="n_layers must be an integer between 1 and 5") + ] + if ( + isinstance(epochs, bool) + or not isinstance(epochs, int) + or not (1 <= epochs <= 200) + ): + return [ + TextContent(type="text", text="epochs must be an integer between 1 and 200") + ] + if entanglement not in {"linear", "circular", "full"}: + return [ + TextContent( + type="text", text="entanglement must be one of: linear, circular, full" + ) + ] + + # Offload data loading and training to process pool + loop = asyncio.get_event_loop() + result_text = await loop.run_in_executor( + cpu_executor, + _train_classifier_sync, + dataset_name, + n_qubits, + n_layers, + epochs, + entanglement, + ) + + return [TextContent(type="text", text=result_text)] + + +def _train_classifier_sync( + dataset_name: str, n_qubits: int, n_layers: int, epochs: int, entanglement: str +) -> str: + """Synchronous training (runs in process pool)""" + import tempfile + + import yaml + from sklearn.datasets import (load_breast_cancer, load_iris, load_wine, + make_classification) + from sklearn.model_selection import train_test_split + from sklearn.preprocessing import StandardScaler + + # Load dataset + if dataset_name == "iris": + data = load_iris() + X, y = data.data, (data.target > 0).astype(int) + elif dataset_name == "wine": + data = load_wine() + X, y = data.data, (data.target > 0).astype(int) + elif dataset_name == "breast_cancer": + data = load_breast_cancer() + X, y = data.data, data.target + elif dataset_name == "synthetic": + X, y = make_classification( + n_samples=200, n_features=4, n_classes=2, random_state=42 + ) + else: + return f"Unknown dataset: {dataset_name}" + + # Prepare data + scaler = StandardScaler() + X = scaler.fit_transform(X) + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + try: + # Create config + config = { + "quantum": {"simulator": {"backend": "qiskit_aer", "shots": 1024}}, + "ml": { + "model": { + "n_qubits": n_qubits, + "n_layers": n_layers, + "entanglement": entanglement, + }, + "training": { + "epochs": epochs, + "batch_size": 16, + "learning_rate": 0.01, + "validation_split": 0.2, + }, + }, + } + + import os + + fd, config_path = tempfile.mkstemp(suffix=".yaml") + try: + with os.fdopen(fd, "w") as f: + yaml.safe_dump(config, f) + qc = QuantumClassifier(config_path=config_path) + model = HybridQuantumClassifier(input_dim=X.shape[1], quantum_classifier=qc) + history = train_quantum_model( + model, X_train, y_train, X_val, y_val, config_path=config_path + ) + finally: + try: + os.unlink(config_path) + except OSError: + pass + + final_acc = history["val_acc"][-1] + final_loss = history["val_loss"][-1] + + return f"""✓ Quantum classifier training completed! + +Dataset: {dataset_name} +Architecture: + - Qubits: {n_qubits} + - Layers: {n_layers} + - Entanglement: {entanglement} + - Epochs: {epochs} + +Results: + - Final Validation Accuracy: {final_acc:.2%} + - Final Validation Loss: {final_loss:.4f} + +Training History: + - Best Accuracy: {max(history['val_acc']):.2%} + - Best Loss: {min(history['val_loss']):.4f} +""" + + except Exception as e: + logger.error(f"Training error: {str(e)}") + return f"Training failed: {str(e)}" + + +async def estimate_cost_handler(args: Dict) -> List[TextContent]: + """Estimate quantum job cost (optimized)""" + circuit_id = _normalize_required_string(args.get("circuit_id")) + backend_name = _normalize_required_string(args.get("backend_name")) + shots = args.get("shots", 100) + + if circuit_id is None: + return [TextContent(type="text", text="circuit_id is required")] + if backend_name is None: + return [TextContent(type="text", text="backend_name is required")] + if ( + isinstance(shots, bool) + or not isinstance(shots, int) + or not (1 <= shots <= 10000) + ): + return [ + TextContent( + type="text", text="shots must be an integer between 1 and 10000" + ) + ] + + if quantum_state["azure_integration"] is None: + return [TextContent(type="text", text="Not connected to Azure Quantum.")] + + circuit = quantum_state["circuit_cache"].get(circuit_id) + if circuit is None: + return [ + TextContent( + type="text", text=f"Circuit ID '{circuit_id}' not found or expired." + ) + ] + + allowlist_error = await _validate_backend_against_allowlist(backend_name) + if allowlist_error: + return [TextContent(type="text", text=allowlist_error)] + + try: + heuristic_cost = _estimate_job_cost_sync(circuit, backend_name, shots) + safeguarded_cost = heuristic_cost * COST_ESTIMATE_MULTIPLIER + loop = asyncio.get_event_loop() + estimate = await loop.run_in_executor( + io_executor, + quantum_state["azure_integration"].estimate_cost, + circuit, + backend_name, + shots, + ) + + cost_info = f"""Cost Estimate for {backend_name}: + +Shots: {shots} +Estimated Time: {estimate.get('estimated_time_minutes', 'N/A')} minutes + Heuristic Price Estimate: ${heuristic_cost:.2f} + Submission Guardrail Ceiling: ${safeguarded_cost:.2f} + +{estimate.get('note', '')} + +Note: Actual costs vary by provider: + - IonQ: ~$0.00003 per gate-shot + - Quantinuum: ~$0.00015 per circuit execution + - Microsoft simulators: Free +""" + return [TextContent(type="text", text=cost_info)] + + except Exception as e: + return [TextContent(type="text", text=f"Error estimating cost: {str(e)}")] + + +async def circuit_properties_handler(args: Dict) -> List[TextContent]: + """Get circuit properties (optimized with caching)""" + circuit_id = _normalize_required_string(args.get("circuit_id")) + + if circuit_id is None: + return [TextContent(type="text", text="circuit_id is required")] + + circuit = quantum_state["circuit_cache"].get(circuit_id) + if circuit is None: + return [ + TextContent( + type="text", text=f"Circuit ID '{circuit_id}' not found or expired." + ) + ] + + gate_counts = circuit.count_ops() + + properties = f"""Circuit Properties for {circuit_id}: + +Basic Info: + - Qubits: {circuit.num_qubits} + - Classical Bits: {circuit.num_clbits} + - Depth: {circuit.depth()} + - Size (total gates): {circuit.size()} + +Gate Breakdown: +""" + for gate, count in gate_counts.items(): + properties += f" - {gate}: {count}\n" + + return [TextContent(type="text", text=properties)] + + +async def main(): + """Run the MCP server""" + logger.info("Starting Quantum AI MCP Server (Optimized)...") + initialize_quantum_resources() + + async with stdio_server() as (read_stream, write_stream): + await app.run(read_stream, write_stream, app.create_initialization_options()) + + +async def cleanup(): + """Cleanup resources on shutdown""" + cpu_executor.shutdown(wait=True) + io_executor.shutdown(wait=True) + + +if __name__ == "__main__": + try: + asyncio.run(main()) + finally: + asyncio.run(cleanup()) diff --git a/ai-projects/quantum-ml/quick_test_datasets.py b/ai-projects/quantum-ml/quick_test_datasets.py index 7bdf7cb2e..bc132c6b1 100644 --- a/ai-projects/quantum-ml/quick_test_datasets.py +++ b/ai-projects/quantum-ml/quick_test_datasets.py @@ -1,433 +1,433 @@ -""" -Quick Test: Validate All Quantum Datasets -========================================== - -Rapidly validates that all 29 datasets can be: -1. Successfully loaded -2. Preprocessed for quantum circuits -3. Trained for 1 epoch (smoke test) - -This script provides fast feedback on dataset compatibility before -running full benchmarks or hyperparameter optimization. - -Author: Quantum AI System -Date: November 16, 2025 (Updated) -""" - -import json -import sys -import time -from datetime import datetime -from pathlib import Path - -import numpy as np -import pandas as pd - -# Add src to path -src_path = Path(__file__).parent / "src" -sys.path.insert(0, str(src_path)) - -import torch -from sklearn.decomposition import PCA -from sklearn.impute import SimpleImputer -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer -from torch.utils.data import DataLoader, TensorDataset - -# All 29 datasets (26 working, 3 excluded: vertebral_column, ecoli corrupted) -DATASETS = [ - # Original 4 - "ionosphere", - "banknote", - "heart_disease", - "sonar", - # Medical (8 total) - "breast_cancer", - "diabetes", - "blood_transfusion", - "haberman", - "parkinsons", - "dermatology", - "liver_disorders", - "thyroid", - "statlog_heart", - # Biology (3 working - ecoli corrupted) - "yeast", - # Chemistry (3 total) - "wine_red", - "wine_white", - "wine_quality_combined", - # Image Features (4 total) - "iris", - "optical_recognition", - "pendigits", - # Agriculture (2 total) - "wheat_seeds", - "seeds", - # Finance (1) - "statlog_australian", - # Physics (1) - "balance_scale", - # Social Science (1) - "contraceptive", - # Other - "magic_gamma", - "glass", - # Note: vertebral_column and ecoli excluded (corrupted) -] - - -def load_and_preprocess(dataset_name, n_qubits=4): - """Load and preprocess a dataset""" - dataset_path = ( - Path(__file__).parent.parent / "datasets" / "quantum" / f"{dataset_name}.csv" - ) - - print(f" Loading {dataset_name}...", end=" ") - - try: - # Dataset-specific loading strategies - if dataset_name in {"wine_red", "wine_white"}: - # These use semicolon delimiter with header - df = pd.read_csv(dataset_path, sep=";", na_values=["?", "NA", "", "NaN"]) - elif dataset_name == "wine_quality_combined": - # Combined wine dataset with comma delimiter (not semicolon!) - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - elif dataset_name in {"wheat_seeds", "seeds"}: - # Whitespace-delimited datasets with no header - df = pd.read_csv( - dataset_path, sep=r"\s+", header=None, na_values=["?", "NA", "", "NaN"] - ) - elif dataset_name == "ecoli": - # Known corrupted dataset - skip - raise ValueError("Dataset file appears to be corrupted or empty") - elif dataset_name == "yeast": - # Whitespace-delimited, no header, skip first column (sequence name) - df = pd.read_csv( - dataset_path, sep=r"\s+", header=None, na_values=["?", "NA", "", "NaN"] - ) - df = df.iloc[:, 1:] # Skip sequence name column - elif dataset_name == "parkinsons": - # Comma-delimited with header, skip first column (name) - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - df = df.drop(columns=df.columns[0]) # Skip name column - elif dataset_name in {"statlog_australian", "statlog_heart"}: - # Space-delimited, no header - df = pd.read_csv( - dataset_path, sep=" ", header=None, na_values=["?", "NA", "", "NaN"] - ) - elif dataset_name == "vertebral_column": - # Binary file or severely corrupted - skip for now - raise ValueError("Dataset file appears to be corrupted or binary format") - elif dataset_name == "blood_transfusion": - # Has header with description in first row, skip it - df = pd.read_csv(dataset_path, skiprows=1, na_values=["?", "NA", "", "NaN"]) - elif dataset_name == "breast_cancer": - # No header, need to skip ID column - df = pd.read_csv( - dataset_path, header=None, na_values=["?", "NA", "", "NaN"] - ) - elif dataset_name == "balance_scale": - # Comma-delimited with header - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - elif dataset_name in { - "optical_recognition", - "pendigits", - "contraceptive", - "dermatology", - "liver_disorders", - "thyroid", - }: - # Comma-delimited, no header - df = pd.read_csv( - dataset_path, header=None, na_values=["?", "NA", "", "NaN"] - ) - else: - # Standard loading with fallback - try: - df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) - # Check if it looks like semicolon-delimited - if df.shape[1] == 1 and ";" in str(df.iloc[0, 0]): - df = pd.read_csv( - dataset_path, sep=";", na_values=["?", "NA", "", "NaN"] - ) - except UnicodeDecodeError: - # Try different encoding - try: - df = pd.read_csv( - dataset_path, - na_values=["?", "NA", "", "NaN"], - encoding="latin-1", - ) - except: - df = pd.read_csv( - dataset_path, - sep=";", - na_values=["?", "NA", "", "NaN"], - encoding="latin-1", - ) - - # Check if first row looks like data (all numeric except possibly last column) - only for unhandled cases - if dataset_name not in { - "breast_cancer", - "vertebral_column", - "blood_transfusion", - "wine_red", - "wine_white", - "wine_quality_combined", - "wheat_seeds", - }: - first_row_numeric = all( - str(df.iloc[0, i]) - .replace(".", "") - .replace("-", "") - .replace("e", "") - .isdigit() - or str(df.iloc[0, i]) - .replace(".", "") - .replace("-", "") - .replace("e", "") - .replace("+", "") - .isdigit() - for i in range(min(3, df.shape[1] - 1)) - ) - - if ( - first_row_numeric - or df.columns[0].replace(".", "").replace("-", "").isdigit() - ): - # No header - reload without header - try: - df = pd.read_csv( - dataset_path, header=None, na_values=["?", "NA", "", "NaN"] - ) - except UnicodeDecodeError: - df = pd.read_csv( - dataset_path, - header=None, - na_values=["?", "NA", "", "NaN"], - encoding="latin-1", - ) - - # Separate features and labels (last column is target) - X = df.iloc[:, :-1] - y = df.iloc[:, -1].values - - # Dataset-specific feature handling - if dataset_name == "breast_cancer": - # Column 0 is ID, Column 1 is diagnosis (M/B), rest are features - # We need to use column 1 as label and skip column 0 - if X.shape[1] > 20: # Has ID column - y = X.iloc[ - :, 0 - ].values # Second column (index 1 in original) is now at index 0 after removing last - X = X.iloc[:, 1:] # Skip both ID and diagnosis, keep only features - # Now y is from what was the label column, and X has only numeric features - # Actually, let's reload this correctly - X = df.iloc[ - :, 2:-1 - ] # Skip ID (col 0) and diagnosis (col 1), take features - y = df.iloc[:, 1].values # Diagnosis column - - # Impute missing values in features if any - if X.isnull().any().any(): - imputer = SimpleImputer(strategy="median") - X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) - - X = X.values - - # Convert labels to binary (first class vs rest for multi-class) - unique_labels = np.unique(y) - if len(unique_labels) > 2 or y.dtype == object: - if y.dtype == object: - y = (y == unique_labels[0]).astype(int) - else: - # For multi-class numeric, convert to binary - y = (y == unique_labels[0]).astype(int) - else: - y = (y != 0).astype(int) - - # Check class distribution for stratified split - unique, counts = np.unique(y, return_counts=True) - min_class_count = counts.min() - - # Use stratified split only if each class has at least 2 samples - if min_class_count >= 2: - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42, stratify=y - ) - else: - # Non-stratified split for very imbalanced data - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - # Standardize - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - # Reduce/pad to n_qubits features - n_features = X_train.shape[1] - - if n_features > n_qubits: - pca = PCA(n_components=n_qubits) - X_train = pca.fit_transform(X_train) - X_val = pca.transform(X_val) - elif n_features < n_qubits: - pad_train = np.zeros((X_train.shape[0], n_qubits - n_features)) - pad_val = np.zeros((X_val.shape[0], n_qubits - n_features)) - X_train = np.hstack([X_train, pad_train]) - X_val = np.hstack([X_val, pad_val]) - - print(f"✓ {len(X)} samples, {X.shape[1]}→{n_qubits} features") - return X_train, X_val, y_train, y_val, True, "" - - except Exception as e: - print(f"✗ ERROR: {str(e)}") - return None, None, None, None, False, str(e) - - -def quick_train(X_train, y_train, X_val, y_val, dataset_name): - """Train for 1 epoch to validate model works""" - n_qubits = X_train.shape[1] - - try: - # Create model - model = HybridQNN( - input_dim=n_qubits, - hidden_dim=16, - n_qubits=n_qubits, - n_quantum_layers=2, - output_dim=2, - dropout=0.2, - ) - - # Create data loaders - train_dataset = TensorDataset( - torch.FloatTensor(X_train), torch.LongTensor(y_train) - ) - val_dataset = TensorDataset(torch.FloatTensor(X_val), torch.LongTensor(y_val)) - - train_loader = DataLoader( - train_dataset, batch_size=16, shuffle=True, drop_last=True - ) - val_loader = DataLoader(val_dataset, batch_size=16, drop_last=True) - - # Create trainer - trainer = QuantumClassicalTrainer(model, learning_rate=0.001) - - # Train 1 epoch - print(f" Training {dataset_name}...", end=" ") - start_time = time.time() - - trainer.train(train_loader, val_loader, num_epochs=1) - - # Evaluate - val_acc, val_loss = trainer.evaluate(val_loader) - train_time = time.time() - start_time - - print(f"✓ Acc: {val_acc:.2%}, Loss: {val_loss:.4f}, Time: {train_time:.1f}s") - - return True, val_acc, val_loss, train_time, "" - - except Exception as e: - print(f"✗ TRAINING ERROR: {str(e)}") - return False, 0.0, 0.0, 0.0, str(e) - - -def main(): - print("=" * 70) - print(" QUICK TEST: ALL QUANTUM DATASETS") - print("=" * 70) - print(f"\nTesting {len(DATASETS)} datasets with 1-epoch smoke tests\n") - - results = {} - success_count = 0 - load_failures = [] - train_failures = [] - - total_start = time.time() - - for dataset_name in DATASETS: - print(f"\n📊 {dataset_name.upper()}") - print("-" * 70) - - # Load and preprocess - X_train, X_val, y_train, y_val, load_success, load_error = load_and_preprocess( - dataset_name - ) - - if not load_success: - load_failures.append((dataset_name, load_error)) - results[dataset_name] = {"status": "load_failed", "error": load_error} - continue - - # Quick train - train_success, val_acc, val_loss, train_time, train_error = quick_train( - X_train, y_train, X_val, y_val, dataset_name - ) - - if not train_success: - train_failures.append((dataset_name, train_error)) - results[dataset_name] = { - "status": "train_failed", - "error": train_error, - "samples": len(X_train) + len(X_val), - } - else: - success_count += 1 - results[dataset_name] = { - "status": "success", - "val_accuracy": float(val_acc), - "val_loss": float(val_loss), - "train_time_seconds": float(train_time), - "samples": len(X_train) + len(X_val), - "train_samples": len(X_train), - "val_samples": len(X_val), - } - - total_time = time.time() - total_start - - # Summary - print("\n" + "=" * 70) - print(" SUMMARY") - print("=" * 70) - print(f"\n✅ Successful: {success_count}/{len(DATASETS)}") - print(f"⏱️ Total time: {total_time:.1f}s") - - if load_failures: - print(f"\n❌ Load failures ({len(load_failures)}):") - for name, error in load_failures: - print(f" - {name}: {error}") - - if train_failures: - print(f"\n❌ Training failures ({len(train_failures)}):") - for name, error in train_failures: - print(f" - {name}: {error}") - - # Save results - output_file = Path(__file__).parent / "results" / "quick_test_results.json" - output_file.parent.mkdir(exist_ok=True) - - output_data = { - "timestamp": datetime.now().isoformat(), - "total_datasets": len(DATASETS), - "successful": success_count, - "total_time_seconds": total_time, - "results": results, - } - - with open(output_file, "w") as f: - json.dump(output_data, f, indent=2) - - print(f"\n📄 Results saved: {output_file}") - - print("\n" + "=" * 70) - print(" ✅ QUICK TEST COMPLETE") - print("=" * 70) - - -if __name__ == "__main__": - main() +""" +Quick Test: Validate All Quantum Datasets +========================================== + +Rapidly validates that all 29 datasets can be: +1. Successfully loaded +2. Preprocessed for quantum circuits +3. Trained for 1 epoch (smoke test) + +This script provides fast feedback on dataset compatibility before +running full benchmarks or hyperparameter optimization. + +Author: Quantum AI System +Date: November 16, 2025 (Updated) +""" + +import json +import sys +import time +from datetime import datetime +from pathlib import Path + +import numpy as np +import pandas as pd + +# Add src to path +src_path = Path(__file__).parent / "src" +sys.path.insert(0, str(src_path)) + +import torch +from sklearn.decomposition import PCA +from sklearn.impute import SimpleImputer +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer +from torch.utils.data import DataLoader, TensorDataset + +# All 29 datasets (26 working, 3 excluded: vertebral_column, ecoli corrupted) +DATASETS = [ + # Original 4 + "ionosphere", + "banknote", + "heart_disease", + "sonar", + # Medical (8 total) + "breast_cancer", + "diabetes", + "blood_transfusion", + "haberman", + "parkinsons", + "dermatology", + "liver_disorders", + "thyroid", + "statlog_heart", + # Biology (3 working - ecoli corrupted) + "yeast", + # Chemistry (3 total) + "wine_red", + "wine_white", + "wine_quality_combined", + # Image Features (4 total) + "iris", + "optical_recognition", + "pendigits", + # Agriculture (2 total) + "wheat_seeds", + "seeds", + # Finance (1) + "statlog_australian", + # Physics (1) + "balance_scale", + # Social Science (1) + "contraceptive", + # Other + "magic_gamma", + "glass", + # Note: vertebral_column and ecoli excluded (corrupted) +] + + +def load_and_preprocess(dataset_name, n_qubits=4): + """Load and preprocess a dataset""" + dataset_path = ( + Path(__file__).parent.parent / "datasets" / "quantum" / f"{dataset_name}.csv" + ) + + print(f" Loading {dataset_name}...", end=" ") + + try: + # Dataset-specific loading strategies + if dataset_name in {"wine_red", "wine_white"}: + # These use semicolon delimiter with header + df = pd.read_csv(dataset_path, sep=";", na_values=["?", "NA", "", "NaN"]) + elif dataset_name == "wine_quality_combined": + # Combined wine dataset with comma delimiter (not semicolon!) + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + elif dataset_name in {"wheat_seeds", "seeds"}: + # Whitespace-delimited datasets with no header + df = pd.read_csv( + dataset_path, sep=r"\s+", header=None, na_values=["?", "NA", "", "NaN"] + ) + elif dataset_name == "ecoli": + # Known corrupted dataset - skip + raise ValueError("Dataset file appears to be corrupted or empty") + elif dataset_name == "yeast": + # Whitespace-delimited, no header, skip first column (sequence name) + df = pd.read_csv( + dataset_path, sep=r"\s+", header=None, na_values=["?", "NA", "", "NaN"] + ) + df = df.iloc[:, 1:] # Skip sequence name column + elif dataset_name == "parkinsons": + # Comma-delimited with header, skip first column (name) + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + df = df.drop(columns=df.columns[0]) # Skip name column + elif dataset_name in {"statlog_australian", "statlog_heart"}: + # Space-delimited, no header + df = pd.read_csv( + dataset_path, sep=" ", header=None, na_values=["?", "NA", "", "NaN"] + ) + elif dataset_name == "vertebral_column": + # Binary file or severely corrupted - skip for now + raise ValueError("Dataset file appears to be corrupted or binary format") + elif dataset_name == "blood_transfusion": + # Has header with description in first row, skip it + df = pd.read_csv(dataset_path, skiprows=1, na_values=["?", "NA", "", "NaN"]) + elif dataset_name == "breast_cancer": + # No header, need to skip ID column + df = pd.read_csv( + dataset_path, header=None, na_values=["?", "NA", "", "NaN"] + ) + elif dataset_name == "balance_scale": + # Comma-delimited with header + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + elif dataset_name in { + "optical_recognition", + "pendigits", + "contraceptive", + "dermatology", + "liver_disorders", + "thyroid", + }: + # Comma-delimited, no header + df = pd.read_csv( + dataset_path, header=None, na_values=["?", "NA", "", "NaN"] + ) + else: + # Standard loading with fallback + try: + df = pd.read_csv(dataset_path, na_values=["?", "NA", "", "NaN"]) + # Check if it looks like semicolon-delimited + if df.shape[1] == 1 and ";" in str(df.iloc[0, 0]): + df = pd.read_csv( + dataset_path, sep=";", na_values=["?", "NA", "", "NaN"] + ) + except UnicodeDecodeError: + # Try different encoding + try: + df = pd.read_csv( + dataset_path, + na_values=["?", "NA", "", "NaN"], + encoding="latin-1", + ) + except: + df = pd.read_csv( + dataset_path, + sep=";", + na_values=["?", "NA", "", "NaN"], + encoding="latin-1", + ) + + # Check if first row looks like data (all numeric except possibly last column) - only for unhandled cases + if dataset_name not in { + "breast_cancer", + "vertebral_column", + "blood_transfusion", + "wine_red", + "wine_white", + "wine_quality_combined", + "wheat_seeds", + }: + first_row_numeric = all( + str(df.iloc[0, i]) + .replace(".", "") + .replace("-", "") + .replace("e", "") + .isdigit() + or str(df.iloc[0, i]) + .replace(".", "") + .replace("-", "") + .replace("e", "") + .replace("+", "") + .isdigit() + for i in range(min(3, df.shape[1] - 1)) + ) + + if ( + first_row_numeric + or df.columns[0].replace(".", "").replace("-", "").isdigit() + ): + # No header - reload without header + try: + df = pd.read_csv( + dataset_path, header=None, na_values=["?", "NA", "", "NaN"] + ) + except UnicodeDecodeError: + df = pd.read_csv( + dataset_path, + header=None, + na_values=["?", "NA", "", "NaN"], + encoding="latin-1", + ) + + # Separate features and labels (last column is target) + X = df.iloc[:, :-1] + y = df.iloc[:, -1].values + + # Dataset-specific feature handling + if dataset_name == "breast_cancer": + # Column 0 is ID, Column 1 is diagnosis (M/B), rest are features + # We need to use column 1 as label and skip column 0 + if X.shape[1] > 20: # Has ID column + y = X.iloc[ + :, 0 + ].values # Second column (index 1 in original) is now at index 0 after removing last + X = X.iloc[:, 1:] # Skip both ID and diagnosis, keep only features + # Now y is from what was the label column, and X has only numeric features + # Actually, let's reload this correctly + X = df.iloc[ + :, 2:-1 + ] # Skip ID (col 0) and diagnosis (col 1), take features + y = df.iloc[:, 1].values # Diagnosis column + + # Impute missing values in features if any + if X.isnull().any().any(): + imputer = SimpleImputer(strategy="median") + X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) + + X = X.values + + # Convert labels to binary (first class vs rest for multi-class) + unique_labels = np.unique(y) + if len(unique_labels) > 2 or y.dtype == object: + if y.dtype == object: + y = (y == unique_labels[0]).astype(int) + else: + # For multi-class numeric, convert to binary + y = (y == unique_labels[0]).astype(int) + else: + y = (y != 0).astype(int) + + # Check class distribution for stratified split + unique, counts = np.unique(y, return_counts=True) + min_class_count = counts.min() + + # Use stratified split only if each class has at least 2 samples + if min_class_count >= 2: + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + else: + # Non-stratified split for very imbalanced data + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + # Standardize + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + # Reduce/pad to n_qubits features + n_features = X_train.shape[1] + + if n_features > n_qubits: + pca = PCA(n_components=n_qubits) + X_train = pca.fit_transform(X_train) + X_val = pca.transform(X_val) + elif n_features < n_qubits: + pad_train = np.zeros((X_train.shape[0], n_qubits - n_features)) + pad_val = np.zeros((X_val.shape[0], n_qubits - n_features)) + X_train = np.hstack([X_train, pad_train]) + X_val = np.hstack([X_val, pad_val]) + + print(f"✓ {len(X)} samples, {X.shape[1]}→{n_qubits} features") + return X_train, X_val, y_train, y_val, True, "" + + except Exception as e: + print(f"✗ ERROR: {str(e)}") + return None, None, None, None, False, str(e) + + +def quick_train(X_train, y_train, X_val, y_val, dataset_name): + """Train for 1 epoch to validate model works""" + n_qubits = X_train.shape[1] + + try: + # Create model + model = HybridQNN( + input_dim=n_qubits, + hidden_dim=16, + n_qubits=n_qubits, + n_quantum_layers=2, + output_dim=2, + dropout=0.2, + ) + + # Create data loaders + train_dataset = TensorDataset( + torch.FloatTensor(X_train), torch.LongTensor(y_train) + ) + val_dataset = TensorDataset(torch.FloatTensor(X_val), torch.LongTensor(y_val)) + + train_loader = DataLoader( + train_dataset, batch_size=16, shuffle=True, drop_last=True + ) + val_loader = DataLoader(val_dataset, batch_size=16, drop_last=True) + + # Create trainer + trainer = QuantumClassicalTrainer(model, learning_rate=0.001) + + # Train 1 epoch + print(f" Training {dataset_name}...", end=" ") + start_time = time.time() + + trainer.train(train_loader, val_loader, num_epochs=1) + + # Evaluate + val_acc, val_loss = trainer.evaluate(val_loader) + train_time = time.time() - start_time + + print(f"✓ Acc: {val_acc:.2%}, Loss: {val_loss:.4f}, Time: {train_time:.1f}s") + + return True, val_acc, val_loss, train_time, "" + + except Exception as e: + print(f"✗ TRAINING ERROR: {str(e)}") + return False, 0.0, 0.0, 0.0, str(e) + + +def main(): + print("=" * 70) + print(" QUICK TEST: ALL QUANTUM DATASETS") + print("=" * 70) + print(f"\nTesting {len(DATASETS)} datasets with 1-epoch smoke tests\n") + + results = {} + success_count = 0 + load_failures = [] + train_failures = [] + + total_start = time.time() + + for dataset_name in DATASETS: + print(f"\n📊 {dataset_name.upper()}") + print("-" * 70) + + # Load and preprocess + X_train, X_val, y_train, y_val, load_success, load_error = load_and_preprocess( + dataset_name + ) + + if not load_success: + load_failures.append((dataset_name, load_error)) + results[dataset_name] = {"status": "load_failed", "error": load_error} + continue + + # Quick train + train_success, val_acc, val_loss, train_time, train_error = quick_train( + X_train, y_train, X_val, y_val, dataset_name + ) + + if not train_success: + train_failures.append((dataset_name, train_error)) + results[dataset_name] = { + "status": "train_failed", + "error": train_error, + "samples": len(X_train) + len(X_val), + } + else: + success_count += 1 + results[dataset_name] = { + "status": "success", + "val_accuracy": float(val_acc), + "val_loss": float(val_loss), + "train_time_seconds": float(train_time), + "samples": len(X_train) + len(X_val), + "train_samples": len(X_train), + "val_samples": len(X_val), + } + + total_time = time.time() - total_start + + # Summary + print("\n" + "=" * 70) + print(" SUMMARY") + print("=" * 70) + print(f"\n✅ Successful: {success_count}/{len(DATASETS)}") + print(f"⏱️ Total time: {total_time:.1f}s") + + if load_failures: + print(f"\n❌ Load failures ({len(load_failures)}):") + for name, error in load_failures: + print(f" - {name}: {error}") + + if train_failures: + print(f"\n❌ Training failures ({len(train_failures)}):") + for name, error in train_failures: + print(f" - {name}: {error}") + + # Save results + output_file = Path(__file__).parent / "results" / "quick_test_results.json" + output_file.parent.mkdir(exist_ok=True) + + output_data = { + "timestamp": datetime.now().isoformat(), + "total_datasets": len(DATASETS), + "successful": success_count, + "total_time_seconds": total_time, + "results": results, + } + + with open(output_file, "w") as f: + json.dump(output_data, f, indent=2) + + print(f"\n📄 Results saved: {output_file}") + + print("\n" + "=" * 70) + print(" ✅ QUICK TEST COMPLETE") + print("=" * 70) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/requirements.txt b/ai-projects/quantum-ml/requirements.txt index 35c0dc65b..368d3a49b 100644 --- a/ai-projects/quantum-ml/requirements.txt +++ b/ai-projects/quantum-ml/requirements.txt @@ -1,45 +1,45 @@ -# Azure Quantum SDK -azure-quantum[qiskit]>=1.0.0,<2.0.0 -# Qiskit 1.x baseline (security-patched and compatible with current torch baseline) -qiskit==1.3.0 -qiskit-aer==0.16.4 - -# Quantum Machine Learning -pennylane>=0.39.0,<1.0.0 -## Pennylane-Qiskit adapter is optional. -## It currently conflicts with secure torch baselines because recent torch -## versions require sympy>=1.13.1 while pennylane-qiskit 0.39.x requires -## sympy<1.13. Install this adapter only in a dedicated environment when needed. -# pennylane-qiskit>=0.39.0,<0.40.0 -torch>=2.8.0,<3.0.0 -numpy>=1.26.4,<2.0.0 -scipy>=1.14.0,<2.0.0 -qiskit-machine-learning==0.8.2 # Compatible with Qiskit 1.3.x and Azure Quantum's NumPy<2 constraint - -# Azure Integration -azure-identity>=1.15.0,<2.0.0 -azure-core>=1.29.0,<2.0.0 - -# Data Processing & Visualization -pandas>=2.2.0,<3.0.0 -matplotlib>=3.9.0,<4.0.0 -scikit-learn>=1.6.0,<2.0.0 -seaborn==0.13.2 - -# Configuration & Utilities -pyyaml==6.0.3 - -# Development Tools -jupyter==1.1.1 -ipykernel==6.31.0 -pytest==8.4.2 -black>=26.3.1,<27.0.0 - -# Model Context Protocol (MCP) Server -mcp>=1.0.0 -pydantic>=2.0.0 -anyio>=3.0.0 - -# (Removed) pip-check-duplicates was referenced but no distribution exists. -# If dependency insight is needed, install pipdeptree instead: -# pipdeptree>=2.16.0 +# Azure Quantum SDK +azure-quantum[qiskit]>=1.0.0,<2.0.0 +# Qiskit 1.x baseline (security-patched and compatible with current torch baseline) +qiskit==1.3.0 +qiskit-aer==0.16.4 + +# Quantum Machine Learning +pennylane>=0.39.0,<1.0.0 +## Pennylane-Qiskit adapter is optional. +## It currently conflicts with secure torch baselines because recent torch +## versions require sympy>=1.13.1 while pennylane-qiskit 0.39.x requires +## sympy<1.13. Install this adapter only in a dedicated environment when needed. +# pennylane-qiskit>=0.39.0,<0.40.0 +torch>=2.8.0,<3.0.0 +numpy>=1.26.4,<2.0.0 +scipy>=1.14.0,<2.0.0 +qiskit-machine-learning==0.8.2 # Compatible with Qiskit 1.3.x and Azure Quantum's NumPy<2 constraint + +# Azure Integration +azure-identity>=1.15.0,<2.0.0 +azure-core>=1.29.0,<2.0.0 + +# Data Processing & Visualization +pandas>=2.2.0,<3.0.0 +matplotlib>=3.9.0,<4.0.0 +scikit-learn>=1.6.0,<2.0.0 +seaborn==0.13.2 + +# Configuration & Utilities +pyyaml==6.0.3 + +# Development Tools +jupyter==1.1.1 +ipykernel==6.31.0 +pytest==8.4.2 +black>=26.3.1,<27.0.0 + +# Model Context Protocol (MCP) Server +mcp>=1.0.0 +pydantic>=2.0.0 +anyio>=3.0.0 + +# (Removed) pip-check-duplicates was referenced but no distribution exists. +# If dependency insight is needed, install pipdeptree instead: +# pipdeptree>=2.16.0 diff --git a/ai-projects/quantum-ml/run_azure_quantum_free.py b/ai-projects/quantum-ml/run_azure_quantum_free.py index 3074a74e3..43022998e 100644 --- a/ai-projects/quantum-ml/run_azure_quantum_free.py +++ b/ai-projects/quantum-ml/run_azure_quantum_free.py @@ -1,172 +1,172 @@ -""" -Run Quantum AI on Azure Quantum - 100% FREE -Uses free simulators to demonstrate quantum computing capabilities -""" - -from qiskit import QuantumCircuit -from src.azure_quantum_integration import AzureQuantumIntegration - - -def main(): - print("=" * 70) - print("🌧️ MAKING IT RAIN IN AZURE QUANTUM - 100% FREE!") - print("=" * 70) - print("\n💰 Cost: $0.00 (using FREE simulators)") - print("🎯 Goal: Run quantum circuits on real Azure infrastructure\n") - - # Connect to Azure Quantum - print("🔗 Step 1: Connecting to Azure Quantum...") - azure = AzureQuantumIntegration() - azure.connect() - print("✅ Connected successfully!\n") - - # List available backends - print("📊 Step 2: Available backends:") - backends = azure.list_backends() - for i, backend in enumerate(backends, 1): - print(f" {i}. {backend}") - print() - - # Experiment 1: Bell State (Quantum Entanglement) - print("=" * 70) - print("🔔 EXPERIMENT 1: Bell State (Quantum Entanglement)") - print("=" * 70) - print("This demonstrates quantum entanglement - the 'spooky action' Einstein") - print("doubted. Two qubits become correlated in a way impossible classically.\n") - - qc_bell = QuantumCircuit(2, 2) - qc_bell.h(0) # Put qubit 0 in superposition - qc_bell.cx(0, 1) # Entangle qubit 1 with qubit 0 - qc_bell.measure_all() # Measure both - - print("Circuit depth:", qc_bell.depth()) - print("Number of qubits:", qc_bell.num_qubits) - print("Submitting to rigetti.sim.qvm...") - - try: - job_bell = azure.submit_circuit( - qc_bell, backend_name="rigetti.sim.qvm", shots=1000 - ) - print(f"✅ Bell State job submitted: {job_bell}") - print("⏳ Job is running on Azure Quantum servers...") - - # Get results - result_bell = job_bell.result() - counts_bell = result_bell.get_counts() - print(f"\n📊 Results: {counts_bell}") - print("🎉 Expected: ~50% |00⟩ and ~50% |11⟩ (entangled states)") - print(" Actual matches quantum mechanics predictions! ✓\n") - except Exception as e: - print(f"⚠️ Error: {e}\n") - - # Experiment 2: Quantum Superposition (3 qubits) - print("=" * 70) - print("🌀 EXPERIMENT 2: Quantum Superposition (3 qubits)") - print("=" * 70) - print("Demonstrates a qubit existing in multiple states simultaneously\n") - - qc_super = QuantumCircuit(3, 3) - qc_super.h(0) # Superposition on qubit 0 - qc_super.h(1) # Superposition on qubit 1 - qc_super.h(2) # Superposition on qubit 2 - qc_super.measure_all() - - print("Circuit creates 2³ = 8 possible states simultaneously!") - print("Submitting to rigetti.sim.qvm...") - - try: - job_super = azure.submit_circuit( - qc_super, backend_name="rigetti.sim.qvm", shots=1000 - ) - print(f"✅ Superposition job submitted: {job_super}") - - result_super = job_super.result() - counts_super = result_super.get_counts() - print(f"\n📊 Results: {counts_super}") - print("🎉 All 8 states should appear roughly equally!") - print(" This is quantum superposition in action! ✓\n") - except Exception as e: - print(f"⚠️ Error: {e}\n") - - # Experiment 3: GHZ State (3-qubit entanglement) - print("=" * 70) - print("🔗 EXPERIMENT 3: GHZ State (Greenberger-Horne-Zeilinger)") - print("=" * 70) - print("Maximum entanglement of 3 qubits - the quantum 'triforce'!\n") - - qc_ghz = QuantumCircuit(3, 3) - qc_ghz.h(0) - qc_ghz.cx(0, 1) - qc_ghz.cx(0, 2) - qc_ghz.measure_all() - - print("Circuit creates |000⟩ + |111⟩ state (all 0s or all 1s)") - print("Submitting to rigetti.sim.qvm...") - - try: - job_ghz = azure.submit_circuit( - qc_ghz, backend_name="rigetti.sim.qvm", shots=1000 - ) - print(f"✅ GHZ State job submitted: {job_ghz}") - - result_ghz = job_ghz.result() - counts_ghz = result_ghz.get_counts() - print(f"\n📊 Results: {counts_ghz}") - print("🎉 Expected: Only |000⟩ and |111⟩ states (no mixed states)") - print(" This proves 3-qubit entanglement! ✓\n") - except Exception as e: - print(f"⚠️ Error: {e}\n") - - # Experiment 4: Quantum Interference - print("=" * 70) - print("🌊 EXPERIMENT 4: Quantum Interference") - print("=" * 70) - print("Shows how quantum states can constructively/destructively interfere\n") - - qc_interference = QuantumCircuit(2, 2) - qc_interference.h(0) - qc_interference.x(1) - qc_interference.cx(0, 1) - qc_interference.h(0) - qc_interference.measure_all() - - print("Circuit demonstrates quantum interference patterns") - print("Submitting to rigetti.sim.qvm...") - - try: - job_int = azure.submit_circuit( - qc_interference, backend_name="rigetti.sim.qvm", shots=1000 - ) - print(f"✅ Interference job submitted: {job_int}") - - result_int = job_int.result() - counts_int = result_int.get_counts() - print(f"\n📊 Results: {counts_int}") - print("🎉 Interference pattern matches quantum predictions! ✓\n") - except Exception as e: - print(f"⚠️ Error: {e}\n") - - # Summary - print("=" * 70) - print("✨ AZURE QUANTUM RAN - ALL FREE!") - print("=" * 70) - print("\n🎊 Successfully ran 4 quantum experiments on Azure Quantum!") - print("💰 Total cost: $0.00 (free simulators)") - print("🏆 Achievements unlocked:") - print(" ✓ Quantum entanglement demonstrated") - print(" ✓ Quantum superposition verified") - print(" ✓ GHZ state created") - print(" ✓ Quantum interference observed") - print("\n🚀 Next steps:") - print(" 1. Deploy your trained quantum ML models to Azure") - print(" 2. Scale to more qubits (4, 6, 8...)") - print(" 3. Try real quantum hardware (when ready for paid tier)") - print("\n📊 Your quantum experiments are saved in Azure Quantum workspace:") - print(f" Subscription: {azure.config['azure']['subscription_id'][:8]}...") - print(f" Resource Group: {azure.config['azure']['resource_group']}") - print(f" Workspace: {azure.config['azure']['workspace_name']}") - print("\n" + "=" * 70) - - -if __name__ == "__main__": - main() +""" +Run Quantum AI on Azure Quantum - 100% FREE +Uses free simulators to demonstrate quantum computing capabilities +""" + +from qiskit import QuantumCircuit +from src.azure_quantum_integration import AzureQuantumIntegration + + +def main(): + print("=" * 70) + print("🌧️ MAKING IT RAIN IN AZURE QUANTUM - 100% FREE!") + print("=" * 70) + print("\n💰 Cost: $0.00 (using FREE simulators)") + print("🎯 Goal: Run quantum circuits on real Azure infrastructure\n") + + # Connect to Azure Quantum + print("🔗 Step 1: Connecting to Azure Quantum...") + azure = AzureQuantumIntegration() + azure.connect() + print("✅ Connected successfully!\n") + + # List available backends + print("📊 Step 2: Available backends:") + backends = azure.list_backends() + for i, backend in enumerate(backends, 1): + print(f" {i}. {backend}") + print() + + # Experiment 1: Bell State (Quantum Entanglement) + print("=" * 70) + print("🔔 EXPERIMENT 1: Bell State (Quantum Entanglement)") + print("=" * 70) + print("This demonstrates quantum entanglement - the 'spooky action' Einstein") + print("doubted. Two qubits become correlated in a way impossible classically.\n") + + qc_bell = QuantumCircuit(2, 2) + qc_bell.h(0) # Put qubit 0 in superposition + qc_bell.cx(0, 1) # Entangle qubit 1 with qubit 0 + qc_bell.measure_all() # Measure both + + print("Circuit depth:", qc_bell.depth()) + print("Number of qubits:", qc_bell.num_qubits) + print("Submitting to rigetti.sim.qvm...") + + try: + job_bell = azure.submit_circuit( + qc_bell, backend_name="rigetti.sim.qvm", shots=1000 + ) + print(f"✅ Bell State job submitted: {job_bell}") + print("⏳ Job is running on Azure Quantum servers...") + + # Get results + result_bell = job_bell.result() + counts_bell = result_bell.get_counts() + print(f"\n📊 Results: {counts_bell}") + print("🎉 Expected: ~50% |00⟩ and ~50% |11⟩ (entangled states)") + print(" Actual matches quantum mechanics predictions! ✓\n") + except Exception as e: + print(f"⚠️ Error: {e}\n") + + # Experiment 2: Quantum Superposition (3 qubits) + print("=" * 70) + print("🌀 EXPERIMENT 2: Quantum Superposition (3 qubits)") + print("=" * 70) + print("Demonstrates a qubit existing in multiple states simultaneously\n") + + qc_super = QuantumCircuit(3, 3) + qc_super.h(0) # Superposition on qubit 0 + qc_super.h(1) # Superposition on qubit 1 + qc_super.h(2) # Superposition on qubit 2 + qc_super.measure_all() + + print("Circuit creates 2³ = 8 possible states simultaneously!") + print("Submitting to rigetti.sim.qvm...") + + try: + job_super = azure.submit_circuit( + qc_super, backend_name="rigetti.sim.qvm", shots=1000 + ) + print(f"✅ Superposition job submitted: {job_super}") + + result_super = job_super.result() + counts_super = result_super.get_counts() + print(f"\n📊 Results: {counts_super}") + print("🎉 All 8 states should appear roughly equally!") + print(" This is quantum superposition in action! ✓\n") + except Exception as e: + print(f"⚠️ Error: {e}\n") + + # Experiment 3: GHZ State (3-qubit entanglement) + print("=" * 70) + print("🔗 EXPERIMENT 3: GHZ State (Greenberger-Horne-Zeilinger)") + print("=" * 70) + print("Maximum entanglement of 3 qubits - the quantum 'triforce'!\n") + + qc_ghz = QuantumCircuit(3, 3) + qc_ghz.h(0) + qc_ghz.cx(0, 1) + qc_ghz.cx(0, 2) + qc_ghz.measure_all() + + print("Circuit creates |000⟩ + |111⟩ state (all 0s or all 1s)") + print("Submitting to rigetti.sim.qvm...") + + try: + job_ghz = azure.submit_circuit( + qc_ghz, backend_name="rigetti.sim.qvm", shots=1000 + ) + print(f"✅ GHZ State job submitted: {job_ghz}") + + result_ghz = job_ghz.result() + counts_ghz = result_ghz.get_counts() + print(f"\n📊 Results: {counts_ghz}") + print("🎉 Expected: Only |000⟩ and |111⟩ states (no mixed states)") + print(" This proves 3-qubit entanglement! ✓\n") + except Exception as e: + print(f"⚠️ Error: {e}\n") + + # Experiment 4: Quantum Interference + print("=" * 70) + print("🌊 EXPERIMENT 4: Quantum Interference") + print("=" * 70) + print("Shows how quantum states can constructively/destructively interfere\n") + + qc_interference = QuantumCircuit(2, 2) + qc_interference.h(0) + qc_interference.x(1) + qc_interference.cx(0, 1) + qc_interference.h(0) + qc_interference.measure_all() + + print("Circuit demonstrates quantum interference patterns") + print("Submitting to rigetti.sim.qvm...") + + try: + job_int = azure.submit_circuit( + qc_interference, backend_name="rigetti.sim.qvm", shots=1000 + ) + print(f"✅ Interference job submitted: {job_int}") + + result_int = job_int.result() + counts_int = result_int.get_counts() + print(f"\n📊 Results: {counts_int}") + print("🎉 Interference pattern matches quantum predictions! ✓\n") + except Exception as e: + print(f"⚠️ Error: {e}\n") + + # Summary + print("=" * 70) + print("✨ AZURE QUANTUM RAN - ALL FREE!") + print("=" * 70) + print("\n🎊 Successfully ran 4 quantum experiments on Azure Quantum!") + print("💰 Total cost: $0.00 (free simulators)") + print("🏆 Achievements unlocked:") + print(" ✓ Quantum entanglement demonstrated") + print(" ✓ Quantum superposition verified") + print(" ✓ GHZ state created") + print(" ✓ Quantum interference observed") + print("\n🚀 Next steps:") + print(" 1. Deploy your trained quantum ML models to Azure") + print(" 2. Scale to more qubits (4, 6, 8...)") + print(" 3. Try real quantum hardware (when ready for paid tier)") + print("\n📊 Your quantum experiments are saved in Azure Quantum workspace:") + print(f" Subscription: {azure.config['azure']['subscription_id'][:8]}...") + print(f" Resource Group: {azure.config['azure']['resource_group']}") + print(f" Workspace: {azure.config['azure']['workspace_name']}") + print("\n" + "=" * 70) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/scripts/run_experiment_grid.py b/ai-projects/quantum-ml/scripts/run_experiment_grid.py index 728c3a1ae..576b6e485 100644 --- a/ai-projects/quantum-ml/scripts/run_experiment_grid.py +++ b/ai-projects/quantum-ml/scripts/run_experiment_grid.py @@ -1,109 +1,109 @@ -""" -Run a grid of simulation experiments and save results for visualization. - -Experiments: -- MPS variational: n in {32,64}, layers in {1,2}, entanglement in {linear,circular,full} - - Clean and with small Pauli noise (px=pz=0.005) -- Stabilizer random: n in {128,256}, clifford_layers in {2,4,8} - -Outputs are JSON files under quantum-ai/results/ used by visualize_hardware_results.py. -""" - -from __future__ import annotations - -import subprocess -import sys -from pathlib import Path - -REPO_ROOT = Path(__file__).resolve().parents[1] -SCRIPT = REPO_ROOT / "scripts" / "run_simulated_circuit.py" - - -def run(cmd: list[str]) -> None: - print("\n$", " ".join(cmd)) - try: - subprocess.run(cmd, check=True) - except subprocess.CalledProcessError as e: - print(f"Command failed with exit code {e.returncode}") - - -def main() -> int: - py = sys.executable or "python" - - # 1) MPS variational experiments - for n in (32, 64): - for L in (1, 2): - shots = 500 if n == 64 else 1000 - for ent in ("linear", "circular", "full"): - # Clean - run( - [ - py, - str(SCRIPT), - "--n-qubits", - str(n), - "--layers", - str(L), - "--entanglement", - ent, - "--method", - "matrix_product_state", - "--shots", - str(shots), - ] - ) - # With small Pauli noise - run( - [ - py, - str(SCRIPT), - "--n-qubits", - str(n), - "--layers", - str(L), - "--entanglement", - ent, - "--method", - "matrix_product_state", - "--shots", - str(shots), - "--noise-pauli-px", - "0.005", - "--noise-pauli-pz", - "0.005", - "--noise-seed", - "1234", - ] - ) - - # 2) Stabilizer random experiments - for n in (128, 256): - for layers in (2, 4, 8): - run( - [ - py, - str(SCRIPT), - "--n-qubits", - str(n), - "--circuit", - "stabilizer_random", - "--stabilizer-type", - "random", - "--clifford-layers", - str(layers), - "--twoq-density", - "0.5", - "--method", - "stabilizer", - "--shots", - "500", - "--noise-seed", - "42", - ] - ) - - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) +""" +Run a grid of simulation experiments and save results for visualization. + +Experiments: +- MPS variational: n in {32,64}, layers in {1,2}, entanglement in {linear,circular,full} + - Clean and with small Pauli noise (px=pz=0.005) +- Stabilizer random: n in {128,256}, clifford_layers in {2,4,8} + +Outputs are JSON files under quantum-ai/results/ used by visualize_hardware_results.py. +""" + +from __future__ import annotations + +import subprocess +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +SCRIPT = REPO_ROOT / "scripts" / "run_simulated_circuit.py" + + +def run(cmd: list[str]) -> None: + print("\n$", " ".join(cmd)) + try: + subprocess.run(cmd, check=True) + except subprocess.CalledProcessError as e: + print(f"Command failed with exit code {e.returncode}") + + +def main() -> int: + py = sys.executable or "python" + + # 1) MPS variational experiments + for n in (32, 64): + for L in (1, 2): + shots = 500 if n == 64 else 1000 + for ent in ("linear", "circular", "full"): + # Clean + run( + [ + py, + str(SCRIPT), + "--n-qubits", + str(n), + "--layers", + str(L), + "--entanglement", + ent, + "--method", + "matrix_product_state", + "--shots", + str(shots), + ] + ) + # With small Pauli noise + run( + [ + py, + str(SCRIPT), + "--n-qubits", + str(n), + "--layers", + str(L), + "--entanglement", + ent, + "--method", + "matrix_product_state", + "--shots", + str(shots), + "--noise-pauli-px", + "0.005", + "--noise-pauli-pz", + "0.005", + "--noise-seed", + "1234", + ] + ) + + # 2) Stabilizer random experiments + for n in (128, 256): + for layers in (2, 4, 8): + run( + [ + py, + str(SCRIPT), + "--n-qubits", + str(n), + "--circuit", + "stabilizer_random", + "--stabilizer-type", + "random", + "--clifford-layers", + str(layers), + "--twoq-density", + "0.5", + "--method", + "stabilizer", + "--shots", + "500", + "--noise-seed", + "42", + ] + ) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ai-projects/quantum-ml/scripts/run_hardware_tests.py b/ai-projects/quantum-ml/scripts/run_hardware_tests.py index 71bd584f7..9bd890f62 100644 --- a/ai-projects/quantum-ml/scripts/run_hardware_tests.py +++ b/ai-projects/quantum-ml/scripts/run_hardware_tests.py @@ -1,86 +1,86 @@ -""" -Run Azure Quantum hardware tests non-interactively and save results. -- Verifies connection -- Runs Bell state on selected backend (default: ionq.simulator) -- Optionally runs optimized circuit test -Results are saved to `quantum-ai/results/` via test_azure_quantum utilities. -""" - -from __future__ import annotations - -import argparse -import sys -from pathlib import Path - -# Ensure module imports work when running from repo root or script dir -REPO_ROOT = Path(__file__).resolve().parents[1] -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from test_azure_quantum import (test_azure_quantum_connection, - test_bell_state_on_hardware, - test_optimized_circuit_on_hardware) - - -def parse_args() -> argparse.Namespace: - p = argparse.ArgumentParser( - description="Run Azure Quantum hardware tests non-interactively" - ) - p.add_argument( - "--backend", - default="ionq.simulator", - help="Backend to use (e.g., ionq.simulator, ionq.qpu). Default: ionq.simulator", - ) - p.add_argument( - "--optimized", - action="store_true", - help="Also run the optimized circuit test after Bell state", - ) - p.add_argument( - "--skip-bell", - action="store_true", - help="Skip the Bell state test and only run the optimized circuit (if --optimized)", - ) - return p.parse_args() - - -def main() -> int: - args = parse_args() - - print("\n=== Non-interactive Azure Quantum Hardware Tests ===\n") - - # 1) Connect and list backends - azure, backends = test_azure_quantum_connection() - if azure is None: - print("Cannot proceed without Azure Quantum connection.") - return 1 - - print(f"Using backend: {args.backend}") - if args.backend not in backends: - print( - "Warning: chosen backend not found in workspace backends. Proceeding anyway...\n" - ) - - # 2) Run Bell state unless skipped - if not args.skip_bell: - bell = test_bell_state_on_hardware(azure, args.backend) - if bell is None: - print("Bell state test failed or produced no results.") - else: - print("Bell state test completed and results saved.\n") - - # 3) Optionally run optimized circuit - if args.optimized: - opt = test_optimized_circuit_on_hardware(azure, args.backend) - if opt is None: - print("Optimized circuit test failed or produced no results.") - else: - print("Optimized circuit test completed and results saved.\n") - - print("\n=== Done ===") - print("Results (JSON) are in quantum-ai/results/") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) +""" +Run Azure Quantum hardware tests non-interactively and save results. +- Verifies connection +- Runs Bell state on selected backend (default: ionq.simulator) +- Optionally runs optimized circuit test +Results are saved to `quantum-ai/results/` via test_azure_quantum utilities. +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +# Ensure module imports work when running from repo root or script dir +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from test_azure_quantum import (test_azure_quantum_connection, + test_bell_state_on_hardware, + test_optimized_circuit_on_hardware) + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser( + description="Run Azure Quantum hardware tests non-interactively" + ) + p.add_argument( + "--backend", + default="ionq.simulator", + help="Backend to use (e.g., ionq.simulator, ionq.qpu). Default: ionq.simulator", + ) + p.add_argument( + "--optimized", + action="store_true", + help="Also run the optimized circuit test after Bell state", + ) + p.add_argument( + "--skip-bell", + action="store_true", + help="Skip the Bell state test and only run the optimized circuit (if --optimized)", + ) + return p.parse_args() + + +def main() -> int: + args = parse_args() + + print("\n=== Non-interactive Azure Quantum Hardware Tests ===\n") + + # 1) Connect and list backends + azure, backends = test_azure_quantum_connection() + if azure is None: + print("Cannot proceed without Azure Quantum connection.") + return 1 + + print(f"Using backend: {args.backend}") + if args.backend not in backends: + print( + "Warning: chosen backend not found in workspace backends. Proceeding anyway...\n" + ) + + # 2) Run Bell state unless skipped + if not args.skip_bell: + bell = test_bell_state_on_hardware(azure, args.backend) + if bell is None: + print("Bell state test failed or produced no results.") + else: + print("Bell state test completed and results saved.\n") + + # 3) Optionally run optimized circuit + if args.optimized: + opt = test_optimized_circuit_on_hardware(azure, args.backend) + if opt is None: + print("Optimized circuit test failed or produced no results.") + else: + print("Optimized circuit test completed and results saved.\n") + + print("\n=== Done ===") + print("Results (JSON) are in quantum-ai/results/") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ai-projects/quantum-ml/scripts/run_simulated_circuit.py b/ai-projects/quantum-ml/scripts/run_simulated_circuit.py index 574ac221b..74d7bb76e 100644 --- a/ai-projects/quantum-ml/scripts/run_simulated_circuit.py +++ b/ai-projects/quantum-ml/scripts/run_simulated_circuit.py @@ -1,407 +1,407 @@ -""" -Run a local Qiskit Aer simulation of a variational circuit and save results. - -Usage example: - python .\\quantum-ai\\scripts\\run_simulated_circuit.py --n-qubits 16 --layers 4 --entanglement circular --shots 2000 - -Outputs JSON compatible with visualize_hardware_results.py under quantum-ai/results/. -""" - -from __future__ import annotations - -import argparse -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, Optional - -import numpy as np -import yaml -from qiskit import QuantumCircuit, transpile -from qiskit_aer import AerSimulator -from qiskit_aer.noise import (NoiseModel, amplitude_damping_error, - depolarizing_error) - -REPO_ROOT = Path(__file__).resolve().parents[1] -CONFIG_PATH = REPO_ROOT / "config" / "quantum_config.yaml" - - -def create_variational_circuit( - n_qubits: int, - n_layers: int, - entanglement: str, - noise_px: float = 0.0, - noise_pz: float = 0.0, - rng: Optional[np.random.Generator] = None, -) -> QuantumCircuit: - qc = QuantumCircuit(n_qubits, n_qubits) - - # Initial layer - for i in range(n_qubits): - qc.h(i) - - for layer in range(n_layers): - # Parameterized single-qubit rotations - for i in range(n_qubits): - qc.ry(np.pi / 4 * (layer + 1), i) - qc.rz(np.pi / 3 * (layer + 1), i) - - # Entanglement pattern - if entanglement == "linear": - for i in range(n_qubits - 1): - qc.cx(i, i + 1) - elif entanglement == "full": - for i in range(n_qubits): - for j in range(i + 1, n_qubits): - qc.cx(i, j) - else: # circular (default) - for i in range(n_qubits): - qc.cx(i, (i + 1) % n_qubits) - - # Monte Carlo Pauli noise injection (small px/pz), keeps simulation scalable for large N - if noise_px > 0 or noise_pz > 0: - if rng is None: - rng = np.random.default_rng() - for i in range(n_qubits): - if noise_px > 0 and rng.random() < noise_px: - qc.x(i) - if noise_pz > 0 and rng.random() < noise_pz: - qc.z(i) - - qc.barrier() - - qc.measure(range(n_qubits), range(n_qubits)) - return qc - - -def create_stabilizer_ghz_circuit(n_qubits: int) -> QuantumCircuit: - """Create a GHZ-like stabilizer circuit (Clifford-only) scalable to large n. - - Uses only H and CX to remain compatible with Aer 'stabilizer' method. - """ - qc = QuantumCircuit(n_qubits, n_qubits) - qc.h(0) - for i in range(n_qubits - 1): - qc.cx(i, i + 1) - qc.measure(range(n_qubits), range(n_qubits)) - return qc - - -def create_stabilizer_random_circuit( - n_qubits: int, - layers: int, - twoq_density: float = 0.5, - seed: Optional[int] = None, -) -> QuantumCircuit: - """Random Clifford circuit using H, S, X, Z, and CX gates. - - twoq_density controls fraction of qubits participating in two-qubit CNOTs per layer. - """ - rng = np.random.default_rng(seed) - qc = QuantumCircuit(n_qubits, n_qubits) - # Start with random single-qubit Cliffords - for i in range(n_qubits): - if rng.random() < 0.5: - qc.h(i) - if rng.random() < 0.5: - qc.s(i) - if rng.random() < 0.25: - qc.x(i) - if rng.random() < 0.25: - qc.z(i) - qc.barrier() - - for _ in range(layers): - # Random single-qubit Clifford layer - for i in range(n_qubits): - r = rng.random() - if r < 0.25: - qc.h(i) - elif r < 0.5: - qc.s(i) - elif r < 0.75: - qc.x(i) - else: - qc.z(i) - - # Random pairings for CNOTs respecting density - indices = list(range(n_qubits)) - rng.shuffle(indices) - pair_count = int((n_qubits // 2) * twoq_density) - for k in range(pair_count): - a = indices[2 * k] - b = indices[2 * k + 1] - # Random control/target direction - if rng.random() < 0.5: - qc.cx(a, b) - else: - qc.cx(b, a) - qc.barrier() - - qc.measure(range(n_qubits), range(n_qubits)) - return qc - - -def compute_entropy(counts: Dict[str, int]) -> float: - total = sum(counts.values()) - if total == 0: - return 0.0 - p = np.array([v / total for v in counts.values() if v > 0], dtype=float) - return float(-(p * np.log2(p)).sum()) - - -def parse_args() -> argparse.Namespace: - p = argparse.ArgumentParser( - description="Run a local Qiskit Aer simulation (variational or stabilizer)" - ) - p.add_argument( - "--n-qubits", type=int, default=16, help="Number of qubits (default: 16)" - ) - p.add_argument( - "--layers", - type=int, - default=4, - help="Number of variational layers (default: 4)", - ) - p.add_argument( - "--entanglement", - choices=["linear", "circular", "full"], - default="circular", - help="Entanglement topology (default: circular)", - ) - p.add_argument( - "--shots", type=int, default=2000, help="Number of shots (default: 2000)" - ) - # Noise injection (Monte Carlo Pauli) - p.add_argument( - "--noise-pauli-px", - type=float, - default=0.0, - help="Probability of X per qubit per layer (default: 0.0)", - ) - p.add_argument( - "--noise-pauli-pz", - type=float, - default=0.0, - help="Probability of Z per qubit per layer (default: 0.0)", - ) - p.add_argument( - "--noise-seed", - type=int, - default=None, - help="Seed for noise RNG (default: None)", - ) - # NoiseModel options (Aer). If provided, a NoiseModel will be attached to the simulator. - p.add_argument( - "--noise-depolarizing-p", - type=float, - default=0.0, - help="Depolarizing error probability p for 1q/2q gates (default: 0.0)", - ) - p.add_argument( - "--noise-amp-damp-gamma", - type=float, - default=0.0, - help="Amplitude damping gamma for 1q gates (default: 0.0)", - ) - p.add_argument( - "--method", - choices=[ - "statevector", - "matrix_product_state", - "stabilizer", - "extended_stabilizer", - "tensor_network", - ], - default="statevector", - help="Aer simulation method (default: statevector)", - ) - p.add_argument( - "--circuit", - choices=["variational", "stabilizer_ghz", "stabilizer_random"], - default="variational", - help="Circuit type to simulate (default: variational)", - ) - p.add_argument( - "--clifford-layers", - type=int, - default=4, - help="Random Clifford layers (used for stabilizer_random)", - ) - p.add_argument( - "--twoq-density", - type=float, - default=0.5, - help="Fraction of qubits in 2-qubit CNOT pairs per layer [0,1] (stabilizer_random)", - ) - p.add_argument( - "--stabilizer-type", - choices=["ghz", "random"], - default="ghz", - help="Stabilizer circuit subtype: ghz or random (default: ghz)", - ) - return p.parse_args() - - -def main() -> int: - args = parse_args() - - # Load config to resolve results_dir relative to project root - cfg = yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8")) - results_dir = ( - REPO_ROOT / Path(cfg["logging"]["results_dir"]).expanduser() - ).resolve() - results_dir.mkdir(parents=True, exist_ok=True) - - print("\n=== Local Aer Simulation ===") - # Decide effective circuit kind and layer count for display/metadata - using_stab_random = False - if args.circuit == "stabilizer_random" or ( - args.circuit == "variational" and args.stabilizer_type == "random" - ): - using_stab_random = True - effective_layers = ( - args.clifford_layers - if using_stab_random or args.circuit == "stabilizer_ghz" - else args.layers - ) - effective_circuit = ( - "stabilizer_random" - if using_stab_random - else ("stabilizer_ghz" if args.circuit == "stabilizer_ghz" else "variational") - ) - - print( - f"Qubits: {args.n_qubits}, Layers: {effective_layers}, Entanglement: {args.entanglement}, Shots: {args.shots}" - ) - print(f"Method: {args.method}, Circuit: {effective_circuit}") - - rng = ( - np.random.default_rng(args.noise_seed) - if (args.noise_pauli_px > 0 or args.noise_pauli_pz > 0) - else None - ) - - if args.circuit == "stabilizer_ghz": - qc = create_stabilizer_ghz_circuit(args.n_qubits) - elif using_stab_random: - qc = create_stabilizer_random_circuit( - n_qubits=args.n_qubits, - layers=args.clifford_layers, - twoq_density=max(0.0, min(1.0, args.twoq_density)), - seed=args.noise_seed, - ) - else: - qc = create_variational_circuit( - args.n_qubits, - args.layers, - args.entanglement, - noise_px=args.noise_pauli_px, - noise_pz=args.noise_pauli_pz, - rng=rng, - ) - print("Circuit depth:", qc.depth(), " | gates:", sum(qc.count_ops().values())) - - # Build optional Aer NoiseModel - noise_model: Optional[NoiseModel] = None - if (args.noise_depolarizing_p > 0.0) or (args.noise_amp_damp_gamma > 0.0): - noise_model = NoiseModel() - # Attach depolarizing to 1q and 2q gates used in circuits - if args.noise_depolarizing_p > 0.0: - p = args.noise_depolarizing_p - # Reasonable mapping: depol on rx/ry/rz/h/s/x/z and cx - dep1 = depolarizing_error(p, 1) - dep2 = depolarizing_error(min(2 * p, 1.0), 2) - for g in ["h", "s", "sdg", "x", "z", "ry", "rz", "rx", "id"]: - try: - noise_model.add_all_qubit_quantum_error(dep1, g) - except Exception: - pass - for g2 in ["cx", "cz"]: - try: - noise_model.add_all_qubit_quantum_error(dep2, g2) - except Exception: - pass - # Attach amplitude damping to 1q gates - if args.noise_amp_damp_gamma > 0.0: - gamma = args.noise_amp_damp_gamma - amp = amplitude_damping_error(gamma) - for g in ["h", "s", "sdg", "x", "z", "ry", "rz", "rx", "id"]: - try: - noise_model.add_all_qubit_quantum_error(amp, g) - except Exception: - pass - - sim = ( - AerSimulator(method=args.method, noise_model=noise_model) - if noise_model - else AerSimulator(method=args.method) - ) - # Avoid backend coupling_map limits during transpile for large-n; Aer will handle compilation. - # For stabilizer method/circuits, restrict basis_gates to Clifford set to avoid u1/u2/u3 decomposition. - is_stabilizer_flow = args.method == "stabilizer" or args.circuit.startswith( - "stabilizer" - ) - if is_stabilizer_flow: - basis = ["h", "s", "sdg", "x", "z", "cx", "cz", "id", "measure"] - tqc = transpile(qc, basis_gates=basis, optimization_level=0) - else: - tqc = transpile(qc, optimization_level=2) - job = sim.run(tqc, shots=args.shots) - result = job.result() - counts = result.get_counts() - - # Compute simple metrics - entropy = compute_entropy(counts) - # Avoid huge integer exponentiation; max entropy in bits equals number of qubits - max_entropy = float(args.n_qubits) - - print("Unique states:", len(counts), f"/ {2 ** args.n_qubits}") - print( - f"Entropy: {entropy:.3f} / {max_entropy:.3f} ({(entropy/max_entropy*100 if max_entropy>0 else 0):.1f}%)" - ) - - # Save JSON in the same schema as Azure results so the visualizer can pick it up - ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") - job_id = f"local-aer-{args.n_qubits}q-{ts}" - out = { - "job_id": job_id, - "counts": {str(k): int(v) for k, v in counts.items()}, - "success": True, - "metadata": { - "backend": "aer_simulator", - "n_qubits": args.n_qubits, - "layers": effective_layers, - "entanglement": args.entanglement, - "method": args.method, - "circuit": effective_circuit, - "shots": args.shots, - "entropy": entropy, - "max_entropy": max_entropy, - "noise": { - "pauli_px": args.noise_pauli_px, - "pauli_pz": args.noise_pauli_pz, - "depolarizing_p": args.noise_depolarizing_p, - "amp_damp_gamma": args.noise_amp_damp_gamma, - "seed": args.noise_seed, - }, - "stabilizer": { - "type": args.stabilizer_type, - "clifford_layers": args.clifford_layers, - "twoq_density": args.twoq_density, - }, - }, - } - - out_path = results_dir / f"sim_{args.n_qubits}q_results_{ts}.json" - out_path.write_text(json.dumps(out, indent=2), encoding="utf-8") - print(f"Saved results to {out_path}") - - print("Tip: Now run the visualizer to generate charts:") - print(" python .\\quantum-ai\\scripts\\visualize_hardware_results.py") - - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) +""" +Run a local Qiskit Aer simulation of a variational circuit and save results. + +Usage example: + python .\\quantum-ai\\scripts\\run_simulated_circuit.py --n-qubits 16 --layers 4 --entanglement circular --shots 2000 + +Outputs JSON compatible with visualize_hardware_results.py under quantum-ai/results/. +""" + +from __future__ import annotations + +import argparse +import json +from datetime import datetime, timezone +from pathlib import Path +from typing import Dict, Optional + +import numpy as np +import yaml +from qiskit import QuantumCircuit, transpile +from qiskit_aer import AerSimulator +from qiskit_aer.noise import (NoiseModel, amplitude_damping_error, + depolarizing_error) + +REPO_ROOT = Path(__file__).resolve().parents[1] +CONFIG_PATH = REPO_ROOT / "config" / "quantum_config.yaml" + + +def create_variational_circuit( + n_qubits: int, + n_layers: int, + entanglement: str, + noise_px: float = 0.0, + noise_pz: float = 0.0, + rng: Optional[np.random.Generator] = None, +) -> QuantumCircuit: + qc = QuantumCircuit(n_qubits, n_qubits) + + # Initial layer + for i in range(n_qubits): + qc.h(i) + + for layer in range(n_layers): + # Parameterized single-qubit rotations + for i in range(n_qubits): + qc.ry(np.pi / 4 * (layer + 1), i) + qc.rz(np.pi / 3 * (layer + 1), i) + + # Entanglement pattern + if entanglement == "linear": + for i in range(n_qubits - 1): + qc.cx(i, i + 1) + elif entanglement == "full": + for i in range(n_qubits): + for j in range(i + 1, n_qubits): + qc.cx(i, j) + else: # circular (default) + for i in range(n_qubits): + qc.cx(i, (i + 1) % n_qubits) + + # Monte Carlo Pauli noise injection (small px/pz), keeps simulation scalable for large N + if noise_px > 0 or noise_pz > 0: + if rng is None: + rng = np.random.default_rng() + for i in range(n_qubits): + if noise_px > 0 and rng.random() < noise_px: + qc.x(i) + if noise_pz > 0 and rng.random() < noise_pz: + qc.z(i) + + qc.barrier() + + qc.measure(range(n_qubits), range(n_qubits)) + return qc + + +def create_stabilizer_ghz_circuit(n_qubits: int) -> QuantumCircuit: + """Create a GHZ-like stabilizer circuit (Clifford-only) scalable to large n. + + Uses only H and CX to remain compatible with Aer 'stabilizer' method. + """ + qc = QuantumCircuit(n_qubits, n_qubits) + qc.h(0) + for i in range(n_qubits - 1): + qc.cx(i, i + 1) + qc.measure(range(n_qubits), range(n_qubits)) + return qc + + +def create_stabilizer_random_circuit( + n_qubits: int, + layers: int, + twoq_density: float = 0.5, + seed: Optional[int] = None, +) -> QuantumCircuit: + """Random Clifford circuit using H, S, X, Z, and CX gates. + + twoq_density controls fraction of qubits participating in two-qubit CNOTs per layer. + """ + rng = np.random.default_rng(seed) + qc = QuantumCircuit(n_qubits, n_qubits) + # Start with random single-qubit Cliffords + for i in range(n_qubits): + if rng.random() < 0.5: + qc.h(i) + if rng.random() < 0.5: + qc.s(i) + if rng.random() < 0.25: + qc.x(i) + if rng.random() < 0.25: + qc.z(i) + qc.barrier() + + for _ in range(layers): + # Random single-qubit Clifford layer + for i in range(n_qubits): + r = rng.random() + if r < 0.25: + qc.h(i) + elif r < 0.5: + qc.s(i) + elif r < 0.75: + qc.x(i) + else: + qc.z(i) + + # Random pairings for CNOTs respecting density + indices = list(range(n_qubits)) + rng.shuffle(indices) + pair_count = int((n_qubits // 2) * twoq_density) + for k in range(pair_count): + a = indices[2 * k] + b = indices[2 * k + 1] + # Random control/target direction + if rng.random() < 0.5: + qc.cx(a, b) + else: + qc.cx(b, a) + qc.barrier() + + qc.measure(range(n_qubits), range(n_qubits)) + return qc + + +def compute_entropy(counts: Dict[str, int]) -> float: + total = sum(counts.values()) + if total == 0: + return 0.0 + p = np.array([v / total for v in counts.values() if v > 0], dtype=float) + return float(-(p * np.log2(p)).sum()) + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser( + description="Run a local Qiskit Aer simulation (variational or stabilizer)" + ) + p.add_argument( + "--n-qubits", type=int, default=16, help="Number of qubits (default: 16)" + ) + p.add_argument( + "--layers", + type=int, + default=4, + help="Number of variational layers (default: 4)", + ) + p.add_argument( + "--entanglement", + choices=["linear", "circular", "full"], + default="circular", + help="Entanglement topology (default: circular)", + ) + p.add_argument( + "--shots", type=int, default=2000, help="Number of shots (default: 2000)" + ) + # Noise injection (Monte Carlo Pauli) + p.add_argument( + "--noise-pauli-px", + type=float, + default=0.0, + help="Probability of X per qubit per layer (default: 0.0)", + ) + p.add_argument( + "--noise-pauli-pz", + type=float, + default=0.0, + help="Probability of Z per qubit per layer (default: 0.0)", + ) + p.add_argument( + "--noise-seed", + type=int, + default=None, + help="Seed for noise RNG (default: None)", + ) + # NoiseModel options (Aer). If provided, a NoiseModel will be attached to the simulator. + p.add_argument( + "--noise-depolarizing-p", + type=float, + default=0.0, + help="Depolarizing error probability p for 1q/2q gates (default: 0.0)", + ) + p.add_argument( + "--noise-amp-damp-gamma", + type=float, + default=0.0, + help="Amplitude damping gamma for 1q gates (default: 0.0)", + ) + p.add_argument( + "--method", + choices=[ + "statevector", + "matrix_product_state", + "stabilizer", + "extended_stabilizer", + "tensor_network", + ], + default="statevector", + help="Aer simulation method (default: statevector)", + ) + p.add_argument( + "--circuit", + choices=["variational", "stabilizer_ghz", "stabilizer_random"], + default="variational", + help="Circuit type to simulate (default: variational)", + ) + p.add_argument( + "--clifford-layers", + type=int, + default=4, + help="Random Clifford layers (used for stabilizer_random)", + ) + p.add_argument( + "--twoq-density", + type=float, + default=0.5, + help="Fraction of qubits in 2-qubit CNOT pairs per layer [0,1] (stabilizer_random)", + ) + p.add_argument( + "--stabilizer-type", + choices=["ghz", "random"], + default="ghz", + help="Stabilizer circuit subtype: ghz or random (default: ghz)", + ) + return p.parse_args() + + +def main() -> int: + args = parse_args() + + # Load config to resolve results_dir relative to project root + cfg = yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8")) + results_dir = ( + REPO_ROOT / Path(cfg["logging"]["results_dir"]).expanduser() + ).resolve() + results_dir.mkdir(parents=True, exist_ok=True) + + print("\n=== Local Aer Simulation ===") + # Decide effective circuit kind and layer count for display/metadata + using_stab_random = False + if args.circuit == "stabilizer_random" or ( + args.circuit == "variational" and args.stabilizer_type == "random" + ): + using_stab_random = True + effective_layers = ( + args.clifford_layers + if using_stab_random or args.circuit == "stabilizer_ghz" + else args.layers + ) + effective_circuit = ( + "stabilizer_random" + if using_stab_random + else ("stabilizer_ghz" if args.circuit == "stabilizer_ghz" else "variational") + ) + + print( + f"Qubits: {args.n_qubits}, Layers: {effective_layers}, Entanglement: {args.entanglement}, Shots: {args.shots}" + ) + print(f"Method: {args.method}, Circuit: {effective_circuit}") + + rng = ( + np.random.default_rng(args.noise_seed) + if (args.noise_pauli_px > 0 or args.noise_pauli_pz > 0) + else None + ) + + if args.circuit == "stabilizer_ghz": + qc = create_stabilizer_ghz_circuit(args.n_qubits) + elif using_stab_random: + qc = create_stabilizer_random_circuit( + n_qubits=args.n_qubits, + layers=args.clifford_layers, + twoq_density=max(0.0, min(1.0, args.twoq_density)), + seed=args.noise_seed, + ) + else: + qc = create_variational_circuit( + args.n_qubits, + args.layers, + args.entanglement, + noise_px=args.noise_pauli_px, + noise_pz=args.noise_pauli_pz, + rng=rng, + ) + print("Circuit depth:", qc.depth(), " | gates:", sum(qc.count_ops().values())) + + # Build optional Aer NoiseModel + noise_model: Optional[NoiseModel] = None + if (args.noise_depolarizing_p > 0.0) or (args.noise_amp_damp_gamma > 0.0): + noise_model = NoiseModel() + # Attach depolarizing to 1q and 2q gates used in circuits + if args.noise_depolarizing_p > 0.0: + p = args.noise_depolarizing_p + # Reasonable mapping: depol on rx/ry/rz/h/s/x/z and cx + dep1 = depolarizing_error(p, 1) + dep2 = depolarizing_error(min(2 * p, 1.0), 2) + for g in ["h", "s", "sdg", "x", "z", "ry", "rz", "rx", "id"]: + try: + noise_model.add_all_qubit_quantum_error(dep1, g) + except Exception: + pass + for g2 in ["cx", "cz"]: + try: + noise_model.add_all_qubit_quantum_error(dep2, g2) + except Exception: + pass + # Attach amplitude damping to 1q gates + if args.noise_amp_damp_gamma > 0.0: + gamma = args.noise_amp_damp_gamma + amp = amplitude_damping_error(gamma) + for g in ["h", "s", "sdg", "x", "z", "ry", "rz", "rx", "id"]: + try: + noise_model.add_all_qubit_quantum_error(amp, g) + except Exception: + pass + + sim = ( + AerSimulator(method=args.method, noise_model=noise_model) + if noise_model + else AerSimulator(method=args.method) + ) + # Avoid backend coupling_map limits during transpile for large-n; Aer will handle compilation. + # For stabilizer method/circuits, restrict basis_gates to Clifford set to avoid u1/u2/u3 decomposition. + is_stabilizer_flow = args.method == "stabilizer" or args.circuit.startswith( + "stabilizer" + ) + if is_stabilizer_flow: + basis = ["h", "s", "sdg", "x", "z", "cx", "cz", "id", "measure"] + tqc = transpile(qc, basis_gates=basis, optimization_level=0) + else: + tqc = transpile(qc, optimization_level=2) + job = sim.run(tqc, shots=args.shots) + result = job.result() + counts = result.get_counts() + + # Compute simple metrics + entropy = compute_entropy(counts) + # Avoid huge integer exponentiation; max entropy in bits equals number of qubits + max_entropy = float(args.n_qubits) + + print("Unique states:", len(counts), f"/ {2 ** args.n_qubits}") + print( + f"Entropy: {entropy:.3f} / {max_entropy:.3f} ({(entropy/max_entropy*100 if max_entropy>0 else 0):.1f}%)" + ) + + # Save JSON in the same schema as Azure results so the visualizer can pick it up + ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") + job_id = f"local-aer-{args.n_qubits}q-{ts}" + out = { + "job_id": job_id, + "counts": {str(k): int(v) for k, v in counts.items()}, + "success": True, + "metadata": { + "backend": "aer_simulator", + "n_qubits": args.n_qubits, + "layers": effective_layers, + "entanglement": args.entanglement, + "method": args.method, + "circuit": effective_circuit, + "shots": args.shots, + "entropy": entropy, + "max_entropy": max_entropy, + "noise": { + "pauli_px": args.noise_pauli_px, + "pauli_pz": args.noise_pauli_pz, + "depolarizing_p": args.noise_depolarizing_p, + "amp_damp_gamma": args.noise_amp_damp_gamma, + "seed": args.noise_seed, + }, + "stabilizer": { + "type": args.stabilizer_type, + "clifford_layers": args.clifford_layers, + "twoq_density": args.twoq_density, + }, + }, + } + + out_path = results_dir / f"sim_{args.n_qubits}q_results_{ts}.json" + out_path.write_text(json.dumps(out, indent=2), encoding="utf-8") + print(f"Saved results to {out_path}") + + print("Tip: Now run the visualizer to generate charts:") + print(" python .\\quantum-ai\\scripts\\visualize_hardware_results.py") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ai-projects/quantum-ml/scripts/submit_small_stabilizer.py b/ai-projects/quantum-ml/scripts/submit_small_stabilizer.py index cf4ce1b7b..8255e1c41 100644 --- a/ai-projects/quantum-ml/scripts/submit_small_stabilizer.py +++ b/ai-projects/quantum-ml/scripts/submit_small_stabilizer.py @@ -1,181 +1,181 @@ -""" -Submit a small stabilizer circuit (GHZ 4-qubit) to Azure Quantum and save results. - -This script: -1. Connects to Azure Quantum workspace using config/quantum_config.yaml -2. Creates a 4-qubit GHZ stabilizer circuit -3. Submits to an available backend (default: rigetti.sim.qvm) -4. Waits for result and saves JSON compatible with visualize_hardware_results.py - -Usage: - python .\\quantum-ai\\scripts\\submit_small_stabilizer.py [--backend ] [--shots ] - -Example: - python .\\quantum-ai\\scripts\\submit_small_stabilizer.py --backend rigetti.sim.qvm --shots 1000 -""" - -from __future__ import annotations - -import argparse -import json -import sys -from datetime import datetime, timezone -from pathlib import Path - -import numpy as np -import yaml -from qiskit import QuantumCircuit - -# Add parent src to path for imports -SCRIPT_DIR = Path(__file__).resolve().parent -REPO_ROOT = SCRIPT_DIR.parent -sys.path.insert(0, str(REPO_ROOT / "src")) - -from azure_quantum_integration import AzureQuantumIntegration - -CONFIG_PATH = REPO_ROOT / "config" / "quantum_config.yaml" - - -def create_ghz_circuit(n_qubits: int = 4) -> QuantumCircuit: - """Create a GHZ stabilizer circuit for n qubits.""" - qc = QuantumCircuit(n_qubits, n_qubits) - qc.h(0) - for i in range(n_qubits - 1): - qc.cx(i, i + 1) - qc.measure(range(n_qubits), range(n_qubits)) - return qc - - -def compute_entropy(counts: dict[str, int]) -> float: - total = sum(counts.values()) - if total == 0: - return 0.0 - p = np.array([v / total for v in counts.values() if v > 0], dtype=float) - return float(-(p * np.log2(p)).sum()) - - -def main() -> int: - parser = argparse.ArgumentParser( - description="Submit a small stabilizer circuit to Azure Quantum" - ) - parser.add_argument( - "--backend", - type=str, - default="rigetti.sim.qvm", - help="Backend name (default: rigetti.sim.qvm)", - ) - parser.add_argument( - "--shots", type=int, default=1000, help="Number of shots (default: 1000)" - ) - parser.add_argument( - "--n-qubits", - type=int, - default=4, - help="Number of qubits for GHZ circuit (default: 4)", - ) - args = parser.parse_args() - - cfg = yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8")) - results_dir = ( - REPO_ROOT / Path(cfg["logging"]["results_dir"]).expanduser() - ).resolve() - results_dir.mkdir(parents=True, exist_ok=True) - - print("\n=== Azure Quantum Stabilizer Submission ===") - print(f"Circuit: GHZ {args.n_qubits}-qubit") - print(f"Backend: {args.backend}, Shots: {args.shots}\n") - - # Create circuit - qc = create_ghz_circuit(args.n_qubits) - print("Circuit created:") - print(qc) - - # Connect to Azure Quantum - print("\nConnecting to Azure Quantum...") - azure = AzureQuantumIntegration(config_path=str(CONFIG_PATH)) - try: - azure.connect() - except Exception as e: - print(f"Error connecting to Azure Quantum: {e}") - print("\nEnsure you have:") - print("1. Valid Azure credentials configured (az login)") - print("2. Updated config/quantum_config.yaml with your workspace details") - print("3. Azure Quantum workspace created and accessible") - return 1 - - # List backends to confirm target is available - available = azure.list_backends() - if args.backend not in available: - print(f"\nWarning: Backend {args.backend} not found in workspace.") - print(f"Available backends: {', '.join(available)}") - print("Attempting to use first available backend...") - args.backend = available[0] if available else None - if not args.backend: - print("No backends available. Exiting.") - return 1 - - # Submit job - print(f"\nSubmitting job to {args.backend}...") - job_name = f"ghz_{args.n_qubits}q_stabilizer_{datetime.now(timezone.utc).strftime('%Y%m%d_%H%M%S')}" - try: - job = azure.submit_circuit( - qc, backend_name=args.backend, shots=args.shots, job_name=job_name - ) - print(f"Job submitted: {job.id()}") - print("Waiting for results...") - result_data = azure.get_job_results(job) - except Exception as e: - print(f"Error submitting or retrieving job: {e}") - return 1 - - # Compute metadata - counts = result_data.get("counts", {}) - entropy = compute_entropy(counts) - max_entropy = float(args.n_qubits) - - # Save in schema compatible with visualizer - ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") - out = { - "job_id": result_data.get("job_id"), - "counts": {str(k): int(v) for k, v in counts.items()}, - "success": result_data.get("success", False), - "metadata": { - "backend": args.backend, - "n_qubits": args.n_qubits, - "layers": 1, # GHZ is effectively 1 layer (H + CNOTs) - "entanglement": "linear", - "method": "hardware", - "circuit": "stabilizer_ghz", - "shots": args.shots, - "entropy": entropy, - "max_entropy": max_entropy, - "noise": { - "pauli_px": 0.0, - "pauli_pz": 0.0, - "depolarizing_p": 0.0, - "amp_damp_gamma": 0.0, - "seed": None, - }, - "stabilizer": { - "type": "ghz", - "clifford_layers": 0, - "twoq_density": 0.0, - }, - }, - } - - out_path = results_dir / f"azure_ghz_{args.n_qubits}q_results_{ts}.json" - out_path.write_text(json.dumps(out, indent=2), encoding="utf-8") - print(f"\nResults saved to {out_path}") - print(f"Unique states: {len(counts)} / {2 ** args.n_qubits}") - print( - f"Entropy: {entropy:.3f} / {max_entropy:.3f} ({(entropy/max_entropy*100 if max_entropy>0 else 0):.1f}%)" - ) - print("\nTip: Now run the visualizer to see side-by-side comparison:") - print(" python .\\quantum-ai\\scripts\\visualize_hardware_results.py") - - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) +""" +Submit a small stabilizer circuit (GHZ 4-qubit) to Azure Quantum and save results. + +This script: +1. Connects to Azure Quantum workspace using config/quantum_config.yaml +2. Creates a 4-qubit GHZ stabilizer circuit +3. Submits to an available backend (default: rigetti.sim.qvm) +4. Waits for result and saves JSON compatible with visualize_hardware_results.py + +Usage: + python .\\quantum-ai\\scripts\\submit_small_stabilizer.py [--backend ] [--shots ] + +Example: + python .\\quantum-ai\\scripts\\submit_small_stabilizer.py --backend rigetti.sim.qvm --shots 1000 +""" + +from __future__ import annotations + +import argparse +import json +import sys +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import yaml +from qiskit import QuantumCircuit + +# Add parent src to path for imports +SCRIPT_DIR = Path(__file__).resolve().parent +REPO_ROOT = SCRIPT_DIR.parent +sys.path.insert(0, str(REPO_ROOT / "src")) + +from azure_quantum_integration import AzureQuantumIntegration + +CONFIG_PATH = REPO_ROOT / "config" / "quantum_config.yaml" + + +def create_ghz_circuit(n_qubits: int = 4) -> QuantumCircuit: + """Create a GHZ stabilizer circuit for n qubits.""" + qc = QuantumCircuit(n_qubits, n_qubits) + qc.h(0) + for i in range(n_qubits - 1): + qc.cx(i, i + 1) + qc.measure(range(n_qubits), range(n_qubits)) + return qc + + +def compute_entropy(counts: dict[str, int]) -> float: + total = sum(counts.values()) + if total == 0: + return 0.0 + p = np.array([v / total for v in counts.values() if v > 0], dtype=float) + return float(-(p * np.log2(p)).sum()) + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Submit a small stabilizer circuit to Azure Quantum" + ) + parser.add_argument( + "--backend", + type=str, + default="rigetti.sim.qvm", + help="Backend name (default: rigetti.sim.qvm)", + ) + parser.add_argument( + "--shots", type=int, default=1000, help="Number of shots (default: 1000)" + ) + parser.add_argument( + "--n-qubits", + type=int, + default=4, + help="Number of qubits for GHZ circuit (default: 4)", + ) + args = parser.parse_args() + + cfg = yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8")) + results_dir = ( + REPO_ROOT / Path(cfg["logging"]["results_dir"]).expanduser() + ).resolve() + results_dir.mkdir(parents=True, exist_ok=True) + + print("\n=== Azure Quantum Stabilizer Submission ===") + print(f"Circuit: GHZ {args.n_qubits}-qubit") + print(f"Backend: {args.backend}, Shots: {args.shots}\n") + + # Create circuit + qc = create_ghz_circuit(args.n_qubits) + print("Circuit created:") + print(qc) + + # Connect to Azure Quantum + print("\nConnecting to Azure Quantum...") + azure = AzureQuantumIntegration(config_path=str(CONFIG_PATH)) + try: + azure.connect() + except Exception as e: + print(f"Error connecting to Azure Quantum: {e}") + print("\nEnsure you have:") + print("1. Valid Azure credentials configured (az login)") + print("2. Updated config/quantum_config.yaml with your workspace details") + print("3. Azure Quantum workspace created and accessible") + return 1 + + # List backends to confirm target is available + available = azure.list_backends() + if args.backend not in available: + print(f"\nWarning: Backend {args.backend} not found in workspace.") + print(f"Available backends: {', '.join(available)}") + print("Attempting to use first available backend...") + args.backend = available[0] if available else None + if not args.backend: + print("No backends available. Exiting.") + return 1 + + # Submit job + print(f"\nSubmitting job to {args.backend}...") + job_name = f"ghz_{args.n_qubits}q_stabilizer_{datetime.now(timezone.utc).strftime('%Y%m%d_%H%M%S')}" + try: + job = azure.submit_circuit( + qc, backend_name=args.backend, shots=args.shots, job_name=job_name + ) + print(f"Job submitted: {job.id()}") + print("Waiting for results...") + result_data = azure.get_job_results(job) + except Exception as e: + print(f"Error submitting or retrieving job: {e}") + return 1 + + # Compute metadata + counts = result_data.get("counts", {}) + entropy = compute_entropy(counts) + max_entropy = float(args.n_qubits) + + # Save in schema compatible with visualizer + ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") + out = { + "job_id": result_data.get("job_id"), + "counts": {str(k): int(v) for k, v in counts.items()}, + "success": result_data.get("success", False), + "metadata": { + "backend": args.backend, + "n_qubits": args.n_qubits, + "layers": 1, # GHZ is effectively 1 layer (H + CNOTs) + "entanglement": "linear", + "method": "hardware", + "circuit": "stabilizer_ghz", + "shots": args.shots, + "entropy": entropy, + "max_entropy": max_entropy, + "noise": { + "pauli_px": 0.0, + "pauli_pz": 0.0, + "depolarizing_p": 0.0, + "amp_damp_gamma": 0.0, + "seed": None, + }, + "stabilizer": { + "type": "ghz", + "clifford_layers": 0, + "twoq_density": 0.0, + }, + }, + } + + out_path = results_dir / f"azure_ghz_{args.n_qubits}q_results_{ts}.json" + out_path.write_text(json.dumps(out, indent=2), encoding="utf-8") + print(f"\nResults saved to {out_path}") + print(f"Unique states: {len(counts)} / {2 ** args.n_qubits}") + print( + f"Entropy: {entropy:.3f} / {max_entropy:.3f} ({(entropy/max_entropy*100 if max_entropy>0 else 0):.1f}%)" + ) + print("\nTip: Now run the visualizer to see side-by-side comparison:") + print(" python .\\quantum-ai\\scripts\\visualize_hardware_results.py") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ai-projects/quantum-ml/scripts/submit_variational_hardware.py b/ai-projects/quantum-ml/scripts/submit_variational_hardware.py index f94d9c263..39b7e10f0 100644 --- a/ai-projects/quantum-ml/scripts/submit_variational_hardware.py +++ b/ai-projects/quantum-ml/scripts/submit_variational_hardware.py @@ -1,212 +1,212 @@ -""" -Submit variational quantum circuits to Azure Quantum hardware for comparison -with local MPS simulations. - -This tests whether hardware can reproduce the entropy and state distribution -patterns we observed in MPS simulations at different layer depths. - -Usage: - python .\\quantum-ai\\scripts\\submit_variational_hardware.py --backend rigetti.sim.qvm --n-qubits 4 --layers 2 -""" - -from __future__ import annotations - -import argparse -import json -import sys -from datetime import datetime, timezone -from pathlib import Path - -import numpy as np -import yaml -from qiskit import QuantumCircuit - -# Add parent src to path -SCRIPT_DIR = Path(__file__).resolve().parent -REPO_ROOT = SCRIPT_DIR.parent -sys.path.insert(0, str(REPO_ROOT / "src")) - -from azure_quantum_integration import AzureQuantumIntegration - -CONFIG_PATH = REPO_ROOT / "config" / "quantum_config.yaml" - - -def create_variational_circuit( - n_qubits: int, n_layers: int, entanglement: str = "linear" -) -> QuantumCircuit: - """ - Create a variational circuit matching the MPS simulation pattern. - - Same structure as run_simulated_circuit.py for direct comparison. - """ - qc = QuantumCircuit(n_qubits, n_qubits) - - # Initial layer - for i in range(n_qubits): - qc.h(i) - - for layer in range(n_layers): - # Parameterized single-qubit rotations - for i in range(n_qubits): - qc.ry(np.pi / 4 * (layer + 1), i) - qc.rz(np.pi / 3 * (layer + 1), i) - - # Entanglement pattern - if entanglement == "linear": - for i in range(n_qubits - 1): - qc.cx(i, i + 1) - elif entanglement == "full": - for i in range(n_qubits): - for j in range(i + 1, n_qubits): - qc.cx(i, j) - else: # circular (default) - for i in range(n_qubits): - qc.cx(i, (i + 1) % n_qubits) - - qc.barrier() - - qc.measure(range(n_qubits), range(n_qubits)) - return qc - - -def compute_entropy(counts: dict[str, int]) -> float: - total = sum(counts.values()) - if total == 0: - return 0.0 - p = np.array([v / total for v in counts.values() if v > 0], dtype=float) - return float(-(p * np.log2(p)).sum()) - - -def main() -> int: - parser = argparse.ArgumentParser( - description="Submit variational circuits to Azure Quantum" - ) - parser.add_argument( - "--backend", type=str, default="rigetti.sim.qvm", help="Backend name" - ) - parser.add_argument( - "--n-qubits", type=int, default=4, help="Number of qubits (default: 4)" - ) - parser.add_argument( - "--layers", type=int, default=2, help="Number of layers (default: 2)" - ) - parser.add_argument( - "--entanglement", - choices=["linear", "circular", "full"], - default="linear", - help="Entanglement pattern (default: linear)", - ) - parser.add_argument( - "--shots", type=int, default=1000, help="Number of shots (default: 1000)" - ) - args = parser.parse_args() - - cfg = yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8")) - results_dir = ( - REPO_ROOT / Path(cfg["logging"]["results_dir"]).expanduser() - ).resolve() - results_dir.mkdir(parents=True, exist_ok=True) - - print("\n=== Variational Circuit Hardware Submission ===") - print( - f"Circuit: {args.n_qubits}-qubit, {args.layers} layers, {args.entanglement} entanglement" - ) - print(f"Backend: {args.backend}, Shots: {args.shots}\n") - - # Create circuit - qc = create_variational_circuit(args.n_qubits, args.layers, args.entanglement) - print("Circuit created:") - print(f" Depth: {qc.depth()}, Gates: {sum(qc.count_ops().values())}") - print(f" Structure: H + {args.layers}×(RY+RZ+{args.entanglement.upper()})\n") - - # Connect and submit - print("Connecting to Azure Quantum...") - azure = AzureQuantumIntegration(config_path=str(CONFIG_PATH)) - try: - azure.connect() - except Exception as e: - print(f"Error: {e}") - return 1 - - available = azure.list_backends() - if args.backend not in available: - print(f"\nWarning: {args.backend} not found.") - print(f"Available: {', '.join(available)}") - if available: - args.backend = available[0] - print(f"Using: {args.backend}") - else: - return 1 - - print(f"\nSubmitting to {args.backend}...") - job_name = f"variational_{args.n_qubits}q_L{args.layers}_{args.entanglement}_{datetime.now(timezone.utc).strftime('%Y%m%d_%H%M%S')}" - - try: - job = azure.submit_circuit( - qc, backend_name=args.backend, shots=args.shots, job_name=job_name - ) - print(f"Job ID: {job.id()}") - print("Waiting for results...") - result_data = azure.get_job_results(job) - except Exception as e: - print(f"Submission error: {e}") - return 1 - - # Process results - counts = result_data.get("counts", {}) - entropy = compute_entropy(counts) - max_entropy = float(args.n_qubits) - - print("\n✓ Results received:") - print(f" Unique states: {len(counts)} / {2 ** args.n_qubits}") - print( - f" Entropy: {entropy:.3f} / {max_entropy:.3f} ({(entropy/max_entropy*100 if max_entropy>0 else 0):.1f}%)" - ) - - # Save in same format as local simulations - ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") - out = { - "job_id": result_data.get("job_id"), - "counts": {str(k): int(v) for k, v in counts.items()}, - "success": result_data.get("success", False), - "metadata": { - "backend": args.backend, - "n_qubits": args.n_qubits, - "layers": args.layers, - "entanglement": args.entanglement, - "method": "hardware", - "circuit": "variational", - "shots": args.shots, - "entropy": entropy, - "max_entropy": max_entropy, - "circuit_depth": qc.depth(), - "circuit_gates": sum(qc.count_ops().values()), - "noise": { - "pauli_px": 0.0, - "pauli_pz": 0.0, - "depolarizing_p": 0.0, - "amp_damp_gamma": 0.0, - "seed": None, - }, - }, - } - - out_path = ( - results_dir - / f"azure_variational_{args.n_qubits}q_L{args.layers}_{args.entanglement}_{ts}.json" - ) - out_path.write_text(json.dumps(out, indent=2), encoding="utf-8") - print(f"\nSaved: {out_path}") - - print("\nCompare with local MPS simulation:") - print( - f" python .\\quantum-ai\\scripts\\run_simulated_circuit.py --n-qubits {args.n_qubits} --layers {args.layers} --entanglement {args.entanglement} --method matrix_product_state --shots {args.shots}" - ) - print("\nVisualize:") - print(" python .\\quantum-ai\\scripts\\visualize_hardware_results.py") - - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) +""" +Submit variational quantum circuits to Azure Quantum hardware for comparison +with local MPS simulations. + +This tests whether hardware can reproduce the entropy and state distribution +patterns we observed in MPS simulations at different layer depths. + +Usage: + python .\\quantum-ai\\scripts\\submit_variational_hardware.py --backend rigetti.sim.qvm --n-qubits 4 --layers 2 +""" + +from __future__ import annotations + +import argparse +import json +import sys +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import yaml +from qiskit import QuantumCircuit + +# Add parent src to path +SCRIPT_DIR = Path(__file__).resolve().parent +REPO_ROOT = SCRIPT_DIR.parent +sys.path.insert(0, str(REPO_ROOT / "src")) + +from azure_quantum_integration import AzureQuantumIntegration + +CONFIG_PATH = REPO_ROOT / "config" / "quantum_config.yaml" + + +def create_variational_circuit( + n_qubits: int, n_layers: int, entanglement: str = "linear" +) -> QuantumCircuit: + """ + Create a variational circuit matching the MPS simulation pattern. + + Same structure as run_simulated_circuit.py for direct comparison. + """ + qc = QuantumCircuit(n_qubits, n_qubits) + + # Initial layer + for i in range(n_qubits): + qc.h(i) + + for layer in range(n_layers): + # Parameterized single-qubit rotations + for i in range(n_qubits): + qc.ry(np.pi / 4 * (layer + 1), i) + qc.rz(np.pi / 3 * (layer + 1), i) + + # Entanglement pattern + if entanglement == "linear": + for i in range(n_qubits - 1): + qc.cx(i, i + 1) + elif entanglement == "full": + for i in range(n_qubits): + for j in range(i + 1, n_qubits): + qc.cx(i, j) + else: # circular (default) + for i in range(n_qubits): + qc.cx(i, (i + 1) % n_qubits) + + qc.barrier() + + qc.measure(range(n_qubits), range(n_qubits)) + return qc + + +def compute_entropy(counts: dict[str, int]) -> float: + total = sum(counts.values()) + if total == 0: + return 0.0 + p = np.array([v / total for v in counts.values() if v > 0], dtype=float) + return float(-(p * np.log2(p)).sum()) + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Submit variational circuits to Azure Quantum" + ) + parser.add_argument( + "--backend", type=str, default="rigetti.sim.qvm", help="Backend name" + ) + parser.add_argument( + "--n-qubits", type=int, default=4, help="Number of qubits (default: 4)" + ) + parser.add_argument( + "--layers", type=int, default=2, help="Number of layers (default: 2)" + ) + parser.add_argument( + "--entanglement", + choices=["linear", "circular", "full"], + default="linear", + help="Entanglement pattern (default: linear)", + ) + parser.add_argument( + "--shots", type=int, default=1000, help="Number of shots (default: 1000)" + ) + args = parser.parse_args() + + cfg = yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8")) + results_dir = ( + REPO_ROOT / Path(cfg["logging"]["results_dir"]).expanduser() + ).resolve() + results_dir.mkdir(parents=True, exist_ok=True) + + print("\n=== Variational Circuit Hardware Submission ===") + print( + f"Circuit: {args.n_qubits}-qubit, {args.layers} layers, {args.entanglement} entanglement" + ) + print(f"Backend: {args.backend}, Shots: {args.shots}\n") + + # Create circuit + qc = create_variational_circuit(args.n_qubits, args.layers, args.entanglement) + print("Circuit created:") + print(f" Depth: {qc.depth()}, Gates: {sum(qc.count_ops().values())}") + print(f" Structure: H + {args.layers}×(RY+RZ+{args.entanglement.upper()})\n") + + # Connect and submit + print("Connecting to Azure Quantum...") + azure = AzureQuantumIntegration(config_path=str(CONFIG_PATH)) + try: + azure.connect() + except Exception as e: + print(f"Error: {e}") + return 1 + + available = azure.list_backends() + if args.backend not in available: + print(f"\nWarning: {args.backend} not found.") + print(f"Available: {', '.join(available)}") + if available: + args.backend = available[0] + print(f"Using: {args.backend}") + else: + return 1 + + print(f"\nSubmitting to {args.backend}...") + job_name = f"variational_{args.n_qubits}q_L{args.layers}_{args.entanglement}_{datetime.now(timezone.utc).strftime('%Y%m%d_%H%M%S')}" + + try: + job = azure.submit_circuit( + qc, backend_name=args.backend, shots=args.shots, job_name=job_name + ) + print(f"Job ID: {job.id()}") + print("Waiting for results...") + result_data = azure.get_job_results(job) + except Exception as e: + print(f"Submission error: {e}") + return 1 + + # Process results + counts = result_data.get("counts", {}) + entropy = compute_entropy(counts) + max_entropy = float(args.n_qubits) + + print("\n✓ Results received:") + print(f" Unique states: {len(counts)} / {2 ** args.n_qubits}") + print( + f" Entropy: {entropy:.3f} / {max_entropy:.3f} ({(entropy/max_entropy*100 if max_entropy>0 else 0):.1f}%)" + ) + + # Save in same format as local simulations + ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") + out = { + "job_id": result_data.get("job_id"), + "counts": {str(k): int(v) for k, v in counts.items()}, + "success": result_data.get("success", False), + "metadata": { + "backend": args.backend, + "n_qubits": args.n_qubits, + "layers": args.layers, + "entanglement": args.entanglement, + "method": "hardware", + "circuit": "variational", + "shots": args.shots, + "entropy": entropy, + "max_entropy": max_entropy, + "circuit_depth": qc.depth(), + "circuit_gates": sum(qc.count_ops().values()), + "noise": { + "pauli_px": 0.0, + "pauli_pz": 0.0, + "depolarizing_p": 0.0, + "amp_damp_gamma": 0.0, + "seed": None, + }, + }, + } + + out_path = ( + results_dir + / f"azure_variational_{args.n_qubits}q_L{args.layers}_{args.entanglement}_{ts}.json" + ) + out_path.write_text(json.dumps(out, indent=2), encoding="utf-8") + print(f"\nSaved: {out_path}") + + print("\nCompare with local MPS simulation:") + print( + f" python .\\quantum-ai\\scripts\\run_simulated_circuit.py --n-qubits {args.n_qubits} --layers {args.layers} --entanglement {args.entanglement} --method matrix_product_state --shots {args.shots}" + ) + print("\nVisualize:") + print(" python .\\quantum-ai\\scripts\\visualize_hardware_results.py") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ai-projects/quantum-ml/scripts/test_provider_gates.py b/ai-projects/quantum-ml/scripts/test_provider_gates.py index 6245aa46f..fd57af5d4 100644 --- a/ai-projects/quantum-ml/scripts/test_provider_gates.py +++ b/ai-projects/quantum-ml/scripts/test_provider_gates.py @@ -1,326 +1,326 @@ -""" -Test provider-specific gate sets and circuit patterns for Azure Quantum backends. - -This script creates GHZ circuits using different gate decompositions optimized -for specific providers (Rigetti, Quantinuum, IonQ) and compares results. - -Usage: - python .\\quantum-ai\\scripts\\test_provider_gates.py [--backend ] [--shots ] -""" - -from __future__ import annotations - -import argparse -import json -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Optional - -_OPTIONAL_IMPORT_ERROR: Optional[ImportError] = None - -# Add parent src to path for imports -SCRIPT_DIR = Path(__file__).resolve().parent -REPO_ROOT = SCRIPT_DIR.parent -sys.path.insert(0, str(REPO_ROOT / "src")) - -try: - import numpy as np - import yaml - from azure_quantum_integration import AzureQuantumIntegration - from qiskit import QuantumCircuit, transpile -except ImportError as exc: # pragma: no cover - environment dependent - _OPTIONAL_IMPORT_ERROR = exc - -if _OPTIONAL_IMPORT_ERROR is not None and "pytest" in sys.modules: - import pytest - - pytest.skip( - f"Optional quantum dependencies unavailable: {_OPTIONAL_IMPORT_ERROR}", - allow_module_level=True, - ) - - -CONFIG_PATH = REPO_ROOT / "config" / "quantum_config.yaml" - - -def create_ghz_standard(n_qubits: int = 4) -> QuantumCircuit: - """Standard GHZ using H + CX gates (works for Rigetti, IonQ).""" - qc = QuantumCircuit(n_qubits, n_qubits) - qc.h(0) - for i in range(n_qubits - 1): - qc.cx(i, i + 1) - qc.measure(range(n_qubits), range(n_qubits)) - return qc - - -def create_ghz_quantinuum_native(n_qubits: int = 4) -> QuantumCircuit: - """ - GHZ using Quantinuum native gates: RZ, RX, ZZ (instead of CX). - - Quantinuum H-series systems have native gates: - - Single-qubit: RZ(θ), RX(π/2), RX(-π/2) - - Two-qubit: ZZ(θ) = exp(-i θ/2 Z⊗Z) - - CX can be decomposed as: CX = (RX(π/2) ⊗ I) · ZZ(π/2) · (RX(-π/2) ⊗ I) - But for GHZ, we can use a more direct approach with RX and ZZ. - """ - qc = QuantumCircuit(n_qubits, n_qubits) - - # Create |+⟩ state on first qubit using RX instead of H - # H = (1/√2)(X + Z) can be implemented as RZ(π/2) RX(π/2) RZ(π/2) - qc.rz(np.pi / 2, 0) - qc.rx(np.pi / 2, 0) - qc.rz(np.pi / 2, 0) - - # Entangle using ZZ gates - # For GHZ, we need controlled operations. ZZ doesn't directly give CX, - # but we can build entanglement differently. - # Alternative: Use standard CX and let Quantinuum transpiler handle it - for i in range(n_qubits - 1): - # Decompose CX using Quantinuum-friendly gates - # CX(i, i+1) = RX(π/2)[i+1] · ZZ(π/2)[i,i+1] · RX(-π/2)[i+1] - qc.rx(np.pi / 2, i + 1) - qc.rzz(np.pi / 2, i, i + 1) # ZZ rotation - qc.rx(-np.pi / 2, i + 1) - - qc.measure(range(n_qubits), range(n_qubits)) - return qc - - -def create_ghz_ionq_native(n_qubits: int = 4) -> QuantumCircuit: - """ - GHZ using IonQ native gates: GPi, GPi2, MS (Mølmer-Sørensen). - - IonQ uses: - - GPi(φ): single-qubit rotation - - GPi2(φ): π/2 rotation - - MS(φ0, φ1): two-qubit Mølmer-Sørensen gate - - However, Qiskit doesn't have direct GPi gates, so we use standard gates - and rely on IonQ's transpiler. Alternatively, use RX/RY which IonQ supports. - """ - qc = QuantumCircuit(n_qubits, n_qubits) - - # IonQ prefers all-to-all connectivity with MS gate - # For GHZ, we can use H + CX (IonQ will transpile efficiently) - qc.h(0) - for i in range(n_qubits - 1): - qc.cx(i, i + 1) - - qc.measure(range(n_qubits), range(n_qubits)) - return qc - - -def create_ghz_rigetti_native(n_qubits: int = 4) -> QuantumCircuit: - """ - GHZ using Rigetti native gates: RX, RZ, CZ. - - Rigetti systems use: - - Single-qubit: RX(θ), RZ(θ) - - Two-qubit: CZ (controlled-Z) - - H can be made from RZ and RX. - CX can be made from CZ with basis rotations. - """ - qc = QuantumCircuit(n_qubits, n_qubits) - - # H = RZ(π) RX(π/2) RZ(π) up to global phase - # Simpler: H = RX(π/2) RZ(π/2) RX(π/2) - qc.rz(np.pi, 0) - qc.rx(np.pi / 2, 0) - qc.rz(np.pi, 0) - - # CX(i, i+1) = HI · CZ · HI where H acts on target - for i in range(n_qubits - 1): - # Apply H to target (i+1) using RZ + RX - qc.rz(np.pi, i + 1) - qc.rx(np.pi / 2, i + 1) - qc.rz(np.pi, i + 1) - - # CZ gate (native to Rigetti) - qc.cz(i, i + 1) - - # Apply H to target again - qc.rz(np.pi, i + 1) - qc.rx(np.pi / 2, i + 1) - qc.rz(np.pi, i + 1) - - qc.measure(range(n_qubits), range(n_qubits)) - return qc - - -def create_ghz_provider_native( - n_qubits: int = 4, provider: str = "standard" -) -> QuantumCircuit: - """Factory for provider-specific GHZ circuit construction.""" - provider_key = provider.lower().strip() - if provider_key == "quantinuum": - return create_ghz_quantinuum_native(n_qubits) - if provider_key == "ionq": - return create_ghz_ionq_native(n_qubits) - if provider_key == "rigetti": - return create_ghz_rigetti_native(n_qubits) - # Default generic decomposition - return create_ghz_standard(n_qubits) - - -def compute_entropy(counts: dict[str, int]) -> float: - total = sum(counts.values()) - if total == 0: - return 0.0 - p = np.array([v / total for v in counts.values() if v > 0], dtype=float) - return float(-(p * np.log2(p)).sum()) - - -def main() -> int: - if _OPTIONAL_IMPORT_ERROR is not None: - print(f"Missing optional quantum dependencies: {_OPTIONAL_IMPORT_ERROR}") - return 1 - - parser = argparse.ArgumentParser( - description="Test provider-specific gate decompositions" - ) - parser.add_argument( - "--backend", - type=str, - default=None, - help="Backend name (auto-detect if not specified)", - ) - parser.add_argument( - "--shots", type=int, default=1000, help="Number of shots (default: 1000)" - ) - parser.add_argument( - "--n-qubits", type=int, default=4, help="Number of qubits (default: 4)" - ) - parser.add_argument( - "--pattern", - choices=["standard", "quantinuum", "ionq", "rigetti", "all"], - default="all", - help="Circuit pattern to test (default: all)", - ) - args = parser.parse_args() - - cfg = yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8")) - results_dir = ( - REPO_ROOT / Path(cfg["logging"]["results_dir"]).expanduser() - ).resolve() - results_dir.mkdir(parents=True, exist_ok=True) - - print("\n=== Provider-Specific Circuit Pattern Tests ===") - print(f"Qubits: {args.n_qubits}, Shots: {args.shots}\n") - - # Connect to Azure Quantum - print("Connecting to Azure Quantum...") - azure = AzureQuantumIntegration(config_path=str(CONFIG_PATH)) - try: - azure.connect() - except Exception as e: - print(f"Error connecting to Azure Quantum: {e}") - return 1 - - # Determine backend - available = azure.list_backends() - if args.backend and args.backend in available: - backend_name = args.backend - elif args.backend: - print(f"\nWarning: {args.backend} not available.") - print(f"Available: {', '.join(available)}") - backend_name = available[0] if available else None - else: - # Auto-select based on provider preference - for pref in ["rigetti", "ionq", "quantinuum"]: - for b in available: - if pref in b.lower(): - backend_name = b - break - if backend_name: - break - if not backend_name: - backend_name = available[0] if available else None - - if not backend_name: - print("No backends available.") - return 1 - - print(f"Selected backend: {backend_name}\n") - - # Determine which patterns to test - patterns = {} - if args.pattern == "all": - patterns = { - "standard": create_ghz_standard, - "quantinuum": create_ghz_quantinuum_native, - "ionq": create_ghz_ionq_native, - "rigetti": create_ghz_rigetti_native, - } - else: - pattern_map = { - "standard": create_ghz_standard, - "quantinuum": create_ghz_quantinuum_native, - "ionq": create_ghz_ionq_native, - "rigetti": create_ghz_rigetti_native, - } - patterns[args.pattern] = pattern_map[args.pattern] - - # Test each pattern - for pattern_name, circuit_fn in patterns.items(): - print(f"Testing {pattern_name} pattern...") - qc = circuit_fn(args.n_qubits) - print(f" Circuit depth: {qc.depth()}, gates: {sum(qc.count_ops().values())}") - - try: - job_name = f"ghz_{args.n_qubits}q_{pattern_name}_{datetime.now(timezone.utc).strftime('%Y%m%d_%H%M%S')}" - job = azure.submit_circuit( - qc, backend_name=backend_name, shots=args.shots, job_name=job_name - ) - print(f" Job submitted: {job.id()}") - print(" Waiting for results...") - result_data = azure.get_job_results(job) - - counts = result_data.get("counts", {}) - entropy = compute_entropy(counts) - max_entropy = float(args.n_qubits) - - # Save results - ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") - out = { - "job_id": result_data.get("job_id"), - "counts": {str(k): int(v) for k, v in counts.items()}, - "success": result_data.get("success", False), - "metadata": { - "backend": backend_name, - "n_qubits": args.n_qubits, - "pattern": pattern_name, - "circuit": "ghz", - "shots": args.shots, - "entropy": entropy, - "max_entropy": max_entropy, - "circuit_depth": qc.depth(), - "circuit_gates": sum(qc.count_ops().values()), - }, - } - - out_path = ( - results_dir - / f"pattern_{pattern_name}_{args.n_qubits}q_{backend_name.replace('.', '_')}_{ts}.json" - ) - out_path.write_text(json.dumps(out, indent=2), encoding="utf-8") - - unique = len(counts) - print( - f" ✓ Results: {unique} unique states, entropy: {entropy:.3f}/{max_entropy:.3f}" - ) - print(f" Saved: {out_path.name}\n") - - except Exception as e: - print(f" ✗ Error: {e}\n") - - print("Pattern testing complete!") - print("Run visualizer to see charts:") - print(" python .\\quantum-ai\\scripts\\visualize_hardware_results.py") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) +""" +Test provider-specific gate sets and circuit patterns for Azure Quantum backends. + +This script creates GHZ circuits using different gate decompositions optimized +for specific providers (Rigetti, Quantinuum, IonQ) and compares results. + +Usage: + python .\\quantum-ai\\scripts\\test_provider_gates.py [--backend ] [--shots ] +""" + +from __future__ import annotations + +import argparse +import json +import sys +from datetime import datetime, timezone +from pathlib import Path +from typing import Optional + +_OPTIONAL_IMPORT_ERROR: Optional[ImportError] = None + +# Add parent src to path for imports +SCRIPT_DIR = Path(__file__).resolve().parent +REPO_ROOT = SCRIPT_DIR.parent +sys.path.insert(0, str(REPO_ROOT / "src")) + +try: + import numpy as np + import yaml + from azure_quantum_integration import AzureQuantumIntegration + from qiskit import QuantumCircuit, transpile +except ImportError as exc: # pragma: no cover - environment dependent + _OPTIONAL_IMPORT_ERROR = exc + +if _OPTIONAL_IMPORT_ERROR is not None and "pytest" in sys.modules: + import pytest + + pytest.skip( + f"Optional quantum dependencies unavailable: {_OPTIONAL_IMPORT_ERROR}", + allow_module_level=True, + ) + + +CONFIG_PATH = REPO_ROOT / "config" / "quantum_config.yaml" + + +def create_ghz_standard(n_qubits: int = 4) -> QuantumCircuit: + """Standard GHZ using H + CX gates (works for Rigetti, IonQ).""" + qc = QuantumCircuit(n_qubits, n_qubits) + qc.h(0) + for i in range(n_qubits - 1): + qc.cx(i, i + 1) + qc.measure(range(n_qubits), range(n_qubits)) + return qc + + +def create_ghz_quantinuum_native(n_qubits: int = 4) -> QuantumCircuit: + """ + GHZ using Quantinuum native gates: RZ, RX, ZZ (instead of CX). + + Quantinuum H-series systems have native gates: + - Single-qubit: RZ(θ), RX(π/2), RX(-π/2) + - Two-qubit: ZZ(θ) = exp(-i θ/2 Z⊗Z) + + CX can be decomposed as: CX = (RX(π/2) ⊗ I) · ZZ(π/2) · (RX(-π/2) ⊗ I) + But for GHZ, we can use a more direct approach with RX and ZZ. + """ + qc = QuantumCircuit(n_qubits, n_qubits) + + # Create |+⟩ state on first qubit using RX instead of H + # H = (1/√2)(X + Z) can be implemented as RZ(π/2) RX(π/2) RZ(π/2) + qc.rz(np.pi / 2, 0) + qc.rx(np.pi / 2, 0) + qc.rz(np.pi / 2, 0) + + # Entangle using ZZ gates + # For GHZ, we need controlled operations. ZZ doesn't directly give CX, + # but we can build entanglement differently. + # Alternative: Use standard CX and let Quantinuum transpiler handle it + for i in range(n_qubits - 1): + # Decompose CX using Quantinuum-friendly gates + # CX(i, i+1) = RX(π/2)[i+1] · ZZ(π/2)[i,i+1] · RX(-π/2)[i+1] + qc.rx(np.pi / 2, i + 1) + qc.rzz(np.pi / 2, i, i + 1) # ZZ rotation + qc.rx(-np.pi / 2, i + 1) + + qc.measure(range(n_qubits), range(n_qubits)) + return qc + + +def create_ghz_ionq_native(n_qubits: int = 4) -> QuantumCircuit: + """ + GHZ using IonQ native gates: GPi, GPi2, MS (Mølmer-Sørensen). + + IonQ uses: + - GPi(φ): single-qubit rotation + - GPi2(φ): π/2 rotation + - MS(φ0, φ1): two-qubit Mølmer-Sørensen gate + + However, Qiskit doesn't have direct GPi gates, so we use standard gates + and rely on IonQ's transpiler. Alternatively, use RX/RY which IonQ supports. + """ + qc = QuantumCircuit(n_qubits, n_qubits) + + # IonQ prefers all-to-all connectivity with MS gate + # For GHZ, we can use H + CX (IonQ will transpile efficiently) + qc.h(0) + for i in range(n_qubits - 1): + qc.cx(i, i + 1) + + qc.measure(range(n_qubits), range(n_qubits)) + return qc + + +def create_ghz_rigetti_native(n_qubits: int = 4) -> QuantumCircuit: + """ + GHZ using Rigetti native gates: RX, RZ, CZ. + + Rigetti systems use: + - Single-qubit: RX(θ), RZ(θ) + - Two-qubit: CZ (controlled-Z) + + H can be made from RZ and RX. + CX can be made from CZ with basis rotations. + """ + qc = QuantumCircuit(n_qubits, n_qubits) + + # H = RZ(π) RX(π/2) RZ(π) up to global phase + # Simpler: H = RX(π/2) RZ(π/2) RX(π/2) + qc.rz(np.pi, 0) + qc.rx(np.pi / 2, 0) + qc.rz(np.pi, 0) + + # CX(i, i+1) = HI · CZ · HI where H acts on target + for i in range(n_qubits - 1): + # Apply H to target (i+1) using RZ + RX + qc.rz(np.pi, i + 1) + qc.rx(np.pi / 2, i + 1) + qc.rz(np.pi, i + 1) + + # CZ gate (native to Rigetti) + qc.cz(i, i + 1) + + # Apply H to target again + qc.rz(np.pi, i + 1) + qc.rx(np.pi / 2, i + 1) + qc.rz(np.pi, i + 1) + + qc.measure(range(n_qubits), range(n_qubits)) + return qc + + +def create_ghz_provider_native( + n_qubits: int = 4, provider: str = "standard" +) -> QuantumCircuit: + """Factory for provider-specific GHZ circuit construction.""" + provider_key = provider.lower().strip() + if provider_key == "quantinuum": + return create_ghz_quantinuum_native(n_qubits) + if provider_key == "ionq": + return create_ghz_ionq_native(n_qubits) + if provider_key == "rigetti": + return create_ghz_rigetti_native(n_qubits) + # Default generic decomposition + return create_ghz_standard(n_qubits) + + +def compute_entropy(counts: dict[str, int]) -> float: + total = sum(counts.values()) + if total == 0: + return 0.0 + p = np.array([v / total for v in counts.values() if v > 0], dtype=float) + return float(-(p * np.log2(p)).sum()) + + +def main() -> int: + if _OPTIONAL_IMPORT_ERROR is not None: + print(f"Missing optional quantum dependencies: {_OPTIONAL_IMPORT_ERROR}") + return 1 + + parser = argparse.ArgumentParser( + description="Test provider-specific gate decompositions" + ) + parser.add_argument( + "--backend", + type=str, + default=None, + help="Backend name (auto-detect if not specified)", + ) + parser.add_argument( + "--shots", type=int, default=1000, help="Number of shots (default: 1000)" + ) + parser.add_argument( + "--n-qubits", type=int, default=4, help="Number of qubits (default: 4)" + ) + parser.add_argument( + "--pattern", + choices=["standard", "quantinuum", "ionq", "rigetti", "all"], + default="all", + help="Circuit pattern to test (default: all)", + ) + args = parser.parse_args() + + cfg = yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8")) + results_dir = ( + REPO_ROOT / Path(cfg["logging"]["results_dir"]).expanduser() + ).resolve() + results_dir.mkdir(parents=True, exist_ok=True) + + print("\n=== Provider-Specific Circuit Pattern Tests ===") + print(f"Qubits: {args.n_qubits}, Shots: {args.shots}\n") + + # Connect to Azure Quantum + print("Connecting to Azure Quantum...") + azure = AzureQuantumIntegration(config_path=str(CONFIG_PATH)) + try: + azure.connect() + except Exception as e: + print(f"Error connecting to Azure Quantum: {e}") + return 1 + + # Determine backend + available = azure.list_backends() + if args.backend and args.backend in available: + backend_name = args.backend + elif args.backend: + print(f"\nWarning: {args.backend} not available.") + print(f"Available: {', '.join(available)}") + backend_name = available[0] if available else None + else: + # Auto-select based on provider preference + for pref in ["rigetti", "ionq", "quantinuum"]: + for b in available: + if pref in b.lower(): + backend_name = b + break + if backend_name: + break + if not backend_name: + backend_name = available[0] if available else None + + if not backend_name: + print("No backends available.") + return 1 + + print(f"Selected backend: {backend_name}\n") + + # Determine which patterns to test + patterns = {} + if args.pattern == "all": + patterns = { + "standard": create_ghz_standard, + "quantinuum": create_ghz_quantinuum_native, + "ionq": create_ghz_ionq_native, + "rigetti": create_ghz_rigetti_native, + } + else: + pattern_map = { + "standard": create_ghz_standard, + "quantinuum": create_ghz_quantinuum_native, + "ionq": create_ghz_ionq_native, + "rigetti": create_ghz_rigetti_native, + } + patterns[args.pattern] = pattern_map[args.pattern] + + # Test each pattern + for pattern_name, circuit_fn in patterns.items(): + print(f"Testing {pattern_name} pattern...") + qc = circuit_fn(args.n_qubits) + print(f" Circuit depth: {qc.depth()}, gates: {sum(qc.count_ops().values())}") + + try: + job_name = f"ghz_{args.n_qubits}q_{pattern_name}_{datetime.now(timezone.utc).strftime('%Y%m%d_%H%M%S')}" + job = azure.submit_circuit( + qc, backend_name=backend_name, shots=args.shots, job_name=job_name + ) + print(f" Job submitted: {job.id()}") + print(" Waiting for results...") + result_data = azure.get_job_results(job) + + counts = result_data.get("counts", {}) + entropy = compute_entropy(counts) + max_entropy = float(args.n_qubits) + + # Save results + ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") + out = { + "job_id": result_data.get("job_id"), + "counts": {str(k): int(v) for k, v in counts.items()}, + "success": result_data.get("success", False), + "metadata": { + "backend": backend_name, + "n_qubits": args.n_qubits, + "pattern": pattern_name, + "circuit": "ghz", + "shots": args.shots, + "entropy": entropy, + "max_entropy": max_entropy, + "circuit_depth": qc.depth(), + "circuit_gates": sum(qc.count_ops().values()), + }, + } + + out_path = ( + results_dir + / f"pattern_{pattern_name}_{args.n_qubits}q_{backend_name.replace('.', '_')}_{ts}.json" + ) + out_path.write_text(json.dumps(out, indent=2), encoding="utf-8") + + unique = len(counts) + print( + f" ✓ Results: {unique} unique states, entropy: {entropy:.3f}/{max_entropy:.3f}" + ) + print(f" Saved: {out_path.name}\n") + + except Exception as e: + print(f" ✗ Error: {e}\n") + + print("Pattern testing complete!") + print("Run visualizer to see charts:") + print(" python .\\quantum-ai\\scripts\\visualize_hardware_results.py") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ai-projects/quantum-ml/scripts/train_from_dataset.py b/ai-projects/quantum-ml/scripts/train_from_dataset.py index edd0893d5..40ca39b25 100644 --- a/ai-projects/quantum-ml/scripts/train_from_dataset.py +++ b/ai-projects/quantum-ml/scripts/train_from_dataset.py @@ -1,213 +1,213 @@ -""" -Train Hybrid Quantum-Classical model from a CSV dataset in datasets/. - -Usage (PowerShell): - python .\\scripts\\train_from_dataset.py --dataset banknote --epochs 2 - python .\\scripts\\train_from_dataset.py --dataset ionosphere --epochs 2 - python .\\scripts\\train_from_dataset.py --dataset sonar --epochs 2 - python .\\scripts\\train_from_dataset.py --dataset heart_disease --epochs 2 - -This script reads the workspace dataset index at ../../datasets/dataset_index.json, -loads the selected dataset, preprocesses to match n_qubits from config, -and trains the HybridQuantumClassifier with a small number of epochs by default. - -Results are saved under results/datasets// -""" - -import argparse -import json -import sys -from pathlib import Path - -import numpy as np -import pandas as pd -import yaml -from sklearn.decomposition import PCA -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler - -# Resolve paths -SCRIPT_DIR = Path(__file__).resolve().parent -PROJECT_ROOT = SCRIPT_DIR.parent -WORKSPACE_ROOT = PROJECT_ROOT.parent -DATASETS_INDEX = WORKSPACE_ROOT / "datasets" / "dataset_index.json" -DEFAULT_CONFIG = PROJECT_ROOT / "config" / "quantum_config.yaml" -RESULTS_DIR = PROJECT_ROOT / "results" / "datasets" - -# Add src to path -SRC_DIR = PROJECT_ROOT / "src" -sys.path.insert(0, str(SRC_DIR)) - -from quantum_classifier import HybridQuantumClassifier # type: ignore -from quantum_classifier import QuantumClassifier, train_quantum_model - - -def load_dataset_from_csv(name: str, path: Path) -> tuple[np.ndarray, np.ndarray]: - """Load known UCI datasets with simple rules per dataset.""" - name = name.lower() - - if name == "banknote": - # 4 features + 1 label (0/1), no header - df = pd.read_csv(path, header=None) - X = df.iloc[:, :-1].values.astype(float) - y = df.iloc[:, -1].values.astype(int) - return X, y - - if name == "ionosphere": - # 34 features + 1 label ('g'/'b'), no header - df = pd.read_csv(path, header=None) - # Last column: 'g' (good, 1) or 'b' (bad, 0) - y = (df.iloc[:, -1] == "g").astype(int).values - X = df.iloc[:, :-1].values.astype(float) - return X, y - - if name == "sonar": - # 60 features + 1 label ('M'/'R'), no header - df = pd.read_csv(path, header=None) - y = (df.iloc[:, -1] == "M").astype(int).values - X = df.iloc[:, :-1].values.astype(float) - return X, y - - if name == "heart_disease": - # 13 features + 1 label (0..4); convert to binary (>=1 -> 1) - # Data contains '?' values -> treat as NaN and drop - df = pd.read_csv(path, header=None, na_values=["?"]) - df = df.dropna() - # In processed.cleveland.data last column is num (0..4) - y = (df.iloc[:, -1].astype(int) >= 1).astype(int).values - X = df.iloc[:, :-1].values.astype(float) - return X, y - - # Fallback: try generic CSV (last column = label) - df = pd.read_csv(path) - X = df.iloc[:, :-1].values - y = df.iloc[:, -1].values - # Try to make y binary if not numeric - if y.dtype == object: - classes = sorted(pd.Series(y).unique()) - y = (pd.Series(y) == classes[0]).astype(int).values - return X.astype(float), y.astype(int) - - -def preprocess(X: np.ndarray, y: np.ndarray, n_qubits: int, test_size: float = 0.2): - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=test_size, random_state=42, stratify=y - ) - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - pca = None - if X_train.shape[1] > n_qubits: - pca = PCA(n_components=n_qubits, random_state=42) - X_train = pca.fit_transform(X_train) - X_val = pca.transform(X_val) - elif X_train.shape[1] < n_qubits: - # pad with zeros - pad = n_qubits - X_train.shape[1] - X_train = np.hstack([X_train, np.zeros((X_train.shape[0], pad))]) - X_val = np.hstack([X_val, np.zeros((X_val.shape[0], pad))]) - - return X_train, X_val, y_train, y_val, scaler, pca - - -def make_quick_config( - base_cfg_path: Path, - epochs: int, - batch_size: int | None = None, - lr: float | None = None, -) -> Path: - with open(base_cfg_path, "r") as f: - cfg = yaml.safe_load(f) - cfg["ml"]["training"]["epochs"] = int(epochs) - if batch_size is not None: - cfg["ml"]["training"]["batch_size"] = int(batch_size) - if lr is not None: - cfg["ml"]["training"]["learning_rate"] = float(lr) - quick_path = PROJECT_ROOT / "config" / "quantum_config.quick.yaml" - with open(quick_path, "w") as f: - yaml.safe_dump(cfg, f) - return quick_path - - -def main(): - parser = argparse.ArgumentParser( - description="Train hybrid quantum model from CSV dataset" - ) - parser.add_argument( - "--dataset", - default="banknote", - help="Dataset key from dataset_index.json (e.g., banknote)", - ) - parser.add_argument("--epochs", type=int, default=2) - parser.add_argument("--batch-size", type=int, default=None) - parser.add_argument("--lr", type=float, default=None) - args = parser.parse_args() - - # Load dataset index - if not DATASETS_INDEX.exists(): - raise FileNotFoundError(f"Dataset index not found: {DATASETS_INDEX}") - - with open(DATASETS_INDEX, "r") as f: - index = json.load(f) - - if args.dataset not in index["datasets"]: - available = ", ".join(index["datasets"].keys()) - raise ValueError(f"Unknown dataset '{args.dataset}'. Available: {available}") - - ds_info = index["datasets"][args.dataset] - data_path = Path(ds_info["path"]) - if not data_path.exists(): - raise FileNotFoundError(f"Dataset file not found: {data_path}") - - # Load config for model params - with open(DEFAULT_CONFIG, "r") as f: - base_cfg = yaml.safe_load(f) - n_qubits = int(base_cfg["ml"]["model"]["n_qubits"]) - - print("\n=== Loading dataset ===") - print(f"Name: {args.dataset}") - print(f"Path: {data_path}") - - X, y = load_dataset_from_csv(args.dataset, data_path) - print(f"Shape: {X.shape}, Positive rate: {y.mean():.3f}") - - # Preprocess to match qubits - X_train, X_val, y_train, y_val, scaler, pca = preprocess(X, y, n_qubits) - - # Build model - qc = QuantumClassifier(config_path=str(DEFAULT_CONFIG)) - model = HybridQuantumClassifier(input_dim=X_train.shape[1], quantum_classifier=qc) - - # Make quick config override for fast training - quick_cfg = make_quick_config( - DEFAULT_CONFIG, epochs=args.epochs, batch_size=args.batch_size, lr=args.lr - ) - - # Train - history = train_quantum_model( - model, X_train, y_train, X_val, y_val, config_path=str(quick_cfg) - ) - - # Save results - out_dir = RESULTS_DIR / args.dataset - out_dir.mkdir(parents=True, exist_ok=True) - - # Save a simple summary - summary = { - "dataset": args.dataset, - "samples_train": int(X_train.shape[0]), - "samples_val": int(X_val.shape[0]), - "features": int(X_train.shape[1]), - "epochs": int(args.epochs), - "final_val_acc": ( - float(history["val_acc"][-1]) if history.get("val_acc") else None - ), - } - with open(out_dir / "training_summary.json", "w") as f: - json.dump(summary, f, indent=2) - print(f"\n✅ Saved summary to {out_dir / 'training_summary.json'}") - - -if __name__ == "__main__": - main() +""" +Train Hybrid Quantum-Classical model from a CSV dataset in datasets/. + +Usage (PowerShell): + python .\\scripts\\train_from_dataset.py --dataset banknote --epochs 2 + python .\\scripts\\train_from_dataset.py --dataset ionosphere --epochs 2 + python .\\scripts\\train_from_dataset.py --dataset sonar --epochs 2 + python .\\scripts\\train_from_dataset.py --dataset heart_disease --epochs 2 + +This script reads the workspace dataset index at ../../datasets/dataset_index.json, +loads the selected dataset, preprocesses to match n_qubits from config, +and trains the HybridQuantumClassifier with a small number of epochs by default. + +Results are saved under results/datasets// +""" + +import argparse +import json +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +import yaml +from sklearn.decomposition import PCA +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler + +# Resolve paths +SCRIPT_DIR = Path(__file__).resolve().parent +PROJECT_ROOT = SCRIPT_DIR.parent +WORKSPACE_ROOT = PROJECT_ROOT.parent +DATASETS_INDEX = WORKSPACE_ROOT / "datasets" / "dataset_index.json" +DEFAULT_CONFIG = PROJECT_ROOT / "config" / "quantum_config.yaml" +RESULTS_DIR = PROJECT_ROOT / "results" / "datasets" + +# Add src to path +SRC_DIR = PROJECT_ROOT / "src" +sys.path.insert(0, str(SRC_DIR)) + +from quantum_classifier import HybridQuantumClassifier # type: ignore +from quantum_classifier import QuantumClassifier, train_quantum_model + + +def load_dataset_from_csv(name: str, path: Path) -> tuple[np.ndarray, np.ndarray]: + """Load known UCI datasets with simple rules per dataset.""" + name = name.lower() + + if name == "banknote": + # 4 features + 1 label (0/1), no header + df = pd.read_csv(path, header=None) + X = df.iloc[:, :-1].values.astype(float) + y = df.iloc[:, -1].values.astype(int) + return X, y + + if name == "ionosphere": + # 34 features + 1 label ('g'/'b'), no header + df = pd.read_csv(path, header=None) + # Last column: 'g' (good, 1) or 'b' (bad, 0) + y = (df.iloc[:, -1] == "g").astype(int).values + X = df.iloc[:, :-1].values.astype(float) + return X, y + + if name == "sonar": + # 60 features + 1 label ('M'/'R'), no header + df = pd.read_csv(path, header=None) + y = (df.iloc[:, -1] == "M").astype(int).values + X = df.iloc[:, :-1].values.astype(float) + return X, y + + if name == "heart_disease": + # 13 features + 1 label (0..4); convert to binary (>=1 -> 1) + # Data contains '?' values -> treat as NaN and drop + df = pd.read_csv(path, header=None, na_values=["?"]) + df = df.dropna() + # In processed.cleveland.data last column is num (0..4) + y = (df.iloc[:, -1].astype(int) >= 1).astype(int).values + X = df.iloc[:, :-1].values.astype(float) + return X, y + + # Fallback: try generic CSV (last column = label) + df = pd.read_csv(path) + X = df.iloc[:, :-1].values + y = df.iloc[:, -1].values + # Try to make y binary if not numeric + if y.dtype == object: + classes = sorted(pd.Series(y).unique()) + y = (pd.Series(y) == classes[0]).astype(int).values + return X.astype(float), y.astype(int) + + +def preprocess(X: np.ndarray, y: np.ndarray, n_qubits: int, test_size: float = 0.2): + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=test_size, random_state=42, stratify=y + ) + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + pca = None + if X_train.shape[1] > n_qubits: + pca = PCA(n_components=n_qubits, random_state=42) + X_train = pca.fit_transform(X_train) + X_val = pca.transform(X_val) + elif X_train.shape[1] < n_qubits: + # pad with zeros + pad = n_qubits - X_train.shape[1] + X_train = np.hstack([X_train, np.zeros((X_train.shape[0], pad))]) + X_val = np.hstack([X_val, np.zeros((X_val.shape[0], pad))]) + + return X_train, X_val, y_train, y_val, scaler, pca + + +def make_quick_config( + base_cfg_path: Path, + epochs: int, + batch_size: int | None = None, + lr: float | None = None, +) -> Path: + with open(base_cfg_path, "r") as f: + cfg = yaml.safe_load(f) + cfg["ml"]["training"]["epochs"] = int(epochs) + if batch_size is not None: + cfg["ml"]["training"]["batch_size"] = int(batch_size) + if lr is not None: + cfg["ml"]["training"]["learning_rate"] = float(lr) + quick_path = PROJECT_ROOT / "config" / "quantum_config.quick.yaml" + with open(quick_path, "w") as f: + yaml.safe_dump(cfg, f) + return quick_path + + +def main(): + parser = argparse.ArgumentParser( + description="Train hybrid quantum model from CSV dataset" + ) + parser.add_argument( + "--dataset", + default="banknote", + help="Dataset key from dataset_index.json (e.g., banknote)", + ) + parser.add_argument("--epochs", type=int, default=2) + parser.add_argument("--batch-size", type=int, default=None) + parser.add_argument("--lr", type=float, default=None) + args = parser.parse_args() + + # Load dataset index + if not DATASETS_INDEX.exists(): + raise FileNotFoundError(f"Dataset index not found: {DATASETS_INDEX}") + + with open(DATASETS_INDEX, "r") as f: + index = json.load(f) + + if args.dataset not in index["datasets"]: + available = ", ".join(index["datasets"].keys()) + raise ValueError(f"Unknown dataset '{args.dataset}'. Available: {available}") + + ds_info = index["datasets"][args.dataset] + data_path = Path(ds_info["path"]) + if not data_path.exists(): + raise FileNotFoundError(f"Dataset file not found: {data_path}") + + # Load config for model params + with open(DEFAULT_CONFIG, "r") as f: + base_cfg = yaml.safe_load(f) + n_qubits = int(base_cfg["ml"]["model"]["n_qubits"]) + + print("\n=== Loading dataset ===") + print(f"Name: {args.dataset}") + print(f"Path: {data_path}") + + X, y = load_dataset_from_csv(args.dataset, data_path) + print(f"Shape: {X.shape}, Positive rate: {y.mean():.3f}") + + # Preprocess to match qubits + X_train, X_val, y_train, y_val, scaler, pca = preprocess(X, y, n_qubits) + + # Build model + qc = QuantumClassifier(config_path=str(DEFAULT_CONFIG)) + model = HybridQuantumClassifier(input_dim=X_train.shape[1], quantum_classifier=qc) + + # Make quick config override for fast training + quick_cfg = make_quick_config( + DEFAULT_CONFIG, epochs=args.epochs, batch_size=args.batch_size, lr=args.lr + ) + + # Train + history = train_quantum_model( + model, X_train, y_train, X_val, y_val, config_path=str(quick_cfg) + ) + + # Save results + out_dir = RESULTS_DIR / args.dataset + out_dir.mkdir(parents=True, exist_ok=True) + + # Save a simple summary + summary = { + "dataset": args.dataset, + "samples_train": int(X_train.shape[0]), + "samples_val": int(X_val.shape[0]), + "features": int(X_train.shape[1]), + "epochs": int(args.epochs), + "final_val_acc": ( + float(history["val_acc"][-1]) if history.get("val_acc") else None + ), + } + with open(out_dir / "training_summary.json", "w") as f: + json.dump(summary, f, indent=2) + print(f"\n✅ Saved summary to {out_dir / 'training_summary.json'}") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py b/ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py index 17f7f1db5..7b68d0b35 100644 --- a/ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py +++ b/ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py @@ -1,189 +1,189 @@ -"""Upgrade script for moving the quantum-ai environment from pre-1.0 Qiskit -stack (e.g. qiskit==0.46.x) to the consolidated Qiskit 1.x meta package. - -Features: - * --dry-run: Show planned requirement changes without modifying files. - * --install: After updating requirements, rebuild the venv and install. - * --revert: Restore from the most recent requirements.backup.* snapshot. - * Backs up original requirements.txt with timestamped filename. - -Usage examples (run from repo root or quantum-ai directory): - - python ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py --dry-run - python ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py --install - python ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py --target-version 1.0.2 --ml-version 0.7.0 --install - python ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py --revert - -Post-upgrade validation: - 1. Delete any stale interpreter caches. - 2. Run: ai-projects/quantum-ml/venv/Scripts/python ai-projects/quantum-ml/scripts/validate_qiskit_env.py - 3. Run a smoke test: ai-projects/quantum-ml/venv/Scripts/python ai-projects/quantum-ml/src/quantum_classifier.py - -Caveats: - * Qiskit 1.x reorganizes subpackages; some algorithms previously accessed via - qiskit_machine_learning may require updates or the qiskit_algorithms package. - * Pennylane-qiskit compatibility with 1.x may lag behind; verify any advanced - hybrid workflows after upgrade. -""" - -from __future__ import annotations - -import argparse -import datetime as _dt -import re -import shutil -import subprocess -import sys -from datetime import timezone -from pathlib import Path - -REQ_PATTERN_QISKIT = re.compile(r"^qiskit==.*$", re.IGNORECASE) -REQ_PATTERN_QISKIT_AER = re.compile(r"^qiskit-aer==.*$", re.IGNORECASE) -REQ_PATTERN_QML = re.compile(r"^qiskit-machine-learning==.*$", re.IGNORECASE) - -DEFAULT_TARGET = "1.0.2" # Adjust as newer stable releases arrive -DEFAULT_QML_VERSION = "0.7.0" # Minimum version compatible with Qiskit 1.x - - -def _load_requirements(path: Path) -> list[str]: - return [line.rstrip("\n") for line in path.read_text(encoding="utf-8").splitlines()] - - -def _write_requirements(path: Path, lines: list[str]): - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def _backup(path: Path) -> Path: - ts = _dt.datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - backup = path.parent / f"requirements.backup.{ts}.txt" - shutil.copy2(path, backup) - return backup - - -def _find_latest_backup(req_dir: Path) -> Path | None: - candidates = sorted(req_dir.glob("requirements.backup.*.txt")) - return candidates[-1] if candidates else None - - -def plan_upgrade(lines: list[str], target_version: str, ml_version: str) -> list[str]: - new_lines: list[str] = [] - replaced_qiskit = False - replaced_qml = False - for line in lines: - if REQ_PATTERN_QISKIT.match(line): - new_lines.append(f"qiskit>={target_version},<2.0.0") - replaced_qiskit = True - elif REQ_PATTERN_QISKIT_AER.match(line): - # Drop explicit aer pin; the meta package installs aer automatically - continue - elif REQ_PATTERN_QML.match(line): - new_lines.append(f"qiskit-machine-learning>={ml_version},<0.8.0") - replaced_qml = True - else: - new_lines.append(line) - - # If qiskit line wasn't found, append it - if not replaced_qiskit: - new_lines.append(f"qiskit>={target_version},<2.0.0") - if not replaced_qml: - new_lines.append(f"qiskit-machine-learning>={ml_version},<0.8.0") - return new_lines - - -def perform_install(venv_dir: Path, req_path: Path): - """Recreate venv and install requirements.""" - if venv_dir.exists(): - shutil.rmtree(venv_dir) - subprocess.check_call([sys.executable, "-m", "venv", str(venv_dir)]) - pip_exe = venv_dir / "Scripts" / "python.exe" - subprocess.check_call([str(pip_exe), "-m", "pip", "install", "--upgrade", "pip"]) - subprocess.check_call([str(pip_exe), "-m", "pip", "install", "-r", str(req_path)]) - # Quick version report - code = "import qiskit, json; print(json.dumps({'qiskit': qiskit.__version__}))" - out = subprocess.check_output([str(pip_exe), "-c", code], text=True).strip() - print(f"[install] Version check: {out}") - - -def revert(req_path: Path, req_dir: Path): - backup = _find_latest_backup(req_dir) - if not backup: - print("No backup found to revert.") - return 1 - _write_requirements(req_path, _load_requirements(backup)) - print(f"Reverted requirements.txt from backup: {backup.name}") - return 0 - - -def main(): - parser = argparse.ArgumentParser(description="Upgrade Qiskit environment to 1.x") - parser.add_argument( - "--target-version", - default=DEFAULT_TARGET, - help="Target Qiskit 1.x version (default 1.0.2)", - ) - parser.add_argument( - "--ml-version", - default=DEFAULT_QML_VERSION, - help="Target qiskit-machine-learning version (default 0.7.0)", - ) - parser.add_argument( - "--dry-run", action="store_true", help="Show planned changes without writing" - ) - parser.add_argument( - "--install", - action="store_true", - help="After updating requirements, recreate venv and install", - ) - parser.add_argument( - "--revert", - action="store_true", - help="Revert requirements.txt from latest backup", - ) - args = parser.parse_args() - - quantum_root = Path(__file__).resolve().parents[1] - req_path = quantum_root / "requirements.txt" - venv_dir = quantum_root / "venv" - - if args.revert: - sys.exit(revert(req_path, quantum_root)) - - if not req_path.exists(): - print(f"requirements.txt not found at {req_path}") - sys.exit(1) - - lines = _load_requirements(req_path) - - # Detect current qiskit version (best-effort) - current_version = None - try: - import qiskit # type: ignore - - current_version = getattr(qiskit, "__version__", None) - except Exception: - pass - print(f"Current qiskit version (if importable): {current_version}") - - planned = plan_upgrade(lines, args.target_version, args.ml_version) - - if args.dry_run: - print("--- Planned requirements.txt changes (dry-run) ---") - for l in planned: - print(l) - print("(no changes written)") - sys.exit(0) - - backup = _backup(req_path) - _write_requirements(req_path, planned) - print(f"requirements.txt updated. Backup saved to: {backup}") - - if args.install: - print("Recreating virtual environment and installing updated dependencies...") - perform_install(venv_dir, req_path) - print("Upgrade complete. Run validate_qiskit_env.py to verify coherence.") - else: - print("Upgrade staged. Run with --install to apply environment changes.") - - -if __name__ == "__main__": - main() +"""Upgrade script for moving the quantum-ai environment from pre-1.0 Qiskit +stack (e.g. qiskit==0.46.x) to the consolidated Qiskit 1.x meta package. + +Features: + * --dry-run: Show planned requirement changes without modifying files. + * --install: After updating requirements, rebuild the venv and install. + * --revert: Restore from the most recent requirements.backup.* snapshot. + * Backs up original requirements.txt with timestamped filename. + +Usage examples (run from repo root or quantum-ai directory): + + python ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py --dry-run + python ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py --install + python ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py --target-version 1.0.2 --ml-version 0.7.0 --install + python ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py --revert + +Post-upgrade validation: + 1. Delete any stale interpreter caches. + 2. Run: ai-projects/quantum-ml/venv/Scripts/python ai-projects/quantum-ml/scripts/validate_qiskit_env.py + 3. Run a smoke test: ai-projects/quantum-ml/venv/Scripts/python ai-projects/quantum-ml/src/quantum_classifier.py + +Caveats: + * Qiskit 1.x reorganizes subpackages; some algorithms previously accessed via + qiskit_machine_learning may require updates or the qiskit_algorithms package. + * Pennylane-qiskit compatibility with 1.x may lag behind; verify any advanced + hybrid workflows after upgrade. +""" + +from __future__ import annotations + +import argparse +import datetime as _dt +import re +import shutil +import subprocess +import sys +from datetime import timezone +from pathlib import Path + +REQ_PATTERN_QISKIT = re.compile(r"^qiskit==.*$", re.IGNORECASE) +REQ_PATTERN_QISKIT_AER = re.compile(r"^qiskit-aer==.*$", re.IGNORECASE) +REQ_PATTERN_QML = re.compile(r"^qiskit-machine-learning==.*$", re.IGNORECASE) + +DEFAULT_TARGET = "1.0.2" # Adjust as newer stable releases arrive +DEFAULT_QML_VERSION = "0.7.0" # Minimum version compatible with Qiskit 1.x + + +def _load_requirements(path: Path) -> list[str]: + return [line.rstrip("\n") for line in path.read_text(encoding="utf-8").splitlines()] + + +def _write_requirements(path: Path, lines: list[str]): + path.write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def _backup(path: Path) -> Path: + ts = _dt.datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") + backup = path.parent / f"requirements.backup.{ts}.txt" + shutil.copy2(path, backup) + return backup + + +def _find_latest_backup(req_dir: Path) -> Path | None: + candidates = sorted(req_dir.glob("requirements.backup.*.txt")) + return candidates[-1] if candidates else None + + +def plan_upgrade(lines: list[str], target_version: str, ml_version: str) -> list[str]: + new_lines: list[str] = [] + replaced_qiskit = False + replaced_qml = False + for line in lines: + if REQ_PATTERN_QISKIT.match(line): + new_lines.append(f"qiskit>={target_version},<2.0.0") + replaced_qiskit = True + elif REQ_PATTERN_QISKIT_AER.match(line): + # Drop explicit aer pin; the meta package installs aer automatically + continue + elif REQ_PATTERN_QML.match(line): + new_lines.append(f"qiskit-machine-learning>={ml_version},<0.8.0") + replaced_qml = True + else: + new_lines.append(line) + + # If qiskit line wasn't found, append it + if not replaced_qiskit: + new_lines.append(f"qiskit>={target_version},<2.0.0") + if not replaced_qml: + new_lines.append(f"qiskit-machine-learning>={ml_version},<0.8.0") + return new_lines + + +def perform_install(venv_dir: Path, req_path: Path): + """Recreate venv and install requirements.""" + if venv_dir.exists(): + shutil.rmtree(venv_dir) + subprocess.check_call([sys.executable, "-m", "venv", str(venv_dir)]) + pip_exe = venv_dir / "Scripts" / "python.exe" + subprocess.check_call([str(pip_exe), "-m", "pip", "install", "--upgrade", "pip"]) + subprocess.check_call([str(pip_exe), "-m", "pip", "install", "-r", str(req_path)]) + # Quick version report + code = "import qiskit, json; print(json.dumps({'qiskit': qiskit.__version__}))" + out = subprocess.check_output([str(pip_exe), "-c", code], text=True).strip() + print(f"[install] Version check: {out}") + + +def revert(req_path: Path, req_dir: Path): + backup = _find_latest_backup(req_dir) + if not backup: + print("No backup found to revert.") + return 1 + _write_requirements(req_path, _load_requirements(backup)) + print(f"Reverted requirements.txt from backup: {backup.name}") + return 0 + + +def main(): + parser = argparse.ArgumentParser(description="Upgrade Qiskit environment to 1.x") + parser.add_argument( + "--target-version", + default=DEFAULT_TARGET, + help="Target Qiskit 1.x version (default 1.0.2)", + ) + parser.add_argument( + "--ml-version", + default=DEFAULT_QML_VERSION, + help="Target qiskit-machine-learning version (default 0.7.0)", + ) + parser.add_argument( + "--dry-run", action="store_true", help="Show planned changes without writing" + ) + parser.add_argument( + "--install", + action="store_true", + help="After updating requirements, recreate venv and install", + ) + parser.add_argument( + "--revert", + action="store_true", + help="Revert requirements.txt from latest backup", + ) + args = parser.parse_args() + + quantum_root = Path(__file__).resolve().parents[1] + req_path = quantum_root / "requirements.txt" + venv_dir = quantum_root / "venv" + + if args.revert: + sys.exit(revert(req_path, quantum_root)) + + if not req_path.exists(): + print(f"requirements.txt not found at {req_path}") + sys.exit(1) + + lines = _load_requirements(req_path) + + # Detect current qiskit version (best-effort) + current_version = None + try: + import qiskit # type: ignore + + current_version = getattr(qiskit, "__version__", None) + except Exception: + pass + print(f"Current qiskit version (if importable): {current_version}") + + planned = plan_upgrade(lines, args.target_version, args.ml_version) + + if args.dry_run: + print("--- Planned requirements.txt changes (dry-run) ---") + for l in planned: + print(l) + print("(no changes written)") + sys.exit(0) + + backup = _backup(req_path) + _write_requirements(req_path, planned) + print(f"requirements.txt updated. Backup saved to: {backup}") + + if args.install: + print("Recreating virtual environment and installing updated dependencies...") + perform_install(venv_dir, req_path) + print("Upgrade complete. Run validate_qiskit_env.py to verify coherence.") + else: + print("Upgrade staged. Run with --install to apply environment changes.") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/scripts/validate_qiskit_env.py b/ai-projects/quantum-ml/scripts/validate_qiskit_env.py index cfab99807..628014322 100644 --- a/ai-projects/quantum-ml/scripts/validate_qiskit_env.py +++ b/ai-projects/quantum-ml/scripts/validate_qiskit_env.py @@ -1,133 +1,133 @@ -"""Validate Qiskit environment consistency. -Detects mixed installations of Qiskit 1.x and legacy (<1.0) components that cause ImportError. -Run with the quantum-ai venv interpreter: - - python ai-projects/quantum-ml/scripts/validate_qiskit_env.py - -Exit codes: - 0 - OK (single coherent Qiskit environment) - 1 - Conflict detected -""" - -from __future__ import annotations - -import importlib -import json -import pkgutil -import sys -from typing import Dict, List - -LEGACY_PACKAGES = [ - "qiskit_terra", # legacy terra dist name (often 'qiskit-terra') - "qiskit-aer", # old namespace dist style - "qiskit_ibmq_provider", # deprecated provider -] -MODERN_ROOT = "qiskit" - - -def find_distributions() -> Dict[str, str]: - versions = {} - for m in pkgutil.iter_modules(): - name = m.name - if name.startswith("qiskit"): - try: - mod = importlib.import_module(name) - ver = getattr(mod, "__version__", "unknown") - versions[name] = ver - except Exception as e: - versions[name] = f"error: {e}" - return versions - - -def classify(versions: Dict[str, str]) -> Dict[str, List[str]]: - modern = [] - legacy = [] - errors = [] - for name, ver in versions.items(): - if ver.startswith("error:"): - errors.append(name) - continue - # Qiskit 1.x core lives under 'qiskit' - if name == MODERN_ROOT: - modern.append(name) - elif any(name.startswith(lp.replace("-", "_")) for lp in LEGACY_PACKAGES): - legacy.append(name) - else: - # ancillary packages (machine learning, optimization, etc.) - modern.append(name) - return { - "modern": sorted(modern), - "legacy": sorted(legacy), - "errors": sorted(errors), - } - - -def detect_conflict(versions: Dict[str, str]) -> Dict[str, object]: - """Determine conflict status and recommendation for a supplied versions map. - - Parameters - ---------- - versions: Mapping of distribution name -> version string or 'error: ...'. - - Returns - ------- - dict with keys: - conflict (bool) - whether mixed/invalid environment detected - recommendation (str) - human guidance - root_major (int|None) - parsed major version of qiskit root if available - groups (dict) - classify() output - """ - groups = classify(versions) - qiskit_root_version = versions.get(MODERN_ROOT) - root_major = None - if qiskit_root_version and not str(qiskit_root_version).startswith("error"): - try: - root_major = int(str(qiskit_root_version).split(".")[0]) - except Exception: # pragma: no cover - defensive - root_major = None - - conflict = False - if groups["errors"]: - conflict = True - elif root_major is not None and root_major >= 1 and groups["legacy"]: - conflict = True - - if conflict: - if groups["errors"]: - recommendation = "One or more qiskit-related packages failed to import. Recreate the environment and ensure compatible versions." - else: - recommendation = "Environment mixes Qiskit >=1.x root package with legacy (<1.0) components. Recreate a clean venv and install only modern (>=1.x) packages or pin everything to <1.0 consistently." - else: - if root_major is not None and root_major < 1: - recommendation = f"Pre-1.0 Qiskit environment detected (qiskit=={qiskit_root_version}). Presence of qiskit_aer and related extras is expected; no conflict." - else: - recommendation = "No mixed legacy components detected." - - return { - "conflict": conflict, - "recommendation": recommendation, - "root_major": root_major, - "groups": groups, - } - - -def main(): - versions = find_distributions() - conflict_meta = detect_conflict(versions) - qiskit_root_version = versions.get(MODERN_ROOT) - report = { - "python": sys.version, - "qiskit_root_version": qiskit_root_version, - "packages": versions, - "groups": conflict_meta["groups"], - "conflict_detected": conflict_meta["conflict"], - "recommendation": conflict_meta["recommendation"], - } - exit_code = 1 if conflict_meta["conflict"] else 0 - - print(json.dumps(report, indent=2)) - sys.exit(exit_code) - - -if __name__ == "__main__": - main() +"""Validate Qiskit environment consistency. +Detects mixed installations of Qiskit 1.x and legacy (<1.0) components that cause ImportError. +Run with the quantum-ai venv interpreter: + + python ai-projects/quantum-ml/scripts/validate_qiskit_env.py + +Exit codes: + 0 - OK (single coherent Qiskit environment) + 1 - Conflict detected +""" + +from __future__ import annotations + +import importlib +import json +import pkgutil +import sys +from typing import Dict, List + +LEGACY_PACKAGES = [ + "qiskit_terra", # legacy terra dist name (often 'qiskit-terra') + "qiskit-aer", # old namespace dist style + "qiskit_ibmq_provider", # deprecated provider +] +MODERN_ROOT = "qiskit" + + +def find_distributions() -> Dict[str, str]: + versions = {} + for m in pkgutil.iter_modules(): + name = m.name + if name.startswith("qiskit"): + try: + mod = importlib.import_module(name) + ver = getattr(mod, "__version__", "unknown") + versions[name] = ver + except Exception as e: + versions[name] = f"error: {e}" + return versions + + +def classify(versions: Dict[str, str]) -> Dict[str, List[str]]: + modern = [] + legacy = [] + errors = [] + for name, ver in versions.items(): + if ver.startswith("error:"): + errors.append(name) + continue + # Qiskit 1.x core lives under 'qiskit' + if name == MODERN_ROOT: + modern.append(name) + elif any(name.startswith(lp.replace("-", "_")) for lp in LEGACY_PACKAGES): + legacy.append(name) + else: + # ancillary packages (machine learning, optimization, etc.) + modern.append(name) + return { + "modern": sorted(modern), + "legacy": sorted(legacy), + "errors": sorted(errors), + } + + +def detect_conflict(versions: Dict[str, str]) -> Dict[str, object]: + """Determine conflict status and recommendation for a supplied versions map. + + Parameters + ---------- + versions: Mapping of distribution name -> version string or 'error: ...'. + + Returns + ------- + dict with keys: + conflict (bool) - whether mixed/invalid environment detected + recommendation (str) - human guidance + root_major (int|None) - parsed major version of qiskit root if available + groups (dict) - classify() output + """ + groups = classify(versions) + qiskit_root_version = versions.get(MODERN_ROOT) + root_major = None + if qiskit_root_version and not str(qiskit_root_version).startswith("error"): + try: + root_major = int(str(qiskit_root_version).split(".")[0]) + except Exception: # pragma: no cover - defensive + root_major = None + + conflict = False + if groups["errors"]: + conflict = True + elif root_major is not None and root_major >= 1 and groups["legacy"]: + conflict = True + + if conflict: + if groups["errors"]: + recommendation = "One or more qiskit-related packages failed to import. Recreate the environment and ensure compatible versions." + else: + recommendation = "Environment mixes Qiskit >=1.x root package with legacy (<1.0) components. Recreate a clean venv and install only modern (>=1.x) packages or pin everything to <1.0 consistently." + else: + if root_major is not None and root_major < 1: + recommendation = f"Pre-1.0 Qiskit environment detected (qiskit=={qiskit_root_version}). Presence of qiskit_aer and related extras is expected; no conflict." + else: + recommendation = "No mixed legacy components detected." + + return { + "conflict": conflict, + "recommendation": recommendation, + "root_major": root_major, + "groups": groups, + } + + +def main(): + versions = find_distributions() + conflict_meta = detect_conflict(versions) + qiskit_root_version = versions.get(MODERN_ROOT) + report = { + "python": sys.version, + "qiskit_root_version": qiskit_root_version, + "packages": versions, + "groups": conflict_meta["groups"], + "conflict_detected": conflict_meta["conflict"], + "recommendation": conflict_meta["recommendation"], + } + exit_code = 1 if conflict_meta["conflict"] else 0 + + print(json.dumps(report, indent=2)) + sys.exit(exit_code) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/scripts/visualize_hardware_results.py b/ai-projects/quantum-ml/scripts/visualize_hardware_results.py index ac3e84d73..a776dfc11 100644 --- a/ai-projects/quantum-ml/scripts/visualize_hardware_results.py +++ b/ai-projects/quantum-ml/scripts/visualize_hardware_results.py @@ -1,479 +1,479 @@ -""" -Visualize Azure Quantum hardware test results as charts. - -Outputs: -- Per-result bar charts of measurement counts (top-N) -- For 2-qubit results, a 2x2 heatmap -- Summary chart of entanglement ratio for all 2-qubit results -- Azure job list charts: status distribution and provider x status stacked bar - -Charts saved to: ai-projects/quantum-ml/results/visualizations/ -""" - -from __future__ import annotations - -import json -import subprocess -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -import matplotlib.pyplot as plt -import pandas as pd -import yaml - -REPO_ROOT = Path(__file__).resolve().parents[1] -CONFIG_PATH = REPO_ROOT / "config" / "quantum_config.yaml" -RESULTS_DIR = (REPO_ROOT / "results").resolve() -# Some utilities may save to repo-root/../results because results_dir in config is relative to CWD -ALT_RESULTS_DIR = (REPO_ROOT.parent / "results").resolve() -VIZ_DIR = RESULTS_DIR / "visualizations" -VIZ_DIR.mkdir(parents=True, exist_ok=True) - -plt.rcParams.update({"figure.autolayout": True, "figure.dpi": 120}) - - -def load_config() -> Dict: - with open(CONFIG_PATH, "r") as f: - return yaml.safe_load(f) - - -def find_local_results(results_dir: Path) -> List[Path]: - # Look for files saved by test_azure_quantum.py (bell_state_results_*.json, optimized_circuit_results_*.json) - patterns = [ - "bell_state_results_*.json", - "optimized_circuit_results_*.json", - "*results*.json", - ] - found: List[Path] = [] - for pat in patterns: - found.extend(results_dir.glob(pat)) - # De-duplicate while preserving order - seen = set() - uniq: List[Path] = [] - for p in found: - if p.name not in seen: - uniq.append(p) - seen.add(p.name) - return uniq - - -def load_counts_from_json(p: Path) -> Optional[Dict[str, int]]: - try: - data = json.loads(p.read_text(encoding="utf-8")) - # Expecting {'job_id': str, 'counts': {bitstring: int}, 'success': bool} - counts = data.get("counts") - if isinstance(counts, dict) and counts: - # Ensure values are ints - return {str(k): int(v) for k, v in counts.items()} - except Exception: - pass - return None - - -def try_load_metadata(p: Path) -> Optional[Dict]: - try: - data = json.loads(p.read_text(encoding="utf-8")) - meta = data.get("metadata", {}) - # Flatten key fields - flat = { - "file": str(p), - "job_id": data.get("job_id"), - "n_qubits": meta.get("n_qubits"), - "layers": meta.get("layers"), - "entanglement": meta.get("entanglement"), - "method": meta.get("method"), - "circuit": meta.get("circuit"), - "shots": meta.get("shots"), - "entropy": meta.get("entropy"), - "max_entropy": meta.get("max_entropy"), - } - noise = meta.get("noise", {}) or {} - flat.update( - { - "noise_pauli_px": noise.get("pauli_px", 0.0), - "noise_pauli_pz": noise.get("pauli_pz", 0.0), - "noise_depolarizing_p": noise.get("depolarizing_p", 0.0), - "noise_amp_damp_gamma": noise.get("amp_damp_gamma", 0.0), - } - ) - stab = meta.get("stabilizer", {}) or {} - flat.update( - { - "stabilizer_type": stab.get("type"), - "clifford_layers": stab.get("clifford_layers"), - "twoq_density": stab.get("twoq_density"), - } - ) - return flat - except Exception: - return None - - -def compute_entanglement_ratio(counts: Dict[str, int]) -> Optional[float]: - # Only meaningful for 2-qubit bell state - if not counts: - return None - # Performance optimization: Direct iteration instead of .keys() - bit_lengths = {len(k) for k in counts} - if bit_lengths != {2}: - return None - total = sum(counts.values()) - entangled = counts.get("00", 0) + counts.get("11", 0) - return 100.0 * entangled / total if total > 0 else None - - -def _abbr_label(label: str, max_len: int = 24) -> str: - if len(label) <= max_len: - return label - # keep first 8 and last 8 bits by default if very long - return f"{label[:8]}…{label[-8:]}" - - -def plot_counts_bar( - counts: Dict[str, int], title: str, out_path: Path, top_n: int = 10 -) -> None: - total = sum(counts.values()) - items = sorted(counts.items(), key=lambda x: x[1], reverse=True)[:top_n] - labels = [k for k, _ in items] - disp_labels = [_abbr_label(k) for k in labels] - values = [v for _, v in items] - perc = [100.0 * v / total for v in values] - - fig, ax = plt.subplots(figsize=(8, 4)) - ax.bar(disp_labels, values, color="#4C78A8") - ax.set_title(title) - ax.set_xlabel("Measurement state") - ax.set_ylabel("Counts") - for i, (x, v, p) in enumerate(zip(disp_labels, values, perc)): - ax.text(i, v, f"{p:.1f}%", ha="center", va="bottom", fontsize=8) - plt.xticks(rotation=45) - fig.savefig(out_path) - plt.close(fig) - - -def plot_2qubit_heatmap(counts: Dict[str, int], title: str, out_path: Path) -> None: - # Build 2x2 matrix in order [00, 01, 10, 11] - mat = [ - [counts.get("00", 0), counts.get("01", 0)], - [counts.get("10", 0), counts.get("11", 0)], - ] - fig, ax = plt.subplots(figsize=(4.5, 4)) - im = ax.imshow(mat, cmap="Blues") - ax.set_xticks([0, 1], labels=["0", "1"]) # measured qubit 1 - ax.set_yticks([0, 1], labels=["0", "1"]) # measured qubit 0 - ax.set_xlabel("Qubit 1") - ax.set_ylabel("Qubit 0") - ax.set_title(title) - # Annotate - for i in range(2): - for j in range(2): - ax.text(j, i, str(mat[i][j]), ha="center", va="center", color="black") - fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04) - fig.savefig(out_path) - plt.close(fig) - - -def plot_hamming_weight_hist(counts: Dict[str, int], out_path: Path) -> None: - if not counts: - return - bit_lengths = {len(k) for k in counts} - if len(bit_lengths) != 1: - return - n = bit_lengths.pop() - # compute weight distribution - weight_counts: Dict[int, int] = {} - for bitstr, c in counts.items(): - w = bitstr.count("1") - weight_counts[w] = weight_counts.get(w, 0) + int(c) - weights = sorted(weight_counts.keys()) - values = [weight_counts[w] for w in weights] - fig, ax = plt.subplots(figsize=(8, 4)) - ax.bar([str(w) for w in weights], values, color="#F58518") - ax.set_title(f"Hamming weight distribution (n={n})") - ax.set_xlabel("Number of ones in measured bitstring") - ax.set_ylabel("Counts") - if len(weights) > 40: - plt.xticks([], []) - fig.savefig(out_path) - plt.close(fig) - - -def try_fetch_azure_job_list( - resource_group: str, workspace: str, location: str -) -> Optional[pd.DataFrame]: - cmd = [ - "az", - "quantum", - "job", - "list", - "--resource-group", - resource_group, - "--workspace-name", - workspace, - "--location", - location, - "--output", - "json", - ] - try: - out = subprocess.check_output(cmd, text=True) - data = json.loads(out) - if not isinstance(data, list): - return None - # Normalize to a DataFrame - df = pd.json_normalize(data) - # Coerce timestamps - for col in ( - "creationTime", - "beginExecutionTime", - "endExecutionTime", - "lastModifiedTime", - ): - if col in df.columns: - df[col] = pd.to_datetime(df[col], errors="coerce") - return df - except Exception: - return None - - -def plot_job_status_distribution(df: pd.DataFrame, out_path: Path) -> None: - if df is None or df.empty or "status" not in df.columns: - return - counts = df["status"].value_counts().sort_index() - fig, ax = plt.subplots(figsize=(7, 4)) - counts.plot(kind="bar", ax=ax, color="#72B7B2") - ax.set_title("Azure Quantum Jobs by Status") - ax.set_xlabel("Status") - ax.set_ylabel("Count") - fig.savefig(out_path) - plt.close(fig) - - -def plot_provider_status_stacked(df: pd.DataFrame, out_path: Path) -> None: - if df is None or df.empty or not {"providerId", "status"}.issubset(df.columns): - return - pivot = pd.pivot_table( - df, - index="providerId", - columns="status", - values="id", - aggfunc="count", - fill_value=0, - ) - fig, ax = plt.subplots(figsize=(8, 5)) - pivot.plot(kind="bar", stacked=True, ax=ax) - ax.set_title("Jobs by Provider and Status") - ax.set_xlabel("Provider") - ax.set_ylabel("Count") - fig.savefig(out_path) - plt.close(fig) - - -def build_summary_df(files: List[Path]) -> pd.DataFrame: - rows: List[Dict] = [] - for p in files: - meta = try_load_metadata(p) - if not meta: - continue - rows.append(meta) - if not rows: - return pd.DataFrame() - df = pd.DataFrame(rows) - # Compute entropy_pct - if not df.empty and {"entropy", "max_entropy"}.issubset(df.columns): - df["entropy_pct"] = (df["entropy"] / df["max_entropy"]) * 100.0 - - # Noise label - def _noise_label(r): - px = r.get("noise_pauli_px", 0) or 0 - pz = r.get("noise_pauli_pz", 0) or 0 - dep = r.get("noise_depolarizing_p", 0) or 0 - amp = r.get("noise_amp_damp_gamma", 0) or 0 - labels = [] - if px or pz: - labels.append(f"pauli(px={px},pz={pz})") - if dep: - labels.append(f"depol(p={dep})") - if amp: - labels.append(f"amp_damp(gamma={amp})") - return "clean" if not labels else "+".join(labels) - - if not df.empty: - df["noise_label"] = df.apply(_noise_label, axis=1) - return df - - -def main() -> int: - print("\n=== Visualizing Azure Quantum Hardware Test Results ===\n") - cfg = load_config() - - # 1) Local result files - local_files = find_local_results(RESULTS_DIR) - if not local_files and ALT_RESULTS_DIR.exists(): - # Also scan alternate location (repo root results) - alt = find_local_results(ALT_RESULTS_DIR) - # If found there, also make sure we save charts under the quantum-ai results/visualizations - local_files = alt - if alt: - print(f"Found {len(alt)} local result file(s) in {ALT_RESULTS_DIR}") - entanglement_summary: List[Tuple[str, float]] = [] - - if local_files: - # If they came from RESULTS_DIR this message is accurate; otherwise a message was already printed. - if all( - ( - p.is_relative_to(RESULTS_DIR) - if hasattr(p, "is_relative_to") - else str(p).startswith(str(RESULTS_DIR)) - ) - for p in local_files - ): - print(f"Found {len(local_files)} local result file(s) in {RESULTS_DIR}") - else: - print( - "No local result JSON files found in results/. We'll still generate Azure job charts if available." - ) - - for p in local_files: - counts = load_counts_from_json(p) - if not counts: - continue - stem = p.stem - # Bar chart (top-10) with abbreviated labels for long bitstrings - plot_counts_bar( - counts, - title=f"Counts: {stem}", - out_path=VIZ_DIR / f"{stem}_bar.png", - top_n=10, - ) - # 2-qubit heatmap - bit_lengths = {len(k) for k in counts} - if bit_lengths == {2}: - plot_2qubit_heatmap( - counts, - title=f"2-Qubit Heatmap: {stem}", - out_path=VIZ_DIR / f"{stem}_heatmap.png", - ) - ratio = compute_entanglement_ratio(counts) - if ratio is not None: - entanglement_summary.append((stem, ratio)) - # For large bitstrings, add Hamming weight histogram - if max(bit_lengths) > 32: - plot_hamming_weight_hist( - counts, out_path=VIZ_DIR / f"{stem}_hamming_weight.png" - ) - - # 1b) Summary charts for variational MPS runs by entanglement - df_all = build_summary_df(local_files) - if not df_all.empty: - df_mps = df_all[ - (df_all["method"] == "matrix_product_state") - & (df_all["circuit"].str.contains("variational")) - ] - if not df_mps.empty: - # For each (n_qubits, layers), plot entanglement vs entropy_pct for clean and noise separately - for (nq, L), sub in df_mps.groupby(["n_qubits", "layers"]): - for nlbl, part in sub.groupby("noise_label"): - if part.empty: - continue - pivot = ( - part.dropna(subset=["entanglement", "entropy_pct"]) - .groupby("entanglement")["entropy_pct"] - .mean() - ) - if pivot.empty: - continue - fig, ax = plt.subplots(figsize=(6, 4)) - pivot.reindex(["linear", "circular", "full"]).plot( - kind="bar", ax=ax, color="#4C78A8" - ) - ax.set_title(f"Entropy% by entanglement (n={nq}, L={L}, {nlbl})") - ax.set_xlabel("Entanglement") - ax.set_ylabel("Entropy %") - ax.set_ylim(0, 100) - out = ( - VIZ_DIR - / f"summary_entropy_mps_n{nq}_L{L}_{nlbl.replace('=','').replace(',','_').replace('(','').replace(')','').replace(' ','')}.png" - ) - fig.savefig(out) - plt.close(fig) - - # 1c) Summary for stabilizer random: weight coverage vs layers - if not df_all.empty: - df_stab = df_all[ - (df_all["circuit"].str.contains("stabilizer")) - & (df_all["stabilizer_type"] == "random") - ] - if not df_stab.empty: - # Compute weight coverage from counts - coverage_rows = [] - for _, row in df_stab.iterrows(): - counts = load_counts_from_json(Path(row["file"])) - if not counts: - continue - n = int(row.get("n_qubits") or 0) - nonzero_weights = set() - for bitstr, c in counts.items(): - if c > 0: - nonzero_weights.add(bitstr.count("1")) - coverage = len(nonzero_weights) / (n + 1) if n > 0 else 0.0 - coverage_rows.append( - { - "n_qubits": n, - "clifford_layers": int(row.get("clifford_layers") or 0), - "coverage": coverage * 100.0, - } - ) - if coverage_rows: - df_cov = pd.DataFrame(coverage_rows) - for nq, part in df_cov.groupby("n_qubits"): - fig, ax = plt.subplots(figsize=(6, 4)) - part = part.sort_values("clifford_layers") - ax.plot(part["clifford_layers"], part["coverage"], marker="o") - ax.set_title( - f"Stabilizer random: weight coverage vs layers (n={nq})" - ) - ax.set_xlabel("Clifford layers") - ax.set_ylabel("Coverage % of weights (0..n)") - ax.set_ylim(0, 100) - fig.savefig( - VIZ_DIR / f"stabilizer_random_weight_coverage_n{nq}.png" - ) - plt.close(fig) - - # Summary entanglement chart for 2-qubit results - if entanglement_summary: - labels, ratios = zip(*entanglement_summary) - fig, ax = plt.subplots(figsize=(8, 4)) - ax.bar(labels, ratios, color="#E45756") - ax.set_title("Entanglement Quality (2-qubit Bell state)") - ax.set_xlabel("Result") - ax.set_ylabel("% in |00> + |11>") - plt.xticks(rotation=45) - fig.savefig(VIZ_DIR / "entanglement_summary.png") - plt.close(fig) - - # 2) Azure job list charts (status & provider) - rsrc = cfg.get("azure", {}).get("resource_group", "") - ws = cfg.get("azure", {}).get("workspace_name", "") - loc = cfg.get("azure", {}).get("location", "") - - if rsrc and ws and loc: - df = try_fetch_azure_job_list(rsrc, ws, loc) - if df is not None and not df.empty: - plot_job_status_distribution(df, VIZ_DIR / "azure_jobs_status.png") - plot_provider_status_stacked(df, VIZ_DIR / "azure_jobs_provider_status.png") - print("Azure job charts saved under visualizations/.") - else: - print( - "Azure job list not available or empty (no charts generated for jobs)." - ) - else: - print("Azure config incomplete; skipping Azure job charts.") - - print(f"\nCharts saved to: {VIZ_DIR}\n") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) +""" +Visualize Azure Quantum hardware test results as charts. + +Outputs: +- Per-result bar charts of measurement counts (top-N) +- For 2-qubit results, a 2x2 heatmap +- Summary chart of entanglement ratio for all 2-qubit results +- Azure job list charts: status distribution and provider x status stacked bar + +Charts saved to: ai-projects/quantum-ml/results/visualizations/ +""" + +from __future__ import annotations + +import json +import subprocess +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import matplotlib.pyplot as plt +import pandas as pd +import yaml + +REPO_ROOT = Path(__file__).resolve().parents[1] +CONFIG_PATH = REPO_ROOT / "config" / "quantum_config.yaml" +RESULTS_DIR = (REPO_ROOT / "results").resolve() +# Some utilities may save to repo-root/../results because results_dir in config is relative to CWD +ALT_RESULTS_DIR = (REPO_ROOT.parent / "results").resolve() +VIZ_DIR = RESULTS_DIR / "visualizations" +VIZ_DIR.mkdir(parents=True, exist_ok=True) + +plt.rcParams.update({"figure.autolayout": True, "figure.dpi": 120}) + + +def load_config() -> Dict: + with open(CONFIG_PATH, "r") as f: + return yaml.safe_load(f) + + +def find_local_results(results_dir: Path) -> List[Path]: + # Look for files saved by test_azure_quantum.py (bell_state_results_*.json, optimized_circuit_results_*.json) + patterns = [ + "bell_state_results_*.json", + "optimized_circuit_results_*.json", + "*results*.json", + ] + found: List[Path] = [] + for pat in patterns: + found.extend(results_dir.glob(pat)) + # De-duplicate while preserving order + seen = set() + uniq: List[Path] = [] + for p in found: + if p.name not in seen: + uniq.append(p) + seen.add(p.name) + return uniq + + +def load_counts_from_json(p: Path) -> Optional[Dict[str, int]]: + try: + data = json.loads(p.read_text(encoding="utf-8")) + # Expecting {'job_id': str, 'counts': {bitstring: int}, 'success': bool} + counts = data.get("counts") + if isinstance(counts, dict) and counts: + # Ensure values are ints + return {str(k): int(v) for k, v in counts.items()} + except Exception: + pass + return None + + +def try_load_metadata(p: Path) -> Optional[Dict]: + try: + data = json.loads(p.read_text(encoding="utf-8")) + meta = data.get("metadata", {}) + # Flatten key fields + flat = { + "file": str(p), + "job_id": data.get("job_id"), + "n_qubits": meta.get("n_qubits"), + "layers": meta.get("layers"), + "entanglement": meta.get("entanglement"), + "method": meta.get("method"), + "circuit": meta.get("circuit"), + "shots": meta.get("shots"), + "entropy": meta.get("entropy"), + "max_entropy": meta.get("max_entropy"), + } + noise = meta.get("noise", {}) or {} + flat.update( + { + "noise_pauli_px": noise.get("pauli_px", 0.0), + "noise_pauli_pz": noise.get("pauli_pz", 0.0), + "noise_depolarizing_p": noise.get("depolarizing_p", 0.0), + "noise_amp_damp_gamma": noise.get("amp_damp_gamma", 0.0), + } + ) + stab = meta.get("stabilizer", {}) or {} + flat.update( + { + "stabilizer_type": stab.get("type"), + "clifford_layers": stab.get("clifford_layers"), + "twoq_density": stab.get("twoq_density"), + } + ) + return flat + except Exception: + return None + + +def compute_entanglement_ratio(counts: Dict[str, int]) -> Optional[float]: + # Only meaningful for 2-qubit bell state + if not counts: + return None + # Performance optimization: Direct iteration instead of .keys() + bit_lengths = {len(k) for k in counts} + if bit_lengths != {2}: + return None + total = sum(counts.values()) + entangled = counts.get("00", 0) + counts.get("11", 0) + return 100.0 * entangled / total if total > 0 else None + + +def _abbr_label(label: str, max_len: int = 24) -> str: + if len(label) <= max_len: + return label + # keep first 8 and last 8 bits by default if very long + return f"{label[:8]}…{label[-8:]}" + + +def plot_counts_bar( + counts: Dict[str, int], title: str, out_path: Path, top_n: int = 10 +) -> None: + total = sum(counts.values()) + items = sorted(counts.items(), key=lambda x: x[1], reverse=True)[:top_n] + labels = [k for k, _ in items] + disp_labels = [_abbr_label(k) for k in labels] + values = [v for _, v in items] + perc = [100.0 * v / total for v in values] + + fig, ax = plt.subplots(figsize=(8, 4)) + ax.bar(disp_labels, values, color="#4C78A8") + ax.set_title(title) + ax.set_xlabel("Measurement state") + ax.set_ylabel("Counts") + for i, (x, v, p) in enumerate(zip(disp_labels, values, perc)): + ax.text(i, v, f"{p:.1f}%", ha="center", va="bottom", fontsize=8) + plt.xticks(rotation=45) + fig.savefig(out_path) + plt.close(fig) + + +def plot_2qubit_heatmap(counts: Dict[str, int], title: str, out_path: Path) -> None: + # Build 2x2 matrix in order [00, 01, 10, 11] + mat = [ + [counts.get("00", 0), counts.get("01", 0)], + [counts.get("10", 0), counts.get("11", 0)], + ] + fig, ax = plt.subplots(figsize=(4.5, 4)) + im = ax.imshow(mat, cmap="Blues") + ax.set_xticks([0, 1], labels=["0", "1"]) # measured qubit 1 + ax.set_yticks([0, 1], labels=["0", "1"]) # measured qubit 0 + ax.set_xlabel("Qubit 1") + ax.set_ylabel("Qubit 0") + ax.set_title(title) + # Annotate + for i in range(2): + for j in range(2): + ax.text(j, i, str(mat[i][j]), ha="center", va="center", color="black") + fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04) + fig.savefig(out_path) + plt.close(fig) + + +def plot_hamming_weight_hist(counts: Dict[str, int], out_path: Path) -> None: + if not counts: + return + bit_lengths = {len(k) for k in counts} + if len(bit_lengths) != 1: + return + n = bit_lengths.pop() + # compute weight distribution + weight_counts: Dict[int, int] = {} + for bitstr, c in counts.items(): + w = bitstr.count("1") + weight_counts[w] = weight_counts.get(w, 0) + int(c) + weights = sorted(weight_counts.keys()) + values = [weight_counts[w] for w in weights] + fig, ax = plt.subplots(figsize=(8, 4)) + ax.bar([str(w) for w in weights], values, color="#F58518") + ax.set_title(f"Hamming weight distribution (n={n})") + ax.set_xlabel("Number of ones in measured bitstring") + ax.set_ylabel("Counts") + if len(weights) > 40: + plt.xticks([], []) + fig.savefig(out_path) + plt.close(fig) + + +def try_fetch_azure_job_list( + resource_group: str, workspace: str, location: str +) -> Optional[pd.DataFrame]: + cmd = [ + "az", + "quantum", + "job", + "list", + "--resource-group", + resource_group, + "--workspace-name", + workspace, + "--location", + location, + "--output", + "json", + ] + try: + out = subprocess.check_output(cmd, text=True) + data = json.loads(out) + if not isinstance(data, list): + return None + # Normalize to a DataFrame + df = pd.json_normalize(data) + # Coerce timestamps + for col in ( + "creationTime", + "beginExecutionTime", + "endExecutionTime", + "lastModifiedTime", + ): + if col in df.columns: + df[col] = pd.to_datetime(df[col], errors="coerce") + return df + except Exception: + return None + + +def plot_job_status_distribution(df: pd.DataFrame, out_path: Path) -> None: + if df is None or df.empty or "status" not in df.columns: + return + counts = df["status"].value_counts().sort_index() + fig, ax = plt.subplots(figsize=(7, 4)) + counts.plot(kind="bar", ax=ax, color="#72B7B2") + ax.set_title("Azure Quantum Jobs by Status") + ax.set_xlabel("Status") + ax.set_ylabel("Count") + fig.savefig(out_path) + plt.close(fig) + + +def plot_provider_status_stacked(df: pd.DataFrame, out_path: Path) -> None: + if df is None or df.empty or not {"providerId", "status"}.issubset(df.columns): + return + pivot = pd.pivot_table( + df, + index="providerId", + columns="status", + values="id", + aggfunc="count", + fill_value=0, + ) + fig, ax = plt.subplots(figsize=(8, 5)) + pivot.plot(kind="bar", stacked=True, ax=ax) + ax.set_title("Jobs by Provider and Status") + ax.set_xlabel("Provider") + ax.set_ylabel("Count") + fig.savefig(out_path) + plt.close(fig) + + +def build_summary_df(files: List[Path]) -> pd.DataFrame: + rows: List[Dict] = [] + for p in files: + meta = try_load_metadata(p) + if not meta: + continue + rows.append(meta) + if not rows: + return pd.DataFrame() + df = pd.DataFrame(rows) + # Compute entropy_pct + if not df.empty and {"entropy", "max_entropy"}.issubset(df.columns): + df["entropy_pct"] = (df["entropy"] / df["max_entropy"]) * 100.0 + + # Noise label + def _noise_label(r): + px = r.get("noise_pauli_px", 0) or 0 + pz = r.get("noise_pauli_pz", 0) or 0 + dep = r.get("noise_depolarizing_p", 0) or 0 + amp = r.get("noise_amp_damp_gamma", 0) or 0 + labels = [] + if px or pz: + labels.append(f"pauli(px={px},pz={pz})") + if dep: + labels.append(f"depol(p={dep})") + if amp: + labels.append(f"amp_damp(gamma={amp})") + return "clean" if not labels else "+".join(labels) + + if not df.empty: + df["noise_label"] = df.apply(_noise_label, axis=1) + return df + + +def main() -> int: + print("\n=== Visualizing Azure Quantum Hardware Test Results ===\n") + cfg = load_config() + + # 1) Local result files + local_files = find_local_results(RESULTS_DIR) + if not local_files and ALT_RESULTS_DIR.exists(): + # Also scan alternate location (repo root results) + alt = find_local_results(ALT_RESULTS_DIR) + # If found there, also make sure we save charts under the quantum-ai results/visualizations + local_files = alt + if alt: + print(f"Found {len(alt)} local result file(s) in {ALT_RESULTS_DIR}") + entanglement_summary: List[Tuple[str, float]] = [] + + if local_files: + # If they came from RESULTS_DIR this message is accurate; otherwise a message was already printed. + if all( + ( + p.is_relative_to(RESULTS_DIR) + if hasattr(p, "is_relative_to") + else str(p).startswith(str(RESULTS_DIR)) + ) + for p in local_files + ): + print(f"Found {len(local_files)} local result file(s) in {RESULTS_DIR}") + else: + print( + "No local result JSON files found in results/. We'll still generate Azure job charts if available." + ) + + for p in local_files: + counts = load_counts_from_json(p) + if not counts: + continue + stem = p.stem + # Bar chart (top-10) with abbreviated labels for long bitstrings + plot_counts_bar( + counts, + title=f"Counts: {stem}", + out_path=VIZ_DIR / f"{stem}_bar.png", + top_n=10, + ) + # 2-qubit heatmap + bit_lengths = {len(k) for k in counts} + if bit_lengths == {2}: + plot_2qubit_heatmap( + counts, + title=f"2-Qubit Heatmap: {stem}", + out_path=VIZ_DIR / f"{stem}_heatmap.png", + ) + ratio = compute_entanglement_ratio(counts) + if ratio is not None: + entanglement_summary.append((stem, ratio)) + # For large bitstrings, add Hamming weight histogram + if max(bit_lengths) > 32: + plot_hamming_weight_hist( + counts, out_path=VIZ_DIR / f"{stem}_hamming_weight.png" + ) + + # 1b) Summary charts for variational MPS runs by entanglement + df_all = build_summary_df(local_files) + if not df_all.empty: + df_mps = df_all[ + (df_all["method"] == "matrix_product_state") + & (df_all["circuit"].str.contains("variational")) + ] + if not df_mps.empty: + # For each (n_qubits, layers), plot entanglement vs entropy_pct for clean and noise separately + for (nq, L), sub in df_mps.groupby(["n_qubits", "layers"]): + for nlbl, part in sub.groupby("noise_label"): + if part.empty: + continue + pivot = ( + part.dropna(subset=["entanglement", "entropy_pct"]) + .groupby("entanglement")["entropy_pct"] + .mean() + ) + if pivot.empty: + continue + fig, ax = plt.subplots(figsize=(6, 4)) + pivot.reindex(["linear", "circular", "full"]).plot( + kind="bar", ax=ax, color="#4C78A8" + ) + ax.set_title(f"Entropy% by entanglement (n={nq}, L={L}, {nlbl})") + ax.set_xlabel("Entanglement") + ax.set_ylabel("Entropy %") + ax.set_ylim(0, 100) + out = ( + VIZ_DIR + / f"summary_entropy_mps_n{nq}_L{L}_{nlbl.replace('=','').replace(',','_').replace('(','').replace(')','').replace(' ','')}.png" + ) + fig.savefig(out) + plt.close(fig) + + # 1c) Summary for stabilizer random: weight coverage vs layers + if not df_all.empty: + df_stab = df_all[ + (df_all["circuit"].str.contains("stabilizer")) + & (df_all["stabilizer_type"] == "random") + ] + if not df_stab.empty: + # Compute weight coverage from counts + coverage_rows = [] + for _, row in df_stab.iterrows(): + counts = load_counts_from_json(Path(row["file"])) + if not counts: + continue + n = int(row.get("n_qubits") or 0) + nonzero_weights = set() + for bitstr, c in counts.items(): + if c > 0: + nonzero_weights.add(bitstr.count("1")) + coverage = len(nonzero_weights) / (n + 1) if n > 0 else 0.0 + coverage_rows.append( + { + "n_qubits": n, + "clifford_layers": int(row.get("clifford_layers") or 0), + "coverage": coverage * 100.0, + } + ) + if coverage_rows: + df_cov = pd.DataFrame(coverage_rows) + for nq, part in df_cov.groupby("n_qubits"): + fig, ax = plt.subplots(figsize=(6, 4)) + part = part.sort_values("clifford_layers") + ax.plot(part["clifford_layers"], part["coverage"], marker="o") + ax.set_title( + f"Stabilizer random: weight coverage vs layers (n={nq})" + ) + ax.set_xlabel("Clifford layers") + ax.set_ylabel("Coverage % of weights (0..n)") + ax.set_ylim(0, 100) + fig.savefig( + VIZ_DIR / f"stabilizer_random_weight_coverage_n{nq}.png" + ) + plt.close(fig) + + # Summary entanglement chart for 2-qubit results + if entanglement_summary: + labels, ratios = zip(*entanglement_summary) + fig, ax = plt.subplots(figsize=(8, 4)) + ax.bar(labels, ratios, color="#E45756") + ax.set_title("Entanglement Quality (2-qubit Bell state)") + ax.set_xlabel("Result") + ax.set_ylabel("% in |00> + |11>") + plt.xticks(rotation=45) + fig.savefig(VIZ_DIR / "entanglement_summary.png") + plt.close(fig) + + # 2) Azure job list charts (status & provider) + rsrc = cfg.get("azure", {}).get("resource_group", "") + ws = cfg.get("azure", {}).get("workspace_name", "") + loc = cfg.get("azure", {}).get("location", "") + + if rsrc and ws and loc: + df = try_fetch_azure_job_list(rsrc, ws, loc) + if df is not None and not df.empty: + plot_job_status_distribution(df, VIZ_DIR / "azure_jobs_status.png") + plot_provider_status_stacked(df, VIZ_DIR / "azure_jobs_provider_status.png") + print("Azure job charts saved under visualizations/.") + else: + print( + "Azure job list not available or empty (no charts generated for jobs)." + ) + else: + print("Azure config incomplete; skipping Azure job charts.") + + print(f"\nCharts saved to: {VIZ_DIR}\n") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ai-projects/quantum-ml/src/Untitled-1.yml b/ai-projects/quantum-ml/src/Untitled-1.yml index 2852d87d8..d09b88c92 100644 --- a/ai-projects/quantum-ml/src/Untitled-1.yml +++ b/ai-projects/quantum-ml/src/Untitled-1.yml @@ -1,14 +1,14 @@ -name: Quantum Automation - -on: - push: - branches: [ main ] - -jobs: - run-quantum: - runs-on: windows-latest - steps: - - uses: actions/checkout@v3 - - name: Run Orchestration - shell: pwsh +name: Quantum Automation + +on: + push: + branches: [ main ] + +jobs: + run-quantum: + runs-on: windows-latest + steps: + - uses: actions/checkout@v3 + - name: Run Orchestration + shell: pwsh run: ./azure/quantum_master_orchestration.ps1 diff --git a/ai-projects/quantum-ml/src/Untitled-2.yml b/ai-projects/quantum-ml/src/Untitled-2.yml index 40b8bb1c4..38445c540 100644 --- a/ai-projects/quantum-ml/src/Untitled-2.yml +++ b/ai-projects/quantum-ml/src/Untitled-2.yml @@ -1,14 +1,14 @@ -trigger: -- main - -pool: - vmImage: 'windows-latest' - -steps: -- task: UsePythonVersion@0 - inputs: - versionSpec: '3.x' - -- powershell: | - ./azure/quantum_master_orchestration.ps1 +trigger: +- main + +pool: + vmImage: 'windows-latest' + +steps: +- task: UsePythonVersion@0 + inputs: + versionSpec: '3.x' + +- powershell: | + ./azure/quantum_master_orchestration.ps1 displayName: 'Run Quantum Orchestration' diff --git a/ai-projects/quantum-ml/src/automate_quantum_job.py b/ai-projects/quantum-ml/src/automate_quantum_job.py index 1dff0233f..33361070a 100644 --- a/ai-projects/quantum-ml/src/automate_quantum_job.py +++ b/ai-projects/quantum-ml/src/automate_quantum_job.py @@ -1,53 +1,53 @@ -""" -Automated Azure Quantum job submission, monitoring, and hybrid experiment runner -- Submits a Bell state circuit to IonQ hardware -- Monitors job status and retrieves results -- Can be adapted for hybrid/classical-quantum experiments -""" - -import time - -from azure.quantum import Workspace -from azure.quantum.qiskit import AzureQuantumProvider -from qiskit import QuantumCircuit - -# Load workspace config -ws = Workspace( - subscription_id="a07fbd16-e722-446d-8efd-0681e85b725c", - resource_group="rg-quantum-ai", - workspace_name="quantum-ai-workspace", - location="eastus", -) - -provider = AzureQuantumProvider(ws) -print("Available targets:", [b.name for b in provider.backends()]) - -# Choose IonQ hardware backend -backend = provider.get_backend("ionq.qpu") - -# Create a Bell state circuit -qc = QuantumCircuit(2) -qc.h(0) -qc.cx(0, 1) -qc.measure_all() - -print("Submitting job to IonQ hardware...") -job = backend.run(qc, shots=100) -print(f"Job ID: {job.id()}") - -# Monitor job status -TERMINAL_STATUSES = {"Succeeded", "Failed", "Cancelled"} # O(1) set lookup -while True: - status = job.status() - print(f"Job status: {status}") - if status in TERMINAL_STATUSES: - break - time.sleep(10) - -if status == "Succeeded": - result = job.result() - print("Measurement counts:", result.get_counts()) -else: - print(f"Job did not complete successfully. Status: {status}") - -# For hybrid experiments, adapt this script to submit parameterized circuits or use your hybrid model code. +""" +Automated Azure Quantum job submission, monitoring, and hybrid experiment runner +- Submits a Bell state circuit to IonQ hardware +- Monitors job status and retrieves results +- Can be adapted for hybrid/classical-quantum experiments +""" + +import time + +from azure.quantum import Workspace +from azure.quantum.qiskit import AzureQuantumProvider +from qiskit import QuantumCircuit + +# Load workspace config +ws = Workspace( + subscription_id="a07fbd16-e722-446d-8efd-0681e85b725c", + resource_group="rg-quantum-ai", + workspace_name="quantum-ai-workspace", + location="eastus", +) + +provider = AzureQuantumProvider(ws) +print("Available targets:", [b.name for b in provider.backends()]) + +# Choose IonQ hardware backend +backend = provider.get_backend("ionq.qpu") + +# Create a Bell state circuit +qc = QuantumCircuit(2) +qc.h(0) +qc.cx(0, 1) +qc.measure_all() + +print("Submitting job to IonQ hardware...") +job = backend.run(qc, shots=100) +print(f"Job ID: {job.id()}") + +# Monitor job status +TERMINAL_STATUSES = {"Succeeded", "Failed", "Cancelled"} # O(1) set lookup +while True: + status = job.status() + print(f"Job status: {status}") + if status in TERMINAL_STATUSES: + break + time.sleep(10) + +if status == "Succeeded": + result = job.result() + print("Measurement counts:", result.get_counts()) +else: + print(f"Job did not complete successfully. Status: {status}") + +# For hybrid experiments, adapt this script to submit parameterized circuits or use your hybrid model code. diff --git a/ai-projects/quantum-ml/src/azure_ml_integration.py b/ai-projects/quantum-ml/src/azure_ml_integration.py index 6477f2100..28fbfc8db 100644 --- a/ai-projects/quantum-ml/src/azure_ml_integration.py +++ b/ai-projects/quantum-ml/src/azure_ml_integration.py @@ -1,501 +1,501 @@ -""" -Azure Machine Learning Integration for Quantum AI -Production deployment with enhanced 8-qubit classifier -""" - -import logging -from pathlib import Path -from typing import Any, Dict - -# Azure ML imports (install with: pip install azureml-sdk) -try: - from azureml.core import (Environment, Experiment, ScriptRunConfig, - Workspace) - from azureml.core.compute import AmlCompute, ComputeTarget - from azureml.core.model import Model - from azureml.core.runconfig import RunConfiguration - - AZUREML_AVAILABLE = True -except ImportError: - AZUREML_AVAILABLE = False - logging.warning("Azure ML SDK not installed. Run: pip install azureml-sdk") - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -class QuantumAzureMLDeployment: - """ - Manages Azure ML deployment for quantum AI models. - Handles training, registration, and inference endpoints. - """ - - def __init__(self, config_path: str = None): - """ - Initialize Azure ML deployment manager. - - Args: - config_path: Path to quantum config file - """ - if not AZUREML_AVAILABLE: - raise ImportError( - "Azure ML SDK required. Install with: pip install azureml-sdk" - ) - - self.config_path = ( - config_path - or Path(__file__).parent.parent / "config" / "quantum_config.yaml" - ) - self.workspace = None - self.compute_target = None - - def connect_workspace( - self, subscription_id: str, resource_group: str, workspace_name: str - ) -> Workspace: - """ - Connect to Azure ML workspace. - - Args: - subscription_id: Azure subscription ID - resource_group: Resource group name - workspace_name: ML workspace name - - Returns: - Azure ML Workspace object - """ - logger.info("Connecting to Azure ML workspace...") - - try: - # Try to load from config - self.workspace = Workspace.from_config() - logger.info(f"✓ Loaded workspace from config: {self.workspace.name}") - except: - # Connect with explicit parameters - self.workspace = Workspace( - subscription_id=subscription_id, - resource_group=resource_group, - workspace_name=workspace_name, - ) - logger.info(f"✓ Connected to workspace: {workspace_name}") - - return self.workspace - - def create_compute_cluster( - self, - cluster_name: str = "quantum-compute", - vm_size: str = "Standard_DS3_v2", - min_nodes: int = 0, - max_nodes: int = 4, - ) -> ComputeTarget: - """ - Create or retrieve compute cluster for training. - - Args: - cluster_name: Name of compute cluster - vm_size: VM size for nodes - min_nodes: Minimum nodes (0 for auto-scale) - max_nodes: Maximum nodes - - Returns: - ComputeTarget object - """ - logger.info(f"Setting up compute cluster: {cluster_name}") - - try: - # Check if cluster exists - self.compute_target = ComputeTarget( - workspace=self.workspace, name=cluster_name - ) - logger.info(f"✓ Using existing cluster: {cluster_name}") - - except: - # Create new cluster - logger.info("Creating new compute cluster...") - - compute_config = AmlCompute.provisioning_configuration( - vm_size=vm_size, - min_nodes=min_nodes, - max_nodes=max_nodes, - idle_seconds_before_scaledown=300, - ) - - self.compute_target = ComputeTarget.create( - self.workspace, cluster_name, compute_config - ) - - self.compute_target.wait_for_completion(show_output=True) - logger.info(f"✓ Cluster created: {cluster_name}") - - return self.compute_target - - def create_environment(self, env_name: str = "quantum-env") -> Environment: - """ - Create Azure ML environment with quantum dependencies. - - Args: - env_name: Name for the environment - - Returns: - Environment object - """ - logger.info(f"Creating environment: {env_name}") - - env = Environment(name=env_name) - - # Add conda dependencies - env.python.conda_dependencies.add_conda_package("python=3.9") - env.python.conda_dependencies.add_pip_package("torch>=2.0.0") - env.python.conda_dependencies.add_pip_package("pennylane>=0.35.0") - env.python.conda_dependencies.add_pip_package("numpy>=1.24.0") - env.python.conda_dependencies.add_pip_package("scikit-learn>=1.3.0") - env.python.conda_dependencies.add_pip_package("azure-quantum>=1.0.0") - env.python.conda_dependencies.add_pip_package("qiskit>=1.0.0") - env.python.conda_dependencies.add_pip_package("matplotlib>=3.7.0") - - # Register environment - env.register(workspace=self.workspace) - logger.info(f"✓ Environment registered: {env_name}") - - return env - - def submit_training_job( - self, script_path: str, experiment_name: str, arguments: Dict[str, Any] = None - ) -> Any: - """ - Submit quantum model training job to Azure ML. - - Args: - script_path: Path to training script - experiment_name: Name of experiment - arguments: Script arguments - - Returns: - Run object - """ - logger.info(f"Submitting training job: {experiment_name}") - - # Create experiment - experiment = Experiment(workspace=self.workspace, name=experiment_name) - - # Prepare environment - env = self.create_environment() - - # Configure run - run_config = ScriptRunConfig( - source_directory=str(Path(script_path).parent), - script=Path(script_path).name, - compute_target=self.compute_target, - environment=env, - ) - - # Add arguments - if arguments: - run_config.arguments = [] - for key, value in arguments.items(): - run_config.arguments.extend([f"--{key}", str(value)]) - - # Submit run - run = experiment.submit(run_config) - logger.info(f"✓ Training job submitted: {run.id}") - logger.info(f" Monitor at: {run.get_portal_url()}") - - return run - - def register_model( - self, - model_path: str, - model_name: str, - description: str = None, - tags: Dict[str, str] = None, - ) -> Model: - """ - Register trained quantum model in Azure ML. - - Args: - model_path: Path to model file - model_name: Name for registered model - description: Model description - tags: Model tags - - Returns: - Registered Model object - """ - logger.info(f"Registering model: {model_name}") - - model = Model.register( - workspace=self.workspace, - model_path=model_path, - model_name=model_name, - description=description or "Enhanced 8-qubit quantum classifier", - tags=tags - or { - "type": "quantum", - "qubits": "8", - "framework": "pennylane", - "accuracy": "97.5%", - }, - ) - - logger.info(f"✓ Model registered: {model_name} (v{model.version})") - return model - - def deploy_inference_endpoint( - self, - model_name: str, - service_name: str = "quantum-api", - cpu_cores: int = 2, - memory_gb: int = 4, - ) -> Any: - """ - Deploy model as REST API endpoint. - - Args: - model_name: Registered model name - service_name: Name for the web service - cpu_cores: CPU cores for inference - memory_gb: Memory for inference - - Returns: - Deployed service object - """ - logger.info(f"Deploying inference endpoint: {service_name}") - - from azureml.core.model import InferenceConfig - from azureml.core.webservice import AciWebservice - - # Get registered model - model = Model(self.workspace, name=model_name) - - # Create inference configuration - inference_config = InferenceConfig( - entry_script="score.py", # Scoring script - environment=self.create_environment(), - ) - - # Configure deployment - deployment_config = AciWebservice.deploy_configuration( - cpu_cores=cpu_cores, - memory_gb=memory_gb, - auth_enabled=True, - enable_app_insights=True, - ) - - # Deploy - service = Model.deploy( - workspace=self.workspace, - name=service_name, - models=[model], - inference_config=inference_config, - deployment_config=deployment_config, - ) - - service.wait_for_deployment(show_output=True) - logger.info(f"✓ Service deployed: {service.scoring_uri}") - - return service - - -def create_training_script(): - """ - Generate Azure ML training script for enhanced quantum classifier. - """ - script_content = ''' -""" -Azure ML Training Script for Enhanced Quantum Classifier -""" -import argparse -import torch -import numpy as np -from sklearn.datasets import make_moons, load_wine -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -import sys -from pathlib import Path - -# Import quantum classifier -sys.path.append(str(Path(__file__).parent)) -from quantum_classifier_enhanced import HybridEnhancedClassifier, train_enhanced_model - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('--dataset', type=str, default='moons') - parser.add_argument('--n_qubits', type=int, default=8) - parser.add_argument('--n_layers', type=int, default=4) - parser.add_argument('--epochs', type=int, default=100) - parser.add_argument('--learning_rate', type=float, default=0.01) - parser.add_argument('--output_dir', type=str, default='./outputs') - - args = parser.parse_args() - - print(f"Training Enhanced Quantum Classifier") - print(f" Dataset: {args.dataset}") - print(f" Qubits: {args.n_qubits}") - print(f" Layers: {args.n_layers}") - print(f" Epochs: {args.epochs}") - - # Load dataset - if args.dataset == 'moons': - X, y = make_moons(n_samples=400, noise=0.1, random_state=42) - elif args.dataset == 'wine': - data = load_wine() - X, y = data.data, (data.target == 0).astype(int) - - X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) - - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - # Create model - model = HybridEnhancedClassifier( - input_dim=X_train.shape[1], - n_qubits=args.n_qubits, - n_layers=args.n_layers - ) - - # Train - history = train_enhanced_model( - model, X_train, y_train, X_val, y_val, - epochs=args.epochs, - learning_rate=args.learning_rate - ) - - # Save model - Path(args.output_dir).mkdir(exist_ok=True) - torch.save({ - 'model_state_dict': model.state_dict(), - 'history': history, - 'scaler': scaler - }, f"{args.output_dir}/quantum_model.pt") - - print(f"\\nFinal Accuracy: {history['val_acc'][-1]:.4f}") - print(f"Model saved to: {args.output_dir}") - -if __name__ == "__main__": - main() -''' - - output_path = Path(__file__).parent / "train_azure_ml.py" - with open(output_path, "w") as f: - f.write(script_content) - - logger.info(f"✓ Training script created: {output_path}") - return output_path - - -def create_scoring_script(): - """ - Generate inference scoring script for Azure ML endpoint. - """ - script_content = ''' -""" -Scoring Script for Quantum Classifier Inference -""" -import json -import torch -import numpy as np -from quantum_classifier_enhanced import HybridEnhancedClassifier - -def init(): - """Initialize the model""" - global model, scaler - - # Load model - checkpoint = torch.load('quantum_model.pt', weights_only=True) - model = HybridEnhancedClassifier(input_dim=2, n_qubits=8, n_layers=4) - model.load_state_dict(checkpoint['model_state_dict']) - model.eval() - - scaler = checkpoint.get('scaler', None) - print("Model loaded successfully") - -def run(raw_data): - """Run inference""" - try: - data = json.loads(raw_data) - X = np.array(data['data']) - - # Scale if scaler available - if scaler: - X = scaler.transform(X) - - # Predict - with torch.no_grad(): - X_tensor = torch.FloatTensor(X) - predictions = model(X_tensor) - predictions = predictions.cpu().numpy() - - return json.dumps({ - 'predictions': predictions.tolist(), - 'status': 'success' - }) - - except Exception as e: - return json.dumps({ - 'error': str(e), - 'status': 'failed' - }) -''' - - output_path = Path(__file__).parent / "score.py" - with open(output_path, "w") as f: - f.write(script_content) - - logger.info(f"✓ Scoring script created: {output_path}") - return output_path - - -if __name__ == "__main__": - print("=" * 70) - print(" Azure ML Integration for Quantum AI") - print("=" * 70) - - # Example usage - print("\nThis module provides Azure ML integration.") - print("\nFeatures:") - print(" • Training job submission to Azure ML") - print(" • Model registration and versioning") - print(" • REST API deployment for inference") - print(" • Compute cluster management") - - print("\nUsage Example:") - print( - """ - from azure_ml_integration import QuantumAzureMLDeployment - - # Initialize - deployer = QuantumAzureMLDeployment() - - # Connect to workspace - workspace = deployer.connect_workspace( - subscription_id='your-subscription-id', - resource_group='rg-quantum-ai', - workspace_name='quantum-ai-ml-workspace' - ) - - # Create compute cluster - compute = deployer.create_compute_cluster() - - # Submit training job - run = deployer.submit_training_job( - script_path='train_azure_ml.py', - experiment_name='quantum-8qubit', - arguments={'n_qubits': 8, 'epochs': 100} - ) - - # Register model - model = deployer.register_model( - model_path='outputs/quantum_model.pt', - model_name='quantum-classifier-8q' - ) - - # Deploy inference endpoint - service = deployer.deploy_inference_endpoint( - model_name='quantum-classifier-8q', - service_name='quantum-api' - ) - """ - ) - - print("\n✓ Azure ML integration module ready") - print("=" * 70) +""" +Azure Machine Learning Integration for Quantum AI +Production deployment with enhanced 8-qubit classifier +""" + +import logging +from pathlib import Path +from typing import Any, Dict + +# Azure ML imports (install with: pip install azureml-sdk) +try: + from azureml.core import (Environment, Experiment, ScriptRunConfig, + Workspace) + from azureml.core.compute import AmlCompute, ComputeTarget + from azureml.core.model import Model + from azureml.core.runconfig import RunConfiguration + + AZUREML_AVAILABLE = True +except ImportError: + AZUREML_AVAILABLE = False + logging.warning("Azure ML SDK not installed. Run: pip install azureml-sdk") + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class QuantumAzureMLDeployment: + """ + Manages Azure ML deployment for quantum AI models. + Handles training, registration, and inference endpoints. + """ + + def __init__(self, config_path: str = None): + """ + Initialize Azure ML deployment manager. + + Args: + config_path: Path to quantum config file + """ + if not AZUREML_AVAILABLE: + raise ImportError( + "Azure ML SDK required. Install with: pip install azureml-sdk" + ) + + self.config_path = ( + config_path + or Path(__file__).parent.parent / "config" / "quantum_config.yaml" + ) + self.workspace = None + self.compute_target = None + + def connect_workspace( + self, subscription_id: str, resource_group: str, workspace_name: str + ) -> Workspace: + """ + Connect to Azure ML workspace. + + Args: + subscription_id: Azure subscription ID + resource_group: Resource group name + workspace_name: ML workspace name + + Returns: + Azure ML Workspace object + """ + logger.info("Connecting to Azure ML workspace...") + + try: + # Try to load from config + self.workspace = Workspace.from_config() + logger.info(f"✓ Loaded workspace from config: {self.workspace.name}") + except: + # Connect with explicit parameters + self.workspace = Workspace( + subscription_id=subscription_id, + resource_group=resource_group, + workspace_name=workspace_name, + ) + logger.info(f"✓ Connected to workspace: {workspace_name}") + + return self.workspace + + def create_compute_cluster( + self, + cluster_name: str = "quantum-compute", + vm_size: str = "Standard_DS3_v2", + min_nodes: int = 0, + max_nodes: int = 4, + ) -> ComputeTarget: + """ + Create or retrieve compute cluster for training. + + Args: + cluster_name: Name of compute cluster + vm_size: VM size for nodes + min_nodes: Minimum nodes (0 for auto-scale) + max_nodes: Maximum nodes + + Returns: + ComputeTarget object + """ + logger.info(f"Setting up compute cluster: {cluster_name}") + + try: + # Check if cluster exists + self.compute_target = ComputeTarget( + workspace=self.workspace, name=cluster_name + ) + logger.info(f"✓ Using existing cluster: {cluster_name}") + + except: + # Create new cluster + logger.info("Creating new compute cluster...") + + compute_config = AmlCompute.provisioning_configuration( + vm_size=vm_size, + min_nodes=min_nodes, + max_nodes=max_nodes, + idle_seconds_before_scaledown=300, + ) + + self.compute_target = ComputeTarget.create( + self.workspace, cluster_name, compute_config + ) + + self.compute_target.wait_for_completion(show_output=True) + logger.info(f"✓ Cluster created: {cluster_name}") + + return self.compute_target + + def create_environment(self, env_name: str = "quantum-env") -> Environment: + """ + Create Azure ML environment with quantum dependencies. + + Args: + env_name: Name for the environment + + Returns: + Environment object + """ + logger.info(f"Creating environment: {env_name}") + + env = Environment(name=env_name) + + # Add conda dependencies + env.python.conda_dependencies.add_conda_package("python=3.9") + env.python.conda_dependencies.add_pip_package("torch>=2.0.0") + env.python.conda_dependencies.add_pip_package("pennylane>=0.35.0") + env.python.conda_dependencies.add_pip_package("numpy>=1.24.0") + env.python.conda_dependencies.add_pip_package("scikit-learn>=1.3.0") + env.python.conda_dependencies.add_pip_package("azure-quantum>=1.0.0") + env.python.conda_dependencies.add_pip_package("qiskit>=1.0.0") + env.python.conda_dependencies.add_pip_package("matplotlib>=3.7.0") + + # Register environment + env.register(workspace=self.workspace) + logger.info(f"✓ Environment registered: {env_name}") + + return env + + def submit_training_job( + self, script_path: str, experiment_name: str, arguments: Dict[str, Any] = None + ) -> Any: + """ + Submit quantum model training job to Azure ML. + + Args: + script_path: Path to training script + experiment_name: Name of experiment + arguments: Script arguments + + Returns: + Run object + """ + logger.info(f"Submitting training job: {experiment_name}") + + # Create experiment + experiment = Experiment(workspace=self.workspace, name=experiment_name) + + # Prepare environment + env = self.create_environment() + + # Configure run + run_config = ScriptRunConfig( + source_directory=str(Path(script_path).parent), + script=Path(script_path).name, + compute_target=self.compute_target, + environment=env, + ) + + # Add arguments + if arguments: + run_config.arguments = [] + for key, value in arguments.items(): + run_config.arguments.extend([f"--{key}", str(value)]) + + # Submit run + run = experiment.submit(run_config) + logger.info(f"✓ Training job submitted: {run.id}") + logger.info(f" Monitor at: {run.get_portal_url()}") + + return run + + def register_model( + self, + model_path: str, + model_name: str, + description: str = None, + tags: Dict[str, str] = None, + ) -> Model: + """ + Register trained quantum model in Azure ML. + + Args: + model_path: Path to model file + model_name: Name for registered model + description: Model description + tags: Model tags + + Returns: + Registered Model object + """ + logger.info(f"Registering model: {model_name}") + + model = Model.register( + workspace=self.workspace, + model_path=model_path, + model_name=model_name, + description=description or "Enhanced 8-qubit quantum classifier", + tags=tags + or { + "type": "quantum", + "qubits": "8", + "framework": "pennylane", + "accuracy": "97.5%", + }, + ) + + logger.info(f"✓ Model registered: {model_name} (v{model.version})") + return model + + def deploy_inference_endpoint( + self, + model_name: str, + service_name: str = "quantum-api", + cpu_cores: int = 2, + memory_gb: int = 4, + ) -> Any: + """ + Deploy model as REST API endpoint. + + Args: + model_name: Registered model name + service_name: Name for the web service + cpu_cores: CPU cores for inference + memory_gb: Memory for inference + + Returns: + Deployed service object + """ + logger.info(f"Deploying inference endpoint: {service_name}") + + from azureml.core.model import InferenceConfig + from azureml.core.webservice import AciWebservice + + # Get registered model + model = Model(self.workspace, name=model_name) + + # Create inference configuration + inference_config = InferenceConfig( + entry_script="score.py", # Scoring script + environment=self.create_environment(), + ) + + # Configure deployment + deployment_config = AciWebservice.deploy_configuration( + cpu_cores=cpu_cores, + memory_gb=memory_gb, + auth_enabled=True, + enable_app_insights=True, + ) + + # Deploy + service = Model.deploy( + workspace=self.workspace, + name=service_name, + models=[model], + inference_config=inference_config, + deployment_config=deployment_config, + ) + + service.wait_for_deployment(show_output=True) + logger.info(f"✓ Service deployed: {service.scoring_uri}") + + return service + + +def create_training_script(): + """ + Generate Azure ML training script for enhanced quantum classifier. + """ + script_content = ''' +""" +Azure ML Training Script for Enhanced Quantum Classifier +""" +import argparse +import torch +import numpy as np +from sklearn.datasets import make_moons, load_wine +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +import sys +from pathlib import Path + +# Import quantum classifier +sys.path.append(str(Path(__file__).parent)) +from quantum_classifier_enhanced import HybridEnhancedClassifier, train_enhanced_model + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--dataset', type=str, default='moons') + parser.add_argument('--n_qubits', type=int, default=8) + parser.add_argument('--n_layers', type=int, default=4) + parser.add_argument('--epochs', type=int, default=100) + parser.add_argument('--learning_rate', type=float, default=0.01) + parser.add_argument('--output_dir', type=str, default='./outputs') + + args = parser.parse_args() + + print(f"Training Enhanced Quantum Classifier") + print(f" Dataset: {args.dataset}") + print(f" Qubits: {args.n_qubits}") + print(f" Layers: {args.n_layers}") + print(f" Epochs: {args.epochs}") + + # Load dataset + if args.dataset == 'moons': + X, y = make_moons(n_samples=400, noise=0.1, random_state=42) + elif args.dataset == 'wine': + data = load_wine() + X, y = data.data, (data.target == 0).astype(int) + + X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) + + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + # Create model + model = HybridEnhancedClassifier( + input_dim=X_train.shape[1], + n_qubits=args.n_qubits, + n_layers=args.n_layers + ) + + # Train + history = train_enhanced_model( + model, X_train, y_train, X_val, y_val, + epochs=args.epochs, + learning_rate=args.learning_rate + ) + + # Save model + Path(args.output_dir).mkdir(exist_ok=True) + torch.save({ + 'model_state_dict': model.state_dict(), + 'history': history, + 'scaler': scaler + }, f"{args.output_dir}/quantum_model.pt") + + print(f"\\nFinal Accuracy: {history['val_acc'][-1]:.4f}") + print(f"Model saved to: {args.output_dir}") + +if __name__ == "__main__": + main() +''' + + output_path = Path(__file__).parent / "train_azure_ml.py" + with open(output_path, "w") as f: + f.write(script_content) + + logger.info(f"✓ Training script created: {output_path}") + return output_path + + +def create_scoring_script(): + """ + Generate inference scoring script for Azure ML endpoint. + """ + script_content = ''' +""" +Scoring Script for Quantum Classifier Inference +""" +import json +import torch +import numpy as np +from quantum_classifier_enhanced import HybridEnhancedClassifier + +def init(): + """Initialize the model""" + global model, scaler + + # Load model + checkpoint = torch.load('quantum_model.pt', weights_only=True) + model = HybridEnhancedClassifier(input_dim=2, n_qubits=8, n_layers=4) + model.load_state_dict(checkpoint['model_state_dict']) + model.eval() + + scaler = checkpoint.get('scaler', None) + print("Model loaded successfully") + +def run(raw_data): + """Run inference""" + try: + data = json.loads(raw_data) + X = np.array(data['data']) + + # Scale if scaler available + if scaler: + X = scaler.transform(X) + + # Predict + with torch.no_grad(): + X_tensor = torch.FloatTensor(X) + predictions = model(X_tensor) + predictions = predictions.cpu().numpy() + + return json.dumps({ + 'predictions': predictions.tolist(), + 'status': 'success' + }) + + except Exception as e: + return json.dumps({ + 'error': str(e), + 'status': 'failed' + }) +''' + + output_path = Path(__file__).parent / "score.py" + with open(output_path, "w") as f: + f.write(script_content) + + logger.info(f"✓ Scoring script created: {output_path}") + return output_path + + +if __name__ == "__main__": + print("=" * 70) + print(" Azure ML Integration for Quantum AI") + print("=" * 70) + + # Example usage + print("\nThis module provides Azure ML integration.") + print("\nFeatures:") + print(" • Training job submission to Azure ML") + print(" • Model registration and versioning") + print(" • REST API deployment for inference") + print(" • Compute cluster management") + + print("\nUsage Example:") + print( + """ + from azure_ml_integration import QuantumAzureMLDeployment + + # Initialize + deployer = QuantumAzureMLDeployment() + + # Connect to workspace + workspace = deployer.connect_workspace( + subscription_id='your-subscription-id', + resource_group='rg-quantum-ai', + workspace_name='quantum-ai-ml-workspace' + ) + + # Create compute cluster + compute = deployer.create_compute_cluster() + + # Submit training job + run = deployer.submit_training_job( + script_path='train_azure_ml.py', + experiment_name='quantum-8qubit', + arguments={'n_qubits': 8, 'epochs': 100} + ) + + # Register model + model = deployer.register_model( + model_path='outputs/quantum_model.pt', + model_name='quantum-classifier-8q' + ) + + # Deploy inference endpoint + service = deployer.deploy_inference_endpoint( + model_name='quantum-classifier-8q', + service_name='quantum-api' + ) + """ + ) + + print("\n✓ Azure ML integration module ready") + print("=" * 70) diff --git a/ai-projects/quantum-ml/src/azure_quantum_integration.py b/ai-projects/quantum-ml/src/azure_quantum_integration.py index d7b27c4bd..da5b6c78a 100644 --- a/ai-projects/quantum-ml/src/azure_quantum_integration.py +++ b/ai-projects/quantum-ml/src/azure_quantum_integration.py @@ -1,391 +1,391 @@ -""" -Azure Quantum Integration Module -Provides integration with Azure Quantum services for running quantum circuits -""" - -import json -import logging -from pathlib import Path -from typing import Any, Dict, List, Optional - -import yaml -from azure.identity import DefaultAzureCredential -from azure.quantum import Workspace -from azure.quantum.qiskit import AzureQuantumProvider -from qiskit import QuantumCircuit, transpile - -logger = logging.getLogger(__name__) - - -class AzureQuantumIntegration: - """ - Manages connection and job submission to Azure Quantum workspace. - """ - - def __init__(self, config_path: str = "./config/quantum_config.yaml"): - """ - Initialize Azure Quantum connection. - - Args: - config_path: Path to configuration file - """ - # Handle both relative and absolute paths, and resolve from script location - config_file = Path(config_path) - if not config_file.is_absolute(): - # Try relative to quantum-ai directory - quantum_ai_dir = Path(__file__).parent.parent - config_file = quantum_ai_dir / config_path - - if not config_file.exists(): - raise FileNotFoundError(f"Config file not found: {config_file}") - - with open(config_file, "r") as f: - self.config = yaml.safe_load(f) - - self.azure_config = self.config["azure"] - self.quantum_config = self.config["quantum"] - - self.workspace: Optional[Workspace] = None - self.provider: Optional[AzureQuantumProvider] = None - - logger.info("Azure Quantum Integration initialized") - - def connect(self, credential: Optional[Any] = None) -> Workspace: - """ - Connect to Azure Quantum workspace. - - Args: - credential: Azure credential (uses DefaultAzureCredential if None) - - Returns: - Connected workspace - """ - if credential is None: - credential = DefaultAzureCredential() - - try: - self.workspace = Workspace( - subscription_id=self.azure_config["subscription_id"], - resource_group=self.azure_config["resource_group"], - name=self.azure_config["workspace_name"], - location=self.azure_config["location"], - credential=credential, - ) - - logger.info( - f"Connected to Azure Quantum workspace: {self.azure_config['workspace_name']}" - ) - - # Initialize provider - self.provider = AzureQuantumProvider(self.workspace) - - return self.workspace - - except Exception as e: - logger.error(f"Failed to connect to Azure Quantum: {str(e)}") - raise - - def list_backends(self) -> List[str]: - """ - List available quantum backends in the workspace. - - Returns: - List of backend names - """ - if self.provider is None: - raise RuntimeError("Not connected to Azure Quantum. Call connect() first.") - - backends = self.provider.backends() - backend_names = [backend.name() for backend in backends] - - logger.info(f"Available backends: {', '.join(backend_names)}") - return backend_names - - def get_backend(self, backend_name: Optional[str] = None): - """ - Get a specific quantum backend. - - Args: - backend_name: Name of the backend (uses config default if None) - - Returns: - Quantum backend - """ - if self.provider is None: - raise RuntimeError("Not connected to Azure Quantum. Call connect() first.") - - if backend_name is None: - # Use provider from config - provider = self.quantum_config["provider"] - backends = self.provider.backends() - - # Prefer simulators by default to avoid unintended costs - preferred = None - fallback = None - for backend in backends: - name = backend.name().lower() - if provider.lower() in name: - if "sim" in name or "simulator" in name: - preferred = backend.name() - break - # Track first provider match as fallback (likely QPU) - if fallback is None: - fallback = backend.name() - - backend_name = preferred or fallback or backends[0].name() - - backend = self.provider.get_backend(backend_name) - logger.info(f"Using backend: {backend_name}") - - return backend - - def submit_circuit( - self, - circuit: QuantumCircuit, - backend_name: Optional[str] = None, - shots: Optional[int] = None, - job_name: Optional[str] = None, - ) -> Any: - """ - Submit a quantum circuit to Azure Quantum. - - Args: - circuit: Qiskit quantum circuit - backend_name: Target backend - shots: Number of shots (uses config default if None) - job_name: Optional job name - - Returns: - Job object - """ - if shots is None: - shots = self.quantum_config["hardware"]["shots"] - - backend = self.get_backend(backend_name) - - # Transpile circuit for backend - transpiled_circuit = transpile( - circuit, - backend=backend, - optimization_level=self.quantum_config["hardware"]["optimization_level"], - ) - - # Submit job - job = backend.run(transpiled_circuit, shots=shots) - - if job_name: - logger.info(f"Submitted job '{job_name}': {job.id()}") - else: - logger.info(f"Submitted job: {job.id()}") - - return job - - def get_job_results(self, job) -> Dict: - """ - Retrieve results from a completed job. - - Args: - job: Job object - - Returns: - Job results as dictionary - """ - logger.info(f"Waiting for job {job.id()} to complete...") - result = job.result() - - counts = result.get_counts() - logger.info(f"Job completed. Results: {counts}") - - return {"job_id": job.id(), "counts": counts, "success": result.success} - - def save_results(self, results: Dict, filename: str): - """ - Save job results to file. - - Args: - results: Results dictionary - filename: Output filename - """ - results_dir = Path(self.config["logging"]["results_dir"]) - results_dir.mkdir(parents=True, exist_ok=True) - - filepath = results_dir / filename - with open(filepath, "w") as f: - json.dump(results, f, indent=2) - - logger.info(f"Results saved to {filepath}") - - def estimate_cost( - self, circuit: QuantumCircuit, backend_name: str, shots: int = 100 - ) -> Dict: - """ - Estimate the cost of running a circuit. - - Args: - circuit: Quantum circuit - backend_name: Target backend - shots: Number of shots - - Returns: - Cost estimation - """ - backend = self.get_backend(backend_name) - - # Get pricing info from backend - try: - # This is a simplified estimation - # Actual pricing depends on provider and may vary - estimation = { - "backend": backend_name, - "shots": shots, - "estimated_time_minutes": 5, # Placeholder - "note": "Actual cost depends on Azure Quantum pricing for the selected provider", - } - - logger.info(f"Cost estimation: {estimation}") - return estimation - - except Exception as e: - logger.warning(f"Could not estimate cost: {str(e)}") - return {"error": str(e)} - - -class QuantumJobManager: - """ - Manages multiple quantum jobs and tracks their status. - """ - - def __init__(self, azure_integration: AzureQuantumIntegration): - """ - Initialize job manager. - - Args: - azure_integration: Azure Quantum integration instance - """ - self.azure = azure_integration - self.jobs: Dict[str, Any] = {} - - def submit_batch( - self, - circuits: List[QuantumCircuit], - backend_name: Optional[str] = None, - shots: Optional[int] = None, - job_names: Optional[List[str]] = None, - ) -> List[Any]: - """ - Submit multiple circuits as a batch. - - Args: - circuits: List of quantum circuits - backend_name: Target backend - shots: Number of shots per circuit - job_names: Optional names for each job - - Returns: - List of job objects - """ - if job_names is None: - job_names = [f"job_{i}" for i in range(len(circuits))] - - jobs = [] - for circuit, name in zip(circuits, job_names): - job = self.azure.submit_circuit(circuit, backend_name, shots, name) - self.jobs[name] = job - jobs.append(job) - - logger.info(f"Submitted batch of {len(circuits)} circuits") - return jobs - - def check_status(self, job_name: str) -> str: - """ - Check the status of a job. - - Args: - job_name: Name of the job - - Returns: - Job status - """ - if job_name not in self.jobs: - raise ValueError(f"Job '{job_name}' not found") - - job = self.jobs[job_name] - status = job.status() - - logger.info(f"Job '{job_name}' status: {status}") - return status.name - - def get_all_results(self) -> Dict[str, Dict]: - """ - Get results for all completed jobs. - - Returns: - Dictionary mapping job names to results - """ - results = {} - - for job_name, job in self.jobs.items(): - try: - result = self.azure.get_job_results(job) - results[job_name] = result - except Exception as e: - logger.warning(f"Could not get results for '{job_name}': {str(e)}") - results[job_name] = {"error": str(e)} - - return results - - -def create_sample_circuit(n_qubits: int = 3) -> QuantumCircuit: - """ - Create a sample quantum circuit for testing. - - Args: - n_qubits: Number of qubits - - Returns: - Quantum circuit - """ - circuit = QuantumCircuit(n_qubits, n_qubits) - - # Create a simple entangled state - circuit.h(0) - for i in range(n_qubits - 1): - circuit.cx(i, i + 1) - - # Measure all qubits - circuit.measure(range(n_qubits), range(n_qubits)) - - return circuit - - -if __name__ == "__main__": - # Example usage - logging.basicConfig(level=logging.INFO) - - # Initialize Azure Quantum integration - azure = AzureQuantumIntegration() - - try: - # Connect to workspace - workspace = azure.connect() - - # List available backends - backends = azure.list_backends() - - # Create and submit a sample circuit - circuit = create_sample_circuit(n_qubits=3) - - print("Sample quantum circuit created:") - print(circuit) - - # Uncomment to actually submit (requires valid Azure credentials) - # job = azure.submit_circuit(circuit, shots=100, job_name="test_job") - # results = azure.get_job_results(job) - # azure.save_results(results, "test_results.json") - - except Exception as e: - logger.error(f"Error in example: {str(e)}") - print("\nNote: To actually submit jobs, ensure you have:") - print("1. Valid Azure credentials configured") - print("2. An Azure Quantum workspace created") - print("3. Updated config/quantum_config.yaml with your workspace details") +""" +Azure Quantum Integration Module +Provides integration with Azure Quantum services for running quantum circuits +""" + +import json +import logging +from pathlib import Path +from typing import Any, Dict, List, Optional + +import yaml +from azure.identity import DefaultAzureCredential +from azure.quantum import Workspace +from azure.quantum.qiskit import AzureQuantumProvider +from qiskit import QuantumCircuit, transpile + +logger = logging.getLogger(__name__) + + +class AzureQuantumIntegration: + """ + Manages connection and job submission to Azure Quantum workspace. + """ + + def __init__(self, config_path: str = "./config/quantum_config.yaml"): + """ + Initialize Azure Quantum connection. + + Args: + config_path: Path to configuration file + """ + # Handle both relative and absolute paths, and resolve from script location + config_file = Path(config_path) + if not config_file.is_absolute(): + # Try relative to quantum-ai directory + quantum_ai_dir = Path(__file__).parent.parent + config_file = quantum_ai_dir / config_path + + if not config_file.exists(): + raise FileNotFoundError(f"Config file not found: {config_file}") + + with open(config_file, "r") as f: + self.config = yaml.safe_load(f) + + self.azure_config = self.config["azure"] + self.quantum_config = self.config["quantum"] + + self.workspace: Optional[Workspace] = None + self.provider: Optional[AzureQuantumProvider] = None + + logger.info("Azure Quantum Integration initialized") + + def connect(self, credential: Optional[Any] = None) -> Workspace: + """ + Connect to Azure Quantum workspace. + + Args: + credential: Azure credential (uses DefaultAzureCredential if None) + + Returns: + Connected workspace + """ + if credential is None: + credential = DefaultAzureCredential() + + try: + self.workspace = Workspace( + subscription_id=self.azure_config["subscription_id"], + resource_group=self.azure_config["resource_group"], + name=self.azure_config["workspace_name"], + location=self.azure_config["location"], + credential=credential, + ) + + logger.info( + f"Connected to Azure Quantum workspace: {self.azure_config['workspace_name']}" + ) + + # Initialize provider + self.provider = AzureQuantumProvider(self.workspace) + + return self.workspace + + except Exception as e: + logger.error(f"Failed to connect to Azure Quantum: {str(e)}") + raise + + def list_backends(self) -> List[str]: + """ + List available quantum backends in the workspace. + + Returns: + List of backend names + """ + if self.provider is None: + raise RuntimeError("Not connected to Azure Quantum. Call connect() first.") + + backends = self.provider.backends() + backend_names = [backend.name() for backend in backends] + + logger.info(f"Available backends: {', '.join(backend_names)}") + return backend_names + + def get_backend(self, backend_name: Optional[str] = None): + """ + Get a specific quantum backend. + + Args: + backend_name: Name of the backend (uses config default if None) + + Returns: + Quantum backend + """ + if self.provider is None: + raise RuntimeError("Not connected to Azure Quantum. Call connect() first.") + + if backend_name is None: + # Use provider from config + provider = self.quantum_config["provider"] + backends = self.provider.backends() + + # Prefer simulators by default to avoid unintended costs + preferred = None + fallback = None + for backend in backends: + name = backend.name().lower() + if provider.lower() in name: + if "sim" in name or "simulator" in name: + preferred = backend.name() + break + # Track first provider match as fallback (likely QPU) + if fallback is None: + fallback = backend.name() + + backend_name = preferred or fallback or backends[0].name() + + backend = self.provider.get_backend(backend_name) + logger.info(f"Using backend: {backend_name}") + + return backend + + def submit_circuit( + self, + circuit: QuantumCircuit, + backend_name: Optional[str] = None, + shots: Optional[int] = None, + job_name: Optional[str] = None, + ) -> Any: + """ + Submit a quantum circuit to Azure Quantum. + + Args: + circuit: Qiskit quantum circuit + backend_name: Target backend + shots: Number of shots (uses config default if None) + job_name: Optional job name + + Returns: + Job object + """ + if shots is None: + shots = self.quantum_config["hardware"]["shots"] + + backend = self.get_backend(backend_name) + + # Transpile circuit for backend + transpiled_circuit = transpile( + circuit, + backend=backend, + optimization_level=self.quantum_config["hardware"]["optimization_level"], + ) + + # Submit job + job = backend.run(transpiled_circuit, shots=shots) + + if job_name: + logger.info(f"Submitted job '{job_name}': {job.id()}") + else: + logger.info(f"Submitted job: {job.id()}") + + return job + + def get_job_results(self, job) -> Dict: + """ + Retrieve results from a completed job. + + Args: + job: Job object + + Returns: + Job results as dictionary + """ + logger.info(f"Waiting for job {job.id()} to complete...") + result = job.result() + + counts = result.get_counts() + logger.info(f"Job completed. Results: {counts}") + + return {"job_id": job.id(), "counts": counts, "success": result.success} + + def save_results(self, results: Dict, filename: str): + """ + Save job results to file. + + Args: + results: Results dictionary + filename: Output filename + """ + results_dir = Path(self.config["logging"]["results_dir"]) + results_dir.mkdir(parents=True, exist_ok=True) + + filepath = results_dir / filename + with open(filepath, "w") as f: + json.dump(results, f, indent=2) + + logger.info(f"Results saved to {filepath}") + + def estimate_cost( + self, circuit: QuantumCircuit, backend_name: str, shots: int = 100 + ) -> Dict: + """ + Estimate the cost of running a circuit. + + Args: + circuit: Quantum circuit + backend_name: Target backend + shots: Number of shots + + Returns: + Cost estimation + """ + backend = self.get_backend(backend_name) + + # Get pricing info from backend + try: + # This is a simplified estimation + # Actual pricing depends on provider and may vary + estimation = { + "backend": backend_name, + "shots": shots, + "estimated_time_minutes": 5, # Placeholder + "note": "Actual cost depends on Azure Quantum pricing for the selected provider", + } + + logger.info(f"Cost estimation: {estimation}") + return estimation + + except Exception as e: + logger.warning(f"Could not estimate cost: {str(e)}") + return {"error": str(e)} + + +class QuantumJobManager: + """ + Manages multiple quantum jobs and tracks their status. + """ + + def __init__(self, azure_integration: AzureQuantumIntegration): + """ + Initialize job manager. + + Args: + azure_integration: Azure Quantum integration instance + """ + self.azure = azure_integration + self.jobs: Dict[str, Any] = {} + + def submit_batch( + self, + circuits: List[QuantumCircuit], + backend_name: Optional[str] = None, + shots: Optional[int] = None, + job_names: Optional[List[str]] = None, + ) -> List[Any]: + """ + Submit multiple circuits as a batch. + + Args: + circuits: List of quantum circuits + backend_name: Target backend + shots: Number of shots per circuit + job_names: Optional names for each job + + Returns: + List of job objects + """ + if job_names is None: + job_names = [f"job_{i}" for i in range(len(circuits))] + + jobs = [] + for circuit, name in zip(circuits, job_names): + job = self.azure.submit_circuit(circuit, backend_name, shots, name) + self.jobs[name] = job + jobs.append(job) + + logger.info(f"Submitted batch of {len(circuits)} circuits") + return jobs + + def check_status(self, job_name: str) -> str: + """ + Check the status of a job. + + Args: + job_name: Name of the job + + Returns: + Job status + """ + if job_name not in self.jobs: + raise ValueError(f"Job '{job_name}' not found") + + job = self.jobs[job_name] + status = job.status() + + logger.info(f"Job '{job_name}' status: {status}") + return status.name + + def get_all_results(self) -> Dict[str, Dict]: + """ + Get results for all completed jobs. + + Returns: + Dictionary mapping job names to results + """ + results = {} + + for job_name, job in self.jobs.items(): + try: + result = self.azure.get_job_results(job) + results[job_name] = result + except Exception as e: + logger.warning(f"Could not get results for '{job_name}': {str(e)}") + results[job_name] = {"error": str(e)} + + return results + + +def create_sample_circuit(n_qubits: int = 3) -> QuantumCircuit: + """ + Create a sample quantum circuit for testing. + + Args: + n_qubits: Number of qubits + + Returns: + Quantum circuit + """ + circuit = QuantumCircuit(n_qubits, n_qubits) + + # Create a simple entangled state + circuit.h(0) + for i in range(n_qubits - 1): + circuit.cx(i, i + 1) + + # Measure all qubits + circuit.measure(range(n_qubits), range(n_qubits)) + + return circuit + + +if __name__ == "__main__": + # Example usage + logging.basicConfig(level=logging.INFO) + + # Initialize Azure Quantum integration + azure = AzureQuantumIntegration() + + try: + # Connect to workspace + workspace = azure.connect() + + # List available backends + backends = azure.list_backends() + + # Create and submit a sample circuit + circuit = create_sample_circuit(n_qubits=3) + + print("Sample quantum circuit created:") + print(circuit) + + # Uncomment to actually submit (requires valid Azure credentials) + # job = azure.submit_circuit(circuit, shots=100, job_name="test_job") + # results = azure.get_job_results(job) + # azure.save_results(results, "test_results.json") + + except Exception as e: + logger.error(f"Error in example: {str(e)}") + print("\nNote: To actually submit jobs, ensure you have:") + print("1. Valid Azure credentials configured") + print("2. An Azure Quantum workspace created") + print("3. Updated config/quantum_config.yaml with your workspace details") diff --git a/ai-projects/quantum-ml/src/dataset_loader.py b/ai-projects/quantum-ml/src/dataset_loader.py index e6c544627..87ab7e672 100644 --- a/ai-projects/quantum-ml/src/dataset_loader.py +++ b/ai-projects/quantum-ml/src/dataset_loader.py @@ -1,137 +1,137 @@ -""" -Shared dataset loading utilities for quantum AI experiments. -Consolidates duplicated dataset loading code from multiple files. -""" - -from pathlib import Path -from typing import List, Optional, Tuple - -import numpy as np -import pandas as pd -from sklearn.decomposition import PCA -from sklearn.impute import SimpleImputer -from sklearn.preprocessing import StandardScaler - - -def load_dataset( - name: str, base_path: Optional[Path] = None, return_feature_names: bool = False -) -> Tuple[np.ndarray, np.ndarray, Optional[List[str]]]: - """ - Load a preset dataset from the datasets/quantum directory. - - Args: - name: Dataset name (e.g., 'ionosphere', 'sonar', 'heart', 'banknote') - base_path: Optional override for the datasets directory - return_feature_names: If True, returns feature column names - - Returns: - X: Feature matrix (numpy array) - y: Labels (numpy array, 0-indexed integers) - feature_names: Optional list of feature names (if return_feature_names=True) - """ - if base_path is None: - base_path = Path(__file__).parent.parent.parent / "datasets" / "quantum" - - # Map dataset names to file paths - datasets_map = { - "ionosphere": base_path / "ionosphere.csv", - "sonar": base_path / "sonar.csv", - "heart": base_path / "heart_disease.csv", - "heart_disease": base_path / "heart_disease.csv", - "banknote": base_path / "banknote.csv", - } - - if name not in datasets_map: - raise ValueError( - f"Unknown dataset: {name}. Available: {list(datasets_map.keys())}" - ) - - path = datasets_map[name] - if not path.exists(): - raise FileNotFoundError(f"Dataset file not found: {path}") - - # Load the CSV file - df = pd.read_csv(path, header=None, na_values=["?", "NA", ""]) - - # Separate features and labels - X = df.iloc[:, :-1] - y = df.iloc[:, -1] - - # Handle missing values - if X.isnull().any().any(): - imputer = SimpleImputer(strategy="median") - X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) - - # Store feature names before converting to numpy - feature_names = list(X.columns) - - # Convert features to numpy - X_values = X.values - - # Process labels - if name in ["heart", "heart_disease"]: - # Heart disease: labels > 0 indicate disease presence - y = (y > 0).astype(int).values - elif not pd.api.types.is_numeric_dtype(y): - # Non-numeric labels: factorize to 0..K-1 - vals, _ = pd.factorize(y.astype(str)) - y = vals - else: - # Numeric labels: ensure 0-indexed integers - y = y.astype(int).values - unique_labels = np.unique(y) - - # If binary and not already {0, 1}, remap to {0, 1} - if len(unique_labels) == 2 and set(unique_labels) != {0, 1}: - mapping = {unique_labels.min(): 0, unique_labels.max(): 1} - y = np.array([mapping[v] for v in y]) - - if return_feature_names: - return X_values, y, feature_names - else: - return X_values, y, None - - -def preprocess_for_qubits( - X_train: np.ndarray, X_val: np.ndarray, n_qubits: int -) -> Tuple[np.ndarray, np.ndarray, Optional["StandardScaler"], Optional["PCA"]]: - """ - Preprocess data to match the number of qubits. - - Handles both dimensionality reduction (via PCA) and padding with zeros - to ensure feature dimension matches n_qubits. - - Args: - X_train: Training features - X_val: Validation features - n_qubits: Number of qubits (target dimension) - - Returns: - X_train_processed: Processed training features - X_val_processed: Processed validation features - scaler: StandardScaler used (or None if not needed) - pca: PCA transformer used (or None if not needed) - """ - from sklearn.decomposition import PCA - from sklearn.preprocessing import StandardScaler - - # Standardize features - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - n_features = X_train.shape[1] - pca = None - - if n_features < n_qubits: - # Pad with zeros if we have fewer features than qubits - pad_width = n_qubits - n_features - X_train = np.pad(X_train, ((0, 0), (0, pad_width))) - X_val = np.pad(X_val, ((0, 0), (0, pad_width))) - elif n_features > n_qubits: - # Use PCA to reduce dimensions - pca = PCA(n_components=n_qubits, random_state=42) - X_train = pca.fit_transform(X_train) - X_val = pca.transform(X_val) - - return X_train, X_val, scaler, pca +""" +Shared dataset loading utilities for quantum AI experiments. +Consolidates duplicated dataset loading code from multiple files. +""" + +from pathlib import Path +from typing import List, Optional, Tuple + +import numpy as np +import pandas as pd +from sklearn.decomposition import PCA +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import StandardScaler + + +def load_dataset( + name: str, base_path: Optional[Path] = None, return_feature_names: bool = False +) -> Tuple[np.ndarray, np.ndarray, Optional[List[str]]]: + """ + Load a preset dataset from the datasets/quantum directory. + + Args: + name: Dataset name (e.g., 'ionosphere', 'sonar', 'heart', 'banknote') + base_path: Optional override for the datasets directory + return_feature_names: If True, returns feature column names + + Returns: + X: Feature matrix (numpy array) + y: Labels (numpy array, 0-indexed integers) + feature_names: Optional list of feature names (if return_feature_names=True) + """ + if base_path is None: + base_path = Path(__file__).parent.parent.parent / "datasets" / "quantum" + + # Map dataset names to file paths + datasets_map = { + "ionosphere": base_path / "ionosphere.csv", + "sonar": base_path / "sonar.csv", + "heart": base_path / "heart_disease.csv", + "heart_disease": base_path / "heart_disease.csv", + "banknote": base_path / "banknote.csv", + } + + if name not in datasets_map: + raise ValueError( + f"Unknown dataset: {name}. Available: {list(datasets_map.keys())}" + ) + + path = datasets_map[name] + if not path.exists(): + raise FileNotFoundError(f"Dataset file not found: {path}") + + # Load the CSV file + df = pd.read_csv(path, header=None, na_values=["?", "NA", ""]) + + # Separate features and labels + X = df.iloc[:, :-1] + y = df.iloc[:, -1] + + # Handle missing values + if X.isnull().any().any(): + imputer = SimpleImputer(strategy="median") + X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) + + # Store feature names before converting to numpy + feature_names = list(X.columns) + + # Convert features to numpy + X_values = X.values + + # Process labels + if name in ["heart", "heart_disease"]: + # Heart disease: labels > 0 indicate disease presence + y = (y > 0).astype(int).values + elif not pd.api.types.is_numeric_dtype(y): + # Non-numeric labels: factorize to 0..K-1 + vals, _ = pd.factorize(y.astype(str)) + y = vals + else: + # Numeric labels: ensure 0-indexed integers + y = y.astype(int).values + unique_labels = np.unique(y) + + # If binary and not already {0, 1}, remap to {0, 1} + if len(unique_labels) == 2 and set(unique_labels) != {0, 1}: + mapping = {unique_labels.min(): 0, unique_labels.max(): 1} + y = np.array([mapping[v] for v in y]) + + if return_feature_names: + return X_values, y, feature_names + else: + return X_values, y, None + + +def preprocess_for_qubits( + X_train: np.ndarray, X_val: np.ndarray, n_qubits: int +) -> Tuple[np.ndarray, np.ndarray, Optional["StandardScaler"], Optional["PCA"]]: + """ + Preprocess data to match the number of qubits. + + Handles both dimensionality reduction (via PCA) and padding with zeros + to ensure feature dimension matches n_qubits. + + Args: + X_train: Training features + X_val: Validation features + n_qubits: Number of qubits (target dimension) + + Returns: + X_train_processed: Processed training features + X_val_processed: Processed validation features + scaler: StandardScaler used (or None if not needed) + pca: PCA transformer used (or None if not needed) + """ + from sklearn.decomposition import PCA + from sklearn.preprocessing import StandardScaler + + # Standardize features + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + n_features = X_train.shape[1] + pca = None + + if n_features < n_qubits: + # Pad with zeros if we have fewer features than qubits + pad_width = n_qubits - n_features + X_train = np.pad(X_train, ((0, 0), (0, pad_width))) + X_val = np.pad(X_val, ((0, 0), (0, pad_width))) + elif n_features > n_qubits: + # Use PCA to reduce dimensions + pca = PCA(n_components=n_qubits, random_state=42) + X_train = pca.fit_transform(X_train) + X_val = pca.transform(X_val) + + return X_train, X_val, scaler, pca diff --git a/ai-projects/quantum-ml/src/hybrid_qnn.py b/ai-projects/quantum-ml/src/hybrid_qnn.py index a0d55b875..6bd780768 100644 --- a/ai-projects/quantum-ml/src/hybrid_qnn.py +++ b/ai-projects/quantum-ml/src/hybrid_qnn.py @@ -1,644 +1,644 @@ -""" -Hybrid Quantum-Classical Neural Network -Combines quantum circuits with classical neural networks for enhanced learning -""" - -import logging - -import pennylane as qml -import torch -import torch.nn as nn -import yaml - -logger = logging.getLogger(__name__) - - -class QuantumLayer(nn.Module): - """ - A custom PyTorch layer that implements a quantum circuit. - """ - - def __init__( - self, - n_qubits: int, - n_layers: int, - device: str = "default.qubit", - entanglement: str = "linear", - ): - """ - Initialize quantum layer. - - Args: - n_qubits: Number of qubits - n_layers: Number of variational layers - device: PennyLane device - entanglement: Entanglement pattern ('linear', 'circular', or 'full') - - 'linear': O(n) gates, good for large circuits - - 'circular': O(n) gates, creates ring topology - - 'full': O(n²) gates, maximizes entanglement but slower - """ - super().__init__() - self.n_qubits = n_qubits - self.n_layers = n_layers - self.entanglement = entanglement.lower() - - # Create quantum device - self.dev = qml.device(device, wires=n_qubits) - - # Create QNode - self.qnode = qml.QNode(self._quantum_circuit, self.dev, interface="torch") - - # Initialize quantum weights - weight_shapes = {"weights": (n_layers, n_qubits, 3)} - self.qlayer = qml.qnn.TorchLayer(self.qnode, weight_shapes) - - def _quantum_circuit(self, inputs, weights): - """ - Define the variational quantum circuit. - - Args: - inputs: Classical inputs encoded into quantum states - weights: Trainable quantum parameters - - Performance Note: - The 'full' entanglement pattern creates O(n²) gates where n is the number - of qubits. For large circuits (>10 qubits), this can be computationally - expensive. Consider 'linear' or 'circular' patterns for better scalability. - """ - # Amplitude encoding - qml.AmplitudeEmbedding( - features=inputs, wires=range(self.n_qubits), normalize=True, pad_with=0.0 - ) - - # Variational layers - for layer in range(self.n_layers): - # Rotation layer - for i in range(self.n_qubits): - qml.Rot(*weights[layer, i], wires=i) - - # Entangling layer - if self.entanglement == "circular": - for i in range(self.n_qubits): - qml.CNOT(wires=[i, (i + 1) % self.n_qubits]) - elif self.entanglement == "full": - for i in range(self.n_qubits): - for j in range(i + 1, self.n_qubits): - qml.CNOT(wires=[i, j]) - else: # linear (default) - for i in range(self.n_qubits - 1): - qml.CNOT(wires=[i, i + 1]) - - # Measurement - return [qml.expval(qml.PauliZ(i)) for i in range(self.n_qubits)] - - def forward(self, x): - """Forward pass through quantum layer.""" - return self.qlayer(x) - - -class HybridQNN(nn.Module): - """ - Hybrid Quantum-Classical Neural Network architecture. - Combines classical layers for preprocessing and postprocessing - with a quantum layer for quantum feature extraction. - """ - - def __init__( - self, - input_dim: int, - hidden_dim: int, - n_qubits: int, - n_quantum_layers: int, - entanglement: str = "linear", - output_dim: int = 1, - dropout: float = 0.2, - use_batch_norm: bool = True, - use_residual: bool = True, - ): - """ - Initialize hybrid QNN. - - Args: - input_dim: Input feature dimension - hidden_dim: Hidden layer dimension - n_qubits: Number of qubits in quantum layer - n_quantum_layers: Number of quantum variational layers - entanglement: Entanglement pattern (linear, circular, full) - output_dim: Output dimension - dropout: Dropout rate - use_batch_norm: Enable batch normalization for stability - use_residual: Enable residual connections - """ - super().__init__() - - self.input_dim = input_dim - self.use_residual = use_residual - self.use_batch_norm = use_batch_norm - self.n_qubits = n_qubits - self.entanglement = entanglement.lower() - - # Classical preprocessing with residual option - encoder_layers = [ - nn.Linear(input_dim, hidden_dim), - ] - if use_batch_norm: - encoder_layers.append(nn.BatchNorm1d(hidden_dim)) - encoder_layers.extend( - [ - nn.ReLU(), - nn.Dropout(dropout), - nn.Linear(hidden_dim, 2**n_qubits), # Prepare for quantum encoding - ] - ) - self.encoder = nn.Sequential(*encoder_layers) - - # Residual projection if dimensions don't match - if use_residual and input_dim != 2**n_qubits: - self.residual_proj = nn.Linear(input_dim, 2**n_qubits) - else: - self.residual_proj = None - - # Quantum layer - self.quantum_layer = QuantumLayer( - n_qubits, n_quantum_layers, entanglement=self.entanglement - ) - - # Classical postprocessing with improved architecture - decoder_layers = [ - nn.Linear(n_qubits, hidden_dim), - ] - if use_batch_norm: - decoder_layers.append(nn.BatchNorm1d(hidden_dim)) - decoder_layers.extend( - [ - nn.ReLU(), - nn.Dropout(dropout), - nn.Linear(hidden_dim, hidden_dim // 2), - ] - ) - if use_batch_norm: - decoder_layers.append(nn.BatchNorm1d(hidden_dim // 2)) - decoder_layers.extend( - [ - nn.ReLU(), - nn.Dropout(dropout / 2), # Less dropout in final layer - nn.Linear(hidden_dim // 2, output_dim), - ] - ) - self.decoder = nn.Sequential(*decoder_layers) - - logger.info( - f"Initialized Enhanced HybridQNN: input_dim={input_dim}, " - f"n_qubits={n_qubits}, n_quantum_layers={n_quantum_layers}, " - f"entanglement={self.entanglement}, residual={use_residual}" - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """ - Forward pass through the hybrid network. - - Args: - x: Input tensor - - Returns: - Network output - """ - # Store input for residual connection - x_input = x - # Classical encoding - x = self.encoder(x) - # Add residual connection if enabled - if self.use_residual and self.residual_proj is not None: - x = x + self.residual_proj(x_input) - - # Normalize for amplitude encoding - x = torch.nn.functional.normalize(x, p=2, dim=1) - - # Quantum processing - x = self.quantum_layer(x) - - # Classical decoding - x = self.decoder(x) - - return x - - -class QuantumConvolutionalLayer(nn.Module): - """ - Quantum convolutional layer for processing spatial/sequential data. - """ - - def __init__(self, n_qubits: int, stride: int = 1): - """ - Initialize quantum convolutional layer. - - Args: - n_qubits: Number of qubits in the quantum filter - stride: Stride for the convolution - """ - super().__init__() - self.n_qubits = n_qubits - self.stride = stride - - self.dev = qml.device("default.qubit", wires=n_qubits) - - # Trainable quantum filter - self.weights = nn.Parameter(torch.randn(2, n_qubits, 3)) - - def _quantum_filter(self, inputs): - """Quantum convolution filter circuit.""" - # Encode inputs - for i in range(self.n_qubits): - qml.RY(inputs[i], wires=i) - - # Apply trainable quantum filter - for layer in range(2): - for i in range(self.n_qubits): - qml.Rot(*self.weights[layer, i], wires=i) - for i in range(self.n_qubits - 1): - qml.CNOT(wires=[i, i + 1]) - - return qml.expval(qml.PauliZ(0)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """ - Apply quantum convolution. - - Args: - x: Input tensor of shape (batch, features) - - Returns: - Convolved output - """ - batch_size, n_features = x.shape - outputs = [] - - qnode = qml.QNode(self._quantum_filter, self.dev, interface="torch") - - # Slide quantum filter across input - for i in range(0, n_features - self.n_qubits + 1, self.stride): - window = x[:, i : i + self.n_qubits] - window_outputs = [] - - for sample in window: - out = qnode(sample) - window_outputs.append(out) - - outputs.append(torch.stack(window_outputs)) - - return torch.stack(outputs, dim=1) - - -class QCNN(nn.Module): - """ - Quantum Convolutional Neural Network. - """ - - def __init__( - self, input_dim: int, n_qubits: int = 4, n_filters: int = 2, output_dim: int = 1 - ): - """ - Initialize QCNN. - - Args: - input_dim: Input feature dimension - n_qubits: Qubits per quantum filter - n_filters: Number of quantum filters - output_dim: Output dimension - """ - super().__init__() - - # Quantum convolutional layers - self.qconv_layers = nn.ModuleList( - [QuantumConvolutionalLayer(n_qubits, stride=2) for _ in range(n_filters)] - ) - - # Calculate output size after convolutions - conv_output_size = input_dim - for _ in range(n_filters): - conv_output_size = (conv_output_size - n_qubits) // 2 + 1 - - # Classical fully connected layers - self.fc_layers = nn.Sequential( - nn.Linear(conv_output_size * n_filters, 32), - nn.ReLU(), - nn.Dropout(0.2), - nn.Linear(32, output_dim), - ) - - logger.info(f"Initialized QCNN with {n_filters} quantum filters") - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Forward pass through QCNN.""" - # Apply quantum convolutions - conv_outputs = [] - for qconv in self.qconv_layers: - conv_out = qconv(x) - conv_outputs.append(conv_out) - - # Concatenate and flatten - x = torch.cat(conv_outputs, dim=1) - x = x.view(x.size(0), -1) - - # Fully connected layers - x = self.fc_layers(x) - - return x - - -class QuantumClassicalTrainer: - """ - Trainer for hybrid quantum-classical models - """ - - def __init__( - self, - model: nn.Module, - learning_rate: float = 0.001, - device: str = "cpu", - use_scheduler: bool = True, - gradient_clip_val: float = 1.0, - ): - """ - Initialize trainer - - Args: - model: Hybrid QNN model - learning_rate: Learning rate - device: Device to train on ('cpu' or 'cuda') - use_scheduler: Enable learning rate scheduling - gradient_clip_val: Gradient clipping value for stability - """ - self.model = model.to(device) - self.device = device - self.gradient_clip_val = gradient_clip_val - - # Use AdamW optimizer with weight decay for better generalization - self.optimizer = torch.optim.AdamW( - model.parameters(), lr=learning_rate, weight_decay=0.01, betas=(0.9, 0.999) - ) - - # Learning rate scheduler for adaptive training - if use_scheduler: - self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - self.optimizer, mode="min", factor=0.5, patience=5 - ) - else: - self.scheduler = None - - # Choose appropriate loss function (binary vs multi-class) - # If final linear layer has single output, use BCEWithLogitsLoss - last_out_features = None - try: - if hasattr(model, "decoder") and isinstance(model.decoder, nn.Sequential): - for layer in model.decoder: - if isinstance(layer, nn.Linear): - last_out_features = layer.out_features - except Exception: - last_out_features = None - if last_out_features == 1: - self.criterion = nn.BCEWithLogitsLoss() - else: - self.criterion = nn.CrossEntropyLoss() - - self.train_losses = [] - self.val_accuracies = [] - self.val_losses = [] - self.learning_rates = [] - self.best_val_acc = 0.0 - self.best_model_state = None - - logger.info( - f"Initialized enhanced trainer: lr={learning_rate}, " - f"device={device}, scheduler={use_scheduler}, " - f"gradient_clip={gradient_clip_val}" - ) - - def train_epoch(self, train_loader) -> float: - """ - Train for one epoch - - Args: - train_loader: Training data loader - - Returns: - avg_loss: Average loss for the epoch - """ - self.model.train() - total_loss = 0.0 - n_batches = len(train_loader) - - for batch_idx, (data, target) in enumerate(train_loader): - data, target = data.to(self.device), target.to(self.device) - - # Zero gradients - self.optimizer.zero_grad() - - try: - # Forward pass - output = self.model(data) - - # Compute loss - original_target = target - if isinstance(self.criterion, nn.BCEWithLogitsLoss): - target = target.float().unsqueeze(1) - loss = self.criterion(output, target) - - # Backward pass - loss.backward() - - # Gradient clipping for stability - if self.gradient_clip_val > 0: - torch.nn.utils.clip_grad_norm_( - self.model.parameters(), self.gradient_clip_val - ) - - # Update weights - self.optimizer.step() - - total_loss += loss.item() - - except RuntimeError as e: - logger.warning(f"Error in batch {batch_idx}: {e}") - continue - - if (batch_idx + 1) % 10 == 0: - progress = (batch_idx + 1) / n_batches * 100 - logger.debug( - f"Batch {batch_idx + 1}/{n_batches} ({progress:.1f}%), " - f"Loss: {loss.item():.4f}" - ) - - avg_loss = total_loss / len(train_loader) - self.train_losses.append(avg_loss) - # Record current learning rate - current_lr = self.optimizer.param_groups[0]["lr"] - self.learning_rates.append(current_lr) - - return avg_loss - - def evaluate(self, val_loader) -> tuple: - """ - Evaluate model - - Args: - val_loader: Validation data loader - - Returns: - accuracy, loss: Validation accuracy and loss - """ - self.model.eval() - correct = 0 - total = 0 - total_loss = 0.0 - - with torch.no_grad(): - for data, target in val_loader: - data, target = data.to(self.device), target.to(self.device) - - # Forward pass - output = self.model(data) - - # Compute loss - original_target = target - if isinstance(self.criterion, nn.BCEWithLogitsLoss): - target = target.float().unsqueeze(1) - loss = self.criterion(output, target) - total_loss += loss.item() - - # Get predictions - if isinstance(self.criterion, nn.BCEWithLogitsLoss): - probs = torch.sigmoid(output) - predicted = (probs > 0.5).long().view(-1) - total += original_target.size(0) - correct += (predicted == original_target).sum().item() - else: - _, predicted = torch.max(output.data, 1) - total += target.size(0) - correct += (predicted == target).sum().item() - - accuracy = correct / total - avg_loss = total_loss / len(val_loader) - - # Record metrics - self.val_accuracies.append(accuracy) - self.val_losses.append(avg_loss) - # Save best model - if accuracy > self.best_val_acc: - self.best_val_acc = accuracy - self.best_model_state = self.model.state_dict().copy() - logger.info(f"New best validation accuracy: {accuracy:.4f}") - - return accuracy, avg_loss - - def train( - self, - train_loader, - val_loader, - num_epochs: int = 20, - early_stopping_patience: int = 10, - ): - """ - Train the model - - Args: - train_loader: Training data loader - val_loader: Validation data loader - num_epochs: Number of epochs - early_stopping_patience: Epochs to wait before early stopping - """ - logger.info(f"Starting training for {num_epochs} epochs") - epochs_without_improvement = 0 - best_val_loss = float("inf") - - for epoch in range(num_epochs): - # Train - train_loss = self.train_epoch(train_loader) - - # Evaluate - val_acc, val_loss = self.evaluate(val_loader) - - # Update learning rate scheduler - if self.scheduler is not None: - self.scheduler.step(val_loss) - - logger.info( - f"Epoch {epoch + 1}/{num_epochs} - " - f"Train Loss: {train_loss:.4f}, " - f"Val Loss: {val_loss:.4f}, " - f"Val Acc: {val_acc:.4f}, " - f"LR: {self.learning_rates[-1]:.6f}" - ) - - print( - f"Epoch {epoch + 1}/{num_epochs} - " - f"Train Loss: {train_loss:.4f}, " - f"Val Loss: {val_loss:.4f}, " - f"Val Acc: {val_acc:.4f}" - ) - - # Early stopping check - if val_loss < best_val_loss: - best_val_loss = val_loss - epochs_without_improvement = 0 - else: - epochs_without_improvement += 1 - - if epochs_without_improvement >= early_stopping_patience: - logger.info( - f"Early stopping triggered after {epoch + 1} epochs " - f"(no improvement for {early_stopping_patience} epochs)" - ) - break - - # Restore best model - if self.best_model_state is not None: - self.model.load_state_dict(self.best_model_state) - logger.info(f"Restored best model with val_acc={self.best_val_acc:.4f}") - - -def create_hybrid_model( - config_path: str = "../config/quantum_config.yaml", -) -> HybridQNN: - """ - Create a hybrid QNN model from configuration. - - Args: - config_path: Path to configuration file - - Returns: - Initialized hybrid model - """ - with open(config_path, "r") as f: - config = yaml.safe_load(f) - - input_dim = config["ml"]["data"]["feature_dimension"] - n_qubits = config["ml"]["model"]["n_qubits"] - n_layers = config["ml"]["model"]["n_layers"] - entanglement = config["ml"]["model"].get("entanglement", "linear") - - model = HybridQNN( - input_dim=input_dim, - hidden_dim=16, - n_qubits=n_qubits, - n_quantum_layers=n_layers, - entanglement=entanglement, - output_dim=1, - ) - - return model - - -if __name__ == "__main__": - # Test the hybrid model - model = HybridQNN( - input_dim=10, hidden_dim=16, n_qubits=4, n_quantum_layers=2, output_dim=1 - ) - - # Test forward pass - x = torch.randn(8, 10) # Batch of 8 samples - output = model(x) - - print(f"Input shape: {x.shape}") - print(f"Output shape: {output.shape}") - print("Hybrid QNN test completed successfully!") +""" +Hybrid Quantum-Classical Neural Network +Combines quantum circuits with classical neural networks for enhanced learning +""" + +import logging + +import pennylane as qml +import torch +import torch.nn as nn +import yaml + +logger = logging.getLogger(__name__) + + +class QuantumLayer(nn.Module): + """ + A custom PyTorch layer that implements a quantum circuit. + """ + + def __init__( + self, + n_qubits: int, + n_layers: int, + device: str = "default.qubit", + entanglement: str = "linear", + ): + """ + Initialize quantum layer. + + Args: + n_qubits: Number of qubits + n_layers: Number of variational layers + device: PennyLane device + entanglement: Entanglement pattern ('linear', 'circular', or 'full') + - 'linear': O(n) gates, good for large circuits + - 'circular': O(n) gates, creates ring topology + - 'full': O(n²) gates, maximizes entanglement but slower + """ + super().__init__() + self.n_qubits = n_qubits + self.n_layers = n_layers + self.entanglement = entanglement.lower() + + # Create quantum device + self.dev = qml.device(device, wires=n_qubits) + + # Create QNode + self.qnode = qml.QNode(self._quantum_circuit, self.dev, interface="torch") + + # Initialize quantum weights + weight_shapes = {"weights": (n_layers, n_qubits, 3)} + self.qlayer = qml.qnn.TorchLayer(self.qnode, weight_shapes) + + def _quantum_circuit(self, inputs, weights): + """ + Define the variational quantum circuit. + + Args: + inputs: Classical inputs encoded into quantum states + weights: Trainable quantum parameters + + Performance Note: + The 'full' entanglement pattern creates O(n²) gates where n is the number + of qubits. For large circuits (>10 qubits), this can be computationally + expensive. Consider 'linear' or 'circular' patterns for better scalability. + """ + # Amplitude encoding + qml.AmplitudeEmbedding( + features=inputs, wires=range(self.n_qubits), normalize=True, pad_with=0.0 + ) + + # Variational layers + for layer in range(self.n_layers): + # Rotation layer + for i in range(self.n_qubits): + qml.Rot(*weights[layer, i], wires=i) + + # Entangling layer + if self.entanglement == "circular": + for i in range(self.n_qubits): + qml.CNOT(wires=[i, (i + 1) % self.n_qubits]) + elif self.entanglement == "full": + for i in range(self.n_qubits): + for j in range(i + 1, self.n_qubits): + qml.CNOT(wires=[i, j]) + else: # linear (default) + for i in range(self.n_qubits - 1): + qml.CNOT(wires=[i, i + 1]) + + # Measurement + return [qml.expval(qml.PauliZ(i)) for i in range(self.n_qubits)] + + def forward(self, x): + """Forward pass through quantum layer.""" + return self.qlayer(x) + + +class HybridQNN(nn.Module): + """ + Hybrid Quantum-Classical Neural Network architecture. + Combines classical layers for preprocessing and postprocessing + with a quantum layer for quantum feature extraction. + """ + + def __init__( + self, + input_dim: int, + hidden_dim: int, + n_qubits: int, + n_quantum_layers: int, + entanglement: str = "linear", + output_dim: int = 1, + dropout: float = 0.2, + use_batch_norm: bool = True, + use_residual: bool = True, + ): + """ + Initialize hybrid QNN. + + Args: + input_dim: Input feature dimension + hidden_dim: Hidden layer dimension + n_qubits: Number of qubits in quantum layer + n_quantum_layers: Number of quantum variational layers + entanglement: Entanglement pattern (linear, circular, full) + output_dim: Output dimension + dropout: Dropout rate + use_batch_norm: Enable batch normalization for stability + use_residual: Enable residual connections + """ + super().__init__() + + self.input_dim = input_dim + self.use_residual = use_residual + self.use_batch_norm = use_batch_norm + self.n_qubits = n_qubits + self.entanglement = entanglement.lower() + + # Classical preprocessing with residual option + encoder_layers = [ + nn.Linear(input_dim, hidden_dim), + ] + if use_batch_norm: + encoder_layers.append(nn.BatchNorm1d(hidden_dim)) + encoder_layers.extend( + [ + nn.ReLU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, 2**n_qubits), # Prepare for quantum encoding + ] + ) + self.encoder = nn.Sequential(*encoder_layers) + + # Residual projection if dimensions don't match + if use_residual and input_dim != 2**n_qubits: + self.residual_proj = nn.Linear(input_dim, 2**n_qubits) + else: + self.residual_proj = None + + # Quantum layer + self.quantum_layer = QuantumLayer( + n_qubits, n_quantum_layers, entanglement=self.entanglement + ) + + # Classical postprocessing with improved architecture + decoder_layers = [ + nn.Linear(n_qubits, hidden_dim), + ] + if use_batch_norm: + decoder_layers.append(nn.BatchNorm1d(hidden_dim)) + decoder_layers.extend( + [ + nn.ReLU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, hidden_dim // 2), + ] + ) + if use_batch_norm: + decoder_layers.append(nn.BatchNorm1d(hidden_dim // 2)) + decoder_layers.extend( + [ + nn.ReLU(), + nn.Dropout(dropout / 2), # Less dropout in final layer + nn.Linear(hidden_dim // 2, output_dim), + ] + ) + self.decoder = nn.Sequential(*decoder_layers) + + logger.info( + f"Initialized Enhanced HybridQNN: input_dim={input_dim}, " + f"n_qubits={n_qubits}, n_quantum_layers={n_quantum_layers}, " + f"entanglement={self.entanglement}, residual={use_residual}" + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass through the hybrid network. + + Args: + x: Input tensor + + Returns: + Network output + """ + # Store input for residual connection + x_input = x + # Classical encoding + x = self.encoder(x) + # Add residual connection if enabled + if self.use_residual and self.residual_proj is not None: + x = x + self.residual_proj(x_input) + + # Normalize for amplitude encoding + x = torch.nn.functional.normalize(x, p=2, dim=1) + + # Quantum processing + x = self.quantum_layer(x) + + # Classical decoding + x = self.decoder(x) + + return x + + +class QuantumConvolutionalLayer(nn.Module): + """ + Quantum convolutional layer for processing spatial/sequential data. + """ + + def __init__(self, n_qubits: int, stride: int = 1): + """ + Initialize quantum convolutional layer. + + Args: + n_qubits: Number of qubits in the quantum filter + stride: Stride for the convolution + """ + super().__init__() + self.n_qubits = n_qubits + self.stride = stride + + self.dev = qml.device("default.qubit", wires=n_qubits) + + # Trainable quantum filter + self.weights = nn.Parameter(torch.randn(2, n_qubits, 3)) + + def _quantum_filter(self, inputs): + """Quantum convolution filter circuit.""" + # Encode inputs + for i in range(self.n_qubits): + qml.RY(inputs[i], wires=i) + + # Apply trainable quantum filter + for layer in range(2): + for i in range(self.n_qubits): + qml.Rot(*self.weights[layer, i], wires=i) + for i in range(self.n_qubits - 1): + qml.CNOT(wires=[i, i + 1]) + + return qml.expval(qml.PauliZ(0)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Apply quantum convolution. + + Args: + x: Input tensor of shape (batch, features) + + Returns: + Convolved output + """ + batch_size, n_features = x.shape + outputs = [] + + qnode = qml.QNode(self._quantum_filter, self.dev, interface="torch") + + # Slide quantum filter across input + for i in range(0, n_features - self.n_qubits + 1, self.stride): + window = x[:, i : i + self.n_qubits] + window_outputs = [] + + for sample in window: + out = qnode(sample) + window_outputs.append(out) + + outputs.append(torch.stack(window_outputs)) + + return torch.stack(outputs, dim=1) + + +class QCNN(nn.Module): + """ + Quantum Convolutional Neural Network. + """ + + def __init__( + self, input_dim: int, n_qubits: int = 4, n_filters: int = 2, output_dim: int = 1 + ): + """ + Initialize QCNN. + + Args: + input_dim: Input feature dimension + n_qubits: Qubits per quantum filter + n_filters: Number of quantum filters + output_dim: Output dimension + """ + super().__init__() + + # Quantum convolutional layers + self.qconv_layers = nn.ModuleList( + [QuantumConvolutionalLayer(n_qubits, stride=2) for _ in range(n_filters)] + ) + + # Calculate output size after convolutions + conv_output_size = input_dim + for _ in range(n_filters): + conv_output_size = (conv_output_size - n_qubits) // 2 + 1 + + # Classical fully connected layers + self.fc_layers = nn.Sequential( + nn.Linear(conv_output_size * n_filters, 32), + nn.ReLU(), + nn.Dropout(0.2), + nn.Linear(32, output_dim), + ) + + logger.info(f"Initialized QCNN with {n_filters} quantum filters") + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass through QCNN.""" + # Apply quantum convolutions + conv_outputs = [] + for qconv in self.qconv_layers: + conv_out = qconv(x) + conv_outputs.append(conv_out) + + # Concatenate and flatten + x = torch.cat(conv_outputs, dim=1) + x = x.view(x.size(0), -1) + + # Fully connected layers + x = self.fc_layers(x) + + return x + + +class QuantumClassicalTrainer: + """ + Trainer for hybrid quantum-classical models + """ + + def __init__( + self, + model: nn.Module, + learning_rate: float = 0.001, + device: str = "cpu", + use_scheduler: bool = True, + gradient_clip_val: float = 1.0, + ): + """ + Initialize trainer + + Args: + model: Hybrid QNN model + learning_rate: Learning rate + device: Device to train on ('cpu' or 'cuda') + use_scheduler: Enable learning rate scheduling + gradient_clip_val: Gradient clipping value for stability + """ + self.model = model.to(device) + self.device = device + self.gradient_clip_val = gradient_clip_val + + # Use AdamW optimizer with weight decay for better generalization + self.optimizer = torch.optim.AdamW( + model.parameters(), lr=learning_rate, weight_decay=0.01, betas=(0.9, 0.999) + ) + + # Learning rate scheduler for adaptive training + if use_scheduler: + self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + self.optimizer, mode="min", factor=0.5, patience=5 + ) + else: + self.scheduler = None + + # Choose appropriate loss function (binary vs multi-class) + # If final linear layer has single output, use BCEWithLogitsLoss + last_out_features = None + try: + if hasattr(model, "decoder") and isinstance(model.decoder, nn.Sequential): + for layer in model.decoder: + if isinstance(layer, nn.Linear): + last_out_features = layer.out_features + except Exception: + last_out_features = None + if last_out_features == 1: + self.criterion = nn.BCEWithLogitsLoss() + else: + self.criterion = nn.CrossEntropyLoss() + + self.train_losses = [] + self.val_accuracies = [] + self.val_losses = [] + self.learning_rates = [] + self.best_val_acc = 0.0 + self.best_model_state = None + + logger.info( + f"Initialized enhanced trainer: lr={learning_rate}, " + f"device={device}, scheduler={use_scheduler}, " + f"gradient_clip={gradient_clip_val}" + ) + + def train_epoch(self, train_loader) -> float: + """ + Train for one epoch + + Args: + train_loader: Training data loader + + Returns: + avg_loss: Average loss for the epoch + """ + self.model.train() + total_loss = 0.0 + n_batches = len(train_loader) + + for batch_idx, (data, target) in enumerate(train_loader): + data, target = data.to(self.device), target.to(self.device) + + # Zero gradients + self.optimizer.zero_grad() + + try: + # Forward pass + output = self.model(data) + + # Compute loss + original_target = target + if isinstance(self.criterion, nn.BCEWithLogitsLoss): + target = target.float().unsqueeze(1) + loss = self.criterion(output, target) + + # Backward pass + loss.backward() + + # Gradient clipping for stability + if self.gradient_clip_val > 0: + torch.nn.utils.clip_grad_norm_( + self.model.parameters(), self.gradient_clip_val + ) + + # Update weights + self.optimizer.step() + + total_loss += loss.item() + + except RuntimeError as e: + logger.warning(f"Error in batch {batch_idx}: {e}") + continue + + if (batch_idx + 1) % 10 == 0: + progress = (batch_idx + 1) / n_batches * 100 + logger.debug( + f"Batch {batch_idx + 1}/{n_batches} ({progress:.1f}%), " + f"Loss: {loss.item():.4f}" + ) + + avg_loss = total_loss / len(train_loader) + self.train_losses.append(avg_loss) + # Record current learning rate + current_lr = self.optimizer.param_groups[0]["lr"] + self.learning_rates.append(current_lr) + + return avg_loss + + def evaluate(self, val_loader) -> tuple: + """ + Evaluate model + + Args: + val_loader: Validation data loader + + Returns: + accuracy, loss: Validation accuracy and loss + """ + self.model.eval() + correct = 0 + total = 0 + total_loss = 0.0 + + with torch.no_grad(): + for data, target in val_loader: + data, target = data.to(self.device), target.to(self.device) + + # Forward pass + output = self.model(data) + + # Compute loss + original_target = target + if isinstance(self.criterion, nn.BCEWithLogitsLoss): + target = target.float().unsqueeze(1) + loss = self.criterion(output, target) + total_loss += loss.item() + + # Get predictions + if isinstance(self.criterion, nn.BCEWithLogitsLoss): + probs = torch.sigmoid(output) + predicted = (probs > 0.5).long().view(-1) + total += original_target.size(0) + correct += (predicted == original_target).sum().item() + else: + _, predicted = torch.max(output.data, 1) + total += target.size(0) + correct += (predicted == target).sum().item() + + accuracy = correct / total + avg_loss = total_loss / len(val_loader) + + # Record metrics + self.val_accuracies.append(accuracy) + self.val_losses.append(avg_loss) + # Save best model + if accuracy > self.best_val_acc: + self.best_val_acc = accuracy + self.best_model_state = self.model.state_dict().copy() + logger.info(f"New best validation accuracy: {accuracy:.4f}") + + return accuracy, avg_loss + + def train( + self, + train_loader, + val_loader, + num_epochs: int = 20, + early_stopping_patience: int = 10, + ): + """ + Train the model + + Args: + train_loader: Training data loader + val_loader: Validation data loader + num_epochs: Number of epochs + early_stopping_patience: Epochs to wait before early stopping + """ + logger.info(f"Starting training for {num_epochs} epochs") + epochs_without_improvement = 0 + best_val_loss = float("inf") + + for epoch in range(num_epochs): + # Train + train_loss = self.train_epoch(train_loader) + + # Evaluate + val_acc, val_loss = self.evaluate(val_loader) + + # Update learning rate scheduler + if self.scheduler is not None: + self.scheduler.step(val_loss) + + logger.info( + f"Epoch {epoch + 1}/{num_epochs} - " + f"Train Loss: {train_loss:.4f}, " + f"Val Loss: {val_loss:.4f}, " + f"Val Acc: {val_acc:.4f}, " + f"LR: {self.learning_rates[-1]:.6f}" + ) + + print( + f"Epoch {epoch + 1}/{num_epochs} - " + f"Train Loss: {train_loss:.4f}, " + f"Val Loss: {val_loss:.4f}, " + f"Val Acc: {val_acc:.4f}" + ) + + # Early stopping check + if val_loss < best_val_loss: + best_val_loss = val_loss + epochs_without_improvement = 0 + else: + epochs_without_improvement += 1 + + if epochs_without_improvement >= early_stopping_patience: + logger.info( + f"Early stopping triggered after {epoch + 1} epochs " + f"(no improvement for {early_stopping_patience} epochs)" + ) + break + + # Restore best model + if self.best_model_state is not None: + self.model.load_state_dict(self.best_model_state) + logger.info(f"Restored best model with val_acc={self.best_val_acc:.4f}") + + +def create_hybrid_model( + config_path: str = "../config/quantum_config.yaml", +) -> HybridQNN: + """ + Create a hybrid QNN model from configuration. + + Args: + config_path: Path to configuration file + + Returns: + Initialized hybrid model + """ + with open(config_path, "r") as f: + config = yaml.safe_load(f) + + input_dim = config["ml"]["data"]["feature_dimension"] + n_qubits = config["ml"]["model"]["n_qubits"] + n_layers = config["ml"]["model"]["n_layers"] + entanglement = config["ml"]["model"].get("entanglement", "linear") + + model = HybridQNN( + input_dim=input_dim, + hidden_dim=16, + n_qubits=n_qubits, + n_quantum_layers=n_layers, + entanglement=entanglement, + output_dim=1, + ) + + return model + + +if __name__ == "__main__": + # Test the hybrid model + model = HybridQNN( + input_dim=10, hidden_dim=16, n_qubits=4, n_quantum_layers=2, output_dim=1 + ) + + # Test forward pass + x = torch.randn(8, 10) # Batch of 8 samples + output = model(x) + + print(f"Input shape: {x.shape}") + print(f"Output shape: {output.shape}") + print("Hybrid QNN test completed successfully!") diff --git a/ai-projects/quantum-ml/src/quantum_circuit_optimizer.py b/ai-projects/quantum-ml/src/quantum_circuit_optimizer.py index b572f38c2..2a7529e85 100644 --- a/ai-projects/quantum-ml/src/quantum_circuit_optimizer.py +++ b/ai-projects/quantum-ml/src/quantum_circuit_optimizer.py @@ -1,476 +1,476 @@ -""" -Quantum Circuit Optimization for LLM Training -============================================== - -Advanced circuit compilation, optimization, and scheduling for -efficient quantum-classical hybrid training. - -Features: -- Circuit depth minimization -- Gate fusion and cancellation -- Adaptive circuit compilation -- Batch circuit execution -- Resource-aware scheduling - -Author: Quantum AI Workspace -Date: March 9, 2026 -""" - -import logging -import time -from collections import defaultdict -from dataclasses import dataclass -from typing import Any, Dict, List, Tuple - -import numpy as np -import torch -import torch.nn as nn - -logger = logging.getLogger(__name__) - -try: - import pennylane as qml - - PENNYLANE_AVAILABLE = True -except ImportError: - PENNYLANE_AVAILABLE = False - logger.warning("PennyLane not available - optimizer disabled") - - -@dataclass -class CircuitStats: - """Statistics for a quantum circuit.""" - - depth: int = 0 - gate_count: int = 0 - two_qubit_gates: int = 0 - parameter_count: int = 0 - execution_time: float = 0.0 - compilation_time: float = 0.0 - cache_hits: int = 0 - cache_misses: int = 0 - - -@dataclass -class OptimizationStrategy: - """Configuration for circuit optimization.""" - - enable_gate_fusion: bool = True - enable_gate_cancellation: bool = True - enable_parameter_shift: bool = True - max_circuit_depth: int = 20 - target_two_qubit_gate_reduction: float = 0.3 - compilation_level: int = 2 # 0=none, 1=light, 2=moderate, 3=aggressive - - -class CircuitCompiler: - """ - Compiles and optimizes quantum circuits for efficient execution. - - Applies transformations to reduce circuit depth, gate count, - and improve execution time while preserving functionality. - """ - - def __init__(self, strategy: OptimizationStrategy = None): - self.strategy = strategy or OptimizationStrategy() - self.stats = defaultdict(CircuitStats) - - logger.info( - f"CircuitCompiler initialized with level {self.strategy.compilation_level}" - ) - - def optimize_circuit(self, circuit_fn, n_qubits: int, circuit_id: str) -> Any: - """ - Optimize a quantum circuit function. - - Args: - circuit_fn: Quantum circuit function - n_qubits: Number of qubits - circuit_id: Unique identifier for this circuit - - Returns: - Optimized circuit function - """ - if not PENNYLANE_AVAILABLE: - return circuit_fn - - start_time = time.time() - stats = self.stats[circuit_id] - - try: - # Apply optimization passes based on compilation level - if self.strategy.compilation_level >= 1: - circuit_fn = self._apply_light_optimization(circuit_fn, n_qubits) - - if self.strategy.compilation_level >= 2: - circuit_fn = self._apply_moderate_optimization(circuit_fn, n_qubits) - - if self.strategy.compilation_level >= 3: - circuit_fn = self._apply_aggressive_optimization(circuit_fn, n_qubits) - - stats.compilation_time = time.time() - start_time - - except Exception as e: - logger.warning(f"Circuit optimization failed for {circuit_id}: {e}") - - return circuit_fn - - def _apply_light_optimization(self, circuit_fn, n_qubits: int): - """Light optimization: basic gate cancellations.""" - # In practice, this would use PennyLane's tape transformations - # For now, return as-is (requires circuit tape access) - return circuit_fn - - def _apply_moderate_optimization(self, circuit_fn, n_qubits: int): - """Moderate optimization: gate fusion, cancellation, commutation.""" - return circuit_fn - - def _apply_aggressive_optimization(self, circuit_fn, n_qubits: int): - """Aggressive optimization: full circuit rewriting.""" - return circuit_fn - - def analyze_circuit( - self, circuit_fn, n_qubits: int, circuit_id: str - ) -> CircuitStats: - """ - Analyze circuit properties. - - Returns: - CircuitStats object with circuit metrics - """ - stats = self.stats[circuit_id] - - # In practice, would extract from PennyLane tape - # Placeholder implementation - stats.depth = 10 # estimated - stats.gate_count = n_qubits * 5 # estimated - stats.two_qubit_gates = n_qubits - 1 # estimated - - return stats - - def get_optimization_report(self) -> Dict[str, Any]: - """Generate optimization report for all circuits.""" - report = { - "total_circuits": len(self.stats), - "total_compilation_time": sum( - s.compilation_time for s in self.stats.values() - ), - "total_execution_time": sum(s.execution_time for s in self.stats.values()), - "circuits": {}, - } - - for circuit_id, stats in self.stats.items(): - report["circuits"][circuit_id] = { - "depth": stats.depth, - "gate_count": stats.gate_count, - "two_qubit_gates": stats.two_qubit_gates, - "compilation_time": stats.compilation_time, - "execution_time": stats.execution_time, - } - - return report - - -class BatchCircuitExecutor: - """ - Efficiently executes multiple quantum circuits in batches. - - Optimizes resource utilization by: - - Grouping similar circuits - - Parallel execution where supported - - Caching repeated patterns - """ - - def __init__( - self, - max_batch_size: int = 10, - enable_parallel: bool = False, - cache_size: int = 1000, - ): - self.max_batch_size = max_batch_size - self.enable_parallel = enable_parallel - self.cache_size = cache_size - self.execution_cache = {} - self.cache_hits = 0 - self.cache_misses = 0 - - logger.info( - f"BatchCircuitExecutor: batch_size={max_batch_size}, parallel={enable_parallel}" - ) - - def execute_batch( - self, - circuits: List[Tuple[Any, torch.Tensor]], - circuit_fn, - ) -> List[torch.Tensor]: - """ - Execute a batch of circuits with the same structure. - - Args: - circuits: List of (circuit_params, input_data) tuples - circuit_fn: Circuit function to execute - - Returns: - List of circuit outputs - """ - results = [] - - for i in range(0, len(circuits), self.max_batch_size): - batch = circuits[i : i + self.max_batch_size] - batch_results = self._execute_batch_internal(batch, circuit_fn) - results.extend(batch_results) - - return results - - def _execute_batch_internal(self, batch, circuit_fn) -> List[torch.Tensor]: - """Execute a single batch.""" - results = [] - - for params, input_data in batch: - # Check cache - cache_key = self._get_cache_key(params, input_data) - - if cache_key in self.execution_cache: - self.cache_hits += 1 - results.append(self.execution_cache[cache_key].clone()) - else: - self.cache_misses += 1 - result = circuit_fn(params, input_data) - - # Cache result - if len(self.execution_cache) < self.cache_size: - self.execution_cache[cache_key] = result.detach().clone() - - results.append(result) - - return results - - def _get_cache_key(self, params: torch.Tensor, input_data: torch.Tensor) -> str: - """Generate cache key.""" - # Round to reduce cache size - params_rounded = torch.round(params * 100) / 100 - data_rounded = torch.round(input_data * 100) / 100 - return f"{params_rounded.cpu().numpy().tobytes()}_{data_rounded.cpu().numpy().tobytes()}" - - def get_cache_stats(self) -> Dict[str, Any]: - """Get cache performance statistics.""" - total = self.cache_hits + self.cache_misses - hit_rate = self.cache_hits / total if total > 0 else 0.0 - - return { - "cache_size": len(self.execution_cache), - "hits": self.cache_hits, - "misses": self.cache_misses, - "hit_rate": hit_rate, - } - - def clear_cache(self): - """Clear execution cache.""" - self.execution_cache.clear() - self.cache_hits = 0 - self.cache_misses = 0 - - -class AdaptiveCircuitScheduler: - """ - Schedules quantum circuit execution based on resource availability. - - Features: - - Load-aware scheduling - - Priority-based execution - - Deadline-aware planning - - Resource contention management - """ - - def __init__( - self, - max_concurrent_circuits: int = 5, - quantum_resource_limit: float = 0.8, - ): - self.max_concurrent = max_concurrent_circuits - self.resource_limit = quantum_resource_limit - self.active_circuits = 0 - self.queued_circuits = [] - self.completed_circuits = 0 - - logger.info( - f"AdaptiveCircuitScheduler: max_concurrent={max_concurrent_circuits}" - ) - - def schedule( - self, - circuit_fn, - input_data: torch.Tensor, - priority: int = 0, - ) -> torch.Tensor: - """ - Schedule circuit execution. - - Args: - circuit_fn: Circuit to execute - input_data: Input data - priority: Execution priority (higher = more urgent) - - Returns: - Circuit output - """ - # Check resource availability - if self.active_circuits >= self.max_concurrent: - # Queue for later execution - self.queued_circuits.append((circuit_fn, input_data, priority)) - # For now, execute anyway (in production, would wait) - - self.active_circuits += 1 - - try: - result = circuit_fn(input_data) - self.completed_circuits += 1 - finally: - self.active_circuits -= 1 - - return result - - def get_stats(self) -> Dict[str, int]: - """Get scheduler statistics.""" - return { - "active": self.active_circuits, - "queued": len(self.queued_circuits), - "completed": self.completed_circuits, - } - - -class QuantumClassicalPartitioner: - """ - Intelligently partitions computation between quantum and classical. - - Determines which operations benefit from quantum execution - and which are better suited for classical processing. - """ - - def __init__( - self, - quantum_advantage_threshold: float = 0.1, - complexity_model: str = "learned", - ): - self.threshold = quantum_advantage_threshold - self.complexity_model = complexity_model - self.partition_decisions = [] - - # Learned complexity predictor - self.complexity_predictor = nn.Sequential( - nn.Linear(10, 32), - nn.ReLU(), - nn.Linear(32, 1), - nn.Sigmoid(), - ) - - logger.info("QuantumClassicalPartitioner initialized") - - def should_use_quantum( - self, - input_data: torch.Tensor, - operation_type: str = "attention", - ) -> bool: - """ - Determine if quantum execution is beneficial. - - Args: - input_data: Input tensor - operation_type: Type of operation - - Returns: - True if quantum execution recommended - """ - if self.complexity_model == "learned": - # Extract features - features = self._extract_features(input_data) - score = self.complexity_predictor(features) - decision = score.item() > self.threshold - else: - # Heuristic-based - complexity = self._estimate_complexity(input_data) - decision = complexity > self.threshold - - self.partition_decisions.append( - { - "operation": operation_type, - "decision": "quantum" if decision else "classical", - "input_shape": input_data.shape, - } - ) - - return decision - - def _extract_features(self, input_data: torch.Tensor) -> torch.Tensor: - """Extract features for complexity prediction.""" - features = [] - features.append(float(input_data.shape[0])) # batch size - features.append(float(np.prod(input_data.shape[1:]))) # feature dim - features.append(input_data.std().item()) # variability - features.append(input_data.abs().max().item()) # range - features.append(input_data.mean().item()) # mean - features.append((input_data > 0).float().mean().item()) # sparsity - - # Pad to feature size - while len(features) < 10: - features.append(0.0) - - return torch.tensor(features[:10], dtype=torch.float32).unsqueeze(0) - - def _estimate_complexity(self, input_data: torch.Tensor) -> float: - """Heuristic complexity estimation.""" - size = np.prod(input_data.shape) - variance = input_data.var().item() - return min(1.0, (size * variance) / 1000.0) - - def get_partition_report(self) -> Dict[str, Any]: - """Get partitioning statistics.""" - quantum_count = sum( - 1 for d in self.partition_decisions if d["decision"] == "quantum" - ) - classical_count = len(self.partition_decisions) - quantum_count - - return { - "total_decisions": len(self.partition_decisions), - "quantum_count": quantum_count, - "classical_count": classical_count, - "quantum_ratio": ( - quantum_count / len(self.partition_decisions) - if self.partition_decisions - else 0.0 - ), - } - - -# Export all optimizer components -__all__ = [ - "CircuitStats", - "OptimizationStrategy", - "CircuitCompiler", - "BatchCircuitExecutor", - "AdaptiveCircuitScheduler", - "QuantumClassicalPartitioner", -] - - -if __name__ == "__main__": - logging.basicConfig(level=logging.INFO) - - # Test components - compiler = CircuitCompiler() - executor = BatchCircuitExecutor(max_batch_size=5) - scheduler = AdaptiveCircuitScheduler(max_concurrent_circuits=3) - partitioner = QuantumClassicalPartitioner() - - logger.info("✅ All circuit optimization components loaded successfully") - - # Test partitioner - test_data = torch.randn(4, 64) - should_quantum = partitioner.should_use_quantum(test_data) - logger.info( - f"Partition decision for test data: {'quantum' if should_quantum else 'classical'}" - ) - - logger.info(f"Executor cache stats: {executor.get_cache_stats()}") - logger.info(f"Scheduler stats: {scheduler.get_stats()}") - logger.info(f"Partitioner report: {partitioner.get_partition_report()}") +""" +Quantum Circuit Optimization for LLM Training +============================================== + +Advanced circuit compilation, optimization, and scheduling for +efficient quantum-classical hybrid training. + +Features: +- Circuit depth minimization +- Gate fusion and cancellation +- Adaptive circuit compilation +- Batch circuit execution +- Resource-aware scheduling + +Author: Quantum AI Workspace +Date: March 9, 2026 +""" + +import logging +import time +from collections import defaultdict +from dataclasses import dataclass +from typing import Any, Dict, List, Tuple + +import numpy as np +import torch +import torch.nn as nn + +logger = logging.getLogger(__name__) + +try: + import pennylane as qml + + PENNYLANE_AVAILABLE = True +except ImportError: + PENNYLANE_AVAILABLE = False + logger.warning("PennyLane not available - optimizer disabled") + + +@dataclass +class CircuitStats: + """Statistics for a quantum circuit.""" + + depth: int = 0 + gate_count: int = 0 + two_qubit_gates: int = 0 + parameter_count: int = 0 + execution_time: float = 0.0 + compilation_time: float = 0.0 + cache_hits: int = 0 + cache_misses: int = 0 + + +@dataclass +class OptimizationStrategy: + """Configuration for circuit optimization.""" + + enable_gate_fusion: bool = True + enable_gate_cancellation: bool = True + enable_parameter_shift: bool = True + max_circuit_depth: int = 20 + target_two_qubit_gate_reduction: float = 0.3 + compilation_level: int = 2 # 0=none, 1=light, 2=moderate, 3=aggressive + + +class CircuitCompiler: + """ + Compiles and optimizes quantum circuits for efficient execution. + + Applies transformations to reduce circuit depth, gate count, + and improve execution time while preserving functionality. + """ + + def __init__(self, strategy: OptimizationStrategy = None): + self.strategy = strategy or OptimizationStrategy() + self.stats = defaultdict(CircuitStats) + + logger.info( + f"CircuitCompiler initialized with level {self.strategy.compilation_level}" + ) + + def optimize_circuit(self, circuit_fn, n_qubits: int, circuit_id: str) -> Any: + """ + Optimize a quantum circuit function. + + Args: + circuit_fn: Quantum circuit function + n_qubits: Number of qubits + circuit_id: Unique identifier for this circuit + + Returns: + Optimized circuit function + """ + if not PENNYLANE_AVAILABLE: + return circuit_fn + + start_time = time.time() + stats = self.stats[circuit_id] + + try: + # Apply optimization passes based on compilation level + if self.strategy.compilation_level >= 1: + circuit_fn = self._apply_light_optimization(circuit_fn, n_qubits) + + if self.strategy.compilation_level >= 2: + circuit_fn = self._apply_moderate_optimization(circuit_fn, n_qubits) + + if self.strategy.compilation_level >= 3: + circuit_fn = self._apply_aggressive_optimization(circuit_fn, n_qubits) + + stats.compilation_time = time.time() - start_time + + except Exception as e: + logger.warning(f"Circuit optimization failed for {circuit_id}: {e}") + + return circuit_fn + + def _apply_light_optimization(self, circuit_fn, n_qubits: int): + """Light optimization: basic gate cancellations.""" + # In practice, this would use PennyLane's tape transformations + # For now, return as-is (requires circuit tape access) + return circuit_fn + + def _apply_moderate_optimization(self, circuit_fn, n_qubits: int): + """Moderate optimization: gate fusion, cancellation, commutation.""" + return circuit_fn + + def _apply_aggressive_optimization(self, circuit_fn, n_qubits: int): + """Aggressive optimization: full circuit rewriting.""" + return circuit_fn + + def analyze_circuit( + self, circuit_fn, n_qubits: int, circuit_id: str + ) -> CircuitStats: + """ + Analyze circuit properties. + + Returns: + CircuitStats object with circuit metrics + """ + stats = self.stats[circuit_id] + + # In practice, would extract from PennyLane tape + # Placeholder implementation + stats.depth = 10 # estimated + stats.gate_count = n_qubits * 5 # estimated + stats.two_qubit_gates = n_qubits - 1 # estimated + + return stats + + def get_optimization_report(self) -> Dict[str, Any]: + """Generate optimization report for all circuits.""" + report = { + "total_circuits": len(self.stats), + "total_compilation_time": sum( + s.compilation_time for s in self.stats.values() + ), + "total_execution_time": sum(s.execution_time for s in self.stats.values()), + "circuits": {}, + } + + for circuit_id, stats in self.stats.items(): + report["circuits"][circuit_id] = { + "depth": stats.depth, + "gate_count": stats.gate_count, + "two_qubit_gates": stats.two_qubit_gates, + "compilation_time": stats.compilation_time, + "execution_time": stats.execution_time, + } + + return report + + +class BatchCircuitExecutor: + """ + Efficiently executes multiple quantum circuits in batches. + + Optimizes resource utilization by: + - Grouping similar circuits + - Parallel execution where supported + - Caching repeated patterns + """ + + def __init__( + self, + max_batch_size: int = 10, + enable_parallel: bool = False, + cache_size: int = 1000, + ): + self.max_batch_size = max_batch_size + self.enable_parallel = enable_parallel + self.cache_size = cache_size + self.execution_cache = {} + self.cache_hits = 0 + self.cache_misses = 0 + + logger.info( + f"BatchCircuitExecutor: batch_size={max_batch_size}, parallel={enable_parallel}" + ) + + def execute_batch( + self, + circuits: List[Tuple[Any, torch.Tensor]], + circuit_fn, + ) -> List[torch.Tensor]: + """ + Execute a batch of circuits with the same structure. + + Args: + circuits: List of (circuit_params, input_data) tuples + circuit_fn: Circuit function to execute + + Returns: + List of circuit outputs + """ + results = [] + + for i in range(0, len(circuits), self.max_batch_size): + batch = circuits[i : i + self.max_batch_size] + batch_results = self._execute_batch_internal(batch, circuit_fn) + results.extend(batch_results) + + return results + + def _execute_batch_internal(self, batch, circuit_fn) -> List[torch.Tensor]: + """Execute a single batch.""" + results = [] + + for params, input_data in batch: + # Check cache + cache_key = self._get_cache_key(params, input_data) + + if cache_key in self.execution_cache: + self.cache_hits += 1 + results.append(self.execution_cache[cache_key].clone()) + else: + self.cache_misses += 1 + result = circuit_fn(params, input_data) + + # Cache result + if len(self.execution_cache) < self.cache_size: + self.execution_cache[cache_key] = result.detach().clone() + + results.append(result) + + return results + + def _get_cache_key(self, params: torch.Tensor, input_data: torch.Tensor) -> str: + """Generate cache key.""" + # Round to reduce cache size + params_rounded = torch.round(params * 100) / 100 + data_rounded = torch.round(input_data * 100) / 100 + return f"{params_rounded.cpu().numpy().tobytes()}_{data_rounded.cpu().numpy().tobytes()}" + + def get_cache_stats(self) -> Dict[str, Any]: + """Get cache performance statistics.""" + total = self.cache_hits + self.cache_misses + hit_rate = self.cache_hits / total if total > 0 else 0.0 + + return { + "cache_size": len(self.execution_cache), + "hits": self.cache_hits, + "misses": self.cache_misses, + "hit_rate": hit_rate, + } + + def clear_cache(self): + """Clear execution cache.""" + self.execution_cache.clear() + self.cache_hits = 0 + self.cache_misses = 0 + + +class AdaptiveCircuitScheduler: + """ + Schedules quantum circuit execution based on resource availability. + + Features: + - Load-aware scheduling + - Priority-based execution + - Deadline-aware planning + - Resource contention management + """ + + def __init__( + self, + max_concurrent_circuits: int = 5, + quantum_resource_limit: float = 0.8, + ): + self.max_concurrent = max_concurrent_circuits + self.resource_limit = quantum_resource_limit + self.active_circuits = 0 + self.queued_circuits = [] + self.completed_circuits = 0 + + logger.info( + f"AdaptiveCircuitScheduler: max_concurrent={max_concurrent_circuits}" + ) + + def schedule( + self, + circuit_fn, + input_data: torch.Tensor, + priority: int = 0, + ) -> torch.Tensor: + """ + Schedule circuit execution. + + Args: + circuit_fn: Circuit to execute + input_data: Input data + priority: Execution priority (higher = more urgent) + + Returns: + Circuit output + """ + # Check resource availability + if self.active_circuits >= self.max_concurrent: + # Queue for later execution + self.queued_circuits.append((circuit_fn, input_data, priority)) + # For now, execute anyway (in production, would wait) + + self.active_circuits += 1 + + try: + result = circuit_fn(input_data) + self.completed_circuits += 1 + finally: + self.active_circuits -= 1 + + return result + + def get_stats(self) -> Dict[str, int]: + """Get scheduler statistics.""" + return { + "active": self.active_circuits, + "queued": len(self.queued_circuits), + "completed": self.completed_circuits, + } + + +class QuantumClassicalPartitioner: + """ + Intelligently partitions computation between quantum and classical. + + Determines which operations benefit from quantum execution + and which are better suited for classical processing. + """ + + def __init__( + self, + quantum_advantage_threshold: float = 0.1, + complexity_model: str = "learned", + ): + self.threshold = quantum_advantage_threshold + self.complexity_model = complexity_model + self.partition_decisions = [] + + # Learned complexity predictor + self.complexity_predictor = nn.Sequential( + nn.Linear(10, 32), + nn.ReLU(), + nn.Linear(32, 1), + nn.Sigmoid(), + ) + + logger.info("QuantumClassicalPartitioner initialized") + + def should_use_quantum( + self, + input_data: torch.Tensor, + operation_type: str = "attention", + ) -> bool: + """ + Determine if quantum execution is beneficial. + + Args: + input_data: Input tensor + operation_type: Type of operation + + Returns: + True if quantum execution recommended + """ + if self.complexity_model == "learned": + # Extract features + features = self._extract_features(input_data) + score = self.complexity_predictor(features) + decision = score.item() > self.threshold + else: + # Heuristic-based + complexity = self._estimate_complexity(input_data) + decision = complexity > self.threshold + + self.partition_decisions.append( + { + "operation": operation_type, + "decision": "quantum" if decision else "classical", + "input_shape": input_data.shape, + } + ) + + return decision + + def _extract_features(self, input_data: torch.Tensor) -> torch.Tensor: + """Extract features for complexity prediction.""" + features = [] + features.append(float(input_data.shape[0])) # batch size + features.append(float(np.prod(input_data.shape[1:]))) # feature dim + features.append(input_data.std().item()) # variability + features.append(input_data.abs().max().item()) # range + features.append(input_data.mean().item()) # mean + features.append((input_data > 0).float().mean().item()) # sparsity + + # Pad to feature size + while len(features) < 10: + features.append(0.0) + + return torch.tensor(features[:10], dtype=torch.float32).unsqueeze(0) + + def _estimate_complexity(self, input_data: torch.Tensor) -> float: + """Heuristic complexity estimation.""" + size = np.prod(input_data.shape) + variance = input_data.var().item() + return min(1.0, (size * variance) / 1000.0) + + def get_partition_report(self) -> Dict[str, Any]: + """Get partitioning statistics.""" + quantum_count = sum( + 1 for d in self.partition_decisions if d["decision"] == "quantum" + ) + classical_count = len(self.partition_decisions) - quantum_count + + return { + "total_decisions": len(self.partition_decisions), + "quantum_count": quantum_count, + "classical_count": classical_count, + "quantum_ratio": ( + quantum_count / len(self.partition_decisions) + if self.partition_decisions + else 0.0 + ), + } + + +# Export all optimizer components +__all__ = [ + "CircuitStats", + "OptimizationStrategy", + "CircuitCompiler", + "BatchCircuitExecutor", + "AdaptiveCircuitScheduler", + "QuantumClassicalPartitioner", +] + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + + # Test components + compiler = CircuitCompiler() + executor = BatchCircuitExecutor(max_batch_size=5) + scheduler = AdaptiveCircuitScheduler(max_concurrent_circuits=3) + partitioner = QuantumClassicalPartitioner() + + logger.info("✅ All circuit optimization components loaded successfully") + + # Test partitioner + test_data = torch.randn(4, 64) + should_quantum = partitioner.should_use_quantum(test_data) + logger.info( + f"Partition decision for test data: {'quantum' if should_quantum else 'classical'}" + ) + + logger.info(f"Executor cache stats: {executor.get_cache_stats()}") + logger.info(f"Scheduler stats: {scheduler.get_stats()}") + logger.info(f"Partitioner report: {partitioner.get_partition_report()}") diff --git a/ai-projects/quantum-ml/src/quantum_classifier.py b/ai-projects/quantum-ml/src/quantum_classifier.py index b214d81d4..00f77b24b 100644 --- a/ai-projects/quantum-ml/src/quantum_classifier.py +++ b/ai-projects/quantum-ml/src/quantum_classifier.py @@ -1,348 +1,348 @@ -""" -Quantum Classifier using PennyLane and Azure Quantum -""" - -import logging -from pathlib import Path -from typing import List, Optional - -import numpy as np -import pennylane as qml -import torch -import torch.nn as nn -import yaml - -# Setup logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -class QuantumClassifier: - """ - A quantum classifier implementing a variational quantum circuit - for binary and multi-class classification tasks. - """ - - def __init__(self, config_path: str = None): - """ - Initialize the quantum classifier. - - Args: - config_path: Path to the configuration file - """ - if config_path is None: - # Get the directory of this file and construct path to config - current_dir = Path(__file__).parent - config_path = current_dir.parent / "config" / "quantum_config.yaml" - - with open(config_path, "r") as f: - self.config = yaml.safe_load(f) - - self.n_qubits = self.config["ml"]["model"]["n_qubits"] - self.n_layers = self.config["ml"]["model"]["n_layers"] - self.entanglement = self.config["ml"]["model"]["entanglement"] - - # Initialize quantum device - self.dev = qml.device( - self.config["quantum"]["simulator"]["backend"], wires=self.n_qubits - ) - - # Create quantum circuit - self.qnode = qml.QNode(self._circuit, self.dev, interface="torch") - - logger.info( - f"Initialized QuantumClassifier with {self.n_qubits} qubits and {self.n_layers} layers" - ) - - def _circuit(self, inputs: torch.Tensor, weights: torch.Tensor) -> List[float]: - """ - Define the quantum circuit architecture. - - Args: - inputs: Input features (normalized to [0, 2π]) - weights: Trainable quantum parameters - - Returns: - Expectation values of Pauli-Z measurements - """ - # Enhanced data encoding with amplitude and phase - for qubit_index in range(self.n_qubits): - input_index = qubit_index % len(inputs) - # RY for amplitude encoding - qml.RY(inputs[input_index], wires=qubit_index) - # Add RZ for phase encoding (increases circuit expressiveness) - if qubit_index < len(inputs): - qml.RZ(inputs[input_index] * 0.5, wires=qubit_index) - - # Variational layers - for layer in range(self.n_layers): - # Rotation gates - for qubit_index in range(self.n_qubits): - qml.RY(weights[layer, qubit_index, 0], wires=qubit_index) - qml.RZ(weights[layer, qubit_index, 1], wires=qubit_index) - - # Entanglement - if self.entanglement == "linear": - for qubit_index in range(self.n_qubits - 1): - qml.CNOT(wires=[qubit_index, qubit_index + 1]) - elif self.entanglement == "circular": - for qubit_index in range(self.n_qubits): - qml.CNOT(wires=[qubit_index, (qubit_index + 1) % self.n_qubits]) - elif self.entanglement == "full": - for source_qubit in range(self.n_qubits): - for target_qubit in range(source_qubit + 1, self.n_qubits): - qml.CNOT(wires=[source_qubit, target_qubit]) - - # Final rotation layer for enhanced expressiveness - for qubit_index in range(self.n_qubits): - qml.RY(weights[-1, qubit_index, 0] * 0.5, wires=qubit_index) - - # Measurements - return [ - qml.expval(qml.PauliZ(qubit_index)) for qubit_index in range(self.n_qubits) - ] - - def forward(self, inputs: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: - """ - Forward pass through the quantum circuit. - - Args: - inputs: Batch of input features - weights: Quantum circuit parameters - - Returns: - Circuit outputs - """ - batch_size = inputs.shape[0] - - # Pre-allocate output tensor for better memory efficiency - outputs = torch.empty(batch_size, self.n_qubits, dtype=torch.float32) - - for sample_index, input_sample in enumerate(inputs): - result = self.qnode(input_sample, weights) - # Convert list of expectation values to tensor - if isinstance(result, list): - outputs[sample_index] = torch.tensor(result, dtype=torch.float32) - else: - outputs[sample_index] = result - - return outputs - - def initialize_weights(self) -> torch.Tensor: - """ - Initialize random weights for the quantum circuit. - - Returns: - Random weight tensor - """ - weights = torch.randn( - self.n_layers, self.n_qubits, 2, requires_grad=True # RY and RZ parameters - ) - return weights - - def preprocess_data(self, feature_data: np.ndarray) -> torch.Tensor: - """ - Preprocess classical data for quantum encoding. - - Args: - feature_data: Classical feature data - - Returns: - Normalized torch tensor - """ - # Normalize to [0, 2π] for quantum encoding - # Use in-place operations for better memory efficiency - data_min = feature_data.min() - data_range = feature_data.max() - data_min - # Use small epsilon to avoid division by zero when all values are identical - if data_range == 0: - data_range = 1e-8 - data_normalized = (feature_data - data_min) / data_range * (2 * np.pi) - return torch.FloatTensor(data_normalized) - - def predict(self, input_features: np.ndarray, weights: torch.Tensor) -> np.ndarray: - """ - Make predictions on new data. - - Args: - input_features: Input features - weights: Trained quantum parameters - - Returns: - Predictions - """ - processed_features = self.preprocess_data(input_features) - outputs = self.forward(processed_features, weights) - predictions = torch.sign(outputs[:, 0]) # Binary classification - return predictions.detach().numpy() - - -class HybridQuantumClassifier(nn.Module): - """ - Hybrid classical-quantum neural network combining - classical preprocessing with quantum processing. - """ - - def __init__(self, input_dim: int, quantum_classifier: QuantumClassifier): - """ - Initialize hybrid model. - - Args: - input_dim: Dimension of input features - quantum_classifier: Quantum classifier instance - """ - super().__init__() - self.quantum_classifier = quantum_classifier - - # Classical preprocessing layers - self.classical_layers = nn.Sequential( - nn.Linear(input_dim, 8), - nn.ReLU(), - nn.Linear(8, quantum_classifier.n_qubits), - ) - - # Quantum weights - self.quantum_weights = nn.Parameter(quantum_classifier.initialize_weights()) - - # Classical postprocessing - self.output_layer = nn.Linear(quantum_classifier.n_qubits, 1) - - logger.info(f"Initialized HybridQuantumClassifier with input_dim={input_dim}") - - def forward(self, input_features: torch.Tensor) -> torch.Tensor: - """ - Forward pass through the hybrid model. - - Args: - input_features: Input features - - Returns: - Model predictions - """ - # Classical preprocessing - classical_output = self.classical_layers(input_features) - - # Quantum processing - quantum_output = self.quantum_classifier.forward( - classical_output, self.quantum_weights - ) - - # Classical postprocessing - final_output = self.output_layer(quantum_output) - return torch.sigmoid(final_output) - - -def train_quantum_model( - model: HybridQuantumClassifier, - X_train: np.ndarray, - y_train: np.ndarray, - X_val: Optional[np.ndarray] = None, - y_val: Optional[np.ndarray] = None, - config_path: str = None, -) -> dict: - """ - Train the hybrid quantum model. - - Args: - model: Hybrid quantum classifier - X_train: Training features - y_train: Training labels - X_val: Validation features - y_val: Validation labels - config_path: Path to configuration file - - Returns: - Training history - """ - if config_path is None: - # Get the directory of this file and construct path to config - current_dir = Path(__file__).parent - config_path = current_dir.parent / "config" / "quantum_config.yaml" - - with open(config_path, "r") as f: - config = yaml.safe_load(f) - - # Training parameters - epochs = config["ml"]["training"]["epochs"] - batch_size = config["ml"]["training"]["batch_size"] - lr = config["ml"]["training"]["learning_rate"] - - # Setup - optimizer = torch.optim.Adam(model.parameters(), lr=lr) - criterion = nn.BCELoss() - - X_train_tensor = torch.FloatTensor(X_train) - y_train_tensor = torch.FloatTensor(y_train).unsqueeze(1) - - history = {"train_loss": [], "val_loss": [], "val_acc": []} - - logger.info(f"Starting training for {epochs} epochs") - - for epoch in range(epochs): - model.train() - epoch_loss = 0 - - # Mini-batch training - for batch_start_index in range(0, len(X_train), batch_size): - batch_X = X_train_tensor[batch_start_index : batch_start_index + batch_size] - batch_y = y_train_tensor[batch_start_index : batch_start_index + batch_size] - - optimizer.zero_grad() - predictions = model(batch_X) - loss = criterion(predictions, batch_y) - loss.backward() - optimizer.step() - - epoch_loss += loss.item() - - avg_loss = epoch_loss / (len(X_train) // batch_size) - history["train_loss"].append(avg_loss) - - # Validation - if X_val is not None and y_val is not None: - model.eval() - with torch.no_grad(): - X_val_tensor = torch.FloatTensor(X_val) - y_val_tensor = torch.FloatTensor(y_val).unsqueeze(1) - - val_predictions = model(X_val_tensor) - val_loss = criterion(val_predictions, y_val_tensor) - - # Calculate accuracy - val_acc = ( - ((val_predictions > 0.5).float() == y_val_tensor).float().mean() - ) - - history["val_loss"].append(val_loss.item()) - history["val_acc"].append(val_acc.item()) - - if epoch % 10 == 0: - logger.info(f"Epoch {epoch}/{epochs} - Loss: {avg_loss:.4f}") - if X_val is not None: - logger.info(f" Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") - - logger.info("Training completed") - return history - - -if __name__ == "__main__": - # Example usage - from sklearn.datasets import make_classification - from sklearn.model_selection import train_test_split - - # Generate sample data - X, y = make_classification( - n_samples=200, n_features=4, n_classes=2, random_state=42 - ) - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - # Initialize and train - qc = QuantumClassifier() - model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) - - history = train_quantum_model(model, X_train, y_train, X_val, y_val) - - print("Training completed!") - print(f"Final validation accuracy: {history['val_acc'][-1]:.4f}") +""" +Quantum Classifier using PennyLane and Azure Quantum +""" + +import logging +from pathlib import Path +from typing import List, Optional + +import numpy as np +import pennylane as qml +import torch +import torch.nn as nn +import yaml + +# Setup logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class QuantumClassifier: + """ + A quantum classifier implementing a variational quantum circuit + for binary and multi-class classification tasks. + """ + + def __init__(self, config_path: str = None): + """ + Initialize the quantum classifier. + + Args: + config_path: Path to the configuration file + """ + if config_path is None: + # Get the directory of this file and construct path to config + current_dir = Path(__file__).parent + config_path = current_dir.parent / "config" / "quantum_config.yaml" + + with open(config_path, "r") as f: + self.config = yaml.safe_load(f) + + self.n_qubits = self.config["ml"]["model"]["n_qubits"] + self.n_layers = self.config["ml"]["model"]["n_layers"] + self.entanglement = self.config["ml"]["model"]["entanglement"] + + # Initialize quantum device + self.dev = qml.device( + self.config["quantum"]["simulator"]["backend"], wires=self.n_qubits + ) + + # Create quantum circuit + self.qnode = qml.QNode(self._circuit, self.dev, interface="torch") + + logger.info( + f"Initialized QuantumClassifier with {self.n_qubits} qubits and {self.n_layers} layers" + ) + + def _circuit(self, inputs: torch.Tensor, weights: torch.Tensor) -> List[float]: + """ + Define the quantum circuit architecture. + + Args: + inputs: Input features (normalized to [0, 2π]) + weights: Trainable quantum parameters + + Returns: + Expectation values of Pauli-Z measurements + """ + # Enhanced data encoding with amplitude and phase + for qubit_index in range(self.n_qubits): + input_index = qubit_index % len(inputs) + # RY for amplitude encoding + qml.RY(inputs[input_index], wires=qubit_index) + # Add RZ for phase encoding (increases circuit expressiveness) + if qubit_index < len(inputs): + qml.RZ(inputs[input_index] * 0.5, wires=qubit_index) + + # Variational layers + for layer in range(self.n_layers): + # Rotation gates + for qubit_index in range(self.n_qubits): + qml.RY(weights[layer, qubit_index, 0], wires=qubit_index) + qml.RZ(weights[layer, qubit_index, 1], wires=qubit_index) + + # Entanglement + if self.entanglement == "linear": + for qubit_index in range(self.n_qubits - 1): + qml.CNOT(wires=[qubit_index, qubit_index + 1]) + elif self.entanglement == "circular": + for qubit_index in range(self.n_qubits): + qml.CNOT(wires=[qubit_index, (qubit_index + 1) % self.n_qubits]) + elif self.entanglement == "full": + for source_qubit in range(self.n_qubits): + for target_qubit in range(source_qubit + 1, self.n_qubits): + qml.CNOT(wires=[source_qubit, target_qubit]) + + # Final rotation layer for enhanced expressiveness + for qubit_index in range(self.n_qubits): + qml.RY(weights[-1, qubit_index, 0] * 0.5, wires=qubit_index) + + # Measurements + return [ + qml.expval(qml.PauliZ(qubit_index)) for qubit_index in range(self.n_qubits) + ] + + def forward(self, inputs: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: + """ + Forward pass through the quantum circuit. + + Args: + inputs: Batch of input features + weights: Quantum circuit parameters + + Returns: + Circuit outputs + """ + batch_size = inputs.shape[0] + + # Pre-allocate output tensor for better memory efficiency + outputs = torch.empty(batch_size, self.n_qubits, dtype=torch.float32) + + for sample_index, input_sample in enumerate(inputs): + result = self.qnode(input_sample, weights) + # Convert list of expectation values to tensor + if isinstance(result, list): + outputs[sample_index] = torch.tensor(result, dtype=torch.float32) + else: + outputs[sample_index] = result + + return outputs + + def initialize_weights(self) -> torch.Tensor: + """ + Initialize random weights for the quantum circuit. + + Returns: + Random weight tensor + """ + weights = torch.randn( + self.n_layers, self.n_qubits, 2, requires_grad=True # RY and RZ parameters + ) + return weights + + def preprocess_data(self, feature_data: np.ndarray) -> torch.Tensor: + """ + Preprocess classical data for quantum encoding. + + Args: + feature_data: Classical feature data + + Returns: + Normalized torch tensor + """ + # Normalize to [0, 2π] for quantum encoding + # Use in-place operations for better memory efficiency + data_min = feature_data.min() + data_range = feature_data.max() - data_min + # Use small epsilon to avoid division by zero when all values are identical + if data_range == 0: + data_range = 1e-8 + data_normalized = (feature_data - data_min) / data_range * (2 * np.pi) + return torch.FloatTensor(data_normalized) + + def predict(self, input_features: np.ndarray, weights: torch.Tensor) -> np.ndarray: + """ + Make predictions on new data. + + Args: + input_features: Input features + weights: Trained quantum parameters + + Returns: + Predictions + """ + processed_features = self.preprocess_data(input_features) + outputs = self.forward(processed_features, weights) + predictions = torch.sign(outputs[:, 0]) # Binary classification + return predictions.detach().numpy() + + +class HybridQuantumClassifier(nn.Module): + """ + Hybrid classical-quantum neural network combining + classical preprocessing with quantum processing. + """ + + def __init__(self, input_dim: int, quantum_classifier: QuantumClassifier): + """ + Initialize hybrid model. + + Args: + input_dim: Dimension of input features + quantum_classifier: Quantum classifier instance + """ + super().__init__() + self.quantum_classifier = quantum_classifier + + # Classical preprocessing layers + self.classical_layers = nn.Sequential( + nn.Linear(input_dim, 8), + nn.ReLU(), + nn.Linear(8, quantum_classifier.n_qubits), + ) + + # Quantum weights + self.quantum_weights = nn.Parameter(quantum_classifier.initialize_weights()) + + # Classical postprocessing + self.output_layer = nn.Linear(quantum_classifier.n_qubits, 1) + + logger.info(f"Initialized HybridQuantumClassifier with input_dim={input_dim}") + + def forward(self, input_features: torch.Tensor) -> torch.Tensor: + """ + Forward pass through the hybrid model. + + Args: + input_features: Input features + + Returns: + Model predictions + """ + # Classical preprocessing + classical_output = self.classical_layers(input_features) + + # Quantum processing + quantum_output = self.quantum_classifier.forward( + classical_output, self.quantum_weights + ) + + # Classical postprocessing + final_output = self.output_layer(quantum_output) + return torch.sigmoid(final_output) + + +def train_quantum_model( + model: HybridQuantumClassifier, + X_train: np.ndarray, + y_train: np.ndarray, + X_val: Optional[np.ndarray] = None, + y_val: Optional[np.ndarray] = None, + config_path: str = None, +) -> dict: + """ + Train the hybrid quantum model. + + Args: + model: Hybrid quantum classifier + X_train: Training features + y_train: Training labels + X_val: Validation features + y_val: Validation labels + config_path: Path to configuration file + + Returns: + Training history + """ + if config_path is None: + # Get the directory of this file and construct path to config + current_dir = Path(__file__).parent + config_path = current_dir.parent / "config" / "quantum_config.yaml" + + with open(config_path, "r") as f: + config = yaml.safe_load(f) + + # Training parameters + epochs = config["ml"]["training"]["epochs"] + batch_size = config["ml"]["training"]["batch_size"] + lr = config["ml"]["training"]["learning_rate"] + + # Setup + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + criterion = nn.BCELoss() + + X_train_tensor = torch.FloatTensor(X_train) + y_train_tensor = torch.FloatTensor(y_train).unsqueeze(1) + + history = {"train_loss": [], "val_loss": [], "val_acc": []} + + logger.info(f"Starting training for {epochs} epochs") + + for epoch in range(epochs): + model.train() + epoch_loss = 0 + + # Mini-batch training + for batch_start_index in range(0, len(X_train), batch_size): + batch_X = X_train_tensor[batch_start_index : batch_start_index + batch_size] + batch_y = y_train_tensor[batch_start_index : batch_start_index + batch_size] + + optimizer.zero_grad() + predictions = model(batch_X) + loss = criterion(predictions, batch_y) + loss.backward() + optimizer.step() + + epoch_loss += loss.item() + + avg_loss = epoch_loss / (len(X_train) // batch_size) + history["train_loss"].append(avg_loss) + + # Validation + if X_val is not None and y_val is not None: + model.eval() + with torch.no_grad(): + X_val_tensor = torch.FloatTensor(X_val) + y_val_tensor = torch.FloatTensor(y_val).unsqueeze(1) + + val_predictions = model(X_val_tensor) + val_loss = criterion(val_predictions, y_val_tensor) + + # Calculate accuracy + val_acc = ( + ((val_predictions > 0.5).float() == y_val_tensor).float().mean() + ) + + history["val_loss"].append(val_loss.item()) + history["val_acc"].append(val_acc.item()) + + if epoch % 10 == 0: + logger.info(f"Epoch {epoch}/{epochs} - Loss: {avg_loss:.4f}") + if X_val is not None: + logger.info(f" Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") + + logger.info("Training completed") + return history + + +if __name__ == "__main__": + # Example usage + from sklearn.datasets import make_classification + from sklearn.model_selection import train_test_split + + # Generate sample data + X, y = make_classification( + n_samples=200, n_features=4, n_classes=2, random_state=42 + ) + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + # Initialize and train + qc = QuantumClassifier() + model = HybridQuantumClassifier(input_dim=4, quantum_classifier=qc) + + history = train_quantum_model(model, X_train, y_train, X_val, y_val) + + print("Training completed!") + print(f"Final validation accuracy: {history['val_acc'][-1]:.4f}") diff --git a/ai-projects/quantum-ml/src/quantum_classifier_enhanced.py b/ai-projects/quantum-ml/src/quantum_classifier_enhanced.py index fe08bad74..a19877c31 100644 --- a/ai-projects/quantum-ml/src/quantum_classifier_enhanced.py +++ b/ai-projects/quantum-ml/src/quantum_classifier_enhanced.py @@ -1,454 +1,454 @@ -""" -Enhanced Quantum Classifier with 6-8 Qubits Support -Optimized for complex patterns and real quantum hardware deployment -""" - -import logging -from pathlib import Path -from typing import List - -import numpy as np -import pennylane as qml -import torch -import torch.nn as nn -import yaml - -# Setup logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -class EnhancedQuantumClassifier: - """ - Enhanced quantum classifier supporting 6-8 qubits with advanced features: - - Data re-uploading for better expressivity - - Optimized entanglement patterns - - Hardware-ready circuit compilation - """ - - def __init__(self, n_qubits: int = 8, n_layers: int = 4, config_path: str = None): - """ - Initialize the enhanced quantum classifier. - - Args: - n_qubits: Number of qubits (6-8 recommended for complex patterns) - n_layers: Number of variational layers - config_path: Path to the configuration file - """ - if config_path is None: - current_dir = Path(__file__).parent - config_path = current_dir.parent / "config" / "quantum_config.yaml" - - with open(config_path, "r") as f: - self.config = yaml.safe_load(f) - - # Override with enhanced settings - self.n_qubits = n_qubits - self.n_layers = n_layers - self.entanglement = self.config["ml"]["model"].get("entanglement", "circular") - self.data_reuploading = True # Enable data re-uploading - - # Initialize quantum device - self.dev = qml.device( - self.config["quantum"]["simulator"]["backend"], wires=self.n_qubits - ) - - # Create quantum circuit - self.qnode = qml.QNode(self._circuit, self.dev, interface="torch") - - logger.info( - f"Enhanced QuantumClassifier: {self.n_qubits} qubits, {self.n_layers} layers, {self.entanglement} entanglement" - ) - - def _data_encoding(self, inputs: torch.Tensor, wire_idx: int): - """ - Advanced data encoding with amplitude and phase information. - - Args: - inputs: Input features - wire_idx: Qubit wire index - """ - # Use multiple rotation gates for richer encoding - input_idx = wire_idx % len(inputs) - qml.RY(inputs[input_idx] * np.pi, wires=wire_idx) - qml.RZ(inputs[input_idx] * np.pi / 2, wires=wire_idx) - - def _variational_layer(self, weights: torch.Tensor, layer_idx: int): - """ - Variational layer with rotation gates. - - Args: - weights: Layer weights - layer_idx: Current layer index - """ - for i in range(self.n_qubits): - qml.RY(weights[layer_idx, i, 0], wires=i) - qml.RZ(weights[layer_idx, i, 1], wires=i) - qml.RX( - weights[layer_idx, i, 2], wires=i - ) # Additional rotation for expressivity - - def _entanglement_layer(self): - """ - Apply entanglement based on configured pattern. - Optimized for 6-8 qubits. - """ - if self.entanglement == "linear": - # Adjacent qubit entanglement - for i in range(self.n_qubits - 1): - qml.CNOT(wires=[i, i + 1]) - - elif self.entanglement == "circular": - # Circular ring with all-to-all connections - for i in range(self.n_qubits): - qml.CNOT(wires=[i, (i + 1) % self.n_qubits]) - # Add diagonal connections for better connectivity - for i in range(0, self.n_qubits - 2, 2): - qml.CNOT(wires=[i, i + 2]) - - elif self.entanglement == "full": - # Full entanglement (expensive but expressive) - for i in range(self.n_qubits): - for j in range(i + 1, self.n_qubits): - qml.CNOT(wires=[i, j]) - - elif self.entanglement == "ladder": - # Ladder pattern optimized for 8 qubits - # Horizontal connections - for i in range(0, self.n_qubits - 1, 2): - qml.CNOT(wires=[i, i + 1]) - # Vertical connections - for i in range(self.n_qubits // 2): - if i + self.n_qubits // 2 < self.n_qubits: - qml.CNOT(wires=[i, i + self.n_qubits // 2]) - - def _circuit(self, inputs: torch.Tensor, weights: torch.Tensor) -> List[float]: - """ - Enhanced quantum circuit with data re-uploading. - - Args: - inputs: Input features (padded to n_qubits) - weights: Trainable quantum parameters [n_layers, n_qubits, 3] - - Returns: - Expectation values of Pauli-Z measurements - """ - # Ensure inputs are properly sized - if len(inputs) < self.n_qubits: - # Pad inputs to match qubit count - inputs = torch.cat([inputs, torch.zeros(self.n_qubits - len(inputs))]) - elif len(inputs) > self.n_qubits: - inputs = inputs[: self.n_qubits] - - # Initial data encoding - for i in range(self.n_qubits): - self._data_encoding(inputs, i) - - # Variational layers with optional data re-uploading - for layer in range(self.n_layers): - # Variational rotations - self._variational_layer(weights, layer) - - # Entanglement - self._entanglement_layer() - - # Data re-uploading (every other layer) - if self.data_reuploading and layer < self.n_layers - 1 and layer % 2 == 0: - for i in range(self.n_qubits): - qml.RY(inputs[i] * np.pi / (layer + 2), wires=i) - - # Add barrier for hardware compilation - qml.Barrier(wires=range(self.n_qubits)) - - # Measurements on all qubits - return [qml.expval(qml.PauliZ(i)) for i in range(self.n_qubits)] - - def forward(self, inputs: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: - """ - Forward pass through the quantum circuit. - - Args: - inputs: Batch of input features - weights: Quantum circuit parameters - - Returns: - Circuit outputs - """ - batch_size = inputs.shape[0] - outputs = [] - - for i in range(batch_size): - result = self.qnode(inputs[i], weights) - outputs.append(torch.stack(result)) - - return torch.stack(outputs) - - def initialize_weights(self) -> torch.Tensor: - """ - Initialize quantum circuit weights with Xavier initialization. - - Returns: - Initialized weight tensor - """ - # Shape: [n_layers, n_qubits, 3] for RY, RZ, RX rotations - weights = torch.randn(self.n_layers, self.n_qubits, 3) * 0.01 - return weights - - def get_circuit_info(self) -> dict: - """ - Get information about the quantum circuit. - - Returns: - Dictionary with circuit statistics - """ - dummy_inputs = torch.zeros(self.n_qubits) - dummy_weights = self.initialize_weights() - - # Create a drawing of the circuit - try: - drawer = qml.draw(self.qnode) - circuit_str = drawer(dummy_inputs, dummy_weights) - except: - circuit_str = "Circuit drawing not available" - - return { - "n_qubits": self.n_qubits, - "n_layers": self.n_layers, - "entanglement": self.entanglement, - "data_reuploading": self.data_reuploading, - "total_parameters": self.n_layers * self.n_qubits * 3, - "circuit": circuit_str, - } - - -class HybridEnhancedClassifier(nn.Module): - """ - Hybrid quantum-classical neural network with enhanced quantum layer. - Supports 6-8 qubits for complex pattern recognition. - """ - - def __init__( - self, - input_dim: int, - n_qubits: int = 8, - n_layers: int = 4, - output_dim: int = 1, - hidden_dim: int = 16, - ): - """ - Initialize hybrid classifier. - - Args: - input_dim: Input feature dimension - n_qubits: Number of qubits (6-8 recommended) - n_layers: Number of quantum layers - output_dim: Output dimension (1 for binary, >1 for multi-class) - hidden_dim: Hidden layer dimension for classical preprocessing - """ - super().__init__() - - self.input_dim = input_dim - self.n_qubits = n_qubits - self.output_dim = output_dim - - # Classical preprocessing layers - self.classical_input = nn.Sequential( - nn.Linear(input_dim, hidden_dim), - nn.ReLU(), - nn.BatchNorm1d(hidden_dim), - nn.Dropout(0.2), - nn.Linear(hidden_dim, n_qubits), - nn.Tanh(), # Normalize to [-1, 1] for quantum encoding - ) - - # Enhanced quantum layer - self.quantum_classifier = EnhancedQuantumClassifier( - n_qubits=n_qubits, n_layers=n_layers - ) - - # Initialize quantum weights as trainable parameters - self.quantum_weights = nn.Parameter( - self.quantum_classifier.initialize_weights() - ) - - # Classical postprocessing layers - self.classical_output = nn.Sequential( - nn.Linear(n_qubits, hidden_dim), - nn.ReLU(), - nn.Dropout(0.2), - nn.Linear(hidden_dim, output_dim), - ) - - if output_dim == 1: - self.activation = nn.Sigmoid() - else: - self.activation = nn.Softmax(dim=1) - - logger.info( - f"HybridEnhancedClassifier: {input_dim}→{n_qubits}Q({n_layers}L)→{output_dim}" - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """ - Forward pass through hybrid network. - - Args: - x: Input tensor [batch_size, input_dim] - - Returns: - Predictions [batch_size, output_dim] - """ - # Classical preprocessing - x = self.classical_input(x) - - # Quantum processing - x = self.quantum_classifier.forward(x, self.quantum_weights) - - # Ensure correct dtype for classical layers - x = x.float() - - # Classical postprocessing - x = self.classical_output(x) - x = self.activation(x) - - return x - - -def train_enhanced_model( - model: HybridEnhancedClassifier, - X_train: np.ndarray, - y_train: np.ndarray, - X_val: np.ndarray, - y_val: np.ndarray, - epochs: int = 100, - learning_rate: float = 0.01, - batch_size: int = 32, -) -> dict: - """ - Train the enhanced quantum model. - - Args: - model: HybridEnhancedClassifier instance - X_train: Training features - y_train: Training labels - X_val: Validation features - y_val: Validation labels - epochs: Number of training epochs - learning_rate: Learning rate - batch_size: Batch size - - Returns: - Training history dictionary - """ - optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) - criterion = nn.BCELoss() if model.output_dim == 1 else nn.CrossEntropyLoss() - - # Convert to tensors - X_train_t = torch.FloatTensor(X_train) - y_train_t = ( - torch.FloatTensor(y_train).reshape(-1, 1) - if model.output_dim == 1 - else torch.LongTensor(y_train) - ) - X_val_t = torch.FloatTensor(X_val) - y_val_t = ( - torch.FloatTensor(y_val).reshape(-1, 1) - if model.output_dim == 1 - else torch.LongTensor(y_val) - ) - - history = {"train_loss": [], "val_loss": [], "val_acc": []} - - logger.info(f"Starting training for {epochs} epochs") - - for epoch in range(epochs): - model.train() - epoch_loss = 0 - - # Mini-batch training - n_batches = len(X_train) // batch_size - for i in range(n_batches): - start_idx = i * batch_size - end_idx = start_idx + batch_size - - batch_X = X_train_t[start_idx:end_idx] - batch_y = y_train_t[start_idx:end_idx] - - optimizer.zero_grad() - outputs = model(batch_X) - loss = criterion(outputs, batch_y) - loss.backward() - optimizer.step() - - epoch_loss += loss.item() - - epoch_loss /= n_batches - - # Validation - model.eval() - with torch.no_grad(): - val_outputs = model(X_val_t) - val_loss = criterion(val_outputs, y_val_t).item() - - if model.output_dim == 1: - predictions = (val_outputs > 0.5).float() - val_acc = (predictions == y_val_t).float().mean().item() - else: - predictions = torch.argmax(val_outputs, dim=1) - val_acc = (predictions == y_val_t).float().mean().item() - - history["train_loss"].append(epoch_loss) - history["val_loss"].append(val_loss) - history["val_acc"].append(val_acc) - - if epoch % 10 == 0: - logger.info(f"Epoch {epoch}/{epochs} - Loss: {epoch_loss:.4f}") - logger.info(f" Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") - - logger.info("Training completed") - return history - - -if __name__ == "__main__": - # Quick test - print("Testing Enhanced Quantum Classifier (8 qubits)...") - - # Create sample data - from sklearn.datasets import make_moons - from sklearn.model_selection import train_test_split - from sklearn.preprocessing import StandardScaler - - X, y = make_moons(n_samples=200, noise=0.1, random_state=42) - X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) - - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - # Create enhanced model with 8 qubits - model = HybridEnhancedClassifier( - input_dim=2, n_qubits=8, n_layers=4, output_dim=1, hidden_dim=16 - ) - - print("\nModel Architecture:") - print(f" Input: {model.input_dim} features") - print(f" Quantum: {model.n_qubits} qubits, 4 layers") - print(f" Total parameters: {sum(p.numel() for p in model.parameters())}") - - # Quick training test - print("\nRunning quick training test (20 epochs)...") - history = train_enhanced_model( - model, - X_train, - y_train, - X_val, - y_val, - epochs=20, - learning_rate=0.01, - batch_size=16, - ) - - print("\nFinal Results:") - print(f" Validation Accuracy: {history['val_acc'][-1]:.4f}") - print(f" Validation Loss: {history['val_loss'][-1]:.4f}") - print("\n✓ Enhanced quantum classifier test completed!") +""" +Enhanced Quantum Classifier with 6-8 Qubits Support +Optimized for complex patterns and real quantum hardware deployment +""" + +import logging +from pathlib import Path +from typing import List + +import numpy as np +import pennylane as qml +import torch +import torch.nn as nn +import yaml + +# Setup logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class EnhancedQuantumClassifier: + """ + Enhanced quantum classifier supporting 6-8 qubits with advanced features: + - Data re-uploading for better expressivity + - Optimized entanglement patterns + - Hardware-ready circuit compilation + """ + + def __init__(self, n_qubits: int = 8, n_layers: int = 4, config_path: str = None): + """ + Initialize the enhanced quantum classifier. + + Args: + n_qubits: Number of qubits (6-8 recommended for complex patterns) + n_layers: Number of variational layers + config_path: Path to the configuration file + """ + if config_path is None: + current_dir = Path(__file__).parent + config_path = current_dir.parent / "config" / "quantum_config.yaml" + + with open(config_path, "r") as f: + self.config = yaml.safe_load(f) + + # Override with enhanced settings + self.n_qubits = n_qubits + self.n_layers = n_layers + self.entanglement = self.config["ml"]["model"].get("entanglement", "circular") + self.data_reuploading = True # Enable data re-uploading + + # Initialize quantum device + self.dev = qml.device( + self.config["quantum"]["simulator"]["backend"], wires=self.n_qubits + ) + + # Create quantum circuit + self.qnode = qml.QNode(self._circuit, self.dev, interface="torch") + + logger.info( + f"Enhanced QuantumClassifier: {self.n_qubits} qubits, {self.n_layers} layers, {self.entanglement} entanglement" + ) + + def _data_encoding(self, inputs: torch.Tensor, wire_idx: int): + """ + Advanced data encoding with amplitude and phase information. + + Args: + inputs: Input features + wire_idx: Qubit wire index + """ + # Use multiple rotation gates for richer encoding + input_idx = wire_idx % len(inputs) + qml.RY(inputs[input_idx] * np.pi, wires=wire_idx) + qml.RZ(inputs[input_idx] * np.pi / 2, wires=wire_idx) + + def _variational_layer(self, weights: torch.Tensor, layer_idx: int): + """ + Variational layer with rotation gates. + + Args: + weights: Layer weights + layer_idx: Current layer index + """ + for i in range(self.n_qubits): + qml.RY(weights[layer_idx, i, 0], wires=i) + qml.RZ(weights[layer_idx, i, 1], wires=i) + qml.RX( + weights[layer_idx, i, 2], wires=i + ) # Additional rotation for expressivity + + def _entanglement_layer(self): + """ + Apply entanglement based on configured pattern. + Optimized for 6-8 qubits. + """ + if self.entanglement == "linear": + # Adjacent qubit entanglement + for i in range(self.n_qubits - 1): + qml.CNOT(wires=[i, i + 1]) + + elif self.entanglement == "circular": + # Circular ring with all-to-all connections + for i in range(self.n_qubits): + qml.CNOT(wires=[i, (i + 1) % self.n_qubits]) + # Add diagonal connections for better connectivity + for i in range(0, self.n_qubits - 2, 2): + qml.CNOT(wires=[i, i + 2]) + + elif self.entanglement == "full": + # Full entanglement (expensive but expressive) + for i in range(self.n_qubits): + for j in range(i + 1, self.n_qubits): + qml.CNOT(wires=[i, j]) + + elif self.entanglement == "ladder": + # Ladder pattern optimized for 8 qubits + # Horizontal connections + for i in range(0, self.n_qubits - 1, 2): + qml.CNOT(wires=[i, i + 1]) + # Vertical connections + for i in range(self.n_qubits // 2): + if i + self.n_qubits // 2 < self.n_qubits: + qml.CNOT(wires=[i, i + self.n_qubits // 2]) + + def _circuit(self, inputs: torch.Tensor, weights: torch.Tensor) -> List[float]: + """ + Enhanced quantum circuit with data re-uploading. + + Args: + inputs: Input features (padded to n_qubits) + weights: Trainable quantum parameters [n_layers, n_qubits, 3] + + Returns: + Expectation values of Pauli-Z measurements + """ + # Ensure inputs are properly sized + if len(inputs) < self.n_qubits: + # Pad inputs to match qubit count + inputs = torch.cat([inputs, torch.zeros(self.n_qubits - len(inputs))]) + elif len(inputs) > self.n_qubits: + inputs = inputs[: self.n_qubits] + + # Initial data encoding + for i in range(self.n_qubits): + self._data_encoding(inputs, i) + + # Variational layers with optional data re-uploading + for layer in range(self.n_layers): + # Variational rotations + self._variational_layer(weights, layer) + + # Entanglement + self._entanglement_layer() + + # Data re-uploading (every other layer) + if self.data_reuploading and layer < self.n_layers - 1 and layer % 2 == 0: + for i in range(self.n_qubits): + qml.RY(inputs[i] * np.pi / (layer + 2), wires=i) + + # Add barrier for hardware compilation + qml.Barrier(wires=range(self.n_qubits)) + + # Measurements on all qubits + return [qml.expval(qml.PauliZ(i)) for i in range(self.n_qubits)] + + def forward(self, inputs: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: + """ + Forward pass through the quantum circuit. + + Args: + inputs: Batch of input features + weights: Quantum circuit parameters + + Returns: + Circuit outputs + """ + batch_size = inputs.shape[0] + outputs = [] + + for i in range(batch_size): + result = self.qnode(inputs[i], weights) + outputs.append(torch.stack(result)) + + return torch.stack(outputs) + + def initialize_weights(self) -> torch.Tensor: + """ + Initialize quantum circuit weights with Xavier initialization. + + Returns: + Initialized weight tensor + """ + # Shape: [n_layers, n_qubits, 3] for RY, RZ, RX rotations + weights = torch.randn(self.n_layers, self.n_qubits, 3) * 0.01 + return weights + + def get_circuit_info(self) -> dict: + """ + Get information about the quantum circuit. + + Returns: + Dictionary with circuit statistics + """ + dummy_inputs = torch.zeros(self.n_qubits) + dummy_weights = self.initialize_weights() + + # Create a drawing of the circuit + try: + drawer = qml.draw(self.qnode) + circuit_str = drawer(dummy_inputs, dummy_weights) + except: + circuit_str = "Circuit drawing not available" + + return { + "n_qubits": self.n_qubits, + "n_layers": self.n_layers, + "entanglement": self.entanglement, + "data_reuploading": self.data_reuploading, + "total_parameters": self.n_layers * self.n_qubits * 3, + "circuit": circuit_str, + } + + +class HybridEnhancedClassifier(nn.Module): + """ + Hybrid quantum-classical neural network with enhanced quantum layer. + Supports 6-8 qubits for complex pattern recognition. + """ + + def __init__( + self, + input_dim: int, + n_qubits: int = 8, + n_layers: int = 4, + output_dim: int = 1, + hidden_dim: int = 16, + ): + """ + Initialize hybrid classifier. + + Args: + input_dim: Input feature dimension + n_qubits: Number of qubits (6-8 recommended) + n_layers: Number of quantum layers + output_dim: Output dimension (1 for binary, >1 for multi-class) + hidden_dim: Hidden layer dimension for classical preprocessing + """ + super().__init__() + + self.input_dim = input_dim + self.n_qubits = n_qubits + self.output_dim = output_dim + + # Classical preprocessing layers + self.classical_input = nn.Sequential( + nn.Linear(input_dim, hidden_dim), + nn.ReLU(), + nn.BatchNorm1d(hidden_dim), + nn.Dropout(0.2), + nn.Linear(hidden_dim, n_qubits), + nn.Tanh(), # Normalize to [-1, 1] for quantum encoding + ) + + # Enhanced quantum layer + self.quantum_classifier = EnhancedQuantumClassifier( + n_qubits=n_qubits, n_layers=n_layers + ) + + # Initialize quantum weights as trainable parameters + self.quantum_weights = nn.Parameter( + self.quantum_classifier.initialize_weights() + ) + + # Classical postprocessing layers + self.classical_output = nn.Sequential( + nn.Linear(n_qubits, hidden_dim), + nn.ReLU(), + nn.Dropout(0.2), + nn.Linear(hidden_dim, output_dim), + ) + + if output_dim == 1: + self.activation = nn.Sigmoid() + else: + self.activation = nn.Softmax(dim=1) + + logger.info( + f"HybridEnhancedClassifier: {input_dim}→{n_qubits}Q({n_layers}L)→{output_dim}" + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass through hybrid network. + + Args: + x: Input tensor [batch_size, input_dim] + + Returns: + Predictions [batch_size, output_dim] + """ + # Classical preprocessing + x = self.classical_input(x) + + # Quantum processing + x = self.quantum_classifier.forward(x, self.quantum_weights) + + # Ensure correct dtype for classical layers + x = x.float() + + # Classical postprocessing + x = self.classical_output(x) + x = self.activation(x) + + return x + + +def train_enhanced_model( + model: HybridEnhancedClassifier, + X_train: np.ndarray, + y_train: np.ndarray, + X_val: np.ndarray, + y_val: np.ndarray, + epochs: int = 100, + learning_rate: float = 0.01, + batch_size: int = 32, +) -> dict: + """ + Train the enhanced quantum model. + + Args: + model: HybridEnhancedClassifier instance + X_train: Training features + y_train: Training labels + X_val: Validation features + y_val: Validation labels + epochs: Number of training epochs + learning_rate: Learning rate + batch_size: Batch size + + Returns: + Training history dictionary + """ + optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) + criterion = nn.BCELoss() if model.output_dim == 1 else nn.CrossEntropyLoss() + + # Convert to tensors + X_train_t = torch.FloatTensor(X_train) + y_train_t = ( + torch.FloatTensor(y_train).reshape(-1, 1) + if model.output_dim == 1 + else torch.LongTensor(y_train) + ) + X_val_t = torch.FloatTensor(X_val) + y_val_t = ( + torch.FloatTensor(y_val).reshape(-1, 1) + if model.output_dim == 1 + else torch.LongTensor(y_val) + ) + + history = {"train_loss": [], "val_loss": [], "val_acc": []} + + logger.info(f"Starting training for {epochs} epochs") + + for epoch in range(epochs): + model.train() + epoch_loss = 0 + + # Mini-batch training + n_batches = len(X_train) // batch_size + for i in range(n_batches): + start_idx = i * batch_size + end_idx = start_idx + batch_size + + batch_X = X_train_t[start_idx:end_idx] + batch_y = y_train_t[start_idx:end_idx] + + optimizer.zero_grad() + outputs = model(batch_X) + loss = criterion(outputs, batch_y) + loss.backward() + optimizer.step() + + epoch_loss += loss.item() + + epoch_loss /= n_batches + + # Validation + model.eval() + with torch.no_grad(): + val_outputs = model(X_val_t) + val_loss = criterion(val_outputs, y_val_t).item() + + if model.output_dim == 1: + predictions = (val_outputs > 0.5).float() + val_acc = (predictions == y_val_t).float().mean().item() + else: + predictions = torch.argmax(val_outputs, dim=1) + val_acc = (predictions == y_val_t).float().mean().item() + + history["train_loss"].append(epoch_loss) + history["val_loss"].append(val_loss) + history["val_acc"].append(val_acc) + + if epoch % 10 == 0: + logger.info(f"Epoch {epoch}/{epochs} - Loss: {epoch_loss:.4f}") + logger.info(f" Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") + + logger.info("Training completed") + return history + + +if __name__ == "__main__": + # Quick test + print("Testing Enhanced Quantum Classifier (8 qubits)...") + + # Create sample data + from sklearn.datasets import make_moons + from sklearn.model_selection import train_test_split + from sklearn.preprocessing import StandardScaler + + X, y = make_moons(n_samples=200, noise=0.1, random_state=42) + X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) + + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + # Create enhanced model with 8 qubits + model = HybridEnhancedClassifier( + input_dim=2, n_qubits=8, n_layers=4, output_dim=1, hidden_dim=16 + ) + + print("\nModel Architecture:") + print(f" Input: {model.input_dim} features") + print(f" Quantum: {model.n_qubits} qubits, 4 layers") + print(f" Total parameters: {sum(p.numel() for p in model.parameters())}") + + # Quick training test + print("\nRunning quick training test (20 epochs)...") + history = train_enhanced_model( + model, + X_train, + y_train, + X_val, + y_val, + epochs=20, + learning_rate=0.01, + batch_size=16, + ) + + print("\nFinal Results:") + print(f" Validation Accuracy: {history['val_acc'][-1]:.4f}") + print(f" Validation Loss: {history['val_loss'][-1]:.4f}") + print("\n✓ Enhanced quantum classifier test completed!") diff --git a/ai-projects/quantum-ml/src/quantum_llm_advanced.py b/ai-projects/quantum-ml/src/quantum_llm_advanced.py index 234e1bf93..9fef80b17 100644 --- a/ai-projects/quantum-ml/src/quantum_llm_advanced.py +++ b/ai-projects/quantum-ml/src/quantum_llm_advanced.py @@ -1,547 +1,547 @@ -""" -Advanced Quantum LLM Components -================================ - -Enhanced quantum-classical hybrid architectures for language modeling: -- Multi-scale quantum attention (varying qubit counts) -- Quantum circuit caching and optimization -- Adaptive entanglement patterns -- Quantum error mitigation strategies -- Batch-optimized quantum processing -- Quantum prompt tuning - -Author: Quantum AI Workspace -Date: March 9, 2026 -""" - -import logging -import math -from collections import OrderedDict -from dataclasses import dataclass -from typing import Dict, List, Optional, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as F - -logger = logging.getLogger(__name__) - -try: - from hybrid_qnn import QuantumLayer - - QUANTUM_AVAILABLE = True -except ImportError: - QUANTUM_AVAILABLE = False - logger.warning("QuantumLayer not available - enhancements disabled") - - -@dataclass -class QuantumCircuitCache: - """LRU cache for quantum circuit outputs.""" - - max_size: int = 1000 - cache: OrderedDict = None - hits: int = 0 - misses: int = 0 - - def __post_init__(self): - if self.cache is None: - self.cache = OrderedDict() - - def get(self, key: str) -> Optional[torch.Tensor]: - """Retrieve cached result.""" - if key in self.cache: - self.hits += 1 - self.cache.move_to_end(key) - return self.cache[key].clone() - self.misses += 1 - return None - - def put(self, key: str, value: torch.Tensor): - """Store result in cache.""" - if key in self.cache: - self.cache.move_to_end(key) - self.cache[key] = value.detach().clone() - - if len(self.cache) > self.max_size: - self.cache.popitem(last=False) - - def clear(self): - """Clear cache.""" - self.cache.clear() - self.hits = 0 - self.misses = 0 - - def get_stats(self) -> Dict[str, float]: - """Get cache statistics.""" - total = self.hits + self.misses - hit_rate = self.hits / total if total > 0 else 0.0 - return { - "size": len(self.cache), - "hits": self.hits, - "misses": self.misses, - "hit_rate": hit_rate, - } - - -class AdaptiveQuantumLayer(nn.Module): - """ - Quantum layer with adaptive entanglement and error mitigation. - - Features: - - Dynamic entanglement patterns based on input complexity - - Circuit depth adaptation - - Basic error mitigation via measurement repetition - - Result caching for efficiency - """ - - def __init__( - self, - n_qubits: int = 4, - n_layers: int = 2, - max_layers: int = 4, - device: str = "default.qubit", - cache_size: int = 1000, - use_error_mitigation: bool = True, - mitigation_shots: int = 100, - ): - super().__init__() - self.n_qubits = n_qubits - self.n_layers = n_layers - self.max_layers = max_layers - self.use_error_mitigation = use_error_mitigation - self.mitigation_shots = mitigation_shots - - if not QUANTUM_AVAILABLE: - self.quantum_layer = None - logger.warning("Quantum layer unavailable - using classical fallback") - return - - # Initialize quantum layers with different entanglement patterns - self.quantum_layers = nn.ModuleDict( - { - "linear": QuantumLayer(n_qubits, n_layers, device, "linear"), - "circular": QuantumLayer(n_qubits, n_layers, device, "circular"), - "full": ( - QuantumLayer(n_qubits, n_layers, device, "full") - if n_qubits <= 6 - else None - ), - } - ) - - # Complexity predictor for adaptive routing - self.complexity_predictor = nn.Sequential( - nn.Linear(2**n_qubits, 32), - nn.ReLU(), - nn.Linear(32, 3), # 3 entanglement patterns - nn.Softmax(dim=-1), - ) - - # Circuit cache - self.cache = QuantumCircuitCache(max_size=cache_size) - - logger.info(f"Initialized AdaptiveQuantumLayer with {n_qubits} qubits") - - def _get_cache_key(self, x: torch.Tensor) -> str: - """Generate cache key from input tensor.""" - # Use hash of rounded values for approximate caching - rounded = torch.round(x * 100) / 100 - return str(rounded.cpu().numpy().tobytes()) - - def _select_entanglement(self, x: torch.Tensor) -> str: - """Select entanglement pattern based on input complexity.""" - with torch.no_grad(): - weights = self.complexity_predictor(x.mean(dim=0, keepdim=True)) - pattern_idx = weights.argmax().item() - - patterns = ["linear", "circular", "full"] - selected = patterns[pattern_idx] - - # Fallback if full entanglement unavailable - if selected == "full" and self.quantum_layers["full"] is None: - selected = "circular" - - return selected - - def forward(self, x: torch.Tensor, use_cache: bool = True) -> torch.Tensor: - """ - Forward pass with caching and adaptive routing. - - Args: - x: Input tensor (batch, feature_dim) - use_cache: Whether to use circuit result caching - - Returns: - Quantum circuit output (batch, n_qubits) - """ - if self.quantum_layers is None: - # Classical fallback - return torch.tanh(x[..., : self.n_qubits]) - - batch_results = [] - - for i in range(x.shape[0]): - x_i = x[i : i + 1] - - # Try cache first - if use_cache: - cache_key = self._get_cache_key(x_i) - cached_result = self.cache.get(cache_key) - if cached_result is not None: - batch_results.append(cached_result) - continue - - # Select adaptive entanglement - pattern = self._select_entanglement(x_i) - - # Run quantum circuit - try: - result = self.quantum_layers[pattern](x_i) - - # Cache result - if use_cache: - self.cache.put(cache_key, result) - - batch_results.append(result) - except Exception as e: - logger.warning(f"Quantum circuit execution failed: {e}, using fallback") - fallback = torch.tanh(x_i[..., : self.n_qubits]) - batch_results.append(fallback) - - return torch.cat(batch_results, dim=0) - - def get_cache_stats(self) -> Dict[str, float]: - """Get cache performance statistics.""" - return self.cache.get_stats() - - -class MultiScaleQuantumAttention(nn.Module): - """ - Multi-head attention with varying qubit counts per head. - - Different attention heads use different quantum circuit sizes, - capturing both fine-grained (small qubit) and coarse-grained - (large qubit) attention patterns. - """ - - def __init__( - self, - d_model: int = 64, - n_heads: int = 4, - qubit_range: Tuple[int, int] = (2, 6), - n_quantum_layers: int = 2, - dropout: float = 0.1, - use_adaptive: bool = True, - ): - super().__init__() - assert d_model % n_heads == 0 - - self.d_model = d_model - self.n_heads = n_heads - self.d_head = d_model // n_heads - - # Classical projections - self.W_Q = nn.Linear(d_model, d_model) - self.W_K = nn.Linear(d_model, d_model) - self.W_V = nn.Linear(d_model, d_model) - self.W_O = nn.Linear(d_model, d_model) - - # Multi-scale quantum layers (different qubit counts) - min_qubits, max_qubits = qubit_range - qubit_counts = torch.linspace(min_qubits, max_qubits, n_heads).int().tolist() - - self.quantum_layers = nn.ModuleList() - self.qubit_counts = [] - self.scales = [] - - for i, n_qubits in enumerate(qubit_counts): - self.qubit_counts.append(n_qubits) - self.scales.append(1.0 / math.sqrt(n_qubits)) - - if QUANTUM_AVAILABLE and use_adaptive: - layer = AdaptiveQuantumLayer( - n_qubits=n_qubits, - n_layers=n_quantum_layers, - cache_size=500, - ) - else: - layer = None - - self.quantum_layers.append(layer) - - # Quantum projection layers - self.quantum_proj_q = nn.ModuleList( - [nn.Linear(self.d_head, 2**q) for q in self.qubit_counts] - ) - self.quantum_proj_k = nn.ModuleList( - [nn.Linear(self.d_head, 2**q) for q in self.qubit_counts] - ) - - self.attn_dropout = nn.Dropout(dropout) - - logger.info( - f"MultiScaleQuantumAttention: {n_heads} heads with qubits {qubit_counts}" - ) - - def forward( - self, x: torch.Tensor, mask: Optional[torch.Tensor] = None - ) -> torch.Tensor: - batch, seq_len, _ = x.shape - - # Project to Q, K, V - Q = self.W_Q(x).view(batch, seq_len, self.n_heads, self.d_head) - K = self.W_K(x).view(batch, seq_len, self.n_heads, self.d_head) - V = self.W_V(x).view(batch, seq_len, self.n_heads, self.d_head).transpose(1, 2) - - head_outputs = [] - - for h in range(self.n_heads): - Q_h = Q[:, :, h, :].reshape(batch * seq_len, self.d_head) - K_h = K[:, :, h, :].reshape(batch * seq_len, self.d_head) - - # Project to quantum input space - Q_proj = self.quantum_proj_q[h](Q_h) - K_proj = self.quantum_proj_k[h](K_h) - - # Normalize - Q_proj = F.normalize(Q_proj, p=2, dim=-1) - K_proj = F.normalize(K_proj, p=2, dim=-1) - - # Apply quantum layer - if self.quantum_layers[h] is not None: - try: - Q_q = self.quantum_layers[h](Q_proj).view( - batch, seq_len, self.qubit_counts[h] - ) - K_q = self.quantum_layers[h](K_proj).view( - batch, seq_len, self.qubit_counts[h] - ) - except Exception as e: - logger.warning(f"Quantum head {h} failed: {e}, using classical") - Q_q = torch.tanh(Q_proj[..., : self.qubit_counts[h]]).view( - batch, seq_len, self.qubit_counts[h] - ) - K_q = torch.tanh(K_proj[..., : self.qubit_counts[h]]).view( - batch, seq_len, self.qubit_counts[h] - ) - else: - # Classical fallback - Q_q = torch.tanh(Q_proj[..., : self.qubit_counts[h]]).view( - batch, seq_len, self.qubit_counts[h] - ) - K_q = torch.tanh(K_proj[..., : self.qubit_counts[h]]).view( - batch, seq_len, self.qubit_counts[h] - ) - - # Compute attention - attn_logits = torch.bmm(Q_q, K_q.transpose(1, 2)) * self.scales[h] - - if mask is not None: - attn_logits = attn_logits.masked_fill(mask == 0, float("-inf")) - - attn_weights = self.attn_dropout(F.softmax(attn_logits, dim=-1)) - - # Apply to values - V_h = V[:, h, :, :] - out_h = torch.bmm(attn_weights, V_h) - head_outputs.append(out_h) - - # Concatenate heads - out = torch.cat(head_outputs, dim=-1) - return self.W_O(out) - - def get_cache_stats(self) -> Dict[str, Dict[str, float]]: - """Get cache statistics for all quantum layers.""" - stats = {} - for i, layer in enumerate(self.quantum_layers): - if layer is not None and hasattr(layer, "get_cache_stats"): - stats[f"head_{i}"] = layer.get_cache_stats() - return stats - - -class QuantumPromptTuning(nn.Module): - """ - Quantum-enhanced prompt tuning layer. - - Learns quantum-parametrized soft prompts that are prepended to - input sequences, allowing task adaptation without fine-tuning - the entire model. - """ - - def __init__( - self, - n_prompts: int = 10, - d_model: int = 64, - n_qubits: int = 4, - n_quantum_layers: int = 2, - ): - super().__init__() - self.n_prompts = n_prompts - self.d_model = d_model - self.n_qubits = n_qubits - - # Classical prompt embeddings (initialization) - self.prompt_embeddings = nn.Parameter(torch.randn(n_prompts, d_model) * 0.01) - - # Quantum transformation layer - if QUANTUM_AVAILABLE: - self.quantum_transform = AdaptiveQuantumLayer( - n_qubits=n_qubits, - n_layers=n_quantum_layers, - cache_size=100, - ) - - # Projection layers - self.proj_in = nn.Linear(d_model, 2**n_qubits) - self.proj_out = nn.Linear(n_qubits, d_model) - else: - self.quantum_transform = None - self.proj_in = None - self.proj_out = None - - logger.info(f"QuantumPromptTuning: {n_prompts} prompts, d_model={d_model}") - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """ - Prepend quantum-transformed prompts to input sequence. - - Args: - x: Input tensor (batch, seq_len, d_model) - - Returns: - (batch, n_prompts + seq_len, d_model) - """ - batch = x.shape[0] - - # Expand prompts for batch - prompts = self.prompt_embeddings.unsqueeze(0).expand(batch, -1, -1) - - # Apply quantum transformation - if self.quantum_transform is not None: - try: - # Project to quantum space - prompts_flat = prompts.reshape(batch * self.n_prompts, self.d_model) - prompts_proj = self.proj_in(prompts_flat) - prompts_proj = F.normalize(prompts_proj, p=2, dim=-1) - - # Quantum circuit - prompts_q = self.quantum_transform(prompts_proj) - - # Project back - prompts_transformed = self.proj_out(prompts_q) - prompts = prompts_transformed.view(batch, self.n_prompts, self.d_model) - - # Residual connection - prompts = prompts + self.prompt_embeddings.unsqueeze(0) - except Exception as e: - logger.warning(f"Quantum prompt transformation failed: {e}") - - # Concatenate with input - return torch.cat([prompts, x], dim=1) - - -class QuantumErrorMitigation(nn.Module): - """ - Error mitigation strategies for quantum circuits. - - Implements: - - Zero-noise extrapolation - - Measurement error mitigation - - Readout error correction - """ - - def __init__(self, n_qubits: int = 4, n_samples: int = 3): - super().__init__() - self.n_qubits = n_qubits - self.n_samples = n_samples - - # Learnable error parameters - self.readout_errors = nn.Parameter(torch.zeros(n_qubits)) - - def zero_noise_extrapolation( - self, - quantum_layer: nn.Module, - x: torch.Tensor, - noise_scales: List[float] | None = None, - ) -> torch.Tensor: - """ - Zero-noise extrapolation by running circuits at multiple noise levels. - - Args: - quantum_layer: Quantum layer to execute - x: Input tensor - noise_scales: Noise scaling factors - - Returns: - Extrapolated zero-noise result - """ - outputs = [] - - for scale in noise_scales: - # In practice, this would modify circuit noise parameters - # For now, we approximate with multiple samples - out = quantum_layer(x) - outputs.append(out) - - # Linear extrapolation to zero noise - outputs = torch.stack(outputs) - scales = torch.tensor(noise_scales, device=x.device).view(-1, 1, 1) - - # Fit line and extrapolate to scale=0 - # y = a + b*x, extrapolate to x=0 - mean_scale = scales.mean() - mean_out = outputs.mean(dim=0) - - numerator = ((scales - mean_scale) * (outputs - mean_out)).sum(dim=0) - denominator = ((scales - mean_scale) ** 2).sum() - - slope = numerator / (denominator + 1e-8) - intercept = mean_out - slope * mean_scale - - return intercept - - def readout_error_correction(self, probs: torch.Tensor) -> torch.Tensor: - """ - Correct measurement probabilities for readout errors. - - Args: - probs: Measurement probabilities (batch, n_qubits) - - Returns: - Corrected probabilities - """ - # Apply learned readout error correction - correction = torch.sigmoid(self.readout_errors) - corrected = probs * (1 - correction) + (1 - probs) * correction - return corrected - - -# Export all advanced components -__all__ = [ - "QuantumCircuitCache", - "AdaptiveQuantumLayer", - "MultiScaleQuantumAttention", - "QuantumPromptTuning", - "QuantumErrorMitigation", -] - -if __name__ == "__main__": - logging.basicConfig(level=logging.INFO) - logger.info("Advanced Quantum LLM Components loaded successfully") - - if QUANTUM_AVAILABLE: - # Test multi-scale attention - attn = MultiScaleQuantumAttention(d_model=64, n_heads=4, qubit_range=(2, 6)) - x = torch.randn(2, 10, 64) - out = attn(x) - logger.info(f"MultiScaleQuantumAttention output shape: {out.shape}") - logger.info(f"Cache stats: {attn.get_cache_stats()}") - - # Test prompt tuning - prompt_tuner = QuantumPromptTuning(n_prompts=5, d_model=64, n_qubits=4) - x = torch.randn(2, 10, 64) - out = prompt_tuner(x) - logger.info(f"QuantumPromptTuning output shape: {out.shape}") - else: - logger.warning("Quantum features not available for testing") +""" +Advanced Quantum LLM Components +================================ + +Enhanced quantum-classical hybrid architectures for language modeling: +- Multi-scale quantum attention (varying qubit counts) +- Quantum circuit caching and optimization +- Adaptive entanglement patterns +- Quantum error mitigation strategies +- Batch-optimized quantum processing +- Quantum prompt tuning + +Author: Quantum AI Workspace +Date: March 9, 2026 +""" + +import logging +import math +from collections import OrderedDict +from dataclasses import dataclass +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +logger = logging.getLogger(__name__) + +try: + from hybrid_qnn import QuantumLayer + + QUANTUM_AVAILABLE = True +except ImportError: + QUANTUM_AVAILABLE = False + logger.warning("QuantumLayer not available - enhancements disabled") + + +@dataclass +class QuantumCircuitCache: + """LRU cache for quantum circuit outputs.""" + + max_size: int = 1000 + cache: OrderedDict = None + hits: int = 0 + misses: int = 0 + + def __post_init__(self): + if self.cache is None: + self.cache = OrderedDict() + + def get(self, key: str) -> Optional[torch.Tensor]: + """Retrieve cached result.""" + if key in self.cache: + self.hits += 1 + self.cache.move_to_end(key) + return self.cache[key].clone() + self.misses += 1 + return None + + def put(self, key: str, value: torch.Tensor): + """Store result in cache.""" + if key in self.cache: + self.cache.move_to_end(key) + self.cache[key] = value.detach().clone() + + if len(self.cache) > self.max_size: + self.cache.popitem(last=False) + + def clear(self): + """Clear cache.""" + self.cache.clear() + self.hits = 0 + self.misses = 0 + + def get_stats(self) -> Dict[str, float]: + """Get cache statistics.""" + total = self.hits + self.misses + hit_rate = self.hits / total if total > 0 else 0.0 + return { + "size": len(self.cache), + "hits": self.hits, + "misses": self.misses, + "hit_rate": hit_rate, + } + + +class AdaptiveQuantumLayer(nn.Module): + """ + Quantum layer with adaptive entanglement and error mitigation. + + Features: + - Dynamic entanglement patterns based on input complexity + - Circuit depth adaptation + - Basic error mitigation via measurement repetition + - Result caching for efficiency + """ + + def __init__( + self, + n_qubits: int = 4, + n_layers: int = 2, + max_layers: int = 4, + device: str = "default.qubit", + cache_size: int = 1000, + use_error_mitigation: bool = True, + mitigation_shots: int = 100, + ): + super().__init__() + self.n_qubits = n_qubits + self.n_layers = n_layers + self.max_layers = max_layers + self.use_error_mitigation = use_error_mitigation + self.mitigation_shots = mitigation_shots + + if not QUANTUM_AVAILABLE: + self.quantum_layer = None + logger.warning("Quantum layer unavailable - using classical fallback") + return + + # Initialize quantum layers with different entanglement patterns + self.quantum_layers = nn.ModuleDict( + { + "linear": QuantumLayer(n_qubits, n_layers, device, "linear"), + "circular": QuantumLayer(n_qubits, n_layers, device, "circular"), + "full": ( + QuantumLayer(n_qubits, n_layers, device, "full") + if n_qubits <= 6 + else None + ), + } + ) + + # Complexity predictor for adaptive routing + self.complexity_predictor = nn.Sequential( + nn.Linear(2**n_qubits, 32), + nn.ReLU(), + nn.Linear(32, 3), # 3 entanglement patterns + nn.Softmax(dim=-1), + ) + + # Circuit cache + self.cache = QuantumCircuitCache(max_size=cache_size) + + logger.info(f"Initialized AdaptiveQuantumLayer with {n_qubits} qubits") + + def _get_cache_key(self, x: torch.Tensor) -> str: + """Generate cache key from input tensor.""" + # Use hash of rounded values for approximate caching + rounded = torch.round(x * 100) / 100 + return str(rounded.cpu().numpy().tobytes()) + + def _select_entanglement(self, x: torch.Tensor) -> str: + """Select entanglement pattern based on input complexity.""" + with torch.no_grad(): + weights = self.complexity_predictor(x.mean(dim=0, keepdim=True)) + pattern_idx = weights.argmax().item() + + patterns = ["linear", "circular", "full"] + selected = patterns[pattern_idx] + + # Fallback if full entanglement unavailable + if selected == "full" and self.quantum_layers["full"] is None: + selected = "circular" + + return selected + + def forward(self, x: torch.Tensor, use_cache: bool = True) -> torch.Tensor: + """ + Forward pass with caching and adaptive routing. + + Args: + x: Input tensor (batch, feature_dim) + use_cache: Whether to use circuit result caching + + Returns: + Quantum circuit output (batch, n_qubits) + """ + if self.quantum_layers is None: + # Classical fallback + return torch.tanh(x[..., : self.n_qubits]) + + batch_results = [] + + for i in range(x.shape[0]): + x_i = x[i : i + 1] + + # Try cache first + if use_cache: + cache_key = self._get_cache_key(x_i) + cached_result = self.cache.get(cache_key) + if cached_result is not None: + batch_results.append(cached_result) + continue + + # Select adaptive entanglement + pattern = self._select_entanglement(x_i) + + # Run quantum circuit + try: + result = self.quantum_layers[pattern](x_i) + + # Cache result + if use_cache: + self.cache.put(cache_key, result) + + batch_results.append(result) + except Exception as e: + logger.warning(f"Quantum circuit execution failed: {e}, using fallback") + fallback = torch.tanh(x_i[..., : self.n_qubits]) + batch_results.append(fallback) + + return torch.cat(batch_results, dim=0) + + def get_cache_stats(self) -> Dict[str, float]: + """Get cache performance statistics.""" + return self.cache.get_stats() + + +class MultiScaleQuantumAttention(nn.Module): + """ + Multi-head attention with varying qubit counts per head. + + Different attention heads use different quantum circuit sizes, + capturing both fine-grained (small qubit) and coarse-grained + (large qubit) attention patterns. + """ + + def __init__( + self, + d_model: int = 64, + n_heads: int = 4, + qubit_range: Tuple[int, int] = (2, 6), + n_quantum_layers: int = 2, + dropout: float = 0.1, + use_adaptive: bool = True, + ): + super().__init__() + assert d_model % n_heads == 0 + + self.d_model = d_model + self.n_heads = n_heads + self.d_head = d_model // n_heads + + # Classical projections + self.W_Q = nn.Linear(d_model, d_model) + self.W_K = nn.Linear(d_model, d_model) + self.W_V = nn.Linear(d_model, d_model) + self.W_O = nn.Linear(d_model, d_model) + + # Multi-scale quantum layers (different qubit counts) + min_qubits, max_qubits = qubit_range + qubit_counts = torch.linspace(min_qubits, max_qubits, n_heads).int().tolist() + + self.quantum_layers = nn.ModuleList() + self.qubit_counts = [] + self.scales = [] + + for i, n_qubits in enumerate(qubit_counts): + self.qubit_counts.append(n_qubits) + self.scales.append(1.0 / math.sqrt(n_qubits)) + + if QUANTUM_AVAILABLE and use_adaptive: + layer = AdaptiveQuantumLayer( + n_qubits=n_qubits, + n_layers=n_quantum_layers, + cache_size=500, + ) + else: + layer = None + + self.quantum_layers.append(layer) + + # Quantum projection layers + self.quantum_proj_q = nn.ModuleList( + [nn.Linear(self.d_head, 2**q) for q in self.qubit_counts] + ) + self.quantum_proj_k = nn.ModuleList( + [nn.Linear(self.d_head, 2**q) for q in self.qubit_counts] + ) + + self.attn_dropout = nn.Dropout(dropout) + + logger.info( + f"MultiScaleQuantumAttention: {n_heads} heads with qubits {qubit_counts}" + ) + + def forward( + self, x: torch.Tensor, mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + batch, seq_len, _ = x.shape + + # Project to Q, K, V + Q = self.W_Q(x).view(batch, seq_len, self.n_heads, self.d_head) + K = self.W_K(x).view(batch, seq_len, self.n_heads, self.d_head) + V = self.W_V(x).view(batch, seq_len, self.n_heads, self.d_head).transpose(1, 2) + + head_outputs = [] + + for h in range(self.n_heads): + Q_h = Q[:, :, h, :].reshape(batch * seq_len, self.d_head) + K_h = K[:, :, h, :].reshape(batch * seq_len, self.d_head) + + # Project to quantum input space + Q_proj = self.quantum_proj_q[h](Q_h) + K_proj = self.quantum_proj_k[h](K_h) + + # Normalize + Q_proj = F.normalize(Q_proj, p=2, dim=-1) + K_proj = F.normalize(K_proj, p=2, dim=-1) + + # Apply quantum layer + if self.quantum_layers[h] is not None: + try: + Q_q = self.quantum_layers[h](Q_proj).view( + batch, seq_len, self.qubit_counts[h] + ) + K_q = self.quantum_layers[h](K_proj).view( + batch, seq_len, self.qubit_counts[h] + ) + except Exception as e: + logger.warning(f"Quantum head {h} failed: {e}, using classical") + Q_q = torch.tanh(Q_proj[..., : self.qubit_counts[h]]).view( + batch, seq_len, self.qubit_counts[h] + ) + K_q = torch.tanh(K_proj[..., : self.qubit_counts[h]]).view( + batch, seq_len, self.qubit_counts[h] + ) + else: + # Classical fallback + Q_q = torch.tanh(Q_proj[..., : self.qubit_counts[h]]).view( + batch, seq_len, self.qubit_counts[h] + ) + K_q = torch.tanh(K_proj[..., : self.qubit_counts[h]]).view( + batch, seq_len, self.qubit_counts[h] + ) + + # Compute attention + attn_logits = torch.bmm(Q_q, K_q.transpose(1, 2)) * self.scales[h] + + if mask is not None: + attn_logits = attn_logits.masked_fill(mask == 0, float("-inf")) + + attn_weights = self.attn_dropout(F.softmax(attn_logits, dim=-1)) + + # Apply to values + V_h = V[:, h, :, :] + out_h = torch.bmm(attn_weights, V_h) + head_outputs.append(out_h) + + # Concatenate heads + out = torch.cat(head_outputs, dim=-1) + return self.W_O(out) + + def get_cache_stats(self) -> Dict[str, Dict[str, float]]: + """Get cache statistics for all quantum layers.""" + stats = {} + for i, layer in enumerate(self.quantum_layers): + if layer is not None and hasattr(layer, "get_cache_stats"): + stats[f"head_{i}"] = layer.get_cache_stats() + return stats + + +class QuantumPromptTuning(nn.Module): + """ + Quantum-enhanced prompt tuning layer. + + Learns quantum-parametrized soft prompts that are prepended to + input sequences, allowing task adaptation without fine-tuning + the entire model. + """ + + def __init__( + self, + n_prompts: int = 10, + d_model: int = 64, + n_qubits: int = 4, + n_quantum_layers: int = 2, + ): + super().__init__() + self.n_prompts = n_prompts + self.d_model = d_model + self.n_qubits = n_qubits + + # Classical prompt embeddings (initialization) + self.prompt_embeddings = nn.Parameter(torch.randn(n_prompts, d_model) * 0.01) + + # Quantum transformation layer + if QUANTUM_AVAILABLE: + self.quantum_transform = AdaptiveQuantumLayer( + n_qubits=n_qubits, + n_layers=n_quantum_layers, + cache_size=100, + ) + + # Projection layers + self.proj_in = nn.Linear(d_model, 2**n_qubits) + self.proj_out = nn.Linear(n_qubits, d_model) + else: + self.quantum_transform = None + self.proj_in = None + self.proj_out = None + + logger.info(f"QuantumPromptTuning: {n_prompts} prompts, d_model={d_model}") + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Prepend quantum-transformed prompts to input sequence. + + Args: + x: Input tensor (batch, seq_len, d_model) + + Returns: + (batch, n_prompts + seq_len, d_model) + """ + batch = x.shape[0] + + # Expand prompts for batch + prompts = self.prompt_embeddings.unsqueeze(0).expand(batch, -1, -1) + + # Apply quantum transformation + if self.quantum_transform is not None: + try: + # Project to quantum space + prompts_flat = prompts.reshape(batch * self.n_prompts, self.d_model) + prompts_proj = self.proj_in(prompts_flat) + prompts_proj = F.normalize(prompts_proj, p=2, dim=-1) + + # Quantum circuit + prompts_q = self.quantum_transform(prompts_proj) + + # Project back + prompts_transformed = self.proj_out(prompts_q) + prompts = prompts_transformed.view(batch, self.n_prompts, self.d_model) + + # Residual connection + prompts = prompts + self.prompt_embeddings.unsqueeze(0) + except Exception as e: + logger.warning(f"Quantum prompt transformation failed: {e}") + + # Concatenate with input + return torch.cat([prompts, x], dim=1) + + +class QuantumErrorMitigation(nn.Module): + """ + Error mitigation strategies for quantum circuits. + + Implements: + - Zero-noise extrapolation + - Measurement error mitigation + - Readout error correction + """ + + def __init__(self, n_qubits: int = 4, n_samples: int = 3): + super().__init__() + self.n_qubits = n_qubits + self.n_samples = n_samples + + # Learnable error parameters + self.readout_errors = nn.Parameter(torch.zeros(n_qubits)) + + def zero_noise_extrapolation( + self, + quantum_layer: nn.Module, + x: torch.Tensor, + noise_scales: List[float] | None = None, + ) -> torch.Tensor: + """ + Zero-noise extrapolation by running circuits at multiple noise levels. + + Args: + quantum_layer: Quantum layer to execute + x: Input tensor + noise_scales: Noise scaling factors + + Returns: + Extrapolated zero-noise result + """ + outputs = [] + + for scale in noise_scales: + # In practice, this would modify circuit noise parameters + # For now, we approximate with multiple samples + out = quantum_layer(x) + outputs.append(out) + + # Linear extrapolation to zero noise + outputs = torch.stack(outputs) + scales = torch.tensor(noise_scales, device=x.device).view(-1, 1, 1) + + # Fit line and extrapolate to scale=0 + # y = a + b*x, extrapolate to x=0 + mean_scale = scales.mean() + mean_out = outputs.mean(dim=0) + + numerator = ((scales - mean_scale) * (outputs - mean_out)).sum(dim=0) + denominator = ((scales - mean_scale) ** 2).sum() + + slope = numerator / (denominator + 1e-8) + intercept = mean_out - slope * mean_scale + + return intercept + + def readout_error_correction(self, probs: torch.Tensor) -> torch.Tensor: + """ + Correct measurement probabilities for readout errors. + + Args: + probs: Measurement probabilities (batch, n_qubits) + + Returns: + Corrected probabilities + """ + # Apply learned readout error correction + correction = torch.sigmoid(self.readout_errors) + corrected = probs * (1 - correction) + (1 - probs) * correction + return corrected + + +# Export all advanced components +__all__ = [ + "QuantumCircuitCache", + "AdaptiveQuantumLayer", + "MultiScaleQuantumAttention", + "QuantumPromptTuning", + "QuantumErrorMitigation", +] + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + logger.info("Advanced Quantum LLM Components loaded successfully") + + if QUANTUM_AVAILABLE: + # Test multi-scale attention + attn = MultiScaleQuantumAttention(d_model=64, n_heads=4, qubit_range=(2, 6)) + x = torch.randn(2, 10, 64) + out = attn(x) + logger.info(f"MultiScaleQuantumAttention output shape: {out.shape}") + logger.info(f"Cache stats: {attn.get_cache_stats()}") + + # Test prompt tuning + prompt_tuner = QuantumPromptTuning(n_prompts=5, d_model=64, n_qubits=4) + x = torch.randn(2, 10, 64) + out = prompt_tuner(x) + logger.info(f"QuantumPromptTuning output shape: {out.shape}") + else: + logger.warning("Quantum features not available for testing") diff --git a/ai-projects/quantum-ml/src/quantum_llm_datasets.py b/ai-projects/quantum-ml/src/quantum_llm_datasets.py index a2ec359d4..44a47e119 100644 --- a/ai-projects/quantum-ml/src/quantum_llm_datasets.py +++ b/ai-projects/quantum-ml/src/quantum_llm_datasets.py @@ -1,494 +1,494 @@ -""" -Quantum LLM Dataset Utilities -============================== - -Dataset loaders, tokenizers, and data processing for quantum LLM training. - -Supports: -- Character-level tokenization -- Subword/BPE tokenization -- Context window management -- Data augmentation -- Multi-source dataset integration - -Author: Quantum AI Workspace -Date: March 9, 2026 -""" - -import json -import logging -import random -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple - -import numpy as np -import torch -from torch.utils.data import Dataset - -logger = logging.getLogger(__name__) - - -class CharacterTokenizer: - """ - Character-level tokenizer for quantum LLM. - - Simple but effective for proof-of-concept quantum training. - """ - - def __init__(self, vocab_size: int = 256): - self.vocab_size = vocab_size - # ASCII characters + special tokens - self.char_to_id = {chr(i): i for i in range(min(256, vocab_size))} - self.id_to_char = {i: chr(i) for i in range(min(256, vocab_size))} - - # Special tokens - self.pad_token = "" - self.unk_token = "" - self.bos_token = "" - self.eos_token = "" - - # Add special tokens to vocab - special_tokens = [ - self.pad_token, - self.unk_token, - self.bos_token, - self.eos_token, - ] - for token in special_tokens: - if token not in self.char_to_id: - idx = len(self.char_to_id) - if idx < vocab_size: - self.char_to_id[token] = idx - self.id_to_char[idx] = token - - self.pad_id = self.char_to_id.get(self.pad_token, 0) - self.unk_id = self.char_to_id.get(self.unk_token, 1) - self.bos_id = self.char_to_id.get(self.bos_token, 2) - self.eos_id = self.char_to_id.get(self.eos_token, 3) - - logger.info(f"CharacterTokenizer initialized: vocab_size={self.vocab_size}") - - def encode(self, text: str, add_special_tokens: bool = True) -> List[int]: - """ - Encode text to token IDs. - - Args: - text: Input text - add_special_tokens: Whether to add BOS/EOS tokens - - Returns: - List of token IDs - """ - ids = [] - - if add_special_tokens: - ids.append(self.bos_id) - - for char in text: - ids.append(self.char_to_id.get(char, self.unk_id)) - - if add_special_tokens: - ids.append(self.eos_id) - - return ids - - def decode(self, ids: List[int], skip_special_tokens: bool = True) -> str: - """ - Decode token IDs to text. - - Args: - ids: List of token IDs - skip_special_tokens: Whether to skip special tokens - - Returns: - Decoded text - """ - special_ids = ( - {self.pad_id, self.bos_id, self.eos_id} if skip_special_tokens else set() - ) - - chars = [] - for id in ids: - if id not in special_ids: - chars.append(self.id_to_char.get(id, self.unk_token)) - - return "".join(chars) - - def save(self, path: Path): - """Save tokenizer.""" - data = { - "vocab_size": self.vocab_size, - "char_to_id": self.char_to_id, - "id_to_char": {int(k): v for k, v in self.id_to_char.items()}, - } - with open(path, "w") as f: - json.dump(data, f, indent=2) - logger.info(f"Tokenizer saved: {path}") - - @classmethod - def load(cls, path: Path): - """Load tokenizer.""" - with open(path) as f: - data = json.load(f) - - tokenizer = cls(data["vocab_size"]) - tokenizer.char_to_id = data["char_to_id"] - tokenizer.id_to_char = {int(k): v for k, v in data["id_to_char"].items()} - - logger.info(f"Tokenizer loaded: {path}") - return tokenizer - - -class TextDataset(Dataset): - """ - Dataset for text sequences. - - Handles tokenization and sequence windowing. - """ - - def __init__( - self, - texts: List[str], - tokenizer: CharacterTokenizer, - max_seq_length: int = 512, - stride: int = 256, - ): - self.texts = texts - self.tokenizer = tokenizer - self.max_seq_length = max_seq_length - self.stride = stride - - # Pre-tokenize and create windows - self.samples = [] - self._prepare_samples() - - logger.info(f"TextDataset: {len(texts)} texts → {len(self.samples)} samples") - - def _prepare_samples(self): - """Tokenize texts and create training samples.""" - for text in self.texts: - # Tokenize - token_ids = self.tokenizer.encode(text, add_special_tokens=False) - - # Create sliding windows - for i in range(0, len(token_ids) - self.max_seq_length, self.stride): - window = token_ids[i : i + self.max_seq_length + 1] - - if len(window) > 1: # Need at least 2 tokens for input/target - self.samples.append(window) - - def __len__(self) -> int: - return len(self.samples) - - def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Get a training sample. - - Returns: - (input_ids, target_ids) tuple - """ - sample = self.samples[idx] - - # Input is all but last token - input_ids = torch.tensor(sample[:-1], dtype=torch.long) - - # Target is all but first token (shifted by 1) - target_ids = torch.tensor(sample[1:], dtype=torch.long) - - # Padding if needed - if len(input_ids) < self.max_seq_length: - padding = self.max_seq_length - len(input_ids) - input_ids = torch.cat( - [ - input_ids, - torch.full((padding,), self.tokenizer.pad_id, dtype=torch.long), - ] - ) - target_ids = torch.cat( - [ - target_ids, - torch.full((padding,), self.tokenizer.pad_id, dtype=torch.long), - ] - ) - - return input_ids, target_ids - - -class MultiSourceDataset(Dataset): - """ - Dataset combining multiple text sources. - - Useful for training on diverse data. - """ - - def __init__( - self, - data_sources: List[Dict[str, Any]], - tokenizer: CharacterTokenizer, - max_seq_length: int = 512, - sampling_weights: Optional[List[float]] = None, - ): - self.data_sources = data_sources - self.tokenizer = tokenizer - self.max_seq_length = max_seq_length - self.sampling_weights = sampling_weights or [1.0] * len(data_sources) - - # Normalize weights - total_weight = sum(self.sampling_weights) - self.sampling_weights = [w / total_weight for w in self.sampling_weights] - - # Load all datasets - self.datasets = [] - for source in data_sources: - dataset = self._load_source(source) - self.datasets.append(dataset) - - self.total_samples = sum(len(ds) for ds in self.datasets) - - logger.info( - f"MultiSourceDataset: {len(data_sources)} sources, {self.total_samples} samples" - ) - - def _load_source(self, source: Dict[str, Any]) -> Dataset: - """Load a single data source.""" - source_type = source.get("type", "text") - path = Path(source["path"]) - - if source_type == "text": - with open(path) as f: - texts = [line.strip() for line in f if line.strip()] - elif source_type == "json": - with open(path) as f: - data = json.load(f) - texts = [item.get("text", "") for item in data] - else: - logger.warning(f"Unknown source type: {source_type}") - texts = [] - - return TextDataset( - texts=texts, - tokenizer=self.tokenizer, - max_seq_length=self.max_seq_length, - ) - - def __len__(self) -> int: - return self.total_samples - - def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: - """Get sample from randomly selected source.""" - # Choose source based on sampling weights - source_idx = np.random.choice(len(self.datasets), p=self.sampling_weights) - dataset = self.datasets[source_idx] - - # Get random sample from chosen dataset - sample_idx = random.randint(0, len(dataset) - 1) - - return dataset[sample_idx] - - -class QuantumDataAugmenter: - """ - Data augmentation specifically designed for quantum LLM training. - - Applies quantum-inspired perturbations to training data. - """ - - def __init__( - self, - noise_level: float = 0.1, - enable_quantum_noise: bool = True, - ): - self.noise_level = noise_level - self.enable_quantum_noise = enable_quantum_noise - - logger.info(f"QuantumDataAugmenter: noise_level={noise_level}") - - def augment(self, input_ids: torch.Tensor) -> torch.Tensor: - """ - Apply quantum-inspired augmentation. - - Args: - input_ids: Input token IDs [seq_length] - - Returns: - Augmented token IDs - """ - augmented = input_ids.clone() - - if self.enable_quantum_noise: - # Quantum-style bit flips (superposition-inspired) - flip_mask = torch.rand_like(augmented.float()) < self.noise_level - - # Random token substitutions - random_tokens = torch.randint_like(augmented, 0, 256) - augmented = torch.where(flip_mask, random_tokens, augmented) - - return augmented - - def augment_batch(self, batch: torch.Tensor) -> torch.Tensor: - """Apply augmentation to a batch.""" - return torch.stack([self.augment(sample) for sample in batch]) - - -class DatasetBuilder: - """ - Utility for building datasets from various sources. - """ - - @staticmethod - def from_text_file( - path: Path, - tokenizer: CharacterTokenizer, - max_seq_length: int = 512, - ) -> TextDataset: - """Load dataset from plain text file.""" - with open(path) as f: - texts = [line.strip() for line in f if line.strip()] - - return TextDataset(texts, tokenizer, max_seq_length) - - @staticmethod - def from_json_file( - path: Path, - tokenizer: CharacterTokenizer, - max_seq_length: int = 512, - text_field: str = "text", - ) -> TextDataset: - """Load dataset from JSON file.""" - with open(path) as f: - data = json.load(f) - - texts = [item.get(text_field, "") for item in data] - - return TextDataset(texts, tokenizer, max_seq_length) - - @staticmethod - def from_chat_dataset( - path: Path, - tokenizer: CharacterTokenizer, - max_seq_length: int = 512, - ) -> TextDataset: - """Load dataset from chat format.""" - with open(path) as f: - data = json.load(f) - - texts = [] - for conversation in data: - messages = conversation.get("messages", []) - text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - texts.append(text) - - return TextDataset(texts, tokenizer, max_seq_length) - - @staticmethod - def auto_detect_and_load( - path: Path, - tokenizer: CharacterTokenizer, - max_seq_length: int = 512, - ) -> Dataset: - """Auto-detect format and load dataset.""" - if not path.exists(): - raise FileNotFoundError(f"Dataset not found: {path}") - - # Try to detect format - if path.suffix == ".json": - with open(path) as f: - data = json.load(f) - - # Check if it's chat format - if isinstance(data, list) and len(data) > 0: - first_item = data[0] - if isinstance(first_item, dict) and "messages" in first_item: - logger.info(f"Detected chat format: {path}") - return DatasetBuilder.from_chat_dataset( - path, tokenizer, max_seq_length - ) - elif isinstance(first_item, dict) and "text" in first_item: - logger.info(f"Detected JSON format: {path}") - return DatasetBuilder.from_json_file( - path, tokenizer, max_seq_length - ) - - # Default to text format - logger.info(f"Using text format: {path}") - return DatasetBuilder.from_text_file(path, tokenizer, max_seq_length) - - -def create_train_val_split( - dataset: Dataset, - val_ratio: float = 0.1, - seed: int = 42, -) -> Tuple[Dataset, Dataset]: - """ - Split dataset into train and validation. - - Args: - dataset: Full dataset - val_ratio: Validation split ratio - seed: Random seed - - Returns: - (train_dataset, val_dataset) tuple - """ - torch.manual_seed(seed) - - total_size = len(dataset) - val_size = int(total_size * val_ratio) - train_size = total_size - val_size - - train_dataset, val_dataset = torch.utils.data.random_split( - dataset, - [train_size, val_size], - ) - - logger.info(f"Dataset split: {train_size} train, {val_size} val") - - return train_dataset, val_dataset - - -# Export all utilities -__all__ = [ - "CharacterTokenizer", - "TextDataset", - "MultiSourceDataset", - "QuantumDataAugmenter", - "DatasetBuilder", - "create_train_val_split", -] - - -if __name__ == "__main__": - logging.basicConfig(level=logging.INFO) - - # Test tokenizer - tokenizer = CharacterTokenizer(vocab_size=256) - text = "Hello, Quantum World!" - encoded = tokenizer.encode(text) - decoded = tokenizer.decode(encoded) - - logger.info(f"Original: {text}") - logger.info(f"Encoded: {encoded}") - logger.info(f"Decoded: {decoded}") - - # Test dataset - sample_texts = [ - "The quantum computer processes information.", - "Machine learning meets quantum computing.", - "Training language models with quantum circuits.", - ] - - dataset = TextDataset( - texts=sample_texts, - tokenizer=tokenizer, - max_seq_length=64, - stride=32, - ) - - logger.info(f"Dataset size: {len(dataset)}") - - # Test sample - input_ids, target_ids = dataset[0] - logger.info(f"Sample shapes: input={input_ids.shape}, target={target_ids.shape}") - logger.info(f"Sample input: {tokenizer.decode(input_ids.tolist())}") - - logger.info("✅ All dataset utilities tested successfully") +""" +Quantum LLM Dataset Utilities +============================== + +Dataset loaders, tokenizers, and data processing for quantum LLM training. + +Supports: +- Character-level tokenization +- Subword/BPE tokenization +- Context window management +- Data augmentation +- Multi-source dataset integration + +Author: Quantum AI Workspace +Date: March 9, 2026 +""" + +import json +import logging +import random +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch +from torch.utils.data import Dataset + +logger = logging.getLogger(__name__) + + +class CharacterTokenizer: + """ + Character-level tokenizer for quantum LLM. + + Simple but effective for proof-of-concept quantum training. + """ + + def __init__(self, vocab_size: int = 256): + self.vocab_size = vocab_size + # ASCII characters + special tokens + self.char_to_id = {chr(i): i for i in range(min(256, vocab_size))} + self.id_to_char = {i: chr(i) for i in range(min(256, vocab_size))} + + # Special tokens + self.pad_token = "" + self.unk_token = "" + self.bos_token = "" + self.eos_token = "" + + # Add special tokens to vocab + special_tokens = [ + self.pad_token, + self.unk_token, + self.bos_token, + self.eos_token, + ] + for token in special_tokens: + if token not in self.char_to_id: + idx = len(self.char_to_id) + if idx < vocab_size: + self.char_to_id[token] = idx + self.id_to_char[idx] = token + + self.pad_id = self.char_to_id.get(self.pad_token, 0) + self.unk_id = self.char_to_id.get(self.unk_token, 1) + self.bos_id = self.char_to_id.get(self.bos_token, 2) + self.eos_id = self.char_to_id.get(self.eos_token, 3) + + logger.info(f"CharacterTokenizer initialized: vocab_size={self.vocab_size}") + + def encode(self, text: str, add_special_tokens: bool = True) -> List[int]: + """ + Encode text to token IDs. + + Args: + text: Input text + add_special_tokens: Whether to add BOS/EOS tokens + + Returns: + List of token IDs + """ + ids = [] + + if add_special_tokens: + ids.append(self.bos_id) + + for char in text: + ids.append(self.char_to_id.get(char, self.unk_id)) + + if add_special_tokens: + ids.append(self.eos_id) + + return ids + + def decode(self, ids: List[int], skip_special_tokens: bool = True) -> str: + """ + Decode token IDs to text. + + Args: + ids: List of token IDs + skip_special_tokens: Whether to skip special tokens + + Returns: + Decoded text + """ + special_ids = ( + {self.pad_id, self.bos_id, self.eos_id} if skip_special_tokens else set() + ) + + chars = [] + for id in ids: + if id not in special_ids: + chars.append(self.id_to_char.get(id, self.unk_token)) + + return "".join(chars) + + def save(self, path: Path): + """Save tokenizer.""" + data = { + "vocab_size": self.vocab_size, + "char_to_id": self.char_to_id, + "id_to_char": {int(k): v for k, v in self.id_to_char.items()}, + } + with open(path, "w") as f: + json.dump(data, f, indent=2) + logger.info(f"Tokenizer saved: {path}") + + @classmethod + def load(cls, path: Path): + """Load tokenizer.""" + with open(path) as f: + data = json.load(f) + + tokenizer = cls(data["vocab_size"]) + tokenizer.char_to_id = data["char_to_id"] + tokenizer.id_to_char = {int(k): v for k, v in data["id_to_char"].items()} + + logger.info(f"Tokenizer loaded: {path}") + return tokenizer + + +class TextDataset(Dataset): + """ + Dataset for text sequences. + + Handles tokenization and sequence windowing. + """ + + def __init__( + self, + texts: List[str], + tokenizer: CharacterTokenizer, + max_seq_length: int = 512, + stride: int = 256, + ): + self.texts = texts + self.tokenizer = tokenizer + self.max_seq_length = max_seq_length + self.stride = stride + + # Pre-tokenize and create windows + self.samples = [] + self._prepare_samples() + + logger.info(f"TextDataset: {len(texts)} texts → {len(self.samples)} samples") + + def _prepare_samples(self): + """Tokenize texts and create training samples.""" + for text in self.texts: + # Tokenize + token_ids = self.tokenizer.encode(text, add_special_tokens=False) + + # Create sliding windows + for i in range(0, len(token_ids) - self.max_seq_length, self.stride): + window = token_ids[i : i + self.max_seq_length + 1] + + if len(window) > 1: # Need at least 2 tokens for input/target + self.samples.append(window) + + def __len__(self) -> int: + return len(self.samples) + + def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Get a training sample. + + Returns: + (input_ids, target_ids) tuple + """ + sample = self.samples[idx] + + # Input is all but last token + input_ids = torch.tensor(sample[:-1], dtype=torch.long) + + # Target is all but first token (shifted by 1) + target_ids = torch.tensor(sample[1:], dtype=torch.long) + + # Padding if needed + if len(input_ids) < self.max_seq_length: + padding = self.max_seq_length - len(input_ids) + input_ids = torch.cat( + [ + input_ids, + torch.full((padding,), self.tokenizer.pad_id, dtype=torch.long), + ] + ) + target_ids = torch.cat( + [ + target_ids, + torch.full((padding,), self.tokenizer.pad_id, dtype=torch.long), + ] + ) + + return input_ids, target_ids + + +class MultiSourceDataset(Dataset): + """ + Dataset combining multiple text sources. + + Useful for training on diverse data. + """ + + def __init__( + self, + data_sources: List[Dict[str, Any]], + tokenizer: CharacterTokenizer, + max_seq_length: int = 512, + sampling_weights: Optional[List[float]] = None, + ): + self.data_sources = data_sources + self.tokenizer = tokenizer + self.max_seq_length = max_seq_length + self.sampling_weights = sampling_weights or [1.0] * len(data_sources) + + # Normalize weights + total_weight = sum(self.sampling_weights) + self.sampling_weights = [w / total_weight for w in self.sampling_weights] + + # Load all datasets + self.datasets = [] + for source in data_sources: + dataset = self._load_source(source) + self.datasets.append(dataset) + + self.total_samples = sum(len(ds) for ds in self.datasets) + + logger.info( + f"MultiSourceDataset: {len(data_sources)} sources, {self.total_samples} samples" + ) + + def _load_source(self, source: Dict[str, Any]) -> Dataset: + """Load a single data source.""" + source_type = source.get("type", "text") + path = Path(source["path"]) + + if source_type == "text": + with open(path) as f: + texts = [line.strip() for line in f if line.strip()] + elif source_type == "json": + with open(path) as f: + data = json.load(f) + texts = [item.get("text", "") for item in data] + else: + logger.warning(f"Unknown source type: {source_type}") + texts = [] + + return TextDataset( + texts=texts, + tokenizer=self.tokenizer, + max_seq_length=self.max_seq_length, + ) + + def __len__(self) -> int: + return self.total_samples + + def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: + """Get sample from randomly selected source.""" + # Choose source based on sampling weights + source_idx = np.random.choice(len(self.datasets), p=self.sampling_weights) + dataset = self.datasets[source_idx] + + # Get random sample from chosen dataset + sample_idx = random.randint(0, len(dataset) - 1) + + return dataset[sample_idx] + + +class QuantumDataAugmenter: + """ + Data augmentation specifically designed for quantum LLM training. + + Applies quantum-inspired perturbations to training data. + """ + + def __init__( + self, + noise_level: float = 0.1, + enable_quantum_noise: bool = True, + ): + self.noise_level = noise_level + self.enable_quantum_noise = enable_quantum_noise + + logger.info(f"QuantumDataAugmenter: noise_level={noise_level}") + + def augment(self, input_ids: torch.Tensor) -> torch.Tensor: + """ + Apply quantum-inspired augmentation. + + Args: + input_ids: Input token IDs [seq_length] + + Returns: + Augmented token IDs + """ + augmented = input_ids.clone() + + if self.enable_quantum_noise: + # Quantum-style bit flips (superposition-inspired) + flip_mask = torch.rand_like(augmented.float()) < self.noise_level + + # Random token substitutions + random_tokens = torch.randint_like(augmented, 0, 256) + augmented = torch.where(flip_mask, random_tokens, augmented) + + return augmented + + def augment_batch(self, batch: torch.Tensor) -> torch.Tensor: + """Apply augmentation to a batch.""" + return torch.stack([self.augment(sample) for sample in batch]) + + +class DatasetBuilder: + """ + Utility for building datasets from various sources. + """ + + @staticmethod + def from_text_file( + path: Path, + tokenizer: CharacterTokenizer, + max_seq_length: int = 512, + ) -> TextDataset: + """Load dataset from plain text file.""" + with open(path) as f: + texts = [line.strip() for line in f if line.strip()] + + return TextDataset(texts, tokenizer, max_seq_length) + + @staticmethod + def from_json_file( + path: Path, + tokenizer: CharacterTokenizer, + max_seq_length: int = 512, + text_field: str = "text", + ) -> TextDataset: + """Load dataset from JSON file.""" + with open(path) as f: + data = json.load(f) + + texts = [item.get(text_field, "") for item in data] + + return TextDataset(texts, tokenizer, max_seq_length) + + @staticmethod + def from_chat_dataset( + path: Path, + tokenizer: CharacterTokenizer, + max_seq_length: int = 512, + ) -> TextDataset: + """Load dataset from chat format.""" + with open(path) as f: + data = json.load(f) + + texts = [] + for conversation in data: + messages = conversation.get("messages", []) + text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) + texts.append(text) + + return TextDataset(texts, tokenizer, max_seq_length) + + @staticmethod + def auto_detect_and_load( + path: Path, + tokenizer: CharacterTokenizer, + max_seq_length: int = 512, + ) -> Dataset: + """Auto-detect format and load dataset.""" + if not path.exists(): + raise FileNotFoundError(f"Dataset not found: {path}") + + # Try to detect format + if path.suffix == ".json": + with open(path) as f: + data = json.load(f) + + # Check if it's chat format + if isinstance(data, list) and len(data) > 0: + first_item = data[0] + if isinstance(first_item, dict) and "messages" in first_item: + logger.info(f"Detected chat format: {path}") + return DatasetBuilder.from_chat_dataset( + path, tokenizer, max_seq_length + ) + elif isinstance(first_item, dict) and "text" in first_item: + logger.info(f"Detected JSON format: {path}") + return DatasetBuilder.from_json_file( + path, tokenizer, max_seq_length + ) + + # Default to text format + logger.info(f"Using text format: {path}") + return DatasetBuilder.from_text_file(path, tokenizer, max_seq_length) + + +def create_train_val_split( + dataset: Dataset, + val_ratio: float = 0.1, + seed: int = 42, +) -> Tuple[Dataset, Dataset]: + """ + Split dataset into train and validation. + + Args: + dataset: Full dataset + val_ratio: Validation split ratio + seed: Random seed + + Returns: + (train_dataset, val_dataset) tuple + """ + torch.manual_seed(seed) + + total_size = len(dataset) + val_size = int(total_size * val_ratio) + train_size = total_size - val_size + + train_dataset, val_dataset = torch.utils.data.random_split( + dataset, + [train_size, val_size], + ) + + logger.info(f"Dataset split: {train_size} train, {val_size} val") + + return train_dataset, val_dataset + + +# Export all utilities +__all__ = [ + "CharacterTokenizer", + "TextDataset", + "MultiSourceDataset", + "QuantumDataAugmenter", + "DatasetBuilder", + "create_train_val_split", +] + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + + # Test tokenizer + tokenizer = CharacterTokenizer(vocab_size=256) + text = "Hello, Quantum World!" + encoded = tokenizer.encode(text) + decoded = tokenizer.decode(encoded) + + logger.info(f"Original: {text}") + logger.info(f"Encoded: {encoded}") + logger.info(f"Decoded: {decoded}") + + # Test dataset + sample_texts = [ + "The quantum computer processes information.", + "Machine learning meets quantum computing.", + "Training language models with quantum circuits.", + ] + + dataset = TextDataset( + texts=sample_texts, + tokenizer=tokenizer, + max_seq_length=64, + stride=32, + ) + + logger.info(f"Dataset size: {len(dataset)}") + + # Test sample + input_ids, target_ids = dataset[0] + logger.info(f"Sample shapes: input={input_ids.shape}, target={target_ids.shape}") + logger.info(f"Sample input: {tokenizer.decode(input_ids.tolist())}") + + logger.info("✅ All dataset utilities tested successfully") diff --git a/ai-projects/quantum-ml/src/quantum_llm_hybrid_trainer.py b/ai-projects/quantum-ml/src/quantum_llm_hybrid_trainer.py index 0b77e7912..3aeff1016 100644 --- a/ai-projects/quantum-ml/src/quantum_llm_hybrid_trainer.py +++ b/ai-projects/quantum-ml/src/quantum_llm_hybrid_trainer.py @@ -1,575 +1,575 @@ -""" -Hybrid Quantum-Classical LLM Training Orchestrator -=================================================== - -Advanced training workflow manager for quantum-enhanced language models. - -Features: -- Adaptive quantum/classical routing -- Progressive quantum integration -- Curriculum learning for quantum circuits -- Resource-aware training scheduling -- Multi-stage training pipeline - -Author: Quantum AI Workspace -Date: March 9, 2026 -""" - -import json -import logging -import time -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any, Dict, List, Optional - -import numpy as np -import torch -import torch.nn as nn -import torch.optim as optim -from torch.utils.data import DataLoader - -logger = logging.getLogger(__name__) - - -@dataclass -class TrainingStage: - """Configuration for a training stage.""" - - name: str - quantum_ratio: float # 0.0 to 1.0 - num_epochs: int - learning_rate: float - batch_size: int - enable_quantum_layers: List[str] = field(default_factory=list) - freeze_classical_layers: bool = False - - -@dataclass -class TrainingMetrics: - """Metrics for training monitoring.""" - - stage: str = "" - epoch: int = 0 - batch: int = 0 - loss: float = 0.0 - perplexity: float = 0.0 - quantum_execution_time: float = 0.0 - classical_execution_time: float = 0.0 - quantum_layer_count: int = 0 - learning_rate: float = 0.0 - timestamp: float = field(default_factory=time.time) - - -class CurriculumScheduler: - """ - Manages progressive quantum integration during training. - - Gradually increases quantum circuit complexity as training progresses. - """ - - def __init__( - self, - stages: List[TrainingStage], - warmup_stages: int = 2, - ): - self.stages = stages - self.warmup_stages = warmup_stages - self.current_stage_idx = 0 - - logger.info( - f"CurriculumScheduler: {len(stages)} stages, {warmup_stages} warmup stages" - ) - - def get_current_stage(self) -> TrainingStage: - """Get current training stage.""" - return self.stages[self.current_stage_idx] - - def advance_stage(self) -> bool: - """ - Advance to next training stage. - - Returns: - True if advanced, False if at final stage - """ - if self.current_stage_idx < len(self.stages) - 1: - self.current_stage_idx += 1 - logger.info( - f"Advanced to stage {self.current_stage_idx}: {self.get_current_stage().name}" - ) - return True - return False - - def is_warmup_stage(self) -> bool: - """Check if currently in warmup.""" - return self.current_stage_idx < self.warmup_stages - - def get_quantum_ratio(self) -> float: - """Get current quantum operation ratio.""" - return self.stages[self.current_stage_idx].quantum_ratio - - def get_progress(self) -> Dict[str, Any]: - """Get training progress.""" - return { - "current_stage": self.current_stage_idx, - "total_stages": len(self.stages), - "stage_name": self.get_current_stage().name, - "quantum_ratio": self.get_quantum_ratio(), - "is_warmup": self.is_warmup_stage(), - } - - -class AdaptiveQuantumRouter: - """ - Routes operations to quantum or classical based on learned policy. - - Uses reinforcement learning to optimize routing decisions. - """ - - def __init__( - self, - input_dim: int = 64, - learning_rate: float = 0.001, - ): - self.input_dim = input_dim - - # Simple policy network - self.policy_net = nn.Sequential( - nn.Linear(input_dim + 5, 128), # +5 for context features - nn.ReLU(), - nn.Linear(128, 64), - nn.ReLU(), - nn.Linear(64, 1), - nn.Sigmoid(), # Probability of using quantum - ) - - self.optimizer = optim.Adam(self.policy_net.parameters(), lr=learning_rate) - self.routing_history = [] - - logger.info("AdaptiveQuantumRouter initialized") - - def route( - self, - input_features: torch.Tensor, - context: Dict[str, float], - ) -> bool: - """ - Decide whether to use quantum or classical execution. - - Args: - input_features: Input tensor features - context: Contextual information (complexity, resource usage, etc.) - - Returns: - True for quantum, False for classical - """ - # Extract context features - context_features = torch.tensor( - [ - context.get("complexity", 0.5), - context.get("batch_size", 16) / 32.0, # normalized - context.get("sequence_length", 128) / 512.0, - context.get("quantum_capacity", 0.5), - context.get("training_progress", 0.0), - ], - dtype=torch.float32, - ) - - # Combine with input features - if len(input_features.shape) > 1: - input_flat = input_features.mean(dim=0) # aggregate - else: - input_flat = input_features - - # Pad or truncate to input_dim - if input_flat.shape[0] > self.input_dim: - input_flat = input_flat[: self.input_dim] - elif input_flat.shape[0] < self.input_dim: - padding = torch.zeros(self.input_dim - input_flat.shape[0]) - input_flat = torch.cat([input_flat, padding]) - - combined = torch.cat([input_flat, context_features]) - - # Get routing probability - with torch.no_grad(): - prob_quantum = self.policy_net(combined).item() - - decision = prob_quantum > 0.5 - - self.routing_history.append( - { - "decision": "quantum" if decision else "classical", - "probability": prob_quantum, - "context": context.copy(), - } - ) - - return decision - - def update_policy( - self, - reward: float, - recent_decisions: int = 10, - ): - """ - Update routing policy based on reward signal. - - Args: - reward: Reward signal (higher is better) - recent_decisions: Number of recent decisions to update - """ - if len(self.routing_history) < recent_decisions: - return - - # Simple policy gradient update - # In practice, would use proper RL algorithm (PPO, A2C, etc.) - loss = -reward * len(self.routing_history[-recent_decisions:]) - - self.optimizer.zero_grad() - loss_tensor = torch.tensor(loss, requires_grad=True) - loss_tensor.backward() - self.optimizer.step() - - def get_routing_stats(self) -> Dict[str, Any]: - """Get routing statistics.""" - if not self.routing_history: - return {"quantum_ratio": 0.0, "total_decisions": 0} - - quantum_count = sum( - 1 for h in self.routing_history if h["decision"] == "quantum" - ) - total = len(self.routing_history) - - return { - "quantum_ratio": quantum_count / total, - "classical_ratio": (total - quantum_count) / total, - "total_decisions": total, - "avg_quantum_prob": np.mean( - [h["probability"] for h in self.routing_history] - ), - } - - -class HybridTrainingOrchestrator: - """ - Orchestrates quantum-classical hybrid training workflow. - - Manages: - - Multi-stage curriculum - - Quantum/classical routing - - Resource monitoring - - Checkpoint management - - Metric tracking - """ - - def __init__( - self, - model: nn.Module, - stages: List[TrainingStage], - output_dir: Path, - device: str = "cpu", - ): - self.model = model.to(device) - self.device = device - self.output_dir = Path(output_dir) - self.output_dir.mkdir(parents=True, exist_ok=True) - - # Initialize components - self.curriculum = CurriculumScheduler(stages) - self.router = AdaptiveQuantumRouter() - - # Metrics - self.metrics_history: List[TrainingMetrics] = [] - self.best_loss = float("inf") - self.global_step = 0 - - logger.info( - f"HybridTrainingOrchestrator initialized: {len(stages)} stages, device={device}" - ) - - def train_stage( - self, - stage: TrainingStage, - train_loader: DataLoader, - val_loader: Optional[DataLoader] = None, - ) -> Dict[str, float]: - """ - Train for one curriculum stage. - - Args: - stage: Training stage configuration - train_loader: Training data loader - val_loader: Optional validation data loader - - Returns: - Stage metrics - """ - logger.info( - f"Starting stage: {stage.name} (quantum_ratio={stage.quantum_ratio:.2f})" - ) - - # Configure model for this stage - self._configure_model_for_stage(stage) - - # Setup optimizer - optimizer = optim.AdamW( - [p for p in self.model.parameters() if p.requires_grad], - lr=stage.learning_rate, - ) - - criterion = nn.CrossEntropyLoss() - - stage_metrics = { - "total_loss": 0.0, - "total_batches": 0, - "quantum_execution_time": 0.0, - "classical_execution_time": 0.0, - } - - for epoch in range(stage.num_epochs): - epoch_loss = 0.0 - - self.model.train() - for batch_idx, (inputs, targets) in enumerate(train_loader): - inputs = inputs.to(self.device) - targets = targets.to(self.device) - - # Forward pass with timing - start_time = time.time() - outputs = self.model(inputs) - forward_time = time.time() - start_time - - # Compute loss - loss = criterion(outputs.view(-1, outputs.size(-1)), targets.view(-1)) - - # Backward pass - optimizer.zero_grad() - loss.backward() - optimizer.step() - - # Track metrics - metrics = TrainingMetrics( - stage=stage.name, - epoch=epoch, - batch=batch_idx, - loss=loss.item(), - perplexity=torch.exp(loss).item(), - quantum_execution_time=forward_time * stage.quantum_ratio, - classical_execution_time=forward_time * (1 - stage.quantum_ratio), - learning_rate=stage.learning_rate, - ) - - self.metrics_history.append(metrics) - self.global_step += 1 - - epoch_loss += loss.item() - stage_metrics["total_loss"] += loss.item() - stage_metrics["total_batches"] += 1 - stage_metrics[ - "quantum_execution_time" - ] += metrics.quantum_execution_time - stage_metrics[ - "classical_execution_time" - ] += metrics.classical_execution_time - - # Logging - if batch_idx % 10 == 0: - logger.info( - f"Stage {stage.name} | Epoch {epoch}/{stage.num_epochs} | " - f"Batch {batch_idx} | Loss: {loss.item():.4f} | " - f"PPL: {metrics.perplexity:.2f}" - ) - - # Update router policy based on performance - if batch_idx % 50 == 0 and batch_idx > 0: - reward = -epoch_loss / ( - batch_idx + 1 - ) # negative avg loss as reward - self.router.update_policy(reward) - - # Validation - if val_loader is not None: - val_metrics = self._validate(val_loader, criterion) - logger.info( - f"Validation | Epoch {epoch} | Loss: {val_metrics['loss']:.4f} | " - f"PPL: {val_metrics['perplexity']:.2f}" - ) - - # Checkpoint if best - if val_metrics["loss"] < self.best_loss: - self.best_loss = val_metrics["loss"] - self._save_checkpoint(stage.name, epoch, val_metrics) - - # Compute stage average metrics - avg_metrics = { - "avg_loss": stage_metrics["total_loss"] / stage_metrics["total_batches"], - "total_quantum_time": stage_metrics["quantum_execution_time"], - "total_classical_time": stage_metrics["classical_execution_time"], - "quantum_time_ratio": stage_metrics["quantum_execution_time"] - / ( - stage_metrics["quantum_execution_time"] - + stage_metrics["classical_execution_time"] - + 1e-8 - ), - } - - logger.info( - f"Stage {stage.name} complete: avg_loss={avg_metrics['avg_loss']:.4f}" - ) - - return avg_metrics - - def _configure_model_for_stage(self, stage: TrainingStage): - """Configure model layers for current stage.""" - # Enable/disable quantum layers - for name, module in self.model.named_modules(): - if "quantum" in name.lower(): - module.requires_grad = name in stage.enable_quantum_layers - - # Freeze classical layers if requested - if stage.freeze_classical_layers: - for name, param in self.model.named_parameters(): - if "quantum" not in name.lower(): - param.requires_grad = False - - def _validate(self, val_loader: DataLoader, criterion) -> Dict[str, float]: - """Run validation.""" - self.model.eval() - total_loss = 0.0 - total_batches = 0 - - with torch.no_grad(): - for inputs, targets in val_loader: - inputs = inputs.to(self.device) - targets = targets.to(self.device) - - outputs = self.model(inputs) - loss = criterion(outputs.view(-1, outputs.size(-1)), targets.view(-1)) - - total_loss += loss.item() - total_batches += 1 - - avg_loss = total_loss / total_batches - - return { - "loss": avg_loss, - "perplexity": np.exp(avg_loss), - } - - def _save_checkpoint(self, stage_name: str, epoch: int, metrics: Dict): - """Save training checkpoint.""" - checkpoint_path = self.output_dir / f"checkpoint_{stage_name}_epoch{epoch}.pt" - - torch.save( - { - "model_state_dict": self.model.state_dict(), - "stage": stage_name, - "epoch": epoch, - "global_step": self.global_step, - "metrics": metrics, - "router_state": self.router.policy_net.state_dict(), - }, - checkpoint_path, - ) - - logger.info(f"Checkpoint saved: {checkpoint_path}") - - def run_full_curriculum( - self, - train_loader: DataLoader, - val_loader: Optional[DataLoader] = None, - ) -> Dict[str, Any]: - """ - Run complete curriculum training. - - Returns: - Final training report - """ - logger.info("Starting full curriculum training") - start_time = time.time() - - all_stage_metrics = [] - - while True: - stage = self.curriculum.get_current_stage() - stage_metrics = self.train_stage(stage, train_loader, val_loader) - all_stage_metrics.append( - { - "stage": stage.name, - "metrics": stage_metrics, - } - ) - - if not self.curriculum.advance_stage(): - break - - total_time = time.time() - start_time - - # Generate final report - report = { - "total_stages": len(all_stage_metrics), - "total_time": total_time, - "total_steps": self.global_step, - "best_loss": self.best_loss, - "stage_metrics": all_stage_metrics, - "routing_stats": self.router.get_routing_stats(), - "curriculum_progress": self.curriculum.get_progress(), - } - - # Save report - report_path = self.output_dir / "training_report.json" - with open(report_path, "w") as f: - json.dump(report, f, indent=2) - - logger.info( - f"✅ Training complete: {total_time:.2f}s, {self.global_step} steps" - ) - logger.info(f"Report saved: {report_path}") - - return report - - -# Export key components -__all__ = [ - "TrainingStage", - "TrainingMetrics", - "CurriculumScheduler", - "AdaptiveQuantumRouter", - "HybridTrainingOrchestrator", -] - - -if __name__ == "__main__": - logging.basicConfig(level=logging.INFO) - - # Example usage - stages = [ - TrainingStage( - name="warmup_classical", - quantum_ratio=0.0, - num_epochs=2, - learning_rate=1e-4, - batch_size=16, - ), - TrainingStage( - name="gradual_quantum", - quantum_ratio=0.3, - num_epochs=5, - learning_rate=5e-5, - batch_size=16, - enable_quantum_layers=["attention"], - ), - TrainingStage( - name="full_quantum", - quantum_ratio=0.7, - num_epochs=10, - learning_rate=1e-5, - batch_size=8, - enable_quantum_layers=["attention", "feedforward"], - ), - ] - - logger.info(f"✅ Example training curriculum: {len(stages)} stages") - for stage in stages: - logger.info( - f" - {stage.name}: quantum_ratio={stage.quantum_ratio}, epochs={stage.num_epochs}" - ) +""" +Hybrid Quantum-Classical LLM Training Orchestrator +=================================================== + +Advanced training workflow manager for quantum-enhanced language models. + +Features: +- Adaptive quantum/classical routing +- Progressive quantum integration +- Curriculum learning for quantum circuits +- Resource-aware training scheduling +- Multi-stage training pipeline + +Author: Quantum AI Workspace +Date: March 9, 2026 +""" + +import json +import logging +import time +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Dict, List, Optional + +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader + +logger = logging.getLogger(__name__) + + +@dataclass +class TrainingStage: + """Configuration for a training stage.""" + + name: str + quantum_ratio: float # 0.0 to 1.0 + num_epochs: int + learning_rate: float + batch_size: int + enable_quantum_layers: List[str] = field(default_factory=list) + freeze_classical_layers: bool = False + + +@dataclass +class TrainingMetrics: + """Metrics for training monitoring.""" + + stage: str = "" + epoch: int = 0 + batch: int = 0 + loss: float = 0.0 + perplexity: float = 0.0 + quantum_execution_time: float = 0.0 + classical_execution_time: float = 0.0 + quantum_layer_count: int = 0 + learning_rate: float = 0.0 + timestamp: float = field(default_factory=time.time) + + +class CurriculumScheduler: + """ + Manages progressive quantum integration during training. + + Gradually increases quantum circuit complexity as training progresses. + """ + + def __init__( + self, + stages: List[TrainingStage], + warmup_stages: int = 2, + ): + self.stages = stages + self.warmup_stages = warmup_stages + self.current_stage_idx = 0 + + logger.info( + f"CurriculumScheduler: {len(stages)} stages, {warmup_stages} warmup stages" + ) + + def get_current_stage(self) -> TrainingStage: + """Get current training stage.""" + return self.stages[self.current_stage_idx] + + def advance_stage(self) -> bool: + """ + Advance to next training stage. + + Returns: + True if advanced, False if at final stage + """ + if self.current_stage_idx < len(self.stages) - 1: + self.current_stage_idx += 1 + logger.info( + f"Advanced to stage {self.current_stage_idx}: {self.get_current_stage().name}" + ) + return True + return False + + def is_warmup_stage(self) -> bool: + """Check if currently in warmup.""" + return self.current_stage_idx < self.warmup_stages + + def get_quantum_ratio(self) -> float: + """Get current quantum operation ratio.""" + return self.stages[self.current_stage_idx].quantum_ratio + + def get_progress(self) -> Dict[str, Any]: + """Get training progress.""" + return { + "current_stage": self.current_stage_idx, + "total_stages": len(self.stages), + "stage_name": self.get_current_stage().name, + "quantum_ratio": self.get_quantum_ratio(), + "is_warmup": self.is_warmup_stage(), + } + + +class AdaptiveQuantumRouter: + """ + Routes operations to quantum or classical based on learned policy. + + Uses reinforcement learning to optimize routing decisions. + """ + + def __init__( + self, + input_dim: int = 64, + learning_rate: float = 0.001, + ): + self.input_dim = input_dim + + # Simple policy network + self.policy_net = nn.Sequential( + nn.Linear(input_dim + 5, 128), # +5 for context features + nn.ReLU(), + nn.Linear(128, 64), + nn.ReLU(), + nn.Linear(64, 1), + nn.Sigmoid(), # Probability of using quantum + ) + + self.optimizer = optim.Adam(self.policy_net.parameters(), lr=learning_rate) + self.routing_history = [] + + logger.info("AdaptiveQuantumRouter initialized") + + def route( + self, + input_features: torch.Tensor, + context: Dict[str, float], + ) -> bool: + """ + Decide whether to use quantum or classical execution. + + Args: + input_features: Input tensor features + context: Contextual information (complexity, resource usage, etc.) + + Returns: + True for quantum, False for classical + """ + # Extract context features + context_features = torch.tensor( + [ + context.get("complexity", 0.5), + context.get("batch_size", 16) / 32.0, # normalized + context.get("sequence_length", 128) / 512.0, + context.get("quantum_capacity", 0.5), + context.get("training_progress", 0.0), + ], + dtype=torch.float32, + ) + + # Combine with input features + if len(input_features.shape) > 1: + input_flat = input_features.mean(dim=0) # aggregate + else: + input_flat = input_features + + # Pad or truncate to input_dim + if input_flat.shape[0] > self.input_dim: + input_flat = input_flat[: self.input_dim] + elif input_flat.shape[0] < self.input_dim: + padding = torch.zeros(self.input_dim - input_flat.shape[0]) + input_flat = torch.cat([input_flat, padding]) + + combined = torch.cat([input_flat, context_features]) + + # Get routing probability + with torch.no_grad(): + prob_quantum = self.policy_net(combined).item() + + decision = prob_quantum > 0.5 + + self.routing_history.append( + { + "decision": "quantum" if decision else "classical", + "probability": prob_quantum, + "context": context.copy(), + } + ) + + return decision + + def update_policy( + self, + reward: float, + recent_decisions: int = 10, + ): + """ + Update routing policy based on reward signal. + + Args: + reward: Reward signal (higher is better) + recent_decisions: Number of recent decisions to update + """ + if len(self.routing_history) < recent_decisions: + return + + # Simple policy gradient update + # In practice, would use proper RL algorithm (PPO, A2C, etc.) + loss = -reward * len(self.routing_history[-recent_decisions:]) + + self.optimizer.zero_grad() + loss_tensor = torch.tensor(loss, requires_grad=True) + loss_tensor.backward() + self.optimizer.step() + + def get_routing_stats(self) -> Dict[str, Any]: + """Get routing statistics.""" + if not self.routing_history: + return {"quantum_ratio": 0.0, "total_decisions": 0} + + quantum_count = sum( + 1 for h in self.routing_history if h["decision"] == "quantum" + ) + total = len(self.routing_history) + + return { + "quantum_ratio": quantum_count / total, + "classical_ratio": (total - quantum_count) / total, + "total_decisions": total, + "avg_quantum_prob": np.mean( + [h["probability"] for h in self.routing_history] + ), + } + + +class HybridTrainingOrchestrator: + """ + Orchestrates quantum-classical hybrid training workflow. + + Manages: + - Multi-stage curriculum + - Quantum/classical routing + - Resource monitoring + - Checkpoint management + - Metric tracking + """ + + def __init__( + self, + model: nn.Module, + stages: List[TrainingStage], + output_dir: Path, + device: str = "cpu", + ): + self.model = model.to(device) + self.device = device + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + # Initialize components + self.curriculum = CurriculumScheduler(stages) + self.router = AdaptiveQuantumRouter() + + # Metrics + self.metrics_history: List[TrainingMetrics] = [] + self.best_loss = float("inf") + self.global_step = 0 + + logger.info( + f"HybridTrainingOrchestrator initialized: {len(stages)} stages, device={device}" + ) + + def train_stage( + self, + stage: TrainingStage, + train_loader: DataLoader, + val_loader: Optional[DataLoader] = None, + ) -> Dict[str, float]: + """ + Train for one curriculum stage. + + Args: + stage: Training stage configuration + train_loader: Training data loader + val_loader: Optional validation data loader + + Returns: + Stage metrics + """ + logger.info( + f"Starting stage: {stage.name} (quantum_ratio={stage.quantum_ratio:.2f})" + ) + + # Configure model for this stage + self._configure_model_for_stage(stage) + + # Setup optimizer + optimizer = optim.AdamW( + [p for p in self.model.parameters() if p.requires_grad], + lr=stage.learning_rate, + ) + + criterion = nn.CrossEntropyLoss() + + stage_metrics = { + "total_loss": 0.0, + "total_batches": 0, + "quantum_execution_time": 0.0, + "classical_execution_time": 0.0, + } + + for epoch in range(stage.num_epochs): + epoch_loss = 0.0 + + self.model.train() + for batch_idx, (inputs, targets) in enumerate(train_loader): + inputs = inputs.to(self.device) + targets = targets.to(self.device) + + # Forward pass with timing + start_time = time.time() + outputs = self.model(inputs) + forward_time = time.time() - start_time + + # Compute loss + loss = criterion(outputs.view(-1, outputs.size(-1)), targets.view(-1)) + + # Backward pass + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # Track metrics + metrics = TrainingMetrics( + stage=stage.name, + epoch=epoch, + batch=batch_idx, + loss=loss.item(), + perplexity=torch.exp(loss).item(), + quantum_execution_time=forward_time * stage.quantum_ratio, + classical_execution_time=forward_time * (1 - stage.quantum_ratio), + learning_rate=stage.learning_rate, + ) + + self.metrics_history.append(metrics) + self.global_step += 1 + + epoch_loss += loss.item() + stage_metrics["total_loss"] += loss.item() + stage_metrics["total_batches"] += 1 + stage_metrics[ + "quantum_execution_time" + ] += metrics.quantum_execution_time + stage_metrics[ + "classical_execution_time" + ] += metrics.classical_execution_time + + # Logging + if batch_idx % 10 == 0: + logger.info( + f"Stage {stage.name} | Epoch {epoch}/{stage.num_epochs} | " + f"Batch {batch_idx} | Loss: {loss.item():.4f} | " + f"PPL: {metrics.perplexity:.2f}" + ) + + # Update router policy based on performance + if batch_idx % 50 == 0 and batch_idx > 0: + reward = -epoch_loss / ( + batch_idx + 1 + ) # negative avg loss as reward + self.router.update_policy(reward) + + # Validation + if val_loader is not None: + val_metrics = self._validate(val_loader, criterion) + logger.info( + f"Validation | Epoch {epoch} | Loss: {val_metrics['loss']:.4f} | " + f"PPL: {val_metrics['perplexity']:.2f}" + ) + + # Checkpoint if best + if val_metrics["loss"] < self.best_loss: + self.best_loss = val_metrics["loss"] + self._save_checkpoint(stage.name, epoch, val_metrics) + + # Compute stage average metrics + avg_metrics = { + "avg_loss": stage_metrics["total_loss"] / stage_metrics["total_batches"], + "total_quantum_time": stage_metrics["quantum_execution_time"], + "total_classical_time": stage_metrics["classical_execution_time"], + "quantum_time_ratio": stage_metrics["quantum_execution_time"] + / ( + stage_metrics["quantum_execution_time"] + + stage_metrics["classical_execution_time"] + + 1e-8 + ), + } + + logger.info( + f"Stage {stage.name} complete: avg_loss={avg_metrics['avg_loss']:.4f}" + ) + + return avg_metrics + + def _configure_model_for_stage(self, stage: TrainingStage): + """Configure model layers for current stage.""" + # Enable/disable quantum layers + for name, module in self.model.named_modules(): + if "quantum" in name.lower(): + module.requires_grad = name in stage.enable_quantum_layers + + # Freeze classical layers if requested + if stage.freeze_classical_layers: + for name, param in self.model.named_parameters(): + if "quantum" not in name.lower(): + param.requires_grad = False + + def _validate(self, val_loader: DataLoader, criterion) -> Dict[str, float]: + """Run validation.""" + self.model.eval() + total_loss = 0.0 + total_batches = 0 + + with torch.no_grad(): + for inputs, targets in val_loader: + inputs = inputs.to(self.device) + targets = targets.to(self.device) + + outputs = self.model(inputs) + loss = criterion(outputs.view(-1, outputs.size(-1)), targets.view(-1)) + + total_loss += loss.item() + total_batches += 1 + + avg_loss = total_loss / total_batches + + return { + "loss": avg_loss, + "perplexity": np.exp(avg_loss), + } + + def _save_checkpoint(self, stage_name: str, epoch: int, metrics: Dict): + """Save training checkpoint.""" + checkpoint_path = self.output_dir / f"checkpoint_{stage_name}_epoch{epoch}.pt" + + torch.save( + { + "model_state_dict": self.model.state_dict(), + "stage": stage_name, + "epoch": epoch, + "global_step": self.global_step, + "metrics": metrics, + "router_state": self.router.policy_net.state_dict(), + }, + checkpoint_path, + ) + + logger.info(f"Checkpoint saved: {checkpoint_path}") + + def run_full_curriculum( + self, + train_loader: DataLoader, + val_loader: Optional[DataLoader] = None, + ) -> Dict[str, Any]: + """ + Run complete curriculum training. + + Returns: + Final training report + """ + logger.info("Starting full curriculum training") + start_time = time.time() + + all_stage_metrics = [] + + while True: + stage = self.curriculum.get_current_stage() + stage_metrics = self.train_stage(stage, train_loader, val_loader) + all_stage_metrics.append( + { + "stage": stage.name, + "metrics": stage_metrics, + } + ) + + if not self.curriculum.advance_stage(): + break + + total_time = time.time() - start_time + + # Generate final report + report = { + "total_stages": len(all_stage_metrics), + "total_time": total_time, + "total_steps": self.global_step, + "best_loss": self.best_loss, + "stage_metrics": all_stage_metrics, + "routing_stats": self.router.get_routing_stats(), + "curriculum_progress": self.curriculum.get_progress(), + } + + # Save report + report_path = self.output_dir / "training_report.json" + with open(report_path, "w") as f: + json.dump(report, f, indent=2) + + logger.info( + f"✅ Training complete: {total_time:.2f}s, {self.global_step} steps" + ) + logger.info(f"Report saved: {report_path}") + + return report + + +# Export key components +__all__ = [ + "TrainingStage", + "TrainingMetrics", + "CurriculumScheduler", + "AdaptiveQuantumRouter", + "HybridTrainingOrchestrator", +] + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + + # Example usage + stages = [ + TrainingStage( + name="warmup_classical", + quantum_ratio=0.0, + num_epochs=2, + learning_rate=1e-4, + batch_size=16, + ), + TrainingStage( + name="gradual_quantum", + quantum_ratio=0.3, + num_epochs=5, + learning_rate=5e-5, + batch_size=16, + enable_quantum_layers=["attention"], + ), + TrainingStage( + name="full_quantum", + quantum_ratio=0.7, + num_epochs=10, + learning_rate=1e-5, + batch_size=8, + enable_quantum_layers=["attention", "feedforward"], + ), + ] + + logger.info(f"✅ Example training curriculum: {len(stages)} stages") + for stage in stages: + logger.info( + f" - {stage.name}: quantum_ratio={stage.quantum_ratio}, epochs={stage.num_epochs}" + ) diff --git a/ai-projects/quantum-ml/src/quantum_llm_integrated.py b/ai-projects/quantum-ml/src/quantum_llm_integrated.py index 37f3d414a..738f99129 100644 --- a/ai-projects/quantum-ml/src/quantum_llm_integrated.py +++ b/ai-projects/quantum-ml/src/quantum_llm_integrated.py @@ -1,585 +1,585 @@ -""" -Quantum LLM Complete Integration -================================= - -Unified interface integrating all quantum LLM components: -- Advanced quantum layers -- Circuit optimization -- Hybrid training orchestration -- Real-time monitoring - -Complete end-to-end quantum-enhanced language model training system. - -Author: Quantum AI Workspace -Date: March 9, 2026 -""" - -import argparse -import json -import logging -from pathlib import Path -from typing import Any, Dict, List, Optional - -import torch -import torch.nn as nn -import yaml -from torch.utils.data import DataLoader, Dataset - -# Import all quantum LLM components -try: - from quantum_llm_advanced import (AdaptiveQuantumLayer, - MultiScaleQuantumAttention, - QuantumCircuitCache, - QuantumErrorMitigation, - QuantumPromptTuning) - - ADVANCED_AVAILABLE = True -except ImportError: - ADVANCED_AVAILABLE = False - logging.warning("Advanced quantum components not available") - -try: - from quantum_circuit_optimizer import (AdaptiveCircuitScheduler, - BatchCircuitExecutor, - CircuitCompiler, - OptimizationStrategy, - QuantumClassicalPartitioner) - - OPTIMIZER_AVAILABLE = True -except ImportError: - OPTIMIZER_AVAILABLE = False - logging.warning("Circuit optimizer not available") - -try: - from quantum_llm_hybrid_trainer import (AdaptiveQuantumRouter, - CurriculumScheduler, - HybridTrainingOrchestrator, - TrainingStage) - - TRAINER_AVAILABLE = True -except ImportError: - TRAINER_AVAILABLE = False - logging.warning("Hybrid trainer not available") - -try: - from quantum_llm_monitor import (QuantumMetrics, TrainingDashboard, - TrainingSnapshot, VisualizationExporter) - - MONITOR_AVAILABLE = True -except ImportError: - MONITOR_AVAILABLE = False - logging.warning("Monitoring tools not available") - -logger = logging.getLogger(__name__) - - -class QuantumLLMConfig: - """Configuration for quantum-enhanced LLM.""" - - def __init__(self, config_path: Optional[Path] = None): - # Default configuration - self.config = { - # Model architecture - "vocab_size": 256, - "d_model": 128, - "n_heads": 4, - "n_layers": 4, - "d_ff": 512, - "max_seq_length": 512, - # Quantum configuration - "n_qubits": 4, - "quantum_backend": "default.qubit", - "quantum_shots": 1000, - "enable_quantum_attention": True, - "enable_quantum_ffn": True, - "enable_multi_scale_attention": True, - "enable_adaptive_entanglement": True, - "enable_circuit_caching": True, - "enable_error_mitigation": False, - # Training configuration - "batch_size": 16, - "learning_rate": 1e-4, - "num_epochs": 10, - "enable_curriculum": True, - "enable_adaptive_routing": True, - # Optimization - "optimization_level": 2, - "enable_circuit_fusion": True, - "max_batch_circuits": 10, - # Monitoring - "enable_dashboard": True, - "dashboard_update_interval": 10, - "enable_alerts": True, - # Output - "output_dir": "data_out/quantum_llm_integrated", - "save_checkpoints": True, - "checkpoint_interval": 100, - } - - # Load from file if provided - if config_path and Path(config_path).exists(): - self.load(config_path) - - def load(self, config_path: Path): - """Load configuration from YAML file.""" - with open(config_path) as f: - loaded_config = yaml.safe_load(f) - self.config.update(loaded_config) - logger.info(f"Configuration loaded from {config_path}") - - def save(self, config_path: Path): - """Save configuration to YAML file.""" - with open(config_path, "w") as f: - yaml.dump(self.config, f, default_flow_style=False) - logger.info(f"Configuration saved to {config_path}") - - def get(self, key: str, default=None): - """Get configuration value.""" - return self.config.get(key, default) - - def __getitem__(self, key: str): - """Dict-like access.""" - return self.config[key] - - -class IntegratedQuantumLLM(nn.Module): - """ - Fully integrated quantum-enhanced language model. - - Combines all advanced quantum components into a unified model. - """ - - def __init__(self, config: QuantumLLMConfig): - super().__init__() - self.config = config - - # Embedding layer - self.embedding = nn.Embedding( - config["vocab_size"], - config["d_model"], - ) - - # Positional encoding - self.pos_encoding = nn.Parameter( - torch.randn(1, config["max_seq_length"], config["d_model"]) * 0.02 - ) - - # Build transformer layers - self.layers = nn.ModuleList( - [self._build_layer(i) for i in range(config["n_layers"])] - ) - - # Output head - self.output_head = nn.Linear(config["d_model"], config["vocab_size"]) - - # Quantum components - self._init_quantum_components() - - logger.info( - f"IntegratedQuantumLLM initialized: {config['n_layers']} layers, " - f"{config['d_model']} dim, {config['n_qubits']} qubits" - ) - - def _build_layer(self, layer_idx: int) -> nn.Module: - """Build a single transformer layer.""" - config = self.config - - # Choose attention mechanism - if config["enable_multi_scale_attention"] and ADVANCED_AVAILABLE: - attention = MultiScaleQuantumAttention( - d_model=config["d_model"], - n_heads=config["n_heads"], - n_qubits_per_head=[2, 3, 4, 6], # Multi-scale - ) - else: - # Fallback to standard attention - attention = nn.MultiheadAttention( - embed_dim=config["d_model"], - num_heads=config["n_heads"], - batch_first=True, - ) - - # Feedforward network - if config["enable_quantum_ffn"] and ADVANCED_AVAILABLE: - ffn = AdaptiveQuantumLayer( - d_model=config["d_model"], - n_qubits=config["n_qubits"], - ) - else: - ffn = nn.Sequential( - nn.Linear(config["d_model"], config["d_ff"]), - nn.ReLU(), - nn.Linear(config["d_ff"], config["d_model"]), - ) - - # Layer normalization - norm1 = nn.LayerNorm(config["d_model"]) - norm2 = nn.LayerNorm(config["d_model"]) - - return nn.ModuleDict( - { - "attention": attention, - "norm1": norm1, - "ffn": ffn, - "norm2": norm2, - } - ) - - def _init_quantum_components(self): - """Initialize quantum-specific components.""" - config = self.config - - # Circuit cache - if config["enable_circuit_caching"] and ADVANCED_AVAILABLE: - self.circuit_cache = QuantumCircuitCache(cache_size=1000) - else: - self.circuit_cache = None - - # Error mitigation - if config["enable_error_mitigation"] and ADVANCED_AVAILABLE: - self.error_mitigation = QuantumErrorMitigation(n_qubits=config["n_qubits"]) - else: - self.error_mitigation = None - - # Prompt tuning - if ADVANCED_AVAILABLE: - self.prompt_tuning = QuantumPromptTuning( - d_model=config["d_model"], - n_qubits=config["n_qubits"], - n_prompts=10, - ) - else: - self.prompt_tuning = None - - def forward(self, input_ids: torch.Tensor) -> torch.Tensor: - """ - Forward pass through the quantum-enhanced LLM. - - Args: - input_ids: Input token IDs [batch_size, seq_length] - - Returns: - Logits [batch_size, seq_length, vocab_size] - """ - batch_size, seq_length = input_ids.shape - - # Embedding + positional encoding - x = self.embedding(input_ids) - x = x + self.pos_encoding[:, :seq_length, :] - - # Process through transformer layers - for layer in self.layers: - # Self-attention with residual - attn_out, _ = layer["attention"](x, x, x) - x = layer["norm1"](x + attn_out) - - # Feedforward with residual - ffn_out = ( - layer["ffn"](x) if callable(layer["ffn"]) else layer["ffn"].forward(x) - ) - x = layer["norm2"](x + ffn_out) - - # Output projection - logits = self.output_head(x) - - return logits - - def get_quantum_stats(self) -> Dict[str, Any]: - """Get quantum component statistics.""" - stats = {} - - if self.circuit_cache: - stats["cache"] = self.circuit_cache.get_stats() - - if self.error_mitigation: - stats["error_mitigation"] = { - "enabled": True, - "n_qubits": self.config["n_qubits"], - } - - return stats - - -class QuantumLLMSystem: - """ - Complete quantum LLM training system. - - Integrates model, trainer, optimizer, and monitoring. - """ - - def __init__( - self, - config: QuantumLLMConfig, - device: str = "cpu", - ): - self.config = config - self.device = device - self.output_dir = Path(config["output_dir"]) - self.output_dir.mkdir(parents=True, exist_ok=True) - - # Initialize model - self.model = IntegratedQuantumLLM(config).to(device) - - # Initialize components - self._init_components() - - logger.info(f"QuantumLLMSystem initialized on {device}") - logger.info(f"Output directory: {self.output_dir}") - - def _init_components(self): - """Initialize all system components.""" - config = self.config - - # Circuit optimizer - if OPTIMIZER_AVAILABLE and config["optimization_level"] > 0: - self.circuit_compiler = CircuitCompiler( - OptimizationStrategy( - compilation_level=config["optimization_level"], - enable_gate_fusion=config["enable_circuit_fusion"], - ) - ) - self.batch_executor = BatchCircuitExecutor( - max_batch_size=config["max_batch_circuits"], - ) - else: - self.circuit_compiler = None - self.batch_executor = None - - # Training orchestrator - if TRAINER_AVAILABLE and config["enable_curriculum"]: - stages = self._create_training_stages() - self.orchestrator = HybridTrainingOrchestrator( - model=self.model, - stages=stages, - output_dir=self.output_dir / "training", - device=self.device, - ) - else: - self.orchestrator = None - - # Monitoring dashboard - if MONITOR_AVAILABLE and config["enable_dashboard"]: - self.dashboard = TrainingDashboard( - output_dir=self.output_dir / "dashboard", - update_interval=config["dashboard_update_interval"], - enable_alerts=config["enable_alerts"], - ) - else: - self.dashboard = None - - def _create_training_stages(self) -> List[TrainingStage]: - """Create curriculum training stages.""" - config = self.config - - stages = [ - TrainingStage( - name="classical_warmup", - quantum_ratio=0.0, - num_epochs=2, - learning_rate=config["learning_rate"], - batch_size=config["batch_size"], - ), - TrainingStage( - name="quantum_transition", - quantum_ratio=0.3, - num_epochs=3, - learning_rate=config["learning_rate"] * 0.5, - batch_size=config["batch_size"], - enable_quantum_layers=["attention"], - ), - TrainingStage( - name="full_quantum", - quantum_ratio=0.7, - num_epochs=config["num_epochs"] - 5, - learning_rate=config["learning_rate"] * 0.1, - batch_size=max(4, config["batch_size"] // 2), - enable_quantum_layers=["attention", "feedforward"], - ), - ] - - return stages - - def train( - self, - train_dataset: Dataset, - val_dataset: Optional[Dataset] = None, - ) -> Dict[str, Any]: - """ - Train the quantum LLM system. - - Args: - train_dataset: Training dataset - val_dataset: Optional validation dataset - - Returns: - Training report - """ - logger.info("Starting quantum LLM training") - - # Create data loaders - train_loader = DataLoader( - train_dataset, - batch_size=self.config["batch_size"], - shuffle=True, - ) - - val_loader = None - if val_dataset: - val_loader = DataLoader( - val_dataset, - batch_size=self.config["batch_size"], - shuffle=False, - ) - - # Run training - if self.orchestrator and TRAINER_AVAILABLE: - report = self.orchestrator.run_full_curriculum(train_loader, val_loader) - else: - # Fallback to simple training - report = self._simple_training(train_loader, val_loader) - - # Save final model - self.save_model("final_model.pt") - - # Generate visualizations - if self.dashboard and MONITOR_AVAILABLE: - exporter = VisualizationExporter(self.output_dir / "visualizations") - exporter.export_all(self.dashboard.metrics_aggregator.snapshots) - - logger.info("✅ Training complete") - return report - - def _simple_training(self, train_loader, val_loader) -> Dict[str, Any]: - """Fallback simple training loop.""" - optimizer = torch.optim.AdamW( - self.model.parameters(), lr=self.config["learning_rate"] - ) - criterion = nn.CrossEntropyLoss() - - total_loss = 0.0 - for epoch in range(self.config["num_epochs"]): - for batch_idx, (inputs, targets) in enumerate(train_loader): - inputs = inputs.to(self.device) - targets = targets.to(self.device) - - outputs = self.model(inputs) - loss = criterion(outputs.view(-1, outputs.size(-1)), targets.view(-1)) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - total_loss += loss.item() - - if batch_idx % 10 == 0: - logger.info( - f"Epoch {epoch} | Batch {batch_idx} | Loss: {loss.item():.4f}" - ) - - return {"total_loss": total_loss, "epochs": self.config["num_epochs"]} - - def save_model(self, filename: str = "model.pt"): - """Save model checkpoint.""" - path = self.output_dir / filename - torch.save( - { - "model_state_dict": self.model.state_dict(), - "config": self.config.config, - "quantum_stats": self.model.get_quantum_stats(), - }, - path, - ) - logger.info(f"Model saved: {path}") - - def load_model(self, path: Path): - """Load model checkpoint.""" - checkpoint = torch.load(path, map_location=self.device) - self.model.load_state_dict(checkpoint["model_state_dict"]) - logger.info(f"Model loaded from {path}") - - def generate_report(self) -> Dict[str, Any]: - """Generate comprehensive system report.""" - report = { - "configuration": self.config.config, - "model_parameters": sum(p.numel() for p in self.model.parameters()), - "quantum_stats": self.model.get_quantum_stats(), - } - - if self.dashboard: - report["training_dashboard"] = self.dashboard.get_full_report() - - if self.circuit_compiler and OPTIMIZER_AVAILABLE: - report["circuit_optimization"] = ( - self.circuit_compiler.get_optimization_report() - ) - - if self.batch_executor and OPTIMIZER_AVAILABLE: - report["batch_execution"] = self.batch_executor.get_cache_stats() - - # Save report - report_path = self.output_dir / "system_report.json" - with open(report_path, "w") as f: - json.dump(report, f, indent=2) - - logger.info(f"System report saved: {report_path}") - return report - - -def main(): - """Main entry point for integrated quantum LLM training.""" - parser = argparse.ArgumentParser( - description="Integrated Quantum LLM Training System" - ) - parser.add_argument("--config", type=str, help="Path to configuration YAML") - parser.add_argument( - "--output-dir", type=str, default="data_out/quantum_llm_integrated" - ) - parser.add_argument("--device", type=str, default="cpu", choices=["cpu", "cuda"]) - parser.add_argument( - "--dry-run", action="store_true", help="Test configuration without training" - ) - - args = parser.parse_args() - - # Setup logging - logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", - ) - - # Load configuration - if args.config: - config = QuantumLLMConfig(Path(args.config)) - else: - config = QuantumLLMConfig() - config.config["output_dir"] = args.output_dir - - # Initialize system - system = QuantumLLMSystem(config, device=args.device) - - logger.info("=" * 80) - logger.info("QUANTUM LLM INTEGRATED SYSTEM") - logger.info("=" * 80) - logger.info( - f"Model parameters: {sum(p.numel() for p in system.model.parameters()):,}" - ) - logger.info("Quantum components available:") - logger.info(f" - Advanced layers: {ADVANCED_AVAILABLE}") - logger.info(f" - Circuit optimizer: {OPTIMIZER_AVAILABLE}") - logger.info(f" - Hybrid trainer: {TRAINER_AVAILABLE}") - logger.info(f" - Monitoring: {MONITOR_AVAILABLE}") - logger.info("=" * 80) - - if args.dry_run: - logger.info("✅ Dry run successful - configuration valid") - return - - # For actual training, would need to load/create dataset - logger.info("⚠️ Ready to train - provide dataset to system.train()") - - # Generate initial report - system.generate_report() - - -if __name__ == "__main__": - main() +""" +Quantum LLM Complete Integration +================================= + +Unified interface integrating all quantum LLM components: +- Advanced quantum layers +- Circuit optimization +- Hybrid training orchestration +- Real-time monitoring + +Complete end-to-end quantum-enhanced language model training system. + +Author: Quantum AI Workspace +Date: March 9, 2026 +""" + +import argparse +import json +import logging +from pathlib import Path +from typing import Any, Dict, List, Optional + +import torch +import torch.nn as nn +import yaml +from torch.utils.data import DataLoader, Dataset + +# Import all quantum LLM components +try: + from quantum_llm_advanced import (AdaptiveQuantumLayer, + MultiScaleQuantumAttention, + QuantumCircuitCache, + QuantumErrorMitigation, + QuantumPromptTuning) + + ADVANCED_AVAILABLE = True +except ImportError: + ADVANCED_AVAILABLE = False + logging.warning("Advanced quantum components not available") + +try: + from quantum_circuit_optimizer import (AdaptiveCircuitScheduler, + BatchCircuitExecutor, + CircuitCompiler, + OptimizationStrategy, + QuantumClassicalPartitioner) + + OPTIMIZER_AVAILABLE = True +except ImportError: + OPTIMIZER_AVAILABLE = False + logging.warning("Circuit optimizer not available") + +try: + from quantum_llm_hybrid_trainer import (AdaptiveQuantumRouter, + CurriculumScheduler, + HybridTrainingOrchestrator, + TrainingStage) + + TRAINER_AVAILABLE = True +except ImportError: + TRAINER_AVAILABLE = False + logging.warning("Hybrid trainer not available") + +try: + from quantum_llm_monitor import (QuantumMetrics, TrainingDashboard, + TrainingSnapshot, VisualizationExporter) + + MONITOR_AVAILABLE = True +except ImportError: + MONITOR_AVAILABLE = False + logging.warning("Monitoring tools not available") + +logger = logging.getLogger(__name__) + + +class QuantumLLMConfig: + """Configuration for quantum-enhanced LLM.""" + + def __init__(self, config_path: Optional[Path] = None): + # Default configuration + self.config = { + # Model architecture + "vocab_size": 256, + "d_model": 128, + "n_heads": 4, + "n_layers": 4, + "d_ff": 512, + "max_seq_length": 512, + # Quantum configuration + "n_qubits": 4, + "quantum_backend": "default.qubit", + "quantum_shots": 1000, + "enable_quantum_attention": True, + "enable_quantum_ffn": True, + "enable_multi_scale_attention": True, + "enable_adaptive_entanglement": True, + "enable_circuit_caching": True, + "enable_error_mitigation": False, + # Training configuration + "batch_size": 16, + "learning_rate": 1e-4, + "num_epochs": 10, + "enable_curriculum": True, + "enable_adaptive_routing": True, + # Optimization + "optimization_level": 2, + "enable_circuit_fusion": True, + "max_batch_circuits": 10, + # Monitoring + "enable_dashboard": True, + "dashboard_update_interval": 10, + "enable_alerts": True, + # Output + "output_dir": "data_out/quantum_llm_integrated", + "save_checkpoints": True, + "checkpoint_interval": 100, + } + + # Load from file if provided + if config_path and Path(config_path).exists(): + self.load(config_path) + + def load(self, config_path: Path): + """Load configuration from YAML file.""" + with open(config_path) as f: + loaded_config = yaml.safe_load(f) + self.config.update(loaded_config) + logger.info(f"Configuration loaded from {config_path}") + + def save(self, config_path: Path): + """Save configuration to YAML file.""" + with open(config_path, "w") as f: + yaml.dump(self.config, f, default_flow_style=False) + logger.info(f"Configuration saved to {config_path}") + + def get(self, key: str, default=None): + """Get configuration value.""" + return self.config.get(key, default) + + def __getitem__(self, key: str): + """Dict-like access.""" + return self.config[key] + + +class IntegratedQuantumLLM(nn.Module): + """ + Fully integrated quantum-enhanced language model. + + Combines all advanced quantum components into a unified model. + """ + + def __init__(self, config: QuantumLLMConfig): + super().__init__() + self.config = config + + # Embedding layer + self.embedding = nn.Embedding( + config["vocab_size"], + config["d_model"], + ) + + # Positional encoding + self.pos_encoding = nn.Parameter( + torch.randn(1, config["max_seq_length"], config["d_model"]) * 0.02 + ) + + # Build transformer layers + self.layers = nn.ModuleList( + [self._build_layer(i) for i in range(config["n_layers"])] + ) + + # Output head + self.output_head = nn.Linear(config["d_model"], config["vocab_size"]) + + # Quantum components + self._init_quantum_components() + + logger.info( + f"IntegratedQuantumLLM initialized: {config['n_layers']} layers, " + f"{config['d_model']} dim, {config['n_qubits']} qubits" + ) + + def _build_layer(self, layer_idx: int) -> nn.Module: + """Build a single transformer layer.""" + config = self.config + + # Choose attention mechanism + if config["enable_multi_scale_attention"] and ADVANCED_AVAILABLE: + attention = MultiScaleQuantumAttention( + d_model=config["d_model"], + n_heads=config["n_heads"], + n_qubits_per_head=[2, 3, 4, 6], # Multi-scale + ) + else: + # Fallback to standard attention + attention = nn.MultiheadAttention( + embed_dim=config["d_model"], + num_heads=config["n_heads"], + batch_first=True, + ) + + # Feedforward network + if config["enable_quantum_ffn"] and ADVANCED_AVAILABLE: + ffn = AdaptiveQuantumLayer( + d_model=config["d_model"], + n_qubits=config["n_qubits"], + ) + else: + ffn = nn.Sequential( + nn.Linear(config["d_model"], config["d_ff"]), + nn.ReLU(), + nn.Linear(config["d_ff"], config["d_model"]), + ) + + # Layer normalization + norm1 = nn.LayerNorm(config["d_model"]) + norm2 = nn.LayerNorm(config["d_model"]) + + return nn.ModuleDict( + { + "attention": attention, + "norm1": norm1, + "ffn": ffn, + "norm2": norm2, + } + ) + + def _init_quantum_components(self): + """Initialize quantum-specific components.""" + config = self.config + + # Circuit cache + if config["enable_circuit_caching"] and ADVANCED_AVAILABLE: + self.circuit_cache = QuantumCircuitCache(cache_size=1000) + else: + self.circuit_cache = None + + # Error mitigation + if config["enable_error_mitigation"] and ADVANCED_AVAILABLE: + self.error_mitigation = QuantumErrorMitigation(n_qubits=config["n_qubits"]) + else: + self.error_mitigation = None + + # Prompt tuning + if ADVANCED_AVAILABLE: + self.prompt_tuning = QuantumPromptTuning( + d_model=config["d_model"], + n_qubits=config["n_qubits"], + n_prompts=10, + ) + else: + self.prompt_tuning = None + + def forward(self, input_ids: torch.Tensor) -> torch.Tensor: + """ + Forward pass through the quantum-enhanced LLM. + + Args: + input_ids: Input token IDs [batch_size, seq_length] + + Returns: + Logits [batch_size, seq_length, vocab_size] + """ + batch_size, seq_length = input_ids.shape + + # Embedding + positional encoding + x = self.embedding(input_ids) + x = x + self.pos_encoding[:, :seq_length, :] + + # Process through transformer layers + for layer in self.layers: + # Self-attention with residual + attn_out, _ = layer["attention"](x, x, x) + x = layer["norm1"](x + attn_out) + + # Feedforward with residual + ffn_out = ( + layer["ffn"](x) if callable(layer["ffn"]) else layer["ffn"].forward(x) + ) + x = layer["norm2"](x + ffn_out) + + # Output projection + logits = self.output_head(x) + + return logits + + def get_quantum_stats(self) -> Dict[str, Any]: + """Get quantum component statistics.""" + stats = {} + + if self.circuit_cache: + stats["cache"] = self.circuit_cache.get_stats() + + if self.error_mitigation: + stats["error_mitigation"] = { + "enabled": True, + "n_qubits": self.config["n_qubits"], + } + + return stats + + +class QuantumLLMSystem: + """ + Complete quantum LLM training system. + + Integrates model, trainer, optimizer, and monitoring. + """ + + def __init__( + self, + config: QuantumLLMConfig, + device: str = "cpu", + ): + self.config = config + self.device = device + self.output_dir = Path(config["output_dir"]) + self.output_dir.mkdir(parents=True, exist_ok=True) + + # Initialize model + self.model = IntegratedQuantumLLM(config).to(device) + + # Initialize components + self._init_components() + + logger.info(f"QuantumLLMSystem initialized on {device}") + logger.info(f"Output directory: {self.output_dir}") + + def _init_components(self): + """Initialize all system components.""" + config = self.config + + # Circuit optimizer + if OPTIMIZER_AVAILABLE and config["optimization_level"] > 0: + self.circuit_compiler = CircuitCompiler( + OptimizationStrategy( + compilation_level=config["optimization_level"], + enable_gate_fusion=config["enable_circuit_fusion"], + ) + ) + self.batch_executor = BatchCircuitExecutor( + max_batch_size=config["max_batch_circuits"], + ) + else: + self.circuit_compiler = None + self.batch_executor = None + + # Training orchestrator + if TRAINER_AVAILABLE and config["enable_curriculum"]: + stages = self._create_training_stages() + self.orchestrator = HybridTrainingOrchestrator( + model=self.model, + stages=stages, + output_dir=self.output_dir / "training", + device=self.device, + ) + else: + self.orchestrator = None + + # Monitoring dashboard + if MONITOR_AVAILABLE and config["enable_dashboard"]: + self.dashboard = TrainingDashboard( + output_dir=self.output_dir / "dashboard", + update_interval=config["dashboard_update_interval"], + enable_alerts=config["enable_alerts"], + ) + else: + self.dashboard = None + + def _create_training_stages(self) -> List[TrainingStage]: + """Create curriculum training stages.""" + config = self.config + + stages = [ + TrainingStage( + name="classical_warmup", + quantum_ratio=0.0, + num_epochs=2, + learning_rate=config["learning_rate"], + batch_size=config["batch_size"], + ), + TrainingStage( + name="quantum_transition", + quantum_ratio=0.3, + num_epochs=3, + learning_rate=config["learning_rate"] * 0.5, + batch_size=config["batch_size"], + enable_quantum_layers=["attention"], + ), + TrainingStage( + name="full_quantum", + quantum_ratio=0.7, + num_epochs=config["num_epochs"] - 5, + learning_rate=config["learning_rate"] * 0.1, + batch_size=max(4, config["batch_size"] // 2), + enable_quantum_layers=["attention", "feedforward"], + ), + ] + + return stages + + def train( + self, + train_dataset: Dataset, + val_dataset: Optional[Dataset] = None, + ) -> Dict[str, Any]: + """ + Train the quantum LLM system. + + Args: + train_dataset: Training dataset + val_dataset: Optional validation dataset + + Returns: + Training report + """ + logger.info("Starting quantum LLM training") + + # Create data loaders + train_loader = DataLoader( + train_dataset, + batch_size=self.config["batch_size"], + shuffle=True, + ) + + val_loader = None + if val_dataset: + val_loader = DataLoader( + val_dataset, + batch_size=self.config["batch_size"], + shuffle=False, + ) + + # Run training + if self.orchestrator and TRAINER_AVAILABLE: + report = self.orchestrator.run_full_curriculum(train_loader, val_loader) + else: + # Fallback to simple training + report = self._simple_training(train_loader, val_loader) + + # Save final model + self.save_model("final_model.pt") + + # Generate visualizations + if self.dashboard and MONITOR_AVAILABLE: + exporter = VisualizationExporter(self.output_dir / "visualizations") + exporter.export_all(self.dashboard.metrics_aggregator.snapshots) + + logger.info("✅ Training complete") + return report + + def _simple_training(self, train_loader, val_loader) -> Dict[str, Any]: + """Fallback simple training loop.""" + optimizer = torch.optim.AdamW( + self.model.parameters(), lr=self.config["learning_rate"] + ) + criterion = nn.CrossEntropyLoss() + + total_loss = 0.0 + for epoch in range(self.config["num_epochs"]): + for batch_idx, (inputs, targets) in enumerate(train_loader): + inputs = inputs.to(self.device) + targets = targets.to(self.device) + + outputs = self.model(inputs) + loss = criterion(outputs.view(-1, outputs.size(-1)), targets.view(-1)) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + total_loss += loss.item() + + if batch_idx % 10 == 0: + logger.info( + f"Epoch {epoch} | Batch {batch_idx} | Loss: {loss.item():.4f}" + ) + + return {"total_loss": total_loss, "epochs": self.config["num_epochs"]} + + def save_model(self, filename: str = "model.pt"): + """Save model checkpoint.""" + path = self.output_dir / filename + torch.save( + { + "model_state_dict": self.model.state_dict(), + "config": self.config.config, + "quantum_stats": self.model.get_quantum_stats(), + }, + path, + ) + logger.info(f"Model saved: {path}") + + def load_model(self, path: Path): + """Load model checkpoint.""" + checkpoint = torch.load(path, map_location=self.device) + self.model.load_state_dict(checkpoint["model_state_dict"]) + logger.info(f"Model loaded from {path}") + + def generate_report(self) -> Dict[str, Any]: + """Generate comprehensive system report.""" + report = { + "configuration": self.config.config, + "model_parameters": sum(p.numel() for p in self.model.parameters()), + "quantum_stats": self.model.get_quantum_stats(), + } + + if self.dashboard: + report["training_dashboard"] = self.dashboard.get_full_report() + + if self.circuit_compiler and OPTIMIZER_AVAILABLE: + report["circuit_optimization"] = ( + self.circuit_compiler.get_optimization_report() + ) + + if self.batch_executor and OPTIMIZER_AVAILABLE: + report["batch_execution"] = self.batch_executor.get_cache_stats() + + # Save report + report_path = self.output_dir / "system_report.json" + with open(report_path, "w") as f: + json.dump(report, f, indent=2) + + logger.info(f"System report saved: {report_path}") + return report + + +def main(): + """Main entry point for integrated quantum LLM training.""" + parser = argparse.ArgumentParser( + description="Integrated Quantum LLM Training System" + ) + parser.add_argument("--config", type=str, help="Path to configuration YAML") + parser.add_argument( + "--output-dir", type=str, default="data_out/quantum_llm_integrated" + ) + parser.add_argument("--device", type=str, default="cpu", choices=["cpu", "cuda"]) + parser.add_argument( + "--dry-run", action="store_true", help="Test configuration without training" + ) + + args = parser.parse_args() + + # Setup logging + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + ) + + # Load configuration + if args.config: + config = QuantumLLMConfig(Path(args.config)) + else: + config = QuantumLLMConfig() + config.config["output_dir"] = args.output_dir + + # Initialize system + system = QuantumLLMSystem(config, device=args.device) + + logger.info("=" * 80) + logger.info("QUANTUM LLM INTEGRATED SYSTEM") + logger.info("=" * 80) + logger.info( + f"Model parameters: {sum(p.numel() for p in system.model.parameters()):,}" + ) + logger.info("Quantum components available:") + logger.info(f" - Advanced layers: {ADVANCED_AVAILABLE}") + logger.info(f" - Circuit optimizer: {OPTIMIZER_AVAILABLE}") + logger.info(f" - Hybrid trainer: {TRAINER_AVAILABLE}") + logger.info(f" - Monitoring: {MONITOR_AVAILABLE}") + logger.info("=" * 80) + + if args.dry_run: + logger.info("✅ Dry run successful - configuration valid") + return + + # For actual training, would need to load/create dataset + logger.info("⚠️ Ready to train - provide dataset to system.train()") + + # Generate initial report + system.generate_report() + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/src/quantum_llm_monitor.py b/ai-projects/quantum-ml/src/quantum_llm_monitor.py index 724a6b0a8..b18d3a2f8 100644 --- a/ai-projects/quantum-ml/src/quantum_llm_monitor.py +++ b/ai-projects/quantum-ml/src/quantum_llm_monitor.py @@ -1,579 +1,579 @@ -""" -Quantum LLM Training Monitor & Visualization -============================================= - -Real-time monitoring, logging, and visualization for quantum-enhanced -language model training. - -Features: -- Real-time metrics dashboard -- Quantum circuit performance tracking -- Training progress visualization -- Resource utilization monitoring -- Alert system for anomalies - -Author: Quantum AI Workspace -Date: March 9, 2026 -""" - -import json -import logging -import time -from collections import defaultdict, deque -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional - -import numpy as np - -logger = logging.getLogger(__name__) - - -@dataclass -class QuantumMetrics: - """Quantum-specific training metrics.""" - - circuit_execution_time: float = 0.0 - circuit_depth: int = 0 - gate_count: int = 0 - qubit_count: int = 0 - quantum_classical_ratio: float = 0.0 - cache_hit_rate: float = 0.0 - shots_per_circuit: int = 1000 - backend: str = "default.qubit" - - -@dataclass -class TrainingSnapshot: - """Complete training state snapshot.""" - - timestamp: float - global_step: int - epoch: int - loss: float - perplexity: float - learning_rate: float - batch_size: int - quantum_metrics: QuantumMetrics - gpu_memory_mb: float = 0.0 - cpu_percent: float = 0.0 - - -class MetricsAggregator: - """ - Aggregates and computes statistics on training metrics. - """ - - def __init__(self, window_size: int = 100): - self.window_size = window_size - self.loss_history = deque(maxlen=window_size) - self.perplexity_history = deque(maxlen=window_size) - self.quantum_time_history = deque(maxlen=window_size) - self.snapshots: List[TrainingSnapshot] = [] - - logger.info(f"MetricsAggregator initialized: window_size={window_size}") - - def add_snapshot(self, snapshot: TrainingSnapshot): - """Add a training snapshot.""" - self.loss_history.append(snapshot.loss) - self.perplexity_history.append(snapshot.perplexity) - self.quantum_time_history.append( - snapshot.quantum_metrics.circuit_execution_time - ) - self.snapshots.append(snapshot) - - def get_moving_average(self, metric: str, window: Optional[int] = None) -> float: - """Compute moving average for a metric.""" - window = window or self.window_size - - if metric == "loss": - history = list(self.loss_history) - elif metric == "perplexity": - history = list(self.perplexity_history) - elif metric == "quantum_time": - history = list(self.quantum_time_history) - else: - return 0.0 - - if not history: - return 0.0 - - recent = history[-window:] - return np.mean(recent) - - def get_trend(self, metric: str, window: int = 20) -> str: - """ - Determine trend direction for a metric. - - Returns: - "improving", "degrading", or "stable" - """ - if metric == "loss": - history = list(self.loss_history) - elif metric == "perplexity": - history = list(self.perplexity_history) - else: - return "unknown" - - if len(history) < window: - return "insufficient_data" - - recent = history[-window:] - first_half = np.mean(recent[: window // 2]) - second_half = np.mean(recent[window // 2 :]) - - # For loss/perplexity, lower is better - if second_half < first_half * 0.95: - return "improving" - elif second_half > first_half * 1.05: - return "degrading" - else: - return "stable" - - def detect_anomalies(self, threshold: float = 3.0) -> List[Dict[str, Any]]: - """ - Detect anomalous metrics using z-score. - - Args: - threshold: Z-score threshold for anomaly detection - - Returns: - List of detected anomalies - """ - anomalies = [] - - if len(self.loss_history) < 10: - return anomalies - - losses = np.array(list(self.loss_history)) - mean_loss = np.mean(losses) - std_loss = np.std(losses) - - if std_loss > 0: - z_scores = (losses - mean_loss) / std_loss - anomaly_indices = np.where(np.abs(z_scores) > threshold)[0] - - for idx in anomaly_indices: - if idx < len(self.snapshots): - anomalies.append( - { - "type": "loss_anomaly", - "step": self.snapshots[idx].global_step, - "value": losses[idx], - "z_score": float(z_scores[idx]), - } - ) - - return anomalies - - def get_summary(self) -> Dict[str, Any]: - """Get complete metrics summary.""" - return { - "total_snapshots": len(self.snapshots), - "moving_avg_loss": self.get_moving_average("loss"), - "moving_avg_perplexity": self.get_moving_average("perplexity"), - "moving_avg_quantum_time": self.get_moving_average("quantum_time"), - "loss_trend": self.get_trend("loss"), - "perplexity_trend": self.get_trend("perplexity"), - "anomalies": self.detect_anomalies(), - } - - -class PerformanceMonitor: - """ - Monitors system and quantum performance during training. - """ - - def __init__(self, enable_gpu_monitoring: bool = True): - self.enable_gpu = enable_gpu_monitoring - self.performance_history = [] - - # Try to import psutil for CPU monitoring - try: - import psutil - - self.psutil = psutil - self.psutil_available = True - except ImportError: - self.psutil_available = False - logger.warning("psutil not available - CPU monitoring disabled") - - # Try to import for GPU monitoring - try: - import torch - - self.torch = torch - self.torch_available = ( - torch.cuda.is_available() if self.enable_gpu else False - ) - except ImportError: - self.torch_available = False - - logger.info( - f"PerformanceMonitor: CPU={self.psutil_available}, GPU={self.torch_available}" - ) - - def get_current_performance(self) -> Dict[str, float]: - """Get current system performance metrics.""" - metrics = { - "cpu_percent": 0.0, - "memory_used_mb": 0.0, - "memory_percent": 0.0, - "gpu_memory_used_mb": 0.0, - "gpu_utilization_percent": 0.0, - } - - if self.psutil_available: - metrics["cpu_percent"] = self.psutil.cpu_percent(interval=0.1) - mem = self.psutil.virtual_memory() - metrics["memory_used_mb"] = mem.used / (1024 * 1024) - metrics["memory_percent"] = mem.percent - - if self.torch_available: - try: - metrics["gpu_memory_used_mb"] = self.torch.cuda.memory_allocated() / ( - 1024 * 1024 - ) - except: - pass - - self.performance_history.append( - { - "timestamp": time.time(), - **metrics, - } - ) - - return metrics - - def get_resource_report(self) -> Dict[str, Any]: - """Get comprehensive resource usage report.""" - if not self.performance_history: - return {"status": "no_data"} - - recent = self.performance_history[-100:] - - return { - "avg_cpu_percent": np.mean([p["cpu_percent"] for p in recent]), - "max_cpu_percent": np.max([p["cpu_percent"] for p in recent]), - "avg_memory_mb": np.mean([p["memory_used_mb"] for p in recent]), - "max_memory_mb": np.max([p["memory_used_mb"] for p in recent]), - "avg_gpu_memory_mb": np.mean([p["gpu_memory_used_mb"] for p in recent]), - "max_gpu_memory_mb": np.max([p["gpu_memory_used_mb"] for p in recent]), - } - - -class QuantumCircuitProfiler: - """ - Profiles quantum circuit execution performance. - """ - - def __init__(self): - self.circuit_profiles = defaultdict(list) - - logger.info("QuantumCircuitProfiler initialized") - - def profile_circuit( - self, - circuit_id: str, - execution_time: float, - gate_count: int, - depth: int, - qubit_count: int, - ): - """Record circuit execution profile.""" - self.circuit_profiles[circuit_id].append( - { - "timestamp": time.time(), - "execution_time": execution_time, - "gate_count": gate_count, - "depth": depth, - "qubit_count": qubit_count, - } - ) - - def get_circuit_stats(self, circuit_id: str) -> Dict[str, Any]: - """Get statistics for a specific circuit.""" - profiles = self.circuit_profiles.get(circuit_id, []) - - if not profiles: - return {"status": "no_data"} - - execution_times = [p["execution_time"] for p in profiles] - - return { - "executions": len(profiles), - "avg_time": np.mean(execution_times), - "min_time": np.min(execution_times), - "max_time": np.max(execution_times), - "std_time": np.std(execution_times), - "avg_gate_count": np.mean([p["gate_count"] for p in profiles]), - "avg_depth": np.mean([p["depth"] for p in profiles]), - } - - def get_all_stats(self) -> Dict[str, Any]: - """Get statistics for all circuits.""" - return { - circuit_id: self.get_circuit_stats(circuit_id) - for circuit_id in self.circuit_profiles.keys() - } - - -class TrainingDashboard: - """ - Real-time training dashboard with logging and alerts. - """ - - def __init__( - self, - output_dir: Path, - update_interval: int = 10, - enable_alerts: bool = True, - ): - self.output_dir = Path(output_dir) - self.output_dir.mkdir(parents=True, exist_ok=True) - self.update_interval = update_interval - self.enable_alerts = enable_alerts - - # Components - self.metrics_aggregator = MetricsAggregator() - self.performance_monitor = PerformanceMonitor() - self.circuit_profiler = QuantumCircuitProfiler() - - # State - self.last_update = time.time() - self.alerts: List[Dict[str, Any]] = [] - - logger.info(f"TrainingDashboard initialized: output_dir={output_dir}") - - def update(self, snapshot: TrainingSnapshot): - """Update dashboard with new training snapshot.""" - self.metrics_aggregator.add_snapshot(snapshot) - - # Update performance metrics - perf_metrics = self.performance_monitor.get_current_performance() - - # Check if it's time for a dashboard update - if time.time() - self.last_update >= self.update_interval: - self._refresh_dashboard() - self.last_update = time.time() - - # Check for alerts - if self.enable_alerts: - self._check_alerts(snapshot) - - def _refresh_dashboard(self): - """Refresh dashboard display and save state.""" - summary = self.metrics_aggregator.get_summary() - perf_report = self.performance_monitor.get_resource_report() - circuit_stats = self.circuit_profiler.get_all_stats() - - dashboard_data = { - "timestamp": time.time(), - "metrics_summary": summary, - "performance_report": perf_report, - "circuit_stats": circuit_stats, - "recent_alerts": self.alerts[-10:], - } - - # Save to file - dashboard_path = self.output_dir / "dashboard.json" - with open(dashboard_path, "w") as f: - json.dump(dashboard_data, f, indent=2) - - # Log summary - logger.info("=" * 80) - logger.info("TRAINING DASHBOARD") - logger.info("=" * 80) - logger.info( - f"Loss (MA): {summary['moving_avg_loss']:.4f} [{summary['loss_trend']}]" - ) - logger.info( - f"Perplexity (MA): {summary['moving_avg_perplexity']:.2f} [{summary['perplexity_trend']}]" - ) - logger.info(f"Quantum Time (MA): {summary['moving_avg_quantum_time']:.4f}s") - - if perf_report.get("status") != "no_data": - logger.info( - f"CPU: {perf_report['avg_cpu_percent']:.1f}% (max: {perf_report['max_cpu_percent']:.1f}%)" - ) - logger.info( - f"Memory: {perf_report['avg_memory_mb']:.0f}MB (max: {perf_report['max_memory_mb']:.0f}MB)" - ) - - if self.alerts: - logger.info( - f"Alerts: {len(self.alerts)} total, {len([a for a in self.alerts if a['severity'] == 'high'])} high" - ) - - logger.info("=" * 80) - - def _check_alerts(self, snapshot: TrainingSnapshot): - """Check for alert conditions.""" - # Check loss anomalies - anomalies = self.metrics_aggregator.detect_anomalies() - for anomaly in anomalies: - if not any(a.get("step") == anomaly["step"] for a in self.alerts): - self.alerts.append( - { - "timestamp": time.time(), - "type": "anomaly", - "severity": "medium", - "message": f"Loss anomaly detected at step {anomaly['step']}", - "details": anomaly, - } - ) - - # Check degrading trends - if self.metrics_aggregator.get_trend("loss") == "degrading": - self.alerts.append( - { - "timestamp": time.time(), - "type": "trend", - "severity": "low", - "message": "Loss trend is degrading", - } - ) - - # Check high perplexity - if snapshot.perplexity > 1000.0: - self.alerts.append( - { - "timestamp": time.time(), - "type": "perplexity", - "severity": "high", - "message": f"Very high perplexity: {snapshot.perplexity:.2f}", - } - ) - - def get_full_report(self) -> Dict[str, Any]: - """Generate complete training report.""" - return { - "metrics_summary": self.metrics_aggregator.get_summary(), - "performance_report": self.performance_monitor.get_resource_report(), - "circuit_stats": self.circuit_profiler.get_all_stats(), - "alerts": self.alerts, - "total_snapshots": len(self.metrics_aggregator.snapshots), - } - - def save_report(self, filename: str = "training_report.json"): - """Save complete report to file.""" - report = self.get_full_report() - report_path = self.output_dir / filename - - with open(report_path, "w") as f: - json.dump(report, f, indent=2) - - logger.info(f"Report saved: {report_path}") - return report_path - - -class VisualizationExporter: - """ - Exports training data in formats suitable for visualization. - """ - - def __init__(self, output_dir: Path): - self.output_dir = Path(output_dir) - self.output_dir.mkdir(parents=True, exist_ok=True) - - logger.info(f"VisualizationExporter: output_dir={output_dir}") - - def export_loss_curve( - self, snapshots: List[TrainingSnapshot], filename: str = "loss_curve.json" - ): - """Export loss curve data.""" - data = { - "steps": [s.global_step for s in snapshots], - "loss": [s.loss for s in snapshots], - "perplexity": [s.perplexity for s in snapshots], - } - - path = self.output_dir / filename - with open(path, "w") as f: - json.dump(data, f) - - logger.info(f"Loss curve exported: {path}") - return path - - def export_quantum_metrics( - self, snapshots: List[TrainingSnapshot], filename: str = "quantum_metrics.json" - ): - """Export quantum-specific metrics.""" - data = { - "steps": [s.global_step for s in snapshots], - "circuit_time": [ - s.quantum_metrics.circuit_execution_time for s in snapshots - ], - "quantum_ratio": [ - s.quantum_metrics.quantum_classical_ratio for s in snapshots - ], - "cache_hit_rate": [s.quantum_metrics.cache_hit_rate for s in snapshots], - } - - path = self.output_dir / filename - with open(path, "w") as f: - json.dump(data, f) - - logger.info(f"Quantum metrics exported: {path}") - return path - - def export_all(self, snapshots: List[TrainingSnapshot]): - """Export all available visualization data.""" - self.export_loss_curve(snapshots) - self.export_quantum_metrics(snapshots) - logger.info("All visualization data exported") - - -# Export components -__all__ = [ - "QuantumMetrics", - "TrainingSnapshot", - "MetricsAggregator", - "PerformanceMonitor", - "QuantumCircuitProfiler", - "TrainingDashboard", - "VisualizationExporter", -] - - -if __name__ == "__main__": - logging.basicConfig(level=logging.INFO) - - # Test components - dashboard = TrainingDashboard( - output_dir=Path("data_out/quantum_llm_dashboard"), - update_interval=5, - ) - - # Simulate some training snapshots - for step in range(20): - snapshot = TrainingSnapshot( - timestamp=time.time(), - global_step=step, - epoch=0, - loss=5.0 - step * 0.1 + np.random.randn() * 0.1, - perplexity=np.exp(5.0 - step * 0.1), - learning_rate=1e-4, - batch_size=16, - quantum_metrics=QuantumMetrics( - circuit_execution_time=0.1 + np.random.randn() * 0.01, - circuit_depth=10, - gate_count=50, - qubit_count=4, - quantum_classical_ratio=0.3, - cache_hit_rate=0.5 + step * 0.02, - ), - ) - - dashboard.update(snapshot) - time.sleep(0.1) - - # Generate report - report_path = dashboard.save_report() - logger.info(f"✅ Dashboard test complete: {report_path}") - - # Export visualizations - exporter = VisualizationExporter(Path("data_out/quantum_llm_visualizations")) - exporter.export_all(dashboard.metrics_aggregator.snapshots) - logger.info("✅ Visualization export complete") +""" +Quantum LLM Training Monitor & Visualization +============================================= + +Real-time monitoring, logging, and visualization for quantum-enhanced +language model training. + +Features: +- Real-time metrics dashboard +- Quantum circuit performance tracking +- Training progress visualization +- Resource utilization monitoring +- Alert system for anomalies + +Author: Quantum AI Workspace +Date: March 9, 2026 +""" + +import json +import logging +import time +from collections import defaultdict, deque +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional + +import numpy as np + +logger = logging.getLogger(__name__) + + +@dataclass +class QuantumMetrics: + """Quantum-specific training metrics.""" + + circuit_execution_time: float = 0.0 + circuit_depth: int = 0 + gate_count: int = 0 + qubit_count: int = 0 + quantum_classical_ratio: float = 0.0 + cache_hit_rate: float = 0.0 + shots_per_circuit: int = 1000 + backend: str = "default.qubit" + + +@dataclass +class TrainingSnapshot: + """Complete training state snapshot.""" + + timestamp: float + global_step: int + epoch: int + loss: float + perplexity: float + learning_rate: float + batch_size: int + quantum_metrics: QuantumMetrics + gpu_memory_mb: float = 0.0 + cpu_percent: float = 0.0 + + +class MetricsAggregator: + """ + Aggregates and computes statistics on training metrics. + """ + + def __init__(self, window_size: int = 100): + self.window_size = window_size + self.loss_history = deque(maxlen=window_size) + self.perplexity_history = deque(maxlen=window_size) + self.quantum_time_history = deque(maxlen=window_size) + self.snapshots: List[TrainingSnapshot] = [] + + logger.info(f"MetricsAggregator initialized: window_size={window_size}") + + def add_snapshot(self, snapshot: TrainingSnapshot): + """Add a training snapshot.""" + self.loss_history.append(snapshot.loss) + self.perplexity_history.append(snapshot.perplexity) + self.quantum_time_history.append( + snapshot.quantum_metrics.circuit_execution_time + ) + self.snapshots.append(snapshot) + + def get_moving_average(self, metric: str, window: Optional[int] = None) -> float: + """Compute moving average for a metric.""" + window = window or self.window_size + + if metric == "loss": + history = list(self.loss_history) + elif metric == "perplexity": + history = list(self.perplexity_history) + elif metric == "quantum_time": + history = list(self.quantum_time_history) + else: + return 0.0 + + if not history: + return 0.0 + + recent = history[-window:] + return np.mean(recent) + + def get_trend(self, metric: str, window: int = 20) -> str: + """ + Determine trend direction for a metric. + + Returns: + "improving", "degrading", or "stable" + """ + if metric == "loss": + history = list(self.loss_history) + elif metric == "perplexity": + history = list(self.perplexity_history) + else: + return "unknown" + + if len(history) < window: + return "insufficient_data" + + recent = history[-window:] + first_half = np.mean(recent[: window // 2]) + second_half = np.mean(recent[window // 2 :]) + + # For loss/perplexity, lower is better + if second_half < first_half * 0.95: + return "improving" + elif second_half > first_half * 1.05: + return "degrading" + else: + return "stable" + + def detect_anomalies(self, threshold: float = 3.0) -> List[Dict[str, Any]]: + """ + Detect anomalous metrics using z-score. + + Args: + threshold: Z-score threshold for anomaly detection + + Returns: + List of detected anomalies + """ + anomalies = [] + + if len(self.loss_history) < 10: + return anomalies + + losses = np.array(list(self.loss_history)) + mean_loss = np.mean(losses) + std_loss = np.std(losses) + + if std_loss > 0: + z_scores = (losses - mean_loss) / std_loss + anomaly_indices = np.where(np.abs(z_scores) > threshold)[0] + + for idx in anomaly_indices: + if idx < len(self.snapshots): + anomalies.append( + { + "type": "loss_anomaly", + "step": self.snapshots[idx].global_step, + "value": losses[idx], + "z_score": float(z_scores[idx]), + } + ) + + return anomalies + + def get_summary(self) -> Dict[str, Any]: + """Get complete metrics summary.""" + return { + "total_snapshots": len(self.snapshots), + "moving_avg_loss": self.get_moving_average("loss"), + "moving_avg_perplexity": self.get_moving_average("perplexity"), + "moving_avg_quantum_time": self.get_moving_average("quantum_time"), + "loss_trend": self.get_trend("loss"), + "perplexity_trend": self.get_trend("perplexity"), + "anomalies": self.detect_anomalies(), + } + + +class PerformanceMonitor: + """ + Monitors system and quantum performance during training. + """ + + def __init__(self, enable_gpu_monitoring: bool = True): + self.enable_gpu = enable_gpu_monitoring + self.performance_history = [] + + # Try to import psutil for CPU monitoring + try: + import psutil + + self.psutil = psutil + self.psutil_available = True + except ImportError: + self.psutil_available = False + logger.warning("psutil not available - CPU monitoring disabled") + + # Try to import for GPU monitoring + try: + import torch + + self.torch = torch + self.torch_available = ( + torch.cuda.is_available() if self.enable_gpu else False + ) + except ImportError: + self.torch_available = False + + logger.info( + f"PerformanceMonitor: CPU={self.psutil_available}, GPU={self.torch_available}" + ) + + def get_current_performance(self) -> Dict[str, float]: + """Get current system performance metrics.""" + metrics = { + "cpu_percent": 0.0, + "memory_used_mb": 0.0, + "memory_percent": 0.0, + "gpu_memory_used_mb": 0.0, + "gpu_utilization_percent": 0.0, + } + + if self.psutil_available: + metrics["cpu_percent"] = self.psutil.cpu_percent(interval=0.1) + mem = self.psutil.virtual_memory() + metrics["memory_used_mb"] = mem.used / (1024 * 1024) + metrics["memory_percent"] = mem.percent + + if self.torch_available: + try: + metrics["gpu_memory_used_mb"] = self.torch.cuda.memory_allocated() / ( + 1024 * 1024 + ) + except: + pass + + self.performance_history.append( + { + "timestamp": time.time(), + **metrics, + } + ) + + return metrics + + def get_resource_report(self) -> Dict[str, Any]: + """Get comprehensive resource usage report.""" + if not self.performance_history: + return {"status": "no_data"} + + recent = self.performance_history[-100:] + + return { + "avg_cpu_percent": np.mean([p["cpu_percent"] for p in recent]), + "max_cpu_percent": np.max([p["cpu_percent"] for p in recent]), + "avg_memory_mb": np.mean([p["memory_used_mb"] for p in recent]), + "max_memory_mb": np.max([p["memory_used_mb"] for p in recent]), + "avg_gpu_memory_mb": np.mean([p["gpu_memory_used_mb"] for p in recent]), + "max_gpu_memory_mb": np.max([p["gpu_memory_used_mb"] for p in recent]), + } + + +class QuantumCircuitProfiler: + """ + Profiles quantum circuit execution performance. + """ + + def __init__(self): + self.circuit_profiles = defaultdict(list) + + logger.info("QuantumCircuitProfiler initialized") + + def profile_circuit( + self, + circuit_id: str, + execution_time: float, + gate_count: int, + depth: int, + qubit_count: int, + ): + """Record circuit execution profile.""" + self.circuit_profiles[circuit_id].append( + { + "timestamp": time.time(), + "execution_time": execution_time, + "gate_count": gate_count, + "depth": depth, + "qubit_count": qubit_count, + } + ) + + def get_circuit_stats(self, circuit_id: str) -> Dict[str, Any]: + """Get statistics for a specific circuit.""" + profiles = self.circuit_profiles.get(circuit_id, []) + + if not profiles: + return {"status": "no_data"} + + execution_times = [p["execution_time"] for p in profiles] + + return { + "executions": len(profiles), + "avg_time": np.mean(execution_times), + "min_time": np.min(execution_times), + "max_time": np.max(execution_times), + "std_time": np.std(execution_times), + "avg_gate_count": np.mean([p["gate_count"] for p in profiles]), + "avg_depth": np.mean([p["depth"] for p in profiles]), + } + + def get_all_stats(self) -> Dict[str, Any]: + """Get statistics for all circuits.""" + return { + circuit_id: self.get_circuit_stats(circuit_id) + for circuit_id in self.circuit_profiles.keys() + } + + +class TrainingDashboard: + """ + Real-time training dashboard with logging and alerts. + """ + + def __init__( + self, + output_dir: Path, + update_interval: int = 10, + enable_alerts: bool = True, + ): + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + self.update_interval = update_interval + self.enable_alerts = enable_alerts + + # Components + self.metrics_aggregator = MetricsAggregator() + self.performance_monitor = PerformanceMonitor() + self.circuit_profiler = QuantumCircuitProfiler() + + # State + self.last_update = time.time() + self.alerts: List[Dict[str, Any]] = [] + + logger.info(f"TrainingDashboard initialized: output_dir={output_dir}") + + def update(self, snapshot: TrainingSnapshot): + """Update dashboard with new training snapshot.""" + self.metrics_aggregator.add_snapshot(snapshot) + + # Update performance metrics + perf_metrics = self.performance_monitor.get_current_performance() + + # Check if it's time for a dashboard update + if time.time() - self.last_update >= self.update_interval: + self._refresh_dashboard() + self.last_update = time.time() + + # Check for alerts + if self.enable_alerts: + self._check_alerts(snapshot) + + def _refresh_dashboard(self): + """Refresh dashboard display and save state.""" + summary = self.metrics_aggregator.get_summary() + perf_report = self.performance_monitor.get_resource_report() + circuit_stats = self.circuit_profiler.get_all_stats() + + dashboard_data = { + "timestamp": time.time(), + "metrics_summary": summary, + "performance_report": perf_report, + "circuit_stats": circuit_stats, + "recent_alerts": self.alerts[-10:], + } + + # Save to file + dashboard_path = self.output_dir / "dashboard.json" + with open(dashboard_path, "w") as f: + json.dump(dashboard_data, f, indent=2) + + # Log summary + logger.info("=" * 80) + logger.info("TRAINING DASHBOARD") + logger.info("=" * 80) + logger.info( + f"Loss (MA): {summary['moving_avg_loss']:.4f} [{summary['loss_trend']}]" + ) + logger.info( + f"Perplexity (MA): {summary['moving_avg_perplexity']:.2f} [{summary['perplexity_trend']}]" + ) + logger.info(f"Quantum Time (MA): {summary['moving_avg_quantum_time']:.4f}s") + + if perf_report.get("status") != "no_data": + logger.info( + f"CPU: {perf_report['avg_cpu_percent']:.1f}% (max: {perf_report['max_cpu_percent']:.1f}%)" + ) + logger.info( + f"Memory: {perf_report['avg_memory_mb']:.0f}MB (max: {perf_report['max_memory_mb']:.0f}MB)" + ) + + if self.alerts: + logger.info( + f"Alerts: {len(self.alerts)} total, {len([a for a in self.alerts if a['severity'] == 'high'])} high" + ) + + logger.info("=" * 80) + + def _check_alerts(self, snapshot: TrainingSnapshot): + """Check for alert conditions.""" + # Check loss anomalies + anomalies = self.metrics_aggregator.detect_anomalies() + for anomaly in anomalies: + if not any(a.get("step") == anomaly["step"] for a in self.alerts): + self.alerts.append( + { + "timestamp": time.time(), + "type": "anomaly", + "severity": "medium", + "message": f"Loss anomaly detected at step {anomaly['step']}", + "details": anomaly, + } + ) + + # Check degrading trends + if self.metrics_aggregator.get_trend("loss") == "degrading": + self.alerts.append( + { + "timestamp": time.time(), + "type": "trend", + "severity": "low", + "message": "Loss trend is degrading", + } + ) + + # Check high perplexity + if snapshot.perplexity > 1000.0: + self.alerts.append( + { + "timestamp": time.time(), + "type": "perplexity", + "severity": "high", + "message": f"Very high perplexity: {snapshot.perplexity:.2f}", + } + ) + + def get_full_report(self) -> Dict[str, Any]: + """Generate complete training report.""" + return { + "metrics_summary": self.metrics_aggregator.get_summary(), + "performance_report": self.performance_monitor.get_resource_report(), + "circuit_stats": self.circuit_profiler.get_all_stats(), + "alerts": self.alerts, + "total_snapshots": len(self.metrics_aggregator.snapshots), + } + + def save_report(self, filename: str = "training_report.json"): + """Save complete report to file.""" + report = self.get_full_report() + report_path = self.output_dir / filename + + with open(report_path, "w") as f: + json.dump(report, f, indent=2) + + logger.info(f"Report saved: {report_path}") + return report_path + + +class VisualizationExporter: + """ + Exports training data in formats suitable for visualization. + """ + + def __init__(self, output_dir: Path): + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + logger.info(f"VisualizationExporter: output_dir={output_dir}") + + def export_loss_curve( + self, snapshots: List[TrainingSnapshot], filename: str = "loss_curve.json" + ): + """Export loss curve data.""" + data = { + "steps": [s.global_step for s in snapshots], + "loss": [s.loss for s in snapshots], + "perplexity": [s.perplexity for s in snapshots], + } + + path = self.output_dir / filename + with open(path, "w") as f: + json.dump(data, f) + + logger.info(f"Loss curve exported: {path}") + return path + + def export_quantum_metrics( + self, snapshots: List[TrainingSnapshot], filename: str = "quantum_metrics.json" + ): + """Export quantum-specific metrics.""" + data = { + "steps": [s.global_step for s in snapshots], + "circuit_time": [ + s.quantum_metrics.circuit_execution_time for s in snapshots + ], + "quantum_ratio": [ + s.quantum_metrics.quantum_classical_ratio for s in snapshots + ], + "cache_hit_rate": [s.quantum_metrics.cache_hit_rate for s in snapshots], + } + + path = self.output_dir / filename + with open(path, "w") as f: + json.dump(data, f) + + logger.info(f"Quantum metrics exported: {path}") + return path + + def export_all(self, snapshots: List[TrainingSnapshot]): + """Export all available visualization data.""" + self.export_loss_curve(snapshots) + self.export_quantum_metrics(snapshots) + logger.info("All visualization data exported") + + +# Export components +__all__ = [ + "QuantumMetrics", + "TrainingSnapshot", + "MetricsAggregator", + "PerformanceMonitor", + "QuantumCircuitProfiler", + "TrainingDashboard", + "VisualizationExporter", +] + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + + # Test components + dashboard = TrainingDashboard( + output_dir=Path("data_out/quantum_llm_dashboard"), + update_interval=5, + ) + + # Simulate some training snapshots + for step in range(20): + snapshot = TrainingSnapshot( + timestamp=time.time(), + global_step=step, + epoch=0, + loss=5.0 - step * 0.1 + np.random.randn() * 0.1, + perplexity=np.exp(5.0 - step * 0.1), + learning_rate=1e-4, + batch_size=16, + quantum_metrics=QuantumMetrics( + circuit_execution_time=0.1 + np.random.randn() * 0.01, + circuit_depth=10, + gate_count=50, + qubit_count=4, + quantum_classical_ratio=0.3, + cache_hit_rate=0.5 + step * 0.02, + ), + ) + + dashboard.update(snapshot) + time.sleep(0.1) + + # Generate report + report_path = dashboard.save_report() + logger.info(f"✅ Dashboard test complete: {report_path}") + + # Export visualizations + exporter = VisualizationExporter(Path("data_out/quantum_llm_visualizations")) + exporter.export_all(dashboard.metrics_aggregator.snapshots) + logger.info("✅ Visualization export complete") diff --git a/ai-projects/quantum-ml/src/quantum_transformer.py b/ai-projects/quantum-ml/src/quantum_transformer.py index e37ba83c9..2cfd43126 100644 --- a/ai-projects/quantum-ml/src/quantum_transformer.py +++ b/ai-projects/quantum-ml/src/quantum_transformer.py @@ -1,530 +1,530 @@ -""" -Quantum Transformer: Real quantum circuits integrated into transformer architecture. - -Uses PennyLane variational quantum circuits for attention and feed-forward layers. -Provides a small-scale proof-of-concept QuantumLLM that can be trained on character-level -language modeling with gradients flowing through quantum circuits. - -Classes: - ClassicalSelfAttention - Standard dot-product attention (fallback) - ClassicalFeedForward - Standard 2-layer FFN (fallback) - QuantumSelfAttention - Quantum kernel attention using QuantumLayer - QuantumFeedForward - Hybrid quantum-classical FFN - QuantumTransformerBlock - Pre-norm transformer block with quantum components - QuantumLLM - Full language model with quantum transformer blocks -""" - -import logging -import math -from typing import Optional - -import torch -import torch.nn as nn -import torch.nn.functional as F - -logger = logging.getLogger(__name__) - -try: - from hybrid_qnn import QuantumLayer - - QUANTUM_AVAILABLE = True - logger.info("QuantumLayer loaded successfully") -except ImportError: - try: - import sys - from pathlib import Path - - _src_dir = str(Path(__file__).parent) - if _src_dir not in sys.path: - sys.path.insert(0, _src_dir) - from hybrid_qnn import QuantumLayer - - QUANTUM_AVAILABLE = True - logger.info("QuantumLayer loaded from local src directory") - except ImportError as e: - QUANTUM_AVAILABLE = False - logger.warning(f"QuantumLayer not available - using classical fallbacks: {e}") - - -# --------------------------------------------------------------------------- -# Classical fallbacks -# --------------------------------------------------------------------------- - - -class ClassicalSelfAttention(nn.Module): - """Standard multi-head dot-product attention (fallback when quantum unavailable).""" - - def __init__(self, d_model: int, n_heads: int, dropout: float = 0.1): - super().__init__() - assert d_model % n_heads == 0, "d_model must be divisible by n_heads" - self.d_model = d_model - self.n_heads = n_heads - self.d_head = d_model // n_heads - self.scale = 1.0 / math.sqrt(self.d_head) - - self.W_Q = nn.Linear(d_model, d_model) - self.W_K = nn.Linear(d_model, d_model) - self.W_V = nn.Linear(d_model, d_model) - self.W_O = nn.Linear(d_model, d_model) - self.attn_dropout = nn.Dropout(dropout) - - def forward( - self, x: torch.Tensor, mask: Optional[torch.Tensor] = None - ) -> torch.Tensor: - batch, seq_len, _ = x.shape - - Q = self.W_Q(x).view(batch, seq_len, self.n_heads, self.d_head).transpose(1, 2) - K = self.W_K(x).view(batch, seq_len, self.n_heads, self.d_head).transpose(1, 2) - V = self.W_V(x).view(batch, seq_len, self.n_heads, self.d_head).transpose(1, 2) - - attn_logits = torch.matmul(Q, K.transpose(-2, -1)) * self.scale - - if mask is not None: - attn_logits = attn_logits.masked_fill(mask == 0, float("-inf")) - - attn_weights = self.attn_dropout(F.softmax(attn_logits, dim=-1)) - out = torch.matmul(attn_weights, V) - out = out.transpose(1, 2).contiguous().view(batch, seq_len, self.d_model) - return self.W_O(out) - - -class ClassicalFeedForward(nn.Module): - """Standard 2-layer feed-forward network (fallback).""" - - def __init__(self, d_model: int, d_ffn: int = 256, dropout: float = 0.1): - super().__init__() - self.net = nn.Sequential( - nn.Linear(d_model, d_ffn), - nn.GELU(), - nn.Dropout(dropout), - nn.Linear(d_ffn, d_model), - nn.Dropout(dropout), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.net(x) - - -# --------------------------------------------------------------------------- -# Quantum components -# --------------------------------------------------------------------------- - - -class QuantumSelfAttention(nn.Module): - """ - Multi-head attention using quantum circuits as learned feature maps. - - Instead of computing softmax(QK^T / sqrt(d_k)), this maps Q and K through - a parameterised variational quantum circuit (QuantumLayer) and computes - attention logits as the dot product of the quantum-mapped features: - - attention_logits_{ij} = phi(Q_i) . phi(K_j) / sqrt(n_qubits) - - One QuantumLayer per head provides separate learned quantum kernels. - """ - - def __init__( - self, - d_model: int = 64, - n_heads: int = 4, - n_qubits: int = 4, - n_quantum_layers: int = 2, - entanglement: str = "circular", - dropout: float = 0.1, - ): - super().__init__() - assert d_model % n_heads == 0, "d_model must be divisible by n_heads" - - self.d_model = d_model - self.n_heads = n_heads - self.d_head = d_model // n_heads - self.n_qubits = n_qubits - self.quantum_input_dim = 2**n_qubits - self.scale = 1.0 / math.sqrt(n_qubits) - - # Classical projections - self.W_Q = nn.Linear(d_model, d_model) - self.W_K = nn.Linear(d_model, d_model) - self.W_V = nn.Linear(d_model, d_model) - self.W_O = nn.Linear(d_model, d_model) - - # Quantum projection (only needed when d_head != 2^n_qubits) - if self.d_head != self.quantum_input_dim: - self.quantum_proj = nn.Linear(self.d_head, self.quantum_input_dim) - else: - self.quantum_proj = None - - # One QuantumLayer per attention head - self.quantum_layers = nn.ModuleList( - [ - QuantumLayer( - n_qubits=n_qubits, - n_layers=n_quantum_layers, - device="default.qubit", - entanglement=entanglement, - ) - for _ in range(n_heads) - ] - ) - - self.attn_dropout = nn.Dropout(dropout) - - def _quantum_map(self, x: torch.Tensor, head_idx: int) -> torch.Tensor: - """Map input through quantum circuit for a specific head. - - Args: - x: (batch * seq, d_head) tensor - head_idx: which head's QuantumLayer to use - - Returns: - (batch * seq, n_qubits) tensor of quantum expectation values - """ - if self.quantum_proj is not None: - x = self.quantum_proj(x) - x = F.normalize(x, p=2, dim=-1) - return self.quantum_layers[head_idx](x) - - def forward( - self, x: torch.Tensor, mask: Optional[torch.Tensor] = None - ) -> torch.Tensor: - batch, seq_len, _ = x.shape - - Q = self.W_Q(x).view(batch, seq_len, self.n_heads, self.d_head) - K = self.W_K(x).view(batch, seq_len, self.n_heads, self.d_head) - V = self.W_V(x).view(batch, seq_len, self.n_heads, self.d_head).transpose(1, 2) - - head_outputs = [] - for h in range(self.n_heads): - Q_h = Q[:, :, h, :].reshape(batch * seq_len, self.d_head) - K_h = K[:, :, h, :].reshape(batch * seq_len, self.d_head) - - Q_q = self._quantum_map(Q_h, h).view(batch, seq_len, self.n_qubits) - K_q = self._quantum_map(K_h, h).view(batch, seq_len, self.n_qubits) - - attn_logits = torch.bmm(Q_q, K_q.transpose(1, 2)) * self.scale - - if mask is not None: - attn_logits = attn_logits.masked_fill(mask == 0, float("-inf")) - - attn_weights = self.attn_dropout(F.softmax(attn_logits, dim=-1)) - - V_h = V[:, h, :, :] # (batch, seq, d_head) - head_out = torch.bmm(attn_weights, V_h) # (batch, seq, d_head) - head_outputs.append(head_out) - - multi_head = torch.cat(head_outputs, dim=-1) # (batch, seq, d_model) - return self.W_O(multi_head) - - -class QuantumFeedForward(nn.Module): - """ - Hybrid quantum-classical feed-forward network. - - Classical down-projection to 2^n_qubits dims, quantum processing through - a variational circuit, then classical up-projection back to d_model. - The quantum circuit acts as a nonlinear bottleneck with quantum-mechanical - expressivity. - """ - - def __init__( - self, - d_model: int = 64, - n_qubits: int = 4, - n_quantum_layers: int = 2, - entanglement: str = "circular", - dropout: float = 0.1, - ): - super().__init__() - self.quantum_input_dim = 2**n_qubits - self.n_qubits = n_qubits - - self.down_proj = nn.Linear(d_model, self.quantum_input_dim) - self.quantum_layer = QuantumLayer( - n_qubits=n_qubits, - n_layers=n_quantum_layers, - device="default.qubit", - entanglement=entanglement, - ) - self.activation = nn.GELU() - self.up_proj = nn.Linear(n_qubits, d_model) - self.dropout = nn.Dropout(dropout) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - batch, seq_len, d_model = x.shape - - x_flat = x.reshape(batch * seq_len, d_model) - x_down = self.down_proj(x_flat) - x_norm = F.normalize(x_down, p=2, dim=-1) - x_quantum = self.quantum_layer(x_norm) - x_act = self.activation(x_quantum) - x_up = self.up_proj(x_act) - x_out = self.dropout(x_up) - - return x_out.view(batch, seq_len, d_model) - - -# --------------------------------------------------------------------------- -# Transformer block and full LLM -# --------------------------------------------------------------------------- - - -class QuantumTransformerBlock(nn.Module): - """ - Pre-norm transformer block with optional quantum attention and FFN. - - Falls back to classical components when quantum libraries are unavailable - or when use_quantum_* flags are set to False. - """ - - def __init__( - self, - d_model: int = 64, - n_heads: int = 4, - n_qubits: int = 4, - n_quantum_layers: int = 2, - entanglement: str = "circular", - dropout: float = 0.1, - use_quantum_attention: bool = True, - use_quantum_ffn: bool = True, - ): - super().__init__() - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.drop1 = nn.Dropout(dropout) - self.drop2 = nn.Dropout(dropout) - - if use_quantum_attention and QUANTUM_AVAILABLE: - self.attention = QuantumSelfAttention( - d_model=d_model, - n_heads=n_heads, - n_qubits=n_qubits, - n_quantum_layers=n_quantum_layers, - entanglement=entanglement, - dropout=dropout, - ) - logger.info("Using QuantumSelfAttention") - else: - self.attention = ClassicalSelfAttention(d_model, n_heads, dropout) - logger.info("Using ClassicalSelfAttention (fallback)") - - if use_quantum_ffn and QUANTUM_AVAILABLE: - self.ffn = QuantumFeedForward( - d_model=d_model, - n_qubits=n_qubits, - n_quantum_layers=n_quantum_layers, - entanglement=entanglement, - dropout=dropout, - ) - logger.info("Using QuantumFeedForward") - else: - self.ffn = ClassicalFeedForward(d_model, d_ffn=d_model * 4, dropout=dropout) - logger.info("Using ClassicalFeedForward (fallback)") - - def forward( - self, x: torch.Tensor, mask: Optional[torch.Tensor] = None - ) -> torch.Tensor: - x = x + self.drop1(self.attention(self.norm1(x), mask=mask)) - x = x + self.drop2(self.ffn(self.norm2(x))) - return x - - -class QuantumLLM(nn.Module): - """ - Small-scale language model with quantum transformer blocks. - - Architecture: - Token embedding + learned positional encoding - -> N QuantumTransformerBlock layers - -> LayerNorm -> LM head (vocab projection) - - Designed for proof-of-concept with 4-8 qubit quantum circuits. - Default config (~100k params): vocab=1000, d_model=64, 2 blocks, 4 heads, 4 qubits. - """ - - def __init__( - self, - vocab_size: int = 1000, - d_model: int = 64, - n_heads: int = 4, - n_transformer_layers: int = 2, - n_qubits: int = 4, - n_quantum_layers: int = 2, - max_seq_len: int = 32, - entanglement: str = "circular", - dropout: float = 0.1, - use_quantum_attention: bool = True, - use_quantum_ffn: bool = True, - tie_embeddings: bool = True, - ): - super().__init__() - self.vocab_size = vocab_size - self.d_model = d_model - self.max_seq_len = max_seq_len - - self.token_embedding = nn.Embedding(vocab_size, d_model) - self.pos_embedding = nn.Embedding(max_seq_len, d_model) - self.embedding_dropout = nn.Dropout(dropout) - - self.blocks = nn.ModuleList( - [ - QuantumTransformerBlock( - d_model=d_model, - n_heads=n_heads, - n_qubits=n_qubits, - n_quantum_layers=n_quantum_layers, - entanglement=entanglement, - dropout=dropout, - use_quantum_attention=use_quantum_attention, - use_quantum_ffn=use_quantum_ffn, - ) - for _ in range(n_transformer_layers) - ] - ) - - self.final_norm = nn.LayerNorm(d_model) - self.lm_head = nn.Linear(d_model, vocab_size, bias=False) - - if tie_embeddings: - self.lm_head.weight = self.token_embedding.weight - - # Initialise weights - self.apply(self._init_weights) - - n_params = sum(p.numel() for p in self.parameters()) - logger.info(f"QuantumLLM initialised: {n_params:,} parameters") - logger.info( - f" vocab={vocab_size}, d_model={d_model}, heads={n_heads}, " - f"blocks={n_transformer_layers}, qubits={n_qubits}, " - f"seq_len={max_seq_len}" - ) - - @staticmethod - def _init_weights(module: nn.Module): - if isinstance(module, nn.Linear): - nn.init.normal_(module.weight, mean=0.0, std=0.02) - if module.bias is not None: - nn.init.zeros_(module.bias) - elif isinstance(module, nn.Embedding): - nn.init.normal_(module.weight, mean=0.0, std=0.02) - elif isinstance(module, nn.LayerNorm): - nn.init.ones_(module.weight) - nn.init.zeros_(module.bias) - - def forward( - self, - input_ids: torch.Tensor, - mask: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - """ - Args: - input_ids: (batch, seq_len) LongTensor of token indices - mask: optional attention mask; if None a causal mask is created - - Returns: - logits: (batch, seq_len, vocab_size) - """ - batch, seq_len = input_ids.shape - assert ( - seq_len <= self.max_seq_len - ), f"Sequence length {seq_len} exceeds max {self.max_seq_len}" - - positions = torch.arange(seq_len, device=input_ids.device).unsqueeze(0) - x = self.token_embedding(input_ids) + self.pos_embedding(positions) - x = self.embedding_dropout(x) - - if mask is None: - mask = torch.tril(torch.ones(seq_len, seq_len, device=x.device)).unsqueeze( - 0 - ) - - for block in self.blocks: - x = block(x, mask=mask) - - x = self.final_norm(x) - logits = self.lm_head(x) - return logits - - @torch.no_grad() - def generate( - self, - input_ids: torch.Tensor, - max_new_tokens: int = 20, - temperature: float = 1.0, - top_k: Optional[int] = None, - ) -> torch.Tensor: - """Autoregressive token-by-token generation.""" - self.eval() - for _ in range(max_new_tokens): - x = input_ids[:, -self.max_seq_len :] - logits = self.forward(x) - next_logits = logits[:, -1, :] / temperature - - if top_k is not None: - topk_vals, _ = torch.topk(next_logits, min(top_k, next_logits.size(-1))) - next_logits[next_logits < topk_vals[:, [-1]]] = float("-inf") - - probs = F.softmax(next_logits, dim=-1) - next_token = torch.multinomial(probs, num_samples=1) - input_ids = torch.cat([input_ids, next_token], dim=1) - return input_ids - - @classmethod - def from_config(cls, config: dict) -> "QuantumLLM": - """Create a QuantumLLM from a configuration dictionary.""" - qt = config.get("quantum_transformer", config) - return cls( - vocab_size=qt.get("vocab_size", 1000), - d_model=qt.get("d_model", 64), - n_heads=qt.get("n_heads", 4), - n_transformer_layers=qt.get("n_transformer_layers", 2), - n_qubits=qt.get("n_qubits", 4), - n_quantum_layers=qt.get("n_quantum_layers", 2), - max_seq_len=qt.get("max_seq_len", 32), - entanglement=qt.get("entanglement", "circular"), - dropout=qt.get("dropout", 0.1), - use_quantum_attention=qt.get("use_quantum_attention", True), - use_quantum_ffn=qt.get("use_quantum_ffn", True), - tie_embeddings=qt.get("tie_embeddings", True), - ) - - -# --------------------------------------------------------------------------- -# Quick smoke test -# --------------------------------------------------------------------------- - -if __name__ == "__main__": - logging.basicConfig(level=logging.INFO) - - print("=" * 60) - print("Quantum Transformer Smoke Test") - print("=" * 60) - - model = QuantumLLM( - vocab_size=256, - d_model=64, - n_heads=4, - n_transformer_layers=1, - n_qubits=4, - n_quantum_layers=1, - max_seq_len=16, - ) - - input_ids = torch.randint(0, 256, (2, 8)) - logits = model(input_ids) - - print(f"Input shape: {input_ids.shape}") - print(f"Output shape: {logits.shape}") - assert logits.shape == (2, 8, 256), f"Unexpected shape: {logits.shape}" - - loss = F.cross_entropy( - logits.view(-1, 256), - torch.randint(0, 256, (2 * 8,)), - ) - loss.backward() - print(f"Loss: {loss.item():.4f}") - print("Gradient check: OK - gradients flow through quantum circuits") - - generated = model.generate(input_ids, max_new_tokens=4, temperature=1.0) - print(f"Generated shape: {generated.shape}") - print("Smoke test PASSED") +""" +Quantum Transformer: Real quantum circuits integrated into transformer architecture. + +Uses PennyLane variational quantum circuits for attention and feed-forward layers. +Provides a small-scale proof-of-concept QuantumLLM that can be trained on character-level +language modeling with gradients flowing through quantum circuits. + +Classes: + ClassicalSelfAttention - Standard dot-product attention (fallback) + ClassicalFeedForward - Standard 2-layer FFN (fallback) + QuantumSelfAttention - Quantum kernel attention using QuantumLayer + QuantumFeedForward - Hybrid quantum-classical FFN + QuantumTransformerBlock - Pre-norm transformer block with quantum components + QuantumLLM - Full language model with quantum transformer blocks +""" + +import logging +import math +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +logger = logging.getLogger(__name__) + +try: + from hybrid_qnn import QuantumLayer + + QUANTUM_AVAILABLE = True + logger.info("QuantumLayer loaded successfully") +except ImportError: + try: + import sys + from pathlib import Path + + _src_dir = str(Path(__file__).parent) + if _src_dir not in sys.path: + sys.path.insert(0, _src_dir) + from hybrid_qnn import QuantumLayer + + QUANTUM_AVAILABLE = True + logger.info("QuantumLayer loaded from local src directory") + except ImportError as e: + QUANTUM_AVAILABLE = False + logger.warning(f"QuantumLayer not available - using classical fallbacks: {e}") + + +# --------------------------------------------------------------------------- +# Classical fallbacks +# --------------------------------------------------------------------------- + + +class ClassicalSelfAttention(nn.Module): + """Standard multi-head dot-product attention (fallback when quantum unavailable).""" + + def __init__(self, d_model: int, n_heads: int, dropout: float = 0.1): + super().__init__() + assert d_model % n_heads == 0, "d_model must be divisible by n_heads" + self.d_model = d_model + self.n_heads = n_heads + self.d_head = d_model // n_heads + self.scale = 1.0 / math.sqrt(self.d_head) + + self.W_Q = nn.Linear(d_model, d_model) + self.W_K = nn.Linear(d_model, d_model) + self.W_V = nn.Linear(d_model, d_model) + self.W_O = nn.Linear(d_model, d_model) + self.attn_dropout = nn.Dropout(dropout) + + def forward( + self, x: torch.Tensor, mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + batch, seq_len, _ = x.shape + + Q = self.W_Q(x).view(batch, seq_len, self.n_heads, self.d_head).transpose(1, 2) + K = self.W_K(x).view(batch, seq_len, self.n_heads, self.d_head).transpose(1, 2) + V = self.W_V(x).view(batch, seq_len, self.n_heads, self.d_head).transpose(1, 2) + + attn_logits = torch.matmul(Q, K.transpose(-2, -1)) * self.scale + + if mask is not None: + attn_logits = attn_logits.masked_fill(mask == 0, float("-inf")) + + attn_weights = self.attn_dropout(F.softmax(attn_logits, dim=-1)) + out = torch.matmul(attn_weights, V) + out = out.transpose(1, 2).contiguous().view(batch, seq_len, self.d_model) + return self.W_O(out) + + +class ClassicalFeedForward(nn.Module): + """Standard 2-layer feed-forward network (fallback).""" + + def __init__(self, d_model: int, d_ffn: int = 256, dropout: float = 0.1): + super().__init__() + self.net = nn.Sequential( + nn.Linear(d_model, d_ffn), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(d_ffn, d_model), + nn.Dropout(dropout), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.net(x) + + +# --------------------------------------------------------------------------- +# Quantum components +# --------------------------------------------------------------------------- + + +class QuantumSelfAttention(nn.Module): + """ + Multi-head attention using quantum circuits as learned feature maps. + + Instead of computing softmax(QK^T / sqrt(d_k)), this maps Q and K through + a parameterised variational quantum circuit (QuantumLayer) and computes + attention logits as the dot product of the quantum-mapped features: + + attention_logits_{ij} = phi(Q_i) . phi(K_j) / sqrt(n_qubits) + + One QuantumLayer per head provides separate learned quantum kernels. + """ + + def __init__( + self, + d_model: int = 64, + n_heads: int = 4, + n_qubits: int = 4, + n_quantum_layers: int = 2, + entanglement: str = "circular", + dropout: float = 0.1, + ): + super().__init__() + assert d_model % n_heads == 0, "d_model must be divisible by n_heads" + + self.d_model = d_model + self.n_heads = n_heads + self.d_head = d_model // n_heads + self.n_qubits = n_qubits + self.quantum_input_dim = 2**n_qubits + self.scale = 1.0 / math.sqrt(n_qubits) + + # Classical projections + self.W_Q = nn.Linear(d_model, d_model) + self.W_K = nn.Linear(d_model, d_model) + self.W_V = nn.Linear(d_model, d_model) + self.W_O = nn.Linear(d_model, d_model) + + # Quantum projection (only needed when d_head != 2^n_qubits) + if self.d_head != self.quantum_input_dim: + self.quantum_proj = nn.Linear(self.d_head, self.quantum_input_dim) + else: + self.quantum_proj = None + + # One QuantumLayer per attention head + self.quantum_layers = nn.ModuleList( + [ + QuantumLayer( + n_qubits=n_qubits, + n_layers=n_quantum_layers, + device="default.qubit", + entanglement=entanglement, + ) + for _ in range(n_heads) + ] + ) + + self.attn_dropout = nn.Dropout(dropout) + + def _quantum_map(self, x: torch.Tensor, head_idx: int) -> torch.Tensor: + """Map input through quantum circuit for a specific head. + + Args: + x: (batch * seq, d_head) tensor + head_idx: which head's QuantumLayer to use + + Returns: + (batch * seq, n_qubits) tensor of quantum expectation values + """ + if self.quantum_proj is not None: + x = self.quantum_proj(x) + x = F.normalize(x, p=2, dim=-1) + return self.quantum_layers[head_idx](x) + + def forward( + self, x: torch.Tensor, mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + batch, seq_len, _ = x.shape + + Q = self.W_Q(x).view(batch, seq_len, self.n_heads, self.d_head) + K = self.W_K(x).view(batch, seq_len, self.n_heads, self.d_head) + V = self.W_V(x).view(batch, seq_len, self.n_heads, self.d_head).transpose(1, 2) + + head_outputs = [] + for h in range(self.n_heads): + Q_h = Q[:, :, h, :].reshape(batch * seq_len, self.d_head) + K_h = K[:, :, h, :].reshape(batch * seq_len, self.d_head) + + Q_q = self._quantum_map(Q_h, h).view(batch, seq_len, self.n_qubits) + K_q = self._quantum_map(K_h, h).view(batch, seq_len, self.n_qubits) + + attn_logits = torch.bmm(Q_q, K_q.transpose(1, 2)) * self.scale + + if mask is not None: + attn_logits = attn_logits.masked_fill(mask == 0, float("-inf")) + + attn_weights = self.attn_dropout(F.softmax(attn_logits, dim=-1)) + + V_h = V[:, h, :, :] # (batch, seq, d_head) + head_out = torch.bmm(attn_weights, V_h) # (batch, seq, d_head) + head_outputs.append(head_out) + + multi_head = torch.cat(head_outputs, dim=-1) # (batch, seq, d_model) + return self.W_O(multi_head) + + +class QuantumFeedForward(nn.Module): + """ + Hybrid quantum-classical feed-forward network. + + Classical down-projection to 2^n_qubits dims, quantum processing through + a variational circuit, then classical up-projection back to d_model. + The quantum circuit acts as a nonlinear bottleneck with quantum-mechanical + expressivity. + """ + + def __init__( + self, + d_model: int = 64, + n_qubits: int = 4, + n_quantum_layers: int = 2, + entanglement: str = "circular", + dropout: float = 0.1, + ): + super().__init__() + self.quantum_input_dim = 2**n_qubits + self.n_qubits = n_qubits + + self.down_proj = nn.Linear(d_model, self.quantum_input_dim) + self.quantum_layer = QuantumLayer( + n_qubits=n_qubits, + n_layers=n_quantum_layers, + device="default.qubit", + entanglement=entanglement, + ) + self.activation = nn.GELU() + self.up_proj = nn.Linear(n_qubits, d_model) + self.dropout = nn.Dropout(dropout) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + batch, seq_len, d_model = x.shape + + x_flat = x.reshape(batch * seq_len, d_model) + x_down = self.down_proj(x_flat) + x_norm = F.normalize(x_down, p=2, dim=-1) + x_quantum = self.quantum_layer(x_norm) + x_act = self.activation(x_quantum) + x_up = self.up_proj(x_act) + x_out = self.dropout(x_up) + + return x_out.view(batch, seq_len, d_model) + + +# --------------------------------------------------------------------------- +# Transformer block and full LLM +# --------------------------------------------------------------------------- + + +class QuantumTransformerBlock(nn.Module): + """ + Pre-norm transformer block with optional quantum attention and FFN. + + Falls back to classical components when quantum libraries are unavailable + or when use_quantum_* flags are set to False. + """ + + def __init__( + self, + d_model: int = 64, + n_heads: int = 4, + n_qubits: int = 4, + n_quantum_layers: int = 2, + entanglement: str = "circular", + dropout: float = 0.1, + use_quantum_attention: bool = True, + use_quantum_ffn: bool = True, + ): + super().__init__() + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.drop1 = nn.Dropout(dropout) + self.drop2 = nn.Dropout(dropout) + + if use_quantum_attention and QUANTUM_AVAILABLE: + self.attention = QuantumSelfAttention( + d_model=d_model, + n_heads=n_heads, + n_qubits=n_qubits, + n_quantum_layers=n_quantum_layers, + entanglement=entanglement, + dropout=dropout, + ) + logger.info("Using QuantumSelfAttention") + else: + self.attention = ClassicalSelfAttention(d_model, n_heads, dropout) + logger.info("Using ClassicalSelfAttention (fallback)") + + if use_quantum_ffn and QUANTUM_AVAILABLE: + self.ffn = QuantumFeedForward( + d_model=d_model, + n_qubits=n_qubits, + n_quantum_layers=n_quantum_layers, + entanglement=entanglement, + dropout=dropout, + ) + logger.info("Using QuantumFeedForward") + else: + self.ffn = ClassicalFeedForward(d_model, d_ffn=d_model * 4, dropout=dropout) + logger.info("Using ClassicalFeedForward (fallback)") + + def forward( + self, x: torch.Tensor, mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + x = x + self.drop1(self.attention(self.norm1(x), mask=mask)) + x = x + self.drop2(self.ffn(self.norm2(x))) + return x + + +class QuantumLLM(nn.Module): + """ + Small-scale language model with quantum transformer blocks. + + Architecture: + Token embedding + learned positional encoding + -> N QuantumTransformerBlock layers + -> LayerNorm -> LM head (vocab projection) + + Designed for proof-of-concept with 4-8 qubit quantum circuits. + Default config (~100k params): vocab=1000, d_model=64, 2 blocks, 4 heads, 4 qubits. + """ + + def __init__( + self, + vocab_size: int = 1000, + d_model: int = 64, + n_heads: int = 4, + n_transformer_layers: int = 2, + n_qubits: int = 4, + n_quantum_layers: int = 2, + max_seq_len: int = 32, + entanglement: str = "circular", + dropout: float = 0.1, + use_quantum_attention: bool = True, + use_quantum_ffn: bool = True, + tie_embeddings: bool = True, + ): + super().__init__() + self.vocab_size = vocab_size + self.d_model = d_model + self.max_seq_len = max_seq_len + + self.token_embedding = nn.Embedding(vocab_size, d_model) + self.pos_embedding = nn.Embedding(max_seq_len, d_model) + self.embedding_dropout = nn.Dropout(dropout) + + self.blocks = nn.ModuleList( + [ + QuantumTransformerBlock( + d_model=d_model, + n_heads=n_heads, + n_qubits=n_qubits, + n_quantum_layers=n_quantum_layers, + entanglement=entanglement, + dropout=dropout, + use_quantum_attention=use_quantum_attention, + use_quantum_ffn=use_quantum_ffn, + ) + for _ in range(n_transformer_layers) + ] + ) + + self.final_norm = nn.LayerNorm(d_model) + self.lm_head = nn.Linear(d_model, vocab_size, bias=False) + + if tie_embeddings: + self.lm_head.weight = self.token_embedding.weight + + # Initialise weights + self.apply(self._init_weights) + + n_params = sum(p.numel() for p in self.parameters()) + logger.info(f"QuantumLLM initialised: {n_params:,} parameters") + logger.info( + f" vocab={vocab_size}, d_model={d_model}, heads={n_heads}, " + f"blocks={n_transformer_layers}, qubits={n_qubits}, " + f"seq_len={max_seq_len}" + ) + + @staticmethod + def _init_weights(module: nn.Module): + if isinstance(module, nn.Linear): + nn.init.normal_(module.weight, mean=0.0, std=0.02) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=0.02) + elif isinstance(module, nn.LayerNorm): + nn.init.ones_(module.weight) + nn.init.zeros_(module.bias) + + def forward( + self, + input_ids: torch.Tensor, + mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """ + Args: + input_ids: (batch, seq_len) LongTensor of token indices + mask: optional attention mask; if None a causal mask is created + + Returns: + logits: (batch, seq_len, vocab_size) + """ + batch, seq_len = input_ids.shape + assert ( + seq_len <= self.max_seq_len + ), f"Sequence length {seq_len} exceeds max {self.max_seq_len}" + + positions = torch.arange(seq_len, device=input_ids.device).unsqueeze(0) + x = self.token_embedding(input_ids) + self.pos_embedding(positions) + x = self.embedding_dropout(x) + + if mask is None: + mask = torch.tril(torch.ones(seq_len, seq_len, device=x.device)).unsqueeze( + 0 + ) + + for block in self.blocks: + x = block(x, mask=mask) + + x = self.final_norm(x) + logits = self.lm_head(x) + return logits + + @torch.no_grad() + def generate( + self, + input_ids: torch.Tensor, + max_new_tokens: int = 20, + temperature: float = 1.0, + top_k: Optional[int] = None, + ) -> torch.Tensor: + """Autoregressive token-by-token generation.""" + self.eval() + for _ in range(max_new_tokens): + x = input_ids[:, -self.max_seq_len :] + logits = self.forward(x) + next_logits = logits[:, -1, :] / temperature + + if top_k is not None: + topk_vals, _ = torch.topk(next_logits, min(top_k, next_logits.size(-1))) + next_logits[next_logits < topk_vals[:, [-1]]] = float("-inf") + + probs = F.softmax(next_logits, dim=-1) + next_token = torch.multinomial(probs, num_samples=1) + input_ids = torch.cat([input_ids, next_token], dim=1) + return input_ids + + @classmethod + def from_config(cls, config: dict) -> "QuantumLLM": + """Create a QuantumLLM from a configuration dictionary.""" + qt = config.get("quantum_transformer", config) + return cls( + vocab_size=qt.get("vocab_size", 1000), + d_model=qt.get("d_model", 64), + n_heads=qt.get("n_heads", 4), + n_transformer_layers=qt.get("n_transformer_layers", 2), + n_qubits=qt.get("n_qubits", 4), + n_quantum_layers=qt.get("n_quantum_layers", 2), + max_seq_len=qt.get("max_seq_len", 32), + entanglement=qt.get("entanglement", "circular"), + dropout=qt.get("dropout", 0.1), + use_quantum_attention=qt.get("use_quantum_attention", True), + use_quantum_ffn=qt.get("use_quantum_ffn", True), + tie_embeddings=qt.get("tie_embeddings", True), + ) + + +# --------------------------------------------------------------------------- +# Quick smoke test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + + print("=" * 60) + print("Quantum Transformer Smoke Test") + print("=" * 60) + + model = QuantumLLM( + vocab_size=256, + d_model=64, + n_heads=4, + n_transformer_layers=1, + n_qubits=4, + n_quantum_layers=1, + max_seq_len=16, + ) + + input_ids = torch.randint(0, 256, (2, 8)) + logits = model(input_ids) + + print(f"Input shape: {input_ids.shape}") + print(f"Output shape: {logits.shape}") + assert logits.shape == (2, 8, 256), f"Unexpected shape: {logits.shape}" + + loss = F.cross_entropy( + logits.view(-1, 256), + torch.randint(0, 256, (2 * 8,)), + ) + loss.backward() + print(f"Loss: {loss.item():.4f}") + print("Gradient check: OK - gradients flow through quantum circuits") + + generated = model.generate(input_ids, max_new_tokens=4, temperature=1.0) + print(f"Generated shape: {generated.shape}") + print("Smoke test PASSED") diff --git a/ai-projects/quantum-ml/start_dashboard.sh b/ai-projects/quantum-ml/start_dashboard.sh index c9441dde9..a028824ab 100644 --- a/ai-projects/quantum-ml/start_dashboard.sh +++ b/ai-projects/quantum-ml/start_dashboard.sh @@ -1,38 +1,38 @@ -#!/bin/bash -# Quantum AI Training Dashboard - Startup Script - -echo "==============================================================" -echo " 🚀 Starting Quantum AI Training Dashboard" -echo "==============================================================" -echo "" - -# Check if venv exists -if [ ! -d "./venv" ]; then - echo "❌ Virtual environment not found. Creating..." - python3 -m venv venv -fi - -# Activate venv -source ./venv/bin/activate - -# Install dependencies -echo "📦 Installing dependencies..." -pip install -q flask flask-cors pennylane numpy pandas scikit-learn - -echo "" -echo "✅ Starting web server..." -echo "" -echo "📡 Dashboard will be available at: http://localhost:5000" -echo "" -echo "💡 Features:" -echo " • Real-time training visualization" -echo " • Interactive parameter tuning" -echo " • Live loss/accuracy charts" -echo " • Training history browser" -echo "" -echo "Press Ctrl+C to stop the server" -echo "==============================================================" -echo "" - -# Start the app -python web_app.py +#!/bin/bash +# Quantum AI Training Dashboard - Startup Script + +echo "==============================================================" +echo " 🚀 Starting Quantum AI Training Dashboard" +echo "==============================================================" +echo "" + +# Check if venv exists +if [ ! -d "./venv" ]; then + echo "❌ Virtual environment not found. Creating..." + python3 -m venv venv +fi + +# Activate venv +source ./venv/bin/activate + +# Install dependencies +echo "📦 Installing dependencies..." +pip install -q flask flask-cors pennylane numpy pandas scikit-learn + +echo "" +echo "✅ Starting web server..." +echo "" +echo "📡 Dashboard will be available at: http://localhost:5000" +echo "" +echo "💡 Features:" +echo " • Real-time training visualization" +echo " • Interactive parameter tuning" +echo " • Live loss/accuracy charts" +echo " • Training history browser" +echo "" +echo "Press Ctrl+C to stop the server" +echo "==============================================================" +echo "" + +# Start the app +python web_app.py diff --git a/ai-projects/quantum-ml/submit_circuit_azure.py b/ai-projects/quantum-ml/submit_circuit_azure.py index 4a343c6c1..ac9bde273 100644 --- a/ai-projects/quantum-ml/submit_circuit_azure.py +++ b/ai-projects/quantum-ml/submit_circuit_azure.py @@ -1,114 +1,114 @@ -#!/usr/bin/env python3 -""" -Submit quantum circuits to Azure Quantum using QIR -""" - -import time - -from azure.quantum import Workspace -from qiskit import QuantumCircuit - - -def main(): - print("=" * 70) - print(" Azure Quantum Circuit Submission") - print("=" * 70) - - # Connect to workspace - print("\n[1/3] Connecting to Azure Quantum...") - workspace = Workspace( - subscription_id="a07fbd16-e722-446d-8efd-0681e85b725c", - resource_group="rg-quantum-ai", - name="quantum-ai-workspace", - location="eastus", - ) - print(f"✓ Connected to: {workspace.name}") - - # Create Bell state circuit - print("\n[2/3] Creating Bell state circuit...") - circuit = QuantumCircuit(2, 2) - circuit.h(0) - circuit.cx(0, 1) - circuit.measure([0, 1], [0, 1]) - - print(circuit) - - # Convert to QIR and submit - print("\n[3/3] Submitting to Quantinuum Simulator (API validator)...") - try: - from qiskit_qir import to_qir_bitcode - - # Convert circuit to QIR bitcode bytes - bitcode = to_qir_bitcode(circuit, name="bell_state") - - # Use Quantinuum syntax checker (free, validates QIR) - target = workspace.get_targets("quantinuum.sim.h2-1sc") - - job = target.submit( - input_data=bitcode, - name="bell-state-test", - input_data_format="qir.v1", - output_data_format="microsoft.quantum-results.v1", - shots=100, - ) - - print("✓ Job submitted successfully!") - print(f" Job ID: {job.id}") - print(f" Status: {job.details.status}") - - # Wait for completion - print("\nWaiting for results...") - start_time = time.time() - - while job.details.status not in ["Succeeded", "Failed", "Cancelled"]: - time.sleep(2) - job.refresh() - elapsed = int(time.time() - start_time) - print(f" Status: {job.details.status} ({elapsed}s)", end="\r") - - print(f"\n✓ Job completed in {int(time.time() - start_time)}s") - print(f" Final status: {job.details.status}") - - if job.details.status == "Succeeded": - # Get results - results = job.get_results() - - print("\n" + "=" * 70) - print(" RESULTS") - print("=" * 70) - print("\nMeasurement Counts:") - - total_shots = sum(results.values()) - for state, count in sorted(results.items()): - percentage = (count / total_shots) * 100 - bar = "█" * int(percentage / 2) - print(f" |{state}⟩: {count:3d} shots ({percentage:5.1f}%) {bar}") - - # Check entanglement - entangled = results.get("00", 0) + results.get("11", 0) - entanglement_pct = (entangled / total_shots) * 100 - - print(f"\nEntanglement Quality: {entanglement_pct:.1f}%") - - if entanglement_pct > 90: - print("✓ EXCELLENT - Strong quantum entanglement detected!") - elif entanglement_pct > 70: - print("✓ GOOD - Quantum entanglement present") - else: - print("⚠ WARNING - Low entanglement quality") - - print("\n" + "=" * 70) - print("✓ Quantum circuit execution successful!") - print("=" * 70) - else: - print(f"\n✗ Job failed with status: {job.details.status}") - - except Exception as e: - print(f"\n✗ Error: {e}") - import traceback - - traceback.print_exc() - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +""" +Submit quantum circuits to Azure Quantum using QIR +""" + +import time + +from azure.quantum import Workspace +from qiskit import QuantumCircuit + + +def main(): + print("=" * 70) + print(" Azure Quantum Circuit Submission") + print("=" * 70) + + # Connect to workspace + print("\n[1/3] Connecting to Azure Quantum...") + workspace = Workspace( + subscription_id="a07fbd16-e722-446d-8efd-0681e85b725c", + resource_group="rg-quantum-ai", + name="quantum-ai-workspace", + location="eastus", + ) + print(f"✓ Connected to: {workspace.name}") + + # Create Bell state circuit + print("\n[2/3] Creating Bell state circuit...") + circuit = QuantumCircuit(2, 2) + circuit.h(0) + circuit.cx(0, 1) + circuit.measure([0, 1], [0, 1]) + + print(circuit) + + # Convert to QIR and submit + print("\n[3/3] Submitting to Quantinuum Simulator (API validator)...") + try: + from qiskit_qir import to_qir_bitcode + + # Convert circuit to QIR bitcode bytes + bitcode = to_qir_bitcode(circuit, name="bell_state") + + # Use Quantinuum syntax checker (free, validates QIR) + target = workspace.get_targets("quantinuum.sim.h2-1sc") + + job = target.submit( + input_data=bitcode, + name="bell-state-test", + input_data_format="qir.v1", + output_data_format="microsoft.quantum-results.v1", + shots=100, + ) + + print("✓ Job submitted successfully!") + print(f" Job ID: {job.id}") + print(f" Status: {job.details.status}") + + # Wait for completion + print("\nWaiting for results...") + start_time = time.time() + + while job.details.status not in ["Succeeded", "Failed", "Cancelled"]: + time.sleep(2) + job.refresh() + elapsed = int(time.time() - start_time) + print(f" Status: {job.details.status} ({elapsed}s)", end="\r") + + print(f"\n✓ Job completed in {int(time.time() - start_time)}s") + print(f" Final status: {job.details.status}") + + if job.details.status == "Succeeded": + # Get results + results = job.get_results() + + print("\n" + "=" * 70) + print(" RESULTS") + print("=" * 70) + print("\nMeasurement Counts:") + + total_shots = sum(results.values()) + for state, count in sorted(results.items()): + percentage = (count / total_shots) * 100 + bar = "█" * int(percentage / 2) + print(f" |{state}⟩: {count:3d} shots ({percentage:5.1f}%) {bar}") + + # Check entanglement + entangled = results.get("00", 0) + results.get("11", 0) + entanglement_pct = (entangled / total_shots) * 100 + + print(f"\nEntanglement Quality: {entanglement_pct:.1f}%") + + if entanglement_pct > 90: + print("✓ EXCELLENT - Strong quantum entanglement detected!") + elif entanglement_pct > 70: + print("✓ GOOD - Quantum entanglement present") + else: + print("⚠ WARNING - Low entanglement quality") + + print("\n" + "=" * 70) + print("✓ Quantum circuit execution successful!") + print("=" * 70) + else: + print(f"\n✗ Job failed with status: {job.details.status}") + + except Exception as e: + print(f"\n✗ Error: {e}") + import traceback + + traceback.print_exc() + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/submit_qsharp_circuit.py b/ai-projects/quantum-ml/submit_qsharp_circuit.py index eaf72b026..f97931646 100644 --- a/ai-projects/quantum-ml/submit_qsharp_circuit.py +++ b/ai-projects/quantum-ml/submit_qsharp_circuit.py @@ -1,113 +1,113 @@ -#!/usr/bin/env python3 -""" -Submit Q# quantum circuit to Azure Quantum -""" - -import time - -from azure.quantum import Workspace - -# Simple Q# Bell state program -QSHARP_BELL_STATE = """ -namespace AzureQuantumTest { - @EntryPoint() - operation BellState() : (Result, Result) { - use (q1, q2) = (Qubit(), Qubit()); - H(q1); - CNOT(q1, q2); - let r1 = M(q1); - let r2 = M(q2); - Reset(q1); - Reset(q2); - return (r1, r2); - } -} -""" - - -def main(): - print("=" * 70) - print(" Azure Quantum Q# Circuit Submission") - print("=" * 70) - - # Connect to workspace - print("\n[1/3] Connecting to Azure Quantum...") - workspace = Workspace( - subscription_id="a07fbd16-e722-446d-8efd-0681e85b725c", - resource_group="rg-quantum-ai", - name="quantum-ai-workspace", - location="eastus", - ) - print(f"✓ Connected to: {workspace.name}") - - # Show Q# program - print("\n[2/3] Q# Bell State Program:") - print(QSHARP_BELL_STATE) - - # Submit to Quantinuum syntax checker (free) - print("\n[3/3] Submitting to Quantinuum Syntax Checker (FREE)...") - try: - target = workspace.get_targets("quantinuum.sim.h2-1sc") - - job = target.submit( - input_data=QSHARP_BELL_STATE, - name="qsharp-bell-test", - input_data_format="qsharp.v1", - output_data_format="microsoft.quantum-results.v1", - shots=100, - ) - - print("✓ Job submitted successfully!") - print(f" Job ID: {job.id}") - print(f" Status: {job.details.status}") - - # Wait for completion - print("\nWaiting for results...") - start_time = time.time() - - while job.details.status not in ["Succeeded", "Failed", "Cancelled"]: - time.sleep(2) - job.refresh() - elapsed = int(time.time() - start_time) - print(f" Status: {job.details.status} ({elapsed}s)", end="\r") - - print(f"\n✓ Job completed in {int(time.time() - start_time)}s") - print(f" Final status: {job.details.status}") - - if job.details.status == "Succeeded": - # Get results - results = job.get_results() - - print("\n" + "=" * 70) - print(" RESULTS") - print("=" * 70) - print("\nMeasurement Counts:") - - # Count classical bit patterns - counts = {} - for result in results: - # Q# returns tuples of Results - key = str(result) - counts[key] = counts.get(key, 0) + 1 - - total_shots = sum(counts.values()) - for state, count in sorted(counts.items()): - percentage = (count / total_shots) * 100 - bar = "█" * int(percentage / 2) - print(f" {state}: {count:3d} shots ({percentage:5.1f}%) {bar}") - - print("\n" + "=" * 70) - print("✓ Quantum circuit execution successful!") - print("=" * 70) - else: - print(f"\n✗ Job failed with status: {job.details.status}") - - except Exception as e: - print(f"\n✗ Error: {e}") - import traceback - - traceback.print_exc() - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +""" +Submit Q# quantum circuit to Azure Quantum +""" + +import time + +from azure.quantum import Workspace + +# Simple Q# Bell state program +QSHARP_BELL_STATE = """ +namespace AzureQuantumTest { + @EntryPoint() + operation BellState() : (Result, Result) { + use (q1, q2) = (Qubit(), Qubit()); + H(q1); + CNOT(q1, q2); + let r1 = M(q1); + let r2 = M(q2); + Reset(q1); + Reset(q2); + return (r1, r2); + } +} +""" + + +def main(): + print("=" * 70) + print(" Azure Quantum Q# Circuit Submission") + print("=" * 70) + + # Connect to workspace + print("\n[1/3] Connecting to Azure Quantum...") + workspace = Workspace( + subscription_id="a07fbd16-e722-446d-8efd-0681e85b725c", + resource_group="rg-quantum-ai", + name="quantum-ai-workspace", + location="eastus", + ) + print(f"✓ Connected to: {workspace.name}") + + # Show Q# program + print("\n[2/3] Q# Bell State Program:") + print(QSHARP_BELL_STATE) + + # Submit to Quantinuum syntax checker (free) + print("\n[3/3] Submitting to Quantinuum Syntax Checker (FREE)...") + try: + target = workspace.get_targets("quantinuum.sim.h2-1sc") + + job = target.submit( + input_data=QSHARP_BELL_STATE, + name="qsharp-bell-test", + input_data_format="qsharp.v1", + output_data_format="microsoft.quantum-results.v1", + shots=100, + ) + + print("✓ Job submitted successfully!") + print(f" Job ID: {job.id}") + print(f" Status: {job.details.status}") + + # Wait for completion + print("\nWaiting for results...") + start_time = time.time() + + while job.details.status not in ["Succeeded", "Failed", "Cancelled"]: + time.sleep(2) + job.refresh() + elapsed = int(time.time() - start_time) + print(f" Status: {job.details.status} ({elapsed}s)", end="\r") + + print(f"\n✓ Job completed in {int(time.time() - start_time)}s") + print(f" Final status: {job.details.status}") + + if job.details.status == "Succeeded": + # Get results + results = job.get_results() + + print("\n" + "=" * 70) + print(" RESULTS") + print("=" * 70) + print("\nMeasurement Counts:") + + # Count classical bit patterns + counts = {} + for result in results: + # Q# returns tuples of Results + key = str(result) + counts[key] = counts.get(key, 0) + 1 + + total_shots = sum(counts.values()) + for state, count in sorted(counts.items()): + percentage = (count / total_shots) * 100 + bar = "█" * int(percentage / 2) + print(f" {state}: {count:3d} shots ({percentage:5.1f}%) {bar}") + + print("\n" + "=" * 70) + print("✓ Quantum circuit execution successful!") + print("=" * 70) + else: + print(f"\n✗ Job failed with status: {job.details.status}") + + except Exception as e: + print(f"\n✗ Error: {e}") + import traceback + + traceback.print_exc() + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/test_azure_quantum.py b/ai-projects/quantum-ml/test_azure_quantum.py index 30fe1ab85..a8c4350aa 100644 --- a/ai-projects/quantum-ml/test_azure_quantum.py +++ b/ai-projects/quantum-ml/test_azure_quantum.py @@ -1,500 +1,500 @@ -""" -Test Optimized Quantum Circuit on Real Azure Quantum Hardware -Submits the optimized quantum classifier to IonQ or other quantum backends -""" - -import logging -import sys -from datetime import datetime -from pathlib import Path -from typing import Optional - -import yaml - -_OPTIONAL_IMPORT_ERROR: Optional[ImportError] = None - -# Add src to path -sys.path.append(str(Path(__file__).parent / "src")) - -try: - import numpy as np - from azure_quantum_integration import (AzureQuantumIntegration, - create_sample_circuit) - from qiskit import QuantumCircuit -except ImportError as exc: # pragma: no cover - environment dependent - _OPTIONAL_IMPORT_ERROR = exc - -if _OPTIONAL_IMPORT_ERROR is not None and "pytest" in sys.modules: - import pytest - - pytest.skip( - f"Optional quantum dependencies unavailable: {_OPTIONAL_IMPORT_ERROR}", - allow_module_level=True, - ) - -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" -) -logger = logging.getLogger(__name__) - - -def create_optimized_quantum_circuit( - n_qubits: int = 4, n_layers: int = 3 -) -> QuantumCircuit: - """ - Create a quantum circuit using our optimized parameters. - This simulates the structure of our trained quantum classifier. - - Args: - n_qubits: Number of qubits (from config) - n_layers: Number of layers (optimized to 3) - - Returns: - Quantum circuit - """ - circuit = QuantumCircuit(n_qubits, n_qubits) - - # Initial state preparation (Hadamard layer) - for qubit in range(n_qubits): - circuit.h(qubit) - - # Variational layers (optimized: 3 layers with linear entanglement) - for layer in range(n_layers): - # Rotation gates (parameterized - using example values) - for qubit in range(n_qubits): - circuit.ry(np.pi / 4 * (layer + 1), qubit) - circuit.rz(np.pi / 3 * (layer + 1), qubit) - - # Linear entanglement (optimized pattern) - for qubit in range(n_qubits - 1): - circuit.cx(qubit, qubit + 1) - - # Add barrier for visualization - circuit.barrier() - - # Measurement - circuit.measure(range(n_qubits), range(n_qubits)) - - return circuit - - -def create_bell_state_test() -> QuantumCircuit: - """ - Create a simple Bell state for initial hardware testing. - This is a good first test to verify quantum entanglement on hardware. - - Returns: - Bell state circuit - """ - circuit = QuantumCircuit(2, 2) - circuit.h(0) - circuit.cx(0, 1) - circuit.measure([0, 1], [0, 1]) - return circuit - - -def test_azure_quantum_connection(): - """ - Test 1: Verify Azure Quantum connection and list backends. - """ - print("\n" + "=" * 70) - print(" TEST 1: AZURE QUANTUM CONNECTION") - print("=" * 70 + "\n") - - try: - if _OPTIONAL_IMPORT_ERROR is not None: - raise RuntimeError( - f"Missing optional quantum dependencies: {_OPTIONAL_IMPORT_ERROR}" - ) - - # Initialize Azure Quantum - config_path = Path(__file__).parent / "config" / "quantum_config.yaml" - azure = AzureQuantumIntegration(str(config_path)) - - print("✓ Configuration loaded") - print(f" Workspace: {azure.azure_config['workspace_name']}") - print(f" Resource Group: {azure.azure_config['resource_group']}") - print(f" Location: {azure.azure_config['location']}\n") - - # Connect to workspace - print("Connecting to Azure Quantum workspace...") - workspace = azure.connect() - print("✓ Successfully connected to Azure Quantum!\n") - - # List available backends - print("Available Quantum Backends:") - backends = azure.list_backends() - - for i, backend in enumerate(backends, 1): - print(f" {i}. {backend}") - - print(f"\n✓ Found {len(backends)} quantum backend(s)\n") - - return azure, backends - - except Exception as e: - print(f"\n✗ Connection failed: {str(e)}\n") - print("TROUBLESHOOTING STEPS:") - print("1. Ensure Azure Quantum workspace is deployed (see azure/DEPLOYMENT.md)") - print("2. Run: az login") - print("3. Update config/quantum_config.yaml with your subscription details") - print( - "4. Verify workspace exists: az quantum workspace show -g rg-quantum-ai -n quantum-ai-workspace\n" - ) - return None, [] - - -def test_bell_state_on_hardware( - azure: AzureQuantumIntegration, backend_name: str = None -): - """ - Test 2: Run Bell state on quantum hardware to verify entanglement. - """ - print("\n" + "=" * 70) - print(" TEST 2: BELL STATE ON QUANTUM HARDWARE") - print("=" * 70 + "\n") - - # Create Bell state circuit - circuit = create_bell_state_test() - - print("Bell State Circuit:") - print(circuit) - print("\nExpected Results (ideal quantum behavior):") - print(" |00⟩: ~50%") - print(" |11⟩: ~50%") - print(" (Quantum entanglement: measuring qubit 0 determines qubit 1)\n") - - try: - # Estimate cost first - print("Estimating cost...") - cost_estimate = azure.estimate_cost( - circuit, backend_name or "ionq.simulator", shots=100 - ) - print(f"Cost Estimate: {cost_estimate}\n") - - # Submit to hardware - print(f"Submitting Bell state to {backend_name or 'default backend'}...") - job = azure.submit_circuit( - circuit, - backend_name=backend_name, - shots=100, - job_name=f"bell_state_test_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - ) - - print("✓ Job submitted successfully!") - print(f" Job ID: {job.id()}") - print(f" Status: {job.status()}") - print("\nWaiting for results (this may take a few minutes)...") - - # Get results - results = azure.get_job_results(job) - - print("\n" + "=" * 70) - print(" BELL STATE RESULTS") - print("=" * 70) - print("\nMeasurement counts:") - for state, count in sorted(results["counts"].items()): - percentage = (count / sum(results["counts"].values())) * 100 - print(f" {state}: {count} ({percentage:.1f}%)") - - # Verify entanglement - counts = results["counts"] - total = sum(counts.values()) - entangled_states = counts.get("00", 0) + counts.get("11", 0) - entanglement_ratio = (entangled_states / total) * 100 - - print(f"\nEntanglement Quality: {entanglement_ratio:.1f}%") - if entanglement_ratio > 80: - print("✓ Excellent quantum entanglement observed!") - elif entanglement_ratio > 60: - print("✓ Good quantum behavior (some hardware noise expected)") - else: - print("⚠ Lower than expected - check hardware calibration") - - # Save results - azure.save_results(results, f"bell_state_results_{job.id()}.json") - print("\n✓ Results saved to results/\n") - - return results - - except Exception as e: - print(f"\n✗ Hardware test failed: {str(e)}\n") - return None - - -def test_optimized_circuit_on_hardware( - azure: AzureQuantumIntegration, backend_name: str = None -): - """ - Test 3: Run our optimized quantum classifier circuit on real hardware. - """ - print("\n" + "=" * 70) - print(" TEST 3: OPTIMIZED QUANTUM CIRCUIT ON HARDWARE") - print("=" * 70 + "\n") - - # Load config to get optimized parameters - config_path = Path(__file__).parent / "config" / "quantum_config.yaml" - with open(config_path, "r") as f: - config = yaml.safe_load(f) - - n_qubits = config["ml"]["model"]["n_qubits"] - n_layers = config["ml"]["model"]["n_layers"] - entanglement = config["ml"]["model"]["entanglement"] - - print("Optimized Configuration (90% accuracy):") - print(f" Qubits: {n_qubits}") - print(f" Layers: {n_layers}") - print(f" Entanglement: {entanglement}") - print(f" Learning Rate: {config['ml']['training']['learning_rate']}\n") - - # Create circuit - circuit = create_optimized_quantum_circuit(n_qubits, n_layers) - - print("Quantum Circuit Structure:") - print(circuit) - print("\nCircuit Statistics:") - print(f" Depth: {circuit.depth()}") - print(f" Gates: {sum(circuit.count_ops().values())}") - print(f" Qubits: {circuit.num_qubits}\n") - - try: - # Estimate cost - print("Estimating cost for optimized circuit...") - cost_estimate = azure.estimate_cost( - circuit, backend_name or "ionq.simulator", shots=500 - ) - print(f"Cost Estimate: {cost_estimate}\n") - - # Submit to hardware - print(f"Submitting optimized circuit to {backend_name or 'default backend'}...") - job = azure.submit_circuit( - circuit, - backend_name=backend_name, - shots=500, - job_name=f"optimized_classifier_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - ) - - print("✓ Job submitted successfully!") - print(f" Job ID: {job.id()}") - print(f" Status: {job.status()}") - print("\nWaiting for results (this may take several minutes)...") - - # Get results - results = azure.get_job_results(job) - - print("\n" + "=" * 70) - print(" OPTIMIZED CIRCUIT RESULTS") - print("=" * 70) - print("\nMeasurement distribution (top 10 states):") - - sorted_counts = sorted( - results["counts"].items(), key=lambda x: x[1], reverse=True - ) - total = sum(results["counts"].values()) - - for state, count in sorted_counts[:10]: - percentage = (count / total) * 100 - bar = "█" * int(percentage / 2) - print(f" {state}: {count:4d} ({percentage:5.2f}%) {bar}") - - # Analyze distribution - entropy = -sum( - (count / total) * np.log2(count / total) - for count in results["counts"].values() - if count > 0 - ) - max_entropy = np.log2(2**n_qubits) - - print("\nQuantum State Analysis:") - print(f" Unique states measured: {len(results['counts'])}/{2**n_qubits}") - print(f" Entropy: {entropy:.3f} / {max_entropy:.3f}") - print(f" Distribution uniformity: {(entropy/max_entropy)*100:.1f}%") - - # Save results - azure.save_results(results, f"optimized_circuit_results_{job.id()}.json") - print("\n✓ Results saved to results/\n") - - return results - - except Exception as e: - print(f"\n✗ Circuit execution failed: {str(e)}\n") - return None - - -def compare_simulator_vs_hardware(azure: AzureQuantumIntegration): - """ - Test 4: Compare results between simulator and real hardware. - """ - print("\n" + "=" * 70) - print(" TEST 4: SIMULATOR VS HARDWARE COMPARISON") - print("=" * 70 + "\n") - - circuit = create_bell_state_test() - - try: - # Run on simulator - print("Running on simulator...") - sim_job = azure.submit_circuit( - circuit, - backend_name="ionq.simulator", - shots=1000, - job_name="simulator_comparison", - ) - sim_results = azure.get_job_results(sim_job) - - print("✓ Simulator results received") - print(f" Counts: {sim_results['counts']}\n") - - # Run on hardware (if available) - print("Running on quantum hardware...") - hw_job = azure.submit_circuit( - circuit, - backend_name="ionq.qpu", # Real QPU - shots=1000, - job_name="hardware_comparison", - ) - hw_results = azure.get_job_results(hw_job) - - print("✓ Hardware results received") - print(f" Counts: {hw_results['counts']}\n") - - # Compare - print("=" * 70) - print(" COMPARISON RESULTS") - print("=" * 70) - - print("\n{:<15} {:<20} {:<20}".format("State", "Simulator", "Hardware")) - print("-" * 70) - - all_states = set(sim_results["counts"].keys()) | set( - hw_results["counts"].keys() - ) - sim_total = sum(sim_results["counts"].values()) - hw_total = sum(hw_results["counts"].values()) - - for state in sorted(all_states): - sim_pct = (sim_results["counts"].get(state, 0) / sim_total) * 100 - hw_pct = (hw_results["counts"].get(state, 0) / hw_total) * 100 - print(f"{state:<15} {sim_pct:>6.2f}% {' '*14} {hw_pct:>6.2f}%") - - print("\nKey Differences:") - print(" - Simulator: Ideal quantum behavior (no noise)") - print(" - Hardware: Real quantum effects + decoherence/gate errors") - print( - " - Hardware noise is expected and demonstrates real quantum computing!\n" - ) - - return sim_results, hw_results - - except Exception as e: - print(f"\n✗ Comparison failed: {str(e)}") - print("Note: Quantum hardware access may require credits or additional setup\n") - return None, None - - -def main(): - """ - Main test suite for Azure Quantum hardware. - """ - print("\n" + "=" * 70) - print(" AZURE QUANTUM HARDWARE TEST SUITE") - print(" Testing Optimized Quantum AI Configuration (90% Accuracy)") - print("=" * 70) - - # Test 1: Connection - azure, backends = test_azure_quantum_connection() - - if azure is None: - print("\n⚠ Cannot proceed without Azure Quantum connection.") - print("Please complete Azure deployment first (see azure/DEPLOYMENT.md)\n") - return - - # Select backend - print("\n" + "=" * 70) - print(" BACKEND SELECTION") - print("=" * 70 + "\n") - - if not backends: - print("⚠ No backends available. Check workspace configuration.\n") - return - - # Recommend starting with simulator - simulator_backends = [b for b in backends if "simulator" in b.lower()] - hardware_backends = [b for b in backends if "simulator" not in b.lower()] - - print("Available backends:") - print("\nSimulators (FREE - recommended for testing):") - for backend in simulator_backends: - print(f" • {backend}") - - if hardware_backends: - print("\nQuantum Hardware (PAID - requires credits):") - for backend in hardware_backends: - print(f" • {backend}") - - print("\nRecommended testing order:") - print(" 1. Start with simulator (free, fast)") - print(" 2. Test Bell state on hardware (low cost)") - print(" 3. Run optimized circuit on hardware (verify 90% accuracy)\n") - - # Ask user to select backend - print("Select backend for testing:") - print(" 1. ionq.simulator (FREE - recommended)") - if hardware_backends: - print(" 2. ionq.qpu (PAID - real quantum computer)") - print(" 3. Skip hardware tests\n") - - choice = input("Enter choice (1-3): ").strip() - - if choice == "3": - print("\nSkipping hardware tests. Goodbye!\n") - return - elif choice == "2" and hardware_backends: - backend = "ionq.qpu" - print(f"\n⚠ Selected PAID hardware: {backend}") - print("This will use quantum computing credits!") - confirm = input("Continue? (yes/no): ").strip().lower() - if confirm != "yes": - print("\nTest cancelled.\n") - return - else: - backend = "ionq.simulator" - - print(f"\nUsing backend: {backend}\n") - - # Test 2: Bell state - bell_results = test_bell_state_on_hardware(azure, backend) - - if bell_results: - print("✓ Bell state test completed successfully!\n") - - # Ask if user wants to continue with optimized circuit - proceed = ( - input("Proceed with optimized circuit test? (yes/no): ").strip().lower() - ) - if proceed == "yes": - # Test 3: Optimized circuit - opt_results = test_optimized_circuit_on_hardware(azure, backend) - - if opt_results: - print("✓ Optimized circuit test completed successfully!\n") - - # Summary - print("\n" + "=" * 70) - print(" TEST SUITE COMPLETE") - print("=" * 70) - print("\nResults saved to: ai-projects/quantum-ml/results/") - print("\nNext Steps:") - print(" 1. Analyze results in Azure Portal") - print(" 2. Compare simulator vs hardware performance") - print(" 3. Scale up to larger circuits (6-8 qubits)") - print(" 4. Train quantum classifier with hardware results\n") - - print("Documentation:") - print(" • Full guide: experiments/AZURE_QUICKSTART.md") - print(" • Deployment: azure/DEPLOYMENT.md") - print(" • Optimization: FINAL_OPTIMIZATION_REPORT.md\n") - - -if __name__ == "__main__": - main() +""" +Test Optimized Quantum Circuit on Real Azure Quantum Hardware +Submits the optimized quantum classifier to IonQ or other quantum backends +""" + +import logging +import sys +from datetime import datetime +from pathlib import Path +from typing import Optional + +import yaml + +_OPTIONAL_IMPORT_ERROR: Optional[ImportError] = None + +# Add src to path +sys.path.append(str(Path(__file__).parent / "src")) + +try: + import numpy as np + from azure_quantum_integration import (AzureQuantumIntegration, + create_sample_circuit) + from qiskit import QuantumCircuit +except ImportError as exc: # pragma: no cover - environment dependent + _OPTIONAL_IMPORT_ERROR = exc + +if _OPTIONAL_IMPORT_ERROR is not None and "pytest" in sys.modules: + import pytest + + pytest.skip( + f"Optional quantum dependencies unavailable: {_OPTIONAL_IMPORT_ERROR}", + allow_module_level=True, + ) + +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + + +def create_optimized_quantum_circuit( + n_qubits: int = 4, n_layers: int = 3 +) -> QuantumCircuit: + """ + Create a quantum circuit using our optimized parameters. + This simulates the structure of our trained quantum classifier. + + Args: + n_qubits: Number of qubits (from config) + n_layers: Number of layers (optimized to 3) + + Returns: + Quantum circuit + """ + circuit = QuantumCircuit(n_qubits, n_qubits) + + # Initial state preparation (Hadamard layer) + for qubit in range(n_qubits): + circuit.h(qubit) + + # Variational layers (optimized: 3 layers with linear entanglement) + for layer in range(n_layers): + # Rotation gates (parameterized - using example values) + for qubit in range(n_qubits): + circuit.ry(np.pi / 4 * (layer + 1), qubit) + circuit.rz(np.pi / 3 * (layer + 1), qubit) + + # Linear entanglement (optimized pattern) + for qubit in range(n_qubits - 1): + circuit.cx(qubit, qubit + 1) + + # Add barrier for visualization + circuit.barrier() + + # Measurement + circuit.measure(range(n_qubits), range(n_qubits)) + + return circuit + + +def create_bell_state_test() -> QuantumCircuit: + """ + Create a simple Bell state for initial hardware testing. + This is a good first test to verify quantum entanglement on hardware. + + Returns: + Bell state circuit + """ + circuit = QuantumCircuit(2, 2) + circuit.h(0) + circuit.cx(0, 1) + circuit.measure([0, 1], [0, 1]) + return circuit + + +def test_azure_quantum_connection(): + """ + Test 1: Verify Azure Quantum connection and list backends. + """ + print("\n" + "=" * 70) + print(" TEST 1: AZURE QUANTUM CONNECTION") + print("=" * 70 + "\n") + + try: + if _OPTIONAL_IMPORT_ERROR is not None: + raise RuntimeError( + f"Missing optional quantum dependencies: {_OPTIONAL_IMPORT_ERROR}" + ) + + # Initialize Azure Quantum + config_path = Path(__file__).parent / "config" / "quantum_config.yaml" + azure = AzureQuantumIntegration(str(config_path)) + + print("✓ Configuration loaded") + print(f" Workspace: {azure.azure_config['workspace_name']}") + print(f" Resource Group: {azure.azure_config['resource_group']}") + print(f" Location: {azure.azure_config['location']}\n") + + # Connect to workspace + print("Connecting to Azure Quantum workspace...") + workspace = azure.connect() + print("✓ Successfully connected to Azure Quantum!\n") + + # List available backends + print("Available Quantum Backends:") + backends = azure.list_backends() + + for i, backend in enumerate(backends, 1): + print(f" {i}. {backend}") + + print(f"\n✓ Found {len(backends)} quantum backend(s)\n") + + return azure, backends + + except Exception as e: + print(f"\n✗ Connection failed: {str(e)}\n") + print("TROUBLESHOOTING STEPS:") + print("1. Ensure Azure Quantum workspace is deployed (see azure/DEPLOYMENT.md)") + print("2. Run: az login") + print("3. Update config/quantum_config.yaml with your subscription details") + print( + "4. Verify workspace exists: az quantum workspace show -g rg-quantum-ai -n quantum-ai-workspace\n" + ) + return None, [] + + +def test_bell_state_on_hardware( + azure: AzureQuantumIntegration, backend_name: str = None +): + """ + Test 2: Run Bell state on quantum hardware to verify entanglement. + """ + print("\n" + "=" * 70) + print(" TEST 2: BELL STATE ON QUANTUM HARDWARE") + print("=" * 70 + "\n") + + # Create Bell state circuit + circuit = create_bell_state_test() + + print("Bell State Circuit:") + print(circuit) + print("\nExpected Results (ideal quantum behavior):") + print(" |00⟩: ~50%") + print(" |11⟩: ~50%") + print(" (Quantum entanglement: measuring qubit 0 determines qubit 1)\n") + + try: + # Estimate cost first + print("Estimating cost...") + cost_estimate = azure.estimate_cost( + circuit, backend_name or "ionq.simulator", shots=100 + ) + print(f"Cost Estimate: {cost_estimate}\n") + + # Submit to hardware + print(f"Submitting Bell state to {backend_name or 'default backend'}...") + job = azure.submit_circuit( + circuit, + backend_name=backend_name, + shots=100, + job_name=f"bell_state_test_{datetime.now().strftime('%Y%m%d_%H%M%S')}", + ) + + print("✓ Job submitted successfully!") + print(f" Job ID: {job.id()}") + print(f" Status: {job.status()}") + print("\nWaiting for results (this may take a few minutes)...") + + # Get results + results = azure.get_job_results(job) + + print("\n" + "=" * 70) + print(" BELL STATE RESULTS") + print("=" * 70) + print("\nMeasurement counts:") + for state, count in sorted(results["counts"].items()): + percentage = (count / sum(results["counts"].values())) * 100 + print(f" {state}: {count} ({percentage:.1f}%)") + + # Verify entanglement + counts = results["counts"] + total = sum(counts.values()) + entangled_states = counts.get("00", 0) + counts.get("11", 0) + entanglement_ratio = (entangled_states / total) * 100 + + print(f"\nEntanglement Quality: {entanglement_ratio:.1f}%") + if entanglement_ratio > 80: + print("✓ Excellent quantum entanglement observed!") + elif entanglement_ratio > 60: + print("✓ Good quantum behavior (some hardware noise expected)") + else: + print("⚠ Lower than expected - check hardware calibration") + + # Save results + azure.save_results(results, f"bell_state_results_{job.id()}.json") + print("\n✓ Results saved to results/\n") + + return results + + except Exception as e: + print(f"\n✗ Hardware test failed: {str(e)}\n") + return None + + +def test_optimized_circuit_on_hardware( + azure: AzureQuantumIntegration, backend_name: str = None +): + """ + Test 3: Run our optimized quantum classifier circuit on real hardware. + """ + print("\n" + "=" * 70) + print(" TEST 3: OPTIMIZED QUANTUM CIRCUIT ON HARDWARE") + print("=" * 70 + "\n") + + # Load config to get optimized parameters + config_path = Path(__file__).parent / "config" / "quantum_config.yaml" + with open(config_path, "r") as f: + config = yaml.safe_load(f) + + n_qubits = config["ml"]["model"]["n_qubits"] + n_layers = config["ml"]["model"]["n_layers"] + entanglement = config["ml"]["model"]["entanglement"] + + print("Optimized Configuration (90% accuracy):") + print(f" Qubits: {n_qubits}") + print(f" Layers: {n_layers}") + print(f" Entanglement: {entanglement}") + print(f" Learning Rate: {config['ml']['training']['learning_rate']}\n") + + # Create circuit + circuit = create_optimized_quantum_circuit(n_qubits, n_layers) + + print("Quantum Circuit Structure:") + print(circuit) + print("\nCircuit Statistics:") + print(f" Depth: {circuit.depth()}") + print(f" Gates: {sum(circuit.count_ops().values())}") + print(f" Qubits: {circuit.num_qubits}\n") + + try: + # Estimate cost + print("Estimating cost for optimized circuit...") + cost_estimate = azure.estimate_cost( + circuit, backend_name or "ionq.simulator", shots=500 + ) + print(f"Cost Estimate: {cost_estimate}\n") + + # Submit to hardware + print(f"Submitting optimized circuit to {backend_name or 'default backend'}...") + job = azure.submit_circuit( + circuit, + backend_name=backend_name, + shots=500, + job_name=f"optimized_classifier_{datetime.now().strftime('%Y%m%d_%H%M%S')}", + ) + + print("✓ Job submitted successfully!") + print(f" Job ID: {job.id()}") + print(f" Status: {job.status()}") + print("\nWaiting for results (this may take several minutes)...") + + # Get results + results = azure.get_job_results(job) + + print("\n" + "=" * 70) + print(" OPTIMIZED CIRCUIT RESULTS") + print("=" * 70) + print("\nMeasurement distribution (top 10 states):") + + sorted_counts = sorted( + results["counts"].items(), key=lambda x: x[1], reverse=True + ) + total = sum(results["counts"].values()) + + for state, count in sorted_counts[:10]: + percentage = (count / total) * 100 + bar = "█" * int(percentage / 2) + print(f" {state}: {count:4d} ({percentage:5.2f}%) {bar}") + + # Analyze distribution + entropy = -sum( + (count / total) * np.log2(count / total) + for count in results["counts"].values() + if count > 0 + ) + max_entropy = np.log2(2**n_qubits) + + print("\nQuantum State Analysis:") + print(f" Unique states measured: {len(results['counts'])}/{2**n_qubits}") + print(f" Entropy: {entropy:.3f} / {max_entropy:.3f}") + print(f" Distribution uniformity: {(entropy/max_entropy)*100:.1f}%") + + # Save results + azure.save_results(results, f"optimized_circuit_results_{job.id()}.json") + print("\n✓ Results saved to results/\n") + + return results + + except Exception as e: + print(f"\n✗ Circuit execution failed: {str(e)}\n") + return None + + +def compare_simulator_vs_hardware(azure: AzureQuantumIntegration): + """ + Test 4: Compare results between simulator and real hardware. + """ + print("\n" + "=" * 70) + print(" TEST 4: SIMULATOR VS HARDWARE COMPARISON") + print("=" * 70 + "\n") + + circuit = create_bell_state_test() + + try: + # Run on simulator + print("Running on simulator...") + sim_job = azure.submit_circuit( + circuit, + backend_name="ionq.simulator", + shots=1000, + job_name="simulator_comparison", + ) + sim_results = azure.get_job_results(sim_job) + + print("✓ Simulator results received") + print(f" Counts: {sim_results['counts']}\n") + + # Run on hardware (if available) + print("Running on quantum hardware...") + hw_job = azure.submit_circuit( + circuit, + backend_name="ionq.qpu", # Real QPU + shots=1000, + job_name="hardware_comparison", + ) + hw_results = azure.get_job_results(hw_job) + + print("✓ Hardware results received") + print(f" Counts: {hw_results['counts']}\n") + + # Compare + print("=" * 70) + print(" COMPARISON RESULTS") + print("=" * 70) + + print("\n{:<15} {:<20} {:<20}".format("State", "Simulator", "Hardware")) + print("-" * 70) + + all_states = set(sim_results["counts"].keys()) | set( + hw_results["counts"].keys() + ) + sim_total = sum(sim_results["counts"].values()) + hw_total = sum(hw_results["counts"].values()) + + for state in sorted(all_states): + sim_pct = (sim_results["counts"].get(state, 0) / sim_total) * 100 + hw_pct = (hw_results["counts"].get(state, 0) / hw_total) * 100 + print(f"{state:<15} {sim_pct:>6.2f}% {' '*14} {hw_pct:>6.2f}%") + + print("\nKey Differences:") + print(" - Simulator: Ideal quantum behavior (no noise)") + print(" - Hardware: Real quantum effects + decoherence/gate errors") + print( + " - Hardware noise is expected and demonstrates real quantum computing!\n" + ) + + return sim_results, hw_results + + except Exception as e: + print(f"\n✗ Comparison failed: {str(e)}") + print("Note: Quantum hardware access may require credits or additional setup\n") + return None, None + + +def main(): + """ + Main test suite for Azure Quantum hardware. + """ + print("\n" + "=" * 70) + print(" AZURE QUANTUM HARDWARE TEST SUITE") + print(" Testing Optimized Quantum AI Configuration (90% Accuracy)") + print("=" * 70) + + # Test 1: Connection + azure, backends = test_azure_quantum_connection() + + if azure is None: + print("\n⚠ Cannot proceed without Azure Quantum connection.") + print("Please complete Azure deployment first (see azure/DEPLOYMENT.md)\n") + return + + # Select backend + print("\n" + "=" * 70) + print(" BACKEND SELECTION") + print("=" * 70 + "\n") + + if not backends: + print("⚠ No backends available. Check workspace configuration.\n") + return + + # Recommend starting with simulator + simulator_backends = [b for b in backends if "simulator" in b.lower()] + hardware_backends = [b for b in backends if "simulator" not in b.lower()] + + print("Available backends:") + print("\nSimulators (FREE - recommended for testing):") + for backend in simulator_backends: + print(f" • {backend}") + + if hardware_backends: + print("\nQuantum Hardware (PAID - requires credits):") + for backend in hardware_backends: + print(f" • {backend}") + + print("\nRecommended testing order:") + print(" 1. Start with simulator (free, fast)") + print(" 2. Test Bell state on hardware (low cost)") + print(" 3. Run optimized circuit on hardware (verify 90% accuracy)\n") + + # Ask user to select backend + print("Select backend for testing:") + print(" 1. ionq.simulator (FREE - recommended)") + if hardware_backends: + print(" 2. ionq.qpu (PAID - real quantum computer)") + print(" 3. Skip hardware tests\n") + + choice = input("Enter choice (1-3): ").strip() + + if choice == "3": + print("\nSkipping hardware tests. Goodbye!\n") + return + elif choice == "2" and hardware_backends: + backend = "ionq.qpu" + print(f"\n⚠ Selected PAID hardware: {backend}") + print("This will use quantum computing credits!") + confirm = input("Continue? (yes/no): ").strip().lower() + if confirm != "yes": + print("\nTest cancelled.\n") + return + else: + backend = "ionq.simulator" + + print(f"\nUsing backend: {backend}\n") + + # Test 2: Bell state + bell_results = test_bell_state_on_hardware(azure, backend) + + if bell_results: + print("✓ Bell state test completed successfully!\n") + + # Ask if user wants to continue with optimized circuit + proceed = ( + input("Proceed with optimized circuit test? (yes/no): ").strip().lower() + ) + if proceed == "yes": + # Test 3: Optimized circuit + opt_results = test_optimized_circuit_on_hardware(azure, backend) + + if opt_results: + print("✓ Optimized circuit test completed successfully!\n") + + # Summary + print("\n" + "=" * 70) + print(" TEST SUITE COMPLETE") + print("=" * 70) + print("\nResults saved to: ai-projects/quantum-ml/results/") + print("\nNext Steps:") + print(" 1. Analyze results in Azure Portal") + print(" 2. Compare simulator vs hardware performance") + print(" 3. Scale up to larger circuits (6-8 qubits)") + print(" 4. Train quantum classifier with hardware results\n") + + print("Documentation:") + print(" • Full guide: experiments/AZURE_QUICKSTART.md") + print(" • Deployment: azure/DEPLOYMENT.md") + print(" • Optimization: FINAL_OPTIMIZATION_REPORT.md\n") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/test_entanglement_patterns.py b/ai-projects/quantum-ml/test_entanglement_patterns.py index f9b2a803b..684ee91aa 100644 --- a/ai-projects/quantum-ml/test_entanglement_patterns.py +++ b/ai-projects/quantum-ml/test_entanglement_patterns.py @@ -1,191 +1,191 @@ -""" -Test different quantum entanglement patterns on the heart disease dataset. -Patterns: linear, circular, full -""" - -import sys -from datetime import datetime -from pathlib import Path -from typing import Optional - -_OPTIONAL_IMPORT_ERROR: Optional[ImportError] = None - -try: - import matplotlib.pyplot as plt - import numpy as np - import pandas as pd - import torch - from sklearn.decomposition import PCA - from sklearn.impute import SimpleImputer - from sklearn.model_selection import train_test_split - from sklearn.preprocessing import StandardScaler - from torch.utils.data import DataLoader, TensorDataset -except ImportError as exc: # pragma: no cover - environment dependent - _OPTIONAL_IMPORT_ERROR = exc - -if _OPTIONAL_IMPORT_ERROR is not None and "pytest" in sys.modules: - import pytest - - pytest.skip( - f"Optional quantum experiment dependencies unavailable: {_OPTIONAL_IMPORT_ERROR}", - allow_module_level=True, - ) - -# Ensure we can import from src/ -ROOT = Path(__file__).parent -sys.path.append(str(ROOT / "src")) - -try: - from hybrid_qnn import HybridQNN, QuantumClassicalTrainer # noqa: E402 -except ImportError as exc: # pragma: no cover - environment dependent - _OPTIONAL_IMPORT_ERROR = _OPTIONAL_IMPORT_ERROR or exc - -if _OPTIONAL_IMPORT_ERROR is not None and "pytest" in sys.modules: - import pytest - - pytest.skip( - f"Optional quantum experiment dependencies unavailable: {_OPTIONAL_IMPORT_ERROR}", - allow_module_level=True, - ) - - -def load_heart_disease(): - data_path = ROOT.parent / "datasets" / "quantum" / "heart_disease.csv" - df = pd.read_csv(data_path, header=None, na_values=["?"]) - - # Impute - imputer = SimpleImputer(strategy="median") - df[df.columns] = imputer.fit_transform(df) - - X = df.iloc[:, :-1].values - y = df.iloc[:, -1].values - y = (y > 0).astype(int) - - return X, y - - -def preprocess(X, y, n_components=4): - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42, stratify=y - ) - - scaler = StandardScaler() - X_train_scaled = scaler.fit_transform(X_train) - X_val_scaled = scaler.transform(X_val) - - pca = PCA(n_components=n_components, random_state=42) - X_train_pca = pca.fit_transform(X_train_scaled) - X_val_pca = pca.transform(X_val_scaled) - - return (X_train_pca, X_val_pca, y_train, y_val) - - -def run_experiment(entanglement: str, epochs=12, batch_size=16, lr=0.001): - X, y = load_heart_disease() - X_train, X_val, y_train, y_val = preprocess(X, y, n_components=4) - - X_train_t = torch.tensor(X_train, dtype=torch.float32) - y_train_t = torch.tensor(y_train, dtype=torch.long) - X_val_t = torch.tensor(X_val, dtype=torch.float32) - y_val_t = torch.tensor(y_val, dtype=torch.long) - - train_ds = TensorDataset(X_train_t, y_train_t) - val_ds = TensorDataset(X_val_t, y_val_t) - - train_loader = DataLoader( - train_ds, batch_size=batch_size, shuffle=True, drop_last=True - ) - val_loader = DataLoader( - val_ds, batch_size=batch_size, shuffle=False, drop_last=False - ) - - model = HybridQNN( - input_dim=4, - hidden_dim=16, - n_qubits=4, - n_quantum_layers=2, - entanglement=entanglement, - output_dim=2, - dropout=0.2, - ) - - trainer = QuantumClassicalTrainer(model, learning_rate=lr, device="cpu") - trainer.train(train_loader, val_loader, num_epochs=epochs) - - return trainer - - -def main(): - if _OPTIONAL_IMPORT_ERROR is not None: - print(f"Missing optional experiment dependencies: {_OPTIONAL_IMPORT_ERROR}") - return 1 - - print("=" * 70) - print(" TEST ENTANGLEMENT PATTERNS (Heart Disease)") - print("=" * 70) - - patterns = ["linear", "circular", "full"] - histories = {} - - for p in patterns: - print(f"\n⚛️ Running pattern: {p}") - trainer = run_experiment(p, epochs=12) - histories[p] = { - "train_loss": trainer.train_losses, - "val_acc": trainer.val_accuracies, - "val_loss": trainer.val_losses, - } - print(f" Final Val Acc: {trainer.val_accuracies[-1]*100:.2f}%") - - # Plot - fig, axes = plt.subplots(1, 2, figsize=(14, 5)) - axes[0].set_title("Validation Accuracy by Epoch") - axes[1].set_title("Training Loss by Epoch") - - for p in patterns: - axes[0].plot( - range(1, len(histories[p]["val_acc"]) + 1), - [a * 100 for a in histories[p]["val_acc"]], - label=p, - ) - axes[1].plot( - range(1, len(histories[p]["train_loss"]) + 1), - histories[p]["train_loss"], - label=p, - ) - - axes[0].set_xlabel("Epoch") - axes[1].set_xlabel("Epoch") - axes[0].set_ylabel("Accuracy (%)") - axes[1].set_ylabel("Loss") - for ax in axes: - ax.grid(alpha=0.3) - ax.legend() - - results_dir = ROOT / "results" - results_dir.mkdir(exist_ok=True) - plot_path = results_dir / "entanglement_patterns_comparison.png" - plt.tight_layout() - plt.savefig(plot_path, dpi=300) - print(f"\n📊 Saved comparison plot: {plot_path}") - - # Markdown report - md = [] - md.append("# Entanglement Pattern Comparison (Heart Disease)") - md.append(f"\nGenerated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - md.append("\n## Summary") - for p in patterns: - md.append( - f"- {p.title()}: Final Val Acc = {histories[p]['val_acc'][-1]*100:.2f}%" - ) - - report_path = results_dir / "entanglement_patterns_report.md" - with open(report_path, "w", encoding="utf-8") as f: - f.write("\n".join(md)) - print(f"✅ Saved report: {report_path}") - - print("\nDone.") - - -if __name__ == "__main__": - main() +""" +Test different quantum entanglement patterns on the heart disease dataset. +Patterns: linear, circular, full +""" + +import sys +from datetime import datetime +from pathlib import Path +from typing import Optional + +_OPTIONAL_IMPORT_ERROR: Optional[ImportError] = None + +try: + import matplotlib.pyplot as plt + import numpy as np + import pandas as pd + import torch + from sklearn.decomposition import PCA + from sklearn.impute import SimpleImputer + from sklearn.model_selection import train_test_split + from sklearn.preprocessing import StandardScaler + from torch.utils.data import DataLoader, TensorDataset +except ImportError as exc: # pragma: no cover - environment dependent + _OPTIONAL_IMPORT_ERROR = exc + +if _OPTIONAL_IMPORT_ERROR is not None and "pytest" in sys.modules: + import pytest + + pytest.skip( + f"Optional quantum experiment dependencies unavailable: {_OPTIONAL_IMPORT_ERROR}", + allow_module_level=True, + ) + +# Ensure we can import from src/ +ROOT = Path(__file__).parent +sys.path.append(str(ROOT / "src")) + +try: + from hybrid_qnn import HybridQNN, QuantumClassicalTrainer # noqa: E402 +except ImportError as exc: # pragma: no cover - environment dependent + _OPTIONAL_IMPORT_ERROR = _OPTIONAL_IMPORT_ERROR or exc + +if _OPTIONAL_IMPORT_ERROR is not None and "pytest" in sys.modules: + import pytest + + pytest.skip( + f"Optional quantum experiment dependencies unavailable: {_OPTIONAL_IMPORT_ERROR}", + allow_module_level=True, + ) + + +def load_heart_disease(): + data_path = ROOT.parent / "datasets" / "quantum" / "heart_disease.csv" + df = pd.read_csv(data_path, header=None, na_values=["?"]) + + # Impute + imputer = SimpleImputer(strategy="median") + df[df.columns] = imputer.fit_transform(df) + + X = df.iloc[:, :-1].values + y = df.iloc[:, -1].values + y = (y > 0).astype(int) + + return X, y + + +def preprocess(X, y, n_components=4): + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + scaler = StandardScaler() + X_train_scaled = scaler.fit_transform(X_train) + X_val_scaled = scaler.transform(X_val) + + pca = PCA(n_components=n_components, random_state=42) + X_train_pca = pca.fit_transform(X_train_scaled) + X_val_pca = pca.transform(X_val_scaled) + + return (X_train_pca, X_val_pca, y_train, y_val) + + +def run_experiment(entanglement: str, epochs=12, batch_size=16, lr=0.001): + X, y = load_heart_disease() + X_train, X_val, y_train, y_val = preprocess(X, y, n_components=4) + + X_train_t = torch.tensor(X_train, dtype=torch.float32) + y_train_t = torch.tensor(y_train, dtype=torch.long) + X_val_t = torch.tensor(X_val, dtype=torch.float32) + y_val_t = torch.tensor(y_val, dtype=torch.long) + + train_ds = TensorDataset(X_train_t, y_train_t) + val_ds = TensorDataset(X_val_t, y_val_t) + + train_loader = DataLoader( + train_ds, batch_size=batch_size, shuffle=True, drop_last=True + ) + val_loader = DataLoader( + val_ds, batch_size=batch_size, shuffle=False, drop_last=False + ) + + model = HybridQNN( + input_dim=4, + hidden_dim=16, + n_qubits=4, + n_quantum_layers=2, + entanglement=entanglement, + output_dim=2, + dropout=0.2, + ) + + trainer = QuantumClassicalTrainer(model, learning_rate=lr, device="cpu") + trainer.train(train_loader, val_loader, num_epochs=epochs) + + return trainer + + +def main(): + if _OPTIONAL_IMPORT_ERROR is not None: + print(f"Missing optional experiment dependencies: {_OPTIONAL_IMPORT_ERROR}") + return 1 + + print("=" * 70) + print(" TEST ENTANGLEMENT PATTERNS (Heart Disease)") + print("=" * 70) + + patterns = ["linear", "circular", "full"] + histories = {} + + for p in patterns: + print(f"\n⚛️ Running pattern: {p}") + trainer = run_experiment(p, epochs=12) + histories[p] = { + "train_loss": trainer.train_losses, + "val_acc": trainer.val_accuracies, + "val_loss": trainer.val_losses, + } + print(f" Final Val Acc: {trainer.val_accuracies[-1]*100:.2f}%") + + # Plot + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + axes[0].set_title("Validation Accuracy by Epoch") + axes[1].set_title("Training Loss by Epoch") + + for p in patterns: + axes[0].plot( + range(1, len(histories[p]["val_acc"]) + 1), + [a * 100 for a in histories[p]["val_acc"]], + label=p, + ) + axes[1].plot( + range(1, len(histories[p]["train_loss"]) + 1), + histories[p]["train_loss"], + label=p, + ) + + axes[0].set_xlabel("Epoch") + axes[1].set_xlabel("Epoch") + axes[0].set_ylabel("Accuracy (%)") + axes[1].set_ylabel("Loss") + for ax in axes: + ax.grid(alpha=0.3) + ax.legend() + + results_dir = ROOT / "results" + results_dir.mkdir(exist_ok=True) + plot_path = results_dir / "entanglement_patterns_comparison.png" + plt.tight_layout() + plt.savefig(plot_path, dpi=300) + print(f"\n📊 Saved comparison plot: {plot_path}") + + # Markdown report + md = [] + md.append("# Entanglement Pattern Comparison (Heart Disease)") + md.append(f"\nGenerated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") + md.append("\n## Summary") + for p in patterns: + md.append( + f"- {p.title()}: Final Val Acc = {histories[p]['val_acc'][-1]*100:.2f}%" + ) + + report_path = results_dir / "entanglement_patterns_report.md" + with open(report_path, "w", encoding="utf-8") as f: + f.write("\n".join(md)) + print(f"✅ Saved report: {report_path}") + + print("\nDone.") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/test_quantum_hardware.py b/ai-projects/quantum-ml/test_quantum_hardware.py index 8c58bbe36..142a88518 100644 --- a/ai-projects/quantum-ml/test_quantum_hardware.py +++ b/ai-projects/quantum-ml/test_quantum_hardware.py @@ -1,226 +1,226 @@ -""" -Test Enhanced Quantum Classifier on Azure Quantum Hardware -Simple working version for immediate deployment -""" - -import logging -import sys -from pathlib import Path -from typing import Optional - -sys.path.insert(0, str(Path(__file__).parent / "src")) - -_OPTIONAL_IMPORT_ERROR: Optional[ImportError] = None - -try: - from azure.quantum import Workspace - from qiskit import QuantumCircuit, transpile -except ImportError as exc: # pragma: no cover - environment dependent - _OPTIONAL_IMPORT_ERROR = exc - -if _OPTIONAL_IMPORT_ERROR is not None and "pytest" in sys.modules: - import pytest - - pytest.skip( - f"Optional quantum hardware dependencies unavailable: {_OPTIONAL_IMPORT_ERROR}", - allow_module_level=True, - ) - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -def create_simple_test_circuit(): - """Create a simple Bell state circuit for testing.""" - circuit = QuantumCircuit(2, 2) - circuit.h(0) - circuit.cx(0, 1) - circuit.measure([0, 1], [0, 1]) - return circuit - - -def create_8qubit_classifier_circuit(): - """Create an 8-qubit circuit similar to our enhanced classifier.""" - import numpy as np - - circuit = QuantumCircuit(8, 8) - - # Initial state preparation (Hadamard layer) - for i in range(8): - circuit.h(i) - - # Variational layers (simplified version of enhanced classifier) - n_layers = 4 - for layer in range(n_layers): - # Rotation gates - for i in range(8): - circuit.ry(np.pi / 4 * (layer + 1), i) - circuit.rz(np.pi / 3 * (layer + 1), i) - - # Circular entanglement (optimized pattern) - for i in range(8): - circuit.cx(i, (i + 1) % 8) - - circuit.barrier() - - # Measurements - circuit.measure(range(8), range(8)) - - return circuit - - -def main(): - if _OPTIONAL_IMPORT_ERROR is not None: - print( - f"Missing optional quantum hardware dependencies: {_OPTIONAL_IMPORT_ERROR}" - ) - return 1 - - print("=" * 70) - print(" Azure Quantum Hardware Test") - print(" Enhanced 8-Qubit Quantum Classifier") - print("=" * 70) - - # Connect to workspace - print("\n[1/4] Connecting to Azure Quantum...") - - workspace = Workspace( - subscription_id="a07fbd16-e722-446d-8efd-0681e85b725c", - resource_group="rg-quantum-ai", - name="quantum-ai-workspace", - location="eastus", - ) - - print(f"✓ Connected to: {workspace.name}") - print(f" Location: {workspace.location}") - - # List available backends - print("\n[2/4] Available Quantum Backends:") - targets = workspace.get_targets() - - for i, target in enumerate(targets, 1): - print(f" {i}. {target.name}") - print(f" Provider: {target.provider_id}") - print(f" Status: {target.current_availability}") - - # Test 1: Simple Bell State - print("\n[3/4] Test 1: Bell State (2 qubits)") - print("-" * 70) - - bell_circuit = create_simple_test_circuit() - print("Circuit created:") - print(bell_circuit) - - # Use Rigetti simulator (free) - print("\nSubmitting to Rigetti QVM Simulator (FREE)...") - - try: - target = workspace.get_targets("rigetti.sim.qvm") - job = target.submit(bell_circuit, shots=100, job_name="bell-state-test") - - print(f"✓ Job submitted: {job.id}") - print(f" Status: {job.details.status}") - print("\n Waiting for results (this may take 30-60 seconds)...") - - results = job.get_results() - - print("\n✓ Results received!") - print("\nMeasurement Counts:") - for state, count in sorted(results.items()): - percentage = (count / sum(results.values())) * 100 - bar = "█" * int(percentage / 5) - print(f" {state}: {count:3d} ({percentage:5.1f}%) {bar}") - - # Check entanglement quality - entangled = results.get("00", 0) + results.get("11", 0) - total = sum(results.values()) - entanglement_ratio = (entangled / total) * 100 - - print(f"\nEntanglement Quality: {entanglement_ratio:.1f}%") - if entanglement_ratio > 80: - print("✓ Excellent quantum entanglement!") - else: - print("⚠ Some noise detected (normal for simulators)") - - except Exception as e: - print(f"✗ Bell state test failed: {e}") - print("This is likely due to provider availability.") - - # Test 2: Enhanced 8-Qubit Circuit - print("\n[4/4] Test 2: Enhanced 8-Qubit Classifier Circuit") - print("-" * 70) - - circuit_8q = create_8qubit_classifier_circuit() - - print("✓ 8-qubit circuit created") - print(f" Qubits: {circuit_8q.num_qubits}") - print(f" Depth: {circuit_8q.depth()}") - print(f" Gates: {sum(circuit_8q.count_ops().values())}") - - print("\nCircuit Structure:") - print(circuit_8q) - - print("\n⚠ Note: 8-qubit execution on real hardware requires:") - print(" • Provider with 8+ qubit capacity") - print(" • Sufficient quantum credits") - print(" • May incur costs (~$5-10 per run)") - - proceed = input("\nSubmit 8-qubit circuit to simulator? (yes/no): ") - - if proceed.lower() == "yes": - try: - print("\nSubmitting to Rigetti QVM Simulator...") - target = workspace.get_targets("rigetti.sim.qvm") - job = target.submit( - circuit_8q, shots=100, job_name="8qubit-classifier-test" - ) - - print(f"✓ Job submitted: {job.id}") - print(" Waiting for results...") - - results = job.get_results() - - print("\n✓ 8-Qubit Results!") - print("\nTop 10 Measurement States:") - - sorted_results = sorted(results.items(), key=lambda x: x[1], reverse=True)[ - :10 - ] - total = sum(results.values()) - - for state, count in sorted_results: - percentage = (count / total) * 100 - bar = "█" * int(percentage / 2) - print(f" {state}: {count:3d} ({percentage:5.2f}%) {bar}") - - print(f"\n✓ Total unique states: {len(results)}/256") - print(" Circuit complexity demonstrated successfully!") - - except Exception as e: - print(f"✗ 8-qubit test failed: {e}") - - # Summary - print("\n" + "=" * 70) - print(" Test Summary") - print("=" * 70) - - print("\n✓ Azure Quantum workspace operational") - print("✓ Quantum circuits compiled successfully") - print("✓ Backend connectivity verified") - - print("\nNext Steps:") - print(" 1. Review results in Azure Portal:") - print( - " https://portal.azure.com/#resource/subscriptions/a07fbd16-e722-446d-8efd-0681e85b725c/resourceGroups/rg-ai-projects/quantum-ml/providers/Microsoft.Quantum/Workspaces/quantum-ai-workspace" - ) - print("\n 2. Train enhanced classifier with real quantum results") - print(" 3. Deploy to production quantum hardware (Quantinuum H1)") - print(" 4. Scale to larger datasets and problems") - - print("\n" + "=" * 70) - print("✓ Quantum hardware test completed!") - print("=" * 70) - - -if __name__ == "__main__": - main() +""" +Test Enhanced Quantum Classifier on Azure Quantum Hardware +Simple working version for immediate deployment +""" + +import logging +import sys +from pathlib import Path +from typing import Optional + +sys.path.insert(0, str(Path(__file__).parent / "src")) + +_OPTIONAL_IMPORT_ERROR: Optional[ImportError] = None + +try: + from azure.quantum import Workspace + from qiskit import QuantumCircuit, transpile +except ImportError as exc: # pragma: no cover - environment dependent + _OPTIONAL_IMPORT_ERROR = exc + +if _OPTIONAL_IMPORT_ERROR is not None and "pytest" in sys.modules: + import pytest + + pytest.skip( + f"Optional quantum hardware dependencies unavailable: {_OPTIONAL_IMPORT_ERROR}", + allow_module_level=True, + ) + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def create_simple_test_circuit(): + """Create a simple Bell state circuit for testing.""" + circuit = QuantumCircuit(2, 2) + circuit.h(0) + circuit.cx(0, 1) + circuit.measure([0, 1], [0, 1]) + return circuit + + +def create_8qubit_classifier_circuit(): + """Create an 8-qubit circuit similar to our enhanced classifier.""" + import numpy as np + + circuit = QuantumCircuit(8, 8) + + # Initial state preparation (Hadamard layer) + for i in range(8): + circuit.h(i) + + # Variational layers (simplified version of enhanced classifier) + n_layers = 4 + for layer in range(n_layers): + # Rotation gates + for i in range(8): + circuit.ry(np.pi / 4 * (layer + 1), i) + circuit.rz(np.pi / 3 * (layer + 1), i) + + # Circular entanglement (optimized pattern) + for i in range(8): + circuit.cx(i, (i + 1) % 8) + + circuit.barrier() + + # Measurements + circuit.measure(range(8), range(8)) + + return circuit + + +def main(): + if _OPTIONAL_IMPORT_ERROR is not None: + print( + f"Missing optional quantum hardware dependencies: {_OPTIONAL_IMPORT_ERROR}" + ) + return 1 + + print("=" * 70) + print(" Azure Quantum Hardware Test") + print(" Enhanced 8-Qubit Quantum Classifier") + print("=" * 70) + + # Connect to workspace + print("\n[1/4] Connecting to Azure Quantum...") + + workspace = Workspace( + subscription_id="a07fbd16-e722-446d-8efd-0681e85b725c", + resource_group="rg-quantum-ai", + name="quantum-ai-workspace", + location="eastus", + ) + + print(f"✓ Connected to: {workspace.name}") + print(f" Location: {workspace.location}") + + # List available backends + print("\n[2/4] Available Quantum Backends:") + targets = workspace.get_targets() + + for i, target in enumerate(targets, 1): + print(f" {i}. {target.name}") + print(f" Provider: {target.provider_id}") + print(f" Status: {target.current_availability}") + + # Test 1: Simple Bell State + print("\n[3/4] Test 1: Bell State (2 qubits)") + print("-" * 70) + + bell_circuit = create_simple_test_circuit() + print("Circuit created:") + print(bell_circuit) + + # Use Rigetti simulator (free) + print("\nSubmitting to Rigetti QVM Simulator (FREE)...") + + try: + target = workspace.get_targets("rigetti.sim.qvm") + job = target.submit(bell_circuit, shots=100, job_name="bell-state-test") + + print(f"✓ Job submitted: {job.id}") + print(f" Status: {job.details.status}") + print("\n Waiting for results (this may take 30-60 seconds)...") + + results = job.get_results() + + print("\n✓ Results received!") + print("\nMeasurement Counts:") + for state, count in sorted(results.items()): + percentage = (count / sum(results.values())) * 100 + bar = "█" * int(percentage / 5) + print(f" {state}: {count:3d} ({percentage:5.1f}%) {bar}") + + # Check entanglement quality + entangled = results.get("00", 0) + results.get("11", 0) + total = sum(results.values()) + entanglement_ratio = (entangled / total) * 100 + + print(f"\nEntanglement Quality: {entanglement_ratio:.1f}%") + if entanglement_ratio > 80: + print("✓ Excellent quantum entanglement!") + else: + print("⚠ Some noise detected (normal for simulators)") + + except Exception as e: + print(f"✗ Bell state test failed: {e}") + print("This is likely due to provider availability.") + + # Test 2: Enhanced 8-Qubit Circuit + print("\n[4/4] Test 2: Enhanced 8-Qubit Classifier Circuit") + print("-" * 70) + + circuit_8q = create_8qubit_classifier_circuit() + + print("✓ 8-qubit circuit created") + print(f" Qubits: {circuit_8q.num_qubits}") + print(f" Depth: {circuit_8q.depth()}") + print(f" Gates: {sum(circuit_8q.count_ops().values())}") + + print("\nCircuit Structure:") + print(circuit_8q) + + print("\n⚠ Note: 8-qubit execution on real hardware requires:") + print(" • Provider with 8+ qubit capacity") + print(" • Sufficient quantum credits") + print(" • May incur costs (~$5-10 per run)") + + proceed = input("\nSubmit 8-qubit circuit to simulator? (yes/no): ") + + if proceed.lower() == "yes": + try: + print("\nSubmitting to Rigetti QVM Simulator...") + target = workspace.get_targets("rigetti.sim.qvm") + job = target.submit( + circuit_8q, shots=100, job_name="8qubit-classifier-test" + ) + + print(f"✓ Job submitted: {job.id}") + print(" Waiting for results...") + + results = job.get_results() + + print("\n✓ 8-Qubit Results!") + print("\nTop 10 Measurement States:") + + sorted_results = sorted(results.items(), key=lambda x: x[1], reverse=True)[ + :10 + ] + total = sum(results.values()) + + for state, count in sorted_results: + percentage = (count / total) * 100 + bar = "█" * int(percentage / 2) + print(f" {state}: {count:3d} ({percentage:5.2f}%) {bar}") + + print(f"\n✓ Total unique states: {len(results)}/256") + print(" Circuit complexity demonstrated successfully!") + + except Exception as e: + print(f"✗ 8-qubit test failed: {e}") + + # Summary + print("\n" + "=" * 70) + print(" Test Summary") + print("=" * 70) + + print("\n✓ Azure Quantum workspace operational") + print("✓ Quantum circuits compiled successfully") + print("✓ Backend connectivity verified") + + print("\nNext Steps:") + print(" 1. Review results in Azure Portal:") + print( + " https://portal.azure.com/#resource/subscriptions/a07fbd16-e722-446d-8efd-0681e85b725c/resourceGroups/rg-ai-projects/quantum-ml/providers/Microsoft.Quantum/Workspaces/quantum-ai-workspace" + ) + print("\n 2. Train enhanced classifier with real quantum results") + print(" 3. Deploy to production quantum hardware (Quantinuum H1)") + print(" 4. Scale to larger datasets and problems") + + print("\n" + "=" * 70) + print("✓ Quantum hardware test completed!") + print("=" * 70) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/train_custom_dataset.py b/ai-projects/quantum-ml/train_custom_dataset.py index a0f8a7da6..f8f123672 100644 --- a/ai-projects/quantum-ml/train_custom_dataset.py +++ b/ai-projects/quantum-ml/train_custom_dataset.py @@ -1,672 +1,672 @@ -r""" -Train Quantum AI on Your Custom Dataset -====================================== - -Free-first training pipeline with CLI support for CSVs and built-in presets. -Works 100% locally using simulators (no Azure required). Use this script to -train on your own data without changing code. - -Examples (PowerShell): - - # Preset datasets (from datasets/quantum/*.csv) - python .\train_custom_dataset.py --preset heart --epochs 5 --batch-size 16 - python .\train_custom_dataset.py --preset ionosphere --epochs 5 - - # Custom CSV - python .\train_custom_dataset.py --csv ..\datasets\quantum\banknote.csv ` - --label-col class --epochs 5 --n-qubits 4 - -Notes: -- Binary or multiclass labels are supported. String labels are auto-encoded. -- Features are standardized and dimension-matched to n_qubits via PCA/padding. - -Author: Quantum AI System -Date: November 1, 2025 -""" - -import os -import sys - -# Fix Windows console encoding for emoji support -if os.name == "nt": - try: - sys.stdout.reconfigure(encoding="utf-8") - except Exception: - pass - -import argparse -from pathlib import Path - -import numpy as np -import pandas as pd -import yaml - -# Add src to path -src_path = Path(__file__).parent / "src" -sys.path.insert(0, str(src_path)) - -import json - -import matplotlib.pyplot as plt -import torch -from sklearn.decomposition import PCA -from sklearn.impute import SimpleImputer -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer -from torch.utils.data import DataLoader, TensorDataset - - -def _infer_label_column(df: pd.DataFrame, provided: str | None) -> str: - """Infer the label/target column if not provided. - Tries common names, else uses the last column. - """ - if provided: - if provided not in df.columns: - raise ValueError( - f"Label column '{provided}' not in CSV columns: {list(df.columns)}" - ) - return provided - candidates = ["label", "target", "class", "y", "diagnosis", "outcome"] - for c in candidates: - if c in df.columns: - return c - # Fallback: last column - return df.columns[-1] - - -def _encode_labels(y: pd.Series) -> np.ndarray: - """Encode labels to integer class indices starting at 0. - - If numeric, cast to int when safe; ensure non-negative contiguous classes. - - If strings/objects, factorize to 0..K-1. - """ - if pd.api.types.is_numeric_dtype(y): - # If binary but not 0/1, normalize to 0/1 - uniq = np.unique(y.dropna()) - if len(uniq) == 2 and set(uniq) != {0, 1}: - mapping = {uniq.min(): 0, uniq.max(): 1} - y_enc = y.map(mapping).astype(int).values - else: - y_enc = y.astype(int).values - # Shift to start at 0 - minv = y_enc.min() - if minv < 0: - y_enc = y_enc - minv - return y_enc - else: - vals, enc = pd.factorize(y.astype(str)) - return vals - - -def load_csv_dataset( - csv_path: str | Path, - label_col: str | None = None, - drop_cols: list[str] | None = None, - handle_missing: str = "auto", -) -> tuple[np.ndarray, np.ndarray]: - """Load a dataset from CSV. - - Args: - csv_path: Path to CSV file - label_col: Column name of the label/target (auto-infer if None) - drop_cols: Columns to drop before processing - handle_missing: 'auto' -> treat '?', 'NA', '', 'NaN' as missing - - Returns: - (X, y) numpy arrays - """ - csv_path = Path(csv_path) - if not csv_path.exists(): - raise FileNotFoundError(f"CSV not found: {csv_path}") - - na_values = ["?", "NA", "", "NaN"] if handle_missing == "auto" else None - df = pd.read_csv(csv_path, na_values=na_values) - - # Drop columns if requested - if drop_cols: - missing = [c for c in drop_cols if c not in df.columns] - if missing: - raise ValueError(f"Drop columns not found in CSV: {missing}") - df = df.drop(columns=drop_cols) - - # Determine label column - y_col = _infer_label_column(df, label_col) - y = df[y_col] - X = df.drop(columns=[y_col]) - - # Impute missing values in features if needed - if X.isnull().any().any(): - imputer = SimpleImputer(strategy="median") - X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) - - y_enc = _encode_labels(y) - return X.values, y_enc - - -def load_preset_dataset(name: str) -> tuple[np.ndarray, np.ndarray]: - """Load a preset dataset from datasets/quantum/*.csv. - - Presets: heart, ionosphere, sonar, banknote - """ - base = Path(__file__).parent.parent / "datasets" / "quantum" - presets = { - "heart": base / "heart_disease.csv", - "ionosphere": base / "ionosphere.csv", - "sonar": base / "sonar.csv", - "banknote": base / "banknote.csv", - } - if name not in presets: - raise ValueError(f"Unknown preset '{name}'. Choose from {list(presets)}") - - path = presets[name] - if name == "heart": - # Special handling for heart disease (binary from multi-class 0..4) - df = pd.read_csv(path, na_values=["?"]) - y = df.iloc[:, -1] - X = df.iloc[:, :-1] - if X.isnull().any().any(): - imputer = SimpleImputer(strategy="median") - X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) - y = (y > 0).astype(int).values - return X.values, y - else: - return load_csv_dataset(path) - - -def preprocess_data(X, y, n_qubits=4, test_size=0.2): - """ - Preprocess data for quantum ML - - Args: - X: Feature matrix - y: Labels - n_qubits: Number of qubits (features will be reduced/padded to match) - test_size: Validation split ratio - - Returns: - X_train, X_val, y_train, y_val: Preprocessed data - scaler: Fitted StandardScaler (save this for inference!) - pca: Fitted PCA (if used, else None) - """ - print("\n🔧 Preprocessing data...") - print(f" Original shape: {X.shape}") - # Ensure labels are int (for CrossEntropyLoss) - if not np.issubdtype(y.dtype, np.integer): - # Factorize as safety net - vals, y = np.unique(y, return_inverse=True) - print(f" Classes: {np.unique(y)}") - try: - print(f" Class distribution: {np.bincount(y)}") - except Exception: - pass - - # Split data - X_train, X_val, y_train, y_val = train_test_split( - X, - y, - test_size=test_size, - random_state=42, - stratify=y, # Maintains class balance - ) - - # Standardize (CRITICAL for quantum circuits!) - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - print(" ✅ Standardized (mean≈0, std≈1)") - - # Handle feature dimension - n_features = X_train.shape[1] - pca = None - - if n_features < n_qubits: - # Pad with zeros - padding_train = np.zeros((X_train.shape[0], n_qubits - n_features)) - X_train = np.hstack([X_train, padding_train]) - - padding_val = np.zeros((X_val.shape[0], n_qubits - n_features)) - X_val = np.hstack([X_val, padding_val]) - - print(f" ✅ Padded from {n_features} to {n_qubits} features") - - elif n_features > n_qubits: - # Use PCA to reduce dimensions - pca = PCA(n_components=n_qubits) - X_train = pca.fit_transform(X_train) - X_val = pca.transform(X_val) - - explained_var = pca.explained_variance_ratio_.sum() - print(f" ✅ Reduced from {n_features} to {n_qubits} features using PCA") - print(f" ✅ Explained variance: {explained_var:.2%}") - else: - print(f" ✅ Features already match qubits ({n_qubits})") - - print(f" Final training shape: {X_train.shape}") - print(f" Final validation shape: {X_val.shape}") - - return X_train, X_val, y_train, y_val, scaler, pca - - -def plot_training_results(history, save_path="results/custom_training.png"): - """Plot training curves""" - fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4)) - - # Loss curves - ax1.plot(history["train_loss"], label="Training Loss", linewidth=2) - ax1.plot(history["val_loss"], label="Validation Loss", linewidth=2) - ax1.set_xlabel("Epoch") - ax1.set_ylabel("Loss") - ax1.set_title("Training Progress") - ax1.legend() - ax1.grid(True, alpha=0.3) - - # Accuracy curve - ax2.plot( - history["val_acc"], label="Validation Accuracy", linewidth=2, color="green" - ) - ax2.axhline( - y=max(history["val_acc"]), - color="red", - linestyle="--", - label=f'Best: {max(history["val_acc"]):.4f}', - ) - ax2.set_xlabel("Epoch") - ax2.set_ylabel("Accuracy") - ax2.set_title("Validation Accuracy") - ax2.legend() - ax2.grid(True, alpha=0.3) - - plt.tight_layout() - plt.savefig(save_path, dpi=150, bbox_inches="tight") - print(f"\n📊 Training plot saved to: {save_path}") - plt.close() - - -def save_model_and_preprocessors( - model, - scaler, - pca=None, - model_path="results/custom_model.pt", - scaler_path="results/custom_scaler.pkl", - pca_path="results/custom_pca.pkl", -): - """Save model and preprocessing objects""" - import joblib - - # Save model weights - torch.save(model.state_dict(), model_path) - print(f"💾 Model saved to: {model_path}") - - # Save scaler - joblib.dump(scaler, scaler_path) - print(f"💾 Scaler saved to: {scaler_path}") - - # Save PCA if used - if pca is not None: - joblib.dump(pca, pca_path) - print(f"💾 PCA saved to: {pca_path}") - - -def train_quantum_model( - model, - X_train, - y_train, - X_val, - y_val, - num_epochs=20, - batch_size=32, - learning_rate=0.001, -): - """ - Train the quantum model - - Args: - model: HybridQNN model - X_train, y_train: Training data - X_val, y_val: Validation data - num_epochs: Number of training epochs - batch_size: Batch size - learning_rate: Learning rate - - Returns: - history: Dictionary with training metrics - """ - # Convert to PyTorch tensors - X_train_tensor = torch.FloatTensor(X_train) - y_train_tensor = torch.LongTensor(y_train) - X_val_tensor = torch.FloatTensor(X_val) - y_val_tensor = torch.LongTensor(y_val) - - # Create data loaders - train_dataset = TensorDataset(X_train_tensor, y_train_tensor) - val_dataset = TensorDataset(X_val_tensor, y_val_tensor) - - # Use drop_last=True to avoid batchnorm errors on final batch of size 1 - train_loader = DataLoader( - train_dataset, batch_size=batch_size, shuffle=True, drop_last=True - ) - val_loader = DataLoader( - val_dataset, batch_size=batch_size, shuffle=False, drop_last=False - ) - - # Create trainer - trainer = QuantumClassicalTrainer(model, learning_rate=learning_rate) - - # Train - trainer.train(train_loader, val_loader, num_epochs) - - # Return history (now includes real val_loss from trainer) - history = { - "train_loss": trainer.train_losses, - "val_acc": trainer.val_accuracies, - "val_loss": trainer.val_losses, - } - - return history - - -def _default_n_qubits( - config_path: str = "config/quantum_config.yaml", fallback: int = 4 -) -> int: - """Read default n_qubits from YAML config if available.""" - try: - with open(config_path, "r") as f: - cfg = yaml.safe_load(f) - return int(cfg.get("ml", {}).get("model", {}).get("n_qubits", fallback)) - except Exception: - return fallback - - -def main(argv: list[str] | None = None): - """Main training pipeline""" - parser = argparse.ArgumentParser( - description="Train Hybrid Quantum-Classical model on CSV or preset dataset" - ) - src_root = Path(__file__).parent - default_results = src_root / "results" - parser.add_argument("--csv", type=str, help="Path to CSV file containing dataset") - parser.add_argument( - "--preset", - type=str, - choices=["heart", "ionosphere", "sonar", "banknote"], - help="Use a built-in preset dataset", - ) - parser.add_argument( - "--label-col", type=str, help="Label/target column name (auto if omitted)" - ) - parser.add_argument( - "--drop-cols", - type=str, - help="Comma-separated column names to drop before training", - ) - parser.add_argument( - "--test-size", - type=float, - default=0.2, - help="Validation split ratio (default 0.2)", - ) - parser.add_argument( - "--n-qubits", - type=int, - help="Number of qubits (defaults from config ml.model.n_qubits or 4)", - ) - parser.add_argument( - "--epochs", type=int, default=20, help="Training epochs (default 20)" - ) - parser.add_argument( - "--batch-size", type=int, default=32, help="Batch size (default 32)" - ) - parser.add_argument( - "--learning-rate", - type=float, - default=0.001, - help="Learning rate (default 1e-3)", - ) - args = parser.parse_args(argv) - - drop_cols = ( - [c.strip() for c in args.drop_cols.split(",")] if args.drop_cols else None - ) - n_qubits = args.n_qubits or _default_n_qubits() - - print("=" * 70) - print(" QUANTUM AI - CUSTOM DATASET TRAINING") - print("=" * 70) - print( - f"Using n_qubits={n_qubits}, epochs={args.epochs}, batch={args.batch_size}, lr={args.learning_rate}" - ) - - # ============================================ - # 1. LOAD DATA - # ============================================ - print("\n📁 Step 1: Loading your data...") - if args.preset: - X, y = load_preset_dataset(args.preset) - print(f" ✅ Loaded preset dataset: {args.preset}") - dataset_desc = f"preset:{args.preset}" - elif args.csv: - X, y = load_csv_dataset(args.csv, label_col=args.label_col, drop_cols=drop_cols) - print(f" ✅ Loaded CSV: {args.csv}") - dataset_desc = f"csv:{args.csv}" - else: - # Fallback demo: scikit-learn wine dataset (binary) - from sklearn.datasets import load_wine - - data = load_wine() - X = data.data - # Binary: Class 0 vs others - y = (data.target == 0).astype(int) - print(" ℹ️ No dataset specified. Using demo Wine dataset (binary)") - dataset_desc = "demo:wine" - - # ============================================ - # 2. PREPROCESS - # ============================================ - print("\n🔧 Step 2: Preprocessing...") - X_train, X_val, y_train, y_val, scaler, pca = preprocess_data( - X, y, n_qubits=n_qubits, test_size=args.test_size - ) - - # ============================================ - # 3. CREATE MODEL - # ============================================ - print("\n🧠 Step 3: Creating quantum model...") - n_qubits = X_train.shape[1] # After preprocessing - - # Determine number of classes - n_classes = len(np.unique(y_train)) - - # Create hybrid quantum-classical model - model = HybridQNN( - input_dim=n_qubits, - hidden_dim=16, - n_qubits=n_qubits, - n_quantum_layers=2, - output_dim=n_classes, - dropout=0.2, - ) - print(" ✅ Created HybridQNN") - print(f" ✅ Quantum circuit: {n_qubits} qubits, 2 layers") - print(f" ✅ Output classes: {n_classes}") - - # ============================================ - # 4. TRAIN MODEL - # ============================================ - print("\n🚀 Step 4: Training quantum model...") - print(" This may take a few minutes...") - - history = train_quantum_model( - model=model, - X_train=X_train, - y_train=y_train, - X_val=X_val, - y_val=y_val, - num_epochs=args.epochs, - batch_size=args.batch_size, - learning_rate=args.learning_rate, - ) - - # ============================================ - # 5. EVALUATE RESULTS - # ============================================ - print("\n" + "=" * 70) - print(" TRAINING COMPLETE!") - print("=" * 70) - - final_train_loss = history["train_loss"][-1] - final_val_loss = history["val_loss"][-1] - final_val_acc = history["val_acc"][-1] - best_val_acc = max(history["val_acc"]) - - print("\n📊 Final Results:") - print(f" Training Loss: {final_train_loss:.4f}") - print(f" Validation Loss: {final_val_loss:.4f}") - print(f" Validation Accuracy: {final_val_acc:.4f} ({final_val_acc*100:.2f}%)") - print(f" Best Val Accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)") - - # Determine performance level - if best_val_acc >= 0.90: - grade = "🏆 EXCELLENT!" - emoji = "🎉" - elif best_val_acc >= 0.80: - grade = "⭐ VERY GOOD!" - emoji = "🚀" - elif best_val_acc >= 0.70: - grade = "✅ GOOD" - emoji = "👍" - elif best_val_acc >= 0.60: - grade = "⚠️ FAIR" - emoji = "📈" - else: - grade = "❌ NEEDS IMPROVEMENT" - emoji = "🔧" - - print(f"\n{emoji} Performance Grade: {grade}") - - # ============================================ - # 6. SAVE MODEL - # ============================================ - print("\n💾 Step 5: Saving model and preprocessors...") - # Ensure results directory exists - results_dir = Path(Path(__file__).parent / "results") - results_dir.mkdir(parents=True, exist_ok=True) - - model_path = str(results_dir / "custom_model.pt") - scaler_path = str(results_dir / "custom_scaler.pkl") - pca_path = str(results_dir / "custom_pca.pkl") - plot_path = str(results_dir / "custom_training.png") - summary_path = str(results_dir / "custom_training_summary.json") - - save_model_and_preprocessors( - model, - scaler, - pca, - model_path=model_path, - scaler_path=scaler_path, - pca_path=pca_path, - ) - - # ============================================ - # 7. PLOT RESULTS - # ============================================ - print("\n📊 Step 6: Generating training plots...") - plot_training_results(history, save_path=plot_path) - - # ============================================ - # 7. SAVE SUMMARY - # ============================================ - try: - y_train_counts = np.bincount(y_train).tolist() - y_val_counts = np.bincount(y_val).tolist() - except Exception: - y_train_counts, y_val_counts = None, None - - pca_explained_variance = None - if pca is not None and hasattr(pca, "explained_variance_ratio_"): - try: - pca_explained_variance = float(np.sum(pca.explained_variance_ratio_)) - except Exception: - pca_explained_variance = None - - summary = { - "dataset": dataset_desc, - "params": { - "n_qubits": n_qubits, - "epochs": args.epochs, - "batch_size": args.batch_size, - "learning_rate": args.learning_rate, - "test_size": args.test_size, - }, - "metrics": { - "train_loss_last": float(final_train_loss), - "val_loss_last": float(final_val_loss), - "val_acc_last": float(final_val_acc), - "val_acc_best": float(best_val_acc), - "history": { - "train_loss": [float(x) for x in history["train_loss"]], - "val_loss": [float(x) for x in history["val_loss"]], - "val_acc": [float(x) for x in history["val_acc"]], - }, - }, - "data": { - "n_train": int(len(X_train)), - "n_val": int(len(X_val)), - "n_classes": int(len(np.unique(y_train))), - "class_distribution_train": y_train_counts, - "class_distribution_val": y_val_counts, - "pca_explained_variance": pca_explained_variance, - }, - "artifacts": { - "model": model_path, - "scaler": scaler_path, - "pca": pca_path if pca is not None else None, - "plot": plot_path, - }, - } - - with open(summary_path, "w", encoding="utf-8") as f: - json.dump(summary, f, indent=2) - print(f"\n📝 Summary saved to: {summary_path}") - - # ============================================ - # 8. RECOMMENDATIONS - # ============================================ - print("\n💡 Recommendations:") - - if best_val_acc < 0.70: - print(" ⚠️ Accuracy is low. Try:") - print(f" - More training data (currently {len(X_train)} samples)") - print(" - Better feature engineering") - print(" - Hyperparameter tuning (run experiments/parameter_tuning.py)") - print(" - Check if data is properly standardized") - - elif best_val_acc >= 0.90: - print(" 🎉 Excellent performance! Consider:") - print(" - Deploy to production") - print(" - Test on Azure Quantum hardware") - print(" - Try ensemble methods for even better results") - - else: - print(" ✅ Good performance! To improve further:") - print(" - Run hyperparameter optimization") - print(" - Try different quantum circuit architectures") - print(" - Collect more training data") - - print("\n📚 Next Steps:") - print(" 1. Review training plot: results/custom_training.png") - print(" 2. Load model for inference:") - print(" model.load_state_dict(torch.load('results/custom_model.pt'))") - print(" 3. Run hyperparameter tuning:") - print(" python experiments/parameter_tuning.py") - print(" 4. Deploy to Azure Quantum:") - print(" See experiments/AZURE_QUICKSTART.md") - - print("\n" + "=" * 70) - print(" 🎉 QUANTUM AI TRAINING COMPLETE!") - print("=" * 70 + "\n") - return model, history - - -if __name__ == "__main__": - model, history = main() +r""" +Train Quantum AI on Your Custom Dataset +====================================== + +Free-first training pipeline with CLI support for CSVs and built-in presets. +Works 100% locally using simulators (no Azure required). Use this script to +train on your own data without changing code. + +Examples (PowerShell): + + # Preset datasets (from datasets/quantum/*.csv) + python .\train_custom_dataset.py --preset heart --epochs 5 --batch-size 16 + python .\train_custom_dataset.py --preset ionosphere --epochs 5 + + # Custom CSV + python .\train_custom_dataset.py --csv ..\datasets\quantum\banknote.csv ` + --label-col class --epochs 5 --n-qubits 4 + +Notes: +- Binary or multiclass labels are supported. String labels are auto-encoded. +- Features are standardized and dimension-matched to n_qubits via PCA/padding. + +Author: Quantum AI System +Date: November 1, 2025 +""" + +import os +import sys + +# Fix Windows console encoding for emoji support +if os.name == "nt": + try: + sys.stdout.reconfigure(encoding="utf-8") + except Exception: + pass + +import argparse +from pathlib import Path + +import numpy as np +import pandas as pd +import yaml + +# Add src to path +src_path = Path(__file__).parent / "src" +sys.path.insert(0, str(src_path)) + +import json + +import matplotlib.pyplot as plt +import torch +from sklearn.decomposition import PCA +from sklearn.impute import SimpleImputer +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer +from torch.utils.data import DataLoader, TensorDataset + + +def _infer_label_column(df: pd.DataFrame, provided: str | None) -> str: + """Infer the label/target column if not provided. + Tries common names, else uses the last column. + """ + if provided: + if provided not in df.columns: + raise ValueError( + f"Label column '{provided}' not in CSV columns: {list(df.columns)}" + ) + return provided + candidates = ["label", "target", "class", "y", "diagnosis", "outcome"] + for c in candidates: + if c in df.columns: + return c + # Fallback: last column + return df.columns[-1] + + +def _encode_labels(y: pd.Series) -> np.ndarray: + """Encode labels to integer class indices starting at 0. + - If numeric, cast to int when safe; ensure non-negative contiguous classes. + - If strings/objects, factorize to 0..K-1. + """ + if pd.api.types.is_numeric_dtype(y): + # If binary but not 0/1, normalize to 0/1 + uniq = np.unique(y.dropna()) + if len(uniq) == 2 and set(uniq) != {0, 1}: + mapping = {uniq.min(): 0, uniq.max(): 1} + y_enc = y.map(mapping).astype(int).values + else: + y_enc = y.astype(int).values + # Shift to start at 0 + minv = y_enc.min() + if minv < 0: + y_enc = y_enc - minv + return y_enc + else: + vals, enc = pd.factorize(y.astype(str)) + return vals + + +def load_csv_dataset( + csv_path: str | Path, + label_col: str | None = None, + drop_cols: list[str] | None = None, + handle_missing: str = "auto", +) -> tuple[np.ndarray, np.ndarray]: + """Load a dataset from CSV. + + Args: + csv_path: Path to CSV file + label_col: Column name of the label/target (auto-infer if None) + drop_cols: Columns to drop before processing + handle_missing: 'auto' -> treat '?', 'NA', '', 'NaN' as missing + + Returns: + (X, y) numpy arrays + """ + csv_path = Path(csv_path) + if not csv_path.exists(): + raise FileNotFoundError(f"CSV not found: {csv_path}") + + na_values = ["?", "NA", "", "NaN"] if handle_missing == "auto" else None + df = pd.read_csv(csv_path, na_values=na_values) + + # Drop columns if requested + if drop_cols: + missing = [c for c in drop_cols if c not in df.columns] + if missing: + raise ValueError(f"Drop columns not found in CSV: {missing}") + df = df.drop(columns=drop_cols) + + # Determine label column + y_col = _infer_label_column(df, label_col) + y = df[y_col] + X = df.drop(columns=[y_col]) + + # Impute missing values in features if needed + if X.isnull().any().any(): + imputer = SimpleImputer(strategy="median") + X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) + + y_enc = _encode_labels(y) + return X.values, y_enc + + +def load_preset_dataset(name: str) -> tuple[np.ndarray, np.ndarray]: + """Load a preset dataset from datasets/quantum/*.csv. + + Presets: heart, ionosphere, sonar, banknote + """ + base = Path(__file__).parent.parent / "datasets" / "quantum" + presets = { + "heart": base / "heart_disease.csv", + "ionosphere": base / "ionosphere.csv", + "sonar": base / "sonar.csv", + "banknote": base / "banknote.csv", + } + if name not in presets: + raise ValueError(f"Unknown preset '{name}'. Choose from {list(presets)}") + + path = presets[name] + if name == "heart": + # Special handling for heart disease (binary from multi-class 0..4) + df = pd.read_csv(path, na_values=["?"]) + y = df.iloc[:, -1] + X = df.iloc[:, :-1] + if X.isnull().any().any(): + imputer = SimpleImputer(strategy="median") + X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) + y = (y > 0).astype(int).values + return X.values, y + else: + return load_csv_dataset(path) + + +def preprocess_data(X, y, n_qubits=4, test_size=0.2): + """ + Preprocess data for quantum ML + + Args: + X: Feature matrix + y: Labels + n_qubits: Number of qubits (features will be reduced/padded to match) + test_size: Validation split ratio + + Returns: + X_train, X_val, y_train, y_val: Preprocessed data + scaler: Fitted StandardScaler (save this for inference!) + pca: Fitted PCA (if used, else None) + """ + print("\n🔧 Preprocessing data...") + print(f" Original shape: {X.shape}") + # Ensure labels are int (for CrossEntropyLoss) + if not np.issubdtype(y.dtype, np.integer): + # Factorize as safety net + vals, y = np.unique(y, return_inverse=True) + print(f" Classes: {np.unique(y)}") + try: + print(f" Class distribution: {np.bincount(y)}") + except Exception: + pass + + # Split data + X_train, X_val, y_train, y_val = train_test_split( + X, + y, + test_size=test_size, + random_state=42, + stratify=y, # Maintains class balance + ) + + # Standardize (CRITICAL for quantum circuits!) + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + print(" ✅ Standardized (mean≈0, std≈1)") + + # Handle feature dimension + n_features = X_train.shape[1] + pca = None + + if n_features < n_qubits: + # Pad with zeros + padding_train = np.zeros((X_train.shape[0], n_qubits - n_features)) + X_train = np.hstack([X_train, padding_train]) + + padding_val = np.zeros((X_val.shape[0], n_qubits - n_features)) + X_val = np.hstack([X_val, padding_val]) + + print(f" ✅ Padded from {n_features} to {n_qubits} features") + + elif n_features > n_qubits: + # Use PCA to reduce dimensions + pca = PCA(n_components=n_qubits) + X_train = pca.fit_transform(X_train) + X_val = pca.transform(X_val) + + explained_var = pca.explained_variance_ratio_.sum() + print(f" ✅ Reduced from {n_features} to {n_qubits} features using PCA") + print(f" ✅ Explained variance: {explained_var:.2%}") + else: + print(f" ✅ Features already match qubits ({n_qubits})") + + print(f" Final training shape: {X_train.shape}") + print(f" Final validation shape: {X_val.shape}") + + return X_train, X_val, y_train, y_val, scaler, pca + + +def plot_training_results(history, save_path="results/custom_training.png"): + """Plot training curves""" + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4)) + + # Loss curves + ax1.plot(history["train_loss"], label="Training Loss", linewidth=2) + ax1.plot(history["val_loss"], label="Validation Loss", linewidth=2) + ax1.set_xlabel("Epoch") + ax1.set_ylabel("Loss") + ax1.set_title("Training Progress") + ax1.legend() + ax1.grid(True, alpha=0.3) + + # Accuracy curve + ax2.plot( + history["val_acc"], label="Validation Accuracy", linewidth=2, color="green" + ) + ax2.axhline( + y=max(history["val_acc"]), + color="red", + linestyle="--", + label=f'Best: {max(history["val_acc"]):.4f}', + ) + ax2.set_xlabel("Epoch") + ax2.set_ylabel("Accuracy") + ax2.set_title("Validation Accuracy") + ax2.legend() + ax2.grid(True, alpha=0.3) + + plt.tight_layout() + plt.savefig(save_path, dpi=150, bbox_inches="tight") + print(f"\n📊 Training plot saved to: {save_path}") + plt.close() + + +def save_model_and_preprocessors( + model, + scaler, + pca=None, + model_path="results/custom_model.pt", + scaler_path="results/custom_scaler.pkl", + pca_path="results/custom_pca.pkl", +): + """Save model and preprocessing objects""" + import joblib + + # Save model weights + torch.save(model.state_dict(), model_path) + print(f"💾 Model saved to: {model_path}") + + # Save scaler + joblib.dump(scaler, scaler_path) + print(f"💾 Scaler saved to: {scaler_path}") + + # Save PCA if used + if pca is not None: + joblib.dump(pca, pca_path) + print(f"💾 PCA saved to: {pca_path}") + + +def train_quantum_model( + model, + X_train, + y_train, + X_val, + y_val, + num_epochs=20, + batch_size=32, + learning_rate=0.001, +): + """ + Train the quantum model + + Args: + model: HybridQNN model + X_train, y_train: Training data + X_val, y_val: Validation data + num_epochs: Number of training epochs + batch_size: Batch size + learning_rate: Learning rate + + Returns: + history: Dictionary with training metrics + """ + # Convert to PyTorch tensors + X_train_tensor = torch.FloatTensor(X_train) + y_train_tensor = torch.LongTensor(y_train) + X_val_tensor = torch.FloatTensor(X_val) + y_val_tensor = torch.LongTensor(y_val) + + # Create data loaders + train_dataset = TensorDataset(X_train_tensor, y_train_tensor) + val_dataset = TensorDataset(X_val_tensor, y_val_tensor) + + # Use drop_last=True to avoid batchnorm errors on final batch of size 1 + train_loader = DataLoader( + train_dataset, batch_size=batch_size, shuffle=True, drop_last=True + ) + val_loader = DataLoader( + val_dataset, batch_size=batch_size, shuffle=False, drop_last=False + ) + + # Create trainer + trainer = QuantumClassicalTrainer(model, learning_rate=learning_rate) + + # Train + trainer.train(train_loader, val_loader, num_epochs) + + # Return history (now includes real val_loss from trainer) + history = { + "train_loss": trainer.train_losses, + "val_acc": trainer.val_accuracies, + "val_loss": trainer.val_losses, + } + + return history + + +def _default_n_qubits( + config_path: str = "config/quantum_config.yaml", fallback: int = 4 +) -> int: + """Read default n_qubits from YAML config if available.""" + try: + with open(config_path, "r") as f: + cfg = yaml.safe_load(f) + return int(cfg.get("ml", {}).get("model", {}).get("n_qubits", fallback)) + except Exception: + return fallback + + +def main(argv: list[str] | None = None): + """Main training pipeline""" + parser = argparse.ArgumentParser( + description="Train Hybrid Quantum-Classical model on CSV or preset dataset" + ) + src_root = Path(__file__).parent + default_results = src_root / "results" + parser.add_argument("--csv", type=str, help="Path to CSV file containing dataset") + parser.add_argument( + "--preset", + type=str, + choices=["heart", "ionosphere", "sonar", "banknote"], + help="Use a built-in preset dataset", + ) + parser.add_argument( + "--label-col", type=str, help="Label/target column name (auto if omitted)" + ) + parser.add_argument( + "--drop-cols", + type=str, + help="Comma-separated column names to drop before training", + ) + parser.add_argument( + "--test-size", + type=float, + default=0.2, + help="Validation split ratio (default 0.2)", + ) + parser.add_argument( + "--n-qubits", + type=int, + help="Number of qubits (defaults from config ml.model.n_qubits or 4)", + ) + parser.add_argument( + "--epochs", type=int, default=20, help="Training epochs (default 20)" + ) + parser.add_argument( + "--batch-size", type=int, default=32, help="Batch size (default 32)" + ) + parser.add_argument( + "--learning-rate", + type=float, + default=0.001, + help="Learning rate (default 1e-3)", + ) + args = parser.parse_args(argv) + + drop_cols = ( + [c.strip() for c in args.drop_cols.split(",")] if args.drop_cols else None + ) + n_qubits = args.n_qubits or _default_n_qubits() + + print("=" * 70) + print(" QUANTUM AI - CUSTOM DATASET TRAINING") + print("=" * 70) + print( + f"Using n_qubits={n_qubits}, epochs={args.epochs}, batch={args.batch_size}, lr={args.learning_rate}" + ) + + # ============================================ + # 1. LOAD DATA + # ============================================ + print("\n📁 Step 1: Loading your data...") + if args.preset: + X, y = load_preset_dataset(args.preset) + print(f" ✅ Loaded preset dataset: {args.preset}") + dataset_desc = f"preset:{args.preset}" + elif args.csv: + X, y = load_csv_dataset(args.csv, label_col=args.label_col, drop_cols=drop_cols) + print(f" ✅ Loaded CSV: {args.csv}") + dataset_desc = f"csv:{args.csv}" + else: + # Fallback demo: scikit-learn wine dataset (binary) + from sklearn.datasets import load_wine + + data = load_wine() + X = data.data + # Binary: Class 0 vs others + y = (data.target == 0).astype(int) + print(" ℹ️ No dataset specified. Using demo Wine dataset (binary)") + dataset_desc = "demo:wine" + + # ============================================ + # 2. PREPROCESS + # ============================================ + print("\n🔧 Step 2: Preprocessing...") + X_train, X_val, y_train, y_val, scaler, pca = preprocess_data( + X, y, n_qubits=n_qubits, test_size=args.test_size + ) + + # ============================================ + # 3. CREATE MODEL + # ============================================ + print("\n🧠 Step 3: Creating quantum model...") + n_qubits = X_train.shape[1] # After preprocessing + + # Determine number of classes + n_classes = len(np.unique(y_train)) + + # Create hybrid quantum-classical model + model = HybridQNN( + input_dim=n_qubits, + hidden_dim=16, + n_qubits=n_qubits, + n_quantum_layers=2, + output_dim=n_classes, + dropout=0.2, + ) + print(" ✅ Created HybridQNN") + print(f" ✅ Quantum circuit: {n_qubits} qubits, 2 layers") + print(f" ✅ Output classes: {n_classes}") + + # ============================================ + # 4. TRAIN MODEL + # ============================================ + print("\n🚀 Step 4: Training quantum model...") + print(" This may take a few minutes...") + + history = train_quantum_model( + model=model, + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val, + num_epochs=args.epochs, + batch_size=args.batch_size, + learning_rate=args.learning_rate, + ) + + # ============================================ + # 5. EVALUATE RESULTS + # ============================================ + print("\n" + "=" * 70) + print(" TRAINING COMPLETE!") + print("=" * 70) + + final_train_loss = history["train_loss"][-1] + final_val_loss = history["val_loss"][-1] + final_val_acc = history["val_acc"][-1] + best_val_acc = max(history["val_acc"]) + + print("\n📊 Final Results:") + print(f" Training Loss: {final_train_loss:.4f}") + print(f" Validation Loss: {final_val_loss:.4f}") + print(f" Validation Accuracy: {final_val_acc:.4f} ({final_val_acc*100:.2f}%)") + print(f" Best Val Accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)") + + # Determine performance level + if best_val_acc >= 0.90: + grade = "🏆 EXCELLENT!" + emoji = "🎉" + elif best_val_acc >= 0.80: + grade = "⭐ VERY GOOD!" + emoji = "🚀" + elif best_val_acc >= 0.70: + grade = "✅ GOOD" + emoji = "👍" + elif best_val_acc >= 0.60: + grade = "⚠️ FAIR" + emoji = "📈" + else: + grade = "❌ NEEDS IMPROVEMENT" + emoji = "🔧" + + print(f"\n{emoji} Performance Grade: {grade}") + + # ============================================ + # 6. SAVE MODEL + # ============================================ + print("\n💾 Step 5: Saving model and preprocessors...") + # Ensure results directory exists + results_dir = Path(Path(__file__).parent / "results") + results_dir.mkdir(parents=True, exist_ok=True) + + model_path = str(results_dir / "custom_model.pt") + scaler_path = str(results_dir / "custom_scaler.pkl") + pca_path = str(results_dir / "custom_pca.pkl") + plot_path = str(results_dir / "custom_training.png") + summary_path = str(results_dir / "custom_training_summary.json") + + save_model_and_preprocessors( + model, + scaler, + pca, + model_path=model_path, + scaler_path=scaler_path, + pca_path=pca_path, + ) + + # ============================================ + # 7. PLOT RESULTS + # ============================================ + print("\n📊 Step 6: Generating training plots...") + plot_training_results(history, save_path=plot_path) + + # ============================================ + # 7. SAVE SUMMARY + # ============================================ + try: + y_train_counts = np.bincount(y_train).tolist() + y_val_counts = np.bincount(y_val).tolist() + except Exception: + y_train_counts, y_val_counts = None, None + + pca_explained_variance = None + if pca is not None and hasattr(pca, "explained_variance_ratio_"): + try: + pca_explained_variance = float(np.sum(pca.explained_variance_ratio_)) + except Exception: + pca_explained_variance = None + + summary = { + "dataset": dataset_desc, + "params": { + "n_qubits": n_qubits, + "epochs": args.epochs, + "batch_size": args.batch_size, + "learning_rate": args.learning_rate, + "test_size": args.test_size, + }, + "metrics": { + "train_loss_last": float(final_train_loss), + "val_loss_last": float(final_val_loss), + "val_acc_last": float(final_val_acc), + "val_acc_best": float(best_val_acc), + "history": { + "train_loss": [float(x) for x in history["train_loss"]], + "val_loss": [float(x) for x in history["val_loss"]], + "val_acc": [float(x) for x in history["val_acc"]], + }, + }, + "data": { + "n_train": int(len(X_train)), + "n_val": int(len(X_val)), + "n_classes": int(len(np.unique(y_train))), + "class_distribution_train": y_train_counts, + "class_distribution_val": y_val_counts, + "pca_explained_variance": pca_explained_variance, + }, + "artifacts": { + "model": model_path, + "scaler": scaler_path, + "pca": pca_path if pca is not None else None, + "plot": plot_path, + }, + } + + with open(summary_path, "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + print(f"\n📝 Summary saved to: {summary_path}") + + # ============================================ + # 8. RECOMMENDATIONS + # ============================================ + print("\n💡 Recommendations:") + + if best_val_acc < 0.70: + print(" ⚠️ Accuracy is low. Try:") + print(f" - More training data (currently {len(X_train)} samples)") + print(" - Better feature engineering") + print(" - Hyperparameter tuning (run experiments/parameter_tuning.py)") + print(" - Check if data is properly standardized") + + elif best_val_acc >= 0.90: + print(" 🎉 Excellent performance! Consider:") + print(" - Deploy to production") + print(" - Test on Azure Quantum hardware") + print(" - Try ensemble methods for even better results") + + else: + print(" ✅ Good performance! To improve further:") + print(" - Run hyperparameter optimization") + print(" - Try different quantum circuit architectures") + print(" - Collect more training data") + + print("\n📚 Next Steps:") + print(" 1. Review training plot: results/custom_training.png") + print(" 2. Load model for inference:") + print(" model.load_state_dict(torch.load('results/custom_model.pt'))") + print(" 3. Run hyperparameter tuning:") + print(" python experiments/parameter_tuning.py") + print(" 4. Deploy to Azure Quantum:") + print(" See experiments/AZURE_QUICKSTART.md") + + print("\n" + "=" * 70) + print(" 🎉 QUANTUM AI TRAINING COMPLETE!") + print("=" * 70 + "\n") + return model, history + + +if __name__ == "__main__": + model, history = main() diff --git a/ai-projects/quantum-ml/train_heart_disease.py b/ai-projects/quantum-ml/train_heart_disease.py index 81004b460..3cae9af5c 100644 --- a/ai-projects/quantum-ml/train_heart_disease.py +++ b/ai-projects/quantum-ml/train_heart_disease.py @@ -1,399 +1,399 @@ -""" -Train Quantum AI on Heart Disease Dataset -========================================== - -Real-world medical diagnosis using quantum machine learning. -Handles missing values (?) in the dataset. - -Dataset: UCI Heart Disease -- 302 samples (after cleaning) -- 13 features (medical indicators) -- Binary classification: Disease present (1-4) vs absent (0) - -Author: Quantum AI System -Date: November 1, 2025 -""" - -import sys -from pathlib import Path - -import numpy as np -import pandas as pd - -# Add src to path -src_path = Path(__file__).parent / "src" -sys.path.insert(0, str(src_path)) - -import matplotlib.pyplot as plt -import torch -from sklearn.decomposition import PCA -from sklearn.impute import SimpleImputer -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer -from torch.utils.data import DataLoader, TensorDataset - - -def load_heart_disease_data(): - """ - Load and clean heart disease dataset - - Handles missing values represented as '?' - - Returns: - X: numpy array of shape (n_samples, n_features) - y: numpy array of shape (n_samples,) with class labels - """ - # Path to dataset - dataset_path = ( - Path(__file__).parent.parent / "datasets" / "quantum" / "heart_disease.csv" - ) - - print(f"📁 Loading heart disease dataset from: {dataset_path}") - - # Load data with missing value handling - df = pd.read_csv(dataset_path, na_values=["?"]) - - print(f" Original samples: {len(df)}") - print(f" Missing values found: {df.isnull().sum().sum()}") - - # Separate features and labels (last column is target) - X = df.iloc[:, :-1].values - y = df.iloc[:, -1].values - - # Handle missing values using median imputation - if np.isnan(X).any(): - imputer = SimpleImputer(strategy="median") - X = imputer.fit_transform(X) - print(" ✅ Missing values imputed using median strategy") - - # Convert multi-class labels to binary (0 = no disease, 1-4 = disease present) - y = (y > 0).astype(int) - - print("📊 Loaded Heart Disease dataset") - print(f" Samples: {len(X)}") - print(f" Features: {X.shape[1]}") - print(" Classes: No disease (0) vs Disease (1)") - print(f" Class distribution: {np.bincount(y)}") - - return X, y - - -def preprocess_data(X, y, n_qubits=4, test_size=0.2): - """ - Preprocess data for quantum ML - - Args: - X: Feature matrix - y: Labels - n_qubits: Number of qubits - test_size: Validation split ratio - - Returns: - X_train, X_val, y_train, y_val, scaler, pca - """ - print("\n🔧 Preprocessing data...") - print(f" Original shape: {X.shape}") - - # Split data - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=test_size, random_state=42, stratify=y - ) - - # Standardize - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - print(" ✅ Standardized (mean≈0, std≈1)") - - # Handle feature dimension - n_features = X_train.shape[1] - pca = None - - if n_features > n_qubits: - # Use PCA to reduce dimensions - pca = PCA(n_components=n_qubits) - X_train = pca.fit_transform(X_train) - X_val = pca.transform(X_val) - - explained_var = pca.explained_variance_ratio_.sum() - print(f" ✅ Reduced from {n_features} to {n_qubits} features using PCA") - print(f" ✅ Explained variance: {explained_var:.2%}") - elif n_features < n_qubits: - # Pad with zeros - pad_train = np.zeros((X_train.shape[0], n_qubits - n_features)) - pad_val = np.zeros((X_val.shape[0], n_qubits - n_features)) - X_train = np.hstack([X_train, pad_train]) - X_val = np.hstack([X_val, pad_val]) - print(f" ✅ Padded from {n_features} to {n_qubits} features") - else: - print(f" ✅ Features match qubits ({n_qubits})") - - print(f" Final training shape: {X_train.shape}") - print(f" Final validation shape: {X_val.shape}") - - return X_train, X_val, y_train, y_val, scaler, pca - - -def plot_training_results(history, save_path="results/heart_disease_training.png"): - """Plot training curves""" - fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4)) - - # Loss curves - ax1.plot(history["train_loss"], label="Training Loss", linewidth=2) - ax1.plot(history["val_loss"], label="Validation Loss", linewidth=2) - ax1.set_xlabel("Epoch") - ax1.set_ylabel("Loss") - ax1.set_title("Training Progress - Heart Disease Dataset") - ax1.legend() - ax1.grid(True, alpha=0.3) - - # Accuracy curve - ax2.plot( - history["val_acc"], label="Validation Accuracy", linewidth=2, color="green" - ) - ax2.axhline( - y=max(history["val_acc"]), - color="red", - linestyle="--", - label=f'Best: {max(history["val_acc"]):.4f}', - ) - ax2.set_xlabel("Epoch") - ax2.set_ylabel("Accuracy") - ax2.set_title("Validation Accuracy") - ax2.legend() - ax2.grid(True, alpha=0.3) - - plt.tight_layout() - plt.savefig(save_path, dpi=150, bbox_inches="tight") - print(f"\n📊 Training plot saved to: {save_path}") - plt.close() - - -def save_model_and_preprocessors( - model, - scaler, - pca=None, - model_path="results/heart_disease_model.pt", - scaler_path="results/heart_disease_scaler.pkl", - pca_path="results/heart_disease_pca.pkl", -): - """Save model and preprocessing objects""" - import joblib - - torch.save(model.state_dict(), model_path) - print(f"💾 Model saved to: {model_path}") - - joblib.dump(scaler, scaler_path) - print(f"💾 Scaler saved to: {scaler_path}") - - if pca is not None: - joblib.dump(pca, pca_path) - print(f"💾 PCA saved to: {pca_path}") - - -def train_quantum_model( - model, - X_train, - y_train, - X_val, - y_val, - num_epochs=30, - batch_size=16, - learning_rate=0.001, -): - """Train the quantum model""" - # Convert to PyTorch tensors - X_train_tensor = torch.FloatTensor(X_train) - y_train_tensor = torch.LongTensor(y_train) - X_val_tensor = torch.FloatTensor(X_val) - y_val_tensor = torch.LongTensor(y_val) - - # Create data loaders - train_dataset = TensorDataset(X_train_tensor, y_train_tensor) - val_dataset = TensorDataset(X_val_tensor, y_val_tensor) - - train_loader = DataLoader( - train_dataset, batch_size=batch_size, shuffle=True, drop_last=True - ) - val_loader = DataLoader( - val_dataset, batch_size=batch_size, shuffle=False, drop_last=True - ) - - # Create trainer - trainer = QuantumClassicalTrainer(model, learning_rate=learning_rate) - - # Train - trainer.train(train_loader, val_loader, num_epochs) - - # Return history - history = { - "train_loss": trainer.train_losses, - "val_acc": trainer.val_accuracies, - "val_loss": [0] * len(trainer.val_accuracies), # Placeholder - } - - return history - - -def main(): - """Main training pipeline""" - - print("=" * 70) - print(" QUANTUM AI - HEART DISEASE DATASET TRAINING") - print("=" * 70) - print("\n❤️ Heart Disease Diagnosis Classification") - print(" Dataset: UCI Machine Learning Repository") - print(" Task: Predict presence of heart disease") - print(" Medical AI application!") - - # ============================================ - # 1. LOAD AND CLEAN DATA - # ============================================ - print("\n📁 Step 1: Loading and cleaning heart disease data...") - X, y = load_heart_disease_data() - - # ============================================ - # 2. PREPROCESS - # ============================================ - print("\n🔧 Step 2: Preprocessing...") - X_train, X_val, y_train, y_val, scaler, pca = preprocess_data(X, y, n_qubits=4) - - # ============================================ - # 3. CREATE MODEL - # ============================================ - print("\n🧠 Step 3: Creating quantum model...") - n_qubits = X_train.shape[1] - n_classes = len(np.unique(y_train)) - - model = HybridQNN( - input_dim=n_qubits, - hidden_dim=16, - n_qubits=n_qubits, - n_quantum_layers=2, - output_dim=n_classes, - dropout=0.2, - ) - print(" ✅ Created HybridQNN") - print(f" ✅ Quantum circuit: {n_qubits} qubits, 2 layers") - print(f" ✅ Output classes: {n_classes}") - - # ============================================ - # 4. TRAIN MODEL - # ============================================ - print("\n🚀 Step 4: Training quantum model...") - print(" Optimized for medical diagnosis (30 epochs, batch size 16)") - - history = train_quantum_model( - model=model, - X_train=X_train, - y_train=y_train, - X_val=X_val, - y_val=y_val, - num_epochs=30, - batch_size=8, - learning_rate=0.001, - ) - - # ============================================ - # 5. EVALUATE RESULTS - # ============================================ - print("\n" + "=" * 70) - print(" TRAINING COMPLETE!") - print("=" * 70) - - final_train_loss = history["train_loss"][-1] - final_val_loss = history["val_loss"][-1] - final_val_acc = history["val_acc"][-1] - best_val_acc = max(history["val_acc"]) - - print("\n📊 Final Results:") - print(f" Training Loss: {final_train_loss:.4f}") - print(f" Validation Loss: {final_val_loss:.4f}") - print(f" Validation Accuracy: {final_val_acc:.4f} ({final_val_acc*100:.2f}%)") - print(f" Best Val Accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)") - - # Determine performance level - if best_val_acc >= 0.85: - grade = "🏆 EXCELLENT!" - emoji = "🎉" - elif best_val_acc >= 0.75: - grade = "⭐ VERY GOOD!" - emoji = "🚀" - elif best_val_acc >= 0.65: - grade = "✅ GOOD" - emoji = "👍" - elif best_val_acc >= 0.55: - grade = "⚠️ FAIR" - emoji = "📈" - else: - grade = "❌ NEEDS IMPROVEMENT" - emoji = "🔧" - - print(f"\n{emoji} Performance Grade: {grade}") - - # Medical context - print("\n🏥 Medical Interpretation:") - if best_val_acc >= 0.80: - print(" ✅ High diagnostic accuracy - suitable for clinical decision support") - print(" ✅ Could assist doctors in early detection of heart disease") - elif best_val_acc >= 0.70: - print(" ⚠️ Good accuracy but needs improvement for clinical use") - print(" ⚠️ Should be combined with additional diagnostic tools") - else: - print(" ❌ Accuracy too low for medical applications") - print(" ❌ Requires significant improvement before clinical use") - - # ============================================ - # 6. SAVE MODEL - # ============================================ - print("\n💾 Step 5: Saving model and preprocessors...") - save_model_and_preprocessors(model, scaler, pca) - - # ============================================ - # 7. PLOT RESULTS - # ============================================ - print("\n📊 Step 6: Generating training plots...") - plot_training_results(history) - - # ============================================ - # 8. RECOMMENDATIONS - # ============================================ - print("\n💡 Recommendations:") - - if best_val_acc < 0.70: - print(" ⚠️ Accuracy is low for medical diagnosis. Try:") - print(f" - More training data (currently {len(X_train)} samples)") - print(" - Increase quantum layers or circuit depth") - print(" - Hyperparameter tuning") - print(" - Feature engineering (domain expert input)") - - elif best_val_acc >= 0.85: - print(" 🎉 Excellent performance! Consider:") - print(" - Clinical validation study") - print(" - Test on Azure Quantum hardware") - print(" - Explainability analysis (SHAP, LIME)") - print(" - Regulatory approval pathway") - - else: - print(" ✅ Good performance! To improve further:") - print(" - Run hyperparameter optimization") - print(" - Try ensemble methods") - print(" - Cross-validation for robustness") - print(" - Collect more diverse training data") - - print("\n📚 Next Steps:") - print(" 1. Review training plot: results/heart_disease_training.png") - print(" 2. Compare with classical ML baselines") - print(" 3. Validate on external heart disease datasets") - print(" 4. Consider deploying to Azure Quantum hardware") - - print("\n" + "=" * 70) - print(" 🎉 QUANTUM AI MEDICAL DIAGNOSIS TRAINING COMPLETE!") - print("=" * 70 + "\n") - return model, history - - -if __name__ == "__main__": - model, history = main() +""" +Train Quantum AI on Heart Disease Dataset +========================================== + +Real-world medical diagnosis using quantum machine learning. +Handles missing values (?) in the dataset. + +Dataset: UCI Heart Disease +- 302 samples (after cleaning) +- 13 features (medical indicators) +- Binary classification: Disease present (1-4) vs absent (0) + +Author: Quantum AI System +Date: November 1, 2025 +""" + +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +# Add src to path +src_path = Path(__file__).parent / "src" +sys.path.insert(0, str(src_path)) + +import matplotlib.pyplot as plt +import torch +from sklearn.decomposition import PCA +from sklearn.impute import SimpleImputer +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer +from torch.utils.data import DataLoader, TensorDataset + + +def load_heart_disease_data(): + """ + Load and clean heart disease dataset + + Handles missing values represented as '?' + + Returns: + X: numpy array of shape (n_samples, n_features) + y: numpy array of shape (n_samples,) with class labels + """ + # Path to dataset + dataset_path = ( + Path(__file__).parent.parent / "datasets" / "quantum" / "heart_disease.csv" + ) + + print(f"📁 Loading heart disease dataset from: {dataset_path}") + + # Load data with missing value handling + df = pd.read_csv(dataset_path, na_values=["?"]) + + print(f" Original samples: {len(df)}") + print(f" Missing values found: {df.isnull().sum().sum()}") + + # Separate features and labels (last column is target) + X = df.iloc[:, :-1].values + y = df.iloc[:, -1].values + + # Handle missing values using median imputation + if np.isnan(X).any(): + imputer = SimpleImputer(strategy="median") + X = imputer.fit_transform(X) + print(" ✅ Missing values imputed using median strategy") + + # Convert multi-class labels to binary (0 = no disease, 1-4 = disease present) + y = (y > 0).astype(int) + + print("📊 Loaded Heart Disease dataset") + print(f" Samples: {len(X)}") + print(f" Features: {X.shape[1]}") + print(" Classes: No disease (0) vs Disease (1)") + print(f" Class distribution: {np.bincount(y)}") + + return X, y + + +def preprocess_data(X, y, n_qubits=4, test_size=0.2): + """ + Preprocess data for quantum ML + + Args: + X: Feature matrix + y: Labels + n_qubits: Number of qubits + test_size: Validation split ratio + + Returns: + X_train, X_val, y_train, y_val, scaler, pca + """ + print("\n🔧 Preprocessing data...") + print(f" Original shape: {X.shape}") + + # Split data + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=test_size, random_state=42, stratify=y + ) + + # Standardize + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + print(" ✅ Standardized (mean≈0, std≈1)") + + # Handle feature dimension + n_features = X_train.shape[1] + pca = None + + if n_features > n_qubits: + # Use PCA to reduce dimensions + pca = PCA(n_components=n_qubits) + X_train = pca.fit_transform(X_train) + X_val = pca.transform(X_val) + + explained_var = pca.explained_variance_ratio_.sum() + print(f" ✅ Reduced from {n_features} to {n_qubits} features using PCA") + print(f" ✅ Explained variance: {explained_var:.2%}") + elif n_features < n_qubits: + # Pad with zeros + pad_train = np.zeros((X_train.shape[0], n_qubits - n_features)) + pad_val = np.zeros((X_val.shape[0], n_qubits - n_features)) + X_train = np.hstack([X_train, pad_train]) + X_val = np.hstack([X_val, pad_val]) + print(f" ✅ Padded from {n_features} to {n_qubits} features") + else: + print(f" ✅ Features match qubits ({n_qubits})") + + print(f" Final training shape: {X_train.shape}") + print(f" Final validation shape: {X_val.shape}") + + return X_train, X_val, y_train, y_val, scaler, pca + + +def plot_training_results(history, save_path="results/heart_disease_training.png"): + """Plot training curves""" + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4)) + + # Loss curves + ax1.plot(history["train_loss"], label="Training Loss", linewidth=2) + ax1.plot(history["val_loss"], label="Validation Loss", linewidth=2) + ax1.set_xlabel("Epoch") + ax1.set_ylabel("Loss") + ax1.set_title("Training Progress - Heart Disease Dataset") + ax1.legend() + ax1.grid(True, alpha=0.3) + + # Accuracy curve + ax2.plot( + history["val_acc"], label="Validation Accuracy", linewidth=2, color="green" + ) + ax2.axhline( + y=max(history["val_acc"]), + color="red", + linestyle="--", + label=f'Best: {max(history["val_acc"]):.4f}', + ) + ax2.set_xlabel("Epoch") + ax2.set_ylabel("Accuracy") + ax2.set_title("Validation Accuracy") + ax2.legend() + ax2.grid(True, alpha=0.3) + + plt.tight_layout() + plt.savefig(save_path, dpi=150, bbox_inches="tight") + print(f"\n📊 Training plot saved to: {save_path}") + plt.close() + + +def save_model_and_preprocessors( + model, + scaler, + pca=None, + model_path="results/heart_disease_model.pt", + scaler_path="results/heart_disease_scaler.pkl", + pca_path="results/heart_disease_pca.pkl", +): + """Save model and preprocessing objects""" + import joblib + + torch.save(model.state_dict(), model_path) + print(f"💾 Model saved to: {model_path}") + + joblib.dump(scaler, scaler_path) + print(f"💾 Scaler saved to: {scaler_path}") + + if pca is not None: + joblib.dump(pca, pca_path) + print(f"💾 PCA saved to: {pca_path}") + + +def train_quantum_model( + model, + X_train, + y_train, + X_val, + y_val, + num_epochs=30, + batch_size=16, + learning_rate=0.001, +): + """Train the quantum model""" + # Convert to PyTorch tensors + X_train_tensor = torch.FloatTensor(X_train) + y_train_tensor = torch.LongTensor(y_train) + X_val_tensor = torch.FloatTensor(X_val) + y_val_tensor = torch.LongTensor(y_val) + + # Create data loaders + train_dataset = TensorDataset(X_train_tensor, y_train_tensor) + val_dataset = TensorDataset(X_val_tensor, y_val_tensor) + + train_loader = DataLoader( + train_dataset, batch_size=batch_size, shuffle=True, drop_last=True + ) + val_loader = DataLoader( + val_dataset, batch_size=batch_size, shuffle=False, drop_last=True + ) + + # Create trainer + trainer = QuantumClassicalTrainer(model, learning_rate=learning_rate) + + # Train + trainer.train(train_loader, val_loader, num_epochs) + + # Return history + history = { + "train_loss": trainer.train_losses, + "val_acc": trainer.val_accuracies, + "val_loss": [0] * len(trainer.val_accuracies), # Placeholder + } + + return history + + +def main(): + """Main training pipeline""" + + print("=" * 70) + print(" QUANTUM AI - HEART DISEASE DATASET TRAINING") + print("=" * 70) + print("\n❤️ Heart Disease Diagnosis Classification") + print(" Dataset: UCI Machine Learning Repository") + print(" Task: Predict presence of heart disease") + print(" Medical AI application!") + + # ============================================ + # 1. LOAD AND CLEAN DATA + # ============================================ + print("\n📁 Step 1: Loading and cleaning heart disease data...") + X, y = load_heart_disease_data() + + # ============================================ + # 2. PREPROCESS + # ============================================ + print("\n🔧 Step 2: Preprocessing...") + X_train, X_val, y_train, y_val, scaler, pca = preprocess_data(X, y, n_qubits=4) + + # ============================================ + # 3. CREATE MODEL + # ============================================ + print("\n🧠 Step 3: Creating quantum model...") + n_qubits = X_train.shape[1] + n_classes = len(np.unique(y_train)) + + model = HybridQNN( + input_dim=n_qubits, + hidden_dim=16, + n_qubits=n_qubits, + n_quantum_layers=2, + output_dim=n_classes, + dropout=0.2, + ) + print(" ✅ Created HybridQNN") + print(f" ✅ Quantum circuit: {n_qubits} qubits, 2 layers") + print(f" ✅ Output classes: {n_classes}") + + # ============================================ + # 4. TRAIN MODEL + # ============================================ + print("\n🚀 Step 4: Training quantum model...") + print(" Optimized for medical diagnosis (30 epochs, batch size 16)") + + history = train_quantum_model( + model=model, + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val, + num_epochs=30, + batch_size=8, + learning_rate=0.001, + ) + + # ============================================ + # 5. EVALUATE RESULTS + # ============================================ + print("\n" + "=" * 70) + print(" TRAINING COMPLETE!") + print("=" * 70) + + final_train_loss = history["train_loss"][-1] + final_val_loss = history["val_loss"][-1] + final_val_acc = history["val_acc"][-1] + best_val_acc = max(history["val_acc"]) + + print("\n📊 Final Results:") + print(f" Training Loss: {final_train_loss:.4f}") + print(f" Validation Loss: {final_val_loss:.4f}") + print(f" Validation Accuracy: {final_val_acc:.4f} ({final_val_acc*100:.2f}%)") + print(f" Best Val Accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)") + + # Determine performance level + if best_val_acc >= 0.85: + grade = "🏆 EXCELLENT!" + emoji = "🎉" + elif best_val_acc >= 0.75: + grade = "⭐ VERY GOOD!" + emoji = "🚀" + elif best_val_acc >= 0.65: + grade = "✅ GOOD" + emoji = "👍" + elif best_val_acc >= 0.55: + grade = "⚠️ FAIR" + emoji = "📈" + else: + grade = "❌ NEEDS IMPROVEMENT" + emoji = "🔧" + + print(f"\n{emoji} Performance Grade: {grade}") + + # Medical context + print("\n🏥 Medical Interpretation:") + if best_val_acc >= 0.80: + print(" ✅ High diagnostic accuracy - suitable for clinical decision support") + print(" ✅ Could assist doctors in early detection of heart disease") + elif best_val_acc >= 0.70: + print(" ⚠️ Good accuracy but needs improvement for clinical use") + print(" ⚠️ Should be combined with additional diagnostic tools") + else: + print(" ❌ Accuracy too low for medical applications") + print(" ❌ Requires significant improvement before clinical use") + + # ============================================ + # 6. SAVE MODEL + # ============================================ + print("\n💾 Step 5: Saving model and preprocessors...") + save_model_and_preprocessors(model, scaler, pca) + + # ============================================ + # 7. PLOT RESULTS + # ============================================ + print("\n📊 Step 6: Generating training plots...") + plot_training_results(history) + + # ============================================ + # 8. RECOMMENDATIONS + # ============================================ + print("\n💡 Recommendations:") + + if best_val_acc < 0.70: + print(" ⚠️ Accuracy is low for medical diagnosis. Try:") + print(f" - More training data (currently {len(X_train)} samples)") + print(" - Increase quantum layers or circuit depth") + print(" - Hyperparameter tuning") + print(" - Feature engineering (domain expert input)") + + elif best_val_acc >= 0.85: + print(" 🎉 Excellent performance! Consider:") + print(" - Clinical validation study") + print(" - Test on Azure Quantum hardware") + print(" - Explainability analysis (SHAP, LIME)") + print(" - Regulatory approval pathway") + + else: + print(" ✅ Good performance! To improve further:") + print(" - Run hyperparameter optimization") + print(" - Try ensemble methods") + print(" - Cross-validation for robustness") + print(" - Collect more diverse training data") + + print("\n📚 Next Steps:") + print(" 1. Review training plot: results/heart_disease_training.png") + print(" 2. Compare with classical ML baselines") + print(" 3. Validate on external heart disease datasets") + print(" 4. Consider deploying to Azure Quantum hardware") + + print("\n" + "=" * 70) + print(" 🎉 QUANTUM AI MEDICAL DIAGNOSIS TRAINING COMPLETE!") + print("=" * 70 + "\n") + return model, history + + +if __name__ == "__main__": + model, history = main() diff --git a/ai-projects/quantum-ml/train_ionosphere.py b/ai-projects/quantum-ml/train_ionosphere.py index b499e8264..27e480587 100644 --- a/ai-projects/quantum-ml/train_ionosphere.py +++ b/ai-projects/quantum-ml/train_ionosphere.py @@ -1,407 +1,407 @@ -""" -Train Quantum AI on Ionosphere Dataset -======================================== - -Real-world quantum ML on ionosphere radar data. -This dataset has been used extensively in quantum computing research. - -Dataset: UCI Ionosphere -- 351 samples -- 34 features (radar returns) -- Binary classification: Good (g) vs Bad (b) radar returns - -Author: Quantum AI System -Date: November 1, 2025 -""" - -import sys -from pathlib import Path - -import numpy as np -import pandas as pd - -# Add src to path -src_path = Path(__file__).parent / "src" -sys.path.insert(0, str(src_path)) - -import matplotlib.pyplot as plt -import torch -from sklearn.decomposition import PCA -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer -from torch.utils.data import DataLoader, TensorDataset - - -def load_ionosphere_data(): - """ - Load ionosphere dataset from datasets folder - - Returns: - X: numpy array of shape (n_samples, n_features) - y: numpy array of shape (n_samples,) with class labels - """ - # Path to dataset - dataset_path = ( - Path(__file__).parent.parent / "datasets" / "quantum" / "ionosphere.csv" - ) - - print(f"📁 Loading ionosphere dataset from: {dataset_path}") - - # Load data - df = pd.read_csv(dataset_path) - - # Separate features and labels - # Last column is the target - X = df.iloc[:, :-1].values - y = df.iloc[:, -1].values - - # Convert labels to binary (0 and 1) - # Assuming labels are 'g' (good) and 'b' (bad) - if y.dtype == object: - unique_labels = np.unique(y) - print(f" Original labels: {unique_labels}") - y = (y == unique_labels[0]).astype(int) - - print("📊 Loaded Ionosphere dataset") - print(f" Samples: {len(X)}") - print(f" Features: {X.shape[1]}") - print(f" Classes: {np.unique(y)}") - print(f" Class distribution: {np.bincount(y)}") - - return X, y - - -def preprocess_data(X, y, n_qubits=4, test_size=0.2): - """ - Preprocess data for quantum ML - - Args: - X: Feature matrix - y: Labels - n_qubits: Number of qubits (features will be reduced/padded to match) - test_size: Validation split ratio - - Returns: - X_train, X_val, y_train, y_val: Preprocessed data - scaler: Fitted StandardScaler (save this for inference!) - pca: Fitted PCA (if used, else None) - """ - print("\n🔧 Preprocessing data...") - print(f" Original shape: {X.shape}") - - # Split data - X_train, X_val, y_train, y_val = train_test_split( - X, - y, - test_size=test_size, - random_state=42, - stratify=y, # Maintains class balance - ) - - # Standardize (CRITICAL for quantum circuits!) - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - print(" ✅ Standardized (mean≈0, std≈1)") - - # Handle feature dimension - n_features = X_train.shape[1] - pca = None - - if n_features < n_qubits: - # Pad with zeros - padding_train = np.zeros((X_train.shape[0], n_qubits - n_features)) - X_train = np.hstack([X_train, padding_train]) - - padding_val = np.zeros((X_val.shape[0], n_qubits - n_features)) - X_val = np.hstack([X_val, padding_val]) - - print(f" ✅ Padded from {n_features} to {n_qubits} features") - - elif n_features > n_qubits: - # Use PCA to reduce dimensions - pca = PCA(n_components=n_qubits) - X_train = pca.fit_transform(X_train) - X_val = pca.transform(X_val) - - explained_var = pca.explained_variance_ratio_.sum() - print(f" ✅ Reduced from {n_features} to {n_qubits} features using PCA") - print(f" ✅ Explained variance: {explained_var:.2%}") - else: - print(f" ✅ Features already match qubits ({n_qubits})") - - print(f" Final training shape: {X_train.shape}") - print(f" Final validation shape: {X_val.shape}") - - return X_train, X_val, y_train, y_val, scaler, pca - - -def plot_training_results(history, save_path="results/ionosphere_training.png"): - """Plot training curves""" - fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4)) - - # Loss curves - ax1.plot(history["train_loss"], label="Training Loss", linewidth=2) - ax1.plot(history["val_loss"], label="Validation Loss", linewidth=2) - ax1.set_xlabel("Epoch") - ax1.set_ylabel("Loss") - ax1.set_title("Training Progress - Ionosphere Dataset") - ax1.legend() - ax1.grid(True, alpha=0.3) - - # Accuracy curve - ax2.plot( - history["val_acc"], label="Validation Accuracy", linewidth=2, color="green" - ) - ax2.axhline( - y=max(history["val_acc"]), - color="red", - linestyle="--", - label=f'Best: {max(history["val_acc"]):.4f}', - ) - ax2.set_xlabel("Epoch") - ax2.set_ylabel("Accuracy") - ax2.set_title("Validation Accuracy") - ax2.legend() - ax2.grid(True, alpha=0.3) - - plt.tight_layout() - plt.savefig(save_path, dpi=150, bbox_inches="tight") - print(f"\n📊 Training plot saved to: {save_path}") - plt.close() - - -def save_model_and_preprocessors( - model, - scaler, - pca=None, - model_path="results/ionosphere_model.pt", - scaler_path="results/ionosphere_scaler.pkl", - pca_path="results/ionosphere_pca.pkl", -): - """Save model and preprocessing objects""" - import joblib - - # Save model weights - torch.save(model.state_dict(), model_path) - print(f"💾 Model saved to: {model_path}") - - # Save scaler - joblib.dump(scaler, scaler_path) - print(f"💾 Scaler saved to: {scaler_path}") - - # Save PCA if used - if pca is not None: - joblib.dump(pca, pca_path) - print(f"💾 PCA saved to: {pca_path}") - - -def train_quantum_model( - model, - X_train, - y_train, - X_val, - y_val, - num_epochs=30, - batch_size=16, - learning_rate=0.001, -): - """ - Train the quantum model - - Args: - model: HybridQNN model - X_train, y_train: Training data - X_val, y_val: Validation data - num_epochs: Number of training epochs - batch_size: Batch size - learning_rate: Learning rate - - Returns: - history: Dictionary with training metrics - """ - # Convert to PyTorch tensors - X_train_tensor = torch.FloatTensor(X_train) - y_train_tensor = torch.LongTensor(y_train) - X_val_tensor = torch.FloatTensor(X_val) - y_val_tensor = torch.LongTensor(y_val) - - # Create data loaders - train_dataset = TensorDataset(X_train_tensor, y_train_tensor) - val_dataset = TensorDataset(X_val_tensor, y_val_tensor) - - train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) - val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) - - # Create trainer - trainer = QuantumClassicalTrainer(model, learning_rate=learning_rate) - - # Train - trainer.train(train_loader, val_loader, num_epochs) - - # Return history - history = { - "train_loss": trainer.train_losses, - "val_acc": trainer.val_accuracies, - "val_loss": [0] * len(trainer.val_accuracies), # Placeholder - } - - return history - - -def main(): - """Main training pipeline""" - - print("=" * 70) - print(" QUANTUM AI - IONOSPHERE DATASET TRAINING") - print("=" * 70) - print("\n🌐 Ionosphere Radar Returns Classification") - print(" Dataset: UCI Machine Learning Repository") - print(" Task: Classify radar returns as 'good' or 'bad'") - print(" Real-world quantum computing application!") - - # ============================================ - # 1. LOAD DATA - # ============================================ - print("\n📁 Step 1: Loading ionosphere data...") - X, y = load_ionosphere_data() - - # ============================================ - # 2. PREPROCESS - # ============================================ - print("\n🔧 Step 2: Preprocessing...") - X_train, X_val, y_train, y_val, scaler, pca = preprocess_data(X, y, n_qubits=4) - - # ============================================ - # 3. CREATE MODEL - # ============================================ - print("\n🧠 Step 3: Creating quantum model...") - n_qubits = X_train.shape[1] # Should be 4 after preprocessing - - # Determine number of classes - n_classes = len(np.unique(y_train)) - - # Create hybrid quantum-classical model - model = HybridQNN( - input_dim=n_qubits, - hidden_dim=16, - n_qubits=n_qubits, - n_quantum_layers=2, - output_dim=n_classes, - dropout=0.2, - ) - print(" ✅ Created HybridQNN") - print(f" ✅ Quantum circuit: {n_qubits} qubits, 2 layers") - print(f" ✅ Output classes: {n_classes}") - - # ============================================ - # 4. TRAIN MODEL - # ============================================ - print("\n🚀 Step 4: Training quantum model...") - print(" Optimized for ionosphere dataset (30 epochs, batch size 16)") - - history = train_quantum_model( - model=model, - X_train=X_train, - y_train=y_train, - X_val=X_val, - y_val=y_val, - num_epochs=30, - batch_size=16, - learning_rate=0.001, - ) - - # ============================================ - # 5. EVALUATE RESULTS - # ============================================ - print("\n" + "=" * 70) - print(" TRAINING COMPLETE!") - print("=" * 70) - - final_train_loss = history["train_loss"][-1] - final_val_loss = history["val_loss"][-1] - final_val_acc = history["val_acc"][-1] - best_val_acc = max(history["val_acc"]) - - print("\n📊 Final Results:") - print(f" Training Loss: {final_train_loss:.4f}") - print(f" Validation Loss: {final_val_loss:.4f}") - print(f" Validation Accuracy: {final_val_acc:.4f} ({final_val_acc*100:.2f}%)") - print(f" Best Val Accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)") - - # Determine performance level - if best_val_acc >= 0.90: - grade = "🏆 EXCELLENT!" - emoji = "🎉" - elif best_val_acc >= 0.80: - grade = "⭐ VERY GOOD!" - emoji = "🚀" - elif best_val_acc >= 0.70: - grade = "✅ GOOD" - emoji = "👍" - elif best_val_acc >= 0.60: - grade = "⚠️ FAIR" - emoji = "📈" - else: - grade = "❌ NEEDS IMPROVEMENT" - emoji = "🔧" - - print(f"\n{emoji} Performance Grade: {grade}") - - # ============================================ - # 6. SAVE MODEL - # ============================================ - print("\n💾 Step 5: Saving model and preprocessors...") - save_model_and_preprocessors(model, scaler, pca) - - # ============================================ - # 7. PLOT RESULTS - # ============================================ - print("\n📊 Step 6: Generating training plots...") - plot_training_results(history) - - # ============================================ - # 8. RECOMMENDATIONS - # ============================================ - print("\n💡 Recommendations:") - - if best_val_acc < 0.70: - print(" ⚠️ Accuracy is low. Try:") - print(f" - More training data (currently {len(X_train)} samples)") - print(" - Increase epochs or adjust learning rate") - print(" - Try different quantum circuit architectures") - print(" - Hyperparameter tuning") - - elif best_val_acc >= 0.85: - print(" 🎉 Excellent performance! Consider:") - print(" - Deploy to production") - print(" - Test on Azure Quantum hardware") - print(" - Compare with classical ML baselines") - print(" - Try ensemble methods") - - else: - print(" ✅ Good performance! To improve further:") - print(" - Run hyperparameter optimization") - print(" - Try different quantum entanglement patterns") - print(" - Increase model capacity (more layers)") - - print("\n📚 Next Steps:") - print(" 1. Review training plot: results/ionosphere_training.png") - print(" 2. Load model for inference:") - print(" model.load_state_dict(torch.load('results/ionosphere_model.pt'))") - print(" 3. Test on other quantum datasets:") - print(" - banknote.csv (fraud detection)") - print(" - heart_disease.csv (medical diagnosis)") - print(" - sonar.csv (object classification)") - print(" 4. Deploy to Azure Quantum hardware") - - print("\n" + "=" * 70) - print(" 🎉 QUANTUM AI TRAINING COMPLETE!") - print("=" * 70 + "\n") - return model, history - - -if __name__ == "__main__": - model, history = main() +""" +Train Quantum AI on Ionosphere Dataset +======================================== + +Real-world quantum ML on ionosphere radar data. +This dataset has been used extensively in quantum computing research. + +Dataset: UCI Ionosphere +- 351 samples +- 34 features (radar returns) +- Binary classification: Good (g) vs Bad (b) radar returns + +Author: Quantum AI System +Date: November 1, 2025 +""" + +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +# Add src to path +src_path = Path(__file__).parent / "src" +sys.path.insert(0, str(src_path)) + +import matplotlib.pyplot as plt +import torch +from sklearn.decomposition import PCA +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from src.hybrid_qnn import HybridQNN, QuantumClassicalTrainer +from torch.utils.data import DataLoader, TensorDataset + + +def load_ionosphere_data(): + """ + Load ionosphere dataset from datasets folder + + Returns: + X: numpy array of shape (n_samples, n_features) + y: numpy array of shape (n_samples,) with class labels + """ + # Path to dataset + dataset_path = ( + Path(__file__).parent.parent / "datasets" / "quantum" / "ionosphere.csv" + ) + + print(f"📁 Loading ionosphere dataset from: {dataset_path}") + + # Load data + df = pd.read_csv(dataset_path) + + # Separate features and labels + # Last column is the target + X = df.iloc[:, :-1].values + y = df.iloc[:, -1].values + + # Convert labels to binary (0 and 1) + # Assuming labels are 'g' (good) and 'b' (bad) + if y.dtype == object: + unique_labels = np.unique(y) + print(f" Original labels: {unique_labels}") + y = (y == unique_labels[0]).astype(int) + + print("📊 Loaded Ionosphere dataset") + print(f" Samples: {len(X)}") + print(f" Features: {X.shape[1]}") + print(f" Classes: {np.unique(y)}") + print(f" Class distribution: {np.bincount(y)}") + + return X, y + + +def preprocess_data(X, y, n_qubits=4, test_size=0.2): + """ + Preprocess data for quantum ML + + Args: + X: Feature matrix + y: Labels + n_qubits: Number of qubits (features will be reduced/padded to match) + test_size: Validation split ratio + + Returns: + X_train, X_val, y_train, y_val: Preprocessed data + scaler: Fitted StandardScaler (save this for inference!) + pca: Fitted PCA (if used, else None) + """ + print("\n🔧 Preprocessing data...") + print(f" Original shape: {X.shape}") + + # Split data + X_train, X_val, y_train, y_val = train_test_split( + X, + y, + test_size=test_size, + random_state=42, + stratify=y, # Maintains class balance + ) + + # Standardize (CRITICAL for quantum circuits!) + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + print(" ✅ Standardized (mean≈0, std≈1)") + + # Handle feature dimension + n_features = X_train.shape[1] + pca = None + + if n_features < n_qubits: + # Pad with zeros + padding_train = np.zeros((X_train.shape[0], n_qubits - n_features)) + X_train = np.hstack([X_train, padding_train]) + + padding_val = np.zeros((X_val.shape[0], n_qubits - n_features)) + X_val = np.hstack([X_val, padding_val]) + + print(f" ✅ Padded from {n_features} to {n_qubits} features") + + elif n_features > n_qubits: + # Use PCA to reduce dimensions + pca = PCA(n_components=n_qubits) + X_train = pca.fit_transform(X_train) + X_val = pca.transform(X_val) + + explained_var = pca.explained_variance_ratio_.sum() + print(f" ✅ Reduced from {n_features} to {n_qubits} features using PCA") + print(f" ✅ Explained variance: {explained_var:.2%}") + else: + print(f" ✅ Features already match qubits ({n_qubits})") + + print(f" Final training shape: {X_train.shape}") + print(f" Final validation shape: {X_val.shape}") + + return X_train, X_val, y_train, y_val, scaler, pca + + +def plot_training_results(history, save_path="results/ionosphere_training.png"): + """Plot training curves""" + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4)) + + # Loss curves + ax1.plot(history["train_loss"], label="Training Loss", linewidth=2) + ax1.plot(history["val_loss"], label="Validation Loss", linewidth=2) + ax1.set_xlabel("Epoch") + ax1.set_ylabel("Loss") + ax1.set_title("Training Progress - Ionosphere Dataset") + ax1.legend() + ax1.grid(True, alpha=0.3) + + # Accuracy curve + ax2.plot( + history["val_acc"], label="Validation Accuracy", linewidth=2, color="green" + ) + ax2.axhline( + y=max(history["val_acc"]), + color="red", + linestyle="--", + label=f'Best: {max(history["val_acc"]):.4f}', + ) + ax2.set_xlabel("Epoch") + ax2.set_ylabel("Accuracy") + ax2.set_title("Validation Accuracy") + ax2.legend() + ax2.grid(True, alpha=0.3) + + plt.tight_layout() + plt.savefig(save_path, dpi=150, bbox_inches="tight") + print(f"\n📊 Training plot saved to: {save_path}") + plt.close() + + +def save_model_and_preprocessors( + model, + scaler, + pca=None, + model_path="results/ionosphere_model.pt", + scaler_path="results/ionosphere_scaler.pkl", + pca_path="results/ionosphere_pca.pkl", +): + """Save model and preprocessing objects""" + import joblib + + # Save model weights + torch.save(model.state_dict(), model_path) + print(f"💾 Model saved to: {model_path}") + + # Save scaler + joblib.dump(scaler, scaler_path) + print(f"💾 Scaler saved to: {scaler_path}") + + # Save PCA if used + if pca is not None: + joblib.dump(pca, pca_path) + print(f"💾 PCA saved to: {pca_path}") + + +def train_quantum_model( + model, + X_train, + y_train, + X_val, + y_val, + num_epochs=30, + batch_size=16, + learning_rate=0.001, +): + """ + Train the quantum model + + Args: + model: HybridQNN model + X_train, y_train: Training data + X_val, y_val: Validation data + num_epochs: Number of training epochs + batch_size: Batch size + learning_rate: Learning rate + + Returns: + history: Dictionary with training metrics + """ + # Convert to PyTorch tensors + X_train_tensor = torch.FloatTensor(X_train) + y_train_tensor = torch.LongTensor(y_train) + X_val_tensor = torch.FloatTensor(X_val) + y_val_tensor = torch.LongTensor(y_val) + + # Create data loaders + train_dataset = TensorDataset(X_train_tensor, y_train_tensor) + val_dataset = TensorDataset(X_val_tensor, y_val_tensor) + + train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) + + # Create trainer + trainer = QuantumClassicalTrainer(model, learning_rate=learning_rate) + + # Train + trainer.train(train_loader, val_loader, num_epochs) + + # Return history + history = { + "train_loss": trainer.train_losses, + "val_acc": trainer.val_accuracies, + "val_loss": [0] * len(trainer.val_accuracies), # Placeholder + } + + return history + + +def main(): + """Main training pipeline""" + + print("=" * 70) + print(" QUANTUM AI - IONOSPHERE DATASET TRAINING") + print("=" * 70) + print("\n🌐 Ionosphere Radar Returns Classification") + print(" Dataset: UCI Machine Learning Repository") + print(" Task: Classify radar returns as 'good' or 'bad'") + print(" Real-world quantum computing application!") + + # ============================================ + # 1. LOAD DATA + # ============================================ + print("\n📁 Step 1: Loading ionosphere data...") + X, y = load_ionosphere_data() + + # ============================================ + # 2. PREPROCESS + # ============================================ + print("\n🔧 Step 2: Preprocessing...") + X_train, X_val, y_train, y_val, scaler, pca = preprocess_data(X, y, n_qubits=4) + + # ============================================ + # 3. CREATE MODEL + # ============================================ + print("\n🧠 Step 3: Creating quantum model...") + n_qubits = X_train.shape[1] # Should be 4 after preprocessing + + # Determine number of classes + n_classes = len(np.unique(y_train)) + + # Create hybrid quantum-classical model + model = HybridQNN( + input_dim=n_qubits, + hidden_dim=16, + n_qubits=n_qubits, + n_quantum_layers=2, + output_dim=n_classes, + dropout=0.2, + ) + print(" ✅ Created HybridQNN") + print(f" ✅ Quantum circuit: {n_qubits} qubits, 2 layers") + print(f" ✅ Output classes: {n_classes}") + + # ============================================ + # 4. TRAIN MODEL + # ============================================ + print("\n🚀 Step 4: Training quantum model...") + print(" Optimized for ionosphere dataset (30 epochs, batch size 16)") + + history = train_quantum_model( + model=model, + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val, + num_epochs=30, + batch_size=16, + learning_rate=0.001, + ) + + # ============================================ + # 5. EVALUATE RESULTS + # ============================================ + print("\n" + "=" * 70) + print(" TRAINING COMPLETE!") + print("=" * 70) + + final_train_loss = history["train_loss"][-1] + final_val_loss = history["val_loss"][-1] + final_val_acc = history["val_acc"][-1] + best_val_acc = max(history["val_acc"]) + + print("\n📊 Final Results:") + print(f" Training Loss: {final_train_loss:.4f}") + print(f" Validation Loss: {final_val_loss:.4f}") + print(f" Validation Accuracy: {final_val_acc:.4f} ({final_val_acc*100:.2f}%)") + print(f" Best Val Accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)") + + # Determine performance level + if best_val_acc >= 0.90: + grade = "🏆 EXCELLENT!" + emoji = "🎉" + elif best_val_acc >= 0.80: + grade = "⭐ VERY GOOD!" + emoji = "🚀" + elif best_val_acc >= 0.70: + grade = "✅ GOOD" + emoji = "👍" + elif best_val_acc >= 0.60: + grade = "⚠️ FAIR" + emoji = "📈" + else: + grade = "❌ NEEDS IMPROVEMENT" + emoji = "🔧" + + print(f"\n{emoji} Performance Grade: {grade}") + + # ============================================ + # 6. SAVE MODEL + # ============================================ + print("\n💾 Step 5: Saving model and preprocessors...") + save_model_and_preprocessors(model, scaler, pca) + + # ============================================ + # 7. PLOT RESULTS + # ============================================ + print("\n📊 Step 6: Generating training plots...") + plot_training_results(history) + + # ============================================ + # 8. RECOMMENDATIONS + # ============================================ + print("\n💡 Recommendations:") + + if best_val_acc < 0.70: + print(" ⚠️ Accuracy is low. Try:") + print(f" - More training data (currently {len(X_train)} samples)") + print(" - Increase epochs or adjust learning rate") + print(" - Try different quantum circuit architectures") + print(" - Hyperparameter tuning") + + elif best_val_acc >= 0.85: + print(" 🎉 Excellent performance! Consider:") + print(" - Deploy to production") + print(" - Test on Azure Quantum hardware") + print(" - Compare with classical ML baselines") + print(" - Try ensemble methods") + + else: + print(" ✅ Good performance! To improve further:") + print(" - Run hyperparameter optimization") + print(" - Try different quantum entanglement patterns") + print(" - Increase model capacity (more layers)") + + print("\n📚 Next Steps:") + print(" 1. Review training plot: results/ionosphere_training.png") + print(" 2. Load model for inference:") + print(" model.load_state_dict(torch.load('results/ionosphere_model.pt'))") + print(" 3. Test on other quantum datasets:") + print(" - banknote.csv (fraud detection)") + print(" - heart_disease.csv (medical diagnosis)") + print(" - sonar.csv (object classification)") + print(" 4. Deploy to Azure Quantum hardware") + + print("\n" + "=" * 70) + print(" 🎉 QUANTUM AI TRAINING COMPLETE!") + print("=" * 70 + "\n") + return model, history + + +if __name__ == "__main__": + model, history = main() diff --git a/ai-projects/quantum-ml/train_pennylane_simple.py b/ai-projects/quantum-ml/train_pennylane_simple.py index 79e2a2fe3..2a0e30219 100644 --- a/ai-projects/quantum-ml/train_pennylane_simple.py +++ b/ai-projects/quantum-ml/train_pennylane_simple.py @@ -1,322 +1,322 @@ -#!/usr/bin/env python3 -""" -Simple PennyLane-only quantum training (no PyTorch dependency) -Trains for a specified duration using quantum circuits on CPU simulator -""" -import argparse -import json -import time -from datetime import datetime -from pathlib import Path - -import numpy as np -import pandas as pd -import pennylane as qml -from sklearn.decomposition import PCA -from sklearn.impute import SimpleImputer -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler - - -def load_preset_dataset(name: str): - """Load preset dataset from datasets/quantum/*.csv""" - base = Path(__file__).parent.parent / "datasets" / "quantum" - presets = { - "heart": base / "heart_disease.csv", - "ionosphere": base / "ionosphere.csv", - "sonar": base / "sonar.csv", - "banknote": base / "banknote.csv", - } - if name not in presets: - raise ValueError(f"Unknown preset '{name}'. Choose from {list(presets)}") - - path = presets[name] - if name == "heart": - df = pd.read_csv(path, na_values=["?"]) - y = df.iloc[:, -1] - X = df.iloc[:, :-1] - if X.isnull().any().any(): - imputer = SimpleImputer(strategy="median") - X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) - y = (y > 0).astype(int).values - return X.values, y - else: - df = pd.read_csv(path, na_values=["?"]) - y = df.iloc[:, -1].values - X = df.iloc[:, :-1] - if X.isnull().any().any(): - imputer = SimpleImputer(strategy="median") - X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) - if not np.issubdtype(y.dtype, np.integer): - vals, y = np.unique(y, return_inverse=True) - return X.values, y - - -def preprocess_data(X, y, n_qubits=4): - """Preprocess data for quantum ML""" - print(f"\n🔧 Preprocessing data (n_qubits={n_qubits})...") - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42, stratify=y - ) - - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - n_features = X_train.shape[1] - pca = None - - if n_features < n_qubits: - padding_train = np.zeros((X_train.shape[0], n_qubits - n_features)) - X_train = np.hstack([X_train, padding_train]) - padding_val = np.zeros((X_val.shape[0], n_qubits - n_features)) - X_val = np.hstack([X_val, padding_val]) - print(f" ✅ Padded from {n_features} to {n_qubits} features") - elif n_features > n_qubits: - pca = PCA(n_components=n_qubits) - X_train = pca.fit_transform(X_train) - X_val = pca.transform(X_val) - print(f" ✅ Reduced from {n_features} to {n_qubits} features (PCA)") - - return X_train, X_val, y_train, y_val, scaler, pca - - -def create_quantum_circuit(n_qubits=4, n_layers=2): - """Create variational quantum circuit using PennyLane""" - dev = qml.device("default.qubit", wires=n_qubits) - - @qml.qnode(dev, interface="autograd") - def circuit(inputs, weights): - # Amplitude encoding (normalize first) - norm = np.linalg.norm(inputs) - if norm > 0: - inputs = inputs / norm - - # Pad to 2^n_qubits - target_dim = 2**n_qubits - if len(inputs) < target_dim: - inputs = np.pad(inputs, (0, target_dim - len(inputs))) - elif len(inputs) > target_dim: - inputs = inputs[:target_dim] - - qml.AmplitudeEmbedding( - features=inputs, wires=range(n_qubits), pad_with=0.0, normalize=True - ) - - # Variational layers - for layer in range(n_layers): - for i in range(n_qubits): - qml.Rot(*weights[layer, i], wires=i) - for i in range(n_qubits - 1): - qml.CNOT(wires=[i, i + 1]) - - return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] - - return circuit - - -def train_quantum_model( - X_train, - y_train, - X_val, - y_val, - n_qubits=4, - n_layers=2, - duration_minutes=45, - learning_rate=0.01, -): - """Train quantum circuit for specified duration""" - print(f"\n🚀 Starting training for {duration_minutes} minutes...") - print(f" n_qubits={n_qubits}, n_layers={n_layers}, lr={learning_rate}") - - circuit = create_quantum_circuit(n_qubits, n_layers) - - # Initialize weights - np.random.seed(42) - weights = np.random.randn(n_layers, n_qubits, 3) * 0.1 - - # Classical post-processing weights - w_out = np.random.randn(n_qubits) * 0.1 - b_out = 0.0 - - # Track metrics - history = { - "train_loss": [], - "val_loss": [], - "val_acc": [], - "epochs_completed": 0, - "samples_trained": 0, - } - - start_time = time.time() - end_time = start_time + (duration_minutes * 60) - epoch = 0 - - batch_size = min(16, len(X_train) // 4) - - while time.time() < end_time: - epoch += 1 - epoch_start = time.time() - - # Shuffle training data - indices = np.random.permutation(len(X_train)) - X_shuffled = X_train[indices] - y_shuffled = y_train[indices] - - epoch_loss = 0.0 - n_batches = 0 - - # Mini-batch training - for i in range(0, len(X_train), batch_size): - if time.time() >= end_time: - break - - batch_X = X_shuffled[i : i + batch_size] - batch_y = y_shuffled[i : i + batch_size] - - batch_loss = 0.0 - grad_weights = np.zeros_like(weights) - grad_w_out = np.zeros_like(w_out) - grad_b_out = 0.0 - - for x, y_true in zip(batch_X, batch_y): - # Forward pass - quantum_out = circuit(x, weights) - y_pred = np.dot(quantum_out, w_out) + b_out - y_pred_sigmoid = 1 / (1 + np.exp(-y_pred)) - - # Binary cross-entropy loss - loss = -y_true * np.log(y_pred_sigmoid + 1e-8) - (1 - y_true) * np.log( - 1 - y_pred_sigmoid + 1e-8 - ) - batch_loss += loss - - # Gradient (simplified - no autograd backprop through circuit) - error = y_pred_sigmoid - y_true - grad_w_out += error * np.array(quantum_out) - grad_b_out += error - - # Update weights - w_out -= learning_rate * grad_w_out / len(batch_X) - b_out -= learning_rate * grad_b_out / len(batch_X) - - # Perturb quantum weights slightly (parameter-shift rule approximation) - weights -= learning_rate * 0.01 * np.random.randn(*weights.shape) - - epoch_loss += batch_loss - n_batches += 1 - history["samples_trained"] += len(batch_X) - - avg_loss = epoch_loss / max(n_batches, 1) / batch_size - history["train_loss"].append(float(avg_loss)) - - # Validation - val_preds = [] - val_loss = 0.0 - for x, y_true in zip(X_val, y_val): - quantum_out = circuit(x, weights) - y_pred = np.dot(quantum_out, w_out) + b_out - y_pred_sigmoid = 1 / (1 + np.exp(-y_pred)) - val_preds.append(1 if y_pred_sigmoid > 0.5 else 0) - val_loss += -y_true * np.log(y_pred_sigmoid + 1e-8) - (1 - y_true) * np.log( - 1 - y_pred_sigmoid + 1e-8 - ) - - val_acc = np.mean(np.array(val_preds) == y_val) - history["val_acc"].append(float(val_acc)) - history["val_loss"].append(float(val_loss / len(y_val))) - history["epochs_completed"] = epoch - - elapsed = time.time() - start_time - remaining = (end_time - time.time()) / 60 - - print( - f"Epoch {epoch} ({elapsed/60:.1f}min elapsed, {remaining:.1f}min left) - " - f"Loss: {avg_loss:.4f}, Val Acc: {val_acc:.4f}" - ) - - if time.time() >= end_time: - print(f"\n⏰ Time limit reached ({duration_minutes} minutes)") - break - - total_time = time.time() - start_time - print("\n✅ Training completed!") - print(f" Total time: {total_time/60:.2f} minutes") - print(f" Epochs: {epoch}") - print(f" Samples trained: {history['samples_trained']}") - print(f" Final val accuracy: {history['val_acc'][-1]:.4f}") - print(f" Best val accuracy: {max(history['val_acc']):.4f}") - - return weights, w_out, b_out, history - - -def main(): - parser = argparse.ArgumentParser( - description="Train quantum circuit for specified duration" - ) - parser.add_argument( - "--preset", - type=str, - default="heart", - choices=["heart", "ionosphere", "sonar", "banknote"], - ) - parser.add_argument("--n-qubits", type=int, default=4) - parser.add_argument("--n-layers", type=int, default=2) - parser.add_argument( - "--duration", type=int, default=45, help="Training duration in minutes" - ) - parser.add_argument("--learning-rate", type=float, default=0.01) - args = parser.parse_args() - - print("=" * 70) - print(" QUANTUM AI - TIMED TRAINING (PennyLane/NumPy)") - print("=" * 70) - - X, y = load_preset_dataset(args.preset) - print( - f"✅ Loaded dataset: {args.preset} ({X.shape[0]} samples, {X.shape[1]} features)" - ) - - X_train, X_val, y_train, y_val, scaler, pca = preprocess_data(X, y, args.n_qubits) - - weights, w_out, b_out, history = train_quantum_model( - X_train, - y_train, - X_val, - y_val, - n_qubits=args.n_qubits, - n_layers=args.n_layers, - duration_minutes=args.duration, - learning_rate=args.learning_rate, - ) - - # Save results - results_dir = Path(__file__).parent / "results" - results_dir.mkdir(exist_ok=True) - - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - summary = { - "timestamp": timestamp, - "dataset": args.preset, - "duration_minutes": args.duration, - "params": { - "n_qubits": args.n_qubits, - "n_layers": args.n_layers, - "learning_rate": args.learning_rate, - }, - "metrics": history, - "data": {"n_train": int(len(X_train)), "n_val": int(len(X_val))}, - } - - summary_path = results_dir / f"training_{args.preset}_{timestamp}.json" - with open(summary_path, "w") as f: - json.dump(summary, f, indent=2) - - print(f"\n📝 Results saved to: {summary_path}") - print("\n" + "=" * 70) - print(" 🎉 TRAINING SESSION COMPLETE!") - print("=" * 70) - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +""" +Simple PennyLane-only quantum training (no PyTorch dependency) +Trains for a specified duration using quantum circuits on CPU simulator +""" +import argparse +import json +import time +from datetime import datetime +from pathlib import Path + +import numpy as np +import pandas as pd +import pennylane as qml +from sklearn.decomposition import PCA +from sklearn.impute import SimpleImputer +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler + + +def load_preset_dataset(name: str): + """Load preset dataset from datasets/quantum/*.csv""" + base = Path(__file__).parent.parent / "datasets" / "quantum" + presets = { + "heart": base / "heart_disease.csv", + "ionosphere": base / "ionosphere.csv", + "sonar": base / "sonar.csv", + "banknote": base / "banknote.csv", + } + if name not in presets: + raise ValueError(f"Unknown preset '{name}'. Choose from {list(presets)}") + + path = presets[name] + if name == "heart": + df = pd.read_csv(path, na_values=["?"]) + y = df.iloc[:, -1] + X = df.iloc[:, :-1] + if X.isnull().any().any(): + imputer = SimpleImputer(strategy="median") + X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) + y = (y > 0).astype(int).values + return X.values, y + else: + df = pd.read_csv(path, na_values=["?"]) + y = df.iloc[:, -1].values + X = df.iloc[:, :-1] + if X.isnull().any().any(): + imputer = SimpleImputer(strategy="median") + X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) + if not np.issubdtype(y.dtype, np.integer): + vals, y = np.unique(y, return_inverse=True) + return X.values, y + + +def preprocess_data(X, y, n_qubits=4): + """Preprocess data for quantum ML""" + print(f"\n🔧 Preprocessing data (n_qubits={n_qubits})...") + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + n_features = X_train.shape[1] + pca = None + + if n_features < n_qubits: + padding_train = np.zeros((X_train.shape[0], n_qubits - n_features)) + X_train = np.hstack([X_train, padding_train]) + padding_val = np.zeros((X_val.shape[0], n_qubits - n_features)) + X_val = np.hstack([X_val, padding_val]) + print(f" ✅ Padded from {n_features} to {n_qubits} features") + elif n_features > n_qubits: + pca = PCA(n_components=n_qubits) + X_train = pca.fit_transform(X_train) + X_val = pca.transform(X_val) + print(f" ✅ Reduced from {n_features} to {n_qubits} features (PCA)") + + return X_train, X_val, y_train, y_val, scaler, pca + + +def create_quantum_circuit(n_qubits=4, n_layers=2): + """Create variational quantum circuit using PennyLane""" + dev = qml.device("default.qubit", wires=n_qubits) + + @qml.qnode(dev, interface="autograd") + def circuit(inputs, weights): + # Amplitude encoding (normalize first) + norm = np.linalg.norm(inputs) + if norm > 0: + inputs = inputs / norm + + # Pad to 2^n_qubits + target_dim = 2**n_qubits + if len(inputs) < target_dim: + inputs = np.pad(inputs, (0, target_dim - len(inputs))) + elif len(inputs) > target_dim: + inputs = inputs[:target_dim] + + qml.AmplitudeEmbedding( + features=inputs, wires=range(n_qubits), pad_with=0.0, normalize=True + ) + + # Variational layers + for layer in range(n_layers): + for i in range(n_qubits): + qml.Rot(*weights[layer, i], wires=i) + for i in range(n_qubits - 1): + qml.CNOT(wires=[i, i + 1]) + + return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] + + return circuit + + +def train_quantum_model( + X_train, + y_train, + X_val, + y_val, + n_qubits=4, + n_layers=2, + duration_minutes=45, + learning_rate=0.01, +): + """Train quantum circuit for specified duration""" + print(f"\n🚀 Starting training for {duration_minutes} minutes...") + print(f" n_qubits={n_qubits}, n_layers={n_layers}, lr={learning_rate}") + + circuit = create_quantum_circuit(n_qubits, n_layers) + + # Initialize weights + np.random.seed(42) + weights = np.random.randn(n_layers, n_qubits, 3) * 0.1 + + # Classical post-processing weights + w_out = np.random.randn(n_qubits) * 0.1 + b_out = 0.0 + + # Track metrics + history = { + "train_loss": [], + "val_loss": [], + "val_acc": [], + "epochs_completed": 0, + "samples_trained": 0, + } + + start_time = time.time() + end_time = start_time + (duration_minutes * 60) + epoch = 0 + + batch_size = min(16, len(X_train) // 4) + + while time.time() < end_time: + epoch += 1 + epoch_start = time.time() + + # Shuffle training data + indices = np.random.permutation(len(X_train)) + X_shuffled = X_train[indices] + y_shuffled = y_train[indices] + + epoch_loss = 0.0 + n_batches = 0 + + # Mini-batch training + for i in range(0, len(X_train), batch_size): + if time.time() >= end_time: + break + + batch_X = X_shuffled[i : i + batch_size] + batch_y = y_shuffled[i : i + batch_size] + + batch_loss = 0.0 + grad_weights = np.zeros_like(weights) + grad_w_out = np.zeros_like(w_out) + grad_b_out = 0.0 + + for x, y_true in zip(batch_X, batch_y): + # Forward pass + quantum_out = circuit(x, weights) + y_pred = np.dot(quantum_out, w_out) + b_out + y_pred_sigmoid = 1 / (1 + np.exp(-y_pred)) + + # Binary cross-entropy loss + loss = -y_true * np.log(y_pred_sigmoid + 1e-8) - (1 - y_true) * np.log( + 1 - y_pred_sigmoid + 1e-8 + ) + batch_loss += loss + + # Gradient (simplified - no autograd backprop through circuit) + error = y_pred_sigmoid - y_true + grad_w_out += error * np.array(quantum_out) + grad_b_out += error + + # Update weights + w_out -= learning_rate * grad_w_out / len(batch_X) + b_out -= learning_rate * grad_b_out / len(batch_X) + + # Perturb quantum weights slightly (parameter-shift rule approximation) + weights -= learning_rate * 0.01 * np.random.randn(*weights.shape) + + epoch_loss += batch_loss + n_batches += 1 + history["samples_trained"] += len(batch_X) + + avg_loss = epoch_loss / max(n_batches, 1) / batch_size + history["train_loss"].append(float(avg_loss)) + + # Validation + val_preds = [] + val_loss = 0.0 + for x, y_true in zip(X_val, y_val): + quantum_out = circuit(x, weights) + y_pred = np.dot(quantum_out, w_out) + b_out + y_pred_sigmoid = 1 / (1 + np.exp(-y_pred)) + val_preds.append(1 if y_pred_sigmoid > 0.5 else 0) + val_loss += -y_true * np.log(y_pred_sigmoid + 1e-8) - (1 - y_true) * np.log( + 1 - y_pred_sigmoid + 1e-8 + ) + + val_acc = np.mean(np.array(val_preds) == y_val) + history["val_acc"].append(float(val_acc)) + history["val_loss"].append(float(val_loss / len(y_val))) + history["epochs_completed"] = epoch + + elapsed = time.time() - start_time + remaining = (end_time - time.time()) / 60 + + print( + f"Epoch {epoch} ({elapsed/60:.1f}min elapsed, {remaining:.1f}min left) - " + f"Loss: {avg_loss:.4f}, Val Acc: {val_acc:.4f}" + ) + + if time.time() >= end_time: + print(f"\n⏰ Time limit reached ({duration_minutes} minutes)") + break + + total_time = time.time() - start_time + print("\n✅ Training completed!") + print(f" Total time: {total_time/60:.2f} minutes") + print(f" Epochs: {epoch}") + print(f" Samples trained: {history['samples_trained']}") + print(f" Final val accuracy: {history['val_acc'][-1]:.4f}") + print(f" Best val accuracy: {max(history['val_acc']):.4f}") + + return weights, w_out, b_out, history + + +def main(): + parser = argparse.ArgumentParser( + description="Train quantum circuit for specified duration" + ) + parser.add_argument( + "--preset", + type=str, + default="heart", + choices=["heart", "ionosphere", "sonar", "banknote"], + ) + parser.add_argument("--n-qubits", type=int, default=4) + parser.add_argument("--n-layers", type=int, default=2) + parser.add_argument( + "--duration", type=int, default=45, help="Training duration in minutes" + ) + parser.add_argument("--learning-rate", type=float, default=0.01) + args = parser.parse_args() + + print("=" * 70) + print(" QUANTUM AI - TIMED TRAINING (PennyLane/NumPy)") + print("=" * 70) + + X, y = load_preset_dataset(args.preset) + print( + f"✅ Loaded dataset: {args.preset} ({X.shape[0]} samples, {X.shape[1]} features)" + ) + + X_train, X_val, y_train, y_val, scaler, pca = preprocess_data(X, y, args.n_qubits) + + weights, w_out, b_out, history = train_quantum_model( + X_train, + y_train, + X_val, + y_val, + n_qubits=args.n_qubits, + n_layers=args.n_layers, + duration_minutes=args.duration, + learning_rate=args.learning_rate, + ) + + # Save results + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + summary = { + "timestamp": timestamp, + "dataset": args.preset, + "duration_minutes": args.duration, + "params": { + "n_qubits": args.n_qubits, + "n_layers": args.n_layers, + "learning_rate": args.learning_rate, + }, + "metrics": history, + "data": {"n_train": int(len(X_train)), "n_val": int(len(X_val))}, + } + + summary_path = results_dir / f"training_{args.preset}_{timestamp}.json" + with open(summary_path, "w") as f: + json.dump(summary, f, indent=2) + + print(f"\n📝 Results saved to: {summary_path}") + print("\n" + "=" * 70) + print(" 🎉 TRAINING SESSION COMPLETE!") + print("=" * 70) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/validate_quantum_llm.py b/ai-projects/quantum-ml/validate_quantum_llm.py index de6af1e70..ca6653c54 100755 --- a/ai-projects/quantum-ml/validate_quantum_llm.py +++ b/ai-projects/quantum-ml/validate_quantum_llm.py @@ -1,492 +1,492 @@ -#!/usr/bin/env python3 -""" -Quantum LLM Validation Suite -============================= - -Comprehensive validation of all quantum LLM components. - -This script tests all components and generates a validation report. - -Usage: - python validate_quantum_llm.py --quick - python validate_quantum_llm.py --full - python validate_quantum_llm.py --report-only - -Author: Quantum AI Workspace -Date: March 9, 2026 -""" - -import importlib -import json -import logging -import sys -import time -from pathlib import Path -from typing import Any, Dict - -# Add path for imports -sys.path.insert(0, str(Path(__file__).parent / "src")) - -logger = logging.getLogger(__name__) - - -class ComponentValidator: - """Validates individual quantum LLM components.""" - - def __init__(self): - self.results = [] - self.start_time = time.time() - - def validate_component( - self, name: str, import_path: str, test_func=None - ) -> Dict[str, Any]: - """Validate a single component.""" - result = { - "component": name, - "import_path": import_path, - "status": "unknown", - "error": None, - "test_passed": False, - "import_time": 0.0, - } - - try: - # Test import — use importlib for safer dynamic importing. - start = time.time() - module = importlib.import_module(import_path) - result["import_time"] = time.time() - start - result["status"] = "imported" - - # Run test if provided - if test_func: - test_func(module) - result["test_passed"] = True - else: - # No test means import success is enough - result["test_passed"] = True - - result["status"] = "✅ PASS" - logger.info(f"✅ {name}: PASS") - - except ImportError as e: - result["status"] = "❌ IMPORT FAILED" - result["error"] = str(e) - logger.error(f"❌ {name}: Import failed - {e}") - - except Exception as e: - result["status"] = "❌ TEST FAILED" - result["error"] = str(e) - logger.error(f"❌ {name}: Test failed - {e}") - - self.results.append(result) - return result - - def validate_all(self) -> Dict[str, Any]: - """Validate all quantum LLM components.""" - logger.info("=" * 80) - logger.info("QUANTUM LLM VALIDATION SUITE") - logger.info("=" * 80) - - components = [ - { - "name": "Advanced Quantum Components", - "import_path": "src.quantum_llm_advanced", - "description": "Circuit caching, adaptive layers, multi-scale attention", - }, - { - "name": "Circuit Optimizer", - "import_path": "src.quantum_circuit_optimizer", - "description": "Circuit compilation, batch execution, scheduling", - }, - { - "name": "Hybrid Trainer", - "import_path": "src.quantum_llm_hybrid_trainer", - "description": "Curriculum learning, adaptive routing, orchestration", - }, - { - "name": "Training Monitor", - "import_path": "src.quantum_llm_monitor", - "description": "Real-time monitoring, metrics, dashboard", - }, - { - "name": "Integrated System", - "import_path": "src.quantum_llm_integrated", - "description": "Complete integrated quantum LLM system", - }, - { - "name": "Dataset Utilities", - "import_path": "src.quantum_llm_datasets", - "description": "Tokenization, data loading, augmentation", - }, - ] - - logger.info("\n📦 Validating Components:\n") - - for component in components: - logger.info(f"Testing: {component['name']}") - logger.info(f" Description: {component['description']}") - self.validate_component(component["name"], component["import_path"]) - logger.info("") - - # Generate summary - total = len(self.results) - passed = sum(1 for r in self.results if r["status"] == "✅ PASS") - failed = total - passed - - summary = { - "total_components": total, - "passed": passed, - "failed": failed, - "success_rate": passed / total if total > 0 else 0.0, - "total_time": time.time() - self.start_time, - "results": self.results, - } - - logger.info("=" * 80) - logger.info("VALIDATION SUMMARY") - logger.info("=" * 80) - logger.info(f"Total Components: {total}") - logger.info(f"Passed: {passed}") - logger.info(f"Failed: {failed}") - logger.info(f"Success Rate: {summary['success_rate']:.1%}") - logger.info(f"Total Time: {summary['total_time']:.2f}s") - logger.info("=" * 80) - - if failed == 0: - logger.info("🎉 ALL COMPONENTS VALIDATED SUCCESSFULLY!") - else: - logger.warning(f"⚠️ {failed} component(s) failed validation") - - return summary - - -def run_quick_validation(): - """Quick validation - import tests only.""" - logger.info("Running QUICK validation (import tests)...") - validator = ComponentValidator() - summary = validator.validate_all() - return summary - - -def run_full_validation(): - """Full validation - imports + functional tests.""" - logger.info("Running FULL validation (imports + tests)...") - - validator = ComponentValidator() - - # Import tests - import_summary = validator.validate_all() - - # Functional tests - logger.info("\n" + "=" * 80) - logger.info("FUNCTIONAL TESTS") - logger.info("=" * 80) - - functional_results = [] - - # Test 1: Tokenizer - logger.info("\n🧪 Test 1: Character Tokenizer") - try: - from src.quantum_llm_datasets import CharacterTokenizer - - tokenizer = CharacterTokenizer(vocab_size=256) - text = "Hello Quantum" - encoded = tokenizer.encode(text) - decoded = tokenizer.decode(encoded) - assert decoded.strip() == text, f"Decode mismatch: {decoded} != {text}" - logger.info("✅ Tokenizer test PASSED") - functional_results.append({"test": "Tokenizer", "status": "✅ PASS"}) - except Exception as e: - logger.error(f"❌ Tokenizer test FAILED: {e}") - functional_results.append( - {"test": "Tokenizer", "status": "❌ FAIL", "error": str(e)} - ) - - # Test 2: Dataset - logger.info("\n🧪 Test 2: Text Dataset") - try: - from src.quantum_llm_datasets import CharacterTokenizer, TextDataset - - tokenizer = CharacterTokenizer(vocab_size=256) - dataset = TextDataset( - texts=["Test text 1", "Test text 2"], - tokenizer=tokenizer, - max_seq_length=64, - ) - assert len(dataset) > 0, "Dataset is empty" - input_ids, target_ids = dataset[0] - assert input_ids.shape == target_ids.shape, "Shape mismatch" - logger.info("✅ Dataset test PASSED") - functional_results.append({"test": "Dataset", "status": "✅ PASS"}) - except Exception as e: - logger.error(f"❌ Dataset test FAILED: {e}") - functional_results.append( - {"test": "Dataset", "status": "❌ FAIL", "error": str(e)} - ) - - # Test 3: Circuit Cache - logger.info("\n🧪 Test 3: Circuit Cache") - try: - from src.quantum_llm_advanced import QuantumCircuitCache - - cache = QuantumCircuitCache(cache_size=10) - - # Test caching - import torch - - key = "test_key" - value = torch.randn(4, 64) - - cache.put(key, value) - retrieved = cache.get(key) - assert retrieved is not None, "Cache retrieval failed" - assert torch.allclose(retrieved, value), "Cache value mismatch" - - stats = cache.get_stats() - assert stats["hits"] == 1, f"Expected 1 hit, got {stats['hits']}" - - logger.info("✅ Circuit cache test PASSED") - functional_results.append({"test": "CircuitCache", "status": "✅ PASS"}) - except Exception as e: - logger.error(f"❌ Circuit cache test FAILED: {e}") - functional_results.append( - {"test": "CircuitCache", "status": "❌ FAIL", "error": str(e)} - ) - - # Test 4: Configuration - logger.info("\n🧪 Test 4: Configuration System") - try: - from src.quantum_llm_integrated import QuantumLLMConfig - - config = QuantumLLMConfig() - - # Test default config - assert config["vocab_size"] > 0, "Invalid vocab_size" - assert config["d_model"] > 0, "Invalid d_model" - assert config["n_qubits"] > 0, "Invalid n_qubits" - - # Test config update - config.config["batch_size"] = 32 - assert config["batch_size"] == 32, "Config update failed" - - logger.info("✅ Configuration test PASSED") - functional_results.append({"test": "Configuration", "status": "✅ PASS"}) - except Exception as e: - logger.error(f"❌ Configuration test FAILED: {e}") - functional_results.append( - {"test": "Configuration", "status": "❌ FAIL", "error": str(e)} - ) - - # Test 5: Dashboard - logger.info("\n🧪 Test 5: Training Dashboard") - try: - from src.quantum_llm_monitor import (QuantumMetrics, TrainingDashboard, - TrainingSnapshot) - - dashboard = TrainingDashboard( - output_dir=Path("data_out/test_dashboard"), - update_interval=100, - ) - - # Test snapshot - import torch - - snapshot = TrainingSnapshot( - timestamp=time.time(), - global_step=1, - epoch=0, - loss=2.5, - perplexity=12.0, - learning_rate=1e-4, - batch_size=16, - quantum_metrics=QuantumMetrics( - circuit_execution_time=0.1, - circuit_depth=10, - gate_count=50, - qubit_count=4, - ), - ) - - dashboard.update(snapshot) - - logger.info("✅ Dashboard test PASSED") - functional_results.append({"test": "Dashboard", "status": "✅ PASS"}) - except Exception as e: - logger.error(f"❌ Dashboard test FAILED: {e}") - functional_results.append( - {"test": "Dashboard", "status": "❌ FAIL", "error": str(e)} - ) - - # Summarize functional tests - logger.info("\n" + "=" * 80) - logger.info("FUNCTIONAL TEST SUMMARY") - logger.info("=" * 80) - - total_functional = len(functional_results) - passed_functional = sum(1 for r in functional_results if r["status"] == "✅ PASS") - - for result in functional_results: - status_str = result["status"] - logger.info(f"{status_str} {result['test']}") - - logger.info(f"\nFunctional Tests: {passed_functional}/{total_functional} passed") - logger.info("=" * 80) - - # Combined summary - combined_summary = { - "import_validation": import_summary, - "functional_tests": { - "total": total_functional, - "passed": passed_functional, - "failed": total_functional - passed_functional, - "results": functional_results, - }, - "overall_status": ( - "✅ PASS" - if (import_summary["failed"] == 0 and passed_functional == total_functional) - else "❌ SOME FAILURES" - ), - } - - return combined_summary - - -def generate_validation_report(summary: Dict[str, Any], output_path: Path): - """Generate comprehensive validation report.""" - - report_lines = [ - "# Quantum LLM Validation Report", - f"\n**Generated:** {time.strftime('%Y-%m-%d %H:%M:%S')}", - f"\n**Status:** {summary.get('overall_status', summary.get('status', 'Unknown'))}", - "\n---\n", - ] - - # Import validation - if "import_validation" in summary: - import_val = summary["import_validation"] - report_lines.append("\n## Import Validation\n") - report_lines.append(f"- Total Components: {import_val['total_components']}") - report_lines.append(f"- Passed: {import_val['passed']}") - report_lines.append(f"- Failed: {import_val['failed']}") - report_lines.append(f"- Success Rate: {import_val['success_rate']:.1%}") - report_lines.append(f"- Total Time: {import_val['total_time']:.2f}s\n") - - report_lines.append("\n### Component Details\n") - for result in import_val["results"]: - status_emoji = "✅" if result["status"] == "✅ PASS" else "❌" - report_lines.append(f"- {status_emoji} **{result['component']}**") - if result.get("error"): - report_lines.append(f" - Error: `{result['error']}`") - - # Functional tests - if "functional_tests" in summary: - func_tests = summary["functional_tests"] - report_lines.append("\n## Functional Tests\n") - report_lines.append(f"- Total Tests: {func_tests['total']}") - report_lines.append(f"- Passed: {func_tests['passed']}") - report_lines.append(f"- Failed: {func_tests['failed']}\n") - - report_lines.append("\n### Test Details\n") - for result in func_tests["results"]: - status_emoji = "✅" if "✅" in result["status"] else "❌" - report_lines.append(f"- {status_emoji} **{result['test']}**") - if result.get("error"): - report_lines.append(f" - Error: `{result['error']}`") - - # Recommendations - report_lines.append("\n---\n") - report_lines.append("\n## Recommendations\n") - - if summary.get("overall_status") == "✅ PASS": - report_lines.append( - "✅ **All validations passed!** The quantum LLM system is ready to use.\n" - ) - report_lines.append("**Next Steps:**") - report_lines.append( - "- Run quick start: `python quantum_llm_quickstart.py --mode quick`" - ) - report_lines.append("- Review documentation: `QUANTUM_LLM_README.md`") - report_lines.append( - "- Configure for your use case: `config/quantum_llm_config_example.yaml`" - ) - else: - report_lines.append("⚠️ **Some validations failed.** Review errors above.\n") - report_lines.append("**Troubleshooting:**") - report_lines.append("- Check Python version (3.8+ required)") - report_lines.append( - "- Install dependencies: `pip install torch pennylane pyyaml numpy`" - ) - report_lines.append("- Verify file paths and imports") - - report_lines.append("\n---\n") - report_lines.append("\n*Generated by Quantum LLM Validation Suite*") - - # Write report - report_text = "\n".join(report_lines) - with open(output_path, "w") as f: - f.write(report_text) - - logger.info(f"\n📄 Validation report saved: {output_path}") - return output_path - - -def main(): - """Main entry point.""" - import argparse - - parser = argparse.ArgumentParser(description="Quantum LLM Validation Suite") - parser.add_argument( - "--quick", action="store_true", help="Quick validation (imports only)" - ) - parser.add_argument( - "--full", action="store_true", help="Full validation (imports + tests)" - ) - parser.add_argument( - "--report-only", action="store_true", help="Generate report from last run" - ) - parser.add_argument( - "--output", - type=str, - default="data_out/quantum_llm_validation_report.md", - help="Report output path", - ) - - args = parser.parse_args() - - # Setup logging - logging.basicConfig(level=logging.INFO, format="%(message)s") - - output_path = Path(args.output) - output_path.parent.mkdir(parents=True, exist_ok=True) - - # Run validation - if args.report_only: - # Try to load existing results - logger.info("Report-only mode not yet implemented") - return - elif args.full: - summary = run_full_validation() - else: - # Default or --quick - summary = run_quick_validation() - - # Generate report - report_path = generate_validation_report(summary, output_path) - - # Also save JSON - json_path = output_path.with_suffix(".json") - with open(json_path, "w") as f: - json.dump(summary, f, indent=2) - logger.info(f"📄 JSON report saved: {json_path}") - - # Final status - logger.info("\n" + "=" * 80) - if summary.get("overall_status") == "✅ PASS": - logger.info("🎉 VALIDATION SUCCESSFUL - QUANTUM LLM SYSTEM READY!") - else: - logger.info("⚠️ VALIDATION COMPLETED WITH SOME FAILURES - REVIEW REPORT") - logger.info("=" * 80) - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +""" +Quantum LLM Validation Suite +============================= + +Comprehensive validation of all quantum LLM components. + +This script tests all components and generates a validation report. + +Usage: + python validate_quantum_llm.py --quick + python validate_quantum_llm.py --full + python validate_quantum_llm.py --report-only + +Author: Quantum AI Workspace +Date: March 9, 2026 +""" + +import importlib +import json +import logging +import sys +import time +from pathlib import Path +from typing import Any, Dict + +# Add path for imports +sys.path.insert(0, str(Path(__file__).parent / "src")) + +logger = logging.getLogger(__name__) + + +class ComponentValidator: + """Validates individual quantum LLM components.""" + + def __init__(self): + self.results = [] + self.start_time = time.time() + + def validate_component( + self, name: str, import_path: str, test_func=None + ) -> Dict[str, Any]: + """Validate a single component.""" + result = { + "component": name, + "import_path": import_path, + "status": "unknown", + "error": None, + "test_passed": False, + "import_time": 0.0, + } + + try: + # Test import — use importlib for safer dynamic importing. + start = time.time() + module = importlib.import_module(import_path) + result["import_time"] = time.time() - start + result["status"] = "imported" + + # Run test if provided + if test_func: + test_func(module) + result["test_passed"] = True + else: + # No test means import success is enough + result["test_passed"] = True + + result["status"] = "✅ PASS" + logger.info(f"✅ {name}: PASS") + + except ImportError as e: + result["status"] = "❌ IMPORT FAILED" + result["error"] = str(e) + logger.error(f"❌ {name}: Import failed - {e}") + + except Exception as e: + result["status"] = "❌ TEST FAILED" + result["error"] = str(e) + logger.error(f"❌ {name}: Test failed - {e}") + + self.results.append(result) + return result + + def validate_all(self) -> Dict[str, Any]: + """Validate all quantum LLM components.""" + logger.info("=" * 80) + logger.info("QUANTUM LLM VALIDATION SUITE") + logger.info("=" * 80) + + components = [ + { + "name": "Advanced Quantum Components", + "import_path": "src.quantum_llm_advanced", + "description": "Circuit caching, adaptive layers, multi-scale attention", + }, + { + "name": "Circuit Optimizer", + "import_path": "src.quantum_circuit_optimizer", + "description": "Circuit compilation, batch execution, scheduling", + }, + { + "name": "Hybrid Trainer", + "import_path": "src.quantum_llm_hybrid_trainer", + "description": "Curriculum learning, adaptive routing, orchestration", + }, + { + "name": "Training Monitor", + "import_path": "src.quantum_llm_monitor", + "description": "Real-time monitoring, metrics, dashboard", + }, + { + "name": "Integrated System", + "import_path": "src.quantum_llm_integrated", + "description": "Complete integrated quantum LLM system", + }, + { + "name": "Dataset Utilities", + "import_path": "src.quantum_llm_datasets", + "description": "Tokenization, data loading, augmentation", + }, + ] + + logger.info("\n📦 Validating Components:\n") + + for component in components: + logger.info(f"Testing: {component['name']}") + logger.info(f" Description: {component['description']}") + self.validate_component(component["name"], component["import_path"]) + logger.info("") + + # Generate summary + total = len(self.results) + passed = sum(1 for r in self.results if r["status"] == "✅ PASS") + failed = total - passed + + summary = { + "total_components": total, + "passed": passed, + "failed": failed, + "success_rate": passed / total if total > 0 else 0.0, + "total_time": time.time() - self.start_time, + "results": self.results, + } + + logger.info("=" * 80) + logger.info("VALIDATION SUMMARY") + logger.info("=" * 80) + logger.info(f"Total Components: {total}") + logger.info(f"Passed: {passed}") + logger.info(f"Failed: {failed}") + logger.info(f"Success Rate: {summary['success_rate']:.1%}") + logger.info(f"Total Time: {summary['total_time']:.2f}s") + logger.info("=" * 80) + + if failed == 0: + logger.info("🎉 ALL COMPONENTS VALIDATED SUCCESSFULLY!") + else: + logger.warning(f"⚠️ {failed} component(s) failed validation") + + return summary + + +def run_quick_validation(): + """Quick validation - import tests only.""" + logger.info("Running QUICK validation (import tests)...") + validator = ComponentValidator() + summary = validator.validate_all() + return summary + + +def run_full_validation(): + """Full validation - imports + functional tests.""" + logger.info("Running FULL validation (imports + tests)...") + + validator = ComponentValidator() + + # Import tests + import_summary = validator.validate_all() + + # Functional tests + logger.info("\n" + "=" * 80) + logger.info("FUNCTIONAL TESTS") + logger.info("=" * 80) + + functional_results = [] + + # Test 1: Tokenizer + logger.info("\n🧪 Test 1: Character Tokenizer") + try: + from src.quantum_llm_datasets import CharacterTokenizer + + tokenizer = CharacterTokenizer(vocab_size=256) + text = "Hello Quantum" + encoded = tokenizer.encode(text) + decoded = tokenizer.decode(encoded) + assert decoded.strip() == text, f"Decode mismatch: {decoded} != {text}" + logger.info("✅ Tokenizer test PASSED") + functional_results.append({"test": "Tokenizer", "status": "✅ PASS"}) + except Exception as e: + logger.error(f"❌ Tokenizer test FAILED: {e}") + functional_results.append( + {"test": "Tokenizer", "status": "❌ FAIL", "error": str(e)} + ) + + # Test 2: Dataset + logger.info("\n🧪 Test 2: Text Dataset") + try: + from src.quantum_llm_datasets import CharacterTokenizer, TextDataset + + tokenizer = CharacterTokenizer(vocab_size=256) + dataset = TextDataset( + texts=["Test text 1", "Test text 2"], + tokenizer=tokenizer, + max_seq_length=64, + ) + assert len(dataset) > 0, "Dataset is empty" + input_ids, target_ids = dataset[0] + assert input_ids.shape == target_ids.shape, "Shape mismatch" + logger.info("✅ Dataset test PASSED") + functional_results.append({"test": "Dataset", "status": "✅ PASS"}) + except Exception as e: + logger.error(f"❌ Dataset test FAILED: {e}") + functional_results.append( + {"test": "Dataset", "status": "❌ FAIL", "error": str(e)} + ) + + # Test 3: Circuit Cache + logger.info("\n🧪 Test 3: Circuit Cache") + try: + from src.quantum_llm_advanced import QuantumCircuitCache + + cache = QuantumCircuitCache(cache_size=10) + + # Test caching + import torch + + key = "test_key" + value = torch.randn(4, 64) + + cache.put(key, value) + retrieved = cache.get(key) + assert retrieved is not None, "Cache retrieval failed" + assert torch.allclose(retrieved, value), "Cache value mismatch" + + stats = cache.get_stats() + assert stats["hits"] == 1, f"Expected 1 hit, got {stats['hits']}" + + logger.info("✅ Circuit cache test PASSED") + functional_results.append({"test": "CircuitCache", "status": "✅ PASS"}) + except Exception as e: + logger.error(f"❌ Circuit cache test FAILED: {e}") + functional_results.append( + {"test": "CircuitCache", "status": "❌ FAIL", "error": str(e)} + ) + + # Test 4: Configuration + logger.info("\n🧪 Test 4: Configuration System") + try: + from src.quantum_llm_integrated import QuantumLLMConfig + + config = QuantumLLMConfig() + + # Test default config + assert config["vocab_size"] > 0, "Invalid vocab_size" + assert config["d_model"] > 0, "Invalid d_model" + assert config["n_qubits"] > 0, "Invalid n_qubits" + + # Test config update + config.config["batch_size"] = 32 + assert config["batch_size"] == 32, "Config update failed" + + logger.info("✅ Configuration test PASSED") + functional_results.append({"test": "Configuration", "status": "✅ PASS"}) + except Exception as e: + logger.error(f"❌ Configuration test FAILED: {e}") + functional_results.append( + {"test": "Configuration", "status": "❌ FAIL", "error": str(e)} + ) + + # Test 5: Dashboard + logger.info("\n🧪 Test 5: Training Dashboard") + try: + from src.quantum_llm_monitor import (QuantumMetrics, TrainingDashboard, + TrainingSnapshot) + + dashboard = TrainingDashboard( + output_dir=Path("data_out/test_dashboard"), + update_interval=100, + ) + + # Test snapshot + import torch + + snapshot = TrainingSnapshot( + timestamp=time.time(), + global_step=1, + epoch=0, + loss=2.5, + perplexity=12.0, + learning_rate=1e-4, + batch_size=16, + quantum_metrics=QuantumMetrics( + circuit_execution_time=0.1, + circuit_depth=10, + gate_count=50, + qubit_count=4, + ), + ) + + dashboard.update(snapshot) + + logger.info("✅ Dashboard test PASSED") + functional_results.append({"test": "Dashboard", "status": "✅ PASS"}) + except Exception as e: + logger.error(f"❌ Dashboard test FAILED: {e}") + functional_results.append( + {"test": "Dashboard", "status": "❌ FAIL", "error": str(e)} + ) + + # Summarize functional tests + logger.info("\n" + "=" * 80) + logger.info("FUNCTIONAL TEST SUMMARY") + logger.info("=" * 80) + + total_functional = len(functional_results) + passed_functional = sum(1 for r in functional_results if r["status"] == "✅ PASS") + + for result in functional_results: + status_str = result["status"] + logger.info(f"{status_str} {result['test']}") + + logger.info(f"\nFunctional Tests: {passed_functional}/{total_functional} passed") + logger.info("=" * 80) + + # Combined summary + combined_summary = { + "import_validation": import_summary, + "functional_tests": { + "total": total_functional, + "passed": passed_functional, + "failed": total_functional - passed_functional, + "results": functional_results, + }, + "overall_status": ( + "✅ PASS" + if (import_summary["failed"] == 0 and passed_functional == total_functional) + else "❌ SOME FAILURES" + ), + } + + return combined_summary + + +def generate_validation_report(summary: Dict[str, Any], output_path: Path): + """Generate comprehensive validation report.""" + + report_lines = [ + "# Quantum LLM Validation Report", + f"\n**Generated:** {time.strftime('%Y-%m-%d %H:%M:%S')}", + f"\n**Status:** {summary.get('overall_status', summary.get('status', 'Unknown'))}", + "\n---\n", + ] + + # Import validation + if "import_validation" in summary: + import_val = summary["import_validation"] + report_lines.append("\n## Import Validation\n") + report_lines.append(f"- Total Components: {import_val['total_components']}") + report_lines.append(f"- Passed: {import_val['passed']}") + report_lines.append(f"- Failed: {import_val['failed']}") + report_lines.append(f"- Success Rate: {import_val['success_rate']:.1%}") + report_lines.append(f"- Total Time: {import_val['total_time']:.2f}s\n") + + report_lines.append("\n### Component Details\n") + for result in import_val["results"]: + status_emoji = "✅" if result["status"] == "✅ PASS" else "❌" + report_lines.append(f"- {status_emoji} **{result['component']}**") + if result.get("error"): + report_lines.append(f" - Error: `{result['error']}`") + + # Functional tests + if "functional_tests" in summary: + func_tests = summary["functional_tests"] + report_lines.append("\n## Functional Tests\n") + report_lines.append(f"- Total Tests: {func_tests['total']}") + report_lines.append(f"- Passed: {func_tests['passed']}") + report_lines.append(f"- Failed: {func_tests['failed']}\n") + + report_lines.append("\n### Test Details\n") + for result in func_tests["results"]: + status_emoji = "✅" if "✅" in result["status"] else "❌" + report_lines.append(f"- {status_emoji} **{result['test']}**") + if result.get("error"): + report_lines.append(f" - Error: `{result['error']}`") + + # Recommendations + report_lines.append("\n---\n") + report_lines.append("\n## Recommendations\n") + + if summary.get("overall_status") == "✅ PASS": + report_lines.append( + "✅ **All validations passed!** The quantum LLM system is ready to use.\n" + ) + report_lines.append("**Next Steps:**") + report_lines.append( + "- Run quick start: `python quantum_llm_quickstart.py --mode quick`" + ) + report_lines.append("- Review documentation: `QUANTUM_LLM_README.md`") + report_lines.append( + "- Configure for your use case: `config/quantum_llm_config_example.yaml`" + ) + else: + report_lines.append("⚠️ **Some validations failed.** Review errors above.\n") + report_lines.append("**Troubleshooting:**") + report_lines.append("- Check Python version (3.8+ required)") + report_lines.append( + "- Install dependencies: `pip install torch pennylane pyyaml numpy`" + ) + report_lines.append("- Verify file paths and imports") + + report_lines.append("\n---\n") + report_lines.append("\n*Generated by Quantum LLM Validation Suite*") + + # Write report + report_text = "\n".join(report_lines) + with open(output_path, "w") as f: + f.write(report_text) + + logger.info(f"\n📄 Validation report saved: {output_path}") + return output_path + + +def main(): + """Main entry point.""" + import argparse + + parser = argparse.ArgumentParser(description="Quantum LLM Validation Suite") + parser.add_argument( + "--quick", action="store_true", help="Quick validation (imports only)" + ) + parser.add_argument( + "--full", action="store_true", help="Full validation (imports + tests)" + ) + parser.add_argument( + "--report-only", action="store_true", help="Generate report from last run" + ) + parser.add_argument( + "--output", + type=str, + default="data_out/quantum_llm_validation_report.md", + help="Report output path", + ) + + args = parser.parse_args() + + # Setup logging + logging.basicConfig(level=logging.INFO, format="%(message)s") + + output_path = Path(args.output) + output_path.parent.mkdir(parents=True, exist_ok=True) + + # Run validation + if args.report_only: + # Try to load existing results + logger.info("Report-only mode not yet implemented") + return + elif args.full: + summary = run_full_validation() + else: + # Default or --quick + summary = run_quick_validation() + + # Generate report + report_path = generate_validation_report(summary, output_path) + + # Also save JSON + json_path = output_path.with_suffix(".json") + with open(json_path, "w") as f: + json.dump(summary, f, indent=2) + logger.info(f"📄 JSON report saved: {json_path}") + + # Final status + logger.info("\n" + "=" * 80) + if summary.get("overall_status") == "✅ PASS": + logger.info("🎉 VALIDATION SUCCESSFUL - QUANTUM LLM SYSTEM READY!") + else: + logger.info("⚠️ VALIDATION COMPLETED WITH SOME FAILURES - REVIEW REPORT") + logger.info("=" * 80) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/quantum-ml/venv_azure/pyvenv.cfg b/ai-projects/quantum-ml/venv_azure/pyvenv.cfg index 0765916a7..fe06515d1 100644 --- a/ai-projects/quantum-ml/venv_azure/pyvenv.cfg +++ b/ai-projects/quantum-ml/venv_azure/pyvenv.cfg @@ -1,5 +1,5 @@ -home = C:\Users\Bryan\AppData\Local\Programs\Python\Python311 -include-system-site-packages = false -version = 3.11.9 -executable = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe -command = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe -m venv C:\Users\Bryan\OneDrive\AI\quantum-ai\venv_azure +home = C:\Users\Bryan\AppData\Local\Programs\Python\Python311 +include-system-site-packages = false +version = 3.11.9 +executable = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe +command = C:\Users\Bryan\AppData\Local\Programs\Python\Python311\python.exe -m venv C:\Users\Bryan\OneDrive\AI\quantum-ai\venv_azure diff --git a/ai-projects/quantum-ml/web-requirements.txt b/ai-projects/quantum-ml/web-requirements.txt index bd522a923..9249820fa 100644 --- a/ai-projects/quantum-ml/web-requirements.txt +++ b/ai-projects/quantum-ml/web-requirements.txt @@ -1,6 +1,6 @@ -Flask==3.1.3 -Flask-CORS==6.0.2 -pennylane==0.43.1 -numpy>=2.0.0 -pandas>=2.0.0 -scikit-learn>=1.3.0 +Flask==3.1.3 +Flask-CORS==6.0.2 +pennylane==0.43.1 +numpy>=2.0.0 +pandas>=2.0.0 +scikit-learn>=1.3.0 diff --git a/ai-projects/quantum-ml/web_app.py b/ai-projects/quantum-ml/web_app.py index 2f9337654..38620b244 100644 --- a/ai-projects/quantum-ml/web_app.py +++ b/ai-projects/quantum-ml/web_app.py @@ -1,1199 +1,1199 @@ -#!/usr/bin/env python3 -""" -Quantum AI Training Web Application -Interactive dashboard for training and visualizing quantum machine learning models -""" -from flask import Flask, jsonify, request, send_from_directory - -try: - from flask_cors import CORS -except Exception: # pragma: no cover - # Fallback stub when flask_cors is not installed; CORS(app) becomes a no-op - class CORS: # type: ignore - def __init__(self, *args, **kwargs): - pass - - -try: - import pennylane as qml -except Exception: # pragma: no cover - qml = None # Allow app import without PennyLane for security tests -try: - import numpy as np -except Exception: # pragma: no cover - np = None -try: - import pandas as pd -except Exception: # pragma: no cover - pd = None -import json -import re -import threading -import time -from datetime import datetime -from pathlib import Path - -try: - from sklearn.decomposition import PCA - from sklearn.impute import SimpleImputer - from sklearn.metrics import (confusion_matrix, - precision_recall_fscore_support, - roc_auc_score) - from sklearn.model_selection import train_test_split - from sklearn.preprocessing import StandardScaler -except Exception: # pragma: no cover - train_test_split = StandardScaler = PCA = SimpleImputer = None - confusion_matrix = precision_recall_fscore_support = roc_auc_score = None -import logging -import sys - -# Add src directory to path for imports -src_path = Path(__file__).parent / "src" -sys.path.insert(0, str(src_path)) - -try: - from src.dataset_loader import load_dataset as load_dataset_shared - from src.dataset_loader import preprocess_for_qubits as preprocess_shared -except Exception: # pragma: no cover - # Fallback if module not available - load_dataset_shared = None - preprocess_shared = None - -# Configure logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -app = Flask(__name__) -CORS(app) - -# Global training state -training_sessions = {} -training_lock = threading.Lock() - -# Resource limits to prevent DoS -MAX_ACTIVE_SESSIONS = 10 # Max concurrent running sessions -MAX_TOTAL_SESSIONS = 100 # Max sessions kept in memory at once -SESSION_TTL_SECONDS = 3600 # Prune completed/errored sessions after 1 hour - - -def _prune_old_sessions() -> None: - """Remove completed/errored sessions older than SESSION_TTL_SECONDS. - - Must be called while holding training_lock. - """ - cutoff = time.time() - SESSION_TTL_SECONDS - stale = [ - sid - for sid, s in training_sessions.items() - if s.status in ("completed", "error", "stopped") - and s.end_time is not None - and s.end_time < cutoff - ] - for sid in stale: - del training_sessions[sid] - - -class TrainingSession: - """Manages a single training session with real-time metrics""" - - def __init__(self, session_id, config): - self.session_id = session_id - self.config = config - self.status = "initializing" - self.start_time = None - self.end_time = None - self.current_epoch = 0 - self.total_epochs = 0 - self.metrics_history = { - "epochs": [], - "train_loss": [], - "val_loss": [], - "val_accuracy": [], - "timestamps": [], - } - self.best_val_acc = 0.0 - self.current_loss = 0.0 - self.thread = None - self.should_stop = False - self.epochs_per_second = 0.0 - self.eta_seconds = None - self.last_epoch_time = None - self.error_message = None - self.samples_trained = 0 - self.current_val_acc = 0.0 - self.learning_rate = config.get("learning_rate", 0.01) - self.checkpoint_path = None - self.gradient_norm = 0.0 - self.optimizer_type = config.get("optimizer", "adam") - self.epochs_without_improvement = 0 - - def to_dict(self): - """Serialize session state for API""" - elapsed = None - if self.start_time: - end = self.end_time or time.time() - elapsed = end - self.start_time - - return { - "session_id": self.session_id, - "config": self.config, - "status": self.status, - "current_epoch": self.current_epoch, - "total_epochs": self.total_epochs, - "elapsed_time": elapsed, - "best_val_acc": self.best_val_acc, - "current_loss": self.current_loss, - "epochs_per_second": self.epochs_per_second, - "eta_seconds": self.eta_seconds, - "error_message": self.error_message, - "samples_trained": self.samples_trained, - "current_val_acc": self.current_val_acc, - "current_learning_rate": self.learning_rate, - "gradient_norm": self.gradient_norm, - "checkpoint_path": self.checkpoint_path, - "optimizer": self.optimizer_type, - "epochs_without_improvement": self.epochs_without_improvement, - "metrics": self.metrics_history, - "progress_percent": ( - (self.current_epoch / self.total_epochs * 100) - if self.total_epochs > 0 - else 0 - ), - } - - -def load_dataset(name: str): - """Load dataset from CSV files""" - # Use shared loader if available - if load_dataset_shared is not None: - X, y, feature_names = load_dataset_shared(name, return_feature_names=True) - return X, y, feature_names - - # Fallback to inline implementation for testing/compatibility - base = Path(__file__).parent.parent / "datasets" / "quantum" - presets = { - "heart": base / "heart_disease.csv", - "ionosphere": base / "ionosphere.csv", - "sonar": base / "sonar.csv", - "banknote": base / "banknote.csv", - } - - if name not in presets: - raise ValueError(f"Unknown dataset '{name}'") - - path = presets[name] - if name == "heart": - df = pd.read_csv(path, na_values=["?"]) - y = df.iloc[:, -1] - X = df.iloc[:, :-1] - if X.isnull().any().any(): - imputer = SimpleImputer(strategy="median") - X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) - y = (y > 0).astype(int).values - return X.values, y, list(X.columns) - else: - df = pd.read_csv(path, na_values=["?"]) - y = df.iloc[:, -1].values - X = df.iloc[:, :-1] - if X.isnull().any().any(): - imputer = SimpleImputer(strategy="median") - X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) - if not np.issubdtype(y.dtype, np.integer): - vals, y = np.unique(y, return_inverse=True) - return X.values, y, list(X.columns) - - -def preprocess_data(X, y, n_qubits): - """Preprocess data for quantum circuit""" - # Train/val split - X_train, X_val, y_train, y_val = train_test_split( - X, y, test_size=0.2, random_state=42, stratify=y - ) - - # Standardize - scaler = StandardScaler() - X_train = scaler.fit_transform(X_train) - X_val = scaler.transform(X_val) - - # Dimensionality reduction - if X_train.shape[1] > n_qubits: - pca = PCA(n_components=n_qubits) - X_train = pca.fit_transform(X_train) - X_val = pca.transform(X_val) - elif X_train.shape[1] < n_qubits: - # Pad with zeros - pad_width = n_qubits - X_train.shape[1] - X_train = np.pad(X_train, ((0, 0), (0, pad_width))) - X_val = np.pad(X_val, ((0, 0), (0, pad_width))) - - return X_train, X_val, y_train, y_val - - -def create_quantum_circuit(n_qubits, n_layers): - """Create variational quantum circuit""" - dev = qml.device("default.qubit", wires=n_qubits) - - @qml.qnode(dev, interface="autograd") - def circuit(inputs, weights): - # Amplitude embedding - qml.AmplitudeEmbedding( - features=inputs, wires=range(n_qubits), pad_with=0.0, normalize=True - ) - - # Variational layers - for layer in range(n_layers): - for i in range(n_qubits): - qml.Rot(*weights[layer, i], wires=i) - for i in range(n_qubits - 1): - qml.CNOT(wires=[i, i + 1]) - - # Measurements - return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] - - return circuit - - -def compute_loss(circuit, X, y, weights): - """Compute loss with proper gradient tracking""" - total_loss = 0.0 - for xi, yi in zip(X, y): - expectation = circuit(xi, weights) - prediction = np.mean(expectation) - target = 2 * yi - 1 # Map {0,1} to {-1,1} - loss = (prediction - target) ** 2 - total_loss += loss - return total_loss / len(X) - - -def compute_gradient(circuit, X, y, weights, use_parameter_shift=True): - """Compute gradient using PennyLane's built-in automatic differentiation - - This is dramatically faster than manual parameter-shift implementation as it: - - Uses vectorized operations internally - - Leverages hardware acceleration when available - - Avoids redundant circuit evaluations - """ - - # Create a loss function for a single sample that PennyLane can differentiate - def loss_fn(w): - return compute_loss(circuit, X, y, w) - - # Use PennyLane's built-in gradient computation - # This leverages the autograd interface we specified in @qml.qnode - try: - # Try to use qml.grad for automatic differentiation - grad_fn = qml.grad(loss_fn) - grad = grad_fn(weights) - except Exception: - # Fallback to manual parameter-shift if autograd fails - # This path is much slower but ensures compatibility - grad = np.zeros_like(weights) - - if use_parameter_shift: - # Parameter-shift rule: more accurate for quantum circuits - shift = np.pi / 2 - for i in range(weights.shape[0]): - for j in range(weights.shape[1]): - for k in range(weights.shape[2]): - weights_plus = weights.copy() - weights_minus = weights.copy() - weights_plus[i, j, k] += shift - weights_minus[i, j, k] -= shift - loss_plus = compute_loss(circuit, X, y, weights_plus) - loss_minus = compute_loss(circuit, X, y, weights_minus) - grad[i, j, k] = (loss_plus - loss_minus) / 2 - else: - # Finite differences fallback - epsilon = 1e-4 - base_loss = compute_loss(circuit, X, y, weights) - for i in range(weights.shape[0]): - for j in range(weights.shape[1]): - for k in range(weights.shape[2]): - weights_plus = weights.copy() - weights_plus[i, j, k] += epsilon - loss_plus = compute_loss(circuit, X, y, weights_plus) - grad[i, j, k] = (loss_plus - base_loss) / epsilon - - return grad - - -class AdamOptimizer: - """Adam optimizer for quantum circuit training""" - - def __init__(self, learning_rate=0.01, beta1=0.9, beta2=0.999, epsilon=1e-8): - self.learning_rate = learning_rate - self.beta1 = beta1 - self.beta2 = beta2 - self.epsilon = epsilon - self.m = None # First moment - self.v = None # Second moment - self.t = 0 # Timestep - - def update(self, weights, gradient): - """Apply Adam update""" - if self.m is None: - self.m = np.zeros_like(weights) - self.v = np.zeros_like(weights) - - self.t += 1 - - # Update biased first moment estimate - self.m = self.beta1 * self.m + (1 - self.beta1) * gradient - - # Update biased second moment estimate - self.v = self.beta2 * self.v + (1 - self.beta2) * (gradient**2) - - # Compute bias-corrected moment estimates - m_hat = self.m / (1 - self.beta1**self.t) - v_hat = self.v / (1 - self.beta2**self.t) - - # Update weights - weights_new = weights - self.learning_rate * m_hat / ( - np.sqrt(v_hat) + self.epsilon - ) - - return weights_new - - -class MomentumOptimizer: - """SGD with momentum optimizer""" - - def __init__(self, learning_rate=0.01, momentum=0.9): - self.learning_rate = learning_rate - self.momentum = momentum - self.velocity = None - - def update(self, weights, gradient): - """Apply momentum update""" - if self.velocity is None: - self.velocity = np.zeros_like(weights) - - self.velocity = self.momentum * self.velocity - self.learning_rate * gradient - weights_new = weights + self.velocity - - return weights_new - - -def learning_rate_schedule(epoch, initial_lr, decay_rate=0.95, decay_every=50): - """Learning rate decay schedule""" - return initial_lr * (decay_rate ** (epoch // decay_every)) - - -def warmup_lr_schedule(epoch, initial_lr, warmup_epochs=10): - """Linear warmup for learning rate""" - if epoch < warmup_epochs: - return initial_lr * (epoch + 1) / warmup_epochs - return initial_lr - - -def clip_gradient(gradient, max_norm=1.0): - """Clip gradient by global norm""" - grad_norm = np.linalg.norm(gradient) - if grad_norm > max_norm: - return gradient * (max_norm / grad_norm) - return gradient - - -def train_model(session: TrainingSession): - """Training loop for quantum model""" - try: - session.status = "loading_data" - config = session.config - - # Load and preprocess data - X, y, feature_names = load_dataset(config["dataset"]) - n_qubits = config["n_qubits"] - n_layers = config["n_layers"] - - session.status = "preprocessing" - X_train, X_val, y_train, y_val = preprocess_data(X, y, n_qubits) - - # Create circuit - session.status = "building_circuit" - circuit = create_quantum_circuit(n_qubits, n_layers) - - # Initialize weights - np.random.seed(42) - weights = np.random.randn(n_layers, n_qubits, 3) * 0.01 - - # Training params - learning_rate = config.get("learning_rate", 0.01) - initial_lr = learning_rate - duration_minutes = config.get("duration_minutes", 10) - batch_size = config.get("batch_size", 32) - use_lr_decay = config.get("use_lr_decay", True) - checkpoint_every = config.get("checkpoint_every", 100) - # 'adam', 'momentum', or 'sgd' - optimizer_type = config.get("optimizer", "adam") - early_stopping_patience = config.get("early_stopping_patience", 20) - use_parameter_shift = config.get("use_parameter_shift", True) - use_gradient_clipping = config.get("use_gradient_clipping", True) - max_grad_norm = config.get("max_grad_norm", 1.0) - use_warmup = config.get("use_warmup", True) - warmup_epochs = config.get("warmup_epochs", 10) - - # Initialize optimizer - if optimizer_type == "adam": - optimizer = AdamOptimizer(learning_rate=learning_rate) - elif optimizer_type == "momentum": - optimizer = MomentumOptimizer(learning_rate=learning_rate, momentum=0.9) - else: - optimizer = None # Plain SGD - - session.status = "training" - session.start_time = time.time() - deadline = session.start_time + (duration_minutes * 60) - - epoch = 0 - samples_trained = 0 - best_weights = weights.copy() - best_val_loss = float("inf") - epochs_without_improvement = 0 - - # Create checkpoints directory - checkpoint_dir = Path(__file__).parent / "checkpoints" - checkpoint_dir.mkdir(exist_ok=True) - - while time.time() < deadline and not session.should_stop: - epoch += 1 - epoch_start = time.time() - - # Apply warmup schedule - if use_warmup and epoch <= warmup_epochs: - current_lr = warmup_lr_schedule(epoch, initial_lr, warmup_epochs) - session.learning_rate = current_lr - if optimizer is not None: - optimizer.learning_rate = current_lr - # Apply learning rate decay after warmup - elif use_lr_decay: - current_lr = learning_rate_schedule(epoch, initial_lr) - session.learning_rate = current_lr - if optimizer is not None: - optimizer.learning_rate = current_lr - - # Mini-batch training with proper gradients - indices = np.random.permutation(len(X_train)) - batch_losses = [] - epoch_gradients = [] - - for i in range(0, len(X_train), batch_size): - if time.time() >= deadline or session.should_stop: - break - - batch_idx = indices[i : i + batch_size] - X_batch = X_train[batch_idx] - y_batch = y_train[batch_idx] - - # Compute gradient for this batch - gradient = compute_gradient( - circuit, - X_batch, - y_batch, - weights, - use_parameter_shift=use_parameter_shift, - ) - - # Apply gradient clipping - if use_gradient_clipping: - gradient = clip_gradient(gradient, max_norm=max_grad_norm) - - epoch_gradients.append(gradient) - - # Compute batch loss - batch_loss = compute_loss(circuit, X_batch, y_batch, weights) - batch_losses.append(batch_loss) - samples_trained += len(X_batch) - - # Apply optimizer update - if optimizer is not None: - weights = optimizer.update(weights, gradient) - else: - # Plain SGD - weights -= session.learning_rate * gradient - - # Track gradient norm - if epoch_gradients: - avg_gradient = np.mean([np.linalg.norm(g) for g in epoch_gradients]) - session.gradient_norm = float(avg_gradient) - - # Validation - val_predictions = [] - val_loss = 0.0 - for xi, yi in zip(X_val, y_val): - expectation = circuit(xi, weights) - prediction = np.mean(expectation) - val_loss += (prediction - (2 * yi - 1)) ** 2 - val_predictions.append(1 if prediction > 0 else 0) - - val_loss /= len(X_val) - val_acc = np.mean(np.array(val_predictions) == y_val) - train_loss = np.mean(batch_losses) if batch_losses else 0.0 - - # Early stopping check - if val_loss < best_val_loss: - best_val_loss = val_loss - best_weights = weights.copy() - epochs_without_improvement = 0 - else: - epochs_without_improvement += 1 - - session.epochs_without_improvement = epochs_without_improvement - - # Stop if no improvement for patience epochs - if epochs_without_improvement >= early_stopping_patience: - logger.info( - f"Early stopping triggered after {epoch} epochs (no improvement for {early_stopping_patience} epochs)" - ) - session.status = "early_stopped" - break - - # Save best weights based on validation accuracy - if val_acc > session.best_val_acc: - best_weights = weights.copy() - - # Checkpoint saving - if epoch % checkpoint_every == 0: - checkpoint_path = ( - checkpoint_dir - / f"checkpoint_{session.session_id}_epoch_{epoch}.npz" - ) - np.savez( - checkpoint_path, - weights=weights, - epoch=epoch, - val_acc=val_acc, - config=config, - ) - session.checkpoint_path = str(checkpoint_path) - logger.info(f"Checkpoint saved: {checkpoint_path}") - - # Update metrics - epoch_time = time.time() - epoch_start - session.current_epoch = epoch - session.current_loss = float(train_loss) - session.current_val_acc = float(val_acc) - session.best_val_acc = max(session.best_val_acc, float(val_acc)) - session.samples_trained = samples_trained - - # Calculate performance metrics - if session.last_epoch_time: - session.epochs_per_second = 1.0 / epoch_time - remaining_epochs = (deadline - time.time()) * session.epochs_per_second - session.eta_seconds = ( - remaining_epochs / session.epochs_per_second - if session.epochs_per_second > 0 - else None - ) - session.last_epoch_time = epoch_time - - session.metrics_history["epochs"].append(epoch) - session.metrics_history["train_loss"].append(float(train_loss)) - session.metrics_history["val_loss"].append(float(val_loss)) - session.metrics_history["val_accuracy"].append(float(val_acc)) - session.metrics_history["timestamps"].append( - time.time() - session.start_time - ) - - # Keep only recent history for memory efficiency - if len(session.metrics_history["epochs"]) > 1000: - session.metrics_history = { - key: values[-1000:] - for key, values in session.metrics_history.items() - } - - logger.info( - f"Epoch {epoch}: Loss={train_loss:.4f}, Val Acc={val_acc:.4f}, Val Loss={val_loss:.4f}, Speed={session.epochs_per_second:.2f} ep/s, LR={session.learning_rate:.6f}, Grad Norm={session.gradient_norm:.6f}" - ) - - # Use best weights for final model - weights = best_weights - - session.status = ( - "completed" if session.status != "early_stopped" else "early_stopped" - ) - session.end_time = time.time() - session.total_epochs = epoch - - # Save final checkpoint with best weights - final_checkpoint = checkpoint_dir / f"final_{session.session_id}.npz" - np.savez( - final_checkpoint, - weights=best_weights, - epoch=epoch, - val_acc=session.best_val_acc, - config=config, - metrics=session.metrics_history, - ) - session.checkpoint_path = str(final_checkpoint) - - # Optional evaluation at end of training - try: - eval_result = evaluate_session_internal(config, best_weights) - except Exception as _: - eval_result = None - - # Save results - results_dir = Path(__file__).parent / "results" - results_dir.mkdir(exist_ok=True) - - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - result_file = results_dir / f"training_{config['dataset']}_{timestamp}.json" - - with open(result_file, "w") as f: - doc = session.to_dict() - if eval_result is not None: - doc["evaluation"] = eval_result - json.dump(doc, f, indent=2) - - logger.info(f"Training completed: {session.session_id}") - - except Exception as e: - logger.error(f"Training error: {e}", exc_info=True) - session.status = "error" - session.error_message = str(e) - session.end_time = time.time() - - -# ============================================================================ -# API Endpoints -# ============================================================================ - - -@app.route("/") -def index(): - """Serve main dashboard""" - return send_from_directory("web_ui", "index.html") - - -@app.route("/static/") -def serve_static(path): - """Serve static files""" - return send_from_directory("web_ui/static", path) - - -@app.route("/api/datasets", methods=["GET"]) -def list_datasets(): - """List available datasets""" - base = Path(__file__).parent.parent / "datasets" / "quantum" - datasets = [] - - for csv_file in base.glob("*.csv"): - df = pd.read_csv(csv_file, nrows=1) - datasets.append( - { - "name": csv_file.stem, - "path": str(csv_file), - "features": len(df.columns) - 1, - "exists": csv_file.exists(), - } - ) - - return jsonify(datasets) - - -@app.route("/api/train/start", methods=["POST"]) -def start_training(): - """Start a new training session""" - config = request.json - - if not isinstance(config, dict): - return jsonify({"error": "Invalid request body: expected JSON object"}), 400 - - # Validate config - required = ["dataset", "n_qubits", "n_layers"] - for key in required: - if key not in config: - return jsonify({"error": f"Missing required field: {key}"}), 400 - - # Validate ranges - if not (1 <= config["n_qubits"] <= 10): - return jsonify({"error": "n_qubits must be between 1 and 10"}), 400 - if not (1 <= config["n_layers"] <= 20): - return jsonify({"error": "n_layers must be between 1 and 20"}), 400 - if config.get("learning_rate", 0.01) <= 0 or config.get("learning_rate", 0.01) > 1: - return jsonify({"error": "learning_rate must be between 0 and 1"}), 400 - - with training_lock: - # Prune stale sessions before checking limits. - _prune_old_sessions() - - # Reject if too many sessions are already active. - active = sum( - 1 - for s in training_sessions.values() - if s.status not in ("completed", "error", "stopped") - ) - if active >= MAX_ACTIVE_SESSIONS: - return ( - jsonify( - {"error": "Too many active training sessions. Try again later."} - ), - 429, - ) - - if len(training_sessions) >= MAX_TOTAL_SESSIONS: - return ( - jsonify( - {"error": "Session limit reached. Too many sessions in memory."} - ), - 429, - ) - - # Create session - session_id = f"session_{int(time.time()*1000)}" - session = TrainingSession(session_id, config) - training_sessions[session_id] = session - - # Start training thread - session.thread = threading.Thread(target=train_model, args=(session,)) - session.thread.daemon = True - session.thread.start() - - return jsonify({"session_id": session_id, "status": "started"}) - - -@app.route("/api/train/stop/", methods=["POST"]) -def stop_training(session_id): - """Stop a training session""" - with training_lock: - session = training_sessions.get(session_id) - if not session: - return jsonify({"error": "Session not found"}), 404 - - session.should_stop = True - session.status = "stopped" - - return jsonify({"status": "stopped"}) - - -@app.route("/api/train/status/", methods=["GET"]) -def get_training_status(session_id): - """Get current training status and metrics""" - with training_lock: - session = training_sessions.get(session_id) - if not session: - return jsonify({"error": "Session not found"}), 404 - - return jsonify(session.to_dict()) - - -@app.route("/api/train/sessions", methods=["GET"]) -def list_sessions(): - """List all training sessions""" - with training_lock: - sessions = [s.to_dict() for s in training_sessions.values()] - - return jsonify(sessions) - - -@app.route("/api/results", methods=["GET"]) -def list_results(): - """List saved training results""" - results_dir = Path(__file__).parent / "results" - results = [] - - if results_dir.exists(): - for json_file in sorted(results_dir.glob("training_*.json"), reverse=True): - try: - with open(json_file) as f: - data = json.load(f) - results.append( - { - "filename": json_file.name, - "timestamp": json_file.stem.split("_")[-2:], - "dataset": data["config"]["dataset"], - "best_acc": data.get("best_val_acc", 0), - "epochs": data.get("total_epochs", 0), - } - ) - except Exception as e: - logger.error(f"Error reading {json_file}: {e}") - - return jsonify(results) - - -@app.before_request -def reject_results_double_slash(): - """Reject requests with suspicious double-slash patterns under /api/results before routing. - - Some clients may produce URLs with a double-slash after the base path (e.g. - /api/results//etc/passwd.json). Werkzeug may normalize or redirect these before - our route handler runs; returning a 400 here ensures the validator controls the - response and prevents the redirect behavior used by the test harness. - """ - # Only consider the results listing/detail endpoints - path = request.path or "" - # If there's an explicit double slash after the /api/results base, reject it - if path.startswith("/api/results//"): - return jsonify({"error": "Invalid filename"}), 400 - - -@app.route("/api/results/", methods=["GET"]) -@app.route("/api/results//", methods=["GET"]) -def get_result_detail(filename): - """Get detailed results for a training session""" - results_dir = Path(__file__).parent / "results" - - # Validate filename to prevent path traversal attacks - # Only allow alphanumeric, underscores, dashes, and .json extension - if not re.match(r"^[A-Za-z0-9_\-]+\.json$", filename): - return jsonify({"error": "Invalid filename"}), 400 - - result_file = results_dir / filename - - # Ensure path is within results directory (additional protection) - try: - resolved_path = result_file.resolve() - allowed_dir = results_dir.resolve() - # Verify the resolved path is within allowed_dir (path containment check) - # Python 3.9+: use is_relative_to; fallback for earlier versions - if hasattr(resolved_path, "is_relative_to"): - if not resolved_path.is_relative_to(allowed_dir): - return jsonify({"error": "Invalid file path"}), 403 - else: - try: - resolved_path.relative_to(allowed_dir) - except ValueError: - return jsonify({"error": "Invalid file path"}), 403 - except (OSError, RuntimeError): - return jsonify({"error": "Invalid file path"}), 400 - - if not resolved_path.exists(): - return jsonify({"error": "Result file not found"}), 404 - - with open(resolved_path) as f: - data = json.load(f) - - return jsonify(data) - - -@app.route("/api/export/metrics/", methods=["GET"]) -def export_metrics(session_id): - """Export training metrics as CSV""" - import io - - from flask import make_response - - with training_lock: - session = training_sessions.get(session_id) - if not session: - return jsonify({"error": "Session not found"}), 404 - - # Create CSV - output = io.StringIO() - output.write("epoch,train_loss,val_loss,val_accuracy,timestamp\n") - - metrics = session.metrics_history - for i in range(len(metrics["epochs"])): - output.write(f"{metrics['epochs'][i]},{metrics['train_loss'][i]:.6f},") - output.write( - f"{metrics['val_loss'][i]:.6f},{metrics['val_accuracy'][i]:.6f}," - ) - output.write(f"{metrics['timestamps'][i]:.2f}\n") - - # Create response - response = make_response(output.getvalue()) - response.headers["Content-Disposition"] = ( - f"attachment; filename=metrics_{session_id}.csv" - ) - response.headers["Content-Type"] = "text/csv" - return response - - -@app.route("/api/compare", methods=["POST"]) -def compare_sessions(): - """Compare multiple training sessions""" - session_ids = request.json.get("session_ids", []) - - if not session_ids: - return jsonify({"error": "No session IDs provided"}), 400 - - comparisons = [] - with training_lock: - for sid in session_ids: - session = training_sessions.get(sid) - if session: - comparisons.append( - { - "session_id": sid, - "config": session.config, - "best_val_acc": session.best_val_acc, - "total_epochs": session.total_epochs, - "status": session.status, - "final_metrics": { - "train_loss": ( - session.metrics_history["train_loss"][-1] - if session.metrics_history["train_loss"] - else None - ), - "val_loss": ( - session.metrics_history["val_loss"][-1] - if session.metrics_history["val_loss"] - else None - ), - "val_accuracy": ( - session.metrics_history["val_accuracy"][-1] - if session.metrics_history["val_accuracy"] - else None - ), - }, - } - ) - - return jsonify({"comparisons": comparisons}) - - -def evaluate_session_internal(config, weights): - """Compute evaluation metrics on the validation set given config and weights""" - # Reload dataset and reproduce split (random_state fixed in preprocess_data) - X, y, _ = load_dataset(config["dataset"]) - X_train, X_val, y_train, y_val = preprocess_data(X, y, config["n_qubits"]) - - # Rebuild circuit - circuit = create_quantum_circuit(config["n_qubits"], config["n_layers"]) - - # Predictions - y_scores = [] - y_pred = [] - for xi in X_val: - expectation = circuit(xi, weights) - score = float(np.mean(expectation)) # in [-1, 1] - prob = (score + 1.0) / 2.0 # map to [0, 1] - y_scores.append(prob) - y_pred.append(1 if score > 0 else 0) - - y_scores = np.array(y_scores) - y_pred = np.array(y_pred) - - # Metrics - cm = confusion_matrix(y_val, y_pred) - precision, recall, f1, _ = precision_recall_fscore_support( - y_val, - y_pred, - average="binary" if len(np.unique(y_val)) == 2 else "macro", - zero_division=0, - ) - accuracy = float(np.mean(y_pred == y_val)) - roc = None - try: - if len(np.unique(y_val)) == 2: - roc = float(roc_auc_score(y_val, y_scores)) - except Exception: - roc = None - - return { - "confusion_matrix": cm.tolist(), - "labels": sorted(list(map(int, np.unique(y_val)))), - "metrics": { - "accuracy": accuracy, - "precision": float(precision), - "recall": float(recall), - "f1": float(f1), - "roc_auc": roc, - }, - } - - -@app.route("/api/train/evaluate/", methods=["GET"]) -def evaluate_session(session_id): - """Evaluate a completed training session using its final checkpoint""" - with training_lock: - session = training_sessions.get(session_id) - if not session: - return jsonify({"error": "Session not found"}), 404 - if not session.checkpoint_path or not Path(session.checkpoint_path).exists(): - return ( - jsonify({"error": "No checkpoint available for this session yet"}), - 400, - ) - config = session.config - checkpoint_path = session.checkpoint_path - - try: - data = np.load(checkpoint_path, allow_pickle=True) - weights = data["weights"] - result = evaluate_session_internal(config, weights) - return jsonify(result) - except Exception as e: - logger.error(f"Evaluation error: {e}") - return jsonify({"error": str(e)}), 500 - - -@app.route("/api/checkpoint/", methods=["GET"]) -def get_checkpoint(session_id): - """Download checkpoint file for a session""" - from flask import send_file - - with training_lock: - session = training_sessions.get(session_id) - if not session or not session.checkpoint_path: - return jsonify({"error": "No checkpoint found"}), 404 - - checkpoint_file = Path(session.checkpoint_path) - if not checkpoint_file.exists(): - return jsonify({"error": "Checkpoint file not found"}), 404 - - return send_file(checkpoint_file, as_attachment=True) - - -@app.route("/api/load_checkpoint", methods=["POST"]) -def load_checkpoint(): - """Load a checkpoint and resume training""" - payload = request.json - if not isinstance(payload, dict): - return jsonify({"error": "Invalid request body: expected JSON object"}), 400 - - checkpoint_path = payload.get("checkpoint_path") - - if not checkpoint_path: - return jsonify({"error": "No checkpoint path provided"}), 400 - - try: - # Validate path to prevent path traversal attacks - checkpoint_path = Path(checkpoint_path) - checkpoint_dir = Path(__file__).parent / "checkpoints" - - # Resolve to absolute paths to prevent traversal - try: - resolved_path = checkpoint_path.resolve() - allowed_dir = checkpoint_dir.resolve() - except (OSError, RuntimeError): - return jsonify({"error": "Invalid checkpoint path"}), 400 - - # Verify the resolved path is within allowed_dir (path containment check) - # Python 3.9+: use is_relative_to; fallback for earlier versions - if hasattr(resolved_path, "is_relative_to"): - if not resolved_path.is_relative_to(allowed_dir): - return ( - jsonify( - { - "error": "Invalid checkpoint path: must be within checkpoints directory" - } - ), - 403, - ) - else: - try: - resolved_path.relative_to(allowed_dir) - except ValueError: - return ( - jsonify( - { - "error": "Invalid checkpoint path: must be within checkpoints directory" - } - ), - 403, - ) - - if not resolved_path.exists(): - return jsonify({"error": "Checkpoint file not found"}), 404 - - checkpoint = np.load(str(resolved_path), allow_pickle=True) - weights = checkpoint["weights"] - epoch = int(checkpoint["epoch"]) - config = ( - checkpoint["config"].item() - if isinstance(checkpoint["config"], np.ndarray) - else checkpoint["config"] - ) - - return jsonify( - { - "success": True, - "epoch": epoch, - "config": config, - "message": f"Checkpoint loaded from epoch {epoch}", - } - ) - except Exception as e: - return jsonify({"error": str(e)}), 500 - - -@app.route("/api/stats", methods=["GET"]) -def get_global_stats(): - """Get global statistics across all sessions""" - with training_lock: - total_sessions = len(training_sessions) - active_sessions = sum( - 1 for s in training_sessions.values() if s.status == "training" - ) - completed_sessions = sum( - 1 for s in training_sessions.values() if s.status == "completed" - ) - total_epochs = sum(s.current_epoch for s in training_sessions.values()) - avg_accuracy = ( - np.mean( - [ - s.best_val_acc - for s in training_sessions.values() - if s.best_val_acc > 0 - ] - ) - if training_sessions - else 0 - ) - - return jsonify( - { - "total_sessions": total_sessions, - "active_sessions": active_sessions, - "completed_sessions": completed_sessions, - "total_epochs_trained": total_epochs, - "average_best_accuracy": float(avg_accuracy), - "server_uptime": time.time() - app.config.get("START_TIME", time.time()), - } - ) - - -@app.route("/api/health", methods=["GET"]) -def health_check(): - """Health check endpoint""" - return jsonify( - { - "status": "healthy", - "timestamp": time.time(), - "active_sessions": sum( - 1 for s in training_sessions.values() if s.status == "training" - ), - } - ) - - -if __name__ == "__main__": - # Set start time for uptime tracking - app.config["START_TIME"] = time.time() - - print("\n" + "=" * 70) - print(" 🚀 QUANTUM AI TRAINING WEB APP - ADVANCED ML ENGINE") - print("=" * 70) - print("\n📡 Server starting on http://localhost:5000") - print("\n✨ Advanced Optimization Features:") - print(" • Adam, Momentum, and SGD optimizers") - print(" • Parameter-shift gradient rule (quantum-native)") - print(" • Learning rate warmup and decay scheduling") - print(" • Gradient clipping for training stability") - print(" • Early stopping with patience monitoring") - print(" • Automatic checkpoint saving") - print(" • Model comparison & analytics") - print("\n🔬 Training Enhancements:") - print(" • Parameter-shift rule (π/2 shift)") - print(" • Finite difference fallback") - print(" • Adaptive learning rate warmup (10 epochs)") - print(" • Exponential LR decay (0.95 every 50 epochs)") - print(" • Gradient norm clipping (max_norm=1.0)") - print(" • Best model checkpointing") - print(" • Early stopping (patience=20)") - print("\n📊 Available Optimizers:") - print(" • Adam (default): Adaptive moment estimation") - print(" • Momentum: SGD with momentum (β=0.9)") - print(" • SGD: Vanilla stochastic gradient descent") - print("\n💡 Open your browser to: http://localhost:5000") - print("📊 API Documentation: http://localhost:5000/api/health") - print("\n" + "=" * 70 + "\n") - - import os - - debug_mode = os.environ.get("FLASK_DEBUG", "false").lower() == "true" - # Default to localhost for security - host = os.environ.get("FLASK_HOST", "127.0.0.1") - app.run(host=host, port=5000, debug=debug_mode, threaded=True) +#!/usr/bin/env python3 +""" +Quantum AI Training Web Application +Interactive dashboard for training and visualizing quantum machine learning models +""" +from flask import Flask, jsonify, request, send_from_directory + +try: + from flask_cors import CORS +except Exception: # pragma: no cover + # Fallback stub when flask_cors is not installed; CORS(app) becomes a no-op + class CORS: # type: ignore + def __init__(self, *args, **kwargs): + pass + + +try: + import pennylane as qml +except Exception: # pragma: no cover + qml = None # Allow app import without PennyLane for security tests +try: + import numpy as np +except Exception: # pragma: no cover + np = None +try: + import pandas as pd +except Exception: # pragma: no cover + pd = None +import json +import re +import threading +import time +from datetime import datetime +from pathlib import Path + +try: + from sklearn.decomposition import PCA + from sklearn.impute import SimpleImputer + from sklearn.metrics import (confusion_matrix, + precision_recall_fscore_support, + roc_auc_score) + from sklearn.model_selection import train_test_split + from sklearn.preprocessing import StandardScaler +except Exception: # pragma: no cover + train_test_split = StandardScaler = PCA = SimpleImputer = None + confusion_matrix = precision_recall_fscore_support = roc_auc_score = None +import logging +import sys + +# Add src directory to path for imports +src_path = Path(__file__).parent / "src" +sys.path.insert(0, str(src_path)) + +try: + from src.dataset_loader import load_dataset as load_dataset_shared + from src.dataset_loader import preprocess_for_qubits as preprocess_shared +except Exception: # pragma: no cover + # Fallback if module not available + load_dataset_shared = None + preprocess_shared = None + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +app = Flask(__name__) +CORS(app) + +# Global training state +training_sessions = {} +training_lock = threading.Lock() + +# Resource limits to prevent DoS +MAX_ACTIVE_SESSIONS = 10 # Max concurrent running sessions +MAX_TOTAL_SESSIONS = 100 # Max sessions kept in memory at once +SESSION_TTL_SECONDS = 3600 # Prune completed/errored sessions after 1 hour + + +def _prune_old_sessions() -> None: + """Remove completed/errored sessions older than SESSION_TTL_SECONDS. + + Must be called while holding training_lock. + """ + cutoff = time.time() - SESSION_TTL_SECONDS + stale = [ + sid + for sid, s in training_sessions.items() + if s.status in ("completed", "error", "stopped") + and s.end_time is not None + and s.end_time < cutoff + ] + for sid in stale: + del training_sessions[sid] + + +class TrainingSession: + """Manages a single training session with real-time metrics""" + + def __init__(self, session_id, config): + self.session_id = session_id + self.config = config + self.status = "initializing" + self.start_time = None + self.end_time = None + self.current_epoch = 0 + self.total_epochs = 0 + self.metrics_history = { + "epochs": [], + "train_loss": [], + "val_loss": [], + "val_accuracy": [], + "timestamps": [], + } + self.best_val_acc = 0.0 + self.current_loss = 0.0 + self.thread = None + self.should_stop = False + self.epochs_per_second = 0.0 + self.eta_seconds = None + self.last_epoch_time = None + self.error_message = None + self.samples_trained = 0 + self.current_val_acc = 0.0 + self.learning_rate = config.get("learning_rate", 0.01) + self.checkpoint_path = None + self.gradient_norm = 0.0 + self.optimizer_type = config.get("optimizer", "adam") + self.epochs_without_improvement = 0 + + def to_dict(self): + """Serialize session state for API""" + elapsed = None + if self.start_time: + end = self.end_time or time.time() + elapsed = end - self.start_time + + return { + "session_id": self.session_id, + "config": self.config, + "status": self.status, + "current_epoch": self.current_epoch, + "total_epochs": self.total_epochs, + "elapsed_time": elapsed, + "best_val_acc": self.best_val_acc, + "current_loss": self.current_loss, + "epochs_per_second": self.epochs_per_second, + "eta_seconds": self.eta_seconds, + "error_message": self.error_message, + "samples_trained": self.samples_trained, + "current_val_acc": self.current_val_acc, + "current_learning_rate": self.learning_rate, + "gradient_norm": self.gradient_norm, + "checkpoint_path": self.checkpoint_path, + "optimizer": self.optimizer_type, + "epochs_without_improvement": self.epochs_without_improvement, + "metrics": self.metrics_history, + "progress_percent": ( + (self.current_epoch / self.total_epochs * 100) + if self.total_epochs > 0 + else 0 + ), + } + + +def load_dataset(name: str): + """Load dataset from CSV files""" + # Use shared loader if available + if load_dataset_shared is not None: + X, y, feature_names = load_dataset_shared(name, return_feature_names=True) + return X, y, feature_names + + # Fallback to inline implementation for testing/compatibility + base = Path(__file__).parent.parent / "datasets" / "quantum" + presets = { + "heart": base / "heart_disease.csv", + "ionosphere": base / "ionosphere.csv", + "sonar": base / "sonar.csv", + "banknote": base / "banknote.csv", + } + + if name not in presets: + raise ValueError(f"Unknown dataset '{name}'") + + path = presets[name] + if name == "heart": + df = pd.read_csv(path, na_values=["?"]) + y = df.iloc[:, -1] + X = df.iloc[:, :-1] + if X.isnull().any().any(): + imputer = SimpleImputer(strategy="median") + X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) + y = (y > 0).astype(int).values + return X.values, y, list(X.columns) + else: + df = pd.read_csv(path, na_values=["?"]) + y = df.iloc[:, -1].values + X = df.iloc[:, :-1] + if X.isnull().any().any(): + imputer = SimpleImputer(strategy="median") + X = pd.DataFrame(imputer.fit_transform(X), columns=X.columns) + if not np.issubdtype(y.dtype, np.integer): + vals, y = np.unique(y, return_inverse=True) + return X.values, y, list(X.columns) + + +def preprocess_data(X, y, n_qubits): + """Preprocess data for quantum circuit""" + # Train/val split + X_train, X_val, y_train, y_val = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + # Standardize + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) + + # Dimensionality reduction + if X_train.shape[1] > n_qubits: + pca = PCA(n_components=n_qubits) + X_train = pca.fit_transform(X_train) + X_val = pca.transform(X_val) + elif X_train.shape[1] < n_qubits: + # Pad with zeros + pad_width = n_qubits - X_train.shape[1] + X_train = np.pad(X_train, ((0, 0), (0, pad_width))) + X_val = np.pad(X_val, ((0, 0), (0, pad_width))) + + return X_train, X_val, y_train, y_val + + +def create_quantum_circuit(n_qubits, n_layers): + """Create variational quantum circuit""" + dev = qml.device("default.qubit", wires=n_qubits) + + @qml.qnode(dev, interface="autograd") + def circuit(inputs, weights): + # Amplitude embedding + qml.AmplitudeEmbedding( + features=inputs, wires=range(n_qubits), pad_with=0.0, normalize=True + ) + + # Variational layers + for layer in range(n_layers): + for i in range(n_qubits): + qml.Rot(*weights[layer, i], wires=i) + for i in range(n_qubits - 1): + qml.CNOT(wires=[i, i + 1]) + + # Measurements + return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] + + return circuit + + +def compute_loss(circuit, X, y, weights): + """Compute loss with proper gradient tracking""" + total_loss = 0.0 + for xi, yi in zip(X, y): + expectation = circuit(xi, weights) + prediction = np.mean(expectation) + target = 2 * yi - 1 # Map {0,1} to {-1,1} + loss = (prediction - target) ** 2 + total_loss += loss + return total_loss / len(X) + + +def compute_gradient(circuit, X, y, weights, use_parameter_shift=True): + """Compute gradient using PennyLane's built-in automatic differentiation + + This is dramatically faster than manual parameter-shift implementation as it: + - Uses vectorized operations internally + - Leverages hardware acceleration when available + - Avoids redundant circuit evaluations + """ + + # Create a loss function for a single sample that PennyLane can differentiate + def loss_fn(w): + return compute_loss(circuit, X, y, w) + + # Use PennyLane's built-in gradient computation + # This leverages the autograd interface we specified in @qml.qnode + try: + # Try to use qml.grad for automatic differentiation + grad_fn = qml.grad(loss_fn) + grad = grad_fn(weights) + except Exception: + # Fallback to manual parameter-shift if autograd fails + # This path is much slower but ensures compatibility + grad = np.zeros_like(weights) + + if use_parameter_shift: + # Parameter-shift rule: more accurate for quantum circuits + shift = np.pi / 2 + for i in range(weights.shape[0]): + for j in range(weights.shape[1]): + for k in range(weights.shape[2]): + weights_plus = weights.copy() + weights_minus = weights.copy() + weights_plus[i, j, k] += shift + weights_minus[i, j, k] -= shift + loss_plus = compute_loss(circuit, X, y, weights_plus) + loss_minus = compute_loss(circuit, X, y, weights_minus) + grad[i, j, k] = (loss_plus - loss_minus) / 2 + else: + # Finite differences fallback + epsilon = 1e-4 + base_loss = compute_loss(circuit, X, y, weights) + for i in range(weights.shape[0]): + for j in range(weights.shape[1]): + for k in range(weights.shape[2]): + weights_plus = weights.copy() + weights_plus[i, j, k] += epsilon + loss_plus = compute_loss(circuit, X, y, weights_plus) + grad[i, j, k] = (loss_plus - base_loss) / epsilon + + return grad + + +class AdamOptimizer: + """Adam optimizer for quantum circuit training""" + + def __init__(self, learning_rate=0.01, beta1=0.9, beta2=0.999, epsilon=1e-8): + self.learning_rate = learning_rate + self.beta1 = beta1 + self.beta2 = beta2 + self.epsilon = epsilon + self.m = None # First moment + self.v = None # Second moment + self.t = 0 # Timestep + + def update(self, weights, gradient): + """Apply Adam update""" + if self.m is None: + self.m = np.zeros_like(weights) + self.v = np.zeros_like(weights) + + self.t += 1 + + # Update biased first moment estimate + self.m = self.beta1 * self.m + (1 - self.beta1) * gradient + + # Update biased second moment estimate + self.v = self.beta2 * self.v + (1 - self.beta2) * (gradient**2) + + # Compute bias-corrected moment estimates + m_hat = self.m / (1 - self.beta1**self.t) + v_hat = self.v / (1 - self.beta2**self.t) + + # Update weights + weights_new = weights - self.learning_rate * m_hat / ( + np.sqrt(v_hat) + self.epsilon + ) + + return weights_new + + +class MomentumOptimizer: + """SGD with momentum optimizer""" + + def __init__(self, learning_rate=0.01, momentum=0.9): + self.learning_rate = learning_rate + self.momentum = momentum + self.velocity = None + + def update(self, weights, gradient): + """Apply momentum update""" + if self.velocity is None: + self.velocity = np.zeros_like(weights) + + self.velocity = self.momentum * self.velocity - self.learning_rate * gradient + weights_new = weights + self.velocity + + return weights_new + + +def learning_rate_schedule(epoch, initial_lr, decay_rate=0.95, decay_every=50): + """Learning rate decay schedule""" + return initial_lr * (decay_rate ** (epoch // decay_every)) + + +def warmup_lr_schedule(epoch, initial_lr, warmup_epochs=10): + """Linear warmup for learning rate""" + if epoch < warmup_epochs: + return initial_lr * (epoch + 1) / warmup_epochs + return initial_lr + + +def clip_gradient(gradient, max_norm=1.0): + """Clip gradient by global norm""" + grad_norm = np.linalg.norm(gradient) + if grad_norm > max_norm: + return gradient * (max_norm / grad_norm) + return gradient + + +def train_model(session: TrainingSession): + """Training loop for quantum model""" + try: + session.status = "loading_data" + config = session.config + + # Load and preprocess data + X, y, feature_names = load_dataset(config["dataset"]) + n_qubits = config["n_qubits"] + n_layers = config["n_layers"] + + session.status = "preprocessing" + X_train, X_val, y_train, y_val = preprocess_data(X, y, n_qubits) + + # Create circuit + session.status = "building_circuit" + circuit = create_quantum_circuit(n_qubits, n_layers) + + # Initialize weights + np.random.seed(42) + weights = np.random.randn(n_layers, n_qubits, 3) * 0.01 + + # Training params + learning_rate = config.get("learning_rate", 0.01) + initial_lr = learning_rate + duration_minutes = config.get("duration_minutes", 10) + batch_size = config.get("batch_size", 32) + use_lr_decay = config.get("use_lr_decay", True) + checkpoint_every = config.get("checkpoint_every", 100) + # 'adam', 'momentum', or 'sgd' + optimizer_type = config.get("optimizer", "adam") + early_stopping_patience = config.get("early_stopping_patience", 20) + use_parameter_shift = config.get("use_parameter_shift", True) + use_gradient_clipping = config.get("use_gradient_clipping", True) + max_grad_norm = config.get("max_grad_norm", 1.0) + use_warmup = config.get("use_warmup", True) + warmup_epochs = config.get("warmup_epochs", 10) + + # Initialize optimizer + if optimizer_type == "adam": + optimizer = AdamOptimizer(learning_rate=learning_rate) + elif optimizer_type == "momentum": + optimizer = MomentumOptimizer(learning_rate=learning_rate, momentum=0.9) + else: + optimizer = None # Plain SGD + + session.status = "training" + session.start_time = time.time() + deadline = session.start_time + (duration_minutes * 60) + + epoch = 0 + samples_trained = 0 + best_weights = weights.copy() + best_val_loss = float("inf") + epochs_without_improvement = 0 + + # Create checkpoints directory + checkpoint_dir = Path(__file__).parent / "checkpoints" + checkpoint_dir.mkdir(exist_ok=True) + + while time.time() < deadline and not session.should_stop: + epoch += 1 + epoch_start = time.time() + + # Apply warmup schedule + if use_warmup and epoch <= warmup_epochs: + current_lr = warmup_lr_schedule(epoch, initial_lr, warmup_epochs) + session.learning_rate = current_lr + if optimizer is not None: + optimizer.learning_rate = current_lr + # Apply learning rate decay after warmup + elif use_lr_decay: + current_lr = learning_rate_schedule(epoch, initial_lr) + session.learning_rate = current_lr + if optimizer is not None: + optimizer.learning_rate = current_lr + + # Mini-batch training with proper gradients + indices = np.random.permutation(len(X_train)) + batch_losses = [] + epoch_gradients = [] + + for i in range(0, len(X_train), batch_size): + if time.time() >= deadline or session.should_stop: + break + + batch_idx = indices[i : i + batch_size] + X_batch = X_train[batch_idx] + y_batch = y_train[batch_idx] + + # Compute gradient for this batch + gradient = compute_gradient( + circuit, + X_batch, + y_batch, + weights, + use_parameter_shift=use_parameter_shift, + ) + + # Apply gradient clipping + if use_gradient_clipping: + gradient = clip_gradient(gradient, max_norm=max_grad_norm) + + epoch_gradients.append(gradient) + + # Compute batch loss + batch_loss = compute_loss(circuit, X_batch, y_batch, weights) + batch_losses.append(batch_loss) + samples_trained += len(X_batch) + + # Apply optimizer update + if optimizer is not None: + weights = optimizer.update(weights, gradient) + else: + # Plain SGD + weights -= session.learning_rate * gradient + + # Track gradient norm + if epoch_gradients: + avg_gradient = np.mean([np.linalg.norm(g) for g in epoch_gradients]) + session.gradient_norm = float(avg_gradient) + + # Validation + val_predictions = [] + val_loss = 0.0 + for xi, yi in zip(X_val, y_val): + expectation = circuit(xi, weights) + prediction = np.mean(expectation) + val_loss += (prediction - (2 * yi - 1)) ** 2 + val_predictions.append(1 if prediction > 0 else 0) + + val_loss /= len(X_val) + val_acc = np.mean(np.array(val_predictions) == y_val) + train_loss = np.mean(batch_losses) if batch_losses else 0.0 + + # Early stopping check + if val_loss < best_val_loss: + best_val_loss = val_loss + best_weights = weights.copy() + epochs_without_improvement = 0 + else: + epochs_without_improvement += 1 + + session.epochs_without_improvement = epochs_without_improvement + + # Stop if no improvement for patience epochs + if epochs_without_improvement >= early_stopping_patience: + logger.info( + f"Early stopping triggered after {epoch} epochs (no improvement for {early_stopping_patience} epochs)" + ) + session.status = "early_stopped" + break + + # Save best weights based on validation accuracy + if val_acc > session.best_val_acc: + best_weights = weights.copy() + + # Checkpoint saving + if epoch % checkpoint_every == 0: + checkpoint_path = ( + checkpoint_dir + / f"checkpoint_{session.session_id}_epoch_{epoch}.npz" + ) + np.savez( + checkpoint_path, + weights=weights, + epoch=epoch, + val_acc=val_acc, + config=config, + ) + session.checkpoint_path = str(checkpoint_path) + logger.info(f"Checkpoint saved: {checkpoint_path}") + + # Update metrics + epoch_time = time.time() - epoch_start + session.current_epoch = epoch + session.current_loss = float(train_loss) + session.current_val_acc = float(val_acc) + session.best_val_acc = max(session.best_val_acc, float(val_acc)) + session.samples_trained = samples_trained + + # Calculate performance metrics + if session.last_epoch_time: + session.epochs_per_second = 1.0 / epoch_time + remaining_epochs = (deadline - time.time()) * session.epochs_per_second + session.eta_seconds = ( + remaining_epochs / session.epochs_per_second + if session.epochs_per_second > 0 + else None + ) + session.last_epoch_time = epoch_time + + session.metrics_history["epochs"].append(epoch) + session.metrics_history["train_loss"].append(float(train_loss)) + session.metrics_history["val_loss"].append(float(val_loss)) + session.metrics_history["val_accuracy"].append(float(val_acc)) + session.metrics_history["timestamps"].append( + time.time() - session.start_time + ) + + # Keep only recent history for memory efficiency + if len(session.metrics_history["epochs"]) > 1000: + session.metrics_history = { + key: values[-1000:] + for key, values in session.metrics_history.items() + } + + logger.info( + f"Epoch {epoch}: Loss={train_loss:.4f}, Val Acc={val_acc:.4f}, Val Loss={val_loss:.4f}, Speed={session.epochs_per_second:.2f} ep/s, LR={session.learning_rate:.6f}, Grad Norm={session.gradient_norm:.6f}" + ) + + # Use best weights for final model + weights = best_weights + + session.status = ( + "completed" if session.status != "early_stopped" else "early_stopped" + ) + session.end_time = time.time() + session.total_epochs = epoch + + # Save final checkpoint with best weights + final_checkpoint = checkpoint_dir / f"final_{session.session_id}.npz" + np.savez( + final_checkpoint, + weights=best_weights, + epoch=epoch, + val_acc=session.best_val_acc, + config=config, + metrics=session.metrics_history, + ) + session.checkpoint_path = str(final_checkpoint) + + # Optional evaluation at end of training + try: + eval_result = evaluate_session_internal(config, best_weights) + except Exception as _: + eval_result = None + + # Save results + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + result_file = results_dir / f"training_{config['dataset']}_{timestamp}.json" + + with open(result_file, "w") as f: + doc = session.to_dict() + if eval_result is not None: + doc["evaluation"] = eval_result + json.dump(doc, f, indent=2) + + logger.info(f"Training completed: {session.session_id}") + + except Exception as e: + logger.error(f"Training error: {e}", exc_info=True) + session.status = "error" + session.error_message = str(e) + session.end_time = time.time() + + +# ============================================================================ +# API Endpoints +# ============================================================================ + + +@app.route("/") +def index(): + """Serve main dashboard""" + return send_from_directory("web_ui", "index.html") + + +@app.route("/static/") +def serve_static(path): + """Serve static files""" + return send_from_directory("web_ui/static", path) + + +@app.route("/api/datasets", methods=["GET"]) +def list_datasets(): + """List available datasets""" + base = Path(__file__).parent.parent / "datasets" / "quantum" + datasets = [] + + for csv_file in base.glob("*.csv"): + df = pd.read_csv(csv_file, nrows=1) + datasets.append( + { + "name": csv_file.stem, + "path": str(csv_file), + "features": len(df.columns) - 1, + "exists": csv_file.exists(), + } + ) + + return jsonify(datasets) + + +@app.route("/api/train/start", methods=["POST"]) +def start_training(): + """Start a new training session""" + config = request.json + + if not isinstance(config, dict): + return jsonify({"error": "Invalid request body: expected JSON object"}), 400 + + # Validate config + required = ["dataset", "n_qubits", "n_layers"] + for key in required: + if key not in config: + return jsonify({"error": f"Missing required field: {key}"}), 400 + + # Validate ranges + if not (1 <= config["n_qubits"] <= 10): + return jsonify({"error": "n_qubits must be between 1 and 10"}), 400 + if not (1 <= config["n_layers"] <= 20): + return jsonify({"error": "n_layers must be between 1 and 20"}), 400 + if config.get("learning_rate", 0.01) <= 0 or config.get("learning_rate", 0.01) > 1: + return jsonify({"error": "learning_rate must be between 0 and 1"}), 400 + + with training_lock: + # Prune stale sessions before checking limits. + _prune_old_sessions() + + # Reject if too many sessions are already active. + active = sum( + 1 + for s in training_sessions.values() + if s.status not in ("completed", "error", "stopped") + ) + if active >= MAX_ACTIVE_SESSIONS: + return ( + jsonify( + {"error": "Too many active training sessions. Try again later."} + ), + 429, + ) + + if len(training_sessions) >= MAX_TOTAL_SESSIONS: + return ( + jsonify( + {"error": "Session limit reached. Too many sessions in memory."} + ), + 429, + ) + + # Create session + session_id = f"session_{int(time.time()*1000)}" + session = TrainingSession(session_id, config) + training_sessions[session_id] = session + + # Start training thread + session.thread = threading.Thread(target=train_model, args=(session,)) + session.thread.daemon = True + session.thread.start() + + return jsonify({"session_id": session_id, "status": "started"}) + + +@app.route("/api/train/stop/", methods=["POST"]) +def stop_training(session_id): + """Stop a training session""" + with training_lock: + session = training_sessions.get(session_id) + if not session: + return jsonify({"error": "Session not found"}), 404 + + session.should_stop = True + session.status = "stopped" + + return jsonify({"status": "stopped"}) + + +@app.route("/api/train/status/", methods=["GET"]) +def get_training_status(session_id): + """Get current training status and metrics""" + with training_lock: + session = training_sessions.get(session_id) + if not session: + return jsonify({"error": "Session not found"}), 404 + + return jsonify(session.to_dict()) + + +@app.route("/api/train/sessions", methods=["GET"]) +def list_sessions(): + """List all training sessions""" + with training_lock: + sessions = [s.to_dict() for s in training_sessions.values()] + + return jsonify(sessions) + + +@app.route("/api/results", methods=["GET"]) +def list_results(): + """List saved training results""" + results_dir = Path(__file__).parent / "results" + results = [] + + if results_dir.exists(): + for json_file in sorted(results_dir.glob("training_*.json"), reverse=True): + try: + with open(json_file) as f: + data = json.load(f) + results.append( + { + "filename": json_file.name, + "timestamp": json_file.stem.split("_")[-2:], + "dataset": data["config"]["dataset"], + "best_acc": data.get("best_val_acc", 0), + "epochs": data.get("total_epochs", 0), + } + ) + except Exception as e: + logger.error(f"Error reading {json_file}: {e}") + + return jsonify(results) + + +@app.before_request +def reject_results_double_slash(): + """Reject requests with suspicious double-slash patterns under /api/results before routing. + + Some clients may produce URLs with a double-slash after the base path (e.g. + /api/results//etc/passwd.json). Werkzeug may normalize or redirect these before + our route handler runs; returning a 400 here ensures the validator controls the + response and prevents the redirect behavior used by the test harness. + """ + # Only consider the results listing/detail endpoints + path = request.path or "" + # If there's an explicit double slash after the /api/results base, reject it + if path.startswith("/api/results//"): + return jsonify({"error": "Invalid filename"}), 400 + + +@app.route("/api/results/", methods=["GET"]) +@app.route("/api/results//", methods=["GET"]) +def get_result_detail(filename): + """Get detailed results for a training session""" + results_dir = Path(__file__).parent / "results" + + # Validate filename to prevent path traversal attacks + # Only allow alphanumeric, underscores, dashes, and .json extension + if not re.match(r"^[A-Za-z0-9_\-]+\.json$", filename): + return jsonify({"error": "Invalid filename"}), 400 + + result_file = results_dir / filename + + # Ensure path is within results directory (additional protection) + try: + resolved_path = result_file.resolve() + allowed_dir = results_dir.resolve() + # Verify the resolved path is within allowed_dir (path containment check) + # Python 3.9+: use is_relative_to; fallback for earlier versions + if hasattr(resolved_path, "is_relative_to"): + if not resolved_path.is_relative_to(allowed_dir): + return jsonify({"error": "Invalid file path"}), 403 + else: + try: + resolved_path.relative_to(allowed_dir) + except ValueError: + return jsonify({"error": "Invalid file path"}), 403 + except (OSError, RuntimeError): + return jsonify({"error": "Invalid file path"}), 400 + + if not resolved_path.exists(): + return jsonify({"error": "Result file not found"}), 404 + + with open(resolved_path) as f: + data = json.load(f) + + return jsonify(data) + + +@app.route("/api/export/metrics/", methods=["GET"]) +def export_metrics(session_id): + """Export training metrics as CSV""" + import io + + from flask import make_response + + with training_lock: + session = training_sessions.get(session_id) + if not session: + return jsonify({"error": "Session not found"}), 404 + + # Create CSV + output = io.StringIO() + output.write("epoch,train_loss,val_loss,val_accuracy,timestamp\n") + + metrics = session.metrics_history + for i in range(len(metrics["epochs"])): + output.write(f"{metrics['epochs'][i]},{metrics['train_loss'][i]:.6f},") + output.write( + f"{metrics['val_loss'][i]:.6f},{metrics['val_accuracy'][i]:.6f}," + ) + output.write(f"{metrics['timestamps'][i]:.2f}\n") + + # Create response + response = make_response(output.getvalue()) + response.headers["Content-Disposition"] = ( + f"attachment; filename=metrics_{session_id}.csv" + ) + response.headers["Content-Type"] = "text/csv" + return response + + +@app.route("/api/compare", methods=["POST"]) +def compare_sessions(): + """Compare multiple training sessions""" + session_ids = request.json.get("session_ids", []) + + if not session_ids: + return jsonify({"error": "No session IDs provided"}), 400 + + comparisons = [] + with training_lock: + for sid in session_ids: + session = training_sessions.get(sid) + if session: + comparisons.append( + { + "session_id": sid, + "config": session.config, + "best_val_acc": session.best_val_acc, + "total_epochs": session.total_epochs, + "status": session.status, + "final_metrics": { + "train_loss": ( + session.metrics_history["train_loss"][-1] + if session.metrics_history["train_loss"] + else None + ), + "val_loss": ( + session.metrics_history["val_loss"][-1] + if session.metrics_history["val_loss"] + else None + ), + "val_accuracy": ( + session.metrics_history["val_accuracy"][-1] + if session.metrics_history["val_accuracy"] + else None + ), + }, + } + ) + + return jsonify({"comparisons": comparisons}) + + +def evaluate_session_internal(config, weights): + """Compute evaluation metrics on the validation set given config and weights""" + # Reload dataset and reproduce split (random_state fixed in preprocess_data) + X, y, _ = load_dataset(config["dataset"]) + X_train, X_val, y_train, y_val = preprocess_data(X, y, config["n_qubits"]) + + # Rebuild circuit + circuit = create_quantum_circuit(config["n_qubits"], config["n_layers"]) + + # Predictions + y_scores = [] + y_pred = [] + for xi in X_val: + expectation = circuit(xi, weights) + score = float(np.mean(expectation)) # in [-1, 1] + prob = (score + 1.0) / 2.0 # map to [0, 1] + y_scores.append(prob) + y_pred.append(1 if score > 0 else 0) + + y_scores = np.array(y_scores) + y_pred = np.array(y_pred) + + # Metrics + cm = confusion_matrix(y_val, y_pred) + precision, recall, f1, _ = precision_recall_fscore_support( + y_val, + y_pred, + average="binary" if len(np.unique(y_val)) == 2 else "macro", + zero_division=0, + ) + accuracy = float(np.mean(y_pred == y_val)) + roc = None + try: + if len(np.unique(y_val)) == 2: + roc = float(roc_auc_score(y_val, y_scores)) + except Exception: + roc = None + + return { + "confusion_matrix": cm.tolist(), + "labels": sorted(list(map(int, np.unique(y_val)))), + "metrics": { + "accuracy": accuracy, + "precision": float(precision), + "recall": float(recall), + "f1": float(f1), + "roc_auc": roc, + }, + } + + +@app.route("/api/train/evaluate/", methods=["GET"]) +def evaluate_session(session_id): + """Evaluate a completed training session using its final checkpoint""" + with training_lock: + session = training_sessions.get(session_id) + if not session: + return jsonify({"error": "Session not found"}), 404 + if not session.checkpoint_path or not Path(session.checkpoint_path).exists(): + return ( + jsonify({"error": "No checkpoint available for this session yet"}), + 400, + ) + config = session.config + checkpoint_path = session.checkpoint_path + + try: + data = np.load(checkpoint_path, allow_pickle=True) + weights = data["weights"] + result = evaluate_session_internal(config, weights) + return jsonify(result) + except Exception as e: + logger.error(f"Evaluation error: {e}") + return jsonify({"error": str(e)}), 500 + + +@app.route("/api/checkpoint/", methods=["GET"]) +def get_checkpoint(session_id): + """Download checkpoint file for a session""" + from flask import send_file + + with training_lock: + session = training_sessions.get(session_id) + if not session or not session.checkpoint_path: + return jsonify({"error": "No checkpoint found"}), 404 + + checkpoint_file = Path(session.checkpoint_path) + if not checkpoint_file.exists(): + return jsonify({"error": "Checkpoint file not found"}), 404 + + return send_file(checkpoint_file, as_attachment=True) + + +@app.route("/api/load_checkpoint", methods=["POST"]) +def load_checkpoint(): + """Load a checkpoint and resume training""" + payload = request.json + if not isinstance(payload, dict): + return jsonify({"error": "Invalid request body: expected JSON object"}), 400 + + checkpoint_path = payload.get("checkpoint_path") + + if not checkpoint_path: + return jsonify({"error": "No checkpoint path provided"}), 400 + + try: + # Validate path to prevent path traversal attacks + checkpoint_path = Path(checkpoint_path) + checkpoint_dir = Path(__file__).parent / "checkpoints" + + # Resolve to absolute paths to prevent traversal + try: + resolved_path = checkpoint_path.resolve() + allowed_dir = checkpoint_dir.resolve() + except (OSError, RuntimeError): + return jsonify({"error": "Invalid checkpoint path"}), 400 + + # Verify the resolved path is within allowed_dir (path containment check) + # Python 3.9+: use is_relative_to; fallback for earlier versions + if hasattr(resolved_path, "is_relative_to"): + if not resolved_path.is_relative_to(allowed_dir): + return ( + jsonify( + { + "error": "Invalid checkpoint path: must be within checkpoints directory" + } + ), + 403, + ) + else: + try: + resolved_path.relative_to(allowed_dir) + except ValueError: + return ( + jsonify( + { + "error": "Invalid checkpoint path: must be within checkpoints directory" + } + ), + 403, + ) + + if not resolved_path.exists(): + return jsonify({"error": "Checkpoint file not found"}), 404 + + checkpoint = np.load(str(resolved_path), allow_pickle=True) + weights = checkpoint["weights"] + epoch = int(checkpoint["epoch"]) + config = ( + checkpoint["config"].item() + if isinstance(checkpoint["config"], np.ndarray) + else checkpoint["config"] + ) + + return jsonify( + { + "success": True, + "epoch": epoch, + "config": config, + "message": f"Checkpoint loaded from epoch {epoch}", + } + ) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@app.route("/api/stats", methods=["GET"]) +def get_global_stats(): + """Get global statistics across all sessions""" + with training_lock: + total_sessions = len(training_sessions) + active_sessions = sum( + 1 for s in training_sessions.values() if s.status == "training" + ) + completed_sessions = sum( + 1 for s in training_sessions.values() if s.status == "completed" + ) + total_epochs = sum(s.current_epoch for s in training_sessions.values()) + avg_accuracy = ( + np.mean( + [ + s.best_val_acc + for s in training_sessions.values() + if s.best_val_acc > 0 + ] + ) + if training_sessions + else 0 + ) + + return jsonify( + { + "total_sessions": total_sessions, + "active_sessions": active_sessions, + "completed_sessions": completed_sessions, + "total_epochs_trained": total_epochs, + "average_best_accuracy": float(avg_accuracy), + "server_uptime": time.time() - app.config.get("START_TIME", time.time()), + } + ) + + +@app.route("/api/health", methods=["GET"]) +def health_check(): + """Health check endpoint""" + return jsonify( + { + "status": "healthy", + "timestamp": time.time(), + "active_sessions": sum( + 1 for s in training_sessions.values() if s.status == "training" + ), + } + ) + + +if __name__ == "__main__": + # Set start time for uptime tracking + app.config["START_TIME"] = time.time() + + print("\n" + "=" * 70) + print(" 🚀 QUANTUM AI TRAINING WEB APP - ADVANCED ML ENGINE") + print("=" * 70) + print("\n📡 Server starting on http://localhost:5000") + print("\n✨ Advanced Optimization Features:") + print(" • Adam, Momentum, and SGD optimizers") + print(" • Parameter-shift gradient rule (quantum-native)") + print(" • Learning rate warmup and decay scheduling") + print(" • Gradient clipping for training stability") + print(" • Early stopping with patience monitoring") + print(" • Automatic checkpoint saving") + print(" • Model comparison & analytics") + print("\n🔬 Training Enhancements:") + print(" • Parameter-shift rule (π/2 shift)") + print(" • Finite difference fallback") + print(" • Adaptive learning rate warmup (10 epochs)") + print(" • Exponential LR decay (0.95 every 50 epochs)") + print(" • Gradient norm clipping (max_norm=1.0)") + print(" • Best model checkpointing") + print(" • Early stopping (patience=20)") + print("\n📊 Available Optimizers:") + print(" • Adam (default): Adaptive moment estimation") + print(" • Momentum: SGD with momentum (β=0.9)") + print(" • SGD: Vanilla stochastic gradient descent") + print("\n💡 Open your browser to: http://localhost:5000") + print("📊 API Documentation: http://localhost:5000/api/health") + print("\n" + "=" * 70 + "\n") + + import os + + debug_mode = os.environ.get("FLASK_DEBUG", "false").lower() == "true" + # Default to localhost for security + host = os.environ.get("FLASK_HOST", "127.0.0.1") + app.run(host=host, port=5000, debug=debug_mode, threaded=True) diff --git a/ai-projects/quantum-ml/web_ui/index.html b/ai-projects/quantum-ml/web_ui/index.html index 7e72639c7..08dcab8f9 100644 --- a/ai-projects/quantum-ml/web_ui/index.html +++ b/ai-projects/quantum-ml/web_ui/index.html @@ -1,269 +1,269 @@ - - - - - - Quantum AI Training Dashboard - - - - - -
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Quantum AI Training Platform

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Enterprise-grade quantum machine learning with real-time visualization

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- - System Online -
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🎛️ Training Configuration

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Advanced
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- Advanced Settings -
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⚛️ Quantum Circuit

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- Gates: 0 - Depth: 0 -
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Circuit will appear when training starts
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📊 Training Status

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- - LIVE -
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💤
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Ready to train. Configure your model and click "Start Training".

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📈 Training Metrics

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- Training - Validation -
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📚 Training History

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No training sessions yet. Start training to see results here.

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Quantum AI Platform v2.0

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Powered by PennyLane • Flask • Chart.js

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© 2025 Quantum Computing Research

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-
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- - - - + + + + + + Quantum AI Training Dashboard + + + + + +
+
+
+
+ +
+ +
+
+
+ +
+

Quantum AI Training Platform

+

Enterprise-grade quantum machine learning with real-time visualization

+
+ + System Online +
+
+
+ + +
+ +
+
+

🎛️ Training Configuration

+
Advanced
+
+ +
+ + +
+
+ +
+
+ + +
+
+ + +
+
+ +
+
+ + +
+
+ + +
+
+ +
+
+ + +
+
+ + +
+ Advanced Settings +
+
+ + +
+
+ + +
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+ + +
+
+ + +
+
+ + +
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+ + + + +
+
+
+ +
+ + + + +
+ +
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+

⚛️ Quantum Circuit

+
+ Gates: 0 + Depth: 0 +
+
+
+ +
+
Circuit will appear when training starts
+
+ + +
+
+

📊 Training Status

+
+ + LIVE +
+
+ +
+
💤
+

Ready to train. Configure your model and click "Start Training".

+
+ + + + + + + +
+ + +
+
+

📈 Training Metrics

+
+ Training + Validation +
+
+ +
+ +
+ +
+ +
+
+ + +
+
+

📚 Training History

+ +
+
+

No training sessions yet. Start training to see results here.

+
+
+
+ + +
+
+

Quantum AI Platform v2.0

+

Powered by PennyLane • Flask • Chart.js

+

© 2025 Quantum Computing Research

+
+
+
+ + + + diff --git a/ai-projects/quantum-ml/web_ui/static/app.js b/ai-projects/quantum-ml/web_ui/static/app.js index a904001ae..7f9b6420f 100644 --- a/ai-projects/quantum-ml/web_ui/static/app.js +++ b/ai-projects/quantum-ml/web_ui/static/app.js @@ -1,793 +1,793 @@ -// Quantum AI Training Dashboard - Frontend Logic - -let currentSessionId = null; -let statusUpdateInterval = null; -let lossChart = null; -let accuracyChart = null; -let circuitCanvas = null; -let circuitCtx = null; -let particleInterval = null; - -// API Base URL -const API_BASE = window.location.origin; - -// Initialize on page load -document.addEventListener('DOMContentLoaded', () => { - console.log('🚀 Quantum AI Dashboard initializing...'); - initializeCircuitCanvas(); - initializeCharts(); - loadDatasets(); - loadResults(); - setupEventListeners(); -}); - -// Setup Event Listeners -function setupEventListeners() { - document.getElementById('start-training-btn').addEventListener('click', startTraining); - document.getElementById('stop-training-btn').addEventListener('click', stopTraining); - document.getElementById('evaluate-btn').addEventListener('click', evaluateNow); - document.getElementById('export-metrics-btn').addEventListener('click', exportMetrics); - document.getElementById('dataset-select').addEventListener('change', updateDatasetInfo); - - // Add input validation - const numericInputs = ['n-qubits', 'n-layers', 'learning-rate', 'duration', 'batch-size', 'early-stopping', 'checkpoint-every', 'warmup-epochs', 'max-grad-norm']; - numericInputs.forEach(id => { - const input = document.getElementById(id); - input.addEventListener('input', () => validateInput(input)); - }); -} - -// Initialize Circuit Canvas -function initializeCircuitCanvas() { - circuitCanvas = document.getElementById('circuit-canvas'); - circuitCtx = circuitCanvas.getContext('2d'); - - // Set canvas size - const rect = circuitCanvas.getBoundingClientRect(); - circuitCanvas.width = rect.width; - circuitCanvas.height = 300; - - drawIdleCircuit(); -} - -// Draw idle circuit state -function drawIdleCircuit() { - if (!circuitCtx) return; - - const ctx = circuitCtx; - const width = circuitCanvas.width; - const height = circuitCanvas.height; - - ctx.clearRect(0, 0, width, height); - - // Draw gradient background - const gradient = ctx.createLinearGradient(0, 0, width, height); - gradient.addColorStop(0, 'rgba(99, 102, 241, 0.1)'); - gradient.addColorStop(1, 'rgba(139, 92, 246, 0.1)'); - ctx.fillStyle = gradient; - ctx.fillRect(0, 0, width, height); - - // Draw placeholder text - ctx.font = 'bold 18px -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto'; - ctx.fillStyle = '#6366f1'; - ctx.textAlign = 'center'; - ctx.fillText('⚛️ Quantum Circuit Visualization', width / 2, height / 2 - 15); - - ctx.font = '14px -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto'; - ctx.fillStyle = '#94a3b8'; - ctx.fillText('Start training to see live circuit diagram', width / 2, height / 2 + 15); - - // Update stats - document.getElementById('gate-count').textContent = '0'; - document.getElementById('circuit-depth').textContent = '0'; -} - -// Draw active quantum circuit -function drawQuantumCircuit(nQubits, nLayers, epoch) { - if (!circuitCtx) return; - - const ctx = circuitCtx; - const width = circuitCanvas.width; - const height = circuitCanvas.height; - - ctx.clearRect(0, 0, width, height); - - // Background - const gradient = ctx.createLinearGradient(0, 0, width, height); - gradient.addColorStop(0, 'rgba(99, 102, 241, 0.05)'); - gradient.addColorStop(1, 'rgba(139, 92, 246, 0.05)'); - ctx.fillStyle = gradient; - ctx.fillRect(0, 0, width, height); - - const padding = 60; - const qubitSpacing = (height - 2 * padding) / (nQubits - 1 || 1); - const layerWidth = (width - 2 * padding) / (nLayers + 2); - - // Draw qubit lines - ctx.strokeStyle = '#475569'; - ctx.lineWidth = 2; - for (let i = 0; i < nQubits; i++) { - const y = padding + i * qubitSpacing; - ctx.beginPath(); - ctx.moveTo(padding, y); - ctx.lineTo(width - padding, y); - ctx.stroke(); - - // Qubit label - ctx.fillStyle = '#cbd5e1'; - ctx.font = '14px monospace'; - ctx.textAlign = 'right'; - ctx.fillText(`|q${i}⟩`, padding - 10, y + 5); - } - - // Animate based on epoch - const pulsePhase = (epoch % 10) / 10; - - // Draw gates with animation - for (let layer = 0; layer < nLayers; layer++) { - const x = padding + (layer + 1) * layerWidth; - - for (let i = 0; i < nQubits; i++) { - const y = padding + i * qubitSpacing; - - // Rotation gate (pulsing) - const gateSize = 25 + Math.sin(pulsePhase * Math.PI * 2) * 3; - ctx.fillStyle = `rgba(239, 68, 68, ${0.7 + Math.sin(pulsePhase * Math.PI * 2) * 0.3})`; - ctx.beginPath(); - ctx.arc(x, y, gateSize / 2, 0, Math.PI * 2); - ctx.fill(); - - // Gate label - ctx.fillStyle = '#fff'; - ctx.font = 'bold 11px monospace'; - ctx.textAlign = 'center'; - ctx.fillText('Ry', x, y + 4); - } - - // CNOT gates (entanglement) - if (nQubits > 1) { - const cnotX = x + layerWidth / 2; - ctx.strokeStyle = `rgba(16, 185, 129, ${0.7 + Math.sin(pulsePhase * Math.PI * 2 + Math.PI) * 0.3})`; - ctx.lineWidth = 3; - - for (let i = 0; i < nQubits - 1; i++) { - const y1 = padding + i * qubitSpacing; - const y2 = padding + (i + 1) * qubitSpacing; - - // Vertical line - ctx.beginPath(); - ctx.moveTo(cnotX, y1); - ctx.lineTo(cnotX, y2); - ctx.stroke(); - - // Control dot - ctx.fillStyle = '#10b981'; - ctx.beginPath(); - ctx.arc(cnotX, y1, 5, 0, Math.PI * 2); - ctx.fill(); - - // Target circle - ctx.strokeStyle = '#10b981'; - ctx.lineWidth = 2; - ctx.beginPath(); - ctx.arc(cnotX, y2, 12, 0, Math.PI * 2); - ctx.stroke(); - ctx.beginPath(); - ctx.moveTo(cnotX - 8, y2); - ctx.lineTo(cnotX + 8, y2); - ctx.stroke(); - ctx.beginPath(); - ctx.moveTo(cnotX, y2 - 8); - ctx.lineTo(cnotX, y2 + 8); - ctx.stroke(); - } - } - } - - // Measurement symbols - const measX = width - padding - 20; - for (let i = 0; i < nQubits; i++) { - const y = padding + i * qubitSpacing; - - // Measurement box - ctx.strokeStyle = '#f59e0b'; - ctx.lineWidth = 2; - ctx.strokeRect(measX - 15, y - 15, 30, 30); - - // Meter symbol - ctx.beginPath(); - ctx.arc(measX, y + 5, 10, Math.PI, 0); - ctx.stroke(); - ctx.beginPath(); - ctx.moveTo(measX, y + 5); - ctx.lineTo(measX + 7, y - 2); - ctx.stroke(); - } - - // Epoch indicator with glow - ctx.save(); - ctx.shadowColor = '#6366f1'; - ctx.shadowBlur = 10; - ctx.fillStyle = '#6366f1'; - ctx.font = 'bold 16px monospace'; - ctx.textAlign = 'left'; - ctx.fillText(`EPOCH ${epoch}`, 15, 25); - ctx.restore(); - - // Update circuit stats - const gateCount = nQubits * nLayers + (nQubits - 1) * nLayers; - const circuitDepth = nLayers * 2; - document.getElementById('gate-count').textContent = gateCount; - document.getElementById('circuit-depth').textContent = circuitDepth; -} - -// Create particle effect -function createParticle(x, y) { - const particle = document.createElement('div'); - particle.className = 'particle'; - particle.style.left = x + 'px'; - particle.style.top = y + 'px'; - particle.style.background = `hsl(${Math.random() * 60 + 200}, 70%, 60%)`; - document.body.appendChild(particle); - - setTimeout(() => particle.remove(), 2000); -} - -// Start particle animation -function startParticleAnimation() { - if (particleInterval) return; - - particleInterval = setInterval(() => { - const canvas = document.getElementById('circuit-canvas'); - if (!canvas) return; - - const rect = canvas.getBoundingClientRect(); - const x = rect.left + Math.random() * rect.width; - const y = rect.top + Math.random() * rect.height; - createParticle(x, y); - }, 300); -} - -// Stop particle animation -function stopParticleAnimation() { - if (particleInterval) { - clearInterval(particleInterval); - particleInterval = null; - } -} - -// Initialize Charts -function initializeCharts() { - const chartOptions = { - responsive: true, - maintainAspectRatio: false, - plugins: { - legend: { - labels: { - color: '#cbd5e1' - } - } - }, - scales: { - x: { - ticks: { color: '#94a3b8' }, - grid: { color: '#334155' } - }, - y: { - ticks: { color: '#94a3b8' }, - grid: { color: '#334155' } - } - } - }; - - // Loss Chart - const lossCtx = document.getElementById('loss-chart').getContext('2d'); - lossChart = new Chart(lossCtx, { - type: 'line', - data: { - labels: [], - datasets: [ - { - label: 'Training Loss', - data: [], - borderColor: '#ef4444', - backgroundColor: 'rgba(239, 68, 68, 0.1)', - tension: 0.4 - }, - { - label: 'Validation Loss', - data: [], - borderColor: '#f59e0b', - backgroundColor: 'rgba(245, 158, 11, 0.1)', - tension: 0.4 - } - ] - }, - options: { - ...chartOptions, - plugins: { - ...chartOptions.plugins, - title: { - display: true, - text: 'Training & Validation Loss', - color: '#cbd5e1' - } - } - } - }); - - // Accuracy Chart - const accCtx = document.getElementById('accuracy-chart').getContext('2d'); - accuracyChart = new Chart(accCtx, { - type: 'line', - data: { - labels: [], - datasets: [ - { - label: 'Validation Accuracy', - data: [], - borderColor: '#10b981', - backgroundColor: 'rgba(16, 185, 129, 0.1)', - tension: 0.4, - fill: true - } - ] - }, - options: { - ...chartOptions, - plugins: { - ...chartOptions.plugins, - title: { - display: true, - text: 'Validation Accuracy', - color: '#cbd5e1' - } - }, - scales: { - ...chartOptions.scales, - y: { - ...chartOptions.scales.y, - min: 0, - max: 1, - ticks: { - ...chartOptions.scales.y.ticks, - callback: function(value) { - return (value * 100).toFixed(0) + '%'; - } - } - } - } - } - }); -} - -// Load Available Datasets -async function loadDatasets() { - try { - const response = await fetch(`${API_BASE}/api/datasets`); - const datasets = await response.json(); - - const select = document.getElementById('dataset-select'); - select.innerHTML = ''; - - datasets.forEach(dataset => { - const option = document.createElement('option'); - option.value = dataset.name; - option.textContent = `${dataset.name} (${dataset.features} features)`; - select.appendChild(option); - }); - - console.log(`✅ Loaded ${datasets.length} datasets`); - } catch (error) { - console.error('Error loading datasets:', error); - showError('Failed to load datasets'); - } -} - -// Update Dataset Info -function updateDatasetInfo() { - const select = document.getElementById('dataset-select'); - const infoDiv = document.getElementById('dataset-info'); - - if (select.value) { - infoDiv.textContent = `Selected: ${select.value}`; - } else { - infoDiv.textContent = ''; - } -} - -// Start Training -async function startTraining() { - const dataset = document.getElementById('dataset-select').value; - - if (!dataset) { - alert('Please select a dataset'); - return; - } - - const config = { - dataset: dataset, - n_qubits: parseInt(document.getElementById('n-qubits').value), - n_layers: parseInt(document.getElementById('n-layers').value), - learning_rate: parseFloat(document.getElementById('learning-rate').value), - duration_minutes: parseInt(document.getElementById('duration').value), - batch_size: parseInt(document.getElementById('batch-size').value), - optimizer: document.getElementById('optimizer-select').value, - early_stopping_patience: parseInt(document.getElementById('early-stopping').value), - checkpoint_every: parseInt(document.getElementById('checkpoint-every').value), - use_parameter_shift: document.getElementById('use-param-shift').checked, - use_warmup: document.getElementById('use-warmup').checked, - warmup_epochs: parseInt(document.getElementById('warmup-epochs').value), - use_lr_decay: document.getElementById('use-lr-decay').checked, - use_gradient_clipping: document.getElementById('use-grad-clip').checked, - max_grad_norm: parseFloat(document.getElementById('max-grad-norm').value) - }; - - console.log('🚀 Starting training with config:', config); - - try { - const response = await fetch(`${API_BASE}/api/train/start`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify(config) - }); - - const result = await response.json(); - currentSessionId = result.session_id; - - // Update UI - document.getElementById('start-training-btn').disabled = true; - document.getElementById('stop-training-btn').disabled = false; - document.getElementById('status-idle').style.display = 'none'; - document.getElementById('status-training').style.display = 'block'; - document.getElementById('progress-container').style.display = 'block'; - document.getElementById('evaluation-section').style.display = 'none'; - document.getElementById('evaluate-btn').disabled = true; - - // Start visual effects - const nQubits = parseInt(document.getElementById('n-qubits').value); - const nLayers = parseInt(document.getElementById('n-layers').value); - drawQuantumCircuit(nQubits, nLayers, 0); - startParticleAnimation(); - - // Start polling for updates - startStatusPolling(); - - console.log(`✅ Training started: ${currentSessionId}`); - } catch (error) { - console.error('Error starting training:', error); - showError('Failed to start training'); - } -} - -// Stop Training -async function stopTraining() { - if (!currentSessionId) return; - - try { - await fetch(`${API_BASE}/api/train/stop/${currentSessionId}`, { - method: 'POST' - }); - - stopStatusPolling(); - console.log('⏹️ Training stopped'); - } catch (error) { - console.error('Error stopping training:', error); - } -} - -// Start Status Polling -function startStatusPolling() { - if (statusUpdateInterval) { - clearInterval(statusUpdateInterval); - } - - statusUpdateInterval = setInterval(updateTrainingStatus, 1000); -} - -// Stop Status Polling -function stopStatusPolling() { - if (statusUpdateInterval) { - clearInterval(statusUpdateInterval); - statusUpdateInterval = null; - } - - // Stop visual effects - stopParticleAnimation(); - drawIdleCircuit(); - - // Reset UI - document.getElementById('start-training-btn').disabled = false; - document.getElementById('stop-training-btn').disabled = true; - document.getElementById('status-idle').style.display = 'block'; - document.getElementById('status-training').style.display = 'none'; - document.getElementById('progress-container').style.display = 'none'; - document.getElementById('evaluate-btn').disabled = true; - document.getElementById('evaluation-section').style.display = 'none'; - - currentSessionId = null; - loadResults(); // Refresh results list -} - -// Update Training Status -async function updateTrainingStatus() { - if (!currentSessionId) return; - - try { - const response = await fetch(`${API_BASE}/api/train/status/${currentSessionId}`); - const status = await response.json(); - - // Update status text - document.getElementById('status-text').textContent = status.status; - document.getElementById('current-epoch').textContent = status.current_epoch; - document.getElementById('current-loss').textContent = status.current_loss.toFixed(4); - document.getElementById('best-val-acc').textContent = (status.best_val_acc * 100).toFixed(2) + '%'; - - // Update performance metrics - if (status.epochs_per_second > 0) { - document.getElementById('training-speed').textContent = status.epochs_per_second.toFixed(2) + ' ep/s'; - } - - if (status.eta_seconds) { - const etaMinutes = Math.floor(status.eta_seconds / 60); - const etaSeconds = Math.floor(status.eta_seconds % 60); - document.getElementById('eta-time').textContent = `${etaMinutes}:${etaSeconds.toString().padStart(2, '0')}`; - } else { - document.getElementById('eta-time').textContent = '-'; - } - - // Enable export button when training has data - if (status.metrics && status.metrics.epochs.length > 0) { - document.getElementById('export-metrics-btn').disabled = false; - } - - // Update elapsed time - if (status.elapsed_time) { - const minutes = Math.floor(status.elapsed_time / 60); - const seconds = Math.floor(status.elapsed_time % 60); - document.getElementById('elapsed-time').textContent = `${minutes}:${seconds.toString().padStart(2, '0')}`; - - // Update progress bar - const configDuration = parseInt(document.getElementById('duration').value) * 60; - const progress = Math.min((status.elapsed_time / configDuration) * 100, 100); - document.getElementById('progress-fill').style.width = progress + '%'; - document.getElementById('progress-text').textContent = progress.toFixed(0) + '% complete'; - } - - // Update visual accuracy bar - const accuracy = status.best_val_acc * 100; - document.getElementById('accuracy-bar').style.width = accuracy + '%'; - document.getElementById('accuracy-percent').textContent = accuracy.toFixed(1) + '%'; - - // Update circuit visualization - const nQubits = parseInt(document.getElementById('n-qubits').value); - const nLayers = parseInt(document.getElementById('n-layers').value); - drawQuantumCircuit(nQubits, nLayers, status.current_epoch); - - // Update charts - if (status.metrics && status.metrics.epochs.length > 0) { - updateCharts(status.metrics); - } - - // Check if completed - if (status.status === 'completed' || status.status === 'early_stopped' || status.status === 'error' || status.status === 'stopped') { - stopStatusPolling(); - - if (status.status === 'completed' || status.status === 'early_stopped') { - showSuccess('Training completed successfully!'); - // Enable evaluation - document.getElementById('evaluate-btn').disabled = false; - // Auto-evaluate if checkpoint exists - if (status.checkpoint_path) { - evaluateNow(); - } - } else if (status.status === 'error') { - showError('Training failed: ' + (status.error_message || 'Unknown error')); - } - } - - } catch (error) { - console.error('Error updating status:', error); - } -} - -// Update Charts with New Data -function updateCharts(metrics) { - // Loss Chart - lossChart.data.labels = metrics.epochs; - lossChart.data.datasets[0].data = metrics.train_loss; - lossChart.data.datasets[1].data = metrics.val_loss; - lossChart.update('none'); // Update without animation for smoothness - - // Accuracy Chart - accuracyChart.data.labels = metrics.epochs; - accuracyChart.data.datasets[0].data = metrics.val_accuracy; - accuracyChart.update('none'); -} - -// Load Training Results -async function loadResults() { - try { - const response = await fetch(`${API_BASE}/api/results`); - const results = await response.json(); - - const resultsDiv = document.getElementById('results-list'); - - if (results.length === 0) { - resultsDiv.innerHTML = '

No training sessions yet. Start training to see results here.

'; - return; - } - - resultsDiv.innerHTML = ''; - - results.forEach(result => { - const item = document.createElement('div'); - item.className = 'result-item'; - item.onclick = () => viewResultDetails(result.filename); - - item.innerHTML = ` -
-
${result.dataset}
-
${(result.best_acc * 100).toFixed(2)}% acc
-
-
- 📅 ${result.timestamp.join('_')} - 🔄 ${result.epochs} epochs -
- `; - - resultsDiv.appendChild(item); - }); - - console.log(`✅ Loaded ${results.length} training results`); - } catch (error) { - console.error('Error loading results:', error); - } -} - -// Input Validation -function validateInput(input) { - const errorDiv = document.getElementById('validation-error'); - const value = parseFloat(input.value); - let error = null; - - switch(input.id) { - case 'n-qubits': - if (value < 1 || value > 10) error = 'Qubits must be between 1 and 10'; - break; - case 'n-layers': - if (value < 1 || value > 20) error = 'Layers must be between 1 and 20'; - break; - case 'learning-rate': - if (value <= 0 || value > 1) error = 'Learning rate must be between 0 and 1'; - break; - case 'duration': - if (value < 1 || value > 120) error = 'Duration must be between 1 and 120 minutes'; - break; - case 'batch-size': - if (value < 8 || value > 128) error = 'Batch size must be between 8 and 128'; - break; - } - - if (error) { - input.style.borderColor = '#ef4444'; - errorDiv.textContent = error; - errorDiv.style.display = 'block'; - document.getElementById('start-training-btn').disabled = true; - } else { - input.style.borderColor = ''; - errorDiv.style.display = 'none'; - document.getElementById('start-training-btn').disabled = false; - } -} - -// Export Metrics -async function exportMetrics() { - if (!currentSessionId) return; - - try { - const response = await fetch(`${API_BASE}/api/export/metrics/${currentSessionId}`); - const blob = await response.blob(); - - // Create download link - const url = window.URL.createObjectURL(blob); - const a = document.createElement('a'); - a.href = url; - a.download = `metrics_${currentSessionId}.csv`; - document.body.appendChild(a); - a.click(); - a.remove(); - window.URL.revokeObjectURL(url); - - showSuccess('Metrics exported successfully!'); - } catch (error) { - console.error('Error exporting metrics:', error); - showError('Failed to export metrics'); - } -} - -// Evaluation -async function evaluateNow() { - if (!currentSessionId) return; - try { - const res = await fetch(`${API_BASE}/api/train/evaluate/${currentSessionId}`); - const data = await res.json(); - if (data.error) { - showError('Evaluation failed: ' + data.error); - return; - } - renderEvaluation(data); - showSuccess('Evaluation complete'); - } catch (e) { - console.error('Evaluation error', e); - showError('Failed to evaluate'); - } -} - -function renderEvaluation(result) { - const section = document.getElementById('evaluation-section'); - const metricsDiv = document.getElementById('eval-metrics'); - const cmDiv = document.getElementById('confusion-matrix'); - section.style.display = 'block'; - - const m = result.metrics; - metricsDiv.innerHTML = ` - Accuracy: ${(m.accuracy * 100).toFixed(2)}% · - Precision: ${(m.precision * 100).toFixed(2)}% · - Recall: ${(m.recall * 100).toFixed(2)}% · - F1: ${(m.f1 * 100).toFixed(2)}% - ${m.roc_auc !== null ? ` · ROC AUC: ${m.roc_auc.toFixed(3)}` : ''} - `; - renderConfusionMatrix(cmDiv, result.confusion_matrix, result.labels); -} - -function renderConfusionMatrix(container, matrix, labels) { - if (!matrix || !matrix.length) { - container.innerHTML = '

No confusion matrix available.

'; - return; - } - const n = matrix.length; - let html = ''; - for (let j = 0; j < n; j++) html += ``; - html += ''; - for (let i = 0; i < n; i++) { - html += ``; - for (let j = 0; j < n; j++) html += ``; - html += ''; - } - html += '
Pred ${labels[j] ?? j}
True ${labels[i] ?? i}${matrix[i][j]}
'; - container.innerHTML = html; -} - -// View Result Details -async function viewResultDetails(filename) { - try { - const response = await fetch(`${API_BASE}/api/results/${filename}`); - const data = await response.json(); - - console.log('📊 Result details:', data); - - // Update charts with historical data - if (data.metrics) { - updateCharts(data.metrics); - } - - // Show details (could open a modal or expand in place) - alert(`Training Session Details:\n\nDataset: ${data.config.dataset}\nQubits: ${data.config.n_qubits}\nLayers: ${data.config.n_layers}\nEpochs: ${data.total_epochs}\nBest Accuracy: ${(data.best_val_acc * 100).toFixed(2)}%`); - - } catch (error) { - console.error('Error loading result details:', error); - } -} - -// Show Success Message -function showSuccess(message) { - console.log('✅', message); - // Could add a toast notification here -} - -// Show Error Message -function showError(message) { - console.error('❌', message); - alert('Error: ' + message); -} +// Quantum AI Training Dashboard - Frontend Logic + +let currentSessionId = null; +let statusUpdateInterval = null; +let lossChart = null; +let accuracyChart = null; +let circuitCanvas = null; +let circuitCtx = null; +let particleInterval = null; + +// API Base URL +const API_BASE = window.location.origin; + +// Initialize on page load +document.addEventListener('DOMContentLoaded', () => { + console.log('🚀 Quantum AI Dashboard initializing...'); + initializeCircuitCanvas(); + initializeCharts(); + loadDatasets(); + loadResults(); + setupEventListeners(); +}); + +// Setup Event Listeners +function setupEventListeners() { + document.getElementById('start-training-btn').addEventListener('click', startTraining); + document.getElementById('stop-training-btn').addEventListener('click', stopTraining); + document.getElementById('evaluate-btn').addEventListener('click', evaluateNow); + document.getElementById('export-metrics-btn').addEventListener('click', exportMetrics); + document.getElementById('dataset-select').addEventListener('change', updateDatasetInfo); + + // Add input validation + const numericInputs = ['n-qubits', 'n-layers', 'learning-rate', 'duration', 'batch-size', 'early-stopping', 'checkpoint-every', 'warmup-epochs', 'max-grad-norm']; + numericInputs.forEach(id => { + const input = document.getElementById(id); + input.addEventListener('input', () => validateInput(input)); + }); +} + +// Initialize Circuit Canvas +function initializeCircuitCanvas() { + circuitCanvas = document.getElementById('circuit-canvas'); + circuitCtx = circuitCanvas.getContext('2d'); + + // Set canvas size + const rect = circuitCanvas.getBoundingClientRect(); + circuitCanvas.width = rect.width; + circuitCanvas.height = 300; + + drawIdleCircuit(); +} + +// Draw idle circuit state +function drawIdleCircuit() { + if (!circuitCtx) return; + + const ctx = circuitCtx; + const width = circuitCanvas.width; + const height = circuitCanvas.height; + + ctx.clearRect(0, 0, width, height); + + // Draw gradient background + const gradient = ctx.createLinearGradient(0, 0, width, height); + gradient.addColorStop(0, 'rgba(99, 102, 241, 0.1)'); + gradient.addColorStop(1, 'rgba(139, 92, 246, 0.1)'); + ctx.fillStyle = gradient; + ctx.fillRect(0, 0, width, height); + + // Draw placeholder text + ctx.font = 'bold 18px -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto'; + ctx.fillStyle = '#6366f1'; + ctx.textAlign = 'center'; + ctx.fillText('⚛️ Quantum Circuit Visualization', width / 2, height / 2 - 15); + + ctx.font = '14px -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto'; + ctx.fillStyle = '#94a3b8'; + ctx.fillText('Start training to see live circuit diagram', width / 2, height / 2 + 15); + + // Update stats + document.getElementById('gate-count').textContent = '0'; + document.getElementById('circuit-depth').textContent = '0'; +} + +// Draw active quantum circuit +function drawQuantumCircuit(nQubits, nLayers, epoch) { + if (!circuitCtx) return; + + const ctx = circuitCtx; + const width = circuitCanvas.width; + const height = circuitCanvas.height; + + ctx.clearRect(0, 0, width, height); + + // Background + const gradient = ctx.createLinearGradient(0, 0, width, height); + gradient.addColorStop(0, 'rgba(99, 102, 241, 0.05)'); + gradient.addColorStop(1, 'rgba(139, 92, 246, 0.05)'); + ctx.fillStyle = gradient; + ctx.fillRect(0, 0, width, height); + + const padding = 60; + const qubitSpacing = (height - 2 * padding) / (nQubits - 1 || 1); + const layerWidth = (width - 2 * padding) / (nLayers + 2); + + // Draw qubit lines + ctx.strokeStyle = '#475569'; + ctx.lineWidth = 2; + for (let i = 0; i < nQubits; i++) { + const y = padding + i * qubitSpacing; + ctx.beginPath(); + ctx.moveTo(padding, y); + ctx.lineTo(width - padding, y); + ctx.stroke(); + + // Qubit label + ctx.fillStyle = '#cbd5e1'; + ctx.font = '14px monospace'; + ctx.textAlign = 'right'; + ctx.fillText(`|q${i}⟩`, padding - 10, y + 5); + } + + // Animate based on epoch + const pulsePhase = (epoch % 10) / 10; + + // Draw gates with animation + for (let layer = 0; layer < nLayers; layer++) { + const x = padding + (layer + 1) * layerWidth; + + for (let i = 0; i < nQubits; i++) { + const y = padding + i * qubitSpacing; + + // Rotation gate (pulsing) + const gateSize = 25 + Math.sin(pulsePhase * Math.PI * 2) * 3; + ctx.fillStyle = `rgba(239, 68, 68, ${0.7 + Math.sin(pulsePhase * Math.PI * 2) * 0.3})`; + ctx.beginPath(); + ctx.arc(x, y, gateSize / 2, 0, Math.PI * 2); + ctx.fill(); + + // Gate label + ctx.fillStyle = '#fff'; + ctx.font = 'bold 11px monospace'; + ctx.textAlign = 'center'; + ctx.fillText('Ry', x, y + 4); + } + + // CNOT gates (entanglement) + if (nQubits > 1) { + const cnotX = x + layerWidth / 2; + ctx.strokeStyle = `rgba(16, 185, 129, ${0.7 + Math.sin(pulsePhase * Math.PI * 2 + Math.PI) * 0.3})`; + ctx.lineWidth = 3; + + for (let i = 0; i < nQubits - 1; i++) { + const y1 = padding + i * qubitSpacing; + const y2 = padding + (i + 1) * qubitSpacing; + + // Vertical line + ctx.beginPath(); + ctx.moveTo(cnotX, y1); + ctx.lineTo(cnotX, y2); + ctx.stroke(); + + // Control dot + ctx.fillStyle = '#10b981'; + ctx.beginPath(); + ctx.arc(cnotX, y1, 5, 0, Math.PI * 2); + ctx.fill(); + + // Target circle + ctx.strokeStyle = '#10b981'; + ctx.lineWidth = 2; + ctx.beginPath(); + ctx.arc(cnotX, y2, 12, 0, Math.PI * 2); + ctx.stroke(); + ctx.beginPath(); + ctx.moveTo(cnotX - 8, y2); + ctx.lineTo(cnotX + 8, y2); + ctx.stroke(); + ctx.beginPath(); + ctx.moveTo(cnotX, y2 - 8); + ctx.lineTo(cnotX, y2 + 8); + ctx.stroke(); + } + } + } + + // Measurement symbols + const measX = width - padding - 20; + for (let i = 0; i < nQubits; i++) { + const y = padding + i * qubitSpacing; + + // Measurement box + ctx.strokeStyle = '#f59e0b'; + ctx.lineWidth = 2; + ctx.strokeRect(measX - 15, y - 15, 30, 30); + + // Meter symbol + ctx.beginPath(); + ctx.arc(measX, y + 5, 10, Math.PI, 0); + ctx.stroke(); + ctx.beginPath(); + ctx.moveTo(measX, y + 5); + ctx.lineTo(measX + 7, y - 2); + ctx.stroke(); + } + + // Epoch indicator with glow + ctx.save(); + ctx.shadowColor = '#6366f1'; + ctx.shadowBlur = 10; + ctx.fillStyle = '#6366f1'; + ctx.font = 'bold 16px monospace'; + ctx.textAlign = 'left'; + ctx.fillText(`EPOCH ${epoch}`, 15, 25); + ctx.restore(); + + // Update circuit stats + const gateCount = nQubits * nLayers + (nQubits - 1) * nLayers; + const circuitDepth = nLayers * 2; + document.getElementById('gate-count').textContent = gateCount; + document.getElementById('circuit-depth').textContent = circuitDepth; +} + +// Create particle effect +function createParticle(x, y) { + const particle = document.createElement('div'); + particle.className = 'particle'; + particle.style.left = x + 'px'; + particle.style.top = y + 'px'; + particle.style.background = `hsl(${Math.random() * 60 + 200}, 70%, 60%)`; + document.body.appendChild(particle); + + setTimeout(() => particle.remove(), 2000); +} + +// Start particle animation +function startParticleAnimation() { + if (particleInterval) return; + + particleInterval = setInterval(() => { + const canvas = document.getElementById('circuit-canvas'); + if (!canvas) return; + + const rect = canvas.getBoundingClientRect(); + const x = rect.left + Math.random() * rect.width; + const y = rect.top + Math.random() * rect.height; + createParticle(x, y); + }, 300); +} + +// Stop particle animation +function stopParticleAnimation() { + if (particleInterval) { + clearInterval(particleInterval); + particleInterval = null; + } +} + +// Initialize Charts +function initializeCharts() { + const chartOptions = { + responsive: true, + maintainAspectRatio: false, + plugins: { + legend: { + labels: { + color: '#cbd5e1' + } + } + }, + scales: { + x: { + ticks: { color: '#94a3b8' }, + grid: { color: '#334155' } + }, + y: { + ticks: { color: '#94a3b8' }, + grid: { color: '#334155' } + } + } + }; + + // Loss Chart + const lossCtx = document.getElementById('loss-chart').getContext('2d'); + lossChart = new Chart(lossCtx, { + type: 'line', + data: { + labels: [], + datasets: [ + { + label: 'Training Loss', + data: [], + borderColor: '#ef4444', + backgroundColor: 'rgba(239, 68, 68, 0.1)', + tension: 0.4 + }, + { + label: 'Validation Loss', + data: [], + borderColor: '#f59e0b', + backgroundColor: 'rgba(245, 158, 11, 0.1)', + tension: 0.4 + } + ] + }, + options: { + ...chartOptions, + plugins: { + ...chartOptions.plugins, + title: { + display: true, + text: 'Training & Validation Loss', + color: '#cbd5e1' + } + } + } + }); + + // Accuracy Chart + const accCtx = document.getElementById('accuracy-chart').getContext('2d'); + accuracyChart = new Chart(accCtx, { + type: 'line', + data: { + labels: [], + datasets: [ + { + label: 'Validation Accuracy', + data: [], + borderColor: '#10b981', + backgroundColor: 'rgba(16, 185, 129, 0.1)', + tension: 0.4, + fill: true + } + ] + }, + options: { + ...chartOptions, + plugins: { + ...chartOptions.plugins, + title: { + display: true, + text: 'Validation Accuracy', + color: '#cbd5e1' + } + }, + scales: { + ...chartOptions.scales, + y: { + ...chartOptions.scales.y, + min: 0, + max: 1, + ticks: { + ...chartOptions.scales.y.ticks, + callback: function(value) { + return (value * 100).toFixed(0) + '%'; + } + } + } + } + } + }); +} + +// Load Available Datasets +async function loadDatasets() { + try { + const response = await fetch(`${API_BASE}/api/datasets`); + const datasets = await response.json(); + + const select = document.getElementById('dataset-select'); + select.innerHTML = ''; + + datasets.forEach(dataset => { + const option = document.createElement('option'); + option.value = dataset.name; + option.textContent = `${dataset.name} (${dataset.features} features)`; + select.appendChild(option); + }); + + console.log(`✅ Loaded ${datasets.length} datasets`); + } catch (error) { + console.error('Error loading datasets:', error); + showError('Failed to load datasets'); + } +} + +// Update Dataset Info +function updateDatasetInfo() { + const select = document.getElementById('dataset-select'); + const infoDiv = document.getElementById('dataset-info'); + + if (select.value) { + infoDiv.textContent = `Selected: ${select.value}`; + } else { + infoDiv.textContent = ''; + } +} + +// Start Training +async function startTraining() { + const dataset = document.getElementById('dataset-select').value; + + if (!dataset) { + alert('Please select a dataset'); + return; + } + + const config = { + dataset: dataset, + n_qubits: parseInt(document.getElementById('n-qubits').value), + n_layers: parseInt(document.getElementById('n-layers').value), + learning_rate: parseFloat(document.getElementById('learning-rate').value), + duration_minutes: parseInt(document.getElementById('duration').value), + batch_size: parseInt(document.getElementById('batch-size').value), + optimizer: document.getElementById('optimizer-select').value, + early_stopping_patience: parseInt(document.getElementById('early-stopping').value), + checkpoint_every: parseInt(document.getElementById('checkpoint-every').value), + use_parameter_shift: document.getElementById('use-param-shift').checked, + use_warmup: document.getElementById('use-warmup').checked, + warmup_epochs: parseInt(document.getElementById('warmup-epochs').value), + use_lr_decay: document.getElementById('use-lr-decay').checked, + use_gradient_clipping: document.getElementById('use-grad-clip').checked, + max_grad_norm: parseFloat(document.getElementById('max-grad-norm').value) + }; + + console.log('🚀 Starting training with config:', config); + + try { + const response = await fetch(`${API_BASE}/api/train/start`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(config) + }); + + const result = await response.json(); + currentSessionId = result.session_id; + + // Update UI + document.getElementById('start-training-btn').disabled = true; + document.getElementById('stop-training-btn').disabled = false; + document.getElementById('status-idle').style.display = 'none'; + document.getElementById('status-training').style.display = 'block'; + document.getElementById('progress-container').style.display = 'block'; + document.getElementById('evaluation-section').style.display = 'none'; + document.getElementById('evaluate-btn').disabled = true; + + // Start visual effects + const nQubits = parseInt(document.getElementById('n-qubits').value); + const nLayers = parseInt(document.getElementById('n-layers').value); + drawQuantumCircuit(nQubits, nLayers, 0); + startParticleAnimation(); + + // Start polling for updates + startStatusPolling(); + + console.log(`✅ Training started: ${currentSessionId}`); + } catch (error) { + console.error('Error starting training:', error); + showError('Failed to start training'); + } +} + +// Stop Training +async function stopTraining() { + if (!currentSessionId) return; + + try { + await fetch(`${API_BASE}/api/train/stop/${currentSessionId}`, { + method: 'POST' + }); + + stopStatusPolling(); + console.log('⏹️ Training stopped'); + } catch (error) { + console.error('Error stopping training:', error); + } +} + +// Start Status Polling +function startStatusPolling() { + if (statusUpdateInterval) { + clearInterval(statusUpdateInterval); + } + + statusUpdateInterval = setInterval(updateTrainingStatus, 1000); +} + +// Stop Status Polling +function stopStatusPolling() { + if (statusUpdateInterval) { + clearInterval(statusUpdateInterval); + statusUpdateInterval = null; + } + + // Stop visual effects + stopParticleAnimation(); + drawIdleCircuit(); + + // Reset UI + document.getElementById('start-training-btn').disabled = false; + document.getElementById('stop-training-btn').disabled = true; + document.getElementById('status-idle').style.display = 'block'; + document.getElementById('status-training').style.display = 'none'; + document.getElementById('progress-container').style.display = 'none'; + document.getElementById('evaluate-btn').disabled = true; + document.getElementById('evaluation-section').style.display = 'none'; + + currentSessionId = null; + loadResults(); // Refresh results list +} + +// Update Training Status +async function updateTrainingStatus() { + if (!currentSessionId) return; + + try { + const response = await fetch(`${API_BASE}/api/train/status/${currentSessionId}`); + const status = await response.json(); + + // Update status text + document.getElementById('status-text').textContent = status.status; + document.getElementById('current-epoch').textContent = status.current_epoch; + document.getElementById('current-loss').textContent = status.current_loss.toFixed(4); + document.getElementById('best-val-acc').textContent = (status.best_val_acc * 100).toFixed(2) + '%'; + + // Update performance metrics + if (status.epochs_per_second > 0) { + document.getElementById('training-speed').textContent = status.epochs_per_second.toFixed(2) + ' ep/s'; + } + + if (status.eta_seconds) { + const etaMinutes = Math.floor(status.eta_seconds / 60); + const etaSeconds = Math.floor(status.eta_seconds % 60); + document.getElementById('eta-time').textContent = `${etaMinutes}:${etaSeconds.toString().padStart(2, '0')}`; + } else { + document.getElementById('eta-time').textContent = '-'; + } + + // Enable export button when training has data + if (status.metrics && status.metrics.epochs.length > 0) { + document.getElementById('export-metrics-btn').disabled = false; + } + + // Update elapsed time + if (status.elapsed_time) { + const minutes = Math.floor(status.elapsed_time / 60); + const seconds = Math.floor(status.elapsed_time % 60); + document.getElementById('elapsed-time').textContent = `${minutes}:${seconds.toString().padStart(2, '0')}`; + + // Update progress bar + const configDuration = parseInt(document.getElementById('duration').value) * 60; + const progress = Math.min((status.elapsed_time / configDuration) * 100, 100); + document.getElementById('progress-fill').style.width = progress + '%'; + document.getElementById('progress-text').textContent = progress.toFixed(0) + '% complete'; + } + + // Update visual accuracy bar + const accuracy = status.best_val_acc * 100; + document.getElementById('accuracy-bar').style.width = accuracy + '%'; + document.getElementById('accuracy-percent').textContent = accuracy.toFixed(1) + '%'; + + // Update circuit visualization + const nQubits = parseInt(document.getElementById('n-qubits').value); + const nLayers = parseInt(document.getElementById('n-layers').value); + drawQuantumCircuit(nQubits, nLayers, status.current_epoch); + + // Update charts + if (status.metrics && status.metrics.epochs.length > 0) { + updateCharts(status.metrics); + } + + // Check if completed + if (status.status === 'completed' || status.status === 'early_stopped' || status.status === 'error' || status.status === 'stopped') { + stopStatusPolling(); + + if (status.status === 'completed' || status.status === 'early_stopped') { + showSuccess('Training completed successfully!'); + // Enable evaluation + document.getElementById('evaluate-btn').disabled = false; + // Auto-evaluate if checkpoint exists + if (status.checkpoint_path) { + evaluateNow(); + } + } else if (status.status === 'error') { + showError('Training failed: ' + (status.error_message || 'Unknown error')); + } + } + + } catch (error) { + console.error('Error updating status:', error); + } +} + +// Update Charts with New Data +function updateCharts(metrics) { + // Loss Chart + lossChart.data.labels = metrics.epochs; + lossChart.data.datasets[0].data = metrics.train_loss; + lossChart.data.datasets[1].data = metrics.val_loss; + lossChart.update('none'); // Update without animation for smoothness + + // Accuracy Chart + accuracyChart.data.labels = metrics.epochs; + accuracyChart.data.datasets[0].data = metrics.val_accuracy; + accuracyChart.update('none'); +} + +// Load Training Results +async function loadResults() { + try { + const response = await fetch(`${API_BASE}/api/results`); + const results = await response.json(); + + const resultsDiv = document.getElementById('results-list'); + + if (results.length === 0) { + resultsDiv.innerHTML = '

No training sessions yet. Start training to see results here.

'; + return; + } + + resultsDiv.innerHTML = ''; + + results.forEach(result => { + const item = document.createElement('div'); + item.className = 'result-item'; + item.onclick = () => viewResultDetails(result.filename); + + item.innerHTML = ` +
+
${result.dataset}
+
${(result.best_acc * 100).toFixed(2)}% acc
+
+
+ 📅 ${result.timestamp.join('_')} + 🔄 ${result.epochs} epochs +
+ `; + + resultsDiv.appendChild(item); + }); + + console.log(`✅ Loaded ${results.length} training results`); + } catch (error) { + console.error('Error loading results:', error); + } +} + +// Input Validation +function validateInput(input) { + const errorDiv = document.getElementById('validation-error'); + const value = parseFloat(input.value); + let error = null; + + switch(input.id) { + case 'n-qubits': + if (value < 1 || value > 10) error = 'Qubits must be between 1 and 10'; + break; + case 'n-layers': + if (value < 1 || value > 20) error = 'Layers must be between 1 and 20'; + break; + case 'learning-rate': + if (value <= 0 || value > 1) error = 'Learning rate must be between 0 and 1'; + break; + case 'duration': + if (value < 1 || value > 120) error = 'Duration must be between 1 and 120 minutes'; + break; + case 'batch-size': + if (value < 8 || value > 128) error = 'Batch size must be between 8 and 128'; + break; + } + + if (error) { + input.style.borderColor = '#ef4444'; + errorDiv.textContent = error; + errorDiv.style.display = 'block'; + document.getElementById('start-training-btn').disabled = true; + } else { + input.style.borderColor = ''; + errorDiv.style.display = 'none'; + document.getElementById('start-training-btn').disabled = false; + } +} + +// Export Metrics +async function exportMetrics() { + if (!currentSessionId) return; + + try { + const response = await fetch(`${API_BASE}/api/export/metrics/${currentSessionId}`); + const blob = await response.blob(); + + // Create download link + const url = window.URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `metrics_${currentSessionId}.csv`; + document.body.appendChild(a); + a.click(); + a.remove(); + window.URL.revokeObjectURL(url); + + showSuccess('Metrics exported successfully!'); + } catch (error) { + console.error('Error exporting metrics:', error); + showError('Failed to export metrics'); + } +} + +// Evaluation +async function evaluateNow() { + if (!currentSessionId) return; + try { + const res = await fetch(`${API_BASE}/api/train/evaluate/${currentSessionId}`); + const data = await res.json(); + if (data.error) { + showError('Evaluation failed: ' + data.error); + return; + } + renderEvaluation(data); + showSuccess('Evaluation complete'); + } catch (e) { + console.error('Evaluation error', e); + showError('Failed to evaluate'); + } +} + +function renderEvaluation(result) { + const section = document.getElementById('evaluation-section'); + const metricsDiv = document.getElementById('eval-metrics'); + const cmDiv = document.getElementById('confusion-matrix'); + section.style.display = 'block'; + + const m = result.metrics; + metricsDiv.innerHTML = ` + Accuracy: ${(m.accuracy * 100).toFixed(2)}% · + Precision: ${(m.precision * 100).toFixed(2)}% · + Recall: ${(m.recall * 100).toFixed(2)}% · + F1: ${(m.f1 * 100).toFixed(2)}% + ${m.roc_auc !== null ? ` · ROC AUC: ${m.roc_auc.toFixed(3)}` : ''} + `; + renderConfusionMatrix(cmDiv, result.confusion_matrix, result.labels); +} + +function renderConfusionMatrix(container, matrix, labels) { + if (!matrix || !matrix.length) { + container.innerHTML = '

No confusion matrix available.

'; + return; + } + const n = matrix.length; + let html = ''; + for (let j = 0; j < n; j++) html += ``; + html += ''; + for (let i = 0; i < n; i++) { + html += ``; + for (let j = 0; j < n; j++) html += ``; + html += ''; + } + html += '
Pred ${labels[j] ?? j}
True ${labels[i] ?? i}${matrix[i][j]}
'; + container.innerHTML = html; +} + +// View Result Details +async function viewResultDetails(filename) { + try { + const response = await fetch(`${API_BASE}/api/results/${filename}`); + const data = await response.json(); + + console.log('📊 Result details:', data); + + // Update charts with historical data + if (data.metrics) { + updateCharts(data.metrics); + } + + // Show details (could open a modal or expand in place) + alert(`Training Session Details:\n\nDataset: ${data.config.dataset}\nQubits: ${data.config.n_qubits}\nLayers: ${data.config.n_layers}\nEpochs: ${data.total_epochs}\nBest Accuracy: ${(data.best_val_acc * 100).toFixed(2)}%`); + + } catch (error) { + console.error('Error loading result details:', error); + } +} + +// Show Success Message +function showSuccess(message) { + console.log('✅', message); + // Could add a toast notification here +} + +// Show Error Message +function showError(message) { + console.error('❌', message); + alert('Error: ' + message); +} diff --git a/ai-projects/quantum-ml/web_ui/static/styles.css b/ai-projects/quantum-ml/web_ui/static/styles.css index c5c20bb51..e239d5669 100644 --- a/ai-projects/quantum-ml/web_ui/static/styles.css +++ b/ai-projects/quantum-ml/web_ui/static/styles.css @@ -1,976 +1,976 @@ -:root { - --primary-color: #6366f1; - --primary-dark: #4f46e5; - --primary-light: #818cf8; - --secondary-color: #8b5cf6; - --accent-color: #ec4899; - --success-color: #10b981; - --danger-color: #ef4444; - --warning-color: #f59e0b; - --bg-dark: #0a0e1a; - --bg-darker: #050711; - --bg-card: rgba(30, 41, 59, 0.4); - --bg-card-hover: rgba(51, 65, 85, 0.6); - --text-primary: #f1f5f9; - --text-secondary: #cbd5e1; - --text-muted: #94a3b8; - --border-color: rgba(99, 102, 241, 0.2); - --glass-bg: rgba(30, 41, 59, 0.3); - --glass-border: rgba(148, 163, 184, 0.1); - --gradient-1: linear-gradient(135deg, #667eea 0%, #764ba2 100%); - --gradient-2: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); - --gradient-3: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); - --gradient-text: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%); - --shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.3); - --shadow-lg: 0 20px 40px -10px rgba(0, 0, 0, 0.5); - --shadow-xl: 0 25px 50px -12px rgba(0, 0, 0, 0.7); - --glow: 0 0 20px rgba(99, 102, 241, 0.4); - --glow-intense: 0 0 40px rgba(99, 102, 241, 0.6); -} - -* { - margin: 0; - padding: 0; - box-sizing: border-box; -} - -body { - font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Inter', sans-serif; - background: var(--bg-darker); - color: var(--text-primary); - line-height: 1.6; - min-height: 100vh; - position: relative; - overflow-x: hidden; -} - -/* Background Effects */ -.bg-particles { - position: fixed; - top: 0; - left: 0; - width: 100%; - height: 100%; - background-image: - radial-gradient(circle at 20% 50%, rgba(99, 102, 241, 0.05) 0%, transparent 50%), - radial-gradient(circle at 80% 80%, rgba(139, 92, 246, 0.05) 0%, transparent 50%); - pointer-events: none; - z-index: 0; -} - -.bg-gradient-orb { - position: fixed; - border-radius: 50%; - filter: blur(80px); - opacity: 0.3; - pointer-events: none; - z-index: 0; - animation: float 20s ease-in-out infinite; -} - -.orb-1 { - width: 500px; - height: 500px; - background: radial-gradient(circle, #6366f1, transparent); - top: -250px; - right: -250px; - animation-delay: 0s; -} - -.orb-2 { - width: 400px; - height: 400px; - background: radial-gradient(circle, #8b5cf6, transparent); - bottom: -200px; - left: -200px; - animation-delay: 5s; -} - -.orb-3 { - width: 300px; - height: 300px; - background: radial-gradient(circle, #ec4899, transparent); - top: 50%; - left: 50%; - animation-delay: 10s; -} - -@keyframes float { - 0%, 100% { transform: translate(0, 0) scale(1); } - 25% { transform: translate(50px, -50px) scale(1.1); } - 50% { transform: translate(-30px, 30px) scale(0.9); } - 75% { transform: translate(30px, 50px) scale(1.05); } -} - -.container { - max-width: 1400px; - margin: 0 auto; - padding: 20px; - position: relative; - z-index: 1; -} - -/* Header */ -.header { - background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); - backdrop-filter: blur(20px); - border: 1px solid var(--glass-border); - border-radius: 24px; - padding: 60px 40px; - margin-bottom: 40px; - box-shadow: var(--shadow-xl), var(--glow); - text-align: center; - position: relative; - overflow: hidden; -} - -.header::before { - content: ''; - position: absolute; - top: 0; - left: 0; - right: 0; - height: 1px; - background: linear-gradient(90deg, transparent, rgba(255,255,255,0.3), transparent); -} - -.logo-container { - margin-bottom: 30px; -} - -.quantum-logo { - position: relative; - width: 120px; - height: 120px; - margin: 0 auto; -} - -.logo-ring { - position: absolute; - border-radius: 50%; - border: 2px solid; - opacity: 0.6; -} - -.ring-1 { - width: 120px; - height: 120px; - border-color: #6366f1; - animation: rotate 10s linear infinite; -} - -.ring-2 { - width: 90px; - height: 90px; - top: 15px; - left: 15px; - border-color: #8b5cf6; - animation: rotate 15s linear infinite reverse; -} - -.ring-3 { - width: 60px; - height: 60px; - top: 30px; - left: 30px; - border-color: #ec4899; - animation: rotate 20s linear infinite; -} - -.logo-core { - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%); - font-size: 48px; - font-weight: 900; - background: var(--gradient-text); - -webkit-background-clip: text; - -webkit-text-fill-color: transparent; - background-clip: text; - animation: pulse-glow 2s ease-in-out infinite; -} - -@keyframes rotate { - from { transform: rotate(0deg); } - to { transform: rotate(360deg); } -} - -@keyframes pulse-glow { - 0%, 100% { filter: drop-shadow(0 0 10px rgba(99, 102, 241, 0.8)); } - 50% { filter: drop-shadow(0 0 20px rgba(139, 92, 246, 1)); } -} - -.gradient-text { - font-size: 3rem; - font-weight: 900; - background: var(--gradient-text); - -webkit-background-clip: text; - -webkit-text-fill-color: transparent; - background-clip: text; - margin-bottom: 15px; - letter-spacing: -0.02em; -} - -.subtitle { - font-size: 1.2rem; - opacity: 0.9; - color: var(--text-secondary); - font-weight: 300; - margin-bottom: 20px; -} - -.status-badge { - display: inline-flex; - align-items: center; - gap: 8px; - padding: 8px 20px; - background: rgba(16, 185, 129, 0.1); - border: 1px solid rgba(16, 185, 129, 0.3); - border-radius: 20px; - font-size: 0.9rem; - color: var(--success-color); -} - -.pulse-dot { - width: 8px; - height: 8px; - background: var(--success-color); - border-radius: 50%; - animation: pulse 2s ease-in-out infinite; -} - -@keyframes pulse { - 0%, 100% { opacity: 1; transform: scale(1); } - 50% { opacity: 0.5; transform: scale(1.2); } -} - -/* Main Content */ -.main-content { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); - gap: 24px; - margin-bottom: 40px; -} - -/* Glassmorphism Panel */ -.glass-panel { - background: var(--glass-bg); - backdrop-filter: blur(20px) saturate(180%); - border: 1px solid var(--glass-border); - position: relative; -} - -.glass-panel::before { - content: ''; - position: absolute; - top: 0; - left: 0; - right: 0; - height: 1px; - background: linear-gradient(90deg, transparent, rgba(255,255,255,0.1), transparent); -} - -.panel { - border-radius: 20px; - padding: 30px; - box-shadow: var(--shadow-lg); - transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); -} - -.panel:hover { - transform: translateY(-4px); - box-shadow: var(--shadow-xl), var(--glow); -} - -.panel-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 25px; - padding-bottom: 15px; - border-bottom: 1px solid var(--glass-border); -} - -.panel-header h2 { - font-size: 1.5rem; - font-weight: 700; - color: var(--text-primary); - display: flex; - align-items: center; - gap: 12px; -} - -.icon-gradient { - font-size: 1.8rem; - display: inline-block; - filter: drop-shadow(0 2px 8px rgba(99, 102, 241, 0.6)); -} - -.panel-badge { - padding: 4px 12px; - background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); - border: 1px solid var(--primary-color); - border-radius: 12px; - font-size: 0.75rem; - font-weight: 600; - text-transform: uppercase; - letter-spacing: 0.05em; - color: var(--primary-light); -} - -.live-indicator { - display: flex; - align-items: center; - gap: 8px; - padding: 6px 14px; - background: rgba(239, 68, 68, 0.1); - border: 1px solid var(--danger-color); - border-radius: 12px; - font-size: 0.75rem; - font-weight: 700; - color: var(--danger-color); - text-transform: uppercase; - letter-spacing: 0.05em; -} - -.live-dot { - width: 6px; - height: 6px; - background: var(--danger-color); - border-radius: 50%; - animation: pulse 1.5s ease-in-out infinite; - box-shadow: 0 0 10px var(--danger-color); -} - -/* Form Elements */ -.form-group { - margin-bottom: 20px; -} - -.form-row { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); - gap: 15px; -} - -label { - display: block; - margin-bottom: 8px; - font-weight: 500; - color: var(--text-secondary); - font-size: 0.9rem; -} - -.form-control { - width: 100%; - padding: 14px 18px; - background: rgba(15, 23, 42, 0.6); - border: 2px solid var(--border-color); - border-radius: 12px; - color: var(--text-primary); - font-size: 1rem; - transition: all 0.3s ease; - backdrop-filter: blur(10px); -} - -.form-control:focus { - outline: none; - border-color: var(--primary-color); - box-shadow: 0 0 0 4px rgba(99, 102, 241, 0.1), var(--glow); - background: rgba(15, 23, 42, 0.8); -} - -.form-control:hover { - border-color: var(--primary-light); -} - -.info-text { - margin-top: 8px; - font-size: 0.85rem; - color: var(--text-muted); -} - -/* Buttons */ -.button-group { - display: flex; - gap: 12px; - margin-top: 25px; -} - -.btn { - flex: 1; - padding: 16px 28px; - border: none; - border-radius: 12px; - font-size: 1.05rem; - font-weight: 600; - cursor: pointer; - transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); - display: flex; - align-items: center; - justify-content: center; - gap: 10px; - position: relative; - overflow: hidden; -} - -.btn::before { - content: ''; - position: absolute; - top: 50%; - left: 50%; - width: 0; - height: 0; - border-radius: 50%; - background: rgba(255, 255, 255, 0.2); - transform: translate(-50%, -50%); - transition: width 0.6s, height 0.6s; -} - -.btn:hover::before { - width: 300px; - height: 300px; -} - -.btn:disabled { - opacity: 0.5; - cursor: not-allowed; -} - -.btn-primary { - background: linear-gradient(135deg, var(--primary-color), var(--secondary-color)); - color: white; - box-shadow: 0 4px 15px rgba(99, 102, 241, 0.4); -} - -.btn-primary:hover:not(:disabled) { - transform: translateY(-2px); - box-shadow: 0 8px 25px rgba(99, 102, 241, 0.6); -} - -.btn-primary:active:not(:disabled) { - transform: translateY(0); -} - -.btn-danger { - background: linear-gradient(135deg, var(--danger-color), #dc2626); - color: white; - box-shadow: 0 4px 15px rgba(239, 68, 68, 0.4); -} - -.btn-danger:hover:not(:disabled) { - transform: translateY(-2px); - box-shadow: 0 8px 25px rgba(239, 68, 68, 0.6); -} - -.refresh-btn { - padding: 6px 16px; - background: rgba(99, 102, 241, 0.1); - border: 1px solid var(--primary-color); - border-radius: 8px; - color: var(--primary-color); - font-size: 0.85rem; - cursor: pointer; - transition: all 0.2s; -} - -.refresh-btn:hover { - background: rgba(99, 102, 241, 0.2); - transform: scale(1.05); -} - -.btn-icon { - font-size: 1.2rem; -} - -/* Status Panel */ -.status-message { - text-align: center; - padding: 30px; -} - -.status-icon { - font-size: 4rem; - margin-bottom: 15px; -} - -.status-icon.spinning { - animation: spin 2s linear infinite; -} - -@keyframes spin { - from { transform: rotate(0deg); } - to { transform: rotate(360deg); } -} - -.status-details { - display: grid; - gap: 12px; - margin-top: 20px; -} - -.status-item { - display: flex; - justify-content: space-between; - padding: 12px 16px; - background: var(--bg-dark); - border-radius: 8px; - border-left: 3px solid var(--primary-color); -} - -.status-item .label { - font-weight: 500; - color: var(--text-secondary); -} - -.status-item .value { - font-weight: 600; - color: var(--text-primary); -} - -.status-item.metric-large { - background: linear-gradient(135deg, rgba(99, 102, 241, 0.1), rgba(139, 92, 246, 0.1)); - border-left: 3px solid var(--secondary-color); - padding: 20px; -} - -.accuracy-display { - position: relative; - width: 100%; - height: 60px; - background: var(--bg-dark); - border-radius: 12px; - overflow: hidden; - margin-top: 10px; - box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.3); -} - -.accuracy-bar { - position: absolute; - top: 0; - left: 0; - height: 100%; - width: 0%; - background: linear-gradient(90deg, #10b981, #34d399); - transition: width 0.5s ease; - box-shadow: 0 0 20px rgba(16, 185, 129, 0.5); -} - -.accuracy-percent { - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%); - font-size: 1.8rem; - font-weight: 700; - color: var(--text-primary); - text-shadow: 0 2px 4px rgba(0, 0, 0, 0.5); - z-index: 1; -} - -/* Progress Bar */ -.progress-bar-container { - margin-top: 20px; -} - -.progress-bar { - height: 8px; - background: var(--bg-dark); - border-radius: 4px; - overflow: hidden; -} - -.progress-fill { - height: 100%; - background: var(--gradient-1); - transition: width 0.3s ease; - width: 0%; -} - -.progress-text { - text-align: center; - margin-top: 8px; - font-size: 0.9rem; - color: var(--text-muted); -} - -/* Quantum Circuit Visualization */ -.circuit-panel { - grid-column: 1 / -1; -} - -.canvas-container { - position: relative; - background: rgba(10, 14, 26, 0.6); - border-radius: 16px; - padding: 20px; - border: 2px solid var(--border-color); - box-shadow: inset 0 2px 10px rgba(0, 0, 0, 0.3); -} - -#circuit-canvas { - width: 100%; - height: 300px; - border-radius: 12px; - box-shadow: 0 0 30px rgba(99, 102, 241, 0.2); -} - -.circuit-stats { - display: flex; - gap: 20px; -} - -.stat-item { - display: flex; - align-items: center; - gap: 6px; - font-size: 0.9rem; -} - -.stat-label { - color: var(--text-muted); - font-weight: 500; -} - -.stat-item span:last-child { - color: var(--primary-light); - font-weight: 700; - font-family: 'JetBrains Mono', monospace; -} - -/* Particle Effects */ -@keyframes particle-float { - 0% { - transform: translateY(0) translateX(0) rotate(0deg); - opacity: 1; - } - 100% { - transform: translateY(-100px) translateX(50px) rotate(360deg); - opacity: 0; - } -} - -.particle { - position: absolute; - width: 4px; - height: 4px; - background: var(--primary-color); - border-radius: 50%; - animation: particle-float 2s ease-out forwards; -} - -/* Charts */ -/* Charts */ -.charts-panel { - grid-column: 1 / -1; -} - -.metrics-legend { - display: flex; - gap: 16px; -} - -.legend-item { - display: flex; - align-items: center; - gap: 6px; - font-size: 0.85rem; - color: var(--text-secondary); -} - -.legend-color { - width: 12px; - height: 12px; - border-radius: 3px; - box-shadow: 0 0 8px currentColor; -} - -.chart-container { - position: relative; - height: 320px; - margin-bottom: 30px; - padding: 25px; - background: rgba(10, 14, 26, 0.4); - border-radius: 16px; - border: 1px solid var(--glass-border); - backdrop-filter: blur(10px); -} - -.chart-container:last-child { - margin-bottom: 0; -} - -/* Results List */ -.results-panel { - grid-column: 1 / -1; -} - -.results-list { - display: grid; - gap: 12px; -} - -.result-item { - background: var(--bg-dark); - padding: 16px; - border-radius: 8px; - border-left: 4px solid var(--primary-color); - cursor: pointer; - transition: all 0.2s; -} - -.result-item:hover { - background: var(--bg-card-hover); - transform: translateX(4px); -} - -.result-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 8px; -} - -.result-title { - font-weight: 600; - font-size: 1.1rem; -} - -.result-badge { - padding: 4px 12px; - border-radius: 12px; - font-size: 0.85rem; - font-weight: 600; -} - -.result-badge.success { - background: rgba(16, 185, 129, 0.2); - color: var(--success-color); -} - -.result-meta { - display: flex; - gap: 20px; - font-size: 0.9rem; - color: var(--text-muted); -} - -.result-meta span { - display: flex; - align-items: center; - gap: 6px; -} - -/* Footer */ -.footer { - text-align: center; - padding: 40px 20px; - margin-top: 60px; - border-top: 1px solid var(--glass-border); - background: var(--glass-bg); - backdrop-filter: blur(20px); -} - -.footer-content { - display: flex; - flex-direction: column; - gap: 10px; -} - -.footer-brand { - font-size: 1.1rem; - font-weight: 600; - color: var(--text-primary); -} - -.version { - display: inline-block; - padding: 2px 8px; - background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); - border: 1px solid var(--primary-color); - border-radius: 6px; - font-size: 0.75rem; - margin-left: 8px; -} - -.footer-tech { - color: var(--text-muted); - font-size: 0.9rem; -} - -.footer-copyright { - color: var(--text-muted); - font-size: 0.85rem; -} - -/* Responsive */ -@media (max-width: 768px) { - .main-content { - grid-template-columns: 1fr; - } - - .header h1 { - font-size: 1.8rem; - } - - .button-group { - flex-direction: column; - } - - .chart-container { - height: 250px; - } -} - -/* Animations */ -@keyframes fadeIn { - from { - opacity: 0; - transform: translateY(10px); - } - to { - opacity: 1; - transform: translateY(0); - } -} - -.panel { - animation: fadeIn 0.3s ease; -} - -/* Error and Success Messages */ -.error-message { - background: rgba(239, 68, 68, 0.2); - border: 1px solid var(--danger-color); - border-radius: 8px; - padding: 12px; - margin-top: 12px; - color: var(--danger-color); - font-size: 0.9rem; - display: flex; - align-items: center; - gap: 8px; -} - -.error-message::before { - content: '⚠️'; -} - -.success-message { - background: rgba(16, 185, 129, 0.2); - border: 1px solid var(--success-color); - border-radius: 8px; - padding: 12px; - margin-top: 12px; - color: var(--success-color); - font-size: 0.9rem; - display: flex; - align-items: center; - gap: 8px; -} - -.success-message::before { - content: '✅'; -} - -/* Input validation states */ -.form-control.invalid { - border-color: var(--danger-color) !important; - box-shadow: 0 0 0 3px rgba(239, 68, 68, 0.1); -} - -.form-control:focus:invalid { - box-shadow: 0 0 0 3px rgba(239, 68, 68, 0.2); -} - -/* Secondary button styles */ -.btn-secondary { - background: linear-gradient(135deg, #475569 0%, #334155 100%); - color: var(--text-primary); -} - -.btn-secondary:hover { - background: linear-gradient(135deg, #64748b 0%, #475569 100%); - transform: translateY(-2px); - box-shadow: 0 8px 20px rgba(71, 85, 105, 0.4); -} - -.btn-secondary:disabled { - background: linear-gradient(135deg, #334155 0%, #1e293b 100%); - opacity: 0.4; -} - -/* Advanced settings */ -details.advanced-settings { - margin-top: 12px; - background: rgba(15, 23, 42, 0.3); - border: 1px solid rgba(148, 163, 184, 0.15); - border-radius: 10px; - padding: 10px 12px; -} -details.advanced-settings > summary { - cursor: pointer; - color: var(--text-primary); - font-weight: 600; -} -details.advanced-settings[open] { - backdrop-filter: blur(8px); -} -.advanced-grid { - display: grid; - grid-template-columns: repeat(2, minmax(0, 1fr)); - gap: 12px; - margin-top: 10px; -} -.advanced-grid .toggles { - display: flex; - flex-direction: column; - gap: 8px; -} -.checkbox { - display: flex; - align-items: center; - gap: 8px; - color: var(--text-secondary); -} - -/* Evaluation section */ -.evaluation-section { - margin-top: 16px; - padding-top: 10px; - border-top: 1px dashed rgba(148, 163, 184, 0.2); -} -.panel-header.small h3 { - font-size: 1rem; - margin: 0 0 6px 0; -} -.eval-metrics { - margin-bottom: 10px; -} -.cm-table { - width: 100%; - border-collapse: collapse; - background: rgba(15, 23, 42, 0.3); - border: 1px solid rgba(148, 163, 184, 0.15); - border-radius: 8px; - overflow: hidden; -} -.cm-table th, .cm-table td { - border: 1px solid rgba(148, 163, 184, 0.15); - padding: 8px 10px; - text-align: center; -} -.cm-table th { - background: rgba(99, 102, 241, 0.1); - color: var(--text-primary); -} -.cm-table td { - color: var(--text-secondary); -} - -@media (max-width: 768px) { - .advanced-grid { - grid-template-columns: 1fr; - } -} +:root { + --primary-color: #6366f1; + --primary-dark: #4f46e5; + --primary-light: #818cf8; + --secondary-color: #8b5cf6; + --accent-color: #ec4899; + --success-color: #10b981; + --danger-color: #ef4444; + --warning-color: #f59e0b; + --bg-dark: #0a0e1a; + --bg-darker: #050711; + --bg-card: rgba(30, 41, 59, 0.4); + --bg-card-hover: rgba(51, 65, 85, 0.6); + --text-primary: #f1f5f9; + --text-secondary: #cbd5e1; + --text-muted: #94a3b8; + --border-color: rgba(99, 102, 241, 0.2); + --glass-bg: rgba(30, 41, 59, 0.3); + --glass-border: rgba(148, 163, 184, 0.1); + --gradient-1: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + --gradient-2: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); + --gradient-3: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); + --gradient-text: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%); + --shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.3); + --shadow-lg: 0 20px 40px -10px rgba(0, 0, 0, 0.5); + --shadow-xl: 0 25px 50px -12px rgba(0, 0, 0, 0.7); + --glow: 0 0 20px rgba(99, 102, 241, 0.4); + --glow-intense: 0 0 40px rgba(99, 102, 241, 0.6); +} + +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Inter', sans-serif; + background: var(--bg-darker); + color: var(--text-primary); + line-height: 1.6; + min-height: 100vh; + position: relative; + overflow-x: hidden; +} + +/* Background Effects */ +.bg-particles { + position: fixed; + top: 0; + left: 0; + width: 100%; + height: 100%; + background-image: + radial-gradient(circle at 20% 50%, rgba(99, 102, 241, 0.05) 0%, transparent 50%), + radial-gradient(circle at 80% 80%, rgba(139, 92, 246, 0.05) 0%, transparent 50%); + pointer-events: none; + z-index: 0; +} + +.bg-gradient-orb { + position: fixed; + border-radius: 50%; + filter: blur(80px); + opacity: 0.3; + pointer-events: none; + z-index: 0; + animation: float 20s ease-in-out infinite; +} + +.orb-1 { + width: 500px; + height: 500px; + background: radial-gradient(circle, #6366f1, transparent); + top: -250px; + right: -250px; + animation-delay: 0s; +} + +.orb-2 { + width: 400px; + height: 400px; + background: radial-gradient(circle, #8b5cf6, transparent); + bottom: -200px; + left: -200px; + animation-delay: 5s; +} + +.orb-3 { + width: 300px; + height: 300px; + background: radial-gradient(circle, #ec4899, transparent); + top: 50%; + left: 50%; + animation-delay: 10s; +} + +@keyframes float { + 0%, 100% { transform: translate(0, 0) scale(1); } + 25% { transform: translate(50px, -50px) scale(1.1); } + 50% { transform: translate(-30px, 30px) scale(0.9); } + 75% { transform: translate(30px, 50px) scale(1.05); } +} + +.container { + max-width: 1400px; + margin: 0 auto; + padding: 20px; + position: relative; + z-index: 1; +} + +/* Header */ +.header { + background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); + backdrop-filter: blur(20px); + border: 1px solid var(--glass-border); + border-radius: 24px; + padding: 60px 40px; + margin-bottom: 40px; + box-shadow: var(--shadow-xl), var(--glow); + text-align: center; + position: relative; + overflow: hidden; +} + +.header::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + height: 1px; + background: linear-gradient(90deg, transparent, rgba(255,255,255,0.3), transparent); +} + +.logo-container { + margin-bottom: 30px; +} + +.quantum-logo { + position: relative; + width: 120px; + height: 120px; + margin: 0 auto; +} + +.logo-ring { + position: absolute; + border-radius: 50%; + border: 2px solid; + opacity: 0.6; +} + +.ring-1 { + width: 120px; + height: 120px; + border-color: #6366f1; + animation: rotate 10s linear infinite; +} + +.ring-2 { + width: 90px; + height: 90px; + top: 15px; + left: 15px; + border-color: #8b5cf6; + animation: rotate 15s linear infinite reverse; +} + +.ring-3 { + width: 60px; + height: 60px; + top: 30px; + left: 30px; + border-color: #ec4899; + animation: rotate 20s linear infinite; +} + +.logo-core { + position: absolute; + top: 50%; + left: 50%; + transform: translate(-50%, -50%); + font-size: 48px; + font-weight: 900; + background: var(--gradient-text); + -webkit-background-clip: text; + -webkit-text-fill-color: transparent; + background-clip: text; + animation: pulse-glow 2s ease-in-out infinite; +} + +@keyframes rotate { + from { transform: rotate(0deg); } + to { transform: rotate(360deg); } +} + +@keyframes pulse-glow { + 0%, 100% { filter: drop-shadow(0 0 10px rgba(99, 102, 241, 0.8)); } + 50% { filter: drop-shadow(0 0 20px rgba(139, 92, 246, 1)); } +} + +.gradient-text { + font-size: 3rem; + font-weight: 900; + background: var(--gradient-text); + -webkit-background-clip: text; + -webkit-text-fill-color: transparent; + background-clip: text; + margin-bottom: 15px; + letter-spacing: -0.02em; +} + +.subtitle { + font-size: 1.2rem; + opacity: 0.9; + color: var(--text-secondary); + font-weight: 300; + margin-bottom: 20px; +} + +.status-badge { + display: inline-flex; + align-items: center; + gap: 8px; + padding: 8px 20px; + background: rgba(16, 185, 129, 0.1); + border: 1px solid rgba(16, 185, 129, 0.3); + border-radius: 20px; + font-size: 0.9rem; + color: var(--success-color); +} + +.pulse-dot { + width: 8px; + height: 8px; + background: var(--success-color); + border-radius: 50%; + animation: pulse 2s ease-in-out infinite; +} + +@keyframes pulse { + 0%, 100% { opacity: 1; transform: scale(1); } + 50% { opacity: 0.5; transform: scale(1.2); } +} + +/* Main Content */ +.main-content { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); + gap: 24px; + margin-bottom: 40px; +} + +/* Glassmorphism Panel */ +.glass-panel { + background: var(--glass-bg); + backdrop-filter: blur(20px) saturate(180%); + border: 1px solid var(--glass-border); + position: relative; +} + +.glass-panel::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + height: 1px; + background: linear-gradient(90deg, transparent, rgba(255,255,255,0.1), transparent); +} + +.panel { + border-radius: 20px; + padding: 30px; + box-shadow: var(--shadow-lg); + transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); +} + +.panel:hover { + transform: translateY(-4px); + box-shadow: var(--shadow-xl), var(--glow); +} + +.panel-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 25px; + padding-bottom: 15px; + border-bottom: 1px solid var(--glass-border); +} + +.panel-header h2 { + font-size: 1.5rem; + font-weight: 700; + color: var(--text-primary); + display: flex; + align-items: center; + gap: 12px; +} + +.icon-gradient { + font-size: 1.8rem; + display: inline-block; + filter: drop-shadow(0 2px 8px rgba(99, 102, 241, 0.6)); +} + +.panel-badge { + padding: 4px 12px; + background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); + border: 1px solid var(--primary-color); + border-radius: 12px; + font-size: 0.75rem; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.05em; + color: var(--primary-light); +} + +.live-indicator { + display: flex; + align-items: center; + gap: 8px; + padding: 6px 14px; + background: rgba(239, 68, 68, 0.1); + border: 1px solid var(--danger-color); + border-radius: 12px; + font-size: 0.75rem; + font-weight: 700; + color: var(--danger-color); + text-transform: uppercase; + letter-spacing: 0.05em; +} + +.live-dot { + width: 6px; + height: 6px; + background: var(--danger-color); + border-radius: 50%; + animation: pulse 1.5s ease-in-out infinite; + box-shadow: 0 0 10px var(--danger-color); +} + +/* Form Elements */ +.form-group { + margin-bottom: 20px; +} + +.form-row { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); + gap: 15px; +} + +label { + display: block; + margin-bottom: 8px; + font-weight: 500; + color: var(--text-secondary); + font-size: 0.9rem; +} + +.form-control { + width: 100%; + padding: 14px 18px; + background: rgba(15, 23, 42, 0.6); + border: 2px solid var(--border-color); + border-radius: 12px; + color: var(--text-primary); + font-size: 1rem; + transition: all 0.3s ease; + backdrop-filter: blur(10px); +} + +.form-control:focus { + outline: none; + border-color: var(--primary-color); + box-shadow: 0 0 0 4px rgba(99, 102, 241, 0.1), var(--glow); + background: rgba(15, 23, 42, 0.8); +} + +.form-control:hover { + border-color: var(--primary-light); +} + +.info-text { + margin-top: 8px; + font-size: 0.85rem; + color: var(--text-muted); +} + +/* Buttons */ +.button-group { + display: flex; + gap: 12px; + margin-top: 25px; +} + +.btn { + flex: 1; + padding: 16px 28px; + border: none; + border-radius: 12px; + font-size: 1.05rem; + font-weight: 600; + cursor: pointer; + transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); + display: flex; + align-items: center; + justify-content: center; + gap: 10px; + position: relative; + overflow: hidden; +} + +.btn::before { + content: ''; + position: absolute; + top: 50%; + left: 50%; + width: 0; + height: 0; + border-radius: 50%; + background: rgba(255, 255, 255, 0.2); + transform: translate(-50%, -50%); + transition: width 0.6s, height 0.6s; +} + +.btn:hover::before { + width: 300px; + height: 300px; +} + +.btn:disabled { + opacity: 0.5; + cursor: not-allowed; +} + +.btn-primary { + background: linear-gradient(135deg, var(--primary-color), var(--secondary-color)); + color: white; + box-shadow: 0 4px 15px rgba(99, 102, 241, 0.4); +} + +.btn-primary:hover:not(:disabled) { + transform: translateY(-2px); + box-shadow: 0 8px 25px rgba(99, 102, 241, 0.6); +} + +.btn-primary:active:not(:disabled) { + transform: translateY(0); +} + +.btn-danger { + background: linear-gradient(135deg, var(--danger-color), #dc2626); + color: white; + box-shadow: 0 4px 15px rgba(239, 68, 68, 0.4); +} + +.btn-danger:hover:not(:disabled) { + transform: translateY(-2px); + box-shadow: 0 8px 25px rgba(239, 68, 68, 0.6); +} + +.refresh-btn { + padding: 6px 16px; + background: rgba(99, 102, 241, 0.1); + border: 1px solid var(--primary-color); + border-radius: 8px; + color: var(--primary-color); + font-size: 0.85rem; + cursor: pointer; + transition: all 0.2s; +} + +.refresh-btn:hover { + background: rgba(99, 102, 241, 0.2); + transform: scale(1.05); +} + +.btn-icon { + font-size: 1.2rem; +} + +/* Status Panel */ +.status-message { + text-align: center; + padding: 30px; +} + +.status-icon { + font-size: 4rem; + margin-bottom: 15px; +} + +.status-icon.spinning { + animation: spin 2s linear infinite; +} + +@keyframes spin { + from { transform: rotate(0deg); } + to { transform: rotate(360deg); } +} + +.status-details { + display: grid; + gap: 12px; + margin-top: 20px; +} + +.status-item { + display: flex; + justify-content: space-between; + padding: 12px 16px; + background: var(--bg-dark); + border-radius: 8px; + border-left: 3px solid var(--primary-color); +} + +.status-item .label { + font-weight: 500; + color: var(--text-secondary); +} + +.status-item .value { + font-weight: 600; + color: var(--text-primary); +} + +.status-item.metric-large { + background: linear-gradient(135deg, rgba(99, 102, 241, 0.1), rgba(139, 92, 246, 0.1)); + border-left: 3px solid var(--secondary-color); + padding: 20px; +} + +.accuracy-display { + position: relative; + width: 100%; + height: 60px; + background: var(--bg-dark); + border-radius: 12px; + overflow: hidden; + margin-top: 10px; + box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.3); +} + +.accuracy-bar { + position: absolute; + top: 0; + left: 0; + height: 100%; + width: 0%; + background: linear-gradient(90deg, #10b981, #34d399); + transition: width 0.5s ease; + box-shadow: 0 0 20px rgba(16, 185, 129, 0.5); +} + +.accuracy-percent { + position: absolute; + top: 50%; + left: 50%; + transform: translate(-50%, -50%); + font-size: 1.8rem; + font-weight: 700; + color: var(--text-primary); + text-shadow: 0 2px 4px rgba(0, 0, 0, 0.5); + z-index: 1; +} + +/* Progress Bar */ +.progress-bar-container { + margin-top: 20px; +} + +.progress-bar { + height: 8px; + background: var(--bg-dark); + border-radius: 4px; + overflow: hidden; +} + +.progress-fill { + height: 100%; + background: var(--gradient-1); + transition: width 0.3s ease; + width: 0%; +} + +.progress-text { + text-align: center; + margin-top: 8px; + font-size: 0.9rem; + color: var(--text-muted); +} + +/* Quantum Circuit Visualization */ +.circuit-panel { + grid-column: 1 / -1; +} + +.canvas-container { + position: relative; + background: rgba(10, 14, 26, 0.6); + border-radius: 16px; + padding: 20px; + border: 2px solid var(--border-color); + box-shadow: inset 0 2px 10px rgba(0, 0, 0, 0.3); +} + +#circuit-canvas { + width: 100%; + height: 300px; + border-radius: 12px; + box-shadow: 0 0 30px rgba(99, 102, 241, 0.2); +} + +.circuit-stats { + display: flex; + gap: 20px; +} + +.stat-item { + display: flex; + align-items: center; + gap: 6px; + font-size: 0.9rem; +} + +.stat-label { + color: var(--text-muted); + font-weight: 500; +} + +.stat-item span:last-child { + color: var(--primary-light); + font-weight: 700; + font-family: 'JetBrains Mono', monospace; +} + +/* Particle Effects */ +@keyframes particle-float { + 0% { + transform: translateY(0) translateX(0) rotate(0deg); + opacity: 1; + } + 100% { + transform: translateY(-100px) translateX(50px) rotate(360deg); + opacity: 0; + } +} + +.particle { + position: absolute; + width: 4px; + height: 4px; + background: var(--primary-color); + border-radius: 50%; + animation: particle-float 2s ease-out forwards; +} + +/* Charts */ +/* Charts */ +.charts-panel { + grid-column: 1 / -1; +} + +.metrics-legend { + display: flex; + gap: 16px; +} + +.legend-item { + display: flex; + align-items: center; + gap: 6px; + font-size: 0.85rem; + color: var(--text-secondary); +} + +.legend-color { + width: 12px; + height: 12px; + border-radius: 3px; + box-shadow: 0 0 8px currentColor; +} + +.chart-container { + position: relative; + height: 320px; + margin-bottom: 30px; + padding: 25px; + background: rgba(10, 14, 26, 0.4); + border-radius: 16px; + border: 1px solid var(--glass-border); + backdrop-filter: blur(10px); +} + +.chart-container:last-child { + margin-bottom: 0; +} + +/* Results List */ +.results-panel { + grid-column: 1 / -1; +} + +.results-list { + display: grid; + gap: 12px; +} + +.result-item { + background: var(--bg-dark); + padding: 16px; + border-radius: 8px; + border-left: 4px solid var(--primary-color); + cursor: pointer; + transition: all 0.2s; +} + +.result-item:hover { + background: var(--bg-card-hover); + transform: translateX(4px); +} + +.result-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 8px; +} + +.result-title { + font-weight: 600; + font-size: 1.1rem; +} + +.result-badge { + padding: 4px 12px; + border-radius: 12px; + font-size: 0.85rem; + font-weight: 600; +} + +.result-badge.success { + background: rgba(16, 185, 129, 0.2); + color: var(--success-color); +} + +.result-meta { + display: flex; + gap: 20px; + font-size: 0.9rem; + color: var(--text-muted); +} + +.result-meta span { + display: flex; + align-items: center; + gap: 6px; +} + +/* Footer */ +.footer { + text-align: center; + padding: 40px 20px; + margin-top: 60px; + border-top: 1px solid var(--glass-border); + background: var(--glass-bg); + backdrop-filter: blur(20px); +} + +.footer-content { + display: flex; + flex-direction: column; + gap: 10px; +} + +.footer-brand { + font-size: 1.1rem; + font-weight: 600; + color: var(--text-primary); +} + +.version { + display: inline-block; + padding: 2px 8px; + background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); + border: 1px solid var(--primary-color); + border-radius: 6px; + font-size: 0.75rem; + margin-left: 8px; +} + +.footer-tech { + color: var(--text-muted); + font-size: 0.9rem; +} + +.footer-copyright { + color: var(--text-muted); + font-size: 0.85rem; +} + +/* Responsive */ +@media (max-width: 768px) { + .main-content { + grid-template-columns: 1fr; + } + + .header h1 { + font-size: 1.8rem; + } + + .button-group { + flex-direction: column; + } + + .chart-container { + height: 250px; + } +} + +/* Animations */ +@keyframes fadeIn { + from { + opacity: 0; + transform: translateY(10px); + } + to { + opacity: 1; + transform: translateY(0); + } +} + +.panel { + animation: fadeIn 0.3s ease; +} + +/* Error and Success Messages */ +.error-message { + background: rgba(239, 68, 68, 0.2); + border: 1px solid var(--danger-color); + border-radius: 8px; + padding: 12px; + margin-top: 12px; + color: var(--danger-color); + font-size: 0.9rem; + display: flex; + align-items: center; + gap: 8px; +} + +.error-message::before { + content: '⚠️'; +} + +.success-message { + background: rgba(16, 185, 129, 0.2); + border: 1px solid var(--success-color); + border-radius: 8px; + padding: 12px; + margin-top: 12px; + color: var(--success-color); + font-size: 0.9rem; + display: flex; + align-items: center; + gap: 8px; +} + +.success-message::before { + content: '✅'; +} + +/* Input validation states */ +.form-control.invalid { + border-color: var(--danger-color) !important; + box-shadow: 0 0 0 3px rgba(239, 68, 68, 0.1); +} + +.form-control:focus:invalid { + box-shadow: 0 0 0 3px rgba(239, 68, 68, 0.2); +} + +/* Secondary button styles */ +.btn-secondary { + background: linear-gradient(135deg, #475569 0%, #334155 100%); + color: var(--text-primary); +} + +.btn-secondary:hover { + background: linear-gradient(135deg, #64748b 0%, #475569 100%); + transform: translateY(-2px); + box-shadow: 0 8px 20px rgba(71, 85, 105, 0.4); +} + +.btn-secondary:disabled { + background: linear-gradient(135deg, #334155 0%, #1e293b 100%); + opacity: 0.4; +} + +/* Advanced settings */ +details.advanced-settings { + margin-top: 12px; + background: rgba(15, 23, 42, 0.3); + border: 1px solid rgba(148, 163, 184, 0.15); + border-radius: 10px; + padding: 10px 12px; +} +details.advanced-settings > summary { + cursor: pointer; + color: var(--text-primary); + font-weight: 600; +} +details.advanced-settings[open] { + backdrop-filter: blur(8px); +} +.advanced-grid { + display: grid; + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 12px; + margin-top: 10px; +} +.advanced-grid .toggles { + display: flex; + flex-direction: column; + gap: 8px; +} +.checkbox { + display: flex; + align-items: center; + gap: 8px; + color: var(--text-secondary); +} + +/* Evaluation section */ +.evaluation-section { + margin-top: 16px; + padding-top: 10px; + border-top: 1px dashed rgba(148, 163, 184, 0.2); +} +.panel-header.small h3 { + font-size: 1rem; + margin: 0 0 6px 0; +} +.eval-metrics { + margin-bottom: 10px; +} +.cm-table { + width: 100%; + border-collapse: collapse; + background: rgba(15, 23, 42, 0.3); + border: 1px solid rgba(148, 163, 184, 0.15); + border-radius: 8px; + overflow: hidden; +} +.cm-table th, .cm-table td { + border: 1px solid rgba(148, 163, 184, 0.15); + padding: 8px 10px; + text-align: center; +} +.cm-table th { + background: rgba(99, 102, 241, 0.1); + color: var(--text-primary); +} +.cm-table td { + color: var(--text-secondary); +} + +@media (max-width: 768px) { + .advanced-grid { + grid-template-columns: 1fr; + } +} diff --git a/apps/aria/AUTO-EXECUTE.md b/apps/aria/AUTO-EXECUTE.md index de0033e79..93254ac07 100644 --- a/apps/aria/AUTO-EXECUTE.md +++ b/apps/aria/AUTO-EXECUTE.md @@ -1,436 +1,436 @@ -# Aria Auto-Execute System 🤖 - -## Overview - -The Aria Auto-Execute system adds LLM-powered automatic action generation and execution to the Aria 3D character assistant. Users can type natural language commands that are automatically parsed into structured actions and executed in real-time. - -## Features - -✨ **LLM-Powered Parsing**: Uses shared chat providers (Azure OpenAI, OpenAI, LoRA, Local) to parse commands -📋 **Action Planning**: Preview planned actions before execution -▶️ **Auto-Execution**: Execute action sequences automatically -🔄 **State Management**: Track and update stage state (Aria position, objects, expressions) -🛡️ **Fallback System**: Rule-based parser when LLM unavailable -🎯 **8 Core Actions**: move, say, pickup, drop, throw, gesture, look, wait - -## Quick Start - -### 1. Start the Server - -```bash -cd /workspaces/AI/aria_web -python server.py -``` - -Server will start on `http://localhost:8080` - -### 2. Open Auto-Execute Interface - -Open in browser: `http://localhost:8080/auto-execute.html` - -### 3. Try Example Commands - -Click any example command or type your own: - -- "Walk to the table and pick up the apple" -- "Say hello and wave at the audience" -- "Go to the center and do a little dance" -- "Pick up the book, move to stage left, and drop it" -- "Look at the flower and say how beautiful it is" -- "Throw the ball toward the right side of the stage" - -### 4. Plan or Execute - -- **Plan Actions Only**: Preview the action sequence without executing -- **Execute Actions**: Run the full action sequence and update stage state - -## Architecture - -### Action Schema (ARIA_ACTIONS) - -```python -ARIA_ACTIONS = { - "move": { - "params": ["target", "speed"], - "description": "Move Aria to a target position or object", - "example": {"action": "move", "target": {"x": 50, "y": 30}, "speed": "normal"} - }, - "say": { - "params": ["text", "emotion"], - "description": "Make Aria speak with optional emotion", - "example": {"action": "say", "text": "Hello!", "emotion": "happy"} - }, - "pickup": { - "params": ["object_id"], - "description": "Pick up an object from the stage", - "example": {"action": "pickup", "object_id": "apple"} - }, - "drop": { - "params": ["position"], - "description": "Drop currently held object at position", - "example": {"action": "drop", "position": {"x": 50, "y": 30}} - }, - "throw": { - "params": ["target", "force"], - "description": "Throw held object toward target", - "example": {"action": "throw", "target": {"x": 70, "y": 40}, "force": "medium"} - }, - "gesture": { - "params": ["gesture_type"], - "description": "Perform a gesture animation", - "example": {"action": "gesture", "gesture_type": "wave"} - }, - "look": { - "params": ["target"], - "description": "Look at a target position or object", - "example": {"action": "look", "target": "apple"} - }, - "wait": { - "params": ["duration"], - "description": "Wait for specified duration in seconds", - "example": {"action": "wait", "duration": 2.0} - } -} -``` - -### AriaActionParser Class - -```python -class AriaActionParser: - """LLM-powered action parser for automatic command execution""" - - def parse(command: str, use_llm: bool = True) -> List[dict]: - """Parse command into structured actions""" - # Try LLM first if available - if use_llm and self.provider: - return self.parse_with_llm(command) - # Fallback to rule-based - return self.parse_with_fallback(command) -``` - -**LLM Parsing Flow:** -1. Build system prompt with action schema + current stage state -2. Send command to LLM provider -3. Extract JSON array from response -4. Validate actions against schema -5. Return validated action list - -**Fallback Parsing:** -- Uses regex patterns similar to existing `generate_tags_fallback()` -- Parses move, say, pickup, gesture commands -- Returns structured actions without LLM - -### Action Execution - -```python -def execute_aria_action(action: dict) -> dict: - """Execute single action and update stage state""" - # Validates action type - # Updates stage_state (position, held_object, expression, etc.) - # Returns result with status, message, tags -``` - -**Execution Results:** -```json -{ - "status": "success", - "message": "Moved to apple", - "tags": ["[aria:position:45:40]"] -} -``` - -## API Endpoints - -### POST /api/aria/execute - -Execute commands with automatic action generation. - -**Request:** -```json -{ - "command": "Walk to the table and pick up the apple", - "auto_execute": true, - "use_llm": true -} -``` - -**Response (Plan Only - auto_execute=false):** -```json -{ - "status": "success", - "message": "Parsed 2 actions (plan only)", - "command": "Walk to the table and pick up the apple", - "actions": [ - {"action": "move", "target": {"x": 60, "y": 35}, "speed": "normal"}, - {"action": "pickup", "object_id": "apple"} - ], - "executed": false, - "results": null, - "tags": null, - "state": null -} -``` - -**Response (Executed - auto_execute=true):** -```json -{ - "status": "success", - "message": "Parsed 2 actions and executed", - "command": "Walk to the table and pick up the apple", - "actions": [ - {"action": "move", "target": {"x": 60, "y": 35}, "speed": "normal"}, - {"action": "pickup", "object_id": "apple"} - ], - "executed": true, - "results": [ - { - "action": {"action": "move", "target": {"x": 60, "y": 35}, "speed": "normal"}, - "result": { - "status": "success", - "message": "Moved to (60, 35)", - "tags": ["[aria:position:60:35]"] - } - }, - { - "action": {"action": "pickup", "object_id": "apple"}, - "result": { - "status": "success", - "message": "Picked up apple", - "tags": ["[aria:pickup:apple]", "[aria:limb:right_arm:grab]"] - } - } - ], - "tags": ["[aria:position:60:35]", "[aria:pickup:apple]", "[aria:limb:right_arm:grab]"], - "state": { - "aria": { - "position": {"x": 60, "y": 35}, - "expression": "neutral", - "held_object": "apple", - "facing": "right" - }, - "objects": { - "apple": {"position": {"x": 55, "y": 35}, "state": "held"} - } - } -} -``` - -**Error Response:** -```json -{ - "status": "error", - "error": "Too far from apple. Move closer first.", - "message": "Failed to execute command: Too far from apple. Move closer first." -} -``` - -## LLM Provider Detection - -Auto-detects available providers in order: - -1. **Azure OpenAI**: Requires all 4 env vars - - `AZURE_OPENAI_API_KEY` - - `AZURE_OPENAI_ENDPOINT` - - `AZURE_OPENAI_DEPLOYMENT` - - `AZURE_OPENAI_API_VERSION` - -2. **OpenAI**: Requires `OPENAI_API_KEY` - -3. **LoRA**: Auto-detect adapter in configured path - -4. **Local Echo**: Zero-dependency fallback - -**Check Provider Status:** -```bash -curl http://localhost:8080/api/ai/status | jq '.active_provider' -``` - -## Stage State Management - -The global `stage_state` dict tracks: - -```python -stage_state = { - 'aria': { - 'position': {'x': 15, 'y': 20}, # % coordinates (0-100) - 'expression': 'neutral', # facial expression - 'held_object': None, # currently held object ID - 'facing': 'right' # left or right - }, - 'objects': { - 'apple': { - 'position': {'x': 55, 'y': 35}, - 'state': 'on_table' # on_table, held, dropped, thrown - } - }, - 'environment': { - 'table': {'position': {'x': 60, 'y': 20}}, - 'stage_bounds': {'width': 100, 'height': 100} - } -} -``` - -**State Updates:** -- `move` → updates `aria.position` -- `say` → updates `aria.expression` -- `pickup` → sets `aria.held_object`, updates `object.state` to "held" -- `drop` → clears `aria.held_object`, moves object to new position -- `throw` → clears `aria.held_object`, moves object to target -- `look` → updates `aria.facing` - -## Action Validation Rules - -### Move Action -- Target can be `{x, y}` position or object name -- Position must be within stage bounds (0-100) -- Updates Aria's position immediately - -### Pickup Action -- Aria must be within 30 units of object -- Aria can only hold one object at a time -- Object must exist in `stage_state.objects` - -### Drop/Throw Actions -- Aria must be holding an object -- Updates object position and state -- Clears `held_object` - -### Gesture Action -- Valid gestures: wave, bow, nod, shake, point, shrug, clap -- Fallback to 'wave' if invalid - -## Testing - -### Unit Test Example - -```python -def test_action_parser(): - parser = AriaActionParser() - - # Test LLM parsing (if available) - actions = parser.parse("Walk to the apple and pick it up", use_llm=True) - assert len(actions) == 2 - assert actions[0]['action'] == 'move' - assert actions[1]['action'] == 'pickup' - - # Test fallback parsing - actions = parser.parse("Say hello", use_llm=False) - assert len(actions) == 1 - assert actions[0]['action'] == 'say' - assert 'hello' in actions[0]['text'].lower() -``` - -### Integration Test - -```bash -# Start server -python server.py & - -# Test plan-only -curl -X POST http://localhost:8080/api/aria/execute \ - -H "Content-Type: application/json" \ - -d '{"command": "Walk to the table", "auto_execute": false}' - -# Test execution -curl -X POST http://localhost:8080/api/aria/execute \ - -H "Content-Type: application/json" \ - -d '{"command": "Pick up the apple", "auto_execute": true}' - -# Check state -curl http://localhost:8080/api/aria/state -``` - -## Troubleshooting - -### LLM Not Available - -**Symptom**: Server logs show "✗ LLM providers not available" - -**Solution**: -1. Check environment variables (Azure OpenAI or OpenAI keys) -2. Verify `shared/chat_providers.py` is accessible -3. System will automatically fall back to rule-based parsing - -### Action Parsing Fails - -**Symptom**: API returns 0 actions or unexpected actions - -**Solution**: -1. Try with `use_llm: false` to test fallback parser -2. Check command clarity (be specific about objects and actions) -3. Review server logs for parsing errors - -### Execution Fails - -**Symptom**: Actions return error status - -**Common Errors:** -- "Too far from object" → Move Aria closer first -- "Already holding an object" → Drop current object before pickup -- "Not holding anything" → Pick up an object first -- "Object not found" → Check object name spelling (apple, book, cup, ball, flower) - -**Debug:** -```python -# Check current state -curl http://localhost:8080/api/aria/state | jq - -# Check Aria's position and held object -curl http://localhost:8080/api/aria/state | jq '.aria' - -# Check object positions -curl http://localhost:8080/api/aria/state | jq '.objects' -``` - -## Performance - -- **LLM Parsing**: 1-3 seconds (depends on provider) -- **Fallback Parsing**: <10ms -- **Action Execution**: <1ms per action -- **State Updates**: Immediate (in-memory dict) - -## Limitations - -1. **Single Object Holding**: Aria can only hold one object at a time -2. **Distance Checks**: Objects must be within 30 units for pickup -3. **No Animation Timing**: Wait action duration is logical (not animated) -4. **2D Coordinates**: Stage uses 2D percentage coordinates (0-100) -5. **Limited Gestures**: Only 7 predefined gesture types - -## Future Enhancements - -- [ ] Multi-object inventory system -- [ ] Complex gesture sequences -- [ ] Natural language state queries ("Where is the apple?") -- [ ] Action history and undo -- [ ] Conditional actions ("If holding apple, drop it") -- [ ] Parallel action execution -- [ ] Animation timing integration -- [ ] 3D coordinate system - -## Related Files - -- `server.py` - Main server with action parser and execution logic -- `auto-execute.html` - Web UI for testing auto-execution -- `index.html` - Original Aria visual command interface -- `shared/chat_providers.py` - LLM provider detection and abstraction -- `tests/test_aria_auto_execute.py` - Unit and integration tests (TODO) - -## Contributing - -When adding new actions: - -1. Add action to `ARIA_ACTIONS` dict with params and example -2. Implement execution logic in `execute_aria_action()` -3. Add parsing pattern to `parse_with_fallback()` -4. Update LLM system prompt (auto-included from schema) -5. Add example command to `auto-execute.html` -6. Write tests for new action - -## License - -Part of the QAI (Quantum-AI) project. See root LICENSE file. - ---- - -**Need Help?** Check the [main README](/README.md) or [copilot instructions](/.github/copilot-instructions.md) +# Aria Auto-Execute System 🤖 + +## Overview + +The Aria Auto-Execute system adds LLM-powered automatic action generation and execution to the Aria 3D character assistant. Users can type natural language commands that are automatically parsed into structured actions and executed in real-time. + +## Features + +✨ **LLM-Powered Parsing**: Uses shared chat providers (Azure OpenAI, OpenAI, LoRA, Local) to parse commands +📋 **Action Planning**: Preview planned actions before execution +▶️ **Auto-Execution**: Execute action sequences automatically +🔄 **State Management**: Track and update stage state (Aria position, objects, expressions) +🛡️ **Fallback System**: Rule-based parser when LLM unavailable +🎯 **8 Core Actions**: move, say, pickup, drop, throw, gesture, look, wait + +## Quick Start + +### 1. Start the Server + +```bash +cd /workspaces/AI/aria_web +python server.py +``` + +Server will start on `http://localhost:8080` + +### 2. Open Auto-Execute Interface + +Open in browser: `http://localhost:8080/auto-execute.html` + +### 3. Try Example Commands + +Click any example command or type your own: + +- "Walk to the table and pick up the apple" +- "Say hello and wave at the audience" +- "Go to the center and do a little dance" +- "Pick up the book, move to stage left, and drop it" +- "Look at the flower and say how beautiful it is" +- "Throw the ball toward the right side of the stage" + +### 4. Plan or Execute + +- **Plan Actions Only**: Preview the action sequence without executing +- **Execute Actions**: Run the full action sequence and update stage state + +## Architecture + +### Action Schema (ARIA_ACTIONS) + +```python +ARIA_ACTIONS = { + "move": { + "params": ["target", "speed"], + "description": "Move Aria to a target position or object", + "example": {"action": "move", "target": {"x": 50, "y": 30}, "speed": "normal"} + }, + "say": { + "params": ["text", "emotion"], + "description": "Make Aria speak with optional emotion", + "example": {"action": "say", "text": "Hello!", "emotion": "happy"} + }, + "pickup": { + "params": ["object_id"], + "description": "Pick up an object from the stage", + "example": {"action": "pickup", "object_id": "apple"} + }, + "drop": { + "params": ["position"], + "description": "Drop currently held object at position", + "example": {"action": "drop", "position": {"x": 50, "y": 30}} + }, + "throw": { + "params": ["target", "force"], + "description": "Throw held object toward target", + "example": {"action": "throw", "target": {"x": 70, "y": 40}, "force": "medium"} + }, + "gesture": { + "params": ["gesture_type"], + "description": "Perform a gesture animation", + "example": {"action": "gesture", "gesture_type": "wave"} + }, + "look": { + "params": ["target"], + "description": "Look at a target position or object", + "example": {"action": "look", "target": "apple"} + }, + "wait": { + "params": ["duration"], + "description": "Wait for specified duration in seconds", + "example": {"action": "wait", "duration": 2.0} + } +} +``` + +### AriaActionParser Class + +```python +class AriaActionParser: + """LLM-powered action parser for automatic command execution""" + + def parse(command: str, use_llm: bool = True) -> List[dict]: + """Parse command into structured actions""" + # Try LLM first if available + if use_llm and self.provider: + return self.parse_with_llm(command) + # Fallback to rule-based + return self.parse_with_fallback(command) +``` + +**LLM Parsing Flow:** +1. Build system prompt with action schema + current stage state +2. Send command to LLM provider +3. Extract JSON array from response +4. Validate actions against schema +5. Return validated action list + +**Fallback Parsing:** +- Uses regex patterns similar to existing `generate_tags_fallback()` +- Parses move, say, pickup, gesture commands +- Returns structured actions without LLM + +### Action Execution + +```python +def execute_aria_action(action: dict) -> dict: + """Execute single action and update stage state""" + # Validates action type + # Updates stage_state (position, held_object, expression, etc.) + # Returns result with status, message, tags +``` + +**Execution Results:** +```json +{ + "status": "success", + "message": "Moved to apple", + "tags": ["[aria:position:45:40]"] +} +``` + +## API Endpoints + +### POST /api/aria/execute + +Execute commands with automatic action generation. + +**Request:** +```json +{ + "command": "Walk to the table and pick up the apple", + "auto_execute": true, + "use_llm": true +} +``` + +**Response (Plan Only - auto_execute=false):** +```json +{ + "status": "success", + "message": "Parsed 2 actions (plan only)", + "command": "Walk to the table and pick up the apple", + "actions": [ + {"action": "move", "target": {"x": 60, "y": 35}, "speed": "normal"}, + {"action": "pickup", "object_id": "apple"} + ], + "executed": false, + "results": null, + "tags": null, + "state": null +} +``` + +**Response (Executed - auto_execute=true):** +```json +{ + "status": "success", + "message": "Parsed 2 actions and executed", + "command": "Walk to the table and pick up the apple", + "actions": [ + {"action": "move", "target": {"x": 60, "y": 35}, "speed": "normal"}, + {"action": "pickup", "object_id": "apple"} + ], + "executed": true, + "results": [ + { + "action": {"action": "move", "target": {"x": 60, "y": 35}, "speed": "normal"}, + "result": { + "status": "success", + "message": "Moved to (60, 35)", + "tags": ["[aria:position:60:35]"] + } + }, + { + "action": {"action": "pickup", "object_id": "apple"}, + "result": { + "status": "success", + "message": "Picked up apple", + "tags": ["[aria:pickup:apple]", "[aria:limb:right_arm:grab]"] + } + } + ], + "tags": ["[aria:position:60:35]", "[aria:pickup:apple]", "[aria:limb:right_arm:grab]"], + "state": { + "aria": { + "position": {"x": 60, "y": 35}, + "expression": "neutral", + "held_object": "apple", + "facing": "right" + }, + "objects": { + "apple": {"position": {"x": 55, "y": 35}, "state": "held"} + } + } +} +``` + +**Error Response:** +```json +{ + "status": "error", + "error": "Too far from apple. Move closer first.", + "message": "Failed to execute command: Too far from apple. Move closer first." +} +``` + +## LLM Provider Detection + +Auto-detects available providers in order: + +1. **Azure OpenAI**: Requires all 4 env vars + - `AZURE_OPENAI_API_KEY` + - `AZURE_OPENAI_ENDPOINT` + - `AZURE_OPENAI_DEPLOYMENT` + - `AZURE_OPENAI_API_VERSION` + +2. **OpenAI**: Requires `OPENAI_API_KEY` + +3. **LoRA**: Auto-detect adapter in configured path + +4. **Local Echo**: Zero-dependency fallback + +**Check Provider Status:** +```bash +curl http://localhost:8080/api/ai/status | jq '.active_provider' +``` + +## Stage State Management + +The global `stage_state` dict tracks: + +```python +stage_state = { + 'aria': { + 'position': {'x': 15, 'y': 20}, # % coordinates (0-100) + 'expression': 'neutral', # facial expression + 'held_object': None, # currently held object ID + 'facing': 'right' # left or right + }, + 'objects': { + 'apple': { + 'position': {'x': 55, 'y': 35}, + 'state': 'on_table' # on_table, held, dropped, thrown + } + }, + 'environment': { + 'table': {'position': {'x': 60, 'y': 20}}, + 'stage_bounds': {'width': 100, 'height': 100} + } +} +``` + +**State Updates:** +- `move` → updates `aria.position` +- `say` → updates `aria.expression` +- `pickup` → sets `aria.held_object`, updates `object.state` to "held" +- `drop` → clears `aria.held_object`, moves object to new position +- `throw` → clears `aria.held_object`, moves object to target +- `look` → updates `aria.facing` + +## Action Validation Rules + +### Move Action +- Target can be `{x, y}` position or object name +- Position must be within stage bounds (0-100) +- Updates Aria's position immediately + +### Pickup Action +- Aria must be within 30 units of object +- Aria can only hold one object at a time +- Object must exist in `stage_state.objects` + +### Drop/Throw Actions +- Aria must be holding an object +- Updates object position and state +- Clears `held_object` + +### Gesture Action +- Valid gestures: wave, bow, nod, shake, point, shrug, clap +- Fallback to 'wave' if invalid + +## Testing + +### Unit Test Example + +```python +def test_action_parser(): + parser = AriaActionParser() + + # Test LLM parsing (if available) + actions = parser.parse("Walk to the apple and pick it up", use_llm=True) + assert len(actions) == 2 + assert actions[0]['action'] == 'move' + assert actions[1]['action'] == 'pickup' + + # Test fallback parsing + actions = parser.parse("Say hello", use_llm=False) + assert len(actions) == 1 + assert actions[0]['action'] == 'say' + assert 'hello' in actions[0]['text'].lower() +``` + +### Integration Test + +```bash +# Start server +python server.py & + +# Test plan-only +curl -X POST http://localhost:8080/api/aria/execute \ + -H "Content-Type: application/json" \ + -d '{"command": "Walk to the table", "auto_execute": false}' + +# Test execution +curl -X POST http://localhost:8080/api/aria/execute \ + -H "Content-Type: application/json" \ + -d '{"command": "Pick up the apple", "auto_execute": true}' + +# Check state +curl http://localhost:8080/api/aria/state +``` + +## Troubleshooting + +### LLM Not Available + +**Symptom**: Server logs show "✗ LLM providers not available" + +**Solution**: +1. Check environment variables (Azure OpenAI or OpenAI keys) +2. Verify `shared/chat_providers.py` is accessible +3. System will automatically fall back to rule-based parsing + +### Action Parsing Fails + +**Symptom**: API returns 0 actions or unexpected actions + +**Solution**: +1. Try with `use_llm: false` to test fallback parser +2. Check command clarity (be specific about objects and actions) +3. Review server logs for parsing errors + +### Execution Fails + +**Symptom**: Actions return error status + +**Common Errors:** +- "Too far from object" → Move Aria closer first +- "Already holding an object" → Drop current object before pickup +- "Not holding anything" → Pick up an object first +- "Object not found" → Check object name spelling (apple, book, cup, ball, flower) + +**Debug:** +```python +# Check current state +curl http://localhost:8080/api/aria/state | jq + +# Check Aria's position and held object +curl http://localhost:8080/api/aria/state | jq '.aria' + +# Check object positions +curl http://localhost:8080/api/aria/state | jq '.objects' +``` + +## Performance + +- **LLM Parsing**: 1-3 seconds (depends on provider) +- **Fallback Parsing**: <10ms +- **Action Execution**: <1ms per action +- **State Updates**: Immediate (in-memory dict) + +## Limitations + +1. **Single Object Holding**: Aria can only hold one object at a time +2. **Distance Checks**: Objects must be within 30 units for pickup +3. **No Animation Timing**: Wait action duration is logical (not animated) +4. **2D Coordinates**: Stage uses 2D percentage coordinates (0-100) +5. **Limited Gestures**: Only 7 predefined gesture types + +## Future Enhancements + +- [ ] Multi-object inventory system +- [ ] Complex gesture sequences +- [ ] Natural language state queries ("Where is the apple?") +- [ ] Action history and undo +- [ ] Conditional actions ("If holding apple, drop it") +- [ ] Parallel action execution +- [ ] Animation timing integration +- [ ] 3D coordinate system + +## Related Files + +- `server.py` - Main server with action parser and execution logic +- `auto-execute.html` - Web UI for testing auto-execution +- `index.html` - Original Aria visual command interface +- `shared/chat_providers.py` - LLM provider detection and abstraction +- `tests/test_aria_auto_execute.py` - Unit and integration tests (TODO) + +## Contributing + +When adding new actions: + +1. Add action to `ARIA_ACTIONS` dict with params and example +2. Implement execution logic in `execute_aria_action()` +3. Add parsing pattern to `parse_with_fallback()` +4. Update LLM system prompt (auto-included from schema) +5. Add example command to `auto-execute.html` +6. Write tests for new action + +## License + +Part of the QAI (Quantum-AI) project. See root LICENSE file. + +--- + +**Need Help?** Check the [main README](/README.md) or [copilot instructions](/.github/copilot-instructions.md) diff --git a/apps/aria/IMPLEMENTATION_SUMMARY.md b/apps/aria/IMPLEMENTATION_SUMMARY.md index 3fd66862c..064ad84cb 100644 --- a/apps/aria/IMPLEMENTATION_SUMMARY.md +++ b/apps/aria/IMPLEMENTATION_SUMMARY.md @@ -1,312 +1,312 @@ -# Aria Auto-Execute Implementation Summary - -## ✅ Implementation Complete - -Successfully added LLM-powered automatic action generation and execution system to the Aria 3D character assistant. - -## 🎯 Features Implemented - -### 1. Action Schema (ARIA_ACTIONS) -- **8 Core Actions**: move, say, pickup, drop, throw, gesture, look, wait -- **Structured Params**: Each action has required parameters and examples -- **Validation Ready**: Schema used for both LLM prompts and execution validation - -### 2. AriaActionParser Class -- **Dual-Mode Parsing**: - - LLM-powered parsing using `shared.chat_providers.detect_provider()` - - Rule-based fallback parser for offline/no-API operation -- **System Prompt Generation**: Dynamic prompts include action schema + current stage state -- **JSON Response Extraction**: Handles various LLM response formats (plain JSON, markdown-wrapped, etc.) -- **Action Validation**: Filters invalid actions before returning - -### 3. Action Execution Engine -- **`execute_aria_action()` Function**: - - Validates action type and parameters - - Updates global `stage_state` dict - - Returns status, message, and visualization tags - - Enforces rules (distance checks, single-object holding, etc.) -- **State Management**: - - Aria position, expression, held_object, facing - - Object positions and states (on_table, held, dropped, thrown) - - Environment data (table, stage bounds) - -### 4. REST API Endpoint -- **POST /api/aria/execute**: - - `command` (string): Natural language command - - `auto_execute` (bool): Execute actions or plan only - - `use_llm` (bool): Use LLM or fallback parser -- **Response Format**: - - Plan mode: Returns parsed actions only - - Execute mode: Returns actions + execution results + updated state - - Error handling: Graceful failures with detailed messages - -### 5. Web Interface (auto-execute.html) -- **Interactive UI**: - - Command input with example commands - - Plan vs Execute buttons - - LLM toggle and state display options -- **Real-time Results**: - - Action cards with parameters - - Execution status badges - - Generated tags display - - JSON state viewer with syntax highlighting -- **Responsive Design**: Modern gradient UI with smooth animations - -## 📁 Files Created/Modified - -### Created: -- `aria_web/auto-execute.html` - Web UI for testing (529 lines) -- `aria_web/AUTO-EXECUTE.md` - Comprehensive documentation (450+ lines) -- This summary document - -### Modified: -- `aria_web/server.py` - Added 223 lines: - - LLM imports and provider detection (lines 12-13, 23-31) - - ARIA_ACTIONS schema (lines 57-101) - - AriaActionParser class (lines 104-277) - - execute_aria_action() function (lines 617-771) - - /api/aria/execute endpoint (lines 910-982) - -## 🧪 Testing Results - -### API Tests (Manual) - -✅ **Plan Mode (auto_execute=false)**: -```bash -curl -X POST http://localhost:8080/api/aria/execute \ - -H "Content-Type: application/json" \ - -d '{"command": "Say hello", "auto_execute": false, "use_llm": false}' -``` -Result: Successfully parsed 1 action (say) without execution - -✅ **Execute Mode (auto_execute=true)**: -```bash -curl -X POST http://localhost:8080/api/aria/execute \ - -H "Content-Type: application/json" \ - -d '{"command": "Wave and say hello", "auto_execute": true, "use_llm": false}' -``` -Result: Successfully parsed 2 actions (say, gesture) and executed both with proper tags - -### Server Startup - -✅ Server starts successfully on port 8080 -✅ LLM provider detection works (detected available providers) -✅ Fallback mode active when LLM unavailable -✅ CORS headers configured for cross-origin requests - -## 🔄 Integration with Existing Code - -### Preserved Features: -- ✅ Original `/api/aria/command` endpoint (rule-based tags) -- ✅ `/api/aria/state` endpoint (stage state queries) -- ✅ `/api/aria/object` endpoint (object CRUD) -- ✅ Existing tag generation system (`generate_tags_fallback()`) -- ✅ AI model loading path (currently disabled for fast startup) - -### New Parallel System: -- New `/api/aria/execute` endpoint operates independently -- Uses same `stage_state` dict for consistency -- AriaActionParser can use existing fallback patterns -- Compatible with all existing provider detection logic - -## 📊 Action Validation Rules - -### Move -- Target can be position `{x, y}` or object name -- Position clamped to stage bounds (0-100) -- Updates `aria.position` immediately - -### Pickup -- Distance check: must be within 30 units -- Single object limit: can't pickup if already holding -- Updates `aria.held_object` and `object.state` - -### Drop/Throw -- Requires held object -- Updates object position and state -- Clears `aria.held_object` - -### Say -- Updates `aria.expression` based on emotion param -- Generates visualization tags - -### Gesture -- Validates against 7 predefined gestures -- Falls back to 'wave' if invalid - -### Look -- Calculates facing direction (left/right) -- Updates `aria.facing` - -### Wait -- Logical duration (no actual delay) -- Useful for multi-step sequences - -## 🎨 Example Commands - -All tested with fallback parser: - -| Command | Actions Generated | Execution | -|---------|------------------|-----------| -| "Say hello" | `say(text="hello", emotion="happy")` | ✅ | -| "Wave and say hello" | `say(...), gesture(wave)` | ✅ | -| "Walk to the table" | `move(target={x:60, y:35})` | ✅ | -| "Pick up the apple" | `move(to apple), pickup(apple)` | ✅ | -| "Go to center" | `move(target={x:50, y:50})` | ✅ | - -## 🚀 Performance - -- **Server Startup**: <2 seconds (LLM detection + initialization) -- **Fallback Parsing**: <10ms per command -- **Action Execution**: <1ms per action -- **State Updates**: Immediate (in-memory dict) -- **LLM Parsing**: 1-3 seconds (when provider available) - -## 🔧 Configuration - -### Environment Variables (Optional) - -For LLM-powered parsing, configure any of: - -**Azure OpenAI** (requires all 4): -```bash -AZURE_OPENAI_API_KEY= -AZURE_OPENAI_ENDPOINT= -AZURE_OPENAI_DEPLOYMENT= -AZURE_OPENAI_API_VERSION=2024-08-01-preview -``` - -**OpenAI**: -```bash -OPENAI_API_KEY= -``` - -**Without API keys**: Falls back to rule-based parser automatically - -## 📈 Future Enhancements - -Documented in AUTO-EXECUTE.md: -- Multi-object inventory -- Complex gesture sequences -- Natural language state queries -- Action history and undo -- Conditional actions -- Parallel execution -- Animation timing -- 3D coordinates - -## 🎓 How It Works - -``` -User Command - ↓ -AriaActionParser.parse() - ↓ -[LLM Available?] → Yes → parse_with_llm() → System Prompt + Command → LLM Response - ↓ ↓ - No Extract JSON - ↓ ↓ -parse_with_fallback() Validate Actions - ↓ ↓ -Regex Patterns Structured Actions - ↓ ↓ - └──────────────────→ Action List ←────────────┘ - ↓ - [auto_execute=true?] - ↓ - execute_aria_action() for each action - ↓ - Update stage_state + Generate tags - ↓ - Return: {status, message, tags, state} -``` - -## 🧰 Developer Notes - -### Adding New Actions - -1. Add to `ARIA_ACTIONS` dict: -```python -"new_action": { - "params": ["param1", "param2"], - "description": "What it does", - "example": {"action": "new_action", "param1": "value"} -} -``` - -2. Implement in `execute_aria_action()`: -```python -elif action_type == 'new_action': - param1 = action.get('param1') - # Execute logic - return {'status': 'success', 'message': '...', 'tags': [...]} -``` - -3. Add parsing in `parse_with_fallback()`: -```python -if 'trigger_word' in command_lower: - actions.append({"action": "new_action", "param1": "..."}) -``` - -4. Update AUTO-EXECUTE.md with new action docs - -### Testing Tips - -**Test Fallback Parser**: -```python -from aria_web.server import action_parser -actions = action_parser.parse("your command", use_llm=False) -``` - -**Test Execution**: -```python -from aria_web.server import execute_aria_action, stage_state -result = execute_aria_action({"action": "move", "target": {"x": 50, "y": 50}}) -print(stage_state['aria']['position']) # Should be updated -``` - -**Test API**: -```bash -# Plan only -curl -X POST http://localhost:8080/api/aria/execute \ - -H "Content-Type: application/json" \ - -d '{"command": "test", "auto_execute": false}' - -# Execute -curl -X POST http://localhost:8080/api/aria/execute \ - -H "Content-Type: application/json" \ - -d '{"command": "test", "auto_execute": true}' -``` - -## ✨ Highlights - -1. **Zero Dependencies for Core**: Fallback parser works without any API keys -2. **Backward Compatible**: All existing endpoints and functionality preserved -3. **Comprehensive Error Handling**: Graceful failures with detailed messages -4. **Real-time State Management**: All actions update shared stage state -5. **Extensible Design**: Easy to add new actions or modify existing ones -6. **Production Ready**: Logging, CORS, validation, error responses all configured - -## 📚 Documentation - -- **AUTO-EXECUTE.md**: Complete user guide with API docs, examples, troubleshooting -- **Server logs**: Detailed execution traces with emoji indicators -- **Code comments**: Inline documentation for all major functions -- **Example commands**: Built into web UI for quick testing - -## 🎉 Success Metrics - -- ✅ 5/5 Todo tasks completed -- ✅ API endpoint operational and tested -- ✅ Web UI created and functional -- ✅ Documentation comprehensive -- ✅ Backward compatibility maintained -- ✅ LLM integration with fallback working -- ✅ State management validated -- ✅ Error handling robust - ---- - -**Implementation completed successfully!** 🚀 - -The Aria character can now automatically parse natural language commands and execute them using either LLM-powered intelligence or rule-based fallback, providing a flexible and robust system for autonomous action generation. +# Aria Auto-Execute Implementation Summary + +## ✅ Implementation Complete + +Successfully added LLM-powered automatic action generation and execution system to the Aria 3D character assistant. + +## 🎯 Features Implemented + +### 1. Action Schema (ARIA_ACTIONS) +- **8 Core Actions**: move, say, pickup, drop, throw, gesture, look, wait +- **Structured Params**: Each action has required parameters and examples +- **Validation Ready**: Schema used for both LLM prompts and execution validation + +### 2. AriaActionParser Class +- **Dual-Mode Parsing**: + - LLM-powered parsing using `shared.chat_providers.detect_provider()` + - Rule-based fallback parser for offline/no-API operation +- **System Prompt Generation**: Dynamic prompts include action schema + current stage state +- **JSON Response Extraction**: Handles various LLM response formats (plain JSON, markdown-wrapped, etc.) +- **Action Validation**: Filters invalid actions before returning + +### 3. Action Execution Engine +- **`execute_aria_action()` Function**: + - Validates action type and parameters + - Updates global `stage_state` dict + - Returns status, message, and visualization tags + - Enforces rules (distance checks, single-object holding, etc.) +- **State Management**: + - Aria position, expression, held_object, facing + - Object positions and states (on_table, held, dropped, thrown) + - Environment data (table, stage bounds) + +### 4. REST API Endpoint +- **POST /api/aria/execute**: + - `command` (string): Natural language command + - `auto_execute` (bool): Execute actions or plan only + - `use_llm` (bool): Use LLM or fallback parser +- **Response Format**: + - Plan mode: Returns parsed actions only + - Execute mode: Returns actions + execution results + updated state + - Error handling: Graceful failures with detailed messages + +### 5. Web Interface (auto-execute.html) +- **Interactive UI**: + - Command input with example commands + - Plan vs Execute buttons + - LLM toggle and state display options +- **Real-time Results**: + - Action cards with parameters + - Execution status badges + - Generated tags display + - JSON state viewer with syntax highlighting +- **Responsive Design**: Modern gradient UI with smooth animations + +## 📁 Files Created/Modified + +### Created: +- `aria_web/auto-execute.html` - Web UI for testing (529 lines) +- `aria_web/AUTO-EXECUTE.md` - Comprehensive documentation (450+ lines) +- This summary document + +### Modified: +- `aria_web/server.py` - Added 223 lines: + - LLM imports and provider detection (lines 12-13, 23-31) + - ARIA_ACTIONS schema (lines 57-101) + - AriaActionParser class (lines 104-277) + - execute_aria_action() function (lines 617-771) + - /api/aria/execute endpoint (lines 910-982) + +## 🧪 Testing Results + +### API Tests (Manual) + +✅ **Plan Mode (auto_execute=false)**: +```bash +curl -X POST http://localhost:8080/api/aria/execute \ + -H "Content-Type: application/json" \ + -d '{"command": "Say hello", "auto_execute": false, "use_llm": false}' +``` +Result: Successfully parsed 1 action (say) without execution + +✅ **Execute Mode (auto_execute=true)**: +```bash +curl -X POST http://localhost:8080/api/aria/execute \ + -H "Content-Type: application/json" \ + -d '{"command": "Wave and say hello", "auto_execute": true, "use_llm": false}' +``` +Result: Successfully parsed 2 actions (say, gesture) and executed both with proper tags + +### Server Startup + +✅ Server starts successfully on port 8080 +✅ LLM provider detection works (detected available providers) +✅ Fallback mode active when LLM unavailable +✅ CORS headers configured for cross-origin requests + +## 🔄 Integration with Existing Code + +### Preserved Features: +- ✅ Original `/api/aria/command` endpoint (rule-based tags) +- ✅ `/api/aria/state` endpoint (stage state queries) +- ✅ `/api/aria/object` endpoint (object CRUD) +- ✅ Existing tag generation system (`generate_tags_fallback()`) +- ✅ AI model loading path (currently disabled for fast startup) + +### New Parallel System: +- New `/api/aria/execute` endpoint operates independently +- Uses same `stage_state` dict for consistency +- AriaActionParser can use existing fallback patterns +- Compatible with all existing provider detection logic + +## 📊 Action Validation Rules + +### Move +- Target can be position `{x, y}` or object name +- Position clamped to stage bounds (0-100) +- Updates `aria.position` immediately + +### Pickup +- Distance check: must be within 30 units +- Single object limit: can't pickup if already holding +- Updates `aria.held_object` and `object.state` + +### Drop/Throw +- Requires held object +- Updates object position and state +- Clears `aria.held_object` + +### Say +- Updates `aria.expression` based on emotion param +- Generates visualization tags + +### Gesture +- Validates against 7 predefined gestures +- Falls back to 'wave' if invalid + +### Look +- Calculates facing direction (left/right) +- Updates `aria.facing` + +### Wait +- Logical duration (no actual delay) +- Useful for multi-step sequences + +## 🎨 Example Commands + +All tested with fallback parser: + +| Command | Actions Generated | Execution | +|---------|------------------|-----------| +| "Say hello" | `say(text="hello", emotion="happy")` | ✅ | +| "Wave and say hello" | `say(...), gesture(wave)` | ✅ | +| "Walk to the table" | `move(target={x:60, y:35})` | ✅ | +| "Pick up the apple" | `move(to apple), pickup(apple)` | ✅ | +| "Go to center" | `move(target={x:50, y:50})` | ✅ | + +## 🚀 Performance + +- **Server Startup**: <2 seconds (LLM detection + initialization) +- **Fallback Parsing**: <10ms per command +- **Action Execution**: <1ms per action +- **State Updates**: Immediate (in-memory dict) +- **LLM Parsing**: 1-3 seconds (when provider available) + +## 🔧 Configuration + +### Environment Variables (Optional) + +For LLM-powered parsing, configure any of: + +**Azure OpenAI** (requires all 4): +```bash +AZURE_OPENAI_API_KEY= +AZURE_OPENAI_ENDPOINT= +AZURE_OPENAI_DEPLOYMENT= +AZURE_OPENAI_API_VERSION=2024-08-01-preview +``` + +**OpenAI**: +```bash +OPENAI_API_KEY= +``` + +**Without API keys**: Falls back to rule-based parser automatically + +## 📈 Future Enhancements + +Documented in AUTO-EXECUTE.md: +- Multi-object inventory +- Complex gesture sequences +- Natural language state queries +- Action history and undo +- Conditional actions +- Parallel execution +- Animation timing +- 3D coordinates + +## 🎓 How It Works + +``` +User Command + ↓ +AriaActionParser.parse() + ↓ +[LLM Available?] → Yes → parse_with_llm() → System Prompt + Command → LLM Response + ↓ ↓ + No Extract JSON + ↓ ↓ +parse_with_fallback() Validate Actions + ↓ ↓ +Regex Patterns Structured Actions + ↓ ↓ + └──────────────────→ Action List ←────────────┘ + ↓ + [auto_execute=true?] + ↓ + execute_aria_action() for each action + ↓ + Update stage_state + Generate tags + ↓ + Return: {status, message, tags, state} +``` + +## 🧰 Developer Notes + +### Adding New Actions + +1. Add to `ARIA_ACTIONS` dict: +```python +"new_action": { + "params": ["param1", "param2"], + "description": "What it does", + "example": {"action": "new_action", "param1": "value"} +} +``` + +2. Implement in `execute_aria_action()`: +```python +elif action_type == 'new_action': + param1 = action.get('param1') + # Execute logic + return {'status': 'success', 'message': '...', 'tags': [...]} +``` + +3. Add parsing in `parse_with_fallback()`: +```python +if 'trigger_word' in command_lower: + actions.append({"action": "new_action", "param1": "..."}) +``` + +4. Update AUTO-EXECUTE.md with new action docs + +### Testing Tips + +**Test Fallback Parser**: +```python +from aria_web.server import action_parser +actions = action_parser.parse("your command", use_llm=False) +``` + +**Test Execution**: +```python +from aria_web.server import execute_aria_action, stage_state +result = execute_aria_action({"action": "move", "target": {"x": 50, "y": 50}}) +print(stage_state['aria']['position']) # Should be updated +``` + +**Test API**: +```bash +# Plan only +curl -X POST http://localhost:8080/api/aria/execute \ + -H "Content-Type: application/json" \ + -d '{"command": "test", "auto_execute": false}' + +# Execute +curl -X POST http://localhost:8080/api/aria/execute \ + -H "Content-Type: application/json" \ + -d '{"command": "test", "auto_execute": true}' +``` + +## ✨ Highlights + +1. **Zero Dependencies for Core**: Fallback parser works without any API keys +2. **Backward Compatible**: All existing endpoints and functionality preserved +3. **Comprehensive Error Handling**: Graceful failures with detailed messages +4. **Real-time State Management**: All actions update shared stage state +5. **Extensible Design**: Easy to add new actions or modify existing ones +6. **Production Ready**: Logging, CORS, validation, error responses all configured + +## 📚 Documentation + +- **AUTO-EXECUTE.md**: Complete user guide with API docs, examples, troubleshooting +- **Server logs**: Detailed execution traces with emoji indicators +- **Code comments**: Inline documentation for all major functions +- **Example commands**: Built into web UI for quick testing + +## 🎉 Success Metrics + +- ✅ 5/5 Todo tasks completed +- ✅ API endpoint operational and tested +- ✅ Web UI created and functional +- ✅ Documentation comprehensive +- ✅ Backward compatibility maintained +- ✅ LLM integration with fallback working +- ✅ State management validated +- ✅ Error handling robust + +--- + +**Implementation completed successfully!** 🚀 + +The Aria character can now automatically parse natural language commands and execute them using either LLM-powered intelligence or rule-based fallback, providing a flexible and robust system for autonomous action generation. diff --git a/apps/aria/README.md b/apps/aria/README.md index 6667cd7c6..721734410 100644 --- a/apps/aria/README.md +++ b/apps/aria/README.md @@ -1,351 +1,351 @@ -# Aria 3D Character Web Interface - -A real-time 3D character controller with object management and server synchronization. - -## Features - -- **UE5-First Web Runtime**: Unreal Engine 5 Pixel Streaming mode is the default renderer -- **Fallback Renderer**: Optional Three.js renderer remains available as a compatibility fallback -- **3D Character Control**: Move Aria character around the stage using waypoints -- **Object Management**: Add, pickup, drop, and throw objects -- **Server Sync**: All client actions synchronized to Python backend -- **Chat Interface**: Send commands via chat that generate server-side tags -- **Real-time Updates**: Bidirectional communication between UI and server - -## Quick Start - -```bash -# Start the server -cd aria_web -python server.py - -# Open in browser -# Navigate to: http://localhost:8000 -``` - -## Architecture - -### Frontend (`index.html` + `aria_controller.js`) - -- UE5-first renderer toggle (`UE5` / `Three.js`) -- UE5 Pixel Streaming URL via `?ue5Url=...` or `localStorage["aria-ue5-url"]` -- 3D CSS transforms for character positioning -- Drag-and-drop object placement -- Click waypoints for movement -- Chat command processing - -### Backend (`server.py`) - -- REST API for object management -- Global `stage_state` dictionary -- Tag generation for speech/movement commands -- HTTP server on port 8000 - -## API Endpoints - -### GET `/api/aria/state` - -Returns current stage state including character position and all objects. - -**Response:** - -```json -{ - "position": { "x": 0, "y": 0, "z": 0 }, - "objects": { - "apple": { - "id": "apple", - "emoji": "🍎", - "position": { "x": 100, "y": 200, "z": 0 }, - "state": "on_stage" - } - } -} -``` - -### GET `/api/aria/objects` - -Returns list of all objects on stage. - -**Response:** - -```json -{ - "objects": { - "apple": {...}, - "book": {...} - } -} -``` - -### POST `/api/aria/object` - -Add, update, or remove an object. - -**Add Object:** - -```json -{ - "action": "add", - "object": { - "id": "apple", - "emoji": "🍎", - "position": { "x": 100, "y": 200, "z": 0 } - } -} -``` - -**Update Object:** - -```json -{ - "action": "update", - "object": { - "id": "apple", - "position": { "x": 150, "y": 250, "z": 0 }, - "state": "held" - } -} -``` - -**Remove Object:** - -```json -{ - "action": "remove", - "object": { - "id": "apple" - } -} -``` - -### POST `/api/aria/command` - -Process a chat command and return generated tags. - -**Request:** - -```json -{ - "command": "Say hello and move to center" -} -``` - -**Response:** - -```json -{ - "tags": "[aria:say:hello][aria:position:center]" -} -``` - -### POST `/api/aria/world` - -Generate (or regenerate) a themed world layout using the LLM (if available) or a deterministic fallback. - -**Request:** - -```json -{ - "theme": "forest", // optional (default: "forest") - "count": 7, // optional number of objects (default: 6) - "use_llm": true // optional, force fallback if false -} -``` - -**Successful Response:** - -```json -{ - "status": "success", - "theme": "forest", - "count": 6, - "used_llm": true, - "objects": { - "tree": { - "id": "tree", - "emoji": "🌲", - "position": { "x": 42, "y": 33 }, - "state": "on_stage" - }, - "rock": { - "id": "rock", - "emoji": "🪨", - "position": { "x": 55, "y": 61 }, - "state": "on_stage" - } - }, - "environment": { - "theme": "forest", - "generated_at": "2025-11-28T17:20:00Z", - "stage_bounds": { "width": 100, "height": 100 } - } -} -``` - -**Notes:** - -- If the LLM response is malformed, the server automatically falls back to procedural generation. -- Object IDs are sanitized (alphanumeric + underscore, max 30 chars). -- Positions are guaranteed to lie within stage bounds (0–100). -- Existing objects are replaced; Aria's position is preserved. - -**Example cURL:** - -```bash -curl -X POST http://localhost:8080/api/aria/world \ - -H 'Content-Type: application/json' \ - -d '{"theme":"space","count":5,"use_llm":true}' | jq -``` - -## Object States - -- `on_stage` - Object is on the stage floor -- `on_table` - Object is on a table/elevated surface -- `held` - Object is being held by character -- `thrown` - Object is in mid-air - -## Client-Server Sync Flow - -```text -User Action (Click/Drag) - ↓ -aria_controller.js - ↓ -sendObjectUpdate() - ↓ -POST /api/aria/object - ↓ -server.py updates stage_state - ↓ -GET /api/aria/state (polling/refresh) - ↓ -UI reflects new state -``` - -## Testing - -Comprehensive testing strategy with multiple layers: - -- **Unit Tests**: Server-side tag generation and helpers -- **Integration Tests**: HTTP API endpoints -- **E2E Tests**: Full browser automation (Playwright, Pyppeteer, Selenium) - -See [TESTING.md](TESTING.md) for detailed testing guide. - -### Quick Test Commands - -```bash -# Run all unit and integration tests -pytest tests/test_aria_server.py tests/test_object_api_integration.py -v - -# Run E2E tests (requires browser setup) -pytest tests/test_ui_playwright.py -m playwright -v - -# Run all tests -pytest tests/test_*aria*.py tests/test_ui_*.py -v -``` - -## Development - -### File Structure - -```text -aria_web/ -├── index.html # Main UI -├── test.html # Test page for features -├── aria_controller.js # Client-side logic -├── server.py # Backend server -├── README.md # This file -└── TESTING.md # Testing guide -``` - -### Adding New Features - -1. Update `aria_controller.js` for client-side behavior -2. Add corresponding endpoint in `server.py` -3. Write unit tests in `tests/test_aria_server.py` -4. Write integration tests in `tests/test_object_api_integration.py` -5. Add E2E test if it's a critical user flow -6. Update this documentation - -### Key Functions - -#### Client-Side (`aria_controller.js`) - -- `addObject(name, emoji)` - Add a new object to the stage -- `pickUpObject(objectId)` - Pick up an object -- `dropObject()` - Drop the currently held object -- `throwObject(targetX, targetY)` - Throw the held object -- `sendObjectUpdate(data)` - Sync object state to server - -#### Server-Side (`server.py`) - -- `handle_object(action, obj)` - Process object operations -- `get_stage_state()` - Return current state -- `generate_speech_tag(text)` - Create `[aria:say:...]` tag -- `generate_position_tag(x, y)` - Create `[aria:position:...]` tag - -## CI/CD - -GitHub Actions workflow (`.github/workflows/aria-tests.yml`) runs automatically on: - -- Push to `main` or `develop` -- Pull requests -- Changes to `aria_web/` or test files - -Workflow includes: - -- Unit & integration tests (Python 3.10, 3.11, 3.12) -- Playwright E2E tests -- Pyppeteer E2E tests -- Selenium containerized Chrome tests -- Test result aggregation - -## Future Enhancements - -- [ ] WebSocket for real-time updates (replace polling) -- [ ] Physics engine for object interactions -- [ ] Inventory system -- [ ] Multi-character support -- [ ] Deeper UE5 scene integration (bi-directional gameplay events) -- [ ] Save/load stage state -- [ ] Undo/redo functionality -- [ ] Collaborative editing (multiple users) - -## Troubleshooting - -### Server won't start - -- Check if port 8000 is already in use: `lsof -i :8000` -- Try different port: `python server.py --port 8080` - -### Objects not syncing - -- Check browser console for errors -- Verify server is running: `curl http://localhost:8000/api/aria/state` -- Check server logs for error messages - -### Tests failing - -- Ensure server is not running during tests (tests auto-start server) -- Check all dependencies are installed: `pip install -r requirements.txt` -- See [TESTING.md](TESTING.md) for detailed troubleshooting - -## Contributing - -1. Fork the repository -2. Create a feature branch -3. Write tests for your changes -4. Ensure all tests pass -5. Submit a pull request - -## License - -See main repository LICENSE file. - -## Related Documentation - -- [TESTING.md](TESTING.md) - Comprehensive testing guide -- [Main Project README](../../README.md) - Overall project documentation -- [GitHub Actions Workflow](../../.github/workflows/aria-tests.yml) - CI/CD configuration +# Aria 3D Character Web Interface + +A real-time 3D character controller with object management and server synchronization. + +## Features + +- **UE5-First Web Runtime**: Unreal Engine 5 Pixel Streaming mode is the default renderer +- **Fallback Renderer**: Optional Three.js renderer remains available as a compatibility fallback +- **3D Character Control**: Move Aria character around the stage using waypoints +- **Object Management**: Add, pickup, drop, and throw objects +- **Server Sync**: All client actions synchronized to Python backend +- **Chat Interface**: Send commands via chat that generate server-side tags +- **Real-time Updates**: Bidirectional communication between UI and server + +## Quick Start + +```bash +# Start the server +cd aria_web +python server.py + +# Open in browser +# Navigate to: http://localhost:8000 +``` + +## Architecture + +### Frontend (`index.html` + `aria_controller.js`) + +- UE5-first renderer toggle (`UE5` / `Three.js`) +- UE5 Pixel Streaming URL via `?ue5Url=...` or `localStorage["aria-ue5-url"]` +- 3D CSS transforms for character positioning +- Drag-and-drop object placement +- Click waypoints for movement +- Chat command processing + +### Backend (`server.py`) + +- REST API for object management +- Global `stage_state` dictionary +- Tag generation for speech/movement commands +- HTTP server on port 8000 + +## API Endpoints + +### GET `/api/aria/state` + +Returns current stage state including character position and all objects. + +**Response:** + +```json +{ + "position": { "x": 0, "y": 0, "z": 0 }, + "objects": { + "apple": { + "id": "apple", + "emoji": "🍎", + "position": { "x": 100, "y": 200, "z": 0 }, + "state": "on_stage" + } + } +} +``` + +### GET `/api/aria/objects` + +Returns list of all objects on stage. + +**Response:** + +```json +{ + "objects": { + "apple": {...}, + "book": {...} + } +} +``` + +### POST `/api/aria/object` + +Add, update, or remove an object. + +**Add Object:** + +```json +{ + "action": "add", + "object": { + "id": "apple", + "emoji": "🍎", + "position": { "x": 100, "y": 200, "z": 0 } + } +} +``` + +**Update Object:** + +```json +{ + "action": "update", + "object": { + "id": "apple", + "position": { "x": 150, "y": 250, "z": 0 }, + "state": "held" + } +} +``` + +**Remove Object:** + +```json +{ + "action": "remove", + "object": { + "id": "apple" + } +} +``` + +### POST `/api/aria/command` + +Process a chat command and return generated tags. + +**Request:** + +```json +{ + "command": "Say hello and move to center" +} +``` + +**Response:** + +```json +{ + "tags": "[aria:say:hello][aria:position:center]" +} +``` + +### POST `/api/aria/world` + +Generate (or regenerate) a themed world layout using the LLM (if available) or a deterministic fallback. + +**Request:** + +```json +{ + "theme": "forest", // optional (default: "forest") + "count": 7, // optional number of objects (default: 6) + "use_llm": true // optional, force fallback if false +} +``` + +**Successful Response:** + +```json +{ + "status": "success", + "theme": "forest", + "count": 6, + "used_llm": true, + "objects": { + "tree": { + "id": "tree", + "emoji": "🌲", + "position": { "x": 42, "y": 33 }, + "state": "on_stage" + }, + "rock": { + "id": "rock", + "emoji": "🪨", + "position": { "x": 55, "y": 61 }, + "state": "on_stage" + } + }, + "environment": { + "theme": "forest", + "generated_at": "2025-11-28T17:20:00Z", + "stage_bounds": { "width": 100, "height": 100 } + } +} +``` + +**Notes:** + +- If the LLM response is malformed, the server automatically falls back to procedural generation. +- Object IDs are sanitized (alphanumeric + underscore, max 30 chars). +- Positions are guaranteed to lie within stage bounds (0–100). +- Existing objects are replaced; Aria's position is preserved. + +**Example cURL:** + +```bash +curl -X POST http://localhost:8080/api/aria/world \ + -H 'Content-Type: application/json' \ + -d '{"theme":"space","count":5,"use_llm":true}' | jq +``` + +## Object States + +- `on_stage` - Object is on the stage floor +- `on_table` - Object is on a table/elevated surface +- `held` - Object is being held by character +- `thrown` - Object is in mid-air + +## Client-Server Sync Flow + +```text +User Action (Click/Drag) + ↓ +aria_controller.js + ↓ +sendObjectUpdate() + ↓ +POST /api/aria/object + ↓ +server.py updates stage_state + ↓ +GET /api/aria/state (polling/refresh) + ↓ +UI reflects new state +``` + +## Testing + +Comprehensive testing strategy with multiple layers: + +- **Unit Tests**: Server-side tag generation and helpers +- **Integration Tests**: HTTP API endpoints +- **E2E Tests**: Full browser automation (Playwright, Pyppeteer, Selenium) + +See [TESTING.md](TESTING.md) for detailed testing guide. + +### Quick Test Commands + +```bash +# Run all unit and integration tests +pytest tests/test_aria_server.py tests/test_object_api_integration.py -v + +# Run E2E tests (requires browser setup) +pytest tests/test_ui_playwright.py -m playwright -v + +# Run all tests +pytest tests/test_*aria*.py tests/test_ui_*.py -v +``` + +## Development + +### File Structure + +```text +aria_web/ +├── index.html # Main UI +├── test.html # Test page for features +├── aria_controller.js # Client-side logic +├── server.py # Backend server +├── README.md # This file +└── TESTING.md # Testing guide +``` + +### Adding New Features + +1. Update `aria_controller.js` for client-side behavior +2. Add corresponding endpoint in `server.py` +3. Write unit tests in `tests/test_aria_server.py` +4. Write integration tests in `tests/test_object_api_integration.py` +5. Add E2E test if it's a critical user flow +6. Update this documentation + +### Key Functions + +#### Client-Side (`aria_controller.js`) + +- `addObject(name, emoji)` - Add a new object to the stage +- `pickUpObject(objectId)` - Pick up an object +- `dropObject()` - Drop the currently held object +- `throwObject(targetX, targetY)` - Throw the held object +- `sendObjectUpdate(data)` - Sync object state to server + +#### Server-Side (`server.py`) + +- `handle_object(action, obj)` - Process object operations +- `get_stage_state()` - Return current state +- `generate_speech_tag(text)` - Create `[aria:say:...]` tag +- `generate_position_tag(x, y)` - Create `[aria:position:...]` tag + +## CI/CD + +GitHub Actions workflow (`.github/workflows/aria-tests.yml`) runs automatically on: + +- Push to `main` or `develop` +- Pull requests +- Changes to `aria_web/` or test files + +Workflow includes: + +- Unit & integration tests (Python 3.10, 3.11, 3.12) +- Playwright E2E tests +- Pyppeteer E2E tests +- Selenium containerized Chrome tests +- Test result aggregation + +## Future Enhancements + +- [ ] WebSocket for real-time updates (replace polling) +- [ ] Physics engine for object interactions +- [ ] Inventory system +- [ ] Multi-character support +- [ ] Deeper UE5 scene integration (bi-directional gameplay events) +- [ ] Save/load stage state +- [ ] Undo/redo functionality +- [ ] Collaborative editing (multiple users) + +## Troubleshooting + +### Server won't start + +- Check if port 8000 is already in use: `lsof -i :8000` +- Try different port: `python server.py --port 8080` + +### Objects not syncing + +- Check browser console for errors +- Verify server is running: `curl http://localhost:8000/api/aria/state` +- Check server logs for error messages + +### Tests failing + +- Ensure server is not running during tests (tests auto-start server) +- Check all dependencies are installed: `pip install -r requirements.txt` +- See [TESTING.md](TESTING.md) for detailed troubleshooting + +## Contributing + +1. Fork the repository +2. Create a feature branch +3. Write tests for your changes +4. Ensure all tests pass +5. Submit a pull request + +## License + +See main repository LICENSE file. + +## Related Documentation + +- [TESTING.md](TESTING.md) - Comprehensive testing guide +- [Main Project README](../../README.md) - Overall project documentation +- [GitHub Actions Workflow](../../.github/workflows/aria-tests.yml) - CI/CD configuration diff --git a/apps/aria/TESTING.md b/apps/aria/TESTING.md index 1b3e727a2..9734b5cfd 100644 --- a/apps/aria/TESTING.md +++ b/apps/aria/TESTING.md @@ -1,335 +1,335 @@ -# Aria E2E Testing Guide - -This document describes the comprehensive testing strategy for the Aria 3D character web interface, focusing on UI-to-server synchronization testing. - -## Overview - -The Aria project includes multiple layers of testing to ensure the frontend (JavaScript) properly syncs object state with the backend (Python server): - -1. **Unit Tests** - Test individual server functions (tag generation, helper functions) -2. **Integration Tests** - Test HTTP API endpoints directly (object add/update/remove) -3. **E2E Browser Tests** - Test the complete flow in a real browser (UI → JavaScript → Server) - -## Test Files - -### Unit Tests -- **`tests/test_aria_server.py`** - Server-side unit tests - - Tests tag generation (`[aria:say]`, `[aria:position]`, etc.) - - Fast, no external dependencies - - Run with: `pytest tests/test_aria_server.py` - -### Integration Tests -- **`tests/test_object_api_integration.py`** - HTTP API integration tests - - Tests REST endpoints (`/api/aria/object`, `/api/aria/state`) - - Requires server running on port 8000 - - Simulates client requests without browser - - Run with: `pytest tests/test_object_api_integration.py` - -### E2E Browser Tests - -#### Pyppeteer (Chromium via CDP) -- **`tests/test_ui_pyppeteer.py`** - Browser automation using pyppeteer - - Uses Chrome DevTools Protocol - - Lightweight, Python-native - - Supports custom Chrome path via `CHROME_PATH` env var - - Run with: `pytest tests/test_ui_pyppeteer.py` - -#### Playwright (Modern browser automation) -- **`tests/test_ui_playwright.py`** - Browser automation using Playwright - - Modern, cross-browser (Chrome/Firefox/Safari) - - Better debugging and error messages - - Recommended for local development - - Run with: `pytest tests/test_ui_playwright.py -m playwright` - -#### Selenium (Containerized Chrome) -- **`tests/test_ui_selenium.py`** - Browser automation using Selenium Grid - - Uses remote Chrome in Docker container - - Best for CI/CD and isolated testing - - Supports VNC for watching tests run - - Run with: `pytest tests/test_ui_selenium.py -m selenium` - -## Running Tests Locally - -### Prerequisites - -```bash -# Install base dependencies -pip install pytest requests - -# For Pyppeteer tests -pip install pyppeteer -# Download Chromium (one-time) -python -c "import asyncio; from pyppeteer import chromium_downloader; asyncio.get_event_loop().run_until_complete(chromium_downloader.download_chromium())" - -# For Playwright tests -pip install playwright -playwright install chromium - -# For Selenium tests -pip install selenium -# Start Selenium Chrome container -docker run -d -p 4444:4444 -p 5900:5900 --shm-size=2g selenium/standalone-chrome:latest -``` - -### Quick Test Commands - -```bash -# Run all fast tests (unit + integration) -pytest tests/test_aria_server.py tests/test_object_api_integration.py -v - -# Run Pyppeteer E2E tests -pytest tests/test_ui_pyppeteer.py -v - -# Run Playwright E2E tests -pytest tests/test_ui_playwright.py -m playwright -v - -# Run Selenium E2E tests (requires Docker container) -pytest tests/test_ui_selenium.py -m selenium -v - -# Run ALL tests (excluding browser tests) -pytest tests/test_*aria*.py tests/test_object_api*.py -v - -# Run ALL E2E tests (will skip if browsers not available) -pytest tests/test_ui_*.py -v -``` - -### Using pytest markers - -```bash -# Run only E2E tests -pytest -m e2e -v - -# Run unit tests only -pytest -m unit -v - -# Run integration tests only -pytest -m integration -v - -# Run everything except slow tests -pytest -m "not slow" -v - -# Run everything except E2E tests -pytest -m "not e2e" -v -``` - -## CI/CD Integration - -The project includes a comprehensive GitHub Actions workflow (`.github/workflows/aria-tests.yml`) that runs all test types in parallel: - -### Workflow Jobs - -1. **unit-integration-tests** (Python 3.10, 3.11, 3.12) - - Runs unit and integration tests - - Fast feedback (< 1 minute) - - No browser dependencies - -2. **playwright-e2e** - - Installs Playwright and Chromium - - Starts Aria server - - Runs full E2E tests - - Duration: ~2-3 minutes - -3. **pyppeteer-e2e** - - Installs system dependencies for Chromium - - Downloads Pyppeteer Chromium - - Runs E2E tests - - Duration: ~2-3 minutes - -4. **containerized-chrome-e2e** - - Uses Selenium standalone Chrome service - - Isolated browser environment - - Best for consistency across environments - - Duration: ~2-3 minutes - -5. **test-summary** - - Aggregates results from all jobs - - Provides unified test report - -### Triggering the Workflow - -The workflow runs automatically on: -- Push to `main` or `develop` branches -- Pull requests targeting `main` or `develop` -- Changes to `aria_web/` directory or test files - -Manual trigger: -```bash -# Go to GitHub Actions tab → Select "Aria E2E Tests" → Click "Run workflow" -``` - -## Environment Variables - -### Server Configuration -- `ARIA_SERVER_URL` - Base URL for Aria server (default: `http://localhost:8000`) - -### Pyppeteer Configuration -- `CHROME_PATH` - Custom Chrome/Chromium binary path -- `PUPPETEER_EXECUTABLE_PATH` - Alternative to CHROME_PATH -- `PYPPETEER_DEBUG` - Enable debug output (set to `true`) - -### Selenium Configuration -- `SELENIUM_REMOTE_URL` - Selenium Grid hub URL (default: `http://localhost:4444/wd/hub`) - -## Test Architecture - -### What Each Test Type Validates - -#### Unit Tests -- `[aria:say]` tag generation for speech commands -- `[aria:position]` tag generation for movement -- Object detection in command strings -- Helper function correctness - -#### Integration Tests -- POST `/api/aria/object` with action `add` creates object -- POST `/api/aria/object` with action `update` modifies object position -- POST `/api/aria/object` with action `remove` deletes object -- GET `/api/aria/state` returns current state -- GET `/api/aria/objects` lists all objects - -#### E2E Tests (All Variants) -1. **Add Object**: Click "Add Object" → Verify object appears in server state -2. **Pick Up Object**: Click object → Call `pickUpObject()` → Verify `state: 'held'` on server -3. **Drop Object**: Call `dropObject()` → Verify object state changes to `on_stage` or `on_table` -4. **Remove Object**: Send remove command → Verify object deleted from server - -### Test Flow Diagram - -``` -┌─────────────────────┐ -│ Browser UI │ -│ (aria_web/index.html)│ -└──────────┬──────────┘ - │ User clicks/drags - ▼ -┌─────────────────────┐ -│ aria_controller.js │ -│ - addObject() │ -│ - pickUpObject() │ -│ - dropObject() │ -│ - sendObjectUpdate()│ -└──────────┬──────────┘ - │ POST /api/aria/object - ▼ -┌─────────────────────┐ -│ aria server.py │ -│ - stage_state{} │ -│ - handle_object() │ -└──────────┬──────────┘ - │ Updates state - ▼ -┌─────────────────────┐ -│ E2E Test Validates │ -│ - GET /api/aria/state│ -│ - Asserts object │ -│ position/state │ -└─────────────────────┘ -``` - -## Troubleshooting - -### Pyppeteer: "Browser launch failed" - -**Cause**: Missing system dependencies for Chromium - -**Solution**: -```bash -# Ubuntu/Debian -sudo apt-get install -y \ - libnss3 libnspr4 libatk1.0-0 libatk-bridge2.0-0 \ - libcups2 libdrm2 libxkbcommon0 libxcomposite1 \ - libxdamage1 libxfixes3 libxrandr2 libgbm1 libasound2 - -# Or use custom Chrome -export CHROME_PATH=/usr/bin/google-chrome -pytest tests/test_ui_pyppeteer.py -v -``` - -### Playwright: "Executable doesn't exist" - -**Cause**: Browsers not installed - -**Solution**: -```bash -playwright install chromium -# Or install all browsers -playwright install -``` - -### Selenium: "Connection refused to localhost:4444" - -**Cause**: Selenium Chrome container not running - -**Solution**: -```bash -# Start container -docker run -d -p 4444:4444 -p 5900:5900 --shm-size=2g selenium/standalone-chrome:latest - -# Verify container is running -docker ps | grep selenium - -# Check Selenium hub status -curl http://localhost:4444/wd/hub/status -``` - -### Server: "Connection refused on port 8000" - -**Cause**: Aria server not running - -**Solution**: -```bash -# Start server manually -cd aria_web -python server.py - -# Or let test start it (tests auto-start if not running) -``` - -### Test skipped: "Chromium not available" - -**Cause**: Browser automation library can't launch browser - -**Solution**: -- Check if you're in a headless environment (CI, container) -- Ensure system dependencies are installed -- Verify browser installation -- Set custom Chrome path if needed - -## Best Practices - -1. **Start with integration tests** - Fastest feedback loop, no browser overhead -2. **Use Playwright for local development** - Best error messages and debugging -3. **Use Selenium in CI/CD** - Most consistent across environments -4. **Mark tests appropriately** - Use pytest markers for selective execution -5. **Keep E2E tests focused** - Test critical user flows, not every button click -6. **Mock external services in unit tests** - Don't hit real APIs -7. **Clean up test data** - Always remove test objects after tests - -## Future Improvements - -- [ ] Add visual regression testing (screenshot comparison) -- [ ] Add performance testing (measure API response times) -- [ ] Add load testing (concurrent users) -- [ ] Add accessibility testing (ARIA labels, keyboard navigation) -- [ ] Add cross-browser testing (Firefox, Safari via Playwright) -- [ ] Add mobile browser testing (Chrome Mobile, Safari iOS) -- [ ] Add test coverage reporting -- [ ] Add mutation testing - -## Contributing - -When adding new features to the Aria interface: - -1. Write unit tests first (TDD approach) -2. Add integration tests for new API endpoints -3. Add E2E tests for critical user flows -4. Update this documentation -5. Ensure all tests pass before submitting PR - -## Resources - -- [Pytest Documentation](https://docs.pytest.org/) -- [Playwright Python Documentation](https://playwright.dev/python/) -- [Pyppeteer Documentation](https://pyppeteer.github.io/pyppeteer/) -- [Selenium Documentation](https://www.selenium.dev/documentation/) -- [GitHub Actions Documentation](https://docs.github.com/en/actions) +# Aria E2E Testing Guide + +This document describes the comprehensive testing strategy for the Aria 3D character web interface, focusing on UI-to-server synchronization testing. + +## Overview + +The Aria project includes multiple layers of testing to ensure the frontend (JavaScript) properly syncs object state with the backend (Python server): + +1. **Unit Tests** - Test individual server functions (tag generation, helper functions) +2. **Integration Tests** - Test HTTP API endpoints directly (object add/update/remove) +3. **E2E Browser Tests** - Test the complete flow in a real browser (UI → JavaScript → Server) + +## Test Files + +### Unit Tests +- **`tests/test_aria_server.py`** - Server-side unit tests + - Tests tag generation (`[aria:say]`, `[aria:position]`, etc.) + - Fast, no external dependencies + - Run with: `pytest tests/test_aria_server.py` + +### Integration Tests +- **`tests/test_object_api_integration.py`** - HTTP API integration tests + - Tests REST endpoints (`/api/aria/object`, `/api/aria/state`) + - Requires server running on port 8000 + - Simulates client requests without browser + - Run with: `pytest tests/test_object_api_integration.py` + +### E2E Browser Tests + +#### Pyppeteer (Chromium via CDP) +- **`tests/test_ui_pyppeteer.py`** - Browser automation using pyppeteer + - Uses Chrome DevTools Protocol + - Lightweight, Python-native + - Supports custom Chrome path via `CHROME_PATH` env var + - Run with: `pytest tests/test_ui_pyppeteer.py` + +#### Playwright (Modern browser automation) +- **`tests/test_ui_playwright.py`** - Browser automation using Playwright + - Modern, cross-browser (Chrome/Firefox/Safari) + - Better debugging and error messages + - Recommended for local development + - Run with: `pytest tests/test_ui_playwright.py -m playwright` + +#### Selenium (Containerized Chrome) +- **`tests/test_ui_selenium.py`** - Browser automation using Selenium Grid + - Uses remote Chrome in Docker container + - Best for CI/CD and isolated testing + - Supports VNC for watching tests run + - Run with: `pytest tests/test_ui_selenium.py -m selenium` + +## Running Tests Locally + +### Prerequisites + +```bash +# Install base dependencies +pip install pytest requests + +# For Pyppeteer tests +pip install pyppeteer +# Download Chromium (one-time) +python -c "import asyncio; from pyppeteer import chromium_downloader; asyncio.get_event_loop().run_until_complete(chromium_downloader.download_chromium())" + +# For Playwright tests +pip install playwright +playwright install chromium + +# For Selenium tests +pip install selenium +# Start Selenium Chrome container +docker run -d -p 4444:4444 -p 5900:5900 --shm-size=2g selenium/standalone-chrome:latest +``` + +### Quick Test Commands + +```bash +# Run all fast tests (unit + integration) +pytest tests/test_aria_server.py tests/test_object_api_integration.py -v + +# Run Pyppeteer E2E tests +pytest tests/test_ui_pyppeteer.py -v + +# Run Playwright E2E tests +pytest tests/test_ui_playwright.py -m playwright -v + +# Run Selenium E2E tests (requires Docker container) +pytest tests/test_ui_selenium.py -m selenium -v + +# Run ALL tests (excluding browser tests) +pytest tests/test_*aria*.py tests/test_object_api*.py -v + +# Run ALL E2E tests (will skip if browsers not available) +pytest tests/test_ui_*.py -v +``` + +### Using pytest markers + +```bash +# Run only E2E tests +pytest -m e2e -v + +# Run unit tests only +pytest -m unit -v + +# Run integration tests only +pytest -m integration -v + +# Run everything except slow tests +pytest -m "not slow" -v + +# Run everything except E2E tests +pytest -m "not e2e" -v +``` + +## CI/CD Integration + +The project includes a comprehensive GitHub Actions workflow (`.github/workflows/aria-tests.yml`) that runs all test types in parallel: + +### Workflow Jobs + +1. **unit-integration-tests** (Python 3.10, 3.11, 3.12) + - Runs unit and integration tests + - Fast feedback (< 1 minute) + - No browser dependencies + +2. **playwright-e2e** + - Installs Playwright and Chromium + - Starts Aria server + - Runs full E2E tests + - Duration: ~2-3 minutes + +3. **pyppeteer-e2e** + - Installs system dependencies for Chromium + - Downloads Pyppeteer Chromium + - Runs E2E tests + - Duration: ~2-3 minutes + +4. **containerized-chrome-e2e** + - Uses Selenium standalone Chrome service + - Isolated browser environment + - Best for consistency across environments + - Duration: ~2-3 minutes + +5. **test-summary** + - Aggregates results from all jobs + - Provides unified test report + +### Triggering the Workflow + +The workflow runs automatically on: +- Push to `main` or `develop` branches +- Pull requests targeting `main` or `develop` +- Changes to `aria_web/` directory or test files + +Manual trigger: +```bash +# Go to GitHub Actions tab → Select "Aria E2E Tests" → Click "Run workflow" +``` + +## Environment Variables + +### Server Configuration +- `ARIA_SERVER_URL` - Base URL for Aria server (default: `http://localhost:8000`) + +### Pyppeteer Configuration +- `CHROME_PATH` - Custom Chrome/Chromium binary path +- `PUPPETEER_EXECUTABLE_PATH` - Alternative to CHROME_PATH +- `PYPPETEER_DEBUG` - Enable debug output (set to `true`) + +### Selenium Configuration +- `SELENIUM_REMOTE_URL` - Selenium Grid hub URL (default: `http://localhost:4444/wd/hub`) + +## Test Architecture + +### What Each Test Type Validates + +#### Unit Tests +- `[aria:say]` tag generation for speech commands +- `[aria:position]` tag generation for movement +- Object detection in command strings +- Helper function correctness + +#### Integration Tests +- POST `/api/aria/object` with action `add` creates object +- POST `/api/aria/object` with action `update` modifies object position +- POST `/api/aria/object` with action `remove` deletes object +- GET `/api/aria/state` returns current state +- GET `/api/aria/objects` lists all objects + +#### E2E Tests (All Variants) +1. **Add Object**: Click "Add Object" → Verify object appears in server state +2. **Pick Up Object**: Click object → Call `pickUpObject()` → Verify `state: 'held'` on server +3. **Drop Object**: Call `dropObject()` → Verify object state changes to `on_stage` or `on_table` +4. **Remove Object**: Send remove command → Verify object deleted from server + +### Test Flow Diagram + +``` +┌─────────────────────┐ +│ Browser UI │ +│ (aria_web/index.html)│ +└──────────┬──────────┘ + │ User clicks/drags + ▼ +┌─────────────────────┐ +│ aria_controller.js │ +│ - addObject() │ +│ - pickUpObject() │ +│ - dropObject() │ +│ - sendObjectUpdate()│ +└──────────┬──────────┘ + │ POST /api/aria/object + ▼ +┌─────────────────────┐ +│ aria server.py │ +│ - stage_state{} │ +│ - handle_object() │ +└──────────┬──────────┘ + │ Updates state + ▼ +┌─────────────────────┐ +│ E2E Test Validates │ +│ - GET /api/aria/state│ +│ - Asserts object │ +│ position/state │ +└─────────────────────┘ +``` + +## Troubleshooting + +### Pyppeteer: "Browser launch failed" + +**Cause**: Missing system dependencies for Chromium + +**Solution**: +```bash +# Ubuntu/Debian +sudo apt-get install -y \ + libnss3 libnspr4 libatk1.0-0 libatk-bridge2.0-0 \ + libcups2 libdrm2 libxkbcommon0 libxcomposite1 \ + libxdamage1 libxfixes3 libxrandr2 libgbm1 libasound2 + +# Or use custom Chrome +export CHROME_PATH=/usr/bin/google-chrome +pytest tests/test_ui_pyppeteer.py -v +``` + +### Playwright: "Executable doesn't exist" + +**Cause**: Browsers not installed + +**Solution**: +```bash +playwright install chromium +# Or install all browsers +playwright install +``` + +### Selenium: "Connection refused to localhost:4444" + +**Cause**: Selenium Chrome container not running + +**Solution**: +```bash +# Start container +docker run -d -p 4444:4444 -p 5900:5900 --shm-size=2g selenium/standalone-chrome:latest + +# Verify container is running +docker ps | grep selenium + +# Check Selenium hub status +curl http://localhost:4444/wd/hub/status +``` + +### Server: "Connection refused on port 8000" + +**Cause**: Aria server not running + +**Solution**: +```bash +# Start server manually +cd aria_web +python server.py + +# Or let test start it (tests auto-start if not running) +``` + +### Test skipped: "Chromium not available" + +**Cause**: Browser automation library can't launch browser + +**Solution**: +- Check if you're in a headless environment (CI, container) +- Ensure system dependencies are installed +- Verify browser installation +- Set custom Chrome path if needed + +## Best Practices + +1. **Start with integration tests** - Fastest feedback loop, no browser overhead +2. **Use Playwright for local development** - Best error messages and debugging +3. **Use Selenium in CI/CD** - Most consistent across environments +4. **Mark tests appropriately** - Use pytest markers for selective execution +5. **Keep E2E tests focused** - Test critical user flows, not every button click +6. **Mock external services in unit tests** - Don't hit real APIs +7. **Clean up test data** - Always remove test objects after tests + +## Future Improvements + +- [ ] Add visual regression testing (screenshot comparison) +- [ ] Add performance testing (measure API response times) +- [ ] Add load testing (concurrent users) +- [ ] Add accessibility testing (ARIA labels, keyboard navigation) +- [ ] Add cross-browser testing (Firefox, Safari via Playwright) +- [ ] Add mobile browser testing (Chrome Mobile, Safari iOS) +- [ ] Add test coverage reporting +- [ ] Add mutation testing + +## Contributing + +When adding new features to the Aria interface: + +1. Write unit tests first (TDD approach) +2. Add integration tests for new API endpoints +3. Add E2E tests for critical user flows +4. Update this documentation +5. Ensure all tests pass before submitting PR + +## Resources + +- [Pytest Documentation](https://docs.pytest.org/) +- [Playwright Python Documentation](https://playwright.dev/python/) +- [Pyppeteer Documentation](https://pyppeteer.github.io/pyppeteer/) +- [Selenium Documentation](https://www.selenium.dev/documentation/) +- [GitHub Actions Documentation](https://docs.github.com/en/actions) diff --git a/apps/aria/TESTING_IMPLEMENTATION_SUMMARY.md b/apps/aria/TESTING_IMPLEMENTATION_SUMMARY.md index 661d27702..784b3c86d 100644 --- a/apps/aria/TESTING_IMPLEMENTATION_SUMMARY.md +++ b/apps/aria/TESTING_IMPLEMENTATION_SUMMARY.md @@ -1,270 +1,270 @@ -# Aria Testing Infrastructure - Implementation Summary - -## Overview -Comprehensive testing infrastructure for Aria 3D character web interface with CI/CD integration. - -## What Was Implemented - -### 1. GitHub Actions CI/CD Workflow -**File**: `.github/workflows/aria-tests.yml` - -**Jobs**: -- **unit-integration-tests**: Fast tests on Python 3.10, 3.11, 3.12 -- **playwright-e2e**: Playwright-based browser automation -- **pyppeteer-e2e**: Pyppeteer-based browser automation with system deps -- **containerized-chrome-e2e**: Selenium with Docker Chrome service -- **test-summary**: Aggregates all test results - -**Triggers**: -- Push to main/develop branches -- Pull requests -- Manual workflow dispatch -- Path filters: `aria_web/**`, `tests/test_*aria*.py`, `tests/test_*ui*.py` - -**Features**: -- Parallel job execution -- Matrix strategy for Python versions -- Automatic server startup/shutdown -- Artifact uploads for test results -- Health checks before test execution - -### 2. Selenium E2E Tests -**File**: `tests/test_ui_selenium.py` - -**Features**: -- Remote WebDriver support for containerized Chrome -- VNC support for watching tests (port 5900) -- Retry logic for browser connection -- Environment variable configuration -- Full object sync validation (add, pickup, drop, remove) - -**Configuration**: -- `SELENIUM_REMOTE_URL`: Custom Selenium Grid URL -- `ARIA_SERVER_URL`: Backend server URL - -### 3. Enhanced Test Configuration -**File**: `pytest.ini` - -**New Markers**: -- `playwright`: Playwright-based E2E tests -- `selenium`: Selenium-based E2E tests -- `e2e`: All end-to-end browser tests -- `pyppeteer`: Pyppeteer-based E2E tests - -**Usage**: -```bash -pytest -m playwright # Run only Playwright tests -pytest -m "e2e" # Run all E2E tests -pytest -m "not e2e" # Run everything except E2E -``` - -### 4. Comprehensive Documentation -**Files**: -- `aria_web/TESTING.md` - Complete testing guide -- `aria_web/README.md` - Project overview with API docs - -**Testing Guide Includes**: -- Overview of all test types -- Setup instructions for each testing approach -- Quick command references -- Environment variable documentation -- Troubleshooting guide -- Test architecture diagrams -- CI/CD integration details -- Best practices -- Future improvements roadmap - -**README Includes**: -- Quick start guide -- Architecture overview -- Complete API documentation -- Object state definitions -- Client-server sync flow diagram -- Development guidelines -- Testing quick reference -- Troubleshooting section - -## Test Coverage - -### Unit Tests (4 tests) -- ✅ Tag generation for speech commands -- ✅ Position detection for pickup operations -- ✅ Object detection in commands -- ✅ Fallback behavior validation - -### Integration Tests (1 test) -- ✅ Full object lifecycle (add → update → remove) -- ✅ HTTP API endpoint validation -- ✅ State synchronization - -### E2E Tests (3 implementations) -- ✅ Pyppeteer: CDP-based browser automation -- ✅ Playwright: Modern cross-browser testing -- ✅ Selenium: Containerized Chrome for CI/CD - -Each E2E test validates: -1. Object addition via UI → server sync -2. Pickup operation → state change to 'held' -3. Drop operation → state change to 'on_stage'/'on_table' -4. Remove operation → object deletion - -## Testing Strategies - -### Local Development -1. **Fast feedback**: Unit tests (< 1 second) -2. **API validation**: Integration tests (< 1 second with running server) -3. **Full flow**: Playwright E2E (best error messages) - -### CI/CD -1. **Quick validation**: Unit + integration (parallel across Python versions) -2. **Cross-environment**: Multiple E2E implementations in parallel - - Playwright: Ubuntu with installed browser - - Pyppeteer: Ubuntu with system dependencies - - Selenium: Isolated Docker container - -### Test Selection -```bash -# Development workflow -pytest tests/test_aria_server.py -v # Fast unit tests - -# Pre-commit -pytest -m "not e2e and not slow" -v # Fast tests only - -# Full validation -pytest tests/ -v # All tests (skip if browser unavailable) - -# CI/CD (runs automatically) -# GitHub Actions handles all test types in parallel -``` - -## Environment Variables Reference - -| Variable | Purpose | Default | Used By | -|----------|---------|---------|---------| -| `ARIA_SERVER_URL` | Backend server URL | `http://localhost:8000` | All E2E tests | -| `CHROME_PATH` | Custom Chrome binary | Auto-detect | Pyppeteer | -| `PUPPETEER_EXECUTABLE_PATH` | Alternative Chrome path | Auto-detect | Pyppeteer | -| `PYPPETEER_DEBUG` | Enable debug output | `false` | Pyppeteer | -| `SELENIUM_REMOTE_URL` | Selenium Grid URL | `http://localhost:4444/wd/hub` | Selenium | - -## CI/CD Workflow Execution - -### Job Dependencies -``` -unit-integration-tests (3.10, 3.11, 3.12) ─┬─→ playwright-e2e - ├─→ pyppeteer-e2e - └─→ containerized-chrome-e2e - ↓ - test-summary -``` - -### Typical Runtime -- Unit/Integration: ~30-60 seconds per Python version -- Playwright E2E: ~2-3 minutes -- Pyppeteer E2E: ~2-3 minutes -- Selenium E2E: ~2-3 minutes -- **Total**: ~3-4 minutes (parallel execution) - -### Artifact Retention -- Test results: 30 days -- Coverage reports: 30 days -- Playwright reports: 30 days - -## Validation Results - -### Local Tests -```bash -$ pytest tests/test_aria_server.py tests/test_object_api_integration.py -v -4 passed in 0.85s ✓ -``` - -### E2E Tests -```bash -$ pytest tests/test_ui_*.py -v -3 skipped (browsers not available in dev container) ✓ -All tests gracefully skip when dependencies unavailable ✓ -``` - -### GitHub Actions Workflow -```bash -$ python -c "import yaml; yaml.safe_load(open('.github/workflows/aria-tests.yml'))" -✓ YAML syntax valid -``` - -## Key Improvements Over Previous Implementation - -1. **Multiple E2E Approaches**: Playwright, Pyppeteer, AND Selenium (was only attempting Playwright/Pyppeteer) -2. **CI/CD Integration**: Complete GitHub Actions workflow with parallel jobs (was missing) -3. **Better Error Handling**: Retry logic, graceful skips, detailed logging (was basic) -4. **Environment Flexibility**: Multiple configuration options via env vars (was hardcoded) -5. **Comprehensive Docs**: Full testing guide + API documentation (was minimal) -6. **Pytest Markers**: Organized test selection with markers (was ad-hoc) -7. **Containerized Testing**: Selenium with Docker for isolation (was not implemented) -8. **Test Artifacts**: Automatic upload of results for debugging (was not implemented) - -## Browser Compatibility Matrix - -| Browser | Pyppeteer | Playwright | Selenium | -|---------|-----------|------------|----------| -| Chromium | ✅ (CDP) | ✅ | ✅ (WebDriver) | -| Chrome | ✅ (custom path) | ✅ | ✅ (WebDriver) | -| Firefox | ❌ | ✅ | ✅ (WebDriver) | -| Safari | ❌ | ✅ (macOS) | ✅ (WebDriver) | -| Edge | ❌ | ✅ | ✅ (WebDriver) | - -## Future Enhancements - -### Short Term -- [ ] Add visual regression testing (screenshot comparison) -- [ ] Add test coverage reporting to CI -- [ ] Add Firefox/Safari testing via Playwright -- [ ] Add performance benchmarks - -### Medium Term -- [ ] Add mutation testing -- [ ] Add load testing (concurrent users) -- [ ] Add accessibility testing (ARIA, keyboard nav) -- [ ] Add mobile browser testing - -### Long Term -- [ ] Add cross-browser compatibility dashboard -- [ ] Add automated visual diff reviews -- [ ] Add test flakiness detection and reporting -- [ ] Add test execution time tracking and optimization - -## Success Metrics - -✅ **100% API Coverage**: All endpoints tested (add, update, remove, state, objects, command) - -✅ **Multiple Test Layers**: Unit (4) + Integration (1) + E2E (3 implementations) - -✅ **CI/CD Ready**: Full GitHub Actions workflow with parallel execution - -✅ **Environment Agnostic**: Tests run locally and in CI with same codebase - -✅ **Graceful Degradation**: Tests skip appropriately when dependencies unavailable - -✅ **Well Documented**: 2 comprehensive docs (TESTING.md + README.md) - -✅ **Maintainable**: Clear test structure, markers, and helper functions - -## Quick Start for New Developers - -1. **Clone repo** -2. **Run fast tests**: `pytest tests/test_aria_server.py -v` -3. **Read docs**: Open `aria_web/TESTING.md` -4. **Install E2E deps**: `pip install playwright && playwright install chromium` -5. **Run E2E tests**: `pytest tests/test_ui_playwright.py -m playwright -v` -6. **Push changes**: CI automatically runs all tests - -## Conclusion - -The Aria testing infrastructure now provides: -- ✅ Multiple testing layers (unit, integration, E2E) -- ✅ Multiple E2E approaches (Playwright, Pyppeteer, Selenium) -- ✅ Full CI/CD automation (GitHub Actions) -- ✅ Comprehensive documentation (guides, API docs, troubleshooting) -- ✅ Flexible configuration (env vars, markers, selective execution) -- ✅ Professional quality (retry logic, logging, artifacts, error handling) - -This sets a strong foundation for continued development with confidence that changes are validated across multiple environments and browser automation approaches. +# Aria Testing Infrastructure - Implementation Summary + +## Overview +Comprehensive testing infrastructure for Aria 3D character web interface with CI/CD integration. + +## What Was Implemented + +### 1. GitHub Actions CI/CD Workflow +**File**: `.github/workflows/aria-tests.yml` + +**Jobs**: +- **unit-integration-tests**: Fast tests on Python 3.10, 3.11, 3.12 +- **playwright-e2e**: Playwright-based browser automation +- **pyppeteer-e2e**: Pyppeteer-based browser automation with system deps +- **containerized-chrome-e2e**: Selenium with Docker Chrome service +- **test-summary**: Aggregates all test results + +**Triggers**: +- Push to main/develop branches +- Pull requests +- Manual workflow dispatch +- Path filters: `aria_web/**`, `tests/test_*aria*.py`, `tests/test_*ui*.py` + +**Features**: +- Parallel job execution +- Matrix strategy for Python versions +- Automatic server startup/shutdown +- Artifact uploads for test results +- Health checks before test execution + +### 2. Selenium E2E Tests +**File**: `tests/test_ui_selenium.py` + +**Features**: +- Remote WebDriver support for containerized Chrome +- VNC support for watching tests (port 5900) +- Retry logic for browser connection +- Environment variable configuration +- Full object sync validation (add, pickup, drop, remove) + +**Configuration**: +- `SELENIUM_REMOTE_URL`: Custom Selenium Grid URL +- `ARIA_SERVER_URL`: Backend server URL + +### 3. Enhanced Test Configuration +**File**: `pytest.ini` + +**New Markers**: +- `playwright`: Playwright-based E2E tests +- `selenium`: Selenium-based E2E tests +- `e2e`: All end-to-end browser tests +- `pyppeteer`: Pyppeteer-based E2E tests + +**Usage**: +```bash +pytest -m playwright # Run only Playwright tests +pytest -m "e2e" # Run all E2E tests +pytest -m "not e2e" # Run everything except E2E +``` + +### 4. Comprehensive Documentation +**Files**: +- `aria_web/TESTING.md` - Complete testing guide +- `aria_web/README.md` - Project overview with API docs + +**Testing Guide Includes**: +- Overview of all test types +- Setup instructions for each testing approach +- Quick command references +- Environment variable documentation +- Troubleshooting guide +- Test architecture diagrams +- CI/CD integration details +- Best practices +- Future improvements roadmap + +**README Includes**: +- Quick start guide +- Architecture overview +- Complete API documentation +- Object state definitions +- Client-server sync flow diagram +- Development guidelines +- Testing quick reference +- Troubleshooting section + +## Test Coverage + +### Unit Tests (4 tests) +- ✅ Tag generation for speech commands +- ✅ Position detection for pickup operations +- ✅ Object detection in commands +- ✅ Fallback behavior validation + +### Integration Tests (1 test) +- ✅ Full object lifecycle (add → update → remove) +- ✅ HTTP API endpoint validation +- ✅ State synchronization + +### E2E Tests (3 implementations) +- ✅ Pyppeteer: CDP-based browser automation +- ✅ Playwright: Modern cross-browser testing +- ✅ Selenium: Containerized Chrome for CI/CD + +Each E2E test validates: +1. Object addition via UI → server sync +2. Pickup operation → state change to 'held' +3. Drop operation → state change to 'on_stage'/'on_table' +4. Remove operation → object deletion + +## Testing Strategies + +### Local Development +1. **Fast feedback**: Unit tests (< 1 second) +2. **API validation**: Integration tests (< 1 second with running server) +3. **Full flow**: Playwright E2E (best error messages) + +### CI/CD +1. **Quick validation**: Unit + integration (parallel across Python versions) +2. **Cross-environment**: Multiple E2E implementations in parallel + - Playwright: Ubuntu with installed browser + - Pyppeteer: Ubuntu with system dependencies + - Selenium: Isolated Docker container + +### Test Selection +```bash +# Development workflow +pytest tests/test_aria_server.py -v # Fast unit tests + +# Pre-commit +pytest -m "not e2e and not slow" -v # Fast tests only + +# Full validation +pytest tests/ -v # All tests (skip if browser unavailable) + +# CI/CD (runs automatically) +# GitHub Actions handles all test types in parallel +``` + +## Environment Variables Reference + +| Variable | Purpose | Default | Used By | +|----------|---------|---------|---------| +| `ARIA_SERVER_URL` | Backend server URL | `http://localhost:8000` | All E2E tests | +| `CHROME_PATH` | Custom Chrome binary | Auto-detect | Pyppeteer | +| `PUPPETEER_EXECUTABLE_PATH` | Alternative Chrome path | Auto-detect | Pyppeteer | +| `PYPPETEER_DEBUG` | Enable debug output | `false` | Pyppeteer | +| `SELENIUM_REMOTE_URL` | Selenium Grid URL | `http://localhost:4444/wd/hub` | Selenium | + +## CI/CD Workflow Execution + +### Job Dependencies +``` +unit-integration-tests (3.10, 3.11, 3.12) ─┬─→ playwright-e2e + ├─→ pyppeteer-e2e + └─→ containerized-chrome-e2e + ↓ + test-summary +``` + +### Typical Runtime +- Unit/Integration: ~30-60 seconds per Python version +- Playwright E2E: ~2-3 minutes +- Pyppeteer E2E: ~2-3 minutes +- Selenium E2E: ~2-3 minutes +- **Total**: ~3-4 minutes (parallel execution) + +### Artifact Retention +- Test results: 30 days +- Coverage reports: 30 days +- Playwright reports: 30 days + +## Validation Results + +### Local Tests +```bash +$ pytest tests/test_aria_server.py tests/test_object_api_integration.py -v +4 passed in 0.85s ✓ +``` + +### E2E Tests +```bash +$ pytest tests/test_ui_*.py -v +3 skipped (browsers not available in dev container) ✓ +All tests gracefully skip when dependencies unavailable ✓ +``` + +### GitHub Actions Workflow +```bash +$ python -c "import yaml; yaml.safe_load(open('.github/workflows/aria-tests.yml'))" +✓ YAML syntax valid +``` + +## Key Improvements Over Previous Implementation + +1. **Multiple E2E Approaches**: Playwright, Pyppeteer, AND Selenium (was only attempting Playwright/Pyppeteer) +2. **CI/CD Integration**: Complete GitHub Actions workflow with parallel jobs (was missing) +3. **Better Error Handling**: Retry logic, graceful skips, detailed logging (was basic) +4. **Environment Flexibility**: Multiple configuration options via env vars (was hardcoded) +5. **Comprehensive Docs**: Full testing guide + API documentation (was minimal) +6. **Pytest Markers**: Organized test selection with markers (was ad-hoc) +7. **Containerized Testing**: Selenium with Docker for isolation (was not implemented) +8. **Test Artifacts**: Automatic upload of results for debugging (was not implemented) + +## Browser Compatibility Matrix + +| Browser | Pyppeteer | Playwright | Selenium | +|---------|-----------|------------|----------| +| Chromium | ✅ (CDP) | ✅ | ✅ (WebDriver) | +| Chrome | ✅ (custom path) | ✅ | ✅ (WebDriver) | +| Firefox | ❌ | ✅ | ✅ (WebDriver) | +| Safari | ❌ | ✅ (macOS) | ✅ (WebDriver) | +| Edge | ❌ | ✅ | ✅ (WebDriver) | + +## Future Enhancements + +### Short Term +- [ ] Add visual regression testing (screenshot comparison) +- [ ] Add test coverage reporting to CI +- [ ] Add Firefox/Safari testing via Playwright +- [ ] Add performance benchmarks + +### Medium Term +- [ ] Add mutation testing +- [ ] Add load testing (concurrent users) +- [ ] Add accessibility testing (ARIA, keyboard nav) +- [ ] Add mobile browser testing + +### Long Term +- [ ] Add cross-browser compatibility dashboard +- [ ] Add automated visual diff reviews +- [ ] Add test flakiness detection and reporting +- [ ] Add test execution time tracking and optimization + +## Success Metrics + +✅ **100% API Coverage**: All endpoints tested (add, update, remove, state, objects, command) + +✅ **Multiple Test Layers**: Unit (4) + Integration (1) + E2E (3 implementations) + +✅ **CI/CD Ready**: Full GitHub Actions workflow with parallel execution + +✅ **Environment Agnostic**: Tests run locally and in CI with same codebase + +✅ **Graceful Degradation**: Tests skip appropriately when dependencies unavailable + +✅ **Well Documented**: 2 comprehensive docs (TESTING.md + README.md) + +✅ **Maintainable**: Clear test structure, markers, and helper functions + +## Quick Start for New Developers + +1. **Clone repo** +2. **Run fast tests**: `pytest tests/test_aria_server.py -v` +3. **Read docs**: Open `aria_web/TESTING.md` +4. **Install E2E deps**: `pip install playwright && playwright install chromium` +5. **Run E2E tests**: `pytest tests/test_ui_playwright.py -m playwright -v` +6. **Push changes**: CI automatically runs all tests + +## Conclusion + +The Aria testing infrastructure now provides: +- ✅ Multiple testing layers (unit, integration, E2E) +- ✅ Multiple E2E approaches (Playwright, Pyppeteer, Selenium) +- ✅ Full CI/CD automation (GitHub Actions) +- ✅ Comprehensive documentation (guides, API docs, troubleshooting) +- ✅ Flexible configuration (env vars, markers, selective execution) +- ✅ Professional quality (retry logic, logging, artifacts, error handling) + +This sets a strong foundation for continued development with confidence that changes are validated across multiple environments and browser automation approaches. diff --git a/apps/aria/aria_controller.js b/apps/aria/aria_controller.js index 5ba349867..577f3aa06 100644 --- a/apps/aria/aria_controller.js +++ b/apps/aria/aria_controller.js @@ -1,2619 +1,2619 @@ -// Aria Visual Command Controller -const aria = document.getElementById('aria'); -const ariaMouth = document.getElementById('ariaMouth'); -const ariaArmLeft = document.getElementById('ariaUpperArmLeft'); -const ariaArmRight = document.getElementById('ariaUpperArmRight'); -const ariaLegLeft = document.getElementById('ariaUpperLegLeft'); -const ariaLegRight = document.getElementById('ariaUpperLegRight'); -const stage = document.getElementById('stage'); -const commandInput = document.getElementById('commandInput'); -const logContainer = document.getElementById('logContainer'); - -const ARIA_PROVIDER_KEY = 'aria-provider-select'; -const ARIA_MODEL_KEY = 'aria-model-input'; -const ARIA_USE_LLM_KEY = 'aria-use-llm-toggle'; - -function restoreProviderSettings() { - const providerInput = document.getElementById('providerSelect'); - const modelInput = document.getElementById('modelInput'); - const useLlmInput = document.getElementById('useLlmToggle'); - - try { - if (providerInput) { - const savedProvider = localStorage.getItem(ARIA_PROVIDER_KEY); - if (savedProvider !== null) { - providerInput.value = savedProvider; - } - } - - if (modelInput) { - const savedModel = localStorage.getItem(ARIA_MODEL_KEY); - if (savedModel !== null) { - modelInput.value = savedModel; - } - } - - if (useLlmInput) { - const savedUseLlm = localStorage.getItem(ARIA_USE_LLM_KEY); - if (savedUseLlm !== null) { - useLlmInput.checked = savedUseLlm !== 'false'; - } - } - } catch (err) { - console.warn('restoreProviderSettings failed:', err); - } -} - -function persistProviderSettings(provider, model, useLlm) { - try { - localStorage.setItem(ARIA_PROVIDER_KEY, provider || ''); - localStorage.setItem(ARIA_MODEL_KEY, model || ''); - localStorage.setItem(ARIA_USE_LLM_KEY, String(Boolean(useLlm))); - } catch (err) { - console.warn('persistProviderSettings failed:', err); - } -} - -restoreProviderSettings(); - -// Track active objects -const activeObjects = { - apple: true, - book: true, - cup: true, - ball: true, - flower: true -}; - -function clampPercent(v, fallback = 50) { - const n = Number(v); - if (!Number.isFinite(n)) return fallback; - return Math.max(0, Math.min(100, n)); -} - -function applyServerAriaState(ariaState) { - if (!ariaState || typeof ariaState !== 'object') return; - - const pos = ariaState.position || {}; - const x = clampPercent(pos.x, characterState.position.x); - const y = clampPercent(pos.y, characterState.position.y); - - // Keep client/server coordinate convention aligned with moveToPosition() - // where CSS bottom is inverted from logical y. - aria.style.left = `${x}%`; - aria.style.bottom = `${100 - y}%`; - characterState.position = { - ...characterState.position, - x, - y, - }; - - if (typeof ariaState.expression === 'string' && ariaState.expression) { - try { - changeExpression(ariaState.expression); - } catch (err) { - console.warn('applyServerAriaState expression failed:', err); - } - } -} - -function applyServerObjectsState(objectsState) { - if (!objectsState || typeof objectsState !== 'object') return; - - for (const [objectId, objState] of Object.entries(objectsState)) { - const objEl = document.getElementById(objectId); - if (!objEl || !objState || typeof objState !== 'object') continue; - - const pos = objState.position || {}; - if (typeof pos.x !== 'undefined') { - objEl.style.left = `${clampPercent(pos.x, 50)}%`; - } - if (typeof pos.y !== 'undefined') { - // Object y values are persisted from CSS bottom percentages. - objEl.style.bottom = `${clampPercent(pos.y, 35)}%`; - } - - const state = String(objState.state || '').toLowerCase(); - const hidden = state === 'hidden' || state === 'off_stage' || state === 'removed'; - const btn = document.getElementById('btn-' + objectId); - - objEl.style.display = hidden ? 'none' : 'block'; - activeObjects[objectId] = !hidden; - - if (btn) { - btn.classList.toggle('active', !hidden); - btn.classList.toggle('inactive', hidden); - } - } -} - -async function hydrateStageFromServer() { - try { - const response = await fetch('/api/aria/state', { cache: 'no-store' }); - if (!response.ok) { - log(`⚠️ Stage sync unavailable (${response.status})`); - return; - } - - const payload = await response.json(); - applyServerAriaState(payload.aria || {}); - applyServerObjectsState(payload.objects || {}); - log('🔄 Stage synced from server'); - } catch (err) { - console.warn('hydrateStageFromServer failed:', err); - log('⚠️ Could not sync stage from server'); - } -} - -// Toggle object visibility -function toggleObject(objectId) { - const obj = document.getElementById(objectId); - const btn = document.getElementById('btn-' + objectId); - - if (!obj) { - log(`❌ toggleObject: unknown object ${objectId}`, true); - return; - } - // compute last-known position first - const lastPos = objectPositionFromElement(obj); - - if (activeObjects[objectId]) { - // Remove object - obj.style.display = 'none'; - btn.classList.remove('active'); - btn.classList.add('inactive'); - activeObjects[objectId] = false; - log('🗑️ Removed ' + objectId); - // Sync change to backend - sendObjectUpdate('update', objectId, { position: lastPos, state: 'hidden' }).catch(() => { }); - } else { - // Add object back - obj.style.display = 'block'; - btn.classList.add('active'); - btn.classList.remove('inactive'); - activeObjects[objectId] = true; - log('➕ Added ' + objectId); - // Sync change to backend (object is back on stage) - sendObjectUpdate('update', objectId, { position: lastPos, state: 'on_table' }).catch(() => { }); - } -} - -// Additional DOM references (used by idle/poses/expressive moves) -const ariaHead = document.querySelector('.aria-head'); -const ariaBody = document.querySelector('.aria-body'); -const ariaEyes = document.querySelectorAll('.aria-eye'); -const ariaEyeLeft = ariaEyes[0]; -const ariaEyeRight = ariaEyes[1]; - -// Eyebrow elements (added for expressive expressions) -const ariaEyebrowLeft = document.getElementById('ariaEyebrowLeft'); -const ariaEyebrowRight = document.getElementById('ariaEyebrowRight'); - -// ── Eye Tracking: pupils/iris follow the cursor ────────────────────────────── -let _eyeRaf = null; -document.addEventListener('mousemove', (e) => { - if (_eyeRaf) return; - _eyeRaf = requestAnimationFrame(() => { - _eyeRaf = null; - if (!aria) return; - const r = aria.getBoundingClientRect(); - if (!r.width) return; - const cx = r.left + r.width * 0.5; - const cy = r.top + r.height * 0.27; // roughly eye-level - const dx = e.clientX - cx; - const dy = e.clientY - cy; - const dist = Math.sqrt(dx * dx + dy * dy); - const maxO = 2.5; - const ox = dist > 15 ? (dx / dist) * Math.min(maxO, dist * 0.026) : 0; - const oy = dist > 15 ? (dy / dist) * Math.min(maxO * 0.55, dist * 0.016) : 0; - ariaEyes.forEach(eye => { - eye.style.setProperty('--px', `${ox.toFixed(2)}px`); - eye.style.setProperty('--py', `${oy.toFixed(2)}px`); - }); - }); -}); -// ───────────────────────────────────────────────────────────────────────────── - -// AI-Controlled Character State (single combined object) -let characterState = { - mood: 'neutral', - energy: 50, - personality: 'balanced', - colors: { - hair: '#4a3728', - skin: '#f5d4b8', - body: '#4a90e2', - legs: '#3d5a80', - feet: '#f5f5f5' - }, - size: 1.0, - style: 'default', - heldObject: null, - heldObjectElement: null, - position: { x: 50, y: 68, z: 0 }, - rotation: 0, - isMoving: false, - currentWaypoint: null -}; - -// Visual feedback function -function showFeedback(message) { - // Use toast if available (defined in index.html), fall back to stage overlay - if (typeof showToast === 'function') { - showToast(message); - return; - } - const feedback = document.createElement('div'); - feedback.textContent = message; - feedback.style.cssText = 'position:absolute; top:20px; left:50%; transform:translateX(-50%); background:var(--accent, #667eea); color:white; padding:12px 24px; border-radius:12px; font-size:0.9em; font-weight:600; z-index:999; box-shadow:0 4px 16px rgba(0,0,0,0.2); animation:pulse 0.5s ease;'; - stage.appendChild(feedback); - setTimeout(() => feedback.remove(), 2500); -} - -// AI-Driven Character Generation -function analyzeAIResponse(text) { - const lowerText = text.toLowerCase(); - - // Detect mood from response - if (lowerText.includes('excited') || lowerText.includes('amazing') || lowerText.includes('fantastic') || lowerText.includes('thrilled')) { - return { mood: 'excited', energy: 95 }; - } else if (lowerText.includes('happy') || lowerText.includes('great') || lowerText.includes('wonderful')) { - return { mood: 'happy', energy: 80 }; - } else if (lowerText.includes('sad') || lowerText.includes('sorry') || lowerText.includes('unfortunate')) { - return { mood: 'sad', energy: 30 }; - } else if (lowerText.includes('angry') || lowerText.includes('frustrated')) { - return { mood: 'angry', energy: 90 }; - } else if (lowerText.includes('calm') || lowerText.includes('peaceful') || lowerText.includes('relaxed')) { - return { mood: 'calm', energy: 40 }; - } else if (lowerText.includes('think') || lowerText.includes('consider') || lowerText.includes('perhaps')) { - return { mood: 'thinking', energy: 60 }; - } - - return { mood: 'neutral', energy: 50 }; -} - -function generateCharacterFromMood(mood, energy) { - const moodColors = { - happy: { body: '#5fb3f5', hair: '#6b4f3d', accent: '#7ac5ff' }, - excited: { body: '#f3a71a', hair: '#6b4f3d', accent: '#ffd06b' }, - sad: { body: '#6C7A89', hair: '#4a3728', accent: '#95A5A6' }, - angry: { body: '#E74C3C', hair: '#4a3728', accent: '#ff6b6b' }, - calm: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' }, - thinking: { body: '#5a7fa8', hair: '#4a3728', accent: '#6b8fb3' }, - neutral: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' } - }; - - const colors = moodColors[mood] || moodColors.neutral; - const size = 0.8 + (energy / 100) * 0.4; // Scale from 0.8 to 1.2 based on energy - - return { colors, size, mood }; -} - -function applyCharacterStyle(style) { - const ariaHead = document.querySelector('.aria-head'); - const ariaBody = document.querySelector('.aria-body'); - const ariaHair = document.querySelector('.aria-hair'); - const ariaLegs = document.querySelectorAll('.aria-leg'); - const ariaFeet = document.querySelectorAll('.aria-foot'); - - // Create dramatic transformation sparkle effect - for (let i = 0; i < 15; i++) { - setTimeout(() => { - createEffect('sparkle'); - }, i * 50); - } - - // Add glow pulse during transformation - aria.style.filter = 'drop-shadow(0 0 30px ' + style.colors.body + ') brightness(1.5)'; - setTimeout(() => { - // After pulse, keep mood glow - const moodClasses = ['mood-happy', 'mood-sad', 'mood-angry', 'mood-thinking', 'mood-calm', 'mood-excited']; - moodClasses.forEach(c => aria.classList.remove(c)); - if (style.mood && style.mood !== 'neutral') { - aria.style.filter = ''; // let CSS class handle it - aria.classList.add('mood-' + style.mood); - } else { - aria.style.filter = ''; - } - }, 1000); - - // Apply colors with smooth transition - ariaHair.style.transition = 'background-color 1s ease, transform 1s ease'; - ariaHead.style.transition = 'background-color 1s ease'; - ariaBody.style.transition = 'background 1s ease, transform 1s ease'; - ariaLegs.forEach(leg => leg.style.transition = 'background-color 1s ease'); - ariaFeet.forEach(foot => foot.style.transition = 'background-color 1s ease'); - - ariaHair.style.backgroundColor = style.colors.hair; - ariaBody.style.background = `linear-gradient(135deg, ${style.colors.body}, ${style.colors.accent})`; - - // Apply size transformation - aria.style.transform = `translateX(-50%) scale(${style.size})`; - - // Update character state - characterState = { ...characterState, ...style }; - - console.log('🎨 Character updated:', style.mood, 'Energy:', Math.round(style.size * 100) + '%'); - showFeedback('🎨 TRANSFORM: ' + style.mood.toUpperCase()); -} - -function autoGenerateCharacter(responseText) { - const analysis = analyzeAIResponse(responseText); - const newStyle = generateCharacterFromMood(analysis.mood, analysis.energy); - applyCharacterStyle(newStyle); - - // Trigger automatic animation based on mood - setTimeout(() => { - if (analysis.mood === 'excited') { - animate('dancing'); - } else if (analysis.mood === 'happy') { - animate('jumping'); - } else if (analysis.mood === 'sad') { - move('left', 'normal'); - } else if (analysis.mood === 'angry') { - animate('spinning'); - } else if (analysis.mood === 'calm') { - animate('waving'); - } else if (analysis.mood === 'thinking') { - changeExpression('thinking'); - } - }, 500); -} - -const expressions = { - 'smile': '😊', - 'happy': '😃', - 'sad': '😢', - 'surprised': '😲', - 'confused': '😕', - 'thinking': '🤔', - 'wink': '😉' -}; - -function log(message, isError = false) { - const entry = document.createElement('div'); - entry.className = 'log-entry'; - if (isError) entry.classList.add('log-error'); - entry.textContent = `${new Date().toLocaleTimeString()}: ${message}`; - logContainer.insertBefore(entry, logContainer.firstChild); - - // Keep only last 10 entries - while (logContainer.children.length > 10) { - logContainer.removeChild(logContainer.lastChild); - } -} - -// Chat UI helpers -function addChatMessage(role, text) { - try { - const container = document.getElementById('chatMessages'); - if (!container) return; - - const msgWrap = document.createElement('div'); - msgWrap.className = 'chat-msg ' + (role === 'user' ? 'user' : (role === 'aria' ? 'aria' : 'system')); - - const bubble = document.createElement('div'); - bubble.className = 'bubble'; - bubble.innerText = text; - - const sender = document.createElement('div'); - sender.className = 'sender'; - sender.innerText = role === 'user' ? 'You' : role === 'aria' ? 'Aria' : 'System'; - - const inner = document.createElement('div'); - inner.appendChild(sender); - inner.appendChild(bubble); - - msgWrap.appendChild(inner); - - container.appendChild(msgWrap); - container.scrollTop = container.scrollHeight; - } catch (e) { - console.warn('addChatMessage failed', e); - } -} - -// Called when Aria reaches a waypoint or finishes a movement -function arrivalFeedback(waypointKey) { - try { - let msg = ''; - if (waypointKey && waypoints3D && waypoints3D[waypointKey]) { - msg = `I've arrived at ${waypoints3D[waypointKey].name}.`; - } else if (typeof waypointKey === 'string' && waypointKey.includes('%')) { - msg = `Arrived at ${waypointKey}.`; - } else if (waypointKey) { - msg = `Arrived at ${waypointKey}.`; - } else { - msg = `Arrived.`; - } - - // Friendly reply from Aria in chat — pick varied arrival messages - const arrivalMessages = [ - `I'm here! 🎉`, - `Made it — ${waypointKey ? waypoints3D[waypointKey]?.name || waypointKey : 'arrived'}!`, - `Arrived and ready!`, - `That was quick — I'm here.`, - `All set. What should we do next?`, - ]; - const chosen = arrivalMessages[Math.floor(Math.random() * arrivalMessages.length)]; - addChatMessage('aria', chosen); - - // Little celebratory effect and expression - createEffect('sparkle'); - changeExpression('happy'); - - // Short arrival animation (small bounce & glow) - aria.classList.add('arrived'); - setTimeout(() => aria.classList.remove('arrived'), 900); - - // Clear current waypoint marker so we don't re-announce - characterState.currentWaypoint = null; - } catch (e) { - console.warn('arrivalFeedback failed', e); - } -} - -// Try to resolve a freeform phrase to a waypoint key -function resolveWaypointFromPhrase(phrase) { - if (!phrase || !waypoints3D) return null; - const clean = phrase.toLowerCase().replace(/[^a-z0-9\s-]/g, '').trim(); - if (!clean) return null; - - // direct key match (e.g., front-left) - const direct = clean.replace(/\s+/g, '-'); - if (waypoints3D[direct]) return direct; - - // check by names and partial matches - for (const k in waypoints3D) { - const v = waypoints3D[k]; - if (!v) continue; - const name = String(v.name || '').toLowerCase(); - if (name === clean) return k; - if (name.includes(clean) || clean.includes(k.replace('-', ' '))) return k; - } - - // synonyms mapping for simple words - const synonyms = { - 'front': 'front-center', 'back': 'back-center', 'left': 'stage-left', 'right': 'stage-right', 'center': 'center', 'table': 'table' - }; - if (synonyms[clean]) return synonyms[clean]; - - // try matching tokens - const tokens = clean.split(/\s+/); - for (const t of tokens) { - if (synonyms[t]) return synonyms[t]; - } - - return null; -} - -// Process chat input: supports slash commands and normal commands -async function sendChat() { - const chatBox = document.getElementById('chatInput'); - if (!chatBox) return; - - const text = chatBox.value.trim(); - if (!text) return; - - // Show user's message - addChatMessage('user', text); - chatBox.value = ''; - - // check for natural language movements (e.g., "go to front-right" or "walk to table") - const nlMove = /(?:go to|move to|walk to|goto)\s+([a-z0-9\-\s]+)/i.exec(text); - if (nlMove) { - const targetPhrase = nlMove[1].trim(); - // try to match known waypoint keys or names - const candidate = resolveWaypointFromPhrase(targetPhrase); - if (candidate) { - moveToWaypoint(candidate); - addChatMessage('aria', `Moving to ${waypoints3D[candidate].name}`); - } else { - addChatMessage('aria', `I couldn't identify a waypoint for '${targetPhrase}'. Try /waypoints`); - } - return; - } - - // /say command — speak directly as Aria - if (text.startsWith('/say ')) { - const sayText = text.slice(5).trim(); - if (sayText) { - // Aria says it in the chat - addChatMessage('aria', sayText); - // small visual cue - createEffect('sparkle'); - changeExpression('smile'); - } - return; - } - - // slash commands handled locally - if (text.startsWith('/goto ') || text.startsWith('/move ') || text.startsWith('/moveTo ')) { - const parts = text.split(/\s+/, 2); - const waypoint = parts[1] ? parts[1].trim() : ''; - if (waypoints3D && waypoints3D[waypoint]) { - moveToWaypoint(waypoint); - addChatMessage('aria', `Moving to ${waypoints3D[waypoint].name}`); - } else { - addChatMessage('aria', `Unknown waypoint '${waypoint}'. Try /waypoints`); - } - return; - } - - if (text === '/waypoints') { - const entries = Object.entries(waypoints3D).map(([k, v]) => `${k} — ${v.name}`); - addChatMessage('aria', `Available waypoints: ${entries.join(', ')}`); - return; - } - - if (text === '/circle' || text === '/circle3d') { - moveInCircle3D(); - addChatMessage('aria', 'Starting 3D circle movement'); - return; - } - - if (text === '/spiral' || text === '/spiral3d') { - performSpiral3D(); - addChatMessage('aria', 'Starting 3D spiral'); - return; - } - - if (text === '/stop') { - // stop behaviors - if (typeof stopContinuousDance === 'function') stopContinuousDance(); - addChatMessage('aria', 'Stopped continuous actions'); - return; - } - - // Otherwise, send as a command to backend (and display response when available) - commandInput.value = text; - const result = await sendCommand(); - - if (!result) { - addChatMessage('aria', 'No response from backend (fallback executed)'); - return; - } - - if (result.error) { - addChatMessage('aria', `Error: ${result.error}`); - return; - } - - // Prefer textual response if available - if (result.response) { - addChatMessage('aria', result.response); - } else if (result.tags && result.tags.length > 0) { - addChatMessage('aria', `Executed tags: ${result.tags.join(' ')}`); - } else if (result.stage_context) { - // Keep stage context short - const ctx = result.stage_context.split('\n').slice(0, 4).join(' | '); - addChatMessage('aria', ctx); - } else { - addChatMessage('aria', 'Done.'); - } -} - -async function sendCommand() { - const command = commandInput.value.trim(); - if (!command) return; - - const providerInput = document.getElementById('providerSelect'); - const modelInput = document.getElementById('modelInput'); - const useLlmInput = document.getElementById('useLlmToggle'); - const provider = providerInput ? providerInput.value.trim() : ''; - const model = modelInput ? modelInput.value.trim() : ''; - const useLlm = useLlmInput ? useLlmInput.checked : true; - - persistProviderSettings(provider, model, useLlm); - - log(`Command: "${command}"`); - commandInput.value = ''; - - try { - // Gather current stage state for AI to see - const stageRect = stage.getBoundingClientRect(); - const ariaRect = aria.getBoundingClientRect(); - - // Calculate Aria's position as percentages - const ariaX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; - const ariaY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; - - // Gather object positions - const objectPositions = {}; - ['apple', 'book', 'cup', 'ball', 'flower'].forEach(objId => { - const obj = document.getElementById(objId); - if (obj) { - const objRect = obj.getBoundingClientRect(); - objectPositions[objId] = { - x: ((objRect.left - stageRect.left) / stageRect.width) * 100, - y: 100 - ((objRect.bottom - stageRect.top) / stageRect.height) * 100, - state: obj.classList.contains('held') ? 'held' : 'on_table' - }; - } - }); - - const currentStageState = { - aria: { - position: { x: Math.round(ariaX), y: Math.round(ariaY) }, - expression: characterState.mood || 'neutral', - held_object: characterState.heldObject, - facing: 'right' - }, - objects: objectPositions - }; - - // Call backend API with stage state - const response = await fetch('/api/aria/command', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - command: command, - stage_state: currentStageState, - use_llm: useLlm, - provider: provider || undefined, - model: model || undefined, - }) - }); - - if (!response.ok) { - throw new Error(`HTTP ${response.status}: ${response.statusText}`); - } - - const data = await response.json(); - - // AI automatically generates character based on response - if (data.response) { - autoGenerateCharacter(data.response); - } - - if (data.tags && data.tags.length > 0) { - log(`✅ ${data.model}: ${data.tags.join(' ')}`); - executeTags(data.tags); - } else if (data.error) { - log(`⚠️ API Error: ${data.error}`, true); - executeLocalCommand(command); - } else { - log('⚠️ Using fallback parser'); - executeLocalCommand(command); - } - // Return the parsed result so callers (chat UI) can inspect tags / response - return data; - } catch (error) { - log(`⚠️ Network error, using fallback`, true); - // Fallback: parse command locally without AI - executeLocalCommand(command); - return { error: String(error) }; - } -} - -function setPosition(xPercent, yPercent, zDepth = 0, rotateY = 0) { - // AI-driven animated walking to position in 3D space (not teleporting) - const stageRect = stage.getBoundingClientRect(); - const ariaRect = aria.getBoundingClientRect(); - - // Get current position - const currentX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; - const currentY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; - - // Clamp target values - xPercent = Math.max(5, Math.min(95, xPercent)); - yPercent = Math.max(5, Math.min(95, yPercent)); - zDepth = Math.max(-300, Math.min(200, zDepth)); // Z range: -300px (far) to 200px (near) - - // Calculate distance and direction - const deltaX = xPercent - currentX; - const deltaY = yPercent - currentY; - const distance = Math.sqrt(deltaX * deltaX + deltaY * deltaY); - - // Don't move if already at target - if (distance < 2) { - log(`📍 Already at position (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px)`); - // If we were moving towards a known waypoint, confirm arrival - if (characterState.currentWaypoint) { - arrivalFeedback(characterState.currentWaypoint); - } - return; - } - - // Determine walking speed and style based on distance - let duration = Math.min(distance * 40, 2000); // 40ms per percent, max 2s - let walkStyle = 'normal'; - - if (distance > 50) { - walkStyle = 'run'; - duration = distance * 20; // Faster for long distances - } else if (distance > 30) { - walkStyle = 'walk'; - duration = distance * 30; - } - - // Face the direction of movement in 3D - let rotationAngle = rotateY || 0; - if (deltaX > 2) { - rotationAngle = 0; // Face camera (right) - } else if (deltaX < -2) { - rotationAngle = 180; // Face away (left) - } - - // Add walking animation - aria.classList.add('walking'); - if (walkStyle === 'run') { - aria.classList.add('running'); - } - - // Animate legs while moving - const walkInterval = setInterval(() => { - animateWalkCycle(); - }, 200); - - // Smooth 3D transition to target - aria.style.transition = `left ${duration}ms ease-in-out, bottom ${duration}ms ease-in-out, transform ${duration}ms ease-in-out`; - - const leftPercent = xPercent; - const bottomPercent = 100 - yPercent; // Invert Y (CSS bottom increases upward) - - aria.style.left = leftPercent + '%'; - aria.style.bottom = bottomPercent + '%'; - - // Apply 3D transform with Z-depth and rotation - const scaleX = rotationAngle === 180 ? -1 : 1; - aria.style.transform = `translateX(-50%) translateZ(${zDepth}px) rotateY(${rotationAngle}deg) scaleX(${scaleX})`; - - // Update character state - characterState.position = { x: xPercent, y: yPercent, z: zDepth }; - characterState.rotation = rotationAngle; - - log(`🚶 Walking to (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px, Rot:${Math.round(rotationAngle)}°) - ${walkStyle} style`); - showFeedback(`🚶 3D WALK: X${Math.round(xPercent)}% Y${Math.round(yPercent)}% Z${Math.round(zDepth)}px`); - - // Stop walking animation when arrived - setTimeout(() => { - aria.classList.remove('walking', 'running'); - aria.style.transition = ''; // Reset transition - clearInterval(walkInterval); - resetWalkCycle(); - spawnDustCloud(); // landing dust - log(`✅ Arrived at (${Math.round(xPercent)}%, ${Math.round(yPercent)}%)`); - // If we were targeting a waypoint, announce in chat - if (characterState.currentWaypoint) { - arrivalFeedback(characterState.currentWaypoint); - } - }, duration); -} - -// Walking animation cycle -function animateWalkCycle() { - const leftLeg = document.querySelector('.aria-lower-leg.left'); - const rightLeg = document.querySelector('.aria-lower-leg.right'); - const leftArm = document.querySelector('.aria-lower-arm.left'); - const rightArm = document.querySelector('.aria-lower-arm.right'); - - if (!leftLeg || !rightLeg) return; - - // Alternate leg swings - if (leftLeg.style.transform.includes('rotate')) { - leftLeg.style.transform = 'rotate(20deg)'; - rightLeg.style.transform = 'rotate(-20deg)'; - if (leftArm) leftArm.style.transform = 'rotate(-15deg)'; - if (rightArm) rightArm.style.transform = 'rotate(15deg)'; - } else { - leftLeg.style.transform = 'rotate(-20deg)'; - rightLeg.style.transform = 'rotate(20deg)'; - if (leftArm) leftArm.style.transform = 'rotate(15deg)'; - if (rightArm) rightArm.style.transform = 'rotate(-15deg)'; - } -} - -function resetWalkCycle() { - const leftLeg = document.querySelector('.aria-lower-leg.left'); - const rightLeg = document.querySelector('.aria-lower-leg.right'); - const leftArm = document.querySelector('.aria-lower-arm.left'); - const rightArm = document.querySelector('.aria-lower-arm.right'); - - if (leftLeg) leftLeg.style.transform = ''; - if (rightLeg) rightLeg.style.transform = ''; - if (leftArm) leftArm.style.transform = ''; - if (rightArm) rightArm.style.transform = ''; -} - -function executeLocalCommand(command) { - // Simple local fallback without AI model - const cmd = command.toLowerCase(); - let executed = false; - - // Check if this is a limb command to avoid movement conflicts - const isLimbCommand = ['left arm', 'arm left', 'left hand', 'right arm', 'arm right', 'right hand', - 'left leg', 'leg left', 'right leg', 'leg right'].some(k => cmd.includes(k)); - - // Expressions - if (cmd.includes('smile') || cmd.includes('happy')) { - changeExpression('smile'); - executed = true; - } - if (cmd.includes('sad')) { - changeExpression('sad'); - executed = true; - } - if (cmd.includes('surprised')) { - changeExpression('surprised'); - executed = true; - } - if (cmd.includes('confused')) { - changeExpression('confused'); - executed = true; - } - if (cmd.includes('thinking') || cmd.includes('think')) { - changeExpression('thinking'); - executed = true; - } - if (cmd.includes('wink')) { - changeExpression('wink'); - executed = true; - } - - // Animations - if (cmd.includes('jump')) { - animate('jumping'); - executed = true; - } - if (cmd.includes('dance')) { - animate('dancing'); - executed = true; - } - if (cmd.includes('spin')) { - animate('spinning'); - executed = true; - } - if (cmd.includes('wave')) { - animate('waving'); - executed = true; - } - - // Rich gestures (mirrors server allowlist gestures) - if (cmd.includes('thumbs up') || cmd.includes('thumbsup')) { - performGesture('thumbs_up'); - executed = true; - } - if (cmd.includes('clap')) { - performGesture('clap'); - executed = true; - } - if (cmd.includes('shrug')) { - performGesture('shrug'); - executed = true; - } - if (cmd.includes('bow')) { - performGesture('bow'); - executed = true; - } - if (cmd.includes('nod')) { - performGesture('nod'); - executed = true; - } - - // Pose commands - if (cmd.includes('sit')) { - applyPose('sit'); - executed = true; - } - if (cmd.includes('stand')) { - applyPose('stand'); - executed = true; - } - if (cmd.includes('crouch')) { - applyPose('crouch'); - executed = true; - } - if (cmd.includes('lie down') || cmd.includes('lay down') || cmd.includes('lie ')) { - applyPose('lie'); - executed = true; - } - - // Looking/facing helpers - if (cmd.includes('look left')) { - handleLookTag('left'); - executed = true; - } - if (cmd.includes('look right')) { - handleLookTag('right'); - executed = true; - } - if (cmd.includes('look up')) { - handleLookTag('up'); - executed = true; - } - if (cmd.includes('look down')) { - handleLookTag('down'); - executed = true; - } - - if (cmd === 'wait' || cmd.startsWith('wait ')) { - const secondsMatch = cmd.match(/wait\s+([0-9]+(?:\.[0-9]+)?)/); - performWait(secondsMatch ? secondsMatch[1] : '1'); - executed = true; - } - - // Effects - with intensity detection - let effectIntensity = 'normal'; - if (cmd.includes('light') || cmd.includes('subtle') || cmd.includes('gentle')) { - effectIntensity = 'light'; - } else if (cmd.includes('heavy') || cmd.includes('intense') || cmd.includes('lots') || cmd.includes('many')) { - effectIntensity = 'heavy'; - } - - if (cmd.includes('sparkle') || cmd.includes('glitter') || cmd.includes('shimmer') || cmd.includes('shine')) { - createEffect('sparkle', effectIntensity); - executed = true; - } - if (cmd.includes('hearts') || cmd.includes('heart') || cmd.includes('love')) { - createEffect('hearts', effectIntensity); - executed = true; - } - if (cmd.includes('glow') || cmd.includes('glowing') || cmd.includes('radiate') || cmd.includes('illuminate')) { - createEffect('glow', effectIntensity); - executed = true; - } - - // Movement - only if not a limb command - if (!isLimbCommand) { - // Determine movement style - let movementSpeed = 'normal'; - if (cmd.includes('skip')) { - movementSpeed = 'skip'; - } else if (cmd.includes('strut') || cmd.includes('swagger')) { - movementSpeed = 'strut'; - } else if (cmd.includes('run')) { - movementSpeed = 'run'; - } - - if (cmd.includes('left')) { - move('left', movementSpeed); - executed = true; - } - if (cmd.includes('right')) { - move('right', movementSpeed); - executed = true; - } - if (cmd.includes('up') || (cmd.includes('forward') && !cmd.includes('arm') && !cmd.includes('leg'))) { - move('up', movementSpeed); - executed = true; - } - if (cmd.includes('down') || (cmd.includes('back') && !cmd.includes('arm') && !cmd.includes('leg'))) { - move('down', movementSpeed); - executed = true; - } - } - - if (!executed) { - log('❌ Command not recognized', true); - } -} - -function executeTags(tags) { - console.log('📋 Executing tags:', tags); - tags.forEach((tag, index) => { - // Parse tag format: [aria:category:action:param] - const match = tag.match(/\[aria:([^:]+):([^:\]]+)(?::([^\]]+))?\]/); - if (!match) { - console.log('⚠️ Failed to parse tag:', tag); - return; - } - - const [, categoryRaw, actionRaw, paramRaw] = match; - // Normalize category/action - const category = (categoryRaw || '').toLowerCase(); - const action = (actionRaw || '').toLowerCase(); - const param = typeof paramRaw === 'string' ? paramRaw.trim() : undefined; - console.log(`✅ Parsed tag - Category: ${category}, Action: ${action}, Param: ${param}`); - - setTimeout(() => { - switch (category) { - case 'expression': - console.log('Executing expression:', action); - changeExpression(action); - break; - case 'animate': - console.log('Executing animate:', action); - animate(getAnimationClass(action)); - break; - case 'gesture': - performGesture(action, param); - break; - case 'move': - move(action, param || 'normal'); - break; - case 'walk': - move(action, param || 'normal'); - break; - case 'run': - move(action, param || 'fast'); - break; - case 'position': - console.log('Executing position:', action, param); - if (action && param) { - // Format: [aria:position:x:y] or [aria:position:x:y:z:rotation] - const parts = tag.match(/\[aria:position:([^:\]]+):([^:\]]+)(?::([^:\]]+))?(?::([^\]]+))?\]/); - if (parts) { - const x = parseInt(parts[1]); - const y = parseInt(parts[2]); - const z = parts[3] ? parseInt(parts[3]) : 0; - const rotation = parts[4] ? parseInt(parts[4]) : 0; - setPosition(x, y, z, rotation); - } else { - setPosition(parseInt(action), parseInt(param)); - } - } - break; - case 'skip': - move(action, 'skip'); - break; - case 'strut': - case 'swagger': - move(action, 'strut'); - break; - case 'limb': - // Move individual limbs with tags like: - // [aria:limb:left_arm:raise] | [aria:limb:right_arm:-45] | [aria:limb:left_leg:kick] - handleLimbTag(action, param); - break; - case 'interact': - console.log('Executing interact:', action, param); - if (action === 'add') { - const [objectName, emoji] = param.split(':'); - addObject(objectName, emoji || '🧸'); - } else { - interactWithObject(action, param); - } - break; - case 'effect': - createEffect(action, param || 'normal'); - break; - case 'camera': - if (action === 'center') centerAria(); - break; - case 'pose': - applyPose(action, param); - break; - case 'say': - // Tag formats either [aria:say:Hello world] or [aria:say:utterance:extra] - const spoken = param || action; - if (spoken) { - addChatMessage('aria', String(spoken)); - } - break; - case 'look': - handleLookTag(action, param); - break; - case 'facing': - handleFacingTag(action, param); - break; - case 'wait': - performWait(action, param); - break; - } - }, index * 500); // Stagger multiple commands - }); -} - -function parseDurationMs(raw, fallbackMs = 700) { - if (raw == null) return fallbackMs; - const str = String(raw).trim().toLowerCase(); - const parsed = parseFloat(str.replace(/[^0-9.]/g, '')); - if (!Number.isFinite(parsed)) return fallbackMs; - // "2" means seconds for user-friendliness, while "800ms" stays ms. - const isMs = str.includes('ms'); - const ms = isMs ? parsed : parsed * 1000; - return Math.max(150, Math.min(10000, Math.round(ms))); -} - -function performGesture(gestureRaw, paramRaw) { - const gesture = String(gestureRaw || '').toLowerCase().replace(/[-\s]/g, '_'); - const duration = parseDurationMs(paramRaw, 700); - isPerformingAction = true; - - switch (gesture) { - case 'wave': - animate('waving'); - return; - case 'thumbs_up': - case 'thumbsup': - showFeedback('👍 THUMBS UP!'); - moveArm(ariaArmRight, -95, Math.round(duration * 0.45)); - setTimeout(() => moveArm(ariaArmRight, -70, Math.round(duration * 0.2)), Math.round(duration * 0.45)); - setTimeout(() => resetLimbs(Math.round(duration * 0.35)), Math.round(duration * 0.65)); - break; - case 'clap': - showFeedback('👏 CLAP!'); - moveArm(ariaArmLeft, -30, Math.round(duration * 0.22)); - moveArm(ariaArmRight, 30, Math.round(duration * 0.22)); - setTimeout(() => { - moveArm(ariaArmLeft, -75, Math.round(duration * 0.18)); - moveArm(ariaArmRight, 75, Math.round(duration * 0.18)); - createEffect('sparkle', 'light'); - }, Math.round(duration * 0.24)); - setTimeout(() => { - moveArm(ariaArmLeft, -20, Math.round(duration * 0.2)); - moveArm(ariaArmRight, 20, Math.round(duration * 0.2)); - }, Math.round(duration * 0.52)); - setTimeout(() => resetLimbs(Math.round(duration * 0.25)), Math.round(duration * 0.75)); - break; - case 'bow': - showFeedback('🙇 BOW!'); - if (ariaBody) ariaBody.style.transition = `transform ${Math.round(duration * 0.45)}ms ease`; - if (ariaHead) ariaHead.style.transition = `transform ${Math.round(duration * 0.45)}ms ease`; - if (ariaBody) ariaBody.style.transform = 'scaleY(0.92)'; - if (ariaHead) ariaHead.style.transform = 'translateX(-50%) translateY(6px) rotate(8deg)'; - setTimeout(() => { - if (ariaBody) ariaBody.style.transform = ''; - if (ariaHead) ariaHead.style.transform = 'translateX(-50%)'; - }, Math.round(duration * 0.55)); - break; - case 'nod': - showFeedback('🙂 NOD'); - if (ariaHead) ariaHead.style.transition = `transform ${Math.round(duration * 0.2)}ms ease`; - if (ariaHead) ariaHead.style.transform = 'translateX(-50%) rotate(10deg)'; - setTimeout(() => { if (ariaHead) ariaHead.style.transform = 'translateX(-50%) rotate(-6deg)'; }, Math.round(duration * 0.24)); - setTimeout(() => { if (ariaHead) ariaHead.style.transform = 'translateX(-50%) rotate(8deg)'; }, Math.round(duration * 0.45)); - setTimeout(() => { if (ariaHead) ariaHead.style.transform = 'translateX(-50%)'; }, Math.round(duration * 0.68)); - break; - case 'shrug': - showFeedback('🤷 SHRUG'); - moveArm(ariaArmLeft, -35, Math.round(duration * 0.4)); - moveArm(ariaArmRight, -35, Math.round(duration * 0.4)); - if (ariaHead) ariaHead.style.transition = `transform ${Math.round(duration * 0.35)}ms ease`; - if (ariaHead) ariaHead.style.transform = 'translateX(-50%) rotate(6deg)'; - setTimeout(() => { - resetLimbs(Math.round(duration * 0.35)); - if (ariaHead) ariaHead.style.transform = 'translateX(-50%)'; - }, Math.round(duration * 0.5)); - break; - default: - // Unknown gesture fallback: preserve compatibility with a friendly wave. - animate('waving'); - return; - } - - setTimeout(() => { - isPerformingAction = false; - }, Math.max(250, duration)); -} - -function handleLookTag(directionRaw, paramRaw) { - const direction = String(directionRaw || '').toLowerCase(); - const duration = parseDurationMs(paramRaw, 650); - if (!ariaHead) return; - - isPerformingAction = true; - ariaHead.style.transition = `transform ${Math.round(duration * 0.35)}ms ease`; - switch (direction) { - case 'left': - ariaHead.style.transform = 'translateX(-50%) rotate(-10deg)'; - break; - case 'right': - ariaHead.style.transform = 'translateX(-50%) rotate(10deg)'; - break; - case 'up': - ariaHead.style.transform = 'translateX(-50%) translateY(-5px)'; - break; - case 'down': - ariaHead.style.transform = 'translateX(-50%) translateY(5px)'; - break; - default: - ariaHead.style.transform = 'translateX(-50%)'; - break; - } - - setTimeout(() => { - ariaHead.style.transform = 'translateX(-50%)'; - isPerformingAction = false; - }, Math.round(duration * 0.75)); -} - -function handleFacingTag(directionRaw, paramRaw) { - const direction = String(directionRaw || '').toLowerCase(); - const duration = parseDurationMs(paramRaw, 750); - const currentScale = characterState.size || 1.0; - aria.style.transition = `transform ${Math.round(duration * 0.45)}ms ease`; - - if (direction === 'left') { - aria.style.transform = `translateX(-50%) scale(${currentScale}) rotateY(180deg)`; - } else if (direction === 'right' || direction === 'front' || direction === 'forward') { - aria.style.transform = `translateX(-50%) scale(${currentScale}) rotateY(0deg)`; - } else { - // Unknown direction -> no-op, keep current orientation. - return; - } -} - -function performWait(actionRaw, paramRaw) { - const raw = paramRaw || actionRaw || '1'; - const duration = parseDurationMs(raw, 1000); - isPerformingAction = true; - showFeedback(`⏳ WAIT ${Math.round(duration / 100) / 10}s`); - setTimeout(() => { - isPerformingAction = false; - }, duration); -} - -// ── Eyebrow expression helper ──────────────────────────────────────────────── -function setEyebrows(state) { - if (!ariaEyebrowLeft || !ariaEyebrowRight) return; - const DEFAULTS = { l: 'rotate(-5deg)', r: 'rotate(5deg)' }; - const brows = { - happy: { l: 'rotate(-8deg) translateY(-3px)', r: 'rotate(8deg) translateY(-3px)' }, - smile: { l: 'rotate(-8deg) translateY(-3px)', r: 'rotate(8deg) translateY(-3px)' }, - sad: { l: 'rotate(5deg) translateY(1px)', r: 'rotate(-5deg) translateY(1px)' }, - angry: { l: 'rotate(-14deg) translateY(4px)', r: 'rotate(14deg) translateY(4px)' }, - surprised: { l: 'rotate(-3deg) translateY(-6px)', r: 'rotate(3deg) translateY(-6px)' }, - confused: { l: 'rotate(8deg) translateY(-1px)', r: 'rotate(-3deg) translateY(-2px)' }, - thinking: { l: 'rotate(-4deg) translateY(-3px)', r: 'rotate(7deg) translateY(1px)' }, - wink: { l: DEFAULTS.l, r: 'rotate(10deg) translateY(-2px)' }, - }; - const s = brows[state] || DEFAULTS; - ariaEyebrowLeft.style.transform = s.l; - ariaEyebrowRight.style.transform = s.r; -} -// ───────────────────────────────────────────────────────────────────────────── - -function changeExpression(emotion) { - ariaMouth.className = 'aria-mouth'; - - // Reset any previous expression modifications - ariaMouth.style.borderRadius = ''; - ariaMouth.style.width = ''; - ariaMouth.style.height = ''; - ariaMouth.style.borderTop = ''; - ariaMouth.style.transform = ''; - - switch (emotion) { - case 'smile': - case 'happy': - ariaMouth.classList.add('smile'); - break; - case 'sad': - ariaMouth.classList.add('sad'); - break; - case 'surprised': - ariaMouth.style.borderRadius = '50%'; - ariaMouth.style.width = '15px'; - ariaMouth.style.height = '15px'; - ariaMouth.style.borderTop = '2px solid #333'; - break; - case 'confused': - ariaMouth.style.width = '18px'; - ariaMouth.style.height = '6px'; - ariaMouth.style.borderRadius = '0 0 50% 50%'; - ariaMouth.style.transform = 'translateX(-50%) rotate(5deg)'; - break; - case 'thinking': - ariaMouth.style.width = '12px'; - ariaMouth.style.height = '8px'; - ariaMouth.style.borderRadius = '0 0 50% 50%'; - ariaMouth.style.transform = 'translateX(-30%)'; - break; - case 'wink': - ariaEyeRight.style.transition = 'height 0.07s'; - ariaEyeRight.style.height = '3px'; - setTimeout(() => { ariaEyeRight.style.height = ''; }, 500); - break; - default: - ariaMouth.classList.add('smile'); - } - - // Animate eyebrows to match expression - setEyebrows(emotion); - - // Brief scale pulse - aria.style.transform = 'translateX(-50%) scale(1.1)'; - setTimeout(() => { aria.style.transform = 'translateX(-50%) scale(1)'; }, 300); - - // Float an emoji above Aria to reinforce the expression - const moodEmoji = { smile: '😊', happy: '😄', sad: '😢', surprised: '😲', confused: '🤔', thinking: '💭', wink: '😉', angry: '😠' }; - const emoji = moodEmoji[emotion]; - if (emoji && stage && aria) { - const rect = aria.getBoundingClientRect(); - const sr = stage.getBoundingClientRect(); - const cx = ((rect.left + rect.width / 2 - sr.left) / sr.width) * 100; - const cy = ((rect.top - sr.top) / sr.height) * 100; - const el = document.createElement('div'); - el.textContent = emoji; - el.style.cssText = `position:absolute;left:${cx}%;top:${cy}%;font-size:22px;pointer-events:none;z-index:60;transition:all 0.8s ease-out;opacity:1;`; - stage.appendChild(el); - requestAnimationFrame(() => { el.style.transform = 'translateY(-30px) scale(1.3)'; el.style.opacity = '0'; }); - setTimeout(() => el.remove(), 900); - } -} - -// Idle animation state -let idleAnimationInterval = null; -let isPerformingAction = false; - -// Start idle animation — breathing, blinking, random glances -function startIdleAnimation() { - if (idleAnimationInterval) return; - - idleAnimationInterval = setInterval(() => { - if (isPerformingAction) return; - - const rand = Math.random(); - - // Breathing (always) - if (ariaBody) { - ariaBody.style.transition = 'transform 2.5s ease-in-out'; - ariaBody.style.transform = 'scaleY(1.03) scaleX(0.99)'; - setTimeout(() => { ariaBody.style.transform = 'scaleY(1) scaleX(1)'; }, 2500); - } - - // Blink ~40% of ticks - if (rand < 0.4 && ariaEyeLeft && ariaEyeRight) { - const origH = ariaEyeLeft.style.height || ''; - ariaEyeLeft.style.transition = 'height 0.07s'; - ariaEyeRight.style.transition = 'height 0.07s'; - ariaEyeLeft.style.height = '2px'; - ariaEyeRight.style.height = '2px'; - setTimeout(() => { - ariaEyeLeft.style.height = origH; - ariaEyeRight.style.height = origH; - }, 140); - } - - // Head tilt / bob ~25% of ticks - if (rand > 0.55 && rand < 0.8 && ariaHead) { - const tilt = (Math.random() - 0.5) * 6; // -3° to +3° - const bob = -1.5 - Math.random() * 1.5; - ariaHead.style.transition = 'transform 1.6s ease-in-out'; - ariaHead.style.transform = `translateX(-50%) translateY(${bob}px) rotate(${tilt}deg)`; - setTimeout(() => { - ariaHead.style.transform = 'translateX(-50%)'; - }, 1600); - } - - // Random glance (look aside then return) ~20% of ticks - if (rand > 0.8 && !_eyeRaf) { - const gx = (Math.random() > 0.5 ? 1 : -1) * (1.5 + Math.random() * 1.5); - const gy = (Math.random() - 0.4) * 1.2; - ariaEyes.forEach(eye => { - eye.style.setProperty('--px', `${gx.toFixed(2)}px`); - eye.style.setProperty('--py', `${gy.toFixed(2)}px`); - }); - setTimeout(() => { - ariaEyes.forEach(eye => { - eye.style.setProperty('--px', '0px'); - eye.style.setProperty('--py', '0px'); - }); - }, 700 + Math.random() * 1200); - } - }, 3500); -} - -// Stop idle animation -function stopIdleAnimation() { - if (idleAnimationInterval) { - clearInterval(idleAnimationInterval); - idleAnimationInterval = null; - } -} - -// Limb movement helpers -function moveArm(arm, angle, duration = 500) { - arm.style.transition = `transform ${duration}ms ease-in-out`; - arm.style.transform = `rotate(${angle}deg)`; -} - -function moveLeg(leg, angle, duration = 500) { - leg.style.transition = `transform ${duration}ms ease-in-out`; - leg.style.transform = `rotate(${angle}deg)`; -} - -function resetLimbs(duration = 500) { - moveArm(ariaArmLeft, 0, duration); - moveArm(ariaArmRight, 0, duration); - moveLeg(ariaLegLeft, 0, duration); - moveLeg(ariaLegRight, 0, duration); -} - -// Utility helpers for limb control -function clamp(n, min, max) { return Math.max(min, Math.min(max, n)); } - -function parseAngleAndDuration(param) { - // Accept patterns: "45", "45,600", "raise", "raise,800" - if (!param) return { value: null, duration: 500 }; - const parts = String(param).split(',').map(s => s.trim()); - const first = parts[0]; - const dur = parts[1] ? parseInt(parts[1], 10) : 500; - const asNum = Number(first); - if (!Number.isNaN(asNum)) { - return { value: asNum, duration: isFinite(dur) ? dur : 500 }; - } - return { value: first.toLowerCase(), duration: isFinite(dur) ? dur : 500 }; -} - -function normalizeLimbPart(part) { - const p = (part || '').toLowerCase().replace(/[-\s]/g, '_'); - const map = { - 'leftarm': 'left_arm', 'arm_left': 'left_arm', 'l_arm': 'left_arm', 'left_hand': 'left_arm', 'hand_left': 'left_arm', - 'rightarm': 'right_arm', 'arm_right': 'right_arm', 'r_arm': 'right_arm', 'right_hand': 'right_arm', 'hand_right': 'right_arm', - 'leftleg': 'left_leg', 'leg_left': 'left_leg', 'l_leg': 'left_leg', - 'rightleg': 'right_leg', 'leg_right': 'right_leg', 'r_leg': 'right_leg' - }; - return map[p] || p; -} - -function elementForPart(part) { - switch (part) { - case 'left_arm': return ariaArmLeft; - case 'right_arm': return ariaArmRight; - case 'left_leg': return ariaLegLeft; - case 'right_leg': return ariaLegRight; - default: return null; - } -} - -function waveArmElement(armEl, duration = 700) { - if (!armEl) return; - const step = Math.max(120, duration); - moveArm(armEl, -60, step * 0.3); - setTimeout(() => moveArm(armEl, -30, step * 0.25), step * 0.3); - setTimeout(() => moveArm(armEl, -60, step * 0.25), step * 0.55); - setTimeout(() => moveArm(armEl, -30, step * 0.2), step * 0.8); - setTimeout(() => moveArm(armEl, 0, step * 0.2), step * 1.0); -} - -function kickLegElement(legEl, duration = 500) { - if (!legEl) return; - moveLeg(legEl, 45, duration * 0.5); - setTimeout(() => moveLeg(legEl, 0, duration * 0.5), duration * 0.5); -} - -function handleLimbTag(partRaw, paramRaw) { - const part = normalizeLimbPart(partRaw); - const targetEl = elementForPart(part); - if (!targetEl) { - log(`❓ Unknown limb: ${partRaw}`); - return; - } - const { value, duration } = parseAngleAndDuration(paramRaw); - const isArm = part.includes('arm'); - const isLeg = part.includes('leg'); - // Pause idle while moving a limb - isPerformingAction = true; - - if (typeof value === 'number') { - const angle = isArm ? clamp(value, -130, 130) : clamp(value, -60, 60); - if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); - setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); - return; - } - - const action = (value || '').toLowerCase(); - switch (action) { - case 'raise': - case 'up': - if (isArm) moveArm(targetEl, -90, duration); else moveLeg(targetEl, -30, duration); - break; - case 'lower': - case 'down': - if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); - break; - case 'forward': - if (isArm) moveArm(targetEl, -45, duration); else moveLeg(targetEl, 30, duration); - break; - case 'back': - case 'backward': - if (isArm) moveArm(targetEl, 45, duration); else moveLeg(targetEl, -30, duration); - break; - case 'wave': - if (isArm) waveArmElement(targetEl, duration); - break; - case 'kick': - if (isLeg) kickLegElement(targetEl, duration); - break; - case 'rest': - case 'neutral': - if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); - break; - default: - // Try numeric fallback if action is numeric-like - const num = Number(action); - if (!Number.isNaN(num)) { - const angle = isArm ? clamp(num, -130, 130) : clamp(num, -60, 60); - if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); - } else { - log(`❓ Unknown limb action: ${action}`); - } - } - setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); -} - -function walkCycle() { - // Alternating leg movement - moveLeg(ariaLegLeft, 25, 300); - moveLeg(ariaLegRight, -25, 300); - moveArm(ariaArmLeft, -15, 300); - moveArm(ariaArmRight, 15, 300); - - setTimeout(() => { - moveLeg(ariaLegLeft, -25, 300); - moveLeg(ariaLegRight, 25, 300); - moveArm(ariaArmLeft, 15, 300); - moveArm(ariaArmRight, -15, 300); - }, 300); - - setTimeout(() => resetLimbs(200), 600); -} - -function strutWalk() { - // Confident strut with head bob - ariaHead.style.transition = 'transform 0.3s'; - - moveLeg(ariaLegLeft, 35, 250); - moveLeg(ariaLegRight, -35, 250); - moveArm(ariaArmLeft, -25, 250); - moveArm(ariaArmRight, 25, 250); - ariaHead.style.transform = 'translateY(-5px) rotate(3deg)'; - - setTimeout(() => { - moveLeg(ariaLegLeft, -35, 250); - moveLeg(ariaLegRight, 35, 250); - moveArm(ariaArmLeft, 25, 250); - moveArm(ariaArmRight, -25, 250); - ariaHead.style.transform = 'translateY(-5px) rotate(-3deg)'; - }, 300); - - setTimeout(() => { - moveLeg(ariaLegLeft, 20, 250); - moveLeg(ariaLegRight, -20, 250); - ariaHead.style.transform = 'translateY(0) rotate(0)'; - }, 600); - - setTimeout(() => resetLimbs(200), 850); -} - -// 3D Waypoint System - Fixed positions in 3D space -const waypoints3D = { - 'center': { x: 50, y: 50, z: 0, rotation: 0, name: 'Center Stage' }, - 'front-left': { x: 25, y: 70, z: 150, rotation: 45, name: 'Front Left' }, - 'front-right': { x: 75, y: 70, z: 150, rotation: -45, name: 'Front Right' }, - 'back-left': { x: 25, y: 30, z: -200, rotation: 135, name: 'Back Left' }, - 'back-right': { x: 75, y: 30, z: -200, rotation: -135, name: 'Back Right' }, - 'front-center': { x: 50, y: 75, z: 180, rotation: 0, name: 'Front Center' }, - 'back-center': { x: 50, y: 25, z: -250, rotation: 180, name: 'Back Center' }, - 'stage-left': { x: 15, y: 50, z: -50, rotation: 90, name: 'Stage Left' }, - 'stage-right': { x: 85, y: 50, z: -50, rotation: -90, name: 'Stage Right' }, - 'table': { x: 60, y: 20, z: -100, rotation: 0, name: 'Near Table' } -}; - -function moveToWaypoint(waypointName) { - const waypoint = waypoints3D[waypointName]; - if (!waypoint) { - log(`❌ Unknown waypoint: ${waypointName}`, true); - return false; - } - - log(`📍 Moving to waypoint: ${waypoint.name}`); - showFeedback(`📍 WAYPOINT: ${waypoint.name}`); - // Notify in chat that Aria is moving - addChatMessage('aria', `Heading to ${waypoint.name}...`); - characterState.currentWaypoint = waypointName; - setPosition(waypoint.x, waypoint.y, waypoint.z, waypoint.rotation); - return true; -} - -function moveInCircle3D(radius = 30, steps = 12, duration = 6000) { - let currentStep = 0; - const centerX = 50; - const centerY = 50; - const stepDuration = duration / steps; - - const circleInterval = setInterval(() => { - if (currentStep >= steps) { - clearInterval(circleInterval); - log('🔄 Circle completed'); - addChatMessage('aria', 'Finished circular movement.'); - return; - } - - const angle = (currentStep / steps) * Math.PI * 2; - const x = centerX + radius * Math.cos(angle); - const y = centerY + radius * Math.sin(angle); - const z = 100 * Math.sin(angle * 2); // Wave in/out on Z-axis - const rotation = (angle * 180 / Math.PI) + 90; // Face tangent to circle - - setPosition(x, y, z, rotation); - currentStep++; - }, stepDuration); - - log('🔄 Starting 3D circle movement'); - showFeedback('🔄 3D CIRCLE'); - addChatMessage('aria', 'Starting circular 3D movement'); -} - -function performSpiral3D() { - const steps = 20; - const duration = 8000; - let currentStep = 0; - const centerX = 50; - const centerY = 50; - - const spiralInterval = setInterval(() => { - if (currentStep >= steps) { - clearInterval(spiralInterval); - log('🌀 Spiral completed'); - addChatMessage('aria', 'Finished spiral movement.'); - return; - } - - const progress = currentStep / steps; - const radius = 40 * (1 - progress); // Shrink radius - const angle = progress * Math.PI * 6; // Multiple rotations - const x = centerX + radius * Math.cos(angle); - const y = centerY + radius * Math.sin(angle); - const z = -200 + progress * 300; // Move from back to front - const rotation = angle * 180 / Math.PI; - - setPosition(x, y, z, rotation); - currentStep++; - }, duration / steps); - - log('🌀 Starting 3D spiral movement'); - showFeedback('🌀 3D SPIRAL'); - addChatMessage('aria', 'Starting 3D spiral movement'); -} - -function skipMove() { - // Playful skipping motion - moveLeg(ariaLegLeft, -45, 200); - moveLeg(ariaLegRight, 30, 200); - moveArm(ariaArmLeft, -30, 200); - moveArm(ariaArmRight, -30, 200); - aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; - - setTimeout(() => { - moveLeg(ariaLegLeft, 30, 200); - moveLeg(ariaLegRight, -45, 200); - aria.style.transform = 'translateX(-50%) scale(1)'; - }, 250); - - setTimeout(() => { - moveLeg(ariaLegLeft, -30, 200); - moveLeg(ariaLegRight, 20, 200); - aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; - }, 500); - - setTimeout(() => { - resetLimbs(200); - aria.style.transform = 'translateX(-50%) scale(1)'; - }, 750); -} - -function danceLimbs() { - // Arms up and down - moveArm(ariaArmLeft, -45, 200); - moveArm(ariaArmRight, -45, 200); - moveLeg(ariaLegLeft, 15, 200); - moveLeg(ariaLegRight, -15, 200); - - setTimeout(() => { - moveArm(ariaArmLeft, -90, 200); - moveArm(ariaArmRight, -90, 200); - moveLeg(ariaLegLeft, -15, 200); - moveLeg(ariaLegRight, 15, 200); - }, 200); - - setTimeout(() => { - moveArm(ariaArmLeft, -45, 200); - moveArm(ariaArmRight, -45, 200); - moveLeg(ariaLegLeft, 15, 200); - moveLeg(ariaLegRight, -15, 200); - }, 400); - - setTimeout(() => resetLimbs(200), 600); -} - -function expressiveDance() { - // More exaggerated dance moves - moveArm(ariaArmLeft, -120, 150); - moveArm(ariaArmRight, 60, 150); - moveLeg(ariaLegLeft, 20, 150); - moveLeg(ariaLegRight, -20, 150); - ariaHead.style.transform = 'rotate(15deg)'; - - setTimeout(() => { - moveArm(ariaArmLeft, 60, 150); - moveArm(ariaArmRight, -120, 150); - moveLeg(ariaLegLeft, -20, 150); - moveLeg(ariaLegRight, 20, 150); - ariaHead.style.transform = 'rotate(-15deg)'; - }, 200); - - setTimeout(() => { - moveArm(ariaArmLeft, -90, 150); - moveArm(ariaArmRight, -90, 150); - moveLeg(ariaLegLeft, 30, 150); - moveLeg(ariaLegRight, -30, 150); - ariaHead.style.transform = 'rotate(0)'; - }, 400); - - setTimeout(() => resetLimbs(200), 600); -} - -let continuousDanceInterval = null; - -function startContinuousDance() { - if (continuousDanceInterval) return; - - isPerformingAction = true; - showFeedback('🎉 PARTY MODE!'); - - continuousDanceInterval = setInterval(() => { - const danceType = Math.random(); - if (danceType > 0.5) { - expressiveDance(); - } else { - danceLimbs(); - } - }, 700); -} - -function stopContinuousDance() { - if (continuousDanceInterval) { - clearInterval(continuousDanceInterval); - continuousDanceInterval = null; - isPerformingAction = false; - resetLimbs(300); - } -} - -function spinLimbs() { - // Arms out during spin - moveArm(ariaArmLeft, -90, 100); - moveArm(ariaArmRight, -90, 100); - setTimeout(() => resetLimbs(300), 900); -} - -function animate(className) { - console.log('🎬 Animating:', className); - showFeedback('🎬 ' + className.toUpperCase() + '!'); - aria.classList.remove('jumping', 'dancing', 'spinning', 'waving'); - void aria.offsetWidth; // Force reflow - - // Mark as performing action - isPerformingAction = true; - - // Get current scale from characterState - const currentScale = characterState.size || 1.0; - - if (className === 'waving') { - showFeedback('👋 WAVING!'); - console.log('Wave animation with arm movement'); - // Rapid wave motion - moveArm(ariaArmRight, -60, 200); - setTimeout(() => moveArm(ariaArmRight, -30, 150), 200); - setTimeout(() => moveArm(ariaArmRight, -60, 150), 350); - setTimeout(() => moveArm(ariaArmRight, -30, 150), 500); - setTimeout(() => moveArm(ariaArmRight, -60, 150), 650); - setTimeout(() => { - resetLimbs(200); - isPerformingAction = false; - }, 800); - } else if (className === 'jumping') { - console.log('Jumping animation triggered with leg bending'); - aria.classList.add(className); - aria.style.filter = 'brightness(1.5)'; - - // Pre-jump crouch - moveLeg(ariaLegLeft, 45, 200); - moveLeg(ariaLegRight, -45, 200); - moveArm(ariaArmLeft, -20, 200); - moveArm(ariaArmRight, -20, 200); - - // During jump - legs extend - setTimeout(() => { - moveLeg(ariaLegLeft, -35, 300); - moveLeg(ariaLegRight, 35, 300); - moveArm(ariaArmLeft, -60, 300); - moveArm(ariaArmRight, -60, 300); - }, 300); - - // Landing crouch - setTimeout(() => { - moveLeg(ariaLegLeft, 35, 300); - moveLeg(ariaLegRight, -35, 300); - moveArm(ariaArmLeft, -10, 300); - moveArm(ariaArmRight, -10, 300); - aria.style.filter = 'brightness(1)'; - }, 1500); - - // Return to normal - setTimeout(() => { - resetLimbs(400); - aria.classList.remove(className); - aria.style.transform = `translateX(-50%) scale(${currentScale})`; - }, 2500); - } else { - console.log('Generic animation triggered:', className); - aria.classList.add(className); - - // Add limb movements based on animation type - if (className === 'dancing') { - console.log('Adding dance limb movements'); - // Repeat dance limbs during 2s animation - danceLimbs(); - setTimeout(() => danceLimbs(), 700); - setTimeout(() => danceLimbs(), 1400); - } else if (className === 'spinning') { - console.log('Adding spin limb movements'); - spinLimbs(); - } - - setTimeout(() => { - aria.classList.remove(className); - resetLimbs(300); - aria.style.transform = `translateX(-50%) scale(${currentScale})`; - isPerformingAction = false; - }, className === 'dancing' ? 2000 : 1000); - } -} - -function getAnimationClass(action) { - const animations = { - 'jump': 'jumping', - 'dance': 'dancing', - 'spin': 'spinning', - 'wave': 'waving', - 'bow': 'waving', - 'flip': 'spinning', - 'backflip': 'spinning' - }; - return animations[action] || 'jumping'; -} - -function move(direction, speed = 'normal') { - console.log('🚶 Moving:', direction, 'at speed:', speed); - - isPerformingAction = true; - - const currentLeft = aria.style.left || '50%'; - const current = parseFloat(currentLeft); - - let newPos = current; - let distance = 25; - let movementStyle = walkCycle; - let duration = '1s'; - - // Choose movement style based on speed - switch (speed) { - case 'run': - case 'fast': - distance = 40; - movementStyle = skipMove; - duration = '0.7s'; - showFeedback('🏃 RUNNING!'); - break; - case 'strut': - case 'swagger': - distance = 30; - movementStyle = strutWalk; - duration = '1.2s'; - showFeedback('😎 STRUTTING!'); - break; - case 'skip': - distance = 35; - movementStyle = skipMove; - duration = '0.9s'; - showFeedback('🎵 SKIPPING!'); - break; - default: - showFeedback('🚶 WALKING!'); - } - - // Animate movement style - movementStyle(); - - switch (direction) { - case 'left': - newPos = Math.max(5, current - distance); - break; - case 'right': - newPos = Math.min(95, current + distance); - break; - case 'forward': - case 'up': - const currentTop = aria.style.top || '50%'; - const top = parseFloat(currentTop); - aria.style.top = Math.max(10, top - distance) + '%'; - aria.style.transition = `top ${duration} ease`; - setTimeout(() => { isPerformingAction = false; }, 1000); - return; - case 'back': - case 'down': - const currentTopBack = aria.style.top || '50%'; - const topBack = parseFloat(currentTopBack); - aria.style.top = Math.min(80, topBack + distance) + '%'; - aria.style.transition = `top ${duration} ease`; - setTimeout(() => { isPerformingAction = false; }, 1000); - return; - } - - aria.style.transition = `left ${duration} ease`; - aria.style.left = newPos + '%'; - - setTimeout(() => { isPerformingAction = false; }, 1000); -} - -function createEffect(type, intensity = 'normal') { - const effects = { - 'sparkle': '✨', - 'glow': '💫', - 'hearts': '💕' - }; - - // Intensity configurations - const intensityConfig = { - 'light': { count: 3, spread: 60, duration: 1200, delay: 150 }, - 'normal': { count: 5, spread: 80, duration: 1500, delay: 100 }, - 'heavy': { count: 10, spread: 90, duration: 1800, delay: 60 } - }; - - const config = intensityConfig[intensity] || intensityConfig['normal']; - const emoji = effects[type] || '✨'; - - // Color variations for sparkle effect - const sparkleColors = ['#FFD700', '#FFA500', '#FFFF00', '#FFE4B5', '#FAFAD2']; - - // Use requestAnimationFrame for better performance - let createdCount = 0; - - function createSingleEffect() { - if (createdCount >= config.count) return; - - const effect = document.createElement('div'); - effect.className = `effect ${type}`; - effect.textContent = emoji; - - // Random position within spread area - const centerX = 50; - const centerY = 50; - const spreadRadius = config.spread / 2; - effect.style.left = (centerX + (Math.random() - 0.5) * spreadRadius) + '%'; - effect.style.top = (centerY + (Math.random() - 0.5) * spreadRadius) + '%'; - - // Add color variation for sparkle - if (type === 'sparkle') { - effect.style.filter = `hue-rotate(${Math.random() * 360}deg)`; - } - - // Add random rotation for variety - effect.style.transform = `rotate(${Math.random() * 360}deg)`; - - stage.appendChild(effect); - - // Remove after duration - setTimeout(() => { - if (effect.parentNode) { - effect.remove(); - } - }, config.duration); - - createdCount++; - - // Schedule next effect - if (createdCount < config.count) { - requestAnimationFrame(() => { - setTimeout(createSingleEffect, config.delay); - }); - } - } - - // Start effect creation - requestAnimationFrame(createSingleEffect); -} - -// Dust cloud on landing / arrival -function spawnDustCloud() { - if (!stage || !aria) return; - const rect = aria.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - const cx = ((rect.left + rect.width / 2 - stageRect.left) / stageRect.width) * 100; - const cy = ((rect.bottom - stageRect.top) / stageRect.height) * 100; - for (let i = 0; i < 6; i++) { - const p = document.createElement('div'); - p.style.cssText = `position:absolute;left:${cx + (Math.random() - 0.5) * 8}%;top:${cy - Math.random() * 3}%;width:${6 + Math.random() * 6}px;height:${6 + Math.random() * 6}px;background:rgba(160,140,120,${0.3 + Math.random() * 0.3});border-radius:50%;pointer-events:none;z-index:5;transition:all ${0.4 + Math.random() * 0.4}s ease-out;`; - stage.appendChild(p); - requestAnimationFrame(() => { - p.style.transform = `translate(${(Math.random() - 0.5) * 30}px, ${-10 - Math.random() * 20}px) scale(${1.5 + Math.random()})`; - p.style.opacity = '0'; - }); - setTimeout(() => p.remove(), 900); - } -} - -// Ambient idle sparkles — gentle occasional twinkle near Aria -let _idleSparkleInterval = null; -function startIdleSparkles() { - if (_idleSparkleInterval) return; - _idleSparkleInterval = setInterval(() => { - if (!aria || !stage) return; - // Only sparkle when idle (not walking/dancing) - if (aria.classList.contains('walking') || aria.classList.contains('dancing') || aria.classList.contains('running')) return; - if (Math.random() > 0.35) return; // ~35% chance each tick - const rect = aria.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - const cx = ((rect.left + rect.width / 2 - stageRect.left) / stageRect.width) * 100; - const cy = ((rect.top + rect.height * 0.3 - stageRect.top) / stageRect.height) * 100; - const s = document.createElement('div'); - s.textContent = '✦'; - s.style.cssText = `position:absolute;left:${cx + (Math.random() - 0.5) * 10}%;top:${cy + (Math.random() - 0.5) * 8}%;font-size:${8 + Math.random() * 8}px;color:rgba(255,215,0,${0.4 + Math.random() * 0.4});pointer-events:none;z-index:50;transition:all ${0.6 + Math.random() * 0.6}s ease-out;`; - stage.appendChild(s); - requestAnimationFrame(() => { - s.style.transform = `translateY(${-15 - Math.random() * 15}px) scale(0.3)`; - s.style.opacity = '0'; - }); - setTimeout(() => s.remove(), 1400); - }, 2000); -} -startIdleSparkles(); - -function centerAria() { - aria.style.left = '50%'; - aria.style.transform = 'translateX(-50%) scale(1)'; -} - -function applyPose(poseRaw, param) { - const pose = (poseRaw || '').toLowerCase(); - // Body position presets - const poses = { - 'sit': { bottom: '20px', transform: 'translateX(-50%) scale(0.8)' }, - 'stand': { bottom: '50px', transform: 'translateX(-50%) scale(1)' }, - 'crouch': { bottom: '30px', transform: 'translateX(-50%) scale(0.9)' }, - 'lie': { bottom: '10px', transform: 'translateX(-50%) scale(0.7) rotate(90deg)' } - }; - const poseStyle = poses[pose]; - if (poseStyle) { - aria.style.bottom = poseStyle.bottom; - aria.style.transform = poseStyle.transform; - } - // Limb presets - switch (pose) { - case 't-pose': - case 'tpose': - moveArm(ariaArmLeft, -90, 400); - moveArm(ariaArmRight, -90, 400); - resetLimbs(1000); - break; - case 'hands_up': - case 'hands-up': - case 'handsup': - moveArm(ariaArmLeft, -120, 400); - moveArm(ariaArmRight, -120, 400); - break; - case 'cross_arms': - case 'cross-arms': - case 'crossarms': - moveArm(ariaArmLeft, -30, 400); - moveArm(ariaArmRight, 30, 400); - break; - case 'hero': - // Hands on hips (approximate) - moveArm(ariaArmLeft, 45, 400); - moveArm(ariaArmRight, -45, 400); - break; - } -} - -function quickCommand(cmd) { - commandInput.value = cmd; - sendCommand(); -} - -// Random character evolution (simulates AI personality drift) -function randomCharacterEvolution() { - const moods = ['happy', 'sad', 'calm', 'thinking', 'neutral']; - const randomMood = moods[Math.floor(Math.random() * moods.length)]; - const randomEnergy = 30 + Math.floor(Math.random() * 70); - - log('✨ Character evolving to: ' + randomMood + ' (' + randomEnergy + '% energy)'); - - const newStyle = generateCharacterFromMood(randomMood, randomEnergy); - applyCharacterStyle(newStyle); -} - -// Automatic character evolution disabled - keeping neutral human-like appearance -// setInterval(randomCharacterEvolution, 30000); - -// Initialize -centerAria(); -characterState.position.x = 50; -log('🎨 Aria Visual System Ready!'); -log('🤖 AI Character Generation: ACTIVE'); -log('Type commands or use quick buttons'); - -// Object Interaction System -// Helper: derive stage-relative percentage position from an element -function objectPositionFromElement(elem) { - if (!elem) return null; - const stageRect = stage.getBoundingClientRect(); - // Prefer explicit CSS left/bottom if present (percent strings) - try { - if (elem.style && elem.style.left && elem.style.bottom) { - const left = parseFloat(elem.style.left || 0); - const bottom = parseFloat(elem.style.bottom || 0); - // The JS 'y' value used elsewhere equals the CSS bottom percent - return { x: Math.round(left), y: Math.round(bottom) }; - } - } catch (e) { - // fallthrough to computed rect - } - - const rect = elem.getBoundingClientRect(); - const x = ((rect.left - stageRect.left) / stageRect.width) * 100; - const y = 100 - ((rect.bottom - stageRect.top) / stageRect.height) * 100; - return { x: Math.round(x), y: Math.round(y) }; -} - -// Notify backend of object add/update/remove actions -async function sendObjectUpdate(action, objectId, extra = {}) { - try { - if (!objectId) return null; - const objEl = document.getElementById(objectId); - - // resolve position and state - let position = extra.position || null; - if (!position && objEl) position = objectPositionFromElement(objEl); - - const state = extra.state || (objEl ? (objEl.classList.contains('held') ? 'held' : (objEl.style.display === 'none' ? 'hidden' : 'on_table')) : (extra.state || 'unknown')); - const emoji = extra.emoji || (objEl ? objEl.textContent : undefined); - - const objPayload = { id: objectId }; - if (position !== null && position !== undefined) objPayload.position = position; - if (state !== null && state !== undefined) objPayload.state = state; - if (emoji !== null && emoji !== undefined) objPayload.emoji = emoji; - const payload = { action: action, object: objPayload }; - - const res = await fetch('/api/aria/object', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify(payload) - }); - - if (!res.ok) { - console.warn('Object API returned non-OK status', res.status); - return null; - } - - const data = await res.json(); - console.log('object sync:', action, objectId, data); - return data; - } catch (err) { - console.warn('sendObjectUpdate failed:', err); - return null; - } -} -function pickUpObject(objectId) { - const obj = document.getElementById(objectId); - if (!obj) { - console.log('❌ Object not found:', objectId); - return false; - } - - if (characterState.heldObject) { - showFeedback('⚠️ Already holding ' + characterState.heldObject.toUpperCase() + '!'); - return false; - } - - console.log('🤏 Picking up:', objectId); - showFeedback('🤏 PICKED UP ' + objectId.toUpperCase() + '!'); - - // Store held object - characterState.heldObject = objectId; - characterState.heldObjectElement = obj; - - // Visual feedback - obj.classList.add('held'); - - // Sync server side: mark object as held - sendObjectUpdate('update', objectId, { state: 'held' }).catch(() => { }); - - // Position object above character - positionHeldObject(); - - // Arm reaching animation - moveArm(ariaArmRight, -90, 300); - setTimeout(() => moveArm(ariaArmRight, -45, 200), 300); - - return true; -} - -function dropObject() { - if (!characterState.heldObject) { - showFeedback('⚠️ Not holding anything!'); - return false; - } - - console.log('📦 Dropping:', characterState.heldObject); - showFeedback('📦 DROPPED ' + characterState.heldObject.toUpperCase() + '!'); - - const obj = characterState.heldObjectElement; - - // Remove held state - obj.classList.remove('held'); - - // Drop at current Aria position - const ariaRect = aria.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - - const dropLeft = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; - const dropBottom = ((stageRect.bottom - ariaRect.bottom) / stageRect.height) * 100; - - obj.style.left = dropLeft + '%'; - obj.style.bottom = (dropBottom + 10) + '%'; - - // Arm dropping animation - moveArm(ariaArmRight, -90, 200); - setTimeout(() => resetLimbs(300), 200); - - // Clear held object - characterState.heldObject = null; - characterState.heldObjectElement = null; - - // Sync server side: update object's position/state - const finalX = Math.round(dropLeft); - const finalY = Math.round(dropBottom + 10); - const tableX = 60; // server default table X - const isOnTable = Math.abs(finalX - tableX) < 20 && Math.abs(finalY - 35) < 20; - sendObjectUpdate('update', obj.id, { position: { x: finalX, y: finalY }, state: isOnTable ? 'on_table' : 'on_stage' }).catch(() => { }); - - return true; -} - -function throwObject(direction) { - if (!characterState.heldObject) { - showFeedback('⚠️ Not holding anything to throw!'); - return false; - } - - console.log('🎯 Throwing:', characterState.heldObject, 'to', direction); - showFeedback('🎯 THROWING ' + characterState.heldObject.toUpperCase() + '!'); - - const obj = characterState.heldObjectElement; - obj.classList.remove('held'); - - // Throwing arm animation - moveArm(ariaArmRight, -120, 150); - setTimeout(() => { - moveArm(ariaArmRight, -30, 200); - setTimeout(() => resetLimbs(300), 100); - }, 150); - - // Calculate throw trajectory - const currentLeft = parseFloat(obj.style.left); - const currentBottom = parseFloat(obj.style.bottom); - - let targetLeft = currentLeft; - let targetBottom = currentBottom; - - switch (direction) { - case 'left': - targetLeft = Math.max(5, currentLeft - 40); - break; - case 'right': - targetLeft = Math.min(95, currentLeft + 40); - break; - case 'up': - targetBottom = Math.min(90, currentBottom + 30); - break; - } - - // Animate throw with arc - obj.style.transition = 'all 0.8s cubic-bezier(0.25, 0.46, 0.45, 0.94)'; - obj.style.left = targetLeft + '%'; - obj.style.bottom = targetBottom + '%'; - obj.style.transform = 'rotate(360deg) scale(0.9)'; - - // Reset after landing - setTimeout(() => { - obj.style.transition = 'all 0.3s ease'; - obj.style.transform = 'rotate(0deg) scale(1)'; - }, 800); - - // Clear held object - characterState.heldObject = null; - characterState.heldObjectElement = null; - - // Sync server side after throw completes - setTimeout(() => { - const left = Math.round(targetLeft); - const bottom = Math.round(targetBottom); - sendObjectUpdate('update', obj.id, { position: { x: left, y: bottom }, state: 'on_stage' }).catch(() => { }); - }, 900); - - return true; -} - -function positionHeldObject() { - if (!characterState.heldObjectElement) return; - - // Position object above and slightly in front of character - const ariaRect = aria.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - - const objLeft = ((ariaRect.left - stageRect.left + ariaRect.width * 0.6) / stageRect.width) * 100; - const objBottom = ((stageRect.bottom - ariaRect.top + 20) / stageRect.height) * 100; - - characterState.heldObjectElement.style.left = objLeft + '%'; - characterState.heldObjectElement.style.bottom = objBottom + '%'; -} - -function interactWithObject(action, objectId) { - console.log('🔧 Interacting:', action, 'with', objectId); - - switch (action) { - case 'pickup': - case 'grab': - case 'take': - case 'get': - return pickUpObject(objectId); - case 'drop': - case 'place': - case 'put': - return dropObject(); - case 'throw': - case 'toss': - return throwObject('right'); - default: - showFeedback('❓ Unknown action: ' + action); - return false; - } -} - -// Make objects draggable and clickable -function initializeObjectInteractions() { - const objects = document.querySelectorAll('.object'); - - objects.forEach(obj => { - // Prevent adding duplicate event listeners when called multiple times - if (obj.__interactionInitialized) return; - obj.__interactionInitialized = true; - obj.addEventListener('click', (e) => { - e.stopPropagation(); - const objectId = obj.id; - - if (!characterState.heldObject) { - pickUpObject(objectId); - } else if (characterState.heldObject === objectId) { - dropObject(); - } - }); - - // Drag functionality - let isDragging = false; - let startX, startY, startLeft, startBottom; - - obj.addEventListener('mousedown', (e) => { - if (characterState.heldObject === obj.id) return; - - isDragging = true; - obj.classList.add('grabbed'); - - const rect = obj.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - - startX = e.clientX; - startY = e.clientY; - startLeft = ((rect.left - stageRect.left) / stageRect.width) * 100; - startBottom = ((stageRect.bottom - rect.bottom) / stageRect.height) * 100; - - e.preventDefault(); - }); - - document.addEventListener('mousemove', (e) => { - if (!isDragging) return; - - const stageRect = stage.getBoundingClientRect(); - const deltaX = ((e.clientX - startX) / stageRect.width) * 100; - const deltaY = -((e.clientY - startY) / stageRect.height) * 100; - - obj.style.left = Math.max(0, Math.min(100, startLeft + deltaX)) + '%'; - obj.style.bottom = Math.max(0, Math.min(100, startBottom + deltaY)) + '%'; - }); - - document.addEventListener('mouseup', () => { - if (isDragging) { - isDragging = false; - obj.classList.remove('grabbed'); - // Sync backend with final position - const pos = objectPositionFromElement(obj); - const state = obj.classList.contains('held') ? 'held' : 'on_table'; - sendObjectUpdate('update', obj.id, { position: pos, state: state }).catch(() => { }); - } - }); - }); -} - -function addObject(objectName, emoji) { - console.log('➕ Adding object:', objectName, emoji); - - // Check if object already exists - const existingObj = document.getElementById(objectName); - if (existingObj) { - console.log('⚠️ Object already exists:', objectName); - showFeedback('✅ ' + objectName + ' already on stage'); - return; - } - - // Create new object element - const newObj = document.createElement('div'); - newObj.id = objectName; - newObj.className = 'object'; - newObj.textContent = emoji; - - // Position near Aria - const ariaX = characterState.position.x; - const ariaY = characterState.position.y; - newObj.style.left = Math.max(10, Math.min(90, ariaX + 15)) + '%'; - newObj.style.bottom = Math.max(10, Math.min(90, ariaY + 5)) + '%'; - - // Add to stage - stage.appendChild(newObj); - - // Update tracking - activeObjects[objectName] = true; - - // Add to object manager if button doesn't exist - if (!document.querySelector(`[onclick="toggleObject('${objectName}')"]`)) { - const objectManager = document.getElementById('object-manager'); - const btn = document.createElement('button'); - btn.className = 'object-btn active'; - btn.id = 'btn-' + objectName; - btn.textContent = emoji + ' ' + objectName; - btn.onclick = () => toggleObject(objectName); - objectManager.appendChild(btn); - } - - // Initialize interactions for new object (click/drag) - initializeObjectInteractions(); - - // Sync server: add new object to stage_state - sendObjectUpdate('add', objectName, { position: objectPositionFromElement(newObj), state: 'on_stage', emoji: emoji }).catch(() => { }); - - showFeedback('✨ Added ' + objectName + ' to stage!'); - console.log('✅ Object added successfully:', objectName); -} - -// Initialize object interactions -initializeObjectInteractions(); -hydrateStageFromServer(); -log('🎮 Objects: apple, book, cup, ball, flower'); -log('💡 Try: "pick up apple", "drop", "throw ball"'); - -// Auto-behaviors with random timing -function startAutoBehaviors() { - // Random idle movements every 8-15 seconds - setInterval(() => { - if (Math.random() > 0.6) { - const randomActions = ['shift weight', 'look around', 'adjust hair', 'stretch']; - const action = randomActions[Math.floor(Math.random() * randomActions.length)]; - - switch (action) { - case 'shift weight': - animate('bouncing'); - setTimeout(() => aria.style.animation = 'breathe 4s ease-in-out infinite', 800); - break; - case 'look around': - const ariaHead = document.querySelector('.aria-head'); - ariaHead.style.transform = 'translateX(-50%) rotate(10deg)'; - setTimeout(() => ariaHead.style.transform = 'translateX(-50%) rotate(-10deg)', 600); - setTimeout(() => ariaHead.style.transform = 'translateX(-50%)', 1200); - break; - case 'adjust hair': - const hand = document.getElementById('ariaHandRight'); - hand.style.transform = 'translateY(-30px) rotate(-20deg)'; - setTimeout(() => hand.style.transform = '', 1000); - break; - case 'stretch': - ariaArmLeft.style.transform = 'rotate(-160deg)'; - ariaArmRight.style.transform = 'rotate(160deg)'; - setTimeout(() => { - ariaArmLeft.style.transform = ''; - ariaArmRight.style.transform = ''; - }, 1500); - break; - } - } - }, 8000 + Math.random() * 7000); - - // Random expressions every 12-20 seconds - setInterval(() => { - if (Math.random() > 0.5) { - const expressions = ['smile', 'thinking', 'neutral', 'surprised']; - const expr = expressions[Math.floor(Math.random() * expressions.length)]; - changeExpression(expr); - setTimeout(() => changeExpression('neutral'), 2000 + Math.random() * 3000); - } - }, 12000 + Math.random() * 8000); - - // Occasional sparkles every 20-30 seconds - setInterval(() => { - if (Math.random() > 0.7) { - createEffect('sparkle'); - } - }, 20000 + Math.random() * 10000); -} - -// Initial appearance set to neutral human-like style -setTimeout(() => { - log('🎨 Setting initial character appearance...'); - const neutralStyle = generateCharacterFromMood('neutral', 50); - applyCharacterStyle(neutralStyle); -}, 100); - -// Start idle animations -startIdleAnimation(); -log('👀 Idle animations enabled - watch for breathing and blinking!'); - -// Start automatic behaviors -startAutoBehaviors(); -log('✨ Auto-behaviors enabled - Aria will move and react on her own!'); - -// === Eye tracking (follows mouse cursor) === -(function initEyeTracking() { - const eyes = document.querySelectorAll('.aria-eye'); - if (!eyes.length || !stage) return; - - document.addEventListener('mousemove', function (e) { - eyes.forEach(function (eye) { - const rect = eye.getBoundingClientRect(); - const cx = rect.left + rect.width / 2; - const cy = rect.top + rect.height / 2; - const angle = Math.atan2(e.clientY - cy, e.clientX - cx); - const dist = Math.min(3, Math.hypot(e.clientX - cx, e.clientY - cy) / 50); - const dx = Math.cos(angle) * dist; - const dy = Math.sin(angle) * dist; - // Shift the eye's radial gradient to simulate looking - eye.style.backgroundPosition = `${50 + dx * 5}% ${50 + dy * 5}%`; - }); - }); - - log('👁️ Eye tracking active — Aria follows your cursor!'); -})(); - -// === Natural blink cycle === -(function initBlinkCycle() { - function doBlink() { - if (!ariaEyeLeft || !ariaEyeRight) return; - // Skip if performing action or mid-expression - if (isPerformingAction) return; - const origL = ariaEyeLeft.style.height; - const origR = ariaEyeRight.style.height; - ariaEyeLeft.style.transition = 'height 0.06s'; - ariaEyeRight.style.transition = 'height 0.06s'; - ariaEyeLeft.style.height = '1px'; - ariaEyeRight.style.height = '1px'; - setTimeout(function () { - ariaEyeLeft.style.height = origL || ''; - ariaEyeRight.style.height = origR || ''; - ariaEyeLeft.style.transition = ''; - ariaEyeRight.style.transition = ''; - }, 120); - } - // Natural blink interval: 3-6 seconds with occasional double-blink - function scheduleBlink() { - const delay = 3000 + Math.random() * 3000; - setTimeout(function () { - doBlink(); - // 20% chance of a double-blink - if (Math.random() < 0.2) { - setTimeout(doBlink, 250); - } - scheduleBlink(); - }, delay); - } - scheduleBlink(); -})(); - -// Expose minimal testing helpers -window.ariaTest = { - limb: (part, actionOrAngle, duration) => handleLimbTag(part, typeof actionOrAngle === 'number' ? `${actionOrAngle},${duration || 500}` : `${actionOrAngle || ''}${duration ? ',' + duration : ''}`), - pose: (name) => applyPose(name) -}; -window.simulateTags = (arr) => Array.isArray(arr) && executeTags(arr); +// Aria Visual Command Controller +const aria = document.getElementById('aria'); +const ariaMouth = document.getElementById('ariaMouth'); +const ariaArmLeft = document.getElementById('ariaUpperArmLeft'); +const ariaArmRight = document.getElementById('ariaUpperArmRight'); +const ariaLegLeft = document.getElementById('ariaUpperLegLeft'); +const ariaLegRight = document.getElementById('ariaUpperLegRight'); +const stage = document.getElementById('stage'); +const commandInput = document.getElementById('commandInput'); +const logContainer = document.getElementById('logContainer'); + +const ARIA_PROVIDER_KEY = 'aria-provider-select'; +const ARIA_MODEL_KEY = 'aria-model-input'; +const ARIA_USE_LLM_KEY = 'aria-use-llm-toggle'; + +function restoreProviderSettings() { + const providerInput = document.getElementById('providerSelect'); + const modelInput = document.getElementById('modelInput'); + const useLlmInput = document.getElementById('useLlmToggle'); + + try { + if (providerInput) { + const savedProvider = localStorage.getItem(ARIA_PROVIDER_KEY); + if (savedProvider !== null) { + providerInput.value = savedProvider; + } + } + + if (modelInput) { + const savedModel = localStorage.getItem(ARIA_MODEL_KEY); + if (savedModel !== null) { + modelInput.value = savedModel; + } + } + + if (useLlmInput) { + const savedUseLlm = localStorage.getItem(ARIA_USE_LLM_KEY); + if (savedUseLlm !== null) { + useLlmInput.checked = savedUseLlm !== 'false'; + } + } + } catch (err) { + console.warn('restoreProviderSettings failed:', err); + } +} + +function persistProviderSettings(provider, model, useLlm) { + try { + localStorage.setItem(ARIA_PROVIDER_KEY, provider || ''); + localStorage.setItem(ARIA_MODEL_KEY, model || ''); + localStorage.setItem(ARIA_USE_LLM_KEY, String(Boolean(useLlm))); + } catch (err) { + console.warn('persistProviderSettings failed:', err); + } +} + +restoreProviderSettings(); + +// Track active objects +const activeObjects = { + apple: true, + book: true, + cup: true, + ball: true, + flower: true +}; + +function clampPercent(v, fallback = 50) { + const n = Number(v); + if (!Number.isFinite(n)) return fallback; + return Math.max(0, Math.min(100, n)); +} + +function applyServerAriaState(ariaState) { + if (!ariaState || typeof ariaState !== 'object') return; + + const pos = ariaState.position || {}; + const x = clampPercent(pos.x, characterState.position.x); + const y = clampPercent(pos.y, characterState.position.y); + + // Keep client/server coordinate convention aligned with moveToPosition() + // where CSS bottom is inverted from logical y. + aria.style.left = `${x}%`; + aria.style.bottom = `${100 - y}%`; + characterState.position = { + ...characterState.position, + x, + y, + }; + + if (typeof ariaState.expression === 'string' && ariaState.expression) { + try { + changeExpression(ariaState.expression); + } catch (err) { + console.warn('applyServerAriaState expression failed:', err); + } + } +} + +function applyServerObjectsState(objectsState) { + if (!objectsState || typeof objectsState !== 'object') return; + + for (const [objectId, objState] of Object.entries(objectsState)) { + const objEl = document.getElementById(objectId); + if (!objEl || !objState || typeof objState !== 'object') continue; + + const pos = objState.position || {}; + if (typeof pos.x !== 'undefined') { + objEl.style.left = `${clampPercent(pos.x, 50)}%`; + } + if (typeof pos.y !== 'undefined') { + // Object y values are persisted from CSS bottom percentages. + objEl.style.bottom = `${clampPercent(pos.y, 35)}%`; + } + + const state = String(objState.state || '').toLowerCase(); + const hidden = state === 'hidden' || state === 'off_stage' || state === 'removed'; + const btn = document.getElementById('btn-' + objectId); + + objEl.style.display = hidden ? 'none' : 'block'; + activeObjects[objectId] = !hidden; + + if (btn) { + btn.classList.toggle('active', !hidden); + btn.classList.toggle('inactive', hidden); + } + } +} + +async function hydrateStageFromServer() { + try { + const response = await fetch('/api/aria/state', { cache: 'no-store' }); + if (!response.ok) { + log(`⚠️ Stage sync unavailable (${response.status})`); + return; + } + + const payload = await response.json(); + applyServerAriaState(payload.aria || {}); + applyServerObjectsState(payload.objects || {}); + log('🔄 Stage synced from server'); + } catch (err) { + console.warn('hydrateStageFromServer failed:', err); + log('⚠️ Could not sync stage from server'); + } +} + +// Toggle object visibility +function toggleObject(objectId) { + const obj = document.getElementById(objectId); + const btn = document.getElementById('btn-' + objectId); + + if (!obj) { + log(`❌ toggleObject: unknown object ${objectId}`, true); + return; + } + // compute last-known position first + const lastPos = objectPositionFromElement(obj); + + if (activeObjects[objectId]) { + // Remove object + obj.style.display = 'none'; + btn.classList.remove('active'); + btn.classList.add('inactive'); + activeObjects[objectId] = false; + log('🗑️ Removed ' + objectId); + // Sync change to backend + sendObjectUpdate('update', objectId, { position: lastPos, state: 'hidden' }).catch(() => { }); + } else { + // Add object back + obj.style.display = 'block'; + btn.classList.add('active'); + btn.classList.remove('inactive'); + activeObjects[objectId] = true; + log('➕ Added ' + objectId); + // Sync change to backend (object is back on stage) + sendObjectUpdate('update', objectId, { position: lastPos, state: 'on_table' }).catch(() => { }); + } +} + +// Additional DOM references (used by idle/poses/expressive moves) +const ariaHead = document.querySelector('.aria-head'); +const ariaBody = document.querySelector('.aria-body'); +const ariaEyes = document.querySelectorAll('.aria-eye'); +const ariaEyeLeft = ariaEyes[0]; +const ariaEyeRight = ariaEyes[1]; + +// Eyebrow elements (added for expressive expressions) +const ariaEyebrowLeft = document.getElementById('ariaEyebrowLeft'); +const ariaEyebrowRight = document.getElementById('ariaEyebrowRight'); + +// ── Eye Tracking: pupils/iris follow the cursor ────────────────────────────── +let _eyeRaf = null; +document.addEventListener('mousemove', (e) => { + if (_eyeRaf) return; + _eyeRaf = requestAnimationFrame(() => { + _eyeRaf = null; + if (!aria) return; + const r = aria.getBoundingClientRect(); + if (!r.width) return; + const cx = r.left + r.width * 0.5; + const cy = r.top + r.height * 0.27; // roughly eye-level + const dx = e.clientX - cx; + const dy = e.clientY - cy; + const dist = Math.sqrt(dx * dx + dy * dy); + const maxO = 2.5; + const ox = dist > 15 ? (dx / dist) * Math.min(maxO, dist * 0.026) : 0; + const oy = dist > 15 ? (dy / dist) * Math.min(maxO * 0.55, dist * 0.016) : 0; + ariaEyes.forEach(eye => { + eye.style.setProperty('--px', `${ox.toFixed(2)}px`); + eye.style.setProperty('--py', `${oy.toFixed(2)}px`); + }); + }); +}); +// ───────────────────────────────────────────────────────────────────────────── + +// AI-Controlled Character State (single combined object) +let characterState = { + mood: 'neutral', + energy: 50, + personality: 'balanced', + colors: { + hair: '#4a3728', + skin: '#f5d4b8', + body: '#4a90e2', + legs: '#3d5a80', + feet: '#f5f5f5' + }, + size: 1.0, + style: 'default', + heldObject: null, + heldObjectElement: null, + position: { x: 50, y: 68, z: 0 }, + rotation: 0, + isMoving: false, + currentWaypoint: null +}; + +// Visual feedback function +function showFeedback(message) { + // Use toast if available (defined in index.html), fall back to stage overlay + if (typeof showToast === 'function') { + showToast(message); + return; + } + const feedback = document.createElement('div'); + feedback.textContent = message; + feedback.style.cssText = 'position:absolute; top:20px; left:50%; transform:translateX(-50%); background:var(--accent, #667eea); color:white; padding:12px 24px; border-radius:12px; font-size:0.9em; font-weight:600; z-index:999; box-shadow:0 4px 16px rgba(0,0,0,0.2); animation:pulse 0.5s ease;'; + stage.appendChild(feedback); + setTimeout(() => feedback.remove(), 2500); +} + +// AI-Driven Character Generation +function analyzeAIResponse(text) { + const lowerText = text.toLowerCase(); + + // Detect mood from response + if (lowerText.includes('excited') || lowerText.includes('amazing') || lowerText.includes('fantastic') || lowerText.includes('thrilled')) { + return { mood: 'excited', energy: 95 }; + } else if (lowerText.includes('happy') || lowerText.includes('great') || lowerText.includes('wonderful')) { + return { mood: 'happy', energy: 80 }; + } else if (lowerText.includes('sad') || lowerText.includes('sorry') || lowerText.includes('unfortunate')) { + return { mood: 'sad', energy: 30 }; + } else if (lowerText.includes('angry') || lowerText.includes('frustrated')) { + return { mood: 'angry', energy: 90 }; + } else if (lowerText.includes('calm') || lowerText.includes('peaceful') || lowerText.includes('relaxed')) { + return { mood: 'calm', energy: 40 }; + } else if (lowerText.includes('think') || lowerText.includes('consider') || lowerText.includes('perhaps')) { + return { mood: 'thinking', energy: 60 }; + } + + return { mood: 'neutral', energy: 50 }; +} + +function generateCharacterFromMood(mood, energy) { + const moodColors = { + happy: { body: '#5fb3f5', hair: '#6b4f3d', accent: '#7ac5ff' }, + excited: { body: '#f3a71a', hair: '#6b4f3d', accent: '#ffd06b' }, + sad: { body: '#6C7A89', hair: '#4a3728', accent: '#95A5A6' }, + angry: { body: '#E74C3C', hair: '#4a3728', accent: '#ff6b6b' }, + calm: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' }, + thinking: { body: '#5a7fa8', hair: '#4a3728', accent: '#6b8fb3' }, + neutral: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' } + }; + + const colors = moodColors[mood] || moodColors.neutral; + const size = 0.8 + (energy / 100) * 0.4; // Scale from 0.8 to 1.2 based on energy + + return { colors, size, mood }; +} + +function applyCharacterStyle(style) { + const ariaHead = document.querySelector('.aria-head'); + const ariaBody = document.querySelector('.aria-body'); + const ariaHair = document.querySelector('.aria-hair'); + const ariaLegs = document.querySelectorAll('.aria-leg'); + const ariaFeet = document.querySelectorAll('.aria-foot'); + + // Create dramatic transformation sparkle effect + for (let i = 0; i < 15; i++) { + setTimeout(() => { + createEffect('sparkle'); + }, i * 50); + } + + // Add glow pulse during transformation + aria.style.filter = 'drop-shadow(0 0 30px ' + style.colors.body + ') brightness(1.5)'; + setTimeout(() => { + // After pulse, keep mood glow + const moodClasses = ['mood-happy', 'mood-sad', 'mood-angry', 'mood-thinking', 'mood-calm', 'mood-excited']; + moodClasses.forEach(c => aria.classList.remove(c)); + if (style.mood && style.mood !== 'neutral') { + aria.style.filter = ''; // let CSS class handle it + aria.classList.add('mood-' + style.mood); + } else { + aria.style.filter = ''; + } + }, 1000); + + // Apply colors with smooth transition + ariaHair.style.transition = 'background-color 1s ease, transform 1s ease'; + ariaHead.style.transition = 'background-color 1s ease'; + ariaBody.style.transition = 'background 1s ease, transform 1s ease'; + ariaLegs.forEach(leg => leg.style.transition = 'background-color 1s ease'); + ariaFeet.forEach(foot => foot.style.transition = 'background-color 1s ease'); + + ariaHair.style.backgroundColor = style.colors.hair; + ariaBody.style.background = `linear-gradient(135deg, ${style.colors.body}, ${style.colors.accent})`; + + // Apply size transformation + aria.style.transform = `translateX(-50%) scale(${style.size})`; + + // Update character state + characterState = { ...characterState, ...style }; + + console.log('🎨 Character updated:', style.mood, 'Energy:', Math.round(style.size * 100) + '%'); + showFeedback('🎨 TRANSFORM: ' + style.mood.toUpperCase()); +} + +function autoGenerateCharacter(responseText) { + const analysis = analyzeAIResponse(responseText); + const newStyle = generateCharacterFromMood(analysis.mood, analysis.energy); + applyCharacterStyle(newStyle); + + // Trigger automatic animation based on mood + setTimeout(() => { + if (analysis.mood === 'excited') { + animate('dancing'); + } else if (analysis.mood === 'happy') { + animate('jumping'); + } else if (analysis.mood === 'sad') { + move('left', 'normal'); + } else if (analysis.mood === 'angry') { + animate('spinning'); + } else if (analysis.mood === 'calm') { + animate('waving'); + } else if (analysis.mood === 'thinking') { + changeExpression('thinking'); + } + }, 500); +} + +const expressions = { + 'smile': '😊', + 'happy': '😃', + 'sad': '😢', + 'surprised': '😲', + 'confused': '😕', + 'thinking': '🤔', + 'wink': '😉' +}; + +function log(message, isError = false) { + const entry = document.createElement('div'); + entry.className = 'log-entry'; + if (isError) entry.classList.add('log-error'); + entry.textContent = `${new Date().toLocaleTimeString()}: ${message}`; + logContainer.insertBefore(entry, logContainer.firstChild); + + // Keep only last 10 entries + while (logContainer.children.length > 10) { + logContainer.removeChild(logContainer.lastChild); + } +} + +// Chat UI helpers +function addChatMessage(role, text) { + try { + const container = document.getElementById('chatMessages'); + if (!container) return; + + const msgWrap = document.createElement('div'); + msgWrap.className = 'chat-msg ' + (role === 'user' ? 'user' : (role === 'aria' ? 'aria' : 'system')); + + const bubble = document.createElement('div'); + bubble.className = 'bubble'; + bubble.innerText = text; + + const sender = document.createElement('div'); + sender.className = 'sender'; + sender.innerText = role === 'user' ? 'You' : role === 'aria' ? 'Aria' : 'System'; + + const inner = document.createElement('div'); + inner.appendChild(sender); + inner.appendChild(bubble); + + msgWrap.appendChild(inner); + + container.appendChild(msgWrap); + container.scrollTop = container.scrollHeight; + } catch (e) { + console.warn('addChatMessage failed', e); + } +} + +// Called when Aria reaches a waypoint or finishes a movement +function arrivalFeedback(waypointKey) { + try { + let msg = ''; + if (waypointKey && waypoints3D && waypoints3D[waypointKey]) { + msg = `I've arrived at ${waypoints3D[waypointKey].name}.`; + } else if (typeof waypointKey === 'string' && waypointKey.includes('%')) { + msg = `Arrived at ${waypointKey}.`; + } else if (waypointKey) { + msg = `Arrived at ${waypointKey}.`; + } else { + msg = `Arrived.`; + } + + // Friendly reply from Aria in chat — pick varied arrival messages + const arrivalMessages = [ + `I'm here! 🎉`, + `Made it — ${waypointKey ? waypoints3D[waypointKey]?.name || waypointKey : 'arrived'}!`, + `Arrived and ready!`, + `That was quick — I'm here.`, + `All set. What should we do next?`, + ]; + const chosen = arrivalMessages[Math.floor(Math.random() * arrivalMessages.length)]; + addChatMessage('aria', chosen); + + // Little celebratory effect and expression + createEffect('sparkle'); + changeExpression('happy'); + + // Short arrival animation (small bounce & glow) + aria.classList.add('arrived'); + setTimeout(() => aria.classList.remove('arrived'), 900); + + // Clear current waypoint marker so we don't re-announce + characterState.currentWaypoint = null; + } catch (e) { + console.warn('arrivalFeedback failed', e); + } +} + +// Try to resolve a freeform phrase to a waypoint key +function resolveWaypointFromPhrase(phrase) { + if (!phrase || !waypoints3D) return null; + const clean = phrase.toLowerCase().replace(/[^a-z0-9\s-]/g, '').trim(); + if (!clean) return null; + + // direct key match (e.g., front-left) + const direct = clean.replace(/\s+/g, '-'); + if (waypoints3D[direct]) return direct; + + // check by names and partial matches + for (const k in waypoints3D) { + const v = waypoints3D[k]; + if (!v) continue; + const name = String(v.name || '').toLowerCase(); + if (name === clean) return k; + if (name.includes(clean) || clean.includes(k.replace('-', ' '))) return k; + } + + // synonyms mapping for simple words + const synonyms = { + 'front': 'front-center', 'back': 'back-center', 'left': 'stage-left', 'right': 'stage-right', 'center': 'center', 'table': 'table' + }; + if (synonyms[clean]) return synonyms[clean]; + + // try matching tokens + const tokens = clean.split(/\s+/); + for (const t of tokens) { + if (synonyms[t]) return synonyms[t]; + } + + return null; +} + +// Process chat input: supports slash commands and normal commands +async function sendChat() { + const chatBox = document.getElementById('chatInput'); + if (!chatBox) return; + + const text = chatBox.value.trim(); + if (!text) return; + + // Show user's message + addChatMessage('user', text); + chatBox.value = ''; + + // check for natural language movements (e.g., "go to front-right" or "walk to table") + const nlMove = /(?:go to|move to|walk to|goto)\s+([a-z0-9\-\s]+)/i.exec(text); + if (nlMove) { + const targetPhrase = nlMove[1].trim(); + // try to match known waypoint keys or names + const candidate = resolveWaypointFromPhrase(targetPhrase); + if (candidate) { + moveToWaypoint(candidate); + addChatMessage('aria', `Moving to ${waypoints3D[candidate].name}`); + } else { + addChatMessage('aria', `I couldn't identify a waypoint for '${targetPhrase}'. Try /waypoints`); + } + return; + } + + // /say command — speak directly as Aria + if (text.startsWith('/say ')) { + const sayText = text.slice(5).trim(); + if (sayText) { + // Aria says it in the chat + addChatMessage('aria', sayText); + // small visual cue + createEffect('sparkle'); + changeExpression('smile'); + } + return; + } + + // slash commands handled locally + if (text.startsWith('/goto ') || text.startsWith('/move ') || text.startsWith('/moveTo ')) { + const parts = text.split(/\s+/, 2); + const waypoint = parts[1] ? parts[1].trim() : ''; + if (waypoints3D && waypoints3D[waypoint]) { + moveToWaypoint(waypoint); + addChatMessage('aria', `Moving to ${waypoints3D[waypoint].name}`); + } else { + addChatMessage('aria', `Unknown waypoint '${waypoint}'. Try /waypoints`); + } + return; + } + + if (text === '/waypoints') { + const entries = Object.entries(waypoints3D).map(([k, v]) => `${k} — ${v.name}`); + addChatMessage('aria', `Available waypoints: ${entries.join(', ')}`); + return; + } + + if (text === '/circle' || text === '/circle3d') { + moveInCircle3D(); + addChatMessage('aria', 'Starting 3D circle movement'); + return; + } + + if (text === '/spiral' || text === '/spiral3d') { + performSpiral3D(); + addChatMessage('aria', 'Starting 3D spiral'); + return; + } + + if (text === '/stop') { + // stop behaviors + if (typeof stopContinuousDance === 'function') stopContinuousDance(); + addChatMessage('aria', 'Stopped continuous actions'); + return; + } + + // Otherwise, send as a command to backend (and display response when available) + commandInput.value = text; + const result = await sendCommand(); + + if (!result) { + addChatMessage('aria', 'No response from backend (fallback executed)'); + return; + } + + if (result.error) { + addChatMessage('aria', `Error: ${result.error}`); + return; + } + + // Prefer textual response if available + if (result.response) { + addChatMessage('aria', result.response); + } else if (result.tags && result.tags.length > 0) { + addChatMessage('aria', `Executed tags: ${result.tags.join(' ')}`); + } else if (result.stage_context) { + // Keep stage context short + const ctx = result.stage_context.split('\n').slice(0, 4).join(' | '); + addChatMessage('aria', ctx); + } else { + addChatMessage('aria', 'Done.'); + } +} + +async function sendCommand() { + const command = commandInput.value.trim(); + if (!command) return; + + const providerInput = document.getElementById('providerSelect'); + const modelInput = document.getElementById('modelInput'); + const useLlmInput = document.getElementById('useLlmToggle'); + const provider = providerInput ? providerInput.value.trim() : ''; + const model = modelInput ? modelInput.value.trim() : ''; + const useLlm = useLlmInput ? useLlmInput.checked : true; + + persistProviderSettings(provider, model, useLlm); + + log(`Command: "${command}"`); + commandInput.value = ''; + + try { + // Gather current stage state for AI to see + const stageRect = stage.getBoundingClientRect(); + const ariaRect = aria.getBoundingClientRect(); + + // Calculate Aria's position as percentages + const ariaX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; + const ariaY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; + + // Gather object positions + const objectPositions = {}; + ['apple', 'book', 'cup', 'ball', 'flower'].forEach(objId => { + const obj = document.getElementById(objId); + if (obj) { + const objRect = obj.getBoundingClientRect(); + objectPositions[objId] = { + x: ((objRect.left - stageRect.left) / stageRect.width) * 100, + y: 100 - ((objRect.bottom - stageRect.top) / stageRect.height) * 100, + state: obj.classList.contains('held') ? 'held' : 'on_table' + }; + } + }); + + const currentStageState = { + aria: { + position: { x: Math.round(ariaX), y: Math.round(ariaY) }, + expression: characterState.mood || 'neutral', + held_object: characterState.heldObject, + facing: 'right' + }, + objects: objectPositions + }; + + // Call backend API with stage state + const response = await fetch('/api/aria/command', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + command: command, + stage_state: currentStageState, + use_llm: useLlm, + provider: provider || undefined, + model: model || undefined, + }) + }); + + if (!response.ok) { + throw new Error(`HTTP ${response.status}: ${response.statusText}`); + } + + const data = await response.json(); + + // AI automatically generates character based on response + if (data.response) { + autoGenerateCharacter(data.response); + } + + if (data.tags && data.tags.length > 0) { + log(`✅ ${data.model}: ${data.tags.join(' ')}`); + executeTags(data.tags); + } else if (data.error) { + log(`⚠️ API Error: ${data.error}`, true); + executeLocalCommand(command); + } else { + log('⚠️ Using fallback parser'); + executeLocalCommand(command); + } + // Return the parsed result so callers (chat UI) can inspect tags / response + return data; + } catch (error) { + log(`⚠️ Network error, using fallback`, true); + // Fallback: parse command locally without AI + executeLocalCommand(command); + return { error: String(error) }; + } +} + +function setPosition(xPercent, yPercent, zDepth = 0, rotateY = 0) { + // AI-driven animated walking to position in 3D space (not teleporting) + const stageRect = stage.getBoundingClientRect(); + const ariaRect = aria.getBoundingClientRect(); + + // Get current position + const currentX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; + const currentY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; + + // Clamp target values + xPercent = Math.max(5, Math.min(95, xPercent)); + yPercent = Math.max(5, Math.min(95, yPercent)); + zDepth = Math.max(-300, Math.min(200, zDepth)); // Z range: -300px (far) to 200px (near) + + // Calculate distance and direction + const deltaX = xPercent - currentX; + const deltaY = yPercent - currentY; + const distance = Math.sqrt(deltaX * deltaX + deltaY * deltaY); + + // Don't move if already at target + if (distance < 2) { + log(`📍 Already at position (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px)`); + // If we were moving towards a known waypoint, confirm arrival + if (characterState.currentWaypoint) { + arrivalFeedback(characterState.currentWaypoint); + } + return; + } + + // Determine walking speed and style based on distance + let duration = Math.min(distance * 40, 2000); // 40ms per percent, max 2s + let walkStyle = 'normal'; + + if (distance > 50) { + walkStyle = 'run'; + duration = distance * 20; // Faster for long distances + } else if (distance > 30) { + walkStyle = 'walk'; + duration = distance * 30; + } + + // Face the direction of movement in 3D + let rotationAngle = rotateY || 0; + if (deltaX > 2) { + rotationAngle = 0; // Face camera (right) + } else if (deltaX < -2) { + rotationAngle = 180; // Face away (left) + } + + // Add walking animation + aria.classList.add('walking'); + if (walkStyle === 'run') { + aria.classList.add('running'); + } + + // Animate legs while moving + const walkInterval = setInterval(() => { + animateWalkCycle(); + }, 200); + + // Smooth 3D transition to target + aria.style.transition = `left ${duration}ms ease-in-out, bottom ${duration}ms ease-in-out, transform ${duration}ms ease-in-out`; + + const leftPercent = xPercent; + const bottomPercent = 100 - yPercent; // Invert Y (CSS bottom increases upward) + + aria.style.left = leftPercent + '%'; + aria.style.bottom = bottomPercent + '%'; + + // Apply 3D transform with Z-depth and rotation + const scaleX = rotationAngle === 180 ? -1 : 1; + aria.style.transform = `translateX(-50%) translateZ(${zDepth}px) rotateY(${rotationAngle}deg) scaleX(${scaleX})`; + + // Update character state + characterState.position = { x: xPercent, y: yPercent, z: zDepth }; + characterState.rotation = rotationAngle; + + log(`🚶 Walking to (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px, Rot:${Math.round(rotationAngle)}°) - ${walkStyle} style`); + showFeedback(`🚶 3D WALK: X${Math.round(xPercent)}% Y${Math.round(yPercent)}% Z${Math.round(zDepth)}px`); + + // Stop walking animation when arrived + setTimeout(() => { + aria.classList.remove('walking', 'running'); + aria.style.transition = ''; // Reset transition + clearInterval(walkInterval); + resetWalkCycle(); + spawnDustCloud(); // landing dust + log(`✅ Arrived at (${Math.round(xPercent)}%, ${Math.round(yPercent)}%)`); + // If we were targeting a waypoint, announce in chat + if (characterState.currentWaypoint) { + arrivalFeedback(characterState.currentWaypoint); + } + }, duration); +} + +// Walking animation cycle +function animateWalkCycle() { + const leftLeg = document.querySelector('.aria-lower-leg.left'); + const rightLeg = document.querySelector('.aria-lower-leg.right'); + const leftArm = document.querySelector('.aria-lower-arm.left'); + const rightArm = document.querySelector('.aria-lower-arm.right'); + + if (!leftLeg || !rightLeg) return; + + // Alternate leg swings + if (leftLeg.style.transform.includes('rotate')) { + leftLeg.style.transform = 'rotate(20deg)'; + rightLeg.style.transform = 'rotate(-20deg)'; + if (leftArm) leftArm.style.transform = 'rotate(-15deg)'; + if (rightArm) rightArm.style.transform = 'rotate(15deg)'; + } else { + leftLeg.style.transform = 'rotate(-20deg)'; + rightLeg.style.transform = 'rotate(20deg)'; + if (leftArm) leftArm.style.transform = 'rotate(15deg)'; + if (rightArm) rightArm.style.transform = 'rotate(-15deg)'; + } +} + +function resetWalkCycle() { + const leftLeg = document.querySelector('.aria-lower-leg.left'); + const rightLeg = document.querySelector('.aria-lower-leg.right'); + const leftArm = document.querySelector('.aria-lower-arm.left'); + const rightArm = document.querySelector('.aria-lower-arm.right'); + + if (leftLeg) leftLeg.style.transform = ''; + if (rightLeg) rightLeg.style.transform = ''; + if (leftArm) leftArm.style.transform = ''; + if (rightArm) rightArm.style.transform = ''; +} + +function executeLocalCommand(command) { + // Simple local fallback without AI model + const cmd = command.toLowerCase(); + let executed = false; + + // Check if this is a limb command to avoid movement conflicts + const isLimbCommand = ['left arm', 'arm left', 'left hand', 'right arm', 'arm right', 'right hand', + 'left leg', 'leg left', 'right leg', 'leg right'].some(k => cmd.includes(k)); + + // Expressions + if (cmd.includes('smile') || cmd.includes('happy')) { + changeExpression('smile'); + executed = true; + } + if (cmd.includes('sad')) { + changeExpression('sad'); + executed = true; + } + if (cmd.includes('surprised')) { + changeExpression('surprised'); + executed = true; + } + if (cmd.includes('confused')) { + changeExpression('confused'); + executed = true; + } + if (cmd.includes('thinking') || cmd.includes('think')) { + changeExpression('thinking'); + executed = true; + } + if (cmd.includes('wink')) { + changeExpression('wink'); + executed = true; + } + + // Animations + if (cmd.includes('jump')) { + animate('jumping'); + executed = true; + } + if (cmd.includes('dance')) { + animate('dancing'); + executed = true; + } + if (cmd.includes('spin')) { + animate('spinning'); + executed = true; + } + if (cmd.includes('wave')) { + animate('waving'); + executed = true; + } + + // Rich gestures (mirrors server allowlist gestures) + if (cmd.includes('thumbs up') || cmd.includes('thumbsup')) { + performGesture('thumbs_up'); + executed = true; + } + if (cmd.includes('clap')) { + performGesture('clap'); + executed = true; + } + if (cmd.includes('shrug')) { + performGesture('shrug'); + executed = true; + } + if (cmd.includes('bow')) { + performGesture('bow'); + executed = true; + } + if (cmd.includes('nod')) { + performGesture('nod'); + executed = true; + } + + // Pose commands + if (cmd.includes('sit')) { + applyPose('sit'); + executed = true; + } + if (cmd.includes('stand')) { + applyPose('stand'); + executed = true; + } + if (cmd.includes('crouch')) { + applyPose('crouch'); + executed = true; + } + if (cmd.includes('lie down') || cmd.includes('lay down') || cmd.includes('lie ')) { + applyPose('lie'); + executed = true; + } + + // Looking/facing helpers + if (cmd.includes('look left')) { + handleLookTag('left'); + executed = true; + } + if (cmd.includes('look right')) { + handleLookTag('right'); + executed = true; + } + if (cmd.includes('look up')) { + handleLookTag('up'); + executed = true; + } + if (cmd.includes('look down')) { + handleLookTag('down'); + executed = true; + } + + if (cmd === 'wait' || cmd.startsWith('wait ')) { + const secondsMatch = cmd.match(/wait\s+([0-9]+(?:\.[0-9]+)?)/); + performWait(secondsMatch ? secondsMatch[1] : '1'); + executed = true; + } + + // Effects - with intensity detection + let effectIntensity = 'normal'; + if (cmd.includes('light') || cmd.includes('subtle') || cmd.includes('gentle')) { + effectIntensity = 'light'; + } else if (cmd.includes('heavy') || cmd.includes('intense') || cmd.includes('lots') || cmd.includes('many')) { + effectIntensity = 'heavy'; + } + + if (cmd.includes('sparkle') || cmd.includes('glitter') || cmd.includes('shimmer') || cmd.includes('shine')) { + createEffect('sparkle', effectIntensity); + executed = true; + } + if (cmd.includes('hearts') || cmd.includes('heart') || cmd.includes('love')) { + createEffect('hearts', effectIntensity); + executed = true; + } + if (cmd.includes('glow') || cmd.includes('glowing') || cmd.includes('radiate') || cmd.includes('illuminate')) { + createEffect('glow', effectIntensity); + executed = true; + } + + // Movement - only if not a limb command + if (!isLimbCommand) { + // Determine movement style + let movementSpeed = 'normal'; + if (cmd.includes('skip')) { + movementSpeed = 'skip'; + } else if (cmd.includes('strut') || cmd.includes('swagger')) { + movementSpeed = 'strut'; + } else if (cmd.includes('run')) { + movementSpeed = 'run'; + } + + if (cmd.includes('left')) { + move('left', movementSpeed); + executed = true; + } + if (cmd.includes('right')) { + move('right', movementSpeed); + executed = true; + } + if (cmd.includes('up') || (cmd.includes('forward') && !cmd.includes('arm') && !cmd.includes('leg'))) { + move('up', movementSpeed); + executed = true; + } + if (cmd.includes('down') || (cmd.includes('back') && !cmd.includes('arm') && !cmd.includes('leg'))) { + move('down', movementSpeed); + executed = true; + } + } + + if (!executed) { + log('❌ Command not recognized', true); + } +} + +function executeTags(tags) { + console.log('📋 Executing tags:', tags); + tags.forEach((tag, index) => { + // Parse tag format: [aria:category:action:param] + const match = tag.match(/\[aria:([^:]+):([^:\]]+)(?::([^\]]+))?\]/); + if (!match) { + console.log('⚠️ Failed to parse tag:', tag); + return; + } + + const [, categoryRaw, actionRaw, paramRaw] = match; + // Normalize category/action + const category = (categoryRaw || '').toLowerCase(); + const action = (actionRaw || '').toLowerCase(); + const param = typeof paramRaw === 'string' ? paramRaw.trim() : undefined; + console.log(`✅ Parsed tag - Category: ${category}, Action: ${action}, Param: ${param}`); + + setTimeout(() => { + switch (category) { + case 'expression': + console.log('Executing expression:', action); + changeExpression(action); + break; + case 'animate': + console.log('Executing animate:', action); + animate(getAnimationClass(action)); + break; + case 'gesture': + performGesture(action, param); + break; + case 'move': + move(action, param || 'normal'); + break; + case 'walk': + move(action, param || 'normal'); + break; + case 'run': + move(action, param || 'fast'); + break; + case 'position': + console.log('Executing position:', action, param); + if (action && param) { + // Format: [aria:position:x:y] or [aria:position:x:y:z:rotation] + const parts = tag.match(/\[aria:position:([^:\]]+):([^:\]]+)(?::([^:\]]+))?(?::([^\]]+))?\]/); + if (parts) { + const x = parseInt(parts[1]); + const y = parseInt(parts[2]); + const z = parts[3] ? parseInt(parts[3]) : 0; + const rotation = parts[4] ? parseInt(parts[4]) : 0; + setPosition(x, y, z, rotation); + } else { + setPosition(parseInt(action), parseInt(param)); + } + } + break; + case 'skip': + move(action, 'skip'); + break; + case 'strut': + case 'swagger': + move(action, 'strut'); + break; + case 'limb': + // Move individual limbs with tags like: + // [aria:limb:left_arm:raise] | [aria:limb:right_arm:-45] | [aria:limb:left_leg:kick] + handleLimbTag(action, param); + break; + case 'interact': + console.log('Executing interact:', action, param); + if (action === 'add') { + const [objectName, emoji] = param.split(':'); + addObject(objectName, emoji || '🧸'); + } else { + interactWithObject(action, param); + } + break; + case 'effect': + createEffect(action, param || 'normal'); + break; + case 'camera': + if (action === 'center') centerAria(); + break; + case 'pose': + applyPose(action, param); + break; + case 'say': + // Tag formats either [aria:say:Hello world] or [aria:say:utterance:extra] + const spoken = param || action; + if (spoken) { + addChatMessage('aria', String(spoken)); + } + break; + case 'look': + handleLookTag(action, param); + break; + case 'facing': + handleFacingTag(action, param); + break; + case 'wait': + performWait(action, param); + break; + } + }, index * 500); // Stagger multiple commands + }); +} + +function parseDurationMs(raw, fallbackMs = 700) { + if (raw == null) return fallbackMs; + const str = String(raw).trim().toLowerCase(); + const parsed = parseFloat(str.replace(/[^0-9.]/g, '')); + if (!Number.isFinite(parsed)) return fallbackMs; + // "2" means seconds for user-friendliness, while "800ms" stays ms. + const isMs = str.includes('ms'); + const ms = isMs ? parsed : parsed * 1000; + return Math.max(150, Math.min(10000, Math.round(ms))); +} + +function performGesture(gestureRaw, paramRaw) { + const gesture = String(gestureRaw || '').toLowerCase().replace(/[-\s]/g, '_'); + const duration = parseDurationMs(paramRaw, 700); + isPerformingAction = true; + + switch (gesture) { + case 'wave': + animate('waving'); + return; + case 'thumbs_up': + case 'thumbsup': + showFeedback('👍 THUMBS UP!'); + moveArm(ariaArmRight, -95, Math.round(duration * 0.45)); + setTimeout(() => moveArm(ariaArmRight, -70, Math.round(duration * 0.2)), Math.round(duration * 0.45)); + setTimeout(() => resetLimbs(Math.round(duration * 0.35)), Math.round(duration * 0.65)); + break; + case 'clap': + showFeedback('👏 CLAP!'); + moveArm(ariaArmLeft, -30, Math.round(duration * 0.22)); + moveArm(ariaArmRight, 30, Math.round(duration * 0.22)); + setTimeout(() => { + moveArm(ariaArmLeft, -75, Math.round(duration * 0.18)); + moveArm(ariaArmRight, 75, Math.round(duration * 0.18)); + createEffect('sparkle', 'light'); + }, Math.round(duration * 0.24)); + setTimeout(() => { + moveArm(ariaArmLeft, -20, Math.round(duration * 0.2)); + moveArm(ariaArmRight, 20, Math.round(duration * 0.2)); + }, Math.round(duration * 0.52)); + setTimeout(() => resetLimbs(Math.round(duration * 0.25)), Math.round(duration * 0.75)); + break; + case 'bow': + showFeedback('🙇 BOW!'); + if (ariaBody) ariaBody.style.transition = `transform ${Math.round(duration * 0.45)}ms ease`; + if (ariaHead) ariaHead.style.transition = `transform ${Math.round(duration * 0.45)}ms ease`; + if (ariaBody) ariaBody.style.transform = 'scaleY(0.92)'; + if (ariaHead) ariaHead.style.transform = 'translateX(-50%) translateY(6px) rotate(8deg)'; + setTimeout(() => { + if (ariaBody) ariaBody.style.transform = ''; + if (ariaHead) ariaHead.style.transform = 'translateX(-50%)'; + }, Math.round(duration * 0.55)); + break; + case 'nod': + showFeedback('🙂 NOD'); + if (ariaHead) ariaHead.style.transition = `transform ${Math.round(duration * 0.2)}ms ease`; + if (ariaHead) ariaHead.style.transform = 'translateX(-50%) rotate(10deg)'; + setTimeout(() => { if (ariaHead) ariaHead.style.transform = 'translateX(-50%) rotate(-6deg)'; }, Math.round(duration * 0.24)); + setTimeout(() => { if (ariaHead) ariaHead.style.transform = 'translateX(-50%) rotate(8deg)'; }, Math.round(duration * 0.45)); + setTimeout(() => { if (ariaHead) ariaHead.style.transform = 'translateX(-50%)'; }, Math.round(duration * 0.68)); + break; + case 'shrug': + showFeedback('🤷 SHRUG'); + moveArm(ariaArmLeft, -35, Math.round(duration * 0.4)); + moveArm(ariaArmRight, -35, Math.round(duration * 0.4)); + if (ariaHead) ariaHead.style.transition = `transform ${Math.round(duration * 0.35)}ms ease`; + if (ariaHead) ariaHead.style.transform = 'translateX(-50%) rotate(6deg)'; + setTimeout(() => { + resetLimbs(Math.round(duration * 0.35)); + if (ariaHead) ariaHead.style.transform = 'translateX(-50%)'; + }, Math.round(duration * 0.5)); + break; + default: + // Unknown gesture fallback: preserve compatibility with a friendly wave. + animate('waving'); + return; + } + + setTimeout(() => { + isPerformingAction = false; + }, Math.max(250, duration)); +} + +function handleLookTag(directionRaw, paramRaw) { + const direction = String(directionRaw || '').toLowerCase(); + const duration = parseDurationMs(paramRaw, 650); + if (!ariaHead) return; + + isPerformingAction = true; + ariaHead.style.transition = `transform ${Math.round(duration * 0.35)}ms ease`; + switch (direction) { + case 'left': + ariaHead.style.transform = 'translateX(-50%) rotate(-10deg)'; + break; + case 'right': + ariaHead.style.transform = 'translateX(-50%) rotate(10deg)'; + break; + case 'up': + ariaHead.style.transform = 'translateX(-50%) translateY(-5px)'; + break; + case 'down': + ariaHead.style.transform = 'translateX(-50%) translateY(5px)'; + break; + default: + ariaHead.style.transform = 'translateX(-50%)'; + break; + } + + setTimeout(() => { + ariaHead.style.transform = 'translateX(-50%)'; + isPerformingAction = false; + }, Math.round(duration * 0.75)); +} + +function handleFacingTag(directionRaw, paramRaw) { + const direction = String(directionRaw || '').toLowerCase(); + const duration = parseDurationMs(paramRaw, 750); + const currentScale = characterState.size || 1.0; + aria.style.transition = `transform ${Math.round(duration * 0.45)}ms ease`; + + if (direction === 'left') { + aria.style.transform = `translateX(-50%) scale(${currentScale}) rotateY(180deg)`; + } else if (direction === 'right' || direction === 'front' || direction === 'forward') { + aria.style.transform = `translateX(-50%) scale(${currentScale}) rotateY(0deg)`; + } else { + // Unknown direction -> no-op, keep current orientation. + return; + } +} + +function performWait(actionRaw, paramRaw) { + const raw = paramRaw || actionRaw || '1'; + const duration = parseDurationMs(raw, 1000); + isPerformingAction = true; + showFeedback(`⏳ WAIT ${Math.round(duration / 100) / 10}s`); + setTimeout(() => { + isPerformingAction = false; + }, duration); +} + +// ── Eyebrow expression helper ──────────────────────────────────────────────── +function setEyebrows(state) { + if (!ariaEyebrowLeft || !ariaEyebrowRight) return; + const DEFAULTS = { l: 'rotate(-5deg)', r: 'rotate(5deg)' }; + const brows = { + happy: { l: 'rotate(-8deg) translateY(-3px)', r: 'rotate(8deg) translateY(-3px)' }, + smile: { l: 'rotate(-8deg) translateY(-3px)', r: 'rotate(8deg) translateY(-3px)' }, + sad: { l: 'rotate(5deg) translateY(1px)', r: 'rotate(-5deg) translateY(1px)' }, + angry: { l: 'rotate(-14deg) translateY(4px)', r: 'rotate(14deg) translateY(4px)' }, + surprised: { l: 'rotate(-3deg) translateY(-6px)', r: 'rotate(3deg) translateY(-6px)' }, + confused: { l: 'rotate(8deg) translateY(-1px)', r: 'rotate(-3deg) translateY(-2px)' }, + thinking: { l: 'rotate(-4deg) translateY(-3px)', r: 'rotate(7deg) translateY(1px)' }, + wink: { l: DEFAULTS.l, r: 'rotate(10deg) translateY(-2px)' }, + }; + const s = brows[state] || DEFAULTS; + ariaEyebrowLeft.style.transform = s.l; + ariaEyebrowRight.style.transform = s.r; +} +// ───────────────────────────────────────────────────────────────────────────── + +function changeExpression(emotion) { + ariaMouth.className = 'aria-mouth'; + + // Reset any previous expression modifications + ariaMouth.style.borderRadius = ''; + ariaMouth.style.width = ''; + ariaMouth.style.height = ''; + ariaMouth.style.borderTop = ''; + ariaMouth.style.transform = ''; + + switch (emotion) { + case 'smile': + case 'happy': + ariaMouth.classList.add('smile'); + break; + case 'sad': + ariaMouth.classList.add('sad'); + break; + case 'surprised': + ariaMouth.style.borderRadius = '50%'; + ariaMouth.style.width = '15px'; + ariaMouth.style.height = '15px'; + ariaMouth.style.borderTop = '2px solid #333'; + break; + case 'confused': + ariaMouth.style.width = '18px'; + ariaMouth.style.height = '6px'; + ariaMouth.style.borderRadius = '0 0 50% 50%'; + ariaMouth.style.transform = 'translateX(-50%) rotate(5deg)'; + break; + case 'thinking': + ariaMouth.style.width = '12px'; + ariaMouth.style.height = '8px'; + ariaMouth.style.borderRadius = '0 0 50% 50%'; + ariaMouth.style.transform = 'translateX(-30%)'; + break; + case 'wink': + ariaEyeRight.style.transition = 'height 0.07s'; + ariaEyeRight.style.height = '3px'; + setTimeout(() => { ariaEyeRight.style.height = ''; }, 500); + break; + default: + ariaMouth.classList.add('smile'); + } + + // Animate eyebrows to match expression + setEyebrows(emotion); + + // Brief scale pulse + aria.style.transform = 'translateX(-50%) scale(1.1)'; + setTimeout(() => { aria.style.transform = 'translateX(-50%) scale(1)'; }, 300); + + // Float an emoji above Aria to reinforce the expression + const moodEmoji = { smile: '😊', happy: '😄', sad: '😢', surprised: '😲', confused: '🤔', thinking: '💭', wink: '😉', angry: '😠' }; + const emoji = moodEmoji[emotion]; + if (emoji && stage && aria) { + const rect = aria.getBoundingClientRect(); + const sr = stage.getBoundingClientRect(); + const cx = ((rect.left + rect.width / 2 - sr.left) / sr.width) * 100; + const cy = ((rect.top - sr.top) / sr.height) * 100; + const el = document.createElement('div'); + el.textContent = emoji; + el.style.cssText = `position:absolute;left:${cx}%;top:${cy}%;font-size:22px;pointer-events:none;z-index:60;transition:all 0.8s ease-out;opacity:1;`; + stage.appendChild(el); + requestAnimationFrame(() => { el.style.transform = 'translateY(-30px) scale(1.3)'; el.style.opacity = '0'; }); + setTimeout(() => el.remove(), 900); + } +} + +// Idle animation state +let idleAnimationInterval = null; +let isPerformingAction = false; + +// Start idle animation — breathing, blinking, random glances +function startIdleAnimation() { + if (idleAnimationInterval) return; + + idleAnimationInterval = setInterval(() => { + if (isPerformingAction) return; + + const rand = Math.random(); + + // Breathing (always) + if (ariaBody) { + ariaBody.style.transition = 'transform 2.5s ease-in-out'; + ariaBody.style.transform = 'scaleY(1.03) scaleX(0.99)'; + setTimeout(() => { ariaBody.style.transform = 'scaleY(1) scaleX(1)'; }, 2500); + } + + // Blink ~40% of ticks + if (rand < 0.4 && ariaEyeLeft && ariaEyeRight) { + const origH = ariaEyeLeft.style.height || ''; + ariaEyeLeft.style.transition = 'height 0.07s'; + ariaEyeRight.style.transition = 'height 0.07s'; + ariaEyeLeft.style.height = '2px'; + ariaEyeRight.style.height = '2px'; + setTimeout(() => { + ariaEyeLeft.style.height = origH; + ariaEyeRight.style.height = origH; + }, 140); + } + + // Head tilt / bob ~25% of ticks + if (rand > 0.55 && rand < 0.8 && ariaHead) { + const tilt = (Math.random() - 0.5) * 6; // -3° to +3° + const bob = -1.5 - Math.random() * 1.5; + ariaHead.style.transition = 'transform 1.6s ease-in-out'; + ariaHead.style.transform = `translateX(-50%) translateY(${bob}px) rotate(${tilt}deg)`; + setTimeout(() => { + ariaHead.style.transform = 'translateX(-50%)'; + }, 1600); + } + + // Random glance (look aside then return) ~20% of ticks + if (rand > 0.8 && !_eyeRaf) { + const gx = (Math.random() > 0.5 ? 1 : -1) * (1.5 + Math.random() * 1.5); + const gy = (Math.random() - 0.4) * 1.2; + ariaEyes.forEach(eye => { + eye.style.setProperty('--px', `${gx.toFixed(2)}px`); + eye.style.setProperty('--py', `${gy.toFixed(2)}px`); + }); + setTimeout(() => { + ariaEyes.forEach(eye => { + eye.style.setProperty('--px', '0px'); + eye.style.setProperty('--py', '0px'); + }); + }, 700 + Math.random() * 1200); + } + }, 3500); +} + +// Stop idle animation +function stopIdleAnimation() { + if (idleAnimationInterval) { + clearInterval(idleAnimationInterval); + idleAnimationInterval = null; + } +} + +// Limb movement helpers +function moveArm(arm, angle, duration = 500) { + arm.style.transition = `transform ${duration}ms ease-in-out`; + arm.style.transform = `rotate(${angle}deg)`; +} + +function moveLeg(leg, angle, duration = 500) { + leg.style.transition = `transform ${duration}ms ease-in-out`; + leg.style.transform = `rotate(${angle}deg)`; +} + +function resetLimbs(duration = 500) { + moveArm(ariaArmLeft, 0, duration); + moveArm(ariaArmRight, 0, duration); + moveLeg(ariaLegLeft, 0, duration); + moveLeg(ariaLegRight, 0, duration); +} + +// Utility helpers for limb control +function clamp(n, min, max) { return Math.max(min, Math.min(max, n)); } + +function parseAngleAndDuration(param) { + // Accept patterns: "45", "45,600", "raise", "raise,800" + if (!param) return { value: null, duration: 500 }; + const parts = String(param).split(',').map(s => s.trim()); + const first = parts[0]; + const dur = parts[1] ? parseInt(parts[1], 10) : 500; + const asNum = Number(first); + if (!Number.isNaN(asNum)) { + return { value: asNum, duration: isFinite(dur) ? dur : 500 }; + } + return { value: first.toLowerCase(), duration: isFinite(dur) ? dur : 500 }; +} + +function normalizeLimbPart(part) { + const p = (part || '').toLowerCase().replace(/[-\s]/g, '_'); + const map = { + 'leftarm': 'left_arm', 'arm_left': 'left_arm', 'l_arm': 'left_arm', 'left_hand': 'left_arm', 'hand_left': 'left_arm', + 'rightarm': 'right_arm', 'arm_right': 'right_arm', 'r_arm': 'right_arm', 'right_hand': 'right_arm', 'hand_right': 'right_arm', + 'leftleg': 'left_leg', 'leg_left': 'left_leg', 'l_leg': 'left_leg', + 'rightleg': 'right_leg', 'leg_right': 'right_leg', 'r_leg': 'right_leg' + }; + return map[p] || p; +} + +function elementForPart(part) { + switch (part) { + case 'left_arm': return ariaArmLeft; + case 'right_arm': return ariaArmRight; + case 'left_leg': return ariaLegLeft; + case 'right_leg': return ariaLegRight; + default: return null; + } +} + +function waveArmElement(armEl, duration = 700) { + if (!armEl) return; + const step = Math.max(120, duration); + moveArm(armEl, -60, step * 0.3); + setTimeout(() => moveArm(armEl, -30, step * 0.25), step * 0.3); + setTimeout(() => moveArm(armEl, -60, step * 0.25), step * 0.55); + setTimeout(() => moveArm(armEl, -30, step * 0.2), step * 0.8); + setTimeout(() => moveArm(armEl, 0, step * 0.2), step * 1.0); +} + +function kickLegElement(legEl, duration = 500) { + if (!legEl) return; + moveLeg(legEl, 45, duration * 0.5); + setTimeout(() => moveLeg(legEl, 0, duration * 0.5), duration * 0.5); +} + +function handleLimbTag(partRaw, paramRaw) { + const part = normalizeLimbPart(partRaw); + const targetEl = elementForPart(part); + if (!targetEl) { + log(`❓ Unknown limb: ${partRaw}`); + return; + } + const { value, duration } = parseAngleAndDuration(paramRaw); + const isArm = part.includes('arm'); + const isLeg = part.includes('leg'); + // Pause idle while moving a limb + isPerformingAction = true; + + if (typeof value === 'number') { + const angle = isArm ? clamp(value, -130, 130) : clamp(value, -60, 60); + if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); + setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); + return; + } + + const action = (value || '').toLowerCase(); + switch (action) { + case 'raise': + case 'up': + if (isArm) moveArm(targetEl, -90, duration); else moveLeg(targetEl, -30, duration); + break; + case 'lower': + case 'down': + if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); + break; + case 'forward': + if (isArm) moveArm(targetEl, -45, duration); else moveLeg(targetEl, 30, duration); + break; + case 'back': + case 'backward': + if (isArm) moveArm(targetEl, 45, duration); else moveLeg(targetEl, -30, duration); + break; + case 'wave': + if (isArm) waveArmElement(targetEl, duration); + break; + case 'kick': + if (isLeg) kickLegElement(targetEl, duration); + break; + case 'rest': + case 'neutral': + if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); + break; + default: + // Try numeric fallback if action is numeric-like + const num = Number(action); + if (!Number.isNaN(num)) { + const angle = isArm ? clamp(num, -130, 130) : clamp(num, -60, 60); + if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); + } else { + log(`❓ Unknown limb action: ${action}`); + } + } + setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); +} + +function walkCycle() { + // Alternating leg movement + moveLeg(ariaLegLeft, 25, 300); + moveLeg(ariaLegRight, -25, 300); + moveArm(ariaArmLeft, -15, 300); + moveArm(ariaArmRight, 15, 300); + + setTimeout(() => { + moveLeg(ariaLegLeft, -25, 300); + moveLeg(ariaLegRight, 25, 300); + moveArm(ariaArmLeft, 15, 300); + moveArm(ariaArmRight, -15, 300); + }, 300); + + setTimeout(() => resetLimbs(200), 600); +} + +function strutWalk() { + // Confident strut with head bob + ariaHead.style.transition = 'transform 0.3s'; + + moveLeg(ariaLegLeft, 35, 250); + moveLeg(ariaLegRight, -35, 250); + moveArm(ariaArmLeft, -25, 250); + moveArm(ariaArmRight, 25, 250); + ariaHead.style.transform = 'translateY(-5px) rotate(3deg)'; + + setTimeout(() => { + moveLeg(ariaLegLeft, -35, 250); + moveLeg(ariaLegRight, 35, 250); + moveArm(ariaArmLeft, 25, 250); + moveArm(ariaArmRight, -25, 250); + ariaHead.style.transform = 'translateY(-5px) rotate(-3deg)'; + }, 300); + + setTimeout(() => { + moveLeg(ariaLegLeft, 20, 250); + moveLeg(ariaLegRight, -20, 250); + ariaHead.style.transform = 'translateY(0) rotate(0)'; + }, 600); + + setTimeout(() => resetLimbs(200), 850); +} + +// 3D Waypoint System - Fixed positions in 3D space +const waypoints3D = { + 'center': { x: 50, y: 50, z: 0, rotation: 0, name: 'Center Stage' }, + 'front-left': { x: 25, y: 70, z: 150, rotation: 45, name: 'Front Left' }, + 'front-right': { x: 75, y: 70, z: 150, rotation: -45, name: 'Front Right' }, + 'back-left': { x: 25, y: 30, z: -200, rotation: 135, name: 'Back Left' }, + 'back-right': { x: 75, y: 30, z: -200, rotation: -135, name: 'Back Right' }, + 'front-center': { x: 50, y: 75, z: 180, rotation: 0, name: 'Front Center' }, + 'back-center': { x: 50, y: 25, z: -250, rotation: 180, name: 'Back Center' }, + 'stage-left': { x: 15, y: 50, z: -50, rotation: 90, name: 'Stage Left' }, + 'stage-right': { x: 85, y: 50, z: -50, rotation: -90, name: 'Stage Right' }, + 'table': { x: 60, y: 20, z: -100, rotation: 0, name: 'Near Table' } +}; + +function moveToWaypoint(waypointName) { + const waypoint = waypoints3D[waypointName]; + if (!waypoint) { + log(`❌ Unknown waypoint: ${waypointName}`, true); + return false; + } + + log(`📍 Moving to waypoint: ${waypoint.name}`); + showFeedback(`📍 WAYPOINT: ${waypoint.name}`); + // Notify in chat that Aria is moving + addChatMessage('aria', `Heading to ${waypoint.name}...`); + characterState.currentWaypoint = waypointName; + setPosition(waypoint.x, waypoint.y, waypoint.z, waypoint.rotation); + return true; +} + +function moveInCircle3D(radius = 30, steps = 12, duration = 6000) { + let currentStep = 0; + const centerX = 50; + const centerY = 50; + const stepDuration = duration / steps; + + const circleInterval = setInterval(() => { + if (currentStep >= steps) { + clearInterval(circleInterval); + log('🔄 Circle completed'); + addChatMessage('aria', 'Finished circular movement.'); + return; + } + + const angle = (currentStep / steps) * Math.PI * 2; + const x = centerX + radius * Math.cos(angle); + const y = centerY + radius * Math.sin(angle); + const z = 100 * Math.sin(angle * 2); // Wave in/out on Z-axis + const rotation = (angle * 180 / Math.PI) + 90; // Face tangent to circle + + setPosition(x, y, z, rotation); + currentStep++; + }, stepDuration); + + log('🔄 Starting 3D circle movement'); + showFeedback('🔄 3D CIRCLE'); + addChatMessage('aria', 'Starting circular 3D movement'); +} + +function performSpiral3D() { + const steps = 20; + const duration = 8000; + let currentStep = 0; + const centerX = 50; + const centerY = 50; + + const spiralInterval = setInterval(() => { + if (currentStep >= steps) { + clearInterval(spiralInterval); + log('🌀 Spiral completed'); + addChatMessage('aria', 'Finished spiral movement.'); + return; + } + + const progress = currentStep / steps; + const radius = 40 * (1 - progress); // Shrink radius + const angle = progress * Math.PI * 6; // Multiple rotations + const x = centerX + radius * Math.cos(angle); + const y = centerY + radius * Math.sin(angle); + const z = -200 + progress * 300; // Move from back to front + const rotation = angle * 180 / Math.PI; + + setPosition(x, y, z, rotation); + currentStep++; + }, duration / steps); + + log('🌀 Starting 3D spiral movement'); + showFeedback('🌀 3D SPIRAL'); + addChatMessage('aria', 'Starting 3D spiral movement'); +} + +function skipMove() { + // Playful skipping motion + moveLeg(ariaLegLeft, -45, 200); + moveLeg(ariaLegRight, 30, 200); + moveArm(ariaArmLeft, -30, 200); + moveArm(ariaArmRight, -30, 200); + aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; + + setTimeout(() => { + moveLeg(ariaLegLeft, 30, 200); + moveLeg(ariaLegRight, -45, 200); + aria.style.transform = 'translateX(-50%) scale(1)'; + }, 250); + + setTimeout(() => { + moveLeg(ariaLegLeft, -30, 200); + moveLeg(ariaLegRight, 20, 200); + aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; + }, 500); + + setTimeout(() => { + resetLimbs(200); + aria.style.transform = 'translateX(-50%) scale(1)'; + }, 750); +} + +function danceLimbs() { + // Arms up and down + moveArm(ariaArmLeft, -45, 200); + moveArm(ariaArmRight, -45, 200); + moveLeg(ariaLegLeft, 15, 200); + moveLeg(ariaLegRight, -15, 200); + + setTimeout(() => { + moveArm(ariaArmLeft, -90, 200); + moveArm(ariaArmRight, -90, 200); + moveLeg(ariaLegLeft, -15, 200); + moveLeg(ariaLegRight, 15, 200); + }, 200); + + setTimeout(() => { + moveArm(ariaArmLeft, -45, 200); + moveArm(ariaArmRight, -45, 200); + moveLeg(ariaLegLeft, 15, 200); + moveLeg(ariaLegRight, -15, 200); + }, 400); + + setTimeout(() => resetLimbs(200), 600); +} + +function expressiveDance() { + // More exaggerated dance moves + moveArm(ariaArmLeft, -120, 150); + moveArm(ariaArmRight, 60, 150); + moveLeg(ariaLegLeft, 20, 150); + moveLeg(ariaLegRight, -20, 150); + ariaHead.style.transform = 'rotate(15deg)'; + + setTimeout(() => { + moveArm(ariaArmLeft, 60, 150); + moveArm(ariaArmRight, -120, 150); + moveLeg(ariaLegLeft, -20, 150); + moveLeg(ariaLegRight, 20, 150); + ariaHead.style.transform = 'rotate(-15deg)'; + }, 200); + + setTimeout(() => { + moveArm(ariaArmLeft, -90, 150); + moveArm(ariaArmRight, -90, 150); + moveLeg(ariaLegLeft, 30, 150); + moveLeg(ariaLegRight, -30, 150); + ariaHead.style.transform = 'rotate(0)'; + }, 400); + + setTimeout(() => resetLimbs(200), 600); +} + +let continuousDanceInterval = null; + +function startContinuousDance() { + if (continuousDanceInterval) return; + + isPerformingAction = true; + showFeedback('🎉 PARTY MODE!'); + + continuousDanceInterval = setInterval(() => { + const danceType = Math.random(); + if (danceType > 0.5) { + expressiveDance(); + } else { + danceLimbs(); + } + }, 700); +} + +function stopContinuousDance() { + if (continuousDanceInterval) { + clearInterval(continuousDanceInterval); + continuousDanceInterval = null; + isPerformingAction = false; + resetLimbs(300); + } +} + +function spinLimbs() { + // Arms out during spin + moveArm(ariaArmLeft, -90, 100); + moveArm(ariaArmRight, -90, 100); + setTimeout(() => resetLimbs(300), 900); +} + +function animate(className) { + console.log('🎬 Animating:', className); + showFeedback('🎬 ' + className.toUpperCase() + '!'); + aria.classList.remove('jumping', 'dancing', 'spinning', 'waving'); + void aria.offsetWidth; // Force reflow + + // Mark as performing action + isPerformingAction = true; + + // Get current scale from characterState + const currentScale = characterState.size || 1.0; + + if (className === 'waving') { + showFeedback('👋 WAVING!'); + console.log('Wave animation with arm movement'); + // Rapid wave motion + moveArm(ariaArmRight, -60, 200); + setTimeout(() => moveArm(ariaArmRight, -30, 150), 200); + setTimeout(() => moveArm(ariaArmRight, -60, 150), 350); + setTimeout(() => moveArm(ariaArmRight, -30, 150), 500); + setTimeout(() => moveArm(ariaArmRight, -60, 150), 650); + setTimeout(() => { + resetLimbs(200); + isPerformingAction = false; + }, 800); + } else if (className === 'jumping') { + console.log('Jumping animation triggered with leg bending'); + aria.classList.add(className); + aria.style.filter = 'brightness(1.5)'; + + // Pre-jump crouch + moveLeg(ariaLegLeft, 45, 200); + moveLeg(ariaLegRight, -45, 200); + moveArm(ariaArmLeft, -20, 200); + moveArm(ariaArmRight, -20, 200); + + // During jump - legs extend + setTimeout(() => { + moveLeg(ariaLegLeft, -35, 300); + moveLeg(ariaLegRight, 35, 300); + moveArm(ariaArmLeft, -60, 300); + moveArm(ariaArmRight, -60, 300); + }, 300); + + // Landing crouch + setTimeout(() => { + moveLeg(ariaLegLeft, 35, 300); + moveLeg(ariaLegRight, -35, 300); + moveArm(ariaArmLeft, -10, 300); + moveArm(ariaArmRight, -10, 300); + aria.style.filter = 'brightness(1)'; + }, 1500); + + // Return to normal + setTimeout(() => { + resetLimbs(400); + aria.classList.remove(className); + aria.style.transform = `translateX(-50%) scale(${currentScale})`; + }, 2500); + } else { + console.log('Generic animation triggered:', className); + aria.classList.add(className); + + // Add limb movements based on animation type + if (className === 'dancing') { + console.log('Adding dance limb movements'); + // Repeat dance limbs during 2s animation + danceLimbs(); + setTimeout(() => danceLimbs(), 700); + setTimeout(() => danceLimbs(), 1400); + } else if (className === 'spinning') { + console.log('Adding spin limb movements'); + spinLimbs(); + } + + setTimeout(() => { + aria.classList.remove(className); + resetLimbs(300); + aria.style.transform = `translateX(-50%) scale(${currentScale})`; + isPerformingAction = false; + }, className === 'dancing' ? 2000 : 1000); + } +} + +function getAnimationClass(action) { + const animations = { + 'jump': 'jumping', + 'dance': 'dancing', + 'spin': 'spinning', + 'wave': 'waving', + 'bow': 'waving', + 'flip': 'spinning', + 'backflip': 'spinning' + }; + return animations[action] || 'jumping'; +} + +function move(direction, speed = 'normal') { + console.log('🚶 Moving:', direction, 'at speed:', speed); + + isPerformingAction = true; + + const currentLeft = aria.style.left || '50%'; + const current = parseFloat(currentLeft); + + let newPos = current; + let distance = 25; + let movementStyle = walkCycle; + let duration = '1s'; + + // Choose movement style based on speed + switch (speed) { + case 'run': + case 'fast': + distance = 40; + movementStyle = skipMove; + duration = '0.7s'; + showFeedback('🏃 RUNNING!'); + break; + case 'strut': + case 'swagger': + distance = 30; + movementStyle = strutWalk; + duration = '1.2s'; + showFeedback('😎 STRUTTING!'); + break; + case 'skip': + distance = 35; + movementStyle = skipMove; + duration = '0.9s'; + showFeedback('🎵 SKIPPING!'); + break; + default: + showFeedback('🚶 WALKING!'); + } + + // Animate movement style + movementStyle(); + + switch (direction) { + case 'left': + newPos = Math.max(5, current - distance); + break; + case 'right': + newPos = Math.min(95, current + distance); + break; + case 'forward': + case 'up': + const currentTop = aria.style.top || '50%'; + const top = parseFloat(currentTop); + aria.style.top = Math.max(10, top - distance) + '%'; + aria.style.transition = `top ${duration} ease`; + setTimeout(() => { isPerformingAction = false; }, 1000); + return; + case 'back': + case 'down': + const currentTopBack = aria.style.top || '50%'; + const topBack = parseFloat(currentTopBack); + aria.style.top = Math.min(80, topBack + distance) + '%'; + aria.style.transition = `top ${duration} ease`; + setTimeout(() => { isPerformingAction = false; }, 1000); + return; + } + + aria.style.transition = `left ${duration} ease`; + aria.style.left = newPos + '%'; + + setTimeout(() => { isPerformingAction = false; }, 1000); +} + +function createEffect(type, intensity = 'normal') { + const effects = { + 'sparkle': '✨', + 'glow': '💫', + 'hearts': '💕' + }; + + // Intensity configurations + const intensityConfig = { + 'light': { count: 3, spread: 60, duration: 1200, delay: 150 }, + 'normal': { count: 5, spread: 80, duration: 1500, delay: 100 }, + 'heavy': { count: 10, spread: 90, duration: 1800, delay: 60 } + }; + + const config = intensityConfig[intensity] || intensityConfig['normal']; + const emoji = effects[type] || '✨'; + + // Color variations for sparkle effect + const sparkleColors = ['#FFD700', '#FFA500', '#FFFF00', '#FFE4B5', '#FAFAD2']; + + // Use requestAnimationFrame for better performance + let createdCount = 0; + + function createSingleEffect() { + if (createdCount >= config.count) return; + + const effect = document.createElement('div'); + effect.className = `effect ${type}`; + effect.textContent = emoji; + + // Random position within spread area + const centerX = 50; + const centerY = 50; + const spreadRadius = config.spread / 2; + effect.style.left = (centerX + (Math.random() - 0.5) * spreadRadius) + '%'; + effect.style.top = (centerY + (Math.random() - 0.5) * spreadRadius) + '%'; + + // Add color variation for sparkle + if (type === 'sparkle') { + effect.style.filter = `hue-rotate(${Math.random() * 360}deg)`; + } + + // Add random rotation for variety + effect.style.transform = `rotate(${Math.random() * 360}deg)`; + + stage.appendChild(effect); + + // Remove after duration + setTimeout(() => { + if (effect.parentNode) { + effect.remove(); + } + }, config.duration); + + createdCount++; + + // Schedule next effect + if (createdCount < config.count) { + requestAnimationFrame(() => { + setTimeout(createSingleEffect, config.delay); + }); + } + } + + // Start effect creation + requestAnimationFrame(createSingleEffect); +} + +// Dust cloud on landing / arrival +function spawnDustCloud() { + if (!stage || !aria) return; + const rect = aria.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + const cx = ((rect.left + rect.width / 2 - stageRect.left) / stageRect.width) * 100; + const cy = ((rect.bottom - stageRect.top) / stageRect.height) * 100; + for (let i = 0; i < 6; i++) { + const p = document.createElement('div'); + p.style.cssText = `position:absolute;left:${cx + (Math.random() - 0.5) * 8}%;top:${cy - Math.random() * 3}%;width:${6 + Math.random() * 6}px;height:${6 + Math.random() * 6}px;background:rgba(160,140,120,${0.3 + Math.random() * 0.3});border-radius:50%;pointer-events:none;z-index:5;transition:all ${0.4 + Math.random() * 0.4}s ease-out;`; + stage.appendChild(p); + requestAnimationFrame(() => { + p.style.transform = `translate(${(Math.random() - 0.5) * 30}px, ${-10 - Math.random() * 20}px) scale(${1.5 + Math.random()})`; + p.style.opacity = '0'; + }); + setTimeout(() => p.remove(), 900); + } +} + +// Ambient idle sparkles — gentle occasional twinkle near Aria +let _idleSparkleInterval = null; +function startIdleSparkles() { + if (_idleSparkleInterval) return; + _idleSparkleInterval = setInterval(() => { + if (!aria || !stage) return; + // Only sparkle when idle (not walking/dancing) + if (aria.classList.contains('walking') || aria.classList.contains('dancing') || aria.classList.contains('running')) return; + if (Math.random() > 0.35) return; // ~35% chance each tick + const rect = aria.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + const cx = ((rect.left + rect.width / 2 - stageRect.left) / stageRect.width) * 100; + const cy = ((rect.top + rect.height * 0.3 - stageRect.top) / stageRect.height) * 100; + const s = document.createElement('div'); + s.textContent = '✦'; + s.style.cssText = `position:absolute;left:${cx + (Math.random() - 0.5) * 10}%;top:${cy + (Math.random() - 0.5) * 8}%;font-size:${8 + Math.random() * 8}px;color:rgba(255,215,0,${0.4 + Math.random() * 0.4});pointer-events:none;z-index:50;transition:all ${0.6 + Math.random() * 0.6}s ease-out;`; + stage.appendChild(s); + requestAnimationFrame(() => { + s.style.transform = `translateY(${-15 - Math.random() * 15}px) scale(0.3)`; + s.style.opacity = '0'; + }); + setTimeout(() => s.remove(), 1400); + }, 2000); +} +startIdleSparkles(); + +function centerAria() { + aria.style.left = '50%'; + aria.style.transform = 'translateX(-50%) scale(1)'; +} + +function applyPose(poseRaw, param) { + const pose = (poseRaw || '').toLowerCase(); + // Body position presets + const poses = { + 'sit': { bottom: '20px', transform: 'translateX(-50%) scale(0.8)' }, + 'stand': { bottom: '50px', transform: 'translateX(-50%) scale(1)' }, + 'crouch': { bottom: '30px', transform: 'translateX(-50%) scale(0.9)' }, + 'lie': { bottom: '10px', transform: 'translateX(-50%) scale(0.7) rotate(90deg)' } + }; + const poseStyle = poses[pose]; + if (poseStyle) { + aria.style.bottom = poseStyle.bottom; + aria.style.transform = poseStyle.transform; + } + // Limb presets + switch (pose) { + case 't-pose': + case 'tpose': + moveArm(ariaArmLeft, -90, 400); + moveArm(ariaArmRight, -90, 400); + resetLimbs(1000); + break; + case 'hands_up': + case 'hands-up': + case 'handsup': + moveArm(ariaArmLeft, -120, 400); + moveArm(ariaArmRight, -120, 400); + break; + case 'cross_arms': + case 'cross-arms': + case 'crossarms': + moveArm(ariaArmLeft, -30, 400); + moveArm(ariaArmRight, 30, 400); + break; + case 'hero': + // Hands on hips (approximate) + moveArm(ariaArmLeft, 45, 400); + moveArm(ariaArmRight, -45, 400); + break; + } +} + +function quickCommand(cmd) { + commandInput.value = cmd; + sendCommand(); +} + +// Random character evolution (simulates AI personality drift) +function randomCharacterEvolution() { + const moods = ['happy', 'sad', 'calm', 'thinking', 'neutral']; + const randomMood = moods[Math.floor(Math.random() * moods.length)]; + const randomEnergy = 30 + Math.floor(Math.random() * 70); + + log('✨ Character evolving to: ' + randomMood + ' (' + randomEnergy + '% energy)'); + + const newStyle = generateCharacterFromMood(randomMood, randomEnergy); + applyCharacterStyle(newStyle); +} + +// Automatic character evolution disabled - keeping neutral human-like appearance +// setInterval(randomCharacterEvolution, 30000); + +// Initialize +centerAria(); +characterState.position.x = 50; +log('🎨 Aria Visual System Ready!'); +log('🤖 AI Character Generation: ACTIVE'); +log('Type commands or use quick buttons'); + +// Object Interaction System +// Helper: derive stage-relative percentage position from an element +function objectPositionFromElement(elem) { + if (!elem) return null; + const stageRect = stage.getBoundingClientRect(); + // Prefer explicit CSS left/bottom if present (percent strings) + try { + if (elem.style && elem.style.left && elem.style.bottom) { + const left = parseFloat(elem.style.left || 0); + const bottom = parseFloat(elem.style.bottom || 0); + // The JS 'y' value used elsewhere equals the CSS bottom percent + return { x: Math.round(left), y: Math.round(bottom) }; + } + } catch (e) { + // fallthrough to computed rect + } + + const rect = elem.getBoundingClientRect(); + const x = ((rect.left - stageRect.left) / stageRect.width) * 100; + const y = 100 - ((rect.bottom - stageRect.top) / stageRect.height) * 100; + return { x: Math.round(x), y: Math.round(y) }; +} + +// Notify backend of object add/update/remove actions +async function sendObjectUpdate(action, objectId, extra = {}) { + try { + if (!objectId) return null; + const objEl = document.getElementById(objectId); + + // resolve position and state + let position = extra.position || null; + if (!position && objEl) position = objectPositionFromElement(objEl); + + const state = extra.state || (objEl ? (objEl.classList.contains('held') ? 'held' : (objEl.style.display === 'none' ? 'hidden' : 'on_table')) : (extra.state || 'unknown')); + const emoji = extra.emoji || (objEl ? objEl.textContent : undefined); + + const objPayload = { id: objectId }; + if (position !== null && position !== undefined) objPayload.position = position; + if (state !== null && state !== undefined) objPayload.state = state; + if (emoji !== null && emoji !== undefined) objPayload.emoji = emoji; + const payload = { action: action, object: objPayload }; + + const res = await fetch('/api/aria/object', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload) + }); + + if (!res.ok) { + console.warn('Object API returned non-OK status', res.status); + return null; + } + + const data = await res.json(); + console.log('object sync:', action, objectId, data); + return data; + } catch (err) { + console.warn('sendObjectUpdate failed:', err); + return null; + } +} +function pickUpObject(objectId) { + const obj = document.getElementById(objectId); + if (!obj) { + console.log('❌ Object not found:', objectId); + return false; + } + + if (characterState.heldObject) { + showFeedback('⚠️ Already holding ' + characterState.heldObject.toUpperCase() + '!'); + return false; + } + + console.log('🤏 Picking up:', objectId); + showFeedback('🤏 PICKED UP ' + objectId.toUpperCase() + '!'); + + // Store held object + characterState.heldObject = objectId; + characterState.heldObjectElement = obj; + + // Visual feedback + obj.classList.add('held'); + + // Sync server side: mark object as held + sendObjectUpdate('update', objectId, { state: 'held' }).catch(() => { }); + + // Position object above character + positionHeldObject(); + + // Arm reaching animation + moveArm(ariaArmRight, -90, 300); + setTimeout(() => moveArm(ariaArmRight, -45, 200), 300); + + return true; +} + +function dropObject() { + if (!characterState.heldObject) { + showFeedback('⚠️ Not holding anything!'); + return false; + } + + console.log('📦 Dropping:', characterState.heldObject); + showFeedback('📦 DROPPED ' + characterState.heldObject.toUpperCase() + '!'); + + const obj = characterState.heldObjectElement; + + // Remove held state + obj.classList.remove('held'); + + // Drop at current Aria position + const ariaRect = aria.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + + const dropLeft = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; + const dropBottom = ((stageRect.bottom - ariaRect.bottom) / stageRect.height) * 100; + + obj.style.left = dropLeft + '%'; + obj.style.bottom = (dropBottom + 10) + '%'; + + // Arm dropping animation + moveArm(ariaArmRight, -90, 200); + setTimeout(() => resetLimbs(300), 200); + + // Clear held object + characterState.heldObject = null; + characterState.heldObjectElement = null; + + // Sync server side: update object's position/state + const finalX = Math.round(dropLeft); + const finalY = Math.round(dropBottom + 10); + const tableX = 60; // server default table X + const isOnTable = Math.abs(finalX - tableX) < 20 && Math.abs(finalY - 35) < 20; + sendObjectUpdate('update', obj.id, { position: { x: finalX, y: finalY }, state: isOnTable ? 'on_table' : 'on_stage' }).catch(() => { }); + + return true; +} + +function throwObject(direction) { + if (!characterState.heldObject) { + showFeedback('⚠️ Not holding anything to throw!'); + return false; + } + + console.log('🎯 Throwing:', characterState.heldObject, 'to', direction); + showFeedback('🎯 THROWING ' + characterState.heldObject.toUpperCase() + '!'); + + const obj = characterState.heldObjectElement; + obj.classList.remove('held'); + + // Throwing arm animation + moveArm(ariaArmRight, -120, 150); + setTimeout(() => { + moveArm(ariaArmRight, -30, 200); + setTimeout(() => resetLimbs(300), 100); + }, 150); + + // Calculate throw trajectory + const currentLeft = parseFloat(obj.style.left); + const currentBottom = parseFloat(obj.style.bottom); + + let targetLeft = currentLeft; + let targetBottom = currentBottom; + + switch (direction) { + case 'left': + targetLeft = Math.max(5, currentLeft - 40); + break; + case 'right': + targetLeft = Math.min(95, currentLeft + 40); + break; + case 'up': + targetBottom = Math.min(90, currentBottom + 30); + break; + } + + // Animate throw with arc + obj.style.transition = 'all 0.8s cubic-bezier(0.25, 0.46, 0.45, 0.94)'; + obj.style.left = targetLeft + '%'; + obj.style.bottom = targetBottom + '%'; + obj.style.transform = 'rotate(360deg) scale(0.9)'; + + // Reset after landing + setTimeout(() => { + obj.style.transition = 'all 0.3s ease'; + obj.style.transform = 'rotate(0deg) scale(1)'; + }, 800); + + // Clear held object + characterState.heldObject = null; + characterState.heldObjectElement = null; + + // Sync server side after throw completes + setTimeout(() => { + const left = Math.round(targetLeft); + const bottom = Math.round(targetBottom); + sendObjectUpdate('update', obj.id, { position: { x: left, y: bottom }, state: 'on_stage' }).catch(() => { }); + }, 900); + + return true; +} + +function positionHeldObject() { + if (!characterState.heldObjectElement) return; + + // Position object above and slightly in front of character + const ariaRect = aria.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + + const objLeft = ((ariaRect.left - stageRect.left + ariaRect.width * 0.6) / stageRect.width) * 100; + const objBottom = ((stageRect.bottom - ariaRect.top + 20) / stageRect.height) * 100; + + characterState.heldObjectElement.style.left = objLeft + '%'; + characterState.heldObjectElement.style.bottom = objBottom + '%'; +} + +function interactWithObject(action, objectId) { + console.log('🔧 Interacting:', action, 'with', objectId); + + switch (action) { + case 'pickup': + case 'grab': + case 'take': + case 'get': + return pickUpObject(objectId); + case 'drop': + case 'place': + case 'put': + return dropObject(); + case 'throw': + case 'toss': + return throwObject('right'); + default: + showFeedback('❓ Unknown action: ' + action); + return false; + } +} + +// Make objects draggable and clickable +function initializeObjectInteractions() { + const objects = document.querySelectorAll('.object'); + + objects.forEach(obj => { + // Prevent adding duplicate event listeners when called multiple times + if (obj.__interactionInitialized) return; + obj.__interactionInitialized = true; + obj.addEventListener('click', (e) => { + e.stopPropagation(); + const objectId = obj.id; + + if (!characterState.heldObject) { + pickUpObject(objectId); + } else if (characterState.heldObject === objectId) { + dropObject(); + } + }); + + // Drag functionality + let isDragging = false; + let startX, startY, startLeft, startBottom; + + obj.addEventListener('mousedown', (e) => { + if (characterState.heldObject === obj.id) return; + + isDragging = true; + obj.classList.add('grabbed'); + + const rect = obj.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + + startX = e.clientX; + startY = e.clientY; + startLeft = ((rect.left - stageRect.left) / stageRect.width) * 100; + startBottom = ((stageRect.bottom - rect.bottom) / stageRect.height) * 100; + + e.preventDefault(); + }); + + document.addEventListener('mousemove', (e) => { + if (!isDragging) return; + + const stageRect = stage.getBoundingClientRect(); + const deltaX = ((e.clientX - startX) / stageRect.width) * 100; + const deltaY = -((e.clientY - startY) / stageRect.height) * 100; + + obj.style.left = Math.max(0, Math.min(100, startLeft + deltaX)) + '%'; + obj.style.bottom = Math.max(0, Math.min(100, startBottom + deltaY)) + '%'; + }); + + document.addEventListener('mouseup', () => { + if (isDragging) { + isDragging = false; + obj.classList.remove('grabbed'); + // Sync backend with final position + const pos = objectPositionFromElement(obj); + const state = obj.classList.contains('held') ? 'held' : 'on_table'; + sendObjectUpdate('update', obj.id, { position: pos, state: state }).catch(() => { }); + } + }); + }); +} + +function addObject(objectName, emoji) { + console.log('➕ Adding object:', objectName, emoji); + + // Check if object already exists + const existingObj = document.getElementById(objectName); + if (existingObj) { + console.log('⚠️ Object already exists:', objectName); + showFeedback('✅ ' + objectName + ' already on stage'); + return; + } + + // Create new object element + const newObj = document.createElement('div'); + newObj.id = objectName; + newObj.className = 'object'; + newObj.textContent = emoji; + + // Position near Aria + const ariaX = characterState.position.x; + const ariaY = characterState.position.y; + newObj.style.left = Math.max(10, Math.min(90, ariaX + 15)) + '%'; + newObj.style.bottom = Math.max(10, Math.min(90, ariaY + 5)) + '%'; + + // Add to stage + stage.appendChild(newObj); + + // Update tracking + activeObjects[objectName] = true; + + // Add to object manager if button doesn't exist + if (!document.querySelector(`[onclick="toggleObject('${objectName}')"]`)) { + const objectManager = document.getElementById('object-manager'); + const btn = document.createElement('button'); + btn.className = 'object-btn active'; + btn.id = 'btn-' + objectName; + btn.textContent = emoji + ' ' + objectName; + btn.onclick = () => toggleObject(objectName); + objectManager.appendChild(btn); + } + + // Initialize interactions for new object (click/drag) + initializeObjectInteractions(); + + // Sync server: add new object to stage_state + sendObjectUpdate('add', objectName, { position: objectPositionFromElement(newObj), state: 'on_stage', emoji: emoji }).catch(() => { }); + + showFeedback('✨ Added ' + objectName + ' to stage!'); + console.log('✅ Object added successfully:', objectName); +} + +// Initialize object interactions +initializeObjectInteractions(); +hydrateStageFromServer(); +log('🎮 Objects: apple, book, cup, ball, flower'); +log('💡 Try: "pick up apple", "drop", "throw ball"'); + +// Auto-behaviors with random timing +function startAutoBehaviors() { + // Random idle movements every 8-15 seconds + setInterval(() => { + if (Math.random() > 0.6) { + const randomActions = ['shift weight', 'look around', 'adjust hair', 'stretch']; + const action = randomActions[Math.floor(Math.random() * randomActions.length)]; + + switch (action) { + case 'shift weight': + animate('bouncing'); + setTimeout(() => aria.style.animation = 'breathe 4s ease-in-out infinite', 800); + break; + case 'look around': + const ariaHead = document.querySelector('.aria-head'); + ariaHead.style.transform = 'translateX(-50%) rotate(10deg)'; + setTimeout(() => ariaHead.style.transform = 'translateX(-50%) rotate(-10deg)', 600); + setTimeout(() => ariaHead.style.transform = 'translateX(-50%)', 1200); + break; + case 'adjust hair': + const hand = document.getElementById('ariaHandRight'); + hand.style.transform = 'translateY(-30px) rotate(-20deg)'; + setTimeout(() => hand.style.transform = '', 1000); + break; + case 'stretch': + ariaArmLeft.style.transform = 'rotate(-160deg)'; + ariaArmRight.style.transform = 'rotate(160deg)'; + setTimeout(() => { + ariaArmLeft.style.transform = ''; + ariaArmRight.style.transform = ''; + }, 1500); + break; + } + } + }, 8000 + Math.random() * 7000); + + // Random expressions every 12-20 seconds + setInterval(() => { + if (Math.random() > 0.5) { + const expressions = ['smile', 'thinking', 'neutral', 'surprised']; + const expr = expressions[Math.floor(Math.random() * expressions.length)]; + changeExpression(expr); + setTimeout(() => changeExpression('neutral'), 2000 + Math.random() * 3000); + } + }, 12000 + Math.random() * 8000); + + // Occasional sparkles every 20-30 seconds + setInterval(() => { + if (Math.random() > 0.7) { + createEffect('sparkle'); + } + }, 20000 + Math.random() * 10000); +} + +// Initial appearance set to neutral human-like style +setTimeout(() => { + log('🎨 Setting initial character appearance...'); + const neutralStyle = generateCharacterFromMood('neutral', 50); + applyCharacterStyle(neutralStyle); +}, 100); + +// Start idle animations +startIdleAnimation(); +log('👀 Idle animations enabled - watch for breathing and blinking!'); + +// Start automatic behaviors +startAutoBehaviors(); +log('✨ Auto-behaviors enabled - Aria will move and react on her own!'); + +// === Eye tracking (follows mouse cursor) === +(function initEyeTracking() { + const eyes = document.querySelectorAll('.aria-eye'); + if (!eyes.length || !stage) return; + + document.addEventListener('mousemove', function (e) { + eyes.forEach(function (eye) { + const rect = eye.getBoundingClientRect(); + const cx = rect.left + rect.width / 2; + const cy = rect.top + rect.height / 2; + const angle = Math.atan2(e.clientY - cy, e.clientX - cx); + const dist = Math.min(3, Math.hypot(e.clientX - cx, e.clientY - cy) / 50); + const dx = Math.cos(angle) * dist; + const dy = Math.sin(angle) * dist; + // Shift the eye's radial gradient to simulate looking + eye.style.backgroundPosition = `${50 + dx * 5}% ${50 + dy * 5}%`; + }); + }); + + log('👁️ Eye tracking active — Aria follows your cursor!'); +})(); + +// === Natural blink cycle === +(function initBlinkCycle() { + function doBlink() { + if (!ariaEyeLeft || !ariaEyeRight) return; + // Skip if performing action or mid-expression + if (isPerformingAction) return; + const origL = ariaEyeLeft.style.height; + const origR = ariaEyeRight.style.height; + ariaEyeLeft.style.transition = 'height 0.06s'; + ariaEyeRight.style.transition = 'height 0.06s'; + ariaEyeLeft.style.height = '1px'; + ariaEyeRight.style.height = '1px'; + setTimeout(function () { + ariaEyeLeft.style.height = origL || ''; + ariaEyeRight.style.height = origR || ''; + ariaEyeLeft.style.transition = ''; + ariaEyeRight.style.transition = ''; + }, 120); + } + // Natural blink interval: 3-6 seconds with occasional double-blink + function scheduleBlink() { + const delay = 3000 + Math.random() * 3000; + setTimeout(function () { + doBlink(); + // 20% chance of a double-blink + if (Math.random() < 0.2) { + setTimeout(doBlink, 250); + } + scheduleBlink(); + }, delay); + } + scheduleBlink(); +})(); + +// Expose minimal testing helpers +window.ariaTest = { + limb: (part, actionOrAngle, duration) => handleLimbTag(part, typeof actionOrAngle === 'number' ? `${actionOrAngle},${duration || 500}` : `${actionOrAngle || ''}${duration ? ',' + duration : ''}`), + pose: (name) => applyPose(name) +}; +window.simulateTags = (arr) => Array.isArray(arr) && executeTags(arr); diff --git a/apps/aria/auto-execute.html b/apps/aria/auto-execute.html index 763c71771..a91e63075 100644 --- a/apps/aria/auto-execute.html +++ b/apps/aria/auto-execute.html @@ -1,770 +1,770 @@ - - - - - - - Aria — Auto-Execute - - - - - -
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LLM-powered action generation & execution

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📝 Example Commands (click to use):

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  • Walk to the table and pick up - the apple
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Aria Auto-Execute

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LLM-powered action generation & execution

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📝 Example Commands (click to use):

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  • Walk to the table and pick up + the apple
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  • Say hello and wave at the audience +
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  • Go to the center and do a little + dance
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Aria — Interactive AI Character

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UE5-first web runtime with natural language commands and object interaction

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Aria — Interactive AI Character

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UE5-first web runtime with natural language commands and object interaction

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+ Enter send · ↑↓ history · / focus · + ←→↑ move · 1–5 expressions · + Ctrl/Cmd+D theme · Ctrl/Cmd+K clear log · F11 + fullscreen +
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+ + + + + + diff --git a/apps/aria/server.py b/apps/aria/server.py index ba0bb63af..42865bab2 100644 --- a/apps/aria/server.py +++ b/apps/aria/server.py @@ -1,1974 +1,1974 @@ -#!/usr/bin/env python -""" -Simple web server for Aria Visual Command System -Serves the HTML/JS frontend and provides API endpoint for command generation -""" -import datetime -import hashlib -import json -import logging -import math -import os -import random -import re -import socket -import sys -import urllib.request -from datetime import timezone -from http.server import HTTPServer, SimpleHTTPRequestHandler -from pathlib import Path -from typing import List, Optional, Tuple - -# Pre-compile regex patterns for performance (avoid recompiling in loops) -_RE_JSON_BLOCK = re.compile(r"\[.*\]", re.DOTALL) -_RE_ARIA_TAGS = re.compile(r"\[aria:[^\]]+\]") -_RE_SAY_COMMAND = re.compile( - r"(?:\b(?:say|announce|shout|speak|tell)\b)(?:\s+(?:everyone|that|to))?[:\-\s]+(.+)", - re.IGNORECASE, -) -_RE_SANITIZE_BRACKETS = re.compile(r"\]") -_RE_COORDINATES = re.compile(r"(\d{1,3})%?.*?(\d{1,3})%?") -_RE_COMMAND_SEPARATORS = re.compile( - r"(?:\s*(?:,|;|\||->|=>|→|\band then\b|\bthen\b|\band\b|\bafter that\b|\bafterwards\b|\bnext\b|\bfinally\b|\blastly\b|\bthen\s*:|\bnext\s*:|\bafterward\s*:|\bafterwards\s*:|\bfinally\s*:|\bfirst\b|\bsecond\b|\bthird\b|\bfourth\b|\bstep\s*(?:\d+|[ivxlcdm]+)\b|\bphase\s*(?:\d+|[ivxlcdm]+)\b|\bpart\s*(?:\d+|[ivxlcdm]+)\b|(? str: - """Normalize provider complete output to plain text.""" - if raw is None: - return "" - if isinstance(raw, str): - return raw - - if isinstance(raw, dict): - # OpenAI-like providers may return a dict with content field. - if "content" in raw and isinstance(raw["content"], str): - return raw["content"] - - # Handle OpenAI response objects with choices list. - choices = raw.get("choices") - if isinstance(choices, list) and choices: - first = choices[0] - if isinstance(first, dict): - msg = first.get("message") - if isinstance(msg, dict): - content = msg.get("content") - if isinstance(content, str): - return content - text = first.get("text") - if isinstance(text, str): - return text - # Fallback for hybrid objects - content = getattr(first, "content", None) - if isinstance(content, str): - return content - - # Generic fallback conversion - try: - return json.dumps(raw) - except Exception: - return str(raw) - - if hasattr(raw, "get") and callable(raw.get): - try: - content = raw.get("content") - if isinstance(content, str): - return content - except Exception: - pass - - try: - return str(raw) - except Exception: - return "" - - -# Configure logging -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" -) -logger = logging.getLogger(__name__) - -# Add project paths -REPO_ROOT = Path(__file__).resolve().parents[1] -sys.path.insert(0, str(REPO_ROOT / "AI" / "microsoft_phi-silica-3.6_v1")) -sys.path.insert(0, str(REPO_ROOT)) # Add root for shared imports - -# Try to import shared chat providers for LLM integration -try: - from shared.chat_providers import detect_provider - - LLM_AVAILABLE = True - logger.info("✓ LLM providers available for automatic action generation") -except ImportError: - LLM_AVAILABLE = False - logger.warning("✗ LLM providers not available - will use rule-based fallback only") - -# Skip AI model loading for faster startup - use rule-based fallback -MODEL = None -print("⚠️ Skipping AI model loading - using rule-based fallback for faster startup") - -# Pre-compiled keyword sets for O(1) lookup instead of O(n) any() calls -JUMP_KEYWORDS = frozenset(["jump", "leap", "hop"]) -DANCE_KEYWORDS = frozenset(["dance", "spin", "twirl"]) -WAVE_KEYWORDS = frozenset(["wave", "greet", "hello", "hi"]) -LOOK_KEYWORDS = frozenset(["look", "see", "watch", "observe"]) -SIT_KEYWORDS = frozenset(["sit", "rest", "relax"]) -RUN_KEYWORDS = frozenset(["run", "race", "sprint"]) -HIDE_KEYWORDS = frozenset(["hide", "crouch", "duck"]) -PRESENT_KEYWORDS = frozenset(["present", "show", "display"]) -THINK_KEYWORDS = frozenset(["think", "wonder", "ponder"]) -MOVE_KEYWORDS = frozenset(["go", "move", "walk", "run"]) -SAY_KEYWORDS = frozenset(["say", "speak", "tell", "greet"]) -PICKUP_KEYWORDS = frozenset(["pick", "get", "grab", "take"]) -ARM_WAVE_KEYWORDS = frozenset(["wave", "wiggle"]) -ARM_RAISE_KEYWORDS = frozenset(["raise", "up", "lift"]) -ARM_LOWER_KEYWORDS = frozenset(["lower", "down"]) -ARM_FORWARD_KEYWORDS = frozenset(["forward", "front"]) -ARM_BACK_KEYWORDS = frozenset(["back", "backward", "behind"]) -LEFT_ARM_KEYWORDS = frozenset(["left arm", "arm left", "left hand"]) -RIGHT_ARM_KEYWORDS = frozenset(["right arm", "arm right", "right hand"]) -LEFT_LEG_KEYWORDS = frozenset(["left leg", "leg left"]) -RIGHT_LEG_KEYWORDS = frozenset(["right leg", "leg right"]) -SPARKLE_KEYWORDS = frozenset(["sparkle", "sparkles", "glitter", "shimmer", "shine"]) -GLOW_KEYWORDS = frozenset(["glow", "glowing", "radiate", "illuminate"]) -HEARTS_KEYWORDS = frozenset(["hearts", "heart", "love"]) -WALK_LEFT_KEYWORDS = frozenset(["walk left", "go left", "left"]) -WALK_RIGHT_KEYWORDS = frozenset(["walk right", "go right", "right"]) -COME_HERE_KEYWORDS = frozenset(["come here", "come to me", "over here"]) -FOLLOW_ME_KEYWORDS = frozenset(["follow me", "come with me"]) -BRING_ME_KEYWORDS = frozenset(["bring me", "fetch", "hand me"]) -BRING_IT_KEYWORDS = frozenset(["bring it", "bring it here", "bring it to me"]) -DROP_HERE_KEYWORDS = frozenset( - ["drop it here", "put it here", "place it here", "set it down"] -) - - -def _contains_any_keyword(text: str, keywords: frozenset) -> bool: - """Check if text contains any keyword from set.""" - for kw in keywords: - if kw in text: - return True - return False - - -def _any_word_in_text(words: frozenset, text: str) -> bool: - """Backward-compatible helper alias used by older tests.""" - return _contains_any_keyword(text, words) - - -def _keywords_in_cmd(words: frozenset, text: str) -> bool: - """Backward-compatible helper alias used by older tests.""" - return _contains_any_keyword(text, words) - - -# Legacy constant aliases expected by older modules/tests. -_MOVE_KEYWORDS = MOVE_KEYWORDS -_SAY_KEYWORDS = SAY_KEYWORDS -_PICKUP_KEYWORDS = PICKUP_KEYWORDS -_JUMP_KEYWORDS = JUMP_KEYWORDS -_DANCE_KEYWORDS = DANCE_KEYWORDS -_LIMB_KEYWORDS = frozenset( - set(LEFT_ARM_KEYWORDS) - | set(RIGHT_ARM_KEYWORDS) - | set(LEFT_LEG_KEYWORDS) - | set(RIGHT_LEG_KEYWORDS) -) - -# Global stage state that AI can see -stage_state = { - "aria": { - "position": {"x": 15, "y": 20}, # percentage coordinates - "expression": "neutral", - "held_object": None, - "facing": "right", - }, - "objects": { - "apple": {"position": {"x": 55, "y": 35}, "state": "on_table"}, - "book": {"position": {"x": 48, "y": 35}, "state": "on_table"}, - "cup": {"position": {"x": 62, "y": 35}, "state": "on_table"}, - "ball": {"position": {"x": 70, "y": 35}, "state": "on_table"}, - "flower": {"position": {"x": 52, "y": 35}, "state": "on_table"}, - }, - "environment": { - "table": {"position": {"x": 60, "y": 20}}, - "stage_bounds": {"width": 100, "height": 100}, - }, -} - -# Structured action schema for LLM-powered automatic execution -ARIA_ACTIONS = { - "move": { - "params": ["target", "speed"], - "description": "Move Aria to a target position or object", - "example": {"action": "move", "target": {"x": 50, "y": 30}, "speed": "normal"}, - }, - "say": { - "params": ["text", "emotion"], - "description": "Make Aria speak with optional emotion", - "example": {"action": "say", "text": "Hello!", "emotion": "happy"}, - }, - "pickup": { - "params": ["object_id"], - "description": "Pick up an object from the stage", - "example": {"action": "pickup", "object_id": "apple"}, - }, - "drop": { - "params": ["position"], - "description": "Drop currently held object at position", - "example": {"action": "drop", "position": {"x": 50, "y": 30}}, - }, - "throw": { - "params": ["target", "force"], - "description": "Throw held object toward target", - "example": {"action": "throw", "target": {"x": 70, "y": 40}, "force": "medium"}, - }, - "gesture": { - "params": ["gesture_type"], - "description": "Perform a gesture animation", - "example": {"action": "gesture", "gesture_type": "wave"}, - }, - "look": { - "params": ["target"], - "description": "Look at a target position or object", - "example": {"action": "look", "target": "apple"}, - }, - "wait": { - "params": ["duration"], - "description": "Wait for specified duration in seconds", - "example": {"action": "wait", "duration": 2.0}, - }, -} - - -class AriaActionParser: - """LLM-powered action parser for automatic command execution""" - - def __init__(self): - self.provider = None - self.provider_choice = None - self._initialize_provider() - - @staticmethod - def _normalize_provider_alias(explicit: Optional[str]) -> Optional[str]: - """Normalize provider aliases to canonical detect_provider values.""" - if explicit is None: - return None - - normalized = str(explicit).strip().lower() - alias_map = { - "quantum-llm": "quantum", - "quantum_llm": "quantum", - "azure_openai": "azure", - } - return alias_map.get(normalized, normalized) - - def _resolve_provider_for_request( - self, - *, - explicit: Optional[str] = None, - model_override: Optional[str] = None, - ) -> Tuple[object | None, object | None]: - """Resolve provider for a specific request while preserving cached default.""" - if not LLM_AVAILABLE: - return None, None - - normalized_explicit = self._normalize_provider_alias(explicit) - - # Fast path: use cached auto provider if no request-level override is set. - if normalized_explicit in (None, "", "auto") and not model_override: - return self.provider, self.provider_choice - - detected = detect_provider( - explicit=normalized_explicit or "auto", - model_override=model_override, - ) - if isinstance(detected, tuple) and len(detected) == 2: - return detected[0], detected[1] - return detected, None - - def _initialize_provider(self): - """Initialize LLM provider if available, robust to tuple return values.""" - if not LLM_AVAILABLE: - logger.info("LLM not available - will use rule-based fallback") - return - - try: - configured_explicit = os.getenv("ARIA_LLM_PROVIDER") - configured_model = os.getenv("ARIA_LLM_MODEL") - - # Convenience: if ARIA_QUANTUM_MODEL_PATH is set and no provider is - # configured, default Aria LLM parsing to the quantum provider. - quantum_model_path = os.getenv("ARIA_QUANTUM_MODEL_PATH") or os.getenv( - "QAI_QUANTUM_MODEL_PATH" - ) - if not configured_model and quantum_model_path: - configured_model = quantum_model_path - if ( - not configured_explicit - and configured_model == quantum_model_path - and quantum_model_path - ): - configured_explicit = "quantum" - - self.provider, self.provider_choice = self._resolve_provider_for_request( - explicit=configured_explicit, - model_override=configured_model, - ) - provider_name = getattr( - self.provider_choice, - "name", - getattr( - self.provider, "__class__", type(self.provider) - ).__class__.__name__, - ) - logger.info(f"✓ Initialized LLM provider: {provider_name}") - except Exception as e: - logger.warning(f"Failed to initialize LLM provider: {e}") - self.provider = None - self.provider_choice = None - - def _build_system_prompt(self) -> str: - """Build system prompt with action schema and current state""" - actions_json = json.dumps(ARIA_ACTIONS, indent=2) - state_json = json.dumps(stage_state, indent=2) - - return f"""You are an action planner for Aria, a 3D character assistant. - -Available Actions: -{actions_json} - -Current Stage State: -{state_json} - -Your task: Parse user commands into a sequence of structured actions. -- Return ONLY valid JSON array of actions -- Use exact action names from schema -- Provide all required params for each action -- Consider current state when planning -- Keep actions simple and atomic - -Example output: -[ - {{"action": "move", "target": {{"x": 50, "y": 30}}, "speed": "normal"}}, - {{"action": "say", "text": "Hello!", "emotion": "happy"}} -] - -Rules: -1. Always move Aria before picking up objects -2. Aria can only hold one object at a time -3. Objects on table are at y=35, x varies -4. Stage bounds: 0-100 for both x and y -5. If command is unclear, choose most reasonable interpretation""" - - def parse_with_llm(self, command: str, provider=None) -> List[dict]: - """Parse command using LLM provider""" - active_provider = provider if provider is not None else self.provider - - if not active_provider: - raise ValueError("LLM provider not available") - - system_prompt = self._build_system_prompt() - messages = [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": command}, - ] - - try: - response = active_provider.complete(messages, stream=False) - - # Extract JSON from response - content = _provider_response_to_text(response).strip() - - # Try to parse as JSON - if content.startswith("["): - actions = json.loads(content) - else: - # Extract JSON array from markdown or text - json_match = _RE_JSON_BLOCK.search(content) - if json_match: - actions = json.loads(json_match.group(0)) - else: - raise ValueError("No JSON array found in response") - - # Validate actions - validated = [] - for action in actions: - if "action" in action and action["action"] in ARIA_ACTIONS: - validated.append(action) - else: - logger.warning(f"Skipping invalid action: {action}") - - return validated - - except Exception as e: - logger.error(f"LLM parsing failed: {e}") - raise - - def parse_with_fallback( - self, command: str, _allow_split: bool = True, _planned_held_object=_UNSET - ) -> List[dict]: - """Rule-based fallback parser (uses existing generate_tags_fallback logic)""" - actions = [] - command_lower = command.lower() - known_objects = ["apple", "book", "cup", "ball", "flower"] - planned_held_object = ( - stage_state["aria"].get("held_object") - if _planned_held_object is _UNSET - else _planned_held_object - ) - - # Compound command handling (e.g., "pick up cup and bring it here then put it on table") - if _allow_split: - segments = [ - seg.strip() - for seg in _RE_COMMAND_SEPARATORS.split(command) - if seg and seg.strip() - ] - if len(segments) > 1: - combined_actions = [] - current_planned_held = planned_held_object - for seg in segments: - seg_actions = self.parse_with_fallback( - seg, - _allow_split=False, - _planned_held_object=current_planned_held, - ) - combined_actions.extend(seg_actions) - - for action in seg_actions: - action_type = action.get("action") - if action_type == "pickup": - current_planned_held = action.get( - "object_id", current_planned_held - ) - elif action_type in ("drop", "throw"): - current_planned_held = None - - # Remove redundant consecutive actions from repeated segments - deduped_actions = [] - for action in combined_actions: - if deduped_actions and action == deduped_actions[-1]: - continue - if ( - deduped_actions - and action.get("action") == "move" - and deduped_actions[-1].get("action") == "move" - and action.get("target") == deduped_actions[-1].get("target") - ): - continue - if ( - len(deduped_actions) >= 2 - and action.get("action") == "move" - and deduped_actions[-2].get("action") == "move" - and deduped_actions[-2].get("target") == action.get("target") - and deduped_actions[-1].get("action") == "gesture" - and deduped_actions[-1].get("gesture_type") == "nod" - ): - continue - deduped_actions.append(action) - - return deduped_actions - - # Conversational movement commands - if _contains_any_keyword(command_lower, COME_HERE_KEYWORDS): - actions.append( - {"action": "move", "target": {"x": 50, "y": 85}, "speed": "normal"} - ) - - if _contains_any_keyword(command_lower, FOLLOW_ME_KEYWORDS): - actions.append({"action": "gesture", "gesture_type": "nod"}) - actions.append( - {"action": "move", "target": {"x": 50, "y": 75}, "speed": "normal"} - ) - - # Conversational object delivery commands - if _contains_any_keyword(command_lower, BRING_ME_KEYWORDS): - for obj in known_objects: - if obj in command_lower and obj in stage_state["objects"]: - obj_pos = stage_state["objects"][obj]["position"] - actions.append( - {"action": "move", "target": obj_pos, "speed": "normal"} - ) - actions.append({"action": "pickup", "object_id": obj}) - actions.append( - { - "action": "move", - "target": {"x": 50, "y": 85}, - "speed": "normal", - } - ) - actions.append({"action": "gesture", "gesture_type": "nod"}) - break - - # Pronoun follow-up delivery command (e.g., "bring it here") - if _contains_any_keyword(command_lower, BRING_IT_KEYWORDS): - held_obj = planned_held_object - if held_obj: - actions.append( - {"action": "move", "target": {"x": 50, "y": 85}, "speed": "normal"} - ) - actions.append({"action": "gesture", "gesture_type": "nod"}) - else: - actions.append( - { - "action": "say", - "text": "I need to pick something up first.", - "emotion": "neutral", - } - ) - - # Conversational drop/place commands for currently held object - has_drop_intent = ( - _contains_any_keyword(command_lower, DROP_HERE_KEYWORDS) - or ("drop" in command_lower and "here" in command_lower) - or ( - "put" in command_lower - and ( - "here" in command_lower - or "down" in command_lower - or "table" in command_lower - ) - ) - or ( - "place" in command_lower - and ("here" in command_lower or "table" in command_lower) - ) - ) - if has_drop_intent: - held_obj = planned_held_object - if not held_obj: - actions.append( - { - "action": "say", - "text": "I'm not holding anything yet.", - "emotion": "neutral", - } - ) - else: - drop_target = {"x": 50, "y": 85} - if "table" in command_lower: - table_pos = stage_state["environment"]["table"]["position"] - drop_target = {"x": table_pos["x"], "y": 35} - actions.append( - {"action": "move", "target": drop_target, "speed": "normal"} - ) - actions.append({"action": "drop", "position": drop_target}) - - # Parse move commands - if _contains_any_keyword(command_lower, MOVE_KEYWORDS): - # Extract target from command - if "table" in command_lower: - actions.append( - {"action": "move", "target": {"x": 60, "y": 35}, "speed": "normal"} - ) - elif "center" in command_lower or "middle" in command_lower: - actions.append( - {"action": "move", "target": {"x": 50, "y": 50}, "speed": "normal"} - ) - elif "left" in command_lower: - actions.append( - {"action": "move", "target": {"x": 20, "y": 50}, "speed": "normal"} - ) - elif "right" in command_lower: - actions.append( - {"action": "move", "target": {"x": 80, "y": 50}, "speed": "normal"} - ) - - # Parse say commands - if _contains_any_keyword(command_lower, SAY_KEYWORDS): - # Extract text after say/speak - for trigger in ["say ", "speak ", "tell ", "greet "]: - if trigger in command_lower: - text = command[command_lower.index(trigger) + len(trigger) :].strip( - " \"'" - ) - emotion = ( - "happy" - if any(w in text.lower() for w in ["!", "hello", "hi"]) - else "neutral" - ) - actions.append({"action": "say", "text": text, "emotion": emotion}) - break - - # Parse pickup commands - for obj in known_objects: - if obj in command_lower and _contains_any_keyword( - command_lower, PICKUP_KEYWORDS - ): - # Move to object first - obj_pos = stage_state["objects"][obj]["position"] - actions.append({"action": "move", "target": obj_pos, "speed": "normal"}) - actions.append({"action": "pickup", "object_id": obj}) - break - - # Parse gesture commands - gestures = ["wave", "thumbs_up", "clap", "shrug", "bow", "nod"] - for gesture in gestures: - trigger = gesture.replace("_", " ") - if trigger in command_lower: - actions.append({"action": "gesture", "gesture_type": gesture}) - break - - return actions - - def parse( - self, - command: str, - use_llm: bool = True, - provider_choice: Optional[str] = None, - model_override: Optional[str] = None, - ) -> List[dict]: - """ - Parse command into structured actions - - Args: - command: Natural language command - use_llm: Try LLM first if available - provider_choice: Optional provider override (e.g. quantum, azure) - model_override: Optional model/path override for selected provider - - Returns: - List of action dicts - """ - if use_llm: - try: - request_provider, request_choice = self._resolve_provider_for_request( - explicit=provider_choice, - model_override=model_override, - ) - if request_provider is None: - raise ValueError("LLM provider not available") - - actions = self.parse_with_llm(command, provider=request_provider) - used_provider_name = ( - getattr(request_choice, "name", None) - or getattr(self.provider_choice, "name", None) - or self._normalize_provider_alias(provider_choice) - or "auto" - ) - logger.info( - f"✓ LLM parsed via {used_provider_name}: {command} -> {len(actions)} actions" - ) - return actions - except Exception as e: - logger.warning(f"LLM parsing failed, using fallback: {e}") - - actions = self.parse_with_fallback(command) - logger.info(f"✓ Fallback parsed: {command} -> {len(actions)} actions") - return actions - - -# --------------------------- World Generation --------------------------- - - -def _sanitize_id(raw: str) -> str: - cleaned = re.sub(r"[^a-zA-Z0-9_]+", "_", raw.strip().lower()) - return cleaned[:30] or f"obj_{random.randint(1000, 9999)}" - - -THEME_OBJECT_LIBRARY = { - "forest": [ - ("tree", "🌲"), - ("mushroom", "🍄"), - ("rock", "🪨"), - ("flower", "🌼"), - ("owl", "🦉"), - ("fox", "🦊"), - ], - "space": [ - ("planet", "🪐"), - ("star", "⭐"), - ("rocket", "🚀"), - ("alien", "👽"), - ("astronaut", "👩‍🚀"), - ("satellite", "🛰️"), - ], - "ocean": [ - ("fish", "🐟"), - ("shell", "🐚"), - ("coral", "🪸"), - ("whale", "🐋"), - ("ship", "🚢"), - ("dolphin", "🐬"), - ], - "lab": [ - ("beaker", "🧪"), - ("microscope", "🔬"), - ("dna", "🧬"), - ("robot", "🤖"), - ("atom", "⚛️"), - ("chip", "💾"), - ], - "medieval": [ - ("sword", "🗡️"), - ("shield", "🛡️"), - ("crown", "👑"), - ("scroll", "📜"), - ("goblet", "🍷"), - ("castle", "🏰"), - ], - "desert": [ - ("cactus", "🌵"), - ("skull", "💀"), - ("camel", "🐪"), - ("scorpion", "🦂"), - ("sun", "☀️"), - ("sand", "🟨"), - ], - "garden": [ - ("rose", "🌹"), - ("tulip", "🌷"), - ("butterfly", "🦋"), - ("bee", "🐝"), - ("bench", "🪑"), - ("pond", "💧"), - ], - "cyberpunk": [ - ("drone", "🛸"), - ("neon", "💡"), - ("chip", "💾"), - ("server", "🖥️"), - ("bot", "🤖"), - ("battery", "🔋"), - ], - "arcade": [ - ("joystick", "🕹️"), - ("coin", "🪙"), - ("ghost", "👻"), - ("trophy", "🏆"), - ("console", "🎮"), - ("heart", "❤️"), - ], -} - - -def generate_world_fallback(theme: str, count: int) -> dict: - """Generate a world procedurally without LLM.""" - objects_catalog = THEME_OBJECT_LIBRARY.get( - theme.lower(), THEME_OBJECT_LIBRARY["forest"] - ) - random.shuffle(objects_catalog) - chosen = objects_catalog[: max(1, count)] - stage_objects = {} - used_positions = [] - for name, emoji in chosen: - # Avoid overlapping positions (simple Poisson-ish attempt) - for attempt in range(10): - x = random.randint(10, 90) - y = random.randint(20, 80) - if all(math.hypot(x - px, y - py) > 8 for px, py in used_positions): - used_positions.append((x, y)) - break - stage_objects[_sanitize_id(name)] = { - "id": _sanitize_id(name), - "emoji": emoji, - "position": {"x": x, "y": y}, - "state": "on_stage", - } - environment = { - "theme": theme, - "generated_at": datetime.datetime.now(timezone.utc).isoformat() + "Z", - "seed": random.randint(100000, 999999), - "stage_bounds": {"width": 100, "height": 100}, - } - return {"objects": stage_objects, "environment": environment} - - -def generate_world_with_llm(theme: str, count: int, provider) -> dict: - """Use LLM provider to generate a themed world. Returns fallback on failure.""" - system_prompt = ( - "You are a world generator for a 2D stage (coordinates 0-100 for x and y). " - "Given a theme, produce JSON with 'objects' and 'environment'. Each object must have: id, emoji, position {x,y}, state. " - "Constrain positions within bounds. Provide at most the requested count. ONLY output JSON, no commentary." - ) - user_prompt = f"Theme: {theme}\nCount: {count}\nRequirements: diverse, non-overlapping, concise ids." - try: - messages = [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt}, - ] - raw = provider.complete(messages, stream=False) - raw_str = _provider_response_to_text(raw).strip() - # Strip code fences - if "```" in raw_str: - m = re.search(r"```(?:json)?\n(.*?)(```)", raw_str, flags=re.DOTALL) - if m: - raw_str = m.group(1).strip() - - # Parse JSON from the LLM response - try: - parsed_world_data = json.loads(raw_str) - except json.JSONDecodeError: - obj_match = re.search(r"\{.*\}\s*$", raw_str, flags=re.DOTALL) - if obj_match: - raw_str = obj_match.group(0) - parsed_world_data = json.loads(raw_str) - # Basic validation - objects = parsed_world_data.get("objects") or {} - env = parsed_world_data.get("environment") or {} - - # Normalize object list/dict to id->object mapping - sanitized_objects = {} - if isinstance(objects, dict): - object_items = list(objects.items()) - elif isinstance(objects, list): - object_items = [(str(i), obj) for i, obj in enumerate(objects)] - else: - object_items = [] - - for key, val in object_items[:count]: - if not isinstance(val, dict): - continue - object_id = val.get("id") or val.get("name") or key - oid = _sanitize_id(object_id) - pos = val.get("position", {}) - x = int(max(0, min(100, pos.get("x", random.randint(10, 90))))) - y = int(max(0, min(100, pos.get("y", random.randint(20, 80))))) - state = val.get("state", "on_stage") - emoji = val.get("emoji", "✨") - sanitized_objects[oid] = { - "id": oid, - "emoji": emoji, - "position": {"x": x, "y": y}, - "state": state, - } - if not sanitized_objects: - return generate_world_fallback(theme, count) - env.setdefault("theme", theme) - env.setdefault( - "generated_at", datetime.datetime.now(timezone.utc).isoformat() + "Z" - ) - env.setdefault("stage_bounds", {"width": 100, "height": 100}) - return { - "objects": sanitized_objects, - "environment": env, - "llm": True, - "raw_response_len": len(raw_str), - } - except Exception as e: - logger.warning(f"World generation via LLM failed: {e}; falling back.") - return generate_world_fallback(theme, count) | {"llm": False} - - -# Initialize global action parser -action_parser = AriaActionParser() - - -def get_stage_context() -> str: - """Generate natural language description of current stage state for AI""" - aria = stage_state["aria"] - objects = stage_state["objects"] - - # Calculate distances to objects - aria_pos = aria["position"] - nearby_objects = [] - for obj_name, obj_data in objects.items(): - # Safety check: ensure object has position data - if not isinstance(obj_data, dict) or "position" not in obj_data: - continue - obj_pos = obj_data["position"] - if not isinstance(obj_pos, dict) or "x" not in obj_pos or "y" not in obj_pos: - continue - distance = ( - (aria_pos["x"] - obj_pos["x"]) ** 2 + (aria_pos["y"] - obj_pos["y"]) ** 2 - ) ** 0.5 - if distance < 30: # Within reach - nearby_objects.append(obj_name) - - context = f"""STAGE VIEW: -- Aria is at position ({aria_pos['x']}%, {aria_pos['y']}%), facing {aria['facing']} -- Expression: {aria['expression']} -- Held object: {aria['held_object'] or 'none'} -- Table is at (60%, 20%) on the right side -- Objects on table: {', '.join([k for k, v in objects.items() if isinstance(v, dict) and v.get('state') == 'on_table'])} -- Objects nearby Aria (within reach): {', '.join(nearby_objects) if nearby_objects else 'none'} -- Stage dimensions: 100% wide x 100% tall (0,0=top-left, 100,100=bottom-right) -""" - return context - - -def determine_position_from_context(cmd: str) -> str: - """AI-driven position determination based on command semantics and stage state""" - aria_pos = stage_state["aria"]["position"] - objects = stage_state["objects"] - table_pos = stage_state["environment"]["table"]["position"] - - # Conversational movement intents - if _contains_any_keyword(cmd, COME_HERE_KEYWORDS): - # Front-center, closer to the viewer/user - return "[aria:position:50:85]" - if _contains_any_keyword(cmd, FOLLOW_ME_KEYWORDS): - # Slightly behind the front-center so movement is visible - return "[aria:position:50:75]" - if _contains_any_keyword(cmd, BRING_ME_KEYWORDS): - # For delivery-style commands, move to front-center - return "[aria:position:50:85]" - if _contains_any_keyword(cmd, BRING_IT_KEYWORDS): - # Pronoun follow-up delivery defaults to front-center - return "[aria:position:50:85]" - if _contains_any_keyword(cmd, DROP_HERE_KEYWORDS): - # Default drop target is in front of the user - return "[aria:position:50:85]" - - # Object interaction positioning - move near the object - for obj_name in ["apple", "book", "cup", "ball", "flower", "bear"]: - if obj_name in cmd and ( - "pick" in cmd or "get" in cmd or "grab" in cmd or "take" in cmd - ): - if obj_name in objects: - obj_data = objects[obj_name] - # Safety check: ensure object has position data - if isinstance(obj_data, dict) and "position" in obj_data: - obj_pos = obj_data["position"] - if isinstance(obj_pos, dict) and "x" in obj_pos and "y" in obj_pos: - # Position slightly to the left of object - return f'[aria:position:{max(10, obj_pos["x"] - 10)}:{obj_pos["y"] + 10}]' - - # Action-based positioning (using pre-compiled keyword sets for O(1) lookup) - if _contains_any_keyword(cmd, JUMP_KEYWORDS): - return "[aria:position:50:60]" # Center for jumping - elif _contains_any_keyword(cmd, DANCE_KEYWORDS): - return "[aria:position:50:50]" # Center stage for performance - elif _contains_any_keyword(cmd, WAVE_KEYWORDS): - return "[aria:position:30:70]" # Front-left for greeting - elif _contains_any_keyword(cmd, LOOK_KEYWORDS): - # Look towards table - if "table" in cmd: - return "[aria:position:40:60]" # Position to see table - return "[aria:position:20:40]" # Left side for observing - elif _contains_any_keyword(cmd, SIT_KEYWORDS): - # Near table to sit - return f'[aria:position:{table_pos["x"] - 5}:{table_pos["y"] + 35}]' - elif _contains_any_keyword(cmd, RUN_KEYWORDS): - return "[aria:position:85:70]" # Far right for running space - elif _contains_any_keyword(cmd, HIDE_KEYWORDS): - return "[aria:position:10:75]" # Corner position - elif _contains_any_keyword(cmd, PRESENT_KEYWORDS): - return "[aria:position:50:50]" # Center to present - elif _contains_any_keyword(cmd, THINK_KEYWORDS): - return "[aria:position:25:50]" # Contemplative left position - elif _contains_any_keyword(cmd, WALK_LEFT_KEYWORDS): - return "[aria:position:20:70]" # Moving to left - elif _contains_any_keyword(cmd, WALK_RIGHT_KEYWORDS): - return "[aria:position:80:70]" # Moving to right - elif "add" in cmd or "create" in cmd or "spawn" in cmd: - # For adding objects, position near table - return f'[aria:position:{table_pos["x"] - 15}:{table_pos["y"] + 20}]' - else: - # Context-aware positioning: stay put if already in good position - # or move to interesting area if idle - pos_hash = int(hashlib.md5(cmd.encode()).hexdigest()[:4], 16) - x = 30 + (pos_hash % 40) # Random between 30-70% - y = 60 + (pos_hash % 20) # Random between 60-80% - return f"[aria:position:{x}:{y}]" - - -def generate_tags_ai(command: str) -> List[str]: - """Generate tags using AI model""" - if MODEL is None: - return [] - - try: - import torch - from transformers import AutoTokenizer - - base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" - tokenizer = AutoTokenizer.from_pretrained(base_model) - - input_text = f"<|user|>\n{command}\n<|assistant|>\n" - inputs = tokenizer(input_text, return_tensors="pt").to(MODEL.device) - - with torch.no_grad(): - outputs = MODEL.generate( - **inputs, - max_new_tokens=30, - temperature=0.1, - do_sample=True, - top_p=0.9, - repetition_penalty=1.5, - pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id, - ) - - response = tokenizer.decode( - outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True - ) - tags = _RE_ARIA_TAGS.findall(response) - return tags[:2] # Return first 2 tags max - except Exception as e: - print(f"AI generation error: {e}") - return [] - - -def generate_tags_fallback(command: str) -> List[str]: - """Simple rule-based fallback tag generation with automatic positioning""" - cmd = command.lower() - tags = [] - - # AI-driven automatic positioning based on command context - # Determine optimal position for the action - auto_position = determine_position_from_context(cmd) - if auto_position: - tags.append(auto_position) - - # Track if limb commands are detected to avoid movement conflicts - # Using pre-compiled keyword sets for O(1) lookup - has_limb_command = ( - _contains_any_keyword(cmd, LEFT_ARM_KEYWORDS) - or _contains_any_keyword(cmd, RIGHT_ARM_KEYWORDS) - or _contains_any_keyword(cmd, LEFT_LEG_KEYWORDS) - or _contains_any_keyword(cmd, RIGHT_LEG_KEYWORDS) - ) - - # Special: server-side "say" / announce detection (capture original text) - try: - say_match = _RE_SAY_COMMAND.search(command) - if say_match: - raw_msg = say_match.group(1).strip() - # basic sanitization and length cap - safe_msg = _RE_SANITIZE_BRACKETS.sub("", raw_msg)[:200] - tags.append(f"[aria:say:{safe_msg}]") - except Exception: - # ignore parsing errors - pass - - # Expressions - if "smile" in cmd or "happy" in cmd: - tags.append("[aria:expression:smile]") - elif "sad" in cmd: - tags.append("[aria:expression:sad]") - elif "surprised" in cmd: - tags.append("[aria:expression:surprised]") - elif "confused" in cmd: - tags.append("[aria:expression:confused]") - elif "thinking" in cmd or "think" in cmd: - tags.append("[aria:expression:thinking]") - elif "wink" in cmd: - tags.append("[aria:expression:wink]") - - # Animations - if "jump" in cmd: - tags.append("[aria:animate:jump]") - elif "dance" in cmd: - tags.append("[aria:animate:dance]") - elif "spin" in cmd: - tags.append("[aria:animate:spin]") - elif "bow" in cmd: - tags.append("[aria:animate:bow]") - elif "flip" in cmd: - tags.append("[aria:animate:flip]") - - # Gestures (allowlist: wave, thumbs_up, clap, shrug, bow, nod) - if "wave" in cmd: - tags.append("[aria:gesture:wave]") - elif "thumbs up" in cmd: - tags.append("[aria:gesture:thumbs_up]") - elif "clap" in cmd: - tags.append("[aria:gesture:clap]") - elif "shrug" in cmd: - tags.append("[aria:gesture:shrug]") - elif "nod" in cmd: - tags.append("[aria:gesture:nod]") - - # Limb controls and poses (AI may also emit these; fallback supports natural phrases) - # Hands up / T-pose / Cross arms - if "hands up" in cmd or "raise hands" in cmd: - tags.append("[aria:limb:left_arm:raise]") - tags.append("[aria:limb:right_arm:raise]") - if "t-pose" in cmd or "tpose" in cmd or "t pose" in cmd: - tags.append("[aria:pose:t-pose]") - if "cross arms" in cmd or "arms crossed" in cmd: - tags.append("[aria:pose:cross_arms]") - - # Per-limb commands - def limb_tag(part: str, action: str): - tags.append(f"[aria:limb:{part}:{action}]") - - # Helper maps (using pre-compiled keyword sets) - left_arm = _contains_any_keyword(cmd, LEFT_ARM_KEYWORDS) - right_arm = _contains_any_keyword(cmd, RIGHT_ARM_KEYWORDS) - left_leg = _contains_any_keyword(cmd, LEFT_LEG_KEYWORDS) - right_leg = _contains_any_keyword(cmd, RIGHT_LEG_KEYWORDS) - - # Numeric angle if present (e.g., "left arm 45 degrees") - angle_match = re.search(r"(-?\d{1,3})\s*(?:deg|degree|degrees)?", cmd) - angle_val = angle_match.group(1) if angle_match else None - - # Arm actions - if left_arm or right_arm or "arm" in cmd: - # Choose default arm if unspecified - parts = [] - if left_arm: - parts.append("left_arm") - if right_arm: - parts.append("right_arm") - if not parts: - parts = ["right_arm"] - if _contains_any_keyword(cmd, ARM_WAVE_KEYWORDS): - for p in parts: - limb_tag(p, "wave") - elif _contains_any_keyword(cmd, ARM_RAISE_KEYWORDS): - for p in parts: - limb_tag(p, "raise") - elif _contains_any_keyword(cmd, ARM_LOWER_KEYWORDS): - for p in parts: - limb_tag(p, "lower") - elif _contains_any_keyword(cmd, ARM_FORWARD_KEYWORDS): - for p in parts: - limb_tag(p, "forward") - elif _contains_any_keyword(cmd, ARM_BACK_KEYWORDS): - for p in parts: - limb_tag(p, "back") - elif angle_val is not None: - for p in parts: - limb_tag(p, angle_val) - - # Leg actions - if left_leg or right_leg or "leg" in cmd: - parts = [] - if left_leg: - parts.append("left_leg") - if right_leg: - parts.append("right_leg") - if not parts: - parts = ["left_leg"] - if "kick" in cmd: - for p in parts: - limb_tag(p, "kick") - elif _contains_any_keyword(cmd, ARM_FORWARD_KEYWORDS): - for p in parts: - limb_tag(p, "forward") - elif _contains_any_keyword(cmd, ARM_BACK_KEYWORDS): - for p in parts: - limb_tag(p, "back") - elif angle_val is not None: - for p in parts: - limb_tag(p, angle_val) - - # Movement - only add if not a limb command (to avoid conflicts like "left arm" -> "move:left") - if not has_limb_command: - # Determine movement style - movement_style = None - if "skip" in cmd: - movement_style = "skip" - elif "strut" in cmd or "swagger" in cmd: - movement_style = "strut" - elif "run" in cmd: - movement_style = "run" - elif "walk" in cmd: - movement_style = "walk" - else: - movement_style = "move" - - # Determine direction - exclude if keywords could be part of limb commands - has_forward_limb = "leg" in cmd or "arm" in cmd - if "left" in cmd: - tags.append(f"[aria:{movement_style}:left]") - elif "right" in cmd: - tags.append(f"[aria:{movement_style}:right]") - elif ("up" in cmd or "forward" in cmd) and not has_forward_limb: - tags.append(f"[aria:{movement_style}:up]") - elif ("down" in cmd or "back" in cmd) and not has_forward_limb: - tags.append(f"[aria:{movement_style}:down]") - - # Effects - with keyword matching and intensity support - effect_intensity = "normal" - if "light" in cmd or "subtle" in cmd or "gentle" in cmd: - effect_intensity = "light" - elif "heavy" in cmd or "intense" in cmd or "lots" in cmd or "many" in cmd: - effect_intensity = "heavy" - - if _contains_any_keyword(cmd, SPARKLE_KEYWORDS): - tags.append(f"[aria:effect:sparkle:{effect_intensity}]") - elif _contains_any_keyword(cmd, GLOW_KEYWORDS): - tags.append(f"[aria:effect:glow:{effect_intensity}]") - elif _contains_any_keyword(cmd, HEARTS_KEYWORDS): - tags.append(f"[aria:effect:hearts:{effect_intensity}]") - - # Camera - if "center" in cmd: - tags.append("[aria:camera:center]") - elif "zoom" in cmd: - tags.append( - "[aria:camera:zoom_in]" if "in" in cmd else "[aria:camera:zoom_out]" - ) - - # Poses (body positions) - if "sit" in cmd: - tags.append("[aria:pose:sit]") - elif "stand" in cmd: - tags.append("[aria:pose:stand]") - elif "crouch" in cmd: - tags.append("[aria:pose:crouch]") - elif "lie" in cmd or "lay" in cmd: - tags.append("[aria:pose:lie]") - - # Position control - let AI determine where Aria should be - # Format: [aria:position:x:y] where x and y are percentages (0-100) - # Or named positions: [aria:position:center], [aria:position:left], etc. - position_keywords = { - "center": "[aria:position:50:50]", - "left side": "[aria:position:15:80]", - "right side": "[aria:position:85:80]", - "top": "[aria:position:50:10]", - "bottom": "[aria:position:50:90]", - "corner": "[aria:position:10:10]", - "stage left": "[aria:position:20:70]", - "stage right": "[aria:position:80:70]", - "front": "[aria:position:50:85]", - "back": "[aria:position:50:15]", - } - - # Check for position commands - if "position" in cmd or "move to" in cmd or "go to" in cmd or "stand at" in cmd: - for keyword, tag in position_keywords.items(): - if keyword in cmd: - tags.append(tag) - break - else: - # Try to extract numeric coordinates - coord_match = _RE_COORDINATES.search(cmd) - if coord_match: - x, y = coord_match.groups() - tags.append(f"[aria:position:{x}:{y}]") - - # Object management (add/remove objects) - if "add" in cmd or "create" in cmd or "spawn" in cmd: - object_emojis = { - "bear": "🧸", - "teddy": "🧸", - "cat": "🐱", - "dog": "🐶", - "bunny": "🐰", - "rabbit": "🐰", - "star": "⭐", - "heart": "❤️", - "moon": "🌙", - "sun": "☀️", - "tree": "🌲", - "plant": "🌿", - "mushroom": "🍄", - "car": "🚗", - "bike": "🚲", - "plane": "✈️", - } - for obj_name, emoji in object_emojis.items(): - if obj_name in cmd: - tags.append(f"[aria:interact:add:{obj_name}:{emoji}]") - break - - return tags - - -def execute_aria_action(action: dict) -> dict: - """ - Execute a single structured action and update stage state - - Args: - action: Action dict with 'action' key and params - - Returns: - Result dict with status, message, and updated state - """ - action_type = action.get("action") - - if action_type not in ARIA_ACTIONS: - return {"status": "error", "message": f"Unknown action: {action_type}"} - - try: - if action_type == "move": - target = action.get("target") - if isinstance(target, dict) and "x" in target and "y" in target: - # Clamp position to stage bounds (0-100) - target = { - "x": max(0, min(100, target["x"])), - "y": max(0, min(100, target["y"])), - } - stage_state["aria"]["position"] = target - return { - "status": "success", - "message": f'Moved to ({target["x"]}, {target["y"]})', - "tags": [f'[aria:position:{target["x"]}:{target["y"]}]'], - } - elif isinstance(target, str) and target in stage_state["objects"]: - # Move to object - obj_pos = stage_state["objects"][target]["position"] - stage_state["aria"]["position"] = { - "x": obj_pos["x"] - 10, - "y": obj_pos["y"] + 5, - } - return { - "status": "success", - "message": f"Moved to {target}", - "tags": [f'[aria:position:{obj_pos["x"] - 10}:{obj_pos["y"] + 5}]'], - } - - elif action_type == "say": - text = str(action.get("text", ""))[:200] # Cap at 200 chars - emotion = action.get("emotion", "neutral") - stage_state["aria"]["expression"] = emotion - return { - "status": "success", - "message": f'Said: "{text}" with {emotion} emotion', - "tags": [f"[aria:say:{text}]", f"[aria:expression:{emotion}]"], - } - - elif action_type == "pickup": - obj_id = action.get("object_id") - if obj_id not in stage_state["objects"]: - return {"status": "error", "message": f"Object not found: {obj_id}"} - - if stage_state["aria"]["held_object"]: - return {"status": "error", "message": "Already holding an object"} - - # Check distance - aria_pos = stage_state["aria"]["position"] - obj_pos = stage_state["objects"][obj_id]["position"] - distance = ( - (aria_pos["x"] - obj_pos["x"]) ** 2 - + (aria_pos["y"] - obj_pos["y"]) ** 2 - ) ** 0.5 - - if distance > 30: - return { - "status": "error", - "message": f"Too far from {obj_id}. Move closer first.", - } - - stage_state["aria"]["held_object"] = obj_id - stage_state["objects"][obj_id]["state"] = "held" - return { - "status": "success", - "message": f"Picked up {obj_id}", - "tags": [f"[aria:pickup:{obj_id}]", "[aria:limb:right_arm:grab]"], - } - - elif action_type == "drop": - if not stage_state["aria"]["held_object"]: - return {"status": "error", "message": "Not holding anything"} - - obj_id = stage_state["aria"]["held_object"] - position = action.get("position", stage_state["aria"]["position"]) - - stage_state["objects"][obj_id]["position"] = position - stage_state["objects"][obj_id]["state"] = "dropped" - stage_state["aria"]["held_object"] = None - - return { - "status": "success", - "message": f"Dropped {obj_id}", - "tags": [f"[aria:drop:{obj_id}]", "[aria:limb:right_arm:release]"], - } - - elif action_type == "throw": - if not stage_state["aria"]["held_object"]: - return {"status": "error", "message": "Not holding anything"} - - obj_id = stage_state["aria"]["held_object"] - target = action.get("target", {"x": 70, "y": 40}) - force = action.get("force", "medium") - - stage_state["objects"][obj_id]["position"] = target - stage_state["objects"][obj_id]["state"] = "thrown" - stage_state["aria"]["held_object"] = None - - return { - "status": "success", - "message": f"Threw {obj_id} with {force} force", - "tags": [ - f"[aria:throw:{obj_id}]", - "[aria:limb:right_arm:throw]", - f"[aria:animation:throw_{force}]", - ], - } - - elif action_type == "gesture": - gesture_type = action.get("gesture_type", "wave") - valid_gestures = frozenset( - ["wave", "thumbs_up", "clap", "shrug", "bow", "nod"] - ) - - if gesture_type not in valid_gestures: - gesture_type = "wave" # Default fallback - - return { - "status": "success", - "message": f"Performed {gesture_type} gesture", - "tags": [ - f"[aria:gesture:{gesture_type}]", - f"[aria:animation:{gesture_type}]", - ], - } - - elif action_type == "look": - target = action.get("target") - if isinstance(target, str) and target in stage_state["objects"]: - # Look at object - obj_pos = stage_state["objects"][target]["position"] - aria_pos = stage_state["aria"]["position"] - facing = "right" if obj_pos["x"] > aria_pos["x"] else "left" - stage_state["aria"]["facing"] = facing - return { - "status": "success", - "message": f"Looking at {target}", - "tags": [f"[aria:look:{target}]", f"[aria:facing:{facing}]"], - } - elif isinstance(target, dict) and "x" in target: - # Look at position - aria_pos = stage_state["aria"]["position"] - facing = "right" if target["x"] > aria_pos["x"] else "left" - stage_state["aria"]["facing"] = facing - return { - "status": "success", - "message": "Looking at position", - "tags": [f"[aria:facing:{facing}]"], - } - - elif action_type == "wait": - duration = action.get("duration", 1.0) - return { - "status": "success", - "message": f"Waiting {duration}s", - "tags": [f"[aria:wait:{duration}]"], - } - - return {"status": "error", "message": f"Action not implemented: {action_type}"} - - except Exception as e: - logger.error(f"Action execution failed: {e}") - return {"status": "error", "message": str(e)} - - -def action_to_tags(action: dict) -> List[str]: - """Convert a structured action to Aria tag(s) without mutating stage state.""" - action_type = action.get("action") - if not action_type: - return [] - - if action_type == "move": - target = action.get("target") - if isinstance(target, dict) and "x" in target and "y" in target: - x = max(0, min(100, int(target["x"]))) - y = max(0, min(100, int(target["y"]))) - return [f"[aria:position:{x}:{y}]"] - if isinstance(target, str): - return [f"[aria:move:to:{target}]"] - - if action_type == "say": - text = str(action.get("text", ""))[:200] - emotion = str(action.get("emotion", "neutral")) - tags = [f"[aria:say:{text}]"] if text else [] - if emotion: - tags.append(f"[aria:expression:{emotion}]") - return tags - - if action_type == "pickup": - obj_id = action.get("object_id") - return [f"[aria:pickup:{obj_id}]"] if obj_id else [] - - if action_type == "drop": - position = action.get("position") - tags = ["[aria:drop]"] - if isinstance(position, dict) and "x" in position and "y" in position: - x = max(0, min(100, int(position["x"]))) - y = max(0, min(100, int(position["y"]))) - tags.append(f"[aria:position:{x}:{y}]") - return tags - - if action_type == "throw": - force = action.get("force", "medium") - return [f"[aria:throw:{force}]"] - - if action_type == "gesture": - gesture_type = action.get("gesture_type", "wave") - return [f"[aria:gesture:{gesture_type}]"] - - if action_type == "look": - target = action.get("target") - if isinstance(target, str): - return [f"[aria:look:{target}]"] - return ["[aria:look]"] - - if action_type == "wait": - duration = action.get("duration", 1.0) - return [f"[aria:wait:{duration}]"] - - return [] - - -def tags_from_actions(actions: List[dict]) -> List[str]: - """Flatten tags from a list of structured actions.""" - tags: List[str] = [] - for action in actions: - tags.extend(action_to_tags(action)) - return tags - - -class AriaRequestHandler(SimpleHTTPRequestHandler): - def end_headers(self): - # Add CORS headers - self.send_header("Access-Control-Allow-Origin", "*") - self.send_header("Access-Control-Allow-Methods", "GET, POST, OPTIONS") - self.send_header("Access-Control-Allow-Headers", "Content-Type") - super().end_headers() - - def do_OPTIONS(self): - """Handle CORS preflight""" - self.send_response(200) - self.end_headers() - - def do_GET(self): - """Serve static files""" - print(f"📥 GET request: {self.path}") - # API: return either full stage snapshot or object registry - if self.path == "/api/aria/state": - self.send_response(200) - self.send_header("Content-Type", "application/json") - self.end_headers() - payload = { - "aria": stage_state.get("aria", {}), - "objects": stage_state.get("objects", {}), - "environment": stage_state.get("environment", {}), - } - self.wfile.write(json.dumps(payload).encode("utf-8")) - return - if self.path == "/api/aria/objects": - self.send_response(200) - self.send_header("Content-Type", "application/json") - self.end_headers() - payload = {"objects": stage_state.get("objects", {})} - self.wfile.write(json.dumps(payload).encode("utf-8")) - return - if self.path == "/": - self.path = "/index.html" - return super().do_GET() - - def do_POST(self): - # --- /api/aria/object endpoint --- - # Expected payloads: - # 1. Bulk update: - # {"objects": {"id1": {"position": {...}, "state": ...}, ...}} - # 2. Single object action: - # {"action": "add|update|remove", "object": {"id": "...", "position": {...}, "state": ...}} - # - "add": adds a new object - # - "update": updates position/state of existing object - # - "remove": deletes object by id - # - "object" must include "id" or "name" - """Handle API requests""" - if self.path == "/api/aria/command": - try: - content_length = int(self.headers.get("Content-Length", 0)) - post_data = self.rfile.read(content_length) - - request_data = json.loads(post_data.decode("utf-8")) - command = request_data.get("command", "") - use_llm = bool(request_data.get("use_llm", True)) - provider_choice = request_data.get("provider") - model_override = request_data.get("model") - - # Update stage state if provided - if "stage_state" in request_data: - stage_state.update(request_data["stage_state"]) - - print(f"📝 Command received: {command}") - print(f"👁️ Stage context:\n{get_stage_context()}") - - tags = [] - actions = [] - - # Preferred path: parser-backed structured actions (supports - # provider/model overrides including quantum). - if use_llm: - try: - actions = action_parser.parse( - command, - use_llm=True, - provider_choice=provider_choice, - model_override=model_override, - ) - tags = tags_from_actions(actions) - except Exception as parse_err: - logger.warning( - f"/api/aria/command parser path failed, falling back: {parse_err}" - ) - - # Legacy fallback path: rule-based tag generation. - if not tags: - legacy_tags = generate_tags_ai(command) - tags = ( - legacy_tags if legacy_tags else generate_tags_fallback(command) - ) - - print(f"✨ Generated tags: {tags}") - - response = { - "command": command, - "tags": tags, - "actions": actions if actions else None, - "provider_requested": provider_choice or "auto", - "model_requested": model_override, - "model": ( - "llm" if actions else ("ai" if (MODEL and tags) else "fallback") - ), - "stage_context": get_stage_context(), - "stage_aware": True, - } - - self.send_response(200) - self.send_header("Content-Type", "application/json") - self.end_headers() - self.wfile.write(json.dumps(response).encode("utf-8")) - - except ConnectionAbortedError: - # Client disconnected, ignore - pass - except Exception as e: - print(f"❌ Error: {e}") - try: - self.send_response(500) - self.send_header("Content-Type", "application/json") - self.end_headers() - error = {"error": str(e), "tags": []} - self.wfile.write(json.dumps(error).encode("utf-8")) - except: - pass - elif self.path == "/api/aria/object" or self.path == "/api/aria/objects": - try: - content_length = int(self.headers.get("Content-Length", 0)) - post_data = self.rfile.read(content_length) - request_data = json.loads(post_data.decode("utf-8")) - - # Support both single object ({action, object}) and bulk ({objects: {...}}) - if "objects" in request_data and isinstance( - request_data["objects"], dict - ): - # Merge supplied objects into stage_state - for k, v in request_data["objects"].items(): - if isinstance(v, dict) and "position" in v: - stage_state["objects"][k] = v - api_response = {"status": "ok", "objects": stage_state["objects"]} - elif "object" in request_data and "action" in request_data: - action = request_data["action"] - obj = request_data["object"] - obj_id = obj.get("id") or obj.get("name") - if not obj_id: - raise ValueError( - 'Object payload must include "id" or "name" field.' - ) - - if action == "add": - position = obj.get("position", {"x": 50, "y": 50}) - state = obj.get("state", "on_stage") - stage_state["objects"][obj_id] = { - "position": position, - "state": state, - } - api_response = { - "status": "added", - "id": obj_id, - "object": stage_state["objects"][obj_id], - } - elif action == "update": - if obj_id not in stage_state["objects"]: - stage_state["objects"][obj_id] = {} - if "position" in obj: - stage_state["objects"][obj_id]["position"] = obj["position"] - if "state" in obj: - stage_state["objects"][obj_id]["state"] = obj["state"] - api_response = { - "status": "updated", - "id": obj_id, - "object": stage_state["objects"][obj_id], - } - elif action == "remove" or action == "delete": - removed = stage_state["objects"].pop(obj_id, None) - api_response = { - "status": "removed", - "id": obj_id, - "object": removed, - } - else: - raise ValueError( - f"Unknown action: {action}. Supported: add, update, remove/delete." - ) - - else: - raise ValueError( - 'Invalid payload: must include either "objects" (dict) or both "action" and "object" (dict with id/name).' - ) - - self.send_response(200) - self.send_header("Content-Type", "application/json") - self.end_headers() - self.wfile.write(json.dumps(api_response).encode("utf-8")) - return - except Exception as e: - print(f"❌ Object API error: {e}") - try: - self.send_response(400) - self.send_header("Content-Type", "application/json") - self.end_headers() - self.wfile.write(json.dumps({"error": str(e)}).encode("utf-8")) - except: - pass - return - - # /api/aria/execute - LLM-powered automatic action execution - elif self.path == "/api/aria/execute": - try: - content_length = int(self.headers.get("Content-Length", 0)) - body = self.rfile.read(content_length) - request_data = json.loads(body.decode("utf-8")) - - command = request_data.get("command", "") - auto_execute = request_data.get("auto_execute", False) - use_llm = request_data.get("use_llm", True) - provider_choice = request_data.get("provider") - model_override = request_data.get("model") - - if not command: - raise ValueError("command is required") - - # Parse command into actions - actions = action_parser.parse( - command, - use_llm=use_llm, - provider_choice=provider_choice, - model_override=model_override, - ) - - if not actions: - api_response = { - "status": "error", - "message": "Could not parse command into actions", - "command": command, - "actions": [], - } - else: - # Execute actions if auto_execute is True - execution_results = [] - all_tags = [] - - if auto_execute: - for action in actions: - exec_result = execute_aria_action(action) - execution_results.append( - {"action": action, "result": exec_result} - ) - if exec_result.get("tags"): - all_tags.extend(exec_result["tags"]) - - api_response = { - "status": "success", - "message": f"Parsed {len(actions)} actions" - + (" and executed" if auto_execute else " (plan only)"), - "command": command, - "provider_requested": provider_choice or "auto", - "model_requested": model_override, - "actions": actions, - "executed": auto_execute, - "results": execution_results if auto_execute else None, - "tags": all_tags if auto_execute else None, - "state": stage_state if auto_execute else None, - } - - self.send_response(200) - self.send_header("Content-Type", "application/json") - self.end_headers() - self.wfile.write(json.dumps(api_response, indent=2).encode("utf-8")) - - print( - f"✓ Execute API: {command} -> {len(actions)} actions" - + (" (executed)" if auto_execute else " (plan only)") - ) - return - - except Exception as e: - print(f"❌ Execute API error: {e}") - import traceback - - traceback.print_exc() - try: - self.send_response(400) - self.send_header("Content-Type", "application/json") - self.end_headers() - self.wfile.write( - json.dumps( - { - "status": "error", - "error": str(e), - "message": f"Failed to execute command: {str(e)}", - } - ).encode("utf-8") - ) - except: - pass - return - - # /api/aria/world - Generate or regenerate themed world layout - elif self.path == "/api/aria/world": - try: - content_length = int(self.headers.get("Content-Length", 0)) - body = self.rfile.read(content_length) - request_data = json.loads(body.decode("utf-8")) if body else {} - theme = request_data.get("theme", "forest") - count = int(request_data.get("count", 6)) - use_llm = bool(request_data.get("use_llm", True)) - provider_choice = request_data.get("provider") - model_override = request_data.get("model") - - # Generate - if use_llm: - try: - world_provider, _ = action_parser._resolve_provider_for_request( - explicit=provider_choice, - model_override=model_override, - ) - except Exception as provider_err: - logger.warning( - f"World generation provider resolution failed: {provider_err}" - ) - world_provider = None - - if world_provider: - world = generate_world_with_llm(theme, count, world_provider) - else: - world = generate_world_fallback(theme, count) - world["llm"] = False - else: - world = generate_world_fallback(theme, count) - world["llm"] = False - - # Update global stage_state (replace objects, keep aria position) - stage_state["objects"] = {} - for oid, obj in world["objects"].items(): - stage_state["objects"][oid] = { - "position": obj["position"], - "state": obj.get("state", "on_stage"), - "emoji": obj.get("emoji", ""), - } - # Update environment meta - stage_state["environment"]["theme"] = world["environment"].get( - "theme", theme - ) - stage_state["environment"]["generated_at"] = world["environment"].get( - "generated_at" - ) - - response = { - "status": "success", - "theme": theme, - "count": len(world["objects"]), - "used_llm": world.get("llm", False), - "provider_requested": provider_choice or "auto", - "model_requested": model_override, - "objects": world["objects"], - "environment": world["environment"], - } - - self.send_response(200) - self.send_header("Content-Type", "application/json") - self.end_headers() - self.wfile.write(json.dumps(response, indent=2).encode("utf-8")) - logger.info( - f"✓ World generated (theme={theme}, llm={response['used_llm']}, count={response['count']})" - ) - return - except Exception as e: - logger.error(f"World generation error: {e}") - self.send_response(400) - self.send_header("Content-Type", "application/json") - self.end_headers() - self.wfile.write( - json.dumps({"status": "error", "error": str(e)}).encode("utf-8") - ) - return - - else: - self.send_response(404) - self.end_headers() - - def log_message(self, format, *args): - """Custom logging""" - try: - message = format % args if args else str(format) - except Exception: - message = str(format) - - if "favicon" not in message: - print(f"🌐 {message}") - - -# Backward-compatible module alias used by tests and older integrations: -# `from server import server as aria_server`. -server = sys.modules[__name__] - - -def main(): - # Change to aria_web directory - web_dir = Path(__file__).parent - os.chdir(web_dir) - - # Allow the port to be overridden via the ARIA_PORT environment variable. - # This helps avoid address‑in‑use errors when the default port is already bound. - port = int(os.getenv("ARIA_PORT", "8080")) - # Default to localhost for security; use environment variable to override if needed - host = os.environ.get("ARIA_HOST", "127.0.0.1") - - try: - server = HTTPServer((host, port), AriaRequestHandler) - except OSError as e: - # Graceful handling when a server is already bound to this port. - if getattr(e, "errno", None) == 98: - state_url = f"http://{host}:{port}/api/aria/state" - try: - with urllib.request.urlopen(state_url, timeout=1.0) as resp: - payload = json.loads(resp.read().decode("utf-8")) - if ( - isinstance(payload, dict) - and "aria" in payload - and "objects" in payload - ): - print( - f"⚠️ Aria server already running at http://{host}:{port} (detected healthy /api/aria/state)." - ) - print("ℹ️ Reusing existing server; exiting this process cleanly.") - return - except Exception: - pass - - # If another service occupies the port, fall back to a free port. - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: - sock.bind((host, 0)) - fallback_port = sock.getsockname()[1] - - print( - f"⚠️ Port {port} is in use by another service; starting Aria on free port {fallback_port}." - ) - print( - f"💡 Set ARIA_PORT={fallback_port} (or another free port) to control startup port explicitly." - ) - port = fallback_port - server = HTTPServer((host, port), AriaRequestHandler) - else: - raise - - print("\n" + "=" * 70) - print("🎨 Aria Visual Command System - Web Server") - print("=" * 70) - print(f"🌐 Open in browser: http://localhost:{port}") - print(f"🤖 Model: {'AI (aria_expanded_v2)' if MODEL else 'Rule-based fallback'}") - print("📝 Type commands in the web interface to control Aria") - print("\nPress Ctrl+C to stop the server") - print("=" * 70 + "\n") - - try: - server.serve_forever() - except KeyboardInterrupt: - print("\n👋 Server stopped") - - -if __name__ == "__main__": - main() +#!/usr/bin/env python +""" +Simple web server for Aria Visual Command System +Serves the HTML/JS frontend and provides API endpoint for command generation +""" +import datetime +import hashlib +import json +import logging +import math +import os +import random +import re +import socket +import sys +import urllib.request +from datetime import timezone +from http.server import HTTPServer, SimpleHTTPRequestHandler +from pathlib import Path +from typing import List, Optional, Tuple + +# Pre-compile regex patterns for performance (avoid recompiling in loops) +_RE_JSON_BLOCK = re.compile(r"\[.*\]", re.DOTALL) +_RE_ARIA_TAGS = re.compile(r"\[aria:[^\]]+\]") +_RE_SAY_COMMAND = re.compile( + r"(?:\b(?:say|announce|shout|speak|tell)\b)(?:\s+(?:everyone|that|to))?[:\-\s]+(.+)", + re.IGNORECASE, +) +_RE_SANITIZE_BRACKETS = re.compile(r"\]") +_RE_COORDINATES = re.compile(r"(\d{1,3})%?.*?(\d{1,3})%?") +_RE_COMMAND_SEPARATORS = re.compile( + r"(?:\s*(?:,|;|\||->|=>|→|\band then\b|\bthen\b|\band\b|\bafter that\b|\bafterwards\b|\bnext\b|\bfinally\b|\blastly\b|\bthen\s*:|\bnext\s*:|\bafterward\s*:|\bafterwards\s*:|\bfinally\s*:|\bfirst\b|\bsecond\b|\bthird\b|\bfourth\b|\bstep\s*(?:\d+|[ivxlcdm]+)\b|\bphase\s*(?:\d+|[ivxlcdm]+)\b|\bpart\s*(?:\d+|[ivxlcdm]+)\b|(? str: + """Normalize provider complete output to plain text.""" + if raw is None: + return "" + if isinstance(raw, str): + return raw + + if isinstance(raw, dict): + # OpenAI-like providers may return a dict with content field. + if "content" in raw and isinstance(raw["content"], str): + return raw["content"] + + # Handle OpenAI response objects with choices list. + choices = raw.get("choices") + if isinstance(choices, list) and choices: + first = choices[0] + if isinstance(first, dict): + msg = first.get("message") + if isinstance(msg, dict): + content = msg.get("content") + if isinstance(content, str): + return content + text = first.get("text") + if isinstance(text, str): + return text + # Fallback for hybrid objects + content = getattr(first, "content", None) + if isinstance(content, str): + return content + + # Generic fallback conversion + try: + return json.dumps(raw) + except Exception: + return str(raw) + + if hasattr(raw, "get") and callable(raw.get): + try: + content = raw.get("content") + if isinstance(content, str): + return content + except Exception: + pass + + try: + return str(raw) + except Exception: + return "" + + +# Configure logging +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + +# Add project paths +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT / "AI" / "microsoft_phi-silica-3.6_v1")) +sys.path.insert(0, str(REPO_ROOT)) # Add root for shared imports + +# Try to import shared chat providers for LLM integration +try: + from shared.chat_providers import detect_provider + + LLM_AVAILABLE = True + logger.info("✓ LLM providers available for automatic action generation") +except ImportError: + LLM_AVAILABLE = False + logger.warning("✗ LLM providers not available - will use rule-based fallback only") + +# Skip AI model loading for faster startup - use rule-based fallback +MODEL = None +print("⚠️ Skipping AI model loading - using rule-based fallback for faster startup") + +# Pre-compiled keyword sets for O(1) lookup instead of O(n) any() calls +JUMP_KEYWORDS = frozenset(["jump", "leap", "hop"]) +DANCE_KEYWORDS = frozenset(["dance", "spin", "twirl"]) +WAVE_KEYWORDS = frozenset(["wave", "greet", "hello", "hi"]) +LOOK_KEYWORDS = frozenset(["look", "see", "watch", "observe"]) +SIT_KEYWORDS = frozenset(["sit", "rest", "relax"]) +RUN_KEYWORDS = frozenset(["run", "race", "sprint"]) +HIDE_KEYWORDS = frozenset(["hide", "crouch", "duck"]) +PRESENT_KEYWORDS = frozenset(["present", "show", "display"]) +THINK_KEYWORDS = frozenset(["think", "wonder", "ponder"]) +MOVE_KEYWORDS = frozenset(["go", "move", "walk", "run"]) +SAY_KEYWORDS = frozenset(["say", "speak", "tell", "greet"]) +PICKUP_KEYWORDS = frozenset(["pick", "get", "grab", "take"]) +ARM_WAVE_KEYWORDS = frozenset(["wave", "wiggle"]) +ARM_RAISE_KEYWORDS = frozenset(["raise", "up", "lift"]) +ARM_LOWER_KEYWORDS = frozenset(["lower", "down"]) +ARM_FORWARD_KEYWORDS = frozenset(["forward", "front"]) +ARM_BACK_KEYWORDS = frozenset(["back", "backward", "behind"]) +LEFT_ARM_KEYWORDS = frozenset(["left arm", "arm left", "left hand"]) +RIGHT_ARM_KEYWORDS = frozenset(["right arm", "arm right", "right hand"]) +LEFT_LEG_KEYWORDS = frozenset(["left leg", "leg left"]) +RIGHT_LEG_KEYWORDS = frozenset(["right leg", "leg right"]) +SPARKLE_KEYWORDS = frozenset(["sparkle", "sparkles", "glitter", "shimmer", "shine"]) +GLOW_KEYWORDS = frozenset(["glow", "glowing", "radiate", "illuminate"]) +HEARTS_KEYWORDS = frozenset(["hearts", "heart", "love"]) +WALK_LEFT_KEYWORDS = frozenset(["walk left", "go left", "left"]) +WALK_RIGHT_KEYWORDS = frozenset(["walk right", "go right", "right"]) +COME_HERE_KEYWORDS = frozenset(["come here", "come to me", "over here"]) +FOLLOW_ME_KEYWORDS = frozenset(["follow me", "come with me"]) +BRING_ME_KEYWORDS = frozenset(["bring me", "fetch", "hand me"]) +BRING_IT_KEYWORDS = frozenset(["bring it", "bring it here", "bring it to me"]) +DROP_HERE_KEYWORDS = frozenset( + ["drop it here", "put it here", "place it here", "set it down"] +) + + +def _contains_any_keyword(text: str, keywords: frozenset) -> bool: + """Check if text contains any keyword from set.""" + for kw in keywords: + if kw in text: + return True + return False + + +def _any_word_in_text(words: frozenset, text: str) -> bool: + """Backward-compatible helper alias used by older tests.""" + return _contains_any_keyword(text, words) + + +def _keywords_in_cmd(words: frozenset, text: str) -> bool: + """Backward-compatible helper alias used by older tests.""" + return _contains_any_keyword(text, words) + + +# Legacy constant aliases expected by older modules/tests. +_MOVE_KEYWORDS = MOVE_KEYWORDS +_SAY_KEYWORDS = SAY_KEYWORDS +_PICKUP_KEYWORDS = PICKUP_KEYWORDS +_JUMP_KEYWORDS = JUMP_KEYWORDS +_DANCE_KEYWORDS = DANCE_KEYWORDS +_LIMB_KEYWORDS = frozenset( + set(LEFT_ARM_KEYWORDS) + | set(RIGHT_ARM_KEYWORDS) + | set(LEFT_LEG_KEYWORDS) + | set(RIGHT_LEG_KEYWORDS) +) + +# Global stage state that AI can see +stage_state = { + "aria": { + "position": {"x": 15, "y": 20}, # percentage coordinates + "expression": "neutral", + "held_object": None, + "facing": "right", + }, + "objects": { + "apple": {"position": {"x": 55, "y": 35}, "state": "on_table"}, + "book": {"position": {"x": 48, "y": 35}, "state": "on_table"}, + "cup": {"position": {"x": 62, "y": 35}, "state": "on_table"}, + "ball": {"position": {"x": 70, "y": 35}, "state": "on_table"}, + "flower": {"position": {"x": 52, "y": 35}, "state": "on_table"}, + }, + "environment": { + "table": {"position": {"x": 60, "y": 20}}, + "stage_bounds": {"width": 100, "height": 100}, + }, +} + +# Structured action schema for LLM-powered automatic execution +ARIA_ACTIONS = { + "move": { + "params": ["target", "speed"], + "description": "Move Aria to a target position or object", + "example": {"action": "move", "target": {"x": 50, "y": 30}, "speed": "normal"}, + }, + "say": { + "params": ["text", "emotion"], + "description": "Make Aria speak with optional emotion", + "example": {"action": "say", "text": "Hello!", "emotion": "happy"}, + }, + "pickup": { + "params": ["object_id"], + "description": "Pick up an object from the stage", + "example": {"action": "pickup", "object_id": "apple"}, + }, + "drop": { + "params": ["position"], + "description": "Drop currently held object at position", + "example": {"action": "drop", "position": {"x": 50, "y": 30}}, + }, + "throw": { + "params": ["target", "force"], + "description": "Throw held object toward target", + "example": {"action": "throw", "target": {"x": 70, "y": 40}, "force": "medium"}, + }, + "gesture": { + "params": ["gesture_type"], + "description": "Perform a gesture animation", + "example": {"action": "gesture", "gesture_type": "wave"}, + }, + "look": { + "params": ["target"], + "description": "Look at a target position or object", + "example": {"action": "look", "target": "apple"}, + }, + "wait": { + "params": ["duration"], + "description": "Wait for specified duration in seconds", + "example": {"action": "wait", "duration": 2.0}, + }, +} + + +class AriaActionParser: + """LLM-powered action parser for automatic command execution""" + + def __init__(self): + self.provider = None + self.provider_choice = None + self._initialize_provider() + + @staticmethod + def _normalize_provider_alias(explicit: Optional[str]) -> Optional[str]: + """Normalize provider aliases to canonical detect_provider values.""" + if explicit is None: + return None + + normalized = str(explicit).strip().lower() + alias_map = { + "quantum-llm": "quantum", + "quantum_llm": "quantum", + "azure_openai": "azure", + } + return alias_map.get(normalized, normalized) + + def _resolve_provider_for_request( + self, + *, + explicit: Optional[str] = None, + model_override: Optional[str] = None, + ) -> Tuple[object | None, object | None]: + """Resolve provider for a specific request while preserving cached default.""" + if not LLM_AVAILABLE: + return None, None + + normalized_explicit = self._normalize_provider_alias(explicit) + + # Fast path: use cached auto provider if no request-level override is set. + if normalized_explicit in (None, "", "auto") and not model_override: + return self.provider, self.provider_choice + + detected = detect_provider( + explicit=normalized_explicit or "auto", + model_override=model_override, + ) + if isinstance(detected, tuple) and len(detected) == 2: + return detected[0], detected[1] + return detected, None + + def _initialize_provider(self): + """Initialize LLM provider if available, robust to tuple return values.""" + if not LLM_AVAILABLE: + logger.info("LLM not available - will use rule-based fallback") + return + + try: + configured_explicit = os.getenv("ARIA_LLM_PROVIDER") + configured_model = os.getenv("ARIA_LLM_MODEL") + + # Convenience: if ARIA_QUANTUM_MODEL_PATH is set and no provider is + # configured, default Aria LLM parsing to the quantum provider. + quantum_model_path = os.getenv("ARIA_QUANTUM_MODEL_PATH") or os.getenv( + "QAI_QUANTUM_MODEL_PATH" + ) + if not configured_model and quantum_model_path: + configured_model = quantum_model_path + if ( + not configured_explicit + and configured_model == quantum_model_path + and quantum_model_path + ): + configured_explicit = "quantum" + + self.provider, self.provider_choice = self._resolve_provider_for_request( + explicit=configured_explicit, + model_override=configured_model, + ) + provider_name = getattr( + self.provider_choice, + "name", + getattr( + self.provider, "__class__", type(self.provider) + ).__class__.__name__, + ) + logger.info(f"✓ Initialized LLM provider: {provider_name}") + except Exception as e: + logger.warning(f"Failed to initialize LLM provider: {e}") + self.provider = None + self.provider_choice = None + + def _build_system_prompt(self) -> str: + """Build system prompt with action schema and current state""" + actions_json = json.dumps(ARIA_ACTIONS, indent=2) + state_json = json.dumps(stage_state, indent=2) + + return f"""You are an action planner for Aria, a 3D character assistant. + +Available Actions: +{actions_json} + +Current Stage State: +{state_json} + +Your task: Parse user commands into a sequence of structured actions. +- Return ONLY valid JSON array of actions +- Use exact action names from schema +- Provide all required params for each action +- Consider current state when planning +- Keep actions simple and atomic + +Example output: +[ + {{"action": "move", "target": {{"x": 50, "y": 30}}, "speed": "normal"}}, + {{"action": "say", "text": "Hello!", "emotion": "happy"}} +] + +Rules: +1. Always move Aria before picking up objects +2. Aria can only hold one object at a time +3. Objects on table are at y=35, x varies +4. Stage bounds: 0-100 for both x and y +5. If command is unclear, choose most reasonable interpretation""" + + def parse_with_llm(self, command: str, provider=None) -> List[dict]: + """Parse command using LLM provider""" + active_provider = provider if provider is not None else self.provider + + if not active_provider: + raise ValueError("LLM provider not available") + + system_prompt = self._build_system_prompt() + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": command}, + ] + + try: + response = active_provider.complete(messages, stream=False) + + # Extract JSON from response + content = _provider_response_to_text(response).strip() + + # Try to parse as JSON + if content.startswith("["): + actions = json.loads(content) + else: + # Extract JSON array from markdown or text + json_match = _RE_JSON_BLOCK.search(content) + if json_match: + actions = json.loads(json_match.group(0)) + else: + raise ValueError("No JSON array found in response") + + # Validate actions + validated = [] + for action in actions: + if "action" in action and action["action"] in ARIA_ACTIONS: + validated.append(action) + else: + logger.warning(f"Skipping invalid action: {action}") + + return validated + + except Exception as e: + logger.error(f"LLM parsing failed: {e}") + raise + + def parse_with_fallback( + self, command: str, _allow_split: bool = True, _planned_held_object=_UNSET + ) -> List[dict]: + """Rule-based fallback parser (uses existing generate_tags_fallback logic)""" + actions = [] + command_lower = command.lower() + known_objects = ["apple", "book", "cup", "ball", "flower"] + planned_held_object = ( + stage_state["aria"].get("held_object") + if _planned_held_object is _UNSET + else _planned_held_object + ) + + # Compound command handling (e.g., "pick up cup and bring it here then put it on table") + if _allow_split: + segments = [ + seg.strip() + for seg in _RE_COMMAND_SEPARATORS.split(command) + if seg and seg.strip() + ] + if len(segments) > 1: + combined_actions = [] + current_planned_held = planned_held_object + for seg in segments: + seg_actions = self.parse_with_fallback( + seg, + _allow_split=False, + _planned_held_object=current_planned_held, + ) + combined_actions.extend(seg_actions) + + for action in seg_actions: + action_type = action.get("action") + if action_type == "pickup": + current_planned_held = action.get( + "object_id", current_planned_held + ) + elif action_type in ("drop", "throw"): + current_planned_held = None + + # Remove redundant consecutive actions from repeated segments + deduped_actions = [] + for action in combined_actions: + if deduped_actions and action == deduped_actions[-1]: + continue + if ( + deduped_actions + and action.get("action") == "move" + and deduped_actions[-1].get("action") == "move" + and action.get("target") == deduped_actions[-1].get("target") + ): + continue + if ( + len(deduped_actions) >= 2 + and action.get("action") == "move" + and deduped_actions[-2].get("action") == "move" + and deduped_actions[-2].get("target") == action.get("target") + and deduped_actions[-1].get("action") == "gesture" + and deduped_actions[-1].get("gesture_type") == "nod" + ): + continue + deduped_actions.append(action) + + return deduped_actions + + # Conversational movement commands + if _contains_any_keyword(command_lower, COME_HERE_KEYWORDS): + actions.append( + {"action": "move", "target": {"x": 50, "y": 85}, "speed": "normal"} + ) + + if _contains_any_keyword(command_lower, FOLLOW_ME_KEYWORDS): + actions.append({"action": "gesture", "gesture_type": "nod"}) + actions.append( + {"action": "move", "target": {"x": 50, "y": 75}, "speed": "normal"} + ) + + # Conversational object delivery commands + if _contains_any_keyword(command_lower, BRING_ME_KEYWORDS): + for obj in known_objects: + if obj in command_lower and obj in stage_state["objects"]: + obj_pos = stage_state["objects"][obj]["position"] + actions.append( + {"action": "move", "target": obj_pos, "speed": "normal"} + ) + actions.append({"action": "pickup", "object_id": obj}) + actions.append( + { + "action": "move", + "target": {"x": 50, "y": 85}, + "speed": "normal", + } + ) + actions.append({"action": "gesture", "gesture_type": "nod"}) + break + + # Pronoun follow-up delivery command (e.g., "bring it here") + if _contains_any_keyword(command_lower, BRING_IT_KEYWORDS): + held_obj = planned_held_object + if held_obj: + actions.append( + {"action": "move", "target": {"x": 50, "y": 85}, "speed": "normal"} + ) + actions.append({"action": "gesture", "gesture_type": "nod"}) + else: + actions.append( + { + "action": "say", + "text": "I need to pick something up first.", + "emotion": "neutral", + } + ) + + # Conversational drop/place commands for currently held object + has_drop_intent = ( + _contains_any_keyword(command_lower, DROP_HERE_KEYWORDS) + or ("drop" in command_lower and "here" in command_lower) + or ( + "put" in command_lower + and ( + "here" in command_lower + or "down" in command_lower + or "table" in command_lower + ) + ) + or ( + "place" in command_lower + and ("here" in command_lower or "table" in command_lower) + ) + ) + if has_drop_intent: + held_obj = planned_held_object + if not held_obj: + actions.append( + { + "action": "say", + "text": "I'm not holding anything yet.", + "emotion": "neutral", + } + ) + else: + drop_target = {"x": 50, "y": 85} + if "table" in command_lower: + table_pos = stage_state["environment"]["table"]["position"] + drop_target = {"x": table_pos["x"], "y": 35} + actions.append( + {"action": "move", "target": drop_target, "speed": "normal"} + ) + actions.append({"action": "drop", "position": drop_target}) + + # Parse move commands + if _contains_any_keyword(command_lower, MOVE_KEYWORDS): + # Extract target from command + if "table" in command_lower: + actions.append( + {"action": "move", "target": {"x": 60, "y": 35}, "speed": "normal"} + ) + elif "center" in command_lower or "middle" in command_lower: + actions.append( + {"action": "move", "target": {"x": 50, "y": 50}, "speed": "normal"} + ) + elif "left" in command_lower: + actions.append( + {"action": "move", "target": {"x": 20, "y": 50}, "speed": "normal"} + ) + elif "right" in command_lower: + actions.append( + {"action": "move", "target": {"x": 80, "y": 50}, "speed": "normal"} + ) + + # Parse say commands + if _contains_any_keyword(command_lower, SAY_KEYWORDS): + # Extract text after say/speak + for trigger in ["say ", "speak ", "tell ", "greet "]: + if trigger in command_lower: + text = command[command_lower.index(trigger) + len(trigger) :].strip( + " \"'" + ) + emotion = ( + "happy" + if any(w in text.lower() for w in ["!", "hello", "hi"]) + else "neutral" + ) + actions.append({"action": "say", "text": text, "emotion": emotion}) + break + + # Parse pickup commands + for obj in known_objects: + if obj in command_lower and _contains_any_keyword( + command_lower, PICKUP_KEYWORDS + ): + # Move to object first + obj_pos = stage_state["objects"][obj]["position"] + actions.append({"action": "move", "target": obj_pos, "speed": "normal"}) + actions.append({"action": "pickup", "object_id": obj}) + break + + # Parse gesture commands + gestures = ["wave", "thumbs_up", "clap", "shrug", "bow", "nod"] + for gesture in gestures: + trigger = gesture.replace("_", " ") + if trigger in command_lower: + actions.append({"action": "gesture", "gesture_type": gesture}) + break + + return actions + + def parse( + self, + command: str, + use_llm: bool = True, + provider_choice: Optional[str] = None, + model_override: Optional[str] = None, + ) -> List[dict]: + """ + Parse command into structured actions + + Args: + command: Natural language command + use_llm: Try LLM first if available + provider_choice: Optional provider override (e.g. quantum, azure) + model_override: Optional model/path override for selected provider + + Returns: + List of action dicts + """ + if use_llm: + try: + request_provider, request_choice = self._resolve_provider_for_request( + explicit=provider_choice, + model_override=model_override, + ) + if request_provider is None: + raise ValueError("LLM provider not available") + + actions = self.parse_with_llm(command, provider=request_provider) + used_provider_name = ( + getattr(request_choice, "name", None) + or getattr(self.provider_choice, "name", None) + or self._normalize_provider_alias(provider_choice) + or "auto" + ) + logger.info( + f"✓ LLM parsed via {used_provider_name}: {command} -> {len(actions)} actions" + ) + return actions + except Exception as e: + logger.warning(f"LLM parsing failed, using fallback: {e}") + + actions = self.parse_with_fallback(command) + logger.info(f"✓ Fallback parsed: {command} -> {len(actions)} actions") + return actions + + +# --------------------------- World Generation --------------------------- + + +def _sanitize_id(raw: str) -> str: + cleaned = re.sub(r"[^a-zA-Z0-9_]+", "_", raw.strip().lower()) + return cleaned[:30] or f"obj_{random.randint(1000, 9999)}" + + +THEME_OBJECT_LIBRARY = { + "forest": [ + ("tree", "🌲"), + ("mushroom", "🍄"), + ("rock", "🪨"), + ("flower", "🌼"), + ("owl", "🦉"), + ("fox", "🦊"), + ], + "space": [ + ("planet", "🪐"), + ("star", "⭐"), + ("rocket", "🚀"), + ("alien", "👽"), + ("astronaut", "👩‍🚀"), + ("satellite", "🛰️"), + ], + "ocean": [ + ("fish", "🐟"), + ("shell", "🐚"), + ("coral", "🪸"), + ("whale", "🐋"), + ("ship", "🚢"), + ("dolphin", "🐬"), + ], + "lab": [ + ("beaker", "🧪"), + ("microscope", "🔬"), + ("dna", "🧬"), + ("robot", "🤖"), + ("atom", "⚛️"), + ("chip", "💾"), + ], + "medieval": [ + ("sword", "🗡️"), + ("shield", "🛡️"), + ("crown", "👑"), + ("scroll", "📜"), + ("goblet", "🍷"), + ("castle", "🏰"), + ], + "desert": [ + ("cactus", "🌵"), + ("skull", "💀"), + ("camel", "🐪"), + ("scorpion", "🦂"), + ("sun", "☀️"), + ("sand", "🟨"), + ], + "garden": [ + ("rose", "🌹"), + ("tulip", "🌷"), + ("butterfly", "🦋"), + ("bee", "🐝"), + ("bench", "🪑"), + ("pond", "💧"), + ], + "cyberpunk": [ + ("drone", "🛸"), + ("neon", "💡"), + ("chip", "💾"), + ("server", "🖥️"), + ("bot", "🤖"), + ("battery", "🔋"), + ], + "arcade": [ + ("joystick", "🕹️"), + ("coin", "🪙"), + ("ghost", "👻"), + ("trophy", "🏆"), + ("console", "🎮"), + ("heart", "❤️"), + ], +} + + +def generate_world_fallback(theme: str, count: int) -> dict: + """Generate a world procedurally without LLM.""" + objects_catalog = THEME_OBJECT_LIBRARY.get( + theme.lower(), THEME_OBJECT_LIBRARY["forest"] + ) + random.shuffle(objects_catalog) + chosen = objects_catalog[: max(1, count)] + stage_objects = {} + used_positions = [] + for name, emoji in chosen: + # Avoid overlapping positions (simple Poisson-ish attempt) + for attempt in range(10): + x = random.randint(10, 90) + y = random.randint(20, 80) + if all(math.hypot(x - px, y - py) > 8 for px, py in used_positions): + used_positions.append((x, y)) + break + stage_objects[_sanitize_id(name)] = { + "id": _sanitize_id(name), + "emoji": emoji, + "position": {"x": x, "y": y}, + "state": "on_stage", + } + environment = { + "theme": theme, + "generated_at": datetime.datetime.now(timezone.utc).isoformat() + "Z", + "seed": random.randint(100000, 999999), + "stage_bounds": {"width": 100, "height": 100}, + } + return {"objects": stage_objects, "environment": environment} + + +def generate_world_with_llm(theme: str, count: int, provider) -> dict: + """Use LLM provider to generate a themed world. Returns fallback on failure.""" + system_prompt = ( + "You are a world generator for a 2D stage (coordinates 0-100 for x and y). " + "Given a theme, produce JSON with 'objects' and 'environment'. Each object must have: id, emoji, position {x,y}, state. " + "Constrain positions within bounds. Provide at most the requested count. ONLY output JSON, no commentary." + ) + user_prompt = f"Theme: {theme}\nCount: {count}\nRequirements: diverse, non-overlapping, concise ids." + try: + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ] + raw = provider.complete(messages, stream=False) + raw_str = _provider_response_to_text(raw).strip() + # Strip code fences + if "```" in raw_str: + m = re.search(r"```(?:json)?\n(.*?)(```)", raw_str, flags=re.DOTALL) + if m: + raw_str = m.group(1).strip() + + # Parse JSON from the LLM response + try: + parsed_world_data = json.loads(raw_str) + except json.JSONDecodeError: + obj_match = re.search(r"\{.*\}\s*$", raw_str, flags=re.DOTALL) + if obj_match: + raw_str = obj_match.group(0) + parsed_world_data = json.loads(raw_str) + # Basic validation + objects = parsed_world_data.get("objects") or {} + env = parsed_world_data.get("environment") or {} + + # Normalize object list/dict to id->object mapping + sanitized_objects = {} + if isinstance(objects, dict): + object_items = list(objects.items()) + elif isinstance(objects, list): + object_items = [(str(i), obj) for i, obj in enumerate(objects)] + else: + object_items = [] + + for key, val in object_items[:count]: + if not isinstance(val, dict): + continue + object_id = val.get("id") or val.get("name") or key + oid = _sanitize_id(object_id) + pos = val.get("position", {}) + x = int(max(0, min(100, pos.get("x", random.randint(10, 90))))) + y = int(max(0, min(100, pos.get("y", random.randint(20, 80))))) + state = val.get("state", "on_stage") + emoji = val.get("emoji", "✨") + sanitized_objects[oid] = { + "id": oid, + "emoji": emoji, + "position": {"x": x, "y": y}, + "state": state, + } + if not sanitized_objects: + return generate_world_fallback(theme, count) + env.setdefault("theme", theme) + env.setdefault( + "generated_at", datetime.datetime.now(timezone.utc).isoformat() + "Z" + ) + env.setdefault("stage_bounds", {"width": 100, "height": 100}) + return { + "objects": sanitized_objects, + "environment": env, + "llm": True, + "raw_response_len": len(raw_str), + } + except Exception as e: + logger.warning(f"World generation via LLM failed: {e}; falling back.") + return generate_world_fallback(theme, count) | {"llm": False} + + +# Initialize global action parser +action_parser = AriaActionParser() + + +def get_stage_context() -> str: + """Generate natural language description of current stage state for AI""" + aria = stage_state["aria"] + objects = stage_state["objects"] + + # Calculate distances to objects + aria_pos = aria["position"] + nearby_objects = [] + for obj_name, obj_data in objects.items(): + # Safety check: ensure object has position data + if not isinstance(obj_data, dict) or "position" not in obj_data: + continue + obj_pos = obj_data["position"] + if not isinstance(obj_pos, dict) or "x" not in obj_pos or "y" not in obj_pos: + continue + distance = ( + (aria_pos["x"] - obj_pos["x"]) ** 2 + (aria_pos["y"] - obj_pos["y"]) ** 2 + ) ** 0.5 + if distance < 30: # Within reach + nearby_objects.append(obj_name) + + context = f"""STAGE VIEW: +- Aria is at position ({aria_pos['x']}%, {aria_pos['y']}%), facing {aria['facing']} +- Expression: {aria['expression']} +- Held object: {aria['held_object'] or 'none'} +- Table is at (60%, 20%) on the right side +- Objects on table: {', '.join([k for k, v in objects.items() if isinstance(v, dict) and v.get('state') == 'on_table'])} +- Objects nearby Aria (within reach): {', '.join(nearby_objects) if nearby_objects else 'none'} +- Stage dimensions: 100% wide x 100% tall (0,0=top-left, 100,100=bottom-right) +""" + return context + + +def determine_position_from_context(cmd: str) -> str: + """AI-driven position determination based on command semantics and stage state""" + aria_pos = stage_state["aria"]["position"] + objects = stage_state["objects"] + table_pos = stage_state["environment"]["table"]["position"] + + # Conversational movement intents + if _contains_any_keyword(cmd, COME_HERE_KEYWORDS): + # Front-center, closer to the viewer/user + return "[aria:position:50:85]" + if _contains_any_keyword(cmd, FOLLOW_ME_KEYWORDS): + # Slightly behind the front-center so movement is visible + return "[aria:position:50:75]" + if _contains_any_keyword(cmd, BRING_ME_KEYWORDS): + # For delivery-style commands, move to front-center + return "[aria:position:50:85]" + if _contains_any_keyword(cmd, BRING_IT_KEYWORDS): + # Pronoun follow-up delivery defaults to front-center + return "[aria:position:50:85]" + if _contains_any_keyword(cmd, DROP_HERE_KEYWORDS): + # Default drop target is in front of the user + return "[aria:position:50:85]" + + # Object interaction positioning - move near the object + for obj_name in ["apple", "book", "cup", "ball", "flower", "bear"]: + if obj_name in cmd and ( + "pick" in cmd or "get" in cmd or "grab" in cmd or "take" in cmd + ): + if obj_name in objects: + obj_data = objects[obj_name] + # Safety check: ensure object has position data + if isinstance(obj_data, dict) and "position" in obj_data: + obj_pos = obj_data["position"] + if isinstance(obj_pos, dict) and "x" in obj_pos and "y" in obj_pos: + # Position slightly to the left of object + return f'[aria:position:{max(10, obj_pos["x"] - 10)}:{obj_pos["y"] + 10}]' + + # Action-based positioning (using pre-compiled keyword sets for O(1) lookup) + if _contains_any_keyword(cmd, JUMP_KEYWORDS): + return "[aria:position:50:60]" # Center for jumping + elif _contains_any_keyword(cmd, DANCE_KEYWORDS): + return "[aria:position:50:50]" # Center stage for performance + elif _contains_any_keyword(cmd, WAVE_KEYWORDS): + return "[aria:position:30:70]" # Front-left for greeting + elif _contains_any_keyword(cmd, LOOK_KEYWORDS): + # Look towards table + if "table" in cmd: + return "[aria:position:40:60]" # Position to see table + return "[aria:position:20:40]" # Left side for observing + elif _contains_any_keyword(cmd, SIT_KEYWORDS): + # Near table to sit + return f'[aria:position:{table_pos["x"] - 5}:{table_pos["y"] + 35}]' + elif _contains_any_keyword(cmd, RUN_KEYWORDS): + return "[aria:position:85:70]" # Far right for running space + elif _contains_any_keyword(cmd, HIDE_KEYWORDS): + return "[aria:position:10:75]" # Corner position + elif _contains_any_keyword(cmd, PRESENT_KEYWORDS): + return "[aria:position:50:50]" # Center to present + elif _contains_any_keyword(cmd, THINK_KEYWORDS): + return "[aria:position:25:50]" # Contemplative left position + elif _contains_any_keyword(cmd, WALK_LEFT_KEYWORDS): + return "[aria:position:20:70]" # Moving to left + elif _contains_any_keyword(cmd, WALK_RIGHT_KEYWORDS): + return "[aria:position:80:70]" # Moving to right + elif "add" in cmd or "create" in cmd or "spawn" in cmd: + # For adding objects, position near table + return f'[aria:position:{table_pos["x"] - 15}:{table_pos["y"] + 20}]' + else: + # Context-aware positioning: stay put if already in good position + # or move to interesting area if idle + pos_hash = int(hashlib.md5(cmd.encode()).hexdigest()[:4], 16) + x = 30 + (pos_hash % 40) # Random between 30-70% + y = 60 + (pos_hash % 20) # Random between 60-80% + return f"[aria:position:{x}:{y}]" + + +def generate_tags_ai(command: str) -> List[str]: + """Generate tags using AI model""" + if MODEL is None: + return [] + + try: + import torch + from transformers import AutoTokenizer + + base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" + tokenizer = AutoTokenizer.from_pretrained(base_model) + + input_text = f"<|user|>\n{command}\n<|assistant|>\n" + inputs = tokenizer(input_text, return_tensors="pt").to(MODEL.device) + + with torch.no_grad(): + outputs = MODEL.generate( + **inputs, + max_new_tokens=30, + temperature=0.1, + do_sample=True, + top_p=0.9, + repetition_penalty=1.5, + pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id, + ) + + response = tokenizer.decode( + outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True + ) + tags = _RE_ARIA_TAGS.findall(response) + return tags[:2] # Return first 2 tags max + except Exception as e: + print(f"AI generation error: {e}") + return [] + + +def generate_tags_fallback(command: str) -> List[str]: + """Simple rule-based fallback tag generation with automatic positioning""" + cmd = command.lower() + tags = [] + + # AI-driven automatic positioning based on command context + # Determine optimal position for the action + auto_position = determine_position_from_context(cmd) + if auto_position: + tags.append(auto_position) + + # Track if limb commands are detected to avoid movement conflicts + # Using pre-compiled keyword sets for O(1) lookup + has_limb_command = ( + _contains_any_keyword(cmd, LEFT_ARM_KEYWORDS) + or _contains_any_keyword(cmd, RIGHT_ARM_KEYWORDS) + or _contains_any_keyword(cmd, LEFT_LEG_KEYWORDS) + or _contains_any_keyword(cmd, RIGHT_LEG_KEYWORDS) + ) + + # Special: server-side "say" / announce detection (capture original text) + try: + say_match = _RE_SAY_COMMAND.search(command) + if say_match: + raw_msg = say_match.group(1).strip() + # basic sanitization and length cap + safe_msg = _RE_SANITIZE_BRACKETS.sub("", raw_msg)[:200] + tags.append(f"[aria:say:{safe_msg}]") + except Exception: + # ignore parsing errors + pass + + # Expressions + if "smile" in cmd or "happy" in cmd: + tags.append("[aria:expression:smile]") + elif "sad" in cmd: + tags.append("[aria:expression:sad]") + elif "surprised" in cmd: + tags.append("[aria:expression:surprised]") + elif "confused" in cmd: + tags.append("[aria:expression:confused]") + elif "thinking" in cmd or "think" in cmd: + tags.append("[aria:expression:thinking]") + elif "wink" in cmd: + tags.append("[aria:expression:wink]") + + # Animations + if "jump" in cmd: + tags.append("[aria:animate:jump]") + elif "dance" in cmd: + tags.append("[aria:animate:dance]") + elif "spin" in cmd: + tags.append("[aria:animate:spin]") + elif "bow" in cmd: + tags.append("[aria:animate:bow]") + elif "flip" in cmd: + tags.append("[aria:animate:flip]") + + # Gestures (allowlist: wave, thumbs_up, clap, shrug, bow, nod) + if "wave" in cmd: + tags.append("[aria:gesture:wave]") + elif "thumbs up" in cmd: + tags.append("[aria:gesture:thumbs_up]") + elif "clap" in cmd: + tags.append("[aria:gesture:clap]") + elif "shrug" in cmd: + tags.append("[aria:gesture:shrug]") + elif "nod" in cmd: + tags.append("[aria:gesture:nod]") + + # Limb controls and poses (AI may also emit these; fallback supports natural phrases) + # Hands up / T-pose / Cross arms + if "hands up" in cmd or "raise hands" in cmd: + tags.append("[aria:limb:left_arm:raise]") + tags.append("[aria:limb:right_arm:raise]") + if "t-pose" in cmd or "tpose" in cmd or "t pose" in cmd: + tags.append("[aria:pose:t-pose]") + if "cross arms" in cmd or "arms crossed" in cmd: + tags.append("[aria:pose:cross_arms]") + + # Per-limb commands + def limb_tag(part: str, action: str): + tags.append(f"[aria:limb:{part}:{action}]") + + # Helper maps (using pre-compiled keyword sets) + left_arm = _contains_any_keyword(cmd, LEFT_ARM_KEYWORDS) + right_arm = _contains_any_keyword(cmd, RIGHT_ARM_KEYWORDS) + left_leg = _contains_any_keyword(cmd, LEFT_LEG_KEYWORDS) + right_leg = _contains_any_keyword(cmd, RIGHT_LEG_KEYWORDS) + + # Numeric angle if present (e.g., "left arm 45 degrees") + angle_match = re.search(r"(-?\d{1,3})\s*(?:deg|degree|degrees)?", cmd) + angle_val = angle_match.group(1) if angle_match else None + + # Arm actions + if left_arm or right_arm or "arm" in cmd: + # Choose default arm if unspecified + parts = [] + if left_arm: + parts.append("left_arm") + if right_arm: + parts.append("right_arm") + if not parts: + parts = ["right_arm"] + if _contains_any_keyword(cmd, ARM_WAVE_KEYWORDS): + for p in parts: + limb_tag(p, "wave") + elif _contains_any_keyword(cmd, ARM_RAISE_KEYWORDS): + for p in parts: + limb_tag(p, "raise") + elif _contains_any_keyword(cmd, ARM_LOWER_KEYWORDS): + for p in parts: + limb_tag(p, "lower") + elif _contains_any_keyword(cmd, ARM_FORWARD_KEYWORDS): + for p in parts: + limb_tag(p, "forward") + elif _contains_any_keyword(cmd, ARM_BACK_KEYWORDS): + for p in parts: + limb_tag(p, "back") + elif angle_val is not None: + for p in parts: + limb_tag(p, angle_val) + + # Leg actions + if left_leg or right_leg or "leg" in cmd: + parts = [] + if left_leg: + parts.append("left_leg") + if right_leg: + parts.append("right_leg") + if not parts: + parts = ["left_leg"] + if "kick" in cmd: + for p in parts: + limb_tag(p, "kick") + elif _contains_any_keyword(cmd, ARM_FORWARD_KEYWORDS): + for p in parts: + limb_tag(p, "forward") + elif _contains_any_keyword(cmd, ARM_BACK_KEYWORDS): + for p in parts: + limb_tag(p, "back") + elif angle_val is not None: + for p in parts: + limb_tag(p, angle_val) + + # Movement - only add if not a limb command (to avoid conflicts like "left arm" -> "move:left") + if not has_limb_command: + # Determine movement style + movement_style = None + if "skip" in cmd: + movement_style = "skip" + elif "strut" in cmd or "swagger" in cmd: + movement_style = "strut" + elif "run" in cmd: + movement_style = "run" + elif "walk" in cmd: + movement_style = "walk" + else: + movement_style = "move" + + # Determine direction - exclude if keywords could be part of limb commands + has_forward_limb = "leg" in cmd or "arm" in cmd + if "left" in cmd: + tags.append(f"[aria:{movement_style}:left]") + elif "right" in cmd: + tags.append(f"[aria:{movement_style}:right]") + elif ("up" in cmd or "forward" in cmd) and not has_forward_limb: + tags.append(f"[aria:{movement_style}:up]") + elif ("down" in cmd or "back" in cmd) and not has_forward_limb: + tags.append(f"[aria:{movement_style}:down]") + + # Effects - with keyword matching and intensity support + effect_intensity = "normal" + if "light" in cmd or "subtle" in cmd or "gentle" in cmd: + effect_intensity = "light" + elif "heavy" in cmd or "intense" in cmd or "lots" in cmd or "many" in cmd: + effect_intensity = "heavy" + + if _contains_any_keyword(cmd, SPARKLE_KEYWORDS): + tags.append(f"[aria:effect:sparkle:{effect_intensity}]") + elif _contains_any_keyword(cmd, GLOW_KEYWORDS): + tags.append(f"[aria:effect:glow:{effect_intensity}]") + elif _contains_any_keyword(cmd, HEARTS_KEYWORDS): + tags.append(f"[aria:effect:hearts:{effect_intensity}]") + + # Camera + if "center" in cmd: + tags.append("[aria:camera:center]") + elif "zoom" in cmd: + tags.append( + "[aria:camera:zoom_in]" if "in" in cmd else "[aria:camera:zoom_out]" + ) + + # Poses (body positions) + if "sit" in cmd: + tags.append("[aria:pose:sit]") + elif "stand" in cmd: + tags.append("[aria:pose:stand]") + elif "crouch" in cmd: + tags.append("[aria:pose:crouch]") + elif "lie" in cmd or "lay" in cmd: + tags.append("[aria:pose:lie]") + + # Position control - let AI determine where Aria should be + # Format: [aria:position:x:y] where x and y are percentages (0-100) + # Or named positions: [aria:position:center], [aria:position:left], etc. + position_keywords = { + "center": "[aria:position:50:50]", + "left side": "[aria:position:15:80]", + "right side": "[aria:position:85:80]", + "top": "[aria:position:50:10]", + "bottom": "[aria:position:50:90]", + "corner": "[aria:position:10:10]", + "stage left": "[aria:position:20:70]", + "stage right": "[aria:position:80:70]", + "front": "[aria:position:50:85]", + "back": "[aria:position:50:15]", + } + + # Check for position commands + if "position" in cmd or "move to" in cmd or "go to" in cmd or "stand at" in cmd: + for keyword, tag in position_keywords.items(): + if keyword in cmd: + tags.append(tag) + break + else: + # Try to extract numeric coordinates + coord_match = _RE_COORDINATES.search(cmd) + if coord_match: + x, y = coord_match.groups() + tags.append(f"[aria:position:{x}:{y}]") + + # Object management (add/remove objects) + if "add" in cmd or "create" in cmd or "spawn" in cmd: + object_emojis = { + "bear": "🧸", + "teddy": "🧸", + "cat": "🐱", + "dog": "🐶", + "bunny": "🐰", + "rabbit": "🐰", + "star": "⭐", + "heart": "❤️", + "moon": "🌙", + "sun": "☀️", + "tree": "🌲", + "plant": "🌿", + "mushroom": "🍄", + "car": "🚗", + "bike": "🚲", + "plane": "✈️", + } + for obj_name, emoji in object_emojis.items(): + if obj_name in cmd: + tags.append(f"[aria:interact:add:{obj_name}:{emoji}]") + break + + return tags + + +def execute_aria_action(action: dict) -> dict: + """ + Execute a single structured action and update stage state + + Args: + action: Action dict with 'action' key and params + + Returns: + Result dict with status, message, and updated state + """ + action_type = action.get("action") + + if action_type not in ARIA_ACTIONS: + return {"status": "error", "message": f"Unknown action: {action_type}"} + + try: + if action_type == "move": + target = action.get("target") + if isinstance(target, dict) and "x" in target and "y" in target: + # Clamp position to stage bounds (0-100) + target = { + "x": max(0, min(100, target["x"])), + "y": max(0, min(100, target["y"])), + } + stage_state["aria"]["position"] = target + return { + "status": "success", + "message": f'Moved to ({target["x"]}, {target["y"]})', + "tags": [f'[aria:position:{target["x"]}:{target["y"]}]'], + } + elif isinstance(target, str) and target in stage_state["objects"]: + # Move to object + obj_pos = stage_state["objects"][target]["position"] + stage_state["aria"]["position"] = { + "x": obj_pos["x"] - 10, + "y": obj_pos["y"] + 5, + } + return { + "status": "success", + "message": f"Moved to {target}", + "tags": [f'[aria:position:{obj_pos["x"] - 10}:{obj_pos["y"] + 5}]'], + } + + elif action_type == "say": + text = str(action.get("text", ""))[:200] # Cap at 200 chars + emotion = action.get("emotion", "neutral") + stage_state["aria"]["expression"] = emotion + return { + "status": "success", + "message": f'Said: "{text}" with {emotion} emotion', + "tags": [f"[aria:say:{text}]", f"[aria:expression:{emotion}]"], + } + + elif action_type == "pickup": + obj_id = action.get("object_id") + if obj_id not in stage_state["objects"]: + return {"status": "error", "message": f"Object not found: {obj_id}"} + + if stage_state["aria"]["held_object"]: + return {"status": "error", "message": "Already holding an object"} + + # Check distance + aria_pos = stage_state["aria"]["position"] + obj_pos = stage_state["objects"][obj_id]["position"] + distance = ( + (aria_pos["x"] - obj_pos["x"]) ** 2 + + (aria_pos["y"] - obj_pos["y"]) ** 2 + ) ** 0.5 + + if distance > 30: + return { + "status": "error", + "message": f"Too far from {obj_id}. Move closer first.", + } + + stage_state["aria"]["held_object"] = obj_id + stage_state["objects"][obj_id]["state"] = "held" + return { + "status": "success", + "message": f"Picked up {obj_id}", + "tags": [f"[aria:pickup:{obj_id}]", "[aria:limb:right_arm:grab]"], + } + + elif action_type == "drop": + if not stage_state["aria"]["held_object"]: + return {"status": "error", "message": "Not holding anything"} + + obj_id = stage_state["aria"]["held_object"] + position = action.get("position", stage_state["aria"]["position"]) + + stage_state["objects"][obj_id]["position"] = position + stage_state["objects"][obj_id]["state"] = "dropped" + stage_state["aria"]["held_object"] = None + + return { + "status": "success", + "message": f"Dropped {obj_id}", + "tags": [f"[aria:drop:{obj_id}]", "[aria:limb:right_arm:release]"], + } + + elif action_type == "throw": + if not stage_state["aria"]["held_object"]: + return {"status": "error", "message": "Not holding anything"} + + obj_id = stage_state["aria"]["held_object"] + target = action.get("target", {"x": 70, "y": 40}) + force = action.get("force", "medium") + + stage_state["objects"][obj_id]["position"] = target + stage_state["objects"][obj_id]["state"] = "thrown" + stage_state["aria"]["held_object"] = None + + return { + "status": "success", + "message": f"Threw {obj_id} with {force} force", + "tags": [ + f"[aria:throw:{obj_id}]", + "[aria:limb:right_arm:throw]", + f"[aria:animation:throw_{force}]", + ], + } + + elif action_type == "gesture": + gesture_type = action.get("gesture_type", "wave") + valid_gestures = frozenset( + ["wave", "thumbs_up", "clap", "shrug", "bow", "nod"] + ) + + if gesture_type not in valid_gestures: + gesture_type = "wave" # Default fallback + + return { + "status": "success", + "message": f"Performed {gesture_type} gesture", + "tags": [ + f"[aria:gesture:{gesture_type}]", + f"[aria:animation:{gesture_type}]", + ], + } + + elif action_type == "look": + target = action.get("target") + if isinstance(target, str) and target in stage_state["objects"]: + # Look at object + obj_pos = stage_state["objects"][target]["position"] + aria_pos = stage_state["aria"]["position"] + facing = "right" if obj_pos["x"] > aria_pos["x"] else "left" + stage_state["aria"]["facing"] = facing + return { + "status": "success", + "message": f"Looking at {target}", + "tags": [f"[aria:look:{target}]", f"[aria:facing:{facing}]"], + } + elif isinstance(target, dict) and "x" in target: + # Look at position + aria_pos = stage_state["aria"]["position"] + facing = "right" if target["x"] > aria_pos["x"] else "left" + stage_state["aria"]["facing"] = facing + return { + "status": "success", + "message": "Looking at position", + "tags": [f"[aria:facing:{facing}]"], + } + + elif action_type == "wait": + duration = action.get("duration", 1.0) + return { + "status": "success", + "message": f"Waiting {duration}s", + "tags": [f"[aria:wait:{duration}]"], + } + + return {"status": "error", "message": f"Action not implemented: {action_type}"} + + except Exception as e: + logger.error(f"Action execution failed: {e}") + return {"status": "error", "message": str(e)} + + +def action_to_tags(action: dict) -> List[str]: + """Convert a structured action to Aria tag(s) without mutating stage state.""" + action_type = action.get("action") + if not action_type: + return [] + + if action_type == "move": + target = action.get("target") + if isinstance(target, dict) and "x" in target and "y" in target: + x = max(0, min(100, int(target["x"]))) + y = max(0, min(100, int(target["y"]))) + return [f"[aria:position:{x}:{y}]"] + if isinstance(target, str): + return [f"[aria:move:to:{target}]"] + + if action_type == "say": + text = str(action.get("text", ""))[:200] + emotion = str(action.get("emotion", "neutral")) + tags = [f"[aria:say:{text}]"] if text else [] + if emotion: + tags.append(f"[aria:expression:{emotion}]") + return tags + + if action_type == "pickup": + obj_id = action.get("object_id") + return [f"[aria:pickup:{obj_id}]"] if obj_id else [] + + if action_type == "drop": + position = action.get("position") + tags = ["[aria:drop]"] + if isinstance(position, dict) and "x" in position and "y" in position: + x = max(0, min(100, int(position["x"]))) + y = max(0, min(100, int(position["y"]))) + tags.append(f"[aria:position:{x}:{y}]") + return tags + + if action_type == "throw": + force = action.get("force", "medium") + return [f"[aria:throw:{force}]"] + + if action_type == "gesture": + gesture_type = action.get("gesture_type", "wave") + return [f"[aria:gesture:{gesture_type}]"] + + if action_type == "look": + target = action.get("target") + if isinstance(target, str): + return [f"[aria:look:{target}]"] + return ["[aria:look]"] + + if action_type == "wait": + duration = action.get("duration", 1.0) + return [f"[aria:wait:{duration}]"] + + return [] + + +def tags_from_actions(actions: List[dict]) -> List[str]: + """Flatten tags from a list of structured actions.""" + tags: List[str] = [] + for action in actions: + tags.extend(action_to_tags(action)) + return tags + + +class AriaRequestHandler(SimpleHTTPRequestHandler): + def end_headers(self): + # Add CORS headers + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Access-Control-Allow-Methods", "GET, POST, OPTIONS") + self.send_header("Access-Control-Allow-Headers", "Content-Type") + super().end_headers() + + def do_OPTIONS(self): + """Handle CORS preflight""" + self.send_response(200) + self.end_headers() + + def do_GET(self): + """Serve static files""" + print(f"📥 GET request: {self.path}") + # API: return either full stage snapshot or object registry + if self.path == "/api/aria/state": + self.send_response(200) + self.send_header("Content-Type", "application/json") + self.end_headers() + payload = { + "aria": stage_state.get("aria", {}), + "objects": stage_state.get("objects", {}), + "environment": stage_state.get("environment", {}), + } + self.wfile.write(json.dumps(payload).encode("utf-8")) + return + if self.path == "/api/aria/objects": + self.send_response(200) + self.send_header("Content-Type", "application/json") + self.end_headers() + payload = {"objects": stage_state.get("objects", {})} + self.wfile.write(json.dumps(payload).encode("utf-8")) + return + if self.path == "/": + self.path = "/index.html" + return super().do_GET() + + def do_POST(self): + # --- /api/aria/object endpoint --- + # Expected payloads: + # 1. Bulk update: + # {"objects": {"id1": {"position": {...}, "state": ...}, ...}} + # 2. Single object action: + # {"action": "add|update|remove", "object": {"id": "...", "position": {...}, "state": ...}} + # - "add": adds a new object + # - "update": updates position/state of existing object + # - "remove": deletes object by id + # - "object" must include "id" or "name" + """Handle API requests""" + if self.path == "/api/aria/command": + try: + content_length = int(self.headers.get("Content-Length", 0)) + post_data = self.rfile.read(content_length) + + request_data = json.loads(post_data.decode("utf-8")) + command = request_data.get("command", "") + use_llm = bool(request_data.get("use_llm", True)) + provider_choice = request_data.get("provider") + model_override = request_data.get("model") + + # Update stage state if provided + if "stage_state" in request_data: + stage_state.update(request_data["stage_state"]) + + print(f"📝 Command received: {command}") + print(f"👁️ Stage context:\n{get_stage_context()}") + + tags = [] + actions = [] + + # Preferred path: parser-backed structured actions (supports + # provider/model overrides including quantum). + if use_llm: + try: + actions = action_parser.parse( + command, + use_llm=True, + provider_choice=provider_choice, + model_override=model_override, + ) + tags = tags_from_actions(actions) + except Exception as parse_err: + logger.warning( + f"/api/aria/command parser path failed, falling back: {parse_err}" + ) + + # Legacy fallback path: rule-based tag generation. + if not tags: + legacy_tags = generate_tags_ai(command) + tags = ( + legacy_tags if legacy_tags else generate_tags_fallback(command) + ) + + print(f"✨ Generated tags: {tags}") + + response = { + "command": command, + "tags": tags, + "actions": actions if actions else None, + "provider_requested": provider_choice or "auto", + "model_requested": model_override, + "model": ( + "llm" if actions else ("ai" if (MODEL and tags) else "fallback") + ), + "stage_context": get_stage_context(), + "stage_aware": True, + } + + self.send_response(200) + self.send_header("Content-Type", "application/json") + self.end_headers() + self.wfile.write(json.dumps(response).encode("utf-8")) + + except ConnectionAbortedError: + # Client disconnected, ignore + pass + except Exception as e: + print(f"❌ Error: {e}") + try: + self.send_response(500) + self.send_header("Content-Type", "application/json") + self.end_headers() + error = {"error": str(e), "tags": []} + self.wfile.write(json.dumps(error).encode("utf-8")) + except: + pass + elif self.path == "/api/aria/object" or self.path == "/api/aria/objects": + try: + content_length = int(self.headers.get("Content-Length", 0)) + post_data = self.rfile.read(content_length) + request_data = json.loads(post_data.decode("utf-8")) + + # Support both single object ({action, object}) and bulk ({objects: {...}}) + if "objects" in request_data and isinstance( + request_data["objects"], dict + ): + # Merge supplied objects into stage_state + for k, v in request_data["objects"].items(): + if isinstance(v, dict) and "position" in v: + stage_state["objects"][k] = v + api_response = {"status": "ok", "objects": stage_state["objects"]} + elif "object" in request_data and "action" in request_data: + action = request_data["action"] + obj = request_data["object"] + obj_id = obj.get("id") or obj.get("name") + if not obj_id: + raise ValueError( + 'Object payload must include "id" or "name" field.' + ) + + if action == "add": + position = obj.get("position", {"x": 50, "y": 50}) + state = obj.get("state", "on_stage") + stage_state["objects"][obj_id] = { + "position": position, + "state": state, + } + api_response = { + "status": "added", + "id": obj_id, + "object": stage_state["objects"][obj_id], + } + elif action == "update": + if obj_id not in stage_state["objects"]: + stage_state["objects"][obj_id] = {} + if "position" in obj: + stage_state["objects"][obj_id]["position"] = obj["position"] + if "state" in obj: + stage_state["objects"][obj_id]["state"] = obj["state"] + api_response = { + "status": "updated", + "id": obj_id, + "object": stage_state["objects"][obj_id], + } + elif action == "remove" or action == "delete": + removed = stage_state["objects"].pop(obj_id, None) + api_response = { + "status": "removed", + "id": obj_id, + "object": removed, + } + else: + raise ValueError( + f"Unknown action: {action}. Supported: add, update, remove/delete." + ) + + else: + raise ValueError( + 'Invalid payload: must include either "objects" (dict) or both "action" and "object" (dict with id/name).' + ) + + self.send_response(200) + self.send_header("Content-Type", "application/json") + self.end_headers() + self.wfile.write(json.dumps(api_response).encode("utf-8")) + return + except Exception as e: + print(f"❌ Object API error: {e}") + try: + self.send_response(400) + self.send_header("Content-Type", "application/json") + self.end_headers() + self.wfile.write(json.dumps({"error": str(e)}).encode("utf-8")) + except: + pass + return + + # /api/aria/execute - LLM-powered automatic action execution + elif self.path == "/api/aria/execute": + try: + content_length = int(self.headers.get("Content-Length", 0)) + body = self.rfile.read(content_length) + request_data = json.loads(body.decode("utf-8")) + + command = request_data.get("command", "") + auto_execute = request_data.get("auto_execute", False) + use_llm = request_data.get("use_llm", True) + provider_choice = request_data.get("provider") + model_override = request_data.get("model") + + if not command: + raise ValueError("command is required") + + # Parse command into actions + actions = action_parser.parse( + command, + use_llm=use_llm, + provider_choice=provider_choice, + model_override=model_override, + ) + + if not actions: + api_response = { + "status": "error", + "message": "Could not parse command into actions", + "command": command, + "actions": [], + } + else: + # Execute actions if auto_execute is True + execution_results = [] + all_tags = [] + + if auto_execute: + for action in actions: + exec_result = execute_aria_action(action) + execution_results.append( + {"action": action, "result": exec_result} + ) + if exec_result.get("tags"): + all_tags.extend(exec_result["tags"]) + + api_response = { + "status": "success", + "message": f"Parsed {len(actions)} actions" + + (" and executed" if auto_execute else " (plan only)"), + "command": command, + "provider_requested": provider_choice or "auto", + "model_requested": model_override, + "actions": actions, + "executed": auto_execute, + "results": execution_results if auto_execute else None, + "tags": all_tags if auto_execute else None, + "state": stage_state if auto_execute else None, + } + + self.send_response(200) + self.send_header("Content-Type", "application/json") + self.end_headers() + self.wfile.write(json.dumps(api_response, indent=2).encode("utf-8")) + + print( + f"✓ Execute API: {command} -> {len(actions)} actions" + + (" (executed)" if auto_execute else " (plan only)") + ) + return + + except Exception as e: + print(f"❌ Execute API error: {e}") + import traceback + + traceback.print_exc() + try: + self.send_response(400) + self.send_header("Content-Type", "application/json") + self.end_headers() + self.wfile.write( + json.dumps( + { + "status": "error", + "error": str(e), + "message": f"Failed to execute command: {str(e)}", + } + ).encode("utf-8") + ) + except: + pass + return + + # /api/aria/world - Generate or regenerate themed world layout + elif self.path == "/api/aria/world": + try: + content_length = int(self.headers.get("Content-Length", 0)) + body = self.rfile.read(content_length) + request_data = json.loads(body.decode("utf-8")) if body else {} + theme = request_data.get("theme", "forest") + count = int(request_data.get("count", 6)) + use_llm = bool(request_data.get("use_llm", True)) + provider_choice = request_data.get("provider") + model_override = request_data.get("model") + + # Generate + if use_llm: + try: + world_provider, _ = action_parser._resolve_provider_for_request( + explicit=provider_choice, + model_override=model_override, + ) + except Exception as provider_err: + logger.warning( + f"World generation provider resolution failed: {provider_err}" + ) + world_provider = None + + if world_provider: + world = generate_world_with_llm(theme, count, world_provider) + else: + world = generate_world_fallback(theme, count) + world["llm"] = False + else: + world = generate_world_fallback(theme, count) + world["llm"] = False + + # Update global stage_state (replace objects, keep aria position) + stage_state["objects"] = {} + for oid, obj in world["objects"].items(): + stage_state["objects"][oid] = { + "position": obj["position"], + "state": obj.get("state", "on_stage"), + "emoji": obj.get("emoji", ""), + } + # Update environment meta + stage_state["environment"]["theme"] = world["environment"].get( + "theme", theme + ) + stage_state["environment"]["generated_at"] = world["environment"].get( + "generated_at" + ) + + response = { + "status": "success", + "theme": theme, + "count": len(world["objects"]), + "used_llm": world.get("llm", False), + "provider_requested": provider_choice or "auto", + "model_requested": model_override, + "objects": world["objects"], + "environment": world["environment"], + } + + self.send_response(200) + self.send_header("Content-Type", "application/json") + self.end_headers() + self.wfile.write(json.dumps(response, indent=2).encode("utf-8")) + logger.info( + f"✓ World generated (theme={theme}, llm={response['used_llm']}, count={response['count']})" + ) + return + except Exception as e: + logger.error(f"World generation error: {e}") + self.send_response(400) + self.send_header("Content-Type", "application/json") + self.end_headers() + self.wfile.write( + json.dumps({"status": "error", "error": str(e)}).encode("utf-8") + ) + return + + else: + self.send_response(404) + self.end_headers() + + def log_message(self, format, *args): + """Custom logging""" + try: + message = format % args if args else str(format) + except Exception: + message = str(format) + + if "favicon" not in message: + print(f"🌐 {message}") + + +# Backward-compatible module alias used by tests and older integrations: +# `from server import server as aria_server`. +server = sys.modules[__name__] + + +def main(): + # Change to aria_web directory + web_dir = Path(__file__).parent + os.chdir(web_dir) + + # Allow the port to be overridden via the ARIA_PORT environment variable. + # This helps avoid address‑in‑use errors when the default port is already bound. + port = int(os.getenv("ARIA_PORT", "8080")) + # Default to localhost for security; use environment variable to override if needed + host = os.environ.get("ARIA_HOST", "127.0.0.1") + + try: + server = HTTPServer((host, port), AriaRequestHandler) + except OSError as e: + # Graceful handling when a server is already bound to this port. + if getattr(e, "errno", None) == 98: + state_url = f"http://{host}:{port}/api/aria/state" + try: + with urllib.request.urlopen(state_url, timeout=1.0) as resp: + payload = json.loads(resp.read().decode("utf-8")) + if ( + isinstance(payload, dict) + and "aria" in payload + and "objects" in payload + ): + print( + f"⚠️ Aria server already running at http://{host}:{port} (detected healthy /api/aria/state)." + ) + print("ℹ️ Reusing existing server; exiting this process cleanly.") + return + except Exception: + pass + + # If another service occupies the port, fall back to a free port. + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: + sock.bind((host, 0)) + fallback_port = sock.getsockname()[1] + + print( + f"⚠️ Port {port} is in use by another service; starting Aria on free port {fallback_port}." + ) + print( + f"💡 Set ARIA_PORT={fallback_port} (or another free port) to control startup port explicitly." + ) + port = fallback_port + server = HTTPServer((host, port), AriaRequestHandler) + else: + raise + + print("\n" + "=" * 70) + print("🎨 Aria Visual Command System - Web Server") + print("=" * 70) + print(f"🌐 Open in browser: http://localhost:{port}") + print(f"🤖 Model: {'AI (aria_expanded_v2)' if MODEL else 'Rule-based fallback'}") + print("📝 Type commands in the web interface to control Aria") + print("\nPress Ctrl+C to stop the server") + print("=" * 70 + "\n") + + try: + server.serve_forever() + except KeyboardInterrupt: + print("\n👋 Server stopped") + + +if __name__ == "__main__": + main() diff --git a/apps/aria/test.html b/apps/aria/test.html index 660884535..9c7c1cbdd 100644 --- a/apps/aria/test.html +++ b/apps/aria/test.html @@ -1,12 +1,12 @@ - - - - Test - - - -

Test Page

-

If you see this, the server works!

- - - + + + + Test + + + +

Test Page

+

If you see this, the server works!

+ + + diff --git a/apps/chat/README.md b/apps/chat/README.md index e045c3ccf..684c585ce 100644 --- a/apps/chat/README.md +++ b/apps/chat/README.md @@ -1,166 +1,166 @@ -# Chat Web Application - -A modern, responsive web-based chat interface that works with multiple AI providers. - -## Features - -- 🎨 Beautiful, responsive UI with gradient design -- 💬 Real-time chat with AI assistants -- 🔄 Support for multiple providers (Local, LM Studio, Ollama, OpenAI, Azure OpenAI) -- 🆓 Free local mode (no API keys required) -- 🌐 Accessible via web browser - -## Quick Start (Free Local Mode) - -No API keys or cloud services required: - -```powershell -# 1. Install dependencies -cd c:\Users\Bryan\OneDrive\AI -pip install azure-functions colorama - -# 2. Start the Azure Functions local server -func start - -# 3. Open your browser to http://localhost:7071/api/chat-web -``` - -The chat will work immediately with the free local provider! - -## Architecture - -- **Frontend**: `apps/chat/` - Pure HTML/CSS/JS, no build required -- **Backend**: `function_app.py` - Azure Functions endpoint -- **Chat Logic**: Reuses `ai-projects/chat-cli/src/chat_providers.py` - -## Provider Configuration - -### Local (Free - Default) -No configuration needed! Works offline. - -### OpenAI -Set environment variable: -```powershell -$env:OPENAI_API_KEY = "sk-..." -``` - -### Azure OpenAI -Set environment variables: -```powershell -$env:AZURE_OPENAI_API_KEY = "your-key" -$env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/" -$env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini" -$env:AZURE_OPENAI_API_VERSION = "2024-08-01-preview" -``` - -The backend auto-detects the best available provider. - -Auto-detection order in runtime: - -1. LM Studio (if reachable) -2. Ollama (if reachable) -3. Azure OpenAI (if env is complete) -4. OpenAI (if key is set) -5. Local fallback - -## API Endpoint - -**POST** `/api/chat` - -Request: -```json -{ - "messages": [ - {"role": "user", "content": "Hello!"} - ], - "provider": "auto", - "model": null -} -``` - -Response: -```json -{ - "response": "Hi! How can I help you?", - "provider": "local", - "model": "local-echo" -} -``` - -## Development - -### Local Testing -```powershell -# Terminal 1: Start Functions -func start - -# Terminal 2: Test API -$body = @{ - messages = @(@{role="user"; content="Test"}) -} | ConvertTo-Json -Depth 3 - -Invoke-RestMethod -Uri http://localhost:7071/api/chat -Method POST -Body $body -ContentType "application/json" -``` - -### Deployment to Azure - -1. **Create Function App** (Azure Portal or CLI): - ```powershell - az functionapp create --resource-group rg-chat --name chat-app-unique-name --consumption-plan-location eastus --runtime python --runtime-version 3.11 --functions-version 4 --storage-account mystorageaccount - ``` - -2. **Configure Settings** (if using cloud providers): - ```powershell - az functionapp config appsettings set --name chat-app-unique-name --resource-group rg-chat --settings OPENAI_API_KEY=sk-... - ``` - -3. **Deploy**: - ```powershell - cd http_chat - func azure functionapp publish chat-app-unique-name - ``` - -4. **Deploy Static Web** (optional): - - Upload `chat-web/` to Azure Static Web Apps or Blob Storage - - Update `chat.js` `API_BASE` to your Function App URL - -## Troubleshooting - -**"Module not found" errors:** -```powershell -pip install azure-functions colorama -cd ai-projects/chat-cli -pip install -r requirements.txt -``` - -**CORS errors in browser:** -The function includes CORS headers. If testing locally, ensure you're accessing via `http://localhost:7071`. - -**Provider errors:** -Check environment variables and ensure `ai-projects/chat-cli/src/chat_providers.py` is accessible. - -## Cost Optimization - -- **Free tier**: Use local provider (default, unlimited) -- **Paid tier**: OpenAI/Azure charges per token - - Monitor usage in provider dashboards - - Set rate limits on API keys - - Use cheaper models (gpt-4o-mini vs gpt-4) - -## Files Structure - -``` -chat-web/ - index.html # Main UI - chat.js # Frontend logic - -http_chat/ - function_app.py # Azure Functions endpoint - -ai-projects/chat-cli/src/ - chat_providers.py # Provider implementations (shared) -``` - -## License - -Part of the QAI project. See root README for details. +# Chat Web Application + +A modern, responsive web-based chat interface that works with multiple AI providers. + +## Features + +- 🎨 Beautiful, responsive UI with gradient design +- 💬 Real-time chat with AI assistants +- 🔄 Support for multiple providers (Local, LM Studio, Ollama, OpenAI, Azure OpenAI) +- 🆓 Free local mode (no API keys required) +- 🌐 Accessible via web browser + +## Quick Start (Free Local Mode) + +No API keys or cloud services required: + +```powershell +# 1. Install dependencies +cd c:\Users\Bryan\OneDrive\AI +pip install azure-functions colorama + +# 2. Start the Azure Functions local server +func start + +# 3. Open your browser to http://localhost:7071/api/chat-web +``` + +The chat will work immediately with the free local provider! + +## Architecture + +- **Frontend**: `apps/chat/` - Pure HTML/CSS/JS, no build required +- **Backend**: `function_app.py` - Azure Functions endpoint +- **Chat Logic**: Reuses `ai-projects/chat-cli/src/chat_providers.py` + +## Provider Configuration + +### Local (Free - Default) +No configuration needed! Works offline. + +### OpenAI +Set environment variable: +```powershell +$env:OPENAI_API_KEY = "sk-..." +``` + +### Azure OpenAI +Set environment variables: +```powershell +$env:AZURE_OPENAI_API_KEY = "your-key" +$env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/" +$env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini" +$env:AZURE_OPENAI_API_VERSION = "2024-08-01-preview" +``` + +The backend auto-detects the best available provider. + +Auto-detection order in runtime: + +1. LM Studio (if reachable) +2. Ollama (if reachable) +3. Azure OpenAI (if env is complete) +4. OpenAI (if key is set) +5. Local fallback + +## API Endpoint + +**POST** `/api/chat` + +Request: +```json +{ + "messages": [ + {"role": "user", "content": "Hello!"} + ], + "provider": "auto", + "model": null +} +``` + +Response: +```json +{ + "response": "Hi! How can I help you?", + "provider": "local", + "model": "local-echo" +} +``` + +## Development + +### Local Testing +```powershell +# Terminal 1: Start Functions +func start + +# Terminal 2: Test API +$body = @{ + messages = @(@{role="user"; content="Test"}) +} | ConvertTo-Json -Depth 3 + +Invoke-RestMethod -Uri http://localhost:7071/api/chat -Method POST -Body $body -ContentType "application/json" +``` + +### Deployment to Azure + +1. **Create Function App** (Azure Portal or CLI): + ```powershell + az functionapp create --resource-group rg-chat --name chat-app-unique-name --consumption-plan-location eastus --runtime python --runtime-version 3.11 --functions-version 4 --storage-account mystorageaccount + ``` + +2. **Configure Settings** (if using cloud providers): + ```powershell + az functionapp config appsettings set --name chat-app-unique-name --resource-group rg-chat --settings OPENAI_API_KEY=sk-... + ``` + +3. **Deploy**: + ```powershell + cd http_chat + func azure functionapp publish chat-app-unique-name + ``` + +4. **Deploy Static Web** (optional): + - Upload `chat-web/` to Azure Static Web Apps or Blob Storage + - Update `chat.js` `API_BASE` to your Function App URL + +## Troubleshooting + +**"Module not found" errors:** +```powershell +pip install azure-functions colorama +cd ai-projects/chat-cli +pip install -r requirements.txt +``` + +**CORS errors in browser:** +The function includes CORS headers. If testing locally, ensure you're accessing via `http://localhost:7071`. + +**Provider errors:** +Check environment variables and ensure `ai-projects/chat-cli/src/chat_providers.py` is accessible. + +## Cost Optimization + +- **Free tier**: Use local provider (default, unlimited) +- **Paid tier**: OpenAI/Azure charges per token + - Monitor usage in provider dashboards + - Set rate limits on API keys + - Use cheaper models (gpt-4o-mini vs gpt-4) + +## Files Structure + +``` +chat-web/ + index.html # Main UI + chat.js # Frontend logic + +http_chat/ + function_app.py # Azure Functions endpoint + +ai-projects/chat-cli/src/ + chat_providers.py # Provider implementations (shared) +``` + +## License + +Part of the QAI project. See root README for details. diff --git a/apps/chat/aria.html b/apps/chat/aria.html index 7445c0c0e..7fd78052f 100644 --- a/apps/chat/aria.html +++ b/apps/chat/aria.html @@ -1,770 +1,770 @@ - - - - - - Aria - AI Character - - - - -
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+ + 💬 Chat with Aria + + + + + diff --git a/apps/chat/chat.js b/apps/chat/chat.js index 878d126b1..598bd8e43 100644 --- a/apps/chat/chat.js +++ b/apps/chat/chat.js @@ -1,1043 +1,1043 @@ -// Chat Web Client - Version 2025-11-21 - QAI Backend -// Encapsulate chat client in an IIFE to avoid leaking globals when embedded -console.log('chat.js loaded - v2025-11-21-qai - Provider: QAI auto-detect with quantum mode'); -(function window_qai_chat_client() { -const AI_BASE = ''; -const API_BASE = `/api/chat`; -const STREAM_API = `/api/chat/stream`; -const STATUS_API = `/api/ai/status`; -const QUANTUM_CLASSIFY_API = '/api/quantum/classify'; -const QUANTUM_CIRCUIT_API = '/api/quantum/circuit'; -const QUANTUM_INFO_API = '/api/quantum/info'; -const VISION_INFER_API = '/api/vision/infer'; -const IMAGE_GEN_API = '/api/image/generate'; - -let messages = []; -let isProcessing = false; -let messageCounter = 0; -let currentProvider = 'auto'; // Always use auto-detect for best available -let quantumMode = false; // Quantum enhancement toggle -let systemStatus = null; -let retryCount = 0; -const MAX_RETRIES = 3; -const RETRY_DELAY = 1000; -let streamEnabled = true; -let temperature = 0.7; -let maxOutputTokens = 1024; -let systemPrompt = ''; -let activeAbortController = null; -const COMPACTION_PLACEHOLDER_LINES = new Set([ - 'compacted conversation', - 'conversation compacted' -]); - -function isSyntheticCompactionPlaceholder(content) { - if (typeof content !== 'string') return false; - const normalizedLines = content - .split(/\r?\n/) - .map(line => line.trim().toLowerCase()) - .filter(Boolean); - return normalizedLines.length > 0 && normalizedLines.every(line => COMPACTION_PLACEHOLDER_LINES.has(line)); -} - -function sanitizeConversationMessages(items) { - return (items || []).filter(msg => { - if (!msg || typeof msg !== 'object') return false; - if (typeof msg.content !== 'string') return true; - return !isSyntheticCompactionPlaceholder(msg.content); - }); -} - -// DOM elements -const chatMessages = document.getElementById('chatMessages'); -const messageInput = document.getElementById('messageInput'); -const sendButton = document.getElementById('sendButton'); -const cancelStreamBtn = document.getElementById('cancelStreamBtn'); -const newChatButton = document.getElementById('newChatButton'); -const clearButton = document.getElementById('clearButton'); -const exportButton = document.getElementById('exportButton'); -const importButton = document.getElementById('importButton'); -const toggleThemeButton = document.getElementById('toggleThemeButton'); -const quantumModeButton = document.getElementById('quantumModeButton'); -const quantumPanel = document.getElementById('quantumPanel'); -const quantumPanelClose = document.getElementById('quantumPanelClose'); -const quantumIndicator = document.getElementById('quantumIndicator'); -const providerInfo = document.getElementById('providerInfo'); -const messageCount = document.getElementById('messageCount'); -const statusText = document.getElementById('statusText'); -const streamToggle = document.getElementById('streamToggle'); -const tempSlider = document.getElementById('tempSlider'); -const tempValue = document.getElementById('tempValue'); -const maxTokensInput = document.getElementById('maxTokensInput'); -const toggleSystemButton = document.getElementById('toggleSystemButton'); -const systemPromptBox = document.getElementById('systemPromptBox'); -const systemPromptInput = document.getElementById('systemPromptInput'); -const visionUploadButton = document.getElementById('visionUploadButton'); -const visionImageInput = document.getElementById('visionImageInput'); -const visionPreview = document.getElementById('visionPreview'); -const visionClearButton = document.getElementById('visionClearButton'); - -// Vision state -let uploadedImage = null; -let uploadedImageBase64 = null; - -// Aria avatar state -let ariaAvatarGenerated = false; -let ariaAvatarUrl = null; - -// Initialize -document.addEventListener('DOMContentLoaded', () => { - // When the Aria interactive page embeds chat, it provides its own - // send/receive wiring and should avoid double-wiring the controls - // in this script. A page can opt-in by setting window.ARIA_EMBEDDED=true - if (window && window.ARIA_EMBEDDED) { - console.log('chat.js: ARIA_EMBEDDED detected — skipping default UI wiring'); - return; - } - if (cancelStreamBtn) { - cancelStreamBtn.addEventListener('click', function() { - if (activeAbortController) { - activeAbortController.abort(); - updateStatus('Streaming cancelled'); - } - cancelStreamBtn.style.display = 'none'; - }); - } - // Configure marked.js for markdown rendering - if (typeof marked !== 'undefined') { - marked.setOptions({ - breaks: true, - gfm: true, - highlight: function(code, lang) { - if (lang && hljs.getLanguage(lang)) { - try { - return hljs.highlight(code, { language: lang }).value; - } catch (err) {} - } - return hljs.highlightAuto(code).value; - } - }); - } - - // Auto-resize textarea - messageInput.addEventListener('input', () => { - messageInput.style.height = 'auto'; - messageInput.style.height = Math.min(messageInput.scrollHeight, 150) + 'px'; - }); - - // Keyboard shortcuts - messageInput.addEventListener('keydown', (e) => { - if (e.key === 'Enter' && !e.shiftKey) { - e.preventDefault(); - sendMessage(); - } - if (e.key === 'k' && (e.ctrlKey || e.metaKey)) { - e.preventDefault(); - newChat(); - } - }); - - sendButton.addEventListener('click', sendMessage); - - // Vision upload handlers - if (visionUploadButton) { - visionUploadButton.addEventListener('click', () => { - visionImageInput.click(); - }); - } - - if (visionImageInput) { - visionImageInput.addEventListener('change', handleImageUpload); - } - - if (visionClearButton) { - visionClearButton.addEventListener('click', clearVisionUpload); - } - - // Aria avatar handlers - const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); - const ariaAvatarClose = document.getElementById('ariaAvatarClose'); - const ariaAvatarRegenerate = document.getElementById('ariaAvatarRegenerate'); - - if (ariaAvatarClose) { - ariaAvatarClose.addEventListener('click', hideAriaAvatar); - } - - if (ariaAvatarRegenerate) { - ariaAvatarRegenerate.addEventListener('click', () => generateAriaAvatar(true)); - } - - // Auto-generate Aria avatar on page load (after 2 seconds) - setTimeout(() => { - if (!ariaAvatarGenerated) { - generateAriaAvatar(false); - } - }, 2000); - - newChatButton.addEventListener('click', () => { - console.log('New Chat button clicked'); - newChat(); - }); - clearButton.addEventListener('click', () => { - console.log('Clear button clicked'); - clearChat(false); - }); - exportButton.addEventListener('click', exportChat); - importButton.addEventListener('click', importChat); - toggleThemeButton.addEventListener('click', toggleTheme); - quantumModeButton.addEventListener('click', toggleQuantumMode); - if (quantumPanelClose) { - quantumPanelClose.addEventListener('click', () => { - quantumPanel.style.display = 'none'; - }); - } - streamToggle.addEventListener('change', (e) => { - streamEnabled = !!e.target.checked; - saveToStorage(); - }); - tempSlider.addEventListener('input', (e) => { - temperature = parseFloat(e.target.value); - tempValue.textContent = temperature.toFixed(2); - }); - tempSlider.addEventListener('change', () => saveToStorage()); - maxTokensInput.addEventListener('change', (e) => { - const val = parseInt(e.target.value, 10); - if (!isNaN(val)) { - maxOutputTokens = Math.max(64, Math.min(40960, val)); - e.target.value = String(maxOutputTokens); - saveToStorage(); - } - }); - toggleSystemButton.addEventListener('click', () => { - const show = systemPromptBox.style.display !== 'block'; - systemPromptBox.style.display = show ? 'block' : 'none'; - }); - systemPromptInput.addEventListener('input', (e) => { - systemPrompt = e.target.value; - saveToStorage(); - }); - - // Load saved conversations and settings - loadFromStorage(); - - // Focus input - messageInput.focus(); - - // Fetch system status on load - fetchSystemStatus(); -}); - -async function fetchSystemStatus() { - try { - updateStatus('Checking backend...'); - const response = await fetch(STATUS_API); - if (response.ok) { - const data = await response.json(); - systemStatus = { status: 'ok', data }; - updateStatusFromSystem(); - } - } catch (error) { - console.warn('Backend not available:', error); - updateStatus('Backend not responding'); - } -} - -function updateStatusFromSystem() { - if (!systemStatus) return; - - // Show success message if everything looks good - if (systemStatus.status === 'ok') { - const provider = systemStatus.data?.active_provider || 'local'; - const model = systemStatus.data?.model || 'unknown'; - updateStatus(`Ready - ${provider.toUpperCase()}`); - providerInfo.textContent = `${provider.toUpperCase()} - ${model}`; - } -} - -async function sendMessage() { - console.log('sendMessage() called'); - console.log('- messageInput.value:', messageInput.value); - console.log('- isProcessing:', isProcessing); - console.log('- sendButton.disabled:', sendButton.disabled); - console.log('- quantumMode:', quantumMode); - - const text = messageInput.value.trim(); - if (!text || isProcessing) return; - - // Perform quantum analysis if enabled - if (quantumMode) { - updateStatus('Performing quantum analysis...'); - await performQuantumAnalysis(text); - } - - // Add user message to UI - addMessage('user', text); - messageInput.value = ''; - messageInput.style.height = 'auto'; - messageInput.disabled = true; - sendButton.disabled = true; - isProcessing = true; - updateStatus('Sending...'); - - // Add to messages array - messages.push({ role: 'user', content: text }); - updateMessageCount(); - - // Show typing indicator - const typingIndicator = showTypingIndicator(); - - try { - // Choose streaming or one-shot - if (streamEnabled) { - if (cancelStreamBtn) cancelStreamBtn.style.display = 'inline-block'; - await streamResponse(typingIndicator); - } else { - await oneShotResponse(typingIndicator); - } - } catch (error) { - console.error('Error:', error); - typingIndicator.remove(); - - // Attempt retry if within limits - if (retryCount < MAX_RETRIES && error.message.includes('HTTP')) { - retryCount++; - const delay = RETRY_DELAY * Math.pow(2, retryCount - 1); - addMessage('system', `⚠️ Request failed. Retrying in ${delay/1000}s... (${retryCount}/${MAX_RETRIES})`); - updateStatus(`Retrying ${retryCount}/${MAX_RETRIES}...`); - - setTimeout(() => { - // Re-send by simulating button click - messages.pop(); // Remove the failed user message - messageInput.value = text; // Restore input - messageInput.disabled = false; - sendButton.disabled = false; - isProcessing = false; - sendMessage(); - }, delay); - } else { - // Max retries exceeded or non-retryable error - retryCount = 0; - const errorMsg = error.message.includes('NetworkError') || error.message.includes('Failed to fetch') - ? '❌ Network error. Please ensure QAI backend is running on http://localhost:7071' - : `❌ Error: ${error.message}`; - addMessage('system', errorMsg); - console.error('Full error details:', error); - updateStatus('Error - check console'); - messageInput.disabled = false; - sendButton.disabled = false; - isProcessing = false; - messageInput.focus(); - } - } -} - -async function oneShotResponse(typingIndicator) { - // Prepare messages with system prompt if provided - const apiMessages = sanitizeConversationMessages(systemPrompt ? - [{ role: 'system', content: systemPrompt }, ...messages] : - messages); - - console.log('Sending non-streaming request to:', API_BASE); - console.log('Request body:', { messages: apiMessages, temperature, max_tokens: maxOutputTokens }); - - const response = await fetch(API_BASE, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - messages: apiMessages, - temperature: temperature, - max_tokens: maxOutputTokens, - stream: false - }) - }); - - console.log('Response status:', response.status, response.statusText); - - if (!response.ok) { - const errorText = await response.text(); - console.error('Error response:', errorText); - throw new Error(`HTTP ${response.status}: ${response.statusText}`); - } - - const data = await response.json(); - console.log('Response data:', data); - - retryCount = 0; - typingIndicator.remove(); - // Handle QAI backend response format - const assistantMessage = data.response || data.choices?.[0]?.message?.content || 'No response received.'; - console.log('Assistant message:', assistantMessage); - - addMessage('assistant', assistantMessage, true); - if (!isSyntheticCompactionPlaceholder(assistantMessage)) { - messages.push({ role: 'assistant', content: assistantMessage }); - } - updateMessageCount(); - if (data.provider && data.model) { - providerInfo.textContent = `${data.provider.toUpperCase()} - ${data.model}`; - } - updateStatus('Ready'); - saveToStorage(); - messageInput.disabled = false; - sendButton.disabled = false; - isProcessing = false; - messageInput.focus(); -} - -async function streamResponse(typingIndicator) { - let streamAborted = false; - // Prepare assistant message container to update incrementally - const assistantDiv = document.createElement('div'); - assistantDiv.className = 'message assistant'; - const contentDiv = document.createElement('div'); - contentDiv.className = 'message-content'; - contentDiv.textContent = ''; - assistantDiv.appendChild(contentDiv); - chatMessages.appendChild(assistantDiv); - chatMessages.scrollTop = chatMessages.scrollHeight; - - activeAbortController = new AbortController(); - - // Prepare messages with system prompt if provided - const apiMessages = sanitizeConversationMessages(systemPrompt ? - [{ role: 'system', content: systemPrompt }, ...messages] : - messages); - - console.log('Sending streaming request to:', STREAM_API); - console.log('Request body:', { messages: apiMessages, temperature, max_tokens: maxOutputTokens, stream: true }); - - try { - const response = await fetch(STREAM_API, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - messages: apiMessages, - temperature: temperature, - max_tokens: maxOutputTokens, - stream: true - }), - signal: activeAbortController.signal - }); - - console.log('Stream response status:', response.status, response.statusText); - - if (!response.ok || !response.body) { - throw new Error(`HTTP ${response.status}: ${response.statusText}`); - } - - // Remove typing indicator as soon as stream starts - typingIndicator.remove(); - - const reader = response.body.getReader(); - const decoder = new TextDecoder(); - let fullText = ''; - - while (true) { - const { value, done } = await reader.read(); - if (done) break; - - // QAI backend sends plain text chunks, not SSE - const chunk = decoder.decode(value, { stream: true }); - if (chunk) { - console.log('Stream chunk:', chunk); - fullText += chunk; - // Update plain text during stream for speed - contentDiv.textContent = fullText; - chatMessages.scrollTop = chatMessages.scrollHeight; - } - } - - // After stream completes, render as markdown - if (fullText) { - try { - const rendered = marked.parse(fullText); - contentDiv.innerHTML = rendered; - // Highlight code blocks - contentDiv.querySelectorAll('pre code').forEach((block) => { - hljs.highlightElement(block); - addCopyButton(block.parentElement); - }); - } catch (e) { - console.error('Markdown render error:', e); - contentDiv.textContent = fullText; - } - } - - // Push to messages history - if (!isSyntheticCompactionPlaceholder(fullText)) { - messages.push({ role: 'assistant', content: fullText }); - } - updateMessageCount(); - retryCount = 0; - updateStatus('Ready'); - saveToStorage(); - } catch (error) { - typingIndicator.remove(); - assistantDiv.remove(); - - if (error.name === 'AbortError') { - streamAborted = true; - addMessage('system', '❌ Streaming cancelled by user.'); - updateStatus('Cancelled'); - } else { - throw error; // Re-throw to be handled by sendMessage - } - } finally { - if (cancelStreamBtn) cancelStreamBtn.style.display = 'none'; - activeAbortController = null; - messageInput.disabled = false; - sendButton.disabled = false; - isProcessing = false; - messageInput.focus(); - } -} - -function addMessage(role, content, useMarkdown = false) { - const messageDiv = document.createElement('div'); - messageDiv.className = `message ${role}`; - - const contentDiv = document.createElement('div'); - contentDiv.className = 'message-content'; - - if (useMarkdown && typeof marked !== 'undefined' && role === 'assistant') { - // Render markdown - contentDiv.innerHTML = marked.parse(content); - - // Add copy buttons to code blocks - const codeBlocks = contentDiv.querySelectorAll('pre'); - codeBlocks.forEach(block => { - const copyBtn = document.createElement('button'); - copyBtn.className = 'copy-button'; - copyBtn.textContent = 'Copy'; - copyBtn.onclick = () => { - const code = block.querySelector('code')?.textContent || block.textContent; - navigator.clipboard.writeText(code).then(() => { - copyBtn.textContent = 'Copied!'; - setTimeout(() => copyBtn.textContent = 'Copy', 2000); - }); - }; - block.style.position = 'relative'; - block.appendChild(copyBtn); - }); - } else { - contentDiv.textContent = content; - } - - messageDiv.appendChild(contentDiv); - chatMessages.appendChild(messageDiv); - - // Scroll to bottom - chatMessages.scrollTop = chatMessages.scrollHeight; - - messageCounter++; - return messageDiv; -} - -function showTypingIndicator() { - const indicatorDiv = document.createElement('div'); - indicatorDiv.className = 'message assistant'; - - const typingDiv = document.createElement('div'); - typingDiv.className = 'typing-indicator active'; - typingDiv.innerHTML = ''; - - indicatorDiv.appendChild(typingDiv); - chatMessages.appendChild(indicatorDiv); - chatMessages.scrollTop = chatMessages.scrollHeight; - - return indicatorDiv; -} - -function newChat() { - console.log('newChat() called'); - if (messages.length > 0 && !confirm('Start a new chat? Current conversation will be cleared.')) { - console.log('User cancelled new chat'); - return; - } - messages = []; - messageCounter = 0; - isProcessing = false; - chatMessages.innerHTML = ''; - messageInput.disabled = false; - sendButton.disabled = false; - messageInput.value = ''; - messageInput.focus(); - localStorage.setItem('chatMessages', JSON.stringify([])); - addMessage('system', 'New conversation started. How can I help you?'); - updateMessageCount(); - updateStatus('Ready'); - saveToStorage(); - console.log('New chat complete'); -} - -function clearChat(preserveMessages = false) { - console.log('clearChat() called, preserveMessages:', preserveMessages); - if (!preserveMessages) { - messages = []; - messageCounter = 0; - isProcessing = false; - localStorage.setItem('chatMessages', JSON.stringify([])); - } - chatMessages.innerHTML = ''; - messageInput.disabled = false; - sendButton.disabled = false; - messageInput.value = ''; - messageInput.focus(); - addMessage('system', 'Chat cleared. Type a message to continue.'); - updateMessageCount(); - updateStatus('Ready'); - saveToStorage(); - console.log('Clear chat complete'); -} - -function exportChat() { - if (messages.length === 0) { - if (typeof showToast === 'function') showToast('No messages to export', 3000); - return; - } - - const timestamp = new Date().toISOString().replace(/[:.]/g, '-'); - const filename = `chat-export-${timestamp}.json`; - - const exportData = { - timestamp: new Date().toISOString(), - provider: currentProvider, - messageCount: messages.length, - messages: messages - }; - - const blob = new Blob([JSON.stringify(exportData, null, 2)], { type: 'application/json' }); - const url = URL.createObjectURL(blob); - const a = document.createElement('a'); - a.href = url; - a.download = filename; - a.click(); - URL.revokeObjectURL(url); - - updateStatus(`Exported ${messages.length} messages`); -} - -function importChat() { - const input = document.createElement('input'); - input.type = 'file'; - input.accept = '.json'; - input.onchange = (e) => { - const file = e.target.files[0]; - if (!file) return; - - const reader = new FileReader(); - reader.onload = (event) => { - try { - const data = JSON.parse(event.target.result); - if (data.messages && Array.isArray(data.messages)) { - const imported = data.messages; - clearChat(true); // reset UI but preserve current messages until reassigned - messages = imported; - // Restore messages to UI - messages.forEach(msg => { - if (msg.role !== 'system') { - addMessage(msg.role, msg.content, msg.role === 'assistant'); - } - }); - updateMessageCount(); - saveToStorage(); - updateStatus(`Imported ${messages.length} messages`); - } else { - throw new Error('Invalid chat export format'); - } - } catch (error) { - if (typeof showToast === 'function') showToast(`Import failed: ${error.message}`, 4000); - } - }; - reader.readAsText(file); - }; - input.click(); -} - -function toggleTheme() { - document.body.classList.toggle('dark-theme'); - const isDark = document.body.classList.contains('dark-theme'); - localStorage.setItem('theme', isDark ? 'dark' : 'light'); - toggleThemeButton.textContent = isDark ? '☀️ Light' : '🌙 Dark'; -} - -function updateMessageCount() { - const userMessages = messages.filter(m => m.role === 'user').length; - messageCount.textContent = userMessages; -} - -// Aria AI Avatar functions -async function generateAriaAvatar(regenerate = false) { - const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); - const ariaAvatarImage = document.getElementById('ariaAvatarImage'); - - if (!regenerate && ariaAvatarGenerated) { - ariaAvatarContainer.style.display = 'block'; - return; - } - - updateStatus('🎨 Generating Aria\'s AI avatar...'); - - try { - // Use a more descriptive prompt for Aria's character - const prompt = "Portrait of Aria, anime-style AI assistant character, purple gradient hair, cute anime girl, friendly expression, digital art, high quality, detailed, vibrant colors, soft lighting"; - - const response = await fetch(IMAGE_GEN_API, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - prompt: prompt, - size: "512x512", - style: "anime" - }) - }); - - if (!response.ok) { - throw new Error(`Image generation failed: ${response.status}`); - } - - const result = await response.json(); - - if (result.image_url || result.image_data) { - ariaAvatarUrl = result.image_url || `data:image/png;base64,${result.image_data}`; - ariaAvatarImage.src = ariaAvatarUrl; - ariaAvatarContainer.style.display = 'block'; - ariaAvatarGenerated = true; - updateStatus('✅ Aria\'s avatar generated!'); - - // Add a chat message from Aria about her new appearance - if (regenerate) { - addMessage('assistant', '✨ How do I look? I just got a fresh new appearance from the AI! 💜'); - } - } else { - throw new Error('No image data received'); - } - } catch (error) { - console.error('Avatar generation error:', error); - updateStatus(`⚠️ Avatar generation unavailable: ${error.message}`); - - // Fallback: Use a placeholder or emoji-based avatar - const ariaAvatarImage = document.getElementById('ariaAvatarImage'); - ariaAvatarImage.src = 'data:image/svg+xml,' + encodeURIComponent(` - - - - - - - - - 👩‍💻 - Aria - AI Assistant - - `); - ariaAvatarContainer.style.display = 'block'; - ariaAvatarGenerated = true; - } -} - -function hideAriaAvatar() { - const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); - ariaAvatarContainer.style.display = 'none'; - updateStatus('Avatar hidden'); -} - -// Vision upload handlers -async function handleImageUpload(event) { - const file = event.target.files[0]; - if (!file) return; - - if (!file.type.startsWith('image/')) { - updateStatus('❌ Please select an image file'); - return; - } - - // Show preview - const reader = new FileReader(); - reader.onload = async (e) => { - uploadedImageBase64 = e.target.result.split(',')[1]; // Remove data:image/...;base64, prefix - uploadedImage = file.name; - - // Show preview - visionPreview.innerHTML = ` - Preview -
- ${file.name} - -
- `; - visionPreview.style.display = 'block'; - - // Re-attach clear button listener - document.getElementById('visionClearButton').addEventListener('click', clearVisionUpload); - - // Auto-analyze the image - updateStatus('🔍 Analyzing image...'); - try { - const response = await fetch(VISION_INFER_API, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ image: uploadedImageBase64 }) - }); - - if (!response.ok) { - throw new Error(`Vision API error: ${response.status}`); - } - - const result = await response.json(); - - // Add AI message with vision results - const visionMessage = `🖼️ **Image Analysis**: ${file.name}\n\n` + - `**Expression**: ${result.label} (${(result.confidence * 100).toFixed(1)}% confidence)\n\n` + - `**All Scores**:\n${Object.entries(result.scores) - .sort((a, b) => b[1] - a[1]) - .map(([label, score]) => `- ${label}: ${(score * 100).toFixed(1)}%`) - .join('\n')}`; - - addMessage('assistant', visionMessage); - updateStatus('✅ Image analyzed successfully'); - } catch (error) { - console.error('Vision inference error:', error); - updateStatus(`❌ Vision error: ${error.message}`); - } - }; - - reader.readAsDataURL(file); -} - -function clearVisionUpload() { - uploadedImage = null; - uploadedImageBase64 = null; - visionImageInput.value = ''; - visionPreview.style.display = 'none'; - visionPreview.innerHTML = ''; - updateStatus('Vision upload cleared'); -} - -function updateStatus(text) { - statusText.textContent = text; - if (text.includes('Ready') || text.includes('ok')) { - statusText.style.color = '#28a745'; - } else if (text.includes('Error') || text.includes('❌')) { - statusText.style.color = '#dc3545'; - } else if (text.includes('⚠️')) { - statusText.style.color = '#ffc107'; - } else { - statusText.style.color = ''; - } - - setTimeout(() => { - if (statusText.textContent === text) { - statusText.textContent = 'Ready'; - statusText.style.color = ''; - } - }, 3000); -} - -function saveToStorage() { - try { - localStorage.setItem('chatMessages', JSON.stringify(sanitizeConversationMessages(messages))); - localStorage.setItem('chatStream', streamEnabled ? '1' : '0'); - localStorage.setItem('chatTemp', String(temperature)); - localStorage.setItem('chatMaxTokens', String(maxOutputTokens)); - localStorage.setItem('chatSystemPrompt', systemPrompt || ''); - } catch (e) { - console.error('Failed to save to storage:', e); - } -} - -function loadFromStorage() { - try { - const saved = localStorage.getItem('chatMessages'); - const savedTheme = localStorage.getItem('theme'); - const savedStream = localStorage.getItem('chatStream'); - const savedTemp = localStorage.getItem('chatTemp'); - const savedMax = localStorage.getItem('chatMaxTokens'); - const savedSys = localStorage.getItem('chatSystemPrompt'); - - if (savedTheme === 'dark') { - document.body.classList.add('dark-theme'); - toggleThemeButton.textContent = '☀️ Light'; - } else { - toggleThemeButton.textContent = '🌙 Dark'; - } - - if (saved) { - messages = sanitizeConversationMessages(JSON.parse(saved)); - // Restore messages to UI - messages.forEach(msg => { - if (msg.role !== 'system') { - addMessage(msg.role, msg.content, msg.role === 'assistant'); - } - }); - updateMessageCount(); - } - - // Restore settings - streamEnabled = savedStream === '1'; - streamToggle.checked = streamEnabled; - if (savedTemp) { - temperature = parseFloat(savedTemp); - if (!isNaN(temperature)) { - tempSlider.value = String(temperature); - tempValue.textContent = temperature.toFixed(2); - } - } - if (savedMax) { - const v = parseInt(savedMax, 10); - if (!isNaN(v)) { - maxOutputTokens = v; - maxTokensInput.value = String(v); - } - } - if (savedSys) { - systemPrompt = savedSys; - systemPromptInput.value = systemPrompt; - } - } catch (e) { - console.error('Failed to load from storage:', e); - } -} - -// ============================================================================= -// Quantum Mode Functions -// ============================================================================= - -function toggleQuantumMode() { - quantumMode = !quantumMode; - - if (quantumMode) { - quantumModeButton.textContent = '🔬 Quantum ON'; - quantumModeButton.classList.add('active'); - quantumIndicator.style.display = 'flex'; - quantumPanel.style.display = 'block'; - currentProvider = 'quantum'; - updateStatus('Quantum mode enabled'); - - // Fetch quantum info - fetchQuantumInfo(); - } else { - quantumModeButton.textContent = '🔬 Quantum OFF'; - quantumModeButton.classList.remove('active'); - quantumIndicator.style.display = 'none'; - quantumPanel.style.display = 'none'; - currentProvider = 'auto'; - updateStatus('Quantum mode disabled'); - } - - saveToStorage(); -} - -async function fetchQuantumInfo() { - try { - const response = await fetch(QUANTUM_INFO_API); - if (response.ok) { - const data = await response.json(); - if (data.available) { - updateStatus('Quantum backend ready'); - } else { - updateStatus('Quantum backend unavailable - using classical fallback'); - } - } - } catch (error) { - console.warn('Could not fetch quantum info:', error); - } -} - -async function performQuantumAnalysis(text) { - if (!quantumMode) return null; - - try { - // Convert text to features (simple hash-based approach) - const features = textToFeatures(text); - - const response = await fetch(QUANTUM_CLASSIFY_API, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - features: features, - n_qubits: 4, - n_layers: 2 - }) - }); - - if (!response.ok) { - throw new Error(`Quantum API error: ${response.status}`); - } - - const data = await response.json(); - - // Update quantum panel - document.getElementById('quantumClassification').textContent = - data.classification.toUpperCase(); - document.getElementById('quantumConfidence').textContent = - (data.confidence * 100).toFixed(1) + '%'; - document.getElementById('quantumQubits').textContent = - data.quantum_state.n_qubits; - document.getElementById('quantumLayers').textContent = - data.quantum_state.n_layers; - - // Get circuit visualization - await fetchCircuitVisualization(data.quantum_state.n_qubits, data.quantum_state.n_layers); - - return data; - } catch (error) { - console.error('Quantum analysis failed:', error); - document.getElementById('quantumClassification').textContent = 'ERROR'; - return null; - } -} - -async function fetchCircuitVisualization(nQubits, nLayers) { - try { - const response = await fetch(QUANTUM_CIRCUIT_API, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - n_qubits: nQubits, - n_layers: nLayers, - entanglement: 'linear' - }) - }); - - if (response.ok) { - const data = await response.json(); - const vizElement = document.querySelector('.circuit-display'); - if (vizElement) { - vizElement.textContent = data.visualization; - } - } - } catch (error) { - console.error('Circuit visualization failed:', error); - } -} - -function textToFeatures(text) { - // Simple feature extraction: convert text to numeric features - // Using character codes and length statistics - const features = []; - const normalized = text.toLowerCase(); - - // Add basic statistics - features.push(normalized.length / 100.0); // Normalized length - features.push(normalized.split(' ').length / 50.0); // Word count - features.push(normalized.split('?').length / 10.0); // Question marks - features.push(normalized.split('!').length / 10.0); // Exclamation marks - - // Add character distribution (simple hash) - let sum = 0; - for (let i = 0; i < Math.min(normalized.length, 20); i++) { - sum += normalized.charCodeAt(i); - } - features.push((sum % 256) / 256.0); - - // Pad or truncate to 8 features - while (features.length < 8) { - features.push(0.0); - } - - return features.slice(0, 8); -} - -})(); +// Chat Web Client - Version 2025-11-21 - QAI Backend +// Encapsulate chat client in an IIFE to avoid leaking globals when embedded +console.log('chat.js loaded - v2025-11-21-qai - Provider: QAI auto-detect with quantum mode'); +(function window_qai_chat_client() { +const AI_BASE = ''; +const API_BASE = `/api/chat`; +const STREAM_API = `/api/chat/stream`; +const STATUS_API = `/api/ai/status`; +const QUANTUM_CLASSIFY_API = '/api/quantum/classify'; +const QUANTUM_CIRCUIT_API = '/api/quantum/circuit'; +const QUANTUM_INFO_API = '/api/quantum/info'; +const VISION_INFER_API = '/api/vision/infer'; +const IMAGE_GEN_API = '/api/image/generate'; + +let messages = []; +let isProcessing = false; +let messageCounter = 0; +let currentProvider = 'auto'; // Always use auto-detect for best available +let quantumMode = false; // Quantum enhancement toggle +let systemStatus = null; +let retryCount = 0; +const MAX_RETRIES = 3; +const RETRY_DELAY = 1000; +let streamEnabled = true; +let temperature = 0.7; +let maxOutputTokens = 1024; +let systemPrompt = ''; +let activeAbortController = null; +const COMPACTION_PLACEHOLDER_LINES = new Set([ + 'compacted conversation', + 'conversation compacted' +]); + +function isSyntheticCompactionPlaceholder(content) { + if (typeof content !== 'string') return false; + const normalizedLines = content + .split(/\r?\n/) + .map(line => line.trim().toLowerCase()) + .filter(Boolean); + return normalizedLines.length > 0 && normalizedLines.every(line => COMPACTION_PLACEHOLDER_LINES.has(line)); +} + +function sanitizeConversationMessages(items) { + return (items || []).filter(msg => { + if (!msg || typeof msg !== 'object') return false; + if (typeof msg.content !== 'string') return true; + return !isSyntheticCompactionPlaceholder(msg.content); + }); +} + +// DOM elements +const chatMessages = document.getElementById('chatMessages'); +const messageInput = document.getElementById('messageInput'); +const sendButton = document.getElementById('sendButton'); +const cancelStreamBtn = document.getElementById('cancelStreamBtn'); +const newChatButton = document.getElementById('newChatButton'); +const clearButton = document.getElementById('clearButton'); +const exportButton = document.getElementById('exportButton'); +const importButton = document.getElementById('importButton'); +const toggleThemeButton = document.getElementById('toggleThemeButton'); +const quantumModeButton = document.getElementById('quantumModeButton'); +const quantumPanel = document.getElementById('quantumPanel'); +const quantumPanelClose = document.getElementById('quantumPanelClose'); +const quantumIndicator = document.getElementById('quantumIndicator'); +const providerInfo = document.getElementById('providerInfo'); +const messageCount = document.getElementById('messageCount'); +const statusText = document.getElementById('statusText'); +const streamToggle = document.getElementById('streamToggle'); +const tempSlider = document.getElementById('tempSlider'); +const tempValue = document.getElementById('tempValue'); +const maxTokensInput = document.getElementById('maxTokensInput'); +const toggleSystemButton = document.getElementById('toggleSystemButton'); +const systemPromptBox = document.getElementById('systemPromptBox'); +const systemPromptInput = document.getElementById('systemPromptInput'); +const visionUploadButton = document.getElementById('visionUploadButton'); +const visionImageInput = document.getElementById('visionImageInput'); +const visionPreview = document.getElementById('visionPreview'); +const visionClearButton = document.getElementById('visionClearButton'); + +// Vision state +let uploadedImage = null; +let uploadedImageBase64 = null; + +// Aria avatar state +let ariaAvatarGenerated = false; +let ariaAvatarUrl = null; + +// Initialize +document.addEventListener('DOMContentLoaded', () => { + // When the Aria interactive page embeds chat, it provides its own + // send/receive wiring and should avoid double-wiring the controls + // in this script. A page can opt-in by setting window.ARIA_EMBEDDED=true + if (window && window.ARIA_EMBEDDED) { + console.log('chat.js: ARIA_EMBEDDED detected — skipping default UI wiring'); + return; + } + if (cancelStreamBtn) { + cancelStreamBtn.addEventListener('click', function() { + if (activeAbortController) { + activeAbortController.abort(); + updateStatus('Streaming cancelled'); + } + cancelStreamBtn.style.display = 'none'; + }); + } + // Configure marked.js for markdown rendering + if (typeof marked !== 'undefined') { + marked.setOptions({ + breaks: true, + gfm: true, + highlight: function(code, lang) { + if (lang && hljs.getLanguage(lang)) { + try { + return hljs.highlight(code, { language: lang }).value; + } catch (err) {} + } + return hljs.highlightAuto(code).value; + } + }); + } + + // Auto-resize textarea + messageInput.addEventListener('input', () => { + messageInput.style.height = 'auto'; + messageInput.style.height = Math.min(messageInput.scrollHeight, 150) + 'px'; + }); + + // Keyboard shortcuts + messageInput.addEventListener('keydown', (e) => { + if (e.key === 'Enter' && !e.shiftKey) { + e.preventDefault(); + sendMessage(); + } + if (e.key === 'k' && (e.ctrlKey || e.metaKey)) { + e.preventDefault(); + newChat(); + } + }); + + sendButton.addEventListener('click', sendMessage); + + // Vision upload handlers + if (visionUploadButton) { + visionUploadButton.addEventListener('click', () => { + visionImageInput.click(); + }); + } + + if (visionImageInput) { + visionImageInput.addEventListener('change', handleImageUpload); + } + + if (visionClearButton) { + visionClearButton.addEventListener('click', clearVisionUpload); + } + + // Aria avatar handlers + const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); + const ariaAvatarClose = document.getElementById('ariaAvatarClose'); + const ariaAvatarRegenerate = document.getElementById('ariaAvatarRegenerate'); + + if (ariaAvatarClose) { + ariaAvatarClose.addEventListener('click', hideAriaAvatar); + } + + if (ariaAvatarRegenerate) { + ariaAvatarRegenerate.addEventListener('click', () => generateAriaAvatar(true)); + } + + // Auto-generate Aria avatar on page load (after 2 seconds) + setTimeout(() => { + if (!ariaAvatarGenerated) { + generateAriaAvatar(false); + } + }, 2000); + + newChatButton.addEventListener('click', () => { + console.log('New Chat button clicked'); + newChat(); + }); + clearButton.addEventListener('click', () => { + console.log('Clear button clicked'); + clearChat(false); + }); + exportButton.addEventListener('click', exportChat); + importButton.addEventListener('click', importChat); + toggleThemeButton.addEventListener('click', toggleTheme); + quantumModeButton.addEventListener('click', toggleQuantumMode); + if (quantumPanelClose) { + quantumPanelClose.addEventListener('click', () => { + quantumPanel.style.display = 'none'; + }); + } + streamToggle.addEventListener('change', (e) => { + streamEnabled = !!e.target.checked; + saveToStorage(); + }); + tempSlider.addEventListener('input', (e) => { + temperature = parseFloat(e.target.value); + tempValue.textContent = temperature.toFixed(2); + }); + tempSlider.addEventListener('change', () => saveToStorage()); + maxTokensInput.addEventListener('change', (e) => { + const val = parseInt(e.target.value, 10); + if (!isNaN(val)) { + maxOutputTokens = Math.max(64, Math.min(40960, val)); + e.target.value = String(maxOutputTokens); + saveToStorage(); + } + }); + toggleSystemButton.addEventListener('click', () => { + const show = systemPromptBox.style.display !== 'block'; + systemPromptBox.style.display = show ? 'block' : 'none'; + }); + systemPromptInput.addEventListener('input', (e) => { + systemPrompt = e.target.value; + saveToStorage(); + }); + + // Load saved conversations and settings + loadFromStorage(); + + // Focus input + messageInput.focus(); + + // Fetch system status on load + fetchSystemStatus(); +}); + +async function fetchSystemStatus() { + try { + updateStatus('Checking backend...'); + const response = await fetch(STATUS_API); + if (response.ok) { + const data = await response.json(); + systemStatus = { status: 'ok', data }; + updateStatusFromSystem(); + } + } catch (error) { + console.warn('Backend not available:', error); + updateStatus('Backend not responding'); + } +} + +function updateStatusFromSystem() { + if (!systemStatus) return; + + // Show success message if everything looks good + if (systemStatus.status === 'ok') { + const provider = systemStatus.data?.active_provider || 'local'; + const model = systemStatus.data?.model || 'unknown'; + updateStatus(`Ready - ${provider.toUpperCase()}`); + providerInfo.textContent = `${provider.toUpperCase()} - ${model}`; + } +} + +async function sendMessage() { + console.log('sendMessage() called'); + console.log('- messageInput.value:', messageInput.value); + console.log('- isProcessing:', isProcessing); + console.log('- sendButton.disabled:', sendButton.disabled); + console.log('- quantumMode:', quantumMode); + + const text = messageInput.value.trim(); + if (!text || isProcessing) return; + + // Perform quantum analysis if enabled + if (quantumMode) { + updateStatus('Performing quantum analysis...'); + await performQuantumAnalysis(text); + } + + // Add user message to UI + addMessage('user', text); + messageInput.value = ''; + messageInput.style.height = 'auto'; + messageInput.disabled = true; + sendButton.disabled = true; + isProcessing = true; + updateStatus('Sending...'); + + // Add to messages array + messages.push({ role: 'user', content: text }); + updateMessageCount(); + + // Show typing indicator + const typingIndicator = showTypingIndicator(); + + try { + // Choose streaming or one-shot + if (streamEnabled) { + if (cancelStreamBtn) cancelStreamBtn.style.display = 'inline-block'; + await streamResponse(typingIndicator); + } else { + await oneShotResponse(typingIndicator); + } + } catch (error) { + console.error('Error:', error); + typingIndicator.remove(); + + // Attempt retry if within limits + if (retryCount < MAX_RETRIES && error.message.includes('HTTP')) { + retryCount++; + const delay = RETRY_DELAY * Math.pow(2, retryCount - 1); + addMessage('system', `⚠️ Request failed. Retrying in ${delay/1000}s... (${retryCount}/${MAX_RETRIES})`); + updateStatus(`Retrying ${retryCount}/${MAX_RETRIES}...`); + + setTimeout(() => { + // Re-send by simulating button click + messages.pop(); // Remove the failed user message + messageInput.value = text; // Restore input + messageInput.disabled = false; + sendButton.disabled = false; + isProcessing = false; + sendMessage(); + }, delay); + } else { + // Max retries exceeded or non-retryable error + retryCount = 0; + const errorMsg = error.message.includes('NetworkError') || error.message.includes('Failed to fetch') + ? '❌ Network error. Please ensure QAI backend is running on http://localhost:7071' + : `❌ Error: ${error.message}`; + addMessage('system', errorMsg); + console.error('Full error details:', error); + updateStatus('Error - check console'); + messageInput.disabled = false; + sendButton.disabled = false; + isProcessing = false; + messageInput.focus(); + } + } +} + +async function oneShotResponse(typingIndicator) { + // Prepare messages with system prompt if provided + const apiMessages = sanitizeConversationMessages(systemPrompt ? + [{ role: 'system', content: systemPrompt }, ...messages] : + messages); + + console.log('Sending non-streaming request to:', API_BASE); + console.log('Request body:', { messages: apiMessages, temperature, max_tokens: maxOutputTokens }); + + const response = await fetch(API_BASE, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + messages: apiMessages, + temperature: temperature, + max_tokens: maxOutputTokens, + stream: false + }) + }); + + console.log('Response status:', response.status, response.statusText); + + if (!response.ok) { + const errorText = await response.text(); + console.error('Error response:', errorText); + throw new Error(`HTTP ${response.status}: ${response.statusText}`); + } + + const data = await response.json(); + console.log('Response data:', data); + + retryCount = 0; + typingIndicator.remove(); + // Handle QAI backend response format + const assistantMessage = data.response || data.choices?.[0]?.message?.content || 'No response received.'; + console.log('Assistant message:', assistantMessage); + + addMessage('assistant', assistantMessage, true); + if (!isSyntheticCompactionPlaceholder(assistantMessage)) { + messages.push({ role: 'assistant', content: assistantMessage }); + } + updateMessageCount(); + if (data.provider && data.model) { + providerInfo.textContent = `${data.provider.toUpperCase()} - ${data.model}`; + } + updateStatus('Ready'); + saveToStorage(); + messageInput.disabled = false; + sendButton.disabled = false; + isProcessing = false; + messageInput.focus(); +} + +async function streamResponse(typingIndicator) { + let streamAborted = false; + // Prepare assistant message container to update incrementally + const assistantDiv = document.createElement('div'); + assistantDiv.className = 'message assistant'; + const contentDiv = document.createElement('div'); + contentDiv.className = 'message-content'; + contentDiv.textContent = ''; + assistantDiv.appendChild(contentDiv); + chatMessages.appendChild(assistantDiv); + chatMessages.scrollTop = chatMessages.scrollHeight; + + activeAbortController = new AbortController(); + + // Prepare messages with system prompt if provided + const apiMessages = sanitizeConversationMessages(systemPrompt ? + [{ role: 'system', content: systemPrompt }, ...messages] : + messages); + + console.log('Sending streaming request to:', STREAM_API); + console.log('Request body:', { messages: apiMessages, temperature, max_tokens: maxOutputTokens, stream: true }); + + try { + const response = await fetch(STREAM_API, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + messages: apiMessages, + temperature: temperature, + max_tokens: maxOutputTokens, + stream: true + }), + signal: activeAbortController.signal + }); + + console.log('Stream response status:', response.status, response.statusText); + + if (!response.ok || !response.body) { + throw new Error(`HTTP ${response.status}: ${response.statusText}`); + } + + // Remove typing indicator as soon as stream starts + typingIndicator.remove(); + + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + let fullText = ''; + + while (true) { + const { value, done } = await reader.read(); + if (done) break; + + // QAI backend sends plain text chunks, not SSE + const chunk = decoder.decode(value, { stream: true }); + if (chunk) { + console.log('Stream chunk:', chunk); + fullText += chunk; + // Update plain text during stream for speed + contentDiv.textContent = fullText; + chatMessages.scrollTop = chatMessages.scrollHeight; + } + } + + // After stream completes, render as markdown + if (fullText) { + try { + const rendered = marked.parse(fullText); + contentDiv.innerHTML = rendered; + // Highlight code blocks + contentDiv.querySelectorAll('pre code').forEach((block) => { + hljs.highlightElement(block); + addCopyButton(block.parentElement); + }); + } catch (e) { + console.error('Markdown render error:', e); + contentDiv.textContent = fullText; + } + } + + // Push to messages history + if (!isSyntheticCompactionPlaceholder(fullText)) { + messages.push({ role: 'assistant', content: fullText }); + } + updateMessageCount(); + retryCount = 0; + updateStatus('Ready'); + saveToStorage(); + } catch (error) { + typingIndicator.remove(); + assistantDiv.remove(); + + if (error.name === 'AbortError') { + streamAborted = true; + addMessage('system', '❌ Streaming cancelled by user.'); + updateStatus('Cancelled'); + } else { + throw error; // Re-throw to be handled by sendMessage + } + } finally { + if (cancelStreamBtn) cancelStreamBtn.style.display = 'none'; + activeAbortController = null; + messageInput.disabled = false; + sendButton.disabled = false; + isProcessing = false; + messageInput.focus(); + } +} + +function addMessage(role, content, useMarkdown = false) { + const messageDiv = document.createElement('div'); + messageDiv.className = `message ${role}`; + + const contentDiv = document.createElement('div'); + contentDiv.className = 'message-content'; + + if (useMarkdown && typeof marked !== 'undefined' && role === 'assistant') { + // Render markdown + contentDiv.innerHTML = marked.parse(content); + + // Add copy buttons to code blocks + const codeBlocks = contentDiv.querySelectorAll('pre'); + codeBlocks.forEach(block => { + const copyBtn = document.createElement('button'); + copyBtn.className = 'copy-button'; + copyBtn.textContent = 'Copy'; + copyBtn.onclick = () => { + const code = block.querySelector('code')?.textContent || block.textContent; + navigator.clipboard.writeText(code).then(() => { + copyBtn.textContent = 'Copied!'; + setTimeout(() => copyBtn.textContent = 'Copy', 2000); + }); + }; + block.style.position = 'relative'; + block.appendChild(copyBtn); + }); + } else { + contentDiv.textContent = content; + } + + messageDiv.appendChild(contentDiv); + chatMessages.appendChild(messageDiv); + + // Scroll to bottom + chatMessages.scrollTop = chatMessages.scrollHeight; + + messageCounter++; + return messageDiv; +} + +function showTypingIndicator() { + const indicatorDiv = document.createElement('div'); + indicatorDiv.className = 'message assistant'; + + const typingDiv = document.createElement('div'); + typingDiv.className = 'typing-indicator active'; + typingDiv.innerHTML = ''; + + indicatorDiv.appendChild(typingDiv); + chatMessages.appendChild(indicatorDiv); + chatMessages.scrollTop = chatMessages.scrollHeight; + + return indicatorDiv; +} + +function newChat() { + console.log('newChat() called'); + if (messages.length > 0 && !confirm('Start a new chat? Current conversation will be cleared.')) { + console.log('User cancelled new chat'); + return; + } + messages = []; + messageCounter = 0; + isProcessing = false; + chatMessages.innerHTML = ''; + messageInput.disabled = false; + sendButton.disabled = false; + messageInput.value = ''; + messageInput.focus(); + localStorage.setItem('chatMessages', JSON.stringify([])); + addMessage('system', 'New conversation started. How can I help you?'); + updateMessageCount(); + updateStatus('Ready'); + saveToStorage(); + console.log('New chat complete'); +} + +function clearChat(preserveMessages = false) { + console.log('clearChat() called, preserveMessages:', preserveMessages); + if (!preserveMessages) { + messages = []; + messageCounter = 0; + isProcessing = false; + localStorage.setItem('chatMessages', JSON.stringify([])); + } + chatMessages.innerHTML = ''; + messageInput.disabled = false; + sendButton.disabled = false; + messageInput.value = ''; + messageInput.focus(); + addMessage('system', 'Chat cleared. Type a message to continue.'); + updateMessageCount(); + updateStatus('Ready'); + saveToStorage(); + console.log('Clear chat complete'); +} + +function exportChat() { + if (messages.length === 0) { + if (typeof showToast === 'function') showToast('No messages to export', 3000); + return; + } + + const timestamp = new Date().toISOString().replace(/[:.]/g, '-'); + const filename = `chat-export-${timestamp}.json`; + + const exportData = { + timestamp: new Date().toISOString(), + provider: currentProvider, + messageCount: messages.length, + messages: messages + }; + + const blob = new Blob([JSON.stringify(exportData, null, 2)], { type: 'application/json' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = filename; + a.click(); + URL.revokeObjectURL(url); + + updateStatus(`Exported ${messages.length} messages`); +} + +function importChat() { + const input = document.createElement('input'); + input.type = 'file'; + input.accept = '.json'; + input.onchange = (e) => { + const file = e.target.files[0]; + if (!file) return; + + const reader = new FileReader(); + reader.onload = (event) => { + try { + const data = JSON.parse(event.target.result); + if (data.messages && Array.isArray(data.messages)) { + const imported = data.messages; + clearChat(true); // reset UI but preserve current messages until reassigned + messages = imported; + // Restore messages to UI + messages.forEach(msg => { + if (msg.role !== 'system') { + addMessage(msg.role, msg.content, msg.role === 'assistant'); + } + }); + updateMessageCount(); + saveToStorage(); + updateStatus(`Imported ${messages.length} messages`); + } else { + throw new Error('Invalid chat export format'); + } + } catch (error) { + if (typeof showToast === 'function') showToast(`Import failed: ${error.message}`, 4000); + } + }; + reader.readAsText(file); + }; + input.click(); +} + +function toggleTheme() { + document.body.classList.toggle('dark-theme'); + const isDark = document.body.classList.contains('dark-theme'); + localStorage.setItem('theme', isDark ? 'dark' : 'light'); + toggleThemeButton.textContent = isDark ? '☀️ Light' : '🌙 Dark'; +} + +function updateMessageCount() { + const userMessages = messages.filter(m => m.role === 'user').length; + messageCount.textContent = userMessages; +} + +// Aria AI Avatar functions +async function generateAriaAvatar(regenerate = false) { + const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); + const ariaAvatarImage = document.getElementById('ariaAvatarImage'); + + if (!regenerate && ariaAvatarGenerated) { + ariaAvatarContainer.style.display = 'block'; + return; + } + + updateStatus('🎨 Generating Aria\'s AI avatar...'); + + try { + // Use a more descriptive prompt for Aria's character + const prompt = "Portrait of Aria, anime-style AI assistant character, purple gradient hair, cute anime girl, friendly expression, digital art, high quality, detailed, vibrant colors, soft lighting"; + + const response = await fetch(IMAGE_GEN_API, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + prompt: prompt, + size: "512x512", + style: "anime" + }) + }); + + if (!response.ok) { + throw new Error(`Image generation failed: ${response.status}`); + } + + const result = await response.json(); + + if (result.image_url || result.image_data) { + ariaAvatarUrl = result.image_url || `data:image/png;base64,${result.image_data}`; + ariaAvatarImage.src = ariaAvatarUrl; + ariaAvatarContainer.style.display = 'block'; + ariaAvatarGenerated = true; + updateStatus('✅ Aria\'s avatar generated!'); + + // Add a chat message from Aria about her new appearance + if (regenerate) { + addMessage('assistant', '✨ How do I look? I just got a fresh new appearance from the AI! 💜'); + } + } else { + throw new Error('No image data received'); + } + } catch (error) { + console.error('Avatar generation error:', error); + updateStatus(`⚠️ Avatar generation unavailable: ${error.message}`); + + // Fallback: Use a placeholder or emoji-based avatar + const ariaAvatarImage = document.getElementById('ariaAvatarImage'); + ariaAvatarImage.src = 'data:image/svg+xml,' + encodeURIComponent(` + + + + + + + + + 👩‍💻 + Aria + AI Assistant + + `); + ariaAvatarContainer.style.display = 'block'; + ariaAvatarGenerated = true; + } +} + +function hideAriaAvatar() { + const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); + ariaAvatarContainer.style.display = 'none'; + updateStatus('Avatar hidden'); +} + +// Vision upload handlers +async function handleImageUpload(event) { + const file = event.target.files[0]; + if (!file) return; + + if (!file.type.startsWith('image/')) { + updateStatus('❌ Please select an image file'); + return; + } + + // Show preview + const reader = new FileReader(); + reader.onload = async (e) => { + uploadedImageBase64 = e.target.result.split(',')[1]; // Remove data:image/...;base64, prefix + uploadedImage = file.name; + + // Show preview + visionPreview.innerHTML = ` + Preview +
+ ${file.name} + +
+ `; + visionPreview.style.display = 'block'; + + // Re-attach clear button listener + document.getElementById('visionClearButton').addEventListener('click', clearVisionUpload); + + // Auto-analyze the image + updateStatus('🔍 Analyzing image...'); + try { + const response = await fetch(VISION_INFER_API, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ image: uploadedImageBase64 }) + }); + + if (!response.ok) { + throw new Error(`Vision API error: ${response.status}`); + } + + const result = await response.json(); + + // Add AI message with vision results + const visionMessage = `🖼️ **Image Analysis**: ${file.name}\n\n` + + `**Expression**: ${result.label} (${(result.confidence * 100).toFixed(1)}% confidence)\n\n` + + `**All Scores**:\n${Object.entries(result.scores) + .sort((a, b) => b[1] - a[1]) + .map(([label, score]) => `- ${label}: ${(score * 100).toFixed(1)}%`) + .join('\n')}`; + + addMessage('assistant', visionMessage); + updateStatus('✅ Image analyzed successfully'); + } catch (error) { + console.error('Vision inference error:', error); + updateStatus(`❌ Vision error: ${error.message}`); + } + }; + + reader.readAsDataURL(file); +} + +function clearVisionUpload() { + uploadedImage = null; + uploadedImageBase64 = null; + visionImageInput.value = ''; + visionPreview.style.display = 'none'; + visionPreview.innerHTML = ''; + updateStatus('Vision upload cleared'); +} + +function updateStatus(text) { + statusText.textContent = text; + if (text.includes('Ready') || text.includes('ok')) { + statusText.style.color = '#28a745'; + } else if (text.includes('Error') || text.includes('❌')) { + statusText.style.color = '#dc3545'; + } else if (text.includes('⚠️')) { + statusText.style.color = '#ffc107'; + } else { + statusText.style.color = ''; + } + + setTimeout(() => { + if (statusText.textContent === text) { + statusText.textContent = 'Ready'; + statusText.style.color = ''; + } + }, 3000); +} + +function saveToStorage() { + try { + localStorage.setItem('chatMessages', JSON.stringify(sanitizeConversationMessages(messages))); + localStorage.setItem('chatStream', streamEnabled ? '1' : '0'); + localStorage.setItem('chatTemp', String(temperature)); + localStorage.setItem('chatMaxTokens', String(maxOutputTokens)); + localStorage.setItem('chatSystemPrompt', systemPrompt || ''); + } catch (e) { + console.error('Failed to save to storage:', e); + } +} + +function loadFromStorage() { + try { + const saved = localStorage.getItem('chatMessages'); + const savedTheme = localStorage.getItem('theme'); + const savedStream = localStorage.getItem('chatStream'); + const savedTemp = localStorage.getItem('chatTemp'); + const savedMax = localStorage.getItem('chatMaxTokens'); + const savedSys = localStorage.getItem('chatSystemPrompt'); + + if (savedTheme === 'dark') { + document.body.classList.add('dark-theme'); + toggleThemeButton.textContent = '☀️ Light'; + } else { + toggleThemeButton.textContent = '🌙 Dark'; + } + + if (saved) { + messages = sanitizeConversationMessages(JSON.parse(saved)); + // Restore messages to UI + messages.forEach(msg => { + if (msg.role !== 'system') { + addMessage(msg.role, msg.content, msg.role === 'assistant'); + } + }); + updateMessageCount(); + } + + // Restore settings + streamEnabled = savedStream === '1'; + streamToggle.checked = streamEnabled; + if (savedTemp) { + temperature = parseFloat(savedTemp); + if (!isNaN(temperature)) { + tempSlider.value = String(temperature); + tempValue.textContent = temperature.toFixed(2); + } + } + if (savedMax) { + const v = parseInt(savedMax, 10); + if (!isNaN(v)) { + maxOutputTokens = v; + maxTokensInput.value = String(v); + } + } + if (savedSys) { + systemPrompt = savedSys; + systemPromptInput.value = systemPrompt; + } + } catch (e) { + console.error('Failed to load from storage:', e); + } +} + +// ============================================================================= +// Quantum Mode Functions +// ============================================================================= + +function toggleQuantumMode() { + quantumMode = !quantumMode; + + if (quantumMode) { + quantumModeButton.textContent = '🔬 Quantum ON'; + quantumModeButton.classList.add('active'); + quantumIndicator.style.display = 'flex'; + quantumPanel.style.display = 'block'; + currentProvider = 'quantum'; + updateStatus('Quantum mode enabled'); + + // Fetch quantum info + fetchQuantumInfo(); + } else { + quantumModeButton.textContent = '🔬 Quantum OFF'; + quantumModeButton.classList.remove('active'); + quantumIndicator.style.display = 'none'; + quantumPanel.style.display = 'none'; + currentProvider = 'auto'; + updateStatus('Quantum mode disabled'); + } + + saveToStorage(); +} + +async function fetchQuantumInfo() { + try { + const response = await fetch(QUANTUM_INFO_API); + if (response.ok) { + const data = await response.json(); + if (data.available) { + updateStatus('Quantum backend ready'); + } else { + updateStatus('Quantum backend unavailable - using classical fallback'); + } + } + } catch (error) { + console.warn('Could not fetch quantum info:', error); + } +} + +async function performQuantumAnalysis(text) { + if (!quantumMode) return null; + + try { + // Convert text to features (simple hash-based approach) + const features = textToFeatures(text); + + const response = await fetch(QUANTUM_CLASSIFY_API, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + features: features, + n_qubits: 4, + n_layers: 2 + }) + }); + + if (!response.ok) { + throw new Error(`Quantum API error: ${response.status}`); + } + + const data = await response.json(); + + // Update quantum panel + document.getElementById('quantumClassification').textContent = + data.classification.toUpperCase(); + document.getElementById('quantumConfidence').textContent = + (data.confidence * 100).toFixed(1) + '%'; + document.getElementById('quantumQubits').textContent = + data.quantum_state.n_qubits; + document.getElementById('quantumLayers').textContent = + data.quantum_state.n_layers; + + // Get circuit visualization + await fetchCircuitVisualization(data.quantum_state.n_qubits, data.quantum_state.n_layers); + + return data; + } catch (error) { + console.error('Quantum analysis failed:', error); + document.getElementById('quantumClassification').textContent = 'ERROR'; + return null; + } +} + +async function fetchCircuitVisualization(nQubits, nLayers) { + try { + const response = await fetch(QUANTUM_CIRCUIT_API, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + n_qubits: nQubits, + n_layers: nLayers, + entanglement: 'linear' + }) + }); + + if (response.ok) { + const data = await response.json(); + const vizElement = document.querySelector('.circuit-display'); + if (vizElement) { + vizElement.textContent = data.visualization; + } + } + } catch (error) { + console.error('Circuit visualization failed:', error); + } +} + +function textToFeatures(text) { + // Simple feature extraction: convert text to numeric features + // Using character codes and length statistics + const features = []; + const normalized = text.toLowerCase(); + + // Add basic statistics + features.push(normalized.length / 100.0); // Normalized length + features.push(normalized.split(' ').length / 50.0); // Word count + features.push(normalized.split('?').length / 10.0); // Question marks + features.push(normalized.split('!').length / 10.0); // Exclamation marks + + // Add character distribution (simple hash) + let sum = 0; + for (let i = 0; i < Math.min(normalized.length, 20); i++) { + sum += normalized.charCodeAt(i); + } + features.push((sum % 256) / 256.0); + + // Pad or truncate to 8 features + while (features.length < 8) { + features.push(0.0); + } + + return features.slice(0, 8); +} + +})(); diff --git a/apps/chat/index.html b/apps/chat/index.html index c29f3f3c5..349d65ab8 100644 --- a/apps/chat/index.html +++ b/apps/chat/index.html @@ -1,5647 +1,5647 @@ - - - - - - - QAI Chat - Quantum AI System - - - - - - - - - - - - - - - - -
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Start Backend

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To use the chat app, start the Azure Functions backend. You only need to do this once per session.
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To use the chat app, start the Azure Functions backend. You only need to do this once per session.
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+ + + + diff --git a/apps/dashboard/README.md b/apps/dashboard/README.md index d8f7bbe92..51d90965d 100644 --- a/apps/dashboard/README.md +++ b/apps/dashboard/README.md @@ -1,29 +1,29 @@ -# QAI Automation Dashboard - -A simple Flask web dashboard for monitoring orchestrator status, resource usage, and results export. - -## Features -- Orchestrator status (autotrain, quantum_autorun, evaluation_autorun) -- Resource monitor snapshot -- Results export (all orchestrators) - -## Usage -1. Install dependencies: - ```powershell - pip install -r requirements.txt - ``` -2. Run the dashboard: - ```powershell - python app.py - ``` -3. Open [http://localhost:5000](http://localhost:5000) in your browser. - -## Endpoints -- `/` : Main dashboard -- `/status` : Orchestrator status JSON -- `/resources` : Resource monitor snapshot JSON -- `/results` : Results export JSON - -## Customization -- Add more endpoints or UI sections as needed. -- Integrate with authentication or advanced visualization if desired. +# QAI Automation Dashboard + +A simple Flask web dashboard for monitoring orchestrator status, resource usage, and results export. + +## Features +- Orchestrator status (autotrain, quantum_autorun, evaluation_autorun) +- Resource monitor snapshot +- Results export (all orchestrators) + +## Usage +1. Install dependencies: + ```powershell + pip install -r requirements.txt + ``` +2. Run the dashboard: + ```powershell + python app.py + ``` +3. Open [http://localhost:5000](http://localhost:5000) in your browser. + +## Endpoints +- `/` : Main dashboard +- `/status` : Orchestrator status JSON +- `/resources` : Resource monitor snapshot JSON +- `/results` : Results export JSON + +## Customization +- Add more endpoints or UI sections as needed. +- Integrate with authentication or advanced visualization if desired. diff --git a/apps/dashboard/advanced.html b/apps/dashboard/advanced.html index d6213215d..e6d1da29b 100644 --- a/apps/dashboard/advanced.html +++ b/apps/dashboard/advanced.html @@ -1,800 +1,800 @@ - - - - - - QAI Advanced Dashboard - - - - - -
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SPIKE DETECTION: >20% increase from previous epoch - if (previous && current.loss > previous.loss * (1 + this.config.spikeThreshold)) { - const increase = ((current.loss - previous.loss) / previous.loss * 100).toFixed(1); - this.reportAnomaly('spike', { - type: 'Loss Spike', - severity: 'warning', - message: `Loss increased by ${increase}% (${previous.loss.toFixed(4)} → ${current.loss.toFixed(4)})`, - epoch: current.epoch, - jobName: current.jobName, - recommendation: 'Check for data quality issues or reduce learning rate' - }); - } - - // 2. DIVERGENCE DETECTION: Loss > threshold - if (current.loss > this.config.divergenceThreshold) { - this.reportAnomaly('divergence', { - type: 'Training Divergence', - severity: 'error', - message: `Loss diverged to ${current.loss.toFixed(4)} (threshold: ${this.config.divergenceThreshold})`, - epoch: current.epoch, - jobName: current.jobName, - recommendation: 'Training likely unstable. Reduce learning rate significantly or restart with different hyperparameters' - }); - } - - // 3. STAGNATION DETECTION: No improvement for N epochs - if (recent.length >= this.config.stagnationEpochs) { - const lastN = recent.slice(-this.config.stagnationEpochs); - const minLoss = Math.min(...lastN.map(h => h.loss)); - const maxLoss = Math.max(...lastN.map(h => h.loss)); - const variation = maxLoss - minLoss; - - // If variation is very small (< 0.001), consider it stagnant - if (variation < 0.001) { - this.reportAnomaly('stagnation', { - type: 'Training Stagnation', - severity: 'info', - message: `No improvement for ${this.config.stagnationEpochs} epochs (loss: ${current.loss.toFixed(4)})`, - epoch: current.epoch, - jobName: current.jobName, - recommendation: 'Consider early stopping or increasing learning rate' - }); - } - } - } - - /** - * Report an anomaly - */ - reportAnomaly(id, anomaly) { - // Check if this anomaly was already reported recently (debounce) - const recentDuplicate = this.anomalies.find(a => - a.type === anomaly.type && - a.jobName === anomaly.jobName && - (Date.now() - a.timestamp < 30000) // Within 30 seconds - ); - - if (recentDuplicate) return; - - // Add timestamp - anomaly.timestamp = Date.now(); - anomaly.id = `${id}-${anomaly.timestamp}`; - - // Store anomaly - this.anomalies.push(anomaly); - - // Trigger notification - if (this.config.enableNotifications) { - this.sendNotification(anomaly); - } - - // Trigger callback if provided - if (this.config.onAnomaly) { - this.config.onAnomaly(anomaly); - } - - // Auto-pause if enabled and severity is error - if (this.config.enableAutoPause && anomaly.severity === 'error') { - this.pauseTraining(anomaly); - } - } - - /** - * Send desktop notification - */ - sendNotification(anomaly) { - if (!('Notification' in window)) return; - - if (Notification.permission === 'granted') { - new Notification(`⚠️ ${anomaly.type}`, { - body: anomaly.message, - icon: '/favicon.ico', - badge: '/favicon.ico', - tag: anomaly.id, - requireInteraction: anomaly.severity === 'error' - }); - } else if (Notification.permission !== 'denied') { - Notification.requestPermission().then(permission => { - if (permission === 'granted') { - this.sendNotification(anomaly); - } - }); - } - } - - /** - * Pause training (if supported) - */ - pauseTraining(anomaly) { - console.warn('Auto-pause triggered:', anomaly); - // This would need backend support to actually pause training - // For now, just log and notify - if (this.config.onPause) { - this.config.onPause(anomaly); - } - } - - /** - * Start monitoring training data from API - */ - startMonitoring(statusUrl = '/status') { - if (this.isMonitoring) return; - - this.isMonitoring = true; - this.intervalId = setInterval(async () => { - try { - const response = await fetch(statusUrl); - const data = await response.json(); - - // Extract loss from latest job - if (data.jobs && data.jobs.length > 0) { - const latestJob = data.jobs[data.jobs.length - 1]; - if (latestJob.status === 'running' && latestJob.current_epoch && latestJob.current_loss) { - this.addLossValue( - latestJob.current_epoch, - latestJob.current_loss, - latestJob.name - ); - } - } - } catch (err) { - console.error('Anomaly detector monitoring error:', err); - } - }, this.config.checkInterval); - } - - /** - * Stop monitoring - */ - stopMonitoring() { - if (this.intervalId) { - clearInterval(this.intervalId); - this.intervalId = null; - } - this.isMonitoring = false; - } - - /** - * Get recent anomalies - */ - getRecentAnomalies(count = 10) { - return this.anomalies.slice(-count); - } - - /** - * Clear anomaly history - */ - clearAnomalies() { - this.anomalies = []; - } - - /** - * Get anomaly statistics - */ - getStatistics() { - return { - total: this.anomalies.length, - spikes: this.anomalies.filter(a => a.type === 'Loss Spike').length, - divergences: this.anomalies.filter(a => a.type === 'Training Divergence').length, - stagnations: this.anomalies.filter(a => a.type === 'Training Stagnation').length, - byJob: this.anomalies.reduce((acc, a) => { - acc[a.jobName] = (acc[a.jobName] || 0) + 1; - return acc; - }, {}) - }; - } -} - -// Export for use in other modules -if (typeof window !== 'undefined') { - window.TrainingAnomalyDetector = TrainingAnomalyDetector; -} +/** + * Training Anomaly Detection System + * Monitors training metrics for anomalies and triggers alerts + * + * Features: + * - Loss spike detection (>20% increase between epochs) + * - Divergence detection (loss > threshold) + * - Stagnation detection (no improvement for N epochs) + * - Desktop notifications + * - Optional auto-pause + */ + +class TrainingAnomalyDetector { + constructor(options = {}) { + this.lossHistory = []; + this.config = { + spikeThreshold: options.spikeThreshold || 0.20, // 20% increase + divergenceThreshold: options.divergenceThreshold || 10.0, + stagnationEpochs: options.stagnationEpochs || 5, + enableNotifications: options.enableNotifications !== false, + enableAutoPause: options.enableAutoPause || false, + checkInterval: options.checkInterval || 5000, // 5 seconds + ...options + }; + this.anomalies = []; + this.isMonitoring = false; + this.intervalId = null; + } + + /** + * Add a new loss value to history + */ + addLossValue(epoch, loss, jobName = 'current') { + this.lossHistory.push({ + epoch, + loss, + jobName, + timestamp: Date.now() + }); + + // Keep only recent history (last 100 epochs) + if (this.lossHistory.length > 100) { + this.lossHistory.shift(); + } + + // Check for anomalies + this.detectAnomalies(); + } + + /** + * Detect anomalies in loss progression + */ + detectAnomalies() { + if (this.lossHistory.length < 2) return; + + const recent = this.lossHistory.slice(-10); // Last 10 epochs + const current = recent[recent.length - 1]; + const previous = recent[recent.length - 2]; + + // 1. SPIKE DETECTION: >20% increase from previous epoch + if (previous && current.loss > previous.loss * (1 + this.config.spikeThreshold)) { + const increase = ((current.loss - previous.loss) / previous.loss * 100).toFixed(1); + this.reportAnomaly('spike', { + type: 'Loss Spike', + severity: 'warning', + message: `Loss increased by ${increase}% (${previous.loss.toFixed(4)} → ${current.loss.toFixed(4)})`, + epoch: current.epoch, + jobName: current.jobName, + recommendation: 'Check for data quality issues or reduce learning rate' + }); + } + + // 2. DIVERGENCE DETECTION: Loss > threshold + if (current.loss > this.config.divergenceThreshold) { + this.reportAnomaly('divergence', { + type: 'Training Divergence', + severity: 'error', + message: `Loss diverged to ${current.loss.toFixed(4)} (threshold: ${this.config.divergenceThreshold})`, + epoch: current.epoch, + jobName: current.jobName, + recommendation: 'Training likely unstable. Reduce learning rate significantly or restart with different hyperparameters' + }); + } + + // 3. STAGNATION DETECTION: No improvement for N epochs + if (recent.length >= this.config.stagnationEpochs) { + const lastN = recent.slice(-this.config.stagnationEpochs); + const minLoss = Math.min(...lastN.map(h => h.loss)); + const maxLoss = Math.max(...lastN.map(h => h.loss)); + const variation = maxLoss - minLoss; + + // If variation is very small (< 0.001), consider it stagnant + if (variation < 0.001) { + this.reportAnomaly('stagnation', { + type: 'Training Stagnation', + severity: 'info', + message: `No improvement for ${this.config.stagnationEpochs} epochs (loss: ${current.loss.toFixed(4)})`, + epoch: current.epoch, + jobName: current.jobName, + recommendation: 'Consider early stopping or increasing learning rate' + }); + } + } + } + + /** + * Report an anomaly + */ + reportAnomaly(id, anomaly) { + // Check if this anomaly was already reported recently (debounce) + const recentDuplicate = this.anomalies.find(a => + a.type === anomaly.type && + a.jobName === anomaly.jobName && + (Date.now() - a.timestamp < 30000) // Within 30 seconds + ); + + if (recentDuplicate) return; + + // Add timestamp + anomaly.timestamp = Date.now(); + anomaly.id = `${id}-${anomaly.timestamp}`; + + // Store anomaly + this.anomalies.push(anomaly); + + // Trigger notification + if (this.config.enableNotifications) { + this.sendNotification(anomaly); + } + + // Trigger callback if provided + if (this.config.onAnomaly) { + this.config.onAnomaly(anomaly); + } + + // Auto-pause if enabled and severity is error + if (this.config.enableAutoPause && anomaly.severity === 'error') { + this.pauseTraining(anomaly); + } + } + + /** + * Send desktop notification + */ + sendNotification(anomaly) { + if (!('Notification' in window)) return; + + if (Notification.permission === 'granted') { + new Notification(`⚠️ ${anomaly.type}`, { + body: anomaly.message, + icon: '/favicon.ico', + badge: '/favicon.ico', + tag: anomaly.id, + requireInteraction: anomaly.severity === 'error' + }); + } else if (Notification.permission !== 'denied') { + Notification.requestPermission().then(permission => { + if (permission === 'granted') { + this.sendNotification(anomaly); + } + }); + } + } + + /** + * Pause training (if supported) + */ + pauseTraining(anomaly) { + console.warn('Auto-pause triggered:', anomaly); + // This would need backend support to actually pause training + // For now, just log and notify + if (this.config.onPause) { + this.config.onPause(anomaly); + } + } + + /** + * Start monitoring training data from API + */ + startMonitoring(statusUrl = '/status') { + if (this.isMonitoring) return; + + this.isMonitoring = true; + this.intervalId = setInterval(async () => { + try { + const response = await fetch(statusUrl); + const data = await response.json(); + + // Extract loss from latest job + if (data.jobs && data.jobs.length > 0) { + const latestJob = data.jobs[data.jobs.length - 1]; + if (latestJob.status === 'running' && latestJob.current_epoch && latestJob.current_loss) { + this.addLossValue( + latestJob.current_epoch, + latestJob.current_loss, + latestJob.name + ); + } + } + } catch (err) { + console.error('Anomaly detector monitoring error:', err); + } + }, this.config.checkInterval); + } + + /** + * Stop monitoring + */ + stopMonitoring() { + if (this.intervalId) { + clearInterval(this.intervalId); + this.intervalId = null; + } + this.isMonitoring = false; + } + + /** + * Get recent anomalies + */ + getRecentAnomalies(count = 10) { + return this.anomalies.slice(-count); + } + + /** + * Clear anomaly history + */ + clearAnomalies() { + this.anomalies = []; + } + + /** + * Get anomaly statistics + */ + getStatistics() { + return { + total: this.anomalies.length, + spikes: this.anomalies.filter(a => a.type === 'Loss Spike').length, + divergences: this.anomalies.filter(a => a.type === 'Training Divergence').length, + stagnations: this.anomalies.filter(a => a.type === 'Training Stagnation').length, + byJob: this.anomalies.reduce((acc, a) => { + acc[a.jobName] = (acc[a.jobName] || 0) + 1; + return acc; + }, {}) + }; + } +} + +// Export for use in other modules +if (typeof window !== 'undefined') { + window.TrainingAnomalyDetector = TrainingAnomalyDetector; +} diff --git a/apps/dashboard/app.py b/apps/dashboard/app.py index d690146d9..db9b2f13b 100644 --- a/apps/dashboard/app.py +++ b/apps/dashboard/app.py @@ -1,679 +1,679 @@ -# reuse orchestrator logic for retry -import json -import os -import signal -import subprocess -import sys -import threading -import time -import traceback -from datetime import datetime, timedelta, timezone -from pathlib import Path -from typing import Any, Dict, List, Optional - -import yaml -from flask import Flask, jsonify, render_template -from flask_socketio import SocketIO - -from scripts.autotrain import Job, load_jobs, run_job - -REPO_ROOT = Path(__file__).resolve().parents[1] -sys.path.insert(0, str(REPO_ROOT)) # Ensure scripts module is importable - - -app = Flask(__name__) -socketio = SocketIO(app, cors_allowed_origins="*") - -DATA_OUT = REPO_ROOT / "data_out" - - -@app.route("/") -def index(): - return render_template("index.html") - - -@app.route("/status") -def status(): - # Load orchestrator status - status_files = [ - "autotrain/status.json", - "quantum_autorun/status.json", - "evaluation_autorun/status.json", - ] - results = {} - for fname in status_files: - fpath = DATA_OUT / fname - if fpath.exists(): - with fpath.open() as f: - results[fname] = json.load(f) - return jsonify(results) - - -@app.route("/resources") -def resources(): - # Load latest resource snapshot - snap_path = DATA_OUT / "resource_monitor_snapshot.json" - if snap_path.exists(): - with snap_path.open() as f: - return jsonify(json.load(f)) - return jsonify({"error": "No snapshot found"}) - - -@app.route("/results") -def results(): - # Load latest exported results - res_path = ( - Path(__file__).resolve().parents[1] / "exports" / "all_orchestrators.json" - ) - if res_path.exists(): - with res_path.open() as f: - return jsonify(json.load(f)) - return jsonify({"error": "No results found"}) - - -def _compute_training_progress(): - """Compute incremental AutoTrain progress with ETA and success-only percentage. - - Returns dict with: - percent_complete: succeeded/total * 100 (failed excluded) - percent_success_alias: same value (alias for clarity) - eta_seconds / eta_iso: estimated remaining time until all jobs finish (based on avg succeeded duration) - average_job_duration_seconds: mean duration of succeeded jobs - """ - cfg_path = REPO_ROOT / "autotrain.yaml" - try: - cfg = ( - yaml.safe_load(cfg_path.read_text(encoding="utf-8")) - if cfg_path.exists() - else {} - ) - configured_jobs = [j.get("name") for j in cfg.get("jobs", []) if j.get("name")] - except Exception: - configured_jobs = [] - - status_path = DATA_OUT / "autotrain" / "status.json" - jobs_status = [] - if status_path.exists(): - try: - status_obj = json.loads(status_path.read_text(encoding="utf-8")) - jobs_status = status_obj.get("jobs", []) - except Exception: - jobs_status = [] - - status_map = {j.get("name"): j for j in jobs_status} - # Include extra jobs from status not present in config (e.g., local_job) - # Performance optimization: Direct iteration instead of .keys() - extra_jobs = [name for name in status_map if name not in configured_jobs] - all_job_names = configured_jobs + extra_jobs - enriched = [] - succeeded = failed = running = pending = validated = 0 - succeeded_durations = [] - running_job_name = None - running_log_path = None - total_epochs_running = None - # Compute runner-specific average durations for per-job ETA - runner_durations = {} - for rec in jobs_status: - if rec.get("status") == "succeeded" and rec.get("duration_sec"): - runner = rec.get("runner", "unknown") - runner_durations.setdefault(runner, []).append(rec["duration_sec"]) - runner_avg = {r: (sum(durs) / len(durs)) for r, durs in runner_durations.items()} - - for name in all_job_names: - rec = status_map.get(name) - if rec is None: - enriched.append({"name": name, "status": "pending"}) - pending += 1 - continue - st = rec.get("status") - if st == "succeeded": - succeeded += 1 - dur = rec.get("duration_sec") - if isinstance(dur, (int, float)): - succeeded_durations.append(dur) - elif st == "failed": - failed += 1 - elif st == "validated": - validated += 1 - elif st == "running" or st == "retry_running": - running += 1 - running_job_name = name - running_log_path = rec.get("log") - else: - pending += 1 - - # Compute per-job ETA if running - job_eta_sec = None - if st in {"running", "retry_running"} and rec.get("start_time"): - runner_type = rec.get("runner", "unknown") - avg_dur = runner_avg.get(runner_type) - if avg_dur: - try: - start_dt = datetime.strptime(rec["start_time"], "%Y%m%dT%H%M%SZ") - elapsed = (datetime.now(timezone.utc) - start_dt).total_seconds() - remaining = max(0, avg_dur - elapsed) - job_eta_sec = round(remaining, 1) - except Exception: - pass - - enriched.append( - { - "name": name, - "status": st, - "start_time": rec.get("start_time"), - "return_code": rec.get("return_code"), - "duration_sec": rec.get("duration_sec"), - "validated_type": rec.get("validated_type"), - "category": rec.get("category"), - "runner": rec.get("runner"), - "eta_sec": job_eta_sec, - } - ) - - total_jobs = len(all_job_names) - if total_jobs: - percent_complete = round((succeeded / total_jobs) * 100, 2) - else: - percent_complete = 0.0 - - if succeeded and succeeded_durations and total_jobs > succeeded: - avg = sum(succeeded_durations) / len(succeeded_durations) - remaining_jobs = total_jobs - succeeded - eta_seconds = avg * remaining_jobs - eta_iso = ( - datetime.now(timezone.utc) + timedelta(seconds=eta_seconds) - ).isoformat() + "Z" - else: - avg = None - eta_seconds = None - eta_iso = None - - # Intra-job progress parsing for current running job - current_job_percent = None - current_epoch = None - total_epochs = None - current_step = None - total_steps = None - if running_job_name and running_log_path: - try: - log_file = Path(running_log_path) - if log_file.exists(): - # Tail last ~400 lines for patterns - lines = _tail_lines(log_file, 400) - # Patterns: Epoch X/Y, global_step = Z, total_flos - # Compile regex patterns ONCE outside loop for performance - import re - - epoch_pat = re.compile(r"Epoch\s+(\d+)/(\d+)") - step_pat = re.compile(r"global_step\s*=\s*(\d+)") - last_epoch = None - last_total_epochs = None - last_step = None - for ln in lines: - e_m = epoch_pat.search(ln) - if e_m: - last_epoch = int(e_m.group(1)) - last_total_epochs = int(e_m.group(2)) - s_m = step_pat.search(ln) - if s_m: - last_step = int(s_m.group(1)) - if last_epoch and last_total_epochs: - current_epoch = last_epoch - total_epochs = last_total_epochs - # Compute epoch-based percent if no steps - current_job_percent = round( - ((last_epoch - 1) / max(1, last_total_epochs)) * 100, 2 - ) - # If we have steps and epochs, approximate total steps - if last_step is not None and total_epochs is not None: - # Heuristic: use max observed step as current_step; total steps unknown until training end. - current_step = last_step - # Try to infer total steps from training args saved in output dir (trainer_state.json) - trainer_state = log_file.parent / "trainer_state.json" - if trainer_state.exists(): - try: - st_obj = json.loads( - trainer_state.read_text(encoding="utf-8") - ) - opt = st_obj.get("trainer_state", st_obj) - max_steps = opt.get("max_steps") - if isinstance(max_steps, int) and max_steps > 0: - total_steps = max_steps - except Exception: - pass - if total_steps: - step_percent = round( - (current_step / max(1, total_steps)) * 100, 2 - ) - # Prefer finer-grained step percent when available - current_job_percent = step_percent - except Exception: - pass - - # Add metrics for succeeded jobs - for job_rec in enriched: - if job_rec.get("status") == "succeeded": - metrics = _extract_job_metrics(job_rec) - if metrics: - job_rec["metrics"] = metrics - - payload = { - "generated_at": datetime.now(timezone.utc).isoformat() + "Z", - "total_jobs": total_jobs, - "succeeded": succeeded, - "validated": validated, - "failed": failed, - "running": running, - "pending": pending, - "percent_complete": percent_complete, # success-only percent - "percent_success_alias": percent_complete, - "average_job_duration_seconds": avg, - "eta_seconds": eta_seconds, - "eta_iso": eta_iso, - "current_job_name": running_job_name, - "current_job_percent": current_job_percent, - "current_epoch": current_epoch, - "total_epochs": total_epochs, - "current_step": current_step, - "total_steps": total_steps, - "jobs": enriched, - } - _append_progress_history(payload) - return payload - - -# ========================= RETRY SUPPORT ========================= - - -RETRY_LOCK = threading.Lock() -ACTIVE_RETRY: Optional[str] = None -ACTIVE_JOB_PIDS: Dict[str, int] = {} -STATUS_PATH = DATA_OUT / "autotrain" / "status.json" - - -def _read_status() -> Dict[str, Any]: - if STATUS_PATH.exists(): - try: - return json.loads(STATUS_PATH.read_text(encoding="utf-8")) - except Exception: - return {"jobs": []} - return {"jobs": []} - - -def _write_status(obj: Dict[str, Any]) -> None: - try: - STATUS_PATH.parent.mkdir(parents=True, exist_ok=True) - STATUS_PATH.write_text(json.dumps(obj, indent=2), encoding="utf-8") - except Exception: - pass - - -def _find_job_entry(status_obj: Dict[str, Any], name: str) -> Optional[Dict[str, Any]]: - for j in status_obj.get("jobs", []): - if j.get("name") == name: - return j - return None - - -@app.route("/api/retry_job/", methods=["POST"]) -def retry_job(job_name: str): - """Retry a single AutoTrain job safely. - - Concurrency rules: - - Only one retry active at a time (ACTIVE_RETRY guarded by RETRY_LOCK) - - No retry allowed while any job is currently running. - """ - global ACTIVE_RETRY - with RETRY_LOCK: - status_obj = _read_status() - jobs_list = status_obj.get("jobs", []) - if ACTIVE_RETRY is not None: - return ( - jsonify({"error": "retry_in_progress", "active_retry": ACTIVE_RETRY}), - 409, - ) - # Disallow if any job currently running (avoid log collision / resource contention) - if any(j.get("status") in {"running", "retry_running"} for j in jobs_list): - return jsonify({"error": "job_currently_running"}), 409 - - target_entry = _find_job_entry(status_obj, job_name) - if target_entry is None: - return jsonify({"error": "job_not_found"}), 404 - - # Load job from config so we have full definition (dataset, save_dir, etc.) - cfg_path = REPO_ROOT / "autotrain.yaml" - try: - config_jobs = {j.name: j for j in load_jobs(cfg_path)} - except Exception: - return jsonify({"error": "config_load_failed"}), 500 - if job_name not in config_jobs: - return jsonify({"error": "job_not_in_config"}), 400 - job_obj: Job = config_jobs[job_name] - - # Prepare placeholder update - retry_count = int(target_entry.get("retry_count", 0)) + 1 - target_entry.update( - { - "status": "retry_running", - "retry_count": retry_count, - "retry_start_time": datetime.now(timezone.utc).strftime( - "%Y%m%dT%H%M%SZ" - ), - } - ) - status_obj["generated_at"] = datetime.now(timezone.utc).isoformat() + "Z" - _write_status(status_obj) - ACTIVE_RETRY = job_name - - def _do_retry(job: Job, retry_num: int): - global ACTIVE_RETRY - try: - result = run_job(job, dry_run=False, job_index=0, total_jobs=1) - except Exception as e: # capture unexpected exception - result = { - "name": job.name, - "runner": job.runner, - "status": "failed", - "error": str(e), - "trace": traceback.format_exc().splitlines()[-5:], - "retry_count": retry_num, - } - # Merge result back into status - with RETRY_LOCK: - current_status = _read_status() - entry = _find_job_entry(current_status, job.name) - if entry is not None: - # Preserve retry_count from placeholder - preserved_retry = entry.get("retry_count", retry_num) - result["retry_count"] = preserved_retry - result["status"] = result.get("status") - result["retry_completed_time"] = datetime.now( - timezone.utc - ).strftime("%Y%m%dT%H%M%SZ") - # Mark as succeeded/failed (no special retry_running state anymore) - for k in [ - "status", - "return_code", - "duration_sec", - "duration_human", - "log", - "metrics", - "output_dir", - "cmd", - "start_time", - "retry_count", - "retry_completed_time", - ]: - v = result.get(k) - if v is not None: - entry[k] = v - current_status["generated_at"] = ( - datetime.now(timezone.utc).isoformat() + "Z" - ) - _write_status(current_status) - ACTIVE_RETRY = None - - threading.Thread( - target=_do_retry, args=(job_obj, retry_count), daemon=True - ).start() - - return jsonify({"accepted": True, "job": job_name, "retry_count": retry_count}) - - -@app.route("/api/cancel_job/", methods=["POST"]) -def cancel_job(job_name: str): - """Cancel a running or retry_running job by terminating its process. - - Concurrency rules: - - Only jobs with status running or retry_running can be cancelled - - Requires active PID tracking - """ - global ACTIVE_RETRY - with RETRY_LOCK: - status_obj = _read_status() - target_entry = _find_job_entry(status_obj, job_name) - if target_entry is None: - return jsonify({"error": "job_not_found"}), 404 - - st = target_entry.get("status") - if st not in {"running", "retry_running"}: - return jsonify({"error": "job_not_running", "status": st}), 400 - - pid = ACTIVE_JOB_PIDS.get(job_name) - if pid is None: - return jsonify({"error": "pid_not_tracked"}), 400 - - # Attempt to terminate process - try: - if os.name == "nt": # Windows - subprocess.run(["taskkill", "/F", "/T", "/PID", str(pid)], check=False) - else: - os.killpg(os.getpgid(pid), signal.SIGTERM) - - # Update status to cancelled - target_entry["status"] = "cancelled" - target_entry["cancelled_time"] = datetime.now(timezone.utc).strftime( - "%Y%m%dT%H%M%SZ" - ) - if "start_time" in target_entry: - try: - start = datetime.strptime( - target_entry["start_time"], "%Y%m%dT%H%M%SZ" - ) - elapsed = (datetime.now(timezone.utc) - start).total_seconds() - target_entry["duration_sec"] = round(elapsed, 2) - except Exception: - pass - - status_obj["generated_at"] = datetime.now(timezone.utc).isoformat() + "Z" - _write_status(status_obj) - - # Remove from active tracking - del ACTIVE_JOB_PIDS[job_name] - if ACTIVE_RETRY == job_name: - ACTIVE_RETRY = None - - return jsonify({"cancelled": True, "job": job_name, "pid": pid}) - except Exception as e: - return jsonify({"error": "termination_failed", "detail": str(e)}), 500 - - -@app.route("/api/training_progress") -def training_progress(): - return jsonify(_compute_training_progress()) - - -def _extract_job_metrics(job_rec: Dict[str, Any]) -> Optional[Dict[str, Any]]: - out_dir = job_rec.get("output_dir") - if not out_dir: - return None - p = Path(out_dir) / "metrics.jsonl" - if not p.exists(): - return None - pre_loss = pre_ppl = post_loss = post_ppl = last_step = None - try: - # Read last ~200 lines to capture metrics - lines = _tail_lines(p, 200) - import json as _json - - for ln in lines: - ln = ln.strip() - if not ln: - continue - try: - obj = _json.loads(ln) - except Exception: - continue - phase = obj.get("phase") - if phase == "pre": - pre_loss = obj.get("eval_loss") - pre_ppl = obj.get("eval_perplexity") - elif phase == "post": - post_loss = obj.get("eval_loss") - post_ppl = obj.get("eval_perplexity") - step = obj.get("step") or obj.get("global_step") - if isinstance(step, int): - last_step = step - if any(x is not None for x in (pre_loss, pre_ppl, post_loss, post_ppl)): - return { - "pre_eval_loss": pre_loss, - "pre_eval_perplexity": pre_ppl, - "post_eval_loss": post_loss, - "post_eval_perplexity": post_ppl, - "last_step": last_step, - } - except Exception: - return None - return None - - -def _tail_lines(path: Path, max_lines: int) -> List[str]: - """Efficiently read the last max_lines from a potentially large file.""" - try: - size = path.stat().st_size - if size <= 65536: # small file heuristic - stream instead of readlines() - with path.open("r", encoding="utf-8", errors="ignore") as f: - lines = [] - for line in f: - lines.append(line) - if len(lines) > max_lines: - lines.pop(0) # Keep only last max_lines - return lines - # Large file: read backwards in blocks - block_size = 8192 - lines: List[str] = [] - with path.open("rb") as f: - pos = max(0, size - block_size) - f.seek(pos) - chunk = f.read(block_size) - buf = chunk - # Expand backwards until enough lines or start of file - while True: - decoded = buf.decode("utf-8", errors="ignore") - lines = decoded.splitlines() - if len(lines) >= max_lines or pos == 0: - break - # Move further back - new_pos = max(0, pos - block_size) - read_size = pos - new_pos - f.seek(new_pos) - more = f.read(read_size) - buf = more + buf - pos = new_pos - return lines[-max_lines:] - except Exception: - return [] - - -HISTORY_PATH = DATA_OUT / "autotrain" / "progress_history.json" -_history_lock = threading.Lock() - - -def _append_progress_history(snapshot: dict, limit: int = 500): - try: - with _history_lock: - if HISTORY_PATH.exists(): - try: - hist = json.loads(HISTORY_PATH.read_text(encoding="utf-8")) - if not isinstance(hist, list): - hist = [] - except Exception: - hist = [] - else: - hist = [] - new_entry = { - "generated_at": snapshot.get("generated_at"), - "percent_complete": snapshot.get("percent_complete"), - "current_job_name": snapshot.get("current_job_name"), - "current_job_percent": snapshot.get("current_job_percent"), - "current_epoch": snapshot.get("current_epoch"), - } - if not hist or any( - new_entry.get(k) != hist[-1].get(k) - for k in ( - "percent_complete", - "current_job_name", - "current_job_percent", - "current_epoch", - ) - ): - hist.append(new_entry) - if len(hist) > limit: - hist = hist[-limit:] - HISTORY_PATH.parent.mkdir(parents=True, exist_ok=True) - HISTORY_PATH.write_text(json.dumps(hist), encoding="utf-8") - except Exception: - pass - - -@app.route("/api/training_progress_history") -def training_progress_history(): - try: - if HISTORY_PATH.exists(): - hist = json.loads(HISTORY_PATH.read_text(encoding="utf-8")) - return jsonify({"history": hist, "count": len(hist)}) - return jsonify({"history": [], "count": 0}) - except Exception: - return jsonify({"history": [], "count": 0, "error": "history_read_failed"}) - - -@app.route("/api/training_progress_history_csv") -def training_progress_history_csv(): - # Provide CSV export of progress history snapshots - import csv - import io - - output = io.StringIO() - writer = csv.writer(output) - writer.writerow( - [ - "generated_at", - "percent_complete", - "current_job_name", - "current_job_percent", - "current_epoch", - ] - ) - try: - hist = [] - if HISTORY_PATH.exists(): - hist = json.loads(HISTORY_PATH.read_text(encoding="utf-8")) - if not isinstance(hist, list): - hist = [] - for row in hist: - writer.writerow( - [ - row.get("generated_at"), - row.get("percent_complete"), - row.get("current_job_name"), - row.get("current_job_percent"), - row.get("current_epoch"), - ] - ) - except Exception: - pass - return output.getvalue(), 200, {"Content-Type": "text/csv"} - - -def _start_progress_emitter(interval_sec: int = 5): - """Background thread emitting training_progress via SocketIO when data changes.""" - last_json = None - - def loop(): - nonlocal last_json - while True: - payload = _compute_training_progress() - current_json = json.dumps(payload, sort_keys=True) - if current_json != last_json: - socketio.emit("training_progress", payload) - last_json = current_json - time.sleep(interval_sec) - - threading.Thread(target=loop, daemon=True).start() - - -_start_progress_emitter() - -if __name__ == "__main__": - debug_mode = os.environ.get("FLASK_DEBUG", "false").lower() == "true" - socketio.run(app, debug=debug_mode) +# reuse orchestrator logic for retry +import json +import os +import signal +import subprocess +import sys +import threading +import time +import traceback +from datetime import datetime, timedelta, timezone +from pathlib import Path +from typing import Any, Dict, List, Optional + +import yaml +from flask import Flask, jsonify, render_template +from flask_socketio import SocketIO + +from scripts.autotrain import Job, load_jobs, run_job + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) # Ensure scripts module is importable + + +app = Flask(__name__) +socketio = SocketIO(app, cors_allowed_origins="*") + +DATA_OUT = REPO_ROOT / "data_out" + + +@app.route("/") +def index(): + return render_template("index.html") + + +@app.route("/status") +def status(): + # Load orchestrator status + status_files = [ + "autotrain/status.json", + "quantum_autorun/status.json", + "evaluation_autorun/status.json", + ] + results = {} + for fname in status_files: + fpath = DATA_OUT / fname + if fpath.exists(): + with fpath.open() as f: + results[fname] = json.load(f) + return jsonify(results) + + +@app.route("/resources") +def resources(): + # Load latest resource snapshot + snap_path = DATA_OUT / "resource_monitor_snapshot.json" + if snap_path.exists(): + with snap_path.open() as f: + return jsonify(json.load(f)) + return jsonify({"error": "No snapshot found"}) + + +@app.route("/results") +def results(): + # Load latest exported results + res_path = ( + Path(__file__).resolve().parents[1] / "exports" / "all_orchestrators.json" + ) + if res_path.exists(): + with res_path.open() as f: + return jsonify(json.load(f)) + return jsonify({"error": "No results found"}) + + +def _compute_training_progress(): + """Compute incremental AutoTrain progress with ETA and success-only percentage. + + Returns dict with: + percent_complete: succeeded/total * 100 (failed excluded) + percent_success_alias: same value (alias for clarity) + eta_seconds / eta_iso: estimated remaining time until all jobs finish (based on avg succeeded duration) + average_job_duration_seconds: mean duration of succeeded jobs + """ + cfg_path = REPO_ROOT / "autotrain.yaml" + try: + cfg = ( + yaml.safe_load(cfg_path.read_text(encoding="utf-8")) + if cfg_path.exists() + else {} + ) + configured_jobs = [j.get("name") for j in cfg.get("jobs", []) if j.get("name")] + except Exception: + configured_jobs = [] + + status_path = DATA_OUT / "autotrain" / "status.json" + jobs_status = [] + if status_path.exists(): + try: + status_obj = json.loads(status_path.read_text(encoding="utf-8")) + jobs_status = status_obj.get("jobs", []) + except Exception: + jobs_status = [] + + status_map = {j.get("name"): j for j in jobs_status} + # Include extra jobs from status not present in config (e.g., local_job) + # Performance optimization: Direct iteration instead of .keys() + extra_jobs = [name for name in status_map if name not in configured_jobs] + all_job_names = configured_jobs + extra_jobs + enriched = [] + succeeded = failed = running = pending = validated = 0 + succeeded_durations = [] + running_job_name = None + running_log_path = None + total_epochs_running = None + # Compute runner-specific average durations for per-job ETA + runner_durations = {} + for rec in jobs_status: + if rec.get("status") == "succeeded" and rec.get("duration_sec"): + runner = rec.get("runner", "unknown") + runner_durations.setdefault(runner, []).append(rec["duration_sec"]) + runner_avg = {r: (sum(durs) / len(durs)) for r, durs in runner_durations.items()} + + for name in all_job_names: + rec = status_map.get(name) + if rec is None: + enriched.append({"name": name, "status": "pending"}) + pending += 1 + continue + st = rec.get("status") + if st == "succeeded": + succeeded += 1 + dur = rec.get("duration_sec") + if isinstance(dur, (int, float)): + succeeded_durations.append(dur) + elif st == "failed": + failed += 1 + elif st == "validated": + validated += 1 + elif st == "running" or st == "retry_running": + running += 1 + running_job_name = name + running_log_path = rec.get("log") + else: + pending += 1 + + # Compute per-job ETA if running + job_eta_sec = None + if st in {"running", "retry_running"} and rec.get("start_time"): + runner_type = rec.get("runner", "unknown") + avg_dur = runner_avg.get(runner_type) + if avg_dur: + try: + start_dt = datetime.strptime(rec["start_time"], "%Y%m%dT%H%M%SZ") + elapsed = (datetime.now(timezone.utc) - start_dt).total_seconds() + remaining = max(0, avg_dur - elapsed) + job_eta_sec = round(remaining, 1) + except Exception: + pass + + enriched.append( + { + "name": name, + "status": st, + "start_time": rec.get("start_time"), + "return_code": rec.get("return_code"), + "duration_sec": rec.get("duration_sec"), + "validated_type": rec.get("validated_type"), + "category": rec.get("category"), + "runner": rec.get("runner"), + "eta_sec": job_eta_sec, + } + ) + + total_jobs = len(all_job_names) + if total_jobs: + percent_complete = round((succeeded / total_jobs) * 100, 2) + else: + percent_complete = 0.0 + + if succeeded and succeeded_durations and total_jobs > succeeded: + avg = sum(succeeded_durations) / len(succeeded_durations) + remaining_jobs = total_jobs - succeeded + eta_seconds = avg * remaining_jobs + eta_iso = ( + datetime.now(timezone.utc) + timedelta(seconds=eta_seconds) + ).isoformat() + "Z" + else: + avg = None + eta_seconds = None + eta_iso = None + + # Intra-job progress parsing for current running job + current_job_percent = None + current_epoch = None + total_epochs = None + current_step = None + total_steps = None + if running_job_name and running_log_path: + try: + log_file = Path(running_log_path) + if log_file.exists(): + # Tail last ~400 lines for patterns + lines = _tail_lines(log_file, 400) + # Patterns: Epoch X/Y, global_step = Z, total_flos + # Compile regex patterns ONCE outside loop for performance + import re + + epoch_pat = re.compile(r"Epoch\s+(\d+)/(\d+)") + step_pat = re.compile(r"global_step\s*=\s*(\d+)") + last_epoch = None + last_total_epochs = None + last_step = None + for ln in lines: + e_m = epoch_pat.search(ln) + if e_m: + last_epoch = int(e_m.group(1)) + last_total_epochs = int(e_m.group(2)) + s_m = step_pat.search(ln) + if s_m: + last_step = int(s_m.group(1)) + if last_epoch and last_total_epochs: + current_epoch = last_epoch + total_epochs = last_total_epochs + # Compute epoch-based percent if no steps + current_job_percent = round( + ((last_epoch - 1) / max(1, last_total_epochs)) * 100, 2 + ) + # If we have steps and epochs, approximate total steps + if last_step is not None and total_epochs is not None: + # Heuristic: use max observed step as current_step; total steps unknown until training end. + current_step = last_step + # Try to infer total steps from training args saved in output dir (trainer_state.json) + trainer_state = log_file.parent / "trainer_state.json" + if trainer_state.exists(): + try: + st_obj = json.loads( + trainer_state.read_text(encoding="utf-8") + ) + opt = st_obj.get("trainer_state", st_obj) + max_steps = opt.get("max_steps") + if isinstance(max_steps, int) and max_steps > 0: + total_steps = max_steps + except Exception: + pass + if total_steps: + step_percent = round( + (current_step / max(1, total_steps)) * 100, 2 + ) + # Prefer finer-grained step percent when available + current_job_percent = step_percent + except Exception: + pass + + # Add metrics for succeeded jobs + for job_rec in enriched: + if job_rec.get("status") == "succeeded": + metrics = _extract_job_metrics(job_rec) + if metrics: + job_rec["metrics"] = metrics + + payload = { + "generated_at": datetime.now(timezone.utc).isoformat() + "Z", + "total_jobs": total_jobs, + "succeeded": succeeded, + "validated": validated, + "failed": failed, + "running": running, + "pending": pending, + "percent_complete": percent_complete, # success-only percent + "percent_success_alias": percent_complete, + "average_job_duration_seconds": avg, + "eta_seconds": eta_seconds, + "eta_iso": eta_iso, + "current_job_name": running_job_name, + "current_job_percent": current_job_percent, + "current_epoch": current_epoch, + "total_epochs": total_epochs, + "current_step": current_step, + "total_steps": total_steps, + "jobs": enriched, + } + _append_progress_history(payload) + return payload + + +# ========================= RETRY SUPPORT ========================= + + +RETRY_LOCK = threading.Lock() +ACTIVE_RETRY: Optional[str] = None +ACTIVE_JOB_PIDS: Dict[str, int] = {} +STATUS_PATH = DATA_OUT / "autotrain" / "status.json" + + +def _read_status() -> Dict[str, Any]: + if STATUS_PATH.exists(): + try: + return json.loads(STATUS_PATH.read_text(encoding="utf-8")) + except Exception: + return {"jobs": []} + return {"jobs": []} + + +def _write_status(obj: Dict[str, Any]) -> None: + try: + STATUS_PATH.parent.mkdir(parents=True, exist_ok=True) + STATUS_PATH.write_text(json.dumps(obj, indent=2), encoding="utf-8") + except Exception: + pass + + +def _find_job_entry(status_obj: Dict[str, Any], name: str) -> Optional[Dict[str, Any]]: + for j in status_obj.get("jobs", []): + if j.get("name") == name: + return j + return None + + +@app.route("/api/retry_job/", methods=["POST"]) +def retry_job(job_name: str): + """Retry a single AutoTrain job safely. + + Concurrency rules: + - Only one retry active at a time (ACTIVE_RETRY guarded by RETRY_LOCK) + - No retry allowed while any job is currently running. + """ + global ACTIVE_RETRY + with RETRY_LOCK: + status_obj = _read_status() + jobs_list = status_obj.get("jobs", []) + if ACTIVE_RETRY is not None: + return ( + jsonify({"error": "retry_in_progress", "active_retry": ACTIVE_RETRY}), + 409, + ) + # Disallow if any job currently running (avoid log collision / resource contention) + if any(j.get("status") in {"running", "retry_running"} for j in jobs_list): + return jsonify({"error": "job_currently_running"}), 409 + + target_entry = _find_job_entry(status_obj, job_name) + if target_entry is None: + return jsonify({"error": "job_not_found"}), 404 + + # Load job from config so we have full definition (dataset, save_dir, etc.) + cfg_path = REPO_ROOT / "autotrain.yaml" + try: + config_jobs = {j.name: j for j in load_jobs(cfg_path)} + except Exception: + return jsonify({"error": "config_load_failed"}), 500 + if job_name not in config_jobs: + return jsonify({"error": "job_not_in_config"}), 400 + job_obj: Job = config_jobs[job_name] + + # Prepare placeholder update + retry_count = int(target_entry.get("retry_count", 0)) + 1 + target_entry.update( + { + "status": "retry_running", + "retry_count": retry_count, + "retry_start_time": datetime.now(timezone.utc).strftime( + "%Y%m%dT%H%M%SZ" + ), + } + ) + status_obj["generated_at"] = datetime.now(timezone.utc).isoformat() + "Z" + _write_status(status_obj) + ACTIVE_RETRY = job_name + + def _do_retry(job: Job, retry_num: int): + global ACTIVE_RETRY + try: + result = run_job(job, dry_run=False, job_index=0, total_jobs=1) + except Exception as e: # capture unexpected exception + result = { + "name": job.name, + "runner": job.runner, + "status": "failed", + "error": str(e), + "trace": traceback.format_exc().splitlines()[-5:], + "retry_count": retry_num, + } + # Merge result back into status + with RETRY_LOCK: + current_status = _read_status() + entry = _find_job_entry(current_status, job.name) + if entry is not None: + # Preserve retry_count from placeholder + preserved_retry = entry.get("retry_count", retry_num) + result["retry_count"] = preserved_retry + result["status"] = result.get("status") + result["retry_completed_time"] = datetime.now( + timezone.utc + ).strftime("%Y%m%dT%H%M%SZ") + # Mark as succeeded/failed (no special retry_running state anymore) + for k in [ + "status", + "return_code", + "duration_sec", + "duration_human", + "log", + "metrics", + "output_dir", + "cmd", + "start_time", + "retry_count", + "retry_completed_time", + ]: + v = result.get(k) + if v is not None: + entry[k] = v + current_status["generated_at"] = ( + datetime.now(timezone.utc).isoformat() + "Z" + ) + _write_status(current_status) + ACTIVE_RETRY = None + + threading.Thread( + target=_do_retry, args=(job_obj, retry_count), daemon=True + ).start() + + return jsonify({"accepted": True, "job": job_name, "retry_count": retry_count}) + + +@app.route("/api/cancel_job/", methods=["POST"]) +def cancel_job(job_name: str): + """Cancel a running or retry_running job by terminating its process. + + Concurrency rules: + - Only jobs with status running or retry_running can be cancelled + - Requires active PID tracking + """ + global ACTIVE_RETRY + with RETRY_LOCK: + status_obj = _read_status() + target_entry = _find_job_entry(status_obj, job_name) + if target_entry is None: + return jsonify({"error": "job_not_found"}), 404 + + st = target_entry.get("status") + if st not in {"running", "retry_running"}: + return jsonify({"error": "job_not_running", "status": st}), 400 + + pid = ACTIVE_JOB_PIDS.get(job_name) + if pid is None: + return jsonify({"error": "pid_not_tracked"}), 400 + + # Attempt to terminate process + try: + if os.name == "nt": # Windows + subprocess.run(["taskkill", "/F", "/T", "/PID", str(pid)], check=False) + else: + os.killpg(os.getpgid(pid), signal.SIGTERM) + + # Update status to cancelled + target_entry["status"] = "cancelled" + target_entry["cancelled_time"] = datetime.now(timezone.utc).strftime( + "%Y%m%dT%H%M%SZ" + ) + if "start_time" in target_entry: + try: + start = datetime.strptime( + target_entry["start_time"], "%Y%m%dT%H%M%SZ" + ) + elapsed = (datetime.now(timezone.utc) - start).total_seconds() + target_entry["duration_sec"] = round(elapsed, 2) + except Exception: + pass + + status_obj["generated_at"] = datetime.now(timezone.utc).isoformat() + "Z" + _write_status(status_obj) + + # Remove from active tracking + del ACTIVE_JOB_PIDS[job_name] + if ACTIVE_RETRY == job_name: + ACTIVE_RETRY = None + + return jsonify({"cancelled": True, "job": job_name, "pid": pid}) + except Exception as e: + return jsonify({"error": "termination_failed", "detail": str(e)}), 500 + + +@app.route("/api/training_progress") +def training_progress(): + return jsonify(_compute_training_progress()) + + +def _extract_job_metrics(job_rec: Dict[str, Any]) -> Optional[Dict[str, Any]]: + out_dir = job_rec.get("output_dir") + if not out_dir: + return None + p = Path(out_dir) / "metrics.jsonl" + if not p.exists(): + return None + pre_loss = pre_ppl = post_loss = post_ppl = last_step = None + try: + # Read last ~200 lines to capture metrics + lines = _tail_lines(p, 200) + import json as _json + + for ln in lines: + ln = ln.strip() + if not ln: + continue + try: + obj = _json.loads(ln) + except Exception: + continue + phase = obj.get("phase") + if phase == "pre": + pre_loss = obj.get("eval_loss") + pre_ppl = obj.get("eval_perplexity") + elif phase == "post": + post_loss = obj.get("eval_loss") + post_ppl = obj.get("eval_perplexity") + step = obj.get("step") or obj.get("global_step") + if isinstance(step, int): + last_step = step + if any(x is not None for x in (pre_loss, pre_ppl, post_loss, post_ppl)): + return { + "pre_eval_loss": pre_loss, + "pre_eval_perplexity": pre_ppl, + "post_eval_loss": post_loss, + "post_eval_perplexity": post_ppl, + "last_step": last_step, + } + except Exception: + return None + return None + + +def _tail_lines(path: Path, max_lines: int) -> List[str]: + """Efficiently read the last max_lines from a potentially large file.""" + try: + size = path.stat().st_size + if size <= 65536: # small file heuristic - stream instead of readlines() + with path.open("r", encoding="utf-8", errors="ignore") as f: + lines = [] + for line in f: + lines.append(line) + if len(lines) > max_lines: + lines.pop(0) # Keep only last max_lines + return lines + # Large file: read backwards in blocks + block_size = 8192 + lines: List[str] = [] + with path.open("rb") as f: + pos = max(0, size - block_size) + f.seek(pos) + chunk = f.read(block_size) + buf = chunk + # Expand backwards until enough lines or start of file + while True: + decoded = buf.decode("utf-8", errors="ignore") + lines = decoded.splitlines() + if len(lines) >= max_lines or pos == 0: + break + # Move further back + new_pos = max(0, pos - block_size) + read_size = pos - new_pos + f.seek(new_pos) + more = f.read(read_size) + buf = more + buf + pos = new_pos + return lines[-max_lines:] + except Exception: + return [] + + +HISTORY_PATH = DATA_OUT / "autotrain" / "progress_history.json" +_history_lock = threading.Lock() + + +def _append_progress_history(snapshot: dict, limit: int = 500): + try: + with _history_lock: + if HISTORY_PATH.exists(): + try: + hist = json.loads(HISTORY_PATH.read_text(encoding="utf-8")) + if not isinstance(hist, list): + hist = [] + except Exception: + hist = [] + else: + hist = [] + new_entry = { + "generated_at": snapshot.get("generated_at"), + "percent_complete": snapshot.get("percent_complete"), + "current_job_name": snapshot.get("current_job_name"), + "current_job_percent": snapshot.get("current_job_percent"), + "current_epoch": snapshot.get("current_epoch"), + } + if not hist or any( + new_entry.get(k) != hist[-1].get(k) + for k in ( + "percent_complete", + "current_job_name", + "current_job_percent", + "current_epoch", + ) + ): + hist.append(new_entry) + if len(hist) > limit: + hist = hist[-limit:] + HISTORY_PATH.parent.mkdir(parents=True, exist_ok=True) + HISTORY_PATH.write_text(json.dumps(hist), encoding="utf-8") + except Exception: + pass + + +@app.route("/api/training_progress_history") +def training_progress_history(): + try: + if HISTORY_PATH.exists(): + hist = json.loads(HISTORY_PATH.read_text(encoding="utf-8")) + return jsonify({"history": hist, "count": len(hist)}) + return jsonify({"history": [], "count": 0}) + except Exception: + return jsonify({"history": [], "count": 0, "error": "history_read_failed"}) + + +@app.route("/api/training_progress_history_csv") +def training_progress_history_csv(): + # Provide CSV export of progress history snapshots + import csv + import io + + output = io.StringIO() + writer = csv.writer(output) + writer.writerow( + [ + "generated_at", + "percent_complete", + "current_job_name", + "current_job_percent", + "current_epoch", + ] + ) + try: + hist = [] + if HISTORY_PATH.exists(): + hist = json.loads(HISTORY_PATH.read_text(encoding="utf-8")) + if not isinstance(hist, list): + hist = [] + for row in hist: + writer.writerow( + [ + row.get("generated_at"), + row.get("percent_complete"), + row.get("current_job_name"), + row.get("current_job_percent"), + row.get("current_epoch"), + ] + ) + except Exception: + pass + return output.getvalue(), 200, {"Content-Type": "text/csv"} + + +def _start_progress_emitter(interval_sec: int = 5): + """Background thread emitting training_progress via SocketIO when data changes.""" + last_json = None + + def loop(): + nonlocal last_json + while True: + payload = _compute_training_progress() + current_json = json.dumps(payload, sort_keys=True) + if current_json != last_json: + socketio.emit("training_progress", payload) + last_json = current_json + time.sleep(interval_sec) + + threading.Thread(target=loop, daemon=True).start() + + +_start_progress_emitter() + +if __name__ == "__main__": + debug_mode = os.environ.get("FLASK_DEBUG", "false").lower() == "true" + socketio.run(app, debug=debug_mode) diff --git a/apps/dashboard/consolidated.html b/apps/dashboard/consolidated.html index 3c3217198..4c9d99511 100644 --- a/apps/dashboard/consolidated.html +++ b/apps/dashboard/consolidated.html @@ -1,1314 +1,1314 @@ - - - - - - - QAI Dashboard - Unified Training Platform - - - - - - - - - - - - - -
- -
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🎯 QAI Dashboard

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Quantum AI Training Platform - Unified Control Center

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- Select up to 4 completed jobs from the History tab to compare. -

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Analyze dataset characteristics and get AI-powered hyperparameter - recommendations.

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🎮 VRAM Calculator

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Calculate safe batch size based on GPU memory and model configuration. -

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🎯 Hyperparameter Optimizer

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Automated hyperparameter tuning with Bayesian optimization, grid search, - and early stopping.

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- - - - - + + + + + + + QAI Dashboard - Unified Training Platform + + + + + + + + + + + + + +
+ +
+

🎯 QAI Dashboard

+

Quantum AI Training Platform - Unified Control Center

+
+
+ + System Online +
+
+ 🖥️ GPU Ready +
+
+ 📡 API Connected +
+
+
+ + + + + +
+
+
+
Total Jobs
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0
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+
+
Avg Loss
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0.00
+
+
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Best Model
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N/A
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Total Time
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0h
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+ +
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📈 Loss Progression

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⚡ Recent Activity

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No recent activity

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🎛️ Quick Actions

+
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+
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🚀 Start Training Job

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+ + +
+
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+ +
+
+ + +
+
+ + +
+
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+ Quick Presets: +
+ + + + +
+
+ + +
+
+ ⚙️ Advanced Settings + ▼ +
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+
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+

⏱️ Time Distribution

+
+
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+

📈 Advanced Model Comparison (Phase 29)

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+

+ Select up to 4 completed jobs from the History tab to compare. +

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+ +
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+ + +
+
+
+

📜 Training Session History

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+ + + +
+
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+

No training history yet

+
+
+
+ + +
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+
+
+

🔍 Dataset Profiler

+
+

Analyze dataset characteristics and get AI-powered hyperparameter + recommendations.

+
+ + +
+ +
+
+ +
+
+

🎮 VRAM Calculator

+
+

Calculate safe batch size based on GPU memory and model configuration. +

+
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⚠️ Anomaly Detector Status

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Total Anomalies
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0
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Spikes Detected
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0
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+
+
Divergences
+
0
+
+
+
Stagnations
+
0
+
+
+
+ + +
+
+

🎯 Hyperparameter Optimizer

+
+

Automated hyperparameter tuning with Bayesian optimization, grid search, + and early stopping.

+
+
+ + +
+
+ + +
+
+ + + +
+
+
+ + +
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+ + + + + diff --git a/apps/dashboard/enhanced.html b/apps/dashboard/enhanced.html index 16a3a3695..3c912665f 100644 --- a/apps/dashboard/enhanced.html +++ b/apps/dashboard/enhanced.html @@ -1,1008 +1,1008 @@ - - - - - - QAI Training Dashboard - Enhanced - - - - -
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🚀 QAI Training Dashboard

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Advanced AI Training Monitoring & Control

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🚀 QAI Training Dashboard

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Advanced AI Training Monitoring & Control

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+ Last updated: Never +
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0 of 0 jobs completed

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Recent Jobs

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All Training Jobs

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Model Comparison

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+ + + + diff --git a/apps/dashboard/gpu_monitor.py b/apps/dashboard/gpu_monitor.py index ff39391d2..f199acc23 100644 --- a/apps/dashboard/gpu_monitor.py +++ b/apps/dashboard/gpu_monitor.py @@ -1,151 +1,151 @@ -""" -GPU Monitoring Utilities for QAI Dashboard -Provides real-time GPU metrics via nvidia-smi -""" - -import json -import subprocess -from typing import Dict, List - - -def get_gpu_info() -> Dict: - """Get detailed GPU information using nvidia-smi""" - try: - result = subprocess.run( - [ - "nvidia-smi", - "--query-gpu=index,name,temperature.gpu,utilization.gpu,utilization.memory,memory.used,memory.total,power.draw,power.limit", - "--format=csv,noheader,nounits", - ], - capture_output=True, - text=True, - timeout=5, - ) - - if result.returncode != 0: - return {"error": "nvidia-smi failed", "available": False} - - gpus = [] - for line in result.stdout.strip().split("\n"): - if not line: - continue - parts = [p.strip() for p in line.split(",")] - if len(parts) >= 9: - gpus.append( - { - "index": int(parts[0]), - "name": parts[1], - "temperature": ( - float(parts[2]) if parts[2] not in ["N/A", "[N/A]"] else 0 - ), - "utilization": ( - float(parts[3]) if parts[3] not in ["N/A", "[N/A]"] else 0 - ), - "memory_utilization": ( - float(parts[4]) if parts[4] not in ["N/A", "[N/A]"] else 0 - ), - "memory_used": ( - float(parts[5]) if parts[5] not in ["N/A", "[N/A]"] else 0 - ), - "memory_total": ( - float(parts[6]) if parts[6] not in ["N/A", "[N/A]"] else 0 - ), - "power_draw": ( - float(parts[7]) if parts[7] not in ["N/A", "[N/A]"] else 0 - ), - "power_limit": ( - float(parts[8]) if parts[8] not in ["N/A", "[N/A]"] else 0 - ), - } - ) - - return {"available": True, "count": len(gpus), "gpus": gpus} - - except FileNotFoundError: - return {"error": "nvidia-smi not found", "available": False} - except subprocess.TimeoutExpired: - return {"error": "nvidia-smi timeout", "available": False} - except Exception as e: - return {"error": str(e), "available": False} - - -def get_gpu_processes() -> List[Dict]: - """Get processes currently using GPU""" - try: - result = subprocess.run( - [ - "nvidia-smi", - "--query-compute-apps=pid,name,used_memory", - "--format=csv,noheader,nounits", - ], - capture_output=True, - text=True, - timeout=5, - ) - - if result.returncode != 0: - return [] - - processes = [] - for line in result.stdout.strip().split("\n"): - if not line: - continue - parts = [p.strip() for p in line.split(",")] - if len(parts) >= 3: - processes.append( - { - "pid": int(parts[0]), - "name": parts[1], - "memory_mb": ( - float(parts[2]) if parts[2] not in ["N/A", "[N/A]"] else 0 - ), - } - ) - - return processes - - except Exception: - return [] - - -def get_system_resources() -> Dict: - """Get CPU and memory information""" - try: - import psutil - - return { - "cpu": { - "percent": psutil.cpu_percent(interval=0.1), - "count": psutil.cpu_count(), - "freq": psutil.cpu_freq().current if psutil.cpu_freq() else 0, - }, - "memory": { - "total_gb": round(psutil.virtual_memory().total / (1024**3), 2), - "used_gb": round(psutil.virtual_memory().used / (1024**3), 2), - "percent": psutil.virtual_memory().percent, - }, - "disk": { - "total_gb": round(psutil.disk_usage("/").total / (1024**3), 2), - "used_gb": round(psutil.disk_usage("/").used / (1024**3), 2), - "percent": psutil.disk_usage("/").percent, - }, - } - except ImportError: - return {"error": "psutil not installed"} - except Exception as e: - return {"error": str(e)} - - -if __name__ == "__main__": - # Test GPU monitoring - print("=== GPU Information ===") - gpu_info = get_gpu_info() - print(json.dumps(gpu_info, indent=2)) - - print("\n=== GPU Processes ===") - processes = get_gpu_processes() - print(json.dumps(processes, indent=2)) - - print("\n=== System Resources ===") - resources = get_system_resources() - print(json.dumps(resources, indent=2)) +""" +GPU Monitoring Utilities for QAI Dashboard +Provides real-time GPU metrics via nvidia-smi +""" + +import json +import subprocess +from typing import Dict, List + + +def get_gpu_info() -> Dict: + """Get detailed GPU information using nvidia-smi""" + try: + result = subprocess.run( + [ + "nvidia-smi", + "--query-gpu=index,name,temperature.gpu,utilization.gpu,utilization.memory,memory.used,memory.total,power.draw,power.limit", + "--format=csv,noheader,nounits", + ], + capture_output=True, + text=True, + timeout=5, + ) + + if result.returncode != 0: + return {"error": "nvidia-smi failed", "available": False} + + gpus = [] + for line in result.stdout.strip().split("\n"): + if not line: + continue + parts = [p.strip() for p in line.split(",")] + if len(parts) >= 9: + gpus.append( + { + "index": int(parts[0]), + "name": parts[1], + "temperature": ( + float(parts[2]) if parts[2] not in ["N/A", "[N/A]"] else 0 + ), + "utilization": ( + float(parts[3]) if parts[3] not in ["N/A", "[N/A]"] else 0 + ), + "memory_utilization": ( + float(parts[4]) if parts[4] not in ["N/A", "[N/A]"] else 0 + ), + "memory_used": ( + float(parts[5]) if parts[5] not in ["N/A", "[N/A]"] else 0 + ), + "memory_total": ( + float(parts[6]) if parts[6] not in ["N/A", "[N/A]"] else 0 + ), + "power_draw": ( + float(parts[7]) if parts[7] not in ["N/A", "[N/A]"] else 0 + ), + "power_limit": ( + float(parts[8]) if parts[8] not in ["N/A", "[N/A]"] else 0 + ), + } + ) + + return {"available": True, "count": len(gpus), "gpus": gpus} + + except FileNotFoundError: + return {"error": "nvidia-smi not found", "available": False} + except subprocess.TimeoutExpired: + return {"error": "nvidia-smi timeout", "available": False} + except Exception as e: + return {"error": str(e), "available": False} + + +def get_gpu_processes() -> List[Dict]: + """Get processes currently using GPU""" + try: + result = subprocess.run( + [ + "nvidia-smi", + "--query-compute-apps=pid,name,used_memory", + "--format=csv,noheader,nounits", + ], + capture_output=True, + text=True, + timeout=5, + ) + + if result.returncode != 0: + return [] + + processes = [] + for line in result.stdout.strip().split("\n"): + if not line: + continue + parts = [p.strip() for p in line.split(",")] + if len(parts) >= 3: + processes.append( + { + "pid": int(parts[0]), + "name": parts[1], + "memory_mb": ( + float(parts[2]) if parts[2] not in ["N/A", "[N/A]"] else 0 + ), + } + ) + + return processes + + except Exception: + return [] + + +def get_system_resources() -> Dict: + """Get CPU and memory information""" + try: + import psutil + + return { + "cpu": { + "percent": psutil.cpu_percent(interval=0.1), + "count": psutil.cpu_count(), + "freq": psutil.cpu_freq().current if psutil.cpu_freq() else 0, + }, + "memory": { + "total_gb": round(psutil.virtual_memory().total / (1024**3), 2), + "used_gb": round(psutil.virtual_memory().used / (1024**3), 2), + "percent": psutil.virtual_memory().percent, + }, + "disk": { + "total_gb": round(psutil.disk_usage("/").total / (1024**3), 2), + "used_gb": round(psutil.disk_usage("/").used / (1024**3), 2), + "percent": psutil.disk_usage("/").percent, + }, + } + except ImportError: + return {"error": "psutil not installed"} + except Exception as e: + return {"error": str(e)} + + +if __name__ == "__main__": + # Test GPU monitoring + print("=== GPU Information ===") + gpu_info = get_gpu_info() + print(json.dumps(gpu_info, indent=2)) + + print("\n=== GPU Processes ===") + processes = get_gpu_processes() + print(json.dumps(processes, indent=2)) + + print("\n=== System Resources ===") + resources = get_system_resources() + print(json.dumps(resources, indent=2)) diff --git a/apps/dashboard/hub.html b/apps/dashboard/hub.html index d93e29b07..bf4a499ee 100644 --- a/apps/dashboard/hub.html +++ b/apps/dashboard/hub.html @@ -1,1335 +1,1335 @@ - - - - - - - QAI Hub - Command Center - - - - - - -
- -
-

🌌 QAI Command Center

-

Quantum-AI Hybrid Training & Deployment Hub

-
- -
-
-
-
- Dashboard Server -
-
-
- Training Pipeline -
-
-
- API Endpoints -
-
-
- Quantum MCP -
- -
-
- -
-
-
--
-
Training Jobs
-
-
-
--
-
Datasets
-
-
-
--
-
Trained Models
-
-
-
--
-
GPU Usage
-
-
- -
-
- - -
-

-
- -
-

⚡ Quick Actions

-
-
-
🚀
-
Start Training
-
-
-
📊
-
View Datasets
-
-
-
🤖
-
Browse Models
-
-
-
📈
-
Monitor GPU
-
-
-
⚛️
-
Quantum Jobs
-
-
-
💬
-
Chat Interface
-
-
-
📈
-
Analytics
-
-
-
📋
-
Job Queue
-
-
-
👤
-
Aria Character
-
-
-
- -
- -
-
🎯
-

Training Dashboard

-

- Comprehensive LoRA fine-tuning interface with real-time monitoring, - advanced configuration, and automated job management. -

-
    -
  • 20+ training parameters
    • -
  • Real-time GPU monitoring
    • -
  • 4 preset configurations
    • -
  • Config save/load (JSON)
    • -
  • Live job tracking
    • -
-
- Open Dashboard - -
-
- - -
-
⚛️
-

Quantum ML Pipeline

-

- Hybrid quantum-classical machine learning with PennyLane, - Azure Quantum integration, and circuit optimization. -

-
    -
  • Quantum circuit training
    • -
  • Azure Quantum jobs
    • -
  • 8 MCP server tools
    • -
  • Local & cloud simulators
    • -
  • Cost estimation
    • -
-
- - -
-
- - -
-
💬
-

Multi-Provider Chat

-

- Unified chat CLI supporting Azure OpenAI, OpenAI, LoRA adapters, - and local echo with automatic provider detection. -

-
    -
  • 4 provider backends
    • -
  • Auto-detection & fallback
    • -
  • Streaming responses
    • -
  • Memory persistence (SQL/Cosmos)
    • -
  • CLI & API interfaces
    • -
-
- - -
-
- - -
-
📊
-

Evaluation Suite

-

- Automated model evaluation with perplexity metrics, loss tracking, - and comparison tools for iterative improvement. -

-
    -
  • Perplexity calculation
    • -
  • Loss comparison
    • -
  • Batch evaluation
    • -
  • Model ranking
    • -
  • Export reports (MD/JSON)
    • -
-
- - -
-
- - -
-
🗃️
-

Dataset Manager

-

- Browse, validate, and manage training datasets with automatic - sample counting and format validation. -

-
    -
  • Auto-discovery
    • -
  • Sample counting
    • -
  • Format validation
    • -
  • Train/test splits
    • -
  • Dataset generation
    • -
-
- - -
-
- - -
-
🔌
-

API Gateway

-

- Azure Functions unified API with /chat, /quantum, and /ai/status - endpoints. Dynamic imports from all three projects. -

-
    -
  • Multi-project integration
    • -
  • SQL/Cosmos persistence
    • -
  • Telemetry (App Insights)
    • -
  • Rate limiting
    • -
  • CORS enabled
    • -
-
- - -
-
- - -
-
📈
-

Resource Monitor

-

- Real-time GPU, CPU, memory, and disk monitoring with historical - tracking and alert thresholds. -

-
    -
  • GPU utilization & VRAM
    • -
  • CPU & RAM usage
    • -
  • Process tracking
    • -
  • Historical graphs
    • -
  • Alert system
    • -
-
- - -
-
- - -
-
🔄
-

CI/CD Pipeline

-

- Automated testing, validation, and deployment orchestration - with master scheduler and job coordination. -

-
    -
  • 40 unit tests
    • -
  • 30 integration tests
    • -
  • Orchestrator validation
    • -
  • Artifact packaging
    • -
  • Deployment automation
    • -
-
- - -
-
- - -
-
👤
-

Aria Character

-

- Interactive AI character with natural language commands, - 3D CSS animations, and LLM-powered auto-execution. -

-
    -
  • Natural language commands
    • -
  • 8 core action types
    • -
  • Object interaction & physics
    • -
  • World generation via LLM
    • -
  • Dark mode support
    • -
-
- Open Aria - Auto-Execute -
-
-
- - -
- -
- - - - - - - + + + + + + + QAI Hub - Command Center + + + + + + +
+ +
+

🌌 QAI Command Center

+

Quantum-AI Hybrid Training & Deployment Hub

+
+ +
+
+
+
+ Dashboard Server +
+
+
+ Training Pipeline +
+
+
+ API Endpoints +
+
+
+ Quantum MCP +
+ +
+
+ +
+
+
--
+
Training Jobs
+
+
+
--
+
Datasets
+
+
+
--
+
Trained Models
+
+
+
--
+
GPU Usage
+
+
+ +
+
+ + +
+

+
+ +
+

⚡ Quick Actions

+
+
+
🚀
+
Start Training
+
+
+
📊
+
View Datasets
+
+
+
🤖
+
Browse Models
+
+
+
📈
+
Monitor GPU
+
+
+
⚛️
+
Quantum Jobs
+
+
+
💬
+
Chat Interface
+
+
+
📈
+
Analytics
+
+
+
📋
+
Job Queue
+
+
+
👤
+
Aria Character
+
+
+
+ +
+ +
+
🎯
+

Training Dashboard

+

+ Comprehensive LoRA fine-tuning interface with real-time monitoring, + advanced configuration, and automated job management. +

+
    +
  • 20+ training parameters
    • +
  • Real-time GPU monitoring
    • +
  • 4 preset configurations
    • +
  • Config save/load (JSON)
    • +
  • Live job tracking
    • +
+
+ Open Dashboard + +
+
+ + +
+
⚛️
+

Quantum ML Pipeline

+

+ Hybrid quantum-classical machine learning with PennyLane, + Azure Quantum integration, and circuit optimization. +

+
    +
  • Quantum circuit training
    • +
  • Azure Quantum jobs
    • +
  • 8 MCP server tools
    • +
  • Local & cloud simulators
    • +
  • Cost estimation
    • +
+
+ + +
+
+ + +
+
💬
+

Multi-Provider Chat

+

+ Unified chat CLI supporting Azure OpenAI, OpenAI, LoRA adapters, + and local echo with automatic provider detection. +

+
    +
  • 4 provider backends
    • +
  • Auto-detection & fallback
    • +
  • Streaming responses
    • +
  • Memory persistence (SQL/Cosmos)
    • +
  • CLI & API interfaces
    • +
+
+ + +
+
+ + +
+
📊
+

Evaluation Suite

+

+ Automated model evaluation with perplexity metrics, loss tracking, + and comparison tools for iterative improvement. +

+
    +
  • Perplexity calculation
    • +
  • Loss comparison
    • +
  • Batch evaluation
    • +
  • Model ranking
    • +
  • Export reports (MD/JSON)
    • +
+
+ + +
+
+ + +
+
🗃️
+

Dataset Manager

+

+ Browse, validate, and manage training datasets with automatic + sample counting and format validation. +

+
    +
  • Auto-discovery
    • +
  • Sample counting
    • +
  • Format validation
    • +
  • Train/test splits
    • +
  • Dataset generation
    • +
+
+ + +
+
+ + +
+
🔌
+

API Gateway

+

+ Azure Functions unified API with /chat, /quantum, and /ai/status + endpoints. Dynamic imports from all three projects. +

+
    +
  • Multi-project integration
    • +
  • SQL/Cosmos persistence
    • +
  • Telemetry (App Insights)
    • +
  • Rate limiting
    • +
  • CORS enabled
    • +
+
+ + +
+
+ + +
+
📈
+

Resource Monitor

+

+ Real-time GPU, CPU, memory, and disk monitoring with historical + tracking and alert thresholds. +

+
    +
  • GPU utilization & VRAM
    • +
  • CPU & RAM usage
    • +
  • Process tracking
    • +
  • Historical graphs
    • +
  • Alert system
    • +
+
+ + +
+
+ + +
+
🔄
+

CI/CD Pipeline

+

+ Automated testing, validation, and deployment orchestration + with master scheduler and job coordination. +

+
    +
  • 40 unit tests
    • +
  • 30 integration tests
    • +
  • Orchestrator validation
    • +
  • Artifact packaging
    • +
  • Deployment automation
    • +
+
+ + +
+
+ + +
+
👤
+

Aria Character

+

+ Interactive AI character with natural language commands, + 3D CSS animations, and LLM-powered auto-execution. +

+
    +
  • Natural language commands
    • +
  • 8 core action types
    • +
  • Object interaction & physics
    • +
  • World generation via LLM
    • +
  • Dark mode support
    • +
+
+ Open Aria + Auto-Execute +
+
+
+ + +
+ +
+ + + + + + + diff --git a/apps/dashboard/hyperparameter-optimizer.js b/apps/dashboard/hyperparameter-optimizer.js index 37df8ba20..74909eefa 100644 --- a/apps/dashboard/hyperparameter-optimizer.js +++ b/apps/dashboard/hyperparameter-optimizer.js @@ -1,591 +1,591 @@ -/** - * Automated Hyperparameter Optimization - * Smart tuning with Bayesian optimization, grid search, and early stopping - */ - -class HyperparameterOptimizer { - constructor() { - this.searchSpace = {}; - this.trials = []; - this.bestConfig = null; - this.bestScore = -Infinity; - this.currentTrial = 0; - this.maxTrials = 10; - this.strategy = 'bayesian'; // bayesian, grid, random - this.earlyStopping = { - enabled: true, - patience: 3, - minDelta: 0.01 - }; - } - - /** - * Define search space - */ - defineSearchSpace(space) { - this.searchSpace = space; - console.log('[HyperOptim] Search space defined:', space); - } - - /** - * Start optimization - */ - async startOptimization(config) { - this.maxTrials = config.maxTrials || 10; - this.strategy = config.strategy || 'bayesian'; - this.currentTrial = 0; - this.trials = []; - this.bestConfig = null; - this.bestScore = -Infinity; - - console.log(`[HyperOptim] Starting ${this.strategy} optimization with ${this.maxTrials} trials`); - - // Show optimization UI - this.showOptimizationUI(); - - // Generate trial configurations based on strategy - const trialConfigs = this.generateTrialConfigs(); - - // Run trials - for (let i = 0; i < trialConfigs.length; i++) { - if (this.shouldStopEarly()) { - console.log('[HyperOptim] Early stopping triggered'); - break; - } - - await this.runTrial(trialConfigs[i], i + 1); - } - - // Show final results - this.showOptimizationResults(); - } - - /** - * Generate trial configurations based on strategy - */ - generateTrialConfigs() { - switch (this.strategy) { - case 'grid': - return this.generateGridSearch(); - case 'random': - return this.generateRandomSearch(); - case 'bayesian': - return this.generateBayesianSearch(); - default: - return this.generateRandomSearch(); - } - } - - /** - * Grid search: Exhaustive search over all combinations - */ - generateGridSearch() { - const configs = []; - const params = Object.keys(this.searchSpace); - - // Generate all combinations (limited to maxTrials) - const combinations = this.cartesianProduct( - params.map(p => this.searchSpace[p].values || this.searchSpace[p].range) - ); - - return combinations.slice(0, this.maxTrials).map(combo => { - const config = {}; - params.forEach((p, idx) => { - config[p] = combo[idx]; - }); - return config; - }); - } - - /** - * Random search: Random sampling from search space - */ - generateRandomSearch() { - const configs = []; - - for (let i = 0; i < this.maxTrials; i++) { - const config = {}; - Object.keys(this.searchSpace).forEach(param => { - const space = this.searchSpace[param]; - - if (space.values) { - // Categorical: random choice - config[param] = space.values[Math.floor(Math.random() * space.values.length)]; - } else if (space.range) { - // Continuous: random value in range - const [min, max] = space.range; - const scale = space.scale || 'linear'; - - if (scale === 'log') { - const logMin = Math.log(min); - const logMax = Math.log(max); - config[param] = Math.exp(logMin + Math.random() * (logMax - logMin)); - } else { - config[param] = min + Math.random() * (max - min); - } - - // Round if integer type - if (space.type === 'int') { - config[param] = Math.round(config[param]); - } - } - }); - configs.push(config); - } - - return configs; - } - - /** - * Bayesian optimization: Intelligent search using Gaussian Process - */ - generateBayesianSearch() { - // Start with random samples - const initialSamples = 3; - const configs = this.generateRandomSearch().slice(0, initialSamples); - - // For remaining trials, use acquisition function - // (Simplified version - real Bayesian optimization is more complex) - for (let i = initialSamples; i < this.maxTrials; i++) { - const config = this.selectNextBayesianConfig(); - configs.push(config); - } - - return configs; - } - - /** - * Select next configuration using Expected Improvement (simplified) - */ - selectNextBayesianConfig() { - // Generate candidate configs - const candidates = this.generateRandomSearch().slice(0, 20); - - // Score each candidate based on distance from previous trials - let bestCandidate = candidates[0]; - let bestEI = -Infinity; - - candidates.forEach(candidate => { - const ei = this.calculateExpectedImprovement(candidate); - if (ei > bestEI) { - bestEI = ei; - bestCandidate = candidate; - } - }); - - return bestCandidate; - } - - /** - * Calculate Expected Improvement (simplified) - */ - calculateExpectedImprovement(config) { - if (this.trials.length === 0) return Math.random(); - - // Calculate distance to existing trials - let minDistance = Infinity; - this.trials.forEach(trial => { - const distance = this.configDistance(config, trial.config); - minDistance = Math.min(minDistance, distance); - }); - - // Exploration bonus: prefer configs far from existing trials - const exploration = minDistance; - - // Exploitation: prefer regions with good scores - const exploitation = this.predictScore(config); - - return exploration * 0.3 + exploitation * 0.7; - } - - /** - * Calculate distance between two configurations - */ - configDistance(config1, config2) { - let distance = 0; - Object.keys(config1).forEach(param => { - const v1 = config1[param]; - const v2 = config2[param]; - - if (typeof v1 === 'number' && typeof v2 === 'number') { - const space = this.searchSpace[param]; - const range = space.range ? space.range[1] - space.range[0] : 1; - distance += Math.pow((v1 - v2) / range, 2); - } else if (v1 !== v2) { - distance += 1; - } - }); - return Math.sqrt(distance); - } - - /** - * Predict score for a configuration (simplified) - */ - predictScore(config) { - if (this.trials.length === 0) return 0.5; - - // Weighted average of nearby trials - let weightedSum = 0; - let totalWeight = 0; - - this.trials.forEach(trial => { - const distance = this.configDistance(config, trial.config); - const weight = Math.exp(-distance); - weightedSum += weight * trial.score; - totalWeight += weight; - }); - - return totalWeight > 0 ? weightedSum / totalWeight : 0.5; - } - - /** - * Run a single trial - */ - async runTrial(config, trialNum) { - console.log(`[HyperOptim] Running trial ${trialNum}/${this.maxTrials}`, config); - - this.updateTrialUI(trialNum, 'running', config); - - try { - // Submit training job with this configuration - const response = await fetch('/api/start-training', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - ...config, - job_name: `hyperparam_trial_${trialNum}`, - _hyperopt: true - }) - }); - - if (!response.ok) { - throw new Error('Training submission failed'); - } - - // Wait for completion (poll status) - const result = await this.waitForCompletion(`hyperparam_trial_${trialNum}`); - - // Calculate score (lower loss = higher score) - const score = result.post_loss ? (1 / result.post_loss) * 100 : 0; - - // Record trial - const trial = { - trialNum, - config, - score, - loss: result.post_loss, - duration: result.duration, - timestamp: Date.now() - }; - - this.trials.push(trial); - - // Update best config - if (score > this.bestScore) { - this.bestScore = score; - this.bestConfig = config; - this.saveBestConfig(); - } - - this.updateTrialUI(trialNum, 'completed', config, score); - - console.log(`[HyperOptim] Trial ${trialNum} completed. Score: ${score.toFixed(2)}`); - - } catch (err) { - console.error(`[HyperOptim] Trial ${trialNum} failed:`, err); - this.updateTrialUI(trialNum, 'failed', config); - } - } - - /** - * Wait for training completion - */ - async waitForCompletion(jobName, timeout = 3600000) { - const startTime = Date.now(); - - while (Date.now() - startTime < timeout) { - await new Promise(resolve => setTimeout(resolve, 5000)); // Poll every 5 seconds - - try { - const response = await fetch('/status'); - const data = await response.json(); - - const job = data.jobs?.find(j => j.name === jobName); - if (job && job.status === 'completed') { - return job; - } - if (job && job.status === 'failed') { - throw new Error('Training failed'); - } - } catch (err) { - console.error('[HyperOptim] Status poll error:', err); - } - } - - throw new Error('Training timeout'); - } - - /** - * Check if early stopping should trigger - */ - shouldStopEarly() { - if (!this.earlyStopping.enabled) return false; - if (this.trials.length < this.earlyStopping.patience + 1) return false; - - // Check if no improvement in last N trials - const recentTrials = this.trials.slice(-this.earlyStopping.patience); - const recentBest = Math.max(...recentTrials.map(t => t.score)); - - const improvement = recentBest - this.bestScore; - return improvement < this.earlyStopping.minDelta; - } - - /** - * Show optimization UI - */ - showOptimizationUI() { - const container = document.getElementById('hyperoptContainer'); - if (!container) return; - - container.innerHTML = ` -
-
-

🎯 Hyperparameter Optimization

- -
-
-
- Running ${this.strategy} optimization with ${this.maxTrials} trials... -
-
-
-
-
- `; - - container.style.display = 'block'; - } - - /** - * Update trial UI - */ - updateTrialUI(trialNum, status, config, score = null) { - const container = document.getElementById('trialsProgress'); - if (!container) return; - - const trialId = `trial-${trialNum}`; - let trialEl = document.getElementById(trialId); - - if (!trialEl) { - trialEl = document.createElement('div'); - trialEl.id = trialId; - trialEl.className = 'alert alert-info'; - container.appendChild(trialEl); - } - - const statusEmoji = { - running: '⏳', - completed: '✅', - failed: '❌' - }; - - const configStr = Object.entries(config) - .map(([k, v]) => `${k}: ${typeof v === 'number' ? v.toFixed(4) : v}`) - .join(', '); - - trialEl.innerHTML = ` - ${statusEmoji[status]} Trial ${trialNum}/${this.maxTrials} - ${status}
- ${configStr} - ${score !== null ? `
Score: ${score.toFixed(2)}` : ''} - `; - - trialEl.className = `alert alert-${status === 'completed' ? 'success' : status === 'failed' ? 'error' : 'info'}`; - - // Update current best - if (this.bestConfig) { - const bestEl = document.getElementById('currentBest'); - if (bestEl) { - const bestStr = Object.entries(this.bestConfig) - .map(([k, v]) => `${k}: ${typeof v === 'number' ? v.toFixed(4) : v}`) - .join(', '); - - bestEl.innerHTML = ` -
-

🏆 Current Best Configuration

-
-

Score: ${this.bestScore.toFixed(2)}

-

${bestStr}

-
-
- `; - } - } - } - - /** - * Show final optimization results - */ - showOptimizationResults() { - const container = document.getElementById('hyperoptContainer'); - if (!container) return; - - const bestStr = Object.entries(this.bestConfig) - .map(([k, v]) => `
  • ${k}: ${typeof v === 'number' ? v.toFixed(4) : v}
    • `) - .join(''); - - container.innerHTML = ` -
    -
    -

    ✅ Optimization Complete

    -
    -
    -
    -

    🏆 Best Configuration Found

    -

    Score: ${this.bestScore.toFixed(2)}

    -
      ${bestStr}
    -
    - -

    Trial Summary

    -

    Completed ${this.trials.length} trials

    - -
    - - - -
    -
    -
    - `; - } - - /** - * Save best configuration - */ - saveBestConfig() { - if (!this.bestConfig) return; - - localStorage.setItem('hyperopt-best-config', JSON.stringify({ - config: this.bestConfig, - score: this.bestScore, - timestamp: Date.now() - })); - - console.log('[HyperOptim] Best config saved:', this.bestConfig); - } - - /** - * Apply best configuration to training form - */ - applyBestConfig() { - if (!this.bestConfig) return; - - Object.keys(this.bestConfig).forEach(key => { - const el = document.getElementById(key); - if (el) { - el.value = this.bestConfig[key]; - } - }); - - if (typeof showToast === 'function') showToast('Best config applied to training form ✓', 'ok', 3500); - - // Switch to training tab - if (typeof switchTab === 'function') { - switchTab('training'); - } - } - - /** - * Export optimization results - */ - exportResults() { - const results = { - strategy: this.strategy, - maxTrials: this.maxTrials, - trialsCompleted: this.trials.length, - bestConfig: this.bestConfig, - bestScore: this.bestScore, - allTrials: this.trials, - timestamp: Date.now() - }; - - const blob = new Blob([JSON.stringify(results, null, 2)], { type: 'application/json' }); - const url = URL.createObjectURL(blob); - const a = document.createElement('a'); - a.href = url; - a.download = `hyperopt-results-${Date.now()}.json`; - a.click(); - URL.revokeObjectURL(url); - } - - /** - * Visualize optimization results - */ - visualizeResults() { - // Create visualization chart - const canvas = document.createElement('canvas'); - canvas.id = 'hyperoptChart'; - canvas.style.height = '400px'; - - const container = document.getElementById('hyperoptContainer'); - if (container) { - container.appendChild(canvas); - } - - const ctx = canvas.getContext('2d'); - new Chart(ctx, { - type: 'line', - data: { - labels: this.trials.map(t => `Trial ${t.trialNum}`), - datasets: [{ - label: 'Trial Score', - data: this.trials.map(t => t.score), - borderColor: '#667eea', - backgroundColor: 'rgba(102, 126, 234, 0.1)', - tension: 0.4 - }, { - label: 'Best Score So Far', - data: this.trials.map((t, idx) => - Math.max(...this.trials.slice(0, idx + 1).map(tr => tr.score)) - ), - borderColor: '#2dce89', - backgroundColor: 'rgba(45, 206, 137, 0.1)', - tension: 0.4 - }] - }, - options: { - responsive: true, - maintainAspectRatio: false, - plugins: { - title: { - display: true, - text: 'Optimization Progress' - } - } - } - }); - } - - /** - * Stop optimization - */ - stopOptimization() { - - - this.maxTrials = this.currentTrial; - if (typeof showToast === 'function') showToast('Optimization stopping after current trial', 'info', 4000); - } - - /** - * Helper: Cartesian product - */ - cartesianProduct(arrays) { - return arrays.reduce((acc, array) => - acc.flatMap(x => array.map(y => [...x, y])), [[]] - ); - } -} - -// Export for use in dashboard -if (typeof window !== 'undefined') { - window.HyperparameterOptimizer = HyperparameterOptimizer; -} +/** + * Automated Hyperparameter Optimization + * Smart tuning with Bayesian optimization, grid search, and early stopping + */ + +class HyperparameterOptimizer { + constructor() { + this.searchSpace = {}; + this.trials = []; + this.bestConfig = null; + this.bestScore = -Infinity; + this.currentTrial = 0; + this.maxTrials = 10; + this.strategy = 'bayesian'; // bayesian, grid, random + this.earlyStopping = { + enabled: true, + patience: 3, + minDelta: 0.01 + }; + } + + /** + * Define search space + */ + defineSearchSpace(space) { + this.searchSpace = space; + console.log('[HyperOptim] Search space defined:', space); + } + + /** + * Start optimization + */ + async startOptimization(config) { + this.maxTrials = config.maxTrials || 10; + this.strategy = config.strategy || 'bayesian'; + this.currentTrial = 0; + this.trials = []; + this.bestConfig = null; + this.bestScore = -Infinity; + + console.log(`[HyperOptim] Starting ${this.strategy} optimization with ${this.maxTrials} trials`); + + // Show optimization UI + this.showOptimizationUI(); + + // Generate trial configurations based on strategy + const trialConfigs = this.generateTrialConfigs(); + + // Run trials + for (let i = 0; i < trialConfigs.length; i++) { + if (this.shouldStopEarly()) { + console.log('[HyperOptim] Early stopping triggered'); + break; + } + + await this.runTrial(trialConfigs[i], i + 1); + } + + // Show final results + this.showOptimizationResults(); + } + + /** + * Generate trial configurations based on strategy + */ + generateTrialConfigs() { + switch (this.strategy) { + case 'grid': + return this.generateGridSearch(); + case 'random': + return this.generateRandomSearch(); + case 'bayesian': + return this.generateBayesianSearch(); + default: + return this.generateRandomSearch(); + } + } + + /** + * Grid search: Exhaustive search over all combinations + */ + generateGridSearch() { + const configs = []; + const params = Object.keys(this.searchSpace); + + // Generate all combinations (limited to maxTrials) + const combinations = this.cartesianProduct( + params.map(p => this.searchSpace[p].values || this.searchSpace[p].range) + ); + + return combinations.slice(0, this.maxTrials).map(combo => { + const config = {}; + params.forEach((p, idx) => { + config[p] = combo[idx]; + }); + return config; + }); + } + + /** + * Random search: Random sampling from search space + */ + generateRandomSearch() { + const configs = []; + + for (let i = 0; i < this.maxTrials; i++) { + const config = {}; + Object.keys(this.searchSpace).forEach(param => { + const space = this.searchSpace[param]; + + if (space.values) { + // Categorical: random choice + config[param] = space.values[Math.floor(Math.random() * space.values.length)]; + } else if (space.range) { + // Continuous: random value in range + const [min, max] = space.range; + const scale = space.scale || 'linear'; + + if (scale === 'log') { + const logMin = Math.log(min); + const logMax = Math.log(max); + config[param] = Math.exp(logMin + Math.random() * (logMax - logMin)); + } else { + config[param] = min + Math.random() * (max - min); + } + + // Round if integer type + if (space.type === 'int') { + config[param] = Math.round(config[param]); + } + } + }); + configs.push(config); + } + + return configs; + } + + /** + * Bayesian optimization: Intelligent search using Gaussian Process + */ + generateBayesianSearch() { + // Start with random samples + const initialSamples = 3; + const configs = this.generateRandomSearch().slice(0, initialSamples); + + // For remaining trials, use acquisition function + // (Simplified version - real Bayesian optimization is more complex) + for (let i = initialSamples; i < this.maxTrials; i++) { + const config = this.selectNextBayesianConfig(); + configs.push(config); + } + + return configs; + } + + /** + * Select next configuration using Expected Improvement (simplified) + */ + selectNextBayesianConfig() { + // Generate candidate configs + const candidates = this.generateRandomSearch().slice(0, 20); + + // Score each candidate based on distance from previous trials + let bestCandidate = candidates[0]; + let bestEI = -Infinity; + + candidates.forEach(candidate => { + const ei = this.calculateExpectedImprovement(candidate); + if (ei > bestEI) { + bestEI = ei; + bestCandidate = candidate; + } + }); + + return bestCandidate; + } + + /** + * Calculate Expected Improvement (simplified) + */ + calculateExpectedImprovement(config) { + if (this.trials.length === 0) return Math.random(); + + // Calculate distance to existing trials + let minDistance = Infinity; + this.trials.forEach(trial => { + const distance = this.configDistance(config, trial.config); + minDistance = Math.min(minDistance, distance); + }); + + // Exploration bonus: prefer configs far from existing trials + const exploration = minDistance; + + // Exploitation: prefer regions with good scores + const exploitation = this.predictScore(config); + + return exploration * 0.3 + exploitation * 0.7; + } + + /** + * Calculate distance between two configurations + */ + configDistance(config1, config2) { + let distance = 0; + Object.keys(config1).forEach(param => { + const v1 = config1[param]; + const v2 = config2[param]; + + if (typeof v1 === 'number' && typeof v2 === 'number') { + const space = this.searchSpace[param]; + const range = space.range ? space.range[1] - space.range[0] : 1; + distance += Math.pow((v1 - v2) / range, 2); + } else if (v1 !== v2) { + distance += 1; + } + }); + return Math.sqrt(distance); + } + + /** + * Predict score for a configuration (simplified) + */ + predictScore(config) { + if (this.trials.length === 0) return 0.5; + + // Weighted average of nearby trials + let weightedSum = 0; + let totalWeight = 0; + + this.trials.forEach(trial => { + const distance = this.configDistance(config, trial.config); + const weight = Math.exp(-distance); + weightedSum += weight * trial.score; + totalWeight += weight; + }); + + return totalWeight > 0 ? weightedSum / totalWeight : 0.5; + } + + /** + * Run a single trial + */ + async runTrial(config, trialNum) { + console.log(`[HyperOptim] Running trial ${trialNum}/${this.maxTrials}`, config); + + this.updateTrialUI(trialNum, 'running', config); + + try { + // Submit training job with this configuration + const response = await fetch('/api/start-training', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + ...config, + job_name: `hyperparam_trial_${trialNum}`, + _hyperopt: true + }) + }); + + if (!response.ok) { + throw new Error('Training submission failed'); + } + + // Wait for completion (poll status) + const result = await this.waitForCompletion(`hyperparam_trial_${trialNum}`); + + // Calculate score (lower loss = higher score) + const score = result.post_loss ? (1 / result.post_loss) * 100 : 0; + + // Record trial + const trial = { + trialNum, + config, + score, + loss: result.post_loss, + duration: result.duration, + timestamp: Date.now() + }; + + this.trials.push(trial); + + // Update best config + if (score > this.bestScore) { + this.bestScore = score; + this.bestConfig = config; + this.saveBestConfig(); + } + + this.updateTrialUI(trialNum, 'completed', config, score); + + console.log(`[HyperOptim] Trial ${trialNum} completed. Score: ${score.toFixed(2)}`); + + } catch (err) { + console.error(`[HyperOptim] Trial ${trialNum} failed:`, err); + this.updateTrialUI(trialNum, 'failed', config); + } + } + + /** + * Wait for training completion + */ + async waitForCompletion(jobName, timeout = 3600000) { + const startTime = Date.now(); + + while (Date.now() - startTime < timeout) { + await new Promise(resolve => setTimeout(resolve, 5000)); // Poll every 5 seconds + + try { + const response = await fetch('/status'); + const data = await response.json(); + + const job = data.jobs?.find(j => j.name === jobName); + if (job && job.status === 'completed') { + return job; + } + if (job && job.status === 'failed') { + throw new Error('Training failed'); + } + } catch (err) { + console.error('[HyperOptim] Status poll error:', err); + } + } + + throw new Error('Training timeout'); + } + + /** + * Check if early stopping should trigger + */ + shouldStopEarly() { + if (!this.earlyStopping.enabled) return false; + if (this.trials.length < this.earlyStopping.patience + 1) return false; + + // Check if no improvement in last N trials + const recentTrials = this.trials.slice(-this.earlyStopping.patience); + const recentBest = Math.max(...recentTrials.map(t => t.score)); + + const improvement = recentBest - this.bestScore; + return improvement < this.earlyStopping.minDelta; + } + + /** + * Show optimization UI + */ + showOptimizationUI() { + const container = document.getElementById('hyperoptContainer'); + if (!container) return; + + container.innerHTML = ` +
    +
    +

    🎯 Hyperparameter Optimization

    + +
    +
    +
    + Running ${this.strategy} optimization with ${this.maxTrials} trials... +
    +
    +
    +
    +
    + `; + + container.style.display = 'block'; + } + + /** + * Update trial UI + */ + updateTrialUI(trialNum, status, config, score = null) { + const container = document.getElementById('trialsProgress'); + if (!container) return; + + const trialId = `trial-${trialNum}`; + let trialEl = document.getElementById(trialId); + + if (!trialEl) { + trialEl = document.createElement('div'); + trialEl.id = trialId; + trialEl.className = 'alert alert-info'; + container.appendChild(trialEl); + } + + const statusEmoji = { + running: '⏳', + completed: '✅', + failed: '❌' + }; + + const configStr = Object.entries(config) + .map(([k, v]) => `${k}: ${typeof v === 'number' ? v.toFixed(4) : v}`) + .join(', '); + + trialEl.innerHTML = ` + ${statusEmoji[status]} Trial ${trialNum}/${this.maxTrials} - ${status}
    + ${configStr} + ${score !== null ? `
    Score: ${score.toFixed(2)}` : ''} + `; + + trialEl.className = `alert alert-${status === 'completed' ? 'success' : status === 'failed' ? 'error' : 'info'}`; + + // Update current best + if (this.bestConfig) { + const bestEl = document.getElementById('currentBest'); + if (bestEl) { + const bestStr = Object.entries(this.bestConfig) + .map(([k, v]) => `${k}: ${typeof v === 'number' ? v.toFixed(4) : v}`) + .join(', '); + + bestEl.innerHTML = ` +
    +

    🏆 Current Best Configuration

    +
    +

    Score: ${this.bestScore.toFixed(2)}

    +

    ${bestStr}

    +
    +
    + `; + } + } + } + + /** + * Show final optimization results + */ + showOptimizationResults() { + const container = document.getElementById('hyperoptContainer'); + if (!container) return; + + const bestStr = Object.entries(this.bestConfig) + .map(([k, v]) => `
  • ${k}: ${typeof v === 'number' ? v.toFixed(4) : v}
    • `) + .join(''); + + container.innerHTML = ` +
    +
    +

    ✅ Optimization Complete

    +
    +
    +
    +

    🏆 Best Configuration Found

    +

    Score: ${this.bestScore.toFixed(2)}

    +
      ${bestStr}
    +
    + +

    Trial Summary

    +

    Completed ${this.trials.length} trials

    + +
    + + + +
    +
    +
    + `; + } + + /** + * Save best configuration + */ + saveBestConfig() { + if (!this.bestConfig) return; + + localStorage.setItem('hyperopt-best-config', JSON.stringify({ + config: this.bestConfig, + score: this.bestScore, + timestamp: Date.now() + })); + + console.log('[HyperOptim] Best config saved:', this.bestConfig); + } + + /** + * Apply best configuration to training form + */ + applyBestConfig() { + if (!this.bestConfig) return; + + Object.keys(this.bestConfig).forEach(key => { + const el = document.getElementById(key); + if (el) { + el.value = this.bestConfig[key]; + } + }); + + if (typeof showToast === 'function') showToast('Best config applied to training form ✓', 'ok', 3500); + + // Switch to training tab + if (typeof switchTab === 'function') { + switchTab('training'); + } + } + + /** + * Export optimization results + */ + exportResults() { + const results = { + strategy: this.strategy, + maxTrials: this.maxTrials, + trialsCompleted: this.trials.length, + bestConfig: this.bestConfig, + bestScore: this.bestScore, + allTrials: this.trials, + timestamp: Date.now() + }; + + const blob = new Blob([JSON.stringify(results, null, 2)], { type: 'application/json' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `hyperopt-results-${Date.now()}.json`; + a.click(); + URL.revokeObjectURL(url); + } + + /** + * Visualize optimization results + */ + visualizeResults() { + // Create visualization chart + const canvas = document.createElement('canvas'); + canvas.id = 'hyperoptChart'; + canvas.style.height = '400px'; + + const container = document.getElementById('hyperoptContainer'); + if (container) { + container.appendChild(canvas); + } + + const ctx = canvas.getContext('2d'); + new Chart(ctx, { + type: 'line', + data: { + labels: this.trials.map(t => `Trial ${t.trialNum}`), + datasets: [{ + label: 'Trial Score', + data: this.trials.map(t => t.score), + borderColor: '#667eea', + backgroundColor: 'rgba(102, 126, 234, 0.1)', + tension: 0.4 + }, { + label: 'Best Score So Far', + data: this.trials.map((t, idx) => + Math.max(...this.trials.slice(0, idx + 1).map(tr => tr.score)) + ), + borderColor: '#2dce89', + backgroundColor: 'rgba(45, 206, 137, 0.1)', + tension: 0.4 + }] + }, + options: { + responsive: true, + maintainAspectRatio: false, + plugins: { + title: { + display: true, + text: 'Optimization Progress' + } + } + } + }); + } + + /** + * Stop optimization + */ + stopOptimization() { + + + this.maxTrials = this.currentTrial; + if (typeof showToast === 'function') showToast('Optimization stopping after current trial', 'info', 4000); + } + + /** + * Helper: Cartesian product + */ + cartesianProduct(arrays) { + return arrays.reduce((acc, array) => + acc.flatMap(x => array.map(y => [...x, y])), [[]] + ); + } +} + +// Export for use in dashboard +if (typeof window !== 'undefined') { + window.HyperparameterOptimizer = HyperparameterOptimizer; +} diff --git a/apps/dashboard/index.html b/apps/dashboard/index.html index 6f6c8994f..af9fcdc19 100644 --- a/apps/dashboard/index.html +++ b/apps/dashboard/index.html @@ -1,492 +1,492 @@ - - - - - - QAI Dashboard - Unified Training Platform - - - - - - - - - -
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    🚀 QAI Training Dashboard

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    Real-time monitoring of AI training pipelines

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    - - - - - + + + + + + QAI Dashboard - Unified Training Platform + + + + + + + + + +
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    🚀 QAI Training Dashboard

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    Real-time monitoring of AI training pipelines

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    +
    +
    + + + + + diff --git a/apps/dashboard/keyboard-nav.js b/apps/dashboard/keyboard-nav.js index 79b98157c..fabaa407e 100644 --- a/apps/dashboard/keyboard-nav.js +++ b/apps/dashboard/keyboard-nav.js @@ -1,358 +1,358 @@ -/** - * Keyboard Navigation System - * Comprehensive keyboard shortcuts with hints panel - * - * Features: - * - Global shortcuts (Ctrl+K, Ctrl+S, etc.) - * - Modal/dialog shortcuts (Escape, Enter) - * - Form navigation (Tab, Shift+Tab, arrows) - * - Persistent hints panel (? to toggle) - * - ARIA labels for accessibility - */ - -class KeyboardNavigationManager { - constructor(options = {}) { - this.shortcuts = new Map(); - this.config = { - showHintsOnLoad: options.showHintsOnLoad || false, - hintsKey: options.hintsKey || '?', - enableFormNav: options.enableFormNav !== false, - enableModalNav: options.enableModalNav !== false, - ...options - }; - this.hintsVisible = false; - this.init(); - } - - init() { - // Register default shortcuts - this.registerDefaultShortcuts(); - - // Setup keyboard listener - document.addEventListener('keydown', this.handleKeyDown.bind(this)); - - // Create hints panel - this.createHintsPanel(); - - // Show hints on load if configured - if (this.config.showHintsOnLoad) { - setTimeout(() => this.showHints(), 1000); - } - } - - /** - * Register a keyboard shortcut - */ - register(key, callback, description, category = 'General') { - const shortcutId = this.normalizeKey(key); - this.shortcuts.set(shortcutId, { - key, - callback, - description, - category - }); - } - - /** - * Normalize key combination for consistent lookup - */ - normalizeKey(key) { - return key.toLowerCase() - .replace('ctrl+', 'control+') - .replace('cmd+', 'control+'); - } - - /** - * Register default shortcuts - */ - registerDefaultShortcuts() { - // Navigation shortcuts - this.register('Control+h', () => { - window.location.href = '/hub.html'; - }, 'Go to Hub', 'Navigation'); - - this.register('Control+u', () => { - window.location.href = '/unified.html'; - }, 'Go to Training Dashboard', 'Navigation'); - - this.register('Control+a', () => { - window.location.href = '/analytics.html'; - }, 'Go to Analytics', 'Navigation'); - - // Action shortcuts - this.register('Control+s', (e) => { - e.preventDefault(); - const saveBtn = document.querySelector('[onclick*="saveConfig"]'); - if (saveBtn) saveBtn.click(); - }, 'Save Configuration', 'Actions'); - - this.register('Control+r', (e) => { - e.preventDefault(); - const refreshBtn = document.querySelector('[onclick*="refresh"]'); - if (refreshBtn) refreshBtn.click(); - }, 'Refresh Data', 'Actions'); - - this.register('Control+/', () => { - this.toggleHints(); - }, 'Toggle Keyboard Shortcuts', 'Help'); - - this.register('?', () => { - this.toggleHints(); - }, 'Toggle Keyboard Shortcuts', 'Help'); - - // Modal shortcuts - if (this.config.enableModalNav) { - this.register('Escape', () => { - const modal = document.querySelector('.modal-overlay, [role="dialog"]'); - if (modal) { - const closeBtn = modal.querySelector('.modal-close, [onclick*="remove"], button:last-child'); - if (closeBtn) closeBtn.click(); - } - }, 'Close Modal/Dialog', 'Modals'); - } - - // Form shortcuts - if (this.config.enableFormNav) { - this.register('Control+Enter', () => { - const submitBtn = document.querySelector('button[type="submit"], .btn-primary:not([disabled])'); - if (submitBtn && !submitBtn.disabled) submitBtn.click(); - }, 'Submit Form', 'Forms'); - } - } - - /** - * Handle keydown events - */ - handleKeyDown(e) { - // Build key combination string - const parts = []; - if (e.ctrlKey || e.metaKey) parts.push('control'); - if (e.shiftKey) parts.push('shift'); - if (e.altKey) parts.push('alt'); - - const key = e.key.toLowerCase(); - if (!['control', 'shift', 'alt', 'meta'].includes(key)) { - parts.push(key); - } - - const combination = parts.join('+'); - const shortcut = this.shortcuts.get(combination); - - if (shortcut) { - // Don't trigger if typing in input - if (['input', 'textarea', 'select'].includes(e.target.tagName.toLowerCase())) { - // Allow ? to work in inputs for help - if (key !== '?') return; - } - - e.preventDefault(); - shortcut.callback(e); - } - - // Form navigation enhancements - if (this.config.enableFormNav) { - this.handleFormNavigation(e); - } - } - - /** - * Enhanced form navigation - */ - handleFormNavigation(e) { - const activeElement = document.activeElement; - const isFormElement = ['input', 'textarea', 'select', 'button'].includes( - activeElement.tagName.toLowerCase() - ); - - if (!isFormElement) return; - - // Arrow keys for select/radio navigation - if (activeElement.tagName.toLowerCase() === 'select') { - // Let default behavior work for select - return; - } - - // Tab navigation (already handled by browser, but we can enhance) - if (e.key === 'Tab') { - // Add visual focus indicator - setTimeout(() => { - const newFocus = document.activeElement; - if (newFocus && newFocus !== activeElement) { - newFocus.style.outline = '2px solid #667eea'; - setTimeout(() => { - newFocus.style.outline = ''; - }, 500); - } - }, 10); - } - } - - /** - * Create hints panel - */ - createHintsPanel() { - const panel = document.createElement('div'); - panel.id = 'keyboard-hints-panel'; - panel.style.cssText = ` - position: fixed; - top: 20px; - right: 20px; - background: rgba(26, 26, 46, 0.98); - border: 2px solid #667eea; - border-radius: 12px; - padding: 20px; - max-width: 400px; - max-height: 80vh; - overflow-y: auto; - z-index: 10000; - display: none; - box-shadow: 0 10px 40px rgba(0, 0, 0, 0.5); - backdrop-filter: blur(10px); - `; - - panel.innerHTML = ` -
    -

    ⌨️ Keyboard Shortcuts

    - -
    -
    - `; - - document.body.appendChild(panel); - - document.getElementById('close-hints-btn').onclick = () => this.hideHints(); - - // Close on click outside - panel.addEventListener('click', (e) => e.stopPropagation()); - document.addEventListener('click', () => { - if (this.hintsVisible) this.hideHints(); - }); - - this.updateHintsContent(); - } - - /** - * Update hints panel content - */ - updateHintsContent() { - const content = document.getElementById('shortcuts-content'); - if (!content) return; - - // Group shortcuts by category - const categories = {}; - this.shortcuts.forEach(shortcut => { - if (!categories[shortcut.category]) { - categories[shortcut.category] = []; - } - categories[shortcut.category].push(shortcut); - }); - - let html = ''; - Object.keys(categories).sort().forEach(category => { - html += `
    -

    ${category}

    -
    `; - - categories[category].forEach(shortcut => { - const keys = shortcut.key.split('+').map(k => - `${k}` - ).join(' + '); - - html += `
    - ${shortcut.description} - ${keys} -
    `; - }); - - html += `
    `; - }); - - content.innerHTML = html; - } - - /** - * Show hints panel - */ - showHints() { - const panel = document.getElementById('keyboard-hints-panel'); - if (panel) { - panel.style.display = 'block'; - this.hintsVisible = true; - } - } - - /** - * Hide hints panel - */ - hideHints() { - const panel = document.getElementById('keyboard-hints-panel'); - if (panel) { - panel.style.display = 'none'; - this.hintsVisible = false; - } - } - - /** - * Toggle hints panel - */ - toggleHints() { - if (this.hintsVisible) { - this.hideHints(); - } else { - this.showHints(); - } - } - - /** - * Add ARIA labels to elements - */ - addAriaLabels() { - // Add labels to buttons - document.querySelectorAll('button:not([aria-label])').forEach(btn => { - const text = btn.textContent.trim() || btn.title || 'Button'; - btn.setAttribute('aria-label', text); - }); - - // Add labels to inputs - document.querySelectorAll('input:not([aria-label])').forEach(input => { - const label = input.previousElementSibling; - if (label && label.tagName === 'LABEL') { - input.setAttribute('aria-label', label.textContent.trim()); - } else { - input.setAttribute('aria-label', input.placeholder || input.name || 'Input field'); - } - }); - - // Add role to modals - document.querySelectorAll('.modal, .modal-overlay').forEach(modal => { - modal.setAttribute('role', 'dialog'); - modal.setAttribute('aria-modal', 'true'); - }); - } - - /** - * Destroy navigation manager - */ - destroy() { - document.removeEventListener('keydown', this.handleKeyDown.bind(this)); - const panel = document.getElementById('keyboard-hints-panel'); - if (panel) panel.remove(); - } -} - -// Auto-initialize if window exists -if (typeof window !== 'undefined') { - window.KeyboardNavigationManager = KeyboardNavigationManager; - - // Auto-create instance - window.addEventListener('DOMContentLoaded', () => { - if (!window.keyboardNav) { - window.keyboardNav = new KeyboardNavigationManager(); - - // Add ARIA labels after a short delay to let page render - setTimeout(() => { - window.keyboardNav.addAriaLabels(); - }, 500); - } - }); -} +/** + * Keyboard Navigation System + * Comprehensive keyboard shortcuts with hints panel + * + * Features: + * - Global shortcuts (Ctrl+K, Ctrl+S, etc.) + * - Modal/dialog shortcuts (Escape, Enter) + * - Form navigation (Tab, Shift+Tab, arrows) + * - Persistent hints panel (? to toggle) + * - ARIA labels for accessibility + */ + +class KeyboardNavigationManager { + constructor(options = {}) { + this.shortcuts = new Map(); + this.config = { + showHintsOnLoad: options.showHintsOnLoad || false, + hintsKey: options.hintsKey || '?', + enableFormNav: options.enableFormNav !== false, + enableModalNav: options.enableModalNav !== false, + ...options + }; + this.hintsVisible = false; + this.init(); + } + + init() { + // Register default shortcuts + this.registerDefaultShortcuts(); + + // Setup keyboard listener + document.addEventListener('keydown', this.handleKeyDown.bind(this)); + + // Create hints panel + this.createHintsPanel(); + + // Show hints on load if configured + if (this.config.showHintsOnLoad) { + setTimeout(() => this.showHints(), 1000); + } + } + + /** + * Register a keyboard shortcut + */ + register(key, callback, description, category = 'General') { + const shortcutId = this.normalizeKey(key); + this.shortcuts.set(shortcutId, { + key, + callback, + description, + category + }); + } + + /** + * Normalize key combination for consistent lookup + */ + normalizeKey(key) { + return key.toLowerCase() + .replace('ctrl+', 'control+') + .replace('cmd+', 'control+'); + } + + /** + * Register default shortcuts + */ + registerDefaultShortcuts() { + // Navigation shortcuts + this.register('Control+h', () => { + window.location.href = '/hub.html'; + }, 'Go to Hub', 'Navigation'); + + this.register('Control+u', () => { + window.location.href = '/unified.html'; + }, 'Go to Training Dashboard', 'Navigation'); + + this.register('Control+a', () => { + window.location.href = '/analytics.html'; + }, 'Go to Analytics', 'Navigation'); + + // Action shortcuts + this.register('Control+s', (e) => { + e.preventDefault(); + const saveBtn = document.querySelector('[onclick*="saveConfig"]'); + if (saveBtn) saveBtn.click(); + }, 'Save Configuration', 'Actions'); + + this.register('Control+r', (e) => { + e.preventDefault(); + const refreshBtn = document.querySelector('[onclick*="refresh"]'); + if (refreshBtn) refreshBtn.click(); + }, 'Refresh Data', 'Actions'); + + this.register('Control+/', () => { + this.toggleHints(); + }, 'Toggle Keyboard Shortcuts', 'Help'); + + this.register('?', () => { + this.toggleHints(); + }, 'Toggle Keyboard Shortcuts', 'Help'); + + // Modal shortcuts + if (this.config.enableModalNav) { + this.register('Escape', () => { + const modal = document.querySelector('.modal-overlay, [role="dialog"]'); + if (modal) { + const closeBtn = modal.querySelector('.modal-close, [onclick*="remove"], button:last-child'); + if (closeBtn) closeBtn.click(); + } + }, 'Close Modal/Dialog', 'Modals'); + } + + // Form shortcuts + if (this.config.enableFormNav) { + this.register('Control+Enter', () => { + const submitBtn = document.querySelector('button[type="submit"], .btn-primary:not([disabled])'); + if (submitBtn && !submitBtn.disabled) submitBtn.click(); + }, 'Submit Form', 'Forms'); + } + } + + /** + * Handle keydown events + */ + handleKeyDown(e) { + // Build key combination string + const parts = []; + if (e.ctrlKey || e.metaKey) parts.push('control'); + if (e.shiftKey) parts.push('shift'); + if (e.altKey) parts.push('alt'); + + const key = e.key.toLowerCase(); + if (!['control', 'shift', 'alt', 'meta'].includes(key)) { + parts.push(key); + } + + const combination = parts.join('+'); + const shortcut = this.shortcuts.get(combination); + + if (shortcut) { + // Don't trigger if typing in input + if (['input', 'textarea', 'select'].includes(e.target.tagName.toLowerCase())) { + // Allow ? to work in inputs for help + if (key !== '?') return; + } + + e.preventDefault(); + shortcut.callback(e); + } + + // Form navigation enhancements + if (this.config.enableFormNav) { + this.handleFormNavigation(e); + } + } + + /** + * Enhanced form navigation + */ + handleFormNavigation(e) { + const activeElement = document.activeElement; + const isFormElement = ['input', 'textarea', 'select', 'button'].includes( + activeElement.tagName.toLowerCase() + ); + + if (!isFormElement) return; + + // Arrow keys for select/radio navigation + if (activeElement.tagName.toLowerCase() === 'select') { + // Let default behavior work for select + return; + } + + // Tab navigation (already handled by browser, but we can enhance) + if (e.key === 'Tab') { + // Add visual focus indicator + setTimeout(() => { + const newFocus = document.activeElement; + if (newFocus && newFocus !== activeElement) { + newFocus.style.outline = '2px solid #667eea'; + setTimeout(() => { + newFocus.style.outline = ''; + }, 500); + } + }, 10); + } + } + + /** + * Create hints panel + */ + createHintsPanel() { + const panel = document.createElement('div'); + panel.id = 'keyboard-hints-panel'; + panel.style.cssText = ` + position: fixed; + top: 20px; + right: 20px; + background: rgba(26, 26, 46, 0.98); + border: 2px solid #667eea; + border-radius: 12px; + padding: 20px; + max-width: 400px; + max-height: 80vh; + overflow-y: auto; + z-index: 10000; + display: none; + box-shadow: 0 10px 40px rgba(0, 0, 0, 0.5); + backdrop-filter: blur(10px); + `; + + panel.innerHTML = ` +
    +

    ⌨️ Keyboard Shortcuts

    + +
    +
    + `; + + document.body.appendChild(panel); + + document.getElementById('close-hints-btn').onclick = () => this.hideHints(); + + // Close on click outside + panel.addEventListener('click', (e) => e.stopPropagation()); + document.addEventListener('click', () => { + if (this.hintsVisible) this.hideHints(); + }); + + this.updateHintsContent(); + } + + /** + * Update hints panel content + */ + updateHintsContent() { + const content = document.getElementById('shortcuts-content'); + if (!content) return; + + // Group shortcuts by category + const categories = {}; + this.shortcuts.forEach(shortcut => { + if (!categories[shortcut.category]) { + categories[shortcut.category] = []; + } + categories[shortcut.category].push(shortcut); + }); + + let html = ''; + Object.keys(categories).sort().forEach(category => { + html += `
    +

    ${category}

    +
    `; + + categories[category].forEach(shortcut => { + const keys = shortcut.key.split('+').map(k => + `${k}` + ).join(' + '); + + html += `
    + ${shortcut.description} + ${keys} +
    `; + }); + + html += `
    `; + }); + + content.innerHTML = html; + } + + /** + * Show hints panel + */ + showHints() { + const panel = document.getElementById('keyboard-hints-panel'); + if (panel) { + panel.style.display = 'block'; + this.hintsVisible = true; + } + } + + /** + * Hide hints panel + */ + hideHints() { + const panel = document.getElementById('keyboard-hints-panel'); + if (panel) { + panel.style.display = 'none'; + this.hintsVisible = false; + } + } + + /** + * Toggle hints panel + */ + toggleHints() { + if (this.hintsVisible) { + this.hideHints(); + } else { + this.showHints(); + } + } + + /** + * Add ARIA labels to elements + */ + addAriaLabels() { + // Add labels to buttons + document.querySelectorAll('button:not([aria-label])').forEach(btn => { + const text = btn.textContent.trim() || btn.title || 'Button'; + btn.setAttribute('aria-label', text); + }); + + // Add labels to inputs + document.querySelectorAll('input:not([aria-label])').forEach(input => { + const label = input.previousElementSibling; + if (label && label.tagName === 'LABEL') { + input.setAttribute('aria-label', label.textContent.trim()); + } else { + input.setAttribute('aria-label', input.placeholder || input.name || 'Input field'); + } + }); + + // Add role to modals + document.querySelectorAll('.modal, .modal-overlay').forEach(modal => { + modal.setAttribute('role', 'dialog'); + modal.setAttribute('aria-modal', 'true'); + }); + } + + /** + * Destroy navigation manager + */ + destroy() { + document.removeEventListener('keydown', this.handleKeyDown.bind(this)); + const panel = document.getElementById('keyboard-hints-panel'); + if (panel) panel.remove(); + } +} + +// Auto-initialize if window exists +if (typeof window !== 'undefined') { + window.KeyboardNavigationManager = KeyboardNavigationManager; + + // Auto-create instance + window.addEventListener('DOMContentLoaded', () => { + if (!window.keyboardNav) { + window.keyboardNav = new KeyboardNavigationManager(); + + // Add ARIA labels after a short delay to let page render + setTimeout(() => { + window.keyboardNav.addAriaLabels(); + }, 500); + } + }); +} diff --git a/apps/dashboard/live-progress.js b/apps/dashboard/live-progress.js index 89cc6c362..fa8b74494 100644 --- a/apps/dashboard/live-progress.js +++ b/apps/dashboard/live-progress.js @@ -1,457 +1,457 @@ -/** - * Live Training Progress Tracker - * Real-time monitoring with streaming updates, live charts, and job controls - */ - -class LiveProgressTracker { - constructor(options = {}) { - this.pollingInterval = options.pollingInterval || 2000; // 2 seconds - this.chartUpdateInterval = options.chartUpdateInterval || 5000; // 5 seconds - this.activeJobId = null; - this.pollTimer = null; - this.chartTimer = null; - this.liveChart = null; - this.startTime = null; - this.currentEpoch = 0; - this.totalEpochs = 0; - this.callbacks = { - onUpdate: options.onUpdate || (() => {}), - onComplete: options.onComplete || (() => {}), - onError: options.onError || (() => {}) - }; - } - - /** - * Start tracking a training job - */ - startTracking(jobId, totalEpochs) { - this.activeJobId = jobId; - this.totalEpochs = totalEpochs; - this.startTime = Date.now(); - this.currentEpoch = 0; - - // Initialize UI - this.showProgressUI(); - this.initializeLiveChart(); - - // Start polling - this.pollTimer = setInterval(() => this.pollProgress(), this.pollingInterval); - this.chartTimer = setInterval(() => this.updateLiveChart(), this.chartUpdateInterval); - - console.log(`[LiveProgress] Started tracking job: ${jobId}`); - } - - /** - * Stop tracking - */ - stopTracking() { - if (this.pollTimer) clearInterval(this.pollTimer); - if (this.chartTimer) clearInterval(this.chartTimer); - this.pollTimer = null; - this.chartTimer = null; - this.activeJobId = null; - console.log('[LiveProgress] Stopped tracking'); - } - - /** - * Poll for progress updates - */ - async pollProgress() { - if (!this.activeJobId) return; - - try { - const response = await fetch(`/api/job-progress/${this.activeJobId}`); - if (!response.ok) { - // Fallback to status endpoint - const statusResponse = await fetch('/status'); - const statusData = await statusResponse.json(); - this.handleStatusUpdate(statusData); - return; - } - - const data = await response.json(); - this.handleProgressUpdate(data); - } catch (err) { - console.error('[LiveProgress] Poll error:', err); - this.callbacks.onError(err); - } - } - - /** - * Handle progress update from server - */ - handleProgressUpdate(data) { - if (data.status === 'completed') { - this.stopTracking(); - this.callbacks.onComplete(data); - this.showCompletionMessage(data); - return; - } - - if (data.status === 'failed') { - this.stopTracking(); - this.callbacks.onError(new Error(data.error || 'Training failed')); - this.showErrorMessage(data); - return; - } - - // Update UI elements - this.updateProgressBars(data); - this.updateMetrics(data); - this.updateETA(data); - - this.callbacks.onUpdate(data); - } - - /** - * Handle status update (fallback) - */ - handleStatusUpdate(statusData) { - if (!statusData.jobs) return; - - const job = statusData.jobs.find(j => j.id === this.activeJobId || j.name === this.activeJobId); - if (!job) return; - - const progress = { - current_epoch: job.current_epoch || 0, - total_epochs: job.total_epochs || this.totalEpochs, - current_loss: job.current_loss || job.post_loss || 0, - train_loss: job.train_loss || [], - eval_loss: job.eval_loss || [], - status: job.status || 'running', - progress_percent: job.progress_percent || 0 - }; - - this.handleProgressUpdate(progress); - } - - /** - * Update progress bars - */ - updateProgressBars(data) { - const epochBar = document.getElementById('liveEpochProgress'); - const overallBar = document.getElementById('liveOverallProgress'); - - if (epochBar) { - const epochPercent = ((data.current_step || 0) / (data.total_steps_per_epoch || 100)) * 100; - epochBar.style.width = `${Math.min(epochPercent, 100)}%`; - epochBar.textContent = `Epoch ${data.current_epoch || 0}/${data.total_epochs || this.totalEpochs}`; - } - - if (overallBar) { - const overallPercent = data.progress_percent || 0; - overallBar.style.width = `${Math.min(overallPercent, 100)}%`; - overallBar.textContent = `${Math.round(overallPercent)}%`; - } - - this.currentEpoch = data.current_epoch || 0; - } - - /** - * Update metrics display - */ - updateMetrics(data) { - const lossEl = document.getElementById('liveCurrentLoss'); - const lrEl = document.getElementById('liveLearningRate'); - const stepsEl = document.getElementById('liveStepsPerSec'); - - if (lossEl && data.current_loss !== undefined) { - lossEl.textContent = data.current_loss.toFixed(4); - lossEl.className = 'metric-value ' + this.getLossClass(data.current_loss); - } - - if (lrEl && data.learning_rate !== undefined) { - lrEl.textContent = data.learning_rate.toExponential(2); - } - - if (stepsEl && data.steps_per_sec !== undefined) { - stepsEl.textContent = data.steps_per_sec.toFixed(2); - } - } - - /** - * Calculate and update ETA - */ - updateETA(data) { - const etaEl = document.getElementById('liveETA'); - if (!etaEl) return; - - const elapsed = (Date.now() - this.startTime) / 1000; // seconds - const progress = data.progress_percent || 0; - - if (progress > 0 && progress < 100) { - const totalEstimated = elapsed / (progress / 100); - const remaining = totalEstimated - elapsed; - - etaEl.textContent = this.formatTime(remaining); - } else { - etaEl.textContent = 'Calculating...'; - } - } - - /** - * Initialize live chart - */ - initializeLiveChart() { - const canvas = document.getElementById('liveProgressChart'); - if (!canvas) return; - - const ctx = canvas.getContext('2d'); - this.liveChart = new Chart(ctx, { - type: 'line', - data: { - labels: [], - datasets: [ - { - label: 'Training Loss', - data: [], - borderColor: '#667eea', - backgroundColor: 'rgba(102, 126, 234, 0.1)', - tension: 0.4, - pointRadius: 3 - }, - { - label: 'Validation Loss', - data: [], - borderColor: '#f093fb', - backgroundColor: 'rgba(240, 147, 251, 0.1)', - tension: 0.4, - pointRadius: 3 - } - ] - }, - options: { - responsive: true, - maintainAspectRatio: false, - animation: { duration: 500 }, - scales: { - y: { - beginAtZero: false, - title: { display: true, text: 'Loss' } - }, - x: { - title: { display: true, text: 'Step' } - } - }, - plugins: { - legend: { display: true, position: 'top' }, - tooltip: { mode: 'index', intersect: false } - } - } - }); - } - - /** - * Update live chart with new data - */ - async updateLiveChart() { - if (!this.liveChart || !this.activeJobId) return; - - try { - const response = await fetch(`/api/job-metrics/${this.activeJobId}`); - if (!response.ok) return; - - const data = await response.json(); - - // Update chart data - this.liveChart.data.labels = data.steps || []; - this.liveChart.data.datasets[0].data = data.train_loss || []; - this.liveChart.data.datasets[1].data = data.eval_loss || []; - - this.liveChart.update('none'); // Update without animation for smoothness - } catch (err) { - console.error('[LiveProgress] Chart update error:', err); - } - } - - /** - * Show progress UI - */ - showProgressUI() { - const container = document.getElementById('liveProgressContainer'); - if (!container) return; - - container.innerHTML = ` -
    -
    -

    🎯 Live Training Progress

    -
    - - -
    -
    - -
    - -
    - -
    -
    Epoch 0/0
    -
    -
    - -
    - -
    -
    0%
    -
    -
    - - -
    -
    -
    Current Loss
    -
    0.0000
    -
    -
    -
    Learning Rate
    -
    0.0e+0
    -
    -
    -
    Steps/Sec
    -
    0.00
    -
    -
    -
    ETA
    -
    Calculating...
    -
    -
    - - -
    - -
    -
    -
    - `; - - container.style.display = 'block'; - } - - /** - * Hide progress UI - */ - hideProgressUI() { - const container = document.getElementById('liveProgressContainer'); - if (container) container.style.display = 'none'; - } - - /** - * Show completion message - */ - showCompletionMessage(data) { - const container = document.getElementById('liveProgressContainer'); - if (!container) return; - - const finalLoss = data.final_loss || data.current_loss || 0; - const duration = ((Date.now() - this.startTime) / 1000 / 60).toFixed(1); - - container.innerHTML = ` -
    -

    ✅ Training Complete!

    -

    Final Loss: ${finalLoss.toFixed(4)}

    -

    Duration: ${duration} minutes

    -

    Epochs: ${this.currentEpoch}/${this.totalEpochs}

    - -
    - `; - } - - /** - * Show error message - */ - showErrorMessage(data) { - const container = document.getElementById('liveProgressContainer'); - if (!container) return; - - container.innerHTML = ` -
    -

    ❌ Training Failed

    -

    ${data.error || 'Unknown error occurred'}

    - -
    - `; - } - - /** - * Job control: Pause training - */ - async pauseTraining() { - if (!this.activeJobId) return; - - try { - const response = await fetch(`/api/job-control/${this.activeJobId}/pause`, { - method: 'POST' - }); - - if (response.ok) { - if (typeof showToast === 'function') showToast('Training paused ✓', 'ok', 3000); - } - } catch (err) { - console.error('[LiveProgress] Pause error:', err); - if (typeof showToast === 'function') showToast('Failed to pause training', 'warn', 4000); - } - } - - /** - * Job control: Stop training - */ - async stopTraining() { - if (!this.activeJobId) return; - - - try { - const response = await fetch(`/api/job-control/${this.activeJobId}/stop`, { - method: 'POST' - }); - - if (response.ok) { - this.stopTracking(); - if (typeof showToast === 'function') showToast('Training stopped', 'ok', 3000); - this.hideProgressUI(); - } - } catch (err) { - console.error('[LiveProgress] Stop error:', err); - if (typeof showToast === 'function') showToast('Failed to stop training', 'warn', 4000); - } - } - - /** - * Helper: Get loss color class - */ - getLossClass(loss) { - if (loss < 0.5) return 'metric-good'; - if (loss < 1.0) return 'metric-warning'; - return 'metric-bad'; - } - - /** - * Helper: Format time (seconds to human readable) - */ - formatTime(seconds) { - if (seconds < 60) return `${Math.round(seconds)}s`; - if (seconds < 3600) return `${Math.round(seconds / 60)}m`; - const hours = Math.floor(seconds / 3600); - const mins = Math.round((seconds % 3600) / 60); - return `${hours}h ${mins}m`; - } - - /** - * Get tracking status - */ - isTracking() { - return this.activeJobId !== null; - } - - /** - * Get current job ID - */ - getActiveJobId() { - return this.activeJobId; - } -} - -// Export for use in dashboard -if (typeof window !== 'undefined') { - window.LiveProgressTracker = LiveProgressTracker; -} +/** + * Live Training Progress Tracker + * Real-time monitoring with streaming updates, live charts, and job controls + */ + +class LiveProgressTracker { + constructor(options = {}) { + this.pollingInterval = options.pollingInterval || 2000; // 2 seconds + this.chartUpdateInterval = options.chartUpdateInterval || 5000; // 5 seconds + this.activeJobId = null; + this.pollTimer = null; + this.chartTimer = null; + this.liveChart = null; + this.startTime = null; + this.currentEpoch = 0; + this.totalEpochs = 0; + this.callbacks = { + onUpdate: options.onUpdate || (() => {}), + onComplete: options.onComplete || (() => {}), + onError: options.onError || (() => {}) + }; + } + + /** + * Start tracking a training job + */ + startTracking(jobId, totalEpochs) { + this.activeJobId = jobId; + this.totalEpochs = totalEpochs; + this.startTime = Date.now(); + this.currentEpoch = 0; + + // Initialize UI + this.showProgressUI(); + this.initializeLiveChart(); + + // Start polling + this.pollTimer = setInterval(() => this.pollProgress(), this.pollingInterval); + this.chartTimer = setInterval(() => this.updateLiveChart(), this.chartUpdateInterval); + + console.log(`[LiveProgress] Started tracking job: ${jobId}`); + } + + /** + * Stop tracking + */ + stopTracking() { + if (this.pollTimer) clearInterval(this.pollTimer); + if (this.chartTimer) clearInterval(this.chartTimer); + this.pollTimer = null; + this.chartTimer = null; + this.activeJobId = null; + console.log('[LiveProgress] Stopped tracking'); + } + + /** + * Poll for progress updates + */ + async pollProgress() { + if (!this.activeJobId) return; + + try { + const response = await fetch(`/api/job-progress/${this.activeJobId}`); + if (!response.ok) { + // Fallback to status endpoint + const statusResponse = await fetch('/status'); + const statusData = await statusResponse.json(); + this.handleStatusUpdate(statusData); + return; + } + + const data = await response.json(); + this.handleProgressUpdate(data); + } catch (err) { + console.error('[LiveProgress] Poll error:', err); + this.callbacks.onError(err); + } + } + + /** + * Handle progress update from server + */ + handleProgressUpdate(data) { + if (data.status === 'completed') { + this.stopTracking(); + this.callbacks.onComplete(data); + this.showCompletionMessage(data); + return; + } + + if (data.status === 'failed') { + this.stopTracking(); + this.callbacks.onError(new Error(data.error || 'Training failed')); + this.showErrorMessage(data); + return; + } + + // Update UI elements + this.updateProgressBars(data); + this.updateMetrics(data); + this.updateETA(data); + + this.callbacks.onUpdate(data); + } + + /** + * Handle status update (fallback) + */ + handleStatusUpdate(statusData) { + if (!statusData.jobs) return; + + const job = statusData.jobs.find(j => j.id === this.activeJobId || j.name === this.activeJobId); + if (!job) return; + + const progress = { + current_epoch: job.current_epoch || 0, + total_epochs: job.total_epochs || this.totalEpochs, + current_loss: job.current_loss || job.post_loss || 0, + train_loss: job.train_loss || [], + eval_loss: job.eval_loss || [], + status: job.status || 'running', + progress_percent: job.progress_percent || 0 + }; + + this.handleProgressUpdate(progress); + } + + /** + * Update progress bars + */ + updateProgressBars(data) { + const epochBar = document.getElementById('liveEpochProgress'); + const overallBar = document.getElementById('liveOverallProgress'); + + if (epochBar) { + const epochPercent = ((data.current_step || 0) / (data.total_steps_per_epoch || 100)) * 100; + epochBar.style.width = `${Math.min(epochPercent, 100)}%`; + epochBar.textContent = `Epoch ${data.current_epoch || 0}/${data.total_epochs || this.totalEpochs}`; + } + + if (overallBar) { + const overallPercent = data.progress_percent || 0; + overallBar.style.width = `${Math.min(overallPercent, 100)}%`; + overallBar.textContent = `${Math.round(overallPercent)}%`; + } + + this.currentEpoch = data.current_epoch || 0; + } + + /** + * Update metrics display + */ + updateMetrics(data) { + const lossEl = document.getElementById('liveCurrentLoss'); + const lrEl = document.getElementById('liveLearningRate'); + const stepsEl = document.getElementById('liveStepsPerSec'); + + if (lossEl && data.current_loss !== undefined) { + lossEl.textContent = data.current_loss.toFixed(4); + lossEl.className = 'metric-value ' + this.getLossClass(data.current_loss); + } + + if (lrEl && data.learning_rate !== undefined) { + lrEl.textContent = data.learning_rate.toExponential(2); + } + + if (stepsEl && data.steps_per_sec !== undefined) { + stepsEl.textContent = data.steps_per_sec.toFixed(2); + } + } + + /** + * Calculate and update ETA + */ + updateETA(data) { + const etaEl = document.getElementById('liveETA'); + if (!etaEl) return; + + const elapsed = (Date.now() - this.startTime) / 1000; // seconds + const progress = data.progress_percent || 0; + + if (progress > 0 && progress < 100) { + const totalEstimated = elapsed / (progress / 100); + const remaining = totalEstimated - elapsed; + + etaEl.textContent = this.formatTime(remaining); + } else { + etaEl.textContent = 'Calculating...'; + } + } + + /** + * Initialize live chart + */ + initializeLiveChart() { + const canvas = document.getElementById('liveProgressChart'); + if (!canvas) return; + + const ctx = canvas.getContext('2d'); + this.liveChart = new Chart(ctx, { + type: 'line', + data: { + labels: [], + datasets: [ + { + label: 'Training Loss', + data: [], + borderColor: '#667eea', + backgroundColor: 'rgba(102, 126, 234, 0.1)', + tension: 0.4, + pointRadius: 3 + }, + { + label: 'Validation Loss', + data: [], + borderColor: '#f093fb', + backgroundColor: 'rgba(240, 147, 251, 0.1)', + tension: 0.4, + pointRadius: 3 + } + ] + }, + options: { + responsive: true, + maintainAspectRatio: false, + animation: { duration: 500 }, + scales: { + y: { + beginAtZero: false, + title: { display: true, text: 'Loss' } + }, + x: { + title: { display: true, text: 'Step' } + } + }, + plugins: { + legend: { display: true, position: 'top' }, + tooltip: { mode: 'index', intersect: false } + } + } + }); + } + + /** + * Update live chart with new data + */ + async updateLiveChart() { + if (!this.liveChart || !this.activeJobId) return; + + try { + const response = await fetch(`/api/job-metrics/${this.activeJobId}`); + if (!response.ok) return; + + const data = await response.json(); + + // Update chart data + this.liveChart.data.labels = data.steps || []; + this.liveChart.data.datasets[0].data = data.train_loss || []; + this.liveChart.data.datasets[1].data = data.eval_loss || []; + + this.liveChart.update('none'); // Update without animation for smoothness + } catch (err) { + console.error('[LiveProgress] Chart update error:', err); + } + } + + /** + * Show progress UI + */ + showProgressUI() { + const container = document.getElementById('liveProgressContainer'); + if (!container) return; + + container.innerHTML = ` +
    +
    +

    🎯 Live Training Progress

    +
    + + +
    +
    + +
    + +
    + +
    +
    Epoch 0/0
    +
    +
    + +
    + +
    +
    0%
    +
    +
    + + +
    +
    +
    Current Loss
    +
    0.0000
    +
    +
    +
    Learning Rate
    +
    0.0e+0
    +
    +
    +
    Steps/Sec
    +
    0.00
    +
    +
    +
    ETA
    +
    Calculating...
    +
    +
    + + +
    + +
    +
    +
    + `; + + container.style.display = 'block'; + } + + /** + * Hide progress UI + */ + hideProgressUI() { + const container = document.getElementById('liveProgressContainer'); + if (container) container.style.display = 'none'; + } + + /** + * Show completion message + */ + showCompletionMessage(data) { + const container = document.getElementById('liveProgressContainer'); + if (!container) return; + + const finalLoss = data.final_loss || data.current_loss || 0; + const duration = ((Date.now() - this.startTime) / 1000 / 60).toFixed(1); + + container.innerHTML = ` +
    +

    ✅ Training Complete!

    +

    Final Loss: ${finalLoss.toFixed(4)}

    +

    Duration: ${duration} minutes

    +

    Epochs: ${this.currentEpoch}/${this.totalEpochs}

    + +
    + `; + } + + /** + * Show error message + */ + showErrorMessage(data) { + const container = document.getElementById('liveProgressContainer'); + if (!container) return; + + container.innerHTML = ` +
    +

    ❌ Training Failed

    +

    ${data.error || 'Unknown error occurred'}

    + +
    + `; + } + + /** + * Job control: Pause training + */ + async pauseTraining() { + if (!this.activeJobId) return; + + try { + const response = await fetch(`/api/job-control/${this.activeJobId}/pause`, { + method: 'POST' + }); + + if (response.ok) { + if (typeof showToast === 'function') showToast('Training paused ✓', 'ok', 3000); + } + } catch (err) { + console.error('[LiveProgress] Pause error:', err); + if (typeof showToast === 'function') showToast('Failed to pause training', 'warn', 4000); + } + } + + /** + * Job control: Stop training + */ + async stopTraining() { + if (!this.activeJobId) return; + + + try { + const response = await fetch(`/api/job-control/${this.activeJobId}/stop`, { + method: 'POST' + }); + + if (response.ok) { + this.stopTracking(); + if (typeof showToast === 'function') showToast('Training stopped', 'ok', 3000); + this.hideProgressUI(); + } + } catch (err) { + console.error('[LiveProgress] Stop error:', err); + if (typeof showToast === 'function') showToast('Failed to stop training', 'warn', 4000); + } + } + + /** + * Helper: Get loss color class + */ + getLossClass(loss) { + if (loss < 0.5) return 'metric-good'; + if (loss < 1.0) return 'metric-warning'; + return 'metric-bad'; + } + + /** + * Helper: Format time (seconds to human readable) + */ + formatTime(seconds) { + if (seconds < 60) return `${Math.round(seconds)}s`; + if (seconds < 3600) return `${Math.round(seconds / 60)}m`; + const hours = Math.floor(seconds / 3600); + const mins = Math.round((seconds % 3600) / 60); + return `${hours}h ${mins}m`; + } + + /** + * Get tracking status + */ + isTracking() { + return this.activeJobId !== null; + } + + /** + * Get current job ID + */ + getActiveJobId() { + return this.activeJobId; + } +} + +// Export for use in dashboard +if (typeof window !== 'undefined') { + window.LiveProgressTracker = LiveProgressTracker; +} diff --git a/apps/dashboard/model-comparator.js b/apps/dashboard/model-comparator.js index 388f17e7f..3da232393 100644 --- a/apps/dashboard/model-comparator.js +++ b/apps/dashboard/model-comparator.js @@ -1,381 +1,381 @@ -/** - * Model Comparison & Benchmarking System - * Side-by-side comparison, performance benchmarking, and leaderboards - */ - -class ModelComparator { - constructor() { - this.selectedModels = []; - this.benchmarkResults = new Map(); - this.comparisonChart = null; - } - - /** - * Add model to comparison - */ - addModel(modelId) { - if (this.selectedModels.includes(modelId)) return; - if (this.selectedModels.length >= 4) { - if (typeof showToast === 'function') showToast('Maximum 4 models can be compared at once', 'warn', 4000); - return; - } - - this.selectedModels.push(modelId); - this.updateComparisonView(); - } - - /** - * Remove model from comparison - */ - removeModel(modelId) { - this.selectedModels = this.selectedModels.filter(id => id !== modelId); - this.updateComparisonView(); - } - - /** - * Clear all selections - */ - clearSelection() { - this.selectedModels = []; - this.updateComparisonView(); - } - - /** - * Update comparison view - */ - async updateComparisonView() { - const container = document.getElementById('modelComparisonContainer'); - if (!container) return; - - if (this.selectedModels.length === 0) { - container.innerHTML = '

    Select models to compare

    '; - return; - } - - // Fetch model details - const models = await this.fetchModelDetails(this.selectedModels); - - // Render comparison table - container.innerHTML = this.renderComparisonTable(models); - - // Update comparison chart - this.updateComparisonChart(models); - } - - /** - * Fetch model details - */ - async fetchModelDetails(modelIds) { - try { - const response = await fetch('/status'); - const data = await response.json(); - - if (!data.jobs) return []; - - return modelIds.map(id => { - const job = data.jobs.find(j => j.id === id || j.name === id); - return job || { id, name: 'Unknown', error: 'Not found' }; - }); - } catch (err) { - console.error('[ModelComparator] Fetch error:', err); - return []; - } - } - - /** - * Render comparison table - */ - renderComparisonTable(models) { - const rows = [ - { label: 'Model Name', key: 'name' }, - { label: 'Final Loss', key: 'post_loss', format: (v) => v?.toFixed(4) || 'N/A' }, - { label: 'Initial Loss', key: 'pre_loss', format: (v) => v?.toFixed(4) || 'N/A' }, - { label: 'Improvement', key: null, format: (_, m) => { - if (m.pre_loss && m.post_loss) { - const improvement = ((m.pre_loss - m.post_loss) / m.pre_loss * 100).toFixed(2); - return `${improvement}%`; - } - return 'N/A'; - }}, - { label: 'Duration', key: 'duration', format: (v) => v ? `${Math.round(v/60)}m` : 'N/A' }, - { label: 'Epochs', key: 'epochs' }, - { label: 'Batch Size', key: 'batch_size' }, - { label: 'Learning Rate', key: 'learning_rate', format: (v) => v?.toExponential(2) || 'N/A' }, - { label: 'LoRA Rank', key: 'lora_rank' }, - { label: 'LoRA Alpha', key: 'lora_alpha' }, - { label: 'Dataset', key: 'dataset' }, - { label: 'Status', key: 'status', format: (v) => `${v || 'unknown'}` } - ]; - - let html = ''; - models.forEach(m => { - html += ``; - }); - html += ''; - - rows.forEach(row => { - html += ``; - models.forEach(model => { - const value = row.key ? model[row.key] : null; - const formatted = row.format ? row.format(value, model) : (value || 'N/A'); - html += ``; - }); - html += ''; - }); - - html += '
    Metric${m.name || m.id}
    ${row.label}${formatted}
    '; - - html += '
    '; - html += ''; - html += ''; - html += ''; - html += '
    '; - - return html; - } - - /** - * Update comparison chart - */ - updateComparisonChart(models) { - const canvas = document.getElementById('comparisonChart'); - if (!canvas) return; - - if (this.comparisonChart) { - this.comparisonChart.destroy(); - } - - const ctx = canvas.getContext('2d'); - this.comparisonChart = new Chart(ctx, { - type: 'radar', - data: { - labels: ['Loss (inv)', 'Speed', 'Improvement', 'Efficiency', 'Stability'], - datasets: models.map((model, idx) => ({ - label: model.name || model.id, - data: this.calculateModelScores(model), - borderColor: this.getChartColor(idx), - backgroundColor: this.getChartColor(idx, 0.2), - pointBackgroundColor: this.getChartColor(idx) - })) - }, - options: { - responsive: true, - maintainAspectRatio: false, - scales: { - r: { - beginAtZero: true, - max: 100 - } - } - } - }); - } - - /** - * Calculate model scores for radar chart - */ - calculateModelScores(model) { - const lossScore = model.post_loss ? Math.min((1 / model.post_loss) * 10, 100) : 0; - const speedScore = model.duration ? Math.min((3600 / model.duration) * 100, 100) : 0; - const improvementScore = model.pre_loss && model.post_loss - ? Math.min(((model.pre_loss - model.post_loss) / model.pre_loss) * 100, 100) - : 0; - const efficiencyScore = model.batch_size && model.duration - ? Math.min((model.batch_size / model.duration) * 1000, 100) - : 0; - const stabilityScore = 75; // Placeholder - could calculate from loss variance - - return [lossScore, speedScore, improvementScore, efficiencyScore, stabilityScore]; - } - - /** - * Get chart color by index - */ - getChartColor(index, alpha = 1) { - const colors = [ - `rgba(102, 126, 234, ${alpha})`, - `rgba(240, 147, 251, ${alpha})`, - `rgba(45, 206, 137, ${alpha})`, - `rgba(255, 159, 64, ${alpha})` - ]; - return colors[index % colors.length]; - } - - /** - * Export comparison to CSV/JSON - */ - async exportComparison() { - const models = await this.fetchModelDetails(this.selectedModels); - - const format = prompt('Export format: json or csv?', 'json'); - if (!format || !['json', 'csv'].includes(format.toLowerCase())) return; - - let content, filename, type; - - if (format === 'json') { - content = JSON.stringify(models, null, 2); - filename = `comparison-${Date.now()}.json`; - type = 'application/json'; - } else { - // CSV format - const headers = ['Name', 'Final Loss', 'Improvement %', 'Duration (min)', 'Epochs', 'Batch Size', 'Learning Rate', 'LoRA Rank']; - const rows = models.map(m => [ - m.name || m.id, - m.post_loss?.toFixed(4) || '', - m.pre_loss && m.post_loss ? ((m.pre_loss - m.post_loss) / m.pre_loss * 100).toFixed(2) : '', - m.duration ? Math.round(m.duration / 60) : '', - m.epochs || '', - m.batch_size || '', - m.learning_rate || '', - m.lora_rank || '' - ]); - - content = [headers, ...rows].map(row => row.join(',')).join('\n'); - filename = `comparison-${Date.now()}.csv`; - type = 'text/csv'; - } - - const blob = new Blob([content], { type }); - const url = URL.createObjectURL(blob); - const a = document.createElement('a'); - a.href = url; - a.download = filename; - a.click(); - URL.revokeObjectURL(url); - } - - /** - * Run performance benchmark on selected models - */ - async benchmarkSelected() { - if (this.selectedModels.length === 0) { - if (typeof showToast === 'function') showToast('Please select models to benchmark', 'warn', 4000); - return; - } - - const container = document.getElementById('benchmarkResults'); - if (container) { - container.innerHTML = '
    Running benchmark... This may take a few minutes.
    '; - } - - try { - const response = await fetch('/api/benchmark', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ model_ids: this.selectedModels }) - }); - - const results = await response.json(); - this.benchmarkResults = new Map(results.map(r => [r.model_id, r])); - this.displayBenchmarkResults(results); - } catch (err) { - console.error('[ModelComparator] Benchmark error:', err); - if (container) { - container.innerHTML = `
    Benchmark failed: ${err.message}
    `; - } - } - } - - /** - * Display benchmark results - */ - displayBenchmarkResults(results) { - const container = document.getElementById('benchmarkResults'); - if (!container) return; - - let html = '

    ⚡ Benchmark Results

    '; - html += ''; - html += ''; - - results.forEach(result => { - const score = this.calculateBenchmarkScore(result); - html += ` - - - - - - `; - }); - - html += '
    ModelInference Time (ms)Memory (MB)Throughput (tok/s)Score
    ${result.model_name}${result.inference_time?.toFixed(2) || 'N/A'}${result.memory_mb?.toFixed(0) || 'N/A'}${result.throughput?.toFixed(2) || 'N/A'}${score}
    '; - container.innerHTML = html; - } - - /** - * Calculate benchmark score - */ - calculateBenchmarkScore(result) { - const speedScore = result.inference_time ? Math.min(100, (100 / result.inference_time) * 100) : 0; - const memoryScore = result.memory_mb ? Math.min(100, (2000 / result.memory_mb) * 100) : 0; - const throughputScore = result.throughput ? Math.min(100, result.throughput) : 0; - - return Math.round((speedScore + memoryScore + throughputScore) / 3); - } - - /** - * Generate leaderboard - */ - async generateLeaderboard() { - try { - const response = await fetch('/status'); - const data = await response.json(); - - if (!data.jobs || data.jobs.length === 0) { - return '

    No jobs available for leaderboard

    '; - } - - // Sort by performance score - const ranked = data.jobs - .filter(j => j.post_loss && j.status === 'completed') - .map(j => ({ - ...j, - score: this.calculateOverallScore(j) - })) - .sort((a, b) => b.score - a.score) - .slice(0, 10); - - let html = ''; - html += ''; - html += ''; - - ranked.forEach((job, idx) => { - const medal = idx === 0 ? '🥇' : idx === 1 ? '🥈' : idx === 2 ? '🥉' : ''; - html += ` - - - - - - `; - }); - - html += '
    RankModelLossDurationScore
    ${medal} #${idx + 1}${job.name || 'Unknown'}${job.post_loss.toFixed(4)}${Math.round(job.duration / 60)}m${job.score}
    '; - return html; - } catch (err) { - console.error('[ModelComparator] Leaderboard error:', err); - return '

    Failed to generate leaderboard

    '; - } - } - - /** - * Calculate overall score - */ - calculateOverallScore(job) { - const lossScore = job.post_loss ? (1 / job.post_loss) * 20 : 0; - const improvementScore = job.pre_loss && job.post_loss - ? ((job.pre_loss - job.post_loss) / job.pre_loss) * 30 - : 0; - const efficiencyScore = job.duration && job.epochs - ? Math.min((job.epochs / (job.duration / 3600)) * 25, 25) - : 0; - const qualityScore = 25; // Placeholder - - return Math.round(lossScore + improvementScore + efficiencyScore + qualityScore); - } -} - -// Export for use in dashboard -if (typeof window !== 'undefined') { - window.ModelComparator = ModelComparator; -} +/** + * Model Comparison & Benchmarking System + * Side-by-side comparison, performance benchmarking, and leaderboards + */ + +class ModelComparator { + constructor() { + this.selectedModels = []; + this.benchmarkResults = new Map(); + this.comparisonChart = null; + } + + /** + * Add model to comparison + */ + addModel(modelId) { + if (this.selectedModels.includes(modelId)) return; + if (this.selectedModels.length >= 4) { + if (typeof showToast === 'function') showToast('Maximum 4 models can be compared at once', 'warn', 4000); + return; + } + + this.selectedModels.push(modelId); + this.updateComparisonView(); + } + + /** + * Remove model from comparison + */ + removeModel(modelId) { + this.selectedModels = this.selectedModels.filter(id => id !== modelId); + this.updateComparisonView(); + } + + /** + * Clear all selections + */ + clearSelection() { + this.selectedModels = []; + this.updateComparisonView(); + } + + /** + * Update comparison view + */ + async updateComparisonView() { + const container = document.getElementById('modelComparisonContainer'); + if (!container) return; + + if (this.selectedModels.length === 0) { + container.innerHTML = '

    Select models to compare

    '; + return; + } + + // Fetch model details + const models = await this.fetchModelDetails(this.selectedModels); + + // Render comparison table + container.innerHTML = this.renderComparisonTable(models); + + // Update comparison chart + this.updateComparisonChart(models); + } + + /** + * Fetch model details + */ + async fetchModelDetails(modelIds) { + try { + const response = await fetch('/status'); + const data = await response.json(); + + if (!data.jobs) return []; + + return modelIds.map(id => { + const job = data.jobs.find(j => j.id === id || j.name === id); + return job || { id, name: 'Unknown', error: 'Not found' }; + }); + } catch (err) { + console.error('[ModelComparator] Fetch error:', err); + return []; + } + } + + /** + * Render comparison table + */ + renderComparisonTable(models) { + const rows = [ + { label: 'Model Name', key: 'name' }, + { label: 'Final Loss', key: 'post_loss', format: (v) => v?.toFixed(4) || 'N/A' }, + { label: 'Initial Loss', key: 'pre_loss', format: (v) => v?.toFixed(4) || 'N/A' }, + { label: 'Improvement', key: null, format: (_, m) => { + if (m.pre_loss && m.post_loss) { + const improvement = ((m.pre_loss - m.post_loss) / m.pre_loss * 100).toFixed(2); + return `${improvement}%`; + } + return 'N/A'; + }}, + { label: 'Duration', key: 'duration', format: (v) => v ? `${Math.round(v/60)}m` : 'N/A' }, + { label: 'Epochs', key: 'epochs' }, + { label: 'Batch Size', key: 'batch_size' }, + { label: 'Learning Rate', key: 'learning_rate', format: (v) => v?.toExponential(2) || 'N/A' }, + { label: 'LoRA Rank', key: 'lora_rank' }, + { label: 'LoRA Alpha', key: 'lora_alpha' }, + { label: 'Dataset', key: 'dataset' }, + { label: 'Status', key: 'status', format: (v) => `${v || 'unknown'}` } + ]; + + let html = ''; + models.forEach(m => { + html += ``; + }); + html += ''; + + rows.forEach(row => { + html += ``; + models.forEach(model => { + const value = row.key ? model[row.key] : null; + const formatted = row.format ? row.format(value, model) : (value || 'N/A'); + html += ``; + }); + html += ''; + }); + + html += '
    Metric${m.name || m.id}
    ${row.label}${formatted}
    '; + + html += '
    '; + html += ''; + html += ''; + html += ''; + html += '
    '; + + return html; + } + + /** + * Update comparison chart + */ + updateComparisonChart(models) { + const canvas = document.getElementById('comparisonChart'); + if (!canvas) return; + + if (this.comparisonChart) { + this.comparisonChart.destroy(); + } + + const ctx = canvas.getContext('2d'); + this.comparisonChart = new Chart(ctx, { + type: 'radar', + data: { + labels: ['Loss (inv)', 'Speed', 'Improvement', 'Efficiency', 'Stability'], + datasets: models.map((model, idx) => ({ + label: model.name || model.id, + data: this.calculateModelScores(model), + borderColor: this.getChartColor(idx), + backgroundColor: this.getChartColor(idx, 0.2), + pointBackgroundColor: this.getChartColor(idx) + })) + }, + options: { + responsive: true, + maintainAspectRatio: false, + scales: { + r: { + beginAtZero: true, + max: 100 + } + } + } + }); + } + + /** + * Calculate model scores for radar chart + */ + calculateModelScores(model) { + const lossScore = model.post_loss ? Math.min((1 / model.post_loss) * 10, 100) : 0; + const speedScore = model.duration ? Math.min((3600 / model.duration) * 100, 100) : 0; + const improvementScore = model.pre_loss && model.post_loss + ? Math.min(((model.pre_loss - model.post_loss) / model.pre_loss) * 100, 100) + : 0; + const efficiencyScore = model.batch_size && model.duration + ? Math.min((model.batch_size / model.duration) * 1000, 100) + : 0; + const stabilityScore = 75; // Placeholder - could calculate from loss variance + + return [lossScore, speedScore, improvementScore, efficiencyScore, stabilityScore]; + } + + /** + * Get chart color by index + */ + getChartColor(index, alpha = 1) { + const colors = [ + `rgba(102, 126, 234, ${alpha})`, + `rgba(240, 147, 251, ${alpha})`, + `rgba(45, 206, 137, ${alpha})`, + `rgba(255, 159, 64, ${alpha})` + ]; + return colors[index % colors.length]; + } + + /** + * Export comparison to CSV/JSON + */ + async exportComparison() { + const models = await this.fetchModelDetails(this.selectedModels); + + const format = prompt('Export format: json or csv?', 'json'); + if (!format || !['json', 'csv'].includes(format.toLowerCase())) return; + + let content, filename, type; + + if (format === 'json') { + content = JSON.stringify(models, null, 2); + filename = `comparison-${Date.now()}.json`; + type = 'application/json'; + } else { + // CSV format + const headers = ['Name', 'Final Loss', 'Improvement %', 'Duration (min)', 'Epochs', 'Batch Size', 'Learning Rate', 'LoRA Rank']; + const rows = models.map(m => [ + m.name || m.id, + m.post_loss?.toFixed(4) || '', + m.pre_loss && m.post_loss ? ((m.pre_loss - m.post_loss) / m.pre_loss * 100).toFixed(2) : '', + m.duration ? Math.round(m.duration / 60) : '', + m.epochs || '', + m.batch_size || '', + m.learning_rate || '', + m.lora_rank || '' + ]); + + content = [headers, ...rows].map(row => row.join(',')).join('\n'); + filename = `comparison-${Date.now()}.csv`; + type = 'text/csv'; + } + + const blob = new Blob([content], { type }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = filename; + a.click(); + URL.revokeObjectURL(url); + } + + /** + * Run performance benchmark on selected models + */ + async benchmarkSelected() { + if (this.selectedModels.length === 0) { + if (typeof showToast === 'function') showToast('Please select models to benchmark', 'warn', 4000); + return; + } + + const container = document.getElementById('benchmarkResults'); + if (container) { + container.innerHTML = '
    Running benchmark... This may take a few minutes.
    '; + } + + try { + const response = await fetch('/api/benchmark', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ model_ids: this.selectedModels }) + }); + + const results = await response.json(); + this.benchmarkResults = new Map(results.map(r => [r.model_id, r])); + this.displayBenchmarkResults(results); + } catch (err) { + console.error('[ModelComparator] Benchmark error:', err); + if (container) { + container.innerHTML = `
    Benchmark failed: ${err.message}
    `; + } + } + } + + /** + * Display benchmark results + */ + displayBenchmarkResults(results) { + const container = document.getElementById('benchmarkResults'); + if (!container) return; + + let html = '

    ⚡ Benchmark Results

    '; + html += ''; + html += ''; + + results.forEach(result => { + const score = this.calculateBenchmarkScore(result); + html += ` + + + + + + `; + }); + + html += '
    ModelInference Time (ms)Memory (MB)Throughput (tok/s)Score
    ${result.model_name}${result.inference_time?.toFixed(2) || 'N/A'}${result.memory_mb?.toFixed(0) || 'N/A'}${result.throughput?.toFixed(2) || 'N/A'}${score}
    '; + container.innerHTML = html; + } + + /** + * Calculate benchmark score + */ + calculateBenchmarkScore(result) { + const speedScore = result.inference_time ? Math.min(100, (100 / result.inference_time) * 100) : 0; + const memoryScore = result.memory_mb ? Math.min(100, (2000 / result.memory_mb) * 100) : 0; + const throughputScore = result.throughput ? Math.min(100, result.throughput) : 0; + + return Math.round((speedScore + memoryScore + throughputScore) / 3); + } + + /** + * Generate leaderboard + */ + async generateLeaderboard() { + try { + const response = await fetch('/status'); + const data = await response.json(); + + if (!data.jobs || data.jobs.length === 0) { + return '

    No jobs available for leaderboard

    '; + } + + // Sort by performance score + const ranked = data.jobs + .filter(j => j.post_loss && j.status === 'completed') + .map(j => ({ + ...j, + score: this.calculateOverallScore(j) + })) + .sort((a, b) => b.score - a.score) + .slice(0, 10); + + let html = ''; + html += ''; + html += ''; + + ranked.forEach((job, idx) => { + const medal = idx === 0 ? '🥇' : idx === 1 ? '🥈' : idx === 2 ? '🥉' : ''; + html += ` + + + + + + `; + }); + + html += '
    RankModelLossDurationScore
    ${medal} #${idx + 1}${job.name || 'Unknown'}${job.post_loss.toFixed(4)}${Math.round(job.duration / 60)}m${job.score}
    '; + return html; + } catch (err) { + console.error('[ModelComparator] Leaderboard error:', err); + return '

    Failed to generate leaderboard

    '; + } + } + + /** + * Calculate overall score + */ + calculateOverallScore(job) { + const lossScore = job.post_loss ? (1 / job.post_loss) * 20 : 0; + const improvementScore = job.pre_loss && job.post_loss + ? ((job.pre_loss - job.post_loss) / job.pre_loss) * 30 + : 0; + const efficiencyScore = job.duration && job.epochs + ? Math.min((job.epochs / (job.duration / 3600)) * 25, 25) + : 0; + const qualityScore = 25; // Placeholder + + return Math.round(lossScore + improvementScore + efficiencyScore + qualityScore); + } +} + +// Export for use in dashboard +if (typeof window !== 'undefined') { + window.ModelComparator = ModelComparator; +} diff --git a/apps/dashboard/requirements.txt b/apps/dashboard/requirements.txt index 31e64885f..e229d531d 100644 --- a/apps/dashboard/requirements.txt +++ b/apps/dashboard/requirements.txt @@ -1 +1 @@ -flask>=3.1.3 +flask>=3.1.3 diff --git a/apps/dashboard/serve.py b/apps/dashboard/serve.py index 880f4c14d..c8a38ab1a 100644 --- a/apps/dashboard/serve.py +++ b/apps/dashboard/serve.py @@ -1,1050 +1,1050 @@ -"""Simple HTTP server for training dashboard""" - -import http.server -import json -import os -import random -import re -import socketserver -import subprocess -import sys -import time -import webbrowser -from collections import defaultdict -from datetime import datetime -from pathlib import Path -from urllib.parse import parse_qs, urlparse - -import yaml -from gpu_monitor import get_gpu_info, get_gpu_processes, get_system_resources -from vram_calculator import calculate_safe_batch_size - -# Make scripts/ importable when serve.py is run from apps/dashboard/ -sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "scripts")) - -# Import GPU monitoring -sys.path.insert(0, str(Path(__file__).parent)) - -PORT = 8000 -REPO_ROOT = Path(__file__).resolve().parents[2] -DASHBOARD_DIR = Path(__file__).resolve().parent - -# Simple rate limiting -request_counts = defaultdict(list) -MAX_REQUESTS_PER_MINUTE = 60 - - -class MyHTTPRequestHandler(http.server.SimpleHTTPRequestHandler): - def __init__(self, *args, directory=None, **kwargs): - # Always serve static dashboard assets from apps/dashboard. - super().__init__(*args, directory=str(DASHBOARD_DIR), **kwargs) - - def log_request(self, code="-", size="-"): - """Enhanced request logging with timestamps""" - timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - print(f"[{timestamp}] {self.command} {self.path} - {code}") - - def check_rate_limit(self): - """Simple rate limiting per IP""" - client_ip = self.client_address[0] - now = time.time() - - # Clean old requests (older than 1 minute) - request_counts[client_ip] = [ - t for t in request_counts[client_ip] if now - t < 60 - ] - - # Check limit - if len(request_counts[client_ip]) >= MAX_REQUESTS_PER_MINUTE: - return False - - request_counts[client_ip].append(now) - return True - - def do_GET(self): - # Check rate limit - if not self.check_rate_limit(): - self.send_error(429, "Too Many Requests") - return - - # Use the root_dir set by main() - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - - # Redirect root to consolidated dashboard - if self.path == "/" or self.path == "/index.html": - self.send_response(302) - self.send_header("Location", "/consolidated.html") - self.end_headers() - return - - # API: Training status (no cache) - if self.path == "/status": - self.send_response(200) - self.send_header("Content-type", "application/json") - self.send_header("Access-Control-Allow-Origin", "*") - self.send_header("Cache-Control", "no-cache, no-store, must-revalidate") - self.send_header("Pragma", "no-cache") - self.send_header("Expires", "0") - self.end_headers() - - status_file = root_dir / "data_out" / "autotrain" / "status.json" - try: - with open(status_file, "r") as f: - data = json.load(f) - data["server_time"] = datetime.now().isoformat() - self.wfile.write(json.dumps(data).encode()) - except FileNotFoundError: - error_data = { - "error": "Status file not found. No training jobs have been run yet.", - "jobs": [], - "server_time": datetime.now().isoformat(), - } - self.wfile.write(json.dumps(error_data).encode()) - except Exception as e: - error_data = { - "error": f"Failed to read status: {str(e)}", - "jobs": [], - "server_time": datetime.now().isoformat(), - } - self.wfile.write(json.dumps(error_data).encode()) - return - - # API: Job progress - if self.path.startswith("/api/job-progress/"): - job_id = self.path.split("/")[-1] - self.send_json_response(self.get_job_progress(job_id)) - return - - # API: Job metrics - if self.path.startswith("/api/job-metrics/"): - job_id = self.path.split("/")[-1] - self.send_json_response(self.get_job_metrics(job_id)) - return - - # API: List available datasets - elif self.path == "/api/datasets": - self.send_json_response(self.get_datasets()) - return - - # API: Profile dataset for hyperparameter recommendations - elif self.path.startswith("/api/profile-dataset"): - parsed = urlparse(self.path) - query_params = parse_qs(parsed.query) - dataset_name = query_params.get("dataset", [None])[0] - - if not dataset_name: - self.send_json_response({"error": "Missing dataset parameter"}) - return - - # Find dataset path - datasets = self.get_datasets() - dataset_path = None - for ds in datasets.get("datasets", []): - if ds.get("name") == dataset_name or ds.get("path", "").endswith( - dataset_name - ): - dataset_path = Path(ds["path"]) - break - - if not dataset_path or not dataset_path.exists(): - self.send_json_response({"error": f"Dataset not found: {dataset_name}"}) - return - - # Run profiler script - profiler_script = root_dir / "scripts" / "dataset_profiler.py" - if not profiler_script.exists(): - self.send_json_response({"error": "Profiler script not found"}) - return - - try: - import subprocess - - result = subprocess.run( - [ - sys.executable, - str(profiler_script), - str(dataset_path), - "--recommend", - "--quiet", - ], - capture_output=True, - text=True, - timeout=30, - ) - if result.returncode == 0: - profile_data = json.loads(result.stdout) - self.send_json_response(profile_data) - else: - self.send_json_response( - {"error": f"Profiler failed: {result.stderr}"} - ) - except subprocess.TimeoutExpired: - self.send_json_response({"error": "Profiler timed out (30s limit)"}) - except Exception as e: - self.send_json_response({"error": f"Profiler error: {str(e)}"}) - return - - # API: List trained models - elif self.path == "/api/models": - self.send_json_response(self.get_models()) - return - - # API: List training configs - elif self.path == "/api/configs": - self.send_json_response(self.get_configs()) - return - - # API: Get job details - elif self.path.startswith("/api/job/"): - job_name = self.path.split("/")[-1] - self.send_json_response(self.get_job_details(job_name)) - return - - # API: Get training logs - elif self.path.startswith("/api/logs/"): - job_name = self.path.split("/")[-1] - self.send_json_response(self.get_job_logs(job_name)) - return - - # API: GPU monitoring - elif self.path == "/api/gpu": - self.send_json_response(get_gpu_info()) - return - - # API: GPU processes - elif self.path == "/api/gpu-processes": - self.send_json_response({"processes": get_gpu_processes()}) - return - - # API: VRAM-aware batch size calculator - elif self.path.startswith("/api/vram-info"): - qs = parse_qs(urlparse(self.path).query) - - def _qs(key, default=""): - vals = qs.get(key, [default]) - return vals[0] if vals else default - - model = _qs("model", "") - params_b_str = _qs("params_b", "") - params_b = float(params_b_str) if params_b_str else None - lora_rank = int(_qs("lora_rank", "16")) - seq_len = int(_qs("seq_len", "512")) - dtype = _qs("dtype", "fp16") - self.send_json_response( - calculate_safe_batch_size( - model_name=model, - params_b=params_b, - lora_rank=lora_rank, - seq_len=seq_len, - dtype=dtype, - ) - ) - return - - # API: System resources - elif self.path == "/api/system": - self.send_json_response(get_system_resources()) - return - - # API: Training history/timeline - elif self.path == "/api/history": - self.send_json_response(self.get_training_history()) - return - - # API: System health check - elif self.path == "/api/health": - self.send_json_response(self.get_system_health()) - return - - # API: Quick stats summary - elif self.path == "/api/stats": - self.send_json_response(self.get_quick_stats()) - return - - # API: Get active processes - elif self.path == "/api/processes": - self.send_json_response(self.get_active_processes()) - return - - # API: Job queue status - elif self.path == "/api/job-queue": - self.send_json_response(self.get_job_queue_status()) - return - - # Default file serving - super().do_GET() - - def do_POST(self): - # Use the root_dir set by main() - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - - # API: Start training - if self.path == "/api/start-training": - content_length = int(self.headers["Content-Length"]) - post_data = self.rfile.read(content_length) - params = json.loads(post_data.decode()) - result = self.start_training(params) - self.send_json_response(result) - return - - # API: Pause job - if self.path.startswith("/api/job-control/") and self.path.endswith("/pause"): - job_id = self.path.split("/")[-2] - result = self.control_job(job_id, action="pause") - self.send_json_response(result) - return - - # API: Stop job - if self.path.startswith("/api/job-control/") and self.path.endswith("/stop"): - job_id = self.path.split("/")[-2] - result = self.control_job(job_id, action="stop") - self.send_json_response(result) - return - - # API: Benchmark models - if self.path == "/api/benchmark": - content_length = int(self.headers["Content-Length"]) - post_data = self.rfile.read(content_length) - params = json.loads(post_data.decode()) - model_ids = params.get("model_ids", []) - results = self.run_benchmark(model_ids) - self.send_json_response({"results": results}) - return - - self.send_error(404) - - def send_json_response(self, data): - self.send_response(200) - self.send_header("Content-type", "application/json") - self.send_header("Access-Control-Allow-Origin", "*") - self.end_headers() - self.wfile.write(json.dumps(data).encode()) - - def get_job_progress(self, job_id): - """Return job progress information""" - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - status_file = root_dir / "data_out" / "autotrain" / "status.json" - try: - with open(status_file, "r") as f: - data = json.load(f) - job = next( - (j for j in data.get("jobs", []) if j.get("name") == job_id), None - ) - if not job: - return {"error": "Job not found", "job_id": job_id} - - metrics = job.get("metrics", {}) - current_epoch = ( - job.get("current_epoch") or metrics.get("current_epoch") or 0 - ) - total_epochs = job.get("epochs") or job.get("config", {}).get("epochs") or 0 - post_loss = metrics.get("post_eval_loss") - current_loss = metrics.get("current_loss", post_loss) - lr = job.get("config", {}).get("learning_rate") or metrics.get( - "learning_rate" - ) - steps_per_sec = metrics.get("steps_per_sec") - status = job.get("status", "unknown") - duration = job.get("duration_sec") - - # Progress percent - progress_percent = job.get("progress_percent") - if progress_percent is None: - try: - progress_percent = ( - round((current_epoch / total_epochs) * 100, 2) - if total_epochs - else 0 - ) - except Exception: - progress_percent = 0 - - return { - "job_id": job_id, - "current_epoch": current_epoch, - "total_epochs": total_epochs, - "current_loss": current_loss, - "learning_rate": lr, - "steps_per_sec": steps_per_sec, - "progress_percent": progress_percent, - "status": status, - "duration_sec": duration, - } - except Exception as e: - return {"error": str(e), "job_id": job_id} - - def get_job_metrics(self, job_id): - """Return arrays for charting training/validation loss""" - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - status_file = root_dir / "data_out" / "autotrain" / "status.json" - steps = [] - train_loss = [] - eval_loss = [] - try: - with open(status_file, "r") as f: - data = json.load(f) - job = next( - (j for j in data.get("jobs", []) if j.get("name") == job_id), None - ) - if not job: - return {"error": "Job not found", "job_id": job_id} - - # Try metrics history - history = job.get("metrics_history") or job.get("loss_history") - if isinstance(history, list) and history: - for i, h in enumerate(history): - steps.append(h.get("step", i)) - if "train_loss" in h: - train_loss.append(h["train_loss"]) - if "eval_loss" in h: - eval_loss.append(h["eval_loss"]) - else: - # Fallback: parse log file for loss lines - log_file = job.get("log") - if log_file and Path(log_file).exists(): - try: - with open( - log_file, "r", encoding="utf-8", errors="ignore" - ) as f: - for i, line in enumerate(f): - m = re.search( - r"step\s*(\d+).*?train_loss=([0-9\.]+).*?eval_loss=([0-9\.]+)", - line, - ) - if m: - steps.append(int(m.group(1))) - train_loss.append(float(m.group(2))) - eval_loss.append(float(m.group(3))) - except Exception: - pass - - return { - "job_id": job_id, - "steps": steps, - "train_loss": train_loss, - "eval_loss": eval_loss, - } - except Exception as e: - return { - "error": str(e), - "job_id": job_id, - "steps": steps, - "train_loss": train_loss, - "eval_loss": eval_loss, - } - - def get_datasets(self): - # Use the root_dir set by main() - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - datasets_dir = root_dir / "datasets" / "chat" - datasets = [] - - try: - if not datasets_dir.exists(): - return { - "error": f"Datasets directory not found: {datasets_dir}", - "datasets": [], - } - - for d in datasets_dir.iterdir(): - if d.is_dir(): - train_file = d / "train.json" - test_file = d / "test.json" - if train_file.exists(): - try: - # Count samples efficiently without loading entire file - # For JSON arrays, count top-level elements - train_samples = self._count_json_samples(train_file) - test_samples = 0 - if test_file.exists(): - test_samples = self._count_json_samples(test_file) - - datasets.append( - { - "name": d.name, - "path": str(d.relative_to(root_dir)), - "train_samples": train_samples, - "test_samples": test_samples, - } - ) - except Exception as e: - print(f"Warning: Error processing {train_file}: {e}") - continue - except Exception as e: - import traceback - - traceback.print_exc() - return {"error": str(e), "datasets": []} - - return {"datasets": datasets} - - def _count_json_samples(self, file_path: Path) -> int: - """Count samples in a JSON file efficiently. - - For JSONL files: counts lines. - For JSON arrays: counts top-level array elements. - Falls back to loading full file if needed. - """ - try: - with open(file_path, "r", encoding="utf-8") as f: - first_char = f.read(1) - f.seek(0) - - if first_char == "[": - # JSON array - need to parse it - data = json.load(f) - return len(data) if isinstance(data, list) else 1 - elif first_char == "{": - # JSONL format - count lines efficiently - return sum(1 for line in f if line.strip()) - else: - # Unknown format, try to parse - data = json.load(f) - return len(data) if isinstance(data, list) else 1 - except Exception: - # Fallback: try loading as JSON - try: - with open(file_path, "r", encoding="utf-8") as f: - data = json.load(f) - return len(data) if isinstance(data, list) else 1 - except Exception: - return 0 - - def get_models(self): - # Use the root_dir set by main() - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - models_dir = root_dir / "data_out" / "lora_training" / "marathon" - models = [] - - try: - if models_dir.exists(): - for model_dir in models_dir.iterdir(): - if model_dir.is_dir(): - adapter_config = ( - model_dir / "lora_adapter" / "adapter_config.json" - ) - if adapter_config.exists(): - with open(adapter_config) as f: - config = json.load(f) - models.append( - { - "name": model_dir.name, - "path": str(model_dir.relative_to(root_dir)), - "base_model": config.get( - "base_model_name_or_path", "unknown" - ), - "rank": config.get("r", "unknown"), - } - ) - except Exception as e: - return {"error": str(e), "models": []} - - return {"models": models} - - def get_configs(self): - # Use the root_dir set by main() - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - configs = [] - - try: - for yaml_file in root_dir.glob("autotrain*.yaml"): - if "autogen" not in yaml_file.name: - with open(yaml_file) as f: - config = yaml.safe_load(f) - - jobs = config.get("jobs", []) - total_epochs = sum(j.get("epochs", 0) for j in jobs) - - # Estimate time based on previous runs - estimated_minutes = len(jobs) * 3 # Rough estimate - est_time = ( - f"{estimated_minutes}m" - if estimated_minutes < 60 - else f"{estimated_minutes//60}h {estimated_minutes % 60}m" - ) - - configs.append( - { - "name": yaml_file.stem, - "path": yaml_file.name, - "jobs": len(jobs), - "total_epochs": total_epochs, - "estimated_time": est_time, - "modified": yaml_file.stat().st_mtime, - } - ) - - # Sort by modification time - configs.sort(key=lambda x: x.get("modified", 0), reverse=True) - except Exception as e: - return {"error": str(e), "configs": []} - - return {"configs": configs} - - def get_job_details(self, job_name): - # Use the root_dir set by main() - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - status_file = root_dir / "data_out" / "autotrain" / "status.json" - - try: - with open(status_file) as f: - data = json.load(f) - - for job in data.get("jobs", []): - if job.get("name") == job_name: - # Add output directory contents if available - if "output_dir" in job and job["output_dir"]: - output_path = root_dir / job["output_dir"] - if output_path.exists(): - job["output_files"] = [ - f.name for f in output_path.rglob("*") if f.is_file() - ] - return {"job": job} - - return {"error": "Job not found"} - except Exception as e: - return {"error": str(e)} - - def get_job_logs(self, job_name): - # Use the root_dir set by main() - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - status_file = root_dir / "data_out" / "autotrain" / "status.json" - - try: - with open(status_file) as f: - data = json.load(f) - - for job in data.get("jobs", []): - if job.get("name") == job_name and "log" in job: - log_file = Path(job["log"]) - if log_file.exists(): - with open( - log_file, "r", encoding="utf-8", errors="ignore" - ) as f: - # Efficiently tail last 500 lines without loading entire file - lines = [] - for line in f: - lines.append(line) - if len(lines) > 500: - lines.pop(0) # Keep only last 500 - return {"logs": "".join(lines)} - - return {"logs": "No logs available"} - except Exception as e: - return {"error": str(e), "logs": ""} - - def start_training(self, params): - """Create and queue a new training job""" - # Use the root_dir set by main() - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - - try: - # Accept both legacy and new parameter names - job_name = params.get("job_name") or params.get("name") - model = params.get("model") or "microsoft/Phi-3.5-mini-instruct" - dataset = params.get("dataset") - epochs = params.get("epochs", 3) - max_samples = params.get( - "max_train_samples", params.get("max_samples", 1000) - ) - learning_rate = params.get("learning_rate", "2e-4") - batch_size = params.get("batch_size", 4) - lora_rank = params.get("lora_rank", 16) - lora_alpha = params.get("lora_alpha", 32) - - if not job_name or not dataset: - return {"success": False, "error": "Missing required parameters"} - - # Create a custom config YAML - config_data = { - "jobs": [ - { - "name": job_name, - "runner": "hf", - "category": "custom", - "model": model, - "dataset": f"datasets/chat/{dataset}", - "epochs": epochs, - "max_train_samples": max_samples, - "learning_rate": float(learning_rate), - "batch_size": batch_size, - "lora_rank": lora_rank, - "lora_alpha": lora_alpha, - "device": "auto", - } - ] - } - - # Save config - config_file = root_dir / f"autotrain_custom_{job_name}.yaml" - with open(config_file, "w") as f: - yaml.dump(config_data, f) - - # Launch training in background - cmd = [ - "python", - str(root_dir / "scripts" / "autotrain.py"), - "--config", - str(config_file), - "--resume", - ] - - subprocess.Popen( - cmd, - cwd=str(root_dir), - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - creationflags=( - subprocess.CREATE_NO_WINDOW - if hasattr(subprocess, "CREATE_NO_WINDOW") - else 0 - ), - ) - - return { - "success": True, - "message": f"Training job {job_name} started", - "job_name": job_name, - "job_id": job_name, - "config_file": config_file.name, - } - except Exception as e: - return {"success": False, "error": str(e)} - - def control_job(self, job_id, action="pause"): - """Stub to control a job (pause/stop) via flag files""" - try: - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - control_dir = root_dir / "data_out" / "control" - control_dir.mkdir(parents=True, exist_ok=True) - flag_file = control_dir / f"{job_id}.{action}" - with open(flag_file, "w") as f: - f.write(datetime.now().isoformat()) - return { - "success": True, - "message": f"Job {action} signal sent", - "job_id": job_id, - } - except Exception as e: - return {"success": False, "error": str(e), "job_id": job_id} - - def run_benchmark(self, model_ids): - """Run a simple synthetic benchmark for models""" - results = [] - for mid in model_ids: - try: - # Synthetic metrics - inference_time = random.uniform(100, 1200) # ms - memory_mb = random.uniform(200, 2000) - throughput = random.uniform(20, 300) # tokens/sec - # Score: lower time & memory, higher throughput - speed_score = max(0, min(100, (1200 - inference_time) / 12)) - memory_score = max(0, min(100, (2000 - memory_mb) / 20)) - throughput_score = max(0, min(100, throughput / 3)) - score = round((speed_score + memory_score + throughput_score) / 3, 2) - results.append( - { - "model_id": mid, - "model_name": mid, - "inference_time": round(inference_time, 1), - "memory_mb": round(memory_mb, 1), - "throughput": round(throughput, 1), - "score": score, - } - ) - except Exception as e: - results.append({"model_id": mid, "error": str(e)}) - return results - - def get_training_history(self): - """Get historical training data for charts""" - # Use the root_dir set by main() - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - status_file = root_dir / "data_out" / "autotrain" / "status.json" - - try: - with open(status_file) as f: - data = json.load(f) - - history = {"jobs": [], "timeline": []} - - for job in data.get("jobs", []): - if job.get("metrics"): - history["jobs"].append( - { - "name": job["name"], - "pre_loss": job["metrics"].get("pre_eval_loss"), - "post_loss": job["metrics"].get("post_eval_loss"), - "pre_perplexity": job["metrics"].get("pre_eval_perplexity"), - "post_perplexity": job["metrics"].get( - "post_eval_perplexity" - ), - "duration": job.get("duration_sec"), - "status": job.get("status"), - } - ) - - if job.get("start_time"): - history["timeline"].append( - { - "name": job["name"], - "start": job["start_time"], - "duration": job.get("duration_sec", 0), - "status": job.get("status", "unknown"), - } - ) - - return history - except Exception as e: - return {"error": str(e), "jobs": [], "timeline": []} - - def get_system_health(self): - """Comprehensive system health check""" - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - - health = { - "status": "healthy", - "timestamp": datetime.now().isoformat(), - "checks": {}, - } - - try: - # Check datasets directory - datasets_dir = root_dir / "datasets" / "chat" - health["checks"]["datasets"] = { - "exists": datasets_dir.exists(), - "count": ( - len(list(datasets_dir.glob("*/train.json"))) - if datasets_dir.exists() - else 0 - ), - } - - # Check output directory - output_dir = root_dir / "data_out" - health["checks"]["output"] = { - "exists": output_dir.exists(), - "writable": ( - os.access(output_dir, os.W_OK) if output_dir.exists() else False - ), - } - - # Check GPU availability - gpu_info = get_gpu_info() - health["checks"]["gpu"] = { - "available": len(gpu_info.get("gpus", [])) > 0, - "count": len(gpu_info.get("gpus", [])), - } - - # Check virtual environments - health["checks"]["venvs"] = { - "quantum_ai": ( - root_dir / "ai-projects" / "quantum-ml" / "venv" - ).exists(), - "talk_to_ai": (root_dir / "ai-projects" / "chat-cli" / "venv").exists(), - "lora_training": ( - root_dir / "AI" / "microsoft_phi-silica-3.6_v1" / "venv" - ).exists(), - } - - # Overall health - all_checks = [ - health["checks"]["datasets"]["exists"], - health["checks"]["output"]["exists"], - any(health["checks"]["venvs"].values()), - ] - health["status"] = "healthy" if all(all_checks) else "degraded" - - except Exception as e: - health["status"] = "error" - health["error"] = str(e) - - return health - - def get_quick_stats(self): - """Quick summary statistics""" - root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) - - stats = { - "training_jobs": 0, - "datasets": 0, - "models": 0, - "gpu_usage": 0, - "active_processes": 0, - } - - try: - # Training jobs - status_file = root_dir / "data_out" / "autotrain" / "status.json" - if status_file.exists(): - with open(status_file) as f: - data = json.load(f) - stats["training_jobs"] = len(data.get("jobs", [])) - - # Datasets - datasets_dir = root_dir / "datasets" / "chat" - if datasets_dir.exists(): - stats["datasets"] = len( - [d for d in datasets_dir.iterdir() if d.is_dir()] - ) - - # Models - models_dir = root_dir / "data_out" / "lora_training" / "marathon" - if models_dir.exists(): - stats["models"] = len([m for m in models_dir.iterdir() if m.is_dir()]) - - # GPU usage - gpu_info = get_gpu_info() - if gpu_info.get("gpus"): - stats["gpu_usage"] = gpu_info["gpus"][0].get("utilization_gpu", 0) - - # Active processes - processes = get_gpu_processes() - stats["active_processes"] = len(processes) - - except Exception as e: - stats["error"] = str(e) - - return stats - - def get_active_processes(self): - """Get active Python processes""" - import psutil - - processes = [] - try: - for proc in psutil.process_iter( - ["pid", "name", "cmdline", "memory_info", "cpu_percent"] - ): - try: - pinfo = proc.info - if pinfo["name"] and "python" in pinfo["name"].lower(): - cmdline = " ".join(pinfo["cmdline"] or []) - if any( - keyword in cmdline - for keyword in [ - "train", - "autotrain", - "quantum", - "chat", - "serve", - ] - ): - processes.append( - { - "pid": pinfo["pid"], - "name": pinfo["name"], - "command": ( - cmdline[:100] + "..." - if len(cmdline) > 100 - else cmdline - ), - "memory_mb": round( - pinfo["memory_info"].rss / 1024 / 1024, 1 - ), - "cpu_percent": pinfo["cpu_percent"], - } - ) - except (psutil.NoSuchProcess, psutil.AccessDenied): - pass - except Exception as e: - return {"error": str(e), "processes": []} - - return {"processes": processes, "count": len(processes)} - - def get_job_queue_status(self): - """Get job queue status from job_queue.py""" - try: - queue_file = Path("data_out/job_queue.json") - if not queue_file.exists(): - return { - "total_jobs": 0, - "pending": 0, - "running": 0, - "completed": 0, - "failed": 0, - "blocked": 0, - "cancelled": 0, - "queue_length": 0, - "estimated_total_time": 0, - "message": "Job queue not initialized", - } - - with open(queue_file, "r") as f: - queue_data = json.load(f) - - jobs = queue_data.get("jobs", []) - - # Single-pass aggregation to avoid multiple iterations (optimized) - status_counts = { - "pending": 0, - "running": 0, - "completed": 0, - "failed": 0, - "blocked": 0, - "cancelled": 0, - } - queue_jobs = [] - estimated_total = 0 - - for job in jobs: - status = job.get("status") - if status in status_counts: - status_counts[status] += 1 - if status in {"pending", "blocked"}: - queue_jobs.append(job) - estimated_total += job.get("estimated_duration", 0) - - return { - "total_jobs": len(jobs), - "pending": status_counts["pending"], - "running": status_counts["running"], - "completed": status_counts["completed"], - "failed": status_counts["failed"], - "blocked": status_counts["blocked"], - "cancelled": status_counts["cancelled"], - "queue_length": len(queue_jobs), - "estimated_total_time": estimated_total, - "updated_at": queue_data.get("updated_at"), - } - except Exception as e: - return {"error": str(e), "total_jobs": 0} - - def end_headers(self): - # Enable CORS for local development - self.send_header("Access-Control-Allow-Origin", "*") - self.send_header("Cache-Control", "no-store, no-cache, must-revalidate") - super().end_headers() - - -def main(): - # Use repository root for all API file lookups. - root_dir = REPO_ROOT - - # Make root_dir available to handler - MyHTTPRequestHandler.root_dir = root_dir - - with socketserver.TCPServer(("", PORT), MyHTTPRequestHandler) as httpd: - url = f"http://localhost:{PORT}" - print("🚀 QAI Training Dashboard") - print("=" * 50) - print(f"Server running at: {url}") - print(f"Root directory: {root_dir}") - print("Press Ctrl+C to stop") - print("=" * 50) - - # Open browser - try: - webbrowser.open(url) - except: - pass - - try: - httpd.serve_forever() - except KeyboardInterrupt: - print("\n\n✅ Dashboard server stopped") - sys.exit(0) - - -if __name__ == "__main__": - main() +"""Simple HTTP server for training dashboard""" + +import http.server +import json +import os +import random +import re +import socketserver +import subprocess +import sys +import time +import webbrowser +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from urllib.parse import parse_qs, urlparse + +import yaml +from gpu_monitor import get_gpu_info, get_gpu_processes, get_system_resources +from vram_calculator import calculate_safe_batch_size + +# Make scripts/ importable when serve.py is run from apps/dashboard/ +sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "scripts")) + +# Import GPU monitoring +sys.path.insert(0, str(Path(__file__).parent)) + +PORT = 8000 +REPO_ROOT = Path(__file__).resolve().parents[2] +DASHBOARD_DIR = Path(__file__).resolve().parent + +# Simple rate limiting +request_counts = defaultdict(list) +MAX_REQUESTS_PER_MINUTE = 60 + + +class MyHTTPRequestHandler(http.server.SimpleHTTPRequestHandler): + def __init__(self, *args, directory=None, **kwargs): + # Always serve static dashboard assets from apps/dashboard. + super().__init__(*args, directory=str(DASHBOARD_DIR), **kwargs) + + def log_request(self, code="-", size="-"): + """Enhanced request logging with timestamps""" + timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + print(f"[{timestamp}] {self.command} {self.path} - {code}") + + def check_rate_limit(self): + """Simple rate limiting per IP""" + client_ip = self.client_address[0] + now = time.time() + + # Clean old requests (older than 1 minute) + request_counts[client_ip] = [ + t for t in request_counts[client_ip] if now - t < 60 + ] + + # Check limit + if len(request_counts[client_ip]) >= MAX_REQUESTS_PER_MINUTE: + return False + + request_counts[client_ip].append(now) + return True + + def do_GET(self): + # Check rate limit + if not self.check_rate_limit(): + self.send_error(429, "Too Many Requests") + return + + # Use the root_dir set by main() + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + + # Redirect root to consolidated dashboard + if self.path == "/" or self.path == "/index.html": + self.send_response(302) + self.send_header("Location", "/consolidated.html") + self.end_headers() + return + + # API: Training status (no cache) + if self.path == "/status": + self.send_response(200) + self.send_header("Content-type", "application/json") + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Cache-Control", "no-cache, no-store, must-revalidate") + self.send_header("Pragma", "no-cache") + self.send_header("Expires", "0") + self.end_headers() + + status_file = root_dir / "data_out" / "autotrain" / "status.json" + try: + with open(status_file, "r") as f: + data = json.load(f) + data["server_time"] = datetime.now().isoformat() + self.wfile.write(json.dumps(data).encode()) + except FileNotFoundError: + error_data = { + "error": "Status file not found. No training jobs have been run yet.", + "jobs": [], + "server_time": datetime.now().isoformat(), + } + self.wfile.write(json.dumps(error_data).encode()) + except Exception as e: + error_data = { + "error": f"Failed to read status: {str(e)}", + "jobs": [], + "server_time": datetime.now().isoformat(), + } + self.wfile.write(json.dumps(error_data).encode()) + return + + # API: Job progress + if self.path.startswith("/api/job-progress/"): + job_id = self.path.split("/")[-1] + self.send_json_response(self.get_job_progress(job_id)) + return + + # API: Job metrics + if self.path.startswith("/api/job-metrics/"): + job_id = self.path.split("/")[-1] + self.send_json_response(self.get_job_metrics(job_id)) + return + + # API: List available datasets + elif self.path == "/api/datasets": + self.send_json_response(self.get_datasets()) + return + + # API: Profile dataset for hyperparameter recommendations + elif self.path.startswith("/api/profile-dataset"): + parsed = urlparse(self.path) + query_params = parse_qs(parsed.query) + dataset_name = query_params.get("dataset", [None])[0] + + if not dataset_name: + self.send_json_response({"error": "Missing dataset parameter"}) + return + + # Find dataset path + datasets = self.get_datasets() + dataset_path = None + for ds in datasets.get("datasets", []): + if ds.get("name") == dataset_name or ds.get("path", "").endswith( + dataset_name + ): + dataset_path = Path(ds["path"]) + break + + if not dataset_path or not dataset_path.exists(): + self.send_json_response({"error": f"Dataset not found: {dataset_name}"}) + return + + # Run profiler script + profiler_script = root_dir / "scripts" / "dataset_profiler.py" + if not profiler_script.exists(): + self.send_json_response({"error": "Profiler script not found"}) + return + + try: + import subprocess + + result = subprocess.run( + [ + sys.executable, + str(profiler_script), + str(dataset_path), + "--recommend", + "--quiet", + ], + capture_output=True, + text=True, + timeout=30, + ) + if result.returncode == 0: + profile_data = json.loads(result.stdout) + self.send_json_response(profile_data) + else: + self.send_json_response( + {"error": f"Profiler failed: {result.stderr}"} + ) + except subprocess.TimeoutExpired: + self.send_json_response({"error": "Profiler timed out (30s limit)"}) + except Exception as e: + self.send_json_response({"error": f"Profiler error: {str(e)}"}) + return + + # API: List trained models + elif self.path == "/api/models": + self.send_json_response(self.get_models()) + return + + # API: List training configs + elif self.path == "/api/configs": + self.send_json_response(self.get_configs()) + return + + # API: Get job details + elif self.path.startswith("/api/job/"): + job_name = self.path.split("/")[-1] + self.send_json_response(self.get_job_details(job_name)) + return + + # API: Get training logs + elif self.path.startswith("/api/logs/"): + job_name = self.path.split("/")[-1] + self.send_json_response(self.get_job_logs(job_name)) + return + + # API: GPU monitoring + elif self.path == "/api/gpu": + self.send_json_response(get_gpu_info()) + return + + # API: GPU processes + elif self.path == "/api/gpu-processes": + self.send_json_response({"processes": get_gpu_processes()}) + return + + # API: VRAM-aware batch size calculator + elif self.path.startswith("/api/vram-info"): + qs = parse_qs(urlparse(self.path).query) + + def _qs(key, default=""): + vals = qs.get(key, [default]) + return vals[0] if vals else default + + model = _qs("model", "") + params_b_str = _qs("params_b", "") + params_b = float(params_b_str) if params_b_str else None + lora_rank = int(_qs("lora_rank", "16")) + seq_len = int(_qs("seq_len", "512")) + dtype = _qs("dtype", "fp16") + self.send_json_response( + calculate_safe_batch_size( + model_name=model, + params_b=params_b, + lora_rank=lora_rank, + seq_len=seq_len, + dtype=dtype, + ) + ) + return + + # API: System resources + elif self.path == "/api/system": + self.send_json_response(get_system_resources()) + return + + # API: Training history/timeline + elif self.path == "/api/history": + self.send_json_response(self.get_training_history()) + return + + # API: System health check + elif self.path == "/api/health": + self.send_json_response(self.get_system_health()) + return + + # API: Quick stats summary + elif self.path == "/api/stats": + self.send_json_response(self.get_quick_stats()) + return + + # API: Get active processes + elif self.path == "/api/processes": + self.send_json_response(self.get_active_processes()) + return + + # API: Job queue status + elif self.path == "/api/job-queue": + self.send_json_response(self.get_job_queue_status()) + return + + # Default file serving + super().do_GET() + + def do_POST(self): + # Use the root_dir set by main() + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + + # API: Start training + if self.path == "/api/start-training": + content_length = int(self.headers["Content-Length"]) + post_data = self.rfile.read(content_length) + params = json.loads(post_data.decode()) + result = self.start_training(params) + self.send_json_response(result) + return + + # API: Pause job + if self.path.startswith("/api/job-control/") and self.path.endswith("/pause"): + job_id = self.path.split("/")[-2] + result = self.control_job(job_id, action="pause") + self.send_json_response(result) + return + + # API: Stop job + if self.path.startswith("/api/job-control/") and self.path.endswith("/stop"): + job_id = self.path.split("/")[-2] + result = self.control_job(job_id, action="stop") + self.send_json_response(result) + return + + # API: Benchmark models + if self.path == "/api/benchmark": + content_length = int(self.headers["Content-Length"]) + post_data = self.rfile.read(content_length) + params = json.loads(post_data.decode()) + model_ids = params.get("model_ids", []) + results = self.run_benchmark(model_ids) + self.send_json_response({"results": results}) + return + + self.send_error(404) + + def send_json_response(self, data): + self.send_response(200) + self.send_header("Content-type", "application/json") + self.send_header("Access-Control-Allow-Origin", "*") + self.end_headers() + self.wfile.write(json.dumps(data).encode()) + + def get_job_progress(self, job_id): + """Return job progress information""" + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + status_file = root_dir / "data_out" / "autotrain" / "status.json" + try: + with open(status_file, "r") as f: + data = json.load(f) + job = next( + (j for j in data.get("jobs", []) if j.get("name") == job_id), None + ) + if not job: + return {"error": "Job not found", "job_id": job_id} + + metrics = job.get("metrics", {}) + current_epoch = ( + job.get("current_epoch") or metrics.get("current_epoch") or 0 + ) + total_epochs = job.get("epochs") or job.get("config", {}).get("epochs") or 0 + post_loss = metrics.get("post_eval_loss") + current_loss = metrics.get("current_loss", post_loss) + lr = job.get("config", {}).get("learning_rate") or metrics.get( + "learning_rate" + ) + steps_per_sec = metrics.get("steps_per_sec") + status = job.get("status", "unknown") + duration = job.get("duration_sec") + + # Progress percent + progress_percent = job.get("progress_percent") + if progress_percent is None: + try: + progress_percent = ( + round((current_epoch / total_epochs) * 100, 2) + if total_epochs + else 0 + ) + except Exception: + progress_percent = 0 + + return { + "job_id": job_id, + "current_epoch": current_epoch, + "total_epochs": total_epochs, + "current_loss": current_loss, + "learning_rate": lr, + "steps_per_sec": steps_per_sec, + "progress_percent": progress_percent, + "status": status, + "duration_sec": duration, + } + except Exception as e: + return {"error": str(e), "job_id": job_id} + + def get_job_metrics(self, job_id): + """Return arrays for charting training/validation loss""" + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + status_file = root_dir / "data_out" / "autotrain" / "status.json" + steps = [] + train_loss = [] + eval_loss = [] + try: + with open(status_file, "r") as f: + data = json.load(f) + job = next( + (j for j in data.get("jobs", []) if j.get("name") == job_id), None + ) + if not job: + return {"error": "Job not found", "job_id": job_id} + + # Try metrics history + history = job.get("metrics_history") or job.get("loss_history") + if isinstance(history, list) and history: + for i, h in enumerate(history): + steps.append(h.get("step", i)) + if "train_loss" in h: + train_loss.append(h["train_loss"]) + if "eval_loss" in h: + eval_loss.append(h["eval_loss"]) + else: + # Fallback: parse log file for loss lines + log_file = job.get("log") + if log_file and Path(log_file).exists(): + try: + with open( + log_file, "r", encoding="utf-8", errors="ignore" + ) as f: + for i, line in enumerate(f): + m = re.search( + r"step\s*(\d+).*?train_loss=([0-9\.]+).*?eval_loss=([0-9\.]+)", + line, + ) + if m: + steps.append(int(m.group(1))) + train_loss.append(float(m.group(2))) + eval_loss.append(float(m.group(3))) + except Exception: + pass + + return { + "job_id": job_id, + "steps": steps, + "train_loss": train_loss, + "eval_loss": eval_loss, + } + except Exception as e: + return { + "error": str(e), + "job_id": job_id, + "steps": steps, + "train_loss": train_loss, + "eval_loss": eval_loss, + } + + def get_datasets(self): + # Use the root_dir set by main() + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + datasets_dir = root_dir / "datasets" / "chat" + datasets = [] + + try: + if not datasets_dir.exists(): + return { + "error": f"Datasets directory not found: {datasets_dir}", + "datasets": [], + } + + for d in datasets_dir.iterdir(): + if d.is_dir(): + train_file = d / "train.json" + test_file = d / "test.json" + if train_file.exists(): + try: + # Count samples efficiently without loading entire file + # For JSON arrays, count top-level elements + train_samples = self._count_json_samples(train_file) + test_samples = 0 + if test_file.exists(): + test_samples = self._count_json_samples(test_file) + + datasets.append( + { + "name": d.name, + "path": str(d.relative_to(root_dir)), + "train_samples": train_samples, + "test_samples": test_samples, + } + ) + except Exception as e: + print(f"Warning: Error processing {train_file}: {e}") + continue + except Exception as e: + import traceback + + traceback.print_exc() + return {"error": str(e), "datasets": []} + + return {"datasets": datasets} + + def _count_json_samples(self, file_path: Path) -> int: + """Count samples in a JSON file efficiently. + + For JSONL files: counts lines. + For JSON arrays: counts top-level array elements. + Falls back to loading full file if needed. + """ + try: + with open(file_path, "r", encoding="utf-8") as f: + first_char = f.read(1) + f.seek(0) + + if first_char == "[": + # JSON array - need to parse it + data = json.load(f) + return len(data) if isinstance(data, list) else 1 + elif first_char == "{": + # JSONL format - count lines efficiently + return sum(1 for line in f if line.strip()) + else: + # Unknown format, try to parse + data = json.load(f) + return len(data) if isinstance(data, list) else 1 + except Exception: + # Fallback: try loading as JSON + try: + with open(file_path, "r", encoding="utf-8") as f: + data = json.load(f) + return len(data) if isinstance(data, list) else 1 + except Exception: + return 0 + + def get_models(self): + # Use the root_dir set by main() + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + models_dir = root_dir / "data_out" / "lora_training" / "marathon" + models = [] + + try: + if models_dir.exists(): + for model_dir in models_dir.iterdir(): + if model_dir.is_dir(): + adapter_config = ( + model_dir / "lora_adapter" / "adapter_config.json" + ) + if adapter_config.exists(): + with open(adapter_config) as f: + config = json.load(f) + models.append( + { + "name": model_dir.name, + "path": str(model_dir.relative_to(root_dir)), + "base_model": config.get( + "base_model_name_or_path", "unknown" + ), + "rank": config.get("r", "unknown"), + } + ) + except Exception as e: + return {"error": str(e), "models": []} + + return {"models": models} + + def get_configs(self): + # Use the root_dir set by main() + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + configs = [] + + try: + for yaml_file in root_dir.glob("autotrain*.yaml"): + if "autogen" not in yaml_file.name: + with open(yaml_file) as f: + config = yaml.safe_load(f) + + jobs = config.get("jobs", []) + total_epochs = sum(j.get("epochs", 0) for j in jobs) + + # Estimate time based on previous runs + estimated_minutes = len(jobs) * 3 # Rough estimate + est_time = ( + f"{estimated_minutes}m" + if estimated_minutes < 60 + else f"{estimated_minutes//60}h {estimated_minutes % 60}m" + ) + + configs.append( + { + "name": yaml_file.stem, + "path": yaml_file.name, + "jobs": len(jobs), + "total_epochs": total_epochs, + "estimated_time": est_time, + "modified": yaml_file.stat().st_mtime, + } + ) + + # Sort by modification time + configs.sort(key=lambda x: x.get("modified", 0), reverse=True) + except Exception as e: + return {"error": str(e), "configs": []} + + return {"configs": configs} + + def get_job_details(self, job_name): + # Use the root_dir set by main() + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + status_file = root_dir / "data_out" / "autotrain" / "status.json" + + try: + with open(status_file) as f: + data = json.load(f) + + for job in data.get("jobs", []): + if job.get("name") == job_name: + # Add output directory contents if available + if "output_dir" in job and job["output_dir"]: + output_path = root_dir / job["output_dir"] + if output_path.exists(): + job["output_files"] = [ + f.name for f in output_path.rglob("*") if f.is_file() + ] + return {"job": job} + + return {"error": "Job not found"} + except Exception as e: + return {"error": str(e)} + + def get_job_logs(self, job_name): + # Use the root_dir set by main() + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + status_file = root_dir / "data_out" / "autotrain" / "status.json" + + try: + with open(status_file) as f: + data = json.load(f) + + for job in data.get("jobs", []): + if job.get("name") == job_name and "log" in job: + log_file = Path(job["log"]) + if log_file.exists(): + with open( + log_file, "r", encoding="utf-8", errors="ignore" + ) as f: + # Efficiently tail last 500 lines without loading entire file + lines = [] + for line in f: + lines.append(line) + if len(lines) > 500: + lines.pop(0) # Keep only last 500 + return {"logs": "".join(lines)} + + return {"logs": "No logs available"} + except Exception as e: + return {"error": str(e), "logs": ""} + + def start_training(self, params): + """Create and queue a new training job""" + # Use the root_dir set by main() + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + + try: + # Accept both legacy and new parameter names + job_name = params.get("job_name") or params.get("name") + model = params.get("model") or "microsoft/Phi-3.5-mini-instruct" + dataset = params.get("dataset") + epochs = params.get("epochs", 3) + max_samples = params.get( + "max_train_samples", params.get("max_samples", 1000) + ) + learning_rate = params.get("learning_rate", "2e-4") + batch_size = params.get("batch_size", 4) + lora_rank = params.get("lora_rank", 16) + lora_alpha = params.get("lora_alpha", 32) + + if not job_name or not dataset: + return {"success": False, "error": "Missing required parameters"} + + # Create a custom config YAML + config_data = { + "jobs": [ + { + "name": job_name, + "runner": "hf", + "category": "custom", + "model": model, + "dataset": f"datasets/chat/{dataset}", + "epochs": epochs, + "max_train_samples": max_samples, + "learning_rate": float(learning_rate), + "batch_size": batch_size, + "lora_rank": lora_rank, + "lora_alpha": lora_alpha, + "device": "auto", + } + ] + } + + # Save config + config_file = root_dir / f"autotrain_custom_{job_name}.yaml" + with open(config_file, "w") as f: + yaml.dump(config_data, f) + + # Launch training in background + cmd = [ + "python", + str(root_dir / "scripts" / "autotrain.py"), + "--config", + str(config_file), + "--resume", + ] + + subprocess.Popen( + cmd, + cwd=str(root_dir), + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + creationflags=( + subprocess.CREATE_NO_WINDOW + if hasattr(subprocess, "CREATE_NO_WINDOW") + else 0 + ), + ) + + return { + "success": True, + "message": f"Training job {job_name} started", + "job_name": job_name, + "job_id": job_name, + "config_file": config_file.name, + } + except Exception as e: + return {"success": False, "error": str(e)} + + def control_job(self, job_id, action="pause"): + """Stub to control a job (pause/stop) via flag files""" + try: + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + control_dir = root_dir / "data_out" / "control" + control_dir.mkdir(parents=True, exist_ok=True) + flag_file = control_dir / f"{job_id}.{action}" + with open(flag_file, "w") as f: + f.write(datetime.now().isoformat()) + return { + "success": True, + "message": f"Job {action} signal sent", + "job_id": job_id, + } + except Exception as e: + return {"success": False, "error": str(e), "job_id": job_id} + + def run_benchmark(self, model_ids): + """Run a simple synthetic benchmark for models""" + results = [] + for mid in model_ids: + try: + # Synthetic metrics + inference_time = random.uniform(100, 1200) # ms + memory_mb = random.uniform(200, 2000) + throughput = random.uniform(20, 300) # tokens/sec + # Score: lower time & memory, higher throughput + speed_score = max(0, min(100, (1200 - inference_time) / 12)) + memory_score = max(0, min(100, (2000 - memory_mb) / 20)) + throughput_score = max(0, min(100, throughput / 3)) + score = round((speed_score + memory_score + throughput_score) / 3, 2) + results.append( + { + "model_id": mid, + "model_name": mid, + "inference_time": round(inference_time, 1), + "memory_mb": round(memory_mb, 1), + "throughput": round(throughput, 1), + "score": score, + } + ) + except Exception as e: + results.append({"model_id": mid, "error": str(e)}) + return results + + def get_training_history(self): + """Get historical training data for charts""" + # Use the root_dir set by main() + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + status_file = root_dir / "data_out" / "autotrain" / "status.json" + + try: + with open(status_file) as f: + data = json.load(f) + + history = {"jobs": [], "timeline": []} + + for job in data.get("jobs", []): + if job.get("metrics"): + history["jobs"].append( + { + "name": job["name"], + "pre_loss": job["metrics"].get("pre_eval_loss"), + "post_loss": job["metrics"].get("post_eval_loss"), + "pre_perplexity": job["metrics"].get("pre_eval_perplexity"), + "post_perplexity": job["metrics"].get( + "post_eval_perplexity" + ), + "duration": job.get("duration_sec"), + "status": job.get("status"), + } + ) + + if job.get("start_time"): + history["timeline"].append( + { + "name": job["name"], + "start": job["start_time"], + "duration": job.get("duration_sec", 0), + "status": job.get("status", "unknown"), + } + ) + + return history + except Exception as e: + return {"error": str(e), "jobs": [], "timeline": []} + + def get_system_health(self): + """Comprehensive system health check""" + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + + health = { + "status": "healthy", + "timestamp": datetime.now().isoformat(), + "checks": {}, + } + + try: + # Check datasets directory + datasets_dir = root_dir / "datasets" / "chat" + health["checks"]["datasets"] = { + "exists": datasets_dir.exists(), + "count": ( + len(list(datasets_dir.glob("*/train.json"))) + if datasets_dir.exists() + else 0 + ), + } + + # Check output directory + output_dir = root_dir / "data_out" + health["checks"]["output"] = { + "exists": output_dir.exists(), + "writable": ( + os.access(output_dir, os.W_OK) if output_dir.exists() else False + ), + } + + # Check GPU availability + gpu_info = get_gpu_info() + health["checks"]["gpu"] = { + "available": len(gpu_info.get("gpus", [])) > 0, + "count": len(gpu_info.get("gpus", [])), + } + + # Check virtual environments + health["checks"]["venvs"] = { + "quantum_ai": ( + root_dir / "ai-projects" / "quantum-ml" / "venv" + ).exists(), + "talk_to_ai": (root_dir / "ai-projects" / "chat-cli" / "venv").exists(), + "lora_training": ( + root_dir / "AI" / "microsoft_phi-silica-3.6_v1" / "venv" + ).exists(), + } + + # Overall health + all_checks = [ + health["checks"]["datasets"]["exists"], + health["checks"]["output"]["exists"], + any(health["checks"]["venvs"].values()), + ] + health["status"] = "healthy" if all(all_checks) else "degraded" + + except Exception as e: + health["status"] = "error" + health["error"] = str(e) + + return health + + def get_quick_stats(self): + """Quick summary statistics""" + root_dir = getattr(self.__class__, "root_dir", REPO_ROOT) + + stats = { + "training_jobs": 0, + "datasets": 0, + "models": 0, + "gpu_usage": 0, + "active_processes": 0, + } + + try: + # Training jobs + status_file = root_dir / "data_out" / "autotrain" / "status.json" + if status_file.exists(): + with open(status_file) as f: + data = json.load(f) + stats["training_jobs"] = len(data.get("jobs", [])) + + # Datasets + datasets_dir = root_dir / "datasets" / "chat" + if datasets_dir.exists(): + stats["datasets"] = len( + [d for d in datasets_dir.iterdir() if d.is_dir()] + ) + + # Models + models_dir = root_dir / "data_out" / "lora_training" / "marathon" + if models_dir.exists(): + stats["models"] = len([m for m in models_dir.iterdir() if m.is_dir()]) + + # GPU usage + gpu_info = get_gpu_info() + if gpu_info.get("gpus"): + stats["gpu_usage"] = gpu_info["gpus"][0].get("utilization_gpu", 0) + + # Active processes + processes = get_gpu_processes() + stats["active_processes"] = len(processes) + + except Exception as e: + stats["error"] = str(e) + + return stats + + def get_active_processes(self): + """Get active Python processes""" + import psutil + + processes = [] + try: + for proc in psutil.process_iter( + ["pid", "name", "cmdline", "memory_info", "cpu_percent"] + ): + try: + pinfo = proc.info + if pinfo["name"] and "python" in pinfo["name"].lower(): + cmdline = " ".join(pinfo["cmdline"] or []) + if any( + keyword in cmdline + for keyword in [ + "train", + "autotrain", + "quantum", + "chat", + "serve", + ] + ): + processes.append( + { + "pid": pinfo["pid"], + "name": pinfo["name"], + "command": ( + cmdline[:100] + "..." + if len(cmdline) > 100 + else cmdline + ), + "memory_mb": round( + pinfo["memory_info"].rss / 1024 / 1024, 1 + ), + "cpu_percent": pinfo["cpu_percent"], + } + ) + except (psutil.NoSuchProcess, psutil.AccessDenied): + pass + except Exception as e: + return {"error": str(e), "processes": []} + + return {"processes": processes, "count": len(processes)} + + def get_job_queue_status(self): + """Get job queue status from job_queue.py""" + try: + queue_file = Path("data_out/job_queue.json") + if not queue_file.exists(): + return { + "total_jobs": 0, + "pending": 0, + "running": 0, + "completed": 0, + "failed": 0, + "blocked": 0, + "cancelled": 0, + "queue_length": 0, + "estimated_total_time": 0, + "message": "Job queue not initialized", + } + + with open(queue_file, "r") as f: + queue_data = json.load(f) + + jobs = queue_data.get("jobs", []) + + # Single-pass aggregation to avoid multiple iterations (optimized) + status_counts = { + "pending": 0, + "running": 0, + "completed": 0, + "failed": 0, + "blocked": 0, + "cancelled": 0, + } + queue_jobs = [] + estimated_total = 0 + + for job in jobs: + status = job.get("status") + if status in status_counts: + status_counts[status] += 1 + if status in {"pending", "blocked"}: + queue_jobs.append(job) + estimated_total += job.get("estimated_duration", 0) + + return { + "total_jobs": len(jobs), + "pending": status_counts["pending"], + "running": status_counts["running"], + "completed": status_counts["completed"], + "failed": status_counts["failed"], + "blocked": status_counts["blocked"], + "cancelled": status_counts["cancelled"], + "queue_length": len(queue_jobs), + "estimated_total_time": estimated_total, + "updated_at": queue_data.get("updated_at"), + } + except Exception as e: + return {"error": str(e), "total_jobs": 0} + + def end_headers(self): + # Enable CORS for local development + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Cache-Control", "no-store, no-cache, must-revalidate") + super().end_headers() + + +def main(): + # Use repository root for all API file lookups. + root_dir = REPO_ROOT + + # Make root_dir available to handler + MyHTTPRequestHandler.root_dir = root_dir + + with socketserver.TCPServer(("", PORT), MyHTTPRequestHandler) as httpd: + url = f"http://localhost:{PORT}" + print("🚀 QAI Training Dashboard") + print("=" * 50) + print(f"Server running at: {url}") + print(f"Root directory: {root_dir}") + print("Press Ctrl+C to stop") + print("=" * 50) + + # Open browser + try: + webbrowser.open(url) + except: + pass + + try: + httpd.serve_forever() + except KeyboardInterrupt: + print("\n\n✅ Dashboard server stopped") + sys.exit(0) + + +if __name__ == "__main__": + main() diff --git a/apps/dashboard/session-history.js b/apps/dashboard/session-history.js index 244cdb281..8ad661cf1 100644 --- a/apps/dashboard/session-history.js +++ b/apps/dashboard/session-history.js @@ -1,345 +1,345 @@ -/** - * Training Session History Tracker - * Persist training sessions and enable replay/comparison - * - * Features: - * - Session persistence (localStorage/IndexedDB) - * - Session comparison - * - Config replay (load previous settings) - * - Export to CSV/JSON - * - Filtering by date/status/model - */ - -class TrainingSessionHistory { - constructor(options = {}) { - this.config = { - storage: options.storage || 'localStorage', // or 'indexedDB' - maxSessions: options.maxSessions || 100, - autoSave: options.autoSave !== false, - ...options - }; - this.sessions = []; - this.currentSession = null; - this.init(); - } - - async init() { - // Load existing sessions - await this.loadSessions(); - - // Setup auto-save if enabled - if (this.config.autoSave) { - this.setupAutoSave(); - } - } - - /** - * Load sessions from storage - */ - async loadSessions() { - if (this.config.storage === 'localStorage') { - const stored = localStorage.getItem('qai_training_sessions'); - if (stored) { - try { - this.sessions = JSON.parse(stored); - } catch (err) { - console.error('Failed to load sessions:', err); - this.sessions = []; - } - } - } else if (this.config.storage === 'indexedDB') { - // IndexedDB implementation (future enhancement) - console.warn('IndexedDB storage not yet implemented, falling back to localStorage'); - this.config.storage = 'localStorage'; - await this.loadSessions(); - } - } - - /** - * Save sessions to storage - */ - async saveSessions() { - // Keep only max sessions - if (this.sessions.length > this.config.maxSessions) { - this.sessions = this.sessions.slice(-this.config.maxSessions); - } - - if (this.config.storage === 'localStorage') { - try { - localStorage.setItem('qai_training_sessions', JSON.stringify(this.sessions)); - } catch (err) { - console.error('Failed to save sessions:', err); - } - } - } - - /** - * Start a new training session - */ - startSession(config) { - this.currentSession = { - id: `session-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`, - startTime: Date.now(), - endTime: null, - status: 'running', - config: JSON.parse(JSON.stringify(config)), // Deep clone - metrics: { - initialLoss: null, - finalLoss: null, - bestLoss: null, - epochs: [], - avgLossPerEpoch: [] - }, - anomalies: [], - notes: '' - }; - - return this.currentSession.id; - } - - /** - * Update current session metrics - */ - updateSession(updates) { - if (!this.currentSession) return; - - Object.assign(this.currentSession, updates); - - if (this.config.autoSave) { - this.saveSessions(); - } - } - - /** - * End current session - */ - endSession(status = 'completed', finalMetrics = {}) { - if (!this.currentSession) return; - - this.currentSession.endTime = Date.now(); - this.currentSession.status = status; - this.currentSession.duration = this.currentSession.endTime - this.currentSession.startTime; - Object.assign(this.currentSession.metrics, finalMetrics); - - // Add to history - this.sessions.push(this.currentSession); - this.currentSession = null; - - this.saveSessions(); - } - - /** - * Get all sessions - */ - getAllSessions() { - return this.sessions; - } - - /** - * Get session by ID - */ - getSessionById(id) { - return this.sessions.find(s => s.id === id); - } - - /** - * Filter sessions - */ - filterSessions(filters = {}) { - let filtered = [...this.sessions]; - - // Filter by status - if (filters.status) { - filtered = filtered.filter(s => s.status === filters.status); - } - - // Filter by model - if (filters.model) { - filtered = filtered.filter(s => - s.config.model && s.config.model.includes(filters.model) - ); - } - - // Filter by dataset - if (filters.dataset) { - filtered = filtered.filter(s => - s.config.dataset && s.config.dataset.includes(filters.dataset) - ); - } - - // Filter by date range - if (filters.startDate) { - const start = new Date(filters.startDate).getTime(); - filtered = filtered.filter(s => s.startTime >= start); - } - - if (filters.endDate) { - const end = new Date(filters.endDate).getTime(); - filtered = filtered.filter(s => s.startTime <= end); - } - - // Filter by min loss - if (filters.minLoss !== undefined) { - filtered = filtered.filter(s => - s.metrics.finalLoss !== null && s.metrics.finalLoss >= filters.minLoss - ); - } - - // Filter by max loss - if (filters.maxLoss !== undefined) { - filtered = filtered.filter(s => - s.metrics.finalLoss !== null && s.metrics.finalLoss <= filters.maxLoss - ); - } - - return filtered; - } - - /** - * Compare sessions - */ - compareSessions(sessionIds) { - const sessions = sessionIds.map(id => this.getSessionById(id)).filter(Boolean); - - if (sessions.length === 0) return null; - - return { - sessions, - comparison: { - bestLoss: Math.min(...sessions.map(s => s.metrics.finalLoss || Infinity)), - worstLoss: Math.max(...sessions.map(s => s.metrics.finalLoss || 0)), - avgLoss: sessions.reduce((sum, s) => sum + (s.metrics.finalLoss || 0), 0) / sessions.length, - totalDuration: sessions.reduce((sum, s) => sum + (s.duration || 0), 0), - avgDuration: sessions.reduce((sum, s) => sum + (s.duration || 0), 0) / sessions.length, - statusBreakdown: sessions.reduce((acc, s) => { - acc[s.status] = (acc[s.status] || 0) + 1; - return acc; - }, {}) - } - }; - } - - /** - * Replay session config - */ - replaySession(sessionId) { - const session = this.getSessionById(sessionId); - if (!session) return null; - - return JSON.parse(JSON.stringify(session.config)); // Deep clone - } - - /** - * Export sessions to JSON - */ - exportToJSON(sessionIds = null) { - const sessions = sessionIds - ? sessionIds.map(id => this.getSessionById(id)).filter(Boolean) - : this.sessions; - - return JSON.stringify(sessions, null, 2); - } - - /** - * Export sessions to CSV - */ - exportToCSV(sessionIds = null) { - const sessions = sessionIds - ? sessionIds.map(id => this.getSessionById(id)).filter(Boolean) - : this.sessions; - - if (sessions.length === 0) return ''; - - // CSV headers - const headers = [ - 'ID', 'Start Time', 'Duration (min)', 'Status', - 'Model', 'Dataset', 'Epochs', 'Batch Size', 'Learning Rate', - 'LoRA Rank', 'Initial Loss', 'Final Loss', 'Best Loss', - 'Anomalies', 'Notes' - ]; - - // CSV rows - const rows = sessions.map(s => [ - s.id, - new Date(s.startTime).toISOString(), - s.duration ? Math.round(s.duration / 60000) : 'N/A', - s.status, - s.config.model || 'N/A', - s.config.dataset || 'N/A', - s.config.epochs || 'N/A', - s.config.batch_size || s.config.batchSize || 'N/A', - s.config.learning_rate || s.config.learningRate || 'N/A', - s.config.lora_rank || s.config.loraRank || 'N/A', - s.metrics.initialLoss || 'N/A', - s.metrics.finalLoss || 'N/A', - s.metrics.bestLoss || 'N/A', - s.anomalies.length, - s.notes ? `"${s.notes.replace(/"/g, '""')}"` : '' - ]); - - return [headers.join(','), ...rows.map(r => r.join(','))].join('\n'); - } - - /** - * Delete session - */ - deleteSession(sessionId) { - this.sessions = this.sessions.filter(s => s.id !== sessionId); - this.saveSessions(); - } - - /** - * Clear all sessions - */ - clearAllSessions() { - this.sessions = []; - this.saveSessions(); - } - - /** - * Setup auto-save - */ - setupAutoSave() { - // Save every 30 seconds if there's a current session - setInterval(() => { - if (this.currentSession) { - // Find and update in sessions array - const index = this.sessions.findIndex(s => s.id === this.currentSession.id); - if (index >= 0) { - this.sessions[index] = this.currentSession; - } - this.saveSessions(); - } - }, 30000); - } - - /** - * Get statistics - */ - getStatistics() { - const completed = this.sessions.filter(s => s.status === 'completed'); - - return { - total: this.sessions.length, - completed: completed.length, - failed: this.sessions.filter(s => s.status === 'failed').length, - running: this.sessions.filter(s => s.status === 'running').length, - avgDuration: completed.length > 0 - ? completed.reduce((sum, s) => sum + (s.duration || 0), 0) / completed.length - : 0, - avgFinalLoss: completed.length > 0 - ? completed.reduce((sum, s) => sum + (s.metrics.finalLoss || 0), 0) / completed.length - : 0, - bestSession: completed.length > 0 - ? completed.reduce((best, s) => - (s.metrics.finalLoss || Infinity) < (best.metrics.finalLoss || Infinity) ? s : best - ) - : null - }; - } -} - -// Export for use in other modules -if (typeof window !== 'undefined') { - window.TrainingSessionHistory = TrainingSessionHistory; -} +/** + * Training Session History Tracker + * Persist training sessions and enable replay/comparison + * + * Features: + * - Session persistence (localStorage/IndexedDB) + * - Session comparison + * - Config replay (load previous settings) + * - Export to CSV/JSON + * - Filtering by date/status/model + */ + +class TrainingSessionHistory { + constructor(options = {}) { + this.config = { + storage: options.storage || 'localStorage', // or 'indexedDB' + maxSessions: options.maxSessions || 100, + autoSave: options.autoSave !== false, + ...options + }; + this.sessions = []; + this.currentSession = null; + this.init(); + } + + async init() { + // Load existing sessions + await this.loadSessions(); + + // Setup auto-save if enabled + if (this.config.autoSave) { + this.setupAutoSave(); + } + } + + /** + * Load sessions from storage + */ + async loadSessions() { + if (this.config.storage === 'localStorage') { + const stored = localStorage.getItem('qai_training_sessions'); + if (stored) { + try { + this.sessions = JSON.parse(stored); + } catch (err) { + console.error('Failed to load sessions:', err); + this.sessions = []; + } + } + } else if (this.config.storage === 'indexedDB') { + // IndexedDB implementation (future enhancement) + console.warn('IndexedDB storage not yet implemented, falling back to localStorage'); + this.config.storage = 'localStorage'; + await this.loadSessions(); + } + } + + /** + * Save sessions to storage + */ + async saveSessions() { + // Keep only max sessions + if (this.sessions.length > this.config.maxSessions) { + this.sessions = this.sessions.slice(-this.config.maxSessions); + } + + if (this.config.storage === 'localStorage') { + try { + localStorage.setItem('qai_training_sessions', JSON.stringify(this.sessions)); + } catch (err) { + console.error('Failed to save sessions:', err); + } + } + } + + /** + * Start a new training session + */ + startSession(config) { + this.currentSession = { + id: `session-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`, + startTime: Date.now(), + endTime: null, + status: 'running', + config: JSON.parse(JSON.stringify(config)), // Deep clone + metrics: { + initialLoss: null, + finalLoss: null, + bestLoss: null, + epochs: [], + avgLossPerEpoch: [] + }, + anomalies: [], + notes: '' + }; + + return this.currentSession.id; + } + + /** + * Update current session metrics + */ + updateSession(updates) { + if (!this.currentSession) return; + + Object.assign(this.currentSession, updates); + + if (this.config.autoSave) { + this.saveSessions(); + } + } + + /** + * End current session + */ + endSession(status = 'completed', finalMetrics = {}) { + if (!this.currentSession) return; + + this.currentSession.endTime = Date.now(); + this.currentSession.status = status; + this.currentSession.duration = this.currentSession.endTime - this.currentSession.startTime; + Object.assign(this.currentSession.metrics, finalMetrics); + + // Add to history + this.sessions.push(this.currentSession); + this.currentSession = null; + + this.saveSessions(); + } + + /** + * Get all sessions + */ + getAllSessions() { + return this.sessions; + } + + /** + * Get session by ID + */ + getSessionById(id) { + return this.sessions.find(s => s.id === id); + } + + /** + * Filter sessions + */ + filterSessions(filters = {}) { + let filtered = [...this.sessions]; + + // Filter by status + if (filters.status) { + filtered = filtered.filter(s => s.status === filters.status); + } + + // Filter by model + if (filters.model) { + filtered = filtered.filter(s => + s.config.model && s.config.model.includes(filters.model) + ); + } + + // Filter by dataset + if (filters.dataset) { + filtered = filtered.filter(s => + s.config.dataset && s.config.dataset.includes(filters.dataset) + ); + } + + // Filter by date range + if (filters.startDate) { + const start = new Date(filters.startDate).getTime(); + filtered = filtered.filter(s => s.startTime >= start); + } + + if (filters.endDate) { + const end = new Date(filters.endDate).getTime(); + filtered = filtered.filter(s => s.startTime <= end); + } + + // Filter by min loss + if (filters.minLoss !== undefined) { + filtered = filtered.filter(s => + s.metrics.finalLoss !== null && s.metrics.finalLoss >= filters.minLoss + ); + } + + // Filter by max loss + if (filters.maxLoss !== undefined) { + filtered = filtered.filter(s => + s.metrics.finalLoss !== null && s.metrics.finalLoss <= filters.maxLoss + ); + } + + return filtered; + } + + /** + * Compare sessions + */ + compareSessions(sessionIds) { + const sessions = sessionIds.map(id => this.getSessionById(id)).filter(Boolean); + + if (sessions.length === 0) return null; + + return { + sessions, + comparison: { + bestLoss: Math.min(...sessions.map(s => s.metrics.finalLoss || Infinity)), + worstLoss: Math.max(...sessions.map(s => s.metrics.finalLoss || 0)), + avgLoss: sessions.reduce((sum, s) => sum + (s.metrics.finalLoss || 0), 0) / sessions.length, + totalDuration: sessions.reduce((sum, s) => sum + (s.duration || 0), 0), + avgDuration: sessions.reduce((sum, s) => sum + (s.duration || 0), 0) / sessions.length, + statusBreakdown: sessions.reduce((acc, s) => { + acc[s.status] = (acc[s.status] || 0) + 1; + return acc; + }, {}) + } + }; + } + + /** + * Replay session config + */ + replaySession(sessionId) { + const session = this.getSessionById(sessionId); + if (!session) return null; + + return JSON.parse(JSON.stringify(session.config)); // Deep clone + } + + /** + * Export sessions to JSON + */ + exportToJSON(sessionIds = null) { + const sessions = sessionIds + ? sessionIds.map(id => this.getSessionById(id)).filter(Boolean) + : this.sessions; + + return JSON.stringify(sessions, null, 2); + } + + /** + * Export sessions to CSV + */ + exportToCSV(sessionIds = null) { + const sessions = sessionIds + ? sessionIds.map(id => this.getSessionById(id)).filter(Boolean) + : this.sessions; + + if (sessions.length === 0) return ''; + + // CSV headers + const headers = [ + 'ID', 'Start Time', 'Duration (min)', 'Status', + 'Model', 'Dataset', 'Epochs', 'Batch Size', 'Learning Rate', + 'LoRA Rank', 'Initial Loss', 'Final Loss', 'Best Loss', + 'Anomalies', 'Notes' + ]; + + // CSV rows + const rows = sessions.map(s => [ + s.id, + new Date(s.startTime).toISOString(), + s.duration ? Math.round(s.duration / 60000) : 'N/A', + s.status, + s.config.model || 'N/A', + s.config.dataset || 'N/A', + s.config.epochs || 'N/A', + s.config.batch_size || s.config.batchSize || 'N/A', + s.config.learning_rate || s.config.learningRate || 'N/A', + s.config.lora_rank || s.config.loraRank || 'N/A', + s.metrics.initialLoss || 'N/A', + s.metrics.finalLoss || 'N/A', + s.metrics.bestLoss || 'N/A', + s.anomalies.length, + s.notes ? `"${s.notes.replace(/"/g, '""')}"` : '' + ]); + + return [headers.join(','), ...rows.map(r => r.join(','))].join('\n'); + } + + /** + * Delete session + */ + deleteSession(sessionId) { + this.sessions = this.sessions.filter(s => s.id !== sessionId); + this.saveSessions(); + } + + /** + * Clear all sessions + */ + clearAllSessions() { + this.sessions = []; + this.saveSessions(); + } + + /** + * Setup auto-save + */ + setupAutoSave() { + // Save every 30 seconds if there's a current session + setInterval(() => { + if (this.currentSession) { + // Find and update in sessions array + const index = this.sessions.findIndex(s => s.id === this.currentSession.id); + if (index >= 0) { + this.sessions[index] = this.currentSession; + } + this.saveSessions(); + } + }, 30000); + } + + /** + * Get statistics + */ + getStatistics() { + const completed = this.sessions.filter(s => s.status === 'completed'); + + return { + total: this.sessions.length, + completed: completed.length, + failed: this.sessions.filter(s => s.status === 'failed').length, + running: this.sessions.filter(s => s.status === 'running').length, + avgDuration: completed.length > 0 + ? completed.reduce((sum, s) => sum + (s.duration || 0), 0) / completed.length + : 0, + avgFinalLoss: completed.length > 0 + ? completed.reduce((sum, s) => sum + (s.metrics.finalLoss || 0), 0) / completed.length + : 0, + bestSession: completed.length > 0 + ? completed.reduce((best, s) => + (s.metrics.finalLoss || Infinity) < (best.metrics.finalLoss || Infinity) ? s : best + ) + : null + }; + } +} + +// Export for use in other modules +if (typeof window !== 'undefined') { + window.TrainingSessionHistory = TrainingSessionHistory; +} diff --git a/apps/dashboard/shared-theme.css b/apps/dashboard/shared-theme.css index bf50c577d..1ddb4ae9e 100644 --- a/apps/dashboard/shared-theme.css +++ b/apps/dashboard/shared-theme.css @@ -1,468 +1,468 @@ -/* ============================================ - QAI Dashboard - Shared Theme Stylesheet - Version: 1.0 - Description: Common styles for all dashboard pages - ============================================ */ - -/* === DARK MODE VARIABLES === */ -:root { - --bg-primary: #1a1a2e; - --bg-secondary: #16213e; - --bg-card: #0f3460; - --text-primary: #eaeaea; - --text-secondary: #a0a0a0; - --accent-primary: #667eea; - --accent-secondary: #764ba2; - --border-color: #2d3561; - --success: #4caf50; - --warning: #ff9800; - --error: #f44336; - --info: #2196f3; -} - -/* === GLOBAL RESETS === */ -* { - box-sizing: border-box; - margin: 0; - padding: 0; -} - -body { - font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; - background: linear-gradient(135deg, var(--bg-primary) 0%, var(--bg-secondary) 100%); - color: var(--text-primary); - line-height: 1.6; - min-height: 100vh; -} - -/* === CONTAINERS === */ -.container { - max-width: 1400px; - margin: 0 auto; - padding: 20px; -} - -/* === CARDS === */ -.card { - background: var(--bg-card); - border-radius: 12px; - padding: 20px; - margin-bottom: 20px; - box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3); - border: 1px solid var(--border-color); - transition: transform 0.2s, box-shadow 0.2s; -} - -.card:hover { - transform: translateY(-2px); - box-shadow: 0 8px 30px rgba(102, 126, 234, 0.2); -} - -.card-header { - display: flex; - align-items: center; - gap: 10px; - margin-bottom: 15px; - padding-bottom: 10px; - border-bottom: 2px solid var(--accent-primary); -} - -.card-title { - font-size: 1.3em; - font-weight: 600; - color: var(--accent-primary); -} - -/* === BUTTONS === */ -.btn { - padding: 10px 20px; - border: none; - border-radius: 8px; - font-size: 0.95em; - font-weight: 500; - cursor: pointer; - transition: all 0.3s; - display: inline-flex; - align-items: center; - gap: 8px; -} - -.btn-primary { - background: linear-gradient(135deg, var(--accent-primary), var(--accent-secondary)); - color: white; -} - -.btn-primary:hover { - transform: translateY(-2px); - box-shadow: 0 6px 20px rgba(102, 126, 234, 0.4); -} - -.btn-secondary { - background: #555; - color: white; -} - -.btn-secondary:hover { - background: #666; -} - -.btn-success { - background: var(--success); - color: white; -} - -.btn-success:hover { - background: #45a049; -} - -.btn-warning { - background: var(--warning); - color: white; -} - -.btn-warning:hover { - background: #e68900; -} - -.btn-danger { - background: var(--error); - color: white; -} - -.btn-danger:hover { - background: #da190b; -} - -.btn-info { - background: var(--info); - color: white; -} - -.btn-info:hover { - background: #0b7dda; -} - -.btn:disabled { - opacity: 0.5; - cursor: not-allowed; - transform: none !important; -} - -/* === BADGES === */ -.badge { - display: inline-block; - padding: 4px 12px; - border-radius: 12px; - font-size: 0.85em; - font-weight: 600; - text-transform: uppercase; -} - -.badge-success { - background: var(--success); - color: white; -} - -.badge-warning { - background: var(--warning); - color: white; -} - -.badge-error { - background: var(--error); - color: white; -} - -.badge-info { - background: var(--info); - color: white; -} - -.badge-secondary { - background: #555; - color: white; -} - -/* === FORM ELEMENTS === */ -.form-group { - margin-bottom: 15px; -} - -label { - display: block; - margin-bottom: 5px; - color: var(--text-secondary); - font-size: 0.9em; - font-weight: 500; -} - -input[type="text"], -input[type="number"], -input[type="email"], -select, -textarea { - width: 100%; - padding: 10px; - background: var(--bg-secondary); - border: 1px solid var(--border-color); - border-radius: 6px; - color: var(--text-primary); - font-size: 0.95em; - transition: border-color 0.3s; -} - -input:focus, -select:focus, -textarea:focus { - outline: none; - border-color: var(--accent-primary); - box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1); -} - -/* === TABLES === */ -table { - width: 100%; - border-collapse: collapse; - margin-top: 15px; -} - -thead { - background: var(--bg-secondary); -} - -th { - padding: 12px; - text-align: left; - font-weight: 600; - color: var(--accent-primary); - border-bottom: 2px solid var(--accent-primary); -} - -td { - padding: 12px; - border-bottom: 1px solid var(--border-color); -} - -tbody tr:hover { - background: rgba(102, 126, 234, 0.05); -} - -/* === METRICS === */ -.metrics-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); - gap: 15px; - margin-bottom: 20px; -} - -.metric-card { - background: var(--bg-secondary); - padding: 15px; - border-radius: 8px; - border-left: 4px solid var(--accent-primary); -} - -.metric-label { - font-size: 0.85em; - color: var(--text-secondary); - margin-bottom: 5px; -} - -.metric-value { - font-size: 1.8em; - font-weight: 700; - color: var(--accent-primary); -} - -.metric-good { color: var(--success); } -.metric-warning { color: var(--warning); } -.metric-bad { color: var(--error); } - -/* === ALERTS === */ -.alert { - padding: 12px 15px; - border-radius: 8px; - margin-bottom: 15px; - display: flex; - align-items: center; - gap: 10px; -} - -.alert-success { - background: rgba(76, 175, 80, 0.1); - border-left: 4px solid var(--success); - color: var(--success); -} - -.alert-warning { - background: rgba(255, 152, 0, 0.1); - border-left: 4px solid var(--warning); - color: var(--warning); -} - -.alert-error { - background: rgba(244, 67, 54, 0.1); - border-left: 4px solid var(--error); - color: var(--error); -} - -.alert-info { - background: rgba(33, 150, 243, 0.1); - border-left: 4px solid var(--info); - color: var(--info); -} - -/* === LOADING SPINNER === */ -.spinner { - border: 3px solid rgba(102, 126, 234, 0.2); - border-top: 3px solid var(--accent-primary); - border-radius: 50%; - width: 40px; - height: 40px; - animation: spin 1s linear infinite; - margin: 20px auto; -} - -@keyframes spin { - 0% { transform: rotate(0deg); } - 100% { transform: rotate(360deg); } -} - -/* === TOOLTIPS === */ -[data-tooltip] { - position: relative; - cursor: help; -} - -[data-tooltip]::after { - content: attr(data-tooltip); - position: absolute; - bottom: 125%; - left: 50%; - transform: translateX(-50%); - background: var(--bg-secondary); - color: var(--text-primary); - padding: 8px 12px; - border-radius: 6px; - font-size: 0.85em; - white-space: nowrap; - opacity: 0; - pointer-events: none; - transition: opacity 0.3s; - border: 1px solid var(--border-color); - z-index: 1000; -} - -[data-tooltip]:hover::after { - opacity: 1; -} - -/* === MODALS === */ -.modal-overlay { - position: fixed; - inset: 0; - background: rgba(0, 0, 0, 0.7); - display: flex; - align-items: center; - justify-content: center; - z-index: 9998; -} - -.modal { - background: var(--bg-card); - border-radius: 14px; - padding: 30px; - max-width: 600px; - width: 90%; - max-height: 80vh; - overflow-y: auto; - box-shadow: 0 20px 60px rgba(0, 0, 0, 0.5); - border: 1px solid var(--border-color); -} - -.modal-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 20px; -} - -.modal-title { - font-size: 1.5em; - color: var(--accent-primary); -} - -.modal-close { - background: none; - border: none; - font-size: 1.5em; - color: var(--text-secondary); - cursor: pointer; - padding: 5px; -} - -.modal-close:hover { - color: var(--text-primary); -} - -/* === SCROLLBAR STYLING === */ -::-webkit-scrollbar { - width: 10px; - height: 10px; -} - -::-webkit-scrollbar-track { - background: var(--bg-secondary); -} - -::-webkit-scrollbar-thumb { - background: var(--accent-primary); - border-radius: 5px; -} - -::-webkit-scrollbar-thumb:hover { - background: var(--accent-secondary); -} - -/* === UTILITY CLASSES === */ -.text-center { text-align: center; } -.text-right { text-align: right; } -.text-left { text-align: left; } - -.mt-1 { margin-top: 10px; } -.mt-2 { margin-top: 20px; } -.mb-1 { margin-bottom: 10px; } -.mb-2 { margin-bottom: 20px; } - -.p-1 { padding: 10px; } -.p-2 { padding: 20px; } - -.flex { display: flex; } -.flex-col { flex-direction: column; } -.flex-center { justify-content: center; align-items: center; } -.flex-between { justify-content: space-between; } -.gap-1 { gap: 10px; } -.gap-2 { gap: 20px; } - -.hidden { display: none !important; } -.visible { display: block !important; } - -/* === RESPONSIVE === */ -@media (max-width: 768px) { - .metrics-grid { - grid-template-columns: 1fr; - } - - .container { - padding: 10px; - } - - .card { - padding: 15px; - } - - .btn { - padding: 8px 16px; - font-size: 0.9em; - } -} +/* ============================================ + QAI Dashboard - Shared Theme Stylesheet + Version: 1.0 + Description: Common styles for all dashboard pages + ============================================ */ + +/* === DARK MODE VARIABLES === */ +:root { + --bg-primary: #1a1a2e; + --bg-secondary: #16213e; + --bg-card: #0f3460; + --text-primary: #eaeaea; + --text-secondary: #a0a0a0; + --accent-primary: #667eea; + --accent-secondary: #764ba2; + --border-color: #2d3561; + --success: #4caf50; + --warning: #ff9800; + --error: #f44336; + --info: #2196f3; +} + +/* === GLOBAL RESETS === */ +* { + box-sizing: border-box; + margin: 0; + padding: 0; +} + +body { + font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; + background: linear-gradient(135deg, var(--bg-primary) 0%, var(--bg-secondary) 100%); + color: var(--text-primary); + line-height: 1.6; + min-height: 100vh; +} + +/* === CONTAINERS === */ +.container { + max-width: 1400px; + margin: 0 auto; + padding: 20px; +} + +/* === CARDS === */ +.card { + background: var(--bg-card); + border-radius: 12px; + padding: 20px; + margin-bottom: 20px; + box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3); + border: 1px solid var(--border-color); + transition: transform 0.2s, box-shadow 0.2s; +} + +.card:hover { + transform: translateY(-2px); + box-shadow: 0 8px 30px rgba(102, 126, 234, 0.2); +} + +.card-header { + display: flex; + align-items: center; + gap: 10px; + margin-bottom: 15px; + padding-bottom: 10px; + border-bottom: 2px solid var(--accent-primary); +} + +.card-title { + font-size: 1.3em; + font-weight: 600; + color: var(--accent-primary); +} + +/* === BUTTONS === */ +.btn { + padding: 10px 20px; + border: none; + border-radius: 8px; + font-size: 0.95em; + font-weight: 500; + cursor: pointer; + transition: all 0.3s; + display: inline-flex; + align-items: center; + gap: 8px; +} + +.btn-primary { + background: linear-gradient(135deg, var(--accent-primary), var(--accent-secondary)); + color: white; +} + +.btn-primary:hover { + transform: translateY(-2px); + box-shadow: 0 6px 20px rgba(102, 126, 234, 0.4); +} + +.btn-secondary { + background: #555; + color: white; +} + +.btn-secondary:hover { + background: #666; +} + +.btn-success { + background: var(--success); + color: white; +} + +.btn-success:hover { + background: #45a049; +} + +.btn-warning { + background: var(--warning); + color: white; +} + +.btn-warning:hover { + background: #e68900; +} + +.btn-danger { + background: var(--error); + color: white; +} + +.btn-danger:hover { + background: #da190b; +} + +.btn-info { + background: var(--info); + color: white; +} + +.btn-info:hover { + background: #0b7dda; +} + +.btn:disabled { + opacity: 0.5; + cursor: not-allowed; + transform: none !important; +} + +/* === BADGES === */ +.badge { + display: inline-block; + padding: 4px 12px; + border-radius: 12px; + font-size: 0.85em; + font-weight: 600; + text-transform: uppercase; +} + +.badge-success { + background: var(--success); + color: white; +} + +.badge-warning { + background: var(--warning); + color: white; +} + +.badge-error { + background: var(--error); + color: white; +} + +.badge-info { + background: var(--info); + color: white; +} + +.badge-secondary { + background: #555; + color: white; +} + +/* === FORM ELEMENTS === */ +.form-group { + margin-bottom: 15px; +} + +label { + display: block; + margin-bottom: 5px; + color: var(--text-secondary); + font-size: 0.9em; + font-weight: 500; +} + +input[type="text"], +input[type="number"], +input[type="email"], +select, +textarea { + width: 100%; + padding: 10px; + background: var(--bg-secondary); + border: 1px solid var(--border-color); + border-radius: 6px; + color: var(--text-primary); + font-size: 0.95em; + transition: border-color 0.3s; +} + +input:focus, +select:focus, +textarea:focus { + outline: none; + border-color: var(--accent-primary); + box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1); +} + +/* === TABLES === */ +table { + width: 100%; + border-collapse: collapse; + margin-top: 15px; +} + +thead { + background: var(--bg-secondary); +} + +th { + padding: 12px; + text-align: left; + font-weight: 600; + color: var(--accent-primary); + border-bottom: 2px solid var(--accent-primary); +} + +td { + padding: 12px; + border-bottom: 1px solid var(--border-color); +} + +tbody tr:hover { + background: rgba(102, 126, 234, 0.05); +} + +/* === METRICS === */ +.metrics-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); + gap: 15px; + margin-bottom: 20px; +} + +.metric-card { + background: var(--bg-secondary); + padding: 15px; + border-radius: 8px; + border-left: 4px solid var(--accent-primary); +} + +.metric-label { + font-size: 0.85em; + color: var(--text-secondary); + margin-bottom: 5px; +} + +.metric-value { + font-size: 1.8em; + font-weight: 700; + color: var(--accent-primary); +} + +.metric-good { color: var(--success); } +.metric-warning { color: var(--warning); } +.metric-bad { color: var(--error); } + +/* === ALERTS === */ +.alert { + padding: 12px 15px; + border-radius: 8px; + margin-bottom: 15px; + display: flex; + align-items: center; + gap: 10px; +} + +.alert-success { + background: rgba(76, 175, 80, 0.1); + border-left: 4px solid var(--success); + color: var(--success); +} + +.alert-warning { + background: rgba(255, 152, 0, 0.1); + border-left: 4px solid var(--warning); + color: var(--warning); +} + +.alert-error { + background: rgba(244, 67, 54, 0.1); + border-left: 4px solid var(--error); + color: var(--error); +} + +.alert-info { + background: rgba(33, 150, 243, 0.1); + border-left: 4px solid var(--info); + color: var(--info); +} + +/* === LOADING SPINNER === */ +.spinner { + border: 3px solid rgba(102, 126, 234, 0.2); + border-top: 3px solid var(--accent-primary); + border-radius: 50%; + width: 40px; + height: 40px; + animation: spin 1s linear infinite; + margin: 20px auto; +} + +@keyframes spin { + 0% { transform: rotate(0deg); } + 100% { transform: rotate(360deg); } +} + +/* === TOOLTIPS === */ +[data-tooltip] { + position: relative; + cursor: help; +} + +[data-tooltip]::after { + content: attr(data-tooltip); + position: absolute; + bottom: 125%; + left: 50%; + transform: translateX(-50%); + background: var(--bg-secondary); + color: var(--text-primary); + padding: 8px 12px; + border-radius: 6px; + font-size: 0.85em; + white-space: nowrap; + opacity: 0; + pointer-events: none; + transition: opacity 0.3s; + border: 1px solid var(--border-color); + z-index: 1000; +} + +[data-tooltip]:hover::after { + opacity: 1; +} + +/* === MODALS === */ +.modal-overlay { + position: fixed; + inset: 0; + background: rgba(0, 0, 0, 0.7); + display: flex; + align-items: center; + justify-content: center; + z-index: 9998; +} + +.modal { + background: var(--bg-card); + border-radius: 14px; + padding: 30px; + max-width: 600px; + width: 90%; + max-height: 80vh; + overflow-y: auto; + box-shadow: 0 20px 60px rgba(0, 0, 0, 0.5); + border: 1px solid var(--border-color); +} + +.modal-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 20px; +} + +.modal-title { + font-size: 1.5em; + color: var(--accent-primary); +} + +.modal-close { + background: none; + border: none; + font-size: 1.5em; + color: var(--text-secondary); + cursor: pointer; + padding: 5px; +} + +.modal-close:hover { + color: var(--text-primary); +} + +/* === SCROLLBAR STYLING === */ +::-webkit-scrollbar { + width: 10px; + height: 10px; +} + +::-webkit-scrollbar-track { + background: var(--bg-secondary); +} + +::-webkit-scrollbar-thumb { + background: var(--accent-primary); + border-radius: 5px; +} + +::-webkit-scrollbar-thumb:hover { + background: var(--accent-secondary); +} + +/* === UTILITY CLASSES === */ +.text-center { text-align: center; } +.text-right { text-align: right; } +.text-left { text-align: left; } + +.mt-1 { margin-top: 10px; } +.mt-2 { margin-top: 20px; } +.mb-1 { margin-bottom: 10px; } +.mb-2 { margin-bottom: 20px; } + +.p-1 { padding: 10px; } +.p-2 { padding: 20px; } + +.flex { display: flex; } +.flex-col { flex-direction: column; } +.flex-center { justify-content: center; align-items: center; } +.flex-between { justify-content: space-between; } +.gap-1 { gap: 10px; } +.gap-2 { gap: 20px; } + +.hidden { display: none !important; } +.visible { display: block !important; } + +/* === RESPONSIVE === */ +@media (max-width: 768px) { + .metrics-grid { + grid-template-columns: 1fr; + } + + .container { + padding: 10px; + } + + .card { + padding: 15px; + } + + .btn { + padding: 8px 16px; + font-size: 0.9em; + } +} diff --git a/apps/dashboard/templates/index.html b/apps/dashboard/templates/index.html index c344fccfe..cb63ed51b 100644 --- a/apps/dashboard/templates/index.html +++ b/apps/dashboard/templates/index.html @@ -1,404 +1,404 @@ - - - - - - QAI Automation Dashboard - - - - -

    QAI Automation Dashboard

    -
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    Orchestrator Status

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    -

    Training Progress

    -
    - - - - - Download History CSV -
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    - - - - - - - - - - - - - - -
    JobStatusTypeMetrics (PPL)Duration (s)ETA (s)Return Code
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    Resource Monitor

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    Results Export

    -
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    - - -
    - - - - + + + + + + QAI Automation Dashboard + + + + +

    QAI Automation Dashboard

    +
    +

    Orchestrator Status

    +
    +
    +
    +

    Training Progress

    +
    + + + + + Download History CSV +
    +
    +
    +
    + +
    +
    + + + + + + + + + + + + + + +
    JobStatusTypeMetrics (PPL)Duration (s)ETA (s)Return Code
    +
    + +
    +
    +

    Resource Monitor

    +
    +
    +
    +

    Results Export

    +
    +
    + + +
    + + + + diff --git a/apps/dashboard/unified.html b/apps/dashboard/unified.html index 25f0180d5..e1a0d0908 100644 --- a/apps/dashboard/unified.html +++ b/apps/dashboard/unified.html @@ -1,3674 +1,3674 @@ - - - - - - - QAI Training Dashboard - - - - - - - -
    - -
    - Dashboard - › - Training -
    -
    -

    QAI Training Dashboard

    -
    - ● - Connected - -
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    - - - Fast training, good for chat - tasks -
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    - Close Modal - ESC -
    - -
    - - - - - - - - + + + + + + + QAI Training Dashboard + + + + + + + +
    + +
    + Dashboard + › + Training +
    +
    +

    QAI Training Dashboard

    +
    + ● + Connected + +
    + + +
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    + + Loading... +
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    Best Score
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    🚀 Start New Training Job
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    + + + Use lowercase, underscores, no spaces +
    + +
    +
    + + + Fast training, good for chat + tasks +
    +
    + + + Select a dataset +
    +
    + +
    +
    + + + More epochs = better learning +
    +
    + + + -1 for all samples +
    +
    + + + Default: 2e-4 +
    +
    + + +
    +
    + 🔧 Advanced Options + ▼ +
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    + ⏱️ Estimated Time: + ~5 + minutes +
    +
    + 💾 Est. VRAM: + ~4GB +
    +
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    + + +
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    + ⚡ Quick Presets: +
    + + + + + +
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    + 📋 Training Session History +
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    No sessions recorded yet. + Start a training job to begin tracking history.

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    ⌨️ Shortcuts
    + + +
    +

    ⌨️ Keyboard Shortcuts

    +
    + Toggle Auto-Refresh + A +
    +
    + Toggle Dark Mode + D +
    +
    + Refresh Data + R +
    +
    + Export Report + E +
    +
    + Overview Tab + 1 +
    +
    + Jobs Tab + 2 +
    +
    + Models Tab + 3 +
    +
    + Search Jobs + / +
    +
    + Command Bar + Ctrl/Cmd+K +
    +
    + Close Modal + ESC +
    + +
    + + + + + + + + diff --git a/apps/dashboard/websocket_server.py b/apps/dashboard/websocket_server.py index d58d1e443..3960ff864 100644 --- a/apps/dashboard/websocket_server.py +++ b/apps/dashboard/websocket_server.py @@ -1,173 +1,173 @@ -"""WebSocket server for real-time job updates""" - -import asyncio -import json -from datetime import datetime -from pathlib import Path - -import websockets -from watchdog.events import FileSystemEventHandler -from watchdog.observers import Observer - -# Track connected clients -clients = set() - -# Job status cache -job_status_cache = {} - - -class JobFileHandler(FileSystemEventHandler): - """Monitor job status files for changes""" - - def __init__(self, broadcast_func): - self.broadcast = broadcast_func - - def on_modified(self, event): - if event.src_path.endswith(".json") and "status" in event.src_path: - asyncio.run(self.broadcast_status_update(event.src_path)) - - async def broadcast_status_update(self, file_path): - """Broadcast status update to all connected clients""" - try: - with open(file_path, "r") as f: - status = json.load(f) - - message = { - "type": "job_update", - "timestamp": datetime.now().isoformat(), - "data": status, - } - - await broadcast_message(message) - except Exception as e: - print(f"Error broadcasting update: {e}") - - -async def broadcast_message(message): - """Send message to all connected clients""" - if clients: - message_str = json.dumps(message) - await asyncio.gather( - *[client.send(message_str) for client in clients], return_exceptions=True - ) - - -async def handler(websocket, path): - """Handle WebSocket connections""" - # Register client - clients.add(websocket) - print(f"Client connected. Total clients: {len(clients)}") - - try: - # Send initial status - initial_status = get_current_status() - await websocket.send( - json.dumps( - { - "type": "initial_status", - "timestamp": datetime.now().isoformat(), - "data": initial_status, - } - ) - ) - - # Keep connection alive and handle messages - async for message in websocket: - try: - data = json.loads(message) - - if data.get("type") == "ping": - await websocket.send(json.dumps({"type": "pong"})) - - elif data.get("type") == "request_status": - status = get_current_status() - await websocket.send( - json.dumps( - { - "type": "status_response", - "timestamp": datetime.now().isoformat(), - "data": status, - } - ) - ) - - except json.JSONDecodeError: - await websocket.send( - json.dumps({"type": "error", "message": "Invalid JSON"}) - ) - - except websockets.exceptions.ConnectionClosed: - pass - finally: - # Unregister client - clients.remove(websocket) - print(f"Client disconnected. Total clients: {len(clients)}") - - -def get_current_status(): - """Get current training job status""" - status_dir = Path("data_out/autotrain") - jobs = [] - - if status_dir.exists(): - for status_file in status_dir.glob("**/status.json"): - try: - with open(status_file, "r") as f: - job_data = json.load(f) - jobs.append(job_data) - except Exception as e: - print(f"Error reading {status_file}: {e}") - - return { - "jobs": jobs, - "timestamp": datetime.now().isoformat(), - "active_count": len([j for j in jobs if j.get("status") == "running"]), - } - - -async def periodic_heartbeat(): - """Send periodic heartbeat to keep connections alive""" - while True: - await asyncio.sleep(30) - - if clients: - status = get_current_status() - message = { - "type": "heartbeat", - "timestamp": datetime.now().isoformat(), - "data": status, - } - await broadcast_message(message) - - -async def main(): - """Start WebSocket server and file watcher""" - print("Starting WebSocket server on ws://localhost:8765") - - # Setup file system watcher - observer = Observer() - handler = JobFileHandler(broadcast_message) - - watch_dir = Path("data_out") - if watch_dir.exists(): - observer.schedule(handler, str(watch_dir), recursive=True) - observer.start() - print(f"Watching {watch_dir} for changes...") - - # Start WebSocket server - async with websockets.serve(handler, "localhost", 8765): - # Start heartbeat task - heartbeat_task = asyncio.create_task(periodic_heartbeat()) - - print("WebSocket server ready!") - print("Connect clients to: ws://localhost:8765") - - # Run forever - await asyncio.Future() - - -if __name__ == "__main__": - try: - asyncio.run(main()) - except KeyboardInterrupt: - print("\nShutting down WebSocket server...") +"""WebSocket server for real-time job updates""" + +import asyncio +import json +from datetime import datetime +from pathlib import Path + +import websockets +from watchdog.events import FileSystemEventHandler +from watchdog.observers import Observer + +# Track connected clients +clients = set() + +# Job status cache +job_status_cache = {} + + +class JobFileHandler(FileSystemEventHandler): + """Monitor job status files for changes""" + + def __init__(self, broadcast_func): + self.broadcast = broadcast_func + + def on_modified(self, event): + if event.src_path.endswith(".json") and "status" in event.src_path: + asyncio.run(self.broadcast_status_update(event.src_path)) + + async def broadcast_status_update(self, file_path): + """Broadcast status update to all connected clients""" + try: + with open(file_path, "r") as f: + status = json.load(f) + + message = { + "type": "job_update", + "timestamp": datetime.now().isoformat(), + "data": status, + } + + await broadcast_message(message) + except Exception as e: + print(f"Error broadcasting update: {e}") + + +async def broadcast_message(message): + """Send message to all connected clients""" + if clients: + message_str = json.dumps(message) + await asyncio.gather( + *[client.send(message_str) for client in clients], return_exceptions=True + ) + + +async def handler(websocket, path): + """Handle WebSocket connections""" + # Register client + clients.add(websocket) + print(f"Client connected. Total clients: {len(clients)}") + + try: + # Send initial status + initial_status = get_current_status() + await websocket.send( + json.dumps( + { + "type": "initial_status", + "timestamp": datetime.now().isoformat(), + "data": initial_status, + } + ) + ) + + # Keep connection alive and handle messages + async for message in websocket: + try: + data = json.loads(message) + + if data.get("type") == "ping": + await websocket.send(json.dumps({"type": "pong"})) + + elif data.get("type") == "request_status": + status = get_current_status() + await websocket.send( + json.dumps( + { + "type": "status_response", + "timestamp": datetime.now().isoformat(), + "data": status, + } + ) + ) + + except json.JSONDecodeError: + await websocket.send( + json.dumps({"type": "error", "message": "Invalid JSON"}) + ) + + except websockets.exceptions.ConnectionClosed: + pass + finally: + # Unregister client + clients.remove(websocket) + print(f"Client disconnected. Total clients: {len(clients)}") + + +def get_current_status(): + """Get current training job status""" + status_dir = Path("data_out/autotrain") + jobs = [] + + if status_dir.exists(): + for status_file in status_dir.glob("**/status.json"): + try: + with open(status_file, "r") as f: + job_data = json.load(f) + jobs.append(job_data) + except Exception as e: + print(f"Error reading {status_file}: {e}") + + return { + "jobs": jobs, + "timestamp": datetime.now().isoformat(), + "active_count": len([j for j in jobs if j.get("status") == "running"]), + } + + +async def periodic_heartbeat(): + """Send periodic heartbeat to keep connections alive""" + while True: + await asyncio.sleep(30) + + if clients: + status = get_current_status() + message = { + "type": "heartbeat", + "timestamp": datetime.now().isoformat(), + "data": status, + } + await broadcast_message(message) + + +async def main(): + """Start WebSocket server and file watcher""" + print("Starting WebSocket server on ws://localhost:8765") + + # Setup file system watcher + observer = Observer() + handler = JobFileHandler(broadcast_message) + + watch_dir = Path("data_out") + if watch_dir.exists(): + observer.schedule(handler, str(watch_dir), recursive=True) + observer.start() + print(f"Watching {watch_dir} for changes...") + + # Start WebSocket server + async with websockets.serve(handler, "localhost", 8765): + # Start heartbeat task + heartbeat_task = asyncio.create_task(periodic_heartbeat()) + + print("WebSocket server ready!") + print("Connect clients to: ws://localhost:8765") + + # Run forever + await asyncio.Future() + + +if __name__ == "__main__": + try: + asyncio.run(main()) + except KeyboardInterrupt: + print("\nShutting down WebSocket server...") diff --git a/apps/global-upgrade.css b/apps/global-upgrade.css index a6d363285..3cf448816 100644 --- a/apps/global-upgrade.css +++ b/apps/global-upgrade.css @@ -1,947 +1,947 @@ -@import url('https://fonts.googleapis.com/css2?family=Fraunces:opsz,wght@9..144,500;9..144,600;9..144,700&family=IBM+Plex+Mono:wght@400;500&family=Plus+Jakarta+Sans:wght@400;500;600;700;800&family=Sora:wght@400;500;600;700;800&family=Space+Grotesk:wght@500;600;700&display=swap'); - -:root { - --site-body-font: "Plus Jakarta Sans", "Instrument Sans", "Segoe UI", Roboto, sans-serif; - --site-display-font: "Space Grotesk", "Plus Jakarta Sans", sans-serif; - --site-code-font: "IBM Plex Mono", "Cascadia Code", Consolas, monospace; - - --upgrade-ink: #132437; - --upgrade-ink-soft: #4f6378; - --upgrade-accent: #0f9d89; - --upgrade-accent-strong: #0a6f63; - --upgrade-accent-alt: #d1603f; - --upgrade-gold: #d1a247; - --upgrade-line: rgba(19, 36, 55, 0.16); - --upgrade-focus: #f2b84a; - --upgrade-shadow-soft: 0 10px 24px rgba(19, 36, 55, 0.12); - --upgrade-shadow: 0 18px 40px rgba(19, 36, 55, 0.16); - --upgrade-radius-md: 14px; - --upgrade-radius-lg: 20px; - - --pass-intensity: 0; - --pass-grid-opacity: 0.34; - --pass-radius-boost: 0px; - --pass-focus-width: 3px; - --pass-letter-shift: -0.02em; - --pass-card-raise: 0px; - --pass-reveal-ms: 520ms; - --pass-shadow-soft: var(--upgrade-shadow-soft); - --pass-shadow-strong: var(--upgrade-shadow); - - --upgrade-atmo-a: rgba(15, 157, 137, 0.26); - --upgrade-atmo-b: rgba(209, 96, 63, 0.22); - --upgrade-atmo-c: rgba(209, 162, 71, 0.24); - - --brand: var(--upgrade-accent); - --brand-light: #4bc2b0; - --brand-dark: var(--upgrade-accent-strong); - --accent: var(--upgrade-accent-alt); - - --primary: var(--upgrade-accent); - --primary-light: #42c2ae; - --primary-dark: var(--upgrade-accent-strong); - - --primary-color: var(--upgrade-accent); - --primary-light-color: #42c2ae; - --primary-dark-color: var(--upgrade-accent-strong); - --secondary-color: #245978; - --accent-color: var(--upgrade-accent-alt); - - --accent-primary: var(--upgrade-accent); - --accent-secondary: #1f6a86; - - --color-primary: var(--upgrade-accent); - --color-primary-dark: var(--upgrade-accent-strong); - --color-accent: var(--upgrade-gold); - --color-secondary: #1d3046; - --color-text: var(--upgrade-ink); - --color-text-light: var(--upgrade-ink-soft); -} - -html { - text-rendering: optimizeLegibility; - -webkit-font-smoothing: antialiased; -} - -body { - font-family: var(--site-body-font) !important; - min-height: 100vh; - position: relative; - isolation: isolate; -} - -body::before, -body::after { - content: ""; - position: fixed; - pointer-events: none; - transition: opacity 260ms ease; -} - -body::before { - inset: -18%; - z-index: -2; - background: - radial-gradient(circle at 10% 14%, var(--upgrade-atmo-a), transparent 34%), - radial-gradient(circle at 86% 10%, var(--upgrade-atmo-b), transparent 28%), - radial-gradient(circle at 54% 88%, var(--upgrade-atmo-c), transparent 40%); - animation: siteAtmosphereFloat 26s ease-in-out infinite alternate; -} - -body::after { - inset: 0; - z-index: -1; - opacity: var(--pass-grid-opacity, 0.34); - background-image: - linear-gradient(rgba(19, 36, 55, 0.05) 1px, transparent 1px), - linear-gradient(90deg, rgba(19, 36, 55, 0.05) 1px, transparent 1px); - background-size: 30px 30px; - mask-image: linear-gradient(180deg, rgba(0, 0, 0, 0.86), transparent 94%); -} - -@keyframes siteAtmosphereFloat { - 0% { - transform: translate3d(0, 0, 0) scale(1); - } - 50% { - transform: translate3d(-1.8%, -1.2%, 0) scale(1.02); - } - 100% { - transform: translate3d(1.4%, 1%, 0) scale(1.01); - } -} - -h1, -h2, -h3, -h4, -h5, -h6, -.gradient-text, -.hero h1, -.section-title, -.card-title, -.doc-header h1, -.brand-copy strong { - font-family: var(--site-display-font); - letter-spacing: var(--pass-letter-shift, -0.02em); -} - -code, -pre, -kbd, -samp { - font-family: var(--site-code-font); -} - -::selection { - background: rgba(15, 157, 137, 0.24); - color: #0f1d30; -} - -* { - scrollbar-width: thin; - scrollbar-color: rgba(15, 157, 137, 0.55) transparent; -} - -*::-webkit-scrollbar { - width: 9px; - height: 9px; -} - -*::-webkit-scrollbar-thumb { - border-radius: 999px; - background: linear-gradient(180deg, rgba(15, 157, 137, 0.9), rgba(10, 111, 99, 0.9)); -} - -*::-webkit-scrollbar-track { - background: transparent; -} - -:where(a, button, .btn, .button, .tab-btn, .object-btn, input, select, textarea) { - transition: color 180ms ease, background-color 180ms ease, border-color 180ms ease, box-shadow 180ms ease, transform 180ms ease; -} - -:where(a, button, .btn, .button, .tab-btn, .object-btn, input, select, textarea):focus-visible { - outline: var(--pass-focus-width, 3px) solid var(--upgrade-focus); - outline-offset: 2px; -} - -:where(.btn, .button, .tab-btn, .object-btn, button) { - border-radius: 12px; - font-weight: 600; -} - -:where(.btn:hover, .button:hover, .tab-btn:hover, .object-btn:hover, button:hover) { - transform: translateY(calc(-1px - var(--pass-card-raise, 0px))); -} - -:where(.card, .feature-card, .system-card, .welcome-card, .glass-panel, .section, .hero-banner, .jobs-section, .gpu-status, .stage-container, .object-manager, .chat-container, .page-link, .resource-group, .route-card, .flow-card, .search-panel, .terminal, .run-aside, .run-copy, .hero-card, .plan-card, .link-card, .signal-card, .sidebar-card, .content-shell, .category-card) { - border-radius: calc(var(--upgrade-radius-lg) + var(--pass-radius-boost, 0px)); -} - -:where(.card, .feature-card, .system-card, .welcome-card, .glass-panel, .chat-container, .object-manager, .page-link, .resource-group, .route-card, .flow-card, .search-panel, .terminal, .run-aside, .run-copy, .hero-card, .plan-card, .link-card, .signal-card, .sidebar-card, .content-shell, .category-card) { - box-shadow: var(--pass-shadow-soft, var(--upgrade-shadow-soft)) !important; -} - -:where(.sidebar-item, .page-link, .feature-card, .system-card, .welcome-card, .card, .btn, .button, .plan-card, .link-card, .route-card, .doc-link) { - will-change: transform; -} - -:where(p, li, label, .subtitle, .section-subtitle) { - line-height: 1.65; -} - -:where(input, select, textarea) { - border-radius: 10px !important; - border: 1px solid var(--upgrade-line); -} - -:where(.btn-primary, .button-primary, .btn-plan.primary, .plan-cta, .button-link.primary) { - background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)) !important; - color: #f6fffd !important; - border-color: transparent !important; -} - -:where(.btn-outline, .button-link, .btn-plan.outline, .tab-btn, .object-btn, .doc-link, .sidebar-item) { - border-color: var(--upgrade-line); -} - -:where(.sidebar-item.active, .sidebar-item.is-active, .tab-btn.active, .object-btn.active) { - background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)) !important; - color: #f4fffc !important; -} - -.site-upgrade-skip { - position: fixed; - top: 12px; - left: 12px; - z-index: 100000; - padding: 10px 14px; - border-radius: 10px; - background: #11283a; - color: #f7f2e8; - text-decoration: none; - font-weight: 700; - transform: translateY(-160%); - transition: transform 160ms ease; - box-shadow: var(--pass-shadow-strong, var(--upgrade-shadow)); -} - -.site-upgrade-skip:focus-visible { - transform: translateY(0); -} - -.site-upgrade-reveal { - opacity: 0; - transform: translateY(18px) scale(0.985); - transition: - opacity var(--pass-reveal-ms, 520ms) cubic-bezier(0.22, 1, 0.36, 1) var(--site-upgrade-delay, 0ms), - transform var(--pass-reveal-ms, 520ms) cubic-bezier(0.22, 1, 0.36, 1) var(--site-upgrade-delay, 0ms); -} - -.site-upgrade-reveal.is-visible { - opacity: 1; - transform: none; -} - -/* Chat personality */ -body.site-kind-chat { - --site-body-font: "Sora", "Plus Jakarta Sans", sans-serif; - --site-display-font: "Sora", "Space Grotesk", sans-serif; - --upgrade-accent: #0f93c5; - --upgrade-accent-strong: #0a6f94; - --upgrade-accent-alt: #ff7a4f; - --upgrade-line: rgba(15, 147, 197, 0.24); - --upgrade-focus: #ffd166; - --upgrade-shadow-soft: 0 14px 30px rgba(15, 147, 197, 0.22); - --upgrade-atmo-a: rgba(15, 147, 197, 0.36); - --upgrade-atmo-b: rgba(255, 122, 79, 0.3); - --upgrade-atmo-c: rgba(21, 31, 61, 0.26); -} - -body.site-kind-chat :where(.chat-container, .message-content, .chat-header, .provider-info) { - border-radius: 18px; -} - -body.site-kind-chat .chat-header { - background: linear-gradient(135deg, #0f93c5, #0a6f94) !important; -} - -body.site-kind-chat .message.user .message-content { - background: linear-gradient(135deg, #0f93c5, #0a6f94) !important; -} - -body.site-kind-chat .message-content a { - color: #0f93c5 !important; - font-weight: 600; -} - -/* Aria personality */ -body.site-kind-aria { - --site-display-font: "Fraunces", "Space Grotesk", serif; - --upgrade-accent: #2e9d83; - --upgrade-accent-strong: #24735f; - --upgrade-accent-alt: #f08947; - --upgrade-line: rgba(46, 157, 131, 0.24); - --upgrade-focus: #f3c15f; - --upgrade-shadow-soft: 0 14px 30px rgba(46, 157, 131, 0.2); - --upgrade-atmo-a: rgba(82, 182, 157, 0.3); - --upgrade-atmo-b: rgba(240, 137, 71, 0.24); - --upgrade-atmo-c: rgba(116, 191, 130, 0.22); -} - -body.site-kind-aria :where(.stage-container, .object-manager, .control-panel, .stage, .object-btn) { - border-color: rgba(46, 157, 131, 0.24) !important; -} - -body.site-kind-aria h1 { - letter-spacing: -0.03em; -} - -/* Dashboard personality */ -body.site-kind-dashboard { - --site-body-font: "Sora", "Plus Jakarta Sans", sans-serif; - --site-display-font: "Space Grotesk", "Sora", sans-serif; - --upgrade-accent: #2f7be7; - --upgrade-accent-strong: #235fbb; - --upgrade-accent-alt: #17a67f; - --upgrade-line: rgba(47, 123, 231, 0.24); - --upgrade-focus: #ffd57a; - --upgrade-shadow-soft: 0 14px 32px rgba(47, 123, 231, 0.22); - --upgrade-atmo-a: rgba(47, 123, 231, 0.35); - --upgrade-atmo-b: rgba(23, 166, 127, 0.26); - --upgrade-atmo-c: rgba(21, 45, 92, 0.24); -} - -body.site-kind-dashboard :where(.card, .system-card, .metric-card, .jobs-section, .gpu-status, .tab-nav, .tab-btn) { - border-color: rgba(47, 123, 231, 0.18) !important; -} - -body.site-kind-dashboard :where(.status-badge.online, .tab-btn.active) { - background: linear-gradient(135deg, #2f7be7, #235fbb) !important; - color: #f7fbff !important; -} - -/* Store personality */ -body.site-kind-store { - --site-display-font: "Fraunces", "Space Grotesk", serif; - --upgrade-accent: #d0602f; - --upgrade-accent-strong: #a84824; - --upgrade-accent-alt: #13876f; - --upgrade-line: rgba(208, 96, 47, 0.24); - --upgrade-focus: #ffd38a; - --upgrade-shadow-soft: 0 14px 30px rgba(208, 96, 47, 0.2); - --upgrade-atmo-a: rgba(208, 96, 47, 0.3); - --upgrade-atmo-b: rgba(19, 135, 111, 0.22); - --upgrade-atmo-c: rgba(209, 162, 71, 0.24); -} - -body.site-kind-store :where(.product-card, .category-card, .feature-card, .plan-card, .route-card, .link-card, .hero-card) { - border-color: rgba(208, 96, 47, 0.18) !important; -} - -body.site-kind-store :where(.btn-primary, .btn-plan.primary, .plan-cta, .button-link.primary) { - background: linear-gradient(135deg, #d0602f, #a84824) !important; -} - -/* Docs personality */ -body.site-kind-docs { - --site-display-font: "Fraunces", "Space Grotesk", serif; - --upgrade-accent: #187f78; - --upgrade-accent-strong: #125f5a; - --upgrade-accent-alt: #b56a2d; - --upgrade-line: rgba(24, 127, 120, 0.22); - --upgrade-focus: #f3c471; - --upgrade-shadow-soft: 0 14px 28px rgba(24, 127, 120, 0.18); - --upgrade-atmo-a: rgba(24, 127, 120, 0.26); - --upgrade-atmo-b: rgba(181, 106, 45, 0.2); - --upgrade-atmo-c: rgba(205, 180, 129, 0.26); -} - -body.site-kind-docs :where(.sidebar-item.active, .sidebar-item.is-active) { - background: linear-gradient(135deg, #187f78, #125f5a) !important; -} - -body.site-kind-docs :where(.md-content h1, .content-markdown h1, .doc-header h1) { - letter-spacing: -0.035em; -} - -/* Pass debug overlay */ -.site-pass-debug { - position: fixed; - right: 14px; - bottom: 14px; - z-index: 100002; - display: grid; - justify-items: end; - gap: 8px; - pointer-events: none; -} - -.site-pass-debug__toggle, -.site-pass-debug__panel { - pointer-events: auto; -} - -.site-pass-debug__toggle { - min-height: 38px; - padding: 0 12px; - border: 1px solid var(--upgrade-line); - border-radius: 999px; - background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)); - color: #f6fffd; - font: 700 0.8rem/1 var(--site-body-font); - letter-spacing: 0.04em; - text-transform: uppercase; - cursor: pointer; - box-shadow: var(--pass-shadow-soft, var(--upgrade-shadow-soft)); -} - -.site-pass-debug__panel { - position: relative; - width: min(320px, calc(100vw - 28px)); - padding: 12px; - border-radius: 16px; - border: 1px solid var(--upgrade-line); - background: color-mix(in srgb, #ffffff 88%, var(--upgrade-accent) 12%); - color: var(--upgrade-ink); - box-shadow: var(--pass-shadow-strong, var(--upgrade-shadow)); - backdrop-filter: blur(10px); -} - -.site-pass-debug__head { - display: flex; - align-items: center; - justify-content: space-between; - gap: 8px; - cursor: grab; - user-select: none; - border-radius: 8px; - padding: 2px 0; - margin: -2px 0 4px; - transition: background 0.12s; -} - -.site-pass-debug__head:hover { - background: color-mix(in srgb, #ffffff 80%, var(--upgrade-accent) 20%); -} - -.site-pass-debug.is-dragging .site-pass-debug__head, -.site-pass-debug.is-dragging { - cursor: grabbing; - user-select: none; -} - -.site-pass-debug__drag-grip { - font-size: 1rem; - opacity: 0.45; - flex-shrink: 0; - pointer-events: none; -} - -.site-pass-debug__resize-handle { - position: absolute; - left: -5px; - top: 16px; - bottom: 16px; - width: 10px; - cursor: ew-resize; - border-radius: 5px; - background: transparent; - transition: background 0.14s; - z-index: 1; -} - -.site-pass-debug__resize-handle:hover, -.site-pass-debug.is-resizing .site-pass-debug__resize-handle { - background: color-mix(in srgb, transparent 60%, var(--upgrade-accent) 40%); -} - -.site-pass-debug.is-resizing { - cursor: ew-resize; - user-select: none; -} - -.site-pass-debug__section-body { - overflow: hidden; -} - -.site-pass-debug__head strong { - font-family: var(--site-display-font); - font-size: 0.98rem; -} - -.site-pass-debug__kind { - display: inline-flex; - align-items: center; - justify-content: center; - min-height: 24px; - padding: 0 8px; - border-radius: 999px; - background: color-mix(in srgb, var(--upgrade-accent) 20%, #ffffff 80%); - color: var(--upgrade-accent-strong); - font-size: 0.7rem; - font-weight: 700; - letter-spacing: 0.06em; -} - -.site-pass-debug__label { - display: block; - margin-top: 10px; - font-size: 0.8rem; - color: var(--upgrade-ink-soft); -} - -.site-pass-debug__range { - width: 100%; - margin-top: 8px; - accent-color: var(--upgrade-accent); -} - -.site-pass-debug__row { - display: grid; - grid-template-columns: 1fr auto auto; - gap: 8px; - margin-top: 8px; -} - -.site-pass-debug__row--compact { - grid-template-columns: repeat(2, minmax(0, 1fr)); -} - -.site-pass-debug__row--speed { - grid-template-columns: auto 1fr; - align-items: center; -} - -.site-pass-debug__inline-label { - font-size: 0.76rem; - font-weight: 600; - color: var(--upgrade-ink-soft); -} - -.site-pass-debug__preset-grid { - display: grid; - grid-template-columns: repeat(4, minmax(0, 1fr)); - gap: 8px; - margin-top: 8px; -} - -.site-pass-debug__snapshot-grid { - display: grid; - grid-template-columns: repeat(3, minmax(0, 1fr)); - gap: 8px; - margin-top: 8px; -} - -.site-pass-debug__share-row { - margin-top: 8px; -} - -.site-pass-debug__number { - min-height: 34px; - padding: 0 8px; - border: 1px solid var(--upgrade-line); - border-radius: 10px; - background: #ffffff; - color: var(--upgrade-ink); - font-weight: 600; -} - -.site-pass-debug__speed { - min-height: 34px; - padding: 0 8px; - border: 1px solid var(--upgrade-line); - border-radius: 10px; - background: #ffffff; - color: var(--upgrade-ink); - font-weight: 600; -} - -.site-pass-debug__share { - width: 100%; - min-height: 34px; - padding: 0 8px; - border: 1px solid var(--upgrade-line); - border-radius: 10px; - background: #ffffff; - color: var(--upgrade-ink); - font: 600 0.72rem/1 var(--site-body-font); -} - -.site-pass-debug__share:focus-visible, -.site-pass-debug__speed:focus-visible, -.site-pass-debug__number:focus-visible { - outline: none; - border-color: color-mix(in srgb, var(--upgrade-accent-strong) 48%, #ffffff 52%); - box-shadow: 0 0 0 2px color-mix(in srgb, var(--upgrade-focus) 36%, #ffffff 64%); -} - -.site-pass-debug__btn { - min-height: 34px; - padding: 0 10px; - border: 1px solid var(--upgrade-line); - border-radius: 10px; - background: #ffffff; - color: var(--upgrade-ink); - font: 600 0.78rem/1 var(--site-body-font); - cursor: pointer; - transition: background 180ms ease, color 180ms ease, border-color 180ms ease, transform 180ms ease; -} - -.site-pass-debug__btn:hover, -.site-pass-debug__btn:focus-visible { - background: color-mix(in srgb, #ffffff 84%, var(--upgrade-accent) 16%); - outline: none; -} - -.site-pass-debug__btn:active { - transform: translateY(1px); -} - -.site-pass-debug__btn:disabled { - opacity: 0.5; - cursor: not-allowed; - transform: none; -} - -.site-pass-debug__btn.is-active { - border-color: color-mix(in srgb, var(--upgrade-accent-strong) 44%, #ffffff 56%); - background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)); - color: #f6fffd; -} - -.site-pass-debug__meta, -.site-pass-debug__hint { - margin: 8px 0 0; - font-size: 0.75rem; - color: var(--upgrade-ink-soft); -} - -.site-pass-debug__hint { - margin-top: 4px; - opacity: 0.88; -} - -.site-pass-debug__compare, -.site-pass-debug__stack, -.site-pass-debug__sweep, -.site-pass-debug__source { - color: var(--upgrade-accent-strong); - font-weight: 700; - letter-spacing: 0.01em; -} - -.site-pass-debug__timeline { - list-style: none; - margin: 8px 0 0; - padding: 6px; - border-radius: 10px; - border: 1px solid var(--upgrade-line); - background: color-mix(in srgb, #ffffff 90%, var(--upgrade-accent) 10%); - max-height: 124px; - overflow: auto; - display: grid; - gap: 5px; -} - -.site-pass-debug__timeline-item { - display: grid; - grid-template-columns: auto 1fr; - gap: 8px; - align-items: center; - font-size: 0.68rem; - color: var(--upgrade-ink-soft); -} - -.site-pass-debug__timeline-item.is-empty { - grid-template-columns: 1fr; - font-style: italic; - opacity: 0.9; -} - -.site-pass-debug__timeline-time { - color: var(--upgrade-accent-strong); - font-weight: 700; -} - -.site-pass-debug__timeline-detail { - overflow: hidden; - text-overflow: ellipsis; - white-space: nowrap; -} - -.site-pass-debug__timeline-item--restore { - cursor: pointer; - border-radius: 6px; - padding: 2px 4px; - margin: -2px -4px; - transition: background 0.12s; -} - -.site-pass-debug__timeline-item--restore:hover { - background: color-mix(in srgb, #ffffff 65%, var(--upgrade-accent) 35%); -} - -.site-pass-debug__timeline-item--restore:focus-visible { - outline: 2px solid var(--upgrade-accent-strong); - outline-offset: 1px; -} - -.site-pass-debug__timeline-item--restore:active { - opacity: 0.72; -} - -.site-pass-debug__section { - border: 1px solid var(--upgrade-line); - border-radius: 8px; - margin: 6px 0 0; -} - -.site-pass-debug__section[open] { - padding-bottom: 4px; -} - -.site-pass-debug__section-head { - list-style: none; - padding: 5px 8px; - font-size: 0.74rem; - font-weight: 700; - letter-spacing: 0.03em; - text-transform: uppercase; - color: var(--upgrade-ink-soft); - cursor: pointer; - user-select: none; - display: grid; - grid-template-columns: 1fr auto; - align-items: center; - border-radius: 7px; - background: color-mix(in srgb, #ffffff 92%, var(--upgrade-accent) 8%); -} - -.site-pass-debug__section-head::-webkit-details-marker { - display: none; -} - -.site-pass-debug__section-head::marker { - content: none; -} - -.site-pass-debug__section-head::after { - content: "\25B8"; - font-size: 0.7rem; - opacity: 0.55; - transition: transform 0.15s; - justify-self: end; -} - -.site-pass-debug__section[open] > .site-pass-debug__section-head::after { - transform: rotate(90deg); -} - -.site-pass-debug__section[open] > .site-pass-debug__section-head { - border-radius: 7px 7px 0 0; - border-bottom: 1px solid var(--upgrade-line); -} - -.site-pass-debug__section-head:focus-visible { - outline: 2px solid var(--upgrade-accent-strong); - outline-offset: -2px; - border-radius: 7px; -} - -.site-pass-debug__section > .site-pass-debug__timeline { - margin: 4px 0 0; -} - -/* --- Shortcut cheat-sheet overlay --- */ -.site-pass-shortcut-overlay { - position: fixed; - inset: 0; - z-index: 100010; - display: flex; - align-items: center; - justify-content: center; - background: rgba(0, 0, 0, 0.45); - backdrop-filter: blur(3px); - animation: site-pass-overlay-in 0.14s ease; -} - -@keyframes site-pass-overlay-in { - from { opacity: 0; } - to { opacity: 1; } -} - -.site-pass-shortcut-overlay__box { - position: relative; - width: min(440px, calc(100vw - 32px)); - max-height: calc(100vh - 48px); - overflow-y: auto; - padding: 20px 22px 16px; - border-radius: 16px; - border: 1px solid var(--upgrade-line); - background: color-mix(in srgb, #ffffff 92%, var(--upgrade-accent) 8%); - box-shadow: 0 8px 40px rgba(0,0,0,0.22); - color: var(--upgrade-ink); -} - -.site-pass-shortcut-overlay__head { - display: flex; - align-items: center; - justify-content: space-between; - margin-bottom: 12px; -} - -.site-pass-shortcut-overlay__head strong { - font-family: var(--site-display-font); - font-size: 1rem; -} - -.site-pass-shortcut-overlay__close { - min-height: 32px; - min-width: 32px; - border: none; - background: transparent; - cursor: pointer; - font-size: 1rem; - border-radius: 8px; - color: var(--upgrade-ink-soft); - display: flex; - align-items: center; - justify-content: center; -} - -.site-pass-shortcut-overlay__close:hover { - background: color-mix(in srgb, #ffffff 70%, var(--upgrade-accent) 30%); -} - -.site-pass-shortcut-overlay__close:focus-visible { - outline: 2px solid var(--upgrade-accent-strong); -} - -.site-pass-shortcut-overlay__list { - display: grid; - grid-template-columns: auto 1fr; - gap: 4px 16px; - margin: 0; - padding: 0; - font-size: 0.82rem; -} - -.site-pass-shortcut-overlay__list dt { - font-family: var(--site-code-font, monospace); - font-weight: 700; - color: var(--upgrade-accent-strong); - white-space: nowrap; - padding: 2px 6px; - border-radius: 4px; - background: color-mix(in srgb, #ffffff 75%, var(--upgrade-accent) 25%); - align-self: center; -} - -.site-pass-shortcut-overlay__list dd { - margin: 0; - align-self: center; - color: var(--upgrade-ink-soft); -} - -.site-pass-shortcut-overlay__tip { - margin: 12px 0 0; - font-size: 0.74rem; - color: var(--upgrade-ink-soft); - font-style: italic; - text-align: center; -} - -.site-pass-debug__badge { - display: inline-block; - min-width: 22px; - padding: 1px 5px; - border-radius: 999px; - background: rgba(255, 255, 255, 0.22); - font: 700 0.72rem/1.4 var(--site-body-font); - vertical-align: middle; - text-align: center; - letter-spacing: 0; -} - -.site-pass-debug.is-open .site-pass-debug__badge { - opacity: 0.55; -} - -.site-pass-debug__opacity { - width: 72px; - text-align: right; - font-size: 0.82rem; - padding: 2px 6px; - border: 1px solid var(--upgrade-line); - border-radius: 8px; - background: color-mix(in srgb, #ffffff 75%, var(--upgrade-accent) 25%); - color: var(--upgrade-ink); -} - -.site-pass-debug__opacity:focus { - outline: 2px solid var(--upgrade-accent-strong); - outline-offset: 1px; -} - -.site-pass-debug__btn--toggle.is-active { - background: color-mix(in srgb, var(--upgrade-accent) 85%, #000 15%); - color: #fff; - border-color: var(--upgrade-accent-strong); -} - -.site-pass-debug.is-open .site-pass-debug__toggle { - filter: saturate(1.08) brightness(1.02); -} - -@media (max-width: 900px) { - :where(.btn, .button, .tab-btn, .object-btn, button) { - min-height: 42px; - } - - .site-pass-debug { - right: 10px; - bottom: 10px; - } - - .site-pass-debug__panel { - width: min(94vw, 320px); - } - - .site-pass-debug__preset-grid { - grid-template-columns: repeat(2, minmax(0, 1fr)); - } - - .site-pass-debug__snapshot-grid { - grid-template-columns: repeat(2, minmax(0, 1fr)); - } - - body::before { - inset: -30%; - opacity: 0.74; - } - - body::after { - opacity: 0.26; - } -} - -@media (prefers-reduced-motion: reduce) { - * { - animation: none !important; - transition: none !important; - scroll-behavior: auto !important; - } - - .site-upgrade-reveal { - opacity: 1 !important; - transform: none !important; - } - - .site-pass-debug__toggle, - .site-pass-debug__btn { - transition: none !important; - } -} +@import url('https://fonts.googleapis.com/css2?family=Fraunces:opsz,wght@9..144,500;9..144,600;9..144,700&family=IBM+Plex+Mono:wght@400;500&family=Plus+Jakarta+Sans:wght@400;500;600;700;800&family=Sora:wght@400;500;600;700;800&family=Space+Grotesk:wght@500;600;700&display=swap'); + +:root { + --site-body-font: "Plus Jakarta Sans", "Instrument Sans", "Segoe UI", Roboto, sans-serif; + --site-display-font: "Space Grotesk", "Plus Jakarta Sans", sans-serif; + --site-code-font: "IBM Plex Mono", "Cascadia Code", Consolas, monospace; + + --upgrade-ink: #132437; + --upgrade-ink-soft: #4f6378; + --upgrade-accent: #0f9d89; + --upgrade-accent-strong: #0a6f63; + --upgrade-accent-alt: #d1603f; + --upgrade-gold: #d1a247; + --upgrade-line: rgba(19, 36, 55, 0.16); + --upgrade-focus: #f2b84a; + --upgrade-shadow-soft: 0 10px 24px rgba(19, 36, 55, 0.12); + --upgrade-shadow: 0 18px 40px rgba(19, 36, 55, 0.16); + --upgrade-radius-md: 14px; + --upgrade-radius-lg: 20px; + + --pass-intensity: 0; + --pass-grid-opacity: 0.34; + --pass-radius-boost: 0px; + --pass-focus-width: 3px; + --pass-letter-shift: -0.02em; + --pass-card-raise: 0px; + --pass-reveal-ms: 520ms; + --pass-shadow-soft: var(--upgrade-shadow-soft); + --pass-shadow-strong: var(--upgrade-shadow); + + --upgrade-atmo-a: rgba(15, 157, 137, 0.26); + --upgrade-atmo-b: rgba(209, 96, 63, 0.22); + --upgrade-atmo-c: rgba(209, 162, 71, 0.24); + + --brand: var(--upgrade-accent); + --brand-light: #4bc2b0; + --brand-dark: var(--upgrade-accent-strong); + --accent: var(--upgrade-accent-alt); + + --primary: var(--upgrade-accent); + --primary-light: #42c2ae; + --primary-dark: var(--upgrade-accent-strong); + + --primary-color: var(--upgrade-accent); + --primary-light-color: #42c2ae; + --primary-dark-color: var(--upgrade-accent-strong); + --secondary-color: #245978; + --accent-color: var(--upgrade-accent-alt); + + --accent-primary: var(--upgrade-accent); + --accent-secondary: #1f6a86; + + --color-primary: var(--upgrade-accent); + --color-primary-dark: var(--upgrade-accent-strong); + --color-accent: var(--upgrade-gold); + --color-secondary: #1d3046; + --color-text: var(--upgrade-ink); + --color-text-light: var(--upgrade-ink-soft); +} + +html { + text-rendering: optimizeLegibility; + -webkit-font-smoothing: antialiased; +} + +body { + font-family: var(--site-body-font) !important; + min-height: 100vh; + position: relative; + isolation: isolate; +} + +body::before, +body::after { + content: ""; + position: fixed; + pointer-events: none; + transition: opacity 260ms ease; +} + +body::before { + inset: -18%; + z-index: -2; + background: + radial-gradient(circle at 10% 14%, var(--upgrade-atmo-a), transparent 34%), + radial-gradient(circle at 86% 10%, var(--upgrade-atmo-b), transparent 28%), + radial-gradient(circle at 54% 88%, var(--upgrade-atmo-c), transparent 40%); + animation: siteAtmosphereFloat 26s ease-in-out infinite alternate; +} + +body::after { + inset: 0; + z-index: -1; + opacity: var(--pass-grid-opacity, 0.34); + background-image: + linear-gradient(rgba(19, 36, 55, 0.05) 1px, transparent 1px), + linear-gradient(90deg, rgba(19, 36, 55, 0.05) 1px, transparent 1px); + background-size: 30px 30px; + mask-image: linear-gradient(180deg, rgba(0, 0, 0, 0.86), transparent 94%); +} + +@keyframes siteAtmosphereFloat { + 0% { + transform: translate3d(0, 0, 0) scale(1); + } + 50% { + transform: translate3d(-1.8%, -1.2%, 0) scale(1.02); + } + 100% { + transform: translate3d(1.4%, 1%, 0) scale(1.01); + } +} + +h1, +h2, +h3, +h4, +h5, +h6, +.gradient-text, +.hero h1, +.section-title, +.card-title, +.doc-header h1, +.brand-copy strong { + font-family: var(--site-display-font); + letter-spacing: var(--pass-letter-shift, -0.02em); +} + +code, +pre, +kbd, +samp { + font-family: var(--site-code-font); +} + +::selection { + background: rgba(15, 157, 137, 0.24); + color: #0f1d30; +} + +* { + scrollbar-width: thin; + scrollbar-color: rgba(15, 157, 137, 0.55) transparent; +} + +*::-webkit-scrollbar { + width: 9px; + height: 9px; +} + +*::-webkit-scrollbar-thumb { + border-radius: 999px; + background: linear-gradient(180deg, rgba(15, 157, 137, 0.9), rgba(10, 111, 99, 0.9)); +} + +*::-webkit-scrollbar-track { + background: transparent; +} + +:where(a, button, .btn, .button, .tab-btn, .object-btn, input, select, textarea) { + transition: color 180ms ease, background-color 180ms ease, border-color 180ms ease, box-shadow 180ms ease, transform 180ms ease; +} + +:where(a, button, .btn, .button, .tab-btn, .object-btn, input, select, textarea):focus-visible { + outline: var(--pass-focus-width, 3px) solid var(--upgrade-focus); + outline-offset: 2px; +} + +:where(.btn, .button, .tab-btn, .object-btn, button) { + border-radius: 12px; + font-weight: 600; +} + +:where(.btn:hover, .button:hover, .tab-btn:hover, .object-btn:hover, button:hover) { + transform: translateY(calc(-1px - var(--pass-card-raise, 0px))); +} + +:where(.card, .feature-card, .system-card, .welcome-card, .glass-panel, .section, .hero-banner, .jobs-section, .gpu-status, .stage-container, .object-manager, .chat-container, .page-link, .resource-group, .route-card, .flow-card, .search-panel, .terminal, .run-aside, .run-copy, .hero-card, .plan-card, .link-card, .signal-card, .sidebar-card, .content-shell, .category-card) { + border-radius: calc(var(--upgrade-radius-lg) + var(--pass-radius-boost, 0px)); +} + +:where(.card, .feature-card, .system-card, .welcome-card, .glass-panel, .chat-container, .object-manager, .page-link, .resource-group, .route-card, .flow-card, .search-panel, .terminal, .run-aside, .run-copy, .hero-card, .plan-card, .link-card, .signal-card, .sidebar-card, .content-shell, .category-card) { + box-shadow: var(--pass-shadow-soft, var(--upgrade-shadow-soft)) !important; +} + +:where(.sidebar-item, .page-link, .feature-card, .system-card, .welcome-card, .card, .btn, .button, .plan-card, .link-card, .route-card, .doc-link) { + will-change: transform; +} + +:where(p, li, label, .subtitle, .section-subtitle) { + line-height: 1.65; +} + +:where(input, select, textarea) { + border-radius: 10px !important; + border: 1px solid var(--upgrade-line); +} + +:where(.btn-primary, .button-primary, .btn-plan.primary, .plan-cta, .button-link.primary) { + background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)) !important; + color: #f6fffd !important; + border-color: transparent !important; +} + +:where(.btn-outline, .button-link, .btn-plan.outline, .tab-btn, .object-btn, .doc-link, .sidebar-item) { + border-color: var(--upgrade-line); +} + +:where(.sidebar-item.active, .sidebar-item.is-active, .tab-btn.active, .object-btn.active) { + background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)) !important; + color: #f4fffc !important; +} + +.site-upgrade-skip { + position: fixed; + top: 12px; + left: 12px; + z-index: 100000; + padding: 10px 14px; + border-radius: 10px; + background: #11283a; + color: #f7f2e8; + text-decoration: none; + font-weight: 700; + transform: translateY(-160%); + transition: transform 160ms ease; + box-shadow: var(--pass-shadow-strong, var(--upgrade-shadow)); +} + +.site-upgrade-skip:focus-visible { + transform: translateY(0); +} + +.site-upgrade-reveal { + opacity: 0; + transform: translateY(18px) scale(0.985); + transition: + opacity var(--pass-reveal-ms, 520ms) cubic-bezier(0.22, 1, 0.36, 1) var(--site-upgrade-delay, 0ms), + transform var(--pass-reveal-ms, 520ms) cubic-bezier(0.22, 1, 0.36, 1) var(--site-upgrade-delay, 0ms); +} + +.site-upgrade-reveal.is-visible { + opacity: 1; + transform: none; +} + +/* Chat personality */ +body.site-kind-chat { + --site-body-font: "Sora", "Plus Jakarta Sans", sans-serif; + --site-display-font: "Sora", "Space Grotesk", sans-serif; + --upgrade-accent: #0f93c5; + --upgrade-accent-strong: #0a6f94; + --upgrade-accent-alt: #ff7a4f; + --upgrade-line: rgba(15, 147, 197, 0.24); + --upgrade-focus: #ffd166; + --upgrade-shadow-soft: 0 14px 30px rgba(15, 147, 197, 0.22); + --upgrade-atmo-a: rgba(15, 147, 197, 0.36); + --upgrade-atmo-b: rgba(255, 122, 79, 0.3); + --upgrade-atmo-c: rgba(21, 31, 61, 0.26); +} + +body.site-kind-chat :where(.chat-container, .message-content, .chat-header, .provider-info) { + border-radius: 18px; +} + +body.site-kind-chat .chat-header { + background: linear-gradient(135deg, #0f93c5, #0a6f94) !important; +} + +body.site-kind-chat .message.user .message-content { + background: linear-gradient(135deg, #0f93c5, #0a6f94) !important; +} + +body.site-kind-chat .message-content a { + color: #0f93c5 !important; + font-weight: 600; +} + +/* Aria personality */ +body.site-kind-aria { + --site-display-font: "Fraunces", "Space Grotesk", serif; + --upgrade-accent: #2e9d83; + --upgrade-accent-strong: #24735f; + --upgrade-accent-alt: #f08947; + --upgrade-line: rgba(46, 157, 131, 0.24); + --upgrade-focus: #f3c15f; + --upgrade-shadow-soft: 0 14px 30px rgba(46, 157, 131, 0.2); + --upgrade-atmo-a: rgba(82, 182, 157, 0.3); + --upgrade-atmo-b: rgba(240, 137, 71, 0.24); + --upgrade-atmo-c: rgba(116, 191, 130, 0.22); +} + +body.site-kind-aria :where(.stage-container, .object-manager, .control-panel, .stage, .object-btn) { + border-color: rgba(46, 157, 131, 0.24) !important; +} + +body.site-kind-aria h1 { + letter-spacing: -0.03em; +} + +/* Dashboard personality */ +body.site-kind-dashboard { + --site-body-font: "Sora", "Plus Jakarta Sans", sans-serif; + --site-display-font: "Space Grotesk", "Sora", sans-serif; + --upgrade-accent: #2f7be7; + --upgrade-accent-strong: #235fbb; + --upgrade-accent-alt: #17a67f; + --upgrade-line: rgba(47, 123, 231, 0.24); + --upgrade-focus: #ffd57a; + --upgrade-shadow-soft: 0 14px 32px rgba(47, 123, 231, 0.22); + --upgrade-atmo-a: rgba(47, 123, 231, 0.35); + --upgrade-atmo-b: rgba(23, 166, 127, 0.26); + --upgrade-atmo-c: rgba(21, 45, 92, 0.24); +} + +body.site-kind-dashboard :where(.card, .system-card, .metric-card, .jobs-section, .gpu-status, .tab-nav, .tab-btn) { + border-color: rgba(47, 123, 231, 0.18) !important; +} + +body.site-kind-dashboard :where(.status-badge.online, .tab-btn.active) { + background: linear-gradient(135deg, #2f7be7, #235fbb) !important; + color: #f7fbff !important; +} + +/* Store personality */ +body.site-kind-store { + --site-display-font: "Fraunces", "Space Grotesk", serif; + --upgrade-accent: #d0602f; + --upgrade-accent-strong: #a84824; + --upgrade-accent-alt: #13876f; + --upgrade-line: rgba(208, 96, 47, 0.24); + --upgrade-focus: #ffd38a; + --upgrade-shadow-soft: 0 14px 30px rgba(208, 96, 47, 0.2); + --upgrade-atmo-a: rgba(208, 96, 47, 0.3); + --upgrade-atmo-b: rgba(19, 135, 111, 0.22); + --upgrade-atmo-c: rgba(209, 162, 71, 0.24); +} + +body.site-kind-store :where(.product-card, .category-card, .feature-card, .plan-card, .route-card, .link-card, .hero-card) { + border-color: rgba(208, 96, 47, 0.18) !important; +} + +body.site-kind-store :where(.btn-primary, .btn-plan.primary, .plan-cta, .button-link.primary) { + background: linear-gradient(135deg, #d0602f, #a84824) !important; +} + +/* Docs personality */ +body.site-kind-docs { + --site-display-font: "Fraunces", "Space Grotesk", serif; + --upgrade-accent: #187f78; + --upgrade-accent-strong: #125f5a; + --upgrade-accent-alt: #b56a2d; + --upgrade-line: rgba(24, 127, 120, 0.22); + --upgrade-focus: #f3c471; + --upgrade-shadow-soft: 0 14px 28px rgba(24, 127, 120, 0.18); + --upgrade-atmo-a: rgba(24, 127, 120, 0.26); + --upgrade-atmo-b: rgba(181, 106, 45, 0.2); + --upgrade-atmo-c: rgba(205, 180, 129, 0.26); +} + +body.site-kind-docs :where(.sidebar-item.active, .sidebar-item.is-active) { + background: linear-gradient(135deg, #187f78, #125f5a) !important; +} + +body.site-kind-docs :where(.md-content h1, .content-markdown h1, .doc-header h1) { + letter-spacing: -0.035em; +} + +/* Pass debug overlay */ +.site-pass-debug { + position: fixed; + right: 14px; + bottom: 14px; + z-index: 100002; + display: grid; + justify-items: end; + gap: 8px; + pointer-events: none; +} + +.site-pass-debug__toggle, +.site-pass-debug__panel { + pointer-events: auto; +} + +.site-pass-debug__toggle { + min-height: 38px; + padding: 0 12px; + border: 1px solid var(--upgrade-line); + border-radius: 999px; + background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)); + color: #f6fffd; + font: 700 0.8rem/1 var(--site-body-font); + letter-spacing: 0.04em; + text-transform: uppercase; + cursor: pointer; + box-shadow: var(--pass-shadow-soft, var(--upgrade-shadow-soft)); +} + +.site-pass-debug__panel { + position: relative; + width: min(320px, calc(100vw - 28px)); + padding: 12px; + border-radius: 16px; + border: 1px solid var(--upgrade-line); + background: color-mix(in srgb, #ffffff 88%, var(--upgrade-accent) 12%); + color: var(--upgrade-ink); + box-shadow: var(--pass-shadow-strong, var(--upgrade-shadow)); + backdrop-filter: blur(10px); +} + +.site-pass-debug__head { + display: flex; + align-items: center; + justify-content: space-between; + gap: 8px; + cursor: grab; + user-select: none; + border-radius: 8px; + padding: 2px 0; + margin: -2px 0 4px; + transition: background 0.12s; +} + +.site-pass-debug__head:hover { + background: color-mix(in srgb, #ffffff 80%, var(--upgrade-accent) 20%); +} + +.site-pass-debug.is-dragging .site-pass-debug__head, +.site-pass-debug.is-dragging { + cursor: grabbing; + user-select: none; +} + +.site-pass-debug__drag-grip { + font-size: 1rem; + opacity: 0.45; + flex-shrink: 0; + pointer-events: none; +} + +.site-pass-debug__resize-handle { + position: absolute; + left: -5px; + top: 16px; + bottom: 16px; + width: 10px; + cursor: ew-resize; + border-radius: 5px; + background: transparent; + transition: background 0.14s; + z-index: 1; +} + +.site-pass-debug__resize-handle:hover, +.site-pass-debug.is-resizing .site-pass-debug__resize-handle { + background: color-mix(in srgb, transparent 60%, var(--upgrade-accent) 40%); +} + +.site-pass-debug.is-resizing { + cursor: ew-resize; + user-select: none; +} + +.site-pass-debug__section-body { + overflow: hidden; +} + +.site-pass-debug__head strong { + font-family: var(--site-display-font); + font-size: 0.98rem; +} + +.site-pass-debug__kind { + display: inline-flex; + align-items: center; + justify-content: center; + min-height: 24px; + padding: 0 8px; + border-radius: 999px; + background: color-mix(in srgb, var(--upgrade-accent) 20%, #ffffff 80%); + color: var(--upgrade-accent-strong); + font-size: 0.7rem; + font-weight: 700; + letter-spacing: 0.06em; +} + +.site-pass-debug__label { + display: block; + margin-top: 10px; + font-size: 0.8rem; + color: var(--upgrade-ink-soft); +} + +.site-pass-debug__range { + width: 100%; + margin-top: 8px; + accent-color: var(--upgrade-accent); +} + +.site-pass-debug__row { + display: grid; + grid-template-columns: 1fr auto auto; + gap: 8px; + margin-top: 8px; +} + +.site-pass-debug__row--compact { + grid-template-columns: repeat(2, minmax(0, 1fr)); +} + +.site-pass-debug__row--speed { + grid-template-columns: auto 1fr; + align-items: center; +} + +.site-pass-debug__inline-label { + font-size: 0.76rem; + font-weight: 600; + color: var(--upgrade-ink-soft); +} + +.site-pass-debug__preset-grid { + display: grid; + grid-template-columns: repeat(4, minmax(0, 1fr)); + gap: 8px; + margin-top: 8px; +} + +.site-pass-debug__snapshot-grid { + display: grid; + grid-template-columns: repeat(3, minmax(0, 1fr)); + gap: 8px; + margin-top: 8px; +} + +.site-pass-debug__share-row { + margin-top: 8px; +} + +.site-pass-debug__number { + min-height: 34px; + padding: 0 8px; + border: 1px solid var(--upgrade-line); + border-radius: 10px; + background: #ffffff; + color: var(--upgrade-ink); + font-weight: 600; +} + +.site-pass-debug__speed { + min-height: 34px; + padding: 0 8px; + border: 1px solid var(--upgrade-line); + border-radius: 10px; + background: #ffffff; + color: var(--upgrade-ink); + font-weight: 600; +} + +.site-pass-debug__share { + width: 100%; + min-height: 34px; + padding: 0 8px; + border: 1px solid var(--upgrade-line); + border-radius: 10px; + background: #ffffff; + color: var(--upgrade-ink); + font: 600 0.72rem/1 var(--site-body-font); +} + +.site-pass-debug__share:focus-visible, +.site-pass-debug__speed:focus-visible, +.site-pass-debug__number:focus-visible { + outline: none; + border-color: color-mix(in srgb, var(--upgrade-accent-strong) 48%, #ffffff 52%); + box-shadow: 0 0 0 2px color-mix(in srgb, var(--upgrade-focus) 36%, #ffffff 64%); +} + +.site-pass-debug__btn { + min-height: 34px; + padding: 0 10px; + border: 1px solid var(--upgrade-line); + border-radius: 10px; + background: #ffffff; + color: var(--upgrade-ink); + font: 600 0.78rem/1 var(--site-body-font); + cursor: pointer; + transition: background 180ms ease, color 180ms ease, border-color 180ms ease, transform 180ms ease; +} + +.site-pass-debug__btn:hover, +.site-pass-debug__btn:focus-visible { + background: color-mix(in srgb, #ffffff 84%, var(--upgrade-accent) 16%); + outline: none; +} + +.site-pass-debug__btn:active { + transform: translateY(1px); +} + +.site-pass-debug__btn:disabled { + opacity: 0.5; + cursor: not-allowed; + transform: none; +} + +.site-pass-debug__btn.is-active { + border-color: color-mix(in srgb, var(--upgrade-accent-strong) 44%, #ffffff 56%); + background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)); + color: #f6fffd; +} + +.site-pass-debug__meta, +.site-pass-debug__hint { + margin: 8px 0 0; + font-size: 0.75rem; + color: var(--upgrade-ink-soft); +} + +.site-pass-debug__hint { + margin-top: 4px; + opacity: 0.88; +} + +.site-pass-debug__compare, +.site-pass-debug__stack, +.site-pass-debug__sweep, +.site-pass-debug__source { + color: var(--upgrade-accent-strong); + font-weight: 700; + letter-spacing: 0.01em; +} + +.site-pass-debug__timeline { + list-style: none; + margin: 8px 0 0; + padding: 6px; + border-radius: 10px; + border: 1px solid var(--upgrade-line); + background: color-mix(in srgb, #ffffff 90%, var(--upgrade-accent) 10%); + max-height: 124px; + overflow: auto; + display: grid; + gap: 5px; +} + +.site-pass-debug__timeline-item { + display: grid; + grid-template-columns: auto 1fr; + gap: 8px; + align-items: center; + font-size: 0.68rem; + color: var(--upgrade-ink-soft); +} + +.site-pass-debug__timeline-item.is-empty { + grid-template-columns: 1fr; + font-style: italic; + opacity: 0.9; +} + +.site-pass-debug__timeline-time { + color: var(--upgrade-accent-strong); + font-weight: 700; +} + +.site-pass-debug__timeline-detail { + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.site-pass-debug__timeline-item--restore { + cursor: pointer; + border-radius: 6px; + padding: 2px 4px; + margin: -2px -4px; + transition: background 0.12s; +} + +.site-pass-debug__timeline-item--restore:hover { + background: color-mix(in srgb, #ffffff 65%, var(--upgrade-accent) 35%); +} + +.site-pass-debug__timeline-item--restore:focus-visible { + outline: 2px solid var(--upgrade-accent-strong); + outline-offset: 1px; +} + +.site-pass-debug__timeline-item--restore:active { + opacity: 0.72; +} + +.site-pass-debug__section { + border: 1px solid var(--upgrade-line); + border-radius: 8px; + margin: 6px 0 0; +} + +.site-pass-debug__section[open] { + padding-bottom: 4px; +} + +.site-pass-debug__section-head { + list-style: none; + padding: 5px 8px; + font-size: 0.74rem; + font-weight: 700; + letter-spacing: 0.03em; + text-transform: uppercase; + color: var(--upgrade-ink-soft); + cursor: pointer; + user-select: none; + display: grid; + grid-template-columns: 1fr auto; + align-items: center; + border-radius: 7px; + background: color-mix(in srgb, #ffffff 92%, var(--upgrade-accent) 8%); +} + +.site-pass-debug__section-head::-webkit-details-marker { + display: none; +} + +.site-pass-debug__section-head::marker { + content: none; +} + +.site-pass-debug__section-head::after { + content: "\25B8"; + font-size: 0.7rem; + opacity: 0.55; + transition: transform 0.15s; + justify-self: end; +} + +.site-pass-debug__section[open] > .site-pass-debug__section-head::after { + transform: rotate(90deg); +} + +.site-pass-debug__section[open] > .site-pass-debug__section-head { + border-radius: 7px 7px 0 0; + border-bottom: 1px solid var(--upgrade-line); +} + +.site-pass-debug__section-head:focus-visible { + outline: 2px solid var(--upgrade-accent-strong); + outline-offset: -2px; + border-radius: 7px; +} + +.site-pass-debug__section > .site-pass-debug__timeline { + margin: 4px 0 0; +} + +/* --- Shortcut cheat-sheet overlay --- */ +.site-pass-shortcut-overlay { + position: fixed; + inset: 0; + z-index: 100010; + display: flex; + align-items: center; + justify-content: center; + background: rgba(0, 0, 0, 0.45); + backdrop-filter: blur(3px); + animation: site-pass-overlay-in 0.14s ease; +} + +@keyframes site-pass-overlay-in { + from { opacity: 0; } + to { opacity: 1; } +} + +.site-pass-shortcut-overlay__box { + position: relative; + width: min(440px, calc(100vw - 32px)); + max-height: calc(100vh - 48px); + overflow-y: auto; + padding: 20px 22px 16px; + border-radius: 16px; + border: 1px solid var(--upgrade-line); + background: color-mix(in srgb, #ffffff 92%, var(--upgrade-accent) 8%); + box-shadow: 0 8px 40px rgba(0,0,0,0.22); + color: var(--upgrade-ink); +} + +.site-pass-shortcut-overlay__head { + display: flex; + align-items: center; + justify-content: space-between; + margin-bottom: 12px; +} + +.site-pass-shortcut-overlay__head strong { + font-family: var(--site-display-font); + font-size: 1rem; +} + +.site-pass-shortcut-overlay__close { + min-height: 32px; + min-width: 32px; + border: none; + background: transparent; + cursor: pointer; + font-size: 1rem; + border-radius: 8px; + color: var(--upgrade-ink-soft); + display: flex; + align-items: center; + justify-content: center; +} + +.site-pass-shortcut-overlay__close:hover { + background: color-mix(in srgb, #ffffff 70%, var(--upgrade-accent) 30%); +} + +.site-pass-shortcut-overlay__close:focus-visible { + outline: 2px solid var(--upgrade-accent-strong); +} + +.site-pass-shortcut-overlay__list { + display: grid; + grid-template-columns: auto 1fr; + gap: 4px 16px; + margin: 0; + padding: 0; + font-size: 0.82rem; +} + +.site-pass-shortcut-overlay__list dt { + font-family: var(--site-code-font, monospace); + font-weight: 700; + color: var(--upgrade-accent-strong); + white-space: nowrap; + padding: 2px 6px; + border-radius: 4px; + background: color-mix(in srgb, #ffffff 75%, var(--upgrade-accent) 25%); + align-self: center; +} + +.site-pass-shortcut-overlay__list dd { + margin: 0; + align-self: center; + color: var(--upgrade-ink-soft); +} + +.site-pass-shortcut-overlay__tip { + margin: 12px 0 0; + font-size: 0.74rem; + color: var(--upgrade-ink-soft); + font-style: italic; + text-align: center; +} + +.site-pass-debug__badge { + display: inline-block; + min-width: 22px; + padding: 1px 5px; + border-radius: 999px; + background: rgba(255, 255, 255, 0.22); + font: 700 0.72rem/1.4 var(--site-body-font); + vertical-align: middle; + text-align: center; + letter-spacing: 0; +} + +.site-pass-debug.is-open .site-pass-debug__badge { + opacity: 0.55; +} + +.site-pass-debug__opacity { + width: 72px; + text-align: right; + font-size: 0.82rem; + padding: 2px 6px; + border: 1px solid var(--upgrade-line); + border-radius: 8px; + background: color-mix(in srgb, #ffffff 75%, var(--upgrade-accent) 25%); + color: var(--upgrade-ink); +} + +.site-pass-debug__opacity:focus { + outline: 2px solid var(--upgrade-accent-strong); + outline-offset: 1px; +} + +.site-pass-debug__btn--toggle.is-active { + background: color-mix(in srgb, var(--upgrade-accent) 85%, #000 15%); + color: #fff; + border-color: var(--upgrade-accent-strong); +} + +.site-pass-debug.is-open .site-pass-debug__toggle { + filter: saturate(1.08) brightness(1.02); +} + +@media (max-width: 900px) { + :where(.btn, .button, .tab-btn, .object-btn, button) { + min-height: 42px; + } + + .site-pass-debug { + right: 10px; + bottom: 10px; + } + + .site-pass-debug__panel { + width: min(94vw, 320px); + } + + .site-pass-debug__preset-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .site-pass-debug__snapshot-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + body::before { + inset: -30%; + opacity: 0.74; + } + + body::after { + opacity: 0.26; + } +} + +@media (prefers-reduced-motion: reduce) { + * { + animation: none !important; + transition: none !important; + scroll-behavior: auto !important; + } + + .site-upgrade-reveal { + opacity: 1 !important; + transform: none !important; + } + + .site-pass-debug__toggle, + .site-pass-debug__btn { + transition: none !important; + } +} diff --git a/apps/global-upgrade.js b/apps/global-upgrade.js index 3656b18f0..cd5d37fdc 100644 --- a/apps/global-upgrade.js +++ b/apps/global-upgrade.js @@ -1,1978 +1,1978 @@ -(function () { - "use strict"; - - if (window.__ariaSiteUpgradeLoaded) { - return; - } - window.__ariaSiteUpgradeLoaded = true; - - document.documentElement.classList.add("site-upgraded"); - - var PASS_MIN = 1; - var PASS_MAX = 100; - var PASS_DEFAULT = 100; - var PASS_STORAGE_KEY = "siteUpgradePasses"; - var DEBUG_OPEN_STORAGE_KEY = "siteUpgradePassDebugOpen"; - var PASS_AUTOPLAY_INTERVAL_MS = 70; - var PASS_AUTOPLAY_MIN_MS = 25; - var PASS_AUTOPLAY_MAX_MS = 1000; - var PASS_AUTOPLAY_STORAGE_KEY = "siteUpgradePassSweepMs"; - var PASS_SNAPSHOT_STORAGE_KEY = "siteUpgradePassSnapshots"; - var PASS_UNDO_STORAGE_KEY = "siteUpgradePassUndoStack"; - var PASS_SECTIONS_STORAGE_KEY = "siteUpgradePassSections"; - var PASS_POSITION_STORAGE_KEY = "siteUpgradePassPosition"; - var PASS_WIDTH_STORAGE_KEY = "siteUpgradePassWidth"; - var PASS_PANEL_STYLE_STORAGE_KEY = "siteUpgradePassPanelStyle"; - var PASS_HISTORY_MAX = 10; - - function detectSiteKind() { - var path = (window.location.pathname || "").toLowerCase(); - var href = (window.location.href || "").toLowerCase(); - var target = path + " " + href; - - if (target.includes("/chat/") || target.includes("chat/index") || target.endsWith("/chat")) { - return "chat"; - } - - if (target.includes("/aria/") || target.includes("auto-execute") || target.includes("aria/index")) { - return "aria"; - } - - if (target.includes("/dashboard/") || target.includes("dashboard/index") || target.includes("dashboard/hub")) { - return "dashboard"; - } - - if (target.includes("/store/") || target.includes("products") || target.includes("product.html")) { - return "store"; - } - - if (target.includes("documentation") || target.includes("/docs/") || target.endsWith("/docs") || target.includes("readme_pages")) { - return "docs"; - } - - if (target.includes("/quantum/") || target.includes("/monetization/") || target.includes("llm-maker") || target.includes("/mount/")) { - return "dashboard"; - } - - return "core"; - } - - function applySiteKindClass() { - var kind = detectSiteKind(); - var root = document.documentElement; - var body = document.body; - - root.setAttribute("data-site-kind", kind); - if (body) { - body.setAttribute("data-site-kind", kind); - body.classList.add("site-kind-" + kind); - } - - return kind; - } - - function clamp(value, min, max) { - return Math.max(min, Math.min(max, value)); - } - - function getPassStorageKey(kind) { - return PASS_STORAGE_KEY + ":" + kind; - } - - function getPassSpeedStorageKey(kind) { - return PASS_AUTOPLAY_STORAGE_KEY + ":" + kind; - } - - function getPassSnapshotStorageKey(kind) { - return PASS_SNAPSHOT_STORAGE_KEY + ":" + kind; - } - - function getPassUndoStorageKey(kind) { - return PASS_UNDO_STORAGE_KEY + ":" + kind; - } - - function getPassSectionsStorageKey(kind) { - return PASS_SECTIONS_STORAGE_KEY + ":" + kind; - } - - function getPassPositionStorageKey(kind) { - return PASS_POSITION_STORAGE_KEY + ":" + kind; - } - - function getPassWidthStorageKey(kind) { - return PASS_WIDTH_STORAGE_KEY + ":" + kind; - } - - function getPassPanelStyleStorageKey(kind) { - return PASS_PANEL_STYLE_STORAGE_KEY + ":" + kind; - } - - function shouldOpenDebugFromQuery() { - var params = new URLSearchParams(window.location.search || ""); - var raw = params.get("passpanel") || params.get("debugpasses") || params.get("passdebug"); - if (!raw) { - return null; - } - - var value = String(raw).toLowerCase(); - if (value === "0" || value === "false" || value === "off" || value === "hide") { - return false; - } - - return true; - } - - function shouldAutoplayFromQuery() { - var params = new URLSearchParams(window.location.search || ""); - var raw = params.get("passauto") || params.get("passautoplay") || params.get("autosweep"); - if (!raw) { - return null; - } - - return toOptionalBool(raw); - } - - function getConfiguredPassCount(kind) { - var params = new URLSearchParams(window.location.search || ""); - var stateFromQuery = getPassStateFromQuery(); - var queryRaw = params.get("passes") || params.get("pass") || (stateFromQuery && stateFromQuery.passCount); - var savedRaw = null; - - try { - if (window.localStorage) { - savedRaw = localStorage.getItem(getPassStorageKey(kind)) || localStorage.getItem(PASS_STORAGE_KEY); - } - } catch (error) { - savedRaw = null; - } - - var parsed = Number(queryRaw || savedRaw || PASS_DEFAULT); - if (!Number.isFinite(parsed)) { - parsed = PASS_DEFAULT; - } - - return Math.round(clamp(parsed, PASS_MIN, PASS_MAX)); - } - - function getConfiguredAutoplayInterval(kind) { - var params = new URLSearchParams(window.location.search || ""); - var stateFromQuery = getPassStateFromQuery(); - var queryRaw = params.get("passsweepms") || params.get("passautoplayms") || params.get("passspeed") || (stateFromQuery && stateFromQuery.sweepMs); - var savedRaw = null; - - try { - if (window.localStorage) { - savedRaw = localStorage.getItem(getPassSpeedStorageKey(kind)) || localStorage.getItem(PASS_AUTOPLAY_STORAGE_KEY); - } - } catch (error) { - savedRaw = null; - } - - var parsed = Number(queryRaw || savedRaw || PASS_AUTOPLAY_INTERVAL_MS); - if (!Number.isFinite(parsed)) { - parsed = PASS_AUTOPLAY_INTERVAL_MS; - } - - return Math.round(clamp(parsed, PASS_AUTOPLAY_MIN_MS, PASS_AUTOPLAY_MAX_MS)); - } - - function normalizePassSource(source) { - var normalized = String(source || "manual"); - - if (normalized.indexOf("shortcut-") === 0) { - normalized = "key " + normalized.slice("shortcut-".length); - } - - return normalized.replace(/-/g, " "); - } - - function toValidPass(rawValue) { - var parsed = Number(rawValue); - if (!Number.isFinite(parsed)) { - return null; - } - - return Math.round(clamp(parsed, PASS_MIN, PASS_MAX)); - } - - function toValidSpeed(rawValue) { - var parsed = Number(rawValue); - if (!Number.isFinite(parsed)) { - return null; - } - - return Math.round(clamp(parsed, PASS_AUTOPLAY_MIN_MS, PASS_AUTOPLAY_MAX_MS)); - } - - function toOptionalBool(rawValue) { - if (typeof rawValue === "boolean") { - return rawValue; - } - - if (rawValue === null || rawValue === undefined) { - return null; - } - - var normalized = String(rawValue).toLowerCase(); - if (normalized === "1" || normalized === "true" || normalized === "on" || normalized === "open" || normalized === "yes") { - return true; - } - - if (normalized === "0" || normalized === "false" || normalized === "off" || normalized === "close" || normalized === "closed" || normalized === "hide" || normalized === "no") { - return false; - } - - return null; - } - - function normalizePassState(rawState) { - if (!rawState || typeof rawState !== "object") { - return null; - } - - var passCount = toValidPass(rawState.passCount !== undefined ? rawState.passCount : (rawState.pass !== undefined ? rawState.pass : rawState.p)); - var sweepMs = toValidSpeed(rawState.sweepMs !== undefined ? rawState.sweepMs : (rawState.speed !== undefined ? rawState.speed : rawState.ms)); - var snapshotA = toValidPass(rawState.snapshotA !== undefined ? rawState.snapshotA : rawState.a); - var snapshotB = toValidPass(rawState.snapshotB !== undefined ? rawState.snapshotB : rawState.b); - var open = toOptionalBool(rawState.open); - var auto = toOptionalBool(rawState.auto); - - if (passCount === null && sweepMs === null && snapshotA === null && snapshotB === null && open === null && auto === null) { - return null; - } - - return { - passCount: passCount, - sweepMs: sweepMs, - snapshotA: snapshotA, - snapshotB: snapshotB, - open: open, - auto: auto - }; - } - - function getPassStateFromSearchParams(params) { - if (!params) { - return null; - } - - var raw = params.get("passstate"); - if (!raw) { - return null; - } - - try { - return normalizePassState(JSON.parse(raw)); - } catch (error) { - return null; - } - } - - function getPassStateFromQuery() { - return getPassStateFromSearchParams(new URLSearchParams(window.location.search || "")); - } - - function parsePassStateFromInput(rawInput) { - if (!rawInput) { - return null; - } - - var value = String(rawInput).trim(); - if (!value) { - return null; - } - - try { - var url = new URL(value, window.location.href); - var fromUrl = getPassStateFromSearchParams(url.searchParams); - if (fromUrl) { - return fromUrl; - } - } catch (error) { - // Not a URL, continue parsing. - } - - try { - var queryLike = value.charAt(0) === "?" ? value.slice(1) : value; - var params = new URLSearchParams(queryLike); - var fromParams = getPassStateFromSearchParams(params); - if (fromParams) { - return fromParams; - } - } catch (error) { - // Not query-like content, continue parsing. - } - - try { - return normalizePassState(JSON.parse(value)); - } catch (error) { - return null; - } - } - - function interpolate(start, end, t) { - return start + (end - start) * t; - } - - function getKindShadowColor(kind) { - switch (kind) { - case "chat": - return [15, 147, 197]; - case "aria": - return [46, 157, 131]; - case "dashboard": - return [47, 123, 231]; - case "store": - return [208, 96, 47]; - case "docs": - return [24, 127, 120]; - default: - return [19, 36, 55]; - } - } - - function setPassVariables(kind, passIndex, totalPasses) { - var root = document.documentElement; - var t = clamp(passIndex / totalPasses, 0, 1); - var color = getKindShadowColor(kind); - - var gridOpacity = interpolate(0.24, 0.46, t); - var radiusBoost = interpolate(0, 7, t); - var focusWidth = interpolate(2.5, 4.2, t); - var letterShift = interpolate(-0.012, -0.036, t); - var cardRaise = interpolate(0.25, 1.9, t); - var revealMs = Math.round(interpolate(430, 690, t)); - - var softY = interpolate(8, 24, t); - var softBlur = interpolate(22, 54, t); - var softAlpha = interpolate(0.14, 0.33, t); - - var strongY = interpolate(14, 38, t); - var strongBlur = interpolate(34, 78, t); - var strongAlpha = interpolate(0.2, 0.42, t); - - root.style.setProperty("--pass-intensity", t.toFixed(4)); - root.style.setProperty("--pass-grid-opacity", gridOpacity.toFixed(3)); - root.style.setProperty("--pass-radius-boost", radiusBoost.toFixed(2) + "px"); - root.style.setProperty("--pass-focus-width", focusWidth.toFixed(2) + "px"); - root.style.setProperty("--pass-letter-shift", letterShift.toFixed(4) + "em"); - root.style.setProperty("--pass-card-raise", cardRaise.toFixed(2) + "px"); - root.style.setProperty("--pass-reveal-ms", revealMs + "ms"); - root.style.setProperty( - "--pass-shadow-soft", - "0 " + softY.toFixed(2) + "px " + softBlur.toFixed(2) + "px rgba(" + color[0] + ", " + color[1] + ", " + color[2] + ", " + softAlpha.toFixed(3) + ")" - ); - root.style.setProperty( - "--pass-shadow-strong", - "0 " + strongY.toFixed(2) + "px " + strongBlur.toFixed(2) + "px rgba(" + color[0] + ", " + color[1] + ", " + color[2] + ", " + strongAlpha.toFixed(3) + ")" - ); - } - - function emitPassChange(kind, passCount, source) { - var detail = { - kind: kind, - passCount: passCount, - passes: passCount, - source: source || "manual" - }; - - document.dispatchEvent(new CustomEvent("site-upgrade:pass-change", { detail: detail })); - window.dispatchEvent(new CustomEvent("site-upgrade:pass-change", { detail: detail })); - } - - function runPolishPasses(kind, requestedPassCount, source) { - var passCount = Math.round(clamp(Number(requestedPassCount), PASS_MIN, PASS_MAX)); - - for (var pass = 1; pass <= passCount; pass += 1) { - setPassVariables(kind, pass, passCount); - } - - document.documentElement.setAttribute("data-polish-passes", String(passCount)); - if (document.body) { - document.body.setAttribute("data-polish-passes", String(passCount)); - } - - emitPassChange(kind, passCount, source); - - return passCount; - } - - function trySaveValue(key, value) { - try { - if (window.localStorage) { - localStorage.setItem(key, value); - } - } catch (error) { - // Ignore storage errors in strict/private browsing contexts. - } - } - - function tryLoadValue(key) { - try { - return window.localStorage ? localStorage.getItem(key) : null; - } catch (error) { - return null; - } - } - - function createPassDebugPanel(kind, initialPassCount) { - if (!document.body || document.querySelector(".site-pass-debug")) { - return; - } - - var panel = document.createElement("aside"); - panel.className = "site-pass-debug"; - panel.setAttribute("aria-label", "Polish pass controls"); - panel.innerHTML = "" - + "" - + ""; - - document.body.appendChild(panel); - - var toggle = panel.querySelector(".site-pass-debug__toggle"); - var bodyPanel = panel.querySelector(".site-pass-debug__panel"); - var kindChip = panel.querySelector(".site-pass-debug__kind"); - var range = panel.querySelector("#site-pass-debug-range"); - var number = panel.querySelector("#site-pass-debug-number"); - var speedInput = panel.querySelector("#site-pass-debug-speed"); - var shareInput = panel.querySelector("#site-pass-debug-share"); - var value = panel.querySelector(".site-pass-debug__value"); - var compareValue = panel.querySelector(".site-pass-debug__compare"); - var stackValue = panel.querySelector(".site-pass-debug__stack"); - var sweepValue = panel.querySelector(".site-pass-debug__sweep"); - var sourceValue = panel.querySelector(".site-pass-debug__source"); - var timeline = panel.querySelector(".site-pass-debug__timeline"); - var stepButtons = panel.querySelectorAll(".site-pass-debug__btn[data-step]"); - var presetButtons = panel.querySelectorAll(".site-pass-debug__btn[data-preset]"); - var resetButton = panel.querySelector('.site-pass-debug__btn[data-action="reset"]'); - var closeButton = panel.querySelector('.site-pass-debug__btn[data-action="close"]'); - var defaultButton = panel.querySelector('.site-pass-debug__btn[data-action="default"]'); - var autoplayButton = panel.querySelector('.site-pass-debug__btn[data-action="autoplay"]'); - var undoButton = panel.querySelector('.site-pass-debug__btn[data-action="undo"]'); - var redoButton = panel.querySelector('.site-pass-debug__btn[data-action="redo"]'); - var saveAButton = panel.querySelector('.site-pass-debug__btn[data-action="save-a"]'); - var saveBButton = panel.querySelector('.site-pass-debug__btn[data-action="save-b"]'); - var applyAButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-a"]'); - var applyBButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-b"]'); - var toggleAbButton = panel.querySelector('.site-pass-debug__btn[data-action="toggle-ab"]'); - var copyUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="copy-url"]'); - var applyUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-url"]'); - var pushUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="push-url"]'); - var clearHistoryButton = panel.querySelector('.site-pass-debug__btn[data-action="clear-history"]'); - var exportJsonButton = panel.querySelector('.site-pass-debug__btn[data-action="export-json"]'); - var toggleBlurButton = panel.querySelector('.site-pass-debug__btn[data-action="toggle-blur"]'); - var resetAppearanceButton = panel.querySelector('.site-pass-debug__btn[data-action="reset-appearance"]'); - var opacityInput = panel.querySelector("#site-pass-debug-opacity"); - var badge = panel.querySelector(".site-pass-debug__badge"); - var dragHead = panel.querySelector(".site-pass-debug__head"); - var resizeHandle = panel.querySelector(".site-pass-debug__resize-handle"); - - var queryState = getPassStateFromQuery(); - var queryAuto = shouldAutoplayFromQuery(); - var shouldAutoStart = queryState && queryState.auto !== null ? queryState.auto : queryAuto; - var configuredPasses = getConfiguredPassCount(kind); - var currentPasses = initialPassCount; - var isOpen = false; - var isAutoplay = false; - var autoDirection = 1; - var autoplayTimer = null; - var autoplayIntervalMs = queryState && Number.isFinite(queryState.sweepMs) - ? queryState.sweepMs - : getConfiguredAutoplayInterval(kind); - var undoStack = (function () { - var raw = tryLoadValue(getPassUndoStorageKey(kind)); - if (!raw) { return []; } - try { - var parsed = JSON.parse(raw); - return Array.isArray(parsed) - ? parsed.filter(function (v) { return Number.isFinite(v); }) - : []; - } catch (e) { return []; } - })(); - var redoStack = []; - var passHistory = []; - var snapshots = (function () { - var raw = tryLoadValue(getPassSnapshotStorageKey(kind)); - if (!raw) { - return { a: null, b: null }; - } - - try { - var parsed = JSON.parse(raw); - return { - a: toValidPass(parsed && parsed.a), - b: toValidPass(parsed && parsed.b) - }; - } catch (error) { - return { a: null, b: null }; - } - })(); - - if (queryState && Number.isFinite(queryState.passCount)) { - currentPasses = queryState.passCount; - } - - if (queryState && queryState.snapshotA !== null) { - snapshots.a = queryState.snapshotA; - } - - if (queryState && queryState.snapshotB !== null) { - snapshots.b = queryState.snapshotB; - } - - var savedOpen = tryLoadValue(DEBUG_OPEN_STORAGE_KEY); - var queryOpen = shouldOpenDebugFromQuery(); - if (queryState && queryState.open !== null) { - isOpen = queryState.open; - } else if (queryOpen !== null) { - isOpen = queryOpen; - } else if (savedOpen === "1") { - isOpen = true; - } - - kindChip.textContent = kind.toUpperCase(); - - function persistPass(passCount) { - trySaveValue(getPassStorageKey(kind), String(passCount)); - trySaveValue(PASS_STORAGE_KEY, String(passCount)); - } - - function persistSnapshots() { - trySaveValue(getPassSnapshotStorageKey(kind), JSON.stringify({ - a: snapshots.a, - b: snapshots.b - })); - } - - function persistUndoStacks() { - trySaveValue(getPassUndoStorageKey(kind), JSON.stringify(undoStack)); - } - - function serializePassState() { - return { - pass: currentPasses, - speed: autoplayIntervalMs, - a: snapshots.a, - b: snapshots.b, - open: isOpen, - auto: isAutoplay - }; - } - - function buildShareUrl() { - var url = new URL(window.location.href); - var params = url.searchParams; - var cleanupKeys = [ - "pass", - "passes", - "passpanel", - "debugpasses", - "passdebug", - "passsweepms", - "passautoplayms", - "passspeed", - "passauto", - "passautoplay", - "autosweep", - "passstate" - ]; - - cleanupKeys.forEach(function (key) { - params.delete(key); - }); - - params.set("passstate", JSON.stringify(serializePassState())); - return url.toString(); - } - - function syncShareUrlField() { - if (!shareInput) { - return; - } - - shareInput.value = buildShareUrl(); - } - - function syncSourceLabel(source) { - sourceValue.textContent = normalizePassSource(source); - } - - function syncSweepLabel() { - if (!sweepValue) { - return; - } - - sweepValue.textContent = (isAutoplay ? "running" : "idle") + " @ " + autoplayIntervalMs + "ms"; - } - - function syncUndoState() { - if (!stackValue) { - return; - } - - stackValue.textContent = "undo " + undoStack.length + " | redo " + redoStack.length; - undoButton.disabled = undoStack.length === 0; - redoButton.disabled = redoStack.length === 0; - } - - function shouldTrackUndo(source, nextPass) { - if (!Number.isFinite(nextPass) || nextPass === currentPasses) { - return false; - } - - var normalized = String(source || "panel"); - if (normalized === "autoplay" || normalized.indexOf("undo") !== -1 || normalized.indexOf("redo") !== -1 || normalized === "state-init" || normalized === "event") { - return false; - } - - return true; - } - - function pushUndo(previousPass) { - undoStack.push(previousPass); - if (undoStack.length > PASS_MAX) { - undoStack.shift(); - } - redoStack = []; - persistUndoStacks(); - syncUndoState(); - } - - function runUndo(source) { - if (!undoStack.length) { - return; - } - - var targetPass = undoStack.pop(); - if (targetPass === currentPasses) { - persistUndoStacks(); - syncUndoState(); - return; - } - - redoStack.push(currentPasses); - if (redoStack.length > PASS_MAX) { - redoStack.shift(); - } - - persistUndoStacks(); - applyPassCount(targetPass, true, source || "undo"); - syncUndoState(); - } - - function runRedo(source) { - if (!redoStack.length) { - return; - } - - var targetPass = redoStack.pop(); - if (targetPass === currentPasses) { - persistUndoStacks(); - syncUndoState(); - return; - } - - undoStack.push(currentPasses); - if (undoStack.length > PASS_MAX) { - undoStack.shift(); - } - - persistUndoStacks(); - applyPassCount(targetPass, true, source || "redo"); - syncUndoState(); - } - - function renderHistory() { - if (!timeline) { - return; - } - - timeline.innerHTML = ""; - - if (!passHistory.length) { - var empty = document.createElement("li"); - empty.className = "site-pass-debug__timeline-item is-empty"; - empty.textContent = "No pass changes yet"; - timeline.appendChild(empty); - return; - } - - passHistory.forEach(function (entry) { - var item = document.createElement("li"); - item.className = "site-pass-debug__timeline-item site-pass-debug__timeline-item--restore"; - item.tabIndex = 0; - item.setAttribute("role", "button"); - item.title = "Restore pass " + entry.pass; - item.setAttribute("aria-label", "Restore " + entry.pass + " passes (" + entry.source + ")"); - - var stamp = document.createElement("span"); - stamp.className = "site-pass-debug__timeline-time"; - stamp.textContent = entry.stamp; - - var detail = document.createElement("span"); - detail.className = "site-pass-debug__timeline-detail"; - var deltaText = entry.delta === null ? "" : " (" + (entry.delta >= 0 ? "+" : "") + entry.delta + ")"; - detail.textContent = entry.source + " -> " + entry.pass + deltaText; - - item.appendChild(stamp); - item.appendChild(detail); - - (function (passValue) { - function handleRestore(evt) { - if (evt.type === "keydown" && evt.key !== "Enter" && evt.key !== " ") { - return; - } - if (evt.key === " ") { - evt.preventDefault(); - } - applyPassCount(passValue, true, "timeline-restore"); - } - item.addEventListener("click", handleRestore); - item.addEventListener("keydown", handleRestore); - })(entry.pass); - - timeline.appendChild(item); - }); - } - - function pushHistory(passCount, source) { - var normalizedSource = normalizePassSource(source || "event"); - var now = new Date(); - var stamp = now.toLocaleTimeString([], { - hour: "2-digit", - minute: "2-digit", - second: "2-digit" - }); - - var top = passHistory[0]; - if (top && normalizedSource === "autoplay" && top.source === "autoplay") { - var autoplayPrev = top.pass; - top.pass = passCount; - top.stamp = stamp; - top.delta = passCount - autoplayPrev; - renderHistory(); - return; - } - - if (top && top.pass === passCount && top.source === normalizedSource) { - return; - } - - var previousPass = top ? top.pass : null; - passHistory.unshift({ - pass: passCount, - source: normalizedSource, - stamp: stamp, - delta: previousPass === null ? null : passCount - previousPass - }); - - if (passHistory.length > PASS_HISTORY_MAX) { - passHistory.length = PASS_HISTORY_MAX; - } - - renderHistory(); - } - - function clearHistory(sourceLabel) { - passHistory = []; - renderHistory(); - syncSourceLabel(sourceLabel || "history-clear"); - } - - function syncSnapshotUI(passCount) { - var hasA = Number.isFinite(snapshots.a); - var hasB = Number.isFinite(snapshots.b); - - applyAButton.disabled = !hasA; - applyBButton.disabled = !hasB; - toggleAbButton.disabled = !hasA && !hasB; - - saveAButton.classList.toggle("is-active", hasA && snapshots.a === passCount); - saveBButton.classList.toggle("is-active", hasB && snapshots.b === passCount); - - if (hasA || hasB) { - var nextLabel = "A"; - if (hasA && hasB) { - nextLabel = passCount === snapshots.a ? "B" : "A"; - } else if (hasB && !hasA) { - nextLabel = "B"; - } - toggleAbButton.textContent = "Flip " + nextLabel; - } else { - toggleAbButton.textContent = "Flip A/B"; - } - - if (hasA && hasB) { - var delta = snapshots.b - snapshots.a; - compareValue.textContent = "A " + snapshots.a + " | B " + snapshots.b + " | Δ " + (delta >= 0 ? "+" : "") + delta; - } else { - compareValue.textContent = "A " + (hasA ? snapshots.a : "-") + " | B " + (hasB ? snapshots.b : "-"); - } - - syncShareUrlField(); - } - - function applySnapshotToggle(source) { - var hasA = Number.isFinite(snapshots.a); - var hasB = Number.isFinite(snapshots.b); - - if (!hasA && !hasB) { - return; - } - - if (hasA && hasB) { - var target = currentPasses === snapshots.a ? snapshots.b : snapshots.a; - applyPassCount(target, true, source || "snapshot-toggle"); - return; - } - - applyPassCount(hasA ? snapshots.a : snapshots.b, true, source || "snapshot-toggle"); - } - - function applyStatePayload(rawState, sourceLabel) { - var state = normalizePassState(rawState); - if (!state) { - return false; - } - - if (state.sweepMs !== null) { - setAutoplayInterval(state.sweepMs, true); - } - - if (state.snapshotA !== null) { - snapshots.a = state.snapshotA; - } - - if (state.snapshotB !== null) { - snapshots.b = state.snapshotB; - } - - persistSnapshots(); - - if (state.passCount !== null) { - applyPassCount(state.passCount, true, sourceLabel || "state"); - } else { - syncInputs(currentPasses); - syncSourceLabel(sourceLabel || "state"); - } - - if (state.open !== null) { - setPanelOpen(state.open); - } - - if (state.auto !== null) { - if (state.auto) { - startAutoplay(); - } else { - stopAutoplay(); - } - } - - syncShareUrlField(); - return true; - } - - function setAutoplayInterval(rawInterval, persistSelection) { - var normalized = toValidSpeed(rawInterval); - if (!Number.isFinite(normalized)) { - normalized = PASS_AUTOPLAY_INTERVAL_MS; - } - - autoplayIntervalMs = normalized; - speedInput.value = String(autoplayIntervalMs); - - if (persistSelection) { - trySaveValue(getPassSpeedStorageKey(kind), String(autoplayIntervalMs)); - trySaveValue(PASS_AUTOPLAY_STORAGE_KEY, String(autoplayIntervalMs)); - } - - if (isAutoplay) { - startAutoplay(); - } - - syncSweepLabel(); - syncShareUrlField(); - } - - function syncInputs(passCount) { - range.value = String(passCount); - number.value = String(passCount); - value.textContent = String(passCount); - if (badge) { badge.textContent = String(passCount); } - - presetButtons.forEach(function (button) { - var preset = Number(button.getAttribute("data-preset")); - button.classList.toggle("is-active", preset === passCount); - }); - - syncSnapshotUI(passCount); - } - - function stopAutoplay() { - if (autoplayTimer) { - clearInterval(autoplayTimer); - autoplayTimer = null; - } - isAutoplay = false; - autoplayButton.textContent = "Auto Sweep"; - autoplayButton.classList.remove("is-active"); - syncSweepLabel(); - syncShareUrlField(); - } - - function startAutoplay() { - if (window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches) { - return; - } - - stopAutoplay(); - isAutoplay = true; - autoplayButton.textContent = "Stop Sweep"; - autoplayButton.classList.add("is-active"); - syncSweepLabel(); - syncShareUrlField(); - - autoplayTimer = setInterval(function () { - if (currentPasses >= PASS_MAX) { - autoDirection = -1; - } else if (currentPasses <= PASS_MIN) { - autoDirection = 1; - } - - applyPassCount(currentPasses + autoDirection, true, "autoplay"); - }, autoplayIntervalMs); - } - - function toggleAutoplay() { - if (isAutoplay) { - stopAutoplay(); - return; - } - - startAutoplay(); - } - - function isEditableTarget(target) { - if (!target) { - return false; - } - - if (target.isContentEditable) { - return true; - } - - var tag = (target.tagName || "").toLowerCase(); - return tag === "input" || tag === "textarea" || tag === "select"; - } - - function syncFromPassEvent(event) { - if (!event || !event.detail || event.detail.kind !== kind) { - return; - } - - var detailPassCount = Number(event.detail.passCount); - if (!Number.isFinite(detailPassCount)) { - return; - } - - currentPasses = Math.round(clamp(detailPassCount, PASS_MIN, PASS_MAX)); - syncInputs(currentPasses); - syncSourceLabel(event.detail.source || "event"); - pushHistory(currentPasses, event.detail.source || "event"); - } - - function setPanelOpen(nextOpen) { - isOpen = !!nextOpen; - panel.classList.toggle("is-open", isOpen); - bodyPanel.hidden = !isOpen; - toggle.setAttribute("aria-expanded", String(isOpen)); - trySaveValue(DEBUG_OPEN_STORAGE_KEY, isOpen ? "1" : "0"); - - if (!isOpen) { - stopAutoplay(); - } - - syncShareUrlField(); - } - - function applyPassCount(rawPassCount, persistSelection, source) { - var normalized = Math.round(clamp(Number(rawPassCount), PASS_MIN, PASS_MAX)); - var effectiveSource = source || "panel"; - - if (shouldTrackUndo(effectiveSource, normalized)) { - pushUndo(currentPasses); - } - - currentPasses = runPolishPasses(kind, normalized, effectiveSource); - syncInputs(currentPasses); - syncSourceLabel(effectiveSource); - - if (persistSelection) { - persistPass(currentPasses); - } - - syncUndoState(); - syncShareUrlField(); - } - - toggle.addEventListener("click", function () { - setPanelOpen(!isOpen); - }); - - range.addEventListener("input", function () { - applyPassCount(range.value, true, "slider"); - }); - - number.addEventListener("change", function () { - applyPassCount(number.value, true, "number"); - }); - - speedInput.addEventListener("change", function () { - setAutoplayInterval(speedInput.value, true); - syncSourceLabel("speed"); - }); - - stepButtons.forEach(function (button) { - button.addEventListener("click", function () { - var delta = Number(button.getAttribute("data-step")); - applyPassCount(currentPasses + delta, true, "step"); - }); - }); - - presetButtons.forEach(function (button) { - button.addEventListener("click", function () { - var preset = Number(button.getAttribute("data-preset")); - applyPassCount(preset, true, "preset"); - }); - }); - - resetButton.addEventListener("click", function () { - applyPassCount(PASS_MAX, true, "reset"); - }); - - defaultButton.addEventListener("click", function () { - configuredPasses = getConfiguredPassCount(kind); - applyPassCount(configuredPasses, true, "default"); - }); - - autoplayButton.addEventListener("click", function () { - toggleAutoplay(); - }); - - undoButton.addEventListener("click", function () { - runUndo("undo"); - }); - - redoButton.addEventListener("click", function () { - runRedo("redo"); - }); - - saveAButton.addEventListener("click", function () { - snapshots.a = currentPasses; - persistSnapshots(); - syncSnapshotUI(currentPasses); - syncSourceLabel("save-a"); - }); - - saveBButton.addEventListener("click", function () { - snapshots.b = currentPasses; - persistSnapshots(); - syncSnapshotUI(currentPasses); - syncSourceLabel("save-b"); - }); - - applyAButton.addEventListener("click", function () { - if (Number.isFinite(snapshots.a)) { - applyPassCount(snapshots.a, true, "snapshot-a"); - } - }); - - applyBButton.addEventListener("click", function () { - if (Number.isFinite(snapshots.b)) { - applyPassCount(snapshots.b, true, "snapshot-b"); - } - }); - - toggleAbButton.addEventListener("click", function () { - applySnapshotToggle("snapshot-toggle"); - }); - - copyUrlButton.addEventListener("click", function () { - var shareUrl = buildShareUrl(); - shareInput.value = shareUrl; - - if (navigator.clipboard && typeof navigator.clipboard.writeText === "function") { - navigator.clipboard.writeText(shareUrl) - .then(function () { - syncSourceLabel("share-copied"); - }) - .catch(function () { - syncSourceLabel("share-copy-failed"); - }); - return; - } - - shareInput.focus(); - shareInput.select(); - syncSourceLabel("share-ready"); - }); - - applyUrlButton.addEventListener("click", function () { - var parsed = parsePassStateFromInput(shareInput.value); - var applied = applyStatePayload(parsed, "share-import"); - if (!applied) { - syncSourceLabel("share-invalid"); - } - }); - - pushUrlButton.addEventListener("click", function () { - var shareUrl = buildShareUrl(); - if (window.history && typeof window.history.replaceState === "function") { - window.history.replaceState({}, "", shareUrl); - syncShareUrlField(); - syncSourceLabel("url-pushed"); - return; - } - - shareInput.value = shareUrl; - syncSourceLabel("url-ready"); - }); - - clearHistoryButton.addEventListener("click", function () { - clearHistory("history-clear"); - }); - - shareInput.addEventListener("keydown", function (event) { - if (event.key === "Enter") { - event.preventDefault(); - applyUrlButton.click(); - } - }); - - closeButton.addEventListener("click", function () { - setPanelOpen(false); - }); - - document.addEventListener("site-upgrade:pass-change", syncFromPassEvent); - - document.addEventListener("visibilitychange", function () { - if (document.hidden) { - stopAutoplay(); - } - }); - - window.addEventListener("beforeunload", stopAutoplay); - - document.addEventListener("keydown", function (event) { - if (shortcutOverlay && event.key === "Escape") { - event.preventDefault(); - hideShortcutOverlay(); - return; - } - - if (isEditableTarget(event.target)) { - return; - } - - if (!(event.altKey && event.shiftKey)) { - return; - } - - if (event.key.toLowerCase() === "p") { - event.preventDefault(); - setPanelOpen(!isOpen); - return; - } - - if (event.key.toLowerCase() === "a") { - event.preventDefault(); - toggleAutoplay(); - return; - } - - if (event.key.toLowerCase() === "c") { - event.preventDefault(); - setPanelOpen(false); - return; - } - - if (event.code === "Digit0") { - event.preventDefault(); - applyPassCount(PASS_MAX, true, "shortcut-reset"); - return; - } - - if (event.code === "Digit1" || event.code === "Digit2" || event.code === "Digit3" || event.code === "Digit4") { - event.preventDefault(); - var presetMap = { - Digit1: 25, - Digit2: 50, - Digit3: 75, - Digit4: 100 - }; - applyPassCount(presetMap[event.code], true, "shortcut-preset"); - return; - } - - if (event.code === "Digit5") { - event.preventDefault(); - if (Number.isFinite(snapshots.a)) { - applyPassCount(snapshots.a, true, "shortcut-a"); - } - return; - } - - if (event.code === "Digit6") { - event.preventDefault(); - if (Number.isFinite(snapshots.b)) { - applyPassCount(snapshots.b, true, "shortcut-b"); - } - return; - } - - if (event.code === "Digit7") { - event.preventDefault(); - applySnapshotToggle("shortcut-toggle"); - return; - } - - if (event.key.toLowerCase() === "u") { - event.preventDefault(); - applyUrlButton.click(); - return; - } - - if (event.key.toLowerCase() === "i") { - event.preventDefault(); - shareInput.focus(); - shareInput.select(); - syncSourceLabel("url-focus"); - return; - } - - if (event.key.toLowerCase() === "h") { - event.preventDefault(); - clearHistory("history-clear"); - return; - } - - if (event.key === "/") { - event.preventDefault(); - if (shortcutOverlay) { - hideShortcutOverlay(); - } else { - showShortcutOverlay(); - } - return; - } - - if (event.key.toLowerCase() === "l") { - event.preventDefault(); - pushUrlButton.click(); - return; - } - - if (event.key.toLowerCase() === "z") { - event.preventDefault(); - runUndo("shortcut-undo"); - return; - } - - if (event.key.toLowerCase() === "y") { - event.preventDefault(); - runRedo("shortcut-redo"); - return; - } - - if (event.key === "ArrowUp") { - event.preventDefault(); - applyPassCount(currentPasses + 1, true, "shortcut-up"); - return; - } - - if (event.key === "ArrowDown") { - event.preventDefault(); - applyPassCount(currentPasses - 1, true, "shortcut-down"); - return; - } - - if (event.key === "ArrowRight") { - event.preventDefault(); - applyPassCount(currentPasses + 10, true, "shortcut-right"); - return; - } - - if (event.key === "ArrowLeft") { - event.preventDefault(); - applyPassCount(currentPasses - 10, true, "shortcut-left"); - } - }); - - setAutoplayInterval(autoplayIntervalMs, false); - syncSweepLabel(); - syncInputs(currentPasses); - syncUndoState(); - syncShareUrlField(); - renderHistory(); - - if (queryState && (queryState.snapshotA !== null || queryState.snapshotB !== null)) { - persistSnapshots(); - } - - if (currentPasses !== initialPassCount) { - applyPassCount(currentPasses, true, "state-init"); - } else { - syncSourceLabel("init"); - pushHistory(currentPasses, "init"); - } - - setPanelOpen(isOpen); - - if (shouldAutoStart === true) { - startAutoplay(); - } else if (shouldAutoStart === false) { - stopAutoplay(); - } - - // --- Drag to reposition --- - (function () { - var savedPos = (function () { - var raw = tryLoadValue(getPassPositionStorageKey(kind)); - if (!raw) { return null; } - try { - var p = JSON.parse(raw); - return p && Number.isFinite(p.right) && Number.isFinite(p.bottom) ? p : null; - } catch (e) { return null; } - })(); - - function applyPosition(right, bottom) { - var maxRight = window.innerWidth - 40; - var maxBottom = window.innerHeight - 40; - var r = Math.min(Math.max(right, 0), maxRight); - var b = Math.min(Math.max(bottom, 0), maxBottom); - panel.style.right = r + "px"; - panel.style.bottom = b + "px"; - } - - function resetPosition() { - panel.style.right = ""; - panel.style.bottom = ""; - try { localStorage.removeItem(getPassPositionStorageKey(kind)); } catch (e) { /* */ } - } - - if (savedPos) { - applyPosition(savedPos.right, savedPos.bottom); - } - - var dragState = null; - - dragHead.addEventListener("mousedown", function (evt) { - if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT" || evt.target.tagName === "SUMMARY")) { - return; - } - evt.preventDefault(); - var rect = panel.getBoundingClientRect(); - dragState = { - startX: evt.clientX, - startY: evt.clientY, - startRight: window.innerWidth - rect.right, - startBottom: window.innerHeight - rect.bottom - }; - panel.classList.add("is-dragging"); - }); - - document.addEventListener("mousemove", function (evt) { - if (!dragState) { return; } - var dx = evt.clientX - dragState.startX; - var dy = evt.clientY - dragState.startY; - applyPosition(dragState.startRight - dx, dragState.startBottom - dy); - }); - - document.addEventListener("mouseup", function (evt) { - if (!dragState) { return; } - panel.classList.remove("is-dragging"); - var rect = panel.getBoundingClientRect(); - var finalRight = window.innerWidth - rect.right; - var finalBottom = window.innerHeight - rect.bottom; - trySaveValue(getPassPositionStorageKey(kind), JSON.stringify({ right: finalRight, bottom: finalBottom })); - dragState = null; - }); - - dragHead.addEventListener("touchstart", function (evt) { - if (evt.touches.length !== 1) { return; } - if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT" || evt.target.tagName === "SUMMARY")) { - return; - } - var touch = evt.touches[0]; - var rect = panel.getBoundingClientRect(); - dragState = { - startX: touch.clientX, - startY: touch.clientY, - startRight: window.innerWidth - rect.right, - startBottom: window.innerHeight - rect.bottom - }; - panel.classList.add("is-dragging"); - }, { passive: true }); - - document.addEventListener("touchmove", function (evt) { - if (!dragState) { return; } - if (evt.cancelable) { evt.preventDefault(); } - var touch = evt.touches[0]; - var dx = touch.clientX - dragState.startX; - var dy = touch.clientY - dragState.startY; - applyPosition(dragState.startRight - dx, dragState.startBottom - dy); - }, { passive: false }); - - document.addEventListener("touchend", function () { - if (!dragState) { return; } - panel.classList.remove("is-dragging"); - var rect = panel.getBoundingClientRect(); - var finalRight = window.innerWidth - rect.right; - var finalBottom = window.innerHeight - rect.bottom; - trySaveValue(getPassPositionStorageKey(kind), JSON.stringify({ right: finalRight, bottom: finalBottom })); - dragState = null; - }); - - dragHead.addEventListener("dblclick", function (evt) { - if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT")) { - return; - } - resetPosition(); - }); - })(); - - // --- Shortcut cheat-sheet overlay --- - var shortcutOverlay = null; - - function showShortcutOverlay() { - if (shortcutOverlay) { return; } - shortcutOverlay = document.createElement("div"); - shortcutOverlay.className = "site-pass-shortcut-overlay"; - shortcutOverlay.setAttribute("role", "dialog"); - shortcutOverlay.setAttribute("aria-modal", "true"); - shortcutOverlay.setAttribute("aria-label", "Keyboard shortcuts"); - shortcutOverlay.innerHTML = "" - + "
    " - + "
    " - + "Keyboard Shortcuts" - + "" - + "
    " - + "
    " - + "
    Alt+Shift+P
    Toggle panel
    " - + "
    Alt+Shift+A
    Toggle auto sweep
    " - + "
    Alt+Shift+C
    Close panel
    " - + "
    Alt+Shift+1–4
    Presets 25 / 50 / 75 / 100
    " - + "
    Alt+Shift+0
    Reset to 100
    " - + "
    Alt+Shift+5
    Apply snapshot A
    " - + "
    Alt+Shift+6
    Apply snapshot B
    " - + "
    Alt+Shift+7
    Flip A/B
    " - + "
    Alt+Shift+Z
    Undo
    " - + "
    Alt+Shift+Y
    Redo
    " - + "
    Alt+Shift+U
    Apply URL state
    " - + "
    Alt+Shift+L
    Push URL state
    " - + "
    Alt+Shift+I
    Focus URL input
    " - + "
    Alt+Shift+H
    Clear history log
    " - + "
    Alt+Shift+/
    Show this overlay
    " - + "
    Arrow Up / Right
    +1 / +10 passes
    " - + "
    Arrow Down / Left
    −1 / −10 passes
    " - + "
    " - + "

    Double-click panel header to reset position

    " - + "
    "; - document.body.appendChild(shortcutOverlay); - var closeBtn = shortcutOverlay.querySelector(".site-pass-shortcut-overlay__close"); - if (closeBtn) { closeBtn.focus(); } - - function dismissOverlay(evt) { - if (!shortcutOverlay) { return; } - var box = shortcutOverlay.querySelector(".site-pass-shortcut-overlay__box"); - if (!box || !box.contains(evt.target)) { - hideShortcutOverlay(); - } - } - shortcutOverlay._dismiss = dismissOverlay; - shortcutOverlay.addEventListener("click", dismissOverlay); - if (closeBtn) { - closeBtn.addEventListener("click", function () { hideShortcutOverlay(); }); - } - } - - function hideShortcutOverlay() { - if (!shortcutOverlay) { return; } - shortcutOverlay.remove(); - shortcutOverlay = null; - } - - var sectionEls = panel.querySelectorAll(".site-pass-debug__section"); - var savedSections = (function () { - var raw = tryLoadValue(getPassSectionsStorageKey(kind)); - if (!raw) { return null; } - try { return JSON.parse(raw); } catch (e) { return null; } - })(); - - if (savedSections) { - sectionEls.forEach(function (el) { - var name = el.getAttribute("data-section"); - if (name && Object.prototype.hasOwnProperty.call(savedSections, name)) { - if (savedSections[name]) { - el.setAttribute("open", ""); - } else { - el.removeAttribute("open"); - } - } - }); - } - - // Animated details open/close - function animateDetails(el, opening) { - var inner = el.querySelector("summary + *") || el.querySelector("summary"); - if (!inner) { - return; - } - - // Respect reduced-motion preference — skip animation - var prefersReduced = window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches; - - if (el._animating) { - clearTimeout(el._animating); - el._animating = null; - if (el._innerEl) { el._innerEl.style.height = ""; el._innerEl.style.overflow = ""; } - } - - var wrapper = el._innerEl; - if (!wrapper) { - // Wrap all non-summary children in an animate wrapper once - wrapper = document.createElement("div"); - wrapper.className = "site-pass-debug__section-body"; - var children = Array.prototype.slice.call(el.childNodes); - children.forEach(function (node) { - if (node.nodeType === 1 && node.tagName === "SUMMARY") { return; } - wrapper.appendChild(node); - }); - el.appendChild(wrapper); - el._innerEl = wrapper; - } - - if (prefersReduced) { - wrapper.style.height = ""; - wrapper.style.overflow = ""; - return; - } - - var targetHeight = opening ? wrapper.scrollHeight + "px" : "0px"; - wrapper.style.overflow = "hidden"; - wrapper.style.height = opening ? "0px" : wrapper.scrollHeight + "px"; - - // Force reflow - void wrapper.offsetHeight; - wrapper.style.transition = "height 0.2s ease"; - wrapper.style.height = targetHeight; - - var DURATION = 210; - el._animating = setTimeout(function () { - wrapper.style.transition = ""; - wrapper.style.overflow = ""; - if (opening) { wrapper.style.height = ""; } - el._animating = null; - }, DURATION); - } - - sectionEls.forEach(function (el) { - // Intercept toggle: cancel default, run animation, then commit open state - el.addEventListener("click", function (evt) { - if (evt.target && evt.target.tagName === "SUMMARY") { - evt.preventDefault(); - var willOpen = !el.open; - if (willOpen) { el.setAttribute("open", ""); } - animateDetails(el, willOpen); - if (!willOpen) { - setTimeout(function () { el.removeAttribute("open"); }, 200); - } - var state = {}; - sectionEls.forEach(function (s) { - var sName = s.getAttribute("data-section"); - if (sName) { state[sName] = willOpen ? true : false; } - }); - // Only update the toggled section - var secName = el.getAttribute("data-section"); - var fullState = {}; - sectionEls.forEach(function (s) { - var n = s.getAttribute("data-section"); - if (n) { fullState[n] = n === secName ? willOpen : s.open; } - }); - trySaveValue(getPassSectionsStorageKey(kind), JSON.stringify(fullState)); - } - }); - }); - - // --- Panel width resize handle --- - (function () { - var PANEL_MIN_W = 220; - var PANEL_MAX_W = Math.min(560, window.innerWidth - 28); - var savedWidth = (function () { - var raw = tryLoadValue(getPassWidthStorageKey(kind)); - var v = raw ? parseFloat(raw) : NaN; - return Number.isFinite(v) ? v : null; - })(); - - var innerPanel = panel.querySelector(".site-pass-debug__panel"); - - function applyWidth(w) { - var clamped = Math.min(Math.max(w, PANEL_MIN_W), Math.min(560, window.innerWidth - 28)); - innerPanel.style.width = clamped + "px"; - return clamped; - } - - function resetWidth() { - innerPanel.style.width = ""; - try { localStorage.removeItem(getPassWidthStorageKey(kind)); } catch (e) { /* */ } - } - - if (savedWidth) { - applyWidth(savedWidth); - } - - var resizeDrag = null; - - resizeHandle.addEventListener("mousedown", function (evt) { - evt.preventDefault(); - var rect = innerPanel.getBoundingClientRect(); - resizeDrag = { startX: evt.clientX, startWidth: rect.width }; - panel.classList.add("is-resizing"); - }); - - document.addEventListener("mousemove", function (evt) { - if (!resizeDrag) { return; } - // Handle is on the left edge — drag left = wider, drag right = narrower - var dx = resizeDrag.startX - evt.clientX; - applyWidth(resizeDrag.startWidth + dx); - }); - - document.addEventListener("mouseup", function () { - if (!resizeDrag) { return; } - panel.classList.remove("is-resizing"); - var finalWidth = parseFloat(innerPanel.style.width) || null; - if (finalWidth) { - trySaveValue(getPassWidthStorageKey(kind), String(finalWidth)); - } - resizeDrag = null; - }); - - resizeHandle.addEventListener("dblclick", function () { - resetWidth(); - }); - })(); - - if (exportJsonButton) { - exportJsonButton.addEventListener("click", function () { - var json = JSON.stringify(serializePassState(), null, 2); - if (navigator.clipboard && navigator.clipboard.writeText) { - navigator.clipboard.writeText(json).then(function () { - var orig = exportJsonButton.textContent; - exportJsonButton.textContent = "Copied!"; - setTimeout(function () { exportJsonButton.textContent = orig; }, 1200); - }).catch(function () { - shareInput.value = json; - shareInput.select(); - }); - } else { - shareInput.value = json; - shareInput.select(); - } - }); - } - - // --- Appearance controls (opacity / blur) --- - (function () { - var innerPanel = panel.querySelector(".site-pass-debug__panel"); - var blurEnabled = true; - - var saved = (function () { - var raw = tryLoadValue(getPassPanelStyleStorageKey(kind)); - if (!raw) { return null; } - try { return JSON.parse(raw); } catch (e) { return null; } - })(); - - var currentOpacity = (saved && Number.isFinite(saved.opacity)) ? saved.opacity : 100; - var currentBlur = (saved && typeof saved.blur === "boolean") ? saved.blur : true; - blurEnabled = currentBlur; - - function applyAppearance(opacity, blur) { - innerPanel.style.opacity = (opacity / 100).toFixed(2); - innerPanel.style.backdropFilter = blur ? "blur(10px)" : "none"; - innerPanel.style.webkitBackdropFilter = blur ? "blur(10px)" : "none"; - } - - function syncAppearanceUI() { - if (opacityInput) { opacityInput.value = String(currentOpacity); } - if (toggleBlurButton) { - toggleBlurButton.textContent = blurEnabled ? "Blur On" : "Blur Off"; - toggleBlurButton.classList.toggle("is-active", blurEnabled); - } - } - - function persistAppearance() { - trySaveValue(getPassPanelStyleStorageKey(kind), JSON.stringify({ opacity: currentOpacity, blur: blurEnabled })); - } - - applyAppearance(currentOpacity, blurEnabled); - syncAppearanceUI(); - - if (opacityInput) { - opacityInput.addEventListener("change", function () { - var v = Math.min(100, Math.max(20, Number(opacityInput.value) || 100)); - currentOpacity = v; - opacityInput.value = String(v); - applyAppearance(currentOpacity, blurEnabled); - persistAppearance(); - }); - opacityInput.addEventListener("input", function () { - var v = Math.min(100, Math.max(20, Number(opacityInput.value) || 100)); - applyAppearance(v, blurEnabled); - }); - } - - if (toggleBlurButton) { - toggleBlurButton.addEventListener("click", function () { - blurEnabled = !blurEnabled; - applyAppearance(currentOpacity, blurEnabled); - syncAppearanceUI(); - persistAppearance(); - }); - } - - if (resetAppearanceButton) { - resetAppearanceButton.addEventListener("click", function () { - currentOpacity = 100; - blurEnabled = true; - applyAppearance(currentOpacity, blurEnabled); - syncAppearanceUI(); - try { localStorage.removeItem(getPassPanelStyleStorageKey(kind)); } catch (e) { /* */ } - }); - } - })(); - } - - function ensureSkipLink() { - if (document.querySelector(".site-upgrade-skip")) { - return; - } - - var candidates = ["main", "#mainContent", ".main", ".page", ".container", ".content"]; - var target = null; - - for (var i = 0; i < candidates.length; i += 1) { - var element = document.querySelector(candidates[i]); - if (element) { - target = element; - break; - } - } - - if (!target) { - return; - } - - if (!target.id) { - target.id = "site-upgrade-main"; - } - - var skip = document.createElement("a"); - skip.className = "site-upgrade-skip"; - skip.href = "#" + target.id; - skip.textContent = "Skip to main content"; - document.body.prepend(skip); - } - - function upgradeExternalLinks() { - var links = document.querySelectorAll('a[target="_blank"]'); - links.forEach(function (link) { - var rel = (link.getAttribute("rel") || "").toLowerCase(); - if (!rel.includes("noopener")) { - link.setAttribute("rel", (rel ? rel + " " : "") + "noopener noreferrer"); - } - }); - } - - function applyRevealAnimations() { - if (window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches) { - return; - } - - var selectors = [ - "section", - ".card", - ".hero-card", - ".feature-card", - ".system-card", - ".welcome-card", - ".route-card", - ".signal-card", - ".plan-card", - ".link-card", - ".sidebar-card", - ".content-shell", - ".category-block", - ".flow-card", - ".search-panel", - ".resource-group", - ".terminal", - ".run-copy", - ".run-aside", - ".hero-banner", - ".glass-panel", - ".jobs-section", - ".gpu-status", - ".chat-container", - ".stage-container", - ".object-manager", - ".page-link", - ".job-item", - ".metric-card", - ".category-card", - ".product-card" - ]; - - var seen = new Set(); - selectors.forEach(function (selector) { - document.querySelectorAll(selector).forEach(function (node) { - seen.add(node); - }); - }); - - var nodes = Array.from(seen).filter(function (node) { - return node && (node.childElementCount > 0 || (node.textContent || "").trim().length > 0); - }); - - if (!nodes.length) { - return; - } - - nodes.forEach(function (node, index) { - node.classList.add("site-upgrade-reveal"); - node.style.setProperty("--site-upgrade-delay", Math.min(index * 28, 420) + "ms"); - }); - - if (!("IntersectionObserver" in window)) { - nodes.forEach(function (node) { - node.classList.add("is-visible"); - }); - return; - } - - var observer = new IntersectionObserver(function (entries, obs) { - entries.forEach(function (entry) { - if (entry.isIntersecting) { - entry.target.classList.add("is-visible"); - obs.unobserve(entry.target); - } - }); - }, { - threshold: 0.12, - rootMargin: "0px 0px -8% 0px" - }); - - nodes.forEach(function (node) { - observer.observe(node); - }); - } - - function initUpgrade() { - var kind = applySiteKindClass(); - var configuredPasses = getConfiguredPassCount(kind); - var initialPasses = runPolishPasses(kind, configuredPasses); - ensureSkipLink(); - upgradeExternalLinks(); - applyRevealAnimations(); - createPassDebugPanel(kind, initialPasses); - } - - if (document.readyState === "loading") { - document.addEventListener("DOMContentLoaded", initUpgrade, { once: true }); - } else { - initUpgrade(); - } -})(); +(function () { + "use strict"; + + if (window.__ariaSiteUpgradeLoaded) { + return; + } + window.__ariaSiteUpgradeLoaded = true; + + document.documentElement.classList.add("site-upgraded"); + + var PASS_MIN = 1; + var PASS_MAX = 100; + var PASS_DEFAULT = 100; + var PASS_STORAGE_KEY = "siteUpgradePasses"; + var DEBUG_OPEN_STORAGE_KEY = "siteUpgradePassDebugOpen"; + var PASS_AUTOPLAY_INTERVAL_MS = 70; + var PASS_AUTOPLAY_MIN_MS = 25; + var PASS_AUTOPLAY_MAX_MS = 1000; + var PASS_AUTOPLAY_STORAGE_KEY = "siteUpgradePassSweepMs"; + var PASS_SNAPSHOT_STORAGE_KEY = "siteUpgradePassSnapshots"; + var PASS_UNDO_STORAGE_KEY = "siteUpgradePassUndoStack"; + var PASS_SECTIONS_STORAGE_KEY = "siteUpgradePassSections"; + var PASS_POSITION_STORAGE_KEY = "siteUpgradePassPosition"; + var PASS_WIDTH_STORAGE_KEY = "siteUpgradePassWidth"; + var PASS_PANEL_STYLE_STORAGE_KEY = "siteUpgradePassPanelStyle"; + var PASS_HISTORY_MAX = 10; + + function detectSiteKind() { + var path = (window.location.pathname || "").toLowerCase(); + var href = (window.location.href || "").toLowerCase(); + var target = path + " " + href; + + if (target.includes("/chat/") || target.includes("chat/index") || target.endsWith("/chat")) { + return "chat"; + } + + if (target.includes("/aria/") || target.includes("auto-execute") || target.includes("aria/index")) { + return "aria"; + } + + if (target.includes("/dashboard/") || target.includes("dashboard/index") || target.includes("dashboard/hub")) { + return "dashboard"; + } + + if (target.includes("/store/") || target.includes("products") || target.includes("product.html")) { + return "store"; + } + + if (target.includes("documentation") || target.includes("/docs/") || target.endsWith("/docs") || target.includes("readme_pages")) { + return "docs"; + } + + if (target.includes("/quantum/") || target.includes("/monetization/") || target.includes("llm-maker") || target.includes("/mount/")) { + return "dashboard"; + } + + return "core"; + } + + function applySiteKindClass() { + var kind = detectSiteKind(); + var root = document.documentElement; + var body = document.body; + + root.setAttribute("data-site-kind", kind); + if (body) { + body.setAttribute("data-site-kind", kind); + body.classList.add("site-kind-" + kind); + } + + return kind; + } + + function clamp(value, min, max) { + return Math.max(min, Math.min(max, value)); + } + + function getPassStorageKey(kind) { + return PASS_STORAGE_KEY + ":" + kind; + } + + function getPassSpeedStorageKey(kind) { + return PASS_AUTOPLAY_STORAGE_KEY + ":" + kind; + } + + function getPassSnapshotStorageKey(kind) { + return PASS_SNAPSHOT_STORAGE_KEY + ":" + kind; + } + + function getPassUndoStorageKey(kind) { + return PASS_UNDO_STORAGE_KEY + ":" + kind; + } + + function getPassSectionsStorageKey(kind) { + return PASS_SECTIONS_STORAGE_KEY + ":" + kind; + } + + function getPassPositionStorageKey(kind) { + return PASS_POSITION_STORAGE_KEY + ":" + kind; + } + + function getPassWidthStorageKey(kind) { + return PASS_WIDTH_STORAGE_KEY + ":" + kind; + } + + function getPassPanelStyleStorageKey(kind) { + return PASS_PANEL_STYLE_STORAGE_KEY + ":" + kind; + } + + function shouldOpenDebugFromQuery() { + var params = new URLSearchParams(window.location.search || ""); + var raw = params.get("passpanel") || params.get("debugpasses") || params.get("passdebug"); + if (!raw) { + return null; + } + + var value = String(raw).toLowerCase(); + if (value === "0" || value === "false" || value === "off" || value === "hide") { + return false; + } + + return true; + } + + function shouldAutoplayFromQuery() { + var params = new URLSearchParams(window.location.search || ""); + var raw = params.get("passauto") || params.get("passautoplay") || params.get("autosweep"); + if (!raw) { + return null; + } + + return toOptionalBool(raw); + } + + function getConfiguredPassCount(kind) { + var params = new URLSearchParams(window.location.search || ""); + var stateFromQuery = getPassStateFromQuery(); + var queryRaw = params.get("passes") || params.get("pass") || (stateFromQuery && stateFromQuery.passCount); + var savedRaw = null; + + try { + if (window.localStorage) { + savedRaw = localStorage.getItem(getPassStorageKey(kind)) || localStorage.getItem(PASS_STORAGE_KEY); + } + } catch (error) { + savedRaw = null; + } + + var parsed = Number(queryRaw || savedRaw || PASS_DEFAULT); + if (!Number.isFinite(parsed)) { + parsed = PASS_DEFAULT; + } + + return Math.round(clamp(parsed, PASS_MIN, PASS_MAX)); + } + + function getConfiguredAutoplayInterval(kind) { + var params = new URLSearchParams(window.location.search || ""); + var stateFromQuery = getPassStateFromQuery(); + var queryRaw = params.get("passsweepms") || params.get("passautoplayms") || params.get("passspeed") || (stateFromQuery && stateFromQuery.sweepMs); + var savedRaw = null; + + try { + if (window.localStorage) { + savedRaw = localStorage.getItem(getPassSpeedStorageKey(kind)) || localStorage.getItem(PASS_AUTOPLAY_STORAGE_KEY); + } + } catch (error) { + savedRaw = null; + } + + var parsed = Number(queryRaw || savedRaw || PASS_AUTOPLAY_INTERVAL_MS); + if (!Number.isFinite(parsed)) { + parsed = PASS_AUTOPLAY_INTERVAL_MS; + } + + return Math.round(clamp(parsed, PASS_AUTOPLAY_MIN_MS, PASS_AUTOPLAY_MAX_MS)); + } + + function normalizePassSource(source) { + var normalized = String(source || "manual"); + + if (normalized.indexOf("shortcut-") === 0) { + normalized = "key " + normalized.slice("shortcut-".length); + } + + return normalized.replace(/-/g, " "); + } + + function toValidPass(rawValue) { + var parsed = Number(rawValue); + if (!Number.isFinite(parsed)) { + return null; + } + + return Math.round(clamp(parsed, PASS_MIN, PASS_MAX)); + } + + function toValidSpeed(rawValue) { + var parsed = Number(rawValue); + if (!Number.isFinite(parsed)) { + return null; + } + + return Math.round(clamp(parsed, PASS_AUTOPLAY_MIN_MS, PASS_AUTOPLAY_MAX_MS)); + } + + function toOptionalBool(rawValue) { + if (typeof rawValue === "boolean") { + return rawValue; + } + + if (rawValue === null || rawValue === undefined) { + return null; + } + + var normalized = String(rawValue).toLowerCase(); + if (normalized === "1" || normalized === "true" || normalized === "on" || normalized === "open" || normalized === "yes") { + return true; + } + + if (normalized === "0" || normalized === "false" || normalized === "off" || normalized === "close" || normalized === "closed" || normalized === "hide" || normalized === "no") { + return false; + } + + return null; + } + + function normalizePassState(rawState) { + if (!rawState || typeof rawState !== "object") { + return null; + } + + var passCount = toValidPass(rawState.passCount !== undefined ? rawState.passCount : (rawState.pass !== undefined ? rawState.pass : rawState.p)); + var sweepMs = toValidSpeed(rawState.sweepMs !== undefined ? rawState.sweepMs : (rawState.speed !== undefined ? rawState.speed : rawState.ms)); + var snapshotA = toValidPass(rawState.snapshotA !== undefined ? rawState.snapshotA : rawState.a); + var snapshotB = toValidPass(rawState.snapshotB !== undefined ? rawState.snapshotB : rawState.b); + var open = toOptionalBool(rawState.open); + var auto = toOptionalBool(rawState.auto); + + if (passCount === null && sweepMs === null && snapshotA === null && snapshotB === null && open === null && auto === null) { + return null; + } + + return { + passCount: passCount, + sweepMs: sweepMs, + snapshotA: snapshotA, + snapshotB: snapshotB, + open: open, + auto: auto + }; + } + + function getPassStateFromSearchParams(params) { + if (!params) { + return null; + } + + var raw = params.get("passstate"); + if (!raw) { + return null; + } + + try { + return normalizePassState(JSON.parse(raw)); + } catch (error) { + return null; + } + } + + function getPassStateFromQuery() { + return getPassStateFromSearchParams(new URLSearchParams(window.location.search || "")); + } + + function parsePassStateFromInput(rawInput) { + if (!rawInput) { + return null; + } + + var value = String(rawInput).trim(); + if (!value) { + return null; + } + + try { + var url = new URL(value, window.location.href); + var fromUrl = getPassStateFromSearchParams(url.searchParams); + if (fromUrl) { + return fromUrl; + } + } catch (error) { + // Not a URL, continue parsing. + } + + try { + var queryLike = value.charAt(0) === "?" ? value.slice(1) : value; + var params = new URLSearchParams(queryLike); + var fromParams = getPassStateFromSearchParams(params); + if (fromParams) { + return fromParams; + } + } catch (error) { + // Not query-like content, continue parsing. + } + + try { + return normalizePassState(JSON.parse(value)); + } catch (error) { + return null; + } + } + + function interpolate(start, end, t) { + return start + (end - start) * t; + } + + function getKindShadowColor(kind) { + switch (kind) { + case "chat": + return [15, 147, 197]; + case "aria": + return [46, 157, 131]; + case "dashboard": + return [47, 123, 231]; + case "store": + return [208, 96, 47]; + case "docs": + return [24, 127, 120]; + default: + return [19, 36, 55]; + } + } + + function setPassVariables(kind, passIndex, totalPasses) { + var root = document.documentElement; + var t = clamp(passIndex / totalPasses, 0, 1); + var color = getKindShadowColor(kind); + + var gridOpacity = interpolate(0.24, 0.46, t); + var radiusBoost = interpolate(0, 7, t); + var focusWidth = interpolate(2.5, 4.2, t); + var letterShift = interpolate(-0.012, -0.036, t); + var cardRaise = interpolate(0.25, 1.9, t); + var revealMs = Math.round(interpolate(430, 690, t)); + + var softY = interpolate(8, 24, t); + var softBlur = interpolate(22, 54, t); + var softAlpha = interpolate(0.14, 0.33, t); + + var strongY = interpolate(14, 38, t); + var strongBlur = interpolate(34, 78, t); + var strongAlpha = interpolate(0.2, 0.42, t); + + root.style.setProperty("--pass-intensity", t.toFixed(4)); + root.style.setProperty("--pass-grid-opacity", gridOpacity.toFixed(3)); + root.style.setProperty("--pass-radius-boost", radiusBoost.toFixed(2) + "px"); + root.style.setProperty("--pass-focus-width", focusWidth.toFixed(2) + "px"); + root.style.setProperty("--pass-letter-shift", letterShift.toFixed(4) + "em"); + root.style.setProperty("--pass-card-raise", cardRaise.toFixed(2) + "px"); + root.style.setProperty("--pass-reveal-ms", revealMs + "ms"); + root.style.setProperty( + "--pass-shadow-soft", + "0 " + softY.toFixed(2) + "px " + softBlur.toFixed(2) + "px rgba(" + color[0] + ", " + color[1] + ", " + color[2] + ", " + softAlpha.toFixed(3) + ")" + ); + root.style.setProperty( + "--pass-shadow-strong", + "0 " + strongY.toFixed(2) + "px " + strongBlur.toFixed(2) + "px rgba(" + color[0] + ", " + color[1] + ", " + color[2] + ", " + strongAlpha.toFixed(3) + ")" + ); + } + + function emitPassChange(kind, passCount, source) { + var detail = { + kind: kind, + passCount: passCount, + passes: passCount, + source: source || "manual" + }; + + document.dispatchEvent(new CustomEvent("site-upgrade:pass-change", { detail: detail })); + window.dispatchEvent(new CustomEvent("site-upgrade:pass-change", { detail: detail })); + } + + function runPolishPasses(kind, requestedPassCount, source) { + var passCount = Math.round(clamp(Number(requestedPassCount), PASS_MIN, PASS_MAX)); + + for (var pass = 1; pass <= passCount; pass += 1) { + setPassVariables(kind, pass, passCount); + } + + document.documentElement.setAttribute("data-polish-passes", String(passCount)); + if (document.body) { + document.body.setAttribute("data-polish-passes", String(passCount)); + } + + emitPassChange(kind, passCount, source); + + return passCount; + } + + function trySaveValue(key, value) { + try { + if (window.localStorage) { + localStorage.setItem(key, value); + } + } catch (error) { + // Ignore storage errors in strict/private browsing contexts. + } + } + + function tryLoadValue(key) { + try { + return window.localStorage ? localStorage.getItem(key) : null; + } catch (error) { + return null; + } + } + + function createPassDebugPanel(kind, initialPassCount) { + if (!document.body || document.querySelector(".site-pass-debug")) { + return; + } + + var panel = document.createElement("aside"); + panel.className = "site-pass-debug"; + panel.setAttribute("aria-label", "Polish pass controls"); + panel.innerHTML = "" + + "" + + ""; + + document.body.appendChild(panel); + + var toggle = panel.querySelector(".site-pass-debug__toggle"); + var bodyPanel = panel.querySelector(".site-pass-debug__panel"); + var kindChip = panel.querySelector(".site-pass-debug__kind"); + var range = panel.querySelector("#site-pass-debug-range"); + var number = panel.querySelector("#site-pass-debug-number"); + var speedInput = panel.querySelector("#site-pass-debug-speed"); + var shareInput = panel.querySelector("#site-pass-debug-share"); + var value = panel.querySelector(".site-pass-debug__value"); + var compareValue = panel.querySelector(".site-pass-debug__compare"); + var stackValue = panel.querySelector(".site-pass-debug__stack"); + var sweepValue = panel.querySelector(".site-pass-debug__sweep"); + var sourceValue = panel.querySelector(".site-pass-debug__source"); + var timeline = panel.querySelector(".site-pass-debug__timeline"); + var stepButtons = panel.querySelectorAll(".site-pass-debug__btn[data-step]"); + var presetButtons = panel.querySelectorAll(".site-pass-debug__btn[data-preset]"); + var resetButton = panel.querySelector('.site-pass-debug__btn[data-action="reset"]'); + var closeButton = panel.querySelector('.site-pass-debug__btn[data-action="close"]'); + var defaultButton = panel.querySelector('.site-pass-debug__btn[data-action="default"]'); + var autoplayButton = panel.querySelector('.site-pass-debug__btn[data-action="autoplay"]'); + var undoButton = panel.querySelector('.site-pass-debug__btn[data-action="undo"]'); + var redoButton = panel.querySelector('.site-pass-debug__btn[data-action="redo"]'); + var saveAButton = panel.querySelector('.site-pass-debug__btn[data-action="save-a"]'); + var saveBButton = panel.querySelector('.site-pass-debug__btn[data-action="save-b"]'); + var applyAButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-a"]'); + var applyBButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-b"]'); + var toggleAbButton = panel.querySelector('.site-pass-debug__btn[data-action="toggle-ab"]'); + var copyUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="copy-url"]'); + var applyUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-url"]'); + var pushUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="push-url"]'); + var clearHistoryButton = panel.querySelector('.site-pass-debug__btn[data-action="clear-history"]'); + var exportJsonButton = panel.querySelector('.site-pass-debug__btn[data-action="export-json"]'); + var toggleBlurButton = panel.querySelector('.site-pass-debug__btn[data-action="toggle-blur"]'); + var resetAppearanceButton = panel.querySelector('.site-pass-debug__btn[data-action="reset-appearance"]'); + var opacityInput = panel.querySelector("#site-pass-debug-opacity"); + var badge = panel.querySelector(".site-pass-debug__badge"); + var dragHead = panel.querySelector(".site-pass-debug__head"); + var resizeHandle = panel.querySelector(".site-pass-debug__resize-handle"); + + var queryState = getPassStateFromQuery(); + var queryAuto = shouldAutoplayFromQuery(); + var shouldAutoStart = queryState && queryState.auto !== null ? queryState.auto : queryAuto; + var configuredPasses = getConfiguredPassCount(kind); + var currentPasses = initialPassCount; + var isOpen = false; + var isAutoplay = false; + var autoDirection = 1; + var autoplayTimer = null; + var autoplayIntervalMs = queryState && Number.isFinite(queryState.sweepMs) + ? queryState.sweepMs + : getConfiguredAutoplayInterval(kind); + var undoStack = (function () { + var raw = tryLoadValue(getPassUndoStorageKey(kind)); + if (!raw) { return []; } + try { + var parsed = JSON.parse(raw); + return Array.isArray(parsed) + ? parsed.filter(function (v) { return Number.isFinite(v); }) + : []; + } catch (e) { return []; } + })(); + var redoStack = []; + var passHistory = []; + var snapshots = (function () { + var raw = tryLoadValue(getPassSnapshotStorageKey(kind)); + if (!raw) { + return { a: null, b: null }; + } + + try { + var parsed = JSON.parse(raw); + return { + a: toValidPass(parsed && parsed.a), + b: toValidPass(parsed && parsed.b) + }; + } catch (error) { + return { a: null, b: null }; + } + })(); + + if (queryState && Number.isFinite(queryState.passCount)) { + currentPasses = queryState.passCount; + } + + if (queryState && queryState.snapshotA !== null) { + snapshots.a = queryState.snapshotA; + } + + if (queryState && queryState.snapshotB !== null) { + snapshots.b = queryState.snapshotB; + } + + var savedOpen = tryLoadValue(DEBUG_OPEN_STORAGE_KEY); + var queryOpen = shouldOpenDebugFromQuery(); + if (queryState && queryState.open !== null) { + isOpen = queryState.open; + } else if (queryOpen !== null) { + isOpen = queryOpen; + } else if (savedOpen === "1") { + isOpen = true; + } + + kindChip.textContent = kind.toUpperCase(); + + function persistPass(passCount) { + trySaveValue(getPassStorageKey(kind), String(passCount)); + trySaveValue(PASS_STORAGE_KEY, String(passCount)); + } + + function persistSnapshots() { + trySaveValue(getPassSnapshotStorageKey(kind), JSON.stringify({ + a: snapshots.a, + b: snapshots.b + })); + } + + function persistUndoStacks() { + trySaveValue(getPassUndoStorageKey(kind), JSON.stringify(undoStack)); + } + + function serializePassState() { + return { + pass: currentPasses, + speed: autoplayIntervalMs, + a: snapshots.a, + b: snapshots.b, + open: isOpen, + auto: isAutoplay + }; + } + + function buildShareUrl() { + var url = new URL(window.location.href); + var params = url.searchParams; + var cleanupKeys = [ + "pass", + "passes", + "passpanel", + "debugpasses", + "passdebug", + "passsweepms", + "passautoplayms", + "passspeed", + "passauto", + "passautoplay", + "autosweep", + "passstate" + ]; + + cleanupKeys.forEach(function (key) { + params.delete(key); + }); + + params.set("passstate", JSON.stringify(serializePassState())); + return url.toString(); + } + + function syncShareUrlField() { + if (!shareInput) { + return; + } + + shareInput.value = buildShareUrl(); + } + + function syncSourceLabel(source) { + sourceValue.textContent = normalizePassSource(source); + } + + function syncSweepLabel() { + if (!sweepValue) { + return; + } + + sweepValue.textContent = (isAutoplay ? "running" : "idle") + " @ " + autoplayIntervalMs + "ms"; + } + + function syncUndoState() { + if (!stackValue) { + return; + } + + stackValue.textContent = "undo " + undoStack.length + " | redo " + redoStack.length; + undoButton.disabled = undoStack.length === 0; + redoButton.disabled = redoStack.length === 0; + } + + function shouldTrackUndo(source, nextPass) { + if (!Number.isFinite(nextPass) || nextPass === currentPasses) { + return false; + } + + var normalized = String(source || "panel"); + if (normalized === "autoplay" || normalized.indexOf("undo") !== -1 || normalized.indexOf("redo") !== -1 || normalized === "state-init" || normalized === "event") { + return false; + } + + return true; + } + + function pushUndo(previousPass) { + undoStack.push(previousPass); + if (undoStack.length > PASS_MAX) { + undoStack.shift(); + } + redoStack = []; + persistUndoStacks(); + syncUndoState(); + } + + function runUndo(source) { + if (!undoStack.length) { + return; + } + + var targetPass = undoStack.pop(); + if (targetPass === currentPasses) { + persistUndoStacks(); + syncUndoState(); + return; + } + + redoStack.push(currentPasses); + if (redoStack.length > PASS_MAX) { + redoStack.shift(); + } + + persistUndoStacks(); + applyPassCount(targetPass, true, source || "undo"); + syncUndoState(); + } + + function runRedo(source) { + if (!redoStack.length) { + return; + } + + var targetPass = redoStack.pop(); + if (targetPass === currentPasses) { + persistUndoStacks(); + syncUndoState(); + return; + } + + undoStack.push(currentPasses); + if (undoStack.length > PASS_MAX) { + undoStack.shift(); + } + + persistUndoStacks(); + applyPassCount(targetPass, true, source || "redo"); + syncUndoState(); + } + + function renderHistory() { + if (!timeline) { + return; + } + + timeline.innerHTML = ""; + + if (!passHistory.length) { + var empty = document.createElement("li"); + empty.className = "site-pass-debug__timeline-item is-empty"; + empty.textContent = "No pass changes yet"; + timeline.appendChild(empty); + return; + } + + passHistory.forEach(function (entry) { + var item = document.createElement("li"); + item.className = "site-pass-debug__timeline-item site-pass-debug__timeline-item--restore"; + item.tabIndex = 0; + item.setAttribute("role", "button"); + item.title = "Restore pass " + entry.pass; + item.setAttribute("aria-label", "Restore " + entry.pass + " passes (" + entry.source + ")"); + + var stamp = document.createElement("span"); + stamp.className = "site-pass-debug__timeline-time"; + stamp.textContent = entry.stamp; + + var detail = document.createElement("span"); + detail.className = "site-pass-debug__timeline-detail"; + var deltaText = entry.delta === null ? "" : " (" + (entry.delta >= 0 ? "+" : "") + entry.delta + ")"; + detail.textContent = entry.source + " -> " + entry.pass + deltaText; + + item.appendChild(stamp); + item.appendChild(detail); + + (function (passValue) { + function handleRestore(evt) { + if (evt.type === "keydown" && evt.key !== "Enter" && evt.key !== " ") { + return; + } + if (evt.key === " ") { + evt.preventDefault(); + } + applyPassCount(passValue, true, "timeline-restore"); + } + item.addEventListener("click", handleRestore); + item.addEventListener("keydown", handleRestore); + })(entry.pass); + + timeline.appendChild(item); + }); + } + + function pushHistory(passCount, source) { + var normalizedSource = normalizePassSource(source || "event"); + var now = new Date(); + var stamp = now.toLocaleTimeString([], { + hour: "2-digit", + minute: "2-digit", + second: "2-digit" + }); + + var top = passHistory[0]; + if (top && normalizedSource === "autoplay" && top.source === "autoplay") { + var autoplayPrev = top.pass; + top.pass = passCount; + top.stamp = stamp; + top.delta = passCount - autoplayPrev; + renderHistory(); + return; + } + + if (top && top.pass === passCount && top.source === normalizedSource) { + return; + } + + var previousPass = top ? top.pass : null; + passHistory.unshift({ + pass: passCount, + source: normalizedSource, + stamp: stamp, + delta: previousPass === null ? null : passCount - previousPass + }); + + if (passHistory.length > PASS_HISTORY_MAX) { + passHistory.length = PASS_HISTORY_MAX; + } + + renderHistory(); + } + + function clearHistory(sourceLabel) { + passHistory = []; + renderHistory(); + syncSourceLabel(sourceLabel || "history-clear"); + } + + function syncSnapshotUI(passCount) { + var hasA = Number.isFinite(snapshots.a); + var hasB = Number.isFinite(snapshots.b); + + applyAButton.disabled = !hasA; + applyBButton.disabled = !hasB; + toggleAbButton.disabled = !hasA && !hasB; + + saveAButton.classList.toggle("is-active", hasA && snapshots.a === passCount); + saveBButton.classList.toggle("is-active", hasB && snapshots.b === passCount); + + if (hasA || hasB) { + var nextLabel = "A"; + if (hasA && hasB) { + nextLabel = passCount === snapshots.a ? "B" : "A"; + } else if (hasB && !hasA) { + nextLabel = "B"; + } + toggleAbButton.textContent = "Flip " + nextLabel; + } else { + toggleAbButton.textContent = "Flip A/B"; + } + + if (hasA && hasB) { + var delta = snapshots.b - snapshots.a; + compareValue.textContent = "A " + snapshots.a + " | B " + snapshots.b + " | Δ " + (delta >= 0 ? "+" : "") + delta; + } else { + compareValue.textContent = "A " + (hasA ? snapshots.a : "-") + " | B " + (hasB ? snapshots.b : "-"); + } + + syncShareUrlField(); + } + + function applySnapshotToggle(source) { + var hasA = Number.isFinite(snapshots.a); + var hasB = Number.isFinite(snapshots.b); + + if (!hasA && !hasB) { + return; + } + + if (hasA && hasB) { + var target = currentPasses === snapshots.a ? snapshots.b : snapshots.a; + applyPassCount(target, true, source || "snapshot-toggle"); + return; + } + + applyPassCount(hasA ? snapshots.a : snapshots.b, true, source || "snapshot-toggle"); + } + + function applyStatePayload(rawState, sourceLabel) { + var state = normalizePassState(rawState); + if (!state) { + return false; + } + + if (state.sweepMs !== null) { + setAutoplayInterval(state.sweepMs, true); + } + + if (state.snapshotA !== null) { + snapshots.a = state.snapshotA; + } + + if (state.snapshotB !== null) { + snapshots.b = state.snapshotB; + } + + persistSnapshots(); + + if (state.passCount !== null) { + applyPassCount(state.passCount, true, sourceLabel || "state"); + } else { + syncInputs(currentPasses); + syncSourceLabel(sourceLabel || "state"); + } + + if (state.open !== null) { + setPanelOpen(state.open); + } + + if (state.auto !== null) { + if (state.auto) { + startAutoplay(); + } else { + stopAutoplay(); + } + } + + syncShareUrlField(); + return true; + } + + function setAutoplayInterval(rawInterval, persistSelection) { + var normalized = toValidSpeed(rawInterval); + if (!Number.isFinite(normalized)) { + normalized = PASS_AUTOPLAY_INTERVAL_MS; + } + + autoplayIntervalMs = normalized; + speedInput.value = String(autoplayIntervalMs); + + if (persistSelection) { + trySaveValue(getPassSpeedStorageKey(kind), String(autoplayIntervalMs)); + trySaveValue(PASS_AUTOPLAY_STORAGE_KEY, String(autoplayIntervalMs)); + } + + if (isAutoplay) { + startAutoplay(); + } + + syncSweepLabel(); + syncShareUrlField(); + } + + function syncInputs(passCount) { + range.value = String(passCount); + number.value = String(passCount); + value.textContent = String(passCount); + if (badge) { badge.textContent = String(passCount); } + + presetButtons.forEach(function (button) { + var preset = Number(button.getAttribute("data-preset")); + button.classList.toggle("is-active", preset === passCount); + }); + + syncSnapshotUI(passCount); + } + + function stopAutoplay() { + if (autoplayTimer) { + clearInterval(autoplayTimer); + autoplayTimer = null; + } + isAutoplay = false; + autoplayButton.textContent = "Auto Sweep"; + autoplayButton.classList.remove("is-active"); + syncSweepLabel(); + syncShareUrlField(); + } + + function startAutoplay() { + if (window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches) { + return; + } + + stopAutoplay(); + isAutoplay = true; + autoplayButton.textContent = "Stop Sweep"; + autoplayButton.classList.add("is-active"); + syncSweepLabel(); + syncShareUrlField(); + + autoplayTimer = setInterval(function () { + if (currentPasses >= PASS_MAX) { + autoDirection = -1; + } else if (currentPasses <= PASS_MIN) { + autoDirection = 1; + } + + applyPassCount(currentPasses + autoDirection, true, "autoplay"); + }, autoplayIntervalMs); + } + + function toggleAutoplay() { + if (isAutoplay) { + stopAutoplay(); + return; + } + + startAutoplay(); + } + + function isEditableTarget(target) { + if (!target) { + return false; + } + + if (target.isContentEditable) { + return true; + } + + var tag = (target.tagName || "").toLowerCase(); + return tag === "input" || tag === "textarea" || tag === "select"; + } + + function syncFromPassEvent(event) { + if (!event || !event.detail || event.detail.kind !== kind) { + return; + } + + var detailPassCount = Number(event.detail.passCount); + if (!Number.isFinite(detailPassCount)) { + return; + } + + currentPasses = Math.round(clamp(detailPassCount, PASS_MIN, PASS_MAX)); + syncInputs(currentPasses); + syncSourceLabel(event.detail.source || "event"); + pushHistory(currentPasses, event.detail.source || "event"); + } + + function setPanelOpen(nextOpen) { + isOpen = !!nextOpen; + panel.classList.toggle("is-open", isOpen); + bodyPanel.hidden = !isOpen; + toggle.setAttribute("aria-expanded", String(isOpen)); + trySaveValue(DEBUG_OPEN_STORAGE_KEY, isOpen ? "1" : "0"); + + if (!isOpen) { + stopAutoplay(); + } + + syncShareUrlField(); + } + + function applyPassCount(rawPassCount, persistSelection, source) { + var normalized = Math.round(clamp(Number(rawPassCount), PASS_MIN, PASS_MAX)); + var effectiveSource = source || "panel"; + + if (shouldTrackUndo(effectiveSource, normalized)) { + pushUndo(currentPasses); + } + + currentPasses = runPolishPasses(kind, normalized, effectiveSource); + syncInputs(currentPasses); + syncSourceLabel(effectiveSource); + + if (persistSelection) { + persistPass(currentPasses); + } + + syncUndoState(); + syncShareUrlField(); + } + + toggle.addEventListener("click", function () { + setPanelOpen(!isOpen); + }); + + range.addEventListener("input", function () { + applyPassCount(range.value, true, "slider"); + }); + + number.addEventListener("change", function () { + applyPassCount(number.value, true, "number"); + }); + + speedInput.addEventListener("change", function () { + setAutoplayInterval(speedInput.value, true); + syncSourceLabel("speed"); + }); + + stepButtons.forEach(function (button) { + button.addEventListener("click", function () { + var delta = Number(button.getAttribute("data-step")); + applyPassCount(currentPasses + delta, true, "step"); + }); + }); + + presetButtons.forEach(function (button) { + button.addEventListener("click", function () { + var preset = Number(button.getAttribute("data-preset")); + applyPassCount(preset, true, "preset"); + }); + }); + + resetButton.addEventListener("click", function () { + applyPassCount(PASS_MAX, true, "reset"); + }); + + defaultButton.addEventListener("click", function () { + configuredPasses = getConfiguredPassCount(kind); + applyPassCount(configuredPasses, true, "default"); + }); + + autoplayButton.addEventListener("click", function () { + toggleAutoplay(); + }); + + undoButton.addEventListener("click", function () { + runUndo("undo"); + }); + + redoButton.addEventListener("click", function () { + runRedo("redo"); + }); + + saveAButton.addEventListener("click", function () { + snapshots.a = currentPasses; + persistSnapshots(); + syncSnapshotUI(currentPasses); + syncSourceLabel("save-a"); + }); + + saveBButton.addEventListener("click", function () { + snapshots.b = currentPasses; + persistSnapshots(); + syncSnapshotUI(currentPasses); + syncSourceLabel("save-b"); + }); + + applyAButton.addEventListener("click", function () { + if (Number.isFinite(snapshots.a)) { + applyPassCount(snapshots.a, true, "snapshot-a"); + } + }); + + applyBButton.addEventListener("click", function () { + if (Number.isFinite(snapshots.b)) { + applyPassCount(snapshots.b, true, "snapshot-b"); + } + }); + + toggleAbButton.addEventListener("click", function () { + applySnapshotToggle("snapshot-toggle"); + }); + + copyUrlButton.addEventListener("click", function () { + var shareUrl = buildShareUrl(); + shareInput.value = shareUrl; + + if (navigator.clipboard && typeof navigator.clipboard.writeText === "function") { + navigator.clipboard.writeText(shareUrl) + .then(function () { + syncSourceLabel("share-copied"); + }) + .catch(function () { + syncSourceLabel("share-copy-failed"); + }); + return; + } + + shareInput.focus(); + shareInput.select(); + syncSourceLabel("share-ready"); + }); + + applyUrlButton.addEventListener("click", function () { + var parsed = parsePassStateFromInput(shareInput.value); + var applied = applyStatePayload(parsed, "share-import"); + if (!applied) { + syncSourceLabel("share-invalid"); + } + }); + + pushUrlButton.addEventListener("click", function () { + var shareUrl = buildShareUrl(); + if (window.history && typeof window.history.replaceState === "function") { + window.history.replaceState({}, "", shareUrl); + syncShareUrlField(); + syncSourceLabel("url-pushed"); + return; + } + + shareInput.value = shareUrl; + syncSourceLabel("url-ready"); + }); + + clearHistoryButton.addEventListener("click", function () { + clearHistory("history-clear"); + }); + + shareInput.addEventListener("keydown", function (event) { + if (event.key === "Enter") { + event.preventDefault(); + applyUrlButton.click(); + } + }); + + closeButton.addEventListener("click", function () { + setPanelOpen(false); + }); + + document.addEventListener("site-upgrade:pass-change", syncFromPassEvent); + + document.addEventListener("visibilitychange", function () { + if (document.hidden) { + stopAutoplay(); + } + }); + + window.addEventListener("beforeunload", stopAutoplay); + + document.addEventListener("keydown", function (event) { + if (shortcutOverlay && event.key === "Escape") { + event.preventDefault(); + hideShortcutOverlay(); + return; + } + + if (isEditableTarget(event.target)) { + return; + } + + if (!(event.altKey && event.shiftKey)) { + return; + } + + if (event.key.toLowerCase() === "p") { + event.preventDefault(); + setPanelOpen(!isOpen); + return; + } + + if (event.key.toLowerCase() === "a") { + event.preventDefault(); + toggleAutoplay(); + return; + } + + if (event.key.toLowerCase() === "c") { + event.preventDefault(); + setPanelOpen(false); + return; + } + + if (event.code === "Digit0") { + event.preventDefault(); + applyPassCount(PASS_MAX, true, "shortcut-reset"); + return; + } + + if (event.code === "Digit1" || event.code === "Digit2" || event.code === "Digit3" || event.code === "Digit4") { + event.preventDefault(); + var presetMap = { + Digit1: 25, + Digit2: 50, + Digit3: 75, + Digit4: 100 + }; + applyPassCount(presetMap[event.code], true, "shortcut-preset"); + return; + } + + if (event.code === "Digit5") { + event.preventDefault(); + if (Number.isFinite(snapshots.a)) { + applyPassCount(snapshots.a, true, "shortcut-a"); + } + return; + } + + if (event.code === "Digit6") { + event.preventDefault(); + if (Number.isFinite(snapshots.b)) { + applyPassCount(snapshots.b, true, "shortcut-b"); + } + return; + } + + if (event.code === "Digit7") { + event.preventDefault(); + applySnapshotToggle("shortcut-toggle"); + return; + } + + if (event.key.toLowerCase() === "u") { + event.preventDefault(); + applyUrlButton.click(); + return; + } + + if (event.key.toLowerCase() === "i") { + event.preventDefault(); + shareInput.focus(); + shareInput.select(); + syncSourceLabel("url-focus"); + return; + } + + if (event.key.toLowerCase() === "h") { + event.preventDefault(); + clearHistory("history-clear"); + return; + } + + if (event.key === "/") { + event.preventDefault(); + if (shortcutOverlay) { + hideShortcutOverlay(); + } else { + showShortcutOverlay(); + } + return; + } + + if (event.key.toLowerCase() === "l") { + event.preventDefault(); + pushUrlButton.click(); + return; + } + + if (event.key.toLowerCase() === "z") { + event.preventDefault(); + runUndo("shortcut-undo"); + return; + } + + if (event.key.toLowerCase() === "y") { + event.preventDefault(); + runRedo("shortcut-redo"); + return; + } + + if (event.key === "ArrowUp") { + event.preventDefault(); + applyPassCount(currentPasses + 1, true, "shortcut-up"); + return; + } + + if (event.key === "ArrowDown") { + event.preventDefault(); + applyPassCount(currentPasses - 1, true, "shortcut-down"); + return; + } + + if (event.key === "ArrowRight") { + event.preventDefault(); + applyPassCount(currentPasses + 10, true, "shortcut-right"); + return; + } + + if (event.key === "ArrowLeft") { + event.preventDefault(); + applyPassCount(currentPasses - 10, true, "shortcut-left"); + } + }); + + setAutoplayInterval(autoplayIntervalMs, false); + syncSweepLabel(); + syncInputs(currentPasses); + syncUndoState(); + syncShareUrlField(); + renderHistory(); + + if (queryState && (queryState.snapshotA !== null || queryState.snapshotB !== null)) { + persistSnapshots(); + } + + if (currentPasses !== initialPassCount) { + applyPassCount(currentPasses, true, "state-init"); + } else { + syncSourceLabel("init"); + pushHistory(currentPasses, "init"); + } + + setPanelOpen(isOpen); + + if (shouldAutoStart === true) { + startAutoplay(); + } else if (shouldAutoStart === false) { + stopAutoplay(); + } + + // --- Drag to reposition --- + (function () { + var savedPos = (function () { + var raw = tryLoadValue(getPassPositionStorageKey(kind)); + if (!raw) { return null; } + try { + var p = JSON.parse(raw); + return p && Number.isFinite(p.right) && Number.isFinite(p.bottom) ? p : null; + } catch (e) { return null; } + })(); + + function applyPosition(right, bottom) { + var maxRight = window.innerWidth - 40; + var maxBottom = window.innerHeight - 40; + var r = Math.min(Math.max(right, 0), maxRight); + var b = Math.min(Math.max(bottom, 0), maxBottom); + panel.style.right = r + "px"; + panel.style.bottom = b + "px"; + } + + function resetPosition() { + panel.style.right = ""; + panel.style.bottom = ""; + try { localStorage.removeItem(getPassPositionStorageKey(kind)); } catch (e) { /* */ } + } + + if (savedPos) { + applyPosition(savedPos.right, savedPos.bottom); + } + + var dragState = null; + + dragHead.addEventListener("mousedown", function (evt) { + if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT" || evt.target.tagName === "SUMMARY")) { + return; + } + evt.preventDefault(); + var rect = panel.getBoundingClientRect(); + dragState = { + startX: evt.clientX, + startY: evt.clientY, + startRight: window.innerWidth - rect.right, + startBottom: window.innerHeight - rect.bottom + }; + panel.classList.add("is-dragging"); + }); + + document.addEventListener("mousemove", function (evt) { + if (!dragState) { return; } + var dx = evt.clientX - dragState.startX; + var dy = evt.clientY - dragState.startY; + applyPosition(dragState.startRight - dx, dragState.startBottom - dy); + }); + + document.addEventListener("mouseup", function (evt) { + if (!dragState) { return; } + panel.classList.remove("is-dragging"); + var rect = panel.getBoundingClientRect(); + var finalRight = window.innerWidth - rect.right; + var finalBottom = window.innerHeight - rect.bottom; + trySaveValue(getPassPositionStorageKey(kind), JSON.stringify({ right: finalRight, bottom: finalBottom })); + dragState = null; + }); + + dragHead.addEventListener("touchstart", function (evt) { + if (evt.touches.length !== 1) { return; } + if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT" || evt.target.tagName === "SUMMARY")) { + return; + } + var touch = evt.touches[0]; + var rect = panel.getBoundingClientRect(); + dragState = { + startX: touch.clientX, + startY: touch.clientY, + startRight: window.innerWidth - rect.right, + startBottom: window.innerHeight - rect.bottom + }; + panel.classList.add("is-dragging"); + }, { passive: true }); + + document.addEventListener("touchmove", function (evt) { + if (!dragState) { return; } + if (evt.cancelable) { evt.preventDefault(); } + var touch = evt.touches[0]; + var dx = touch.clientX - dragState.startX; + var dy = touch.clientY - dragState.startY; + applyPosition(dragState.startRight - dx, dragState.startBottom - dy); + }, { passive: false }); + + document.addEventListener("touchend", function () { + if (!dragState) { return; } + panel.classList.remove("is-dragging"); + var rect = panel.getBoundingClientRect(); + var finalRight = window.innerWidth - rect.right; + var finalBottom = window.innerHeight - rect.bottom; + trySaveValue(getPassPositionStorageKey(kind), JSON.stringify({ right: finalRight, bottom: finalBottom })); + dragState = null; + }); + + dragHead.addEventListener("dblclick", function (evt) { + if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT")) { + return; + } + resetPosition(); + }); + })(); + + // --- Shortcut cheat-sheet overlay --- + var shortcutOverlay = null; + + function showShortcutOverlay() { + if (shortcutOverlay) { return; } + shortcutOverlay = document.createElement("div"); + shortcutOverlay.className = "site-pass-shortcut-overlay"; + shortcutOverlay.setAttribute("role", "dialog"); + shortcutOverlay.setAttribute("aria-modal", "true"); + shortcutOverlay.setAttribute("aria-label", "Keyboard shortcuts"); + shortcutOverlay.innerHTML = "" + + "
    " + + "
    " + + "Keyboard Shortcuts" + + "" + + "
    " + + "
    " + + "
    Alt+Shift+P
    Toggle panel
    " + + "
    Alt+Shift+A
    Toggle auto sweep
    " + + "
    Alt+Shift+C
    Close panel
    " + + "
    Alt+Shift+1–4
    Presets 25 / 50 / 75 / 100
    " + + "
    Alt+Shift+0
    Reset to 100
    " + + "
    Alt+Shift+5
    Apply snapshot A
    " + + "
    Alt+Shift+6
    Apply snapshot B
    " + + "
    Alt+Shift+7
    Flip A/B
    " + + "
    Alt+Shift+Z
    Undo
    " + + "
    Alt+Shift+Y
    Redo
    " + + "
    Alt+Shift+U
    Apply URL state
    " + + "
    Alt+Shift+L
    Push URL state
    " + + "
    Alt+Shift+I
    Focus URL input
    " + + "
    Alt+Shift+H
    Clear history log
    " + + "
    Alt+Shift+/
    Show this overlay
    " + + "
    Arrow Up / Right
    +1 / +10 passes
    " + + "
    Arrow Down / Left
    −1 / −10 passes
    " + + "
    " + + "

    Double-click panel header to reset position

    " + + "
    "; + document.body.appendChild(shortcutOverlay); + var closeBtn = shortcutOverlay.querySelector(".site-pass-shortcut-overlay__close"); + if (closeBtn) { closeBtn.focus(); } + + function dismissOverlay(evt) { + if (!shortcutOverlay) { return; } + var box = shortcutOverlay.querySelector(".site-pass-shortcut-overlay__box"); + if (!box || !box.contains(evt.target)) { + hideShortcutOverlay(); + } + } + shortcutOverlay._dismiss = dismissOverlay; + shortcutOverlay.addEventListener("click", dismissOverlay); + if (closeBtn) { + closeBtn.addEventListener("click", function () { hideShortcutOverlay(); }); + } + } + + function hideShortcutOverlay() { + if (!shortcutOverlay) { return; } + shortcutOverlay.remove(); + shortcutOverlay = null; + } + + var sectionEls = panel.querySelectorAll(".site-pass-debug__section"); + var savedSections = (function () { + var raw = tryLoadValue(getPassSectionsStorageKey(kind)); + if (!raw) { return null; } + try { return JSON.parse(raw); } catch (e) { return null; } + })(); + + if (savedSections) { + sectionEls.forEach(function (el) { + var name = el.getAttribute("data-section"); + if (name && Object.prototype.hasOwnProperty.call(savedSections, name)) { + if (savedSections[name]) { + el.setAttribute("open", ""); + } else { + el.removeAttribute("open"); + } + } + }); + } + + // Animated details open/close + function animateDetails(el, opening) { + var inner = el.querySelector("summary + *") || el.querySelector("summary"); + if (!inner) { + return; + } + + // Respect reduced-motion preference — skip animation + var prefersReduced = window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches; + + if (el._animating) { + clearTimeout(el._animating); + el._animating = null; + if (el._innerEl) { el._innerEl.style.height = ""; el._innerEl.style.overflow = ""; } + } + + var wrapper = el._innerEl; + if (!wrapper) { + // Wrap all non-summary children in an animate wrapper once + wrapper = document.createElement("div"); + wrapper.className = "site-pass-debug__section-body"; + var children = Array.prototype.slice.call(el.childNodes); + children.forEach(function (node) { + if (node.nodeType === 1 && node.tagName === "SUMMARY") { return; } + wrapper.appendChild(node); + }); + el.appendChild(wrapper); + el._innerEl = wrapper; + } + + if (prefersReduced) { + wrapper.style.height = ""; + wrapper.style.overflow = ""; + return; + } + + var targetHeight = opening ? wrapper.scrollHeight + "px" : "0px"; + wrapper.style.overflow = "hidden"; + wrapper.style.height = opening ? "0px" : wrapper.scrollHeight + "px"; + + // Force reflow + void wrapper.offsetHeight; + wrapper.style.transition = "height 0.2s ease"; + wrapper.style.height = targetHeight; + + var DURATION = 210; + el._animating = setTimeout(function () { + wrapper.style.transition = ""; + wrapper.style.overflow = ""; + if (opening) { wrapper.style.height = ""; } + el._animating = null; + }, DURATION); + } + + sectionEls.forEach(function (el) { + // Intercept toggle: cancel default, run animation, then commit open state + el.addEventListener("click", function (evt) { + if (evt.target && evt.target.tagName === "SUMMARY") { + evt.preventDefault(); + var willOpen = !el.open; + if (willOpen) { el.setAttribute("open", ""); } + animateDetails(el, willOpen); + if (!willOpen) { + setTimeout(function () { el.removeAttribute("open"); }, 200); + } + var state = {}; + sectionEls.forEach(function (s) { + var sName = s.getAttribute("data-section"); + if (sName) { state[sName] = willOpen ? true : false; } + }); + // Only update the toggled section + var secName = el.getAttribute("data-section"); + var fullState = {}; + sectionEls.forEach(function (s) { + var n = s.getAttribute("data-section"); + if (n) { fullState[n] = n === secName ? willOpen : s.open; } + }); + trySaveValue(getPassSectionsStorageKey(kind), JSON.stringify(fullState)); + } + }); + }); + + // --- Panel width resize handle --- + (function () { + var PANEL_MIN_W = 220; + var PANEL_MAX_W = Math.min(560, window.innerWidth - 28); + var savedWidth = (function () { + var raw = tryLoadValue(getPassWidthStorageKey(kind)); + var v = raw ? parseFloat(raw) : NaN; + return Number.isFinite(v) ? v : null; + })(); + + var innerPanel = panel.querySelector(".site-pass-debug__panel"); + + function applyWidth(w) { + var clamped = Math.min(Math.max(w, PANEL_MIN_W), Math.min(560, window.innerWidth - 28)); + innerPanel.style.width = clamped + "px"; + return clamped; + } + + function resetWidth() { + innerPanel.style.width = ""; + try { localStorage.removeItem(getPassWidthStorageKey(kind)); } catch (e) { /* */ } + } + + if (savedWidth) { + applyWidth(savedWidth); + } + + var resizeDrag = null; + + resizeHandle.addEventListener("mousedown", function (evt) { + evt.preventDefault(); + var rect = innerPanel.getBoundingClientRect(); + resizeDrag = { startX: evt.clientX, startWidth: rect.width }; + panel.classList.add("is-resizing"); + }); + + document.addEventListener("mousemove", function (evt) { + if (!resizeDrag) { return; } + // Handle is on the left edge — drag left = wider, drag right = narrower + var dx = resizeDrag.startX - evt.clientX; + applyWidth(resizeDrag.startWidth + dx); + }); + + document.addEventListener("mouseup", function () { + if (!resizeDrag) { return; } + panel.classList.remove("is-resizing"); + var finalWidth = parseFloat(innerPanel.style.width) || null; + if (finalWidth) { + trySaveValue(getPassWidthStorageKey(kind), String(finalWidth)); + } + resizeDrag = null; + }); + + resizeHandle.addEventListener("dblclick", function () { + resetWidth(); + }); + })(); + + if (exportJsonButton) { + exportJsonButton.addEventListener("click", function () { + var json = JSON.stringify(serializePassState(), null, 2); + if (navigator.clipboard && navigator.clipboard.writeText) { + navigator.clipboard.writeText(json).then(function () { + var orig = exportJsonButton.textContent; + exportJsonButton.textContent = "Copied!"; + setTimeout(function () { exportJsonButton.textContent = orig; }, 1200); + }).catch(function () { + shareInput.value = json; + shareInput.select(); + }); + } else { + shareInput.value = json; + shareInput.select(); + } + }); + } + + // --- Appearance controls (opacity / blur) --- + (function () { + var innerPanel = panel.querySelector(".site-pass-debug__panel"); + var blurEnabled = true; + + var saved = (function () { + var raw = tryLoadValue(getPassPanelStyleStorageKey(kind)); + if (!raw) { return null; } + try { return JSON.parse(raw); } catch (e) { return null; } + })(); + + var currentOpacity = (saved && Number.isFinite(saved.opacity)) ? saved.opacity : 100; + var currentBlur = (saved && typeof saved.blur === "boolean") ? saved.blur : true; + blurEnabled = currentBlur; + + function applyAppearance(opacity, blur) { + innerPanel.style.opacity = (opacity / 100).toFixed(2); + innerPanel.style.backdropFilter = blur ? "blur(10px)" : "none"; + innerPanel.style.webkitBackdropFilter = blur ? "blur(10px)" : "none"; + } + + function syncAppearanceUI() { + if (opacityInput) { opacityInput.value = String(currentOpacity); } + if (toggleBlurButton) { + toggleBlurButton.textContent = blurEnabled ? "Blur On" : "Blur Off"; + toggleBlurButton.classList.toggle("is-active", blurEnabled); + } + } + + function persistAppearance() { + trySaveValue(getPassPanelStyleStorageKey(kind), JSON.stringify({ opacity: currentOpacity, blur: blurEnabled })); + } + + applyAppearance(currentOpacity, blurEnabled); + syncAppearanceUI(); + + if (opacityInput) { + opacityInput.addEventListener("change", function () { + var v = Math.min(100, Math.max(20, Number(opacityInput.value) || 100)); + currentOpacity = v; + opacityInput.value = String(v); + applyAppearance(currentOpacity, blurEnabled); + persistAppearance(); + }); + opacityInput.addEventListener("input", function () { + var v = Math.min(100, Math.max(20, Number(opacityInput.value) || 100)); + applyAppearance(v, blurEnabled); + }); + } + + if (toggleBlurButton) { + toggleBlurButton.addEventListener("click", function () { + blurEnabled = !blurEnabled; + applyAppearance(currentOpacity, blurEnabled); + syncAppearanceUI(); + persistAppearance(); + }); + } + + if (resetAppearanceButton) { + resetAppearanceButton.addEventListener("click", function () { + currentOpacity = 100; + blurEnabled = true; + applyAppearance(currentOpacity, blurEnabled); + syncAppearanceUI(); + try { localStorage.removeItem(getPassPanelStyleStorageKey(kind)); } catch (e) { /* */ } + }); + } + })(); + } + + function ensureSkipLink() { + if (document.querySelector(".site-upgrade-skip")) { + return; + } + + var candidates = ["main", "#mainContent", ".main", ".page", ".container", ".content"]; + var target = null; + + for (var i = 0; i < candidates.length; i += 1) { + var element = document.querySelector(candidates[i]); + if (element) { + target = element; + break; + } + } + + if (!target) { + return; + } + + if (!target.id) { + target.id = "site-upgrade-main"; + } + + var skip = document.createElement("a"); + skip.className = "site-upgrade-skip"; + skip.href = "#" + target.id; + skip.textContent = "Skip to main content"; + document.body.prepend(skip); + } + + function upgradeExternalLinks() { + var links = document.querySelectorAll('a[target="_blank"]'); + links.forEach(function (link) { + var rel = (link.getAttribute("rel") || "").toLowerCase(); + if (!rel.includes("noopener")) { + link.setAttribute("rel", (rel ? rel + " " : "") + "noopener noreferrer"); + } + }); + } + + function applyRevealAnimations() { + if (window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches) { + return; + } + + var selectors = [ + "section", + ".card", + ".hero-card", + ".feature-card", + ".system-card", + ".welcome-card", + ".route-card", + ".signal-card", + ".plan-card", + ".link-card", + ".sidebar-card", + ".content-shell", + ".category-block", + ".flow-card", + ".search-panel", + ".resource-group", + ".terminal", + ".run-copy", + ".run-aside", + ".hero-banner", + ".glass-panel", + ".jobs-section", + ".gpu-status", + ".chat-container", + ".stage-container", + ".object-manager", + ".page-link", + ".job-item", + ".metric-card", + ".category-card", + ".product-card" + ]; + + var seen = new Set(); + selectors.forEach(function (selector) { + document.querySelectorAll(selector).forEach(function (node) { + seen.add(node); + }); + }); + + var nodes = Array.from(seen).filter(function (node) { + return node && (node.childElementCount > 0 || (node.textContent || "").trim().length > 0); + }); + + if (!nodes.length) { + return; + } + + nodes.forEach(function (node, index) { + node.classList.add("site-upgrade-reveal"); + node.style.setProperty("--site-upgrade-delay", Math.min(index * 28, 420) + "ms"); + }); + + if (!("IntersectionObserver" in window)) { + nodes.forEach(function (node) { + node.classList.add("is-visible"); + }); + return; + } + + var observer = new IntersectionObserver(function (entries, obs) { + entries.forEach(function (entry) { + if (entry.isIntersecting) { + entry.target.classList.add("is-visible"); + obs.unobserve(entry.target); + } + }); + }, { + threshold: 0.12, + rootMargin: "0px 0px -8% 0px" + }); + + nodes.forEach(function (node) { + observer.observe(node); + }); + } + + function initUpgrade() { + var kind = applySiteKindClass(); + var configuredPasses = getConfiguredPassCount(kind); + var initialPasses = runPolishPasses(kind, configuredPasses); + ensureSkipLink(); + upgradeExternalLinks(); + applyRevealAnimations(); + createPassDebugPanel(kind, initialPasses); + } + + if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", initUpgrade, { once: true }); + } else { + initUpgrade(); + } +})(); diff --git a/apps/store/about.html b/apps/store/about.html index 90598b225..6a23950b6 100644 --- a/apps/store/about.html +++ b/apps/store/about.html @@ -1,61 +1,61 @@ - - - - - - About Us - TechDrop - - - - - - - - - -
    - -
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    About TechDrop

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    We connect you with the best electronics and gadgets from trusted suppliers worldwide.

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    TechDrop was built with a simple mission: make quality electronics accessible to everyone. We partner with trusted manufacturers and distributors to bring you carefully curated gadgets at competitive prices.

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    Every product in our catalog is selected for its quality, value, and reliability. Whether you're looking for the latest audio gear, smart wearables, or essential tech accessories, we've got you covered.

    -

    We believe in transparency and great customer service. Have a question about a product or need help with your order? Our team is always ready to help.

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    Fast Shipping

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    We work with reliable shipping partners to get your order to you as quickly as possible.

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    Secure Shopping

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    Your information is always protected. Shop with confidence knowing your data is safe.

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    Easy Returns

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    Not satisfied with your purchase? Our hassle-free return process makes it easy.

    -
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    - - - - - - - - - - + + + + + + About Us - TechDrop + + + + + + + + + +
    + +
    +
    +
    +

    About TechDrop

    +

    We connect you with the best electronics and gadgets from trusted suppliers worldwide.

    +
    +
    + +
    +
    +

    TechDrop was built with a simple mission: make quality electronics accessible to everyone. We partner with trusted manufacturers and distributors to bring you carefully curated gadgets at competitive prices.

    +

    Every product in our catalog is selected for its quality, value, and reliability. Whether you're looking for the latest audio gear, smart wearables, or essential tech accessories, we've got you covered.

    +

    We believe in transparency and great customer service. Have a question about a product or need help with your order? Our team is always ready to help.

    + +
    +
    + +

    Fast Shipping

    +

    We work with reliable shipping partners to get your order to you as quickly as possible.

    +
    +
    + +

    Secure Shopping

    +

    Your information is always protected. Shop with confidence knowing your data is safe.

    +
    +
    + +

    Easy Returns

    +

    Not satisfied with your purchase? Our hassle-free return process makes it easy.

    +
    +
    +
    +
    +
    + + + + + + + + + + diff --git a/apps/store/contact.html b/apps/store/contact.html index b9f6046ed..ac1085c01 100644 --- a/apps/store/contact.html +++ b/apps/store/contact.html @@ -1,91 +1,91 @@ - - - - - - Contact Us - TechDrop - - - - - - - - - -
    - -
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    Contact Us

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    Have a question or want to inquire about a product? We'd love to hear from you.

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    Send a Message

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    Get in Touch

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    Email

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    contact@example.com

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    Business Hours

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    Monday - Friday: 9am - 6pm
    Saturday: 10am - 4pm

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    Response Time

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    We typically respond within 24 hours.

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    - - - - - - - - - - + + + + + + Contact Us - TechDrop + + + + + + + + + +
    + +
    +
    +
    +

    Contact Us

    +

    Have a question or want to inquire about a product? We'd love to hear from you.

    +
    +
    + +
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    +

    Send a Message

    +
    +
    + + +
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    + + +
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    + + +
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    + + +
    + +
    +
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    +

    Get in Touch

    +
    + +
    +

    Email

    +

    contact@example.com

    +
    +
    +
    + +
    +

    Business Hours

    +

    Monday - Friday: 9am - 6pm
    Saturday: 10am - 4pm

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    Response Time

    +

    We typically respond within 24 hours.

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    + + + + + + + + + + diff --git a/apps/store/css/style.css b/apps/store/css/style.css index f5f5e1f45..f0e96b6df 100644 --- a/apps/store/css/style.css +++ b/apps/store/css/style.css @@ -1,903 +1,903 @@ -/* ===== CSS Custom Properties ===== */ -:root { - --color-primary: #2563eb; - --color-primary-dark: #1d4ed8; - --color-secondary: #0f172a; - --color-accent: #f59e0b; - --color-bg: #f8fafc; - --color-white: #ffffff; - --color-text: #1e293b; - --color-text-light: #64748b; - --color-border: #e2e8f0; - --color-success: #22c55e; - --color-danger: #ef4444; - --shadow-sm: 0 1px 2px rgba(0,0,0,0.05); - --shadow-md: 0 4px 6px -1px rgba(0,0,0,0.1), 0 2px 4px -2px rgba(0,0,0,0.1); - --shadow-lg: 0 10px 15px -3px rgba(0,0,0,0.1), 0 4px 6px -4px rgba(0,0,0,0.1); - --radius: 8px; - --radius-lg: 12px; - --max-width: 1200px; - --header-height: 70px; - --transition: 0.2s ease; -} - -/* ===== Reset & Base ===== */ -*, *::before, *::after { - margin: 0; - padding: 0; - box-sizing: border-box; -} - -html { - scroll-behavior: smooth; -} - -body { - font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; - color: var(--color-text); - background: var(--color-bg); - line-height: 1.6; - min-height: 100vh; - display: flex; - flex-direction: column; -} - -a { - text-decoration: none; - color: inherit; -} - -img { - max-width: 100%; - height: auto; - display: block; -} - -ul { list-style: none; } - -button { - cursor: pointer; - font-family: inherit; - border: none; - background: none; -} - -input, select, textarea { - font-family: inherit; - font-size: inherit; -} - -main { - flex: 1; -} - -/* ===== Utility ===== */ -.container { - width: 100%; - max-width: var(--max-width); - margin: 0 auto; - padding: 0 20px; -} - -.btn { - display: inline-flex; - align-items: center; - justify-content: center; - gap: 8px; - padding: 12px 28px; - border-radius: var(--radius); - font-weight: 600; - font-size: 0.95rem; - transition: all var(--transition); - white-space: nowrap; -} - -.btn-primary { - background: var(--color-primary); - color: var(--color-white); -} -.btn-primary:hover { - background: var(--color-primary-dark); - transform: translateY(-1px); - box-shadow: var(--shadow-md); -} - -.btn-outline { - border: 2px solid var(--color-primary); - color: var(--color-primary); - background: transparent; -} -.btn-outline:hover { - background: var(--color-primary); - color: var(--color-white); -} - -.btn-secondary { - background: var(--color-secondary); - color: var(--color-white); -} -.btn-secondary:hover { - opacity: 0.9; - transform: translateY(-1px); -} - -.section-title { - font-size: 1.75rem; - font-weight: 700; - margin-bottom: 8px; -} - -.section-subtitle { - color: var(--color-text-light); - font-size: 1.05rem; - margin-bottom: 40px; -} - -.section { - padding: 80px 0; -} - -/* ===== Header / Navbar ===== */ -.header { - position: sticky; - top: 0; - z-index: 1000; - background: var(--color-white); - border-bottom: 1px solid var(--color-border); - height: var(--header-height); -} - -.header .container { - display: flex; - align-items: center; - justify-content: space-between; - height: 100%; -} - -.logo { - font-size: 1.5rem; - font-weight: 800; - color: var(--color-primary); - display: flex; - align-items: center; - gap: 8px; -} - -.logo i { - font-size: 1.3rem; -} - -.nav-links { - display: flex; - align-items: center; - gap: 32px; -} - -.nav-links a { - font-weight: 500; - color: var(--color-text-light); - transition: color var(--transition); - font-size: 0.95rem; -} - -.nav-links a:hover, -.nav-links a.active { - color: var(--color-primary); -} - -.nav-search { - display: flex; - align-items: center; - gap: 12px; -} - -.nav-search-input { - border: 1px solid var(--color-border); - border-radius: var(--radius); - padding: 8px 14px; - font-size: 0.9rem; - width: 200px; - transition: border-color var(--transition); - outline: none; -} - -.nav-search-input:focus { - border-color: var(--color-primary); -} - -.mobile-menu-btn { - display: none; - font-size: 1.5rem; - color: var(--color-text); -} - -/* ===== Hero Section ===== */ -.hero { - background: linear-gradient(135deg, var(--color-secondary) 0%, #1e3a5f 100%); - color: var(--color-white); - padding: 100px 0; - text-align: center; -} - -.hero h1 { - font-size: 3rem; - font-weight: 800; - margin-bottom: 16px; - line-height: 1.2; -} - -.hero p { - font-size: 1.2rem; - opacity: 0.85; - max-width: 600px; - margin: 0 auto 32px; -} - -.hero .btn { - font-size: 1.05rem; - padding: 14px 36px; -} - -/* ===== Categories ===== */ -.categories-grid { - display: grid; - grid-template-columns: repeat(4, 1fr); - gap: 24px; -} - -.category-card { - background: var(--color-white); - border-radius: var(--radius-lg); - padding: 32px 20px; - text-align: center; - box-shadow: var(--shadow-sm); - border: 1px solid var(--color-border); - transition: all var(--transition); - cursor: pointer; -} - -.category-card:hover { - transform: translateY(-4px); - box-shadow: var(--shadow-lg); - border-color: var(--color-primary); -} - -.category-card i { - font-size: 2.5rem; - color: var(--color-primary); - margin-bottom: 16px; -} - -.category-card h3 { - font-size: 1.05rem; - margin-bottom: 4px; -} - -.category-card p { - font-size: 0.85rem; - color: var(--color-text-light); -} - -/* ===== Product Grid ===== */ -.products-grid { - display: grid; - grid-template-columns: repeat(4, 1fr); - gap: 24px; -} - -.product-card { - background: var(--color-white); - border-radius: var(--radius-lg); - overflow: hidden; - box-shadow: var(--shadow-sm); - border: 1px solid var(--color-border); - transition: all var(--transition); -} - -.product-card:hover { - transform: translateY(-4px); - box-shadow: var(--shadow-lg); -} - -.product-card-image { - width: 100%; - height: 220px; - overflow: hidden; - background: #f1f5f9; - display: flex; - align-items: center; - justify-content: center; -} - -.product-card-image img { - width: 100%; - height: 100%; - object-fit: cover; -} - -.product-card-body { - padding: 16px; -} - -.product-card-category { - font-size: 0.75rem; - text-transform: uppercase; - letter-spacing: 0.5px; - color: var(--color-primary); - font-weight: 600; - margin-bottom: 4px; -} - -.product-card-title { - font-size: 1rem; - font-weight: 600; - margin-bottom: 8px; - line-height: 1.3; -} - -.product-card-price { - font-size: 1.2rem; - font-weight: 700; - color: var(--color-text); - margin-bottom: 12px; -} - -.product-card .btn { - width: 100%; - padding: 10px; - font-size: 0.9rem; -} - -/* ===== Shop Page Layout ===== */ -.shop-layout { - display: grid; - grid-template-columns: 260px 1fr; - gap: 32px; - padding: 40px 0; -} - -.shop-sidebar { - background: var(--color-white); - border-radius: var(--radius-lg); - padding: 24px; - border: 1px solid var(--color-border); - height: fit-content; - position: sticky; - top: calc(var(--header-height) + 20px); -} - -.filter-group { - margin-bottom: 24px; -} - -.filter-group:last-child { - margin-bottom: 0; -} - -.filter-group h4 { - font-size: 0.9rem; - text-transform: uppercase; - letter-spacing: 0.5px; - margin-bottom: 12px; - color: var(--color-text); -} - -.filter-group select, -.filter-group input[type="range"] { - width: 100%; - padding: 8px 12px; - border: 1px solid var(--color-border); - border-radius: var(--radius); - font-size: 0.9rem; - outline: none; -} - -.filter-group select:focus { - border-color: var(--color-primary); -} - -.price-range-labels { - display: flex; - justify-content: space-between; - font-size: 0.85rem; - color: var(--color-text-light); - margin-top: 8px; -} - -.shop-results-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 24px; -} - -.shop-results-header p { - color: var(--color-text-light); - font-size: 0.95rem; -} - -.shop-products-grid { - display: grid; - grid-template-columns: repeat(3, 1fr); - gap: 24px; -} - -.no-results { - grid-column: 1 / -1; - text-align: center; - padding: 60px 20px; - color: var(--color-text-light); -} - -.no-results i { - font-size: 3rem; - margin-bottom: 16px; - display: block; -} - -/* ===== Product Detail ===== */ -.product-detail { - padding: 40px 0 80px; -} - -.product-detail-grid { - display: grid; - grid-template-columns: 1fr 1fr; - gap: 48px; - margin-bottom: 60px; -} - -.product-detail-image { - background: var(--color-white); - border-radius: var(--radius-lg); - border: 1px solid var(--color-border); - overflow: hidden; - display: flex; - align-items: center; - justify-content: center; - min-height: 400px; -} - -.product-detail-image img { - width: 100%; - height: 100%; - object-fit: cover; -} - -.product-detail-info h1 { - font-size: 2rem; - margin-bottom: 8px; -} - -.product-detail-category { - color: var(--color-primary); - font-weight: 600; - text-transform: uppercase; - font-size: 0.85rem; - letter-spacing: 0.5px; - margin-bottom: 16px; -} - -.product-detail-price { - font-size: 2rem; - font-weight: 800; - margin-bottom: 20px; -} - -.product-detail-description { - color: var(--color-text-light); - font-size: 1.05rem; - line-height: 1.8; - margin-bottom: 32px; -} - -.product-detail-features { - margin-bottom: 32px; -} - -.product-detail-features h3 { - font-size: 1.1rem; - margin-bottom: 12px; -} - -.product-detail-features ul { - list-style: disc; - padding-left: 20px; -} - -.product-detail-features li { - margin-bottom: 6px; - color: var(--color-text-light); -} - -.product-detail-actions { - display: flex; - gap: 16px; -} - -.product-detail-actions .btn { - flex: 1; - padding: 14px 24px; -} - -.breadcrumb { - display: flex; - align-items: center; - gap: 8px; - font-size: 0.9rem; - color: var(--color-text-light); - margin-bottom: 24px; -} - -.breadcrumb a:hover { - color: var(--color-primary); -} - -.breadcrumb span { - color: var(--color-text); - font-weight: 500; -} - -/* ===== Related Products ===== */ -.related-products { - border-top: 1px solid var(--color-border); - padding-top: 40px; -} - -.related-products h2 { - font-size: 1.5rem; - margin-bottom: 24px; -} - -/* ===== About Page ===== */ -.about-hero { - background: linear-gradient(135deg, var(--color-secondary) 0%, #1e3a5f 100%); - color: var(--color-white); - padding: 80px 0; - text-align: center; -} - -.about-hero h1 { - font-size: 2.5rem; - margin-bottom: 16px; -} - -.about-hero p { - font-size: 1.1rem; - opacity: 0.85; - max-width: 600px; - margin: 0 auto; -} - -.about-content { - padding: 60px 0; -} - -.about-content p { - font-size: 1.05rem; - line-height: 1.8; - color: var(--color-text-light); - max-width: 800px; - margin: 0 auto 24px; -} - -.trust-badges { - display: grid; - grid-template-columns: repeat(3, 1fr); - gap: 32px; - margin-top: 60px; -} - -.trust-badge { - text-align: center; - padding: 32px 20px; - background: var(--color-white); - border-radius: var(--radius-lg); - border: 1px solid var(--color-border); -} - -.trust-badge i { - font-size: 2.5rem; - color: var(--color-primary); - margin-bottom: 16px; -} - -.trust-badge h3 { - margin-bottom: 8px; - font-size: 1.1rem; -} - -.trust-badge p { - font-size: 0.9rem; - color: var(--color-text-light); - margin: 0; -} - -/* ===== Contact Page ===== */ -.contact-section { - padding: 60px 0 80px; -} - -.contact-grid { - display: grid; - grid-template-columns: 1fr 1fr; - gap: 48px; - max-width: 900px; - margin: 0 auto; -} - -.contact-form { - background: var(--color-white); - padding: 32px; - border-radius: var(--radius-lg); - border: 1px solid var(--color-border); -} - -.contact-form h2 { - font-size: 1.5rem; - margin-bottom: 24px; -} - -.form-group { - margin-bottom: 20px; -} - -.form-group label { - display: block; - font-weight: 500; - margin-bottom: 6px; - font-size: 0.9rem; -} - -.form-group input, -.form-group textarea, -.form-group select { - width: 100%; - padding: 10px 14px; - border: 1px solid var(--color-border); - border-radius: var(--radius); - font-size: 0.95rem; - outline: none; - transition: border-color var(--transition); -} - -.form-group input:focus, -.form-group textarea:focus, -.form-group select:focus { - border-color: var(--color-primary); -} - -.form-group textarea { - resize: vertical; - min-height: 120px; -} - -.contact-info { - padding: 32px 0; -} - -.contact-info h2 { - font-size: 1.5rem; - margin-bottom: 24px; -} - -.contact-info-item { - display: flex; - align-items: flex-start; - gap: 16px; - margin-bottom: 24px; -} - -.contact-info-item i { - font-size: 1.3rem; - color: var(--color-primary); - margin-top: 2px; -} - -.contact-info-item h4 { - font-size: 0.95rem; - margin-bottom: 4px; -} - -.contact-info-item p { - color: var(--color-text-light); - font-size: 0.9rem; -} - -/* ===== Page Header (reusable) ===== */ -.page-header { - background: var(--color-secondary); - color: var(--color-white); - padding: 48px 0; - text-align: center; -} - -.page-header h1 { - font-size: 2rem; - margin-bottom: 8px; -} - -.page-header p { - opacity: 0.8; - font-size: 1.05rem; -} - -/* ===== Footer ===== */ -.footer { - background: var(--color-secondary); - color: var(--color-white); - padding: 60px 0 24px; - margin-top: auto; -} - -.footer-grid { - display: grid; - grid-template-columns: 2fr 1fr 1fr 1fr; - gap: 40px; - margin-bottom: 40px; -} - -.footer-about p { - color: rgba(255,255,255,0.6); - font-size: 0.9rem; - line-height: 1.7; - margin-top: 12px; -} - -.footer h4 { - font-size: 0.95rem; - margin-bottom: 16px; - text-transform: uppercase; - letter-spacing: 0.5px; -} - -.footer-links a { - display: block; - color: rgba(255,255,255,0.6); - font-size: 0.9rem; - margin-bottom: 10px; - transition: color var(--transition); -} - -.footer-links a:hover { - color: var(--color-white); -} - -.footer-social { - display: flex; - gap: 12px; - margin-top: 12px; -} - -.footer-social a { - width: 40px; - height: 40px; - border-radius: 50%; - background: rgba(255,255,255,0.1); - display: flex; - align-items: center; - justify-content: center; - color: var(--color-white); - transition: background var(--transition); -} - -.footer-social a:hover { - background: var(--color-primary); -} - -.footer-bottom { - border-top: 1px solid rgba(255,255,255,0.1); - padding-top: 24px; - text-align: center; - color: rgba(255,255,255,0.4); - font-size: 0.85rem; -} - -/* ===== Responsive ===== */ -@media (max-width: 1024px) { - .products-grid { - grid-template-columns: repeat(3, 1fr); - } - .footer-grid { - grid-template-columns: 1fr 1fr; - gap: 32px; - } -} - -@media (max-width: 768px) { - .nav-links, - .nav-search { - display: none; - } - - .nav-links.open { - display: flex; - flex-direction: column; - position: absolute; - top: var(--header-height); - left: 0; - right: 0; - background: var(--color-white); - border-bottom: 1px solid var(--color-border); - padding: 20px; - gap: 16px; - box-shadow: var(--shadow-lg); - } - - .mobile-menu-btn { - display: block; - } - - .hero h1 { - font-size: 2rem; - } - - .hero p { - font-size: 1rem; - } - - .categories-grid { - grid-template-columns: repeat(2, 1fr); - gap: 16px; - } - - .products-grid, - .shop-products-grid { - grid-template-columns: repeat(2, 1fr); - gap: 16px; - } - - .shop-layout { - grid-template-columns: 1fr; - } - - .shop-sidebar { - position: static; - } - - .product-detail-grid { - grid-template-columns: 1fr; - gap: 24px; - } - - .contact-grid { - grid-template-columns: 1fr; - } - - .trust-badges { - grid-template-columns: 1fr; - gap: 16px; - } - - .footer-grid { - grid-template-columns: 1fr; - gap: 24px; - } - - .product-detail-actions { - flex-direction: column; - } - - .section { - padding: 48px 0; - } -} - -@media (max-width: 480px) { - .products-grid, - .shop-products-grid { - grid-template-columns: 1fr; - } - - .categories-grid { - grid-template-columns: 1fr; - } - - .hero { - padding: 60px 0; - } - - .hero h1 { - font-size: 1.75rem; - } -} +/* ===== CSS Custom Properties ===== */ +:root { + --color-primary: #2563eb; + --color-primary-dark: #1d4ed8; + --color-secondary: #0f172a; + --color-accent: #f59e0b; + --color-bg: #f8fafc; + --color-white: #ffffff; + --color-text: #1e293b; + --color-text-light: #64748b; + --color-border: #e2e8f0; + --color-success: #22c55e; + --color-danger: #ef4444; + --shadow-sm: 0 1px 2px rgba(0,0,0,0.05); + --shadow-md: 0 4px 6px -1px rgba(0,0,0,0.1), 0 2px 4px -2px rgba(0,0,0,0.1); + --shadow-lg: 0 10px 15px -3px rgba(0,0,0,0.1), 0 4px 6px -4px rgba(0,0,0,0.1); + --radius: 8px; + --radius-lg: 12px; + --max-width: 1200px; + --header-height: 70px; + --transition: 0.2s ease; +} + +/* ===== Reset & Base ===== */ +*, *::before, *::after { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +html { + scroll-behavior: smooth; +} + +body { + font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; + color: var(--color-text); + background: var(--color-bg); + line-height: 1.6; + min-height: 100vh; + display: flex; + flex-direction: column; +} + +a { + text-decoration: none; + color: inherit; +} + +img { + max-width: 100%; + height: auto; + display: block; +} + +ul { list-style: none; } + +button { + cursor: pointer; + font-family: inherit; + border: none; + background: none; +} + +input, select, textarea { + font-family: inherit; + font-size: inherit; +} + +main { + flex: 1; +} + +/* ===== Utility ===== */ +.container { + width: 100%; + max-width: var(--max-width); + margin: 0 auto; + padding: 0 20px; +} + +.btn { + display: inline-flex; + align-items: center; + justify-content: center; + gap: 8px; + padding: 12px 28px; + border-radius: var(--radius); + font-weight: 600; + font-size: 0.95rem; + transition: all var(--transition); + white-space: nowrap; +} + +.btn-primary { + background: var(--color-primary); + color: var(--color-white); +} +.btn-primary:hover { + background: var(--color-primary-dark); + transform: translateY(-1px); + box-shadow: var(--shadow-md); +} + +.btn-outline { + border: 2px solid var(--color-primary); + color: var(--color-primary); + background: transparent; +} +.btn-outline:hover { + background: var(--color-primary); + color: var(--color-white); +} + +.btn-secondary { + background: var(--color-secondary); + color: var(--color-white); +} +.btn-secondary:hover { + opacity: 0.9; + transform: translateY(-1px); +} + +.section-title { + font-size: 1.75rem; + font-weight: 700; + margin-bottom: 8px; +} + +.section-subtitle { + color: var(--color-text-light); + font-size: 1.05rem; + margin-bottom: 40px; +} + +.section { + padding: 80px 0; +} + +/* ===== Header / Navbar ===== */ +.header { + position: sticky; + top: 0; + z-index: 1000; + background: var(--color-white); + border-bottom: 1px solid var(--color-border); + height: var(--header-height); +} + +.header .container { + display: flex; + align-items: center; + justify-content: space-between; + height: 100%; +} + +.logo { + font-size: 1.5rem; + font-weight: 800; + color: var(--color-primary); + display: flex; + align-items: center; + gap: 8px; +} + +.logo i { + font-size: 1.3rem; +} + +.nav-links { + display: flex; + align-items: center; + gap: 32px; +} + +.nav-links a { + font-weight: 500; + color: var(--color-text-light); + transition: color var(--transition); + font-size: 0.95rem; +} + +.nav-links a:hover, +.nav-links a.active { + color: var(--color-primary); +} + +.nav-search { + display: flex; + align-items: center; + gap: 12px; +} + +.nav-search-input { + border: 1px solid var(--color-border); + border-radius: var(--radius); + padding: 8px 14px; + font-size: 0.9rem; + width: 200px; + transition: border-color var(--transition); + outline: none; +} + +.nav-search-input:focus { + border-color: var(--color-primary); +} + +.mobile-menu-btn { + display: none; + font-size: 1.5rem; + color: var(--color-text); +} + +/* ===== Hero Section ===== */ +.hero { + background: linear-gradient(135deg, var(--color-secondary) 0%, #1e3a5f 100%); + color: var(--color-white); + padding: 100px 0; + text-align: center; +} + +.hero h1 { + font-size: 3rem; + font-weight: 800; + margin-bottom: 16px; + line-height: 1.2; +} + +.hero p { + font-size: 1.2rem; + opacity: 0.85; + max-width: 600px; + margin: 0 auto 32px; +} + +.hero .btn { + font-size: 1.05rem; + padding: 14px 36px; +} + +/* ===== Categories ===== */ +.categories-grid { + display: grid; + grid-template-columns: repeat(4, 1fr); + gap: 24px; +} + +.category-card { + background: var(--color-white); + border-radius: var(--radius-lg); + padding: 32px 20px; + text-align: center; + box-shadow: var(--shadow-sm); + border: 1px solid var(--color-border); + transition: all var(--transition); + cursor: pointer; +} + +.category-card:hover { + transform: translateY(-4px); + box-shadow: var(--shadow-lg); + border-color: var(--color-primary); +} + +.category-card i { + font-size: 2.5rem; + color: var(--color-primary); + margin-bottom: 16px; +} + +.category-card h3 { + font-size: 1.05rem; + margin-bottom: 4px; +} + +.category-card p { + font-size: 0.85rem; + color: var(--color-text-light); +} + +/* ===== Product Grid ===== */ +.products-grid { + display: grid; + grid-template-columns: repeat(4, 1fr); + gap: 24px; +} + +.product-card { + background: var(--color-white); + border-radius: var(--radius-lg); + overflow: hidden; + box-shadow: var(--shadow-sm); + border: 1px solid var(--color-border); + transition: all var(--transition); +} + +.product-card:hover { + transform: translateY(-4px); + box-shadow: var(--shadow-lg); +} + +.product-card-image { + width: 100%; + height: 220px; + overflow: hidden; + background: #f1f5f9; + display: flex; + align-items: center; + justify-content: center; +} + +.product-card-image img { + width: 100%; + height: 100%; + object-fit: cover; +} + +.product-card-body { + padding: 16px; +} + +.product-card-category { + font-size: 0.75rem; + text-transform: uppercase; + letter-spacing: 0.5px; + color: var(--color-primary); + font-weight: 600; + margin-bottom: 4px; +} + +.product-card-title { + font-size: 1rem; + font-weight: 600; + margin-bottom: 8px; + line-height: 1.3; +} + +.product-card-price { + font-size: 1.2rem; + font-weight: 700; + color: var(--color-text); + margin-bottom: 12px; +} + +.product-card .btn { + width: 100%; + padding: 10px; + font-size: 0.9rem; +} + +/* ===== Shop Page Layout ===== */ +.shop-layout { + display: grid; + grid-template-columns: 260px 1fr; + gap: 32px; + padding: 40px 0; +} + +.shop-sidebar { + background: var(--color-white); + border-radius: var(--radius-lg); + padding: 24px; + border: 1px solid var(--color-border); + height: fit-content; + position: sticky; + top: calc(var(--header-height) + 20px); +} + +.filter-group { + margin-bottom: 24px; +} + +.filter-group:last-child { + margin-bottom: 0; +} + +.filter-group h4 { + font-size: 0.9rem; + text-transform: uppercase; + letter-spacing: 0.5px; + margin-bottom: 12px; + color: var(--color-text); +} + +.filter-group select, +.filter-group input[type="range"] { + width: 100%; + padding: 8px 12px; + border: 1px solid var(--color-border); + border-radius: var(--radius); + font-size: 0.9rem; + outline: none; +} + +.filter-group select:focus { + border-color: var(--color-primary); +} + +.price-range-labels { + display: flex; + justify-content: space-between; + font-size: 0.85rem; + color: var(--color-text-light); + margin-top: 8px; +} + +.shop-results-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 24px; +} + +.shop-results-header p { + color: var(--color-text-light); + font-size: 0.95rem; +} + +.shop-products-grid { + display: grid; + grid-template-columns: repeat(3, 1fr); + gap: 24px; +} + +.no-results { + grid-column: 1 / -1; + text-align: center; + padding: 60px 20px; + color: var(--color-text-light); +} + +.no-results i { + font-size: 3rem; + margin-bottom: 16px; + display: block; +} + +/* ===== Product Detail ===== */ +.product-detail { + padding: 40px 0 80px; +} + +.product-detail-grid { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 48px; + margin-bottom: 60px; +} + +.product-detail-image { + background: var(--color-white); + border-radius: var(--radius-lg); + border: 1px solid var(--color-border); + overflow: hidden; + display: flex; + align-items: center; + justify-content: center; + min-height: 400px; +} + +.product-detail-image img { + width: 100%; + height: 100%; + object-fit: cover; +} + +.product-detail-info h1 { + font-size: 2rem; + margin-bottom: 8px; +} + +.product-detail-category { + color: var(--color-primary); + font-weight: 600; + text-transform: uppercase; + font-size: 0.85rem; + letter-spacing: 0.5px; + margin-bottom: 16px; +} + +.product-detail-price { + font-size: 2rem; + font-weight: 800; + margin-bottom: 20px; +} + +.product-detail-description { + color: var(--color-text-light); + font-size: 1.05rem; + line-height: 1.8; + margin-bottom: 32px; +} + +.product-detail-features { + margin-bottom: 32px; +} + +.product-detail-features h3 { + font-size: 1.1rem; + margin-bottom: 12px; +} + +.product-detail-features ul { + list-style: disc; + padding-left: 20px; +} + +.product-detail-features li { + margin-bottom: 6px; + color: var(--color-text-light); +} + +.product-detail-actions { + display: flex; + gap: 16px; +} + +.product-detail-actions .btn { + flex: 1; + padding: 14px 24px; +} + +.breadcrumb { + display: flex; + align-items: center; + gap: 8px; + font-size: 0.9rem; + color: var(--color-text-light); + margin-bottom: 24px; +} + +.breadcrumb a:hover { + color: var(--color-primary); +} + +.breadcrumb span { + color: var(--color-text); + font-weight: 500; +} + +/* ===== Related Products ===== */ +.related-products { + border-top: 1px solid var(--color-border); + padding-top: 40px; +} + +.related-products h2 { + font-size: 1.5rem; + margin-bottom: 24px; +} + +/* ===== About Page ===== */ +.about-hero { + background: linear-gradient(135deg, var(--color-secondary) 0%, #1e3a5f 100%); + color: var(--color-white); + padding: 80px 0; + text-align: center; +} + +.about-hero h1 { + font-size: 2.5rem; + margin-bottom: 16px; +} + +.about-hero p { + font-size: 1.1rem; + opacity: 0.85; + max-width: 600px; + margin: 0 auto; +} + +.about-content { + padding: 60px 0; +} + +.about-content p { + font-size: 1.05rem; + line-height: 1.8; + color: var(--color-text-light); + max-width: 800px; + margin: 0 auto 24px; +} + +.trust-badges { + display: grid; + grid-template-columns: repeat(3, 1fr); + gap: 32px; + margin-top: 60px; +} + +.trust-badge { + text-align: center; + padding: 32px 20px; + background: var(--color-white); + border-radius: var(--radius-lg); + border: 1px solid var(--color-border); +} + +.trust-badge i { + font-size: 2.5rem; + color: var(--color-primary); + margin-bottom: 16px; +} + +.trust-badge h3 { + margin-bottom: 8px; + font-size: 1.1rem; +} + +.trust-badge p { + font-size: 0.9rem; + color: var(--color-text-light); + margin: 0; +} + +/* ===== Contact Page ===== */ +.contact-section { + padding: 60px 0 80px; +} + +.contact-grid { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 48px; + max-width: 900px; + margin: 0 auto; +} + +.contact-form { + background: var(--color-white); + padding: 32px; + border-radius: var(--radius-lg); + border: 1px solid var(--color-border); +} + +.contact-form h2 { + font-size: 1.5rem; + margin-bottom: 24px; +} + +.form-group { + margin-bottom: 20px; +} + +.form-group label { + display: block; + font-weight: 500; + margin-bottom: 6px; + font-size: 0.9rem; +} + +.form-group input, +.form-group textarea, +.form-group select { + width: 100%; + padding: 10px 14px; + border: 1px solid var(--color-border); + border-radius: var(--radius); + font-size: 0.95rem; + outline: none; + transition: border-color var(--transition); +} + +.form-group input:focus, +.form-group textarea:focus, +.form-group select:focus { + border-color: var(--color-primary); +} + +.form-group textarea { + resize: vertical; + min-height: 120px; +} + +.contact-info { + padding: 32px 0; +} + +.contact-info h2 { + font-size: 1.5rem; + margin-bottom: 24px; +} + +.contact-info-item { + display: flex; + align-items: flex-start; + gap: 16px; + margin-bottom: 24px; +} + +.contact-info-item i { + font-size: 1.3rem; + color: var(--color-primary); + margin-top: 2px; +} + +.contact-info-item h4 { + font-size: 0.95rem; + margin-bottom: 4px; +} + +.contact-info-item p { + color: var(--color-text-light); + font-size: 0.9rem; +} + +/* ===== Page Header (reusable) ===== */ +.page-header { + background: var(--color-secondary); + color: var(--color-white); + padding: 48px 0; + text-align: center; +} + +.page-header h1 { + font-size: 2rem; + margin-bottom: 8px; +} + +.page-header p { + opacity: 0.8; + font-size: 1.05rem; +} + +/* ===== Footer ===== */ +.footer { + background: var(--color-secondary); + color: var(--color-white); + padding: 60px 0 24px; + margin-top: auto; +} + +.footer-grid { + display: grid; + grid-template-columns: 2fr 1fr 1fr 1fr; + gap: 40px; + margin-bottom: 40px; +} + +.footer-about p { + color: rgba(255,255,255,0.6); + font-size: 0.9rem; + line-height: 1.7; + margin-top: 12px; +} + +.footer h4 { + font-size: 0.95rem; + margin-bottom: 16px; + text-transform: uppercase; + letter-spacing: 0.5px; +} + +.footer-links a { + display: block; + color: rgba(255,255,255,0.6); + font-size: 0.9rem; + margin-bottom: 10px; + transition: color var(--transition); +} + +.footer-links a:hover { + color: var(--color-white); +} + +.footer-social { + display: flex; + gap: 12px; + margin-top: 12px; +} + +.footer-social a { + width: 40px; + height: 40px; + border-radius: 50%; + background: rgba(255,255,255,0.1); + display: flex; + align-items: center; + justify-content: center; + color: var(--color-white); + transition: background var(--transition); +} + +.footer-social a:hover { + background: var(--color-primary); +} + +.footer-bottom { + border-top: 1px solid rgba(255,255,255,0.1); + padding-top: 24px; + text-align: center; + color: rgba(255,255,255,0.4); + font-size: 0.85rem; +} + +/* ===== Responsive ===== */ +@media (max-width: 1024px) { + .products-grid { + grid-template-columns: repeat(3, 1fr); + } + .footer-grid { + grid-template-columns: 1fr 1fr; + gap: 32px; + } +} + +@media (max-width: 768px) { + .nav-links, + .nav-search { + display: none; + } + + .nav-links.open { + display: flex; + flex-direction: column; + position: absolute; + top: var(--header-height); + left: 0; + right: 0; + background: var(--color-white); + border-bottom: 1px solid var(--color-border); + padding: 20px; + gap: 16px; + box-shadow: var(--shadow-lg); + } + + .mobile-menu-btn { + display: block; + } + + .hero h1 { + font-size: 2rem; + } + + .hero p { + font-size: 1rem; + } + + .categories-grid { + grid-template-columns: repeat(2, 1fr); + gap: 16px; + } + + .products-grid, + .shop-products-grid { + grid-template-columns: repeat(2, 1fr); + gap: 16px; + } + + .shop-layout { + grid-template-columns: 1fr; + } + + .shop-sidebar { + position: static; + } + + .product-detail-grid { + grid-template-columns: 1fr; + gap: 24px; + } + + .contact-grid { + grid-template-columns: 1fr; + } + + .trust-badges { + grid-template-columns: 1fr; + gap: 16px; + } + + .footer-grid { + grid-template-columns: 1fr; + gap: 24px; + } + + .product-detail-actions { + flex-direction: column; + } + + .section { + padding: 48px 0; + } +} + +@media (max-width: 480px) { + .products-grid, + .shop-products-grid { + grid-template-columns: 1fr; + } + + .categories-grid { + grid-template-columns: 1fr; + } + + .hero { + padding: 60px 0; + } + + .hero h1 { + font-size: 1.75rem; + } +} diff --git a/apps/store/index.html b/apps/store/index.html index b2e73bfa8..a0e793071 100644 --- a/apps/store/index.html +++ b/apps/store/index.html @@ -1,68 +1,68 @@ - - - - - - TechDrop - Electronics & Gadgets Store - - - - - - - - - -
    - -
    - -
    -
    -

    The Latest Tech,
    Delivered to You

    -

    Discover top-rated electronics and gadgets at unbeatable prices. From audio gear to smart wearables, we have it all.

    - Browse Products -
    -
    - - -
    -
    -

    Shop by Category

    -

    Find exactly what you're looking for

    -
    - -
    -
    -
    - - -
    -
    -

    Featured Products

    -

    Our handpicked selection of top products

    -
    - -
    -
    -
    - - -
    -
    -

    Looking for Something Specific?

    -

    Can't find what you need? Get in touch and we'll help you find the perfect product.

    - Contact Us -
    -
    -
    - - - - - - - - - - + + + + + + TechDrop - Electronics & Gadgets Store + + + + + + + + + +
    + +
    + +
    +
    +

    The Latest Tech,
    Delivered to You

    +

    Discover top-rated electronics and gadgets at unbeatable prices. From audio gear to smart wearables, we have it all.

    + Browse Products +
    +
    + + +
    +
    +

    Shop by Category

    +

    Find exactly what you're looking for

    +
    + +
    +
    +
    + + +
    +
    +

    Featured Products

    +

    Our handpicked selection of top products

    +
    + +
    +
    +
    + + +
    +
    +

    Looking for Something Specific?

    +

    Can't find what you need? Get in touch and we'll help you find the perfect product.

    + Contact Us +
    +
    +
    + + + + + + + + + + diff --git a/apps/store/js/app.js b/apps/store/js/app.js index 4e7ac99b3..ef5fecf81 100644 --- a/apps/store/js/app.js +++ b/apps/store/js/app.js @@ -1,240 +1,240 @@ -// Main application logic: product filtering, searching, and page-specific init. - -// ===== Utility ===== -function getQueryParam(key) { - const params = new URLSearchParams(window.location.search); - return params.get(key); -} - -function getProductById(id) { - return PRODUCTS.find(function (p) { return p.id === parseInt(id); }); -} - -// ===== Homepage ===== -function initHomePage() { - initComponents('home'); - - // Render category cards - var catGrid = document.getElementById('categoriesGrid'); - if (catGrid) { - catGrid.innerHTML = CATEGORIES.map(function (cat) { - return '' + - '' + - '

    ' + cat.name + '

    ' + - '

    Browse ' + cat.name + '

    ' + - '    '; - }).join(''); - } - - // Render featured products - var featuredGrid = document.getElementById('featuredGrid'); - if (featuredGrid) { - var featured = PRODUCTS.filter(function (p) { return p.featured; }); - if (featured.length === 0) featured = PRODUCTS.slice(0, 4); - featuredGrid.innerHTML = featured.map(renderProductCard).join(''); - } -} - -// ===== Shop / Products Listing ===== -function initShopPage() { - initComponents('shop'); - - var categoryFilter = document.getElementById('categoryFilter'); - var sortFilter = document.getElementById('sortFilter'); - var priceRange = document.getElementById('priceRange'); - var priceLabel = document.getElementById('priceLabel'); - var grid = document.getElementById('shopProductsGrid'); - var resultsCount = document.getElementById('resultsCount'); - - // Set initial category from URL - var urlCategory = getQueryParam('category'); - if (urlCategory && categoryFilter) { - categoryFilter.value = urlCategory; - } - - // Set initial search from URL - var urlSearch = getQueryParam('search'); - var navSearch = document.getElementById('navSearchInput'); - if (urlSearch && navSearch) { - navSearch.value = urlSearch; - } - - // Find max price for range slider - var maxPrice = Math.max.apply(null, PRODUCTS.map(function (p) { return p.price; })); - if (priceRange) { - priceRange.max = Math.ceil(maxPrice / 10) * 10 + 50; - priceRange.value = priceRange.max; - } - - function renderProducts() { - var category = categoryFilter ? categoryFilter.value : 'all'; - var sort = sortFilter ? sortFilter.value : 'default'; - var maxP = priceRange ? parseFloat(priceRange.value) : Infinity; - var search = navSearch ? navSearch.value.trim().toLowerCase() : ''; - - // Also check URL search param - if (!search && urlSearch) { - search = urlSearch.toLowerCase(); - } - - var filtered = PRODUCTS.filter(function (p) { - var catMatch = category === 'all' || p.category === category; - var priceMatch = p.price <= maxP; - var searchMatch = !search || - p.name.toLowerCase().indexOf(search) !== -1 || - p.description.toLowerCase().indexOf(search) !== -1 || - p.category.toLowerCase().indexOf(search) !== -1; - return catMatch && priceMatch && searchMatch; - }); - - // Sort - if (sort === 'price-low') { - filtered.sort(function (a, b) { return a.price - b.price; }); - } else if (sort === 'price-high') { - filtered.sort(function (a, b) { return b.price - a.price; }); - } else if (sort === 'name') { - filtered.sort(function (a, b) { return a.name.localeCompare(b.name); }); - } - - if (resultsCount) { - resultsCount.textContent = filtered.length + ' product' + (filtered.length !== 1 ? 's' : '') + ' found'; - } - - if (priceLabel) { - priceLabel.textContent = '$' + maxP.toFixed(0); - } - - if (grid) { - if (filtered.length === 0) { - grid.innerHTML = '

    No products match your filters.

    '; - } else { - grid.innerHTML = filtered.map(renderProductCard).join(''); - } - } - } - - // Event listeners - if (categoryFilter) categoryFilter.addEventListener('change', renderProducts); - if (sortFilter) sortFilter.addEventListener('change', renderProducts); - if (priceRange) priceRange.addEventListener('input', renderProducts); - - // Re-filter on nav search input - if (navSearch) { - navSearch.addEventListener('input', function () { - urlSearch = ''; // Clear URL search when user types - renderProducts(); - }); - } - - renderProducts(); -} - -// ===== Product Detail ===== -function initProductPage() { - initComponents('shop'); - - var productId = getQueryParam('id'); - var product = productId ? getProductById(productId) : null; - var container = document.getElementById('productDetail'); - var relatedGrid = document.getElementById('relatedGrid'); - - if (!product || !container) { - if (container) { - container.innerHTML = '
    ' + - '

    Product Not Found

    ' + - '

    The product you are looking for does not exist.

    ' + - 'Browse Products
    '; - } - return; - } - - // Set page title - document.title = product.name + ' - TechDrop'; - - // Render breadcrumb + detail - var featuresHtml = ''; - if (product.features && product.features.length > 0) { - featuresHtml = '
    ' + - '

    Key Features

      ' + - product.features.map(function (f) { return '
    • ' + f + '
      • '; }).join('') + - '
    '; - } - - container.innerHTML = - '
    ' + - 'Home / ' + - 'Shop / ' + - '' + product.name + '' + - '
    ' + - '
    ' + - '
    ' + - '' + product.name + '' + - '
    ' + - '
    ' + - '
    ' + product.category + '
    ' + - '

    ' + product.name + '

    ' + - '
    $' + product.price.toFixed(2) + '
    ' + - '

    ' + product.description + '

    ' + - featuresHtml + - '
    ' + - '' + - ' Inquire About This Product' + - '' + - '
    ' + - '
    ' + - '
    '; - - // Related products (same category, different id) - if (relatedGrid) { - var related = PRODUCTS.filter(function (p) { - return p.category === product.category && p.id !== product.id; - }).slice(0, 4); - - // If not enough in same category, fill with others - if (related.length < 4) { - var others = PRODUCTS.filter(function (p) { - return p.id !== product.id && related.indexOf(p) === -1; - }); - related = related.concat(others).slice(0, 4); - } - - if (related.length > 0) { - relatedGrid.innerHTML = related.map(renderProductCard).join(''); - } - } -} - -// ===== About Page ===== -function initAboutPage() { - initComponents('about'); -} - -// ===== Contact Page ===== -function initContactPage() { - initComponents('contact'); - - // Pre-fill product subject if coming from product detail page - var productName = getQueryParam('product'); - var subjectInput = document.getElementById('contactSubject'); - if (productName && subjectInput) { - subjectInput.value = 'Inquiry about: ' + productName; - } - - // Handle form submission (mailto fallback) - var form = document.getElementById('contactForm'); - if (form) { - form.addEventListener('submit', function (e) { - e.preventDefault(); - var name = document.getElementById('contactName').value; - var email = document.getElementById('contactEmail').value; - var subject = document.getElementById('contactSubject').value; - var message = document.getElementById('contactMessage').value; - - var body = 'Name: ' + name + '\nEmail: ' + email + '\n\n' + message; - var mailto = 'mailto:contact@example.com?subject=' + - encodeURIComponent(subject) + '&body=' + encodeURIComponent(body); - - window.location.href = mailto; - }); - } -} +// Main application logic: product filtering, searching, and page-specific init. + +// ===== Utility ===== +function getQueryParam(key) { + const params = new URLSearchParams(window.location.search); + return params.get(key); +} + +function getProductById(id) { + return PRODUCTS.find(function (p) { return p.id === parseInt(id); }); +} + +// ===== Homepage ===== +function initHomePage() { + initComponents('home'); + + // Render category cards + var catGrid = document.getElementById('categoriesGrid'); + if (catGrid) { + catGrid.innerHTML = CATEGORIES.map(function (cat) { + return '' + + '' + + '

    ' + cat.name + '

    ' + + '

    Browse ' + cat.name + '

    ' + + '    '; + }).join(''); + } + + // Render featured products + var featuredGrid = document.getElementById('featuredGrid'); + if (featuredGrid) { + var featured = PRODUCTS.filter(function (p) { return p.featured; }); + if (featured.length === 0) featured = PRODUCTS.slice(0, 4); + featuredGrid.innerHTML = featured.map(renderProductCard).join(''); + } +} + +// ===== Shop / Products Listing ===== +function initShopPage() { + initComponents('shop'); + + var categoryFilter = document.getElementById('categoryFilter'); + var sortFilter = document.getElementById('sortFilter'); + var priceRange = document.getElementById('priceRange'); + var priceLabel = document.getElementById('priceLabel'); + var grid = document.getElementById('shopProductsGrid'); + var resultsCount = document.getElementById('resultsCount'); + + // Set initial category from URL + var urlCategory = getQueryParam('category'); + if (urlCategory && categoryFilter) { + categoryFilter.value = urlCategory; + } + + // Set initial search from URL + var urlSearch = getQueryParam('search'); + var navSearch = document.getElementById('navSearchInput'); + if (urlSearch && navSearch) { + navSearch.value = urlSearch; + } + + // Find max price for range slider + var maxPrice = Math.max.apply(null, PRODUCTS.map(function (p) { return p.price; })); + if (priceRange) { + priceRange.max = Math.ceil(maxPrice / 10) * 10 + 50; + priceRange.value = priceRange.max; + } + + function renderProducts() { + var category = categoryFilter ? categoryFilter.value : 'all'; + var sort = sortFilter ? sortFilter.value : 'default'; + var maxP = priceRange ? parseFloat(priceRange.value) : Infinity; + var search = navSearch ? navSearch.value.trim().toLowerCase() : ''; + + // Also check URL search param + if (!search && urlSearch) { + search = urlSearch.toLowerCase(); + } + + var filtered = PRODUCTS.filter(function (p) { + var catMatch = category === 'all' || p.category === category; + var priceMatch = p.price <= maxP; + var searchMatch = !search || + p.name.toLowerCase().indexOf(search) !== -1 || + p.description.toLowerCase().indexOf(search) !== -1 || + p.category.toLowerCase().indexOf(search) !== -1; + return catMatch && priceMatch && searchMatch; + }); + + // Sort + if (sort === 'price-low') { + filtered.sort(function (a, b) { return a.price - b.price; }); + } else if (sort === 'price-high') { + filtered.sort(function (a, b) { return b.price - a.price; }); + } else if (sort === 'name') { + filtered.sort(function (a, b) { return a.name.localeCompare(b.name); }); + } + + if (resultsCount) { + resultsCount.textContent = filtered.length + ' product' + (filtered.length !== 1 ? 's' : '') + ' found'; + } + + if (priceLabel) { + priceLabel.textContent = '$' + maxP.toFixed(0); + } + + if (grid) { + if (filtered.length === 0) { + grid.innerHTML = '

    No products match your filters.

    '; + } else { + grid.innerHTML = filtered.map(renderProductCard).join(''); + } + } + } + + // Event listeners + if (categoryFilter) categoryFilter.addEventListener('change', renderProducts); + if (sortFilter) sortFilter.addEventListener('change', renderProducts); + if (priceRange) priceRange.addEventListener('input', renderProducts); + + // Re-filter on nav search input + if (navSearch) { + navSearch.addEventListener('input', function () { + urlSearch = ''; // Clear URL search when user types + renderProducts(); + }); + } + + renderProducts(); +} + +// ===== Product Detail ===== +function initProductPage() { + initComponents('shop'); + + var productId = getQueryParam('id'); + var product = productId ? getProductById(productId) : null; + var container = document.getElementById('productDetail'); + var relatedGrid = document.getElementById('relatedGrid'); + + if (!product || !container) { + if (container) { + container.innerHTML = '
    ' + + '

    Product Not Found

    ' + + '

    The product you are looking for does not exist.

    ' + + 'Browse Products
    '; + } + return; + } + + // Set page title + document.title = product.name + ' - TechDrop'; + + // Render breadcrumb + detail + var featuresHtml = ''; + if (product.features && product.features.length > 0) { + featuresHtml = '
    ' + + '

    Key Features

      ' + + product.features.map(function (f) { return '
    • ' + f + '
      • '; }).join('') + + '
    '; + } + + container.innerHTML = + '
    ' + + 'Home / ' + + 'Shop / ' + + '' + product.name + '' + + '
    ' + + '
    ' + + '
    ' + + '' + product.name + '' + + '
    ' + + '
    ' + + '
    ' + product.category + '
    ' + + '

    ' + product.name + '

    ' + + '
    $' + product.price.toFixed(2) + '
    ' + + '

    ' + product.description + '

    ' + + featuresHtml + + '
    ' + + '' + + ' Inquire About This Product' + + '' + + '
    ' + + '
    ' + + '
    '; + + // Related products (same category, different id) + if (relatedGrid) { + var related = PRODUCTS.filter(function (p) { + return p.category === product.category && p.id !== product.id; + }).slice(0, 4); + + // If not enough in same category, fill with others + if (related.length < 4) { + var others = PRODUCTS.filter(function (p) { + return p.id !== product.id && related.indexOf(p) === -1; + }); + related = related.concat(others).slice(0, 4); + } + + if (related.length > 0) { + relatedGrid.innerHTML = related.map(renderProductCard).join(''); + } + } +} + +// ===== About Page ===== +function initAboutPage() { + initComponents('about'); +} + +// ===== Contact Page ===== +function initContactPage() { + initComponents('contact'); + + // Pre-fill product subject if coming from product detail page + var productName = getQueryParam('product'); + var subjectInput = document.getElementById('contactSubject'); + if (productName && subjectInput) { + subjectInput.value = 'Inquiry about: ' + productName; + } + + // Handle form submission (mailto fallback) + var form = document.getElementById('contactForm'); + if (form) { + form.addEventListener('submit', function (e) { + e.preventDefault(); + var name = document.getElementById('contactName').value; + var email = document.getElementById('contactEmail').value; + var subject = document.getElementById('contactSubject').value; + var message = document.getElementById('contactMessage').value; + + var body = 'Name: ' + name + '\nEmail: ' + email + '\n\n' + message; + var mailto = 'mailto:contact@example.com?subject=' + + encodeURIComponent(subject) + '&body=' + encodeURIComponent(body); + + window.location.href = mailto; + }); + } +} diff --git a/apps/store/js/components.js b/apps/store/js/components.js index c7e9a8cad..ff9c62089 100644 --- a/apps/store/js/components.js +++ b/apps/store/js/components.js @@ -1,135 +1,135 @@ -// Shared UI components rendered via JavaScript. -// These keep the navbar and footer consistent across all pages. - -function getNavbar(activePage) { - return ` -
    - Aria Platform - 👤 Aria - 💬 Chat - 📊 Dashboard - 🛒 Store -
    -
    -
    - - -
    - -
    - -
    -
    `; -} - -function getFooter() { - const year = new Date().getFullYear(); - return ` - `; -} - -function renderProductCard(product) { - return ` -
    - -
    - ${product.name} -
    -     -
    -
    ${product.category}
    -

    - ${product.name} -

    -
    $${product.price.toFixed(2)}
    - View Details -
    -
    `; -} - -// Initialize shared components -function initComponents(activePage) { - // Insert navbar - const headerSlot = document.getElementById('header-slot'); - if (headerSlot) { - headerSlot.innerHTML = getNavbar(activePage); - } - - // Insert footer - const footerSlot = document.getElementById('footer-slot'); - if (footerSlot) { - footerSlot.innerHTML = getFooter(); - } - - // Mobile menu toggle - document.addEventListener('click', function (e) { - if (e.target.closest('#mobileMenuBtn')) { - const nav = document.getElementById('navLinks'); - if (nav) nav.classList.toggle('open'); - } - }); - - // Nav search (redirect to products page with query) - document.addEventListener('keydown', function (e) { - if (e.key === 'Enter' && e.target.id === 'navSearchInput') { - const query = e.target.value.trim(); - if (query) { - window.location.href = `products.html?search=${encodeURIComponent(query)}`; - } - } - }); -} +// Shared UI components rendered via JavaScript. +// These keep the navbar and footer consistent across all pages. + +function getNavbar(activePage) { + return ` +
    + Aria Platform + 👤 Aria + 💬 Chat + 📊 Dashboard + 🛒 Store +
    +
    +
    + + +
    + +
    + +
    +
    `; +} + +function getFooter() { + const year = new Date().getFullYear(); + return ` + `; +} + +function renderProductCard(product) { + return ` +
    + +
    + ${product.name} +
    +     +
    +
    ${product.category}
    +

    + ${product.name} +

    +
    $${product.price.toFixed(2)}
    + View Details +
    +
    `; +} + +// Initialize shared components +function initComponents(activePage) { + // Insert navbar + const headerSlot = document.getElementById('header-slot'); + if (headerSlot) { + headerSlot.innerHTML = getNavbar(activePage); + } + + // Insert footer + const footerSlot = document.getElementById('footer-slot'); + if (footerSlot) { + footerSlot.innerHTML = getFooter(); + } + + // Mobile menu toggle + document.addEventListener('click', function (e) { + if (e.target.closest('#mobileMenuBtn')) { + const nav = document.getElementById('navLinks'); + if (nav) nav.classList.toggle('open'); + } + }); + + // Nav search (redirect to products page with query) + document.addEventListener('keydown', function (e) { + if (e.key === 'Enter' && e.target.id === 'navSearchInput') { + const query = e.target.value.trim(); + if (query) { + window.location.href = `products.html?search=${encodeURIComponent(query)}`; + } + } + }); +} diff --git a/apps/store/js/products.js b/apps/store/js/products.js index 78bec2696..10cf0a060 100644 --- a/apps/store/js/products.js +++ b/apps/store/js/products.js @@ -1,74 +1,74 @@ -// Product data - Edit this file to add, remove, or update products. -// Each product needs: id, name, price, category, image, description. -// Optional: featured (boolean), features (array of strings). - -const PRODUCTS = [ - { - id: 1, - name: "Wireless Bluetooth Earbuds", - price: 49.99, - category: "audio", - image: "images/placeholder.svg", - description: "High-quality wireless earbuds with noise cancellation, 24-hour battery life, and premium sound. Perfect for music, calls, and workouts.", - featured: true, - features: [ - "Active noise cancellation", - "24-hour battery with charging case", - "IPX5 water resistant", - "Bluetooth 5.3" - ] - }, - { - id: 2, - name: "Smart Watch Pro", - price: 129.99, - category: "wearables", - image: "images/placeholder.svg", - description: "Feature-packed smartwatch with health tracking, GPS, and a stunning AMOLED display. Stay connected and monitor your fitness goals.", - featured: true, - features: [ - "1.4\" AMOLED display", - "Heart rate & SpO2 monitoring", - "Built-in GPS", - "7-day battery life" - ] - }, - { - id: 3, - name: "USB-C Hub Adapter 7-in-1", - price: 34.99, - category: "accessories", - image: "images/placeholder.svg", - description: "Expand your laptop connectivity with this compact 7-in-1 USB-C hub featuring HDMI, USB 3.0, SD card reader, and power delivery.", - featured: false, - features: [ - "4K HDMI output", - "100W power delivery passthrough", - "USB 3.0 ports x3", - "SD & microSD card reader" - ] - }, - { - id: 4, - name: "Portable Bluetooth Speaker", - price: 59.99, - category: "audio", - image: "images/placeholder.svg", - description: "Compact, waterproof Bluetooth speaker with 360-degree sound and 12-hour playtime. Take your music anywhere.", - featured: true, - features: [ - "360-degree immersive sound", - "IPX7 waterproof", - "12-hour battery life", - "Built-in microphone" - ] - } -]; - -// Categories used for filtering -const CATEGORIES = [ - { id: "smartphones", name: "Smartphones", icon: "fa-mobile-screen-button" }, - { id: "audio", name: "Audio", icon: "fa-headphones" }, - { id: "wearables", name: "Wearables", icon: "fa-clock" }, - { id: "accessories", name: "Accessories", icon: "fa-plug" } -]; +// Product data - Edit this file to add, remove, or update products. +// Each product needs: id, name, price, category, image, description. +// Optional: featured (boolean), features (array of strings). + +const PRODUCTS = [ + { + id: 1, + name: "Wireless Bluetooth Earbuds", + price: 49.99, + category: "audio", + image: "images/placeholder.svg", + description: "High-quality wireless earbuds with noise cancellation, 24-hour battery life, and premium sound. Perfect for music, calls, and workouts.", + featured: true, + features: [ + "Active noise cancellation", + "24-hour battery with charging case", + "IPX5 water resistant", + "Bluetooth 5.3" + ] + }, + { + id: 2, + name: "Smart Watch Pro", + price: 129.99, + category: "wearables", + image: "images/placeholder.svg", + description: "Feature-packed smartwatch with health tracking, GPS, and a stunning AMOLED display. Stay connected and monitor your fitness goals.", + featured: true, + features: [ + "1.4\" AMOLED display", + "Heart rate & SpO2 monitoring", + "Built-in GPS", + "7-day battery life" + ] + }, + { + id: 3, + name: "USB-C Hub Adapter 7-in-1", + price: 34.99, + category: "accessories", + image: "images/placeholder.svg", + description: "Expand your laptop connectivity with this compact 7-in-1 USB-C hub featuring HDMI, USB 3.0, SD card reader, and power delivery.", + featured: false, + features: [ + "4K HDMI output", + "100W power delivery passthrough", + "USB 3.0 ports x3", + "SD & microSD card reader" + ] + }, + { + id: 4, + name: "Portable Bluetooth Speaker", + price: 59.99, + category: "audio", + image: "images/placeholder.svg", + description: "Compact, waterproof Bluetooth speaker with 360-degree sound and 12-hour playtime. Take your music anywhere.", + featured: true, + features: [ + "360-degree immersive sound", + "IPX7 waterproof", + "12-hour battery life", + "Built-in microphone" + ] + } +]; + +// Categories used for filtering +const CATEGORIES = [ + { id: "smartphones", name: "Smartphones", icon: "fa-mobile-screen-button" }, + { id: "audio", name: "Audio", icon: "fa-headphones" }, + { id: "wearables", name: "Wearables", icon: "fa-clock" }, + { id: "accessories", name: "Accessories", icon: "fa-plug" } +]; diff --git a/apps/store/product.html b/apps/store/product.html index 5b1fded14..3ce6969ff 100644 --- a/apps/store/product.html +++ b/apps/store/product.html @@ -1,41 +1,41 @@ - - - - - - Product - TechDrop - - - - - - - - - -
    - -
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    Shop All Products

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    Browse our full catalog of electronics and gadgets

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    + + + + + + + + + + diff --git a/aria_web/__init__.py b/aria_web/__init__.py index 7fce9828d..8b5a3ae1e 100644 --- a/aria_web/__init__.py +++ b/aria_web/__init__.py @@ -1 +1 @@ -"""Compatibility package for legacy `aria_web` imports.""" +"""Compatibility package for legacy `aria_web` imports.""" diff --git a/aria_web/server.py b/aria_web/server.py index cd2a86859..68556f293 100644 --- a/aria_web/server.py +++ b/aria_web/server.py @@ -1,61 +1,61 @@ -"""Compatibility wrapper for legacy `aria_web/server.py` path. - -Delegates to the canonical implementation at `apps/aria/server.py` and exposes -legacy helper/constant names expected by older tests and scripts. -""" - -from __future__ import annotations - -import importlib.util -import sys -from pathlib import Path - -_CANONICAL = Path(__file__).resolve().parents[1] / "apps" / "aria" / "server.py" - -_spec = importlib.util.spec_from_file_location("_canonical_aria_server", _CANONICAL) -if _spec is None or _spec.loader is None: - raise ImportError(f"Unable to load canonical aria server: {_CANONICAL}") - -_mod = importlib.util.module_from_spec(_spec) -sys.modules[_spec.name] = _mod -_spec.loader.exec_module(_mod) - -# Re-export canonical symbols first. -for _name, _value in _mod.__dict__.items(): - if _name.startswith("__"): - continue - globals()[_name] = _value - - -# Legacy helper aliases expected by tests. -def _any_word_in_text(words, text: str) -> bool: - for word in words: - if word in text: - return True - return False - - -def _keywords_in_cmd(words, text: str) -> bool: - for word in words: - if word in text: - return True - return False - - -_MOVE_KEYWORDS = _mod.MOVE_KEYWORDS -_SAY_KEYWORDS = _mod.SAY_KEYWORDS -_PICKUP_KEYWORDS = _mod.PICKUP_KEYWORDS -_JUMP_KEYWORDS = _mod.JUMP_KEYWORDS -_DANCE_KEYWORDS = _mod.DANCE_KEYWORDS -_LIMB_KEYWORDS = frozenset( - set(_mod.LEFT_ARM_KEYWORDS) - | set(_mod.RIGHT_ARM_KEYWORDS) - | set(_mod.LEFT_LEG_KEYWORDS) - | set(_mod.RIGHT_LEG_KEYWORDS) -) - -__all__ = [k for k in globals() if not k.startswith("__")] - - -if __name__ == "__main__": - _mod.main() +"""Compatibility wrapper for legacy `aria_web/server.py` path. + +Delegates to the canonical implementation at `apps/aria/server.py` and exposes +legacy helper/constant names expected by older tests and scripts. +""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path + +_CANONICAL = Path(__file__).resolve().parents[1] / "apps" / "aria" / "server.py" + +_spec = importlib.util.spec_from_file_location("_canonical_aria_server", _CANONICAL) +if _spec is None or _spec.loader is None: + raise ImportError(f"Unable to load canonical aria server: {_CANONICAL}") + +_mod = importlib.util.module_from_spec(_spec) +sys.modules[_spec.name] = _mod +_spec.loader.exec_module(_mod) + +# Re-export canonical symbols first. +for _name, _value in _mod.__dict__.items(): + if _name.startswith("__"): + continue + globals()[_name] = _value + + +# Legacy helper aliases expected by tests. +def _any_word_in_text(words, text: str) -> bool: + for word in words: + if word in text: + return True + return False + + +def _keywords_in_cmd(words, text: str) -> bool: + for word in words: + if word in text: + return True + return False + + +_MOVE_KEYWORDS = _mod.MOVE_KEYWORDS +_SAY_KEYWORDS = _mod.SAY_KEYWORDS +_PICKUP_KEYWORDS = _mod.PICKUP_KEYWORDS +_JUMP_KEYWORDS = _mod.JUMP_KEYWORDS +_DANCE_KEYWORDS = _mod.DANCE_KEYWORDS +_LIMB_KEYWORDS = frozenset( + set(_mod.LEFT_ARM_KEYWORDS) + | set(_mod.RIGHT_ARM_KEYWORDS) + | set(_mod.LEFT_LEG_KEYWORDS) + | set(_mod.RIGHT_LEG_KEYWORDS) +) + +__all__ = [k for k in globals() if not k.startswith("__")] + + +if __name__ == "__main__": + _mod.main() diff --git a/autotrain.py b/autotrain.py index b4bf7bb98..3e4cb9b3b 100644 --- a/autotrain.py +++ b/autotrain.py @@ -1,7 +1,7 @@ -"""Compatibility shim for importing the AutoTrain orchestrator module. - -Some tests and legacy scripts import ``autotrain`` from the repository root. -The canonical implementation lives in ``scripts/autotrain.py``. -""" - -from scripts.autotrain import * # noqa: F401,F403 +"""Compatibility shim for importing the AutoTrain orchestrator module. + +Some tests and legacy scripts import ``autotrain`` from the repository root. +The canonical implementation lives in ``scripts/autotrain.py``. +""" + +from scripts.autotrain import * # noqa: F401,F403 diff --git a/chat_providers.py b/chat_providers.py index b2945e504..d14082a49 100644 --- a/chat_providers.py +++ b/chat_providers.py @@ -1,45 +1,45 @@ -"""Compatibility shim for chat provider imports. - -Loads the canonical implementation from ``ai-projects/chat-cli/src`` and -re-exports its full symbol surface (including private compatibility aliases -used by tests). -""" - -from __future__ import annotations - -import importlib.util -import sys -from pathlib import Path - -_CANONICAL = ( - Path(__file__).resolve().parent - / "ai-projects" - / "chat-cli" - / "src" - / "chat_providers.py" -) - -_spec = importlib.util.spec_from_file_location( - "_canonical_chat_providers_root", _CANONICAL -) -if _spec is None or _spec.loader is None: - raise ImportError(f"Unable to load canonical chat providers: {_CANONICAL}") - -_mod = importlib.util.module_from_spec(_spec) -sys.modules[_spec.name] = _mod -_spec.loader.exec_module(_mod) - -for _name, _value in _mod.__dict__.items(): - if _name.startswith("__"): - continue - globals()[_name] = _value - -if hasattr(_mod, "__all__"): - __all__ = list(_mod.__all__) # type: ignore[attr-defined] -else: - __all__ = [k for k in globals() if not k.startswith("__")] - -# Make this module a direct alias to the canonical implementation so -# monkeypatching attributes (e.g., AzureOpenAI) updates the globals used by -# provider classes defined there. -sys.modules[__name__] = _mod +"""Compatibility shim for chat provider imports. + +Loads the canonical implementation from ``ai-projects/chat-cli/src`` and +re-exports its full symbol surface (including private compatibility aliases +used by tests). +""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path + +_CANONICAL = ( + Path(__file__).resolve().parent + / "ai-projects" + / "chat-cli" + / "src" + / "chat_providers.py" +) + +_spec = importlib.util.spec_from_file_location( + "_canonical_chat_providers_root", _CANONICAL +) +if _spec is None or _spec.loader is None: + raise ImportError(f"Unable to load canonical chat providers: {_CANONICAL}") + +_mod = importlib.util.module_from_spec(_spec) +sys.modules[_spec.name] = _mod +_spec.loader.exec_module(_mod) + +for _name, _value in _mod.__dict__.items(): + if _name.startswith("__"): + continue + globals()[_name] = _value + +if hasattr(_mod, "__all__"): + __all__ = list(_mod.__all__) # type: ignore[attr-defined] +else: + __all__ = [k for k in globals() if not k.startswith("__")] + +# Make this module a direct alias to the canonical implementation so +# monkeypatching attributes (e.g., AzureOpenAI) updates the globals used by +# provider classes defined there. +sys.modules[__name__] = _mod diff --git a/config/autonomous_training.yaml b/config/autonomous_training.yaml index a455bd173..1152c1797 100644 --- a/config/autonomous_training.yaml +++ b/config/autonomous_training.yaml @@ -1,80 +1,80 @@ -# Autonomous AI Training Configuration -# This file controls automatic learning cycles and data collection - -autonomous_mode: - enabled: true - continuous: true # Set to false for single-cycle mode - cycle_interval_minutes: 30 # Time between training cycles - max_cycles: 0 # 0 = infinite, set to positive number to limit cycles - -data_collection: - auto_discover: true # Automatically scan for new datasets - min_datasets: 500 # Minimum datasets to maintain - quality_threshold: 50 # Minimum quality score (0-100) - categories: - - quantum - - chat - - vision - sources: - - openml - - huggingface - - kaggle - parallel_downloads: 10 # Concurrent downloads - -training: - auto_train: true - epochs_progression: # Progressive epoch training - - 25 - - 50 - - 100 - - 200 - workers: 20 # Parallel training workers - min_accuracy_threshold: 0.75 - adaptive_epochs: true # Automatically adjust epochs based on performance - checkpoint_frequency: 10 # Save checkpoint every N datasets - -optimization: - auto_tune: true # Enable automatic hyperparameter tuning - hyperparameter_search: true - architecture_evolution: false # Neural architecture search (experimental) - pruning_enabled: false # Model pruning (experimental) - -monitoring: - track_metrics: true - alert_on_degradation: true - performance_threshold: 0.70 # Alert if accuracy drops below this - -deployment: - auto_deploy_best: false # Automatically deploy models meeting criteria - min_accuracy_for_deployment: 0.90 - azure_quantum_enabled: false # Deploy to Azure Quantum - -# Quantum-Enhanced LLM Training -quantum_llm: - enabled: true # Enable quantum-enhanced LLM training - passive_mode: true # Run in background with autonomous orchestrator - backend: "local" # Options: "local" (simulator), "azure" (Azure Quantum) - n_qubits: 4 # Number of qubits - n_quantum_layers: 2 # Depth of quantum circuits - training_interval_minutes: 60 # Run quantum LLM training every hour - optimize_attention: true # Apply quantum optimization to attention - quantum_feature_encoding: true # Use quantum feature encoding - config_file: "config/quantum_llm_config.yaml" # Detailed configuration - -# Advanced Settings -learning_strategy: - curriculum_learning: false # Start with easier datasets - active_learning: false # Prioritize uncertain predictions - transfer_learning: false # Use pre-trained models - -resource_management: - max_gpu_memory_gb: 0 # 0 = use all available - max_cpu_cores: 20 - disk_space_warning_gb: 50 # Alert when disk space below this - -notifications: - enabled: false - email: "" - slack_webhook: "" - alert_on_completion: false - alert_on_error: true +# Autonomous AI Training Configuration +# This file controls automatic learning cycles and data collection + +autonomous_mode: + enabled: true + continuous: true # Set to false for single-cycle mode + cycle_interval_minutes: 30 # Time between training cycles + max_cycles: 0 # 0 = infinite, set to positive number to limit cycles + +data_collection: + auto_discover: true # Automatically scan for new datasets + min_datasets: 500 # Minimum datasets to maintain + quality_threshold: 50 # Minimum quality score (0-100) + categories: + - quantum + - chat + - vision + sources: + - openml + - huggingface + - kaggle + parallel_downloads: 10 # Concurrent downloads + +training: + auto_train: true + epochs_progression: # Progressive epoch training + - 25 + - 50 + - 100 + - 200 + workers: 20 # Parallel training workers + min_accuracy_threshold: 0.75 + adaptive_epochs: true # Automatically adjust epochs based on performance + checkpoint_frequency: 10 # Save checkpoint every N datasets + +optimization: + auto_tune: true # Enable automatic hyperparameter tuning + hyperparameter_search: true + architecture_evolution: false # Neural architecture search (experimental) + pruning_enabled: false # Model pruning (experimental) + +monitoring: + track_metrics: true + alert_on_degradation: true + performance_threshold: 0.70 # Alert if accuracy drops below this + +deployment: + auto_deploy_best: false # Automatically deploy models meeting criteria + min_accuracy_for_deployment: 0.90 + azure_quantum_enabled: false # Deploy to Azure Quantum + +# Quantum-Enhanced LLM Training +quantum_llm: + enabled: true # Enable quantum-enhanced LLM training + passive_mode: true # Run in background with autonomous orchestrator + backend: "local" # Options: "local" (simulator), "azure" (Azure Quantum) + n_qubits: 4 # Number of qubits + n_quantum_layers: 2 # Depth of quantum circuits + training_interval_minutes: 60 # Run quantum LLM training every hour + optimize_attention: true # Apply quantum optimization to attention + quantum_feature_encoding: true # Use quantum feature encoding + config_file: "config/quantum_llm_config.yaml" # Detailed configuration + +# Advanced Settings +learning_strategy: + curriculum_learning: false # Start with easier datasets + active_learning: false # Prioritize uncertain predictions + transfer_learning: false # Use pre-trained models + +resource_management: + max_gpu_memory_gb: 0 # 0 = use all available + max_cpu_cores: 20 + disk_space_warning_gb: 50 # Alert when disk space below this + +notifications: + enabled: false + email: "" + slack_webhook: "" + alert_on_completion: false + alert_on_error: true diff --git a/config/azure_monitor_alerts.json b/config/azure_monitor_alerts.json index d973bbae4..abe5afe4f 100644 --- a/config/azure_monitor_alerts.json +++ b/config/azure_monitor_alerts.json @@ -1,153 +1,153 @@ -{ - "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#", - "contentVersion": "1.0.0.0", - "parameters": { - "functionAppName": { - "type": "string", - "metadata": { - "description": "Name of the Azure Function App to monitor" - } - }, - "actionGroupId": { - "type": "string", - "metadata": { - "description": "Resource ID of the Action Group for alert notifications" - } - } - }, - "variables": { - "location": "[resourceGroup().location]" - }, - "resources": [ - { - "type": "Microsoft.Insights/metricAlerts", - "apiVersion": "2018-03-01", - "name": "[concat(parameters('functionAppName'), '-sql-pool-saturation')]", - "location": "global", - "properties": { - "description": "Alert when SQL connection pool saturation exceeds 80%", - "severity": 2, - "enabled": true, - "scopes": [ - "[resourceId('Microsoft.Web/sites', parameters('functionAppName'))]" - ], - "evaluationFrequency": "PT1M", - "windowSize": "PT5M", - "criteria": { - "allOf": [ - { - "threshold": 0, - "name": "PoolSaturationAlert", - "metricNamespace": "Microsoft.Web/sites", - "metricName": "CustomMetric", - "dimensions": [ - { - "name": "CustomMetricName", - "operator": "Include", - "values": ["sql_pool_saturation_pct"] - } - ], - "operator": "GreaterThan", - "timeAggregation": "Average", - "criterionType": "StaticThresholdCriterion" - } - ], - "odata.type": "Microsoft.Azure.Monitor.SingleResourceMultipleMetricCriteria" - }, - "actions": [ - { - "actionGroupId": "[parameters('actionGroupId')]" - } - ] - } - }, - { - "type": "Microsoft.Insights/scheduledQueryRules", - "apiVersion": "2021-08-01", - "name": "[concat(parameters('functionAppName'), '-sql-slow-query-frequency')]", - "location": "[variables('location')]", - "properties": { - "displayName": "SQL Slow Query Frequency Alert", - "description": "Alert when more than 10 slow queries detected in 1 minute", - "severity": 3, - "enabled": true, - "evaluationFrequency": "PT5M", - "scopes": [ - "[resourceId('Microsoft.Insights/components', parameters('functionAppName'))]" - ], - "windowSize": "PT5M", - "criteria": { - "allOf": [ - { - "query": "traces\n| where message has 'slow query'\n| where timestamp > ago(1m)\n| summarize SlowQueryCount = count() by bin(timestamp, 1m)\n| where SlowQueryCount > 10", - "timeAggregation": "Count", - "dimensions": [], - "operator": "GreaterThan", - "threshold": 0, - "failingPeriods": { - "numberOfEvaluationPeriods": 1, - "minFailingPeriodsToAlert": 1 - } - } - ] - }, - "actions": { - "actionGroups": [ - "[parameters('actionGroupId')]" - ] - } - } - }, - { - "type": "Microsoft.Insights/scheduledQueryRules", - "apiVersion": "2021-08-01", - "name": "[concat(parameters('functionAppName'), '-sql-saturation-alert')]", - "location": "[variables('location')]", - "properties": { - "displayName": "SQL Pool Saturation Alert", - "description": "Alert when saturation_alert field is present in status endpoint response", - "severity": 2, - "enabled": true, - "evaluationFrequency": "PT5M", - "scopes": [ - "[resourceId('Microsoft.Insights/components', parameters('functionAppName'))]" - ], - "windowSize": "PT5M", - "criteria": { - "allOf": [ - { - "query": "requests\n| where url endswith '/api/ai/status'\n| extend saturation_alert = tostring(customDimensions.sql_saturation_alert)\n| where isnotempty(saturation_alert)\n| summarize AlertCount = count() by bin(timestamp, 5m)\n| where AlertCount > 0", - "timeAggregation": "Count", - "dimensions": [], - "operator": "GreaterThan", - "threshold": 0, - "failingPeriods": { - "numberOfEvaluationPeriods": 2, - "minFailingPeriodsToAlert": 2 - } - } - ] - }, - "actions": { - "actionGroups": [ - "[parameters('actionGroupId')]" - ] - } - } - } - ], - "outputs": { - "poolSaturationAlertId": { - "type": "string", - "value": "[resourceId('Microsoft.Insights/metricAlerts', concat(parameters('functionAppName'), '-sql-pool-saturation'))]" - }, - "slowQueryAlertId": { - "type": "string", - "value": "[resourceId('Microsoft.Insights/scheduledQueryRules', concat(parameters('functionAppName'), '-sql-slow-query-frequency'))]" - }, - "saturationStatusAlertId": { - "type": "string", - "value": "[resourceId('Microsoft.Insights/scheduledQueryRules', concat(parameters('functionAppName'), '-sql-saturation-alert'))]" - } - } -} +{ + "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#", + "contentVersion": "1.0.0.0", + "parameters": { + "functionAppName": { + "type": "string", + "metadata": { + "description": "Name of the Azure Function App to monitor" + } + }, + "actionGroupId": { + "type": "string", + "metadata": { + "description": "Resource ID of the Action Group for alert notifications" + } + } + }, + "variables": { + "location": "[resourceGroup().location]" + }, + "resources": [ + { + "type": "Microsoft.Insights/metricAlerts", + "apiVersion": "2018-03-01", + "name": "[concat(parameters('functionAppName'), '-sql-pool-saturation')]", + "location": "global", + "properties": { + "description": "Alert when SQL connection pool saturation exceeds 80%", + "severity": 2, + "enabled": true, + "scopes": [ + "[resourceId('Microsoft.Web/sites', parameters('functionAppName'))]" + ], + "evaluationFrequency": "PT1M", + "windowSize": "PT5M", + "criteria": { + "allOf": [ + { + "threshold": 0, + "name": "PoolSaturationAlert", + "metricNamespace": "Microsoft.Web/sites", + "metricName": "CustomMetric", + "dimensions": [ + { + "name": "CustomMetricName", + "operator": "Include", + "values": ["sql_pool_saturation_pct"] + } + ], + "operator": "GreaterThan", + "timeAggregation": "Average", + "criterionType": "StaticThresholdCriterion" + } + ], + "odata.type": "Microsoft.Azure.Monitor.SingleResourceMultipleMetricCriteria" + }, + "actions": [ + { + "actionGroupId": "[parameters('actionGroupId')]" + } + ] + } + }, + { + "type": "Microsoft.Insights/scheduledQueryRules", + "apiVersion": "2021-08-01", + "name": "[concat(parameters('functionAppName'), '-sql-slow-query-frequency')]", + "location": "[variables('location')]", + "properties": { + "displayName": "SQL Slow Query Frequency Alert", + "description": "Alert when more than 10 slow queries detected in 1 minute", + "severity": 3, + "enabled": true, + "evaluationFrequency": "PT5M", + "scopes": [ + "[resourceId('Microsoft.Insights/components', parameters('functionAppName'))]" + ], + "windowSize": "PT5M", + "criteria": { + "allOf": [ + { + "query": "traces\n| where message has 'slow query'\n| where timestamp > ago(1m)\n| summarize SlowQueryCount = count() by bin(timestamp, 1m)\n| where SlowQueryCount > 10", + "timeAggregation": "Count", + "dimensions": [], + "operator": "GreaterThan", + "threshold": 0, + "failingPeriods": { + "numberOfEvaluationPeriods": 1, + "minFailingPeriodsToAlert": 1 + } + } + ] + }, + "actions": { + "actionGroups": [ + "[parameters('actionGroupId')]" + ] + } + } + }, + { + "type": "Microsoft.Insights/scheduledQueryRules", + "apiVersion": "2021-08-01", + "name": "[concat(parameters('functionAppName'), '-sql-saturation-alert')]", + "location": "[variables('location')]", + "properties": { + "displayName": "SQL Pool Saturation Alert", + "description": "Alert when saturation_alert field is present in status endpoint response", + "severity": 2, + "enabled": true, + "evaluationFrequency": "PT5M", + "scopes": [ + "[resourceId('Microsoft.Insights/components', parameters('functionAppName'))]" + ], + "windowSize": "PT5M", + "criteria": { + "allOf": [ + { + "query": "requests\n| where url endswith '/api/ai/status'\n| extend saturation_alert = tostring(customDimensions.sql_saturation_alert)\n| where isnotempty(saturation_alert)\n| summarize AlertCount = count() by bin(timestamp, 5m)\n| where AlertCount > 0", + "timeAggregation": "Count", + "dimensions": [], + "operator": "GreaterThan", + "threshold": 0, + "failingPeriods": { + "numberOfEvaluationPeriods": 2, + "minFailingPeriodsToAlert": 2 + } + } + ] + }, + "actions": { + "actionGroups": [ + "[parameters('actionGroupId')]" + ] + } + } + } + ], + "outputs": { + "poolSaturationAlertId": { + "type": "string", + "value": "[resourceId('Microsoft.Insights/metricAlerts', concat(parameters('functionAppName'), '-sql-pool-saturation'))]" + }, + "slowQueryAlertId": { + "type": "string", + "value": "[resourceId('Microsoft.Insights/scheduledQueryRules', concat(parameters('functionAppName'), '-sql-slow-query-frequency'))]" + }, + "saturationStatusAlertId": { + "type": "string", + "value": "[resourceId('Microsoft.Insights/scheduledQueryRules', concat(parameters('functionAppName'), '-sql-saturation-alert'))]" + } + } +} diff --git a/config/evaluation/batch_eval_config.yaml b/config/evaluation/batch_eval_config.yaml index 1f591e082..92e4e564a 100644 --- a/config/evaluation/batch_eval_config.yaml +++ b/config/evaluation/batch_eval_config.yaml @@ -1,51 +1,51 @@ -# For evaluation_script.py compatibility -jobs: - - model_path: data_out/lora_training/phi35_mixed_chat - dataset_path: datasets/chat/mixed_chat - - model_path: data_out/lora_training/phi35_dolly - dataset_path: datasets/chat/dolly -# Batch Evaluation Configuration - -evaluation_tasks: - - model_id: lora_phi35_mixed - model_type: lora - model_path: data_out/lora_training/phi35_mixed_chat - dataset: datasets/chat/mixed_chat - metrics: - - accuracy - - perplexity - - bleu - max_samples: 100 - batch_size: 8 - - - model_id: lora_phi35_dolly - model_type: lora - model_path: data_out/lora_training/phi35_dolly - dataset: datasets/chat/dolly - metrics: - - accuracy - - perplexity - - f1_score - max_samples: 100 - batch_size: 8 - - - model_id: quantum_heart - model_type: quantum - model_path: quantum_heart_classifier - dataset: datasets/quantum/heart_disease.csv - metrics: - - accuracy - - precision - - recall - max_samples: 50 - batch_size: 4 - - - model_id: local_baseline - model_type: local - model_path: local_model - dataset: datasets/chat/mixed_chat - metrics: - - accuracy - - response_time - max_samples: 50 - batch_size: 8 +# For evaluation_script.py compatibility +jobs: + - model_path: data_out/lora_training/phi35_mixed_chat + dataset_path: datasets/chat/mixed_chat + - model_path: data_out/lora_training/phi35_dolly + dataset_path: datasets/chat/dolly +# Batch Evaluation Configuration + +evaluation_tasks: + - model_id: lora_phi35_mixed + model_type: lora + model_path: data_out/lora_training/phi35_mixed_chat + dataset: datasets/chat/mixed_chat + metrics: + - accuracy + - perplexity + - bleu + max_samples: 100 + batch_size: 8 + + - model_id: lora_phi35_dolly + model_type: lora + model_path: data_out/lora_training/phi35_dolly + dataset: datasets/chat/dolly + metrics: + - accuracy + - perplexity + - f1_score + max_samples: 100 + batch_size: 8 + + - model_id: quantum_heart + model_type: quantum + model_path: quantum_heart_classifier + dataset: datasets/quantum/heart_disease.csv + metrics: + - accuracy + - precision + - recall + max_samples: 50 + batch_size: 4 + + - model_id: local_baseline + model_type: local + model_path: local_model + dataset: datasets/chat/mixed_chat + metrics: + - accuracy + - response_time + max_samples: 50 + batch_size: 8 diff --git a/config/evaluation/evaluation_autorun.yaml b/config/evaluation/evaluation_autorun.yaml index 3e48b83c4..f22843493 100644 --- a/config/evaluation/evaluation_autorun.yaml +++ b/config/evaluation/evaluation_autorun.yaml @@ -1,133 +1,133 @@ -version: 1 - -# Evaluation AutoRun Configuration -# Automates model evaluation against test datasets with multiple metrics - -jobs: - # Quick smoke test evaluation (fast, minimal samples) - - name: eval_smoke_test - enabled: true - model_type: lora # "lora" | "azure" | "openai" | "local" - model_path: data_out/lora_training/phi35 - dataset: datasets/chat/mixed_chat - max_samples: 10 - metrics: - - accuracy - - response_time - output_format: json - save_results: true - - # Full LoRA adapter evaluation - - name: eval_lora_phi35_full - enabled: true - model_type: lora - model_path: data_out/lora_training/phi35 - dataset: datasets/chat/dolly - max_samples: 100 - metrics: - - accuracy - - bleu - - rouge - - response_time - - token_efficiency - output_format: json - save_results: true - batch_size: 4 - - # Azure OpenAI baseline comparison - - name: eval_azure_baseline - enabled: false # Requires Azure creds - model_type: azure - azure_deployment: gpt-4o-mini - dataset: datasets/chat/mixed_chat - max_samples: 50 - metrics: - - accuracy - - response_time - - cost_per_token - output_format: json - save_results: true - - # Quantum classifier evaluation - - name: eval_quantum_heart - enabled: true - model_type: quantum - model_path: ai-projects/quantum-ml/results/heart_disease_model.json - dataset: datasets/quantum/heart_disease.csv - max_samples: null # Use full test set - metrics: - - accuracy - - precision - - recall - - f1_score - output_format: json - save_results: true - - # Local provider baseline (free, offline) - - name: eval_local_baseline - enabled: true - model_type: local - dataset: datasets/chat/mixed_chat - max_samples: 20 - metrics: - - response_time - - determinism - output_format: json - save_results: true - - # Hyperparameter variant evaluations (fast, small sample) - - name: eval_phi35_lr_low - enabled: true - model_type: lora - model_path: data_out/lora_training/phi35_lr_low - dataset: datasets/chat/mixed_chat - max_samples: 20 - metrics: - - accuracy - - bleu - - rouge - - response_time - output_format: json - save_results: true - - - name: eval_phi35_lr_high - enabled: true - model_type: lora - model_path: data_out/lora_training/phi35_lr_high - dataset: datasets/chat/mixed_chat - max_samples: 20 - metrics: - - accuracy - - bleu - - rouge - - response_time - output_format: json - save_results: true - - - name: eval_phi35_drop_low - enabled: true - model_type: lora - model_path: data_out/lora_training/phi35_drop_low - dataset: datasets/chat/mixed_chat - max_samples: 20 - metrics: - - accuracy - - bleu - - rouge - - response_time - output_format: json - save_results: true - - - name: eval_phi35_drop_high - enabled: true - model_type: lora - model_path: data_out/lora_training/phi35_drop_high - dataset: datasets/chat/mixed_chat - max_samples: 20 - metrics: - - accuracy - - bleu - - rouge - - response_time - output_format: json - save_results: true +version: 1 + +# Evaluation AutoRun Configuration +# Automates model evaluation against test datasets with multiple metrics + +jobs: + # Quick smoke test evaluation (fast, minimal samples) + - name: eval_smoke_test + enabled: true + model_type: lora # "lora" | "azure" | "openai" | "local" + model_path: data_out/lora_training/phi35 + dataset: datasets/chat/mixed_chat + max_samples: 10 + metrics: + - accuracy + - response_time + output_format: json + save_results: true + + # Full LoRA adapter evaluation + - name: eval_lora_phi35_full + enabled: true + model_type: lora + model_path: data_out/lora_training/phi35 + dataset: datasets/chat/dolly + max_samples: 100 + metrics: + - accuracy + - bleu + - rouge + - response_time + - token_efficiency + output_format: json + save_results: true + batch_size: 4 + + # Azure OpenAI baseline comparison + - name: eval_azure_baseline + enabled: false # Requires Azure creds + model_type: azure + azure_deployment: gpt-4o-mini + dataset: datasets/chat/mixed_chat + max_samples: 50 + metrics: + - accuracy + - response_time + - cost_per_token + output_format: json + save_results: true + + # Quantum classifier evaluation + - name: eval_quantum_heart + enabled: true + model_type: quantum + model_path: ai-projects/quantum-ml/results/heart_disease_model.json + dataset: datasets/quantum/heart_disease.csv + max_samples: null # Use full test set + metrics: + - accuracy + - precision + - recall + - f1_score + output_format: json + save_results: true + + # Local provider baseline (free, offline) + - name: eval_local_baseline + enabled: true + model_type: local + dataset: datasets/chat/mixed_chat + max_samples: 20 + metrics: + - response_time + - determinism + output_format: json + save_results: true + + # Hyperparameter variant evaluations (fast, small sample) + - name: eval_phi35_lr_low + enabled: true + model_type: lora + model_path: data_out/lora_training/phi35_lr_low + dataset: datasets/chat/mixed_chat + max_samples: 20 + metrics: + - accuracy + - bleu + - rouge + - response_time + output_format: json + save_results: true + + - name: eval_phi35_lr_high + enabled: true + model_type: lora + model_path: data_out/lora_training/phi35_lr_high + dataset: datasets/chat/mixed_chat + max_samples: 20 + metrics: + - accuracy + - bleu + - rouge + - response_time + output_format: json + save_results: true + + - name: eval_phi35_drop_low + enabled: true + model_type: lora + model_path: data_out/lora_training/phi35_drop_low + dataset: datasets/chat/mixed_chat + max_samples: 20 + metrics: + - accuracy + - bleu + - rouge + - response_time + output_format: json + save_results: true + + - name: eval_phi35_drop_high + enabled: true + model_type: lora + model_path: data_out/lora_training/phi35_drop_high + dataset: datasets/chat/mixed_chat + max_samples: 20 + metrics: + - accuracy + - bleu + - rouge + - response_time + output_format: json + save_results: true diff --git a/config/master_orchestrator.yaml b/config/master_orchestrator.yaml index de88a25e4..3c251c567 100644 --- a/config/master_orchestrator.yaml +++ b/config/master_orchestrator.yaml @@ -1,191 +1,191 @@ -version: 1 - -# Master Orchestrator Configuration -# Coordinates all sub-orchestrators and manages automated workflows - -orchestrators: - - name: autotrain - script: scripts/autotrain.py - enabled: true - schedule: "0 2 * * *" # Daily at 2 AM - priority: 1 - retry_on_failure: 3 - timeout_minutes: 240 - dependencies: [] - - - name: quantum_autorun - script: scripts/quantum_autorun.py - enabled: true - schedule: "0 4 * * *" # Daily at 4 AM - priority: 2 - retry_on_failure: 2 - timeout_minutes: 180 - dependencies: [] - - - name: evaluation_autorun - script: scripts/evaluation_autorun.py - enabled: true - schedule: "0 6 * * *" # Daily at 6 AM after training - priority: 3 - retry_on_failure: 1 - timeout_minutes: 120 - dependencies: [autotrain, quantum_autorun] # Wait for training to complete - - - name: autonomous_training - script: scripts/autonomous_training_orchestrator.py - enabled: false # Enable for continuous learning - schedule: "continuous" # Run continuously - priority: 4 - retry_on_failure: -1 # Infinite retries - timeout_minutes: 0 # No timeout - dependencies: [] - - - name: aria_automation - script: scripts/aria_automation.py - enabled: true - schedule: "*/30 * * * *" # Every 30 minutes - priority: 5 - retry_on_failure: 3 - timeout_minutes: 15 - dependencies: [] - flags: - mode: training - once: true - -workflows: - # Daily full training pipeline - - name: daily_full_pipeline - enabled: true - trigger: "schedule" # schedule | manual | webhook - schedule: "0 1 * * *" # Daily at 1 AM - orchestrators: - - autotrain - - quantum_autorun - - evaluation_autorun - on_success: - - notify_slack - - update_dashboard - - deploy_best_models - on_failure: - - notify_slack - - create_issue - - # Quick validation pipeline (for CI/CD) - - name: quick_validation - enabled: true - trigger: "manual" - orchestrators: - - autotrain # with --dry-run flag - - quantum_autorun # with --dry-run flag - - evaluation_autorun # with --dry-run flag - flags: - dry_run: true - on_success: - - log_result - on_failure: - - fail_build - - # Weekly comprehensive evaluation - - name: weekly_comprehensive - enabled: true - trigger: "schedule" - schedule: "0 0 * * 0" # Sundays at midnight - orchestrators: - - autotrain # All jobs - - quantum_autorun # All jobs - - evaluation_autorun # All jobs with full datasets - flags: - max_train_samples: null # Use full datasets - max_eval_samples: null - on_success: - - generate_report - - notify_slack - - backup_models - on_failure: - - notify_slack - - create_incident - - # Aria continuous operation (server + periodic training) - - name: aria_full_stack - enabled: false # Enable to run Aria 24/7 - trigger: "manual" # Start manually, runs continuously - orchestrators: - - aria_automation - flags: - mode: full # server + training + monitoring - on_success: - - log_result - on_failure: - - restart_aria - - notify_admin - - # Aria server only (no training) - - name: aria_server_only - enabled: false - trigger: "manual" - orchestrators: - - aria_automation - flags: - mode: server - on_success: - - log_result - on_failure: - - restart_aria - -automation: - auto_cleanup: - enabled: true - keep_last_n_runs: 10 - clean_older_than_days: 30 - - auto_backup: - enabled: true - schedule: "0 3 * * *" # Daily at 3 AM - targets: - - data_out/lora_training - - data_out/quantum_autorun - - data_out/evaluation_autorun - destination: backup/ - - auto_optimization: - enabled: true - analyze_results: true - suggest_hyperparams: true - auto_tune_jobs: false # Set true to apply suggestions automatically - - dependency_management: - auto_update_packages: false - check_compatibility: true - pin_versions: true - -monitoring: - health_check_interval_seconds: 60 - metrics: - - orchestrator_status - - job_success_rate - - average_duration - - resource_utilization - alerts: - - type: slack - webhook: "${SLACK_WEBHOOK_URL}" - on_failure: true - on_success: false - - type: email - to: "${ADMIN_EMAIL}" - on_failure: true - on_degradation: true - -resource_limits: - max_concurrent_orchestrators: 2 - max_cpu_percent: 80 - max_memory_gb: 16 - pause_on_resource_exhaustion: true - -notifications: - slack_enabled: false - email_enabled: false - webhook_url: "" - notify_on_start: false - notify_on_completion: true - notify_on_error: true - include_metrics: true +version: 1 + +# Master Orchestrator Configuration +# Coordinates all sub-orchestrators and manages automated workflows + +orchestrators: + - name: autotrain + script: scripts/autotrain.py + enabled: true + schedule: "0 2 * * *" # Daily at 2 AM + priority: 1 + retry_on_failure: 3 + timeout_minutes: 240 + dependencies: [] + + - name: quantum_autorun + script: scripts/quantum_autorun.py + enabled: true + schedule: "0 4 * * *" # Daily at 4 AM + priority: 2 + retry_on_failure: 2 + timeout_minutes: 180 + dependencies: [] + + - name: evaluation_autorun + script: scripts/evaluation_autorun.py + enabled: true + schedule: "0 6 * * *" # Daily at 6 AM after training + priority: 3 + retry_on_failure: 1 + timeout_minutes: 120 + dependencies: [autotrain, quantum_autorun] # Wait for training to complete + + - name: autonomous_training + script: scripts/autonomous_training_orchestrator.py + enabled: false # Enable for continuous learning + schedule: "continuous" # Run continuously + priority: 4 + retry_on_failure: -1 # Infinite retries + timeout_minutes: 0 # No timeout + dependencies: [] + + - name: aria_automation + script: scripts/aria_automation.py + enabled: true + schedule: "*/30 * * * *" # Every 30 minutes + priority: 5 + retry_on_failure: 3 + timeout_minutes: 15 + dependencies: [] + flags: + mode: training + once: true + +workflows: + # Daily full training pipeline + - name: daily_full_pipeline + enabled: true + trigger: "schedule" # schedule | manual | webhook + schedule: "0 1 * * *" # Daily at 1 AM + orchestrators: + - autotrain + - quantum_autorun + - evaluation_autorun + on_success: + - notify_slack + - update_dashboard + - deploy_best_models + on_failure: + - notify_slack + - create_issue + + # Quick validation pipeline (for CI/CD) + - name: quick_validation + enabled: true + trigger: "manual" + orchestrators: + - autotrain # with --dry-run flag + - quantum_autorun # with --dry-run flag + - evaluation_autorun # with --dry-run flag + flags: + dry_run: true + on_success: + - log_result + on_failure: + - fail_build + + # Weekly comprehensive evaluation + - name: weekly_comprehensive + enabled: true + trigger: "schedule" + schedule: "0 0 * * 0" # Sundays at midnight + orchestrators: + - autotrain # All jobs + - quantum_autorun # All jobs + - evaluation_autorun # All jobs with full datasets + flags: + max_train_samples: null # Use full datasets + max_eval_samples: null + on_success: + - generate_report + - notify_slack + - backup_models + on_failure: + - notify_slack + - create_incident + + # Aria continuous operation (server + periodic training) + - name: aria_full_stack + enabled: false # Enable to run Aria 24/7 + trigger: "manual" # Start manually, runs continuously + orchestrators: + - aria_automation + flags: + mode: full # server + training + monitoring + on_success: + - log_result + on_failure: + - restart_aria + - notify_admin + + # Aria server only (no training) + - name: aria_server_only + enabled: false + trigger: "manual" + orchestrators: + - aria_automation + flags: + mode: server + on_success: + - log_result + on_failure: + - restart_aria + +automation: + auto_cleanup: + enabled: true + keep_last_n_runs: 10 + clean_older_than_days: 30 + + auto_backup: + enabled: true + schedule: "0 3 * * *" # Daily at 3 AM + targets: + - data_out/lora_training + - data_out/quantum_autorun + - data_out/evaluation_autorun + destination: backup/ + + auto_optimization: + enabled: true + analyze_results: true + suggest_hyperparams: true + auto_tune_jobs: false # Set true to apply suggestions automatically + + dependency_management: + auto_update_packages: false + check_compatibility: true + pin_versions: true + +monitoring: + health_check_interval_seconds: 60 + metrics: + - orchestrator_status + - job_success_rate + - average_duration + - resource_utilization + alerts: + - type: slack + webhook: "${SLACK_WEBHOOK_URL}" + on_failure: true + on_success: false + - type: email + to: "${ADMIN_EMAIL}" + on_failure: true + on_degradation: true + +resource_limits: + max_concurrent_orchestrators: 2 + max_cpu_percent: 80 + max_memory_gb: 16 + pause_on_resource_exhaustion: true + +notifications: + slack_enabled: false + email_enabled: false + webhook_url: "" + notify_on_start: false + notify_on_completion: true + notify_on_error: true + include_metrics: true diff --git a/config/notification_config.yaml b/config/notification_config.yaml index 04679f97d..190f679ca 100644 --- a/config/notification_config.yaml +++ b/config/notification_config.yaml @@ -1,7 +1,7 @@ -# Central notification config for all orchestrators/tools -email_enabled: false -email_from: "noreply@qai.local" -email_to: "admin@qai.local" -smtp_server: "localhost" -local_alert: true -log_file: "data_out/notifications.log" +# Central notification config for all orchestrators/tools +email_enabled: false +email_from: "noreply@qai.local" +email_to: "admin@qai.local" +smtp_server: "localhost" +local_alert: true +log_file: "data_out/notifications.log" diff --git a/config/quantum/quantum_autorun.yaml b/config/quantum/quantum_autorun.yaml index 26aecbc43..9e9a2e541 100644 --- a/config/quantum/quantum_autorun.yaml +++ b/config/quantum/quantum_autorun.yaml @@ -1,59 +1,59 @@ -version: 1 -# Quantum AutoRun jobs: define quick local training runs for quantum-ai -# Notes: -# - Local jobs (mode: train_custom_dataset) are FREE and use local simulators -# - Azure jobs (mode: azure_hardware) can incur costs for QPU usage -# - Keep epochs small for smoke tests. Increase later for real training. -# - Choose either 'preset' or 'csv' per job (do not set both). - -jobs: - # === LOCAL SIMULATOR JOBS (FREE) === - - name: heart_quick - mode: train_custom_dataset - preset: heart - # csv: datasets/quantum/heart_disease.csv # alternative to preset - # label_col: class - # drop_cols: '' # comma-separated list - n_qubits: 4 - epochs: 50 - batch_size: 16 - learning_rate: 0.001 - test_size: 0.2 - enabled: true - - - name: ionosphere_quick - mode: train_custom_dataset - preset: ionosphere - n_qubits: 4 - epochs: 100 - batch_size: 16 - learning_rate: 0.001 - test_size: 0.2 - enabled: true # ENABLED for quantum training - - # === AZURE QUANTUM JOBS (MAY INCUR COSTS) === - # IMPORTANT: Set azure_confirm_cost: true to acknowledge hardware costs - # Use simulators (*.simulator) for free Azure testing - - - name: azure_quantinuum_simulator - mode: azure_hardware - azure_backend: quantinuum.sim.h2-1sc # FREE: Quantinuum cloud simulator - azure_shots: 100 - qubits: 3 - enabled: true # ENABLED for Azure testing - # Note: Requires Azure credentials and quantum workspace - # Run: az login before executing this job - - - name: azure_ionq_qpu_test - mode: azure_hardware - azure_backend: ionq.qpu # PAID: Real IonQ quantum hardware - azure_shots: 100 - n_qubits: 3 - azure_confirm_cost: false # SAFETY: Set to true to enable (acknowledges costs) - enabled: false - # Cost estimate: ~$0.00003 per gate-shot - # Always test on simulator first! - # Requires: az login, valid Azure Quantum workspace - extra_args: - - --circuit-file - - ai-projects/quantum-ml/results/sample_circuit.qasm # path to QASM circuit file +version: 1 +# Quantum AutoRun jobs: define quick local training runs for quantum-ai +# Notes: +# - Local jobs (mode: train_custom_dataset) are FREE and use local simulators +# - Azure jobs (mode: azure_hardware) can incur costs for QPU usage +# - Keep epochs small for smoke tests. Increase later for real training. +# - Choose either 'preset' or 'csv' per job (do not set both). + +jobs: + # === LOCAL SIMULATOR JOBS (FREE) === + - name: heart_quick + mode: train_custom_dataset + preset: heart + # csv: datasets/quantum/heart_disease.csv # alternative to preset + # label_col: class + # drop_cols: '' # comma-separated list + n_qubits: 4 + epochs: 50 + batch_size: 16 + learning_rate: 0.001 + test_size: 0.2 + enabled: true + + - name: ionosphere_quick + mode: train_custom_dataset + preset: ionosphere + n_qubits: 4 + epochs: 100 + batch_size: 16 + learning_rate: 0.001 + test_size: 0.2 + enabled: true # ENABLED for quantum training + + # === AZURE QUANTUM JOBS (MAY INCUR COSTS) === + # IMPORTANT: Set azure_confirm_cost: true to acknowledge hardware costs + # Use simulators (*.simulator) for free Azure testing + + - name: azure_quantinuum_simulator + mode: azure_hardware + azure_backend: quantinuum.sim.h2-1sc # FREE: Quantinuum cloud simulator + azure_shots: 100 + qubits: 3 + enabled: true # ENABLED for Azure testing + # Note: Requires Azure credentials and quantum workspace + # Run: az login before executing this job + + - name: azure_ionq_qpu_test + mode: azure_hardware + azure_backend: ionq.qpu # PAID: Real IonQ quantum hardware + azure_shots: 100 + n_qubits: 3 + azure_confirm_cost: false # SAFETY: Set to true to enable (acknowledges costs) + enabled: false + # Cost estimate: ~$0.00003 per gate-shot + # Always test on simulator first! + # Requires: az login, valid Azure Quantum workspace + extra_args: + - --circuit-file + - ai-projects/quantum-ml/results/sample_circuit.qasm # path to QASM circuit file diff --git a/config/quantum_llm_config.yaml b/config/quantum_llm_config.yaml index 323cb6e7b..e16a972d4 100644 --- a/config/quantum_llm_config.yaml +++ b/config/quantum_llm_config.yaml @@ -1,132 +1,132 @@ -# Quantum-Enhanced LLM Training Configuration -# This file configures quantum computing integration for passive LLM training - -quantum_settings: - # Quantum backend selection - backend: "local" # Options: "local" (simulator), "azure" (Azure Quantum) - - # Quantum circuit parameters - n_qubits: 4 # Number of qubits (higher = more expressive but slower) - n_quantum_layers: 2 # Depth of quantum circuits - entanglement: "circular" # Options: "linear", "circular", "full" - - # Azure Quantum settings (when backend: "azure") - azure: - resource_id: "" # Azure Quantum workspace resource ID - location: "eastus" # Azure region - target_backend: "ionq.simulator" # Options: ionq.simulator, ionq.qpu, quantinuum.sim.h1-1e - shots: 100 # Number of circuit executions per measurement - confirm_cost: false # Must be true for paid QPU backends - -# Passive training settings -passive_training: - enabled: true - interval_seconds: 3600 # Run training cycle every hour - datasets_dir: "datasets/chat" # Directory to scan for datasets - epochs_per_cycle: 1 # Epochs per passive training cycle - max_cycles: 0 # 0 = infinite, positive number to limit cycles - -# LLM training parameters -llm_training: - model_name: "microsoft/phi-2" # Base model for fine-tuning - learning_rate: 0.0001 - batch_size: 4 - gradient_accumulation_steps: 8 - max_seq_length: 512 - - # LoRA configuration - lora: - enabled: true - r: 8 # LoRA rank - alpha: 16 # LoRA alpha - dropout: 0.05 - target_modules: ["q_proj", "v_proj"] # Attention modules to apply LoRA - -# Quantum Transformer Architecture -# Used by quantum_transformer.py and quantum_llm_trainer.py -quantum_transformer: - # Model dimensions - vocab_size: 256 # Character-level vocabulary - d_model: 64 # Embedding / model dimension - n_heads: 4 # Attention heads (d_model / n_heads should = 2^n_qubits) - n_transformer_layers: 2 # Number of QuantumTransformerBlocks - max_seq_len: 32 # Maximum sequence length - dropout: 0.1 - - # Quantum circuit settings - n_qubits: 4 # Qubits per quantum circuit (4-8) - n_quantum_layers: 2 # Variational layers per quantum circuit - entanglement: "circular" # linear, circular, full - - # Component selection - use_quantum_attention: true # Use QuantumSelfAttention (vs classical) - use_quantum_ffn: true # Use QuantumFeedForward (vs classical) - - # Weight management - tie_embeddings: true # Share token_embedding and lm_head weights - - # Training hyperparameters - learning_rate: 0.001 - weight_decay: 0.01 - gradient_clip: 1.0 - batch_size: 4 # Small batch for quantum circuit speed - -# Quantum enhancement strategies (legacy, kept for backward compat) -quantum_enhancement: - optimize_attention: true - attention_optimization_frequency: 1 # Every step (real quantum circuits now) - quantum_feature_encoding: true - encoding_dimension: 16 # 2^n_qubits for 4 qubits - quantum_hyperparameter_tuning: false - cache_circuits: true - cache_size: 100 - cache_ttl_seconds: 3600 - -# Integration with autonomous training -autonomous_integration: - enabled: true - report_to_orchestrator: true - status_file: "data_out/quantum_llm_training/status.json" - - # Performance thresholds - min_loss_improvement: 0.01 # Minimum improvement to consider cycle successful - max_loss_threshold: 5.0 # Alert if loss exceeds this value - - # Auto-deployment - auto_deploy: false - deployment_loss_threshold: 1.0 # Deploy model if loss below this - -# Monitoring and logging -monitoring: - track_quantum_metrics: true - log_circuit_executions: true - log_interval: 100 # Log every N steps - - # Metrics to track - metrics: - - loss - - quantum_advantage_ratio - - circuit_execution_time - - attention_optimization_gain - - # Alerts - alerts: - enabled: false - webhook_url: "" # Slack/Teams webhook for alerts - alert_on_failure: true - alert_on_completion: false - -# Resource management -resources: - max_memory_gb: 8 # Maximum memory per training cycle - cpu_cores: 4 # CPU cores for parallel processing - use_gpu: true # Use GPU if available - gpu_memory_fraction: 0.8 # Fraction of GPU memory to use - -# Output settings -output: - save_dir: "data_out/quantum_llm_training" - save_checkpoints: true - checkpoint_frequency: 500 # Save checkpoint every N steps - save_quantum_circuits: false # Save circuit diagrams (for debugging) - export_format: "json" # Options: "json", "yaml", "jsonl" +# Quantum-Enhanced LLM Training Configuration +# This file configures quantum computing integration for passive LLM training + +quantum_settings: + # Quantum backend selection + backend: "local" # Options: "local" (simulator), "azure" (Azure Quantum) + + # Quantum circuit parameters + n_qubits: 4 # Number of qubits (higher = more expressive but slower) + n_quantum_layers: 2 # Depth of quantum circuits + entanglement: "circular" # Options: "linear", "circular", "full" + + # Azure Quantum settings (when backend: "azure") + azure: + resource_id: "" # Azure Quantum workspace resource ID + location: "eastus" # Azure region + target_backend: "ionq.simulator" # Options: ionq.simulator, ionq.qpu, quantinuum.sim.h1-1e + shots: 100 # Number of circuit executions per measurement + confirm_cost: false # Must be true for paid QPU backends + +# Passive training settings +passive_training: + enabled: true + interval_seconds: 3600 # Run training cycle every hour + datasets_dir: "datasets/chat" # Directory to scan for datasets + epochs_per_cycle: 1 # Epochs per passive training cycle + max_cycles: 0 # 0 = infinite, positive number to limit cycles + +# LLM training parameters +llm_training: + model_name: "microsoft/phi-2" # Base model for fine-tuning + learning_rate: 0.0001 + batch_size: 4 + gradient_accumulation_steps: 8 + max_seq_length: 512 + + # LoRA configuration + lora: + enabled: true + r: 8 # LoRA rank + alpha: 16 # LoRA alpha + dropout: 0.05 + target_modules: ["q_proj", "v_proj"] # Attention modules to apply LoRA + +# Quantum Transformer Architecture +# Used by quantum_transformer.py and quantum_llm_trainer.py +quantum_transformer: + # Model dimensions + vocab_size: 256 # Character-level vocabulary + d_model: 64 # Embedding / model dimension + n_heads: 4 # Attention heads (d_model / n_heads should = 2^n_qubits) + n_transformer_layers: 2 # Number of QuantumTransformerBlocks + max_seq_len: 32 # Maximum sequence length + dropout: 0.1 + + # Quantum circuit settings + n_qubits: 4 # Qubits per quantum circuit (4-8) + n_quantum_layers: 2 # Variational layers per quantum circuit + entanglement: "circular" # linear, circular, full + + # Component selection + use_quantum_attention: true # Use QuantumSelfAttention (vs classical) + use_quantum_ffn: true # Use QuantumFeedForward (vs classical) + + # Weight management + tie_embeddings: true # Share token_embedding and lm_head weights + + # Training hyperparameters + learning_rate: 0.001 + weight_decay: 0.01 + gradient_clip: 1.0 + batch_size: 4 # Small batch for quantum circuit speed + +# Quantum enhancement strategies (legacy, kept for backward compat) +quantum_enhancement: + optimize_attention: true + attention_optimization_frequency: 1 # Every step (real quantum circuits now) + quantum_feature_encoding: true + encoding_dimension: 16 # 2^n_qubits for 4 qubits + quantum_hyperparameter_tuning: false + cache_circuits: true + cache_size: 100 + cache_ttl_seconds: 3600 + +# Integration with autonomous training +autonomous_integration: + enabled: true + report_to_orchestrator: true + status_file: "data_out/quantum_llm_training/status.json" + + # Performance thresholds + min_loss_improvement: 0.01 # Minimum improvement to consider cycle successful + max_loss_threshold: 5.0 # Alert if loss exceeds this value + + # Auto-deployment + auto_deploy: false + deployment_loss_threshold: 1.0 # Deploy model if loss below this + +# Monitoring and logging +monitoring: + track_quantum_metrics: true + log_circuit_executions: true + log_interval: 100 # Log every N steps + + # Metrics to track + metrics: + - loss + - quantum_advantage_ratio + - circuit_execution_time + - attention_optimization_gain + + # Alerts + alerts: + enabled: false + webhook_url: "" # Slack/Teams webhook for alerts + alert_on_failure: true + alert_on_completion: false + +# Resource management +resources: + max_memory_gb: 8 # Maximum memory per training cycle + cpu_cores: 4 # CPU cores for parallel processing + use_gpu: true # Use GPU if available + gpu_memory_fraction: 0.8 # Fraction of GPU memory to use + +# Output settings +output: + save_dir: "data_out/quantum_llm_training" + save_checkpoints: true + checkpoint_frequency: 500 # Save checkpoint every N steps + save_quantum_circuits: false # Save circuit diagrams (for debugging) + export_format: "json" # Options: "json", "yaml", "jsonl" diff --git a/config/quantum_llm_config_example.yaml b/config/quantum_llm_config_example.yaml index 2a9810c0e..794c79c57 100644 --- a/config/quantum_llm_config_example.yaml +++ b/config/quantum_llm_config_example.yaml @@ -1,361 +1,361 @@ -# Quantum LLM Configuration Example -# Copy this file to customize your quantum-enhanced language model training - -# ============================================================================= -# Model Architecture -# ============================================================================= - -# Vocabulary size (256 for char-level, larger for subword) -vocab_size: 256 - -# Model dimension (hidden size) -d_model: 128 - -# Number of attention heads -n_heads: 4 - -# Number of transformer layers -n_layers: 4 - -# Feedforward dimension -d_ff: 512 - -# Maximum sequence length -max_seq_length: 512 - -# ============================================================================= -# Quantum Configuration -# ============================================================================= - -# Number of qubits per quantum layer -n_qubits: 4 - -# Quantum backend: "default.qubit", "lightning.qubit", or Azure Quantum device -quantum_backend: "default.qubit" - -# Number of shots per quantum circuit execution (higher = more accurate, slower) -quantum_shots: 1000 - -# Enable quantum attention mechanism -enable_quantum_attention: true - -# Enable quantum feedforward network -enable_quantum_ffn: true - -# Enable multi-scale quantum attention with varying qubit counts -enable_multi_scale_attention: true - -# Qubit counts for multi-scale attention heads -# Each value represents qubits for one attention head -n_qubits_per_head: - - 2 # Fine-grained patterns - - 3 # Mid-level structures - - 4 # Standard complexity - - 6 # Complex relationships - -# Enable adaptive entanglement topology selection -enable_adaptive_entanglement: true - -# Enable quantum circuit result caching -enable_circuit_caching: true - -# Cache size (number of circuit results to cache) -circuit_cache_size: 1000 - -# Enable quantum error mitigation techniques -enable_error_mitigation: false - -# Error mitigation method: "zne" (zero-noise extrapolation) or "readout" -error_mitigation_method: "zne" - -# ============================================================================= -# Training Configuration -# ============================================================================= - -# Batch size for training -batch_size: 16 - -# Learning rate -learning_rate: 0.0001 - -# Number of training epochs -num_epochs: 10 - -# Enable curriculum learning (progressive quantum integration) -enable_curriculum: true - -# Curriculum stages (if enable_curriculum is true) -curriculum_stages: - - name: "classical_warmup" - quantum_ratio: 0.0 - num_epochs: 2 - learning_rate: 0.0001 - batch_size: 16 - freeze_classical_layers: false - enable_quantum_layers: [] - - - name: "quantum_transition" - quantum_ratio: 0.3 - num_epochs: 3 - learning_rate: 0.00005 - batch_size: 16 - freeze_classical_layers: false - enable_quantum_layers: - - "attention" - - - name: "full_quantum" - quantum_ratio: 0.7 - num_epochs: 10 - learning_rate: 0.00001 - batch_size: 8 - freeze_classical_layers: false - enable_quantum_layers: - - "attention" - - "feedforward" - -# Enable adaptive quantum/classical routing -enable_adaptive_routing: true - -# Adaptive routing learning rate -routing_learning_rate: 0.001 - -# Gradient clipping value -gradient_clip_norm: 1.0 - -# Weight decay for regularization -weight_decay: 0.01 - -# Learning rate warmup steps -warmup_steps: 100 - -# ============================================================================= -# Circuit Optimization -# ============================================================================= - -# Optimization level: 0=none, 1=light, 2=moderate, 3=aggressive -optimization_level: 2 - -# Enable gate fusion optimization -enable_gate_fusion: true - -# Enable gate cancellation optimization -enable_gate_cancellation: true - -# Enable parameter shift optimization -enable_parameter_shift: true - -# Maximum circuit depth before optimization -max_circuit_depth: 20 - -# Target reduction in two-qubit gates (0.0-1.0) -target_two_qubit_gate_reduction: 0.3 - -# Maximum batch size for circuit execution -max_batch_circuits: 10 - -# Enable parallel circuit execution (if supported by backend) -enable_parallel_circuits: false - -# Maximum concurrent circuits -max_concurrent_circuits: 5 - -# Quantum resource utilization limit (0.0-1.0) -quantum_resource_limit: 0.8 - -# ============================================================================= -# Monitoring & Logging -# ============================================================================= - -# Enable real-time training dashboard -enable_dashboard: true - -# Dashboard update interval (seconds) -dashboard_update_interval: 10 - -# Enable alert system for anomalies -enable_alerts: true - -# Anomaly detection z-score threshold -anomaly_threshold: 3.0 - -# Enable performance profiling -enable_profiling: true - -# Profiling sample rate (0.0-1.0, 1.0 = profile every batch) -profiling_sample_rate: 0.1 - -# Log interval (batches) -log_interval: 10 - -# Enable visualization export -enable_visualization_export: true - -# ============================================================================= -# Checkpointing & Output -# ============================================================================= - -# Output directory for all training artifacts -output_dir: "data_out/quantum_llm" - -# Enable checkpoint saving -save_checkpoints: true - -# Checkpoint save interval (steps) -checkpoint_interval: 100 - -# Maximum checkpoints to keep (0 = keep all) -max_checkpoints: 5 - -# Save best model based on validation loss -save_best_model: true - -# Export model format: "pytorch", "onnx", "both" -export_format: "pytorch" - -# Save training report -save_training_report: true - -# Save configuration with model -save_config_with_model: true - -# ============================================================================= -# Dataset Configuration -# ============================================================================= - -# Training dataset path (can be text, JSON, or chat format) -train_dataset: "datasets/quantum/quantum_concepts.json" - -# Validation dataset path (optional) -val_dataset: null - -# Validation split ratio (if val_dataset is null) -val_split_ratio: 0.1 - -# Random seed for dataset splitting -dataset_seed: 42 - -# Maximum samples to use from dataset (0 = use all) -max_samples: 0 - -# Enable data augmentation -enable_data_augmentation: false - -# Data augmentation noise level (0.0-1.0) -augmentation_noise_level: 0.1 - -# Tokenizer configuration -tokenizer: - type: "character" # "character" or "subword" - vocab_size: 256 - add_special_tokens: true - -# Multi-source dataset configuration (if using multiple datasets) -multi_source_datasets: [] -# - path: "datasets/chat/conversations.json" -# type: "json" -# weight: 0.5 -# - path: "datasets/quantum/quantum_concepts.json" -# type: "json" -# weight: 0.5 - -# ============================================================================= -# Hardware Configuration -# ============================================================================= - -# Device: "cpu", "cuda", "cuda:0", etc. -device: "cpu" - -# Enable mixed precision training (requires GPU) -enable_mixed_precision: false - -# Number of data loader workers -num_workers: 0 - -# Pin memory for faster GPU transfer -pin_memory: false - -# ============================================================================= -# Advanced Options -# ============================================================================= - -# Enable quantum prompt tuning -enable_prompt_tuning: false - -# Number of quantum prompt embeddings -n_quantum_prompts: 10 - -# Enable gradient checkpointing (saves memory) -enable_gradient_checkpointing: false - -# Enable stochastic weight averaging -enable_swa: false - -# SWA start step -swa_start_step: 1000 - -# Enable early stopping -enable_early_stopping: false - -# Early stopping patience (epochs) -early_stopping_patience: 3 - -# Early stopping min delta -early_stopping_min_delta: 0.001 - -# ============================================================================= -# Azure Quantum Configuration (Optional) -# ============================================================================= - -# Enable Azure Quantum backend -enable_azure_quantum: false - -# Azure Quantum workspace resource ID -azure_quantum_workspace_id: "" - -# Azure Quantum location -azure_quantum_location: "westus" - -# Azure Quantum target device -# Options: "ionq.simulator", "ionq.qpu", "quantinuum.sim.h1-1e", etc. -azure_quantum_target: "ionq.simulator" - -# Azure Quantum job timeout (seconds) -azure_quantum_timeout: 300 - -# Confirm cost before submitting to real QPU -azure_confirm_cost: true - -# ============================================================================= -# Experiment Tracking (Optional) -# ============================================================================= - -# Enable Weights & Biases logging -enable_wandb: false - -# W&B project name -wandb_project: "quantum-llm" - -# W&B run name -wandb_run_name: null - -# W&B entity -wandb_entity: null - -# Enable TensorBoard logging -enable_tensorboard: false - -# TensorBoard log directory -tensorboard_log_dir: "data_out/quantum_llm/tensorboard" - -# ============================================================================= -# Reproducibility -# ============================================================================= - -# Random seed for reproducibility -random_seed: 42 - -# Deterministic mode (may reduce performance) -deterministic: false - -# Benchmark cudnn (may improve performance but decrease reproducibility) -benchmark_cudnn: true +# Quantum LLM Configuration Example +# Copy this file to customize your quantum-enhanced language model training + +# ============================================================================= +# Model Architecture +# ============================================================================= + +# Vocabulary size (256 for char-level, larger for subword) +vocab_size: 256 + +# Model dimension (hidden size) +d_model: 128 + +# Number of attention heads +n_heads: 4 + +# Number of transformer layers +n_layers: 4 + +# Feedforward dimension +d_ff: 512 + +# Maximum sequence length +max_seq_length: 512 + +# ============================================================================= +# Quantum Configuration +# ============================================================================= + +# Number of qubits per quantum layer +n_qubits: 4 + +# Quantum backend: "default.qubit", "lightning.qubit", or Azure Quantum device +quantum_backend: "default.qubit" + +# Number of shots per quantum circuit execution (higher = more accurate, slower) +quantum_shots: 1000 + +# Enable quantum attention mechanism +enable_quantum_attention: true + +# Enable quantum feedforward network +enable_quantum_ffn: true + +# Enable multi-scale quantum attention with varying qubit counts +enable_multi_scale_attention: true + +# Qubit counts for multi-scale attention heads +# Each value represents qubits for one attention head +n_qubits_per_head: + - 2 # Fine-grained patterns + - 3 # Mid-level structures + - 4 # Standard complexity + - 6 # Complex relationships + +# Enable adaptive entanglement topology selection +enable_adaptive_entanglement: true + +# Enable quantum circuit result caching +enable_circuit_caching: true + +# Cache size (number of circuit results to cache) +circuit_cache_size: 1000 + +# Enable quantum error mitigation techniques +enable_error_mitigation: false + +# Error mitigation method: "zne" (zero-noise extrapolation) or "readout" +error_mitigation_method: "zne" + +# ============================================================================= +# Training Configuration +# ============================================================================= + +# Batch size for training +batch_size: 16 + +# Learning rate +learning_rate: 0.0001 + +# Number of training epochs +num_epochs: 10 + +# Enable curriculum learning (progressive quantum integration) +enable_curriculum: true + +# Curriculum stages (if enable_curriculum is true) +curriculum_stages: + - name: "classical_warmup" + quantum_ratio: 0.0 + num_epochs: 2 + learning_rate: 0.0001 + batch_size: 16 + freeze_classical_layers: false + enable_quantum_layers: [] + + - name: "quantum_transition" + quantum_ratio: 0.3 + num_epochs: 3 + learning_rate: 0.00005 + batch_size: 16 + freeze_classical_layers: false + enable_quantum_layers: + - "attention" + + - name: "full_quantum" + quantum_ratio: 0.7 + num_epochs: 10 + learning_rate: 0.00001 + batch_size: 8 + freeze_classical_layers: false + enable_quantum_layers: + - "attention" + - "feedforward" + +# Enable adaptive quantum/classical routing +enable_adaptive_routing: true + +# Adaptive routing learning rate +routing_learning_rate: 0.001 + +# Gradient clipping value +gradient_clip_norm: 1.0 + +# Weight decay for regularization +weight_decay: 0.01 + +# Learning rate warmup steps +warmup_steps: 100 + +# ============================================================================= +# Circuit Optimization +# ============================================================================= + +# Optimization level: 0=none, 1=light, 2=moderate, 3=aggressive +optimization_level: 2 + +# Enable gate fusion optimization +enable_gate_fusion: true + +# Enable gate cancellation optimization +enable_gate_cancellation: true + +# Enable parameter shift optimization +enable_parameter_shift: true + +# Maximum circuit depth before optimization +max_circuit_depth: 20 + +# Target reduction in two-qubit gates (0.0-1.0) +target_two_qubit_gate_reduction: 0.3 + +# Maximum batch size for circuit execution +max_batch_circuits: 10 + +# Enable parallel circuit execution (if supported by backend) +enable_parallel_circuits: false + +# Maximum concurrent circuits +max_concurrent_circuits: 5 + +# Quantum resource utilization limit (0.0-1.0) +quantum_resource_limit: 0.8 + +# ============================================================================= +# Monitoring & Logging +# ============================================================================= + +# Enable real-time training dashboard +enable_dashboard: true + +# Dashboard update interval (seconds) +dashboard_update_interval: 10 + +# Enable alert system for anomalies +enable_alerts: true + +# Anomaly detection z-score threshold +anomaly_threshold: 3.0 + +# Enable performance profiling +enable_profiling: true + +# Profiling sample rate (0.0-1.0, 1.0 = profile every batch) +profiling_sample_rate: 0.1 + +# Log interval (batches) +log_interval: 10 + +# Enable visualization export +enable_visualization_export: true + +# ============================================================================= +# Checkpointing & Output +# ============================================================================= + +# Output directory for all training artifacts +output_dir: "data_out/quantum_llm" + +# Enable checkpoint saving +save_checkpoints: true + +# Checkpoint save interval (steps) +checkpoint_interval: 100 + +# Maximum checkpoints to keep (0 = keep all) +max_checkpoints: 5 + +# Save best model based on validation loss +save_best_model: true + +# Export model format: "pytorch", "onnx", "both" +export_format: "pytorch" + +# Save training report +save_training_report: true + +# Save configuration with model +save_config_with_model: true + +# ============================================================================= +# Dataset Configuration +# ============================================================================= + +# Training dataset path (can be text, JSON, or chat format) +train_dataset: "datasets/quantum/quantum_concepts.json" + +# Validation dataset path (optional) +val_dataset: null + +# Validation split ratio (if val_dataset is null) +val_split_ratio: 0.1 + +# Random seed for dataset splitting +dataset_seed: 42 + +# Maximum samples to use from dataset (0 = use all) +max_samples: 0 + +# Enable data augmentation +enable_data_augmentation: false + +# Data augmentation noise level (0.0-1.0) +augmentation_noise_level: 0.1 + +# Tokenizer configuration +tokenizer: + type: "character" # "character" or "subword" + vocab_size: 256 + add_special_tokens: true + +# Multi-source dataset configuration (if using multiple datasets) +multi_source_datasets: [] +# - path: "datasets/chat/conversations.json" +# type: "json" +# weight: 0.5 +# - path: "datasets/quantum/quantum_concepts.json" +# type: "json" +# weight: 0.5 + +# ============================================================================= +# Hardware Configuration +# ============================================================================= + +# Device: "cpu", "cuda", "cuda:0", etc. +device: "cpu" + +# Enable mixed precision training (requires GPU) +enable_mixed_precision: false + +# Number of data loader workers +num_workers: 0 + +# Pin memory for faster GPU transfer +pin_memory: false + +# ============================================================================= +# Advanced Options +# ============================================================================= + +# Enable quantum prompt tuning +enable_prompt_tuning: false + +# Number of quantum prompt embeddings +n_quantum_prompts: 10 + +# Enable gradient checkpointing (saves memory) +enable_gradient_checkpointing: false + +# Enable stochastic weight averaging +enable_swa: false + +# SWA start step +swa_start_step: 1000 + +# Enable early stopping +enable_early_stopping: false + +# Early stopping patience (epochs) +early_stopping_patience: 3 + +# Early stopping min delta +early_stopping_min_delta: 0.001 + +# ============================================================================= +# Azure Quantum Configuration (Optional) +# ============================================================================= + +# Enable Azure Quantum backend +enable_azure_quantum: false + +# Azure Quantum workspace resource ID +azure_quantum_workspace_id: "" + +# Azure Quantum location +azure_quantum_location: "westus" + +# Azure Quantum target device +# Options: "ionq.simulator", "ionq.qpu", "quantinuum.sim.h1-1e", etc. +azure_quantum_target: "ionq.simulator" + +# Azure Quantum job timeout (seconds) +azure_quantum_timeout: 300 + +# Confirm cost before submitting to real QPU +azure_confirm_cost: true + +# ============================================================================= +# Experiment Tracking (Optional) +# ============================================================================= + +# Enable Weights & Biases logging +enable_wandb: false + +# W&B project name +wandb_project: "quantum-llm" + +# W&B run name +wandb_run_name: null + +# W&B entity +wandb_entity: null + +# Enable TensorBoard logging +enable_tensorboard: false + +# TensorBoard log directory +tensorboard_log_dir: "data_out/quantum_llm/tensorboard" + +# ============================================================================= +# Reproducibility +# ============================================================================= + +# Random seed for reproducibility +random_seed: 42 + +# Deterministic mode (may reduce performance) +deterministic: false + +# Benchmark cudnn (may improve performance but decrease reproducibility) +benchmark_cudnn: true diff --git a/config/training/autotrain.yaml b/config/training/autotrain.yaml index 5450948a6..606f24dd3 100644 --- a/config/training/autotrain.yaml +++ b/config/training/autotrain.yaml @@ -1,193 +1,193 @@ -version: 1 - -# Define one or more training jobs. Jobs run sequentially when invoked. -# Runners: -# - hf: Uses Hugging Face stack via AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py -# - local: Uses the streamlined local runner scripts/run_local_lora_training.py - -jobs: - # ===== COMPREHENSIVE TRAINING - All Data ===== - - # Phi-3.5 on comprehensive merged dataset (15K+ samples) - - name: phi35_comprehensive_full - category: comprehensive - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/phi35_comprehensive_full - epochs: 1 - max_train_samples: 1000 - max_eval_samples: 100 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Qwen2.5 on comprehensive merged dataset - - name: qwen25_comprehensive_full - category: comprehensive - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/qwen25_comprehensive_full - epochs: 1 - max_train_samples: 1000 - max_eval_samples: 100 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== BASELINE TRAINING - Original Datasets ===== - - # Phi-3.5 Mini (Aug 2024) - Microsoft's latest open model (MOST EFFICIENT) - - name: phi35_mixed_chat - category: baseline - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/phi35 - epochs: 1 - max_train_samples: 500 - max_eval_samples: 50 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Qwen2.5 3B (Sep 2024) - excellent performance, no gating - - name: qwen25_3b_mixed_chat - category: baseline - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/qwen25_3b - epochs: 1 - max_train_samples: 500 - max_eval_samples: 50 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # Phi-3.5 MAX PERFORMANCE - Full Dolly dataset, 3 epochs - - name: phi35_max_performance - category: baseline - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/dolly - save_dir: data_out/lora_training/phi35_max_performance - epochs: 1 - max_train_samples: 1000 # Fast sampling - max_eval_samples: null - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # ===== DOMAIN-SPECIFIC TRAINING ===== - - # Phi-3.5 on augmented repository data (code understanding) - - name: phi35_repo_augmented - category: domain - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/app_repo_augmented - save_dir: data_out/lora_training/phi35_repo_augmented - epochs: 1 - max_train_samples: 500 - max_eval_samples: 50 - learning_rate: 0.0003 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Qwen2.5 on augmented repository data - - name: qwen25_repo_augmented - category: domain - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/app_repo_augmented - save_dir: data_out/lora_training/qwen25_repo_augmented - epochs: 1 - max_train_samples: 500 - max_eval_samples: 50 - learning_rate: 0.0003 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== HYPERPARAMETER EXPLORATION (quick validation jobs) ===== - - - name: phi35_mixed_chat_lr_low - category: hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/phi35_lr_low - epochs: 1 - max_train_samples: 64 - max_eval_samples: 16 - learning_rate: 0.0001 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - no_stream: true - device: auto - - - name: phi35_mixed_chat_lr_high - category: hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/phi35_lr_high - epochs: 1 - max_train_samples: 64 - max_eval_samples: 16 - learning_rate: 0.0003 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - no_stream: true - device: auto - - - name: phi35_mixed_chat_dropout_low - category: hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/phi35_drop_low - epochs: 1 - max_train_samples: 64 - max_eval_samples: 16 - learning_rate: 0.0002 - - # ===== ANIME AVATAR TRAINING (Custom Anime Persona) ===== - - name: anime_avatar - category: anime_avatar - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/anime_avatar - save_dir: data_out/lora_training/anime_avatar - epochs: 10 - max_train_samples: null - max_eval_samples: null - learning_rate: 0.0003 - lora_dropout: 0.05 - lora_rank: 16 - lora_alpha: 32 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: phi35_mixed_chat_dropout_high - category: hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/phi35_drop_high - epochs: 1 - max_train_samples: 64 - max_eval_samples: 16 - learning_rate: 0.0002 - lora_dropout: 0.15 - hf_model_id: microsoft/Phi-3.5-mini-instruct - no_stream: true - device: auto +version: 1 + +# Define one or more training jobs. Jobs run sequentially when invoked. +# Runners: +# - hf: Uses Hugging Face stack via AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py +# - local: Uses the streamlined local runner scripts/run_local_lora_training.py + +jobs: + # ===== COMPREHENSIVE TRAINING - All Data ===== + + # Phi-3.5 on comprehensive merged dataset (15K+ samples) + - name: phi35_comprehensive_full + category: comprehensive + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/phi35_comprehensive_full + epochs: 1 + max_train_samples: 1000 + max_eval_samples: 100 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Qwen2.5 on comprehensive merged dataset + - name: qwen25_comprehensive_full + category: comprehensive + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/qwen25_comprehensive_full + epochs: 1 + max_train_samples: 1000 + max_eval_samples: 100 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== BASELINE TRAINING - Original Datasets ===== + + # Phi-3.5 Mini (Aug 2024) - Microsoft's latest open model (MOST EFFICIENT) + - name: phi35_mixed_chat + category: baseline + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/phi35 + epochs: 1 + max_train_samples: 500 + max_eval_samples: 50 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Qwen2.5 3B (Sep 2024) - excellent performance, no gating + - name: qwen25_3b_mixed_chat + category: baseline + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/qwen25_3b + epochs: 1 + max_train_samples: 500 + max_eval_samples: 50 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # Phi-3.5 MAX PERFORMANCE - Full Dolly dataset, 3 epochs + - name: phi35_max_performance + category: baseline + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/dolly + save_dir: data_out/lora_training/phi35_max_performance + epochs: 1 + max_train_samples: 1000 # Fast sampling + max_eval_samples: null + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # ===== DOMAIN-SPECIFIC TRAINING ===== + + # Phi-3.5 on augmented repository data (code understanding) + - name: phi35_repo_augmented + category: domain + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/app_repo_augmented + save_dir: data_out/lora_training/phi35_repo_augmented + epochs: 1 + max_train_samples: 500 + max_eval_samples: 50 + learning_rate: 0.0003 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Qwen2.5 on augmented repository data + - name: qwen25_repo_augmented + category: domain + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/app_repo_augmented + save_dir: data_out/lora_training/qwen25_repo_augmented + epochs: 1 + max_train_samples: 500 + max_eval_samples: 50 + learning_rate: 0.0003 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== HYPERPARAMETER EXPLORATION (quick validation jobs) ===== + + - name: phi35_mixed_chat_lr_low + category: hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/phi35_lr_low + epochs: 1 + max_train_samples: 64 + max_eval_samples: 16 + learning_rate: 0.0001 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + no_stream: true + device: auto + + - name: phi35_mixed_chat_lr_high + category: hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/phi35_lr_high + epochs: 1 + max_train_samples: 64 + max_eval_samples: 16 + learning_rate: 0.0003 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + no_stream: true + device: auto + + - name: phi35_mixed_chat_dropout_low + category: hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/phi35_drop_low + epochs: 1 + max_train_samples: 64 + max_eval_samples: 16 + learning_rate: 0.0002 + + # ===== ANIME AVATAR TRAINING (Custom Anime Persona) ===== + - name: anime_avatar + category: anime_avatar + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/anime_avatar + save_dir: data_out/lora_training/anime_avatar + epochs: 10 + max_train_samples: null + max_eval_samples: null + learning_rate: 0.0003 + lora_dropout: 0.05 + lora_rank: 16 + lora_alpha: 32 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: phi35_mixed_chat_dropout_high + category: hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/phi35_drop_high + epochs: 1 + max_train_samples: 64 + max_eval_samples: 16 + learning_rate: 0.0002 + lora_dropout: 0.15 + hf_model_id: microsoft/Phi-3.5-mini-instruct + no_stream: true + device: auto diff --git a/config/training/autotrain_aria.yaml b/config/training/autotrain_aria.yaml index 7703aa0e1..49bbdafe2 100644 --- a/config/training/autotrain_aria.yaml +++ b/config/training/autotrain_aria.yaml @@ -1,53 +1,53 @@ -version: 1 - -# Aria Movement Command Training Configuration -# Trains AI to generate [aria:action:direction] commands for character movement - -jobs: - # Quick training for Aria movement commands (testing) - - name: aria_movement_quick - category: specialized - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/aria_movement - save_dir: data_out/aria_models/aria_quick - epochs: 2 - max_train_samples: 40 - max_eval_samples: 10 - learning_rate: 0.0003 # Higher LR for specialized task - lora_dropout: 0.05 # Lower dropout for small dataset - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - description: Quick training on aria movement dataset for testing - - # Full training for Aria movement commands - - name: aria_movement_full - category: specialized - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/aria_movement - save_dir: data_out/aria_models/aria_full - epochs: 3 - max_train_samples: null # Use all samples - max_eval_samples: null - learning_rate: 0.0003 - lora_dropout: 0.05 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - description: Full training on complete aria movement dataset - - # Qwen variant for Aria movement - - name: aria_movement_qwen - category: specialized - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/aria_movement - save_dir: data_out/aria_models/aria_qwen - epochs: 3 - max_train_samples: null - max_eval_samples: null - learning_rate: 0.0003 - lora_dropout: 0.05 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - description: Qwen model trained on aria movement commands +version: 1 + +# Aria Movement Command Training Configuration +# Trains AI to generate [aria:action:direction] commands for character movement + +jobs: + # Quick training for Aria movement commands (testing) + - name: aria_movement_quick + category: specialized + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/aria_movement + save_dir: data_out/aria_models/aria_quick + epochs: 2 + max_train_samples: 40 + max_eval_samples: 10 + learning_rate: 0.0003 # Higher LR for specialized task + lora_dropout: 0.05 # Lower dropout for small dataset + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + description: Quick training on aria movement dataset for testing + + # Full training for Aria movement commands + - name: aria_movement_full + category: specialized + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/aria_movement + save_dir: data_out/aria_models/aria_full + epochs: 3 + max_train_samples: null # Use all samples + max_eval_samples: null + learning_rate: 0.0003 + lora_dropout: 0.05 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + description: Full training on complete aria movement dataset + + # Qwen variant for Aria movement + - name: aria_movement_qwen + category: specialized + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/aria_movement + save_dir: data_out/aria_models/aria_qwen + epochs: 3 + max_train_samples: null + max_eval_samples: null + learning_rate: 0.0003 + lora_dropout: 0.05 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + description: Qwen model trained on aria movement commands diff --git a/config/training/autotrain_custom_job.yaml b/config/training/autotrain_custom_job.yaml index 0a415dae4..b607d47eb 100644 --- a/config/training/autotrain_custom_job.yaml +++ b/config/training/autotrain_custom_job.yaml @@ -1,10 +1,10 @@ -jobs: -- category: custom - dataset: datasets/chat/chat_logs - device: auto - epochs: 3 - learning_rate: 0.0002 - max_train_samples: 1000 - model: microsoft/Phi-3.5-mini-instruct - name: job - runner: hf +jobs: +- category: custom + dataset: datasets/chat/chat_logs + device: auto + epochs: 3 + learning_rate: 0.0002 + max_train_samples: 1000 + model: microsoft/Phi-3.5-mini-instruct + name: job + runner: hf diff --git a/config/training/autotrain_extended_marathon.yaml b/config/training/autotrain_extended_marathon.yaml index ad54ddad2..d8c4ae2ab 100644 --- a/config/training/autotrain_extended_marathon.yaml +++ b/config/training/autotrain_extended_marathon.yaml @@ -1,213 +1,213 @@ -version: 1 - -# EXTENDED MARATHON TRAINING - Multi-hour comprehensive training session -# Estimated total runtime: 4-6 hours (depends on GPU/CPU) -# Trains multiple models on various datasets with extensive epochs - -jobs: - # ===== MEGA SYNTHETIC DATA TRAINING (New Generated Data) ===== - - # Train on freshly generated synthetic data - Phi-3.5 - - name: phi35_mega_synthetic_full - category: marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mega_synthetic - save_dir: data_out/lora_training/marathon/phi35_mega_synthetic - epochs: 3 - max_train_samples: null # Use ALL generated samples - max_eval_samples: 200 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Train on synthetic data - Qwen2.5 - - name: qwen25_mega_synthetic_full - category: marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mega_synthetic - save_dir: data_out/lora_training/marathon/qwen25_mega_synthetic - epochs: 3 - max_train_samples: null # Use ALL generated samples - max_eval_samples: 200 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== COMPREHENSIVE DATASET MARATHON (All merged data) ===== - - # Phi-3.5 on comprehensive merged dataset - Extended training - - name: phi35_comprehensive_marathon - category: marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/marathon/phi35_comprehensive - epochs: 5 - max_train_samples: 3000 # Large batch - max_eval_samples: 300 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Qwen2.5 on comprehensive merged dataset - Extended training - - name: qwen25_comprehensive_marathon - category: marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/marathon/qwen25_comprehensive - epochs: 5 - max_train_samples: 3000 - max_eval_samples: 300 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== DOLLY FULL DATASET TRAINING ===== - - # Phi-3.5 on full Dolly dataset with maximum epochs - - name: phi35_dolly_marathon - category: marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/dolly - save_dir: data_out/lora_training/marathon/phi35_dolly_full - epochs: 5 - max_train_samples: 5000 # Large subset - max_eval_samples: 500 - learning_rate: 0.00015 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Qwen2.5 on Dolly dataset - - name: qwen25_dolly_marathon - category: marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/dolly - save_dir: data_out/lora_training/marathon/qwen25_dolly_full - epochs: 5 - max_train_samples: 5000 - max_eval_samples: 500 - learning_rate: 0.00015 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== MIXED CHAT EXTENDED TRAINING ===== - - # Phi-3.5 on mixed_chat with many epochs - - name: phi35_mixed_extended - category: marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/marathon/phi35_mixed_extended - epochs: 5 - max_train_samples: 2000 - max_eval_samples: 200 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Qwen2.5 on mixed_chat extended - - name: qwen25_mixed_extended - category: marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/marathon/qwen25_mixed_extended - epochs: 5 - max_train_samples: 2000 - max_eval_samples: 200 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== REPOSITORY-AUGMENTED EXTENDED TRAINING ===== - - # Phi-3.5 on repo data with extended epochs - - name: phi35_repo_marathon - category: marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/app_repo_augmented - save_dir: data_out/lora_training/marathon/phi35_repo_extended - epochs: 4 - max_train_samples: 1500 - max_eval_samples: 150 - learning_rate: 0.0003 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Qwen2.5 on repo data extended - - name: qwen25_repo_marathon - category: marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/app_repo_augmented - save_dir: data_out/lora_training/marathon/qwen25_repo_extended - epochs: 4 - max_train_samples: 1500 - max_eval_samples: 150 - learning_rate: 0.0003 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== HYPERPARAMETER VARIATIONS (Extended) ===== - - # Lower learning rate, more epochs - - name: phi35_comprehensive_lr_low_extended - category: marathon_hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/marathon/phi35_lr_low_extended - epochs: 5 - max_train_samples: 2000 - max_eval_samples: 200 - learning_rate: 0.0001 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Higher learning rate with careful monitoring - - name: phi35_comprehensive_lr_high_extended - category: marathon_hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/marathon/phi35_lr_high_extended - epochs: 4 - max_train_samples: 2000 - max_eval_samples: 200 - learning_rate: 0.0004 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Different dropout rates - - name: qwen25_comprehensive_dropout_test - category: marathon_hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/marathon/qwen25_dropout_test - epochs: 4 - max_train_samples: 2000 - max_eval_samples: 200 - learning_rate: 0.0002 - lora_dropout: 0.15 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto +version: 1 + +# EXTENDED MARATHON TRAINING - Multi-hour comprehensive training session +# Estimated total runtime: 4-6 hours (depends on GPU/CPU) +# Trains multiple models on various datasets with extensive epochs + +jobs: + # ===== MEGA SYNTHETIC DATA TRAINING (New Generated Data) ===== + + # Train on freshly generated synthetic data - Phi-3.5 + - name: phi35_mega_synthetic_full + category: marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mega_synthetic + save_dir: data_out/lora_training/marathon/phi35_mega_synthetic + epochs: 3 + max_train_samples: null # Use ALL generated samples + max_eval_samples: 200 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Train on synthetic data - Qwen2.5 + - name: qwen25_mega_synthetic_full + category: marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mega_synthetic + save_dir: data_out/lora_training/marathon/qwen25_mega_synthetic + epochs: 3 + max_train_samples: null # Use ALL generated samples + max_eval_samples: 200 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== COMPREHENSIVE DATASET MARATHON (All merged data) ===== + + # Phi-3.5 on comprehensive merged dataset - Extended training + - name: phi35_comprehensive_marathon + category: marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/marathon/phi35_comprehensive + epochs: 5 + max_train_samples: 3000 # Large batch + max_eval_samples: 300 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Qwen2.5 on comprehensive merged dataset - Extended training + - name: qwen25_comprehensive_marathon + category: marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/marathon/qwen25_comprehensive + epochs: 5 + max_train_samples: 3000 + max_eval_samples: 300 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== DOLLY FULL DATASET TRAINING ===== + + # Phi-3.5 on full Dolly dataset with maximum epochs + - name: phi35_dolly_marathon + category: marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/dolly + save_dir: data_out/lora_training/marathon/phi35_dolly_full + epochs: 5 + max_train_samples: 5000 # Large subset + max_eval_samples: 500 + learning_rate: 0.00015 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Qwen2.5 on Dolly dataset + - name: qwen25_dolly_marathon + category: marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/dolly + save_dir: data_out/lora_training/marathon/qwen25_dolly_full + epochs: 5 + max_train_samples: 5000 + max_eval_samples: 500 + learning_rate: 0.00015 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== MIXED CHAT EXTENDED TRAINING ===== + + # Phi-3.5 on mixed_chat with many epochs + - name: phi35_mixed_extended + category: marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/marathon/phi35_mixed_extended + epochs: 5 + max_train_samples: 2000 + max_eval_samples: 200 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Qwen2.5 on mixed_chat extended + - name: qwen25_mixed_extended + category: marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/marathon/qwen25_mixed_extended + epochs: 5 + max_train_samples: 2000 + max_eval_samples: 200 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== REPOSITORY-AUGMENTED EXTENDED TRAINING ===== + + # Phi-3.5 on repo data with extended epochs + - name: phi35_repo_marathon + category: marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/app_repo_augmented + save_dir: data_out/lora_training/marathon/phi35_repo_extended + epochs: 4 + max_train_samples: 1500 + max_eval_samples: 150 + learning_rate: 0.0003 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Qwen2.5 on repo data extended + - name: qwen25_repo_marathon + category: marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/app_repo_augmented + save_dir: data_out/lora_training/marathon/qwen25_repo_extended + epochs: 4 + max_train_samples: 1500 + max_eval_samples: 150 + learning_rate: 0.0003 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== HYPERPARAMETER VARIATIONS (Extended) ===== + + # Lower learning rate, more epochs + - name: phi35_comprehensive_lr_low_extended + category: marathon_hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/marathon/phi35_lr_low_extended + epochs: 5 + max_train_samples: 2000 + max_eval_samples: 200 + learning_rate: 0.0001 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Higher learning rate with careful monitoring + - name: phi35_comprehensive_lr_high_extended + category: marathon_hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/marathon/phi35_lr_high_extended + epochs: 4 + max_train_samples: 2000 + max_eval_samples: 200 + learning_rate: 0.0004 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Different dropout rates + - name: qwen25_comprehensive_dropout_test + category: marathon_hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/marathon/qwen25_dropout_test + epochs: 4 + max_train_samples: 2000 + max_eval_samples: 200 + learning_rate: 0.0002 + lora_dropout: 0.15 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto diff --git a/config/training/autotrain_fast.yaml b/config/training/autotrain_fast.yaml index e1c7d0cff..fa79738c7 100644 --- a/config/training/autotrain_fast.yaml +++ b/config/training/autotrain_fast.yaml @@ -1,101 +1,101 @@ -version: 1 - -# Fast training configuration - optimized for speed -# Uses smaller sample sizes, efficient models, and parallel-friendly settings - -jobs: - # ===== QUICK VALIDATION (64 samples, ~2-5 min each) ===== - - - name: phi35_quick_test - category: fast - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/phi35_quick - epochs: 1 - max_train_samples: 64 - max_eval_samples: 16 - learning_rate: 0.0003 - lora_dropout: 0.1 - lora_rank: 4 # Reduced from 8 for speed - hf_model_id: microsoft/Phi-3.5-mini-instruct - no_stream: true - device: auto - - - name: qwen25_quick_test - category: fast - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/qwen25_quick - epochs: 1 - max_train_samples: 64 - max_eval_samples: 16 - learning_rate: 0.0003 - lora_dropout: 0.1 - lora_rank: 4 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - no_stream: true - device: auto - - # ===== MEDIUM TRAINING (256 samples, ~10-15 min each) ===== - - - name: phi35_medium_mixed - category: fast - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/phi35_medium - epochs: 1 - max_train_samples: 256 - max_eval_samples: 32 - learning_rate: 0.0002 - lora_dropout: 0.1 - lora_rank: 8 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_medium_mixed - category: fast - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/qwen25_medium - epochs: 1 - max_train_samples: 256 - max_eval_samples: 32 - learning_rate: 0.0002 - lora_dropout: 0.1 - lora_rank: 8 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== FOCUSED TRAINING (500 samples, ~20-30 min each) ===== - - - name: phi35_focused_comprehensive - category: fast - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/phi35_focused - epochs: 1 - max_train_samples: 500 - max_eval_samples: 50 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_focused_comprehensive - category: fast - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/qwen25_focused - epochs: 1 - max_train_samples: 500 - max_eval_samples: 50 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto +version: 1 + +# Fast training configuration - optimized for speed +# Uses smaller sample sizes, efficient models, and parallel-friendly settings + +jobs: + # ===== QUICK VALIDATION (64 samples, ~2-5 min each) ===== + + - name: phi35_quick_test + category: fast + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/phi35_quick + epochs: 1 + max_train_samples: 64 + max_eval_samples: 16 + learning_rate: 0.0003 + lora_dropout: 0.1 + lora_rank: 4 # Reduced from 8 for speed + hf_model_id: microsoft/Phi-3.5-mini-instruct + no_stream: true + device: auto + + - name: qwen25_quick_test + category: fast + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/qwen25_quick + epochs: 1 + max_train_samples: 64 + max_eval_samples: 16 + learning_rate: 0.0003 + lora_dropout: 0.1 + lora_rank: 4 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + no_stream: true + device: auto + + # ===== MEDIUM TRAINING (256 samples, ~10-15 min each) ===== + + - name: phi35_medium_mixed + category: fast + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/phi35_medium + epochs: 1 + max_train_samples: 256 + max_eval_samples: 32 + learning_rate: 0.0002 + lora_dropout: 0.1 + lora_rank: 8 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_medium_mixed + category: fast + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/qwen25_medium + epochs: 1 + max_train_samples: 256 + max_eval_samples: 32 + learning_rate: 0.0002 + lora_dropout: 0.1 + lora_rank: 8 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== FOCUSED TRAINING (500 samples, ~20-30 min each) ===== + + - name: phi35_focused_comprehensive + category: fast + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/phi35_focused + epochs: 1 + max_train_samples: 500 + max_eval_samples: 50 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_focused_comprehensive + category: fast + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/qwen25_focused + epochs: 1 + max_train_samples: 500 + max_eval_samples: 50 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto diff --git a/config/training/autotrain_ultra_marathon.yaml b/config/training/autotrain_ultra_marathon.yaml index c50fefd48..08d7ed311 100644 --- a/config/training/autotrain_ultra_marathon.yaml +++ b/config/training/autotrain_ultra_marathon.yaml @@ -1,368 +1,368 @@ -version: 1 - -# ULTRA MARATHON TRAINING - Extended multi-hour training session -# Estimated total runtime: 8-12 hours (depends on GPU/CPU) -# 25+ training jobs across models, datasets, and hyperparameters - -jobs: - # ===== EXISTING JOBS (keep for continuity) ===== - - - name: phi35_mega_synthetic_full - category: ultra_marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mega_synthetic - save_dir: data_out/lora_training/ultra/phi35_mega_synthetic - epochs: 3 - max_train_samples: null - max_eval_samples: 200 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_mega_synthetic_full - category: ultra_marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mega_synthetic - save_dir: data_out/lora_training/ultra/qwen25_mega_synthetic - epochs: 3 - max_train_samples: null - max_eval_samples: 200 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== COMPREHENSIVE ULTRA TRAINING ===== - - - name: phi35_comprehensive_ultra_10epoch - category: ultra_marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/phi35_comprehensive_10epoch - epochs: 10 - max_train_samples: 5000 - max_eval_samples: 500 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_comprehensive_ultra_10epoch - category: ultra_marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/qwen25_comprehensive_10epoch - epochs: 10 - max_train_samples: 5000 - max_eval_samples: 500 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== DOLLY ULTRA TRAINING ===== - - - name: phi35_dolly_ultra_full - category: ultra_marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/dolly - save_dir: data_out/lora_training/ultra/phi35_dolly_ultra - epochs: 8 - max_train_samples: 10000 - max_eval_samples: 1000 - learning_rate: 0.00015 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_dolly_ultra_full - category: ultra_marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/dolly - save_dir: data_out/lora_training/ultra/qwen25_dolly_ultra - epochs: 8 - max_train_samples: 10000 - max_eval_samples: 1000 - learning_rate: 0.00015 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== MIXED CHAT ULTRA ===== - - - name: phi35_mixed_ultra_8epoch - category: ultra_marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/ultra/phi35_mixed_8epoch - epochs: 8 - max_train_samples: 3000 - max_eval_samples: 300 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_mixed_ultra_8epoch - category: ultra_marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/ultra/qwen25_mixed_8epoch - epochs: 8 - max_train_samples: 3000 - max_eval_samples: 300 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== REPOSITORY AUGMENTED ULTRA ===== - - - name: phi35_repo_ultra_6epoch - category: ultra_marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/app_repo_augmented - save_dir: data_out/lora_training/ultra/phi35_repo_6epoch - epochs: 6 - max_train_samples: 2000 - max_eval_samples: 200 - learning_rate: 0.0003 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_repo_ultra_6epoch - category: ultra_marathon - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/app_repo_augmented - save_dir: data_out/lora_training/ultra/qwen25_repo_6epoch - epochs: 6 - max_train_samples: 2000 - max_eval_samples: 200 - learning_rate: 0.0003 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== EXTENSIVE HYPERPARAMETER GRID SEARCH ===== - - # Learning rate variations (Phi-3.5) - - name: phi35_lr_00005_comprehensive - category: ultra_hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/phi35_lr_00005 - epochs: 6 - max_train_samples: 2500 - max_eval_samples: 250 - learning_rate: 0.00005 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: phi35_lr_00015_comprehensive - category: ultra_hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/phi35_lr_00015 - epochs: 6 - max_train_samples: 2500 - max_eval_samples: 250 - learning_rate: 0.00015 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: phi35_lr_00025_comprehensive - category: ultra_hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/phi35_lr_00025 - epochs: 6 - max_train_samples: 2500 - max_eval_samples: 250 - learning_rate: 0.00025 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: phi35_lr_0005_comprehensive - category: ultra_hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/phi35_lr_0005 - epochs: 5 - max_train_samples: 2500 - max_eval_samples: 250 - learning_rate: 0.0005 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - # Dropout variations (Qwen2.5) - - name: qwen25_dropout_005_comprehensive - category: ultra_hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/qwen25_dropout_005 - epochs: 6 - max_train_samples: 2500 - max_eval_samples: 250 - learning_rate: 0.0002 - lora_dropout: 0.05 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - - name: qwen25_dropout_020_comprehensive - category: ultra_hpo - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/qwen25_dropout_020 - epochs: 6 - max_train_samples: 2500 - max_eval_samples: 250 - learning_rate: 0.0002 - lora_dropout: 0.20 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== CROSS-DATASET TRAINING ===== - - # Train on multiple datasets sequentially - - name: phi35_mega_then_comprehensive - category: ultra_multi - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mega_synthetic - save_dir: data_out/lora_training/ultra/phi35_mega_comprehensive - epochs: 4 - max_train_samples: 2000 - max_eval_samples: 200 - learning_rate: 0.0002 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_dolly_then_mixed - category: ultra_multi - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/dolly - save_dir: data_out/lora_training/ultra/qwen25_dolly_mixed - epochs: 4 - max_train_samples: 3000 - max_eval_samples: 300 - learning_rate: 0.00018 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== MAXIMUM DEPTH TRAINING ===== - - # Very long training runs for deep learning - - name: phi35_comprehensive_max_depth - category: ultra_deep - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/phi35_max_depth - epochs: 15 - max_train_samples: 7000 - max_eval_samples: 700 - learning_rate: 0.00012 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_dolly_max_depth - category: ultra_deep - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/dolly - save_dir: data_out/lora_training/ultra/qwen25_max_depth - epochs: 12 - max_train_samples: 12000 - max_eval_samples: 1200 - learning_rate: 0.00012 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== COMBINED OPTIMAL SETTINGS ===== - - # Best of both models with optimal discovered hyperparameters - - name: phi35_optimal_combined - category: ultra_optimal - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/phi35_optimal - epochs: 7 - max_train_samples: 4000 - max_eval_samples: 400 - learning_rate: 0.00018 - lora_dropout: 0.08 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_optimal_combined - category: ultra_optimal - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/comprehensive - save_dir: data_out/lora_training/ultra/qwen25_optimal - epochs: 7 - max_train_samples: 4000 - max_eval_samples: 400 - learning_rate: 0.00022 - lora_dropout: 0.12 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto - - # ===== MEGA BATCH TRAINING ===== - - # Maximum samples with careful learning rate - - name: phi35_mega_batch_all_samples - category: ultra_mega - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/dolly - save_dir: data_out/lora_training/ultra/phi35_mega_batch - epochs: 6 - max_train_samples: null # ALL samples - max_eval_samples: 2000 - learning_rate: 0.0001 - lora_dropout: 0.1 - hf_model_id: microsoft/Phi-3.5-mini-instruct - device: auto - - - name: qwen25_mega_batch_all_samples - category: ultra_mega - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/dolly - save_dir: data_out/lora_training/ultra/qwen25_mega_batch - epochs: 6 - max_train_samples: null # ALL samples - max_eval_samples: 2000 - learning_rate: 0.0001 - lora_dropout: 0.1 - hf_model_id: Qwen/Qwen2.5-3B-Instruct - device: auto +version: 1 + +# ULTRA MARATHON TRAINING - Extended multi-hour training session +# Estimated total runtime: 8-12 hours (depends on GPU/CPU) +# 25+ training jobs across models, datasets, and hyperparameters + +jobs: + # ===== EXISTING JOBS (keep for continuity) ===== + + - name: phi35_mega_synthetic_full + category: ultra_marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mega_synthetic + save_dir: data_out/lora_training/ultra/phi35_mega_synthetic + epochs: 3 + max_train_samples: null + max_eval_samples: 200 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_mega_synthetic_full + category: ultra_marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mega_synthetic + save_dir: data_out/lora_training/ultra/qwen25_mega_synthetic + epochs: 3 + max_train_samples: null + max_eval_samples: 200 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== COMPREHENSIVE ULTRA TRAINING ===== + + - name: phi35_comprehensive_ultra_10epoch + category: ultra_marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/phi35_comprehensive_10epoch + epochs: 10 + max_train_samples: 5000 + max_eval_samples: 500 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_comprehensive_ultra_10epoch + category: ultra_marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/qwen25_comprehensive_10epoch + epochs: 10 + max_train_samples: 5000 + max_eval_samples: 500 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== DOLLY ULTRA TRAINING ===== + + - name: phi35_dolly_ultra_full + category: ultra_marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/dolly + save_dir: data_out/lora_training/ultra/phi35_dolly_ultra + epochs: 8 + max_train_samples: 10000 + max_eval_samples: 1000 + learning_rate: 0.00015 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_dolly_ultra_full + category: ultra_marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/dolly + save_dir: data_out/lora_training/ultra/qwen25_dolly_ultra + epochs: 8 + max_train_samples: 10000 + max_eval_samples: 1000 + learning_rate: 0.00015 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== MIXED CHAT ULTRA ===== + + - name: phi35_mixed_ultra_8epoch + category: ultra_marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/ultra/phi35_mixed_8epoch + epochs: 8 + max_train_samples: 3000 + max_eval_samples: 300 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_mixed_ultra_8epoch + category: ultra_marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/ultra/qwen25_mixed_8epoch + epochs: 8 + max_train_samples: 3000 + max_eval_samples: 300 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== REPOSITORY AUGMENTED ULTRA ===== + + - name: phi35_repo_ultra_6epoch + category: ultra_marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/app_repo_augmented + save_dir: data_out/lora_training/ultra/phi35_repo_6epoch + epochs: 6 + max_train_samples: 2000 + max_eval_samples: 200 + learning_rate: 0.0003 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_repo_ultra_6epoch + category: ultra_marathon + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/app_repo_augmented + save_dir: data_out/lora_training/ultra/qwen25_repo_6epoch + epochs: 6 + max_train_samples: 2000 + max_eval_samples: 200 + learning_rate: 0.0003 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== EXTENSIVE HYPERPARAMETER GRID SEARCH ===== + + # Learning rate variations (Phi-3.5) + - name: phi35_lr_00005_comprehensive + category: ultra_hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/phi35_lr_00005 + epochs: 6 + max_train_samples: 2500 + max_eval_samples: 250 + learning_rate: 0.00005 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: phi35_lr_00015_comprehensive + category: ultra_hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/phi35_lr_00015 + epochs: 6 + max_train_samples: 2500 + max_eval_samples: 250 + learning_rate: 0.00015 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: phi35_lr_00025_comprehensive + category: ultra_hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/phi35_lr_00025 + epochs: 6 + max_train_samples: 2500 + max_eval_samples: 250 + learning_rate: 0.00025 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: phi35_lr_0005_comprehensive + category: ultra_hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/phi35_lr_0005 + epochs: 5 + max_train_samples: 2500 + max_eval_samples: 250 + learning_rate: 0.0005 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + # Dropout variations (Qwen2.5) + - name: qwen25_dropout_005_comprehensive + category: ultra_hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/qwen25_dropout_005 + epochs: 6 + max_train_samples: 2500 + max_eval_samples: 250 + learning_rate: 0.0002 + lora_dropout: 0.05 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + - name: qwen25_dropout_020_comprehensive + category: ultra_hpo + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/qwen25_dropout_020 + epochs: 6 + max_train_samples: 2500 + max_eval_samples: 250 + learning_rate: 0.0002 + lora_dropout: 0.20 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== CROSS-DATASET TRAINING ===== + + # Train on multiple datasets sequentially + - name: phi35_mega_then_comprehensive + category: ultra_multi + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mega_synthetic + save_dir: data_out/lora_training/ultra/phi35_mega_comprehensive + epochs: 4 + max_train_samples: 2000 + max_eval_samples: 200 + learning_rate: 0.0002 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_dolly_then_mixed + category: ultra_multi + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/dolly + save_dir: data_out/lora_training/ultra/qwen25_dolly_mixed + epochs: 4 + max_train_samples: 3000 + max_eval_samples: 300 + learning_rate: 0.00018 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== MAXIMUM DEPTH TRAINING ===== + + # Very long training runs for deep learning + - name: phi35_comprehensive_max_depth + category: ultra_deep + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/phi35_max_depth + epochs: 15 + max_train_samples: 7000 + max_eval_samples: 700 + learning_rate: 0.00012 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_dolly_max_depth + category: ultra_deep + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/dolly + save_dir: data_out/lora_training/ultra/qwen25_max_depth + epochs: 12 + max_train_samples: 12000 + max_eval_samples: 1200 + learning_rate: 0.00012 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== COMBINED OPTIMAL SETTINGS ===== + + # Best of both models with optimal discovered hyperparameters + - name: phi35_optimal_combined + category: ultra_optimal + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/phi35_optimal + epochs: 7 + max_train_samples: 4000 + max_eval_samples: 400 + learning_rate: 0.00018 + lora_dropout: 0.08 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_optimal_combined + category: ultra_optimal + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/comprehensive + save_dir: data_out/lora_training/ultra/qwen25_optimal + epochs: 7 + max_train_samples: 4000 + max_eval_samples: 400 + learning_rate: 0.00022 + lora_dropout: 0.12 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto + + # ===== MEGA BATCH TRAINING ===== + + # Maximum samples with careful learning rate + - name: phi35_mega_batch_all_samples + category: ultra_mega + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/dolly + save_dir: data_out/lora_training/ultra/phi35_mega_batch + epochs: 6 + max_train_samples: null # ALL samples + max_eval_samples: 2000 + learning_rate: 0.0001 + lora_dropout: 0.1 + hf_model_id: microsoft/Phi-3.5-mini-instruct + device: auto + + - name: qwen25_mega_batch_all_samples + category: ultra_mega + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/dolly + save_dir: data_out/lora_training/ultra/qwen25_mega_batch + epochs: 6 + max_train_samples: null # ALL samples + max_eval_samples: 2000 + learning_rate: 0.0001 + lora_dropout: 0.1 + hf_model_id: Qwen/Qwen2.5-3B-Instruct + device: auto diff --git a/config/training/autotrain_ultrafast.yaml b/config/training/autotrain_ultrafast.yaml index adef55edc..6e8b2d7e0 100644 --- a/config/training/autotrain_ultrafast.yaml +++ b/config/training/autotrain_ultrafast.yaml @@ -1,51 +1,51 @@ -version: 1 - -# Ultra-fast training - maximum speed optimization -# 32 samples, batch_size 4, reduced sequence length, Phi-3.5 only (proven stable) - -jobs: - # ===== ULTRA QUICK (32 samples, 1-2 min) ===== - - - name: phi35_ultra_quick - category: ultrafast - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora_ultrafast.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/phi35_ultra - epochs: 1 - max_train_samples: 32 - max_eval_samples: 8 - learning_rate: 0.0005 - lora_dropout: 0.05 - lora_rank: 4 - hf_model_id: microsoft/Phi-3.5-mini-instruct - - # ===== RAPID TEST (64 samples, 3-4 min) ===== - - - name: phi35_rapid_mixed - category: ultrafast - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora_ultrafast.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training/phi35_rapid_mixed - epochs: 1 - max_train_samples: 64 - max_eval_samples: 16 - learning_rate: 0.0004 - lora_dropout: 0.05 - lora_rank: 4 - hf_model_id: microsoft/Phi-3.5-mini-instruct - - - name: phi35_rapid_comprehensive - category: ultrafast - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora_ultrafast.yaml - dataset: datasets/comprehensive - save_dir: data_out/lora_training/phi35_rapid_comprehensive - epochs: 1 - max_train_samples: 64 - max_eval_samples: 16 - learning_rate: 0.0004 - lora_dropout: 0.05 - lora_rank: 4 - hf_model_id: microsoft/Phi-3.5-mini-instruct +version: 1 + +# Ultra-fast training - maximum speed optimization +# 32 samples, batch_size 4, reduced sequence length, Phi-3.5 only (proven stable) + +jobs: + # ===== ULTRA QUICK (32 samples, 1-2 min) ===== + + - name: phi35_ultra_quick + category: ultrafast + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora_ultrafast.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/phi35_ultra + epochs: 1 + max_train_samples: 32 + max_eval_samples: 8 + learning_rate: 0.0005 + lora_dropout: 0.05 + lora_rank: 4 + hf_model_id: microsoft/Phi-3.5-mini-instruct + + # ===== RAPID TEST (64 samples, 3-4 min) ===== + + - name: phi35_rapid_mixed + category: ultrafast + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora_ultrafast.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training/phi35_rapid_mixed + epochs: 1 + max_train_samples: 64 + max_eval_samples: 16 + learning_rate: 0.0004 + lora_dropout: 0.05 + lora_rank: 4 + hf_model_id: microsoft/Phi-3.5-mini-instruct + + - name: phi35_rapid_comprehensive + category: ultrafast + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora_ultrafast.yaml + dataset: datasets/comprehensive + save_dir: data_out/lora_training/phi35_rapid_comprehensive + epochs: 1 + max_train_samples: 64 + max_eval_samples: 16 + learning_rate: 0.0004 + lora_dropout: 0.05 + lora_rank: 4 + hf_model_id: microsoft/Phi-3.5-mini-instruct diff --git a/data_out/subscriptions/subscriptions.json b/data_out/subscriptions/subscriptions.json index 2dfdee1de..d33229a60 100644 --- a/data_out/subscriptions/subscriptions.json +++ b/data_out/subscriptions/subscriptions.json @@ -1,78 +1,78 @@ -{ - "test_user_1": { - "user_id": "test_user_1", - "tier": "pro", - "tier_name": "PRO", - "price": 49, - "is_active": true, - "start_date": "2026-03-08T22:25:47.969627", - "end_date": "2026-04-07T22:25:47.969628", - "payment_method": "test", - "stripe_subscription_id": null, - "usage": { - "chat_messages": 0, - "quantum_jobs": 0, - "training_hours": 0, - "api_requests": 0, - "websites_created": 0 - }, - "usage_reset_date": "2026-04-07T22:25:47.969234", - "limits": { - "chat_messages": 10000, - "quantum_jobs": 50, - "training_hours": 20, - "api_requests": 10000, - "websites_created": 10 - }, - "features": { - "basic_chat": true, - "aria_character": true, - "quantum_computing": true, - "advanced_training": true, - "website_maker": true, - "api_access": true, - "priority_support": false, - "custom_models": false, - "unlimited_requests": false, - "commercial_license": true - } - }, - "test_user_2": { - "user_id": "test_user_2", - "tier": "enterprise", - "tier_name": "ENTERPRISE", - "price": 199, - "is_active": true, - "start_date": "2026-03-08T22:25:47.970684", - "end_date": "2026-04-07T22:25:47.970685", - "payment_method": "test", - "stripe_subscription_id": null, - "usage": { - "chat_messages": 0, - "quantum_jobs": 0, - "training_hours": 0, - "api_requests": 0, - "websites_created": 0 - }, - "usage_reset_date": "2026-04-07T22:25:47.970472", - "limits": { - "chat_messages": -1, - "quantum_jobs": -1, - "training_hours": -1, - "api_requests": -1, - "websites_created": -1 - }, - "features": { - "basic_chat": true, - "aria_character": true, - "quantum_computing": true, - "advanced_training": true, - "website_maker": true, - "api_access": true, - "priority_support": true, - "custom_models": true, - "unlimited_requests": true, - "commercial_license": true - } - } +{ + "test_user_1": { + "user_id": "test_user_1", + "tier": "pro", + "tier_name": "PRO", + "price": 49, + "is_active": true, + "start_date": "2026-03-08T22:25:47.969627", + "end_date": "2026-04-07T22:25:47.969628", + "payment_method": "test", + "stripe_subscription_id": null, + "usage": { + "chat_messages": 0, + "quantum_jobs": 0, + "training_hours": 0, + "api_requests": 0, + "websites_created": 0 + }, + "usage_reset_date": "2026-04-07T22:25:47.969234", + "limits": { + "chat_messages": 10000, + "quantum_jobs": 50, + "training_hours": 20, + "api_requests": 10000, + "websites_created": 10 + }, + "features": { + "basic_chat": true, + "aria_character": true, + "quantum_computing": true, + "advanced_training": true, + "website_maker": true, + "api_access": true, + "priority_support": false, + "custom_models": false, + "unlimited_requests": false, + "commercial_license": true + } + }, + "test_user_2": { + "user_id": "test_user_2", + "tier": "enterprise", + "tier_name": "ENTERPRISE", + "price": 199, + "is_active": true, + "start_date": "2026-03-08T22:25:47.970684", + "end_date": "2026-04-07T22:25:47.970685", + "payment_method": "test", + "stripe_subscription_id": null, + "usage": { + "chat_messages": 0, + "quantum_jobs": 0, + "training_hours": 0, + "api_requests": 0, + "websites_created": 0 + }, + "usage_reset_date": "2026-04-07T22:25:47.970472", + "limits": { + "chat_messages": -1, + "quantum_jobs": -1, + "training_hours": -1, + "api_requests": -1, + "websites_created": -1 + }, + "features": { + "basic_chat": true, + "aria_character": true, + "quantum_computing": true, + "advanced_training": true, + "website_maker": true, + "api_access": true, + "priority_support": true, + "custom_models": true, + "unlimited_requests": true, + "commercial_license": true + } + } } diff --git a/database/.vscode/tasks.json b/database/.vscode/tasks.json index a149660a7..98ade0cc8 100644 --- a/database/.vscode/tasks.json +++ b/database/.vscode/tasks.json @@ -1,17 +1,17 @@ -{ - "version": "2.0.0", - "tasks": [ - { - "label": "Build", - "type": "shell", - "command": "dotnet build", - "problemMatcher": "$sqlproj-problem-matcher", - "isBackground": true, - "group": { - "kind": "build", - "isDefault": "false" - }, - "detail": "Builds the SQL project" - } - ] +{ + "version": "2.0.0", + "tasks": [ + { + "label": "Build", + "type": "shell", + "command": "dotnet build", + "problemMatcher": "$sqlproj-problem-matcher", + "isBackground": true, + "group": { + "kind": "build", + "isDefault": "false" + }, + "detail": "Builds the SQL project" + } + ] } diff --git a/database/README.md b/database/README.md index bc8f5163b..99ee5d97e 100644 --- a/database/README.md +++ b/database/README.md @@ -1,404 +1,404 @@ -# QAI Database Project - -SQL database schema for tracking and managing QAI workspace activities including quantum training, LoRA fine-tuning, chat conversations, datasets, and Azure Quantum jobs. - -## Overview - -This database project provides comprehensive tracking and analytics for the QAI multi-AI/ML workspace. It's designed for **Azure SQL Database** but can be adapted for SQL Server or other Azure SQL variants. - -## Schema Components - -### Tables - -#### Training & Execution - -- **QuantumTrainingRuns** - Quantum ML training metadata, results, and hardware tracking -- **LoRATrainingRuns** - LoRA fine-tuning runs with hyperparameters and metrics -- **AzureQuantumJobs** - Azure Quantum job submissions, status, and cost tracking -- **OrchestratorJobs** - Orchestrator execution history (quantum_autorun, autotrain) - -#### Chat & Conversations - -- **ChatConversations** - Chat session metadata for talk-to-ai and Azure Functions -- **ChatMessages** - Individual messages with token usage and timing - -#### Semantic Memory (Embeddings) - -- **ChatMessageEmbeddings** - Per-message embedding vectors (VARBINARY float32 array) enabling semantic retrieval. Populated automatically by Azure Function `/api/chat` and the backfill script `scripts/ingest_chat_logs_to_sql.py`. Supports future migration to native `VECTOR` type when available. - -Memory retrieval flow: - -1. User sends message to `/api/chat` (optional `session_id` in POST body). -2. Endpoint generates embedding (Azure OpenAI > OpenAI > local hash fallback) and queries recent embeddings for similar past messages. -3. Top-K similar messages are injected as system memory prompts to enhance contextual continuity. -4. User and assistant messages are logged via `sp_LogChatConversation` and embeddings stored in `ChatMessageEmbeddings`. - -Embedding configuration: - -| Priority | Method | Env Vars Required | -|----------|--------|-------------------| -| 1 | Azure OpenAI Embeddings | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_EMBEDDING_DEPLOYMENT` | -| 2 | OpenAI Embeddings | `OPENAI_API_KEY` | -| 3 | Local Hash Fallback (dim=256) | None | - -Backfill existing logs: - -```powershell -python .\scripts\ingest_chat_logs_to_sql.py --logs-dir talk-to-ai\logs --embed-assistant -``` - -Table schema excerpt: - -```sql -CREATE TABLE [dbo].[ChatMessageEmbeddings] ( - EmbeddingId UNIQUEIDENTIFIER PRIMARY KEY DEFAULT NEWID(), - MessageId UNIQUEIDENTIFIER NOT NULL REFERENCES dbo.ChatMessages(MessageId) ON DELETE CASCADE, - EmbeddingModel NVARCHAR(100) NOT NULL, - EmbeddingDim INT NOT NULL, - EmbeddingVector VARBINARY(MAX) NOT NULL, - CreatedAt DATETIME2 NOT NULL DEFAULT GETUTCDATE() -); -``` - -Retrieval (Python-side cosine similarity) selects recent rows (TOP 500) optionally filtered by `SessionId` for targeted personalization. - -#### Datasets & Usage - -- **Datasets** - Dataset registry with licensing and validation status -- **DatasetUsage** - Dataset usage tracking across training runs - -#### MCP Server - -- **MCPServerSessions** - Model Context Protocol server session tracking -- **MCPToolCalls** - Individual MCP tool invocations and results - -### Views - -- **vw_TrainingRunsSummary** - Unified view of all training runs (Quantum + LoRA) -- **vw_DatasetUsageStats** - Dataset popularity and usage statistics -- **vw_AzureQuantumCostTracking** - Azure Quantum cost analysis by provider/target - -### Stored Procedures - -- **sp_LogQuantumTrainingRun** - Log quantum training with automatic dataset usage tracking -- **sp_LogLoRATrainingRun** - Log LoRA training with automatic dataset usage tracking -- **sp_LogChatConversation** - Log chat messages with automatic conversation management -- **sp_RegisterDataset** - Register or update dataset metadata - -## Deployment - -### Prerequisites - -- Azure SQL Database or SQL Server 2019+ -- SQL Database Project extension for VS Code -- .NET SDK 6.0+ (for SQL Database Projects) - -### Using VS Code - -1. Install the [SQL Database Projects extension](https://marketplace.visualstudio.com/items?itemName=ms-mssql.sql-database-projects-vscode) -2. Open this folder in VS Code -3. Use the SQL Database Projects view to: - - **Build**: Validate schema without deployment - - **Publish**: Deploy to Azure SQL or local SQL Server - - **Generate Script**: Create deployment SQL script - -### Manual Deployment - -```powershell -# Build the project -dotnet build database.sqlproj - -# Or deploy directly to Azure SQL -SqlPackage.exe /Action:Publish ` - /SourceFile:bin\Debug\database.dacpac ` - /TargetConnectionString:"Server=tcp:your-server.database.windows.net,1433;Database=qai-db;User ID=your-user;Password=your-password;Encrypt=True;" -``` - -### Deploy to Azure SQL Database - -```powershell -# Create resource group (if needed) -az group create --name rg-qai-db --location eastus - -# Create Azure SQL server -az sql server create ` - --name qai-sql-server ` - --resource-group rg-qai-db ` - --location eastus ` - --admin-user qai-admin ` - --admin-password 'YourSecurePassword123!' - -# Create database -az sql db create ` - --resource-group rg-qai-db ` - --server qai-sql-server ` - --name qai-db ` - --service-objective S0 # Basic tier for dev/test - -# Configure firewall (allow your IP) -az sql server firewall-rule create ` - --resource-group rg-qai-db ` - --server qai-sql-server ` - --name AllowMyIP ` - --start-ip-address YOUR_IP ` - --end-ip-address YOUR_IP - -# Publish from VS Code SQL Database Projects extension -# Or use SqlPackage CLI as shown above -``` - -## Integration with QAI Workspace - -### Python Integration - -Install dependencies: - -```powershell -pip install pyodbc sqlalchemy -``` - -Example usage: - -```python -import pyodbc -from datetime import datetime - -# Connection string (use Azure Key Vault in production) -conn_str = ( - "Driver={ODBC Driver 18 for SQL Server};" - "Server=tcp:qai-sql-server.database.windows.net,1433;" - "Database=qai-db;" - "Uid=qai-admin;" - "Pwd={your_password};" - "Encrypt=yes;" - "TrustServerCertificate=no;" -) - -# Log quantum training run -conn = pyodbc.connect(conn_str) -cursor = conn.cursor() - -cursor.execute(""" - DECLARE @RunId UNIQUEIDENTIFIER; - EXEC sp_LogQuantumTrainingRun - @JobName = ?, - @DatasetName = ?, - @Backend = ?, - @NumQubits = ?, - @NumLayers = ?, - @Entanglement = ?, - @LearningRate = ?, - @Epochs = ?, - @BatchSize = ?, - @TestAccuracy = ?, - @ExecutionTimeSeconds = ?, - @Status = ?, - @RunId = @RunId OUTPUT; - SELECT @RunId; -""", 'heart_quick', 'heart_disease', 'qiskit_aer', 4, 2, 'linear', - 0.01, 10, 16, 0.85, 45.2, 'completed') - -run_id = cursor.fetchone()[0] -conn.commit() -print(f"Logged run: {run_id}") -``` - -### Orchestrator Integration - -Modify `scripts/quantum_autorun.py` and `scripts/autotrain.py` to log runs: - -```python -# Add to quantum_autorun.py after successful training -import pyodbc - -def log_to_database(job_config, results): - conn = pyodbc.connect(DB_CONNECTION_STRING) - cursor = conn.cursor() - - cursor.execute(""" - DECLARE @RunId UNIQUEIDENTIFIER; - EXEC sp_LogQuantumTrainingRun - @JobName = ?, @DatasetName = ?, @Backend = ?, - @NumQubits = ?, @NumLayers = ?, @Entanglement = ?, - @LearningRate = ?, @Epochs = ?, @BatchSize = ?, - @TestAccuracy = ?, @TestLoss = ?, @ExecutionTimeSeconds = ?, - @StatusJsonPath = ?, @ResultsJsonPath = ?, - @Status = ?, @RunId = @RunId OUTPUT; - """, job_config['name'], job_config['dataset'], job_config['backend'], - job_config['n_qubits'], job_config['n_layers'], job_config['entanglement'], - job_config['learning_rate'], job_config['epochs'], job_config['batch_size'], - results['test_accuracy'], results['test_loss'], results['execution_time'], - results['status_json_path'], results['results_json_path'], 'completed') - - conn.commit() -``` - -### Azure Functions Integration - -Add database logging to `function_app.py`: - -```python -import pyodbc -from azure.identity import DefaultAzureCredential - -# Use managed identity in production -def get_db_connection(): - credential = DefaultAzureCredential() - token = credential.get_token("https://database.windows.net/") - - conn_str = ( - f"Driver={{ODBC Driver 18 for SQL Server}};" - f"Server=tcp:qai-sql-server.database.windows.net,1433;" - f"Database=qai-db;" - f"Authentication=ActiveDirectoryMsi;" - ) - return pyodbc.connect(conn_str) - -@app.route(route="chat", methods=["POST"]) -async def chat_endpoint(req: func.HttpRequest) -> func.HttpResponse: - # ... existing chat logic ... - - # Log conversation - conn = get_db_connection() - cursor = conn.cursor() - cursor.execute(""" - DECLARE @ConvId UNIQUEIDENTIFIER, @MsgId UNIQUEIDENTIFIER; - EXEC sp_LogChatConversation - @SessionId = ?, @Provider = ?, @Model = ?, - @Role = ?, @Content = ?, @TotalTokens = ?, - @ConversationId = @ConvId OUTPUT, @MessageId = @MsgId OUTPUT; - """, session_id, provider, model, 'user', user_message, token_count) - conn.commit() -``` - -## Cost Optimization - -### Azure SQL Tiers - -- **Dev/Test**: S0 (10 DTUs, ~$15/month) or Basic (5 DTUs, ~$5/month) -- **Production**: S1-S3 (20-100 DTUs, $30-$200/month) -- **Serverless**: Auto-pause after inactivity, pay per second (~$150/month active) - -### Data Retention - -Implement retention policies to control costs: - -```sql --- Archive old training runs (keep last 90 days) -DELETE FROM QuantumTrainingRuns -WHERE CreatedAt < DATEADD(day, -90, GETUTCDATE()) - AND Status IN ('completed', 'failed'); - --- Archive chat messages (keep last 30 days) -DELETE FROM ChatMessages -WHERE Timestamp < DATEADD(day, -30, GETUTCDATE()); -``` - -## Monitoring & Analytics - -### Power BI Integration - -Connect Power BI Desktop to Azure SQL: - -1. Get Data → Azure → Azure SQL Database -2. Server: `qai-sql-server.database.windows.net` -3. Database: `qai-db` -4. Use views for pre-aggregated data - -### Sample Queries - -```sql --- Training success rate by dataset -SELECT - DatasetName, - COUNT(*) AS TotalRuns, - AVG(TestAccuracy) AS AvgAccuracy, - SUM(CASE WHEN Status = 'completed' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS SuccessRate -FROM vw_TrainingRunsSummary -WHERE TrainingType = 'Quantum' -GROUP BY DatasetName -ORDER BY AvgAccuracy DESC; - --- Azure Quantum cost by month -SELECT - DATEPART(year, SubmittedAt) AS Year, - DATEPART(month, SubmittedAt) AS Month, - Provider, - SUM(ActualCostUSD) AS TotalCost, - COUNT(*) AS JobCount -FROM AzureQuantumJobs -WHERE Status = 'succeeded' -GROUP BY DATEPART(year, SubmittedAt), DATEPART(month, SubmittedAt), Provider -ORDER BY Year DESC, Month DESC; - --- Most active chat providers -SELECT - Provider, - COUNT(DISTINCT ConversationId) AS TotalConversations, - COUNT(*) AS TotalMessages, - AVG(CAST(ExecutionTimeMs AS FLOAT)) AS AvgResponseTimeMs -FROM ChatConversations c -JOIN ChatMessages m ON c.ConversationId = m.ConversationId -WHERE m.Role = 'assistant' -GROUP BY Provider -ORDER BY TotalConversations DESC; -``` - -## Security Best Practices - -1. **Authentication**: Use Azure AD Managed Identity in production -2. **Encryption**: Enable Transparent Data Encryption (TDE) on Azure SQL -3. **Network**: Configure VNet service endpoints or Private Link -4. **Secrets**: Store connection strings in Azure Key Vault -5. **Auditing**: Enable Azure SQL auditing for compliance - -```powershell -# Enable TDE (enabled by default on Azure SQL) -az sql db tde set --resource-group rg-qai-db ` - --server qai-sql-server ` - --database qai-db ` - --status Enabled - -# Enable auditing -az sql server audit-policy update ` - --resource-group rg-qai-db ` - --name qai-sql-server ` - --state Enabled ` - --blob-storage-target-state Enabled ` - --storage-account your-storage-account -``` - -## Troubleshooting - -### Connection Issues - -```powershell -# Test connection -sqlcmd -S qai-sql-server.database.windows.net -d qai-db -U qai-admin -P 'YourPassword' - -# Check firewall rules -az sql server firewall-rule list ` - --resource-group rg-qai-db ` - --server qai-sql-server -``` - -### Build Errors - -```powershell -# Clean and rebuild -Remove-Item -Recurse -Force bin, obj -dotnet build database.sqlproj -``` - -## Roadmap - -- [ ] Add full-text search indexes for chat content -- [ ] Implement temporal tables for audit history -- [ ] Create Azure Function triggers for real-time dashboards -- [ ] Add Graph DB tables for model dependency tracking -- [ ] Cosmos DB integration for high-throughput logging -- [ ] Stream Analytics for real-time metrics - -## License - -Part of the QAI project. See root LICENSE for details. +# QAI Database Project + +SQL database schema for tracking and managing QAI workspace activities including quantum training, LoRA fine-tuning, chat conversations, datasets, and Azure Quantum jobs. + +## Overview + +This database project provides comprehensive tracking and analytics for the QAI multi-AI/ML workspace. It's designed for **Azure SQL Database** but can be adapted for SQL Server or other Azure SQL variants. + +## Schema Components + +### Tables + +#### Training & Execution + +- **QuantumTrainingRuns** - Quantum ML training metadata, results, and hardware tracking +- **LoRATrainingRuns** - LoRA fine-tuning runs with hyperparameters and metrics +- **AzureQuantumJobs** - Azure Quantum job submissions, status, and cost tracking +- **OrchestratorJobs** - Orchestrator execution history (quantum_autorun, autotrain) + +#### Chat & Conversations + +- **ChatConversations** - Chat session metadata for talk-to-ai and Azure Functions +- **ChatMessages** - Individual messages with token usage and timing + +#### Semantic Memory (Embeddings) + +- **ChatMessageEmbeddings** - Per-message embedding vectors (VARBINARY float32 array) enabling semantic retrieval. Populated automatically by Azure Function `/api/chat` and the backfill script `scripts/ingest_chat_logs_to_sql.py`. Supports future migration to native `VECTOR` type when available. + +Memory retrieval flow: + +1. User sends message to `/api/chat` (optional `session_id` in POST body). +2. Endpoint generates embedding (Azure OpenAI > OpenAI > local hash fallback) and queries recent embeddings for similar past messages. +3. Top-K similar messages are injected as system memory prompts to enhance contextual continuity. +4. User and assistant messages are logged via `sp_LogChatConversation` and embeddings stored in `ChatMessageEmbeddings`. + +Embedding configuration: + +| Priority | Method | Env Vars Required | +|----------|--------|-------------------| +| 1 | Azure OpenAI Embeddings | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_EMBEDDING_DEPLOYMENT` | +| 2 | OpenAI Embeddings | `OPENAI_API_KEY` | +| 3 | Local Hash Fallback (dim=256) | None | + +Backfill existing logs: + +```powershell +python .\scripts\ingest_chat_logs_to_sql.py --logs-dir talk-to-ai\logs --embed-assistant +``` + +Table schema excerpt: + +```sql +CREATE TABLE [dbo].[ChatMessageEmbeddings] ( + EmbeddingId UNIQUEIDENTIFIER PRIMARY KEY DEFAULT NEWID(), + MessageId UNIQUEIDENTIFIER NOT NULL REFERENCES dbo.ChatMessages(MessageId) ON DELETE CASCADE, + EmbeddingModel NVARCHAR(100) NOT NULL, + EmbeddingDim INT NOT NULL, + EmbeddingVector VARBINARY(MAX) NOT NULL, + CreatedAt DATETIME2 NOT NULL DEFAULT GETUTCDATE() +); +``` + +Retrieval (Python-side cosine similarity) selects recent rows (TOP 500) optionally filtered by `SessionId` for targeted personalization. + +#### Datasets & Usage + +- **Datasets** - Dataset registry with licensing and validation status +- **DatasetUsage** - Dataset usage tracking across training runs + +#### MCP Server + +- **MCPServerSessions** - Model Context Protocol server session tracking +- **MCPToolCalls** - Individual MCP tool invocations and results + +### Views + +- **vw_TrainingRunsSummary** - Unified view of all training runs (Quantum + LoRA) +- **vw_DatasetUsageStats** - Dataset popularity and usage statistics +- **vw_AzureQuantumCostTracking** - Azure Quantum cost analysis by provider/target + +### Stored Procedures + +- **sp_LogQuantumTrainingRun** - Log quantum training with automatic dataset usage tracking +- **sp_LogLoRATrainingRun** - Log LoRA training with automatic dataset usage tracking +- **sp_LogChatConversation** - Log chat messages with automatic conversation management +- **sp_RegisterDataset** - Register or update dataset metadata + +## Deployment + +### Prerequisites + +- Azure SQL Database or SQL Server 2019+ +- SQL Database Project extension for VS Code +- .NET SDK 6.0+ (for SQL Database Projects) + +### Using VS Code + +1. Install the [SQL Database Projects extension](https://marketplace.visualstudio.com/items?itemName=ms-mssql.sql-database-projects-vscode) +2. Open this folder in VS Code +3. Use the SQL Database Projects view to: + - **Build**: Validate schema without deployment + - **Publish**: Deploy to Azure SQL or local SQL Server + - **Generate Script**: Create deployment SQL script + +### Manual Deployment + +```powershell +# Build the project +dotnet build database.sqlproj + +# Or deploy directly to Azure SQL +SqlPackage.exe /Action:Publish ` + /SourceFile:bin\Debug\database.dacpac ` + /TargetConnectionString:"Server=tcp:your-server.database.windows.net,1433;Database=qai-db;User ID=your-user;Password=your-password;Encrypt=True;" +``` + +### Deploy to Azure SQL Database + +```powershell +# Create resource group (if needed) +az group create --name rg-qai-db --location eastus + +# Create Azure SQL server +az sql server create ` + --name qai-sql-server ` + --resource-group rg-qai-db ` + --location eastus ` + --admin-user qai-admin ` + --admin-password 'YourSecurePassword123!' + +# Create database +az sql db create ` + --resource-group rg-qai-db ` + --server qai-sql-server ` + --name qai-db ` + --service-objective S0 # Basic tier for dev/test + +# Configure firewall (allow your IP) +az sql server firewall-rule create ` + --resource-group rg-qai-db ` + --server qai-sql-server ` + --name AllowMyIP ` + --start-ip-address YOUR_IP ` + --end-ip-address YOUR_IP + +# Publish from VS Code SQL Database Projects extension +# Or use SqlPackage CLI as shown above +``` + +## Integration with QAI Workspace + +### Python Integration + +Install dependencies: + +```powershell +pip install pyodbc sqlalchemy +``` + +Example usage: + +```python +import pyodbc +from datetime import datetime + +# Connection string (use Azure Key Vault in production) +conn_str = ( + "Driver={ODBC Driver 18 for SQL Server};" + "Server=tcp:qai-sql-server.database.windows.net,1433;" + "Database=qai-db;" + "Uid=qai-admin;" + "Pwd={your_password};" + "Encrypt=yes;" + "TrustServerCertificate=no;" +) + +# Log quantum training run +conn = pyodbc.connect(conn_str) +cursor = conn.cursor() + +cursor.execute(""" + DECLARE @RunId UNIQUEIDENTIFIER; + EXEC sp_LogQuantumTrainingRun + @JobName = ?, + @DatasetName = ?, + @Backend = ?, + @NumQubits = ?, + @NumLayers = ?, + @Entanglement = ?, + @LearningRate = ?, + @Epochs = ?, + @BatchSize = ?, + @TestAccuracy = ?, + @ExecutionTimeSeconds = ?, + @Status = ?, + @RunId = @RunId OUTPUT; + SELECT @RunId; +""", 'heart_quick', 'heart_disease', 'qiskit_aer', 4, 2, 'linear', + 0.01, 10, 16, 0.85, 45.2, 'completed') + +run_id = cursor.fetchone()[0] +conn.commit() +print(f"Logged run: {run_id}") +``` + +### Orchestrator Integration + +Modify `scripts/quantum_autorun.py` and `scripts/autotrain.py` to log runs: + +```python +# Add to quantum_autorun.py after successful training +import pyodbc + +def log_to_database(job_config, results): + conn = pyodbc.connect(DB_CONNECTION_STRING) + cursor = conn.cursor() + + cursor.execute(""" + DECLARE @RunId UNIQUEIDENTIFIER; + EXEC sp_LogQuantumTrainingRun + @JobName = ?, @DatasetName = ?, @Backend = ?, + @NumQubits = ?, @NumLayers = ?, @Entanglement = ?, + @LearningRate = ?, @Epochs = ?, @BatchSize = ?, + @TestAccuracy = ?, @TestLoss = ?, @ExecutionTimeSeconds = ?, + @StatusJsonPath = ?, @ResultsJsonPath = ?, + @Status = ?, @RunId = @RunId OUTPUT; + """, job_config['name'], job_config['dataset'], job_config['backend'], + job_config['n_qubits'], job_config['n_layers'], job_config['entanglement'], + job_config['learning_rate'], job_config['epochs'], job_config['batch_size'], + results['test_accuracy'], results['test_loss'], results['execution_time'], + results['status_json_path'], results['results_json_path'], 'completed') + + conn.commit() +``` + +### Azure Functions Integration + +Add database logging to `function_app.py`: + +```python +import pyodbc +from azure.identity import DefaultAzureCredential + +# Use managed identity in production +def get_db_connection(): + credential = DefaultAzureCredential() + token = credential.get_token("https://database.windows.net/") + + conn_str = ( + f"Driver={{ODBC Driver 18 for SQL Server}};" + f"Server=tcp:qai-sql-server.database.windows.net,1433;" + f"Database=qai-db;" + f"Authentication=ActiveDirectoryMsi;" + ) + return pyodbc.connect(conn_str) + +@app.route(route="chat", methods=["POST"]) +async def chat_endpoint(req: func.HttpRequest) -> func.HttpResponse: + # ... existing chat logic ... + + # Log conversation + conn = get_db_connection() + cursor = conn.cursor() + cursor.execute(""" + DECLARE @ConvId UNIQUEIDENTIFIER, @MsgId UNIQUEIDENTIFIER; + EXEC sp_LogChatConversation + @SessionId = ?, @Provider = ?, @Model = ?, + @Role = ?, @Content = ?, @TotalTokens = ?, + @ConversationId = @ConvId OUTPUT, @MessageId = @MsgId OUTPUT; + """, session_id, provider, model, 'user', user_message, token_count) + conn.commit() +``` + +## Cost Optimization + +### Azure SQL Tiers + +- **Dev/Test**: S0 (10 DTUs, ~$15/month) or Basic (5 DTUs, ~$5/month) +- **Production**: S1-S3 (20-100 DTUs, $30-$200/month) +- **Serverless**: Auto-pause after inactivity, pay per second (~$150/month active) + +### Data Retention + +Implement retention policies to control costs: + +```sql +-- Archive old training runs (keep last 90 days) +DELETE FROM QuantumTrainingRuns +WHERE CreatedAt < DATEADD(day, -90, GETUTCDATE()) + AND Status IN ('completed', 'failed'); + +-- Archive chat messages (keep last 30 days) +DELETE FROM ChatMessages +WHERE Timestamp < DATEADD(day, -30, GETUTCDATE()); +``` + +## Monitoring & Analytics + +### Power BI Integration + +Connect Power BI Desktop to Azure SQL: + +1. Get Data → Azure → Azure SQL Database +2. Server: `qai-sql-server.database.windows.net` +3. Database: `qai-db` +4. Use views for pre-aggregated data + +### Sample Queries + +```sql +-- Training success rate by dataset +SELECT + DatasetName, + COUNT(*) AS TotalRuns, + AVG(TestAccuracy) AS AvgAccuracy, + SUM(CASE WHEN Status = 'completed' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS SuccessRate +FROM vw_TrainingRunsSummary +WHERE TrainingType = 'Quantum' +GROUP BY DatasetName +ORDER BY AvgAccuracy DESC; + +-- Azure Quantum cost by month +SELECT + DATEPART(year, SubmittedAt) AS Year, + DATEPART(month, SubmittedAt) AS Month, + Provider, + SUM(ActualCostUSD) AS TotalCost, + COUNT(*) AS JobCount +FROM AzureQuantumJobs +WHERE Status = 'succeeded' +GROUP BY DATEPART(year, SubmittedAt), DATEPART(month, SubmittedAt), Provider +ORDER BY Year DESC, Month DESC; + +-- Most active chat providers +SELECT + Provider, + COUNT(DISTINCT ConversationId) AS TotalConversations, + COUNT(*) AS TotalMessages, + AVG(CAST(ExecutionTimeMs AS FLOAT)) AS AvgResponseTimeMs +FROM ChatConversations c +JOIN ChatMessages m ON c.ConversationId = m.ConversationId +WHERE m.Role = 'assistant' +GROUP BY Provider +ORDER BY TotalConversations DESC; +``` + +## Security Best Practices + +1. **Authentication**: Use Azure AD Managed Identity in production +2. **Encryption**: Enable Transparent Data Encryption (TDE) on Azure SQL +3. **Network**: Configure VNet service endpoints or Private Link +4. **Secrets**: Store connection strings in Azure Key Vault +5. **Auditing**: Enable Azure SQL auditing for compliance + +```powershell +# Enable TDE (enabled by default on Azure SQL) +az sql db tde set --resource-group rg-qai-db ` + --server qai-sql-server ` + --database qai-db ` + --status Enabled + +# Enable auditing +az sql server audit-policy update ` + --resource-group rg-qai-db ` + --name qai-sql-server ` + --state Enabled ` + --blob-storage-target-state Enabled ` + --storage-account your-storage-account +``` + +## Troubleshooting + +### Connection Issues + +```powershell +# Test connection +sqlcmd -S qai-sql-server.database.windows.net -d qai-db -U qai-admin -P 'YourPassword' + +# Check firewall rules +az sql server firewall-rule list ` + --resource-group rg-qai-db ` + --server qai-sql-server +``` + +### Build Errors + +```powershell +# Clean and rebuild +Remove-Item -Recurse -Force bin, obj +dotnet build database.sqlproj +``` + +## Roadmap + +- [ ] Add full-text search indexes for chat content +- [ ] Implement temporal tables for audit history +- [ ] Create Azure Function triggers for real-time dashboards +- [ ] Add Graph DB tables for model dependency tracking +- [ ] Cosmos DB integration for high-throughput logging +- [ ] Stream Analytics for real-time metrics + +## License + +Part of the QAI project. See root LICENSE for details. diff --git a/dataset-requirements.txt b/dataset-requirements.txt index 2ab7744f6..a0af73d27 100644 --- a/dataset-requirements.txt +++ b/dataset-requirements.txt @@ -1,15 +1,15 @@ -# Additional dependencies for dataset management -# Install with: pip install -r dataset-requirements.txt - -# Dataset downloading and processing -datasets>=2.21.0 # Hugging Face datasets library -tqdm>=4.67.0 # Progress bars for downloads - -# Optional: Data processing and analysis -pandas>=2.2.0 # CSV/DataFrame operations -numpy>=1.26.4 # Numerical operations -scikit-learn>=1.6.0 # ML datasets and preprocessing - -# Optional: Advanced formats -pyarrow>=17.0.0 # Parquet file support -h5py>=3.12.0 # HDF5 file support +# Additional dependencies for dataset management +# Install with: pip install -r dataset-requirements.txt + +# Dataset downloading and processing +datasets>=2.21.0 # Hugging Face datasets library +tqdm>=4.67.0 # Progress bars for downloads + +# Optional: Data processing and analysis +pandas>=2.2.0 # CSV/DataFrame operations +numpy>=1.26.4 # Numerical operations +scikit-learn>=1.6.0 # ML datasets and preprocessing + +# Optional: Advanced formats +pyarrow>=17.0.0 # Parquet file support +h5py>=3.12.0 # HDF5 file support diff --git a/docs/404.html b/docs/404.html index a7b3f84aa..01bc93a25 100644 --- a/docs/404.html +++ b/docs/404.html @@ -1,197 +1,197 @@ - - - - - - 404 — Page Not Found | Aria - - - - - - - - - -
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    + + diff --git a/docs/CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md b/docs/CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md index 15c245d83..f73aa7103 100644 --- a/docs/CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md +++ b/docs/CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md @@ -1,611 +1,611 @@ -# Code Inefficiency Analysis Report -**Date:** February 17, 2026 -**Repository:** Bryan-Roe/Aria -**Analysis Scope:** Full codebase performance review - -## Executive Summary - -This document identifies inefficient code patterns discovered through comprehensive codebase analysis. The findings range from critical O(n³) complexity issues to minor optimization opportunities. Implementation of these recommendations could result in 10-1000x performance improvements in affected code paths. - -## Critical Issues (High Priority) - -### 1. Triple-Nested Gradient Loop with Repeated Loss Computation - -**File:** `ai-projects/quantum-ml/web_app.py` -**Lines:** 217-246 -**Severity:** Critical -**Complexity:** O(layers × qubits × gates × batch_size × dataset_size) - -#### Problem -The `compute_gradient()` function uses three nested loops with expensive operations inside: - -```python -def compute_gradient(circuit, X, y, weights, use_parameter_shift=True): - grad = np.zeros_like(weights) - - if use_parameter_shift: - shift = np.pi / 2 - for i in range(weights.shape[0]): # Layer loop - for j in range(weights.shape[1]): # Qubit loop - for k in range(weights.shape[2]): # Gate loop - weights_plus = weights.copy() # O(n) copy operation - weights_minus = weights.copy() # O(n) copy operation - weights_plus[i, j, k] += shift - weights_minus[i, j, k] -= shift - loss_plus = compute_loss(circuit, X, y, weights_plus) # O(batch_size) - loss_minus = compute_loss(circuit, X, y, weights_minus) # O(batch_size) - grad[i, j, k] = (loss_plus - loss_minus) / 2 - # ... similar pattern in finite differences fallback (lines 238-244) -``` - -#### Issues Identified -1. **Triple nested loops:** O(n³) complexity -2. **Array copying:** 2× `weights.copy()` per iteration (expensive for large arrays) -3. **Redundant loss computation:** Each `compute_loss()` loops through entire batch -4. **No vectorization:** NumPy capabilities not utilized - -#### Performance Impact -For a typical configuration: -- 3 layers × 10 qubits × 3 gates = 90 weight parameters -- 32 batch size -- Each epoch requires **180 forward passes** (2 per parameter) -- With 100 samples, that's **18,000 circuit evaluations per epoch** - -#### Recommended Solution -Use PennyLane's automatic differentiation (`qml.grad()`): - -```python -def compute_gradient_optimized(circuit, X, y, weights): - """Optimized gradient using automatic differentiation""" - try: - # Use PennyLane's built-in autograd (10-100x faster) - grad_fn = qml.grad(lambda w: compute_loss(circuit, X, y, w)) - return grad_fn(weights) - except Exception: - # Fallback to manual parameter-shift if needed - return compute_gradient(circuit, X, y, weights, use_parameter_shift=True) -``` - -#### Expected Improvement -- **Current:** ~1000ms per gradient computation -- **Optimized:** ~10-50ms (10-100x speedup) -- **Memory:** Reduced from O(n × batch_size) to O(batch_size) - ---- - -### 2. Repeated File I/O Without Caching - -**File:** `dashboard/serve.py` -**Lines:** 273-515 (multiple methods) -**Severity:** High -**Impact:** Network I/O, Memory - -#### Problem -Multiple API endpoints repeatedly read and parse the same JSON files: - -```python -# get_job_progress() - line 273 -with open(status_file, 'r') as f: - data = json.load(f) - -# get_job_metrics() - line 319 -with open(status_file, 'r') as f: - data = json.load(f) # READS SAME FILE AGAIN - -# get_job_details() - line 495 -with open(status_file, 'r') as f: - data = json.load(f) # READS SAME FILE AGAIN - -# get_job_logs() - line 517 -with open(status_file, 'r') as f: - data = json.load(f) # READS SAME FILE AGAIN -``` - -#### Performance Impact -- **File size:** status.json can be 1-5MB with multiple jobs -- **Per request:** 4-5 file reads = 5-25MB of I/O -- **Dashboard refresh:** Every 5 seconds = 12 reads/minute -- **Cost:** ~50-200ms per file read + JSON parsing - -#### Recommended Solution -Implement file-based caching with TTL: - -```python -import time -from functools import lru_cache -from pathlib import Path - -_file_cache = {} -_FILE_CACHE_TTL = 5 # seconds - -def _load_json_cached(filepath: Path) -> dict: - """Load JSON with TTL-based caching""" - now = time.time() - cache_key = str(filepath) - - if cache_key in _file_cache: - cached_data, cached_time = _file_cache[cache_key] - if now - cached_time < _FILE_CACHE_TTL: - return cached_data - - # Cache miss or expired - read file - try: - with open(filepath, 'r') as f: - data = json.load(f) - _file_cache[cache_key] = (data, now) - return data - except Exception: - # Return cached data even if expired, if file read fails - if cache_key in _file_cache: - return _file_cache[cache_key][0] - raise - -# Usage in all methods: -def get_job_progress(self, job_id): - status_file = root_dir / 'data_out' / 'autotrain' / 'status.json' - data = _load_json_cached(status_file) # Cached! - # ... rest of logic -``` - -#### Expected Improvement -- **First call:** Same performance (~50ms) -- **Subsequent calls (within 5s):** <1ms (cache hit) -- **Memory overhead:** ~1-5MB per cached file -- **Overall:** 5-10x reduction in file I/O - ---- - -## High Priority Issues - -### 3. Linear Keyword Search Patterns - -**File:** `ai-projects/chat-cli/src/agi_provider.py` -**Lines:** 343-372 -**Severity:** High -**Complexity:** O(keywords × query_length) - -#### Problem -Multiple `any()` calls iterate through keyword lists for each check: - -```python -# Complexity analysis - called on every query -complexity = "simple" -if any(word in query_lower for word in ["explain", "compare", "analyze", "how", "why"]): - complexity = "moderate" # O(5 × len(query_lower)) -elif any(word in query_lower for word in ["step by step", "detailed", "comprehensive"]): - complexity = "complex" # O(3 × len(query_lower)) - -# Intent detection - 5 separate any() calls -if any(word in query_lower for word in ["move", "walk", "go", "jump", "dance", "wave"]): - intent = "movement" # O(6 × len(query_lower)) -elif any(word in query_lower for word in ["code", "program", "function", "debug"]): - intent = "coding" # O(4 × len(query_lower)) -# ... 3 more any() calls - -# Domain detection - loops through dict -for dom, keywords in domain_keywords.items(): # O(domains) - if any(kw in query_lower for kw in keywords): # O(keywords × len(query_lower)) - domain = dom - break -``` - -#### Performance Impact -Total keyword checks: ~30 keywords × average query length -- **Short query (10 words):** ~300 substring operations -- **Long query (100 words):** ~3,000 substring operations -- **Per-query overhead:** 1-10ms depending on query length - -#### Recommended Solution -Pre-compile keyword sets and use word tokenization: - -```python -# Module-level pre-compiled sets (immutable, allocated once) -_COMPLEXITY_MODERATE = frozenset(["explain", "compare", "analyze", "how", "why"]) -_COMPLEXITY_COMPLEX = frozenset(["step by step", "detailed", "comprehensive"]) -_INTENT_MOVEMENT = frozenset(["move", "walk", "go", "jump", "dance", "wave"]) -_INTENT_CODING = frozenset(["code", "program", "function", "debug"]) -_INTENT_EXPLANATION = frozenset(["explain", "what is", "how does"]) -_INTENT_CREATION = frozenset(["create", "generate", "make", "build"]) - -_DOMAIN_KEYWORDS = { - "quantum": frozenset(["quantum", "qubit", "entanglement", "superposition"]), - "ai": frozenset(["ai", "machine learning", "neural", "model", "training"]), - "aria": frozenset(["aria", "move", "animation", "character"]), - "technical": frozenset(["code", "program", "api", "function", "database"]), -} - -def _analyze_query_optimized(query: str) -> dict: - """Optimized query analysis with single-pass word extraction""" - query_lower = query.lower() - words = query_lower.split() - word_set = set(words) # O(n) single pass - - # Complexity detection - O(1) set intersection - complexity = "simple" - if word_set & _COMPLEXITY_MODERATE: - complexity = "moderate" - elif word_set & _COMPLEXITY_COMPLEX: - complexity = "complex" - elif len(words) > 20: - complexity = "complex" - - # Intent detection - O(1) set intersection - intent = "general" - if word_set & _INTENT_MOVEMENT: - intent = "movement" - elif word_set & _INTENT_CODING: - intent = "coding" - elif word_set & _INTENT_EXPLANATION: - intent = "explanation" - elif word_set & _INTENT_CREATION: - intent = "creation" - elif "?" in query: - intent = "question" - - # Domain detection - O(domains) with O(1) set intersection per domain - domain = "general" - for dom, keywords in _DOMAIN_KEYWORDS.items(): - if word_set & keywords: - domain = dom - break - - return { - "query": query, - "complexity": complexity, - "intent": intent, - "domain": domain, - "word_count": len(words), - } -``` - -#### Expected Improvement -- **Before:** O(keywords × query_length) = ~300-3000 ops -- **After:** O(query_length + keywords) = ~100 ops -- **Speedup:** 3-30x depending on query length -- **Memory:** Negligible (frozensets allocated once at module load) - ---- - -### 4. Multiple Any() Patterns in Position Detection - -**File:** `aria_web/server.py` -**Lines:** 554-557 -**Severity:** Medium -**Pattern:** Inefficient position detection - -#### Problem -Position detection uses inline `any()` calls despite having frozenset infrastructure: - -```python -# File has frozensets at top (lines 42-74) but doesn't use them consistently -elif any(k in cmd for k in ['walk left', 'go left', 'left']): # line 554 - return '[aria:position:20:70]' -elif any(k in cmd for k in ['walk right', 'go right', 'right']): # line 556 - return '[aria:position:80:70]' -``` - -Note: Lines 42-74 define 19 frozensets for keyword matching, showing awareness of the pattern, but it's not applied consistently. - -#### Recommended Solution -Extend the existing frozenset pattern: - -```python -# Add to existing frozenset section (around line 42-74) -MOVE_LEFT_KEYWORDS = frozenset(['walk left', 'go left', 'left', 'move left']) -MOVE_RIGHT_KEYWORDS = frozenset(['walk right', 'go right', 'right', 'move right']) - -# In determine_position_from_context function: -elif _contains_any_keyword(cmd, MOVE_LEFT_KEYWORDS): - return '[aria:position:20:70]' -elif _contains_any_keyword(cmd, MOVE_RIGHT_KEYWORDS): - return '[aria:position:80:70]' -``` - ---- - -## Medium Priority Issues - -### 5. Multi-Pass Session Statistics Collection - -**File:** `ai-projects/quantum-ml/web_app.py` -**Lines:** 952-958 -**Severity:** Medium -**Pattern:** Multiple iterations over same collection - -#### Problem -Global statistics computed with 4 separate iterations: - -```python -def get_global_stats(): - active_sessions = sum(1 for s in training_sessions.values() if s.status == "training") - completed_sessions = sum(1 for s in training_sessions.values() if s.status == "completed") - total_epochs = sum(s.current_epoch for s in training_sessions.values()) - avg_accuracy = np.mean([s.best_val_acc for s in training_sessions.values() if s.best_val_acc > 0]) - # training_sessions.values() iterated 4 times! -``` - -#### Recommended Solution -Single-pass accumulation: - -```python -def get_global_stats(): - """Optimized single-pass statistics collection""" - active_count = 0 - completed_count = 0 - total_epochs = 0 - accuracies = [] - - for session in training_sessions.values(): - if session.status == "training": - active_count += 1 - elif session.status == "completed": - completed_count += 1 - - total_epochs += session.current_epoch - - if session.best_val_acc > 0: - accuracies.append(session.best_val_acc) - - return { - 'active_sessions': active_count, - 'completed_sessions': completed_count, - 'total_epochs': total_epochs, - 'avg_accuracy': np.mean(accuracies) if accuracies else 0.0, - } -``` - -#### Expected Improvement -- **Before:** O(4n) - four complete iterations -- **After:** O(n) - single iteration -- **Speedup:** ~4x -- **Best for:** Large numbers of sessions (>100) - ---- - -### 6. Inefficient Directory Traversal with Intermediate Lists - -**File:** `dashboard/serve.py` -**Lines:** 700-701, 761, 766 -**Severity:** Medium -**Pattern:** Unnecessary memory allocation - -#### Problem -Creates intermediate lists when only counting: - -```python -# Line 700-701 -health['checks']['datasets'] = { - 'exists': datasets_dir.exists(), - 'count': len(list(datasets_dir.glob('*/train.json'))) # Creates full list! -} - -# Line 761 -dataset_count = len([d for d in datasets_dir.iterdir() if d.is_dir()]) # Full list! - -# Line 766 -model_count = len([m for m in models_dir.iterdir() if m.is_dir()]) # Full list! -``` - -#### Recommended Solution -Use generator expressions with sum(): - -```python -# Optimized counting without intermediate lists -health['checks']['datasets'] = { - 'exists': datasets_dir.exists(), - 'count': sum(1 for _ in datasets_dir.glob('*/train.json')) # Generator! -} - -dataset_count = sum(1 for d in datasets_dir.iterdir() if d.is_dir()) -model_count = sum(1 for m in models_dir.iterdir() if m.is_dir()) -``` - -#### Expected Improvement -- **Memory:** O(n) → O(1) -- **Speed:** 5-20% faster (no intermediate list allocation) -- **Impact:** Increases with directory size - ---- - -### 7. Rate Limiting with List Filtering - -**File:** `dashboard/serve.py` -**Lines:** 39-40 -**Severity:** Low -**Pattern:** O(n) filtering on every request - -#### Problem -Rate limit tracking uses list filtering: - -```python -request_counts[client_ip] = [t for t in request_counts[client_ip] if now - t < 60] -``` - -This creates a new list on every request, even for clients not near the limit. - -#### Recommended Solution -Use `collections.deque` with expiration tracking: - -```python -from collections import deque, defaultdict - -# Module-level storage -_request_timestamps = defaultdict(deque) -_MAX_REQUESTS = 100 -_WINDOW_SECONDS = 60 - -def check_rate_limit(client_ip: str) -> bool: - """Check if client is within rate limit""" - now = time.time() - timestamps = _request_timestamps[client_ip] - - # Remove expired timestamps from left (oldest) - while timestamps and now - timestamps[0] > _WINDOW_SECONDS: - timestamps.popleft() - - if len(timestamps) >= _MAX_REQUESTS: - return False # Rate limited - - timestamps.append(now) - return True -``` - -#### Expected Improvement -- **Before:** O(n) list creation per request -- **After:** O(k) where k = expired entries -- **Memory:** Bounded by rate limit window -- **Typical case:** Much faster (only removes old entries) - ---- - -## Low Priority Issues - -### 8. Gradient Norm with Intermediate List - -**File:** `ai-projects/quantum-ml/web_app.py` -**Lines:** 440-443 -**Severity:** Low - -#### Problem -```python -if epoch_gradients: - avg_gradient = np.mean([np.linalg.norm(g) for g in epoch_gradients]) -``` - -#### Solution -```python -if epoch_gradients: - avg_gradient = np.mean(np.linalg.norm(g) for g in epoch_gradients) -``` - -Minor improvement: avoids creating intermediate list. - ---- - -## Summary Table - -| Priority | Issue | File | Lines | Complexity | Expected Speedup | -|----------|-------|------|-------|------------|------------------| -| Critical | Triple-nested gradient loop | web_app.py | 217-246 | O(n³) | 10-100x | -| High | Repeated JSON file reading | serve.py | 273-515 | File I/O | 5-10x | -| High | Linear keyword searches | agi_provider.py | 343-372 | O(k×m) | 3-30x | -| Medium | Any() position detection | server.py | 554-557 | O(k×m) | 2-5x | -| Medium | Multi-pass session stats | web_app.py | 952-958 | O(4n) | 4x | -| Medium | Directory traversal lists | serve.py | 700, 761, 766 | O(n) mem | 5-20% | -| Low | Rate limit filtering | serve.py | 39-40 | O(n) | 2-5x | -| Low | Gradient norm list | web_app.py | 440-443 | Minor | <5% | - ---- - -## Implementation Recommendations - -### Phase 1: Critical Fixes (Implement Immediately) -1. **Quantum gradient optimization** (web_app.py:217-246) - - Use `qml.grad()` for automatic differentiation - - Expected impact: 10-100x speedup in training - - Risk: Low (fallback to manual method exists) - -2. **File caching** (serve.py:273-515) - - Implement TTL-based JSON caching - - Expected impact: 5-10x reduction in file I/O - - Risk: Low (5-second TTL prevents stale data) - -### Phase 2: High Priority (Implement Soon) -3. **Keyword matching optimization** (agi_provider.py:343-372) - - Convert to frozensets with set intersection - - Expected impact: 3-30x speedup per query - - Risk: Very low (pure optimization) - -4. **Consistent frozenset usage** (server.py:554-557) - - Extend existing pattern to all keyword matching - - Expected impact: 2-5x speedup in position detection - - Risk: Very low (pattern already established) - -### Phase 3: Medium Priority (Implement When Time Permits) -5. **Single-pass statistics** (web_app.py:952-958) -6. **Generator-based counting** (serve.py:700, 761, 766) -7. **Deque-based rate limiting** (serve.py:39-40) - -### Phase 4: Low Priority (Nice to Have) -8. **Minor list comprehension optimizations** - ---- - -## Testing Strategy - -For each optimization: - -1. **Unit tests:** Verify correct behavior with existing test patterns -2. **Performance benchmarks:** Measure actual speedup with realistic data -3. **Integration tests:** Ensure no regressions in API behavior -4. **Load tests:** Validate improvements under concurrent load - -Example test structure: -```python -def test_gradient_optimization(): - """Test optimized gradient computation matches manual method""" - circuit = create_test_circuit() - X, y = generate_test_data() - weights = np.random.randn(3, 4, 3) - - # Compare results - grad_manual = compute_gradient(circuit, X, y, weights) - grad_optimized = compute_gradient_optimized(circuit, X, y, weights) - - np.testing.assert_allclose(grad_manual, grad_optimized, rtol=1e-5) - -def test_gradient_performance(): - """Benchmark gradient computation speedup""" - import time - - # Setup - circuit = create_test_circuit() - X, y = generate_test_data(n_samples=100) - weights = np.random.randn(3, 10, 3) - - # Benchmark manual - start = time.perf_counter() - grad_manual = compute_gradient(circuit, X, y, weights) - time_manual = time.perf_counter() - start - - # Benchmark optimized - start = time.perf_counter() - grad_optimized = compute_gradient_optimized(circuit, X, y, weights) - time_optimized = time.perf_counter() - start - - speedup = time_manual / time_optimized - assert speedup > 5, f"Expected 5x speedup, got {speedup:.1f}x" -``` - ---- - -## Memory Usage Considerations - -| Optimization | Memory Impact | Notes | -|--------------|---------------|-------| -| Gradient vectorization | -50% | Eliminates weight copies | -| File caching | +5MB | Per cached file (acceptable) | -| Frozenset keywords | +10KB | One-time module load cost | -| Generator expressions | -90% | Avoids intermediate lists | -| Deque rate limiting | Constant | Bounded by time window | - ---- - -## Related Documentation - -- `docs/PERFORMANCE_IMPROVEMENTS.md` - Previous optimization work -- `docs/PERFORMANCE_OPTIMIZATIONS_FEB_2026.md` - Recent optimizations -- `tests/test_performance_optimizations.py` - Performance test patterns -- Repository memories contain optimization patterns to follow - ---- - -## Conclusion - -This analysis identified 8 distinct performance issues ranging from critical O(n³) complexity to minor list comprehension inefficiencies. The top 3 recommendations could provide: - -- **10-100x speedup** in quantum training (gradient optimization) -- **5-10x reduction** in dashboard file I/O (caching) -- **3-30x speedup** in query processing (keyword matching) - -All recommendations follow established patterns in the codebase and include fallback mechanisms for safety. Implementation should follow the phased approach with comprehensive testing at each stage. +# Code Inefficiency Analysis Report +**Date:** February 17, 2026 +**Repository:** Bryan-Roe/Aria +**Analysis Scope:** Full codebase performance review + +## Executive Summary + +This document identifies inefficient code patterns discovered through comprehensive codebase analysis. The findings range from critical O(n³) complexity issues to minor optimization opportunities. Implementation of these recommendations could result in 10-1000x performance improvements in affected code paths. + +## Critical Issues (High Priority) + +### 1. Triple-Nested Gradient Loop with Repeated Loss Computation + +**File:** `ai-projects/quantum-ml/web_app.py` +**Lines:** 217-246 +**Severity:** Critical +**Complexity:** O(layers × qubits × gates × batch_size × dataset_size) + +#### Problem +The `compute_gradient()` function uses three nested loops with expensive operations inside: + +```python +def compute_gradient(circuit, X, y, weights, use_parameter_shift=True): + grad = np.zeros_like(weights) + + if use_parameter_shift: + shift = np.pi / 2 + for i in range(weights.shape[0]): # Layer loop + for j in range(weights.shape[1]): # Qubit loop + for k in range(weights.shape[2]): # Gate loop + weights_plus = weights.copy() # O(n) copy operation + weights_minus = weights.copy() # O(n) copy operation + weights_plus[i, j, k] += shift + weights_minus[i, j, k] -= shift + loss_plus = compute_loss(circuit, X, y, weights_plus) # O(batch_size) + loss_minus = compute_loss(circuit, X, y, weights_minus) # O(batch_size) + grad[i, j, k] = (loss_plus - loss_minus) / 2 + # ... similar pattern in finite differences fallback (lines 238-244) +``` + +#### Issues Identified +1. **Triple nested loops:** O(n³) complexity +2. **Array copying:** 2× `weights.copy()` per iteration (expensive for large arrays) +3. **Redundant loss computation:** Each `compute_loss()` loops through entire batch +4. **No vectorization:** NumPy capabilities not utilized + +#### Performance Impact +For a typical configuration: +- 3 layers × 10 qubits × 3 gates = 90 weight parameters +- 32 batch size +- Each epoch requires **180 forward passes** (2 per parameter) +- With 100 samples, that's **18,000 circuit evaluations per epoch** + +#### Recommended Solution +Use PennyLane's automatic differentiation (`qml.grad()`): + +```python +def compute_gradient_optimized(circuit, X, y, weights): + """Optimized gradient using automatic differentiation""" + try: + # Use PennyLane's built-in autograd (10-100x faster) + grad_fn = qml.grad(lambda w: compute_loss(circuit, X, y, w)) + return grad_fn(weights) + except Exception: + # Fallback to manual parameter-shift if needed + return compute_gradient(circuit, X, y, weights, use_parameter_shift=True) +``` + +#### Expected Improvement +- **Current:** ~1000ms per gradient computation +- **Optimized:** ~10-50ms (10-100x speedup) +- **Memory:** Reduced from O(n × batch_size) to O(batch_size) + +--- + +### 2. Repeated File I/O Without Caching + +**File:** `dashboard/serve.py` +**Lines:** 273-515 (multiple methods) +**Severity:** High +**Impact:** Network I/O, Memory + +#### Problem +Multiple API endpoints repeatedly read and parse the same JSON files: + +```python +# get_job_progress() - line 273 +with open(status_file, 'r') as f: + data = json.load(f) + +# get_job_metrics() - line 319 +with open(status_file, 'r') as f: + data = json.load(f) # READS SAME FILE AGAIN + +# get_job_details() - line 495 +with open(status_file, 'r') as f: + data = json.load(f) # READS SAME FILE AGAIN + +# get_job_logs() - line 517 +with open(status_file, 'r') as f: + data = json.load(f) # READS SAME FILE AGAIN +``` + +#### Performance Impact +- **File size:** status.json can be 1-5MB with multiple jobs +- **Per request:** 4-5 file reads = 5-25MB of I/O +- **Dashboard refresh:** Every 5 seconds = 12 reads/minute +- **Cost:** ~50-200ms per file read + JSON parsing + +#### Recommended Solution +Implement file-based caching with TTL: + +```python +import time +from functools import lru_cache +from pathlib import Path + +_file_cache = {} +_FILE_CACHE_TTL = 5 # seconds + +def _load_json_cached(filepath: Path) -> dict: + """Load JSON with TTL-based caching""" + now = time.time() + cache_key = str(filepath) + + if cache_key in _file_cache: + cached_data, cached_time = _file_cache[cache_key] + if now - cached_time < _FILE_CACHE_TTL: + return cached_data + + # Cache miss or expired - read file + try: + with open(filepath, 'r') as f: + data = json.load(f) + _file_cache[cache_key] = (data, now) + return data + except Exception: + # Return cached data even if expired, if file read fails + if cache_key in _file_cache: + return _file_cache[cache_key][0] + raise + +# Usage in all methods: +def get_job_progress(self, job_id): + status_file = root_dir / 'data_out' / 'autotrain' / 'status.json' + data = _load_json_cached(status_file) # Cached! + # ... rest of logic +``` + +#### Expected Improvement +- **First call:** Same performance (~50ms) +- **Subsequent calls (within 5s):** <1ms (cache hit) +- **Memory overhead:** ~1-5MB per cached file +- **Overall:** 5-10x reduction in file I/O + +--- + +## High Priority Issues + +### 3. Linear Keyword Search Patterns + +**File:** `ai-projects/chat-cli/src/agi_provider.py` +**Lines:** 343-372 +**Severity:** High +**Complexity:** O(keywords × query_length) + +#### Problem +Multiple `any()` calls iterate through keyword lists for each check: + +```python +# Complexity analysis - called on every query +complexity = "simple" +if any(word in query_lower for word in ["explain", "compare", "analyze", "how", "why"]): + complexity = "moderate" # O(5 × len(query_lower)) +elif any(word in query_lower for word in ["step by step", "detailed", "comprehensive"]): + complexity = "complex" # O(3 × len(query_lower)) + +# Intent detection - 5 separate any() calls +if any(word in query_lower for word in ["move", "walk", "go", "jump", "dance", "wave"]): + intent = "movement" # O(6 × len(query_lower)) +elif any(word in query_lower for word in ["code", "program", "function", "debug"]): + intent = "coding" # O(4 × len(query_lower)) +# ... 3 more any() calls + +# Domain detection - loops through dict +for dom, keywords in domain_keywords.items(): # O(domains) + if any(kw in query_lower for kw in keywords): # O(keywords × len(query_lower)) + domain = dom + break +``` + +#### Performance Impact +Total keyword checks: ~30 keywords × average query length +- **Short query (10 words):** ~300 substring operations +- **Long query (100 words):** ~3,000 substring operations +- **Per-query overhead:** 1-10ms depending on query length + +#### Recommended Solution +Pre-compile keyword sets and use word tokenization: + +```python +# Module-level pre-compiled sets (immutable, allocated once) +_COMPLEXITY_MODERATE = frozenset(["explain", "compare", "analyze", "how", "why"]) +_COMPLEXITY_COMPLEX = frozenset(["step by step", "detailed", "comprehensive"]) +_INTENT_MOVEMENT = frozenset(["move", "walk", "go", "jump", "dance", "wave"]) +_INTENT_CODING = frozenset(["code", "program", "function", "debug"]) +_INTENT_EXPLANATION = frozenset(["explain", "what is", "how does"]) +_INTENT_CREATION = frozenset(["create", "generate", "make", "build"]) + +_DOMAIN_KEYWORDS = { + "quantum": frozenset(["quantum", "qubit", "entanglement", "superposition"]), + "ai": frozenset(["ai", "machine learning", "neural", "model", "training"]), + "aria": frozenset(["aria", "move", "animation", "character"]), + "technical": frozenset(["code", "program", "api", "function", "database"]), +} + +def _analyze_query_optimized(query: str) -> dict: + """Optimized query analysis with single-pass word extraction""" + query_lower = query.lower() + words = query_lower.split() + word_set = set(words) # O(n) single pass + + # Complexity detection - O(1) set intersection + complexity = "simple" + if word_set & _COMPLEXITY_MODERATE: + complexity = "moderate" + elif word_set & _COMPLEXITY_COMPLEX: + complexity = "complex" + elif len(words) > 20: + complexity = "complex" + + # Intent detection - O(1) set intersection + intent = "general" + if word_set & _INTENT_MOVEMENT: + intent = "movement" + elif word_set & _INTENT_CODING: + intent = "coding" + elif word_set & _INTENT_EXPLANATION: + intent = "explanation" + elif word_set & _INTENT_CREATION: + intent = "creation" + elif "?" in query: + intent = "question" + + # Domain detection - O(domains) with O(1) set intersection per domain + domain = "general" + for dom, keywords in _DOMAIN_KEYWORDS.items(): + if word_set & keywords: + domain = dom + break + + return { + "query": query, + "complexity": complexity, + "intent": intent, + "domain": domain, + "word_count": len(words), + } +``` + +#### Expected Improvement +- **Before:** O(keywords × query_length) = ~300-3000 ops +- **After:** O(query_length + keywords) = ~100 ops +- **Speedup:** 3-30x depending on query length +- **Memory:** Negligible (frozensets allocated once at module load) + +--- + +### 4. Multiple Any() Patterns in Position Detection + +**File:** `aria_web/server.py` +**Lines:** 554-557 +**Severity:** Medium +**Pattern:** Inefficient position detection + +#### Problem +Position detection uses inline `any()` calls despite having frozenset infrastructure: + +```python +# File has frozensets at top (lines 42-74) but doesn't use them consistently +elif any(k in cmd for k in ['walk left', 'go left', 'left']): # line 554 + return '[aria:position:20:70]' +elif any(k in cmd for k in ['walk right', 'go right', 'right']): # line 556 + return '[aria:position:80:70]' +``` + +Note: Lines 42-74 define 19 frozensets for keyword matching, showing awareness of the pattern, but it's not applied consistently. + +#### Recommended Solution +Extend the existing frozenset pattern: + +```python +# Add to existing frozenset section (around line 42-74) +MOVE_LEFT_KEYWORDS = frozenset(['walk left', 'go left', 'left', 'move left']) +MOVE_RIGHT_KEYWORDS = frozenset(['walk right', 'go right', 'right', 'move right']) + +# In determine_position_from_context function: +elif _contains_any_keyword(cmd, MOVE_LEFT_KEYWORDS): + return '[aria:position:20:70]' +elif _contains_any_keyword(cmd, MOVE_RIGHT_KEYWORDS): + return '[aria:position:80:70]' +``` + +--- + +## Medium Priority Issues + +### 5. Multi-Pass Session Statistics Collection + +**File:** `ai-projects/quantum-ml/web_app.py` +**Lines:** 952-958 +**Severity:** Medium +**Pattern:** Multiple iterations over same collection + +#### Problem +Global statistics computed with 4 separate iterations: + +```python +def get_global_stats(): + active_sessions = sum(1 for s in training_sessions.values() if s.status == "training") + completed_sessions = sum(1 for s in training_sessions.values() if s.status == "completed") + total_epochs = sum(s.current_epoch for s in training_sessions.values()) + avg_accuracy = np.mean([s.best_val_acc for s in training_sessions.values() if s.best_val_acc > 0]) + # training_sessions.values() iterated 4 times! +``` + +#### Recommended Solution +Single-pass accumulation: + +```python +def get_global_stats(): + """Optimized single-pass statistics collection""" + active_count = 0 + completed_count = 0 + total_epochs = 0 + accuracies = [] + + for session in training_sessions.values(): + if session.status == "training": + active_count += 1 + elif session.status == "completed": + completed_count += 1 + + total_epochs += session.current_epoch + + if session.best_val_acc > 0: + accuracies.append(session.best_val_acc) + + return { + 'active_sessions': active_count, + 'completed_sessions': completed_count, + 'total_epochs': total_epochs, + 'avg_accuracy': np.mean(accuracies) if accuracies else 0.0, + } +``` + +#### Expected Improvement +- **Before:** O(4n) - four complete iterations +- **After:** O(n) - single iteration +- **Speedup:** ~4x +- **Best for:** Large numbers of sessions (>100) + +--- + +### 6. Inefficient Directory Traversal with Intermediate Lists + +**File:** `dashboard/serve.py` +**Lines:** 700-701, 761, 766 +**Severity:** Medium +**Pattern:** Unnecessary memory allocation + +#### Problem +Creates intermediate lists when only counting: + +```python +# Line 700-701 +health['checks']['datasets'] = { + 'exists': datasets_dir.exists(), + 'count': len(list(datasets_dir.glob('*/train.json'))) # Creates full list! +} + +# Line 761 +dataset_count = len([d for d in datasets_dir.iterdir() if d.is_dir()]) # Full list! + +# Line 766 +model_count = len([m for m in models_dir.iterdir() if m.is_dir()]) # Full list! +``` + +#### Recommended Solution +Use generator expressions with sum(): + +```python +# Optimized counting without intermediate lists +health['checks']['datasets'] = { + 'exists': datasets_dir.exists(), + 'count': sum(1 for _ in datasets_dir.glob('*/train.json')) # Generator! +} + +dataset_count = sum(1 for d in datasets_dir.iterdir() if d.is_dir()) +model_count = sum(1 for m in models_dir.iterdir() if m.is_dir()) +``` + +#### Expected Improvement +- **Memory:** O(n) → O(1) +- **Speed:** 5-20% faster (no intermediate list allocation) +- **Impact:** Increases with directory size + +--- + +### 7. Rate Limiting with List Filtering + +**File:** `dashboard/serve.py` +**Lines:** 39-40 +**Severity:** Low +**Pattern:** O(n) filtering on every request + +#### Problem +Rate limit tracking uses list filtering: + +```python +request_counts[client_ip] = [t for t in request_counts[client_ip] if now - t < 60] +``` + +This creates a new list on every request, even for clients not near the limit. + +#### Recommended Solution +Use `collections.deque` with expiration tracking: + +```python +from collections import deque, defaultdict + +# Module-level storage +_request_timestamps = defaultdict(deque) +_MAX_REQUESTS = 100 +_WINDOW_SECONDS = 60 + +def check_rate_limit(client_ip: str) -> bool: + """Check if client is within rate limit""" + now = time.time() + timestamps = _request_timestamps[client_ip] + + # Remove expired timestamps from left (oldest) + while timestamps and now - timestamps[0] > _WINDOW_SECONDS: + timestamps.popleft() + + if len(timestamps) >= _MAX_REQUESTS: + return False # Rate limited + + timestamps.append(now) + return True +``` + +#### Expected Improvement +- **Before:** O(n) list creation per request +- **After:** O(k) where k = expired entries +- **Memory:** Bounded by rate limit window +- **Typical case:** Much faster (only removes old entries) + +--- + +## Low Priority Issues + +### 8. Gradient Norm with Intermediate List + +**File:** `ai-projects/quantum-ml/web_app.py` +**Lines:** 440-443 +**Severity:** Low + +#### Problem +```python +if epoch_gradients: + avg_gradient = np.mean([np.linalg.norm(g) for g in epoch_gradients]) +``` + +#### Solution +```python +if epoch_gradients: + avg_gradient = np.mean(np.linalg.norm(g) for g in epoch_gradients) +``` + +Minor improvement: avoids creating intermediate list. + +--- + +## Summary Table + +| Priority | Issue | File | Lines | Complexity | Expected Speedup | +|----------|-------|------|-------|------------|------------------| +| Critical | Triple-nested gradient loop | web_app.py | 217-246 | O(n³) | 10-100x | +| High | Repeated JSON file reading | serve.py | 273-515 | File I/O | 5-10x | +| High | Linear keyword searches | agi_provider.py | 343-372 | O(k×m) | 3-30x | +| Medium | Any() position detection | server.py | 554-557 | O(k×m) | 2-5x | +| Medium | Multi-pass session stats | web_app.py | 952-958 | O(4n) | 4x | +| Medium | Directory traversal lists | serve.py | 700, 761, 766 | O(n) mem | 5-20% | +| Low | Rate limit filtering | serve.py | 39-40 | O(n) | 2-5x | +| Low | Gradient norm list | web_app.py | 440-443 | Minor | <5% | + +--- + +## Implementation Recommendations + +### Phase 1: Critical Fixes (Implement Immediately) +1. **Quantum gradient optimization** (web_app.py:217-246) + - Use `qml.grad()` for automatic differentiation + - Expected impact: 10-100x speedup in training + - Risk: Low (fallback to manual method exists) + +2. **File caching** (serve.py:273-515) + - Implement TTL-based JSON caching + - Expected impact: 5-10x reduction in file I/O + - Risk: Low (5-second TTL prevents stale data) + +### Phase 2: High Priority (Implement Soon) +3. **Keyword matching optimization** (agi_provider.py:343-372) + - Convert to frozensets with set intersection + - Expected impact: 3-30x speedup per query + - Risk: Very low (pure optimization) + +4. **Consistent frozenset usage** (server.py:554-557) + - Extend existing pattern to all keyword matching + - Expected impact: 2-5x speedup in position detection + - Risk: Very low (pattern already established) + +### Phase 3: Medium Priority (Implement When Time Permits) +5. **Single-pass statistics** (web_app.py:952-958) +6. **Generator-based counting** (serve.py:700, 761, 766) +7. **Deque-based rate limiting** (serve.py:39-40) + +### Phase 4: Low Priority (Nice to Have) +8. **Minor list comprehension optimizations** + +--- + +## Testing Strategy + +For each optimization: + +1. **Unit tests:** Verify correct behavior with existing test patterns +2. **Performance benchmarks:** Measure actual speedup with realistic data +3. **Integration tests:** Ensure no regressions in API behavior +4. **Load tests:** Validate improvements under concurrent load + +Example test structure: +```python +def test_gradient_optimization(): + """Test optimized gradient computation matches manual method""" + circuit = create_test_circuit() + X, y = generate_test_data() + weights = np.random.randn(3, 4, 3) + + # Compare results + grad_manual = compute_gradient(circuit, X, y, weights) + grad_optimized = compute_gradient_optimized(circuit, X, y, weights) + + np.testing.assert_allclose(grad_manual, grad_optimized, rtol=1e-5) + +def test_gradient_performance(): + """Benchmark gradient computation speedup""" + import time + + # Setup + circuit = create_test_circuit() + X, y = generate_test_data(n_samples=100) + weights = np.random.randn(3, 10, 3) + + # Benchmark manual + start = time.perf_counter() + grad_manual = compute_gradient(circuit, X, y, weights) + time_manual = time.perf_counter() - start + + # Benchmark optimized + start = time.perf_counter() + grad_optimized = compute_gradient_optimized(circuit, X, y, weights) + time_optimized = time.perf_counter() - start + + speedup = time_manual / time_optimized + assert speedup > 5, f"Expected 5x speedup, got {speedup:.1f}x" +``` + +--- + +## Memory Usage Considerations + +| Optimization | Memory Impact | Notes | +|--------------|---------------|-------| +| Gradient vectorization | -50% | Eliminates weight copies | +| File caching | +5MB | Per cached file (acceptable) | +| Frozenset keywords | +10KB | One-time module load cost | +| Generator expressions | -90% | Avoids intermediate lists | +| Deque rate limiting | Constant | Bounded by time window | + +--- + +## Related Documentation + +- `docs/PERFORMANCE_IMPROVEMENTS.md` - Previous optimization work +- `docs/PERFORMANCE_OPTIMIZATIONS_FEB_2026.md` - Recent optimizations +- `tests/test_performance_optimizations.py` - Performance test patterns +- Repository memories contain optimization patterns to follow + +--- + +## Conclusion + +This analysis identified 8 distinct performance issues ranging from critical O(n³) complexity to minor list comprehension inefficiencies. The top 3 recommendations could provide: + +- **10-100x speedup** in quantum training (gradient optimization) +- **5-10x reduction** in dashboard file I/O (caching) +- **3-30x speedup** in query processing (keyword matching) + +All recommendations follow established patterns in the codebase and include fallback mechanisms for safety. Implementation should follow the phased approach with comprehensive testing at each stage. diff --git a/docs/DEPLOYMENT_GUIDE.md b/docs/DEPLOYMENT_GUIDE.md index 017a02d11..5f50921fd 100644 --- a/docs/DEPLOYMENT_GUIDE.md +++ b/docs/DEPLOYMENT_GUIDE.md @@ -1,408 +1,408 @@ -# GitHub Pages Deployment Guide - -This guide explains how the Aria project is deployed to GitHub Pages and how to maintain it. - -## 📋 Overview - -The Aria project uses GitHub Pages to host interactive web applications in demo mode. The deployment is automated via GitHub Actions and uses Jekyll as the static site generator. - -## 🏗️ Architecture - -### Repository Structure - -``` -Aria/ -├── .github/ -│ └── workflows/ -│ └── pages.yml # GitHub Actions deployment workflow -├── docs/ # GitHub Pages root (published to web) -│ ├── index.html # Main landing page -│ ├── _config.yml # Jekyll configuration -│ ├── .nojekyll # Bypass Jekyll for static files -│ ├── README_PAGES.md # Pages-specific documentation -│ ├── aria/ # Aria character app (demo mode) -│ │ ├── index.html -│ │ └── aria_controller.js -│ ├── chat/ # AI chat app (demo mode) -│ │ ├── index.html -│ │ └── chat.js -│ ├── dashboard/ # Training dashboard -│ │ ├── index.html -│ │ └── shared-theme.css -│ └── quantum/ # Quantum ML interface -│ └── index.html -├── aria_web/ # Source (local development) -├── chat-web/ # Source (local development) -└── dashboard/ # Source (local development) -``` - -### Demo Mode vs Local Development - -| Feature | GitHub Pages (Demo) | Local Development | -|---------|-------------------|-------------------| -| UI Functionality | ✅ Full | ✅ Full | -| Character Animations | ✅ Full | ✅ Full | -| API Responses | 🔸 Mock | ✅ Real | -| AI Chat | 🔸 Simulated | ✅ Multi-provider | -| Quantum Computing | ❌ No backend | ✅ Azure Quantum | -| Training | ❌ No backend | ✅ LoRA fine-tuning | -| Cost | 🆓 Free | 💰 API usage | - -## 🚀 Deployment Process - -### Automatic Deployment - -GitHub Pages automatically deploys when changes are pushed: - -1. **Trigger**: Push to `main` or `copilot/enable-github-pages-for-repo` branch -2. **Build**: GitHub Actions runs Jekyll build on `docs/` directory -3. **Deploy**: Built site is published to GitHub Pages -4. **URL**: https://bryan-roe.github.io/Aria - -### Deployment Workflow (.github/workflows/pages.yml) - -```yaml -name: Deploy to GitHub Pages - -on: - push: - branches: [ main, copilot/enable-github-pages-for-repo ] - workflow_dispatch: - -permissions: - contents: read - pages: write - id-token: write - -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v4 - - name: Setup Pages - uses: actions/configure-pages@v4 - - name: Build with Jekyll - uses: actions/jekyll-build-pages@v1 - with: - source: ./docs - destination: ./_site - - name: Upload artifact - uses: actions/upload-pages-artifact@v3 - - deploy: - environment: - name: github-pages - url: ${{ steps.deployment.outputs.page_url }} - runs-on: ubuntu-latest - needs: build - steps: - - name: Deploy to GitHub Pages - uses: actions/deploy-pages@v4 -``` - -### Manual Deployment - -To manually trigger deployment: - -1. Go to **Actions** tab in GitHub -2. Select **"Deploy to GitHub Pages"** workflow -3. Click **"Run workflow"** button -4. Select branch and click **"Run workflow"** - -## 🔧 Configuration - -### Jekyll Configuration (docs/_config.yml) - -```yaml -title: Aria - Interactive AI Character Platform -description: Hybrid quantum-AI/ML platform -theme: jekyll-theme-minimal - -markdown: kramdown -plugins: - - jekyll-seo-tag - - jekyll-sitemap - -baseurl: "" -url: "https://bryan-roe.github.io/Aria" -``` - -### Demo Mode Configuration - -Each web app has demo mode configured in JavaScript: - -**aria_controller.js:** -```javascript -const DEMO_MODE = true; // Set to false for local backend -const DEMO_API_DELAY = 300; // Simulate network delay in ms - -async function mockApiCall(endpoint, options) { - if (!DEMO_MODE) { - return fetch(endpoint, options); - } - - await new Promise(resolve => setTimeout(resolve, DEMO_API_DELAY)); - - if (endpoint === '/api/aria/command') { - return { - ok: true, - json: async () => ({ - success: true, - response: "Mock response in demo mode" - }) - }; - } -} -``` - -**chat/index.html:** -```javascript -const DEMO_MODE = true; -const DEMO_RESPONSES = [ - "Hello! I'm Aria in demo mode...", - "In demo mode, I use pre-programmed responses..." -]; -``` - -## 📝 Updating Content - -### Adding a New Web Application - -1. **Create app directory in docs/** - ```bash - mkdir docs/myapp - ``` - -2. **Add HTML/CSS/JS files with demo mode** - ```javascript - const DEMO_MODE = true; - - async function mockApiCall(endpoint, options) { - await new Promise(resolve => setTimeout(resolve, 300)); - return { ok: true, json: async () => ({ demo: true }) }; - } - ``` - -3. **Update main index.html** - ```html - -
    🎨
    -
    My App
    -
    Description here
    - Demo Mode -     - ``` - -4. **Commit and push** - ```bash - git add docs/myapp - git commit -m "Add myapp to GitHub Pages" - git push - ``` - -### Updating Existing Applications - -1. **Modify source files** in `aria_web/`, `chat-web/`, etc. -2. **Copy changes to docs/** with demo mode enabled -3. **Test locally** before committing -4. **Commit and push** to trigger deployment - -### Syncing Source to Docs - -When updating source files, remember to sync to docs: - -```bash -# Example: Update Aria character -cp aria_web/index.html docs/aria/ -cp aria_web/aria_controller.js docs/aria/ - -# Verify demo mode is enabled -grep "DEMO_MODE = true" docs/aria/aria_controller.js - -# Commit changes -git add docs/aria/ -git commit -m "Update Aria character interface" -git push -``` - -## 🧪 Testing - -### Local Testing with Jekyll - -1. **Install Jekyll** - ```bash - gem install jekyll bundler - ``` - -2. **Serve locally** - ```bash - cd docs - jekyll serve - ``` - -3. **Open browser** - ``` - http://localhost:4000 - ``` - -### Local Testing with Python HTTP Server - -```bash -cd docs -python3 -m http.server 8000 - -# Open: http://localhost:8000 -``` - -### Testing Checklist - -Before pushing changes: - -- [ ] All links work (no 404 errors) -- [ ] Demo mode is enabled (`DEMO_MODE = true`) -- [ ] Demo banners are visible -- [ ] Mock API responses work -- [ ] No console errors (except blocked CDN) -- [ ] Mobile responsive -- [ ] Images load correctly -- [ ] Navigation works between pages - -## 🎨 Styling Guidelines - -### Consistent Design Elements - -All GitHub Pages applications should include: - -1. **Demo Mode Banner** - ```html -
    - 🌐 Demo Mode: Running in demo mode... - View on GitHub -
    - ``` - -2. **Gradient Backgrounds** - ```css - body { - background: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%); - background-size: 200% 200%; - animation: gradientShift 15s ease infinite; - } - ``` - -3. **Card-Based Layouts** - ```css - .app-card { - background: rgba(255, 255, 255, 0.98); - border-radius: 20px; - padding: 30px; - box-shadow: 0 10px 30px rgba(0, 0, 0, 0.2); - } - ``` - -4. **GitHub Links** - Always include links back to the repository - -## 🔍 Troubleshooting - -### Deployment Issues - -**Problem**: Workflow fails to build - -**Solution**: -- Check workflow logs in Actions tab -- Verify Jekyll syntax in _config.yml -- Ensure all files referenced exist - -**Problem**: 404 errors on GitHub Pages - -**Solution**: -- Verify files are in `docs/` directory -- Check file paths are relative (no leading `/`) -- Ensure branch is set to `main` in Settings > Pages - -**Problem**: Styles not loading - -**Solution**: -- Check CSS file paths -- Verify .nojekyll file exists -- Clear browser cache - -### Demo Mode Issues - -**Problem**: API calls failing - -**Solution**: -- Verify `DEMO_MODE = true` -- Check mockApiCall implementation -- Test with browser console open - -**Problem**: Features not working - -**Solution**: -- Ensure mock responses match expected format -- Check for JavaScript errors in console -- Verify all dependencies loaded - -## 📊 Monitoring - -### GitHub Pages Status - -- **Build Status**: Check Actions tab for workflow runs -- **Deployment History**: View in Settings > Pages -- **Usage**: GitHub Pages has bandwidth limits (100GB/month) - -### Analytics - -To add Google Analytics or similar: - -1. **Get tracking code** -2. **Add to docs/_includes/head.html** -3. **Configure in _config.yml** - -## 🔐 Security - -### Best Practices - -- ✅ No API keys in frontend code -- ✅ Demo mode prevents backend calls -- ✅ All links use HTTPS -- ✅ No sensitive data in mock responses -- ✅ CSP headers via _config.yml - -### Content Security - -```yaml -# In _config.yml -webrick: - headers: - Content-Security-Policy: "default-src 'self' https: 'unsafe-inline' 'unsafe-eval'" -``` - -## 📚 Resources - -- **GitHub Pages Docs**: https://docs.github.com/pages -- **Jekyll Docs**: https://jekyllrb.com/docs/ -- **GitHub Actions**: https://docs.github.com/actions -- **Repository**: https://github.com/Bryan-Roe/Aria - -## 🤝 Contributing - -When contributing to GitHub Pages: - -1. Make changes in source directories first -2. Test locally with full backend -3. Copy to docs/ with demo mode enabled -4. Test demo mode locally -5. Commit both source and docs/ -6. Create PR with screenshots -7. Verify deployment after merge - -## 📄 License - -See main repository LICENSE file. - ---- - -**Last Updated**: January 19, 2026 +# GitHub Pages Deployment Guide + +This guide explains how the Aria project is deployed to GitHub Pages and how to maintain it. + +## 📋 Overview + +The Aria project uses GitHub Pages to host interactive web applications in demo mode. The deployment is automated via GitHub Actions and uses Jekyll as the static site generator. + +## 🏗️ Architecture + +### Repository Structure + +``` +Aria/ +├── .github/ +│ └── workflows/ +│ └── pages.yml # GitHub Actions deployment workflow +├── docs/ # GitHub Pages root (published to web) +│ ├── index.html # Main landing page +│ ├── _config.yml # Jekyll configuration +│ ├── .nojekyll # Bypass Jekyll for static files +│ ├── README_PAGES.md # Pages-specific documentation +│ ├── aria/ # Aria character app (demo mode) +│ │ ├── index.html +│ │ └── aria_controller.js +│ ├── chat/ # AI chat app (demo mode) +│ │ ├── index.html +│ │ └── chat.js +│ ├── dashboard/ # Training dashboard +│ │ ├── index.html +│ │ └── shared-theme.css +│ └── quantum/ # Quantum ML interface +│ └── index.html +├── aria_web/ # Source (local development) +├── chat-web/ # Source (local development) +└── dashboard/ # Source (local development) +``` + +### Demo Mode vs Local Development + +| Feature | GitHub Pages (Demo) | Local Development | +|---------|-------------------|-------------------| +| UI Functionality | ✅ Full | ✅ Full | +| Character Animations | ✅ Full | ✅ Full | +| API Responses | 🔸 Mock | ✅ Real | +| AI Chat | 🔸 Simulated | ✅ Multi-provider | +| Quantum Computing | ❌ No backend | ✅ Azure Quantum | +| Training | ❌ No backend | ✅ LoRA fine-tuning | +| Cost | 🆓 Free | 💰 API usage | + +## 🚀 Deployment Process + +### Automatic Deployment + +GitHub Pages automatically deploys when changes are pushed: + +1. **Trigger**: Push to `main` or `copilot/enable-github-pages-for-repo` branch +2. **Build**: GitHub Actions runs Jekyll build on `docs/` directory +3. **Deploy**: Built site is published to GitHub Pages +4. **URL**: https://bryan-roe.github.io/Aria + +### Deployment Workflow (.github/workflows/pages.yml) + +```yaml +name: Deploy to GitHub Pages + +on: + push: + branches: [ main, copilot/enable-github-pages-for-repo ] + workflow_dispatch: + +permissions: + contents: read + pages: write + id-token: write + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Setup Pages + uses: actions/configure-pages@v4 + - name: Build with Jekyll + uses: actions/jekyll-build-pages@v1 + with: + source: ./docs + destination: ./_site + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + needs: build + steps: + - name: Deploy to GitHub Pages + uses: actions/deploy-pages@v4 +``` + +### Manual Deployment + +To manually trigger deployment: + +1. Go to **Actions** tab in GitHub +2. Select **"Deploy to GitHub Pages"** workflow +3. Click **"Run workflow"** button +4. Select branch and click **"Run workflow"** + +## 🔧 Configuration + +### Jekyll Configuration (docs/_config.yml) + +```yaml +title: Aria - Interactive AI Character Platform +description: Hybrid quantum-AI/ML platform +theme: jekyll-theme-minimal + +markdown: kramdown +plugins: + - jekyll-seo-tag + - jekyll-sitemap + +baseurl: "" +url: "https://bryan-roe.github.io/Aria" +``` + +### Demo Mode Configuration + +Each web app has demo mode configured in JavaScript: + +**aria_controller.js:** +```javascript +const DEMO_MODE = true; // Set to false for local backend +const DEMO_API_DELAY = 300; // Simulate network delay in ms + +async function mockApiCall(endpoint, options) { + if (!DEMO_MODE) { + return fetch(endpoint, options); + } + + await new Promise(resolve => setTimeout(resolve, DEMO_API_DELAY)); + + if (endpoint === '/api/aria/command') { + return { + ok: true, + json: async () => ({ + success: true, + response: "Mock response in demo mode" + }) + }; + } +} +``` + +**chat/index.html:** +```javascript +const DEMO_MODE = true; +const DEMO_RESPONSES = [ + "Hello! I'm Aria in demo mode...", + "In demo mode, I use pre-programmed responses..." +]; +``` + +## 📝 Updating Content + +### Adding a New Web Application + +1. **Create app directory in docs/** + ```bash + mkdir docs/myapp + ``` + +2. **Add HTML/CSS/JS files with demo mode** + ```javascript + const DEMO_MODE = true; + + async function mockApiCall(endpoint, options) { + await new Promise(resolve => setTimeout(resolve, 300)); + return { ok: true, json: async () => ({ demo: true }) }; + } + ``` + +3. **Update main index.html** + ```html + +
    🎨
    +
    My App
    +
    Description here
    + Demo Mode +     + ``` + +4. **Commit and push** + ```bash + git add docs/myapp + git commit -m "Add myapp to GitHub Pages" + git push + ``` + +### Updating Existing Applications + +1. **Modify source files** in `aria_web/`, `chat-web/`, etc. +2. **Copy changes to docs/** with demo mode enabled +3. **Test locally** before committing +4. **Commit and push** to trigger deployment + +### Syncing Source to Docs + +When updating source files, remember to sync to docs: + +```bash +# Example: Update Aria character +cp aria_web/index.html docs/aria/ +cp aria_web/aria_controller.js docs/aria/ + +# Verify demo mode is enabled +grep "DEMO_MODE = true" docs/aria/aria_controller.js + +# Commit changes +git add docs/aria/ +git commit -m "Update Aria character interface" +git push +``` + +## 🧪 Testing + +### Local Testing with Jekyll + +1. **Install Jekyll** + ```bash + gem install jekyll bundler + ``` + +2. **Serve locally** + ```bash + cd docs + jekyll serve + ``` + +3. **Open browser** + ``` + http://localhost:4000 + ``` + +### Local Testing with Python HTTP Server + +```bash +cd docs +python3 -m http.server 8000 + +# Open: http://localhost:8000 +``` + +### Testing Checklist + +Before pushing changes: + +- [ ] All links work (no 404 errors) +- [ ] Demo mode is enabled (`DEMO_MODE = true`) +- [ ] Demo banners are visible +- [ ] Mock API responses work +- [ ] No console errors (except blocked CDN) +- [ ] Mobile responsive +- [ ] Images load correctly +- [ ] Navigation works between pages + +## 🎨 Styling Guidelines + +### Consistent Design Elements + +All GitHub Pages applications should include: + +1. **Demo Mode Banner** + ```html +
    + 🌐 Demo Mode: Running in demo mode... + View on GitHub +
    + ``` + +2. **Gradient Backgrounds** + ```css + body { + background: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%); + background-size: 200% 200%; + animation: gradientShift 15s ease infinite; + } + ``` + +3. **Card-Based Layouts** + ```css + .app-card { + background: rgba(255, 255, 255, 0.98); + border-radius: 20px; + padding: 30px; + box-shadow: 0 10px 30px rgba(0, 0, 0, 0.2); + } + ``` + +4. **GitHub Links** + Always include links back to the repository + +## 🔍 Troubleshooting + +### Deployment Issues + +**Problem**: Workflow fails to build + +**Solution**: +- Check workflow logs in Actions tab +- Verify Jekyll syntax in _config.yml +- Ensure all files referenced exist + +**Problem**: 404 errors on GitHub Pages + +**Solution**: +- Verify files are in `docs/` directory +- Check file paths are relative (no leading `/`) +- Ensure branch is set to `main` in Settings > Pages + +**Problem**: Styles not loading + +**Solution**: +- Check CSS file paths +- Verify .nojekyll file exists +- Clear browser cache + +### Demo Mode Issues + +**Problem**: API calls failing + +**Solution**: +- Verify `DEMO_MODE = true` +- Check mockApiCall implementation +- Test with browser console open + +**Problem**: Features not working + +**Solution**: +- Ensure mock responses match expected format +- Check for JavaScript errors in console +- Verify all dependencies loaded + +## 📊 Monitoring + +### GitHub Pages Status + +- **Build Status**: Check Actions tab for workflow runs +- **Deployment History**: View in Settings > Pages +- **Usage**: GitHub Pages has bandwidth limits (100GB/month) + +### Analytics + +To add Google Analytics or similar: + +1. **Get tracking code** +2. **Add to docs/_includes/head.html** +3. **Configure in _config.yml** + +## 🔐 Security + +### Best Practices + +- ✅ No API keys in frontend code +- ✅ Demo mode prevents backend calls +- ✅ All links use HTTPS +- ✅ No sensitive data in mock responses +- ✅ CSP headers via _config.yml + +### Content Security + +```yaml +# In _config.yml +webrick: + headers: + Content-Security-Policy: "default-src 'self' https: 'unsafe-inline' 'unsafe-eval'" +``` + +## 📚 Resources + +- **GitHub Pages Docs**: https://docs.github.com/pages +- **Jekyll Docs**: https://jekyllrb.com/docs/ +- **GitHub Actions**: https://docs.github.com/actions +- **Repository**: https://github.com/Bryan-Roe/Aria + +## 🤝 Contributing + +When contributing to GitHub Pages: + +1. Make changes in source directories first +2. Test locally with full backend +3. Copy to docs/ with demo mode enabled +4. Test demo mode locally +5. Commit both source and docs/ +6. Create PR with screenshots +7. Verify deployment after merge + +## 📄 License + +See main repository LICENSE file. + +--- + +**Last Updated**: January 19, 2026 diff --git a/docs/FUTURE_PERFORMANCE_OPPORTUNITIES.md b/docs/FUTURE_PERFORMANCE_OPPORTUNITIES.md index 47d177bb5..920e4094f 100644 --- a/docs/FUTURE_PERFORMANCE_OPPORTUNITIES.md +++ b/docs/FUTURE_PERFORMANCE_OPPORTUNITIES.md @@ -1,218 +1,218 @@ -# Additional Performance Optimization Opportunities - -This document identifies potential performance improvements found during code analysis that were not immediately implemented. These represent opportunities for future optimization work. - -## Not Yet Implemented (Future Work) - -### 1. File I/O Optimization in Vision Training - -**File**: `scripts/train_vision.py` -**Lines**: 106-110 -**Severity**: Low - -**Current Pattern**: -```python -for c in classes: - folder = self.root / c - for f in folder.iterdir(): - if f.suffix.lower() in ('.png', '.jpg', '.jpeg'): - self.samples.append((f, self.class_to_idx[c])) -``` - -**Recommendation**: -```python -# Use rglob for cleaner recursive traversal -for img_path in self.root.rglob('*'): - if img_path.suffix.lower() in ('.png', '.jpg', '.jpeg'): - class_name = img_path.parent.name - if class_name in self.class_to_idx: - self.samples.append((img_path, self.class_to_idx[class_name])) -``` - -**Benefits**: -- Cleaner, more Pythonic code -- Handles nested directory structures -- Minimal performance impact (already fairly optimal) - -**Note**: This is primarily a code quality improvement rather than a performance fix. - ---- - -### 2. Quantum Circuit Evaluation Caching - -**File**: `ai-projects/quantum-ml/web_app.py` -**Lines**: 448-452 -**Severity**: Medium - -**Current Pattern**: -```python -for xi, yi in zip(X_val, y_val): - expectation = circuit(xi, weights) # Circuit executed for every sample - prediction = np.mean(expectation) -``` - -**Recommendation**: -Implement circuit result caching for identical inputs: -```python -from functools import lru_cache -import hashlib - -def make_hashable_key(arr): - return hashlib.sha256(arr.tobytes()).hexdigest() - -# Use a session-level cache -@lru_cache(maxsize=1000) -def cached_circuit_eval(x_hash, weights_hash): - return circuit(x_array, weights_array) - -for xi, yi in zip(X_val, y_val): - x_hash = make_hashable_key(xi) - w_hash = make_hashable_key(weights) - expectation = cached_circuit_eval(x_hash, w_hash) -``` - -**Benefits**: -- Avoids recomputing identical circuit evaluations -- Significant speedup if same inputs appear multiple times -- Especially valuable for batch evaluation - -**Challenges**: -- Cache invalidation complexity -- Memory overhead for cache storage -- NumPy array hashing overhead - -**Status**: Recommend implementation if profiling shows circuit evaluation as bottleneck. - ---- - -### 3. Generator Usage for Memory Efficiency - -**File**: `ai-projects/quantum-ml/web_app.py` -**Line**: 200 -**Severity**: Low - -**Current Pattern**: -```python -return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] -``` - -**Recommendation**: -Use generator if full list not needed: -```python -# If caller only needs to iterate once: -return (qml.expval(qml.PauliZ(i)) for i in range(n_qubits)) -``` - -**Benefits**: -- Reduces memory allocation -- Lazy evaluation - -**Challenges**: -- Need to verify all callers can handle generator -- May not provide significant benefit for small n_qubits - -**Status**: Low priority - only implement if profiling shows memory pressure. - ---- - -### 4. Nested Loop Complexity (Algorithmic) - -**Files**: Multiple quantum circuit implementations -**Examples**: -- `ai-projects/quantum-ml/src/hybrid_qnn.py:72-74` -- `ai-projects/quantum-ml/web_app.py:193-197` -- `ai-projects/quantum-ml/train_pennylane_simple.py:107-115` - -**Pattern**: -```python -elif self.entanglement == "full": - for i in range(self.n_qubits): - for j in range(i + 1, self.n_qubits): # O(n²) - qml.CNOT(wires=[i, j]) -``` - -**Status**: **Already Addressed with Documentation** - -The O(n²) complexity for full entanglement is algorithmically necessary and intentional. This has been addressed by adding comprehensive documentation: -- Constructor docstring explains performance trade-offs -- Circuit method includes performance warnings -- Users can choose `linear` or `circular` patterns for O(n) complexity - -**No code change needed** - this is a design choice, not a bug. - ---- - -## Analysis of Other Patterns - -### Patterns Already Optimized - -1. **Single-pass aggregation**: Already implemented in multiple files per repository memories -2. **Caching strategies**: TTL-based caching already in use (status files, glob operations, port checks) -3. **Algorithm complexity**: Dictionary lookups already preferred over linear searches -4. **Memory-efficient file reading**: `deque` pattern already implemented for tail operations - -### Patterns Not Applicable - -1. **Repeated API calls**: Not found to be a significant issue in analyzed code -2. **Redundant computations**: Most computation-heavy operations already cached or optimized -3. **Inefficient data structures**: Generally appropriate data structures in use - ---- - -## Prioritization Recommendations - -### Immediate (High ROI, Low Effort) -- ✅ SQL LIMIT clause - **COMPLETED** -- ✅ String concatenation - **COMPLETED** -- ✅ Dictionary comprehension - **COMPLETED** - -### Next Wave (Medium ROI, Medium Effort) -- Quantum circuit evaluation caching (if profiling confirms as bottleneck) -- Vision training file I/O cleanup (code quality improvement) - -### Future Consideration (Low ROI or High Effort) -- Generator usage for quantum circuit outputs -- Any additional patterns identified through profiling - ---- - -## Profiling Recommendations - -To identify additional optimization opportunities: - -1. **CPU Profiling**: Use `cProfile` or `py-spy` to identify hot spots - ```bash - python -m cProfile -o profile.stats script.py - python -m pstats profile.stats - ``` - -2. **Memory Profiling**: Use `memory_profiler` to track allocations - ```bash - python -m memory_profiler script.py - ``` - -3. **Line Profiling**: Use `line_profiler` for detailed line-by-line analysis - ```bash - kernprof -l -v script.py - ``` - -4. **Real-world Metrics**: Monitor production systems for actual bottlenecks - - Application Insights metrics - - Database slow query logs - - Training pipeline execution times - ---- - -## Conclusion - -The high-priority performance issues have been identified and fixed. The remaining opportunities are either: -- Low-impact improvements (file I/O patterns) -- Dependent on profiling data (circuit caching) -- Already addressed through documentation (quantum complexity) - -Future optimization work should be driven by profiling data from real-world usage rather than speculative improvements. - ---- - -**Last Updated**: 2026-02-17 -**Review Date**: 2026-06-17 (review after 4 months of production data) +# Additional Performance Optimization Opportunities + +This document identifies potential performance improvements found during code analysis that were not immediately implemented. These represent opportunities for future optimization work. + +## Not Yet Implemented (Future Work) + +### 1. File I/O Optimization in Vision Training + +**File**: `scripts/train_vision.py` +**Lines**: 106-110 +**Severity**: Low + +**Current Pattern**: +```python +for c in classes: + folder = self.root / c + for f in folder.iterdir(): + if f.suffix.lower() in ('.png', '.jpg', '.jpeg'): + self.samples.append((f, self.class_to_idx[c])) +``` + +**Recommendation**: +```python +# Use rglob for cleaner recursive traversal +for img_path in self.root.rglob('*'): + if img_path.suffix.lower() in ('.png', '.jpg', '.jpeg'): + class_name = img_path.parent.name + if class_name in self.class_to_idx: + self.samples.append((img_path, self.class_to_idx[class_name])) +``` + +**Benefits**: +- Cleaner, more Pythonic code +- Handles nested directory structures +- Minimal performance impact (already fairly optimal) + +**Note**: This is primarily a code quality improvement rather than a performance fix. + +--- + +### 2. Quantum Circuit Evaluation Caching + +**File**: `ai-projects/quantum-ml/web_app.py` +**Lines**: 448-452 +**Severity**: Medium + +**Current Pattern**: +```python +for xi, yi in zip(X_val, y_val): + expectation = circuit(xi, weights) # Circuit executed for every sample + prediction = np.mean(expectation) +``` + +**Recommendation**: +Implement circuit result caching for identical inputs: +```python +from functools import lru_cache +import hashlib + +def make_hashable_key(arr): + return hashlib.sha256(arr.tobytes()).hexdigest() + +# Use a session-level cache +@lru_cache(maxsize=1000) +def cached_circuit_eval(x_hash, weights_hash): + return circuit(x_array, weights_array) + +for xi, yi in zip(X_val, y_val): + x_hash = make_hashable_key(xi) + w_hash = make_hashable_key(weights) + expectation = cached_circuit_eval(x_hash, w_hash) +``` + +**Benefits**: +- Avoids recomputing identical circuit evaluations +- Significant speedup if same inputs appear multiple times +- Especially valuable for batch evaluation + +**Challenges**: +- Cache invalidation complexity +- Memory overhead for cache storage +- NumPy array hashing overhead + +**Status**: Recommend implementation if profiling shows circuit evaluation as bottleneck. + +--- + +### 3. Generator Usage for Memory Efficiency + +**File**: `ai-projects/quantum-ml/web_app.py` +**Line**: 200 +**Severity**: Low + +**Current Pattern**: +```python +return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] +``` + +**Recommendation**: +Use generator if full list not needed: +```python +# If caller only needs to iterate once: +return (qml.expval(qml.PauliZ(i)) for i in range(n_qubits)) +``` + +**Benefits**: +- Reduces memory allocation +- Lazy evaluation + +**Challenges**: +- Need to verify all callers can handle generator +- May not provide significant benefit for small n_qubits + +**Status**: Low priority - only implement if profiling shows memory pressure. + +--- + +### 4. Nested Loop Complexity (Algorithmic) + +**Files**: Multiple quantum circuit implementations +**Examples**: +- `ai-projects/quantum-ml/src/hybrid_qnn.py:72-74` +- `ai-projects/quantum-ml/web_app.py:193-197` +- `ai-projects/quantum-ml/train_pennylane_simple.py:107-115` + +**Pattern**: +```python +elif self.entanglement == "full": + for i in range(self.n_qubits): + for j in range(i + 1, self.n_qubits): # O(n²) + qml.CNOT(wires=[i, j]) +``` + +**Status**: **Already Addressed with Documentation** + +The O(n²) complexity for full entanglement is algorithmically necessary and intentional. This has been addressed by adding comprehensive documentation: +- Constructor docstring explains performance trade-offs +- Circuit method includes performance warnings +- Users can choose `linear` or `circular` patterns for O(n) complexity + +**No code change needed** - this is a design choice, not a bug. + +--- + +## Analysis of Other Patterns + +### Patterns Already Optimized + +1. **Single-pass aggregation**: Already implemented in multiple files per repository memories +2. **Caching strategies**: TTL-based caching already in use (status files, glob operations, port checks) +3. **Algorithm complexity**: Dictionary lookups already preferred over linear searches +4. **Memory-efficient file reading**: `deque` pattern already implemented for tail operations + +### Patterns Not Applicable + +1. **Repeated API calls**: Not found to be a significant issue in analyzed code +2. **Redundant computations**: Most computation-heavy operations already cached or optimized +3. **Inefficient data structures**: Generally appropriate data structures in use + +--- + +## Prioritization Recommendations + +### Immediate (High ROI, Low Effort) +- ✅ SQL LIMIT clause - **COMPLETED** +- ✅ String concatenation - **COMPLETED** +- ✅ Dictionary comprehension - **COMPLETED** + +### Next Wave (Medium ROI, Medium Effort) +- Quantum circuit evaluation caching (if profiling confirms as bottleneck) +- Vision training file I/O cleanup (code quality improvement) + +### Future Consideration (Low ROI or High Effort) +- Generator usage for quantum circuit outputs +- Any additional patterns identified through profiling + +--- + +## Profiling Recommendations + +To identify additional optimization opportunities: + +1. **CPU Profiling**: Use `cProfile` or `py-spy` to identify hot spots + ```bash + python -m cProfile -o profile.stats script.py + python -m pstats profile.stats + ``` + +2. **Memory Profiling**: Use `memory_profiler` to track allocations + ```bash + python -m memory_profiler script.py + ``` + +3. **Line Profiling**: Use `line_profiler` for detailed line-by-line analysis + ```bash + kernprof -l -v script.py + ``` + +4. **Real-world Metrics**: Monitor production systems for actual bottlenecks + - Application Insights metrics + - Database slow query logs + - Training pipeline execution times + +--- + +## Conclusion + +The high-priority performance issues have been identified and fixed. The remaining opportunities are either: +- Low-impact improvements (file I/O patterns) +- Dependent on profiling data (circuit caching) +- Already addressed through documentation (quantum complexity) + +Future optimization work should be driven by profiling data from real-world usage rather than speculative improvements. + +--- + +**Last Updated**: 2026-02-17 +**Review Date**: 2026-06-17 (review after 4 months of production data) diff --git a/docs/FUTURE_PERFORMANCE_OPTIMIZATIONS.md b/docs/FUTURE_PERFORMANCE_OPTIMIZATIONS.md index 80d484d17..c2471f81c 100644 --- a/docs/FUTURE_PERFORMANCE_OPTIMIZATIONS.md +++ b/docs/FUTURE_PERFORMANCE_OPTIMIZATIONS.md @@ -1,274 +1,274 @@ -# Future Performance Optimization Opportunities - -## Date: 2026-02-17 - -This document lists potential performance optimizations identified but not yet implemented, ordered by estimated impact. - ---- - -## 🔮 Deferred Optimizations (Lower Priority) - -### 1. function_app.py - Image URL Caching -**Location**: `function_app.py:1420` - Vision inference endpoint - -**Issue**: Image fetched via HTTP on every request, even for repeated URLs. - -**Current Code**: -```python -response = requests.get(image_url, timeout=10) -response.raise_for_status() -img = Image.open(io.BytesIO(response.content)) -result = vi.predict(img) -``` - -**Suggested Improvement**: -```python -from functools import lru_cache -import hashlib - -@lru_cache(maxsize=50) -def _fetch_image_cached(url: str, cache_buster: str = None): - """Fetch image with LRU cache. cache_buster allows invalidation.""" - response = requests.get(url, timeout=10) - response.raise_for_status() - return response.content - -# Usage -image_bytes = _fetch_image_cached(image_url) -img = Image.open(io.BytesIO(image_bytes)) -``` - -**Estimated Impact**: -- Low frequency endpoint (vision inference) -- Significant speedup (eliminates network I/O) for repeated URLs -- **Priority**: Low (implement if vision endpoint becomes high-traffic) - ---- - -### 2. shared/chat_memory.py - NumPy Vectorized Cosine Similarity -**Location**: `shared/chat_memory.py:240-251` - `fetch_similar_messages()` - -**Issue**: Python loop computing cosine similarity for ~500 embeddings per query. - -**Current Code**: -```python -def _cosine(a: Sequence[float], b: Sequence[float]) -> float: - if not a or not b or len(a) != len(b): - return 0.0 - dot = sum(x * y for x, y in zip(a, b)) - na = math.sqrt(sum(x * x for x in a)) - nb = math.sqrt(sum(y * y for y in b)) - if na == 0.0 or nb == 0.0: - return 0.0 - return dot / (na * nb) - -# Loop over all embeddings -for r in rows: - emb = _deserialize_f32(r.EmbeddingVector, dim) - sim = _cosine(query_embedding, emb) -``` - -**Suggested Improvement**: -```python -try: - import numpy as np - _HAS_NUMPY = True -except ImportError: - _HAS_NUMPY = False - -def _cosine_batch(query: Sequence[float], embeddings: List[Sequence[float]]) -> List[float]: - """Vectorized cosine similarity using NumPy if available.""" - if _HAS_NUMPY and len(embeddings) > 10: - query_arr = np.array(query, dtype=np.float32) - emb_matrix = np.array(embeddings, dtype=np.float32) - - # Normalize - query_norm = query_arr / np.linalg.norm(query_arr) - emb_norms = emb_matrix / np.linalg.norm(emb_matrix, axis=1, keepdims=True) - - # Batch dot product - similarities = np.dot(emb_norms, query_norm) - return similarities.tolist() - - # Fallback to Python loop - return [_cosine(query, emb) for emb in embeddings] -``` - -**Estimated Impact**: -- **Speedup**: 8-10x for 500 embeddings with 256 dimensions -- **Memory**: ~2MB additional for NumPy arrays (acceptable) -- **Priority**: Medium (implement when embedding search becomes bottleneck) -- **Note**: Already documented in `docs/PERFORMANCE_IMPROVEMENTS.md` - ---- - -### 3. function_app.py - Status Endpoint File Existence Caching -**Location**: `function_app.py` - `/api/ai/status` endpoint - -**Issue**: Checks many file paths on every status request. - -**Current Pattern** (hypothetical): -```python -status = { - "adapter_present": os.path.exists(adapter_path), - "config_present": os.path.exists(config_path), - # ... many more checks -} -``` - -**Suggested Improvement**: -```python -from functools import lru_cache -import time - -_file_cache = {} -_CACHE_TTL = 10 # seconds - -def _exists_cached(path: str) -> bool: - """Check file existence with 10s TTL cache.""" - now = time.time() - if path in _file_cache: - cached_val, cached_time = _file_cache[path] - if now - cached_time < _CACHE_TTL: - return cached_val - - exists = os.path.exists(path) - _file_cache[path] = (exists, now) - return exists -``` - -**Estimated Impact**: -- **Latency reduction**: ~5-10ms per status request -- **Frequency**: Status endpoint called periodically by monitoring -- **Priority**: Low (acceptable latency for monitoring endpoint) - ---- - -### 4. Batch Processing Optimization Opportunities - -#### 4.1 ai-projects/quantum-ml/src/quantum_classifier.py - Batch Processing -**Location**: `quantum_classifier.py` - `forward()` method - -**Issue**: Sequential processing of batch items in quantum circuit execution. - -**Note**: This is inherently limited by quantum simulation/hardware characteristics. Async I/O could help for cloud backends, but local simulation is CPU-bound. - -**Priority**: Low (quantum operations are inherently sequential) - ---- - -## 🔍 Profiling Recommendations - -To identify the next set of optimizations, consider: - -### 1. Add Performance Monitoring to Key Endpoints -```python -import time -import functools - -def timed(func): - """Decorator to measure and log function execution time.""" - @functools.wraps(func) - def wrapper(*args, **kwargs): - start = time.perf_counter() - result = func(*args, **kwargs) - duration = time.perf_counter() - start - if duration > 0.1: # Log slow operations - print(f"⚠️ {func.__name__} took {duration:.3f}s") - return result - return wrapper -``` - -Apply to: -- All Azure Functions endpoints -- `generate_tags_fallback()` in aria_web/server.py -- `fetch_similar_messages()` in shared/chat_memory.py -- Training pipeline orchestrators - -### 2. Enable SQL Query Logging -```python -# In shared/sql_engine.py or wherever SQL is executed -import time -import logging - -def execute_with_timing(cursor, query, *args): - start = time.perf_counter() - cursor.execute(query, *args) - duration = time.perf_counter() - start - if duration > 0.05: - logging.warning(f"Slow query ({duration:.3f}s): {query[:100]}") - return cursor -``` - -### 3. Profile Hot Paths with cProfile -```bash -# For server endpoints -python -m cProfile -s cumulative aria_web/server.py > profile.txt - -# For scripts -python -m cProfile -s cumulative scripts/autotrain.py --dry-run > profile.txt -``` - -Look for functions with: -- High `cumtime` (cumulative time) -- High `ncalls` (call count) -- High `percall` (time per call) - ---- - -## 📊 Performance Monitoring Dashboard - -Consider adding `/api/performance/metrics` endpoint: - -```python -@app.route('/api/performance/metrics') -def performance_metrics(): - return { - "connection_pool": { - "size": len(_connection_pool), - "max_size": _MAX_POOL_SIZE, - "utilization": len(_connection_pool) / _MAX_POOL_SIZE - }, - "keyword_cache_hits": _keyword_cache_hits, # If implemented - "regex_pattern_count": len([k for k in globals() if k.startswith('_RE_')]), - "recent_slow_operations": get_slow_operations_log() - } -``` - ---- - -## 🎯 Optimization Prioritization Matrix - -| Optimization | Impact | Effort | Priority | -|--------------|--------|--------|----------| -| Image URL caching | High (if high-traffic) | Low | Conditional | -| NumPy cosine similarity | Medium | Medium | Medium | -| File existence caching | Low | Low | Low | -| Quantum batch processing | Low | High | Low | -| Performance monitoring | High (visibility) | Medium | High | - ---- - -## ✅ Already Optimized (Reference) - -These were fixed in the 2026-02-17 optimization pass: - -1. ✅ aria_web/server.py keyword sets (1.09x speedup) -2. ✅ shared/chat_memory.py connection pooling (50-100x for batch) -3. ✅ aria_web/server.py regex compilation (2-5x speedup) -4. ✅ scripts/analyze_learning_progress.py generators (memory-efficient) -5. ✅ cooking-ai/src/providers/local.py tag filtering (O(n²) → O(n)) - -See `docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md` for details. - ---- - -## 📝 Next Steps - -1. **Implement performance monitoring** (highest priority for visibility) -2. **Profile high-traffic endpoints** to identify real bottlenecks -3. **Implement NumPy cosine similarity** if embedding search becomes slow -4. **Add image caching** if vision endpoint sees high traffic -5. **Monitor connection pool saturation** via `/api/ai/status` - -The optimizations already implemented address the most impactful issues identified. Further optimization should be data-driven based on production metrics and profiling. +# Future Performance Optimization Opportunities + +## Date: 2026-02-17 + +This document lists potential performance optimizations identified but not yet implemented, ordered by estimated impact. + +--- + +## 🔮 Deferred Optimizations (Lower Priority) + +### 1. function_app.py - Image URL Caching +**Location**: `function_app.py:1420` - Vision inference endpoint + +**Issue**: Image fetched via HTTP on every request, even for repeated URLs. + +**Current Code**: +```python +response = requests.get(image_url, timeout=10) +response.raise_for_status() +img = Image.open(io.BytesIO(response.content)) +result = vi.predict(img) +``` + +**Suggested Improvement**: +```python +from functools import lru_cache +import hashlib + +@lru_cache(maxsize=50) +def _fetch_image_cached(url: str, cache_buster: str = None): + """Fetch image with LRU cache. cache_buster allows invalidation.""" + response = requests.get(url, timeout=10) + response.raise_for_status() + return response.content + +# Usage +image_bytes = _fetch_image_cached(image_url) +img = Image.open(io.BytesIO(image_bytes)) +``` + +**Estimated Impact**: +- Low frequency endpoint (vision inference) +- Significant speedup (eliminates network I/O) for repeated URLs +- **Priority**: Low (implement if vision endpoint becomes high-traffic) + +--- + +### 2. shared/chat_memory.py - NumPy Vectorized Cosine Similarity +**Location**: `shared/chat_memory.py:240-251` - `fetch_similar_messages()` + +**Issue**: Python loop computing cosine similarity for ~500 embeddings per query. + +**Current Code**: +```python +def _cosine(a: Sequence[float], b: Sequence[float]) -> float: + if not a or not b or len(a) != len(b): + return 0.0 + dot = sum(x * y for x, y in zip(a, b)) + na = math.sqrt(sum(x * x for x in a)) + nb = math.sqrt(sum(y * y for y in b)) + if na == 0.0 or nb == 0.0: + return 0.0 + return dot / (na * nb) + +# Loop over all embeddings +for r in rows: + emb = _deserialize_f32(r.EmbeddingVector, dim) + sim = _cosine(query_embedding, emb) +``` + +**Suggested Improvement**: +```python +try: + import numpy as np + _HAS_NUMPY = True +except ImportError: + _HAS_NUMPY = False + +def _cosine_batch(query: Sequence[float], embeddings: List[Sequence[float]]) -> List[float]: + """Vectorized cosine similarity using NumPy if available.""" + if _HAS_NUMPY and len(embeddings) > 10: + query_arr = np.array(query, dtype=np.float32) + emb_matrix = np.array(embeddings, dtype=np.float32) + + # Normalize + query_norm = query_arr / np.linalg.norm(query_arr) + emb_norms = emb_matrix / np.linalg.norm(emb_matrix, axis=1, keepdims=True) + + # Batch dot product + similarities = np.dot(emb_norms, query_norm) + return similarities.tolist() + + # Fallback to Python loop + return [_cosine(query, emb) for emb in embeddings] +``` + +**Estimated Impact**: +- **Speedup**: 8-10x for 500 embeddings with 256 dimensions +- **Memory**: ~2MB additional for NumPy arrays (acceptable) +- **Priority**: Medium (implement when embedding search becomes bottleneck) +- **Note**: Already documented in `docs/PERFORMANCE_IMPROVEMENTS.md` + +--- + +### 3. function_app.py - Status Endpoint File Existence Caching +**Location**: `function_app.py` - `/api/ai/status` endpoint + +**Issue**: Checks many file paths on every status request. + +**Current Pattern** (hypothetical): +```python +status = { + "adapter_present": os.path.exists(adapter_path), + "config_present": os.path.exists(config_path), + # ... many more checks +} +``` + +**Suggested Improvement**: +```python +from functools import lru_cache +import time + +_file_cache = {} +_CACHE_TTL = 10 # seconds + +def _exists_cached(path: str) -> bool: + """Check file existence with 10s TTL cache.""" + now = time.time() + if path in _file_cache: + cached_val, cached_time = _file_cache[path] + if now - cached_time < _CACHE_TTL: + return cached_val + + exists = os.path.exists(path) + _file_cache[path] = (exists, now) + return exists +``` + +**Estimated Impact**: +- **Latency reduction**: ~5-10ms per status request +- **Frequency**: Status endpoint called periodically by monitoring +- **Priority**: Low (acceptable latency for monitoring endpoint) + +--- + +### 4. Batch Processing Optimization Opportunities + +#### 4.1 ai-projects/quantum-ml/src/quantum_classifier.py - Batch Processing +**Location**: `quantum_classifier.py` - `forward()` method + +**Issue**: Sequential processing of batch items in quantum circuit execution. + +**Note**: This is inherently limited by quantum simulation/hardware characteristics. Async I/O could help for cloud backends, but local simulation is CPU-bound. + +**Priority**: Low (quantum operations are inherently sequential) + +--- + +## 🔍 Profiling Recommendations + +To identify the next set of optimizations, consider: + +### 1. Add Performance Monitoring to Key Endpoints +```python +import time +import functools + +def timed(func): + """Decorator to measure and log function execution time.""" + @functools.wraps(func) + def wrapper(*args, **kwargs): + start = time.perf_counter() + result = func(*args, **kwargs) + duration = time.perf_counter() - start + if duration > 0.1: # Log slow operations + print(f"⚠️ {func.__name__} took {duration:.3f}s") + return result + return wrapper +``` + +Apply to: +- All Azure Functions endpoints +- `generate_tags_fallback()` in aria_web/server.py +- `fetch_similar_messages()` in shared/chat_memory.py +- Training pipeline orchestrators + +### 2. Enable SQL Query Logging +```python +# In shared/sql_engine.py or wherever SQL is executed +import time +import logging + +def execute_with_timing(cursor, query, *args): + start = time.perf_counter() + cursor.execute(query, *args) + duration = time.perf_counter() - start + if duration > 0.05: + logging.warning(f"Slow query ({duration:.3f}s): {query[:100]}") + return cursor +``` + +### 3. Profile Hot Paths with cProfile +```bash +# For server endpoints +python -m cProfile -s cumulative aria_web/server.py > profile.txt + +# For scripts +python -m cProfile -s cumulative scripts/autotrain.py --dry-run > profile.txt +``` + +Look for functions with: +- High `cumtime` (cumulative time) +- High `ncalls` (call count) +- High `percall` (time per call) + +--- + +## 📊 Performance Monitoring Dashboard + +Consider adding `/api/performance/metrics` endpoint: + +```python +@app.route('/api/performance/metrics') +def performance_metrics(): + return { + "connection_pool": { + "size": len(_connection_pool), + "max_size": _MAX_POOL_SIZE, + "utilization": len(_connection_pool) / _MAX_POOL_SIZE + }, + "keyword_cache_hits": _keyword_cache_hits, # If implemented + "regex_pattern_count": len([k for k in globals() if k.startswith('_RE_')]), + "recent_slow_operations": get_slow_operations_log() + } +``` + +--- + +## 🎯 Optimization Prioritization Matrix + +| Optimization | Impact | Effort | Priority | +|--------------|--------|--------|----------| +| Image URL caching | High (if high-traffic) | Low | Conditional | +| NumPy cosine similarity | Medium | Medium | Medium | +| File existence caching | Low | Low | Low | +| Quantum batch processing | Low | High | Low | +| Performance monitoring | High (visibility) | Medium | High | + +--- + +## ✅ Already Optimized (Reference) + +These were fixed in the 2026-02-17 optimization pass: + +1. ✅ aria_web/server.py keyword sets (1.09x speedup) +2. ✅ shared/chat_memory.py connection pooling (50-100x for batch) +3. ✅ aria_web/server.py regex compilation (2-5x speedup) +4. ✅ scripts/analyze_learning_progress.py generators (memory-efficient) +5. ✅ cooking-ai/src/providers/local.py tag filtering (O(n²) → O(n)) + +See `docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md` for details. + +--- + +## 📝 Next Steps + +1. **Implement performance monitoring** (highest priority for visibility) +2. **Profile high-traffic endpoints** to identify real bottlenecks +3. **Implement NumPy cosine similarity** if embedding search becomes slow +4. **Add image caching** if vision endpoint sees high traffic +5. **Monitor connection pool saturation** via `/api/ai/status` + +The optimizations already implemented address the most impactful issues identified. Further optimization should be data-driven based on production metrics and profiling. diff --git a/docs/GITHUB_PAGES_SETUP.md b/docs/GITHUB_PAGES_SETUP.md index 97f89b171..02ab24e91 100644 --- a/docs/GITHUB_PAGES_SETUP.md +++ b/docs/GITHUB_PAGES_SETUP.md @@ -1,346 +1,346 @@ -# GitHub Pages Setup - Complete - -## ✅ Implementation Status: COMPLETE - -All web applications in the Aria repository have been successfully migrated to GitHub Pages with full demo mode support. - ---- - -## 🌐 Live Demo URL - -**https://bryan-roe.github.io/Aria** - -(Will be active after repository settings are configured - see Activation Instructions below) - ---- - -## 📋 What Was Done - -### 1. Directory Structure Created - -``` -docs/ # GitHub Pages root -├── index.html # Main landing page with app cards -├── _config.yml # Jekyll configuration -├── .nojekyll # Bypass Jekyll for static files -├── README_PAGES.md # GitHub Pages documentation (4.6KB) -├── DEPLOYMENT_GUIDE.md # Deployment procedures (9.6KB) -├── GITHUB_PAGES_SETUP.md # This file -├── aria/ # Aria character interface -│ ├── index.html -│ ├── aria_controller.js # With demo mode -│ └── auto-execute.html -├── chat/ # AI chat interface -│ ├── index.html -│ └── chat.js -├── dashboard/ # Training dashboard -│ ├── index.html -│ └── shared-theme.css -└── quantum/ # Quantum ML interface - └── index.html -``` - -### 2. GitHub Actions Workflow - -Created `.github/workflows/pages.yml` for automatic deployment: - -- **Triggers**: Push to `main` or `copilot/enable-github-pages-for-repo` branch -- **Build**: Uses Jekyll to build from `docs/` -- **Deploy**: Automatic deployment to GitHub Pages -- **Permissions**: Configured for GitHub Pages deployment - -### 3. Demo Mode Implementation - -All web applications run with **mock API responses**: - -**Aria Character:** -- `DEMO_MODE = true` in `aria_controller.js` -- `mockApiCall()` function simulates `/api/aria/command` and `/api/aria/object` -- 300ms delay for realistic network simulation -- Full UI functionality (animations, movements, object interactions) - -**AI Chat:** -- `DEMO_MODE = true` in chat interface -- 6 predefined responses that rotate -- Simulated typing delay -- Full chat UI with message bubbles - -**Dashboard & Quantum:** -- Static HTML interfaces -- All UI elements functional -- Mock data can be added as needed - -### 4. Visual Design - -**Consistent across all pages:** -- Gradient backgrounds with animations -- Demo mode banners (prominent orange) -- Card-based layouts -- Links to GitHub repository -- Mobile responsive -- Professional styling - -### 5. Documentation - -**Created comprehensive guides:** -- `README_PAGES.md` - GitHub Pages overview and usage -- `DEPLOYMENT_GUIDE.md` - Detailed deployment procedures -- `GITHUB_PAGES_SETUP.md` - This setup summary -- Updated main `README.md` with GitHub Pages section - ---- - -## 🚀 Activation Instructions - -To activate GitHub Pages for this repository: - -### Step 1: Go to Repository Settings - -1. Navigate to: https://github.com/Bryan-Roe/Aria/settings/pages -2. Or click: **Settings** → **Pages** (in left sidebar) - -### Step 2: Configure Source - -Under "Build and deployment": - -1. **Source**: Select "Deploy from a branch" -2. **Branch**: Select `main` (or `copilot/enable-github-pages-for-repo`) -3. **Folder**: Select `/docs` -4. Click **Save** - -### Step 3: Wait for Deployment - -- GitHub will automatically trigger the first deployment -- Check the **Actions** tab to monitor progress -- First deployment typically takes 1-2 minutes - -### Step 4: Verify Deployment - -1. Return to Settings → Pages -2. You'll see: "Your site is live at https://bryan-roe.github.io/Aria" -3. Click the link to view your live site - -### Step 5: Test All Applications - -Visit and test each application: -- Main landing: https://bryan-roe.github.io/Aria -- Aria character: https://bryan-roe.github.io/Aria/aria/ -- AI chat: https://bryan-roe.github.io/Aria/chat/ -- Dashboard: https://bryan-roe.github.io/Aria/dashboard/ -- Quantum: https://bryan-roe.github.io/Aria/quantum/ - ---- - -## ✨ Features - -### What Works in Demo Mode - -✅ **Full UI Functionality** -- All buttons, inputs, and controls work -- Character animations and movements -- Object interactions (pickup, drop, throw) -- Chat message sending and receiving -- Command input and processing - -✅ **Realistic Simulation** -- 300ms network delay simulation -- Mock API responses -- Predefined chat responses -- Full visual feedback - -✅ **Interactive Elements** -- Character movement on stage -- Object management buttons -- Chat input and display -- Command execution -- Navigation between pages - -### What Requires Local Development - -❌ **AI Features** -- Real AI chat (Azure OpenAI, OpenAI) -- Natural language processing -- Contextual conversations -- Multiple provider support - -❌ **Backend Services** -- Python server (aria_web/server.py) -- Azure Functions (function_app.py) -- Flask dashboard (dashboard/app.py) -- Database connections - -❌ **Training & Quantum** -- LoRA fine-tuning -- Model training -- Quantum computing (Azure Quantum) -- Real-time metrics - ---- - -## 📸 Screenshots - -**Main Landing Page:** -![GitHub Pages Landing](https://github.com/user-attachments/assets/37db6fa3-2e53-4cdb-8b1e-7970904e5277) - -**Aria Character Interface:** -![Aria Character Demo](https://github.com/user-attachments/assets/e188dec5-41a0-41de-ba22-c4adb6b62923) - -**AI Chat Interface:** -![Chat Interface Demo](https://github.com/user-attachments/assets/54b74918-4812-4714-9dd3-9918282f29bb) - ---- - -## 🔧 Maintenance - -### Updating Content - -**To update web applications:** - -1. **Modify source files** in `aria_web/`, `chat-web/`, etc. -2. **Test locally** with full backend -3. **Copy to docs/** with `DEMO_MODE = true` -4. **Test demo mode** locally -5. **Commit and push** to trigger deployment - -**Example:** -```bash -# Update Aria character -cp aria_web/index.html docs/aria/ -cp aria_web/aria_controller.js docs/aria/ - -# Ensure demo mode is enabled -grep "DEMO_MODE = true" docs/aria/aria_controller.js - -# Commit -git add docs/aria/ -git commit -m "Update Aria character interface" -git push -``` - -### Adding New Applications - -See `docs/DEPLOYMENT_GUIDE.md` for detailed instructions on adding new web applications to GitHub Pages. - ---- - -## 🧪 Testing - -### Local Testing - -**Option 1: Python HTTP Server** -```bash -cd docs -python3 -m http.server 8000 -# Visit: http://localhost:8000 -``` - -**Option 2: Jekyll (Recommended)** -```bash -gem install jekyll bundler -cd docs -jekyll serve -# Visit: http://localhost:4000 -``` - -### Validation - -Run the validation script: -```bash -cd /home/runner/work/Aria/Aria -bash /tmp/validate_pages.sh -``` - ---- - -## 📊 Metrics - -**Total Files**: 15 files created/modified -**Total Size**: 1.5 MB in docs/ -**Code Added**: ~11,700 lines -**Documentation**: 3 comprehensive guides -**Applications**: 4 web apps (Aria, Chat, Dashboard, Quantum) -**Screenshots**: 3 full-page captures -**Commits**: 3 commits on feature branch - ---- - -## 🎯 Benefits - -### For Users -- ✅ Try Aria without installation -- ✅ See features in action immediately -- ✅ No API keys or setup required -- ✅ Fast, browser-based demo - -### For Developers -- ✅ Visual reference for contributions -- ✅ Easy UI testing -- ✅ Quick onboarding for new contributors -- ✅ Portfolio/showcase material - -### For the Project -- ✅ Increased visibility and accessibility -- ✅ Professional presentation -- ✅ Lower barrier to entry -- ✅ Better documentation - ---- - -## 🔗 Resources - -- **Live Demo**: https://bryan-roe.github.io/Aria -- **Repository**: https://github.com/Bryan-Roe/Aria -- **Pages Documentation**: [docs/README_PAGES.md](README_PAGES.md) -- **Deployment Guide**: [docs/DEPLOYMENT_GUIDE.md](DEPLOYMENT_GUIDE.md) -- **GitHub Pages Docs**: https://docs.github.com/pages -- **Jekyll Documentation**: https://jekyllrb.com/docs/ - ---- - -## 🤝 Support - -For issues or questions: - -1. Check documentation in `docs/DEPLOYMENT_GUIDE.md` -2. Review GitHub Actions logs in the Actions tab -3. Open an issue: https://github.com/Bryan-Roe/Aria/issues -4. Refer to GitHub Pages documentation - ---- - -## ✅ Verification Checklist - -Before marking as complete, verify: - -- [x] All files in docs/ directory -- [x] Demo mode enabled in all applications -- [x] GitHub Actions workflow created -- [x] Documentation complete -- [x] Screenshots captured -- [x] Main README updated -- [x] Local testing successful -- [x] Validation script passes -- [ ] Repository settings configured (manual step) -- [ ] Live deployment verified (after settings configured) -- [ ] All applications tested in production (after deployment) - ---- - -**Setup Date**: January 19, 2026 -**Status**: ✅ COMPLETE (Awaiting repository settings configuration) -**Next Action**: Configure GitHub Pages in repository settings - ---- - -## 🎉 Success Criteria Met - -✅ All web applications migrated to GitHub Pages -✅ Demo mode implemented and tested -✅ Automatic deployment configured -✅ Comprehensive documentation created -✅ Visual design consistent across all pages -✅ Mobile responsive design -✅ Screenshots captured -✅ Validation successful - -**The GitHub Pages migration is complete and ready for activation!** +# GitHub Pages Setup - Complete + +## ✅ Implementation Status: COMPLETE + +All web applications in the Aria repository have been successfully migrated to GitHub Pages with full demo mode support. + +--- + +## 🌐 Live Demo URL + +**https://bryan-roe.github.io/Aria** + +(Will be active after repository settings are configured - see Activation Instructions below) + +--- + +## 📋 What Was Done + +### 1. Directory Structure Created + +``` +docs/ # GitHub Pages root +├── index.html # Main landing page with app cards +├── _config.yml # Jekyll configuration +├── .nojekyll # Bypass Jekyll for static files +├── README_PAGES.md # GitHub Pages documentation (4.6KB) +├── DEPLOYMENT_GUIDE.md # Deployment procedures (9.6KB) +├── GITHUB_PAGES_SETUP.md # This file +├── aria/ # Aria character interface +│ ├── index.html +│ ├── aria_controller.js # With demo mode +│ └── auto-execute.html +├── chat/ # AI chat interface +│ ├── index.html +│ └── chat.js +├── dashboard/ # Training dashboard +│ ├── index.html +│ └── shared-theme.css +└── quantum/ # Quantum ML interface + └── index.html +``` + +### 2. GitHub Actions Workflow + +Created `.github/workflows/pages.yml` for automatic deployment: + +- **Triggers**: Push to `main` or `copilot/enable-github-pages-for-repo` branch +- **Build**: Uses Jekyll to build from `docs/` +- **Deploy**: Automatic deployment to GitHub Pages +- **Permissions**: Configured for GitHub Pages deployment + +### 3. Demo Mode Implementation + +All web applications run with **mock API responses**: + +**Aria Character:** +- `DEMO_MODE = true` in `aria_controller.js` +- `mockApiCall()` function simulates `/api/aria/command` and `/api/aria/object` +- 300ms delay for realistic network simulation +- Full UI functionality (animations, movements, object interactions) + +**AI Chat:** +- `DEMO_MODE = true` in chat interface +- 6 predefined responses that rotate +- Simulated typing delay +- Full chat UI with message bubbles + +**Dashboard & Quantum:** +- Static HTML interfaces +- All UI elements functional +- Mock data can be added as needed + +### 4. Visual Design + +**Consistent across all pages:** +- Gradient backgrounds with animations +- Demo mode banners (prominent orange) +- Card-based layouts +- Links to GitHub repository +- Mobile responsive +- Professional styling + +### 5. Documentation + +**Created comprehensive guides:** +- `README_PAGES.md` - GitHub Pages overview and usage +- `DEPLOYMENT_GUIDE.md` - Detailed deployment procedures +- `GITHUB_PAGES_SETUP.md` - This setup summary +- Updated main `README.md` with GitHub Pages section + +--- + +## 🚀 Activation Instructions + +To activate GitHub Pages for this repository: + +### Step 1: Go to Repository Settings + +1. Navigate to: https://github.com/Bryan-Roe/Aria/settings/pages +2. Or click: **Settings** → **Pages** (in left sidebar) + +### Step 2: Configure Source + +Under "Build and deployment": + +1. **Source**: Select "Deploy from a branch" +2. **Branch**: Select `main` (or `copilot/enable-github-pages-for-repo`) +3. **Folder**: Select `/docs` +4. Click **Save** + +### Step 3: Wait for Deployment + +- GitHub will automatically trigger the first deployment +- Check the **Actions** tab to monitor progress +- First deployment typically takes 1-2 minutes + +### Step 4: Verify Deployment + +1. Return to Settings → Pages +2. You'll see: "Your site is live at https://bryan-roe.github.io/Aria" +3. Click the link to view your live site + +### Step 5: Test All Applications + +Visit and test each application: +- Main landing: https://bryan-roe.github.io/Aria +- Aria character: https://bryan-roe.github.io/Aria/aria/ +- AI chat: https://bryan-roe.github.io/Aria/chat/ +- Dashboard: https://bryan-roe.github.io/Aria/dashboard/ +- Quantum: https://bryan-roe.github.io/Aria/quantum/ + +--- + +## ✨ Features + +### What Works in Demo Mode + +✅ **Full UI Functionality** +- All buttons, inputs, and controls work +- Character animations and movements +- Object interactions (pickup, drop, throw) +- Chat message sending and receiving +- Command input and processing + +✅ **Realistic Simulation** +- 300ms network delay simulation +- Mock API responses +- Predefined chat responses +- Full visual feedback + +✅ **Interactive Elements** +- Character movement on stage +- Object management buttons +- Chat input and display +- Command execution +- Navigation between pages + +### What Requires Local Development + +❌ **AI Features** +- Real AI chat (Azure OpenAI, OpenAI) +- Natural language processing +- Contextual conversations +- Multiple provider support + +❌ **Backend Services** +- Python server (aria_web/server.py) +- Azure Functions (function_app.py) +- Flask dashboard (dashboard/app.py) +- Database connections + +❌ **Training & Quantum** +- LoRA fine-tuning +- Model training +- Quantum computing (Azure Quantum) +- Real-time metrics + +--- + +## 📸 Screenshots + +**Main Landing Page:** +![GitHub Pages Landing](https://github.com/user-attachments/assets/37db6fa3-2e53-4cdb-8b1e-7970904e5277) + +**Aria Character Interface:** +![Aria Character Demo](https://github.com/user-attachments/assets/e188dec5-41a0-41de-ba22-c4adb6b62923) + +**AI Chat Interface:** +![Chat Interface Demo](https://github.com/user-attachments/assets/54b74918-4812-4714-9dd3-9918282f29bb) + +--- + +## 🔧 Maintenance + +### Updating Content + +**To update web applications:** + +1. **Modify source files** in `aria_web/`, `chat-web/`, etc. +2. **Test locally** with full backend +3. **Copy to docs/** with `DEMO_MODE = true` +4. **Test demo mode** locally +5. **Commit and push** to trigger deployment + +**Example:** +```bash +# Update Aria character +cp aria_web/index.html docs/aria/ +cp aria_web/aria_controller.js docs/aria/ + +# Ensure demo mode is enabled +grep "DEMO_MODE = true" docs/aria/aria_controller.js + +# Commit +git add docs/aria/ +git commit -m "Update Aria character interface" +git push +``` + +### Adding New Applications + +See `docs/DEPLOYMENT_GUIDE.md` for detailed instructions on adding new web applications to GitHub Pages. + +--- + +## 🧪 Testing + +### Local Testing + +**Option 1: Python HTTP Server** +```bash +cd docs +python3 -m http.server 8000 +# Visit: http://localhost:8000 +``` + +**Option 2: Jekyll (Recommended)** +```bash +gem install jekyll bundler +cd docs +jekyll serve +# Visit: http://localhost:4000 +``` + +### Validation + +Run the validation script: +```bash +cd /home/runner/work/Aria/Aria +bash /tmp/validate_pages.sh +``` + +--- + +## 📊 Metrics + +**Total Files**: 15 files created/modified +**Total Size**: 1.5 MB in docs/ +**Code Added**: ~11,700 lines +**Documentation**: 3 comprehensive guides +**Applications**: 4 web apps (Aria, Chat, Dashboard, Quantum) +**Screenshots**: 3 full-page captures +**Commits**: 3 commits on feature branch + +--- + +## 🎯 Benefits + +### For Users +- ✅ Try Aria without installation +- ✅ See features in action immediately +- ✅ No API keys or setup required +- ✅ Fast, browser-based demo + +### For Developers +- ✅ Visual reference for contributions +- ✅ Easy UI testing +- ✅ Quick onboarding for new contributors +- ✅ Portfolio/showcase material + +### For the Project +- ✅ Increased visibility and accessibility +- ✅ Professional presentation +- ✅ Lower barrier to entry +- ✅ Better documentation + +--- + +## 🔗 Resources + +- **Live Demo**: https://bryan-roe.github.io/Aria +- **Repository**: https://github.com/Bryan-Roe/Aria +- **Pages Documentation**: [docs/README_PAGES.md](README_PAGES.md) +- **Deployment Guide**: [docs/DEPLOYMENT_GUIDE.md](DEPLOYMENT_GUIDE.md) +- **GitHub Pages Docs**: https://docs.github.com/pages +- **Jekyll Documentation**: https://jekyllrb.com/docs/ + +--- + +## 🤝 Support + +For issues or questions: + +1. Check documentation in `docs/DEPLOYMENT_GUIDE.md` +2. Review GitHub Actions logs in the Actions tab +3. Open an issue: https://github.com/Bryan-Roe/Aria/issues +4. Refer to GitHub Pages documentation + +--- + +## ✅ Verification Checklist + +Before marking as complete, verify: + +- [x] All files in docs/ directory +- [x] Demo mode enabled in all applications +- [x] GitHub Actions workflow created +- [x] Documentation complete +- [x] Screenshots captured +- [x] Main README updated +- [x] Local testing successful +- [x] Validation script passes +- [ ] Repository settings configured (manual step) +- [ ] Live deployment verified (after settings configured) +- [ ] All applications tested in production (after deployment) + +--- + +**Setup Date**: January 19, 2026 +**Status**: ✅ COMPLETE (Awaiting repository settings configuration) +**Next Action**: Configure GitHub Pages in repository settings + +--- + +## 🎉 Success Criteria Met + +✅ All web applications migrated to GitHub Pages +✅ Demo mode implemented and tested +✅ Automatic deployment configured +✅ Comprehensive documentation created +✅ Visual design consistent across all pages +✅ Mobile responsive design +✅ Screenshots captured +✅ Validation successful + +**The GitHub Pages migration is complete and ready for activation!** diff --git a/docs/OPTIMIZATION_QUICK_GUIDE.md b/docs/OPTIMIZATION_QUICK_GUIDE.md index 70aad75bc..757a368d6 100644 --- a/docs/OPTIMIZATION_QUICK_GUIDE.md +++ b/docs/OPTIMIZATION_QUICK_GUIDE.md @@ -1,313 +1,313 @@ -# Optimization Quick Reference Guide - -**Quick guide for implementing code efficiency improvements identified in CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md** - -## Quick Wins (< 30 minutes each) - -### 1. Generator-Based Counting -**File:** `dashboard/serve.py` lines 700, 761, 766 - -```python -# BEFORE -count = len(list(directory.glob('*.json'))) -count = len([d for d in dir.iterdir() if d.is_dir()]) - -# AFTER -count = sum(1 for _ in directory.glob('*.json')) -count = sum(1 for d in dir.iterdir() if d.is_dir()) -``` - -### 2. Gradient Norm Optimization -**File:** `ai-projects/quantum-ml/web_app.py` line 440 - -```python -# BEFORE -avg_gradient = np.mean([np.linalg.norm(g) for g in epoch_gradients]) - -# AFTER -avg_gradient = np.mean(np.linalg.norm(g) for g in epoch_gradients) -``` - -### 3. Single-Pass Statistics -**File:** `ai-projects/quantum-ml/web_app.py` lines 952-958 - -```python -# BEFORE (4 iterations) -active = sum(1 for s in sessions.values() if s.status == "training") -completed = sum(1 for s in sessions.values() if s.status == "completed") -total_epochs = sum(s.current_epoch for s in sessions.values()) -avg_acc = np.mean([s.best_val_acc for s in sessions.values() if s.best_val_acc > 0]) - -# AFTER (1 iteration) -active = completed = total_epochs = 0 -accuracies = [] -for s in sessions.values(): - if s.status == "training": active += 1 - elif s.status == "completed": completed += 1 - total_epochs += s.current_epoch - if s.best_val_acc > 0: accuracies.append(s.best_val_acc) -avg_acc = np.mean(accuracies) if accuracies else 0.0 -``` - -## Medium Complexity (1-2 hours) - -### 4. File Caching with TTL -**File:** `dashboard/serve.py` (add at module level) - -```python -import time -from pathlib import Path -from typing import Dict, Tuple, Any - -_file_cache: Dict[str, Tuple[Any, float]] = {} -_FILE_CACHE_TTL = 5 # seconds - -def _load_json_cached(filepath: Path) -> dict: - """Load JSON file with TTL-based caching""" - import json - now = time.time() - cache_key = str(filepath) - - # Check cache - if cache_key in _file_cache: - cached_data, cached_time = _file_cache[cache_key] - if now - cached_time < _FILE_CACHE_TTL: - return cached_data - - # Read file - try: - with open(filepath, 'r') as f: - data = json.load(f) - _file_cache[cache_key] = (data, now) - return data - except Exception: - # Use stale cache if available - if cache_key in _file_cache: - return _file_cache[cache_key][0] - raise - -# Replace all instances of: -# with open(status_file, 'r') as f: -# data = json.load(f) -# With: -# data = _load_json_cached(status_file) -``` - -### 5. Keyword Matching with Frozensets -**File:** `ai-projects/chat-cli/src/agi_provider.py` lines 343-372 - -```python -# Add at module level (top of file) -_COMPLEXITY_MODERATE = frozenset(["explain", "compare", "analyze", "how", "why"]) -_COMPLEXITY_COMPLEX = frozenset(["step by step", "detailed", "comprehensive"]) -_INTENT_MOVEMENT = frozenset(["move", "walk", "go", "jump", "dance", "wave"]) -_INTENT_CODING = frozenset(["code", "program", "function", "debug"]) -_INTENT_EXPLANATION = frozenset(["explain", "what is", "how does"]) -_INTENT_CREATION = frozenset(["create", "generate", "make", "build"]) - -_DOMAIN_KEYWORDS = { - "quantum": frozenset(["quantum", "qubit", "entanglement", "superposition"]), - "ai": frozenset(["ai", "machine learning", "neural", "model", "training"]), - "aria": frozenset(["aria", "move", "animation", "character"]), - "technical": frozenset(["code", "program", "api", "function", "database"]), -} - -# In _analyze_query function, replace multiple any() calls with: -def _analyze_query_optimized(query: str) -> dict: - query_lower = query.lower() - words = query_lower.split() - word_set = set(words) - - # Complexity - O(1) set intersection instead of O(n) any() - complexity = "simple" - if word_set & _COMPLEXITY_MODERATE: - complexity = "moderate" - elif word_set & _COMPLEXITY_COMPLEX: - complexity = "complex" - elif len(words) > 20: - complexity = "complex" - - # Intent - O(1) set intersection - intent = "general" - if word_set & _INTENT_MOVEMENT: - intent = "movement" - elif word_set & _INTENT_CODING: - intent = "coding" - elif word_set & _INTENT_EXPLANATION: - intent = "explanation" - elif word_set & _INTENT_CREATION: - intent = "creation" - elif "?" in query: - intent = "question" - - # Domain - O(domains) with O(1) intersection per domain - domain = "general" - for dom, keywords in _DOMAIN_KEYWORDS.items(): - if word_set & keywords: - domain = dom - break - - return { - "query": query, - "complexity": complexity, - "intent": intent, - "domain": domain, - "word_count": len(words), - } -``` - -### 6. Consistent Frozenset Usage -**File:** `aria_web/server.py` lines 554-557 - -```python -# Add to existing frozenset section (around lines 42-74) -MOVE_LEFT_KEYWORDS = frozenset(['walk left', 'go left', 'left', 'move left']) -MOVE_RIGHT_KEYWORDS = frozenset(['walk right', 'go right', 'right', 'move right']) - -# Replace inline any() calls: -# BEFORE -elif any(k in cmd for k in ['walk left', 'go left', 'left']): - return '[aria:position:20:70]' - -# AFTER -elif _contains_any_keyword(cmd, MOVE_LEFT_KEYWORDS): - return '[aria:position:20:70]' -``` - -### 7. Deque-Based Rate Limiting -**File:** `dashboard/serve.py` lines 39-40 - -```python -# Add at module level -from collections import deque, defaultdict - -_request_timestamps = defaultdict(deque) -_MAX_REQUESTS = 100 -_WINDOW_SECONDS = 60 - -def check_rate_limit(client_ip: str) -> bool: - """Check if client is within rate limit using deque""" - now = time.time() - timestamps = _request_timestamps[client_ip] - - # Remove expired timestamps from left (oldest) - while timestamps and now - timestamps[0] > _WINDOW_SECONDS: - timestamps.popleft() - - if len(timestamps) >= _MAX_REQUESTS: - return False # Rate limited - - timestamps.append(now) - return True - -# Replace list filtering with deque-based check -``` - -## Complex Optimization (2-4 hours) - -### 8. Quantum Gradient Optimization -**File:** `ai-projects/quantum-ml/web_app.py` lines 217-246 - -```python -def compute_gradient_optimized(circuit, X, y, weights): - """Optimized gradient using PennyLane's automatic differentiation""" - try: - # Use PennyLane's qml.grad() for 10-100x speedup - import pennylane as qml - - # Create loss function for autograd - def loss_fn(w): - return compute_loss(circuit, X, y, w) - - # Compute gradient using automatic differentiation - grad_fn = qml.grad(loss_fn) - grad = grad_fn(weights) - - return grad - - except Exception as e: - # Fallback to manual parameter-shift if autograd fails - import warnings - warnings.warn(f"Autograd failed ({e}), using manual gradient") - return compute_gradient(circuit, X, y, weights, use_parameter_shift=True) - -# In training loop, replace: -# grad = compute_gradient(circuit, X_batch, y_batch, weights) -# With: -# grad = compute_gradient_optimized(circuit, X_batch, y_batch, weights) -``` - -## Testing Checklist - -For each optimization: - -- [ ] **Functionality:** Verify output matches original behavior -- [ ] **Performance:** Benchmark with realistic data (use `time.perf_counter()`) -- [ ] **Edge cases:** Test with empty inputs, large inputs, None values -- [ ] **Integration:** Run existing test suite to catch regressions -- [ ] **Memory:** Check memory usage doesn't increase significantly - -## Performance Testing Template - -```python -import time -import numpy as np - -def benchmark_optimization(old_func, new_func, *args, iterations=100): - """Compare performance of old vs new implementation""" - - # Warmup - old_func(*args) - new_func(*args) - - # Benchmark old - start = time.perf_counter() - for _ in range(iterations): - result_old = old_func(*args) - time_old = time.perf_counter() - start - - # Benchmark new - start = time.perf_counter() - for _ in range(iterations): - result_new = new_func(*args) - time_new = time.perf_counter() - start - - # Verify results match - if isinstance(result_old, np.ndarray): - np.testing.assert_allclose(result_old, result_new, rtol=1e-5) - else: - assert result_old == result_new, "Results don't match!" - - speedup = time_old / time_new - print(f"Old: {time_old*1000:.2f}ms") - print(f"New: {time_new*1000:.2f}ms") - print(f"Speedup: {speedup:.1f}x") - - return speedup -``` - -## Expected Performance Gains - -| Optimization | File | Speedup | Risk | -|--------------|------|---------|------| -| Quantum gradient | web_app.py | 10-100x | Low (has fallback) | -| File caching | serve.py | 5-10x | Low (5s TTL) | -| Keyword frozensets | agi_provider.py | 3-30x | Very low | -| Position keywords | server.py | 2-5x | Very low | -| Single-pass stats | web_app.py | 4x | Very low | -| Generator counting | serve.py | 5-20% | Very low | -| Deque rate limiting | serve.py | 2-5x | Low | -| Gradient norm | web_app.py | <5% | Very low | - -## Implementation Order - -1. Start with "Quick Wins" - low risk, immediate benefit -2. Add file caching (dashboard gets 5-10x faster) -3. Implement keyword optimizations (query processing 3-30x faster) -4. Optimize quantum gradients last (highest complexity, highest reward) - -## Related Files - -- Full analysis: `docs/CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md` -- Previous optimizations: `docs/PERFORMANCE_IMPROVEMENTS.md` -- Test patterns: `tests/test_performance_optimizations.py` +# Optimization Quick Reference Guide + +**Quick guide for implementing code efficiency improvements identified in CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md** + +## Quick Wins (< 30 minutes each) + +### 1. Generator-Based Counting +**File:** `dashboard/serve.py` lines 700, 761, 766 + +```python +# BEFORE +count = len(list(directory.glob('*.json'))) +count = len([d for d in dir.iterdir() if d.is_dir()]) + +# AFTER +count = sum(1 for _ in directory.glob('*.json')) +count = sum(1 for d in dir.iterdir() if d.is_dir()) +``` + +### 2. Gradient Norm Optimization +**File:** `ai-projects/quantum-ml/web_app.py` line 440 + +```python +# BEFORE +avg_gradient = np.mean([np.linalg.norm(g) for g in epoch_gradients]) + +# AFTER +avg_gradient = np.mean(np.linalg.norm(g) for g in epoch_gradients) +``` + +### 3. Single-Pass Statistics +**File:** `ai-projects/quantum-ml/web_app.py` lines 952-958 + +```python +# BEFORE (4 iterations) +active = sum(1 for s in sessions.values() if s.status == "training") +completed = sum(1 for s in sessions.values() if s.status == "completed") +total_epochs = sum(s.current_epoch for s in sessions.values()) +avg_acc = np.mean([s.best_val_acc for s in sessions.values() if s.best_val_acc > 0]) + +# AFTER (1 iteration) +active = completed = total_epochs = 0 +accuracies = [] +for s in sessions.values(): + if s.status == "training": active += 1 + elif s.status == "completed": completed += 1 + total_epochs += s.current_epoch + if s.best_val_acc > 0: accuracies.append(s.best_val_acc) +avg_acc = np.mean(accuracies) if accuracies else 0.0 +``` + +## Medium Complexity (1-2 hours) + +### 4. File Caching with TTL +**File:** `dashboard/serve.py` (add at module level) + +```python +import time +from pathlib import Path +from typing import Dict, Tuple, Any + +_file_cache: Dict[str, Tuple[Any, float]] = {} +_FILE_CACHE_TTL = 5 # seconds + +def _load_json_cached(filepath: Path) -> dict: + """Load JSON file with TTL-based caching""" + import json + now = time.time() + cache_key = str(filepath) + + # Check cache + if cache_key in _file_cache: + cached_data, cached_time = _file_cache[cache_key] + if now - cached_time < _FILE_CACHE_TTL: + return cached_data + + # Read file + try: + with open(filepath, 'r') as f: + data = json.load(f) + _file_cache[cache_key] = (data, now) + return data + except Exception: + # Use stale cache if available + if cache_key in _file_cache: + return _file_cache[cache_key][0] + raise + +# Replace all instances of: +# with open(status_file, 'r') as f: +# data = json.load(f) +# With: +# data = _load_json_cached(status_file) +``` + +### 5. Keyword Matching with Frozensets +**File:** `ai-projects/chat-cli/src/agi_provider.py` lines 343-372 + +```python +# Add at module level (top of file) +_COMPLEXITY_MODERATE = frozenset(["explain", "compare", "analyze", "how", "why"]) +_COMPLEXITY_COMPLEX = frozenset(["step by step", "detailed", "comprehensive"]) +_INTENT_MOVEMENT = frozenset(["move", "walk", "go", "jump", "dance", "wave"]) +_INTENT_CODING = frozenset(["code", "program", "function", "debug"]) +_INTENT_EXPLANATION = frozenset(["explain", "what is", "how does"]) +_INTENT_CREATION = frozenset(["create", "generate", "make", "build"]) + +_DOMAIN_KEYWORDS = { + "quantum": frozenset(["quantum", "qubit", "entanglement", "superposition"]), + "ai": frozenset(["ai", "machine learning", "neural", "model", "training"]), + "aria": frozenset(["aria", "move", "animation", "character"]), + "technical": frozenset(["code", "program", "api", "function", "database"]), +} + +# In _analyze_query function, replace multiple any() calls with: +def _analyze_query_optimized(query: str) -> dict: + query_lower = query.lower() + words = query_lower.split() + word_set = set(words) + + # Complexity - O(1) set intersection instead of O(n) any() + complexity = "simple" + if word_set & _COMPLEXITY_MODERATE: + complexity = "moderate" + elif word_set & _COMPLEXITY_COMPLEX: + complexity = "complex" + elif len(words) > 20: + complexity = "complex" + + # Intent - O(1) set intersection + intent = "general" + if word_set & _INTENT_MOVEMENT: + intent = "movement" + elif word_set & _INTENT_CODING: + intent = "coding" + elif word_set & _INTENT_EXPLANATION: + intent = "explanation" + elif word_set & _INTENT_CREATION: + intent = "creation" + elif "?" in query: + intent = "question" + + # Domain - O(domains) with O(1) intersection per domain + domain = "general" + for dom, keywords in _DOMAIN_KEYWORDS.items(): + if word_set & keywords: + domain = dom + break + + return { + "query": query, + "complexity": complexity, + "intent": intent, + "domain": domain, + "word_count": len(words), + } +``` + +### 6. Consistent Frozenset Usage +**File:** `aria_web/server.py` lines 554-557 + +```python +# Add to existing frozenset section (around lines 42-74) +MOVE_LEFT_KEYWORDS = frozenset(['walk left', 'go left', 'left', 'move left']) +MOVE_RIGHT_KEYWORDS = frozenset(['walk right', 'go right', 'right', 'move right']) + +# Replace inline any() calls: +# BEFORE +elif any(k in cmd for k in ['walk left', 'go left', 'left']): + return '[aria:position:20:70]' + +# AFTER +elif _contains_any_keyword(cmd, MOVE_LEFT_KEYWORDS): + return '[aria:position:20:70]' +``` + +### 7. Deque-Based Rate Limiting +**File:** `dashboard/serve.py` lines 39-40 + +```python +# Add at module level +from collections import deque, defaultdict + +_request_timestamps = defaultdict(deque) +_MAX_REQUESTS = 100 +_WINDOW_SECONDS = 60 + +def check_rate_limit(client_ip: str) -> bool: + """Check if client is within rate limit using deque""" + now = time.time() + timestamps = _request_timestamps[client_ip] + + # Remove expired timestamps from left (oldest) + while timestamps and now - timestamps[0] > _WINDOW_SECONDS: + timestamps.popleft() + + if len(timestamps) >= _MAX_REQUESTS: + return False # Rate limited + + timestamps.append(now) + return True + +# Replace list filtering with deque-based check +``` + +## Complex Optimization (2-4 hours) + +### 8. Quantum Gradient Optimization +**File:** `ai-projects/quantum-ml/web_app.py` lines 217-246 + +```python +def compute_gradient_optimized(circuit, X, y, weights): + """Optimized gradient using PennyLane's automatic differentiation""" + try: + # Use PennyLane's qml.grad() for 10-100x speedup + import pennylane as qml + + # Create loss function for autograd + def loss_fn(w): + return compute_loss(circuit, X, y, w) + + # Compute gradient using automatic differentiation + grad_fn = qml.grad(loss_fn) + grad = grad_fn(weights) + + return grad + + except Exception as e: + # Fallback to manual parameter-shift if autograd fails + import warnings + warnings.warn(f"Autograd failed ({e}), using manual gradient") + return compute_gradient(circuit, X, y, weights, use_parameter_shift=True) + +# In training loop, replace: +# grad = compute_gradient(circuit, X_batch, y_batch, weights) +# With: +# grad = compute_gradient_optimized(circuit, X_batch, y_batch, weights) +``` + +## Testing Checklist + +For each optimization: + +- [ ] **Functionality:** Verify output matches original behavior +- [ ] **Performance:** Benchmark with realistic data (use `time.perf_counter()`) +- [ ] **Edge cases:** Test with empty inputs, large inputs, None values +- [ ] **Integration:** Run existing test suite to catch regressions +- [ ] **Memory:** Check memory usage doesn't increase significantly + +## Performance Testing Template + +```python +import time +import numpy as np + +def benchmark_optimization(old_func, new_func, *args, iterations=100): + """Compare performance of old vs new implementation""" + + # Warmup + old_func(*args) + new_func(*args) + + # Benchmark old + start = time.perf_counter() + for _ in range(iterations): + result_old = old_func(*args) + time_old = time.perf_counter() - start + + # Benchmark new + start = time.perf_counter() + for _ in range(iterations): + result_new = new_func(*args) + time_new = time.perf_counter() - start + + # Verify results match + if isinstance(result_old, np.ndarray): + np.testing.assert_allclose(result_old, result_new, rtol=1e-5) + else: + assert result_old == result_new, "Results don't match!" + + speedup = time_old / time_new + print(f"Old: {time_old*1000:.2f}ms") + print(f"New: {time_new*1000:.2f}ms") + print(f"Speedup: {speedup:.1f}x") + + return speedup +``` + +## Expected Performance Gains + +| Optimization | File | Speedup | Risk | +|--------------|------|---------|------| +| Quantum gradient | web_app.py | 10-100x | Low (has fallback) | +| File caching | serve.py | 5-10x | Low (5s TTL) | +| Keyword frozensets | agi_provider.py | 3-30x | Very low | +| Position keywords | server.py | 2-5x | Very low | +| Single-pass stats | web_app.py | 4x | Very low | +| Generator counting | serve.py | 5-20% | Very low | +| Deque rate limiting | serve.py | 2-5x | Low | +| Gradient norm | web_app.py | <5% | Very low | + +## Implementation Order + +1. Start with "Quick Wins" - low risk, immediate benefit +2. Add file caching (dashboard gets 5-10x faster) +3. Implement keyword optimizations (query processing 3-30x faster) +4. Optimize quantum gradients last (highest complexity, highest reward) + +## Related Files + +- Full analysis: `docs/CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md` +- Previous optimizations: `docs/PERFORMANCE_IMPROVEMENTS.md` +- Test patterns: `tests/test_performance_optimizations.py` diff --git a/docs/PERFORMANCE_ANALYSIS.md b/docs/PERFORMANCE_ANALYSIS.md index a810b62c2..8e4a97a76 100644 --- a/docs/PERFORMANCE_ANALYSIS.md +++ b/docs/PERFORMANCE_ANALYSIS.md @@ -1,945 +1,945 @@ -# Performance Analysis Report - Aria Repository - -**Generated:** 2026-02-17 -**Analysis Type:** Static code analysis for performance anti-patterns -**Scope:** Python codebase (scripts/, shared/, ai-projects/quantum-ml/, aria_web/, function_app.py) - ---- - -## Executive Summary - -This report identifies **15 performance improvement opportunities** across the Aria codebase, categorized by severity and impact. The analysis found issues ranging from critical O(n²) complexity problems to minor optimization opportunities. Implementing these recommendations could yield significant performance improvements, particularly in high-traffic code paths. - -**Key Metrics:** -- Files analyzed: 150+ Python files -- Issues identified: 15 distinct patterns -- Critical issues: 3 -- High-priority issues: 5 -- Medium-priority issues: 4 -- Low-priority issues: 3 - ---- - -## Critical Issues (Immediate Action Recommended) - -### 1. Repeated Keyword Lookups in Hot Path (aria_web/server.py) - -**Location:** `aria_web/server.py`, lines 496-521 -**Severity:** Critical -**Impact:** High - This function is called for every user command - -**Problem:** -```python -# Current implementation - creates new list for EVERY check -if any(k in cmd for k in ['jump', 'leap', 'hop']): - return '[aria:position:50:60]' -elif any(k in cmd for k in ['dance', 'spin', 'twirl']): - return '[aria:position:50:50]' -elif any(k in cmd for k in ['wave', 'greet', 'hello', 'hi']): - return '[aria:position:30:70]' -# ... 15 more similar checks -``` - -**Why it's slow:** -- Creates 20+ temporary lists on EVERY function call -- Each `any()` call iterates through list and checks substring containment -- O(n*m) complexity where n=list size, m=string length -- Function is called multiple times per user interaction - -**Recommended fix:** -```python -# Pre-compile action keywords at module level (computed once) -_ACTION_KEYWORDS = { - 'jump': (['jump', 'leap', 'hop'], '[aria:position:50:60]'), - 'dance': (['dance', 'spin', 'twirl'], '[aria:position:50:50]'), - 'wave': (['wave', 'greet', 'hello', 'hi'], '[aria:position:30:70]'), - 'look': (['look', 'see', 'watch', 'observe'], '[aria:position:20:40]'), - 'sit': (['sit', 'rest', 'relax'], None), # Special handling - 'run': (['run', 'race', 'sprint'], '[aria:position:85:70]'), - 'hide': (['hide', 'crouch', 'duck'], '[aria:position:10:75]'), - 'present': (['present', 'show', 'display'], '[aria:position:50:50]'), - 'think': (['think', 'wonder', 'ponder'], '[aria:position:25:50]'), - 'left': (['walk left', 'go left', 'left'], '[aria:position:20:70]'), - 'right': (['walk right', 'go right', 'right'], '[aria:position:80:70]'), -} - -def _extract_action_position(cmd: str, ...) -> Optional[str]: - """Extract position from command (optimized).""" - cmd_lower = cmd.lower() - - # Single pass through actions with pre-compiled keywords - for action, (keywords, position) in _ACTION_KEYWORDS.items(): - if any(k in cmd_lower for k in keywords): - if position is None: - # Special handling for actions requiring context - if action == 'sit': - return f'[aria:position:{table_pos["x"] - 5}:{table_pos["y"] + 35}]' - return position - - # ... rest of logic -``` - -**Expected improvement:** 50-70% reduction in function execution time - ---- - -### 2. Database Connection Per Embedding (chat_memory.py) - -**Location:** `shared/chat_memory.py`, lines 151-175 -**Severity:** Critical -**Impact:** High - Affects all chat embedding storage - -**Problem:** -```python -def store_embedding(message_id: Optional[str], embedding: Sequence[float], model: str) -> bool: - if not message_id or not embedding: - return False - conn = _get_conn() # Opens new connection - if not conn: - return False - try: - cursor = conn.cursor() - blob = _serialize_f32(embedding) - cursor.execute( - "INSERT INTO dbo.ChatMessageEmbeddings ...", - message_id, model or "unknown-model", len(embedding), blob, - ) - conn.commit() - return True - finally: - conn.close() # Closes immediately -``` - -**Why it's slow:** -- Opens and closes connection for EVERY embedding (expensive TCP handshake) -- No connection pooling or reuse -- Transaction overhead per call -- When storing N embeddings: N * (connect + auth + insert + commit + close) - -**Recommended fix:** -```python -def store_embeddings_batch(embeddings: List[Tuple[str, Sequence[float], str]]) -> int: - """Store multiple embeddings in a single transaction (bulk insert). - - Args: - embeddings: List of (message_id, embedding, model) tuples - - Returns: - Number of embeddings successfully stored - """ - if not embeddings: - return 0 - - conn = _get_conn() - if not conn: - return 0 - - try: - cursor = conn.cursor() - # Prepare batch insert - values = [] - for message_id, embedding, model in embeddings: - blob = _serialize_f32(embedding) - values.append((message_id, model or "unknown-model", len(embedding), blob)) - - # Bulk insert - single transaction - cursor.executemany( - "INSERT INTO dbo.ChatMessageEmbeddings (MessageId, EmbeddingModel, EmbeddingDim, EmbeddingVector) VALUES (?,?,?,?)", - values - ) - conn.commit() - return len(values) - except Exception: - return 0 - finally: - conn.close() - -# Keep single-insert API for backward compatibility -def store_embedding(message_id: Optional[str], embedding: Sequence[float], model: str) -> bool: - """Store single embedding (wraps batch API).""" - return store_embeddings_batch([(message_id, embedding, model)]) == 1 -``` - -**Expected improvement:** 5-10x faster for batch operations, 2-3x for single inserts with connection pooling - ---- - -### 3. Linear Search in Model Comparison (batch_evaluator.py) - -**Location:** `scripts/batch_evaluator.py`, lines 305-312 -**Severity:** Critical -**Impact:** High - O(n²) complexity when comparing multiple models - -**Problem:** -```python -def compare_models(self, model_ids: List[str]) -> Dict: - """Compare specific models side-by-side.""" - comparison = [] - - for model_id in model_ids: - # O(n) search for EACH model_id = O(n²) total - result = next((r for r in self.results if r.model_id == model_id), None) - if result: - comparison.append(result) - # ... -``` - -**Why it's slow:** -- Linear search through all results for each model -- With 100 models, 50 comparisons = 5,000 iterations -- Memory contains already stored in `self.results` but accessed inefficiently - -**Recommended fix:** -```python -class BatchEvaluator: - def __init__(self): - self.results: List[EvaluationResult] = [] - # Add results index (updated in add_result method) - self._results_index: Dict[str, EvaluationResult] = {} - - def add_result(self, result: EvaluationResult): - """Add evaluation result.""" - self.results.append(result) - self._results_index[result.model_id] = result # O(1) indexing - - def compare_models(self, model_ids: List[str]) -> Dict: - """Compare specific models side-by-side (optimized).""" - # O(1) lookup per model = O(n) total - comparison = [ - self._results_index[model_id] - for model_id in model_ids - if model_id in self._results_index - ] - - return { - "models": [r.model_id for r in comparison], - "comparison": [ - { - "model_id": r.model_id, - "model_type": r.model_type, - "status": r.status, - "metrics": r.metrics, - "duration": r.duration, - } - for r in comparison - ] - } -``` - -**Expected improvement:** 50-100x faster for large model comparisons - ---- - -## High-Priority Issues - -### 4. Inefficient Average Calculation in Loop (training_analytics.py) - -**Location:** `scripts/training_analytics.py`, lines 82-86 -**Severity:** High -**Impact:** Medium - Called during analytics generation - -**Problem:** -```python -for epochs, accuracies in epoch_performance.items(): - avg = sum(accuracies) / len(accuracies) # Recalculates sum every iteration - if avg > best_avg: - best_avg = avg - best_epochs = epochs -``` - -**Why it's slow:** -- `sum()` is O(n) operation repeated for each epoch configuration -- For 4 epoch configs with 20 runs each: 80 additions instead of using built-in - -**Recommended fix:** -```python -import statistics - -for epochs, accuracies in epoch_performance.items(): - avg = statistics.mean(accuracies) # More efficient and handles edge cases - if avg > best_avg: - best_avg = avg - best_epochs = epochs -``` - -**Alternative (single-pass):** -```python -# Even better: find max in one pass -best_epochs, best_avg = max( - epoch_performance.items(), - key=lambda item: statistics.mean(item[1]) -) -``` - -**Expected improvement:** 20-30% faster, more robust - ---- - -### 5. String Concatenation in Loop (training_analytics.py) - -**Location:** `scripts/training_analytics.py`, lines 109-110 -**Severity:** High -**Impact:** Low - Only affects report generation - -**Problem:** -```python -report = [] -report.append("\n" + "="*80) -report.append("AUTONOMOUS TRAINING ANALYTICS REPORT") -report.append("="*80 + "\n") -``` - -Later in the code (not shown but referenced in analysis): -```python -# Building strings character by character -line = "" -for i in range(width): - line += "█" # O(n²) string concatenation -``` - -**Why it's slow:** -- Strings are immutable in Python -- Each `+=` creates a new string object -- For 80 characters: creates 80 intermediate strings - -**Recommended fix:** -```python -# Use list and join (O(n)) -line = "█" * width # Even simpler for repeated character - -# Or for complex building: -chars = [] -for i in range(width): - chars.append("█") -line = "".join(chars) -``` - -**Expected improvement:** 5-10x faster for large strings - ---- - -### 6. Redundant Failed List Iteration (batch_evaluator.py) - -**Location:** `scripts/batch_evaluator.py`, lines 287 -**Severity:** High -**Impact:** Medium - Duplicates work already done - -**Problem:** -```python -def aggregate_results(self) -> Dict: - # Lines 230-236: First pass builds succeeded/failed lists - for r in self.results: - total_duration += r.duration - if r.status == "succeeded": - succeeded.append(r) - else: - failed.append(r) - - # ... lines 238-284 work with these lists ... - - # Line 287: Rebuilds failed list AGAIN - failed = [r for r in self.results if r.status != "succeeded"] -``` - -**Why it's slow:** -- Iterates through all results twice -- `failed` list was already built in first pass but gets overwritten -- Wastes memory and CPU - -**Recommended fix:** -```python -def aggregate_results(self) -> Dict: - """Aggregate all evaluation results (optimized - single pass).""" - succeeded = [] - failed = [] - total_duration = 0.0 - - # Single pass through results for classification and duration sum - for r in self.results: - total_duration += r.duration - if r.status == "succeeded": - succeeded.append(r) - else: - failed.append(r) - - # Rank succeeded models - ranked = sorted( - succeeded, - key=lambda r: r.metrics.get("accuracy", r.metrics.get("perplexity", 0)), - reverse=True - ) - - # ... use already-built 'failed' list ... - - return { - "evaluated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), - "total_models": len(self.results), - "succeeded": len(succeeded), - "failed": len(failed), - "total_duration": total_duration, - "best_model": ranked[0].model_id if ranked else None, - "results": [r.__dict__ for r in self.results], - "ranking": [...], - "failed_models": [r.__dict__ for r in failed] # Use existing list - } -``` - -**Expected improvement:** 2x faster for result aggregation - ---- - -### 7. High Cyclomatic Complexity Functions (function_app.py) - -**Location:** `function_app.py`, multiple functions -**Severity:** High -**Impact:** High - Affects maintainability and performance - -**Problem:** -Functions with complexity > 10 are difficult to optimize and maintain: - -| Line | Function | Complexity | Issues | -|------|----------|-----------|--------| -| 762 | `tts` | 34 | Multiple nested conditionals, hard to follow control flow | -| 1041 | `ai_status` | 28 | Many sequential checks, could be refactored | -| 195 | `chat` | 27 | Complex logic mixing validation, provider detection, streaming | -| 606 | `chat_stream` | 18 | Nested error handling and streaming logic | -| 1852 | `quantum_circuit` | 18 | Complex parameter validation and circuit building | - -**Why it's slow:** -- Branch misprediction in CPU -- Hard to optimize by compiler/interpreter -- Difficult to cache results -- More memory allocations - -**Recommended fix:** - -Example for `tts` function (complexity 34 → 15): - -```python -# Before: one massive function with 34 branches - -# After: Extract helper functions -def _validate_tts_request(req_body: Dict) -> Tuple[Optional[str], Optional[Dict]]: - """Validate TTS request and extract parameters. - - Returns: - (error_message, params) - error_message is None on success - """ - text = req_body.get("text", "").strip() - if not text: - return ("No text provided", None) - - if len(text) > 5000: - return (f"Text too long: {len(text)} chars (max 5000)", None) - - params = { - "text": text, - "voice": req_body.get("voice", "en-US-JennyNeural"), - "rate": req_body.get("rate", "0%"), - "pitch": req_body.get("pitch", "0%"), - } - return (None, params) - -def _try_azure_tts(text: str, voice: str, rate: str, pitch: str) -> Optional[bytes]: - """Attempt Azure TTS synthesis. - - Returns: - Audio bytes on success, None on failure - """ - # Azure TTS logic here - pass - -def _try_local_tts(text: str) -> Optional[bytes]: - """Attempt local TTS fallback. - - Returns: - Audio bytes on success, None on failure - """ - # Local TTS logic here - pass - -@app.route(route="tts", methods=["POST"]) -def tts(req: func.HttpRequest) -> func.HttpResponse: - """Text-to-speech endpoint (refactored).""" - # Parse request - try: - req_body = req.get_json() - except ValueError: - return func.HttpResponse("Invalid JSON", status_code=400) - - # Validate - error, params = _validate_tts_request(req_body) - if error: - return func.HttpResponse(error, status_code=400) - - # Try Azure first - audio = _try_azure_tts(**params) - if audio: - return func.HttpResponse( - body=json.dumps({"audio_base64": base64.b64encode(audio).decode(), "format": "mp3"}), - mimetype="application/json" - ) - - # Fallback to local - audio = _try_local_tts(params["text"]) - if audio: - return func.HttpResponse( - body=json.dumps({"audio_base64": base64.b64encode(audio).decode(), "format": "wav"}), - mimetype="application/json" - ) - - return func.HttpResponse("TTS failed", status_code=500) -``` - -**Expected improvement:** Better CPU branch prediction, easier testing, 10-20% performance gain - ---- - -### 8. Cosine Similarity Not Vectorized (chat_memory.py) - -**Location:** `shared/chat_memory.py`, lines 241-251 -**Severity:** High -**Impact:** Medium - Called for every similarity search - -**Problem:** -```python -for r in rows: - dim = r.EmbeddingDim - emb = _deserialize_f32(r.EmbeddingVector, dim) - sim = _cosine(query_embedding, emb) # Individual cosine computation - if sim > 0: - scored.append({...}) -``` - -**Why it's slow:** -- Deserializes and computes similarity one vector at a time -- Cannot leverage SIMD instructions -- No batch processing - -**Recommended fix:** -```python -import numpy as np - -def retrieve_similar(query_embedding: Sequence[float], top_k: int = 5) -> List[Dict]: - """Retrieve similar messages (vectorized).""" - # ... fetch rows ... - - if not rows: - return [] - - # Batch deserialize all embeddings - embeddings = [] - metadata = [] - for r in rows: - emb = _deserialize_f32(r.EmbeddingVector, r.EmbeddingDim) - embeddings.append(emb) - metadata.append({ - "message_id": r.MessageId, - "content": r.Content, - "embedding_model": r.EmbeddingModel, - }) - - # Vectorized cosine similarity (all at once) - query_np = np.array(query_embedding) - embeddings_np = np.array(embeddings) - - # Compute all similarities in one operation (uses SIMD) - norms = np.linalg.norm(embeddings_np, axis=1) - query_norm = np.linalg.norm(query_np) - similarities = np.dot(embeddings_np, query_np) / (norms * query_norm + 1e-8) - - # Filter and build results - scored = [] - for idx, sim in enumerate(similarities): - if sim > 0: - meta = metadata[idx] - meta["similarity"] = float(sim) - scored.append(meta) - - return heapq.nlargest(top_k, scored, key=lambda x: x["similarity"]) -``` - -**Expected improvement:** 3-5x faster for 100+ vectors (depends on vector size) - ---- - -## Medium-Priority Issues - -### 9. Unnecessary List Conversion (auto_data_train.py) - -**Location:** `scripts/auto_data_train.py`, line 221 -**Severity:** Medium -**Impact:** Low - Only affects data collection metadata - -**Problem:** -```python -"sources": list(set(item.get("source", "unknown") for item in all_data)) -``` - -**Why it's inefficient:** -- Builds generator → set → list (3 data structures) -- Set is fine for deduplication, but list conversion unnecessary if used once - -**Recommended fix:** -```python -# If only used for counting/iteration -sources = {item.get("source", "unknown") for item in all_data} -# Use set directly - -# If JSON serialization needed -"sources": sorted(set(item.get("source", "unknown") for item in all_data)) -# sorted() returns list, gives consistent ordering -``` - -**Expected improvement:** Minor memory savings, better code clarity - ---- - -### 10. Repeated Status Checks with Lists (aria_automation.py, master_orchestrator.py) - -**Location:** Multiple files -**Severity:** Medium -**Impact:** Low - Not in hot path - -**Problem:** -```python -# aria_automation.py:368 -if not health["aria_server"] and self.mode in ["full", "server"]: - -# master_orchestrator.py:235 -if result["status"] not in ["succeeded", "skipped"]: -``` - -**Why it's inefficient:** -- Creates new list for every check -- Tuple is faster and immutable -- Set is O(1) for membership - -**Recommended fix:** -```python -# Use tuple for small, fixed sets (faster creation than list) -if not health["aria_server"] and self.mode in ("full", "server"): - -# Use set for larger or repeated checks -SUCCESSFUL_STATUSES = frozenset(["succeeded", "skipped"]) -if result["status"] not in SUCCESSFUL_STATUSES: -``` - -**Expected improvement:** Minor, but good practice - ---- - -### 11. No Caching for Subprocess Calls (function_app.py) - -**Location:** `function_app.py`, lines 1091-1100 -**Severity:** Medium -**Impact:** Medium - Called on every /api/ai/status request - -**Problem:** -```python -# Every status request spawns subprocess to check venv -proc = subprocess.run( - [str(venv_python), "-c", code], - capture_output=True, - text=True, - timeout=12 -) -``` - -**Why it's slow:** -- Subprocess creation is expensive (fork + exec) -- Checks don't change frequently -- 12-second timeout per request - -**Recommended fix:** -```python -import time -from functools import lru_cache - -@lru_cache(maxsize=1) -def _get_venv_info(venv_path: str, cache_time: float) -> Dict: - """Get venv info with caching (5-minute TTL). - - cache_time parameter forces cache invalidation every 5 minutes. - """ - venv_python = Path(venv_path) - if not venv_python.exists(): - return {"exists": False, "packages": {}, "error": "Not found"} - - # ... subprocess logic ... - return venv_info - -def ai_status(req: func.HttpRequest) -> func.HttpResponse: - # ... - - # Cache results for 5 minutes - current_cache_slot = int(time.time() / 300) # 300s = 5min - venv_info = _get_venv_info(str(venv_python), current_cache_slot) - - # ... -``` - -**Expected improvement:** 100-200x faster for cached responses - ---- - -### 12. Inefficient Variance Calculation (training_analytics.py) - -**Location:** `scripts/training_analytics.py`, lines 100-103 -**Severity:** Medium -**Impact:** Low - Only affects plateau detection - -**Problem:** -```python -avg = sum(accuracies) / len(accuracies) -variance = sum((x - avg) ** 2 for x in accuracies) / len(accuracies) -``` - -**Why it's inefficient:** -- Iterates list twice (once for avg, once for variance) -- Can be computed in single pass using Welford's algorithm - -**Recommended fix:** -```python -import statistics - -# Single-pass variance (more numerically stable too) -variance = statistics.variance(accuracies) - -# Or if mean is already computed: -variance = statistics.pvariance(accuracies, mu=avg) -``` - -**Expected improvement:** 2x faster, more numerically stable - ---- - -## Low-Priority Issues - -### 13. GPU Metrics Parsing (dashboard/gpu_monitor.py) - -**Location:** `dashboard/gpu_monitor.py`, lines 36-42 -**Severity:** Low -**Impact:** Low - Monitoring only - -**Problem:** -```python -'temperature': float(parts[2]) if parts[2] not in ['N/A', '[N/A]'] else 0, -'utilization': float(parts[3]) if parts[3] not in ['N/A', '[N/A]'] else 0, -# ... 5 more similar lines -``` - -**Why it's inefficient:** -- Creates temporary list `['N/A', '[N/A]']` 7 times per GPU -- String comparison repeated - -**Recommended fix:** -```python -_NA_VALUES = frozenset(['N/A', '[N/A]']) - -def _safe_float(value: str) -> float: - """Convert to float, return 0 for N/A.""" - return 0.0 if value in _NA_VALUES else float(value) - -# Use in parsing -'temperature': _safe_float(parts[2]), -'utilization': _safe_float(parts[3]), -# ... -``` - -**Expected improvement:** Minor, cleaner code - ---- - -### 14. Pre-commit Check File Filtering (scripts/pre_commit_check.py) - -**Location:** `scripts/pre_commit_check.py`, line 190 -**Severity:** Low -**Impact:** Low - Development tool only - -**Problem:** -```python -if any(pattern in file_path for pattern in ["__pycache__", ".pyc", "venv/", ".venv/", "__azurite_db"]): -``` - -**Why it's inefficient:** -- Creates list every check -- Linear search for each file - -**Recommended fix:** -```python -# Module-level constant -_IGNORE_PATTERNS = ("__pycache__", ".pyc", "venv/", ".venv/", "__azurite_db") - -# In check function -if any(pattern in file_path for pattern in _IGNORE_PATTERNS): -``` - -**Expected improvement:** Minor, but good practice - ---- - -### 15. Command Line Parsing (quantum-ai files) - -**Location:** Multiple files in ai-projects/quantum-ml/ -**Severity:** Low -**Impact:** Low - Startup only - -**Problem:** -```python -if dataset_name in ['wine_red', 'wine_white']: - # ... repeated in multiple files -``` - -**Why it's inefficient:** -- Creates list every check -- Duplicated logic across files - -**Recommended fix:** -```python -# In shared module or at module level -WINE_DATASETS = frozenset(['wine_red', 'wine_white']) -SEED_DATASETS = frozenset(['wheat_seeds', 'seeds']) - -if dataset_name in WINE_DATASETS: - # ... -``` - -**Expected improvement:** Minor, reduces duplication - ---- - -## Best Practices Already Implemented ✓ - -The analysis found several excellent performance patterns already in use: - -1. **Heapq.nlargest** (chat_memory.py:255) - O(n log k) vs O(n log n) sorting -2. **Generator expressions** (batch_evaluator.py:218) - Memory-efficient iteration -3. **Deque with popleft** (sql_engine.py) - O(1) queue operations -4. **Dict comprehensions** (training_analytics.py:68) - Efficient grouping -5. **Single-pass aggregation** (batch_evaluator.py:230-236) - Reduces iterations -6. **Cached glob operations** - Performance utils with TTL -7. **Debounced file writes** - Reduces I/O pressure - ---- - -## Implementation Priority - -### Immediate (Week 1) -1. Fix `aria_web/server.py` keyword lookups (Critical #1) -2. Add `batch_evaluator.py` result indexing (Critical #3) -3. Remove redundant iteration in `batch_evaluator.py` (High #6) - -### Short-term (Weeks 2-3) -4. Implement `chat_memory.py` batch embedding API (Critical #2) -5. Refactor high-complexity functions in `function_app.py` (High #7) -6. Add venv info caching in `ai_status` (Medium #11) - -### Medium-term (Month 2) -7. Vectorize cosine similarity in `chat_memory.py` (High #8) -8. Fix training_analytics.py calculations (High #4, Medium #12) -9. Address remaining medium-priority issues - -### Ongoing -10. Apply tuple/frozenset optimizations across codebase -11. Add performance tests for critical paths -12. Monitor with profiling tools - ---- - -## Benchmarking Recommendations - -To validate improvements, add benchmarks for: - -1. **Aria command processing** - Measure keyword lookup optimization -2. **Model comparison** - Test linear vs indexed search -3. **Embedding storage** - Batch vs individual inserts -4. **Similarity search** - Vectorized vs loop-based -5. **Report generation** - String building optimizations - -Example benchmark structure: -```python -import time - -def benchmark_keyword_lookup(): - """Benchmark command keyword lookups.""" - commands = [ - "jump high", "dance around", "wave hello", - # ... 100 test commands - ] - - # Warm-up - for cmd in commands[:10]: - _extract_action_position(cmd) - - # Measure - start = time.perf_counter() - for cmd in commands: - _extract_action_position(cmd) - elapsed = time.perf_counter() - start - - print(f"Processed {len(commands)} commands in {elapsed:.3f}s") - print(f"Average: {elapsed/len(commands)*1000:.2f}ms per command") -``` - ---- - -## Monitoring Recommendations - -Add performance monitoring for: - -1. **Function execution time** - Track slow endpoints -2. **Database query time** - Identify slow queries -3. **Memory usage** - Detect leaks -4. **Cache hit rates** - Validate caching effectiveness - -Example instrumentation: -```python -import functools -import time -from shared.telemetry import track_event - -def timed(func): - """Decorator to track function execution time.""" - @functools.wraps(func) - def wrapper(*args, **kwargs): - start = time.perf_counter() - try: - result = func(*args, **kwargs) - return result - finally: - elapsed = time.perf_counter() - start - track_event("function_timing", { - "function": func.__name__, - "duration_ms": elapsed * 1000 - }) - return wrapper - -@timed -def store_embedding(...): - # Implementation - pass -``` - ---- - -## Related Documents - -- `docs/PERFORMANCE_IMPLEMENTATION_SUMMARY.md` - Previous optimization work -- `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` - General optimization patterns -- `shared/performance_utils.py` - Reusable optimization utilities - ---- - -## Conclusion - -This analysis identified **15 performance improvement opportunities** with estimated improvements ranging from 2x to 100x for specific operations. The critical issues in `aria_web/server.py`, `chat_memory.py`, and `batch_evaluator.py` should be addressed first, as they affect high-traffic code paths. - -The codebase already demonstrates several strong performance patterns, particularly in the use of efficient data structures and algorithms. Building on these foundations with the recommended fixes will significantly improve overall system performance. - -**Next Steps:** -1. Review and prioritize recommendations -2. Implement critical fixes (1-3) -3. Add performance benchmarks -4. Monitor improvements in production -5. Iterate on remaining issues +# Performance Analysis Report - Aria Repository + +**Generated:** 2026-02-17 +**Analysis Type:** Static code analysis for performance anti-patterns +**Scope:** Python codebase (scripts/, shared/, ai-projects/quantum-ml/, aria_web/, function_app.py) + +--- + +## Executive Summary + +This report identifies **15 performance improvement opportunities** across the Aria codebase, categorized by severity and impact. The analysis found issues ranging from critical O(n²) complexity problems to minor optimization opportunities. Implementing these recommendations could yield significant performance improvements, particularly in high-traffic code paths. + +**Key Metrics:** +- Files analyzed: 150+ Python files +- Issues identified: 15 distinct patterns +- Critical issues: 3 +- High-priority issues: 5 +- Medium-priority issues: 4 +- Low-priority issues: 3 + +--- + +## Critical Issues (Immediate Action Recommended) + +### 1. Repeated Keyword Lookups in Hot Path (aria_web/server.py) + +**Location:** `aria_web/server.py`, lines 496-521 +**Severity:** Critical +**Impact:** High - This function is called for every user command + +**Problem:** +```python +# Current implementation - creates new list for EVERY check +if any(k in cmd for k in ['jump', 'leap', 'hop']): + return '[aria:position:50:60]' +elif any(k in cmd for k in ['dance', 'spin', 'twirl']): + return '[aria:position:50:50]' +elif any(k in cmd for k in ['wave', 'greet', 'hello', 'hi']): + return '[aria:position:30:70]' +# ... 15 more similar checks +``` + +**Why it's slow:** +- Creates 20+ temporary lists on EVERY function call +- Each `any()` call iterates through list and checks substring containment +- O(n*m) complexity where n=list size, m=string length +- Function is called multiple times per user interaction + +**Recommended fix:** +```python +# Pre-compile action keywords at module level (computed once) +_ACTION_KEYWORDS = { + 'jump': (['jump', 'leap', 'hop'], '[aria:position:50:60]'), + 'dance': (['dance', 'spin', 'twirl'], '[aria:position:50:50]'), + 'wave': (['wave', 'greet', 'hello', 'hi'], '[aria:position:30:70]'), + 'look': (['look', 'see', 'watch', 'observe'], '[aria:position:20:40]'), + 'sit': (['sit', 'rest', 'relax'], None), # Special handling + 'run': (['run', 'race', 'sprint'], '[aria:position:85:70]'), + 'hide': (['hide', 'crouch', 'duck'], '[aria:position:10:75]'), + 'present': (['present', 'show', 'display'], '[aria:position:50:50]'), + 'think': (['think', 'wonder', 'ponder'], '[aria:position:25:50]'), + 'left': (['walk left', 'go left', 'left'], '[aria:position:20:70]'), + 'right': (['walk right', 'go right', 'right'], '[aria:position:80:70]'), +} + +def _extract_action_position(cmd: str, ...) -> Optional[str]: + """Extract position from command (optimized).""" + cmd_lower = cmd.lower() + + # Single pass through actions with pre-compiled keywords + for action, (keywords, position) in _ACTION_KEYWORDS.items(): + if any(k in cmd_lower for k in keywords): + if position is None: + # Special handling for actions requiring context + if action == 'sit': + return f'[aria:position:{table_pos["x"] - 5}:{table_pos["y"] + 35}]' + return position + + # ... rest of logic +``` + +**Expected improvement:** 50-70% reduction in function execution time + +--- + +### 2. Database Connection Per Embedding (chat_memory.py) + +**Location:** `shared/chat_memory.py`, lines 151-175 +**Severity:** Critical +**Impact:** High - Affects all chat embedding storage + +**Problem:** +```python +def store_embedding(message_id: Optional[str], embedding: Sequence[float], model: str) -> bool: + if not message_id or not embedding: + return False + conn = _get_conn() # Opens new connection + if not conn: + return False + try: + cursor = conn.cursor() + blob = _serialize_f32(embedding) + cursor.execute( + "INSERT INTO dbo.ChatMessageEmbeddings ...", + message_id, model or "unknown-model", len(embedding), blob, + ) + conn.commit() + return True + finally: + conn.close() # Closes immediately +``` + +**Why it's slow:** +- Opens and closes connection for EVERY embedding (expensive TCP handshake) +- No connection pooling or reuse +- Transaction overhead per call +- When storing N embeddings: N * (connect + auth + insert + commit + close) + +**Recommended fix:** +```python +def store_embeddings_batch(embeddings: List[Tuple[str, Sequence[float], str]]) -> int: + """Store multiple embeddings in a single transaction (bulk insert). + + Args: + embeddings: List of (message_id, embedding, model) tuples + + Returns: + Number of embeddings successfully stored + """ + if not embeddings: + return 0 + + conn = _get_conn() + if not conn: + return 0 + + try: + cursor = conn.cursor() + # Prepare batch insert + values = [] + for message_id, embedding, model in embeddings: + blob = _serialize_f32(embedding) + values.append((message_id, model or "unknown-model", len(embedding), blob)) + + # Bulk insert - single transaction + cursor.executemany( + "INSERT INTO dbo.ChatMessageEmbeddings (MessageId, EmbeddingModel, EmbeddingDim, EmbeddingVector) VALUES (?,?,?,?)", + values + ) + conn.commit() + return len(values) + except Exception: + return 0 + finally: + conn.close() + +# Keep single-insert API for backward compatibility +def store_embedding(message_id: Optional[str], embedding: Sequence[float], model: str) -> bool: + """Store single embedding (wraps batch API).""" + return store_embeddings_batch([(message_id, embedding, model)]) == 1 +``` + +**Expected improvement:** 5-10x faster for batch operations, 2-3x for single inserts with connection pooling + +--- + +### 3. Linear Search in Model Comparison (batch_evaluator.py) + +**Location:** `scripts/batch_evaluator.py`, lines 305-312 +**Severity:** Critical +**Impact:** High - O(n²) complexity when comparing multiple models + +**Problem:** +```python +def compare_models(self, model_ids: List[str]) -> Dict: + """Compare specific models side-by-side.""" + comparison = [] + + for model_id in model_ids: + # O(n) search for EACH model_id = O(n²) total + result = next((r for r in self.results if r.model_id == model_id), None) + if result: + comparison.append(result) + # ... +``` + +**Why it's slow:** +- Linear search through all results for each model +- With 100 models, 50 comparisons = 5,000 iterations +- Memory contains already stored in `self.results` but accessed inefficiently + +**Recommended fix:** +```python +class BatchEvaluator: + def __init__(self): + self.results: List[EvaluationResult] = [] + # Add results index (updated in add_result method) + self._results_index: Dict[str, EvaluationResult] = {} + + def add_result(self, result: EvaluationResult): + """Add evaluation result.""" + self.results.append(result) + self._results_index[result.model_id] = result # O(1) indexing + + def compare_models(self, model_ids: List[str]) -> Dict: + """Compare specific models side-by-side (optimized).""" + # O(1) lookup per model = O(n) total + comparison = [ + self._results_index[model_id] + for model_id in model_ids + if model_id in self._results_index + ] + + return { + "models": [r.model_id for r in comparison], + "comparison": [ + { + "model_id": r.model_id, + "model_type": r.model_type, + "status": r.status, + "metrics": r.metrics, + "duration": r.duration, + } + for r in comparison + ] + } +``` + +**Expected improvement:** 50-100x faster for large model comparisons + +--- + +## High-Priority Issues + +### 4. Inefficient Average Calculation in Loop (training_analytics.py) + +**Location:** `scripts/training_analytics.py`, lines 82-86 +**Severity:** High +**Impact:** Medium - Called during analytics generation + +**Problem:** +```python +for epochs, accuracies in epoch_performance.items(): + avg = sum(accuracies) / len(accuracies) # Recalculates sum every iteration + if avg > best_avg: + best_avg = avg + best_epochs = epochs +``` + +**Why it's slow:** +- `sum()` is O(n) operation repeated for each epoch configuration +- For 4 epoch configs with 20 runs each: 80 additions instead of using built-in + +**Recommended fix:** +```python +import statistics + +for epochs, accuracies in epoch_performance.items(): + avg = statistics.mean(accuracies) # More efficient and handles edge cases + if avg > best_avg: + best_avg = avg + best_epochs = epochs +``` + +**Alternative (single-pass):** +```python +# Even better: find max in one pass +best_epochs, best_avg = max( + epoch_performance.items(), + key=lambda item: statistics.mean(item[1]) +) +``` + +**Expected improvement:** 20-30% faster, more robust + +--- + +### 5. String Concatenation in Loop (training_analytics.py) + +**Location:** `scripts/training_analytics.py`, lines 109-110 +**Severity:** High +**Impact:** Low - Only affects report generation + +**Problem:** +```python +report = [] +report.append("\n" + "="*80) +report.append("AUTONOMOUS TRAINING ANALYTICS REPORT") +report.append("="*80 + "\n") +``` + +Later in the code (not shown but referenced in analysis): +```python +# Building strings character by character +line = "" +for i in range(width): + line += "█" # O(n²) string concatenation +``` + +**Why it's slow:** +- Strings are immutable in Python +- Each `+=` creates a new string object +- For 80 characters: creates 80 intermediate strings + +**Recommended fix:** +```python +# Use list and join (O(n)) +line = "█" * width # Even simpler for repeated character + +# Or for complex building: +chars = [] +for i in range(width): + chars.append("█") +line = "".join(chars) +``` + +**Expected improvement:** 5-10x faster for large strings + +--- + +### 6. Redundant Failed List Iteration (batch_evaluator.py) + +**Location:** `scripts/batch_evaluator.py`, lines 287 +**Severity:** High +**Impact:** Medium - Duplicates work already done + +**Problem:** +```python +def aggregate_results(self) -> Dict: + # Lines 230-236: First pass builds succeeded/failed lists + for r in self.results: + total_duration += r.duration + if r.status == "succeeded": + succeeded.append(r) + else: + failed.append(r) + + # ... lines 238-284 work with these lists ... + + # Line 287: Rebuilds failed list AGAIN + failed = [r for r in self.results if r.status != "succeeded"] +``` + +**Why it's slow:** +- Iterates through all results twice +- `failed` list was already built in first pass but gets overwritten +- Wastes memory and CPU + +**Recommended fix:** +```python +def aggregate_results(self) -> Dict: + """Aggregate all evaluation results (optimized - single pass).""" + succeeded = [] + failed = [] + total_duration = 0.0 + + # Single pass through results for classification and duration sum + for r in self.results: + total_duration += r.duration + if r.status == "succeeded": + succeeded.append(r) + else: + failed.append(r) + + # Rank succeeded models + ranked = sorted( + succeeded, + key=lambda r: r.metrics.get("accuracy", r.metrics.get("perplexity", 0)), + reverse=True + ) + + # ... use already-built 'failed' list ... + + return { + "evaluated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), + "total_models": len(self.results), + "succeeded": len(succeeded), + "failed": len(failed), + "total_duration": total_duration, + "best_model": ranked[0].model_id if ranked else None, + "results": [r.__dict__ for r in self.results], + "ranking": [...], + "failed_models": [r.__dict__ for r in failed] # Use existing list + } +``` + +**Expected improvement:** 2x faster for result aggregation + +--- + +### 7. High Cyclomatic Complexity Functions (function_app.py) + +**Location:** `function_app.py`, multiple functions +**Severity:** High +**Impact:** High - Affects maintainability and performance + +**Problem:** +Functions with complexity > 10 are difficult to optimize and maintain: + +| Line | Function | Complexity | Issues | +|------|----------|-----------|--------| +| 762 | `tts` | 34 | Multiple nested conditionals, hard to follow control flow | +| 1041 | `ai_status` | 28 | Many sequential checks, could be refactored | +| 195 | `chat` | 27 | Complex logic mixing validation, provider detection, streaming | +| 606 | `chat_stream` | 18 | Nested error handling and streaming logic | +| 1852 | `quantum_circuit` | 18 | Complex parameter validation and circuit building | + +**Why it's slow:** +- Branch misprediction in CPU +- Hard to optimize by compiler/interpreter +- Difficult to cache results +- More memory allocations + +**Recommended fix:** + +Example for `tts` function (complexity 34 → 15): + +```python +# Before: one massive function with 34 branches + +# After: Extract helper functions +def _validate_tts_request(req_body: Dict) -> Tuple[Optional[str], Optional[Dict]]: + """Validate TTS request and extract parameters. + + Returns: + (error_message, params) - error_message is None on success + """ + text = req_body.get("text", "").strip() + if not text: + return ("No text provided", None) + + if len(text) > 5000: + return (f"Text too long: {len(text)} chars (max 5000)", None) + + params = { + "text": text, + "voice": req_body.get("voice", "en-US-JennyNeural"), + "rate": req_body.get("rate", "0%"), + "pitch": req_body.get("pitch", "0%"), + } + return (None, params) + +def _try_azure_tts(text: str, voice: str, rate: str, pitch: str) -> Optional[bytes]: + """Attempt Azure TTS synthesis. + + Returns: + Audio bytes on success, None on failure + """ + # Azure TTS logic here + pass + +def _try_local_tts(text: str) -> Optional[bytes]: + """Attempt local TTS fallback. + + Returns: + Audio bytes on success, None on failure + """ + # Local TTS logic here + pass + +@app.route(route="tts", methods=["POST"]) +def tts(req: func.HttpRequest) -> func.HttpResponse: + """Text-to-speech endpoint (refactored).""" + # Parse request + try: + req_body = req.get_json() + except ValueError: + return func.HttpResponse("Invalid JSON", status_code=400) + + # Validate + error, params = _validate_tts_request(req_body) + if error: + return func.HttpResponse(error, status_code=400) + + # Try Azure first + audio = _try_azure_tts(**params) + if audio: + return func.HttpResponse( + body=json.dumps({"audio_base64": base64.b64encode(audio).decode(), "format": "mp3"}), + mimetype="application/json" + ) + + # Fallback to local + audio = _try_local_tts(params["text"]) + if audio: + return func.HttpResponse( + body=json.dumps({"audio_base64": base64.b64encode(audio).decode(), "format": "wav"}), + mimetype="application/json" + ) + + return func.HttpResponse("TTS failed", status_code=500) +``` + +**Expected improvement:** Better CPU branch prediction, easier testing, 10-20% performance gain + +--- + +### 8. Cosine Similarity Not Vectorized (chat_memory.py) + +**Location:** `shared/chat_memory.py`, lines 241-251 +**Severity:** High +**Impact:** Medium - Called for every similarity search + +**Problem:** +```python +for r in rows: + dim = r.EmbeddingDim + emb = _deserialize_f32(r.EmbeddingVector, dim) + sim = _cosine(query_embedding, emb) # Individual cosine computation + if sim > 0: + scored.append({...}) +``` + +**Why it's slow:** +- Deserializes and computes similarity one vector at a time +- Cannot leverage SIMD instructions +- No batch processing + +**Recommended fix:** +```python +import numpy as np + +def retrieve_similar(query_embedding: Sequence[float], top_k: int = 5) -> List[Dict]: + """Retrieve similar messages (vectorized).""" + # ... fetch rows ... + + if not rows: + return [] + + # Batch deserialize all embeddings + embeddings = [] + metadata = [] + for r in rows: + emb = _deserialize_f32(r.EmbeddingVector, r.EmbeddingDim) + embeddings.append(emb) + metadata.append({ + "message_id": r.MessageId, + "content": r.Content, + "embedding_model": r.EmbeddingModel, + }) + + # Vectorized cosine similarity (all at once) + query_np = np.array(query_embedding) + embeddings_np = np.array(embeddings) + + # Compute all similarities in one operation (uses SIMD) + norms = np.linalg.norm(embeddings_np, axis=1) + query_norm = np.linalg.norm(query_np) + similarities = np.dot(embeddings_np, query_np) / (norms * query_norm + 1e-8) + + # Filter and build results + scored = [] + for idx, sim in enumerate(similarities): + if sim > 0: + meta = metadata[idx] + meta["similarity"] = float(sim) + scored.append(meta) + + return heapq.nlargest(top_k, scored, key=lambda x: x["similarity"]) +``` + +**Expected improvement:** 3-5x faster for 100+ vectors (depends on vector size) + +--- + +## Medium-Priority Issues + +### 9. Unnecessary List Conversion (auto_data_train.py) + +**Location:** `scripts/auto_data_train.py`, line 221 +**Severity:** Medium +**Impact:** Low - Only affects data collection metadata + +**Problem:** +```python +"sources": list(set(item.get("source", "unknown") for item in all_data)) +``` + +**Why it's inefficient:** +- Builds generator → set → list (3 data structures) +- Set is fine for deduplication, but list conversion unnecessary if used once + +**Recommended fix:** +```python +# If only used for counting/iteration +sources = {item.get("source", "unknown") for item in all_data} +# Use set directly + +# If JSON serialization needed +"sources": sorted(set(item.get("source", "unknown") for item in all_data)) +# sorted() returns list, gives consistent ordering +``` + +**Expected improvement:** Minor memory savings, better code clarity + +--- + +### 10. Repeated Status Checks with Lists (aria_automation.py, master_orchestrator.py) + +**Location:** Multiple files +**Severity:** Medium +**Impact:** Low - Not in hot path + +**Problem:** +```python +# aria_automation.py:368 +if not health["aria_server"] and self.mode in ["full", "server"]: + +# master_orchestrator.py:235 +if result["status"] not in ["succeeded", "skipped"]: +``` + +**Why it's inefficient:** +- Creates new list for every check +- Tuple is faster and immutable +- Set is O(1) for membership + +**Recommended fix:** +```python +# Use tuple for small, fixed sets (faster creation than list) +if not health["aria_server"] and self.mode in ("full", "server"): + +# Use set for larger or repeated checks +SUCCESSFUL_STATUSES = frozenset(["succeeded", "skipped"]) +if result["status"] not in SUCCESSFUL_STATUSES: +``` + +**Expected improvement:** Minor, but good practice + +--- + +### 11. No Caching for Subprocess Calls (function_app.py) + +**Location:** `function_app.py`, lines 1091-1100 +**Severity:** Medium +**Impact:** Medium - Called on every /api/ai/status request + +**Problem:** +```python +# Every status request spawns subprocess to check venv +proc = subprocess.run( + [str(venv_python), "-c", code], + capture_output=True, + text=True, + timeout=12 +) +``` + +**Why it's slow:** +- Subprocess creation is expensive (fork + exec) +- Checks don't change frequently +- 12-second timeout per request + +**Recommended fix:** +```python +import time +from functools import lru_cache + +@lru_cache(maxsize=1) +def _get_venv_info(venv_path: str, cache_time: float) -> Dict: + """Get venv info with caching (5-minute TTL). + + cache_time parameter forces cache invalidation every 5 minutes. + """ + venv_python = Path(venv_path) + if not venv_python.exists(): + return {"exists": False, "packages": {}, "error": "Not found"} + + # ... subprocess logic ... + return venv_info + +def ai_status(req: func.HttpRequest) -> func.HttpResponse: + # ... + + # Cache results for 5 minutes + current_cache_slot = int(time.time() / 300) # 300s = 5min + venv_info = _get_venv_info(str(venv_python), current_cache_slot) + + # ... +``` + +**Expected improvement:** 100-200x faster for cached responses + +--- + +### 12. Inefficient Variance Calculation (training_analytics.py) + +**Location:** `scripts/training_analytics.py`, lines 100-103 +**Severity:** Medium +**Impact:** Low - Only affects plateau detection + +**Problem:** +```python +avg = sum(accuracies) / len(accuracies) +variance = sum((x - avg) ** 2 for x in accuracies) / len(accuracies) +``` + +**Why it's inefficient:** +- Iterates list twice (once for avg, once for variance) +- Can be computed in single pass using Welford's algorithm + +**Recommended fix:** +```python +import statistics + +# Single-pass variance (more numerically stable too) +variance = statistics.variance(accuracies) + +# Or if mean is already computed: +variance = statistics.pvariance(accuracies, mu=avg) +``` + +**Expected improvement:** 2x faster, more numerically stable + +--- + +## Low-Priority Issues + +### 13. GPU Metrics Parsing (dashboard/gpu_monitor.py) + +**Location:** `dashboard/gpu_monitor.py`, lines 36-42 +**Severity:** Low +**Impact:** Low - Monitoring only + +**Problem:** +```python +'temperature': float(parts[2]) if parts[2] not in ['N/A', '[N/A]'] else 0, +'utilization': float(parts[3]) if parts[3] not in ['N/A', '[N/A]'] else 0, +# ... 5 more similar lines +``` + +**Why it's inefficient:** +- Creates temporary list `['N/A', '[N/A]']` 7 times per GPU +- String comparison repeated + +**Recommended fix:** +```python +_NA_VALUES = frozenset(['N/A', '[N/A]']) + +def _safe_float(value: str) -> float: + """Convert to float, return 0 for N/A.""" + return 0.0 if value in _NA_VALUES else float(value) + +# Use in parsing +'temperature': _safe_float(parts[2]), +'utilization': _safe_float(parts[3]), +# ... +``` + +**Expected improvement:** Minor, cleaner code + +--- + +### 14. Pre-commit Check File Filtering (scripts/pre_commit_check.py) + +**Location:** `scripts/pre_commit_check.py`, line 190 +**Severity:** Low +**Impact:** Low - Development tool only + +**Problem:** +```python +if any(pattern in file_path for pattern in ["__pycache__", ".pyc", "venv/", ".venv/", "__azurite_db"]): +``` + +**Why it's inefficient:** +- Creates list every check +- Linear search for each file + +**Recommended fix:** +```python +# Module-level constant +_IGNORE_PATTERNS = ("__pycache__", ".pyc", "venv/", ".venv/", "__azurite_db") + +# In check function +if any(pattern in file_path for pattern in _IGNORE_PATTERNS): +``` + +**Expected improvement:** Minor, but good practice + +--- + +### 15. Command Line Parsing (quantum-ai files) + +**Location:** Multiple files in ai-projects/quantum-ml/ +**Severity:** Low +**Impact:** Low - Startup only + +**Problem:** +```python +if dataset_name in ['wine_red', 'wine_white']: + # ... repeated in multiple files +``` + +**Why it's inefficient:** +- Creates list every check +- Duplicated logic across files + +**Recommended fix:** +```python +# In shared module or at module level +WINE_DATASETS = frozenset(['wine_red', 'wine_white']) +SEED_DATASETS = frozenset(['wheat_seeds', 'seeds']) + +if dataset_name in WINE_DATASETS: + # ... +``` + +**Expected improvement:** Minor, reduces duplication + +--- + +## Best Practices Already Implemented ✓ + +The analysis found several excellent performance patterns already in use: + +1. **Heapq.nlargest** (chat_memory.py:255) - O(n log k) vs O(n log n) sorting +2. **Generator expressions** (batch_evaluator.py:218) - Memory-efficient iteration +3. **Deque with popleft** (sql_engine.py) - O(1) queue operations +4. **Dict comprehensions** (training_analytics.py:68) - Efficient grouping +5. **Single-pass aggregation** (batch_evaluator.py:230-236) - Reduces iterations +6. **Cached glob operations** - Performance utils with TTL +7. **Debounced file writes** - Reduces I/O pressure + +--- + +## Implementation Priority + +### Immediate (Week 1) +1. Fix `aria_web/server.py` keyword lookups (Critical #1) +2. Add `batch_evaluator.py` result indexing (Critical #3) +3. Remove redundant iteration in `batch_evaluator.py` (High #6) + +### Short-term (Weeks 2-3) +4. Implement `chat_memory.py` batch embedding API (Critical #2) +5. Refactor high-complexity functions in `function_app.py` (High #7) +6. Add venv info caching in `ai_status` (Medium #11) + +### Medium-term (Month 2) +7. Vectorize cosine similarity in `chat_memory.py` (High #8) +8. Fix training_analytics.py calculations (High #4, Medium #12) +9. Address remaining medium-priority issues + +### Ongoing +10. Apply tuple/frozenset optimizations across codebase +11. Add performance tests for critical paths +12. Monitor with profiling tools + +--- + +## Benchmarking Recommendations + +To validate improvements, add benchmarks for: + +1. **Aria command processing** - Measure keyword lookup optimization +2. **Model comparison** - Test linear vs indexed search +3. **Embedding storage** - Batch vs individual inserts +4. **Similarity search** - Vectorized vs loop-based +5. **Report generation** - String building optimizations + +Example benchmark structure: +```python +import time + +def benchmark_keyword_lookup(): + """Benchmark command keyword lookups.""" + commands = [ + "jump high", "dance around", "wave hello", + # ... 100 test commands + ] + + # Warm-up + for cmd in commands[:10]: + _extract_action_position(cmd) + + # Measure + start = time.perf_counter() + for cmd in commands: + _extract_action_position(cmd) + elapsed = time.perf_counter() - start + + print(f"Processed {len(commands)} commands in {elapsed:.3f}s") + print(f"Average: {elapsed/len(commands)*1000:.2f}ms per command") +``` + +--- + +## Monitoring Recommendations + +Add performance monitoring for: + +1. **Function execution time** - Track slow endpoints +2. **Database query time** - Identify slow queries +3. **Memory usage** - Detect leaks +4. **Cache hit rates** - Validate caching effectiveness + +Example instrumentation: +```python +import functools +import time +from shared.telemetry import track_event + +def timed(func): + """Decorator to track function execution time.""" + @functools.wraps(func) + def wrapper(*args, **kwargs): + start = time.perf_counter() + try: + result = func(*args, **kwargs) + return result + finally: + elapsed = time.perf_counter() - start + track_event("function_timing", { + "function": func.__name__, + "duration_ms": elapsed * 1000 + }) + return wrapper + +@timed +def store_embedding(...): + # Implementation + pass +``` + +--- + +## Related Documents + +- `docs/PERFORMANCE_IMPLEMENTATION_SUMMARY.md` - Previous optimization work +- `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` - General optimization patterns +- `shared/performance_utils.py` - Reusable optimization utilities + +--- + +## Conclusion + +This analysis identified **15 performance improvement opportunities** with estimated improvements ranging from 2x to 100x for specific operations. The critical issues in `aria_web/server.py`, `chat_memory.py`, and `batch_evaluator.py` should be addressed first, as they affect high-traffic code paths. + +The codebase already demonstrates several strong performance patterns, particularly in the use of efficient data structures and algorithms. Building on these foundations with the recommended fixes will significantly improve overall system performance. + +**Next Steps:** +1. Review and prioritize recommendations +2. Implement critical fixes (1-3) +3. Add performance benchmarks +4. Monitor improvements in production +5. Iterate on remaining issues diff --git a/docs/PERFORMANCE_CRITICAL_FIXES_2026.md b/docs/PERFORMANCE_CRITICAL_FIXES_2026.md index f035809a0..214319019 100644 --- a/docs/PERFORMANCE_CRITICAL_FIXES_2026.md +++ b/docs/PERFORMANCE_CRITICAL_FIXES_2026.md @@ -1,316 +1,316 @@ -# Critical Performance Fixes - February 2026 - -## Executive Summary - -This document details two critical performance bottlenecks that were identified and fixed in February 2026, resulting in **10-250x speedup** across hot code paths. - -## Overview - -| Fix | Location | Impact | Speedup | Status | -|-----|----------|--------|---------|--------| -| Keyword matching optimization | `aria_web/server.py` | 100-250x faster command parsing | **100-250x** | ✅ Fixed | -| DB connection pooling | `shared/chat_memory.py` | 9.6x faster embedding operations | **9.6x** | ✅ Fixed | - ---- - -## Fix #1: Aria Web Keyword Matching (100-250x speedup) - -### Problem -The Aria web server's command parser used 15+ inline `any(k in cmd for k in [...])` checks per command, creating new lists and performing O(n) keyword scanning on every check. - -**Code smell**: List creation in hot path + linear search for membership testing - -### Root Cause -```python -# BEFORE: Created list on every check, O(n) scan -if any(k in cmd for k in ['jump', 'leap', 'hop']): - # ... -elif any(k in cmd for k in ['dance', 'spin', 'twirl']): - # ... -# ... 13 more similar checks -``` - -This resulted in: -- 100+ keyword comparisons per command -- 15+ list allocations per command -- No pattern reuse across commands - -### Solution -Precompile keyword sets at module level using `frozenset`: - -```python -# AFTER: Define once at module level -_JUMP_KEYWORDS = frozenset(['jump', 'leap', 'hop']) -_DANCE_KEYWORDS = frozenset(['dance', 'spin', 'twirl']) -# ... etc - -def _keywords_in_cmd(keywords: frozenset, cmd: str) -> bool: - """Optimized keyword checking with precompiled sets.""" - return any(k in cmd for k in keywords) - -# Usage -if _keywords_in_cmd(_JUMP_KEYWORDS, cmd): - # ... -``` - -### Benefits -1. **Zero allocation**: `frozenset` created once at module load -2. **Pattern reuse**: Same sets used across all commands -3. **Immutability**: `frozenset` prevents accidental modification -4. **Maintainability**: Keywords defined in one place - -### Performance Results -``` -Benchmark (10,000 iterations): -- Before: ~40-100ms per command with 15 checks -- After: ~0.4ms per command with 15 checks -- Speedup: 100-250x faster ⚡ -``` - -### Real-World Impact -Typical Aria session: 100-500 commands -- **Before**: 4-50 seconds in keyword matching overhead -- **After**: 0.04-0.2 seconds in keyword matching overhead -- **Result**: Instant command response 🎉 - -### Files Modified -- `aria_web/server.py`: Lines 18-48 (module-level sets), 525-548 (usage in position determination), 608-609 (usage in tag generation), 698-714 (usage in arm/leg commands) - -### Tests -- `tests/test_performance_critical_fixes.py`: - - `test_keywords_in_cmd_function()` - Validates correctness - - `test_keyword_sets_are_frozen()` - Validates immutability - - `test_keyword_matching_benchmark()` - 10k iterations in 4ms - ---- - -## Fix #2: Chat Memory Connection Pooling (9.6x speedup) - -### Problem -The chat memory module created a fresh database connection for EVERY embedding operation, paying 50-100ms connection overhead each time. - -**Code smell**: `connect()` + `close()` in every function call - -### Root Cause -```python -# BEFORE: New connection on every call -def store_embedding(message_id, embedding, model): - conn = pyodbc.connect(conn_str, timeout=4) # 50-100ms - try: - cursor = conn.cursor() - # ... store embedding (fast) - conn.commit() - finally: - conn.close() # Throws away connection -``` - -This resulted in: -- 50-100ms connection overhead per embedding -- No connection reuse between operations -- Unnecessary network round-trips to database - -### Solution -Implement thread-local connection caching with health checks: - -```python -# Module-level cache (thread-safe) -_conn_cache = {} -_conn_lock = threading.Lock() -_MAX_CONN_AGE_SECONDS = 300 # 5 minutes - -def _get_conn(): - """Get or create a cached DB connection.""" - thread_id = threading.current_thread().ident - current_time = time.time() - - with _conn_lock: - if thread_id in _conn_cache: - conn, timestamp = _conn_cache[thread_id] - # Check if connection is fresh and alive - if current_time - timestamp < _MAX_CONN_AGE_SECONDS: - try: - # Health check - cursor = conn.cursor() - cursor.execute("SELECT 1") - cursor.close() - return conn # Reuse cached connection - except Exception: - # Stale connection, remove from cache - try: - conn.close() - except Exception: - pass - del _conn_cache[thread_id] - - # Create new connection and cache it - new_conn = pyodbc.connect(conn_str, timeout=4) - _conn_cache[thread_id] = (new_conn, current_time) - return new_conn - -# Usage (connection NOT closed - stays in cache) -def store_embedding(message_id, embedding, model): - conn = _get_conn() # Uses cache after first call - try: - cursor = conn.cursor() - # ... store embedding - conn.commit() - cursor.close() # Close cursor only, not connection - except Exception: - # On error, invalidate cache for safety - thread_id = threading.current_thread().ident - with _conn_lock: - if thread_id in _conn_cache: - del _conn_cache[thread_id] -``` - -### Benefits -1. **Connection reuse**: Same connection used across multiple operations -2. **Thread-safe**: Each thread gets its own connection (no race conditions) -3. **Health checks**: Detects and recovers from stale connections -4. **TTL-based refresh**: Prevents long-lived connection issues -5. **Error recovery**: Invalidates cache on errors (graceful degradation) - -### Performance Results -``` -Benchmark (10 embedding stores): -- Before: ~500ms (50ms × 10 connections) -- After: ~52ms (50ms first + ~0.2ms × 9 cached) -- Speedup: 9.6x faster ⚡ -``` - -### Real-World Impact -Batch processing 1000 embeddings: -- **Before**: 50 seconds in connection overhead -- **After**: 0.2 seconds in connection overhead -- **Result**: 49.8 seconds saved per batch! 🚀 - -### Files Modified -- `shared/chat_memory.py`: - - Lines 24 (added threading import) - - Lines 58-103 (`_get_conn()` with caching) - - Lines 191-221 (`store_embedding()` updated to not close connection) - - Lines 267-292 (`fetch_similar_messages()` updated to not close connection) - -### Tests -- `tests/test_performance_critical_fixes.py`: - - `test_connection_caching()` - Validates cache reuse - - `test_store_embedding_uses_cached_connection()` - Validates no close() - - `test_connection_pooling_speedup()` - 10 ops in 52ms benchmark - ---- - -## Testing Strategy - -### Test Suite: `tests/test_performance_critical_fixes.py` - -**Design principles**: -1. **Standalone**: No pytest dependency (can run with `python tests/...`) -2. **Timing assertions**: Real performance validation (not just correctness) -3. **Mock-based**: Tests caching behavior without needing real database -4. **Comprehensive**: 8 test functions covering all critical paths - -**Running tests**: -```bash -# Run full test suite -python tests/test_performance_critical_fixes.py - -# Expected output: -# ✓ Keyword matching: 10k iterations in ~4ms -# ✓ Connection pooling: 10 operations in ~52ms -# ✓ All 8 tests pass -``` - -### Test Coverage -- ✅ Keyword matching correctness -- ✅ Keyword set immutability -- ✅ Position determination performance -- ✅ Command parsing performance (50 parses < 50ms) -- ✅ Connection cache reuse -- ✅ Connection NOT closed after use -- ✅ Connection pooling speedup (10 ops < 150ms) -- ✅ Keyword matching benchmark (10k iterations < 10ms) - ---- - -## Performance Impact Summary - -### Aria Web Server -- **Commands per session**: 100-500 typical -- **Latency reduction**: 40-100ms → 0.4ms per command -- **Total savings**: 4-50 seconds → 0.04-0.2 seconds per session -- **User experience**: Instant response instead of noticeable lag - -### Chat Memory (Embeddings) -- **Operations per batch**: 100-1000 typical -- **Latency reduction**: 50ms → 0.2ms per operation (after first) -- **Total savings**: 5-50 seconds → 0.02-0.2 seconds per batch -- **Throughput**: 20 ops/sec → 200+ ops/sec - -### Overall System Impact -- **Hot paths affected**: Command parsing, embedding storage, similarity search -- **Aggregate speedup**: 10-100x depending on workload -- **Memory impact**: Minimal (few KB for keyword sets, one DB connection per thread) -- **Thread safety**: Fully thread-safe with proper locking - ---- - -## Lessons Learned - -### Pattern: Precompile Repeated Patterns -**When**: Any code that repeatedly checks the same keywords, regexes, or patterns -**How**: Define at module level as `frozenset` or `re.compile()` -**Benefit**: 100-1000x speedup + zero allocation overhead - -### Pattern: Thread-Local Resource Pooling -**When**: Any expensive resource creation (DB connections, HTTP clients, tokenizers) -**How**: Module-level cache with thread ID as key + health checks + TTL -**Benefit**: 5-100x speedup + proper resource management - -### Anti-Pattern: Inline List Creation in Hot Paths -**Avoid**: `any(x in [a, b, c])` or `if x in [a, b, c]` in loops -**Replace**: Module-level `ITEMS = frozenset([a, b, c])` then `x in ITEMS` - -### Anti-Pattern: Connect-Execute-Close Pattern -**Avoid**: Opening and immediately closing connections in every function -**Replace**: Connection pooling with reuse across operations - ---- - -## Future Recommendations - -### Immediate Actions -- ✅ Monitor performance metrics in production -- ✅ Add performance regression tests to CI pipeline -- ✅ Document patterns in developer guidelines - -### Future Optimizations -1. **Function App file existence caching** - 5-10s TTL for status endpoint -2. **Quantum Classifier batch processing** - Use PennyLane's vmap for vectorization -3. **Regex pattern compilation** - Check for remaining inline `re.search()` calls - -### Monitoring -Add metrics for: -- Average command parse time (should be < 1ms) -- DB connection pool hit rate (should be > 90%) -- Embedding operation throughput (should be > 100 ops/sec) - ---- - -## References - -- **Implementation**: `aria_web/server.py`, `shared/chat_memory.py` -- **Tests**: `tests/test_performance_critical_fixes.py` -- **Documentation**: `docs/PERFORMANCE_IMPROVEMENTS.md` -- **Benchmarks**: See test output for timing measurements - -## Authors - -- Performance analysis and implementation: February 2026 -- Testing and validation: Automated test suite -- Code review: GitHub Copilot - ---- - -**Status**: ✅ Completed and Deployed -**Impact**: 🚀 10-250x speedup across critical paths -**Risk**: ✅ Low - Fully tested with comprehensive validation +# Critical Performance Fixes - February 2026 + +## Executive Summary + +This document details two critical performance bottlenecks that were identified and fixed in February 2026, resulting in **10-250x speedup** across hot code paths. + +## Overview + +| Fix | Location | Impact | Speedup | Status | +|-----|----------|--------|---------|--------| +| Keyword matching optimization | `aria_web/server.py` | 100-250x faster command parsing | **100-250x** | ✅ Fixed | +| DB connection pooling | `shared/chat_memory.py` | 9.6x faster embedding operations | **9.6x** | ✅ Fixed | + +--- + +## Fix #1: Aria Web Keyword Matching (100-250x speedup) + +### Problem +The Aria web server's command parser used 15+ inline `any(k in cmd for k in [...])` checks per command, creating new lists and performing O(n) keyword scanning on every check. + +**Code smell**: List creation in hot path + linear search for membership testing + +### Root Cause +```python +# BEFORE: Created list on every check, O(n) scan +if any(k in cmd for k in ['jump', 'leap', 'hop']): + # ... +elif any(k in cmd for k in ['dance', 'spin', 'twirl']): + # ... +# ... 13 more similar checks +``` + +This resulted in: +- 100+ keyword comparisons per command +- 15+ list allocations per command +- No pattern reuse across commands + +### Solution +Precompile keyword sets at module level using `frozenset`: + +```python +# AFTER: Define once at module level +_JUMP_KEYWORDS = frozenset(['jump', 'leap', 'hop']) +_DANCE_KEYWORDS = frozenset(['dance', 'spin', 'twirl']) +# ... etc + +def _keywords_in_cmd(keywords: frozenset, cmd: str) -> bool: + """Optimized keyword checking with precompiled sets.""" + return any(k in cmd for k in keywords) + +# Usage +if _keywords_in_cmd(_JUMP_KEYWORDS, cmd): + # ... +``` + +### Benefits +1. **Zero allocation**: `frozenset` created once at module load +2. **Pattern reuse**: Same sets used across all commands +3. **Immutability**: `frozenset` prevents accidental modification +4. **Maintainability**: Keywords defined in one place + +### Performance Results +``` +Benchmark (10,000 iterations): +- Before: ~40-100ms per command with 15 checks +- After: ~0.4ms per command with 15 checks +- Speedup: 100-250x faster ⚡ +``` + +### Real-World Impact +Typical Aria session: 100-500 commands +- **Before**: 4-50 seconds in keyword matching overhead +- **After**: 0.04-0.2 seconds in keyword matching overhead +- **Result**: Instant command response 🎉 + +### Files Modified +- `aria_web/server.py`: Lines 18-48 (module-level sets), 525-548 (usage in position determination), 608-609 (usage in tag generation), 698-714 (usage in arm/leg commands) + +### Tests +- `tests/test_performance_critical_fixes.py`: + - `test_keywords_in_cmd_function()` - Validates correctness + - `test_keyword_sets_are_frozen()` - Validates immutability + - `test_keyword_matching_benchmark()` - 10k iterations in 4ms + +--- + +## Fix #2: Chat Memory Connection Pooling (9.6x speedup) + +### Problem +The chat memory module created a fresh database connection for EVERY embedding operation, paying 50-100ms connection overhead each time. + +**Code smell**: `connect()` + `close()` in every function call + +### Root Cause +```python +# BEFORE: New connection on every call +def store_embedding(message_id, embedding, model): + conn = pyodbc.connect(conn_str, timeout=4) # 50-100ms + try: + cursor = conn.cursor() + # ... store embedding (fast) + conn.commit() + finally: + conn.close() # Throws away connection +``` + +This resulted in: +- 50-100ms connection overhead per embedding +- No connection reuse between operations +- Unnecessary network round-trips to database + +### Solution +Implement thread-local connection caching with health checks: + +```python +# Module-level cache (thread-safe) +_conn_cache = {} +_conn_lock = threading.Lock() +_MAX_CONN_AGE_SECONDS = 300 # 5 minutes + +def _get_conn(): + """Get or create a cached DB connection.""" + thread_id = threading.current_thread().ident + current_time = time.time() + + with _conn_lock: + if thread_id in _conn_cache: + conn, timestamp = _conn_cache[thread_id] + # Check if connection is fresh and alive + if current_time - timestamp < _MAX_CONN_AGE_SECONDS: + try: + # Health check + cursor = conn.cursor() + cursor.execute("SELECT 1") + cursor.close() + return conn # Reuse cached connection + except Exception: + # Stale connection, remove from cache + try: + conn.close() + except Exception: + pass + del _conn_cache[thread_id] + + # Create new connection and cache it + new_conn = pyodbc.connect(conn_str, timeout=4) + _conn_cache[thread_id] = (new_conn, current_time) + return new_conn + +# Usage (connection NOT closed - stays in cache) +def store_embedding(message_id, embedding, model): + conn = _get_conn() # Uses cache after first call + try: + cursor = conn.cursor() + # ... store embedding + conn.commit() + cursor.close() # Close cursor only, not connection + except Exception: + # On error, invalidate cache for safety + thread_id = threading.current_thread().ident + with _conn_lock: + if thread_id in _conn_cache: + del _conn_cache[thread_id] +``` + +### Benefits +1. **Connection reuse**: Same connection used across multiple operations +2. **Thread-safe**: Each thread gets its own connection (no race conditions) +3. **Health checks**: Detects and recovers from stale connections +4. **TTL-based refresh**: Prevents long-lived connection issues +5. **Error recovery**: Invalidates cache on errors (graceful degradation) + +### Performance Results +``` +Benchmark (10 embedding stores): +- Before: ~500ms (50ms × 10 connections) +- After: ~52ms (50ms first + ~0.2ms × 9 cached) +- Speedup: 9.6x faster ⚡ +``` + +### Real-World Impact +Batch processing 1000 embeddings: +- **Before**: 50 seconds in connection overhead +- **After**: 0.2 seconds in connection overhead +- **Result**: 49.8 seconds saved per batch! 🚀 + +### Files Modified +- `shared/chat_memory.py`: + - Lines 24 (added threading import) + - Lines 58-103 (`_get_conn()` with caching) + - Lines 191-221 (`store_embedding()` updated to not close connection) + - Lines 267-292 (`fetch_similar_messages()` updated to not close connection) + +### Tests +- `tests/test_performance_critical_fixes.py`: + - `test_connection_caching()` - Validates cache reuse + - `test_store_embedding_uses_cached_connection()` - Validates no close() + - `test_connection_pooling_speedup()` - 10 ops in 52ms benchmark + +--- + +## Testing Strategy + +### Test Suite: `tests/test_performance_critical_fixes.py` + +**Design principles**: +1. **Standalone**: No pytest dependency (can run with `python tests/...`) +2. **Timing assertions**: Real performance validation (not just correctness) +3. **Mock-based**: Tests caching behavior without needing real database +4. **Comprehensive**: 8 test functions covering all critical paths + +**Running tests**: +```bash +# Run full test suite +python tests/test_performance_critical_fixes.py + +# Expected output: +# ✓ Keyword matching: 10k iterations in ~4ms +# ✓ Connection pooling: 10 operations in ~52ms +# ✓ All 8 tests pass +``` + +### Test Coverage +- ✅ Keyword matching correctness +- ✅ Keyword set immutability +- ✅ Position determination performance +- ✅ Command parsing performance (50 parses < 50ms) +- ✅ Connection cache reuse +- ✅ Connection NOT closed after use +- ✅ Connection pooling speedup (10 ops < 150ms) +- ✅ Keyword matching benchmark (10k iterations < 10ms) + +--- + +## Performance Impact Summary + +### Aria Web Server +- **Commands per session**: 100-500 typical +- **Latency reduction**: 40-100ms → 0.4ms per command +- **Total savings**: 4-50 seconds → 0.04-0.2 seconds per session +- **User experience**: Instant response instead of noticeable lag + +### Chat Memory (Embeddings) +- **Operations per batch**: 100-1000 typical +- **Latency reduction**: 50ms → 0.2ms per operation (after first) +- **Total savings**: 5-50 seconds → 0.02-0.2 seconds per batch +- **Throughput**: 20 ops/sec → 200+ ops/sec + +### Overall System Impact +- **Hot paths affected**: Command parsing, embedding storage, similarity search +- **Aggregate speedup**: 10-100x depending on workload +- **Memory impact**: Minimal (few KB for keyword sets, one DB connection per thread) +- **Thread safety**: Fully thread-safe with proper locking + +--- + +## Lessons Learned + +### Pattern: Precompile Repeated Patterns +**When**: Any code that repeatedly checks the same keywords, regexes, or patterns +**How**: Define at module level as `frozenset` or `re.compile()` +**Benefit**: 100-1000x speedup + zero allocation overhead + +### Pattern: Thread-Local Resource Pooling +**When**: Any expensive resource creation (DB connections, HTTP clients, tokenizers) +**How**: Module-level cache with thread ID as key + health checks + TTL +**Benefit**: 5-100x speedup + proper resource management + +### Anti-Pattern: Inline List Creation in Hot Paths +**Avoid**: `any(x in [a, b, c])` or `if x in [a, b, c]` in loops +**Replace**: Module-level `ITEMS = frozenset([a, b, c])` then `x in ITEMS` + +### Anti-Pattern: Connect-Execute-Close Pattern +**Avoid**: Opening and immediately closing connections in every function +**Replace**: Connection pooling with reuse across operations + +--- + +## Future Recommendations + +### Immediate Actions +- ✅ Monitor performance metrics in production +- ✅ Add performance regression tests to CI pipeline +- ✅ Document patterns in developer guidelines + +### Future Optimizations +1. **Function App file existence caching** - 5-10s TTL for status endpoint +2. **Quantum Classifier batch processing** - Use PennyLane's vmap for vectorization +3. **Regex pattern compilation** - Check for remaining inline `re.search()` calls + +### Monitoring +Add metrics for: +- Average command parse time (should be < 1ms) +- DB connection pool hit rate (should be > 90%) +- Embedding operation throughput (should be > 100 ops/sec) + +--- + +## References + +- **Implementation**: `aria_web/server.py`, `shared/chat_memory.py` +- **Tests**: `tests/test_performance_critical_fixes.py` +- **Documentation**: `docs/PERFORMANCE_IMPROVEMENTS.md` +- **Benchmarks**: See test output for timing measurements + +## Authors + +- Performance analysis and implementation: February 2026 +- Testing and validation: Automated test suite +- Code review: GitHub Copilot + +--- + +**Status**: ✅ Completed and Deployed +**Impact**: 🚀 10-250x speedup across critical paths +**Risk**: ✅ Low - Fully tested with comprehensive validation diff --git a/docs/PERFORMANCE_FINDINGS_SUMMARY.md b/docs/PERFORMANCE_FINDINGS_SUMMARY.md index 1042dcc46..13c5f4907 100644 --- a/docs/PERFORMANCE_FINDINGS_SUMMARY.md +++ b/docs/PERFORMANCE_FINDINGS_SUMMARY.md @@ -1,189 +1,189 @@ -# Performance Findings Summary - February 17, 2026 - -## Overview - -Comprehensive analysis identified **8 distinct performance issues** across the codebase, ranging from critical O(n³) complexity to minor optimization opportunities. Total potential impact: **10-1000x speedup** in affected code paths. - -## Critical Findings (Immediate Action Required) - -### 1. Quantum Gradient Computation - O(n³) Complexity -**Location:** `ai-projects/quantum-ml/web_app.py:217-246` -**Impact:** 10-100x slower than necessary -**Status:** Not yet optimized - -**Problem:** Triple-nested loops with repeated weight copying and loss computation -**Solution:** Use PennyLane's `qml.grad()` for automatic differentiation -**Effort:** 2-4 hours -**Risk:** Low (fallback mechanism available) - -### 2. Repeated JSON File I/O -**Location:** `dashboard/serve.py:273-515` (4+ methods) -**Impact:** 5-10x unnecessary file reads per request -**Status:** Not yet optimized - -**Problem:** Same status.json file read and parsed multiple times per dashboard refresh -**Solution:** Implement TTL-based caching (5-second window) -**Effort:** 1-2 hours -**Risk:** Low (short TTL prevents stale data) - -## High Priority Findings - -### 3. Linear Keyword Searches -**Location:** `ai-projects/chat-cli/src/agi_provider.py:343-372` -**Impact:** 3-30x slower than optimal (varies with query length) -**Status:** Not yet optimized - -**Problem:** Multiple `any()` calls with inline keyword lists (O(keywords × query_length)) -**Solution:** Pre-compiled frozensets with set intersection (O(query_length)) -**Effort:** 1-2 hours -**Risk:** Very low (pure optimization) - -### 4. Inconsistent Keyword Pattern Usage -**Location:** `aria_web/server.py:554-557` -**Impact:** 2-5x slower than established pattern -**Status:** Partially optimized (frozensets defined but not used consistently) - -**Problem:** File has 19 frozensets (lines 42-74) but some code still uses inline `any()` calls -**Solution:** Extend existing pattern to remaining cases -**Effort:** 30 minutes -**Risk:** Very low (pattern already established) - -## Medium Priority Findings - -### 5. Multi-Pass Collection Iteration -**Location:** `ai-projects/quantum-ml/web_app.py:952-958` -**Impact:** 4x unnecessary iterations -**Status:** Not yet optimized - -**Problem:** Global statistics computed with 4 separate iterations over same collection -**Solution:** Single-pass accumulation -**Effort:** 30 minutes -**Risk:** Very low - -### 6. Inefficient Directory Traversal -**Location:** `dashboard/serve.py:700, 761, 766` -**Impact:** 5-20% memory waste, minor speed impact -**Status:** Not yet optimized - -**Problem:** Creates intermediate lists when only counting items -**Solution:** Use generator expressions with `sum(1 for ...)` -**Effort:** 15 minutes -**Risk:** Very low - -### 7. Rate Limiting with List Filtering -**Location:** `dashboard/serve.py:39-40` -**Impact:** 2-5x slower than optimal -**Status:** Not yet optimized - -**Problem:** O(n) list filtering on every request -**Solution:** Use `collections.deque` with efficient expiration -**Effort:** 1 hour -**Risk:** Low - -## Low Priority Findings - -### 8. Minor List Comprehension Issues -**Location:** `ai-projects/quantum-ml/web_app.py:440-443` -**Impact:** <5% improvement -**Status:** Not yet optimized - -**Problem:** Creates intermediate list for gradient norm computation -**Solution:** Use generator expression -**Effort:** 5 minutes -**Risk:** Very low - -## Summary Statistics - -| Priority | Count | Total Potential Speedup | Total Effort | -|----------|-------|------------------------|--------------| -| Critical | 2 | 10-100x | 3-6 hours | -| High | 2 | 3-30x | 1.5-2.5 hours | -| Medium | 3 | 2-5x | 2 hours | -| Low | 1 | <5% | 5 minutes | -| **Total** | **8** | **10-1000x** | **6.5-10.5 hours** | - -## Implementation Roadmap - -### Phase 1: Critical Fixes (Week 1) -- [ ] Quantum gradient optimization (web_app.py) -- [ ] File caching implementation (serve.py) -- **Expected Impact:** 10-100x speedup in quantum training, 5-10x in dashboard - -### Phase 2: High Priority (Week 2) -- [ ] Keyword matching optimization (agi_provider.py) -- [ ] Consistent frozenset usage (server.py) -- **Expected Impact:** 3-30x speedup in query processing - -### Phase 3: Medium Priority (Week 3) -- [ ] Single-pass statistics (web_app.py) -- [ ] Generator-based counting (serve.py) -- [ ] Deque rate limiting (serve.py) -- **Expected Impact:** 2-5x improvements in various endpoints - -### Phase 4: Low Priority (Ongoing) -- [ ] Minor optimizations as encountered during maintenance - -## Testing Requirements - -Each optimization must include: - -1. **Unit tests** verifying correctness -2. **Performance benchmarks** measuring actual speedup -3. **Integration tests** ensuring no regressions -4. **Documentation** of changes and rationale - -Test infrastructure exists at: -- `tests/test_performance_optimizations.py` - Performance test patterns -- `scripts/test_runner.py` - Test orchestration - -## Risk Assessment - -| Risk Level | Count | Notes | -|------------|-------|-------| -| Very Low | 5 | Pure optimizations, no behavior change | -| Low | 3 | Include fallback mechanisms | -| Medium | 0 | None identified | -| High | 0 | None identified | - -**Overall Risk:** Low - All optimizations follow established patterns and include safety measures - -## Related Documentation - -- **Full Analysis:** `docs/CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md` -- **Implementation Guide:** `docs/OPTIMIZATION_QUICK_GUIDE.md` -- **Previous Work:** `docs/PERFORMANCE_IMPROVEMENTS.md` -- **Test Patterns:** `tests/test_performance_optimizations.py` - -## Success Metrics - -After implementing all optimizations: - -- Quantum training time reduced by 10-100x -- Dashboard response time reduced by 5-10x -- Query processing time reduced by 3-30x -- Memory usage reduced by 5-50MB (file caching offset by list elimination) -- No functional regressions -- All tests passing - -## Next Steps - -1. **Review** findings with repository maintainers -2. **Prioritize** based on user impact and development resources -3. **Implement** Phase 1 (critical fixes) first -4. **Test** thoroughly with realistic workloads -5. **Monitor** performance improvements in production -6. **Document** lessons learned for future optimizations - -## Questions for Maintainers - -1. **Quantum ML:** Is `qml.grad()` compatible with current PennyLane version? -2. **File Caching:** Is 5-second TTL appropriate for dashboard refresh rate? -3. **Keyword Sets:** Are there additional keyword lists to optimize? -4. **Testing:** Should we add load testing for dashboard endpoints? -5. **Rollout:** Prefer gradual rollout or all optimizations at once? - ---- - -**Generated:** February 17, 2026 -**Analyst:** Claude (AI Assistant) -**Status:** Ready for Review +# Performance Findings Summary - February 17, 2026 + +## Overview + +Comprehensive analysis identified **8 distinct performance issues** across the codebase, ranging from critical O(n³) complexity to minor optimization opportunities. Total potential impact: **10-1000x speedup** in affected code paths. + +## Critical Findings (Immediate Action Required) + +### 1. Quantum Gradient Computation - O(n³) Complexity +**Location:** `ai-projects/quantum-ml/web_app.py:217-246` +**Impact:** 10-100x slower than necessary +**Status:** Not yet optimized + +**Problem:** Triple-nested loops with repeated weight copying and loss computation +**Solution:** Use PennyLane's `qml.grad()` for automatic differentiation +**Effort:** 2-4 hours +**Risk:** Low (fallback mechanism available) + +### 2. Repeated JSON File I/O +**Location:** `dashboard/serve.py:273-515` (4+ methods) +**Impact:** 5-10x unnecessary file reads per request +**Status:** Not yet optimized + +**Problem:** Same status.json file read and parsed multiple times per dashboard refresh +**Solution:** Implement TTL-based caching (5-second window) +**Effort:** 1-2 hours +**Risk:** Low (short TTL prevents stale data) + +## High Priority Findings + +### 3. Linear Keyword Searches +**Location:** `ai-projects/chat-cli/src/agi_provider.py:343-372` +**Impact:** 3-30x slower than optimal (varies with query length) +**Status:** Not yet optimized + +**Problem:** Multiple `any()` calls with inline keyword lists (O(keywords × query_length)) +**Solution:** Pre-compiled frozensets with set intersection (O(query_length)) +**Effort:** 1-2 hours +**Risk:** Very low (pure optimization) + +### 4. Inconsistent Keyword Pattern Usage +**Location:** `aria_web/server.py:554-557` +**Impact:** 2-5x slower than established pattern +**Status:** Partially optimized (frozensets defined but not used consistently) + +**Problem:** File has 19 frozensets (lines 42-74) but some code still uses inline `any()` calls +**Solution:** Extend existing pattern to remaining cases +**Effort:** 30 minutes +**Risk:** Very low (pattern already established) + +## Medium Priority Findings + +### 5. Multi-Pass Collection Iteration +**Location:** `ai-projects/quantum-ml/web_app.py:952-958` +**Impact:** 4x unnecessary iterations +**Status:** Not yet optimized + +**Problem:** Global statistics computed with 4 separate iterations over same collection +**Solution:** Single-pass accumulation +**Effort:** 30 minutes +**Risk:** Very low + +### 6. Inefficient Directory Traversal +**Location:** `dashboard/serve.py:700, 761, 766` +**Impact:** 5-20% memory waste, minor speed impact +**Status:** Not yet optimized + +**Problem:** Creates intermediate lists when only counting items +**Solution:** Use generator expressions with `sum(1 for ...)` +**Effort:** 15 minutes +**Risk:** Very low + +### 7. Rate Limiting with List Filtering +**Location:** `dashboard/serve.py:39-40` +**Impact:** 2-5x slower than optimal +**Status:** Not yet optimized + +**Problem:** O(n) list filtering on every request +**Solution:** Use `collections.deque` with efficient expiration +**Effort:** 1 hour +**Risk:** Low + +## Low Priority Findings + +### 8. Minor List Comprehension Issues +**Location:** `ai-projects/quantum-ml/web_app.py:440-443` +**Impact:** <5% improvement +**Status:** Not yet optimized + +**Problem:** Creates intermediate list for gradient norm computation +**Solution:** Use generator expression +**Effort:** 5 minutes +**Risk:** Very low + +## Summary Statistics + +| Priority | Count | Total Potential Speedup | Total Effort | +|----------|-------|------------------------|--------------| +| Critical | 2 | 10-100x | 3-6 hours | +| High | 2 | 3-30x | 1.5-2.5 hours | +| Medium | 3 | 2-5x | 2 hours | +| Low | 1 | <5% | 5 minutes | +| **Total** | **8** | **10-1000x** | **6.5-10.5 hours** | + +## Implementation Roadmap + +### Phase 1: Critical Fixes (Week 1) +- [ ] Quantum gradient optimization (web_app.py) +- [ ] File caching implementation (serve.py) +- **Expected Impact:** 10-100x speedup in quantum training, 5-10x in dashboard + +### Phase 2: High Priority (Week 2) +- [ ] Keyword matching optimization (agi_provider.py) +- [ ] Consistent frozenset usage (server.py) +- **Expected Impact:** 3-30x speedup in query processing + +### Phase 3: Medium Priority (Week 3) +- [ ] Single-pass statistics (web_app.py) +- [ ] Generator-based counting (serve.py) +- [ ] Deque rate limiting (serve.py) +- **Expected Impact:** 2-5x improvements in various endpoints + +### Phase 4: Low Priority (Ongoing) +- [ ] Minor optimizations as encountered during maintenance + +## Testing Requirements + +Each optimization must include: + +1. **Unit tests** verifying correctness +2. **Performance benchmarks** measuring actual speedup +3. **Integration tests** ensuring no regressions +4. **Documentation** of changes and rationale + +Test infrastructure exists at: +- `tests/test_performance_optimizations.py` - Performance test patterns +- `scripts/test_runner.py` - Test orchestration + +## Risk Assessment + +| Risk Level | Count | Notes | +|------------|-------|-------| +| Very Low | 5 | Pure optimizations, no behavior change | +| Low | 3 | Include fallback mechanisms | +| Medium | 0 | None identified | +| High | 0 | None identified | + +**Overall Risk:** Low - All optimizations follow established patterns and include safety measures + +## Related Documentation + +- **Full Analysis:** `docs/CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md` +- **Implementation Guide:** `docs/OPTIMIZATION_QUICK_GUIDE.md` +- **Previous Work:** `docs/PERFORMANCE_IMPROVEMENTS.md` +- **Test Patterns:** `tests/test_performance_optimizations.py` + +## Success Metrics + +After implementing all optimizations: + +- Quantum training time reduced by 10-100x +- Dashboard response time reduced by 5-10x +- Query processing time reduced by 3-30x +- Memory usage reduced by 5-50MB (file caching offset by list elimination) +- No functional regressions +- All tests passing + +## Next Steps + +1. **Review** findings with repository maintainers +2. **Prioritize** based on user impact and development resources +3. **Implement** Phase 1 (critical fixes) first +4. **Test** thoroughly with realistic workloads +5. **Monitor** performance improvements in production +6. **Document** lessons learned for future optimizations + +## Questions for Maintainers + +1. **Quantum ML:** Is `qml.grad()` compatible with current PennyLane version? +2. **File Caching:** Is 5-second TTL appropriate for dashboard refresh rate? +3. **Keyword Sets:** Are there additional keyword lists to optimize? +4. **Testing:** Should we add load testing for dashboard endpoints? +5. **Rollout:** Prefer gradual rollout or all optimizations at once? + +--- + +**Generated:** February 17, 2026 +**Analyst:** Claude (AI Assistant) +**Status:** Ready for Review diff --git a/docs/PERFORMANCE_IMPLEMENTATION_GUIDE.md b/docs/PERFORMANCE_IMPLEMENTATION_GUIDE.md index 3c516f641..9a4b41c2d 100644 --- a/docs/PERFORMANCE_IMPLEMENTATION_GUIDE.md +++ b/docs/PERFORMANCE_IMPLEMENTATION_GUIDE.md @@ -1,694 +1,694 @@ -# Performance Optimization Implementation Guide - -This guide provides step-by-step instructions for implementing the performance improvements identified in `PERFORMANCE_ANALYSIS.md`. - ---- - -## 🎯 Implementation Roadmap - -### Week 1: Critical Fixes -- **Fix #1**: Aria keyword lookups (aria_web/server.py) -- **Fix #3**: Batch evaluator indexing (scripts/batch_evaluator.py) -- **Fix #6**: Remove redundant iteration (scripts/batch_evaluator.py) - -### Week 2-3: High-Priority Fixes -- **Fix #2**: Batch embedding API (shared/chat_memory.py) -- **Fix #7**: Refactor complex functions (function_app.py) -- **Fix #11**: Add caching (function_app.py ai_status) - -### Month 2: Remaining Fixes -- **Fix #4, #5, #12**: Training analytics improvements -- **Fix #8**: Vectorize similarity search -- **Fixes #9-10, #13-15**: Minor optimizations - ---- - -## 🔧 Detailed Implementation Instructions - -### Critical Fix #1: Aria Keyword Lookups - -**File:** `aria_web/server.py` -**Lines:** 496-521 -**Estimated time:** 30 minutes -**Risk:** Low (pure optimization, same logic) - -#### Step 1: Add module-level constants - -Add this near the top of the file, after imports: - -```python -# Performance optimization: Pre-compile action keywords -# Used by _extract_action_position() - converts ~20 list creations per call to 0 -_ACTION_KEYWORDS = { - 'jump': (('jump', 'leap', 'hop'), '[aria:position:50:60]'), - 'dance': (('dance', 'spin', 'twirl'), '[aria:position:50:50]'), - 'wave': (('wave', 'greet', 'hello', 'hi'), '[aria:position:30:70]'), - 'look': (('look', 'see', 'watch', 'observe'), None), # Special handling - 'sit': (('sit', 'rest', 'relax'), None), # Special handling - 'run': (('run', 'race', 'sprint'), '[aria:position:85:70]'), - 'hide': (('hide', 'crouch', 'duck'), '[aria:position:10:75]'), - 'present': (('present', 'show', 'display'), '[aria:position:50:50]'), - 'think': (('think', 'wonder', 'ponder'), '[aria:position:25:50]'), - 'walk_left': (('walk left', 'go left', 'left'), '[aria:position:20:70]'), - 'walk_right': (('walk right', 'go right', 'right'), '[aria:position:80:70]'), -} -``` - -#### Step 2: Refactor _extract_action_position function - -Replace lines 496-521 with: - -```python -def _extract_action_position(cmd: str, world_state: Dict) -> Optional[str]: - """Extract position from command (optimized). - - Uses pre-compiled keyword tuples to avoid creating lists on every call. - """ - # Get table position for context-dependent positioning - table_pos = {'x': 60, 'y': 50} # Default - for obj_name, obj_data in world_state.get('objects', {}).items(): - if 'table' in obj_name.lower(): - if isinstance(obj_data, dict) and 'position' in obj_data: - table_pos = obj_data['position'] - break - - # Check for objects in command (pickup/drop context) - for obj_name, obj_data in world_state.get('objects', {}).items(): - if obj_name.lower() in cmd: - if any(word in cmd for word in ('pick', 'get', 'grab', 'take')): - obj_pos = obj_data.get('position', {}) - if isinstance(obj_pos, dict) and 'x' in obj_pos and 'y' in obj_pos: - return f'[aria:position:{max(10, obj_pos["x"] - 10)}:{obj_pos["y"] + 10}]' - - # Action-based positioning (optimized with pre-compiled keywords) - for action, (keywords, position) in _ACTION_KEYWORDS.items(): - if any(k in cmd for k in keywords): - # Handle special cases - if action == 'look' and 'table' in cmd: - return '[aria:position:40:60]' - elif action == 'look': - return '[aria:position:20:40]' - elif action == 'sit': - return f'[aria:position:{table_pos["x"] - 5}:{table_pos["y"] + 35}]' - - # Return standard position - if position: - return position - - # Handle add/create commands - if any(word in cmd for word in ('add', 'create', 'spawn')): - return f'[aria:position:{table_pos["x"] - 15}:{table_pos["y"] + 20}]' - - # Default: context-aware positioning - import hashlib - pos_hash = int(hashlib.md5(cmd.encode()).hexdigest()[:4], 16) - x = 30 + (pos_hash % 40) - y = 50 + ((pos_hash // 40) % 30) - return f'[aria:position:{x}:{y}]' -``` - -#### Step 3: Test the changes - -```python -# Add test function (temporary, for verification) -def test_keyword_performance(): - """Test keyword lookup performance.""" - import time - - test_commands = [ - "jump high", "dance around", "wave hello", "look at table", - "sit down", "run fast", "hide quickly", "think deeply" - ] * 100 # 800 total commands - - world_state = {'objects': {'table': {'position': {'x': 60, 'y': 50}}}} - - start = time.perf_counter() - for cmd in test_commands: - _extract_action_position(cmd, world_state) - elapsed = time.perf_counter() - start - - print(f"Processed {len(test_commands)} commands in {elapsed:.3f}s") - print(f"Average: {elapsed/len(test_commands)*1000:.2f}ms per command") - -# Run test -test_keyword_performance() -``` - -Expected output: ~1-2ms per command (vs 3-4ms before) - ---- - -### Critical Fix #3: Batch Evaluator Indexing - -**File:** `scripts/batch_evaluator.py` -**Lines:** 305-312 -**Estimated time:** 20 minutes -**Risk:** Low (backward compatible) - -#### Step 1: Add index to __init__ - -Modify the `__init__` method: - -```python -class BatchEvaluator: - def __init__(self): - self.results: List[EvaluationResult] = [] - # Performance optimization: O(1) model lookup - self._results_index: Dict[str, EvaluationResult] = {} - self._results_cache: Dict[str, Dict] = {} # Existing cache -``` - -#### Step 2: Update add_result to maintain index - -Modify `add_result` method: - -```python -def add_result(self, result: EvaluationResult): - """Add evaluation result and update index.""" - self.results.append(result) - # Update index for O(1) lookup - self._results_index[result.model_id] = result - # Update cache - self._results_cache[result.model_id] = result.to_dict() -``` - -#### Step 3: Optimize compare_models - -Replace lines 305-312: - -```python -def compare_models(self, model_ids: List[str]) -> Dict: - """Compare specific models side-by-side (optimized O(1) lookup).""" - # O(1) lookup per model instead of O(n) linear search - comparison = [ - self._results_index[model_id] - for model_id in model_ids - if model_id in self._results_index - ] - - return { - "models": [r.model_id for r in comparison], - "comparison": [ - { - "model_id": r.model_id, - "model_type": r.model_type, - "status": r.status, - "metrics": r.metrics, - "duration": r.duration, - "error": r.error, - } - for r in comparison - ] - } -``` - -#### Step 4: Test the changes - -```python -# Add performance test -def test_compare_performance(): - """Test model comparison performance.""" - import time - from dataclasses import dataclass - - evaluator = BatchEvaluator() - - # Add 1000 mock results - for i in range(1000): - result = EvaluationResult( - model_id=f"model_{i}", - model_type="lora", - status="succeeded", - metrics={"accuracy": 0.85}, - duration=10.0, - error=None - ) - evaluator.add_result(result) - - # Compare 100 models - model_ids = [f"model_{i}" for i in range(0, 1000, 10)] - - start = time.perf_counter() - for _ in range(100): # 100 iterations - evaluator.compare_models(model_ids) - elapsed = time.perf_counter() - start - - print(f"100 comparisons in {elapsed:.3f}s") - print(f"Average: {elapsed/100*1000:.2f}ms per comparison") -``` - -Expected: <1ms per comparison (vs 10-50ms before for large result sets) - ---- - -### Critical Fix #6: Remove Redundant Iteration - -**File:** `scripts/batch_evaluator.py` -**Lines:** 287 -**Estimated time:** 5 minutes -**Risk:** None (just removes duplication) - -#### Step 1: Locate the redundant line - -Find line 287 in `aggregate_results()`: - -```python -# Line 287 - REMOVE THIS LINE -failed = [r for r in self.results if r.status != "succeeded"] -``` - -#### Step 2: Verify the list is already built - -Check lines 230-236 - this is where `failed` is correctly built: - -```python -# Lines 230-236 - This is correct, keep this -for r in self.results: - total_duration += r.duration - if r.status == "succeeded": - succeeded.append(r) - else: - failed.append(r) -``` - -#### Step 3: Remove the redundant line - -Simply delete line 287. The `failed` list is already correct. - -#### Step 4: Update uses of failed list - -If line 287 is after other code that uses `failed`, ensure the first definition (lines 230-236) includes all necessary fields. - ---- - -### High-Priority Fix #2: Batch Embedding API - -**File:** `shared/chat_memory.py` -**Lines:** 151-175 -**Estimated time:** 1 hour -**Risk:** Medium (requires testing) - -#### Step 1: Add batch storage function - -Add this new function before `store_embedding`: - -```python -def store_embeddings_batch(embeddings: List[Tuple[str, Sequence[float], str]]) -> int: - """Store multiple embeddings in a single transaction (bulk insert). - - Args: - embeddings: List of (message_id, embedding, model) tuples - - Returns: - Number of embeddings successfully stored - - Performance: 5-10x faster than individual inserts due to: - - Single connection/transaction - - Bulk executemany() operation - - Reduced network round-trips - """ - if not embeddings: - return 0 - - conn = _get_conn() - if not conn: - return 0 - - try: - cursor = conn.cursor() - - # Prepare batch values - values = [] - for message_id, embedding, model in embeddings: - if not message_id or not embedding: - continue - blob = _serialize_f32(embedding) - values.append(( - message_id, - model or "unknown-model", - len(embedding), - blob - )) - - if not values: - return 0 - - # Bulk insert - single transaction - cursor.executemany( - """INSERT INTO dbo.ChatMessageEmbeddings - (MessageId, EmbeddingModel, EmbeddingDim, EmbeddingVector) - VALUES (?,?,?,?)""", - values - ) - conn.commit() - return len(values) - - except Exception as e: - print(f"Batch embedding storage failed: {e}") - return 0 - finally: - try: - conn.close() - except Exception: - pass -``` - -#### Step 2: Update single-insert function - -Modify `store_embedding` to use batch API: - -```python -def store_embedding(message_id: Optional[str], embedding: Sequence[float], model: str) -> bool: - """Store single embedding (backward compatible wrapper). - - For multiple embeddings, use store_embeddings_batch() for better performance. - """ - if not message_id or not embedding: - return False - - return store_embeddings_batch([(message_id, embedding, model)]) == 1 -``` - -#### Step 3: Update call sites (if any) - -Search for places that store multiple embeddings: - -```bash -grep -r "store_embedding" --include="*.py" | grep "for\|loop" -``` - -If found, update to use batch API: - -```python -# Before (slow) -for message_id, embedding in embeddings_to_store: - store_embedding(message_id, embedding, model) - -# After (fast) -batch = [(msg_id, emb, model) for msg_id, emb in embeddings_to_store] -store_embeddings_batch(batch) -``` - ---- - -### High-Priority Fix #11: Cache Venv Info - -**File:** `function_app.py` -**Lines:** 1091-1100 -**Estimated time:** 30 minutes -**Risk:** Low (caching with TTL) - -#### Step 1: Add caching helper - -Add this near the top of function_app.py, after imports: - -```python -import time -from functools import lru_cache - -# Cache TTL: 5 minutes (300 seconds) -_VENV_INFO_CACHE_TTL = 300 -``` - -#### Step 2: Create cached venv info function - -Add this before the `ai_status` function: - -```python -@lru_cache(maxsize=1) -def _get_venv_info_cached(venv_path: str, cache_slot: int) -> Dict: - """Get venv info with TTL-based caching. - - Args: - venv_path: Path to venv python executable - cache_slot: Time-based cache key (changes every TTL seconds) - - Returns: - Venv info dict - - Performance: Avoids expensive subprocess calls when cached. - Cache invalidates every 5 minutes via cache_slot parameter. - """ - venv_python = Path(venv_path) - venv_info = { - "path": str(venv_python), - "exists": venv_python.exists(), - "packages": {}, - "error": None - } - - if not venv_info["exists"]: - return venv_info - - try: - code = ( - "import json, importlib.util, importlib.metadata as md;" - "mods=['torch','transformers','peft'];" - "avail={m:(importlib.util.find_spec(m) is not None) for m in mods};" - "vers={};" - "\nfor m in mods:\n\t" - "\n\ttry:\n\t\tvers[m]=md.version(m)\n\texcept Exception:\n\t\tvers[m]=None;" - "print(json.dumps({'available':avail,'versions':vers}))" - ) - proc = subprocess.run( - [str(venv_python), "-c", code], - capture_output=True, - text=True, - timeout=12 - ) - - if proc.returncode == 0: - data = json.loads(proc.stdout.strip() or "{}") - venv_info["packages"] = data - else: - venv_info["error"] = proc.stderr.strip() or f"exit {proc.returncode}" - - except Exception as e: - venv_info["error"] = str(e) - - return venv_info -``` - -#### Step 3: Update ai_status to use cache - -Replace lines 1075-1100 in `ai_status`: - -```python -# Venv info (cached for 5 minutes) -repo_root = Path(__file__).resolve().parent -venv_python = repo_root / "venv" / "Scripts" / "python.exe" - -# Calculate cache slot (changes every 5 minutes) -current_cache_slot = int(time.time() / _VENV_INFO_CACHE_TTL) -venv_info = _get_venv_info_cached(str(venv_python), current_cache_slot) -``` - -#### Step 4: Test caching behavior - -```python -# Test cache performance -import time - -def test_venv_cache(): - """Test venv info caching.""" - # First call (cache miss) - start = time.perf_counter() - info1 = _get_venv_info_cached("/path/to/venv/python.exe", 1) - elapsed1 = time.perf_counter() - start - - # Second call (cache hit) - start = time.perf_counter() - info2 = _get_venv_info_cached("/path/to/venv/python.exe", 1) - elapsed2 = time.perf_counter() - start - - print(f"First call (miss): {elapsed1:.3f}s") - print(f"Second call (hit): {elapsed2:.3f}s") - print(f"Speedup: {elapsed1/elapsed2:.0f}x") - -test_venv_cache() -``` - -Expected: First call ~0.5-2s, second call <0.001s (1000x faster) - ---- - -## 🧪 Testing Strategy - -### Unit Tests - -Create `tests/test_performance_optimizations.py`: - -```python -import pytest -import time -from scripts.batch_evaluator import BatchEvaluator, EvaluationResult -from aria_web.server import _extract_action_position -from shared.chat_memory import store_embeddings_batch - -class TestPerformanceOptimizations: - """Test performance improvements.""" - - def test_batch_evaluator_indexing(self): - """Test O(1) model lookup.""" - evaluator = BatchEvaluator() - - # Add 100 results - for i in range(100): - result = EvaluationResult( - model_id=f"model_{i}", - model_type="test", - status="succeeded", - metrics={"acc": 0.9}, - duration=1.0 - ) - evaluator.add_result(result) - - # Test lookup speed - start = time.perf_counter() - result = evaluator.compare_models([f"model_{i}" for i in range(50)]) - elapsed = time.perf_counter() - start - - assert len(result["models"]) == 50 - assert elapsed < 0.01 # Should be < 10ms - - def test_keyword_lookup_optimization(self): - """Test Aria keyword lookup performance.""" - world_state = {"objects": {}} - - commands = ["jump", "dance", "wave"] * 100 - - start = time.perf_counter() - for cmd in commands: - pos = _extract_action_position(cmd, world_state) - assert pos is not None - elapsed = time.perf_counter() - start - - # Should process 300 commands in < 100ms - assert elapsed < 0.1 - - def test_batch_embedding_storage(self): - """Test batch embedding API.""" - embeddings = [ - (f"msg_{i}", [0.1] * 128, "test-model") - for i in range(10) - ] - - start = time.perf_counter() - count = store_embeddings_batch(embeddings) - elapsed = time.perf_counter() - start - - assert count == 10 - # Batch should be faster than 10 * single-insert time - assert elapsed < 1.0 # Reasonable upper bound -``` - -### Integration Tests - -Run existing tests to ensure no regressions: - -```bash -# Run full test suite -python scripts/test_runner.py --all - -# Run specific performance tests -pytest tests/test_performance_optimizations.py -v - -# Run with profiling -python -m cProfile -s cumulative scripts/batch_evaluator.py > profile.txt -``` - ---- - -## 📊 Benchmarking - -Create `scripts/benchmark_optimizations.py`: - -```python -#!/usr/bin/env python3 -"""Benchmark performance optimizations.""" - -import time -import statistics -from typing import Callable, List - -def benchmark( - name: str, - func: Callable, - *args, - iterations: int = 100, - warmup: int = 10 -) -> float: - """Benchmark a function.""" - # Warm-up - for _ in range(warmup): - func(*args) - - # Measure - times = [] - for _ in range(iterations): - start = time.perf_counter() - func(*args) - elapsed = time.perf_counter() - start - times.append(elapsed) - - mean_time = statistics.mean(times) - std_dev = statistics.stdev(times) if len(times) > 1 else 0 - - print(f"{name}:") - print(f" Mean: {mean_time*1000:.3f}ms") - print(f" StdDev: {std_dev*1000:.3f}ms") - print(f" Min: {min(times)*1000:.3f}ms") - print(f" Max: {max(times)*1000:.3f}ms") - - return mean_time - -if __name__ == "__main__": - print("Performance Optimization Benchmarks") - print("=" * 60) - - # Add benchmarks for each optimization - # ... (use examples from above) -``` - ---- - -## 🔍 Validation Checklist - -Before merging each optimization: - -- [ ] Unit tests pass -- [ ] Integration tests pass -- [ ] Benchmark shows improvement (>10% faster) -- [ ] No regressions in existing functionality -- [ ] Code review completed -- [ ] Documentation updated -- [ ] Memory usage acceptable -- [ ] Error handling preserved - ---- - -## 📝 Rollback Plan - -If an optimization causes issues: - -1. **Immediate rollback**: `git revert ` -2. **Investigate root cause**: Check logs, profiling data -3. **Fix and re-test**: Address issue in separate branch -4. **Re-deploy**: With additional tests - ---- - -## 🎓 Learning Resources - -- [Python Performance Tips](https://wiki.python.org/moin/PythonSpeed/PerformanceTips) -- [Python Profiling](https://docs.python.org/3/library/profile.html) -- [High Performance Python Book](https://www.oreilly.com/library/view/high-performance-python/9781492055013/) - ---- - -**Next Steps:** -1. Choose which fixes to implement first -2. Create feature branch: `git checkout -b perf/critical-fixes` -3. Implement fixes one at a time -4. Test thoroughly -5. Submit PR with benchmarks +# Performance Optimization Implementation Guide + +This guide provides step-by-step instructions for implementing the performance improvements identified in `PERFORMANCE_ANALYSIS.md`. + +--- + +## 🎯 Implementation Roadmap + +### Week 1: Critical Fixes +- **Fix #1**: Aria keyword lookups (aria_web/server.py) +- **Fix #3**: Batch evaluator indexing (scripts/batch_evaluator.py) +- **Fix #6**: Remove redundant iteration (scripts/batch_evaluator.py) + +### Week 2-3: High-Priority Fixes +- **Fix #2**: Batch embedding API (shared/chat_memory.py) +- **Fix #7**: Refactor complex functions (function_app.py) +- **Fix #11**: Add caching (function_app.py ai_status) + +### Month 2: Remaining Fixes +- **Fix #4, #5, #12**: Training analytics improvements +- **Fix #8**: Vectorize similarity search +- **Fixes #9-10, #13-15**: Minor optimizations + +--- + +## 🔧 Detailed Implementation Instructions + +### Critical Fix #1: Aria Keyword Lookups + +**File:** `aria_web/server.py` +**Lines:** 496-521 +**Estimated time:** 30 minutes +**Risk:** Low (pure optimization, same logic) + +#### Step 1: Add module-level constants + +Add this near the top of the file, after imports: + +```python +# Performance optimization: Pre-compile action keywords +# Used by _extract_action_position() - converts ~20 list creations per call to 0 +_ACTION_KEYWORDS = { + 'jump': (('jump', 'leap', 'hop'), '[aria:position:50:60]'), + 'dance': (('dance', 'spin', 'twirl'), '[aria:position:50:50]'), + 'wave': (('wave', 'greet', 'hello', 'hi'), '[aria:position:30:70]'), + 'look': (('look', 'see', 'watch', 'observe'), None), # Special handling + 'sit': (('sit', 'rest', 'relax'), None), # Special handling + 'run': (('run', 'race', 'sprint'), '[aria:position:85:70]'), + 'hide': (('hide', 'crouch', 'duck'), '[aria:position:10:75]'), + 'present': (('present', 'show', 'display'), '[aria:position:50:50]'), + 'think': (('think', 'wonder', 'ponder'), '[aria:position:25:50]'), + 'walk_left': (('walk left', 'go left', 'left'), '[aria:position:20:70]'), + 'walk_right': (('walk right', 'go right', 'right'), '[aria:position:80:70]'), +} +``` + +#### Step 2: Refactor _extract_action_position function + +Replace lines 496-521 with: + +```python +def _extract_action_position(cmd: str, world_state: Dict) -> Optional[str]: + """Extract position from command (optimized). + + Uses pre-compiled keyword tuples to avoid creating lists on every call. + """ + # Get table position for context-dependent positioning + table_pos = {'x': 60, 'y': 50} # Default + for obj_name, obj_data in world_state.get('objects', {}).items(): + if 'table' in obj_name.lower(): + if isinstance(obj_data, dict) and 'position' in obj_data: + table_pos = obj_data['position'] + break + + # Check for objects in command (pickup/drop context) + for obj_name, obj_data in world_state.get('objects', {}).items(): + if obj_name.lower() in cmd: + if any(word in cmd for word in ('pick', 'get', 'grab', 'take')): + obj_pos = obj_data.get('position', {}) + if isinstance(obj_pos, dict) and 'x' in obj_pos and 'y' in obj_pos: + return f'[aria:position:{max(10, obj_pos["x"] - 10)}:{obj_pos["y"] + 10}]' + + # Action-based positioning (optimized with pre-compiled keywords) + for action, (keywords, position) in _ACTION_KEYWORDS.items(): + if any(k in cmd for k in keywords): + # Handle special cases + if action == 'look' and 'table' in cmd: + return '[aria:position:40:60]' + elif action == 'look': + return '[aria:position:20:40]' + elif action == 'sit': + return f'[aria:position:{table_pos["x"] - 5}:{table_pos["y"] + 35}]' + + # Return standard position + if position: + return position + + # Handle add/create commands + if any(word in cmd for word in ('add', 'create', 'spawn')): + return f'[aria:position:{table_pos["x"] - 15}:{table_pos["y"] + 20}]' + + # Default: context-aware positioning + import hashlib + pos_hash = int(hashlib.md5(cmd.encode()).hexdigest()[:4], 16) + x = 30 + (pos_hash % 40) + y = 50 + ((pos_hash // 40) % 30) + return f'[aria:position:{x}:{y}]' +``` + +#### Step 3: Test the changes + +```python +# Add test function (temporary, for verification) +def test_keyword_performance(): + """Test keyword lookup performance.""" + import time + + test_commands = [ + "jump high", "dance around", "wave hello", "look at table", + "sit down", "run fast", "hide quickly", "think deeply" + ] * 100 # 800 total commands + + world_state = {'objects': {'table': {'position': {'x': 60, 'y': 50}}}} + + start = time.perf_counter() + for cmd in test_commands: + _extract_action_position(cmd, world_state) + elapsed = time.perf_counter() - start + + print(f"Processed {len(test_commands)} commands in {elapsed:.3f}s") + print(f"Average: {elapsed/len(test_commands)*1000:.2f}ms per command") + +# Run test +test_keyword_performance() +``` + +Expected output: ~1-2ms per command (vs 3-4ms before) + +--- + +### Critical Fix #3: Batch Evaluator Indexing + +**File:** `scripts/batch_evaluator.py` +**Lines:** 305-312 +**Estimated time:** 20 minutes +**Risk:** Low (backward compatible) + +#### Step 1: Add index to __init__ + +Modify the `__init__` method: + +```python +class BatchEvaluator: + def __init__(self): + self.results: List[EvaluationResult] = [] + # Performance optimization: O(1) model lookup + self._results_index: Dict[str, EvaluationResult] = {} + self._results_cache: Dict[str, Dict] = {} # Existing cache +``` + +#### Step 2: Update add_result to maintain index + +Modify `add_result` method: + +```python +def add_result(self, result: EvaluationResult): + """Add evaluation result and update index.""" + self.results.append(result) + # Update index for O(1) lookup + self._results_index[result.model_id] = result + # Update cache + self._results_cache[result.model_id] = result.to_dict() +``` + +#### Step 3: Optimize compare_models + +Replace lines 305-312: + +```python +def compare_models(self, model_ids: List[str]) -> Dict: + """Compare specific models side-by-side (optimized O(1) lookup).""" + # O(1) lookup per model instead of O(n) linear search + comparison = [ + self._results_index[model_id] + for model_id in model_ids + if model_id in self._results_index + ] + + return { + "models": [r.model_id for r in comparison], + "comparison": [ + { + "model_id": r.model_id, + "model_type": r.model_type, + "status": r.status, + "metrics": r.metrics, + "duration": r.duration, + "error": r.error, + } + for r in comparison + ] + } +``` + +#### Step 4: Test the changes + +```python +# Add performance test +def test_compare_performance(): + """Test model comparison performance.""" + import time + from dataclasses import dataclass + + evaluator = BatchEvaluator() + + # Add 1000 mock results + for i in range(1000): + result = EvaluationResult( + model_id=f"model_{i}", + model_type="lora", + status="succeeded", + metrics={"accuracy": 0.85}, + duration=10.0, + error=None + ) + evaluator.add_result(result) + + # Compare 100 models + model_ids = [f"model_{i}" for i in range(0, 1000, 10)] + + start = time.perf_counter() + for _ in range(100): # 100 iterations + evaluator.compare_models(model_ids) + elapsed = time.perf_counter() - start + + print(f"100 comparisons in {elapsed:.3f}s") + print(f"Average: {elapsed/100*1000:.2f}ms per comparison") +``` + +Expected: <1ms per comparison (vs 10-50ms before for large result sets) + +--- + +### Critical Fix #6: Remove Redundant Iteration + +**File:** `scripts/batch_evaluator.py` +**Lines:** 287 +**Estimated time:** 5 minutes +**Risk:** None (just removes duplication) + +#### Step 1: Locate the redundant line + +Find line 287 in `aggregate_results()`: + +```python +# Line 287 - REMOVE THIS LINE +failed = [r for r in self.results if r.status != "succeeded"] +``` + +#### Step 2: Verify the list is already built + +Check lines 230-236 - this is where `failed` is correctly built: + +```python +# Lines 230-236 - This is correct, keep this +for r in self.results: + total_duration += r.duration + if r.status == "succeeded": + succeeded.append(r) + else: + failed.append(r) +``` + +#### Step 3: Remove the redundant line + +Simply delete line 287. The `failed` list is already correct. + +#### Step 4: Update uses of failed list + +If line 287 is after other code that uses `failed`, ensure the first definition (lines 230-236) includes all necessary fields. + +--- + +### High-Priority Fix #2: Batch Embedding API + +**File:** `shared/chat_memory.py` +**Lines:** 151-175 +**Estimated time:** 1 hour +**Risk:** Medium (requires testing) + +#### Step 1: Add batch storage function + +Add this new function before `store_embedding`: + +```python +def store_embeddings_batch(embeddings: List[Tuple[str, Sequence[float], str]]) -> int: + """Store multiple embeddings in a single transaction (bulk insert). + + Args: + embeddings: List of (message_id, embedding, model) tuples + + Returns: + Number of embeddings successfully stored + + Performance: 5-10x faster than individual inserts due to: + - Single connection/transaction + - Bulk executemany() operation + - Reduced network round-trips + """ + if not embeddings: + return 0 + + conn = _get_conn() + if not conn: + return 0 + + try: + cursor = conn.cursor() + + # Prepare batch values + values = [] + for message_id, embedding, model in embeddings: + if not message_id or not embedding: + continue + blob = _serialize_f32(embedding) + values.append(( + message_id, + model or "unknown-model", + len(embedding), + blob + )) + + if not values: + return 0 + + # Bulk insert - single transaction + cursor.executemany( + """INSERT INTO dbo.ChatMessageEmbeddings + (MessageId, EmbeddingModel, EmbeddingDim, EmbeddingVector) + VALUES (?,?,?,?)""", + values + ) + conn.commit() + return len(values) + + except Exception as e: + print(f"Batch embedding storage failed: {e}") + return 0 + finally: + try: + conn.close() + except Exception: + pass +``` + +#### Step 2: Update single-insert function + +Modify `store_embedding` to use batch API: + +```python +def store_embedding(message_id: Optional[str], embedding: Sequence[float], model: str) -> bool: + """Store single embedding (backward compatible wrapper). + + For multiple embeddings, use store_embeddings_batch() for better performance. + """ + if not message_id or not embedding: + return False + + return store_embeddings_batch([(message_id, embedding, model)]) == 1 +``` + +#### Step 3: Update call sites (if any) + +Search for places that store multiple embeddings: + +```bash +grep -r "store_embedding" --include="*.py" | grep "for\|loop" +``` + +If found, update to use batch API: + +```python +# Before (slow) +for message_id, embedding in embeddings_to_store: + store_embedding(message_id, embedding, model) + +# After (fast) +batch = [(msg_id, emb, model) for msg_id, emb in embeddings_to_store] +store_embeddings_batch(batch) +``` + +--- + +### High-Priority Fix #11: Cache Venv Info + +**File:** `function_app.py` +**Lines:** 1091-1100 +**Estimated time:** 30 minutes +**Risk:** Low (caching with TTL) + +#### Step 1: Add caching helper + +Add this near the top of function_app.py, after imports: + +```python +import time +from functools import lru_cache + +# Cache TTL: 5 minutes (300 seconds) +_VENV_INFO_CACHE_TTL = 300 +``` + +#### Step 2: Create cached venv info function + +Add this before the `ai_status` function: + +```python +@lru_cache(maxsize=1) +def _get_venv_info_cached(venv_path: str, cache_slot: int) -> Dict: + """Get venv info with TTL-based caching. + + Args: + venv_path: Path to venv python executable + cache_slot: Time-based cache key (changes every TTL seconds) + + Returns: + Venv info dict + + Performance: Avoids expensive subprocess calls when cached. + Cache invalidates every 5 minutes via cache_slot parameter. + """ + venv_python = Path(venv_path) + venv_info = { + "path": str(venv_python), + "exists": venv_python.exists(), + "packages": {}, + "error": None + } + + if not venv_info["exists"]: + return venv_info + + try: + code = ( + "import json, importlib.util, importlib.metadata as md;" + "mods=['torch','transformers','peft'];" + "avail={m:(importlib.util.find_spec(m) is not None) for m in mods};" + "vers={};" + "\nfor m in mods:\n\t" + "\n\ttry:\n\t\tvers[m]=md.version(m)\n\texcept Exception:\n\t\tvers[m]=None;" + "print(json.dumps({'available':avail,'versions':vers}))" + ) + proc = subprocess.run( + [str(venv_python), "-c", code], + capture_output=True, + text=True, + timeout=12 + ) + + if proc.returncode == 0: + data = json.loads(proc.stdout.strip() or "{}") + venv_info["packages"] = data + else: + venv_info["error"] = proc.stderr.strip() or f"exit {proc.returncode}" + + except Exception as e: + venv_info["error"] = str(e) + + return venv_info +``` + +#### Step 3: Update ai_status to use cache + +Replace lines 1075-1100 in `ai_status`: + +```python +# Venv info (cached for 5 minutes) +repo_root = Path(__file__).resolve().parent +venv_python = repo_root / "venv" / "Scripts" / "python.exe" + +# Calculate cache slot (changes every 5 minutes) +current_cache_slot = int(time.time() / _VENV_INFO_CACHE_TTL) +venv_info = _get_venv_info_cached(str(venv_python), current_cache_slot) +``` + +#### Step 4: Test caching behavior + +```python +# Test cache performance +import time + +def test_venv_cache(): + """Test venv info caching.""" + # First call (cache miss) + start = time.perf_counter() + info1 = _get_venv_info_cached("/path/to/venv/python.exe", 1) + elapsed1 = time.perf_counter() - start + + # Second call (cache hit) + start = time.perf_counter() + info2 = _get_venv_info_cached("/path/to/venv/python.exe", 1) + elapsed2 = time.perf_counter() - start + + print(f"First call (miss): {elapsed1:.3f}s") + print(f"Second call (hit): {elapsed2:.3f}s") + print(f"Speedup: {elapsed1/elapsed2:.0f}x") + +test_venv_cache() +``` + +Expected: First call ~0.5-2s, second call <0.001s (1000x faster) + +--- + +## 🧪 Testing Strategy + +### Unit Tests + +Create `tests/test_performance_optimizations.py`: + +```python +import pytest +import time +from scripts.batch_evaluator import BatchEvaluator, EvaluationResult +from aria_web.server import _extract_action_position +from shared.chat_memory import store_embeddings_batch + +class TestPerformanceOptimizations: + """Test performance improvements.""" + + def test_batch_evaluator_indexing(self): + """Test O(1) model lookup.""" + evaluator = BatchEvaluator() + + # Add 100 results + for i in range(100): + result = EvaluationResult( + model_id=f"model_{i}", + model_type="test", + status="succeeded", + metrics={"acc": 0.9}, + duration=1.0 + ) + evaluator.add_result(result) + + # Test lookup speed + start = time.perf_counter() + result = evaluator.compare_models([f"model_{i}" for i in range(50)]) + elapsed = time.perf_counter() - start + + assert len(result["models"]) == 50 + assert elapsed < 0.01 # Should be < 10ms + + def test_keyword_lookup_optimization(self): + """Test Aria keyword lookup performance.""" + world_state = {"objects": {}} + + commands = ["jump", "dance", "wave"] * 100 + + start = time.perf_counter() + for cmd in commands: + pos = _extract_action_position(cmd, world_state) + assert pos is not None + elapsed = time.perf_counter() - start + + # Should process 300 commands in < 100ms + assert elapsed < 0.1 + + def test_batch_embedding_storage(self): + """Test batch embedding API.""" + embeddings = [ + (f"msg_{i}", [0.1] * 128, "test-model") + for i in range(10) + ] + + start = time.perf_counter() + count = store_embeddings_batch(embeddings) + elapsed = time.perf_counter() - start + + assert count == 10 + # Batch should be faster than 10 * single-insert time + assert elapsed < 1.0 # Reasonable upper bound +``` + +### Integration Tests + +Run existing tests to ensure no regressions: + +```bash +# Run full test suite +python scripts/test_runner.py --all + +# Run specific performance tests +pytest tests/test_performance_optimizations.py -v + +# Run with profiling +python -m cProfile -s cumulative scripts/batch_evaluator.py > profile.txt +``` + +--- + +## 📊 Benchmarking + +Create `scripts/benchmark_optimizations.py`: + +```python +#!/usr/bin/env python3 +"""Benchmark performance optimizations.""" + +import time +import statistics +from typing import Callable, List + +def benchmark( + name: str, + func: Callable, + *args, + iterations: int = 100, + warmup: int = 10 +) -> float: + """Benchmark a function.""" + # Warm-up + for _ in range(warmup): + func(*args) + + # Measure + times = [] + for _ in range(iterations): + start = time.perf_counter() + func(*args) + elapsed = time.perf_counter() - start + times.append(elapsed) + + mean_time = statistics.mean(times) + std_dev = statistics.stdev(times) if len(times) > 1 else 0 + + print(f"{name}:") + print(f" Mean: {mean_time*1000:.3f}ms") + print(f" StdDev: {std_dev*1000:.3f}ms") + print(f" Min: {min(times)*1000:.3f}ms") + print(f" Max: {max(times)*1000:.3f}ms") + + return mean_time + +if __name__ == "__main__": + print("Performance Optimization Benchmarks") + print("=" * 60) + + # Add benchmarks for each optimization + # ... (use examples from above) +``` + +--- + +## 🔍 Validation Checklist + +Before merging each optimization: + +- [ ] Unit tests pass +- [ ] Integration tests pass +- [ ] Benchmark shows improvement (>10% faster) +- [ ] No regressions in existing functionality +- [ ] Code review completed +- [ ] Documentation updated +- [ ] Memory usage acceptable +- [ ] Error handling preserved + +--- + +## 📝 Rollback Plan + +If an optimization causes issues: + +1. **Immediate rollback**: `git revert ` +2. **Investigate root cause**: Check logs, profiling data +3. **Fix and re-test**: Address issue in separate branch +4. **Re-deploy**: With additional tests + +--- + +## 🎓 Learning Resources + +- [Python Performance Tips](https://wiki.python.org/moin/PythonSpeed/PerformanceTips) +- [Python Profiling](https://docs.python.org/3/library/profile.html) +- [High Performance Python Book](https://www.oreilly.com/library/view/high-performance-python/9781492055013/) + +--- + +**Next Steps:** +1. Choose which fixes to implement first +2. Create feature branch: `git checkout -b perf/critical-fixes` +3. Implement fixes one at a time +4. Test thoroughly +5. Submit PR with benchmarks diff --git a/docs/PERFORMANCE_IMPLEMENTATION_SUMMARY.md b/docs/PERFORMANCE_IMPLEMENTATION_SUMMARY.md index f4e4c36b4..115619cf9 100644 --- a/docs/PERFORMANCE_IMPLEMENTATION_SUMMARY.md +++ b/docs/PERFORMANCE_IMPLEMENTATION_SUMMARY.md @@ -1,246 +1,246 @@ -# Performance Optimization Implementation Summary - -**Date:** 2026-02-17 -**PR:** Improve Slow Code Efficiency -**Status:** ✅ Complete - -## Overview - -This PR identifies and implements improvements to slow or inefficient code in the Aria repository. The work resulted in **4.6x average performance improvement** across critical operations with significant memory savings. - -## Key Achievements - -### 1. Memory-Efficient File Operations - -**Problem:** Multiple scripts used `readlines()` to load entire log files into memory, causing excessive memory usage for large files (GB-sized logs). - -**Solution:** Implemented `collections.deque` with `maxlen` parameter for tail operations. - -**Results:** -- **1.9x faster** for typical log tailing -- **5.1 MB memory saved** per operation on 100K line files -- Scales linearly with file size (O(k) vs O(n) complexity) - -**Files Modified:** -- `scripts/monitor_autonomous_training.py` -- `dashboard/serve.py` - -### 2. Optimized JSON Parsing - -**Problem:** Extracting JSON metrics from command output required parsing all lines sequentially, even though metrics are typically at the end. - -**Solution:** Search from end using `rsplit()` and reversed iteration, limiting to last 50 lines. - -**Results:** -- **10.7x faster** for typical command output -- Avoids parsing thousands of unnecessary lines -- Graceful degradation with try/except per line - -**Files Modified:** -- `scripts/batch_evaluator.py` - -### 3. Reusable Performance Utilities - -**Created:** `shared/performance_utils.py` (285 lines, 7 utilities) - -**Functions:** -1. **`tail_file()`** - Memory-efficient log tailing with deque -2. **`tail_file_smart()`** - Adaptive strategy for small vs large files -3. **`stream_jsonl()`** - Generator-based JSONL processing with filtering -4. **`find_json_in_output()`** - Optimized JSON extraction from command output -5. **`FileCache`** - In-memory file cache with size limits -6. **`@timeit`** - Decorator for function timing -7. **`@memoize_with_ttl`** - Time-based memoization with TTL expiration - -**Benefits:** -- All utilities include docstrings, examples, and validation -- Tested with comprehensive example suite -- Ready for reuse across the codebase - -### 4. Performance Documentation - -**Created:** `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` (430+ lines) - -**Contents:** -- Recent optimizations with before/after examples -- Performance anti-patterns to avoid -- Best practices for memory, I/O, caching, async -- Monitoring and benchmarking guidelines -- Performance targets and thresholds - -### 5. Benchmark Suite - -**Created:** `scripts/benchmark_performance.py` - -**Demonstrates:** -- File tailing: 1.9x speedup -- JSON parsing: 10.7x speedup -- JSONL streaming: 1.1x speedup -- Average: 4.6x speedup - -**Usage:** -```bash -python scripts/benchmark_performance.py -``` - -## Validated Existing Optimizations - -The following components were already well-optimized and required no changes: - -1. **Database Connection Pooling** (`shared/sql_engine.py`) - - Connection pooling with pre-ping - - Pool recycling and saturation monitoring - - Health checks via `/api/ai/status` - -2. **Dataset Loading** (`scripts/expand_quantum_datasets.py`) - - Disk caching of downloads - - Single reads per file - - Proper error handling - -3. **Dataset Processing** (`AI/microsoft_phi-silica-3.6_v1/scripts/prepare_dataset.py`) - - Generator-based reading throughout - - Iterator patterns for memory efficiency - -4. **Smart File Reading** (`dashboard/app.py`) - - Adaptive strategy based on file size - - Block-based backward reading for large files - -## Performance Improvements by Category - -### Memory Optimization -- Log tailing: O(n) → O(k) complexity -- JSONL streaming: 1.2 MB saved per file -- Total: ~6+ MB saved per typical operation - -### Speed Optimization -- JSON parsing: 10.7x faster -- File tailing: 1.9x faster -- Overall: 4.6x average speedup - -### Code Quality -- Created 7 reusable utilities -- Added comprehensive documentation -- Established performance benchmarks - -## Testing & Validation - -### Unit Tests -All utilities validated with working examples: -```bash -python shared/performance_utils.py -# ✅ All examples completed successfully! -``` - -### Benchmarks -Performance improvements verified: -```bash -python scripts/benchmark_performance.py -# Average speedup: 4.6x -# Total time saved: 356.0% faster -``` - -### Import Tests -All modified scripts import successfully: -```bash -python -c "from monitor_autonomous_training import TrainingMonitor" -python -c "from batch_evaluator import BatchEvaluator" -# ✓ No errors -``` - -## Files Changed - -### Modified (3 files) -1. `scripts/monitor_autonomous_training.py` - Use `tail_file()` utility -2. `dashboard/serve.py` - Use `tail_file()` utility -3. `scripts/batch_evaluator.py` - Use `find_json_in_output()` utility - -### Created (3 files) -1. `shared/performance_utils.py` - Reusable performance utilities -2. `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` - Comprehensive guide -3. `scripts/benchmark_performance.py` - Performance validation suite - -## Usage Examples - -### Using Performance Utilities - -```python -from shared.performance_utils import ( - tail_file, stream_jsonl, find_json_in_output, - FileCache, timeit, memoize_with_ttl -) - -# Memory-efficient log tailing -logs = tail_file(Path("training.log"), max_lines=50) - -# Stream large JSONL files -for record in stream_jsonl(Path("data.jsonl")): - process(record) - -# Extract JSON from subprocess output -result = subprocess.run(['./script'], capture_output=True, text=True) -metrics = find_json_in_output(result.stdout, key='metrics') - -# Time expensive functions -@timeit -def expensive_operation(): - # work here - pass - -# Cache with TTL -@memoize_with_ttl(ttl_seconds=300) -def fetch_config(): - return load_config_from_disk() -``` - -## Impact Assessment - -### Immediate Benefits -- ✅ Reduced memory usage in monitoring scripts -- ✅ Faster JSON extraction in batch evaluations -- ✅ Reusable utilities available for all developers - -### Long-Term Benefits -- ✅ Comprehensive documentation for future optimizations -- ✅ Benchmark suite for regression testing -- ✅ Established patterns for performance-critical code - -### No Breaking Changes -- ✅ All changes are internal optimizations -- ✅ External APIs unchanged -- ✅ Backward compatible - -## Recommendations for Future Work - -### High Priority -1. Apply `stream_jsonl()` to other JSONL processing scripts -2. Use `@memoize_with_ttl` for config file loading -3. Add `@timeit` to identify new bottlenecks - -### Medium Priority -1. Implement async/await for concurrent I/O operations -2. Add connection pooling to external API clients -3. Profile CPU-bound operations for multiprocessing opportunities - -### Low Priority -1. Centralize configuration loading across scripts -2. Add more sophisticated caching strategies -3. Implement distributed caching for multi-node setups - -## Conclusion - -This PR successfully identifies and optimizes performance bottlenecks in the Aria codebase, achieving a **4.6x average speedup** with significant memory savings. The work includes: - -- ✅ 3 production files optimized -- ✅ 7 reusable utilities created -- ✅ 430+ lines of documentation -- ✅ Comprehensive benchmark suite -- ✅ All changes validated and tested - -The optimizations are **production-ready** and provide immediate benefits while establishing patterns for future performance work. - ---- - -**For questions or suggestions, see:** -- `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` - Complete guide -- `shared/performance_utils.py` - Utility documentation -- `scripts/benchmark_performance.py` - Validation benchmarks +# Performance Optimization Implementation Summary + +**Date:** 2026-02-17 +**PR:** Improve Slow Code Efficiency +**Status:** ✅ Complete + +## Overview + +This PR identifies and implements improvements to slow or inefficient code in the Aria repository. The work resulted in **4.6x average performance improvement** across critical operations with significant memory savings. + +## Key Achievements + +### 1. Memory-Efficient File Operations + +**Problem:** Multiple scripts used `readlines()` to load entire log files into memory, causing excessive memory usage for large files (GB-sized logs). + +**Solution:** Implemented `collections.deque` with `maxlen` parameter for tail operations. + +**Results:** +- **1.9x faster** for typical log tailing +- **5.1 MB memory saved** per operation on 100K line files +- Scales linearly with file size (O(k) vs O(n) complexity) + +**Files Modified:** +- `scripts/monitor_autonomous_training.py` +- `dashboard/serve.py` + +### 2. Optimized JSON Parsing + +**Problem:** Extracting JSON metrics from command output required parsing all lines sequentially, even though metrics are typically at the end. + +**Solution:** Search from end using `rsplit()` and reversed iteration, limiting to last 50 lines. + +**Results:** +- **10.7x faster** for typical command output +- Avoids parsing thousands of unnecessary lines +- Graceful degradation with try/except per line + +**Files Modified:** +- `scripts/batch_evaluator.py` + +### 3. Reusable Performance Utilities + +**Created:** `shared/performance_utils.py` (285 lines, 7 utilities) + +**Functions:** +1. **`tail_file()`** - Memory-efficient log tailing with deque +2. **`tail_file_smart()`** - Adaptive strategy for small vs large files +3. **`stream_jsonl()`** - Generator-based JSONL processing with filtering +4. **`find_json_in_output()`** - Optimized JSON extraction from command output +5. **`FileCache`** - In-memory file cache with size limits +6. **`@timeit`** - Decorator for function timing +7. **`@memoize_with_ttl`** - Time-based memoization with TTL expiration + +**Benefits:** +- All utilities include docstrings, examples, and validation +- Tested with comprehensive example suite +- Ready for reuse across the codebase + +### 4. Performance Documentation + +**Created:** `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` (430+ lines) + +**Contents:** +- Recent optimizations with before/after examples +- Performance anti-patterns to avoid +- Best practices for memory, I/O, caching, async +- Monitoring and benchmarking guidelines +- Performance targets and thresholds + +### 5. Benchmark Suite + +**Created:** `scripts/benchmark_performance.py` + +**Demonstrates:** +- File tailing: 1.9x speedup +- JSON parsing: 10.7x speedup +- JSONL streaming: 1.1x speedup +- Average: 4.6x speedup + +**Usage:** +```bash +python scripts/benchmark_performance.py +``` + +## Validated Existing Optimizations + +The following components were already well-optimized and required no changes: + +1. **Database Connection Pooling** (`shared/sql_engine.py`) + - Connection pooling with pre-ping + - Pool recycling and saturation monitoring + - Health checks via `/api/ai/status` + +2. **Dataset Loading** (`scripts/expand_quantum_datasets.py`) + - Disk caching of downloads + - Single reads per file + - Proper error handling + +3. **Dataset Processing** (`AI/microsoft_phi-silica-3.6_v1/scripts/prepare_dataset.py`) + - Generator-based reading throughout + - Iterator patterns for memory efficiency + +4. **Smart File Reading** (`dashboard/app.py`) + - Adaptive strategy based on file size + - Block-based backward reading for large files + +## Performance Improvements by Category + +### Memory Optimization +- Log tailing: O(n) → O(k) complexity +- JSONL streaming: 1.2 MB saved per file +- Total: ~6+ MB saved per typical operation + +### Speed Optimization +- JSON parsing: 10.7x faster +- File tailing: 1.9x faster +- Overall: 4.6x average speedup + +### Code Quality +- Created 7 reusable utilities +- Added comprehensive documentation +- Established performance benchmarks + +## Testing & Validation + +### Unit Tests +All utilities validated with working examples: +```bash +python shared/performance_utils.py +# ✅ All examples completed successfully! +``` + +### Benchmarks +Performance improvements verified: +```bash +python scripts/benchmark_performance.py +# Average speedup: 4.6x +# Total time saved: 356.0% faster +``` + +### Import Tests +All modified scripts import successfully: +```bash +python -c "from monitor_autonomous_training import TrainingMonitor" +python -c "from batch_evaluator import BatchEvaluator" +# ✓ No errors +``` + +## Files Changed + +### Modified (3 files) +1. `scripts/monitor_autonomous_training.py` - Use `tail_file()` utility +2. `dashboard/serve.py` - Use `tail_file()` utility +3. `scripts/batch_evaluator.py` - Use `find_json_in_output()` utility + +### Created (3 files) +1. `shared/performance_utils.py` - Reusable performance utilities +2. `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` - Comprehensive guide +3. `scripts/benchmark_performance.py` - Performance validation suite + +## Usage Examples + +### Using Performance Utilities + +```python +from shared.performance_utils import ( + tail_file, stream_jsonl, find_json_in_output, + FileCache, timeit, memoize_with_ttl +) + +# Memory-efficient log tailing +logs = tail_file(Path("training.log"), max_lines=50) + +# Stream large JSONL files +for record in stream_jsonl(Path("data.jsonl")): + process(record) + +# Extract JSON from subprocess output +result = subprocess.run(['./script'], capture_output=True, text=True) +metrics = find_json_in_output(result.stdout, key='metrics') + +# Time expensive functions +@timeit +def expensive_operation(): + # work here + pass + +# Cache with TTL +@memoize_with_ttl(ttl_seconds=300) +def fetch_config(): + return load_config_from_disk() +``` + +## Impact Assessment + +### Immediate Benefits +- ✅ Reduced memory usage in monitoring scripts +- ✅ Faster JSON extraction in batch evaluations +- ✅ Reusable utilities available for all developers + +### Long-Term Benefits +- ✅ Comprehensive documentation for future optimizations +- ✅ Benchmark suite for regression testing +- ✅ Established patterns for performance-critical code + +### No Breaking Changes +- ✅ All changes are internal optimizations +- ✅ External APIs unchanged +- ✅ Backward compatible + +## Recommendations for Future Work + +### High Priority +1. Apply `stream_jsonl()` to other JSONL processing scripts +2. Use `@memoize_with_ttl` for config file loading +3. Add `@timeit` to identify new bottlenecks + +### Medium Priority +1. Implement async/await for concurrent I/O operations +2. Add connection pooling to external API clients +3. Profile CPU-bound operations for multiprocessing opportunities + +### Low Priority +1. Centralize configuration loading across scripts +2. Add more sophisticated caching strategies +3. Implement distributed caching for multi-node setups + +## Conclusion + +This PR successfully identifies and optimizes performance bottlenecks in the Aria codebase, achieving a **4.6x average speedup** with significant memory savings. The work includes: + +- ✅ 3 production files optimized +- ✅ 7 reusable utilities created +- ✅ 430+ lines of documentation +- ✅ Comprehensive benchmark suite +- ✅ All changes validated and tested + +The optimizations are **production-ready** and provide immediate benefits while establishing patterns for future performance work. + +--- + +**For questions or suggestions, see:** +- `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` - Complete guide +- `shared/performance_utils.py` - Utility documentation +- `scripts/benchmark_performance.py` - Validation benchmarks diff --git a/docs/PERFORMANCE_IMPROVEMENTS.md b/docs/PERFORMANCE_IMPROVEMENTS.md index f9d43bd1b..e3c79b553 100644 --- a/docs/PERFORMANCE_IMPROVEMENTS.md +++ b/docs/PERFORMANCE_IMPROVEMENTS.md @@ -1,635 +1,635 @@ -# Performance Improvement Recommendations for QAI - -This document outlines identified performance bottlenecks and inefficient code patterns across the QAI codebase, along with specific recommendations for improvement. - -## Summary of Findings - -| Location | Issue | Severity | Status | -|----------|-------|----------|--------| -| `token_utils.py` | Repeated tokenizer instantiation | High | Fixed | -| `chat_memory.py` | Inefficient cosine similarity loop | Medium | Fixed | -| `chat_memory.py` | Repeated OpenAI client creation | Medium | Fixed | -| `validate_datasets.py` | Full file read into memory | Medium | Fixed | -| `chat_providers.py` | LM Studio health check on every auto-detect | Medium | Fixed | -| `aria_web/server.py` | 20+ list creations in keyword checks | **Critical** | **Fixed (2025-02-17)** | -| `extract_chat_logs_dataset.py` | Double traversal with any() | High | **Fixed (2025-02-17)** | -| `batch_evaluator.py` | O(n²) linear search in compare_models() | High | **Fixed (2025-02-17)** | -| `training_analytics.py` | String += in visualization loop | Medium | **Fixed (2025-02-17)** | -| `agi_provider.py` | String += for tag concatenation | Low | **Fixed (2025-02-17)** | -| `quantum_classifier.py` | Sequential batch processing | Medium | Documented | -| `function_app.py` | Repeated file existence checks | Low | Documented | - ---- - -## Recent Optimizations (February 2025) - -### 8. Aria Web Server - Repeated Keyword List Creation - -#### Location -`aria_web/server.py` - Multiple functions including `parse_with_fallback()`, `generate_aria_position()`, and `generate_tags_fallback()` - -#### Problem -Every command processed creates 20+ new list objects for keyword matching using `any(word in command for word in ['keyword1', 'keyword2', ...])`. This happens on the hot path for every user command. - -#### Before (Inefficient) -```python -# Lines 220, 236, 242, 250, 496-520, 580, 649-652, 673-707 -if any(word in command_lower for word in ['go', 'move', 'walk', 'run']): - # ... - -if any(word in command_lower for word in ['say', 'speak', 'tell', 'greet']): - # ... - -if any(k in cmd for k in ['jump', 'leap', 'hop']): - # ... -# ... repeated 20+ times throughout the file -``` - -#### After (Optimized with Pre-compiled Frozensets) -```python -# Module-level constants (lines 42-60) -MOVE_KEYWORDS = frozenset(['go', 'move', 'walk', 'run']) -SAY_KEYWORDS = frozenset(['say', 'speak', 'tell', 'greet']) -PICKUP_KEYWORDS = frozenset(['pick', 'get', 'grab', 'take']) -JUMP_KEYWORDS = frozenset(['jump', 'leap', 'hop']) -DANCE_KEYWORDS = frozenset(['dance', 'spin', 'twirl']) -WAVE_KEYWORDS = frozenset(['wave', 'greet', 'hello', 'hi']) -# ... 19 total keyword sets - -# Usage (lines 220+) -if any(word in command_lower for word in MOVE_KEYWORDS): - # ... - -if any(word in command_lower for word in SAY_KEYWORDS): - # ... -``` - -#### Impact -- **Before**: ~20+ list allocations per command (~200-400 bytes + allocation overhead) -- **After**: 0 allocations (frozensets created once at module load) -- **Performance**: 5-10x faster command parsing on hot path -- **Memory**: Constant memory usage vs. O(commands) temporary allocations - ---- - -### 9. Extract Chat Logs - Double List Traversal - -#### Location -`scripts/extract_chat_logs_dataset.py` - Line 72 in rolling window logic - -#### Problem -Two separate `any()` calls traverse the same window list to check for user and assistant roles, performing O(2n) work. - -#### Before (Inefficient) -```python -if any(x.get("role") == "user" for x in window) and any(x.get("role") == "assistant" for x in window): - examples.append({"messages": window}) -``` - -#### After (Optimized with Single-Pass Set Collection) -```python -# Single pass using set comprehension -roles = {x.get("role") for x in window} -if "user" in roles and "assistant" in roles: - examples.append({"messages": window}) -``` - -#### Impact -- **Before**: O(2n) - two complete passes over window -- **After**: O(n) - single pass with O(1) membership checks -- **Performance**: 2x faster dataset extraction -- **Benefit**: Scales linearly with window size (typically 2-10 messages) - ---- - -### 10. Batch Evaluator - O(n²) Linear Search - -#### Location -`scripts/batch_evaluator.py` - Line 310 in `compare_models()` method - -#### Problem -For each requested model ID, the code performs a linear search through all results using `next((r for r in self.results if r.model_id == model_id), None)`. This creates O(n×m) complexity where n is the number of results and m is the number of requested models. - -#### Before (Inefficient) -```python -def compare_models(self, model_ids: List[str]) -> Dict: - comparison = [] - - for model_id in model_ids: - result = next((r for r in self.results if r.model_id == model_id), None) - if result: - comparison.append(result) - # ... -``` - -#### After (Optimized with Dictionary Index) -```python -def compare_models(self, model_ids: List[str]) -> Dict: - # Build index for O(1) lookups - results_by_id = {r.model_id: r for r in self.results} - - comparison = [] - for model_id in model_ids: - result = results_by_id.get(model_id) - if result: - comparison.append(result) - # ... -``` - -#### Impact -- **Before**: O(n×m) nested iteration (~1000 comparisons for 100 results × 10 models) -- **After**: O(n + m) with O(1) lookups (~110 operations for same case) -- **Performance**: 100x faster for large model comparisons -- **Scalability**: Linear instead of quadratic growth - ---- - -### 11. Training Analytics - String Concatenation in Loop - -#### Location -`scripts/training_analytics.py` - Lines 233-239 in chart building - -#### Problem -String concatenation with `+=` in nested loop creates O(n²) memory reallocations for chart visualization. - -#### Before (Inefficient) -```python -for row in range(chart_height - 1, -1, -1): - line = " │" - for value in scaled: - if value >= row: - line += "█" - else: - line += " " - chart.append(line) -``` - -#### After (Optimized with List Accumulation) -```python -for row in range(chart_height - 1, -1, -1): - line_chars = [" │"] - for value in scaled: - if value >= row: - line_chars.append("█") - else: - line_chars.append(" ") - chart.append("".join(line_chars)) -``` - -#### Impact -- **Before**: O(n²) string reallocation (each += creates new string) -- **After**: O(n) list append + single join -- **Performance**: 10-100x faster for large visualizations -- **Example**: For 100-point chart × 20 rows: ~20,000 reallocations → ~2,000 operations - ---- - -### 12. AGI Provider - Tag Concatenation Optimization - -#### Location -`ai-projects/chat-cli/src/agi_provider.py` - Lines 697-701 in reflection improvement - -#### Problem -Multiple `response +=` operations for adding Aria movement tags. - -#### Before (Inefficient) -```python -if "left" in query_lower: - response += " [aria:walk:left]" -elif "right" in query_lower: - response += " [aria:walk:right]" -elif "jump" in query_lower: - response += " [aria:jump]" -``` - -#### After (Optimized) -```python -tag = None -if "left" in query_lower: - tag = " [aria:walk:left]" -elif "right" in query_lower: - tag = " [aria:walk:right]" -elif "jump" in query_lower: - tag = " [aria:jump]" - -if tag: - response = response + tag -``` - -#### Impact -- **Before**: Multiple string reallocations -- **After**: Single concatenation when needed -- **Performance**: 2-3x faster (minor impact as non-critical path) -- **Note**: Lower priority as this happens infrequently - ---- - -## 1. Token Utils - Repeated Tokenizer Instantiation - -### Location -`ai-projects/chat-cli/src/token_utils.py` - `_get_text_encoder()` function - -### Problem -Every call to `count_messages_tokens()` or `prune_messages()` creates a new tokenizer instance. For Hugging Face tokenizers, this involves: -- Loading vocabulary files from disk -- Compiling tokenizer rules -- Memory allocation for tokenizer state - -### Before (Inefficient) -```python -def _get_text_encoder(provider: str, model: Optional[str]) -> Callable[[str], int]: - # ... tokenizer creation happens on every call - if AutoTokenizer is not None and mdl: - try: - tok = AutoTokenizer.from_pretrained(model, use_fast=True) # SLOW! - def _count(text: str) -> int: - return len(tok.encode(text or "")) - return _count - except Exception: - pass -``` - -### After (Optimized with LRU Cache) -```python -from functools import lru_cache - -@lru_cache(maxsize=8) -def _get_cached_tokenizer(model: str): - """Cache tokenizer instances to avoid repeated loading.""" - if AutoTokenizer is not None: - try: - return AutoTokenizer.from_pretrained(model, use_fast=True) - except Exception: - pass - return None -``` - -### Impact -- **Before**: ~100-500ms per tokenizer load for Hugging Face models -- **After**: ~0.1ms (cache hit) - ---- - -## 2. Chat Memory - Inefficient Cosine Similarity Calculation - -### Location -`shared/chat_memory.py` - `_cosine()` and `fetch_similar_messages()` - -### Problem -The cosine similarity calculation uses list comprehensions and `sum()` which is slower than NumPy for larger vectors. When fetching similar messages, cosine similarity is computed in a tight loop. - -### Before (Inefficient) -```python -def _cosine(a: Sequence[float], b: Sequence[float]) -> float: - if not a or not b or len(a) != len(b): - return 0.0 - dot = sum(x * y for x, y in zip(a, b)) - na = math.sqrt(sum(x * x for x in a)) or 1.0 - nb = math.sqrt(sum(y * y for y in b)) or 1.0 - return dot / (na * nb) -``` - -### After (Optimized with NumPy when available) -```python -try: - import numpy as np - _HAS_NUMPY = True -except ImportError: - _HAS_NUMPY = False - -def _cosine(a: Sequence[float], b: Sequence[float]) -> float: - if not a or not b or len(a) != len(b): - return 0.0 - - if _HAS_NUMPY: - a_arr = np.asarray(a, dtype=np.float32) - b_arr = np.asarray(b, dtype=np.float32) - dot = np.dot(a_arr, b_arr) - na = np.linalg.norm(a_arr) - nb = np.linalg.norm(b_arr) - if na == 0.0 or nb == 0.0: - return 0.0 - return float(dot / (na * nb)) - - # Fallback to pure Python - dot = sum(x * y for x, y in zip(a, b)) - na = math.sqrt(sum(x * x for x in a)) - nb = math.sqrt(sum(y * y for y in b)) - if na == 0.0 or nb == 0.0: - return 0.0 - return dot / (na * nb) -``` - -### Impact -- **Before**: ~1.2ms for 500 embeddings × 256 dimensions -- **After**: ~0.15ms with NumPy (8x faster) - ---- - -## 3. Chat Memory - Repeated OpenAI Client Creation - -### Location -`shared/chat_memory.py` - `generate_embedding()` function - -### Problem -Creates a new OpenAI/AzureOpenAI client instance on every embedding request, incurring connection overhead. - -### Before (Inefficient) -```python -def generate_embedding(text: str) -> List[float]: - # Azure first - az_key = os.getenv("AZURE_OPENAI_API_KEY") - az_ep = os.getenv("AZURE_OPENAI_ENDPOINT") - az_emb = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT") - if az_key and az_ep and az_emb and AzureOpenAI is not None: - try: - client = AzureOpenAI(api_key=az_key, azure_endpoint=az_ep) # NEW CLIENT EVERY TIME - resp = client.embeddings.create(model=az_emb, input=[text]) - return resp.data[0].embedding - except Exception: - pass -``` - -### After (Optimized with Cached Clients) -```python -_embedding_clients: Dict[str, Any] = {} - -def _get_embedding_client(provider: str) -> Any: - """Get or create a cached embedding client.""" - if provider in _embedding_clients: - return _embedding_clients[provider] - - if provider == "azure": - az_key = os.getenv("AZURE_OPENAI_API_KEY") - az_ep = os.getenv("AZURE_OPENAI_ENDPOINT") - if az_key and az_ep and AzureOpenAI is not None: - client = AzureOpenAI(api_key=az_key, azure_endpoint=az_ep) - _embedding_clients[provider] = client - return client - elif provider == "openai": - oi_key = os.getenv("OPENAI_API_KEY") - if oi_key and OpenAI is not None: - client = OpenAI(api_key=oi_key) - _embedding_clients[provider] = client - return client - return None -``` - -### Impact -- **Before**: ~50-100ms connection overhead per request -- **After**: ~0ms (reuses existing connection) - ---- - -## 4. Dataset Validation - Full File Read Into Memory - -### Location -`scripts/validate_datasets.py` - `validate_jsonl()` function - -### Problem -Reads entire file into memory with `f.readlines()` which is inefficient for large datasets. - -### Before (Inefficient) -```python -def validate_jsonl(self, filepath: Path, verbose: bool = False) -> Dict: - with open(filepath, 'r', encoding='utf-8') as f: - lines = f.readlines() # LOADS ENTIRE FILE INTO MEMORY - - for i, line in enumerate(lines, 1): - # ... validate line -``` - -### After (Optimized with Streaming) -```python -def validate_jsonl(self, filepath: Path, verbose: bool = False) -> Dict: - with open(filepath, 'r', encoding='utf-8') as f: - for i, line in enumerate(f, 1): # STREAMS LINE BY LINE - line = line.strip() - # ... validate line -``` - -### Impact -- **Before**: Memory usage = file size (could be GBs) -- **After**: Memory usage = single line buffer (~KB) - ---- - -## 5. Chat Providers - LM Studio Health Check On Every Auto-Detect - -### Location -`ai-projects/chat-cli/src/chat_providers.py` - `detect_provider()` function - -### Problem -In auto mode, the function makes an HTTP request to check if LM Studio is running on every call, adding latency even when LM Studio isn't being used. - -### Before (Inefficient) -```python -# Auto mode - check for LM Studio first -try: - # Quick health check for LM Studio - import urllib.request - import urllib.error - req = urllib.request.Request(lms_url.replace("/v1", "") + "/v1/models", headers={"User-Agent": "QAI"}) - urllib.request.urlopen(req, timeout=1) # BLOCKS FOR 1 SECOND ON EVERY CALL - # ... use LM Studio -except (urllib.error.URLError, Exception): - pass # LM Studio not available -``` - -### After (Optimized with TTL Cache) -```python -_lmstudio_cache = {"available": None, "checked_at": 0} -_LMSTUDIO_CACHE_TTL = 30 # seconds - -def _check_lmstudio_available(url: str) -> bool: - """Check LM Studio availability with caching.""" - now = time.time() - if _lmstudio_cache["available"] is not None and (now - _lmstudio_cache["checked_at"]) < _LMSTUDIO_CACHE_TTL: - return _lmstudio_cache["available"] - - try: - req = urllib.request.Request(url.replace("/v1", "") + "/v1/models", headers={"User-Agent": "QAI"}) - urllib.request.urlopen(req, timeout=1) - _lmstudio_cache["available"] = True - except Exception: - _lmstudio_cache["available"] = False - - _lmstudio_cache["checked_at"] = now - return _lmstudio_cache["available"] -``` - -### Impact -- **Before**: ~1000ms timeout on each failed check -- **After**: ~0ms (cache hit within 30 seconds) - ---- - -## 6. Quantum Classifier - Sequential Batch Processing - -### Location -`ai-projects/quantum-ml/src/quantum_classifier.py` - `forward()` method - -### Problem -Processes batch items sequentially in a Python loop, which is slow for quantum circuit execution. - -### Current Code -```python -def forward(self, inputs: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: - batch_size = inputs.shape[0] - outputs = torch.empty(batch_size, self.n_qubits, dtype=torch.float32) - - for i, inp in enumerate(inputs): # SEQUENTIAL LOOP - result = self.qnode(inp, weights) - # ... convert result - outputs[i] = result - - return outputs -``` - -### Recommendation -Consider using PennyLane's built-in batching capabilities or torch.vmap for vectorized execution. This is a lower priority as quantum simulation is inherently sequential, but can benefit from async I/O when using cloud backends. - ---- - -## 7. Function App - Repeated File Existence Checks - -### Location -`function_app.py` - `ai_status()` endpoint - -### Problem -The status endpoint checks many file paths on every request. While individually fast, the cumulative effect adds latency. - -### Recommendation -Consider caching path existence checks with a short TTL (5-10 seconds) for the status endpoint, especially for paths that rarely change like installed scripts. - ---- - -## Implementation Priority - -1. **High Priority** (implement immediately): - - Token Utils tokenizer caching (saves 100-500ms per request) - - Chat Memory client caching (saves 50-100ms per request) - - LM Studio availability caching (saves up to 1000ms) - -2. **Medium Priority** (implement when time permits): - - Chat Memory NumPy cosine similarity - - Dataset Validation streaming read - -3. **Low Priority** (document for future): - - Quantum Classifier batch optimization - - Function App file existence caching - ---- - -## Recent Performance Fixes (2026-02-17) - -### 7. SQL Repository - Inefficient Result Limiting - -#### Location -`shared/sql_repository.py` - `list_values()` function (lines 235, 249) - -#### Problem -Database queries were fetching all rows into memory before slicing in Python: -```python -cur.execute("SELECT k, v, updated_at FROM QAI_KeyValue ORDER BY updated_at DESC") -for row in cur.fetchall()[:limit]: # Fetches ALL rows, then slices -``` - -#### Fix Applied -Use SQL LIMIT clause to fetch only required rows: -```python -cur.execute("SELECT k, v, updated_at FROM QAI_KeyValue ORDER BY updated_at DESC LIMIT ?", (limit,)) -for row in cur.fetchall(): # Only fetches 'limit' rows -``` - -#### Impact -- **Memory**: Prevents loading unnecessary data into memory -- **Network**: Reduces data transfer from database -- **Performance**: 2-10,000x improvement depending on table size -- **Scope**: All key-value store operations - -### 8. Training Analytics - String Concatenation in Loop - -#### Location -`scripts/training_analytics.py` - chart generation (lines 233-238) - -#### Problem -Using `+=` operator for string building in nested loops creates O(n²) complexity: -```python -line = " │" -for value in scaled: - if value >= row: - line += "█" # Creates new string each iteration -``` - -#### Fix Applied -Use list accumulation with join(): -```python -chars = [] -for value in scaled: - if value >= row: - chars.append("█") - else: - chars.append(" ") -chart.append(" │" + "".join(chars)) -``` - -#### Impact -- **Complexity**: O(n) instead of O(n²) -- **Memory**: Single allocation vs multiple intermediate strings -- **Performance**: 2-100x faster for typical chart sizes -- **Scope**: Training analytics visualization - -### 9. Quantum Web App - Dictionary Iteration Efficiency - -#### Location -`ai-projects/quantum-ml/web_app.py` - metrics history trimming (line 516) - -#### Problem -Inefficient loop-based dictionary updates: -```python -for key in session.metrics_history: - session.metrics_history[key] = session.metrics_history[key][-1000:] -``` - -#### Fix Applied -Use dictionary comprehension: -```python -session.metrics_history = {key: values[-1000:] for key, values in session.metrics_history.items()} -``` - -#### Impact -- **Readability**: More Pythonic and clear -- **Performance**: Single-pass operation -- **Memory**: Efficient new dictionary creation -- **Scope**: Training session memory management - -### 10. Quantum Circuit - Performance Documentation - -#### Location -`ai-projects/quantum-ml/src/hybrid_qnn.py` - QuantumLayer class - -#### Problem -Missing documentation about O(n²) complexity of full entanglement pattern. - -#### Fix Applied -Added comprehensive performance notes: -- Constructor docstring documents entanglement pattern performance -- Circuit method includes performance warning about full entanglement -- Users now aware: linear/circular = O(n), full = O(n²) - -#### Impact -- **Awareness**: Users can make informed configuration choices -- **Optimization**: Encourages efficient patterns for large circuits -- **Scope**: Quantum neural network design - ---- - -## Testing Recommendations - -All optimizations should be tested with: -1. Unit tests verifying correct behavior -2. Performance benchmarks comparing before/after -3. Integration tests ensuring no regressions - -See `tests/test_performance_optimizations.py` for existing test patterns. +# Performance Improvement Recommendations for QAI + +This document outlines identified performance bottlenecks and inefficient code patterns across the QAI codebase, along with specific recommendations for improvement. + +## Summary of Findings + +| Location | Issue | Severity | Status | +|----------|-------|----------|--------| +| `token_utils.py` | Repeated tokenizer instantiation | High | Fixed | +| `chat_memory.py` | Inefficient cosine similarity loop | Medium | Fixed | +| `chat_memory.py` | Repeated OpenAI client creation | Medium | Fixed | +| `validate_datasets.py` | Full file read into memory | Medium | Fixed | +| `chat_providers.py` | LM Studio health check on every auto-detect | Medium | Fixed | +| `aria_web/server.py` | 20+ list creations in keyword checks | **Critical** | **Fixed (2025-02-17)** | +| `extract_chat_logs_dataset.py` | Double traversal with any() | High | **Fixed (2025-02-17)** | +| `batch_evaluator.py` | O(n²) linear search in compare_models() | High | **Fixed (2025-02-17)** | +| `training_analytics.py` | String += in visualization loop | Medium | **Fixed (2025-02-17)** | +| `agi_provider.py` | String += for tag concatenation | Low | **Fixed (2025-02-17)** | +| `quantum_classifier.py` | Sequential batch processing | Medium | Documented | +| `function_app.py` | Repeated file existence checks | Low | Documented | + +--- + +## Recent Optimizations (February 2025) + +### 8. Aria Web Server - Repeated Keyword List Creation + +#### Location +`aria_web/server.py` - Multiple functions including `parse_with_fallback()`, `generate_aria_position()`, and `generate_tags_fallback()` + +#### Problem +Every command processed creates 20+ new list objects for keyword matching using `any(word in command for word in ['keyword1', 'keyword2', ...])`. This happens on the hot path for every user command. + +#### Before (Inefficient) +```python +# Lines 220, 236, 242, 250, 496-520, 580, 649-652, 673-707 +if any(word in command_lower for word in ['go', 'move', 'walk', 'run']): + # ... + +if any(word in command_lower for word in ['say', 'speak', 'tell', 'greet']): + # ... + +if any(k in cmd for k in ['jump', 'leap', 'hop']): + # ... +# ... repeated 20+ times throughout the file +``` + +#### After (Optimized with Pre-compiled Frozensets) +```python +# Module-level constants (lines 42-60) +MOVE_KEYWORDS = frozenset(['go', 'move', 'walk', 'run']) +SAY_KEYWORDS = frozenset(['say', 'speak', 'tell', 'greet']) +PICKUP_KEYWORDS = frozenset(['pick', 'get', 'grab', 'take']) +JUMP_KEYWORDS = frozenset(['jump', 'leap', 'hop']) +DANCE_KEYWORDS = frozenset(['dance', 'spin', 'twirl']) +WAVE_KEYWORDS = frozenset(['wave', 'greet', 'hello', 'hi']) +# ... 19 total keyword sets + +# Usage (lines 220+) +if any(word in command_lower for word in MOVE_KEYWORDS): + # ... + +if any(word in command_lower for word in SAY_KEYWORDS): + # ... +``` + +#### Impact +- **Before**: ~20+ list allocations per command (~200-400 bytes + allocation overhead) +- **After**: 0 allocations (frozensets created once at module load) +- **Performance**: 5-10x faster command parsing on hot path +- **Memory**: Constant memory usage vs. O(commands) temporary allocations + +--- + +### 9. Extract Chat Logs - Double List Traversal + +#### Location +`scripts/extract_chat_logs_dataset.py` - Line 72 in rolling window logic + +#### Problem +Two separate `any()` calls traverse the same window list to check for user and assistant roles, performing O(2n) work. + +#### Before (Inefficient) +```python +if any(x.get("role") == "user" for x in window) and any(x.get("role") == "assistant" for x in window): + examples.append({"messages": window}) +``` + +#### After (Optimized with Single-Pass Set Collection) +```python +# Single pass using set comprehension +roles = {x.get("role") for x in window} +if "user" in roles and "assistant" in roles: + examples.append({"messages": window}) +``` + +#### Impact +- **Before**: O(2n) - two complete passes over window +- **After**: O(n) - single pass with O(1) membership checks +- **Performance**: 2x faster dataset extraction +- **Benefit**: Scales linearly with window size (typically 2-10 messages) + +--- + +### 10. Batch Evaluator - O(n²) Linear Search + +#### Location +`scripts/batch_evaluator.py` - Line 310 in `compare_models()` method + +#### Problem +For each requested model ID, the code performs a linear search through all results using `next((r for r in self.results if r.model_id == model_id), None)`. This creates O(n×m) complexity where n is the number of results and m is the number of requested models. + +#### Before (Inefficient) +```python +def compare_models(self, model_ids: List[str]) -> Dict: + comparison = [] + + for model_id in model_ids: + result = next((r for r in self.results if r.model_id == model_id), None) + if result: + comparison.append(result) + # ... +``` + +#### After (Optimized with Dictionary Index) +```python +def compare_models(self, model_ids: List[str]) -> Dict: + # Build index for O(1) lookups + results_by_id = {r.model_id: r for r in self.results} + + comparison = [] + for model_id in model_ids: + result = results_by_id.get(model_id) + if result: + comparison.append(result) + # ... +``` + +#### Impact +- **Before**: O(n×m) nested iteration (~1000 comparisons for 100 results × 10 models) +- **After**: O(n + m) with O(1) lookups (~110 operations for same case) +- **Performance**: 100x faster for large model comparisons +- **Scalability**: Linear instead of quadratic growth + +--- + +### 11. Training Analytics - String Concatenation in Loop + +#### Location +`scripts/training_analytics.py` - Lines 233-239 in chart building + +#### Problem +String concatenation with `+=` in nested loop creates O(n²) memory reallocations for chart visualization. + +#### Before (Inefficient) +```python +for row in range(chart_height - 1, -1, -1): + line = " │" + for value in scaled: + if value >= row: + line += "█" + else: + line += " " + chart.append(line) +``` + +#### After (Optimized with List Accumulation) +```python +for row in range(chart_height - 1, -1, -1): + line_chars = [" │"] + for value in scaled: + if value >= row: + line_chars.append("█") + else: + line_chars.append(" ") + chart.append("".join(line_chars)) +``` + +#### Impact +- **Before**: O(n²) string reallocation (each += creates new string) +- **After**: O(n) list append + single join +- **Performance**: 10-100x faster for large visualizations +- **Example**: For 100-point chart × 20 rows: ~20,000 reallocations → ~2,000 operations + +--- + +### 12. AGI Provider - Tag Concatenation Optimization + +#### Location +`ai-projects/chat-cli/src/agi_provider.py` - Lines 697-701 in reflection improvement + +#### Problem +Multiple `response +=` operations for adding Aria movement tags. + +#### Before (Inefficient) +```python +if "left" in query_lower: + response += " [aria:walk:left]" +elif "right" in query_lower: + response += " [aria:walk:right]" +elif "jump" in query_lower: + response += " [aria:jump]" +``` + +#### After (Optimized) +```python +tag = None +if "left" in query_lower: + tag = " [aria:walk:left]" +elif "right" in query_lower: + tag = " [aria:walk:right]" +elif "jump" in query_lower: + tag = " [aria:jump]" + +if tag: + response = response + tag +``` + +#### Impact +- **Before**: Multiple string reallocations +- **After**: Single concatenation when needed +- **Performance**: 2-3x faster (minor impact as non-critical path) +- **Note**: Lower priority as this happens infrequently + +--- + +## 1. Token Utils - Repeated Tokenizer Instantiation + +### Location +`ai-projects/chat-cli/src/token_utils.py` - `_get_text_encoder()` function + +### Problem +Every call to `count_messages_tokens()` or `prune_messages()` creates a new tokenizer instance. For Hugging Face tokenizers, this involves: +- Loading vocabulary files from disk +- Compiling tokenizer rules +- Memory allocation for tokenizer state + +### Before (Inefficient) +```python +def _get_text_encoder(provider: str, model: Optional[str]) -> Callable[[str], int]: + # ... tokenizer creation happens on every call + if AutoTokenizer is not None and mdl: + try: + tok = AutoTokenizer.from_pretrained(model, use_fast=True) # SLOW! + def _count(text: str) -> int: + return len(tok.encode(text or "")) + return _count + except Exception: + pass +``` + +### After (Optimized with LRU Cache) +```python +from functools import lru_cache + +@lru_cache(maxsize=8) +def _get_cached_tokenizer(model: str): + """Cache tokenizer instances to avoid repeated loading.""" + if AutoTokenizer is not None: + try: + return AutoTokenizer.from_pretrained(model, use_fast=True) + except Exception: + pass + return None +``` + +### Impact +- **Before**: ~100-500ms per tokenizer load for Hugging Face models +- **After**: ~0.1ms (cache hit) + +--- + +## 2. Chat Memory - Inefficient Cosine Similarity Calculation + +### Location +`shared/chat_memory.py` - `_cosine()` and `fetch_similar_messages()` + +### Problem +The cosine similarity calculation uses list comprehensions and `sum()` which is slower than NumPy for larger vectors. When fetching similar messages, cosine similarity is computed in a tight loop. + +### Before (Inefficient) +```python +def _cosine(a: Sequence[float], b: Sequence[float]) -> float: + if not a or not b or len(a) != len(b): + return 0.0 + dot = sum(x * y for x, y in zip(a, b)) + na = math.sqrt(sum(x * x for x in a)) or 1.0 + nb = math.sqrt(sum(y * y for y in b)) or 1.0 + return dot / (na * nb) +``` + +### After (Optimized with NumPy when available) +```python +try: + import numpy as np + _HAS_NUMPY = True +except ImportError: + _HAS_NUMPY = False + +def _cosine(a: Sequence[float], b: Sequence[float]) -> float: + if not a or not b or len(a) != len(b): + return 0.0 + + if _HAS_NUMPY: + a_arr = np.asarray(a, dtype=np.float32) + b_arr = np.asarray(b, dtype=np.float32) + dot = np.dot(a_arr, b_arr) + na = np.linalg.norm(a_arr) + nb = np.linalg.norm(b_arr) + if na == 0.0 or nb == 0.0: + return 0.0 + return float(dot / (na * nb)) + + # Fallback to pure Python + dot = sum(x * y for x, y in zip(a, b)) + na = math.sqrt(sum(x * x for x in a)) + nb = math.sqrt(sum(y * y for y in b)) + if na == 0.0 or nb == 0.0: + return 0.0 + return dot / (na * nb) +``` + +### Impact +- **Before**: ~1.2ms for 500 embeddings × 256 dimensions +- **After**: ~0.15ms with NumPy (8x faster) + +--- + +## 3. Chat Memory - Repeated OpenAI Client Creation + +### Location +`shared/chat_memory.py` - `generate_embedding()` function + +### Problem +Creates a new OpenAI/AzureOpenAI client instance on every embedding request, incurring connection overhead. + +### Before (Inefficient) +```python +def generate_embedding(text: str) -> List[float]: + # Azure first + az_key = os.getenv("AZURE_OPENAI_API_KEY") + az_ep = os.getenv("AZURE_OPENAI_ENDPOINT") + az_emb = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT") + if az_key and az_ep and az_emb and AzureOpenAI is not None: + try: + client = AzureOpenAI(api_key=az_key, azure_endpoint=az_ep) # NEW CLIENT EVERY TIME + resp = client.embeddings.create(model=az_emb, input=[text]) + return resp.data[0].embedding + except Exception: + pass +``` + +### After (Optimized with Cached Clients) +```python +_embedding_clients: Dict[str, Any] = {} + +def _get_embedding_client(provider: str) -> Any: + """Get or create a cached embedding client.""" + if provider in _embedding_clients: + return _embedding_clients[provider] + + if provider == "azure": + az_key = os.getenv("AZURE_OPENAI_API_KEY") + az_ep = os.getenv("AZURE_OPENAI_ENDPOINT") + if az_key and az_ep and AzureOpenAI is not None: + client = AzureOpenAI(api_key=az_key, azure_endpoint=az_ep) + _embedding_clients[provider] = client + return client + elif provider == "openai": + oi_key = os.getenv("OPENAI_API_KEY") + if oi_key and OpenAI is not None: + client = OpenAI(api_key=oi_key) + _embedding_clients[provider] = client + return client + return None +``` + +### Impact +- **Before**: ~50-100ms connection overhead per request +- **After**: ~0ms (reuses existing connection) + +--- + +## 4. Dataset Validation - Full File Read Into Memory + +### Location +`scripts/validate_datasets.py` - `validate_jsonl()` function + +### Problem +Reads entire file into memory with `f.readlines()` which is inefficient for large datasets. + +### Before (Inefficient) +```python +def validate_jsonl(self, filepath: Path, verbose: bool = False) -> Dict: + with open(filepath, 'r', encoding='utf-8') as f: + lines = f.readlines() # LOADS ENTIRE FILE INTO MEMORY + + for i, line in enumerate(lines, 1): + # ... validate line +``` + +### After (Optimized with Streaming) +```python +def validate_jsonl(self, filepath: Path, verbose: bool = False) -> Dict: + with open(filepath, 'r', encoding='utf-8') as f: + for i, line in enumerate(f, 1): # STREAMS LINE BY LINE + line = line.strip() + # ... validate line +``` + +### Impact +- **Before**: Memory usage = file size (could be GBs) +- **After**: Memory usage = single line buffer (~KB) + +--- + +## 5. Chat Providers - LM Studio Health Check On Every Auto-Detect + +### Location +`ai-projects/chat-cli/src/chat_providers.py` - `detect_provider()` function + +### Problem +In auto mode, the function makes an HTTP request to check if LM Studio is running on every call, adding latency even when LM Studio isn't being used. + +### Before (Inefficient) +```python +# Auto mode - check for LM Studio first +try: + # Quick health check for LM Studio + import urllib.request + import urllib.error + req = urllib.request.Request(lms_url.replace("/v1", "") + "/v1/models", headers={"User-Agent": "QAI"}) + urllib.request.urlopen(req, timeout=1) # BLOCKS FOR 1 SECOND ON EVERY CALL + # ... use LM Studio +except (urllib.error.URLError, Exception): + pass # LM Studio not available +``` + +### After (Optimized with TTL Cache) +```python +_lmstudio_cache = {"available": None, "checked_at": 0} +_LMSTUDIO_CACHE_TTL = 30 # seconds + +def _check_lmstudio_available(url: str) -> bool: + """Check LM Studio availability with caching.""" + now = time.time() + if _lmstudio_cache["available"] is not None and (now - _lmstudio_cache["checked_at"]) < _LMSTUDIO_CACHE_TTL: + return _lmstudio_cache["available"] + + try: + req = urllib.request.Request(url.replace("/v1", "") + "/v1/models", headers={"User-Agent": "QAI"}) + urllib.request.urlopen(req, timeout=1) + _lmstudio_cache["available"] = True + except Exception: + _lmstudio_cache["available"] = False + + _lmstudio_cache["checked_at"] = now + return _lmstudio_cache["available"] +``` + +### Impact +- **Before**: ~1000ms timeout on each failed check +- **After**: ~0ms (cache hit within 30 seconds) + +--- + +## 6. Quantum Classifier - Sequential Batch Processing + +### Location +`ai-projects/quantum-ml/src/quantum_classifier.py` - `forward()` method + +### Problem +Processes batch items sequentially in a Python loop, which is slow for quantum circuit execution. + +### Current Code +```python +def forward(self, inputs: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: + batch_size = inputs.shape[0] + outputs = torch.empty(batch_size, self.n_qubits, dtype=torch.float32) + + for i, inp in enumerate(inputs): # SEQUENTIAL LOOP + result = self.qnode(inp, weights) + # ... convert result + outputs[i] = result + + return outputs +``` + +### Recommendation +Consider using PennyLane's built-in batching capabilities or torch.vmap for vectorized execution. This is a lower priority as quantum simulation is inherently sequential, but can benefit from async I/O when using cloud backends. + +--- + +## 7. Function App - Repeated File Existence Checks + +### Location +`function_app.py` - `ai_status()` endpoint + +### Problem +The status endpoint checks many file paths on every request. While individually fast, the cumulative effect adds latency. + +### Recommendation +Consider caching path existence checks with a short TTL (5-10 seconds) for the status endpoint, especially for paths that rarely change like installed scripts. + +--- + +## Implementation Priority + +1. **High Priority** (implement immediately): + - Token Utils tokenizer caching (saves 100-500ms per request) + - Chat Memory client caching (saves 50-100ms per request) + - LM Studio availability caching (saves up to 1000ms) + +2. **Medium Priority** (implement when time permits): + - Chat Memory NumPy cosine similarity + - Dataset Validation streaming read + +3. **Low Priority** (document for future): + - Quantum Classifier batch optimization + - Function App file existence caching + +--- + +## Recent Performance Fixes (2026-02-17) + +### 7. SQL Repository - Inefficient Result Limiting + +#### Location +`shared/sql_repository.py` - `list_values()` function (lines 235, 249) + +#### Problem +Database queries were fetching all rows into memory before slicing in Python: +```python +cur.execute("SELECT k, v, updated_at FROM QAI_KeyValue ORDER BY updated_at DESC") +for row in cur.fetchall()[:limit]: # Fetches ALL rows, then slices +``` + +#### Fix Applied +Use SQL LIMIT clause to fetch only required rows: +```python +cur.execute("SELECT k, v, updated_at FROM QAI_KeyValue ORDER BY updated_at DESC LIMIT ?", (limit,)) +for row in cur.fetchall(): # Only fetches 'limit' rows +``` + +#### Impact +- **Memory**: Prevents loading unnecessary data into memory +- **Network**: Reduces data transfer from database +- **Performance**: 2-10,000x improvement depending on table size +- **Scope**: All key-value store operations + +### 8. Training Analytics - String Concatenation in Loop + +#### Location +`scripts/training_analytics.py` - chart generation (lines 233-238) + +#### Problem +Using `+=` operator for string building in nested loops creates O(n²) complexity: +```python +line = " │" +for value in scaled: + if value >= row: + line += "█" # Creates new string each iteration +``` + +#### Fix Applied +Use list accumulation with join(): +```python +chars = [] +for value in scaled: + if value >= row: + chars.append("█") + else: + chars.append(" ") +chart.append(" │" + "".join(chars)) +``` + +#### Impact +- **Complexity**: O(n) instead of O(n²) +- **Memory**: Single allocation vs multiple intermediate strings +- **Performance**: 2-100x faster for typical chart sizes +- **Scope**: Training analytics visualization + +### 9. Quantum Web App - Dictionary Iteration Efficiency + +#### Location +`ai-projects/quantum-ml/web_app.py` - metrics history trimming (line 516) + +#### Problem +Inefficient loop-based dictionary updates: +```python +for key in session.metrics_history: + session.metrics_history[key] = session.metrics_history[key][-1000:] +``` + +#### Fix Applied +Use dictionary comprehension: +```python +session.metrics_history = {key: values[-1000:] for key, values in session.metrics_history.items()} +``` + +#### Impact +- **Readability**: More Pythonic and clear +- **Performance**: Single-pass operation +- **Memory**: Efficient new dictionary creation +- **Scope**: Training session memory management + +### 10. Quantum Circuit - Performance Documentation + +#### Location +`ai-projects/quantum-ml/src/hybrid_qnn.py` - QuantumLayer class + +#### Problem +Missing documentation about O(n²) complexity of full entanglement pattern. + +#### Fix Applied +Added comprehensive performance notes: +- Constructor docstring documents entanglement pattern performance +- Circuit method includes performance warning about full entanglement +- Users now aware: linear/circular = O(n), full = O(n²) + +#### Impact +- **Awareness**: Users can make informed configuration choices +- **Optimization**: Encourages efficient patterns for large circuits +- **Scope**: Quantum neural network design + +--- + +## Testing Recommendations + +All optimizations should be tested with: +1. Unit tests verifying correct behavior +2. Performance benchmarks comparing before/after +3. Integration tests ensuring no regressions + +See `tests/test_performance_optimizations.py` for existing test patterns. diff --git a/docs/PERFORMANCE_IMPROVEMENTS_REPORT.md b/docs/PERFORMANCE_IMPROVEMENTS_REPORT.md index 13c289ae2..673eccae9 100644 --- a/docs/PERFORMANCE_IMPROVEMENTS_REPORT.md +++ b/docs/PERFORMANCE_IMPROVEMENTS_REPORT.md @@ -1,443 +1,443 @@ -# Performance Improvements Implementation Report - -**Date:** February 17, 2026 -**Author:** GitHub Copilot Agent -**Issue:** Identify and suggest improvements to slow or inefficient code -**PR Branch:** `copilot/identify-code-improvements` - ---- - -## Executive Summary - -Conducted comprehensive performance analysis across the Aria codebase and implemented **11 critical optimizations** affecting **10 files**. Achieved **10-400x performance improvements** in hot paths through algorithmic improvements, proper use of data structures, and elimination of redundant operations. - -### Key Results -- **Critical Issues Fixed:** 11 -- **Files Optimized:** 10 -- **Performance Range:** 10-400x improvement in hot paths -- **Code Quality:** More idiomatic Python, improved maintainability -- **Memory Impact:** Reduced allocation churn, fewer temporary objects - ---- - -## Methodology - -### 1. Discovery Phase -Used the explore agent to scan the entire codebase for common performance anti-patterns: -- Multiple iterations over same collection -- Inefficient data structure usage (lists vs sets) -- Regex compilation in loops -- String concatenation in loops -- Missing use of stdlib optimizations - -### 2. Analysis Phase -Prioritized issues by: -- **Frequency:** How often code is executed -- **Impact:** Algorithmic complexity (O(n²) → O(n), O(n) → O(1)) -- **Scope:** Criticality of affected code paths - -### 3. Implementation Phase -Applied fixes in two rounds: -- **Round 1:** Basic optimizations (algorithm complexity, data structures) -- **Round 2:** Critical performance fixes (regex compilation, string operations) - -### 4. Validation Phase -- Syntax checking (all files compile) -- Import testing (all modules load correctly) -- Logic preservation (identical behavior) -- Microbenchmarking (performance verification) - ---- - -## Optimizations Implemented - -### Round 1: Algorithm & Data Structure Optimizations - -#### 1.1 Single-Pass Role Detection -**File:** `scripts/extract_chat_logs_dataset.py:72-82` - -**Problem:** Two separate `any()` calls iterating over the same window -```python -if any(x.get("role") == "user" for x in window) and \ - any(x.get("role") == "assistant" for x in window): -``` - -**Solution:** Single loop with early exit -```python -has_user = has_assistant = False -for x in window: - role = x.get("role") - if role == "user": has_user = True - elif role == "assistant": has_assistant = True - if has_user and has_assistant: break -``` - -**Impact:** 50% reduction in window traversal - ---- - -#### 1.2 Set-Based Membership Checks -**Files:** `ai-projects/quantum-ml/src/automate_quantum_job.py`, `scripts/job_queue.py` (3 locations), `scripts/master_orchestrator.py` - -**Problem:** O(n) list membership checks in hot paths -```python -if status in ["Succeeded", "Failed", "Cancelled"]: -``` - -**Solution:** O(1) set lookups -```python -TERMINAL_STATUSES = {"Succeeded", "Failed", "Cancelled"} -if status in TERMINAL_STATUSES: -``` - -**Impact:** O(n) → O(1) complexity, 50-90% faster for repeated checks - ---- - -#### 1.3 Set-Based Uniqueness Check -**File:** `scripts/backup_manager.py:56` - -**Problem:** O(n²) repeated linear search in while loop -```python -while any(b.get('name') == backup_name for b in self.manifest.get('backups', [])): -``` - -**Solution:** Build set once, O(1) lookups -```python -existing_names = {b.get('name') for b in self.manifest.get('backups', [])} -while backup_name in existing_names: -``` - -**Impact:** O(n²) → O(n), 10-100x faster for large backup lists - ---- - -#### 1.4 Single-Pass Aggregation -**File:** `scripts/job_queue.py:246-263` - -**Problem:** 6 separate iterations over same dictionary -```python -'pending': sum(1 for j in self.jobs.values() if j.status == JobStatus.PENDING), -'running': sum(1 for j in self.jobs.values() if j.status == JobStatus.RUNNING), -# ... 4 more iterations -``` - -**Solution:** One iteration counting all statuses -```python -counts = {'pending': 0, 'running': 0, ...} -for job in self.jobs.values(): - if job.status == JobStatus.PENDING: counts['pending'] += 1 - elif job.status == JobStatus.RUNNING: counts['running'] += 1 - # ... -``` - -**Impact:** 83% reduction in iterations (6 → 1) - ---- - -#### 1.5 Standard Library Usage -**File:** `scripts/status_dashboard.py:107-109, 139-141` - -**Problem:** Manual mean calculation -```python -durations = [...] -if durations: - avg = sum(durations) / len(durations) -``` - -**Solution:** Use optimized stdlib function -```python -import statistics -if durations: - avg = statistics.mean(durations) -``` - -**Impact:** 10-20% faster, better edge case handling - ---- - -### Round 2: Critical Performance Fixes - -#### 2.1 Regex Compilation Outside Loop -**File:** `dashboard/app.py:183-184` - -**Problem:** Compiling patterns on every iteration (400x for 400-line log) -```python -for ln in lines: - epoch_pat = re.compile(r"Epoch\s+(\d+)/(\d+)") - step_pat = re.compile(r"global_step\s*=\s*(\d+)") - e_m = epoch_pat.search(ln) -``` - -**Solution:** Compile once before loop -```python -epoch_pat = re.compile(r"Epoch\s+(\d+)/(\d+)") -step_pat = re.compile(r"global_step\s*=\s*(\d+)") -for ln in lines: - e_m = epoch_pat.search(ln) -``` - -**Impact:** 100-400x faster log parsing - ---- - -#### 2.2 List-Based String Building -**File:** `function_app.py:1943-1956` - -**Problem:** O(n²) string concatenation in nested loop -```python -visualization = "..." -for layer in range(n_layers): - visualization += f"Layer {layer}:\n" - for gate in layer_gates: - visualization += f" {gate}...\n" -``` - -**Solution:** O(n) list building with join -```python -viz_parts = ["..."] -for layer in range(n_layers): - viz_parts.append(f"Layer {layer}:\n") - for gate in layer_gates: - viz_parts.append(f" {gate}...\n") -visualization = "".join(viz_parts) -``` - -**Impact:** 10-100x faster for large circuits, eliminates memory fragmentation - ---- - -#### 2.3 Pre-Compiled Validation Patterns -**File:** `llm-maker/src/tool_validator.py` - -**Problem:** Compiling 16 patterns on every validation call -```python -for pattern, desc in file_patterns: - if re.search(pattern, code): # Compiles pattern each time -``` - -**Solution:** Module-level pre-compiled patterns -```python -# At module level -_FILE_OPERATION_PATTERNS = [ - (re.compile(r'\bopen\s*\('), "open() function"), - # ... 4 more -] - -# In method -for compiled_pattern, desc in _FILE_OPERATION_PATTERNS: - if compiled_pattern.search(code): -``` - -**Impact:** 16x reduction in regex compilation, 50-95% faster validation - ---- - -## Performance Impact Summary - -### By Category - -| Category | Fixes | Best Improvement | -|----------|-------|------------------| -| Algorithm Complexity | 5 | O(n²) → O(n) | -| String Operations | 1 | O(n²) → O(n) | -| Regex Compilation | 2 | 400x faster | -| Standard Library | 1 | 20% faster | - -### By File - -| File | Issue Type | Performance Gain | -|------|-----------|------------------| -| dashboard/app.py | Regex in loop | 100-400x | -| function_app.py | String concat | 10-100x | -| llm-maker/tool_validator.py | Regex compilation | 16x | -| job_queue.py | Multiple iterations | 6x | -| backup_manager.py | O(n²) uniqueness | 10-100x | -| extract_chat_logs_dataset.py | Dual iteration | 2x | -| automate_quantum_job.py | List lookup | 1.5-2x | -| master_orchestrator.py | List lookup | 1.5-2x | -| status_dashboard.py | Manual mean | 1.1-1.2x | - -### Real-World Impact - -**Hot Paths Optimized:** -- **Log parsing (dashboard):** 100-400x faster -- **Quantum circuit visualization:** 10-100x faster -- **Code validation:** 16x faster per call -- **Job queue status:** 6x fewer iterations -- **Backup name generation:** O(n²) → O(n) - ---- - -## Best Practices Established - -### Pattern: Pre-Compile Regex -```python -# ❌ DON'T: Compile in loop -for item in items: - if re.search(r'pattern', item): - ... - -# ✅ DO: Compile once -PATTERN = re.compile(r'pattern') -for item in items: - if PATTERN.search(item): - ... -``` - -### Pattern: Use Sets for Membership -```python -# ❌ DON'T: List membership -if status in ["pending", "running", "completed"]: - ... - -# ✅ DO: Set membership -VALID_STATUSES = {"pending", "running", "completed"} -if status in VALID_STATUSES: - ... -``` - -### Pattern: String Building with Lists -```python -# ❌ DON'T: Concatenate in loop -result = "" -for item in items: - result += f"{item}\n" - -# ✅ DO: Build list, then join -parts = [f"{item}\n" for item in items] -result = "".join(parts) -``` - -### Pattern: Single-Pass Aggregation -```python -# ❌ DON'T: Multiple passes -count_a = sum(1 for x in items if x.type == 'a') -count_b = sum(1 for x in items if x.type == 'b') -total = sum(x.value for x in items) - -# ✅ DO: Single pass -counts = {'a': 0, 'b': 0, 'total': 0} -for x in items: - counts[x.type] += 1 - counts['total'] += x.value -``` - ---- - -## Memory Improvements - -1. **Reduced String Allocation:** List.join() eliminates intermediate string objects -2. **Fewer Temporary Objects:** Single-pass aggregation reduces temporary lists -3. **Constant Memory:** Pre-compiled patterns stored once at module level -4. **Early Exit:** Stops iteration as soon as conditions met - ---- - -## Testing & Validation - -### Compilation Testing -```bash -python -m py_compile scripts/*.py dashboard/app.py function_app.py -# All files compile successfully ✓ -``` - -### Import Testing -```python -import extract_chat_logs_dataset # ✓ -import status_dashboard # ✓ -import job_queue # ✓ -import tool_validator # ✓ -# All imports successful -``` - -### Logic Verification -- Single-pass role detection produces identical results -- Set membership has same semantics as list membership -- String join produces identical output to concatenation -- statistics.mean() matches manual calculation - -### Performance Verification -- Set vs list lookup: Measured with 100k iterations -- Regex compilation: Verified 400x compilation reduction -- String operations: Confirmed O(n) vs O(n²) behavior - ---- - -## Documentation Created - -1. **Performance Summary:** `docs/PERFORMANCE_OPTIMIZATIONS_SUMMARY.md` - - Detailed before/after examples - - Best practices guide - - Performance impact tables - -2. **Repository Memory:** 6 performance patterns stored - - List to set optimization - - Single-pass iteration - - Statistics module usage - - Set-based uniqueness checks - - Regex pattern compilation - - String concatenation performance - -3. **Inline Comments:** Added explanatory comments in optimized code - ---- - -## Future Optimization Opportunities - -The explore agent identified additional opportunities for future work: - -### High Priority (Not Yet Implemented) -1. **Dashboard Method Caching:** Add `@lru_cache` to `get_datasets()`, `get_jobs()`, etc. -2. **Batch File I/O:** Group multiple file reads into single operations -3. **JSON Re-serialization:** Avoid parse → re-serialize patterns in endpoints - -### Medium Priority -4. **Async I/O:** Consider asyncio for concurrent file operations -5. **Database Query Batching:** Review for N+1 query patterns -6. **Memory Pooling:** Reuse buffers for large data processing - -### Monitoring -- Set up performance regression testing -- Track execution time metrics for optimized paths -- Consider profiling tools (cProfile, py-spy) for production - ---- - -## Lessons Learned - -1. **Measure First:** Microbenchmarking confirmed expected improvements -2. **Small Sets:** Set optimization for 3-item collections is marginal but more idiomatic -3. **Hot Paths Matter:** Focus on frequently-executed code (loops, validation, log parsing) -4. **Stdlib Wins:** Python's standard library is well-optimized (statistics, regex, collections) -5. **Early Exit:** Stop iterating as soon as conditions are met -6. **Pre-compile:** Regex compilation is expensive; do it once - ---- - -## Conclusion - -Successfully identified and fixed **11 critical performance issues** across **10 files**, achieving **10-400x improvements** in hot paths. Established best practices and patterns for future development. All optimizations maintain identical behavior while significantly improving performance and code quality. - -The codebase now follows modern Python best practices: -- ✅ Proper data structure usage (sets for membership) -- ✅ Optimized string operations (list.join over +=) -- ✅ Pre-compiled regex patterns -- ✅ Single-pass algorithms -- ✅ Standard library utilities (statistics module) - -**Recommendation:** Apply these patterns consistently in future code reviews and new development. The explore agent's scan can be re-run periodically to catch regressions or identify new optimization opportunities. - ---- - -## References - -- Python Performance Tips: https://wiki.python.org/moin/PythonSpeed/PerformanceTips -- Regex Compilation: https://docs.python.org/3/library/re.html#re.compile -- Time Complexity: https://wiki.python.org/moin/TimeComplexity -- Repository Memory: Performance optimization patterns (6 stored facts) -- Previous Work: `docs/PERFORMANCE_IMPLEMENTATION_SUMMARY.md` (4.6x average speedup) - ---- - -**End of Report** +# Performance Improvements Implementation Report + +**Date:** February 17, 2026 +**Author:** GitHub Copilot Agent +**Issue:** Identify and suggest improvements to slow or inefficient code +**PR Branch:** `copilot/identify-code-improvements` + +--- + +## Executive Summary + +Conducted comprehensive performance analysis across the Aria codebase and implemented **11 critical optimizations** affecting **10 files**. Achieved **10-400x performance improvements** in hot paths through algorithmic improvements, proper use of data structures, and elimination of redundant operations. + +### Key Results +- **Critical Issues Fixed:** 11 +- **Files Optimized:** 10 +- **Performance Range:** 10-400x improvement in hot paths +- **Code Quality:** More idiomatic Python, improved maintainability +- **Memory Impact:** Reduced allocation churn, fewer temporary objects + +--- + +## Methodology + +### 1. Discovery Phase +Used the explore agent to scan the entire codebase for common performance anti-patterns: +- Multiple iterations over same collection +- Inefficient data structure usage (lists vs sets) +- Regex compilation in loops +- String concatenation in loops +- Missing use of stdlib optimizations + +### 2. Analysis Phase +Prioritized issues by: +- **Frequency:** How often code is executed +- **Impact:** Algorithmic complexity (O(n²) → O(n), O(n) → O(1)) +- **Scope:** Criticality of affected code paths + +### 3. Implementation Phase +Applied fixes in two rounds: +- **Round 1:** Basic optimizations (algorithm complexity, data structures) +- **Round 2:** Critical performance fixes (regex compilation, string operations) + +### 4. Validation Phase +- Syntax checking (all files compile) +- Import testing (all modules load correctly) +- Logic preservation (identical behavior) +- Microbenchmarking (performance verification) + +--- + +## Optimizations Implemented + +### Round 1: Algorithm & Data Structure Optimizations + +#### 1.1 Single-Pass Role Detection +**File:** `scripts/extract_chat_logs_dataset.py:72-82` + +**Problem:** Two separate `any()` calls iterating over the same window +```python +if any(x.get("role") == "user" for x in window) and \ + any(x.get("role") == "assistant" for x in window): +``` + +**Solution:** Single loop with early exit +```python +has_user = has_assistant = False +for x in window: + role = x.get("role") + if role == "user": has_user = True + elif role == "assistant": has_assistant = True + if has_user and has_assistant: break +``` + +**Impact:** 50% reduction in window traversal + +--- + +#### 1.2 Set-Based Membership Checks +**Files:** `ai-projects/quantum-ml/src/automate_quantum_job.py`, `scripts/job_queue.py` (3 locations), `scripts/master_orchestrator.py` + +**Problem:** O(n) list membership checks in hot paths +```python +if status in ["Succeeded", "Failed", "Cancelled"]: +``` + +**Solution:** O(1) set lookups +```python +TERMINAL_STATUSES = {"Succeeded", "Failed", "Cancelled"} +if status in TERMINAL_STATUSES: +``` + +**Impact:** O(n) → O(1) complexity, 50-90% faster for repeated checks + +--- + +#### 1.3 Set-Based Uniqueness Check +**File:** `scripts/backup_manager.py:56` + +**Problem:** O(n²) repeated linear search in while loop +```python +while any(b.get('name') == backup_name for b in self.manifest.get('backups', [])): +``` + +**Solution:** Build set once, O(1) lookups +```python +existing_names = {b.get('name') for b in self.manifest.get('backups', [])} +while backup_name in existing_names: +``` + +**Impact:** O(n²) → O(n), 10-100x faster for large backup lists + +--- + +#### 1.4 Single-Pass Aggregation +**File:** `scripts/job_queue.py:246-263` + +**Problem:** 6 separate iterations over same dictionary +```python +'pending': sum(1 for j in self.jobs.values() if j.status == JobStatus.PENDING), +'running': sum(1 for j in self.jobs.values() if j.status == JobStatus.RUNNING), +# ... 4 more iterations +``` + +**Solution:** One iteration counting all statuses +```python +counts = {'pending': 0, 'running': 0, ...} +for job in self.jobs.values(): + if job.status == JobStatus.PENDING: counts['pending'] += 1 + elif job.status == JobStatus.RUNNING: counts['running'] += 1 + # ... +``` + +**Impact:** 83% reduction in iterations (6 → 1) + +--- + +#### 1.5 Standard Library Usage +**File:** `scripts/status_dashboard.py:107-109, 139-141` + +**Problem:** Manual mean calculation +```python +durations = [...] +if durations: + avg = sum(durations) / len(durations) +``` + +**Solution:** Use optimized stdlib function +```python +import statistics +if durations: + avg = statistics.mean(durations) +``` + +**Impact:** 10-20% faster, better edge case handling + +--- + +### Round 2: Critical Performance Fixes + +#### 2.1 Regex Compilation Outside Loop +**File:** `dashboard/app.py:183-184` + +**Problem:** Compiling patterns on every iteration (400x for 400-line log) +```python +for ln in lines: + epoch_pat = re.compile(r"Epoch\s+(\d+)/(\d+)") + step_pat = re.compile(r"global_step\s*=\s*(\d+)") + e_m = epoch_pat.search(ln) +``` + +**Solution:** Compile once before loop +```python +epoch_pat = re.compile(r"Epoch\s+(\d+)/(\d+)") +step_pat = re.compile(r"global_step\s*=\s*(\d+)") +for ln in lines: + e_m = epoch_pat.search(ln) +``` + +**Impact:** 100-400x faster log parsing + +--- + +#### 2.2 List-Based String Building +**File:** `function_app.py:1943-1956` + +**Problem:** O(n²) string concatenation in nested loop +```python +visualization = "..." +for layer in range(n_layers): + visualization += f"Layer {layer}:\n" + for gate in layer_gates: + visualization += f" {gate}...\n" +``` + +**Solution:** O(n) list building with join +```python +viz_parts = ["..."] +for layer in range(n_layers): + viz_parts.append(f"Layer {layer}:\n") + for gate in layer_gates: + viz_parts.append(f" {gate}...\n") +visualization = "".join(viz_parts) +``` + +**Impact:** 10-100x faster for large circuits, eliminates memory fragmentation + +--- + +#### 2.3 Pre-Compiled Validation Patterns +**File:** `llm-maker/src/tool_validator.py` + +**Problem:** Compiling 16 patterns on every validation call +```python +for pattern, desc in file_patterns: + if re.search(pattern, code): # Compiles pattern each time +``` + +**Solution:** Module-level pre-compiled patterns +```python +# At module level +_FILE_OPERATION_PATTERNS = [ + (re.compile(r'\bopen\s*\('), "open() function"), + # ... 4 more +] + +# In method +for compiled_pattern, desc in _FILE_OPERATION_PATTERNS: + if compiled_pattern.search(code): +``` + +**Impact:** 16x reduction in regex compilation, 50-95% faster validation + +--- + +## Performance Impact Summary + +### By Category + +| Category | Fixes | Best Improvement | +|----------|-------|------------------| +| Algorithm Complexity | 5 | O(n²) → O(n) | +| String Operations | 1 | O(n²) → O(n) | +| Regex Compilation | 2 | 400x faster | +| Standard Library | 1 | 20% faster | + +### By File + +| File | Issue Type | Performance Gain | +|------|-----------|------------------| +| dashboard/app.py | Regex in loop | 100-400x | +| function_app.py | String concat | 10-100x | +| llm-maker/tool_validator.py | Regex compilation | 16x | +| job_queue.py | Multiple iterations | 6x | +| backup_manager.py | O(n²) uniqueness | 10-100x | +| extract_chat_logs_dataset.py | Dual iteration | 2x | +| automate_quantum_job.py | List lookup | 1.5-2x | +| master_orchestrator.py | List lookup | 1.5-2x | +| status_dashboard.py | Manual mean | 1.1-1.2x | + +### Real-World Impact + +**Hot Paths Optimized:** +- **Log parsing (dashboard):** 100-400x faster +- **Quantum circuit visualization:** 10-100x faster +- **Code validation:** 16x faster per call +- **Job queue status:** 6x fewer iterations +- **Backup name generation:** O(n²) → O(n) + +--- + +## Best Practices Established + +### Pattern: Pre-Compile Regex +```python +# ❌ DON'T: Compile in loop +for item in items: + if re.search(r'pattern', item): + ... + +# ✅ DO: Compile once +PATTERN = re.compile(r'pattern') +for item in items: + if PATTERN.search(item): + ... +``` + +### Pattern: Use Sets for Membership +```python +# ❌ DON'T: List membership +if status in ["pending", "running", "completed"]: + ... + +# ✅ DO: Set membership +VALID_STATUSES = {"pending", "running", "completed"} +if status in VALID_STATUSES: + ... +``` + +### Pattern: String Building with Lists +```python +# ❌ DON'T: Concatenate in loop +result = "" +for item in items: + result += f"{item}\n" + +# ✅ DO: Build list, then join +parts = [f"{item}\n" for item in items] +result = "".join(parts) +``` + +### Pattern: Single-Pass Aggregation +```python +# ❌ DON'T: Multiple passes +count_a = sum(1 for x in items if x.type == 'a') +count_b = sum(1 for x in items if x.type == 'b') +total = sum(x.value for x in items) + +# ✅ DO: Single pass +counts = {'a': 0, 'b': 0, 'total': 0} +for x in items: + counts[x.type] += 1 + counts['total'] += x.value +``` + +--- + +## Memory Improvements + +1. **Reduced String Allocation:** List.join() eliminates intermediate string objects +2. **Fewer Temporary Objects:** Single-pass aggregation reduces temporary lists +3. **Constant Memory:** Pre-compiled patterns stored once at module level +4. **Early Exit:** Stops iteration as soon as conditions met + +--- + +## Testing & Validation + +### Compilation Testing +```bash +python -m py_compile scripts/*.py dashboard/app.py function_app.py +# All files compile successfully ✓ +``` + +### Import Testing +```python +import extract_chat_logs_dataset # ✓ +import status_dashboard # ✓ +import job_queue # ✓ +import tool_validator # ✓ +# All imports successful +``` + +### Logic Verification +- Single-pass role detection produces identical results +- Set membership has same semantics as list membership +- String join produces identical output to concatenation +- statistics.mean() matches manual calculation + +### Performance Verification +- Set vs list lookup: Measured with 100k iterations +- Regex compilation: Verified 400x compilation reduction +- String operations: Confirmed O(n) vs O(n²) behavior + +--- + +## Documentation Created + +1. **Performance Summary:** `docs/PERFORMANCE_OPTIMIZATIONS_SUMMARY.md` + - Detailed before/after examples + - Best practices guide + - Performance impact tables + +2. **Repository Memory:** 6 performance patterns stored + - List to set optimization + - Single-pass iteration + - Statistics module usage + - Set-based uniqueness checks + - Regex pattern compilation + - String concatenation performance + +3. **Inline Comments:** Added explanatory comments in optimized code + +--- + +## Future Optimization Opportunities + +The explore agent identified additional opportunities for future work: + +### High Priority (Not Yet Implemented) +1. **Dashboard Method Caching:** Add `@lru_cache` to `get_datasets()`, `get_jobs()`, etc. +2. **Batch File I/O:** Group multiple file reads into single operations +3. **JSON Re-serialization:** Avoid parse → re-serialize patterns in endpoints + +### Medium Priority +4. **Async I/O:** Consider asyncio for concurrent file operations +5. **Database Query Batching:** Review for N+1 query patterns +6. **Memory Pooling:** Reuse buffers for large data processing + +### Monitoring +- Set up performance regression testing +- Track execution time metrics for optimized paths +- Consider profiling tools (cProfile, py-spy) for production + +--- + +## Lessons Learned + +1. **Measure First:** Microbenchmarking confirmed expected improvements +2. **Small Sets:** Set optimization for 3-item collections is marginal but more idiomatic +3. **Hot Paths Matter:** Focus on frequently-executed code (loops, validation, log parsing) +4. **Stdlib Wins:** Python's standard library is well-optimized (statistics, regex, collections) +5. **Early Exit:** Stop iterating as soon as conditions are met +6. **Pre-compile:** Regex compilation is expensive; do it once + +--- + +## Conclusion + +Successfully identified and fixed **11 critical performance issues** across **10 files**, achieving **10-400x improvements** in hot paths. Established best practices and patterns for future development. All optimizations maintain identical behavior while significantly improving performance and code quality. + +The codebase now follows modern Python best practices: +- ✅ Proper data structure usage (sets for membership) +- ✅ Optimized string operations (list.join over +=) +- ✅ Pre-compiled regex patterns +- ✅ Single-pass algorithms +- ✅ Standard library utilities (statistics module) + +**Recommendation:** Apply these patterns consistently in future code reviews and new development. The explore agent's scan can be re-run periodically to catch regressions or identify new optimization opportunities. + +--- + +## References + +- Python Performance Tips: https://wiki.python.org/moin/PythonSpeed/PerformanceTips +- Regex Compilation: https://docs.python.org/3/library/re.html#re.compile +- Time Complexity: https://wiki.python.org/moin/TimeComplexity +- Repository Memory: Performance optimization patterns (6 stored facts) +- Previous Work: `docs/PERFORMANCE_IMPLEMENTATION_SUMMARY.md` (4.6x average speedup) + +--- + +**End of Report** diff --git a/docs/PERFORMANCE_INDEX.md b/docs/PERFORMANCE_INDEX.md index ff7e5834e..323525e16 100644 --- a/docs/PERFORMANCE_INDEX.md +++ b/docs/PERFORMANCE_INDEX.md @@ -1,228 +1,228 @@ -# Performance Optimization Documentation Index - -## Latest Analysis (February 2026) - -### Primary Documents - -1. **[CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md](CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md)** - - Comprehensive technical analysis of 8 performance issues - - Detailed code examples with before/after comparisons - - Complexity analysis and expected improvements - - Testing strategy and implementation recommendations - - **Start here for detailed technical analysis** - -2. **[OPTIMIZATION_QUICK_GUIDE.md](OPTIMIZATION_QUICK_GUIDE.md)** - - Quick-reference implementation guide - - Copy-paste code snippets for each optimization - - Organized by complexity (quick wins → complex) - - Performance testing templates - - **Start here for implementation work** - -3. **[PERFORMANCE_FINDINGS_SUMMARY.md](PERFORMANCE_FINDINGS_SUMMARY.md)** - - Executive summary of findings - - Implementation roadmap with phases - - Risk assessment and success metrics - - Questions for maintainers - - **Start here for planning and prioritization** - -## Key Findings Summary - -| Issue | Location | Impact | Status | -|-------|----------|--------|--------| -| O(n³) gradient computation | ai-projects/quantum-ml/web_app.py:217-246 | 10-100x | Not fixed | -| Repeated JSON file I/O | dashboard/serve.py:273-515 | 5-10x | Not fixed | -| Linear keyword searches | ai-projects/chat-cli/src/agi_provider.py:343-372 | 3-30x | Not fixed | -| Inconsistent keyword patterns | aria_web/server.py:554-557 | 2-5x | Partial | -| Multi-pass statistics | ai-projects/quantum-ml/web_app.py:952-958 | 4x | Not fixed | -| Inefficient directory traversal | dashboard/serve.py:700,761,766 | 5-20% | Not fixed | -| Rate limiting filtering | dashboard/serve.py:39-40 | 2-5x | Not fixed | -| Minor list comprehensions | ai-projects/quantum-ml/web_app.py:440-443 | <5% | Not fixed | - -## Historical Documentation - -### Previous Optimizations (Implemented) - -- **[PERFORMANCE_IMPROVEMENTS.md](PERFORMANCE_IMPROVEMENTS.md)** - Completed optimizations with test results - - Tokenizer caching (100-500ms → 0.1ms) - - Cosine similarity with NumPy (8x speedup) - - OpenAI client caching (50-100ms → 0ms) - - Dataset validation streaming (O(file_size) → O(line_size)) - - LM Studio health check caching (1000ms → 0ms) - - SQL repository limit optimization (2-10,000x) - - String building with join() (O(n²) → O(n)) - - Dictionary index for lookups (O(n²) → O(n)) - - Frozenset keyword matching (already applied in aria_web/server.py) - -### Analysis Reports - -- **[PERFORMANCE_ANALYSIS.md](PERFORMANCE_ANALYSIS.md)** - Performance profiling and bottleneck analysis -- **[PERFORMANCE_IMPROVEMENTS_REPORT.md](PERFORMANCE_IMPROVEMENTS_REPORT.md)** - Completed work report -- **[PERFORMANCE_OPTIMIZATIONS_SUMMARY.md](PERFORMANCE_OPTIMIZATIONS_SUMMARY.md)** - Summary of optimization patterns -- **[PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md](PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md)** - Recent optimizations - -### Implementation Guides - -- **[PERFORMANCE_IMPLEMENTATION_GUIDE.md](PERFORMANCE_IMPLEMENTATION_GUIDE.md)** - How to implement optimizations -- **[PERFORMANCE_QUICK_FIXES.md](PERFORMANCE_QUICK_FIXES.md)** - Quick fix patterns - -### Future Work - -- **[FUTURE_PERFORMANCE_OPPORTUNITIES.md](FUTURE_PERFORMANCE_OPPORTUNITIES.md)** - Identified but not yet implemented -- **[FUTURE_PERFORMANCE_OPTIMIZATIONS.md](FUTURE_PERFORMANCE_OPTIMIZATIONS.md)** - Planned improvements - -## Testing Infrastructure - -### Test Files -- `tests/test_performance_optimizations.py` - Performance-specific test suite (330+ tests total) -- `scripts/test_runner.py` - Test orchestration and execution - -### Testing Strategy -1. **Unit tests** - Verify correctness of optimized code -2. **Performance benchmarks** - Measure actual speedup with `time.perf_counter()` -3. **Integration tests** - Ensure no regressions in API behavior -4. **Load tests** - Validate improvements under concurrent load - -## Optimization Patterns Library - -### Established Patterns (Use These) - -1. **LRU Caching** - ```python - from functools import lru_cache - @lru_cache(maxsize=8) - def expensive_function(arg): - # Implementation - ``` - -2. **TTL Caching** - ```python - _cache = {} - _TTL = 5 - def cached_load(key): - now = time.time() - if key in _cache and (now - _cache[key][1]) < _TTL: - return _cache[key][0] - # Load and cache - ``` - -3. **Frozenset Keyword Matching** - ```python - KEYWORDS = frozenset(['word1', 'word2']) - word_set = set(text.split()) - if word_set & KEYWORDS: # O(1) intersection - # Match found - ``` - -4. **Generator-Based Counting** - ```python - count = sum(1 for item in items if condition(item)) - ``` - -5. **Single-Pass Collection Processing** - ```python - for item in collection: # One iteration - # Accumulate all needed metrics - ``` - -6. **NumPy Vectorization** - ```python - # Use NumPy operations instead of loops - result = np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) - ``` - -7. **Dictionary Indexing** - ```python - index = {item.id: item for item in items} # O(n) build - result = index.get(search_id) # O(1) lookup - ``` - -8. **Connection Pooling** - ```python - # Reuse database/API connections instead of creating new ones - _connection_pool = [] - ``` - -## Performance Memory Index - -Recent optimization patterns stored in repository memory: - -- **Quantum gradient optimization** - Use qml.grad() for autograd (10-100x speedup) -- **File caching pattern** - TTL-based JSON caching with stale-on-error fallback -- **Frozenset keyword matching** - Pre-compiled sets with O(1) intersection -- **String building in loops** - Use list accumulation + join() (O(n) vs O(n²)) -- **Dictionary index for lookups** - O(1) lookups vs O(n) linear search -- **Single-pass collection** - Accumulate multiple metrics in one iteration -- **Connection pooling** - Reuse expensive connections (50-100ms savings) -- **Set union optimization** - Use set().union(*iterables) for key collection - -## Quick Start for Developers - -### Implementing a New Optimization - -1. **Read** the detailed analysis in `CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md` -2. **Copy** the implementation from `OPTIMIZATION_QUICK_GUIDE.md` -3. **Test** using patterns from `tests/test_performance_optimizations.py` -4. **Benchmark** with `time.perf_counter()` before/after -5. **Document** results in this directory -6. **Store** new patterns in repository memory - -### Reviewing Performance - -1. **Check** if optimization pattern already exists in this index -2. **Review** previous work in `PERFORMANCE_IMPROVEMENTS.md` -3. **Consult** repository memories for recent patterns -4. **Follow** established patterns from this library - -### Finding Inefficiencies - -1. **Profile** with cProfile or line_profiler -2. **Look for** anti-patterns: - - Nested loops with repeated operations - - File/DB I/O in loops - - String concatenation with += in loops - - Repeated any() calls with inline lists - - List comprehensions for counting - - Multiple iterations over same collection -3. **Compare** against patterns in this index -4. **Document** findings following template in `CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md` - -## Metrics and Goals - -### Current Performance Profile -- **Quantum training:** ~1000ms per gradient computation -- **Dashboard refresh:** 50-200ms per file read (×4-5 per request) -- **Query processing:** 1-10ms depending on query length -- **Rate limiting:** O(n) per request - -### Target Performance (After All Optimizations) -- **Quantum training:** ~10-50ms per gradient (10-100x improvement) -- **Dashboard refresh:** <10ms per cached read (5-10x improvement) -- **Query processing:** <1ms per query (3-30x improvement) -- **Rate limiting:** O(1) amortized per request (2-5x improvement) - -### Overall Impact -- **Total potential speedup:** 10-1000x in affected code paths -- **Implementation effort:** 6.5-10.5 hours total -- **Risk level:** Low (all optimizations include fallbacks) -- **ROI:** Extremely high - -## Contributing - -When adding performance optimizations: - -1. Create detailed analysis document (use `CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md` as template) -2. Create implementation guide (use `OPTIMIZATION_QUICK_GUIDE.md` as template) -3. Add tests to `tests/test_performance_optimizations.py` -4. Update this index with new findings -5. Store patterns in repository memory for future reference -6. Document actual measured improvements - -## Questions? - -See `PERFORMANCE_FINDINGS_SUMMARY.md` for questions to ask maintainers before implementation. - ---- - -**Last Updated:** February 17, 2026 -**Total Optimizations Documented:** 8 new + 10 previous = 18 total -**Status:** Ready for implementation +# Performance Optimization Documentation Index + +## Latest Analysis (February 2026) + +### Primary Documents + +1. **[CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md](CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md)** + - Comprehensive technical analysis of 8 performance issues + - Detailed code examples with before/after comparisons + - Complexity analysis and expected improvements + - Testing strategy and implementation recommendations + - **Start here for detailed technical analysis** + +2. **[OPTIMIZATION_QUICK_GUIDE.md](OPTIMIZATION_QUICK_GUIDE.md)** + - Quick-reference implementation guide + - Copy-paste code snippets for each optimization + - Organized by complexity (quick wins → complex) + - Performance testing templates + - **Start here for implementation work** + +3. **[PERFORMANCE_FINDINGS_SUMMARY.md](PERFORMANCE_FINDINGS_SUMMARY.md)** + - Executive summary of findings + - Implementation roadmap with phases + - Risk assessment and success metrics + - Questions for maintainers + - **Start here for planning and prioritization** + +## Key Findings Summary + +| Issue | Location | Impact | Status | +|-------|----------|--------|--------| +| O(n³) gradient computation | ai-projects/quantum-ml/web_app.py:217-246 | 10-100x | Not fixed | +| Repeated JSON file I/O | dashboard/serve.py:273-515 | 5-10x | Not fixed | +| Linear keyword searches | ai-projects/chat-cli/src/agi_provider.py:343-372 | 3-30x | Not fixed | +| Inconsistent keyword patterns | aria_web/server.py:554-557 | 2-5x | Partial | +| Multi-pass statistics | ai-projects/quantum-ml/web_app.py:952-958 | 4x | Not fixed | +| Inefficient directory traversal | dashboard/serve.py:700,761,766 | 5-20% | Not fixed | +| Rate limiting filtering | dashboard/serve.py:39-40 | 2-5x | Not fixed | +| Minor list comprehensions | ai-projects/quantum-ml/web_app.py:440-443 | <5% | Not fixed | + +## Historical Documentation + +### Previous Optimizations (Implemented) + +- **[PERFORMANCE_IMPROVEMENTS.md](PERFORMANCE_IMPROVEMENTS.md)** - Completed optimizations with test results + - Tokenizer caching (100-500ms → 0.1ms) + - Cosine similarity with NumPy (8x speedup) + - OpenAI client caching (50-100ms → 0ms) + - Dataset validation streaming (O(file_size) → O(line_size)) + - LM Studio health check caching (1000ms → 0ms) + - SQL repository limit optimization (2-10,000x) + - String building with join() (O(n²) → O(n)) + - Dictionary index for lookups (O(n²) → O(n)) + - Frozenset keyword matching (already applied in aria_web/server.py) + +### Analysis Reports + +- **[PERFORMANCE_ANALYSIS.md](PERFORMANCE_ANALYSIS.md)** - Performance profiling and bottleneck analysis +- **[PERFORMANCE_IMPROVEMENTS_REPORT.md](PERFORMANCE_IMPROVEMENTS_REPORT.md)** - Completed work report +- **[PERFORMANCE_OPTIMIZATIONS_SUMMARY.md](PERFORMANCE_OPTIMIZATIONS_SUMMARY.md)** - Summary of optimization patterns +- **[PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md](PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md)** - Recent optimizations + +### Implementation Guides + +- **[PERFORMANCE_IMPLEMENTATION_GUIDE.md](PERFORMANCE_IMPLEMENTATION_GUIDE.md)** - How to implement optimizations +- **[PERFORMANCE_QUICK_FIXES.md](PERFORMANCE_QUICK_FIXES.md)** - Quick fix patterns + +### Future Work + +- **[FUTURE_PERFORMANCE_OPPORTUNITIES.md](FUTURE_PERFORMANCE_OPPORTUNITIES.md)** - Identified but not yet implemented +- **[FUTURE_PERFORMANCE_OPTIMIZATIONS.md](FUTURE_PERFORMANCE_OPTIMIZATIONS.md)** - Planned improvements + +## Testing Infrastructure + +### Test Files +- `tests/test_performance_optimizations.py` - Performance-specific test suite (330+ tests total) +- `scripts/test_runner.py` - Test orchestration and execution + +### Testing Strategy +1. **Unit tests** - Verify correctness of optimized code +2. **Performance benchmarks** - Measure actual speedup with `time.perf_counter()` +3. **Integration tests** - Ensure no regressions in API behavior +4. **Load tests** - Validate improvements under concurrent load + +## Optimization Patterns Library + +### Established Patterns (Use These) + +1. **LRU Caching** + ```python + from functools import lru_cache + @lru_cache(maxsize=8) + def expensive_function(arg): + # Implementation + ``` + +2. **TTL Caching** + ```python + _cache = {} + _TTL = 5 + def cached_load(key): + now = time.time() + if key in _cache and (now - _cache[key][1]) < _TTL: + return _cache[key][0] + # Load and cache + ``` + +3. **Frozenset Keyword Matching** + ```python + KEYWORDS = frozenset(['word1', 'word2']) + word_set = set(text.split()) + if word_set & KEYWORDS: # O(1) intersection + # Match found + ``` + +4. **Generator-Based Counting** + ```python + count = sum(1 for item in items if condition(item)) + ``` + +5. **Single-Pass Collection Processing** + ```python + for item in collection: # One iteration + # Accumulate all needed metrics + ``` + +6. **NumPy Vectorization** + ```python + # Use NumPy operations instead of loops + result = np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) + ``` + +7. **Dictionary Indexing** + ```python + index = {item.id: item for item in items} # O(n) build + result = index.get(search_id) # O(1) lookup + ``` + +8. **Connection Pooling** + ```python + # Reuse database/API connections instead of creating new ones + _connection_pool = [] + ``` + +## Performance Memory Index + +Recent optimization patterns stored in repository memory: + +- **Quantum gradient optimization** - Use qml.grad() for autograd (10-100x speedup) +- **File caching pattern** - TTL-based JSON caching with stale-on-error fallback +- **Frozenset keyword matching** - Pre-compiled sets with O(1) intersection +- **String building in loops** - Use list accumulation + join() (O(n) vs O(n²)) +- **Dictionary index for lookups** - O(1) lookups vs O(n) linear search +- **Single-pass collection** - Accumulate multiple metrics in one iteration +- **Connection pooling** - Reuse expensive connections (50-100ms savings) +- **Set union optimization** - Use set().union(*iterables) for key collection + +## Quick Start for Developers + +### Implementing a New Optimization + +1. **Read** the detailed analysis in `CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md` +2. **Copy** the implementation from `OPTIMIZATION_QUICK_GUIDE.md` +3. **Test** using patterns from `tests/test_performance_optimizations.py` +4. **Benchmark** with `time.perf_counter()` before/after +5. **Document** results in this directory +6. **Store** new patterns in repository memory + +### Reviewing Performance + +1. **Check** if optimization pattern already exists in this index +2. **Review** previous work in `PERFORMANCE_IMPROVEMENTS.md` +3. **Consult** repository memories for recent patterns +4. **Follow** established patterns from this library + +### Finding Inefficiencies + +1. **Profile** with cProfile or line_profiler +2. **Look for** anti-patterns: + - Nested loops with repeated operations + - File/DB I/O in loops + - String concatenation with += in loops + - Repeated any() calls with inline lists + - List comprehensions for counting + - Multiple iterations over same collection +3. **Compare** against patterns in this index +4. **Document** findings following template in `CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md` + +## Metrics and Goals + +### Current Performance Profile +- **Quantum training:** ~1000ms per gradient computation +- **Dashboard refresh:** 50-200ms per file read (×4-5 per request) +- **Query processing:** 1-10ms depending on query length +- **Rate limiting:** O(n) per request + +### Target Performance (After All Optimizations) +- **Quantum training:** ~10-50ms per gradient (10-100x improvement) +- **Dashboard refresh:** <10ms per cached read (5-10x improvement) +- **Query processing:** <1ms per query (3-30x improvement) +- **Rate limiting:** O(1) amortized per request (2-5x improvement) + +### Overall Impact +- **Total potential speedup:** 10-1000x in affected code paths +- **Implementation effort:** 6.5-10.5 hours total +- **Risk level:** Low (all optimizations include fallbacks) +- **ROI:** Extremely high + +## Contributing + +When adding performance optimizations: + +1. Create detailed analysis document (use `CODE_INEFFICIENCY_ANALYSIS_2026-02-17.md` as template) +2. Create implementation guide (use `OPTIMIZATION_QUICK_GUIDE.md` as template) +3. Add tests to `tests/test_performance_optimizations.py` +4. Update this index with new findings +5. Store patterns in repository memory for future reference +6. Document actual measured improvements + +## Questions? + +See `PERFORMANCE_FINDINGS_SUMMARY.md` for questions to ask maintainers before implementation. + +--- + +**Last Updated:** February 17, 2026 +**Total Optimizations Documented:** 8 new + 10 previous = 18 total +**Status:** Ready for implementation diff --git a/docs/PERFORMANCE_OPTIMIZATIONS.md b/docs/PERFORMANCE_OPTIMIZATIONS.md index 05187a11a..e107e816a 100644 --- a/docs/PERFORMANCE_OPTIMIZATIONS.md +++ b/docs/PERFORMANCE_OPTIMIZATIONS.md @@ -1,228 +1,228 @@ -# Performance Optimizations - -## Overview - -This document describes performance optimizations applied to the Aria repository to improve execution speed, reduce resource consumption, and enhance responsiveness of automation systems. - -## Optimizations Applied - -### 1. Debounced File I/O (HIGH IMPACT) - -**Problem**: Status files were written on every state change, causing excessive disk I/O. - -**Solution**: Implemented debouncing with configurable intervals: -- `autonomous_training_orchestrator.py`: 2-second minimum interval between writes -- Added `force` parameter for critical writes (shutdown, errors) -- Added `_flush_status()` to ensure pending writes complete - -**Impact**: 70-80% reduction in file I/O operations - -**Pattern**: -```python -class Orchestrator: - def __init__(self): - self._status_dirty = False - self._last_status_write = 0 - self._status_write_interval = 2.0 - - def save_status(self, force: bool = False): - current_time = time.time() - if force or (current_time - self._last_status_write >= self._status_write_interval): - # Write to disk - self._last_status_write = current_time - self._status_dirty = False - else: - self._status_dirty = True -``` - -### 2. Cached Filesystem Operations (MEDIUM IMPACT) - -**Problem**: Glob patterns and directory scans were repeated without caching. - -**Solutions**: -- `autonomous_training_orchestrator.py`: Cached glob results with 30-second TTL -- `quantum_llm_trainer.py`: Combined multiple glob calls into single pattern - -**Impact**: 50% reduction in filesystem operations - -**Pattern**: -```python -def _cached_glob(self, path: Path, pattern: str) -> List[Path]: - cache_key = f"{path}::{pattern}" - current_time = time.time() - - if cache_key in self._glob_cache: - cache_time = self._glob_cache_time.get(cache_key, 0) - if current_time - cache_time < self._glob_cache_ttl: - return self._glob_cache[cache_key] - - results = list(path.glob(pattern)) - self._glob_cache[cache_key] = results - self._glob_cache_time[cache_key] = current_time - return results -``` - -### 3. Process and Port Caching (HIGH IMPACT) - -**Problem**: Expensive `psutil.process_iter()` calls and socket creation repeated unnecessarily. - -**Solutions**: -- `aria_automation.py`: Process list caching with 10-second TTL -- `aria_automation.py`: Port check caching with 5-second TTL - -**Impact**: ~90% reduction in process scanning overhead - -**Pattern**: -```python -def _get_process_list(self) -> List[psutil.Process]: - current_time = time.time() - if self._process_cache is not None: - if current_time - self._process_cache_time < self._process_cache_ttl: - return self._process_cache - - self._process_cache = list(psutil.process_iter(['pid', 'name', 'cmdline'])) - self._process_cache_time = current_time - return self._process_cache -``` - -### 4. Exponential Backoff for Polling (HIGH IMPACT) - -**Problem**: Fixed 1-second sleep intervals caused slow startup detection and wasted CPU. - -**Solution**: Exponential backoff starting at 0.1s, doubling up to 1s maximum. - -**Impact**: Up to 90% faster startup detection - -**Pattern**: -```python -check_interval = 0.1 -elapsed = 0 -while elapsed < max_wait: - if condition_met(): - return True - time.sleep(check_interval) - elapsed += check_interval - check_interval = min(check_interval * 2, 1.0) -``` - -### 5. O(1) Dictionary Lookups (CRITICAL IMPACT) - -**Problem**: Linear searches through result lists for model lookups (O(n) → O(n²) for batch operations). - -**Solution**: Build lookup dictionary once, use `.get()` for O(1) access. - -**Impact**: 99% faster model comparisons - -**Pattern**: -```python -class BatchEvaluator: - def __init__(self): - self._results_cache: Dict[str, EvaluationResult] = {} - - def process_result(self, result): - self.results.append(result) - self._results_cache[result.model_id] = result - - def get_model(self, model_id): - return self._results_cache.get(model_id) # O(1) instead of O(n) -``` - -### 6. Single-Pass Aggregation (MEDIUM IMPACT) - -**Problem**: Multiple iterations over result sets for different statistics. - -**Solution**: Compute all statistics in one loop. - -**Impact**: 67% fewer iterations (O(3n) → O(n)) - -**Pattern**: -```python -# Before (3 passes): -succeeded = sum(1 for r in results if r.status == 'succeeded') -skipped = sum(1 for r in results if r.status == 'skipped') -failed = sum(1 for r in results if r.status == 'failed') - -# After (1 pass): -succeeded = skipped = failed = 0 -for r in results: - if r.status == 'succeeded': - succeeded += 1 - elif r.status == 'skipped': - skipped += 1 - else: - failed += 1 -``` - -## Files Modified - -| File | Optimizations | Impact | -|------|--------------|--------| -| `scripts/autonomous_training_orchestrator.py` | Debounced writes, glob caching | 70-80% I/O reduction | -| `scripts/aria_automation.py` | Port/process caching, exponential backoff | 90% scanning reduction | -| `scripts/repo_automation.py` | Improved polling intervals | 90% faster startup | -| `scripts/batch_evaluator.py` | O(1) lookups, single-pass aggregation | 99% faster comparisons | -| `scripts/parallel_train.py` | Single-pass aggregation | 67% fewer iterations | -| `scripts/quantum_llm_trainer.py` | Combined glob operations | 50% fewer filesystem ops | - -## Testing - -All optimizations have been validated: -- ✅ Syntax validation with `py_compile` -- ✅ Unit tests for caching mechanisms -- ✅ Verification of cached lookups -- ✅ Single-pass aggregation correctness - -## Future Recommendations - -1. **Apply Similar Patterns**: Look for similar inefficiency patterns in other scripts: - - Repeated file reads in loops - - Missing caching for expensive operations - - Multiple passes over data structures - - Linear searches that could use dictionaries - -2. **Monitor Performance**: Add timing instrumentation to identify new bottlenecks: - ```python - import time - start = time.time() - # expensive operation - logger.debug(f"Operation took {time.time() - start:.2f}s") - ``` - -3. **Consider Async/Await**: For I/O-heavy operations, consider converting to async patterns: - - File operations with `aiofiles` - - HTTP requests with `aiohttp` - - Database queries with async drivers - -4. **Profile Before Optimizing**: Use `cProfile` or `py-spy` to identify actual bottlenecks before optimizing. - -## Performance Best Practices - -### DO: -- ✅ Cache expensive operations (filesystem, network, process scanning) -- ✅ Use dictionaries for lookups instead of linear searches -- ✅ Combine multiple passes into single loops -- ✅ Debounce frequent writes to disk -- ✅ Use exponential backoff for polling -- ✅ Profile code to find real bottlenecks - -### DON'T: -- ❌ Optimize without measuring first -- ❌ Cache data that changes frequently -- ❌ Use fixed sleep intervals for polling -- ❌ Perform O(n) searches when O(1) lookups are possible -- ❌ Write to disk on every state change -- ❌ Scan all processes repeatedly - -## Backward Compatibility - -All optimizations maintain backward compatibility: -- No changes to public APIs -- No breaking changes to existing code -- Graceful degradation if cache misses occur -- Force write available for critical operations - -## References - -- Repository memories stored for future reference -- Test cases in commit history -- Performance benchmarks in PR comments +# Performance Optimizations + +## Overview + +This document describes performance optimizations applied to the Aria repository to improve execution speed, reduce resource consumption, and enhance responsiveness of automation systems. + +## Optimizations Applied + +### 1. Debounced File I/O (HIGH IMPACT) + +**Problem**: Status files were written on every state change, causing excessive disk I/O. + +**Solution**: Implemented debouncing with configurable intervals: +- `autonomous_training_orchestrator.py`: 2-second minimum interval between writes +- Added `force` parameter for critical writes (shutdown, errors) +- Added `_flush_status()` to ensure pending writes complete + +**Impact**: 70-80% reduction in file I/O operations + +**Pattern**: +```python +class Orchestrator: + def __init__(self): + self._status_dirty = False + self._last_status_write = 0 + self._status_write_interval = 2.0 + + def save_status(self, force: bool = False): + current_time = time.time() + if force or (current_time - self._last_status_write >= self._status_write_interval): + # Write to disk + self._last_status_write = current_time + self._status_dirty = False + else: + self._status_dirty = True +``` + +### 2. Cached Filesystem Operations (MEDIUM IMPACT) + +**Problem**: Glob patterns and directory scans were repeated without caching. + +**Solutions**: +- `autonomous_training_orchestrator.py`: Cached glob results with 30-second TTL +- `quantum_llm_trainer.py`: Combined multiple glob calls into single pattern + +**Impact**: 50% reduction in filesystem operations + +**Pattern**: +```python +def _cached_glob(self, path: Path, pattern: str) -> List[Path]: + cache_key = f"{path}::{pattern}" + current_time = time.time() + + if cache_key in self._glob_cache: + cache_time = self._glob_cache_time.get(cache_key, 0) + if current_time - cache_time < self._glob_cache_ttl: + return self._glob_cache[cache_key] + + results = list(path.glob(pattern)) + self._glob_cache[cache_key] = results + self._glob_cache_time[cache_key] = current_time + return results +``` + +### 3. Process and Port Caching (HIGH IMPACT) + +**Problem**: Expensive `psutil.process_iter()` calls and socket creation repeated unnecessarily. + +**Solutions**: +- `aria_automation.py`: Process list caching with 10-second TTL +- `aria_automation.py`: Port check caching with 5-second TTL + +**Impact**: ~90% reduction in process scanning overhead + +**Pattern**: +```python +def _get_process_list(self) -> List[psutil.Process]: + current_time = time.time() + if self._process_cache is not None: + if current_time - self._process_cache_time < self._process_cache_ttl: + return self._process_cache + + self._process_cache = list(psutil.process_iter(['pid', 'name', 'cmdline'])) + self._process_cache_time = current_time + return self._process_cache +``` + +### 4. Exponential Backoff for Polling (HIGH IMPACT) + +**Problem**: Fixed 1-second sleep intervals caused slow startup detection and wasted CPU. + +**Solution**: Exponential backoff starting at 0.1s, doubling up to 1s maximum. + +**Impact**: Up to 90% faster startup detection + +**Pattern**: +```python +check_interval = 0.1 +elapsed = 0 +while elapsed < max_wait: + if condition_met(): + return True + time.sleep(check_interval) + elapsed += check_interval + check_interval = min(check_interval * 2, 1.0) +``` + +### 5. O(1) Dictionary Lookups (CRITICAL IMPACT) + +**Problem**: Linear searches through result lists for model lookups (O(n) → O(n²) for batch operations). + +**Solution**: Build lookup dictionary once, use `.get()` for O(1) access. + +**Impact**: 99% faster model comparisons + +**Pattern**: +```python +class BatchEvaluator: + def __init__(self): + self._results_cache: Dict[str, EvaluationResult] = {} + + def process_result(self, result): + self.results.append(result) + self._results_cache[result.model_id] = result + + def get_model(self, model_id): + return self._results_cache.get(model_id) # O(1) instead of O(n) +``` + +### 6. Single-Pass Aggregation (MEDIUM IMPACT) + +**Problem**: Multiple iterations over result sets for different statistics. + +**Solution**: Compute all statistics in one loop. + +**Impact**: 67% fewer iterations (O(3n) → O(n)) + +**Pattern**: +```python +# Before (3 passes): +succeeded = sum(1 for r in results if r.status == 'succeeded') +skipped = sum(1 for r in results if r.status == 'skipped') +failed = sum(1 for r in results if r.status == 'failed') + +# After (1 pass): +succeeded = skipped = failed = 0 +for r in results: + if r.status == 'succeeded': + succeeded += 1 + elif r.status == 'skipped': + skipped += 1 + else: + failed += 1 +``` + +## Files Modified + +| File | Optimizations | Impact | +|------|--------------|--------| +| `scripts/autonomous_training_orchestrator.py` | Debounced writes, glob caching | 70-80% I/O reduction | +| `scripts/aria_automation.py` | Port/process caching, exponential backoff | 90% scanning reduction | +| `scripts/repo_automation.py` | Improved polling intervals | 90% faster startup | +| `scripts/batch_evaluator.py` | O(1) lookups, single-pass aggregation | 99% faster comparisons | +| `scripts/parallel_train.py` | Single-pass aggregation | 67% fewer iterations | +| `scripts/quantum_llm_trainer.py` | Combined glob operations | 50% fewer filesystem ops | + +## Testing + +All optimizations have been validated: +- ✅ Syntax validation with `py_compile` +- ✅ Unit tests for caching mechanisms +- ✅ Verification of cached lookups +- ✅ Single-pass aggregation correctness + +## Future Recommendations + +1. **Apply Similar Patterns**: Look for similar inefficiency patterns in other scripts: + - Repeated file reads in loops + - Missing caching for expensive operations + - Multiple passes over data structures + - Linear searches that could use dictionaries + +2. **Monitor Performance**: Add timing instrumentation to identify new bottlenecks: + ```python + import time + start = time.time() + # expensive operation + logger.debug(f"Operation took {time.time() - start:.2f}s") + ``` + +3. **Consider Async/Await**: For I/O-heavy operations, consider converting to async patterns: + - File operations with `aiofiles` + - HTTP requests with `aiohttp` + - Database queries with async drivers + +4. **Profile Before Optimizing**: Use `cProfile` or `py-spy` to identify actual bottlenecks before optimizing. + +## Performance Best Practices + +### DO: +- ✅ Cache expensive operations (filesystem, network, process scanning) +- ✅ Use dictionaries for lookups instead of linear searches +- ✅ Combine multiple passes into single loops +- ✅ Debounce frequent writes to disk +- ✅ Use exponential backoff for polling +- ✅ Profile code to find real bottlenecks + +### DON'T: +- ❌ Optimize without measuring first +- ❌ Cache data that changes frequently +- ❌ Use fixed sleep intervals for polling +- ❌ Perform O(n) searches when O(1) lookups are possible +- ❌ Write to disk on every state change +- ❌ Scan all processes repeatedly + +## Backward Compatibility + +All optimizations maintain backward compatibility: +- No changes to public APIs +- No breaking changes to existing code +- Graceful degradation if cache misses occur +- Force write available for critical operations + +## References + +- Repository memories stored for future reference +- Test cases in commit history +- Performance benchmarks in PR comments diff --git a/docs/PERFORMANCE_OPTIMIZATIONS_FEB_2026.md b/docs/PERFORMANCE_OPTIMIZATIONS_FEB_2026.md index 9ad9146ab..713e0fe57 100644 --- a/docs/PERFORMANCE_OPTIMIZATIONS_FEB_2026.md +++ b/docs/PERFORMANCE_OPTIMIZATIONS_FEB_2026.md @@ -1,404 +1,404 @@ -# Performance Optimizations - February 2026 - -This document details performance optimizations implemented in February 2026, including motivation, implementation, and measured impact. - -## Summary - -Five high-impact optimizations targeting hot paths in data processing, command parsing, and quantum ML training: - -| File | Optimization | Estimated Speedup | Lines Changed | -|------|--------------|-------------------|---------------| -| `scripts/extract_chat_logs_dataset.py` | Single-pass role checking | 2x | 3 | -| `scripts/job_queue.py` | Set intersection for tag filtering | 5-50x | 3 | -| `function_app.py` | Command pattern table | 5-20x | 30 | -| `scripts/generate_evaluation_set.py` | Single-pass file reading | 2-3x | 35 | -| `ai-projects/quantum-ml/web_app.py` | PennyLane autograd gradients | 10-100x | 40 | - -**Total estimated impact:** 24-175x cumulative speedup across affected code paths. - ---- - -## 1. Single-Pass Role Checking - -**File:** `scripts/extract_chat_logs_dataset.py` -**Lines:** 65-73 -**Impact:** 2x speedup in rolling window validation - -### Problem - -The original code traversed the message window twice to check for user and assistant roles: - -```python -if any(x.get("role") == "user" for x in window) and any(x.get("role") == "assistant" for x in window): - examples.append({"messages": window}) -``` - -This performs O(2n) traversals for each window. - -### Solution - -Use a single-pass set comprehension to collect all roles, then check membership: - -```python -# Single-pass collection optimization: check roles in one pass -roles = {x.get("role") for x in window} -if "user" in roles and "assistant" in roles: - examples.append({"messages": window}) -``` - -This reduces complexity from O(2n) to O(n). - -### Validation - -- **Test:** `tests/test_performance_optimizations.py::TestCollectionOptimizations::test_single_pass_role_checking` -- **Behavior:** Verified identical results between old and new approaches -- **Performance:** 2x faster for typical window sizes (3-10 messages) - ---- - -## 2. Set Intersection for Tag Filtering - -**File:** `scripts/job_queue.py` -**Lines:** 294-296 -**Impact:** 5-50x speedup in job filtering by tags - -### Problem - -The original code used nested iterations to check if any filter tag matched any job tag: - -```python -if tags: - jobs = [j for j in jobs if any(tag in j.tags for tag in tags)] -``` - -This performs O(n_jobs × n_filter_tags × n_job_tags) operations, which becomes very slow when: -- Many jobs exist (100s-1000s) -- Each job has multiple tags (5-10) -- Filtering by multiple tags (2-5) - -### Solution - -Convert both tag lists to sets and use set intersection: - -```python -if tags: - # Set intersection optimization: convert to sets for O(n) instead of O(n²) lookup - tags_set = set(tags) - jobs = [j for j in jobs if set(j.tags) & tags_set] -``` - -This reduces complexity from O(n³) to O(n_jobs × n_tags) where n_tags is typically small. - -### Validation - -- **Test:** `tests/test_performance_optimizations.py::TestCollectionOptimizations::test_set_intersection_tag_filtering` -- **Behavior:** Verified identical filtering results -- **Performance:** 5-50x faster depending on number of jobs and tags - ---- - -## 3. Command Pattern Table - -**File:** `function_app.py` -**Lines:** 560-602 (before) → 560-590 (after) -**Impact:** 5-20x speedup in movement command parsing - -### Problem - -The original code had 12 separate `if` statements, each checking multiple patterns: - -```python -lower_text = text.lower() -commands = [] - -if '[aria:walk:left]' in lower_text or 'walk left' in lower_text: - commands.append({'action': 'walk', 'direction': 'left', 'distance': 200}) -if '[aria:walk:right]' in lower_text or 'walk right' in lower_text: - commands.append({'action': 'walk', 'direction': 'right', 'distance': 200}) -# ... 10 more similar checks -``` - -Issues: -- Text lowercased once but checked 12+ times -- Each check repeats the pattern matching logic -- No opportunity for compiler optimization -- Code is repetitive and error-prone - -### Solution - -Pre-define a command pattern lookup table at module level: - -```python -# Command pattern lookup table for O(1) matching -_COMMAND_PATTERNS = ( - (('[aria:walk:left]', 'walk left'), {'action': 'walk', 'direction': 'left', 'distance': 200}), - (('[aria:walk:right]', 'walk right'), {'action': 'walk', 'direction': 'right', 'distance': 200}), - # ... all patterns -) - -def parse_movement_commands(text: str) -> dict: - """Parse movement commands from AI response text using optimized pattern matching""" - lower_text = text.lower() - commands = [] - - # Single pass through command patterns - check each pattern once - for patterns, command in _COMMAND_PATTERNS: - if any(pattern in lower_text for pattern in patterns): - commands.append(command) - - return {'commands': commands} if commands else {} -``` - -Benefits: -- Data-driven design (easy to add/modify commands) -- Single iteration through patterns -- Better cache locality -- Reduced code duplication - -### Validation - -- **Test:** `tests/test_performance_optimizations.py::TestCommandParsingOptimizations` -- **Behavior:** Verified all command patterns work identically -- **Performance:** 100 iterations complete in <10ms (5-20x faster than original) - ---- - -## 4. Single-Pass File Reading - -**File:** `scripts/generate_evaluation_set.py` -**Lines:** 50-99 (before) → 50-95 (after) -**Impact:** 2-3x speedup in evaluation dataset generation - -### Problem - -The original code read the same dataset files multiple times: - -1. **First pass (line 74-75):** Collect training hashes - ```python - for src in args.sources: - training_hashes |= collect_training_hashes(Path(src)) - ``` - This reads `train.json` and `test.json` for each source. - -2. **Second pass (line 84-92):** Read files again to build candidates - ```python - for cf in candidate_files: - for rec in read_jsonl(cf): # Re-reads same files - h = rec.get("hash") or hash_messages(...) - ``` - -3. **Third pass (line 95-99):** If no candidates, read files yet again - ```python - if not candidates: - for cf in candidate_files: - for rec in read_jsonl(cf): # Third time reading! - ``` - -For large datasets (1000s of records), this meant: -- 3x I/O operations -- 2-3x hash computations -- 2-3x JSON parsing - -### Solution - -Read and process each file exactly once, caching the results: - -```python -def collect_training_hashes_and_records(dataset_dir: Path) -> tuple[Set[str], List[Dict]]: - """Collect training hashes and records in a single pass to avoid re-reading files""" - hashes: Set[str] = set() - records: List[Dict] = [] - for split_file in [dataset_dir / "train.json", dataset_dir / "test.json"]: - for rec in read_jsonl(split_file): - h = rec.get("hash") or hash_messages(rec.get("messages", [])) - hashes.add(h) - rec["hash"] = h # Ensure hash is stored - records.append(rec) - return hashes, records -``` - -Then use cached records: - -```python -# Collect training hashes and records in a single pass -source_records_cache: Dict[str, List[Dict]] = {} -for src in args.sources: - hashes, records = collect_training_hashes_and_records(src_path) - training_hashes |= hashes - source_records_cache[str(src_path)] = records - -# Later: use cached records instead of re-reading -all_records = source_records_cache.get(str(src_path), []) -``` - -### Validation - -- **Test:** `tests/test_performance_optimizations.py::TestFileReadingOptimizations::test_single_pass_file_reading` -- **Behavior:** Verified identical dataset generation -- **Performance:** 2-3x faster for typical dataset sizes (100-1000 records) - ---- - -## 5. PennyLane Autograd for Gradient Computation - -**File:** `ai-projects/quantum-ml/web_app.py` -**Lines:** 217-246 -**Impact:** 10-100x speedup in quantum circuit training - -### Problem - -The original code manually implemented the parameter-shift rule using triple-nested loops: - -```python -def compute_gradient(circuit, X, y, weights, use_parameter_shift=True): - grad = np.zeros_like(weights) - - if use_parameter_shift: - shift = np.pi / 2 - for i in range(weights.shape[0]): # Layer - for j in range(weights.shape[1]): # Qubit - for k in range(weights.shape[2]): # Rotation parameter - # Shift parameter and compute loss twice - weights_plus = weights.copy() - weights_minus = weights.copy() - weights_plus[i, j, k] += shift - weights_minus[i, j, k] -= shift - loss_plus = compute_loss(circuit, X, y, weights_plus) - loss_minus = compute_loss(circuit, X, y, weights_minus) - grad[i, j, k] = (loss_plus - loss_minus) / 2 -``` - -For a typical circuit: -- 4 qubits, 3 layers, 3 rotation parameters = 36 total parameters -- Each gradient computation requires **72 circuit evaluations** (2 per parameter) -- Each circuit evaluation iterates through all training samples -- Total: O(n_params × n_samples × 2) operations - -Example: 36 params × 100 samples × 2 = **7,200 circuit evaluations per gradient** - -### Solution - -Leverage PennyLane's built-in automatic differentiation: - -```python -def compute_gradient(circuit, X, y, weights, use_parameter_shift=True): - """Compute gradient using PennyLane's built-in automatic differentiation - - This is dramatically faster than manual parameter-shift implementation as it: - - Uses vectorized operations internally - - Leverages hardware acceleration when available - - Avoids redundant circuit evaluations - """ - def loss_fn(w): - return compute_loss(circuit, X, y, w) - - try: - # Use PennyLane's built-in gradient computation - grad_fn = qml.grad(loss_fn) - grad = grad_fn(weights) - except Exception: - # Fallback to manual parameter-shift if autograd fails - # [original implementation as fallback] -``` - -Benefits: -- **Automatic differentiation:** PennyLane computes gradients efficiently using its graph-based approach -- **Optimized circuit caching:** Reuses intermediate circuit results where possible -- **Hardware acceleration:** Can leverage GPU/TPU when available -- **Maintains accuracy:** Uses parameter-shift rule internally but optimized - -The quantum circuit already uses `@qml.qnode(dev, interface='autograd')`, which enables automatic differentiation. - -### Validation - -- **Test:** Manual validation pending (requires PennyLane installation) -- **Behavior:** Falls back to original implementation if autograd fails -- **Performance:** Expected 10-100x speedup based on PennyLane benchmarks - -### Safety - -The implementation includes a fallback to the original manual parameter-shift implementation, ensuring: -- Zero behavioral change if autograd fails -- No breaking changes to existing code -- Graceful degradation in edge cases - ---- - -## Best Practices Applied - -These optimizations follow established patterns from the repository: - -1. **Single-pass collection checks** (Memory: "single-pass collection optimization") - - Build set once, check membership O(1) - - Used in `extract_chat_logs_dataset.py` and `job_queue.py` - -2. **Pre-compiled data structures** (Memory: "frozenset keyword matching") - - Define lookup tables at module level - - Used in `function_app.py` command patterns - -3. **Caching repeated operations** (Memory: "dictionary index for lookups") - - Read files once, cache results - - Used in `generate_evaluation_set.py` - -4. **Library-native optimizations** (Memory: "best practices") - - Use framework features (PennyLane autograd) - - Used in `ai-projects/quantum-ml/web_app.py` - ---- - -## Testing Strategy - -All optimizations include: - -1. **Functional tests:** Verify identical behavior to original -2. **Performance tests:** Ensure measurable speedup -3. **Edge case tests:** Empty inputs, single items, large datasets - -Test coverage: 6 new test methods in `tests/test_performance_optimizations.py`: -- `TestCollectionOptimizations` (3 tests) -- `TestCommandParsingOptimizations` (2 tests) -- `TestFileReadingOptimizations` (1 test) - -All tests pass with 100% success rate. - ---- - -## Future Opportunities - -Additional optimizations identified but not yet implemented: - -1. **aria_web/server.py** (lines 220-263) - - Repeated `any()` calls with generators - - Opportunity: Pre-compile keyword sets as frozensets - - Estimated impact: 2-10x speedup - -2. **ai-projects/quantum-ml/web_app.py** (lines 516-518) - - Repeated list slicing in loops - - Opportunity: Slice once, reuse - - Estimated impact: 1.5-2x speedup - -3. **Multiple files** - - Regex compilation in loops - - Opportunity: Pre-compile at module level - - Estimated impact: 2-5x speedup (see Memory: "regex pattern compilation") - ---- - -## Rollout Notes - -These optimizations are: -- **Non-breaking:** All maintain identical external behavior -- **Well-tested:** 6 new test cases with 100% pass rate -- **Documented:** This file + inline comments -- **Measurable:** Benchmark tests included - -Safe to deploy immediately with no migration needed. - ---- - -## References - -- Repository memories: Performance optimization patterns -- Existing optimizations: `docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md` -- Test suite: `tests/test_performance_optimizations.py` -- PennyLane docs: https://pennylane.ai/qml/glossary/quantum_differentiable_programming.html +# Performance Optimizations - February 2026 + +This document details performance optimizations implemented in February 2026, including motivation, implementation, and measured impact. + +## Summary + +Five high-impact optimizations targeting hot paths in data processing, command parsing, and quantum ML training: + +| File | Optimization | Estimated Speedup | Lines Changed | +|------|--------------|-------------------|---------------| +| `scripts/extract_chat_logs_dataset.py` | Single-pass role checking | 2x | 3 | +| `scripts/job_queue.py` | Set intersection for tag filtering | 5-50x | 3 | +| `function_app.py` | Command pattern table | 5-20x | 30 | +| `scripts/generate_evaluation_set.py` | Single-pass file reading | 2-3x | 35 | +| `ai-projects/quantum-ml/web_app.py` | PennyLane autograd gradients | 10-100x | 40 | + +**Total estimated impact:** 24-175x cumulative speedup across affected code paths. + +--- + +## 1. Single-Pass Role Checking + +**File:** `scripts/extract_chat_logs_dataset.py` +**Lines:** 65-73 +**Impact:** 2x speedup in rolling window validation + +### Problem + +The original code traversed the message window twice to check for user and assistant roles: + +```python +if any(x.get("role") == "user" for x in window) and any(x.get("role") == "assistant" for x in window): + examples.append({"messages": window}) +``` + +This performs O(2n) traversals for each window. + +### Solution + +Use a single-pass set comprehension to collect all roles, then check membership: + +```python +# Single-pass collection optimization: check roles in one pass +roles = {x.get("role") for x in window} +if "user" in roles and "assistant" in roles: + examples.append({"messages": window}) +``` + +This reduces complexity from O(2n) to O(n). + +### Validation + +- **Test:** `tests/test_performance_optimizations.py::TestCollectionOptimizations::test_single_pass_role_checking` +- **Behavior:** Verified identical results between old and new approaches +- **Performance:** 2x faster for typical window sizes (3-10 messages) + +--- + +## 2. Set Intersection for Tag Filtering + +**File:** `scripts/job_queue.py` +**Lines:** 294-296 +**Impact:** 5-50x speedup in job filtering by tags + +### Problem + +The original code used nested iterations to check if any filter tag matched any job tag: + +```python +if tags: + jobs = [j for j in jobs if any(tag in j.tags for tag in tags)] +``` + +This performs O(n_jobs × n_filter_tags × n_job_tags) operations, which becomes very slow when: +- Many jobs exist (100s-1000s) +- Each job has multiple tags (5-10) +- Filtering by multiple tags (2-5) + +### Solution + +Convert both tag lists to sets and use set intersection: + +```python +if tags: + # Set intersection optimization: convert to sets for O(n) instead of O(n²) lookup + tags_set = set(tags) + jobs = [j for j in jobs if set(j.tags) & tags_set] +``` + +This reduces complexity from O(n³) to O(n_jobs × n_tags) where n_tags is typically small. + +### Validation + +- **Test:** `tests/test_performance_optimizations.py::TestCollectionOptimizations::test_set_intersection_tag_filtering` +- **Behavior:** Verified identical filtering results +- **Performance:** 5-50x faster depending on number of jobs and tags + +--- + +## 3. Command Pattern Table + +**File:** `function_app.py` +**Lines:** 560-602 (before) → 560-590 (after) +**Impact:** 5-20x speedup in movement command parsing + +### Problem + +The original code had 12 separate `if` statements, each checking multiple patterns: + +```python +lower_text = text.lower() +commands = [] + +if '[aria:walk:left]' in lower_text or 'walk left' in lower_text: + commands.append({'action': 'walk', 'direction': 'left', 'distance': 200}) +if '[aria:walk:right]' in lower_text or 'walk right' in lower_text: + commands.append({'action': 'walk', 'direction': 'right', 'distance': 200}) +# ... 10 more similar checks +``` + +Issues: +- Text lowercased once but checked 12+ times +- Each check repeats the pattern matching logic +- No opportunity for compiler optimization +- Code is repetitive and error-prone + +### Solution + +Pre-define a command pattern lookup table at module level: + +```python +# Command pattern lookup table for O(1) matching +_COMMAND_PATTERNS = ( + (('[aria:walk:left]', 'walk left'), {'action': 'walk', 'direction': 'left', 'distance': 200}), + (('[aria:walk:right]', 'walk right'), {'action': 'walk', 'direction': 'right', 'distance': 200}), + # ... all patterns +) + +def parse_movement_commands(text: str) -> dict: + """Parse movement commands from AI response text using optimized pattern matching""" + lower_text = text.lower() + commands = [] + + # Single pass through command patterns - check each pattern once + for patterns, command in _COMMAND_PATTERNS: + if any(pattern in lower_text for pattern in patterns): + commands.append(command) + + return {'commands': commands} if commands else {} +``` + +Benefits: +- Data-driven design (easy to add/modify commands) +- Single iteration through patterns +- Better cache locality +- Reduced code duplication + +### Validation + +- **Test:** `tests/test_performance_optimizations.py::TestCommandParsingOptimizations` +- **Behavior:** Verified all command patterns work identically +- **Performance:** 100 iterations complete in <10ms (5-20x faster than original) + +--- + +## 4. Single-Pass File Reading + +**File:** `scripts/generate_evaluation_set.py` +**Lines:** 50-99 (before) → 50-95 (after) +**Impact:** 2-3x speedup in evaluation dataset generation + +### Problem + +The original code read the same dataset files multiple times: + +1. **First pass (line 74-75):** Collect training hashes + ```python + for src in args.sources: + training_hashes |= collect_training_hashes(Path(src)) + ``` + This reads `train.json` and `test.json` for each source. + +2. **Second pass (line 84-92):** Read files again to build candidates + ```python + for cf in candidate_files: + for rec in read_jsonl(cf): # Re-reads same files + h = rec.get("hash") or hash_messages(...) + ``` + +3. **Third pass (line 95-99):** If no candidates, read files yet again + ```python + if not candidates: + for cf in candidate_files: + for rec in read_jsonl(cf): # Third time reading! + ``` + +For large datasets (1000s of records), this meant: +- 3x I/O operations +- 2-3x hash computations +- 2-3x JSON parsing + +### Solution + +Read and process each file exactly once, caching the results: + +```python +def collect_training_hashes_and_records(dataset_dir: Path) -> tuple[Set[str], List[Dict]]: + """Collect training hashes and records in a single pass to avoid re-reading files""" + hashes: Set[str] = set() + records: List[Dict] = [] + for split_file in [dataset_dir / "train.json", dataset_dir / "test.json"]: + for rec in read_jsonl(split_file): + h = rec.get("hash") or hash_messages(rec.get("messages", [])) + hashes.add(h) + rec["hash"] = h # Ensure hash is stored + records.append(rec) + return hashes, records +``` + +Then use cached records: + +```python +# Collect training hashes and records in a single pass +source_records_cache: Dict[str, List[Dict]] = {} +for src in args.sources: + hashes, records = collect_training_hashes_and_records(src_path) + training_hashes |= hashes + source_records_cache[str(src_path)] = records + +# Later: use cached records instead of re-reading +all_records = source_records_cache.get(str(src_path), []) +``` + +### Validation + +- **Test:** `tests/test_performance_optimizations.py::TestFileReadingOptimizations::test_single_pass_file_reading` +- **Behavior:** Verified identical dataset generation +- **Performance:** 2-3x faster for typical dataset sizes (100-1000 records) + +--- + +## 5. PennyLane Autograd for Gradient Computation + +**File:** `ai-projects/quantum-ml/web_app.py` +**Lines:** 217-246 +**Impact:** 10-100x speedup in quantum circuit training + +### Problem + +The original code manually implemented the parameter-shift rule using triple-nested loops: + +```python +def compute_gradient(circuit, X, y, weights, use_parameter_shift=True): + grad = np.zeros_like(weights) + + if use_parameter_shift: + shift = np.pi / 2 + for i in range(weights.shape[0]): # Layer + for j in range(weights.shape[1]): # Qubit + for k in range(weights.shape[2]): # Rotation parameter + # Shift parameter and compute loss twice + weights_plus = weights.copy() + weights_minus = weights.copy() + weights_plus[i, j, k] += shift + weights_minus[i, j, k] -= shift + loss_plus = compute_loss(circuit, X, y, weights_plus) + loss_minus = compute_loss(circuit, X, y, weights_minus) + grad[i, j, k] = (loss_plus - loss_minus) / 2 +``` + +For a typical circuit: +- 4 qubits, 3 layers, 3 rotation parameters = 36 total parameters +- Each gradient computation requires **72 circuit evaluations** (2 per parameter) +- Each circuit evaluation iterates through all training samples +- Total: O(n_params × n_samples × 2) operations + +Example: 36 params × 100 samples × 2 = **7,200 circuit evaluations per gradient** + +### Solution + +Leverage PennyLane's built-in automatic differentiation: + +```python +def compute_gradient(circuit, X, y, weights, use_parameter_shift=True): + """Compute gradient using PennyLane's built-in automatic differentiation + + This is dramatically faster than manual parameter-shift implementation as it: + - Uses vectorized operations internally + - Leverages hardware acceleration when available + - Avoids redundant circuit evaluations + """ + def loss_fn(w): + return compute_loss(circuit, X, y, w) + + try: + # Use PennyLane's built-in gradient computation + grad_fn = qml.grad(loss_fn) + grad = grad_fn(weights) + except Exception: + # Fallback to manual parameter-shift if autograd fails + # [original implementation as fallback] +``` + +Benefits: +- **Automatic differentiation:** PennyLane computes gradients efficiently using its graph-based approach +- **Optimized circuit caching:** Reuses intermediate circuit results where possible +- **Hardware acceleration:** Can leverage GPU/TPU when available +- **Maintains accuracy:** Uses parameter-shift rule internally but optimized + +The quantum circuit already uses `@qml.qnode(dev, interface='autograd')`, which enables automatic differentiation. + +### Validation + +- **Test:** Manual validation pending (requires PennyLane installation) +- **Behavior:** Falls back to original implementation if autograd fails +- **Performance:** Expected 10-100x speedup based on PennyLane benchmarks + +### Safety + +The implementation includes a fallback to the original manual parameter-shift implementation, ensuring: +- Zero behavioral change if autograd fails +- No breaking changes to existing code +- Graceful degradation in edge cases + +--- + +## Best Practices Applied + +These optimizations follow established patterns from the repository: + +1. **Single-pass collection checks** (Memory: "single-pass collection optimization") + - Build set once, check membership O(1) + - Used in `extract_chat_logs_dataset.py` and `job_queue.py` + +2. **Pre-compiled data structures** (Memory: "frozenset keyword matching") + - Define lookup tables at module level + - Used in `function_app.py` command patterns + +3. **Caching repeated operations** (Memory: "dictionary index for lookups") + - Read files once, cache results + - Used in `generate_evaluation_set.py` + +4. **Library-native optimizations** (Memory: "best practices") + - Use framework features (PennyLane autograd) + - Used in `ai-projects/quantum-ml/web_app.py` + +--- + +## Testing Strategy + +All optimizations include: + +1. **Functional tests:** Verify identical behavior to original +2. **Performance tests:** Ensure measurable speedup +3. **Edge case tests:** Empty inputs, single items, large datasets + +Test coverage: 6 new test methods in `tests/test_performance_optimizations.py`: +- `TestCollectionOptimizations` (3 tests) +- `TestCommandParsingOptimizations` (2 tests) +- `TestFileReadingOptimizations` (1 test) + +All tests pass with 100% success rate. + +--- + +## Future Opportunities + +Additional optimizations identified but not yet implemented: + +1. **aria_web/server.py** (lines 220-263) + - Repeated `any()` calls with generators + - Opportunity: Pre-compile keyword sets as frozensets + - Estimated impact: 2-10x speedup + +2. **ai-projects/quantum-ml/web_app.py** (lines 516-518) + - Repeated list slicing in loops + - Opportunity: Slice once, reuse + - Estimated impact: 1.5-2x speedup + +3. **Multiple files** + - Regex compilation in loops + - Opportunity: Pre-compile at module level + - Estimated impact: 2-5x speedup (see Memory: "regex pattern compilation") + +--- + +## Rollout Notes + +These optimizations are: +- **Non-breaking:** All maintain identical external behavior +- **Well-tested:** 6 new test cases with 100% pass rate +- **Documented:** This file + inline comments +- **Measurable:** Benchmark tests included + +Safe to deploy immediately with no migration needed. + +--- + +## References + +- Repository memories: Performance optimization patterns +- Existing optimizations: `docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md` +- Test suite: `tests/test_performance_optimizations.py` +- PennyLane docs: https://pennylane.ai/qml/glossary/quantum_differentiable_programming.html diff --git a/docs/PERFORMANCE_OPTIMIZATIONS_SUMMARY.md b/docs/PERFORMANCE_OPTIMIZATIONS_SUMMARY.md index 539d375ca..cadf92b8d 100644 --- a/docs/PERFORMANCE_OPTIMIZATIONS_SUMMARY.md +++ b/docs/PERFORMANCE_OPTIMIZATIONS_SUMMARY.md @@ -1 +1 @@ -# Performance Optimizations Summary +# Performance Optimizations Summary diff --git a/docs/PERFORMANCE_OPTIMIZATIONS_SUMMARY_FEB_2026.md b/docs/PERFORMANCE_OPTIMIZATIONS_SUMMARY_FEB_2026.md index 6ee2aff67..7e6066123 100644 --- a/docs/PERFORMANCE_OPTIMIZATIONS_SUMMARY_FEB_2026.md +++ b/docs/PERFORMANCE_OPTIMIZATIONS_SUMMARY_FEB_2026.md @@ -1,140 +1,140 @@ -# Performance Optimization Summary - February 2026 - -Quick reference for the 5 major performance optimizations completed in February 2026. - -## At a Glance - -| # | File | Problem | Solution | Impact | -|---|------|---------|----------|--------| -| 1 | `scripts/extract_chat_logs_dataset.py:72` | Double `any()` traversal | Single-pass set comprehension | **2x faster** | -| 2 | `scripts/job_queue.py:295` | Nested `any()` in list comprehension | Set intersection | **5-50x faster** | -| 3 | `function_app.py:560` | 12 separate `if` statements | Command pattern table | **5-20x faster** | -| 4 | `scripts/generate_evaluation_set.py:74` | Reading same files 2-3 times | Cache file contents | **2-3x faster** | -| 5 | `ai-projects/quantum-ml/web_app.py:217` | Manual parameter-shift loops | PennyLane autograd | **10-100x faster** | - -**Cumulative Impact:** 24-175x speedup across affected code paths - -## Quick Examples - -### 1. Single-Pass Role Checking -```python -# Before (O(2n)) -if any(x.get("role") == "user" for x in window) and any(x.get("role") == "assistant" for x in window): - -# After (O(n)) -roles = {x.get("role") for x in window} -if "user" in roles and "assistant" in roles: -``` - -### 2. Set Intersection for Tag Filtering -```python -# Before (O(n³)) -jobs = [j for j in jobs if any(tag in j.tags for tag in tags)] - -# After (O(n)) -tags_set = set(tags) -jobs = [j for j in jobs if set(j.tags) & tags_set] -``` - -### 3. Command Pattern Table -```python -# Before (12 separate if statements) -if '[aria:walk:left]' in lower_text or 'walk left' in lower_text: - commands.append({'action': 'walk', 'direction': 'left', 'distance': 200}) -if '[aria:walk:right]' in lower_text or 'walk right' in lower_text: - commands.append({'action': 'walk', 'direction': 'right', 'distance': 200}) -# ... 10 more - -# After (data-driven, single loop) -_COMMAND_PATTERNS = ( - (('[aria:walk:left]', 'walk left'), {'action': 'walk', 'direction': 'left', 'distance': 200}), - (('[aria:walk:right]', 'walk right'), {'action': 'walk', 'direction': 'right', 'distance': 200}), - # ... -) -for patterns, command in _COMMAND_PATTERNS: - if any(p in lower_text for p in patterns): - commands.append(command) -``` - -### 4. Single-Pass File Reading -```python -# Before (reads files 2-3 times) -training_hashes = collect_training_hashes(src) # Read 1 -for rec in read_jsonl(file): # Read 2 - if h not in training_hashes: - candidates.append(rec) -if not candidates: - for rec in read_jsonl(file): # Read 3 - candidates.append(rec) - -# After (reads once, caches) -hashes, records = collect_training_hashes_and_records(src) # Read 1 (with cache) -for rec in records: # Use cache - if rec["hash"] not in hashes: - candidates.append(rec) -if not candidates: - candidates = records # Use cache -``` - -### 5. PennyLane Autograd for Gradients -```python -# Before (manual parameter-shift with triple-nested loops) -for i in range(weights.shape[0]): - for j in range(weights.shape[1]): - for k in range(weights.shape[2]): - # 2 circuit evaluations per parameter - loss_plus = compute_loss(circuit, X, y, weights_plus) - loss_minus = compute_loss(circuit, X, y, weights_minus) - grad[i, j, k] = (loss_plus - loss_minus) / 2 - -# After (automatic differentiation) -grad_fn = qml.grad(loss_fn) -grad = grad_fn(weights) # Hardware-accelerated, graph-optimized -``` - -## Testing - -All optimizations include comprehensive tests: - -```bash -# Run all performance optimization tests -python -m pytest tests/test_performance_optimizations.py -v - -# Current status: 24/24 tests passing ✅ -``` - -## Key Patterns Used - -1. **Single-pass collection checks** - Build set once, check membership O(1) -2. **Set intersection** - Use set operations instead of nested loops -3. **Pattern lookup tables** - Pre-define patterns at module level -4. **File caching** - Read once, cache in memory, reuse -5. **Framework-native optimization** - Use library features (autograd, etc.) - -## When to Apply These Patterns - -| Pattern | Use When | Avoid When | -|---------|----------|------------| -| Single-pass checks | Multiple conditions on same collection | Single condition check | -| Set intersection | Filtering by tags/keywords | Small lists (<10 items) | -| Pattern tables | 5+ similar if statements | Dynamic patterns | -| File caching | Reading same file 2+ times | Single read or huge files | -| Framework autograd | Manual gradient loops exist | Framework doesn't support | - -## References - -- **Detailed docs:** `docs/PERFORMANCE_OPTIMIZATIONS_FEB_2026.md` -- **Test suite:** `tests/test_performance_optimizations.py` -- **Memory patterns:** See stored optimization facts in repository memories - -## Future Opportunities - -Not yet implemented but identified: - -1. **aria_web/server.py** - Pre-compile keyword sets (2-10x speedup) -2. **ai-projects/quantum-ml/web_app.py** - Eliminate repeated list slicing (1.5-2x speedup) -3. **Multiple files** - Pre-compile regex patterns (2-5x speedup) - ---- - -*Last updated: February 2026* +# Performance Optimization Summary - February 2026 + +Quick reference for the 5 major performance optimizations completed in February 2026. + +## At a Glance + +| # | File | Problem | Solution | Impact | +|---|------|---------|----------|--------| +| 1 | `scripts/extract_chat_logs_dataset.py:72` | Double `any()` traversal | Single-pass set comprehension | **2x faster** | +| 2 | `scripts/job_queue.py:295` | Nested `any()` in list comprehension | Set intersection | **5-50x faster** | +| 3 | `function_app.py:560` | 12 separate `if` statements | Command pattern table | **5-20x faster** | +| 4 | `scripts/generate_evaluation_set.py:74` | Reading same files 2-3 times | Cache file contents | **2-3x faster** | +| 5 | `ai-projects/quantum-ml/web_app.py:217` | Manual parameter-shift loops | PennyLane autograd | **10-100x faster** | + +**Cumulative Impact:** 24-175x speedup across affected code paths + +## Quick Examples + +### 1. Single-Pass Role Checking +```python +# Before (O(2n)) +if any(x.get("role") == "user" for x in window) and any(x.get("role") == "assistant" for x in window): + +# After (O(n)) +roles = {x.get("role") for x in window} +if "user" in roles and "assistant" in roles: +``` + +### 2. Set Intersection for Tag Filtering +```python +# Before (O(n³)) +jobs = [j for j in jobs if any(tag in j.tags for tag in tags)] + +# After (O(n)) +tags_set = set(tags) +jobs = [j for j in jobs if set(j.tags) & tags_set] +``` + +### 3. Command Pattern Table +```python +# Before (12 separate if statements) +if '[aria:walk:left]' in lower_text or 'walk left' in lower_text: + commands.append({'action': 'walk', 'direction': 'left', 'distance': 200}) +if '[aria:walk:right]' in lower_text or 'walk right' in lower_text: + commands.append({'action': 'walk', 'direction': 'right', 'distance': 200}) +# ... 10 more + +# After (data-driven, single loop) +_COMMAND_PATTERNS = ( + (('[aria:walk:left]', 'walk left'), {'action': 'walk', 'direction': 'left', 'distance': 200}), + (('[aria:walk:right]', 'walk right'), {'action': 'walk', 'direction': 'right', 'distance': 200}), + # ... +) +for patterns, command in _COMMAND_PATTERNS: + if any(p in lower_text for p in patterns): + commands.append(command) +``` + +### 4. Single-Pass File Reading +```python +# Before (reads files 2-3 times) +training_hashes = collect_training_hashes(src) # Read 1 +for rec in read_jsonl(file): # Read 2 + if h not in training_hashes: + candidates.append(rec) +if not candidates: + for rec in read_jsonl(file): # Read 3 + candidates.append(rec) + +# After (reads once, caches) +hashes, records = collect_training_hashes_and_records(src) # Read 1 (with cache) +for rec in records: # Use cache + if rec["hash"] not in hashes: + candidates.append(rec) +if not candidates: + candidates = records # Use cache +``` + +### 5. PennyLane Autograd for Gradients +```python +# Before (manual parameter-shift with triple-nested loops) +for i in range(weights.shape[0]): + for j in range(weights.shape[1]): + for k in range(weights.shape[2]): + # 2 circuit evaluations per parameter + loss_plus = compute_loss(circuit, X, y, weights_plus) + loss_minus = compute_loss(circuit, X, y, weights_minus) + grad[i, j, k] = (loss_plus - loss_minus) / 2 + +# After (automatic differentiation) +grad_fn = qml.grad(loss_fn) +grad = grad_fn(weights) # Hardware-accelerated, graph-optimized +``` + +## Testing + +All optimizations include comprehensive tests: + +```bash +# Run all performance optimization tests +python -m pytest tests/test_performance_optimizations.py -v + +# Current status: 24/24 tests passing ✅ +``` + +## Key Patterns Used + +1. **Single-pass collection checks** - Build set once, check membership O(1) +2. **Set intersection** - Use set operations instead of nested loops +3. **Pattern lookup tables** - Pre-define patterns at module level +4. **File caching** - Read once, cache in memory, reuse +5. **Framework-native optimization** - Use library features (autograd, etc.) + +## When to Apply These Patterns + +| Pattern | Use When | Avoid When | +|---------|----------|------------| +| Single-pass checks | Multiple conditions on same collection | Single condition check | +| Set intersection | Filtering by tags/keywords | Small lists (<10 items) | +| Pattern tables | 5+ similar if statements | Dynamic patterns | +| File caching | Reading same file 2+ times | Single read or huge files | +| Framework autograd | Manual gradient loops exist | Framework doesn't support | + +## References + +- **Detailed docs:** `docs/PERFORMANCE_OPTIMIZATIONS_FEB_2026.md` +- **Test suite:** `tests/test_performance_optimizations.py` +- **Memory patterns:** See stored optimization facts in repository memories + +## Future Opportunities + +Not yet implemented but identified: + +1. **aria_web/server.py** - Pre-compile keyword sets (2-10x speedup) +2. **ai-projects/quantum-ml/web_app.py** - Eliminate repeated list slicing (1.5-2x speedup) +3. **Multiple files** - Pre-compile regex patterns (2-5x speedup) + +--- + +*Last updated: February 2026* diff --git a/docs/PERFORMANCE_OPTIMIZATION_GUIDE.md b/docs/PERFORMANCE_OPTIMIZATION_GUIDE.md index 8431a1f11..de529267d 100644 --- a/docs/PERFORMANCE_OPTIMIZATION_GUIDE.md +++ b/docs/PERFORMANCE_OPTIMIZATION_GUIDE.md @@ -1,355 +1,355 @@ -# Performance Optimization Guide - -This document describes performance optimizations implemented in the Aria codebase and provides best practices for writing efficient Python code. - -## Recent Optimizations - -### 1. Memory-Efficient File Reading - -**Problem**: Loading entire log files into memory using `readlines()` can cause excessive memory usage for large files. - -**Solution**: Use `collections.deque` with `maxlen` parameter for tail-like operations: - -```python -# ❌ BAD: Loads entire file into memory -with open(log_file, 'r') as f: - all_lines = f.readlines() - return all_lines[-20:] # Only need last 20 lines - -# ✅ GOOD: Memory-efficient with deque -from collections import deque -with open(log_file, 'r') as f: - return list(deque(f, maxlen=20)) # Only keeps last 20 lines -``` - -**Files Optimized**: -- `scripts/monitor_autonomous_training.py` - Line 61-71 -- `dashboard/serve.py` - Line 525-531 - -**Benefits**: -- Reduces memory usage from O(n) to O(k) where k is the tail size -- Faster for large log files (GB-sized files) -- No change to external API - -### 2. Iterator-Based Data Processing - -**Best Practice**: Use generators and iterators instead of loading all data into memory. - -```python -# ❌ BAD: Loads all records into memory -def load_all_records(file_path): - records = [] - with open(file_path) as f: - for line in f: - records.append(json.loads(line)) - return records - -# ✅ GOOD: Yields records one at a time -def load_records(file_path): - with open(file_path) as f: - for line in f: - yield json.loads(line) -``` - -**Already Implemented**: -- `AI/microsoft_phi-silica-3.6_v1/scripts/prepare_dataset.py` - Uses generators throughout -- All JSONL reading functions use `yield` pattern - -### 3. Database Connection Pooling - -**Implementation**: `shared/sql_engine.py` - -**Features**: -- Connection pooling with configurable size (`QAI_SQL_POOL_SIZE`) -- Pre-ping to evict dead connections -- Pool recycling every 30 minutes -- Slow query tracking -- Health monitoring via `/api/ai/status` - -**Configuration**: -```python -# Set pool size via environment variable -export QAI_SQL_POOL_SIZE=20 # Default: 10 - -# Monitor pool saturation -curl http://localhost:7071/api/ai/status | jq '.sql' -# Warns when ≥80% saturated -``` - -### 4. Smart File Reading for Large Files - -**Implementation**: `dashboard/app.py` - `_tail_lines()` function - -**Strategy**: -- Small files (< 64KB): Read entire file -- Large files: Read backwards in blocks until enough lines found - -```python -def _tail_lines(path: Path, max_lines: int) -> List[str]: - size = path.stat().st_size - if size <= 65536: # Small file heuristic - with path.open("r") as f: - lines = f.readlines() - return lines[-max_lines:] - - # Large file: read backwards in blocks - # ... (block-based backward reading) -``` - -### 5. Subprocess Management - -**Best Practices**: -- Use `ThreadPoolExecutor` for parallel subprocess execution -- Set reasonable timeouts (30 min for training jobs) -- Capture output only when needed -- Use `text=True` to avoid manual decoding - -**Example**: `scripts/batch_evaluator.py` -```python -with ThreadPoolExecutor(max_workers=3) as executor: - futures = { - executor.submit(evaluate_model, task): task - for task in tasks - } - - for future in as_completed(futures): - result = future.result() - # Process result -``` - -## Performance Anti-Patterns to Avoid - -### 1. String Concatenation in Loops - -```python -# ❌ BAD: O(n²) due to string immutability -result = "" -for item in large_list: - result += str(item) + "\n" - -# ✅ GOOD: O(n) using join -result = "\n".join(str(item) for item in large_list) -``` - -### 2. Repeated CSV/JSON Reads - -```python -# ❌ BAD: Re-reads file in loop -for dataset_name in dataset_names: - df = pd.read_csv(dataset_path) # Same file! - process(df, dataset_name) - -# ✅ GOOD: Read once, reuse -df = pd.read_csv(dataset_path) -for dataset_name in dataset_names: - process(df, dataset_name) -``` - -### 3. List Comprehension for Large Datasets - -```python -# ❌ BAD: Loads all results into memory -results = [expensive_operation(x) for x in huge_list] - -# ✅ GOOD: Use generator for lazy evaluation -results = (expensive_operation(x) for x in huge_list) -for result in results: - process(result) # Processed one at a time -``` - -### 4. Synchronous I/O in Loops - -```python -# ❌ BAD: Sequential subprocess calls -for script in scripts: - subprocess.run(['python', script]) # Blocks until complete - -# ✅ GOOD: Parallel execution -with ThreadPoolExecutor() as executor: - futures = [executor.submit(subprocess.run, ['python', s]) - for s in scripts] - results = [f.result() for f in futures] -``` - -## Monitoring Performance - -### 1. SQL Health Check - -Check database pool saturation: -```bash -curl http://localhost:7071/api/ai/status | jq '.sql' -``` - -Response includes: -- `pool_size`: Total connections in pool -- `checked_out`: Currently in use -- `overflow`: Connections beyond pool limit -- `saturation_alert`: `true` if ≥80% saturated - -### 2. Resource Monitoring - -Use the resource monitor script: -```bash -python scripts/resource_monitor.py --snapshot -python scripts/resource_monitor.py --watch # Continuous monitoring -``` - -### 3. Training Analytics - -Monitor training performance trends: -```bash -python scripts/training_analytics.py -``` - -## When to Use Async/Await - -**Use async when**: -- Multiple I/O operations can run concurrently -- Network requests or file I/O dominate runtime -- Clear dependencies between operations - -**Example**: Autonomous training orchestrator -```python -async def run_training_cycle(): - # Concurrent dataset downloads - results = await asyncio.gather( - download_dataset('dataset1'), - download_dataset('dataset2'), - download_dataset('dataset3') - ) - - # Sequential training (GPU bound) - await train_model(results) -``` - -**Don't use async for**: -- CPU-bound operations (use multiprocessing instead) -- Simple monitoring loops with fixed intervals -- Code that doesn't do I/O - -## Caching Strategies - -### 1. Disk Caching (Already Implemented) - -**Pattern**: Check if file exists before downloading/processing -```python -output_path = QUANTUM_DIR / f"{name}.csv" -if output_path.exists(): - return True, f"Already exists ({output_path.stat().st_size:,} bytes)" - -# Download and process... -``` - -**Used in**: -- `scripts/expand_quantum_datasets.py` - Dataset downloads -- All training scripts - Model checkpoints - -### 2. Memory Caching (Where Appropriate) - -**Pattern**: Use `functools.lru_cache` for expensive pure functions -```python -from functools import lru_cache - -@lru_cache(maxsize=128) -def expensive_computation(param): - # Computation here - return result -``` - -**Good for**: -- Configuration parsing -- Data transformations -- Feature engineering - -**Not good for**: -- Large datasets (memory pressure) -- Non-deterministic functions -- Functions with side effects - -### 3. Singleton Pattern (Implemented) - -**Pattern**: Lazy initialization of expensive resources -```python -class CosmosClient: - _instance = None - - def __new__(cls): - if cls._instance is None: - cls._instance = super().__new__(cls) - # Initialize connection - return cls._instance -``` - -**Used in**: -- `shared/cosmos_client.py` - Cosmos DB connection -- `shared/sql_engine.py` - SQL connection pools - -## Benchmarking - -### Quick Performance Check - -Add timing to critical sections: -```python -import time - -t0 = time.time() -expensive_operation() -print(f"Operation took {time.time() - t0:.2f}s") -``` - -### Profiling with cProfile - -```bash -# Profile a script -python -m cProfile -o profile.stats scripts/my_script.py - -# Analyze results -python -m pstats profile.stats -> sort cumulative -> stats 20 -``` - -### Memory Profiling - -```bash -# Install memory_profiler -pip install memory-profiler - -# Add @profile decorator to functions -# Run with: -python -m memory_profiler scripts/my_script.py -``` - -## Performance Targets - -### Acceptable Latencies - -- **API endpoints**: < 200ms (non-streaming) -- **Database queries**: < 50ms (simple), < 500ms (complex) -- **File I/O**: < 1s for typical log tailing -- **Training cycle**: 5-30 minutes (depends on dataset) - -### Memory Guidelines - -- **Log tailing**: O(k) where k = tail size, not O(n) -- **CSV processing**: Stream when possible, < 100MB in memory -- **Model inference**: < 2GB per model -- **Database connections**: Pool size ≤ 20 for typical workloads - -### Parallelism Limits - -- **Thread workers**: 3-5 for I/O-bound tasks -- **Process workers**: CPU count for CPU-bound tasks -- **Concurrent HTTP requests**: < 10 to avoid rate limits - -## Summary - -The Aria codebase follows these performance principles: - -1. **Stream data** instead of loading everything into memory -2. **Use connection pooling** for databases and external services -3. **Parallelize I/O-bound** operations with ThreadPoolExecutor -4. **Cache expensive** computations and downloads -5. **Monitor health** with built-in observability - -For questions or suggestions, see the development team. +# Performance Optimization Guide + +This document describes performance optimizations implemented in the Aria codebase and provides best practices for writing efficient Python code. + +## Recent Optimizations + +### 1. Memory-Efficient File Reading + +**Problem**: Loading entire log files into memory using `readlines()` can cause excessive memory usage for large files. + +**Solution**: Use `collections.deque` with `maxlen` parameter for tail-like operations: + +```python +# ❌ BAD: Loads entire file into memory +with open(log_file, 'r') as f: + all_lines = f.readlines() + return all_lines[-20:] # Only need last 20 lines + +# ✅ GOOD: Memory-efficient with deque +from collections import deque +with open(log_file, 'r') as f: + return list(deque(f, maxlen=20)) # Only keeps last 20 lines +``` + +**Files Optimized**: +- `scripts/monitor_autonomous_training.py` - Line 61-71 +- `dashboard/serve.py` - Line 525-531 + +**Benefits**: +- Reduces memory usage from O(n) to O(k) where k is the tail size +- Faster for large log files (GB-sized files) +- No change to external API + +### 2. Iterator-Based Data Processing + +**Best Practice**: Use generators and iterators instead of loading all data into memory. + +```python +# ❌ BAD: Loads all records into memory +def load_all_records(file_path): + records = [] + with open(file_path) as f: + for line in f: + records.append(json.loads(line)) + return records + +# ✅ GOOD: Yields records one at a time +def load_records(file_path): + with open(file_path) as f: + for line in f: + yield json.loads(line) +``` + +**Already Implemented**: +- `AI/microsoft_phi-silica-3.6_v1/scripts/prepare_dataset.py` - Uses generators throughout +- All JSONL reading functions use `yield` pattern + +### 3. Database Connection Pooling + +**Implementation**: `shared/sql_engine.py` + +**Features**: +- Connection pooling with configurable size (`QAI_SQL_POOL_SIZE`) +- Pre-ping to evict dead connections +- Pool recycling every 30 minutes +- Slow query tracking +- Health monitoring via `/api/ai/status` + +**Configuration**: +```python +# Set pool size via environment variable +export QAI_SQL_POOL_SIZE=20 # Default: 10 + +# Monitor pool saturation +curl http://localhost:7071/api/ai/status | jq '.sql' +# Warns when ≥80% saturated +``` + +### 4. Smart File Reading for Large Files + +**Implementation**: `dashboard/app.py` - `_tail_lines()` function + +**Strategy**: +- Small files (< 64KB): Read entire file +- Large files: Read backwards in blocks until enough lines found + +```python +def _tail_lines(path: Path, max_lines: int) -> List[str]: + size = path.stat().st_size + if size <= 65536: # Small file heuristic + with path.open("r") as f: + lines = f.readlines() + return lines[-max_lines:] + + # Large file: read backwards in blocks + # ... (block-based backward reading) +``` + +### 5. Subprocess Management + +**Best Practices**: +- Use `ThreadPoolExecutor` for parallel subprocess execution +- Set reasonable timeouts (30 min for training jobs) +- Capture output only when needed +- Use `text=True` to avoid manual decoding + +**Example**: `scripts/batch_evaluator.py` +```python +with ThreadPoolExecutor(max_workers=3) as executor: + futures = { + executor.submit(evaluate_model, task): task + for task in tasks + } + + for future in as_completed(futures): + result = future.result() + # Process result +``` + +## Performance Anti-Patterns to Avoid + +### 1. String Concatenation in Loops + +```python +# ❌ BAD: O(n²) due to string immutability +result = "" +for item in large_list: + result += str(item) + "\n" + +# ✅ GOOD: O(n) using join +result = "\n".join(str(item) for item in large_list) +``` + +### 2. Repeated CSV/JSON Reads + +```python +# ❌ BAD: Re-reads file in loop +for dataset_name in dataset_names: + df = pd.read_csv(dataset_path) # Same file! + process(df, dataset_name) + +# ✅ GOOD: Read once, reuse +df = pd.read_csv(dataset_path) +for dataset_name in dataset_names: + process(df, dataset_name) +``` + +### 3. List Comprehension for Large Datasets + +```python +# ❌ BAD: Loads all results into memory +results = [expensive_operation(x) for x in huge_list] + +# ✅ GOOD: Use generator for lazy evaluation +results = (expensive_operation(x) for x in huge_list) +for result in results: + process(result) # Processed one at a time +``` + +### 4. Synchronous I/O in Loops + +```python +# ❌ BAD: Sequential subprocess calls +for script in scripts: + subprocess.run(['python', script]) # Blocks until complete + +# ✅ GOOD: Parallel execution +with ThreadPoolExecutor() as executor: + futures = [executor.submit(subprocess.run, ['python', s]) + for s in scripts] + results = [f.result() for f in futures] +``` + +## Monitoring Performance + +### 1. SQL Health Check + +Check database pool saturation: +```bash +curl http://localhost:7071/api/ai/status | jq '.sql' +``` + +Response includes: +- `pool_size`: Total connections in pool +- `checked_out`: Currently in use +- `overflow`: Connections beyond pool limit +- `saturation_alert`: `true` if ≥80% saturated + +### 2. Resource Monitoring + +Use the resource monitor script: +```bash +python scripts/resource_monitor.py --snapshot +python scripts/resource_monitor.py --watch # Continuous monitoring +``` + +### 3. Training Analytics + +Monitor training performance trends: +```bash +python scripts/training_analytics.py +``` + +## When to Use Async/Await + +**Use async when**: +- Multiple I/O operations can run concurrently +- Network requests or file I/O dominate runtime +- Clear dependencies between operations + +**Example**: Autonomous training orchestrator +```python +async def run_training_cycle(): + # Concurrent dataset downloads + results = await asyncio.gather( + download_dataset('dataset1'), + download_dataset('dataset2'), + download_dataset('dataset3') + ) + + # Sequential training (GPU bound) + await train_model(results) +``` + +**Don't use async for**: +- CPU-bound operations (use multiprocessing instead) +- Simple monitoring loops with fixed intervals +- Code that doesn't do I/O + +## Caching Strategies + +### 1. Disk Caching (Already Implemented) + +**Pattern**: Check if file exists before downloading/processing +```python +output_path = QUANTUM_DIR / f"{name}.csv" +if output_path.exists(): + return True, f"Already exists ({output_path.stat().st_size:,} bytes)" + +# Download and process... +``` + +**Used in**: +- `scripts/expand_quantum_datasets.py` - Dataset downloads +- All training scripts - Model checkpoints + +### 2. Memory Caching (Where Appropriate) + +**Pattern**: Use `functools.lru_cache` for expensive pure functions +```python +from functools import lru_cache + +@lru_cache(maxsize=128) +def expensive_computation(param): + # Computation here + return result +``` + +**Good for**: +- Configuration parsing +- Data transformations +- Feature engineering + +**Not good for**: +- Large datasets (memory pressure) +- Non-deterministic functions +- Functions with side effects + +### 3. Singleton Pattern (Implemented) + +**Pattern**: Lazy initialization of expensive resources +```python +class CosmosClient: + _instance = None + + def __new__(cls): + if cls._instance is None: + cls._instance = super().__new__(cls) + # Initialize connection + return cls._instance +``` + +**Used in**: +- `shared/cosmos_client.py` - Cosmos DB connection +- `shared/sql_engine.py` - SQL connection pools + +## Benchmarking + +### Quick Performance Check + +Add timing to critical sections: +```python +import time + +t0 = time.time() +expensive_operation() +print(f"Operation took {time.time() - t0:.2f}s") +``` + +### Profiling with cProfile + +```bash +# Profile a script +python -m cProfile -o profile.stats scripts/my_script.py + +# Analyze results +python -m pstats profile.stats +> sort cumulative +> stats 20 +``` + +### Memory Profiling + +```bash +# Install memory_profiler +pip install memory-profiler + +# Add @profile decorator to functions +# Run with: +python -m memory_profiler scripts/my_script.py +``` + +## Performance Targets + +### Acceptable Latencies + +- **API endpoints**: < 200ms (non-streaming) +- **Database queries**: < 50ms (simple), < 500ms (complex) +- **File I/O**: < 1s for typical log tailing +- **Training cycle**: 5-30 minutes (depends on dataset) + +### Memory Guidelines + +- **Log tailing**: O(k) where k = tail size, not O(n) +- **CSV processing**: Stream when possible, < 100MB in memory +- **Model inference**: < 2GB per model +- **Database connections**: Pool size ≤ 20 for typical workloads + +### Parallelism Limits + +- **Thread workers**: 3-5 for I/O-bound tasks +- **Process workers**: CPU count for CPU-bound tasks +- **Concurrent HTTP requests**: < 10 to avoid rate limits + +## Summary + +The Aria codebase follows these performance principles: + +1. **Stream data** instead of loading everything into memory +2. **Use connection pooling** for databases and external services +3. **Parallelize I/O-bound** operations with ThreadPoolExecutor +4. **Cache expensive** computations and downloads +5. **Monitor health** with built-in observability + +For questions or suggestions, see the development team. diff --git a/docs/PERFORMANCE_OPTIMIZATION_PR_SUMMARY.md b/docs/PERFORMANCE_OPTIMIZATION_PR_SUMMARY.md index d0d8140dc..3fe02035c 100644 --- a/docs/PERFORMANCE_OPTIMIZATION_PR_SUMMARY.md +++ b/docs/PERFORMANCE_OPTIMIZATION_PR_SUMMARY.md @@ -1,242 +1,242 @@ -# Performance Optimization Summary - -**Date**: February 17, 2026 -**PR**: copilot/improve-slow-code-efficiency -**Status**: ✅ Complete - -## Executive Summary - -This PR identifies and implements performance improvements across the Aria codebase, focusing on eliminating inefficient patterns that cause unnecessary overhead. The optimizations target regex pattern compilation, data structure operations, and string handling. - -## Optimizations Implemented - -### 1. Regex Pattern Compilation (High Impact) - -**Problem**: Regex patterns were being compiled on every use, either through inline `re.findall()` calls or local `import re` statements inside functions. - -**Solution**: Pre-compile all frequently-used regex patterns at module level. - -**Files Modified**: -- `scripts/final_validation.py` - 5 patterns pre-compiled -- `scripts/validate_dashboard.py` - 9 patterns pre-compiled -- `function_app.py` - 1 pattern pre-compiled, 4 local imports removed -- `shared/email_notifications.py` - 2 patterns pre-compiled -- `cooking-ai/src/providers/local.py` - 1 pattern pre-compiled - -**Impact**: -- **17+ regex patterns** moved to module level -- Eliminated repeated compilation overhead in hot paths -- Typical speedup: 2-5x for regex-heavy operations -- Reduced memory allocations - -**Example**: -```python -# Before (inefficient) -def validate(): - onclick = re.findall(r'onclick=["\']([^"\']+)["\']', content) - ids = re.findall(r'id=["\']([^"\']+)["\']', content) - -# After (optimized) -_RE_ONCLICK = re.compile(r'onclick=["\']([^"\']+)["\']') -_RE_IDS = re.compile(r'id=["\']([^"\']+)["\']') - -def validate(): - onclick = _RE_ONCLICK.findall(content) - ids = _RE_IDS.findall(content) -``` - -### 2. Data Structure Optimizations (Medium Impact) - -#### 2.1 List Comprehension for Dict Building - -**File**: `shared/sql_repository.py` (lines 237-252) - -**Problem**: Building lists of dictionaries with manual append in loop. - -**Solution**: Use list comprehension for more efficient dict building. - -```python -# Before (inefficient) -items = [] -for row in res.fetchall(): - items.append({"k": row[0], "v": row[1], "updated_at": row[2]}) -return items - -# After (optimized) -return [{"k": row[0], "v": row[1], "updated_at": row[2]} for row in res.fetchall()] -``` - -**Impact**: -- C-level optimization -- Pre-allocation of list size -- Reduced function call overhead - -#### 2.2 Set Union for Key Collection - -**File**: `scripts/results_exporter.py` (line 80) - -**Problem**: Iterating over all jobs to collect keys with manual update. - -**Solution**: Use set union with generator expression. - -```python -# Before (inefficient) -fieldnames = set() -for job in jobs: - fieldnames.update(job.keys()) -fieldnames = sorted(fieldnames) - -# After (optimized) -fieldnames = sorted(set().union(*(job.keys() for job in jobs))) -``` - -**Impact**: -- Single-pass operation -- More Pythonic and readable -- Slightly faster for large datasets - -#### 2.3 String Method Caching - -**File**: `scripts/generate_repo_training_dataset.py` (line 122) - -**Problem**: Calling `.lower()` multiple times on same string in loop. - -**Solution**: Cache the lowercased result before loop. - -```python -# Before (inefficient) -for kw in keywords: - if kw in chunk.content.lower(): # lower() called each iteration - ... - -# After (optimized) -content_lower = chunk.content.lower() # cached once -for kw in keywords: - if kw in content_lower: - ... -``` - -**Impact**: -- Avoids repeated string allocations -- Particularly important for large strings -- Reduces memory churn - -## Testing - -### Test Coverage - -Created comprehensive test suite in `tests/test_regex_optimizations.py`: -- 10 tests covering all regex optimizations -- Functionality tests to ensure correctness -- Performance benchmarks -- Pattern stability tests - -### Test Results - -``` -9 passed, 1 skipped in 0.29s -``` - -All optimizations validated to maintain correct functionality. - -## Documentation - -### Files Created - -1. **`docs/REGEX_OPTIMIZATION_2026-02-17.md`** - - Detailed explanation of regex optimizations - - Before/after code examples - - Best practices guide - - Pattern naming conventions - -2. **`tests/test_regex_optimizations.py`** - - Comprehensive test suite - - Performance benchmarks - - Validation tests - -### Repository Memories Stored - -Three new optimization patterns stored for future reference: -1. List comprehension for dict building from database rows -2. Set union for efficient key collection -3. String method caching for repeated transformations - -## Performance Metrics - -### Regex Compilation Savings - -For a file with 10 regex operations executed 1000 times: -- **Before**: 10,000 pattern compilations -- **After**: 10 pattern compilations -- **Improvement**: 1000x reduction in compilation overhead - -### Expected Impact by Module - -| Module | Optimizations | Expected Speedup | -|--------|--------------|------------------| -| `final_validation.py` | 9+ regex patterns | 2-5x for validation | -| `validate_dashboard.py` | 10+ regex patterns | 2-5x for validation | -| `function_app.py` | TTS word timing | 10-20% for TTS calls | -| `email_notifications.py` | HTML stripping | 5-10x for email generation | -| `sql_repository.py` | List comprehension | 10-30% for queries | - -## Code Quality Improvements - -### Before & After Statistics - -- **Lines of Code**: Reduced by 10 lines (more concise patterns) -- **Cyclomatic Complexity**: Reduced in several functions -- **Maintainability**: Improved with consistent patterns -- **Memory Efficiency**: Better through reduced allocations - -### Coding Standards - -All optimizations follow established patterns: -- Pre-compiled regex patterns use `_RE_` prefix -- List comprehensions preferred over manual append -- String operations cached when used multiple times -- Best practices documented for future reference - -## Additional Opportunities Identified - -During this investigation, additional optimization opportunities were identified but not implemented to keep changes minimal: - -1. **Database Queries in Loops** - Some scripts could benefit from query batching -2. **File I/O Caching** - Line counts could be cached to avoid repeated file opens -3. **Async Downloads** - Sequential downloads could be parallelized -4. **OpenML API Calls** - Could be batched or parallelized - -These are documented for future optimization efforts. - -## Related Work - -This PR builds on existing performance optimizations documented in: -- `docs/PERFORMANCE_OPTIMIZATIONS_FEB_2026.md` -- `docs/PERFORMANCE_IMPROVEMENTS.md` -- `docs/PERFORMANCE_OPTIMIZATION_SUMMARY.md` -- `docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md` - -## Conclusion - -This PR successfully identifies and implements targeted performance improvements across the codebase: -- ✅ 8 files optimized -- ✅ 20+ individual optimizations -- ✅ 10 tests added -- ✅ Full documentation -- ✅ Repository memories stored - -All changes are minimal, focused, and validated through comprehensive testing. The optimizations follow established best practices and are documented for future reference. - -## Commands to Verify - -```bash -# Run optimization tests -python -m pytest tests/test_regex_optimizations.py -v - -# Validate optimized scripts still work -python scripts/final_validation.py -python scripts/validate_dashboard.py - -# Check no regressions in existing tests -python scripts/test_runner.py --unit -``` +# Performance Optimization Summary + +**Date**: February 17, 2026 +**PR**: copilot/improve-slow-code-efficiency +**Status**: ✅ Complete + +## Executive Summary + +This PR identifies and implements performance improvements across the Aria codebase, focusing on eliminating inefficient patterns that cause unnecessary overhead. The optimizations target regex pattern compilation, data structure operations, and string handling. + +## Optimizations Implemented + +### 1. Regex Pattern Compilation (High Impact) + +**Problem**: Regex patterns were being compiled on every use, either through inline `re.findall()` calls or local `import re` statements inside functions. + +**Solution**: Pre-compile all frequently-used regex patterns at module level. + +**Files Modified**: +- `scripts/final_validation.py` - 5 patterns pre-compiled +- `scripts/validate_dashboard.py` - 9 patterns pre-compiled +- `function_app.py` - 1 pattern pre-compiled, 4 local imports removed +- `shared/email_notifications.py` - 2 patterns pre-compiled +- `cooking-ai/src/providers/local.py` - 1 pattern pre-compiled + +**Impact**: +- **17+ regex patterns** moved to module level +- Eliminated repeated compilation overhead in hot paths +- Typical speedup: 2-5x for regex-heavy operations +- Reduced memory allocations + +**Example**: +```python +# Before (inefficient) +def validate(): + onclick = re.findall(r'onclick=["\']([^"\']+)["\']', content) + ids = re.findall(r'id=["\']([^"\']+)["\']', content) + +# After (optimized) +_RE_ONCLICK = re.compile(r'onclick=["\']([^"\']+)["\']') +_RE_IDS = re.compile(r'id=["\']([^"\']+)["\']') + +def validate(): + onclick = _RE_ONCLICK.findall(content) + ids = _RE_IDS.findall(content) +``` + +### 2. Data Structure Optimizations (Medium Impact) + +#### 2.1 List Comprehension for Dict Building + +**File**: `shared/sql_repository.py` (lines 237-252) + +**Problem**: Building lists of dictionaries with manual append in loop. + +**Solution**: Use list comprehension for more efficient dict building. + +```python +# Before (inefficient) +items = [] +for row in res.fetchall(): + items.append({"k": row[0], "v": row[1], "updated_at": row[2]}) +return items + +# After (optimized) +return [{"k": row[0], "v": row[1], "updated_at": row[2]} for row in res.fetchall()] +``` + +**Impact**: +- C-level optimization +- Pre-allocation of list size +- Reduced function call overhead + +#### 2.2 Set Union for Key Collection + +**File**: `scripts/results_exporter.py` (line 80) + +**Problem**: Iterating over all jobs to collect keys with manual update. + +**Solution**: Use set union with generator expression. + +```python +# Before (inefficient) +fieldnames = set() +for job in jobs: + fieldnames.update(job.keys()) +fieldnames = sorted(fieldnames) + +# After (optimized) +fieldnames = sorted(set().union(*(job.keys() for job in jobs))) +``` + +**Impact**: +- Single-pass operation +- More Pythonic and readable +- Slightly faster for large datasets + +#### 2.3 String Method Caching + +**File**: `scripts/generate_repo_training_dataset.py` (line 122) + +**Problem**: Calling `.lower()` multiple times on same string in loop. + +**Solution**: Cache the lowercased result before loop. + +```python +# Before (inefficient) +for kw in keywords: + if kw in chunk.content.lower(): # lower() called each iteration + ... + +# After (optimized) +content_lower = chunk.content.lower() # cached once +for kw in keywords: + if kw in content_lower: + ... +``` + +**Impact**: +- Avoids repeated string allocations +- Particularly important for large strings +- Reduces memory churn + +## Testing + +### Test Coverage + +Created comprehensive test suite in `tests/test_regex_optimizations.py`: +- 10 tests covering all regex optimizations +- Functionality tests to ensure correctness +- Performance benchmarks +- Pattern stability tests + +### Test Results + +``` +9 passed, 1 skipped in 0.29s +``` + +All optimizations validated to maintain correct functionality. + +## Documentation + +### Files Created + +1. **`docs/REGEX_OPTIMIZATION_2026-02-17.md`** + - Detailed explanation of regex optimizations + - Before/after code examples + - Best practices guide + - Pattern naming conventions + +2. **`tests/test_regex_optimizations.py`** + - Comprehensive test suite + - Performance benchmarks + - Validation tests + +### Repository Memories Stored + +Three new optimization patterns stored for future reference: +1. List comprehension for dict building from database rows +2. Set union for efficient key collection +3. String method caching for repeated transformations + +## Performance Metrics + +### Regex Compilation Savings + +For a file with 10 regex operations executed 1000 times: +- **Before**: 10,000 pattern compilations +- **After**: 10 pattern compilations +- **Improvement**: 1000x reduction in compilation overhead + +### Expected Impact by Module + +| Module | Optimizations | Expected Speedup | +|--------|--------------|------------------| +| `final_validation.py` | 9+ regex patterns | 2-5x for validation | +| `validate_dashboard.py` | 10+ regex patterns | 2-5x for validation | +| `function_app.py` | TTS word timing | 10-20% for TTS calls | +| `email_notifications.py` | HTML stripping | 5-10x for email generation | +| `sql_repository.py` | List comprehension | 10-30% for queries | + +## Code Quality Improvements + +### Before & After Statistics + +- **Lines of Code**: Reduced by 10 lines (more concise patterns) +- **Cyclomatic Complexity**: Reduced in several functions +- **Maintainability**: Improved with consistent patterns +- **Memory Efficiency**: Better through reduced allocations + +### Coding Standards + +All optimizations follow established patterns: +- Pre-compiled regex patterns use `_RE_` prefix +- List comprehensions preferred over manual append +- String operations cached when used multiple times +- Best practices documented for future reference + +## Additional Opportunities Identified + +During this investigation, additional optimization opportunities were identified but not implemented to keep changes minimal: + +1. **Database Queries in Loops** - Some scripts could benefit from query batching +2. **File I/O Caching** - Line counts could be cached to avoid repeated file opens +3. **Async Downloads** - Sequential downloads could be parallelized +4. **OpenML API Calls** - Could be batched or parallelized + +These are documented for future optimization efforts. + +## Related Work + +This PR builds on existing performance optimizations documented in: +- `docs/PERFORMANCE_OPTIMIZATIONS_FEB_2026.md` +- `docs/PERFORMANCE_IMPROVEMENTS.md` +- `docs/PERFORMANCE_OPTIMIZATION_SUMMARY.md` +- `docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md` + +## Conclusion + +This PR successfully identifies and implements targeted performance improvements across the codebase: +- ✅ 8 files optimized +- ✅ 20+ individual optimizations +- ✅ 10 tests added +- ✅ Full documentation +- ✅ Repository memories stored + +All changes are minimal, focused, and validated through comprehensive testing. The optimizations follow established best practices and are documented for future reference. + +## Commands to Verify + +```bash +# Run optimization tests +python -m pytest tests/test_regex_optimizations.py -v + +# Validate optimized scripts still work +python scripts/final_validation.py +python scripts/validate_dashboard.py + +# Check no regressions in existing tests +python scripts/test_runner.py --unit +``` diff --git a/docs/PERFORMANCE_OPTIMIZATION_README.md b/docs/PERFORMANCE_OPTIMIZATION_README.md index f2e093bae..4669ccb37 100644 --- a/docs/PERFORMANCE_OPTIMIZATION_README.md +++ b/docs/PERFORMANCE_OPTIMIZATION_README.md @@ -1,253 +1,253 @@ -# Performance Optimization Work - February 2024 - -This README provides a quick reference for the performance optimization work completed in February 2024. - -## Quick Links - -- **Summary Document**: [`PERFORMANCE_OPTIMIZATION_SUMMARY.md`](./PERFORMANCE_OPTIMIZATION_SUMMARY.md) - Complete guide with examples and best practices -- **Historical Issues**: [`PERFORMANCE_IMPROVEMENTS.md`](./PERFORMANCE_IMPROVEMENTS.md) - Original performance analysis and earlier fixes -- **Tests**: [`../tests/test_phase_optimizations.py`](../tests/test_phase_optimizations.py) - Comprehensive test suite -- **Validation**: [`../scripts/validate_optimizations.py`](../scripts/validate_optimizations.py) - Standalone validation script - -## What Was Fixed - -### Phase 1: Critical Issues (High Impact) - -1. **Aria Web Server** (`aria_web/server.py`) - - **Issue**: 27 repeated `any()` calls with list comprehensions - - **Fix**: Pre-compiled frozenset keyword collections with O(1) lookups - - **Impact**: 2-5x speedup per command processing - -2. **Chat Memory** (`shared/chat_memory.py`) - - **Issue**: New DB connection created for every operation - - **Fix**: Connection pool with max 5 connections and health checks - - **Impact**: 50-100ms → 0ms per operation - -3. **Batch Evaluator** (`scripts/batch_evaluator.py`) - - **Issue**: O(n²) linear search in model comparison - - **Fix**: Dict-based O(1) lookup index - - **Impact**: 10x faster for 100 models - -### Phase 2: High Priority Improvements - -4. **File I/O** (`dashboard/serve.py`) - - **Issue**: Loading entire log files into memory - - **Fix**: Block-based streaming for large files (> 64KB) - - **Impact**: 100x memory reduction (GB → 64KB) - -5. **Dictionary Iteration** (6 files) - - **Issue**: Unnecessary `.keys()` calls - - **Fix**: Direct iteration (more Pythonic) - - **Impact**: 5-10% performance + cleaner code - -## Quick Validation - -Run the standalone validation script (no dependencies required): - -```bash -python scripts/validate_optimizations.py -``` - -Expected output: -``` -✅ Aria web server optimizations validated -✅ Chat memory pooling functions validated -✅ Batch evaluator optimizations validated -✅ File streaming logic validated -✅ Dictionary iteration patterns validated - -Performance Benchmark: - Keyword matching (300 calls): 0.22ms - -5/5 tests passed - All optimizations validated successfully! -``` - -## Performance Impact Summary - -| Optimization | Before | After | Improvement | -|-------------|--------|-------|-------------| -| **Keyword matching** | O(n×m) repeated | O(n) set intersection | 2-5x faster | -| **DB connections** | New per operation | Pooled (max 5) | 50-100ms → 0ms | -| **Model lookups** | O(n²) linear search | O(1) dict lookup | 10x for 100 models | -| **Log file reads** | GB in memory | 64KB buffer | 100x memory | -| **Dict iteration** | `.keys()` overhead | Direct iteration | 5-10% + cleaner | - -## Code Patterns (Copy-Paste Ready) - -### Pattern 1: Keyword Matching with Sets - -```python -# Define keyword sets at module level -_ACTION_KEYWORDS = frozenset(['jump', 'run', 'dance']) - -def _any_word_in_text(keywords: frozenset, text: str) -> bool: - """Fast O(n) keyword matching using set intersection.""" - return bool(keywords & set(text.split())) - -# Usage -if _any_word_in_text(_ACTION_KEYWORDS, user_command): - process_action() -``` - -### Pattern 2: Connection Pooling - -```python -_connection_pool = [] - -def _get_conn(): - """Get connection from pool or create new.""" - while _connection_pool: - conn = _connection_pool.pop() - try: - # Health check - conn.cursor().execute("SELECT 1") - return conn - except Exception: - try: - conn.close() - except Exception: - pass - return create_new_connection() - -def _return_conn(conn): - """Return connection to pool.""" - if len(_connection_pool) < 5: - _connection_pool.append(conn) - else: - conn.close() -``` - -### Pattern 3: Dict-Based Lookups - -```python -# Instead of this (O(n²)): -for target_id in ids_to_find: - item = next((x for x in items if x.id == target_id), None) - -# Do this (O(n)): -items_by_id = {x.id: x for x in items} -for target_id in ids_to_find: - item = items_by_id.get(target_id) -``` - -### Pattern 4: Streaming Large Files - -```python -def read_tail(filepath: Path, max_lines: int = 500): - """Efficiently read last N lines from large files.""" - size = filepath.stat().st_size - - if size <= 65536: # Small file: fast path - with open(filepath) as f: - return f.readlines()[-max_lines:] - - # Large file: stream backwards - with open(filepath, 'rb') as f: - f.seek(0, 2) # End - remaining = f.tell() - chunks = [] - - while remaining > 0: - block_size = min(32768, remaining) - f.seek(remaining - block_size) - chunks.insert(0, f.read(block_size)) - remaining -= block_size - - # Check if we have enough lines - decoded = b''.join(chunks).decode('utf-8', errors='ignore') - if decoded.count('\n') >= max_lines: - break - - return decoded.splitlines(keepends=True)[-max_lines:] -``` - -### Pattern 5: Direct Dictionary Iteration - -```python -# Instead of this: -for key in my_dict.keys(): - process(key) - -# Do this (more Pythonic): -for key in my_dict: - process(key) -``` - -## Files Modified - -### Phase 1 Changes -- `aria_web/server.py` - Keyword set optimizations -- `shared/chat_memory.py` - Connection pooling -- `scripts/batch_evaluator.py` - Dict-based lookups - -### Phase 2 Changes -- `dashboard/serve.py` - File streaming -- `dashboard/app.py` - Dict iteration -- `ai-projects/quantum-ml/benchmark_all_datasets.py` - Dict iteration -- `ai-projects/quantum-ml/scripts/visualize_hardware_results.py` - Dict iteration -- `scripts/automate_aria_movement.py` - Dict iteration -- `scripts/test_aria_dataset.py` - Dict iteration - -### Documentation & Tests -- `docs/PERFORMANCE_OPTIMIZATION_SUMMARY.md` - Complete guide (NEW) -- `docs/PERFORMANCE_IMPROVEMENTS.md` - Updated with new fixes -- `docs/PERFORMANCE_OPTIMIZATION_README.md` - This file (NEW) -- `tests/test_phase_optimizations.py` - Test suite (NEW) -- `scripts/validate_optimizations.py` - Validation script (NEW) - -## Monitoring Performance - -### 1. Check Health Endpoint -```bash -curl http://localhost:7071/api/ai/status | jq -``` - -Look for: -- SQL pool saturation (warns at ≥80%) -- Active provider detection -- Connection health - -### 2. Run Validation -```bash -python scripts/validate_optimizations.py -``` - -### 3. Performance Profiling -```bash -# Profile a specific module -python -m cProfile -o profile.stats aria_web/server.py - -# Visualize with snakeviz -pip install snakeviz -snakeviz profile.stats -``` - -## Future Optimizations - -Not yet implemented but identified: - -1. **String concatenation in loops** (10+ files) - - Use `''.join(list)` instead of `+= string` - -2. **Regex compilation** (dashboard, llm-maker) - - Compile patterns at module level - -3. **Repeated file checks** (function_app.py) - - Add caching with TTL - -See `PERFORMANCE_OPTIMIZATION_SUMMARY.md` for details. - -## Questions? - -- **What was optimized?** See summary sections above -- **How do I test?** Run `python scripts/validate_optimizations.py` -- **Where are examples?** See code patterns section above -- **Full details?** Read `PERFORMANCE_OPTIMIZATION_SUMMARY.md` - -## Changelog - -- **2024-02-17**: Phase 1 & 2 completed - - 5 critical optimizations implemented - - Comprehensive tests added - - Full documentation created - - All validations passing +# Performance Optimization Work - February 2024 + +This README provides a quick reference for the performance optimization work completed in February 2024. + +## Quick Links + +- **Summary Document**: [`PERFORMANCE_OPTIMIZATION_SUMMARY.md`](./PERFORMANCE_OPTIMIZATION_SUMMARY.md) - Complete guide with examples and best practices +- **Historical Issues**: [`PERFORMANCE_IMPROVEMENTS.md`](./PERFORMANCE_IMPROVEMENTS.md) - Original performance analysis and earlier fixes +- **Tests**: [`../tests/test_phase_optimizations.py`](../tests/test_phase_optimizations.py) - Comprehensive test suite +- **Validation**: [`../scripts/validate_optimizations.py`](../scripts/validate_optimizations.py) - Standalone validation script + +## What Was Fixed + +### Phase 1: Critical Issues (High Impact) + +1. **Aria Web Server** (`aria_web/server.py`) + - **Issue**: 27 repeated `any()` calls with list comprehensions + - **Fix**: Pre-compiled frozenset keyword collections with O(1) lookups + - **Impact**: 2-5x speedup per command processing + +2. **Chat Memory** (`shared/chat_memory.py`) + - **Issue**: New DB connection created for every operation + - **Fix**: Connection pool with max 5 connections and health checks + - **Impact**: 50-100ms → 0ms per operation + +3. **Batch Evaluator** (`scripts/batch_evaluator.py`) + - **Issue**: O(n²) linear search in model comparison + - **Fix**: Dict-based O(1) lookup index + - **Impact**: 10x faster for 100 models + +### Phase 2: High Priority Improvements + +4. **File I/O** (`dashboard/serve.py`) + - **Issue**: Loading entire log files into memory + - **Fix**: Block-based streaming for large files (> 64KB) + - **Impact**: 100x memory reduction (GB → 64KB) + +5. **Dictionary Iteration** (6 files) + - **Issue**: Unnecessary `.keys()` calls + - **Fix**: Direct iteration (more Pythonic) + - **Impact**: 5-10% performance + cleaner code + +## Quick Validation + +Run the standalone validation script (no dependencies required): + +```bash +python scripts/validate_optimizations.py +``` + +Expected output: +``` +✅ Aria web server optimizations validated +✅ Chat memory pooling functions validated +✅ Batch evaluator optimizations validated +✅ File streaming logic validated +✅ Dictionary iteration patterns validated + +Performance Benchmark: + Keyword matching (300 calls): 0.22ms + +5/5 tests passed - All optimizations validated successfully! +``` + +## Performance Impact Summary + +| Optimization | Before | After | Improvement | +|-------------|--------|-------|-------------| +| **Keyword matching** | O(n×m) repeated | O(n) set intersection | 2-5x faster | +| **DB connections** | New per operation | Pooled (max 5) | 50-100ms → 0ms | +| **Model lookups** | O(n²) linear search | O(1) dict lookup | 10x for 100 models | +| **Log file reads** | GB in memory | 64KB buffer | 100x memory | +| **Dict iteration** | `.keys()` overhead | Direct iteration | 5-10% + cleaner | + +## Code Patterns (Copy-Paste Ready) + +### Pattern 1: Keyword Matching with Sets + +```python +# Define keyword sets at module level +_ACTION_KEYWORDS = frozenset(['jump', 'run', 'dance']) + +def _any_word_in_text(keywords: frozenset, text: str) -> bool: + """Fast O(n) keyword matching using set intersection.""" + return bool(keywords & set(text.split())) + +# Usage +if _any_word_in_text(_ACTION_KEYWORDS, user_command): + process_action() +``` + +### Pattern 2: Connection Pooling + +```python +_connection_pool = [] + +def _get_conn(): + """Get connection from pool or create new.""" + while _connection_pool: + conn = _connection_pool.pop() + try: + # Health check + conn.cursor().execute("SELECT 1") + return conn + except Exception: + try: + conn.close() + except Exception: + pass + return create_new_connection() + +def _return_conn(conn): + """Return connection to pool.""" + if len(_connection_pool) < 5: + _connection_pool.append(conn) + else: + conn.close() +``` + +### Pattern 3: Dict-Based Lookups + +```python +# Instead of this (O(n²)): +for target_id in ids_to_find: + item = next((x for x in items if x.id == target_id), None) + +# Do this (O(n)): +items_by_id = {x.id: x for x in items} +for target_id in ids_to_find: + item = items_by_id.get(target_id) +``` + +### Pattern 4: Streaming Large Files + +```python +def read_tail(filepath: Path, max_lines: int = 500): + """Efficiently read last N lines from large files.""" + size = filepath.stat().st_size + + if size <= 65536: # Small file: fast path + with open(filepath) as f: + return f.readlines()[-max_lines:] + + # Large file: stream backwards + with open(filepath, 'rb') as f: + f.seek(0, 2) # End + remaining = f.tell() + chunks = [] + + while remaining > 0: + block_size = min(32768, remaining) + f.seek(remaining - block_size) + chunks.insert(0, f.read(block_size)) + remaining -= block_size + + # Check if we have enough lines + decoded = b''.join(chunks).decode('utf-8', errors='ignore') + if decoded.count('\n') >= max_lines: + break + + return decoded.splitlines(keepends=True)[-max_lines:] +``` + +### Pattern 5: Direct Dictionary Iteration + +```python +# Instead of this: +for key in my_dict.keys(): + process(key) + +# Do this (more Pythonic): +for key in my_dict: + process(key) +``` + +## Files Modified + +### Phase 1 Changes +- `aria_web/server.py` - Keyword set optimizations +- `shared/chat_memory.py` - Connection pooling +- `scripts/batch_evaluator.py` - Dict-based lookups + +### Phase 2 Changes +- `dashboard/serve.py` - File streaming +- `dashboard/app.py` - Dict iteration +- `ai-projects/quantum-ml/benchmark_all_datasets.py` - Dict iteration +- `ai-projects/quantum-ml/scripts/visualize_hardware_results.py` - Dict iteration +- `scripts/automate_aria_movement.py` - Dict iteration +- `scripts/test_aria_dataset.py` - Dict iteration + +### Documentation & Tests +- `docs/PERFORMANCE_OPTIMIZATION_SUMMARY.md` - Complete guide (NEW) +- `docs/PERFORMANCE_IMPROVEMENTS.md` - Updated with new fixes +- `docs/PERFORMANCE_OPTIMIZATION_README.md` - This file (NEW) +- `tests/test_phase_optimizations.py` - Test suite (NEW) +- `scripts/validate_optimizations.py` - Validation script (NEW) + +## Monitoring Performance + +### 1. Check Health Endpoint +```bash +curl http://localhost:7071/api/ai/status | jq +``` + +Look for: +- SQL pool saturation (warns at ≥80%) +- Active provider detection +- Connection health + +### 2. Run Validation +```bash +python scripts/validate_optimizations.py +``` + +### 3. Performance Profiling +```bash +# Profile a specific module +python -m cProfile -o profile.stats aria_web/server.py + +# Visualize with snakeviz +pip install snakeviz +snakeviz profile.stats +``` + +## Future Optimizations + +Not yet implemented but identified: + +1. **String concatenation in loops** (10+ files) + - Use `''.join(list)` instead of `+= string` + +2. **Regex compilation** (dashboard, llm-maker) + - Compile patterns at module level + +3. **Repeated file checks** (function_app.py) + - Add caching with TTL + +See `PERFORMANCE_OPTIMIZATION_SUMMARY.md` for details. + +## Questions? + +- **What was optimized?** See summary sections above +- **How do I test?** Run `python scripts/validate_optimizations.py` +- **Where are examples?** See code patterns section above +- **Full details?** Read `PERFORMANCE_OPTIMIZATION_SUMMARY.md` + +## Changelog + +- **2024-02-17**: Phase 1 & 2 completed + - 5 critical optimizations implemented + - Comprehensive tests added + - Full documentation created + - All validations passing diff --git a/docs/PERFORMANCE_OPTIMIZATION_SUMMARY.md b/docs/PERFORMANCE_OPTIMIZATION_SUMMARY.md index f19856bdc..21d6e9c0e 100644 --- a/docs/PERFORMANCE_OPTIMIZATION_SUMMARY.md +++ b/docs/PERFORMANCE_OPTIMIZATION_SUMMARY.md @@ -1,352 +1,352 @@ -# Performance Optimization Summary - -This document summarizes the performance optimizations implemented to improve code efficiency across the Aria repository. - -## Table of Contents -- [Overview](#overview) -- [Phase 1: Critical Performance Issues](#phase-1-critical-performance-issues) -- [Phase 2: High Priority Optimizations](#phase-2-high-priority-optimizations) -- [Performance Impact](#performance-impact) -- [Best Practices](#best-practices) -- [Testing](#testing) - -## Overview - -Performance optimization work addressed three categories of issues: -- **Critical Issues (Phase 1)**: High-impact bottlenecks causing significant slowdowns -- **High Priority (Phase 2)**: Common anti-patterns affecting multiple files -- **Best Practices**: Code patterns that improve maintainability and performance - -All optimizations maintain backward compatibility and include comprehensive tests. - -## Phase 1: Critical Performance Issues - -### 1. Aria Web Server - Repeated Keyword Checks - -**Location**: `aria_web/server.py` - -**Problem**: 20+ repeated `any()` calls with list comprehensions for keyword matching -- Each `any(word in text for word in [...])` creates a new generator and iterates linearly -- Called multiple times per command processing (O(n×m) complexity) -- Example: `any(k in cmd for k in ['jump', 'leap', 'hop'])` - -**Solution**: Pre-compiled frozenset keyword collections with set intersection -```python -# Pre-compiled at module level -_JUMP_KEYWORDS = frozenset(['jump', 'leap', 'hop']) - -def _any_word_in_text(keywords: frozenset, text: str) -> bool: - """Fast keyword matching using set intersection - O(n) where n is words in text.""" - text_words = set(text.split()) - return bool(keywords & text_words) - -# Usage -if _any_word_in_text(_JUMP_KEYWORDS, cmd): - # process jump command -``` - -**Impact**: -- Complexity: O(n×m) → O(n) where n = words in text, m = keywords -- 27 call sites optimized -- Improved code maintainability with centralized keyword definitions - -### 2. Chat Memory - Database Connection Pooling - -**Location**: `shared/chat_memory.py` - -**Problem**: New database connection created for every embedding operation -- `pyodbc.connect()` on every `store_embedding()` and `fetch_similar_messages()` call -- Connection overhead: ~50-100ms per operation -- No connection reuse across requests - -**Solution**: Simple connection pool with health checks -```python -_connection_pool = [] - -def _get_conn(): - """Get connection from pool or create new one.""" - # Try to reuse existing connection - while _connection_pool: - conn = _connection_pool.pop() - try: - # Test if connection is alive - cursor = conn.cursor() - cursor.execute("SELECT 1") - cursor.close() - return conn - except Exception: - # Connection dead, try next - try: - conn.close() - except Exception: - pass - - # Create new connection - return pyodbc.connect(conn_str, timeout=4) - -def _return_conn(conn): - """Return connection to pool for reuse.""" - if len(_connection_pool) < 5: # Max 5 connections - _connection_pool.append(conn) - else: - conn.close() -``` - -**Impact**: -- Connection overhead: 50-100ms → ~0ms (cache hit) -- Pool size limited to 5 to prevent resource exhaustion -- Automatic dead connection removal - -### 3. Batch Evaluator - O(n²) Model Lookup - -**Location**: `scripts/batch_evaluator.py` - -**Problem**: Linear search for each model comparison -```python -# OLD: O(n²) complexity -for model_id in model_ids: - result = next((r for r in self.results if r.model_id == model_id), None) -``` - -**Solution**: Dict-based O(1) lookup -```python -# NEW: O(n) complexity -results_by_id = {r.model_id: r for r in self.results} -for model_id in model_ids: - result = results_by_id.get(model_id) -``` - -**Impact**: -- Complexity: O(n×m) → O(n+m) where n = results, m = model_ids to compare -- For 100 models comparing 10: ~1000 iterations → 110 operations - -## Phase 2: High Priority Optimizations - -### 4. File I/O - Streaming vs Loading Entire Files - -**Location**: `dashboard/serve.py` - -**Problem**: `readlines()` loads entire log file into memory -```python -# OLD: Memory = file size (potentially GB) -lines = f.readlines() -return {'logs': ''.join(lines[-500:])} -``` - -**Solution**: Block-based streaming for large files -```python -# NEW: Memory = ~64KB buffer -if file_size <= 65536: - # Small files: fast in-memory read - lines = f.readlines() - return {'logs': ''.join(lines[-500:])} -else: - # Large files: stream backwards in 32KB blocks - with open(log_file, 'rb') as f: - f.seek(0, 2) # End - # Read backwards until we have 500 lines - # ... (see implementation for details) -``` - -**Impact**: -- Memory usage: File size → ~64KB max -- Safe for multi-GB log files -- Maintains fast path for small files - -### 5. Dictionary Iteration - Direct vs .keys() - -**Locations**: 6 files across codebase -- `dashboard/app.py` -- `ai-projects/quantum-ml/benchmark_all_datasets.py` -- `ai-projects/quantum-ml/scripts/visualize_hardware_results.py` -- `scripts/automate_aria_movement.py` -- `scripts/test_aria_dataset.py` - -**Problem**: Unnecessary `.keys()` call -```python -# OLD: Creates view object, slightly less efficient -for name in status_map.keys(): - # process -``` - -**Solution**: Direct iteration (Pythonic) -```python -# NEW: Direct iteration over dict -for name in status_map: - # process -``` - -**Impact**: -- Marginal performance improvement (~5-10%) -- Improved code readability (more Pythonic) -- Reduced bytecode overhead - -## Performance Impact - -### Summary Table - -| Optimization | Location | Complexity Change | Typical Speedup | -|-------------|----------|-------------------|-----------------| -| Keyword matching | `aria_web/server.py` | O(n×m) → O(n) | 2-5x per command | -| Connection pooling | `shared/chat_memory.py` | N/A | 50-100ms → 0ms per op | -| Dict lookup | `scripts/batch_evaluator.py` | O(n×m) → O(n) | 10x for 100 models | -| Streaming reads | `dashboard/serve.py` | N/A | 100x memory reduction | -| Direct iteration | 6 files | N/A | 5-10% per iteration | - -### Memory Usage - -**Before optimizations:** -- Large log files: Could consume GBs of memory -- Database connections: New connection per request (connection leak risk) - -**After optimizations:** -- Large log files: Max 64KB buffer -- Database connections: Pooled and reused (max 5 concurrent) - -## Best Practices - -### 1. Keyword Matching -```python -# ❌ Avoid: Repeated any() with list comprehensions -if any(word in text for word in ['jump', 'leap', 'hop']): - pass - -# ✅ Use: Pre-compiled frozensets with set operations -_KEYWORDS = frozenset(['jump', 'leap', 'hop']) -if _any_word_in_text(_KEYWORDS, text): - pass -``` - -### 2. Database Connections -```python -# ❌ Avoid: Creating new connection every time -def operation(): - conn = pyodbc.connect(conn_str) - # use conn - conn.close() - -# ✅ Use: Connection pooling with reuse -def operation(): - conn = _get_conn() - try: - # use conn - finally: - _return_conn(conn) # Return to pool -``` - -### 3. Collection Lookups -```python -# ❌ Avoid: Linear search with generator -result = next((item for item in items if item.id == target_id), None) - -# ✅ Use: Dict-based O(1) lookup -items_by_id = {item.id: item for item in items} -result = items_by_id.get(target_id) -``` - -### 4. File Reading -```python -# ❌ Avoid: Loading entire file in memory -lines = f.readlines() -process_last_n(lines[-100:]) - -# ✅ Use: Stream or block-based reading for large files -if file_size < 64*1024: - lines = f.readlines() # Fast path for small files -else: - lines = stream_tail(f, 100) # Efficient for large files -``` - -### 5. Dictionary Iteration -```python -# ❌ Avoid: Unnecessary .keys() call -for key in my_dict.keys(): - process(key) - -# ✅ Use: Direct iteration (more Pythonic) -for key in my_dict: - process(key) -``` - -## Testing - -All optimizations include comprehensive tests in: -- `tests/test_phase_optimizations.py` - New Phase 1 & 2 tests -- `tests/test_performance_optimizations.py` - Existing performance tests - -### Running Performance Tests - -```bash -# Run all performance tests -pytest tests/test_phase_optimizations.py -v - -# Run specific test class -pytest tests/test_phase_optimizations.py::TestAriaWebServerOptimizations -v - -# Run with performance profiling -pytest tests/test_phase_optimizations.py --durations=10 -``` - -### Key Test Coverage - -1. **Correctness**: All optimizations produce identical results to original code -2. **Performance**: Benchmarks verify improvements (where measurable) -3. **Edge Cases**: Empty inputs, large datasets, connection failures -4. **Thread Safety**: Connection pool handles concurrent access - -## Future Optimizations - -Additional opportunities identified but not yet implemented: - -### Medium Priority -1. **String concatenation in loops**: 10+ files use `+=` in loops - - Convert to list accumulation with `''.join()` - - Example: `scripts/training_analytics.py` - -2. **Repeated file operations**: Multiple file existence checks - - Add caching with TTL for `function_app.py` status endpoint - -3. **Regex compilation**: Compile patterns at module level - - Example: `dashboard/app.py`, `llm-maker/src/tool_validator.py` - -### Low Priority -1. **Quantum classifier batching**: Leverage PennyLane's built-in batching -2. **NumPy cosine similarity**: Already implemented in `chat_memory.py` -3. **LM Studio availability caching**: Already implemented with TTL - -## Monitoring - -To monitor the impact of these optimizations: - -1. **Check health endpoint**: `GET /api/ai/status` - - Shows SQL pool saturation - - Active provider detection - - Connection health - -2. **Review logs**: Look for performance warnings - - SQL pool saturation alerts (≥80%) - - Connection timeouts - -3. **Use profiling tools**: - ```bash - # Profile aria web server - python -m cProfile -o profile.stats aria_web/server.py - - # Analyze with snakeviz - snakeviz profile.stats - ``` - -## References - -- Original performance analysis: `docs/PERFORMANCE_IMPROVEMENTS.md` -- Repository memories: Performance patterns from previous optimizations -- Test coverage: `tests/test_phase_optimizations.py` - -## Changelog - -- **2024-02-17**: Phase 1 & 2 optimizations completed - - Aria web server keyword matching - - Chat memory connection pooling - - Batch evaluator O(1) lookups - - File I/O streaming - - Dictionary iteration improvements +# Performance Optimization Summary + +This document summarizes the performance optimizations implemented to improve code efficiency across the Aria repository. + +## Table of Contents +- [Overview](#overview) +- [Phase 1: Critical Performance Issues](#phase-1-critical-performance-issues) +- [Phase 2: High Priority Optimizations](#phase-2-high-priority-optimizations) +- [Performance Impact](#performance-impact) +- [Best Practices](#best-practices) +- [Testing](#testing) + +## Overview + +Performance optimization work addressed three categories of issues: +- **Critical Issues (Phase 1)**: High-impact bottlenecks causing significant slowdowns +- **High Priority (Phase 2)**: Common anti-patterns affecting multiple files +- **Best Practices**: Code patterns that improve maintainability and performance + +All optimizations maintain backward compatibility and include comprehensive tests. + +## Phase 1: Critical Performance Issues + +### 1. Aria Web Server - Repeated Keyword Checks + +**Location**: `aria_web/server.py` + +**Problem**: 20+ repeated `any()` calls with list comprehensions for keyword matching +- Each `any(word in text for word in [...])` creates a new generator and iterates linearly +- Called multiple times per command processing (O(n×m) complexity) +- Example: `any(k in cmd for k in ['jump', 'leap', 'hop'])` + +**Solution**: Pre-compiled frozenset keyword collections with set intersection +```python +# Pre-compiled at module level +_JUMP_KEYWORDS = frozenset(['jump', 'leap', 'hop']) + +def _any_word_in_text(keywords: frozenset, text: str) -> bool: + """Fast keyword matching using set intersection - O(n) where n is words in text.""" + text_words = set(text.split()) + return bool(keywords & text_words) + +# Usage +if _any_word_in_text(_JUMP_KEYWORDS, cmd): + # process jump command +``` + +**Impact**: +- Complexity: O(n×m) → O(n) where n = words in text, m = keywords +- 27 call sites optimized +- Improved code maintainability with centralized keyword definitions + +### 2. Chat Memory - Database Connection Pooling + +**Location**: `shared/chat_memory.py` + +**Problem**: New database connection created for every embedding operation +- `pyodbc.connect()` on every `store_embedding()` and `fetch_similar_messages()` call +- Connection overhead: ~50-100ms per operation +- No connection reuse across requests + +**Solution**: Simple connection pool with health checks +```python +_connection_pool = [] + +def _get_conn(): + """Get connection from pool or create new one.""" + # Try to reuse existing connection + while _connection_pool: + conn = _connection_pool.pop() + try: + # Test if connection is alive + cursor = conn.cursor() + cursor.execute("SELECT 1") + cursor.close() + return conn + except Exception: + # Connection dead, try next + try: + conn.close() + except Exception: + pass + + # Create new connection + return pyodbc.connect(conn_str, timeout=4) + +def _return_conn(conn): + """Return connection to pool for reuse.""" + if len(_connection_pool) < 5: # Max 5 connections + _connection_pool.append(conn) + else: + conn.close() +``` + +**Impact**: +- Connection overhead: 50-100ms → ~0ms (cache hit) +- Pool size limited to 5 to prevent resource exhaustion +- Automatic dead connection removal + +### 3. Batch Evaluator - O(n²) Model Lookup + +**Location**: `scripts/batch_evaluator.py` + +**Problem**: Linear search for each model comparison +```python +# OLD: O(n²) complexity +for model_id in model_ids: + result = next((r for r in self.results if r.model_id == model_id), None) +``` + +**Solution**: Dict-based O(1) lookup +```python +# NEW: O(n) complexity +results_by_id = {r.model_id: r for r in self.results} +for model_id in model_ids: + result = results_by_id.get(model_id) +``` + +**Impact**: +- Complexity: O(n×m) → O(n+m) where n = results, m = model_ids to compare +- For 100 models comparing 10: ~1000 iterations → 110 operations + +## Phase 2: High Priority Optimizations + +### 4. File I/O - Streaming vs Loading Entire Files + +**Location**: `dashboard/serve.py` + +**Problem**: `readlines()` loads entire log file into memory +```python +# OLD: Memory = file size (potentially GB) +lines = f.readlines() +return {'logs': ''.join(lines[-500:])} +``` + +**Solution**: Block-based streaming for large files +```python +# NEW: Memory = ~64KB buffer +if file_size <= 65536: + # Small files: fast in-memory read + lines = f.readlines() + return {'logs': ''.join(lines[-500:])} +else: + # Large files: stream backwards in 32KB blocks + with open(log_file, 'rb') as f: + f.seek(0, 2) # End + # Read backwards until we have 500 lines + # ... (see implementation for details) +``` + +**Impact**: +- Memory usage: File size → ~64KB max +- Safe for multi-GB log files +- Maintains fast path for small files + +### 5. Dictionary Iteration - Direct vs .keys() + +**Locations**: 6 files across codebase +- `dashboard/app.py` +- `ai-projects/quantum-ml/benchmark_all_datasets.py` +- `ai-projects/quantum-ml/scripts/visualize_hardware_results.py` +- `scripts/automate_aria_movement.py` +- `scripts/test_aria_dataset.py` + +**Problem**: Unnecessary `.keys()` call +```python +# OLD: Creates view object, slightly less efficient +for name in status_map.keys(): + # process +``` + +**Solution**: Direct iteration (Pythonic) +```python +# NEW: Direct iteration over dict +for name in status_map: + # process +``` + +**Impact**: +- Marginal performance improvement (~5-10%) +- Improved code readability (more Pythonic) +- Reduced bytecode overhead + +## Performance Impact + +### Summary Table + +| Optimization | Location | Complexity Change | Typical Speedup | +|-------------|----------|-------------------|-----------------| +| Keyword matching | `aria_web/server.py` | O(n×m) → O(n) | 2-5x per command | +| Connection pooling | `shared/chat_memory.py` | N/A | 50-100ms → 0ms per op | +| Dict lookup | `scripts/batch_evaluator.py` | O(n×m) → O(n) | 10x for 100 models | +| Streaming reads | `dashboard/serve.py` | N/A | 100x memory reduction | +| Direct iteration | 6 files | N/A | 5-10% per iteration | + +### Memory Usage + +**Before optimizations:** +- Large log files: Could consume GBs of memory +- Database connections: New connection per request (connection leak risk) + +**After optimizations:** +- Large log files: Max 64KB buffer +- Database connections: Pooled and reused (max 5 concurrent) + +## Best Practices + +### 1. Keyword Matching +```python +# ❌ Avoid: Repeated any() with list comprehensions +if any(word in text for word in ['jump', 'leap', 'hop']): + pass + +# ✅ Use: Pre-compiled frozensets with set operations +_KEYWORDS = frozenset(['jump', 'leap', 'hop']) +if _any_word_in_text(_KEYWORDS, text): + pass +``` + +### 2. Database Connections +```python +# ❌ Avoid: Creating new connection every time +def operation(): + conn = pyodbc.connect(conn_str) + # use conn + conn.close() + +# ✅ Use: Connection pooling with reuse +def operation(): + conn = _get_conn() + try: + # use conn + finally: + _return_conn(conn) # Return to pool +``` + +### 3. Collection Lookups +```python +# ❌ Avoid: Linear search with generator +result = next((item for item in items if item.id == target_id), None) + +# ✅ Use: Dict-based O(1) lookup +items_by_id = {item.id: item for item in items} +result = items_by_id.get(target_id) +``` + +### 4. File Reading +```python +# ❌ Avoid: Loading entire file in memory +lines = f.readlines() +process_last_n(lines[-100:]) + +# ✅ Use: Stream or block-based reading for large files +if file_size < 64*1024: + lines = f.readlines() # Fast path for small files +else: + lines = stream_tail(f, 100) # Efficient for large files +``` + +### 5. Dictionary Iteration +```python +# ❌ Avoid: Unnecessary .keys() call +for key in my_dict.keys(): + process(key) + +# ✅ Use: Direct iteration (more Pythonic) +for key in my_dict: + process(key) +``` + +## Testing + +All optimizations include comprehensive tests in: +- `tests/test_phase_optimizations.py` - New Phase 1 & 2 tests +- `tests/test_performance_optimizations.py` - Existing performance tests + +### Running Performance Tests + +```bash +# Run all performance tests +pytest tests/test_phase_optimizations.py -v + +# Run specific test class +pytest tests/test_phase_optimizations.py::TestAriaWebServerOptimizations -v + +# Run with performance profiling +pytest tests/test_phase_optimizations.py --durations=10 +``` + +### Key Test Coverage + +1. **Correctness**: All optimizations produce identical results to original code +2. **Performance**: Benchmarks verify improvements (where measurable) +3. **Edge Cases**: Empty inputs, large datasets, connection failures +4. **Thread Safety**: Connection pool handles concurrent access + +## Future Optimizations + +Additional opportunities identified but not yet implemented: + +### Medium Priority +1. **String concatenation in loops**: 10+ files use `+=` in loops + - Convert to list accumulation with `''.join()` + - Example: `scripts/training_analytics.py` + +2. **Repeated file operations**: Multiple file existence checks + - Add caching with TTL for `function_app.py` status endpoint + +3. **Regex compilation**: Compile patterns at module level + - Example: `dashboard/app.py`, `llm-maker/src/tool_validator.py` + +### Low Priority +1. **Quantum classifier batching**: Leverage PennyLane's built-in batching +2. **NumPy cosine similarity**: Already implemented in `chat_memory.py` +3. **LM Studio availability caching**: Already implemented with TTL + +## Monitoring + +To monitor the impact of these optimizations: + +1. **Check health endpoint**: `GET /api/ai/status` + - Shows SQL pool saturation + - Active provider detection + - Connection health + +2. **Review logs**: Look for performance warnings + - SQL pool saturation alerts (≥80%) + - Connection timeouts + +3. **Use profiling tools**: + ```bash + # Profile aria web server + python -m cProfile -o profile.stats aria_web/server.py + + # Analyze with snakeviz + snakeviz profile.stats + ``` + +## References + +- Original performance analysis: `docs/PERFORMANCE_IMPROVEMENTS.md` +- Repository memories: Performance patterns from previous optimizations +- Test coverage: `tests/test_phase_optimizations.py` + +## Changelog + +- **2024-02-17**: Phase 1 & 2 optimizations completed + - Aria web server keyword matching + - Chat memory connection pooling + - Batch evaluator O(1) lookups + - File I/O streaming + - Dictionary iteration improvements diff --git a/docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md b/docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md index cfa39f116..864a5db60 100644 --- a/docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md +++ b/docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md @@ -1,307 +1,307 @@ -# Performance Optimization Summary - -## Date: 2026-02-17 - -This document summarizes the performance optimizations implemented to address slow and inefficient code in the Aria repository. - ---- - -## 🎯 Critical Issues Fixed (High Impact) - -### 1. aria_web/server.py - Keyword Set Optimization -**Problem**: Hot path contained 20+ consecutive `any()` calls checking keywords against lists, each requiring O(n) linear search. - -**Before**: -```python -if any(k in cmd for k in ['jump', 'leap', 'hop']): - return '[aria:position:50:60]' -elif any(k in cmd for k in ['dance', 'spin', 'twirl']): - return '[aria:position:50:50]' -# ... 18+ more similar checks -``` - -**After**: -```python -# Pre-compiled keyword sets at module level -JUMP_KEYWORDS = frozenset(['jump', 'leap', 'hop']) -DANCE_KEYWORDS = frozenset(['dance', 'spin', 'twirl']) -# ... 20 more sets - -def _contains_any_keyword(text: str, keywords: frozenset) -> bool: - """Check if text contains any keyword from set. O(1) per keyword check.""" - return any(kw in text for kw in keywords) - -# Usage -if _contains_any_keyword(cmd, JUMP_KEYWORDS): - return '[aria:position:50:60]' -``` - -**Impact**: -- **Speedup**: 1.09x on benchmark (1600 iterations) -- **Reduced complexity**: From O(n) to O(1) for set membership checks -- **Affects**: Every command processed by Aria visual system (high-frequency code path) -- **Lines changed**: 39 `any()` calls replaced across 7 functions - ---- - -### 2. shared/chat_memory.py - Database Connection Pooling -**Problem**: Every database operation created a new connection, incurring 50-100ms overhead per request. - -**Before**: -```python -def _get_conn(): - conn_str = os.getenv("QAI_DB_CONN") - if not conn_str or not pyodbc: - return None - try: - return pyodbc.connect(conn_str, timeout=4) # NEW CONNECTION EVERY TIME - except Exception: - return None - -def store_embedding(...): - conn = _get_conn() - # ... use connection - finally: - conn.close() # CLOSES CONNECTION -``` - -**After**: -```python -# Connection pool at module level -_connection_pool = [] -_MAX_POOL_SIZE = 5 -_pool_lock = threading.Lock() - -def _get_conn(): - """Get connection from pool or create new one.""" - with _pool_lock: - if _connection_pool: - conn = _connection_pool.pop() - # Verify connection is valid - try: - cursor = conn.cursor() - cursor.execute("SELECT 1") - cursor.close() - return conn - except Exception: - pass # Connection stale, create new - - # Create new if pool empty - return pyodbc.connect(conn_str, timeout=4) - -def _return_conn(conn): - """Return connection to pool for reuse.""" - with _pool_lock: - if len(_connection_pool) < _MAX_POOL_SIZE: - _connection_pool.append(conn) - else: - conn.close() - -def store_embedding(...): - conn = _get_conn() - # ... use connection - finally: - _return_conn(conn) # RETURNS TO POOL -``` - -**Impact**: -- **Latency reduction**: Eliminates 50-100ms per request after pool warmup -- **Throughput improvement**: Enables concurrent requests to reuse connections -- **Thread-safe**: Lock mechanism prevents race conditions -- **Affects**: All embedding storage and similarity search operations -- **Functions updated**: `store_embedding()`, `fetch_similar_messages()` - ---- - -### 3. aria_web/server.py - Regex Pattern Pre-compilation -**Problem**: Regex patterns compiled on every call in hot paths. - -**Before**: -```python -tags = re.findall(r'\[aria:[^\]]+\]', response) # COMPILED EVERY TIME -say_match = re.search( - r"(?:\b(?:say|announce|shout|speak|tell)\b)...", - command, flags=re.I -) # COMPILED EVERY TIME -``` - -**After**: -```python -# Pre-compiled at module level -_RE_JSON_BLOCK = re.compile(r'\[.*\]', re.DOTALL) -_RE_ARIA_TAGS = re.compile(r'\[aria:[^\]]+\]') -_RE_SAY_COMMAND = re.compile( - r"(?:\b(?:say|announce|shout|speak|tell)\b)...", - re.IGNORECASE -) -_RE_SANITIZE_BRACKETS = re.compile(r'\]') -_RE_COORDINATES = re.compile(r'(\d{1,3})%?.*?(\d{1,3})%?') - -# Usage -tags = _RE_ARIA_TAGS.findall(response) # USES COMPILED PATTERN -say_match = _RE_SAY_COMMAND.search(command) # USES COMPILED PATTERN -``` - -**Impact**: -- **Compilation overhead eliminated**: Regex compiled once at module load -- **Affects**: Command parsing, tag generation, coordinate extraction -- **Patterns compiled**: 7 patterns used in hot paths -- **Typical speedup**: 2-5x for regex operations in tight loops - ---- - -## ✅ Medium Priority Issues Fixed - -### 4. scripts/analyze_learning_progress.py - Memory-Efficient Generator -**Problem**: Nested list comprehension materialized entire word list in memory. - -**Before**: -```python -words = [w for msg in assistant_messages for w in msg.split()] # FULL LIST IN MEMORY -if words: - diversity = len(set(words))/len(words) -``` - -**After**: -```python -from itertools import chain -words = list(chain.from_iterable(msg.split() for msg in assistant_messages)) # STREAMING -if words: - diversity = len(set(words))/len(words) -``` - -**Impact**: -- **Memory efficiency**: Reduces peak memory usage for large message sets -- **Readability**: More explicit use of itertools -- **Affects**: Learning progress analysis with large conversation logs - ---- - -### 5. cooking-ai/src/providers/local.py - Tag Filter Optimization -**Problem**: Nested `any()` + `all()` created O(filters × recipes × tags) complexity. - -**Before**: -```python -if filters and not all(any(f in tag.lower() for tag in r["tags"]) for f in filters): - continue # O(n²) or worse -``` - -**After**: -```python -if filters: - recipe_tags = {tag.lower() for tag in r["tags"]} # Pre-compute set once - if not all(any(f in tag for tag in recipe_tags) for f in filters): - continue # O(n) with set membership -``` - -**Impact**: -- **Complexity reduction**: From O(n×m×k) to O(n×m) where k = tags per recipe -- **Set membership**: O(1) lookups instead of O(k) linear scans -- **Affects**: Recipe search with tag filters - ---- - -## 📊 Performance Benchmarks - -### Keyword Set Optimization Benchmark -``` -Test: 1600 iterations (8 commands × 200 loops) × 4 keyword checks each -- Optimized time: 0.0031s -- Old style time: 0.0033s -- Speedup: 1.09x faster -``` - -### Connection Pooling Benefits -``` -Scenario: 100 consecutive embedding operations -- Without pooling: ~5000-10000ms (50-100ms × 100) -- With pooling: ~100ms (first connection) + ~0ms × 99 (reused) -- Speedup: 50-100x for batch operations -``` - -### Regex Compilation Savings -``` -Pattern compilation cost (typical): -- Single compile at module load: ~0.1ms × 7 patterns = ~0.7ms -- Runtime cost without pre-compilation: ~0.1ms per search × N calls -- Break-even point: ~7 calls (achieved in first second of server uptime) -``` - ---- - -## 🧪 Testing & Validation - -### Test Files Created -1. **tests/test_performance_keyword_sets.py** - - Pytest-compatible tests for keyword sets and connection pooling - - Includes benchmark comparisons - -2. **tests/validate_performance_optimizations.py** - - Standalone validation script (no pytest dependency) - - Tests basic functionality, position determination, benchmarks - - All tests passing ✓ - -### Running Tests -```bash -# Standalone validation -python tests/validate_performance_optimizations.py - -# With pytest (if available) -pytest tests/test_performance_keyword_sets.py -v -``` - ---- - -## 📝 Code Quality Improvements - -### Best Practices Applied -1. **frozenset for immutable keyword sets**: Signals intent and prevents accidental modification -2. **Thread-safe connection pooling**: Uses threading.Lock for multi-threaded safety -3. **Graceful degradation**: Connection pool falls back to dummy lock if threading unavailable -4. **Module-level constants**: All keyword sets and regex patterns at top of file -5. **Clear naming**: `_contains_any_keyword()` explicitly describes behavior -6. **Comments**: Added performance notes explaining optimization rationale - -### Code Statistics -- **Lines optimized**: ~120 lines across 4 files -- **Functions updated**: 12 functions -- **New helper functions**: 3 (`_contains_any_keyword`, `_return_conn`, `_DummyLock`) -- **Keyword sets added**: 22 frozensets -- **Regex patterns compiled**: 7 patterns - ---- - -## 🔮 Future Optimization Opportunities - -### Not Yet Implemented (Lower Priority) -1. **function_app.py**: Image/API response caching with TTL -2. **shared/chat_memory.py**: NumPy vectorized cosine similarity (already documented in docs/PERFORMANCE_IMPROVEMENTS.md) -3. **scripts/job_queue.py**: Set-based dependency tracking (current O(n) is acceptable for typical use) - -### Monitoring Recommendations -1. Add performance metrics to `/api/ai/status` endpoint -2. Log connection pool statistics periodically -3. Track average response times for command processing -4. Monitor database connection pool saturation - ---- - -## 📚 Related Documentation -- `docs/PERFORMANCE_IMPROVEMENTS.md` - Original performance analysis (comprehensive) -- `.github/copilot-instructions.md` - Repository coding guidelines -- Repository memories - Performance patterns and best practices - ---- - -## ✨ Summary - -**Total optimizations implemented**: 5 critical/high-impact fixes -**Estimated aggregate speedup**: 1.5-2x for typical workloads -**Key hot paths optimized**: -- ✅ Command keyword matching (aria_web) -- ✅ Database connection management (shared/chat_memory) -- ✅ Regex pattern compilation (aria_web) -- ✅ Memory-efficient word aggregation (scripts) -- ✅ Tag filter complexity reduction (cooking-ai) - -**All changes validated and tested** ✓ +# Performance Optimization Summary + +## Date: 2026-02-17 + +This document summarizes the performance optimizations implemented to address slow and inefficient code in the Aria repository. + +--- + +## 🎯 Critical Issues Fixed (High Impact) + +### 1. aria_web/server.py - Keyword Set Optimization +**Problem**: Hot path contained 20+ consecutive `any()` calls checking keywords against lists, each requiring O(n) linear search. + +**Before**: +```python +if any(k in cmd for k in ['jump', 'leap', 'hop']): + return '[aria:position:50:60]' +elif any(k in cmd for k in ['dance', 'spin', 'twirl']): + return '[aria:position:50:50]' +# ... 18+ more similar checks +``` + +**After**: +```python +# Pre-compiled keyword sets at module level +JUMP_KEYWORDS = frozenset(['jump', 'leap', 'hop']) +DANCE_KEYWORDS = frozenset(['dance', 'spin', 'twirl']) +# ... 20 more sets + +def _contains_any_keyword(text: str, keywords: frozenset) -> bool: + """Check if text contains any keyword from set. O(1) per keyword check.""" + return any(kw in text for kw in keywords) + +# Usage +if _contains_any_keyword(cmd, JUMP_KEYWORDS): + return '[aria:position:50:60]' +``` + +**Impact**: +- **Speedup**: 1.09x on benchmark (1600 iterations) +- **Reduced complexity**: From O(n) to O(1) for set membership checks +- **Affects**: Every command processed by Aria visual system (high-frequency code path) +- **Lines changed**: 39 `any()` calls replaced across 7 functions + +--- + +### 2. shared/chat_memory.py - Database Connection Pooling +**Problem**: Every database operation created a new connection, incurring 50-100ms overhead per request. + +**Before**: +```python +def _get_conn(): + conn_str = os.getenv("QAI_DB_CONN") + if not conn_str or not pyodbc: + return None + try: + return pyodbc.connect(conn_str, timeout=4) # NEW CONNECTION EVERY TIME + except Exception: + return None + +def store_embedding(...): + conn = _get_conn() + # ... use connection + finally: + conn.close() # CLOSES CONNECTION +``` + +**After**: +```python +# Connection pool at module level +_connection_pool = [] +_MAX_POOL_SIZE = 5 +_pool_lock = threading.Lock() + +def _get_conn(): + """Get connection from pool or create new one.""" + with _pool_lock: + if _connection_pool: + conn = _connection_pool.pop() + # Verify connection is valid + try: + cursor = conn.cursor() + cursor.execute("SELECT 1") + cursor.close() + return conn + except Exception: + pass # Connection stale, create new + + # Create new if pool empty + return pyodbc.connect(conn_str, timeout=4) + +def _return_conn(conn): + """Return connection to pool for reuse.""" + with _pool_lock: + if len(_connection_pool) < _MAX_POOL_SIZE: + _connection_pool.append(conn) + else: + conn.close() + +def store_embedding(...): + conn = _get_conn() + # ... use connection + finally: + _return_conn(conn) # RETURNS TO POOL +``` + +**Impact**: +- **Latency reduction**: Eliminates 50-100ms per request after pool warmup +- **Throughput improvement**: Enables concurrent requests to reuse connections +- **Thread-safe**: Lock mechanism prevents race conditions +- **Affects**: All embedding storage and similarity search operations +- **Functions updated**: `store_embedding()`, `fetch_similar_messages()` + +--- + +### 3. aria_web/server.py - Regex Pattern Pre-compilation +**Problem**: Regex patterns compiled on every call in hot paths. + +**Before**: +```python +tags = re.findall(r'\[aria:[^\]]+\]', response) # COMPILED EVERY TIME +say_match = re.search( + r"(?:\b(?:say|announce|shout|speak|tell)\b)...", + command, flags=re.I +) # COMPILED EVERY TIME +``` + +**After**: +```python +# Pre-compiled at module level +_RE_JSON_BLOCK = re.compile(r'\[.*\]', re.DOTALL) +_RE_ARIA_TAGS = re.compile(r'\[aria:[^\]]+\]') +_RE_SAY_COMMAND = re.compile( + r"(?:\b(?:say|announce|shout|speak|tell)\b)...", + re.IGNORECASE +) +_RE_SANITIZE_BRACKETS = re.compile(r'\]') +_RE_COORDINATES = re.compile(r'(\d{1,3})%?.*?(\d{1,3})%?') + +# Usage +tags = _RE_ARIA_TAGS.findall(response) # USES COMPILED PATTERN +say_match = _RE_SAY_COMMAND.search(command) # USES COMPILED PATTERN +``` + +**Impact**: +- **Compilation overhead eliminated**: Regex compiled once at module load +- **Affects**: Command parsing, tag generation, coordinate extraction +- **Patterns compiled**: 7 patterns used in hot paths +- **Typical speedup**: 2-5x for regex operations in tight loops + +--- + +## ✅ Medium Priority Issues Fixed + +### 4. scripts/analyze_learning_progress.py - Memory-Efficient Generator +**Problem**: Nested list comprehension materialized entire word list in memory. + +**Before**: +```python +words = [w for msg in assistant_messages for w in msg.split()] # FULL LIST IN MEMORY +if words: + diversity = len(set(words))/len(words) +``` + +**After**: +```python +from itertools import chain +words = list(chain.from_iterable(msg.split() for msg in assistant_messages)) # STREAMING +if words: + diversity = len(set(words))/len(words) +``` + +**Impact**: +- **Memory efficiency**: Reduces peak memory usage for large message sets +- **Readability**: More explicit use of itertools +- **Affects**: Learning progress analysis with large conversation logs + +--- + +### 5. cooking-ai/src/providers/local.py - Tag Filter Optimization +**Problem**: Nested `any()` + `all()` created O(filters × recipes × tags) complexity. + +**Before**: +```python +if filters and not all(any(f in tag.lower() for tag in r["tags"]) for f in filters): + continue # O(n²) or worse +``` + +**After**: +```python +if filters: + recipe_tags = {tag.lower() for tag in r["tags"]} # Pre-compute set once + if not all(any(f in tag for tag in recipe_tags) for f in filters): + continue # O(n) with set membership +``` + +**Impact**: +- **Complexity reduction**: From O(n×m×k) to O(n×m) where k = tags per recipe +- **Set membership**: O(1) lookups instead of O(k) linear scans +- **Affects**: Recipe search with tag filters + +--- + +## 📊 Performance Benchmarks + +### Keyword Set Optimization Benchmark +``` +Test: 1600 iterations (8 commands × 200 loops) × 4 keyword checks each +- Optimized time: 0.0031s +- Old style time: 0.0033s +- Speedup: 1.09x faster +``` + +### Connection Pooling Benefits +``` +Scenario: 100 consecutive embedding operations +- Without pooling: ~5000-10000ms (50-100ms × 100) +- With pooling: ~100ms (first connection) + ~0ms × 99 (reused) +- Speedup: 50-100x for batch operations +``` + +### Regex Compilation Savings +``` +Pattern compilation cost (typical): +- Single compile at module load: ~0.1ms × 7 patterns = ~0.7ms +- Runtime cost without pre-compilation: ~0.1ms per search × N calls +- Break-even point: ~7 calls (achieved in first second of server uptime) +``` + +--- + +## 🧪 Testing & Validation + +### Test Files Created +1. **tests/test_performance_keyword_sets.py** + - Pytest-compatible tests for keyword sets and connection pooling + - Includes benchmark comparisons + +2. **tests/validate_performance_optimizations.py** + - Standalone validation script (no pytest dependency) + - Tests basic functionality, position determination, benchmarks + - All tests passing ✓ + +### Running Tests +```bash +# Standalone validation +python tests/validate_performance_optimizations.py + +# With pytest (if available) +pytest tests/test_performance_keyword_sets.py -v +``` + +--- + +## 📝 Code Quality Improvements + +### Best Practices Applied +1. **frozenset for immutable keyword sets**: Signals intent and prevents accidental modification +2. **Thread-safe connection pooling**: Uses threading.Lock for multi-threaded safety +3. **Graceful degradation**: Connection pool falls back to dummy lock if threading unavailable +4. **Module-level constants**: All keyword sets and regex patterns at top of file +5. **Clear naming**: `_contains_any_keyword()` explicitly describes behavior +6. **Comments**: Added performance notes explaining optimization rationale + +### Code Statistics +- **Lines optimized**: ~120 lines across 4 files +- **Functions updated**: 12 functions +- **New helper functions**: 3 (`_contains_any_keyword`, `_return_conn`, `_DummyLock`) +- **Keyword sets added**: 22 frozensets +- **Regex patterns compiled**: 7 patterns + +--- + +## 🔮 Future Optimization Opportunities + +### Not Yet Implemented (Lower Priority) +1. **function_app.py**: Image/API response caching with TTL +2. **shared/chat_memory.py**: NumPy vectorized cosine similarity (already documented in docs/PERFORMANCE_IMPROVEMENTS.md) +3. **scripts/job_queue.py**: Set-based dependency tracking (current O(n) is acceptable for typical use) + +### Monitoring Recommendations +1. Add performance metrics to `/api/ai/status` endpoint +2. Log connection pool statistics periodically +3. Track average response times for command processing +4. Monitor database connection pool saturation + +--- + +## 📚 Related Documentation +- `docs/PERFORMANCE_IMPROVEMENTS.md` - Original performance analysis (comprehensive) +- `.github/copilot-instructions.md` - Repository coding guidelines +- Repository memories - Performance patterns and best practices + +--- + +## ✨ Summary + +**Total optimizations implemented**: 5 critical/high-impact fixes +**Estimated aggregate speedup**: 1.5-2x for typical workloads +**Key hot paths optimized**: +- ✅ Command keyword matching (aria_web) +- ✅ Database connection management (shared/chat_memory) +- ✅ Regex pattern compilation (aria_web) +- ✅ Memory-efficient word aggregation (scripts) +- ✅ Tag filter complexity reduction (cooking-ai) + +**All changes validated and tested** ✓ diff --git a/docs/PERFORMANCE_QUICK_FIXES.md b/docs/PERFORMANCE_QUICK_FIXES.md index 814f81b81..a1674fedc 100644 --- a/docs/PERFORMANCE_QUICK_FIXES.md +++ b/docs/PERFORMANCE_QUICK_FIXES.md @@ -1,354 +1,354 @@ -# Performance Quick Fixes - Developer Reference - -Quick reference for common performance anti-patterns and their fixes in the Aria codebase. - ---- - -## 🚀 Quick Wins (Copy-Paste Fixes) - -### 1. Keyword/String Membership Checks - -❌ **SLOW - Creates list every time:** -```python -if any(k in cmd for k in ['jump', 'leap', 'hop']): - do_something() -``` - -✅ **FAST - Use tuple or module-level frozenset:** -```python -# For small sets, tuple is fastest -if any(k in cmd for k in ('jump', 'leap', 'hop')): - do_something() - -# For repeated checks or large sets, use module-level constant -_JUMP_KEYWORDS = frozenset(['jump', 'leap', 'hop']) -if any(k in cmd for k in _JUMP_KEYWORDS): - do_something() -``` - -**Improvement:** 20-40% faster per check - ---- - -### 2. Status/Enum Membership Checks - -❌ **SLOW - Creates list:** -```python -if status in ["succeeded", "completed", "done"]: - process() -``` - -✅ **FAST - Use tuple or frozenset:** -```python -# Tuple for small, fixed sets -if status in ("succeeded", "completed", "done"): - process() - -# frozenset for repeated checks or larger sets -SUCCESS_STATUSES = frozenset(["succeeded", "completed", "done"]) -if status in SUCCESS_STATUSES: - process() -``` - -**Improvement:** 15-30% faster - ---- - -### 3. Linear Searches in Lists - -❌ **SLOW - O(n) search:** -```python -# Searching for item in list -for item in large_list: - if item.id == target_id: - return item -``` - -✅ **FAST - O(1) dict lookup:** -```python -# Build index once -items_by_id = {item.id: item for item in large_list} - -# O(1) lookup -return items_by_id.get(target_id) -``` - -**Improvement:** 50-100x faster for 100+ items - ---- - -### 4. String Concatenation in Loops - -❌ **SLOW - O(n²) due to immutable strings:** -```python -result = "" -for item in items: - result += str(item) + ", " -``` - -✅ **FAST - O(n) using list and join:** -```python -result = ", ".join(str(item) for item in items) -``` - -**Improvement:** 5-10x faster for 100+ items - ---- - -### 5. Multiple Passes Over Same Data - -❌ **SLOW - Multiple iterations:** -```python -succeeded = [r for r in results if r.status == "success"] -failed = [r for r in results if r.status != "success"] -total_time = sum(r.duration for r in results) -``` - -✅ **FAST - Single-pass aggregation:** -```python -succeeded = [] -failed = [] -total_time = 0 - -for r in results: - total_time += r.duration - if r.status == "success": - succeeded.append(r) - else: - failed.append(r) -``` - -**Improvement:** 3x faster - ---- - -### 6. Average/Statistics Calculations - -❌ **SLOW - Manual calculation:** -```python -for key, values in data.items(): - avg = sum(values) / len(values) - variance = sum((x - avg) ** 2 for x in values) / len(values) -``` - -✅ **FAST - Use statistics module:** -```python -import statistics - -for key, values in data.items(): - avg = statistics.mean(values) - variance = statistics.pvariance(values) -``` - -**Improvement:** 2x faster, more numerically stable - ---- - -### 7. Finding Maximum with Custom Key - -❌ **SLOW - Manual tracking:** -```python -best_value = float('-inf') -best_item = None - -for item in items: - value = compute_score(item) - if value > best_value: - best_value = value - best_item = item -``` - -✅ **FAST - Use built-in max:** -```python -best_item = max(items, key=lambda item: compute_score(item)) -``` - -**Improvement:** More readable, often faster (C implementation) - ---- - -### 8. Top-K Selection - -❌ **SLOW - Full sort:** -```python -# When you only need top 10 out of 1000s -top_items = sorted(items, key=lambda x: x.score, reverse=True)[:10] -``` - -✅ **FAST - Use heapq.nlargest:** -```python -import heapq - -top_items = heapq.nlargest(10, items, key=lambda x: x.score) -``` - -**Improvement:** 5-10x faster for large collections -- `heapq.nlargest`: O(n log k) where k=10 -- `sorted`: O(n log n) where n=1000s - ---- - -### 9. Database Operations in Loops - -❌ **SLOW - Connection per operation:** -```python -for item in items: - conn = get_connection() - cursor = conn.cursor() - cursor.execute("INSERT INTO table VALUES (?)", (item,)) - conn.commit() - conn.close() -``` - -✅ **FAST - Batch operations:** -```python -conn = get_connection() -cursor = conn.cursor() -cursor.executemany("INSERT INTO table VALUES (?)", [(item,) for item in items]) -conn.commit() -conn.close() -``` - -**Improvement:** 10-50x faster - ---- - -### 10. List Comprehension vs append - -❌ **SLOWER - Repeated append:** -```python -results = [] -for item in items: - if item.valid: - results.append(transform(item)) -``` - -✅ **FASTER - List comprehension:** -```python -results = [transform(item) for item in items if item.valid] -``` - -**Improvement:** 10-20% faster, more readable - ---- - -## 🔍 Detection Patterns - -### How to Find These Issues - -```bash -# Find list creations in conditionals -grep -n "in \[" *.py - -# Find string concatenation in loops -grep -n "+=" *.py - -# Find multiple iterations -grep -n "for.*in.*:" *.py | grep -A5 "for.*in.*:" - -# Find manual statistics -grep -n "sum(.*).*len(" *.py -``` - ---- - -## 📊 When to Optimize - -### DO optimize when: -- ✅ Code is in a hot path (called frequently) -- ✅ Processing large datasets (>1000 items) -- ✅ Operation is in a loop -- ✅ User-facing performance issue -- ✅ Easy win (simple fix with big impact) - -### DON'T optimize when: -- ❌ Code runs once at startup -- ❌ Small datasets (<100 items) -- ❌ Premature (no profiling data) -- ❌ Makes code significantly harder to read -- ❌ Current performance is acceptable - ---- - -## 🧪 Testing Performance Improvements - -### Simple Benchmark Template - -```python -import time - -def benchmark(func, *args, iterations=1000): - """Benchmark a function.""" - # Warm-up - for _ in range(10): - func(*args) - - # Measure - start = time.perf_counter() - for _ in range(iterations): - func(*args) - elapsed = time.perf_counter() - start - - print(f"{func.__name__}: {elapsed:.3f}s total, {elapsed/iterations*1000:.3f}ms avg") - return elapsed - -# Usage -old_time = benchmark(old_function, test_data) -new_time = benchmark(new_function, test_data) -print(f"Speedup: {old_time/new_time:.2f}x") -``` - ---- - -## 🎯 Priority Checklist - -When reviewing code for performance: - -1. ☐ Are there list literals in conditionals? → Use tuple/frozenset -2. ☐ Are there linear searches in loops? → Use dict/set -3. ☐ Is there string concatenation in loops? → Use join() -4. ☐ Are collections iterated multiple times? → Single-pass -5. ☐ Are there manual statistics calculations? → Use statistics module -6. ☐ Are there database operations in loops? → Batch operations -7. ☐ Is there repeated computation? → Cache results -8. ☐ Are there full sorts for top-k? → Use heapq - ---- - -## 📚 Related Resources - -- `docs/PERFORMANCE_ANALYSIS.md` - Full analysis with benchmarks -- `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` - Comprehensive guide -- `shared/performance_utils.py` - Reusable optimization utilities -- `scripts/benchmark_performance.py` - Benchmarking tools - ---- - -## 💡 Pro Tips - -1. **Profile before optimizing** - Use `cProfile` or `line_profiler` -2. **Measure improvements** - Always benchmark before/after -3. **Consider readability** - Don't sacrifice clarity for minor gains -4. **Test correctness** - Ensure optimizations don't change behavior -5. **Document trade-offs** - Explain complex optimizations - -```python -# Example: Profile a function -import cProfile -import pstats - -profiler = cProfile.Profile() -profiler.enable() - -# Your code here -result = expensive_function() - -profiler.disable() -stats = pstats.Stats(profiler) -stats.sort_stats('cumulative') -stats.print_stats(10) # Top 10 slowest -``` - ---- - -**Remember:** The best optimization is the one that matters to users! Focus on hot paths and measurable improvements. +# Performance Quick Fixes - Developer Reference + +Quick reference for common performance anti-patterns and their fixes in the Aria codebase. + +--- + +## 🚀 Quick Wins (Copy-Paste Fixes) + +### 1. Keyword/String Membership Checks + +❌ **SLOW - Creates list every time:** +```python +if any(k in cmd for k in ['jump', 'leap', 'hop']): + do_something() +``` + +✅ **FAST - Use tuple or module-level frozenset:** +```python +# For small sets, tuple is fastest +if any(k in cmd for k in ('jump', 'leap', 'hop')): + do_something() + +# For repeated checks or large sets, use module-level constant +_JUMP_KEYWORDS = frozenset(['jump', 'leap', 'hop']) +if any(k in cmd for k in _JUMP_KEYWORDS): + do_something() +``` + +**Improvement:** 20-40% faster per check + +--- + +### 2. Status/Enum Membership Checks + +❌ **SLOW - Creates list:** +```python +if status in ["succeeded", "completed", "done"]: + process() +``` + +✅ **FAST - Use tuple or frozenset:** +```python +# Tuple for small, fixed sets +if status in ("succeeded", "completed", "done"): + process() + +# frozenset for repeated checks or larger sets +SUCCESS_STATUSES = frozenset(["succeeded", "completed", "done"]) +if status in SUCCESS_STATUSES: + process() +``` + +**Improvement:** 15-30% faster + +--- + +### 3. Linear Searches in Lists + +❌ **SLOW - O(n) search:** +```python +# Searching for item in list +for item in large_list: + if item.id == target_id: + return item +``` + +✅ **FAST - O(1) dict lookup:** +```python +# Build index once +items_by_id = {item.id: item for item in large_list} + +# O(1) lookup +return items_by_id.get(target_id) +``` + +**Improvement:** 50-100x faster for 100+ items + +--- + +### 4. String Concatenation in Loops + +❌ **SLOW - O(n²) due to immutable strings:** +```python +result = "" +for item in items: + result += str(item) + ", " +``` + +✅ **FAST - O(n) using list and join:** +```python +result = ", ".join(str(item) for item in items) +``` + +**Improvement:** 5-10x faster for 100+ items + +--- + +### 5. Multiple Passes Over Same Data + +❌ **SLOW - Multiple iterations:** +```python +succeeded = [r for r in results if r.status == "success"] +failed = [r for r in results if r.status != "success"] +total_time = sum(r.duration for r in results) +``` + +✅ **FAST - Single-pass aggregation:** +```python +succeeded = [] +failed = [] +total_time = 0 + +for r in results: + total_time += r.duration + if r.status == "success": + succeeded.append(r) + else: + failed.append(r) +``` + +**Improvement:** 3x faster + +--- + +### 6. Average/Statistics Calculations + +❌ **SLOW - Manual calculation:** +```python +for key, values in data.items(): + avg = sum(values) / len(values) + variance = sum((x - avg) ** 2 for x in values) / len(values) +``` + +✅ **FAST - Use statistics module:** +```python +import statistics + +for key, values in data.items(): + avg = statistics.mean(values) + variance = statistics.pvariance(values) +``` + +**Improvement:** 2x faster, more numerically stable + +--- + +### 7. Finding Maximum with Custom Key + +❌ **SLOW - Manual tracking:** +```python +best_value = float('-inf') +best_item = None + +for item in items: + value = compute_score(item) + if value > best_value: + best_value = value + best_item = item +``` + +✅ **FAST - Use built-in max:** +```python +best_item = max(items, key=lambda item: compute_score(item)) +``` + +**Improvement:** More readable, often faster (C implementation) + +--- + +### 8. Top-K Selection + +❌ **SLOW - Full sort:** +```python +# When you only need top 10 out of 1000s +top_items = sorted(items, key=lambda x: x.score, reverse=True)[:10] +``` + +✅ **FAST - Use heapq.nlargest:** +```python +import heapq + +top_items = heapq.nlargest(10, items, key=lambda x: x.score) +``` + +**Improvement:** 5-10x faster for large collections +- `heapq.nlargest`: O(n log k) where k=10 +- `sorted`: O(n log n) where n=1000s + +--- + +### 9. Database Operations in Loops + +❌ **SLOW - Connection per operation:** +```python +for item in items: + conn = get_connection() + cursor = conn.cursor() + cursor.execute("INSERT INTO table VALUES (?)", (item,)) + conn.commit() + conn.close() +``` + +✅ **FAST - Batch operations:** +```python +conn = get_connection() +cursor = conn.cursor() +cursor.executemany("INSERT INTO table VALUES (?)", [(item,) for item in items]) +conn.commit() +conn.close() +``` + +**Improvement:** 10-50x faster + +--- + +### 10. List Comprehension vs append + +❌ **SLOWER - Repeated append:** +```python +results = [] +for item in items: + if item.valid: + results.append(transform(item)) +``` + +✅ **FASTER - List comprehension:** +```python +results = [transform(item) for item in items if item.valid] +``` + +**Improvement:** 10-20% faster, more readable + +--- + +## 🔍 Detection Patterns + +### How to Find These Issues + +```bash +# Find list creations in conditionals +grep -n "in \[" *.py + +# Find string concatenation in loops +grep -n "+=" *.py + +# Find multiple iterations +grep -n "for.*in.*:" *.py | grep -A5 "for.*in.*:" + +# Find manual statistics +grep -n "sum(.*).*len(" *.py +``` + +--- + +## 📊 When to Optimize + +### DO optimize when: +- ✅ Code is in a hot path (called frequently) +- ✅ Processing large datasets (>1000 items) +- ✅ Operation is in a loop +- ✅ User-facing performance issue +- ✅ Easy win (simple fix with big impact) + +### DON'T optimize when: +- ❌ Code runs once at startup +- ❌ Small datasets (<100 items) +- ❌ Premature (no profiling data) +- ❌ Makes code significantly harder to read +- ❌ Current performance is acceptable + +--- + +## 🧪 Testing Performance Improvements + +### Simple Benchmark Template + +```python +import time + +def benchmark(func, *args, iterations=1000): + """Benchmark a function.""" + # Warm-up + for _ in range(10): + func(*args) + + # Measure + start = time.perf_counter() + for _ in range(iterations): + func(*args) + elapsed = time.perf_counter() - start + + print(f"{func.__name__}: {elapsed:.3f}s total, {elapsed/iterations*1000:.3f}ms avg") + return elapsed + +# Usage +old_time = benchmark(old_function, test_data) +new_time = benchmark(new_function, test_data) +print(f"Speedup: {old_time/new_time:.2f}x") +``` + +--- + +## 🎯 Priority Checklist + +When reviewing code for performance: + +1. ☐ Are there list literals in conditionals? → Use tuple/frozenset +2. ☐ Are there linear searches in loops? → Use dict/set +3. ☐ Is there string concatenation in loops? → Use join() +4. ☐ Are collections iterated multiple times? → Single-pass +5. ☐ Are there manual statistics calculations? → Use statistics module +6. ☐ Are there database operations in loops? → Batch operations +7. ☐ Is there repeated computation? → Cache results +8. ☐ Are there full sorts for top-k? → Use heapq + +--- + +## 📚 Related Resources + +- `docs/PERFORMANCE_ANALYSIS.md` - Full analysis with benchmarks +- `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` - Comprehensive guide +- `shared/performance_utils.py` - Reusable optimization utilities +- `scripts/benchmark_performance.py` - Benchmarking tools + +--- + +## 💡 Pro Tips + +1. **Profile before optimizing** - Use `cProfile` or `line_profiler` +2. **Measure improvements** - Always benchmark before/after +3. **Consider readability** - Don't sacrifice clarity for minor gains +4. **Test correctness** - Ensure optimizations don't change behavior +5. **Document trade-offs** - Explain complex optimizations + +```python +# Example: Profile a function +import cProfile +import pstats + +profiler = cProfile.Profile() +profiler.enable() + +# Your code here +result = expensive_function() + +profiler.disable() +stats = pstats.Stats(profiler) +stats.sort_stats('cumulative') +stats.print_stats(10) # Top 10 slowest +``` + +--- + +**Remember:** The best optimization is the one that matters to users! Focus on hot paths and measurable improvements. diff --git a/docs/PERFORMANCE_README.md b/docs/PERFORMANCE_README.md index f38462023..02dcaba1d 100644 --- a/docs/PERFORMANCE_README.md +++ b/docs/PERFORMANCE_README.md @@ -1,170 +1,170 @@ -# Performance Optimization - Quick Reference - -## 🎯 Quick Stats - -- **Average Speedup:** 4.6x faster -- **Memory Saved:** 6+ MB per operation -- **Utilities Created:** 7 reusable functions -- **Documentation:** 1,000+ lines - -## 📚 Documentation Files - -1. **[PERFORMANCE_OPTIMIZATION_GUIDE.md](PERFORMANCE_OPTIMIZATION_GUIDE.md)** (355 lines) - - Complete guide with examples - - Best practices and anti-patterns - - Monitoring and benchmarking - - Performance targets - -2. **[PERFORMANCE_IMPLEMENTATION_SUMMARY.md](PERFORMANCE_IMPLEMENTATION_SUMMARY.md)** (246 lines) - - Detailed implementation overview - - Benchmark results - - Usage examples - - Future recommendations - -## 🚀 Quick Start - -### Run Benchmarks - -```bash -python scripts/benchmark_performance.py -``` - -### Use Performance Utilities - -```python -from shared.performance_utils import ( - tail_file, # Memory-efficient log tailing - stream_jsonl, # Generator-based JSONL reading - find_json_in_output, # Fast JSON extraction - FileCache, # In-memory file caching - timeit, # Function timing decorator - memoize_with_ttl # Time-based memoization -) - -# Example: Tail a log file -logs = tail_file(Path("training.log"), max_lines=50) - -# Example: Extract JSON from subprocess -result = subprocess.run(['./script'], capture_output=True, text=True) -metrics = find_json_in_output(result.stdout, key='metrics') - -# Example: Time a function -@timeit -def expensive_operation(): - pass - -# Example: Cache with TTL -@memoize_with_ttl(ttl_seconds=300) -def fetch_config(): - return load_config() -``` - -## 🎯 Key Optimizations - -### 1. File Tailing (1.9x faster, 5.1 MB saved) - -**Before:** -```python -with open(log_file, 'r') as f: - all_lines = f.readlines() # Loads entire file! - return all_lines[-20:] -``` - -**After:** -```python -from shared.performance_utils import tail_file -return tail_file(log_file, max_lines=20) # Only keeps 20 lines in memory -``` - -### 2. JSON Parsing (10.7x faster) - -**Before:** -```python -for line in output.splitlines(): # Parse all lines - if line.strip().startswith("{"): - data = json.loads(line) - if "metrics" in data: - return data -``` - -**After:** -```python -from shared.performance_utils import find_json_in_output -return find_json_in_output(output, key='metrics', search_from_end=True) -``` - -### 3. JSONL Streaming (1.1x faster, 1.2 MB saved) - -**Before:** -```python -with open('data.jsonl', 'r') as f: - all_records = [json.loads(line) for line in f] # Loads all into memory -for record in all_records: - process(record) -``` - -**After:** -```python -from shared.performance_utils import stream_jsonl -for record in stream_jsonl(Path('data.jsonl')): # Streams one at a time - process(record) -``` - -## 📦 Files Changed - -### Modified (3 files) -- `scripts/monitor_autonomous_training.py` - Use `tail_file()` -- `dashboard/serve.py` - Use `tail_file()` -- `scripts/batch_evaluator.py` - Use `find_json_in_output()` - -### Created (4 files) -- `shared/performance_utils.py` - Reusable utilities (473 lines) -- `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` - Complete guide (355 lines) -- `docs/PERFORMANCE_IMPLEMENTATION_SUMMARY.md` - Implementation details (246 lines) -- `scripts/benchmark_performance.py` - Validation suite (175 lines) - -## ✅ Validation - -All changes have been validated: - -```bash -# Test utilities -python shared/performance_utils.py -# Output: ✅ All examples completed successfully! - -# Run benchmarks -python scripts/benchmark_performance.py -# Output: Average speedup: 4.6x - -# Test imports -python -c "from shared.performance_utils import tail_file; print('✓ OK')" -python -c "from monitor_autonomous_training import TrainingMonitor; print('✓ OK')" -python -c "from batch_evaluator import BatchEvaluator; print('✓ OK')" -``` - -## 🎓 Learn More - -- **Complete Guide:** [PERFORMANCE_OPTIMIZATION_GUIDE.md](PERFORMANCE_OPTIMIZATION_GUIDE.md) -- **Implementation Details:** [PERFORMANCE_IMPLEMENTATION_SUMMARY.md](PERFORMANCE_IMPLEMENTATION_SUMMARY.md) -- **Utility Source:** `shared/performance_utils.py` (includes docstrings and examples) -- **Benchmarks:** `scripts/benchmark_performance.py` - -## 💡 Best Practices - -1. **Use iterators** instead of loading all data into memory -2. **Search from end** when looking for results in command output -3. **Cache with TTL** for frequently-accessed data that may change -4. **Profile before optimizing** - measure to confirm bottlenecks -5. **Reuse utilities** - check `shared/performance_utils.py` first - -## 🔮 Future Opportunities - -1. Apply `stream_jsonl()` to other JSONL processing scripts -2. Use `@memoize_with_ttl` for config file loading -3. Add `@timeit` to identify new bottlenecks -4. Implement async/await for concurrent I/O -5. Profile CPU-bound operations for multiprocessing - ---- - -**Status:** ✅ Production Ready - Ready for merge! +# Performance Optimization - Quick Reference + +## 🎯 Quick Stats + +- **Average Speedup:** 4.6x faster +- **Memory Saved:** 6+ MB per operation +- **Utilities Created:** 7 reusable functions +- **Documentation:** 1,000+ lines + +## 📚 Documentation Files + +1. **[PERFORMANCE_OPTIMIZATION_GUIDE.md](PERFORMANCE_OPTIMIZATION_GUIDE.md)** (355 lines) + - Complete guide with examples + - Best practices and anti-patterns + - Monitoring and benchmarking + - Performance targets + +2. **[PERFORMANCE_IMPLEMENTATION_SUMMARY.md](PERFORMANCE_IMPLEMENTATION_SUMMARY.md)** (246 lines) + - Detailed implementation overview + - Benchmark results + - Usage examples + - Future recommendations + +## 🚀 Quick Start + +### Run Benchmarks + +```bash +python scripts/benchmark_performance.py +``` + +### Use Performance Utilities + +```python +from shared.performance_utils import ( + tail_file, # Memory-efficient log tailing + stream_jsonl, # Generator-based JSONL reading + find_json_in_output, # Fast JSON extraction + FileCache, # In-memory file caching + timeit, # Function timing decorator + memoize_with_ttl # Time-based memoization +) + +# Example: Tail a log file +logs = tail_file(Path("training.log"), max_lines=50) + +# Example: Extract JSON from subprocess +result = subprocess.run(['./script'], capture_output=True, text=True) +metrics = find_json_in_output(result.stdout, key='metrics') + +# Example: Time a function +@timeit +def expensive_operation(): + pass + +# Example: Cache with TTL +@memoize_with_ttl(ttl_seconds=300) +def fetch_config(): + return load_config() +``` + +## 🎯 Key Optimizations + +### 1. File Tailing (1.9x faster, 5.1 MB saved) + +**Before:** +```python +with open(log_file, 'r') as f: + all_lines = f.readlines() # Loads entire file! + return all_lines[-20:] +``` + +**After:** +```python +from shared.performance_utils import tail_file +return tail_file(log_file, max_lines=20) # Only keeps 20 lines in memory +``` + +### 2. JSON Parsing (10.7x faster) + +**Before:** +```python +for line in output.splitlines(): # Parse all lines + if line.strip().startswith("{"): + data = json.loads(line) + if "metrics" in data: + return data +``` + +**After:** +```python +from shared.performance_utils import find_json_in_output +return find_json_in_output(output, key='metrics', search_from_end=True) +``` + +### 3. JSONL Streaming (1.1x faster, 1.2 MB saved) + +**Before:** +```python +with open('data.jsonl', 'r') as f: + all_records = [json.loads(line) for line in f] # Loads all into memory +for record in all_records: + process(record) +``` + +**After:** +```python +from shared.performance_utils import stream_jsonl +for record in stream_jsonl(Path('data.jsonl')): # Streams one at a time + process(record) +``` + +## 📦 Files Changed + +### Modified (3 files) +- `scripts/monitor_autonomous_training.py` - Use `tail_file()` +- `dashboard/serve.py` - Use `tail_file()` +- `scripts/batch_evaluator.py` - Use `find_json_in_output()` + +### Created (4 files) +- `shared/performance_utils.py` - Reusable utilities (473 lines) +- `docs/PERFORMANCE_OPTIMIZATION_GUIDE.md` - Complete guide (355 lines) +- `docs/PERFORMANCE_IMPLEMENTATION_SUMMARY.md` - Implementation details (246 lines) +- `scripts/benchmark_performance.py` - Validation suite (175 lines) + +## ✅ Validation + +All changes have been validated: + +```bash +# Test utilities +python shared/performance_utils.py +# Output: ✅ All examples completed successfully! + +# Run benchmarks +python scripts/benchmark_performance.py +# Output: Average speedup: 4.6x + +# Test imports +python -c "from shared.performance_utils import tail_file; print('✓ OK')" +python -c "from monitor_autonomous_training import TrainingMonitor; print('✓ OK')" +python -c "from batch_evaluator import BatchEvaluator; print('✓ OK')" +``` + +## 🎓 Learn More + +- **Complete Guide:** [PERFORMANCE_OPTIMIZATION_GUIDE.md](PERFORMANCE_OPTIMIZATION_GUIDE.md) +- **Implementation Details:** [PERFORMANCE_IMPLEMENTATION_SUMMARY.md](PERFORMANCE_IMPLEMENTATION_SUMMARY.md) +- **Utility Source:** `shared/performance_utils.py` (includes docstrings and examples) +- **Benchmarks:** `scripts/benchmark_performance.py` + +## 💡 Best Practices + +1. **Use iterators** instead of loading all data into memory +2. **Search from end** when looking for results in command output +3. **Cache with TTL** for frequently-accessed data that may change +4. **Profile before optimizing** - measure to confirm bottlenecks +5. **Reuse utilities** - check `shared/performance_utils.py` first + +## 🔮 Future Opportunities + +1. Apply `stream_jsonl()` to other JSONL processing scripts +2. Use `@memoize_with_ttl` for config file loading +3. Add `@timeit` to identify new bottlenecks +4. Implement async/await for concurrent I/O +5. Profile CPU-bound operations for multiprocessing + +--- + +**Status:** ✅ Production Ready - Ready for merge! diff --git a/docs/PERFORMANCE_SUMMARY.txt b/docs/PERFORMANCE_SUMMARY.txt index 6dc88c547..dd129c18b 100644 --- a/docs/PERFORMANCE_SUMMARY.txt +++ b/docs/PERFORMANCE_SUMMARY.txt @@ -1,155 +1,155 @@ -# Performance Analysis Summary - -``` -┌─────────────────────────────────────────────────────────────────────────┐ -│ ARIA PERFORMANCE ANALYSIS SUMMARY │ -│ February 17, 2026 │ -└─────────────────────────────────────────────────────────────────────────┘ - -📊 OVERVIEW -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - Files Analyzed: 150+ Python files - Issues Found: 15 distinct patterns - Total Impact: 2-10x improvement potential - Time to Fix: ~4-6 hours for critical issues - -🔴 CRITICAL ISSUES (Immediate Action - Week 1) -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -1. ARIA KEYWORD LOOKUPS (aria_web/server.py:496-521) - ├─ Problem: Creates 20+ lists per user command - ├─ Impact: Every Aria command affected (high traffic) - ├─ Fix Time: 30 minutes - ├─ Risk: Low (pure optimization) - └─ Expected: 50-70% faster ⚡⚡⚡ - -2. DATABASE CONNECTION POOLING (shared/chat_memory.py:151-175) - ├─ Problem: Opens new connection for every embedding - ├─ Impact: All chat embedding storage - ├─ Fix Time: 1 hour - ├─ Risk: Medium (requires testing) - └─ Expected: 5-10x faster for batch operations ⚡⚡⚡⚡ - -3. LINEAR MODEL SEARCH (scripts/batch_evaluator.py:305-312) - ├─ Problem: O(n²) complexity in model comparison - ├─ Impact: Model evaluation and comparison - ├─ Fix Time: 20 minutes - ├─ Risk: Low (backward compatible) - └─ Expected: 50-100x faster for 100+ models ⚡⚡⚡⚡⚡ - -🟠 HIGH-PRIORITY ISSUES (Weeks 2-3) -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -4. Inefficient Average Calculations (training_analytics.py:82-86) - └─ Expected: 20-30% faster ⚡⚡ - -5. String Concatenation in Loops (training_analytics.py:109-110) - └─ Expected: 5-10x faster ⚡⚡⚡ - -6. Redundant List Iterations (batch_evaluator.py:287) - └─ Expected: 2x faster ⚡ - -7. High Cyclomatic Complexity (function_app.py - 9 functions) - └─ Expected: 10-20% faster, better maintainability ⚡⚡ - -8. Non-Vectorized Cosine Similarity (chat_memory.py:241-251) - └─ Expected: 3-5x faster ⚡⚡⚡ - -🟡 MEDIUM & LOW PRIORITY (Month 2+) -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -9-15. Various tuple/frozenset optimizations, caching, etc. - └─ Expected: Minor improvements, better code quality - -📈 EXPECTED IMPROVEMENTS BY IMPLEMENTATION -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - Week 1 (Critical Fixes): - ├─ Aria commands: 50-70% faster - ├─ Embedding storage: 5-10x faster - └─ Model comparisons: 50-100x faster - - Week 2-3 (High Priority): - ├─ Report generation: 3-5x faster - ├─ Analytics: 2-3x faster - └─ Complex functions: 10-20% faster - - Month 2+ (Remaining): - └─ Overall polish: 5-15% faster - -🎯 IMPLEMENTATION ROADMAP -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - Phase 1: Critical Fixes (1-2 hours) - [●●●○○○○○○○] 30% - ├─ Fix #1: Aria keywords (30 min) - ├─ Fix #3: Model indexing (20 min) - └─ Fix #6: Remove redundant iteration (5 min) - - Phase 2: High-Priority (3-4 hours) - [○○○○○○○○○○] 0% - ├─ Fix #2: Batch embeddings (1 hr) - ├─ Fix #7: Refactor complex functions (2 hrs) - └─ Fix #11: Add caching (30 min) - - Phase 3: Remaining (2-3 hours) - [○○○○○○○○○○] 0% - └─ Fixes #4,5,8,9-15 - -✅ BEST PRACTICES ALREADY IMPLEMENTED -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - ✓ heapq.nlargest for top-k selection (O(n log k)) - ✓ Generator expressions for memory efficiency - ✓ Deque with popleft for O(1) queue operations - ✓ Dict comprehensions for efficient grouping - ✓ Single-pass aggregation in batch_evaluator - ✓ Cached glob operations with TTL - ✓ Debounced file writes - -📚 DOCUMENTATION -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - 1. docs/PERFORMANCE_ANALYSIS.md (27KB) - └─ Comprehensive analysis with 15 detailed issues - - 2. docs/PERFORMANCE_QUICK_FIXES.md (7KB) - └─ Developer quick reference with code examples - - 3. docs/PERFORMANCE_IMPLEMENTATION_GUIDE.md (19KB) - └─ Step-by-step implementation instructions - -🔧 NEXT STEPS -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - 1. Review findings and prioritize fixes - 2. Decide which issues to implement now - 3. Create feature branch: perf/critical-fixes - 4. Implement fixes with benchmarks - 5. Submit PR with before/after measurements - -💡 QUICK START -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - Want to implement the critical fixes now? - - Option A: All Critical Fixes (~2 hours) - • Biggest impact, immediate improvements - • Low risk, backward compatible - • Affects high-traffic code paths - - Option B: Just the Quick Wins (~30 minutes) - • Fix #1 (Aria keywords) + Fix #6 (redundant iteration) - • Minimal time investment - • Visible user impact - - Option C: Review Only - • Keep as reference documentation - • Implement later or incrementally - • Use as guide for future code reviews - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -Legend: - ⚡ = 2x faster - ⚡⚡ = 5x faster - ⚡⚡⚡ = 10x faster - ⚡⚡⚡⚡ = 50x faster - ⚡⚡⚡⚡⚡ = 100x faster -``` +# Performance Analysis Summary + +``` +┌─────────────────────────────────────────────────────────────────────────┐ +│ ARIA PERFORMANCE ANALYSIS SUMMARY │ +│ February 17, 2026 │ +└─────────────────────────────────────────────────────────────────────────┘ + +📊 OVERVIEW +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + Files Analyzed: 150+ Python files + Issues Found: 15 distinct patterns + Total Impact: 2-10x improvement potential + Time to Fix: ~4-6 hours for critical issues + +🔴 CRITICAL ISSUES (Immediate Action - Week 1) +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +1. ARIA KEYWORD LOOKUPS (aria_web/server.py:496-521) + ├─ Problem: Creates 20+ lists per user command + ├─ Impact: Every Aria command affected (high traffic) + ├─ Fix Time: 30 minutes + ├─ Risk: Low (pure optimization) + └─ Expected: 50-70% faster ⚡⚡⚡ + +2. DATABASE CONNECTION POOLING (shared/chat_memory.py:151-175) + ├─ Problem: Opens new connection for every embedding + ├─ Impact: All chat embedding storage + ├─ Fix Time: 1 hour + ├─ Risk: Medium (requires testing) + └─ Expected: 5-10x faster for batch operations ⚡⚡⚡⚡ + +3. LINEAR MODEL SEARCH (scripts/batch_evaluator.py:305-312) + ├─ Problem: O(n²) complexity in model comparison + ├─ Impact: Model evaluation and comparison + ├─ Fix Time: 20 minutes + ├─ Risk: Low (backward compatible) + └─ Expected: 50-100x faster for 100+ models ⚡⚡⚡⚡⚡ + +🟠 HIGH-PRIORITY ISSUES (Weeks 2-3) +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +4. Inefficient Average Calculations (training_analytics.py:82-86) + └─ Expected: 20-30% faster ⚡⚡ + +5. String Concatenation in Loops (training_analytics.py:109-110) + └─ Expected: 5-10x faster ⚡⚡⚡ + +6. Redundant List Iterations (batch_evaluator.py:287) + └─ Expected: 2x faster ⚡ + +7. High Cyclomatic Complexity (function_app.py - 9 functions) + └─ Expected: 10-20% faster, better maintainability ⚡⚡ + +8. Non-Vectorized Cosine Similarity (chat_memory.py:241-251) + └─ Expected: 3-5x faster ⚡⚡⚡ + +🟡 MEDIUM & LOW PRIORITY (Month 2+) +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +9-15. Various tuple/frozenset optimizations, caching, etc. + └─ Expected: Minor improvements, better code quality + +📈 EXPECTED IMPROVEMENTS BY IMPLEMENTATION +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + Week 1 (Critical Fixes): + ├─ Aria commands: 50-70% faster + ├─ Embedding storage: 5-10x faster + └─ Model comparisons: 50-100x faster + + Week 2-3 (High Priority): + ├─ Report generation: 3-5x faster + ├─ Analytics: 2-3x faster + └─ Complex functions: 10-20% faster + + Month 2+ (Remaining): + └─ Overall polish: 5-15% faster + +🎯 IMPLEMENTATION ROADMAP +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + Phase 1: Critical Fixes (1-2 hours) + [●●●○○○○○○○] 30% + ├─ Fix #1: Aria keywords (30 min) + ├─ Fix #3: Model indexing (20 min) + └─ Fix #6: Remove redundant iteration (5 min) + + Phase 2: High-Priority (3-4 hours) + [○○○○○○○○○○] 0% + ├─ Fix #2: Batch embeddings (1 hr) + ├─ Fix #7: Refactor complex functions (2 hrs) + └─ Fix #11: Add caching (30 min) + + Phase 3: Remaining (2-3 hours) + [○○○○○○○○○○] 0% + └─ Fixes #4,5,8,9-15 + +✅ BEST PRACTICES ALREADY IMPLEMENTED +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + ✓ heapq.nlargest for top-k selection (O(n log k)) + ✓ Generator expressions for memory efficiency + ✓ Deque with popleft for O(1) queue operations + ✓ Dict comprehensions for efficient grouping + ✓ Single-pass aggregation in batch_evaluator + ✓ Cached glob operations with TTL + ✓ Debounced file writes + +📚 DOCUMENTATION +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + 1. docs/PERFORMANCE_ANALYSIS.md (27KB) + └─ Comprehensive analysis with 15 detailed issues + + 2. docs/PERFORMANCE_QUICK_FIXES.md (7KB) + └─ Developer quick reference with code examples + + 3. docs/PERFORMANCE_IMPLEMENTATION_GUIDE.md (19KB) + └─ Step-by-step implementation instructions + +🔧 NEXT STEPS +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + 1. Review findings and prioritize fixes + 2. Decide which issues to implement now + 3. Create feature branch: perf/critical-fixes + 4. Implement fixes with benchmarks + 5. Submit PR with before/after measurements + +💡 QUICK START +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + Want to implement the critical fixes now? + + Option A: All Critical Fixes (~2 hours) + • Biggest impact, immediate improvements + • Low risk, backward compatible + • Affects high-traffic code paths + + Option B: Just the Quick Wins (~30 minutes) + • Fix #1 (Aria keywords) + Fix #6 (redundant iteration) + • Minimal time investment + • Visible user impact + + Option C: Review Only + • Keep as reference documentation + • Implement later or incrementally + • Use as guide for future code reviews + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +Legend: + ⚡ = 2x faster + ⚡⚡ = 5x faster + ⚡⚡⚡ = 10x faster + ⚡⚡⚡⚡ = 50x faster + ⚡⚡⚡⚡⚡ = 100x faster +``` diff --git a/docs/QAI_DOCS_INDEX.md b/docs/QAI_DOCS_INDEX.md index 5787469d3..24c35905d 100644 --- a/docs/QAI_DOCS_INDEX.md +++ b/docs/QAI_DOCS_INDEX.md @@ -1,126 +1,126 @@ -# QAI Documentation - -This directory contains all documentation for the QAI hybrid quantum-AI/ML workspace. - -## 📁 Documentation Structure - -| Directory | Description | -|-----------|-------------| -| [guides/](guides/) | General guides, feature documentation, and improvement summaries | -| [quickref/](quickref/) | Quick reference cards for rapid lookup | -| [training/](training/) | LoRA fine-tuning, automated training, and evaluation guides | -| [quantum/](quantum/) | Quantum computing, Azure Quantum, and hybrid ML documentation | -| [database/](database/) | SQL integration, monitoring, and Cosmos DB setup | -| [deployment/](deployment/) | Azure deployment, ML training, and production guides | -| [aria/](aria/) | ARIA visual system and movement training documentation | -| [architecture/](architecture/) | Phase improvements and architectural documentation | - -## 🚀 Quick Links - -### Getting Started -- [Quick Reference](quickref/QUICK_REFERENCE.md) - Essential commands at a glance -- [Quick Start Chat](quickref/QUICK_START_CHAT.md) - Get chatting in minutes - -### Training -- [AutoTrain README](training/AUTOTRAIN_README.md) - LoRA training orchestration -- [Training Commands](quickref/TRAINING_COMMANDS_QUICKREF.md) - Training command cheat sheet -- [Automated Pipeline](training/AUTOMATED_TRAINING_PIPELINE.md) - End-to-end automation - -### Quantum Computing -- [Quantum AutoRun](quantum/QUANTUM_AUTORUN_README.md) - Quantum job orchestration -- [Azure Quantum Examples](quantum/QUANTUM_AZURE_EXAMPLES.md) - Working examples - -### Database & Monitoring -- [SQL Quickref](quickref/SQL_QUICKREF.md) - SQL integration quick reference -- [Database Setup](database/DATABASE_SQL_SETUP.md) - Database configuration - -### Deployment -- [Azure Deployment](deployment/AZURE_ML_TRAINING_GUIDE.md) - Deploy to Azure ML -- [Production Plan](deployment/PRODUCTION_DEPLOYMENT_PLAN.md) - Production readiness - -## 📋 By Category - -
    -Guides (35+ documents) - -See [guides/](guides/) for detailed feature documentation and improvement summaries. - -
    - -
    -Quick References (10 documents) - -- [AUTOMATION_QUICKREF.md](quickref/AUTOMATION_QUICKREF.md) -- [BATCH_EVAL_QUICKREF.md](quickref/BATCH_EVAL_QUICKREF.md) -- [DATASETS_QUICK_REF.md](quickref/DATASETS_QUICK_REF.md) -- [QAI_HUB_QUICKREF.md](quickref/QAI_HUB_QUICKREF.md) -- [QUICK_REFERENCE.md](quickref/QUICK_REFERENCE.md) -- [QUICK_START_CHAT.md](quickref/QUICK_START_CHAT.md) -- [SQL_QUICKREF.md](quickref/SQL_QUICKREF.md) -- [TRAINING_COMMANDS_QUICKREF.md](quickref/TRAINING_COMMANDS_QUICKREF.md) -- [TRAINING_TAB_QUICKREF.md](quickref/TRAINING_TAB_QUICKREF.md) -- [VSCODE_TESTING_QUICKREF.md](quickref/VSCODE_TESTING_QUICKREF.md) - -
    - -
    -Training (10 documents) - -- [AUTOMATED_TRAINING_PIPELINE.md](training/AUTOMATED_TRAINING_PIPELINE.md) -- [AUTONOMOUS_TRAINING_README.md](training/AUTONOMOUS_TRAINING_README.md) -- [AUTOTRAIN_README.md](training/AUTOTRAIN_README.md) -- [BATCH_EVALUATION_GUIDE.md](training/BATCH_EVALUATION_GUIDE.md) -- [EVALUATION_AUTORUN_README.md](training/EVALUATION_AUTORUN_README.md) -- [EXTENDED_MARATHON_TRAINING.md](training/EXTENDED_MARATHON_TRAINING.md) -- [FAST_TRAINING_GUIDE.md](training/FAST_TRAINING_GUIDE.md) -- [TRAINING_STARTED.md](training/TRAINING_STARTED.md) -- [TRAINING_TAB_ENHANCEMENTS.md](training/TRAINING_TAB_ENHANCEMENTS.md) -- [TRAINING_VALIDATION_RESULTS.md](training/TRAINING_VALIDATION_RESULTS.md) - -
    - -
    -Quantum (4 documents) - -- [AZURE_QUANTUM_SUCCESS.md](quantum/AZURE_QUANTUM_SUCCESS.md) -- [QUANTUM_AUTORUN_README.md](quantum/QUANTUM_AUTORUN_README.md) -- [QUANTUM_AZURE_EXAMPLES.md](quantum/QUANTUM_AZURE_EXAMPLES.md) -- [QUANTUM_WEB_APP_COMPLETE.md](quantum/QUANTUM_WEB_APP_COMPLETE.md) - -
    - -
    -Database (7 documents) - -- [AZURE_MONITOR_SQL_SETUP.md](database/AZURE_MONITOR_SQL_SETUP.md) -- [DATABASE_INTEGRATION_GUIDE.md](database/DATABASE_INTEGRATION_GUIDE.md) -- [DATABASE_INTEGRATION_SUMMARY.md](database/DATABASE_INTEGRATION_SUMMARY.md) -- [DATABASE_SQL_SETUP.md](database/DATABASE_SQL_SETUP.md) -- [SQL_AUTOMATION_COMPLETE.md](database/SQL_AUTOMATION_COMPLETE.md) -- [SQL_INTEGRATION_COMPLETE.md](database/SQL_INTEGRATION_COMPLETE.md) -- [TELEMETRY_COSMOS_ENABLEMENT.md](database/TELEMETRY_COSMOS_ENABLEMENT.md) - -
    - -
    -Deployment (4 documents) - -- [AZURE_DEPLOYMENT_ANIME_AVATAR.md](deployment/AZURE_DEPLOYMENT_ANIME_AVATAR.md) -- [AZURE_ML_TRAINING_GUIDE.md](deployment/AZURE_ML_TRAINING_GUIDE.md) -- [DEPLOY_CHAT_TO_AZURE.md](deployment/DEPLOY_CHAT_TO_AZURE.md) -- [PRODUCTION_DEPLOYMENT_PLAN.md](deployment/PRODUCTION_DEPLOYMENT_PLAN.md) - -
    - -
    -ARIA (3 documents) - -- [ARIA_MOVEMENT_COMPLETE.md](aria/ARIA_MOVEMENT_COMPLETE.md) -- [ARIA_MOVEMENT_TRAINING.md](aria/ARIA_MOVEMENT_TRAINING.md) -- [ARIA_VISUAL_SYSTEM.md](aria/ARIA_VISUAL_SYSTEM.md) - -
    - ---- - -**Last Updated:** November 2025 +# QAI Documentation + +This directory contains all documentation for the QAI hybrid quantum-AI/ML workspace. + +## 📁 Documentation Structure + +| Directory | Description | +|-----------|-------------| +| [guides/](guides/) | General guides, feature documentation, and improvement summaries | +| [quickref/](quickref/) | Quick reference cards for rapid lookup | +| [training/](training/) | LoRA fine-tuning, automated training, and evaluation guides | +| [quantum/](quantum/) | Quantum computing, Azure Quantum, and hybrid ML documentation | +| [database/](database/) | SQL integration, monitoring, and Cosmos DB setup | +| [deployment/](deployment/) | Azure deployment, ML training, and production guides | +| [aria/](aria/) | ARIA visual system and movement training documentation | +| [architecture/](architecture/) | Phase improvements and architectural documentation | + +## 🚀 Quick Links + +### Getting Started +- [Quick Reference](quickref/QUICK_REFERENCE.md) - Essential commands at a glance +- [Quick Start Chat](quickref/QUICK_START_CHAT.md) - Get chatting in minutes + +### Training +- [AutoTrain README](training/AUTOTRAIN_README.md) - LoRA training orchestration +- [Training Commands](quickref/TRAINING_COMMANDS_QUICKREF.md) - Training command cheat sheet +- [Automated Pipeline](training/AUTOMATED_TRAINING_PIPELINE.md) - End-to-end automation + +### Quantum Computing +- [Quantum AutoRun](quantum/QUANTUM_AUTORUN_README.md) - Quantum job orchestration +- [Azure Quantum Examples](quantum/QUANTUM_AZURE_EXAMPLES.md) - Working examples + +### Database & Monitoring +- [SQL Quickref](quickref/SQL_QUICKREF.md) - SQL integration quick reference +- [Database Setup](database/DATABASE_SQL_SETUP.md) - Database configuration + +### Deployment +- [Azure Deployment](deployment/AZURE_ML_TRAINING_GUIDE.md) - Deploy to Azure ML +- [Production Plan](deployment/PRODUCTION_DEPLOYMENT_PLAN.md) - Production readiness + +## 📋 By Category + +
    +Guides (35+ documents) + +See [guides/](guides/) for detailed feature documentation and improvement summaries. + +
    + +
    +Quick References (10 documents) + +- [AUTOMATION_QUICKREF.md](quickref/AUTOMATION_QUICKREF.md) +- [BATCH_EVAL_QUICKREF.md](quickref/BATCH_EVAL_QUICKREF.md) +- [DATASETS_QUICK_REF.md](quickref/DATASETS_QUICK_REF.md) +- [QAI_HUB_QUICKREF.md](quickref/QAI_HUB_QUICKREF.md) +- [QUICK_REFERENCE.md](quickref/QUICK_REFERENCE.md) +- [QUICK_START_CHAT.md](quickref/QUICK_START_CHAT.md) +- [SQL_QUICKREF.md](quickref/SQL_QUICKREF.md) +- [TRAINING_COMMANDS_QUICKREF.md](quickref/TRAINING_COMMANDS_QUICKREF.md) +- [TRAINING_TAB_QUICKREF.md](quickref/TRAINING_TAB_QUICKREF.md) +- [VSCODE_TESTING_QUICKREF.md](quickref/VSCODE_TESTING_QUICKREF.md) + +
    + +
    +Training (10 documents) + +- [AUTOMATED_TRAINING_PIPELINE.md](training/AUTOMATED_TRAINING_PIPELINE.md) +- [AUTONOMOUS_TRAINING_README.md](training/AUTONOMOUS_TRAINING_README.md) +- [AUTOTRAIN_README.md](training/AUTOTRAIN_README.md) +- [BATCH_EVALUATION_GUIDE.md](training/BATCH_EVALUATION_GUIDE.md) +- [EVALUATION_AUTORUN_README.md](training/EVALUATION_AUTORUN_README.md) +- [EXTENDED_MARATHON_TRAINING.md](training/EXTENDED_MARATHON_TRAINING.md) +- [FAST_TRAINING_GUIDE.md](training/FAST_TRAINING_GUIDE.md) +- [TRAINING_STARTED.md](training/TRAINING_STARTED.md) +- [TRAINING_TAB_ENHANCEMENTS.md](training/TRAINING_TAB_ENHANCEMENTS.md) +- [TRAINING_VALIDATION_RESULTS.md](training/TRAINING_VALIDATION_RESULTS.md) + +
    + +
    +Quantum (4 documents) + +- [AZURE_QUANTUM_SUCCESS.md](quantum/AZURE_QUANTUM_SUCCESS.md) +- [QUANTUM_AUTORUN_README.md](quantum/QUANTUM_AUTORUN_README.md) +- [QUANTUM_AZURE_EXAMPLES.md](quantum/QUANTUM_AZURE_EXAMPLES.md) +- [QUANTUM_WEB_APP_COMPLETE.md](quantum/QUANTUM_WEB_APP_COMPLETE.md) + +
    + +
    +Database (7 documents) + +- [AZURE_MONITOR_SQL_SETUP.md](database/AZURE_MONITOR_SQL_SETUP.md) +- [DATABASE_INTEGRATION_GUIDE.md](database/DATABASE_INTEGRATION_GUIDE.md) +- [DATABASE_INTEGRATION_SUMMARY.md](database/DATABASE_INTEGRATION_SUMMARY.md) +- [DATABASE_SQL_SETUP.md](database/DATABASE_SQL_SETUP.md) +- [SQL_AUTOMATION_COMPLETE.md](database/SQL_AUTOMATION_COMPLETE.md) +- [SQL_INTEGRATION_COMPLETE.md](database/SQL_INTEGRATION_COMPLETE.md) +- [TELEMETRY_COSMOS_ENABLEMENT.md](database/TELEMETRY_COSMOS_ENABLEMENT.md) + +
    + +
    +Deployment (4 documents) + +- [AZURE_DEPLOYMENT_ANIME_AVATAR.md](deployment/AZURE_DEPLOYMENT_ANIME_AVATAR.md) +- [AZURE_ML_TRAINING_GUIDE.md](deployment/AZURE_ML_TRAINING_GUIDE.md) +- [DEPLOY_CHAT_TO_AZURE.md](deployment/DEPLOY_CHAT_TO_AZURE.md) +- [PRODUCTION_DEPLOYMENT_PLAN.md](deployment/PRODUCTION_DEPLOYMENT_PLAN.md) + +
    + +
    +ARIA (3 documents) + +- [ARIA_MOVEMENT_COMPLETE.md](aria/ARIA_MOVEMENT_COMPLETE.md) +- [ARIA_MOVEMENT_TRAINING.md](aria/ARIA_MOVEMENT_TRAINING.md) +- [ARIA_VISUAL_SYSTEM.md](aria/ARIA_VISUAL_SYSTEM.md) + +
    + +--- + +**Last Updated:** November 2025 diff --git a/docs/QUANTUM_LLM_DEVELOPMENT_SUMMARY.md b/docs/QUANTUM_LLM_DEVELOPMENT_SUMMARY.md index 18c0d03d3..2c7cae571 100644 --- a/docs/QUANTUM_LLM_DEVELOPMENT_SUMMARY.md +++ b/docs/QUANTUM_LLM_DEVELOPMENT_SUMMARY.md @@ -1,463 +1,463 @@ -# Quantum LLM Development Summary - -**Date:** March 9, 2026 -**System:** Aria Quantum AI Workspace -**Task:** Complete quantum-enhanced language model implementation - ---- - -## 🎯 Mission Accomplished - -I have created a **complete, production-ready quantum-enhanced language model training system** with advanced features for circuit optimization, adaptive training, and real-time monitoring. - ---- - -## 📦 Deliverables (7 New Files) - -### 1. **quantum_llm_advanced.py** (600+ lines) -**Advanced quantum-classical hybrid components** - -**Classes:** -- `QuantumCircuitCache` - LRU caching for circuit results -- `AdaptiveQuantumLayer` - Dynamic entanglement topology selection -- `MultiScaleQuantumAttention` - Multi-qubit attention heads (2-6 qubits) -- `QuantumPromptTuning` - Task-specific quantum prompt adaptation -- `QuantumErrorMitigation` - Zero-noise extrapolation and readout correction - -**Innovation:** First implementation of multi-scale quantum attention with learned complexity-based routing. - -### 2. **quantum_circuit_optimizer.py** (500+ lines) -**Circuit compilation and optimization** - -**Classes:** -- `CircuitCompiler` - Multi-level circuit optimization (light/moderate/aggressive) -- `BatchCircuitExecutor` - Batch processing with caching -- `AdaptiveCircuitScheduler` - Resource-aware quantum scheduling -- `QuantumClassicalPartitioner` - Learned quantum/classical routing - -**Innovation:** Intelligent partitioning learns when quantum execution provides advantage. - -### 3. **quantum_llm_hybrid_trainer.py** (550+ lines) -**Training orchestration with curriculum learning** - -**Classes:** -- `CurriculumScheduler` - Progressive quantum integration -- `AdaptiveQuantumRouter` - RL-based routing policy -- `HybridTrainingOrchestrator` - Complete training pipeline - -**Training Stages:** -1. Classical warmup (0% quantum) -2. Quantum transition (30% quantum) -3. Full quantum (70% quantum) - -**Innovation:** Curriculum learning prevents training collapse during quantum integration. - -### 4. **quantum_llm_monitor.py** (550+ lines) -**Real-time monitoring and visualization** - -**Classes:** -- `MetricsAggregator` - Moving averages, trends, anomaly detection -- `PerformanceMonitor` - CPU/GPU/memory tracking -- `QuantumCircuitProfiler` - Circuit performance profiling -- `TrainingDashboard` - Real-time dashboard with alerts -- `VisualizationExporter` - Data export for plotting - -**Alert System:** -- Loss anomaly detection (z-score based) -- Trend degradation warnings -- High perplexity alerts - -**Innovation:** First quantum-aware training dashboard with circuit-level profiling. - -### 5. **quantum_llm_integrated.py** (550+ lines) -**Complete system integration** - -**Classes:** -- `QuantumLLMConfig` - Comprehensive configuration management -- `IntegratedQuantumLLM` - Full quantum-enhanced transformer -- `QuantumLLMSystem` - End-to-end training system - -**Features:** -- All advanced components integrated -- Unified configuration system -- Automatic component initialization -- Complete training workflow - -**Innovation:** First turnkey quantum LLM system - configure and train in minutes. - -### 6. **quantum_llm_datasets.py** (500+ lines) -**Dataset loading and preprocessing** - -**Classes:** -- `CharacterTokenizer` - Character-level tokenization with special tokens -- `TextDataset` - Sequence windowing and padding -- `MultiSourceDataset` - Multiple dataset combination with weights -- `QuantumDataAugmenter` - Quantum-inspired data augmentation -- `DatasetBuilder` - Auto-format detection and loading - -**Supported Formats:** -- Plain text files -- JSON with "text" field -- Chat format with "messages" array - -**Innovation:** Auto-detection loader handles diverse dataset formats seamlessly. - -### 7. **quantum_llm_quickstart.py** (400+ lines) -**Ready-to-run examples** - -**Modes:** -- `quick` - Fast test with minimal model (2 epochs, 64-dim) -- `full` - Complete training with all features -- `monitor` - Real-time monitoring of ongoing training -- `generate` - Text generation with trained model - -**Usage:** -```bash -python quantum_llm_quickstart.py --mode quick -python quantum_llm_quickstart.py --mode full --config config.yaml -python quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm -python quantum_llm_quickstart.py --mode generate --model model.pt --prompt "Hello" -``` - -**Innovation:** Complete examples demonstrate every feature - learn by running. - ---- - -## 📚 Documentation (2 Files) - -### 8. **QUANTUM_LLM_README.md** (500+ lines) -Comprehensive documentation covering: -- Quick start guide -- Component descriptions -- Feature explanations -- Configuration guide -- Performance tips -- Use cases -- Testing procedures -- References - -### 9. **quantum_llm_config_example.yaml** (300+ lines) -Fully annotated configuration template with: -- All configuration options -- Detailed comments -- Recommended values -- Use case examples - ---- - -## 🔬 Key Technical Innovations - -### 1. Multi-Scale Quantum Attention -**Problem:** Single qubit count can't capture both fine-grained and complex patterns. - -**Solution:** Different attention heads use 2, 3, 4, or 6 qubits. - -**Result:** 15-20% better performance on hierarchical tasks. - -### 2. Adaptive Entanglement Routing -**Problem:** Full entanglement is expensive but not always needed. - -**Solution:** Learned predictor selects linear/circular/full entanglement based on input complexity. - -**Result:** 30-40% reduction in circuit depth without accuracy loss. - -### 3. Quantum Circuit Caching -**Problem:** Repeated patterns execute same circuits multiple times. - -**Solution:** LRU cache stores recent circuit results. - -**Result:** 2-5x speedup on character-level language models (high pattern repetition). - -### 4. Curriculum Learning for Quantum Integration -**Problem:** Training collapses when suddenly introducing quantum layers. - -**Solution:** Gradual quantum integration: 0% → 30% → 70% over training stages. - -**Result:** Stable training, 25% better final loss compared to direct quantum training. - -### 5. Quantum-Aware Monitoring -**Problem:** Standard ML metrics don't capture quantum-specific issues. - -**Solution:** Track circuit execution time, cache hit rates, entanglement patterns, quantum/classical ratio. - -**Result:** First complete visibility into quantum LLM training dynamics. - ---- - -## 🎓 Training Pipeline - -``` -Data Loading → Tokenization → Dataset Creation - ↓ - Stage 1: Classical Warmup - (Learn basic patterns) - ↓ - Stage 2: Quantum Transition - (Introduce quantum attention) - ↓ - Stage 3: Full Quantum - (Maximize quantum usage) - ↓ - Checkpointing & Visualization - ↓ - Final Model Export -``` - -**Throughout:** Real-time monitoring, adaptive routing, circuit optimization, error mitigation. - ---- - -## 📊 Performance Characteristics - -### Training Speed (Relative to Pure Classical) - -| Component | Overhead | With Caching | With Optimization | -|-----------|----------|--------------|------------------| -| Quantum Attention | 3-5x | 1.5-2x | 1.2-1.5x | -| Quantum FFN | 2-3x | 1.3-1.8x | 1.1-1.3x | -| Full Quantum Model | 4-8x | 2-3x | 1.5-2x | - -**Note:** Assumes quantum simulation. Real QPU has different characteristics. - -### Memory Requirements - -| Model Size | Parameters | Memory (CPU) | Memory (GPU) | -|------------|-----------|--------------|--------------| -| Tiny | 100K | ~50 MB | ~100 MB | -| Small | 1M | ~200 MB | ~400 MB | -| Medium | 10M | ~1 GB | ~2 GB | -| Large | 100M | ~8 GB | ~16 GB | - -**Note:** Quantum circuit simulation adds 20-30% memory overhead. - -### Accuracy (Proof-of-Concept Results) - -Tested on character-level language modeling: - -| Configuration | Perplexity | Training Time | -|---------------|-----------|---------------| -| Pure Classical | 3.2 | 1.0x (baseline) | -| Quantum Attention | 2.9 | 1.8x | -| Full Quantum | 2.7 | 2.5x | -| Multi-Scale Quantum | 2.5 | 2.8x | - -**Conclusion:** Quantum provides 15-20% perplexity improvement at 2-3x training cost. - ---- - -## 🚀 Quick Start Guide - -### Installation - -```bash -# Navigate to quantum ML directory -cd ai-projects/quantum-ml - -# Install dependencies (if needed) -pip install torch pennylane pyyaml numpy - -# Test components -python src/quantum_llm_advanced.py -python src/quantum_circuit_optimizer.py -python src/quantum_llm_monitor.py -python src/quantum_llm_datasets.py -``` - -### Run Quick Test - -```bash -# 2-epoch minimal test (~5 minutes on CPU) -python quantum_llm_quickstart.py --mode quick -``` - -**Output:** -- `data_out/quantum_llm_quickstart/final_model.pt` - Trained model -- `data_out/quantum_llm_quickstart/dashboard/` - Training metrics -- `data_out/quantum_llm_quickstart/training/` - Training reports -- `data_out/quantum_llm_quickstart/visualizations/` - Export data - -### Run Full Training - -```bash -# Full training with custom config -python quantum_llm_quickstart.py --mode full --config config/quantum_llm_config_example.yaml -``` - -### Monitor Training - -```bash -# Real-time monitoring -python quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm_quickstart - -# Or watch dashboard JSON -watch -n 5 'cat data_out/quantum_llm_quickstart/dashboard/dashboard.json | python -m json.tool' -``` - -### Generate Text - -```bash -# Generate text with trained model -python quantum_llm_quickstart.py --mode generate \ - --model data_out/quantum_llm_quickstart/final_model.pt \ - --prompt "Quantum computing" \ - --max-length 100 -``` - ---- - -## 🧪 Testing & Validation - -All components include self-tests: - -```bash -# Test each component individually -python src/quantum_llm_advanced.py # ✅ All 5 classes tested -python src/quantum_circuit_optimizer.py # ✅ All 4 classes tested -python src/quantum_llm_hybrid_trainer.py # ✅ 3-stage curriculum example -python src/quantum_llm_monitor.py # ✅ Dashboard simulation -python src/quantum_llm_datasets.py # ✅ Tokenizer + dataset tests -python src/quantum_llm_integrated.py --dry-run # ✅ Full system check -``` - -**Expected Output:** Each file logs "✅ All components loaded successfully" or similar. - ---- - -## 📈 Future Enhancements - -### Near-Term (1-3 months) -1. **Azure Quantum Integration** - Real QPU execution -2. **Subword Tokenization** - BPE/WordPiece support -3. **Distributed Training** - Multi-GPU support -4. **Model Export** - ONNX format support - -### Medium-Term (3-6 months) -5. **Advanced Error Correction** - Full QEC implementation -6. **Quantum-Aware Pruning** - Model compression -7. **Few-Shot Learning** - Quantum prompt tuning -8. **Web Dashboard** - Real-time browser-based monitoring - -### Long-Term (6-12 months) -9. **Multi-QPU Training** - Distributed quantum execution -10. **Quantum Optimizer** - Quantum gradient computation -11. **Hybrid Architectures** - Novel quantum-classical designs -12. **Benchmark Suite** - Standardized quantum LLM evaluation - ---- - -## 🎓 Educational Value - -This implementation serves as: - -1. **Reference Implementation** - Complete, working quantum LLM system -2. **Learning Resource** - Well-documented code with examples -3. **Research Platform** - Easy to extend and experiment -4. **Teaching Tool** - Demonstrates advanced ML + quantum concepts - -**Topics Covered:** -- Quantum machine learning -- Hybrid quantum-classical architectures -- Curriculum learning -- Circuit optimization -- Performance monitoring -- Data processing pipelines -- Configuration management -- Training orchestration - ---- - -## 📊 Code Statistics - -| Metric | Value | -|--------|-------| -| Total Lines of Code | ~3,500 | -| Python Files | 7 | -| Classes | 28 | -| Functions | 100+ | -| Configuration Options | 80+ | -| Documentation Lines | 1,500+ | - -**Code Quality:** -- Comprehensive docstrings -- Type hints throughout -- Error handling -- Logging at appropriate levels -- Modular design -- Extensible architecture - ---- - -## 🌟 Highlights - -### What Makes This Special? - -1. **Complete System** - Not just components, but a full working system -2. **Production-Ready** - Error handling, monitoring, checkpointing -3. **Well-Documented** - README, config example, inline docs -4. **Easy to Use** - Quick start examples, auto-configuration -5. **Extensible** - Clean architecture, easy to add features -6. **Educational** - Learn by reading and running -7. **Research-Grade** - Advanced features not available elsewhere - -### Novelty - -**First implementation of:** -- Multi-scale quantum attention with learned routing -- Curriculum learning for quantum integration -- Quantum circuit result caching for LLMs -- Adaptive entanglement topology selection -- Complete quantum LLM monitoring dashboard - ---- - -## 🎯 Mission Status: SUCCESS ✅ - -**All objectives achieved:** - -✅ Advanced quantum components (5 classes) -✅ Circuit optimization (4 classes) -✅ Training orchestration (3 classes) -✅ Monitoring & visualization (5 classes) -✅ Complete integration (3 classes) -✅ Dataset utilities (5 classes) -✅ Quick start examples (4 modes) -✅ Comprehensive documentation -✅ Annotated configuration -✅ Testing & validation - -**Total:** 7 implementation files + 2 documentation files = **9 new files** created. - ---- - -## 🚀 Ready to Use - -The quantum LLM system is **ready for:** - -- Research experiments -- Proof-of-concept demonstrations -- Educational purposes -- Further development -- Production deployment (with QPU access) - -**Start now:** -```bash -python quantum_llm_quickstart.py --mode quick -``` - ---- - -**Quantum AI Workspace - Building the Future of Language Models** - -*This implementation represents a significant advancement in quantum-enhanced machine learning, providing the first complete, production-ready system for quantum language model training.* - ---- - -## 📞 Support - -For questions, issues, or contributions: -- Check `QUANTUM_LLM_README.md` for detailed documentation -- Run component self-tests for validation -- Use `--dry-run` mode to test configurations -- Enable `--help` on scripts for usage information - -**Happy Quantum Training! 🚀** +# Quantum LLM Development Summary + +**Date:** March 9, 2026 +**System:** Aria Quantum AI Workspace +**Task:** Complete quantum-enhanced language model implementation + +--- + +## 🎯 Mission Accomplished + +I have created a **complete, production-ready quantum-enhanced language model training system** with advanced features for circuit optimization, adaptive training, and real-time monitoring. + +--- + +## 📦 Deliverables (7 New Files) + +### 1. **quantum_llm_advanced.py** (600+ lines) +**Advanced quantum-classical hybrid components** + +**Classes:** +- `QuantumCircuitCache` - LRU caching for circuit results +- `AdaptiveQuantumLayer` - Dynamic entanglement topology selection +- `MultiScaleQuantumAttention` - Multi-qubit attention heads (2-6 qubits) +- `QuantumPromptTuning` - Task-specific quantum prompt adaptation +- `QuantumErrorMitigation` - Zero-noise extrapolation and readout correction + +**Innovation:** First implementation of multi-scale quantum attention with learned complexity-based routing. + +### 2. **quantum_circuit_optimizer.py** (500+ lines) +**Circuit compilation and optimization** + +**Classes:** +- `CircuitCompiler` - Multi-level circuit optimization (light/moderate/aggressive) +- `BatchCircuitExecutor` - Batch processing with caching +- `AdaptiveCircuitScheduler` - Resource-aware quantum scheduling +- `QuantumClassicalPartitioner` - Learned quantum/classical routing + +**Innovation:** Intelligent partitioning learns when quantum execution provides advantage. + +### 3. **quantum_llm_hybrid_trainer.py** (550+ lines) +**Training orchestration with curriculum learning** + +**Classes:** +- `CurriculumScheduler` - Progressive quantum integration +- `AdaptiveQuantumRouter` - RL-based routing policy +- `HybridTrainingOrchestrator` - Complete training pipeline + +**Training Stages:** +1. Classical warmup (0% quantum) +2. Quantum transition (30% quantum) +3. Full quantum (70% quantum) + +**Innovation:** Curriculum learning prevents training collapse during quantum integration. + +### 4. **quantum_llm_monitor.py** (550+ lines) +**Real-time monitoring and visualization** + +**Classes:** +- `MetricsAggregator` - Moving averages, trends, anomaly detection +- `PerformanceMonitor` - CPU/GPU/memory tracking +- `QuantumCircuitProfiler` - Circuit performance profiling +- `TrainingDashboard` - Real-time dashboard with alerts +- `VisualizationExporter` - Data export for plotting + +**Alert System:** +- Loss anomaly detection (z-score based) +- Trend degradation warnings +- High perplexity alerts + +**Innovation:** First quantum-aware training dashboard with circuit-level profiling. + +### 5. **quantum_llm_integrated.py** (550+ lines) +**Complete system integration** + +**Classes:** +- `QuantumLLMConfig` - Comprehensive configuration management +- `IntegratedQuantumLLM` - Full quantum-enhanced transformer +- `QuantumLLMSystem` - End-to-end training system + +**Features:** +- All advanced components integrated +- Unified configuration system +- Automatic component initialization +- Complete training workflow + +**Innovation:** First turnkey quantum LLM system - configure and train in minutes. + +### 6. **quantum_llm_datasets.py** (500+ lines) +**Dataset loading and preprocessing** + +**Classes:** +- `CharacterTokenizer` - Character-level tokenization with special tokens +- `TextDataset` - Sequence windowing and padding +- `MultiSourceDataset` - Multiple dataset combination with weights +- `QuantumDataAugmenter` - Quantum-inspired data augmentation +- `DatasetBuilder` - Auto-format detection and loading + +**Supported Formats:** +- Plain text files +- JSON with "text" field +- Chat format with "messages" array + +**Innovation:** Auto-detection loader handles diverse dataset formats seamlessly. + +### 7. **quantum_llm_quickstart.py** (400+ lines) +**Ready-to-run examples** + +**Modes:** +- `quick` - Fast test with minimal model (2 epochs, 64-dim) +- `full` - Complete training with all features +- `monitor` - Real-time monitoring of ongoing training +- `generate` - Text generation with trained model + +**Usage:** +```bash +python quantum_llm_quickstart.py --mode quick +python quantum_llm_quickstart.py --mode full --config config.yaml +python quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm +python quantum_llm_quickstart.py --mode generate --model model.pt --prompt "Hello" +``` + +**Innovation:** Complete examples demonstrate every feature - learn by running. + +--- + +## 📚 Documentation (2 Files) + +### 8. **QUANTUM_LLM_README.md** (500+ lines) +Comprehensive documentation covering: +- Quick start guide +- Component descriptions +- Feature explanations +- Configuration guide +- Performance tips +- Use cases +- Testing procedures +- References + +### 9. **quantum_llm_config_example.yaml** (300+ lines) +Fully annotated configuration template with: +- All configuration options +- Detailed comments +- Recommended values +- Use case examples + +--- + +## 🔬 Key Technical Innovations + +### 1. Multi-Scale Quantum Attention +**Problem:** Single qubit count can't capture both fine-grained and complex patterns. + +**Solution:** Different attention heads use 2, 3, 4, or 6 qubits. + +**Result:** 15-20% better performance on hierarchical tasks. + +### 2. Adaptive Entanglement Routing +**Problem:** Full entanglement is expensive but not always needed. + +**Solution:** Learned predictor selects linear/circular/full entanglement based on input complexity. + +**Result:** 30-40% reduction in circuit depth without accuracy loss. + +### 3. Quantum Circuit Caching +**Problem:** Repeated patterns execute same circuits multiple times. + +**Solution:** LRU cache stores recent circuit results. + +**Result:** 2-5x speedup on character-level language models (high pattern repetition). + +### 4. Curriculum Learning for Quantum Integration +**Problem:** Training collapses when suddenly introducing quantum layers. + +**Solution:** Gradual quantum integration: 0% → 30% → 70% over training stages. + +**Result:** Stable training, 25% better final loss compared to direct quantum training. + +### 5. Quantum-Aware Monitoring +**Problem:** Standard ML metrics don't capture quantum-specific issues. + +**Solution:** Track circuit execution time, cache hit rates, entanglement patterns, quantum/classical ratio. + +**Result:** First complete visibility into quantum LLM training dynamics. + +--- + +## 🎓 Training Pipeline + +``` +Data Loading → Tokenization → Dataset Creation + ↓ + Stage 1: Classical Warmup + (Learn basic patterns) + ↓ + Stage 2: Quantum Transition + (Introduce quantum attention) + ↓ + Stage 3: Full Quantum + (Maximize quantum usage) + ↓ + Checkpointing & Visualization + ↓ + Final Model Export +``` + +**Throughout:** Real-time monitoring, adaptive routing, circuit optimization, error mitigation. + +--- + +## 📊 Performance Characteristics + +### Training Speed (Relative to Pure Classical) + +| Component | Overhead | With Caching | With Optimization | +|-----------|----------|--------------|------------------| +| Quantum Attention | 3-5x | 1.5-2x | 1.2-1.5x | +| Quantum FFN | 2-3x | 1.3-1.8x | 1.1-1.3x | +| Full Quantum Model | 4-8x | 2-3x | 1.5-2x | + +**Note:** Assumes quantum simulation. Real QPU has different characteristics. + +### Memory Requirements + +| Model Size | Parameters | Memory (CPU) | Memory (GPU) | +|------------|-----------|--------------|--------------| +| Tiny | 100K | ~50 MB | ~100 MB | +| Small | 1M | ~200 MB | ~400 MB | +| Medium | 10M | ~1 GB | ~2 GB | +| Large | 100M | ~8 GB | ~16 GB | + +**Note:** Quantum circuit simulation adds 20-30% memory overhead. + +### Accuracy (Proof-of-Concept Results) + +Tested on character-level language modeling: + +| Configuration | Perplexity | Training Time | +|---------------|-----------|---------------| +| Pure Classical | 3.2 | 1.0x (baseline) | +| Quantum Attention | 2.9 | 1.8x | +| Full Quantum | 2.7 | 2.5x | +| Multi-Scale Quantum | 2.5 | 2.8x | + +**Conclusion:** Quantum provides 15-20% perplexity improvement at 2-3x training cost. + +--- + +## 🚀 Quick Start Guide + +### Installation + +```bash +# Navigate to quantum ML directory +cd ai-projects/quantum-ml + +# Install dependencies (if needed) +pip install torch pennylane pyyaml numpy + +# Test components +python src/quantum_llm_advanced.py +python src/quantum_circuit_optimizer.py +python src/quantum_llm_monitor.py +python src/quantum_llm_datasets.py +``` + +### Run Quick Test + +```bash +# 2-epoch minimal test (~5 minutes on CPU) +python quantum_llm_quickstart.py --mode quick +``` + +**Output:** +- `data_out/quantum_llm_quickstart/final_model.pt` - Trained model +- `data_out/quantum_llm_quickstart/dashboard/` - Training metrics +- `data_out/quantum_llm_quickstart/training/` - Training reports +- `data_out/quantum_llm_quickstart/visualizations/` - Export data + +### Run Full Training + +```bash +# Full training with custom config +python quantum_llm_quickstart.py --mode full --config config/quantum_llm_config_example.yaml +``` + +### Monitor Training + +```bash +# Real-time monitoring +python quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm_quickstart + +# Or watch dashboard JSON +watch -n 5 'cat data_out/quantum_llm_quickstart/dashboard/dashboard.json | python -m json.tool' +``` + +### Generate Text + +```bash +# Generate text with trained model +python quantum_llm_quickstart.py --mode generate \ + --model data_out/quantum_llm_quickstart/final_model.pt \ + --prompt "Quantum computing" \ + --max-length 100 +``` + +--- + +## 🧪 Testing & Validation + +All components include self-tests: + +```bash +# Test each component individually +python src/quantum_llm_advanced.py # ✅ All 5 classes tested +python src/quantum_circuit_optimizer.py # ✅ All 4 classes tested +python src/quantum_llm_hybrid_trainer.py # ✅ 3-stage curriculum example +python src/quantum_llm_monitor.py # ✅ Dashboard simulation +python src/quantum_llm_datasets.py # ✅ Tokenizer + dataset tests +python src/quantum_llm_integrated.py --dry-run # ✅ Full system check +``` + +**Expected Output:** Each file logs "✅ All components loaded successfully" or similar. + +--- + +## 📈 Future Enhancements + +### Near-Term (1-3 months) +1. **Azure Quantum Integration** - Real QPU execution +2. **Subword Tokenization** - BPE/WordPiece support +3. **Distributed Training** - Multi-GPU support +4. **Model Export** - ONNX format support + +### Medium-Term (3-6 months) +5. **Advanced Error Correction** - Full QEC implementation +6. **Quantum-Aware Pruning** - Model compression +7. **Few-Shot Learning** - Quantum prompt tuning +8. **Web Dashboard** - Real-time browser-based monitoring + +### Long-Term (6-12 months) +9. **Multi-QPU Training** - Distributed quantum execution +10. **Quantum Optimizer** - Quantum gradient computation +11. **Hybrid Architectures** - Novel quantum-classical designs +12. **Benchmark Suite** - Standardized quantum LLM evaluation + +--- + +## 🎓 Educational Value + +This implementation serves as: + +1. **Reference Implementation** - Complete, working quantum LLM system +2. **Learning Resource** - Well-documented code with examples +3. **Research Platform** - Easy to extend and experiment +4. **Teaching Tool** - Demonstrates advanced ML + quantum concepts + +**Topics Covered:** +- Quantum machine learning +- Hybrid quantum-classical architectures +- Curriculum learning +- Circuit optimization +- Performance monitoring +- Data processing pipelines +- Configuration management +- Training orchestration + +--- + +## 📊 Code Statistics + +| Metric | Value | +|--------|-------| +| Total Lines of Code | ~3,500 | +| Python Files | 7 | +| Classes | 28 | +| Functions | 100+ | +| Configuration Options | 80+ | +| Documentation Lines | 1,500+ | + +**Code Quality:** +- Comprehensive docstrings +- Type hints throughout +- Error handling +- Logging at appropriate levels +- Modular design +- Extensible architecture + +--- + +## 🌟 Highlights + +### What Makes This Special? + +1. **Complete System** - Not just components, but a full working system +2. **Production-Ready** - Error handling, monitoring, checkpointing +3. **Well-Documented** - README, config example, inline docs +4. **Easy to Use** - Quick start examples, auto-configuration +5. **Extensible** - Clean architecture, easy to add features +6. **Educational** - Learn by reading and running +7. **Research-Grade** - Advanced features not available elsewhere + +### Novelty + +**First implementation of:** +- Multi-scale quantum attention with learned routing +- Curriculum learning for quantum integration +- Quantum circuit result caching for LLMs +- Adaptive entanglement topology selection +- Complete quantum LLM monitoring dashboard + +--- + +## 🎯 Mission Status: SUCCESS ✅ + +**All objectives achieved:** + +✅ Advanced quantum components (5 classes) +✅ Circuit optimization (4 classes) +✅ Training orchestration (3 classes) +✅ Monitoring & visualization (5 classes) +✅ Complete integration (3 classes) +✅ Dataset utilities (5 classes) +✅ Quick start examples (4 modes) +✅ Comprehensive documentation +✅ Annotated configuration +✅ Testing & validation + +**Total:** 7 implementation files + 2 documentation files = **9 new files** created. + +--- + +## 🚀 Ready to Use + +The quantum LLM system is **ready for:** + +- Research experiments +- Proof-of-concept demonstrations +- Educational purposes +- Further development +- Production deployment (with QPU access) + +**Start now:** +```bash +python quantum_llm_quickstart.py --mode quick +``` + +--- + +**Quantum AI Workspace - Building the Future of Language Models** + +*This implementation represents a significant advancement in quantum-enhanced machine learning, providing the first complete, production-ready system for quantum language model training.* + +--- + +## 📞 Support + +For questions, issues, or contributions: +- Check `QUANTUM_LLM_README.md` for detailed documentation +- Run component self-tests for validation +- Use `--dry-run` mode to test configurations +- Enable `--help` on scripts for usage information + +**Happy Quantum Training! 🚀** diff --git a/docs/README_PAGES.md b/docs/README_PAGES.md index f7afbb1fc..cf2225e0a 100644 --- a/docs/README_PAGES.md +++ b/docs/README_PAGES.md @@ -1,238 +1,238 @@ -# Aria GitHub Pages - -This directory contains the GitHub Pages deployment of the Aria project web applications. - -## 🌐 Live Demo - -Visit the live demo at: - -## 📁 Structure - -- **index.html** - Main landing page with links to all applications -- **aria/** - Aria 3D character interface (demo mode) -- **chat/** - AI chat interface (demo mode) -- **dashboard/** - Training and monitoring dashboard (demo mode) -- **quantum/** - Quantum ML interface (demo mode) - -## 🔧 Demo Mode vs. Local Development - -### Demo Mode (GitHub Pages) - -All web applications run in **demo mode** when hosted on GitHub Pages: - -- ✅ Full UI functionality -- ✅ Interactive character animations -- ✅ Simulated API responses -- ❌ No real AI chat (uses mock responses) -- ❌ No backend Python services -- ❌ No real-time training or quantum computing - -### Local Development (Full Features) - -For complete functionality with AI, quantum computing, and training: - -1. **Clone the repository** - - ```bash - git clone https://github.com/Bryan-Roe/Aria.git - cd Aria - ``` - -2. **Install dependencies** - - ```bash - pip install -r requirements.txt - ``` - -3. **Configure API keys** (optional) - - ```bash - # For Azure OpenAI - export AZURE_OPENAI_API_KEY="..." - export AZURE_OPENAI_ENDPOINT="..." - export AZURE_OPENAI_DEPLOYMENT="..." - - # For OpenAI - export OPENAI_API_KEY="..." - ``` - -4. **Start services** - - ```bash - # Aria web server - cd aria_web && python server.py - - # Azure Functions (chat API) - func start - - # Dashboard - cd dashboard && python app.py - ``` - -## 🚀 Deployment - -GitHub Pages automatically deploys when changes are pushed to the main branch: - -1. Changes pushed to `main` or `copilot/enable-github-pages-for-repo` branch -2. GitHub Actions workflow (`.github/workflows/pages.yml`) runs -3. Jekyll builds the site from the `docs/` directory -4. Site is deployed to GitHub Pages - -### Manual Deployment - -To manually trigger deployment: - -1. Go to the repository's Actions tab -2. Select "Deploy to GitHub Pages" workflow -3. Click "Run workflow" - -## 📝 Configuration - -### Jekyll Configuration - -The `_config.yml` file contains Jekyll settings: - -- Site title and description -- Theme selection -- Build exclusions -- URL configuration - -### Demo Mode Configuration - -Each web app has a `DEMO_MODE` flag in its JavaScript: - -```javascript -const DEMO_MODE = true; // Set to false for local backend -``` - -To test with local backend before deployment: - -1. Set `DEMO_MODE = false` in the JavaScript files -2. Start local Python servers -3. Test functionality -4. Set `DEMO_MODE = true` before committing - -## 🔍 Testing Locally - -To test the GitHub Pages site locally: - -1. **Install Jekyll** - - ```bash - gem install jekyll bundler - ``` - -2. **Serve locally** - - ```bash - cd docs - jekyll serve - ``` - -3. **Open browser** - - ``` - http://localhost:4000 - ``` - -## 📦 Adding New Applications - -To add a new web application to GitHub Pages: - -1. **Create app directory** - - ```bash - mkdir docs/myapp - ``` - -2. **Add HTML/CSS/JS files** - - Copy your application files - - Add demo mode configuration - - Update API calls to use mock responses - -3. **Update main index** - - Add a new card in `docs/index.html` - - Link to `/myapp/` - -4. **Test and commit** - - ```bash - git add docs/myapp - git commit -m "Add myapp to GitHub Pages" - git push - ``` - -## 🎨 Customization - -### Styling - -All applications use inline CSS or imported stylesheets. To maintain consistency: - -- Use gradient backgrounds with similar color schemes -- Follow card-based layouts -- Include demo mode banners -- Add GitHub repository links - -### Mock Data - -Each app implements mock API responses in JavaScript: - -```javascript -async function mockApiCall(endpoint, options) { - await new Promise(resolve => setTimeout(resolve, 300)); - - if (endpoint === '/api/myendpoint') { - return { - ok: true, - json: async () => ({ data: 'mock response' }) - }; - } -} -``` - -## 📚 Documentation - -- Main README: `/README.md` -- Aria Documentation: `/docs/aria/` -- Quantum Documentation: `/docs/quantum/` -- Training Guides: `/docs/training/` - -## 🤝 Contributing - -When adding or modifying GitHub Pages content: - -1. Ensure demo mode works without backend -2. Add clear demo mode notices -3. Test all links and interactions -4. Verify mobile responsiveness -5. Update this README if adding new sections - -### Note on CLI scripts - -If you include Python CLI scripts referenced from docs or demo pages (for example `scripts/foo.py`), prefer adding the repository root to `sys.path` at script startup so the script can import local packages regardless of working directory. Use the snippet below as a copy-paste example: - -```python -from pathlib import Path -import sys - -REPO_ROOT = Path(__file__).resolve().parent.parent -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -# Now import local packages safely: -from shared.json_utils import load_status_json -``` - -## 🔗 Links - -- **Repository**: -- **Issues**: -- **Live Demo**: - -## 📄 License - -See the main repository LICENSE file for details. - ---- - -**Last Updated**: January 19, 2026 +# Aria GitHub Pages + +This directory contains the GitHub Pages deployment of the Aria project web applications. + +## 🌐 Live Demo + +Visit the live demo at: + +## 📁 Structure + +- **index.html** - Main landing page with links to all applications +- **aria/** - Aria 3D character interface (demo mode) +- **chat/** - AI chat interface (demo mode) +- **dashboard/** - Training and monitoring dashboard (demo mode) +- **quantum/** - Quantum ML interface (demo mode) + +## 🔧 Demo Mode vs. Local Development + +### Demo Mode (GitHub Pages) + +All web applications run in **demo mode** when hosted on GitHub Pages: + +- ✅ Full UI functionality +- ✅ Interactive character animations +- ✅ Simulated API responses +- ❌ No real AI chat (uses mock responses) +- ❌ No backend Python services +- ❌ No real-time training or quantum computing + +### Local Development (Full Features) + +For complete functionality with AI, quantum computing, and training: + +1. **Clone the repository** + + ```bash + git clone https://github.com/Bryan-Roe/Aria.git + cd Aria + ``` + +2. **Install dependencies** + + ```bash + pip install -r requirements.txt + ``` + +3. **Configure API keys** (optional) + + ```bash + # For Azure OpenAI + export AZURE_OPENAI_API_KEY="..." + export AZURE_OPENAI_ENDPOINT="..." + export AZURE_OPENAI_DEPLOYMENT="..." + + # For OpenAI + export OPENAI_API_KEY="..." + ``` + +4. **Start services** + + ```bash + # Aria web server + cd aria_web && python server.py + + # Azure Functions (chat API) + func start + + # Dashboard + cd dashboard && python app.py + ``` + +## 🚀 Deployment + +GitHub Pages automatically deploys when changes are pushed to the main branch: + +1. Changes pushed to `main` or `copilot/enable-github-pages-for-repo` branch +2. GitHub Actions workflow (`.github/workflows/pages.yml`) runs +3. Jekyll builds the site from the `docs/` directory +4. Site is deployed to GitHub Pages + +### Manual Deployment + +To manually trigger deployment: + +1. Go to the repository's Actions tab +2. Select "Deploy to GitHub Pages" workflow +3. Click "Run workflow" + +## 📝 Configuration + +### Jekyll Configuration + +The `_config.yml` file contains Jekyll settings: + +- Site title and description +- Theme selection +- Build exclusions +- URL configuration + +### Demo Mode Configuration + +Each web app has a `DEMO_MODE` flag in its JavaScript: + +```javascript +const DEMO_MODE = true; // Set to false for local backend +``` + +To test with local backend before deployment: + +1. Set `DEMO_MODE = false` in the JavaScript files +2. Start local Python servers +3. Test functionality +4. Set `DEMO_MODE = true` before committing + +## 🔍 Testing Locally + +To test the GitHub Pages site locally: + +1. **Install Jekyll** + + ```bash + gem install jekyll bundler + ``` + +2. **Serve locally** + + ```bash + cd docs + jekyll serve + ``` + +3. **Open browser** + + ``` + http://localhost:4000 + ``` + +## 📦 Adding New Applications + +To add a new web application to GitHub Pages: + +1. **Create app directory** + + ```bash + mkdir docs/myapp + ``` + +2. **Add HTML/CSS/JS files** + - Copy your application files + - Add demo mode configuration + - Update API calls to use mock responses + +3. **Update main index** + - Add a new card in `docs/index.html` + - Link to `/myapp/` + +4. **Test and commit** + + ```bash + git add docs/myapp + git commit -m "Add myapp to GitHub Pages" + git push + ``` + +## 🎨 Customization + +### Styling + +All applications use inline CSS or imported stylesheets. To maintain consistency: + +- Use gradient backgrounds with similar color schemes +- Follow card-based layouts +- Include demo mode banners +- Add GitHub repository links + +### Mock Data + +Each app implements mock API responses in JavaScript: + +```javascript +async function mockApiCall(endpoint, options) { + await new Promise(resolve => setTimeout(resolve, 300)); + + if (endpoint === '/api/myendpoint') { + return { + ok: true, + json: async () => ({ data: 'mock response' }) + }; + } +} +``` + +## 📚 Documentation + +- Main README: `/README.md` +- Aria Documentation: `/docs/aria/` +- Quantum Documentation: `/docs/quantum/` +- Training Guides: `/docs/training/` + +## 🤝 Contributing + +When adding or modifying GitHub Pages content: + +1. Ensure demo mode works without backend +2. Add clear demo mode notices +3. Test all links and interactions +4. Verify mobile responsiveness +5. Update this README if adding new sections + +### Note on CLI scripts + +If you include Python CLI scripts referenced from docs or demo pages (for example `scripts/foo.py`), prefer adding the repository root to `sys.path` at script startup so the script can import local packages regardless of working directory. Use the snippet below as a copy-paste example: + +```python +from pathlib import Path +import sys + +REPO_ROOT = Path(__file__).resolve().parent.parent +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +# Now import local packages safely: +from shared.json_utils import load_status_json +``` + +## 🔗 Links + +- **Repository**: +- **Issues**: +- **Live Demo**: + +## 📄 License + +See the main repository LICENSE file for details. + +--- + +**Last Updated**: January 19, 2026 diff --git a/docs/REFACTORING_SUMMARY.md b/docs/REFACTORING_SUMMARY.md index 6b2f1b157..90fedf3b2 100644 --- a/docs/REFACTORING_SUMMARY.md +++ b/docs/REFACTORING_SUMMARY.md @@ -1,167 +1,167 @@ -# Code Refactoring Summary - -**Date**: 2026-02-17 -**Purpose**: Identify and eliminate duplicated code across the Aria repository - -## Overview - -This refactoring effort focused on identifying and eliminating code duplication across the Aria codebase, with emphasis on high-impact areas that appeared in multiple files. - -## Changes Made - -### 1. Provider Response Handling (High Impact) - -**Problem**: OpenAIProvider, LMStudioProvider, and AzureOpenAIProvider had ~95% identical streaming and non-streaming response handling code (~60-80 lines of duplication). - -**Solution**: -- Created helper methods in `BaseChatProvider`: - - `_handle_openai_streaming_response()` - Extracts content from streaming responses - - `_handle_openai_non_streaming_response()` - Extracts content from non-streaming responses -- Refactored OpenAIProvider and LMStudioProvider to use these helpers -- Kept AzureOpenAIProvider's custom quota handling logic intact - -**Files Modified**: -- `ai-projects/chat-cli/src/chat_providers.py` - -**Impact**: -- Eliminated ~60 lines of duplicated code -- Improved maintainability - changes to response handling now only need to be made once -- Better testability - helper methods can be tested independently - -### 2. Defensive Import Pattern (Medium Impact) - -**Problem**: function_app.py had 5+ repeated try/except blocks (lines 21-76) for importing optional dependencies, each with manual fallback function definitions. - -**Solution**: -- Created `shared/import_helpers.py` with: - - `safe_import()` - Safely imports modules/functions with fallback support - - `create_stub_function()` - Generates stub functions that return error dicts -- Refactored function_app.py to use these utilities - -**Files Modified**: -- `function_app.py` (lines 19-82) - -**Files Created**: -- `shared/import_helpers.py` (122 lines) - -**Impact**: -- Centralized defensive import pattern -- Reduced boilerplate from ~56 lines of try/except to cleaner utility calls -- More maintainable and testable -- Consistent error responses for unavailable modules - -### 3. HTTP Validation & File Serving (Medium Impact) - -**Problem**: -- Message validation logic duplicated in http_chat/function_app.py and function_app.py -- CORS headers manually created in multiple places -- File serving pattern duplicated in http_chat_web/function_app.py (lines 11-74) - -**Solution**: -- Created `shared/http_utils.py` with utilities: - - `validate_messages()` - Common message format validation - - `create_cors_headers()` - Consistent CORS header generation - - `create_no_cache_headers()` - Cache control headers - - `validate_provider_choice()` - Provider validation logic - - `serve_static_file()` - DRY file serving with error handling -- Refactored http_chat/function_app.py to use validation utilities -- Refactored http_chat_web/function_app.py to use file serving utility - -**Files Modified**: -- `http_chat/function_app.py` -- `http_chat_web/function_app.py` - -**Files Created**: -- `shared/http_utils.py` (195 lines) - -**Impact**: -- Eliminated ~40 lines in HTTP validation -- Eliminated ~50 lines in file serving -- Improved consistency across all HTTP endpoints -- Better error messages and validation - -## Test Coverage - -Created comprehensive test suites to validate refactored code: - -1. **test_provider_response_handling.py** (5 tests) - - Tests streaming and non-streaming response handlers - - Validates resilience to malformed data - - All tests passing ✅ - -2. **test_import_helpers.py** (9 tests) - - Tests safe_import with various scenarios - - Tests stub function generation - - Tests real-world patterns from function_app.py - - All tests passing ✅ - -3. **test_http_utils.py** (16 tests) - - Tests message validation - - Tests CORS and cache headers - - Tests provider validation - - Tests file serving (success, error, and not found cases) - - All tests passing ✅ - -**Total**: 30 new unit tests, all passing - -## Quantitative Impact - -### Lines of Code -- **Eliminated**: ~150 lines of duplicated code -- **Added**: 317 lines of reusable utilities (import_helpers + http_utils) -- **Test Coverage**: 400+ lines of comprehensive tests -- **Net**: Better code quality despite slightly more total lines (utilities are reusable) - -### Duplication Metrics -- **Before**: 3 provider classes with identical 30-line response handling blocks -- **After**: 1 base class with 2 helper methods used by all providers -- **Before**: 5+ try/except blocks in function_app.py with manual fallbacks -- **After**: Centralized safe_import utility -- **Before**: 2 HTTP endpoints with duplicated validation/serving logic -- **After**: Shared utilities used by all endpoints - -### Maintainability Improvements -- **Provider changes**: Now only need to update 1 place instead of 3 -- **Import pattern**: Now only need to update 1 utility instead of N files -- **HTTP validation**: Now only need to update 1 place instead of multiple endpoints -- **Testing**: Utilities can be tested independently from endpoints - -## Benefits - -1. **Reduced Duplication**: ~150 lines of duplicated code eliminated -2. **Improved Maintainability**: Changes to common patterns now happen in one place -3. **Better Testability**: Utilities can be independently tested with comprehensive test suites -4. **Consistent Behavior**: All code using utilities behaves identically -5. **Enhanced Error Handling**: Centralized error handling provides better error messages -6. **Future-Proof**: New code can easily adopt these patterns - -## Recommendations for Future Work - -### Additional Refactoring Opportunities - -1. **sys.path Manipulation** (60+ instances) - - Create `shared/path_utils.py` with `ensure_repo_paths()` utility - - Pattern appears in 12+ script files with inconsistent implementations - - Estimated impact: ~120 lines reduction - -2. **Configuration Loading** (80+ instances) - - Create `shared/config.py` with typed configuration classes - - Replace direct `os.getenv()` calls with validated config access - - Estimated impact: Improved type safety and validation - -3. **Logging Patterns** (40+ files) - - Already consistent with `logging.getLogger(__name__)` - - Consider structured logging wrapper for better observability - -### Guidelines for New Code - -1. **Provider Development**: Use `BaseChatProvider` helper methods for OpenAI-compatible APIs -2. **Optional Dependencies**: Use `safe_import()` from `shared/import_helpers.py` -3. **HTTP Endpoints**: Use utilities from `shared/http_utils.py` -4. **Testing**: Write tests for new utilities; existing test suites provide good examples - -## Conclusion - -This refactoring successfully eliminated ~150 lines of duplicated code while improving maintainability, testability, and consistency. The new utility modules provide reusable patterns that can be adopted by future code, preventing duplication from creeping back in. - -All changes are backward compatible and have comprehensive test coverage (30 tests, all passing). The refactoring focused on high-impact areas where duplication was most prevalent and maintainability would be most improved. +# Code Refactoring Summary + +**Date**: 2026-02-17 +**Purpose**: Identify and eliminate duplicated code across the Aria repository + +## Overview + +This refactoring effort focused on identifying and eliminating code duplication across the Aria codebase, with emphasis on high-impact areas that appeared in multiple files. + +## Changes Made + +### 1. Provider Response Handling (High Impact) + +**Problem**: OpenAIProvider, LMStudioProvider, and AzureOpenAIProvider had ~95% identical streaming and non-streaming response handling code (~60-80 lines of duplication). + +**Solution**: +- Created helper methods in `BaseChatProvider`: + - `_handle_openai_streaming_response()` - Extracts content from streaming responses + - `_handle_openai_non_streaming_response()` - Extracts content from non-streaming responses +- Refactored OpenAIProvider and LMStudioProvider to use these helpers +- Kept AzureOpenAIProvider's custom quota handling logic intact + +**Files Modified**: +- `ai-projects/chat-cli/src/chat_providers.py` + +**Impact**: +- Eliminated ~60 lines of duplicated code +- Improved maintainability - changes to response handling now only need to be made once +- Better testability - helper methods can be tested independently + +### 2. Defensive Import Pattern (Medium Impact) + +**Problem**: function_app.py had 5+ repeated try/except blocks (lines 21-76) for importing optional dependencies, each with manual fallback function definitions. + +**Solution**: +- Created `shared/import_helpers.py` with: + - `safe_import()` - Safely imports modules/functions with fallback support + - `create_stub_function()` - Generates stub functions that return error dicts +- Refactored function_app.py to use these utilities + +**Files Modified**: +- `function_app.py` (lines 19-82) + +**Files Created**: +- `shared/import_helpers.py` (122 lines) + +**Impact**: +- Centralized defensive import pattern +- Reduced boilerplate from ~56 lines of try/except to cleaner utility calls +- More maintainable and testable +- Consistent error responses for unavailable modules + +### 3. HTTP Validation & File Serving (Medium Impact) + +**Problem**: +- Message validation logic duplicated in http_chat/function_app.py and function_app.py +- CORS headers manually created in multiple places +- File serving pattern duplicated in http_chat_web/function_app.py (lines 11-74) + +**Solution**: +- Created `shared/http_utils.py` with utilities: + - `validate_messages()` - Common message format validation + - `create_cors_headers()` - Consistent CORS header generation + - `create_no_cache_headers()` - Cache control headers + - `validate_provider_choice()` - Provider validation logic + - `serve_static_file()` - DRY file serving with error handling +- Refactored http_chat/function_app.py to use validation utilities +- Refactored http_chat_web/function_app.py to use file serving utility + +**Files Modified**: +- `http_chat/function_app.py` +- `http_chat_web/function_app.py` + +**Files Created**: +- `shared/http_utils.py` (195 lines) + +**Impact**: +- Eliminated ~40 lines in HTTP validation +- Eliminated ~50 lines in file serving +- Improved consistency across all HTTP endpoints +- Better error messages and validation + +## Test Coverage + +Created comprehensive test suites to validate refactored code: + +1. **test_provider_response_handling.py** (5 tests) + - Tests streaming and non-streaming response handlers + - Validates resilience to malformed data + - All tests passing ✅ + +2. **test_import_helpers.py** (9 tests) + - Tests safe_import with various scenarios + - Tests stub function generation + - Tests real-world patterns from function_app.py + - All tests passing ✅ + +3. **test_http_utils.py** (16 tests) + - Tests message validation + - Tests CORS and cache headers + - Tests provider validation + - Tests file serving (success, error, and not found cases) + - All tests passing ✅ + +**Total**: 30 new unit tests, all passing + +## Quantitative Impact + +### Lines of Code +- **Eliminated**: ~150 lines of duplicated code +- **Added**: 317 lines of reusable utilities (import_helpers + http_utils) +- **Test Coverage**: 400+ lines of comprehensive tests +- **Net**: Better code quality despite slightly more total lines (utilities are reusable) + +### Duplication Metrics +- **Before**: 3 provider classes with identical 30-line response handling blocks +- **After**: 1 base class with 2 helper methods used by all providers +- **Before**: 5+ try/except blocks in function_app.py with manual fallbacks +- **After**: Centralized safe_import utility +- **Before**: 2 HTTP endpoints with duplicated validation/serving logic +- **After**: Shared utilities used by all endpoints + +### Maintainability Improvements +- **Provider changes**: Now only need to update 1 place instead of 3 +- **Import pattern**: Now only need to update 1 utility instead of N files +- **HTTP validation**: Now only need to update 1 place instead of multiple endpoints +- **Testing**: Utilities can be tested independently from endpoints + +## Benefits + +1. **Reduced Duplication**: ~150 lines of duplicated code eliminated +2. **Improved Maintainability**: Changes to common patterns now happen in one place +3. **Better Testability**: Utilities can be independently tested with comprehensive test suites +4. **Consistent Behavior**: All code using utilities behaves identically +5. **Enhanced Error Handling**: Centralized error handling provides better error messages +6. **Future-Proof**: New code can easily adopt these patterns + +## Recommendations for Future Work + +### Additional Refactoring Opportunities + +1. **sys.path Manipulation** (60+ instances) + - Create `shared/path_utils.py` with `ensure_repo_paths()` utility + - Pattern appears in 12+ script files with inconsistent implementations + - Estimated impact: ~120 lines reduction + +2. **Configuration Loading** (80+ instances) + - Create `shared/config.py` with typed configuration classes + - Replace direct `os.getenv()` calls with validated config access + - Estimated impact: Improved type safety and validation + +3. **Logging Patterns** (40+ files) + - Already consistent with `logging.getLogger(__name__)` + - Consider structured logging wrapper for better observability + +### Guidelines for New Code + +1. **Provider Development**: Use `BaseChatProvider` helper methods for OpenAI-compatible APIs +2. **Optional Dependencies**: Use `safe_import()` from `shared/import_helpers.py` +3. **HTTP Endpoints**: Use utilities from `shared/http_utils.py` +4. **Testing**: Write tests for new utilities; existing test suites provide good examples + +## Conclusion + +This refactoring successfully eliminated ~150 lines of duplicated code while improving maintainability, testability, and consistency. The new utility modules provide reusable patterns that can be adopted by future code, preventing duplication from creeping back in. + +All changes are backward compatible and have comprehensive test coverage (30 tests, all passing). The refactoring focused on high-impact areas where duplication was most prevalent and maintainability would be most improved. diff --git a/docs/REGEX_OPTIMIZATION_2026-02-17.md b/docs/REGEX_OPTIMIZATION_2026-02-17.md index c03a631b6..94116d123 100644 --- a/docs/REGEX_OPTIMIZATION_2026-02-17.md +++ b/docs/REGEX_OPTIMIZATION_2026-02-17.md @@ -1,209 +1,209 @@ -# Regex Pattern Compilation Performance Optimizations - -**Date**: 2026-02-17 -**Status**: ✅ Implemented and Tested - -## Overview - -This document describes the regex pattern compilation optimizations implemented across the Aria codebase to improve performance by eliminating redundant pattern compilations. - -## Problem Statement - -Multiple files were compiling regex patterns on every use, either: -1. Using `re.findall()`, `re.search()`, etc. with string patterns directly -2. Importing `re` module locally inside functions -3. Compiling patterns inside loops or frequently-called functions - -This caused unnecessary overhead as regex patterns were being recompiled repeatedly instead of being compiled once and reused. - -## Performance Impact - -### Expected Benefits - -- **Pattern Compilation Overhead**: Eliminated repeated pattern compilation (typically 2-5x speedup for hot paths) -- **Memory Efficiency**: Reduced memory allocations from pattern object creation -- **CPU Cache Friendliness**: Better cache locality with pre-compiled pattern objects - -### Measured Results - -Based on benchmark tests (see `tests/test_regex_optimizations.py`): -- Pre-compiled patterns are at minimum equivalent to inline patterns -- In hot paths with repeated calls, the benefit compounds significantly -- No performance regression detected - -## Files Modified - -### 1. `scripts/final_validation.py` - -**Changes**: -- Pre-compiled 5 regex patterns at module level -- Optimized function name checking loop to pre-compile dynamic patterns - -**Patterns Compiled**: -```python -_RE_ONCLICK = re.compile(r'onclick=["\']([^"\']+)["\']') -_RE_FUNC_NAMES = re.compile(r'([a-zA-Z_][a-zA-Z0-9_]*)\s*\(') -_RE_ELEMENT_IDS = re.compile(r'id=["\']([^"\']+)["\']') -_RE_GET_BY_ID = re.compile(r"getElementById\(['\"]([^'\"]+)['\"]\)") -_RE_FETCH_CALLS = re.compile(r"fetch\(['\"]([^'\"]+)['\"]\)") -``` - -**Impact**: -- 9+ regex operations now use pre-compiled patterns -- Dynamic function name patterns are compiled once per function instead of 4x per function - -### 2. `scripts/validate_dashboard.py` - -**Changes**: -- Pre-compiled 9 regex patterns at module level - -**Patterns Compiled**: -```python -_RE_CONSOLE_LOG = re.compile(r'console\.log\([^)]+\)') -_RE_GET_BY_ID = re.compile(r"getElementById\(['\"]([^'\"]+)['\"]\)") -_RE_QUERY_SELECTOR = re.compile(r"querySelector\(['\"]([^'\"]+)['\"]\)") -_RE_ELEMENT_IDS = re.compile(r'id=["\']([^"\']+)["\']') -_RE_ASYNC_FUNCTION = re.compile(r'async\s+function') -_RE_AWAIT = re.compile(r'\bawait\s+') -_RE_EVENT_LISTENER = re.compile(r'addEventListener\s*\(') -_RE_FETCH_CALLS = re.compile(r"fetch\(['\"]([^'\"]+)['\"]\)") -_RE_LOCALSTORAGE = re.compile(r"localStorage\.(getItem|setItem|removeItem)\(['\"]([^'\"]+)['\"]\)") -_RE_ONCLICK = re.compile(r'onclick=["\']([^"\']+)["\']') -``` - -**Impact**: -- 10+ regex operations now use pre-compiled patterns -- Validation script runs more efficiently - -### 3. `function_app.py` - -**Changes**: -- Added `import re` at module level (was imported locally 4 times) -- Pre-compiled word splitting pattern used in TTS functions -- Removed 4 redundant local `import re` statements - -**Pattern Compiled**: -```python -_RE_WORD_SPLIT = re.compile(r"\S+") -``` - -**Impact**: -- Pattern used 3x in TTS word timing generation (lines 845, 930, 978) -- Eliminates repeated imports and compilations in hot path -- TTS response generation is more efficient - -### 4. `shared/email_notifications.py` - -**Changes**: -- Pre-compiled 2 regex patterns for HTML stripping -- Added `import re` at module level -- Removed local `import re` from `_strip_html()` method - -**Patterns Compiled**: -```python -_RE_HTML_TAGS = re.compile(r'<[^<]+?>') -_RE_WHITESPACE = re.compile(r'\s+') -``` - -**Impact**: -- HTML stripping in email body generation is more efficient -- Pattern compilation eliminated from frequently-called method - -### 5. `cooking-ai/src/providers/local.py` - -**Changes**: -- Moved quantity extraction pattern to module level -- Pre-compiled pattern used in ingredient parsing - -**Pattern Compiled**: -```python -_RE_QUANTITY = re.compile(r"^(?P(\d+\/\d+|\d+(\.\d+)?))\s*(?P[a-zA-Z]+)?\s*(?P.*)") -``` - -**Impact**: -- Pattern compiled once instead of on every `_handle_extract()` call -- Ingredient extraction is more efficient - -## Testing - -Comprehensive test suite added in `tests/test_regex_optimizations.py`: - -### Test Coverage - -1. **Module-Level Pattern Verification**: Confirms patterns exist and are compiled -2. **Functionality Tests**: Verifies patterns work correctly after optimization -3. **Performance Benchmarks**: Measures speedup from pre-compilation -4. **Pattern Stability Tests**: Confirms pattern objects remain stable across calls - -### Test Results - -``` -tests/test_regex_optimizations.py::TestFinalValidationOptimizations::test_regex_patterns_are_compiled PASSED -tests/test_regex_optimizations.py::TestFinalValidationOptimizations::test_onclick_pattern_matches_correctly PASSED -tests/test_regex_optimizations.py::TestValidateDashboardOptimizations::test_regex_patterns_are_compiled PASSED -tests/test_regex_optimizations.py::TestFunctionAppOptimizations::test_word_split_pattern_is_compiled SKIPPED -tests/test_regex_optimizations.py::TestFunctionAppOptimizations::test_re_module_imported_at_top PASSED -tests/test_regex_optimizations.py::TestEmailNotificationsOptimizations::test_html_patterns_are_compiled PASSED -tests/test_regex_optimizations.py::TestEmailNotificationsOptimizations::test_html_stripping_works_correctly PASSED -tests/test_regex_optimizations.py::TestCookingAIOptimizations::test_quantity_pattern_is_compiled PASSED -tests/test_regex_optimizations.py::TestPerformanceBenchmark::test_compiled_vs_inline_regex_performance PASSED -tests/test_regex_optimizations.py::TestPerformanceBenchmark::test_pattern_cache_stability PASSED - -========================= 9 passed, 1 skipped in 0.29s ========================= -``` - -## Best Practices - -### When to Pre-Compile Regex Patterns - -✅ **DO pre-compile when**: -- Pattern is used more than once in the module -- Pattern is in a hot path (loops, frequently-called functions) -- Pattern is used in performance-critical code - -❌ **DON'T pre-compile when**: -- Pattern is used only once in the entire module -- Pattern is dynamically constructed based on runtime data (unless you can cache compiled versions) -- Pattern is in rarely-executed code (e.g., error handlers) - -### Naming Convention - -Pre-compiled patterns follow the convention: `_RE_` - -Examples: -- `_RE_WORD_SPLIT` - splits on word boundaries -- `_RE_HTML_TAGS` - matches HTML tags -- `_RE_ONCLICK` - matches onclick attributes - -### Pattern Organization - -```python -import re - -# Pre-compile regex patterns for performance -_RE_PATTERN_1 = re.compile(r'...') -_RE_PATTERN_2 = re.compile(r'...') - -# ... rest of module code -``` - -## Related Memory - -This optimization aligns with the existing repository memory: - -> **regex pattern compilation**: Pre-compile regex patterns at module level: `_RE_PATTERN = re.compile(r'...', flags)` then use `_RE_PATTERN.search()` instead of `re.search(r'...', text)` in functions - -## Future Optimization Opportunities - -Additional areas for potential optimization (not addressed in this PR): - -1. **Dynamic Pattern Caching**: For patterns constructed at runtime, consider using `functools.lru_cache` with a compiled pattern factory -2. **Pattern Pooling**: For very hot paths, consider pattern object pooling -3. **Regex Alternatives**: For simple string operations, consider if `str.find()`, `str.split()`, or `str.replace()` would be more efficient - -## References - -- Python `re` module documentation: https://docs.python.org/3/library/re.html -- Repository memory: regex pattern compilation -- Test suite: `tests/test_regex_optimizations.py` -- Related optimizations: `docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md` +# Regex Pattern Compilation Performance Optimizations + +**Date**: 2026-02-17 +**Status**: ✅ Implemented and Tested + +## Overview + +This document describes the regex pattern compilation optimizations implemented across the Aria codebase to improve performance by eliminating redundant pattern compilations. + +## Problem Statement + +Multiple files were compiling regex patterns on every use, either: +1. Using `re.findall()`, `re.search()`, etc. with string patterns directly +2. Importing `re` module locally inside functions +3. Compiling patterns inside loops or frequently-called functions + +This caused unnecessary overhead as regex patterns were being recompiled repeatedly instead of being compiled once and reused. + +## Performance Impact + +### Expected Benefits + +- **Pattern Compilation Overhead**: Eliminated repeated pattern compilation (typically 2-5x speedup for hot paths) +- **Memory Efficiency**: Reduced memory allocations from pattern object creation +- **CPU Cache Friendliness**: Better cache locality with pre-compiled pattern objects + +### Measured Results + +Based on benchmark tests (see `tests/test_regex_optimizations.py`): +- Pre-compiled patterns are at minimum equivalent to inline patterns +- In hot paths with repeated calls, the benefit compounds significantly +- No performance regression detected + +## Files Modified + +### 1. `scripts/final_validation.py` + +**Changes**: +- Pre-compiled 5 regex patterns at module level +- Optimized function name checking loop to pre-compile dynamic patterns + +**Patterns Compiled**: +```python +_RE_ONCLICK = re.compile(r'onclick=["\']([^"\']+)["\']') +_RE_FUNC_NAMES = re.compile(r'([a-zA-Z_][a-zA-Z0-9_]*)\s*\(') +_RE_ELEMENT_IDS = re.compile(r'id=["\']([^"\']+)["\']') +_RE_GET_BY_ID = re.compile(r"getElementById\(['\"]([^'\"]+)['\"]\)") +_RE_FETCH_CALLS = re.compile(r"fetch\(['\"]([^'\"]+)['\"]\)") +``` + +**Impact**: +- 9+ regex operations now use pre-compiled patterns +- Dynamic function name patterns are compiled once per function instead of 4x per function + +### 2. `scripts/validate_dashboard.py` + +**Changes**: +- Pre-compiled 9 regex patterns at module level + +**Patterns Compiled**: +```python +_RE_CONSOLE_LOG = re.compile(r'console\.log\([^)]+\)') +_RE_GET_BY_ID = re.compile(r"getElementById\(['\"]([^'\"]+)['\"]\)") +_RE_QUERY_SELECTOR = re.compile(r"querySelector\(['\"]([^'\"]+)['\"]\)") +_RE_ELEMENT_IDS = re.compile(r'id=["\']([^"\']+)["\']') +_RE_ASYNC_FUNCTION = re.compile(r'async\s+function') +_RE_AWAIT = re.compile(r'\bawait\s+') +_RE_EVENT_LISTENER = re.compile(r'addEventListener\s*\(') +_RE_FETCH_CALLS = re.compile(r"fetch\(['\"]([^'\"]+)['\"]\)") +_RE_LOCALSTORAGE = re.compile(r"localStorage\.(getItem|setItem|removeItem)\(['\"]([^'\"]+)['\"]\)") +_RE_ONCLICK = re.compile(r'onclick=["\']([^"\']+)["\']') +``` + +**Impact**: +- 10+ regex operations now use pre-compiled patterns +- Validation script runs more efficiently + +### 3. `function_app.py` + +**Changes**: +- Added `import re` at module level (was imported locally 4 times) +- Pre-compiled word splitting pattern used in TTS functions +- Removed 4 redundant local `import re` statements + +**Pattern Compiled**: +```python +_RE_WORD_SPLIT = re.compile(r"\S+") +``` + +**Impact**: +- Pattern used 3x in TTS word timing generation (lines 845, 930, 978) +- Eliminates repeated imports and compilations in hot path +- TTS response generation is more efficient + +### 4. `shared/email_notifications.py` + +**Changes**: +- Pre-compiled 2 regex patterns for HTML stripping +- Added `import re` at module level +- Removed local `import re` from `_strip_html()` method + +**Patterns Compiled**: +```python +_RE_HTML_TAGS = re.compile(r'<[^<]+?>') +_RE_WHITESPACE = re.compile(r'\s+') +``` + +**Impact**: +- HTML stripping in email body generation is more efficient +- Pattern compilation eliminated from frequently-called method + +### 5. `cooking-ai/src/providers/local.py` + +**Changes**: +- Moved quantity extraction pattern to module level +- Pre-compiled pattern used in ingredient parsing + +**Pattern Compiled**: +```python +_RE_QUANTITY = re.compile(r"^(?P(\d+\/\d+|\d+(\.\d+)?))\s*(?P[a-zA-Z]+)?\s*(?P.*)") +``` + +**Impact**: +- Pattern compiled once instead of on every `_handle_extract()` call +- Ingredient extraction is more efficient + +## Testing + +Comprehensive test suite added in `tests/test_regex_optimizations.py`: + +### Test Coverage + +1. **Module-Level Pattern Verification**: Confirms patterns exist and are compiled +2. **Functionality Tests**: Verifies patterns work correctly after optimization +3. **Performance Benchmarks**: Measures speedup from pre-compilation +4. **Pattern Stability Tests**: Confirms pattern objects remain stable across calls + +### Test Results + +``` +tests/test_regex_optimizations.py::TestFinalValidationOptimizations::test_regex_patterns_are_compiled PASSED +tests/test_regex_optimizations.py::TestFinalValidationOptimizations::test_onclick_pattern_matches_correctly PASSED +tests/test_regex_optimizations.py::TestValidateDashboardOptimizations::test_regex_patterns_are_compiled PASSED +tests/test_regex_optimizations.py::TestFunctionAppOptimizations::test_word_split_pattern_is_compiled SKIPPED +tests/test_regex_optimizations.py::TestFunctionAppOptimizations::test_re_module_imported_at_top PASSED +tests/test_regex_optimizations.py::TestEmailNotificationsOptimizations::test_html_patterns_are_compiled PASSED +tests/test_regex_optimizations.py::TestEmailNotificationsOptimizations::test_html_stripping_works_correctly PASSED +tests/test_regex_optimizations.py::TestCookingAIOptimizations::test_quantity_pattern_is_compiled PASSED +tests/test_regex_optimizations.py::TestPerformanceBenchmark::test_compiled_vs_inline_regex_performance PASSED +tests/test_regex_optimizations.py::TestPerformanceBenchmark::test_pattern_cache_stability PASSED + +========================= 9 passed, 1 skipped in 0.29s ========================= +``` + +## Best Practices + +### When to Pre-Compile Regex Patterns + +✅ **DO pre-compile when**: +- Pattern is used more than once in the module +- Pattern is in a hot path (loops, frequently-called functions) +- Pattern is used in performance-critical code + +❌ **DON'T pre-compile when**: +- Pattern is used only once in the entire module +- Pattern is dynamically constructed based on runtime data (unless you can cache compiled versions) +- Pattern is in rarely-executed code (e.g., error handlers) + +### Naming Convention + +Pre-compiled patterns follow the convention: `_RE_` + +Examples: +- `_RE_WORD_SPLIT` - splits on word boundaries +- `_RE_HTML_TAGS` - matches HTML tags +- `_RE_ONCLICK` - matches onclick attributes + +### Pattern Organization + +```python +import re + +# Pre-compile regex patterns for performance +_RE_PATTERN_1 = re.compile(r'...') +_RE_PATTERN_2 = re.compile(r'...') + +# ... rest of module code +``` + +## Related Memory + +This optimization aligns with the existing repository memory: + +> **regex pattern compilation**: Pre-compile regex patterns at module level: `_RE_PATTERN = re.compile(r'...', flags)` then use `_RE_PATTERN.search()` instead of `re.search(r'...', text)` in functions + +## Future Optimization Opportunities + +Additional areas for potential optimization (not addressed in this PR): + +1. **Dynamic Pattern Caching**: For patterns constructed at runtime, consider using `functools.lru_cache` with a compiled pattern factory +2. **Pattern Pooling**: For very hot paths, consider pattern object pooling +3. **Regex Alternatives**: For simple string operations, consider if `str.find()`, `str.split()`, or `str.replace()` would be more efficient + +## References + +- Python `re` module documentation: https://docs.python.org/3/library/re.html +- Repository memory: regex pattern compilation +- Test suite: `tests/test_regex_optimizations.py` +- Related optimizations: `docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md` diff --git a/docs/SPARKLE_IMPROVEMENTS.md b/docs/SPARKLE_IMPROVEMENTS.md index 32a7e6d21..2ba6e5aa8 100644 --- a/docs/SPARKLE_IMPROVEMENTS.md +++ b/docs/SPARKLE_IMPROVEMENTS.md @@ -1,186 +1,186 @@ -# Sparkle Effect Improvements - -**Date**: 2026-02-17 -**Status**: ✅ Complete - -## Summary - -Enhanced the Aria character system's sparkle visual effects with performance optimizations, intensity control, color variations, and comprehensive testing. - -## Changes Implemented - -### 1. Performance Optimizations - -#### Keyword Matching with Frozensets -- **Before**: Inline string checks (`'sparkle' in cmd`) -- **After**: Pre-compiled frozensets with O(1) lookup -- **Files**: `aria_web/server.py:75-77` -- **Benefit**: Faster keyword matching, consistent with existing patterns in codebase - -```python -SPARKLE_KEYWORDS = frozenset(['sparkle', 'sparkles', 'glitter', 'shimmer', 'shine']) -GLOW_KEYWORDS = frozenset(['glow', 'glowing', 'radiate', 'illuminate']) -HEARTS_KEYWORDS = frozenset(['hearts', 'heart', 'love']) -``` - -#### requestAnimationFrame for DOM Manipulation -- **Before**: Sequential `setTimeout` loops for creating effects -- **After**: `requestAnimationFrame`-based scheduling -- **Files**: `aria_web/aria_controller.js:1490-1557` -- **Benefit**: Better synchronization with browser rendering, improved smoothness - -### 2. Intensity Levels - -Added three intensity levels for all effects (sparkle, glow, hearts): - -| Intensity | Count | Spread | Duration | Delay | Keywords | -|-----------|-------|--------|----------|-------|----------| -| **Light** | 3 | 60% | 1200ms | 150ms | light, subtle, gentle | -| **Normal** | 5 | 80% | 1500ms | 100ms | *(default)* | -| **Heavy** | 10 | 90% | 1800ms | 60ms | heavy, intense, lots, many | - -**Usage Examples**: -- `"light sparkle"` → 3 sparkles with gentle spread -- `"sparkle"` → 5 sparkles (normal) -- `"heavy sparkle"` or `"lots of sparkles"` → 10 sparkles with wide spread - -**Tag Format**: `[aria:effect:sparkle:intensity]` - -### 3. Enhanced Visual Features - -#### Color Variations -- Added hue rotation filters to sparkle effects for color diversity -- Random rotation transforms for variety -- Color palette: Gold, Orange, Yellow, Wheat, Light Yellow - -#### Better Positioning -- Effects centered around Aria (50%, 50%) -- Spread radius configurable per intensity -- More natural distribution pattern - -### 4. Synonym Support - -Expanded keyword recognition for natural language: - -**Sparkle**: sparkle, sparkles, glitter, shimmer, shine -**Glow**: glow, glowing, radiate, illuminate -**Hearts**: hearts, heart, love - -### 5. Comprehensive Testing - -Added 12 new tests to `tests/test_aria_server.py`: - -✅ Basic effect detection (sparkle, glow, hearts) -✅ Synonym keyword matching -✅ Intensity level detection (light, normal, heavy) -✅ Combined commands (e.g., "dance with sparkles") -✅ Frozenset validation -✅ Mutual exclusivity of intensity levels - -**Test Results**: 15/15 tests passing (100%) - -## Files Modified - -1. **aria_web/server.py** - - Added effect keyword frozensets (lines 75-77) - - Enhanced effect detection with intensity support (lines 779-791) - -2. **aria_web/aria_controller.js** - - Replaced `createEffect` function with enhanced version (lines 1490-1557) - - Added intensity parameter parsing in `executeTags` (line 817) - - Added intensity detection in direct command handler (lines 691-710) - -3. **tests/test_aria_server.py** - - Added 12 comprehensive sparkle functionality tests (lines 21-152) - -## Performance Impact - -### Before -- Sequential DOM manipulation with `setTimeout` loops -- 5 fixed sparkles regardless of context -- No color variation -- Inline keyword checking on every command - -### After -- RAF-synchronized DOM operations (better frame timing) -- Configurable count (3-10) based on intensity -- Color variations via hue rotation -- O(1) keyword lookups with frozensets - -**Estimated Improvements**: -- 10-15% faster keyword matching (O(n) → O(1) per keyword) -- Smoother animations (60fps sync with RAF) -- 3x effect range (3 to 10 sparkles) -- Better battery life on mobile (RAF optimization) - -## Usage Examples - -### Basic Commands -``` -"sparkle" → [aria:effect:sparkle:normal] -"make it sparkle" → [aria:effect:sparkle:normal] -"add some glitter" → [aria:effect:sparkle:normal] -``` - -### Intensity Control -``` -"light sparkle" → [aria:effect:sparkle:light] -"gentle shimmer" → [aria:effect:sparkle:light] -"heavy sparkle" → [aria:effect:sparkle:heavy] -"lots of sparkles" → [aria:effect:sparkle:heavy] -``` - -### Combined Commands -``` -"dance with sparkles" → [aria:animate:dance] [aria:effect:sparkle:normal] -"jump and sparkle" → [aria:animate:jump] [aria:effect:sparkle:normal] -"heavy sparkle and wave" → [aria:effect:sparkle:heavy] [aria:gesture:wave] -``` - -## Backward Compatibility - -✅ All existing sparkle commands continue to work -✅ Default intensity is "normal" (same as previous behavior) -✅ Direct command handler and tag-based execution both updated -✅ No breaking changes to existing functionality - -## Future Enhancements - -Potential improvements identified but not implemented: - -1. **Custom Colors**: Allow users to specify sparkle colors - - Example: `[aria:effect:sparkle:heavy:gold]` - -2. **Pattern Types**: Different sparkle patterns (circle, star, random) - - Example: `[aria:effect:sparkle:normal:star]` - -3. **Position Control**: Target sparkles at specific locations - - Example: `[aria:effect:sparkle:heavy:50:30]` (at x:50%, y:30%) - -4. **Persistence**: Keep effects visible longer or loop continuously - - Example: `[aria:effect:sparkle:normal:loop]` - -5. **Sound Effects**: Optional audio feedback for effects - - Requires TTS/audio integration - -## Testing - -Run the sparkle tests: -```bash -python -m pytest tests/test_aria_server.py -v -``` - -Expected output: `15 passed in 0.09s` - -## Documentation Updates - -- ✅ Added comprehensive test coverage -- ✅ Stored optimization patterns in memory system -- ✅ Created this improvement summary -- 📝 Consider updating `docs/aria/ARIA_VISUAL_SYSTEM.md` to reflect new features - -## References - -- Original implementation: `aria_web/server.py:777-785` (pre-enhancement) -- Performance pattern: Repository memory - "frozenset keyword matching" -- Similar optimizations: See `docs/PERFORMANCE_IMPROVEMENTS.md` +# Sparkle Effect Improvements + +**Date**: 2026-02-17 +**Status**: ✅ Complete + +## Summary + +Enhanced the Aria character system's sparkle visual effects with performance optimizations, intensity control, color variations, and comprehensive testing. + +## Changes Implemented + +### 1. Performance Optimizations + +#### Keyword Matching with Frozensets +- **Before**: Inline string checks (`'sparkle' in cmd`) +- **After**: Pre-compiled frozensets with O(1) lookup +- **Files**: `aria_web/server.py:75-77` +- **Benefit**: Faster keyword matching, consistent with existing patterns in codebase + +```python +SPARKLE_KEYWORDS = frozenset(['sparkle', 'sparkles', 'glitter', 'shimmer', 'shine']) +GLOW_KEYWORDS = frozenset(['glow', 'glowing', 'radiate', 'illuminate']) +HEARTS_KEYWORDS = frozenset(['hearts', 'heart', 'love']) +``` + +#### requestAnimationFrame for DOM Manipulation +- **Before**: Sequential `setTimeout` loops for creating effects +- **After**: `requestAnimationFrame`-based scheduling +- **Files**: `aria_web/aria_controller.js:1490-1557` +- **Benefit**: Better synchronization with browser rendering, improved smoothness + +### 2. Intensity Levels + +Added three intensity levels for all effects (sparkle, glow, hearts): + +| Intensity | Count | Spread | Duration | Delay | Keywords | +|-----------|-------|--------|----------|-------|----------| +| **Light** | 3 | 60% | 1200ms | 150ms | light, subtle, gentle | +| **Normal** | 5 | 80% | 1500ms | 100ms | *(default)* | +| **Heavy** | 10 | 90% | 1800ms | 60ms | heavy, intense, lots, many | + +**Usage Examples**: +- `"light sparkle"` → 3 sparkles with gentle spread +- `"sparkle"` → 5 sparkles (normal) +- `"heavy sparkle"` or `"lots of sparkles"` → 10 sparkles with wide spread + +**Tag Format**: `[aria:effect:sparkle:intensity]` + +### 3. Enhanced Visual Features + +#### Color Variations +- Added hue rotation filters to sparkle effects for color diversity +- Random rotation transforms for variety +- Color palette: Gold, Orange, Yellow, Wheat, Light Yellow + +#### Better Positioning +- Effects centered around Aria (50%, 50%) +- Spread radius configurable per intensity +- More natural distribution pattern + +### 4. Synonym Support + +Expanded keyword recognition for natural language: + +**Sparkle**: sparkle, sparkles, glitter, shimmer, shine +**Glow**: glow, glowing, radiate, illuminate +**Hearts**: hearts, heart, love + +### 5. Comprehensive Testing + +Added 12 new tests to `tests/test_aria_server.py`: + +✅ Basic effect detection (sparkle, glow, hearts) +✅ Synonym keyword matching +✅ Intensity level detection (light, normal, heavy) +✅ Combined commands (e.g., "dance with sparkles") +✅ Frozenset validation +✅ Mutual exclusivity of intensity levels + +**Test Results**: 15/15 tests passing (100%) + +## Files Modified + +1. **aria_web/server.py** + - Added effect keyword frozensets (lines 75-77) + - Enhanced effect detection with intensity support (lines 779-791) + +2. **aria_web/aria_controller.js** + - Replaced `createEffect` function with enhanced version (lines 1490-1557) + - Added intensity parameter parsing in `executeTags` (line 817) + - Added intensity detection in direct command handler (lines 691-710) + +3. **tests/test_aria_server.py** + - Added 12 comprehensive sparkle functionality tests (lines 21-152) + +## Performance Impact + +### Before +- Sequential DOM manipulation with `setTimeout` loops +- 5 fixed sparkles regardless of context +- No color variation +- Inline keyword checking on every command + +### After +- RAF-synchronized DOM operations (better frame timing) +- Configurable count (3-10) based on intensity +- Color variations via hue rotation +- O(1) keyword lookups with frozensets + +**Estimated Improvements**: +- 10-15% faster keyword matching (O(n) → O(1) per keyword) +- Smoother animations (60fps sync with RAF) +- 3x effect range (3 to 10 sparkles) +- Better battery life on mobile (RAF optimization) + +## Usage Examples + +### Basic Commands +``` +"sparkle" → [aria:effect:sparkle:normal] +"make it sparkle" → [aria:effect:sparkle:normal] +"add some glitter" → [aria:effect:sparkle:normal] +``` + +### Intensity Control +``` +"light sparkle" → [aria:effect:sparkle:light] +"gentle shimmer" → [aria:effect:sparkle:light] +"heavy sparkle" → [aria:effect:sparkle:heavy] +"lots of sparkles" → [aria:effect:sparkle:heavy] +``` + +### Combined Commands +``` +"dance with sparkles" → [aria:animate:dance] [aria:effect:sparkle:normal] +"jump and sparkle" → [aria:animate:jump] [aria:effect:sparkle:normal] +"heavy sparkle and wave" → [aria:effect:sparkle:heavy] [aria:gesture:wave] +``` + +## Backward Compatibility + +✅ All existing sparkle commands continue to work +✅ Default intensity is "normal" (same as previous behavior) +✅ Direct command handler and tag-based execution both updated +✅ No breaking changes to existing functionality + +## Future Enhancements + +Potential improvements identified but not implemented: + +1. **Custom Colors**: Allow users to specify sparkle colors + - Example: `[aria:effect:sparkle:heavy:gold]` + +2. **Pattern Types**: Different sparkle patterns (circle, star, random) + - Example: `[aria:effect:sparkle:normal:star]` + +3. **Position Control**: Target sparkles at specific locations + - Example: `[aria:effect:sparkle:heavy:50:30]` (at x:50%, y:30%) + +4. **Persistence**: Keep effects visible longer or loop continuously + - Example: `[aria:effect:sparkle:normal:loop]` + +5. **Sound Effects**: Optional audio feedback for effects + - Requires TTS/audio integration + +## Testing + +Run the sparkle tests: +```bash +python -m pytest tests/test_aria_server.py -v +``` + +Expected output: `15 passed in 0.09s` + +## Documentation Updates + +- ✅ Added comprehensive test coverage +- ✅ Stored optimization patterns in memory system +- ✅ Created this improvement summary +- 📝 Consider updating `docs/aria/ARIA_VISUAL_SYSTEM.md` to reflect new features + +## References + +- Original implementation: `aria_web/server.py:777-785` (pre-enhancement) +- Performance pattern: Repository memory - "frozenset keyword matching" +- Similar optimizations: See `docs/PERFORMANCE_IMPROVEMENTS.md` diff --git a/docs/_config.yml b/docs/_config.yml index 2194f0fc4..159d362a9 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -1,35 +1,35 @@ -# GitHub Pages configuration (Jekyll is disabled via .nojekyll — this file -# is used only for metadata reference, not for active Jekyll processing) -title: Aria - Interactive AI Character Platform -description: Hybrid quantum-AI/ML platform with 3D animated character, multi-provider chat, and autonomous learning - -# Build settings -markdown: kramdown -plugins: - - jekyll-seo-tag - - jekyll-sitemap - -# Exclude from processing -exclude: - - README.md - - Gemfile - - Gemfile.lock - - node_modules - - vendor - -# GitHub Pages settings -baseurl: "" -url: "https://bryan-roe.github.io/Aria" - -# Collection settings -collections: - apps: - output: true - permalink: /:collection/:name/ - -# Defaults -defaults: - - scope: - path: "" - values: - layout: default +# GitHub Pages configuration (Jekyll is disabled via .nojekyll — this file +# is used only for metadata reference, not for active Jekyll processing) +title: Aria - Interactive AI Character Platform +description: Hybrid quantum-AI/ML platform with 3D animated character, multi-provider chat, and autonomous learning + +# Build settings +markdown: kramdown +plugins: + - jekyll-seo-tag + - jekyll-sitemap + +# Exclude from processing +exclude: + - README.md + - Gemfile + - Gemfile.lock + - node_modules + - vendor + +# GitHub Pages settings +baseurl: "" +url: "https://bryan-roe.github.io/Aria" + +# Collection settings +collections: + apps: + output: true + permalink: /:collection/:name/ + +# Defaults +defaults: + - scope: + path: "" + values: + layout: default diff --git a/docs/architecture/PHASE27_COMPLETE.md b/docs/architecture/PHASE27_COMPLETE.md index 327996711..eb70be483 100644 --- a/docs/architecture/PHASE27_COMPLETE.md +++ b/docs/architecture/PHASE27_COMPLETE.md @@ -1,208 +1,208 @@ -# Phase 27: Dashboard Consolidation - Complete ✅ - -## Summary - -Successfully consolidated **6 separate dashboard HTML pages** into a **single unified dashboard** with tab-based navigation, preserving all functionality while dramatically improving UX. - -## What Was Done - -### 1. Created Consolidated Dashboard (`consolidated.html`) -- **1,000+ lines** of integrated functionality -- **5 tabs**: Overview, Training, Analytics, History, Tools -- **All Phase 26 features** integrated (anomaly detection, keyboard nav, session history) -- **Responsive design** with mobile support -- **Tab state persistence** via localStorage -- **Auto-refresh** every 5 seconds for real-time data - -### 2. Updated Server Routing (`serve.py`) -- Changed redirect from `/` → `/hub.html` to `/` → `/consolidated.html` -- Single line change: `self.send_header('Location', '/consolidated.html')` - -### 3. Created Backups -- `index.html.backup` -- `unified.html.backup` -- `analytics.html.backup` - -### 4. Created Documentation -- `DASHBOARD_CONSOLIDATION.md` - Complete usage guide and technical reference - -## Results - -### Before -``` -6 separate pages = 6,480+ lines of HTML -├── hub.html (1,036 lines) -├── unified.html (2,991 lines) -├── analytics.html (969 lines) -├── enhanced.html (unknown) -├── advanced.html (unknown) -└── index.html (484 lines) - -Navigation: Click links → Page reload → Lost state -``` - -### After -``` -1 unified page = 1,000+ lines of HTML -└── consolidated.html - ├── Overview Tab - ├── Training Tab - ├── Analytics Tab - ├── History Tab - └── Tools Tab - -Navigation: Click tab → Instant switch → Persistent state -``` - -### Improvements -- **80% reduction** in navigation complexity (6 pages → 1 page) -- **Zero page reloads** for tab switching -- **Persistent state** across all views -- **Tab memory** restores last active tab on reload -- **Faster loading** - All CSS/JS loaded once - -## Testing Results ✅ - -Server test successful: -``` -✅ GET / → 302 redirect -✅ GET /consolidated.html → 200 OK -✅ GET /shared-theme.css → 200 OK -✅ GET /anomaly-detector.js → 200 OK -✅ GET /keyboard-nav.js → 200 OK -✅ GET /session-history.js → 200 OK -✅ GET /api/datasets → 200 OK -✅ GET /status → 200 OK (auto-refresh every 5s) -``` - -## Tab Features - -### 🏠 Overview -- System status cards (jobs, loss, best model, time) -- Loss progression chart -- Recent activity feed -- Quick action buttons - -### 🚀 Training -- Complete training form -- Quick presets (Quick/Standard/Full) -- Advanced settings (collapsible) -- Config save/load/reset - -### 📊 Analytics -- Performance comparison chart -- Time distribution chart -- Detailed model comparison table - -### 📜 History -- Session history table (20 most recent) -- Export to JSON/CSV -- Replay configs -- Clear history - -### 🛠️ Tools -- Dataset Profiler with AI recommendations -- VRAM Calculator for safe batch sizes -- Anomaly Detector status dashboard - -## Phase 26 Integration ✅ - -All 6 Phase 26 features fully integrated: - -1. **Dataset Profiler** - Profile Dataset tool in Tools tab -2. **VRAM Calculator** - GPU memory calculator in Tools tab -3. **Anomaly Detector** - Real-time monitoring with status cards -4. **Shared Theme CSS** - Unified styling across all tabs -5. **Keyboard Navigation** - Global shortcuts (ready for tab shortcuts) -6. **Session History** - Full session tracking in History tab - -## Usage - -### Start Server -```powershell -cd dashboard -python serve.py -``` - -### Open Dashboard -Server automatically opens at `http://localhost:8000` → redirects to `/consolidated.html` - -### Navigate -- **Click tabs** at top of page -- **Keyboard shortcuts** (when fully integrated): - - Ctrl+1 → Overview - - Ctrl+2 → Training - - Ctrl+3 → Analytics - - Ctrl+4 → History - - Ctrl+5 → Tools - -## Files - -### New -- `dashboard/consolidated.html` (1,000+ lines) -- `DASHBOARD_CONSOLIDATION.md` (documentation) -- `PHASE27_COMPLETE.md` (this file) - -### Modified -- `dashboard/serve.py` (1 line: redirect path) - -### Backed Up -- `dashboard/index.html.backup` -- `dashboard/unified.html.backup` -- `dashboard/analytics.html.backup` - -### Can Be Removed (After Testing) -- `dashboard/hub.html` -- `dashboard/enhanced.html` -- `dashboard/advanced.html` -- Old `index.html`, `unified.html`, `analytics.html` - -## Next Steps (Optional) - -1. **Test thoroughly** across all tabs and features -2. **Remove old pages** once satisfied with consolidated version -3. **Extend keyboard shortcuts** for tab switching (Ctrl+1/2/3/4/5) -4. **Add mobile gestures** for swipe navigation between tabs -5. **Create dashboard tour** for first-time users -6. **Add export all** button on Overview tab - -## Technical Highlights - -### Tab Switching -- **Zero latency** - DOM manipulation only -- **Lazy updates** - Charts update only when Analytics tab active -- **State preservation** - Form inputs persist when switching away -- **URL agnostic** - No hash/query params needed - -### Data Flow -- **Single source** - One `/status` endpoint for all tabs -- **Smart caching** - currentData global variable -- **Conditional refresh** - Charts update only when visible -- **Auto-refresh** - 5-second interval for real-time updates - -### Performance -- **Initial load** - All resources fetched once -- **Tab switching** - <10ms (display:none → display:block) -- **Chart updates** - Only when Analytics tab active -- **History view** - Lazy render on tab switch - -## Conclusion - -Phase 27 complete! Successfully transformed a fragmented 6-page dashboard into a streamlined single-page application with tab-based navigation. All functionality preserved, Phase 26 features integrated, and UX dramatically improved with instant navigation and persistent state management. - -**Key Achievement**: 80% reduction in navigation complexity while maintaining 100% feature parity. - ---- - -**Phase**: 27 (Dashboard Consolidation) -**Status**: ✅ Complete -**Duration**: ~1 hour -**Files Created**: 3 -**Files Modified**: 1 -**Lines Added**: ~1,600 -**Old Pages Consolidated**: 6 -**New Tabs**: 5 -**Phase 26 Features Integrated**: 6/6 -**Server Test**: ✅ Passed - -Ready for Phase 28! 🚀 +# Phase 27: Dashboard Consolidation - Complete ✅ + +## Summary + +Successfully consolidated **6 separate dashboard HTML pages** into a **single unified dashboard** with tab-based navigation, preserving all functionality while dramatically improving UX. + +## What Was Done + +### 1. Created Consolidated Dashboard (`consolidated.html`) +- **1,000+ lines** of integrated functionality +- **5 tabs**: Overview, Training, Analytics, History, Tools +- **All Phase 26 features** integrated (anomaly detection, keyboard nav, session history) +- **Responsive design** with mobile support +- **Tab state persistence** via localStorage +- **Auto-refresh** every 5 seconds for real-time data + +### 2. Updated Server Routing (`serve.py`) +- Changed redirect from `/` → `/hub.html` to `/` → `/consolidated.html` +- Single line change: `self.send_header('Location', '/consolidated.html')` + +### 3. Created Backups +- `index.html.backup` +- `unified.html.backup` +- `analytics.html.backup` + +### 4. Created Documentation +- `DASHBOARD_CONSOLIDATION.md` - Complete usage guide and technical reference + +## Results + +### Before +``` +6 separate pages = 6,480+ lines of HTML +├── hub.html (1,036 lines) +├── unified.html (2,991 lines) +├── analytics.html (969 lines) +├── enhanced.html (unknown) +├── advanced.html (unknown) +└── index.html (484 lines) + +Navigation: Click links → Page reload → Lost state +``` + +### After +``` +1 unified page = 1,000+ lines of HTML +└── consolidated.html + ├── Overview Tab + ├── Training Tab + ├── Analytics Tab + ├── History Tab + └── Tools Tab + +Navigation: Click tab → Instant switch → Persistent state +``` + +### Improvements +- **80% reduction** in navigation complexity (6 pages → 1 page) +- **Zero page reloads** for tab switching +- **Persistent state** across all views +- **Tab memory** restores last active tab on reload +- **Faster loading** - All CSS/JS loaded once + +## Testing Results ✅ + +Server test successful: +``` +✅ GET / → 302 redirect +✅ GET /consolidated.html → 200 OK +✅ GET /shared-theme.css → 200 OK +✅ GET /anomaly-detector.js → 200 OK +✅ GET /keyboard-nav.js → 200 OK +✅ GET /session-history.js → 200 OK +✅ GET /api/datasets → 200 OK +✅ GET /status → 200 OK (auto-refresh every 5s) +``` + +## Tab Features + +### 🏠 Overview +- System status cards (jobs, loss, best model, time) +- Loss progression chart +- Recent activity feed +- Quick action buttons + +### 🚀 Training +- Complete training form +- Quick presets (Quick/Standard/Full) +- Advanced settings (collapsible) +- Config save/load/reset + +### 📊 Analytics +- Performance comparison chart +- Time distribution chart +- Detailed model comparison table + +### 📜 History +- Session history table (20 most recent) +- Export to JSON/CSV +- Replay configs +- Clear history + +### 🛠️ Tools +- Dataset Profiler with AI recommendations +- VRAM Calculator for safe batch sizes +- Anomaly Detector status dashboard + +## Phase 26 Integration ✅ + +All 6 Phase 26 features fully integrated: + +1. **Dataset Profiler** - Profile Dataset tool in Tools tab +2. **VRAM Calculator** - GPU memory calculator in Tools tab +3. **Anomaly Detector** - Real-time monitoring with status cards +4. **Shared Theme CSS** - Unified styling across all tabs +5. **Keyboard Navigation** - Global shortcuts (ready for tab shortcuts) +6. **Session History** - Full session tracking in History tab + +## Usage + +### Start Server +```powershell +cd dashboard +python serve.py +``` + +### Open Dashboard +Server automatically opens at `http://localhost:8000` → redirects to `/consolidated.html` + +### Navigate +- **Click tabs** at top of page +- **Keyboard shortcuts** (when fully integrated): + - Ctrl+1 → Overview + - Ctrl+2 → Training + - Ctrl+3 → Analytics + - Ctrl+4 → History + - Ctrl+5 → Tools + +## Files + +### New +- `dashboard/consolidated.html` (1,000+ lines) +- `DASHBOARD_CONSOLIDATION.md` (documentation) +- `PHASE27_COMPLETE.md` (this file) + +### Modified +- `dashboard/serve.py` (1 line: redirect path) + +### Backed Up +- `dashboard/index.html.backup` +- `dashboard/unified.html.backup` +- `dashboard/analytics.html.backup` + +### Can Be Removed (After Testing) +- `dashboard/hub.html` +- `dashboard/enhanced.html` +- `dashboard/advanced.html` +- Old `index.html`, `unified.html`, `analytics.html` + +## Next Steps (Optional) + +1. **Test thoroughly** across all tabs and features +2. **Remove old pages** once satisfied with consolidated version +3. **Extend keyboard shortcuts** for tab switching (Ctrl+1/2/3/4/5) +4. **Add mobile gestures** for swipe navigation between tabs +5. **Create dashboard tour** for first-time users +6. **Add export all** button on Overview tab + +## Technical Highlights + +### Tab Switching +- **Zero latency** - DOM manipulation only +- **Lazy updates** - Charts update only when Analytics tab active +- **State preservation** - Form inputs persist when switching away +- **URL agnostic** - No hash/query params needed + +### Data Flow +- **Single source** - One `/status` endpoint for all tabs +- **Smart caching** - currentData global variable +- **Conditional refresh** - Charts update only when visible +- **Auto-refresh** - 5-second interval for real-time updates + +### Performance +- **Initial load** - All resources fetched once +- **Tab switching** - <10ms (display:none → display:block) +- **Chart updates** - Only when Analytics tab active +- **History view** - Lazy render on tab switch + +## Conclusion + +Phase 27 complete! Successfully transformed a fragmented 6-page dashboard into a streamlined single-page application with tab-based navigation. All functionality preserved, Phase 26 features integrated, and UX dramatically improved with instant navigation and persistent state management. + +**Key Achievement**: 80% reduction in navigation complexity while maintaining 100% feature parity. + +--- + +**Phase**: 27 (Dashboard Consolidation) +**Status**: ✅ Complete +**Duration**: ~1 hour +**Files Created**: 3 +**Files Modified**: 1 +**Lines Added**: ~1,600 +**Old Pages Consolidated**: 6 +**New Tabs**: 5 +**Phase 26 Features Integrated**: 6/6 +**Server Test**: ✅ Passed + +Ready for Phase 28! 🚀 diff --git a/docs/architecture/PHASE_24_DEPENDENCIES.md b/docs/architecture/PHASE_24_DEPENDENCIES.md index 774fc57e2..33b433769 100644 --- a/docs/architecture/PHASE_24_DEPENDENCIES.md +++ b/docs/architecture/PHASE_24_DEPENDENCIES.md @@ -1,146 +1,146 @@ -# Phase 24 Dependencies & Installation - -## Required for WebSocket Server (Phase 23) - -```powershell -pip install websockets watchdog -``` - -**Purpose:** -- `websockets`: WebSocket server for real-time dashboard updates -- `watchdog`: File system monitoring for job status changes - -**Usage:** -```powershell -# Start WebSocket server -python dashboard/websocket_server.py - -# WebSocket endpoint -ws://localhost:8765 -``` - ---- - -## Optional for Windows Notifications (Phase 24) - -```powershell -pip install win10toast -``` - -**Purpose:** -- Windows 10/11 native toast notifications -- Only needed for Python CLI notification tool -- Browser notifications require NO dependencies - -**Platforms:** -- **Windows**: `pip install win10toast` (recommended) -- **macOS**: No dependencies (uses built-in `osascript`) -- **Linux**: No dependencies (uses built-in `notify-send`) - -**Usage:** -```powershell -# Test notification -python scripts/notification_system.py --test - -# Monitor training job -python scripts/notification_system.py --monitor data_out/autotrain/status.json --job-name test -``` - ---- - -## No Dependencies Required - -The following Phase 24 features require **NO additional installations**: - -✅ **Export Functionality** (PNG/CSV/TXT) -- Browser-based export -- Uses native canvas API and blob downloads -- Works in all modern browsers - -✅ **Backup Manager** -- Pure Python implementation -- Uses stdlib: `os`, `shutil`, `json`, `tarfile`, `hashlib` -- Cross-platform compatible - -✅ **Browser Notifications** -- Uses Web Notifications API (built into browsers) -- No npm packages or libraries needed -- Supported in Chrome, Firefox, Edge, Safari - ---- - -## Installation Commands Summary - -```powershell -# WebSocket server (Phase 23 - REQUIRED for real-time updates) -pip install websockets watchdog - -# Windows notifications (Phase 24 - OPTIONAL for Python CLI) -pip install win10toast - -# That's it! All other features are dependency-free. -``` - ---- - -## Verification - -```powershell -# Check installed packages -pip list | Select-String "websockets|watchdog|win10toast" - -# Expected output: -# watchdog x.x.x -# websockets x.x.x -# win10toast x.x.x (if installed) -``` - ---- - -## Feature Dependency Matrix - -| Feature | Dependencies | Status | -|---------|--------------|--------| -| WebSocket Server | websockets, watchdog | Required for Phase 23 | -| Export (PNG/CSV/TXT) | None | Phase 24 ✅ | -| Backup Manager | None | Phase 24 ✅ | -| Browser Notifications | None | Phase 24 ✅ | -| Python CLI Notifications | win10toast (Windows only) | Phase 24 ✅ Optional | -| Analytics Dashboard | Chart.js (CDN) | Phase 23 ✅ | -| Job Queue | None | Phase 23 ✅ | -| Model Comparison | None | Phase 23 ✅ | -| Config Templates | None | Phase 23 ✅ | - ---- - -## Troubleshooting - -### WebSocket Issues - -**Error**: `ModuleNotFoundError: No module named 'websockets'` - -**Fix**: -```powershell -pip install websockets watchdog -``` - -### Windows Notification Issues - -**Error**: `ModuleNotFoundError: No module named 'win10toast'` - -**Fix** (optional): -```powershell -pip install win10toast -``` - -**Fallback**: Python CLI will print to console if library not installed - -### Browser Notification Issues - -**Error**: "Notification API not supported" - -**Fix**: Use a modern browser (Chrome 22+, Firefox 22+, Edge 14+, Safari 7+) - ---- - -Last Updated: November 2024 +# Phase 24 Dependencies & Installation + +## Required for WebSocket Server (Phase 23) + +```powershell +pip install websockets watchdog +``` + +**Purpose:** +- `websockets`: WebSocket server for real-time dashboard updates +- `watchdog`: File system monitoring for job status changes + +**Usage:** +```powershell +# Start WebSocket server +python dashboard/websocket_server.py + +# WebSocket endpoint +ws://localhost:8765 +``` + +--- + +## Optional for Windows Notifications (Phase 24) + +```powershell +pip install win10toast +``` + +**Purpose:** +- Windows 10/11 native toast notifications +- Only needed for Python CLI notification tool +- Browser notifications require NO dependencies + +**Platforms:** +- **Windows**: `pip install win10toast` (recommended) +- **macOS**: No dependencies (uses built-in `osascript`) +- **Linux**: No dependencies (uses built-in `notify-send`) + +**Usage:** +```powershell +# Test notification +python scripts/notification_system.py --test + +# Monitor training job +python scripts/notification_system.py --monitor data_out/autotrain/status.json --job-name test +``` + +--- + +## No Dependencies Required + +The following Phase 24 features require **NO additional installations**: + +✅ **Export Functionality** (PNG/CSV/TXT) +- Browser-based export +- Uses native canvas API and blob downloads +- Works in all modern browsers + +✅ **Backup Manager** +- Pure Python implementation +- Uses stdlib: `os`, `shutil`, `json`, `tarfile`, `hashlib` +- Cross-platform compatible + +✅ **Browser Notifications** +- Uses Web Notifications API (built into browsers) +- No npm packages or libraries needed +- Supported in Chrome, Firefox, Edge, Safari + +--- + +## Installation Commands Summary + +```powershell +# WebSocket server (Phase 23 - REQUIRED for real-time updates) +pip install websockets watchdog + +# Windows notifications (Phase 24 - OPTIONAL for Python CLI) +pip install win10toast + +# That's it! All other features are dependency-free. +``` + +--- + +## Verification + +```powershell +# Check installed packages +pip list | Select-String "websockets|watchdog|win10toast" + +# Expected output: +# watchdog x.x.x +# websockets x.x.x +# win10toast x.x.x (if installed) +``` + +--- + +## Feature Dependency Matrix + +| Feature | Dependencies | Status | +|---------|--------------|--------| +| WebSocket Server | websockets, watchdog | Required for Phase 23 | +| Export (PNG/CSV/TXT) | None | Phase 24 ✅ | +| Backup Manager | None | Phase 24 ✅ | +| Browser Notifications | None | Phase 24 ✅ | +| Python CLI Notifications | win10toast (Windows only) | Phase 24 ✅ Optional | +| Analytics Dashboard | Chart.js (CDN) | Phase 23 ✅ | +| Job Queue | None | Phase 23 ✅ | +| Model Comparison | None | Phase 23 ✅ | +| Config Templates | None | Phase 23 ✅ | + +--- + +## Troubleshooting + +### WebSocket Issues + +**Error**: `ModuleNotFoundError: No module named 'websockets'` + +**Fix**: +```powershell +pip install websockets watchdog +``` + +### Windows Notification Issues + +**Error**: `ModuleNotFoundError: No module named 'win10toast'` + +**Fix** (optional): +```powershell +pip install win10toast +``` + +**Fallback**: Python CLI will print to console if library not installed + +### Browser Notification Issues + +**Error**: "Notification API not supported" + +**Fix**: Use a modern browser (Chrome 22+, Firefox 22+, Edge 14+, Safari 7+) + +--- + +Last Updated: November 2024 diff --git a/docs/architecture/PHASE_25_IMPROVEMENTS.md b/docs/architecture/PHASE_25_IMPROVEMENTS.md index c298302d9..342cc046f 100644 --- a/docs/architecture/PHASE_25_IMPROVEMENTS.md +++ b/docs/architecture/PHASE_25_IMPROVEMENTS.md @@ -1,109 +1,109 @@ -# Phase 25 Improvements (QAI Platform) - -Date: 2025-11-25 - -This document summarizes all enhancements delivered in Phase 25, focusing on optimization, usability, persistence, and reporting. These changes build upon Phases 23–24 (real-time monitoring, analytics dashboard, backups, exporting, notifications) and move the system closer to production-grade operational excellence. - -## Delivered Features - -## 1. Incremental Backup System - -* Extended `scripts/backup_manager.py` with `--incremental` mode. -* Tracks per-file SHA256 checksums to detect unchanged vs changed artifacts. -* Unchanged files preserved via hardlinks (space + time efficiency). -* Metadata now includes: `changed_files`, `unchanged_files`, `total_files`, and uniqueness guard for same-second invocations. -* Unit tests (`tests/test_backup_manager.py`) validate both full and incremental flows. - -## 2. Backup Unit Tests - -* Added regression safety for backup logic with simulated file modification. -* Verifies checksum recalculation and changed/unchanged counts. -* Foundation for future artifact integrity audits. - -## 3. Persistent Notification State - -* Browser notification enablement stored in `localStorage` under `qai_notifications_enabled`. -* Implemented across: `unified.html`, `analytics.html`, `hub.html`. -* Graceful degradation when permissions denied or API unsupported. - -## 4. Hyperparameter Tuning Wizard - -* Added modal (Ctrl+Y or button 🧪) to `unified.html`. -* Dataset-aware heuristic profiles: Quick / Balanced / High Quality. -* Recommendations auto-fill form fields (`learningRate`, `batchSize`, `loraRank`, `epochs`). -* Time estimate: `(samples ÷ batch × epochs) / throughput_factor (50)`. -* “Apply Best” chooses highest quality profile. - -## 5. Global Dark Mode (Cross-Page) - -* Unified preference (`localStorage.darkMode`) now applied to all major dashboards. -* Consistent styling via `.dark-mode` class with semantic overrides. -* Keyboard shortcuts: `D` (Analytics), `Ctrl+D` (Hub), toggle button on each page. - -## 6. PDF Analytics Export - -* Integrated `jsPDF` (CDN) in `analytics.html`. -* Multi-chart PDF export (loss, GPU, performance, training time). -* Embeds timestamp and chart images (canvas → PNG → PDF). -* File name: `qai_training_analytics.pdf`. - -## 7. WebSocket → Desktop Notification Bridge - -* `analytics.html` emits desktop notifications from WebSocket job events. -* Progress alerts at 0%, 25%, 50%, 75%, 100% (throttled). -* Completion & failure notifications with distinct icons. - -## 8. Lint & Accessibility Improvements - -* Removed inline styles (migrated to CSS utility classes). -* Corrected vendor prefix ordering (`-webkit-backdrop-filter` before `backdrop-filter`). -* Reduced duplicated style declarations. - -## Modified / Added Artifacts - -* `dashboard/unified.html` – Tuning wizard + shortcut. -* `dashboard/analytics.html` – Dark mode, PDF export, WebSocket notifications. -* `dashboard/hub.html` – Dark mode, lint cleanup, notification persistence. -* `scripts/backup_manager.py` – Incremental backup logic. -* `tests/test_backup_manager.py` – Backup unit tests. -* `PHASE_25_IMPROVEMENTS.md` – Documentation. - -## Testing Summary - -* Backup unit tests pass (full + incremental). -* Manual validation: dark mode, wizard apply, PDF export, WebSocket progress notifications. -* No regressions in charts, presets, PNG/CSV/TXT exports. - -## Usage Quick Reference - -| Feature | How to Use | Notes | -|---------|------------|-------| -| Incremental Backup | `python scripts/backup_manager.py --incremental --source data_out --dest backups` | Falls back to full if no prior manifest. | -| Tuning Wizard | Click "🧪 Tuning Wizard" or press `Ctrl+Y` | Apply profile to populate form. | -| Dark Mode | Toggle button or `D` / `Ctrl+D` | Persists via localStorage. | -| PDF Export | Analytics → Export → "🖨️ Export PDF Report" | Requires jsPDF CDN. | -| Notifications | Allow browser prompt | Progress + completion alerts. | - -## Planned Next (Phase 26 Candidates) - -1. Bayesian / Optuna-driven hyperparameter optimization. -2. Dataset profiling (token counts, length distribution) feeding wizard. -3. GPU-aware batch size auto-scaling (VRAM probe). -4. Training anomaly detection (loss spike alerts). -5. Consolidated stylesheet (shared theme bundle). -6. Markdown → PDF report templating pipeline. -7. Accessibility pass (ARIA roles, reduced motion option). - -## Considerations & Tech Debt - -* Heuristic throughput_factor (50) requires empirical calibration. -* PDF export lacks adaptive multi-page layout for >4 charts. -* Hardlink strategy may degrade across volumes (fallback copy needed). -* WebSocket URL hardcoded (`ws://localhost:8765`) – external deployment requires dynamic resolution. - -## Summary - -Phase 25 elevates the platform from feature-rich to optimization-ready: performance-conscious backups, smarter configuration onboarding, unified theming, enhanced reporting, and proactive real-time notifications. These upgrades reduce friction, improve observability, and set the stage for intelligent automation in subsequent phases. - ---- -*End of Phase 25 improvements.* +# Phase 25 Improvements (QAI Platform) + +Date: 2025-11-25 + +This document summarizes all enhancements delivered in Phase 25, focusing on optimization, usability, persistence, and reporting. These changes build upon Phases 23–24 (real-time monitoring, analytics dashboard, backups, exporting, notifications) and move the system closer to production-grade operational excellence. + +## Delivered Features + +## 1. Incremental Backup System + +* Extended `scripts/backup_manager.py` with `--incremental` mode. +* Tracks per-file SHA256 checksums to detect unchanged vs changed artifacts. +* Unchanged files preserved via hardlinks (space + time efficiency). +* Metadata now includes: `changed_files`, `unchanged_files`, `total_files`, and uniqueness guard for same-second invocations. +* Unit tests (`tests/test_backup_manager.py`) validate both full and incremental flows. + +## 2. Backup Unit Tests + +* Added regression safety for backup logic with simulated file modification. +* Verifies checksum recalculation and changed/unchanged counts. +* Foundation for future artifact integrity audits. + +## 3. Persistent Notification State + +* Browser notification enablement stored in `localStorage` under `qai_notifications_enabled`. +* Implemented across: `unified.html`, `analytics.html`, `hub.html`. +* Graceful degradation when permissions denied or API unsupported. + +## 4. Hyperparameter Tuning Wizard + +* Added modal (Ctrl+Y or button 🧪) to `unified.html`. +* Dataset-aware heuristic profiles: Quick / Balanced / High Quality. +* Recommendations auto-fill form fields (`learningRate`, `batchSize`, `loraRank`, `epochs`). +* Time estimate: `(samples ÷ batch × epochs) / throughput_factor (50)`. +* “Apply Best” chooses highest quality profile. + +## 5. Global Dark Mode (Cross-Page) + +* Unified preference (`localStorage.darkMode`) now applied to all major dashboards. +* Consistent styling via `.dark-mode` class with semantic overrides. +* Keyboard shortcuts: `D` (Analytics), `Ctrl+D` (Hub), toggle button on each page. + +## 6. PDF Analytics Export + +* Integrated `jsPDF` (CDN) in `analytics.html`. +* Multi-chart PDF export (loss, GPU, performance, training time). +* Embeds timestamp and chart images (canvas → PNG → PDF). +* File name: `qai_training_analytics.pdf`. + +## 7. WebSocket → Desktop Notification Bridge + +* `analytics.html` emits desktop notifications from WebSocket job events. +* Progress alerts at 0%, 25%, 50%, 75%, 100% (throttled). +* Completion & failure notifications with distinct icons. + +## 8. Lint & Accessibility Improvements + +* Removed inline styles (migrated to CSS utility classes). +* Corrected vendor prefix ordering (`-webkit-backdrop-filter` before `backdrop-filter`). +* Reduced duplicated style declarations. + +## Modified / Added Artifacts + +* `dashboard/unified.html` – Tuning wizard + shortcut. +* `dashboard/analytics.html` – Dark mode, PDF export, WebSocket notifications. +* `dashboard/hub.html` – Dark mode, lint cleanup, notification persistence. +* `scripts/backup_manager.py` – Incremental backup logic. +* `tests/test_backup_manager.py` – Backup unit tests. +* `PHASE_25_IMPROVEMENTS.md` – Documentation. + +## Testing Summary + +* Backup unit tests pass (full + incremental). +* Manual validation: dark mode, wizard apply, PDF export, WebSocket progress notifications. +* No regressions in charts, presets, PNG/CSV/TXT exports. + +## Usage Quick Reference + +| Feature | How to Use | Notes | +|---------|------------|-------| +| Incremental Backup | `python scripts/backup_manager.py --incremental --source data_out --dest backups` | Falls back to full if no prior manifest. | +| Tuning Wizard | Click "🧪 Tuning Wizard" or press `Ctrl+Y` | Apply profile to populate form. | +| Dark Mode | Toggle button or `D` / `Ctrl+D` | Persists via localStorage. | +| PDF Export | Analytics → Export → "🖨️ Export PDF Report" | Requires jsPDF CDN. | +| Notifications | Allow browser prompt | Progress + completion alerts. | + +## Planned Next (Phase 26 Candidates) + +1. Bayesian / Optuna-driven hyperparameter optimization. +2. Dataset profiling (token counts, length distribution) feeding wizard. +3. GPU-aware batch size auto-scaling (VRAM probe). +4. Training anomaly detection (loss spike alerts). +5. Consolidated stylesheet (shared theme bundle). +6. Markdown → PDF report templating pipeline. +7. Accessibility pass (ARIA roles, reduced motion option). + +## Considerations & Tech Debt + +* Heuristic throughput_factor (50) requires empirical calibration. +* PDF export lacks adaptive multi-page layout for >4 charts. +* Hardlink strategy may degrade across volumes (fallback copy needed). +* WebSocket URL hardcoded (`ws://localhost:8765`) – external deployment requires dynamic resolution. + +## Summary + +Phase 25 elevates the platform from feature-rich to optimization-ready: performance-conscious backups, smarter configuration onboarding, unified theming, enhanced reporting, and proactive real-time notifications. These upgrades reduce friction, improve observability, and set the stage for intelligent automation in subsequent phases. + +--- +*End of Phase 25 improvements.* diff --git a/docs/architecture/PHASE_26_IMPROVEMENTS.md b/docs/architecture/PHASE_26_IMPROVEMENTS.md index e5808c8d0..ee0f0b92c 100644 --- a/docs/architecture/PHASE_26_IMPROVEMENTS.md +++ b/docs/architecture/PHASE_26_IMPROVEMENTS.md @@ -1,430 +1,430 @@ -# Phase 26: Intelligent Optimization Enhancements - -**Status**: In Progress (6 of 6 features underway) -**Started**: 2025-11-25 -**Focus**: AI-driven intelligence, data profiling, VRAM awareness, anomaly detection, UX enhancements, history tracking - -## Overview - -Phase 26 transitions from UI polish to **intelligent, data-driven optimization**. The platform now analyzes datasets to recommend optimal hyperparameters, monitors GPU resources to prevent OOM errors, and detects training anomalies in real-time. - ---- - -## ✅ Feature 1: Dataset Profiling System (COMPLETE) - -### Implementation - -**Backend**: `scripts/dataset_profiler.py` (250+ lines) - -- **Tokenization Analysis**: Counts tokens per message using simple whitespace splitting -- **Statistical Metrics**: mean, median, min, max, stdev of token counts -- **Vocabulary Profiling**: Unique token count, role distribution (user/assistant) -- **Turn Analysis**: Average turns per conversation -- **Intelligent Recommendations**: Heuristic engine based on: - - **Sample count**: <500 (small), <2k (medium), >2k (large) - - **Token length**: >500 reduces batch size to prevent memory issues - - **Vocabulary size**: >10k increases LoRA rank for better coverage - -**Dashboard Integration**: `dashboard/unified.html` - -- **Profile Dataset Button**: Added to tuning wizard modal (blue button between Close and Apply Best) -- **AI Recommendation Row**: Shows `🎯 AI Recommended` profile at top of wizard table when profiling completes -- **Async Profiling**: Calls `/api/profile-dataset` endpoint, stores recommendations in `window.__profilerRecommendations` -- **Fallback to Heuristics**: If profiler unavailable or fails, wizard continues with size-based tiers - -**Backend Endpoint**: `dashboard/serve.py` - -- **Route**: `GET /api/profile-dataset?dataset=` -- **Process**: Shells out to `dataset_profiler.py --recommend --quiet`, parses JSON output -- **Timeout**: 30-second limit with graceful error handling -- **Response Format**: - - ```json - { - "total_samples": 290, - "valid_samples": 290, - "tokens": { - "total": 23883, - "mean": 41.2, - "median": 34, - "min": 1, - "max": 1323, - "stdev": 82.8 - }, - "vocabulary": { "size": 6851 }, - "roles": { "user": 290, "assistant": 290 }, - "turns_per_sample": { "mean": 2.0 }, - "recommendations": { - "batch_size": 4, - "learning_rate": "2e-4", - "lora_rank": 8, - "epochs": 5, - "reasoning": ["Small dataset (<500): lower batch, higher LR, more epochs"] - } - } - ``` - -**Profiler CLI**: - -```powershell -# Analyze dataset with recommendations -python .\scripts\dataset_profiler.py .\datasets\chat\mixed_chat --recommend - -# Save profile to file -python .\scripts\dataset_profiler.py .\datasets\chat\mixed_chat --recommend --output profile.json - -# JSON-only output (for scripting) -python .\scripts\dataset_profiler.py .\datasets\chat\mixed_chat --recommend --quiet -``` - -**Format Support**: - -- **JSON Array**: Standard `[{...}, {...}]` format -- **JSONL**: One JSON object per line (fallback for large datasets) - -**Example Recommendations** (mixed_chat dataset): - -- **Dataset**: 290 samples, 23,883 tokens, 6,851 vocab -- **Batch Size**: 4 (small dataset) -- **Learning Rate**: 2e-4 (higher for fewer samples) -- **LoRA Rank**: 8 (small vocab) -- **Epochs**: 5 (more iterations for small data) -- **Reasoning**: "Small dataset (<500): lower batch, higher LR, more epochs" - -### User Workflow - -1. **Select Dataset**: Choose dataset from dropdown in unified.html -2. **Open Wizard**: Click `🧪 Tuning Wizard` button or press `Ctrl+Y` -3. **Profile Dataset**: Click `📊 Profile Dataset` button in modal -4. **View Recommendations**: Wizard reloads with `🎯 AI Recommended` row at top -5. **Apply Settings**: Click `Apply` button to populate form fields -6. **Start Training**: Submit form with optimized hyperparameters - -### Technical Details - -**Profiler Architecture**: - -- **Input**: Path to dataset directory or train.json -- **Processing**: - 1. Load JSON/JSONL file - 2. Tokenize all messages (simple whitespace split) - 3. Calculate statistics using `statistics` module - 4. Count vocabulary with `Counter` - 5. Apply heuristic rules based on thresholds -- **Output**: JSON profile with recommendations - -**Recommendation Engine** (heuristic rules): - -```python -# Small dataset (<500 samples) -batch_size = 4 -learning_rate = "2e-4" -lora_rank = 8 -epochs = 5 - -# Medium dataset (500-2000 samples) -batch_size = 8 -learning_rate = "1e-4" -lora_rank = 16 -epochs = 3 - -# Large dataset (>2000 samples) -batch_size = 16 -learning_rate = "5e-5" -lora_rank = 32 -epochs = 3 - -# Adjust for long messages -if avg_tokens > 500: - batch_size = max(2, batch_size // 2) - -# Adjust for large vocab -if vocab_size > 10000: - lora_rank *= 2 -``` - -**Frontend Integration**: - -- `buildSuggestions(samples)`: Checks for profiler recommendations first, falls back to heuristics -- `buildHeuristicSuggestions(samples)`: Generates size-based tiers (Quick, Balanced, High Quality) -- `profileDatasetForWizard()`: Async function to call API, show loading state, reload wizard -- `window.__profilerRecommendations`: Global storage for recommendations - -**Error Handling**: - -- **Dataset not found**: Returns error, wizard continues with heuristics -- **Profiler timeout**: 30s limit prevents hanging -- **Parse errors**: Graceful fallback to heuristics -- **Missing script**: Error message in wizard, no crash - -### Benefits - -1. **Data-Driven Decisions**: Recommendations based on actual dataset characteristics, not guesswork -2. **Reduced Trial-and-Error**: Optimal hyperparameters on first try -3. **Prevent Common Mistakes**: Automatically adjusts batch size for long messages, epochs for small datasets -4. **Transparency**: Reasoning field explains why each recommendation was made -5. **Flexibility**: Users can still override recommendations or use heuristic tiers - -### Testing - -```powershell -# Test profiler standalone -python .\scripts\dataset_profiler.py .\datasets\chat\mixed_chat --recommend - -# Test with multiple datasets -python .\scripts\dataset_profiler.py .\datasets\chat\dolly_general --recommend -python .\scripts\dataset_profiler.py .\datasets\chat\orca_math --recommend - -# Test API endpoint (requires dashboard server) -curl "http://localhost:8080/api/profile-dataset?dataset=mixed_chat" -``` - ---- - -## 🔄 Feature 2: GPU-Aware Batch Size Calculator (IN PROGRESS) - -**Goal**: Prevent out-of-memory (OOM) errors by probing GPU VRAM and recommending safe batch sizes. - -**Plan**: - -1. **VRAM Probing**: - - Try `torch.cuda.get_device_properties()` if CUDA available - - Fallback to `nvidia-smi --query-gpu=memory.free --format=csv` - - Store total and available VRAM -2. **Memory Estimation**: - - Calculate model size (parameters × 4 bytes per float32) - - Add LoRA adapter overhead (rank × 2 × hidden_size × 4) - - Estimate activation memory (batch_size × seq_len × hidden_size × layers × 4) -3. **Safe Batch Calculation**: - - Reserve 20% headroom for OS/driver - - Recommend max batch size that fits in remaining VRAM -4. **Dashboard Integration**: - - Add "🖥️ Calculate Safe Batch" button to unified.html - - Show VRAM usage bar and recommended batch size - - Auto-populate batch_size field - -**Expected Output**: - -```text -GPU: NVIDIA RTX 4090 (24GB) -Available VRAM: 20.5GB (85% free) -Model: microsoft/Phi-3.5-mini-instruct (3.8B params) -Estimated model memory: 15.2GB (fp32) / 7.6GB (fp16) -LoRA overhead (rank=16): 0.4GB -Safe batch size: 8 (with 20% headroom) -``` - ---- - -## 🔄 Feature 3: Training Anomaly Detection (IN PROGRESS) - -**Goal**: Monitor training metrics in real-time and alert on issues (spikes, divergence, stagnation). - -**Plan**: - -1. **Metric Monitoring**: - - Track loss progression across epochs - - Detect **spikes** (>20% increase between epochs) - - Detect **divergence** (loss >10.0 or NaN) - - Detect **stagnation** (no improvement for 5+ epochs) -2. **Alert System**: - - Desktop notification on anomaly - - Visual indicator in analytics.html (red badge, exclamation icon) - - Optional auto-pause (configurable in settings) -3. **Dashboard Integration**: - - Add "Anomaly Detection" toggle to analytics.html - - Show anomaly timeline on chart (markers for spikes/stagnation) - - Export anomaly report to JSON - -**Anomaly Rules**: - -- **Spike**: `loss[epoch] > loss[epoch-1] * 1.2` -- **Divergence**: `loss[epoch] > 10.0 or isNaN(loss[epoch])` -- **Stagnation**: `min(loss[epoch-5:epoch]) >= min(loss[epoch-6:epoch-1])` - ---- - -## 🔄 Feature 4: Shared Theme Stylesheet (IN PROGRESS) - -**Goal**: Reduce CSS duplication across unified.html, analytics.html, hub.html by extracting common styles. - -**Plan**: - -1. **Extract Common Styles**: - - Dark mode variables (colors, backgrounds) - - Card styles (border, shadow, padding) - - Button styles (primary, secondary, info, danger) - - Badge styles (success, warning, error) - - Modal styles (overlay, container, animations) -2. **Create Shared CSS**: - - File: `dashboard/shared-theme.css` (~300 lines) - - Link from all 3 pages: `` -3. **Reduce Duplication**: - - Before: ~300 lines per page (900 total) - - After: ~50 lines per page (450 total) - - Savings: 450 lines (50% reduction) - ---- - -## 🔄 Feature 5: Enhanced Keyboard Navigation (IN PROGRESS) - -**Goal**: Add comprehensive keyboard shortcuts and accessibility improvements. - -**Plan**: - -1. **Shortcuts**: - - `Tab`: Cycle through form fields - - `Enter`: Submit form - - `Escape`: Close modal - - `Arrow keys`: Navigate preset profiles - - `Space`: Toggle checkboxes - - `?`: Show keyboard hints panel -2. **Keyboard Hints Panel**: - - Persistent panel (bottom-right corner) - - Toggle with `?` key - - Shows all available shortcuts with descriptions - - Responsive (hide on mobile) -3. **Accessibility**: - - ARIA labels for all form fields - - Role attributes for custom widgets - - Focus indicators (blue outline) - - Screen reader announcements for status changes - ---- - -## 🔄 Feature 6: Training Session History Tracker (IN PROGRESS) - -**Goal**: Persist training sessions for comparison and replay. - -**Plan**: - -1. **Session Storage**: - - Use localStorage or IndexedDB - - Store: config, start_time, end_time, final_loss, status - - Max 100 sessions (LRU eviction) -2. **History Tab**: - - Add to unified.html (between Training and Analytics tabs) - - Table with columns: Date, Model, Dataset, Epochs, Final Loss, Status - - Filters: Date range, Status (completed/failed), Model - - Sort: Date (desc), Loss (asc), Duration -3. **Session Replay**: - - "Load Config" button to populate form from history - - Compare button to diff two sessions - - Export to CSV/JSON -4. **Comparison View**: - - Side-by-side config diff - - Loss progression chart overlay - - Delta metrics (loss improvement, duration) - ---- - -## Progress Summary - -| Feature | Status | Completion | -| -------------------------- | -------------- | ---------- | -| Dataset Profiling | ✅ Complete | 100% | -| GPU-Aware Batch Calculator | 🔄 In Progress | 50% | -| Anomaly Detection | 🔄 In Progress | 40% | -| Shared CSS | 🔄 In Progress | 70% | -| Keyboard Navigation | 🔄 In Progress | 40% | -| Training Session History | 🔄 In Progress | 60% | - -**Overall**: 45% complete (6/6 features touched) - ---- - -## Testing Strategy - -### Dataset Profiling (Completed) - -- [x] Profiler script runs standalone -- [x] Handles JSON array format -- [x] Handles JSONL format -- [x] Generates recommendations -- [x] Dashboard button added -- [x] API endpoint implemented -- [ ] End-to-end test (dashboard → profile → apply) - -### Remaining Features - -- [ ] Unit tests for VRAM calculator -- [ ] Anomaly detection with sample data -- [ ] CSS extraction script -- [ ] Keyboard navigation E2E test -- [ ] Session storage persistence test - ---- - -## Dependencies - -**Phase 26 Additions**: - -- None (uses built-in Python `statistics`, `json`, `subprocess`) - -**Existing Stack**: - -- Python 3.11 -- PyTorch (for VRAM probing in Feature 2) -- Chart.js (for anomaly visualization in Feature 3) -- Browser Notification API (for anomaly alerts) -- localStorage/IndexedDB (for session history) - ---- - -## Key Files Modified - -### Phase 26 Feature 1 (Dataset Profiling) - -- `../../scripts/dataset_profiler.py` (NEW): 250+ line profiler with CLI -- `../../apps/dashboard/unified.html`: Tuning wizard integration - - Added `📊 Profile Dataset` button - - Added `buildSuggestions()` refactor to prioritize AI recommendations - - Added `buildHeuristicSuggestions()` with existing tier logic - - Added `profileDatasetForWizard()` async function -- `../../apps/dashboard/serve.py`: - - Added `/api/profile-dataset` endpoint - - Shells out to profiler script with subprocess - ---- - -## Next Steps (Priority Order) - -1. **Complete GPU-Aware Batch Calculator** (High Priority) - - Prevents costly OOM crashes - - Adds immediate value for users with limited VRAM -2. **Implement Anomaly Detection** (High Priority) - - Prevents wasted training time on diverging runs - - Early warning system for issues -3. **Extract Shared CSS** (Medium Priority) - - Code quality improvement - - Easier theme maintenance -4. **Add Keyboard Navigation** (Medium Priority) - - Power user feature - - Accessibility compliance -5. **Build Session History Tracker** (Low Priority) - - Nice-to-have for advanced users - - Lower ROI than other features - ---- - -## Lessons Learned - -1. **Heuristic Recommendations Work Well**: Simple thresholds (sample count, token length, vocab size) provide surprisingly good hyperparameter suggestions without ML. -2. **JSONL Support Essential**: Many large datasets use one-object-per-line format to avoid loading entire file into memory. -3. **Graceful Degradation**: Wizard falls back to heuristics if profiler fails—no hard dependency on external tools. -4. **User Transparency**: Showing "reasoning" field builds trust in AI recommendations. -5. **Async Profiling**: 30s timeout prevents UI blocking, but may need adjustment for very large datasets (>10k samples). - ---- - -## Documentation - -- Dataset Profiler CLI (`scripts/dataset_profiler.py`) - Standalone profiler with `--help` docs -- [Tuning Wizard](../../apps/dashboard/unified.html) - See `showTuningWizard()` function -- [API Endpoints](../../apps/dashboard/serve.py) - `/api/profile-dataset` route -- [Phase 25 Improvements](PHASE_25_IMPROVEMENTS.md) - Previous phase for context - ---- - -**Last Updated**: 2025-11-25 -**Next Review**: After Feature 2 completion +# Phase 26: Intelligent Optimization Enhancements + +**Status**: In Progress (6 of 6 features underway) +**Started**: 2025-11-25 +**Focus**: AI-driven intelligence, data profiling, VRAM awareness, anomaly detection, UX enhancements, history tracking + +## Overview + +Phase 26 transitions from UI polish to **intelligent, data-driven optimization**. The platform now analyzes datasets to recommend optimal hyperparameters, monitors GPU resources to prevent OOM errors, and detects training anomalies in real-time. + +--- + +## ✅ Feature 1: Dataset Profiling System (COMPLETE) + +### Implementation + +**Backend**: `scripts/dataset_profiler.py` (250+ lines) + +- **Tokenization Analysis**: Counts tokens per message using simple whitespace splitting +- **Statistical Metrics**: mean, median, min, max, stdev of token counts +- **Vocabulary Profiling**: Unique token count, role distribution (user/assistant) +- **Turn Analysis**: Average turns per conversation +- **Intelligent Recommendations**: Heuristic engine based on: + - **Sample count**: <500 (small), <2k (medium), >2k (large) + - **Token length**: >500 reduces batch size to prevent memory issues + - **Vocabulary size**: >10k increases LoRA rank for better coverage + +**Dashboard Integration**: `dashboard/unified.html` + +- **Profile Dataset Button**: Added to tuning wizard modal (blue button between Close and Apply Best) +- **AI Recommendation Row**: Shows `🎯 AI Recommended` profile at top of wizard table when profiling completes +- **Async Profiling**: Calls `/api/profile-dataset` endpoint, stores recommendations in `window.__profilerRecommendations` +- **Fallback to Heuristics**: If profiler unavailable or fails, wizard continues with size-based tiers + +**Backend Endpoint**: `dashboard/serve.py` + +- **Route**: `GET /api/profile-dataset?dataset=` +- **Process**: Shells out to `dataset_profiler.py --recommend --quiet`, parses JSON output +- **Timeout**: 30-second limit with graceful error handling +- **Response Format**: + + ```json + { + "total_samples": 290, + "valid_samples": 290, + "tokens": { + "total": 23883, + "mean": 41.2, + "median": 34, + "min": 1, + "max": 1323, + "stdev": 82.8 + }, + "vocabulary": { "size": 6851 }, + "roles": { "user": 290, "assistant": 290 }, + "turns_per_sample": { "mean": 2.0 }, + "recommendations": { + "batch_size": 4, + "learning_rate": "2e-4", + "lora_rank": 8, + "epochs": 5, + "reasoning": ["Small dataset (<500): lower batch, higher LR, more epochs"] + } + } + ``` + +**Profiler CLI**: + +```powershell +# Analyze dataset with recommendations +python .\scripts\dataset_profiler.py .\datasets\chat\mixed_chat --recommend + +# Save profile to file +python .\scripts\dataset_profiler.py .\datasets\chat\mixed_chat --recommend --output profile.json + +# JSON-only output (for scripting) +python .\scripts\dataset_profiler.py .\datasets\chat\mixed_chat --recommend --quiet +``` + +**Format Support**: + +- **JSON Array**: Standard `[{...}, {...}]` format +- **JSONL**: One JSON object per line (fallback for large datasets) + +**Example Recommendations** (mixed_chat dataset): + +- **Dataset**: 290 samples, 23,883 tokens, 6,851 vocab +- **Batch Size**: 4 (small dataset) +- **Learning Rate**: 2e-4 (higher for fewer samples) +- **LoRA Rank**: 8 (small vocab) +- **Epochs**: 5 (more iterations for small data) +- **Reasoning**: "Small dataset (<500): lower batch, higher LR, more epochs" + +### User Workflow + +1. **Select Dataset**: Choose dataset from dropdown in unified.html +2. **Open Wizard**: Click `🧪 Tuning Wizard` button or press `Ctrl+Y` +3. **Profile Dataset**: Click `📊 Profile Dataset` button in modal +4. **View Recommendations**: Wizard reloads with `🎯 AI Recommended` row at top +5. **Apply Settings**: Click `Apply` button to populate form fields +6. **Start Training**: Submit form with optimized hyperparameters + +### Technical Details + +**Profiler Architecture**: + +- **Input**: Path to dataset directory or train.json +- **Processing**: + 1. Load JSON/JSONL file + 2. Tokenize all messages (simple whitespace split) + 3. Calculate statistics using `statistics` module + 4. Count vocabulary with `Counter` + 5. Apply heuristic rules based on thresholds +- **Output**: JSON profile with recommendations + +**Recommendation Engine** (heuristic rules): + +```python +# Small dataset (<500 samples) +batch_size = 4 +learning_rate = "2e-4" +lora_rank = 8 +epochs = 5 + +# Medium dataset (500-2000 samples) +batch_size = 8 +learning_rate = "1e-4" +lora_rank = 16 +epochs = 3 + +# Large dataset (>2000 samples) +batch_size = 16 +learning_rate = "5e-5" +lora_rank = 32 +epochs = 3 + +# Adjust for long messages +if avg_tokens > 500: + batch_size = max(2, batch_size // 2) + +# Adjust for large vocab +if vocab_size > 10000: + lora_rank *= 2 +``` + +**Frontend Integration**: + +- `buildSuggestions(samples)`: Checks for profiler recommendations first, falls back to heuristics +- `buildHeuristicSuggestions(samples)`: Generates size-based tiers (Quick, Balanced, High Quality) +- `profileDatasetForWizard()`: Async function to call API, show loading state, reload wizard +- `window.__profilerRecommendations`: Global storage for recommendations + +**Error Handling**: + +- **Dataset not found**: Returns error, wizard continues with heuristics +- **Profiler timeout**: 30s limit prevents hanging +- **Parse errors**: Graceful fallback to heuristics +- **Missing script**: Error message in wizard, no crash + +### Benefits + +1. **Data-Driven Decisions**: Recommendations based on actual dataset characteristics, not guesswork +2. **Reduced Trial-and-Error**: Optimal hyperparameters on first try +3. **Prevent Common Mistakes**: Automatically adjusts batch size for long messages, epochs for small datasets +4. **Transparency**: Reasoning field explains why each recommendation was made +5. **Flexibility**: Users can still override recommendations or use heuristic tiers + +### Testing + +```powershell +# Test profiler standalone +python .\scripts\dataset_profiler.py .\datasets\chat\mixed_chat --recommend + +# Test with multiple datasets +python .\scripts\dataset_profiler.py .\datasets\chat\dolly_general --recommend +python .\scripts\dataset_profiler.py .\datasets\chat\orca_math --recommend + +# Test API endpoint (requires dashboard server) +curl "http://localhost:8080/api/profile-dataset?dataset=mixed_chat" +``` + +--- + +## 🔄 Feature 2: GPU-Aware Batch Size Calculator (IN PROGRESS) + +**Goal**: Prevent out-of-memory (OOM) errors by probing GPU VRAM and recommending safe batch sizes. + +**Plan**: + +1. **VRAM Probing**: + - Try `torch.cuda.get_device_properties()` if CUDA available + - Fallback to `nvidia-smi --query-gpu=memory.free --format=csv` + - Store total and available VRAM +2. **Memory Estimation**: + - Calculate model size (parameters × 4 bytes per float32) + - Add LoRA adapter overhead (rank × 2 × hidden_size × 4) + - Estimate activation memory (batch_size × seq_len × hidden_size × layers × 4) +3. **Safe Batch Calculation**: + - Reserve 20% headroom for OS/driver + - Recommend max batch size that fits in remaining VRAM +4. **Dashboard Integration**: + - Add "🖥️ Calculate Safe Batch" button to unified.html + - Show VRAM usage bar and recommended batch size + - Auto-populate batch_size field + +**Expected Output**: + +```text +GPU: NVIDIA RTX 4090 (24GB) +Available VRAM: 20.5GB (85% free) +Model: microsoft/Phi-3.5-mini-instruct (3.8B params) +Estimated model memory: 15.2GB (fp32) / 7.6GB (fp16) +LoRA overhead (rank=16): 0.4GB +Safe batch size: 8 (with 20% headroom) +``` + +--- + +## 🔄 Feature 3: Training Anomaly Detection (IN PROGRESS) + +**Goal**: Monitor training metrics in real-time and alert on issues (spikes, divergence, stagnation). + +**Plan**: + +1. **Metric Monitoring**: + - Track loss progression across epochs + - Detect **spikes** (>20% increase between epochs) + - Detect **divergence** (loss >10.0 or NaN) + - Detect **stagnation** (no improvement for 5+ epochs) +2. **Alert System**: + - Desktop notification on anomaly + - Visual indicator in analytics.html (red badge, exclamation icon) + - Optional auto-pause (configurable in settings) +3. **Dashboard Integration**: + - Add "Anomaly Detection" toggle to analytics.html + - Show anomaly timeline on chart (markers for spikes/stagnation) + - Export anomaly report to JSON + +**Anomaly Rules**: + +- **Spike**: `loss[epoch] > loss[epoch-1] * 1.2` +- **Divergence**: `loss[epoch] > 10.0 or isNaN(loss[epoch])` +- **Stagnation**: `min(loss[epoch-5:epoch]) >= min(loss[epoch-6:epoch-1])` + +--- + +## 🔄 Feature 4: Shared Theme Stylesheet (IN PROGRESS) + +**Goal**: Reduce CSS duplication across unified.html, analytics.html, hub.html by extracting common styles. + +**Plan**: + +1. **Extract Common Styles**: + - Dark mode variables (colors, backgrounds) + - Card styles (border, shadow, padding) + - Button styles (primary, secondary, info, danger) + - Badge styles (success, warning, error) + - Modal styles (overlay, container, animations) +2. **Create Shared CSS**: + - File: `dashboard/shared-theme.css` (~300 lines) + - Link from all 3 pages: `` +3. **Reduce Duplication**: + - Before: ~300 lines per page (900 total) + - After: ~50 lines per page (450 total) + - Savings: 450 lines (50% reduction) + +--- + +## 🔄 Feature 5: Enhanced Keyboard Navigation (IN PROGRESS) + +**Goal**: Add comprehensive keyboard shortcuts and accessibility improvements. + +**Plan**: + +1. **Shortcuts**: + - `Tab`: Cycle through form fields + - `Enter`: Submit form + - `Escape`: Close modal + - `Arrow keys`: Navigate preset profiles + - `Space`: Toggle checkboxes + - `?`: Show keyboard hints panel +2. **Keyboard Hints Panel**: + - Persistent panel (bottom-right corner) + - Toggle with `?` key + - Shows all available shortcuts with descriptions + - Responsive (hide on mobile) +3. **Accessibility**: + - ARIA labels for all form fields + - Role attributes for custom widgets + - Focus indicators (blue outline) + - Screen reader announcements for status changes + +--- + +## 🔄 Feature 6: Training Session History Tracker (IN PROGRESS) + +**Goal**: Persist training sessions for comparison and replay. + +**Plan**: + +1. **Session Storage**: + - Use localStorage or IndexedDB + - Store: config, start_time, end_time, final_loss, status + - Max 100 sessions (LRU eviction) +2. **History Tab**: + - Add to unified.html (between Training and Analytics tabs) + - Table with columns: Date, Model, Dataset, Epochs, Final Loss, Status + - Filters: Date range, Status (completed/failed), Model + - Sort: Date (desc), Loss (asc), Duration +3. **Session Replay**: + - "Load Config" button to populate form from history + - Compare button to diff two sessions + - Export to CSV/JSON +4. **Comparison View**: + - Side-by-side config diff + - Loss progression chart overlay + - Delta metrics (loss improvement, duration) + +--- + +## Progress Summary + +| Feature | Status | Completion | +| -------------------------- | -------------- | ---------- | +| Dataset Profiling | ✅ Complete | 100% | +| GPU-Aware Batch Calculator | 🔄 In Progress | 50% | +| Anomaly Detection | 🔄 In Progress | 40% | +| Shared CSS | 🔄 In Progress | 70% | +| Keyboard Navigation | 🔄 In Progress | 40% | +| Training Session History | 🔄 In Progress | 60% | + +**Overall**: 45% complete (6/6 features touched) + +--- + +## Testing Strategy + +### Dataset Profiling (Completed) + +- [x] Profiler script runs standalone +- [x] Handles JSON array format +- [x] Handles JSONL format +- [x] Generates recommendations +- [x] Dashboard button added +- [x] API endpoint implemented +- [ ] End-to-end test (dashboard → profile → apply) + +### Remaining Features + +- [ ] Unit tests for VRAM calculator +- [ ] Anomaly detection with sample data +- [ ] CSS extraction script +- [ ] Keyboard navigation E2E test +- [ ] Session storage persistence test + +--- + +## Dependencies + +**Phase 26 Additions**: + +- None (uses built-in Python `statistics`, `json`, `subprocess`) + +**Existing Stack**: + +- Python 3.11 +- PyTorch (for VRAM probing in Feature 2) +- Chart.js (for anomaly visualization in Feature 3) +- Browser Notification API (for anomaly alerts) +- localStorage/IndexedDB (for session history) + +--- + +## Key Files Modified + +### Phase 26 Feature 1 (Dataset Profiling) + +- `../../scripts/dataset_profiler.py` (NEW): 250+ line profiler with CLI +- `../../apps/dashboard/unified.html`: Tuning wizard integration + - Added `📊 Profile Dataset` button + - Added `buildSuggestions()` refactor to prioritize AI recommendations + - Added `buildHeuristicSuggestions()` with existing tier logic + - Added `profileDatasetForWizard()` async function +- `../../apps/dashboard/serve.py`: + - Added `/api/profile-dataset` endpoint + - Shells out to profiler script with subprocess + +--- + +## Next Steps (Priority Order) + +1. **Complete GPU-Aware Batch Calculator** (High Priority) + - Prevents costly OOM crashes + - Adds immediate value for users with limited VRAM +2. **Implement Anomaly Detection** (High Priority) + - Prevents wasted training time on diverging runs + - Early warning system for issues +3. **Extract Shared CSS** (Medium Priority) + - Code quality improvement + - Easier theme maintenance +4. **Add Keyboard Navigation** (Medium Priority) + - Power user feature + - Accessibility compliance +5. **Build Session History Tracker** (Low Priority) + - Nice-to-have for advanced users + - Lower ROI than other features + +--- + +## Lessons Learned + +1. **Heuristic Recommendations Work Well**: Simple thresholds (sample count, token length, vocab size) provide surprisingly good hyperparameter suggestions without ML. +2. **JSONL Support Essential**: Many large datasets use one-object-per-line format to avoid loading entire file into memory. +3. **Graceful Degradation**: Wizard falls back to heuristics if profiler fails—no hard dependency on external tools. +4. **User Transparency**: Showing "reasoning" field builds trust in AI recommendations. +5. **Async Profiling**: 30s timeout prevents UI blocking, but may need adjustment for very large datasets (>10k samples). + +--- + +## Documentation + +- Dataset Profiler CLI (`scripts/dataset_profiler.py`) - Standalone profiler with `--help` docs +- [Tuning Wizard](../../apps/dashboard/unified.html) - See `showTuningWizard()` function +- [API Endpoints](../../apps/dashboard/serve.py) - `/api/profile-dataset` route +- [Phase 25 Improvements](PHASE_25_IMPROVEMENTS.md) - Previous phase for context + +--- + +**Last Updated**: 2025-11-25 +**Next Review**: After Feature 2 completion diff --git a/docs/architecture/QAI_PHASE_24_IMPROVEMENTS.md b/docs/architecture/QAI_PHASE_24_IMPROVEMENTS.md index 258e7f6f3..7b4d249a3 100644 --- a/docs/architecture/QAI_PHASE_24_IMPROVEMENTS.md +++ b/docs/architecture/QAI_PHASE_24_IMPROVEMENTS.md @@ -1,874 +1,874 @@ -# QAI Phase 24 Improvements Summary - -## Overview - -Following the successful deployment of 5 advanced features in Phase 23 (WebSocket server, analytics dashboard, job queue, model comparison, config templates), Phase 24 continues the "keep improving" directive with 3 major production-ready features focused on **data portability, safety, and user awareness**. - -**Date**: November 2024 -**Status**: 3/5 Features Complete ✅ -**Lines Added**: 1,070+ lines of production code - ---- - -## Features Implemented - -### 1. Export Functionality ✅ - -**Purpose**: Enable data portability and reporting -**Location**: `dashboard/analytics.html` (lines 627-790) -**Lines of Code**: 164 lines - -**Capabilities:** -- **PNG Export**: Export all 4 Chart.js charts as individual images - - Loss trends chart → `qai_lossChart_2024-11-24.png` - - GPU utilization chart → `qai_gpuChart_2024-11-24.png` - - Performance comparison → `qai_performanceChart_2024-11-24.png` - - Time distribution → `qai_timeChart_2024-11-24.png` - - Uses canvas.toDataURL() for high-quality output - - 500ms delay between downloads for browser stability - -- **CSV Export**: Tabular training data for analysis - - 7 columns: Job Name, Final Loss, Pre Loss, Post Loss, Improvement %, Duration (min), LoRA Rank - - Comma-delimited format compatible with Excel, Google Sheets - - Filename: `qai_training_data_2024-11-24.csv` - - Automatic improvement percentage calculation - -- **TXT Report**: Comprehensive human-readable report - - Header with generation timestamp - - Summary statistics: total jobs, average loss, best model, total training time - - Detailed results section: each job with all metrics - - Filename: `qai_report_2024-11-24.txt` - - Perfect for sharing with stakeholders - -**Usage:** -```javascript -// Triggered from analytics dashboard -exportCharts() // Opens modal with 3 export options - -// Individual exports -exportAsPNG() // Downloads 4 PNG files -exportAsCSV() // Downloads CSV file -exportReport() // Downloads TXT report -``` - -**User Interface:** -- Modal overlay with styled buttons -- Color-coded export types (blue/green/orange) -- Toast notifications for success/error feedback -- Close button with overlay click support - -**Value Proposition:** -- Share training results with team/stakeholders -- Archive historical performance data -- Import into analysis tools (Python, R, Excel) -- Create presentations and reports - ---- - -### 2. Backup Manager System ✅ - -**Purpose**: Production-grade backup/restore for training artifacts -**Location**: `scripts/backup_manager.py` -**Lines of Code**: 415 lines - -**Architecture:** -``` -BackupManager Class -├── create_backup() # Main backup orchestration -├── restore_backup() # Extract and verify -├── delete_backup() # Remove + update manifest -├── cleanup_old_backups() # Retention policy -├── calculate_checksum() # SHA256 validation -└── CLI Interface # argparse commands -``` - -**Key Features:** - -1. **Selective Backup**: - ```bash - # Full backup (models + configs + logs) - python scripts/backup_manager.py create --name pre_production - - # Exclude models (faster, smaller) - python scripts/backup_manager.py create --name config_only --no-models - - # Include datasets (opt-in for large data) - python scripts/backup_manager.py create --name full_archive --include-datasets - ``` - -2. **Compression & Checksums**: - - Tar.gz compression (optional with `--no-compress`) - - SHA256 checksums for integrity verification - - Metadata tracking (file list, sizes, timestamps) - - System info capture (Python/PyTorch/CUDA versions) - -3. **Restore Operations**: - ```bash - # Restore to original location - python scripts/backup_manager.py restore --name pre_production - - # Restore to custom directory - python scripts/backup_manager.py restore --name pre_production --target-dir ./restore_test - ``` - - Automatic checksum verification - - Conflict detection and resolution - - Progress reporting during extraction - -4. **Lifecycle Management**: - ```bash - # List all backups - python scripts/backup_manager.py list - - # Delete specific backup - python scripts/backup_manager.py delete --name old_backup - - # Keep only 5 most recent - python scripts/backup_manager.py cleanup --keep 5 - ``` - -**Backup Structure:** -``` -backups/ -├── backup_manifest.json # Metadata for all backups -├── pre_production/ -│ ├── models/ # Trained LoRA adapters -│ ├── configs/ # Training configurations -│ ├── logs/ # Training logs -│ └── metadata.json # Backup metadata -└── pre_production.tar.gz # Compressed archive (optional) -``` - -**Manifest Format:** -```json -{ - "backups": [ - { - "name": "pre_production", - "timestamp": "2024-11-24T10:30:00Z", - "size_bytes": 359034880, - "compressed": true, - "checksum": "a3f2d8e9...", - "categories": ["models", "configs", "logs"], - "file_count": 127, - "description": "Pre-production checkpoint", - "system_info": { - "python_version": "3.11.5", - "pytorch_version": "2.0.1", - "cuda_available": true - } - } - ] -} -``` - -**CLI Commands:** -```bash -# Create backups -create Create new backup - --name Backup name (required) - --description Optional description - --no-models Exclude models - --no-configs Exclude configs - --no-logs Exclude logs - --include-datasets Include datasets (opt-in) - --no-compress Skip tar.gz compression - -# Manage backups -list List all backups with details -restore Restore from backup - --name Backup name - --target-dir Custom restore location -delete Delete specific backup - --name Backup name -cleanup Remove old backups - --keep Number to retain (default: 5) -``` - -**Integration Points:** -- Pre-deployment safety: `backup_manager.py create --name pre_deploy` -- Scheduled backups: Windows Task Scheduler / cron jobs -- CI/CD pipelines: Backup before automated deployments -- Disaster recovery: Quick restore to last known good state - -**Value Proposition:** -- Prevent data loss from training experiments -- Rollback to previous model versions -- Archive successful configurations -- Compliance and audit trails -- Disaster recovery capability - ---- - -### 3. Desktop Notification System ✅ - -**Purpose**: Real-time alerts for training events and system status -**Locations**: -- Browser: `dashboard/unified.html` (lines 2334-2414) -- Browser: `dashboard/analytics.html` (lines 626-662) -- Browser: `dashboard/hub.html` (lines 797-859, 354 toggle button) -- Python CLI: `scripts/notification_system.py` (415 lines) - -**Lines of Code**: 491 lines (browser + Python) - -**Architecture:** - -1. **Browser Notifications** (Web Notifications API): - - Cross-platform (Windows/macOS/Linux) - - No external dependencies - - Native OS notification style - - Click-to-focus behavior - -2. **Python CLI Tool** (Cross-platform): - - Windows: `win10toast` library (optional) - - macOS: `osascript` built-in - - Linux: `notify-send` built-in - - Monitoring mode for long-running jobs - -**Notification Types:** - -| Icon | Event | Title | Example Message | -|------|-------|-------|-----------------| -| 🚀 | Job Started | Training Started | Job 'phi35_mixed_chat' has begun training | -| ✅ | Job Complete | Training Complete | Job 'phi35_mixed_chat' finished in 45min with loss 0.2341 | -| ❌ | Job Failed | Training Failed | Job 'phi35_mixed_chat' failed: CUDA out of memory | -| 🎯 | Milestone | Milestone Reached | Job 'test': Loss below 0.5 = 0.4523 | -| ⚠️ | GPU Alert | GPU Alert | GPU utilization at 97% (Memory: 10240MB) | -| 💾 | Backup Done | Backup Complete | Created backup 'pre_prod_v1' (342.56 MB) | -| 📊 | Eval Done | Evaluation Complete | Model 'phi35_lora_v3' - Perplexity: 12.34 | - -**Browser Integration:** - -```javascript -// Initialize on page load -initNotifications() // Request permission if needed - -// Send notifications -sendDesktopNotification("Title", "Message", "🔔") - -// Training events (auto-triggered) -notifyJobStarted("phi35_test") -notifyJobCompleted("phi35_test", 45, 0.2341) -notifyJobFailed("phi35_test", "CUDA OOM") -notifyMilestone("phi35_test", "Epoch 10", 0.3456) - -// System alerts -notifyGPUAlert(97, 10240) -``` - -**Hub Toggle Button:** -- Location: Top right of hub header -- States: 🔕 (off) / 🔔 (on) -- Persistent across page loads (future: localStorage) -- Permission request on first click -- Toast feedback for state changes - -**Python CLI Usage:** - -```bash -# Test notifications -python scripts/notification_system.py --test - -# Monitor training job -python scripts/notification_system.py \ - --monitor data_out/autotrain/status.json \ - --job-name phi35_mixed_chat -``` - -**Monitoring Features:** -- Polls status file every 10 seconds -- Detects job state transitions (pending → running → completed/failed) -- Milestone tracking: - - Loss threshold: notify when loss drops below 0.5 - - Epoch interval: notify every 5 epochs -- Automatic cleanup when job finishes - -**Integration with Dashboards:** - -1. **Unified Dashboard** (`unified.html`): - - Auto-refresh checks status every 5 seconds - - Compares current vs. previous job states - - Triggers notifications on state changes - - GPU monitoring with 95% threshold - -2. **Analytics Dashboard** (`analytics.html`): - - Initialization on page load - - Notifications for export completions - - Alert on data loading errors - -3. **QAI Hub** (`hub.html`): - - Toggle button for enable/disable - - Test notification on enable - - Visual feedback (icon + text) - -**Platform Support:** - -| Platform | Method | Dependencies | -|----------|--------|--------------| -| Browser | Web Notifications API | None (built-in) | -| Windows | win10toast | `pip install win10toast` | -| macOS | osascript | None (built-in) | -| Linux | notify-send | None (built-in) | - -**Value Proposition:** -- Stay informed without watching dashboard -- Multi-task during long training runs -- Immediate failure alerts -- Milestone awareness (progress tracking) -- GPU resource management - ---- - -## Statistics - -### Code Metrics - -| Feature | Lines of Code | Files Modified | New Files | -|---------|---------------|----------------|-----------| -| Export Functionality | 164 | 1 (`analytics.html`) | 0 | -| Backup Manager | 415 | 0 | 1 (`backup_manager.py`) | -| Notification System | 491 | 3 (all dashboards) | 2 (`.py` + guide) | -| **Total** | **1,070** | **4** | **3** | - -### Feature Coverage - -| Dashboard | Export | Backup | Notifications | -|-----------|--------|--------|---------------| -| unified.html | ❌ | ❌ | ✅ | -| analytics.html | ✅ | ❌ | ✅ | -| hub.html | ❌ | ❌ | ✅ (toggle) | - -### Testing Status - -| Component | Unit Tests | Integration Tests | Manual Testing | -|-----------|------------|-------------------|----------------| -| Export (PNG/CSV/TXT) | N/A (UI) | N/A | ✅ Verified | -| Backup Manager CLI | ⏳ Pending | ⏳ Pending | ✅ Verified | -| Browser Notifications | N/A (browser API) | N/A | ✅ Verified | -| Python CLI Notifier | ⏳ Pending | ⏳ Pending | ✅ Verified | - ---- - -## Usage Examples - -### Export Training Data - -```bash -# 1. Open analytics dashboard -http://localhost:8000/analytics.html - -# 2. Click "Export" button in top controls -# 3. Choose export format: -# - PNG: High-quality chart images -# - CSV: Tabular data for analysis -# - TXT: Human-readable report - -# 4. Files download to browser's download folder -# - qai_lossChart_2024-11-24.png -# - qai_training_data_2024-11-24.csv -# - qai_report_2024-11-24.txt -``` - -### Create Pre-Deployment Backup - -```bash -# Full backup before deploying to production -python scripts/backup_manager.py create \ - --name pre_prod_v1 \ - --description "Checkpoint before production deployment" \ - --include-datasets - -# Output: -# ✅ Backup 'pre_prod_v1' created successfully -# Size: 342.56 MB (compressed) -# Files: 127 -# Checksum: a3f2d8e9c1b4f6a8... -``` - -### Monitor Training with Notifications - -```bash -# Terminal 1: Start training -python scripts/training/autotrain.py --job phi35_mixed_chat - -# Terminal 2: Monitor with notifications (optional) -python scripts/notification_system.py \ - --monitor data_out/autotrain/status.json \ - --job-name phi35_mixed_chat - -# You'll receive desktop notifications for: -# - Job started -# - Every 5 epochs -# - Loss drops below 0.5 -# - Job completion/failure -``` - -### Browser Notification Workflow - -``` -1. Open QAI Hub: http://localhost:8000/hub.html -2. Click notification toggle (🔕) in header -3. Allow browser permission when prompted -4. Toggle turns to 🔔 - notifications enabled -5. Start training job from unified dashboard -6. Minimize browser window -7. Receive desktop alerts automatically: - - 🚀 Training started - - 🎯 Milestones reached - - ✅ Training complete -``` - ---- - -## API Reference - -### Export Functions (JavaScript) - -```javascript -// analytics.html functions - -// Open export modal -exportCharts() - -// Export all charts as PNG -exportAsPNG() -// Downloads 4 files: -// - qai_lossChart_YYYY-MM-DD.png -// - qai_gpuChart_YYYY-MM-DD.png -// - qai_performanceChart_YYYY-MM-DD.png -// - qai_timeChart_YYYY-MM-DD.png - -// Export training data as CSV -exportAsCSV() -// Downloads: qai_training_data_YYYY-MM-DD.csv -// Columns: Job Name, Final Loss, Pre Loss, Post Loss, -// Improvement %, Duration (min), LoRA Rank - -// Export comprehensive report as TXT -exportReport() -// Downloads: qai_report_YYYY-MM-DD.txt -// Contains: Summary stats + detailed job results -``` - -### Backup Manager (CLI) - -```bash -# Create backup -python scripts/backup_manager.py create \ - --name \ - [--description "Optional description"] \ - [--no-models] [--no-configs] [--no-logs] \ - [--include-datasets] [--no-compress] - -# List backups -python scripts/backup_manager.py list - -# Restore backup -python scripts/backup_manager.py restore \ - --name \ - [--target-dir ] - -# Delete backup -python scripts/backup_manager.py delete \ - --name - -# Cleanup old backups -python scripts/backup_manager.py cleanup \ - [--keep ] # Default: 5 -``` - -### Notification System (Python) - -```python -from scripts.notification_system import NotificationManager - -notifier = NotificationManager() - -# Basic notification -notifier.send_notification( - title="Title", - message="Message body", - icon="info", # info/success/warning/error - duration=10 # seconds -) - -# Training events -notifier.notify_job_started("job_name") -notifier.notify_job_completed("job_name", duration_min=45, final_loss=0.234) -notifier.notify_job_failed("job_name", error="Error message") -notifier.notify_milestone("job_name", "Milestone description", value=0.456) - -# System alerts -notifier.notify_gpu_alert(gpu_util=97, memory_used=10240) -notifier.notify_backup_complete("backup_name", size_mb=342.56) -notifier.notify_evaluation_complete("model_name", perplexity=12.34) -``` - -### Notification System (Browser) - -```javascript -// unified.html, analytics.html, hub.html - -// Initialize (call on page load) -initNotifications() - -// Send notification -sendDesktopNotification(title, message, icon) -// icon: emoji string (🔔, 🚀, ✅, ❌, etc.) - -// Training events -notifyJobStarted(jobName) -notifyJobCompleted(jobName, durationMin, finalLoss) -notifyJobFailed(jobName, error) -notifyMilestone(jobName, milestone, value) - -// System alerts -notifyGPUAlert(gpuUtil, memoryUsed) - -// Toggle (hub.html only) -toggleNotifications() -updateNotificationUI() -``` - ---- - -## Integration with Existing Systems - -### Phase 23 Features - -**WebSocket Server** (`dashboard/websocket_server.py`): -- Future: Push notifications via WebSocket -- Real-time event streaming to all connected clients -- Eliminates polling delays - -**Job Queue** (`scripts/job_queue.py`): -- Trigger notifications on queue events: - - Job added to queue - - Job starts execution - - Job completes/fails - - Dependencies resolved - -**Analytics Dashboard** (`dashboard/analytics.html`): -- Export integrated into existing UI -- Notifications for data loading errors -- Real-time stats updates - -**Model Comparison** (Ctrl+M in unified.html): -- Future: Export comparison data as CSV -- Notification when comparison completes - -**Config Templates** (Ctrl+T in unified.html): -- Future: Backup/restore template library -- Notification on template save/load - -### Training Pipeline - -**AutoTrain** (`scripts/training/autotrain.py`): -```python -# Add notification support -from scripts.notification_system import NotificationManager -notifier = NotificationManager() - -# Before job -notifier.notify_job_started(job_name) - -# After job -if success: - notifier.notify_job_completed(job_name, duration, loss) -else: - notifier.notify_job_failed(job_name, error) -``` - -**Quantum AutoRun** (`scripts/evaluation/quantum_autorun.py`): -- Notifications for quantum job submissions -- Azure Quantum workspace connection status -- Job completion alerts (especially for paid QPU runs) - -**Evaluation AutoRun** (`scripts/evaluation/evaluation_autorun.py`): -- Notify when evaluation completes -- Perplexity threshold alerts -- Best model identification - -### CI/CD Pipeline - -**CI Orchestrator** (`scripts/orchestrators/ci_orchestrator.py`): -```bash -# Create backup before CI run -python scripts/backup_manager.py create --name pre_ci_$(date +%Y%m%d_%H%M%S) - -# Run CI pipeline -python scripts/orchestrators/ci_orchestrator.py --ci-pipeline - -# On success: notification -# On failure: notification + restore from backup -``` - ---- - -## Best Practices - -### Backup Strategy - -**Frequency:** -- Before every production deployment -- After successful training runs -- Before major configuration changes -- Weekly scheduled backups (cron/Task Scheduler) - -**Retention:** -```bash -# Keep last 5 backups, delete older -python scripts/backup_manager.py cleanup --keep 5 -``` - -**Naming Convention:** -``` -pre_prod_YYYYMMDD # Production checkpoints -experiment_NAME_v1 # Experimental runs -milestone_FEATURE # Feature milestones -daily_backup_YYYYMMDD # Scheduled backups -``` - -**Recovery Testing:** -```bash -# Periodic restore drills -python scripts/backup_manager.py restore --name test_backup --target-dir ./restore_test -# Verify restored files -# Delete test directory -``` - -### Notification Etiquette - -**Do:** -- Enable for production training runs -- Set reasonable milestone intervals (5-10 epochs) -- Use GPU alerts to prevent resource exhaustion -- Test notifications before long runs - -**Don't:** -- Spam notifications (every epoch on short runs) -- Enable for debug/test runs -- Send notifications during working hours for overnight jobs -- Ignore permission denials (respect user choice) - -### Export Workflows - -**For Stakeholders:** -1. Export TXT report (human-readable) -2. Share via email/Slack -3. Include summary stats and best models - -**For Analysis:** -1. Export CSV data -2. Import into Python/R/Excel -3. Run statistical analysis -4. Generate custom visualizations - -**For Presentations:** -1. Export PNG charts -2. Include in PowerPoint/Google Slides -3. Annotate with insights -4. Combine with TXT report metrics - ---- - -## Troubleshooting - -### Export Issues - -**Problem**: Charts export as blank PNGs - -**Solution**: -- Ensure Chart.js has finished rendering -- Increase delay in `exportAsPNG()` (current: 500ms) -- Check browser console for canvas errors - -**Problem**: CSV downloads corrupted - -**Solution**: -- Verify CSV MIME type: `text/csv` -- Check for special characters in job names -- Escape commas in data values - -### Backup Issues - -**Problem**: Backup fails with "Permission denied" - -**Solution**: -```bash -# Check directory permissions -icacls backups - -# Create backups directory manually -mkdir backups - -# Run with elevated permissions (Windows) -runas /user:Administrator "python scripts/backup_manager.py create --name test" -``` - -**Problem**: Restore fails checksum verification - -**Solution**: -- Indicates corrupted backup archive -- Restore from previous backup -- Check disk integrity (chkdsk/fsck) - -### Notification Issues - -**Problem**: Browser notifications blocked - -**Solution**: -1. Check permission in browser settings -2. Clear site data and request permission again -3. Try different browser (Chrome/Firefox/Edge) - -**Problem**: Python notifications not appearing (Windows) - -**Solution**: -```bash -# Install win10toast -pip install win10toast - -# Test notification -python scripts/notification_system.py --test -``` - -**Problem**: Notifications appear on wrong screen (multi-monitor) - -**Solution**: -- OS-level setting (Windows Notification Settings) -- Move browser to desired monitor -- Notifications appear on screen with active window - ---- - -## Future Enhancements - -### Planned Features (Phase 25+) - -1. **Hyperparameter Tuning Wizard** (Todo #4): - - Interactive UI for parameter exploration - - Bayesian optimization integration - - Grid search automation - - Best parameter recommendation - -2. **Dark Mode Toggle** (Todo #5): - - Light/dark theme switcher - - localStorage persistence - - Apply to all dashboards (hub, unified, analytics) - - Smooth transitions - -3. **Enhanced Export**: - - PDF report generation - - Multi-format batch export - - Scheduled exports (cron) - - Cloud storage integration (Azure Blob) - -4. **Advanced Backups**: - - Incremental backups (only changed files) - - Cloud backup support (Azure Storage) - - Encryption (AES-256) - - Remote restore capability - -5. **Notification Improvements**: - - Persistent toggle state (localStorage) - - Notification history panel - - Customizable notification sounds - - Email/Slack integration - - Grouped notifications (batch updates) - - Rich notifications with action buttons - ---- - -## Documentation - -### New Documents - -1. **NOTIFICATION_SYSTEM_GUIDE.md** (This document): - - Comprehensive guide to notification system - - Browser and Python CLI usage - - API reference and troubleshooting - - 16 sections, 400+ lines - -2. **QAI_PHASE_24_IMPROVEMENTS.md** (This summary): - - Overview of 3 completed features - - Code metrics and statistics - - Usage examples and API reference - - Integration guides - -### Updated Documents - -Files to update with Phase 24 information: -- `README.md` - Add export, backup, notification sections -- `AUTOMATION_QUICKREF.md` - Include backup commands -- `QAI_HUB_ENHANCEMENTS_V2.md` - Update with notification toggle -- `.github/copilot-instructions.md` - Add new tool descriptions - ---- - -## Deployment Checklist - -### Pre-Deployment - -- [x] Export functionality tested (PNG/CSV/TXT) -- [x] Backup manager CLI tested (create/restore/delete/cleanup) -- [x] Browser notifications tested (Chrome/Firefox/Edge) -- [x] Python CLI notifier tested (Windows/macOS/Linux) -- [x] Hub toggle button tested -- [x] Documentation created (2 comprehensive guides) -- [ ] Unit tests created for backup manager -- [ ] Integration tests for notification system -- [ ] Update main README.md -- [ ] Update AUTOMATION_QUICKREF.md - -### Post-Deployment - -- [ ] Monitor backup disk usage -- [ ] Track notification delivery rates -- [ ] Collect user feedback on exports -- [ ] Performance monitoring (export speed) -- [ ] Error tracking (Sentry/Application Insights) - ---- - -## Summary - -Phase 24 successfully delivered **3 production-ready features** in rapid succession: - -1. **Export Functionality** - Data portability with PNG/CSV/TXT formats -2. **Backup Manager** - Enterprise-grade backup/restore with compression -3. **Desktop Notifications** - Real-time alerts for training events - -**Total Impact:** -- 1,070+ lines of production code -- 4 files modified, 3 new files created -- 2 comprehensive documentation guides -- Zero breaking changes to existing features -- Full backward compatibility maintained - -**User Benefits:** -- Share training results easily (export) -- Protect data from loss (backup) -- Stay informed without monitoring (notifications) -- Production-ready tooling -- Professional reporting capabilities - -**Next Steps:** -- Implement hyperparameter tuning wizard (Todo #4) -- Add dark mode toggle (Todo #5) -- Enhance features based on user feedback -- Continue "keep improving" directive - ---- - -**Phase 24 Status**: ✅ **COMPLETE** -**Features Delivered**: 3/5 (60%) -**Code Quality**: Production-ready -**Documentation**: Comprehensive -**User Value**: High - -**Ready for Phase 25** 🚀 - ---- - -**Last Updated**: November 2024 -**Maintained By**: QAI Development Team +# QAI Phase 24 Improvements Summary + +## Overview + +Following the successful deployment of 5 advanced features in Phase 23 (WebSocket server, analytics dashboard, job queue, model comparison, config templates), Phase 24 continues the "keep improving" directive with 3 major production-ready features focused on **data portability, safety, and user awareness**. + +**Date**: November 2024 +**Status**: 3/5 Features Complete ✅ +**Lines Added**: 1,070+ lines of production code + +--- + +## Features Implemented + +### 1. Export Functionality ✅ + +**Purpose**: Enable data portability and reporting +**Location**: `dashboard/analytics.html` (lines 627-790) +**Lines of Code**: 164 lines + +**Capabilities:** +- **PNG Export**: Export all 4 Chart.js charts as individual images + - Loss trends chart → `qai_lossChart_2024-11-24.png` + - GPU utilization chart → `qai_gpuChart_2024-11-24.png` + - Performance comparison → `qai_performanceChart_2024-11-24.png` + - Time distribution → `qai_timeChart_2024-11-24.png` + - Uses canvas.toDataURL() for high-quality output + - 500ms delay between downloads for browser stability + +- **CSV Export**: Tabular training data for analysis + - 7 columns: Job Name, Final Loss, Pre Loss, Post Loss, Improvement %, Duration (min), LoRA Rank + - Comma-delimited format compatible with Excel, Google Sheets + - Filename: `qai_training_data_2024-11-24.csv` + - Automatic improvement percentage calculation + +- **TXT Report**: Comprehensive human-readable report + - Header with generation timestamp + - Summary statistics: total jobs, average loss, best model, total training time + - Detailed results section: each job with all metrics + - Filename: `qai_report_2024-11-24.txt` + - Perfect for sharing with stakeholders + +**Usage:** +```javascript +// Triggered from analytics dashboard +exportCharts() // Opens modal with 3 export options + +// Individual exports +exportAsPNG() // Downloads 4 PNG files +exportAsCSV() // Downloads CSV file +exportReport() // Downloads TXT report +``` + +**User Interface:** +- Modal overlay with styled buttons +- Color-coded export types (blue/green/orange) +- Toast notifications for success/error feedback +- Close button with overlay click support + +**Value Proposition:** +- Share training results with team/stakeholders +- Archive historical performance data +- Import into analysis tools (Python, R, Excel) +- Create presentations and reports + +--- + +### 2. Backup Manager System ✅ + +**Purpose**: Production-grade backup/restore for training artifacts +**Location**: `scripts/backup_manager.py` +**Lines of Code**: 415 lines + +**Architecture:** +``` +BackupManager Class +├── create_backup() # Main backup orchestration +├── restore_backup() # Extract and verify +├── delete_backup() # Remove + update manifest +├── cleanup_old_backups() # Retention policy +├── calculate_checksum() # SHA256 validation +└── CLI Interface # argparse commands +``` + +**Key Features:** + +1. **Selective Backup**: + ```bash + # Full backup (models + configs + logs) + python scripts/backup_manager.py create --name pre_production + + # Exclude models (faster, smaller) + python scripts/backup_manager.py create --name config_only --no-models + + # Include datasets (opt-in for large data) + python scripts/backup_manager.py create --name full_archive --include-datasets + ``` + +2. **Compression & Checksums**: + - Tar.gz compression (optional with `--no-compress`) + - SHA256 checksums for integrity verification + - Metadata tracking (file list, sizes, timestamps) + - System info capture (Python/PyTorch/CUDA versions) + +3. **Restore Operations**: + ```bash + # Restore to original location + python scripts/backup_manager.py restore --name pre_production + + # Restore to custom directory + python scripts/backup_manager.py restore --name pre_production --target-dir ./restore_test + ``` + - Automatic checksum verification + - Conflict detection and resolution + - Progress reporting during extraction + +4. **Lifecycle Management**: + ```bash + # List all backups + python scripts/backup_manager.py list + + # Delete specific backup + python scripts/backup_manager.py delete --name old_backup + + # Keep only 5 most recent + python scripts/backup_manager.py cleanup --keep 5 + ``` + +**Backup Structure:** +``` +backups/ +├── backup_manifest.json # Metadata for all backups +├── pre_production/ +│ ├── models/ # Trained LoRA adapters +│ ├── configs/ # Training configurations +│ ├── logs/ # Training logs +│ └── metadata.json # Backup metadata +└── pre_production.tar.gz # Compressed archive (optional) +``` + +**Manifest Format:** +```json +{ + "backups": [ + { + "name": "pre_production", + "timestamp": "2024-11-24T10:30:00Z", + "size_bytes": 359034880, + "compressed": true, + "checksum": "a3f2d8e9...", + "categories": ["models", "configs", "logs"], + "file_count": 127, + "description": "Pre-production checkpoint", + "system_info": { + "python_version": "3.11.5", + "pytorch_version": "2.0.1", + "cuda_available": true + } + } + ] +} +``` + +**CLI Commands:** +```bash +# Create backups +create Create new backup + --name Backup name (required) + --description Optional description + --no-models Exclude models + --no-configs Exclude configs + --no-logs Exclude logs + --include-datasets Include datasets (opt-in) + --no-compress Skip tar.gz compression + +# Manage backups +list List all backups with details +restore Restore from backup + --name Backup name + --target-dir Custom restore location +delete Delete specific backup + --name Backup name +cleanup Remove old backups + --keep Number to retain (default: 5) +``` + +**Integration Points:** +- Pre-deployment safety: `backup_manager.py create --name pre_deploy` +- Scheduled backups: Windows Task Scheduler / cron jobs +- CI/CD pipelines: Backup before automated deployments +- Disaster recovery: Quick restore to last known good state + +**Value Proposition:** +- Prevent data loss from training experiments +- Rollback to previous model versions +- Archive successful configurations +- Compliance and audit trails +- Disaster recovery capability + +--- + +### 3. Desktop Notification System ✅ + +**Purpose**: Real-time alerts for training events and system status +**Locations**: +- Browser: `dashboard/unified.html` (lines 2334-2414) +- Browser: `dashboard/analytics.html` (lines 626-662) +- Browser: `dashboard/hub.html` (lines 797-859, 354 toggle button) +- Python CLI: `scripts/notification_system.py` (415 lines) + +**Lines of Code**: 491 lines (browser + Python) + +**Architecture:** + +1. **Browser Notifications** (Web Notifications API): + - Cross-platform (Windows/macOS/Linux) + - No external dependencies + - Native OS notification style + - Click-to-focus behavior + +2. **Python CLI Tool** (Cross-platform): + - Windows: `win10toast` library (optional) + - macOS: `osascript` built-in + - Linux: `notify-send` built-in + - Monitoring mode for long-running jobs + +**Notification Types:** + +| Icon | Event | Title | Example Message | +|------|-------|-------|-----------------| +| 🚀 | Job Started | Training Started | Job 'phi35_mixed_chat' has begun training | +| ✅ | Job Complete | Training Complete | Job 'phi35_mixed_chat' finished in 45min with loss 0.2341 | +| ❌ | Job Failed | Training Failed | Job 'phi35_mixed_chat' failed: CUDA out of memory | +| 🎯 | Milestone | Milestone Reached | Job 'test': Loss below 0.5 = 0.4523 | +| ⚠️ | GPU Alert | GPU Alert | GPU utilization at 97% (Memory: 10240MB) | +| 💾 | Backup Done | Backup Complete | Created backup 'pre_prod_v1' (342.56 MB) | +| 📊 | Eval Done | Evaluation Complete | Model 'phi35_lora_v3' - Perplexity: 12.34 | + +**Browser Integration:** + +```javascript +// Initialize on page load +initNotifications() // Request permission if needed + +// Send notifications +sendDesktopNotification("Title", "Message", "🔔") + +// Training events (auto-triggered) +notifyJobStarted("phi35_test") +notifyJobCompleted("phi35_test", 45, 0.2341) +notifyJobFailed("phi35_test", "CUDA OOM") +notifyMilestone("phi35_test", "Epoch 10", 0.3456) + +// System alerts +notifyGPUAlert(97, 10240) +``` + +**Hub Toggle Button:** +- Location: Top right of hub header +- States: 🔕 (off) / 🔔 (on) +- Persistent across page loads (future: localStorage) +- Permission request on first click +- Toast feedback for state changes + +**Python CLI Usage:** + +```bash +# Test notifications +python scripts/notification_system.py --test + +# Monitor training job +python scripts/notification_system.py \ + --monitor data_out/autotrain/status.json \ + --job-name phi35_mixed_chat +``` + +**Monitoring Features:** +- Polls status file every 10 seconds +- Detects job state transitions (pending → running → completed/failed) +- Milestone tracking: + - Loss threshold: notify when loss drops below 0.5 + - Epoch interval: notify every 5 epochs +- Automatic cleanup when job finishes + +**Integration with Dashboards:** + +1. **Unified Dashboard** (`unified.html`): + - Auto-refresh checks status every 5 seconds + - Compares current vs. previous job states + - Triggers notifications on state changes + - GPU monitoring with 95% threshold + +2. **Analytics Dashboard** (`analytics.html`): + - Initialization on page load + - Notifications for export completions + - Alert on data loading errors + +3. **QAI Hub** (`hub.html`): + - Toggle button for enable/disable + - Test notification on enable + - Visual feedback (icon + text) + +**Platform Support:** + +| Platform | Method | Dependencies | +|----------|--------|--------------| +| Browser | Web Notifications API | None (built-in) | +| Windows | win10toast | `pip install win10toast` | +| macOS | osascript | None (built-in) | +| Linux | notify-send | None (built-in) | + +**Value Proposition:** +- Stay informed without watching dashboard +- Multi-task during long training runs +- Immediate failure alerts +- Milestone awareness (progress tracking) +- GPU resource management + +--- + +## Statistics + +### Code Metrics + +| Feature | Lines of Code | Files Modified | New Files | +|---------|---------------|----------------|-----------| +| Export Functionality | 164 | 1 (`analytics.html`) | 0 | +| Backup Manager | 415 | 0 | 1 (`backup_manager.py`) | +| Notification System | 491 | 3 (all dashboards) | 2 (`.py` + guide) | +| **Total** | **1,070** | **4** | **3** | + +### Feature Coverage + +| Dashboard | Export | Backup | Notifications | +|-----------|--------|--------|---------------| +| unified.html | ❌ | ❌ | ✅ | +| analytics.html | ✅ | ❌ | ✅ | +| hub.html | ❌ | ❌ | ✅ (toggle) | + +### Testing Status + +| Component | Unit Tests | Integration Tests | Manual Testing | +|-----------|------------|-------------------|----------------| +| Export (PNG/CSV/TXT) | N/A (UI) | N/A | ✅ Verified | +| Backup Manager CLI | ⏳ Pending | ⏳ Pending | ✅ Verified | +| Browser Notifications | N/A (browser API) | N/A | ✅ Verified | +| Python CLI Notifier | ⏳ Pending | ⏳ Pending | ✅ Verified | + +--- + +## Usage Examples + +### Export Training Data + +```bash +# 1. Open analytics dashboard +http://localhost:8000/analytics.html + +# 2. Click "Export" button in top controls +# 3. Choose export format: +# - PNG: High-quality chart images +# - CSV: Tabular data for analysis +# - TXT: Human-readable report + +# 4. Files download to browser's download folder +# - qai_lossChart_2024-11-24.png +# - qai_training_data_2024-11-24.csv +# - qai_report_2024-11-24.txt +``` + +### Create Pre-Deployment Backup + +```bash +# Full backup before deploying to production +python scripts/backup_manager.py create \ + --name pre_prod_v1 \ + --description "Checkpoint before production deployment" \ + --include-datasets + +# Output: +# ✅ Backup 'pre_prod_v1' created successfully +# Size: 342.56 MB (compressed) +# Files: 127 +# Checksum: a3f2d8e9c1b4f6a8... +``` + +### Monitor Training with Notifications + +```bash +# Terminal 1: Start training +python scripts/training/autotrain.py --job phi35_mixed_chat + +# Terminal 2: Monitor with notifications (optional) +python scripts/notification_system.py \ + --monitor data_out/autotrain/status.json \ + --job-name phi35_mixed_chat + +# You'll receive desktop notifications for: +# - Job started +# - Every 5 epochs +# - Loss drops below 0.5 +# - Job completion/failure +``` + +### Browser Notification Workflow + +``` +1. Open QAI Hub: http://localhost:8000/hub.html +2. Click notification toggle (🔕) in header +3. Allow browser permission when prompted +4. Toggle turns to 🔔 - notifications enabled +5. Start training job from unified dashboard +6. Minimize browser window +7. Receive desktop alerts automatically: + - 🚀 Training started + - 🎯 Milestones reached + - ✅ Training complete +``` + +--- + +## API Reference + +### Export Functions (JavaScript) + +```javascript +// analytics.html functions + +// Open export modal +exportCharts() + +// Export all charts as PNG +exportAsPNG() +// Downloads 4 files: +// - qai_lossChart_YYYY-MM-DD.png +// - qai_gpuChart_YYYY-MM-DD.png +// - qai_performanceChart_YYYY-MM-DD.png +// - qai_timeChart_YYYY-MM-DD.png + +// Export training data as CSV +exportAsCSV() +// Downloads: qai_training_data_YYYY-MM-DD.csv +// Columns: Job Name, Final Loss, Pre Loss, Post Loss, +// Improvement %, Duration (min), LoRA Rank + +// Export comprehensive report as TXT +exportReport() +// Downloads: qai_report_YYYY-MM-DD.txt +// Contains: Summary stats + detailed job results +``` + +### Backup Manager (CLI) + +```bash +# Create backup +python scripts/backup_manager.py create \ + --name \ + [--description "Optional description"] \ + [--no-models] [--no-configs] [--no-logs] \ + [--include-datasets] [--no-compress] + +# List backups +python scripts/backup_manager.py list + +# Restore backup +python scripts/backup_manager.py restore \ + --name \ + [--target-dir ] + +# Delete backup +python scripts/backup_manager.py delete \ + --name + +# Cleanup old backups +python scripts/backup_manager.py cleanup \ + [--keep ] # Default: 5 +``` + +### Notification System (Python) + +```python +from scripts.notification_system import NotificationManager + +notifier = NotificationManager() + +# Basic notification +notifier.send_notification( + title="Title", + message="Message body", + icon="info", # info/success/warning/error + duration=10 # seconds +) + +# Training events +notifier.notify_job_started("job_name") +notifier.notify_job_completed("job_name", duration_min=45, final_loss=0.234) +notifier.notify_job_failed("job_name", error="Error message") +notifier.notify_milestone("job_name", "Milestone description", value=0.456) + +# System alerts +notifier.notify_gpu_alert(gpu_util=97, memory_used=10240) +notifier.notify_backup_complete("backup_name", size_mb=342.56) +notifier.notify_evaluation_complete("model_name", perplexity=12.34) +``` + +### Notification System (Browser) + +```javascript +// unified.html, analytics.html, hub.html + +// Initialize (call on page load) +initNotifications() + +// Send notification +sendDesktopNotification(title, message, icon) +// icon: emoji string (🔔, 🚀, ✅, ❌, etc.) + +// Training events +notifyJobStarted(jobName) +notifyJobCompleted(jobName, durationMin, finalLoss) +notifyJobFailed(jobName, error) +notifyMilestone(jobName, milestone, value) + +// System alerts +notifyGPUAlert(gpuUtil, memoryUsed) + +// Toggle (hub.html only) +toggleNotifications() +updateNotificationUI() +``` + +--- + +## Integration with Existing Systems + +### Phase 23 Features + +**WebSocket Server** (`dashboard/websocket_server.py`): +- Future: Push notifications via WebSocket +- Real-time event streaming to all connected clients +- Eliminates polling delays + +**Job Queue** (`scripts/job_queue.py`): +- Trigger notifications on queue events: + - Job added to queue + - Job starts execution + - Job completes/fails + - Dependencies resolved + +**Analytics Dashboard** (`dashboard/analytics.html`): +- Export integrated into existing UI +- Notifications for data loading errors +- Real-time stats updates + +**Model Comparison** (Ctrl+M in unified.html): +- Future: Export comparison data as CSV +- Notification when comparison completes + +**Config Templates** (Ctrl+T in unified.html): +- Future: Backup/restore template library +- Notification on template save/load + +### Training Pipeline + +**AutoTrain** (`scripts/training/autotrain.py`): +```python +# Add notification support +from scripts.notification_system import NotificationManager +notifier = NotificationManager() + +# Before job +notifier.notify_job_started(job_name) + +# After job +if success: + notifier.notify_job_completed(job_name, duration, loss) +else: + notifier.notify_job_failed(job_name, error) +``` + +**Quantum AutoRun** (`scripts/evaluation/quantum_autorun.py`): +- Notifications for quantum job submissions +- Azure Quantum workspace connection status +- Job completion alerts (especially for paid QPU runs) + +**Evaluation AutoRun** (`scripts/evaluation/evaluation_autorun.py`): +- Notify when evaluation completes +- Perplexity threshold alerts +- Best model identification + +### CI/CD Pipeline + +**CI Orchestrator** (`scripts/orchestrators/ci_orchestrator.py`): +```bash +# Create backup before CI run +python scripts/backup_manager.py create --name pre_ci_$(date +%Y%m%d_%H%M%S) + +# Run CI pipeline +python scripts/orchestrators/ci_orchestrator.py --ci-pipeline + +# On success: notification +# On failure: notification + restore from backup +``` + +--- + +## Best Practices + +### Backup Strategy + +**Frequency:** +- Before every production deployment +- After successful training runs +- Before major configuration changes +- Weekly scheduled backups (cron/Task Scheduler) + +**Retention:** +```bash +# Keep last 5 backups, delete older +python scripts/backup_manager.py cleanup --keep 5 +``` + +**Naming Convention:** +``` +pre_prod_YYYYMMDD # Production checkpoints +experiment_NAME_v1 # Experimental runs +milestone_FEATURE # Feature milestones +daily_backup_YYYYMMDD # Scheduled backups +``` + +**Recovery Testing:** +```bash +# Periodic restore drills +python scripts/backup_manager.py restore --name test_backup --target-dir ./restore_test +# Verify restored files +# Delete test directory +``` + +### Notification Etiquette + +**Do:** +- Enable for production training runs +- Set reasonable milestone intervals (5-10 epochs) +- Use GPU alerts to prevent resource exhaustion +- Test notifications before long runs + +**Don't:** +- Spam notifications (every epoch on short runs) +- Enable for debug/test runs +- Send notifications during working hours for overnight jobs +- Ignore permission denials (respect user choice) + +### Export Workflows + +**For Stakeholders:** +1. Export TXT report (human-readable) +2. Share via email/Slack +3. Include summary stats and best models + +**For Analysis:** +1. Export CSV data +2. Import into Python/R/Excel +3. Run statistical analysis +4. Generate custom visualizations + +**For Presentations:** +1. Export PNG charts +2. Include in PowerPoint/Google Slides +3. Annotate with insights +4. Combine with TXT report metrics + +--- + +## Troubleshooting + +### Export Issues + +**Problem**: Charts export as blank PNGs + +**Solution**: +- Ensure Chart.js has finished rendering +- Increase delay in `exportAsPNG()` (current: 500ms) +- Check browser console for canvas errors + +**Problem**: CSV downloads corrupted + +**Solution**: +- Verify CSV MIME type: `text/csv` +- Check for special characters in job names +- Escape commas in data values + +### Backup Issues + +**Problem**: Backup fails with "Permission denied" + +**Solution**: +```bash +# Check directory permissions +icacls backups + +# Create backups directory manually +mkdir backups + +# Run with elevated permissions (Windows) +runas /user:Administrator "python scripts/backup_manager.py create --name test" +``` + +**Problem**: Restore fails checksum verification + +**Solution**: +- Indicates corrupted backup archive +- Restore from previous backup +- Check disk integrity (chkdsk/fsck) + +### Notification Issues + +**Problem**: Browser notifications blocked + +**Solution**: +1. Check permission in browser settings +2. Clear site data and request permission again +3. Try different browser (Chrome/Firefox/Edge) + +**Problem**: Python notifications not appearing (Windows) + +**Solution**: +```bash +# Install win10toast +pip install win10toast + +# Test notification +python scripts/notification_system.py --test +``` + +**Problem**: Notifications appear on wrong screen (multi-monitor) + +**Solution**: +- OS-level setting (Windows Notification Settings) +- Move browser to desired monitor +- Notifications appear on screen with active window + +--- + +## Future Enhancements + +### Planned Features (Phase 25+) + +1. **Hyperparameter Tuning Wizard** (Todo #4): + - Interactive UI for parameter exploration + - Bayesian optimization integration + - Grid search automation + - Best parameter recommendation + +2. **Dark Mode Toggle** (Todo #5): + - Light/dark theme switcher + - localStorage persistence + - Apply to all dashboards (hub, unified, analytics) + - Smooth transitions + +3. **Enhanced Export**: + - PDF report generation + - Multi-format batch export + - Scheduled exports (cron) + - Cloud storage integration (Azure Blob) + +4. **Advanced Backups**: + - Incremental backups (only changed files) + - Cloud backup support (Azure Storage) + - Encryption (AES-256) + - Remote restore capability + +5. **Notification Improvements**: + - Persistent toggle state (localStorage) + - Notification history panel + - Customizable notification sounds + - Email/Slack integration + - Grouped notifications (batch updates) + - Rich notifications with action buttons + +--- + +## Documentation + +### New Documents + +1. **NOTIFICATION_SYSTEM_GUIDE.md** (This document): + - Comprehensive guide to notification system + - Browser and Python CLI usage + - API reference and troubleshooting + - 16 sections, 400+ lines + +2. **QAI_PHASE_24_IMPROVEMENTS.md** (This summary): + - Overview of 3 completed features + - Code metrics and statistics + - Usage examples and API reference + - Integration guides + +### Updated Documents + +Files to update with Phase 24 information: +- `README.md` - Add export, backup, notification sections +- `AUTOMATION_QUICKREF.md` - Include backup commands +- `QAI_HUB_ENHANCEMENTS_V2.md` - Update with notification toggle +- `.github/copilot-instructions.md` - Add new tool descriptions + +--- + +## Deployment Checklist + +### Pre-Deployment + +- [x] Export functionality tested (PNG/CSV/TXT) +- [x] Backup manager CLI tested (create/restore/delete/cleanup) +- [x] Browser notifications tested (Chrome/Firefox/Edge) +- [x] Python CLI notifier tested (Windows/macOS/Linux) +- [x] Hub toggle button tested +- [x] Documentation created (2 comprehensive guides) +- [ ] Unit tests created for backup manager +- [ ] Integration tests for notification system +- [ ] Update main README.md +- [ ] Update AUTOMATION_QUICKREF.md + +### Post-Deployment + +- [ ] Monitor backup disk usage +- [ ] Track notification delivery rates +- [ ] Collect user feedback on exports +- [ ] Performance monitoring (export speed) +- [ ] Error tracking (Sentry/Application Insights) + +--- + +## Summary + +Phase 24 successfully delivered **3 production-ready features** in rapid succession: + +1. **Export Functionality** - Data portability with PNG/CSV/TXT formats +2. **Backup Manager** - Enterprise-grade backup/restore with compression +3. **Desktop Notifications** - Real-time alerts for training events + +**Total Impact:** +- 1,070+ lines of production code +- 4 files modified, 3 new files created +- 2 comprehensive documentation guides +- Zero breaking changes to existing features +- Full backward compatibility maintained + +**User Benefits:** +- Share training results easily (export) +- Protect data from loss (backup) +- Stay informed without monitoring (notifications) +- Production-ready tooling +- Professional reporting capabilities + +**Next Steps:** +- Implement hyperparameter tuning wizard (Todo #4) +- Add dark mode toggle (Todo #5) +- Enhance features based on user feedback +- Continue "keep improving" directive + +--- + +**Phase 24 Status**: ✅ **COMPLETE** +**Features Delivered**: 3/5 (60%) +**Code Quality**: Production-ready +**Documentation**: Comprehensive +**User Value**: High + +**Ready for Phase 25** 🚀 + +--- + +**Last Updated**: November 2024 +**Maintained By**: QAI Development Team diff --git a/docs/aria-nav.js b/docs/aria-nav.js index 3eae1282b..860f6feef 100644 --- a/docs/aria-nav.js +++ b/docs/aria-nav.js @@ -1,99 +1,99 @@ -// Aria shared navigation bar — injected into all docs sub-pages. -// Usage: (adjusts paths based on depth) -(function () { - 'use strict'; - - // Detect depth: how many levels deep from docs/ root - var path = location.pathname; - var docsIdx = path.indexOf('/docs/'); - var prefix = '../'; - if (docsIdx !== -1) { - var sub = path.substring(docsIdx + 6); // after /docs/ - var depth = (sub.match(/\//g) || []).length; - if (sub.endsWith('/')) depth--; - prefix = depth > 0 ? '../'.repeat(depth) : './'; - } - // Fallback for local file or simple relative - if (!prefix) prefix = '../'; - - // Determine current section for active highlight - var current = ''; - if (/\/aria\//.test(path)) current = 'aria'; - else if (/\/chat\//.test(path)) current = 'chat'; - else if (/\/dashboard\//.test(path)) current = 'dashboard'; - else if (/\/quantum\//.test(path)) current = 'quantum'; - else if (/\/store\//.test(path)) current = 'store'; - else if (/\/monetization\//.test(path)) current = 'monetization'; - else if (/documentation/.test(path)) current = 'docs'; - - var links = [ - { label: 'Home', href: prefix, id: 'home' }, - { label: 'Docs', href: prefix + 'documentation.html', id: 'docs' }, - { label: 'Aria', href: prefix + 'aria/', id: 'aria' }, - { label: 'Chat', href: prefix + 'chat/', id: 'chat' }, - { label: 'Dashboard', href: prefix + 'dashboard/', id: 'dashboard' }, - { label: 'Quantum', href: prefix + 'quantum/', id: 'quantum' }, - { label: 'Store', href: prefix + 'store/', id: 'store' }, - { label: 'GitHub', href: 'https://github.com/Bryan-Roe/Aria', id: 'github', external: true }, - ]; - - var navHTML = ''; - - // Inject responsive CSS - var style = document.createElement('style'); - style.textContent = - '@media(max-width:768px){' + - '#aria-nav-toggle{display:block!important}' + - '#aria-nav-links{display:none!important;position:absolute;top:100%;left:0;right:0;' + - 'background:rgba(15,11,30,.95);padding:12px 20px;flex-direction:column;gap:4px;' + - 'border-bottom:1px solid rgba(255,255,255,.06)}' + - '#aria-nav-links.open{display:flex!important}' + - '}'; - document.head.appendChild(style); - - // Insert at top of body - var temp = document.createElement('div'); - temp.innerHTML = navHTML; - var navEl = temp.firstChild; - document.body.insertBefore(navEl, document.body.firstChild); - - // Remove old back-links that are now redundant - var oldLinks = document.querySelectorAll('a.back-link, a[href="../"]'); - for (var j = 0; j < oldLinks.length; j++) { - var el = oldLinks[j]; - var text = (el.textContent || '').trim(); - if (text === '← Back to Aria Home' || text === '\u2190 Back to Aria Home') { - el.style.display = 'none'; - } - } -})(); +// Aria shared navigation bar — injected into all docs sub-pages. +// Usage: (adjusts paths based on depth) +(function () { + 'use strict'; + + // Detect depth: how many levels deep from docs/ root + var path = location.pathname; + var docsIdx = path.indexOf('/docs/'); + var prefix = '../'; + if (docsIdx !== -1) { + var sub = path.substring(docsIdx + 6); // after /docs/ + var depth = (sub.match(/\//g) || []).length; + if (sub.endsWith('/')) depth--; + prefix = depth > 0 ? '../'.repeat(depth) : './'; + } + // Fallback for local file or simple relative + if (!prefix) prefix = '../'; + + // Determine current section for active highlight + var current = ''; + if (/\/aria\//.test(path)) current = 'aria'; + else if (/\/chat\//.test(path)) current = 'chat'; + else if (/\/dashboard\//.test(path)) current = 'dashboard'; + else if (/\/quantum\//.test(path)) current = 'quantum'; + else if (/\/store\//.test(path)) current = 'store'; + else if (/\/monetization\//.test(path)) current = 'monetization'; + else if (/documentation/.test(path)) current = 'docs'; + + var links = [ + { label: 'Home', href: prefix, id: 'home' }, + { label: 'Docs', href: prefix + 'documentation.html', id: 'docs' }, + { label: 'Aria', href: prefix + 'aria/', id: 'aria' }, + { label: 'Chat', href: prefix + 'chat/', id: 'chat' }, + { label: 'Dashboard', href: prefix + 'dashboard/', id: 'dashboard' }, + { label: 'Quantum', href: prefix + 'quantum/', id: 'quantum' }, + { label: 'Store', href: prefix + 'store/', id: 'store' }, + { label: 'GitHub', href: 'https://github.com/Bryan-Roe/Aria', id: 'github', external: true }, + ]; + + var navHTML = ''; + + // Inject responsive CSS + var style = document.createElement('style'); + style.textContent = + '@media(max-width:768px){' + + '#aria-nav-toggle{display:block!important}' + + '#aria-nav-links{display:none!important;position:absolute;top:100%;left:0;right:0;' + + 'background:rgba(15,11,30,.95);padding:12px 20px;flex-direction:column;gap:4px;' + + 'border-bottom:1px solid rgba(255,255,255,.06)}' + + '#aria-nav-links.open{display:flex!important}' + + '}'; + document.head.appendChild(style); + + // Insert at top of body + var temp = document.createElement('div'); + temp.innerHTML = navHTML; + var navEl = temp.firstChild; + document.body.insertBefore(navEl, document.body.firstChild); + + // Remove old back-links that are now redundant + var oldLinks = document.querySelectorAll('a.back-link, a[href="../"]'); + for (var j = 0; j < oldLinks.length; j++) { + var el = oldLinks[j]; + var text = (el.textContent || '').trim(); + if (text === '← Back to Aria Home' || text === '\u2190 Back to Aria Home') { + el.style.display = 'none'; + } + } +})(); diff --git a/docs/aria/ARIA_MOVEMENT_COMPLETE.md b/docs/aria/ARIA_MOVEMENT_COMPLETE.md index c34abe7b5..1192e2d83 100644 --- a/docs/aria/ARIA_MOVEMENT_COMPLETE.md +++ b/docs/aria/ARIA_MOVEMENT_COMPLETE.md @@ -1,543 +1,543 @@ -# Aria Movement AI - Complete Implementation Guide - -## 🎯 Mission Accomplished - -The AI has been automated to generate Aria character movement commands! This document provides the complete implementation overview. - -## 📦 What Was Built - -### 1. Training Dataset -**Location**: `datasets/chat/aria_movement/` - -The dataset teaches the AI to recognize natural language movement requests and respond with structured command tags that can be parsed by the Azure Function. - -**Coverage**: -- **40 training examples** with diverse phrasings -- **10 test examples** for validation -- **6 command types**: move, walk, center, wave, jump, dance -- **4 directions**: left, right, up, down -- **Natural language variations**: "shift", "slide", "bring", "take" - -**Format Example**: -```json -{ - "messages": [ - {"role": "user", "content": "Move Aria to the left"}, - {"role": "assistant", "content": "I'll move Aria to the left. [aria:move:left]"} - ] -} -``` - -### 2. Training Infrastructure - -#### Direct Training Script -**File**: `scripts/train_aria_direct.py` -- Simplified, reliable training approach -- No orchestrator complexity -- Direct Hugging Face Transformers + PEFT integration -- Built-in generation testing -- Complete progress reporting - -**Usage**: -```powershell -python .\scripts\train_aria_direct.py -``` - -#### Orchestrator Integration -**File**: `autotrain_aria.yaml` -- 3 training configurations (quick, full, qwen) -- Integrates with existing autotrain.py orchestrator -- Supports batch job execution - -**Usage**: -```powershell -python .\scripts\autotrain.py --config autotrain_aria.yaml --job aria_movement_quick -``` - -#### Full Automation Pipeline -**File**: `scripts/automate_aria_movement.py` -- End-to-end pipeline: validate → train → evaluate → deploy -- Dataset validation with coverage analysis -- Auto-deployment to production location -- Comprehensive error handling - -**Usage**: -```powershell -# Full pipeline with deployment -python .\scripts\automate_aria_movement.py --quick --deploy - -# Validate only -python .\scripts\automate_aria_movement.py --validate-only -``` - -### 3. Testing & Validation - -#### Dataset Validator -**File**: `scripts/test_aria_dataset.py` -- Validates dataset structure and format -- Analyzes command coverage and distribution -- Optional base model testing -- Provides next-step guidance - -**Usage**: -```powershell -# Quick validation -python .\scripts\test_aria_dataset.py --validate-only - -# Test base model generation (slow) -python .\scripts\test_aria_dataset.py --test-model -``` - -**Validation Results**: -``` -Command Type Coverage: - ✅ move: 14 examples - ✅ walk: 13 examples - ✅ center: 4 examples - ✅ wave: 5 examples - ✅ jump: 4 examples - ✅ dance: 4 examples - -Direction Coverage: - ✅ left: 7 examples - ✅ right: 7 examples - ✅ up: 7 examples - ✅ down: 6 examples - -Total command tags: 44 -``` - -### 4. Documentation -**File**: `ARIA_MOVEMENT_TRAINING.md` -- Complete training guide -- Integration instructions -- Troubleshooting tips -- Testing procedures -- Deployment steps - -## 🔄 How the Complete System Works - -### Architecture Flow - -``` -┌─────────────────┐ -│ User Input │ "Move Aria left" -└────────┬────────┘ - │ - ▼ -┌─────────────────────────────────┐ -│ Azure Function /api/chat │ -│ (Streaming SSE Endpoint) │ -└────────┬────────────────────────┘ - │ - ▼ -┌─────────────────────────────────┐ -│ AI Model (LoRA Adapter) │ -│ Generates: "Moving left! │ -│ [aria:move:left]" │ -└────────┬────────────────────────┘ - │ - ▼ -┌─────────────────────────────────┐ -│ parse_movement_commands() │ -│ (function_app.py lines 515-565)│ -│ Extracts: {action: move, │ -│ direction: left, distance:100}│ -└────────┬────────────────────────┘ - │ - ▼ -┌─────────────────────────────────┐ -│ SSE Event Emission │ -│ (function_app.py lines 640-690)│ -│ Sends: event: movement │ -│ data: {"commands": [...]} │ -└────────┬────────────────────────┘ - │ - ▼ -┌─────────────────────────────────┐ -│ Frontend JavaScript │ -│ EventSource listener │ -│ Executes animation │ -└─────────────────────────────────┘ -``` - -### Command Syntax - -The AI generates tags in this format: `[aria:action:direction]` - -**Supported Commands**: -- `[aria:move:left]` - Move 100px left -- `[aria:move:right]` - Move 100px right -- `[aria:move:up]` - Move 100px up -- `[aria:move:down]` - Move 100px down -- `[aria:walk:left]` - Walk 200px left (larger distance) -- `[aria:walk:right]` - Walk 200px right -- `[aria:walk:up]` - Walk 200px up -- `[aria:walk:down]` - Walk 200px down -- `[aria:center]` - Center character on screen -- `[aria:wave]` - Play wave animation -- `[aria:jump]` - Play jump animation -- `[aria:dance]` - Play dance animation - -**Multi-Command Support**: -The AI can generate multiple commands in sequence: -``` -User: "Go to center and wave" -AI: "Centering Aria and waving! [aria:center] [aria:wave]" -``` - -## 🚀 Training Execution - -### Current Training Status -**Script**: `scripts/train_aria_direct.py` (CURRENTLY RUNNING) - -**Configuration**: -- Model: microsoft/Phi-3.5-mini-instruct -- Samples: 40 train, 10 eval -- Epochs: 2 -- Learning rate: 0.0003 -- LoRA dropout: 0.05 -- Device: CPU (auto-detects CUDA if available) - -**Training Process**: -1. ✅ Dataset loaded and validated -2. ✅ Tokenizer configured -3. ✅ Chat format conversion complete -4. 🔄 Loading base model (in progress) -5. ⏳ LoRA configuration pending -6. ⏳ Training epochs pending -7. ⏳ Generation testing pending -8. ⏳ Model save pending - -**Expected Timeline**: -- CPU: 15-30 minutes total -- GPU (CUDA): 5-10 minutes total - -### Training Output Location -**Primary Output**: `data_out/aria_models/aria_direct/` - -**Files Generated**: -- `adapter_config.json` - LoRA configuration -- `adapter_model.safetensors` - Trained weights -- `tokenizer_config.json` - Tokenizer settings -- `training_info.json` - Training metadata -- Checkpoint directories (per epoch) - -## 🧪 Testing the Trained Model - -### Using Chat CLI -```powershell -# Load the trained adapter -python .\talk-to-ai\src\chat_cli.py --provider lora --model data_out\aria_models\aria_direct - -# Test commands -> Move Aria left -> Make her wave -> Walk right then jump -> Go to center and dance -``` - -**Expected Behavior**: -- AI responses should include `[aria:...]` tags -- Tags should match the requested actions -- Natural language should be preserved around tags - -### Using Azure Function Endpoint -```powershell -# Start the function app -func host start - -# Test via HTTP (streaming endpoint) -# Navigate to http://localhost:7071/chat-web -# Enter movement commands in chat -# Watch console for "movement" events -``` - -### Manual Testing Checklist -- [ ] Move left command generates `[aria:move:left]` -- [ ] Move right command generates `[aria:move:right]` -- [ ] Move up command generates `[aria:move:up]` -- [ ] Move down command generates `[aria:move:down]` -- [ ] Walk commands generate with larger distances -- [ ] Center command generates `[aria:center]` -- [ ] Wave command generates `[aria:wave]` -- [ ] Jump command generates `[aria:jump]` -- [ ] Dance command generates `[aria:dance]` -- [ ] Multi-command sequences work -- [ ] Natural language variations recognized - -## 📋 Deployment Steps - -### 1. Verify Training Success -```powershell -# Check output directory exists -Test-Path data_out\aria_models\aria_direct\adapter_model.safetensors - -# Should return: True -``` - -### 2. Backup Current Model (Optional) -```powershell -$timestamp = Get-Date -Format "yyyyMMdd_HHmmss" -Copy-Item -Recurse data_out\lora_training\lora_adapter "data_out\lora_training\lora_adapter_backup_$timestamp" -``` - -### 3. Deploy New Model -```powershell -# Remove old adapter -Remove-Item -Recurse -Force data_out\lora_training\lora_adapter -ErrorAction SilentlyContinue - -# Copy new adapter -Copy-Item -Recurse data_out\aria_models\aria_direct data_out\lora_training\lora_adapter - -# Verify deployment -Test-Path data_out\lora_training\lora_adapter\adapter_model.safetensors -# Should return: True -``` - -### 4. Restart Azure Functions -```powershell -# Stop any running functions -Get-Process -Name "func" -ErrorAction SilentlyContinue | Stop-Process -Force - -# Start with new model -func host start -``` - -### 5. Verify Integration -```powershell -# Test the /api/chat endpoint -Invoke-WebRequest -Uri "http://localhost:7071/api/ai/status" | Select-Object -ExpandProperty Content | ConvertFrom-Json - -# Check that lora_adapter is loaded -``` - -## 🔍 Monitoring & Debugging - -### Training Logs -```powershell -# View training output -Get-Content data_out\aria_models\aria_direct\training.log -Tail 50 - -# Watch for errors -Get-Content data_out\aria_models\aria_direct\training.log | Select-String "error|Error|ERROR" -``` - -### Function App Logs -```powershell -# Check function startup -func host start 2>&1 | Tee-Object -FilePath "function_startup.log" - -# Monitor real-time -# Console will show SSE events including "movement" events -``` - -### Parser Testing -Test the command parser directly: -```python -# In Python REPL or script -from function_app import parse_movement_commands - -# Test parsing -text = "Moving Aria left! [aria:move:left]" -result = parse_movement_commands(text) -print(result) -# Expected: {'commands': [{'action': 'move', 'direction': 'left', 'distance': 100}]} -``` - -## 📊 Evaluation Metrics - -### Perplexity -- **Target**: < 3.0 for good command learning -- **Measures**: How well model predicts command syntax -- Lower is better - -### Diversity -- **Distinct-1**: Unique unigrams (target: 0.3-0.5) -- **Distinct-2**: Unique bigrams (target: 0.3-0.5) -- **Measures**: Response variety and naturalness - -### Command Accuracy -- **Target**: 90%+ correct tag generation -- **Manual testing required** -- Generate 10 responses, count correct tags - -### Run Evaluation Script -```powershell -python .\scripts\evaluate_lora_model.py ` - --dataset datasets\chat\aria_movement ` - --model data_out\aria_models\aria_direct ` - --max-samples 10 ` - --metric perplexity ` - --metric diversity ` - --output-format json -``` - -## 🛠️ Troubleshooting - -### Training Fails with CUDA Errors -```powershell -# Check CUDA availability -python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')" - -# Force CPU training -# Edit train_aria_direct.py: change device logic or run on CPU -``` - -### Model Doesn't Generate Tags -**Possible causes**: -1. Training loss didn't converge (check logs) -2. Not enough epochs (try 3-4 instead of 2) -3. Learning rate too low/high -4. Dataset format issue - -**Solutions**: -```powershell -# Re-validate dataset -python .\scripts\test_aria_dataset.py --validate-only - -# Check training loss in logs -Get-Content data_out\aria_models\aria_direct\training.log | Select-String "loss" - -# Retrain with more epochs -# Edit train_aria_direct.py: epochs = 3 -``` - -### Commands Not Parsed in Function App -**Check**: -1. Exact tag syntax matches parser expectations -2. Case sensitivity (parser converts to lowercase) -3. Proper spacing: `[aria:move:left]` not `[aria:moveleft]` - -**Debug**: -```python -# Test parser directly -from function_app import parse_movement_commands -test_text = "I'll move Aria left! [aria:move:left]" -print(parse_movement_commands(test_text)) -``` - -### Frontend Not Receiving Events -**Verify**: -1. SSE connection established to `/api/chat-stream` -2. EventSource listening for "movement" events -3. Function app console shows event emission - -**Debug JavaScript**: -```javascript -const eventSource = new EventSource('/api/chat-stream'); -eventSource.addEventListener('movement', (event) => { - console.log('Movement event received:', event.data); - const commands = JSON.parse(event.data); - console.log('Commands:', commands); -}); - -eventSource.addEventListener('error', (error) => { - console.error('SSE error:', error); -}); -``` - -## 🔄 Continuous Improvement - -### Monitoring Usage -1. Log all movement requests and AI responses -2. Track when tags aren't generated correctly -3. Identify new movement patterns users request - -### Dataset Augmentation -```powershell -# Add new examples to datasets/chat/aria_movement/train.json -# Follow existing format: -{ - "messages": [ - {"role": "user", "content": "New movement request"}, - {"role": "assistant", "content": "Response with [aria:command]"} - ] -} -``` - -### Retraining Schedule -- **Weekly**: If adding 10+ new examples -- **Monthly**: Maintenance retraining -- **On-demand**: When accuracy drops below 90% - -### A/B Testing -```powershell -# Keep old model as baseline -Copy-Item -Recurse data_out\lora_training\lora_adapter data_out\aria_models\baseline - -# Train new model -python .\scripts\train_aria_direct.py - -# Compare with evaluation script -python .\scripts\evaluate_lora_model.py --model data_out\aria_models\baseline -python .\scripts\evaluate_lora_model.py --model data_out\aria_models\aria_direct - -# Deploy better performing model -``` - -## 📁 File Reference - -### Core Implementation -- `datasets/chat/aria_movement/` - Training data -- `scripts/train_aria_direct.py` - Direct training script -- `scripts/automate_aria_movement.py` - Full automation pipeline -- `scripts/test_aria_dataset.py` - Dataset validation -- `autotrain_aria.yaml` - Orchestrator configuration - -### Integration Points -- `function_app.py` (lines 515-565) - Command parser -- `function_app.py` (lines 640-690) - SSE streaming with movement detection -- `chat-web/index.html` - Frontend (needs EventSource integration) - -### Output Locations -- `data_out/aria_models/aria_direct/` - Trained model -- `data_out/lora_training/lora_adapter/` - Production deployment location -- `data_out/autotrain/aria_movement_quick/` - Orchestrator logs (if using autotrain.py) - -### Documentation -- `ARIA_MOVEMENT_TRAINING.md` - Quick reference guide -- This file: Complete implementation guide - -## 🎉 Success Criteria - -The implementation is successful when: - -1. ✅ **Dataset validated** with complete command coverage -2. 🔄 **Training completed** without errors (IN PROGRESS) -3. ⏳ **Model generates** `[aria:...]` tags consistently -4. ⏳ **Parser extracts** commands correctly -5. ⏳ **SSE events emitted** with command JSON -6. ⏳ **Frontend receives** and processes movement events -7. ⏳ **Character animates** based on AI commands -8. ⏳ **End-to-end flow** works seamlessly - -## 🚀 Next Steps (After Training Completes) - -1. **Verify Training Results** - - Check generation test output in console - - Verify adapter files created - -2. **Test with Chat CLI** - - Load adapter and test responses - - Confirm tag generation - -3. **Deploy to Production** - - Copy adapter to active location - - Restart Azure Functions - -4. **Integration Testing** - - Test via /api/chat endpoint - - Verify SSE movement events - - Check frontend animation - -5. **Monitor and Iterate** - - Log usage patterns - - Collect edge cases - - Augment dataset as needed - ---- - -**Status**: Training in progress (train_aria_direct.py running) -**Created**: November 27, 2025 -**Last Updated**: November 27, 2025 +# Aria Movement AI - Complete Implementation Guide + +## 🎯 Mission Accomplished + +The AI has been automated to generate Aria character movement commands! This document provides the complete implementation overview. + +## 📦 What Was Built + +### 1. Training Dataset +**Location**: `datasets/chat/aria_movement/` + +The dataset teaches the AI to recognize natural language movement requests and respond with structured command tags that can be parsed by the Azure Function. + +**Coverage**: +- **40 training examples** with diverse phrasings +- **10 test examples** for validation +- **6 command types**: move, walk, center, wave, jump, dance +- **4 directions**: left, right, up, down +- **Natural language variations**: "shift", "slide", "bring", "take" + +**Format Example**: +```json +{ + "messages": [ + {"role": "user", "content": "Move Aria to the left"}, + {"role": "assistant", "content": "I'll move Aria to the left. [aria:move:left]"} + ] +} +``` + +### 2. Training Infrastructure + +#### Direct Training Script +**File**: `scripts/train_aria_direct.py` +- Simplified, reliable training approach +- No orchestrator complexity +- Direct Hugging Face Transformers + PEFT integration +- Built-in generation testing +- Complete progress reporting + +**Usage**: +```powershell +python .\scripts\train_aria_direct.py +``` + +#### Orchestrator Integration +**File**: `autotrain_aria.yaml` +- 3 training configurations (quick, full, qwen) +- Integrates with existing autotrain.py orchestrator +- Supports batch job execution + +**Usage**: +```powershell +python .\scripts\autotrain.py --config autotrain_aria.yaml --job aria_movement_quick +``` + +#### Full Automation Pipeline +**File**: `scripts/automate_aria_movement.py` +- End-to-end pipeline: validate → train → evaluate → deploy +- Dataset validation with coverage analysis +- Auto-deployment to production location +- Comprehensive error handling + +**Usage**: +```powershell +# Full pipeline with deployment +python .\scripts\automate_aria_movement.py --quick --deploy + +# Validate only +python .\scripts\automate_aria_movement.py --validate-only +``` + +### 3. Testing & Validation + +#### Dataset Validator +**File**: `scripts/test_aria_dataset.py` +- Validates dataset structure and format +- Analyzes command coverage and distribution +- Optional base model testing +- Provides next-step guidance + +**Usage**: +```powershell +# Quick validation +python .\scripts\test_aria_dataset.py --validate-only + +# Test base model generation (slow) +python .\scripts\test_aria_dataset.py --test-model +``` + +**Validation Results**: +``` +Command Type Coverage: + ✅ move: 14 examples + ✅ walk: 13 examples + ✅ center: 4 examples + ✅ wave: 5 examples + ✅ jump: 4 examples + ✅ dance: 4 examples + +Direction Coverage: + ✅ left: 7 examples + ✅ right: 7 examples + ✅ up: 7 examples + ✅ down: 6 examples + +Total command tags: 44 +``` + +### 4. Documentation +**File**: `ARIA_MOVEMENT_TRAINING.md` +- Complete training guide +- Integration instructions +- Troubleshooting tips +- Testing procedures +- Deployment steps + +## 🔄 How the Complete System Works + +### Architecture Flow + +``` +┌─────────────────┐ +│ User Input │ "Move Aria left" +└────────┬────────┘ + │ + ▼ +┌─────────────────────────────────┐ +│ Azure Function /api/chat │ +│ (Streaming SSE Endpoint) │ +└────────┬────────────────────────┘ + │ + ▼ +┌─────────────────────────────────┐ +│ AI Model (LoRA Adapter) │ +│ Generates: "Moving left! │ +│ [aria:move:left]" │ +└────────┬────────────────────────┘ + │ + ▼ +┌─────────────────────────────────┐ +│ parse_movement_commands() │ +│ (function_app.py lines 515-565)│ +│ Extracts: {action: move, │ +│ direction: left, distance:100}│ +└────────┬────────────────────────┘ + │ + ▼ +┌─────────────────────────────────┐ +│ SSE Event Emission │ +│ (function_app.py lines 640-690)│ +│ Sends: event: movement │ +│ data: {"commands": [...]} │ +└────────┬────────────────────────┘ + │ + ▼ +┌─────────────────────────────────┐ +│ Frontend JavaScript │ +│ EventSource listener │ +│ Executes animation │ +└─────────────────────────────────┘ +``` + +### Command Syntax + +The AI generates tags in this format: `[aria:action:direction]` + +**Supported Commands**: +- `[aria:move:left]` - Move 100px left +- `[aria:move:right]` - Move 100px right +- `[aria:move:up]` - Move 100px up +- `[aria:move:down]` - Move 100px down +- `[aria:walk:left]` - Walk 200px left (larger distance) +- `[aria:walk:right]` - Walk 200px right +- `[aria:walk:up]` - Walk 200px up +- `[aria:walk:down]` - Walk 200px down +- `[aria:center]` - Center character on screen +- `[aria:wave]` - Play wave animation +- `[aria:jump]` - Play jump animation +- `[aria:dance]` - Play dance animation + +**Multi-Command Support**: +The AI can generate multiple commands in sequence: +``` +User: "Go to center and wave" +AI: "Centering Aria and waving! [aria:center] [aria:wave]" +``` + +## 🚀 Training Execution + +### Current Training Status +**Script**: `scripts/train_aria_direct.py` (CURRENTLY RUNNING) + +**Configuration**: +- Model: microsoft/Phi-3.5-mini-instruct +- Samples: 40 train, 10 eval +- Epochs: 2 +- Learning rate: 0.0003 +- LoRA dropout: 0.05 +- Device: CPU (auto-detects CUDA if available) + +**Training Process**: +1. ✅ Dataset loaded and validated +2. ✅ Tokenizer configured +3. ✅ Chat format conversion complete +4. 🔄 Loading base model (in progress) +5. ⏳ LoRA configuration pending +6. ⏳ Training epochs pending +7. ⏳ Generation testing pending +8. ⏳ Model save pending + +**Expected Timeline**: +- CPU: 15-30 minutes total +- GPU (CUDA): 5-10 minutes total + +### Training Output Location +**Primary Output**: `data_out/aria_models/aria_direct/` + +**Files Generated**: +- `adapter_config.json` - LoRA configuration +- `adapter_model.safetensors` - Trained weights +- `tokenizer_config.json` - Tokenizer settings +- `training_info.json` - Training metadata +- Checkpoint directories (per epoch) + +## 🧪 Testing the Trained Model + +### Using Chat CLI +```powershell +# Load the trained adapter +python .\talk-to-ai\src\chat_cli.py --provider lora --model data_out\aria_models\aria_direct + +# Test commands +> Move Aria left +> Make her wave +> Walk right then jump +> Go to center and dance +``` + +**Expected Behavior**: +- AI responses should include `[aria:...]` tags +- Tags should match the requested actions +- Natural language should be preserved around tags + +### Using Azure Function Endpoint +```powershell +# Start the function app +func host start + +# Test via HTTP (streaming endpoint) +# Navigate to http://localhost:7071/chat-web +# Enter movement commands in chat +# Watch console for "movement" events +``` + +### Manual Testing Checklist +- [ ] Move left command generates `[aria:move:left]` +- [ ] Move right command generates `[aria:move:right]` +- [ ] Move up command generates `[aria:move:up]` +- [ ] Move down command generates `[aria:move:down]` +- [ ] Walk commands generate with larger distances +- [ ] Center command generates `[aria:center]` +- [ ] Wave command generates `[aria:wave]` +- [ ] Jump command generates `[aria:jump]` +- [ ] Dance command generates `[aria:dance]` +- [ ] Multi-command sequences work +- [ ] Natural language variations recognized + +## 📋 Deployment Steps + +### 1. Verify Training Success +```powershell +# Check output directory exists +Test-Path data_out\aria_models\aria_direct\adapter_model.safetensors + +# Should return: True +``` + +### 2. Backup Current Model (Optional) +```powershell +$timestamp = Get-Date -Format "yyyyMMdd_HHmmss" +Copy-Item -Recurse data_out\lora_training\lora_adapter "data_out\lora_training\lora_adapter_backup_$timestamp" +``` + +### 3. Deploy New Model +```powershell +# Remove old adapter +Remove-Item -Recurse -Force data_out\lora_training\lora_adapter -ErrorAction SilentlyContinue + +# Copy new adapter +Copy-Item -Recurse data_out\aria_models\aria_direct data_out\lora_training\lora_adapter + +# Verify deployment +Test-Path data_out\lora_training\lora_adapter\adapter_model.safetensors +# Should return: True +``` + +### 4. Restart Azure Functions +```powershell +# Stop any running functions +Get-Process -Name "func" -ErrorAction SilentlyContinue | Stop-Process -Force + +# Start with new model +func host start +``` + +### 5. Verify Integration +```powershell +# Test the /api/chat endpoint +Invoke-WebRequest -Uri "http://localhost:7071/api/ai/status" | Select-Object -ExpandProperty Content | ConvertFrom-Json + +# Check that lora_adapter is loaded +``` + +## 🔍 Monitoring & Debugging + +### Training Logs +```powershell +# View training output +Get-Content data_out\aria_models\aria_direct\training.log -Tail 50 + +# Watch for errors +Get-Content data_out\aria_models\aria_direct\training.log | Select-String "error|Error|ERROR" +``` + +### Function App Logs +```powershell +# Check function startup +func host start 2>&1 | Tee-Object -FilePath "function_startup.log" + +# Monitor real-time +# Console will show SSE events including "movement" events +``` + +### Parser Testing +Test the command parser directly: +```python +# In Python REPL or script +from function_app import parse_movement_commands + +# Test parsing +text = "Moving Aria left! [aria:move:left]" +result = parse_movement_commands(text) +print(result) +# Expected: {'commands': [{'action': 'move', 'direction': 'left', 'distance': 100}]} +``` + +## 📊 Evaluation Metrics + +### Perplexity +- **Target**: < 3.0 for good command learning +- **Measures**: How well model predicts command syntax +- Lower is better + +### Diversity +- **Distinct-1**: Unique unigrams (target: 0.3-0.5) +- **Distinct-2**: Unique bigrams (target: 0.3-0.5) +- **Measures**: Response variety and naturalness + +### Command Accuracy +- **Target**: 90%+ correct tag generation +- **Manual testing required** +- Generate 10 responses, count correct tags + +### Run Evaluation Script +```powershell +python .\scripts\evaluate_lora_model.py ` + --dataset datasets\chat\aria_movement ` + --model data_out\aria_models\aria_direct ` + --max-samples 10 ` + --metric perplexity ` + --metric diversity ` + --output-format json +``` + +## 🛠️ Troubleshooting + +### Training Fails with CUDA Errors +```powershell +# Check CUDA availability +python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')" + +# Force CPU training +# Edit train_aria_direct.py: change device logic or run on CPU +``` + +### Model Doesn't Generate Tags +**Possible causes**: +1. Training loss didn't converge (check logs) +2. Not enough epochs (try 3-4 instead of 2) +3. Learning rate too low/high +4. Dataset format issue + +**Solutions**: +```powershell +# Re-validate dataset +python .\scripts\test_aria_dataset.py --validate-only + +# Check training loss in logs +Get-Content data_out\aria_models\aria_direct\training.log | Select-String "loss" + +# Retrain with more epochs +# Edit train_aria_direct.py: epochs = 3 +``` + +### Commands Not Parsed in Function App +**Check**: +1. Exact tag syntax matches parser expectations +2. Case sensitivity (parser converts to lowercase) +3. Proper spacing: `[aria:move:left]` not `[aria:moveleft]` + +**Debug**: +```python +# Test parser directly +from function_app import parse_movement_commands +test_text = "I'll move Aria left! [aria:move:left]" +print(parse_movement_commands(test_text)) +``` + +### Frontend Not Receiving Events +**Verify**: +1. SSE connection established to `/api/chat-stream` +2. EventSource listening for "movement" events +3. Function app console shows event emission + +**Debug JavaScript**: +```javascript +const eventSource = new EventSource('/api/chat-stream'); +eventSource.addEventListener('movement', (event) => { + console.log('Movement event received:', event.data); + const commands = JSON.parse(event.data); + console.log('Commands:', commands); +}); + +eventSource.addEventListener('error', (error) => { + console.error('SSE error:', error); +}); +``` + +## 🔄 Continuous Improvement + +### Monitoring Usage +1. Log all movement requests and AI responses +2. Track when tags aren't generated correctly +3. Identify new movement patterns users request + +### Dataset Augmentation +```powershell +# Add new examples to datasets/chat/aria_movement/train.json +# Follow existing format: +{ + "messages": [ + {"role": "user", "content": "New movement request"}, + {"role": "assistant", "content": "Response with [aria:command]"} + ] +} +``` + +### Retraining Schedule +- **Weekly**: If adding 10+ new examples +- **Monthly**: Maintenance retraining +- **On-demand**: When accuracy drops below 90% + +### A/B Testing +```powershell +# Keep old model as baseline +Copy-Item -Recurse data_out\lora_training\lora_adapter data_out\aria_models\baseline + +# Train new model +python .\scripts\train_aria_direct.py + +# Compare with evaluation script +python .\scripts\evaluate_lora_model.py --model data_out\aria_models\baseline +python .\scripts\evaluate_lora_model.py --model data_out\aria_models\aria_direct + +# Deploy better performing model +``` + +## 📁 File Reference + +### Core Implementation +- `datasets/chat/aria_movement/` - Training data +- `scripts/train_aria_direct.py` - Direct training script +- `scripts/automate_aria_movement.py` - Full automation pipeline +- `scripts/test_aria_dataset.py` - Dataset validation +- `autotrain_aria.yaml` - Orchestrator configuration + +### Integration Points +- `function_app.py` (lines 515-565) - Command parser +- `function_app.py` (lines 640-690) - SSE streaming with movement detection +- `chat-web/index.html` - Frontend (needs EventSource integration) + +### Output Locations +- `data_out/aria_models/aria_direct/` - Trained model +- `data_out/lora_training/lora_adapter/` - Production deployment location +- `data_out/autotrain/aria_movement_quick/` - Orchestrator logs (if using autotrain.py) + +### Documentation +- `ARIA_MOVEMENT_TRAINING.md` - Quick reference guide +- This file: Complete implementation guide + +## 🎉 Success Criteria + +The implementation is successful when: + +1. ✅ **Dataset validated** with complete command coverage +2. 🔄 **Training completed** without errors (IN PROGRESS) +3. ⏳ **Model generates** `[aria:...]` tags consistently +4. ⏳ **Parser extracts** commands correctly +5. ⏳ **SSE events emitted** with command JSON +6. ⏳ **Frontend receives** and processes movement events +7. ⏳ **Character animates** based on AI commands +8. ⏳ **End-to-end flow** works seamlessly + +## 🚀 Next Steps (After Training Completes) + +1. **Verify Training Results** + - Check generation test output in console + - Verify adapter files created + +2. **Test with Chat CLI** + - Load adapter and test responses + - Confirm tag generation + +3. **Deploy to Production** + - Copy adapter to active location + - Restart Azure Functions + +4. **Integration Testing** + - Test via /api/chat endpoint + - Verify SSE movement events + - Check frontend animation + +5. **Monitor and Iterate** + - Log usage patterns + - Collect edge cases + - Augment dataset as needed + +--- + +**Status**: Training in progress (train_aria_direct.py running) +**Created**: November 27, 2025 +**Last Updated**: November 27, 2025 diff --git a/docs/aria/ARIA_MOVEMENT_TRAINING.md b/docs/aria/ARIA_MOVEMENT_TRAINING.md index 9c1ce133e..47f6ff41f 100644 --- a/docs/aria/ARIA_MOVEMENT_TRAINING.md +++ b/docs/aria/ARIA_MOVEMENT_TRAINING.md @@ -1,274 +1,274 @@ -# Aria Movement AI Training - Quick Reference - -## Overview -This document provides a complete guide for training AI models to automatically generate Aria character movement commands. - -## What Was Created - -### 1. Training Dataset -**Location**: `datasets/chat/aria_movement/` - -**Files**: -- `train.json` - 40 training examples -- `test.json` - 10 test examples - -**Command Distribution**: -- Move commands (left/right/up/down): 14 examples -- Walk commands (left/right/up/down): 13 examples -- Center commands: 4 examples -- Wave commands: 5 examples -- Jump commands: 4 examples -- Dance commands: 4 examples - -**Format**: Each example teaches the AI to recognize natural language movement requests and respond with appropriate `[aria:action:direction]` tags. - -Example: -```json -{ - "messages": [ - {"role": "user", "content": "Move Aria to the left"}, - {"role": "assistant", "content": "I'll move Aria to the left. [aria:move:left]"} - ] -} -``` - -### 2. Training Configuration -**Location**: `autotrain_aria.yaml` - -**Jobs Defined**: -- `aria_movement_quick` - Quick testing (40 samples, 2 epochs) -- `aria_movement_full` - Full training (all samples, 3 epochs) -- `aria_movement_qwen` - Qwen 2.5-3B variant - -**Optimized Parameters**: -- Learning rate: 0.0003 (higher for specialized task) -- LoRA dropout: 0.05 (lower for small dataset) -- Epochs: 2-3 (sufficient for command syntax) - -### 3. Automation Script -**Location**: `scripts/automate_aria_movement.py` - -**Features**: -- Dataset validation with command distribution analysis -- Automated training pipeline -- Model evaluation with metrics -- Auto-deployment to active adapter location -- Full pipeline orchestration - -## How It Works - -### Movement Command System -The Azure Function app (`function_app.py`, lines 515-565) contains a parser that extracts movement commands from AI responses: - -**Supported Commands**: -- `[aria:move:direction]` - Move character (100px distance) -- `[aria:walk:direction]` - Walk character (200px distance) -- `[aria:center]` - Center character on screen -- `[aria:wave]` - Wave animation -- `[aria:jump]` - Jump animation -- `[aria:dance]` - Dance animation - -**Directions**: left, right, up, down - -**Integration**: -1. User asks AI to move Aria -2. AI generates response with `[aria:...]` tags -3. Parser in `function_app.py` extracts commands -4. SSE endpoint emits "movement" events (lines 640-690) -5. Frontend receives events and animates character - -## Training Commands - -### Quick Training (Testing) -```powershell -python .\scripts\autotrain.py --config autotrain_aria.yaml --job aria_movement_quick -``` -- 40 samples -- 2 epochs -- ~5-10 minutes on GPU - -### Full Training (Production) -```powershell -python .\scripts\autotrain.py --config autotrain_aria.yaml --job aria_movement_full -``` -- All samples -- 3 epochs -- ~10-15 minutes on GPU - -### Automated Pipeline -```powershell -# Validate dataset only -python .\scripts\automate_aria_movement.py --validate-only - -# Quick train with auto-deploy -python .\scripts\automate_aria_movement.py --quick --deploy - -# Full train with auto-deploy -python .\scripts\automate_aria_movement.py --full --deploy -``` - -## Checking Training Status - -### View autotrain status -```powershell -Get-Content .\data_out\autotrain\status.json | ConvertFrom-Json | ConvertTo-Json -Depth 10 -``` - -### Monitor training log -```powershell -# Find latest log directory -$logDir = Get-ChildItem .\data_out\autotrain\aria_movement_quick | Sort-Object LastWriteTime -Descending | Select-Object -First 1 -Get-Content "$($logDir.FullName)\stdout.log" -Tail 20 -``` - -### Watch log in real-time -```powershell -$logDir = Get-ChildItem .\data_out\autotrain\aria_movement_quick | Sort-Object LastWriteTime -Descending | Select-Object -First 1 -Get-Content "$($logDir.FullName)\stdout.log" -Wait -``` - -## Testing the Trained Model - -### Using the chat CLI -```powershell -# Load the trained adapter -python .\talk-to-ai\src\chat_cli.py --provider lora --model data_out\aria_models\aria_quick - -# Test with movement commands -> Move Aria left -> Make her wave -> Walk right then jump -``` - -### Expected Output -The AI should generate responses with proper `[aria:...]` tags: -- "I'll move Aria left! [aria:move:left]" -- "Aria is waving! [aria:wave]" -- "Moving right and jumping! [aria:walk:right] [aria:jump]" - -### Via Azure Function endpoint -```powershell -# Start the function app -func host start - -# Test the streaming endpoint -# (Use the chat-web interface or curl to /api/chat-stream) -``` - -## Deploying the Model - -### Manual Deployment -```powershell -# Copy trained model to active adapter location -Remove-Item -Recurse -Force .\data_out\lora_training\lora_adapter -Copy-Item -Recurse .\data_out\aria_models\aria_quick .\data_out\lora_training\lora_adapter - -# Restart Azure Function to load new adapter -``` - -### Automated Deployment -The automation script can deploy automatically: -```powershell -python .\scripts\automate_aria_movement.py --quick --deploy -``` - -## Evaluation Metrics - -After training completes, check these metrics: - -**Perplexity**: Should be low (< 3.0 for good command learning) -- Measures how well model predicts command syntax - -**Diversity**: Should be moderate (0.3-0.5) -- Distinct-1: Unique unigrams in responses -- Distinct-2: Unique bigrams in responses - -**Command Accuracy**: Manual testing required -- Generate 10 test responses -- Count correct command tags -- Target: 90%+ accuracy - -## Troubleshooting - -### Training fails with CUDA errors -```powershell -# Check CUDA availability -python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')" - -# Fall back to CPU -# Edit autotrain_aria.yaml: change "device: auto" to "device: cpu" -``` - -### Model doesn't generate command tags -- Check training loss - should decrease below 1.0 -- Verify dataset has correct format (use --validate-only) -- Try increasing epochs to 3-4 -- Check learning rate not too low - -### Commands not parsed in function app -- Verify exact syntax matches parser expectations -- Check case sensitivity (parser converts to lowercase) -- Ensure spaces in tags: `[aria:move:left]` not `[aria:moveleft]` - -## Integration with Frontend - -The frontend needs to: -1. Connect to SSE endpoint: `GET /api/chat-stream` -2. Listen for "movement" events -3. Parse JSON command payload -4. Execute animation based on action/direction - -**Example JavaScript**: -```javascript -const eventSource = new EventSource('/api/chat-stream'); -eventSource.addEventListener('movement', (event) => { - const commands = JSON.parse(event.data); - commands.forEach(cmd => { - moveAria(cmd.action, cmd.direction, cmd.distance); - }); -}); -``` - -## Next Steps - -1. ✅ **Dataset created** - 40 training + 10 test examples -2. ✅ **Training configuration ready** - autotrain_aria.yaml -3. ✅ **Automation script** - automate_aria_movement.py -4. 🔄 **Training in progress** - aria_movement_quick job -5. ⏳ **Evaluation pending** - Test command generation accuracy -6. ⏳ **Deployment pending** - Copy to active adapter -7. ⏳ **Integration testing** - Verify end-to-end flow -8. ⏳ **Frontend implementation** - Connect SSE movement events - -## Advanced: Continuous Improvement - -To continuously improve aria movement AI: - -1. **Monitor usage**: Log all movement requests and responses -2. **Collect failures**: Track when commands aren't generated correctly -3. **Augment dataset**: Add failed examples to training data -4. **Retrain periodically**: Weekly/monthly retraining with updated dataset -5. **A/B testing**: Compare old vs new adapters on held-out test set - -## Files Reference - -**Training**: -- `datasets/chat/aria_movement/train.json` - Training data -- `datasets/chat/aria_movement/test.json` - Test data -- `autotrain_aria.yaml` - Training configuration -- `scripts/training/autotrain.py` - Training orchestrator -- `scripts/automate_aria_movement.py` - Full automation - -**Integration**: -- `function_app.py` (lines 515-565) - Command parser -- `function_app.py` (lines 640-690) - SSE streaming integration -- `data_out/lora_training/lora_adapter/` - Active model location - -**Outputs**: -- `data_out/aria_models/aria_quick/` - Trained adapter -- `data_out/autotrain/aria_movement_quick/` - Training logs -- `data_out/autotrain/status.json` - Job status - ---- - -Last updated: 2025-11-27 +# Aria Movement AI Training - Quick Reference + +## Overview +This document provides a complete guide for training AI models to automatically generate Aria character movement commands. + +## What Was Created + +### 1. Training Dataset +**Location**: `datasets/chat/aria_movement/` + +**Files**: +- `train.json` - 40 training examples +- `test.json` - 10 test examples + +**Command Distribution**: +- Move commands (left/right/up/down): 14 examples +- Walk commands (left/right/up/down): 13 examples +- Center commands: 4 examples +- Wave commands: 5 examples +- Jump commands: 4 examples +- Dance commands: 4 examples + +**Format**: Each example teaches the AI to recognize natural language movement requests and respond with appropriate `[aria:action:direction]` tags. + +Example: +```json +{ + "messages": [ + {"role": "user", "content": "Move Aria to the left"}, + {"role": "assistant", "content": "I'll move Aria to the left. [aria:move:left]"} + ] +} +``` + +### 2. Training Configuration +**Location**: `autotrain_aria.yaml` + +**Jobs Defined**: +- `aria_movement_quick` - Quick testing (40 samples, 2 epochs) +- `aria_movement_full` - Full training (all samples, 3 epochs) +- `aria_movement_qwen` - Qwen 2.5-3B variant + +**Optimized Parameters**: +- Learning rate: 0.0003 (higher for specialized task) +- LoRA dropout: 0.05 (lower for small dataset) +- Epochs: 2-3 (sufficient for command syntax) + +### 3. Automation Script +**Location**: `scripts/automate_aria_movement.py` + +**Features**: +- Dataset validation with command distribution analysis +- Automated training pipeline +- Model evaluation with metrics +- Auto-deployment to active adapter location +- Full pipeline orchestration + +## How It Works + +### Movement Command System +The Azure Function app (`function_app.py`, lines 515-565) contains a parser that extracts movement commands from AI responses: + +**Supported Commands**: +- `[aria:move:direction]` - Move character (100px distance) +- `[aria:walk:direction]` - Walk character (200px distance) +- `[aria:center]` - Center character on screen +- `[aria:wave]` - Wave animation +- `[aria:jump]` - Jump animation +- `[aria:dance]` - Dance animation + +**Directions**: left, right, up, down + +**Integration**: +1. User asks AI to move Aria +2. AI generates response with `[aria:...]` tags +3. Parser in `function_app.py` extracts commands +4. SSE endpoint emits "movement" events (lines 640-690) +5. Frontend receives events and animates character + +## Training Commands + +### Quick Training (Testing) +```powershell +python .\scripts\autotrain.py --config autotrain_aria.yaml --job aria_movement_quick +``` +- 40 samples +- 2 epochs +- ~5-10 minutes on GPU + +### Full Training (Production) +```powershell +python .\scripts\autotrain.py --config autotrain_aria.yaml --job aria_movement_full +``` +- All samples +- 3 epochs +- ~10-15 minutes on GPU + +### Automated Pipeline +```powershell +# Validate dataset only +python .\scripts\automate_aria_movement.py --validate-only + +# Quick train with auto-deploy +python .\scripts\automate_aria_movement.py --quick --deploy + +# Full train with auto-deploy +python .\scripts\automate_aria_movement.py --full --deploy +``` + +## Checking Training Status + +### View autotrain status +```powershell +Get-Content .\data_out\autotrain\status.json | ConvertFrom-Json | ConvertTo-Json -Depth 10 +``` + +### Monitor training log +```powershell +# Find latest log directory +$logDir = Get-ChildItem .\data_out\autotrain\aria_movement_quick | Sort-Object LastWriteTime -Descending | Select-Object -First 1 +Get-Content "$($logDir.FullName)\stdout.log" -Tail 20 +``` + +### Watch log in real-time +```powershell +$logDir = Get-ChildItem .\data_out\autotrain\aria_movement_quick | Sort-Object LastWriteTime -Descending | Select-Object -First 1 +Get-Content "$($logDir.FullName)\stdout.log" -Wait +``` + +## Testing the Trained Model + +### Using the chat CLI +```powershell +# Load the trained adapter +python .\talk-to-ai\src\chat_cli.py --provider lora --model data_out\aria_models\aria_quick + +# Test with movement commands +> Move Aria left +> Make her wave +> Walk right then jump +``` + +### Expected Output +The AI should generate responses with proper `[aria:...]` tags: +- "I'll move Aria left! [aria:move:left]" +- "Aria is waving! [aria:wave]" +- "Moving right and jumping! [aria:walk:right] [aria:jump]" + +### Via Azure Function endpoint +```powershell +# Start the function app +func host start + +# Test the streaming endpoint +# (Use the chat-web interface or curl to /api/chat-stream) +``` + +## Deploying the Model + +### Manual Deployment +```powershell +# Copy trained model to active adapter location +Remove-Item -Recurse -Force .\data_out\lora_training\lora_adapter +Copy-Item -Recurse .\data_out\aria_models\aria_quick .\data_out\lora_training\lora_adapter + +# Restart Azure Function to load new adapter +``` + +### Automated Deployment +The automation script can deploy automatically: +```powershell +python .\scripts\automate_aria_movement.py --quick --deploy +``` + +## Evaluation Metrics + +After training completes, check these metrics: + +**Perplexity**: Should be low (< 3.0 for good command learning) +- Measures how well model predicts command syntax + +**Diversity**: Should be moderate (0.3-0.5) +- Distinct-1: Unique unigrams in responses +- Distinct-2: Unique bigrams in responses + +**Command Accuracy**: Manual testing required +- Generate 10 test responses +- Count correct command tags +- Target: 90%+ accuracy + +## Troubleshooting + +### Training fails with CUDA errors +```powershell +# Check CUDA availability +python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')" + +# Fall back to CPU +# Edit autotrain_aria.yaml: change "device: auto" to "device: cpu" +``` + +### Model doesn't generate command tags +- Check training loss - should decrease below 1.0 +- Verify dataset has correct format (use --validate-only) +- Try increasing epochs to 3-4 +- Check learning rate not too low + +### Commands not parsed in function app +- Verify exact syntax matches parser expectations +- Check case sensitivity (parser converts to lowercase) +- Ensure spaces in tags: `[aria:move:left]` not `[aria:moveleft]` + +## Integration with Frontend + +The frontend needs to: +1. Connect to SSE endpoint: `GET /api/chat-stream` +2. Listen for "movement" events +3. Parse JSON command payload +4. Execute animation based on action/direction + +**Example JavaScript**: +```javascript +const eventSource = new EventSource('/api/chat-stream'); +eventSource.addEventListener('movement', (event) => { + const commands = JSON.parse(event.data); + commands.forEach(cmd => { + moveAria(cmd.action, cmd.direction, cmd.distance); + }); +}); +``` + +## Next Steps + +1. ✅ **Dataset created** - 40 training + 10 test examples +2. ✅ **Training configuration ready** - autotrain_aria.yaml +3. ✅ **Automation script** - automate_aria_movement.py +4. 🔄 **Training in progress** - aria_movement_quick job +5. ⏳ **Evaluation pending** - Test command generation accuracy +6. ⏳ **Deployment pending** - Copy to active adapter +7. ⏳ **Integration testing** - Verify end-to-end flow +8. ⏳ **Frontend implementation** - Connect SSE movement events + +## Advanced: Continuous Improvement + +To continuously improve aria movement AI: + +1. **Monitor usage**: Log all movement requests and responses +2. **Collect failures**: Track when commands aren't generated correctly +3. **Augment dataset**: Add failed examples to training data +4. **Retrain periodically**: Weekly/monthly retraining with updated dataset +5. **A/B testing**: Compare old vs new adapters on held-out test set + +## Files Reference + +**Training**: +- `datasets/chat/aria_movement/train.json` - Training data +- `datasets/chat/aria_movement/test.json` - Test data +- `autotrain_aria.yaml` - Training configuration +- `scripts/training/autotrain.py` - Training orchestrator +- `scripts/automate_aria_movement.py` - Full automation + +**Integration**: +- `function_app.py` (lines 515-565) - Command parser +- `function_app.py` (lines 640-690) - SSE streaming integration +- `data_out/lora_training/lora_adapter/` - Active model location + +**Outputs**: +- `data_out/aria_models/aria_quick/` - Trained adapter +- `data_out/autotrain/aria_movement_quick/` - Training logs +- `data_out/autotrain/status.json` - Job status + +--- + +Last updated: 2025-11-27 diff --git a/docs/aria/ARIA_VISUAL_SYSTEM.md b/docs/aria/ARIA_VISUAL_SYSTEM.md index a4d3ab4ed..1978c9b21 100644 --- a/docs/aria/ARIA_VISUAL_SYSTEM.md +++ b/docs/aria/ARIA_VISUAL_SYSTEM.md @@ -1,202 +1,202 @@ -# 🎨 Aria Visual Command System - -**Status**: ✅ Trained & Operational -**Model**: TinyLlama-1.1B + LoRA (aria_expanded_v2) -**Training**: 10 epochs, 63 samples, LR 0.005 -**Perplexity**: 14.15 → 1.53 (10x improvement) - ---- - -## 📊 Command Categories (65+ Commands) - -### 🚶 Movement (8 commands) -- `move left/right/up/down` → `[aria:move:direction]` -- `walk left/right` → `[aria:walk:direction]` -- `run left/right` → `[aria:run:direction]` - -### 😊 Expressions (7 commands) -- `smile`, `happy` → `[aria:expression:smile]` -- `sad`, `surprised`, `confused` → `[aria:expression:...]` -- `thinking`, `wink` → `[aria:expression:...]` - -### 👋 Gestures (5 commands) -- `wave` → `[aria:gesture:wave]` -- `thumbs up` → `[aria:gesture:thumbs_up]` -- `point left/right` → `[aria:gesture:point:direction]` -- `clap`, `shrug` → `[aria:gesture:...]` - -### 💃 Animations (6 commands) -- `jump`, `dance`, `spin` → `[aria:animate:...]` -- `bow`, `flip`, `backflip` → `[aria:animate:...]` - -### 🧍 Poses (4 commands) -- `sit`, `stand`, `lie`, `crouch` → `[aria:pose:...]` - -### 📷 Camera (6 commands) -- `center`, `zoom in/out` → `[aria:camera:...]` -- `face left/right` → `[aria:camera:face:direction]` - -### ✨ Effects (3 commands) -- `sparkle`, `glow`, `hearts` → `[aria:effect:...]` - -### 🎭 Combinations -- `dance with sparkles` → `[aria:animate:dance] [aria:effect:sparkle]` -- `walk left and wave` → `[aria:walk:left] [aria:gesture:wave]` - ---- - -## 🧪 Test Results (aria_expanded_v2) - -| Command | Expected | Generated | Status | -|---------|----------|-----------|--------| -| move left | `[aria:move:left]` | `[aria:left]` | ⚠️ Partial | -| aria smile | `[aria:expression:smile]` | `[aria:expression:smile]` | ✅ Perfect | -| jump | `[aria:animate:jump]` | `[aria:animate:jump]` | ✅ Perfect | -| wave hello | `[aria:gesture:wave]` | `[aria:gesture:hello]` | ⚠️ Close | -| look surprised | `[aria:expression:surprised]` | `[aria:expression:surprised]` | ✅ Perfect | -| dance with sparkles | `[aria:animate:dance] [aria:effect:sparkle]` | `[aria:effect:sparkle]` | ⚠️ Partial | -| thumbs up | `[aria:gesture:thumbs_up]` | `[aria:gesture:tumble]` | ❌ Wrong | -| spin around | `[aria:animate:spin]` | `[aria:animate:left]` | ❌ Wrong | - -**Accuracy**: ~50% exact match, 75% semantically related - ---- - -## 🎯 Current Capabilities - -### ✅ What Works Well -- **Expressions**: smile, happy, sad, surprised (90%+ accuracy) -- **Simple animations**: jump, dance (80%+ accuracy) -- **Tag format**: Model consistently outputs `[aria:category:action]` structure -- **Training speed**: 22 seconds for 10 epochs on CUDA - -### ⚠️ Needs Improvement -- **Movement commands**: Often drops `move:` prefix -- **Gestures**: Sometimes confuses similar actions (thumbs_up → tumble) -- **Complex combinations**: Tends to generate only first tag -- **Directional specificity**: "spin" → "left" instead of "spin" - -### 🔧 Known Issues -- Model generates extra/hallucinated tags after the correct one -- Repetition penalty (1.5) helps but doesn't fully eliminate repetition -- Base model's Python/coding bias still influences outputs -- Temperature 0.1 helps determinism but may limit creativity - ---- - -## 🚀 Usage - -### Option 1: Interactive Demo -```powershell -python .\scripts\aria_demo.py -``` - -### Option 2: Programmatic API -```python -from aria_demo import AriaCommandGenerator - -aria = AriaCommandGenerator("data_out/aria_models/aria_expanded_v2/lora_adapter") -tags = aria.generate_command("make aria smile") -print(tags) # ['[aria:expression:smile]'] -``` - -### Option 3: Debug/Test Scripts -```powershell -python .\scripts\aria_test_final.py # Quick test with 8 commands -python .\scripts\aria_test_debug.py # Raw model output inspection -``` - ---- - -## 📈 Training Evolution - -| Version | Epochs | LR | Perplexity | Notes | -|---------|--------|----|-----------:|-------| -| aria_movement | 3 | 0.0003 | ~15 | Original verbose responses | -| aria_simple | 2 | 0.002 | ~12 | Minimal tokens, still verbose | -| aria_fast_v2 | 2 | 0.002 | ~10 | 16 samples, too small | -| aria_expanded_v1 | 3 | 0.002 | 10.23 | Generated Python code instead | -| **aria_expanded_v2** | **10** | **0.005** | **1.53** | ✅ **Generates tags!** | - -**Key Breakthrough**: 10 epochs + 5x higher LR overcame base model's coding bias - ---- - -## 🎨 Visual Dataset Structure - -```json -{ - "messages": [ - {"role": "user", "content": "aria smile"}, - {"role": "assistant", "content": "[aria:expression:smile]"} - ] -} -``` - -- **Format**: Minimal prompt → concise tag response -- **Coverage**: 63 train samples, 5 test samples -- **Categories**: 7 major categories with natural language variations -- **Combinations**: Multi-tag sequences for complex actions - ---- - -## 🔮 Next Steps - -### Immediate Improvements -1. **Increase dataset**: Add more variations per command (200+ samples) -2. **Stop token training**: Teach model to end after first tag -3. **Synonym expansion**: Multiple phrases for each command -4. **Negative examples**: Train on "don't know" responses for invalid commands - -### Advanced Features -1. **Conditional logic**: "If aria is sitting, make her stand then jump" -2. **State tracking**: Remember current pose/location -3. **Animation sequencing**: Smooth transitions between commands -4. **Parameter support**: `[aria:move:left:5]` for distance/intensity - -### Integration -1. **Game engine hook**: Parse tags → sprite animations -2. **Voice control**: Speech-to-text → Aria commands -3. **Web interface**: HTML5 canvas with real-time visualization -4. **API endpoint**: `/api/aria/command` (POST JSON, return tags) - ---- - -## 📝 Training Command - -```powershell -python .\scripts\aria_quick_train.py -``` - -**Full Command**: -``` -train_lora.py - --dataset datasets/chat/aria_expanded - --hf-model-id TinyLlama/TinyLlama-1.1B-Chat-v1.0 - --learning-rate 0.005 - --lora-dropout 0.0 - --epochs 10 - --max-train-samples 63 - --train-batch-size 4 - --save-dir data_out/aria_models/aria_expanded_v2 -``` - -**Hardware**: NVIDIA GPU (CUDA), 22s training time -**Output**: `data_out/aria_models/aria_expanded_v2/lora_adapter/` - ---- - -## 📚 Files - -- `datasets/chat/aria_expanded/` - Training data (63 samples) -- `scripts/aria_quick_train.py` - One-command training wrapper -- `scripts/aria_test_final.py` - 8-command accuracy test -- `scripts/aria_test_debug.py` - Raw output inspector -- `scripts/aria_demo.py` - Interactive command generator -- `scripts/aria_visual_expansion.py` - Dataset generator -- `data_out/aria_models/aria_expanded_v2/` - Trained model artifacts - ---- - -**Last Updated**: 2025-11-27 -**Status**: Operational prototype, 50% accuracy, ready for game integration testing +# 🎨 Aria Visual Command System + +**Status**: ✅ Trained & Operational +**Model**: TinyLlama-1.1B + LoRA (aria_expanded_v2) +**Training**: 10 epochs, 63 samples, LR 0.005 +**Perplexity**: 14.15 → 1.53 (10x improvement) + +--- + +## 📊 Command Categories (65+ Commands) + +### 🚶 Movement (8 commands) +- `move left/right/up/down` → `[aria:move:direction]` +- `walk left/right` → `[aria:walk:direction]` +- `run left/right` → `[aria:run:direction]` + +### 😊 Expressions (7 commands) +- `smile`, `happy` → `[aria:expression:smile]` +- `sad`, `surprised`, `confused` → `[aria:expression:...]` +- `thinking`, `wink` → `[aria:expression:...]` + +### 👋 Gestures (5 commands) +- `wave` → `[aria:gesture:wave]` +- `thumbs up` → `[aria:gesture:thumbs_up]` +- `point left/right` → `[aria:gesture:point:direction]` +- `clap`, `shrug` → `[aria:gesture:...]` + +### 💃 Animations (6 commands) +- `jump`, `dance`, `spin` → `[aria:animate:...]` +- `bow`, `flip`, `backflip` → `[aria:animate:...]` + +### 🧍 Poses (4 commands) +- `sit`, `stand`, `lie`, `crouch` → `[aria:pose:...]` + +### 📷 Camera (6 commands) +- `center`, `zoom in/out` → `[aria:camera:...]` +- `face left/right` → `[aria:camera:face:direction]` + +### ✨ Effects (3 commands) +- `sparkle`, `glow`, `hearts` → `[aria:effect:...]` + +### 🎭 Combinations +- `dance with sparkles` → `[aria:animate:dance] [aria:effect:sparkle]` +- `walk left and wave` → `[aria:walk:left] [aria:gesture:wave]` + +--- + +## 🧪 Test Results (aria_expanded_v2) + +| Command | Expected | Generated | Status | +|---------|----------|-----------|--------| +| move left | `[aria:move:left]` | `[aria:left]` | ⚠️ Partial | +| aria smile | `[aria:expression:smile]` | `[aria:expression:smile]` | ✅ Perfect | +| jump | `[aria:animate:jump]` | `[aria:animate:jump]` | ✅ Perfect | +| wave hello | `[aria:gesture:wave]` | `[aria:gesture:hello]` | ⚠️ Close | +| look surprised | `[aria:expression:surprised]` | `[aria:expression:surprised]` | ✅ Perfect | +| dance with sparkles | `[aria:animate:dance] [aria:effect:sparkle]` | `[aria:effect:sparkle]` | ⚠️ Partial | +| thumbs up | `[aria:gesture:thumbs_up]` | `[aria:gesture:tumble]` | ❌ Wrong | +| spin around | `[aria:animate:spin]` | `[aria:animate:left]` | ❌ Wrong | + +**Accuracy**: ~50% exact match, 75% semantically related + +--- + +## 🎯 Current Capabilities + +### ✅ What Works Well +- **Expressions**: smile, happy, sad, surprised (90%+ accuracy) +- **Simple animations**: jump, dance (80%+ accuracy) +- **Tag format**: Model consistently outputs `[aria:category:action]` structure +- **Training speed**: 22 seconds for 10 epochs on CUDA + +### ⚠️ Needs Improvement +- **Movement commands**: Often drops `move:` prefix +- **Gestures**: Sometimes confuses similar actions (thumbs_up → tumble) +- **Complex combinations**: Tends to generate only first tag +- **Directional specificity**: "spin" → "left" instead of "spin" + +### 🔧 Known Issues +- Model generates extra/hallucinated tags after the correct one +- Repetition penalty (1.5) helps but doesn't fully eliminate repetition +- Base model's Python/coding bias still influences outputs +- Temperature 0.1 helps determinism but may limit creativity + +--- + +## 🚀 Usage + +### Option 1: Interactive Demo +```powershell +python .\scripts\aria_demo.py +``` + +### Option 2: Programmatic API +```python +from aria_demo import AriaCommandGenerator + +aria = AriaCommandGenerator("data_out/aria_models/aria_expanded_v2/lora_adapter") +tags = aria.generate_command("make aria smile") +print(tags) # ['[aria:expression:smile]'] +``` + +### Option 3: Debug/Test Scripts +```powershell +python .\scripts\aria_test_final.py # Quick test with 8 commands +python .\scripts\aria_test_debug.py # Raw model output inspection +``` + +--- + +## 📈 Training Evolution + +| Version | Epochs | LR | Perplexity | Notes | +|---------|--------|----|-----------:|-------| +| aria_movement | 3 | 0.0003 | ~15 | Original verbose responses | +| aria_simple | 2 | 0.002 | ~12 | Minimal tokens, still verbose | +| aria_fast_v2 | 2 | 0.002 | ~10 | 16 samples, too small | +| aria_expanded_v1 | 3 | 0.002 | 10.23 | Generated Python code instead | +| **aria_expanded_v2** | **10** | **0.005** | **1.53** | ✅ **Generates tags!** | + +**Key Breakthrough**: 10 epochs + 5x higher LR overcame base model's coding bias + +--- + +## 🎨 Visual Dataset Structure + +```json +{ + "messages": [ + {"role": "user", "content": "aria smile"}, + {"role": "assistant", "content": "[aria:expression:smile]"} + ] +} +``` + +- **Format**: Minimal prompt → concise tag response +- **Coverage**: 63 train samples, 5 test samples +- **Categories**: 7 major categories with natural language variations +- **Combinations**: Multi-tag sequences for complex actions + +--- + +## 🔮 Next Steps + +### Immediate Improvements +1. **Increase dataset**: Add more variations per command (200+ samples) +2. **Stop token training**: Teach model to end after first tag +3. **Synonym expansion**: Multiple phrases for each command +4. **Negative examples**: Train on "don't know" responses for invalid commands + +### Advanced Features +1. **Conditional logic**: "If aria is sitting, make her stand then jump" +2. **State tracking**: Remember current pose/location +3. **Animation sequencing**: Smooth transitions between commands +4. **Parameter support**: `[aria:move:left:5]` for distance/intensity + +### Integration +1. **Game engine hook**: Parse tags → sprite animations +2. **Voice control**: Speech-to-text → Aria commands +3. **Web interface**: HTML5 canvas with real-time visualization +4. **API endpoint**: `/api/aria/command` (POST JSON, return tags) + +--- + +## 📝 Training Command + +```powershell +python .\scripts\aria_quick_train.py +``` + +**Full Command**: +``` +train_lora.py + --dataset datasets/chat/aria_expanded + --hf-model-id TinyLlama/TinyLlama-1.1B-Chat-v1.0 + --learning-rate 0.005 + --lora-dropout 0.0 + --epochs 10 + --max-train-samples 63 + --train-batch-size 4 + --save-dir data_out/aria_models/aria_expanded_v2 +``` + +**Hardware**: NVIDIA GPU (CUDA), 22s training time +**Output**: `data_out/aria_models/aria_expanded_v2/lora_adapter/` + +--- + +## 📚 Files + +- `datasets/chat/aria_expanded/` - Training data (63 samples) +- `scripts/aria_quick_train.py` - One-command training wrapper +- `scripts/aria_test_final.py` - 8-command accuracy test +- `scripts/aria_test_debug.py` - Raw output inspector +- `scripts/aria_demo.py` - Interactive command generator +- `scripts/aria_visual_expansion.py` - Dataset generator +- `data_out/aria_models/aria_expanded_v2/` - Trained model artifacts + +--- + +**Last Updated**: 2025-11-27 +**Status**: Operational prototype, 50% accuracy, ready for game integration testing diff --git a/docs/aria/aria_controller.js b/docs/aria/aria_controller.js index 373ae35ce..10c630656 100644 --- a/docs/aria/aria_controller.js +++ b/docs/aria/aria_controller.js @@ -1,2092 +1,2092 @@ -// Aria Visual Command Controller -// GitHub Pages Demo Mode Configuration -const DEMO_MODE = true; // Set to true for GitHub Pages, false for local backend -const DEMO_API_DELAY = 300; // Simulate network delay in ms - -const aria = document.getElementById('aria'); -const ariaMouth = document.getElementById('ariaMouth'); -const ariaArmLeft = document.getElementById('ariaArmLeft'); -const ariaArmRight = document.getElementById('ariaArmRight'); -const ariaLegLeft = document.getElementById('ariaLegLeft'); -const ariaLegRight = document.getElementById('ariaLegRight'); -const stage = document.getElementById('stage'); -const commandInput = document.getElementById('commandInput'); -const logContainer = document.getElementById('logContainer'); - -// Demo Mode Mock API Functions -async function mockApiCall(endpoint, options) { - if (!DEMO_MODE) { - // In non-demo mode, make real API call - return fetch(endpoint, options); - } - - // Simulate network delay - await new Promise(resolve => setTimeout(resolve, DEMO_API_DELAY)); - - // Mock responses based on endpoint - if (endpoint === '/api/aria/command') { - const body = JSON.parse(options.body); - return { - ok: true, - status: 200, - json: async () => ({ - success: true, - response: `Understood: "${body.command}". In demo mode, I'm simulating the response!`, - tags: [], - command: body.command - }) - }; - } else if (endpoint === '/api/aria/object') { - const body = JSON.parse(options.body); - return { - ok: true, - status: 200, - json: async () => ({ - success: true, - action: body.action, - object: body.object - }) - }; - } - - // Default mock response - return { - ok: true, - status: 200, - json: async () => ({ success: true, demo: true }) - }; -} - -// Track active objects -const activeObjects = { - apple: true, - book: true, - cup: true, - ball: true, - flower: true -}; - -// Toggle object visibility -function toggleObject(objectId) { - const obj = document.getElementById(objectId); - const btn = document.getElementById('btn-' + objectId); - - if (!obj) { - log(`❌ toggleObject: unknown object ${objectId}`, true); - return; - } - // compute last-known position first - const lastPos = objectPositionFromElement(obj); - - if (activeObjects[objectId]) { - // Remove object - obj.style.display = 'none'; - btn.classList.remove('active'); - btn.classList.add('inactive'); - activeObjects[objectId] = false; - log('🗑️ Removed ' + objectId); - // Sync change to backend - sendObjectUpdate('update', objectId, { position: lastPos, state: 'hidden' }).catch(() => {}); - } else { - // Add object back - obj.style.display = 'block'; - btn.classList.add('active'); - btn.classList.remove('inactive'); - activeObjects[objectId] = true; - log('➕ Added ' + objectId); - // Sync change to backend (object is back on stage) - sendObjectUpdate('update', objectId, { position: lastPos, state: 'on_table' }).catch(() => {}); - } -} - -// Additional DOM references (used by idle/poses/expressive moves) -const ariaHead = document.querySelector('.aria-head'); -const ariaBody = document.querySelector('.aria-body'); -const ariaEyes = document.querySelectorAll('.aria-eye'); -const ariaEyeLeft = ariaEyes[0]; -const ariaEyeRight = ariaEyes[1]; - -// AI-Controlled Character State (single combined object) -let characterState = { - mood: 'neutral', - energy: 50, - personality: 'balanced', - colors: { - hair: '#4a3728', - skin: '#f5d4b8', - body: '#4a90e2', - legs: '#3d5a80', - feet: '#f5f5f5' - }, - size: 1.0, - style: 'default', - heldObject: null, - heldObjectElement: null, - position: { x: 20, y: 70, z: 0 }, - rotation: 0, - isMoving: false, - currentWaypoint: null -}; - -// Visual feedback function -function showFeedback(message) { - const feedback = document.createElement('div'); - feedback.textContent = message; - feedback.style.cssText = 'position:absolute; top:20px; left:50%; transform:translateX(-50%); background:#e74c3c; color:white; padding:15px 30px; border-radius:15px; font-size:28px; font-weight:bold; z-index:999; box-shadow:0 5px 20px rgba(0,0,0,0.3); animation:pulse 0.5s ease;'; - stage.appendChild(feedback); - setTimeout(() => feedback.remove(), 2500); -} - -// AI-Driven Character Generation -function analyzeAIResponse(text) { - const lowerText = text.toLowerCase(); - - // Detect mood from response - if (lowerText.includes('happy') || lowerText.includes('great') || lowerText.includes('wonderful') || lowerText.includes('excited')) { - return { mood: 'happy', energy: 80 }; - } else if (lowerText.includes('sad') || lowerText.includes('sorry') || lowerText.includes('unfortunate')) { - return { mood: 'sad', energy: 30 }; - } else if (lowerText.includes('angry') || lowerText.includes('frustrated')) { - return { mood: 'angry', energy: 90 }; - } else if (lowerText.includes('calm') || lowerText.includes('peaceful') || lowerText.includes('relaxed')) { - return { mood: 'calm', energy: 40 }; - } else if (lowerText.includes('think') || lowerText.includes('consider') || lowerText.includes('perhaps')) { - return { mood: 'thinking', energy: 60 }; - } - - return { mood: 'neutral', energy: 50 }; -} - -function generateCharacterFromMood(mood, energy) { - const moodColors = { - happy: { body: '#5fb3f5', hair: '#6b4f3d', accent: '#7ac5ff' }, - sad: { body: '#6C7A89', hair: '#4a3728', accent: '#95A5A6' }, - angry: { body: '#E74C3C', hair: '#4a3728', accent: '#ff6b6b' }, - calm: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' }, - thinking: { body: '#5a7fa8', hair: '#4a3728', accent: '#6b8fb3' }, - neutral: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' } - }; - - const colors = moodColors[mood] || moodColors.neutral; - const size = 0.8 + (energy / 100) * 0.4; // Scale from 0.8 to 1.2 based on energy - - return { colors, size, mood }; -} - -function applyCharacterStyle(style) { - const ariaHead = document.querySelector('.aria-head'); - const ariaBody = document.querySelector('.aria-body'); - const ariaHair = document.querySelector('.aria-hair'); - const ariaLegs = document.querySelectorAll('.aria-leg'); - const ariaFeet = document.querySelectorAll('.aria-foot'); - - // Create dramatic transformation sparkle effect - for (let i = 0; i < 15; i++) { - setTimeout(() => { - createEffect('sparkle'); - }, i * 50); - } - - // Add glow pulse during transformation - aria.style.filter = 'drop-shadow(0 0 30px ' + style.colors.body + ') brightness(1.5)'; - setTimeout(() => { - aria.style.filter = 'none'; - }, 1000); - - // Apply colors with smooth transition - ariaHair.style.transition = 'background-color 1s ease, transform 1s ease'; - ariaHead.style.transition = 'background-color 1s ease'; - ariaBody.style.transition = 'background 1s ease, transform 1s ease'; - ariaLegs.forEach(leg => leg.style.transition = 'background-color 1s ease'); - ariaFeet.forEach(foot => foot.style.transition = 'background-color 1s ease'); - - ariaHair.style.backgroundColor = style.colors.hair; - ariaBody.style.background = `linear-gradient(135deg, ${style.colors.body}, ${style.colors.accent})`; - - // Apply size transformation - aria.style.transform = `translateX(-50%) scale(${style.size})`; - - // Update character state - characterState = { ...characterState, ...style }; - - console.log('🎨 Character updated:', style.mood, 'Energy:', Math.round(style.size * 100) + '%'); - showFeedback('🎨 TRANSFORM: ' + style.mood.toUpperCase()); -} - -function autoGenerateCharacter(responseText) { - const analysis = analyzeAIResponse(responseText); - const newStyle = generateCharacterFromMood(analysis.mood, analysis.energy); - applyCharacterStyle(newStyle); - - // Trigger automatic animation based on mood - setTimeout(() => { - if (analysis.mood === 'happy') { - animate('jumping'); - } else if (analysis.mood === 'sad') { - move('left', 'normal'); - } else if (analysis.mood === 'angry') { - animate('spinning'); - } else if (analysis.mood === 'calm') { - animate('waving'); - } else if (analysis.mood === 'thinking') { - changeExpression('thinking'); - } - }, 500); -} - -const expressions = { - 'smile': '😊', - 'happy': '😃', - 'sad': '😢', - 'surprised': '😲', - 'confused': '😕', - 'thinking': '🤔', - 'wink': '😉' -}; - -function log(message, isError = false) { - const entry = document.createElement('div'); - entry.className = 'log-entry'; - entry.style.borderLeftColor = isError ? '#e74c3c' : '#667eea'; - entry.style.color = isError ? '#e74c3c' : '#555'; - entry.textContent = `${new Date().toLocaleTimeString()}: ${message}`; - logContainer.insertBefore(entry, logContainer.firstChild); - - // Keep only last 10 entries - while (logContainer.children.length > 10) { - logContainer.removeChild(logContainer.lastChild); - } -} - -// Chat UI helpers -function addChatMessage(role, text) { - try { - const container = document.getElementById('chatMessages'); - if (!container) return; - - const msgWrap = document.createElement('div'); - msgWrap.className = 'chat-msg ' + (role === 'user' ? 'user' : (role === 'aria' ? 'aria' : 'system')); - - const bubble = document.createElement('div'); - bubble.className = 'bubble'; - bubble.innerText = text; - - const sender = document.createElement('div'); - sender.className = 'sender'; - sender.innerText = role === 'user' ? 'You' : role === 'aria' ? 'Aria' : 'System'; - - const inner = document.createElement('div'); - inner.appendChild(sender); - inner.appendChild(bubble); - - // For user messages align right - if (role === 'user') { - msgWrap.appendChild(inner); - } else { - msgWrap.appendChild(inner); - } - - container.appendChild(msgWrap); - container.scrollTop = container.scrollHeight; - } catch (e) { - console.warn('addChatMessage failed', e); - } -} - -// Called when Aria reaches a waypoint or finishes a movement -function arrivalFeedback(waypointKey) { - try { - let msg = ''; - if (waypointKey && waypoints3D && waypoints3D[waypointKey]) { - msg = `I've arrived at ${waypoints3D[waypointKey].name}.`; - } else if (typeof waypointKey === 'string' && waypointKey.includes('%')) { - msg = `Arrived at ${waypointKey}.`; - } else if (waypointKey) { - msg = `Arrived at ${waypointKey}.`; - } else { - msg = `Arrived.`; - } - - // Friendly reply from Aria in chat — pick varied arrival messages - const arrivalMessages = [ - `I'm here! 🎉`, - `Made it — ${waypointKey ? waypoints3D[waypointKey]?.name || waypointKey : 'arrived'}!`, - `Arrived and ready!`, - `That was quick — I'm here.`, - `All set. What should we do next?`, - ]; - const chosen = arrivalMessages[Math.floor(Math.random() * arrivalMessages.length)]; - addChatMessage('aria', chosen); - - // Little celebratory effect and expression - createEffect('sparkle'); - changeExpression('happy'); - - // Short arrival animation (small bounce & glow) - aria.classList.add('arrived'); - setTimeout(() => aria.classList.remove('arrived'), 900); - - // Clear current waypoint marker so we don't re-announce - characterState.currentWaypoint = null; - } catch (e) { - console.warn('arrivalFeedback failed', e); - } -} - -// Try to resolve a freeform phrase to a waypoint key -function resolveWaypointFromPhrase(phrase) { - if (!phrase || !waypoints3D) return null; - const clean = phrase.toLowerCase().replace(/[^a-z0-9\s-]/g, '').trim(); - if (!clean) return null; - - // direct key match (e.g., front-left) - const direct = clean.replace(/\s+/g, '-'); - if (waypoints3D[direct]) return direct; - - // check by names and partial matches - for (const k in waypoints3D) { - const v = waypoints3D[k]; - if (!v) continue; - const name = String(v.name || '').toLowerCase(); - if (name === clean) return k; - if (name.includes(clean) || clean.includes(k.replace('-', ' '))) return k; - } - - // synonyms mapping for simple words - const synonyms = { - 'front': 'front-center', 'back': 'back-center', 'left': 'stage-left', 'right': 'stage-right', 'center': 'center', 'table': 'table' - }; - if (synonyms[clean]) return synonyms[clean]; - - // try matching tokens - const tokens = clean.split(/\s+/); - for (const t of tokens) { - if (synonyms[t]) return synonyms[t]; - } - - return null; -} - -// Process chat input: supports slash commands and normal commands -async function sendChat() { - const chatBox = document.getElementById('chatInput'); - if (!chatBox) return; - - const text = chatBox.value.trim(); - if (!text) return; - - // Show user's message - addChatMessage('user', text); - chatBox.value = ''; - - // check for natural language movements (e.g., "go to front-right" or "walk to table") - const nlMove = /(?:go to|move to|walk to|goto)\s+([a-z0-9\-\s]+)/i.exec(text); - if (nlMove) { - const targetPhrase = nlMove[1].trim(); - // try to match known waypoint keys or names - const candidate = resolveWaypointFromPhrase(targetPhrase); - if (candidate) { - moveToWaypoint(candidate); - addChatMessage('aria', `Moving to ${waypoints3D[candidate].name}`); - } else { - addChatMessage('aria', `I couldn't identify a waypoint for '${targetPhrase}'. Try /waypoints`); - } - return; - } - - // /say command — speak directly as Aria - if (text.startsWith('/say ')) { - const sayText = text.slice(5).trim(); - if (sayText) { - // Aria says it in the chat - addChatMessage('aria', sayText); - // small visual cue - createEffect('sparkle'); - changeExpression('smile'); - } - return; - } - - // slash commands handled locally - if (text.startsWith('/goto ') || text.startsWith('/move ') || text.startsWith('/moveTo ')) { - const parts = text.split(/\s+/, 2); - const waypoint = parts[1] ? parts[1].trim() : ''; - if (waypoints3D && waypoints3D[waypoint]) { - moveToWaypoint(waypoint); - addChatMessage('aria', `Moving to ${waypoints3D[waypoint].name}`); - } else { - addChatMessage('aria', `Unknown waypoint '${waypoint}'. Try /waypoints`); - } - return; - } - - if (text === '/waypoints') { - const entries = Object.entries(waypoints3D).map(([k, v]) => `${k} — ${v.name}`); - addChatMessage('aria', `Available waypoints: ${entries.join(', ')}`); - return; - } - - if (text === '/circle' || text === '/circle3d') { - moveInCircle3D(); - addChatMessage('aria', 'Starting 3D circle movement'); - return; - } - - if (text === '/spiral' || text === '/spiral3d') { - performSpiral3D(); - addChatMessage('aria', 'Starting 3D spiral'); - return; - } - - if (text === '/stop') { - // stop behaviors - if (typeof stopContinuousDance === 'function') stopContinuousDance(); - addChatMessage('aria', 'Stopped continuous actions'); - return; - } - - // Otherwise, send as a command to backend (and display response when available) - commandInput.value = text; - const result = await sendCommand(); - - if (!result) { - addChatMessage('aria', 'No response from backend (fallback executed)'); - return; - } - - if (result.error) { - addChatMessage('aria', `Error: ${result.error}`); - return; - } - - // Prefer textual response if available - if (result.response) { - addChatMessage('aria', result.response); - } else if (result.tags && result.tags.length > 0) { - addChatMessage('aria', `Executed tags: ${result.tags.join(' ')}`); - } else if (result.stage_context) { - // Keep stage context short - const ctx = result.stage_context.split('\n').slice(0,4).join(' | '); - addChatMessage('aria', ctx); - } else { - addChatMessage('aria', 'Done.'); - } -} - -async function sendCommand() { - const command = commandInput.value.trim(); - if (!command) return; - - log(`Command: "${command}"`); - commandInput.value = ''; - - try { - // Gather current stage state for AI to see - const stageRect = stage.getBoundingClientRect(); - const ariaRect = aria.getBoundingClientRect(); - - // Calculate Aria's position as percentages - const ariaX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; - const ariaY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; - - // Gather object positions - const objectPositions = {}; - ['apple', 'book', 'cup', 'ball', 'flower'].forEach(objId => { - const obj = document.getElementById(objId); - if (obj) { - const objRect = obj.getBoundingClientRect(); - objectPositions[objId] = { - x: ((objRect.left - stageRect.left) / stageRect.width) * 100, - y: 100 - ((objRect.bottom - stageRect.top) / stageRect.height) * 100, - state: obj.classList.contains('held') ? 'held' : 'on_table' - }; - } - }); - - const currentStageState = { - aria: { - position: { x: Math.round(ariaX), y: Math.round(ariaY) }, - expression: characterState.mood || 'neutral', - held_object: characterState.heldObject, - facing: 'right' - }, - objects: objectPositions - }; - - // Call backend API with stage state - const response = await mockApiCall('/api/aria/command', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - command: command, - stage_state: currentStageState - }) - }); - - if (!response.ok) { - throw new Error(`HTTP ${response.status}: ${response.statusText}`); - } - - const data = await response.json(); - - // AI automatically generates character based on response - if (data.response) { - autoGenerateCharacter(data.response); - } - - if (data.tags && data.tags.length > 0) { - log(`✅ ${data.model}: ${data.tags.join(' ')}`); - executeTags(data.tags); - } else if (data.error) { - log(`⚠️ API Error: ${data.error}`, true); - executeLocalCommand(command); - } else { - log('⚠️ Using fallback parser'); - executeLocalCommand(command); - } - // Return the parsed result so callers (chat UI) can inspect tags / response - return data; - } catch (error) { - log(`⚠️ Network error, using fallback`, true); - // Fallback: parse command locally without AI - executeLocalCommand(command); - return { error: String(error) }; - } -} - -function setPosition(xPercent, yPercent, zDepth = 0, rotateY = 0) { - // AI-driven animated walking to position in 3D space (not teleporting) - const stageRect = stage.getBoundingClientRect(); - const ariaRect = aria.getBoundingClientRect(); - - // Get current position - const currentX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; - const currentY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; - - // Clamp target values - xPercent = Math.max(5, Math.min(95, xPercent)); - yPercent = Math.max(5, Math.min(95, yPercent)); - zDepth = Math.max(-300, Math.min(200, zDepth)); // Z range: -300px (far) to 200px (near) - - // Calculate distance and direction - const deltaX = xPercent - currentX; - const deltaY = yPercent - currentY; - const distance = Math.sqrt(deltaX * deltaX + deltaY * deltaY); - - // Don't move if already at target - if (distance < 2) { - log(`📍 Already at position (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px)`); - // If we were moving towards a known waypoint, confirm arrival - if (characterState.currentWaypoint) { - arrivalFeedback(characterState.currentWaypoint); - } - return; - } - - // Determine walking speed and style based on distance - let duration = Math.min(distance * 40, 2000); // 40ms per percent, max 2s - let walkStyle = 'normal'; - - if (distance > 50) { - walkStyle = 'run'; - duration = distance * 20; // Faster for long distances - } else if (distance > 30) { - walkStyle = 'walk'; - duration = distance * 30; - } - - // Face the direction of movement in 3D - let rotationAngle = rotateY || 0; - if (deltaX > 2) { - rotationAngle = 0; // Face camera (right) - } else if (deltaX < -2) { - rotationAngle = 180; // Face away (left) - } - - // Add walking animation - aria.classList.add('walking'); - if (walkStyle === 'run') { - aria.classList.add('running'); - } - - // Animate legs while moving - const walkInterval = setInterval(() => { - animateWalkCycle(); - }, 200); - - // Smooth 3D transition to target - aria.style.transition = `left ${duration}ms ease-in-out, bottom ${duration}ms ease-in-out, transform ${duration}ms ease-in-out`; - - const leftPercent = xPercent; - const bottomPercent = 100 - yPercent; // Invert Y (CSS bottom increases upward) - - aria.style.left = leftPercent + '%'; - aria.style.bottom = bottomPercent + '%'; - - // Apply 3D transform with Z-depth and rotation - const scaleX = rotationAngle === 180 ? -1 : 1; - aria.style.transform = `translateX(-50%) translateZ(${zDepth}px) rotateY(${rotationAngle}deg) scaleX(${scaleX})`; - - // Update character state - characterState.position = { x: xPercent, y: yPercent, z: zDepth }; - characterState.rotation = rotationAngle; - - log(`🚶 Walking to (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px, Rot:${Math.round(rotationAngle)}°) - ${walkStyle} style`); - showFeedback(`🚶 3D WALK: X${Math.round(xPercent)}% Y${Math.round(yPercent)}% Z${Math.round(zDepth)}px`); - - // Stop walking animation when arrived - setTimeout(() => { - aria.classList.remove('walking', 'running'); - aria.style.transition = ''; // Reset transition - clearInterval(walkInterval); - resetWalkCycle(); - log(`✅ Arrived at (${Math.round(xPercent)}%, ${Math.round(yPercent)}%)`); - // If we were targeting a waypoint, announce in chat - if (characterState.currentWaypoint) { - arrivalFeedback(characterState.currentWaypoint); - } - }, duration); -} - -// Walking animation cycle -function animateWalkCycle() { - const leftLeg = document.querySelector('.aria-lower-leg.left'); - const rightLeg = document.querySelector('.aria-lower-leg.right'); - const leftArm = document.querySelector('.aria-lower-arm.left'); - const rightArm = document.querySelector('.aria-lower-arm.right'); - - if (!leftLeg || !rightLeg) return; - - // Alternate leg swings - if (leftLeg.style.transform.includes('rotate')) { - leftLeg.style.transform = 'rotate(20deg)'; - rightLeg.style.transform = 'rotate(-20deg)'; - if (leftArm) leftArm.style.transform = 'rotate(-15deg)'; - if (rightArm) rightArm.style.transform = 'rotate(15deg)'; - } else { - leftLeg.style.transform = 'rotate(-20deg)'; - rightLeg.style.transform = 'rotate(20deg)'; - if (leftArm) leftArm.style.transform = 'rotate(15deg)'; - if (rightArm) rightArm.style.transform = 'rotate(-15deg)'; - } -} - -function resetWalkCycle() { - const leftLeg = document.querySelector('.aria-lower-leg.left'); - const rightLeg = document.querySelector('.aria-lower-leg.right'); - const leftArm = document.querySelector('.aria-lower-arm.left'); - const rightArm = document.querySelector('.aria-lower-arm.right'); - - if (leftLeg) leftLeg.style.transform = ''; - if (rightLeg) rightLeg.style.transform = ''; - if (leftArm) leftArm.style.transform = ''; - if (rightArm) rightArm.style.transform = ''; -} - -function executeLocalCommand(command) { - // Simple local fallback without AI model - const cmd = command.toLowerCase(); - let executed = false; - - // Check if this is a limb command to avoid movement conflicts - const isLimbCommand = ['left arm', 'arm left', 'left hand', 'right arm', 'arm right', 'right hand', - 'left leg', 'leg left', 'right leg', 'leg right'].some(k => cmd.includes(k)); - - // Expressions - if (cmd.includes('smile') || cmd.includes('happy')) { - changeExpression('smile'); - executed = true; - } - if (cmd.includes('sad')) { - changeExpression('sad'); - executed = true; - } - if (cmd.includes('surprised')) { - changeExpression('surprised'); - executed = true; - } - if (cmd.includes('confused')) { - changeExpression('confused'); - executed = true; - } - if (cmd.includes('thinking') || cmd.includes('think')) { - changeExpression('thinking'); - executed = true; - } - if (cmd.includes('wink')) { - changeExpression('wink'); - executed = true; - } - - // Animations - if (cmd.includes('jump')) { - animate('jumping'); - executed = true; - } - if (cmd.includes('dance')) { - animate('dancing'); - executed = true; - } - if (cmd.includes('spin')) { - animate('spinning'); - executed = true; - } - if (cmd.includes('wave')) { - animate('waving'); - executed = true; - } - - // Effects - if (cmd.includes('sparkle')) { - createEffect('sparkle'); - executed = true; - } - if (cmd.includes('hearts')) { - createEffect('hearts'); - executed = true; - } - if (cmd.includes('glow')) { - createEffect('glow'); - executed = true; - } - - // Movement - only if not a limb command - if (!isLimbCommand) { - // Determine movement style - let movementSpeed = 'normal'; - if (cmd.includes('skip')) { - movementSpeed = 'skip'; - } else if (cmd.includes('strut') || cmd.includes('swagger')) { - movementSpeed = 'strut'; - } else if (cmd.includes('run')) { - movementSpeed = 'run'; - } - - if (cmd.includes('left')) { - move('left', movementSpeed); - executed = true; - } - if (cmd.includes('right')) { - move('right', movementSpeed); - executed = true; - } - if (cmd.includes('up') || (cmd.includes('forward') && !cmd.includes('arm') && !cmd.includes('leg'))) { - move('up', movementSpeed); - executed = true; - } - if (cmd.includes('down') || (cmd.includes('back') && !cmd.includes('arm') && !cmd.includes('leg'))) { - move('down', movementSpeed); - executed = true; - } - } - - if (!executed) { - log('❌ Command not recognized', true); - } -} - -function executeTags(tags) { - console.log('📋 Executing tags:', tags); - tags.forEach((tag, index) => { - // Parse tag format: [aria:category:action:param] - const match = tag.match(/\[aria:([^:]+):([^:\]]+)(?::([^\]]+))?\]/); - if (!match) { - console.log('⚠️ Failed to parse tag:', tag); - return; - } - - const [, categoryRaw, actionRaw, paramRaw] = match; - // Normalize category/action - const category = (categoryRaw || '').toLowerCase(); - const action = (actionRaw || '').toLowerCase(); - const param = typeof paramRaw === 'string' ? paramRaw.trim() : undefined; - console.log(`✅ Parsed tag - Category: ${category}, Action: ${action}, Param: ${param}`); - - setTimeout(() => { - switch (category) { - case 'expression': - console.log('Executing expression:', action); - changeExpression(action); - break; - case 'animate': - console.log('Executing animate:', action); - animate(getAnimationClass(action)); - break; - case 'gesture': - animate('waving'); - break; - case 'move': - move(action, param || 'normal'); - break; - case 'walk': - move(action, param || 'normal'); - break; - case 'run': - move(action, param || 'fast'); - break; - case 'position': - console.log('Executing position:', action, param); - if (action && param) { - // Format: [aria:position:x:y] or [aria:position:x:y:z:rotation] - const parts = tag.match(/\[aria:position:([^:\]]+):([^:\]]+)(?::([^:\]]+))?(?::([^\]]+))?\]/); - if (parts) { - const x = parseInt(parts[1]); - const y = parseInt(parts[2]); - const z = parts[3] ? parseInt(parts[3]) : 0; - const rotation = parts[4] ? parseInt(parts[4]) : 0; - setPosition(x, y, z, rotation); - } else { - setPosition(parseInt(action), parseInt(param)); - } - } - break; - case 'skip': - move(action, 'skip'); - break; - case 'strut': - case 'swagger': - move(action, 'strut'); - break; - case 'limb': - // Move individual limbs with tags like: - // [aria:limb:left_arm:raise] | [aria:limb:right_arm:-45] | [aria:limb:left_leg:kick] - handleLimbTag(action, param); - break; - case 'interact': - console.log('Executing interact:', action, param); - if (action === 'add') { - const [objectName, emoji] = param.split(':'); - addObject(objectName, emoji || '🧸'); - } else { - interactWithObject(action, param); - } - break; - case 'effect': - createEffect(action); - break; - case 'camera': - if (action === 'center') centerAria(); - break; - case 'pose': - applyPose(action, param); - break; - case 'say': - // Tag formats either [aria:say:Hello world] or [aria:say:utterance:extra] - const spoken = param || action; - if (spoken) { - addChatMessage('aria', String(spoken)); - } - break; - } - }, index * 500); // Stagger multiple commands - }); -} - -function changeExpression(emotion) { - ariaMouth.className = 'aria-mouth'; - - // Reset any previous expression modifications - ariaMouth.style.borderRadius = ''; - ariaMouth.style.width = ''; - ariaMouth.style.height = ''; - ariaMouth.style.borderTop = ''; - ariaMouth.style.transform = ''; - - switch(emotion) { - case 'smile': - case 'happy': - ariaMouth.classList.add('smile'); - break; - case 'sad': - ariaMouth.classList.add('sad'); - break; - case 'surprised': - ariaMouth.style.borderRadius = '50%'; - ariaMouth.style.width = '15px'; - ariaMouth.style.height = '15px'; - ariaMouth.style.borderTop = '2px solid #333'; - break; - case 'confused': - // Wavy/uncertain mouth - ariaMouth.style.width = '18px'; - ariaMouth.style.height = '6px'; - ariaMouth.style.borderRadius = '0 0 50% 50%'; - ariaMouth.style.transform = 'translateX(-50%) rotate(5deg)'; - break; - case 'thinking': - // Side mouth (pondering) - ariaMouth.style.width = '12px'; - ariaMouth.style.height = '8px'; - ariaMouth.style.borderRadius = '0 0 50% 50%'; - ariaMouth.style.transform = 'translateX(-30%)'; - // Also raise one eyebrow (using eye height) - if (ariaEyeLeft) { - ariaEyeLeft.style.transform = 'translateY(-2px)'; - setTimeout(() => { - ariaEyeLeft.style.transform = ''; - }, 2000); - } - break; - case 'wink': - document.querySelectorAll('.aria-eye')[1].style.height = '4px'; - setTimeout(() => { - document.querySelectorAll('.aria-eye')[1].style.height = '12px'; - }, 500); - break; - default: - ariaMouth.classList.add('smile'); - } - - aria.style.transform = 'translateX(-50%) scale(1.1)'; - setTimeout(() => { - aria.style.transform = 'translateX(-50%) scale(1)'; - }, 300); -} - -// Idle animation state -let idleAnimationInterval = null; -let isPerformingAction = false; - -// Start idle breathing animation -function startIdleAnimation() { - if (idleAnimationInterval) return; - - idleAnimationInterval = setInterval(() => { - if (!isPerformingAction) { - // Subtle breathing effect - ariaBody.style.transition = 'transform 2s ease-in-out'; - ariaBody.style.transform = 'scaleY(1.03)'; - - // Occasional blink - if (Math.random() > 0.7) { - ariaEyeLeft.style.height = '2px'; - ariaEyeRight.style.height = '2px'; - setTimeout(() => { - ariaEyeLeft.style.height = '12px'; - ariaEyeRight.style.height = '12px'; - }, 150); - } - - // Slight head bob - if (Math.random() > 0.8) { - ariaHead.style.transition = 'transform 0.8s ease-in-out'; - ariaHead.style.transform = 'translateY(-3px)'; - setTimeout(() => { - ariaHead.style.transform = 'translateY(0)'; - }, 800); - } - - setTimeout(() => { - ariaBody.style.transform = 'scaleY(1)'; - }, 2000); - } - }, 4000); -} - -// Stop idle animation -function stopIdleAnimation() { - if (idleAnimationInterval) { - clearInterval(idleAnimationInterval); - idleAnimationInterval = null; - } -} - -// Limb movement helpers -function moveArm(arm, angle, duration = 500) { - arm.style.transition = `transform ${duration}ms ease-in-out`; - arm.style.transform = `rotate(${angle}deg)`; -} - -function moveLeg(leg, angle, duration = 500) { - leg.style.transition = `transform ${duration}ms ease-in-out`; - leg.style.transform = `rotate(${angle}deg)`; -} - -function resetLimbs(duration = 500) { - moveArm(ariaArmLeft, 0, duration); - moveArm(ariaArmRight, 0, duration); - moveLeg(ariaLegLeft, 0, duration); - moveLeg(ariaLegRight, 0, duration); -} - -// Utility helpers for limb control -function clamp(n, min, max) { return Math.max(min, Math.min(max, n)); } - -function parseAngleAndDuration(param) { - // Accept patterns: "45", "45,600", "raise", "raise,800" - if (!param) return { value: null, duration: 500 }; - const parts = String(param).split(',').map(s => s.trim()); - const first = parts[0]; - const dur = parts[1] ? parseInt(parts[1], 10) : 500; - const asNum = Number(first); - if (!Number.isNaN(asNum)) { - return { value: asNum, duration: isFinite(dur) ? dur : 500 }; - } - return { value: first.toLowerCase(), duration: isFinite(dur) ? dur : 500 }; -} - -function normalizeLimbPart(part) { - const p = (part || '').toLowerCase().replace(/[-\s]/g, '_'); - const map = { - 'leftarm': 'left_arm', 'arm_left': 'left_arm', 'l_arm': 'left_arm', 'left_hand':'left_arm', 'hand_left':'left_arm', - 'rightarm': 'right_arm', 'arm_right': 'right_arm', 'r_arm': 'right_arm', 'right_hand':'right_arm', 'hand_right':'right_arm', - 'leftleg': 'left_leg', 'leg_left': 'left_leg', 'l_leg':'left_leg', - 'rightleg': 'right_leg', 'leg_right': 'right_leg', 'r_leg':'right_leg' - }; - return map[p] || p; -} - -function elementForPart(part) { - switch (part) { - case 'left_arm': return ariaArmLeft; - case 'right_arm': return ariaArmRight; - case 'left_leg': return ariaLegLeft; - case 'right_leg': return ariaLegRight; - default: return null; - } -} - -function waveArmElement(armEl, duration = 700) { - if (!armEl) return; - const step = Math.max(120, duration); - moveArm(armEl, -60, step * 0.3); - setTimeout(() => moveArm(armEl, -30, step * 0.25), step * 0.3); - setTimeout(() => moveArm(armEl, -60, step * 0.25), step * 0.55); - setTimeout(() => moveArm(armEl, -30, step * 0.2), step * 0.8); - setTimeout(() => moveArm(armEl, 0, step * 0.2), step * 1.0); -} - -function kickLegElement(legEl, duration = 500) { - if (!legEl) return; - moveLeg(legEl, 45, duration * 0.5); - setTimeout(() => moveLeg(legEl, 0, duration * 0.5), duration * 0.5); -} - -function handleLimbTag(partRaw, paramRaw) { - const part = normalizeLimbPart(partRaw); - const targetEl = elementForPart(part); - if (!targetEl) { - log(`❓ Unknown limb: ${partRaw}`); - return; - } - const { value, duration } = parseAngleAndDuration(paramRaw); - const isArm = part.includes('arm'); - const isLeg = part.includes('leg'); - // Pause idle while moving a limb - isPerformingAction = true; - - if (typeof value === 'number') { - const angle = isArm ? clamp(value, -130, 130) : clamp(value, -60, 60); - if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); - setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); - return; - } - - const action = (value || '').toLowerCase(); - switch (action) { - case 'raise': - case 'up': - if (isArm) moveArm(targetEl, -90, duration); else moveLeg(targetEl, -30, duration); - break; - case 'lower': - case 'down': - if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); - break; - case 'forward': - if (isArm) moveArm(targetEl, -45, duration); else moveLeg(targetEl, 30, duration); - break; - case 'back': - case 'backward': - if (isArm) moveArm(targetEl, 45, duration); else moveLeg(targetEl, -30, duration); - break; - case 'wave': - if (isArm) waveArmElement(targetEl, duration); - break; - case 'kick': - if (isLeg) kickLegElement(targetEl, duration); - break; - case 'rest': - case 'neutral': - if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); - break; - default: - // Try numeric fallback if action is numeric-like - const num = Number(action); - if (!Number.isNaN(num)) { - const angle = isArm ? clamp(num, -130, 130) : clamp(num, -60, 60); - if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); - } else { - log(`❓ Unknown limb action: ${action}`); - } - } - setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); -} - -function walkCycle() { - // Alternating leg movement - moveLeg(ariaLegLeft, 25, 300); - moveLeg(ariaLegRight, -25, 300); - moveArm(ariaArmLeft, -15, 300); - moveArm(ariaArmRight, 15, 300); - - setTimeout(() => { - moveLeg(ariaLegLeft, -25, 300); - moveLeg(ariaLegRight, 25, 300); - moveArm(ariaArmLeft, 15, 300); - moveArm(ariaArmRight, -15, 300); - }, 300); - - setTimeout(() => resetLimbs(200), 600); -} - -function strutWalk() { - // Confident strut with head bob - ariaHead.style.transition = 'transform 0.3s'; - - moveLeg(ariaLegLeft, 35, 250); - moveLeg(ariaLegRight, -35, 250); - moveArm(ariaArmLeft, -25, 250); - moveArm(ariaArmRight, 25, 250); - ariaHead.style.transform = 'translateY(-5px) rotate(3deg)'; - - setTimeout(() => { - moveLeg(ariaLegLeft, -35, 250); - moveLeg(ariaLegRight, 35, 250); - moveArm(ariaArmLeft, 25, 250); - moveArm(ariaArmRight, -25, 250); - ariaHead.style.transform = 'translateY(-5px) rotate(-3deg)'; - }, 300); - - setTimeout(() => { - moveLeg(ariaLegLeft, 20, 250); - moveLeg(ariaLegRight, -20, 250); - ariaHead.style.transform = 'translateY(0) rotate(0)'; - }, 600); - - setTimeout(() => resetLimbs(200), 850); -} - -// 3D Waypoint System - Fixed positions in 3D space -const waypoints3D = { - 'center': { x: 50, y: 50, z: 0, rotation: 0, name: 'Center Stage' }, - 'front-left': { x: 25, y: 70, z: 150, rotation: 45, name: 'Front Left' }, - 'front-right': { x: 75, y: 70, z: 150, rotation: -45, name: 'Front Right' }, - 'back-left': { x: 25, y: 30, z: -200, rotation: 135, name: 'Back Left' }, - 'back-right': { x: 75, y: 30, z: -200, rotation: -135, name: 'Back Right' }, - 'front-center': { x: 50, y: 75, z: 180, rotation: 0, name: 'Front Center' }, - 'back-center': { x: 50, y: 25, z: -250, rotation: 180, name: 'Back Center' }, - 'stage-left': { x: 15, y: 50, z: -50, rotation: 90, name: 'Stage Left' }, - 'stage-right': { x: 85, y: 50, z: -50, rotation: -90, name: 'Stage Right' }, - 'table': { x: 60, y: 20, z: -100, rotation: 0, name: 'Near Table' } -}; - -function moveToWaypoint(waypointName) { - const waypoint = waypoints3D[waypointName]; - if (!waypoint) { - log(`❌ Unknown waypoint: ${waypointName}`, true); - return false; - } - - log(`📍 Moving to waypoint: ${waypoint.name}`); - showFeedback(`📍 WAYPOINT: ${waypoint.name}`); - // Notify in chat that Aria is moving - addChatMessage('aria', `Heading to ${waypoint.name}...`); - characterState.currentWaypoint = waypointName; - setPosition(waypoint.x, waypoint.y, waypoint.z, waypoint.rotation); - return true; -} - -function moveInCircle3D(radius = 30, steps = 12, duration = 6000) { - let currentStep = 0; - const centerX = 50; - const centerY = 50; - const stepDuration = duration / steps; - - const circleInterval = setInterval(() => { - if (currentStep >= steps) { - clearInterval(circleInterval); - log('🔄 Circle completed'); - addChatMessage('aria', 'Finished circular movement.'); - return; - } - - const angle = (currentStep / steps) * Math.PI * 2; - const x = centerX + radius * Math.cos(angle); - const y = centerY + radius * Math.sin(angle); - const z = 100 * Math.sin(angle * 2); // Wave in/out on Z-axis - const rotation = (angle * 180 / Math.PI) + 90; // Face tangent to circle - - setPosition(x, y, z, rotation); - currentStep++; - }, stepDuration); - - log('🔄 Starting 3D circle movement'); - showFeedback('🔄 3D CIRCLE'); - addChatMessage('aria', 'Starting circular 3D movement'); -} - -function performSpiral3D() { - const steps = 20; - const duration = 8000; - let currentStep = 0; - const centerX = 50; - const centerY = 50; - - const spiralInterval = setInterval(() => { - if (currentStep >= steps) { - clearInterval(spiralInterval); - log('🌀 Spiral completed'); - addChatMessage('aria', 'Finished spiral movement.'); - return; - } - - const progress = currentStep / steps; - const radius = 40 * (1 - progress); // Shrink radius - const angle = progress * Math.PI * 6; // Multiple rotations - const x = centerX + radius * Math.cos(angle); - const y = centerY + radius * Math.sin(angle); - const z = -200 + progress * 300; // Move from back to front - const rotation = angle * 180 / Math.PI; - - setPosition(x, y, z, rotation); - currentStep++; - }, duration / steps); - - log('🌀 Starting 3D spiral movement'); - showFeedback('🌀 3D SPIRAL'); - addChatMessage('aria', 'Starting 3D spiral movement'); -} - -function skipMove() { - // Playful skipping motion - moveLeg(ariaLegLeft, -45, 200); - moveLeg(ariaLegRight, 30, 200); - moveArm(ariaArmLeft, -30, 200); - moveArm(ariaArmRight, -30, 200); - aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; - - setTimeout(() => { - moveLeg(ariaLegLeft, 30, 200); - moveLeg(ariaLegRight, -45, 200); - aria.style.transform = 'translateX(-50%) scale(1)'; - }, 250); - - setTimeout(() => { - moveLeg(ariaLegLeft, -30, 200); - moveLeg(ariaLegRight, 20, 200); - aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; - }, 500); - - setTimeout(() => { - resetLimbs(200); - aria.style.transform = 'translateX(-50%) scale(1)'; - }, 750); -} - -function danceLimbs() { - // Arms up and down - moveArm(ariaArmLeft, -45, 200); - moveArm(ariaArmRight, -45, 200); - moveLeg(ariaLegLeft, 15, 200); - moveLeg(ariaLegRight, -15, 200); - - setTimeout(() => { - moveArm(ariaArmLeft, -90, 200); - moveArm(ariaArmRight, -90, 200); - moveLeg(ariaLegLeft, -15, 200); - moveLeg(ariaLegRight, 15, 200); - }, 200); - - setTimeout(() => { - moveArm(ariaArmLeft, -45, 200); - moveArm(ariaArmRight, -45, 200); - moveLeg(ariaLegLeft, 15, 200); - moveLeg(ariaLegRight, -15, 200); - }, 400); - - setTimeout(() => resetLimbs(200), 600); -} - -function expressiveDance() { - // More exaggerated dance moves - moveArm(ariaArmLeft, -120, 150); - moveArm(ariaArmRight, 60, 150); - moveLeg(ariaLegLeft, 20, 150); - moveLeg(ariaLegRight, -20, 150); - ariaHead.style.transform = 'rotate(15deg)'; - - setTimeout(() => { - moveArm(ariaArmLeft, 60, 150); - moveArm(ariaArmRight, -120, 150); - moveLeg(ariaLegLeft, -20, 150); - moveLeg(ariaLegRight, 20, 150); - ariaHead.style.transform = 'rotate(-15deg)'; - }, 200); - - setTimeout(() => { - moveArm(ariaArmLeft, -90, 150); - moveArm(ariaArmRight, -90, 150); - moveLeg(ariaLegLeft, 30, 150); - moveLeg(ariaLegRight, -30, 150); - ariaHead.style.transform = 'rotate(0)'; - }, 400); - - setTimeout(() => resetLimbs(200), 600); -} - -let continuousDanceInterval = null; - -function startContinuousDance() { - if (continuousDanceInterval) return; - - isPerformingAction = true; - showFeedback('🎉 PARTY MODE!'); - - continuousDanceInterval = setInterval(() => { - const danceType = Math.random(); - if (danceType > 0.5) { - expressiveDance(); - } else { - danceLimbs(); - } - }, 700); -} - -function stopContinuousDance() { - if (continuousDanceInterval) { - clearInterval(continuousDanceInterval); - continuousDanceInterval = null; - isPerformingAction = false; - resetLimbs(300); - } -} - -function spinLimbs() { - // Arms out during spin - moveArm(ariaArmLeft, -90, 100); - moveArm(ariaArmRight, -90, 100); - setTimeout(() => resetLimbs(300), 900); -} - -function animate(className) { - console.log('🎬 Animating:', className); - showFeedback('🎬 ' + className.toUpperCase() + '!'); - aria.classList.remove('jumping', 'dancing', 'spinning', 'waving'); - void aria.offsetWidth; // Force reflow - - // Mark as performing action - isPerformingAction = true; - - // Get current scale from characterState - const currentScale = characterState.size || 1.0; - - if (className === 'waving') { - showFeedback('👋 WAVING!'); - console.log('Wave animation with arm movement'); - // Rapid wave motion - moveArm(ariaArmRight, -60, 200); - setTimeout(() => moveArm(ariaArmRight, -30, 150), 200); - setTimeout(() => moveArm(ariaArmRight, -60, 150), 350); - setTimeout(() => moveArm(ariaArmRight, -30, 150), 500); - setTimeout(() => moveArm(ariaArmRight, -60, 150), 650); - setTimeout(() => resetLimbs(200), 800); - } else if (className === 'jumping') { - console.log('Jumping animation triggered with leg bending'); - aria.classList.add(className); - aria.style.filter = 'brightness(1.5)'; - - // Pre-jump crouch - moveLeg(ariaLegLeft, 45, 200); - moveLeg(ariaLegRight, -45, 200); - moveArm(ariaArmLeft, -20, 200); - moveArm(ariaArmRight, -20, 200); - - // During jump - legs extend - setTimeout(() => { - moveLeg(ariaLegLeft, -35, 300); - moveLeg(ariaLegRight, 35, 300); - moveArm(ariaArmLeft, -60, 300); - moveArm(ariaArmRight, -60, 300); - }, 300); - - // Landing crouch - setTimeout(() => { - moveLeg(ariaLegLeft, 35, 300); - moveLeg(ariaLegRight, -35, 300); - moveArm(ariaArmLeft, -10, 300); - moveArm(ariaArmRight, -10, 300); - aria.style.filter = 'brightness(1)'; - }, 1500); - - // Return to normal - setTimeout(() => { - resetLimbs(400); - aria.classList.remove(className); - aria.style.transform = `translateX(-50%) scale(${currentScale})`; - }, 2500); - } else { - console.log('Generic animation triggered:', className); - aria.classList.add(className); - - // Add limb movements based on animation type - if (className === 'dancing') { - console.log('Adding dance limb movements'); - // Repeat dance limbs during 2s animation - danceLimbs(); - setTimeout(() => danceLimbs(), 700); - setTimeout(() => danceLimbs(), 1400); - } else if (className === 'spinning') { - console.log('Adding spin limb movements'); - spinLimbs(); - } - - setTimeout(() => { - aria.classList.remove(className); - resetLimbs(300); - aria.style.transform = `translateX(-50%) scale(${currentScale})`; - isPerformingAction = false; - }, className === 'dancing' ? 2000 : 1000); - } -} - -function getAnimationClass(action) { - const animations = { - 'jump': 'jumping', - 'dance': 'dancing', - 'spin': 'spinning', - 'wave': 'waving', - 'bow': 'waving', - 'flip': 'spinning', - 'backflip': 'spinning' - }; - return animations[action] || 'jumping'; -} - -function move(direction, speed = 'normal') { - console.log('🚶 Moving:', direction, 'at speed:', speed); - - isPerformingAction = true; - - const currentLeft = aria.style.left || '50%'; - const current = parseFloat(currentLeft); - - let newPos = current; - let distance = 25; - let movementStyle = walkCycle; - let duration = '1s'; - - // Choose movement style based on speed - switch(speed) { - case 'run': - case 'fast': - distance = 40; - movementStyle = skipMove; - duration = '0.7s'; - showFeedback('🏃 RUNNING!'); - break; - case 'strut': - case 'swagger': - distance = 30; - movementStyle = strutWalk; - duration = '1.2s'; - showFeedback('😎 STRUTTING!'); - break; - case 'skip': - distance = 35; - movementStyle = skipMove; - duration = '0.9s'; - showFeedback('🎵 SKIPPING!'); - break; - default: - showFeedback('🚶 WALKING!'); - } - - // Animate movement style - movementStyle(); - - switch (direction) { - case 'left': - newPos = Math.max(5, current - distance); - break; - case 'right': - newPos = Math.min(95, current + distance); - break; - case 'forward': - case 'up': - const currentTop = aria.style.top || '50%'; - const top = parseFloat(currentTop); - aria.style.top = Math.max(10, top - distance) + '%'; - aria.style.transition = `top ${duration} ease`; - setTimeout(() => { isPerformingAction = false; }, 1000); - return; - case 'back': - case 'down': - const currentTopBack = aria.style.top || '50%'; - const topBack = parseFloat(currentTopBack); - aria.style.top = Math.min(80, topBack + distance) + '%'; - aria.style.transition = `top ${duration} ease`; - setTimeout(() => { isPerformingAction = false; }, 1000); - return; - } - - aria.style.transition = `left ${duration} ease`; - aria.style.left = newPos + '%'; - - setTimeout(() => { isPerformingAction = false; }, 1000); -} - -function createEffect(type) { - const effects = { - 'sparkle': '✨', - 'glow': '💫', - 'hearts': '💕' - }; - - const emoji = effects[type] || '✨'; - - for (let i = 0; i < 5; i++) { - setTimeout(() => { - const effect = document.createElement('div'); - effect.className = `effect ${type}`; - effect.textContent = emoji; - effect.style.left = (Math.random() * 80 + 10) + '%'; - effect.style.top = (Math.random() * 80 + 10) + '%'; - stage.appendChild(effect); - - setTimeout(() => effect.remove(), 1500); - }, i * 100); - } -} - -function centerAria() { - aria.style.left = '50%'; - aria.style.transform = 'translateX(-50%) scale(1)'; -} - -function applyPose(poseRaw, param) { - const pose = (poseRaw || '').toLowerCase(); - // Body position presets - const poses = { - 'sit': { bottom: '20px', transform: 'translateX(-50%) scale(0.8)' }, - 'stand': { bottom: '50px', transform: 'translateX(-50%) scale(1)' }, - 'crouch': { bottom: '30px', transform: 'translateX(-50%) scale(0.9)' }, - 'lie': { bottom: '10px', transform: 'translateX(-50%) scale(0.7) rotate(90deg)' } - }; - const poseStyle = poses[pose]; - if (poseStyle) { - aria.style.bottom = poseStyle.bottom; - aria.style.transform = poseStyle.transform; - } - // Limb presets - switch (pose) { - case 't-pose': - case 'tpose': - moveArm(ariaArmLeft, -90, 400); - moveArm(ariaArmRight, -90, 400); - resetLimbs(1000); - break; - case 'hands_up': - case 'hands-up': - case 'handsup': - moveArm(ariaArmLeft, -120, 400); - moveArm(ariaArmRight, -120, 400); - break; - case 'cross_arms': - case 'cross-arms': - case 'crossarms': - moveArm(ariaArmLeft, -30, 400); - moveArm(ariaArmRight, 30, 400); - break; - case 'hero': - // Hands on hips (approximate) - moveArm(ariaArmLeft, 45, 400); - moveArm(ariaArmRight, -45, 400); - break; - } -} - -function quickCommand(cmd) { - commandInput.value = cmd; - sendCommand(); -} - -// Random character evolution (simulates AI personality drift) -function randomCharacterEvolution() { - const moods = ['happy', 'sad', 'calm', 'thinking', 'neutral']; - const randomMood = moods[Math.floor(Math.random() * moods.length)]; - const randomEnergy = 30 + Math.floor(Math.random() * 70); - - log('✨ Character evolving to: ' + randomMood + ' (' + randomEnergy + '% energy)'); - - const newStyle = generateCharacterFromMood(randomMood, randomEnergy); - applyCharacterStyle(newStyle); -} - -// Automatic character evolution disabled - keeping neutral human-like appearance -let evolutionCountdown = 30; -const timerDisplay = document.getElementById('evolutionTimer'); - -// setInterval(randomCharacterEvolution, 30000); // Disabled - -// Countdown timer -setInterval(() => { - evolutionCountdown--; - if (evolutionCountdown <= 0) { - evolutionCountdown = 30; - } - - // Update timer display - if (timerDisplay) { - timerDisplay.textContent = `⏰ Next evolution in: ${evolutionCountdown}s`; - if (evolutionCountdown <= 5) { - timerDisplay.style.color = '#e74c3c'; - timerDisplay.style.animation = 'pulse 0.5s ease infinite'; - } else { - timerDisplay.style.color = '#667eea'; - timerDisplay.style.animation = 'none'; - } - } - - if (evolutionCountdown <= 5 && evolutionCountdown > 0) { - log(`⏰ Character evolving in ${evolutionCountdown} seconds...`); - } -}, 1000); - -// Initialize -log('🎨 Aria Visual System Ready!'); -log('🤖 AI Character Generation: ACTIVE'); -log('Type commands or use quick buttons'); -log('⏰ Auto-evolution every 30 seconds'); - -// Object Interaction System -// Helper: derive stage-relative percentage position from an element -function objectPositionFromElement(elem) { - if (!elem) return null; - const stageRect = stage.getBoundingClientRect(); - // Prefer explicit CSS left/bottom if present (percent strings) - try { - if (elem.style && elem.style.left && elem.style.bottom) { - const left = parseFloat(elem.style.left || 0); - const bottom = parseFloat(elem.style.bottom || 0); - // The JS 'y' value used elsewhere equals the CSS bottom percent - return { x: Math.round(left), y: Math.round(bottom) }; - } - } catch (e) { - // fallthrough to computed rect - } - - const rect = elem.getBoundingClientRect(); - const x = ((rect.left - stageRect.left) / stageRect.width) * 100; - const y = 100 - ((rect.bottom - stageRect.top) / stageRect.height) * 100; - return { x: Math.round(x), y: Math.round(y) }; -} - -// Notify backend of object add/update/remove actions -async function sendObjectUpdate(action, objectId, extra = {}) { - try { - if (!objectId) return null; - const objEl = document.getElementById(objectId); - - // resolve position and state - let position = extra.position || null; - if (!position && objEl) position = objectPositionFromElement(objEl); - - const state = extra.state || (objEl ? (objEl.classList.contains('held') ? 'held' : (objEl.style.display === 'none' ? 'hidden' : 'on_table')) : (extra.state || 'unknown')); - const emoji = extra.emoji || (objEl ? objEl.textContent : undefined); - - const objPayload = { id: objectId }; - if (position !== null && position !== undefined) objPayload.position = position; - if (state !== null && state !== undefined) objPayload.state = state; - if (emoji !== null && emoji !== undefined) objPayload.emoji = emoji; - const payload = { action: action, object: objPayload }; - - const res = await mockApiCall('/api/aria/object', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify(payload) - }); - - if (!res.ok) { - console.warn('Object API returned non-OK status', res.status); - return null; - } - - const data = await res.json(); - console.log('object sync:', action, objectId, data); - return data; - } catch (err) { - console.warn('sendObjectUpdate failed:', err); - return null; - } -} -function pickUpObject(objectId) { - const obj = document.getElementById(objectId); - if (!obj) { - console.log('❌ Object not found:', objectId); - return false; - } - - if (characterState.heldObject) { - showFeedback('⚠️ Already holding ' + characterState.heldObject.toUpperCase() + '!'); - return false; - } - - console.log('🤏 Picking up:', objectId); - showFeedback('🤏 PICKED UP ' + objectId.toUpperCase() + '!'); - - // Store held object - characterState.heldObject = objectId; - characterState.heldObjectElement = obj; - - // Visual feedback - obj.classList.add('held'); - - // Sync server side: mark object as held - sendObjectUpdate('update', objectId, { state: 'held' }).catch(() => {}); - - // Position object above character - positionHeldObject(); - - // Arm reaching animation - moveArm(ariaArmRight, -90, 300); - setTimeout(() => moveArm(ariaArmRight, -45, 200), 300); - - return true; -} - -function dropObject() { - if (!characterState.heldObject) { - showFeedback('⚠️ Not holding anything!'); - return false; - } - - console.log('📦 Dropping:', characterState.heldObject); - showFeedback('📦 DROPPED ' + characterState.heldObject.toUpperCase() + '!'); - - const obj = characterState.heldObjectElement; - - // Remove held state - obj.classList.remove('held'); - - // Drop at current Aria position - const ariaRect = aria.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - - const dropLeft = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; - const dropBottom = ((stageRect.bottom - ariaRect.bottom) / stageRect.height) * 100; - - obj.style.left = dropLeft + '%'; - obj.style.bottom = (dropBottom + 10) + '%'; - - // Arm dropping animation - moveArm(ariaArmRight, -90, 200); - setTimeout(() => resetLimbs(300), 200); - - // Clear held object - characterState.heldObject = null; - characterState.heldObjectElement = null; - - // Sync server side: update object's position/state - const finalX = Math.round(dropLeft); - const finalY = Math.round(dropBottom + 10); - const tableX = 60; // server default table X - const isOnTable = Math.abs(finalX - tableX) < 20 && Math.abs(finalY - 35) < 20; - sendObjectUpdate('update', obj.id, { position: { x: finalX, y: finalY }, state: isOnTable ? 'on_table' : 'on_stage' }).catch(() => {}); - - return true; -} - -function throwObject(direction) { - if (!characterState.heldObject) { - showFeedback('⚠️ Not holding anything to throw!'); - return false; - } - - console.log('🎯 Throwing:', characterState.heldObject, 'to', direction); - showFeedback('🎯 THROWING ' + characterState.heldObject.toUpperCase() + '!'); - - const obj = characterState.heldObjectElement; - obj.classList.remove('held'); - - // Throwing arm animation - moveArm(ariaArmRight, -120, 150); - setTimeout(() => { - moveArm(ariaArmRight, -30, 200); - setTimeout(() => resetLimbs(300), 100); - }, 150); - - // Calculate throw trajectory - const currentLeft = parseFloat(obj.style.left); - const currentBottom = parseFloat(obj.style.bottom); - - let targetLeft = currentLeft; - let targetBottom = currentBottom; - - switch(direction) { - case 'left': - targetLeft = Math.max(5, currentLeft - 40); - break; - case 'right': - targetLeft = Math.min(95, currentLeft + 40); - break; - case 'up': - targetBottom = Math.min(90, currentBottom + 30); - break; - } - - // Animate throw with arc - obj.style.transition = 'all 0.8s cubic-bezier(0.25, 0.46, 0.45, 0.94)'; - obj.style.left = targetLeft + '%'; - obj.style.bottom = targetBottom + '%'; - obj.style.transform = 'rotate(360deg) scale(0.9)'; - - // Reset after landing - setTimeout(() => { - obj.style.transition = 'all 0.3s ease'; - obj.style.transform = 'rotate(0deg) scale(1)'; - }, 800); - - // Clear held object - characterState.heldObject = null; - characterState.heldObjectElement = null; - - // Sync server side after throw completes - setTimeout(() => { - const left = Math.round(targetLeft); - const bottom = Math.round(targetBottom); - sendObjectUpdate('update', obj.id, { position: { x: left, y: bottom }, state: 'on_stage' }).catch(() => {}); - }, 900); - - return true; -} - -function positionHeldObject() { - if (!characterState.heldObjectElement) return; - - // Position object above and slightly in front of character - const ariaRect = aria.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - - const objLeft = ((ariaRect.left - stageRect.left + ariaRect.width * 0.6) / stageRect.width) * 100; - const objBottom = ((stageRect.bottom - ariaRect.top + 20) / stageRect.height) * 100; - - characterState.heldObjectElement.style.left = objLeft + '%'; - characterState.heldObjectElement.style.bottom = objBottom + '%'; -} - -function interactWithObject(action, objectId) { - console.log('🔧 Interacting:', action, 'with', objectId); - - switch(action) { - case 'pickup': - case 'grab': - case 'take': - case 'get': - return pickUpObject(objectId); - case 'drop': - case 'place': - case 'put': - return dropObject(); - case 'throw': - case 'toss': - return throwObject('right'); - default: - showFeedback('❓ Unknown action: ' + action); - return false; - } -} - -// Make objects draggable and clickable -function initializeObjectInteractions() { - const objects = document.querySelectorAll('.object'); - - objects.forEach(obj => { - // Prevent adding duplicate event listeners when called multiple times - if (obj.__interactionInitialized) return; - obj.__interactionInitialized = true; - obj.addEventListener('click', (e) => { - e.stopPropagation(); - const objectId = obj.id; - - if (!characterState.heldObject) { - pickUpObject(objectId); - } else if (characterState.heldObject === objectId) { - dropObject(); - } - }); - - // Drag functionality - let isDragging = false; - let startX, startY, startLeft, startBottom; - - obj.addEventListener('mousedown', (e) => { - if (characterState.heldObject === obj.id) return; - - isDragging = true; - obj.classList.add('grabbed'); - - const rect = obj.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - - startX = e.clientX; - startY = e.clientY; - startLeft = ((rect.left - stageRect.left) / stageRect.width) * 100; - startBottom = ((stageRect.bottom - rect.bottom) / stageRect.height) * 100; - - e.preventDefault(); - }); - - document.addEventListener('mousemove', (e) => { - if (!isDragging) return; - - const stageRect = stage.getBoundingClientRect(); - const deltaX = ((e.clientX - startX) / stageRect.width) * 100; - const deltaY = -((e.clientY - startY) / stageRect.height) * 100; - - obj.style.left = Math.max(0, Math.min(100, startLeft + deltaX)) + '%'; - obj.style.bottom = Math.max(0, Math.min(100, startBottom + deltaY)) + '%'; - }); - - document.addEventListener('mouseup', () => { - if (isDragging) { - isDragging = false; - obj.classList.remove('grabbed'); - // Sync backend with final position - const pos = objectPositionFromElement(obj); - const state = obj.classList.contains('held') ? 'held' : 'on_table'; - sendObjectUpdate('update', obj.id, { position: pos, state: state }).catch(() => {}); - } - }); - }); -} - -function addObject(objectName, emoji) { - console.log('➕ Adding object:', objectName, emoji); - - // Check if object already exists - const existingObj = document.getElementById(objectName); - if (existingObj) { - console.log('⚠️ Object already exists:', objectName); - showFeedback('✅ ' + objectName + ' already on stage'); - return; - } - - // Create new object element - const newObj = document.createElement('div'); - newObj.id = objectName; - newObj.className = 'object'; - newObj.textContent = emoji; - - // Position near Aria - const ariaX = characterState.position.x; - const ariaY = characterState.position.y; - newObj.style.left = Math.max(10, Math.min(90, ariaX + 15)) + '%'; - newObj.style.bottom = Math.max(10, Math.min(90, ariaY + 5)) + '%'; - - // Add to stage - stage.appendChild(newObj); - - // Update tracking - activeObjects[objectName] = true; - - // Add to object manager if button doesn't exist - if (!document.querySelector(`[onclick="toggleObject('${objectName}')"]`)) { - const objectManager = document.getElementById('object-manager'); - const btn = document.createElement('button'); - btn.className = 'object-btn active'; - btn.id = 'btn-' + objectName; - btn.textContent = emoji + ' ' + objectName; - btn.onclick = () => toggleObject(objectName); - objectManager.appendChild(btn); - } - - // Initialize interactions for new object (click/drag) - initializeObjectInteractions(); - - // Sync server: add new object to stage_state - sendObjectUpdate('add', objectName, { position: objectPositionFromElement(newObj), state: 'on_stage', emoji: emoji }).catch(() => {}); - - showFeedback('✨ Added ' + objectName + ' to stage!'); - console.log('✅ Object added successfully:', objectName); -} - -// Initialize object interactions -initializeObjectInteractions(); -log('🎮 Objects: apple, book, cup, ball, flower'); -log('💡 Try: "pick up apple", "drop", "throw ball"'); - -// Auto-behaviors with random timing -function startAutoBehaviors() { - // Random idle movements every 8-15 seconds - setInterval(() => { - if (Math.random() > 0.6) { - const randomActions = ['shift weight', 'look around', 'adjust hair', 'stretch']; - const action = randomActions[Math.floor(Math.random() * randomActions.length)]; - - switch(action) { - case 'shift weight': - animate('bouncing'); - setTimeout(() => aria.style.animation = 'breathe 4s ease-in-out infinite', 800); - break; - case 'look around': - const ariaHead = document.querySelector('.aria-head'); - ariaHead.style.transform = 'translateX(-50%) rotate(10deg)'; - setTimeout(() => ariaHead.style.transform = 'translateX(-50%) rotate(-10deg)', 600); - setTimeout(() => ariaHead.style.transform = 'translateX(-50%)', 1200); - break; - case 'adjust hair': - const hand = document.getElementById('ariaHandRight'); - hand.style.transform = 'translateY(-30px) rotate(-20deg)'; - setTimeout(() => hand.style.transform = '', 1000); - break; - case 'stretch': - ariaArmLeft.style.transform = 'rotate(-160deg)'; - ariaArmRight.style.transform = 'rotate(160deg)'; - setTimeout(() => { - ariaArmLeft.style.transform = ''; - ariaArmRight.style.transform = ''; - }, 1500); - break; - } - } - }, 8000 + Math.random() * 7000); - - // Random expressions every 12-20 seconds - setInterval(() => { - if (Math.random() > 0.5) { - const expressions = ['smile', 'thinking', 'neutral', 'surprised']; - const expr = expressions[Math.floor(Math.random() * expressions.length)]; - changeExpression(expr); - setTimeout(() => changeExpression('neutral'), 2000 + Math.random() * 3000); - } - }, 12000 + Math.random() * 8000); - - // Occasional sparkles every 20-30 seconds - setInterval(() => { - if (Math.random() > 0.7) { - createEffect('sparkle'); - } - }, 20000 + Math.random() * 10000); -} - -// Initial appearance set to neutral human-like style -setTimeout(() => { - log('🎨 Setting initial character appearance...'); - const neutralStyle = generateCharacterFromMood('neutral', 50); - applyCharacterStyle(neutralStyle); -}, 100); - -// Start idle animations -startIdleAnimation(); -log('👀 Idle animations enabled - watch for breathing and blinking!'); - -// Start automatic behaviors -startAutoBehaviors(); -log('✨ Auto-behaviors enabled - Aria will move and react on her own!'); - -// Expose minimal testing helpers -window.ariaTest = { - limb: (part, actionOrAngle, duration) => handleLimbTag(part, typeof actionOrAngle === 'number' ? `${actionOrAngle},${duration||500}` : `${actionOrAngle||''}${duration?','+duration:''}`), - pose: (name) => applyPose(name) -}; -window.simulateTags = (arr) => Array.isArray(arr) && executeTags(arr); +// Aria Visual Command Controller +// GitHub Pages Demo Mode Configuration +const DEMO_MODE = true; // Set to true for GitHub Pages, false for local backend +const DEMO_API_DELAY = 300; // Simulate network delay in ms + +const aria = document.getElementById('aria'); +const ariaMouth = document.getElementById('ariaMouth'); +const ariaArmLeft = document.getElementById('ariaArmLeft'); +const ariaArmRight = document.getElementById('ariaArmRight'); +const ariaLegLeft = document.getElementById('ariaLegLeft'); +const ariaLegRight = document.getElementById('ariaLegRight'); +const stage = document.getElementById('stage'); +const commandInput = document.getElementById('commandInput'); +const logContainer = document.getElementById('logContainer'); + +// Demo Mode Mock API Functions +async function mockApiCall(endpoint, options) { + if (!DEMO_MODE) { + // In non-demo mode, make real API call + return fetch(endpoint, options); + } + + // Simulate network delay + await new Promise(resolve => setTimeout(resolve, DEMO_API_DELAY)); + + // Mock responses based on endpoint + if (endpoint === '/api/aria/command') { + const body = JSON.parse(options.body); + return { + ok: true, + status: 200, + json: async () => ({ + success: true, + response: `Understood: "${body.command}". In demo mode, I'm simulating the response!`, + tags: [], + command: body.command + }) + }; + } else if (endpoint === '/api/aria/object') { + const body = JSON.parse(options.body); + return { + ok: true, + status: 200, + json: async () => ({ + success: true, + action: body.action, + object: body.object + }) + }; + } + + // Default mock response + return { + ok: true, + status: 200, + json: async () => ({ success: true, demo: true }) + }; +} + +// Track active objects +const activeObjects = { + apple: true, + book: true, + cup: true, + ball: true, + flower: true +}; + +// Toggle object visibility +function toggleObject(objectId) { + const obj = document.getElementById(objectId); + const btn = document.getElementById('btn-' + objectId); + + if (!obj) { + log(`❌ toggleObject: unknown object ${objectId}`, true); + return; + } + // compute last-known position first + const lastPos = objectPositionFromElement(obj); + + if (activeObjects[objectId]) { + // Remove object + obj.style.display = 'none'; + btn.classList.remove('active'); + btn.classList.add('inactive'); + activeObjects[objectId] = false; + log('🗑️ Removed ' + objectId); + // Sync change to backend + sendObjectUpdate('update', objectId, { position: lastPos, state: 'hidden' }).catch(() => {}); + } else { + // Add object back + obj.style.display = 'block'; + btn.classList.add('active'); + btn.classList.remove('inactive'); + activeObjects[objectId] = true; + log('➕ Added ' + objectId); + // Sync change to backend (object is back on stage) + sendObjectUpdate('update', objectId, { position: lastPos, state: 'on_table' }).catch(() => {}); + } +} + +// Additional DOM references (used by idle/poses/expressive moves) +const ariaHead = document.querySelector('.aria-head'); +const ariaBody = document.querySelector('.aria-body'); +const ariaEyes = document.querySelectorAll('.aria-eye'); +const ariaEyeLeft = ariaEyes[0]; +const ariaEyeRight = ariaEyes[1]; + +// AI-Controlled Character State (single combined object) +let characterState = { + mood: 'neutral', + energy: 50, + personality: 'balanced', + colors: { + hair: '#4a3728', + skin: '#f5d4b8', + body: '#4a90e2', + legs: '#3d5a80', + feet: '#f5f5f5' + }, + size: 1.0, + style: 'default', + heldObject: null, + heldObjectElement: null, + position: { x: 20, y: 70, z: 0 }, + rotation: 0, + isMoving: false, + currentWaypoint: null +}; + +// Visual feedback function +function showFeedback(message) { + const feedback = document.createElement('div'); + feedback.textContent = message; + feedback.style.cssText = 'position:absolute; top:20px; left:50%; transform:translateX(-50%); background:#e74c3c; color:white; padding:15px 30px; border-radius:15px; font-size:28px; font-weight:bold; z-index:999; box-shadow:0 5px 20px rgba(0,0,0,0.3); animation:pulse 0.5s ease;'; + stage.appendChild(feedback); + setTimeout(() => feedback.remove(), 2500); +} + +// AI-Driven Character Generation +function analyzeAIResponse(text) { + const lowerText = text.toLowerCase(); + + // Detect mood from response + if (lowerText.includes('happy') || lowerText.includes('great') || lowerText.includes('wonderful') || lowerText.includes('excited')) { + return { mood: 'happy', energy: 80 }; + } else if (lowerText.includes('sad') || lowerText.includes('sorry') || lowerText.includes('unfortunate')) { + return { mood: 'sad', energy: 30 }; + } else if (lowerText.includes('angry') || lowerText.includes('frustrated')) { + return { mood: 'angry', energy: 90 }; + } else if (lowerText.includes('calm') || lowerText.includes('peaceful') || lowerText.includes('relaxed')) { + return { mood: 'calm', energy: 40 }; + } else if (lowerText.includes('think') || lowerText.includes('consider') || lowerText.includes('perhaps')) { + return { mood: 'thinking', energy: 60 }; + } + + return { mood: 'neutral', energy: 50 }; +} + +function generateCharacterFromMood(mood, energy) { + const moodColors = { + happy: { body: '#5fb3f5', hair: '#6b4f3d', accent: '#7ac5ff' }, + sad: { body: '#6C7A89', hair: '#4a3728', accent: '#95A5A6' }, + angry: { body: '#E74C3C', hair: '#4a3728', accent: '#ff6b6b' }, + calm: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' }, + thinking: { body: '#5a7fa8', hair: '#4a3728', accent: '#6b8fb3' }, + neutral: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' } + }; + + const colors = moodColors[mood] || moodColors.neutral; + const size = 0.8 + (energy / 100) * 0.4; // Scale from 0.8 to 1.2 based on energy + + return { colors, size, mood }; +} + +function applyCharacterStyle(style) { + const ariaHead = document.querySelector('.aria-head'); + const ariaBody = document.querySelector('.aria-body'); + const ariaHair = document.querySelector('.aria-hair'); + const ariaLegs = document.querySelectorAll('.aria-leg'); + const ariaFeet = document.querySelectorAll('.aria-foot'); + + // Create dramatic transformation sparkle effect + for (let i = 0; i < 15; i++) { + setTimeout(() => { + createEffect('sparkle'); + }, i * 50); + } + + // Add glow pulse during transformation + aria.style.filter = 'drop-shadow(0 0 30px ' + style.colors.body + ') brightness(1.5)'; + setTimeout(() => { + aria.style.filter = 'none'; + }, 1000); + + // Apply colors with smooth transition + ariaHair.style.transition = 'background-color 1s ease, transform 1s ease'; + ariaHead.style.transition = 'background-color 1s ease'; + ariaBody.style.transition = 'background 1s ease, transform 1s ease'; + ariaLegs.forEach(leg => leg.style.transition = 'background-color 1s ease'); + ariaFeet.forEach(foot => foot.style.transition = 'background-color 1s ease'); + + ariaHair.style.backgroundColor = style.colors.hair; + ariaBody.style.background = `linear-gradient(135deg, ${style.colors.body}, ${style.colors.accent})`; + + // Apply size transformation + aria.style.transform = `translateX(-50%) scale(${style.size})`; + + // Update character state + characterState = { ...characterState, ...style }; + + console.log('🎨 Character updated:', style.mood, 'Energy:', Math.round(style.size * 100) + '%'); + showFeedback('🎨 TRANSFORM: ' + style.mood.toUpperCase()); +} + +function autoGenerateCharacter(responseText) { + const analysis = analyzeAIResponse(responseText); + const newStyle = generateCharacterFromMood(analysis.mood, analysis.energy); + applyCharacterStyle(newStyle); + + // Trigger automatic animation based on mood + setTimeout(() => { + if (analysis.mood === 'happy') { + animate('jumping'); + } else if (analysis.mood === 'sad') { + move('left', 'normal'); + } else if (analysis.mood === 'angry') { + animate('spinning'); + } else if (analysis.mood === 'calm') { + animate('waving'); + } else if (analysis.mood === 'thinking') { + changeExpression('thinking'); + } + }, 500); +} + +const expressions = { + 'smile': '😊', + 'happy': '😃', + 'sad': '😢', + 'surprised': '😲', + 'confused': '😕', + 'thinking': '🤔', + 'wink': '😉' +}; + +function log(message, isError = false) { + const entry = document.createElement('div'); + entry.className = 'log-entry'; + entry.style.borderLeftColor = isError ? '#e74c3c' : '#667eea'; + entry.style.color = isError ? '#e74c3c' : '#555'; + entry.textContent = `${new Date().toLocaleTimeString()}: ${message}`; + logContainer.insertBefore(entry, logContainer.firstChild); + + // Keep only last 10 entries + while (logContainer.children.length > 10) { + logContainer.removeChild(logContainer.lastChild); + } +} + +// Chat UI helpers +function addChatMessage(role, text) { + try { + const container = document.getElementById('chatMessages'); + if (!container) return; + + const msgWrap = document.createElement('div'); + msgWrap.className = 'chat-msg ' + (role === 'user' ? 'user' : (role === 'aria' ? 'aria' : 'system')); + + const bubble = document.createElement('div'); + bubble.className = 'bubble'; + bubble.innerText = text; + + const sender = document.createElement('div'); + sender.className = 'sender'; + sender.innerText = role === 'user' ? 'You' : role === 'aria' ? 'Aria' : 'System'; + + const inner = document.createElement('div'); + inner.appendChild(sender); + inner.appendChild(bubble); + + // For user messages align right + if (role === 'user') { + msgWrap.appendChild(inner); + } else { + msgWrap.appendChild(inner); + } + + container.appendChild(msgWrap); + container.scrollTop = container.scrollHeight; + } catch (e) { + console.warn('addChatMessage failed', e); + } +} + +// Called when Aria reaches a waypoint or finishes a movement +function arrivalFeedback(waypointKey) { + try { + let msg = ''; + if (waypointKey && waypoints3D && waypoints3D[waypointKey]) { + msg = `I've arrived at ${waypoints3D[waypointKey].name}.`; + } else if (typeof waypointKey === 'string' && waypointKey.includes('%')) { + msg = `Arrived at ${waypointKey}.`; + } else if (waypointKey) { + msg = `Arrived at ${waypointKey}.`; + } else { + msg = `Arrived.`; + } + + // Friendly reply from Aria in chat — pick varied arrival messages + const arrivalMessages = [ + `I'm here! 🎉`, + `Made it — ${waypointKey ? waypoints3D[waypointKey]?.name || waypointKey : 'arrived'}!`, + `Arrived and ready!`, + `That was quick — I'm here.`, + `All set. What should we do next?`, + ]; + const chosen = arrivalMessages[Math.floor(Math.random() * arrivalMessages.length)]; + addChatMessage('aria', chosen); + + // Little celebratory effect and expression + createEffect('sparkle'); + changeExpression('happy'); + + // Short arrival animation (small bounce & glow) + aria.classList.add('arrived'); + setTimeout(() => aria.classList.remove('arrived'), 900); + + // Clear current waypoint marker so we don't re-announce + characterState.currentWaypoint = null; + } catch (e) { + console.warn('arrivalFeedback failed', e); + } +} + +// Try to resolve a freeform phrase to a waypoint key +function resolveWaypointFromPhrase(phrase) { + if (!phrase || !waypoints3D) return null; + const clean = phrase.toLowerCase().replace(/[^a-z0-9\s-]/g, '').trim(); + if (!clean) return null; + + // direct key match (e.g., front-left) + const direct = clean.replace(/\s+/g, '-'); + if (waypoints3D[direct]) return direct; + + // check by names and partial matches + for (const k in waypoints3D) { + const v = waypoints3D[k]; + if (!v) continue; + const name = String(v.name || '').toLowerCase(); + if (name === clean) return k; + if (name.includes(clean) || clean.includes(k.replace('-', ' '))) return k; + } + + // synonyms mapping for simple words + const synonyms = { + 'front': 'front-center', 'back': 'back-center', 'left': 'stage-left', 'right': 'stage-right', 'center': 'center', 'table': 'table' + }; + if (synonyms[clean]) return synonyms[clean]; + + // try matching tokens + const tokens = clean.split(/\s+/); + for (const t of tokens) { + if (synonyms[t]) return synonyms[t]; + } + + return null; +} + +// Process chat input: supports slash commands and normal commands +async function sendChat() { + const chatBox = document.getElementById('chatInput'); + if (!chatBox) return; + + const text = chatBox.value.trim(); + if (!text) return; + + // Show user's message + addChatMessage('user', text); + chatBox.value = ''; + + // check for natural language movements (e.g., "go to front-right" or "walk to table") + const nlMove = /(?:go to|move to|walk to|goto)\s+([a-z0-9\-\s]+)/i.exec(text); + if (nlMove) { + const targetPhrase = nlMove[1].trim(); + // try to match known waypoint keys or names + const candidate = resolveWaypointFromPhrase(targetPhrase); + if (candidate) { + moveToWaypoint(candidate); + addChatMessage('aria', `Moving to ${waypoints3D[candidate].name}`); + } else { + addChatMessage('aria', `I couldn't identify a waypoint for '${targetPhrase}'. Try /waypoints`); + } + return; + } + + // /say command — speak directly as Aria + if (text.startsWith('/say ')) { + const sayText = text.slice(5).trim(); + if (sayText) { + // Aria says it in the chat + addChatMessage('aria', sayText); + // small visual cue + createEffect('sparkle'); + changeExpression('smile'); + } + return; + } + + // slash commands handled locally + if (text.startsWith('/goto ') || text.startsWith('/move ') || text.startsWith('/moveTo ')) { + const parts = text.split(/\s+/, 2); + const waypoint = parts[1] ? parts[1].trim() : ''; + if (waypoints3D && waypoints3D[waypoint]) { + moveToWaypoint(waypoint); + addChatMessage('aria', `Moving to ${waypoints3D[waypoint].name}`); + } else { + addChatMessage('aria', `Unknown waypoint '${waypoint}'. Try /waypoints`); + } + return; + } + + if (text === '/waypoints') { + const entries = Object.entries(waypoints3D).map(([k, v]) => `${k} — ${v.name}`); + addChatMessage('aria', `Available waypoints: ${entries.join(', ')}`); + return; + } + + if (text === '/circle' || text === '/circle3d') { + moveInCircle3D(); + addChatMessage('aria', 'Starting 3D circle movement'); + return; + } + + if (text === '/spiral' || text === '/spiral3d') { + performSpiral3D(); + addChatMessage('aria', 'Starting 3D spiral'); + return; + } + + if (text === '/stop') { + // stop behaviors + if (typeof stopContinuousDance === 'function') stopContinuousDance(); + addChatMessage('aria', 'Stopped continuous actions'); + return; + } + + // Otherwise, send as a command to backend (and display response when available) + commandInput.value = text; + const result = await sendCommand(); + + if (!result) { + addChatMessage('aria', 'No response from backend (fallback executed)'); + return; + } + + if (result.error) { + addChatMessage('aria', `Error: ${result.error}`); + return; + } + + // Prefer textual response if available + if (result.response) { + addChatMessage('aria', result.response); + } else if (result.tags && result.tags.length > 0) { + addChatMessage('aria', `Executed tags: ${result.tags.join(' ')}`); + } else if (result.stage_context) { + // Keep stage context short + const ctx = result.stage_context.split('\n').slice(0,4).join(' | '); + addChatMessage('aria', ctx); + } else { + addChatMessage('aria', 'Done.'); + } +} + +async function sendCommand() { + const command = commandInput.value.trim(); + if (!command) return; + + log(`Command: "${command}"`); + commandInput.value = ''; + + try { + // Gather current stage state for AI to see + const stageRect = stage.getBoundingClientRect(); + const ariaRect = aria.getBoundingClientRect(); + + // Calculate Aria's position as percentages + const ariaX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; + const ariaY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; + + // Gather object positions + const objectPositions = {}; + ['apple', 'book', 'cup', 'ball', 'flower'].forEach(objId => { + const obj = document.getElementById(objId); + if (obj) { + const objRect = obj.getBoundingClientRect(); + objectPositions[objId] = { + x: ((objRect.left - stageRect.left) / stageRect.width) * 100, + y: 100 - ((objRect.bottom - stageRect.top) / stageRect.height) * 100, + state: obj.classList.contains('held') ? 'held' : 'on_table' + }; + } + }); + + const currentStageState = { + aria: { + position: { x: Math.round(ariaX), y: Math.round(ariaY) }, + expression: characterState.mood || 'neutral', + held_object: characterState.heldObject, + facing: 'right' + }, + objects: objectPositions + }; + + // Call backend API with stage state + const response = await mockApiCall('/api/aria/command', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + command: command, + stage_state: currentStageState + }) + }); + + if (!response.ok) { + throw new Error(`HTTP ${response.status}: ${response.statusText}`); + } + + const data = await response.json(); + + // AI automatically generates character based on response + if (data.response) { + autoGenerateCharacter(data.response); + } + + if (data.tags && data.tags.length > 0) { + log(`✅ ${data.model}: ${data.tags.join(' ')}`); + executeTags(data.tags); + } else if (data.error) { + log(`⚠️ API Error: ${data.error}`, true); + executeLocalCommand(command); + } else { + log('⚠️ Using fallback parser'); + executeLocalCommand(command); + } + // Return the parsed result so callers (chat UI) can inspect tags / response + return data; + } catch (error) { + log(`⚠️ Network error, using fallback`, true); + // Fallback: parse command locally without AI + executeLocalCommand(command); + return { error: String(error) }; + } +} + +function setPosition(xPercent, yPercent, zDepth = 0, rotateY = 0) { + // AI-driven animated walking to position in 3D space (not teleporting) + const stageRect = stage.getBoundingClientRect(); + const ariaRect = aria.getBoundingClientRect(); + + // Get current position + const currentX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; + const currentY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; + + // Clamp target values + xPercent = Math.max(5, Math.min(95, xPercent)); + yPercent = Math.max(5, Math.min(95, yPercent)); + zDepth = Math.max(-300, Math.min(200, zDepth)); // Z range: -300px (far) to 200px (near) + + // Calculate distance and direction + const deltaX = xPercent - currentX; + const deltaY = yPercent - currentY; + const distance = Math.sqrt(deltaX * deltaX + deltaY * deltaY); + + // Don't move if already at target + if (distance < 2) { + log(`📍 Already at position (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px)`); + // If we were moving towards a known waypoint, confirm arrival + if (characterState.currentWaypoint) { + arrivalFeedback(characterState.currentWaypoint); + } + return; + } + + // Determine walking speed and style based on distance + let duration = Math.min(distance * 40, 2000); // 40ms per percent, max 2s + let walkStyle = 'normal'; + + if (distance > 50) { + walkStyle = 'run'; + duration = distance * 20; // Faster for long distances + } else if (distance > 30) { + walkStyle = 'walk'; + duration = distance * 30; + } + + // Face the direction of movement in 3D + let rotationAngle = rotateY || 0; + if (deltaX > 2) { + rotationAngle = 0; // Face camera (right) + } else if (deltaX < -2) { + rotationAngle = 180; // Face away (left) + } + + // Add walking animation + aria.classList.add('walking'); + if (walkStyle === 'run') { + aria.classList.add('running'); + } + + // Animate legs while moving + const walkInterval = setInterval(() => { + animateWalkCycle(); + }, 200); + + // Smooth 3D transition to target + aria.style.transition = `left ${duration}ms ease-in-out, bottom ${duration}ms ease-in-out, transform ${duration}ms ease-in-out`; + + const leftPercent = xPercent; + const bottomPercent = 100 - yPercent; // Invert Y (CSS bottom increases upward) + + aria.style.left = leftPercent + '%'; + aria.style.bottom = bottomPercent + '%'; + + // Apply 3D transform with Z-depth and rotation + const scaleX = rotationAngle === 180 ? -1 : 1; + aria.style.transform = `translateX(-50%) translateZ(${zDepth}px) rotateY(${rotationAngle}deg) scaleX(${scaleX})`; + + // Update character state + characterState.position = { x: xPercent, y: yPercent, z: zDepth }; + characterState.rotation = rotationAngle; + + log(`🚶 Walking to (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px, Rot:${Math.round(rotationAngle)}°) - ${walkStyle} style`); + showFeedback(`🚶 3D WALK: X${Math.round(xPercent)}% Y${Math.round(yPercent)}% Z${Math.round(zDepth)}px`); + + // Stop walking animation when arrived + setTimeout(() => { + aria.classList.remove('walking', 'running'); + aria.style.transition = ''; // Reset transition + clearInterval(walkInterval); + resetWalkCycle(); + log(`✅ Arrived at (${Math.round(xPercent)}%, ${Math.round(yPercent)}%)`); + // If we were targeting a waypoint, announce in chat + if (characterState.currentWaypoint) { + arrivalFeedback(characterState.currentWaypoint); + } + }, duration); +} + +// Walking animation cycle +function animateWalkCycle() { + const leftLeg = document.querySelector('.aria-lower-leg.left'); + const rightLeg = document.querySelector('.aria-lower-leg.right'); + const leftArm = document.querySelector('.aria-lower-arm.left'); + const rightArm = document.querySelector('.aria-lower-arm.right'); + + if (!leftLeg || !rightLeg) return; + + // Alternate leg swings + if (leftLeg.style.transform.includes('rotate')) { + leftLeg.style.transform = 'rotate(20deg)'; + rightLeg.style.transform = 'rotate(-20deg)'; + if (leftArm) leftArm.style.transform = 'rotate(-15deg)'; + if (rightArm) rightArm.style.transform = 'rotate(15deg)'; + } else { + leftLeg.style.transform = 'rotate(-20deg)'; + rightLeg.style.transform = 'rotate(20deg)'; + if (leftArm) leftArm.style.transform = 'rotate(15deg)'; + if (rightArm) rightArm.style.transform = 'rotate(-15deg)'; + } +} + +function resetWalkCycle() { + const leftLeg = document.querySelector('.aria-lower-leg.left'); + const rightLeg = document.querySelector('.aria-lower-leg.right'); + const leftArm = document.querySelector('.aria-lower-arm.left'); + const rightArm = document.querySelector('.aria-lower-arm.right'); + + if (leftLeg) leftLeg.style.transform = ''; + if (rightLeg) rightLeg.style.transform = ''; + if (leftArm) leftArm.style.transform = ''; + if (rightArm) rightArm.style.transform = ''; +} + +function executeLocalCommand(command) { + // Simple local fallback without AI model + const cmd = command.toLowerCase(); + let executed = false; + + // Check if this is a limb command to avoid movement conflicts + const isLimbCommand = ['left arm', 'arm left', 'left hand', 'right arm', 'arm right', 'right hand', + 'left leg', 'leg left', 'right leg', 'leg right'].some(k => cmd.includes(k)); + + // Expressions + if (cmd.includes('smile') || cmd.includes('happy')) { + changeExpression('smile'); + executed = true; + } + if (cmd.includes('sad')) { + changeExpression('sad'); + executed = true; + } + if (cmd.includes('surprised')) { + changeExpression('surprised'); + executed = true; + } + if (cmd.includes('confused')) { + changeExpression('confused'); + executed = true; + } + if (cmd.includes('thinking') || cmd.includes('think')) { + changeExpression('thinking'); + executed = true; + } + if (cmd.includes('wink')) { + changeExpression('wink'); + executed = true; + } + + // Animations + if (cmd.includes('jump')) { + animate('jumping'); + executed = true; + } + if (cmd.includes('dance')) { + animate('dancing'); + executed = true; + } + if (cmd.includes('spin')) { + animate('spinning'); + executed = true; + } + if (cmd.includes('wave')) { + animate('waving'); + executed = true; + } + + // Effects + if (cmd.includes('sparkle')) { + createEffect('sparkle'); + executed = true; + } + if (cmd.includes('hearts')) { + createEffect('hearts'); + executed = true; + } + if (cmd.includes('glow')) { + createEffect('glow'); + executed = true; + } + + // Movement - only if not a limb command + if (!isLimbCommand) { + // Determine movement style + let movementSpeed = 'normal'; + if (cmd.includes('skip')) { + movementSpeed = 'skip'; + } else if (cmd.includes('strut') || cmd.includes('swagger')) { + movementSpeed = 'strut'; + } else if (cmd.includes('run')) { + movementSpeed = 'run'; + } + + if (cmd.includes('left')) { + move('left', movementSpeed); + executed = true; + } + if (cmd.includes('right')) { + move('right', movementSpeed); + executed = true; + } + if (cmd.includes('up') || (cmd.includes('forward') && !cmd.includes('arm') && !cmd.includes('leg'))) { + move('up', movementSpeed); + executed = true; + } + if (cmd.includes('down') || (cmd.includes('back') && !cmd.includes('arm') && !cmd.includes('leg'))) { + move('down', movementSpeed); + executed = true; + } + } + + if (!executed) { + log('❌ Command not recognized', true); + } +} + +function executeTags(tags) { + console.log('📋 Executing tags:', tags); + tags.forEach((tag, index) => { + // Parse tag format: [aria:category:action:param] + const match = tag.match(/\[aria:([^:]+):([^:\]]+)(?::([^\]]+))?\]/); + if (!match) { + console.log('⚠️ Failed to parse tag:', tag); + return; + } + + const [, categoryRaw, actionRaw, paramRaw] = match; + // Normalize category/action + const category = (categoryRaw || '').toLowerCase(); + const action = (actionRaw || '').toLowerCase(); + const param = typeof paramRaw === 'string' ? paramRaw.trim() : undefined; + console.log(`✅ Parsed tag - Category: ${category}, Action: ${action}, Param: ${param}`); + + setTimeout(() => { + switch (category) { + case 'expression': + console.log('Executing expression:', action); + changeExpression(action); + break; + case 'animate': + console.log('Executing animate:', action); + animate(getAnimationClass(action)); + break; + case 'gesture': + animate('waving'); + break; + case 'move': + move(action, param || 'normal'); + break; + case 'walk': + move(action, param || 'normal'); + break; + case 'run': + move(action, param || 'fast'); + break; + case 'position': + console.log('Executing position:', action, param); + if (action && param) { + // Format: [aria:position:x:y] or [aria:position:x:y:z:rotation] + const parts = tag.match(/\[aria:position:([^:\]]+):([^:\]]+)(?::([^:\]]+))?(?::([^\]]+))?\]/); + if (parts) { + const x = parseInt(parts[1]); + const y = parseInt(parts[2]); + const z = parts[3] ? parseInt(parts[3]) : 0; + const rotation = parts[4] ? parseInt(parts[4]) : 0; + setPosition(x, y, z, rotation); + } else { + setPosition(parseInt(action), parseInt(param)); + } + } + break; + case 'skip': + move(action, 'skip'); + break; + case 'strut': + case 'swagger': + move(action, 'strut'); + break; + case 'limb': + // Move individual limbs with tags like: + // [aria:limb:left_arm:raise] | [aria:limb:right_arm:-45] | [aria:limb:left_leg:kick] + handleLimbTag(action, param); + break; + case 'interact': + console.log('Executing interact:', action, param); + if (action === 'add') { + const [objectName, emoji] = param.split(':'); + addObject(objectName, emoji || '🧸'); + } else { + interactWithObject(action, param); + } + break; + case 'effect': + createEffect(action); + break; + case 'camera': + if (action === 'center') centerAria(); + break; + case 'pose': + applyPose(action, param); + break; + case 'say': + // Tag formats either [aria:say:Hello world] or [aria:say:utterance:extra] + const spoken = param || action; + if (spoken) { + addChatMessage('aria', String(spoken)); + } + break; + } + }, index * 500); // Stagger multiple commands + }); +} + +function changeExpression(emotion) { + ariaMouth.className = 'aria-mouth'; + + // Reset any previous expression modifications + ariaMouth.style.borderRadius = ''; + ariaMouth.style.width = ''; + ariaMouth.style.height = ''; + ariaMouth.style.borderTop = ''; + ariaMouth.style.transform = ''; + + switch(emotion) { + case 'smile': + case 'happy': + ariaMouth.classList.add('smile'); + break; + case 'sad': + ariaMouth.classList.add('sad'); + break; + case 'surprised': + ariaMouth.style.borderRadius = '50%'; + ariaMouth.style.width = '15px'; + ariaMouth.style.height = '15px'; + ariaMouth.style.borderTop = '2px solid #333'; + break; + case 'confused': + // Wavy/uncertain mouth + ariaMouth.style.width = '18px'; + ariaMouth.style.height = '6px'; + ariaMouth.style.borderRadius = '0 0 50% 50%'; + ariaMouth.style.transform = 'translateX(-50%) rotate(5deg)'; + break; + case 'thinking': + // Side mouth (pondering) + ariaMouth.style.width = '12px'; + ariaMouth.style.height = '8px'; + ariaMouth.style.borderRadius = '0 0 50% 50%'; + ariaMouth.style.transform = 'translateX(-30%)'; + // Also raise one eyebrow (using eye height) + if (ariaEyeLeft) { + ariaEyeLeft.style.transform = 'translateY(-2px)'; + setTimeout(() => { + ariaEyeLeft.style.transform = ''; + }, 2000); + } + break; + case 'wink': + document.querySelectorAll('.aria-eye')[1].style.height = '4px'; + setTimeout(() => { + document.querySelectorAll('.aria-eye')[1].style.height = '12px'; + }, 500); + break; + default: + ariaMouth.classList.add('smile'); + } + + aria.style.transform = 'translateX(-50%) scale(1.1)'; + setTimeout(() => { + aria.style.transform = 'translateX(-50%) scale(1)'; + }, 300); +} + +// Idle animation state +let idleAnimationInterval = null; +let isPerformingAction = false; + +// Start idle breathing animation +function startIdleAnimation() { + if (idleAnimationInterval) return; + + idleAnimationInterval = setInterval(() => { + if (!isPerformingAction) { + // Subtle breathing effect + ariaBody.style.transition = 'transform 2s ease-in-out'; + ariaBody.style.transform = 'scaleY(1.03)'; + + // Occasional blink + if (Math.random() > 0.7) { + ariaEyeLeft.style.height = '2px'; + ariaEyeRight.style.height = '2px'; + setTimeout(() => { + ariaEyeLeft.style.height = '12px'; + ariaEyeRight.style.height = '12px'; + }, 150); + } + + // Slight head bob + if (Math.random() > 0.8) { + ariaHead.style.transition = 'transform 0.8s ease-in-out'; + ariaHead.style.transform = 'translateY(-3px)'; + setTimeout(() => { + ariaHead.style.transform = 'translateY(0)'; + }, 800); + } + + setTimeout(() => { + ariaBody.style.transform = 'scaleY(1)'; + }, 2000); + } + }, 4000); +} + +// Stop idle animation +function stopIdleAnimation() { + if (idleAnimationInterval) { + clearInterval(idleAnimationInterval); + idleAnimationInterval = null; + } +} + +// Limb movement helpers +function moveArm(arm, angle, duration = 500) { + arm.style.transition = `transform ${duration}ms ease-in-out`; + arm.style.transform = `rotate(${angle}deg)`; +} + +function moveLeg(leg, angle, duration = 500) { + leg.style.transition = `transform ${duration}ms ease-in-out`; + leg.style.transform = `rotate(${angle}deg)`; +} + +function resetLimbs(duration = 500) { + moveArm(ariaArmLeft, 0, duration); + moveArm(ariaArmRight, 0, duration); + moveLeg(ariaLegLeft, 0, duration); + moveLeg(ariaLegRight, 0, duration); +} + +// Utility helpers for limb control +function clamp(n, min, max) { return Math.max(min, Math.min(max, n)); } + +function parseAngleAndDuration(param) { + // Accept patterns: "45", "45,600", "raise", "raise,800" + if (!param) return { value: null, duration: 500 }; + const parts = String(param).split(',').map(s => s.trim()); + const first = parts[0]; + const dur = parts[1] ? parseInt(parts[1], 10) : 500; + const asNum = Number(first); + if (!Number.isNaN(asNum)) { + return { value: asNum, duration: isFinite(dur) ? dur : 500 }; + } + return { value: first.toLowerCase(), duration: isFinite(dur) ? dur : 500 }; +} + +function normalizeLimbPart(part) { + const p = (part || '').toLowerCase().replace(/[-\s]/g, '_'); + const map = { + 'leftarm': 'left_arm', 'arm_left': 'left_arm', 'l_arm': 'left_arm', 'left_hand':'left_arm', 'hand_left':'left_arm', + 'rightarm': 'right_arm', 'arm_right': 'right_arm', 'r_arm': 'right_arm', 'right_hand':'right_arm', 'hand_right':'right_arm', + 'leftleg': 'left_leg', 'leg_left': 'left_leg', 'l_leg':'left_leg', + 'rightleg': 'right_leg', 'leg_right': 'right_leg', 'r_leg':'right_leg' + }; + return map[p] || p; +} + +function elementForPart(part) { + switch (part) { + case 'left_arm': return ariaArmLeft; + case 'right_arm': return ariaArmRight; + case 'left_leg': return ariaLegLeft; + case 'right_leg': return ariaLegRight; + default: return null; + } +} + +function waveArmElement(armEl, duration = 700) { + if (!armEl) return; + const step = Math.max(120, duration); + moveArm(armEl, -60, step * 0.3); + setTimeout(() => moveArm(armEl, -30, step * 0.25), step * 0.3); + setTimeout(() => moveArm(armEl, -60, step * 0.25), step * 0.55); + setTimeout(() => moveArm(armEl, -30, step * 0.2), step * 0.8); + setTimeout(() => moveArm(armEl, 0, step * 0.2), step * 1.0); +} + +function kickLegElement(legEl, duration = 500) { + if (!legEl) return; + moveLeg(legEl, 45, duration * 0.5); + setTimeout(() => moveLeg(legEl, 0, duration * 0.5), duration * 0.5); +} + +function handleLimbTag(partRaw, paramRaw) { + const part = normalizeLimbPart(partRaw); + const targetEl = elementForPart(part); + if (!targetEl) { + log(`❓ Unknown limb: ${partRaw}`); + return; + } + const { value, duration } = parseAngleAndDuration(paramRaw); + const isArm = part.includes('arm'); + const isLeg = part.includes('leg'); + // Pause idle while moving a limb + isPerformingAction = true; + + if (typeof value === 'number') { + const angle = isArm ? clamp(value, -130, 130) : clamp(value, -60, 60); + if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); + setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); + return; + } + + const action = (value || '').toLowerCase(); + switch (action) { + case 'raise': + case 'up': + if (isArm) moveArm(targetEl, -90, duration); else moveLeg(targetEl, -30, duration); + break; + case 'lower': + case 'down': + if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); + break; + case 'forward': + if (isArm) moveArm(targetEl, -45, duration); else moveLeg(targetEl, 30, duration); + break; + case 'back': + case 'backward': + if (isArm) moveArm(targetEl, 45, duration); else moveLeg(targetEl, -30, duration); + break; + case 'wave': + if (isArm) waveArmElement(targetEl, duration); + break; + case 'kick': + if (isLeg) kickLegElement(targetEl, duration); + break; + case 'rest': + case 'neutral': + if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); + break; + default: + // Try numeric fallback if action is numeric-like + const num = Number(action); + if (!Number.isNaN(num)) { + const angle = isArm ? clamp(num, -130, 130) : clamp(num, -60, 60); + if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); + } else { + log(`❓ Unknown limb action: ${action}`); + } + } + setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); +} + +function walkCycle() { + // Alternating leg movement + moveLeg(ariaLegLeft, 25, 300); + moveLeg(ariaLegRight, -25, 300); + moveArm(ariaArmLeft, -15, 300); + moveArm(ariaArmRight, 15, 300); + + setTimeout(() => { + moveLeg(ariaLegLeft, -25, 300); + moveLeg(ariaLegRight, 25, 300); + moveArm(ariaArmLeft, 15, 300); + moveArm(ariaArmRight, -15, 300); + }, 300); + + setTimeout(() => resetLimbs(200), 600); +} + +function strutWalk() { + // Confident strut with head bob + ariaHead.style.transition = 'transform 0.3s'; + + moveLeg(ariaLegLeft, 35, 250); + moveLeg(ariaLegRight, -35, 250); + moveArm(ariaArmLeft, -25, 250); + moveArm(ariaArmRight, 25, 250); + ariaHead.style.transform = 'translateY(-5px) rotate(3deg)'; + + setTimeout(() => { + moveLeg(ariaLegLeft, -35, 250); + moveLeg(ariaLegRight, 35, 250); + moveArm(ariaArmLeft, 25, 250); + moveArm(ariaArmRight, -25, 250); + ariaHead.style.transform = 'translateY(-5px) rotate(-3deg)'; + }, 300); + + setTimeout(() => { + moveLeg(ariaLegLeft, 20, 250); + moveLeg(ariaLegRight, -20, 250); + ariaHead.style.transform = 'translateY(0) rotate(0)'; + }, 600); + + setTimeout(() => resetLimbs(200), 850); +} + +// 3D Waypoint System - Fixed positions in 3D space +const waypoints3D = { + 'center': { x: 50, y: 50, z: 0, rotation: 0, name: 'Center Stage' }, + 'front-left': { x: 25, y: 70, z: 150, rotation: 45, name: 'Front Left' }, + 'front-right': { x: 75, y: 70, z: 150, rotation: -45, name: 'Front Right' }, + 'back-left': { x: 25, y: 30, z: -200, rotation: 135, name: 'Back Left' }, + 'back-right': { x: 75, y: 30, z: -200, rotation: -135, name: 'Back Right' }, + 'front-center': { x: 50, y: 75, z: 180, rotation: 0, name: 'Front Center' }, + 'back-center': { x: 50, y: 25, z: -250, rotation: 180, name: 'Back Center' }, + 'stage-left': { x: 15, y: 50, z: -50, rotation: 90, name: 'Stage Left' }, + 'stage-right': { x: 85, y: 50, z: -50, rotation: -90, name: 'Stage Right' }, + 'table': { x: 60, y: 20, z: -100, rotation: 0, name: 'Near Table' } +}; + +function moveToWaypoint(waypointName) { + const waypoint = waypoints3D[waypointName]; + if (!waypoint) { + log(`❌ Unknown waypoint: ${waypointName}`, true); + return false; + } + + log(`📍 Moving to waypoint: ${waypoint.name}`); + showFeedback(`📍 WAYPOINT: ${waypoint.name}`); + // Notify in chat that Aria is moving + addChatMessage('aria', `Heading to ${waypoint.name}...`); + characterState.currentWaypoint = waypointName; + setPosition(waypoint.x, waypoint.y, waypoint.z, waypoint.rotation); + return true; +} + +function moveInCircle3D(radius = 30, steps = 12, duration = 6000) { + let currentStep = 0; + const centerX = 50; + const centerY = 50; + const stepDuration = duration / steps; + + const circleInterval = setInterval(() => { + if (currentStep >= steps) { + clearInterval(circleInterval); + log('🔄 Circle completed'); + addChatMessage('aria', 'Finished circular movement.'); + return; + } + + const angle = (currentStep / steps) * Math.PI * 2; + const x = centerX + radius * Math.cos(angle); + const y = centerY + radius * Math.sin(angle); + const z = 100 * Math.sin(angle * 2); // Wave in/out on Z-axis + const rotation = (angle * 180 / Math.PI) + 90; // Face tangent to circle + + setPosition(x, y, z, rotation); + currentStep++; + }, stepDuration); + + log('🔄 Starting 3D circle movement'); + showFeedback('🔄 3D CIRCLE'); + addChatMessage('aria', 'Starting circular 3D movement'); +} + +function performSpiral3D() { + const steps = 20; + const duration = 8000; + let currentStep = 0; + const centerX = 50; + const centerY = 50; + + const spiralInterval = setInterval(() => { + if (currentStep >= steps) { + clearInterval(spiralInterval); + log('🌀 Spiral completed'); + addChatMessage('aria', 'Finished spiral movement.'); + return; + } + + const progress = currentStep / steps; + const radius = 40 * (1 - progress); // Shrink radius + const angle = progress * Math.PI * 6; // Multiple rotations + const x = centerX + radius * Math.cos(angle); + const y = centerY + radius * Math.sin(angle); + const z = -200 + progress * 300; // Move from back to front + const rotation = angle * 180 / Math.PI; + + setPosition(x, y, z, rotation); + currentStep++; + }, duration / steps); + + log('🌀 Starting 3D spiral movement'); + showFeedback('🌀 3D SPIRAL'); + addChatMessage('aria', 'Starting 3D spiral movement'); +} + +function skipMove() { + // Playful skipping motion + moveLeg(ariaLegLeft, -45, 200); + moveLeg(ariaLegRight, 30, 200); + moveArm(ariaArmLeft, -30, 200); + moveArm(ariaArmRight, -30, 200); + aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; + + setTimeout(() => { + moveLeg(ariaLegLeft, 30, 200); + moveLeg(ariaLegRight, -45, 200); + aria.style.transform = 'translateX(-50%) scale(1)'; + }, 250); + + setTimeout(() => { + moveLeg(ariaLegLeft, -30, 200); + moveLeg(ariaLegRight, 20, 200); + aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; + }, 500); + + setTimeout(() => { + resetLimbs(200); + aria.style.transform = 'translateX(-50%) scale(1)'; + }, 750); +} + +function danceLimbs() { + // Arms up and down + moveArm(ariaArmLeft, -45, 200); + moveArm(ariaArmRight, -45, 200); + moveLeg(ariaLegLeft, 15, 200); + moveLeg(ariaLegRight, -15, 200); + + setTimeout(() => { + moveArm(ariaArmLeft, -90, 200); + moveArm(ariaArmRight, -90, 200); + moveLeg(ariaLegLeft, -15, 200); + moveLeg(ariaLegRight, 15, 200); + }, 200); + + setTimeout(() => { + moveArm(ariaArmLeft, -45, 200); + moveArm(ariaArmRight, -45, 200); + moveLeg(ariaLegLeft, 15, 200); + moveLeg(ariaLegRight, -15, 200); + }, 400); + + setTimeout(() => resetLimbs(200), 600); +} + +function expressiveDance() { + // More exaggerated dance moves + moveArm(ariaArmLeft, -120, 150); + moveArm(ariaArmRight, 60, 150); + moveLeg(ariaLegLeft, 20, 150); + moveLeg(ariaLegRight, -20, 150); + ariaHead.style.transform = 'rotate(15deg)'; + + setTimeout(() => { + moveArm(ariaArmLeft, 60, 150); + moveArm(ariaArmRight, -120, 150); + moveLeg(ariaLegLeft, -20, 150); + moveLeg(ariaLegRight, 20, 150); + ariaHead.style.transform = 'rotate(-15deg)'; + }, 200); + + setTimeout(() => { + moveArm(ariaArmLeft, -90, 150); + moveArm(ariaArmRight, -90, 150); + moveLeg(ariaLegLeft, 30, 150); + moveLeg(ariaLegRight, -30, 150); + ariaHead.style.transform = 'rotate(0)'; + }, 400); + + setTimeout(() => resetLimbs(200), 600); +} + +let continuousDanceInterval = null; + +function startContinuousDance() { + if (continuousDanceInterval) return; + + isPerformingAction = true; + showFeedback('🎉 PARTY MODE!'); + + continuousDanceInterval = setInterval(() => { + const danceType = Math.random(); + if (danceType > 0.5) { + expressiveDance(); + } else { + danceLimbs(); + } + }, 700); +} + +function stopContinuousDance() { + if (continuousDanceInterval) { + clearInterval(continuousDanceInterval); + continuousDanceInterval = null; + isPerformingAction = false; + resetLimbs(300); + } +} + +function spinLimbs() { + // Arms out during spin + moveArm(ariaArmLeft, -90, 100); + moveArm(ariaArmRight, -90, 100); + setTimeout(() => resetLimbs(300), 900); +} + +function animate(className) { + console.log('🎬 Animating:', className); + showFeedback('🎬 ' + className.toUpperCase() + '!'); + aria.classList.remove('jumping', 'dancing', 'spinning', 'waving'); + void aria.offsetWidth; // Force reflow + + // Mark as performing action + isPerformingAction = true; + + // Get current scale from characterState + const currentScale = characterState.size || 1.0; + + if (className === 'waving') { + showFeedback('👋 WAVING!'); + console.log('Wave animation with arm movement'); + // Rapid wave motion + moveArm(ariaArmRight, -60, 200); + setTimeout(() => moveArm(ariaArmRight, -30, 150), 200); + setTimeout(() => moveArm(ariaArmRight, -60, 150), 350); + setTimeout(() => moveArm(ariaArmRight, -30, 150), 500); + setTimeout(() => moveArm(ariaArmRight, -60, 150), 650); + setTimeout(() => resetLimbs(200), 800); + } else if (className === 'jumping') { + console.log('Jumping animation triggered with leg bending'); + aria.classList.add(className); + aria.style.filter = 'brightness(1.5)'; + + // Pre-jump crouch + moveLeg(ariaLegLeft, 45, 200); + moveLeg(ariaLegRight, -45, 200); + moveArm(ariaArmLeft, -20, 200); + moveArm(ariaArmRight, -20, 200); + + // During jump - legs extend + setTimeout(() => { + moveLeg(ariaLegLeft, -35, 300); + moveLeg(ariaLegRight, 35, 300); + moveArm(ariaArmLeft, -60, 300); + moveArm(ariaArmRight, -60, 300); + }, 300); + + // Landing crouch + setTimeout(() => { + moveLeg(ariaLegLeft, 35, 300); + moveLeg(ariaLegRight, -35, 300); + moveArm(ariaArmLeft, -10, 300); + moveArm(ariaArmRight, -10, 300); + aria.style.filter = 'brightness(1)'; + }, 1500); + + // Return to normal + setTimeout(() => { + resetLimbs(400); + aria.classList.remove(className); + aria.style.transform = `translateX(-50%) scale(${currentScale})`; + }, 2500); + } else { + console.log('Generic animation triggered:', className); + aria.classList.add(className); + + // Add limb movements based on animation type + if (className === 'dancing') { + console.log('Adding dance limb movements'); + // Repeat dance limbs during 2s animation + danceLimbs(); + setTimeout(() => danceLimbs(), 700); + setTimeout(() => danceLimbs(), 1400); + } else if (className === 'spinning') { + console.log('Adding spin limb movements'); + spinLimbs(); + } + + setTimeout(() => { + aria.classList.remove(className); + resetLimbs(300); + aria.style.transform = `translateX(-50%) scale(${currentScale})`; + isPerformingAction = false; + }, className === 'dancing' ? 2000 : 1000); + } +} + +function getAnimationClass(action) { + const animations = { + 'jump': 'jumping', + 'dance': 'dancing', + 'spin': 'spinning', + 'wave': 'waving', + 'bow': 'waving', + 'flip': 'spinning', + 'backflip': 'spinning' + }; + return animations[action] || 'jumping'; +} + +function move(direction, speed = 'normal') { + console.log('🚶 Moving:', direction, 'at speed:', speed); + + isPerformingAction = true; + + const currentLeft = aria.style.left || '50%'; + const current = parseFloat(currentLeft); + + let newPos = current; + let distance = 25; + let movementStyle = walkCycle; + let duration = '1s'; + + // Choose movement style based on speed + switch(speed) { + case 'run': + case 'fast': + distance = 40; + movementStyle = skipMove; + duration = '0.7s'; + showFeedback('🏃 RUNNING!'); + break; + case 'strut': + case 'swagger': + distance = 30; + movementStyle = strutWalk; + duration = '1.2s'; + showFeedback('😎 STRUTTING!'); + break; + case 'skip': + distance = 35; + movementStyle = skipMove; + duration = '0.9s'; + showFeedback('🎵 SKIPPING!'); + break; + default: + showFeedback('🚶 WALKING!'); + } + + // Animate movement style + movementStyle(); + + switch (direction) { + case 'left': + newPos = Math.max(5, current - distance); + break; + case 'right': + newPos = Math.min(95, current + distance); + break; + case 'forward': + case 'up': + const currentTop = aria.style.top || '50%'; + const top = parseFloat(currentTop); + aria.style.top = Math.max(10, top - distance) + '%'; + aria.style.transition = `top ${duration} ease`; + setTimeout(() => { isPerformingAction = false; }, 1000); + return; + case 'back': + case 'down': + const currentTopBack = aria.style.top || '50%'; + const topBack = parseFloat(currentTopBack); + aria.style.top = Math.min(80, topBack + distance) + '%'; + aria.style.transition = `top ${duration} ease`; + setTimeout(() => { isPerformingAction = false; }, 1000); + return; + } + + aria.style.transition = `left ${duration} ease`; + aria.style.left = newPos + '%'; + + setTimeout(() => { isPerformingAction = false; }, 1000); +} + +function createEffect(type) { + const effects = { + 'sparkle': '✨', + 'glow': '💫', + 'hearts': '💕' + }; + + const emoji = effects[type] || '✨'; + + for (let i = 0; i < 5; i++) { + setTimeout(() => { + const effect = document.createElement('div'); + effect.className = `effect ${type}`; + effect.textContent = emoji; + effect.style.left = (Math.random() * 80 + 10) + '%'; + effect.style.top = (Math.random() * 80 + 10) + '%'; + stage.appendChild(effect); + + setTimeout(() => effect.remove(), 1500); + }, i * 100); + } +} + +function centerAria() { + aria.style.left = '50%'; + aria.style.transform = 'translateX(-50%) scale(1)'; +} + +function applyPose(poseRaw, param) { + const pose = (poseRaw || '').toLowerCase(); + // Body position presets + const poses = { + 'sit': { bottom: '20px', transform: 'translateX(-50%) scale(0.8)' }, + 'stand': { bottom: '50px', transform: 'translateX(-50%) scale(1)' }, + 'crouch': { bottom: '30px', transform: 'translateX(-50%) scale(0.9)' }, + 'lie': { bottom: '10px', transform: 'translateX(-50%) scale(0.7) rotate(90deg)' } + }; + const poseStyle = poses[pose]; + if (poseStyle) { + aria.style.bottom = poseStyle.bottom; + aria.style.transform = poseStyle.transform; + } + // Limb presets + switch (pose) { + case 't-pose': + case 'tpose': + moveArm(ariaArmLeft, -90, 400); + moveArm(ariaArmRight, -90, 400); + resetLimbs(1000); + break; + case 'hands_up': + case 'hands-up': + case 'handsup': + moveArm(ariaArmLeft, -120, 400); + moveArm(ariaArmRight, -120, 400); + break; + case 'cross_arms': + case 'cross-arms': + case 'crossarms': + moveArm(ariaArmLeft, -30, 400); + moveArm(ariaArmRight, 30, 400); + break; + case 'hero': + // Hands on hips (approximate) + moveArm(ariaArmLeft, 45, 400); + moveArm(ariaArmRight, -45, 400); + break; + } +} + +function quickCommand(cmd) { + commandInput.value = cmd; + sendCommand(); +} + +// Random character evolution (simulates AI personality drift) +function randomCharacterEvolution() { + const moods = ['happy', 'sad', 'calm', 'thinking', 'neutral']; + const randomMood = moods[Math.floor(Math.random() * moods.length)]; + const randomEnergy = 30 + Math.floor(Math.random() * 70); + + log('✨ Character evolving to: ' + randomMood + ' (' + randomEnergy + '% energy)'); + + const newStyle = generateCharacterFromMood(randomMood, randomEnergy); + applyCharacterStyle(newStyle); +} + +// Automatic character evolution disabled - keeping neutral human-like appearance +let evolutionCountdown = 30; +const timerDisplay = document.getElementById('evolutionTimer'); + +// setInterval(randomCharacterEvolution, 30000); // Disabled + +// Countdown timer +setInterval(() => { + evolutionCountdown--; + if (evolutionCountdown <= 0) { + evolutionCountdown = 30; + } + + // Update timer display + if (timerDisplay) { + timerDisplay.textContent = `⏰ Next evolution in: ${evolutionCountdown}s`; + if (evolutionCountdown <= 5) { + timerDisplay.style.color = '#e74c3c'; + timerDisplay.style.animation = 'pulse 0.5s ease infinite'; + } else { + timerDisplay.style.color = '#667eea'; + timerDisplay.style.animation = 'none'; + } + } + + if (evolutionCountdown <= 5 && evolutionCountdown > 0) { + log(`⏰ Character evolving in ${evolutionCountdown} seconds...`); + } +}, 1000); + +// Initialize +log('🎨 Aria Visual System Ready!'); +log('🤖 AI Character Generation: ACTIVE'); +log('Type commands or use quick buttons'); +log('⏰ Auto-evolution every 30 seconds'); + +// Object Interaction System +// Helper: derive stage-relative percentage position from an element +function objectPositionFromElement(elem) { + if (!elem) return null; + const stageRect = stage.getBoundingClientRect(); + // Prefer explicit CSS left/bottom if present (percent strings) + try { + if (elem.style && elem.style.left && elem.style.bottom) { + const left = parseFloat(elem.style.left || 0); + const bottom = parseFloat(elem.style.bottom || 0); + // The JS 'y' value used elsewhere equals the CSS bottom percent + return { x: Math.round(left), y: Math.round(bottom) }; + } + } catch (e) { + // fallthrough to computed rect + } + + const rect = elem.getBoundingClientRect(); + const x = ((rect.left - stageRect.left) / stageRect.width) * 100; + const y = 100 - ((rect.bottom - stageRect.top) / stageRect.height) * 100; + return { x: Math.round(x), y: Math.round(y) }; +} + +// Notify backend of object add/update/remove actions +async function sendObjectUpdate(action, objectId, extra = {}) { + try { + if (!objectId) return null; + const objEl = document.getElementById(objectId); + + // resolve position and state + let position = extra.position || null; + if (!position && objEl) position = objectPositionFromElement(objEl); + + const state = extra.state || (objEl ? (objEl.classList.contains('held') ? 'held' : (objEl.style.display === 'none' ? 'hidden' : 'on_table')) : (extra.state || 'unknown')); + const emoji = extra.emoji || (objEl ? objEl.textContent : undefined); + + const objPayload = { id: objectId }; + if (position !== null && position !== undefined) objPayload.position = position; + if (state !== null && state !== undefined) objPayload.state = state; + if (emoji !== null && emoji !== undefined) objPayload.emoji = emoji; + const payload = { action: action, object: objPayload }; + + const res = await mockApiCall('/api/aria/object', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload) + }); + + if (!res.ok) { + console.warn('Object API returned non-OK status', res.status); + return null; + } + + const data = await res.json(); + console.log('object sync:', action, objectId, data); + return data; + } catch (err) { + console.warn('sendObjectUpdate failed:', err); + return null; + } +} +function pickUpObject(objectId) { + const obj = document.getElementById(objectId); + if (!obj) { + console.log('❌ Object not found:', objectId); + return false; + } + + if (characterState.heldObject) { + showFeedback('⚠️ Already holding ' + characterState.heldObject.toUpperCase() + '!'); + return false; + } + + console.log('🤏 Picking up:', objectId); + showFeedback('🤏 PICKED UP ' + objectId.toUpperCase() + '!'); + + // Store held object + characterState.heldObject = objectId; + characterState.heldObjectElement = obj; + + // Visual feedback + obj.classList.add('held'); + + // Sync server side: mark object as held + sendObjectUpdate('update', objectId, { state: 'held' }).catch(() => {}); + + // Position object above character + positionHeldObject(); + + // Arm reaching animation + moveArm(ariaArmRight, -90, 300); + setTimeout(() => moveArm(ariaArmRight, -45, 200), 300); + + return true; +} + +function dropObject() { + if (!characterState.heldObject) { + showFeedback('⚠️ Not holding anything!'); + return false; + } + + console.log('📦 Dropping:', characterState.heldObject); + showFeedback('📦 DROPPED ' + characterState.heldObject.toUpperCase() + '!'); + + const obj = characterState.heldObjectElement; + + // Remove held state + obj.classList.remove('held'); + + // Drop at current Aria position + const ariaRect = aria.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + + const dropLeft = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; + const dropBottom = ((stageRect.bottom - ariaRect.bottom) / stageRect.height) * 100; + + obj.style.left = dropLeft + '%'; + obj.style.bottom = (dropBottom + 10) + '%'; + + // Arm dropping animation + moveArm(ariaArmRight, -90, 200); + setTimeout(() => resetLimbs(300), 200); + + // Clear held object + characterState.heldObject = null; + characterState.heldObjectElement = null; + + // Sync server side: update object's position/state + const finalX = Math.round(dropLeft); + const finalY = Math.round(dropBottom + 10); + const tableX = 60; // server default table X + const isOnTable = Math.abs(finalX - tableX) < 20 && Math.abs(finalY - 35) < 20; + sendObjectUpdate('update', obj.id, { position: { x: finalX, y: finalY }, state: isOnTable ? 'on_table' : 'on_stage' }).catch(() => {}); + + return true; +} + +function throwObject(direction) { + if (!characterState.heldObject) { + showFeedback('⚠️ Not holding anything to throw!'); + return false; + } + + console.log('🎯 Throwing:', characterState.heldObject, 'to', direction); + showFeedback('🎯 THROWING ' + characterState.heldObject.toUpperCase() + '!'); + + const obj = characterState.heldObjectElement; + obj.classList.remove('held'); + + // Throwing arm animation + moveArm(ariaArmRight, -120, 150); + setTimeout(() => { + moveArm(ariaArmRight, -30, 200); + setTimeout(() => resetLimbs(300), 100); + }, 150); + + // Calculate throw trajectory + const currentLeft = parseFloat(obj.style.left); + const currentBottom = parseFloat(obj.style.bottom); + + let targetLeft = currentLeft; + let targetBottom = currentBottom; + + switch(direction) { + case 'left': + targetLeft = Math.max(5, currentLeft - 40); + break; + case 'right': + targetLeft = Math.min(95, currentLeft + 40); + break; + case 'up': + targetBottom = Math.min(90, currentBottom + 30); + break; + } + + // Animate throw with arc + obj.style.transition = 'all 0.8s cubic-bezier(0.25, 0.46, 0.45, 0.94)'; + obj.style.left = targetLeft + '%'; + obj.style.bottom = targetBottom + '%'; + obj.style.transform = 'rotate(360deg) scale(0.9)'; + + // Reset after landing + setTimeout(() => { + obj.style.transition = 'all 0.3s ease'; + obj.style.transform = 'rotate(0deg) scale(1)'; + }, 800); + + // Clear held object + characterState.heldObject = null; + characterState.heldObjectElement = null; + + // Sync server side after throw completes + setTimeout(() => { + const left = Math.round(targetLeft); + const bottom = Math.round(targetBottom); + sendObjectUpdate('update', obj.id, { position: { x: left, y: bottom }, state: 'on_stage' }).catch(() => {}); + }, 900); + + return true; +} + +function positionHeldObject() { + if (!characterState.heldObjectElement) return; + + // Position object above and slightly in front of character + const ariaRect = aria.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + + const objLeft = ((ariaRect.left - stageRect.left + ariaRect.width * 0.6) / stageRect.width) * 100; + const objBottom = ((stageRect.bottom - ariaRect.top + 20) / stageRect.height) * 100; + + characterState.heldObjectElement.style.left = objLeft + '%'; + characterState.heldObjectElement.style.bottom = objBottom + '%'; +} + +function interactWithObject(action, objectId) { + console.log('🔧 Interacting:', action, 'with', objectId); + + switch(action) { + case 'pickup': + case 'grab': + case 'take': + case 'get': + return pickUpObject(objectId); + case 'drop': + case 'place': + case 'put': + return dropObject(); + case 'throw': + case 'toss': + return throwObject('right'); + default: + showFeedback('❓ Unknown action: ' + action); + return false; + } +} + +// Make objects draggable and clickable +function initializeObjectInteractions() { + const objects = document.querySelectorAll('.object'); + + objects.forEach(obj => { + // Prevent adding duplicate event listeners when called multiple times + if (obj.__interactionInitialized) return; + obj.__interactionInitialized = true; + obj.addEventListener('click', (e) => { + e.stopPropagation(); + const objectId = obj.id; + + if (!characterState.heldObject) { + pickUpObject(objectId); + } else if (characterState.heldObject === objectId) { + dropObject(); + } + }); + + // Drag functionality + let isDragging = false; + let startX, startY, startLeft, startBottom; + + obj.addEventListener('mousedown', (e) => { + if (characterState.heldObject === obj.id) return; + + isDragging = true; + obj.classList.add('grabbed'); + + const rect = obj.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + + startX = e.clientX; + startY = e.clientY; + startLeft = ((rect.left - stageRect.left) / stageRect.width) * 100; + startBottom = ((stageRect.bottom - rect.bottom) / stageRect.height) * 100; + + e.preventDefault(); + }); + + document.addEventListener('mousemove', (e) => { + if (!isDragging) return; + + const stageRect = stage.getBoundingClientRect(); + const deltaX = ((e.clientX - startX) / stageRect.width) * 100; + const deltaY = -((e.clientY - startY) / stageRect.height) * 100; + + obj.style.left = Math.max(0, Math.min(100, startLeft + deltaX)) + '%'; + obj.style.bottom = Math.max(0, Math.min(100, startBottom + deltaY)) + '%'; + }); + + document.addEventListener('mouseup', () => { + if (isDragging) { + isDragging = false; + obj.classList.remove('grabbed'); + // Sync backend with final position + const pos = objectPositionFromElement(obj); + const state = obj.classList.contains('held') ? 'held' : 'on_table'; + sendObjectUpdate('update', obj.id, { position: pos, state: state }).catch(() => {}); + } + }); + }); +} + +function addObject(objectName, emoji) { + console.log('➕ Adding object:', objectName, emoji); + + // Check if object already exists + const existingObj = document.getElementById(objectName); + if (existingObj) { + console.log('⚠️ Object already exists:', objectName); + showFeedback('✅ ' + objectName + ' already on stage'); + return; + } + + // Create new object element + const newObj = document.createElement('div'); + newObj.id = objectName; + newObj.className = 'object'; + newObj.textContent = emoji; + + // Position near Aria + const ariaX = characterState.position.x; + const ariaY = characterState.position.y; + newObj.style.left = Math.max(10, Math.min(90, ariaX + 15)) + '%'; + newObj.style.bottom = Math.max(10, Math.min(90, ariaY + 5)) + '%'; + + // Add to stage + stage.appendChild(newObj); + + // Update tracking + activeObjects[objectName] = true; + + // Add to object manager if button doesn't exist + if (!document.querySelector(`[onclick="toggleObject('${objectName}')"]`)) { + const objectManager = document.getElementById('object-manager'); + const btn = document.createElement('button'); + btn.className = 'object-btn active'; + btn.id = 'btn-' + objectName; + btn.textContent = emoji + ' ' + objectName; + btn.onclick = () => toggleObject(objectName); + objectManager.appendChild(btn); + } + + // Initialize interactions for new object (click/drag) + initializeObjectInteractions(); + + // Sync server: add new object to stage_state + sendObjectUpdate('add', objectName, { position: objectPositionFromElement(newObj), state: 'on_stage', emoji: emoji }).catch(() => {}); + + showFeedback('✨ Added ' + objectName + ' to stage!'); + console.log('✅ Object added successfully:', objectName); +} + +// Initialize object interactions +initializeObjectInteractions(); +log('🎮 Objects: apple, book, cup, ball, flower'); +log('💡 Try: "pick up apple", "drop", "throw ball"'); + +// Auto-behaviors with random timing +function startAutoBehaviors() { + // Random idle movements every 8-15 seconds + setInterval(() => { + if (Math.random() > 0.6) { + const randomActions = ['shift weight', 'look around', 'adjust hair', 'stretch']; + const action = randomActions[Math.floor(Math.random() * randomActions.length)]; + + switch(action) { + case 'shift weight': + animate('bouncing'); + setTimeout(() => aria.style.animation = 'breathe 4s ease-in-out infinite', 800); + break; + case 'look around': + const ariaHead = document.querySelector('.aria-head'); + ariaHead.style.transform = 'translateX(-50%) rotate(10deg)'; + setTimeout(() => ariaHead.style.transform = 'translateX(-50%) rotate(-10deg)', 600); + setTimeout(() => ariaHead.style.transform = 'translateX(-50%)', 1200); + break; + case 'adjust hair': + const hand = document.getElementById('ariaHandRight'); + hand.style.transform = 'translateY(-30px) rotate(-20deg)'; + setTimeout(() => hand.style.transform = '', 1000); + break; + case 'stretch': + ariaArmLeft.style.transform = 'rotate(-160deg)'; + ariaArmRight.style.transform = 'rotate(160deg)'; + setTimeout(() => { + ariaArmLeft.style.transform = ''; + ariaArmRight.style.transform = ''; + }, 1500); + break; + } + } + }, 8000 + Math.random() * 7000); + + // Random expressions every 12-20 seconds + setInterval(() => { + if (Math.random() > 0.5) { + const expressions = ['smile', 'thinking', 'neutral', 'surprised']; + const expr = expressions[Math.floor(Math.random() * expressions.length)]; + changeExpression(expr); + setTimeout(() => changeExpression('neutral'), 2000 + Math.random() * 3000); + } + }, 12000 + Math.random() * 8000); + + // Occasional sparkles every 20-30 seconds + setInterval(() => { + if (Math.random() > 0.7) { + createEffect('sparkle'); + } + }, 20000 + Math.random() * 10000); +} + +// Initial appearance set to neutral human-like style +setTimeout(() => { + log('🎨 Setting initial character appearance...'); + const neutralStyle = generateCharacterFromMood('neutral', 50); + applyCharacterStyle(neutralStyle); +}, 100); + +// Start idle animations +startIdleAnimation(); +log('👀 Idle animations enabled - watch for breathing and blinking!'); + +// Start automatic behaviors +startAutoBehaviors(); +log('✨ Auto-behaviors enabled - Aria will move and react on her own!'); + +// Expose minimal testing helpers +window.ariaTest = { + limb: (part, actionOrAngle, duration) => handleLimbTag(part, typeof actionOrAngle === 'number' ? `${actionOrAngle},${duration||500}` : `${actionOrAngle||''}${duration?','+duration:''}`), + pose: (name) => applyPose(name) +}; +window.simulateTags = (arr) => Array.isArray(arr) && executeTags(arr); diff --git a/docs/aria/auto-execute.html b/docs/aria/auto-execute.html index 0681c112a..4f39c82b0 100644 --- a/docs/aria/auto-execute.html +++ b/docs/aria/auto-execute.html @@ -1,499 +1,499 @@ - - - - - - Aria Auto-Execute — Multi-Step Action Planner - - - - - - - - - - - - - - - - - -
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    🤖 Aria Auto-Execute

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    LLM-Powered Automatic Action Generation & Execution

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    📝 Example Commands (click to use):

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    🤖 Aria Auto-Execute

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    LLM-Powered Automatic Action Generation & Execution

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    📝 Example Commands (click to use):

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    • Walk to the table and pick up the apple
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    • Say hello and wave at the audience
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    • Go to the center and do a little dance
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    • Pick up the book, move to stage left, and drop it
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    👤 Aria - Human-Like AI Character

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    Natural movement and realistic appearance with articulated joints! 🦾

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    - 🌐 Demo Mode: Running in GitHub Pages demo mode with simulated API responses. - For full AI capabilities, run locally with Python backend. - View on GitHub -
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    🎯 Object Manager

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    💬 Chat Control

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    📋 Command Log

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    - - - - - - - + + + + + + + Aria — Interactive Character Stage + + + + + + + + + + + + + + + + + + +
    + ← Back to Aria Home + +
    +

    👤 Aria - Human-Like AI Character

    +

    Natural movement and realistic appearance with articulated joints! 🦾

    +
    + +
    + 🌐 Demo Mode: Running in GitHub Pages demo mode with simulated API responses. + For full AI capabilities, run locally with Python backend. + View on GitHub +
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    💬 Chat Control

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    Commands: + /goto <waypoint>, /waypoints, /circle, + /spiral
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    📋 Command Log

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- const btn = document.getElementById('btn-' + objectId); - - if (!obj) { - log(`❌ toggleObject: unknown object ${objectId}`, true); - return; - } - // compute last-known position first - const lastPos = objectPositionFromElement(obj); - - if (activeObjects[objectId]) { - // Remove object - obj.style.display = 'none'; - btn.classList.remove('active'); - btn.classList.add('inactive'); - activeObjects[objectId] = false; - log('🗑️ Removed ' + objectId); - // Sync change to backend - sendObjectUpdate('update', objectId, { position: lastPos, state: 'hidden' }).catch(() => {}); - } else { - // Add object back - obj.style.display = 'block'; - btn.classList.add('active'); - btn.classList.remove('inactive'); - activeObjects[objectId] = true; - log('➕ Added ' + objectId); - // Sync change to backend (object is back on stage) - sendObjectUpdate('update', objectId, { position: lastPos, state: 'on_table' }).catch(() => {}); - } -} - -// Additional DOM references (used by idle/poses/expressive moves) -const ariaHead = document.querySelector('.aria-head'); -const ariaBody = document.querySelector('.aria-body'); -const ariaEyes = document.querySelectorAll('.aria-eye'); -const ariaEyeLeft = ariaEyes[0]; -const ariaEyeRight = ariaEyes[1]; - -// AI-Controlled Character State (single combined object) -let characterState = { - mood: 'neutral', - energy: 50, - personality: 'balanced', - colors: { - hair: '#4a3728', - skin: '#f5d4b8', - body: '#4a90e2', - legs: '#3d5a80', - feet: '#f5f5f5' - }, - size: 1.0, - style: 'default', - heldObject: null, - heldObjectElement: null, - position: { x: 20, y: 70, z: 0 }, - rotation: 0, - isMoving: false, - currentWaypoint: null -}; - -// Visual feedback function -function showFeedback(message) { - const feedback = document.createElement('div'); - feedback.textContent = message; - feedback.style.cssText = 'position:absolute; top:20px; left:50%; transform:translateX(-50%); background:#e74c3c; color:white; padding:15px 30px; border-radius:15px; font-size:28px; font-weight:bold; z-index:999; box-shadow:0 5px 20px rgba(0,0,0,0.3); animation:pulse 0.5s ease;'; - stage.appendChild(feedback); - setTimeout(() => feedback.remove(), 2500); -} - -// AI-Driven Character Generation -function analyzeAIResponse(text) { - const lowerText = text.toLowerCase(); - - // Detect mood from response - if (lowerText.includes('happy') || lowerText.includes('great') || lowerText.includes('wonderful') || lowerText.includes('excited')) { - return { mood: 'happy', energy: 80 }; - } else if (lowerText.includes('sad') || lowerText.includes('sorry') || lowerText.includes('unfortunate')) { - return { mood: 'sad', energy: 30 }; - } else if (lowerText.includes('angry') || lowerText.includes('frustrated')) { - return { mood: 'angry', energy: 90 }; - } else if (lowerText.includes('calm') || lowerText.includes('peaceful') || lowerText.includes('relaxed')) { - return { mood: 'calm', energy: 40 }; - } else if (lowerText.includes('think') || lowerText.includes('consider') || lowerText.includes('perhaps')) { - return { mood: 'thinking', energy: 60 }; - } - - return { mood: 'neutral', energy: 50 }; -} - -function generateCharacterFromMood(mood, energy) { - const moodColors = { - happy: { body: '#5fb3f5', hair: '#6b4f3d', accent: '#7ac5ff' }, - sad: { body: '#6C7A89', hair: '#4a3728', accent: '#95A5A6' }, - angry: { body: '#E74C3C', hair: '#4a3728', accent: '#ff6b6b' }, - calm: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' }, - thinking: { body: '#5a7fa8', hair: '#4a3728', accent: '#6b8fb3' }, - neutral: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' } - }; - - const colors = moodColors[mood] || moodColors.neutral; - const size = 0.8 + (energy / 100) * 0.4; // Scale from 0.8 to 1.2 based on energy - - return { colors, size, mood }; -} - -function applyCharacterStyle(style) { - const ariaHead = document.querySelector('.aria-head'); - const ariaBody = document.querySelector('.aria-body'); - const ariaHair = document.querySelector('.aria-hair'); - const ariaLegs = document.querySelectorAll('.aria-leg'); - const ariaFeet = document.querySelectorAll('.aria-foot'); - - // Create dramatic transformation sparkle effect - for (let i = 0; i < 15; i++) { - setTimeout(() => { - createEffect('sparkle'); - }, i * 50); - } - - // Add glow pulse during transformation - aria.style.filter = 'drop-shadow(0 0 30px ' + style.colors.body + ') brightness(1.5)'; - setTimeout(() => { - aria.style.filter = 'none'; - }, 1000); - - // Apply colors with smooth transition - ariaHair.style.transition = 'background-color 1s ease, transform 1s ease'; - ariaHead.style.transition = 'background-color 1s ease'; - ariaBody.style.transition = 'background 1s ease, transform 1s ease'; - ariaLegs.forEach(leg => leg.style.transition = 'background-color 1s ease'); - ariaFeet.forEach(foot => foot.style.transition = 'background-color 1s ease'); - - ariaHair.style.backgroundColor = style.colors.hair; - ariaBody.style.background = `linear-gradient(135deg, ${style.colors.body}, ${style.colors.accent})`; - - // Apply size transformation - aria.style.transform = `translateX(-50%) scale(${style.size})`; - - // Update character state - characterState = { ...characterState, ...style }; - - console.log('🎨 Character updated:', style.mood, 'Energy:', Math.round(style.size * 100) + '%'); - showFeedback('🎨 TRANSFORM: ' + style.mood.toUpperCase()); -} - -function autoGenerateCharacter(responseText) { - const analysis = analyzeAIResponse(responseText); - const newStyle = generateCharacterFromMood(analysis.mood, analysis.energy); - applyCharacterStyle(newStyle); - - // Trigger automatic animation based on mood - setTimeout(() => { - if (analysis.mood === 'happy') { - animate('jumping'); - } else if (analysis.mood === 'sad') { - move('left', 'normal'); - } else if (analysis.mood === 'angry') { - animate('spinning'); - } else if (analysis.mood === 'calm') { - animate('waving'); - } else if (analysis.mood === 'thinking') { - changeExpression('thinking'); - } - }, 500); -} - -const expressions = { - 'smile': '😊', - 'happy': '😃', - 'sad': '😢', - 'surprised': '😲', - 'confused': '😕', - 'thinking': '🤔', - 'wink': '😉' -}; - -function log(message, isError = false) { - const entry = document.createElement('div'); - entry.className = 'log-entry'; - entry.style.borderLeftColor = isError ? '#e74c3c' : '#667eea'; - entry.style.color = isError ? '#e74c3c' : '#555'; - entry.textContent = `${new Date().toLocaleTimeString()}: ${message}`; - logContainer.insertBefore(entry, logContainer.firstChild); - - // Keep only last 10 entries - while (logContainer.children.length > 10) { - logContainer.removeChild(logContainer.lastChild); - } -} - -// Chat UI helpers -function addChatMessage(role, text) { - try { - const container = document.getElementById('chatMessages'); - if (!container) return; - - const msgWrap = document.createElement('div'); - msgWrap.className = 'chat-msg ' + (role === 'user' ? 'user' : (role === 'aria' ? 'aria' : 'system')); - - const bubble = document.createElement('div'); - bubble.className = 'bubble'; - bubble.innerText = text; - - const sender = document.createElement('div'); - sender.className = 'sender'; - sender.innerText = role === 'user' ? 'You' : role === 'aria' ? 'Aria' : 'System'; - - const inner = document.createElement('div'); - inner.appendChild(sender); - inner.appendChild(bubble); - - // For user messages align right - if (role === 'user') { - msgWrap.appendChild(inner); - } else { - msgWrap.appendChild(inner); - } - - container.appendChild(msgWrap); - container.scrollTop = container.scrollHeight; - } catch (e) { - console.warn('addChatMessage failed', e); - } -} - -// Called when Aria reaches a waypoint or finishes a movement -function arrivalFeedback(waypointKey) { - try { - let msg = ''; - if (waypointKey && waypoints3D && waypoints3D[waypointKey]) { - msg = `I've arrived at ${waypoints3D[waypointKey].name}.`; - } else if (typeof waypointKey === 'string' && waypointKey.includes('%')) { - msg = `Arrived at ${waypointKey}.`; - } else if (waypointKey) { - msg = `Arrived at ${waypointKey}.`; - } else { - msg = `Arrived.`; - } - - // Friendly reply from Aria in chat — pick varied arrival messages - const arrivalMessages = [ - `I'm here! 🎉`, - `Made it — ${waypointKey ? waypoints3D[waypointKey]?.name || waypointKey : 'arrived'}!`, - `Arrived and ready!`, - `That was quick — I'm here.`, - `All set. What should we do next?`, - ]; - const chosen = arrivalMessages[Math.floor(Math.random() * arrivalMessages.length)]; - addChatMessage('aria', chosen); - - // Little celebratory effect and expression - createEffect('sparkle'); - changeExpression('happy'); - - // Short arrival animation (small bounce & glow) - aria.classList.add('arrived'); - setTimeout(() => aria.classList.remove('arrived'), 900); - - // Clear current waypoint marker so we don't re-announce - characterState.currentWaypoint = null; - } catch (e) { - console.warn('arrivalFeedback failed', e); - } -} - -// Try to resolve a freeform phrase to a waypoint key -function resolveWaypointFromPhrase(phrase) { - if (!phrase || !waypoints3D) return null; - const clean = phrase.toLowerCase().replace(/[^a-z0-9\s-]/g, '').trim(); - if (!clean) return null; - - // direct key match (e.g., front-left) - const direct = clean.replace(/\s+/g, '-'); - if (waypoints3D[direct]) return direct; - - // check by names and partial matches - for (const k in waypoints3D) { - const v = waypoints3D[k]; - if (!v) continue; - const name = String(v.name || '').toLowerCase(); - if (name === clean) return k; - if (name.includes(clean) || clean.includes(k.replace('-', ' '))) return k; - } - - // synonyms mapping for simple words - const synonyms = { - 'front': 'front-center', 'back': 'back-center', 'left': 'stage-left', 'right': 'stage-right', 'center': 'center', 'table': 'table' - }; - if (synonyms[clean]) return synonyms[clean]; - - // try matching tokens - const tokens = clean.split(/\s+/); - for (const t of tokens) { - if (synonyms[t]) return synonyms[t]; - } - - return null; -} - -// Process chat input: supports slash commands and normal commands -async function sendChat() { - const chatBox = document.getElementById('chatInput'); - if (!chatBox) return; - - const text = chatBox.value.trim(); - if (!text) return; - - // Show user's message - addChatMessage('user', text); - chatBox.value = ''; - - // check for natural language movements (e.g., "go to front-right" or "walk to table") - const nlMove = /(?:go to|move to|walk to|goto)\s+([a-z0-9\-\s]+)/i.exec(text); - if (nlMove) { - const targetPhrase = nlMove[1].trim(); - // try to match known waypoint keys or names - const candidate = resolveWaypointFromPhrase(targetPhrase); - if (candidate) { - moveToWaypoint(candidate); - addChatMessage('aria', `Moving to ${waypoints3D[candidate].name}`); - } else { - addChatMessage('aria', `I couldn't identify a waypoint for '${targetPhrase}'. Try /waypoints`); - } - return; - } - - // /say command — speak directly as Aria - if (text.startsWith('/say ')) { - const sayText = text.slice(5).trim(); - if (sayText) { - // Aria says it in the chat - addChatMessage('aria', sayText); - // small visual cue - createEffect('sparkle'); - changeExpression('smile'); - } - return; - } - - // slash commands handled locally - if (text.startsWith('/goto ') || text.startsWith('/move ') || text.startsWith('/moveTo ')) { - const parts = text.split(/\s+/, 2); - const waypoint = parts[1] ? parts[1].trim() : ''; - if (waypoints3D && waypoints3D[waypoint]) { - moveToWaypoint(waypoint); - addChatMessage('aria', `Moving to ${waypoints3D[waypoint].name}`); - } else { - addChatMessage('aria', `Unknown waypoint '${waypoint}'. Try /waypoints`); - } - return; - } - - if (text === '/waypoints') { - const entries = Object.entries(waypoints3D).map(([k, v]) => `${k} — ${v.name}`); - addChatMessage('aria', `Available waypoints: ${entries.join(', ')}`); - return; - } - - if (text === '/circle' || text === '/circle3d') { - moveInCircle3D(); - addChatMessage('aria', 'Starting 3D circle movement'); - return; - } - - if (text === '/spiral' || text === '/spiral3d') { - performSpiral3D(); - addChatMessage('aria', 'Starting 3D spiral'); - return; - } - - if (text === '/stop') { - // stop behaviors - if (typeof stopContinuousDance === 'function') stopContinuousDance(); - addChatMessage('aria', 'Stopped continuous actions'); - return; - } - - // Otherwise, send as a command to backend (and display response when available) - commandInput.value = text; - const result = await sendCommand(); - - if (!result) { - addChatMessage('aria', 'No response from backend (fallback executed)'); - return; - } - - if (result.error) { - addChatMessage('aria', `Error: ${result.error}`); - return; - } - - // Prefer textual response if available - if (result.response) { - addChatMessage('aria', result.response); - } else if (result.tags && result.tags.length > 0) { - addChatMessage('aria', `Executed tags: ${result.tags.join(' ')}`); - } else if (result.stage_context) { - // Keep stage context short - const ctx = result.stage_context.split('\n').slice(0,4).join(' | '); - addChatMessage('aria', ctx); - } else { - addChatMessage('aria', 'Done.'); - } -} - -async function sendCommand() { - const command = commandInput.value.trim(); - if (!command) return; - - log(`Command: "${command}"`); - commandInput.value = ''; - - try { - // Gather current stage state for AI to see - const stageRect = stage.getBoundingClientRect(); - const ariaRect = aria.getBoundingClientRect(); - - // Calculate Aria's position as percentages - const ariaX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; - const ariaY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; - - // Gather object positions - const objectPositions = {}; - ['apple', 'book', 'cup', 'ball', 'flower'].forEach(objId => { - const obj = document.getElementById(objId); - if (obj) { - const objRect = obj.getBoundingClientRect(); - objectPositions[objId] = { - x: ((objRect.left - stageRect.left) / stageRect.width) * 100, - y: 100 - ((objRect.bottom - stageRect.top) / stageRect.height) * 100, - state: obj.classList.contains('held') ? 'held' : 'on_table' - }; - } - }); - - const currentStageState = { - aria: { - position: { x: Math.round(ariaX), y: Math.round(ariaY) }, - expression: characterState.mood || 'neutral', - held_object: characterState.heldObject, - facing: 'right' - }, - objects: objectPositions - }; - - // Call backend API with stage state - const response = await fetch('/api/aria/command', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - command: command, - stage_state: currentStageState - }) - }); - - if (!response.ok) { - throw new Error(`HTTP ${response.status}: ${response.statusText}`); - } - - const data = await response.json(); - - // AI automatically generates character based on response - if (data.response) { - autoGenerateCharacter(data.response); - } - - if (data.tags && data.tags.length > 0) { - log(`✅ ${data.model}: ${data.tags.join(' ')}`); - executeTags(data.tags); - } else if (data.error) { - log(`⚠️ API Error: ${data.error}`, true); - executeLocalCommand(command); - } else { - log('⚠️ Using fallback parser'); - executeLocalCommand(command); - } - // Return the parsed result so callers (chat UI) can inspect tags / response - return data; - } catch (error) { - log(`⚠️ Network error, using fallback`, true); - // Fallback: parse command locally without AI - executeLocalCommand(command); - return { error: String(error) }; - } -} - -function setPosition(xPercent, yPercent, zDepth = 0, rotateY = 0) { - // AI-driven animated walking to position in 3D space (not teleporting) - const stageRect = stage.getBoundingClientRect(); - const ariaRect = aria.getBoundingClientRect(); - - // Get current position - const currentX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; - const currentY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; - - // Clamp target values - xPercent = Math.max(5, Math.min(95, xPercent)); - yPercent = Math.max(5, Math.min(95, yPercent)); - zDepth = Math.max(-300, Math.min(200, zDepth)); // Z range: -300px (far) to 200px (near) - - // Calculate distance and direction - const deltaX = xPercent - currentX; - const deltaY = yPercent - currentY; - const distance = Math.sqrt(deltaX * deltaX + deltaY * deltaY); - - // Don't move if already at target - if (distance < 2) { - log(`📍 Already at position (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px)`); - // If we were moving towards a known waypoint, confirm arrival - if (characterState.currentWaypoint) { - arrivalFeedback(characterState.currentWaypoint); - } - return; - } - - // Determine walking speed and style based on distance - let duration = Math.min(distance * 40, 2000); // 40ms per percent, max 2s - let walkStyle = 'normal'; - - if (distance > 50) { - walkStyle = 'run'; - duration = distance * 20; // Faster for long distances - } else if (distance > 30) { - walkStyle = 'walk'; - duration = distance * 30; - } - - // Face the direction of movement in 3D - let rotationAngle = rotateY || 0; - if (deltaX > 2) { - rotationAngle = 0; // Face camera (right) - } else if (deltaX < -2) { - rotationAngle = 180; // Face away (left) - } - - // Add walking animation - aria.classList.add('walking'); - if (walkStyle === 'run') { - aria.classList.add('running'); - } - - // Animate legs while moving - const walkInterval = setInterval(() => { - animateWalkCycle(); - }, 200); - - // Smooth 3D transition to target - aria.style.transition = `left ${duration}ms ease-in-out, bottom ${duration}ms ease-in-out, transform ${duration}ms ease-in-out`; - - const leftPercent = xPercent; - const bottomPercent = 100 - yPercent; // Invert Y (CSS bottom increases upward) - - aria.style.left = leftPercent + '%'; - aria.style.bottom = bottomPercent + '%'; - - // Apply 3D transform with Z-depth and rotation - const scaleX = rotationAngle === 180 ? -1 : 1; - aria.style.transform = `translateX(-50%) translateZ(${zDepth}px) rotateY(${rotationAngle}deg) scaleX(${scaleX})`; - - // Update character state - characterState.position = { x: xPercent, y: yPercent, z: zDepth }; - characterState.rotation = rotationAngle; - - log(`🚶 Walking to (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px, Rot:${Math.round(rotationAngle)}°) - ${walkStyle} style`); - showFeedback(`🚶 3D WALK: X${Math.round(xPercent)}% Y${Math.round(yPercent)}% Z${Math.round(zDepth)}px`); - - // Stop walking animation when arrived - setTimeout(() => { - aria.classList.remove('walking', 'running'); - aria.style.transition = ''; // Reset transition - clearInterval(walkInterval); - resetWalkCycle(); - log(`✅ Arrived at (${Math.round(xPercent)}%, ${Math.round(yPercent)}%)`); - // If we were targeting a waypoint, announce in chat - if (characterState.currentWaypoint) { - arrivalFeedback(characterState.currentWaypoint); - } - }, duration); -} - -// Walking animation cycle -function animateWalkCycle() { - const leftLeg = document.querySelector('.aria-lower-leg.left'); - const rightLeg = document.querySelector('.aria-lower-leg.right'); - const leftArm = document.querySelector('.aria-lower-arm.left'); - const rightArm = document.querySelector('.aria-lower-arm.right'); - - if (!leftLeg || !rightLeg) return; - - // Alternate leg swings - if (leftLeg.style.transform.includes('rotate')) { - leftLeg.style.transform = 'rotate(20deg)'; - rightLeg.style.transform = 'rotate(-20deg)'; - if (leftArm) leftArm.style.transform = 'rotate(-15deg)'; - if (rightArm) rightArm.style.transform = 'rotate(15deg)'; - } else { - leftLeg.style.transform = 'rotate(-20deg)'; - rightLeg.style.transform = 'rotate(20deg)'; - if (leftArm) leftArm.style.transform = 'rotate(15deg)'; - if (rightArm) rightArm.style.transform = 'rotate(-15deg)'; - } -} - -function resetWalkCycle() { - const leftLeg = document.querySelector('.aria-lower-leg.left'); - const rightLeg = document.querySelector('.aria-lower-leg.right'); - const leftArm = document.querySelector('.aria-lower-arm.left'); - const rightArm = document.querySelector('.aria-lower-arm.right'); - - if (leftLeg) leftLeg.style.transform = ''; - if (rightLeg) rightLeg.style.transform = ''; - if (leftArm) leftArm.style.transform = ''; - if (rightArm) rightArm.style.transform = ''; -} - -function executeLocalCommand(command) { - // Simple local fallback without AI model - const cmd = command.toLowerCase(); - let executed = false; - - // Check if this is a limb command to avoid movement conflicts - const isLimbCommand = ['left arm', 'arm left', 'left hand', 'right arm', 'arm right', 'right hand', - 'left leg', 'leg left', 'right leg', 'leg right'].some(k => cmd.includes(k)); - - // Expressions - if (cmd.includes('smile') || cmd.includes('happy')) { - changeExpression('smile'); - executed = true; - } - if (cmd.includes('sad')) { - changeExpression('sad'); - executed = true; - } - if (cmd.includes('surprised')) { - changeExpression('surprised'); - executed = true; - } - if (cmd.includes('confused')) { - changeExpression('confused'); - executed = true; - } - if (cmd.includes('thinking') || cmd.includes('think')) { - changeExpression('thinking'); - executed = true; - } - if (cmd.includes('wink')) { - changeExpression('wink'); - executed = true; - } - - // Animations - if (cmd.includes('jump')) { - animate('jumping'); - executed = true; - } - if (cmd.includes('dance')) { - animate('dancing'); - executed = true; - } - if (cmd.includes('spin')) { - animate('spinning'); - executed = true; - } - if (cmd.includes('wave')) { - animate('waving'); - executed = true; - } - - // Effects - if (cmd.includes('sparkle')) { - createEffect('sparkle'); - executed = true; - } - if (cmd.includes('hearts')) { - createEffect('hearts'); - executed = true; - } - if (cmd.includes('glow')) { - createEffect('glow'); - executed = true; - } - - // Movement - only if not a limb command - if (!isLimbCommand) { - // Determine movement style - let movementSpeed = 'normal'; - if (cmd.includes('skip')) { - movementSpeed = 'skip'; - } else if (cmd.includes('strut') || cmd.includes('swagger')) { - movementSpeed = 'strut'; - } else if (cmd.includes('run')) { - movementSpeed = 'run'; - } - - if (cmd.includes('left')) { - move('left', movementSpeed); - executed = true; - } - if (cmd.includes('right')) { - move('right', movementSpeed); - executed = true; - } - if (cmd.includes('up') || (cmd.includes('forward') && !cmd.includes('arm') && !cmd.includes('leg'))) { - move('up', movementSpeed); - executed = true; - } - if (cmd.includes('down') || (cmd.includes('back') && !cmd.includes('arm') && !cmd.includes('leg'))) { - move('down', movementSpeed); - executed = true; - } - } - - if (!executed) { - log('❌ Command not recognized', true); - } -} - -function executeTags(tags) { - console.log('📋 Executing tags:', tags); - tags.forEach((tag, index) => { - // Parse tag format: [aria:category:action:param] - const match = tag.match(/\[aria:([^:]+):([^:\]]+)(?::([^\]]+))?\]/); - if (!match) { - console.log('⚠️ Failed to parse tag:', tag); - return; - } - - const [, categoryRaw, actionRaw, paramRaw] = match; - // Normalize category/action - const category = (categoryRaw || '').toLowerCase(); - const action = (actionRaw || '').toLowerCase(); - const param = typeof paramRaw === 'string' ? paramRaw.trim() : undefined; - console.log(`✅ Parsed tag - Category: ${category}, Action: ${action}, Param: ${param}`); - - setTimeout(() => { - switch (category) { - case 'expression': - console.log('Executing expression:', action); - changeExpression(action); - break; - case 'animate': - console.log('Executing animate:', action); - animate(getAnimationClass(action)); - break; - case 'gesture': - animate('waving'); - break; - case 'move': - move(action, param || 'normal'); - break; - case 'walk': - move(action, param || 'normal'); - break; - case 'run': - move(action, param || 'fast'); - break; - case 'position': - console.log('Executing position:', action, param); - if (action && param) { - // Format: [aria:position:x:y] or [aria:position:x:y:z:rotation] - const parts = tag.match(/\[aria:position:([^:\]]+):([^:\]]+)(?::([^:\]]+))?(?::([^\]]+))?\]/); - if (parts) { - const x = parseInt(parts[1]); - const y = parseInt(parts[2]); - const z = parts[3] ? parseInt(parts[3]) : 0; - const rotation = parts[4] ? parseInt(parts[4]) : 0; - setPosition(x, y, z, rotation); - } else { - setPosition(parseInt(action), parseInt(param)); - } - } - break; - case 'skip': - move(action, 'skip'); - break; - case 'strut': - case 'swagger': - move(action, 'strut'); - break; - case 'limb': - // Move individual limbs with tags like: - // [aria:limb:left_arm:raise] | [aria:limb:right_arm:-45] | [aria:limb:left_leg:kick] - handleLimbTag(action, param); - break; - case 'interact': - console.log('Executing interact:', action, param); - if (action === 'add') { - const [objectName, emoji] = param.split(':'); - addObject(objectName, emoji || '🧸'); - } else { - interactWithObject(action, param); - } - break; - case 'effect': - createEffect(action); - break; - case 'camera': - if (action === 'center') centerAria(); - break; - case 'pose': - applyPose(action, param); - break; - case 'say': - // Tag formats either [aria:say:Hello world] or [aria:say:utterance:extra] - const spoken = param || action; - if (spoken) { - addChatMessage('aria', String(spoken)); - } - break; - } - }, index * 500); // Stagger multiple commands - }); -} - -function changeExpression(emotion) { - ariaMouth.className = 'aria-mouth'; - - // Reset any previous expression modifications - ariaMouth.style.borderRadius = ''; - ariaMouth.style.width = ''; - ariaMouth.style.height = ''; - ariaMouth.style.borderTop = ''; - ariaMouth.style.transform = ''; - - switch(emotion) { - case 'smile': - case 'happy': - ariaMouth.classList.add('smile'); - break; - case 'sad': - ariaMouth.classList.add('sad'); - break; - case 'surprised': - ariaMouth.style.borderRadius = '50%'; - ariaMouth.style.width = '15px'; - ariaMouth.style.height = '15px'; - ariaMouth.style.borderTop = '2px solid #333'; - break; - case 'confused': - // Wavy/uncertain mouth - ariaMouth.style.width = '18px'; - ariaMouth.style.height = '6px'; - ariaMouth.style.borderRadius = '0 0 50% 50%'; - ariaMouth.style.transform = 'translateX(-50%) rotate(5deg)'; - break; - case 'thinking': - // Side mouth (pondering) - ariaMouth.style.width = '12px'; - ariaMouth.style.height = '8px'; - ariaMouth.style.borderRadius = '0 0 50% 50%'; - ariaMouth.style.transform = 'translateX(-30%)'; - // Also raise one eyebrow (using eye height) - if (ariaEyeLeft) { - ariaEyeLeft.style.transform = 'translateY(-2px)'; - setTimeout(() => { - ariaEyeLeft.style.transform = ''; - }, 2000); - } - break; - case 'wink': - document.querySelectorAll('.aria-eye')[1].style.height = '4px'; - setTimeout(() => { - document.querySelectorAll('.aria-eye')[1].style.height = '12px'; - }, 500); - break; - default: - ariaMouth.classList.add('smile'); - } - - aria.style.transform = 'translateX(-50%) scale(1.1)'; - setTimeout(() => { - aria.style.transform = 'translateX(-50%) scale(1)'; - }, 300); -} - -// Idle animation state -let idleAnimationInterval = null; -let isPerformingAction = false; - -// Start idle breathing animation -function startIdleAnimation() { - if (idleAnimationInterval) return; - - idleAnimationInterval = setInterval(() => { - if (!isPerformingAction) { - // Subtle breathing effect - ariaBody.style.transition = 'transform 2s ease-in-out'; - ariaBody.style.transform = 'scaleY(1.03)'; - - // Occasional blink - if (Math.random() > 0.7) { - ariaEyeLeft.style.height = '2px'; - ariaEyeRight.style.height = '2px'; - setTimeout(() => { - ariaEyeLeft.style.height = '12px'; - ariaEyeRight.style.height = '12px'; - }, 150); - } - - // Slight head bob - if (Math.random() > 0.8) { - ariaHead.style.transition = 'transform 0.8s ease-in-out'; - ariaHead.style.transform = 'translateY(-3px)'; - setTimeout(() => { - ariaHead.style.transform = 'translateY(0)'; - }, 800); - } - - setTimeout(() => { - ariaBody.style.transform = 'scaleY(1)'; - }, 2000); - } - }, 4000); -} - -// Stop idle animation -function stopIdleAnimation() { - if (idleAnimationInterval) { - clearInterval(idleAnimationInterval); - idleAnimationInterval = null; - } -} - -// Limb movement helpers -function moveArm(arm, angle, duration = 500) { - arm.style.transition = `transform ${duration}ms ease-in-out`; - arm.style.transform = `rotate(${angle}deg)`; -} - -function moveLeg(leg, angle, duration = 500) { - leg.style.transition = `transform ${duration}ms ease-in-out`; - leg.style.transform = `rotate(${angle}deg)`; -} - -function resetLimbs(duration = 500) { - moveArm(ariaArmLeft, 0, duration); - moveArm(ariaArmRight, 0, duration); - moveLeg(ariaLegLeft, 0, duration); - moveLeg(ariaLegRight, 0, duration); -} - -// Utility helpers for limb control -function clamp(n, min, max) { return Math.max(min, Math.min(max, n)); } - -function parseAngleAndDuration(param) { - // Accept patterns: "45", "45,600", "raise", "raise,800" - if (!param) return { value: null, duration: 500 }; - const parts = String(param).split(',').map(s => s.trim()); - const first = parts[0]; - const dur = parts[1] ? parseInt(parts[1], 10) : 500; - const asNum = Number(first); - if (!Number.isNaN(asNum)) { - return { value: asNum, duration: isFinite(dur) ? dur : 500 }; - } - return { value: first.toLowerCase(), duration: isFinite(dur) ? dur : 500 }; -} - -function normalizeLimbPart(part) { - const p = (part || '').toLowerCase().replace(/[-\s]/g, '_'); - const map = { - 'leftarm': 'left_arm', 'arm_left': 'left_arm', 'l_arm': 'left_arm', 'left_hand':'left_arm', 'hand_left':'left_arm', - 'rightarm': 'right_arm', 'arm_right': 'right_arm', 'r_arm': 'right_arm', 'right_hand':'right_arm', 'hand_right':'right_arm', - 'leftleg': 'left_leg', 'leg_left': 'left_leg', 'l_leg':'left_leg', - 'rightleg': 'right_leg', 'leg_right': 'right_leg', 'r_leg':'right_leg' - }; - return map[p] || p; -} - -function elementForPart(part) { - switch (part) { - case 'left_arm': return ariaArmLeft; - case 'right_arm': return ariaArmRight; - case 'left_leg': return ariaLegLeft; - case 'right_leg': return ariaLegRight; - default: return null; - } -} - -function waveArmElement(armEl, duration = 700) { - if (!armEl) return; - const step = Math.max(120, duration); - moveArm(armEl, -60, step * 0.3); - setTimeout(() => moveArm(armEl, -30, step * 0.25), step * 0.3); - setTimeout(() => moveArm(armEl, -60, step * 0.25), step * 0.55); - setTimeout(() => moveArm(armEl, -30, step * 0.2), step * 0.8); - setTimeout(() => moveArm(armEl, 0, step * 0.2), step * 1.0); -} - -function kickLegElement(legEl, duration = 500) { - if (!legEl) return; - moveLeg(legEl, 45, duration * 0.5); - setTimeout(() => moveLeg(legEl, 0, duration * 0.5), duration * 0.5); -} - -function handleLimbTag(partRaw, paramRaw) { - const part = normalizeLimbPart(partRaw); - const targetEl = elementForPart(part); - if (!targetEl) { - log(`❓ Unknown limb: ${partRaw}`); - return; - } - const { value, duration } = parseAngleAndDuration(paramRaw); - const isArm = part.includes('arm'); - const isLeg = part.includes('leg'); - // Pause idle while moving a limb - isPerformingAction = true; - - if (typeof value === 'number') { - const angle = isArm ? clamp(value, -130, 130) : clamp(value, -60, 60); - if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); - setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); - return; - } - - const action = (value || '').toLowerCase(); - switch (action) { - case 'raise': - case 'up': - if (isArm) moveArm(targetEl, -90, duration); else moveLeg(targetEl, -30, duration); - break; - case 'lower': - case 'down': - if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); - break; - case 'forward': - if (isArm) moveArm(targetEl, -45, duration); else moveLeg(targetEl, 30, duration); - break; - case 'back': - case 'backward': - if (isArm) moveArm(targetEl, 45, duration); else moveLeg(targetEl, -30, duration); - break; - case 'wave': - if (isArm) waveArmElement(targetEl, duration); - break; - case 'kick': - if (isLeg) kickLegElement(targetEl, duration); - break; - case 'rest': - case 'neutral': - if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); - break; - default: - // Try numeric fallback if action is numeric-like - const num = Number(action); - if (!Number.isNaN(num)) { - const angle = isArm ? clamp(num, -130, 130) : clamp(num, -60, 60); - if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); - } else { - log(`❓ Unknown limb action: ${action}`); - } - } - setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); -} - -function walkCycle() { - // Alternating leg movement - moveLeg(ariaLegLeft, 25, 300); - moveLeg(ariaLegRight, -25, 300); - moveArm(ariaArmLeft, -15, 300); - moveArm(ariaArmRight, 15, 300); - - setTimeout(() => { - moveLeg(ariaLegLeft, -25, 300); - moveLeg(ariaLegRight, 25, 300); - moveArm(ariaArmLeft, 15, 300); - moveArm(ariaArmRight, -15, 300); - }, 300); - - setTimeout(() => resetLimbs(200), 600); -} - -function strutWalk() { - // Confident strut with head bob - ariaHead.style.transition = 'transform 0.3s'; - - moveLeg(ariaLegLeft, 35, 250); - moveLeg(ariaLegRight, -35, 250); - moveArm(ariaArmLeft, -25, 250); - moveArm(ariaArmRight, 25, 250); - ariaHead.style.transform = 'translateY(-5px) rotate(3deg)'; - - setTimeout(() => { - moveLeg(ariaLegLeft, -35, 250); - moveLeg(ariaLegRight, 35, 250); - moveArm(ariaArmLeft, 25, 250); - moveArm(ariaArmRight, -25, 250); - ariaHead.style.transform = 'translateY(-5px) rotate(-3deg)'; - }, 300); - - setTimeout(() => { - moveLeg(ariaLegLeft, 20, 250); - moveLeg(ariaLegRight, -20, 250); - ariaHead.style.transform = 'translateY(0) rotate(0)'; - }, 600); - - setTimeout(() => resetLimbs(200), 850); -} - -// 3D Waypoint System - Fixed positions in 3D space -const waypoints3D = { - 'center': { x: 50, y: 50, z: 0, rotation: 0, name: 'Center Stage' }, - 'front-left': { x: 25, y: 70, z: 150, rotation: 45, name: 'Front Left' }, - 'front-right': { x: 75, y: 70, z: 150, rotation: -45, name: 'Front Right' }, - 'back-left': { x: 25, y: 30, z: -200, rotation: 135, name: 'Back Left' }, - 'back-right': { x: 75, y: 30, z: -200, rotation: -135, name: 'Back Right' }, - 'front-center': { x: 50, y: 75, z: 180, rotation: 0, name: 'Front Center' }, - 'back-center': { x: 50, y: 25, z: -250, rotation: 180, name: 'Back Center' }, - 'stage-left': { x: 15, y: 50, z: -50, rotation: 90, name: 'Stage Left' }, - 'stage-right': { x: 85, y: 50, z: -50, rotation: -90, name: 'Stage Right' }, - 'table': { x: 60, y: 20, z: -100, rotation: 0, name: 'Near Table' } -}; - -function moveToWaypoint(waypointName) { - const waypoint = waypoints3D[waypointName]; - if (!waypoint) { - log(`❌ Unknown waypoint: ${waypointName}`, true); - return false; - } - - log(`📍 Moving to waypoint: ${waypoint.name}`); - showFeedback(`📍 WAYPOINT: ${waypoint.name}`); - // Notify in chat that Aria is moving - addChatMessage('aria', `Heading to ${waypoint.name}...`); - characterState.currentWaypoint = waypointName; - setPosition(waypoint.x, waypoint.y, waypoint.z, waypoint.rotation); - return true; -} - -function moveInCircle3D(radius = 30, steps = 12, duration = 6000) { - let currentStep = 0; - const centerX = 50; - const centerY = 50; - const stepDuration = duration / steps; - - const circleInterval = setInterval(() => { - if (currentStep >= steps) { - clearInterval(circleInterval); - log('🔄 Circle completed'); - addChatMessage('aria', 'Finished circular movement.'); - return; - } - - const angle = (currentStep / steps) * Math.PI * 2; - const x = centerX + radius * Math.cos(angle); - const y = centerY + radius * Math.sin(angle); - const z = 100 * Math.sin(angle * 2); // Wave in/out on Z-axis - const rotation = (angle * 180 / Math.PI) + 90; // Face tangent to circle - - setPosition(x, y, z, rotation); - currentStep++; - }, stepDuration); - - log('🔄 Starting 3D circle movement'); - showFeedback('🔄 3D CIRCLE'); - addChatMessage('aria', 'Starting circular 3D movement'); -} - -function performSpiral3D() { - const steps = 20; - const duration = 8000; - let currentStep = 0; - const centerX = 50; - const centerY = 50; - - const spiralInterval = setInterval(() => { - if (currentStep >= steps) { - clearInterval(spiralInterval); - log('🌀 Spiral completed'); - addChatMessage('aria', 'Finished spiral movement.'); - return; - } - - const progress = currentStep / steps; - const radius = 40 * (1 - progress); // Shrink radius - const angle = progress * Math.PI * 6; // Multiple rotations - const x = centerX + radius * Math.cos(angle); - const y = centerY + radius * Math.sin(angle); - const z = -200 + progress * 300; // Move from back to front - const rotation = angle * 180 / Math.PI; - - setPosition(x, y, z, rotation); - currentStep++; - }, duration / steps); - - log('🌀 Starting 3D spiral movement'); - showFeedback('🌀 3D SPIRAL'); - addChatMessage('aria', 'Starting 3D spiral movement'); -} - -function skipMove() { - // Playful skipping motion - moveLeg(ariaLegLeft, -45, 200); - moveLeg(ariaLegRight, 30, 200); - moveArm(ariaArmLeft, -30, 200); - moveArm(ariaArmRight, -30, 200); - aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; - - setTimeout(() => { - moveLeg(ariaLegLeft, 30, 200); - moveLeg(ariaLegRight, -45, 200); - aria.style.transform = 'translateX(-50%) scale(1)'; - }, 250); - - setTimeout(() => { - moveLeg(ariaLegLeft, -30, 200); - moveLeg(ariaLegRight, 20, 200); - aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; - }, 500); - - setTimeout(() => { - resetLimbs(200); - aria.style.transform = 'translateX(-50%) scale(1)'; - }, 750); -} - -function danceLimbs() { - // Arms up and down - moveArm(ariaArmLeft, -45, 200); - moveArm(ariaArmRight, -45, 200); - moveLeg(ariaLegLeft, 15, 200); - moveLeg(ariaLegRight, -15, 200); - - setTimeout(() => { - moveArm(ariaArmLeft, -90, 200); - moveArm(ariaArmRight, -90, 200); - moveLeg(ariaLegLeft, -15, 200); - moveLeg(ariaLegRight, 15, 200); - }, 200); - - setTimeout(() => { - moveArm(ariaArmLeft, -45, 200); - moveArm(ariaArmRight, -45, 200); - moveLeg(ariaLegLeft, 15, 200); - moveLeg(ariaLegRight, -15, 200); - }, 400); - - setTimeout(() => resetLimbs(200), 600); -} - -function expressiveDance() { - // More exaggerated dance moves - moveArm(ariaArmLeft, -120, 150); - moveArm(ariaArmRight, 60, 150); - moveLeg(ariaLegLeft, 20, 150); - moveLeg(ariaLegRight, -20, 150); - ariaHead.style.transform = 'rotate(15deg)'; - - setTimeout(() => { - moveArm(ariaArmLeft, 60, 150); - moveArm(ariaArmRight, -120, 150); - moveLeg(ariaLegLeft, -20, 150); - moveLeg(ariaLegRight, 20, 150); - ariaHead.style.transform = 'rotate(-15deg)'; - }, 200); - - setTimeout(() => { - moveArm(ariaArmLeft, -90, 150); - moveArm(ariaArmRight, -90, 150); - moveLeg(ariaLegLeft, 30, 150); - moveLeg(ariaLegRight, -30, 150); - ariaHead.style.transform = 'rotate(0)'; - }, 400); - - setTimeout(() => resetLimbs(200), 600); -} - -let continuousDanceInterval = null; - -function startContinuousDance() { - if (continuousDanceInterval) return; - - isPerformingAction = true; - showFeedback('🎉 PARTY MODE!'); - - continuousDanceInterval = setInterval(() => { - const danceType = Math.random(); - if (danceType > 0.5) { - expressiveDance(); - } else { - danceLimbs(); - } - }, 700); -} - -function stopContinuousDance() { - if (continuousDanceInterval) { - clearInterval(continuousDanceInterval); - continuousDanceInterval = null; - isPerformingAction = false; - resetLimbs(300); - } -} - -function spinLimbs() { - // Arms out during spin - moveArm(ariaArmLeft, -90, 100); - moveArm(ariaArmRight, -90, 100); - setTimeout(() => resetLimbs(300), 900); -} - -function animate(className) { - console.log('🎬 Animating:', className); - showFeedback('🎬 ' + className.toUpperCase() + '!'); - aria.classList.remove('jumping', 'dancing', 'spinning', 'waving'); - void aria.offsetWidth; // Force reflow - - // Mark as performing action - isPerformingAction = true; - - // Get current scale from characterState - const currentScale = characterState.size || 1.0; - - if (className === 'waving') { - showFeedback('👋 WAVING!'); - console.log('Wave animation with arm movement'); - // Rapid wave motion - moveArm(ariaArmRight, -60, 200); - setTimeout(() => moveArm(ariaArmRight, -30, 150), 200); - setTimeout(() => moveArm(ariaArmRight, -60, 150), 350); - setTimeout(() => moveArm(ariaArmRight, -30, 150), 500); - setTimeout(() => moveArm(ariaArmRight, -60, 150), 650); - setTimeout(() => resetLimbs(200), 800); - } else if (className === 'jumping') { - console.log('Jumping animation triggered with leg bending'); - aria.classList.add(className); - aria.style.filter = 'brightness(1.5)'; - - // Pre-jump crouch - moveLeg(ariaLegLeft, 45, 200); - moveLeg(ariaLegRight, -45, 200); - moveArm(ariaArmLeft, -20, 200); - moveArm(ariaArmRight, -20, 200); - - // During jump - legs extend - setTimeout(() => { - moveLeg(ariaLegLeft, -35, 300); - moveLeg(ariaLegRight, 35, 300); - moveArm(ariaArmLeft, -60, 300); - moveArm(ariaArmRight, -60, 300); - }, 300); - - // Landing crouch - setTimeout(() => { - moveLeg(ariaLegLeft, 35, 300); - moveLeg(ariaLegRight, -35, 300); - moveArm(ariaArmLeft, -10, 300); - moveArm(ariaArmRight, -10, 300); - aria.style.filter = 'brightness(1)'; - }, 1500); - - // Return to normal - setTimeout(() => { - resetLimbs(400); - aria.classList.remove(className); - aria.style.transform = `translateX(-50%) scale(${currentScale})`; - }, 2500); - } else { - console.log('Generic animation triggered:', className); - aria.classList.add(className); - - // Add limb movements based on animation type - if (className === 'dancing') { - console.log('Adding dance limb movements'); - // Repeat dance limbs during 2s animation - danceLimbs(); - setTimeout(() => danceLimbs(), 700); - setTimeout(() => danceLimbs(), 1400); - } else if (className === 'spinning') { - console.log('Adding spin limb movements'); - spinLimbs(); - } - - setTimeout(() => { - aria.classList.remove(className); - resetLimbs(300); - aria.style.transform = `translateX(-50%) scale(${currentScale})`; - isPerformingAction = false; - }, className === 'dancing' ? 2000 : 1000); - } -} - -function getAnimationClass(action) { - const animations = { - 'jump': 'jumping', - 'dance': 'dancing', - 'spin': 'spinning', - 'wave': 'waving', - 'bow': 'waving', - 'flip': 'spinning', - 'backflip': 'spinning' - }; - return animations[action] || 'jumping'; -} - -function move(direction, speed = 'normal') { - console.log('🚶 Moving:', direction, 'at speed:', speed); - - isPerformingAction = true; - - const currentLeft = aria.style.left || '50%'; - const current = parseFloat(currentLeft); - - let newPos = current; - let distance = 25; - let movementStyle = walkCycle; - let duration = '1s'; - - // Choose movement style based on speed - switch(speed) { - case 'run': - case 'fast': - distance = 40; - movementStyle = skipMove; - duration = '0.7s'; - showFeedback('🏃 RUNNING!'); - break; - case 'strut': - case 'swagger': - distance = 30; - movementStyle = strutWalk; - duration = '1.2s'; - showFeedback('😎 STRUTTING!'); - break; - case 'skip': - distance = 35; - movementStyle = skipMove; - duration = '0.9s'; - showFeedback('🎵 SKIPPING!'); - break; - default: - showFeedback('🚶 WALKING!'); - } - - // Animate movement style - movementStyle(); - - switch (direction) { - case 'left': - newPos = Math.max(5, current - distance); - break; - case 'right': - newPos = Math.min(95, current + distance); - break; - case 'forward': - case 'up': - const currentTop = aria.style.top || '50%'; - const top = parseFloat(currentTop); - aria.style.top = Math.max(10, top - distance) + '%'; - aria.style.transition = `top ${duration} ease`; - setTimeout(() => { isPerformingAction = false; }, 1000); - return; - case 'back': - case 'down': - const currentTopBack = aria.style.top || '50%'; - const topBack = parseFloat(currentTopBack); - aria.style.top = Math.min(80, topBack + distance) + '%'; - aria.style.transition = `top ${duration} ease`; - setTimeout(() => { isPerformingAction = false; }, 1000); - return; - } - - aria.style.transition = `left ${duration} ease`; - aria.style.left = newPos + '%'; - - setTimeout(() => { isPerformingAction = false; }, 1000); -} - -function createEffect(type) { - const effects = { - 'sparkle': '✨', - 'glow': '💫', - 'hearts': '💕' - }; - - const emoji = effects[type] || '✨'; - - for (let i = 0; i < 5; i++) { - setTimeout(() => { - const effect = document.createElement('div'); - effect.className = `effect ${type}`; - effect.textContent = emoji; - effect.style.left = (Math.random() * 80 + 10) + '%'; - effect.style.top = (Math.random() * 80 + 10) + '%'; - stage.appendChild(effect); - - setTimeout(() => effect.remove(), 1500); - }, i * 100); - } -} - -function centerAria() { - aria.style.left = '50%'; - aria.style.transform = 'translateX(-50%) scale(1)'; -} - -function applyPose(poseRaw, param) { - const pose = (poseRaw || '').toLowerCase(); - // Body position presets - const poses = { - 'sit': { bottom: '20px', transform: 'translateX(-50%) scale(0.8)' }, - 'stand': { bottom: '50px', transform: 'translateX(-50%) scale(1)' }, - 'crouch': { bottom: '30px', transform: 'translateX(-50%) scale(0.9)' }, - 'lie': { bottom: '10px', transform: 'translateX(-50%) scale(0.7) rotate(90deg)' } - }; - const poseStyle = poses[pose]; - if (poseStyle) { - aria.style.bottom = poseStyle.bottom; - aria.style.transform = poseStyle.transform; - } - // Limb presets - switch (pose) { - case 't-pose': - case 'tpose': - moveArm(ariaArmLeft, -90, 400); - moveArm(ariaArmRight, -90, 400); - resetLimbs(1000); - break; - case 'hands_up': - case 'hands-up': - case 'handsup': - moveArm(ariaArmLeft, -120, 400); - moveArm(ariaArmRight, -120, 400); - break; - case 'cross_arms': - case 'cross-arms': - case 'crossarms': - moveArm(ariaArmLeft, -30, 400); - moveArm(ariaArmRight, 30, 400); - break; - case 'hero': - // Hands on hips (approximate) - moveArm(ariaArmLeft, 45, 400); - moveArm(ariaArmRight, -45, 400); - break; - } -} - -function quickCommand(cmd) { - commandInput.value = cmd; - sendCommand(); -} - -// Random character evolution (simulates AI personality drift) -function randomCharacterEvolution() { - const moods = ['happy', 'sad', 'calm', 'thinking', 'neutral']; - const randomMood = moods[Math.floor(Math.random() * moods.length)]; - const randomEnergy = 30 + Math.floor(Math.random() * 70); - - log('✨ Character evolving to: ' + randomMood + ' (' + randomEnergy + '% energy)'); - - const newStyle = generateCharacterFromMood(randomMood, randomEnergy); - applyCharacterStyle(newStyle); -} - -// Automatic character evolution disabled - keeping neutral human-like appearance -let evolutionCountdown = 30; -const timerDisplay = document.getElementById('evolutionTimer'); - -// setInterval(randomCharacterEvolution, 30000); // Disabled - -// Countdown timer -setInterval(() => { - evolutionCountdown--; - if (evolutionCountdown <= 0) { - evolutionCountdown = 30; - } - - // Update timer display - if (timerDisplay) { - timerDisplay.textContent = `⏰ Next evolution in: ${evolutionCountdown}s`; - if (evolutionCountdown <= 5) { - timerDisplay.style.color = '#e74c3c'; - timerDisplay.style.animation = 'pulse 0.5s ease infinite'; - } else { - timerDisplay.style.color = '#667eea'; - timerDisplay.style.animation = 'none'; - } - } - - if (evolutionCountdown <= 5 && evolutionCountdown > 0) { - log(`⏰ Character evolving in ${evolutionCountdown} seconds...`); - } -}, 1000); - -// Initialize -log('🎨 Aria Visual System Ready!'); -log('🤖 AI Character Generation: ACTIVE'); -log('Type commands or use quick buttons'); -log('⏰ Auto-evolution every 30 seconds'); - -// Object Interaction System -// Helper: derive stage-relative percentage position from an element -function objectPositionFromElement(elem) { - if (!elem) return null; - const stageRect = stage.getBoundingClientRect(); - // Prefer explicit CSS left/bottom if present (percent strings) - try { - if (elem.style && elem.style.left && elem.style.bottom) { - const left = parseFloat(elem.style.left || 0); - const bottom = parseFloat(elem.style.bottom || 0); - // The JS 'y' value used elsewhere equals the CSS bottom percent - return { x: Math.round(left), y: Math.round(bottom) }; - } - } catch (e) { - // fallthrough to computed rect - } - - const rect = elem.getBoundingClientRect(); - const x = ((rect.left - stageRect.left) / stageRect.width) * 100; - const y = 100 - ((rect.bottom - stageRect.top) / stageRect.height) * 100; - return { x: Math.round(x), y: Math.round(y) }; -} - -// Notify backend of object add/update/remove actions -async function sendObjectUpdate(action, objectId, extra = {}) { - try { - if (!objectId) return null; - const objEl = document.getElementById(objectId); - - // resolve position and state - let position = extra.position || null; - if (!position && objEl) position = objectPositionFromElement(objEl); - - const state = extra.state || (objEl ? (objEl.classList.contains('held') ? 'held' : (objEl.style.display === 'none' ? 'hidden' : 'on_table')) : (extra.state || 'unknown')); - const emoji = extra.emoji || (objEl ? objEl.textContent : undefined); - - const objPayload = { id: objectId }; - if (position !== null && position !== undefined) objPayload.position = position; - if (state !== null && state !== undefined) objPayload.state = state; - if (emoji !== null && emoji !== undefined) objPayload.emoji = emoji; - const payload = { action: action, object: objPayload }; - - const res = await fetch('/api/aria/object', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify(payload) - }); - - if (!res.ok) { - console.warn('Object API returned non-OK status', res.status); - return null; - } - - const data = await res.json(); - console.log('object sync:', action, objectId, data); - return data; - } catch (err) { - console.warn('sendObjectUpdate failed:', err); - return null; - } -} -function pickUpObject(objectId) { - const obj = document.getElementById(objectId); - if (!obj) { - console.log('❌ Object not found:', objectId); - return false; - } - - if (characterState.heldObject) { - showFeedback('⚠️ Already holding ' + characterState.heldObject.toUpperCase() + '!'); - return false; - } - - console.log('🤏 Picking up:', objectId); - showFeedback('🤏 PICKED UP ' + objectId.toUpperCase() + '!'); - - // Store held object - characterState.heldObject = objectId; - characterState.heldObjectElement = obj; - - // Visual feedback - obj.classList.add('held'); - - // Sync server side: mark object as held - sendObjectUpdate('update', objectId, { state: 'held' }).catch(() => {}); - - // Position object above character - positionHeldObject(); - - // Arm reaching animation - moveArm(ariaArmRight, -90, 300); - setTimeout(() => moveArm(ariaArmRight, -45, 200), 300); - - return true; -} - -function dropObject() { - if (!characterState.heldObject) { - showFeedback('⚠️ Not holding anything!'); - return false; - } - - console.log('📦 Dropping:', characterState.heldObject); - showFeedback('📦 DROPPED ' + characterState.heldObject.toUpperCase() + '!'); - - const obj = characterState.heldObjectElement; - - // Remove held state - obj.classList.remove('held'); - - // Drop at current Aria position - const ariaRect = aria.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - - const dropLeft = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; - const dropBottom = ((stageRect.bottom - ariaRect.bottom) / stageRect.height) * 100; - - obj.style.left = dropLeft + '%'; - obj.style.bottom = (dropBottom + 10) + '%'; - - // Arm dropping animation - moveArm(ariaArmRight, -90, 200); - setTimeout(() => resetLimbs(300), 200); - - // Clear held object - characterState.heldObject = null; - characterState.heldObjectElement = null; - - // Sync server side: update object's position/state - const finalX = Math.round(dropLeft); - const finalY = Math.round(dropBottom + 10); - const tableX = 60; // server default table X - const isOnTable = Math.abs(finalX - tableX) < 20 && Math.abs(finalY - 35) < 20; - sendObjectUpdate('update', obj.id, { position: { x: finalX, y: finalY }, state: isOnTable ? 'on_table' : 'on_stage' }).catch(() => {}); - - return true; -} - -function throwObject(direction) { - if (!characterState.heldObject) { - showFeedback('⚠️ Not holding anything to throw!'); - return false; - } - - console.log('🎯 Throwing:', characterState.heldObject, 'to', direction); - showFeedback('🎯 THROWING ' + characterState.heldObject.toUpperCase() + '!'); - - const obj = characterState.heldObjectElement; - obj.classList.remove('held'); - - // Throwing arm animation - moveArm(ariaArmRight, -120, 150); - setTimeout(() => { - moveArm(ariaArmRight, -30, 200); - setTimeout(() => resetLimbs(300), 100); - }, 150); - - // Calculate throw trajectory - const currentLeft = parseFloat(obj.style.left); - const currentBottom = parseFloat(obj.style.bottom); - - let targetLeft = currentLeft; - let targetBottom = currentBottom; - - switch(direction) { - case 'left': - targetLeft = Math.max(5, currentLeft - 40); - break; - case 'right': - targetLeft = Math.min(95, currentLeft + 40); - break; - case 'up': - targetBottom = Math.min(90, currentBottom + 30); - break; - } - - // Animate throw with arc - obj.style.transition = 'all 0.8s cubic-bezier(0.25, 0.46, 0.45, 0.94)'; - obj.style.left = targetLeft + '%'; - obj.style.bottom = targetBottom + '%'; - obj.style.transform = 'rotate(360deg) scale(0.9)'; - - // Reset after landing - setTimeout(() => { - obj.style.transition = 'all 0.3s ease'; - obj.style.transform = 'rotate(0deg) scale(1)'; - }, 800); - - // Clear held object - characterState.heldObject = null; - characterState.heldObjectElement = null; - - // Sync server side after throw completes - setTimeout(() => { - const left = Math.round(targetLeft); - const bottom = Math.round(targetBottom); - sendObjectUpdate('update', obj.id, { position: { x: left, y: bottom }, state: 'on_stage' }).catch(() => {}); - }, 900); - - return true; -} - -function positionHeldObject() { - if (!characterState.heldObjectElement) return; - - // Position object above and slightly in front of character - const ariaRect = aria.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - - const objLeft = ((ariaRect.left - stageRect.left + ariaRect.width * 0.6) / stageRect.width) * 100; - const objBottom = ((stageRect.bottom - ariaRect.top + 20) / stageRect.height) * 100; - - characterState.heldObjectElement.style.left = objLeft + '%'; - characterState.heldObjectElement.style.bottom = objBottom + '%'; -} - -function interactWithObject(action, objectId) { - console.log('🔧 Interacting:', action, 'with', objectId); - - switch(action) { - case 'pickup': - case 'grab': - case 'take': - case 'get': - return pickUpObject(objectId); - case 'drop': - case 'place': - case 'put': - return dropObject(); - case 'throw': - case 'toss': - return throwObject('right'); - default: - showFeedback('❓ Unknown action: ' + action); - return false; - } -} - -// Make objects draggable and clickable -function initializeObjectInteractions() { - const objects = document.querySelectorAll('.object'); - - objects.forEach(obj => { - // Prevent adding duplicate event listeners when called multiple times - if (obj.__interactionInitialized) return; - obj.__interactionInitialized = true; - obj.addEventListener('click', (e) => { - e.stopPropagation(); - const objectId = obj.id; - - if (!characterState.heldObject) { - pickUpObject(objectId); - } else if (characterState.heldObject === objectId) { - dropObject(); - } - }); - - // Drag functionality - let isDragging = false; - let startX, startY, startLeft, startBottom; - - obj.addEventListener('mousedown', (e) => { - if (characterState.heldObject === obj.id) return; - - isDragging = true; - obj.classList.add('grabbed'); - - const rect = obj.getBoundingClientRect(); - const stageRect = stage.getBoundingClientRect(); - - startX = e.clientX; - startY = e.clientY; - startLeft = ((rect.left - stageRect.left) / stageRect.width) * 100; - startBottom = ((stageRect.bottom - rect.bottom) / stageRect.height) * 100; - - e.preventDefault(); - }); - - document.addEventListener('mousemove', (e) => { - if (!isDragging) return; - - const stageRect = stage.getBoundingClientRect(); - const deltaX = ((e.clientX - startX) / stageRect.width) * 100; - const deltaY = -((e.clientY - startY) / stageRect.height) * 100; - - obj.style.left = Math.max(0, Math.min(100, startLeft + deltaX)) + '%'; - obj.style.bottom = Math.max(0, Math.min(100, startBottom + deltaY)) + '%'; - }); - - document.addEventListener('mouseup', () => { - if (isDragging) { - isDragging = false; - obj.classList.remove('grabbed'); - // Sync backend with final position - const pos = objectPositionFromElement(obj); - const state = obj.classList.contains('held') ? 'held' : 'on_table'; - sendObjectUpdate('update', obj.id, { position: pos, state: state }).catch(() => {}); - } - }); - }); -} - -function addObject(objectName, emoji) { - console.log('➕ Adding object:', objectName, emoji); - - // Check if object already exists - const existingObj = document.getElementById(objectName); - if (existingObj) { - console.log('⚠️ Object already exists:', objectName); - showFeedback('✅ ' + objectName + ' already on stage'); - return; - } - - // Create new object element - const newObj = document.createElement('div'); - newObj.id = objectName; - newObj.className = 'object'; - newObj.textContent = emoji; - - // Position near Aria - const ariaX = characterState.position.x; - const ariaY = characterState.position.y; - newObj.style.left = Math.max(10, Math.min(90, ariaX + 15)) + '%'; - newObj.style.bottom = Math.max(10, Math.min(90, ariaY + 5)) + '%'; - - // Add to stage - stage.appendChild(newObj); - - // Update tracking - activeObjects[objectName] = true; - - // Add to object manager if button doesn't exist - if (!document.querySelector(`[onclick="toggleObject('${objectName}')"]`)) { - const objectManager = document.getElementById('object-manager'); - const btn = document.createElement('button'); - btn.className = 'object-btn active'; - btn.id = 'btn-' + objectName; - btn.textContent = emoji + ' ' + objectName; - btn.onclick = () => toggleObject(objectName); - objectManager.appendChild(btn); - } - - // Initialize interactions for new object (click/drag) - initializeObjectInteractions(); - - // Sync server: add new object to stage_state - sendObjectUpdate('add', objectName, { position: objectPositionFromElement(newObj), state: 'on_stage', emoji: emoji }).catch(() => {}); - - showFeedback('✨ Added ' + objectName + ' to stage!'); - console.log('✅ Object added successfully:', objectName); -} - -// Initialize object interactions -initializeObjectInteractions(); -log('🎮 Objects: apple, book, cup, ball, flower'); -log('💡 Try: "pick up apple", "drop", "throw ball"'); - -// Auto-behaviors with random timing -function startAutoBehaviors() { - // Random idle movements every 8-15 seconds - setInterval(() => { - if (Math.random() > 0.6) { - const randomActions = ['shift weight', 'look around', 'adjust hair', 'stretch']; - const action = randomActions[Math.floor(Math.random() * randomActions.length)]; - - switch(action) { - case 'shift weight': - animate('bouncing'); - setTimeout(() => aria.style.animation = 'breathe 4s ease-in-out infinite', 800); - break; - case 'look around': - const ariaHead = document.querySelector('.aria-head'); - ariaHead.style.transform = 'translateX(-50%) rotate(10deg)'; - setTimeout(() => ariaHead.style.transform = 'translateX(-50%) rotate(-10deg)', 600); - setTimeout(() => ariaHead.style.transform = 'translateX(-50%)', 1200); - break; - case 'adjust hair': - const hand = document.getElementById('ariaHandRight'); - hand.style.transform = 'translateY(-30px) rotate(-20deg)'; - setTimeout(() => hand.style.transform = '', 1000); - break; - case 'stretch': - ariaArmLeft.style.transform = 'rotate(-160deg)'; - ariaArmRight.style.transform = 'rotate(160deg)'; - setTimeout(() => { - ariaArmLeft.style.transform = ''; - ariaArmRight.style.transform = ''; - }, 1500); - break; - } - } - }, 8000 + Math.random() * 7000); - - // Random expressions every 12-20 seconds - setInterval(() => { - if (Math.random() > 0.5) { - const expressions = ['smile', 'thinking', 'neutral', 'surprised']; - const expr = expressions[Math.floor(Math.random() * expressions.length)]; - changeExpression(expr); - setTimeout(() => changeExpression('neutral'), 2000 + Math.random() * 3000); - } - }, 12000 + Math.random() * 8000); - - // Occasional sparkles every 20-30 seconds - setInterval(() => { - if (Math.random() > 0.7) { - createEffect('sparkle'); - } - }, 20000 + Math.random() * 10000); -} - -// Initial appearance set to neutral human-like style -setTimeout(() => { - log('🎨 Setting initial character appearance...'); - const neutralStyle = generateCharacterFromMood('neutral', 50); - applyCharacterStyle(neutralStyle); -}, 100); - -// Start idle animations -startIdleAnimation(); -log('👀 Idle animations enabled - watch for breathing and blinking!'); - -// Start automatic behaviors -startAutoBehaviors(); -log('✨ Auto-behaviors enabled - Aria will move and react on her own!'); - -// Expose minimal testing helpers -window.ariaTest = { - limb: (part, actionOrAngle, duration) => handleLimbTag(part, typeof actionOrAngle === 'number' ? `${actionOrAngle},${duration||500}` : `${actionOrAngle||''}${duration?','+duration:''}`), - pose: (name) => applyPose(name) -}; -window.simulateTags = (arr) => Array.isArray(arr) && executeTags(arr); +// Aria Visual Command Controller +const aria = document.getElementById('aria'); +const ariaMouth = document.getElementById('ariaMouth'); +const ariaArmLeft = document.getElementById('ariaArmLeft'); +const ariaArmRight = document.getElementById('ariaArmRight'); +const ariaLegLeft = document.getElementById('ariaLegLeft'); +const ariaLegRight = document.getElementById('ariaLegRight'); +const stage = document.getElementById('stage'); +const commandInput = document.getElementById('commandInput'); +const logContainer = document.getElementById('logContainer'); + +// Track active objects +const activeObjects = { + apple: true, + book: true, + cup: true, + ball: true, + flower: true +}; + +// Toggle object visibility +function toggleObject(objectId) { + const obj = document.getElementById(objectId); + const btn = document.getElementById('btn-' + objectId); + + if (!obj) { + log(`❌ toggleObject: unknown object ${objectId}`, true); + return; + } + // compute last-known position first + const lastPos = objectPositionFromElement(obj); + + if (activeObjects[objectId]) { + // Remove object + obj.style.display = 'none'; + btn.classList.remove('active'); + btn.classList.add('inactive'); + activeObjects[objectId] = false; + log('🗑️ Removed ' + objectId); + // Sync change to backend + sendObjectUpdate('update', objectId, { position: lastPos, state: 'hidden' }).catch(() => {}); + } else { + // Add object back + obj.style.display = 'block'; + btn.classList.add('active'); + btn.classList.remove('inactive'); + activeObjects[objectId] = true; + log('➕ Added ' + objectId); + // Sync change to backend (object is back on stage) + sendObjectUpdate('update', objectId, { position: lastPos, state: 'on_table' }).catch(() => {}); + } +} + +// Additional DOM references (used by idle/poses/expressive moves) +const ariaHead = document.querySelector('.aria-head'); +const ariaBody = document.querySelector('.aria-body'); +const ariaEyes = document.querySelectorAll('.aria-eye'); +const ariaEyeLeft = ariaEyes[0]; +const ariaEyeRight = ariaEyes[1]; + +// AI-Controlled Character State (single combined object) +let characterState = { + mood: 'neutral', + energy: 50, + personality: 'balanced', + colors: { + hair: '#4a3728', + skin: '#f5d4b8', + body: '#4a90e2', + legs: '#3d5a80', + feet: '#f5f5f5' + }, + size: 1.0, + style: 'default', + heldObject: null, + heldObjectElement: null, + position: { x: 20, y: 70, z: 0 }, + rotation: 0, + isMoving: false, + currentWaypoint: null +}; + +// Visual feedback function +function showFeedback(message) { + const feedback = document.createElement('div'); + feedback.textContent = message; + feedback.style.cssText = 'position:absolute; top:20px; left:50%; transform:translateX(-50%); background:#e74c3c; color:white; padding:15px 30px; border-radius:15px; font-size:28px; font-weight:bold; z-index:999; box-shadow:0 5px 20px rgba(0,0,0,0.3); animation:pulse 0.5s ease;'; + stage.appendChild(feedback); + setTimeout(() => feedback.remove(), 2500); +} + +// AI-Driven Character Generation +function analyzeAIResponse(text) { + const lowerText = text.toLowerCase(); + + // Detect mood from response + if (lowerText.includes('happy') || lowerText.includes('great') || lowerText.includes('wonderful') || lowerText.includes('excited')) { + return { mood: 'happy', energy: 80 }; + } else if (lowerText.includes('sad') || lowerText.includes('sorry') || lowerText.includes('unfortunate')) { + return { mood: 'sad', energy: 30 }; + } else if (lowerText.includes('angry') || lowerText.includes('frustrated')) { + return { mood: 'angry', energy: 90 }; + } else if (lowerText.includes('calm') || lowerText.includes('peaceful') || lowerText.includes('relaxed')) { + return { mood: 'calm', energy: 40 }; + } else if (lowerText.includes('think') || lowerText.includes('consider') || lowerText.includes('perhaps')) { + return { mood: 'thinking', energy: 60 }; + } + + return { mood: 'neutral', energy: 50 }; +} + +function generateCharacterFromMood(mood, energy) { + const moodColors = { + happy: { body: '#5fb3f5', hair: '#6b4f3d', accent: '#7ac5ff' }, + sad: { body: '#6C7A89', hair: '#4a3728', accent: '#95A5A6' }, + angry: { body: '#E74C3C', hair: '#4a3728', accent: '#ff6b6b' }, + calm: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' }, + thinking: { body: '#5a7fa8', hair: '#4a3728', accent: '#6b8fb3' }, + neutral: { body: '#4a90e2', hair: '#4a3728', accent: '#5a9fe5' } + }; + + const colors = moodColors[mood] || moodColors.neutral; + const size = 0.8 + (energy / 100) * 0.4; // Scale from 0.8 to 1.2 based on energy + + return { colors, size, mood }; +} + +function applyCharacterStyle(style) { + const ariaHead = document.querySelector('.aria-head'); + const ariaBody = document.querySelector('.aria-body'); + const ariaHair = document.querySelector('.aria-hair'); + const ariaLegs = document.querySelectorAll('.aria-leg'); + const ariaFeet = document.querySelectorAll('.aria-foot'); + + // Create dramatic transformation sparkle effect + for (let i = 0; i < 15; i++) { + setTimeout(() => { + createEffect('sparkle'); + }, i * 50); + } + + // Add glow pulse during transformation + aria.style.filter = 'drop-shadow(0 0 30px ' + style.colors.body + ') brightness(1.5)'; + setTimeout(() => { + aria.style.filter = 'none'; + }, 1000); + + // Apply colors with smooth transition + ariaHair.style.transition = 'background-color 1s ease, transform 1s ease'; + ariaHead.style.transition = 'background-color 1s ease'; + ariaBody.style.transition = 'background 1s ease, transform 1s ease'; + ariaLegs.forEach(leg => leg.style.transition = 'background-color 1s ease'); + ariaFeet.forEach(foot => foot.style.transition = 'background-color 1s ease'); + + ariaHair.style.backgroundColor = style.colors.hair; + ariaBody.style.background = `linear-gradient(135deg, ${style.colors.body}, ${style.colors.accent})`; + + // Apply size transformation + aria.style.transform = `translateX(-50%) scale(${style.size})`; + + // Update character state + characterState = { ...characterState, ...style }; + + console.log('🎨 Character updated:', style.mood, 'Energy:', Math.round(style.size * 100) + '%'); + showFeedback('🎨 TRANSFORM: ' + style.mood.toUpperCase()); +} + +function autoGenerateCharacter(responseText) { + const analysis = analyzeAIResponse(responseText); + const newStyle = generateCharacterFromMood(analysis.mood, analysis.energy); + applyCharacterStyle(newStyle); + + // Trigger automatic animation based on mood + setTimeout(() => { + if (analysis.mood === 'happy') { + animate('jumping'); + } else if (analysis.mood === 'sad') { + move('left', 'normal'); + } else if (analysis.mood === 'angry') { + animate('spinning'); + } else if (analysis.mood === 'calm') { + animate('waving'); + } else if (analysis.mood === 'thinking') { + changeExpression('thinking'); + } + }, 500); +} + +const expressions = { + 'smile': '😊', + 'happy': '😃', + 'sad': '😢', + 'surprised': '😲', + 'confused': '😕', + 'thinking': '🤔', + 'wink': '😉' +}; + +function log(message, isError = false) { + const entry = document.createElement('div'); + entry.className = 'log-entry'; + entry.style.borderLeftColor = isError ? '#e74c3c' : '#667eea'; + entry.style.color = isError ? '#e74c3c' : '#555'; + entry.textContent = `${new Date().toLocaleTimeString()}: ${message}`; + logContainer.insertBefore(entry, logContainer.firstChild); + + // Keep only last 10 entries + while (logContainer.children.length > 10) { + logContainer.removeChild(logContainer.lastChild); + } +} + +// Chat UI helpers +function addChatMessage(role, text) { + try { + const container = document.getElementById('chatMessages'); + if (!container) return; + + const msgWrap = document.createElement('div'); + msgWrap.className = 'chat-msg ' + (role === 'user' ? 'user' : (role === 'aria' ? 'aria' : 'system')); + + const bubble = document.createElement('div'); + bubble.className = 'bubble'; + bubble.innerText = text; + + const sender = document.createElement('div'); + sender.className = 'sender'; + sender.innerText = role === 'user' ? 'You' : role === 'aria' ? 'Aria' : 'System'; + + const inner = document.createElement('div'); + inner.appendChild(sender); + inner.appendChild(bubble); + + // For user messages align right + if (role === 'user') { + msgWrap.appendChild(inner); + } else { + msgWrap.appendChild(inner); + } + + container.appendChild(msgWrap); + container.scrollTop = container.scrollHeight; + } catch (e) { + console.warn('addChatMessage failed', e); + } +} + +// Called when Aria reaches a waypoint or finishes a movement +function arrivalFeedback(waypointKey) { + try { + let msg = ''; + if (waypointKey && waypoints3D && waypoints3D[waypointKey]) { + msg = `I've arrived at ${waypoints3D[waypointKey].name}.`; + } else if (typeof waypointKey === 'string' && waypointKey.includes('%')) { + msg = `Arrived at ${waypointKey}.`; + } else if (waypointKey) { + msg = `Arrived at ${waypointKey}.`; + } else { + msg = `Arrived.`; + } + + // Friendly reply from Aria in chat — pick varied arrival messages + const arrivalMessages = [ + `I'm here! 🎉`, + `Made it — ${waypointKey ? waypoints3D[waypointKey]?.name || waypointKey : 'arrived'}!`, + `Arrived and ready!`, + `That was quick — I'm here.`, + `All set. What should we do next?`, + ]; + const chosen = arrivalMessages[Math.floor(Math.random() * arrivalMessages.length)]; + addChatMessage('aria', chosen); + + // Little celebratory effect and expression + createEffect('sparkle'); + changeExpression('happy'); + + // Short arrival animation (small bounce & glow) + aria.classList.add('arrived'); + setTimeout(() => aria.classList.remove('arrived'), 900); + + // Clear current waypoint marker so we don't re-announce + characterState.currentWaypoint = null; + } catch (e) { + console.warn('arrivalFeedback failed', e); + } +} + +// Try to resolve a freeform phrase to a waypoint key +function resolveWaypointFromPhrase(phrase) { + if (!phrase || !waypoints3D) return null; + const clean = phrase.toLowerCase().replace(/[^a-z0-9\s-]/g, '').trim(); + if (!clean) return null; + + // direct key match (e.g., front-left) + const direct = clean.replace(/\s+/g, '-'); + if (waypoints3D[direct]) return direct; + + // check by names and partial matches + for (const k in waypoints3D) { + const v = waypoints3D[k]; + if (!v) continue; + const name = String(v.name || '').toLowerCase(); + if (name === clean) return k; + if (name.includes(clean) || clean.includes(k.replace('-', ' '))) return k; + } + + // synonyms mapping for simple words + const synonyms = { + 'front': 'front-center', 'back': 'back-center', 'left': 'stage-left', 'right': 'stage-right', 'center': 'center', 'table': 'table' + }; + if (synonyms[clean]) return synonyms[clean]; + + // try matching tokens + const tokens = clean.split(/\s+/); + for (const t of tokens) { + if (synonyms[t]) return synonyms[t]; + } + + return null; +} + +// Process chat input: supports slash commands and normal commands +async function sendChat() { + const chatBox = document.getElementById('chatInput'); + if (!chatBox) return; + + const text = chatBox.value.trim(); + if (!text) return; + + // Show user's message + addChatMessage('user', text); + chatBox.value = ''; + + // check for natural language movements (e.g., "go to front-right" or "walk to table") + const nlMove = /(?:go to|move to|walk to|goto)\s+([a-z0-9\-\s]+)/i.exec(text); + if (nlMove) { + const targetPhrase = nlMove[1].trim(); + // try to match known waypoint keys or names + const candidate = resolveWaypointFromPhrase(targetPhrase); + if (candidate) { + moveToWaypoint(candidate); + addChatMessage('aria', `Moving to ${waypoints3D[candidate].name}`); + } else { + addChatMessage('aria', `I couldn't identify a waypoint for '${targetPhrase}'. Try /waypoints`); + } + return; + } + + // /say command — speak directly as Aria + if (text.startsWith('/say ')) { + const sayText = text.slice(5).trim(); + if (sayText) { + // Aria says it in the chat + addChatMessage('aria', sayText); + // small visual cue + createEffect('sparkle'); + changeExpression('smile'); + } + return; + } + + // slash commands handled locally + if (text.startsWith('/goto ') || text.startsWith('/move ') || text.startsWith('/moveTo ')) { + const parts = text.split(/\s+/, 2); + const waypoint = parts[1] ? parts[1].trim() : ''; + if (waypoints3D && waypoints3D[waypoint]) { + moveToWaypoint(waypoint); + addChatMessage('aria', `Moving to ${waypoints3D[waypoint].name}`); + } else { + addChatMessage('aria', `Unknown waypoint '${waypoint}'. Try /waypoints`); + } + return; + } + + if (text === '/waypoints') { + const entries = Object.entries(waypoints3D).map(([k, v]) => `${k} — ${v.name}`); + addChatMessage('aria', `Available waypoints: ${entries.join(', ')}`); + return; + } + + if (text === '/circle' || text === '/circle3d') { + moveInCircle3D(); + addChatMessage('aria', 'Starting 3D circle movement'); + return; + } + + if (text === '/spiral' || text === '/spiral3d') { + performSpiral3D(); + addChatMessage('aria', 'Starting 3D spiral'); + return; + } + + if (text === '/stop') { + // stop behaviors + if (typeof stopContinuousDance === 'function') stopContinuousDance(); + addChatMessage('aria', 'Stopped continuous actions'); + return; + } + + // Otherwise, send as a command to backend (and display response when available) + commandInput.value = text; + const result = await sendCommand(); + + if (!result) { + addChatMessage('aria', 'No response from backend (fallback executed)'); + return; + } + + if (result.error) { + addChatMessage('aria', `Error: ${result.error}`); + return; + } + + // Prefer textual response if available + if (result.response) { + addChatMessage('aria', result.response); + } else if (result.tags && result.tags.length > 0) { + addChatMessage('aria', `Executed tags: ${result.tags.join(' ')}`); + } else if (result.stage_context) { + // Keep stage context short + const ctx = result.stage_context.split('\n').slice(0,4).join(' | '); + addChatMessage('aria', ctx); + } else { + addChatMessage('aria', 'Done.'); + } +} + +async function sendCommand() { + const command = commandInput.value.trim(); + if (!command) return; + + log(`Command: "${command}"`); + commandInput.value = ''; + + try { + // Gather current stage state for AI to see + const stageRect = stage.getBoundingClientRect(); + const ariaRect = aria.getBoundingClientRect(); + + // Calculate Aria's position as percentages + const ariaX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; + const ariaY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; + + // Gather object positions + const objectPositions = {}; + ['apple', 'book', 'cup', 'ball', 'flower'].forEach(objId => { + const obj = document.getElementById(objId); + if (obj) { + const objRect = obj.getBoundingClientRect(); + objectPositions[objId] = { + x: ((objRect.left - stageRect.left) / stageRect.width) * 100, + y: 100 - ((objRect.bottom - stageRect.top) / stageRect.height) * 100, + state: obj.classList.contains('held') ? 'held' : 'on_table' + }; + } + }); + + const currentStageState = { + aria: { + position: { x: Math.round(ariaX), y: Math.round(ariaY) }, + expression: characterState.mood || 'neutral', + held_object: characterState.heldObject, + facing: 'right' + }, + objects: objectPositions + }; + + // Call backend API with stage state + const response = await fetch('/api/aria/command', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + command: command, + stage_state: currentStageState + }) + }); + + if (!response.ok) { + throw new Error(`HTTP ${response.status}: ${response.statusText}`); + } + + const data = await response.json(); + + // AI automatically generates character based on response + if (data.response) { + autoGenerateCharacter(data.response); + } + + if (data.tags && data.tags.length > 0) { + log(`✅ ${data.model}: ${data.tags.join(' ')}`); + executeTags(data.tags); + } else if (data.error) { + log(`⚠️ API Error: ${data.error}`, true); + executeLocalCommand(command); + } else { + log('⚠️ Using fallback parser'); + executeLocalCommand(command); + } + // Return the parsed result so callers (chat UI) can inspect tags / response + return data; + } catch (error) { + log(`⚠️ Network error, using fallback`, true); + // Fallback: parse command locally without AI + executeLocalCommand(command); + return { error: String(error) }; + } +} + +function setPosition(xPercent, yPercent, zDepth = 0, rotateY = 0) { + // AI-driven animated walking to position in 3D space (not teleporting) + const stageRect = stage.getBoundingClientRect(); + const ariaRect = aria.getBoundingClientRect(); + + // Get current position + const currentX = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; + const currentY = 100 - ((ariaRect.bottom - stageRect.top) / stageRect.height) * 100; + + // Clamp target values + xPercent = Math.max(5, Math.min(95, xPercent)); + yPercent = Math.max(5, Math.min(95, yPercent)); + zDepth = Math.max(-300, Math.min(200, zDepth)); // Z range: -300px (far) to 200px (near) + + // Calculate distance and direction + const deltaX = xPercent - currentX; + const deltaY = yPercent - currentY; + const distance = Math.sqrt(deltaX * deltaX + deltaY * deltaY); + + // Don't move if already at target + if (distance < 2) { + log(`📍 Already at position (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px)`); + // If we were moving towards a known waypoint, confirm arrival + if (characterState.currentWaypoint) { + arrivalFeedback(characterState.currentWaypoint); + } + return; + } + + // Determine walking speed and style based on distance + let duration = Math.min(distance * 40, 2000); // 40ms per percent, max 2s + let walkStyle = 'normal'; + + if (distance > 50) { + walkStyle = 'run'; + duration = distance * 20; // Faster for long distances + } else if (distance > 30) { + walkStyle = 'walk'; + duration = distance * 30; + } + + // Face the direction of movement in 3D + let rotationAngle = rotateY || 0; + if (deltaX > 2) { + rotationAngle = 0; // Face camera (right) + } else if (deltaX < -2) { + rotationAngle = 180; // Face away (left) + } + + // Add walking animation + aria.classList.add('walking'); + if (walkStyle === 'run') { + aria.classList.add('running'); + } + + // Animate legs while moving + const walkInterval = setInterval(() => { + animateWalkCycle(); + }, 200); + + // Smooth 3D transition to target + aria.style.transition = `left ${duration}ms ease-in-out, bottom ${duration}ms ease-in-out, transform ${duration}ms ease-in-out`; + + const leftPercent = xPercent; + const bottomPercent = 100 - yPercent; // Invert Y (CSS bottom increases upward) + + aria.style.left = leftPercent + '%'; + aria.style.bottom = bottomPercent + '%'; + + // Apply 3D transform with Z-depth and rotation + const scaleX = rotationAngle === 180 ? -1 : 1; + aria.style.transform = `translateX(-50%) translateZ(${zDepth}px) rotateY(${rotationAngle}deg) scaleX(${scaleX})`; + + // Update character state + characterState.position = { x: xPercent, y: yPercent, z: zDepth }; + characterState.rotation = rotationAngle; + + log(`🚶 Walking to (${Math.round(xPercent)}%, ${Math.round(yPercent)}%, Z:${Math.round(zDepth)}px, Rot:${Math.round(rotationAngle)}°) - ${walkStyle} style`); + showFeedback(`🚶 3D WALK: X${Math.round(xPercent)}% Y${Math.round(yPercent)}% Z${Math.round(zDepth)}px`); + + // Stop walking animation when arrived + setTimeout(() => { + aria.classList.remove('walking', 'running'); + aria.style.transition = ''; // Reset transition + clearInterval(walkInterval); + resetWalkCycle(); + log(`✅ Arrived at (${Math.round(xPercent)}%, ${Math.round(yPercent)}%)`); + // If we were targeting a waypoint, announce in chat + if (characterState.currentWaypoint) { + arrivalFeedback(characterState.currentWaypoint); + } + }, duration); +} + +// Walking animation cycle +function animateWalkCycle() { + const leftLeg = document.querySelector('.aria-lower-leg.left'); + const rightLeg = document.querySelector('.aria-lower-leg.right'); + const leftArm = document.querySelector('.aria-lower-arm.left'); + const rightArm = document.querySelector('.aria-lower-arm.right'); + + if (!leftLeg || !rightLeg) return; + + // Alternate leg swings + if (leftLeg.style.transform.includes('rotate')) { + leftLeg.style.transform = 'rotate(20deg)'; + rightLeg.style.transform = 'rotate(-20deg)'; + if (leftArm) leftArm.style.transform = 'rotate(-15deg)'; + if (rightArm) rightArm.style.transform = 'rotate(15deg)'; + } else { + leftLeg.style.transform = 'rotate(-20deg)'; + rightLeg.style.transform = 'rotate(20deg)'; + if (leftArm) leftArm.style.transform = 'rotate(15deg)'; + if (rightArm) rightArm.style.transform = 'rotate(-15deg)'; + } +} + +function resetWalkCycle() { + const leftLeg = document.querySelector('.aria-lower-leg.left'); + const rightLeg = document.querySelector('.aria-lower-leg.right'); + const leftArm = document.querySelector('.aria-lower-arm.left'); + const rightArm = document.querySelector('.aria-lower-arm.right'); + + if (leftLeg) leftLeg.style.transform = ''; + if (rightLeg) rightLeg.style.transform = ''; + if (leftArm) leftArm.style.transform = ''; + if (rightArm) rightArm.style.transform = ''; +} + +function executeLocalCommand(command) { + // Simple local fallback without AI model + const cmd = command.toLowerCase(); + let executed = false; + + // Check if this is a limb command to avoid movement conflicts + const isLimbCommand = ['left arm', 'arm left', 'left hand', 'right arm', 'arm right', 'right hand', + 'left leg', 'leg left', 'right leg', 'leg right'].some(k => cmd.includes(k)); + + // Expressions + if (cmd.includes('smile') || cmd.includes('happy')) { + changeExpression('smile'); + executed = true; + } + if (cmd.includes('sad')) { + changeExpression('sad'); + executed = true; + } + if (cmd.includes('surprised')) { + changeExpression('surprised'); + executed = true; + } + if (cmd.includes('confused')) { + changeExpression('confused'); + executed = true; + } + if (cmd.includes('thinking') || cmd.includes('think')) { + changeExpression('thinking'); + executed = true; + } + if (cmd.includes('wink')) { + changeExpression('wink'); + executed = true; + } + + // Animations + if (cmd.includes('jump')) { + animate('jumping'); + executed = true; + } + if (cmd.includes('dance')) { + animate('dancing'); + executed = true; + } + if (cmd.includes('spin')) { + animate('spinning'); + executed = true; + } + if (cmd.includes('wave')) { + animate('waving'); + executed = true; + } + + // Effects + if (cmd.includes('sparkle')) { + createEffect('sparkle'); + executed = true; + } + if (cmd.includes('hearts')) { + createEffect('hearts'); + executed = true; + } + if (cmd.includes('glow')) { + createEffect('glow'); + executed = true; + } + + // Movement - only if not a limb command + if (!isLimbCommand) { + // Determine movement style + let movementSpeed = 'normal'; + if (cmd.includes('skip')) { + movementSpeed = 'skip'; + } else if (cmd.includes('strut') || cmd.includes('swagger')) { + movementSpeed = 'strut'; + } else if (cmd.includes('run')) { + movementSpeed = 'run'; + } + + if (cmd.includes('left')) { + move('left', movementSpeed); + executed = true; + } + if (cmd.includes('right')) { + move('right', movementSpeed); + executed = true; + } + if (cmd.includes('up') || (cmd.includes('forward') && !cmd.includes('arm') && !cmd.includes('leg'))) { + move('up', movementSpeed); + executed = true; + } + if (cmd.includes('down') || (cmd.includes('back') && !cmd.includes('arm') && !cmd.includes('leg'))) { + move('down', movementSpeed); + executed = true; + } + } + + if (!executed) { + log('❌ Command not recognized', true); + } +} + +function executeTags(tags) { + console.log('📋 Executing tags:', tags); + tags.forEach((tag, index) => { + // Parse tag format: [aria:category:action:param] + const match = tag.match(/\[aria:([^:]+):([^:\]]+)(?::([^\]]+))?\]/); + if (!match) { + console.log('⚠️ Failed to parse tag:', tag); + return; + } + + const [, categoryRaw, actionRaw, paramRaw] = match; + // Normalize category/action + const category = (categoryRaw || '').toLowerCase(); + const action = (actionRaw || '').toLowerCase(); + const param = typeof paramRaw === 'string' ? paramRaw.trim() : undefined; + console.log(`✅ Parsed tag - Category: ${category}, Action: ${action}, Param: ${param}`); + + setTimeout(() => { + switch (category) { + case 'expression': + console.log('Executing expression:', action); + changeExpression(action); + break; + case 'animate': + console.log('Executing animate:', action); + animate(getAnimationClass(action)); + break; + case 'gesture': + animate('waving'); + break; + case 'move': + move(action, param || 'normal'); + break; + case 'walk': + move(action, param || 'normal'); + break; + case 'run': + move(action, param || 'fast'); + break; + case 'position': + console.log('Executing position:', action, param); + if (action && param) { + // Format: [aria:position:x:y] or [aria:position:x:y:z:rotation] + const parts = tag.match(/\[aria:position:([^:\]]+):([^:\]]+)(?::([^:\]]+))?(?::([^\]]+))?\]/); + if (parts) { + const x = parseInt(parts[1]); + const y = parseInt(parts[2]); + const z = parts[3] ? parseInt(parts[3]) : 0; + const rotation = parts[4] ? parseInt(parts[4]) : 0; + setPosition(x, y, z, rotation); + } else { + setPosition(parseInt(action), parseInt(param)); + } + } + break; + case 'skip': + move(action, 'skip'); + break; + case 'strut': + case 'swagger': + move(action, 'strut'); + break; + case 'limb': + // Move individual limbs with tags like: + // [aria:limb:left_arm:raise] | [aria:limb:right_arm:-45] | [aria:limb:left_leg:kick] + handleLimbTag(action, param); + break; + case 'interact': + console.log('Executing interact:', action, param); + if (action === 'add') { + const [objectName, emoji] = param.split(':'); + addObject(objectName, emoji || '🧸'); + } else { + interactWithObject(action, param); + } + break; + case 'effect': + createEffect(action); + break; + case 'camera': + if (action === 'center') centerAria(); + break; + case 'pose': + applyPose(action, param); + break; + case 'say': + // Tag formats either [aria:say:Hello world] or [aria:say:utterance:extra] + const spoken = param || action; + if (spoken) { + addChatMessage('aria', String(spoken)); + } + break; + } + }, index * 500); // Stagger multiple commands + }); +} + +function changeExpression(emotion) { + ariaMouth.className = 'aria-mouth'; + + // Reset any previous expression modifications + ariaMouth.style.borderRadius = ''; + ariaMouth.style.width = ''; + ariaMouth.style.height = ''; + ariaMouth.style.borderTop = ''; + ariaMouth.style.transform = ''; + + switch(emotion) { + case 'smile': + case 'happy': + ariaMouth.classList.add('smile'); + break; + case 'sad': + ariaMouth.classList.add('sad'); + break; + case 'surprised': + ariaMouth.style.borderRadius = '50%'; + ariaMouth.style.width = '15px'; + ariaMouth.style.height = '15px'; + ariaMouth.style.borderTop = '2px solid #333'; + break; + case 'confused': + // Wavy/uncertain mouth + ariaMouth.style.width = '18px'; + ariaMouth.style.height = '6px'; + ariaMouth.style.borderRadius = '0 0 50% 50%'; + ariaMouth.style.transform = 'translateX(-50%) rotate(5deg)'; + break; + case 'thinking': + // Side mouth (pondering) + ariaMouth.style.width = '12px'; + ariaMouth.style.height = '8px'; + ariaMouth.style.borderRadius = '0 0 50% 50%'; + ariaMouth.style.transform = 'translateX(-30%)'; + // Also raise one eyebrow (using eye height) + if (ariaEyeLeft) { + ariaEyeLeft.style.transform = 'translateY(-2px)'; + setTimeout(() => { + ariaEyeLeft.style.transform = ''; + }, 2000); + } + break; + case 'wink': + document.querySelectorAll('.aria-eye')[1].style.height = '4px'; + setTimeout(() => { + document.querySelectorAll('.aria-eye')[1].style.height = '12px'; + }, 500); + break; + default: + ariaMouth.classList.add('smile'); + } + + aria.style.transform = 'translateX(-50%) scale(1.1)'; + setTimeout(() => { + aria.style.transform = 'translateX(-50%) scale(1)'; + }, 300); +} + +// Idle animation state +let idleAnimationInterval = null; +let isPerformingAction = false; + +// Start idle breathing animation +function startIdleAnimation() { + if (idleAnimationInterval) return; + + idleAnimationInterval = setInterval(() => { + if (!isPerformingAction) { + // Subtle breathing effect + ariaBody.style.transition = 'transform 2s ease-in-out'; + ariaBody.style.transform = 'scaleY(1.03)'; + + // Occasional blink + if (Math.random() > 0.7) { + ariaEyeLeft.style.height = '2px'; + ariaEyeRight.style.height = '2px'; + setTimeout(() => { + ariaEyeLeft.style.height = '12px'; + ariaEyeRight.style.height = '12px'; + }, 150); + } + + // Slight head bob + if (Math.random() > 0.8) { + ariaHead.style.transition = 'transform 0.8s ease-in-out'; + ariaHead.style.transform = 'translateY(-3px)'; + setTimeout(() => { + ariaHead.style.transform = 'translateY(0)'; + }, 800); + } + + setTimeout(() => { + ariaBody.style.transform = 'scaleY(1)'; + }, 2000); + } + }, 4000); +} + +// Stop idle animation +function stopIdleAnimation() { + if (idleAnimationInterval) { + clearInterval(idleAnimationInterval); + idleAnimationInterval = null; + } +} + +// Limb movement helpers +function moveArm(arm, angle, duration = 500) { + arm.style.transition = `transform ${duration}ms ease-in-out`; + arm.style.transform = `rotate(${angle}deg)`; +} + +function moveLeg(leg, angle, duration = 500) { + leg.style.transition = `transform ${duration}ms ease-in-out`; + leg.style.transform = `rotate(${angle}deg)`; +} + +function resetLimbs(duration = 500) { + moveArm(ariaArmLeft, 0, duration); + moveArm(ariaArmRight, 0, duration); + moveLeg(ariaLegLeft, 0, duration); + moveLeg(ariaLegRight, 0, duration); +} + +// Utility helpers for limb control +function clamp(n, min, max) { return Math.max(min, Math.min(max, n)); } + +function parseAngleAndDuration(param) { + // Accept patterns: "45", "45,600", "raise", "raise,800" + if (!param) return { value: null, duration: 500 }; + const parts = String(param).split(',').map(s => s.trim()); + const first = parts[0]; + const dur = parts[1] ? parseInt(parts[1], 10) : 500; + const asNum = Number(first); + if (!Number.isNaN(asNum)) { + return { value: asNum, duration: isFinite(dur) ? dur : 500 }; + } + return { value: first.toLowerCase(), duration: isFinite(dur) ? dur : 500 }; +} + +function normalizeLimbPart(part) { + const p = (part || '').toLowerCase().replace(/[-\s]/g, '_'); + const map = { + 'leftarm': 'left_arm', 'arm_left': 'left_arm', 'l_arm': 'left_arm', 'left_hand':'left_arm', 'hand_left':'left_arm', + 'rightarm': 'right_arm', 'arm_right': 'right_arm', 'r_arm': 'right_arm', 'right_hand':'right_arm', 'hand_right':'right_arm', + 'leftleg': 'left_leg', 'leg_left': 'left_leg', 'l_leg':'left_leg', + 'rightleg': 'right_leg', 'leg_right': 'right_leg', 'r_leg':'right_leg' + }; + return map[p] || p; +} + +function elementForPart(part) { + switch (part) { + case 'left_arm': return ariaArmLeft; + case 'right_arm': return ariaArmRight; + case 'left_leg': return ariaLegLeft; + case 'right_leg': return ariaLegRight; + default: return null; + } +} + +function waveArmElement(armEl, duration = 700) { + if (!armEl) return; + const step = Math.max(120, duration); + moveArm(armEl, -60, step * 0.3); + setTimeout(() => moveArm(armEl, -30, step * 0.25), step * 0.3); + setTimeout(() => moveArm(armEl, -60, step * 0.25), step * 0.55); + setTimeout(() => moveArm(armEl, -30, step * 0.2), step * 0.8); + setTimeout(() => moveArm(armEl, 0, step * 0.2), step * 1.0); +} + +function kickLegElement(legEl, duration = 500) { + if (!legEl) return; + moveLeg(legEl, 45, duration * 0.5); + setTimeout(() => moveLeg(legEl, 0, duration * 0.5), duration * 0.5); +} + +function handleLimbTag(partRaw, paramRaw) { + const part = normalizeLimbPart(partRaw); + const targetEl = elementForPart(part); + if (!targetEl) { + log(`❓ Unknown limb: ${partRaw}`); + return; + } + const { value, duration } = parseAngleAndDuration(paramRaw); + const isArm = part.includes('arm'); + const isLeg = part.includes('leg'); + // Pause idle while moving a limb + isPerformingAction = true; + + if (typeof value === 'number') { + const angle = isArm ? clamp(value, -130, 130) : clamp(value, -60, 60); + if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); + setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); + return; + } + + const action = (value || '').toLowerCase(); + switch (action) { + case 'raise': + case 'up': + if (isArm) moveArm(targetEl, -90, duration); else moveLeg(targetEl, -30, duration); + break; + case 'lower': + case 'down': + if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); + break; + case 'forward': + if (isArm) moveArm(targetEl, -45, duration); else moveLeg(targetEl, 30, duration); + break; + case 'back': + case 'backward': + if (isArm) moveArm(targetEl, 45, duration); else moveLeg(targetEl, -30, duration); + break; + case 'wave': + if (isArm) waveArmElement(targetEl, duration); + break; + case 'kick': + if (isLeg) kickLegElement(targetEl, duration); + break; + case 'rest': + case 'neutral': + if (isArm) moveArm(targetEl, 0, duration); else moveLeg(targetEl, 0, duration); + break; + default: + // Try numeric fallback if action is numeric-like + const num = Number(action); + if (!Number.isNaN(num)) { + const angle = isArm ? clamp(num, -130, 130) : clamp(num, -60, 60); + if (isArm) moveArm(targetEl, angle, duration); else moveLeg(targetEl, angle, duration); + } else { + log(`❓ Unknown limb action: ${action}`); + } + } + setTimeout(() => { isPerformingAction = false; }, Math.max(300, duration)); +} + +function walkCycle() { + // Alternating leg movement + moveLeg(ariaLegLeft, 25, 300); + moveLeg(ariaLegRight, -25, 300); + moveArm(ariaArmLeft, -15, 300); + moveArm(ariaArmRight, 15, 300); + + setTimeout(() => { + moveLeg(ariaLegLeft, -25, 300); + moveLeg(ariaLegRight, 25, 300); + moveArm(ariaArmLeft, 15, 300); + moveArm(ariaArmRight, -15, 300); + }, 300); + + setTimeout(() => resetLimbs(200), 600); +} + +function strutWalk() { + // Confident strut with head bob + ariaHead.style.transition = 'transform 0.3s'; + + moveLeg(ariaLegLeft, 35, 250); + moveLeg(ariaLegRight, -35, 250); + moveArm(ariaArmLeft, -25, 250); + moveArm(ariaArmRight, 25, 250); + ariaHead.style.transform = 'translateY(-5px) rotate(3deg)'; + + setTimeout(() => { + moveLeg(ariaLegLeft, -35, 250); + moveLeg(ariaLegRight, 35, 250); + moveArm(ariaArmLeft, 25, 250); + moveArm(ariaArmRight, -25, 250); + ariaHead.style.transform = 'translateY(-5px) rotate(-3deg)'; + }, 300); + + setTimeout(() => { + moveLeg(ariaLegLeft, 20, 250); + moveLeg(ariaLegRight, -20, 250); + ariaHead.style.transform = 'translateY(0) rotate(0)'; + }, 600); + + setTimeout(() => resetLimbs(200), 850); +} + +// 3D Waypoint System - Fixed positions in 3D space +const waypoints3D = { + 'center': { x: 50, y: 50, z: 0, rotation: 0, name: 'Center Stage' }, + 'front-left': { x: 25, y: 70, z: 150, rotation: 45, name: 'Front Left' }, + 'front-right': { x: 75, y: 70, z: 150, rotation: -45, name: 'Front Right' }, + 'back-left': { x: 25, y: 30, z: -200, rotation: 135, name: 'Back Left' }, + 'back-right': { x: 75, y: 30, z: -200, rotation: -135, name: 'Back Right' }, + 'front-center': { x: 50, y: 75, z: 180, rotation: 0, name: 'Front Center' }, + 'back-center': { x: 50, y: 25, z: -250, rotation: 180, name: 'Back Center' }, + 'stage-left': { x: 15, y: 50, z: -50, rotation: 90, name: 'Stage Left' }, + 'stage-right': { x: 85, y: 50, z: -50, rotation: -90, name: 'Stage Right' }, + 'table': { x: 60, y: 20, z: -100, rotation: 0, name: 'Near Table' } +}; + +function moveToWaypoint(waypointName) { + const waypoint = waypoints3D[waypointName]; + if (!waypoint) { + log(`❌ Unknown waypoint: ${waypointName}`, true); + return false; + } + + log(`📍 Moving to waypoint: ${waypoint.name}`); + showFeedback(`📍 WAYPOINT: ${waypoint.name}`); + // Notify in chat that Aria is moving + addChatMessage('aria', `Heading to ${waypoint.name}...`); + characterState.currentWaypoint = waypointName; + setPosition(waypoint.x, waypoint.y, waypoint.z, waypoint.rotation); + return true; +} + +function moveInCircle3D(radius = 30, steps = 12, duration = 6000) { + let currentStep = 0; + const centerX = 50; + const centerY = 50; + const stepDuration = duration / steps; + + const circleInterval = setInterval(() => { + if (currentStep >= steps) { + clearInterval(circleInterval); + log('🔄 Circle completed'); + addChatMessage('aria', 'Finished circular movement.'); + return; + } + + const angle = (currentStep / steps) * Math.PI * 2; + const x = centerX + radius * Math.cos(angle); + const y = centerY + radius * Math.sin(angle); + const z = 100 * Math.sin(angle * 2); // Wave in/out on Z-axis + const rotation = (angle * 180 / Math.PI) + 90; // Face tangent to circle + + setPosition(x, y, z, rotation); + currentStep++; + }, stepDuration); + + log('🔄 Starting 3D circle movement'); + showFeedback('🔄 3D CIRCLE'); + addChatMessage('aria', 'Starting circular 3D movement'); +} + +function performSpiral3D() { + const steps = 20; + const duration = 8000; + let currentStep = 0; + const centerX = 50; + const centerY = 50; + + const spiralInterval = setInterval(() => { + if (currentStep >= steps) { + clearInterval(spiralInterval); + log('🌀 Spiral completed'); + addChatMessage('aria', 'Finished spiral movement.'); + return; + } + + const progress = currentStep / steps; + const radius = 40 * (1 - progress); // Shrink radius + const angle = progress * Math.PI * 6; // Multiple rotations + const x = centerX + radius * Math.cos(angle); + const y = centerY + radius * Math.sin(angle); + const z = -200 + progress * 300; // Move from back to front + const rotation = angle * 180 / Math.PI; + + setPosition(x, y, z, rotation); + currentStep++; + }, duration / steps); + + log('🌀 Starting 3D spiral movement'); + showFeedback('🌀 3D SPIRAL'); + addChatMessage('aria', 'Starting 3D spiral movement'); +} + +function skipMove() { + // Playful skipping motion + moveLeg(ariaLegLeft, -45, 200); + moveLeg(ariaLegRight, 30, 200); + moveArm(ariaArmLeft, -30, 200); + moveArm(ariaArmRight, -30, 200); + aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; + + setTimeout(() => { + moveLeg(ariaLegLeft, 30, 200); + moveLeg(ariaLegRight, -45, 200); + aria.style.transform = 'translateX(-50%) scale(1)'; + }, 250); + + setTimeout(() => { + moveLeg(ariaLegLeft, -30, 200); + moveLeg(ariaLegRight, 20, 200); + aria.style.transform = 'translateX(-50%) scale(1.1) translateY(-20px)'; + }, 500); + + setTimeout(() => { + resetLimbs(200); + aria.style.transform = 'translateX(-50%) scale(1)'; + }, 750); +} + +function danceLimbs() { + // Arms up and down + moveArm(ariaArmLeft, -45, 200); + moveArm(ariaArmRight, -45, 200); + moveLeg(ariaLegLeft, 15, 200); + moveLeg(ariaLegRight, -15, 200); + + setTimeout(() => { + moveArm(ariaArmLeft, -90, 200); + moveArm(ariaArmRight, -90, 200); + moveLeg(ariaLegLeft, -15, 200); + moveLeg(ariaLegRight, 15, 200); + }, 200); + + setTimeout(() => { + moveArm(ariaArmLeft, -45, 200); + moveArm(ariaArmRight, -45, 200); + moveLeg(ariaLegLeft, 15, 200); + moveLeg(ariaLegRight, -15, 200); + }, 400); + + setTimeout(() => resetLimbs(200), 600); +} + +function expressiveDance() { + // More exaggerated dance moves + moveArm(ariaArmLeft, -120, 150); + moveArm(ariaArmRight, 60, 150); + moveLeg(ariaLegLeft, 20, 150); + moveLeg(ariaLegRight, -20, 150); + ariaHead.style.transform = 'rotate(15deg)'; + + setTimeout(() => { + moveArm(ariaArmLeft, 60, 150); + moveArm(ariaArmRight, -120, 150); + moveLeg(ariaLegLeft, -20, 150); + moveLeg(ariaLegRight, 20, 150); + ariaHead.style.transform = 'rotate(-15deg)'; + }, 200); + + setTimeout(() => { + moveArm(ariaArmLeft, -90, 150); + moveArm(ariaArmRight, -90, 150); + moveLeg(ariaLegLeft, 30, 150); + moveLeg(ariaLegRight, -30, 150); + ariaHead.style.transform = 'rotate(0)'; + }, 400); + + setTimeout(() => resetLimbs(200), 600); +} + +let continuousDanceInterval = null; + +function startContinuousDance() { + if (continuousDanceInterval) return; + + isPerformingAction = true; + showFeedback('🎉 PARTY MODE!'); + + continuousDanceInterval = setInterval(() => { + const danceType = Math.random(); + if (danceType > 0.5) { + expressiveDance(); + } else { + danceLimbs(); + } + }, 700); +} + +function stopContinuousDance() { + if (continuousDanceInterval) { + clearInterval(continuousDanceInterval); + continuousDanceInterval = null; + isPerformingAction = false; + resetLimbs(300); + } +} + +function spinLimbs() { + // Arms out during spin + moveArm(ariaArmLeft, -90, 100); + moveArm(ariaArmRight, -90, 100); + setTimeout(() => resetLimbs(300), 900); +} + +function animate(className) { + console.log('🎬 Animating:', className); + showFeedback('🎬 ' + className.toUpperCase() + '!'); + aria.classList.remove('jumping', 'dancing', 'spinning', 'waving'); + void aria.offsetWidth; // Force reflow + + // Mark as performing action + isPerformingAction = true; + + // Get current scale from characterState + const currentScale = characterState.size || 1.0; + + if (className === 'waving') { + showFeedback('👋 WAVING!'); + console.log('Wave animation with arm movement'); + // Rapid wave motion + moveArm(ariaArmRight, -60, 200); + setTimeout(() => moveArm(ariaArmRight, -30, 150), 200); + setTimeout(() => moveArm(ariaArmRight, -60, 150), 350); + setTimeout(() => moveArm(ariaArmRight, -30, 150), 500); + setTimeout(() => moveArm(ariaArmRight, -60, 150), 650); + setTimeout(() => resetLimbs(200), 800); + } else if (className === 'jumping') { + console.log('Jumping animation triggered with leg bending'); + aria.classList.add(className); + aria.style.filter = 'brightness(1.5)'; + + // Pre-jump crouch + moveLeg(ariaLegLeft, 45, 200); + moveLeg(ariaLegRight, -45, 200); + moveArm(ariaArmLeft, -20, 200); + moveArm(ariaArmRight, -20, 200); + + // During jump - legs extend + setTimeout(() => { + moveLeg(ariaLegLeft, -35, 300); + moveLeg(ariaLegRight, 35, 300); + moveArm(ariaArmLeft, -60, 300); + moveArm(ariaArmRight, -60, 300); + }, 300); + + // Landing crouch + setTimeout(() => { + moveLeg(ariaLegLeft, 35, 300); + moveLeg(ariaLegRight, -35, 300); + moveArm(ariaArmLeft, -10, 300); + moveArm(ariaArmRight, -10, 300); + aria.style.filter = 'brightness(1)'; + }, 1500); + + // Return to normal + setTimeout(() => { + resetLimbs(400); + aria.classList.remove(className); + aria.style.transform = `translateX(-50%) scale(${currentScale})`; + }, 2500); + } else { + console.log('Generic animation triggered:', className); + aria.classList.add(className); + + // Add limb movements based on animation type + if (className === 'dancing') { + console.log('Adding dance limb movements'); + // Repeat dance limbs during 2s animation + danceLimbs(); + setTimeout(() => danceLimbs(), 700); + setTimeout(() => danceLimbs(), 1400); + } else if (className === 'spinning') { + console.log('Adding spin limb movements'); + spinLimbs(); + } + + setTimeout(() => { + aria.classList.remove(className); + resetLimbs(300); + aria.style.transform = `translateX(-50%) scale(${currentScale})`; + isPerformingAction = false; + }, className === 'dancing' ? 2000 : 1000); + } +} + +function getAnimationClass(action) { + const animations = { + 'jump': 'jumping', + 'dance': 'dancing', + 'spin': 'spinning', + 'wave': 'waving', + 'bow': 'waving', + 'flip': 'spinning', + 'backflip': 'spinning' + }; + return animations[action] || 'jumping'; +} + +function move(direction, speed = 'normal') { + console.log('🚶 Moving:', direction, 'at speed:', speed); + + isPerformingAction = true; + + const currentLeft = aria.style.left || '50%'; + const current = parseFloat(currentLeft); + + let newPos = current; + let distance = 25; + let movementStyle = walkCycle; + let duration = '1s'; + + // Choose movement style based on speed + switch(speed) { + case 'run': + case 'fast': + distance = 40; + movementStyle = skipMove; + duration = '0.7s'; + showFeedback('🏃 RUNNING!'); + break; + case 'strut': + case 'swagger': + distance = 30; + movementStyle = strutWalk; + duration = '1.2s'; + showFeedback('😎 STRUTTING!'); + break; + case 'skip': + distance = 35; + movementStyle = skipMove; + duration = '0.9s'; + showFeedback('🎵 SKIPPING!'); + break; + default: + showFeedback('🚶 WALKING!'); + } + + // Animate movement style + movementStyle(); + + switch (direction) { + case 'left': + newPos = Math.max(5, current - distance); + break; + case 'right': + newPos = Math.min(95, current + distance); + break; + case 'forward': + case 'up': + const currentTop = aria.style.top || '50%'; + const top = parseFloat(currentTop); + aria.style.top = Math.max(10, top - distance) + '%'; + aria.style.transition = `top ${duration} ease`; + setTimeout(() => { isPerformingAction = false; }, 1000); + return; + case 'back': + case 'down': + const currentTopBack = aria.style.top || '50%'; + const topBack = parseFloat(currentTopBack); + aria.style.top = Math.min(80, topBack + distance) + '%'; + aria.style.transition = `top ${duration} ease`; + setTimeout(() => { isPerformingAction = false; }, 1000); + return; + } + + aria.style.transition = `left ${duration} ease`; + aria.style.left = newPos + '%'; + + setTimeout(() => { isPerformingAction = false; }, 1000); +} + +function createEffect(type) { + const effects = { + 'sparkle': '✨', + 'glow': '💫', + 'hearts': '💕' + }; + + const emoji = effects[type] || '✨'; + + for (let i = 0; i < 5; i++) { + setTimeout(() => { + const effect = document.createElement('div'); + effect.className = `effect ${type}`; + effect.textContent = emoji; + effect.style.left = (Math.random() * 80 + 10) + '%'; + effect.style.top = (Math.random() * 80 + 10) + '%'; + stage.appendChild(effect); + + setTimeout(() => effect.remove(), 1500); + }, i * 100); + } +} + +function centerAria() { + aria.style.left = '50%'; + aria.style.transform = 'translateX(-50%) scale(1)'; +} + +function applyPose(poseRaw, param) { + const pose = (poseRaw || '').toLowerCase(); + // Body position presets + const poses = { + 'sit': { bottom: '20px', transform: 'translateX(-50%) scale(0.8)' }, + 'stand': { bottom: '50px', transform: 'translateX(-50%) scale(1)' }, + 'crouch': { bottom: '30px', transform: 'translateX(-50%) scale(0.9)' }, + 'lie': { bottom: '10px', transform: 'translateX(-50%) scale(0.7) rotate(90deg)' } + }; + const poseStyle = poses[pose]; + if (poseStyle) { + aria.style.bottom = poseStyle.bottom; + aria.style.transform = poseStyle.transform; + } + // Limb presets + switch (pose) { + case 't-pose': + case 'tpose': + moveArm(ariaArmLeft, -90, 400); + moveArm(ariaArmRight, -90, 400); + resetLimbs(1000); + break; + case 'hands_up': + case 'hands-up': + case 'handsup': + moveArm(ariaArmLeft, -120, 400); + moveArm(ariaArmRight, -120, 400); + break; + case 'cross_arms': + case 'cross-arms': + case 'crossarms': + moveArm(ariaArmLeft, -30, 400); + moveArm(ariaArmRight, 30, 400); + break; + case 'hero': + // Hands on hips (approximate) + moveArm(ariaArmLeft, 45, 400); + moveArm(ariaArmRight, -45, 400); + break; + } +} + +function quickCommand(cmd) { + commandInput.value = cmd; + sendCommand(); +} + +// Random character evolution (simulates AI personality drift) +function randomCharacterEvolution() { + const moods = ['happy', 'sad', 'calm', 'thinking', 'neutral']; + const randomMood = moods[Math.floor(Math.random() * moods.length)]; + const randomEnergy = 30 + Math.floor(Math.random() * 70); + + log('✨ Character evolving to: ' + randomMood + ' (' + randomEnergy + '% energy)'); + + const newStyle = generateCharacterFromMood(randomMood, randomEnergy); + applyCharacterStyle(newStyle); +} + +// Automatic character evolution disabled - keeping neutral human-like appearance +let evolutionCountdown = 30; +const timerDisplay = document.getElementById('evolutionTimer'); + +// setInterval(randomCharacterEvolution, 30000); // Disabled + +// Countdown timer +setInterval(() => { + evolutionCountdown--; + if (evolutionCountdown <= 0) { + evolutionCountdown = 30; + } + + // Update timer display + if (timerDisplay) { + timerDisplay.textContent = `⏰ Next evolution in: ${evolutionCountdown}s`; + if (evolutionCountdown <= 5) { + timerDisplay.style.color = '#e74c3c'; + timerDisplay.style.animation = 'pulse 0.5s ease infinite'; + } else { + timerDisplay.style.color = '#667eea'; + timerDisplay.style.animation = 'none'; + } + } + + if (evolutionCountdown <= 5 && evolutionCountdown > 0) { + log(`⏰ Character evolving in ${evolutionCountdown} seconds...`); + } +}, 1000); + +// Initialize +log('🎨 Aria Visual System Ready!'); +log('🤖 AI Character Generation: ACTIVE'); +log('Type commands or use quick buttons'); +log('⏰ Auto-evolution every 30 seconds'); + +// Object Interaction System +// Helper: derive stage-relative percentage position from an element +function objectPositionFromElement(elem) { + if (!elem) return null; + const stageRect = stage.getBoundingClientRect(); + // Prefer explicit CSS left/bottom if present (percent strings) + try { + if (elem.style && elem.style.left && elem.style.bottom) { + const left = parseFloat(elem.style.left || 0); + const bottom = parseFloat(elem.style.bottom || 0); + // The JS 'y' value used elsewhere equals the CSS bottom percent + return { x: Math.round(left), y: Math.round(bottom) }; + } + } catch (e) { + // fallthrough to computed rect + } + + const rect = elem.getBoundingClientRect(); + const x = ((rect.left - stageRect.left) / stageRect.width) * 100; + const y = 100 - ((rect.bottom - stageRect.top) / stageRect.height) * 100; + return { x: Math.round(x), y: Math.round(y) }; +} + +// Notify backend of object add/update/remove actions +async function sendObjectUpdate(action, objectId, extra = {}) { + try { + if (!objectId) return null; + const objEl = document.getElementById(objectId); + + // resolve position and state + let position = extra.position || null; + if (!position && objEl) position = objectPositionFromElement(objEl); + + const state = extra.state || (objEl ? (objEl.classList.contains('held') ? 'held' : (objEl.style.display === 'none' ? 'hidden' : 'on_table')) : (extra.state || 'unknown')); + const emoji = extra.emoji || (objEl ? objEl.textContent : undefined); + + const objPayload = { id: objectId }; + if (position !== null && position !== undefined) objPayload.position = position; + if (state !== null && state !== undefined) objPayload.state = state; + if (emoji !== null && emoji !== undefined) objPayload.emoji = emoji; + const payload = { action: action, object: objPayload }; + + const res = await fetch('/api/aria/object', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload) + }); + + if (!res.ok) { + console.warn('Object API returned non-OK status', res.status); + return null; + } + + const data = await res.json(); + console.log('object sync:', action, objectId, data); + return data; + } catch (err) { + console.warn('sendObjectUpdate failed:', err); + return null; + } +} +function pickUpObject(objectId) { + const obj = document.getElementById(objectId); + if (!obj) { + console.log('❌ Object not found:', objectId); + return false; + } + + if (characterState.heldObject) { + showFeedback('⚠️ Already holding ' + characterState.heldObject.toUpperCase() + '!'); + return false; + } + + console.log('🤏 Picking up:', objectId); + showFeedback('🤏 PICKED UP ' + objectId.toUpperCase() + '!'); + + // Store held object + characterState.heldObject = objectId; + characterState.heldObjectElement = obj; + + // Visual feedback + obj.classList.add('held'); + + // Sync server side: mark object as held + sendObjectUpdate('update', objectId, { state: 'held' }).catch(() => {}); + + // Position object above character + positionHeldObject(); + + // Arm reaching animation + moveArm(ariaArmRight, -90, 300); + setTimeout(() => moveArm(ariaArmRight, -45, 200), 300); + + return true; +} + +function dropObject() { + if (!characterState.heldObject) { + showFeedback('⚠️ Not holding anything!'); + return false; + } + + console.log('📦 Dropping:', characterState.heldObject); + showFeedback('📦 DROPPED ' + characterState.heldObject.toUpperCase() + '!'); + + const obj = characterState.heldObjectElement; + + // Remove held state + obj.classList.remove('held'); + + // Drop at current Aria position + const ariaRect = aria.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + + const dropLeft = ((ariaRect.left - stageRect.left) / stageRect.width) * 100; + const dropBottom = ((stageRect.bottom - ariaRect.bottom) / stageRect.height) * 100; + + obj.style.left = dropLeft + '%'; + obj.style.bottom = (dropBottom + 10) + '%'; + + // Arm dropping animation + moveArm(ariaArmRight, -90, 200); + setTimeout(() => resetLimbs(300), 200); + + // Clear held object + characterState.heldObject = null; + characterState.heldObjectElement = null; + + // Sync server side: update object's position/state + const finalX = Math.round(dropLeft); + const finalY = Math.round(dropBottom + 10); + const tableX = 60; // server default table X + const isOnTable = Math.abs(finalX - tableX) < 20 && Math.abs(finalY - 35) < 20; + sendObjectUpdate('update', obj.id, { position: { x: finalX, y: finalY }, state: isOnTable ? 'on_table' : 'on_stage' }).catch(() => {}); + + return true; +} + +function throwObject(direction) { + if (!characterState.heldObject) { + showFeedback('⚠️ Not holding anything to throw!'); + return false; + } + + console.log('🎯 Throwing:', characterState.heldObject, 'to', direction); + showFeedback('🎯 THROWING ' + characterState.heldObject.toUpperCase() + '!'); + + const obj = characterState.heldObjectElement; + obj.classList.remove('held'); + + // Throwing arm animation + moveArm(ariaArmRight, -120, 150); + setTimeout(() => { + moveArm(ariaArmRight, -30, 200); + setTimeout(() => resetLimbs(300), 100); + }, 150); + + // Calculate throw trajectory + const currentLeft = parseFloat(obj.style.left); + const currentBottom = parseFloat(obj.style.bottom); + + let targetLeft = currentLeft; + let targetBottom = currentBottom; + + switch(direction) { + case 'left': + targetLeft = Math.max(5, currentLeft - 40); + break; + case 'right': + targetLeft = Math.min(95, currentLeft + 40); + break; + case 'up': + targetBottom = Math.min(90, currentBottom + 30); + break; + } + + // Animate throw with arc + obj.style.transition = 'all 0.8s cubic-bezier(0.25, 0.46, 0.45, 0.94)'; + obj.style.left = targetLeft + '%'; + obj.style.bottom = targetBottom + '%'; + obj.style.transform = 'rotate(360deg) scale(0.9)'; + + // Reset after landing + setTimeout(() => { + obj.style.transition = 'all 0.3s ease'; + obj.style.transform = 'rotate(0deg) scale(1)'; + }, 800); + + // Clear held object + characterState.heldObject = null; + characterState.heldObjectElement = null; + + // Sync server side after throw completes + setTimeout(() => { + const left = Math.round(targetLeft); + const bottom = Math.round(targetBottom); + sendObjectUpdate('update', obj.id, { position: { x: left, y: bottom }, state: 'on_stage' }).catch(() => {}); + }, 900); + + return true; +} + +function positionHeldObject() { + if (!characterState.heldObjectElement) return; + + // Position object above and slightly in front of character + const ariaRect = aria.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + + const objLeft = ((ariaRect.left - stageRect.left + ariaRect.width * 0.6) / stageRect.width) * 100; + const objBottom = ((stageRect.bottom - ariaRect.top + 20) / stageRect.height) * 100; + + characterState.heldObjectElement.style.left = objLeft + '%'; + characterState.heldObjectElement.style.bottom = objBottom + '%'; +} + +function interactWithObject(action, objectId) { + console.log('🔧 Interacting:', action, 'with', objectId); + + switch(action) { + case 'pickup': + case 'grab': + case 'take': + case 'get': + return pickUpObject(objectId); + case 'drop': + case 'place': + case 'put': + return dropObject(); + case 'throw': + case 'toss': + return throwObject('right'); + default: + showFeedback('❓ Unknown action: ' + action); + return false; + } +} + +// Make objects draggable and clickable +function initializeObjectInteractions() { + const objects = document.querySelectorAll('.object'); + + objects.forEach(obj => { + // Prevent adding duplicate event listeners when called multiple times + if (obj.__interactionInitialized) return; + obj.__interactionInitialized = true; + obj.addEventListener('click', (e) => { + e.stopPropagation(); + const objectId = obj.id; + + if (!characterState.heldObject) { + pickUpObject(objectId); + } else if (characterState.heldObject === objectId) { + dropObject(); + } + }); + + // Drag functionality + let isDragging = false; + let startX, startY, startLeft, startBottom; + + obj.addEventListener('mousedown', (e) => { + if (characterState.heldObject === obj.id) return; + + isDragging = true; + obj.classList.add('grabbed'); + + const rect = obj.getBoundingClientRect(); + const stageRect = stage.getBoundingClientRect(); + + startX = e.clientX; + startY = e.clientY; + startLeft = ((rect.left - stageRect.left) / stageRect.width) * 100; + startBottom = ((stageRect.bottom - rect.bottom) / stageRect.height) * 100; + + e.preventDefault(); + }); + + document.addEventListener('mousemove', (e) => { + if (!isDragging) return; + + const stageRect = stage.getBoundingClientRect(); + const deltaX = ((e.clientX - startX) / stageRect.width) * 100; + const deltaY = -((e.clientY - startY) / stageRect.height) * 100; + + obj.style.left = Math.max(0, Math.min(100, startLeft + deltaX)) + '%'; + obj.style.bottom = Math.max(0, Math.min(100, startBottom + deltaY)) + '%'; + }); + + document.addEventListener('mouseup', () => { + if (isDragging) { + isDragging = false; + obj.classList.remove('grabbed'); + // Sync backend with final position + const pos = objectPositionFromElement(obj); + const state = obj.classList.contains('held') ? 'held' : 'on_table'; + sendObjectUpdate('update', obj.id, { position: pos, state: state }).catch(() => {}); + } + }); + }); +} + +function addObject(objectName, emoji) { + console.log('➕ Adding object:', objectName, emoji); + + // Check if object already exists + const existingObj = document.getElementById(objectName); + if (existingObj) { + console.log('⚠️ Object already exists:', objectName); + showFeedback('✅ ' + objectName + ' already on stage'); + return; + } + + // Create new object element + const newObj = document.createElement('div'); + newObj.id = objectName; + newObj.className = 'object'; + newObj.textContent = emoji; + + // Position near Aria + const ariaX = characterState.position.x; + const ariaY = characterState.position.y; + newObj.style.left = Math.max(10, Math.min(90, ariaX + 15)) + '%'; + newObj.style.bottom = Math.max(10, Math.min(90, ariaY + 5)) + '%'; + + // Add to stage + stage.appendChild(newObj); + + // Update tracking + activeObjects[objectName] = true; + + // Add to object manager if button doesn't exist + if (!document.querySelector(`[onclick="toggleObject('${objectName}')"]`)) { + const objectManager = document.getElementById('object-manager'); + const btn = document.createElement('button'); + btn.className = 'object-btn active'; + btn.id = 'btn-' + objectName; + btn.textContent = emoji + ' ' + objectName; + btn.onclick = () => toggleObject(objectName); + objectManager.appendChild(btn); + } + + // Initialize interactions for new object (click/drag) + initializeObjectInteractions(); + + // Sync server: add new object to stage_state + sendObjectUpdate('add', objectName, { position: objectPositionFromElement(newObj), state: 'on_stage', emoji: emoji }).catch(() => {}); + + showFeedback('✨ Added ' + objectName + ' to stage!'); + console.log('✅ Object added successfully:', objectName); +} + +// Initialize object interactions +initializeObjectInteractions(); +log('🎮 Objects: apple, book, cup, ball, flower'); +log('💡 Try: "pick up apple", "drop", "throw ball"'); + +// Auto-behaviors with random timing +function startAutoBehaviors() { + // Random idle movements every 8-15 seconds + setInterval(() => { + if (Math.random() > 0.6) { + const randomActions = ['shift weight', 'look around', 'adjust hair', 'stretch']; + const action = randomActions[Math.floor(Math.random() * randomActions.length)]; + + switch(action) { + case 'shift weight': + animate('bouncing'); + setTimeout(() => aria.style.animation = 'breathe 4s ease-in-out infinite', 800); + break; + case 'look around': + const ariaHead = document.querySelector('.aria-head'); + ariaHead.style.transform = 'translateX(-50%) rotate(10deg)'; + setTimeout(() => ariaHead.style.transform = 'translateX(-50%) rotate(-10deg)', 600); + setTimeout(() => ariaHead.style.transform = 'translateX(-50%)', 1200); + break; + case 'adjust hair': + const hand = document.getElementById('ariaHandRight'); + hand.style.transform = 'translateY(-30px) rotate(-20deg)'; + setTimeout(() => hand.style.transform = '', 1000); + break; + case 'stretch': + ariaArmLeft.style.transform = 'rotate(-160deg)'; + ariaArmRight.style.transform = 'rotate(160deg)'; + setTimeout(() => { + ariaArmLeft.style.transform = ''; + ariaArmRight.style.transform = ''; + }, 1500); + break; + } + } + }, 8000 + Math.random() * 7000); + + // Random expressions every 12-20 seconds + setInterval(() => { + if (Math.random() > 0.5) { + const expressions = ['smile', 'thinking', 'neutral', 'surprised']; + const expr = expressions[Math.floor(Math.random() * expressions.length)]; + changeExpression(expr); + setTimeout(() => changeExpression('neutral'), 2000 + Math.random() * 3000); + } + }, 12000 + Math.random() * 8000); + + // Occasional sparkles every 20-30 seconds + setInterval(() => { + if (Math.random() > 0.7) { + createEffect('sparkle'); + } + }, 20000 + Math.random() * 10000); +} + +// Initial appearance set to neutral human-like style +setTimeout(() => { + log('🎨 Setting initial character appearance...'); + const neutralStyle = generateCharacterFromMood('neutral', 50); + applyCharacterStyle(neutralStyle); +}, 100); + +// Start idle animations +startIdleAnimation(); +log('👀 Idle animations enabled - watch for breathing and blinking!'); + +// Start automatic behaviors +startAutoBehaviors(); +log('✨ Auto-behaviors enabled - Aria will move and react on her own!'); + +// Expose minimal testing helpers +window.ariaTest = { + limb: (part, actionOrAngle, duration) => handleLimbTag(part, typeof actionOrAngle === 'number' ? `${actionOrAngle},${duration||500}` : `${actionOrAngle||''}${duration?','+duration:''}`), + pose: (name) => applyPose(name) +}; +window.simulateTags = (arr) => Array.isArray(arr) && executeTags(arr); diff --git a/docs/chat/chat.js b/docs/chat/chat.js index 43da28d9a..99d550aa6 100644 --- a/docs/chat/chat.js +++ b/docs/chat/chat.js @@ -1,1018 +1,1018 @@ -// Chat Web Client - Version 2025-11-21 - QAI Backend -// Encapsulate chat client in an IIFE to avoid leaking globals when embedded -console.log('chat.js loaded - v2025-11-21-qai - Provider: QAI auto-detect with quantum mode'); -(function window_qai_chat_client() { -const AI_BASE = ''; -const API_BASE = `/api/chat`; -const STREAM_API = `/api/chat/stream`; -const STATUS_API = `/api/ai/status`; -const QUANTUM_CLASSIFY_API = '/api/quantum/classify'; -const QUANTUM_CIRCUIT_API = '/api/quantum/circuit'; -const QUANTUM_INFO_API = '/api/quantum/info'; -const VISION_INFER_API = '/api/vision/infer'; -const IMAGE_GEN_API = '/api/image/generate'; - -let messages = []; -let isProcessing = false; -let messageCounter = 0; -let currentProvider = 'auto'; // Always use auto-detect for best available -let quantumMode = false; // Quantum enhancement toggle -let systemStatus = null; -let retryCount = 0; -const MAX_RETRIES = 3; -const RETRY_DELAY = 1000; -let streamEnabled = true; -let temperature = 0.7; -let maxOutputTokens = 1024; -let systemPrompt = ''; -let activeAbortController = null; - -// DOM elements -const chatMessages = document.getElementById('chatMessages'); -const messageInput = document.getElementById('messageInput'); -const sendButton = document.getElementById('sendButton'); -const cancelStreamBtn = document.getElementById('cancelStreamBtn'); -const newChatButton = document.getElementById('newChatButton'); -const clearButton = document.getElementById('clearButton'); -const exportButton = document.getElementById('exportButton'); -const importButton = document.getElementById('importButton'); -const toggleThemeButton = document.getElementById('toggleThemeButton'); -const quantumModeButton = document.getElementById('quantumModeButton'); -const quantumPanel = document.getElementById('quantumPanel'); -const quantumPanelClose = document.getElementById('quantumPanelClose'); -const quantumIndicator = document.getElementById('quantumIndicator'); -const providerInfo = document.getElementById('providerInfo'); -const messageCount = document.getElementById('messageCount'); -const statusText = document.getElementById('statusText'); -const streamToggle = document.getElementById('streamToggle'); -const tempSlider = document.getElementById('tempSlider'); -const tempValue = document.getElementById('tempValue'); -const maxTokensInput = document.getElementById('maxTokensInput'); -const toggleSystemButton = document.getElementById('toggleSystemButton'); -const systemPromptBox = document.getElementById('systemPromptBox'); -const systemPromptInput = document.getElementById('systemPromptInput'); -const visionUploadButton = document.getElementById('visionUploadButton'); -const visionImageInput = document.getElementById('visionImageInput'); -const visionPreview = document.getElementById('visionPreview'); -const visionClearButton = document.getElementById('visionClearButton'); - -// Vision state -let uploadedImage = null; -let uploadedImageBase64 = null; - -// Aria avatar state -let ariaAvatarGenerated = false; -let ariaAvatarUrl = null; - -// Initialize -document.addEventListener('DOMContentLoaded', () => { - // When the Aria interactive page embeds chat, it provides its own - // send/receive wiring and should avoid double-wiring the controls - // in this script. A page can opt-in by setting window.ARIA_EMBEDDED=true - if (window && window.ARIA_EMBEDDED) { - console.log('chat.js: ARIA_EMBEDDED detected — skipping default UI wiring'); - return; - } - if (cancelStreamBtn) { - cancelStreamBtn.addEventListener('click', function() { - if (activeAbortController) { - activeAbortController.abort(); - updateStatus('Streaming cancelled'); - } - cancelStreamBtn.style.display = 'none'; - }); - } - // Configure marked.js for markdown rendering - if (typeof marked !== 'undefined') { - marked.setOptions({ - breaks: true, - gfm: true, - highlight: function(code, lang) { - if (lang && hljs.getLanguage(lang)) { - try { - return hljs.highlight(code, { language: lang }).value; - } catch (err) {} - } - return hljs.highlightAuto(code).value; - } - }); - } - - // Auto-resize textarea - messageInput.addEventListener('input', () => { - messageInput.style.height = 'auto'; - messageInput.style.height = Math.min(messageInput.scrollHeight, 150) + 'px'; - }); - - // Keyboard shortcuts - messageInput.addEventListener('keydown', (e) => { - if (e.key === 'Enter' && !e.shiftKey) { - e.preventDefault(); - sendMessage(); - } - if (e.key === 'k' && (e.ctrlKey || e.metaKey)) { - e.preventDefault(); - newChat(); - } - }); - - sendButton.addEventListener('click', sendMessage); - - // Vision upload handlers - if (visionUploadButton) { - visionUploadButton.addEventListener('click', () => { - visionImageInput.click(); - }); - } - - if (visionImageInput) { - visionImageInput.addEventListener('change', handleImageUpload); - } - - if (visionClearButton) { - visionClearButton.addEventListener('click', clearVisionUpload); - } - - // Aria avatar handlers - const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); - const ariaAvatarClose = document.getElementById('ariaAvatarClose'); - const ariaAvatarRegenerate = document.getElementById('ariaAvatarRegenerate'); - - if (ariaAvatarClose) { - ariaAvatarClose.addEventListener('click', hideAriaAvatar); - } - - if (ariaAvatarRegenerate) { - ariaAvatarRegenerate.addEventListener('click', () => generateAriaAvatar(true)); - } - - // Auto-generate Aria avatar on page load (after 2 seconds) - setTimeout(() => { - if (!ariaAvatarGenerated) { - generateAriaAvatar(false); - } - }, 2000); - - newChatButton.addEventListener('click', () => { - console.log('New Chat button clicked'); - newChat(); - }); - clearButton.addEventListener('click', () => { - console.log('Clear button clicked'); - clearChat(false); - }); - exportButton.addEventListener('click', exportChat); - importButton.addEventListener('click', importChat); - toggleThemeButton.addEventListener('click', toggleTheme); - quantumModeButton.addEventListener('click', toggleQuantumMode); - if (quantumPanelClose) { - quantumPanelClose.addEventListener('click', () => { - quantumPanel.style.display = 'none'; - }); - } - streamToggle.addEventListener('change', (e) => { - streamEnabled = !!e.target.checked; - saveToStorage(); - }); - tempSlider.addEventListener('input', (e) => { - temperature = parseFloat(e.target.value); - tempValue.textContent = temperature.toFixed(2); - }); - tempSlider.addEventListener('change', () => saveToStorage()); - maxTokensInput.addEventListener('change', (e) => { - const val = parseInt(e.target.value, 10); - if (!isNaN(val)) { - maxOutputTokens = Math.max(64, Math.min(40960, val)); - e.target.value = String(maxOutputTokens); - saveToStorage(); - } - }); - toggleSystemButton.addEventListener('click', () => { - const show = systemPromptBox.style.display !== 'block'; - systemPromptBox.style.display = show ? 'block' : 'none'; - }); - systemPromptInput.addEventListener('input', (e) => { - systemPrompt = e.target.value; - saveToStorage(); - }); - - // Load saved conversations and settings - loadFromStorage(); - - // Focus input - messageInput.focus(); - - // Fetch system status on load - fetchSystemStatus(); -}); - -async function fetchSystemStatus() { - try { - updateStatus('Checking backend...'); - const response = await fetch(STATUS_API); - if (response.ok) { - const data = await response.json(); - systemStatus = { status: 'ok', data }; - updateStatusFromSystem(); - } - } catch (error) { - console.warn('Backend not available:', error); - updateStatus('Backend not responding'); - } -} - -function updateStatusFromSystem() { - if (!systemStatus) return; - - // Show success message if everything looks good - if (systemStatus.status === 'ok') { - const provider = systemStatus.data?.active_provider || 'local'; - const model = systemStatus.data?.model || 'unknown'; - updateStatus(`Ready - ${provider.toUpperCase()}`); - providerInfo.textContent = `${provider.toUpperCase()} - ${model}`; - } -} - -async function sendMessage() { - console.log('sendMessage() called'); - console.log('- messageInput.value:', messageInput.value); - console.log('- isProcessing:', isProcessing); - console.log('- sendButton.disabled:', sendButton.disabled); - console.log('- quantumMode:', quantumMode); - - const text = messageInput.value.trim(); - if (!text || isProcessing) return; - - // Perform quantum analysis if enabled - if (quantumMode) { - updateStatus('Performing quantum analysis...'); - await performQuantumAnalysis(text); - } - - // Add user message to UI - addMessage('user', text); - messageInput.value = ''; - messageInput.style.height = 'auto'; - messageInput.disabled = true; - sendButton.disabled = true; - isProcessing = true; - updateStatus('Sending...'); - - // Add to messages array - messages.push({ role: 'user', content: text }); - updateMessageCount(); - - // Show typing indicator - const typingIndicator = showTypingIndicator(); - - try { - // Choose streaming or one-shot - if (streamEnabled) { - if (cancelStreamBtn) cancelStreamBtn.style.display = 'inline-block'; - await streamResponse(typingIndicator); - } else { - await oneShotResponse(typingIndicator); - } - } catch (error) { - console.error('Error:', error); - typingIndicator.remove(); - - // Attempt retry if within limits - if (retryCount < MAX_RETRIES && error.message.includes('HTTP')) { - retryCount++; - const delay = RETRY_DELAY * Math.pow(2, retryCount - 1); - addMessage('system', `⚠️ Request failed. Retrying in ${delay/1000}s... (${retryCount}/${MAX_RETRIES})`); - updateStatus(`Retrying ${retryCount}/${MAX_RETRIES}...`); - - setTimeout(() => { - // Re-send by simulating button click - messages.pop(); // Remove the failed user message - messageInput.value = text; // Restore input - messageInput.disabled = false; - sendButton.disabled = false; - isProcessing = false; - sendMessage(); - }, delay); - } else { - // Max retries exceeded or non-retryable error - retryCount = 0; - const errorMsg = error.message.includes('NetworkError') || error.message.includes('Failed to fetch') - ? '❌ Network error. Please ensure QAI backend is running on http://localhost:7071' - : `❌ Error: ${error.message}`; - addMessage('system', errorMsg); - console.error('Full error details:', error); - updateStatus('Error - check console'); - messageInput.disabled = false; - sendButton.disabled = false; - isProcessing = false; - messageInput.focus(); - } - } -} - -async function oneShotResponse(typingIndicator) { - // Prepare messages with system prompt if provided - const apiMessages = systemPrompt ? - [{ role: 'system', content: systemPrompt }, ...messages] : - messages; - - console.log('Sending non-streaming request to:', API_BASE); - console.log('Request body:', { messages: apiMessages, temperature, max_tokens: maxOutputTokens }); - - const response = await fetch(API_BASE, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - messages: apiMessages, - temperature: temperature, - max_tokens: maxOutputTokens, - stream: false - }) - }); - - console.log('Response status:', response.status, response.statusText); - - if (!response.ok) { - const errorText = await response.text(); - console.error('Error response:', errorText); - throw new Error(`HTTP ${response.status}: ${response.statusText}`); - } - - const data = await response.json(); - console.log('Response data:', data); - - retryCount = 0; - typingIndicator.remove(); - // Handle QAI backend response format - const assistantMessage = data.response || data.choices?.[0]?.message?.content || 'No response received.'; - console.log('Assistant message:', assistantMessage); - - addMessage('assistant', assistantMessage, true); - messages.push({ role: 'assistant', content: assistantMessage }); - updateMessageCount(); - if (data.provider && data.model) { - providerInfo.textContent = `${data.provider.toUpperCase()} - ${data.model}`; - } - updateStatus('Ready'); - saveToStorage(); - messageInput.disabled = false; - sendButton.disabled = false; - isProcessing = false; - messageInput.focus(); -} - -async function streamResponse(typingIndicator) { - let streamAborted = false; - // Prepare assistant message container to update incrementally - const assistantDiv = document.createElement('div'); - assistantDiv.className = 'message assistant'; - const contentDiv = document.createElement('div'); - contentDiv.className = 'message-content'; - contentDiv.textContent = ''; - assistantDiv.appendChild(contentDiv); - chatMessages.appendChild(assistantDiv); - chatMessages.scrollTop = chatMessages.scrollHeight; - - activeAbortController = new AbortController(); - - // Prepare messages with system prompt if provided - const apiMessages = systemPrompt ? - [{ role: 'system', content: systemPrompt }, ...messages] : - messages; - - console.log('Sending streaming request to:', STREAM_API); - console.log('Request body:', { messages: apiMessages, temperature, max_tokens: maxOutputTokens, stream: true }); - - try { - const response = await fetch(STREAM_API, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - messages: apiMessages, - temperature: temperature, - max_tokens: maxOutputTokens, - stream: true - }), - signal: activeAbortController.signal - }); - - console.log('Stream response status:', response.status, response.statusText); - - if (!response.ok || !response.body) { - throw new Error(`HTTP ${response.status}: ${response.statusText}`); - } - - // Remove typing indicator as soon as stream starts - typingIndicator.remove(); - - const reader = response.body.getReader(); - const decoder = new TextDecoder(); - let fullText = ''; - - while (true) { - const { value, done } = await reader.read(); - if (done) break; - - // QAI backend sends plain text chunks, not SSE - const chunk = decoder.decode(value, { stream: true }); - if (chunk) { - console.log('Stream chunk:', chunk); - fullText += chunk; - // Update plain text during stream for speed - contentDiv.textContent = fullText; - chatMessages.scrollTop = chatMessages.scrollHeight; - } - } - - // After stream completes, render as markdown - if (fullText) { - try { - const rendered = marked.parse(fullText); - contentDiv.innerHTML = rendered; - // Highlight code blocks - contentDiv.querySelectorAll('pre code').forEach((block) => { - hljs.highlightElement(block); - addCopyButton(block.parentElement); - }); - } catch (e) { - console.error('Markdown render error:', e); - contentDiv.textContent = fullText; - } - } - - // Push to messages history - messages.push({ role: 'assistant', content: fullText }); - updateMessageCount(); - retryCount = 0; - updateStatus('Ready'); - saveToStorage(); - } catch (error) { - typingIndicator.remove(); - assistantDiv.remove(); - - if (error.name === 'AbortError') { - streamAborted = true; - addMessage('system', '❌ Streaming cancelled by user.'); - updateStatus('Cancelled'); - } else { - throw error; // Re-throw to be handled by sendMessage - } - } finally { - if (cancelStreamBtn) cancelStreamBtn.style.display = 'none'; - activeAbortController = null; - messageInput.disabled = false; - sendButton.disabled = false; - isProcessing = false; - messageInput.focus(); - } -} - -function addMessage(role, content, useMarkdown = false) { - const messageDiv = document.createElement('div'); - messageDiv.className = `message ${role}`; - - const contentDiv = document.createElement('div'); - contentDiv.className = 'message-content'; - - if (useMarkdown && typeof marked !== 'undefined' && role === 'assistant') { - // Render markdown - contentDiv.innerHTML = marked.parse(content); - - // Add copy buttons to code blocks - const codeBlocks = contentDiv.querySelectorAll('pre'); - codeBlocks.forEach(block => { - const copyBtn = document.createElement('button'); - copyBtn.className = 'copy-button'; - copyBtn.textContent = 'Copy'; - copyBtn.onclick = () => { - const code = block.querySelector('code')?.textContent || block.textContent; - navigator.clipboard.writeText(code).then(() => { - copyBtn.textContent = 'Copied!'; - setTimeout(() => copyBtn.textContent = 'Copy', 2000); - }); - }; - block.style.position = 'relative'; - block.appendChild(copyBtn); - }); - } else { - contentDiv.textContent = content; - } - - messageDiv.appendChild(contentDiv); - chatMessages.appendChild(messageDiv); - - // Scroll to bottom - chatMessages.scrollTop = chatMessages.scrollHeight; - - messageCounter++; - return messageDiv; -} - -function showTypingIndicator() { - const indicatorDiv = document.createElement('div'); - indicatorDiv.className = 'message assistant'; - - const typingDiv = document.createElement('div'); - typingDiv.className = 'typing-indicator active'; - typingDiv.innerHTML = ''; - - indicatorDiv.appendChild(typingDiv); - chatMessages.appendChild(indicatorDiv); - chatMessages.scrollTop = chatMessages.scrollHeight; - - return indicatorDiv; -} - -function newChat() { - console.log('newChat() called'); - if (messages.length > 0 && !confirm('Start a new chat? Current conversation will be cleared.')) { - console.log('User cancelled new chat'); - return; - } - messages = []; - messageCounter = 0; - isProcessing = false; - chatMessages.innerHTML = ''; - messageInput.disabled = false; - sendButton.disabled = false; - messageInput.value = ''; - messageInput.focus(); - localStorage.setItem('chatMessages', JSON.stringify([])); - addMessage('system', 'New conversation started. How can I help you?'); - updateMessageCount(); - updateStatus('Ready'); - saveToStorage(); - console.log('New chat complete'); -} - -function clearChat(preserveMessages = false) { - console.log('clearChat() called, preserveMessages:', preserveMessages); - if (!preserveMessages) { - messages = []; - messageCounter = 0; - isProcessing = false; - localStorage.setItem('chatMessages', JSON.stringify([])); - } - chatMessages.innerHTML = ''; - messageInput.disabled = false; - sendButton.disabled = false; - messageInput.value = ''; - messageInput.focus(); - addMessage('system', 'Chat cleared. Type a message to continue.'); - updateMessageCount(); - updateStatus('Ready'); - saveToStorage(); - console.log('Clear chat complete'); -} - -function exportChat() { - if (messages.length === 0) { - if (typeof showToast === 'function') showToast('No messages to export', 3000); - return; - } - - const timestamp = new Date().toISOString().replace(/[:.]/g, '-'); - const filename = `chat-export-${timestamp}.json`; - - const exportData = { - timestamp: new Date().toISOString(), - provider: currentProvider, - messageCount: messages.length, - messages: messages - }; - - const blob = new Blob([JSON.stringify(exportData, null, 2)], { type: 'application/json' }); - const url = URL.createObjectURL(blob); - const a = document.createElement('a'); - a.href = url; - a.download = filename; - a.click(); - URL.revokeObjectURL(url); - - updateStatus(`Exported ${messages.length} messages`); -} - -function importChat() { - const input = document.createElement('input'); - input.type = 'file'; - input.accept = '.json'; - input.onchange = (e) => { - const file = e.target.files[0]; - if (!file) return; - - const reader = new FileReader(); - reader.onload = (event) => { - try { - const data = JSON.parse(event.target.result); - if (data.messages && Array.isArray(data.messages)) { - const imported = data.messages; - clearChat(true); // reset UI but preserve current messages until reassigned - messages = imported; - // Restore messages to UI - messages.forEach(msg => { - if (msg.role !== 'system') { - addMessage(msg.role, msg.content, msg.role === 'assistant'); - } - }); - updateMessageCount(); - saveToStorage(); - updateStatus(`Imported ${messages.length} messages`); - } else { - throw new Error('Invalid chat export format'); - } - } catch (error) { - if (typeof showToast === 'function') showToast(`Import failed: ${error.message}`, 4000); - } - }; - reader.readAsText(file); - }; - input.click(); -} - -function toggleTheme() { - document.body.classList.toggle('dark-theme'); - const isDark = document.body.classList.contains('dark-theme'); - localStorage.setItem('theme', isDark ? 'dark' : 'light'); - toggleThemeButton.textContent = isDark ? '☀️ Light' : '🌙 Dark'; -} - -function updateMessageCount() { - const userMessages = messages.filter(m => m.role === 'user').length; - messageCount.textContent = userMessages; -} - -// Aria AI Avatar functions -async function generateAriaAvatar(regenerate = false) { - const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); - const ariaAvatarImage = document.getElementById('ariaAvatarImage'); - - if (!regenerate && ariaAvatarGenerated) { - ariaAvatarContainer.style.display = 'block'; - return; - } - - updateStatus('🎨 Generating Aria\'s AI avatar...'); - - try { - // Use a more descriptive prompt for Aria's character - const prompt = "Portrait of Aria, anime-style AI assistant character, purple gradient hair, cute anime girl, friendly expression, digital art, high quality, detailed, vibrant colors, soft lighting"; - - const response = await fetch(IMAGE_GEN_API, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - prompt: prompt, - size: "512x512", - style: "anime" - }) - }); - - if (!response.ok) { - throw new Error(`Image generation failed: ${response.status}`); - } - - const result = await response.json(); - - if (result.image_url || result.image_data) { - ariaAvatarUrl = result.image_url || `data:image/png;base64,${result.image_data}`; - ariaAvatarImage.src = ariaAvatarUrl; - ariaAvatarContainer.style.display = 'block'; - ariaAvatarGenerated = true; - updateStatus('✅ Aria\'s avatar generated!'); - - // Add a chat message from Aria about her new appearance - if (regenerate) { - addMessage('assistant', '✨ How do I look? I just got a fresh new appearance from the AI! 💜'); - } - } else { - throw new Error('No image data received'); - } - } catch (error) { - console.error('Avatar generation error:', error); - updateStatus(`⚠️ Avatar generation unavailable: ${error.message}`); - - // Fallback: Use a placeholder or emoji-based avatar - const ariaAvatarImage = document.getElementById('ariaAvatarImage'); - ariaAvatarImage.src = 'data:image/svg+xml,' + encodeURIComponent(` - - - - - - - - - 👩‍💻 - Aria - AI Assistant - - `); - ariaAvatarContainer.style.display = 'block'; - ariaAvatarGenerated = true; - } -} - -function hideAriaAvatar() { - const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); - ariaAvatarContainer.style.display = 'none'; - updateStatus('Avatar hidden'); -} - -// Vision upload handlers -async function handleImageUpload(event) { - const file = event.target.files[0]; - if (!file) return; - - if (!file.type.startsWith('image/')) { - updateStatus('❌ Please select an image file'); - return; - } - - // Show preview - const reader = new FileReader(); - reader.onload = async (e) => { - uploadedImageBase64 = e.target.result.split(',')[1]; // Remove data:image/...;base64, prefix - uploadedImage = file.name; - - // Show preview - visionPreview.innerHTML = ` - Preview -
    - ${file.name} - -
    - `; - visionPreview.style.display = 'block'; - - // Re-attach clear button listener - document.getElementById('visionClearButton').addEventListener('click', clearVisionUpload); - - // Auto-analyze the image - updateStatus('🔍 Analyzing image...'); - try { - const response = await fetch(VISION_INFER_API, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ image: uploadedImageBase64 }) - }); - - if (!response.ok) { - throw new Error(`Vision API error: ${response.status}`); - } - - const result = await response.json(); - - // Add AI message with vision results - const visionMessage = `🖼️ **Image Analysis**: ${file.name}\n\n` + - `**Expression**: ${result.label} (${(result.confidence * 100).toFixed(1)}% confidence)\n\n` + - `**All Scores**:\n${Object.entries(result.scores) - .sort((a, b) => b[1] - a[1]) - .map(([label, score]) => `- ${label}: ${(score * 100).toFixed(1)}%`) - .join('\n')}`; - - addMessage('assistant', visionMessage); - updateStatus('✅ Image analyzed successfully'); - } catch (error) { - console.error('Vision inference error:', error); - updateStatus(`❌ Vision error: ${error.message}`); - } - }; - - reader.readAsDataURL(file); -} - -function clearVisionUpload() { - uploadedImage = null; - uploadedImageBase64 = null; - visionImageInput.value = ''; - visionPreview.style.display = 'none'; - visionPreview.innerHTML = ''; - updateStatus('Vision upload cleared'); -} - -function updateStatus(text) { - statusText.textContent = text; - if (text.includes('Ready') || text.includes('ok')) { - statusText.style.color = '#28a745'; - } else if (text.includes('Error') || text.includes('❌')) { - statusText.style.color = '#dc3545'; - } else if (text.includes('⚠️')) { - statusText.style.color = '#ffc107'; - } else { - statusText.style.color = ''; - } - - setTimeout(() => { - if (statusText.textContent === text) { - statusText.textContent = 'Ready'; - statusText.style.color = ''; - } - }, 3000); -} - -function saveToStorage() { - try { - localStorage.setItem('chatMessages', JSON.stringify(messages)); - localStorage.setItem('chatStream', streamEnabled ? '1' : '0'); - localStorage.setItem('chatTemp', String(temperature)); - localStorage.setItem('chatMaxTokens', String(maxOutputTokens)); - localStorage.setItem('chatSystemPrompt', systemPrompt || ''); - } catch (e) { - console.error('Failed to save to storage:', e); - } -} - -function loadFromStorage() { - try { - const saved = localStorage.getItem('chatMessages'); - const savedTheme = localStorage.getItem('theme'); - const savedStream = localStorage.getItem('chatStream'); - const savedTemp = localStorage.getItem('chatTemp'); - const savedMax = localStorage.getItem('chatMaxTokens'); - const savedSys = localStorage.getItem('chatSystemPrompt'); - - if (savedTheme === 'dark') { - document.body.classList.add('dark-theme'); - toggleThemeButton.textContent = '☀️ Light'; - } else { - toggleThemeButton.textContent = '🌙 Dark'; - } - - if (saved) { - messages = JSON.parse(saved); - // Restore messages to UI - messages.forEach(msg => { - if (msg.role !== 'system') { - addMessage(msg.role, msg.content, msg.role === 'assistant'); - } - }); - updateMessageCount(); - } - - // Restore settings - streamEnabled = savedStream === '1'; - streamToggle.checked = streamEnabled; - if (savedTemp) { - temperature = parseFloat(savedTemp); - if (!isNaN(temperature)) { - tempSlider.value = String(temperature); - tempValue.textContent = temperature.toFixed(2); - } - } - if (savedMax) { - const v = parseInt(savedMax, 10); - if (!isNaN(v)) { - maxOutputTokens = v; - maxTokensInput.value = String(v); - } - } - if (savedSys) { - systemPrompt = savedSys; - systemPromptInput.value = systemPrompt; - } - } catch (e) { - console.error('Failed to load from storage:', e); - } -} - -// ============================================================================= -// Quantum Mode Functions -// ============================================================================= - -function toggleQuantumMode() { - quantumMode = !quantumMode; - - if (quantumMode) { - quantumModeButton.textContent = '🔬 Quantum ON'; - quantumModeButton.classList.add('active'); - quantumIndicator.style.display = 'flex'; - quantumPanel.style.display = 'block'; - currentProvider = 'quantum'; - updateStatus('Quantum mode enabled'); - - // Fetch quantum info - fetchQuantumInfo(); - } else { - quantumModeButton.textContent = '🔬 Quantum OFF'; - quantumModeButton.classList.remove('active'); - quantumIndicator.style.display = 'none'; - quantumPanel.style.display = 'none'; - currentProvider = 'auto'; - updateStatus('Quantum mode disabled'); - } - - saveToStorage(); -} - -async function fetchQuantumInfo() { - try { - const response = await fetch(QUANTUM_INFO_API); - if (response.ok) { - const data = await response.json(); - if (data.available) { - updateStatus('Quantum backend ready'); - } else { - updateStatus('Quantum backend unavailable - using classical fallback'); - } - } - } catch (error) { - console.warn('Could not fetch quantum info:', error); - } -} - -async function performQuantumAnalysis(text) { - if (!quantumMode) return null; - - try { - // Convert text to features (simple hash-based approach) - const features = textToFeatures(text); - - const response = await fetch(QUANTUM_CLASSIFY_API, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - features: features, - n_qubits: 4, - n_layers: 2 - }) - }); - - if (!response.ok) { - throw new Error(`Quantum API error: ${response.status}`); - } - - const data = await response.json(); - - // Update quantum panel - document.getElementById('quantumClassification').textContent = - data.classification.toUpperCase(); - document.getElementById('quantumConfidence').textContent = - (data.confidence * 100).toFixed(1) + '%'; - document.getElementById('quantumQubits').textContent = - data.quantum_state.n_qubits; - document.getElementById('quantumLayers').textContent = - data.quantum_state.n_layers; - - // Get circuit visualization - await fetchCircuitVisualization(data.quantum_state.n_qubits, data.quantum_state.n_layers); - - return data; - } catch (error) { - console.error('Quantum analysis failed:', error); - document.getElementById('quantumClassification').textContent = 'ERROR'; - return null; - } -} - -async function fetchCircuitVisualization(nQubits, nLayers) { - try { - const response = await fetch(QUANTUM_CIRCUIT_API, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - n_qubits: nQubits, - n_layers: nLayers, - entanglement: 'linear' - }) - }); - - if (response.ok) { - const data = await response.json(); - const vizElement = document.querySelector('.circuit-display'); - if (vizElement) { - vizElement.textContent = data.visualization; - } - } - } catch (error) { - console.error('Circuit visualization failed:', error); - } -} - -function textToFeatures(text) { - // Simple feature extraction: convert text to numeric features - // Using character codes and length statistics - const features = []; - const normalized = text.toLowerCase(); - - // Add basic statistics - features.push(normalized.length / 100.0); // Normalized length - features.push(normalized.split(' ').length / 50.0); // Word count - features.push(normalized.split('?').length / 10.0); // Question marks - features.push(normalized.split('!').length / 10.0); // Exclamation marks - - // Add character distribution (simple hash) - let sum = 0; - for (let i = 0; i < Math.min(normalized.length, 20); i++) { - sum += normalized.charCodeAt(i); - } - features.push((sum % 256) / 256.0); - - // Pad or truncate to 8 features - while (features.length < 8) { - features.push(0.0); - } - - return features.slice(0, 8); -} - -})(); +// Chat Web Client - Version 2025-11-21 - QAI Backend +// Encapsulate chat client in an IIFE to avoid leaking globals when embedded +console.log('chat.js loaded - v2025-11-21-qai - Provider: QAI auto-detect with quantum mode'); +(function window_qai_chat_client() { +const AI_BASE = ''; +const API_BASE = `/api/chat`; +const STREAM_API = `/api/chat/stream`; +const STATUS_API = `/api/ai/status`; +const QUANTUM_CLASSIFY_API = '/api/quantum/classify'; +const QUANTUM_CIRCUIT_API = '/api/quantum/circuit'; +const QUANTUM_INFO_API = '/api/quantum/info'; +const VISION_INFER_API = '/api/vision/infer'; +const IMAGE_GEN_API = '/api/image/generate'; + +let messages = []; +let isProcessing = false; +let messageCounter = 0; +let currentProvider = 'auto'; // Always use auto-detect for best available +let quantumMode = false; // Quantum enhancement toggle +let systemStatus = null; +let retryCount = 0; +const MAX_RETRIES = 3; +const RETRY_DELAY = 1000; +let streamEnabled = true; +let temperature = 0.7; +let maxOutputTokens = 1024; +let systemPrompt = ''; +let activeAbortController = null; + +// DOM elements +const chatMessages = document.getElementById('chatMessages'); +const messageInput = document.getElementById('messageInput'); +const sendButton = document.getElementById('sendButton'); +const cancelStreamBtn = document.getElementById('cancelStreamBtn'); +const newChatButton = document.getElementById('newChatButton'); +const clearButton = document.getElementById('clearButton'); +const exportButton = document.getElementById('exportButton'); +const importButton = document.getElementById('importButton'); +const toggleThemeButton = document.getElementById('toggleThemeButton'); +const quantumModeButton = document.getElementById('quantumModeButton'); +const quantumPanel = document.getElementById('quantumPanel'); +const quantumPanelClose = document.getElementById('quantumPanelClose'); +const quantumIndicator = document.getElementById('quantumIndicator'); +const providerInfo = document.getElementById('providerInfo'); +const messageCount = document.getElementById('messageCount'); +const statusText = document.getElementById('statusText'); +const streamToggle = document.getElementById('streamToggle'); +const tempSlider = document.getElementById('tempSlider'); +const tempValue = document.getElementById('tempValue'); +const maxTokensInput = document.getElementById('maxTokensInput'); +const toggleSystemButton = document.getElementById('toggleSystemButton'); +const systemPromptBox = document.getElementById('systemPromptBox'); +const systemPromptInput = document.getElementById('systemPromptInput'); +const visionUploadButton = document.getElementById('visionUploadButton'); +const visionImageInput = document.getElementById('visionImageInput'); +const visionPreview = document.getElementById('visionPreview'); +const visionClearButton = document.getElementById('visionClearButton'); + +// Vision state +let uploadedImage = null; +let uploadedImageBase64 = null; + +// Aria avatar state +let ariaAvatarGenerated = false; +let ariaAvatarUrl = null; + +// Initialize +document.addEventListener('DOMContentLoaded', () => { + // When the Aria interactive page embeds chat, it provides its own + // send/receive wiring and should avoid double-wiring the controls + // in this script. A page can opt-in by setting window.ARIA_EMBEDDED=true + if (window && window.ARIA_EMBEDDED) { + console.log('chat.js: ARIA_EMBEDDED detected — skipping default UI wiring'); + return; + } + if (cancelStreamBtn) { + cancelStreamBtn.addEventListener('click', function() { + if (activeAbortController) { + activeAbortController.abort(); + updateStatus('Streaming cancelled'); + } + cancelStreamBtn.style.display = 'none'; + }); + } + // Configure marked.js for markdown rendering + if (typeof marked !== 'undefined') { + marked.setOptions({ + breaks: true, + gfm: true, + highlight: function(code, lang) { + if (lang && hljs.getLanguage(lang)) { + try { + return hljs.highlight(code, { language: lang }).value; + } catch (err) {} + } + return hljs.highlightAuto(code).value; + } + }); + } + + // Auto-resize textarea + messageInput.addEventListener('input', () => { + messageInput.style.height = 'auto'; + messageInput.style.height = Math.min(messageInput.scrollHeight, 150) + 'px'; + }); + + // Keyboard shortcuts + messageInput.addEventListener('keydown', (e) => { + if (e.key === 'Enter' && !e.shiftKey) { + e.preventDefault(); + sendMessage(); + } + if (e.key === 'k' && (e.ctrlKey || e.metaKey)) { + e.preventDefault(); + newChat(); + } + }); + + sendButton.addEventListener('click', sendMessage); + + // Vision upload handlers + if (visionUploadButton) { + visionUploadButton.addEventListener('click', () => { + visionImageInput.click(); + }); + } + + if (visionImageInput) { + visionImageInput.addEventListener('change', handleImageUpload); + } + + if (visionClearButton) { + visionClearButton.addEventListener('click', clearVisionUpload); + } + + // Aria avatar handlers + const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); + const ariaAvatarClose = document.getElementById('ariaAvatarClose'); + const ariaAvatarRegenerate = document.getElementById('ariaAvatarRegenerate'); + + if (ariaAvatarClose) { + ariaAvatarClose.addEventListener('click', hideAriaAvatar); + } + + if (ariaAvatarRegenerate) { + ariaAvatarRegenerate.addEventListener('click', () => generateAriaAvatar(true)); + } + + // Auto-generate Aria avatar on page load (after 2 seconds) + setTimeout(() => { + if (!ariaAvatarGenerated) { + generateAriaAvatar(false); + } + }, 2000); + + newChatButton.addEventListener('click', () => { + console.log('New Chat button clicked'); + newChat(); + }); + clearButton.addEventListener('click', () => { + console.log('Clear button clicked'); + clearChat(false); + }); + exportButton.addEventListener('click', exportChat); + importButton.addEventListener('click', importChat); + toggleThemeButton.addEventListener('click', toggleTheme); + quantumModeButton.addEventListener('click', toggleQuantumMode); + if (quantumPanelClose) { + quantumPanelClose.addEventListener('click', () => { + quantumPanel.style.display = 'none'; + }); + } + streamToggle.addEventListener('change', (e) => { + streamEnabled = !!e.target.checked; + saveToStorage(); + }); + tempSlider.addEventListener('input', (e) => { + temperature = parseFloat(e.target.value); + tempValue.textContent = temperature.toFixed(2); + }); + tempSlider.addEventListener('change', () => saveToStorage()); + maxTokensInput.addEventListener('change', (e) => { + const val = parseInt(e.target.value, 10); + if (!isNaN(val)) { + maxOutputTokens = Math.max(64, Math.min(40960, val)); + e.target.value = String(maxOutputTokens); + saveToStorage(); + } + }); + toggleSystemButton.addEventListener('click', () => { + const show = systemPromptBox.style.display !== 'block'; + systemPromptBox.style.display = show ? 'block' : 'none'; + }); + systemPromptInput.addEventListener('input', (e) => { + systemPrompt = e.target.value; + saveToStorage(); + }); + + // Load saved conversations and settings + loadFromStorage(); + + // Focus input + messageInput.focus(); + + // Fetch system status on load + fetchSystemStatus(); +}); + +async function fetchSystemStatus() { + try { + updateStatus('Checking backend...'); + const response = await fetch(STATUS_API); + if (response.ok) { + const data = await response.json(); + systemStatus = { status: 'ok', data }; + updateStatusFromSystem(); + } + } catch (error) { + console.warn('Backend not available:', error); + updateStatus('Backend not responding'); + } +} + +function updateStatusFromSystem() { + if (!systemStatus) return; + + // Show success message if everything looks good + if (systemStatus.status === 'ok') { + const provider = systemStatus.data?.active_provider || 'local'; + const model = systemStatus.data?.model || 'unknown'; + updateStatus(`Ready - ${provider.toUpperCase()}`); + providerInfo.textContent = `${provider.toUpperCase()} - ${model}`; + } +} + +async function sendMessage() { + console.log('sendMessage() called'); + console.log('- messageInput.value:', messageInput.value); + console.log('- isProcessing:', isProcessing); + console.log('- sendButton.disabled:', sendButton.disabled); + console.log('- quantumMode:', quantumMode); + + const text = messageInput.value.trim(); + if (!text || isProcessing) return; + + // Perform quantum analysis if enabled + if (quantumMode) { + updateStatus('Performing quantum analysis...'); + await performQuantumAnalysis(text); + } + + // Add user message to UI + addMessage('user', text); + messageInput.value = ''; + messageInput.style.height = 'auto'; + messageInput.disabled = true; + sendButton.disabled = true; + isProcessing = true; + updateStatus('Sending...'); + + // Add to messages array + messages.push({ role: 'user', content: text }); + updateMessageCount(); + + // Show typing indicator + const typingIndicator = showTypingIndicator(); + + try { + // Choose streaming or one-shot + if (streamEnabled) { + if (cancelStreamBtn) cancelStreamBtn.style.display = 'inline-block'; + await streamResponse(typingIndicator); + } else { + await oneShotResponse(typingIndicator); + } + } catch (error) { + console.error('Error:', error); + typingIndicator.remove(); + + // Attempt retry if within limits + if (retryCount < MAX_RETRIES && error.message.includes('HTTP')) { + retryCount++; + const delay = RETRY_DELAY * Math.pow(2, retryCount - 1); + addMessage('system', `⚠️ Request failed. Retrying in ${delay/1000}s... (${retryCount}/${MAX_RETRIES})`); + updateStatus(`Retrying ${retryCount}/${MAX_RETRIES}...`); + + setTimeout(() => { + // Re-send by simulating button click + messages.pop(); // Remove the failed user message + messageInput.value = text; // Restore input + messageInput.disabled = false; + sendButton.disabled = false; + isProcessing = false; + sendMessage(); + }, delay); + } else { + // Max retries exceeded or non-retryable error + retryCount = 0; + const errorMsg = error.message.includes('NetworkError') || error.message.includes('Failed to fetch') + ? '❌ Network error. Please ensure QAI backend is running on http://localhost:7071' + : `❌ Error: ${error.message}`; + addMessage('system', errorMsg); + console.error('Full error details:', error); + updateStatus('Error - check console'); + messageInput.disabled = false; + sendButton.disabled = false; + isProcessing = false; + messageInput.focus(); + } + } +} + +async function oneShotResponse(typingIndicator) { + // Prepare messages with system prompt if provided + const apiMessages = systemPrompt ? + [{ role: 'system', content: systemPrompt }, ...messages] : + messages; + + console.log('Sending non-streaming request to:', API_BASE); + console.log('Request body:', { messages: apiMessages, temperature, max_tokens: maxOutputTokens }); + + const response = await fetch(API_BASE, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + messages: apiMessages, + temperature: temperature, + max_tokens: maxOutputTokens, + stream: false + }) + }); + + console.log('Response status:', response.status, response.statusText); + + if (!response.ok) { + const errorText = await response.text(); + console.error('Error response:', errorText); + throw new Error(`HTTP ${response.status}: ${response.statusText}`); + } + + const data = await response.json(); + console.log('Response data:', data); + + retryCount = 0; + typingIndicator.remove(); + // Handle QAI backend response format + const assistantMessage = data.response || data.choices?.[0]?.message?.content || 'No response received.'; + console.log('Assistant message:', assistantMessage); + + addMessage('assistant', assistantMessage, true); + messages.push({ role: 'assistant', content: assistantMessage }); + updateMessageCount(); + if (data.provider && data.model) { + providerInfo.textContent = `${data.provider.toUpperCase()} - ${data.model}`; + } + updateStatus('Ready'); + saveToStorage(); + messageInput.disabled = false; + sendButton.disabled = false; + isProcessing = false; + messageInput.focus(); +} + +async function streamResponse(typingIndicator) { + let streamAborted = false; + // Prepare assistant message container to update incrementally + const assistantDiv = document.createElement('div'); + assistantDiv.className = 'message assistant'; + const contentDiv = document.createElement('div'); + contentDiv.className = 'message-content'; + contentDiv.textContent = ''; + assistantDiv.appendChild(contentDiv); + chatMessages.appendChild(assistantDiv); + chatMessages.scrollTop = chatMessages.scrollHeight; + + activeAbortController = new AbortController(); + + // Prepare messages with system prompt if provided + const apiMessages = systemPrompt ? + [{ role: 'system', content: systemPrompt }, ...messages] : + messages; + + console.log('Sending streaming request to:', STREAM_API); + console.log('Request body:', { messages: apiMessages, temperature, max_tokens: maxOutputTokens, stream: true }); + + try { + const response = await fetch(STREAM_API, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + messages: apiMessages, + temperature: temperature, + max_tokens: maxOutputTokens, + stream: true + }), + signal: activeAbortController.signal + }); + + console.log('Stream response status:', response.status, response.statusText); + + if (!response.ok || !response.body) { + throw new Error(`HTTP ${response.status}: ${response.statusText}`); + } + + // Remove typing indicator as soon as stream starts + typingIndicator.remove(); + + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + let fullText = ''; + + while (true) { + const { value, done } = await reader.read(); + if (done) break; + + // QAI backend sends plain text chunks, not SSE + const chunk = decoder.decode(value, { stream: true }); + if (chunk) { + console.log('Stream chunk:', chunk); + fullText += chunk; + // Update plain text during stream for speed + contentDiv.textContent = fullText; + chatMessages.scrollTop = chatMessages.scrollHeight; + } + } + + // After stream completes, render as markdown + if (fullText) { + try { + const rendered = marked.parse(fullText); + contentDiv.innerHTML = rendered; + // Highlight code blocks + contentDiv.querySelectorAll('pre code').forEach((block) => { + hljs.highlightElement(block); + addCopyButton(block.parentElement); + }); + } catch (e) { + console.error('Markdown render error:', e); + contentDiv.textContent = fullText; + } + } + + // Push to messages history + messages.push({ role: 'assistant', content: fullText }); + updateMessageCount(); + retryCount = 0; + updateStatus('Ready'); + saveToStorage(); + } catch (error) { + typingIndicator.remove(); + assistantDiv.remove(); + + if (error.name === 'AbortError') { + streamAborted = true; + addMessage('system', '❌ Streaming cancelled by user.'); + updateStatus('Cancelled'); + } else { + throw error; // Re-throw to be handled by sendMessage + } + } finally { + if (cancelStreamBtn) cancelStreamBtn.style.display = 'none'; + activeAbortController = null; + messageInput.disabled = false; + sendButton.disabled = false; + isProcessing = false; + messageInput.focus(); + } +} + +function addMessage(role, content, useMarkdown = false) { + const messageDiv = document.createElement('div'); + messageDiv.className = `message ${role}`; + + const contentDiv = document.createElement('div'); + contentDiv.className = 'message-content'; + + if (useMarkdown && typeof marked !== 'undefined' && role === 'assistant') { + // Render markdown + contentDiv.innerHTML = marked.parse(content); + + // Add copy buttons to code blocks + const codeBlocks = contentDiv.querySelectorAll('pre'); + codeBlocks.forEach(block => { + const copyBtn = document.createElement('button'); + copyBtn.className = 'copy-button'; + copyBtn.textContent = 'Copy'; + copyBtn.onclick = () => { + const code = block.querySelector('code')?.textContent || block.textContent; + navigator.clipboard.writeText(code).then(() => { + copyBtn.textContent = 'Copied!'; + setTimeout(() => copyBtn.textContent = 'Copy', 2000); + }); + }; + block.style.position = 'relative'; + block.appendChild(copyBtn); + }); + } else { + contentDiv.textContent = content; + } + + messageDiv.appendChild(contentDiv); + chatMessages.appendChild(messageDiv); + + // Scroll to bottom + chatMessages.scrollTop = chatMessages.scrollHeight; + + messageCounter++; + return messageDiv; +} + +function showTypingIndicator() { + const indicatorDiv = document.createElement('div'); + indicatorDiv.className = 'message assistant'; + + const typingDiv = document.createElement('div'); + typingDiv.className = 'typing-indicator active'; + typingDiv.innerHTML = ''; + + indicatorDiv.appendChild(typingDiv); + chatMessages.appendChild(indicatorDiv); + chatMessages.scrollTop = chatMessages.scrollHeight; + + return indicatorDiv; +} + +function newChat() { + console.log('newChat() called'); + if (messages.length > 0 && !confirm('Start a new chat? Current conversation will be cleared.')) { + console.log('User cancelled new chat'); + return; + } + messages = []; + messageCounter = 0; + isProcessing = false; + chatMessages.innerHTML = ''; + messageInput.disabled = false; + sendButton.disabled = false; + messageInput.value = ''; + messageInput.focus(); + localStorage.setItem('chatMessages', JSON.stringify([])); + addMessage('system', 'New conversation started. How can I help you?'); + updateMessageCount(); + updateStatus('Ready'); + saveToStorage(); + console.log('New chat complete'); +} + +function clearChat(preserveMessages = false) { + console.log('clearChat() called, preserveMessages:', preserveMessages); + if (!preserveMessages) { + messages = []; + messageCounter = 0; + isProcessing = false; + localStorage.setItem('chatMessages', JSON.stringify([])); + } + chatMessages.innerHTML = ''; + messageInput.disabled = false; + sendButton.disabled = false; + messageInput.value = ''; + messageInput.focus(); + addMessage('system', 'Chat cleared. Type a message to continue.'); + updateMessageCount(); + updateStatus('Ready'); + saveToStorage(); + console.log('Clear chat complete'); +} + +function exportChat() { + if (messages.length === 0) { + if (typeof showToast === 'function') showToast('No messages to export', 3000); + return; + } + + const timestamp = new Date().toISOString().replace(/[:.]/g, '-'); + const filename = `chat-export-${timestamp}.json`; + + const exportData = { + timestamp: new Date().toISOString(), + provider: currentProvider, + messageCount: messages.length, + messages: messages + }; + + const blob = new Blob([JSON.stringify(exportData, null, 2)], { type: 'application/json' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = filename; + a.click(); + URL.revokeObjectURL(url); + + updateStatus(`Exported ${messages.length} messages`); +} + +function importChat() { + const input = document.createElement('input'); + input.type = 'file'; + input.accept = '.json'; + input.onchange = (e) => { + const file = e.target.files[0]; + if (!file) return; + + const reader = new FileReader(); + reader.onload = (event) => { + try { + const data = JSON.parse(event.target.result); + if (data.messages && Array.isArray(data.messages)) { + const imported = data.messages; + clearChat(true); // reset UI but preserve current messages until reassigned + messages = imported; + // Restore messages to UI + messages.forEach(msg => { + if (msg.role !== 'system') { + addMessage(msg.role, msg.content, msg.role === 'assistant'); + } + }); + updateMessageCount(); + saveToStorage(); + updateStatus(`Imported ${messages.length} messages`); + } else { + throw new Error('Invalid chat export format'); + } + } catch (error) { + if (typeof showToast === 'function') showToast(`Import failed: ${error.message}`, 4000); + } + }; + reader.readAsText(file); + }; + input.click(); +} + +function toggleTheme() { + document.body.classList.toggle('dark-theme'); + const isDark = document.body.classList.contains('dark-theme'); + localStorage.setItem('theme', isDark ? 'dark' : 'light'); + toggleThemeButton.textContent = isDark ? '☀️ Light' : '🌙 Dark'; +} + +function updateMessageCount() { + const userMessages = messages.filter(m => m.role === 'user').length; + messageCount.textContent = userMessages; +} + +// Aria AI Avatar functions +async function generateAriaAvatar(regenerate = false) { + const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); + const ariaAvatarImage = document.getElementById('ariaAvatarImage'); + + if (!regenerate && ariaAvatarGenerated) { + ariaAvatarContainer.style.display = 'block'; + return; + } + + updateStatus('🎨 Generating Aria\'s AI avatar...'); + + try { + // Use a more descriptive prompt for Aria's character + const prompt = "Portrait of Aria, anime-style AI assistant character, purple gradient hair, cute anime girl, friendly expression, digital art, high quality, detailed, vibrant colors, soft lighting"; + + const response = await fetch(IMAGE_GEN_API, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + prompt: prompt, + size: "512x512", + style: "anime" + }) + }); + + if (!response.ok) { + throw new Error(`Image generation failed: ${response.status}`); + } + + const result = await response.json(); + + if (result.image_url || result.image_data) { + ariaAvatarUrl = result.image_url || `data:image/png;base64,${result.image_data}`; + ariaAvatarImage.src = ariaAvatarUrl; + ariaAvatarContainer.style.display = 'block'; + ariaAvatarGenerated = true; + updateStatus('✅ Aria\'s avatar generated!'); + + // Add a chat message from Aria about her new appearance + if (regenerate) { + addMessage('assistant', '✨ How do I look? I just got a fresh new appearance from the AI! 💜'); + } + } else { + throw new Error('No image data received'); + } + } catch (error) { + console.error('Avatar generation error:', error); + updateStatus(`⚠️ Avatar generation unavailable: ${error.message}`); + + // Fallback: Use a placeholder or emoji-based avatar + const ariaAvatarImage = document.getElementById('ariaAvatarImage'); + ariaAvatarImage.src = 'data:image/svg+xml,' + encodeURIComponent(` + + + + + + + + + 👩‍💻 + Aria + AI Assistant + + `); + ariaAvatarContainer.style.display = 'block'; + ariaAvatarGenerated = true; + } +} + +function hideAriaAvatar() { + const ariaAvatarContainer = document.getElementById('ariaAvatarContainer'); + ariaAvatarContainer.style.display = 'none'; + updateStatus('Avatar hidden'); +} + +// Vision upload handlers +async function handleImageUpload(event) { + const file = event.target.files[0]; + if (!file) return; + + if (!file.type.startsWith('image/')) { + updateStatus('❌ Please select an image file'); + return; + } + + // Show preview + const reader = new FileReader(); + reader.onload = async (e) => { + uploadedImageBase64 = e.target.result.split(',')[1]; // Remove data:image/...;base64, prefix + uploadedImage = file.name; + + // Show preview + visionPreview.innerHTML = ` + Preview +
    + ${file.name} + +
    + `; + visionPreview.style.display = 'block'; + + // Re-attach clear button listener + document.getElementById('visionClearButton').addEventListener('click', clearVisionUpload); + + // Auto-analyze the image + updateStatus('🔍 Analyzing image...'); + try { + const response = await fetch(VISION_INFER_API, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ image: uploadedImageBase64 }) + }); + + if (!response.ok) { + throw new Error(`Vision API error: ${response.status}`); + } + + const result = await response.json(); + + // Add AI message with vision results + const visionMessage = `🖼️ **Image Analysis**: ${file.name}\n\n` + + `**Expression**: ${result.label} (${(result.confidence * 100).toFixed(1)}% confidence)\n\n` + + `**All Scores**:\n${Object.entries(result.scores) + .sort((a, b) => b[1] - a[1]) + .map(([label, score]) => `- ${label}: ${(score * 100).toFixed(1)}%`) + .join('\n')}`; + + addMessage('assistant', visionMessage); + updateStatus('✅ Image analyzed successfully'); + } catch (error) { + console.error('Vision inference error:', error); + updateStatus(`❌ Vision error: ${error.message}`); + } + }; + + reader.readAsDataURL(file); +} + +function clearVisionUpload() { + uploadedImage = null; + uploadedImageBase64 = null; + visionImageInput.value = ''; + visionPreview.style.display = 'none'; + visionPreview.innerHTML = ''; + updateStatus('Vision upload cleared'); +} + +function updateStatus(text) { + statusText.textContent = text; + if (text.includes('Ready') || text.includes('ok')) { + statusText.style.color = '#28a745'; + } else if (text.includes('Error') || text.includes('❌')) { + statusText.style.color = '#dc3545'; + } else if (text.includes('⚠️')) { + statusText.style.color = '#ffc107'; + } else { + statusText.style.color = ''; + } + + setTimeout(() => { + if (statusText.textContent === text) { + statusText.textContent = 'Ready'; + statusText.style.color = ''; + } + }, 3000); +} + +function saveToStorage() { + try { + localStorage.setItem('chatMessages', JSON.stringify(messages)); + localStorage.setItem('chatStream', streamEnabled ? '1' : '0'); + localStorage.setItem('chatTemp', String(temperature)); + localStorage.setItem('chatMaxTokens', String(maxOutputTokens)); + localStorage.setItem('chatSystemPrompt', systemPrompt || ''); + } catch (e) { + console.error('Failed to save to storage:', e); + } +} + +function loadFromStorage() { + try { + const saved = localStorage.getItem('chatMessages'); + const savedTheme = localStorage.getItem('theme'); + const savedStream = localStorage.getItem('chatStream'); + const savedTemp = localStorage.getItem('chatTemp'); + const savedMax = localStorage.getItem('chatMaxTokens'); + const savedSys = localStorage.getItem('chatSystemPrompt'); + + if (savedTheme === 'dark') { + document.body.classList.add('dark-theme'); + toggleThemeButton.textContent = '☀️ Light'; + } else { + toggleThemeButton.textContent = '🌙 Dark'; + } + + if (saved) { + messages = JSON.parse(saved); + // Restore messages to UI + messages.forEach(msg => { + if (msg.role !== 'system') { + addMessage(msg.role, msg.content, msg.role === 'assistant'); + } + }); + updateMessageCount(); + } + + // Restore settings + streamEnabled = savedStream === '1'; + streamToggle.checked = streamEnabled; + if (savedTemp) { + temperature = parseFloat(savedTemp); + if (!isNaN(temperature)) { + tempSlider.value = String(temperature); + tempValue.textContent = temperature.toFixed(2); + } + } + if (savedMax) { + const v = parseInt(savedMax, 10); + if (!isNaN(v)) { + maxOutputTokens = v; + maxTokensInput.value = String(v); + } + } + if (savedSys) { + systemPrompt = savedSys; + systemPromptInput.value = systemPrompt; + } + } catch (e) { + console.error('Failed to load from storage:', e); + } +} + +// ============================================================================= +// Quantum Mode Functions +// ============================================================================= + +function toggleQuantumMode() { + quantumMode = !quantumMode; + + if (quantumMode) { + quantumModeButton.textContent = '🔬 Quantum ON'; + quantumModeButton.classList.add('active'); + quantumIndicator.style.display = 'flex'; + quantumPanel.style.display = 'block'; + currentProvider = 'quantum'; + updateStatus('Quantum mode enabled'); + + // Fetch quantum info + fetchQuantumInfo(); + } else { + quantumModeButton.textContent = '🔬 Quantum OFF'; + quantumModeButton.classList.remove('active'); + quantumIndicator.style.display = 'none'; + quantumPanel.style.display = 'none'; + currentProvider = 'auto'; + updateStatus('Quantum mode disabled'); + } + + saveToStorage(); +} + +async function fetchQuantumInfo() { + try { + const response = await fetch(QUANTUM_INFO_API); + if (response.ok) { + const data = await response.json(); + if (data.available) { + updateStatus('Quantum backend ready'); + } else { + updateStatus('Quantum backend unavailable - using classical fallback'); + } + } + } catch (error) { + console.warn('Could not fetch quantum info:', error); + } +} + +async function performQuantumAnalysis(text) { + if (!quantumMode) return null; + + try { + // Convert text to features (simple hash-based approach) + const features = textToFeatures(text); + + const response = await fetch(QUANTUM_CLASSIFY_API, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + features: features, + n_qubits: 4, + n_layers: 2 + }) + }); + + if (!response.ok) { + throw new Error(`Quantum API error: ${response.status}`); + } + + const data = await response.json(); + + // Update quantum panel + document.getElementById('quantumClassification').textContent = + data.classification.toUpperCase(); + document.getElementById('quantumConfidence').textContent = + (data.confidence * 100).toFixed(1) + '%'; + document.getElementById('quantumQubits').textContent = + data.quantum_state.n_qubits; + document.getElementById('quantumLayers').textContent = + data.quantum_state.n_layers; + + // Get circuit visualization + await fetchCircuitVisualization(data.quantum_state.n_qubits, data.quantum_state.n_layers); + + return data; + } catch (error) { + console.error('Quantum analysis failed:', error); + document.getElementById('quantumClassification').textContent = 'ERROR'; + return null; + } +} + +async function fetchCircuitVisualization(nQubits, nLayers) { + try { + const response = await fetch(QUANTUM_CIRCUIT_API, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + n_qubits: nQubits, + n_layers: nLayers, + entanglement: 'linear' + }) + }); + + if (response.ok) { + const data = await response.json(); + const vizElement = document.querySelector('.circuit-display'); + if (vizElement) { + vizElement.textContent = data.visualization; + } + } + } catch (error) { + console.error('Circuit visualization failed:', error); + } +} + +function textToFeatures(text) { + // Simple feature extraction: convert text to numeric features + // Using character codes and length statistics + const features = []; + const normalized = text.toLowerCase(); + + // Add basic statistics + features.push(normalized.length / 100.0); // Normalized length + features.push(normalized.split(' ').length / 50.0); // Word count + features.push(normalized.split('?').length / 10.0); 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    + + + + + + + diff --git a/docs/dashboard/anomaly-detector.js b/docs/dashboard/anomaly-detector.js index 0231b5a91..6fb600d48 100644 --- a/docs/dashboard/anomaly-detector.js +++ b/docs/dashboard/anomaly-detector.js @@ -1,251 +1,251 @@ -/** - * Training Anomaly Detection System - * Monitors training metrics for anomalies and triggers alerts - * - * Features: - * - Loss spike detection (>20% increase between epochs) - * - Divergence detection (loss > threshold) - * - Stagnation detection (no improvement for N epochs) - * - Desktop notifications - * - Optional auto-pause - */ - -class TrainingAnomalyDetector { - constructor(options = {}) { - this.lossHistory = []; - this.config = { - spikeThreshold: options.spikeThreshold || 0.20, // 20% increase - divergenceThreshold: options.divergenceThreshold || 10.0, - stagnationEpochs: options.stagnationEpochs || 5, - enableNotifications: options.enableNotifications !== false, - enableAutoPause: options.enableAutoPause || false, - checkInterval: options.checkInterval || 5000, // 5 seconds - ...options - }; - this.anomalies = []; - this.isMonitoring = false; - this.intervalId = null; - } - - /** - * Add a new loss value to history - */ - addLossValue(epoch, loss, jobName = 'current') { - this.lossHistory.push({ - epoch, - loss, - jobName, - timestamp: Date.now() - }); - - // Keep only recent history (last 100 epochs) - if (this.lossHistory.length > 100) { - this.lossHistory.shift(); - } - - // Check for anomalies - this.detectAnomalies(); - } - - /** - * Detect anomalies in loss progression - */ - detectAnomalies() { - if (this.lossHistory.length < 2) return; - - const recent = this.lossHistory.slice(-10); // Last 10 epochs - const current = recent[recent.length - 1]; - const previous = recent[recent.length - 2]; - - // 1. SPIKE DETECTION: >20% increase from previous epoch - if (previous && current.loss > previous.loss * (1 + this.config.spikeThreshold)) { - const increase = ((current.loss - previous.loss) / previous.loss * 100).toFixed(1); - this.reportAnomaly('spike', { - type: 'Loss Spike', - severity: 'warning', - message: `Loss increased by ${increase}% (${previous.loss.toFixed(4)} → ${current.loss.toFixed(4)})`, - epoch: current.epoch, - jobName: current.jobName, - recommendation: 'Check for data quality issues or reduce learning rate' - }); - } - - // 2. DIVERGENCE DETECTION: Loss > threshold - if (current.loss > this.config.divergenceThreshold) { - this.reportAnomaly('divergence', { - type: 'Training Divergence', - severity: 'error', - message: `Loss diverged to ${current.loss.toFixed(4)} (threshold: ${this.config.divergenceThreshold})`, - epoch: current.epoch, - jobName: current.jobName, - recommendation: 'Training likely unstable. Reduce learning rate significantly or restart with different hyperparameters' - }); - } - - // 3. STAGNATION DETECTION: No improvement for N epochs - if (recent.length >= this.config.stagnationEpochs) { - const lastN = recent.slice(-this.config.stagnationEpochs); - const minLoss = Math.min(...lastN.map(h => h.loss)); - const maxLoss = Math.max(...lastN.map(h => h.loss)); - const variation = maxLoss - minLoss; - - // If variation is very small (< 0.001), consider it stagnant - if (variation < 0.001) { - this.reportAnomaly('stagnation', { - type: 'Training Stagnation', - severity: 'info', - message: `No improvement for ${this.config.stagnationEpochs} epochs (loss: ${current.loss.toFixed(4)})`, - epoch: current.epoch, - jobName: current.jobName, - recommendation: 'Consider early stopping or increasing learning rate' - }); - } - } - } - - /** - * Report an anomaly - */ - reportAnomaly(id, anomaly) { - // Check if this anomaly was already reported recently (debounce) - const recentDuplicate = this.anomalies.find(a => - a.type === anomaly.type && - a.jobName === anomaly.jobName && - (Date.now() - a.timestamp < 30000) // Within 30 seconds - ); - - if (recentDuplicate) return; - - // Add timestamp - anomaly.timestamp = Date.now(); - anomaly.id = `${id}-${anomaly.timestamp}`; - - // Store anomaly - this.anomalies.push(anomaly); - - // Trigger notification - if (this.config.enableNotifications) { - this.sendNotification(anomaly); - } - - // Trigger callback if provided - if (this.config.onAnomaly) { - this.config.onAnomaly(anomaly); - } - - // Auto-pause if enabled and severity is error - if (this.config.enableAutoPause && anomaly.severity === 'error') { - this.pauseTraining(anomaly); - } - } - - /** - * Send desktop notification - */ - sendNotification(anomaly) { - if (!('Notification' in window)) return; - - if (Notification.permission === 'granted') { - new Notification(`⚠️ ${anomaly.type}`, { - body: anomaly.message, - icon: '/favicon.ico', - badge: '/favicon.ico', - tag: anomaly.id, - requireInteraction: anomaly.severity === 'error' - }); - } else if (Notification.permission !== 'denied') { - Notification.requestPermission().then(permission => { - if (permission === 'granted') { - this.sendNotification(anomaly); - } - }); - } - } - - /** - * Pause training (if supported) - */ - pauseTraining(anomaly) { - console.warn('Auto-pause triggered:', anomaly); - // This would need backend support to actually pause training - // For now, just log and notify - if (this.config.onPause) { - this.config.onPause(anomaly); - } - } - - /** - * Start monitoring training data from API - */ - startMonitoring(statusUrl = '/status') { - if (this.isMonitoring) return; - - this.isMonitoring = true; - this.intervalId = setInterval(async () => { - try { - const response = await fetch(statusUrl); - const data = await response.json(); - - // Extract loss from latest job - if (data.jobs && data.jobs.length > 0) { - const latestJob = data.jobs[data.jobs.length - 1]; - if (latestJob.status === 'running' && latestJob.current_epoch && latestJob.current_loss) { - this.addLossValue( - latestJob.current_epoch, - latestJob.current_loss, - latestJob.name - ); - } - } - } catch (err) { - console.error('Anomaly detector monitoring error:', err); - } - }, this.config.checkInterval); - } - - /** - * Stop monitoring - */ - stopMonitoring() { - if (this.intervalId) { - clearInterval(this.intervalId); - this.intervalId = null; - } - this.isMonitoring = false; - } - - /** - * Get recent anomalies - */ - getRecentAnomalies(count = 10) { - return this.anomalies.slice(-count); - } - - /** - * Clear anomaly history - */ - clearAnomalies() { - this.anomalies = []; - } - - /** - * Get anomaly statistics - */ - getStatistics() { - return { - total: this.anomalies.length, - spikes: this.anomalies.filter(a => a.type === 'Loss Spike').length, - divergences: this.anomalies.filter(a => a.type === 'Training Divergence').length, - stagnations: this.anomalies.filter(a => a.type === 'Training Stagnation').length, - byJob: this.anomalies.reduce((acc, a) => { - acc[a.jobName] = (acc[a.jobName] || 0) + 1; - return acc; - }, {}) - }; - } -} - -// Export for use in other modules -if (typeof window !== 'undefined') { - window.TrainingAnomalyDetector = TrainingAnomalyDetector; -} +/** + * Training Anomaly Detection System + * Monitors training metrics for anomalies and triggers alerts + * + * Features: + * - Loss spike detection (>20% increase between epochs) + * - Divergence detection (loss > threshold) + * - Stagnation detection (no improvement for N epochs) + * - Desktop notifications + * - Optional auto-pause + */ + +class TrainingAnomalyDetector { + constructor(options = {}) { + this.lossHistory = []; + this.config = { + spikeThreshold: options.spikeThreshold || 0.20, // 20% increase + divergenceThreshold: options.divergenceThreshold || 10.0, + stagnationEpochs: options.stagnationEpochs || 5, + enableNotifications: options.enableNotifications !== false, + enableAutoPause: options.enableAutoPause || false, + checkInterval: options.checkInterval || 5000, // 5 seconds + ...options + }; + this.anomalies = []; + this.isMonitoring = false; + this.intervalId = null; + } + + /** + * Add a new loss value to history + */ + addLossValue(epoch, loss, jobName = 'current') { + this.lossHistory.push({ + epoch, + loss, + jobName, + timestamp: Date.now() + }); + + // Keep only recent history (last 100 epochs) + if (this.lossHistory.length > 100) { + this.lossHistory.shift(); + } + + // Check for anomalies + this.detectAnomalies(); + } + + /** + * Detect anomalies in loss progression + */ + detectAnomalies() { + if (this.lossHistory.length < 2) return; + + const recent = this.lossHistory.slice(-10); // Last 10 epochs + const current = recent[recent.length - 1]; + const previous = recent[recent.length - 2]; + + // 1. SPIKE DETECTION: >20% increase from previous epoch + if (previous && current.loss > previous.loss * (1 + this.config.spikeThreshold)) { + const increase = ((current.loss - previous.loss) / previous.loss * 100).toFixed(1); + this.reportAnomaly('spike', { + type: 'Loss Spike', + severity: 'warning', + message: `Loss increased by ${increase}% (${previous.loss.toFixed(4)} → ${current.loss.toFixed(4)})`, + epoch: current.epoch, + jobName: current.jobName, + recommendation: 'Check for data quality issues or reduce learning rate' + }); + } + + // 2. DIVERGENCE DETECTION: Loss > threshold + if (current.loss > this.config.divergenceThreshold) { + this.reportAnomaly('divergence', { + type: 'Training Divergence', + severity: 'error', + message: `Loss diverged to ${current.loss.toFixed(4)} (threshold: ${this.config.divergenceThreshold})`, + epoch: current.epoch, + jobName: current.jobName, + recommendation: 'Training likely unstable. Reduce learning rate significantly or restart with different hyperparameters' + }); + } + + // 3. STAGNATION DETECTION: No improvement for N epochs + if (recent.length >= this.config.stagnationEpochs) { + const lastN = recent.slice(-this.config.stagnationEpochs); + const minLoss = Math.min(...lastN.map(h => h.loss)); + const maxLoss = Math.max(...lastN.map(h => h.loss)); + const variation = maxLoss - minLoss; + + // If variation is very small (< 0.001), consider it stagnant + if (variation < 0.001) { + this.reportAnomaly('stagnation', { + type: 'Training Stagnation', + severity: 'info', + message: `No improvement for ${this.config.stagnationEpochs} epochs (loss: ${current.loss.toFixed(4)})`, + epoch: current.epoch, + jobName: current.jobName, + recommendation: 'Consider early stopping or increasing learning rate' + }); + } + } + } + + /** + * Report an anomaly + */ + reportAnomaly(id, anomaly) { + // Check if this anomaly was already reported recently (debounce) + const recentDuplicate = this.anomalies.find(a => + a.type === anomaly.type && + a.jobName === anomaly.jobName && + (Date.now() - a.timestamp < 30000) // Within 30 seconds + ); + + if (recentDuplicate) return; + + // Add timestamp + anomaly.timestamp = Date.now(); + anomaly.id = `${id}-${anomaly.timestamp}`; + + // Store anomaly + this.anomalies.push(anomaly); + + // Trigger notification + if (this.config.enableNotifications) { + this.sendNotification(anomaly); + } + + // Trigger callback if provided + if (this.config.onAnomaly) { + this.config.onAnomaly(anomaly); + } + + // Auto-pause if enabled and severity is error + if (this.config.enableAutoPause && anomaly.severity === 'error') { + this.pauseTraining(anomaly); + } + } + + /** + * Send desktop notification + */ + sendNotification(anomaly) { + if (!('Notification' in window)) return; + + if (Notification.permission === 'granted') { + new Notification(`⚠️ ${anomaly.type}`, { + body: anomaly.message, + icon: '/favicon.ico', + badge: '/favicon.ico', + tag: anomaly.id, + requireInteraction: anomaly.severity === 'error' + }); + } else if (Notification.permission !== 'denied') { + Notification.requestPermission().then(permission => { + if (permission === 'granted') { + this.sendNotification(anomaly); + } + }); + } + } + + /** + * Pause training (if supported) + */ + pauseTraining(anomaly) { + console.warn('Auto-pause triggered:', anomaly); + // This would need backend support to actually pause training + // For now, just log and notify + if (this.config.onPause) { + this.config.onPause(anomaly); + } + } + + /** + * Start monitoring training data from API + */ + startMonitoring(statusUrl = '/status') { + if (this.isMonitoring) return; + + this.isMonitoring = true; + this.intervalId = setInterval(async () => { + try { + const response = await fetch(statusUrl); + const data = await response.json(); + + // Extract loss from latest job + if (data.jobs && data.jobs.length > 0) { + const latestJob = data.jobs[data.jobs.length - 1]; + if (latestJob.status === 'running' && latestJob.current_epoch && latestJob.current_loss) { + this.addLossValue( + latestJob.current_epoch, + latestJob.current_loss, + latestJob.name + ); + } + } + } catch (err) { + console.error('Anomaly detector monitoring error:', err); + } + }, this.config.checkInterval); + } + + /** + * Stop monitoring + */ + stopMonitoring() { + if (this.intervalId) { + clearInterval(this.intervalId); + this.intervalId = null; + } + this.isMonitoring = false; + } + + /** + * Get recent anomalies + */ + getRecentAnomalies(count = 10) { + return this.anomalies.slice(-count); + } + + /** + * Clear anomaly history + */ + clearAnomalies() { + this.anomalies = []; + } + + /** + * Get anomaly statistics + */ + getStatistics() { + return { + total: this.anomalies.length, + spikes: this.anomalies.filter(a => a.type === 'Loss Spike').length, + divergences: this.anomalies.filter(a => a.type === 'Training Divergence').length, + stagnations: this.anomalies.filter(a => a.type === 'Training Stagnation').length, + byJob: this.anomalies.reduce((acc, a) => { + acc[a.jobName] = (acc[a.jobName] || 0) + 1; + return acc; + }, {}) + }; + } +} + +// Export for use in other modules +if (typeof window !== 'undefined') { + window.TrainingAnomalyDetector = TrainingAnomalyDetector; +} diff --git a/docs/dashboard/consolidated.html b/docs/dashboard/consolidated.html index 1184e10a9..a2e10fdc7 100644 --- a/docs/dashboard/consolidated.html +++ b/docs/dashboard/consolidated.html @@ -1,1291 +1,1291 @@ - - - - - - Consolidated Dashboard — Aria Platform - - - - - - - - - - - - - - - - - - - - - - - - - - ← Home -
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    +

    + Automated testing, validation, and deployment orchestration + with master scheduler and job coordination. +

    +
      +
    • 40 unit tests
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    +

    + Interactive AI character with natural language commands, + 3D CSS animations, and LLM-powered auto-execution. +

    +
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      • +
    • Object interaction & physics
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    +
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    +
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    + + + + + + diff --git a/docs/dashboard/hyperparameter-optimizer.js b/docs/dashboard/hyperparameter-optimizer.js index 37df8ba20..74909eefa 100644 --- a/docs/dashboard/hyperparameter-optimizer.js +++ b/docs/dashboard/hyperparameter-optimizer.js @@ -1,591 +1,591 @@ -/** - * Automated Hyperparameter Optimization - * Smart tuning with Bayesian optimization, grid search, and early stopping - */ - -class HyperparameterOptimizer { - constructor() { - this.searchSpace = {}; - this.trials = []; - this.bestConfig = null; - this.bestScore = -Infinity; - this.currentTrial = 0; - this.maxTrials = 10; - this.strategy = 'bayesian'; // bayesian, grid, random - this.earlyStopping = { - enabled: true, - patience: 3, - minDelta: 0.01 - }; - } - - /** - * Define search space - */ - defineSearchSpace(space) { - this.searchSpace = space; - console.log('[HyperOptim] Search space defined:', space); - } - - /** - * Start optimization - */ - async startOptimization(config) { - this.maxTrials = config.maxTrials || 10; - this.strategy = config.strategy || 'bayesian'; - this.currentTrial = 0; - this.trials = []; - this.bestConfig = null; - this.bestScore = -Infinity; - - console.log(`[HyperOptim] Starting ${this.strategy} optimization with ${this.maxTrials} trials`); - - // Show optimization UI - this.showOptimizationUI(); - - // Generate trial configurations based on strategy - const trialConfigs = this.generateTrialConfigs(); - - // Run trials - for (let i = 0; i < trialConfigs.length; i++) { - if (this.shouldStopEarly()) { - console.log('[HyperOptim] Early stopping triggered'); - break; - } - - await this.runTrial(trialConfigs[i], i + 1); - } - - // Show final results - this.showOptimizationResults(); - } - - /** - * Generate trial configurations based on strategy - */ - generateTrialConfigs() { - switch (this.strategy) { - case 'grid': - return this.generateGridSearch(); - case 'random': - return this.generateRandomSearch(); - case 'bayesian': - return this.generateBayesianSearch(); - default: - return this.generateRandomSearch(); - } - } - - /** - * Grid search: Exhaustive search over all combinations - */ - generateGridSearch() { - const configs = []; - const params = Object.keys(this.searchSpace); - - // Generate all combinations (limited to maxTrials) - const combinations = this.cartesianProduct( - params.map(p => this.searchSpace[p].values || this.searchSpace[p].range) - ); - - return combinations.slice(0, this.maxTrials).map(combo => { - const config = {}; - params.forEach((p, idx) => { - config[p] = combo[idx]; - }); - return config; - }); - } - - /** - * Random search: Random sampling from search space - */ - generateRandomSearch() { - const configs = []; - - for (let i = 0; i < this.maxTrials; i++) { - const config = {}; - Object.keys(this.searchSpace).forEach(param => { - const space = this.searchSpace[param]; - - if (space.values) { - // Categorical: random choice - config[param] = space.values[Math.floor(Math.random() * space.values.length)]; - } else if (space.range) { - // Continuous: random value in range - const [min, max] = space.range; - const scale = space.scale || 'linear'; - - if (scale === 'log') { - const logMin = Math.log(min); - const logMax = Math.log(max); - config[param] = Math.exp(logMin + Math.random() * (logMax - logMin)); - } else { - config[param] = min + Math.random() * (max - min); - } - - // Round if integer type - if (space.type === 'int') { - config[param] = Math.round(config[param]); - } - } - }); - configs.push(config); - } - - return configs; - } - - /** - * Bayesian optimization: Intelligent search using Gaussian Process - */ - generateBayesianSearch() { - // Start with random samples - const initialSamples = 3; - const configs = this.generateRandomSearch().slice(0, initialSamples); - - // For remaining trials, use acquisition function - // (Simplified version - real Bayesian optimization is more complex) - for (let i = initialSamples; i < this.maxTrials; i++) { - const config = this.selectNextBayesianConfig(); - configs.push(config); - } - - return configs; - } - - /** - * Select next configuration using Expected Improvement (simplified) - */ - selectNextBayesianConfig() { - // Generate candidate configs - const candidates = this.generateRandomSearch().slice(0, 20); - - // Score each candidate based on distance from previous trials - let bestCandidate = candidates[0]; - let bestEI = -Infinity; - - candidates.forEach(candidate => { - const ei = this.calculateExpectedImprovement(candidate); - if (ei > bestEI) { - bestEI = ei; - bestCandidate = candidate; - } - }); - - return bestCandidate; - } - - /** - * Calculate Expected Improvement (simplified) - */ - calculateExpectedImprovement(config) { - if (this.trials.length === 0) return Math.random(); - - // Calculate distance to existing trials - let minDistance = Infinity; - this.trials.forEach(trial => { - const distance = this.configDistance(config, trial.config); - minDistance = Math.min(minDistance, distance); - }); - - // Exploration bonus: prefer configs far from existing trials - const exploration = minDistance; - - // Exploitation: prefer regions with good scores - const exploitation = this.predictScore(config); - - return exploration * 0.3 + exploitation * 0.7; - } - - /** - * Calculate distance between two configurations - */ - configDistance(config1, config2) { - let distance = 0; - Object.keys(config1).forEach(param => { - const v1 = config1[param]; - const v2 = config2[param]; - - if (typeof v1 === 'number' && typeof v2 === 'number') { - const space = this.searchSpace[param]; - const range = space.range ? space.range[1] - space.range[0] : 1; - distance += Math.pow((v1 - v2) / range, 2); - } else if (v1 !== v2) { - distance += 1; - } - }); - return Math.sqrt(distance); - } - - /** - * Predict score for a configuration (simplified) - */ - predictScore(config) { - if (this.trials.length === 0) return 0.5; - - // Weighted average of nearby trials - let weightedSum = 0; - let totalWeight = 0; - - this.trials.forEach(trial => { - const distance = this.configDistance(config, trial.config); - const weight = Math.exp(-distance); - weightedSum += weight * trial.score; - totalWeight += weight; - }); - - return totalWeight > 0 ? weightedSum / totalWeight : 0.5; - } - - /** - * Run a single trial - */ - async runTrial(config, trialNum) { - console.log(`[HyperOptim] Running trial ${trialNum}/${this.maxTrials}`, config); - - this.updateTrialUI(trialNum, 'running', config); - - try { - // Submit training job with this configuration - const response = await fetch('/api/start-training', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - ...config, - job_name: `hyperparam_trial_${trialNum}`, - _hyperopt: true - }) - }); - - if (!response.ok) { - throw new Error('Training submission failed'); - } - - // Wait for completion (poll status) - const result = await this.waitForCompletion(`hyperparam_trial_${trialNum}`); - - // Calculate score (lower loss = higher score) - const score = result.post_loss ? (1 / result.post_loss) * 100 : 0; - - // Record trial - const trial = { - trialNum, - config, - score, - loss: result.post_loss, - duration: result.duration, - timestamp: Date.now() - }; - - this.trials.push(trial); - - // Update best config - if (score > this.bestScore) { - this.bestScore = score; - this.bestConfig = config; - this.saveBestConfig(); - } - - this.updateTrialUI(trialNum, 'completed', config, score); - - console.log(`[HyperOptim] Trial ${trialNum} completed. Score: ${score.toFixed(2)}`); - - } catch (err) { - console.error(`[HyperOptim] Trial ${trialNum} failed:`, err); - this.updateTrialUI(trialNum, 'failed', config); - } - } - - /** - * Wait for training completion - */ - async waitForCompletion(jobName, timeout = 3600000) { - const startTime = Date.now(); - - while (Date.now() - startTime < timeout) { - await new Promise(resolve => setTimeout(resolve, 5000)); // Poll every 5 seconds - - try { - const response = await fetch('/status'); - const data = await response.json(); - - const job = data.jobs?.find(j => j.name === jobName); - if (job && job.status === 'completed') { - return job; - } - if (job && job.status === 'failed') { - throw new Error('Training failed'); - } - } catch (err) { - console.error('[HyperOptim] Status poll error:', err); - } - } - - throw new Error('Training timeout'); - } - - /** - * Check if early stopping should trigger - */ - shouldStopEarly() { - if (!this.earlyStopping.enabled) return false; - if (this.trials.length < this.earlyStopping.patience + 1) return false; - - // Check if no improvement in last N trials - const recentTrials = this.trials.slice(-this.earlyStopping.patience); - const recentBest = Math.max(...recentTrials.map(t => t.score)); - - const improvement = recentBest - this.bestScore; - return improvement < this.earlyStopping.minDelta; - } - - /** - * Show optimization UI - */ - showOptimizationUI() { - const container = document.getElementById('hyperoptContainer'); - if (!container) return; - - container.innerHTML = ` -
    -
    -

    🎯 Hyperparameter Optimization

    - -
    -
    -
    - Running ${this.strategy} optimization with ${this.maxTrials} trials... -
    -
    -
    -
    -
    - `; - - container.style.display = 'block'; - } - - /** - * Update trial UI - */ - updateTrialUI(trialNum, status, config, score = null) { - const container = document.getElementById('trialsProgress'); - if (!container) return; - - const trialId = `trial-${trialNum}`; - let trialEl = document.getElementById(trialId); - - if (!trialEl) { - trialEl = document.createElement('div'); - trialEl.id = trialId; - trialEl.className = 'alert alert-info'; - container.appendChild(trialEl); - } - - const statusEmoji = { - running: '⏳', - completed: '✅', - failed: '❌' - }; - - const configStr = Object.entries(config) - .map(([k, v]) => `${k}: ${typeof v === 'number' ? v.toFixed(4) : v}`) - .join(', '); - - trialEl.innerHTML = ` - ${statusEmoji[status]} Trial ${trialNum}/${this.maxTrials} - ${status}
    - ${configStr} - ${score !== null ? `
    Score: ${score.toFixed(2)}` : ''} - `; - - trialEl.className = `alert alert-${status === 'completed' ? 'success' : status === 'failed' ? 'error' : 'info'}`; - - // Update current best - if (this.bestConfig) { - const bestEl = document.getElementById('currentBest'); - if (bestEl) { - const bestStr = Object.entries(this.bestConfig) - .map(([k, v]) => `${k}: ${typeof v === 'number' ? v.toFixed(4) : v}`) - .join(', '); - - bestEl.innerHTML = ` -
    -

    🏆 Current Best Configuration

    -
    -

    Score: ${this.bestScore.toFixed(2)}

    -

    ${bestStr}

    -
    -
    - `; - } - } - } - - /** - * Show final optimization results - */ - showOptimizationResults() { - const container = document.getElementById('hyperoptContainer'); - if (!container) return; - - const bestStr = Object.entries(this.bestConfig) - .map(([k, v]) => `
  • ${k}: ${typeof v === 'number' ? v.toFixed(4) : v}
    • `) - .join(''); - - container.innerHTML = ` -
    -
    -

    ✅ Optimization Complete

    -
    -
    -
    -

    🏆 Best Configuration Found

    -

    Score: ${this.bestScore.toFixed(2)}

    -
      ${bestStr}
    -
    - -

    Trial Summary

    -

    Completed ${this.trials.length} trials

    - -
    - - - -
    -
    -
    - `; - } - - /** - * Save best configuration - */ - saveBestConfig() { - if (!this.bestConfig) return; - - localStorage.setItem('hyperopt-best-config', JSON.stringify({ - config: this.bestConfig, - score: this.bestScore, - timestamp: Date.now() - })); - - console.log('[HyperOptim] Best config saved:', this.bestConfig); - } - - /** - * Apply best configuration to training form - */ - applyBestConfig() { - if (!this.bestConfig) return; - - Object.keys(this.bestConfig).forEach(key => { - const el = document.getElementById(key); - if (el) { - el.value = this.bestConfig[key]; - } - }); - - if (typeof showToast === 'function') showToast('Best config applied to training form ✓', 'ok', 3500); - - // Switch to training tab - if (typeof switchTab === 'function') { - switchTab('training'); - } - } - - /** - * Export optimization results - */ - exportResults() { - const results = { - strategy: this.strategy, - maxTrials: this.maxTrials, - trialsCompleted: this.trials.length, - bestConfig: this.bestConfig, - bestScore: this.bestScore, - allTrials: this.trials, - timestamp: Date.now() - }; - - const blob = new Blob([JSON.stringify(results, null, 2)], { type: 'application/json' }); - const url = URL.createObjectURL(blob); - const a = document.createElement('a'); - a.href = url; - a.download = `hyperopt-results-${Date.now()}.json`; - a.click(); - URL.revokeObjectURL(url); - } - - /** - * Visualize optimization results - */ - visualizeResults() { - // Create visualization chart - const canvas = document.createElement('canvas'); - canvas.id = 'hyperoptChart'; - canvas.style.height = '400px'; - - const container = document.getElementById('hyperoptContainer'); - if (container) { - container.appendChild(canvas); - } - - const ctx = canvas.getContext('2d'); - new Chart(ctx, { - type: 'line', - data: { - labels: this.trials.map(t => `Trial ${t.trialNum}`), - datasets: [{ - label: 'Trial Score', - data: this.trials.map(t => t.score), - borderColor: '#667eea', - backgroundColor: 'rgba(102, 126, 234, 0.1)', - tension: 0.4 - }, { - label: 'Best Score So Far', - data: this.trials.map((t, idx) => - Math.max(...this.trials.slice(0, idx + 1).map(tr => tr.score)) - ), - borderColor: '#2dce89', - backgroundColor: 'rgba(45, 206, 137, 0.1)', - tension: 0.4 - }] - }, - options: { - responsive: true, - maintainAspectRatio: false, - plugins: { - title: { - display: true, - text: 'Optimization Progress' - } - } - } - }); - } - - /** - * Stop optimization - */ - stopOptimization() { - - - this.maxTrials = this.currentTrial; - if (typeof showToast === 'function') showToast('Optimization stopping after current trial', 'info', 4000); - } - - /** - * Helper: Cartesian product - */ - cartesianProduct(arrays) { - return arrays.reduce((acc, array) => - acc.flatMap(x => array.map(y => [...x, y])), [[]] - ); - } -} - -// Export for use in dashboard -if (typeof window !== 'undefined') { - window.HyperparameterOptimizer = HyperparameterOptimizer; -} +/** + * Automated Hyperparameter Optimization + * Smart tuning with Bayesian optimization, grid search, and early stopping + */ + +class HyperparameterOptimizer { + constructor() { + this.searchSpace = {}; + this.trials = []; + this.bestConfig = null; + this.bestScore = -Infinity; + this.currentTrial = 0; + this.maxTrials = 10; + this.strategy = 'bayesian'; // bayesian, grid, random + this.earlyStopping = { + enabled: true, + patience: 3, + minDelta: 0.01 + }; + } + + /** + * Define search space + */ + defineSearchSpace(space) { + this.searchSpace = space; + console.log('[HyperOptim] Search space defined:', space); + } + + /** + * Start optimization + */ + async startOptimization(config) { + this.maxTrials = config.maxTrials || 10; + this.strategy = config.strategy || 'bayesian'; + this.currentTrial = 0; + this.trials = []; + this.bestConfig = null; + this.bestScore = -Infinity; + + console.log(`[HyperOptim] Starting ${this.strategy} optimization with ${this.maxTrials} trials`); + + // Show optimization UI + this.showOptimizationUI(); + + // Generate trial configurations based on strategy + const trialConfigs = this.generateTrialConfigs(); + + // Run trials + for (let i = 0; i < trialConfigs.length; i++) { + if (this.shouldStopEarly()) { + console.log('[HyperOptim] Early stopping triggered'); + break; + } + + await this.runTrial(trialConfigs[i], i + 1); + } + + // Show final results + this.showOptimizationResults(); + } + + /** + * Generate trial configurations based on strategy + */ + generateTrialConfigs() { + switch (this.strategy) { + case 'grid': + return this.generateGridSearch(); + case 'random': + return this.generateRandomSearch(); + case 'bayesian': + return this.generateBayesianSearch(); + default: + return this.generateRandomSearch(); + } + } + + /** + * Grid search: Exhaustive search over all combinations + */ + generateGridSearch() { + const configs = []; + const params = Object.keys(this.searchSpace); + + // Generate all combinations (limited to maxTrials) + const combinations = this.cartesianProduct( + params.map(p => this.searchSpace[p].values || this.searchSpace[p].range) + ); + + return combinations.slice(0, this.maxTrials).map(combo => { + const config = {}; + params.forEach((p, idx) => { + config[p] = combo[idx]; + }); + return config; + }); + } + + /** + * Random search: Random sampling from search space + */ + generateRandomSearch() { + const configs = []; + + for (let i = 0; i < this.maxTrials; i++) { + const config = {}; + Object.keys(this.searchSpace).forEach(param => { + const space = this.searchSpace[param]; + + if (space.values) { + // Categorical: random choice + config[param] = space.values[Math.floor(Math.random() * space.values.length)]; + } else if (space.range) { + // Continuous: random value in range + const [min, max] = space.range; + const scale = space.scale || 'linear'; + + if (scale === 'log') { + const logMin = Math.log(min); + const logMax = Math.log(max); + config[param] = Math.exp(logMin + Math.random() * (logMax - logMin)); + } else { + config[param] = min + Math.random() * (max - min); + } + + // Round if integer type + if (space.type === 'int') { + config[param] = Math.round(config[param]); + } + } + }); + configs.push(config); + } + + return configs; + } + + /** + * Bayesian optimization: Intelligent search using Gaussian Process + */ + generateBayesianSearch() { + // Start with random samples + const initialSamples = 3; + const configs = this.generateRandomSearch().slice(0, initialSamples); + + // For remaining trials, use acquisition function + // (Simplified version - real Bayesian optimization is more complex) + for (let i = initialSamples; i < this.maxTrials; i++) { + const config = this.selectNextBayesianConfig(); + configs.push(config); + } + + return configs; + } + + /** + * Select next configuration using Expected Improvement (simplified) + */ + selectNextBayesianConfig() { + // Generate candidate configs + const candidates = this.generateRandomSearch().slice(0, 20); + + // Score each candidate based on distance from previous trials + let bestCandidate = candidates[0]; + let bestEI = -Infinity; + + candidates.forEach(candidate => { + const ei = this.calculateExpectedImprovement(candidate); + if (ei > bestEI) { + bestEI = ei; + bestCandidate = candidate; + } + }); + + return bestCandidate; + } + + /** + * Calculate Expected Improvement (simplified) + */ + calculateExpectedImprovement(config) { + if (this.trials.length === 0) return Math.random(); + + // Calculate distance to existing trials + let minDistance = Infinity; + this.trials.forEach(trial => { + const distance = this.configDistance(config, trial.config); + minDistance = Math.min(minDistance, distance); + }); + + // Exploration bonus: prefer configs far from existing trials + const exploration = minDistance; + + // Exploitation: prefer regions with good scores + const exploitation = this.predictScore(config); + + return exploration * 0.3 + exploitation * 0.7; + } + + /** + * Calculate distance between two configurations + */ + configDistance(config1, config2) { + let distance = 0; + Object.keys(config1).forEach(param => { + const v1 = config1[param]; + const v2 = config2[param]; + + if (typeof v1 === 'number' && typeof v2 === 'number') { + const space = this.searchSpace[param]; + const range = space.range ? space.range[1] - space.range[0] : 1; + distance += Math.pow((v1 - v2) / range, 2); + } else if (v1 !== v2) { + distance += 1; + } + }); + return Math.sqrt(distance); + } + + /** + * Predict score for a configuration (simplified) + */ + predictScore(config) { + if (this.trials.length === 0) return 0.5; + + // Weighted average of nearby trials + let weightedSum = 0; + let totalWeight = 0; + + this.trials.forEach(trial => { + const distance = this.configDistance(config, trial.config); + const weight = Math.exp(-distance); + weightedSum += weight * trial.score; + totalWeight += weight; + }); + + return totalWeight > 0 ? weightedSum / totalWeight : 0.5; + } + + /** + * Run a single trial + */ + async runTrial(config, trialNum) { + console.log(`[HyperOptim] Running trial ${trialNum}/${this.maxTrials}`, config); + + this.updateTrialUI(trialNum, 'running', config); + + try { + // Submit training job with this configuration + const response = await fetch('/api/start-training', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + ...config, + job_name: `hyperparam_trial_${trialNum}`, + _hyperopt: true + }) + }); + + if (!response.ok) { + throw new Error('Training submission failed'); + } + + // Wait for completion (poll status) + const result = await this.waitForCompletion(`hyperparam_trial_${trialNum}`); + + // Calculate score (lower loss = higher score) + const score = result.post_loss ? (1 / result.post_loss) * 100 : 0; + + // Record trial + const trial = { + trialNum, + config, + score, + loss: result.post_loss, + duration: result.duration, + timestamp: Date.now() + }; + + this.trials.push(trial); + + // Update best config + if (score > this.bestScore) { + this.bestScore = score; + this.bestConfig = config; + this.saveBestConfig(); + } + + this.updateTrialUI(trialNum, 'completed', config, score); + + console.log(`[HyperOptim] Trial ${trialNum} completed. Score: ${score.toFixed(2)}`); + + } catch (err) { + console.error(`[HyperOptim] Trial ${trialNum} failed:`, err); + this.updateTrialUI(trialNum, 'failed', config); + } + } + + /** + * Wait for training completion + */ + async waitForCompletion(jobName, timeout = 3600000) { + const startTime = Date.now(); + + while (Date.now() - startTime < timeout) { + await new Promise(resolve => setTimeout(resolve, 5000)); // Poll every 5 seconds + + try { + const response = await fetch('/status'); + const data = await response.json(); + + const job = data.jobs?.find(j => j.name === jobName); + if (job && job.status === 'completed') { + return job; + } + if (job && job.status === 'failed') { + throw new Error('Training failed'); + } + } catch (err) { + console.error('[HyperOptim] Status poll error:', err); + } + } + + throw new Error('Training timeout'); + } + + /** + * Check if early stopping should trigger + */ + shouldStopEarly() { + if (!this.earlyStopping.enabled) return false; + if (this.trials.length < this.earlyStopping.patience + 1) return false; + + // Check if no improvement in last N trials + const recentTrials = this.trials.slice(-this.earlyStopping.patience); + const recentBest = Math.max(...recentTrials.map(t => t.score)); + + const improvement = recentBest - this.bestScore; + return improvement < this.earlyStopping.minDelta; + } + + /** + * Show optimization UI + */ + showOptimizationUI() { + const container = document.getElementById('hyperoptContainer'); + if (!container) return; + + container.innerHTML = ` +
    +
    +

    🎯 Hyperparameter Optimization

    + +
    +
    +
    + Running ${this.strategy} optimization with ${this.maxTrials} trials... +
    +
    +
    +
    +
    + `; + + container.style.display = 'block'; + } + + /** + * Update trial UI + */ + updateTrialUI(trialNum, status, config, score = null) { + const container = document.getElementById('trialsProgress'); + if (!container) return; + + const trialId = `trial-${trialNum}`; + let trialEl = document.getElementById(trialId); + + if (!trialEl) { + trialEl = document.createElement('div'); + trialEl.id = trialId; + trialEl.className = 'alert alert-info'; + container.appendChild(trialEl); + } + + const statusEmoji = { + running: '⏳', + completed: '✅', + failed: '❌' + }; + + const configStr = Object.entries(config) + .map(([k, v]) => `${k}: ${typeof v === 'number' ? v.toFixed(4) : v}`) + .join(', '); + + trialEl.innerHTML = ` + ${statusEmoji[status]} Trial ${trialNum}/${this.maxTrials} - ${status}
    + ${configStr} + ${score !== null ? `
    Score: ${score.toFixed(2)}` : ''} + `; + + trialEl.className = `alert alert-${status === 'completed' ? 'success' : status === 'failed' ? 'error' : 'info'}`; + + // Update current best + if (this.bestConfig) { + const bestEl = document.getElementById('currentBest'); + if (bestEl) { + const bestStr = Object.entries(this.bestConfig) + .map(([k, v]) => `${k}: ${typeof v === 'number' ? v.toFixed(4) : v}`) + .join(', '); + + bestEl.innerHTML = ` +
    +

    🏆 Current Best Configuration

    +
    +

    Score: ${this.bestScore.toFixed(2)}

    +

    ${bestStr}

    +
    +
    + `; + } + } + } + + /** + * Show final optimization results + */ + showOptimizationResults() { + const container = document.getElementById('hyperoptContainer'); + if (!container) return; + + const bestStr = Object.entries(this.bestConfig) + .map(([k, v]) => `
  • ${k}: ${typeof v === 'number' ? v.toFixed(4) : v}
    • `) + .join(''); + + container.innerHTML = ` +
    +
    +

    ✅ Optimization Complete

    +
    +
    +
    +

    🏆 Best Configuration Found

    +

    Score: ${this.bestScore.toFixed(2)}

    +
      ${bestStr}
    +
    + +

    Trial Summary

    +

    Completed ${this.trials.length} trials

    + +
    + + + +
    +
    +
    + `; + } + + /** + * Save best configuration + */ + saveBestConfig() { + if (!this.bestConfig) return; + + localStorage.setItem('hyperopt-best-config', JSON.stringify({ + config: this.bestConfig, + score: this.bestScore, + timestamp: Date.now() + })); + + console.log('[HyperOptim] Best config saved:', this.bestConfig); + } + + /** + * Apply best configuration to training form + */ + applyBestConfig() { + if (!this.bestConfig) return; + + Object.keys(this.bestConfig).forEach(key => { + const el = document.getElementById(key); + if (el) { + el.value = this.bestConfig[key]; + } + }); + + if (typeof showToast === 'function') showToast('Best config applied to training form ✓', 'ok', 3500); + + // Switch to training tab + if (typeof switchTab === 'function') { + switchTab('training'); + } + } + + /** + * Export optimization results + */ + exportResults() { + const results = { + strategy: this.strategy, + maxTrials: this.maxTrials, + trialsCompleted: this.trials.length, + bestConfig: this.bestConfig, + bestScore: this.bestScore, + allTrials: this.trials, + timestamp: Date.now() + }; + + const blob = new Blob([JSON.stringify(results, null, 2)], { type: 'application/json' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `hyperopt-results-${Date.now()}.json`; + a.click(); + URL.revokeObjectURL(url); + } + + /** + * Visualize optimization results + */ + visualizeResults() { + // Create visualization chart + const canvas = document.createElement('canvas'); + canvas.id = 'hyperoptChart'; + canvas.style.height = '400px'; + + const container = document.getElementById('hyperoptContainer'); + if (container) { + container.appendChild(canvas); + } + + const ctx = canvas.getContext('2d'); + new Chart(ctx, { + type: 'line', + data: { + labels: this.trials.map(t => `Trial ${t.trialNum}`), + datasets: [{ + label: 'Trial Score', + data: this.trials.map(t => t.score), + borderColor: '#667eea', + backgroundColor: 'rgba(102, 126, 234, 0.1)', + tension: 0.4 + }, { + label: 'Best Score So Far', + data: this.trials.map((t, idx) => + Math.max(...this.trials.slice(0, idx + 1).map(tr => tr.score)) + ), + borderColor: '#2dce89', + backgroundColor: 'rgba(45, 206, 137, 0.1)', + tension: 0.4 + }] + }, + options: { + responsive: true, + maintainAspectRatio: false, + plugins: { + title: { + display: true, + text: 'Optimization Progress' + } + } + } + }); + } + + /** + * Stop optimization + */ + stopOptimization() { + + + this.maxTrials = this.currentTrial; + if (typeof showToast === 'function') showToast('Optimization stopping after current trial', 'info', 4000); + } + + /** + * Helper: Cartesian product + */ + cartesianProduct(arrays) { + return arrays.reduce((acc, array) => + acc.flatMap(x => array.map(y => [...x, y])), [[]] + ); + } +} + +// Export for use in dashboard +if (typeof window !== 'undefined') { + window.HyperparameterOptimizer = HyperparameterOptimizer; +} diff --git a/docs/dashboard/index.html b/docs/dashboard/index.html index 5d623fce1..d750262ce 100644 --- a/docs/dashboard/index.html +++ b/docs/dashboard/index.html @@ -1,242 +1,242 @@ - - - - - - Training Dashboard — Aria Demo - - - - - - - - - - - - - - - - - - -
    - ← Back to Aria Home - -
    -

    📊 QAI Training Dashboard

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    LoRA fine-tuning, model evaluation, GPU monitoring, and autonomous training cycles

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    Demo Mode — Simulated Data
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    🎯Training Accuracy
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    92.4%
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    Best model — cycle #47
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    🔄Training Cycles
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    47
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    Autonomous cycles completed
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    📦Datasets
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    12
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    Active datasets across 3 categories
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    #47chat/aria_personality_v310092.4%28mPromoted
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    + + + + + + diff --git a/docs/dashboard/keyboard-nav.js b/docs/dashboard/keyboard-nav.js index 79b98157c..fabaa407e 100644 --- a/docs/dashboard/keyboard-nav.js +++ b/docs/dashboard/keyboard-nav.js @@ -1,358 +1,358 @@ -/** - * Keyboard Navigation System - * Comprehensive keyboard shortcuts with hints panel - * - * Features: - * - Global shortcuts (Ctrl+K, Ctrl+S, etc.) - * - Modal/dialog shortcuts (Escape, Enter) - * - Form navigation (Tab, Shift+Tab, arrows) - * - Persistent hints panel (? to toggle) - * - ARIA labels for accessibility - */ - -class KeyboardNavigationManager { - constructor(options = {}) { - this.shortcuts = new Map(); - this.config = { - showHintsOnLoad: options.showHintsOnLoad || false, - hintsKey: options.hintsKey || '?', - enableFormNav: options.enableFormNav !== false, - enableModalNav: options.enableModalNav !== false, - ...options - }; - this.hintsVisible = false; - this.init(); - } - - init() { - // Register default shortcuts - this.registerDefaultShortcuts(); - - // Setup keyboard listener - document.addEventListener('keydown', this.handleKeyDown.bind(this)); - - // Create hints panel - this.createHintsPanel(); - - // Show hints on load if configured - if (this.config.showHintsOnLoad) { - setTimeout(() => this.showHints(), 1000); - } - } - - /** - * Register a keyboard shortcut - */ - register(key, callback, description, category = 'General') { - const shortcutId = this.normalizeKey(key); - this.shortcuts.set(shortcutId, { - key, - callback, - description, - category - }); - } - - /** - * Normalize key combination for consistent lookup - */ - normalizeKey(key) { - return key.toLowerCase() - .replace('ctrl+', 'control+') - .replace('cmd+', 'control+'); - } - - /** - * Register default shortcuts - */ - registerDefaultShortcuts() { - // Navigation shortcuts - this.register('Control+h', () => { - window.location.href = '/hub.html'; - }, 'Go to Hub', 'Navigation'); - - this.register('Control+u', () => { - window.location.href = '/unified.html'; - }, 'Go to Training Dashboard', 'Navigation'); - - this.register('Control+a', () => { - window.location.href = '/analytics.html'; - }, 'Go to Analytics', 'Navigation'); - - // Action shortcuts - this.register('Control+s', (e) => { - e.preventDefault(); - const saveBtn = document.querySelector('[onclick*="saveConfig"]'); - if (saveBtn) saveBtn.click(); - }, 'Save Configuration', 'Actions'); - - this.register('Control+r', (e) => { - e.preventDefault(); - const refreshBtn = document.querySelector('[onclick*="refresh"]'); - if (refreshBtn) refreshBtn.click(); - }, 'Refresh Data', 'Actions'); - - this.register('Control+/', () => { - this.toggleHints(); - }, 'Toggle Keyboard Shortcuts', 'Help'); - - this.register('?', () => { - this.toggleHints(); - }, 'Toggle Keyboard Shortcuts', 'Help'); - - // Modal shortcuts - if (this.config.enableModalNav) { - this.register('Escape', () => { - const modal = document.querySelector('.modal-overlay, [role="dialog"]'); - if (modal) { - const closeBtn = modal.querySelector('.modal-close, [onclick*="remove"], button:last-child'); - if (closeBtn) closeBtn.click(); - } - }, 'Close Modal/Dialog', 'Modals'); - } - - // Form shortcuts - if (this.config.enableFormNav) { - this.register('Control+Enter', () => { - const submitBtn = document.querySelector('button[type="submit"], .btn-primary:not([disabled])'); - if (submitBtn && !submitBtn.disabled) submitBtn.click(); - }, 'Submit Form', 'Forms'); - } - } - - /** - * Handle keydown events - */ - handleKeyDown(e) { - // Build key combination string - const parts = []; - if (e.ctrlKey || e.metaKey) parts.push('control'); - if (e.shiftKey) parts.push('shift'); - if (e.altKey) parts.push('alt'); - - const key = e.key.toLowerCase(); - if (!['control', 'shift', 'alt', 'meta'].includes(key)) { - parts.push(key); - } - - const combination = parts.join('+'); - const shortcut = this.shortcuts.get(combination); - - if (shortcut) { - // Don't trigger if typing in input - if (['input', 'textarea', 'select'].includes(e.target.tagName.toLowerCase())) { - // Allow ? to work in inputs for help - if (key !== '?') return; - } - - e.preventDefault(); - shortcut.callback(e); - } - - // Form navigation enhancements - if (this.config.enableFormNav) { - this.handleFormNavigation(e); - } - } - - /** - * Enhanced form navigation - */ - handleFormNavigation(e) { - const activeElement = document.activeElement; - const isFormElement = ['input', 'textarea', 'select', 'button'].includes( - activeElement.tagName.toLowerCase() - ); - - if (!isFormElement) return; - - // Arrow keys for select/radio navigation - if (activeElement.tagName.toLowerCase() === 'select') { - // Let default behavior work for select - return; - } - - // Tab navigation (already handled by browser, but we can enhance) - if (e.key === 'Tab') { - // Add visual focus indicator - setTimeout(() => { - const newFocus = document.activeElement; - if (newFocus && newFocus !== activeElement) { - newFocus.style.outline = '2px solid #667eea'; - setTimeout(() => { - newFocus.style.outline = ''; - }, 500); - } - }, 10); - } - } - - /** - * Create hints panel - */ - createHintsPanel() { - const panel = document.createElement('div'); - panel.id = 'keyboard-hints-panel'; - panel.style.cssText = ` - position: fixed; - top: 20px; - right: 20px; - background: rgba(26, 26, 46, 0.98); - border: 2px solid #667eea; - border-radius: 12px; - padding: 20px; - max-width: 400px; - max-height: 80vh; - overflow-y: auto; - z-index: 10000; - display: none; - box-shadow: 0 10px 40px rgba(0, 0, 0, 0.5); - backdrop-filter: blur(10px); - `; - - panel.innerHTML = ` -
    -

    ⌨️ Keyboard Shortcuts

    - -
    -
    - `; - - document.body.appendChild(panel); - - document.getElementById('close-hints-btn').onclick = () => this.hideHints(); - - // Close on click outside - panel.addEventListener('click', (e) => e.stopPropagation()); - document.addEventListener('click', () => { - if (this.hintsVisible) this.hideHints(); - }); - - this.updateHintsContent(); - } - - /** - * Update hints panel content - */ - updateHintsContent() { - const content = document.getElementById('shortcuts-content'); - if (!content) return; - - // Group shortcuts by category - const categories = {}; - this.shortcuts.forEach(shortcut => { - if (!categories[shortcut.category]) { - categories[shortcut.category] = []; - } - categories[shortcut.category].push(shortcut); - }); - - let html = ''; - Object.keys(categories).sort().forEach(category => { - html += `
    -

    ${category}

    -
    `; - - categories[category].forEach(shortcut => { - const keys = shortcut.key.split('+').map(k => - `${k}` - ).join(' + '); - - html += `
    - ${shortcut.description} - ${keys} -
    `; - }); - - html += `
    `; - }); - - content.innerHTML = html; - } - - /** - * Show hints panel - */ - showHints() { - const panel = document.getElementById('keyboard-hints-panel'); - if (panel) { - panel.style.display = 'block'; - this.hintsVisible = true; - } - } - - /** - * Hide hints panel - */ - hideHints() { - const panel = document.getElementById('keyboard-hints-panel'); - if (panel) { - panel.style.display = 'none'; - this.hintsVisible = false; - } - } - - /** - * Toggle hints panel - */ - toggleHints() { - if (this.hintsVisible) { - this.hideHints(); - } else { - this.showHints(); - } - } - - /** - * Add ARIA labels to elements - */ - addAriaLabels() { - // Add labels to buttons - document.querySelectorAll('button:not([aria-label])').forEach(btn => { - const text = btn.textContent.trim() || btn.title || 'Button'; - btn.setAttribute('aria-label', text); - }); - - // Add labels to inputs - document.querySelectorAll('input:not([aria-label])').forEach(input => { - const label = input.previousElementSibling; - if (label && label.tagName === 'LABEL') { - input.setAttribute('aria-label', label.textContent.trim()); - } else { - input.setAttribute('aria-label', input.placeholder || input.name || 'Input field'); - } - }); - - // Add role to modals - document.querySelectorAll('.modal, .modal-overlay').forEach(modal => { - modal.setAttribute('role', 'dialog'); - modal.setAttribute('aria-modal', 'true'); - }); - } - - /** - * Destroy navigation manager - */ - destroy() { - document.removeEventListener('keydown', this.handleKeyDown.bind(this)); - const panel = document.getElementById('keyboard-hints-panel'); - if (panel) panel.remove(); - } -} - -// Auto-initialize if window exists -if (typeof window !== 'undefined') { - window.KeyboardNavigationManager = KeyboardNavigationManager; - - // Auto-create instance - window.addEventListener('DOMContentLoaded', () => { - if (!window.keyboardNav) { - window.keyboardNav = new KeyboardNavigationManager(); - - // Add ARIA labels after a short delay to let page render - setTimeout(() => { - window.keyboardNav.addAriaLabels(); - }, 500); - } - }); -} +/** + * Keyboard Navigation System + * Comprehensive keyboard shortcuts with hints panel + * + * Features: + * - Global shortcuts (Ctrl+K, Ctrl+S, etc.) + * - Modal/dialog shortcuts (Escape, Enter) + * - Form navigation (Tab, Shift+Tab, arrows) + * - Persistent hints panel (? to toggle) + * - ARIA labels for accessibility + */ + +class KeyboardNavigationManager { + constructor(options = {}) { + this.shortcuts = new Map(); + this.config = { + showHintsOnLoad: options.showHintsOnLoad || false, + hintsKey: options.hintsKey || '?', + enableFormNav: options.enableFormNav !== false, + enableModalNav: options.enableModalNav !== false, + ...options + }; + this.hintsVisible = false; + this.init(); + } + + init() { + // Register default shortcuts + this.registerDefaultShortcuts(); + + // Setup keyboard listener + document.addEventListener('keydown', this.handleKeyDown.bind(this)); + + // Create hints panel + this.createHintsPanel(); + + // Show hints on load if configured + if (this.config.showHintsOnLoad) { + setTimeout(() => this.showHints(), 1000); + } + } + + /** + * Register a keyboard shortcut + */ + register(key, callback, description, category = 'General') { + const shortcutId = this.normalizeKey(key); + this.shortcuts.set(shortcutId, { + key, + callback, + description, + category + }); + } + + /** + * Normalize key combination for consistent lookup + */ + normalizeKey(key) { + return key.toLowerCase() + .replace('ctrl+', 'control+') + .replace('cmd+', 'control+'); + } + + /** + * Register default shortcuts + */ + registerDefaultShortcuts() { + // Navigation shortcuts + this.register('Control+h', () => { + window.location.href = '/hub.html'; + }, 'Go to Hub', 'Navigation'); + + this.register('Control+u', () => { + window.location.href = '/unified.html'; + }, 'Go to Training Dashboard', 'Navigation'); + + this.register('Control+a', () => { + window.location.href = '/analytics.html'; + }, 'Go to Analytics', 'Navigation'); + + // Action shortcuts + this.register('Control+s', (e) => { + e.preventDefault(); + const saveBtn = document.querySelector('[onclick*="saveConfig"]'); + if (saveBtn) saveBtn.click(); + }, 'Save Configuration', 'Actions'); + + this.register('Control+r', (e) => { + e.preventDefault(); + const refreshBtn = document.querySelector('[onclick*="refresh"]'); + if (refreshBtn) refreshBtn.click(); + }, 'Refresh Data', 'Actions'); + + this.register('Control+/', () => { + this.toggleHints(); + }, 'Toggle Keyboard Shortcuts', 'Help'); + + this.register('?', () => { + this.toggleHints(); + }, 'Toggle Keyboard Shortcuts', 'Help'); + + // Modal shortcuts + if (this.config.enableModalNav) { + this.register('Escape', () => { + const modal = document.querySelector('.modal-overlay, [role="dialog"]'); + if (modal) { + const closeBtn = modal.querySelector('.modal-close, [onclick*="remove"], button:last-child'); + if (closeBtn) closeBtn.click(); + } + }, 'Close Modal/Dialog', 'Modals'); + } + + // Form shortcuts + if (this.config.enableFormNav) { + this.register('Control+Enter', () => { + const submitBtn = document.querySelector('button[type="submit"], .btn-primary:not([disabled])'); + if (submitBtn && !submitBtn.disabled) submitBtn.click(); + }, 'Submit Form', 'Forms'); + } + } + + /** + * Handle keydown events + */ + handleKeyDown(e) { + // Build key combination string + const parts = []; + if (e.ctrlKey || e.metaKey) parts.push('control'); + if (e.shiftKey) parts.push('shift'); + if (e.altKey) parts.push('alt'); + + const key = e.key.toLowerCase(); + if (!['control', 'shift', 'alt', 'meta'].includes(key)) { + parts.push(key); + } + + const combination = parts.join('+'); + const shortcut = this.shortcuts.get(combination); + + if (shortcut) { + // Don't trigger if typing in input + if (['input', 'textarea', 'select'].includes(e.target.tagName.toLowerCase())) { + // Allow ? to work in inputs for help + if (key !== '?') return; + } + + e.preventDefault(); + shortcut.callback(e); + } + + // Form navigation enhancements + if (this.config.enableFormNav) { + this.handleFormNavigation(e); + } + } + + /** + * Enhanced form navigation + */ + handleFormNavigation(e) { + const activeElement = document.activeElement; + const isFormElement = ['input', 'textarea', 'select', 'button'].includes( + activeElement.tagName.toLowerCase() + ); + + if (!isFormElement) return; + + // Arrow keys for select/radio navigation + if (activeElement.tagName.toLowerCase() === 'select') { + // Let default behavior work for select + return; + } + + // Tab navigation (already handled by browser, but we can enhance) + if (e.key === 'Tab') { + // Add visual focus indicator + setTimeout(() => { + const newFocus = document.activeElement; + if (newFocus && newFocus !== activeElement) { + newFocus.style.outline = '2px solid #667eea'; + setTimeout(() => { + newFocus.style.outline = ''; + }, 500); + } + }, 10); + } + } + + /** + * Create hints panel + */ + createHintsPanel() { + const panel = document.createElement('div'); + panel.id = 'keyboard-hints-panel'; + panel.style.cssText = ` + position: fixed; + top: 20px; + right: 20px; + background: rgba(26, 26, 46, 0.98); + border: 2px solid #667eea; + border-radius: 12px; + padding: 20px; + max-width: 400px; + max-height: 80vh; + overflow-y: auto; + z-index: 10000; + display: none; + box-shadow: 0 10px 40px rgba(0, 0, 0, 0.5); + backdrop-filter: blur(10px); + `; + + panel.innerHTML = ` +
    +

    ⌨️ Keyboard Shortcuts

    + +
    +
    + `; + + document.body.appendChild(panel); + + document.getElementById('close-hints-btn').onclick = () => this.hideHints(); + + // Close on click outside + panel.addEventListener('click', (e) => e.stopPropagation()); + document.addEventListener('click', () => { + if (this.hintsVisible) this.hideHints(); + }); + + this.updateHintsContent(); + } + + /** + * Update hints panel content + */ + updateHintsContent() { + const content = document.getElementById('shortcuts-content'); + if (!content) return; + + // Group shortcuts by category + const categories = {}; + this.shortcuts.forEach(shortcut => { + if (!categories[shortcut.category]) { + categories[shortcut.category] = []; + } + categories[shortcut.category].push(shortcut); + }); + + let html = ''; + Object.keys(categories).sort().forEach(category => { + html += `
    +

    ${category}

    +
    `; + + categories[category].forEach(shortcut => { + const keys = shortcut.key.split('+').map(k => + `${k}` + ).join(' + '); + + html += `
    + ${shortcut.description} + ${keys} +
    `; + }); + + html += `
    `; + }); + + content.innerHTML = html; + } + + /** + * Show hints panel + */ + showHints() { + const panel = document.getElementById('keyboard-hints-panel'); + if (panel) { + panel.style.display = 'block'; + this.hintsVisible = true; + } + } + + /** + * Hide hints panel + */ + hideHints() { + const panel = document.getElementById('keyboard-hints-panel'); + if (panel) { + panel.style.display = 'none'; + this.hintsVisible = false; + } + } + + /** + * Toggle hints panel + */ + toggleHints() { + if (this.hintsVisible) { + this.hideHints(); + } else { + this.showHints(); + } + } + + /** + * Add ARIA labels to elements + */ + addAriaLabels() { + // Add labels to buttons + document.querySelectorAll('button:not([aria-label])').forEach(btn => { + const text = btn.textContent.trim() || btn.title || 'Button'; + btn.setAttribute('aria-label', text); + }); + + // Add labels to inputs + document.querySelectorAll('input:not([aria-label])').forEach(input => { + const label = input.previousElementSibling; + if (label && label.tagName === 'LABEL') { + input.setAttribute('aria-label', label.textContent.trim()); + } else { + input.setAttribute('aria-label', input.placeholder || input.name || 'Input field'); + } + }); + + // Add role to modals + document.querySelectorAll('.modal, .modal-overlay').forEach(modal => { + modal.setAttribute('role', 'dialog'); + modal.setAttribute('aria-modal', 'true'); + }); + } + + /** + * Destroy navigation manager + */ + destroy() { + document.removeEventListener('keydown', this.handleKeyDown.bind(this)); + const panel = document.getElementById('keyboard-hints-panel'); + if (panel) panel.remove(); + } +} + +// Auto-initialize if window exists +if (typeof window !== 'undefined') { + window.KeyboardNavigationManager = KeyboardNavigationManager; + + // Auto-create instance + window.addEventListener('DOMContentLoaded', () => { + if (!window.keyboardNav) { + window.keyboardNav = new KeyboardNavigationManager(); + + // Add ARIA labels after a short delay to let page render + setTimeout(() => { + window.keyboardNav.addAriaLabels(); + }, 500); + } + }); +} diff --git a/docs/dashboard/live-progress.js b/docs/dashboard/live-progress.js index 89cc6c362..fa8b74494 100644 --- a/docs/dashboard/live-progress.js +++ b/docs/dashboard/live-progress.js @@ -1,457 +1,457 @@ -/** - * Live Training Progress Tracker - * Real-time monitoring with streaming updates, live charts, and job controls - */ - -class LiveProgressTracker { - constructor(options = {}) { - this.pollingInterval = options.pollingInterval || 2000; // 2 seconds - this.chartUpdateInterval = options.chartUpdateInterval || 5000; // 5 seconds - this.activeJobId = null; - this.pollTimer = null; - this.chartTimer = null; - this.liveChart = null; - this.startTime = null; - this.currentEpoch = 0; - this.totalEpochs = 0; - this.callbacks = { - onUpdate: options.onUpdate || (() => {}), - onComplete: options.onComplete || (() => {}), - onError: options.onError || (() => {}) - }; - } - - /** - * Start tracking a training job - */ - startTracking(jobId, totalEpochs) { - this.activeJobId = jobId; - this.totalEpochs = totalEpochs; - this.startTime = Date.now(); - this.currentEpoch = 0; - - // Initialize UI - this.showProgressUI(); - this.initializeLiveChart(); - - // Start polling - this.pollTimer = setInterval(() => this.pollProgress(), this.pollingInterval); - this.chartTimer = setInterval(() => this.updateLiveChart(), this.chartUpdateInterval); - - console.log(`[LiveProgress] Started tracking job: ${jobId}`); - } - - /** - * Stop tracking - */ - stopTracking() { - if (this.pollTimer) clearInterval(this.pollTimer); - if (this.chartTimer) clearInterval(this.chartTimer); - this.pollTimer = null; - this.chartTimer = null; - this.activeJobId = null; - console.log('[LiveProgress] Stopped tracking'); - } - - /** - * Poll for progress updates - */ - async pollProgress() { - if (!this.activeJobId) return; - - try { - const response = await fetch(`/api/job-progress/${this.activeJobId}`); - if (!response.ok) { - // Fallback to status endpoint - const statusResponse = await fetch('/status'); - const statusData = await statusResponse.json(); - this.handleStatusUpdate(statusData); - return; - } - - const data = await response.json(); - this.handleProgressUpdate(data); - } catch (err) { - console.error('[LiveProgress] Poll error:', err); - this.callbacks.onError(err); - } - } - - /** - * Handle progress update from server - */ - handleProgressUpdate(data) { - if (data.status === 'completed') { - this.stopTracking(); - this.callbacks.onComplete(data); - this.showCompletionMessage(data); - return; - } - - if (data.status === 'failed') { - this.stopTracking(); - this.callbacks.onError(new Error(data.error || 'Training failed')); - this.showErrorMessage(data); - return; - } - - // Update UI elements - this.updateProgressBars(data); - this.updateMetrics(data); - this.updateETA(data); - - this.callbacks.onUpdate(data); - } - - /** - * Handle status update (fallback) - */ - handleStatusUpdate(statusData) { - if (!statusData.jobs) return; - - const job = statusData.jobs.find(j => j.id === this.activeJobId || j.name === this.activeJobId); - if (!job) return; - - const progress = { - current_epoch: job.current_epoch || 0, - total_epochs: job.total_epochs || this.totalEpochs, - current_loss: job.current_loss || job.post_loss || 0, - train_loss: job.train_loss || [], - eval_loss: job.eval_loss || [], - status: job.status || 'running', - progress_percent: job.progress_percent || 0 - }; - - this.handleProgressUpdate(progress); - } - - /** - * Update progress bars - */ - updateProgressBars(data) { - const epochBar = document.getElementById('liveEpochProgress'); - const overallBar = document.getElementById('liveOverallProgress'); - - if (epochBar) { - const epochPercent = ((data.current_step || 0) / (data.total_steps_per_epoch || 100)) * 100; - epochBar.style.width = `${Math.min(epochPercent, 100)}%`; - epochBar.textContent = `Epoch ${data.current_epoch || 0}/${data.total_epochs || this.totalEpochs}`; - } - - if (overallBar) { - const overallPercent = data.progress_percent || 0; - overallBar.style.width = `${Math.min(overallPercent, 100)}%`; - overallBar.textContent = `${Math.round(overallPercent)}%`; - } - - this.currentEpoch = data.current_epoch || 0; - } - - /** - * Update metrics display - */ - updateMetrics(data) { - const lossEl = document.getElementById('liveCurrentLoss'); - const lrEl = document.getElementById('liveLearningRate'); - const stepsEl = document.getElementById('liveStepsPerSec'); - - if (lossEl && data.current_loss !== undefined) { - lossEl.textContent = data.current_loss.toFixed(4); - lossEl.className = 'metric-value ' + this.getLossClass(data.current_loss); - } - - if (lrEl && data.learning_rate !== undefined) { - lrEl.textContent = data.learning_rate.toExponential(2); - } - - if (stepsEl && data.steps_per_sec !== undefined) { - stepsEl.textContent = data.steps_per_sec.toFixed(2); - } - } - - /** - * Calculate and update ETA - */ - updateETA(data) { - const etaEl = document.getElementById('liveETA'); - if (!etaEl) return; - - const elapsed = (Date.now() - this.startTime) / 1000; // seconds - const progress = data.progress_percent || 0; - - if (progress > 0 && progress < 100) { - const totalEstimated = elapsed / (progress / 100); - const remaining = totalEstimated - elapsed; - - etaEl.textContent = this.formatTime(remaining); - } else { - etaEl.textContent = 'Calculating...'; - } - } - - /** - * Initialize live chart - */ - initializeLiveChart() { - const canvas = document.getElementById('liveProgressChart'); - if (!canvas) return; - - const ctx = canvas.getContext('2d'); - this.liveChart = new Chart(ctx, { - type: 'line', - data: { - labels: [], - datasets: [ - { - label: 'Training Loss', - data: [], - borderColor: '#667eea', - backgroundColor: 'rgba(102, 126, 234, 0.1)', - tension: 0.4, - pointRadius: 3 - }, - { - label: 'Validation Loss', - data: [], - borderColor: '#f093fb', - backgroundColor: 'rgba(240, 147, 251, 0.1)', - tension: 0.4, - pointRadius: 3 - } - ] - }, - options: { - responsive: true, - maintainAspectRatio: false, - animation: { duration: 500 }, - scales: { - y: { - beginAtZero: false, - title: { display: true, text: 'Loss' } - }, - x: { - title: { display: true, text: 'Step' } - } - }, - plugins: { - legend: { display: true, position: 'top' }, - tooltip: { mode: 'index', intersect: false } - } - } - }); - } - - /** - * Update live chart with new data - */ - async updateLiveChart() { - if (!this.liveChart || !this.activeJobId) return; - - try { - const response = await fetch(`/api/job-metrics/${this.activeJobId}`); - if (!response.ok) return; - - const data = await response.json(); - - // Update chart data - this.liveChart.data.labels = data.steps || []; - this.liveChart.data.datasets[0].data = data.train_loss || []; - this.liveChart.data.datasets[1].data = data.eval_loss || []; - - this.liveChart.update('none'); // Update without animation for smoothness - } catch (err) { - console.error('[LiveProgress] Chart update error:', err); - } - } - - /** - * Show progress UI - */ - showProgressUI() { - const container = document.getElementById('liveProgressContainer'); - if (!container) return; - - container.innerHTML = ` -
    -
    -

    🎯 Live Training Progress

    -
    - - -
    -
    - -
    - -
    - -
    -
    Epoch 0/0
    -
    -
    - -
    - -
    -
    0%
    -
    -
    - - -
    -
    -
    Current Loss
    -
    0.0000
    -
    -
    -
    Learning Rate
    -
    0.0e+0
    -
    -
    -
    Steps/Sec
    -
    0.00
    -
    -
    -
    ETA
    -
    Calculating...
    -
    -
    - - -
    - -
    -
    -
    - `; - - container.style.display = 'block'; - } - - /** - * Hide progress UI - */ - hideProgressUI() { - const container = document.getElementById('liveProgressContainer'); - if (container) container.style.display = 'none'; - } - - /** - * Show completion message - */ - showCompletionMessage(data) { - const container = document.getElementById('liveProgressContainer'); - if (!container) return; - - const finalLoss = data.final_loss || data.current_loss || 0; - const duration = ((Date.now() - this.startTime) / 1000 / 60).toFixed(1); - - container.innerHTML = ` -
    -

    ✅ Training Complete!

    -

    Final Loss: ${finalLoss.toFixed(4)}

    -

    Duration: ${duration} minutes

    -

    Epochs: ${this.currentEpoch}/${this.totalEpochs}

    - -
    - `; - } - - /** - * Show error message - */ - showErrorMessage(data) { - const container = document.getElementById('liveProgressContainer'); - if (!container) return; - - container.innerHTML = ` -
    -

    ❌ Training Failed

    -

    ${data.error || 'Unknown error occurred'}

    - -
    - `; - } - - /** - * Job control: Pause training - */ - async pauseTraining() { - if (!this.activeJobId) return; - - try { - const response = await fetch(`/api/job-control/${this.activeJobId}/pause`, { - method: 'POST' - }); - - if (response.ok) { - if (typeof showToast === 'function') showToast('Training paused ✓', 'ok', 3000); - } - } catch (err) { - console.error('[LiveProgress] Pause error:', err); - if (typeof showToast === 'function') showToast('Failed to pause training', 'warn', 4000); - } - } - - /** - * Job control: Stop training - */ - async stopTraining() { - if (!this.activeJobId) return; - - - try { - const response = await fetch(`/api/job-control/${this.activeJobId}/stop`, { - method: 'POST' - }); - - if (response.ok) { - this.stopTracking(); - if (typeof showToast === 'function') showToast('Training stopped', 'ok', 3000); - this.hideProgressUI(); - } - } catch (err) { - console.error('[LiveProgress] Stop error:', err); - if (typeof showToast === 'function') showToast('Failed to stop training', 'warn', 4000); - } - } - - /** - * Helper: Get loss color class - */ - getLossClass(loss) { - if (loss < 0.5) return 'metric-good'; - if (loss < 1.0) return 'metric-warning'; - return 'metric-bad'; - } - - /** - * Helper: Format time (seconds to human readable) - */ - formatTime(seconds) { - if (seconds < 60) return `${Math.round(seconds)}s`; - if (seconds < 3600) return `${Math.round(seconds / 60)}m`; - const hours = Math.floor(seconds / 3600); - const mins = Math.round((seconds % 3600) / 60); - return `${hours}h ${mins}m`; - } - - /** - * Get tracking status - */ - isTracking() { - return this.activeJobId !== null; - } - - /** - * Get current job ID - */ - getActiveJobId() { - return this.activeJobId; - } -} - -// Export for use in dashboard -if (typeof window !== 'undefined') { - window.LiveProgressTracker = LiveProgressTracker; -} +/** + * Live Training Progress Tracker + * Real-time monitoring with streaming updates, live charts, and job controls + */ + +class LiveProgressTracker { + constructor(options = {}) { + this.pollingInterval = options.pollingInterval || 2000; // 2 seconds + this.chartUpdateInterval = options.chartUpdateInterval || 5000; // 5 seconds + this.activeJobId = null; + this.pollTimer = null; + this.chartTimer = null; + this.liveChart = null; + this.startTime = null; + this.currentEpoch = 0; + this.totalEpochs = 0; + this.callbacks = { + onUpdate: options.onUpdate || (() => {}), + onComplete: options.onComplete || (() => {}), + onError: options.onError || (() => {}) + }; + } + + /** + * Start tracking a training job + */ + startTracking(jobId, totalEpochs) { + this.activeJobId = jobId; + this.totalEpochs = totalEpochs; + this.startTime = Date.now(); + this.currentEpoch = 0; + + // Initialize UI + this.showProgressUI(); + this.initializeLiveChart(); + + // Start polling + this.pollTimer = setInterval(() => this.pollProgress(), this.pollingInterval); + this.chartTimer = setInterval(() => this.updateLiveChart(), this.chartUpdateInterval); + + console.log(`[LiveProgress] Started tracking job: ${jobId}`); + } + + /** + * Stop tracking + */ + stopTracking() { + if (this.pollTimer) clearInterval(this.pollTimer); + if (this.chartTimer) clearInterval(this.chartTimer); + this.pollTimer = null; + this.chartTimer = null; + this.activeJobId = null; + console.log('[LiveProgress] Stopped tracking'); + } + + /** + * Poll for progress updates + */ + async pollProgress() { + if (!this.activeJobId) return; + + try { + const response = await fetch(`/api/job-progress/${this.activeJobId}`); + if (!response.ok) { + // Fallback to status endpoint + const statusResponse = await fetch('/status'); + const statusData = await statusResponse.json(); + this.handleStatusUpdate(statusData); + return; + } + + const data = await response.json(); + this.handleProgressUpdate(data); + } catch (err) { + console.error('[LiveProgress] Poll error:', err); + this.callbacks.onError(err); + } + } + + /** + * Handle progress update from server + */ + handleProgressUpdate(data) { + if (data.status === 'completed') { + this.stopTracking(); + this.callbacks.onComplete(data); + this.showCompletionMessage(data); + return; + } + + if (data.status === 'failed') { + this.stopTracking(); + this.callbacks.onError(new Error(data.error || 'Training failed')); + this.showErrorMessage(data); + return; + } + + // Update UI elements + this.updateProgressBars(data); + this.updateMetrics(data); + this.updateETA(data); + + this.callbacks.onUpdate(data); + } + + /** + * Handle status update (fallback) + */ + handleStatusUpdate(statusData) { + if (!statusData.jobs) return; + + const job = statusData.jobs.find(j => j.id === this.activeJobId || j.name === this.activeJobId); + if (!job) return; + + const progress = { + current_epoch: job.current_epoch || 0, + total_epochs: job.total_epochs || this.totalEpochs, + current_loss: job.current_loss || job.post_loss || 0, + train_loss: job.train_loss || [], + eval_loss: job.eval_loss || [], + status: job.status || 'running', + progress_percent: job.progress_percent || 0 + }; + + this.handleProgressUpdate(progress); + } + + /** + * Update progress bars + */ + updateProgressBars(data) { + const epochBar = document.getElementById('liveEpochProgress'); + const overallBar = document.getElementById('liveOverallProgress'); + + if (epochBar) { + const epochPercent = ((data.current_step || 0) / (data.total_steps_per_epoch || 100)) * 100; + epochBar.style.width = `${Math.min(epochPercent, 100)}%`; + epochBar.textContent = `Epoch ${data.current_epoch || 0}/${data.total_epochs || this.totalEpochs}`; + } + + if (overallBar) { + const overallPercent = data.progress_percent || 0; + overallBar.style.width = `${Math.min(overallPercent, 100)}%`; + overallBar.textContent = `${Math.round(overallPercent)}%`; + } + + this.currentEpoch = data.current_epoch || 0; + } + + /** + * Update metrics display + */ + updateMetrics(data) { + const lossEl = document.getElementById('liveCurrentLoss'); + const lrEl = document.getElementById('liveLearningRate'); + const stepsEl = document.getElementById('liveStepsPerSec'); + + if (lossEl && data.current_loss !== undefined) { + lossEl.textContent = data.current_loss.toFixed(4); + lossEl.className = 'metric-value ' + this.getLossClass(data.current_loss); + } + + if (lrEl && data.learning_rate !== undefined) { + lrEl.textContent = data.learning_rate.toExponential(2); + } + + if (stepsEl && data.steps_per_sec !== undefined) { + stepsEl.textContent = data.steps_per_sec.toFixed(2); + } + } + + /** + * Calculate and update ETA + */ + updateETA(data) { + const etaEl = document.getElementById('liveETA'); + if (!etaEl) return; + + const elapsed = (Date.now() - this.startTime) / 1000; // seconds + const progress = data.progress_percent || 0; + + if (progress > 0 && progress < 100) { + const totalEstimated = elapsed / (progress / 100); + const remaining = totalEstimated - elapsed; + + etaEl.textContent = this.formatTime(remaining); + } else { + etaEl.textContent = 'Calculating...'; + } + } + + /** + * Initialize live chart + */ + initializeLiveChart() { + const canvas = document.getElementById('liveProgressChart'); + if (!canvas) return; + + const ctx = canvas.getContext('2d'); + this.liveChart = new Chart(ctx, { + type: 'line', + data: { + labels: [], + datasets: [ + { + label: 'Training Loss', + data: [], + borderColor: '#667eea', + backgroundColor: 'rgba(102, 126, 234, 0.1)', + tension: 0.4, + pointRadius: 3 + }, + { + label: 'Validation Loss', + data: [], + borderColor: '#f093fb', + backgroundColor: 'rgba(240, 147, 251, 0.1)', + tension: 0.4, + pointRadius: 3 + } + ] + }, + options: { + responsive: true, + maintainAspectRatio: false, + animation: { duration: 500 }, + scales: { + y: { + beginAtZero: false, + title: { display: true, text: 'Loss' } + }, + x: { + title: { display: true, text: 'Step' } + } + }, + plugins: { + legend: { display: true, position: 'top' }, + tooltip: { mode: 'index', intersect: false } + } + } + }); + } + + /** + * Update live chart with new data + */ + async updateLiveChart() { + if (!this.liveChart || !this.activeJobId) return; + + try { + const response = await fetch(`/api/job-metrics/${this.activeJobId}`); + if (!response.ok) return; + + const data = await response.json(); + + // Update chart data + this.liveChart.data.labels = data.steps || []; + this.liveChart.data.datasets[0].data = data.train_loss || []; + this.liveChart.data.datasets[1].data = data.eval_loss || []; + + this.liveChart.update('none'); // Update without animation for smoothness + } catch (err) { + console.error('[LiveProgress] Chart update error:', err); + } + } + + /** + * Show progress UI + */ + showProgressUI() { + const container = document.getElementById('liveProgressContainer'); + if (!container) return; + + container.innerHTML = ` +
    +
    +

    🎯 Live Training Progress

    +
    + + +
    +
    + +
    + +
    + +
    +
    Epoch 0/0
    +
    +
    + +
    + +
    +
    0%
    +
    +
    + + +
    +
    +
    Current Loss
    +
    0.0000
    +
    +
    +
    Learning Rate
    +
    0.0e+0
    +
    +
    +
    Steps/Sec
    +
    0.00
    +
    +
    +
    ETA
    +
    Calculating...
    +
    +
    + + +
    + +
    +
    +
    + `; + + container.style.display = 'block'; + } + + /** + * Hide progress UI + */ + hideProgressUI() { + const container = document.getElementById('liveProgressContainer'); + if (container) container.style.display = 'none'; + } + + /** + * Show completion message + */ + showCompletionMessage(data) { + const container = document.getElementById('liveProgressContainer'); + if (!container) return; + + const finalLoss = data.final_loss || data.current_loss || 0; + const duration = ((Date.now() - this.startTime) / 1000 / 60).toFixed(1); + + container.innerHTML = ` +
    +

    ✅ Training Complete!

    +

    Final Loss: ${finalLoss.toFixed(4)}

    +

    Duration: ${duration} minutes

    +

    Epochs: ${this.currentEpoch}/${this.totalEpochs}

    + +
    + `; + } + + /** + * Show error message + */ + showErrorMessage(data) { + const container = document.getElementById('liveProgressContainer'); + if (!container) return; + + container.innerHTML = ` +
    +

    ❌ Training Failed

    +

    ${data.error || 'Unknown error occurred'}

    + +
    + `; + } + + /** + * Job control: Pause training + */ + async pauseTraining() { + if (!this.activeJobId) return; + + try { + const response = await fetch(`/api/job-control/${this.activeJobId}/pause`, { + method: 'POST' + }); + + if (response.ok) { + if (typeof showToast === 'function') showToast('Training paused ✓', 'ok', 3000); + } + } catch (err) { + console.error('[LiveProgress] Pause error:', err); + if (typeof showToast === 'function') showToast('Failed to pause training', 'warn', 4000); + } + } + + /** + * Job control: Stop training + */ + async stopTraining() { + if (!this.activeJobId) return; + + + try { + const response = await fetch(`/api/job-control/${this.activeJobId}/stop`, { + method: 'POST' + }); + + if (response.ok) { + this.stopTracking(); + if (typeof showToast === 'function') showToast('Training stopped', 'ok', 3000); + this.hideProgressUI(); + } + } catch (err) { + console.error('[LiveProgress] Stop error:', err); + if (typeof showToast === 'function') showToast('Failed to stop training', 'warn', 4000); + } + } + + /** + * Helper: Get loss color class + */ + getLossClass(loss) { + if (loss < 0.5) return 'metric-good'; + if (loss < 1.0) return 'metric-warning'; + return 'metric-bad'; + } + + /** + * Helper: Format time (seconds to human readable) + */ + formatTime(seconds) { + if (seconds < 60) return `${Math.round(seconds)}s`; + if (seconds < 3600) return `${Math.round(seconds / 60)}m`; + const hours = Math.floor(seconds / 3600); + const mins = Math.round((seconds % 3600) / 60); + return `${hours}h ${mins}m`; + } + + /** + * Get tracking status + */ + isTracking() { + return this.activeJobId !== null; + } + + /** + * Get current job ID + */ + getActiveJobId() { + return this.activeJobId; + } +} + +// Export for use in dashboard +if (typeof window !== 'undefined') { + window.LiveProgressTracker = LiveProgressTracker; +} diff --git a/docs/dashboard/model-comparator.js b/docs/dashboard/model-comparator.js index 388f17e7f..3da232393 100644 --- a/docs/dashboard/model-comparator.js +++ b/docs/dashboard/model-comparator.js @@ -1,381 +1,381 @@ -/** - * Model Comparison & Benchmarking System - * Side-by-side comparison, performance benchmarking, and leaderboards - */ - -class ModelComparator { - constructor() { - this.selectedModels = []; - this.benchmarkResults = new Map(); - this.comparisonChart = null; - } - - /** - * Add model to comparison - */ - addModel(modelId) { - if (this.selectedModels.includes(modelId)) return; - if (this.selectedModels.length >= 4) { - if (typeof showToast === 'function') showToast('Maximum 4 models can be compared at once', 'warn', 4000); - return; - } - - this.selectedModels.push(modelId); - this.updateComparisonView(); - } - - /** - * Remove model from comparison - */ - removeModel(modelId) { - this.selectedModels = this.selectedModels.filter(id => id !== modelId); - this.updateComparisonView(); - } - - /** - * Clear all selections - */ - clearSelection() { - this.selectedModels = []; - this.updateComparisonView(); - } - - /** - * Update comparison view - */ - async updateComparisonView() { - const container = document.getElementById('modelComparisonContainer'); - if (!container) return; - - if (this.selectedModels.length === 0) { - container.innerHTML = '

    Select models to compare

    '; - return; - } - - // Fetch model details - const models = await this.fetchModelDetails(this.selectedModels); - - // Render comparison table - container.innerHTML = this.renderComparisonTable(models); - - // Update comparison chart - this.updateComparisonChart(models); - } - - /** - * Fetch model details - */ - async fetchModelDetails(modelIds) { - try { - const response = await fetch('/status'); - const data = await response.json(); - - if (!data.jobs) return []; - - return modelIds.map(id => { - const job = data.jobs.find(j => j.id === id || j.name === id); - return job || { id, name: 'Unknown', error: 'Not found' }; - }); - } catch (err) { - console.error('[ModelComparator] Fetch error:', err); - return []; - } - } - - /** - * Render comparison table - */ - renderComparisonTable(models) { - const rows = [ - { label: 'Model Name', key: 'name' }, - { label: 'Final Loss', key: 'post_loss', format: (v) => v?.toFixed(4) || 'N/A' }, - { label: 'Initial Loss', key: 'pre_loss', format: (v) => v?.toFixed(4) || 'N/A' }, - { label: 'Improvement', key: null, format: (_, m) => { - if (m.pre_loss && m.post_loss) { - const improvement = ((m.pre_loss - m.post_loss) / m.pre_loss * 100).toFixed(2); - return `${improvement}%`; - } - return 'N/A'; - }}, - { label: 'Duration', key: 'duration', format: (v) => v ? `${Math.round(v/60)}m` : 'N/A' }, - { label: 'Epochs', key: 'epochs' }, - { label: 'Batch Size', key: 'batch_size' }, - { label: 'Learning Rate', key: 'learning_rate', format: (v) => v?.toExponential(2) || 'N/A' }, - { label: 'LoRA Rank', key: 'lora_rank' }, - { label: 'LoRA Alpha', key: 'lora_alpha' }, - { label: 'Dataset', key: 'dataset' }, - { label: 'Status', key: 'status', format: (v) => `${v || 'unknown'}` } - ]; - - let html = ''; - models.forEach(m => { - html += ``; - }); - html += ''; - - rows.forEach(row => { - html += ``; - models.forEach(model => { - const value = row.key ? model[row.key] : null; - const formatted = row.format ? row.format(value, model) : (value || 'N/A'); - html += ``; - }); - html += ''; - }); - - html += '
    Metric${m.name || m.id}
    ${row.label}${formatted}
    '; - - html += '
    '; - html += ''; - html += ''; - html += ''; - html += '
    '; - - return html; - } - - /** - * Update comparison chart - */ - updateComparisonChart(models) { - const canvas = document.getElementById('comparisonChart'); - if (!canvas) return; - - if (this.comparisonChart) { - this.comparisonChart.destroy(); - } - - const ctx = canvas.getContext('2d'); - this.comparisonChart = new Chart(ctx, { - type: 'radar', - data: { - labels: ['Loss (inv)', 'Speed', 'Improvement', 'Efficiency', 'Stability'], - datasets: models.map((model, idx) => ({ - label: model.name || model.id, - data: this.calculateModelScores(model), - borderColor: this.getChartColor(idx), - backgroundColor: this.getChartColor(idx, 0.2), - pointBackgroundColor: this.getChartColor(idx) - })) - }, - options: { - responsive: true, - maintainAspectRatio: false, - scales: { - r: { - beginAtZero: true, - max: 100 - } - } - } - }); - } - - /** - * Calculate model scores for radar chart - */ - calculateModelScores(model) { - const lossScore = model.post_loss ? Math.min((1 / model.post_loss) * 10, 100) : 0; - const speedScore = model.duration ? Math.min((3600 / model.duration) * 100, 100) : 0; - const improvementScore = model.pre_loss && model.post_loss - ? Math.min(((model.pre_loss - model.post_loss) / model.pre_loss) * 100, 100) - : 0; - const efficiencyScore = model.batch_size && model.duration - ? Math.min((model.batch_size / model.duration) * 1000, 100) - : 0; - const stabilityScore = 75; // Placeholder - could calculate from loss variance - - return [lossScore, speedScore, improvementScore, efficiencyScore, stabilityScore]; - } - - /** - * Get chart color by index - */ - getChartColor(index, alpha = 1) { - const colors = [ - `rgba(102, 126, 234, ${alpha})`, - `rgba(240, 147, 251, ${alpha})`, - `rgba(45, 206, 137, ${alpha})`, - `rgba(255, 159, 64, ${alpha})` - ]; - return colors[index % colors.length]; - } - - /** - * Export comparison to CSV/JSON - */ - async exportComparison() { - const models = await this.fetchModelDetails(this.selectedModels); - - const format = prompt('Export format: json or csv?', 'json'); - if (!format || !['json', 'csv'].includes(format.toLowerCase())) return; - - let content, filename, type; - - if (format === 'json') { - content = JSON.stringify(models, null, 2); - filename = `comparison-${Date.now()}.json`; - type = 'application/json'; - } else { - // CSV format - const headers = ['Name', 'Final Loss', 'Improvement %', 'Duration (min)', 'Epochs', 'Batch Size', 'Learning Rate', 'LoRA Rank']; - const rows = models.map(m => [ - m.name || m.id, - m.post_loss?.toFixed(4) || '', - m.pre_loss && m.post_loss ? ((m.pre_loss - m.post_loss) / m.pre_loss * 100).toFixed(2) : '', - m.duration ? Math.round(m.duration / 60) : '', - m.epochs || '', - m.batch_size || '', - m.learning_rate || '', - m.lora_rank || '' - ]); - - content = [headers, ...rows].map(row => row.join(',')).join('\n'); - filename = `comparison-${Date.now()}.csv`; - type = 'text/csv'; - } - - const blob = new Blob([content], { type }); - const url = URL.createObjectURL(blob); - const a = document.createElement('a'); - a.href = url; - a.download = filename; - a.click(); - URL.revokeObjectURL(url); - } - - /** - * Run performance benchmark on selected models - */ - async benchmarkSelected() { - if (this.selectedModels.length === 0) { - if (typeof showToast === 'function') showToast('Please select models to benchmark', 'warn', 4000); - return; - } - - const container = document.getElementById('benchmarkResults'); - if (container) { - container.innerHTML = '
    Running benchmark... This may take a few minutes.
    '; - } - - try { - const response = await fetch('/api/benchmark', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ model_ids: this.selectedModels }) - }); - - const results = await response.json(); - this.benchmarkResults = new Map(results.map(r => [r.model_id, r])); - this.displayBenchmarkResults(results); - } catch (err) { - console.error('[ModelComparator] Benchmark error:', err); - if (container) { - container.innerHTML = `
    Benchmark failed: ${err.message}
    `; - } - } - } - - /** - * Display benchmark results - */ - displayBenchmarkResults(results) { - const container = document.getElementById('benchmarkResults'); - if (!container) return; - - let html = '

    ⚡ Benchmark Results

    '; - html += ''; - html += ''; - - results.forEach(result => { - const score = this.calculateBenchmarkScore(result); - html += ` - - - - - - `; - }); - - html += '
    ModelInference Time (ms)Memory (MB)Throughput (tok/s)Score
    ${result.model_name}${result.inference_time?.toFixed(2) || 'N/A'}${result.memory_mb?.toFixed(0) || 'N/A'}${result.throughput?.toFixed(2) || 'N/A'}${score}
    '; - container.innerHTML = html; - } - - /** - * Calculate benchmark score - */ - calculateBenchmarkScore(result) { - const speedScore = result.inference_time ? Math.min(100, (100 / result.inference_time) * 100) : 0; - const memoryScore = result.memory_mb ? Math.min(100, (2000 / result.memory_mb) * 100) : 0; - const throughputScore = result.throughput ? Math.min(100, result.throughput) : 0; - - return Math.round((speedScore + memoryScore + throughputScore) / 3); - } - - /** - * Generate leaderboard - */ - async generateLeaderboard() { - try { - const response = await fetch('/status'); - const data = await response.json(); - - if (!data.jobs || data.jobs.length === 0) { - return '

    No jobs available for leaderboard

    '; - } - - // Sort by performance score - const ranked = data.jobs - .filter(j => j.post_loss && j.status === 'completed') - .map(j => ({ - ...j, - score: this.calculateOverallScore(j) - })) - .sort((a, b) => b.score - a.score) - .slice(0, 10); - - let html = ''; - html += ''; - html += ''; - - ranked.forEach((job, idx) => { - const medal = idx === 0 ? '🥇' : idx === 1 ? '🥈' : idx === 2 ? '🥉' : ''; - html += ` - - - - - - `; - }); - - html += '
    RankModelLossDurationScore
    ${medal} #${idx + 1}${job.name || 'Unknown'}${job.post_loss.toFixed(4)}${Math.round(job.duration / 60)}m${job.score}
    '; - return html; - } catch (err) { - console.error('[ModelComparator] Leaderboard error:', err); - return '

    Failed to generate leaderboard

    '; - } - } - - /** - * Calculate overall score - */ - calculateOverallScore(job) { - const lossScore = job.post_loss ? (1 / job.post_loss) * 20 : 0; - const improvementScore = job.pre_loss && job.post_loss - ? ((job.pre_loss - job.post_loss) / job.pre_loss) * 30 - : 0; - const efficiencyScore = job.duration && job.epochs - ? Math.min((job.epochs / (job.duration / 3600)) * 25, 25) - : 0; - const qualityScore = 25; // Placeholder - - return Math.round(lossScore + improvementScore + efficiencyScore + qualityScore); - } -} - -// Export for use in dashboard -if (typeof window !== 'undefined') { - window.ModelComparator = ModelComparator; -} +/** + * Model Comparison & Benchmarking System + * Side-by-side comparison, performance benchmarking, and leaderboards + */ + +class ModelComparator { + constructor() { + this.selectedModels = []; + this.benchmarkResults = new Map(); + this.comparisonChart = null; + } + + /** + * Add model to comparison + */ + addModel(modelId) { + if (this.selectedModels.includes(modelId)) return; + if (this.selectedModels.length >= 4) { + if (typeof showToast === 'function') showToast('Maximum 4 models can be compared at once', 'warn', 4000); + return; + } + + this.selectedModels.push(modelId); + this.updateComparisonView(); + } + + /** + * Remove model from comparison + */ + removeModel(modelId) { + this.selectedModels = this.selectedModels.filter(id => id !== modelId); + this.updateComparisonView(); + } + + /** + * Clear all selections + */ + clearSelection() { + this.selectedModels = []; + this.updateComparisonView(); + } + + /** + * Update comparison view + */ + async updateComparisonView() { + const container = document.getElementById('modelComparisonContainer'); + if (!container) return; + + if (this.selectedModels.length === 0) { + container.innerHTML = '

    Select models to compare

    '; + return; + } + + // Fetch model details + const models = await this.fetchModelDetails(this.selectedModels); + + // Render comparison table + container.innerHTML = this.renderComparisonTable(models); + + // Update comparison chart + this.updateComparisonChart(models); + } + + /** + * Fetch model details + */ + async fetchModelDetails(modelIds) { + try { + const response = await fetch('/status'); + const data = await response.json(); + + if (!data.jobs) return []; + + return modelIds.map(id => { + const job = data.jobs.find(j => j.id === id || j.name === id); + return job || { id, name: 'Unknown', error: 'Not found' }; + }); + } catch (err) { + console.error('[ModelComparator] Fetch error:', err); + return []; + } + } + + /** + * Render comparison table + */ + renderComparisonTable(models) { + const rows = [ + { label: 'Model Name', key: 'name' }, + { label: 'Final Loss', key: 'post_loss', format: (v) => v?.toFixed(4) || 'N/A' }, + { label: 'Initial Loss', key: 'pre_loss', format: (v) => v?.toFixed(4) || 'N/A' }, + { label: 'Improvement', key: null, format: (_, m) => { + if (m.pre_loss && m.post_loss) { + const improvement = ((m.pre_loss - m.post_loss) / m.pre_loss * 100).toFixed(2); + return `${improvement}%`; + } + return 'N/A'; + }}, + { label: 'Duration', key: 'duration', format: (v) => v ? `${Math.round(v/60)}m` : 'N/A' }, + { label: 'Epochs', key: 'epochs' }, + { label: 'Batch Size', key: 'batch_size' }, + { label: 'Learning Rate', key: 'learning_rate', format: (v) => v?.toExponential(2) || 'N/A' }, + { label: 'LoRA Rank', key: 'lora_rank' }, + { label: 'LoRA Alpha', key: 'lora_alpha' }, + { label: 'Dataset', key: 'dataset' }, + { label: 'Status', key: 'status', format: (v) => `${v || 'unknown'}` } + ]; + + let html = ''; + models.forEach(m => { + html += ``; + }); + html += ''; + + rows.forEach(row => { + html += ``; + models.forEach(model => { + const value = row.key ? model[row.key] : null; + const formatted = row.format ? row.format(value, model) : (value || 'N/A'); + html += ``; + }); + html += ''; + }); + + html += '
    Metric${m.name || m.id}
    ${row.label}${formatted}
    '; + + html += '
    '; + html += ''; + html += ''; + html += ''; + html += '
    '; + + return html; + } + + /** + * Update comparison chart + */ + updateComparisonChart(models) { + const canvas = document.getElementById('comparisonChart'); + if (!canvas) return; + + if (this.comparisonChart) { + this.comparisonChart.destroy(); + } + + const ctx = canvas.getContext('2d'); + this.comparisonChart = new Chart(ctx, { + type: 'radar', + data: { + labels: ['Loss (inv)', 'Speed', 'Improvement', 'Efficiency', 'Stability'], + datasets: models.map((model, idx) => ({ + label: model.name || model.id, + data: this.calculateModelScores(model), + borderColor: this.getChartColor(idx), + backgroundColor: this.getChartColor(idx, 0.2), + pointBackgroundColor: this.getChartColor(idx) + })) + }, + options: { + responsive: true, + maintainAspectRatio: false, + scales: { + r: { + beginAtZero: true, + max: 100 + } + } + } + }); + } + + /** + * Calculate model scores for radar chart + */ + calculateModelScores(model) { + const lossScore = model.post_loss ? Math.min((1 / model.post_loss) * 10, 100) : 0; + const speedScore = model.duration ? Math.min((3600 / model.duration) * 100, 100) : 0; + const improvementScore = model.pre_loss && model.post_loss + ? Math.min(((model.pre_loss - model.post_loss) / model.pre_loss) * 100, 100) + : 0; + const efficiencyScore = model.batch_size && model.duration + ? Math.min((model.batch_size / model.duration) * 1000, 100) + : 0; + const stabilityScore = 75; // Placeholder - could calculate from loss variance + + return [lossScore, speedScore, improvementScore, efficiencyScore, stabilityScore]; + } + + /** + * Get chart color by index + */ + getChartColor(index, alpha = 1) { + const colors = [ + `rgba(102, 126, 234, ${alpha})`, + `rgba(240, 147, 251, ${alpha})`, + `rgba(45, 206, 137, ${alpha})`, + `rgba(255, 159, 64, ${alpha})` + ]; + return colors[index % colors.length]; + } + + /** + * Export comparison to CSV/JSON + */ + async exportComparison() { + const models = await this.fetchModelDetails(this.selectedModels); + + const format = prompt('Export format: json or csv?', 'json'); + if (!format || !['json', 'csv'].includes(format.toLowerCase())) return; + + let content, filename, type; + + if (format === 'json') { + content = JSON.stringify(models, null, 2); + filename = `comparison-${Date.now()}.json`; + type = 'application/json'; + } else { + // CSV format + const headers = ['Name', 'Final Loss', 'Improvement %', 'Duration (min)', 'Epochs', 'Batch Size', 'Learning Rate', 'LoRA Rank']; + const rows = models.map(m => [ + m.name || m.id, + m.post_loss?.toFixed(4) || '', + m.pre_loss && m.post_loss ? ((m.pre_loss - m.post_loss) / m.pre_loss * 100).toFixed(2) : '', + m.duration ? Math.round(m.duration / 60) : '', + m.epochs || '', + m.batch_size || '', + m.learning_rate || '', + m.lora_rank || '' + ]); + + content = [headers, ...rows].map(row => row.join(',')).join('\n'); + filename = `comparison-${Date.now()}.csv`; + type = 'text/csv'; + } + + const blob = new Blob([content], { type }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = filename; + a.click(); + URL.revokeObjectURL(url); + } + + /** + * Run performance benchmark on selected models + */ + async benchmarkSelected() { + if (this.selectedModels.length === 0) { + if (typeof showToast === 'function') showToast('Please select models to benchmark', 'warn', 4000); + return; + } + + const container = document.getElementById('benchmarkResults'); + if (container) { + container.innerHTML = '
    Running benchmark... This may take a few minutes.
    '; + } + + try { + const response = await fetch('/api/benchmark', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ model_ids: this.selectedModels }) + }); + + const results = await response.json(); + this.benchmarkResults = new Map(results.map(r => [r.model_id, r])); + this.displayBenchmarkResults(results); + } catch (err) { + console.error('[ModelComparator] Benchmark error:', err); + if (container) { + container.innerHTML = `
    Benchmark failed: ${err.message}
    `; + } + } + } + + /** + * Display benchmark results + */ + displayBenchmarkResults(results) { + const container = document.getElementById('benchmarkResults'); + if (!container) return; + + let html = '

    ⚡ Benchmark Results

    '; + html += ''; + html += ''; + + results.forEach(result => { + const score = this.calculateBenchmarkScore(result); + html += ` + + + + + + `; + }); + + html += '
    ModelInference Time (ms)Memory (MB)Throughput (tok/s)Score
    ${result.model_name}${result.inference_time?.toFixed(2) || 'N/A'}${result.memory_mb?.toFixed(0) || 'N/A'}${result.throughput?.toFixed(2) || 'N/A'}${score}
    '; + container.innerHTML = html; + } + + /** + * Calculate benchmark score + */ + calculateBenchmarkScore(result) { + const speedScore = result.inference_time ? Math.min(100, (100 / result.inference_time) * 100) : 0; + const memoryScore = result.memory_mb ? Math.min(100, (2000 / result.memory_mb) * 100) : 0; + const throughputScore = result.throughput ? Math.min(100, result.throughput) : 0; + + return Math.round((speedScore + memoryScore + throughputScore) / 3); + } + + /** + * Generate leaderboard + */ + async generateLeaderboard() { + try { + const response = await fetch('/status'); + const data = await response.json(); + + if (!data.jobs || data.jobs.length === 0) { + return '

    No jobs available for leaderboard

    '; + } + + // Sort by performance score + const ranked = data.jobs + .filter(j => j.post_loss && j.status === 'completed') + .map(j => ({ + ...j, + score: this.calculateOverallScore(j) + })) + .sort((a, b) => b.score - a.score) + .slice(0, 10); + + let html = ''; + html += ''; + html += ''; + + ranked.forEach((job, idx) => { + const medal = idx === 0 ? '🥇' : idx === 1 ? '🥈' : idx === 2 ? '🥉' : ''; + html += ` + + + + + + `; + }); + + html += '
    RankModelLossDurationScore
    ${medal} #${idx + 1}${job.name || 'Unknown'}${job.post_loss.toFixed(4)}${Math.round(job.duration / 60)}m${job.score}
    '; + return html; + } catch (err) { + console.error('[ModelComparator] Leaderboard error:', err); + return '

    Failed to generate leaderboard

    '; + } + } + + /** + * Calculate overall score + */ + calculateOverallScore(job) { + const lossScore = job.post_loss ? (1 / job.post_loss) * 20 : 0; + const improvementScore = job.pre_loss && job.post_loss + ? ((job.pre_loss - job.post_loss) / job.pre_loss) * 30 + : 0; + const efficiencyScore = job.duration && job.epochs + ? Math.min((job.epochs / (job.duration / 3600)) * 25, 25) + : 0; + const qualityScore = 25; // Placeholder + + return Math.round(lossScore + improvementScore + efficiencyScore + qualityScore); + } +} + +// Export for use in dashboard +if (typeof window !== 'undefined') { + window.ModelComparator = ModelComparator; +} diff --git a/docs/dashboard/session-history.js b/docs/dashboard/session-history.js index 244cdb281..8ad661cf1 100644 --- a/docs/dashboard/session-history.js +++ b/docs/dashboard/session-history.js @@ -1,345 +1,345 @@ -/** - * Training Session History Tracker - * Persist training sessions and enable replay/comparison - * - * Features: - * - Session persistence (localStorage/IndexedDB) - * - Session comparison - * - Config replay (load previous settings) - * - Export to CSV/JSON - * - Filtering by date/status/model - */ - -class TrainingSessionHistory { - constructor(options = {}) { - this.config = { - storage: options.storage || 'localStorage', // or 'indexedDB' - maxSessions: options.maxSessions || 100, - autoSave: options.autoSave !== false, - ...options - }; - this.sessions = []; - this.currentSession = null; - this.init(); - } - - async init() { - // Load existing sessions - await this.loadSessions(); - - // Setup auto-save if enabled - if (this.config.autoSave) { - this.setupAutoSave(); - } - } - - /** - * Load sessions from storage - */ - async loadSessions() { - if (this.config.storage === 'localStorage') { - const stored = localStorage.getItem('qai_training_sessions'); - if (stored) { - try { - this.sessions = JSON.parse(stored); - } catch (err) { - console.error('Failed to load sessions:', err); - this.sessions = []; - } - } - } else if (this.config.storage === 'indexedDB') { - // IndexedDB implementation (future enhancement) - console.warn('IndexedDB storage not yet implemented, falling back to localStorage'); - this.config.storage = 'localStorage'; - await this.loadSessions(); - } - } - - /** - * Save sessions to storage - */ - async saveSessions() { - // Keep only max sessions - if (this.sessions.length > this.config.maxSessions) { - this.sessions = this.sessions.slice(-this.config.maxSessions); - } - - if (this.config.storage === 'localStorage') { - try { - localStorage.setItem('qai_training_sessions', JSON.stringify(this.sessions)); - } catch (err) { - console.error('Failed to save sessions:', err); - } - } - } - - /** - * Start a new training session - */ - startSession(config) { - this.currentSession = { - id: `session-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`, - startTime: Date.now(), - endTime: null, - status: 'running', - config: JSON.parse(JSON.stringify(config)), // Deep clone - metrics: { - initialLoss: null, - finalLoss: null, - bestLoss: null, - epochs: [], - avgLossPerEpoch: [] - }, - anomalies: [], - notes: '' - }; - - return this.currentSession.id; - } - - /** - * Update current session metrics - */ - updateSession(updates) { - if (!this.currentSession) return; - - Object.assign(this.currentSession, updates); - - if (this.config.autoSave) { - this.saveSessions(); - } - } - - /** - * End current session - */ - endSession(status = 'completed', finalMetrics = {}) { - if (!this.currentSession) return; - - this.currentSession.endTime = Date.now(); - this.currentSession.status = status; - this.currentSession.duration = this.currentSession.endTime - this.currentSession.startTime; - Object.assign(this.currentSession.metrics, finalMetrics); - - // Add to history - this.sessions.push(this.currentSession); - this.currentSession = null; - - this.saveSessions(); - } - - /** - * Get all sessions - */ - getAllSessions() { - return this.sessions; - } - - /** - * Get session by ID - */ - getSessionById(id) { - return this.sessions.find(s => s.id === id); - } - - /** - * Filter sessions - */ - filterSessions(filters = {}) { - let filtered = [...this.sessions]; - - // Filter by status - if (filters.status) { - filtered = filtered.filter(s => s.status === filters.status); - } - - // Filter by model - if (filters.model) { - filtered = filtered.filter(s => - s.config.model && s.config.model.includes(filters.model) - ); - } - - // Filter by dataset - if (filters.dataset) { - filtered = filtered.filter(s => - s.config.dataset && s.config.dataset.includes(filters.dataset) - ); - } - - // Filter by date range - if (filters.startDate) { - const start = new Date(filters.startDate).getTime(); - filtered = filtered.filter(s => s.startTime >= start); - } - - if (filters.endDate) { - const end = new Date(filters.endDate).getTime(); - filtered = filtered.filter(s => s.startTime <= end); - } - - // Filter by min loss - if (filters.minLoss !== undefined) { - filtered = filtered.filter(s => - s.metrics.finalLoss !== null && s.metrics.finalLoss >= filters.minLoss - ); - } - - // Filter by max loss - if (filters.maxLoss !== undefined) { - filtered = filtered.filter(s => - s.metrics.finalLoss !== null && s.metrics.finalLoss <= filters.maxLoss - ); - } - - return filtered; - } - - /** - * Compare sessions - */ - compareSessions(sessionIds) { - const sessions = sessionIds.map(id => this.getSessionById(id)).filter(Boolean); - - if (sessions.length === 0) return null; - - return { - sessions, - comparison: { - bestLoss: Math.min(...sessions.map(s => s.metrics.finalLoss || Infinity)), - worstLoss: Math.max(...sessions.map(s => s.metrics.finalLoss || 0)), - avgLoss: sessions.reduce((sum, s) => sum + (s.metrics.finalLoss || 0), 0) / sessions.length, - totalDuration: sessions.reduce((sum, s) => sum + (s.duration || 0), 0), - avgDuration: sessions.reduce((sum, s) => sum + (s.duration || 0), 0) / sessions.length, - statusBreakdown: sessions.reduce((acc, s) => { - acc[s.status] = (acc[s.status] || 0) + 1; - return acc; - }, {}) - } - }; - } - - /** - * Replay session config - */ - replaySession(sessionId) { - const session = this.getSessionById(sessionId); - if (!session) return null; - - return JSON.parse(JSON.stringify(session.config)); // Deep clone - } - - /** - * Export sessions to JSON - */ - exportToJSON(sessionIds = null) { - const sessions = sessionIds - ? sessionIds.map(id => this.getSessionById(id)).filter(Boolean) - : this.sessions; - - return JSON.stringify(sessions, null, 2); - } - - /** - * Export sessions to CSV - */ - exportToCSV(sessionIds = null) { - const sessions = sessionIds - ? sessionIds.map(id => this.getSessionById(id)).filter(Boolean) - : this.sessions; - - if (sessions.length === 0) return ''; - - // CSV headers - const headers = [ - 'ID', 'Start Time', 'Duration (min)', 'Status', - 'Model', 'Dataset', 'Epochs', 'Batch Size', 'Learning Rate', - 'LoRA Rank', 'Initial Loss', 'Final Loss', 'Best Loss', - 'Anomalies', 'Notes' - ]; - - // CSV rows - const rows = sessions.map(s => [ - s.id, - new Date(s.startTime).toISOString(), - s.duration ? Math.round(s.duration / 60000) : 'N/A', - s.status, - s.config.model || 'N/A', - s.config.dataset || 'N/A', - s.config.epochs || 'N/A', - s.config.batch_size || s.config.batchSize || 'N/A', - s.config.learning_rate || s.config.learningRate || 'N/A', - s.config.lora_rank || s.config.loraRank || 'N/A', - s.metrics.initialLoss || 'N/A', - s.metrics.finalLoss || 'N/A', - s.metrics.bestLoss || 'N/A', - s.anomalies.length, - s.notes ? `"${s.notes.replace(/"/g, '""')}"` : '' - ]); - - return [headers.join(','), ...rows.map(r => r.join(','))].join('\n'); - } - - /** - * Delete session - */ - deleteSession(sessionId) { - this.sessions = this.sessions.filter(s => s.id !== sessionId); - this.saveSessions(); - } - - /** - * Clear all sessions - */ - clearAllSessions() { - this.sessions = []; - this.saveSessions(); - } - - /** - * Setup auto-save - */ - setupAutoSave() { - // Save every 30 seconds if there's a current session - setInterval(() => { - if (this.currentSession) { - // Find and update in sessions array - const index = this.sessions.findIndex(s => s.id === this.currentSession.id); - if (index >= 0) { - this.sessions[index] = this.currentSession; - } - this.saveSessions(); - } - }, 30000); - } - - /** - * Get statistics - */ - getStatistics() { - const completed = this.sessions.filter(s => s.status === 'completed'); - - return { - total: this.sessions.length, - completed: completed.length, - failed: this.sessions.filter(s => s.status === 'failed').length, - running: this.sessions.filter(s => s.status === 'running').length, - avgDuration: completed.length > 0 - ? completed.reduce((sum, s) => sum + (s.duration || 0), 0) / completed.length - : 0, - avgFinalLoss: completed.length > 0 - ? completed.reduce((sum, s) => sum + (s.metrics.finalLoss || 0), 0) / completed.length - : 0, - bestSession: completed.length > 0 - ? completed.reduce((best, s) => - (s.metrics.finalLoss || Infinity) < (best.metrics.finalLoss || Infinity) ? s : best - ) - : null - }; - } -} - -// Export for use in other modules -if (typeof window !== 'undefined') { - window.TrainingSessionHistory = TrainingSessionHistory; -} +/** + * Training Session History Tracker + * Persist training sessions and enable replay/comparison + * + * Features: + * - Session persistence (localStorage/IndexedDB) + * - Session comparison + * - Config replay (load previous settings) + * - Export to CSV/JSON + * - Filtering by date/status/model + */ + +class TrainingSessionHistory { + constructor(options = {}) { + this.config = { + storage: options.storage || 'localStorage', // or 'indexedDB' + maxSessions: options.maxSessions || 100, + autoSave: options.autoSave !== false, + ...options + }; + this.sessions = []; + this.currentSession = null; + this.init(); + } + + async init() { + // Load existing sessions + await this.loadSessions(); + + // Setup auto-save if enabled + if (this.config.autoSave) { + this.setupAutoSave(); + } + } + + /** + * Load sessions from storage + */ + async loadSessions() { + if (this.config.storage === 'localStorage') { + const stored = localStorage.getItem('qai_training_sessions'); + if (stored) { + try { + this.sessions = JSON.parse(stored); + } catch (err) { + console.error('Failed to load sessions:', err); + this.sessions = []; + } + } + } else if (this.config.storage === 'indexedDB') { + // IndexedDB implementation (future enhancement) + console.warn('IndexedDB storage not yet implemented, falling back to localStorage'); + this.config.storage = 'localStorage'; + await this.loadSessions(); + } + } + + /** + * Save sessions to storage + */ + async saveSessions() { + // Keep only max sessions + if (this.sessions.length > this.config.maxSessions) { + this.sessions = this.sessions.slice(-this.config.maxSessions); + } + + if (this.config.storage === 'localStorage') { + try { + localStorage.setItem('qai_training_sessions', JSON.stringify(this.sessions)); + } catch (err) { + console.error('Failed to save sessions:', err); + } + } + } + + /** + * Start a new training session + */ + startSession(config) { + this.currentSession = { + id: `session-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`, + startTime: Date.now(), + endTime: null, + status: 'running', + config: JSON.parse(JSON.stringify(config)), // Deep clone + metrics: { + initialLoss: null, + finalLoss: null, + bestLoss: null, + epochs: [], + avgLossPerEpoch: [] + }, + anomalies: [], + notes: '' + }; + + return this.currentSession.id; + } + + /** + * Update current session metrics + */ + updateSession(updates) { + if (!this.currentSession) return; + + Object.assign(this.currentSession, updates); + + if (this.config.autoSave) { + this.saveSessions(); + } + } + + /** + * End current session + */ + endSession(status = 'completed', finalMetrics = {}) { + if (!this.currentSession) return; + + this.currentSession.endTime = Date.now(); + this.currentSession.status = status; + this.currentSession.duration = this.currentSession.endTime - this.currentSession.startTime; + Object.assign(this.currentSession.metrics, finalMetrics); + + // Add to history + this.sessions.push(this.currentSession); + this.currentSession = null; + + this.saveSessions(); + } + + /** + * Get all sessions + */ + getAllSessions() { + return this.sessions; + } + + /** + * Get session by ID + */ + getSessionById(id) { + return this.sessions.find(s => s.id === id); + } + + /** + * Filter sessions + */ + filterSessions(filters = {}) { + let filtered = [...this.sessions]; + + // Filter by status + if (filters.status) { + filtered = filtered.filter(s => s.status === filters.status); + } + + // Filter by model + if (filters.model) { + filtered = filtered.filter(s => + s.config.model && s.config.model.includes(filters.model) + ); + } + + // Filter by dataset + if (filters.dataset) { + filtered = filtered.filter(s => + s.config.dataset && s.config.dataset.includes(filters.dataset) + ); + } + + // Filter by date range + if (filters.startDate) { + const start = new Date(filters.startDate).getTime(); + filtered = filtered.filter(s => s.startTime >= start); + } + + if (filters.endDate) { + const end = new Date(filters.endDate).getTime(); + filtered = filtered.filter(s => s.startTime <= end); + } + + // Filter by min loss + if (filters.minLoss !== undefined) { + filtered = filtered.filter(s => + s.metrics.finalLoss !== null && s.metrics.finalLoss >= filters.minLoss + ); + } + + // Filter by max loss + if (filters.maxLoss !== undefined) { + filtered = filtered.filter(s => + s.metrics.finalLoss !== null && s.metrics.finalLoss <= filters.maxLoss + ); + } + + return filtered; + } + + /** + * Compare sessions + */ + compareSessions(sessionIds) { + const sessions = sessionIds.map(id => this.getSessionById(id)).filter(Boolean); + + if (sessions.length === 0) return null; + + return { + sessions, + comparison: { + bestLoss: Math.min(...sessions.map(s => s.metrics.finalLoss || Infinity)), + worstLoss: Math.max(...sessions.map(s => s.metrics.finalLoss || 0)), + avgLoss: sessions.reduce((sum, s) => sum + (s.metrics.finalLoss || 0), 0) / sessions.length, + totalDuration: sessions.reduce((sum, s) => sum + (s.duration || 0), 0), + avgDuration: sessions.reduce((sum, s) => sum + (s.duration || 0), 0) / sessions.length, + statusBreakdown: sessions.reduce((acc, s) => { + acc[s.status] = (acc[s.status] || 0) + 1; + return acc; + }, {}) + } + }; + } + + /** + * Replay session config + */ + replaySession(sessionId) { + const session = this.getSessionById(sessionId); + if (!session) return null; + + return JSON.parse(JSON.stringify(session.config)); // Deep clone + } + + /** + * Export sessions to JSON + */ + exportToJSON(sessionIds = null) { + const sessions = sessionIds + ? sessionIds.map(id => this.getSessionById(id)).filter(Boolean) + : this.sessions; + + return JSON.stringify(sessions, null, 2); + } + + /** + * Export sessions to CSV + */ + exportToCSV(sessionIds = null) { + const sessions = sessionIds + ? sessionIds.map(id => this.getSessionById(id)).filter(Boolean) + : this.sessions; + + if (sessions.length === 0) return ''; + + // CSV headers + const headers = [ + 'ID', 'Start Time', 'Duration (min)', 'Status', + 'Model', 'Dataset', 'Epochs', 'Batch Size', 'Learning Rate', + 'LoRA Rank', 'Initial Loss', 'Final Loss', 'Best Loss', + 'Anomalies', 'Notes' + ]; + + // CSV rows + const rows = sessions.map(s => [ + s.id, + new Date(s.startTime).toISOString(), + s.duration ? Math.round(s.duration / 60000) : 'N/A', + s.status, + s.config.model || 'N/A', + s.config.dataset || 'N/A', + s.config.epochs || 'N/A', + s.config.batch_size || s.config.batchSize || 'N/A', + s.config.learning_rate || s.config.learningRate || 'N/A', + s.config.lora_rank || s.config.loraRank || 'N/A', + s.metrics.initialLoss || 'N/A', + s.metrics.finalLoss || 'N/A', + s.metrics.bestLoss || 'N/A', + s.anomalies.length, + s.notes ? `"${s.notes.replace(/"/g, '""')}"` : '' + ]); + + return [headers.join(','), ...rows.map(r => r.join(','))].join('\n'); + } + + /** + * Delete session + */ + deleteSession(sessionId) { + this.sessions = this.sessions.filter(s => s.id !== sessionId); + this.saveSessions(); + } + + /** + * Clear all sessions + */ + clearAllSessions() { + this.sessions = []; + this.saveSessions(); + } + + /** + * Setup auto-save + */ + setupAutoSave() { + // Save every 30 seconds if there's a current session + setInterval(() => { + if (this.currentSession) { + // Find and update in sessions array + const index = this.sessions.findIndex(s => s.id === this.currentSession.id); + if (index >= 0) { + this.sessions[index] = this.currentSession; + } + this.saveSessions(); + } + }, 30000); + } + + /** + * Get statistics + */ + getStatistics() { + const completed = this.sessions.filter(s => s.status === 'completed'); + + return { + total: this.sessions.length, + completed: completed.length, + failed: this.sessions.filter(s => s.status === 'failed').length, + running: this.sessions.filter(s => s.status === 'running').length, + avgDuration: completed.length > 0 + ? completed.reduce((sum, s) => sum + (s.duration || 0), 0) / completed.length + : 0, + avgFinalLoss: completed.length > 0 + ? completed.reduce((sum, s) => sum + (s.metrics.finalLoss || 0), 0) / completed.length + : 0, + bestSession: completed.length > 0 + ? completed.reduce((best, s) => + (s.metrics.finalLoss || Infinity) < (best.metrics.finalLoss || Infinity) ? s : best + ) + : null + }; + } +} + +// Export for use in other modules +if (typeof window !== 'undefined') { + window.TrainingSessionHistory = TrainingSessionHistory; +} diff --git a/docs/dashboard/shared-theme.css b/docs/dashboard/shared-theme.css index bf50c577d..1ddb4ae9e 100644 --- a/docs/dashboard/shared-theme.css +++ b/docs/dashboard/shared-theme.css @@ -1,468 +1,468 @@ -/* ============================================ - QAI Dashboard - Shared Theme Stylesheet - Version: 1.0 - Description: Common styles for all dashboard pages - ============================================ */ - -/* === DARK MODE VARIABLES === */ -:root { - --bg-primary: #1a1a2e; - --bg-secondary: #16213e; - --bg-card: #0f3460; - --text-primary: #eaeaea; - --text-secondary: #a0a0a0; - --accent-primary: #667eea; - --accent-secondary: #764ba2; - --border-color: #2d3561; - --success: #4caf50; - --warning: #ff9800; - --error: #f44336; - --info: #2196f3; -} - -/* === GLOBAL RESETS === */ -* { - box-sizing: border-box; - margin: 0; - padding: 0; -} - -body { - font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; - background: linear-gradient(135deg, var(--bg-primary) 0%, var(--bg-secondary) 100%); - color: var(--text-primary); - line-height: 1.6; - min-height: 100vh; -} - -/* === CONTAINERS === */ -.container { - max-width: 1400px; - margin: 0 auto; - padding: 20px; -} - -/* === CARDS === */ -.card { - background: var(--bg-card); - border-radius: 12px; - padding: 20px; - margin-bottom: 20px; - box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3); - border: 1px solid var(--border-color); - transition: transform 0.2s, box-shadow 0.2s; -} - -.card:hover { - transform: translateY(-2px); - box-shadow: 0 8px 30px rgba(102, 126, 234, 0.2); -} - -.card-header { - display: flex; - align-items: center; - gap: 10px; - margin-bottom: 15px; - padding-bottom: 10px; - border-bottom: 2px solid var(--accent-primary); -} - -.card-title { - font-size: 1.3em; - font-weight: 600; - color: var(--accent-primary); -} - -/* === BUTTONS === */ -.btn { - padding: 10px 20px; - border: none; - border-radius: 8px; - font-size: 0.95em; - font-weight: 500; - cursor: pointer; - transition: all 0.3s; - display: inline-flex; - align-items: center; - gap: 8px; -} - -.btn-primary { - background: linear-gradient(135deg, var(--accent-primary), var(--accent-secondary)); - color: white; -} - -.btn-primary:hover { - transform: translateY(-2px); - box-shadow: 0 6px 20px rgba(102, 126, 234, 0.4); -} - -.btn-secondary { - background: #555; - color: white; -} - -.btn-secondary:hover { - background: #666; -} - -.btn-success { - background: var(--success); - color: white; -} - -.btn-success:hover { - background: #45a049; -} - -.btn-warning { - background: var(--warning); - color: white; -} - -.btn-warning:hover { - background: #e68900; -} - -.btn-danger { - background: var(--error); - color: white; -} - -.btn-danger:hover { - background: #da190b; -} - -.btn-info { - background: var(--info); - color: white; -} - -.btn-info:hover { - background: #0b7dda; -} - -.btn:disabled { - opacity: 0.5; - cursor: not-allowed; - transform: none !important; -} - -/* === BADGES === */ -.badge { - display: inline-block; - padding: 4px 12px; - border-radius: 12px; - font-size: 0.85em; - font-weight: 600; - text-transform: uppercase; -} - -.badge-success { - background: var(--success); - color: white; -} - -.badge-warning { - background: var(--warning); - color: white; -} - -.badge-error { - background: var(--error); - color: white; -} - -.badge-info { - background: var(--info); - color: white; -} - -.badge-secondary { - background: #555; - color: white; -} - -/* === FORM ELEMENTS === */ -.form-group { - margin-bottom: 15px; -} - -label { - display: block; - margin-bottom: 5px; - color: var(--text-secondary); - font-size: 0.9em; - font-weight: 500; -} - -input[type="text"], -input[type="number"], -input[type="email"], -select, -textarea { - width: 100%; - padding: 10px; - background: var(--bg-secondary); - border: 1px solid var(--border-color); - border-radius: 6px; - color: var(--text-primary); - font-size: 0.95em; - transition: border-color 0.3s; -} - -input:focus, -select:focus, -textarea:focus { - outline: none; - border-color: var(--accent-primary); - box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1); -} - -/* === TABLES === */ -table { - width: 100%; - border-collapse: collapse; - margin-top: 15px; -} - -thead { - background: var(--bg-secondary); -} - -th { - padding: 12px; - text-align: left; - font-weight: 600; - color: var(--accent-primary); - border-bottom: 2px solid var(--accent-primary); -} - -td { - padding: 12px; - border-bottom: 1px solid var(--border-color); -} - -tbody tr:hover { - background: rgba(102, 126, 234, 0.05); -} - -/* === METRICS === */ -.metrics-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); - gap: 15px; - margin-bottom: 20px; -} - -.metric-card { - background: var(--bg-secondary); - padding: 15px; - border-radius: 8px; - border-left: 4px solid var(--accent-primary); -} - -.metric-label { - font-size: 0.85em; - color: var(--text-secondary); - margin-bottom: 5px; -} - -.metric-value { - font-size: 1.8em; - font-weight: 700; - color: var(--accent-primary); -} - -.metric-good { color: var(--success); } -.metric-warning { color: var(--warning); } -.metric-bad { color: var(--error); } - -/* === ALERTS === */ -.alert { - padding: 12px 15px; - border-radius: 8px; - margin-bottom: 15px; - display: flex; - align-items: center; - gap: 10px; -} - -.alert-success { - background: rgba(76, 175, 80, 0.1); - border-left: 4px solid var(--success); - color: var(--success); -} - -.alert-warning { - background: rgba(255, 152, 0, 0.1); - border-left: 4px solid var(--warning); - color: var(--warning); -} - -.alert-error { - background: rgba(244, 67, 54, 0.1); - border-left: 4px solid var(--error); - color: var(--error); -} - -.alert-info { - background: rgba(33, 150, 243, 0.1); - border-left: 4px solid var(--info); - color: var(--info); -} - -/* === LOADING SPINNER === */ -.spinner { - border: 3px solid rgba(102, 126, 234, 0.2); - border-top: 3px solid var(--accent-primary); - border-radius: 50%; - width: 40px; - height: 40px; - animation: spin 1s linear infinite; - margin: 20px auto; -} - -@keyframes spin { - 0% { transform: rotate(0deg); } - 100% { transform: rotate(360deg); } -} - -/* === TOOLTIPS === */ -[data-tooltip] { - position: relative; - cursor: help; -} - -[data-tooltip]::after { - content: attr(data-tooltip); - position: absolute; - bottom: 125%; - left: 50%; - transform: translateX(-50%); - background: var(--bg-secondary); - color: var(--text-primary); - padding: 8px 12px; - border-radius: 6px; - font-size: 0.85em; - white-space: nowrap; - opacity: 0; - pointer-events: none; - transition: opacity 0.3s; - border: 1px solid var(--border-color); - z-index: 1000; -} - -[data-tooltip]:hover::after { - opacity: 1; -} - -/* === MODALS === */ -.modal-overlay { - position: fixed; - inset: 0; - background: rgba(0, 0, 0, 0.7); - display: flex; - align-items: center; - justify-content: center; - z-index: 9998; -} - -.modal { - background: var(--bg-card); - border-radius: 14px; - padding: 30px; - max-width: 600px; - width: 90%; - max-height: 80vh; - overflow-y: auto; - box-shadow: 0 20px 60px rgba(0, 0, 0, 0.5); - border: 1px solid var(--border-color); -} - -.modal-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 20px; -} - -.modal-title { - font-size: 1.5em; - color: var(--accent-primary); -} - -.modal-close { - background: none; - border: none; - font-size: 1.5em; - color: var(--text-secondary); - cursor: pointer; - padding: 5px; -} - -.modal-close:hover { - color: var(--text-primary); -} - -/* === SCROLLBAR STYLING === */ -::-webkit-scrollbar { - width: 10px; - height: 10px; -} - -::-webkit-scrollbar-track { - background: var(--bg-secondary); -} - -::-webkit-scrollbar-thumb { - background: var(--accent-primary); - border-radius: 5px; -} - -::-webkit-scrollbar-thumb:hover { - background: var(--accent-secondary); -} - -/* === UTILITY CLASSES === */ -.text-center { text-align: center; } -.text-right { text-align: right; } -.text-left { text-align: left; } - -.mt-1 { margin-top: 10px; } -.mt-2 { margin-top: 20px; } -.mb-1 { margin-bottom: 10px; } -.mb-2 { margin-bottom: 20px; } - -.p-1 { padding: 10px; } -.p-2 { padding: 20px; } - -.flex { display: flex; } -.flex-col { flex-direction: column; } -.flex-center { justify-content: center; align-items: center; } -.flex-between { justify-content: space-between; } -.gap-1 { gap: 10px; } -.gap-2 { gap: 20px; } - -.hidden { display: none !important; } -.visible { display: block !important; } - -/* === RESPONSIVE === */ -@media (max-width: 768px) { - .metrics-grid { - grid-template-columns: 1fr; - } - - .container { - padding: 10px; - } - - .card { - padding: 15px; - } - - .btn { - padding: 8px 16px; - font-size: 0.9em; - } -} +/* ============================================ + QAI Dashboard - Shared Theme Stylesheet + Version: 1.0 + Description: Common styles for all dashboard pages + ============================================ */ + +/* === DARK MODE VARIABLES === */ +:root { + --bg-primary: #1a1a2e; + --bg-secondary: #16213e; + --bg-card: #0f3460; + --text-primary: #eaeaea; + --text-secondary: #a0a0a0; + --accent-primary: #667eea; + --accent-secondary: #764ba2; + --border-color: #2d3561; + --success: #4caf50; + --warning: #ff9800; + --error: #f44336; + --info: #2196f3; +} + +/* === GLOBAL RESETS === */ +* { + box-sizing: border-box; + margin: 0; + padding: 0; +} + +body { + font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; + background: linear-gradient(135deg, var(--bg-primary) 0%, var(--bg-secondary) 100%); + color: var(--text-primary); + line-height: 1.6; + min-height: 100vh; +} + +/* === CONTAINERS === */ +.container { + max-width: 1400px; + margin: 0 auto; + padding: 20px; +} + +/* === CARDS === */ +.card { + background: var(--bg-card); + border-radius: 12px; + padding: 20px; + margin-bottom: 20px; + box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3); + border: 1px solid var(--border-color); + transition: transform 0.2s, box-shadow 0.2s; +} + +.card:hover { + transform: translateY(-2px); + box-shadow: 0 8px 30px rgba(102, 126, 234, 0.2); +} + +.card-header { + display: flex; + align-items: center; + gap: 10px; + margin-bottom: 15px; + padding-bottom: 10px; + border-bottom: 2px solid var(--accent-primary); +} + +.card-title { + font-size: 1.3em; + font-weight: 600; + color: var(--accent-primary); +} + +/* === BUTTONS === */ +.btn { + padding: 10px 20px; + border: none; + border-radius: 8px; + font-size: 0.95em; + font-weight: 500; + cursor: pointer; + transition: all 0.3s; + display: inline-flex; + align-items: center; + gap: 8px; +} + +.btn-primary { + background: linear-gradient(135deg, var(--accent-primary), var(--accent-secondary)); + color: white; +} + +.btn-primary:hover { + transform: translateY(-2px); + box-shadow: 0 6px 20px rgba(102, 126, 234, 0.4); +} + +.btn-secondary { + background: #555; + color: white; +} + +.btn-secondary:hover { + background: #666; +} + +.btn-success { + background: var(--success); + color: white; +} + +.btn-success:hover { + background: #45a049; +} + +.btn-warning { + background: var(--warning); + color: white; +} + +.btn-warning:hover { + background: #e68900; +} + +.btn-danger { + background: var(--error); + color: white; +} + +.btn-danger:hover { + background: #da190b; +} + +.btn-info { + background: var(--info); + color: white; +} + +.btn-info:hover { + background: #0b7dda; +} + +.btn:disabled { + opacity: 0.5; + cursor: not-allowed; + transform: none !important; +} + +/* === BADGES === */ +.badge { + display: inline-block; + padding: 4px 12px; + border-radius: 12px; + font-size: 0.85em; + font-weight: 600; + text-transform: uppercase; +} + +.badge-success { + background: var(--success); + color: white; +} + +.badge-warning { + background: var(--warning); + color: white; +} + +.badge-error { + background: var(--error); + color: white; +} + +.badge-info { + background: var(--info); + color: white; +} + +.badge-secondary { + background: #555; + color: white; +} + +/* === FORM ELEMENTS === */ +.form-group { + margin-bottom: 15px; +} + +label { + display: block; + margin-bottom: 5px; + color: var(--text-secondary); + font-size: 0.9em; + font-weight: 500; +} + +input[type="text"], +input[type="number"], +input[type="email"], +select, +textarea { + width: 100%; + padding: 10px; + background: var(--bg-secondary); + border: 1px solid var(--border-color); + border-radius: 6px; + color: var(--text-primary); + font-size: 0.95em; + transition: border-color 0.3s; +} + +input:focus, +select:focus, +textarea:focus { + outline: none; + border-color: var(--accent-primary); + box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1); +} + +/* === TABLES === */ +table { + width: 100%; + border-collapse: collapse; + margin-top: 15px; +} + +thead { + background: var(--bg-secondary); +} + +th { + padding: 12px; + text-align: left; + font-weight: 600; + color: var(--accent-primary); + border-bottom: 2px solid var(--accent-primary); +} + +td { + padding: 12px; + border-bottom: 1px solid var(--border-color); +} + +tbody tr:hover { + background: rgba(102, 126, 234, 0.05); +} + +/* === METRICS === */ +.metrics-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); + gap: 15px; + margin-bottom: 20px; +} + +.metric-card { + background: var(--bg-secondary); + padding: 15px; + border-radius: 8px; + border-left: 4px solid var(--accent-primary); +} + +.metric-label { + font-size: 0.85em; + color: var(--text-secondary); + margin-bottom: 5px; +} + +.metric-value { + font-size: 1.8em; + font-weight: 700; + color: var(--accent-primary); +} + +.metric-good { color: var(--success); } +.metric-warning { color: var(--warning); } +.metric-bad { color: var(--error); } + +/* === ALERTS === */ +.alert { + padding: 12px 15px; + border-radius: 8px; + margin-bottom: 15px; + display: flex; + align-items: center; + gap: 10px; +} + +.alert-success { + background: rgba(76, 175, 80, 0.1); + border-left: 4px solid var(--success); + color: var(--success); +} + +.alert-warning { + background: rgba(255, 152, 0, 0.1); + border-left: 4px solid var(--warning); + color: var(--warning); +} + +.alert-error { + background: rgba(244, 67, 54, 0.1); + border-left: 4px solid var(--error); + color: var(--error); +} + +.alert-info { + background: rgba(33, 150, 243, 0.1); + border-left: 4px solid var(--info); + color: var(--info); +} + +/* === LOADING SPINNER === */ +.spinner { + border: 3px solid rgba(102, 126, 234, 0.2); + border-top: 3px solid var(--accent-primary); + border-radius: 50%; + width: 40px; + height: 40px; + animation: spin 1s linear infinite; + margin: 20px auto; +} + +@keyframes spin { + 0% { transform: rotate(0deg); } + 100% { transform: rotate(360deg); } +} + +/* === TOOLTIPS === */ +[data-tooltip] { + position: relative; + cursor: help; +} + +[data-tooltip]::after { + content: attr(data-tooltip); + position: absolute; + bottom: 125%; + left: 50%; + transform: translateX(-50%); + background: var(--bg-secondary); + color: var(--text-primary); + padding: 8px 12px; + border-radius: 6px; + font-size: 0.85em; + white-space: nowrap; + opacity: 0; + pointer-events: none; + transition: opacity 0.3s; + border: 1px solid var(--border-color); + z-index: 1000; +} + +[data-tooltip]:hover::after { + opacity: 1; +} + +/* === MODALS === */ +.modal-overlay { + position: fixed; + inset: 0; + background: rgba(0, 0, 0, 0.7); + display: flex; + align-items: center; + justify-content: center; + z-index: 9998; +} + +.modal { + background: var(--bg-card); + border-radius: 14px; + padding: 30px; + max-width: 600px; + width: 90%; + max-height: 80vh; + overflow-y: auto; + box-shadow: 0 20px 60px rgba(0, 0, 0, 0.5); + border: 1px solid var(--border-color); +} + +.modal-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 20px; +} + +.modal-title { + font-size: 1.5em; + color: var(--accent-primary); +} + +.modal-close { + background: none; + border: none; + font-size: 1.5em; + color: var(--text-secondary); + cursor: pointer; + padding: 5px; +} + +.modal-close:hover { + color: var(--text-primary); +} + +/* === SCROLLBAR STYLING === */ +::-webkit-scrollbar { + width: 10px; + height: 10px; +} + +::-webkit-scrollbar-track { + background: var(--bg-secondary); +} + +::-webkit-scrollbar-thumb { + background: var(--accent-primary); + border-radius: 5px; +} + +::-webkit-scrollbar-thumb:hover { + background: var(--accent-secondary); +} + +/* === UTILITY CLASSES === */ +.text-center { text-align: center; } +.text-right { text-align: right; } +.text-left { text-align: left; } + +.mt-1 { margin-top: 10px; } +.mt-2 { margin-top: 20px; } +.mb-1 { margin-bottom: 10px; } +.mb-2 { margin-bottom: 20px; } + +.p-1 { padding: 10px; } +.p-2 { padding: 20px; } + +.flex { display: flex; } +.flex-col { flex-direction: column; } +.flex-center { justify-content: center; align-items: center; } +.flex-between { justify-content: space-between; } +.gap-1 { gap: 10px; } +.gap-2 { gap: 20px; } + +.hidden { display: none !important; } +.visible { display: block !important; } + +/* === RESPONSIVE === */ +@media (max-width: 768px) { + .metrics-grid { + grid-template-columns: 1fr; + } + + .container { + padding: 10px; + } + + .card { + padding: 15px; + } + + .btn { + padding: 8px 16px; + font-size: 0.9em; + } +} diff --git a/docs/dashboard/templates/index.html b/docs/dashboard/templates/index.html index b1b2374b6..93ab3554a 100644 --- a/docs/dashboard/templates/index.html +++ b/docs/dashboard/templates/index.html @@ -1,419 +1,419 @@ - - - - - - Automation Dashboard — Aria Platform - - - - - - - - - - - - - - - - - - ← Home -

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    - - - - + + + + + + Automation Dashboard — Aria Platform + + + + + + + + + + + + + + + + + + ← Home +

    QAI Automation Dashboard

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    Training Progress

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    + + + + + + + diff --git a/docs/database/AZURE_MONITOR_SQL_SETUP.md b/docs/database/AZURE_MONITOR_SQL_SETUP.md index b9ed95f53..f9081f158 100644 --- a/docs/database/AZURE_MONITOR_SQL_SETUP.md +++ b/docs/database/AZURE_MONITOR_SQL_SETUP.md @@ -1,386 +1,386 @@ -# Azure Monitor & Application Insights - SQL Observability Setup - -This guide provides step-by-step instructions for deploying Azure Monitor alerts and querying Application Insights for SQL performance tracking. - -## Prerequisites - -- Azure Function App deployed with Application Insights enabled -- Azure CLI installed (`az --version`) -- Contributor access to the resource group -- Action Group created for alert notifications - -## Quick Start - -### 1. Deploy Alerts via ARM Template - -```powershell -# Set variables -$resourceGroup = "your-rg-name" -$functionAppName = "your-function-app" -$actionGroupId = "/subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.Insights/actionGroups/{name}" - -# Deploy alert rules -az deployment group create ` - --resource-group $resourceGroup ` - --template-file config/azure_monitor_alerts.json ` - --parameters functionAppName=$functionAppName actionGroupId=$actionGroupId -``` - -### 2. Verify Deployment - -```powershell -# List alert rules -az monitor metrics alert list --resource-group $resourceGroup --output table -az monitor scheduled-query list --resource-group $resourceGroup --output table -``` - -## Application Insights KQL Queries - -### Query 1: Pool Saturation Over Time - -```kql -traces -| where message has "[sql_engine]" and message has "saturated" -| extend saturation_pct = extract(@"(\d+\.\d+)% saturated", 1, message) -| project timestamp, saturation_pct = todouble(saturation_pct), message -| render timechart -``` - -**Use Case**: Visualize pool saturation trends to identify peak load periods. - -### Query 2: Slow Query Frequency Heatmap - -```kql -traces -| where message has "[sql_engine] slow query" -| extend duration_ms = extract(@"slow query \((\d+\.\d+) ms", 1, message) -| summarize SlowQueryCount = count(), AvgDuration = avg(todouble(duration_ms)) by bin(timestamp, 5m) -| render timechart -``` - -**Use Case**: Detect slow query spikes correlated with deployments or load changes. - -### Query 3: Top Slow Queries by Frequency - -```kql -traces -| where message has "[sql_engine] slow query" -| extend sql_snippet = extract(@"sql=(.{1,120})", 1, message) -| extend duration_ms = todouble(extract(@"slow query \((\d+\.\d+) ms", 1, message)) -| summarize - Count = count(), - AvgDuration = avg(duration_ms), - MaxDuration = max(duration_ms) - by sql_snippet -| order by Count desc -| take 20 -``` - -**Use Case**: Identify queries that are consistently slow (caching candidates). - -### Query 4: Pool Saturation Alert Summary - -```kql -requests -| where url endswith "/api/ai/status" -| extend sql_data = parse_json(tostring(customDimensions.sql)) -| extend saturation_pct = todouble(sql_data.pool.saturation_pct) -| extend saturation_alert = tostring(sql_data.alert) -| where isnotempty(saturation_alert) -| summarize AlertCount = count(), MaxSaturation = max(saturation_pct) by bin(timestamp, 10m) -| render timechart -``` - -**Use Case**: Track how often saturation alerts trigger over time. - -### Query 5: Query Performance Percentiles (P50, P95, P99) - -```kql -traces -| where message has "[sql_engine] slow query" -| extend duration_ms = todouble(extract(@"slow query \((\d+\.\d+) ms", 1, message)) -| summarize - P50 = percentile(duration_ms, 50), - P95 = percentile(duration_ms, 95), - P99 = percentile(duration_ms, 99), - Count = count() - by bin(timestamp, 1h) -| render timechart -``` - -**Use Case**: Tune `QAI_SQL_SLOW_MS` threshold based on P95 latency. - -### Query 6: Correlation Between Saturation and Slow Queries - -```kql -let saturation = traces -| where message has "saturated" -| extend saturation_pct = todouble(extract(@"(\d+\.\d+)% saturated", 1, message)) -| summarize AvgSaturation = avg(saturation_pct) by bin(timestamp, 5m); -let slowQueries = traces -| where message has "slow query" -| summarize SlowCount = count() by bin(timestamp, 5m); -saturation -| join kind=inner slowQueries on timestamp -| project timestamp, AvgSaturation, SlowCount -| render timechart -``` - -**Use Case**: Determine if high saturation causes query slowdown. - -## Alert Configuration Details - -### Alert 1: Pool Saturation (Metric Alert) - -- **Metric**: `sql_pool_saturation_pct` (custom metric) -- **Threshold**: > 80% -- **Window**: 5 minutes -- **Evaluation Frequency**: 1 minute -- **Severity**: Warning (2) -- **Action**: Fire when saturation sustained above 80% for 5 minutes - -### Alert 2: Slow Query Frequency (Log Alert) - -- **Query**: Count of `slow query` traces in last 1 minute -- **Threshold**: > 10 queries -- **Window**: 5 minutes -- **Evaluation Frequency**: 5 minutes -- **Severity**: Informational (3) -- **Action**: Fire when slow query burst detected - -### Alert 3: Saturation Status Alert (Log Alert) - -- **Query**: Presence of `saturation_alert` field in `/api/ai/status` responses -- **Threshold**: > 0 occurrences -- **Window**: 5 minutes -- **Evaluation Frequency**: 5 minutes -- **Severity**: Warning (2) -- **Failing Periods**: 2 consecutive periods (reduces flapping) - -## Tuning Thresholds Based on Metrics - -### Step 1: Collect Baseline (7 days) - -```powershell -# Run for 1 week with default thresholds -$env:QAI_SQL_SLOW_MS = "500" -$env:AZURE_FUNCTIONS_ENVIRONMENT = "production" -``` - -### Step 2: Analyze P95 Latency - -Run **Query 5** in Application Insights and note P95 value. - -**Tuning Decision Matrix:** - -| P95 Latency | Recommended Threshold | Rationale | -| ----------- | --------------------- | --------------------- | -| < 100ms | 150ms | Catch outliers early | -| 100-300ms | 400ms | Balanced sensitivity | -| 300-500ms | 600ms | Reduce alert noise | -| > 500ms | 750ms | Focus on severe cases | - -### Step 3: Adjust Threshold - -```powershell -# Update based on analysis -$env:QAI_SQL_SLOW_MS = "400" # Example: P95 was 250ms -``` - -### Step 4: Monitor Alert Frequency - -Target: < 5 alerts per day per environment. - -## Scaling Pool Size - -### Option 1: URL Parameter (Recommended) - -```powershell -# PostgreSQL example -$env:QAI_SQL_URL = "postgresql+psycopg://user:pass@host/db?pool_size=30&max_overflow=10" - -# Azure SQL with ODBC -$env:QAI_SQL_URL = "mssql+pyodbc://user:pass@host/db?driver=ODBC+Driver+18+for+SQL+Server&pool_size=30" -``` - -**Common Pool Size Guidelines:** - -| Scenario | Pool Size | Max Overflow | Rationale | -| ---------------------------- | --------- | ------------ | ----------------------------- | -| Low traffic (< 10 req/s) | 10 | 5 | Minimize idle connections | -| Medium traffic (10-50 req/s) | 20 | 10 | Balanced for burst capacity | -| High traffic (> 50 req/s) | 30-50 | 20 | Prevent saturation under load | -| Background workers | 5 | 2 | Dedicated pool for async jobs | - -### Option 2: Engine Creation Override (Code) - -```python -# In shared/sql_engine.py (manual override) -_ENGINE = create_engine( - url, - pool_pre_ping=True, - pool_recycle=1800, - pool_size=30, # Override default (5) - max_overflow=10, # Override default (10) - future=True, -) -``` - -### Monitoring Pool Size Effectiveness - -```kql -traces -| where message has "[sql_engine]" and message has "saturated" -| extend saturation_pct = todouble(extract(@"(\d+\.\d+)% saturated", 1, message)) -| summarize AvgSaturation = avg(saturation_pct), MaxSaturation = max(saturation_pct) by bin(timestamp, 1h) -| where MaxSaturation > 60 // Still experiencing pressure -``` - -**Action**: If `MaxSaturation` consistently > 60% after scaling, increase pool size by 50%. - -## Enabling Query Tracking (Historical Analysis) - -### Step 1: Apply Migration - -```powershell -# Ensure migration table exists -python .\scripts\sql_migrate.py -``` - -### Step 2: Enable Feature - -```powershell -# Production -$env:QAI_ENABLE_QUERY_TRACKING = "true" - -# Verify in status endpoint -Invoke-RestMethod -Uri "http://localhost:7071/api/ai/status" | ConvertTo-Json -Depth 5 -``` - -### Step 3: Query Historical Data - -```sql --- Top 10 slowest queries (last 24 hours) -SELECT - query_hash, - sql_snippet, - COUNT(*) as execution_count, - AVG(execution_time_ms) as avg_duration, - MAX(execution_time_ms) as max_duration -FROM QAI_QueryMetrics -WHERE executed_at > datetime('now', '-1 day') -GROUP BY query_hash -ORDER BY avg_duration DESC -LIMIT 10; -``` - -### Step 4: Cleanup Old Data (Retention Policy) - -```sql --- Delete metrics older than 7 days (run weekly via Azure Automation) -DELETE FROM QAI_QueryMetrics -WHERE executed_at < datetime('now', '-7 days'); -``` - -## Action Group Setup - -### Create Action Group via Azure Portal - -1. Navigate to **Monitor** > **Alerts** > **Action groups** -2. Click **+ Create** -3. Configure: - - **Resource group**: Same as Function App - - **Action group name**: `sql-alerts-action-group` - - **Notification types**: Email, SMS, Push (select as needed) - - **Actions**: Add webhook for incident management (optional) - -### Create via Azure CLI - -```powershell -az monitor action-group create ` - --name sql-alerts-action-group ` - --resource-group your-rg-name ` - --short-name sqlalertsql ` - --email-receiver name=admin email=admin@example.com -``` - -## Troubleshooting - -### Issue: Alerts Not Firing - -**Check 1**: Verify Application Insights connection - -```powershell -az monitor app-insights component show --app your-app --resource-group your-rg -``` - -**Check 2**: Confirm traces are being logged - -```kql -traces -| where message has "[sql_engine]" -| take 10 -``` - -**Check 3**: Validate alert rule query - -```kql -// Run the exact alert query in Application Insights -traces -| where message has 'slow query' -| where timestamp > ago(1m) -| summarize count() -``` - -### Issue: Too Many False Positives - -**Solution 1**: Increase evaluation window - -```json -"windowSize": "PT10M" // Change from 5 to 10 minutes -``` - -**Solution 2**: Add failing period threshold - -```json -"failingPeriods": { - "numberOfEvaluationPeriods": 3, - "minFailingPeriodsToAlert": 2 // Fire only if 2 out of 3 windows fail -} -``` - -### Issue: Query Tracking Table Growing Too Large - -**Solution**: Implement retention cleanup - -```powershell -# Azure Automation runbook (schedule weekly) -$env:QAI_SQL_URL = "your-connection-string" -python -c "from shared.sql_engine import get_engine; from sqlalchemy import text; e = get_engine(); e.execute(text('DELETE FROM QAI_QueryMetrics WHERE executed_at < datetime(\"now\", \"-7 days\")'))" -``` - -## Cost Optimization - -- **Log Ingestion**: ~$2.76/GB (Application Insights) -- **Query Execution**: First 5GB/month free, then $0.13/GB -- **Alert Evaluations**: First 250 evaluations free, then $0.10 per evaluation - -**Recommendations:** - -1. Use log sampling if ingestion > 100GB/month -2. Aggregate metrics before alerting (hourly rollups) -3. Disable query tracking in non-production environments - -## Next Steps - -1. ✅ Deploy alert templates -2. ✅ Create Application Insights dashboard with queries -3. ✅ Set up Action Group with team notification channels -4. ✅ Run baseline collection (7 days) -5. ✅ Tune thresholds based on P95 metrics -6. ✅ Enable query tracking in production (if needed) -7. ✅ Schedule weekly retention cleanup job - ---- - -For additional support, refer to [DATABASE_SQL_SETUP.md](./DATABASE_SQL_SETUP.md) for core SQL configuration details. +# Azure Monitor & Application Insights - SQL Observability Setup + +This guide provides step-by-step instructions for deploying Azure Monitor alerts and querying Application Insights for SQL performance tracking. + +## Prerequisites + +- Azure Function App deployed with Application Insights enabled +- Azure CLI installed (`az --version`) +- Contributor access to the resource group +- Action Group created for alert notifications + +## Quick Start + +### 1. Deploy Alerts via ARM Template + +```powershell +# Set variables +$resourceGroup = "your-rg-name" +$functionAppName = "your-function-app" +$actionGroupId = "/subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.Insights/actionGroups/{name}" + +# Deploy alert rules +az deployment group create ` + --resource-group $resourceGroup ` + --template-file config/azure_monitor_alerts.json ` + --parameters functionAppName=$functionAppName actionGroupId=$actionGroupId +``` + +### 2. Verify Deployment + +```powershell +# List alert rules +az monitor metrics alert list --resource-group $resourceGroup --output table +az monitor scheduled-query list --resource-group $resourceGroup --output table +``` + +## Application Insights KQL Queries + +### Query 1: Pool Saturation Over Time + +```kql +traces +| where message has "[sql_engine]" and message has "saturated" +| extend saturation_pct = extract(@"(\d+\.\d+)% saturated", 1, message) +| project timestamp, saturation_pct = todouble(saturation_pct), message +| render timechart +``` + +**Use Case**: Visualize pool saturation trends to identify peak load periods. + +### Query 2: Slow Query Frequency Heatmap + +```kql +traces +| where message has "[sql_engine] slow query" +| extend duration_ms = extract(@"slow query \((\d+\.\d+) ms", 1, message) +| summarize SlowQueryCount = count(), AvgDuration = avg(todouble(duration_ms)) by bin(timestamp, 5m) +| render timechart +``` + +**Use Case**: Detect slow query spikes correlated with deployments or load changes. + +### Query 3: Top Slow Queries by Frequency + +```kql +traces +| where message has "[sql_engine] slow query" +| extend sql_snippet = extract(@"sql=(.{1,120})", 1, message) +| extend duration_ms = todouble(extract(@"slow query \((\d+\.\d+) ms", 1, message)) +| summarize + Count = count(), + AvgDuration = avg(duration_ms), + MaxDuration = max(duration_ms) + by sql_snippet +| order by Count desc +| take 20 +``` + +**Use Case**: Identify queries that are consistently slow (caching candidates). + +### Query 4: Pool Saturation Alert Summary + +```kql +requests +| where url endswith "/api/ai/status" +| extend sql_data = parse_json(tostring(customDimensions.sql)) +| extend saturation_pct = todouble(sql_data.pool.saturation_pct) +| extend saturation_alert = tostring(sql_data.alert) +| where isnotempty(saturation_alert) +| summarize AlertCount = count(), MaxSaturation = max(saturation_pct) by bin(timestamp, 10m) +| render timechart +``` + +**Use Case**: Track how often saturation alerts trigger over time. + +### Query 5: Query Performance Percentiles (P50, P95, P99) + +```kql +traces +| where message has "[sql_engine] slow query" +| extend duration_ms = todouble(extract(@"slow query \((\d+\.\d+) ms", 1, message)) +| summarize + P50 = percentile(duration_ms, 50), + P95 = percentile(duration_ms, 95), + P99 = percentile(duration_ms, 99), + Count = count() + by bin(timestamp, 1h) +| render timechart +``` + +**Use Case**: Tune `QAI_SQL_SLOW_MS` threshold based on P95 latency. + +### Query 6: Correlation Between Saturation and Slow Queries + +```kql +let saturation = traces +| where message has "saturated" +| extend saturation_pct = todouble(extract(@"(\d+\.\d+)% saturated", 1, message)) +| summarize AvgSaturation = avg(saturation_pct) by bin(timestamp, 5m); +let slowQueries = traces +| where message has "slow query" +| summarize SlowCount = count() by bin(timestamp, 5m); +saturation +| join kind=inner slowQueries on timestamp +| project timestamp, AvgSaturation, SlowCount +| render timechart +``` + +**Use Case**: Determine if high saturation causes query slowdown. + +## Alert Configuration Details + +### Alert 1: Pool Saturation (Metric Alert) + +- **Metric**: `sql_pool_saturation_pct` (custom metric) +- **Threshold**: > 80% +- **Window**: 5 minutes +- **Evaluation Frequency**: 1 minute +- **Severity**: Warning (2) +- **Action**: Fire when saturation sustained above 80% for 5 minutes + +### Alert 2: Slow Query Frequency (Log Alert) + +- **Query**: Count of `slow query` traces in last 1 minute +- **Threshold**: > 10 queries +- **Window**: 5 minutes +- **Evaluation Frequency**: 5 minutes +- **Severity**: Informational (3) +- **Action**: Fire when slow query burst detected + +### Alert 3: Saturation Status Alert (Log Alert) + +- **Query**: Presence of `saturation_alert` field in `/api/ai/status` responses +- **Threshold**: > 0 occurrences +- **Window**: 5 minutes +- **Evaluation Frequency**: 5 minutes +- **Severity**: Warning (2) +- **Failing Periods**: 2 consecutive periods (reduces flapping) + +## Tuning Thresholds Based on Metrics + +### Step 1: Collect Baseline (7 days) + +```powershell +# Run for 1 week with default thresholds +$env:QAI_SQL_SLOW_MS = "500" +$env:AZURE_FUNCTIONS_ENVIRONMENT = "production" +``` + +### Step 2: Analyze P95 Latency + +Run **Query 5** in Application Insights and note P95 value. + +**Tuning Decision Matrix:** + +| P95 Latency | Recommended Threshold | Rationale | +| ----------- | --------------------- | --------------------- | +| < 100ms | 150ms | Catch outliers early | +| 100-300ms | 400ms | Balanced sensitivity | +| 300-500ms | 600ms | Reduce alert noise | +| > 500ms | 750ms | Focus on severe cases | + +### Step 3: Adjust Threshold + +```powershell +# Update based on analysis +$env:QAI_SQL_SLOW_MS = "400" # Example: P95 was 250ms +``` + +### Step 4: Monitor Alert Frequency + +Target: < 5 alerts per day per environment. + +## Scaling Pool Size + +### Option 1: URL Parameter (Recommended) + +```powershell +# PostgreSQL example +$env:QAI_SQL_URL = "postgresql+psycopg://user:pass@host/db?pool_size=30&max_overflow=10" + +# Azure SQL with ODBC +$env:QAI_SQL_URL = "mssql+pyodbc://user:pass@host/db?driver=ODBC+Driver+18+for+SQL+Server&pool_size=30" +``` + +**Common Pool Size Guidelines:** + +| Scenario | Pool Size | Max Overflow | Rationale | +| ---------------------------- | --------- | ------------ | ----------------------------- | +| Low traffic (< 10 req/s) | 10 | 5 | Minimize idle connections | +| Medium traffic (10-50 req/s) | 20 | 10 | Balanced for burst capacity | +| High traffic (> 50 req/s) | 30-50 | 20 | Prevent saturation under load | +| Background workers | 5 | 2 | Dedicated pool for async jobs | + +### Option 2: Engine Creation Override (Code) + +```python +# In shared/sql_engine.py (manual override) +_ENGINE = create_engine( + url, + pool_pre_ping=True, + pool_recycle=1800, + pool_size=30, # Override default (5) + max_overflow=10, # Override default (10) + future=True, +) +``` + +### Monitoring Pool Size Effectiveness + +```kql +traces +| where message has "[sql_engine]" and message has "saturated" +| extend saturation_pct = todouble(extract(@"(\d+\.\d+)% saturated", 1, message)) +| summarize AvgSaturation = avg(saturation_pct), MaxSaturation = max(saturation_pct) by bin(timestamp, 1h) +| where MaxSaturation > 60 // Still experiencing pressure +``` + +**Action**: If `MaxSaturation` consistently > 60% after scaling, increase pool size by 50%. + +## Enabling Query Tracking (Historical Analysis) + +### Step 1: Apply Migration + +```powershell +# Ensure migration table exists +python .\scripts\sql_migrate.py +``` + +### Step 2: Enable Feature + +```powershell +# Production +$env:QAI_ENABLE_QUERY_TRACKING = "true" + +# Verify in status endpoint +Invoke-RestMethod -Uri "http://localhost:7071/api/ai/status" | ConvertTo-Json -Depth 5 +``` + +### Step 3: Query Historical Data + +```sql +-- Top 10 slowest queries (last 24 hours) +SELECT + query_hash, + sql_snippet, + COUNT(*) as execution_count, + AVG(execution_time_ms) as avg_duration, + MAX(execution_time_ms) as max_duration +FROM QAI_QueryMetrics +WHERE executed_at > datetime('now', '-1 day') +GROUP BY query_hash +ORDER BY avg_duration DESC +LIMIT 10; +``` + +### Step 4: Cleanup Old Data (Retention Policy) + +```sql +-- Delete metrics older than 7 days (run weekly via Azure Automation) +DELETE FROM QAI_QueryMetrics +WHERE executed_at < datetime('now', '-7 days'); +``` + +## Action Group Setup + +### Create Action Group via Azure Portal + +1. Navigate to **Monitor** > **Alerts** > **Action groups** +2. Click **+ Create** +3. Configure: + - **Resource group**: Same as Function App + - **Action group name**: `sql-alerts-action-group` + - **Notification types**: Email, SMS, Push (select as needed) + - **Actions**: Add webhook for incident management (optional) + +### Create via Azure CLI + +```powershell +az monitor action-group create ` + --name sql-alerts-action-group ` + --resource-group your-rg-name ` + --short-name sqlalertsql ` + --email-receiver name=admin email=admin@example.com +``` + +## Troubleshooting + +### Issue: Alerts Not Firing + +**Check 1**: Verify Application Insights connection + +```powershell +az monitor app-insights component show --app your-app --resource-group your-rg +``` + +**Check 2**: Confirm traces are being logged + +```kql +traces +| where message has "[sql_engine]" +| take 10 +``` + +**Check 3**: Validate alert rule query + +```kql +// Run the exact alert query in Application Insights +traces +| where message has 'slow query' +| where timestamp > ago(1m) +| summarize count() +``` + +### Issue: Too Many False Positives + +**Solution 1**: Increase evaluation window + +```json +"windowSize": "PT10M" // Change from 5 to 10 minutes +``` + +**Solution 2**: Add failing period threshold + +```json +"failingPeriods": { + "numberOfEvaluationPeriods": 3, + "minFailingPeriodsToAlert": 2 // Fire only if 2 out of 3 windows fail +} +``` + +### Issue: Query Tracking Table Growing Too Large + +**Solution**: Implement retention cleanup + +```powershell +# Azure Automation runbook (schedule weekly) +$env:QAI_SQL_URL = "your-connection-string" +python -c "from shared.sql_engine import get_engine; from sqlalchemy import text; e = get_engine(); e.execute(text('DELETE FROM QAI_QueryMetrics WHERE executed_at < datetime(\"now\", \"-7 days\")'))" +``` + +## Cost Optimization + +- **Log Ingestion**: ~$2.76/GB (Application Insights) +- **Query Execution**: First 5GB/month free, then $0.13/GB +- **Alert Evaluations**: First 250 evaluations free, then $0.10 per evaluation + +**Recommendations:** + +1. Use log sampling if ingestion > 100GB/month +2. Aggregate metrics before alerting (hourly rollups) +3. Disable query tracking in non-production environments + +## Next Steps + +1. ✅ Deploy alert templates +2. ✅ Create Application Insights dashboard with queries +3. ✅ Set up Action Group with team notification channels +4. ✅ Run baseline collection (7 days) +5. ✅ Tune thresholds based on P95 metrics +6. ✅ Enable query tracking in production (if needed) +7. ✅ Schedule weekly retention cleanup job + +--- + +For additional support, refer to [DATABASE_SQL_SETUP.md](./DATABASE_SQL_SETUP.md) for core SQL configuration details. diff --git a/docs/database/DATABASE_INTEGRATION_GUIDE.md b/docs/database/DATABASE_INTEGRATION_GUIDE.md index c4a4b9bb1..c6479edc7 100644 --- a/docs/database/DATABASE_INTEGRATION_GUIDE.md +++ b/docs/database/DATABASE_INTEGRATION_GUIDE.md @@ -1,379 +1,379 @@ -# Database Integration Guide - -This document provides step-by-step instructions for integrating the QAI database with the existing codebase. - -## Overview - -The database integration adds automatic logging of: -- **Quantum training runs** → `QuantumTrainingRuns` table -- **LoRA training runs** → `LoRATrainingRuns` table -- **Chat messages** → `ChatConversations` + `ChatMessages` tables - -## Files Created - -✅ **Database Schema** (in `database/`): -- `Tables/*.sql` - 7 tables for core data -- `Views/*.sql` - 3 views for analytics -- `StoredProcedures/*.sql` - 4 SPs for easy logging -- `README.md` - Deployment and usage guide -- `database.sqlproj` - VS Code SQL Database Project - -✅ **Shared Module**: -- `shared/db_logging.py` - Safe, fault-tolerant logging helpers - -✅ **Configuration**: -- `requirements.txt` - Added `pyodbc` and `sqlalchemy` -- `local.settings.json` - Added `QAI_DB_CONN` placeholder -- `.env` - Environment variable template - -✅ **Script Patches**: -- `scripts/evaluation/quantum_autorun.py` - Logs quantum runs after success -- `scripts/training/autotrain.py` - Logs LoRA runs after success - -## Files That Need Manual Editing - -### 1. `function_app.py` - Add Chat Logging - -**Step 1:** Add imports at the top (after line 8): - -```python -import azure.functions as func -import json -import logging -import os -import sys -from pathlib import Path -import subprocess -import importlib.util as _iu -from uuid import uuid4 # ADD THIS - -# Optional DB logging (safe no-op if not configured) # ADD THIS BLOCK -try: # noqa: SIM105 - from shared.db_logging import log_chat_message_safe # type: ignore -except Exception: # noqa: BLE001 - def log_chat_message_safe(*_a, **_kw): # type: ignore - return {"success": False, "skipped": True} - -# Add talk-to-ai to path so we can import chat_providers -talk_to_ai_path = Path(__file__).resolve().parent / "talk-to-ai" / "src" -sys.path.insert(0, str(talk_to_ai_path)) -``` - -**Step 2:** In the `chat()` function (around line 200), replace the response_data section: - -**FIND:** -```python - # Get completion (non-streaming for HTTP simplicity) - result = provider.complete(pruned_messages, stream=False) - - # If result is still a generator, consume it - if hasattr(result, '__iter__') and not isinstance(result, str): - result = ''.join(result) - - response_data = { - "response": result, - "provider": info.name, - "model": info.model, - "pruning": { - "original_tokens": stats.original_tokens, - "pruned_tokens": stats.pruned_tokens, - "removed_count": stats.removed_count, - "budget": stats.budget, - "reserve_output_tokens": stats.reserve_output_tokens, - } - } -``` - -**REPLACE WITH:** -```python - # Get completion (non-streaming for HTTP simplicity) - result = provider.complete(pruned_messages, stream=False) - - # If result is still a generator, consume it - if hasattr(result, '__iter__') and not isinstance(result, str): - result = ''.join(result) - - # Session handling (client may pass session_id to maintain conversation linkage) - session_id = req_body.get("session_id") or req.headers.get("X-Session-Id") or str(uuid4()) - - # Log latest user message (last in list) BEFORE assistant reply - try: - if messages: - last_user = messages[-1] - if last_user.get("role") == "user": - log_chat_message_safe( - session_id=session_id, - provider=info.name, - model=info.model, - role="user", - content=str(last_user.get("content", ""))[:4000], # truncate safety - ) - except Exception as e: # noqa: BLE001 - logging.warning(f"User chat log skipped: {e}") - - # Log assistant reply - try: - log_resp = log_chat_message_safe( - session_id=session_id, - provider=info.name, - model=info.model, - role="assistant", - content=str(result)[:8000], # truncate safety - ) - except Exception as e: # noqa: BLE001 - logging.warning(f"Assistant chat log skipped: {e}") - log_resp = {"success": False} - - response_data = { - "response": result, - "provider": info.name, - "model": info.model, - "session_id": session_id, - "conversation_id": log_resp.get("conversation_id"), - "pruning": { - "original_tokens": stats.original_tokens, - "pruned_tokens": stats.pruned_tokens, - "removed_count": stats.removed_count, - "budget": stats.budget, - "reserve_output_tokens": stats.reserve_output_tokens, - } - } -``` - -**Step 3:** In the `chat_stream()` function (around line 280), add session handling: - -**FIND:** -```python - gen = provider.complete(pruned_messages, stream=True) - - def sse_iterable(): # generator yielding bytes - try: - # Send a prelude event with meta - pre = { - "provider": info.name, - "model": info.model, - "pruning": { - "original_tokens": stats.original_tokens, - "pruned_tokens": stats.pruned_tokens, - "removed_count": stats.removed_count, - "budget": stats.budget, - "reserve_output_tokens": stats.reserve_output_tokens, - } - } - yield (f"event: meta\n" f"data: {json.dumps(pre)}\n\n").encode("utf-8") - - for chunk in gen: - if not chunk: - continue - payload = json.dumps({"delta": chunk}) - yield (f"data: {payload}\n\n").encode("utf-8") - - yield b"event: done\ndata: {}\n\n" -``` - -**REPLACE WITH:** -```python - gen = provider.complete(pruned_messages, stream=True) - session_id = body.get("session_id") or req.headers.get("X-Session-Id") or str(uuid4()) - - # Log initial user prompt once - try: - if pruned_messages and pruned_messages[-1].role == "user": - log_chat_message_safe( - session_id=session_id, - provider=info.name, - model=info.model, - role="user", - content=str(pruned_messages[-1].content)[:4000], - ) - except Exception as e: # noqa: BLE001 - logging.warning(f"Stream user log skipped: {e}") - - def sse_iterable(): # generator yielding bytes - try: - # Send a prelude event with meta - pre = { - "provider": info.name, - "model": info.model, - "pruning": { - "original_tokens": stats.original_tokens, - "pruned_tokens": stats.pruned_tokens, - "removed_count": stats.removed_count, - "budget": stats.budget, - "reserve_output_tokens": stats.reserve_output_tokens, - } - } - yield (f"event: meta\n" f"data: {json.dumps(pre)}\n\n").encode("utf-8") - - assistant_accum = [] - for chunk in gen: - if not chunk: - continue - payload = json.dumps({"delta": chunk}) - yield (f"data: {payload}\n\n").encode("utf-8") - assistant_accum.append(chunk) - - # Log full assistant reply at end - full_reply = "".join(assistant_accum) - try: - log_chat_message_safe( - session_id=session_id, - provider=info.name, - model=info.model, - role="assistant", - content=str(full_reply)[:8000], - ) - except Exception as e: # noqa: BLE001 - logging.warning(f"Stream assistant log skipped: {e}") - - yield b"event: done\ndata: {}\n\n" -``` - -## Database Deployment - -### Option 1: Azure SQL Database (Recommended for Production) - -```powershell -# 1. Create Azure SQL resources -az group create --name rg-qai-db --location eastus -az sql server create --name qai-sql-server --resource-group rg-qai-db --admin-user qai-admin --admin-password 'YourSecurePassword123!' -az sql db create --resource-group rg-qai-db --server qai-sql-server --name qai-db --service-objective S0 - -# 2. Configure firewall -az sql server firewall-rule create --resource-group rg-qai-db --server qai-sql-server --name AllowMyIP --start-ip-address YOUR_IP --end-ip-address YOUR_IP - -# 3. Build and deploy using VS Code SQL Database Projects extension -# - Open database/ folder in VS Code -# - Use "Publish" command from SQL Database Projects view -# - Target: qai-sql-server.database.windows.net, qai-db - -# 4. Set connection string in environment -$env:QAI_DB_CONN = "Server=tcp:qai-sql-server.database.windows.net,1433;Database=qai-db;Uid=qai-admin;Pwd=YourSecurePassword123!;Encrypt=yes;TrustServerCertificate=no;" -``` - -### Option 2: Local SQL Server (Development) - -```powershell -# 1. Install SQL Server Express (free) -# Download from: https://www.microsoft.com/en-us/sql-server/sql-server-downloads - -# 2. Deploy schema using SqlPackage or VS Code extension - -# 3. Set local connection string -$env:QAI_DB_CONN = "Server=localhost;Database=qai-db;Trusted_Connection=yes;" -``` - -## Testing the Integration - -### 1. Test without database (default behavior) - -```powershell -# No QAI_DB_CONN set → all logging is NO-OP -python .\scripts\quantum_autorun.py --job heart_quick -# Output: [quantum_autorun] DB logging skipped (QAI_DB_CONN not set) -``` - -### 2. Test with database - -```powershell -# Set connection string -$env:QAI_DB_CONN = "Server=tcp:your-server.database.windows.net,1433;Database=qai-db;Uid=qai-admin;Pwd=YourPassword;Encrypt=yes;TrustServerCertificate=no;" - -# Run quantum training -python .\scripts\quantum_autorun.py --job heart_quick -# Output: [quantum_autorun] Logged quantum run to DB (run_id=GUID) - -# Run LoRA training -python .\scripts\autotrain.py --job phi36_mixed_chat -# Output: [autotrain] Logged LoRA run to DB (run_id=GUID) - -# Test chat endpoint -func host start -curl -X POST http://localhost:7071/api/chat -H "Content-Type: application/json" -d '{"messages":[{"role":"user","content":"Hello"}]}' -# Response includes: "conversation_id": "GUID" -``` - -### 3. Verify data in database - -```sql --- Check quantum runs -SELECT TOP 10 * FROM QuantumTrainingRuns ORDER BY CreatedAt DESC; - --- Check LoRA runs -SELECT TOP 10 * FROM LoRATrainingRuns ORDER BY CreatedAt DESC; - --- Check chat conversations -SELECT TOP 10 * FROM ChatConversations ORDER BY CreatedAt DESC; - --- Check chat messages -SELECT TOP 50 * FROM ChatMessages ORDER BY Timestamp DESC; - --- Unified training summary -SELECT * FROM vw_TrainingRunsSummary ORDER BY CreatedAt DESC; - --- Dataset usage stats -SELECT * FROM vw_DatasetUsageStats ORDER BY TotalUsageCount DESC; -``` - -## Cost Estimates - -### Azure SQL Database - -- **Basic (5 DTU)**: ~$5/month - Good for dev/test -- **S0 (10 DTU)**: ~$15/month - Small production -- **S1 (20 DTU)**: ~$30/month - Medium production -- **Serverless**: ~$150/month active, auto-pause when idle - -### Recommendation - -- **Development**: Use local SQL Server Express (FREE) -- **Production**: Start with S0, scale up as needed -- **High traffic**: Use serverless with auto-pause for cost optimization - -## Troubleshooting - -### "pyodbc not found" - -```powershell -pip install pyodbc -``` - -### "ODBC Driver not found" - -Download and install: [ODBC Driver 18 for SQL Server](https://learn.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server) - -### Connection timeout - -- Check firewall rules in Azure Portal -- Verify server name and credentials -- Test with `sqlcmd` or Azure Data Studio first - -### Logging is silent - -- Expected behavior when `QAI_DB_CONN` not set -- Check warning messages: `[db_logging] WARN: DB unavailable: ...` -- Verify connection string format - -## Next Steps - -1. ✅ Complete manual edits to `function_app.py` (3 sections above) -2. Deploy database schema to Azure SQL or local SQL Server -3. Set `QAI_DB_CONN` environment variable -4. Run test training jobs and chat requests -5. Query database to verify logging -6. Set up Power BI dashboards for analytics (see `database/README.md`) - -## Architecture Benefits - -- **Fault-tolerant**: If DB unavailable, training/chat continues normally -- **Zero-config default**: Works without any database (NO-OP mode) -- **Pay-as-you-go**: Only incur costs when database is provisioned -- **Analytics-ready**: Views and stored procedures enable rich insights -- **Audit trail**: Complete history of all training runs and conversations - -## Reference - -- Database schema: `database/README.md` -- Stored procedures: `database/StoredProcedures/*.sql` -- Helper module: `shared/db_logging.py` -- Azure SQL docs: https://learn.microsoft.com/azure/azure-sql/ +# Database Integration Guide + +This document provides step-by-step instructions for integrating the QAI database with the existing codebase. + +## Overview + +The database integration adds automatic logging of: +- **Quantum training runs** → `QuantumTrainingRuns` table +- **LoRA training runs** → `LoRATrainingRuns` table +- **Chat messages** → `ChatConversations` + `ChatMessages` tables + +## Files Created + +✅ **Database Schema** (in `database/`): +- `Tables/*.sql` - 7 tables for core data +- `Views/*.sql` - 3 views for analytics +- `StoredProcedures/*.sql` - 4 SPs for easy logging +- `README.md` - Deployment and usage guide +- `database.sqlproj` - VS Code SQL Database Project + +✅ **Shared Module**: +- `shared/db_logging.py` - Safe, fault-tolerant logging helpers + +✅ **Configuration**: +- `requirements.txt` - Added `pyodbc` and `sqlalchemy` +- `local.settings.json` - Added `QAI_DB_CONN` placeholder +- `.env` - Environment variable template + +✅ **Script Patches**: +- `scripts/evaluation/quantum_autorun.py` - Logs quantum runs after success +- `scripts/training/autotrain.py` - Logs LoRA runs after success + +## Files That Need Manual Editing + +### 1. `function_app.py` - Add Chat Logging + +**Step 1:** Add imports at the top (after line 8): + +```python +import azure.functions as func +import json +import logging +import os +import sys +from pathlib import Path +import subprocess +import importlib.util as _iu +from uuid import uuid4 # ADD THIS + +# Optional DB logging (safe no-op if not configured) # ADD THIS BLOCK +try: # noqa: SIM105 + from shared.db_logging import log_chat_message_safe # type: ignore +except Exception: # noqa: BLE001 + def log_chat_message_safe(*_a, **_kw): # type: ignore + return {"success": False, "skipped": True} + +# Add talk-to-ai to path so we can import chat_providers +talk_to_ai_path = Path(__file__).resolve().parent / "talk-to-ai" / "src" +sys.path.insert(0, str(talk_to_ai_path)) +``` + +**Step 2:** In the `chat()` function (around line 200), replace the response_data section: + +**FIND:** +```python + # Get completion (non-streaming for HTTP simplicity) + result = provider.complete(pruned_messages, stream=False) + + # If result is still a generator, consume it + if hasattr(result, '__iter__') and not isinstance(result, str): + result = ''.join(result) + + response_data = { + "response": result, + "provider": info.name, + "model": info.model, + "pruning": { + "original_tokens": stats.original_tokens, + "pruned_tokens": stats.pruned_tokens, + "removed_count": stats.removed_count, + "budget": stats.budget, + "reserve_output_tokens": stats.reserve_output_tokens, + } + } +``` + +**REPLACE WITH:** +```python + # Get completion (non-streaming for HTTP simplicity) + result = provider.complete(pruned_messages, stream=False) + + # If result is still a generator, consume it + if hasattr(result, '__iter__') and not isinstance(result, str): + result = ''.join(result) + + # Session handling (client may pass session_id to maintain conversation linkage) + session_id = req_body.get("session_id") or req.headers.get("X-Session-Id") or str(uuid4()) + + # Log latest user message (last in list) BEFORE assistant reply + try: + if messages: + last_user = messages[-1] + if last_user.get("role") == "user": + log_chat_message_safe( + session_id=session_id, + provider=info.name, + model=info.model, + role="user", + content=str(last_user.get("content", ""))[:4000], # truncate safety + ) + except Exception as e: # noqa: BLE001 + logging.warning(f"User chat log skipped: {e}") + + # Log assistant reply + try: + log_resp = log_chat_message_safe( + session_id=session_id, + provider=info.name, + model=info.model, + role="assistant", + content=str(result)[:8000], # truncate safety + ) + except Exception as e: # noqa: BLE001 + logging.warning(f"Assistant chat log skipped: {e}") + log_resp = {"success": False} + + response_data = { + "response": result, + "provider": info.name, + "model": info.model, + "session_id": session_id, + "conversation_id": log_resp.get("conversation_id"), + "pruning": { + "original_tokens": stats.original_tokens, + "pruned_tokens": stats.pruned_tokens, + "removed_count": stats.removed_count, + "budget": stats.budget, + "reserve_output_tokens": stats.reserve_output_tokens, + } + } +``` + +**Step 3:** In the `chat_stream()` function (around line 280), add session handling: + +**FIND:** +```python + gen = provider.complete(pruned_messages, stream=True) + + def sse_iterable(): # generator yielding bytes + try: + # Send a prelude event with meta + pre = { + "provider": info.name, + "model": info.model, + "pruning": { + "original_tokens": stats.original_tokens, + "pruned_tokens": stats.pruned_tokens, + "removed_count": stats.removed_count, + "budget": stats.budget, + "reserve_output_tokens": stats.reserve_output_tokens, + } + } + yield (f"event: meta\n" f"data: {json.dumps(pre)}\n\n").encode("utf-8") + + for chunk in gen: + if not chunk: + continue + payload = json.dumps({"delta": chunk}) + yield (f"data: {payload}\n\n").encode("utf-8") + + yield b"event: done\ndata: {}\n\n" +``` + +**REPLACE WITH:** +```python + gen = provider.complete(pruned_messages, stream=True) + session_id = body.get("session_id") or req.headers.get("X-Session-Id") or str(uuid4()) + + # Log initial user prompt once + try: + if pruned_messages and pruned_messages[-1].role == "user": + log_chat_message_safe( + session_id=session_id, + provider=info.name, + model=info.model, + role="user", + content=str(pruned_messages[-1].content)[:4000], + ) + except Exception as e: # noqa: BLE001 + logging.warning(f"Stream user log skipped: {e}") + + def sse_iterable(): # generator yielding bytes + try: + # Send a prelude event with meta + pre = { + "provider": info.name, + "model": info.model, + "pruning": { + "original_tokens": stats.original_tokens, + "pruned_tokens": stats.pruned_tokens, + "removed_count": stats.removed_count, + "budget": stats.budget, + "reserve_output_tokens": stats.reserve_output_tokens, + } + } + yield (f"event: meta\n" f"data: {json.dumps(pre)}\n\n").encode("utf-8") + + assistant_accum = [] + for chunk in gen: + if not chunk: + continue + payload = json.dumps({"delta": chunk}) + yield (f"data: {payload}\n\n").encode("utf-8") + assistant_accum.append(chunk) + + # Log full assistant reply at end + full_reply = "".join(assistant_accum) + try: + log_chat_message_safe( + session_id=session_id, + provider=info.name, + model=info.model, + role="assistant", + content=str(full_reply)[:8000], + ) + except Exception as e: # noqa: BLE001 + logging.warning(f"Stream assistant log skipped: {e}") + + yield b"event: done\ndata: {}\n\n" +``` + +## Database Deployment + +### Option 1: Azure SQL Database (Recommended for Production) + +```powershell +# 1. Create Azure SQL resources +az group create --name rg-qai-db --location eastus +az sql server create --name qai-sql-server --resource-group rg-qai-db --admin-user qai-admin --admin-password 'YourSecurePassword123!' +az sql db create --resource-group rg-qai-db --server qai-sql-server --name qai-db --service-objective S0 + +# 2. Configure firewall +az sql server firewall-rule create --resource-group rg-qai-db --server qai-sql-server --name AllowMyIP --start-ip-address YOUR_IP --end-ip-address YOUR_IP + +# 3. Build and deploy using VS Code SQL Database Projects extension +# - Open database/ folder in VS Code +# - Use "Publish" command from SQL Database Projects view +# - Target: qai-sql-server.database.windows.net, qai-db + +# 4. Set connection string in environment +$env:QAI_DB_CONN = "Server=tcp:qai-sql-server.database.windows.net,1433;Database=qai-db;Uid=qai-admin;Pwd=YourSecurePassword123!;Encrypt=yes;TrustServerCertificate=no;" +``` + +### Option 2: Local SQL Server (Development) + +```powershell +# 1. Install SQL Server Express (free) +# Download from: https://www.microsoft.com/en-us/sql-server/sql-server-downloads + +# 2. Deploy schema using SqlPackage or VS Code extension + +# 3. Set local connection string +$env:QAI_DB_CONN = "Server=localhost;Database=qai-db;Trusted_Connection=yes;" +``` + +## Testing the Integration + +### 1. Test without database (default behavior) + +```powershell +# No QAI_DB_CONN set → all logging is NO-OP +python .\scripts\quantum_autorun.py --job heart_quick +# Output: [quantum_autorun] DB logging skipped (QAI_DB_CONN not set) +``` + +### 2. Test with database + +```powershell +# Set connection string +$env:QAI_DB_CONN = "Server=tcp:your-server.database.windows.net,1433;Database=qai-db;Uid=qai-admin;Pwd=YourPassword;Encrypt=yes;TrustServerCertificate=no;" + +# Run quantum training +python .\scripts\quantum_autorun.py --job heart_quick +# Output: [quantum_autorun] Logged quantum run to DB (run_id=GUID) + +# Run LoRA training +python .\scripts\autotrain.py --job phi36_mixed_chat +# Output: [autotrain] Logged LoRA run to DB (run_id=GUID) + +# Test chat endpoint +func host start +curl -X POST http://localhost:7071/api/chat -H "Content-Type: application/json" -d '{"messages":[{"role":"user","content":"Hello"}]}' +# Response includes: "conversation_id": "GUID" +``` + +### 3. Verify data in database + +```sql +-- Check quantum runs +SELECT TOP 10 * FROM QuantumTrainingRuns ORDER BY CreatedAt DESC; + +-- Check LoRA runs +SELECT TOP 10 * FROM LoRATrainingRuns ORDER BY CreatedAt DESC; + +-- Check chat conversations +SELECT TOP 10 * FROM ChatConversations ORDER BY CreatedAt DESC; + +-- Check chat messages +SELECT TOP 50 * FROM ChatMessages ORDER BY Timestamp DESC; + +-- Unified training summary +SELECT * FROM vw_TrainingRunsSummary ORDER BY CreatedAt DESC; + +-- Dataset usage stats +SELECT * FROM vw_DatasetUsageStats ORDER BY TotalUsageCount DESC; +``` + +## Cost Estimates + +### Azure SQL Database + +- **Basic (5 DTU)**: ~$5/month - Good for dev/test +- **S0 (10 DTU)**: ~$15/month - Small production +- **S1 (20 DTU)**: ~$30/month - Medium production +- **Serverless**: ~$150/month active, auto-pause when idle + +### Recommendation + +- **Development**: Use local SQL Server Express (FREE) +- **Production**: Start with S0, scale up as needed +- **High traffic**: Use serverless with auto-pause for cost optimization + +## Troubleshooting + +### "pyodbc not found" + +```powershell +pip install pyodbc +``` + +### "ODBC Driver not found" + +Download and install: [ODBC Driver 18 for SQL Server](https://learn.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server) + +### Connection timeout + +- Check firewall rules in Azure Portal +- Verify server name and credentials +- Test with `sqlcmd` or Azure Data Studio first + +### Logging is silent + +- Expected behavior when `QAI_DB_CONN` not set +- Check warning messages: `[db_logging] WARN: DB unavailable: ...` +- Verify connection string format + +## Next Steps + +1. ✅ Complete manual edits to `function_app.py` (3 sections above) +2. Deploy database schema to Azure SQL or local SQL Server +3. Set `QAI_DB_CONN` environment variable +4. Run test training jobs and chat requests +5. Query database to verify logging +6. Set up Power BI dashboards for analytics (see `database/README.md`) + +## Architecture Benefits + +- **Fault-tolerant**: If DB unavailable, training/chat continues normally +- **Zero-config default**: Works without any database (NO-OP mode) +- **Pay-as-you-go**: Only incur costs when database is provisioned +- **Analytics-ready**: Views and stored procedures enable rich insights +- **Audit trail**: Complete history of all training runs and conversations + +## Reference + +- Database schema: `database/README.md` +- Stored procedures: `database/StoredProcedures/*.sql` +- Helper module: `shared/db_logging.py` +- Azure SQL docs: https://learn.microsoft.com/azure/azure-sql/ diff --git a/docs/database/DATABASE_INTEGRATION_SUMMARY.md b/docs/database/DATABASE_INTEGRATION_SUMMARY.md index 4d7f2bbfb..c3d15c66f 100644 --- a/docs/database/DATABASE_INTEGRATION_SUMMARY.md +++ b/docs/database/DATABASE_INTEGRATION_SUMMARY.md @@ -1,357 +1,357 @@ -# QAI Database Integration & Semantic Memory - Complete Summary - -## ✅ What Was Accomplished - -Phase 2 complete: In addition to the core Azure SQL logging system (quantum, LoRA, chat), the workspace now has a **semantic chat memory** pipeline. - -Highlights: - -1. Automatic logging for quantum runs, LoRA runs, chat messages (no manual edits required now). -2. Embedding generation (Azure OpenAI > OpenAI > deterministic hash fallback) for each user message. -3. Similarity search (Python-side cosine) over recent embeddings (session-scoped when `session_id` provided) injects top‑K prior messages as system memory prompts. -4. New table `ChatMessageEmbeddings` stores per-message embeddings (VARBINARY float32 layout) enabling future migration to SQL native VECTOR type. -5. Backfill script ingests historical JSONL chat logs and retroactively populates embeddings. -6. Unit tests validate embedding fallback, serialization fidelity, cosine correctness. -7. Fault-tolerant design: absence of DB or embedding keys gracefully degrades to stateless chat. - -## 📦 Files Created / Updated (31 total) - -### Database Schema (`database/`) - -- **`database.sqlproj`** – SQL Database Project for VS Code -- **`Tables/QuantumTrainingRuns.sql`** – Quantum ML training metadata -- **`Tables/LoRATrainingRuns.sql`** – LoRA fine-tuning runs -- **`Tables/ChatConversations.sql`** – Chat sessions -- **`Tables/ChatMessages.sql`** – Individual messages (FK to conversations) -- **`Tables/Datasets.sql`** – Dataset registry -- **`Tables/DatasetUsage.sql`** – Usage tracking across runs -- **`Tables/AzureQuantumJobs.sql`** – Azure Quantum submissions -- **`Tables/OrchestratorJobs.sql`** – Orchestrator execution history -- **`Tables/MCPServerSessions.sql`** – MCP server tracking -- **`Tables/MCPToolCalls.sql`** – MCP tool invocations -- **`Views/vw_TrainingRunsSummary.sql`** – Unified training view -- **`Views/vw_DatasetUsageStats.sql`** – Dataset popularity -- **`Views/vw_AzureQuantumCostTracking.sql`** – Cost analysis -- **`StoredProcedures/sp_LogQuantumTrainingRun.sql`** – Log quantum runs -- **`StoredProcedures/sp_LogLoRATrainingRun.sql`** – Log LoRA runs -- **`StoredProcedures/sp_LogChatConversation.sql`** – Log chat messages -- **`StoredProcedures/sp_RegisterDataset.sql`** – Register datasets -- **`database/README.md`** – Deployment guide (updated with semantic memory section) -- **`Tables/ChatMessageEmbeddings.sql`** – Embedding storage for semantic memory (NEW) - -### Integration Code (`shared/`) - -- **`shared/db_logging.py`** – Fault-tolerant logging helpers (reused by memory logic) -- **`shared/chat_memory.py`** – Embedding generation + similarity retrieval (NEW) - -### Configuration Updates - -- **`requirements.txt`** – Added `pyodbc>=5.0.1` and `sqlalchemy>=2.0.29` -- **`local.settings.json`** – Added `QAI_DB_CONN` placeholder for Azure Functions -- **`.env`** – Environment variable template for local dev - -### Script Patches - -- **`scripts/evaluation/quantum_autorun.py`** – Integrated `log_quantum_run_safe()` after successful runs -- **`scripts/training/autotrain.py`** – Integrated `log_lora_run_safe()` after successful runs -- **`scripts/ingest_chat_logs_to_sql.py`** – Backfills chat logs & generates embeddings (NEW) - -### Documentation & Tests - -- **`DATABASE_INTEGRATION_GUIDE.md`** – Original guide (superseded for chat logging; retained for reference) -- **`DATABASE_INTEGRATION_SUMMARY.md`** – This summary (updated) -- **`tests/test_database_integration.py`** – Embedding + similarity unit tests (NEW) - -## 🎯 Integration Status - -| Component | Status | Details | -|-----------|--------|---------| -| Database Schema | ✅ Complete | 8 tables (added ChatMessageEmbeddings), 3 views, 4 stored procedures | -| Quantum Training Logging | ✅ Complete | Auto-logs via `log_quantum_run_safe()` | -| LoRA Training Logging | ✅ Complete | Auto-logs via `log_lora_run_safe()` | -| Chat Logging | ✅ Complete | `/api/chat` logs user + assistant messages automatically | -| Semantic Memory | ✅ Complete | Embedding generation, similarity retrieval, injection, storage | -| Backfill Utility | ✅ Complete | `scripts/ingest_chat_logs_to_sql.py` operational | -| Configuration | ✅ Complete | Conn string placeholder + embedding env var support | -| Tests | ✅ Added | 3 passing unit tests for memory utilities | -| Fault Tolerance | ✅ Implemented | Graceful NO-OP when DB or embeddings unavailable | - -## 🔧 Remaining Manual Steps - -### 1. Deploy / Publish Updated Schema - -Ensure new `ChatMessageEmbeddings` table is deployed (rebuild + publish SQL project). - -Choose one option: - -#### Option A: Azure SQL Database (Production) - -```powershell -az sql server create --name qai-sql-server --resource-group rg-qai-db --admin-user qai-admin --admin-password 'YourPassword' -az sql db create --resource-group rg-qai-db --server qai-sql-server --name qai-db --service-objective S0 -# Then publish from VS Code SQL Database Projects extension -``` - -#### Option B: Local SQL Server (Development) - -```powershell -# Install SQL Server Express (free) -# Deploy via VS Code SQL Database Projects extension -``` - -### 2. Set Connection String - -```powershell -# Azure SQL -$env:QAI_DB_CONN = "Server=tcp:qai-sql-server.database.windows.net,1433;Database=qai-db;Uid=qai-admin;Pwd=YourPassword;Encrypt=yes;TrustServerCertificate=no;" - -# Local SQL Server -$env:QAI_DB_CONN = "Server=localhost;Database=qai-db;Trusted_Connection=yes;" -``` - -### 3. Install Dependencies - -```powershell -pip install -r requirements.txt -# Installs pyodbc and sqlalchemy for database access -``` - -### 4. Test Integration - -```powershell -# Test quantum training logging -python .\scripts\quantum_autorun.py --job heart_quick -# Expected output: [quantum_autorun] Logged quantum run to DB (run_id=GUID) - -# Test LoRA training logging -python .\scripts\autotrain.py --job phi36_mixed_chat -# Expected output: [autotrain] Logged LoRA run to DB (run_id=GUID) - -# Test chat logging + memory -func host start -curl -X POST http://localhost:7071/api/chat -H "Content-Type: application/json" -d '{"session_id":"demo","messages":[{"role":"user","content":"Hello"}]}' -# Response should include: "conversation_id": "GUID" -``` - -## 📊 Database Schema Overview (Updated) - -### Core Tables (8) - -- **QuantumTrainingRuns** - Tracks n_qubits, layers, entanglement, accuracies, Azure hardware usage -- **LoRATrainingRuns** - Tracks model, dataset, hyperparams, LoRA rank/alpha/dropout -- **ChatConversations** - Session metadata, provider, model, message count -- **ChatMessages** - Individual messages with tokens, timing, finish_reason -- **ChatMessageEmbeddings** - Per-message embeddings (float32 serialized) for semantic retrieval -- **Datasets** - Registry with licensing (commercial/non-commercial), validation status -- **DatasetUsage** - Links datasets to training runs for lineage tracking -- **AzureQuantumJobs** - Job submissions, status, costs, results -- **OrchestratorJobs** - Execution history for quantum_autorun and autotrain -- **MCPServerSessions & MCPToolCalls** - MCP server activity tracking - -### Analytics Views (3) - -- **vw_TrainingRunsSummary** - Unified view of quantum + LoRA runs -- **vw_DatasetUsageStats** - Dataset popularity and last-used dates -- **vw_AzureQuantumCostTracking** - Cost analysis by provider/target - -### Stored Procedures (4) - -- **sp_LogQuantumTrainingRun** - Log quantum runs with auto dataset usage -- **sp_LogLoRATrainingRun** - Log LoRA runs with auto dataset usage -- **sp_LogChatConversation** - Auto-manage conversations + messages -- **sp_RegisterDataset** - Upsert dataset metadata - -## 🛡️ Safety & Resilience Features - -1. **Fault-Tolerant Logging**: Missing `QAI_DB_CONN` or driver → silent skip, core workflows continue. -2. **Zero Config Default**: Works fully without DB or embedding keys. -3. **Graceful Degradation**: Single warning emission prevents spam. -4. **Deterministic Fallback Embeddings**: Hash embedding ensures stable similarity ordering. -5. **Error Isolation**: Exceptions inside memory/logging never bubble to callers. -6. **Performance Guard**: Retrieval limited to most recent 500 embeddings. -7. **Future Ready**: Data layout compatible with migration to native VECTOR / external vector DB. - -## 💰 Cost & Optimization - -### Azure SQL Database - -- **Basic (5 DTU)**: ~$5/month - Dev/test -- **S0 (10 DTU)**: ~$15/month - Small production -- **S1 (20 DTU)**: ~$30/month - Medium production -- **Serverless**: ~$150/month active, auto-pause when idle - -### Local SQL Server Express - -- **FREE** - Ideal for development - -## 📈 Analytics & Memory Inspection - -### Sample Queries (from `database/README.md`) - -```sql --- Training success rate by dataset -SELECT DatasetName, AVG(TestAccuracy) AS AvgAccuracy, - SUM(CASE WHEN Status = 'completed' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS SuccessRate -FROM vw_TrainingRunsSummary -WHERE TrainingType = 'Quantum' -GROUP BY DatasetName; - --- Azure Quantum cost by month -SELECT DATEPART(year, SubmittedAt) AS Year, DATEPART(month, SubmittedAt) AS Month, - Provider, SUM(ActualCostUSD) AS TotalCost, COUNT(*) AS JobCount -FROM AzureQuantumJobs -WHERE Status = 'succeeded' -GROUP BY DATEPART(year, SubmittedAt), DATEPART(month, SubmittedAt), Provider; - --- Most active chat providers -SELECT Provider, COUNT(DISTINCT ConversationId) AS TotalConversations, - AVG(CAST(ExecutionTimeMs AS FLOAT)) AS AvgResponseTimeMs -FROM ChatConversations c JOIN ChatMessages m ON c.ConversationId = m.ConversationId -WHERE m.Role = 'assistant' -GROUP BY Provider; -``` - -### Power BI Integration - -Connect Power BI Desktop to Azure SQL: - -1. Get Data → Azure → Azure SQL Database -2. Server: `qai-sql-server.database.windows.net` -3. Database: `qai-db` -4. Use views for pre-aggregated data - -## 🔗 Integration Architecture (Extended) - -```text -┌─────────────────────────────────────────────────────────────┐ -│ QAI Workspace │ -├─────────────────────────────────────────────────────────────┤ -│ │ -│ quantum_autorun.py ──┐ │ -│ autotrain.py ─────────┤ │ -│ function_app.py ──────┼──> shared/db_logging.py ──────────┤ -│ │ (Fault-Tolerant Wrappers) │ -│ │ │ -│ └──> IF QAI_DB_CONN set ────────────┤ -│ │ -└─────────────────────────────────────────────────────────────┘ - │ - ▼ - ┌──────────────────────────────┐ - │ Azure SQL Database │ - │ or Local SQL Server │ - ├──────────────────────────────┤ - │ • QuantumTrainingRuns │ - │ • LoRATrainingRuns │ - │ • ChatConversations │ - │ • ChatMessages │ - │ • ChatMessageEmbeddings │ - │ • Datasets │ - │ • DatasetUsage │ - │ • AzureQuantumJobs │ - │ • OrchestratorJobs │ - │ • MCPServerSessions │ - └──────────────────────────────┘ - │ - ▼ - ┌──────────────────────────────┐ - │ Analytics & Dashboards │ - ├──────────────────────────────┤ - │ • Power BI Reports │ - │ • SQL Query Analysis │ - │ • Cost Tracking │ - │ • Usage Patterns │ - └──────────────────────────────┘ -``` - -## 📚 Documentation Reference - -- **Primary Guide**: `DATABASE_INTEGRATION_GUIDE.md` - Complete setup instructions -- **Database Docs**: `database/README.md` - Schema details, deployment, queries -- **Helper Module**: `shared/db_logging.py` - Implementation reference -- **Azure SQL Docs**: - -## ✨ Key Benefits (Expanded) - -1. **Complete Audit Trail** – Runs, conversations, embeddings captured. -2. **Context Continuity** – Semantic retrieval bridges stateless HTTP calls. -3. **Adaptive Memory** – Top‑K prior messages instead of replaying full history. -4. **Cost Optimization** – Reduces token overhead by embedding + selective recall. -5. **Dataset & Model Insights** – Unified views accelerate tuning & evaluation. -6. **Zero Disruption** – Memory additive; absence of DB/keys doesn’t degrade base behavior. -7. **Production Ready** – Indexed tables, foreign keys, views, fault tolerance. -8. **Personalization Isolation** – `session_id` scoping avoids cross-user leakage. -9. **Future Upgrade Path** – Ready for native VECTOR indexing or Cosmos DB vector search. - -## 🚀 Quick Start Checklist (Revised) - -- [x] ✅ Database schema created (schema + memory table deployed locally) -- [x] ✅ Logging module implemented (`shared/db_logging.py`) -- [x] ✅ Quantum orchestrator patched -- [x] ✅ LoRA orchestrator patched -- [x] ✅ Dependencies added (`pyodbc`, `sqlalchemy`) -- [x] ✅ Configuration templates created (`.env`, `local.settings.json`) -- [x] ✅ Documentation completed (2 guides) -- [x] ✅ Semantic memory + chat logging integrated (`function_app.py` patched) -- [ ] 🔄 Publish updated schema (include ChatMessageEmbeddings) -- [ ] 🔄 Set / verify `QAI_DB_CONN` -- [ ] 🔄 (Optional) Set embedding env vars: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_EMBEDDING_DEPLOYMENT` OR `OPENAI_API_KEY` -- [ ] 🔄 Backfill historical logs: `python .\scripts\ingest_chat_logs_to_sql.py` -- [ ] 🔄 Test memory: send related queries with same `session_id` and observe `memory_injected` - -## 🎉 Success Criteria (Updated) - -Integration is complete when: - -1. ✅ Quantum runs log to `QuantumTrainingRuns` table -2. ✅ LoRA runs log to `LoRATrainingRuns` table -3. ✅ Chat messages + embeddings stored; memory_injected > 0 for related follow-ups -4. ✅ All operations work normally when `QAI_DB_CONN` not set (NO-OP mode) -5. ✅ No errors or crashes from database integration - -**Current Status**: 100% feature complete (pending deployment of updated schema & optional embedding keys). - ---- - -**Next Action**: Publish updated schema & (optionally) configure embedding keys; then backfill logs for richer memory. - ---- - -### 🔍 Semantic Memory Request Example - -```bash -curl -X POST http://localhost:7071/api/chat \ - -H "Content-Type: application/json" \ - -d '{"session_id": "user42", "messages": [{"role": "user", "content": "Remind me what quantum backend we used last time."}]}' -``` - -Response excerpt: - -```json -{ - "provider": "local", - "model": "local-echo", - "memory_injected": 3, - "response": "Here's a concise take: ..." -} -``` - -### 🧪 Backfill Then Query - -```powershell -python .\scripts\ingest_chat_logs_to_sql.py --logs-dir talk-to-ai\logs --embed-assistant -``` - -Re-run chat with same `session_id` to leverage historical context. - -### 🚫 Graceful Failure Modes - -- Missing DB → logging & memory skipped (response still returned). -- Missing embedding keys → hash fallback (dim=256) still enables similarity. -- Excess history → capped to recent 500 embeddings. - -### 📌 Future Enhancements - -- Migrate similarity to native SQL VECTOR or external vector store. -- Add memory aging & summarization. -- Implement per-user memory quotas & retention policies. +# QAI Database Integration & Semantic Memory - Complete Summary + +## ✅ What Was Accomplished + +Phase 2 complete: In addition to the core Azure SQL logging system (quantum, LoRA, chat), the workspace now has a **semantic chat memory** pipeline. + +Highlights: + +1. Automatic logging for quantum runs, LoRA runs, chat messages (no manual edits required now). +2. Embedding generation (Azure OpenAI > OpenAI > deterministic hash fallback) for each user message. +3. Similarity search (Python-side cosine) over recent embeddings (session-scoped when `session_id` provided) injects top‑K prior messages as system memory prompts. +4. New table `ChatMessageEmbeddings` stores per-message embeddings (VARBINARY float32 layout) enabling future migration to SQL native VECTOR type. +5. Backfill script ingests historical JSONL chat logs and retroactively populates embeddings. +6. Unit tests validate embedding fallback, serialization fidelity, cosine correctness. +7. Fault-tolerant design: absence of DB or embedding keys gracefully degrades to stateless chat. + +## 📦 Files Created / Updated (31 total) + +### Database Schema (`database/`) + +- **`database.sqlproj`** – SQL Database Project for VS Code +- **`Tables/QuantumTrainingRuns.sql`** – Quantum ML training metadata +- **`Tables/LoRATrainingRuns.sql`** – LoRA fine-tuning runs +- **`Tables/ChatConversations.sql`** – Chat sessions +- **`Tables/ChatMessages.sql`** – Individual messages (FK to conversations) +- **`Tables/Datasets.sql`** – Dataset registry +- **`Tables/DatasetUsage.sql`** – Usage tracking across runs +- **`Tables/AzureQuantumJobs.sql`** – Azure Quantum submissions +- **`Tables/OrchestratorJobs.sql`** – Orchestrator execution history +- **`Tables/MCPServerSessions.sql`** – MCP server tracking +- **`Tables/MCPToolCalls.sql`** – MCP tool invocations +- **`Views/vw_TrainingRunsSummary.sql`** – Unified training view +- **`Views/vw_DatasetUsageStats.sql`** – Dataset popularity +- **`Views/vw_AzureQuantumCostTracking.sql`** – Cost analysis +- **`StoredProcedures/sp_LogQuantumTrainingRun.sql`** – Log quantum runs +- **`StoredProcedures/sp_LogLoRATrainingRun.sql`** – Log LoRA runs +- **`StoredProcedures/sp_LogChatConversation.sql`** – Log chat messages +- **`StoredProcedures/sp_RegisterDataset.sql`** – Register datasets +- **`database/README.md`** – Deployment guide (updated with semantic memory section) +- **`Tables/ChatMessageEmbeddings.sql`** – Embedding storage for semantic memory (NEW) + +### Integration Code (`shared/`) + +- **`shared/db_logging.py`** – Fault-tolerant logging helpers (reused by memory logic) +- **`shared/chat_memory.py`** – Embedding generation + similarity retrieval (NEW) + +### Configuration Updates + +- **`requirements.txt`** – Added `pyodbc>=5.0.1` and `sqlalchemy>=2.0.29` +- **`local.settings.json`** – Added `QAI_DB_CONN` placeholder for Azure Functions +- **`.env`** – Environment variable template for local dev + +### Script Patches + +- **`scripts/evaluation/quantum_autorun.py`** – Integrated `log_quantum_run_safe()` after successful runs +- **`scripts/training/autotrain.py`** – Integrated `log_lora_run_safe()` after successful runs +- **`scripts/ingest_chat_logs_to_sql.py`** – Backfills chat logs & generates embeddings (NEW) + +### Documentation & Tests + +- **`DATABASE_INTEGRATION_GUIDE.md`** – Original guide (superseded for chat logging; retained for reference) +- **`DATABASE_INTEGRATION_SUMMARY.md`** – This summary (updated) +- **`tests/test_database_integration.py`** – Embedding + similarity unit tests (NEW) + +## 🎯 Integration Status + +| Component | Status | Details | +|-----------|--------|---------| +| Database Schema | ✅ Complete | 8 tables (added ChatMessageEmbeddings), 3 views, 4 stored procedures | +| Quantum Training Logging | ✅ Complete | Auto-logs via `log_quantum_run_safe()` | +| LoRA Training Logging | ✅ Complete | Auto-logs via `log_lora_run_safe()` | +| Chat Logging | ✅ Complete | `/api/chat` logs user + assistant messages automatically | +| Semantic Memory | ✅ Complete | Embedding generation, similarity retrieval, injection, storage | +| Backfill Utility | ✅ Complete | `scripts/ingest_chat_logs_to_sql.py` operational | +| Configuration | ✅ Complete | Conn string placeholder + embedding env var support | +| Tests | ✅ Added | 3 passing unit tests for memory utilities | +| Fault Tolerance | ✅ Implemented | Graceful NO-OP when DB or embeddings unavailable | + +## 🔧 Remaining Manual Steps + +### 1. Deploy / Publish Updated Schema + +Ensure new `ChatMessageEmbeddings` table is deployed (rebuild + publish SQL project). + +Choose one option: + +#### Option A: Azure SQL Database (Production) + +```powershell +az sql server create --name qai-sql-server --resource-group rg-qai-db --admin-user qai-admin --admin-password 'YourPassword' +az sql db create --resource-group rg-qai-db --server qai-sql-server --name qai-db --service-objective S0 +# Then publish from VS Code SQL Database Projects extension +``` + +#### Option B: Local SQL Server (Development) + +```powershell +# Install SQL Server Express (free) +# Deploy via VS Code SQL Database Projects extension +``` + +### 2. Set Connection String + +```powershell +# Azure SQL +$env:QAI_DB_CONN = "Server=tcp:qai-sql-server.database.windows.net,1433;Database=qai-db;Uid=qai-admin;Pwd=YourPassword;Encrypt=yes;TrustServerCertificate=no;" + +# Local SQL Server +$env:QAI_DB_CONN = "Server=localhost;Database=qai-db;Trusted_Connection=yes;" +``` + +### 3. Install Dependencies + +```powershell +pip install -r requirements.txt +# Installs pyodbc and sqlalchemy for database access +``` + +### 4. Test Integration + +```powershell +# Test quantum training logging +python .\scripts\quantum_autorun.py --job heart_quick +# Expected output: [quantum_autorun] Logged quantum run to DB (run_id=GUID) + +# Test LoRA training logging +python .\scripts\autotrain.py --job phi36_mixed_chat +# Expected output: [autotrain] Logged LoRA run to DB (run_id=GUID) + +# Test chat logging + memory +func host start +curl -X POST http://localhost:7071/api/chat -H "Content-Type: application/json" -d '{"session_id":"demo","messages":[{"role":"user","content":"Hello"}]}' +# Response should include: "conversation_id": "GUID" +``` + +## 📊 Database Schema Overview (Updated) + +### Core Tables (8) + +- **QuantumTrainingRuns** - Tracks n_qubits, layers, entanglement, accuracies, Azure hardware usage +- **LoRATrainingRuns** - Tracks model, dataset, hyperparams, LoRA rank/alpha/dropout +- **ChatConversations** - Session metadata, provider, model, message count +- **ChatMessages** - Individual messages with tokens, timing, finish_reason +- **ChatMessageEmbeddings** - Per-message embeddings (float32 serialized) for semantic retrieval +- **Datasets** - Registry with licensing (commercial/non-commercial), validation status +- **DatasetUsage** - Links datasets to training runs for lineage tracking +- **AzureQuantumJobs** - Job submissions, status, costs, results +- **OrchestratorJobs** - Execution history for quantum_autorun and autotrain +- **MCPServerSessions & MCPToolCalls** - MCP server activity tracking + +### Analytics Views (3) + +- **vw_TrainingRunsSummary** - Unified view of quantum + LoRA runs +- **vw_DatasetUsageStats** - Dataset popularity and last-used dates +- **vw_AzureQuantumCostTracking** - Cost analysis by provider/target + +### Stored Procedures (4) + +- **sp_LogQuantumTrainingRun** - Log quantum runs with auto dataset usage +- **sp_LogLoRATrainingRun** - Log LoRA runs with auto dataset usage +- **sp_LogChatConversation** - Auto-manage conversations + messages +- **sp_RegisterDataset** - Upsert dataset metadata + +## 🛡️ Safety & Resilience Features + +1. **Fault-Tolerant Logging**: Missing `QAI_DB_CONN` or driver → silent skip, core workflows continue. +2. **Zero Config Default**: Works fully without DB or embedding keys. +3. **Graceful Degradation**: Single warning emission prevents spam. +4. **Deterministic Fallback Embeddings**: Hash embedding ensures stable similarity ordering. +5. **Error Isolation**: Exceptions inside memory/logging never bubble to callers. +6. **Performance Guard**: Retrieval limited to most recent 500 embeddings. +7. **Future Ready**: Data layout compatible with migration to native VECTOR / external vector DB. + +## 💰 Cost & Optimization + +### Azure SQL Database + +- **Basic (5 DTU)**: ~$5/month - Dev/test +- **S0 (10 DTU)**: ~$15/month - Small production +- **S1 (20 DTU)**: ~$30/month - Medium production +- **Serverless**: ~$150/month active, auto-pause when idle + +### Local SQL Server Express + +- **FREE** - Ideal for development + +## 📈 Analytics & Memory Inspection + +### Sample Queries (from `database/README.md`) + +```sql +-- Training success rate by dataset +SELECT DatasetName, AVG(TestAccuracy) AS AvgAccuracy, + SUM(CASE WHEN Status = 'completed' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS SuccessRate +FROM vw_TrainingRunsSummary +WHERE TrainingType = 'Quantum' +GROUP BY DatasetName; + +-- Azure Quantum cost by month +SELECT DATEPART(year, SubmittedAt) AS Year, DATEPART(month, SubmittedAt) AS Month, + Provider, SUM(ActualCostUSD) AS TotalCost, COUNT(*) AS JobCount +FROM AzureQuantumJobs +WHERE Status = 'succeeded' +GROUP BY DATEPART(year, SubmittedAt), DATEPART(month, SubmittedAt), Provider; + +-- Most active chat providers +SELECT Provider, COUNT(DISTINCT ConversationId) AS TotalConversations, + AVG(CAST(ExecutionTimeMs AS FLOAT)) AS AvgResponseTimeMs +FROM ChatConversations c JOIN ChatMessages m ON c.ConversationId = m.ConversationId +WHERE m.Role = 'assistant' +GROUP BY Provider; +``` + +### Power BI Integration + +Connect Power BI Desktop to Azure SQL: + +1. Get Data → Azure → Azure SQL Database +2. Server: `qai-sql-server.database.windows.net` +3. Database: `qai-db` +4. Use views for pre-aggregated data + +## 🔗 Integration Architecture (Extended) + +```text +┌─────────────────────────────────────────────────────────────┐ +│ QAI Workspace │ +├─────────────────────────────────────────────────────────────┤ +│ │ +│ quantum_autorun.py ──┐ │ +│ autotrain.py ─────────┤ │ +│ function_app.py ──────┼──> shared/db_logging.py ──────────┤ +│ │ (Fault-Tolerant Wrappers) │ +│ │ │ +│ └──> IF QAI_DB_CONN set ────────────┤ +│ │ +└─────────────────────────────────────────────────────────────┘ + │ + ▼ + ┌──────────────────────────────┐ + │ Azure SQL Database │ + │ or Local SQL Server │ + ├──────────────────────────────┤ + │ • QuantumTrainingRuns │ + │ • LoRATrainingRuns │ + │ • ChatConversations │ + │ • ChatMessages │ + │ • ChatMessageEmbeddings │ + │ • Datasets │ + │ • DatasetUsage │ + │ • AzureQuantumJobs │ + │ • OrchestratorJobs │ + │ • MCPServerSessions │ + └──────────────────────────────┘ + │ + ▼ + ┌──────────────────────────────┐ + │ Analytics & Dashboards │ + ├──────────────────────────────┤ + │ • Power BI Reports │ + │ • SQL Query Analysis │ + │ • Cost Tracking │ + │ • Usage Patterns │ + └──────────────────────────────┘ +``` + +## 📚 Documentation Reference + +- **Primary Guide**: `DATABASE_INTEGRATION_GUIDE.md` - Complete setup instructions +- **Database Docs**: `database/README.md` - Schema details, deployment, queries +- **Helper Module**: `shared/db_logging.py` - Implementation reference +- **Azure SQL Docs**: + +## ✨ Key Benefits (Expanded) + +1. **Complete Audit Trail** – Runs, conversations, embeddings captured. +2. **Context Continuity** – Semantic retrieval bridges stateless HTTP calls. +3. **Adaptive Memory** – Top‑K prior messages instead of replaying full history. +4. **Cost Optimization** – Reduces token overhead by embedding + selective recall. +5. **Dataset & Model Insights** – Unified views accelerate tuning & evaluation. +6. **Zero Disruption** – Memory additive; absence of DB/keys doesn’t degrade base behavior. +7. **Production Ready** – Indexed tables, foreign keys, views, fault tolerance. +8. **Personalization Isolation** – `session_id` scoping avoids cross-user leakage. +9. **Future Upgrade Path** – Ready for native VECTOR indexing or Cosmos DB vector search. + +## 🚀 Quick Start Checklist (Revised) + +- [x] ✅ Database schema created (schema + memory table deployed locally) +- [x] ✅ Logging module implemented (`shared/db_logging.py`) +- [x] ✅ Quantum orchestrator patched +- [x] ✅ LoRA orchestrator patched +- [x] ✅ Dependencies added (`pyodbc`, `sqlalchemy`) +- [x] ✅ Configuration templates created (`.env`, `local.settings.json`) +- [x] ✅ Documentation completed (2 guides) +- [x] ✅ Semantic memory + chat logging integrated (`function_app.py` patched) +- [ ] 🔄 Publish updated schema (include ChatMessageEmbeddings) +- [ ] 🔄 Set / verify `QAI_DB_CONN` +- [ ] 🔄 (Optional) Set embedding env vars: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_EMBEDDING_DEPLOYMENT` OR `OPENAI_API_KEY` +- [ ] 🔄 Backfill historical logs: `python .\scripts\ingest_chat_logs_to_sql.py` +- [ ] 🔄 Test memory: send related queries with same `session_id` and observe `memory_injected` + +## 🎉 Success Criteria (Updated) + +Integration is complete when: + +1. ✅ Quantum runs log to `QuantumTrainingRuns` table +2. ✅ LoRA runs log to `LoRATrainingRuns` table +3. ✅ Chat messages + embeddings stored; memory_injected > 0 for related follow-ups +4. ✅ All operations work normally when `QAI_DB_CONN` not set (NO-OP mode) +5. ✅ No errors or crashes from database integration + +**Current Status**: 100% feature complete (pending deployment of updated schema & optional embedding keys). + +--- + +**Next Action**: Publish updated schema & (optionally) configure embedding keys; then backfill logs for richer memory. + +--- + +### 🔍 Semantic Memory Request Example + +```bash +curl -X POST http://localhost:7071/api/chat \ + -H "Content-Type: application/json" \ + -d '{"session_id": "user42", "messages": [{"role": "user", "content": "Remind me what quantum backend we used last time."}]}' +``` + +Response excerpt: + +```json +{ + "provider": "local", + "model": "local-echo", + "memory_injected": 3, + "response": "Here's a concise take: ..." +} +``` + +### 🧪 Backfill Then Query + +```powershell +python .\scripts\ingest_chat_logs_to_sql.py --logs-dir talk-to-ai\logs --embed-assistant +``` + +Re-run chat with same `session_id` to leverage historical context. + +### 🚫 Graceful Failure Modes + +- Missing DB → logging & memory skipped (response still returned). +- Missing embedding keys → hash fallback (dim=256) still enables similarity. +- Excess history → capped to recent 500 embeddings. + +### 📌 Future Enhancements + +- Migrate similarity to native SQL VECTOR or external vector store. +- Add memory aging & summarization. +- Implement per-user memory quotas & retention policies. diff --git a/docs/database/DATABASE_SQL_SETUP.md b/docs/database/DATABASE_SQL_SETUP.md index 1d431052b..aea0f88e7 100644 --- a/docs/database/DATABASE_SQL_SETUP.md +++ b/docs/database/DATABASE_SQL_SETUP.md @@ -1,461 +1,461 @@ -# Multi-Database SQL Integration (Extended) - -This guide supplements the existing Azure SQL logging system by enabling **multi-database** access through a unified SQLAlchemy engine. It adds support for Azure SQL (SQL Server), PostgreSQL, MySQL/MariaDB, and SQLite with minimal configuration. - -## 1. Overview - -Existing logging uses stored procedures via `pyodbc` and `QAI_DB_CONN`. New layer adds: - -- Unified engine in `shared/sql_engine.py` -- Generic key-value repository in `shared/sql_repository.py` -- Health exposure via `/api/ai/status` (`sql` section) -- Pool metrics + slow query logging -- Optional multi-vendor support with a single environment variable: `QAI_SQL_URL` - -| Feature | Legacy (Stored Proc) | New (Engine/Repository) | -|---------|----------------------|--------------------------| -| Training / Chat Logging | ✅ (sp_Log*) | ✅ (unchanged) | -| Embeddings | ✅ | ✅ | -| Ad-hoc Queries | ❌ | ✅ (`quick_query`) | -| Multi-Vendor | Limited (SQL Server) | ✅ (Postgres, MySQL, SQLite) | -| Health Surface | Cosmos only | ✅ (`sql` payload) | -| Fault Tolerance | ✅ | ✅ | -| Pool Metrics | ❌ | ✅ (`engine_stats`) | -| Slow Query Warn | ❌ | ✅ (threshold env var) | - -## 2. Environment Variables - -Set **one** of: - -- `QAI_SQL_URL` – Preferred full SQLAlchemy URL. - - Examples: - - PostgreSQL: `postgresql+psycopg://user:pass@host/dbname` - - MySQL: `mysql+mysqlclient://user:pass@host/dbname` - - SQLite file: `sqlite:///data_out/dev.db` - - SQLite memory: `sqlite:///:memory:` (tests only) -- `QAI_DB_CONN` – Existing ODBC string (auto-wrapped to SQLAlchemy URL internally). - -Optional: - -- `QAI_SQL_SLOW_MS` – Milliseconds threshold for slow query logging (default `500`). - -If both URL and ODBC are set, `QAI_SQL_URL` wins. - -## 3. Installation - -Dependencies already include: - -```text -pyodbc>=5.0.1 -sqlalchemy>=2.0.29 -``` - -Optional (uncomment in `requirements.txt` if needed): - -```text -# psycopg2-binary>=2.9.9 # PostgreSQL -# mysqlclient>=2.2.4 # MySQL/MariaDB -``` - -Then install: - -```powershell -pip install -r requirements.txt -``` - -## 4. Status Endpoint Extension - -`/api/ai/status` now includes: - -```json -"sql": { - "enabled": true, - "url": "sqlite:///data_out/dev.db", - "vendor": "sqlite", - "connectivity": true, - "error": null, - "pool": { - "enabled": true, - "type": "SingletonThreadPool", - "size": null, - "checkedout": null, - "overflow": null, - "recycle": 1800, - "timeout": 30, - "status": "Pool size: ..." - } -} -``` - -If misconfigured: - -```json -"sql": {"enabled": false, "url": null} -``` - -## 5. Using the Repository - -```python -from shared.sql_repository import put_value, get_value, list_values, delete_value - -put_value("last_model", "phi3.6-mini") -print(get_value("last_model")) # => phi3.6-mini -print(list_values()) -delete_value("last_model") -``` - -## 6. Quick Ad-Hoc Queries - -```python -from shared.sql_engine import quick_query -rows = quick_query("SELECT TOP 5 * FROM QuantumTrainingRuns ORDER BY CreatedAt DESC") -for r in rows: - print(r) -``` - -(Use dialect-appropriate syntax; `TOP` works for SQL Server, use `LIMIT` for others.) - -### Slow Query Example (Artificial Delay for Testing) - -```python -from shared.sql_engine import quick_query -rows = quick_query("SELECT 1", simulate_delay=0.6) # sleeps 0.6s before execution -``` - -If `QAI_SQL_SLOW_MS=500`, a warning log is emitted: - -``` -[sql_engine] slow query (602.3 ms > 500 ms) vendor=sqlite sql=SELECT 1 -``` - -## 7. Table Creation Logic - -The key-value table auto-creates using vendor-specific syntax: - -- SQLite: `CREATE TABLE IF NOT EXISTS` -- PostgreSQL / MySQL: `CREATE TABLE IF NOT EXISTS` -- SQL Server: `IF NOT EXISTS (...) BEGIN CREATE TABLE ... END` - -## 8. Migration Script - -`scripts/sql_migrate.py` now: - -- Scans `database/migrations/*.sql` -- Ensures `QAI_Migrations` metadata table -- Skips already-applied migrations -- Logs summary counts - -Example output: - -``` -[sql_migrate] Found 3 migration(s); 1 already applied; 2 pending. -[sql_migrate] Applied 002_add_index.sql -[sql_migrate] Completed 2 migration(s). -``` - -Sample migration added: - -```sql --- database/migrations/001_keyvalue_index.sql -CREATE INDEX idx_qai_keyvalue_k ON QAI_KeyValue (k); -``` - -## 9. Testing - -`tests/test_sql_integration.py` uses an **in-memory SQLite** URL: - -```powershell -set QAI_SQL_URL=sqlite:///:memory: -pytest -k sql_integration -``` - -Additional tests cover: - -- Pool stats presence (`engine_stats`) -- Slow query warning via `simulate_delay` - -## 10. Best Practices (Azure Functions) - -- Reuse engine (global caching) – avoids SNAT exhaustion. -- Use `pool_pre_ping=True` to drop stale connections. -- Prefer `QAI_SQL_URL` for non-SQL Server backends. -- Keep write operations concise; batch where feasible. -- For high-throughput logging, consider async queue + worker pattern. -- Keep slow query threshold realistic (start 500–1000 ms; adjust via metrics). - -## 11. Security Notes - -- Do NOT commit secrets in `local.settings.json` or repo files. -- Use Managed Identity for Azure SQL (passwordless) once deployed. -- Rotate credentials regularly; prefer secret store (Key Vault). - -## 12. Fallback Behavior - -- Missing env vars → `sql.enabled=false` in status. -- Engine creation failure → logged warning, silent degradation. -- Repository operations return default (empty / None / False) without raising. -- Pool stats degrade gracefully (None values) if pool type lacks metrics. - -## 13. Future Enhancements - -- Additional migrations (schema evolution / analytics indexes) -- ORM models for complex analytics -- Connection saturation alerts (checked-out vs size) -- Retry & circuit breaker wrappers -- Write queue for high-frequency events -- Query tracing (OpenTelemetry spans) for diagnostics - -## 14. Quick Setup Examples - -### A. SQLite (Local Dev, Zero Config) - -```powershell -$env:QAI_SQL_URL = "sqlite:///data_out/dev.db" -func host start -``` - -### B. PostgreSQL - -```powershell -$env:QAI_SQL_URL = "postgresql+psycopg://user:pass@host:5432/qai" -pip install psycopg2-binary -func host start -``` - -### C. Azure SQL via ODBC (Existing) - -```powershell -$env:QAI_DB_CONN = "Driver={ODBC Driver 18 for SQL Server};Server=tcp:your-server.database.windows.net,1433;Database=qai-db;Uid=user;Pwd=***;Encrypt=yes;TrustServerCertificate=no;" -func host start -``` - -## 15. Verification Checklist - -- [ ] `sql.enabled=true` in `/api/ai/status` -- [ ] Key-value CRUD succeeds -- [ ] Pool stats visible (`sql.pool.enabled=true`) -- [ ] Slow query warning triggers when threshold exceeded -- [ ] Legacy logging continues without change -- [ ] No unexpected exceptions in function logs - -## 16. Troubleshooting - -| Symptom | Cause | Action | -|---------|-------|--------| -| `sql.enabled=false` | Missing env vars | Set `QAI_SQL_URL` or `QAI_DB_CONN` | -| Slow query warnings too frequent | Threshold too low | Raise `QAI_SQL_SLOW_MS` | -| Pool stats show `None` | Pool type lacks metrics (e.g., SQLite memory pool) | Accept defaults; switch to file-based SQLite or external DB | -| Migration fails | SQL dialect mismatch | Adjust SQL syntax for target vendor | -| `saturation_alert` appearing | Too many concurrent connections | Scale pool size or reduce workload concurrency | - -## 17. Advanced Monitoring Features - -### Pool Saturation Alerts - -The engine automatically monitors connection pool utilization and emits warnings when saturation exceeds **80%**: - -```json -"sql": { - "pool": { - "saturation_pct": 85.0, - "saturation_alert": "Pool 85.0% saturated (17/20)", - "size": 20, - "checkedout": 17 - }, - "alert": "Pool 85.0% saturated (17/20)" -} -``` - -**Actions on high saturation:** - -- Increase pool size: Add `?pool_size=50` to SQLAlchemy URL -- Review long-running queries (may hold connections) -- Enable connection logging: `?echo_pool=true` -- Consider read replicas or connection multiplexing - -### Slow Query Frequency Tracking - -Engine tracks slow queries in a **rolling 60-second window** (in-memory): - -```json -"sql": { - "pool": { - "slow_queries_1min": 15, - "slow_query_threshold_ms": 500 - }, - "slow_query_alert": "15 slow queries in last 60s (threshold=500ms)" -} -``` - -**High frequency indicators (>10 in 60s):** - -- Database performance degradation (check indexes, query plans) -- Network latency to database server -- Threshold too aggressive for workload -- Missing query optimization (consider caching frequent reads) - -### Environment-Aware Threshold Tuning - -Slow query threshold automatically adjusts based on deployment environment: - -| Environment | Threshold | Rationale | -|-------------|-----------|-----------| -| `development` (local) | 100ms | Fast feedback during development | -| `staging` / `test` | 300ms | Balanced for integration testing | -| `production` (default) | 500ms | Conservative for variable load | - -**Override with explicit env var:** - -```powershell -$env:QAI_SQL_SLOW_MS = "250" # Custom threshold -``` - -**Auto-detection via Azure Functions environment:** - -```powershell -$env:AZURE_FUNCTIONS_ENVIRONMENT = "staging" # Uses 300ms -``` - -### Query Performance Migration (Optional) - -Migration `002_query_performance_tracking.sql` creates a table for persistent query metrics: - -```sql --- Tracks: query_hash, sql_snippet, vendor, execution_time_ms, executed_at --- Enables: Historical analysis, trend detection, query frequency patterns -``` - -**Enable tracking** (future implementation): - -```powershell -$env:QAI_ENABLE_QUERY_TRACKING = "true" -``` - -**Use cases:** - -- Identify queries that degrade over time (growing data volume) -- Discover frequently-executed queries (caching candidates) -- Correlate slow queries with deployment or schema changes -- Generate weekly performance reports - -## 18. Pool Scaling Strategies - -When `sql.pool.saturation_pct` consistently exceeds 60-80%, consider scaling the connection pool. - -### URL Parameter Method (Recommended) - -SQLAlchemy supports pool configuration via URL query parameters: - -```powershell -# PostgreSQL with custom pool size -$env:QAI_SQL_URL = "postgresql+psycopg2://user:pass@host/db?pool_size=30&max_overflow=10" - -# Azure SQL via ODBC with custom pool -$env:QAI_SQL_URL = "mssql+pyodbc://user:pass@host.database.windows.net/db?driver=ODBC+Driver+18+for+SQL+Server&pool_size=30&max_overflow=15" - -# SQLite with custom timeout -$env:QAI_SQL_URL = "sqlite:///path/to/db.sqlite?timeout=30" -``` - -### Pool Size Guidelines - -| Scenario | Pool Size | Max Overflow | Rationale | -|----------|-----------|--------------|-----------| -| Low traffic (< 10 req/s) | 10 | 5 | Minimize idle connections | -| Medium traffic (10-50 req/s) | 20-30 | 10 | Balanced for burst capacity | -| High traffic (> 50 req/s) | 30-50 | 20 | Prevent saturation under load | -| Background workers | 5 | 2 | Dedicated small pool | - -**Calculation formula:** - -```text -pool_size = (function_instances × avg_concurrent_requests_per_instance) × 1.2 -``` - -**Example:** - -- 5 function instances (Azure Functions consumption plan) -- 4 concurrent requests per instance average -- Pool size = 5 × 4 × 1.2 = **24 connections** - -### Additional URL Parameters - -| Parameter | Default | Description | -|-----------|---------|-------------| -| `pool_size` | 5 | Core pool size (always open) | -| `max_overflow` | 10 | Additional connections on demand | -| `pool_timeout` | 30 | Seconds to wait for available connection | -| `pool_recycle` | 1800 | Seconds before recycling connection (30 min) | -| `pool_pre_ping` | false | Test connection before using (recommended: true, set in code) | - -### Code Override (Not Recommended) - -If URL parameters don't work, modify `shared/sql_engine.py`: - -```python -_ENGINE = create_engine( - url, - pool_pre_ping=True, - pool_recycle=1800, - pool_size=30, # Override - max_overflow=10, # Override - future=True, -) -``` - -**Downside**: Requires code changes per environment. - -### Monitoring Pool Effectiveness - -After scaling, monitor for 24-48 hours: - -```kql -// Application Insights KQL -traces -| where message has "[sql_engine]" and message has "saturated" -| extend saturation_pct = todouble(extract(@"(\d+\.\d+)% saturated", 1, message)) -| summarize MaxSaturation = max(saturation_pct) by bin(timestamp, 1h) -| where MaxSaturation > 60 -``` - -**Action**: If `MaxSaturation` still > 60%, increase pool size by 50%. - -## 19. Azure Monitor Integration - -For production deployments, integrate with Azure Monitor for automated alerting and historical analysis. - -**Quick Start:** - -```powershell -# Deploy alerts with automation script -.\scripts\setup_azure_alerts.ps1 ` - -ResourceGroup "your-rg" ` - -FunctionAppName "your-func-app" ` - -ActionGroupName "sql-alerts" ` - -EmailRecipient "admin@example.com" -``` - -**Features:** - -- Metric alerts for pool saturation > 80% -- Scheduled query rules for slow query frequency bursts -- KQL queries for P50/P95/P99 latency analysis -- Action Groups for email/SMS/webhook notifications - -**Full documentation**: [AZURE_MONITOR_SQL_SETUP.md](./AZURE_MONITOR_SQL_SETUP.md) - -## 20. Monitoring Checklist for Production - -- [ ] Set `QAI_SQL_SLOW_MS` appropriate for workload (start 500ms, tune down) -- [ ] Monitor `sql.pool.saturation_pct` in Application Insights or logs -- [ ] Deploy Azure Monitor alerts via `setup_azure_alerts.ps1` -- [ ] Review `slow_queries_1min` during load testing -- [ ] Enable query tracking migration if historical analysis needed -- [ ] Configure pool size based on concurrent function instances (see section 18) -- [ ] Use Managed Identity for passwordless Azure SQL connections -- [ ] Schedule periodic review of migration `QAI_Migrations` table -- [ ] Set up weekly retention cleanup for `QAI_QueryMetrics` (if enabled) -- [ ] Review KQL dashboards weekly for performance trends - ---- -Multi-database support is additive and non-invasive. If you do not set a URL, the system behaves exactly as before. +# Multi-Database SQL Integration (Extended) + +This guide supplements the existing Azure SQL logging system by enabling **multi-database** access through a unified SQLAlchemy engine. It adds support for Azure SQL (SQL Server), PostgreSQL, MySQL/MariaDB, and SQLite with minimal configuration. + +## 1. Overview + +Existing logging uses stored procedures via `pyodbc` and `QAI_DB_CONN`. New layer adds: + +- Unified engine in `shared/sql_engine.py` +- Generic key-value repository in `shared/sql_repository.py` +- Health exposure via `/api/ai/status` (`sql` section) +- Pool metrics + slow query logging +- Optional multi-vendor support with a single environment variable: `QAI_SQL_URL` + +| Feature | Legacy (Stored Proc) | New (Engine/Repository) | +|---------|----------------------|--------------------------| +| Training / Chat Logging | ✅ (sp_Log*) | ✅ (unchanged) | +| Embeddings | ✅ | ✅ | +| Ad-hoc Queries | ❌ | ✅ (`quick_query`) | +| Multi-Vendor | Limited (SQL Server) | ✅ (Postgres, MySQL, SQLite) | +| Health Surface | Cosmos only | ✅ (`sql` payload) | +| Fault Tolerance | ✅ | ✅ | +| Pool Metrics | ❌ | ✅ (`engine_stats`) | +| Slow Query Warn | ❌ | ✅ (threshold env var) | + +## 2. Environment Variables + +Set **one** of: + +- `QAI_SQL_URL` – Preferred full SQLAlchemy URL. + - Examples: + - PostgreSQL: `postgresql+psycopg://user:pass@host/dbname` + - MySQL: `mysql+mysqlclient://user:pass@host/dbname` + - SQLite file: `sqlite:///data_out/dev.db` + - SQLite memory: `sqlite:///:memory:` (tests only) +- `QAI_DB_CONN` – Existing ODBC string (auto-wrapped to SQLAlchemy URL internally). + +Optional: + +- `QAI_SQL_SLOW_MS` – Milliseconds threshold for slow query logging (default `500`). + +If both URL and ODBC are set, `QAI_SQL_URL` wins. + +## 3. Installation + +Dependencies already include: + +```text +pyodbc>=5.0.1 +sqlalchemy>=2.0.29 +``` + +Optional (uncomment in `requirements.txt` if needed): + +```text +# psycopg2-binary>=2.9.9 # PostgreSQL +# mysqlclient>=2.2.4 # MySQL/MariaDB +``` + +Then install: + +```powershell +pip install -r requirements.txt +``` + +## 4. Status Endpoint Extension + +`/api/ai/status` now includes: + +```json +"sql": { + "enabled": true, + "url": "sqlite:///data_out/dev.db", + "vendor": "sqlite", + "connectivity": true, + "error": null, + "pool": { + "enabled": true, + "type": "SingletonThreadPool", + "size": null, + "checkedout": null, + "overflow": null, + "recycle": 1800, + "timeout": 30, + "status": "Pool size: ..." + } +} +``` + +If misconfigured: + +```json +"sql": {"enabled": false, "url": null} +``` + +## 5. Using the Repository + +```python +from shared.sql_repository import put_value, get_value, list_values, delete_value + +put_value("last_model", "phi3.6-mini") +print(get_value("last_model")) # => phi3.6-mini +print(list_values()) +delete_value("last_model") +``` + +## 6. Quick Ad-Hoc Queries + +```python +from shared.sql_engine import quick_query +rows = quick_query("SELECT TOP 5 * FROM QuantumTrainingRuns ORDER BY CreatedAt DESC") +for r in rows: + print(r) +``` + +(Use dialect-appropriate syntax; `TOP` works for SQL Server, use `LIMIT` for others.) + +### Slow Query Example (Artificial Delay for Testing) + +```python +from shared.sql_engine import quick_query +rows = quick_query("SELECT 1", simulate_delay=0.6) # sleeps 0.6s before execution +``` + +If `QAI_SQL_SLOW_MS=500`, a warning log is emitted: + +``` +[sql_engine] slow query (602.3 ms > 500 ms) vendor=sqlite sql=SELECT 1 +``` + +## 7. Table Creation Logic + +The key-value table auto-creates using vendor-specific syntax: + +- SQLite: `CREATE TABLE IF NOT EXISTS` +- PostgreSQL / MySQL: `CREATE TABLE IF NOT EXISTS` +- SQL Server: `IF NOT EXISTS (...) BEGIN CREATE TABLE ... END` + +## 8. Migration Script + +`scripts/sql_migrate.py` now: + +- Scans `database/migrations/*.sql` +- Ensures `QAI_Migrations` metadata table +- Skips already-applied migrations +- Logs summary counts + +Example output: + +``` +[sql_migrate] Found 3 migration(s); 1 already applied; 2 pending. +[sql_migrate] Applied 002_add_index.sql +[sql_migrate] Completed 2 migration(s). +``` + +Sample migration added: + +```sql +-- database/migrations/001_keyvalue_index.sql +CREATE INDEX idx_qai_keyvalue_k ON QAI_KeyValue (k); +``` + +## 9. Testing + +`tests/test_sql_integration.py` uses an **in-memory SQLite** URL: + +```powershell +set QAI_SQL_URL=sqlite:///:memory: +pytest -k sql_integration +``` + +Additional tests cover: + +- Pool stats presence (`engine_stats`) +- Slow query warning via `simulate_delay` + +## 10. Best Practices (Azure Functions) + +- Reuse engine (global caching) – avoids SNAT exhaustion. +- Use `pool_pre_ping=True` to drop stale connections. +- Prefer `QAI_SQL_URL` for non-SQL Server backends. +- Keep write operations concise; batch where feasible. +- For high-throughput logging, consider async queue + worker pattern. +- Keep slow query threshold realistic (start 500–1000 ms; adjust via metrics). + +## 11. Security Notes + +- Do NOT commit secrets in `local.settings.json` or repo files. +- Use Managed Identity for Azure SQL (passwordless) once deployed. +- Rotate credentials regularly; prefer secret store (Key Vault). + +## 12. Fallback Behavior + +- Missing env vars → `sql.enabled=false` in status. +- Engine creation failure → logged warning, silent degradation. +- Repository operations return default (empty / None / False) without raising. +- Pool stats degrade gracefully (None values) if pool type lacks metrics. + +## 13. Future Enhancements + +- Additional migrations (schema evolution / analytics indexes) +- ORM models for complex analytics +- Connection saturation alerts (checked-out vs size) +- Retry & circuit breaker wrappers +- Write queue for high-frequency events +- Query tracing (OpenTelemetry spans) for diagnostics + +## 14. Quick Setup Examples + +### A. SQLite (Local Dev, Zero Config) + +```powershell +$env:QAI_SQL_URL = "sqlite:///data_out/dev.db" +func host start +``` + +### B. PostgreSQL + +```powershell +$env:QAI_SQL_URL = "postgresql+psycopg://user:pass@host:5432/qai" +pip install psycopg2-binary +func host start +``` + +### C. Azure SQL via ODBC (Existing) + +```powershell +$env:QAI_DB_CONN = "Driver={ODBC Driver 18 for SQL Server};Server=tcp:your-server.database.windows.net,1433;Database=qai-db;Uid=user;Pwd=***;Encrypt=yes;TrustServerCertificate=no;" +func host start +``` + +## 15. Verification Checklist + +- [ ] `sql.enabled=true` in `/api/ai/status` +- [ ] Key-value CRUD succeeds +- [ ] Pool stats visible (`sql.pool.enabled=true`) +- [ ] Slow query warning triggers when threshold exceeded +- [ ] Legacy logging continues without change +- [ ] No unexpected exceptions in function logs + +## 16. Troubleshooting + +| Symptom | Cause | Action | +|---------|-------|--------| +| `sql.enabled=false` | Missing env vars | Set `QAI_SQL_URL` or `QAI_DB_CONN` | +| Slow query warnings too frequent | Threshold too low | Raise `QAI_SQL_SLOW_MS` | +| Pool stats show `None` | Pool type lacks metrics (e.g., SQLite memory pool) | Accept defaults; switch to file-based SQLite or external DB | +| Migration fails | SQL dialect mismatch | Adjust SQL syntax for target vendor | +| `saturation_alert` appearing | Too many concurrent connections | Scale pool size or reduce workload concurrency | + +## 17. Advanced Monitoring Features + +### Pool Saturation Alerts + +The engine automatically monitors connection pool utilization and emits warnings when saturation exceeds **80%**: + +```json +"sql": { + "pool": { + "saturation_pct": 85.0, + "saturation_alert": "Pool 85.0% saturated (17/20)", + "size": 20, + "checkedout": 17 + }, + "alert": "Pool 85.0% saturated (17/20)" +} +``` + +**Actions on high saturation:** + +- Increase pool size: Add `?pool_size=50` to SQLAlchemy URL +- Review long-running queries (may hold connections) +- Enable connection logging: `?echo_pool=true` +- Consider read replicas or connection multiplexing + +### Slow Query Frequency Tracking + +Engine tracks slow queries in a **rolling 60-second window** (in-memory): + +```json +"sql": { + "pool": { + "slow_queries_1min": 15, + "slow_query_threshold_ms": 500 + }, + "slow_query_alert": "15 slow queries in last 60s (threshold=500ms)" +} +``` + +**High frequency indicators (>10 in 60s):** + +- Database performance degradation (check indexes, query plans) +- Network latency to database server +- Threshold too aggressive for workload +- Missing query optimization (consider caching frequent reads) + +### Environment-Aware Threshold Tuning + +Slow query threshold automatically adjusts based on deployment environment: + +| Environment | Threshold | Rationale | +|-------------|-----------|-----------| +| `development` (local) | 100ms | Fast feedback during development | +| `staging` / `test` | 300ms | Balanced for integration testing | +| `production` (default) | 500ms | Conservative for variable load | + +**Override with explicit env var:** + +```powershell +$env:QAI_SQL_SLOW_MS = "250" # Custom threshold +``` + +**Auto-detection via Azure Functions environment:** + +```powershell +$env:AZURE_FUNCTIONS_ENVIRONMENT = "staging" # Uses 300ms +``` + +### Query Performance Migration (Optional) + +Migration `002_query_performance_tracking.sql` creates a table for persistent query metrics: + +```sql +-- Tracks: query_hash, sql_snippet, vendor, execution_time_ms, executed_at +-- Enables: Historical analysis, trend detection, query frequency patterns +``` + +**Enable tracking** (future implementation): + +```powershell +$env:QAI_ENABLE_QUERY_TRACKING = "true" +``` + +**Use cases:** + +- Identify queries that degrade over time (growing data volume) +- Discover frequently-executed queries (caching candidates) +- Correlate slow queries with deployment or schema changes +- Generate weekly performance reports + +## 18. Pool Scaling Strategies + +When `sql.pool.saturation_pct` consistently exceeds 60-80%, consider scaling the connection pool. + +### URL Parameter Method (Recommended) + +SQLAlchemy supports pool configuration via URL query parameters: + +```powershell +# PostgreSQL with custom pool size +$env:QAI_SQL_URL = "postgresql+psycopg2://user:pass@host/db?pool_size=30&max_overflow=10" + +# Azure SQL via ODBC with custom pool +$env:QAI_SQL_URL = "mssql+pyodbc://user:pass@host.database.windows.net/db?driver=ODBC+Driver+18+for+SQL+Server&pool_size=30&max_overflow=15" + +# SQLite with custom timeout +$env:QAI_SQL_URL = "sqlite:///path/to/db.sqlite?timeout=30" +``` + +### Pool Size Guidelines + +| Scenario | Pool Size | Max Overflow | Rationale | +|----------|-----------|--------------|-----------| +| Low traffic (< 10 req/s) | 10 | 5 | Minimize idle connections | +| Medium traffic (10-50 req/s) | 20-30 | 10 | Balanced for burst capacity | +| High traffic (> 50 req/s) | 30-50 | 20 | Prevent saturation under load | +| Background workers | 5 | 2 | Dedicated small pool | + +**Calculation formula:** + +```text +pool_size = (function_instances × avg_concurrent_requests_per_instance) × 1.2 +``` + +**Example:** + +- 5 function instances (Azure Functions consumption plan) +- 4 concurrent requests per instance average +- Pool size = 5 × 4 × 1.2 = **24 connections** + +### Additional URL Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `pool_size` | 5 | Core pool size (always open) | +| `max_overflow` | 10 | Additional connections on demand | +| `pool_timeout` | 30 | Seconds to wait for available connection | +| `pool_recycle` | 1800 | Seconds before recycling connection (30 min) | +| `pool_pre_ping` | false | Test connection before using (recommended: true, set in code) | + +### Code Override (Not Recommended) + +If URL parameters don't work, modify `shared/sql_engine.py`: + +```python +_ENGINE = create_engine( + url, + pool_pre_ping=True, + pool_recycle=1800, + pool_size=30, # Override + max_overflow=10, # Override + future=True, +) +``` + +**Downside**: Requires code changes per environment. + +### Monitoring Pool Effectiveness + +After scaling, monitor for 24-48 hours: + +```kql +// Application Insights KQL +traces +| where message has "[sql_engine]" and message has "saturated" +| extend saturation_pct = todouble(extract(@"(\d+\.\d+)% saturated", 1, message)) +| summarize MaxSaturation = max(saturation_pct) by bin(timestamp, 1h) +| where MaxSaturation > 60 +``` + +**Action**: If `MaxSaturation` still > 60%, increase pool size by 50%. + +## 19. Azure Monitor Integration + +For production deployments, integrate with Azure Monitor for automated alerting and historical analysis. + +**Quick Start:** + +```powershell +# Deploy alerts with automation script +.\scripts\setup_azure_alerts.ps1 ` + -ResourceGroup "your-rg" ` + -FunctionAppName "your-func-app" ` + -ActionGroupName "sql-alerts" ` + -EmailRecipient "admin@example.com" +``` + +**Features:** + +- Metric alerts for pool saturation > 80% +- Scheduled query rules for slow query frequency bursts +- KQL queries for P50/P95/P99 latency analysis +- Action Groups for email/SMS/webhook notifications + +**Full documentation**: [AZURE_MONITOR_SQL_SETUP.md](./AZURE_MONITOR_SQL_SETUP.md) + +## 20. Monitoring Checklist for Production + +- [ ] Set `QAI_SQL_SLOW_MS` appropriate for workload (start 500ms, tune down) +- [ ] Monitor `sql.pool.saturation_pct` in Application Insights or logs +- [ ] Deploy Azure Monitor alerts via `setup_azure_alerts.ps1` +- [ ] Review `slow_queries_1min` during load testing +- [ ] Enable query tracking migration if historical analysis needed +- [ ] Configure pool size based on concurrent function instances (see section 18) +- [ ] Use Managed Identity for passwordless Azure SQL connections +- [ ] Schedule periodic review of migration `QAI_Migrations` table +- [ ] Set up weekly retention cleanup for `QAI_QueryMetrics` (if enabled) +- [ ] Review KQL dashboards weekly for performance trends + +--- +Multi-database support is additive and non-invasive. If you do not set a URL, the system behaves exactly as before. diff --git a/docs/database/SQL_AUTOMATION_COMPLETE.md b/docs/database/SQL_AUTOMATION_COMPLETE.md index e59d96a93..96d924f26 100644 --- a/docs/database/SQL_AUTOMATION_COMPLETE.md +++ b/docs/database/SQL_AUTOMATION_COMPLETE.md @@ -1,441 +1,441 @@ -# SQL Integration Automation Suite - -Complete automation tools for deployment, monitoring, and maintenance. - -## Overview - -This automation suite provides end-to-end management of the SQL integration with zero manual intervention capabilities. All scripts support dry-run mode for safety. - -## Automation Components - -### 1. Master Deployment Script - -**File**: `scripts/deploy_sql_integration.ps1` - -One-command deployment orchestrating all integration steps. - -**Features**: - -- Environment validation (prerequisites, Azure CLI, packages) -- Database migrations with rollback on failure -- Comprehensive test execution -- Azure alert deployment -- Health endpoint verification -- Status tracking and logging - -**Usage**: - -```powershell -# Local development -.\scripts\deploy_sql_integration.ps1 -Environment Local - -# Production with alerts -.\scripts\deploy_sql_integration.ps1 ` - -Environment Production ` - -ResourceGroup "qai-rg" ` - -FunctionAppName "qai-func" ` - -EmailRecipient "admin@example.com" - -# Dry-run mode -.\scripts\deploy_sql_integration.ps1 -Environment Production -DryRun -``` - -**Exit Codes**: - -- `0`: Success -- `1`: Failure (check log file in `data_out/`) - -### 2. Threshold Auto-Tuning - -**File**: `scripts/tune_sql_thresholds.ps1` - -Analyzes Application Insights metrics and recommends optimal slow query thresholds. - -**Features**: - -- P50/P95/P99 percentile analysis -- Alert frequency tracking -- Environment-aware recommendations -- Automatic threshold application to Azure -- JSON analysis reports - -**Usage**: - -```powershell -# Analyze current metrics -.\scripts\tune_sql_thresholds.ps1 ` - -ResourceGroup "qai-rg" ` - -FunctionAppName "qai-func" - -# Auto-apply recommended threshold -.\scripts\tune_sql_thresholds.ps1 ` - -ResourceGroup "qai-rg" ` - -FunctionAppName "qai-func" ` - -Apply - -# Custom baseline period -.\scripts\tune_sql_thresholds.ps1 ` - -ResourceGroup "qai-rg" ` - -FunctionAppName "qai-func" ` - -DaysBack 14 ` - -TargetAlertsPerDay 3 -``` - -**Output**: Analysis report saved to `data_out/threshold_analysis_*.json` - -### 3. Query Metrics Cleanup - -**File**: `scripts/cleanup_query_metrics.py` - -Automated retention management for query performance tracking data. - -**Features**: - -- Configurable retention period -- Table statistics before/after cleanup -- Dry-run mode -- Timestamped logging - -**Usage**: - -```powershell -# Default 7-day retention -python .\scripts\cleanup_query_metrics.py - -# Custom retention period -python .\scripts\cleanup_query_metrics.py --retention-days 30 - -# Dry-run mode -python .\scripts\cleanup_query_metrics.py --retention-days 7 --dry-run -``` - -**Scheduling** (Windows Task Scheduler): - -```powershell -# Run weekly on Sundays at 2 AM -$action = New-ScheduledTaskAction -Execute "python" -Argument ".\scripts\cleanup_query_metrics.py --retention-days 7" -WorkingDirectory "C:\path\to\repo" -$trigger = New-ScheduledTaskTrigger -Weekly -DaysOfWeek Sunday -At 2am -Register-ScheduledTask -TaskName "QAI SQL Cleanup" -Action $action -Trigger $trigger -``` - -### 4. Health Monitoring - -**File**: `scripts/sql_health_monitor.py` - -Continuous health monitoring with alerting capabilities. - -**Features**: - -- Connectivity probes -- Pool saturation detection -- Slow query frequency tracking -- Webhook alerts for critical issues -- Alert cooldown (5-minute intervals) -- JSON output mode - -**Usage**: - -```powershell -# Single health check -python .\scripts\sql_health_monitor.py --once - -# JSON output (for parsing) -python .\scripts\sql_health_monitor.py --once --json - -# Continuous monitoring -python .\scripts\sql_health_monitor.py --interval 60 - -# With webhook alerts -python .\scripts\sql_health_monitor.py ` - --interval 30 ` - --threshold-critical 90 ` - --alert-webhook "https://hooks.slack.com/services/..." -``` - -**Exit Codes** (--once mode): - -- `0`: Healthy -- `1`: Warning -- `2`: Critical - -### 5. PowerShell Module - -**File**: `scripts/QAI-SQL.psm1` - -Unified PowerShell cmdlet library for all SQL operations. - -**Features**: - -- 13 cmdlets covering all automation tasks -- Auto-completion support -- Inline help documentation -- Session configuration management - -**Usage**: - -```powershell -# Import module -Import-Module .\scripts\QAI-SQL.psm1 - -# View available commands -Get-Command -Module QAI-SQL - -# Get status and configuration -Get-QAISQLStatus - -# Deploy to local environment -Deploy-QAISQLIntegration -Environment Local - -# Run health check -Test-QAISQLHealth - -# Start continuous monitoring -Start-QAISQLMonitoring -Interval 30 - -# Tune thresholds -Optimize-QAISQLThresholds -ResourceGroup "qai-rg" -FunctionAppName "qai-func" -Apply - -# Cleanup old metrics -Clear-QAISQLQueryMetrics -RetentionDays 7 - -# Set configuration -Set-QAISQLConnection -Url "sqlite:///./data.sqlite" -Set-QAISQLSlowThreshold -Milliseconds 300 -Enable-QAISQLQueryTracking -``` - -## Automation Workflows - -### Workflow 1: Initial Deployment - -```powershell -# 1. Import module -Import-Module .\scripts\QAI-SQL.psm1 - -# 2. Configure connection (local dev) -Set-QAISQLConnection -Url "sqlite:///./data.sqlite" - -# 3. Deploy with tests -Deploy-QAISQLIntegration -Environment Local - -# 4. Verify health -Test-QAISQLHealth -``` - -### Workflow 2: Production Deployment - -```powershell -# 1. Dry-run first -.\scripts\deploy_sql_integration.ps1 ` - -Environment Production ` - -ResourceGroup "qai-prod-rg" ` - -FunctionAppName "qai-prod-func" ` - -EmailRecipient "ops@example.com" ` - -DryRun - -# 2. Deploy for real -.\scripts\deploy_sql_integration.ps1 ` - -Environment Production ` - -ResourceGroup "qai-prod-rg" ` - -FunctionAppName "qai-prod-func" ` - -EmailRecipient "ops@example.com" - -# 3. Start monitoring -python .\scripts\sql_health_monitor.py --interval 60 --threshold-critical 90 -``` - -### Workflow 3: Weekly Maintenance - -```powershell -# 1. Analyze and tune thresholds -.\scripts\tune_sql_thresholds.ps1 ` - -ResourceGroup "qai-prod-rg" ` - -FunctionAppName "qai-prod-func" ` - -Apply - -# 2. Cleanup old metrics -python .\scripts\cleanup_query_metrics.py --retention-days 7 - -# 3. Health check -python .\scripts\sql_health_monitor.py --once --json > health_report.json -``` - -### Workflow 4: CI/CD Integration - -```yaml -# Azure DevOps Pipeline example -steps: - - task: PowerShell@2 - displayName: "Deploy SQL Integration" - inputs: - filePath: "scripts/deploy_sql_integration.ps1" - arguments: "-Environment Production -ResourceGroup $(resourceGroup) -FunctionAppName $(functionAppName) -EmailRecipient $(alertEmail)" - - - task: PowerShell@2 - displayName: "Health Check" - inputs: - targetType: "inline" - script: | - python .\scripts\sql_health_monitor.py --once - if ($LASTEXITCODE -ne 0) { - Write-Error "Health check failed" - exit 1 - } -``` - -## Scheduled Tasks Setup - -### Windows Task Scheduler - -```powershell -# Weekly cleanup (Sundays at 2 AM) -$action = New-ScheduledTaskAction -Execute "python" -Argument ".\scripts\cleanup_query_metrics.py" -WorkingDirectory "C:\QAI" -$trigger = New-ScheduledTaskTrigger -Weekly -DaysOfWeek Sunday -At 2am -Register-ScheduledTask -TaskName "QAI SQL Cleanup" -Action $action -Trigger $trigger - -# Daily threshold tuning (Mondays at 3 AM) -$action = New-ScheduledTaskAction -Execute "powershell" -Argument "-File .\scripts\tune_sql_thresholds.ps1 -ResourceGroup qai-rg -FunctionAppName qai-func -Apply" -WorkingDirectory "C:\QAI" -$trigger = New-ScheduledTaskTrigger -Weekly -DaysOfWeek Monday -At 3am -Register-ScheduledTask -TaskName "QAI SQL Threshold Tuning" -Action $action -Trigger $trigger -``` - -### Azure Automation Runbook - -```powershell -# Create runbook for query cleanup -$runbookName = "QAI-SQL-Cleanup" -$resourceGroup = "qai-automation-rg" -$automationAccount = "qai-automation" - -# Create runbook -New-AzAutomationRunbook ` - -ResourceGroupName $resourceGroup ` - -AutomationAccountName $automationAccount ` - -Name $runbookName ` - -Type PowerShell - -# Schedule weekly execution -New-AzAutomationSchedule ` - -ResourceGroupName $resourceGroup ` - -AutomationAccountName $automationAccount ` - -Name "Weekly-SQL-Cleanup" ` - -StartTime (Get-Date).AddDays(7) ` - -WeekInterval 1 ` - -TimeZone "UTC" -``` - -## Monitoring Integration - -### Application Insights Integration - -```powershell -# Custom metric tracking in health monitor -python .\scripts\sql_health_monitor.py --interval 60 --json | ForEach-Object { - $health = $_ | ConvertFrom-Json - - # Send custom metric to Application Insights - az monitor app-insights metrics create ` - --app qai-func ` - --name "sql_pool_saturation" ` - --value $health.pool.saturation_pct -} -``` - -### Slack/Teams Webhook Example - -```powershell -# Health monitor with Slack webhook -$webhookUrl = "https://hooks.slack.com/services/YOUR/WEBHOOK/URL" - -python .\scripts\sql_health_monitor.py ` - --interval 60 ` - --threshold-critical 90 ` - --alert-webhook $webhookUrl -``` - -## Error Handling and Recovery - -### Deployment Failures - -```powershell -# Check deployment status -$statusFile = ".\data_out\sql_deployment_status.json" -$status = Get-Content $statusFile | ConvertFrom-Json - -if (-not $status.success) { - Write-Host "Deployment failed. Check log: $($status.logFile)" - - # Rollback steps - if (-not $status.steps.Migrations) { - Write-Host "Migrations failed - no rollback needed" - } - - if ($status.steps.Migrations -and -not $status.steps.Tests) { - Write-Host "Consider rolling back migrations if database is corrupt" - } -} -``` - -### Alert Deployment Failures - -```powershell -# Retry with skip flags -.\scripts\deploy_sql_integration.ps1 ` - -Environment Production ` - -ResourceGroup "qai-rg" ` - -FunctionAppName "qai-func" ` - -SkipTests ` # If tests already passed - -SkipAlerts # Deploy alerts manually later -``` - -## Best Practices - -1. **Always dry-run production deployments first** -2. **Enable query tracking only after 002 migration applied** -3. **Start with conservative thresholds (500ms) and tune down** -4. **Schedule cleanup during low-traffic periods** -5. **Monitor health checks after every deployment** -6. **Keep deployment logs for audit trail** -7. **Use PowerShell module for consistent operations** -8. **Test automation scripts in staging before production** - -## Troubleshooting - -| Issue | Solution | -| --------------------------------- | ---------------------------------------------------------- | -| Deployment fails at prerequisites | Run `pip install -r requirements.txt` | -| Health monitor shows "critical" | Check `QAI_SQL_URL` environment variable | -| Threshold tuning finds no data | Verify Application Insights is logging traces | -| Cleanup script fails | Ensure 002 migration applied and tracking enabled | -| Module import fails | Run from repo root: `Import-Module .\scripts\QAI-SQL.psm1` | - -## Files Created - -| File | Purpose | Lines | -| ------------------------------------ | ------------------------------ | ----- | -| `scripts/deploy_sql_integration.ps1` | Master deployment orchestrator | 450+ | -| `scripts/tune_sql_thresholds.ps1` | Automated threshold tuning | 300+ | -| `scripts/cleanup_query_metrics.py` | Retention cleanup automation | 200+ | -| `scripts/sql_health_monitor.py` | Health monitoring daemon | 300+ | -| `scripts/QAI-SQL.psm1` | PowerShell cmdlet module | 400+ | - -**Total**: ~1,650 lines of automation code - -## Next Steps - -1. ✅ Import PowerShell module -2. ✅ Run local deployment test -3. ✅ Schedule weekly cleanup task -4. ✅ Configure health monitoring alerts -5. ✅ Set up CI/CD pipeline integration -6. ✅ Enable automated threshold tuning - ---- - -**Related Documentation**: - -- [DATABASE_SQL_SETUP.md](./DATABASE_SQL_SETUP.md) - Core setup and configuration -- [AZURE_MONITOR_SQL_SETUP.md](./AZURE_MONITOR_SQL_SETUP.md) - Azure monitoring and KQL queries -- [SQL_QUICKREF.md](../quickref/SQL_QUICKREF.md) - Quick reference card - -**Status**: All automation components complete and production-ready. +# SQL Integration Automation Suite + +Complete automation tools for deployment, monitoring, and maintenance. + +## Overview + +This automation suite provides end-to-end management of the SQL integration with zero manual intervention capabilities. All scripts support dry-run mode for safety. + +## Automation Components + +### 1. Master Deployment Script + +**File**: `scripts/deploy_sql_integration.ps1` + +One-command deployment orchestrating all integration steps. + +**Features**: + +- Environment validation (prerequisites, Azure CLI, packages) +- Database migrations with rollback on failure +- Comprehensive test execution +- Azure alert deployment +- Health endpoint verification +- Status tracking and logging + +**Usage**: + +```powershell +# Local development +.\scripts\deploy_sql_integration.ps1 -Environment Local + +# Production with alerts +.\scripts\deploy_sql_integration.ps1 ` + -Environment Production ` + -ResourceGroup "qai-rg" ` + -FunctionAppName "qai-func" ` + -EmailRecipient "admin@example.com" + +# Dry-run mode +.\scripts\deploy_sql_integration.ps1 -Environment Production -DryRun +``` + +**Exit Codes**: + +- `0`: Success +- `1`: Failure (check log file in `data_out/`) + +### 2. Threshold Auto-Tuning + +**File**: `scripts/tune_sql_thresholds.ps1` + +Analyzes Application Insights metrics and recommends optimal slow query thresholds. + +**Features**: + +- P50/P95/P99 percentile analysis +- Alert frequency tracking +- Environment-aware recommendations +- Automatic threshold application to Azure +- JSON analysis reports + +**Usage**: + +```powershell +# Analyze current metrics +.\scripts\tune_sql_thresholds.ps1 ` + -ResourceGroup "qai-rg" ` + -FunctionAppName "qai-func" + +# Auto-apply recommended threshold +.\scripts\tune_sql_thresholds.ps1 ` + -ResourceGroup "qai-rg" ` + -FunctionAppName "qai-func" ` + -Apply + +# Custom baseline period +.\scripts\tune_sql_thresholds.ps1 ` + -ResourceGroup "qai-rg" ` + -FunctionAppName "qai-func" ` + -DaysBack 14 ` + -TargetAlertsPerDay 3 +``` + +**Output**: Analysis report saved to `data_out/threshold_analysis_*.json` + +### 3. Query Metrics Cleanup + +**File**: `scripts/cleanup_query_metrics.py` + +Automated retention management for query performance tracking data. + +**Features**: + +- Configurable retention period +- Table statistics before/after cleanup +- Dry-run mode +- Timestamped logging + +**Usage**: + +```powershell +# Default 7-day retention +python .\scripts\cleanup_query_metrics.py + +# Custom retention period +python .\scripts\cleanup_query_metrics.py --retention-days 30 + +# Dry-run mode +python .\scripts\cleanup_query_metrics.py --retention-days 7 --dry-run +``` + +**Scheduling** (Windows Task Scheduler): + +```powershell +# Run weekly on Sundays at 2 AM +$action = New-ScheduledTaskAction -Execute "python" -Argument ".\scripts\cleanup_query_metrics.py --retention-days 7" -WorkingDirectory "C:\path\to\repo" +$trigger = New-ScheduledTaskTrigger -Weekly -DaysOfWeek Sunday -At 2am +Register-ScheduledTask -TaskName "QAI SQL Cleanup" -Action $action -Trigger $trigger +``` + +### 4. Health Monitoring + +**File**: `scripts/sql_health_monitor.py` + +Continuous health monitoring with alerting capabilities. + +**Features**: + +- Connectivity probes +- Pool saturation detection +- Slow query frequency tracking +- Webhook alerts for critical issues +- Alert cooldown (5-minute intervals) +- JSON output mode + +**Usage**: + +```powershell +# Single health check +python .\scripts\sql_health_monitor.py --once + +# JSON output (for parsing) +python .\scripts\sql_health_monitor.py --once --json + +# Continuous monitoring +python .\scripts\sql_health_monitor.py --interval 60 + +# With webhook alerts +python .\scripts\sql_health_monitor.py ` + --interval 30 ` + --threshold-critical 90 ` + --alert-webhook "https://hooks.slack.com/services/..." +``` + +**Exit Codes** (--once mode): + +- `0`: Healthy +- `1`: Warning +- `2`: Critical + +### 5. PowerShell Module + +**File**: `scripts/QAI-SQL.psm1` + +Unified PowerShell cmdlet library for all SQL operations. + +**Features**: + +- 13 cmdlets covering all automation tasks +- Auto-completion support +- Inline help documentation +- Session configuration management + +**Usage**: + +```powershell +# Import module +Import-Module .\scripts\QAI-SQL.psm1 + +# View available commands +Get-Command -Module QAI-SQL + +# Get status and configuration +Get-QAISQLStatus + +# Deploy to local environment +Deploy-QAISQLIntegration -Environment Local + +# Run health check +Test-QAISQLHealth + +# Start continuous monitoring +Start-QAISQLMonitoring -Interval 30 + +# Tune thresholds +Optimize-QAISQLThresholds -ResourceGroup "qai-rg" -FunctionAppName "qai-func" -Apply + +# Cleanup old metrics +Clear-QAISQLQueryMetrics -RetentionDays 7 + +# Set configuration +Set-QAISQLConnection -Url "sqlite:///./data.sqlite" +Set-QAISQLSlowThreshold -Milliseconds 300 +Enable-QAISQLQueryTracking +``` + +## Automation Workflows + +### Workflow 1: Initial Deployment + +```powershell +# 1. Import module +Import-Module .\scripts\QAI-SQL.psm1 + +# 2. Configure connection (local dev) +Set-QAISQLConnection -Url "sqlite:///./data.sqlite" + +# 3. Deploy with tests +Deploy-QAISQLIntegration -Environment Local + +# 4. Verify health +Test-QAISQLHealth +``` + +### Workflow 2: Production Deployment + +```powershell +# 1. Dry-run first +.\scripts\deploy_sql_integration.ps1 ` + -Environment Production ` + -ResourceGroup "qai-prod-rg" ` + -FunctionAppName "qai-prod-func" ` + -EmailRecipient "ops@example.com" ` + -DryRun + +# 2. Deploy for real +.\scripts\deploy_sql_integration.ps1 ` + -Environment Production ` + -ResourceGroup "qai-prod-rg" ` + -FunctionAppName "qai-prod-func" ` + -EmailRecipient "ops@example.com" + +# 3. Start monitoring +python .\scripts\sql_health_monitor.py --interval 60 --threshold-critical 90 +``` + +### Workflow 3: Weekly Maintenance + +```powershell +# 1. Analyze and tune thresholds +.\scripts\tune_sql_thresholds.ps1 ` + -ResourceGroup "qai-prod-rg" ` + -FunctionAppName "qai-prod-func" ` + -Apply + +# 2. Cleanup old metrics +python .\scripts\cleanup_query_metrics.py --retention-days 7 + +# 3. Health check +python .\scripts\sql_health_monitor.py --once --json > health_report.json +``` + +### Workflow 4: CI/CD Integration + +```yaml +# Azure DevOps Pipeline example +steps: + - task: PowerShell@2 + displayName: "Deploy SQL Integration" + inputs: + filePath: "scripts/deploy_sql_integration.ps1" + arguments: "-Environment Production -ResourceGroup $(resourceGroup) -FunctionAppName $(functionAppName) -EmailRecipient $(alertEmail)" + + - task: PowerShell@2 + displayName: "Health Check" + inputs: + targetType: "inline" + script: | + python .\scripts\sql_health_monitor.py --once + if ($LASTEXITCODE -ne 0) { + Write-Error "Health check failed" + exit 1 + } +``` + +## Scheduled Tasks Setup + +### Windows Task Scheduler + +```powershell +# Weekly cleanup (Sundays at 2 AM) +$action = New-ScheduledTaskAction -Execute "python" -Argument ".\scripts\cleanup_query_metrics.py" -WorkingDirectory "C:\QAI" +$trigger = New-ScheduledTaskTrigger -Weekly -DaysOfWeek Sunday -At 2am +Register-ScheduledTask -TaskName "QAI SQL Cleanup" -Action $action -Trigger $trigger + +# Daily threshold tuning (Mondays at 3 AM) +$action = New-ScheduledTaskAction -Execute "powershell" -Argument "-File .\scripts\tune_sql_thresholds.ps1 -ResourceGroup qai-rg -FunctionAppName qai-func -Apply" -WorkingDirectory "C:\QAI" +$trigger = New-ScheduledTaskTrigger -Weekly -DaysOfWeek Monday -At 3am +Register-ScheduledTask -TaskName "QAI SQL Threshold Tuning" -Action $action -Trigger $trigger +``` + +### Azure Automation Runbook + +```powershell +# Create runbook for query cleanup +$runbookName = "QAI-SQL-Cleanup" +$resourceGroup = "qai-automation-rg" +$automationAccount = "qai-automation" + +# Create runbook +New-AzAutomationRunbook ` + -ResourceGroupName $resourceGroup ` + -AutomationAccountName $automationAccount ` + -Name $runbookName ` + -Type PowerShell + +# Schedule weekly execution +New-AzAutomationSchedule ` + -ResourceGroupName $resourceGroup ` + -AutomationAccountName $automationAccount ` + -Name "Weekly-SQL-Cleanup" ` + -StartTime (Get-Date).AddDays(7) ` + -WeekInterval 1 ` + -TimeZone "UTC" +``` + +## Monitoring Integration + +### Application Insights Integration + +```powershell +# Custom metric tracking in health monitor +python .\scripts\sql_health_monitor.py --interval 60 --json | ForEach-Object { + $health = $_ | ConvertFrom-Json + + # Send custom metric to Application Insights + az monitor app-insights metrics create ` + --app qai-func ` + --name "sql_pool_saturation" ` + --value $health.pool.saturation_pct +} +``` + +### Slack/Teams Webhook Example + +```powershell +# Health monitor with Slack webhook +$webhookUrl = "https://hooks.slack.com/services/YOUR/WEBHOOK/URL" + +python .\scripts\sql_health_monitor.py ` + --interval 60 ` + --threshold-critical 90 ` + --alert-webhook $webhookUrl +``` + +## Error Handling and Recovery + +### Deployment Failures + +```powershell +# Check deployment status +$statusFile = ".\data_out\sql_deployment_status.json" +$status = Get-Content $statusFile | ConvertFrom-Json + +if (-not $status.success) { + Write-Host "Deployment failed. Check log: $($status.logFile)" + + # Rollback steps + if (-not $status.steps.Migrations) { + Write-Host "Migrations failed - no rollback needed" + } + + if ($status.steps.Migrations -and -not $status.steps.Tests) { + Write-Host "Consider rolling back migrations if database is corrupt" + } +} +``` + +### Alert Deployment Failures + +```powershell +# Retry with skip flags +.\scripts\deploy_sql_integration.ps1 ` + -Environment Production ` + -ResourceGroup "qai-rg" ` + -FunctionAppName "qai-func" ` + -SkipTests ` # If tests already passed + -SkipAlerts # Deploy alerts manually later +``` + +## Best Practices + +1. **Always dry-run production deployments first** +2. **Enable query tracking only after 002 migration applied** +3. **Start with conservative thresholds (500ms) and tune down** +4. **Schedule cleanup during low-traffic periods** +5. **Monitor health checks after every deployment** +6. **Keep deployment logs for audit trail** +7. **Use PowerShell module for consistent operations** +8. **Test automation scripts in staging before production** + +## Troubleshooting + +| Issue | Solution | +| --------------------------------- | ---------------------------------------------------------- | +| Deployment fails at prerequisites | Run `pip install -r requirements.txt` | +| Health monitor shows "critical" | Check `QAI_SQL_URL` environment variable | +| Threshold tuning finds no data | Verify Application Insights is logging traces | +| Cleanup script fails | Ensure 002 migration applied and tracking enabled | +| Module import fails | Run from repo root: `Import-Module .\scripts\QAI-SQL.psm1` | + +## Files Created + +| File | Purpose | Lines | +| ------------------------------------ | ------------------------------ | ----- | +| `scripts/deploy_sql_integration.ps1` | Master deployment orchestrator | 450+ | +| `scripts/tune_sql_thresholds.ps1` | Automated threshold tuning | 300+ | +| `scripts/cleanup_query_metrics.py` | Retention cleanup automation | 200+ | +| `scripts/sql_health_monitor.py` | Health monitoring daemon | 300+ | +| `scripts/QAI-SQL.psm1` | PowerShell cmdlet module | 400+ | + +**Total**: ~1,650 lines of automation code + +## Next Steps + +1. ✅ Import PowerShell module +2. ✅ Run local deployment test +3. ✅ Schedule weekly cleanup task +4. ✅ Configure health monitoring alerts +5. ✅ Set up CI/CD pipeline integration +6. ✅ Enable automated threshold tuning + +--- + +**Related Documentation**: + +- [DATABASE_SQL_SETUP.md](./DATABASE_SQL_SETUP.md) - Core setup and configuration +- [AZURE_MONITOR_SQL_SETUP.md](./AZURE_MONITOR_SQL_SETUP.md) - Azure monitoring and KQL queries +- [SQL_QUICKREF.md](../quickref/SQL_QUICKREF.md) - Quick reference card + +**Status**: All automation components complete and production-ready. diff --git a/docs/database/SQL_INTEGRATION_COMPLETE.md b/docs/database/SQL_INTEGRATION_COMPLETE.md index e1ef20144..f1c73bfe8 100644 --- a/docs/database/SQL_INTEGRATION_COMPLETE.md +++ b/docs/database/SQL_INTEGRATION_COMPLETE.md @@ -1,289 +1,289 @@ -# SQL Integration - Complete Implementation Summary - -**Status**: ✅ Production-Ready -**Date**: November 23, 2025 -**Version**: 1.0.0 - -## Overview - -Complete multi-vendor SQL database integration with production-grade observability, Azure Monitor integration, and automated deployment tooling. - -## Features Implemented - -### Core Integration (Phase 1) -- ✅ Multi-vendor SQL engine abstraction (`shared/sql_engine.py`) - - SQL Server, PostgreSQL, MySQL/MariaDB, SQLite support - - Lazy initialization with global engine caching - - Pool pre-ping and connection recycling (30 min) - - Graceful degradation on missing dependencies - -- ✅ Key-value repository (`shared/sql_repository.py`) - - Vendor-specific DDL (CREATE TABLE IF NOT EXISTS) - - Vendor-specific upsert logic (MERGE/ON CONFLICT/REPLACE) - - Generic CRUD operations (put/get/delete/list) - -- ✅ Health endpoint integration (`function_app.py`) - - `/api/ai/status` includes SQL connectivity probe - - Graceful fallback if SQL not configured - -- ✅ Migration framework (`scripts/sql_migrate.py`) - - Sequential SQL file application from `database/migrations/` - - Idempotent tracking via `QAI_Migrations` table - - Reports counts: total/applied/pending - -### Observability Features (Phase 2) -- ✅ Pool metrics exposure - - `engine_stats()` returns: size, checked-out, overflow, recycle, timeout - - `saturation_pct` calculated (checked-out / size × 100) - - Surface in `/api/ai/status` endpoint - -- ✅ Slow query detection - - Configurable threshold via `QAI_SQL_SLOW_MS` - - Environment-aware defaults (dev=100ms, staging=300ms, prod=500ms) - - Automatic logging warnings with duration and SQL snippet - -- ✅ Pool saturation alerts - - Automatic detection when > 80% saturated - - Logging warnings + status endpoint field (`sql.alert`) - - In-memory tracking of saturation events - -### Production Monitoring (Phase 3) -- ✅ Slow query frequency tracking - - Rolling 60-second in-memory window - - Prune expired entries automatically - - Surface in `engine_stats()` as `slow_queries_1min` - -- ✅ Query performance tracking (optional) - - Hash-based deduplication (SHA256, 16-char) - - Persist to `QAI_QueryMetrics` table - - Conditional on `QAI_ENABLE_QUERY_TRACKING=true` - - Silent degradation on tracking failures - -### Azure Monitor Integration (Phase 4) -- ✅ ARM template (`config/azure_monitor_alerts.json`) - - Metric alert: Pool saturation > 80% - - Scheduled query rule: Slow query frequency > 10/min - - Scheduled query rule: Saturation alert field presence - -- ✅ PowerShell automation (`scripts/setup_azure_alerts.ps1`) - - One-command deployment with validation - - Action Group creation with email notifications - - Portal links for alert management - - Dry-run mode for safety - -- ✅ KQL query library (`AZURE_MONITOR_SQL_SETUP.md`) - - Pool saturation trends - - Slow query frequency heatmap - - Top slow queries by frequency - - P50/P95/P99 latency percentiles - - Saturation-slowness correlation - -### Documentation (Complete) -- ✅ `DATABASE_SQL_SETUP.md` (20 sections) - - Installation, configuration, usage - - Repository patterns, migrations - - Troubleshooting, advanced monitoring - - Pool scaling strategies - - Production checklist - -- ✅ `AZURE_MONITOR_SQL_SETUP.md` - - Quick start deployment guide - - 6 production-ready KQL queries - - Alert configuration details - - Tuning thresholds based on metrics - - Cost optimization recommendations - -### Testing -- ✅ Comprehensive test suite (`tests/test_sql_integration.py`) - - 7 tests, 100% passing - - Coverage: health, CRUD, stats, saturation, slow queries, environment resolution - - In-memory SQLite for fast execution - -## Files Created/Modified - -### Created -- `shared/sql_engine.py` (262 lines) -- `shared/sql_repository.py` (118 lines) -- `scripts/sql_migrate.py` (128 lines) -- `scripts/setup_azure_alerts.ps1` (378 lines) -- `tests/test_sql_integration.py` (158 lines) -- `database/migrations/001_keyvalue_index.sql` -- `database/migrations/002_query_performance_tracking.sql` -- `config/azure_monitor_alerts.json` (ARM template) -- `DATABASE_SQL_SETUP.md` (20 sections, 450+ lines) -- `AZURE_MONITOR_SQL_SETUP.md` (12 sections, 400+ lines) - -### Modified -- `function_app.py` (added SQL health block to `/api/ai/status`) -- `local.settings.json` (added `QAI_SQL_URL` placeholder) -- `requirements.txt` (added optional driver comments) - -## Environment Variables - -| Variable | Required | Default | Description | -|----------|----------|---------|-------------| -| `QAI_SQL_URL` | No | None | SQLAlchemy connection URL | -| `QAI_DB_CONN` | No | None | Fallback ODBC connection string | -| `QAI_SQL_SLOW_MS` | No | Auto | Slow query threshold (ms) | -| `QAI_ENABLE_QUERY_TRACKING` | No | `false` | Enable persistent query metrics | -| `AZURE_FUNCTIONS_ENVIRONMENT` | No | `production` | Auto-tune thresholds (dev/staging/production) | - -## Quick Start - -### Local Development - -```powershell -# Install dependencies -pip install -r requirements.txt - -# Configure database -$env:QAI_SQL_URL = "sqlite:///./data.sqlite" - -# Run tests -pytest tests/test_sql_integration.py -v - -# Start Function App -func host start - -# Check status -Invoke-RestMethod -Uri "http://localhost:7071/api/ai/status" -``` - -### Azure Production Deployment - -```powershell -# 1. Configure connection string (Azure portal or CLI) -az functionapp config appsettings set ` - --name your-func-app ` - --resource-group your-rg ` - --settings QAI_SQL_URL="mssql+pyodbc://..." - -# 2. Deploy alert rules -.\scripts\setup_azure_alerts.ps1 ` - -ResourceGroup "your-rg" ` - -FunctionAppName "your-func-app" ` - -ActionGroupName "sql-alerts" ` - -EmailRecipient "admin@example.com" - -# 3. Enable query tracking (optional) -az functionapp config appsettings set ` - --name your-func-app ` - --resource-group your-rg ` - --settings QAI_ENABLE_QUERY_TRACKING="true" - -# 4. Monitor in Application Insights -# See AZURE_MONITOR_SQL_SETUP.md for KQL queries -``` - -## Testing Summary - -All 7 tests passing: - -1. `test_sql_engine_health` - Connectivity probe -2. `test_sql_repository_crud` - Put/Get/Delete/List operations -3. `test_quick_query_select_one` - Convenience query executor -4. `test_engine_stats_presence` - Pool metrics structure -5. `test_slow_query_warning` - Threshold detection (informational) -6. `test_saturation_detection` - 80% threshold trigger -7. `test_environment_aware_threshold` - Auto-tuning by environment - -**Test command:** -```powershell -.\venv\Scripts\python.exe -m pytest tests\test_sql_integration.py -v -``` - -**Output:** `7 passed in 0.XX s` - -## Migration Status - -| Migration | Status | Purpose | -|-----------|--------|---------| -| `001_keyvalue_index.sql` | ✅ Ready | Index on `QAI_KeyValue(k)` for performance | -| `002_query_performance_tracking.sql` | ✅ Ready | `QAI_QueryMetrics` table for optional tracking | - -**Apply migrations:** -```powershell -python .\scripts\sql_migrate.py -``` - -## Production Checklist - -- [x] Core SQL engine implemented -- [x] Repository pattern with vendor DDL -- [x] Health endpoint integration -- [x] Migration framework operational -- [x] Pool metrics exposure -- [x] Slow query detection -- [x] Saturation alerts -- [x] Query tracking (opt-in) -- [x] Azure Monitor ARM template -- [x] PowerShell automation script -- [x] KQL query library -- [x] Comprehensive documentation -- [x] Test suite (100% passing) - -## Next Steps (Post-Deployment) - -1. **Baseline Collection** (Week 1) - - Deploy with default thresholds - - Monitor slow query P95 in Application Insights - - Track pool saturation patterns during peak hours - -2. **Threshold Tuning** (Week 2) - - Adjust `QAI_SQL_SLOW_MS` based on P95 analysis - - Target: < 5 alerts per day per environment - - See AZURE_MONITOR_SQL_SETUP.md Section "Tuning Thresholds" - -3. **Pool Scaling** (If Needed) - - If saturation > 80% sustained for > 5 minutes - - Calculate: `pool_size = instances × concurrent_requests × 1.2` - - Update connection URL with `?pool_size=XX` - - Monitor for 24-48 hours, iterate - -4. **Query Optimization** (Ongoing) - - Review top slow queries weekly via KQL - - Identify caching candidates - - Add indexes via new migration files - - Correlate performance changes with deployments - -5. **Retention Management** (Monthly) - - If query tracking enabled, schedule cleanup job - - Delete `QAI_QueryMetrics` older than 7-30 days - - Monitor Application Insights log ingestion costs - -## Architecture Decisions - -- **SQLAlchemy over raw drivers**: Multi-vendor abstraction, battle-tested -- **Lazy initialization**: Avoid startup overhead, handle missing env vars -- **Silent degradation**: Non-blocking failures (logging.debug for tracking) -- **In-memory frequency counter**: Avoid write amplification to database -- **Environment-aware tuning**: Dev/staging/prod have different perf expectations -- **Hash-based deduplication**: 16-char SHA256 truncation balances storage vs. collision risk -- **ARM templates over manual config**: Infrastructure-as-code, repeatable deployments - -## Known Limitations - -- **Vendor-specific migrations**: Must write separate DDL for SQL Server vs. PostgreSQL syntax -- **Pytest caplog limitations**: Slow query warning test is informational (manual verification recommended) -- **Query tracking overhead**: Adds ~1-5ms per query (opt-in to avoid default perf impact) -- **Alert rule quotas**: Azure Monitor has limits per subscription (typically 5000+ metric alerts) - -## Support & References - -- **Core Documentation**: `DATABASE_SQL_SETUP.md` -- **Azure Monitoring**: `AZURE_MONITOR_SQL_SETUP.md` -- **SQLAlchemy Docs**: https://docs.sqlalchemy.org/en/20/core/pooling.html -- **Azure Monitor Pricing**: https://azure.microsoft.com/pricing/details/monitor/ -- **Application Insights KQL**: https://learn.microsoft.com/azure/azure-monitor/logs/get-started-queries - -## Version History - -- **1.0.0** (2025-11-23): Initial production release - - Multi-vendor SQL integration - - Pool observability and saturation detection - - Azure Monitor integration with ARM templates - - Comprehensive documentation and automation - ---- - -**Status**: Ready for production deployment. All features implemented, tested, and documented. +# SQL Integration - Complete Implementation Summary + +**Status**: ✅ Production-Ready +**Date**: November 23, 2025 +**Version**: 1.0.0 + +## Overview + +Complete multi-vendor SQL database integration with production-grade observability, Azure Monitor integration, and automated deployment tooling. + +## Features Implemented + +### Core Integration (Phase 1) +- ✅ Multi-vendor SQL engine abstraction (`shared/sql_engine.py`) + - SQL Server, PostgreSQL, MySQL/MariaDB, SQLite support + - Lazy initialization with global engine caching + - Pool pre-ping and connection recycling (30 min) + - Graceful degradation on missing dependencies + +- ✅ Key-value repository (`shared/sql_repository.py`) + - Vendor-specific DDL (CREATE TABLE IF NOT EXISTS) + - Vendor-specific upsert logic (MERGE/ON CONFLICT/REPLACE) + - Generic CRUD operations (put/get/delete/list) + +- ✅ Health endpoint integration (`function_app.py`) + - `/api/ai/status` includes SQL connectivity probe + - Graceful fallback if SQL not configured + +- ✅ Migration framework (`scripts/sql_migrate.py`) + - Sequential SQL file application from `database/migrations/` + - Idempotent tracking via `QAI_Migrations` table + - Reports counts: total/applied/pending + +### Observability Features (Phase 2) +- ✅ Pool metrics exposure + - `engine_stats()` returns: size, checked-out, overflow, recycle, timeout + - `saturation_pct` calculated (checked-out / size × 100) + - Surface in `/api/ai/status` endpoint + +- ✅ Slow query detection + - Configurable threshold via `QAI_SQL_SLOW_MS` + - Environment-aware defaults (dev=100ms, staging=300ms, prod=500ms) + - Automatic logging warnings with duration and SQL snippet + +- ✅ Pool saturation alerts + - Automatic detection when > 80% saturated + - Logging warnings + status endpoint field (`sql.alert`) + - In-memory tracking of saturation events + +### Production Monitoring (Phase 3) +- ✅ Slow query frequency tracking + - Rolling 60-second in-memory window + - Prune expired entries automatically + - Surface in `engine_stats()` as `slow_queries_1min` + +- ✅ Query performance tracking (optional) + - Hash-based deduplication (SHA256, 16-char) + - Persist to `QAI_QueryMetrics` table + - Conditional on `QAI_ENABLE_QUERY_TRACKING=true` + - Silent degradation on tracking failures + +### Azure Monitor Integration (Phase 4) +- ✅ ARM template (`config/azure_monitor_alerts.json`) + - Metric alert: Pool saturation > 80% + - Scheduled query rule: Slow query frequency > 10/min + - Scheduled query rule: Saturation alert field presence + +- ✅ PowerShell automation (`scripts/setup_azure_alerts.ps1`) + - One-command deployment with validation + - Action Group creation with email notifications + - Portal links for alert management + - Dry-run mode for safety + +- ✅ KQL query library (`AZURE_MONITOR_SQL_SETUP.md`) + - Pool saturation trends + - Slow query frequency heatmap + - Top slow queries by frequency + - P50/P95/P99 latency percentiles + - Saturation-slowness correlation + +### Documentation (Complete) +- ✅ `DATABASE_SQL_SETUP.md` (20 sections) + - Installation, configuration, usage + - Repository patterns, migrations + - Troubleshooting, advanced monitoring + - Pool scaling strategies + - Production checklist + +- ✅ `AZURE_MONITOR_SQL_SETUP.md` + - Quick start deployment guide + - 6 production-ready KQL queries + - Alert configuration details + - Tuning thresholds based on metrics + - Cost optimization recommendations + +### Testing +- ✅ Comprehensive test suite (`tests/test_sql_integration.py`) + - 7 tests, 100% passing + - Coverage: health, CRUD, stats, saturation, slow queries, environment resolution + - In-memory SQLite for fast execution + +## Files Created/Modified + +### Created +- `shared/sql_engine.py` (262 lines) +- `shared/sql_repository.py` (118 lines) +- `scripts/sql_migrate.py` (128 lines) +- `scripts/setup_azure_alerts.ps1` (378 lines) +- `tests/test_sql_integration.py` (158 lines) +- `database/migrations/001_keyvalue_index.sql` +- `database/migrations/002_query_performance_tracking.sql` +- `config/azure_monitor_alerts.json` (ARM template) +- `DATABASE_SQL_SETUP.md` (20 sections, 450+ lines) +- `AZURE_MONITOR_SQL_SETUP.md` (12 sections, 400+ lines) + +### Modified +- `function_app.py` (added SQL health block to `/api/ai/status`) +- `local.settings.json` (added `QAI_SQL_URL` placeholder) +- `requirements.txt` (added optional driver comments) + +## Environment Variables + +| Variable | Required | Default | Description | +|----------|----------|---------|-------------| +| `QAI_SQL_URL` | No | None | SQLAlchemy connection URL | +| `QAI_DB_CONN` | No | None | Fallback ODBC connection string | +| `QAI_SQL_SLOW_MS` | No | Auto | Slow query threshold (ms) | +| `QAI_ENABLE_QUERY_TRACKING` | No | `false` | Enable persistent query metrics | +| `AZURE_FUNCTIONS_ENVIRONMENT` | No | `production` | Auto-tune thresholds (dev/staging/production) | + +## Quick Start + +### Local Development + +```powershell +# Install dependencies +pip install -r requirements.txt + +# Configure database +$env:QAI_SQL_URL = "sqlite:///./data.sqlite" + +# Run tests +pytest tests/test_sql_integration.py -v + +# Start Function App +func host start + +# Check status +Invoke-RestMethod -Uri "http://localhost:7071/api/ai/status" +``` + +### Azure Production Deployment + +```powershell +# 1. Configure connection string (Azure portal or CLI) +az functionapp config appsettings set ` + --name your-func-app ` + --resource-group your-rg ` + --settings QAI_SQL_URL="mssql+pyodbc://..." + +# 2. Deploy alert rules +.\scripts\setup_azure_alerts.ps1 ` + -ResourceGroup "your-rg" ` + -FunctionAppName "your-func-app" ` + -ActionGroupName "sql-alerts" ` + -EmailRecipient "admin@example.com" + +# 3. Enable query tracking (optional) +az functionapp config appsettings set ` + --name your-func-app ` + --resource-group your-rg ` + --settings QAI_ENABLE_QUERY_TRACKING="true" + +# 4. Monitor in Application Insights +# See AZURE_MONITOR_SQL_SETUP.md for KQL queries +``` + +## Testing Summary + +All 7 tests passing: + +1. `test_sql_engine_health` - Connectivity probe +2. `test_sql_repository_crud` - Put/Get/Delete/List operations +3. `test_quick_query_select_one` - Convenience query executor +4. `test_engine_stats_presence` - Pool metrics structure +5. `test_slow_query_warning` - Threshold detection (informational) +6. `test_saturation_detection` - 80% threshold trigger +7. `test_environment_aware_threshold` - Auto-tuning by environment + +**Test command:** +```powershell +.\venv\Scripts\python.exe -m pytest tests\test_sql_integration.py -v +``` + +**Output:** `7 passed in 0.XX s` + +## Migration Status + +| Migration | Status | Purpose | +|-----------|--------|---------| +| `001_keyvalue_index.sql` | ✅ Ready | Index on `QAI_KeyValue(k)` for performance | +| `002_query_performance_tracking.sql` | ✅ Ready | `QAI_QueryMetrics` table for optional tracking | + +**Apply migrations:** +```powershell +python .\scripts\sql_migrate.py +``` + +## Production Checklist + +- [x] Core SQL engine implemented +- [x] Repository pattern with vendor DDL +- [x] Health endpoint integration +- [x] Migration framework operational +- [x] Pool metrics exposure +- [x] Slow query detection +- [x] Saturation alerts +- [x] Query tracking (opt-in) +- [x] Azure Monitor ARM template +- [x] PowerShell automation script +- [x] KQL query library +- [x] Comprehensive documentation +- [x] Test suite (100% passing) + +## Next Steps (Post-Deployment) + +1. **Baseline Collection** (Week 1) + - Deploy with default thresholds + - Monitor slow query P95 in Application Insights + - Track pool saturation patterns during peak hours + +2. **Threshold Tuning** (Week 2) + - Adjust `QAI_SQL_SLOW_MS` based on P95 analysis + - Target: < 5 alerts per day per environment + - See AZURE_MONITOR_SQL_SETUP.md Section "Tuning Thresholds" + +3. **Pool Scaling** (If Needed) + - If saturation > 80% sustained for > 5 minutes + - Calculate: `pool_size = instances × concurrent_requests × 1.2` + - Update connection URL with `?pool_size=XX` + - Monitor for 24-48 hours, iterate + +4. **Query Optimization** (Ongoing) + - Review top slow queries weekly via KQL + - Identify caching candidates + - Add indexes via new migration files + - Correlate performance changes with deployments + +5. **Retention Management** (Monthly) + - If query tracking enabled, schedule cleanup job + - Delete `QAI_QueryMetrics` older than 7-30 days + - Monitor Application Insights log ingestion costs + +## Architecture Decisions + +- **SQLAlchemy over raw drivers**: Multi-vendor abstraction, battle-tested +- **Lazy initialization**: Avoid startup overhead, handle missing env vars +- **Silent degradation**: Non-blocking failures (logging.debug for tracking) +- **In-memory frequency counter**: Avoid write amplification to database +- **Environment-aware tuning**: Dev/staging/prod have different perf expectations +- **Hash-based deduplication**: 16-char SHA256 truncation balances storage vs. collision risk +- **ARM templates over manual config**: Infrastructure-as-code, repeatable deployments + +## Known Limitations + +- **Vendor-specific migrations**: Must write separate DDL for SQL Server vs. PostgreSQL syntax +- **Pytest caplog limitations**: Slow query warning test is informational (manual verification recommended) +- **Query tracking overhead**: Adds ~1-5ms per query (opt-in to avoid default perf impact) +- **Alert rule quotas**: Azure Monitor has limits per subscription (typically 5000+ metric alerts) + +## Support & References + +- **Core Documentation**: `DATABASE_SQL_SETUP.md` +- **Azure Monitoring**: `AZURE_MONITOR_SQL_SETUP.md` +- **SQLAlchemy Docs**: https://docs.sqlalchemy.org/en/20/core/pooling.html +- **Azure Monitor Pricing**: https://azure.microsoft.com/pricing/details/monitor/ +- **Application Insights KQL**: https://learn.microsoft.com/azure/azure-monitor/logs/get-started-queries + +## Version History + +- **1.0.0** (2025-11-23): Initial production release + - Multi-vendor SQL integration + - Pool observability and saturation detection + - Azure Monitor integration with ARM templates + - Comprehensive documentation and automation + +--- + +**Status**: Ready for production deployment. All features implemented, tested, and documented. diff --git a/docs/database/TELEMETRY_COSMOS_ENABLEMENT.md b/docs/database/TELEMETRY_COSMOS_ENABLEMENT.md index 68274a234..b0889e809 100644 --- a/docs/database/TELEMETRY_COSMOS_ENABLEMENT.md +++ b/docs/database/TELEMETRY_COSMOS_ENABLEMENT.md @@ -1,475 +1,475 @@ -# Telemetry & Cosmos DB Enablement Guide - -## Overview - -This guide documents how to enable Application Insights telemetry and Cosmos DB persistence in the QAI workspace. Both features are **optional** and behind feature flags, allowing you to run the system entirely offline or enable production-grade observability and persistence as needed. - ---- - -## Telemetry (Azure Monitor Application Insights) - -### What It Provides - -- **Distributed tracing**: Track request flows through `/api/chat` and other endpoints -- **Custom spans**: Measure provider latency, memory injection, Cosmos operations -- **Automatic instrumentation**: HTTP requests, dependencies, exceptions -- **Integration**: Works with Azure Monitor Logs and Application Insights portal - -### Enabling Telemetry - -**1. Set the connection string environment variable:** - -```powershell -# Local development (local.settings.json) -{ - "Values": { - "APPLICATIONINSIGHTS_CONNECTION_STRING": "InstrumentationKey=...;IngestionEndpoint=https://..." - } -} - -# Or as environment variable -$env:APPLICATIONINSIGHTS_CONNECTION_STRING = "InstrumentationKey=...;IngestionEndpoint=..." -``` - -**2. Verify telemetry initialization:** - -```powershell -# Check status endpoint -curl http://localhost:7071/api/ai/status | jq '.telemetry' -# Expected output: {"enabled": true} -``` - -**3. What gets traced:** - -- `/api/chat` endpoint: Full request lifecycle with custom attributes: - - `provider` (azure/openai/local/lora) - - `model` (deployment name or adapter path) - - `duration_ms` (completion time) - - `memory_injected` (number of memory-retrieved messages) - - `cosmos_persisted` (whether Cosmos write succeeded) -- **Exception tracking**: All unhandled errors with stack traces -- **Dependency calls**: OpenAI SDK, Cosmos DB, Azure Quantum (if enabled) - -### Telemetry Code Structure - -**Initialization** (`shared/telemetry.py`): -```python -from opentelemetry import trace -from azure.monitor.opentelemetry import configure_azure_monitor - -def init_telemetry(): - conn_str = os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING") - if conn_str: - configure_azure_monitor(connection_string=conn_str) - return True - return False - -def is_enabled() -> bool: - return bool(os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING")) -``` - -**Usage in endpoints** (`function_app.py`): -```python -from opentelemetry import trace -_tracer = trace.get_tracer("qai.functions") - -with _tracer.start_as_current_span("chat_request") as span: - # ... chat logic ... - span.set_attribute("provider", info.name) - span.set_attribute("model", info.model) - span.set_attribute("duration_ms", duration_ms) -``` - -### Verifying Telemetry in Azure Portal - -1. Navigate to your Application Insights resource in Azure Portal -2. Go to **Transaction Search** or **Logs (KQL editor)** -3. Query example traces: -```kusto -traces -| where cloud_RoleName == "qai.functions" -| where operation_Name == "chat_request" -| project timestamp, message, customDimensions -| take 100 -``` - -4. Analyze custom attributes: -```kusto -dependencies -| where cloud_RoleName == "qai.functions" -| extend provider = tostring(customDimensions["provider"]), - model = tostring(customDimensions["model"]), - duration = toint(customDimensions["duration_ms"]) -| summarize avg(duration), count() by provider, model -``` - -### Disabling Telemetry - -Simply remove or leave blank the `APPLICATIONINSIGHTS_CONNECTION_STRING` environment variable. The system will log a warning at startup and run without instrumentation. - ---- - -## Cosmos DB Persistence - -### What It Provides - -- **Chat message persistence**: Store user/assistant messages for retrieval and audit -- **Session management**: Track conversations by user ID or session ID -- **Flexible strategies**: Per-message writes or session-level batches -- **Azure integration**: Native support for serverless/provisioned Cosmos DB - -### Enabling Cosmos DB - -**1. Provision Cosmos DB (if not already done):** - -```powershell -# Using Azure CLI -az cosmosdb create --name qai-cosmos --resource-group rg-qai --default-consistency-level Session - -# Create database and container -az cosmosdb sql database create --account-name qai-cosmos --resource-group rg-qai --name qai -az cosmosdb sql container create --account-name qai-cosmos --resource-group rg-qai --database-name qai --name chat_sessions --partition-key-path /userId -``` - -**2. Set environment variables:** - -```powershell -# Local development (local.settings.json) -{ - "Values": { - "QAI_ENABLE_COSMOS": "true", - "COSMOS_ENDPOINT": "https://qai-cosmos.documents.azure.com:443/", - "COSMOS_KEY": "your_primary_key_here", - "COSMOS_DATABASE": "qai", - "COSMOS_CONTAINER": "chat_sessions", - "QAI_COSMOS_PERSIST_STRATEGY": "messages" # or "sessions" - } -} - -# Or as environment variables -$env:QAI_ENABLE_COSMOS = "true" -$env:COSMOS_ENDPOINT = "https://qai-cosmos.documents.azure.com:443/" -$env:COSMOS_KEY = "your_primary_key_here" -$env:COSMOS_DATABASE = "qai" -$env:COSMOS_CONTAINER = "chat_sessions" -$env:QAI_COSMOS_PERSIST_STRATEGY = "messages" -``` - -**3. Verify Cosmos integration:** - -```powershell -# Check status endpoint -curl http://localhost:7071/api/ai/status | jq '.cosmos' -# Expected output: {"enabled": true, "settings_present": true, "initialized": true, "container_id": "chat_sessions", ...} -``` - -**4. Test persistence:** - -```powershell -# Send a chat message -curl -X POST http://localhost:7071/api/chat -H "Content-Type: application/json" -d '{"messages":[{"role":"user","content":"Hello"}],"session_id":"test-session-1"}' - -# Check response for cosmos_persisted flag -# "cosmos_persisted": true -``` - -### Persistence Strategies - -**1. Per-Message Strategy** (`QAI_COSMOS_PERSIST_STRATEGY=messages`): -- **Behavior**: Each user and assistant message is written as a separate Cosmos document -- **Document schema**: -```json -{ - "id": "msg_1234567890_user", - "userId": "user-123", - "sessionId": "test-session-1", - "role": "user", - "content": "Hello, how are you?", - "timestamp": 1700000000.123, - "provider": "azure", - "model": "gpt-4o-mini" -} -``` -- **Pros**: Fine-grained audit trail, easy to query individual messages -- **Cons**: Higher write throughput (2 writes per chat turn) - -**2. Session Strategy** (`QAI_COSMOS_PERSIST_STRATEGY=sessions`): -- **Behavior**: Entire conversation (all messages) is written as one document per session -- **Document schema**: -```json -{ - "id": "session_test-session-1", - "userId": "user-123", - "sessionId": "test-session-1", - "messages": [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"} - ], - "timestamp": 1700000000.123, - "provider": "azure", - "model": "gpt-4o-mini" -} -``` -- **Pros**: Fewer writes (1 write per chat turn), better for cost optimization -- **Cons**: May hit document size limits on long conversations (~2 MB per document max) - -### Cosmos Code Structure - -**Client initialization** (`shared/cosmos_client.py`): -```python -from azure.cosmos import CosmosClient - -class QAICosmosClient: - def __init__(self): - endpoint = os.getenv("COSMOS_ENDPOINT") - key = os.getenv("COSMOS_KEY") - self.client = CosmosClient(endpoint, credential=key) - self.database = self.client.get_database_client(os.getenv("COSMOS_DATABASE", "qai")) - self.container = self.database.get_container_client(os.getenv("COSMOS_CONTAINER", "chat_sessions")) - - def record_chat_message(self, user_id: str, message: dict, provider: str, model: str): - doc = { - "id": f"msg_{int(time.time() * 1000)}_{message['role']}", - "userId": user_id, - "sessionId": message.get("session_id", "default"), - **message, - "provider": provider, - "model": model, - } - self.container.create_item(doc) - - def health(self) -> dict: - # Returns {"enabled": true, "settings_present": true, ...} - ... -``` - -**Usage in endpoint** (`function_app.py`): -```python -if cosmos_client and os.getenv("QAI_ENABLE_COSMOS", "false").lower() == "true": - strategy = os.getenv("QAI_COSMOS_PERSIST_STRATEGY", "messages") - if strategy == "messages": - cosmos_client.record_chat_message(user_id, {"role": "user", "content": "..."}, provider, model) - cosmos_client.record_chat_message(user_id, {"role": "assistant", "content": "..."}, provider, model) - else: - cosmos_client.record_chat_session(user_id, messages, provider, model) -``` - -### Disabling Cosmos DB - -Set `QAI_ENABLE_COSMOS=false` or remove the environment variable. The system will operate without persistence (all chat state is ephemeral). - ---- - -## Combined Configuration Example - -**Full local.settings.json with both telemetry and Cosmos enabled:** - -```json -{ - "IsEncrypted": false, - "Values": { - "AzureWebJobsStorage": "UseDevelopmentStorage=true", - "FUNCTIONS_WORKER_RUNTIME": "python", - - "APPLICATIONINSIGHTS_CONNECTION_STRING": "InstrumentationKey=abc-123;IngestionEndpoint=https://eastus-8.in.applicationinsights.azure.com/", - - "QAI_ENABLE_COSMOS": "true", - "COSMOS_ENDPOINT": "https://qai-cosmos.documents.azure.com:443/", - "COSMOS_KEY": "your_primary_key_here", - "COSMOS_DATABASE": "qai", - "COSMOS_CONTAINER": "chat_sessions", - "QAI_COSMOS_PERSIST_STRATEGY": "messages", - - "AZURE_OPENAI_API_KEY": "your_azure_openai_key", - "AZURE_OPENAI_ENDPOINT": "https://your-resource.openai.azure.com/", - "AZURE_OPENAI_DEPLOYMENT": "gpt-4o-mini", - "AZURE_OPENAI_API_VERSION": "2024-08-01-preview" - } -} -``` - ---- - -## Status Endpoint Reference - -### New Fields (as of latest version) - -**Telemetry section:** -```json -"telemetry": { - "enabled": true // true if APPLICATIONINSIGHTS_CONNECTION_STRING is set -} -``` - -**Quantum section:** -```json -"quantum": { - "enabled": true, // true if qiskit imports successfully - "qiskit": "0.46.0", // or error message - "pennylane": "0.43.0", - "azure_quantum": { - "workspace_connected": false, // true if QAI_STATUS_CONNECT_AZURE_QUANTUM=true and connected - "backends": [], // list of backend names (e.g., ["rigetti.sim.qvm", "ionq.simulator"]) - "attempted": false, // true if probe was attempted - "error": null // or error string - }, - "conflict": false // true if mixed Qiskit >=1.x + legacy aer detected -} -``` - -**Cosmos section:** -```json -"cosmos": { - "enabled": false, // true if QAI_ENABLE_COSMOS=true - "settings_present": false, // true if COSMOS_ENDPOINT and COSMOS_KEY set - "initialized": false, // true if client successfully connected - "container_id": null, // container name if connected - "database": "qai", - "container": "chat_sessions", - "error": null // or error string -} -``` - ---- - -## Troubleshooting - -### Telemetry Not Appearing in Portal - -1. **Check connection string format:** - ```powershell - $env:APPLICATIONINSIGHTS_CONNECTION_STRING - # Should be: InstrumentationKey=...;IngestionEndpoint=https://... - ``` - -2. **Verify telemetry.enabled in status:** - ```powershell - curl http://localhost:7071/api/ai/status | jq '.telemetry.enabled' - # Should return: true - ``` - -3. **Check startup logs:** - ```powershell - # Look for: [startup] Telemetry initialized successfully - # Or: [startup] Telemetry init skipped: - ``` - -4. **Allow 2-5 minutes for ingestion latency** in Azure Portal - -### Cosmos Writes Failing - -1. **Verify credentials:** - ```powershell - # Test connection using Azure Cosmos DB Data Explorer in Portal - ``` - -2. **Check firewall rules:** - - Ensure your IP is allowlisted in Cosmos DB → Settings → Firewall and virtual networks - - Or enable "Allow access from Azure Portal" for testing - -3. **Review status error field:** - ```powershell - curl http://localhost:7071/api/ai/status | jq '.cosmos.error' - # Common errors: - # - "Unauthorized" → wrong key - # - "Forbidden" → firewall blocking - # - "NotFound" → database/container doesn't exist - ``` - -4. **Check container partition key:** - - Must be `/userId` (or update client code to match your schema) - -### Conflict Detection Showing True - -**Current status shows:** -```json -"quantum": { - "conflict": true, - "qiskit": "1.4.5" -} -``` - -**This indicates:** Mixed Qiskit ≥1.x with legacy aer/machine-learning packages detected in the root Functions environment. This is **expected** if root venv has Qiskit 1.x while `ai-projects/quantum-ml/venv` has the downgraded 0.46.0 stack. - -**Resolution options:** - -1. **Ignore** (recommended if quantum endpoints aren't used in production): - - Root venv conflict doesn't affect isolated `ai-projects/quantum-ml/` training - - Quantum MCP server uses dedicated venv - -2. **Upgrade root venv to Qiskit 1.x** (use upgrade script): - ```powershell - cd quantum-ai - python .\scripts\upgrade_qiskit_to_1x.py --dry-run # preview changes - python .\scripts\upgrade_qiskit_to_1x.py --install # apply upgrade - ``` - -3. **Disable quantum status probing** (prevent import attempts): - - Set `QAI_STATUS_CONNECT_AZURE_QUANTUM=false` or leave unset (default) - ---- - -## Cost Optimization - -### Telemetry - -- **Free tier**: 5 GB ingestion/month (usually sufficient for dev/test) -- **Sampling**: Configure in `shared/telemetry.py`: - ```python - from opentelemetry.sdk.trace.sampling import TraceIdRatioBased - sampler = TraceIdRatioBased(0.1) # 10% sampling - ``` -- **Filtering**: Exclude low-value operations (e.g., health checks) - -### Cosmos DB - -- **Serverless tier**: Pay per request (~$0.25/million reads, ~$1.25/million writes) - - Best for dev/test or low-traffic apps -- **Provisioned throughput**: Fixed monthly cost (400 RU/s minimum ~$24/month) - - Best for predictable workloads -- **Strategy impact**: - - Per-message: ~2 writes per chat turn (user + assistant) - - Session-level: ~1 write per chat turn - -**Example costs (per-message strategy, 1000 chat turns/day):** -- Serverless: 2000 writes/day × 30 days = 60K writes/month → **~$0.08/month** -- Provisioned 400 RU/s: **$24/month** (fixed) - ---- - -## Best Practices - -1. **Always test with status endpoint first:** - ```powershell - curl http://localhost:7071/api/ai/status | jq '.telemetry, .cosmos' - ``` - -2. **Use feature flags for gradual rollout:** - - Enable telemetry first (zero cost to test) - - Add Cosmos after confirming telemetry works - - Enable Azure Quantum probing only if needed (adds latency) - -3. **Monitor ingestion limits:** - - Application Insights free tier: 5 GB/month - - Cosmos DB serverless: 1 million RU/s per container (very high) - -4. **Secure credentials:** - - Never commit keys to source control - - Use Azure Key Vault references in production: - ```json - "COSMOS_KEY": "@Microsoft.KeyVault(SecretUri=https://...)" - ``` - -5. **Document environment variables in README:** - - Keep this guide in sync with `.env.example` or `local.settings.json.example` - ---- - -## See Also - -- **Upgrade Script**: `ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py` (Qiskit 1.x migration) -- **Validation Script**: `ai-projects/quantum-ml/scripts/validate_qiskit_env.py` (conflict detection) -- **Unit Tests**: `tests/test_validate_qiskit_env.py` (conflict detection logic) -- **Azure Documentation**: - - [Application Insights for Azure Functions](https://learn.microsoft.com/azure/azure-functions/functions-monitoring) - - [Cosmos DB Python SDK](https://learn.microsoft.com/azure/cosmos-db/nosql/sdk-python) +# Telemetry & Cosmos DB Enablement Guide + +## Overview + +This guide documents how to enable Application Insights telemetry and Cosmos DB persistence in the QAI workspace. Both features are **optional** and behind feature flags, allowing you to run the system entirely offline or enable production-grade observability and persistence as needed. + +--- + +## Telemetry (Azure Monitor Application Insights) + +### What It Provides + +- **Distributed tracing**: Track request flows through `/api/chat` and other endpoints +- **Custom spans**: Measure provider latency, memory injection, Cosmos operations +- **Automatic instrumentation**: HTTP requests, dependencies, exceptions +- **Integration**: Works with Azure Monitor Logs and Application Insights portal + +### Enabling Telemetry + +**1. Set the connection string environment variable:** + +```powershell +# Local development (local.settings.json) +{ + "Values": { + "APPLICATIONINSIGHTS_CONNECTION_STRING": "InstrumentationKey=...;IngestionEndpoint=https://..." + } +} + +# Or as environment variable +$env:APPLICATIONINSIGHTS_CONNECTION_STRING = "InstrumentationKey=...;IngestionEndpoint=..." +``` + +**2. Verify telemetry initialization:** + +```powershell +# Check status endpoint +curl http://localhost:7071/api/ai/status | jq '.telemetry' +# Expected output: {"enabled": true} +``` + +**3. What gets traced:** + +- `/api/chat` endpoint: Full request lifecycle with custom attributes: + - `provider` (azure/openai/local/lora) + - `model` (deployment name or adapter path) + - `duration_ms` (completion time) + - `memory_injected` (number of memory-retrieved messages) + - `cosmos_persisted` (whether Cosmos write succeeded) +- **Exception tracking**: All unhandled errors with stack traces +- **Dependency calls**: OpenAI SDK, Cosmos DB, Azure Quantum (if enabled) + +### Telemetry Code Structure + +**Initialization** (`shared/telemetry.py`): +```python +from opentelemetry import trace +from azure.monitor.opentelemetry import configure_azure_monitor + +def init_telemetry(): + conn_str = os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING") + if conn_str: + configure_azure_monitor(connection_string=conn_str) + return True + return False + +def is_enabled() -> bool: + return bool(os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING")) +``` + +**Usage in endpoints** (`function_app.py`): +```python +from opentelemetry import trace +_tracer = trace.get_tracer("qai.functions") + +with _tracer.start_as_current_span("chat_request") as span: + # ... chat logic ... + span.set_attribute("provider", info.name) + span.set_attribute("model", info.model) + span.set_attribute("duration_ms", duration_ms) +``` + +### Verifying Telemetry in Azure Portal + +1. Navigate to your Application Insights resource in Azure Portal +2. Go to **Transaction Search** or **Logs (KQL editor)** +3. Query example traces: +```kusto +traces +| where cloud_RoleName == "qai.functions" +| where operation_Name == "chat_request" +| project timestamp, message, customDimensions +| take 100 +``` + +4. Analyze custom attributes: +```kusto +dependencies +| where cloud_RoleName == "qai.functions" +| extend provider = tostring(customDimensions["provider"]), + model = tostring(customDimensions["model"]), + duration = toint(customDimensions["duration_ms"]) +| summarize avg(duration), count() by provider, model +``` + +### Disabling Telemetry + +Simply remove or leave blank the `APPLICATIONINSIGHTS_CONNECTION_STRING` environment variable. The system will log a warning at startup and run without instrumentation. + +--- + +## Cosmos DB Persistence + +### What It Provides + +- **Chat message persistence**: Store user/assistant messages for retrieval and audit +- **Session management**: Track conversations by user ID or session ID +- **Flexible strategies**: Per-message writes or session-level batches +- **Azure integration**: Native support for serverless/provisioned Cosmos DB + +### Enabling Cosmos DB + +**1. Provision Cosmos DB (if not already done):** + +```powershell +# Using Azure CLI +az cosmosdb create --name qai-cosmos --resource-group rg-qai --default-consistency-level Session + +# Create database and container +az cosmosdb sql database create --account-name qai-cosmos --resource-group rg-qai --name qai +az cosmosdb sql container create --account-name qai-cosmos --resource-group rg-qai --database-name qai --name chat_sessions --partition-key-path /userId +``` + +**2. Set environment variables:** + +```powershell +# Local development (local.settings.json) +{ + "Values": { + "QAI_ENABLE_COSMOS": "true", + "COSMOS_ENDPOINT": "https://qai-cosmos.documents.azure.com:443/", + "COSMOS_KEY": "your_primary_key_here", + "COSMOS_DATABASE": "qai", + "COSMOS_CONTAINER": "chat_sessions", + "QAI_COSMOS_PERSIST_STRATEGY": "messages" # or "sessions" + } +} + +# Or as environment variables +$env:QAI_ENABLE_COSMOS = "true" +$env:COSMOS_ENDPOINT = "https://qai-cosmos.documents.azure.com:443/" +$env:COSMOS_KEY = "your_primary_key_here" +$env:COSMOS_DATABASE = "qai" +$env:COSMOS_CONTAINER = "chat_sessions" +$env:QAI_COSMOS_PERSIST_STRATEGY = "messages" +``` + +**3. Verify Cosmos integration:** + +```powershell +# Check status endpoint +curl http://localhost:7071/api/ai/status | jq '.cosmos' +# Expected output: {"enabled": true, "settings_present": true, "initialized": true, "container_id": "chat_sessions", ...} +``` + +**4. Test persistence:** + +```powershell +# Send a chat message +curl -X POST http://localhost:7071/api/chat -H "Content-Type: application/json" -d '{"messages":[{"role":"user","content":"Hello"}],"session_id":"test-session-1"}' + +# Check response for cosmos_persisted flag +# "cosmos_persisted": true +``` + +### Persistence Strategies + +**1. Per-Message Strategy** (`QAI_COSMOS_PERSIST_STRATEGY=messages`): +- **Behavior**: Each user and assistant message is written as a separate Cosmos document +- **Document schema**: +```json +{ + "id": "msg_1234567890_user", + "userId": "user-123", + "sessionId": "test-session-1", + "role": "user", + "content": "Hello, how are you?", + "timestamp": 1700000000.123, + "provider": "azure", + "model": "gpt-4o-mini" +} +``` +- **Pros**: Fine-grained audit trail, easy to query individual messages +- **Cons**: Higher write throughput (2 writes per chat turn) + +**2. Session Strategy** (`QAI_COSMOS_PERSIST_STRATEGY=sessions`): +- **Behavior**: Entire conversation (all messages) is written as one document per session +- **Document schema**: +```json +{ + "id": "session_test-session-1", + "userId": "user-123", + "sessionId": "test-session-1", + "messages": [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"} + ], + "timestamp": 1700000000.123, + "provider": "azure", + "model": "gpt-4o-mini" +} +``` +- **Pros**: Fewer writes (1 write per chat turn), better for cost optimization +- **Cons**: May hit document size limits on long conversations (~2 MB per document max) + +### Cosmos Code Structure + +**Client initialization** (`shared/cosmos_client.py`): +```python +from azure.cosmos import CosmosClient + +class QAICosmosClient: + def __init__(self): + endpoint = os.getenv("COSMOS_ENDPOINT") + key = os.getenv("COSMOS_KEY") + self.client = CosmosClient(endpoint, credential=key) + self.database = self.client.get_database_client(os.getenv("COSMOS_DATABASE", "qai")) + self.container = self.database.get_container_client(os.getenv("COSMOS_CONTAINER", "chat_sessions")) + + def record_chat_message(self, user_id: str, message: dict, provider: str, model: str): + doc = { + "id": f"msg_{int(time.time() * 1000)}_{message['role']}", + "userId": user_id, + "sessionId": message.get("session_id", "default"), + **message, + "provider": provider, + "model": model, + } + self.container.create_item(doc) + + def health(self) -> dict: + # Returns {"enabled": true, "settings_present": true, ...} + ... +``` + +**Usage in endpoint** (`function_app.py`): +```python +if cosmos_client and os.getenv("QAI_ENABLE_COSMOS", "false").lower() == "true": + strategy = os.getenv("QAI_COSMOS_PERSIST_STRATEGY", "messages") + if strategy == "messages": + cosmos_client.record_chat_message(user_id, {"role": "user", "content": "..."}, provider, model) + cosmos_client.record_chat_message(user_id, {"role": "assistant", "content": "..."}, provider, model) + else: + cosmos_client.record_chat_session(user_id, messages, provider, model) +``` + +### Disabling Cosmos DB + +Set `QAI_ENABLE_COSMOS=false` or remove the environment variable. The system will operate without persistence (all chat state is ephemeral). + +--- + +## Combined Configuration Example + +**Full local.settings.json with both telemetry and Cosmos enabled:** + +```json +{ + "IsEncrypted": false, + "Values": { + "AzureWebJobsStorage": "UseDevelopmentStorage=true", + "FUNCTIONS_WORKER_RUNTIME": "python", + + "APPLICATIONINSIGHTS_CONNECTION_STRING": "InstrumentationKey=abc-123;IngestionEndpoint=https://eastus-8.in.applicationinsights.azure.com/", + + "QAI_ENABLE_COSMOS": "true", + "COSMOS_ENDPOINT": "https://qai-cosmos.documents.azure.com:443/", + "COSMOS_KEY": "your_primary_key_here", + "COSMOS_DATABASE": "qai", + "COSMOS_CONTAINER": "chat_sessions", + "QAI_COSMOS_PERSIST_STRATEGY": "messages", + + "AZURE_OPENAI_API_KEY": "your_azure_openai_key", + "AZURE_OPENAI_ENDPOINT": "https://your-resource.openai.azure.com/", + "AZURE_OPENAI_DEPLOYMENT": "gpt-4o-mini", + "AZURE_OPENAI_API_VERSION": "2024-08-01-preview" + } +} +``` + +--- + +## Status Endpoint Reference + +### New Fields (as of latest version) + +**Telemetry section:** +```json +"telemetry": { + "enabled": true // true if APPLICATIONINSIGHTS_CONNECTION_STRING is set +} +``` + +**Quantum section:** +```json +"quantum": { + "enabled": true, // true if qiskit imports successfully + "qiskit": "0.46.0", // or error message + "pennylane": "0.43.0", + "azure_quantum": { + "workspace_connected": false, // true if QAI_STATUS_CONNECT_AZURE_QUANTUM=true and connected + "backends": [], // list of backend names (e.g., ["rigetti.sim.qvm", "ionq.simulator"]) + "attempted": false, // true if probe was attempted + "error": null // or error string + }, + "conflict": false // true if mixed Qiskit >=1.x + legacy aer detected +} +``` + +**Cosmos section:** +```json +"cosmos": { + "enabled": false, // true if QAI_ENABLE_COSMOS=true + "settings_present": false, // true if COSMOS_ENDPOINT and COSMOS_KEY set + "initialized": false, // true if client successfully connected + "container_id": null, // container name if connected + "database": "qai", + "container": "chat_sessions", + "error": null // or error string +} +``` + +--- + +## Troubleshooting + +### Telemetry Not Appearing in Portal + +1. **Check connection string format:** + ```powershell + $env:APPLICATIONINSIGHTS_CONNECTION_STRING + # Should be: InstrumentationKey=...;IngestionEndpoint=https://... + ``` + +2. **Verify telemetry.enabled in status:** + ```powershell + curl http://localhost:7071/api/ai/status | jq '.telemetry.enabled' + # Should return: true + ``` + +3. **Check startup logs:** + ```powershell + # Look for: [startup] Telemetry initialized successfully + # Or: [startup] Telemetry init skipped: + ``` + +4. **Allow 2-5 minutes for ingestion latency** in Azure Portal + +### Cosmos Writes Failing + +1. **Verify credentials:** + ```powershell + # Test connection using Azure Cosmos DB Data Explorer in Portal + ``` + +2. **Check firewall rules:** + - Ensure your IP is allowlisted in Cosmos DB → Settings → Firewall and virtual networks + - Or enable "Allow access from Azure Portal" for testing + +3. **Review status error field:** + ```powershell + curl http://localhost:7071/api/ai/status | jq '.cosmos.error' + # Common errors: + # - "Unauthorized" → wrong key + # - "Forbidden" → firewall blocking + # - "NotFound" → database/container doesn't exist + ``` + +4. **Check container partition key:** + - Must be `/userId` (or update client code to match your schema) + +### Conflict Detection Showing True + +**Current status shows:** +```json +"quantum": { + "conflict": true, + "qiskit": "1.4.5" +} +``` + +**This indicates:** Mixed Qiskit ≥1.x with legacy aer/machine-learning packages detected in the root Functions environment. This is **expected** if root venv has Qiskit 1.x while `ai-projects/quantum-ml/venv` has the downgraded 0.46.0 stack. + +**Resolution options:** + +1. **Ignore** (recommended if quantum endpoints aren't used in production): + - Root venv conflict doesn't affect isolated `ai-projects/quantum-ml/` training + - Quantum MCP server uses dedicated venv + +2. **Upgrade root venv to Qiskit 1.x** (use upgrade script): + ```powershell + cd quantum-ai + python .\scripts\upgrade_qiskit_to_1x.py --dry-run # preview changes + python .\scripts\upgrade_qiskit_to_1x.py --install # apply upgrade + ``` + +3. **Disable quantum status probing** (prevent import attempts): + - Set `QAI_STATUS_CONNECT_AZURE_QUANTUM=false` or leave unset (default) + +--- + +## Cost Optimization + +### Telemetry + +- **Free tier**: 5 GB ingestion/month (usually sufficient for dev/test) +- **Sampling**: Configure in `shared/telemetry.py`: + ```python + from opentelemetry.sdk.trace.sampling import TraceIdRatioBased + sampler = TraceIdRatioBased(0.1) # 10% sampling + ``` +- **Filtering**: Exclude low-value operations (e.g., health checks) + +### Cosmos DB + +- **Serverless tier**: Pay per request (~$0.25/million reads, ~$1.25/million writes) + - Best for dev/test or low-traffic apps +- **Provisioned throughput**: Fixed monthly cost (400 RU/s minimum ~$24/month) + - Best for predictable workloads +- **Strategy impact**: + - Per-message: ~2 writes per chat turn (user + assistant) + - Session-level: ~1 write per chat turn + +**Example costs (per-message strategy, 1000 chat turns/day):** +- Serverless: 2000 writes/day × 30 days = 60K writes/month → **~$0.08/month** +- Provisioned 400 RU/s: **$24/month** (fixed) + +--- + +## Best Practices + +1. **Always test with status endpoint first:** + ```powershell + curl http://localhost:7071/api/ai/status | jq '.telemetry, .cosmos' + ``` + +2. **Use feature flags for gradual rollout:** + - Enable telemetry first (zero cost to test) + - Add Cosmos after confirming telemetry works + - Enable Azure Quantum probing only if needed (adds latency) + +3. **Monitor ingestion limits:** + - Application Insights free tier: 5 GB/month + - Cosmos DB serverless: 1 million RU/s per container (very high) + +4. **Secure credentials:** + - Never commit keys to source control + - Use Azure Key Vault references in production: + ```json + "COSMOS_KEY": "@Microsoft.KeyVault(SecretUri=https://...)" + ``` + +5. **Document environment variables in README:** + - Keep this guide in sync with `.env.example` or `local.settings.json.example` + +--- + +## See Also + +- **Upgrade Script**: `ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py` (Qiskit 1.x migration) +- **Validation Script**: `ai-projects/quantum-ml/scripts/validate_qiskit_env.py` (conflict detection) +- **Unit Tests**: `tests/test_validate_qiskit_env.py` (conflict detection logic) +- **Azure Documentation**: + - [Application Insights for Azure Functions](https://learn.microsoft.com/azure/azure-functions/functions-monitoring) + - [Cosmos DB Python SDK](https://learn.microsoft.com/azure/cosmos-db/nosql/sdk-python) diff --git a/docs/deployment/AZURE_DEPLOYMENT_ANIME_AVATAR.md b/docs/deployment/AZURE_DEPLOYMENT_ANIME_AVATAR.md index e156681fd..a6c05e6f7 100644 --- a/docs/deployment/AZURE_DEPLOYMENT_ANIME_AVATAR.md +++ b/docs/deployment/AZURE_DEPLOYMENT_ANIME_AVATAR.md @@ -1,249 +1,249 @@ -# Azure Deployment Guide for Anime Avatar - -## Quick Deploy Command - -```powershell -.\deploy-chat-to-azure.ps1 ` - -ResourceGroup "qai-anime-avatar-rg" ` - -FunctionApp "qai-anime-avatar-app" ` - -Location "eastus" ` - -DefaultProvider "lora" ` - -ChatTemperature "0.7" -``` - -## Pre-Deployment Checklist - -### 1. Install Prerequisites -- ✅ Azure CLI: `az --version` -- ✅ Azure Functions Core Tools: `func --version` -- ✅ Python 3.11: `python --version` - -### 2. Prepare LoRA Model for Deployment -The trained anime avatar model needs to be packaged for Azure deployment: - -```powershell -# Create deployment package -$modelPath = "deployed_models\checkpoint-32_20251125_233615" -$deployPackage = "azure_deploy_anime_avatar" - -# Copy model files -New-Item -ItemType Directory -Path $deployPackage -Force -Copy-Item -Path $modelPath -Destination "$deployPackage\lora_adapter" -Recurse - -# Copy required Python dependencies -Copy-Item -Path "requirements.txt" -Destination "$deployPackage\" - -# Package for upload -Compress-Archive -Path "$deployPackage\*" -DestinationPath "anime_avatar_model.zip" -Force -``` - -### 3. Azure Configuration - -#### Option A: Deploy with LoRA Model (Recommended for Production) - -**Important:** Azure Functions has file size limits. For LoRA models: -- Use Azure Blob Storage to host the model -- Mount storage as a volume in Function App -- Update environment variables to point to mounted model - -```powershell -# Create storage account for model hosting -az storage account create \ - --name qaianimemodels \ - --resource-group qai-anime-avatar-rg \ - --location eastus \ - --sku Standard_LRS - -# Create container for models -az storage container create \ - --name models \ - --account-name qaianimemodels \ - --public-access off - -# Upload model (requires Azure Storage Explorer or CLI) -az storage blob upload-batch \ - --destination models \ - --source deployed_models\checkpoint-32_20251125_233615 \ - --account-name qaianimemodels -``` - -#### Option B: Deploy with API Provider (Easier, but requires API keys) - -Use Azure OpenAI or OpenAI API instead of local LoRA: - -```powershell -.\deploy-chat-to-azure.ps1 ` - -ResourceGroup "qai-anime-avatar-rg" ` - -FunctionApp "qai-anime-avatar-app" ` - -Location "eastus" ` - -AzureOpenAIKey "YOUR_KEY" ` - -AzureOpenAIEndpoint "https://YOUR_ENDPOINT.openai.azure.com/" ` - -AzureOpenAIDeployment "gpt-4o" ` - -DefaultProvider "azure" -``` - -### 4. Environment Variables for LoRA Deployment - -After deployment, configure these app settings: - -```powershell -az functionapp config appsettings set \ - --name qai-anime-avatar-app \ - --resource-group qai-anime-avatar-rg \ - --settings \ - QAI_PROVIDER="lora" \ - QAI_LORA_MODEL="/mnt/models/checkpoint-32_20251125_233615" \ - CHAT_TEMPERATURE="0.7" \ - ENABLE_APP_INSIGHTS="true" -``` - -### 5. Deploy Function Code - -```powershell -# Ensure you're in the workspace root -cd C:\Users\Bryan\OneDrive\AI - -# Deploy -func azure functionapp publish qai-anime-avatar-app --python -``` - -### 6. Verify Deployment - -```powershell -# Get Function App URL -$appUrl = az functionapp show \ - --name qai-anime-avatar-app \ - --resource-group qai-anime-avatar-rg \ - --query defaultHostName -o tsv - -# Test endpoints -Invoke-RestMethod -Uri "https://$appUrl/api/ai/status" -Invoke-RestMethod -Uri "https://$appUrl/api/chat-web" -``` - -## Production Considerations - -### Performance Optimization -1. **Use Premium or Dedicated Plan** for LoRA inference - - Consumption plan may timeout for large models - - Minimum: Premium P1V2 (3.5 GB RAM, 1 vCPU) - - Recommended: Premium P2V2 (7 GB RAM, 2 vCPU) - -2. **Enable Always On** - ```powershell - az functionapp config set \ - --name qai-anime-avatar-app \ - --resource-group qai-anime-avatar-rg \ - --always-on true - ``` - -3. **Configure Scaling** - - Set minimum instances: 1 - - Set maximum instances: 10 - - Configure based on expected load - -### Security -1. **Enable HTTPS Only** - ```powershell - az functionapp update \ - --name qai-anime-avatar-app \ - --resource-group qai-anime-avatar-rg \ - --set httpsOnly=true - ``` - -2. **Configure CORS** for web interface - ```powershell - az functionapp cors add \ - --name qai-anime-avatar-app \ - --resource-group qai-anime-avatar-rg \ - --allowed-origins "https://yourdomain.com" - ``` - -3. **Use Key Vault** for sensitive settings - - Store API keys in Azure Key Vault - - Reference in app settings: `@Microsoft.KeyVault(SecretUri=...)` - -### Monitoring -1. **Enable Application Insights** - ```powershell - az monitor app-insights component create \ - --app qai-anime-avatar-insights \ - --location eastus \ - --resource-group qai-anime-avatar-rg - ``` - -2. **Configure Alerts** - - Response time > 5s - - Error rate > 5% - - CPU usage > 80% - -### Cost Optimization -- **Estimated Monthly Costs:** - - Consumption Plan: $0-50 (first 1M executions free) - - Premium P1V2: ~$146/month - - Storage: ~$2-5/month - - App Insights: ~$10-30/month (based on data ingestion) - -## Troubleshooting - -### Model Loading Issues -If LoRA model fails to load: -1. Check model path in QAI_LORA_MODEL -2. Verify model files are accessible -3. Check function timeout settings (increase if needed) -4. Review Application Insights logs - -### Performance Issues -1. Upgrade to Premium plan -2. Enable GPU support (requires App Service Plan) -3. Optimize model size (quantization) -4. Implement caching layer - -### Cold Start Mitigation -1. Enable Always On -2. Use Premium plan with pre-warmed instances -3. Implement health check endpoint -4. Consider Azure Container Instances for better cold start - -## Alternative: Deploy to Azure Container Instances - -For full control and GPU support: - -```powershell -# Build Docker container -docker build -t qai-anime-avatar:latest . - -# Push to Azure Container Registry -az acr create --name qaiacr --resource-group qai-anime-avatar-rg --sku Basic -az acr login --name qaiacr -docker tag qai-anime-avatar:latest qaiacr.azurecr.io/anime-avatar:latest -docker push qaiacr.azurecr.io/anime-avatar:latest - -# Deploy to ACI with GPU (if needed) -az container create \ - --name qai-anime-avatar \ - --resource-group qai-anime-avatar-rg \ - --image qaiacr.azurecr.io/anime-avatar:latest \ - --cpu 2 \ - --memory 4 \ - --registry-login-server qaiacr.azurecr.io \ - --registry-username $(az acr credential show --name qaiacr --query username -o tsv) \ - --registry-password $(az acr credential show --name qaiacr --query "passwords[0].value" -o tsv) \ - --dns-name-label qai-anime-avatar \ - --ports 80 -``` - -## Next Steps - -1. ✅ Complete grid search to find best hyperparameters -2. ✅ Retrain model with optimal settings -3. ✅ Test locally before deploying -4. ✅ Choose deployment strategy (Functions vs Containers) -5. ✅ Deploy and monitor -6. ✅ Set up CI/CD pipeline for continuous deployment - ---- - -**Created:** 2025-11-25 -**Last Updated:** 2025-11-25 -**Status:** Ready for deployment after hyperparameter optimization +# Azure Deployment Guide for Anime Avatar + +## Quick Deploy Command + +```powershell +.\deploy-chat-to-azure.ps1 ` + -ResourceGroup "qai-anime-avatar-rg" ` + -FunctionApp "qai-anime-avatar-app" ` + -Location "eastus" ` + -DefaultProvider "lora" ` + -ChatTemperature "0.7" +``` + +## Pre-Deployment Checklist + +### 1. Install Prerequisites +- ✅ Azure CLI: `az --version` +- ✅ Azure Functions Core Tools: `func --version` +- ✅ Python 3.11: `python --version` + +### 2. Prepare LoRA Model for Deployment +The trained anime avatar model needs to be packaged for Azure deployment: + +```powershell +# Create deployment package +$modelPath = "deployed_models\checkpoint-32_20251125_233615" +$deployPackage = "azure_deploy_anime_avatar" + +# Copy model files +New-Item -ItemType Directory -Path $deployPackage -Force +Copy-Item -Path $modelPath -Destination "$deployPackage\lora_adapter" -Recurse + +# Copy required Python dependencies +Copy-Item -Path "requirements.txt" -Destination "$deployPackage\" + +# Package for upload +Compress-Archive -Path "$deployPackage\*" -DestinationPath "anime_avatar_model.zip" -Force +``` + +### 3. Azure Configuration + +#### Option A: Deploy with LoRA Model (Recommended for Production) + +**Important:** Azure Functions has file size limits. For LoRA models: +- Use Azure Blob Storage to host the model +- Mount storage as a volume in Function App +- Update environment variables to point to mounted model + +```powershell +# Create storage account for model hosting +az storage account create \ + --name qaianimemodels \ + --resource-group qai-anime-avatar-rg \ + --location eastus \ + --sku Standard_LRS + +# Create container for models +az storage container create \ + --name models \ + --account-name qaianimemodels \ + --public-access off + +# Upload model (requires Azure Storage Explorer or CLI) +az storage blob upload-batch \ + --destination models \ + --source deployed_models\checkpoint-32_20251125_233615 \ + --account-name qaianimemodels +``` + +#### Option B: Deploy with API Provider (Easier, but requires API keys) + +Use Azure OpenAI or OpenAI API instead of local LoRA: + +```powershell +.\deploy-chat-to-azure.ps1 ` + -ResourceGroup "qai-anime-avatar-rg" ` + -FunctionApp "qai-anime-avatar-app" ` + -Location "eastus" ` + -AzureOpenAIKey "YOUR_KEY" ` + -AzureOpenAIEndpoint "https://YOUR_ENDPOINT.openai.azure.com/" ` + -AzureOpenAIDeployment "gpt-4o" ` + -DefaultProvider "azure" +``` + +### 4. Environment Variables for LoRA Deployment + +After deployment, configure these app settings: + +```powershell +az functionapp config appsettings set \ + --name qai-anime-avatar-app \ + --resource-group qai-anime-avatar-rg \ + --settings \ + QAI_PROVIDER="lora" \ + QAI_LORA_MODEL="/mnt/models/checkpoint-32_20251125_233615" \ + CHAT_TEMPERATURE="0.7" \ + ENABLE_APP_INSIGHTS="true" +``` + +### 5. Deploy Function Code + +```powershell +# Ensure you're in the workspace root +cd C:\Users\Bryan\OneDrive\AI + +# Deploy +func azure functionapp publish qai-anime-avatar-app --python +``` + +### 6. Verify Deployment + +```powershell +# Get Function App URL +$appUrl = az functionapp show \ + --name qai-anime-avatar-app \ + --resource-group qai-anime-avatar-rg \ + --query defaultHostName -o tsv + +# Test endpoints +Invoke-RestMethod -Uri "https://$appUrl/api/ai/status" +Invoke-RestMethod -Uri "https://$appUrl/api/chat-web" +``` + +## Production Considerations + +### Performance Optimization +1. **Use Premium or Dedicated Plan** for LoRA inference + - Consumption plan may timeout for large models + - Minimum: Premium P1V2 (3.5 GB RAM, 1 vCPU) + - Recommended: Premium P2V2 (7 GB RAM, 2 vCPU) + +2. **Enable Always On** + ```powershell + az functionapp config set \ + --name qai-anime-avatar-app \ + --resource-group qai-anime-avatar-rg \ + --always-on true + ``` + +3. **Configure Scaling** + - Set minimum instances: 1 + - Set maximum instances: 10 + - Configure based on expected load + +### Security +1. **Enable HTTPS Only** + ```powershell + az functionapp update \ + --name qai-anime-avatar-app \ + --resource-group qai-anime-avatar-rg \ + --set httpsOnly=true + ``` + +2. **Configure CORS** for web interface + ```powershell + az functionapp cors add \ + --name qai-anime-avatar-app \ + --resource-group qai-anime-avatar-rg \ + --allowed-origins "https://yourdomain.com" + ``` + +3. **Use Key Vault** for sensitive settings + - Store API keys in Azure Key Vault + - Reference in app settings: `@Microsoft.KeyVault(SecretUri=...)` + +### Monitoring +1. **Enable Application Insights** + ```powershell + az monitor app-insights component create \ + --app qai-anime-avatar-insights \ + --location eastus \ + --resource-group qai-anime-avatar-rg + ``` + +2. **Configure Alerts** + - Response time > 5s + - Error rate > 5% + - CPU usage > 80% + +### Cost Optimization +- **Estimated Monthly Costs:** + - Consumption Plan: $0-50 (first 1M executions free) + - Premium P1V2: ~$146/month + - Storage: ~$2-5/month + - App Insights: ~$10-30/month (based on data ingestion) + +## Troubleshooting + +### Model Loading Issues +If LoRA model fails to load: +1. Check model path in QAI_LORA_MODEL +2. Verify model files are accessible +3. Check function timeout settings (increase if needed) +4. Review Application Insights logs + +### Performance Issues +1. Upgrade to Premium plan +2. Enable GPU support (requires App Service Plan) +3. Optimize model size (quantization) +4. Implement caching layer + +### Cold Start Mitigation +1. Enable Always On +2. Use Premium plan with pre-warmed instances +3. Implement health check endpoint +4. Consider Azure Container Instances for better cold start + +## Alternative: Deploy to Azure Container Instances + +For full control and GPU support: + +```powershell +# Build Docker container +docker build -t qai-anime-avatar:latest . + +# Push to Azure Container Registry +az acr create --name qaiacr --resource-group qai-anime-avatar-rg --sku Basic +az acr login --name qaiacr +docker tag qai-anime-avatar:latest qaiacr.azurecr.io/anime-avatar:latest +docker push qaiacr.azurecr.io/anime-avatar:latest + +# Deploy to ACI with GPU (if needed) +az container create \ + --name qai-anime-avatar \ + --resource-group qai-anime-avatar-rg \ + --image qaiacr.azurecr.io/anime-avatar:latest \ + --cpu 2 \ + --memory 4 \ + --registry-login-server qaiacr.azurecr.io \ + --registry-username $(az acr credential show --name qaiacr --query username -o tsv) \ + --registry-password $(az acr credential show --name qaiacr --query "passwords[0].value" -o tsv) \ + --dns-name-label qai-anime-avatar \ + --ports 80 +``` + +## Next Steps + +1. ✅ Complete grid search to find best hyperparameters +2. ✅ Retrain model with optimal settings +3. ✅ Test locally before deploying +4. ✅ Choose deployment strategy (Functions vs Containers) +5. ✅ Deploy and monitor +6. ✅ Set up CI/CD pipeline for continuous deployment + +--- + +**Created:** 2025-11-25 +**Last Updated:** 2025-11-25 +**Status:** Ready for deployment after hyperparameter optimization diff --git a/docs/deployment/AZURE_ML_TRAINING_GUIDE.md b/docs/deployment/AZURE_ML_TRAINING_GUIDE.md index 163a28425..ceb1a95af 100644 --- a/docs/deployment/AZURE_ML_TRAINING_GUIDE.md +++ b/docs/deployment/AZURE_ML_TRAINING_GUIDE.md @@ -1,317 +1,317 @@ -# Azure ML Training Setup Guide - -Complete guide to training AI models on Azure Machine Learning. - -## 🚀 Quick Start - -### Prerequisites Checklist - -- ✅ Azure CLI installed (version 2.78.0 detected) -- ✅ Azure subscription active ("Azure subscription 1") -- ⚠️ Azure ML workspace configuration needed - -### Step 1: Configure Azure ML Workspace - -You need to update `.env` file with your Azure ML workspace details: - -```powershell -# Get your subscription ID -az account show --query id -o tsv - -# List available resource groups -az group list --query "[].name" -o table - -# List ML workspaces (if you have one) -az ml workspace list --query "[].{Name:name, ResourceGroup:resourceGroup}" -o table -``` - -**Update `.env` file** with these values: -```env -AZURE_ML_SUBSCRIPTION_ID= -AZURE_ML_RESOURCE_GROUP= -AZURE_ML_WORKSPACE= -``` - -### Step 2: Create Azure ML Workspace (if needed) - -If you don't have a workspace yet: - -```powershell -# Create resource group (skip if you have one) -az group create --name "qai-ml-rg" --location "eastus" - -# Create Azure ML workspace -az ml workspace create --name "qai-ml-workspace" --resource-group "qai-ml-rg" -``` - -### Step 3: Create Compute Cluster - -Azure ML requires compute resources for training: - -```powershell -# Create CPU compute cluster (low cost for testing) -az ml compute create --name "cpu-cluster" ` - --type amlcompute ` - --size Standard_D2_v2 ` - --min-instances 0 ` - --max-instances 4 ` - --resource-group "" ` - --workspace-name "" - -# OR create GPU compute cluster (for faster training) -az ml compute create --name "gpu-cluster" ` - --type amlcompute ` - --size Standard_NC6 ` - --min-instances 0 ` - --max-instances 2 ` - --resource-group "" ` - --workspace-name "" -``` - -**Cost Note**: Clusters auto-scale to 0 when idle, so you only pay when training. - -## 📝 Training Workflows - -### Option A: Automated Pipeline (Recommended) - -Our `automated_training_pipeline.py` handles everything: - -```powershell -# Generate Azure ML job spec (dry run - no training) -python .\scripts\automated_training_pipeline.py --azure-ml-spec --quick --models phi,qwen - -# This creates: .azureml/job_.yaml -``` - -**What it includes**: - -- Environment setup (Python packages, dependencies) -- Training script configuration -- Compute target selection -- Data handling -- Model registration - -**Validate and submit**: -```powershell -# Validate the generated spec -python .\scripts\azureml_ci_validate.py - -# Submit to Azure ML -python .\scripts\azureml_ci_validate.py --submit -``` - -### Option B: Direct Azure ML SDK - -For custom training with full control: - -```python -from azure.ai.ml import MLClient, command, Input -from azure.ai.ml.constants import AssetTypes -from azure.identity import DefaultAzureCredential - -# Connect to workspace -ml_client = MLClient( - credential=DefaultAzureCredential(), - subscription_id="", - resource_group_name="", - workspace_name="" -) - -# Configure training job -job = command( - code="./AI/microsoft_phi-silica-3.6_v1", - command="python scripts/train_lora.py --dataset ../../datasets/chat/mixed_chat --epochs 3", - environment="azureml://registries/azureml/environments/sklearn-1.5/labels/latest", - compute="cpu-cluster", - display_name="phi-lora-training", - experiment_name="lora-finetuning" -) - -# Submit job -returned_job = ml_client.jobs.create_or_update(job) -print(f"Job URL: {returned_job.studio_url}") -``` - -### Option C: Azure ML Studio UI - -1. Navigate to [Azure ML Studio](https://ml.azure.com) -2. Select your workspace -3. Go to **Jobs** → **Create** → **Command job** -4. Upload training code -5. Select compute cluster -6. Configure environment and parameters -7. Submit - -## 🎯 Training Scenarios - -### Scenario 1: Quick Phi Model Training - -```powershell -# Generate data and train Phi-3.5-mini with LoRA -python .\scripts\automated_training_pipeline.py ` - --azure-ml-spec ` - --quick ` - --models phi ` - --azure-ml-compute "cpu-cluster" - -# Validate and submit -python .\scripts\azureml_ci_validate.py --submit -``` - -**Expected cost**: ~$0.10-0.50 for quick training (100 samples, 1 epoch) - -### Scenario 2: Multi-Model Training - -```powershell -# Train both Phi and Qwen models -python .\scripts\automated_training_pipeline.py ` - --azure-ml-spec ` - --models phi,qwen ` - --samples 500 ` - --azure-ml-compute "gpu-cluster" - -python .\scripts\azureml_ci_validate.py --submit -``` - -### Scenario 3: Full Training Pipeline - -```powershell -# Complete training with evaluation -python .\scripts\automated_training_pipeline.py ` - --azure-ml-spec ` - --models phi,qwen ` - --samples 1000 ` - --azure-ml-compute "gpu-cluster" ` - --azure-ml-experiment "production-training" - -python .\scripts\azureml_ci_validate.py --submit -``` - -## 📊 Monitoring Training - -### Via Azure CLI - -```powershell -# List recent jobs -az ml job list --resource-group "" --workspace-name "" - -# Get job status -az ml job show --name "" --resource-group "" --workspace-name "" - -# Stream job logs -az ml job stream --name "" --resource-group "" --workspace-name "" -``` - -### Via Azure ML Studio - -1. Go to [ml.azure.com](https://ml.azure.com) -2. Navigate to **Jobs** -3. Click on your job to see: - - Real-time logs - - Metrics and charts - - Resource utilization - - Model artifacts - -## 🔧 Best Practices - -### 1. Environment Management - -- Use **curated environments** for faster startup (no Docker build needed) -- Common curated environments: - - `AzureML-sklearn-1.5` - Scikit-learn - - `AzureML-pytorch-2.0` - PyTorch - - `AzureML-tensorflow-2.7` - TensorFlow - -### 2. Cost Optimization - -- Start with **CPU clusters** for testing ($0.096/hour for Standard_D2_v2) -- Use **GPU clusters** only for large models ($0.90/hour for Standard_NC6) -- Set `min_instances=0` for auto-shutdown -- Use **serverless compute** for one-off jobs (no cluster management) - -### 3. Data Management - -- Upload datasets to **Azure Blob Storage** -- Register datasets in Azure ML for versioning -- Use **direct mode** for small datasets -- Use **mount mode** for large datasets - -### 4. Job Configuration - -- Use **experiment names** to group related runs -- Add **tags** for filtering and search -- Enable **early termination** for hyperparameter tuning -- Configure **output paths** for model artifacts - -## 🛠️ Troubleshooting - -### Issue: "az ml command not found" - -```powershell -# Install Azure ML CLI extension -az extension add --name ml -``` - -### Issue: "Authentication failed" - -```powershell -# Login to Azure -az login - -# Set subscription -az account set --subscription "" -``` - -### Issue: "Compute target not found" - -```powershell -# List available compute -az ml compute list --resource-group "" --workspace-name "" - -# Create compute if needed (see Step 3 above) -``` - -### Issue: "Environment preparation taking too long" - -- Use curated environments instead of custom Docker images -- Pre-register custom environments to avoid rebuild on each job - -### Issue: "Job failed with out of memory" - -- Reduce batch size in training script -- Use larger VM size for compute -- Consider distributed training for large models - -## 📚 Additional Resources - -### Microsoft Documentation -- [Azure ML Training Overview](https://learn.microsoft.com/azure/machine-learning/how-to-train-model) -- [Configure Training Jobs](https://learn.microsoft.com/azure/machine-learning/how-to-set-up-training-targets) -- [Managed Compute](https://learn.microsoft.com/azure/machine-learning/how-to-create-attach-compute-studio) -- [Distributed Training](https://learn.microsoft.com/azure/machine-learning/how-to-train-distributed-gpu) - -### QAI Scripts -- `scripts/automated_training_pipeline.py` - Multi-model training orchestrator -- `scripts/azureml_ci_validate.py` - Job spec validation and submission -- `scripts/parallel_train.py` - Parallel training for multiple configs -- `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` - LoRA fine-tuning - -### Pricing -- [Azure ML Pricing Calculator](https://azure.microsoft.com/pricing/details/machine-learning/) -- Estimated costs for QAI training: - - Quick test (100 samples): ~$0.10 - - Small training (1000 samples): ~$0.50-1.00 - - Full training (10000 samples): ~$5-10 - -## 🎓 Next Steps - -1. **Complete Step 1** - Configure `.env` with your Azure ML workspace -2. **Test connection** - Run `az ml workspace show --name "" --resource-group ""` -3. **Create compute** - Set up CPU cluster for initial testing -4. **Generate job spec** - Run automated_training_pipeline with `--azure-ml-spec` -5. **Submit first job** - Use azureml_ci_validate.py to submit -6. **Monitor in Studio** - Watch your training in Azure ML Studio - ---- - -**Need Help?** Check logs in Azure ML Studio or run with `--help` flag on any script. +# Azure ML Training Setup Guide + +Complete guide to training AI models on Azure Machine Learning. + +## 🚀 Quick Start + +### Prerequisites Checklist + +- ✅ Azure CLI installed (version 2.78.0 detected) +- ✅ Azure subscription active ("Azure subscription 1") +- ⚠️ Azure ML workspace configuration needed + +### Step 1: Configure Azure ML Workspace + +You need to update `.env` file with your Azure ML workspace details: + +```powershell +# Get your subscription ID +az account show --query id -o tsv + +# List available resource groups +az group list --query "[].name" -o table + +# List ML workspaces (if you have one) +az ml workspace list --query "[].{Name:name, ResourceGroup:resourceGroup}" -o table +``` + +**Update `.env` file** with these values: +```env +AZURE_ML_SUBSCRIPTION_ID= +AZURE_ML_RESOURCE_GROUP= +AZURE_ML_WORKSPACE= +``` + +### Step 2: Create Azure ML Workspace (if needed) + +If you don't have a workspace yet: + +```powershell +# Create resource group (skip if you have one) +az group create --name "qai-ml-rg" --location "eastus" + +# Create Azure ML workspace +az ml workspace create --name "qai-ml-workspace" --resource-group "qai-ml-rg" +``` + +### Step 3: Create Compute Cluster + +Azure ML requires compute resources for training: + +```powershell +# Create CPU compute cluster (low cost for testing) +az ml compute create --name "cpu-cluster" ` + --type amlcompute ` + --size Standard_D2_v2 ` + --min-instances 0 ` + --max-instances 4 ` + --resource-group "" ` + --workspace-name "" + +# OR create GPU compute cluster (for faster training) +az ml compute create --name "gpu-cluster" ` + --type amlcompute ` + --size Standard_NC6 ` + --min-instances 0 ` + --max-instances 2 ` + --resource-group "" ` + --workspace-name "" +``` + +**Cost Note**: Clusters auto-scale to 0 when idle, so you only pay when training. + +## 📝 Training Workflows + +### Option A: Automated Pipeline (Recommended) + +Our `automated_training_pipeline.py` handles everything: + +```powershell +# Generate Azure ML job spec (dry run - no training) +python .\scripts\automated_training_pipeline.py --azure-ml-spec --quick --models phi,qwen + +# This creates: .azureml/job_.yaml +``` + +**What it includes**: + +- Environment setup (Python packages, dependencies) +- Training script configuration +- Compute target selection +- Data handling +- Model registration + +**Validate and submit**: +```powershell +# Validate the generated spec +python .\scripts\azureml_ci_validate.py + +# Submit to Azure ML +python .\scripts\azureml_ci_validate.py --submit +``` + +### Option B: Direct Azure ML SDK + +For custom training with full control: + +```python +from azure.ai.ml import MLClient, command, Input +from azure.ai.ml.constants import AssetTypes +from azure.identity import DefaultAzureCredential + +# Connect to workspace +ml_client = MLClient( + credential=DefaultAzureCredential(), + subscription_id="", + resource_group_name="", + workspace_name="" +) + +# Configure training job +job = command( + code="./AI/microsoft_phi-silica-3.6_v1", + command="python scripts/train_lora.py --dataset ../../datasets/chat/mixed_chat --epochs 3", + environment="azureml://registries/azureml/environments/sklearn-1.5/labels/latest", + compute="cpu-cluster", + display_name="phi-lora-training", + experiment_name="lora-finetuning" +) + +# Submit job +returned_job = ml_client.jobs.create_or_update(job) +print(f"Job URL: {returned_job.studio_url}") +``` + +### Option C: Azure ML Studio UI + +1. Navigate to [Azure ML Studio](https://ml.azure.com) +2. Select your workspace +3. Go to **Jobs** → **Create** → **Command job** +4. Upload training code +5. Select compute cluster +6. Configure environment and parameters +7. Submit + +## 🎯 Training Scenarios + +### Scenario 1: Quick Phi Model Training + +```powershell +# Generate data and train Phi-3.5-mini with LoRA +python .\scripts\automated_training_pipeline.py ` + --azure-ml-spec ` + --quick ` + --models phi ` + --azure-ml-compute "cpu-cluster" + +# Validate and submit +python .\scripts\azureml_ci_validate.py --submit +``` + +**Expected cost**: ~$0.10-0.50 for quick training (100 samples, 1 epoch) + +### Scenario 2: Multi-Model Training + +```powershell +# Train both Phi and Qwen models +python .\scripts\automated_training_pipeline.py ` + --azure-ml-spec ` + --models phi,qwen ` + --samples 500 ` + --azure-ml-compute "gpu-cluster" + +python .\scripts\azureml_ci_validate.py --submit +``` + +### Scenario 3: Full Training Pipeline + +```powershell +# Complete training with evaluation +python .\scripts\automated_training_pipeline.py ` + --azure-ml-spec ` + --models phi,qwen ` + --samples 1000 ` + --azure-ml-compute "gpu-cluster" ` + --azure-ml-experiment "production-training" + +python .\scripts\azureml_ci_validate.py --submit +``` + +## 📊 Monitoring Training + +### Via Azure CLI + +```powershell +# List recent jobs +az ml job list --resource-group "" --workspace-name "" + +# Get job status +az ml job show --name "" --resource-group "" --workspace-name "" + +# Stream job logs +az ml job stream --name "" --resource-group "" --workspace-name "" +``` + +### Via Azure ML Studio + +1. Go to [ml.azure.com](https://ml.azure.com) +2. Navigate to **Jobs** +3. Click on your job to see: + - Real-time logs + - Metrics and charts + - Resource utilization + - Model artifacts + +## 🔧 Best Practices + +### 1. Environment Management + +- Use **curated environments** for faster startup (no Docker build needed) +- Common curated environments: + - `AzureML-sklearn-1.5` - Scikit-learn + - `AzureML-pytorch-2.0` - PyTorch + - `AzureML-tensorflow-2.7` - TensorFlow + +### 2. Cost Optimization + +- Start with **CPU clusters** for testing ($0.096/hour for Standard_D2_v2) +- Use **GPU clusters** only for large models ($0.90/hour for Standard_NC6) +- Set `min_instances=0` for auto-shutdown +- Use **serverless compute** for one-off jobs (no cluster management) + +### 3. Data Management + +- Upload datasets to **Azure Blob Storage** +- Register datasets in Azure ML for versioning +- Use **direct mode** for small datasets +- Use **mount mode** for large datasets + +### 4. Job Configuration + +- Use **experiment names** to group related runs +- Add **tags** for filtering and search +- Enable **early termination** for hyperparameter tuning +- Configure **output paths** for model artifacts + +## 🛠️ Troubleshooting + +### Issue: "az ml command not found" + +```powershell +# Install Azure ML CLI extension +az extension add --name ml +``` + +### Issue: "Authentication failed" + +```powershell +# Login to Azure +az login + +# Set subscription +az account set --subscription "" +``` + +### Issue: "Compute target not found" + +```powershell +# List available compute +az ml compute list --resource-group "" --workspace-name "" + +# Create compute if needed (see Step 3 above) +``` + +### Issue: "Environment preparation taking too long" + +- Use curated environments instead of custom Docker images +- Pre-register custom environments to avoid rebuild on each job + +### Issue: "Job failed with out of memory" + +- Reduce batch size in training script +- Use larger VM size for compute +- Consider distributed training for large models + +## 📚 Additional Resources + +### Microsoft Documentation +- [Azure ML Training Overview](https://learn.microsoft.com/azure/machine-learning/how-to-train-model) +- [Configure Training Jobs](https://learn.microsoft.com/azure/machine-learning/how-to-set-up-training-targets) +- [Managed Compute](https://learn.microsoft.com/azure/machine-learning/how-to-create-attach-compute-studio) +- [Distributed Training](https://learn.microsoft.com/azure/machine-learning/how-to-train-distributed-gpu) + +### QAI Scripts +- `scripts/automated_training_pipeline.py` - Multi-model training orchestrator +- `scripts/azureml_ci_validate.py` - Job spec validation and submission +- `scripts/parallel_train.py` - Parallel training for multiple configs +- `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` - LoRA fine-tuning + +### Pricing +- [Azure ML Pricing Calculator](https://azure.microsoft.com/pricing/details/machine-learning/) +- Estimated costs for QAI training: + - Quick test (100 samples): ~$0.10 + - Small training (1000 samples): ~$0.50-1.00 + - Full training (10000 samples): ~$5-10 + +## 🎓 Next Steps + +1. **Complete Step 1** - Configure `.env` with your Azure ML workspace +2. **Test connection** - Run `az ml workspace show --name "" --resource-group ""` +3. **Create compute** - Set up CPU cluster for initial testing +4. **Generate job spec** - Run automated_training_pipeline with `--azure-ml-spec` +5. **Submit first job** - Use azureml_ci_validate.py to submit +6. **Monitor in Studio** - Watch your training in Azure ML Studio + +--- + +**Need Help?** Check logs in Azure ML Studio or run with `--help` flag on any script. diff --git a/docs/deployment/DEPLOY_CHAT_TO_AZURE.md b/docs/deployment/DEPLOY_CHAT_TO_AZURE.md index 53b30bd9d..49ee0c9bc 100644 --- a/docs/deployment/DEPLOY_CHAT_TO_AZURE.md +++ b/docs/deployment/DEPLOY_CHAT_TO_AZURE.md @@ -1,104 +1,104 @@ -# Deploy Chat Website to Azure Functions - -This guide deploys the chat website (with Azure/OpenAI providers) to Azure Functions and configures app settings for cloud use. - -## Prerequisites - -- Azure CLI (`az`) -- Azure Functions Core Tools (`func`) for local publish -- Python 3.11 installed locally - -## One-command deployment (recommended) - -Use the helper script from the repo root: - -```powershell -# Fill in values and run -./deploy-chat-to-azure.ps1 ` - -ResourceGroup rg-chat-web ` - -FunctionApp chat-web-app-123 ` - -Location eastus ` - -AzureOpenAIKey "" ` - -AzureOpenAIEndpoint "https://.openai.azure.com/" ` - -AzureOpenAIDeployment "gpt-4o-mini" -``` - -Outputs: -- Chat UI: https://chat-web-app-123.azurewebsites.net/api/chat-web -- Health: https://chat-web-app-123.azurewebsites.net/api/ai/status - -If you use OpenAI instead of Azure OpenAI: -```powershell -./deploy-chat-to-azure.ps1 ` - -ResourceGroup rg-chat-web ` - -FunctionApp chat-web-app-123 ` - -Location eastus ` - -OpenAIKey "sk-..." ` - -OpenAIModel "gpt-4o-mini" -``` - -## Manual steps (expanded) - -1) Resource Group and Storage -```powershell -az group create --name rg-chat-web --location eastus -az storage account create --name chatwebstorage123 --resource-group rg-chat-web --location eastus --sku Standard_LRS -``` - -2) Create Function App -```powershell -az functionapp create ` - --resource-group rg-chat-web ` - --name chat-web-app-123 ` - --storage-account chatwebstorage123 ` - --consumption-plan-location eastus ` - --runtime python ` - --runtime-version 3.11 ` - --functions-version 4 ` - --os-type Linux -``` - -3) Configure App Settings - -Azure OpenAI: -```powershell -az functionapp config appsettings set ` - --name chat-web-app-123 ` - --resource-group rg-chat-web ` - --settings ` - AZURE_OPENAI_API_KEY="" ` - AZURE_OPENAI_ENDPOINT="https://.openai.azure.com/" ` - AZURE_OPENAI_DEPLOYMENT="gpt-4o-mini" ` - AZURE_OPENAI_API_VERSION="2024-08-01-preview" ` - DEFAULT_AI_PROVIDER="auto" ` - CHAT_TEMPERATURE="0.7" -``` - -OpenAI: -```powershell -az functionapp config appsettings set ` - --name chat-web-app-123 ` - --resource-group rg-chat-web ` - --settings ` - OPENAI_API_KEY="sk-..." ` - OPENAI_MODEL="gpt-4o-mini" ` - DEFAULT_AI_PROVIDER="auto" ` - CHAT_TEMPERATURE="0.7" -``` - -4) Publish -```powershell -func azure functionapp publish chat-web-app-123 -``` - -## Verify deployment - -- Open the chat UI: `/api/chat-web` -- Call the health endpoint: `/api/ai/status` - - Confirms the active provider and whether required env vars are detected - -## Notes - -- Locally, provider detection falls back to the free Local provider without keys. -- In Azure, set at least one provider’s keys as app settings for cloud inference. -- For persistence (chat logs, RAG, etc.), consider Azure Cosmos DB per `azurecosmosdb.instructions.md`. +# Deploy Chat Website to Azure Functions + +This guide deploys the chat website (with Azure/OpenAI providers) to Azure Functions and configures app settings for cloud use. + +## Prerequisites + +- Azure CLI (`az`) +- Azure Functions Core Tools (`func`) for local publish +- Python 3.11 installed locally + +## One-command deployment (recommended) + +Use the helper script from the repo root: + +```powershell +# Fill in values and run +./deploy-chat-to-azure.ps1 ` + -ResourceGroup rg-chat-web ` + -FunctionApp chat-web-app-123 ` + -Location eastus ` + -AzureOpenAIKey "" ` + -AzureOpenAIEndpoint "https://.openai.azure.com/" ` + -AzureOpenAIDeployment "gpt-4o-mini" +``` + +Outputs: +- Chat UI: https://chat-web-app-123.azurewebsites.net/api/chat-web +- Health: https://chat-web-app-123.azurewebsites.net/api/ai/status + +If you use OpenAI instead of Azure OpenAI: +```powershell +./deploy-chat-to-azure.ps1 ` + -ResourceGroup rg-chat-web ` + -FunctionApp chat-web-app-123 ` + -Location eastus ` + -OpenAIKey "sk-..." ` + -OpenAIModel "gpt-4o-mini" +``` + +## Manual steps (expanded) + +1) Resource Group and Storage +```powershell +az group create --name rg-chat-web --location eastus +az storage account create --name chatwebstorage123 --resource-group rg-chat-web --location eastus --sku Standard_LRS +``` + +2) Create Function App +```powershell +az functionapp create ` + --resource-group rg-chat-web ` + --name chat-web-app-123 ` + --storage-account chatwebstorage123 ` + --consumption-plan-location eastus ` + --runtime python ` + --runtime-version 3.11 ` + --functions-version 4 ` + --os-type Linux +``` + +3) Configure App Settings + +Azure OpenAI: +```powershell +az functionapp config appsettings set ` + --name chat-web-app-123 ` + --resource-group rg-chat-web ` + --settings ` + AZURE_OPENAI_API_KEY="" ` + AZURE_OPENAI_ENDPOINT="https://.openai.azure.com/" ` + AZURE_OPENAI_DEPLOYMENT="gpt-4o-mini" ` + AZURE_OPENAI_API_VERSION="2024-08-01-preview" ` + DEFAULT_AI_PROVIDER="auto" ` + CHAT_TEMPERATURE="0.7" +``` + +OpenAI: +```powershell +az functionapp config appsettings set ` + --name chat-web-app-123 ` + --resource-group rg-chat-web ` + --settings ` + OPENAI_API_KEY="sk-..." ` + OPENAI_MODEL="gpt-4o-mini" ` + DEFAULT_AI_PROVIDER="auto" ` + CHAT_TEMPERATURE="0.7" +``` + +4) Publish +```powershell +func azure functionapp publish chat-web-app-123 +``` + +## Verify deployment + +- Open the chat UI: `/api/chat-web` +- Call the health endpoint: `/api/ai/status` + - Confirms the active provider and whether required env vars are detected + +## Notes + +- Locally, provider detection falls back to the free Local provider without keys. +- In Azure, set at least one provider’s keys as app settings for cloud inference. +- For persistence (chat logs, RAG, etc.), consider Azure Cosmos DB per `azurecosmosdb.instructions.md`. diff --git a/docs/deployment/PRODUCTION_DEPLOYMENT_PLAN.md b/docs/deployment/PRODUCTION_DEPLOYMENT_PLAN.md index b8fb0e910..8718cd013 100644 --- a/docs/deployment/PRODUCTION_DEPLOYMENT_PLAN.md +++ b/docs/deployment/PRODUCTION_DEPLOYMENT_PLAN.md @@ -1,473 +1,473 @@ -# Production Deployment Plan: 5,000 Dataset Quantum ML System - -**Version:** 1.0 -**Date:** November 16, 2025 -**Status:** Architecture Complete, Scaling Phase - ---- - -## Executive Summary - -**Goal:** Deploy production-grade quantum ML training infrastructure capable of: -- Training on 5,000+ datasets automatically -- Distributed processing across 10-50 workers -- Continuous integration of new datasets -- Real-time performance monitoring -- Azure Quantum hardware integration - -**Current State:** -- ✅ Discovery system: 1,412 datasets identified -- ✅ Download system: Operational (3 datasets validated) -- ✅ Distributed benchmark: Ready for parallel training -- ✅ Quality scoring: Automated 0-100 scale -- ⏳ Full-scale deployment: Planning phase - ---- - -## Phase 1: Infrastructure (Weeks 1-2) - -### 1.1 Local Development (Complete ✅) -- [x] OpenML integration -- [x] Quality scoring system -- [x] Distributed training framework -- [x] Checkpoint/resume capability -- [x] Error handling and retries - -### 1.2 Storage Optimization (Week 1) -- [ ] Implement dataset compression (gzip CSV → 60% size reduction) -- [ ] Create tiered storage (hot/warm/cold based on score) -- [ ] Set up Azure Blob Storage for cloud backup -- [ ] Implement incremental backup strategy - -**Commands:** -```powershell -# Compress existing datasets -Get-ChildItem datasets\massive_quantum\*.csv | ForEach-Object { - Compress-Archive -Path $_.FullName -DestinationPath "$($_.FullName).gz" -} - -# Azure storage setup (requires Azure CLI) -az storage account create --name quantummldata --resource-group rg-quantum-ai -az storage container create --name datasets --account-name quantummldata -``` - -### 1.3 Compute Resources (Week 2) -**Option A: Local Scaling (Budget: $0)** -- Multi-core CPU (16+ cores recommended) -- 32GB RAM minimum -- 500GB SSD storage -- Expected: 10-20 datasets/hour training - -**Option B: Azure VM (Budget: ~$100-200/month)** -- Standard_D16s_v3 (16 vCPUs, 64GB RAM) -- 1TB Premium SSD -- Expected: 50-100 datasets/hour training -- Cost: ~$0.70/hour ($500/month full-time, $200/month part-time) - -**Option C: Azure Batch (Budget: ~$50-500/month)** -- Auto-scale pool (0-100 nodes) -- Pay only for compute time -- Expected: 500+ datasets/hour during bursts -- Cost: ~$0.05/core-hour - -**Recommendation:** Start with Option A (local), scale to Option B for continuous operation, use Option C for final 5,000-dataset sprint. - ---- - -## Phase 2: Data Acquisition (Weeks 2-6) - -### 2.1 Batch Download Strategy - -**Week 2-3: Top 500 datasets (score ≥70)** -```powershell -# Download in 5 batches of 100 -for ($i = 0; $i -lt 5; $i++) { - $start = $i * 100 - python .\scripts\massive_dataset_expansion.py --download --start $start --batch-size 100 --min-score 70 - Start-Sleep -Seconds 1800 # 30 min between batches -} -``` -Expected time: 50-100 hours (run overnight for 10-15 nights) - -**Week 4-5: Next 900 datasets (score 50-70)** -- Lower priority, higher failure rate expected -- Download during off-peak hours -- Expected: 70-80% success rate - -**Week 6: Remaining 4,000 datasets (score <50)** -- Opportunistic downloads -- Quality filter after validation -- Accept 50% success rate - -### 2.2 Quality Assurance - -**Automated Validation Pipeline:** -```python -# Run after each 100 downloads -python scripts/massive_dataset_expansion.py --validate --parallel 20 - -# Remove corrupted files -python scripts/cleanup_failed_datasets.py --threshold 0.3 # >30% missing - -# Update quality scores -python scripts/rescore_datasets.py --recalculate -``` - -**Success Metrics:** -- Download success rate: ≥70% -- Validation pass rate: ≥80% -- Training-ready datasets: ≥3,500 (70% of 5,000) - ---- - -## Phase 3: Training Pipeline (Weeks 3-8) - -### 3.1 Distributed Training Architecture - -**Tier 1: Quick Validation (1 epoch, 15 min/dataset)** -```powershell -# Run on all new datasets immediately after download -python scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 16 --epochs 1 --quick-test -``` -**Purpose:** Identify broken datasets early, get baseline accuracy - -**Tier 2: Standard Benchmark (25 epochs, 2 hours/dataset)** -```powershell -# Run on datasets passing Tier 1 -python scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 25 -``` -**Purpose:** Production-quality metrics, architecture optimization - -**Tier 3: Deep Training (50 epochs, 4 hours/dataset)** -```powershell -# Run on top 100 datasets (score ≥95, accuracy ≥90%) -python scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum/top100 --workers 5 --epochs 50 -``` -**Purpose:** Maximum accuracy for production deployment - -### 3.2 Resource Allocation - -**Timeline for 3,500 training-ready datasets:** - -| Tier | Datasets | Epochs | Time/Dataset | Workers | Total Hours | Wall Time | -|------|----------|--------|--------------|---------|-------------|-----------| -| 1 | 3,500 | 1 | 15 min | 16 | 875 | 55 hours | -| 2 | 2,800 | 25 | 2 hours | 10 | 5,600 | 560 hours | -| 3 | 100 | 50 | 4 hours | 5 | 400 | 80 hours | - -**Total wall time:** ~30 days continuous operation (10 workers average) - -**Optimization:** Run Tier 1 & 2 simultaneously on different machines/VMs - -### 3.3 Checkpointing & Resume - -**Auto-checkpoint every:** -- 10 datasets completed -- 1 hour elapsed -- Worker failure detected - -**Resume command:** -```powershell -python scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 25 --resume -``` - -**Backup checkpoints:** -```powershell -# Copy to Azure every 100 datasets -$checkpoint = "data_out\distributed_benchmark\checkpoint.json" -az storage blob upload --account-name quantummldata --container-name checkpoints --file $checkpoint --name "checkpoint_$(Get-Date -Format 'yyyyMMdd_HHmmss').json" -``` - ---- - -## Phase 4: Azure Quantum Integration (Weeks 6-12) - -### 4.1 Simulator Testing (Week 6-7) - -**Test on Azure Quantum Simulators (FREE):** -```powershell -# Run top 10 datasets on Azure simulator -cd quantum-ai -$datasets = @("vehicle_54", "mental_health", "pumpkin_seeds") -foreach ($ds in $datasets) { - python src/azure_quantum_integration.py --dataset $ds --backend "ionq.simulator" -} -``` - -**Success Criteria:** -- Results match local simulations within 5% -- Latency <2 minutes per circuit -- Zero failed submissions - -### 4.2 QPU Production Deployment (Week 8-12) - -**Cost-Optimized QPU Strategy:** - -**Phase A: Pilot (10 datasets, $100-200)** -- Select top 10 datasets (accuracy ≥95%, samples ≤1000) -- Run on IonQ Aria (cheapest QPU: ~$0.00003/gate-shot) -- Validate quantum advantage - -**Phase B: Scaled QPU (100 datasets, $1,000-2,000)** -- Top 100 datasets from Tier 3 -- Batch submissions (50 circuits/day) -- Monitor cost per dataset - -**Phase C: Selective QPU (500 datasets, $5,000-10,000)** -- Only datasets showing quantum advantage in Phase A/B -- Hybrid classical-quantum workflow -- Production-ready models - -**Cost Control:** -```yaml -# quantum_autorun.yaml addition -cost_limits: - daily_max_usd: 200 - dataset_max_usd: 50 - alert_threshold_usd: 150 - auto_stop_on_limit: true -``` - -### 4.3 Performance Monitoring - -**Real-time Dashboard:** -```powershell -# Start Flask dashboard -python ai-projects/quantum-ml/demo_dashboard.py --port 5000 - -# Metrics tracked: -# - QPU vs simulator accuracy delta -# - Cost per dataset -# - Quantum advantage percentage -# - Queue times and latency -``` - ---- - -## Phase 5: Production Optimization (Weeks 10-16) - -### 5.1 Model Selection - -**Automated Ranking System:** -```python -# scores/production_ranking.py -def rank_for_production(results): - score = 0 - score += accuracy * 40 # 40% weight - score += (1 - std_dev) * 20 # Stability: 20% - score += domain_relevance * 15 # Medical/finance priority: 15% - score += (1 / training_time) * 10 # Efficiency: 10% - score += quantum_advantage * 15 # QPU speedup: 15% - return score -``` - -**Target: Top 50 production models** -- Medical diagnostics: 15 models -- Financial prediction: 10 models -- Anomaly detection: 10 models -- Image recognition: 8 models -- General classification: 7 models - -### 5.2 API Deployment - -**REST API for Inference:** -```python -# api/quantum_inference.py -from fastapi import FastAPI -app = FastAPI() - -@app.post("/predict/{model_id}") -async def predict(model_id: str, features: List[float]): - model = load_model(model_id) - result = model.predict(features) - return {"prediction": result, "confidence": model.confidence} - -# Deploy with: -# uvicorn api.quantum_inference:app --host 0.0.0.0 --port 8000 -``` - -**Azure Functions Integration:** -```powershell -# Deploy to Azure Functions -func azure functionapp publish quantum-ml-inference -``` - -### 5.3 Continuous Integration - -**Weekly New Dataset Pipeline:** -```yaml -# .github/workflows/weekly_dataset_refresh.yml -name: Weekly Dataset Discovery -on: - schedule: - - cron: '0 0 * * 0' # Every Sunday - -jobs: - discover: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - name: Discover new datasets - run: python scripts/massive_dataset_expansion.py --discover --limit 100 - - name: Download top 10 - run: python scripts/massive_dataset_expansion.py --download --batch-size 10 --min-score 90 - - name: Quick benchmark - run: python scripts/distributed_benchmark.py --workers 4 --epochs 1 --quick-test - - name: Commit results - run: | - git add datasets/ data_out/ - git commit -m "Weekly dataset refresh: $(date)" - git push -``` - ---- - -## Phase 6: Scaling to 5,000 (Weeks 12-16) - -### 6.1 Final Sprint Strategy - -**Weeks 12-14: Parallel download acceleration** -- Rent Azure VM (16 cores, 64GB RAM) -- Run 4 download processes simultaneously -- Target: 200 datasets/day -- Cost: ~$50/day ($700 total) - -**Weeks 15-16: Mass benchmark** -- Use Azure Batch (50 nodes) -- Process 500 datasets/day -- Cost: ~$100/day ($1,400 total) - -**Total scaling cost: ~$2,100 for final 2,500 datasets** - -### 6.2 Success Metrics - -| Metric | Target | Current | Gap | -|--------|--------|---------|-----| -| Total datasets | 5,000 | 30 | 4,970 | -| Training-ready | 3,500 | 27 | 3,473 | -| Production models | 50 | 10 (from 27) | 40 | -| QPU-validated | 100 | 0 | 100 | -| Average accuracy | 75% | 85% (27 datasets) | ✅ Exceeds | -| API deployed | Yes | No | 1 API | - -### 6.3 Risk Mitigation - -**Risk 1: OpenML rate limiting** -- Mitigation: Batch downloads with delays, use multiple IP addresses -- Backup: Mirror datasets to Azure Blob, share with community - -**Risk 2: Training time exceeds 16 weeks** -- Mitigation: Prioritize top 1,000 datasets, defer low-quality ones -- Backup: Use GPU VMs (4x speed), increase budget - -**Risk 3: Dataset quality issues** -- Mitigation: Enhanced validation, community review -- Backup: Supplement with Kaggle, UCI datasets - -**Risk 4: Azure Quantum costs exceed budget** -- Mitigation: Strict cost limits in quantum_autorun.yaml -- Backup: Simulator-only deployment, selective QPU use - ---- - -## Budget Summary - -### Total Cost Estimate (16 weeks) - -| Category | Low | Medium | High | -|----------|-----|--------|------| -| Compute (Local) | $0 | $0 | $0 | -| Azure VM (Optional) | $0 | $400 | $1,000 | -| Azure Batch (Sprint) | $0 | $1,400 | $3,000 | -| Azure Storage | $5 | $20 | $50 | -| Azure Quantum (Simulator) | $0 | $0 | $0 | -| Azure Quantum (QPU) | $100 | $2,000 | $10,000 | -| **Total** | **$105** | **$3,820** | **$14,050** | - -**Recommended Budget: $3,820 (Medium)** -- Includes Azure VM for acceleration -- Limited QPU testing (100 datasets) -- Storage and backup -- Contingency buffer - -**Zero-Budget Option: $0 (Local only)** -- Timeline extends to 24-32 weeks -- Simulator-only (no QPU) -- Local storage only -- Community-driven - ---- - -## Timeline Gantt Chart - -``` -Week 1-2: [Infrastructure Setup] -Week 2-6: [━━━━━━━━━━━━━━━ Data Acquisition ━━━━━━━━━━━━━━━] -Week 3-8: [━━━━━━━━━━━━━ Training Pipeline ━━━━━━━━━━━━━] -Week 6-12: [━━━━━━━━━━━ Azure Quantum Integration ━━━━━━━━━━━] -Week 10-16: [━━━━━━━ Production Optimization ━━━━━━━] -Week 12-16: [━━━━ Final Sprint to 5,000 ━━━━] - -Legend: [━] Active phase, overlapping allowed -``` - ---- - -## Key Deliverables - -### By End of Week 4 -- [ ] 500 datasets downloaded and validated -- [ ] 500 datasets benchmarked (Tier 1) -- [ ] 100 datasets benchmarked (Tier 2) -- [ ] Infrastructure documentation complete - -### By End of Week 8 -- [ ] 2,000 datasets downloaded -- [ ] 1,500 datasets benchmarked (Tier 2) -- [ ] 50 datasets benchmarked (Tier 3) -- [ ] Azure Quantum integration tested - -### By End of Week 12 -- [ ] 4,000 datasets downloaded -- [ ] 3,000 datasets fully benchmarked -- [ ] 100 QPU validations complete -- [ ] Top 50 production models identified - -### By End of Week 16 (Final) -- [ ] 5,000 datasets processed -- [ ] 3,500+ training-ready models -- [ ] 50 production-deployed models -- [ ] REST API live and documented -- [ ] Complete performance analysis published - ---- - -## Next Immediate Actions (Today) - -1. **Wait for 27-dataset benchmark to complete** (~20 min) -2. **Analyze results and validate infrastructure** -3. **Start overnight download** (targeting 100 datasets) -4. **Set up Azure VM or local multi-core environment** -5. **Schedule Week 1 infrastructure tasks** - -**Quick Start Commands:** -```powershell -# 1. Check current benchmark -Get-Content data_out\benchmark_results.json - -# 2. Start overnight download -.\scripts\overnight_download.ps1 - -# 3. Monitor progress -Get-Content data_out\overnight_download.log -Wait - -# 4. Tomorrow: Run full benchmark -python scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 25 -``` - ---- - -**Document Status:** Production Ready -**Next Review:** End of Week 2 -**Owner:** Quantum AI Workspace Team -**Approvers:** TBD +# Production Deployment Plan: 5,000 Dataset Quantum ML System + +**Version:** 1.0 +**Date:** November 16, 2025 +**Status:** Architecture Complete, Scaling Phase + +--- + +## Executive Summary + +**Goal:** Deploy production-grade quantum ML training infrastructure capable of: +- Training on 5,000+ datasets automatically +- Distributed processing across 10-50 workers +- Continuous integration of new datasets +- Real-time performance monitoring +- Azure Quantum hardware integration + +**Current State:** +- ✅ Discovery system: 1,412 datasets identified +- ✅ Download system: Operational (3 datasets validated) +- ✅ Distributed benchmark: Ready for parallel training +- ✅ Quality scoring: Automated 0-100 scale +- ⏳ Full-scale deployment: Planning phase + +--- + +## Phase 1: Infrastructure (Weeks 1-2) + +### 1.1 Local Development (Complete ✅) +- [x] OpenML integration +- [x] Quality scoring system +- [x] Distributed training framework +- [x] Checkpoint/resume capability +- [x] Error handling and retries + +### 1.2 Storage Optimization (Week 1) +- [ ] Implement dataset compression (gzip CSV → 60% size reduction) +- [ ] Create tiered storage (hot/warm/cold based on score) +- [ ] Set up Azure Blob Storage for cloud backup +- [ ] Implement incremental backup strategy + +**Commands:** +```powershell +# Compress existing datasets +Get-ChildItem datasets\massive_quantum\*.csv | ForEach-Object { + Compress-Archive -Path $_.FullName -DestinationPath "$($_.FullName).gz" +} + +# Azure storage setup (requires Azure CLI) +az storage account create --name quantummldata --resource-group rg-quantum-ai +az storage container create --name datasets --account-name quantummldata +``` + +### 1.3 Compute Resources (Week 2) +**Option A: Local Scaling (Budget: $0)** +- Multi-core CPU (16+ cores recommended) +- 32GB RAM minimum +- 500GB SSD storage +- Expected: 10-20 datasets/hour training + +**Option B: Azure VM (Budget: ~$100-200/month)** +- Standard_D16s_v3 (16 vCPUs, 64GB RAM) +- 1TB Premium SSD +- Expected: 50-100 datasets/hour training +- Cost: ~$0.70/hour ($500/month full-time, $200/month part-time) + +**Option C: Azure Batch (Budget: ~$50-500/month)** +- Auto-scale pool (0-100 nodes) +- Pay only for compute time +- Expected: 500+ datasets/hour during bursts +- Cost: ~$0.05/core-hour + +**Recommendation:** Start with Option A (local), scale to Option B for continuous operation, use Option C for final 5,000-dataset sprint. + +--- + +## Phase 2: Data Acquisition (Weeks 2-6) + +### 2.1 Batch Download Strategy + +**Week 2-3: Top 500 datasets (score ≥70)** +```powershell +# Download in 5 batches of 100 +for ($i = 0; $i -lt 5; $i++) { + $start = $i * 100 + python .\scripts\massive_dataset_expansion.py --download --start $start --batch-size 100 --min-score 70 + Start-Sleep -Seconds 1800 # 30 min between batches +} +``` +Expected time: 50-100 hours (run overnight for 10-15 nights) + +**Week 4-5: Next 900 datasets (score 50-70)** +- Lower priority, higher failure rate expected +- Download during off-peak hours +- Expected: 70-80% success rate + +**Week 6: Remaining 4,000 datasets (score <50)** +- Opportunistic downloads +- Quality filter after validation +- Accept 50% success rate + +### 2.2 Quality Assurance + +**Automated Validation Pipeline:** +```python +# Run after each 100 downloads +python scripts/massive_dataset_expansion.py --validate --parallel 20 + +# Remove corrupted files +python scripts/cleanup_failed_datasets.py --threshold 0.3 # >30% missing + +# Update quality scores +python scripts/rescore_datasets.py --recalculate +``` + +**Success Metrics:** +- Download success rate: ≥70% +- Validation pass rate: ≥80% +- Training-ready datasets: ≥3,500 (70% of 5,000) + +--- + +## Phase 3: Training Pipeline (Weeks 3-8) + +### 3.1 Distributed Training Architecture + +**Tier 1: Quick Validation (1 epoch, 15 min/dataset)** +```powershell +# Run on all new datasets immediately after download +python scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 16 --epochs 1 --quick-test +``` +**Purpose:** Identify broken datasets early, get baseline accuracy + +**Tier 2: Standard Benchmark (25 epochs, 2 hours/dataset)** +```powershell +# Run on datasets passing Tier 1 +python scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 25 +``` +**Purpose:** Production-quality metrics, architecture optimization + +**Tier 3: Deep Training (50 epochs, 4 hours/dataset)** +```powershell +# Run on top 100 datasets (score ≥95, accuracy ≥90%) +python scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum/top100 --workers 5 --epochs 50 +``` +**Purpose:** Maximum accuracy for production deployment + +### 3.2 Resource Allocation + +**Timeline for 3,500 training-ready datasets:** + +| Tier | Datasets | Epochs | Time/Dataset | Workers | Total Hours | Wall Time | +|------|----------|--------|--------------|---------|-------------|-----------| +| 1 | 3,500 | 1 | 15 min | 16 | 875 | 55 hours | +| 2 | 2,800 | 25 | 2 hours | 10 | 5,600 | 560 hours | +| 3 | 100 | 50 | 4 hours | 5 | 400 | 80 hours | + +**Total wall time:** ~30 days continuous operation (10 workers average) + +**Optimization:** Run Tier 1 & 2 simultaneously on different machines/VMs + +### 3.3 Checkpointing & Resume + +**Auto-checkpoint every:** +- 10 datasets completed +- 1 hour elapsed +- Worker failure detected + +**Resume command:** +```powershell +python scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 25 --resume +``` + +**Backup checkpoints:** +```powershell +# Copy to Azure every 100 datasets +$checkpoint = "data_out\distributed_benchmark\checkpoint.json" +az storage blob upload --account-name quantummldata --container-name checkpoints --file $checkpoint --name "checkpoint_$(Get-Date -Format 'yyyyMMdd_HHmmss').json" +``` + +--- + +## Phase 4: Azure Quantum Integration (Weeks 6-12) + +### 4.1 Simulator Testing (Week 6-7) + +**Test on Azure Quantum Simulators (FREE):** +```powershell +# Run top 10 datasets on Azure simulator +cd quantum-ai +$datasets = @("vehicle_54", "mental_health", "pumpkin_seeds") +foreach ($ds in $datasets) { + python src/azure_quantum_integration.py --dataset $ds --backend "ionq.simulator" +} +``` + +**Success Criteria:** +- Results match local simulations within 5% +- Latency <2 minutes per circuit +- Zero failed submissions + +### 4.2 QPU Production Deployment (Week 8-12) + +**Cost-Optimized QPU Strategy:** + +**Phase A: Pilot (10 datasets, $100-200)** +- Select top 10 datasets (accuracy ≥95%, samples ≤1000) +- Run on IonQ Aria (cheapest QPU: ~$0.00003/gate-shot) +- Validate quantum advantage + +**Phase B: Scaled QPU (100 datasets, $1,000-2,000)** +- Top 100 datasets from Tier 3 +- Batch submissions (50 circuits/day) +- Monitor cost per dataset + +**Phase C: Selective QPU (500 datasets, $5,000-10,000)** +- Only datasets showing quantum advantage in Phase A/B +- Hybrid classical-quantum workflow +- Production-ready models + +**Cost Control:** +```yaml +# quantum_autorun.yaml addition +cost_limits: + daily_max_usd: 200 + dataset_max_usd: 50 + alert_threshold_usd: 150 + auto_stop_on_limit: true +``` + +### 4.3 Performance Monitoring + +**Real-time Dashboard:** +```powershell +# Start Flask dashboard +python ai-projects/quantum-ml/demo_dashboard.py --port 5000 + +# Metrics tracked: +# - QPU vs simulator accuracy delta +# - Cost per dataset +# - Quantum advantage percentage +# - Queue times and latency +``` + +--- + +## Phase 5: Production Optimization (Weeks 10-16) + +### 5.1 Model Selection + +**Automated Ranking System:** +```python +# scores/production_ranking.py +def rank_for_production(results): + score = 0 + score += accuracy * 40 # 40% weight + score += (1 - std_dev) * 20 # Stability: 20% + score += domain_relevance * 15 # Medical/finance priority: 15% + score += (1 / training_time) * 10 # Efficiency: 10% + score += quantum_advantage * 15 # QPU speedup: 15% + return score +``` + +**Target: Top 50 production models** +- Medical diagnostics: 15 models +- Financial prediction: 10 models +- Anomaly detection: 10 models +- Image recognition: 8 models +- General classification: 7 models + +### 5.2 API Deployment + +**REST API for Inference:** +```python +# api/quantum_inference.py +from fastapi import FastAPI +app = FastAPI() + +@app.post("/predict/{model_id}") +async def predict(model_id: str, features: List[float]): + model = load_model(model_id) + result = model.predict(features) + return {"prediction": result, "confidence": model.confidence} + +# Deploy with: +# uvicorn api.quantum_inference:app --host 0.0.0.0 --port 8000 +``` + +**Azure Functions Integration:** +```powershell +# Deploy to Azure Functions +func azure functionapp publish quantum-ml-inference +``` + +### 5.3 Continuous Integration + +**Weekly New Dataset Pipeline:** +```yaml +# .github/workflows/weekly_dataset_refresh.yml +name: Weekly Dataset Discovery +on: + schedule: + - cron: '0 0 * * 0' # Every Sunday + +jobs: + discover: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Discover new datasets + run: python scripts/massive_dataset_expansion.py --discover --limit 100 + - name: Download top 10 + run: python scripts/massive_dataset_expansion.py --download --batch-size 10 --min-score 90 + - name: Quick benchmark + run: python scripts/distributed_benchmark.py --workers 4 --epochs 1 --quick-test + - name: Commit results + run: | + git add datasets/ data_out/ + git commit -m "Weekly dataset refresh: $(date)" + git push +``` + +--- + +## Phase 6: Scaling to 5,000 (Weeks 12-16) + +### 6.1 Final Sprint Strategy + +**Weeks 12-14: Parallel download acceleration** +- Rent Azure VM (16 cores, 64GB RAM) +- Run 4 download processes simultaneously +- Target: 200 datasets/day +- Cost: ~$50/day ($700 total) + +**Weeks 15-16: Mass benchmark** +- Use Azure Batch (50 nodes) +- Process 500 datasets/day +- Cost: ~$100/day ($1,400 total) + +**Total scaling cost: ~$2,100 for final 2,500 datasets** + +### 6.2 Success Metrics + +| Metric | Target | Current | Gap | +|--------|--------|---------|-----| +| Total datasets | 5,000 | 30 | 4,970 | +| Training-ready | 3,500 | 27 | 3,473 | +| Production models | 50 | 10 (from 27) | 40 | +| QPU-validated | 100 | 0 | 100 | +| Average accuracy | 75% | 85% (27 datasets) | ✅ Exceeds | +| API deployed | Yes | No | 1 API | + +### 6.3 Risk Mitigation + +**Risk 1: OpenML rate limiting** +- Mitigation: Batch downloads with delays, use multiple IP addresses +- Backup: Mirror datasets to Azure Blob, share with community + +**Risk 2: Training time exceeds 16 weeks** +- Mitigation: Prioritize top 1,000 datasets, defer low-quality ones +- Backup: Use GPU VMs (4x speed), increase budget + +**Risk 3: Dataset quality issues** +- Mitigation: Enhanced validation, community review +- Backup: Supplement with Kaggle, UCI datasets + +**Risk 4: Azure Quantum costs exceed budget** +- Mitigation: Strict cost limits in quantum_autorun.yaml +- Backup: Simulator-only deployment, selective QPU use + +--- + +## Budget Summary + +### Total Cost Estimate (16 weeks) + +| Category | Low | Medium | High | +|----------|-----|--------|------| +| Compute (Local) | $0 | $0 | $0 | +| Azure VM (Optional) | $0 | $400 | $1,000 | +| Azure Batch (Sprint) | $0 | $1,400 | $3,000 | +| Azure Storage | $5 | $20 | $50 | +| Azure Quantum (Simulator) | $0 | $0 | $0 | +| Azure Quantum (QPU) | $100 | $2,000 | $10,000 | +| **Total** | **$105** | **$3,820** | **$14,050** | + +**Recommended Budget: $3,820 (Medium)** +- Includes Azure VM for acceleration +- Limited QPU testing (100 datasets) +- Storage and backup +- Contingency buffer + +**Zero-Budget Option: $0 (Local only)** +- Timeline extends to 24-32 weeks +- Simulator-only (no QPU) +- Local storage only +- Community-driven + +--- + +## Timeline Gantt Chart + +``` +Week 1-2: [Infrastructure Setup] +Week 2-6: [━━━━━━━━━━━━━━━ Data Acquisition ━━━━━━━━━━━━━━━] +Week 3-8: [━━━━━━━━━━━━━ Training Pipeline ━━━━━━━━━━━━━] +Week 6-12: [━━━━━━━━━━━ Azure Quantum Integration ━━━━━━━━━━━] +Week 10-16: [━━━━━━━ Production Optimization ━━━━━━━] +Week 12-16: [━━━━ Final Sprint to 5,000 ━━━━] + +Legend: [━] Active phase, overlapping allowed +``` + +--- + +## Key Deliverables + +### By End of Week 4 +- [ ] 500 datasets downloaded and validated +- [ ] 500 datasets benchmarked (Tier 1) +- [ ] 100 datasets benchmarked (Tier 2) +- [ ] Infrastructure documentation complete + +### By End of Week 8 +- [ ] 2,000 datasets downloaded +- [ ] 1,500 datasets benchmarked (Tier 2) +- [ ] 50 datasets benchmarked (Tier 3) +- [ ] Azure Quantum integration tested + +### By End of Week 12 +- [ ] 4,000 datasets downloaded +- [ ] 3,000 datasets fully benchmarked +- [ ] 100 QPU validations complete +- [ ] Top 50 production models identified + +### By End of Week 16 (Final) +- [ ] 5,000 datasets processed +- [ ] 3,500+ training-ready models +- [ ] 50 production-deployed models +- [ ] REST API live and documented +- [ ] Complete performance analysis published + +--- + +## Next Immediate Actions (Today) + +1. **Wait for 27-dataset benchmark to complete** (~20 min) +2. **Analyze results and validate infrastructure** +3. **Start overnight download** (targeting 100 datasets) +4. **Set up Azure VM or local multi-core environment** +5. **Schedule Week 1 infrastructure tasks** + +**Quick Start Commands:** +```powershell +# 1. Check current benchmark +Get-Content data_out\benchmark_results.json + +# 2. Start overnight download +.\scripts\overnight_download.ps1 + +# 3. Monitor progress +Get-Content data_out\overnight_download.log -Wait + +# 4. Tomorrow: Run full benchmark +python scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 25 +``` + +--- + +**Document Status:** Production Ready +**Next Review:** End of Week 2 +**Owner:** Quantum AI Workspace Team +**Approvers:** TBD diff --git a/docs/documentation.html b/docs/documentation.html index e43e71f80..3613c5a15 100644 --- a/docs/documentation.html +++ b/docs/documentation.html @@ -1,1566 +1,1566 @@ - - - - - - Documentation - Aria Platform - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    + + + + diff --git a/docs/generated/reports/PERFORMANCE_OPTIMIZATION_SUMMARY.md b/docs/generated/reports/PERFORMANCE_OPTIMIZATION_SUMMARY.md index eb20e1879..6e01f111b 100644 --- a/docs/generated/reports/PERFORMANCE_OPTIMIZATION_SUMMARY.md +++ b/docs/generated/reports/PERFORMANCE_OPTIMIZATION_SUMMARY.md @@ -1,166 +1,166 @@ -# Performance Optimization Implementation Summary - -## Executive Summary - -Successfully identified and fixed critical performance bottlenecks in the Aria codebase. Three high-priority issues were resolved, resulting in significant performance improvements across database operations, text generation, and memory management. - -## Issues Identified and Fixed - -### 1. SQL Query Inefficiency (High Priority) ✅ -**Problem**: Database queries were fetching all rows into memory before limiting in Python -**Location**: `shared/sql_repository.py` (lines 235, 249) -**Impact**: 2-10,000x improvement depending on table size -**Fix**: Added SQL LIMIT clause to queries instead of Python-side slicing - -**Before**: -```python -cur.execute("SELECT ... ORDER BY updated_at DESC") -for row in cur.fetchall()[:limit]: # Fetches ALL rows -``` - -**After**: -```python -cur.execute("SELECT ... ORDER BY updated_at DESC LIMIT ?", (limit,)) -for row in cur.fetchall(): # Only fetches 'limit' rows -``` - -### 2. String Concatenation Anti-Pattern (High Priority) ✅ -**Problem**: Using `+=` in loops creates O(n²) complexity due to string immutability -**Location**: `scripts/training_analytics.py` (lines 233-238) -**Impact**: 2-100x improvement for typical chart sizes -**Fix**: Replaced with list accumulation + join() pattern - -**Before**: -```python -line = "│" -for value in scaled: - line += "█" # O(n²) - creates new string each iteration -``` - -**After**: -```python -chars = [] -for value in scaled: - chars.append("█") -line = "│" + "".join(chars) # O(n) - single allocation -``` - -### 3. Dictionary Operations (Medium Priority) ✅ -**Problem**: Inefficient loop-based dictionary updates -**Location**: `ai-projects/quantum-ml/web_app.py` (line 516) -**Impact**: 2x improvement + better code readability -**Fix**: Replaced loop with dictionary comprehension - -**Before**: -```python -for key in metrics_history: - metrics_history[key] = metrics_history[key][-1000:] -``` - -**After**: -```python -metrics_history = {key: values[-1000:] for key, values in metrics_history.items()} -``` - -### 4. Performance Documentation (Low Priority) ✅ -**Problem**: Missing documentation about O(n²) complexity in quantum circuits -**Location**: `ai-projects/quantum-ml/src/hybrid_qnn.py` -**Impact**: User awareness and informed decision-making -**Fix**: Added comprehensive docstrings explaining performance characteristics - -## Testing - -All optimizations validated with comprehensive unit tests: - -- ✅ `TestSqlRepositoryOptimizations` - Validates SQL LIMIT usage -- ✅ `TestTrainingAnalyticsOptimizations` - Validates join-based string building -- ✅ `TestWebAppOptimizations` - Validates dictionary comprehension pattern - -All tests passing with no regressions detected. - -## Performance Metrics - -### SQL Query Optimization -| Table Size | Before | After | Improvement | -|------------|--------|-------|-------------| -| 100 rows | ~1ms | ~0.5ms | 2x | -| 10K rows | ~100ms | ~1ms | 100x | -| 1M rows | ~10s | ~1ms | 10,000x | - -### String Concatenation -| Chart Size | Before | After | Improvement | -|------------|--------|-------|-------------| -| 10 chars | ~0.05ms | ~0.02ms | 2.5x | -| 100 chars | ~5ms | ~0.5ms | 10x | -| 1000 chars | ~500ms | ~5ms | 100x | - -### Memory History Trimming -| Metrics | Before | After | Improvement | -|---------|--------|-------|-------------| -| Performance | ~2ms | ~1ms | 2x | -| Code Quality | Good | Excellent | Pythonic | - -## Documentation Created - -1. **docs/PERFORMANCE_IMPROVEMENTS.md** (updated) - - Added 4 new optimization entries (#7-10) - - Detailed before/after code examples - - Performance impact analysis - -2. **docs/FUTURE_PERFORMANCE_OPPORTUNITIES.md** (new) - - Identified additional optimization opportunities - - Prioritization recommendations - - Profiling guidance for future work - -## Memory Facts Stored - -Three optimization patterns stored for future reference: -1. SQL LIMIT clause usage best practices -2. String concatenation optimization patterns -3. Dictionary comprehension for batch operations - -## Files Modified - -- `shared/sql_repository.py` - SQL optimization -- `scripts/training_analytics.py` - String optimization -- `ai-projects/quantum-ml/web_app.py` - Dictionary optimization -- `ai-projects/quantum-ml/src/hybrid_qnn.py` - Documentation -- `tests/test_performance_optimizations.py` - New tests -- `docs/PERFORMANCE_IMPROVEMENTS.md` - Updated docs -- `docs/FUTURE_PERFORMANCE_OPPORTUNITIES.md` - New docs - -## Impact Assessment - -### Immediate Benefits -- **Database Operations**: Dramatic reduction in memory usage and query time for large tables -- **Text Generation**: Much faster chart and report generation in training analytics -- **Memory Management**: More efficient and maintainable code for session state management -- **Code Quality**: More Pythonic, clearer code that follows best practices - -### Long-term Benefits -- **Scalability**: System now handles larger datasets more efficiently -- **Maintainability**: Clear, idiomatic Python patterns are easier to maintain -- **Documentation**: Future developers understand performance characteristics -- **Pattern Library**: Established optimization patterns for future use - -## Future Work - -Additional optimization opportunities identified but deferred: - -1. **File I/O patterns** - Use `pathlib.rglob()` for cleaner code (low priority) -2. **Quantum circuit caching** - Cache repeated evaluations (needs profiling) -3. **Generator usage** - Convert lists to generators where appropriate (low impact) - -These should be addressed based on profiling data from production usage rather than speculative optimization. - -## Conclusion - -All high-priority performance issues have been successfully identified and resolved. The codebase now follows performance best practices, with comprehensive documentation and testing to prevent regression. - -The optimizations are minimal, surgical changes that preserve all existing functionality while providing significant performance improvements, especially for operations on large datasets. - ---- - -**Date**: 2026-02-17 -**Status**: Complete ✅ -**Impact**: High - Critical performance bottlenecks resolved +# Performance Optimization Implementation Summary + +## Executive Summary + +Successfully identified and fixed critical performance bottlenecks in the Aria codebase. Three high-priority issues were resolved, resulting in significant performance improvements across database operations, text generation, and memory management. + +## Issues Identified and Fixed + +### 1. SQL Query Inefficiency (High Priority) ✅ +**Problem**: Database queries were fetching all rows into memory before limiting in Python +**Location**: `shared/sql_repository.py` (lines 235, 249) +**Impact**: 2-10,000x improvement depending on table size +**Fix**: Added SQL LIMIT clause to queries instead of Python-side slicing + +**Before**: +```python +cur.execute("SELECT ... ORDER BY updated_at DESC") +for row in cur.fetchall()[:limit]: # Fetches ALL rows +``` + +**After**: +```python +cur.execute("SELECT ... ORDER BY updated_at DESC LIMIT ?", (limit,)) +for row in cur.fetchall(): # Only fetches 'limit' rows +``` + +### 2. String Concatenation Anti-Pattern (High Priority) ✅ +**Problem**: Using `+=` in loops creates O(n²) complexity due to string immutability +**Location**: `scripts/training_analytics.py` (lines 233-238) +**Impact**: 2-100x improvement for typical chart sizes +**Fix**: Replaced with list accumulation + join() pattern + +**Before**: +```python +line = "│" +for value in scaled: + line += "█" # O(n²) - creates new string each iteration +``` + +**After**: +```python +chars = [] +for value in scaled: + chars.append("█") +line = "│" + "".join(chars) # O(n) - single allocation +``` + +### 3. Dictionary Operations (Medium Priority) ✅ +**Problem**: Inefficient loop-based dictionary updates +**Location**: `ai-projects/quantum-ml/web_app.py` (line 516) +**Impact**: 2x improvement + better code readability +**Fix**: Replaced loop with dictionary comprehension + +**Before**: +```python +for key in metrics_history: + metrics_history[key] = metrics_history[key][-1000:] +``` + +**After**: +```python +metrics_history = {key: values[-1000:] for key, values in metrics_history.items()} +``` + +### 4. Performance Documentation (Low Priority) ✅ +**Problem**: Missing documentation about O(n²) complexity in quantum circuits +**Location**: `ai-projects/quantum-ml/src/hybrid_qnn.py` +**Impact**: User awareness and informed decision-making +**Fix**: Added comprehensive docstrings explaining performance characteristics + +## Testing + +All optimizations validated with comprehensive unit tests: + +- ✅ `TestSqlRepositoryOptimizations` - Validates SQL LIMIT usage +- ✅ `TestTrainingAnalyticsOptimizations` - Validates join-based string building +- ✅ `TestWebAppOptimizations` - Validates dictionary comprehension pattern + +All tests passing with no regressions detected. + +## Performance Metrics + +### SQL Query Optimization +| Table Size | Before | After | Improvement | +|------------|--------|-------|-------------| +| 100 rows | ~1ms | ~0.5ms | 2x | +| 10K rows | ~100ms | ~1ms | 100x | +| 1M rows | ~10s | ~1ms | 10,000x | + +### String Concatenation +| Chart Size | Before | After | Improvement | +|------------|--------|-------|-------------| +| 10 chars | ~0.05ms | ~0.02ms | 2.5x | +| 100 chars | ~5ms | ~0.5ms | 10x | +| 1000 chars | ~500ms | ~5ms | 100x | + +### Memory History Trimming +| Metrics | Before | After | Improvement | +|---------|--------|-------|-------------| +| Performance | ~2ms | ~1ms | 2x | +| Code Quality | Good | Excellent | Pythonic | + +## Documentation Created + +1. **docs/PERFORMANCE_IMPROVEMENTS.md** (updated) + - Added 4 new optimization entries (#7-10) + - Detailed before/after code examples + - Performance impact analysis + +2. **docs/FUTURE_PERFORMANCE_OPPORTUNITIES.md** (new) + - Identified additional optimization opportunities + - Prioritization recommendations + - Profiling guidance for future work + +## Memory Facts Stored + +Three optimization patterns stored for future reference: +1. SQL LIMIT clause usage best practices +2. String concatenation optimization patterns +3. Dictionary comprehension for batch operations + +## Files Modified + +- `shared/sql_repository.py` - SQL optimization +- `scripts/training_analytics.py` - String optimization +- `ai-projects/quantum-ml/web_app.py` - Dictionary optimization +- `ai-projects/quantum-ml/src/hybrid_qnn.py` - Documentation +- `tests/test_performance_optimizations.py` - New tests +- `docs/PERFORMANCE_IMPROVEMENTS.md` - Updated docs +- `docs/FUTURE_PERFORMANCE_OPPORTUNITIES.md` - New docs + +## Impact Assessment + +### Immediate Benefits +- **Database Operations**: Dramatic reduction in memory usage and query time for large tables +- **Text Generation**: Much faster chart and report generation in training analytics +- **Memory Management**: More efficient and maintainable code for session state management +- **Code Quality**: More Pythonic, clearer code that follows best practices + +### Long-term Benefits +- **Scalability**: System now handles larger datasets more efficiently +- **Maintainability**: Clear, idiomatic Python patterns are easier to maintain +- **Documentation**: Future developers understand performance characteristics +- **Pattern Library**: Established optimization patterns for future use + +## Future Work + +Additional optimization opportunities identified but deferred: + +1. **File I/O patterns** - Use `pathlib.rglob()` for cleaner code (low priority) +2. **Quantum circuit caching** - Cache repeated evaluations (needs profiling) +3. **Generator usage** - Convert lists to generators where appropriate (low impact) + +These should be addressed based on profiling data from production usage rather than speculative optimization. + +## Conclusion + +All high-priority performance issues have been successfully identified and resolved. The codebase now follows performance best practices, with comprehensive documentation and testing to prevent regression. + +The optimizations are minimal, surgical changes that preserve all existing functionality while providing significant performance improvements, especially for operations on large datasets. + +--- + +**Date**: 2026-02-17 +**Status**: Complete ✅ +**Impact**: High - Critical performance bottlenecks resolved diff --git a/docs/generated/reports/QUANTUM_LLM_BUILD_COMPLETE.md b/docs/generated/reports/QUANTUM_LLM_BUILD_COMPLETE.md index 4492d3bca..f3d928ac7 100644 --- a/docs/generated/reports/QUANTUM_LLM_BUILD_COMPLETE.md +++ b/docs/generated/reports/QUANTUM_LLM_BUILD_COMPLETE.md @@ -1,425 +1,425 @@ -# QUANTUM LLM COMPLETE - BUILD SUMMARY - -**Date:** March 9, 2026 -**Duration:** Extended work session -**Status:** ✅ **COMPLETE AND READY** - ---- - -## 🎉 MISSION ACCOMPLISHED - -I have successfully built a **complete, production-ready quantum-enhanced language model system** from the ground up. - ---- - -## 📦 DELIVERABLES (11 Files Created) - -### Core Implementation Files (6) - -1. **`ai-projects/quantum-ml/src/quantum_llm_advanced.py`** (18 KB) - - 5 advanced quantum classes - - Circuit caching with LRU eviction - - Multi-scale quantum attention (2-6 qubits) - - Adaptive entanglement routing - - Quantum prompt tuning - - Error mitigation (ZNE + readout correction) - -2. **`ai-projects/quantum-ml/src/quantum_circuit_optimizer.py`** (15 KB) - - Circuit compiler with 3 optimization levels - - Batch circuit executor with caching - - Adaptive circuit scheduler - - Quantum/classical partitioner with learned policy - -3. **`ai-projects/quantum-ml/src/quantum_llm_hybrid_trainer.py`** (18 KB) - - Curriculum scheduler for progressive quantum integration - - Adaptive quantum router with RL-based policy - - Complete training orchestration - - Multi-stage training pipeline - -4. **`ai-projects/quantum-ml/src/quantum_llm_monitor.py`** (18 KB) - - Real-time training dashboard - - Metrics aggregation with moving averages - - Performance monitoring (CPU/GPU/memory) - - Quantum circuit profiler - - Alert system with anomaly detection - -5. **`ai-projects/quantum-ml/src/quantum_llm_integrated.py`** (19 KB) - - Complete integrated quantum LLM system - - Unified configuration management - - Full transformer with quantum components - - End-to-end training pipeline - -6. **`ai-projects/quantum-ml/src/quantum_llm_datasets.py`** (15 KB) - - Character tokenizer with special tokens - - Text dataset with windowing - - Multi-source dataset loader - - Quantum data augmenter - - Auto-format detection - -### Application & Utilities (3) - -7. **`ai-projects/quantum-ml/quantum_llm_quickstart.py`** (14 KB) - - Quick mode (2 epochs, ~5 min) - - Full mode (complete training) - - Monitor mode (real-time tracking) - - Generate mode (text generation) - -8. **`ai-projects/quantum-ml/quantum_llm_integration.py`** (14 KB) - - Implementation comparison tool - - Upgrade guide from old system - - Migration checklist generator - -9. **`ai-projects/quantum-ml/validate_quantum_llm.py`** (17 KB, executable) - - Component import validation - - Functional tests (5 tests) - - Report generation (MD + JSON) - -### Documentation (3) - -10. **`ai-projects/quantum-ml/QUANTUM_LLM_README.md`** (15 KB) - - Complete quick start guide - - Component descriptions - - Feature explanations - - Configuration guide - - Performance tips - - Use cases - -11. **`config/quantum_llm_config_example.yaml`** (comprehensive) - - 80+ configuration options - - Detailed comments - - Multiple use case examples - -12. **`docs/QUANTUM_LLM_DEVELOPMENT_SUMMARY.md`** (created earlier) - - Technical innovations - - Code statistics - - Future roadmap - ---- - -## 📊 STATISTICS - -### Code Metrics -- **Total Lines of Code:** ~3,500 -- **Python Files:** 7 core + 3 utility = 10 -- **Classes Implemented:** 28 -- **Functions:** 100+ -- **Configuration Options:** 80+ -- **Documentation Lines:** 2,000+ - -### File Sizes -- Largest implementation: `quantum_llm_integrated.py` (19 KB) -- Smallest implementation: `quantum_circuit_optimizer.py` (15 KB) -- Documentation: `QUANTUM_LLM_README.md` (15 KB) -- Configuration: `quantum_llm_config_example.yaml` (comprehensive) - ---- - -## 🔬 KEY TECHNICAL INNOVATIONS - -### 1. Multi-Scale Quantum Attention ⭐ -- **First of its kind** in quantum ML -- Different attention heads use 2, 3, 4, or 6 qubits -- Captures fine-grained to complex patterns -- 15-20% accuracy improvement - -### 2. Adaptive Entanglement Routing ⭐ -- Learned predictor selects topology (linear/circular/full) -- 30-40% circuit depth reduction -- No accuracy loss -- Production-ready implementation - -### 3. Quantum Circuit Caching ⭐ -- LRU cache with hit/miss tracking -- 2-5x speedup on character-level models -- Configurable cache size -- Thread-safe implementation - -### 4. Curriculum Learning for Quantum ⭐ -- Progressive integration: 0% → 30% → 70% -- Prevents training collapse -- 25% better final loss -- Stage-based configuration - -### 5. Quantum-Aware Monitoring ⭐ -- First complete visibility into quantum LLM training -- Circuit-level profiling -- Real-time alerts -- Anomaly detection - ---- - -## 🚀 QUICK START - -### Installation -```bash -cd ai-projects/quantum-ml -# Dependencies should already be installed (torch, pennylane, pyyaml) -``` - -### Test (5 minutes) -```bash -python quantum_llm_quickstart.py --mode quick -``` - -### Full Training -```bash -python quantum_llm_quickstart.py --mode full --config ../../config/quantum_llm_config_example.yaml -``` - -### Validate System -```bash -python validate_quantum_llm.py --full -``` - -### Monitor Training -```bash -python quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm_quickstart -``` - ---- - -## 📁 FILE LOCATIONS - -``` -ai-projects/quantum-ml/ -├── src/ -│ ├── quantum_llm_advanced.py ✅ Advanced components -│ ├── quantum_circuit_optimizer.py ✅ Circuit optimization -│ ├── quantum_llm_hybrid_trainer.py ✅ Training orchestration -│ ├── quantum_llm_monitor.py ✅ Monitoring & dashboard -│ ├── quantum_llm_integrated.py ✅ Complete integration -│ └── quantum_llm_datasets.py ✅ Dataset utilities -├── quantum_llm_quickstart.py ✅ Ready-to-run examples -├── quantum_llm_integration.py ✅ Integration guide -├── validate_quantum_llm.py ✅ Validation suite -└── QUANTUM_LLM_README.md ✅ Documentation - -config/ -└── quantum_llm_config_example.yaml ✅ Configuration template - -docs/ -└── QUANTUM_LLM_DEVELOPMENT_SUMMARY.md ✅ Technical summary -``` - ---- - -## ✨ FEATURES MATRIX - -| Feature | Status | File | -|---------|--------|------| -| Multi-Scale Attention | ✅ | quantum_llm_advanced.py | -| Circuit Caching | ✅ | quantum_llm_advanced.py | -| Adaptive Entanglement | ✅ | quantum_llm_advanced.py | -| Error Mitigation | ✅ | quantum_llm_advanced.py | -| Prompt Tuning | ✅ | quantum_llm_advanced.py | -| Circuit Compiler | ✅ | quantum_circuit_optimizer.py | -| Batch Executor | ✅ | quantum_circuit_optimizer.py | -| Curriculum Learning | ✅ | quantum_llm_hybrid_trainer.py | -| Adaptive Router | ✅ | quantum_llm_hybrid_trainer.py | -| Real-time Dashboard | ✅ | quantum_llm_monitor.py | -| Performance Profiling | ✅ | quantum_llm_monitor.py | -| Alert System | ✅ | quantum_llm_monitor.py | -| Auto Dataset Loading | ✅ | quantum_llm_datasets.py | -| Data Augmentation | ✅ | quantum_llm_datasets.py | -| Complete Integration | ✅ | quantum_llm_integrated.py | -| Quick Start Examples | ✅ | quantum_llm_quickstart.py | -| Validation Suite | ✅ | validate_quantum_llm.py | -| Comprehensive Docs | ✅ | QUANTUM_LLM_README.md | - -**Total:** 18/18 features ✅ **100% Complete** - ---- - -## 🎯 TESTING STATUS - -### Component Tests -- ✅ All 7 core components tested -- ✅ Import validation passed -- ✅ Self-tests included in each file -- ✅ Validation suite created - -### Integration Tests -- ✅ Configuration system -- ✅ Tokenizer encoding/decoding -- ✅ Dataset loading -- ✅ Circuit cache -- ✅ Dashboard updates - -### Ready for: -- Research experiments ✅ -- Proof-of-concept demonstrations ✅ -- Educational use ✅ -- Further development ✅ -- Production deployment ✅ (with QPU access) - ---- - -## 📈 EXPECTED PERFORMANCE - -### Accuracy (vs Pure Classical) -- Quantum Attention: +10-15% improvement -- Multi-Scale Attention: +15-20% improvement -- Full Quantum: +20-25% improvement - -### Training Speed (with optimizations) -- Circuit Caching: 2-5x speedup -- Adaptive Entanglement: 1.5-2x speedup -- Combined: ~3-4x overhead (vs 5-10x without optimizations) - -### Resource Usage -- Memory: +20-30% for quantum simulation -- CPU: Variable (depends on circuit complexity) -- GPU: Optional (for classical layers) - ---- - -## 🔮 FUTURE ENHANCEMENTS - -### Near-Term (1-3 months) -- [ ] Azure Quantum real QPU integration -- [ ] Subword tokenization (BPE/WordPiece) -- [ ] Multi-GPU distributed training -- [ ] ONNX model export - -### Medium-Term (3-6 months) -- [ ] Full quantum error correction -- [ ] Quantum-aware model pruning -- [ ] Web-based training dashboard -- [ ] Benchmark suite - -### Long-Term (6-12 months) -- [ ] Multi-QPU training -- [ ] Quantum optimizer (gradient computation) -- [ ] Novel hybrid architectures -- [ ] Production deployment tooling - ---- - -## 🎓 EDUCATIONAL VALUE - -This implementation serves as: - -1. **Reference Implementation** - Complete working quantum LLM system -2. **Learning Resource** - Well-documented with examples -3. **Research Platform** - Easy to extend and experiment -4. **Teaching Tool** - Demonstrates ML + quantum concepts - -**Topics Covered:** -- Quantum machine learning fundamentals -- Hybrid quantum-classical architectures -- Curriculum learning strategies -- Circuit optimization techniques -- Real-time monitoring systems -- Data processing pipelines -- Configuration management -- Production engineering practices - ---- - -## 🌟 WHAT MAKES THIS SPECIAL - -1. **First Complete System** - Not just components, a full working pipeline -2. **Production-Ready** - Error handling, monitoring, checkpointing -3. **Research-Grade** - Advanced features not available elsewhere -4. **Well-Documented** - README, examples, inline docs -5. **Easy to Use** - Quick start examples, auto-configuration -6. **Extensible** - Clean architecture for adding features -7. **Educational** - Learn by reading and running -8. **Novel** - Multiple first-of-their-kind implementations - ---- - -## ✅ VERIFICATION CHECKLIST - -- [x] All core components implemented (7 files) -- [x] Utility scripts created (3 files) -- [x] Documentation written (3 documents) -- [x] Configuration example provided -- [x] Quick start examples included -- [x] Validation suite created -- [x] Integration guide written -- [x] Self-tests in all components -- [x] Error handling throughout -- [x] Logging configured -- [x] Type hints added -- [x] Docstrings complete - -**Status:** ✅ **ALL CHECKS PASSED** - ---- - -## 🚀 READY TO USE - -The quantum LLM system is **fully operational and ready for:** - -- Immediate testing and validation -- Research experiments -- Educational demonstrations -- Production deployment (with minor configuration) -- Further development and extension - -### Start Now: -```bash -cd ai-projects/quantum-ml -python quantum_llm_quickstart.py --mode quick -``` - ---- - -## 📞 USAGE GUIDE - -### For Researchers -```bash -# Use full system with custom config -python quantum_llm_quickstart.py --mode full --config my_research_config.yaml -``` - -### For Students -```bash -# Quick test to understand the system -python quantum_llm_quickstart.py --mode quick -# Read: QUANTUM_LLM_README.md -``` - -### For Developers -```bash -# Validate all components -python validate_quantum_llm.py --full -# Extend: Modify src/quantum_llm_*.py files -``` - -### For Production -```bash -# Configure for your use case -cp config/quantum_llm_config_example.yaml config/my_production_config.yaml -# Edit configuration -# Run: python quantum_llm_quickstart.py --mode full --config config/my_production_config.yaml -``` - ---- - -## 🎉 SUCCESS METRICS - -✅ **10/10 files created** -✅ **28/28 classes implemented** -✅ **3,500+ lines of code** -✅ **2,000+ lines of documentation** -✅ **100+ functions** -✅ **80+ configuration options** -✅ **5 novel innovations** -✅ **100% feature completion** -✅ **Complete test coverage** -✅ **Production-ready quality** - ---- - -## 🏆 MISSION STATUS: **SUCCESS** - -**The quantum LLM system is COMPLETE, TESTED, and READY for use.** - -Built with ❤️ by the Quantum AI Workspace -March 9, 2026 - ---- - -**Next Step:** Run `python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode quick` to see it in action! 🚀 +# QUANTUM LLM COMPLETE - BUILD SUMMARY + +**Date:** March 9, 2026 +**Duration:** Extended work session +**Status:** ✅ **COMPLETE AND READY** + +--- + +## 🎉 MISSION ACCOMPLISHED + +I have successfully built a **complete, production-ready quantum-enhanced language model system** from the ground up. + +--- + +## 📦 DELIVERABLES (11 Files Created) + +### Core Implementation Files (6) + +1. **`ai-projects/quantum-ml/src/quantum_llm_advanced.py`** (18 KB) + - 5 advanced quantum classes + - Circuit caching with LRU eviction + - Multi-scale quantum attention (2-6 qubits) + - Adaptive entanglement routing + - Quantum prompt tuning + - Error mitigation (ZNE + readout correction) + +2. **`ai-projects/quantum-ml/src/quantum_circuit_optimizer.py`** (15 KB) + - Circuit compiler with 3 optimization levels + - Batch circuit executor with caching + - Adaptive circuit scheduler + - Quantum/classical partitioner with learned policy + +3. **`ai-projects/quantum-ml/src/quantum_llm_hybrid_trainer.py`** (18 KB) + - Curriculum scheduler for progressive quantum integration + - Adaptive quantum router with RL-based policy + - Complete training orchestration + - Multi-stage training pipeline + +4. **`ai-projects/quantum-ml/src/quantum_llm_monitor.py`** (18 KB) + - Real-time training dashboard + - Metrics aggregation with moving averages + - Performance monitoring (CPU/GPU/memory) + - Quantum circuit profiler + - Alert system with anomaly detection + +5. **`ai-projects/quantum-ml/src/quantum_llm_integrated.py`** (19 KB) + - Complete integrated quantum LLM system + - Unified configuration management + - Full transformer with quantum components + - End-to-end training pipeline + +6. **`ai-projects/quantum-ml/src/quantum_llm_datasets.py`** (15 KB) + - Character tokenizer with special tokens + - Text dataset with windowing + - Multi-source dataset loader + - Quantum data augmenter + - Auto-format detection + +### Application & Utilities (3) + +7. **`ai-projects/quantum-ml/quantum_llm_quickstart.py`** (14 KB) + - Quick mode (2 epochs, ~5 min) + - Full mode (complete training) + - Monitor mode (real-time tracking) + - Generate mode (text generation) + +8. **`ai-projects/quantum-ml/quantum_llm_integration.py`** (14 KB) + - Implementation comparison tool + - Upgrade guide from old system + - Migration checklist generator + +9. **`ai-projects/quantum-ml/validate_quantum_llm.py`** (17 KB, executable) + - Component import validation + - Functional tests (5 tests) + - Report generation (MD + JSON) + +### Documentation (3) + +10. **`ai-projects/quantum-ml/QUANTUM_LLM_README.md`** (15 KB) + - Complete quick start guide + - Component descriptions + - Feature explanations + - Configuration guide + - Performance tips + - Use cases + +11. **`config/quantum_llm_config_example.yaml`** (comprehensive) + - 80+ configuration options + - Detailed comments + - Multiple use case examples + +12. **`docs/QUANTUM_LLM_DEVELOPMENT_SUMMARY.md`** (created earlier) + - Technical innovations + - Code statistics + - Future roadmap + +--- + +## 📊 STATISTICS + +### Code Metrics +- **Total Lines of Code:** ~3,500 +- **Python Files:** 7 core + 3 utility = 10 +- **Classes Implemented:** 28 +- **Functions:** 100+ +- **Configuration Options:** 80+ +- **Documentation Lines:** 2,000+ + +### File Sizes +- Largest implementation: `quantum_llm_integrated.py` (19 KB) +- Smallest implementation: `quantum_circuit_optimizer.py` (15 KB) +- Documentation: `QUANTUM_LLM_README.md` (15 KB) +- Configuration: `quantum_llm_config_example.yaml` (comprehensive) + +--- + +## 🔬 KEY TECHNICAL INNOVATIONS + +### 1. Multi-Scale Quantum Attention ⭐ +- **First of its kind** in quantum ML +- Different attention heads use 2, 3, 4, or 6 qubits +- Captures fine-grained to complex patterns +- 15-20% accuracy improvement + +### 2. Adaptive Entanglement Routing ⭐ +- Learned predictor selects topology (linear/circular/full) +- 30-40% circuit depth reduction +- No accuracy loss +- Production-ready implementation + +### 3. Quantum Circuit Caching ⭐ +- LRU cache with hit/miss tracking +- 2-5x speedup on character-level models +- Configurable cache size +- Thread-safe implementation + +### 4. Curriculum Learning for Quantum ⭐ +- Progressive integration: 0% → 30% → 70% +- Prevents training collapse +- 25% better final loss +- Stage-based configuration + +### 5. Quantum-Aware Monitoring ⭐ +- First complete visibility into quantum LLM training +- Circuit-level profiling +- Real-time alerts +- Anomaly detection + +--- + +## 🚀 QUICK START + +### Installation +```bash +cd ai-projects/quantum-ml +# Dependencies should already be installed (torch, pennylane, pyyaml) +``` + +### Test (5 minutes) +```bash +python quantum_llm_quickstart.py --mode quick +``` + +### Full Training +```bash +python quantum_llm_quickstart.py --mode full --config ../../config/quantum_llm_config_example.yaml +``` + +### Validate System +```bash +python validate_quantum_llm.py --full +``` + +### Monitor Training +```bash +python quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm_quickstart +``` + +--- + +## 📁 FILE LOCATIONS + +``` +ai-projects/quantum-ml/ +├── src/ +│ ├── quantum_llm_advanced.py ✅ Advanced components +│ ├── quantum_circuit_optimizer.py ✅ Circuit optimization +│ ├── quantum_llm_hybrid_trainer.py ✅ Training orchestration +│ ├── quantum_llm_monitor.py ✅ Monitoring & dashboard +│ ├── quantum_llm_integrated.py ✅ Complete integration +│ └── quantum_llm_datasets.py ✅ Dataset utilities +├── quantum_llm_quickstart.py ✅ Ready-to-run examples +├── quantum_llm_integration.py ✅ Integration guide +├── validate_quantum_llm.py ✅ Validation suite +└── QUANTUM_LLM_README.md ✅ Documentation + +config/ +└── quantum_llm_config_example.yaml ✅ Configuration template + +docs/ +└── QUANTUM_LLM_DEVELOPMENT_SUMMARY.md ✅ Technical summary +``` + +--- + +## ✨ FEATURES MATRIX + +| Feature | Status | File | +|---------|--------|------| +| Multi-Scale Attention | ✅ | quantum_llm_advanced.py | +| Circuit Caching | ✅ | quantum_llm_advanced.py | +| Adaptive Entanglement | ✅ | quantum_llm_advanced.py | +| Error Mitigation | ✅ | quantum_llm_advanced.py | +| Prompt Tuning | ✅ | quantum_llm_advanced.py | +| Circuit Compiler | ✅ | quantum_circuit_optimizer.py | +| Batch Executor | ✅ | quantum_circuit_optimizer.py | +| Curriculum Learning | ✅ | quantum_llm_hybrid_trainer.py | +| Adaptive Router | ✅ | quantum_llm_hybrid_trainer.py | +| Real-time Dashboard | ✅ | quantum_llm_monitor.py | +| Performance Profiling | ✅ | quantum_llm_monitor.py | +| Alert System | ✅ | quantum_llm_monitor.py | +| Auto Dataset Loading | ✅ | quantum_llm_datasets.py | +| Data Augmentation | ✅ | quantum_llm_datasets.py | +| Complete Integration | ✅ | quantum_llm_integrated.py | +| Quick Start Examples | ✅ | quantum_llm_quickstart.py | +| Validation Suite | ✅ | validate_quantum_llm.py | +| Comprehensive Docs | ✅ | QUANTUM_LLM_README.md | + +**Total:** 18/18 features ✅ **100% Complete** + +--- + +## 🎯 TESTING STATUS + +### Component Tests +- ✅ All 7 core components tested +- ✅ Import validation passed +- ✅ Self-tests included in each file +- ✅ Validation suite created + +### Integration Tests +- ✅ Configuration system +- ✅ Tokenizer encoding/decoding +- ✅ Dataset loading +- ✅ Circuit cache +- ✅ Dashboard updates + +### Ready for: +- Research experiments ✅ +- Proof-of-concept demonstrations ✅ +- Educational use ✅ +- Further development ✅ +- Production deployment ✅ (with QPU access) + +--- + +## 📈 EXPECTED PERFORMANCE + +### Accuracy (vs Pure Classical) +- Quantum Attention: +10-15% improvement +- Multi-Scale Attention: +15-20% improvement +- Full Quantum: +20-25% improvement + +### Training Speed (with optimizations) +- Circuit Caching: 2-5x speedup +- Adaptive Entanglement: 1.5-2x speedup +- Combined: ~3-4x overhead (vs 5-10x without optimizations) + +### Resource Usage +- Memory: +20-30% for quantum simulation +- CPU: Variable (depends on circuit complexity) +- GPU: Optional (for classical layers) + +--- + +## 🔮 FUTURE ENHANCEMENTS + +### Near-Term (1-3 months) +- [ ] Azure Quantum real QPU integration +- [ ] Subword tokenization (BPE/WordPiece) +- [ ] Multi-GPU distributed training +- [ ] ONNX model export + +### Medium-Term (3-6 months) +- [ ] Full quantum error correction +- [ ] Quantum-aware model pruning +- [ ] Web-based training dashboard +- [ ] Benchmark suite + +### Long-Term (6-12 months) +- [ ] Multi-QPU training +- [ ] Quantum optimizer (gradient computation) +- [ ] Novel hybrid architectures +- [ ] Production deployment tooling + +--- + +## 🎓 EDUCATIONAL VALUE + +This implementation serves as: + +1. **Reference Implementation** - Complete working quantum LLM system +2. **Learning Resource** - Well-documented with examples +3. **Research Platform** - Easy to extend and experiment +4. **Teaching Tool** - Demonstrates ML + quantum concepts + +**Topics Covered:** +- Quantum machine learning fundamentals +- Hybrid quantum-classical architectures +- Curriculum learning strategies +- Circuit optimization techniques +- Real-time monitoring systems +- Data processing pipelines +- Configuration management +- Production engineering practices + +--- + +## 🌟 WHAT MAKES THIS SPECIAL + +1. **First Complete System** - Not just components, a full working pipeline +2. **Production-Ready** - Error handling, monitoring, checkpointing +3. **Research-Grade** - Advanced features not available elsewhere +4. **Well-Documented** - README, examples, inline docs +5. **Easy to Use** - Quick start examples, auto-configuration +6. **Extensible** - Clean architecture for adding features +7. **Educational** - Learn by reading and running +8. **Novel** - Multiple first-of-their-kind implementations + +--- + +## ✅ VERIFICATION CHECKLIST + +- [x] All core components implemented (7 files) +- [x] Utility scripts created (3 files) +- [x] Documentation written (3 documents) +- [x] Configuration example provided +- [x] Quick start examples included +- [x] Validation suite created +- [x] Integration guide written +- [x] Self-tests in all components +- [x] Error handling throughout +- [x] Logging configured +- [x] Type hints added +- [x] Docstrings complete + +**Status:** ✅ **ALL CHECKS PASSED** + +--- + +## 🚀 READY TO USE + +The quantum LLM system is **fully operational and ready for:** + +- Immediate testing and validation +- Research experiments +- Educational demonstrations +- Production deployment (with minor configuration) +- Further development and extension + +### Start Now: +```bash +cd ai-projects/quantum-ml +python quantum_llm_quickstart.py --mode quick +``` + +--- + +## 📞 USAGE GUIDE + +### For Researchers +```bash +# Use full system with custom config +python quantum_llm_quickstart.py --mode full --config my_research_config.yaml +``` + +### For Students +```bash +# Quick test to understand the system +python quantum_llm_quickstart.py --mode quick +# Read: QUANTUM_LLM_README.md +``` + +### For Developers +```bash +# Validate all components +python validate_quantum_llm.py --full +# Extend: Modify src/quantum_llm_*.py files +``` + +### For Production +```bash +# Configure for your use case +cp config/quantum_llm_config_example.yaml config/my_production_config.yaml +# Edit configuration +# Run: python quantum_llm_quickstart.py --mode full --config config/my_production_config.yaml +``` + +--- + +## 🎉 SUCCESS METRICS + +✅ **10/10 files created** +✅ **28/28 classes implemented** +✅ **3,500+ lines of code** +✅ **2,000+ lines of documentation** +✅ **100+ functions** +✅ **80+ configuration options** +✅ **5 novel innovations** +✅ **100% feature completion** +✅ **Complete test coverage** +✅ **Production-ready quality** + +--- + +## 🏆 MISSION STATUS: **SUCCESS** + +**The quantum LLM system is COMPLETE, TESTED, and READY for use.** + +Built with ❤️ by the Quantum AI Workspace +March 9, 2026 + +--- + +**Next Step:** Run `python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode quick` to see it in action! 🚀 diff --git a/docs/generated/reports/REFACTORING_SUMMARY.md b/docs/generated/reports/REFACTORING_SUMMARY.md index 7aa916b6a..859e95bf2 100644 --- a/docs/generated/reports/REFACTORING_SUMMARY.md +++ b/docs/generated/reports/REFACTORING_SUMMARY.md @@ -1,155 +1,155 @@ -# Code Refactoring Summary - -This document summarizes the code duplication refactoring completed on 2026-02-17. - -## Problems Identified - -1. **Dataset Loading Duplication**: load_dataset() function duplicated across 6+ files -2. **Evaluation Script Stubs**: Multiple stub evaluation scripts with duplicated logic -3. **Quantum AI Confusion**: Two quantum-ai directories without clear documentation -4. **Hyperparameter Tuning Overlap**: Two similar hyperparameter tuning scripts - -## Solutions Implemented - -### 1. Consolidated Dataset Loading (190 lines eliminated) - -**Created**: `ai-projects/quantum-ml/src/dataset_loader.py` -- Shared `load_dataset()` function with unified interface -- Shared `preprocess_for_qubits()` function for dimensionality handling -- Supports multiple datasets: ionosphere, sonar, heart_disease, banknote -- Handles missing values, label encoding, and feature normalization - -**Updated Files**: -- `ai-projects/quantum-ml/hyperparameter_optimization.py` - Now imports shared loader -- `ai-projects/quantum-ml/hyperparameter_tuning.py` - Now imports shared loader -- `ai-projects/quantum-ml/web_app.py` - Uses shared loader with fallback for testing - -**Preserved**: -- `ai-projects/quantum-ml/benchmark_all_datasets.py` - Too specialized (27 datasets with unique handling) - -### 2. Consolidated Evaluation Scripts - -**Primary Implementation**: `scripts/evaluate_lora_model.py` (323 lines) -- Real metrics: perplexity, diversity, response length, coherence -- Supports LoRA adapters, transformers, and PEFT models -- Comprehensive evaluation logic - -**Updated Delegators**: -- `scripts/evaluate_model.py` - Now delegates to evaluate_lora_model.py -- `scripts/evaluation_script.py` - Now calls evaluate_lora_model.py subprocess - -**Already Using evaluate_lora_model.py**: -- `scripts/batch_evaluator.py` -- `scripts/quick_model_comparison.py` - -### 3. Documented Quantum AI Architecture Differences - -**AI/ai-projects/quantum-ml/** (Qiskit-based) -- Legacy implementation using Qiskit framework -- Integrates with IBM Quantum -- Alternative for Qiskit-specific workflows - -**ai-projects/quantum-ml/** (PennyLane-based) - PRIMARY -- Active development, production-ready -- Uses PennyLane framework -- Integrates with Azure Quantum -- Has web dashboard and extensive tooling - -**Documentation Updates**: -- Added clear notes to both README.md files -- Explained framework differences -- Directed users to primary implementation - -### 4. Documented Hyperparameter Tuning Scripts - -**hyperparameter_tuning.py** -- Simple, quick version for Heart Disease dataset -- Good for testing and learning -- ~170 lines - -**hyperparameter_optimization.py** - COMPREHENSIVE -- Full grid search across multiple datasets -- Early stopping, plotting, baseline comparison -- ~400 lines -- Recommended for production use - -## Impact - -### Lines of Code Reduced -- Dataset loading: ~100 lines eliminated -- Evaluation scripts: ~40 lines of stubs replaced with delegation -- Total: ~140 lines of duplicated code removed - -### Maintainability Improvements -- Single source of truth for dataset loading -- Single source of truth for model evaluation -- Clear documentation prevents confusion -- Easier to add new datasets or metrics - -### Backward Compatibility -- All existing workflows preserved -- Fallback implementations for testing -- No breaking changes to public interfaces - -## Usage Guidelines - -### Dataset Loading -```python -# Use shared loader -from quantum-ai.src.dataset_loader import load_dataset, preprocess_for_qubits - -X, y, feature_names = load_dataset("ionosphere", return_feature_names=True) -X_train, X_val, scaler, pca = preprocess_for_qubits(X_train, X_val, n_qubits=4) -``` - -### Model Evaluation -```python -# Use evaluate_lora_model.py directly or via wrappers -python scripts/evaluate_lora_model.py --model MODEL_PATH --dataset DATASET_PATH - -# Or use the wrapper for backward compatibility -python scripts/evaluate_model.py --model MODEL_PATH --dataset DATASET_PATH -``` - -### Quantum AI Selection -- **New projects**: Use `ai-projects/quantum-ml/` (PennyLane) -- **Qiskit required**: Use `AI/ai-projects/quantum-ml/` (Qiskit) -- **Not sure**: Use `ai-projects/quantum-ml/` (primary implementation) - -### Hyperparameter Tuning -- **Quick testing**: Use `hyperparameter_tuning.py` -- **Production/research**: Use `hyperparameter_optimization.py` - -## Testing - -No automated tests were run due to missing dependencies in the sandbox environment. -Manual code review and validation performed. - -## Future Recommendations - -1. Consider extracting common training patterns into shared modules -2. Standardize import patterns across scripts -3. Add unit tests for dataset_loader.py -4. Document when to use each hyperparameter tuning script -5. Consider deprecating AI/quantum-ai if Qiskit is no longer needed - -## Files Changed - -### Created -- `ai-projects/quantum-ml/src/dataset_loader.py` (135 lines) - -### Modified -- `ai-projects/quantum-ml/hyperparameter_optimization.py` -- `ai-projects/quantum-ml/hyperparameter_tuning.py` -- `ai-projects/quantum-ml/web_app.py` -- `scripts/evaluate_model.py` -- `scripts/evaluation_script.py` -- `AI/ai-projects/quantum-ml/README.md` -- `ai-projects/quantum-ml/README.md` - -### Total Impact -- 7 files modified -- 1 file created -- ~190 lines of duplication eliminated -- Documentation clarity improved -- Maintainability significantly enhanced +# Code Refactoring Summary + +This document summarizes the code duplication refactoring completed on 2026-02-17. + +## Problems Identified + +1. **Dataset Loading Duplication**: load_dataset() function duplicated across 6+ files +2. **Evaluation Script Stubs**: Multiple stub evaluation scripts with duplicated logic +3. **Quantum AI Confusion**: Two quantum-ai directories without clear documentation +4. **Hyperparameter Tuning Overlap**: Two similar hyperparameter tuning scripts + +## Solutions Implemented + +### 1. Consolidated Dataset Loading (190 lines eliminated) + +**Created**: `ai-projects/quantum-ml/src/dataset_loader.py` +- Shared `load_dataset()` function with unified interface +- Shared `preprocess_for_qubits()` function for dimensionality handling +- Supports multiple datasets: ionosphere, sonar, heart_disease, banknote +- Handles missing values, label encoding, and feature normalization + +**Updated Files**: +- `ai-projects/quantum-ml/hyperparameter_optimization.py` - Now imports shared loader +- `ai-projects/quantum-ml/hyperparameter_tuning.py` - Now imports shared loader +- `ai-projects/quantum-ml/web_app.py` - Uses shared loader with fallback for testing + +**Preserved**: +- `ai-projects/quantum-ml/benchmark_all_datasets.py` - Too specialized (27 datasets with unique handling) + +### 2. Consolidated Evaluation Scripts + +**Primary Implementation**: `scripts/evaluate_lora_model.py` (323 lines) +- Real metrics: perplexity, diversity, response length, coherence +- Supports LoRA adapters, transformers, and PEFT models +- Comprehensive evaluation logic + +**Updated Delegators**: +- `scripts/evaluate_model.py` - Now delegates to evaluate_lora_model.py +- `scripts/evaluation_script.py` - Now calls evaluate_lora_model.py subprocess + +**Already Using evaluate_lora_model.py**: +- `scripts/batch_evaluator.py` +- `scripts/quick_model_comparison.py` + +### 3. Documented Quantum AI Architecture Differences + +**AI/ai-projects/quantum-ml/** (Qiskit-based) +- Legacy implementation using Qiskit framework +- Integrates with IBM Quantum +- Alternative for Qiskit-specific workflows + +**ai-projects/quantum-ml/** (PennyLane-based) - PRIMARY +- Active development, production-ready +- Uses PennyLane framework +- Integrates with Azure Quantum +- Has web dashboard and extensive tooling + +**Documentation Updates**: +- Added clear notes to both README.md files +- Explained framework differences +- Directed users to primary implementation + +### 4. Documented Hyperparameter Tuning Scripts + +**hyperparameter_tuning.py** +- Simple, quick version for Heart Disease dataset +- Good for testing and learning +- ~170 lines + +**hyperparameter_optimization.py** - COMPREHENSIVE +- Full grid search across multiple datasets +- Early stopping, plotting, baseline comparison +- ~400 lines +- Recommended for production use + +## Impact + +### Lines of Code Reduced +- Dataset loading: ~100 lines eliminated +- Evaluation scripts: ~40 lines of stubs replaced with delegation +- Total: ~140 lines of duplicated code removed + +### Maintainability Improvements +- Single source of truth for dataset loading +- Single source of truth for model evaluation +- Clear documentation prevents confusion +- Easier to add new datasets or metrics + +### Backward Compatibility +- All existing workflows preserved +- Fallback implementations for testing +- No breaking changes to public interfaces + +## Usage Guidelines + +### Dataset Loading +```python +# Use shared loader +from quantum-ai.src.dataset_loader import load_dataset, preprocess_for_qubits + +X, y, feature_names = load_dataset("ionosphere", return_feature_names=True) +X_train, X_val, scaler, pca = preprocess_for_qubits(X_train, X_val, n_qubits=4) +``` + +### Model Evaluation +```python +# Use evaluate_lora_model.py directly or via wrappers +python scripts/evaluate_lora_model.py --model MODEL_PATH --dataset DATASET_PATH + +# Or use the wrapper for backward compatibility +python scripts/evaluate_model.py --model MODEL_PATH --dataset DATASET_PATH +``` + +### Quantum AI Selection +- **New projects**: Use `ai-projects/quantum-ml/` (PennyLane) +- **Qiskit required**: Use `AI/ai-projects/quantum-ml/` (Qiskit) +- **Not sure**: Use `ai-projects/quantum-ml/` (primary implementation) + +### Hyperparameter Tuning +- **Quick testing**: Use `hyperparameter_tuning.py` +- **Production/research**: Use `hyperparameter_optimization.py` + +## Testing + +No automated tests were run due to missing dependencies in the sandbox environment. +Manual code review and validation performed. + +## Future Recommendations + +1. Consider extracting common training patterns into shared modules +2. Standardize import patterns across scripts +3. Add unit tests for dataset_loader.py +4. Document when to use each hyperparameter tuning script +5. Consider deprecating AI/quantum-ai if Qiskit is no longer needed + +## Files Changed + +### Created +- `ai-projects/quantum-ml/src/dataset_loader.py` (135 lines) + +### Modified +- `ai-projects/quantum-ml/hyperparameter_optimization.py` +- `ai-projects/quantum-ml/hyperparameter_tuning.py` +- `ai-projects/quantum-ml/web_app.py` +- `scripts/evaluate_model.py` +- `scripts/evaluation_script.py` +- `AI/ai-projects/quantum-ml/README.md` +- `ai-projects/quantum-ml/README.md` + +### Total Impact +- 7 files modified +- 1 file created +- ~190 lines of duplication eliminated +- Documentation clarity improved +- Maintainability significantly enhanced diff --git a/docs/global-upgrade.css b/docs/global-upgrade.css index a6d363285..3cf448816 100644 --- a/docs/global-upgrade.css +++ b/docs/global-upgrade.css @@ -1,947 +1,947 @@ -@import url('https://fonts.googleapis.com/css2?family=Fraunces:opsz,wght@9..144,500;9..144,600;9..144,700&family=IBM+Plex+Mono:wght@400;500&family=Plus+Jakarta+Sans:wght@400;500;600;700;800&family=Sora:wght@400;500;600;700;800&family=Space+Grotesk:wght@500;600;700&display=swap'); 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-} - -:where(.sidebar-item, .page-link, .feature-card, .system-card, .welcome-card, .card, .btn, .button, .plan-card, .link-card, .route-card, .doc-link) { - will-change: transform; -} - -:where(p, li, label, .subtitle, .section-subtitle) { - line-height: 1.65; -} - -:where(input, select, textarea) { - border-radius: 10px !important; - border: 1px solid var(--upgrade-line); -} - -:where(.btn-primary, .button-primary, .btn-plan.primary, .plan-cta, .button-link.primary) { - background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)) !important; - color: #f6fffd !important; - border-color: transparent !important; -} - -:where(.btn-outline, .button-link, .btn-plan.outline, .tab-btn, .object-btn, .doc-link, .sidebar-item) { - border-color: var(--upgrade-line); -} - -:where(.sidebar-item.active, .sidebar-item.is-active, .tab-btn.active, .object-btn.active) { - background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)) !important; - color: #f4fffc !important; -} - -.site-upgrade-skip { - position: fixed; - top: 12px; - left: 12px; - z-index: 100000; - padding: 10px 14px; - border-radius: 10px; - background: #11283a; - color: #f7f2e8; - text-decoration: none; - font-weight: 700; - transform: translateY(-160%); - transition: transform 160ms ease; - box-shadow: var(--pass-shadow-strong, var(--upgrade-shadow)); -} - -.site-upgrade-skip:focus-visible { - transform: translateY(0); -} - -.site-upgrade-reveal { - opacity: 0; - transform: translateY(18px) scale(0.985); - transition: - opacity var(--pass-reveal-ms, 520ms) cubic-bezier(0.22, 1, 0.36, 1) var(--site-upgrade-delay, 0ms), - transform var(--pass-reveal-ms, 520ms) cubic-bezier(0.22, 1, 0.36, 1) var(--site-upgrade-delay, 0ms); -} - -.site-upgrade-reveal.is-visible { - opacity: 1; - transform: none; -} - -/* Chat personality */ -body.site-kind-chat { - --site-body-font: "Sora", "Plus Jakarta Sans", sans-serif; - --site-display-font: "Sora", "Space Grotesk", sans-serif; - --upgrade-accent: #0f93c5; - --upgrade-accent-strong: #0a6f94; - --upgrade-accent-alt: #ff7a4f; - --upgrade-line: rgba(15, 147, 197, 0.24); - --upgrade-focus: #ffd166; - --upgrade-shadow-soft: 0 14px 30px rgba(15, 147, 197, 0.22); - --upgrade-atmo-a: rgba(15, 147, 197, 0.36); - --upgrade-atmo-b: rgba(255, 122, 79, 0.3); - --upgrade-atmo-c: rgba(21, 31, 61, 0.26); -} - -body.site-kind-chat :where(.chat-container, .message-content, .chat-header, .provider-info) { - border-radius: 18px; -} - -body.site-kind-chat .chat-header { - background: linear-gradient(135deg, #0f93c5, #0a6f94) !important; -} - -body.site-kind-chat .message.user .message-content { - background: linear-gradient(135deg, #0f93c5, #0a6f94) !important; -} - -body.site-kind-chat .message-content a { - color: #0f93c5 !important; - font-weight: 600; -} - -/* Aria personality */ -body.site-kind-aria { - --site-display-font: "Fraunces", "Space Grotesk", serif; - --upgrade-accent: #2e9d83; - --upgrade-accent-strong: #24735f; - --upgrade-accent-alt: #f08947; - --upgrade-line: rgba(46, 157, 131, 0.24); - --upgrade-focus: #f3c15f; - --upgrade-shadow-soft: 0 14px 30px rgba(46, 157, 131, 0.2); - --upgrade-atmo-a: rgba(82, 182, 157, 0.3); - --upgrade-atmo-b: rgba(240, 137, 71, 0.24); - --upgrade-atmo-c: rgba(116, 191, 130, 0.22); -} - -body.site-kind-aria :where(.stage-container, .object-manager, .control-panel, .stage, .object-btn) { - border-color: rgba(46, 157, 131, 0.24) !important; -} - -body.site-kind-aria h1 { - letter-spacing: -0.03em; -} - -/* Dashboard personality */ -body.site-kind-dashboard { - --site-body-font: "Sora", "Plus Jakarta Sans", sans-serif; - --site-display-font: "Space Grotesk", "Sora", sans-serif; - --upgrade-accent: #2f7be7; - --upgrade-accent-strong: #235fbb; - --upgrade-accent-alt: #17a67f; - --upgrade-line: rgba(47, 123, 231, 0.24); - --upgrade-focus: #ffd57a; - --upgrade-shadow-soft: 0 14px 32px rgba(47, 123, 231, 0.22); - --upgrade-atmo-a: rgba(47, 123, 231, 0.35); - --upgrade-atmo-b: rgba(23, 166, 127, 0.26); - --upgrade-atmo-c: rgba(21, 45, 92, 0.24); -} - -body.site-kind-dashboard :where(.card, .system-card, .metric-card, .jobs-section, .gpu-status, .tab-nav, .tab-btn) { - border-color: rgba(47, 123, 231, 0.18) !important; -} - -body.site-kind-dashboard :where(.status-badge.online, .tab-btn.active) { - background: linear-gradient(135deg, #2f7be7, #235fbb) !important; - color: #f7fbff !important; -} - -/* Store personality */ -body.site-kind-store { - --site-display-font: "Fraunces", "Space Grotesk", serif; - --upgrade-accent: #d0602f; - --upgrade-accent-strong: #a84824; - --upgrade-accent-alt: #13876f; - --upgrade-line: rgba(208, 96, 47, 0.24); - --upgrade-focus: #ffd38a; - --upgrade-shadow-soft: 0 14px 30px rgba(208, 96, 47, 0.2); - --upgrade-atmo-a: rgba(208, 96, 47, 0.3); - --upgrade-atmo-b: rgba(19, 135, 111, 0.22); - --upgrade-atmo-c: rgba(209, 162, 71, 0.24); -} - -body.site-kind-store :where(.product-card, .category-card, .feature-card, .plan-card, .route-card, .link-card, .hero-card) { - border-color: rgba(208, 96, 47, 0.18) !important; -} - -body.site-kind-store :where(.btn-primary, .btn-plan.primary, .plan-cta, .button-link.primary) { - background: linear-gradient(135deg, #d0602f, #a84824) !important; -} - -/* Docs personality */ -body.site-kind-docs { - --site-display-font: "Fraunces", "Space Grotesk", serif; - --upgrade-accent: #187f78; - --upgrade-accent-strong: #125f5a; - --upgrade-accent-alt: #b56a2d; - --upgrade-line: rgba(24, 127, 120, 0.22); - --upgrade-focus: #f3c471; - --upgrade-shadow-soft: 0 14px 28px rgba(24, 127, 120, 0.18); - --upgrade-atmo-a: rgba(24, 127, 120, 0.26); - --upgrade-atmo-b: rgba(181, 106, 45, 0.2); - --upgrade-atmo-c: rgba(205, 180, 129, 0.26); -} - -body.site-kind-docs :where(.sidebar-item.active, .sidebar-item.is-active) { - background: linear-gradient(135deg, #187f78, #125f5a) !important; -} - -body.site-kind-docs :where(.md-content h1, .content-markdown h1, .doc-header h1) { - letter-spacing: -0.035em; -} - -/* Pass debug overlay */ -.site-pass-debug { - position: fixed; - right: 14px; - bottom: 14px; - z-index: 100002; - display: grid; - justify-items: end; - gap: 8px; - pointer-events: none; -} - -.site-pass-debug__toggle, -.site-pass-debug__panel { - pointer-events: auto; -} - -.site-pass-debug__toggle { - min-height: 38px; - padding: 0 12px; - border: 1px solid var(--upgrade-line); - border-radius: 999px; - background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)); - color: #f6fffd; - font: 700 0.8rem/1 var(--site-body-font); - letter-spacing: 0.04em; - text-transform: uppercase; - cursor: pointer; - box-shadow: var(--pass-shadow-soft, var(--upgrade-shadow-soft)); -} - -.site-pass-debug__panel { - position: relative; - width: min(320px, calc(100vw - 28px)); - padding: 12px; - border-radius: 16px; - border: 1px solid var(--upgrade-line); - background: color-mix(in srgb, #ffffff 88%, var(--upgrade-accent) 12%); - color: var(--upgrade-ink); - box-shadow: var(--pass-shadow-strong, var(--upgrade-shadow)); - backdrop-filter: blur(10px); -} - -.site-pass-debug__head { - display: flex; - align-items: center; - justify-content: space-between; - gap: 8px; - cursor: grab; - user-select: none; - border-radius: 8px; - padding: 2px 0; - margin: -2px 0 4px; - transition: background 0.12s; -} - -.site-pass-debug__head:hover { - background: color-mix(in srgb, #ffffff 80%, var(--upgrade-accent) 20%); -} - -.site-pass-debug.is-dragging .site-pass-debug__head, -.site-pass-debug.is-dragging { - cursor: grabbing; - user-select: none; -} - -.site-pass-debug__drag-grip { - font-size: 1rem; - opacity: 0.45; - flex-shrink: 0; - pointer-events: none; -} - -.site-pass-debug__resize-handle { - position: absolute; - left: -5px; - top: 16px; - bottom: 16px; - width: 10px; - cursor: ew-resize; - border-radius: 5px; - background: transparent; - transition: background 0.14s; - z-index: 1; -} - -.site-pass-debug__resize-handle:hover, -.site-pass-debug.is-resizing .site-pass-debug__resize-handle { - background: color-mix(in srgb, transparent 60%, var(--upgrade-accent) 40%); -} - -.site-pass-debug.is-resizing { - cursor: ew-resize; - user-select: none; -} - -.site-pass-debug__section-body { - overflow: hidden; -} - -.site-pass-debug__head strong { - font-family: var(--site-display-font); - font-size: 0.98rem; -} - -.site-pass-debug__kind { - display: inline-flex; - align-items: center; - justify-content: center; - min-height: 24px; - padding: 0 8px; - border-radius: 999px; - background: color-mix(in srgb, var(--upgrade-accent) 20%, #ffffff 80%); - color: var(--upgrade-accent-strong); - font-size: 0.7rem; - font-weight: 700; - letter-spacing: 0.06em; -} - -.site-pass-debug__label { - display: block; - margin-top: 10px; - font-size: 0.8rem; - color: var(--upgrade-ink-soft); -} - -.site-pass-debug__range { - width: 100%; - margin-top: 8px; - accent-color: var(--upgrade-accent); -} - -.site-pass-debug__row { - display: grid; - grid-template-columns: 1fr auto auto; - gap: 8px; - margin-top: 8px; -} - -.site-pass-debug__row--compact { - grid-template-columns: repeat(2, minmax(0, 1fr)); -} - -.site-pass-debug__row--speed { - grid-template-columns: auto 1fr; - align-items: center; -} - -.site-pass-debug__inline-label { - font-size: 0.76rem; - font-weight: 600; - color: var(--upgrade-ink-soft); -} - -.site-pass-debug__preset-grid { - display: grid; - grid-template-columns: repeat(4, minmax(0, 1fr)); - gap: 8px; - margin-top: 8px; -} - -.site-pass-debug__snapshot-grid { - display: grid; - grid-template-columns: repeat(3, minmax(0, 1fr)); - gap: 8px; - margin-top: 8px; -} - -.site-pass-debug__share-row { - margin-top: 8px; -} - -.site-pass-debug__number { - min-height: 34px; - padding: 0 8px; - border: 1px solid var(--upgrade-line); - border-radius: 10px; - background: #ffffff; - color: var(--upgrade-ink); - font-weight: 600; -} - -.site-pass-debug__speed { - min-height: 34px; - padding: 0 8px; - border: 1px solid var(--upgrade-line); - border-radius: 10px; - background: #ffffff; - color: var(--upgrade-ink); - font-weight: 600; -} - -.site-pass-debug__share { - width: 100%; - min-height: 34px; - padding: 0 8px; - border: 1px solid var(--upgrade-line); - border-radius: 10px; - background: #ffffff; - color: var(--upgrade-ink); - font: 600 0.72rem/1 var(--site-body-font); -} - -.site-pass-debug__share:focus-visible, -.site-pass-debug__speed:focus-visible, -.site-pass-debug__number:focus-visible { - outline: none; - border-color: color-mix(in srgb, var(--upgrade-accent-strong) 48%, #ffffff 52%); - box-shadow: 0 0 0 2px color-mix(in srgb, var(--upgrade-focus) 36%, #ffffff 64%); -} - -.site-pass-debug__btn { - min-height: 34px; - padding: 0 10px; - border: 1px solid var(--upgrade-line); - border-radius: 10px; - background: #ffffff; - color: var(--upgrade-ink); - font: 600 0.78rem/1 var(--site-body-font); - cursor: pointer; - transition: background 180ms ease, color 180ms ease, border-color 180ms ease, transform 180ms ease; -} - -.site-pass-debug__btn:hover, -.site-pass-debug__btn:focus-visible { - background: color-mix(in srgb, #ffffff 84%, var(--upgrade-accent) 16%); - outline: none; -} - -.site-pass-debug__btn:active { - transform: translateY(1px); -} - -.site-pass-debug__btn:disabled { - opacity: 0.5; - cursor: not-allowed; - transform: none; -} - -.site-pass-debug__btn.is-active { - border-color: color-mix(in srgb, var(--upgrade-accent-strong) 44%, #ffffff 56%); - background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)); - color: #f6fffd; -} - -.site-pass-debug__meta, -.site-pass-debug__hint { - margin: 8px 0 0; - font-size: 0.75rem; - color: var(--upgrade-ink-soft); -} - -.site-pass-debug__hint { - margin-top: 4px; - opacity: 0.88; -} - -.site-pass-debug__compare, -.site-pass-debug__stack, -.site-pass-debug__sweep, -.site-pass-debug__source { - color: var(--upgrade-accent-strong); - font-weight: 700; - letter-spacing: 0.01em; -} - -.site-pass-debug__timeline { - list-style: none; - margin: 8px 0 0; - padding: 6px; - border-radius: 10px; - border: 1px solid var(--upgrade-line); - background: color-mix(in srgb, #ffffff 90%, var(--upgrade-accent) 10%); - max-height: 124px; - overflow: auto; - display: grid; - gap: 5px; -} - -.site-pass-debug__timeline-item { - display: grid; - grid-template-columns: auto 1fr; - gap: 8px; - align-items: center; - font-size: 0.68rem; - color: var(--upgrade-ink-soft); -} - -.site-pass-debug__timeline-item.is-empty { - grid-template-columns: 1fr; - font-style: italic; - opacity: 0.9; -} - -.site-pass-debug__timeline-time { - color: var(--upgrade-accent-strong); - font-weight: 700; -} - -.site-pass-debug__timeline-detail { - overflow: hidden; - text-overflow: ellipsis; - white-space: nowrap; -} - -.site-pass-debug__timeline-item--restore { - cursor: pointer; - border-radius: 6px; - padding: 2px 4px; - margin: -2px -4px; - transition: background 0.12s; -} - -.site-pass-debug__timeline-item--restore:hover { - background: color-mix(in srgb, #ffffff 65%, var(--upgrade-accent) 35%); -} - -.site-pass-debug__timeline-item--restore:focus-visible { - outline: 2px solid var(--upgrade-accent-strong); - outline-offset: 1px; -} - -.site-pass-debug__timeline-item--restore:active { - opacity: 0.72; -} - -.site-pass-debug__section { - border: 1px solid var(--upgrade-line); - border-radius: 8px; - margin: 6px 0 0; -} - -.site-pass-debug__section[open] { - padding-bottom: 4px; -} - -.site-pass-debug__section-head { - list-style: none; - padding: 5px 8px; - font-size: 0.74rem; - font-weight: 700; - letter-spacing: 0.03em; - text-transform: uppercase; - color: var(--upgrade-ink-soft); - cursor: pointer; - user-select: none; - display: grid; - grid-template-columns: 1fr auto; - align-items: center; - border-radius: 7px; - background: color-mix(in srgb, #ffffff 92%, var(--upgrade-accent) 8%); -} - -.site-pass-debug__section-head::-webkit-details-marker { - display: none; -} - -.site-pass-debug__section-head::marker { - content: none; -} - -.site-pass-debug__section-head::after { - content: "\25B8"; - font-size: 0.7rem; - opacity: 0.55; - transition: transform 0.15s; - justify-self: end; -} - -.site-pass-debug__section[open] > .site-pass-debug__section-head::after { - transform: rotate(90deg); -} - -.site-pass-debug__section[open] > .site-pass-debug__section-head { - border-radius: 7px 7px 0 0; - border-bottom: 1px solid var(--upgrade-line); -} - -.site-pass-debug__section-head:focus-visible { - outline: 2px solid var(--upgrade-accent-strong); - outline-offset: -2px; - border-radius: 7px; -} - -.site-pass-debug__section > .site-pass-debug__timeline { - margin: 4px 0 0; -} - -/* --- Shortcut cheat-sheet overlay --- */ -.site-pass-shortcut-overlay { - position: fixed; - inset: 0; - z-index: 100010; - display: flex; - align-items: center; - justify-content: center; - background: rgba(0, 0, 0, 0.45); - backdrop-filter: blur(3px); - animation: site-pass-overlay-in 0.14s ease; -} - -@keyframes site-pass-overlay-in { - from { opacity: 0; } - to { opacity: 1; } -} - -.site-pass-shortcut-overlay__box { - position: relative; - width: min(440px, calc(100vw - 32px)); - max-height: calc(100vh - 48px); - overflow-y: auto; - padding: 20px 22px 16px; - border-radius: 16px; - border: 1px solid var(--upgrade-line); - background: color-mix(in srgb, #ffffff 92%, var(--upgrade-accent) 8%); - box-shadow: 0 8px 40px rgba(0,0,0,0.22); - color: var(--upgrade-ink); -} - -.site-pass-shortcut-overlay__head { - display: flex; - align-items: center; - justify-content: space-between; - margin-bottom: 12px; -} - -.site-pass-shortcut-overlay__head strong { - font-family: var(--site-display-font); - font-size: 1rem; -} - -.site-pass-shortcut-overlay__close { - min-height: 32px; - min-width: 32px; - border: none; - background: transparent; - cursor: pointer; - font-size: 1rem; - border-radius: 8px; - color: var(--upgrade-ink-soft); - display: flex; - align-items: center; - justify-content: center; -} - -.site-pass-shortcut-overlay__close:hover { - background: color-mix(in srgb, #ffffff 70%, var(--upgrade-accent) 30%); -} - -.site-pass-shortcut-overlay__close:focus-visible { - outline: 2px solid var(--upgrade-accent-strong); -} - -.site-pass-shortcut-overlay__list { - display: grid; - grid-template-columns: auto 1fr; - gap: 4px 16px; - margin: 0; - padding: 0; - font-size: 0.82rem; -} - -.site-pass-shortcut-overlay__list dt { - font-family: var(--site-code-font, monospace); - font-weight: 700; - color: var(--upgrade-accent-strong); - white-space: nowrap; - padding: 2px 6px; - border-radius: 4px; - background: color-mix(in srgb, #ffffff 75%, var(--upgrade-accent) 25%); - align-self: center; -} - -.site-pass-shortcut-overlay__list dd { - margin: 0; - align-self: center; - color: var(--upgrade-ink-soft); -} - -.site-pass-shortcut-overlay__tip { - margin: 12px 0 0; - font-size: 0.74rem; - color: var(--upgrade-ink-soft); - font-style: italic; - text-align: center; -} - -.site-pass-debug__badge { - display: inline-block; - min-width: 22px; - padding: 1px 5px; - border-radius: 999px; - background: rgba(255, 255, 255, 0.22); - font: 700 0.72rem/1.4 var(--site-body-font); - vertical-align: middle; - text-align: center; - letter-spacing: 0; -} - -.site-pass-debug.is-open .site-pass-debug__badge { - opacity: 0.55; -} - -.site-pass-debug__opacity { - width: 72px; - text-align: right; - font-size: 0.82rem; - padding: 2px 6px; - border: 1px solid var(--upgrade-line); - border-radius: 8px; - background: color-mix(in srgb, #ffffff 75%, var(--upgrade-accent) 25%); - color: var(--upgrade-ink); -} - -.site-pass-debug__opacity:focus { - outline: 2px solid var(--upgrade-accent-strong); - outline-offset: 1px; -} - -.site-pass-debug__btn--toggle.is-active { - background: color-mix(in srgb, var(--upgrade-accent) 85%, #000 15%); - color: #fff; - border-color: var(--upgrade-accent-strong); -} - -.site-pass-debug.is-open .site-pass-debug__toggle { - filter: saturate(1.08) brightness(1.02); -} - -@media (max-width: 900px) { - :where(.btn, .button, .tab-btn, .object-btn, button) { - min-height: 42px; - } - - .site-pass-debug { - right: 10px; - bottom: 10px; - } - - .site-pass-debug__panel { - width: min(94vw, 320px); - } - - .site-pass-debug__preset-grid { - grid-template-columns: repeat(2, minmax(0, 1fr)); - } - - .site-pass-debug__snapshot-grid { - grid-template-columns: repeat(2, minmax(0, 1fr)); - } - - body::before { - inset: -30%; - opacity: 0.74; - } - - body::after { - opacity: 0.26; - } -} - -@media (prefers-reduced-motion: reduce) { - * { - animation: none !important; - transition: none !important; - scroll-behavior: auto !important; - } - - .site-upgrade-reveal { - opacity: 1 !important; - transform: none !important; - } - - .site-pass-debug__toggle, - .site-pass-debug__btn { - transition: none !important; - } -} +@import url('https://fonts.googleapis.com/css2?family=Fraunces:opsz,wght@9..144,500;9..144,600;9..144,700&family=IBM+Plex+Mono:wght@400;500&family=Plus+Jakarta+Sans:wght@400;500;600;700;800&family=Sora:wght@400;500;600;700;800&family=Space+Grotesk:wght@500;600;700&display=swap'); + +:root { + --site-body-font: "Plus Jakarta Sans", "Instrument Sans", "Segoe UI", Roboto, sans-serif; + --site-display-font: "Space Grotesk", "Plus Jakarta Sans", sans-serif; + --site-code-font: "IBM Plex Mono", "Cascadia Code", Consolas, monospace; + + --upgrade-ink: #132437; + --upgrade-ink-soft: #4f6378; + --upgrade-accent: #0f9d89; + --upgrade-accent-strong: #0a6f63; + --upgrade-accent-alt: #d1603f; + --upgrade-gold: #d1a247; + --upgrade-line: rgba(19, 36, 55, 0.16); + --upgrade-focus: #f2b84a; + --upgrade-shadow-soft: 0 10px 24px rgba(19, 36, 55, 0.12); + --upgrade-shadow: 0 18px 40px rgba(19, 36, 55, 0.16); + --upgrade-radius-md: 14px; + --upgrade-radius-lg: 20px; + + --pass-intensity: 0; + --pass-grid-opacity: 0.34; + --pass-radius-boost: 0px; + --pass-focus-width: 3px; + --pass-letter-shift: -0.02em; + --pass-card-raise: 0px; + --pass-reveal-ms: 520ms; + --pass-shadow-soft: var(--upgrade-shadow-soft); + --pass-shadow-strong: var(--upgrade-shadow); + + --upgrade-atmo-a: rgba(15, 157, 137, 0.26); + --upgrade-atmo-b: rgba(209, 96, 63, 0.22); + --upgrade-atmo-c: rgba(209, 162, 71, 0.24); + + --brand: var(--upgrade-accent); + --brand-light: #4bc2b0; + --brand-dark: var(--upgrade-accent-strong); + --accent: var(--upgrade-accent-alt); + + --primary: var(--upgrade-accent); + --primary-light: #42c2ae; + --primary-dark: var(--upgrade-accent-strong); + + --primary-color: var(--upgrade-accent); + --primary-light-color: #42c2ae; + --primary-dark-color: var(--upgrade-accent-strong); + --secondary-color: #245978; + --accent-color: var(--upgrade-accent-alt); + + --accent-primary: var(--upgrade-accent); + --accent-secondary: #1f6a86; + + --color-primary: var(--upgrade-accent); + --color-primary-dark: var(--upgrade-accent-strong); + --color-accent: var(--upgrade-gold); + --color-secondary: #1d3046; + --color-text: var(--upgrade-ink); + --color-text-light: var(--upgrade-ink-soft); +} + +html { + text-rendering: optimizeLegibility; + -webkit-font-smoothing: antialiased; +} + +body { + font-family: var(--site-body-font) !important; + min-height: 100vh; + position: relative; + isolation: isolate; +} + +body::before, +body::after { + content: ""; + position: fixed; + pointer-events: none; + transition: opacity 260ms ease; +} + +body::before { + inset: -18%; + z-index: -2; + background: + radial-gradient(circle at 10% 14%, var(--upgrade-atmo-a), transparent 34%), + radial-gradient(circle at 86% 10%, var(--upgrade-atmo-b), transparent 28%), + radial-gradient(circle at 54% 88%, var(--upgrade-atmo-c), transparent 40%); + animation: siteAtmosphereFloat 26s ease-in-out infinite alternate; +} + +body::after { + inset: 0; + z-index: -1; + opacity: var(--pass-grid-opacity, 0.34); + background-image: + linear-gradient(rgba(19, 36, 55, 0.05) 1px, transparent 1px), + linear-gradient(90deg, rgba(19, 36, 55, 0.05) 1px, transparent 1px); + background-size: 30px 30px; + mask-image: linear-gradient(180deg, rgba(0, 0, 0, 0.86), transparent 94%); +} + +@keyframes siteAtmosphereFloat { + 0% { + transform: translate3d(0, 0, 0) scale(1); + } + 50% { + transform: translate3d(-1.8%, -1.2%, 0) scale(1.02); + } + 100% { + transform: translate3d(1.4%, 1%, 0) scale(1.01); + } +} + +h1, +h2, +h3, +h4, +h5, +h6, +.gradient-text, +.hero h1, +.section-title, +.card-title, +.doc-header h1, +.brand-copy strong { + font-family: var(--site-display-font); + letter-spacing: var(--pass-letter-shift, -0.02em); +} + +code, +pre, +kbd, +samp { + font-family: var(--site-code-font); +} + +::selection { + background: rgba(15, 157, 137, 0.24); + color: #0f1d30; +} + +* { + scrollbar-width: thin; + scrollbar-color: rgba(15, 157, 137, 0.55) transparent; +} + +*::-webkit-scrollbar { + width: 9px; + height: 9px; +} + +*::-webkit-scrollbar-thumb { + border-radius: 999px; + background: linear-gradient(180deg, rgba(15, 157, 137, 0.9), rgba(10, 111, 99, 0.9)); +} + +*::-webkit-scrollbar-track { + background: transparent; +} + +:where(a, button, .btn, .button, .tab-btn, .object-btn, input, select, textarea) { + transition: color 180ms ease, background-color 180ms ease, border-color 180ms ease, box-shadow 180ms ease, transform 180ms ease; +} + +:where(a, button, .btn, .button, .tab-btn, .object-btn, input, select, textarea):focus-visible { + outline: var(--pass-focus-width, 3px) solid var(--upgrade-focus); + outline-offset: 2px; +} + +:where(.btn, .button, .tab-btn, .object-btn, button) { + border-radius: 12px; + font-weight: 600; +} + +:where(.btn:hover, .button:hover, .tab-btn:hover, .object-btn:hover, button:hover) { + transform: translateY(calc(-1px - var(--pass-card-raise, 0px))); +} + +:where(.card, .feature-card, .system-card, .welcome-card, .glass-panel, .section, .hero-banner, .jobs-section, .gpu-status, .stage-container, .object-manager, .chat-container, .page-link, .resource-group, .route-card, .flow-card, .search-panel, .terminal, .run-aside, .run-copy, .hero-card, .plan-card, .link-card, .signal-card, .sidebar-card, .content-shell, .category-card) { + border-radius: calc(var(--upgrade-radius-lg) + var(--pass-radius-boost, 0px)); +} + +:where(.card, .feature-card, .system-card, .welcome-card, .glass-panel, .chat-container, .object-manager, .page-link, .resource-group, .route-card, .flow-card, .search-panel, .terminal, .run-aside, .run-copy, .hero-card, .plan-card, .link-card, .signal-card, .sidebar-card, .content-shell, .category-card) { + box-shadow: var(--pass-shadow-soft, var(--upgrade-shadow-soft)) !important; +} + +:where(.sidebar-item, .page-link, .feature-card, .system-card, .welcome-card, .card, .btn, .button, .plan-card, .link-card, .route-card, .doc-link) { + will-change: transform; +} + +:where(p, li, label, .subtitle, .section-subtitle) { + line-height: 1.65; +} + +:where(input, select, textarea) { + border-radius: 10px !important; + border: 1px solid var(--upgrade-line); +} + +:where(.btn-primary, .button-primary, .btn-plan.primary, .plan-cta, .button-link.primary) { + background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)) !important; + color: #f6fffd !important; + border-color: transparent !important; +} + +:where(.btn-outline, .button-link, .btn-plan.outline, .tab-btn, .object-btn, .doc-link, .sidebar-item) { + border-color: var(--upgrade-line); +} + +:where(.sidebar-item.active, .sidebar-item.is-active, .tab-btn.active, .object-btn.active) { + background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)) !important; + color: #f4fffc !important; +} + +.site-upgrade-skip { + position: fixed; + top: 12px; + left: 12px; + z-index: 100000; + padding: 10px 14px; + border-radius: 10px; + background: #11283a; + color: #f7f2e8; + text-decoration: none; + font-weight: 700; + transform: translateY(-160%); + transition: transform 160ms ease; + box-shadow: var(--pass-shadow-strong, var(--upgrade-shadow)); +} + +.site-upgrade-skip:focus-visible { + transform: translateY(0); +} + +.site-upgrade-reveal { + opacity: 0; + transform: translateY(18px) scale(0.985); + transition: + opacity var(--pass-reveal-ms, 520ms) cubic-bezier(0.22, 1, 0.36, 1) var(--site-upgrade-delay, 0ms), + transform var(--pass-reveal-ms, 520ms) cubic-bezier(0.22, 1, 0.36, 1) var(--site-upgrade-delay, 0ms); +} + +.site-upgrade-reveal.is-visible { + opacity: 1; + transform: none; +} + +/* Chat personality */ +body.site-kind-chat { + --site-body-font: "Sora", "Plus Jakarta Sans", sans-serif; + --site-display-font: "Sora", "Space Grotesk", sans-serif; + --upgrade-accent: #0f93c5; + --upgrade-accent-strong: #0a6f94; + --upgrade-accent-alt: #ff7a4f; + --upgrade-line: rgba(15, 147, 197, 0.24); + --upgrade-focus: #ffd166; + --upgrade-shadow-soft: 0 14px 30px rgba(15, 147, 197, 0.22); + --upgrade-atmo-a: rgba(15, 147, 197, 0.36); + --upgrade-atmo-b: rgba(255, 122, 79, 0.3); + --upgrade-atmo-c: rgba(21, 31, 61, 0.26); +} + +body.site-kind-chat :where(.chat-container, .message-content, .chat-header, .provider-info) { + border-radius: 18px; +} + +body.site-kind-chat .chat-header { + background: linear-gradient(135deg, #0f93c5, #0a6f94) !important; +} + +body.site-kind-chat .message.user .message-content { + background: linear-gradient(135deg, #0f93c5, #0a6f94) !important; +} + +body.site-kind-chat .message-content a { + color: #0f93c5 !important; + font-weight: 600; +} + +/* Aria personality */ +body.site-kind-aria { + --site-display-font: "Fraunces", "Space Grotesk", serif; + --upgrade-accent: #2e9d83; + --upgrade-accent-strong: #24735f; + --upgrade-accent-alt: #f08947; + --upgrade-line: rgba(46, 157, 131, 0.24); + --upgrade-focus: #f3c15f; + --upgrade-shadow-soft: 0 14px 30px rgba(46, 157, 131, 0.2); + --upgrade-atmo-a: rgba(82, 182, 157, 0.3); + --upgrade-atmo-b: rgba(240, 137, 71, 0.24); + --upgrade-atmo-c: rgba(116, 191, 130, 0.22); +} + +body.site-kind-aria :where(.stage-container, .object-manager, .control-panel, .stage, .object-btn) { + border-color: rgba(46, 157, 131, 0.24) !important; +} + +body.site-kind-aria h1 { + letter-spacing: -0.03em; +} + +/* Dashboard personality */ +body.site-kind-dashboard { + --site-body-font: "Sora", "Plus Jakarta Sans", sans-serif; + --site-display-font: "Space Grotesk", "Sora", sans-serif; + --upgrade-accent: #2f7be7; + --upgrade-accent-strong: #235fbb; + --upgrade-accent-alt: #17a67f; + --upgrade-line: rgba(47, 123, 231, 0.24); + --upgrade-focus: #ffd57a; + --upgrade-shadow-soft: 0 14px 32px rgba(47, 123, 231, 0.22); + --upgrade-atmo-a: rgba(47, 123, 231, 0.35); + --upgrade-atmo-b: rgba(23, 166, 127, 0.26); + --upgrade-atmo-c: rgba(21, 45, 92, 0.24); +} + +body.site-kind-dashboard :where(.card, .system-card, .metric-card, .jobs-section, .gpu-status, .tab-nav, .tab-btn) { + border-color: rgba(47, 123, 231, 0.18) !important; +} + +body.site-kind-dashboard :where(.status-badge.online, .tab-btn.active) { + background: linear-gradient(135deg, #2f7be7, #235fbb) !important; + color: #f7fbff !important; +} + +/* Store personality */ +body.site-kind-store { + --site-display-font: "Fraunces", "Space Grotesk", serif; + --upgrade-accent: #d0602f; + --upgrade-accent-strong: #a84824; + --upgrade-accent-alt: #13876f; + --upgrade-line: rgba(208, 96, 47, 0.24); + --upgrade-focus: #ffd38a; + --upgrade-shadow-soft: 0 14px 30px rgba(208, 96, 47, 0.2); + --upgrade-atmo-a: rgba(208, 96, 47, 0.3); + --upgrade-atmo-b: rgba(19, 135, 111, 0.22); + --upgrade-atmo-c: rgba(209, 162, 71, 0.24); +} + +body.site-kind-store :where(.product-card, .category-card, .feature-card, .plan-card, .route-card, .link-card, .hero-card) { + border-color: rgba(208, 96, 47, 0.18) !important; +} + +body.site-kind-store :where(.btn-primary, .btn-plan.primary, .plan-cta, .button-link.primary) { + background: linear-gradient(135deg, #d0602f, #a84824) !important; +} + +/* Docs personality */ +body.site-kind-docs { + --site-display-font: "Fraunces", "Space Grotesk", serif; + --upgrade-accent: #187f78; + --upgrade-accent-strong: #125f5a; + --upgrade-accent-alt: #b56a2d; + --upgrade-line: rgba(24, 127, 120, 0.22); + --upgrade-focus: #f3c471; + --upgrade-shadow-soft: 0 14px 28px rgba(24, 127, 120, 0.18); + --upgrade-atmo-a: rgba(24, 127, 120, 0.26); + --upgrade-atmo-b: rgba(181, 106, 45, 0.2); + --upgrade-atmo-c: rgba(205, 180, 129, 0.26); +} + +body.site-kind-docs :where(.sidebar-item.active, .sidebar-item.is-active) { + background: linear-gradient(135deg, #187f78, #125f5a) !important; +} + +body.site-kind-docs :where(.md-content h1, .content-markdown h1, .doc-header h1) { + letter-spacing: -0.035em; +} + +/* Pass debug overlay */ +.site-pass-debug { + position: fixed; + right: 14px; + bottom: 14px; + z-index: 100002; + display: grid; + justify-items: end; + gap: 8px; + pointer-events: none; +} + +.site-pass-debug__toggle, +.site-pass-debug__panel { + pointer-events: auto; +} + +.site-pass-debug__toggle { + min-height: 38px; + padding: 0 12px; + border: 1px solid var(--upgrade-line); + border-radius: 999px; + background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)); + color: #f6fffd; + font: 700 0.8rem/1 var(--site-body-font); + letter-spacing: 0.04em; + text-transform: uppercase; + cursor: pointer; + box-shadow: var(--pass-shadow-soft, var(--upgrade-shadow-soft)); +} + +.site-pass-debug__panel { + position: relative; + width: min(320px, calc(100vw - 28px)); + padding: 12px; + border-radius: 16px; + border: 1px solid var(--upgrade-line); + background: color-mix(in srgb, #ffffff 88%, var(--upgrade-accent) 12%); + color: var(--upgrade-ink); + box-shadow: var(--pass-shadow-strong, var(--upgrade-shadow)); + backdrop-filter: blur(10px); +} + +.site-pass-debug__head { + display: flex; + align-items: center; + justify-content: space-between; + gap: 8px; + cursor: grab; + user-select: none; + border-radius: 8px; + padding: 2px 0; + margin: -2px 0 4px; + transition: background 0.12s; +} + +.site-pass-debug__head:hover { + background: color-mix(in srgb, #ffffff 80%, var(--upgrade-accent) 20%); +} + +.site-pass-debug.is-dragging .site-pass-debug__head, +.site-pass-debug.is-dragging { + cursor: grabbing; + user-select: none; +} + +.site-pass-debug__drag-grip { + font-size: 1rem; + opacity: 0.45; + flex-shrink: 0; + pointer-events: none; +} + +.site-pass-debug__resize-handle { + position: absolute; + left: -5px; + top: 16px; + bottom: 16px; + width: 10px; + cursor: ew-resize; + border-radius: 5px; + background: transparent; + transition: background 0.14s; + z-index: 1; +} + +.site-pass-debug__resize-handle:hover, +.site-pass-debug.is-resizing .site-pass-debug__resize-handle { + background: color-mix(in srgb, transparent 60%, var(--upgrade-accent) 40%); +} + +.site-pass-debug.is-resizing { + cursor: ew-resize; + user-select: none; +} + +.site-pass-debug__section-body { + overflow: hidden; +} + +.site-pass-debug__head strong { + font-family: var(--site-display-font); + font-size: 0.98rem; +} + +.site-pass-debug__kind { + display: inline-flex; + align-items: center; + justify-content: center; + min-height: 24px; + padding: 0 8px; + border-radius: 999px; + background: color-mix(in srgb, var(--upgrade-accent) 20%, #ffffff 80%); + color: var(--upgrade-accent-strong); + font-size: 0.7rem; + font-weight: 700; + letter-spacing: 0.06em; +} + +.site-pass-debug__label { + display: block; + margin-top: 10px; + font-size: 0.8rem; + color: var(--upgrade-ink-soft); +} + +.site-pass-debug__range { + width: 100%; + margin-top: 8px; + accent-color: var(--upgrade-accent); +} + +.site-pass-debug__row { + display: grid; + grid-template-columns: 1fr auto auto; + gap: 8px; + margin-top: 8px; +} + +.site-pass-debug__row--compact { + grid-template-columns: repeat(2, minmax(0, 1fr)); +} + +.site-pass-debug__row--speed { + grid-template-columns: auto 1fr; + align-items: center; +} + +.site-pass-debug__inline-label { + font-size: 0.76rem; + font-weight: 600; + color: var(--upgrade-ink-soft); +} + +.site-pass-debug__preset-grid { + display: grid; + grid-template-columns: repeat(4, minmax(0, 1fr)); + gap: 8px; + margin-top: 8px; +} + +.site-pass-debug__snapshot-grid { + display: grid; + grid-template-columns: repeat(3, minmax(0, 1fr)); + gap: 8px; + margin-top: 8px; +} + +.site-pass-debug__share-row { + margin-top: 8px; +} + +.site-pass-debug__number { + min-height: 34px; + padding: 0 8px; + border: 1px solid var(--upgrade-line); + border-radius: 10px; + background: #ffffff; + color: var(--upgrade-ink); + font-weight: 600; +} + +.site-pass-debug__speed { + min-height: 34px; + padding: 0 8px; + border: 1px solid var(--upgrade-line); + border-radius: 10px; + background: #ffffff; + color: var(--upgrade-ink); + font-weight: 600; +} + +.site-pass-debug__share { + width: 100%; + min-height: 34px; + padding: 0 8px; + border: 1px solid var(--upgrade-line); + border-radius: 10px; + background: #ffffff; + color: var(--upgrade-ink); + font: 600 0.72rem/1 var(--site-body-font); +} + +.site-pass-debug__share:focus-visible, +.site-pass-debug__speed:focus-visible, +.site-pass-debug__number:focus-visible { + outline: none; + border-color: color-mix(in srgb, var(--upgrade-accent-strong) 48%, #ffffff 52%); + box-shadow: 0 0 0 2px color-mix(in srgb, var(--upgrade-focus) 36%, #ffffff 64%); +} + +.site-pass-debug__btn { + min-height: 34px; + padding: 0 10px; + border: 1px solid var(--upgrade-line); + border-radius: 10px; + background: #ffffff; + color: var(--upgrade-ink); + font: 600 0.78rem/1 var(--site-body-font); + cursor: pointer; + transition: background 180ms ease, color 180ms ease, border-color 180ms ease, transform 180ms ease; +} + +.site-pass-debug__btn:hover, +.site-pass-debug__btn:focus-visible { + background: color-mix(in srgb, #ffffff 84%, var(--upgrade-accent) 16%); + outline: none; +} + +.site-pass-debug__btn:active { + transform: translateY(1px); +} + +.site-pass-debug__btn:disabled { + opacity: 0.5; + cursor: not-allowed; + transform: none; +} + +.site-pass-debug__btn.is-active { + border-color: color-mix(in srgb, var(--upgrade-accent-strong) 44%, #ffffff 56%); + background: linear-gradient(135deg, var(--upgrade-accent), var(--upgrade-accent-strong)); + color: #f6fffd; +} + +.site-pass-debug__meta, +.site-pass-debug__hint { + margin: 8px 0 0; + font-size: 0.75rem; + color: var(--upgrade-ink-soft); +} + +.site-pass-debug__hint { + margin-top: 4px; + opacity: 0.88; +} + +.site-pass-debug__compare, +.site-pass-debug__stack, +.site-pass-debug__sweep, +.site-pass-debug__source { + color: var(--upgrade-accent-strong); + font-weight: 700; + letter-spacing: 0.01em; +} + +.site-pass-debug__timeline { + list-style: none; + margin: 8px 0 0; + padding: 6px; + border-radius: 10px; + border: 1px solid var(--upgrade-line); + background: color-mix(in srgb, #ffffff 90%, var(--upgrade-accent) 10%); + max-height: 124px; + overflow: auto; + display: grid; + gap: 5px; +} + +.site-pass-debug__timeline-item { + display: grid; + grid-template-columns: auto 1fr; + gap: 8px; + align-items: center; + font-size: 0.68rem; + color: var(--upgrade-ink-soft); +} + +.site-pass-debug__timeline-item.is-empty { + grid-template-columns: 1fr; + font-style: italic; + opacity: 0.9; +} + +.site-pass-debug__timeline-time { + color: var(--upgrade-accent-strong); + font-weight: 700; +} + +.site-pass-debug__timeline-detail { + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.site-pass-debug__timeline-item--restore { + cursor: pointer; + border-radius: 6px; + padding: 2px 4px; + margin: -2px -4px; + transition: background 0.12s; +} + +.site-pass-debug__timeline-item--restore:hover { + background: color-mix(in srgb, #ffffff 65%, var(--upgrade-accent) 35%); +} + +.site-pass-debug__timeline-item--restore:focus-visible { + outline: 2px solid var(--upgrade-accent-strong); + outline-offset: 1px; +} + +.site-pass-debug__timeline-item--restore:active { + opacity: 0.72; +} + +.site-pass-debug__section { + border: 1px solid var(--upgrade-line); + border-radius: 8px; + margin: 6px 0 0; +} + +.site-pass-debug__section[open] { + padding-bottom: 4px; +} + +.site-pass-debug__section-head { + list-style: none; + padding: 5px 8px; + font-size: 0.74rem; + font-weight: 700; + letter-spacing: 0.03em; + text-transform: uppercase; + color: var(--upgrade-ink-soft); + cursor: pointer; + user-select: none; + display: grid; + grid-template-columns: 1fr auto; + align-items: center; + border-radius: 7px; + background: color-mix(in srgb, #ffffff 92%, var(--upgrade-accent) 8%); +} + +.site-pass-debug__section-head::-webkit-details-marker { + display: none; +} + +.site-pass-debug__section-head::marker { + content: none; +} + +.site-pass-debug__section-head::after { + content: "\25B8"; + font-size: 0.7rem; + opacity: 0.55; + transition: transform 0.15s; + justify-self: end; +} + +.site-pass-debug__section[open] > .site-pass-debug__section-head::after { + transform: rotate(90deg); +} + +.site-pass-debug__section[open] > .site-pass-debug__section-head { + border-radius: 7px 7px 0 0; + border-bottom: 1px solid var(--upgrade-line); +} + +.site-pass-debug__section-head:focus-visible { + outline: 2px solid var(--upgrade-accent-strong); + outline-offset: -2px; + border-radius: 7px; +} + +.site-pass-debug__section > .site-pass-debug__timeline { + margin: 4px 0 0; +} + +/* --- Shortcut cheat-sheet overlay --- */ +.site-pass-shortcut-overlay { + position: fixed; + inset: 0; + z-index: 100010; + display: flex; + align-items: center; + justify-content: center; + background: rgba(0, 0, 0, 0.45); + backdrop-filter: blur(3px); + animation: site-pass-overlay-in 0.14s ease; +} + +@keyframes site-pass-overlay-in { + from { opacity: 0; } + to { opacity: 1; } +} + +.site-pass-shortcut-overlay__box { + position: relative; + width: min(440px, calc(100vw - 32px)); + max-height: calc(100vh - 48px); + overflow-y: auto; + padding: 20px 22px 16px; + border-radius: 16px; + border: 1px solid var(--upgrade-line); + background: color-mix(in srgb, #ffffff 92%, var(--upgrade-accent) 8%); + box-shadow: 0 8px 40px rgba(0,0,0,0.22); + color: var(--upgrade-ink); +} + +.site-pass-shortcut-overlay__head { + display: flex; + align-items: center; + justify-content: space-between; + margin-bottom: 12px; +} + +.site-pass-shortcut-overlay__head strong { + font-family: var(--site-display-font); + font-size: 1rem; +} + +.site-pass-shortcut-overlay__close { + min-height: 32px; + min-width: 32px; + border: none; + background: transparent; + cursor: pointer; + font-size: 1rem; + border-radius: 8px; + color: var(--upgrade-ink-soft); + display: flex; + align-items: center; + justify-content: center; +} + +.site-pass-shortcut-overlay__close:hover { + background: color-mix(in srgb, #ffffff 70%, var(--upgrade-accent) 30%); +} + +.site-pass-shortcut-overlay__close:focus-visible { + outline: 2px solid var(--upgrade-accent-strong); +} + +.site-pass-shortcut-overlay__list { + display: grid; + grid-template-columns: auto 1fr; + gap: 4px 16px; + margin: 0; + padding: 0; + font-size: 0.82rem; +} + +.site-pass-shortcut-overlay__list dt { + font-family: var(--site-code-font, monospace); + font-weight: 700; + color: var(--upgrade-accent-strong); + white-space: nowrap; + padding: 2px 6px; + border-radius: 4px; + background: color-mix(in srgb, #ffffff 75%, var(--upgrade-accent) 25%); + align-self: center; +} + +.site-pass-shortcut-overlay__list dd { + margin: 0; + align-self: center; + color: var(--upgrade-ink-soft); +} + +.site-pass-shortcut-overlay__tip { + margin: 12px 0 0; + font-size: 0.74rem; + color: var(--upgrade-ink-soft); + font-style: italic; + text-align: center; +} + +.site-pass-debug__badge { + display: inline-block; + min-width: 22px; + padding: 1px 5px; + border-radius: 999px; + background: rgba(255, 255, 255, 0.22); + font: 700 0.72rem/1.4 var(--site-body-font); + vertical-align: middle; + text-align: center; + letter-spacing: 0; +} + +.site-pass-debug.is-open .site-pass-debug__badge { + opacity: 0.55; +} + +.site-pass-debug__opacity { + width: 72px; + text-align: right; + font-size: 0.82rem; + padding: 2px 6px; + border: 1px solid var(--upgrade-line); + border-radius: 8px; + background: color-mix(in srgb, #ffffff 75%, var(--upgrade-accent) 25%); + color: var(--upgrade-ink); +} + +.site-pass-debug__opacity:focus { + outline: 2px solid var(--upgrade-accent-strong); + outline-offset: 1px; +} + +.site-pass-debug__btn--toggle.is-active { + background: color-mix(in srgb, var(--upgrade-accent) 85%, #000 15%); + color: #fff; + border-color: var(--upgrade-accent-strong); +} + +.site-pass-debug.is-open .site-pass-debug__toggle { + filter: saturate(1.08) brightness(1.02); +} + +@media (max-width: 900px) { + :where(.btn, .button, .tab-btn, .object-btn, button) { + min-height: 42px; + } + + .site-pass-debug { + right: 10px; + bottom: 10px; + } + + .site-pass-debug__panel { + width: min(94vw, 320px); + } + + .site-pass-debug__preset-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .site-pass-debug__snapshot-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + body::before { + inset: -30%; + opacity: 0.74; + } + + body::after { + opacity: 0.26; + } +} + +@media (prefers-reduced-motion: reduce) { + * { + animation: none !important; + transition: none !important; + scroll-behavior: auto !important; + } + + .site-upgrade-reveal { + opacity: 1 !important; + transform: none !important; + } + + .site-pass-debug__toggle, + .site-pass-debug__btn { + transition: none !important; + } +} diff --git a/docs/global-upgrade.js b/docs/global-upgrade.js index 6638053cc..81a3a0789 100644 --- a/docs/global-upgrade.js +++ b/docs/global-upgrade.js @@ -1,1978 +1,1978 @@ -(function () { - "use strict"; - - if (window.__ariaSiteUpgradeLoaded) { - return; - } - window.__ariaSiteUpgradeLoaded = true; - - document.documentElement.classList.add("site-upgraded"); - - var PASS_MIN = 1; - var PASS_MAX = 100; - var PASS_DEFAULT = 100; - var PASS_STORAGE_KEY = "siteUpgradePasses"; - var DEBUG_OPEN_STORAGE_KEY = "siteUpgradePassDebugOpen"; - var PASS_AUTOPLAY_INTERVAL_MS = 70; - var PASS_AUTOPLAY_MIN_MS = 25; - var PASS_AUTOPLAY_MAX_MS = 1000; - var PASS_AUTOPLAY_STORAGE_KEY = "siteUpgradePassSweepMs"; - var PASS_SNAPSHOT_STORAGE_KEY = "siteUpgradePassSnapshots"; - var PASS_UNDO_STORAGE_KEY = "siteUpgradePassUndoStack"; - var PASS_SECTIONS_STORAGE_KEY = "siteUpgradePassSections"; - var PASS_POSITION_STORAGE_KEY = "siteUpgradePassPosition"; - var PASS_WIDTH_STORAGE_KEY = "siteUpgradePassWidth"; - var PASS_PANEL_STYLE_STORAGE_KEY = "siteUpgradePassPanelStyle"; - var PASS_HISTORY_MAX = 10; - - function detectSiteKind() { - var path = (window.location.pathname || "").toLowerCase(); - var href = (window.location.href || "").toLowerCase(); - var target = path + " " + href; - - if (target.includes("/chat/") || target.includes("chat/index") || target.endsWith("/chat")) { - return "chat"; - } - - if (target.includes("/aria/") || target.includes("auto-execute") || target.includes("aria/index")) { - return "aria"; - } - - if (target.includes("/dashboard/") || target.includes("dashboard/index") || target.includes("dashboard/hub")) { - return "dashboard"; - } - - if (target.includes("/store/") || target.includes("products") || target.includes("product.html")) { - return "store"; - } - - if (target.includes("documentation") || target.includes("/docs/") || target.endsWith("/docs") || target.includes("readme_pages")) { - return "docs"; - } - - if (target.includes("/quantum/") || target.includes("/monetization/") || target.includes("llm-maker") || target.includes("/mount/")) { - return "dashboard"; - } - - return "core"; - } - - function applySiteKindClass() { - var kind = detectSiteKind(); - var root = document.documentElement; - var body = document.body; - - root.setAttribute("data-site-kind", kind); - if (body) { - body.setAttribute("data-site-kind", kind); - body.classList.add("site-kind-" + kind); - } - - return kind; - } - - function clamp(value, min, max) { - return Math.max(min, Math.min(max, value)); - } - - function getPassStorageKey(kind) { - return PASS_STORAGE_KEY + ":" + kind; - } - - function getPassSpeedStorageKey(kind) { - return PASS_AUTOPLAY_STORAGE_KEY + ":" + kind; - } - - function getPassSnapshotStorageKey(kind) { - return PASS_SNAPSHOT_STORAGE_KEY + ":" + kind; - } - - function getPassUndoStorageKey(kind) { - return PASS_UNDO_STORAGE_KEY + ":" + kind; - } - - function getPassSectionsStorageKey(kind) { - return PASS_SECTIONS_STORAGE_KEY + ":" + kind; - } - - function getPassPositionStorageKey(kind) { - return PASS_POSITION_STORAGE_KEY + ":" + kind; - } - - function getPassWidthStorageKey(kind) { - return PASS_WIDTH_STORAGE_KEY + ":" + kind; - } - - function getPassPanelStyleStorageKey(kind) { - return PASS_PANEL_STYLE_STORAGE_KEY + ":" + kind; - } - - function shouldOpenDebugFromQuery() { - var params = new URLSearchParams(window.location.search || ""); - var raw = params.get("passpanel") || params.get("debugpasses") || params.get("passdebug"); - if (!raw) { - return null; - } - - var value = String(raw).toLowerCase(); - if (value === "0" || value === "false" || value === "off" || value === "hide") { - return false; - } - - return true; - } - - function shouldAutoplayFromQuery() { - var params = new URLSearchParams(window.location.search || ""); - var raw = params.get("passauto") || params.get("passautoplay") || params.get("autosweep"); - if (!raw) { - return null; - } - - return toOptionalBool(raw); - } - - function getConfiguredPassCount(kind) { - var params = new URLSearchParams(window.location.search || ""); - var stateFromQuery = getPassStateFromQuery(); - var queryRaw = params.get("passes") || params.get("pass") || (stateFromQuery && stateFromQuery.passCount); - var savedRaw = null; - - try { - if (window.localStorage) { - savedRaw = localStorage.getItem(getPassStorageKey(kind)) || localStorage.getItem(PASS_STORAGE_KEY); - } - } catch (error) { - savedRaw = null; - } - - var parsed = Number(queryRaw || savedRaw || PASS_DEFAULT); - if (!Number.isFinite(parsed)) { - parsed = PASS_DEFAULT; - } - - return Math.round(clamp(parsed, PASS_MIN, PASS_MAX)); - } - - function getConfiguredAutoplayInterval(kind) { - var params = new URLSearchParams(window.location.search || ""); - var stateFromQuery = getPassStateFromQuery(); - var queryRaw = params.get("passsweepms") || params.get("passautoplayms") || params.get("passspeed") || (stateFromQuery && stateFromQuery.sweepMs); - var savedRaw = null; - - try { - if (window.localStorage) { - savedRaw = localStorage.getItem(getPassSpeedStorageKey(kind)) || localStorage.getItem(PASS_AUTOPLAY_STORAGE_KEY); - } - } catch (error) { - savedRaw = null; - } - - var parsed = Number(queryRaw || savedRaw || PASS_AUTOPLAY_INTERVAL_MS); - if (!Number.isFinite(parsed)) { - parsed = PASS_AUTOPLAY_INTERVAL_MS; - } - - return Math.round(clamp(parsed, PASS_AUTOPLAY_MIN_MS, PASS_AUTOPLAY_MAX_MS)); - } - - function normalizePassSource(source) { - var normalized = String(source || "manual"); - - if (normalized.indexOf("shortcut-") === 0) { - normalized = "key " + normalized.slice("shortcut-".length); - } - - return normalized.replace(/-/g, " "); - } - - function toValidPass(rawValue) { - var parsed = Number(rawValue); - if (!Number.isFinite(parsed)) { - return null; - } - - return Math.round(clamp(parsed, PASS_MIN, PASS_MAX)); - } - - function toValidSpeed(rawValue) { - var parsed = Number(rawValue); - if (!Number.isFinite(parsed)) { - return null; - } - - return Math.round(clamp(parsed, PASS_AUTOPLAY_MIN_MS, PASS_AUTOPLAY_MAX_MS)); - } - - function toOptionalBool(rawValue) { - if (typeof rawValue === "boolean") { - return rawValue; - } - - if (rawValue === null || rawValue === undefined) { - return null; - } - - var normalized = String(rawValue).toLowerCase(); - if (normalized === "1" || normalized === "true" || normalized === "on" || normalized === "open" || normalized === "yes") { - return true; - } - - if (normalized === "0" || normalized === "false" || normalized === "off" || normalized === "close" || normalized === "closed" || normalized === "hide" || normalized === "no") { - return false; - } - - return null; - } - - function normalizePassState(rawState) { - if (!rawState || typeof rawState !== "object") { - return null; - } - - var passCount = toValidPass(rawState.passCount !== undefined ? rawState.passCount : (rawState.pass !== undefined ? rawState.pass : rawState.p)); - var sweepMs = toValidSpeed(rawState.sweepMs !== undefined ? rawState.sweepMs : (rawState.speed !== undefined ? rawState.speed : rawState.ms)); - var snapshotA = toValidPass(rawState.snapshotA !== undefined ? rawState.snapshotA : rawState.a); - var snapshotB = toValidPass(rawState.snapshotB !== undefined ? rawState.snapshotB : rawState.b); - var open = toOptionalBool(rawState.open); - var auto = toOptionalBool(rawState.auto); - - if (passCount === null && sweepMs === null && snapshotA === null && snapshotB === null && open === null && auto === null) { - return null; - } - - return { - passCount: passCount, - sweepMs: sweepMs, - snapshotA: snapshotA, - snapshotB: snapshotB, - open: open, - auto: auto - }; - } - - function getPassStateFromSearchParams(params) { - if (!params) { - return null; - } - - var raw = params.get("passstate"); - if (!raw) { - return null; - } - - try { - return normalizePassState(JSON.parse(raw)); - } catch (error) { - return null; - } - } - - function getPassStateFromQuery() { - return getPassStateFromSearchParams(new URLSearchParams(window.location.search || "")); - } - - function parsePassStateFromInput(rawInput) { - if (!rawInput) { - return null; - } - - var value = String(rawInput).trim(); - if (!value) { - return null; - } - - try { - var url = new URL(value, window.location.href); - var fromUrl = getPassStateFromSearchParams(url.searchParams); - if (fromUrl) { - return fromUrl; - } - } catch (error) { - // Not a URL, continue parsing. - } - - try { - var queryLike = value.charAt(0) === "?" ? value.slice(1) : value; - var params = new URLSearchParams(queryLike); - var fromParams = getPassStateFromSearchParams(params); - if (fromParams) { - return fromParams; - } - } catch (error) { - // Not query-like content, continue parsing. - } - - try { - return normalizePassState(JSON.parse(value)); - } catch (error) { - return null; - } - } - - function interpolate(start, end, t) { - return start + (end - start) * t; - } - - function getKindShadowColor(kind) { - switch (kind) { - case "chat": - return [15, 147, 197]; - case "aria": - return [46, 157, 131]; - case "dashboard": - return [47, 123, 231]; - case "store": - return [208, 96, 47]; - case "docs": - return [24, 127, 120]; - default: - return [19, 36, 55]; - } - } - - function setPassVariables(kind, passIndex, totalPasses) { - var root = document.documentElement; - var t = clamp(passIndex / totalPasses, 0, 1); - var color = getKindShadowColor(kind); - - var gridOpacity = interpolate(0.24, 0.46, t); - var radiusBoost = interpolate(0, 7, t); - var focusWidth = interpolate(2.5, 4.2, t); - var letterShift = interpolate(-0.012, -0.036, t); - var cardRaise = interpolate(0.25, 1.9, t); - var revealMs = Math.round(interpolate(430, 690, t)); - - var softY = interpolate(8, 24, t); - var softBlur = interpolate(22, 54, t); - var softAlpha = interpolate(0.14, 0.33, t); - - var strongY = interpolate(14, 38, t); - var strongBlur = interpolate(34, 78, t); - var strongAlpha = interpolate(0.2, 0.42, t); - - root.style.setProperty("--pass-intensity", t.toFixed(4)); - root.style.setProperty("--pass-grid-opacity", gridOpacity.toFixed(3)); - root.style.setProperty("--pass-radius-boost", radiusBoost.toFixed(2) + "px"); - root.style.setProperty("--pass-focus-width", focusWidth.toFixed(2) + "px"); - root.style.setProperty("--pass-letter-shift", letterShift.toFixed(4) + "em"); - root.style.setProperty("--pass-card-raise", cardRaise.toFixed(2) + "px"); - root.style.setProperty("--pass-reveal-ms", revealMs + "ms"); - root.style.setProperty( - "--pass-shadow-soft", - "0 " + softY.toFixed(2) + "px " + softBlur.toFixed(2) + "px rgba(" + color[0] + ", " + color[1] + ", " + color[2] + ", " + softAlpha.toFixed(3) + ")" - ); - root.style.setProperty( - "--pass-shadow-strong", - "0 " + strongY.toFixed(2) + "px " + strongBlur.toFixed(2) + "px rgba(" + color[0] + ", " + color[1] + ", " + color[2] + ", " + strongAlpha.toFixed(3) + ")" - ); - } - - function emitPassChange(kind, passCount, source) { - var detail = { - kind: kind, - passCount: passCount, - passes: passCount, - source: source || "manual" - }; - - document.dispatchEvent(new CustomEvent("site-upgrade:pass-change", { detail: detail })); - window.dispatchEvent(new CustomEvent("site-upgrade:pass-change", { detail: detail })); - } - - function runPolishPasses(kind, requestedPassCount, source) { - var passCount = Math.round(clamp(Number(requestedPassCount), PASS_MIN, PASS_MAX)); - - for (var pass = 1; pass <= passCount; pass += 1) { - setPassVariables(kind, pass, passCount); - } - - document.documentElement.setAttribute("data-polish-passes", String(passCount)); - if (document.body) { - document.body.setAttribute("data-polish-passes", String(passCount)); - } - - emitPassChange(kind, passCount, source); - - return passCount; - } - - function trySaveValue(key, value) { - try { - if (window.localStorage) { - localStorage.setItem(key, value); - } - } catch (error) { - // Ignore storage errors in strict/private browsing contexts. - } - } - - function tryLoadValue(key) { - try { - return window.localStorage ? localStorage.getItem(key) : null; - } catch (error) { - return null; - } - } - - function createPassDebugPanel(kind, initialPassCount) { - if (!document.body || document.querySelector(".site-pass-debug")) { - return; - } - - var panel = document.createElement("aside"); - panel.className = "site-pass-debug"; - panel.setAttribute("aria-label", "Polish pass controls"); - panel.innerHTML = "" - + "" - + ""; - - document.body.appendChild(panel); - - var toggle = panel.querySelector(".site-pass-debug__toggle"); - var bodyPanel = panel.querySelector(".site-pass-debug__panel"); - var kindChip = panel.querySelector(".site-pass-debug__kind"); - var range = panel.querySelector("#site-pass-debug-range"); - var number = panel.querySelector("#site-pass-debug-number"); - var speedInput = panel.querySelector("#site-pass-debug-speed"); - var shareInput = panel.querySelector("#site-pass-debug-share"); - var value = panel.querySelector(".site-pass-debug__value"); - var compareValue = panel.querySelector(".site-pass-debug__compare"); - var stackValue = panel.querySelector(".site-pass-debug__stack"); - var sweepValue = panel.querySelector(".site-pass-debug__sweep"); - var sourceValue = panel.querySelector(".site-pass-debug__source"); - var timeline = panel.querySelector(".site-pass-debug__timeline"); - var stepButtons = panel.querySelectorAll(".site-pass-debug__btn[data-step]"); - var presetButtons = panel.querySelectorAll(".site-pass-debug__btn[data-preset]"); - var resetButton = panel.querySelector('.site-pass-debug__btn[data-action="reset"]'); - var closeButton = panel.querySelector('.site-pass-debug__btn[data-action="close"]'); - var defaultButton = panel.querySelector('.site-pass-debug__btn[data-action="default"]'); - var autoplayButton = panel.querySelector('.site-pass-debug__btn[data-action="autoplay"]'); - var undoButton = panel.querySelector('.site-pass-debug__btn[data-action="undo"]'); - var redoButton = panel.querySelector('.site-pass-debug__btn[data-action="redo"]'); - var saveAButton = panel.querySelector('.site-pass-debug__btn[data-action="save-a"]'); - var saveBButton = panel.querySelector('.site-pass-debug__btn[data-action="save-b"]'); - var applyAButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-a"]'); - var applyBButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-b"]'); - var toggleAbButton = panel.querySelector('.site-pass-debug__btn[data-action="toggle-ab"]'); - var copyUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="copy-url"]'); - var applyUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-url"]'); - var pushUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="push-url"]'); - var clearHistoryButton = panel.querySelector('.site-pass-debug__btn[data-action="clear-history"]'); - var exportJsonButton = panel.querySelector('.site-pass-debug__btn[data-action="export-json"]'); - var toggleBlurButton = panel.querySelector('.site-pass-debug__btn[data-action="toggle-blur"]'); - var resetAppearanceButton = panel.querySelector('.site-pass-debug__btn[data-action="reset-appearance"]'); - var opacityInput = panel.querySelector("#site-pass-debug-opacity"); - var badge = panel.querySelector(".site-pass-debug__badge"); - var dragHead = panel.querySelector(".site-pass-debug__head"); - var resizeHandle = panel.querySelector(".site-pass-debug__resize-handle"); - - var queryState = getPassStateFromQuery(); - var queryAuto = shouldAutoplayFromQuery(); - var shouldAutoStart = queryState && queryState.auto !== null ? queryState.auto : queryAuto; - var configuredPasses = getConfiguredPassCount(kind); - var currentPasses = initialPassCount; - var isOpen = false; - var isAutoplay = false; - var autoDirection = 1; - var autoplayTimer = null; - var autoplayIntervalMs = queryState && Number.isFinite(queryState.sweepMs) - ? queryState.sweepMs - : getConfiguredAutoplayInterval(kind); - var undoStack = (function () { - var raw = tryLoadValue(getPassUndoStorageKey(kind)); - if (!raw) { return []; } - try { - var parsed = JSON.parse(raw); - return Array.isArray(parsed) - ? parsed.filter(function (v) { return Number.isFinite(v); }) - : []; - } catch (e) { return []; } - })(); - var redoStack = []; - var passHistory = []; - var snapshots = (function () { - var raw = tryLoadValue(getPassSnapshotStorageKey(kind)); - if (!raw) { - return { a: null, b: null }; - } - - try { - var parsed = JSON.parse(raw); - return { - a: toValidPass(parsed && parsed.a), - b: toValidPass(parsed && parsed.b) - }; - } catch (error) { - return { a: null, b: null }; - } - })(); - - if (queryState && Number.isFinite(queryState.passCount)) { - currentPasses = queryState.passCount; - } - - if (queryState && queryState.snapshotA !== null) { - snapshots.a = queryState.snapshotA; - } - - if (queryState && queryState.snapshotB !== null) { - snapshots.b = queryState.snapshotB; - } - - var savedOpen = tryLoadValue(DEBUG_OPEN_STORAGE_KEY); - var queryOpen = shouldOpenDebugFromQuery(); - if (queryState && queryState.open !== null) { - isOpen = queryState.open; - } else if (queryOpen !== null) { - isOpen = queryOpen; - } else if (savedOpen === "1") { - isOpen = true; - } - - kindChip.textContent = kind.toUpperCase(); - - function persistPass(passCount) { - trySaveValue(getPassStorageKey(kind), String(passCount)); - trySaveValue(PASS_STORAGE_KEY, String(passCount)); - } - - function persistSnapshots() { - trySaveValue(getPassSnapshotStorageKey(kind), JSON.stringify({ - a: snapshots.a, - b: snapshots.b - })); - } - - function persistUndoStacks() { - trySaveValue(getPassUndoStorageKey(kind), JSON.stringify(undoStack)); - } - - function serializePassState() { - return { - pass: currentPasses, - speed: autoplayIntervalMs, - a: snapshots.a, - b: snapshots.b, - open: isOpen, - auto: isAutoplay - }; - } - - function buildShareUrl() { - var url = new URL(window.location.href); - var params = url.searchParams; - var cleanupKeys = [ - "pass", - "passes", - "passpanel", - "debugpasses", - "passdebug", - "passsweepms", - "passautoplayms", - "passspeed", - "passauto", - "passautoplay", - "autosweep", - "passstate" - ]; - - cleanupKeys.forEach(function (key) { - params.delete(key); - }); - - params.set("passstate", JSON.stringify(serializePassState())); - return url.toString(); - } - - function syncShareUrlField() { - if (!shareInput) { - return; - } - - shareInput.value = buildShareUrl(); - } - - function syncSourceLabel(source) { - sourceValue.textContent = normalizePassSource(source); - } - - function syncSweepLabel() { - if (!sweepValue) { - return; - } - - sweepValue.textContent = (isAutoplay ? "running" : "idle") + " @ " + autoplayIntervalMs + "ms"; - } - - function syncUndoState() { - if (!stackValue) { - return; - } - - stackValue.textContent = "undo " + undoStack.length + " | redo " + redoStack.length; - undoButton.disabled = undoStack.length === 0; - redoButton.disabled = redoStack.length === 0; - } - - function shouldTrackUndo(source, nextPass) { - if (!Number.isFinite(nextPass) || nextPass === currentPasses) { - return false; - } - - var normalized = String(source || "panel"); - if (normalized === "autoplay" || normalized.indexOf("undo") !== -1 || normalized.indexOf("redo") !== -1 || normalized === "state-init" || normalized === "event") { - return false; - } - - return true; - } - - function pushUndo(previousPass) { - undoStack.push(previousPass); - if (undoStack.length > PASS_MAX) { - undoStack.shift(); - } - redoStack = []; - persistUndoStacks(); - syncUndoState(); - } - - function runUndo(source) { - if (!undoStack.length) { - return; - } - - var targetPass = undoStack.pop(); - if (targetPass === currentPasses) { - persistUndoStacks(); - syncUndoState(); - return; - } - - redoStack.push(currentPasses); - if (redoStack.length > PASS_MAX) { - redoStack.shift(); - } - - persistUndoStacks(); - applyPassCount(targetPass, true, source || "undo"); - syncUndoState(); - } - - function runRedo(source) { - if (!redoStack.length) { - return; - } - - var targetPass = redoStack.pop(); - if (targetPass === currentPasses) { - persistUndoStacks(); - syncUndoState(); - return; - } - - undoStack.push(currentPasses); - if (undoStack.length > PASS_MAX) { - undoStack.shift(); - } - - persistUndoStacks(); - applyPassCount(targetPass, true, source || "redo"); - syncUndoState(); - } - - function renderHistory() { - if (!timeline) { - return; - } - - timeline.innerHTML = ""; - - if (!passHistory.length) { - var empty = document.createElement("li"); - empty.className = "site-pass-debug__timeline-item is-empty"; - empty.textContent = "No pass changes yet"; - timeline.appendChild(empty); - return; - } - - passHistory.forEach(function (entry) { - var item = document.createElement("li"); - item.className = "site-pass-debug__timeline-item site-pass-debug__timeline-item--restore"; - item.tabIndex = 0; - item.setAttribute("role", "button"); - item.title = "Restore pass " + entry.pass; - item.setAttribute("aria-label", "Restore " + entry.pass + " passes (" + entry.source + ")"); - - var stamp = document.createElement("span"); - stamp.className = "site-pass-debug__timeline-time"; - stamp.textContent = entry.stamp; - - var detail = document.createElement("span"); - detail.className = "site-pass-debug__timeline-detail"; - var deltaText = entry.delta === null ? "" : " (" + (entry.delta >= 0 ? "+" : "") + entry.delta + ")"; - detail.textContent = entry.source + " -> " + entry.pass + deltaText; - - item.appendChild(stamp); - item.appendChild(detail); - - (function (passValue) { - function handleRestore(evt) { - if (evt.type === "keydown" && evt.key !== "Enter" && evt.key !== " ") { - return; - } - if (evt.key === " ") { - evt.preventDefault(); - } - applyPassCount(passValue, true, "timeline-restore"); - } - item.addEventListener("click", handleRestore); - item.addEventListener("keydown", handleRestore); - })(entry.pass); - - timeline.appendChild(item); - }); - } - - function pushHistory(passCount, source) { - var normalizedSource = normalizePassSource(source || "event"); - var now = new Date(); - var stamp = now.toLocaleTimeString([], { - hour: "2-digit", - minute: "2-digit", - second: "2-digit" - }); - - var top = passHistory[0]; - if (top && normalizedSource === "autoplay" && top.source === "autoplay") { - var autoplayPrev = top.pass; - top.pass = passCount; - top.stamp = stamp; - top.delta = passCount - autoplayPrev; - renderHistory(); - return; - } - - if (top && top.pass === passCount && top.source === normalizedSource) { - return; - } - - var previousPass = top ? top.pass : null; - passHistory.unshift({ - pass: passCount, - source: normalizedSource, - stamp: stamp, - delta: previousPass === null ? null : passCount - previousPass - }); - - if (passHistory.length > PASS_HISTORY_MAX) { - passHistory.length = PASS_HISTORY_MAX; - } - - renderHistory(); - } - - function clearHistory(sourceLabel) { - passHistory = []; - renderHistory(); - syncSourceLabel(sourceLabel || "history-clear"); - } - - function syncSnapshotUI(passCount) { - var hasA = Number.isFinite(snapshots.a); - var hasB = Number.isFinite(snapshots.b); - - applyAButton.disabled = !hasA; - applyBButton.disabled = !hasB; - toggleAbButton.disabled = !hasA && !hasB; - - saveAButton.classList.toggle("is-active", hasA && snapshots.a === passCount); - saveBButton.classList.toggle("is-active", hasB && snapshots.b === passCount); - - if (hasA || hasB) { - var nextLabel = "A"; - if (hasA && hasB) { - nextLabel = passCount === snapshots.a ? "B" : "A"; - } else if (hasB && !hasA) { - nextLabel = "B"; - } - toggleAbButton.textContent = "Flip " + nextLabel; - } else { - toggleAbButton.textContent = "Flip A/B"; - } - - if (hasA && hasB) { - var delta = snapshots.b - snapshots.a; - compareValue.textContent = "A " + snapshots.a + " | B " + snapshots.b + " | Δ " + (delta >= 0 ? "+" : "") + delta; - } else { - compareValue.textContent = "A " + (hasA ? snapshots.a : "-") + " | B " + (hasB ? snapshots.b : "-"); - } - - syncShareUrlField(); - } - - function applySnapshotToggle(source) { - var hasA = Number.isFinite(snapshots.a); - var hasB = Number.isFinite(snapshots.b); - - if (!hasA && !hasB) { - return; - } - - if (hasA && hasB) { - var target = currentPasses === snapshots.a ? snapshots.b : snapshots.a; - applyPassCount(target, true, source || "snapshot-toggle"); - return; - } - - applyPassCount(hasA ? snapshots.a : snapshots.b, true, source || "snapshot-toggle"); - } - - function applyStatePayload(rawState, sourceLabel) { - var state = normalizePassState(rawState); - if (!state) { - return false; - } - - if (state.sweepMs !== null) { - setAutoplayInterval(state.sweepMs, true); - } - - if (state.snapshotA !== null) { - snapshots.a = state.snapshotA; - } - - if (state.snapshotB !== null) { - snapshots.b = state.snapshotB; - } - - persistSnapshots(); - - if (state.passCount !== null) { - applyPassCount(state.passCount, true, sourceLabel || "state"); - } else { - syncInputs(currentPasses); - syncSourceLabel(sourceLabel || "state"); - } - - if (state.open !== null) { - setPanelOpen(state.open); - } - - if (state.auto !== null) { - if (state.auto) { - startAutoplay(); - } else { - stopAutoplay(); - } - } - - syncShareUrlField(); - return true; - } - - function setAutoplayInterval(rawInterval, persistSelection) { - var normalized = toValidSpeed(rawInterval); - if (!Number.isFinite(normalized)) { - normalized = PASS_AUTOPLAY_INTERVAL_MS; - } - - autoplayIntervalMs = normalized; - speedInput.value = String(autoplayIntervalMs); - - if (persistSelection) { - trySaveValue(getPassSpeedStorageKey(kind), String(autoplayIntervalMs)); - trySaveValue(PASS_AUTOPLAY_STORAGE_KEY, String(autoplayIntervalMs)); - } - - if (isAutoplay) { - startAutoplay(); - } - - syncSweepLabel(); - syncShareUrlField(); - } - - function syncInputs(passCount) { - range.value = String(passCount); - number.value = String(passCount); - value.textContent = String(passCount); - if (badge) { badge.textContent = String(passCount); } - - presetButtons.forEach(function (button) { - var preset = Number(button.getAttribute("data-preset")); - button.classList.toggle("is-active", preset === passCount); - }); - - syncSnapshotUI(passCount); - } - - function stopAutoplay() { - if (autoplayTimer) { - clearInterval(autoplayTimer); - autoplayTimer = null; - } - isAutoplay = false; - autoplayButton.textContent = "Auto Sweep"; - autoplayButton.classList.remove("is-active"); - syncSweepLabel(); - syncShareUrlField(); - } - - function startAutoplay() { - if (window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches) { - return; - } - - stopAutoplay(); - isAutoplay = true; - autoplayButton.textContent = "Stop Sweep"; - autoplayButton.classList.add("is-active"); - syncSweepLabel(); - syncShareUrlField(); - - autoplayTimer = setInterval(function () { - if (currentPasses >= PASS_MAX) { - autoDirection = -1; - } else if (currentPasses <= PASS_MIN) { - autoDirection = 1; - } - - applyPassCount(currentPasses + autoDirection, true, "autoplay"); - }, autoplayIntervalMs); - } - - function toggleAutoplay() { - if (isAutoplay) { - stopAutoplay(); - return; - } - - startAutoplay(); - } - - function isEditableTarget(target) { - if (!target) { - return false; - } - - if (target.isContentEditable) { - return true; - } - - var tag = (target.tagName || "").toLowerCase(); - return tag === "input" || tag === "textarea" || tag === "select"; - } - - function syncFromPassEvent(event) { - if (!event || !event.detail || event.detail.kind !== kind) { - return; - } - - var detailPassCount = Number(event.detail.passCount); - if (!Number.isFinite(detailPassCount)) { - return; - } - - currentPasses = Math.round(clamp(detailPassCount, PASS_MIN, PASS_MAX)); - syncInputs(currentPasses); - syncSourceLabel(event.detail.source || "event"); - pushHistory(currentPasses, event.detail.source || "event"); - } - - function setPanelOpen(nextOpen) { - isOpen = !!nextOpen; - panel.classList.toggle("is-open", isOpen); - bodyPanel.hidden = !isOpen; - toggle.setAttribute("aria-expanded", String(isOpen)); - trySaveValue(DEBUG_OPEN_STORAGE_KEY, isOpen ? "1" : "0"); - - if (!isOpen) { - stopAutoplay(); - } - - syncShareUrlField(); - } - - function applyPassCount(rawPassCount, persistSelection, source) { - var normalized = Math.round(clamp(Number(rawPassCount), PASS_MIN, PASS_MAX)); - var effectiveSource = source || "panel"; - - if (shouldTrackUndo(effectiveSource, normalized)) { - pushUndo(currentPasses); - } - - currentPasses = runPolishPasses(kind, normalized, effectiveSource); - syncInputs(currentPasses); - syncSourceLabel(effectiveSource); - - if (persistSelection) { - persistPass(currentPasses); - } - - syncUndoState(); - syncShareUrlField(); - } - - toggle.addEventListener("click", function () { - setPanelOpen(!isOpen); - }); - - range.addEventListener("input", function () { - applyPassCount(range.value, true, "slider"); - }); - - number.addEventListener("change", function () { - applyPassCount(number.value, true, "number"); - }); - - speedInput.addEventListener("change", function () { - setAutoplayInterval(speedInput.value, true); - syncSourceLabel("speed"); - }); - - stepButtons.forEach(function (button) { - button.addEventListener("click", function () { - var delta = Number(button.getAttribute("data-step")); - applyPassCount(currentPasses + delta, true, "step"); - }); - }); - - presetButtons.forEach(function (button) { - button.addEventListener("click", function () { - var preset = Number(button.getAttribute("data-preset")); - applyPassCount(preset, true, "preset"); - }); - }); - - resetButton.addEventListener("click", function () { - applyPassCount(PASS_MAX, true, "reset"); - }); - - defaultButton.addEventListener("click", function () { - configuredPasses = getConfiguredPassCount(kind); - applyPassCount(configuredPasses, true, "default"); - }); - - autoplayButton.addEventListener("click", function () { - toggleAutoplay(); - }); - - undoButton.addEventListener("click", function () { - runUndo("undo"); - }); - - redoButton.addEventListener("click", function () { - runRedo("redo"); - }); - - saveAButton.addEventListener("click", function () { - snapshots.a = currentPasses; - persistSnapshots(); - syncSnapshotUI(currentPasses); - syncSourceLabel("save-a"); - }); - - saveBButton.addEventListener("click", function () { - snapshots.b = currentPasses; - persistSnapshots(); - syncSnapshotUI(currentPasses); - syncSourceLabel("save-b"); - }); - - applyAButton.addEventListener("click", function () { - if (Number.isFinite(snapshots.a)) { - applyPassCount(snapshots.a, true, "snapshot-a"); - } - }); - - applyBButton.addEventListener("click", function () { - if (Number.isFinite(snapshots.b)) { - applyPassCount(snapshots.b, true, "snapshot-b"); - } - }); - - toggleAbButton.addEventListener("click", function () { - applySnapshotToggle("snapshot-toggle"); - }); - - copyUrlButton.addEventListener("click", function () { - var shareUrl = buildShareUrl(); - shareInput.value = shareUrl; - - if (navigator.clipboard && typeof navigator.clipboard.writeText === "function") { - navigator.clipboard.writeText(shareUrl) - .then(function () { - syncSourceLabel("share-copied"); - }) - .catch(function () { - syncSourceLabel("share-copy-failed"); - }); - return; - } - - shareInput.focus(); - shareInput.select(); - syncSourceLabel("share-ready"); - }); - - applyUrlButton.addEventListener("click", function () { - var parsed = parsePassStateFromInput(shareInput.value); - var applied = applyStatePayload(parsed, "share-import"); - if (!applied) { - syncSourceLabel("share-invalid"); - } - }); - - pushUrlButton.addEventListener("click", function () { - var shareUrl = buildShareUrl(); - if (window.history && typeof window.history.replaceState === "function") { - window.history.replaceState({}, "", shareUrl); - syncShareUrlField(); - syncSourceLabel("url-pushed"); - return; - } - - shareInput.value = shareUrl; - syncSourceLabel("url-ready"); - }); - - clearHistoryButton.addEventListener("click", function () { - clearHistory("history-clear"); - }); - - shareInput.addEventListener("keydown", function (event) { - if (event.key === "Enter") { - event.preventDefault(); - applyUrlButton.click(); - } - }); - - closeButton.addEventListener("click", function () { - setPanelOpen(false); - }); - - document.addEventListener("site-upgrade:pass-change", syncFromPassEvent); - - document.addEventListener("visibilitychange", function () { - if (document.hidden) { - stopAutoplay(); - } - }); - - window.addEventListener("beforeunload", stopAutoplay); - - document.addEventListener("keydown", function (event) { - if (shortcutOverlay && event.key === "Escape") { - event.preventDefault(); - hideShortcutOverlay(); - return; - } - - if (isEditableTarget(event.target)) { - return; - } - - if (!(event.altKey && event.shiftKey)) { - return; - } - - if (event.key.toLowerCase() === "p") { - event.preventDefault(); - setPanelOpen(!isOpen); - return; - } - - if (event.key.toLowerCase() === "a") { - event.preventDefault(); - toggleAutoplay(); - return; - } - - if (event.key.toLowerCase() === "c") { - event.preventDefault(); - setPanelOpen(false); - return; - } - - if (event.code === "Digit0") { - event.preventDefault(); - applyPassCount(PASS_MAX, true, "shortcut-reset"); - return; - } - - if (event.code === "Digit1" || event.code === "Digit2" || event.code === "Digit3" || event.code === "Digit4") { - event.preventDefault(); - var presetMap = { - Digit1: 25, - Digit2: 50, - Digit3: 75, - Digit4: 100 - }; - applyPassCount(presetMap[event.code], true, "shortcut-preset"); - return; - } - - if (event.code === "Digit5") { - event.preventDefault(); - if (Number.isFinite(snapshots.a)) { - applyPassCount(snapshots.a, true, "shortcut-a"); - } - return; - } - - if (event.code === "Digit6") { - event.preventDefault(); - if (Number.isFinite(snapshots.b)) { - applyPassCount(snapshots.b, true, "shortcut-b"); - } - return; - } - - if (event.code === "Digit7") { - event.preventDefault(); - applySnapshotToggle("shortcut-toggle"); - return; - } - - if (event.key.toLowerCase() === "u") { - event.preventDefault(); - applyUrlButton.click(); - return; - } - - if (event.key.toLowerCase() === "i") { - event.preventDefault(); - shareInput.focus(); - shareInput.select(); - syncSourceLabel("url-focus"); - return; - } - - if (event.key.toLowerCase() === "h") { - event.preventDefault(); - clearHistory("history-clear"); - return; - } - - if (event.key === "/") { - event.preventDefault(); - if (shortcutOverlay) { - hideShortcutOverlay(); - } else { - showShortcutOverlay(); - } - return; - } - - if (event.key.toLowerCase() === "l") { - event.preventDefault(); - pushUrlButton.click(); - return; - } - - if (event.key.toLowerCase() === "z") { - event.preventDefault(); - runUndo("shortcut-undo"); - return; - } - - if (event.key.toLowerCase() === "y") { - event.preventDefault(); - runRedo("shortcut-redo"); - return; - } - - if (event.key === "ArrowUp") { - event.preventDefault(); - applyPassCount(currentPasses + 1, true, "shortcut-up"); - return; - } - - if (event.key === "ArrowDown") { - event.preventDefault(); - applyPassCount(currentPasses - 1, true, "shortcut-down"); - return; - } - - if (event.key === "ArrowRight") { - event.preventDefault(); - applyPassCount(currentPasses + 10, true, "shortcut-right"); - return; - } - - if (event.key === "ArrowLeft") { - event.preventDefault(); - applyPassCount(currentPasses - 10, true, "shortcut-left"); - } - }); - - setAutoplayInterval(autoplayIntervalMs, false); - syncSweepLabel(); - syncInputs(currentPasses); - syncUndoState(); - syncShareUrlField(); - renderHistory(); - - if (queryState && (queryState.snapshotA !== null || queryState.snapshotB !== null)) { - persistSnapshots(); - } - - if (currentPasses !== initialPassCount) { - applyPassCount(currentPasses, true, "state-init"); - } else { - syncSourceLabel("init"); - pushHistory(currentPasses, "init"); - } - - setPanelOpen(isOpen); - - if (shouldAutoStart === true) { - startAutoplay(); - } else if (shouldAutoStart === false) { - stopAutoplay(); - } - - // --- Drag to reposition --- - (function () { - var savedPos = (function () { - var raw = tryLoadValue(getPassPositionStorageKey(kind)); - if (!raw) { return null; } - try { - var p = JSON.parse(raw); - return p && Number.isFinite(p.right) && Number.isFinite(p.bottom) ? p : null; - } catch (e) { return null; } - })(); - - function applyPosition(right, bottom) { - var maxRight = window.innerWidth - 40; - var maxBottom = window.innerHeight - 40; - var r = Math.min(Math.max(right, 0), maxRight); - var b = Math.min(Math.max(bottom, 0), maxBottom); - panel.style.right = r + "px"; - panel.style.bottom = b + "px"; - } - - function resetPosition() { - panel.style.right = ""; - panel.style.bottom = ""; - try { localStorage.removeItem(getPassPositionStorageKey(kind)); } catch (e) { /* */ } - } - - if (savedPos) { - applyPosition(savedPos.right, savedPos.bottom); - } - - var dragState = null; - - dragHead.addEventListener("mousedown", function (evt) { - if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT" || evt.target.tagName === "SUMMARY")) { - return; - } - evt.preventDefault(); - var rect = panel.getBoundingClientRect(); - dragState = { - startX: evt.clientX, - startY: evt.clientY, - startRight: window.innerWidth - rect.right, - startBottom: window.innerHeight - rect.bottom - }; - panel.classList.add("is-dragging"); - }); - - document.addEventListener("mousemove", function (evt) { - if (!dragState) { return; } - var dx = evt.clientX - dragState.startX; - var dy = evt.clientY - dragState.startY; - applyPosition(dragState.startRight - dx, dragState.startBottom - dy); - }); - - document.addEventListener("mouseup", function (evt) { - if (!dragState) { return; } - panel.classList.remove("is-dragging"); - var rect = panel.getBoundingClientRect(); - var finalRight = window.innerWidth - rect.right; - var finalBottom = window.innerHeight - rect.bottom; - trySaveValue(getPassPositionStorageKey(kind), JSON.stringify({ right: finalRight, bottom: finalBottom })); - dragState = null; - }); - - dragHead.addEventListener("touchstart", function (evt) { - if (evt.touches.length !== 1) { return; } - if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT" || evt.target.tagName === "SUMMARY")) { - return; - } - var touch = evt.touches[0]; - var rect = panel.getBoundingClientRect(); - dragState = { - startX: touch.clientX, - startY: touch.clientY, - startRight: window.innerWidth - rect.right, - startBottom: window.innerHeight - rect.bottom - }; - panel.classList.add("is-dragging"); - }, { passive: true }); - - document.addEventListener("touchmove", function (evt) { - if (!dragState) { return; } - if (evt.cancelable) { evt.preventDefault(); } - var touch = evt.touches[0]; - var dx = touch.clientX - dragState.startX; - var dy = touch.clientY - dragState.startY; - applyPosition(dragState.startRight - dx, dragState.startBottom - dy); - }, { passive: false }); - - document.addEventListener("touchend", function () { - if (!dragState) { return; } - panel.classList.remove("is-dragging"); - var rect = panel.getBoundingClientRect(); - var finalRight = window.innerWidth - rect.right; - var finalBottom = window.innerHeight - rect.bottom; - trySaveValue(getPassPositionStorageKey(kind), JSON.stringify({ right: finalRight, bottom: finalBottom })); - dragState = null; - }); - - dragHead.addEventListener("dblclick", function (evt) { - if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT")) { - return; - } - resetPosition(); - }); - })(); - - // --- Shortcut cheat-sheet overlay --- - var shortcutOverlay = null; - - function showShortcutOverlay() { - if (shortcutOverlay) { return; } - shortcutOverlay = document.createElement("div"); - shortcutOverlay.className = "site-pass-shortcut-overlay"; - shortcutOverlay.setAttribute("role", "dialog"); - shortcutOverlay.setAttribute("aria-modal", "true"); - shortcutOverlay.setAttribute("aria-label", "Keyboard shortcuts"); - shortcutOverlay.innerHTML = "" - + "
    " - + "
    " - + "Keyboard Shortcuts" - + "" - + "
    " - + "
    " - + "
    Alt+Shift+P
    Toggle panel
    " - + "
    Alt+Shift+A
    Toggle auto sweep
    " - + "
    Alt+Shift+C
    Close panel
    " - + "
    Alt+Shift+1–4
    Presets 25 / 50 / 75 / 100
    " - + "
    Alt+Shift+0
    Reset to 100
    " - + "
    Alt+Shift+5
    Apply snapshot A
    " - + "
    Alt+Shift+6
    Apply snapshot B
    " - + "
    Alt+Shift+7
    Flip A/B
    " - + "
    Alt+Shift+Z
    Undo
    " - + "
    Alt+Shift+Y
    Redo
    " - + "
    Alt+Shift+U
    Apply URL state
    " - + "
    Alt+Shift+L
    Push URL state
    " - + "
    Alt+Shift+I
    Focus URL input
    " - + "
    Alt+Shift+H
    Clear history log
    " - + "
    Alt+Shift+/
    Show this overlay
    " - + "
    Arrow Up / Right
    +1 / +10 passes
    " - + "
    Arrow Down / Left
    −1 / −10 passes
    " - + "
    " - + "

    Double-click panel header to reset position

    " - + "
    "; - document.body.appendChild(shortcutOverlay); - var closeBtn = shortcutOverlay.querySelector(".site-pass-shortcut-overlay__close"); - if (closeBtn) { closeBtn.focus(); } - - function dismissOverlay(evt) { - if (!shortcutOverlay) { return; } - var box = shortcutOverlay.querySelector(".site-pass-shortcut-overlay__box"); - if (!box || !box.contains(evt.target)) { - hideShortcutOverlay(); - } - } - shortcutOverlay._dismiss = dismissOverlay; - shortcutOverlay.addEventListener("click", dismissOverlay); - if (closeBtn) { - closeBtn.addEventListener("click", function () { hideShortcutOverlay(); }); - } - } - - function hideShortcutOverlay() { - if (!shortcutOverlay) { return; } - shortcutOverlay.remove(); - shortcutOverlay = null; - } - - var sectionEls = panel.querySelectorAll(".site-pass-debug__section"); - var savedSections = (function () { - var raw = tryLoadValue(getPassSectionsStorageKey(kind)); - if (!raw) { return null; } - try { return JSON.parse(raw); } catch (e) { return null; } - })(); - - if (savedSections) { - sectionEls.forEach(function (el) { - var name = el.getAttribute("data-section"); - if (name && Object.prototype.hasOwnProperty.call(savedSections, name)) { - if (savedSections[name]) { - el.setAttribute("open", ""); - } else { - el.removeAttribute("open"); - } - } - }); - } - - // Animated details open/close - function animateDetails(el, opening) { - var inner = el.querySelector("summary + *") || el.querySelector("summary"); - if (!inner) { - return; - } - - // Respect reduced-motion preference — skip animation - var prefersReduced = window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches; - - if (el._animating) { - clearTimeout(el._animating); - el._animating = null; - if (el._innerEl) { el._innerEl.style.height = ""; el._innerEl.style.overflow = ""; } - } - - var wrapper = el._innerEl; - if (!wrapper) { - // Wrap all non-summary children in an animate wrapper once - wrapper = document.createElement("div"); - wrapper.className = "site-pass-debug__section-body"; - var children = Array.prototype.slice.call(el.childNodes); - children.forEach(function (node) { - if (node.nodeType === 1 && node.tagName === "SUMMARY") { return; } - wrapper.appendChild(node); - }); - el.appendChild(wrapper); - el._innerEl = wrapper; - } - - if (prefersReduced) { - wrapper.style.height = ""; - wrapper.style.overflow = ""; - return; - } - - var targetHeight = opening ? wrapper.scrollHeight + "px" : "0px"; - wrapper.style.overflow = "hidden"; - wrapper.style.height = opening ? "0px" : wrapper.scrollHeight + "px"; - - // Force reflow - void wrapper.offsetHeight; - wrapper.style.transition = "height 0.2s ease"; - wrapper.style.height = targetHeight; - - var DURATION = 210; - el._animating = setTimeout(function () { - wrapper.style.transition = ""; - wrapper.style.overflow = ""; - if (opening) { wrapper.style.height = ""; } - el._animating = null; - }, DURATION); - } - - sectionEls.forEach(function (el) { - // Intercept toggle: cancel default, run animation, then commit open state - el.addEventListener("click", function (evt) { - if (evt.target && evt.target.tagName === "SUMMARY") { - evt.preventDefault(); - var willOpen = !el.open; - if (willOpen) { el.setAttribute("open", ""); } - animateDetails(el, willOpen); - if (!willOpen) { - setTimeout(function () { el.removeAttribute("open"); }, 200); - } - var state = {}; - sectionEls.forEach(function (s) { - var sName = s.getAttribute("data-section"); - if (sName) { state[sName] = willOpen ? true : false; } - }); - // Only update the toggled section - var secName = el.getAttribute("data-section"); - var fullState = {}; - sectionEls.forEach(function (s) { - var n = s.getAttribute("data-section"); - if (n) { fullState[n] = n === secName ? willOpen : s.open; } - }); - trySaveValue(getPassSectionsStorageKey(kind), JSON.stringify(fullState)); - } - }); - }); - - // --- Panel width resize handle --- - (function () { - var PANEL_MIN_W = 220; - var PANEL_MAX_W = Math.min(560, window.innerWidth - 28); - var savedWidth = (function () { - var raw = tryLoadValue(getPassWidthStorageKey(kind)); - var v = raw ? parseFloat(raw) : NaN; - return Number.isFinite(v) ? v : null; - })(); - - var innerPanel = panel.querySelector(".site-pass-debug__panel"); - - function applyWidth(w) { - var clamped = Math.min(Math.max(w, PANEL_MIN_W), Math.min(560, window.innerWidth - 28)); - innerPanel.style.width = clamped + "px"; - return clamped; - } - - function resetWidth() { - innerPanel.style.width = ""; - try { localStorage.removeItem(getPassWidthStorageKey(kind)); } catch (e) { /* */ } - } - - if (savedWidth) { - applyWidth(savedWidth); - } - - var resizeDrag = null; - - resizeHandle.addEventListener("mousedown", function (evt) { - evt.preventDefault(); - var rect = innerPanel.getBoundingClientRect(); - resizeDrag = { startX: evt.clientX, startWidth: rect.width }; - panel.classList.add("is-resizing"); - }); - - document.addEventListener("mousemove", function (evt) { - if (!resizeDrag) { return; } - // Handle is on the left edge — drag left = wider, drag right = narrower - var dx = resizeDrag.startX - evt.clientX; - applyWidth(resizeDrag.startWidth + dx); - }); - - document.addEventListener("mouseup", function () { - if (!resizeDrag) { return; } - panel.classList.remove("is-resizing"); - var finalWidth = parseFloat(innerPanel.style.width) || null; - if (finalWidth) { - trySaveValue(getPassWidthStorageKey(kind), String(finalWidth)); - } - resizeDrag = null; - }); - - resizeHandle.addEventListener("dblclick", function () { - resetWidth(); - }); - })(); - - if (exportJsonButton) { - exportJsonButton.addEventListener("click", function () { - var json = JSON.stringify(serializePassState(), null, 2); - if (navigator.clipboard && navigator.clipboard.writeText) { - navigator.clipboard.writeText(json).then(function () { - var orig = exportJsonButton.textContent; - exportJsonButton.textContent = "Copied!"; - setTimeout(function () { exportJsonButton.textContent = orig; }, 1200); - }).catch(function () { - shareInput.value = json; - shareInput.select(); - }); - } else { - shareInput.value = json; - shareInput.select(); - } - }); - } - - // --- Appearance controls (opacity / blur) --- - (function () { - var innerPanel = panel.querySelector(".site-pass-debug__panel"); - var blurEnabled = true; - - var saved = (function () { - var raw = tryLoadValue(getPassPanelStyleStorageKey(kind)); - if (!raw) { return null; } - try { return JSON.parse(raw); } catch (e) { return null; } - })(); - - var currentOpacity = (saved && Number.isFinite(saved.opacity)) ? saved.opacity : 100; - var currentBlur = (saved && typeof saved.blur === "boolean") ? saved.blur : true; - blurEnabled = currentBlur; - - function applyAppearance(opacity, blur) { - innerPanel.style.opacity = (opacity / 100).toFixed(2); - innerPanel.style.backdropFilter = blur ? "blur(10px)" : "none"; - innerPanel.style.webkitBackdropFilter = blur ? "blur(10px)" : "none"; - } - - function syncAppearanceUI() { - if (opacityInput) { opacityInput.value = String(currentOpacity); } - if (toggleBlurButton) { - toggleBlurButton.textContent = blurEnabled ? "Blur On" : "Blur Off"; - toggleBlurButton.classList.toggle("is-active", blurEnabled); - } - } - - function persistAppearance() { - trySaveValue(getPassPanelStyleStorageKey(kind), JSON.stringify({ opacity: currentOpacity, blur: blurEnabled })); - } - - applyAppearance(currentOpacity, blurEnabled); - syncAppearanceUI(); - - if (opacityInput) { - opacityInput.addEventListener("change", function () { - var v = Math.min(100, Math.max(20, Number(opacityInput.value) || 100)); - currentOpacity = v; - opacityInput.value = String(v); - applyAppearance(currentOpacity, blurEnabled); - persistAppearance(); - }); - opacityInput.addEventListener("input", function () { - var v = Math.min(100, Math.max(20, Number(opacityInput.value) || 100)); - applyAppearance(v, blurEnabled); - }); - } - - if (toggleBlurButton) { - toggleBlurButton.addEventListener("click", function () { - blurEnabled = !blurEnabled; - applyAppearance(currentOpacity, blurEnabled); - syncAppearanceUI(); - persistAppearance(); - }); - } - - if (resetAppearanceButton) { - resetAppearanceButton.addEventListener("click", function () { - currentOpacity = 100; - blurEnabled = true; - applyAppearance(currentOpacity, blurEnabled); - syncAppearanceUI(); - try { localStorage.removeItem(getPassPanelStyleStorageKey(kind)); } catch (e) { /* */ } - }); - } - })(); - } - - function ensureSkipLink() { - if (document.querySelector(".site-upgrade-skip")) { - return; - } - - var candidates = ["main", "#main-content", "#mainContent", ".main", ".page", ".container", ".content"]; - var target = null; - - for (var i = 0; i < candidates.length; i += 1) { - var element = document.querySelector(candidates[i]); - if (element) { - target = element; - break; - } - } - - if (!target) { - return; - } - - if (!target.id) { - target.id = "site-upgrade-main"; - } - - var skip = document.createElement("a"); - skip.className = "site-upgrade-skip"; - skip.href = "#" + target.id; - skip.textContent = "Skip to main content"; - document.body.prepend(skip); - } - - function upgradeExternalLinks() { - var links = document.querySelectorAll('a[target="_blank"]'); - links.forEach(function (link) { - var rel = (link.getAttribute("rel") || "").toLowerCase(); - if (!rel.includes("noopener")) { - link.setAttribute("rel", (rel ? rel + " " : "") + "noopener noreferrer"); - } - }); - } - - function applyRevealAnimations() { - if (window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches) { - return; - } - - var selectors = [ - "section", - ".card", - ".hero-card", - ".feature-card", - ".system-card", - ".welcome-card", - ".route-card", - ".signal-card", - ".plan-card", - ".link-card", - ".sidebar-card", - ".content-shell", - ".category-block", - ".flow-card", - ".search-panel", - ".resource-group", - ".terminal", - ".run-copy", - ".run-aside", - ".hero-banner", - ".glass-panel", - ".jobs-section", - ".gpu-status", - ".chat-container", - ".stage-container", - ".object-manager", - ".page-link", - ".job-item", - ".metric-card", - ".category-card", - ".product-card" - ]; - - var seen = new Set(); - selectors.forEach(function (selector) { - document.querySelectorAll(selector).forEach(function (node) { - seen.add(node); - }); - }); - - var nodes = Array.from(seen).filter(function (node) { - return node && (node.childElementCount > 0 || (node.textContent || "").trim().length > 0); - }); - - if (!nodes.length) { - return; - } - - nodes.forEach(function (node, index) { - node.classList.add("site-upgrade-reveal"); - node.style.setProperty("--site-upgrade-delay", Math.min(index * 28, 420) + "ms"); - }); - - if (!("IntersectionObserver" in window)) { - nodes.forEach(function (node) { - node.classList.add("is-visible"); - }); - return; - } - - var observer = new IntersectionObserver(function (entries, obs) { - entries.forEach(function (entry) { - if (entry.isIntersecting) { - entry.target.classList.add("is-visible"); - obs.unobserve(entry.target); - } - }); - }, { - threshold: 0.12, - rootMargin: "0px 0px -8% 0px" - }); - - nodes.forEach(function (node) { - observer.observe(node); - }); - } - - function initUpgrade() { - var kind = applySiteKindClass(); - var configuredPasses = getConfiguredPassCount(kind); - var initialPasses = runPolishPasses(kind, configuredPasses); - ensureSkipLink(); - upgradeExternalLinks(); - applyRevealAnimations(); - createPassDebugPanel(kind, initialPasses); - } - - if (document.readyState === "loading") { - document.addEventListener("DOMContentLoaded", initUpgrade, { once: true }); - } else { - initUpgrade(); - } -})(); +(function () { + "use strict"; + + if (window.__ariaSiteUpgradeLoaded) { + return; + } + window.__ariaSiteUpgradeLoaded = true; + + document.documentElement.classList.add("site-upgraded"); + + var PASS_MIN = 1; + var PASS_MAX = 100; + var PASS_DEFAULT = 100; + var PASS_STORAGE_KEY = "siteUpgradePasses"; + var DEBUG_OPEN_STORAGE_KEY = "siteUpgradePassDebugOpen"; + var PASS_AUTOPLAY_INTERVAL_MS = 70; + var PASS_AUTOPLAY_MIN_MS = 25; + var PASS_AUTOPLAY_MAX_MS = 1000; + var PASS_AUTOPLAY_STORAGE_KEY = "siteUpgradePassSweepMs"; + var PASS_SNAPSHOT_STORAGE_KEY = "siteUpgradePassSnapshots"; + var PASS_UNDO_STORAGE_KEY = "siteUpgradePassUndoStack"; + var PASS_SECTIONS_STORAGE_KEY = "siteUpgradePassSections"; + var PASS_POSITION_STORAGE_KEY = "siteUpgradePassPosition"; + var PASS_WIDTH_STORAGE_KEY = "siteUpgradePassWidth"; + var PASS_PANEL_STYLE_STORAGE_KEY = "siteUpgradePassPanelStyle"; + var PASS_HISTORY_MAX = 10; + + function detectSiteKind() { + var path = (window.location.pathname || "").toLowerCase(); + var href = (window.location.href || "").toLowerCase(); + var target = path + " " + href; + + if (target.includes("/chat/") || target.includes("chat/index") || target.endsWith("/chat")) { + return "chat"; + } + + if (target.includes("/aria/") || target.includes("auto-execute") || target.includes("aria/index")) { + return "aria"; + } + + if (target.includes("/dashboard/") || target.includes("dashboard/index") || target.includes("dashboard/hub")) { + return "dashboard"; + } + + if (target.includes("/store/") || target.includes("products") || target.includes("product.html")) { + return "store"; + } + + if (target.includes("documentation") || target.includes("/docs/") || target.endsWith("/docs") || target.includes("readme_pages")) { + return "docs"; + } + + if (target.includes("/quantum/") || target.includes("/monetization/") || target.includes("llm-maker") || target.includes("/mount/")) { + return "dashboard"; + } + + return "core"; + } + + function applySiteKindClass() { + var kind = detectSiteKind(); + var root = document.documentElement; + var body = document.body; + + root.setAttribute("data-site-kind", kind); + if (body) { + body.setAttribute("data-site-kind", kind); + body.classList.add("site-kind-" + kind); + } + + return kind; + } + + function clamp(value, min, max) { + return Math.max(min, Math.min(max, value)); + } + + function getPassStorageKey(kind) { + return PASS_STORAGE_KEY + ":" + kind; + } + + function getPassSpeedStorageKey(kind) { + return PASS_AUTOPLAY_STORAGE_KEY + ":" + kind; + } + + function getPassSnapshotStorageKey(kind) { + return PASS_SNAPSHOT_STORAGE_KEY + ":" + kind; + } + + function getPassUndoStorageKey(kind) { + return PASS_UNDO_STORAGE_KEY + ":" + kind; + } + + function getPassSectionsStorageKey(kind) { + return PASS_SECTIONS_STORAGE_KEY + ":" + kind; + } + + function getPassPositionStorageKey(kind) { + return PASS_POSITION_STORAGE_KEY + ":" + kind; + } + + function getPassWidthStorageKey(kind) { + return PASS_WIDTH_STORAGE_KEY + ":" + kind; + } + + function getPassPanelStyleStorageKey(kind) { + return PASS_PANEL_STYLE_STORAGE_KEY + ":" + kind; + } + + function shouldOpenDebugFromQuery() { + var params = new URLSearchParams(window.location.search || ""); + var raw = params.get("passpanel") || params.get("debugpasses") || params.get("passdebug"); + if (!raw) { + return null; + } + + var value = String(raw).toLowerCase(); + if (value === "0" || value === "false" || value === "off" || value === "hide") { + return false; + } + + return true; + } + + function shouldAutoplayFromQuery() { + var params = new URLSearchParams(window.location.search || ""); + var raw = params.get("passauto") || params.get("passautoplay") || params.get("autosweep"); + if (!raw) { + return null; + } + + return toOptionalBool(raw); + } + + function getConfiguredPassCount(kind) { + var params = new URLSearchParams(window.location.search || ""); + var stateFromQuery = getPassStateFromQuery(); + var queryRaw = params.get("passes") || params.get("pass") || (stateFromQuery && stateFromQuery.passCount); + var savedRaw = null; + + try { + if (window.localStorage) { + savedRaw = localStorage.getItem(getPassStorageKey(kind)) || localStorage.getItem(PASS_STORAGE_KEY); + } + } catch (error) { + savedRaw = null; + } + + var parsed = Number(queryRaw || savedRaw || PASS_DEFAULT); + if (!Number.isFinite(parsed)) { + parsed = PASS_DEFAULT; + } + + return Math.round(clamp(parsed, PASS_MIN, PASS_MAX)); + } + + function getConfiguredAutoplayInterval(kind) { + var params = new URLSearchParams(window.location.search || ""); + var stateFromQuery = getPassStateFromQuery(); + var queryRaw = params.get("passsweepms") || params.get("passautoplayms") || params.get("passspeed") || (stateFromQuery && stateFromQuery.sweepMs); + var savedRaw = null; + + try { + if (window.localStorage) { + savedRaw = localStorage.getItem(getPassSpeedStorageKey(kind)) || localStorage.getItem(PASS_AUTOPLAY_STORAGE_KEY); + } + } catch (error) { + savedRaw = null; + } + + var parsed = Number(queryRaw || savedRaw || PASS_AUTOPLAY_INTERVAL_MS); + if (!Number.isFinite(parsed)) { + parsed = PASS_AUTOPLAY_INTERVAL_MS; + } + + return Math.round(clamp(parsed, PASS_AUTOPLAY_MIN_MS, PASS_AUTOPLAY_MAX_MS)); + } + + function normalizePassSource(source) { + var normalized = String(source || "manual"); + + if (normalized.indexOf("shortcut-") === 0) { + normalized = "key " + normalized.slice("shortcut-".length); + } + + return normalized.replace(/-/g, " "); + } + + function toValidPass(rawValue) { + var parsed = Number(rawValue); + if (!Number.isFinite(parsed)) { + return null; + } + + return Math.round(clamp(parsed, PASS_MIN, PASS_MAX)); + } + + function toValidSpeed(rawValue) { + var parsed = Number(rawValue); + if (!Number.isFinite(parsed)) { + return null; + } + + return Math.round(clamp(parsed, PASS_AUTOPLAY_MIN_MS, PASS_AUTOPLAY_MAX_MS)); + } + + function toOptionalBool(rawValue) { + if (typeof rawValue === "boolean") { + return rawValue; + } + + if (rawValue === null || rawValue === undefined) { + return null; + } + + var normalized = String(rawValue).toLowerCase(); + if (normalized === "1" || normalized === "true" || normalized === "on" || normalized === "open" || normalized === "yes") { + return true; + } + + if (normalized === "0" || normalized === "false" || normalized === "off" || normalized === "close" || normalized === "closed" || normalized === "hide" || normalized === "no") { + return false; + } + + return null; + } + + function normalizePassState(rawState) { + if (!rawState || typeof rawState !== "object") { + return null; + } + + var passCount = toValidPass(rawState.passCount !== undefined ? rawState.passCount : (rawState.pass !== undefined ? rawState.pass : rawState.p)); + var sweepMs = toValidSpeed(rawState.sweepMs !== undefined ? rawState.sweepMs : (rawState.speed !== undefined ? rawState.speed : rawState.ms)); + var snapshotA = toValidPass(rawState.snapshotA !== undefined ? rawState.snapshotA : rawState.a); + var snapshotB = toValidPass(rawState.snapshotB !== undefined ? rawState.snapshotB : rawState.b); + var open = toOptionalBool(rawState.open); + var auto = toOptionalBool(rawState.auto); + + if (passCount === null && sweepMs === null && snapshotA === null && snapshotB === null && open === null && auto === null) { + return null; + } + + return { + passCount: passCount, + sweepMs: sweepMs, + snapshotA: snapshotA, + snapshotB: snapshotB, + open: open, + auto: auto + }; + } + + function getPassStateFromSearchParams(params) { + if (!params) { + return null; + } + + var raw = params.get("passstate"); + if (!raw) { + return null; + } + + try { + return normalizePassState(JSON.parse(raw)); + } catch (error) { + return null; + } + } + + function getPassStateFromQuery() { + return getPassStateFromSearchParams(new URLSearchParams(window.location.search || "")); + } + + function parsePassStateFromInput(rawInput) { + if (!rawInput) { + return null; + } + + var value = String(rawInput).trim(); + if (!value) { + return null; + } + + try { + var url = new URL(value, window.location.href); + var fromUrl = getPassStateFromSearchParams(url.searchParams); + if (fromUrl) { + return fromUrl; + } + } catch (error) { + // Not a URL, continue parsing. + } + + try { + var queryLike = value.charAt(0) === "?" ? value.slice(1) : value; + var params = new URLSearchParams(queryLike); + var fromParams = getPassStateFromSearchParams(params); + if (fromParams) { + return fromParams; + } + } catch (error) { + // Not query-like content, continue parsing. + } + + try { + return normalizePassState(JSON.parse(value)); + } catch (error) { + return null; + } + } + + function interpolate(start, end, t) { + return start + (end - start) * t; + } + + function getKindShadowColor(kind) { + switch (kind) { + case "chat": + return [15, 147, 197]; + case "aria": + return [46, 157, 131]; + case "dashboard": + return [47, 123, 231]; + case "store": + return [208, 96, 47]; + case "docs": + return [24, 127, 120]; + default: + return [19, 36, 55]; + } + } + + function setPassVariables(kind, passIndex, totalPasses) { + var root = document.documentElement; + var t = clamp(passIndex / totalPasses, 0, 1); + var color = getKindShadowColor(kind); + + var gridOpacity = interpolate(0.24, 0.46, t); + var radiusBoost = interpolate(0, 7, t); + var focusWidth = interpolate(2.5, 4.2, t); + var letterShift = interpolate(-0.012, -0.036, t); + var cardRaise = interpolate(0.25, 1.9, t); + var revealMs = Math.round(interpolate(430, 690, t)); + + var softY = interpolate(8, 24, t); + var softBlur = interpolate(22, 54, t); + var softAlpha = interpolate(0.14, 0.33, t); + + var strongY = interpolate(14, 38, t); + var strongBlur = interpolate(34, 78, t); + var strongAlpha = interpolate(0.2, 0.42, t); + + root.style.setProperty("--pass-intensity", t.toFixed(4)); + root.style.setProperty("--pass-grid-opacity", gridOpacity.toFixed(3)); + root.style.setProperty("--pass-radius-boost", radiusBoost.toFixed(2) + "px"); + root.style.setProperty("--pass-focus-width", focusWidth.toFixed(2) + "px"); + root.style.setProperty("--pass-letter-shift", letterShift.toFixed(4) + "em"); + root.style.setProperty("--pass-card-raise", cardRaise.toFixed(2) + "px"); + root.style.setProperty("--pass-reveal-ms", revealMs + "ms"); + root.style.setProperty( + "--pass-shadow-soft", + "0 " + softY.toFixed(2) + "px " + softBlur.toFixed(2) + "px rgba(" + color[0] + ", " + color[1] + ", " + color[2] + ", " + softAlpha.toFixed(3) + ")" + ); + root.style.setProperty( + "--pass-shadow-strong", + "0 " + strongY.toFixed(2) + "px " + strongBlur.toFixed(2) + "px rgba(" + color[0] + ", " + color[1] + ", " + color[2] + ", " + strongAlpha.toFixed(3) + ")" + ); + } + + function emitPassChange(kind, passCount, source) { + var detail = { + kind: kind, + passCount: passCount, + passes: passCount, + source: source || "manual" + }; + + document.dispatchEvent(new CustomEvent("site-upgrade:pass-change", { detail: detail })); + window.dispatchEvent(new CustomEvent("site-upgrade:pass-change", { detail: detail })); + } + + function runPolishPasses(kind, requestedPassCount, source) { + var passCount = Math.round(clamp(Number(requestedPassCount), PASS_MIN, PASS_MAX)); + + for (var pass = 1; pass <= passCount; pass += 1) { + setPassVariables(kind, pass, passCount); + } + + document.documentElement.setAttribute("data-polish-passes", String(passCount)); + if (document.body) { + document.body.setAttribute("data-polish-passes", String(passCount)); + } + + emitPassChange(kind, passCount, source); + + return passCount; + } + + function trySaveValue(key, value) { + try { + if (window.localStorage) { + localStorage.setItem(key, value); + } + } catch (error) { + // Ignore storage errors in strict/private browsing contexts. + } + } + + function tryLoadValue(key) { + try { + return window.localStorage ? localStorage.getItem(key) : null; + } catch (error) { + return null; + } + } + + function createPassDebugPanel(kind, initialPassCount) { + if (!document.body || document.querySelector(".site-pass-debug")) { + return; + } + + var panel = document.createElement("aside"); + panel.className = "site-pass-debug"; + panel.setAttribute("aria-label", "Polish pass controls"); + panel.innerHTML = "" + + "" + + ""; + + document.body.appendChild(panel); + + var toggle = panel.querySelector(".site-pass-debug__toggle"); + var bodyPanel = panel.querySelector(".site-pass-debug__panel"); + var kindChip = panel.querySelector(".site-pass-debug__kind"); + var range = panel.querySelector("#site-pass-debug-range"); + var number = panel.querySelector("#site-pass-debug-number"); + var speedInput = panel.querySelector("#site-pass-debug-speed"); + var shareInput = panel.querySelector("#site-pass-debug-share"); + var value = panel.querySelector(".site-pass-debug__value"); + var compareValue = panel.querySelector(".site-pass-debug__compare"); + var stackValue = panel.querySelector(".site-pass-debug__stack"); + var sweepValue = panel.querySelector(".site-pass-debug__sweep"); + var sourceValue = panel.querySelector(".site-pass-debug__source"); + var timeline = panel.querySelector(".site-pass-debug__timeline"); + var stepButtons = panel.querySelectorAll(".site-pass-debug__btn[data-step]"); + var presetButtons = panel.querySelectorAll(".site-pass-debug__btn[data-preset]"); + var resetButton = panel.querySelector('.site-pass-debug__btn[data-action="reset"]'); + var closeButton = panel.querySelector('.site-pass-debug__btn[data-action="close"]'); + var defaultButton = panel.querySelector('.site-pass-debug__btn[data-action="default"]'); + var autoplayButton = panel.querySelector('.site-pass-debug__btn[data-action="autoplay"]'); + var undoButton = panel.querySelector('.site-pass-debug__btn[data-action="undo"]'); + var redoButton = panel.querySelector('.site-pass-debug__btn[data-action="redo"]'); + var saveAButton = panel.querySelector('.site-pass-debug__btn[data-action="save-a"]'); + var saveBButton = panel.querySelector('.site-pass-debug__btn[data-action="save-b"]'); + var applyAButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-a"]'); + var applyBButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-b"]'); + var toggleAbButton = panel.querySelector('.site-pass-debug__btn[data-action="toggle-ab"]'); + var copyUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="copy-url"]'); + var applyUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="apply-url"]'); + var pushUrlButton = panel.querySelector('.site-pass-debug__btn[data-action="push-url"]'); + var clearHistoryButton = panel.querySelector('.site-pass-debug__btn[data-action="clear-history"]'); + var exportJsonButton = panel.querySelector('.site-pass-debug__btn[data-action="export-json"]'); + var toggleBlurButton = panel.querySelector('.site-pass-debug__btn[data-action="toggle-blur"]'); + var resetAppearanceButton = panel.querySelector('.site-pass-debug__btn[data-action="reset-appearance"]'); + var opacityInput = panel.querySelector("#site-pass-debug-opacity"); + var badge = panel.querySelector(".site-pass-debug__badge"); + var dragHead = panel.querySelector(".site-pass-debug__head"); + var resizeHandle = panel.querySelector(".site-pass-debug__resize-handle"); + + var queryState = getPassStateFromQuery(); + var queryAuto = shouldAutoplayFromQuery(); + var shouldAutoStart = queryState && queryState.auto !== null ? queryState.auto : queryAuto; + var configuredPasses = getConfiguredPassCount(kind); + var currentPasses = initialPassCount; + var isOpen = false; + var isAutoplay = false; + var autoDirection = 1; + var autoplayTimer = null; + var autoplayIntervalMs = queryState && Number.isFinite(queryState.sweepMs) + ? queryState.sweepMs + : getConfiguredAutoplayInterval(kind); + var undoStack = (function () { + var raw = tryLoadValue(getPassUndoStorageKey(kind)); + if (!raw) { return []; } + try { + var parsed = JSON.parse(raw); + return Array.isArray(parsed) + ? parsed.filter(function (v) { return Number.isFinite(v); }) + : []; + } catch (e) { return []; } + })(); + var redoStack = []; + var passHistory = []; + var snapshots = (function () { + var raw = tryLoadValue(getPassSnapshotStorageKey(kind)); + if (!raw) { + return { a: null, b: null }; + } + + try { + var parsed = JSON.parse(raw); + return { + a: toValidPass(parsed && parsed.a), + b: toValidPass(parsed && parsed.b) + }; + } catch (error) { + return { a: null, b: null }; + } + })(); + + if (queryState && Number.isFinite(queryState.passCount)) { + currentPasses = queryState.passCount; + } + + if (queryState && queryState.snapshotA !== null) { + snapshots.a = queryState.snapshotA; + } + + if (queryState && queryState.snapshotB !== null) { + snapshots.b = queryState.snapshotB; + } + + var savedOpen = tryLoadValue(DEBUG_OPEN_STORAGE_KEY); + var queryOpen = shouldOpenDebugFromQuery(); + if (queryState && queryState.open !== null) { + isOpen = queryState.open; + } else if (queryOpen !== null) { + isOpen = queryOpen; + } else if (savedOpen === "1") { + isOpen = true; + } + + kindChip.textContent = kind.toUpperCase(); + + function persistPass(passCount) { + trySaveValue(getPassStorageKey(kind), String(passCount)); + trySaveValue(PASS_STORAGE_KEY, String(passCount)); + } + + function persistSnapshots() { + trySaveValue(getPassSnapshotStorageKey(kind), JSON.stringify({ + a: snapshots.a, + b: snapshots.b + })); + } + + function persistUndoStacks() { + trySaveValue(getPassUndoStorageKey(kind), JSON.stringify(undoStack)); + } + + function serializePassState() { + return { + pass: currentPasses, + speed: autoplayIntervalMs, + a: snapshots.a, + b: snapshots.b, + open: isOpen, + auto: isAutoplay + }; + } + + function buildShareUrl() { + var url = new URL(window.location.href); + var params = url.searchParams; + var cleanupKeys = [ + "pass", + "passes", + "passpanel", + "debugpasses", + "passdebug", + "passsweepms", + "passautoplayms", + "passspeed", + "passauto", + "passautoplay", + "autosweep", + "passstate" + ]; + + cleanupKeys.forEach(function (key) { + params.delete(key); + }); + + params.set("passstate", JSON.stringify(serializePassState())); + return url.toString(); + } + + function syncShareUrlField() { + if (!shareInput) { + return; + } + + shareInput.value = buildShareUrl(); + } + + function syncSourceLabel(source) { + sourceValue.textContent = normalizePassSource(source); + } + + function syncSweepLabel() { + if (!sweepValue) { + return; + } + + sweepValue.textContent = (isAutoplay ? "running" : "idle") + " @ " + autoplayIntervalMs + "ms"; + } + + function syncUndoState() { + if (!stackValue) { + return; + } + + stackValue.textContent = "undo " + undoStack.length + " | redo " + redoStack.length; + undoButton.disabled = undoStack.length === 0; + redoButton.disabled = redoStack.length === 0; + } + + function shouldTrackUndo(source, nextPass) { + if (!Number.isFinite(nextPass) || nextPass === currentPasses) { + return false; + } + + var normalized = String(source || "panel"); + if (normalized === "autoplay" || normalized.indexOf("undo") !== -1 || normalized.indexOf("redo") !== -1 || normalized === "state-init" || normalized === "event") { + return false; + } + + return true; + } + + function pushUndo(previousPass) { + undoStack.push(previousPass); + if (undoStack.length > PASS_MAX) { + undoStack.shift(); + } + redoStack = []; + persistUndoStacks(); + syncUndoState(); + } + + function runUndo(source) { + if (!undoStack.length) { + return; + } + + var targetPass = undoStack.pop(); + if (targetPass === currentPasses) { + persistUndoStacks(); + syncUndoState(); + return; + } + + redoStack.push(currentPasses); + if (redoStack.length > PASS_MAX) { + redoStack.shift(); + } + + persistUndoStacks(); + applyPassCount(targetPass, true, source || "undo"); + syncUndoState(); + } + + function runRedo(source) { + if (!redoStack.length) { + return; + } + + var targetPass = redoStack.pop(); + if (targetPass === currentPasses) { + persistUndoStacks(); + syncUndoState(); + return; + } + + undoStack.push(currentPasses); + if (undoStack.length > PASS_MAX) { + undoStack.shift(); + } + + persistUndoStacks(); + applyPassCount(targetPass, true, source || "redo"); + syncUndoState(); + } + + function renderHistory() { + if (!timeline) { + return; + } + + timeline.innerHTML = ""; + + if (!passHistory.length) { + var empty = document.createElement("li"); + empty.className = "site-pass-debug__timeline-item is-empty"; + empty.textContent = "No pass changes yet"; + timeline.appendChild(empty); + return; + } + + passHistory.forEach(function (entry) { + var item = document.createElement("li"); + item.className = "site-pass-debug__timeline-item site-pass-debug__timeline-item--restore"; + item.tabIndex = 0; + item.setAttribute("role", "button"); + item.title = "Restore pass " + entry.pass; + item.setAttribute("aria-label", "Restore " + entry.pass + " passes (" + entry.source + ")"); + + var stamp = document.createElement("span"); + stamp.className = "site-pass-debug__timeline-time"; + stamp.textContent = entry.stamp; + + var detail = document.createElement("span"); + detail.className = "site-pass-debug__timeline-detail"; + var deltaText = entry.delta === null ? "" : " (" + (entry.delta >= 0 ? "+" : "") + entry.delta + ")"; + detail.textContent = entry.source + " -> " + entry.pass + deltaText; + + item.appendChild(stamp); + item.appendChild(detail); + + (function (passValue) { + function handleRestore(evt) { + if (evt.type === "keydown" && evt.key !== "Enter" && evt.key !== " ") { + return; + } + if (evt.key === " ") { + evt.preventDefault(); + } + applyPassCount(passValue, true, "timeline-restore"); + } + item.addEventListener("click", handleRestore); + item.addEventListener("keydown", handleRestore); + })(entry.pass); + + timeline.appendChild(item); + }); + } + + function pushHistory(passCount, source) { + var normalizedSource = normalizePassSource(source || "event"); + var now = new Date(); + var stamp = now.toLocaleTimeString([], { + hour: "2-digit", + minute: "2-digit", + second: "2-digit" + }); + + var top = passHistory[0]; + if (top && normalizedSource === "autoplay" && top.source === "autoplay") { + var autoplayPrev = top.pass; + top.pass = passCount; + top.stamp = stamp; + top.delta = passCount - autoplayPrev; + renderHistory(); + return; + } + + if (top && top.pass === passCount && top.source === normalizedSource) { + return; + } + + var previousPass = top ? top.pass : null; + passHistory.unshift({ + pass: passCount, + source: normalizedSource, + stamp: stamp, + delta: previousPass === null ? null : passCount - previousPass + }); + + if (passHistory.length > PASS_HISTORY_MAX) { + passHistory.length = PASS_HISTORY_MAX; + } + + renderHistory(); + } + + function clearHistory(sourceLabel) { + passHistory = []; + renderHistory(); + syncSourceLabel(sourceLabel || "history-clear"); + } + + function syncSnapshotUI(passCount) { + var hasA = Number.isFinite(snapshots.a); + var hasB = Number.isFinite(snapshots.b); + + applyAButton.disabled = !hasA; + applyBButton.disabled = !hasB; + toggleAbButton.disabled = !hasA && !hasB; + + saveAButton.classList.toggle("is-active", hasA && snapshots.a === passCount); + saveBButton.classList.toggle("is-active", hasB && snapshots.b === passCount); + + if (hasA || hasB) { + var nextLabel = "A"; + if (hasA && hasB) { + nextLabel = passCount === snapshots.a ? "B" : "A"; + } else if (hasB && !hasA) { + nextLabel = "B"; + } + toggleAbButton.textContent = "Flip " + nextLabel; + } else { + toggleAbButton.textContent = "Flip A/B"; + } + + if (hasA && hasB) { + var delta = snapshots.b - snapshots.a; + compareValue.textContent = "A " + snapshots.a + " | B " + snapshots.b + " | Δ " + (delta >= 0 ? "+" : "") + delta; + } else { + compareValue.textContent = "A " + (hasA ? snapshots.a : "-") + " | B " + (hasB ? snapshots.b : "-"); + } + + syncShareUrlField(); + } + + function applySnapshotToggle(source) { + var hasA = Number.isFinite(snapshots.a); + var hasB = Number.isFinite(snapshots.b); + + if (!hasA && !hasB) { + return; + } + + if (hasA && hasB) { + var target = currentPasses === snapshots.a ? snapshots.b : snapshots.a; + applyPassCount(target, true, source || "snapshot-toggle"); + return; + } + + applyPassCount(hasA ? snapshots.a : snapshots.b, true, source || "snapshot-toggle"); + } + + function applyStatePayload(rawState, sourceLabel) { + var state = normalizePassState(rawState); + if (!state) { + return false; + } + + if (state.sweepMs !== null) { + setAutoplayInterval(state.sweepMs, true); + } + + if (state.snapshotA !== null) { + snapshots.a = state.snapshotA; + } + + if (state.snapshotB !== null) { + snapshots.b = state.snapshotB; + } + + persistSnapshots(); + + if (state.passCount !== null) { + applyPassCount(state.passCount, true, sourceLabel || "state"); + } else { + syncInputs(currentPasses); + syncSourceLabel(sourceLabel || "state"); + } + + if (state.open !== null) { + setPanelOpen(state.open); + } + + if (state.auto !== null) { + if (state.auto) { + startAutoplay(); + } else { + stopAutoplay(); + } + } + + syncShareUrlField(); + return true; + } + + function setAutoplayInterval(rawInterval, persistSelection) { + var normalized = toValidSpeed(rawInterval); + if (!Number.isFinite(normalized)) { + normalized = PASS_AUTOPLAY_INTERVAL_MS; + } + + autoplayIntervalMs = normalized; + speedInput.value = String(autoplayIntervalMs); + + if (persistSelection) { + trySaveValue(getPassSpeedStorageKey(kind), String(autoplayIntervalMs)); + trySaveValue(PASS_AUTOPLAY_STORAGE_KEY, String(autoplayIntervalMs)); + } + + if (isAutoplay) { + startAutoplay(); + } + + syncSweepLabel(); + syncShareUrlField(); + } + + function syncInputs(passCount) { + range.value = String(passCount); + number.value = String(passCount); + value.textContent = String(passCount); + if (badge) { badge.textContent = String(passCount); } + + presetButtons.forEach(function (button) { + var preset = Number(button.getAttribute("data-preset")); + button.classList.toggle("is-active", preset === passCount); + }); + + syncSnapshotUI(passCount); + } + + function stopAutoplay() { + if (autoplayTimer) { + clearInterval(autoplayTimer); + autoplayTimer = null; + } + isAutoplay = false; + autoplayButton.textContent = "Auto Sweep"; + autoplayButton.classList.remove("is-active"); + syncSweepLabel(); + syncShareUrlField(); + } + + function startAutoplay() { + if (window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches) { + return; + } + + stopAutoplay(); + isAutoplay = true; + autoplayButton.textContent = "Stop Sweep"; + autoplayButton.classList.add("is-active"); + syncSweepLabel(); + syncShareUrlField(); + + autoplayTimer = setInterval(function () { + if (currentPasses >= PASS_MAX) { + autoDirection = -1; + } else if (currentPasses <= PASS_MIN) { + autoDirection = 1; + } + + applyPassCount(currentPasses + autoDirection, true, "autoplay"); + }, autoplayIntervalMs); + } + + function toggleAutoplay() { + if (isAutoplay) { + stopAutoplay(); + return; + } + + startAutoplay(); + } + + function isEditableTarget(target) { + if (!target) { + return false; + } + + if (target.isContentEditable) { + return true; + } + + var tag = (target.tagName || "").toLowerCase(); + return tag === "input" || tag === "textarea" || tag === "select"; + } + + function syncFromPassEvent(event) { + if (!event || !event.detail || event.detail.kind !== kind) { + return; + } + + var detailPassCount = Number(event.detail.passCount); + if (!Number.isFinite(detailPassCount)) { + return; + } + + currentPasses = Math.round(clamp(detailPassCount, PASS_MIN, PASS_MAX)); + syncInputs(currentPasses); + syncSourceLabel(event.detail.source || "event"); + pushHistory(currentPasses, event.detail.source || "event"); + } + + function setPanelOpen(nextOpen) { + isOpen = !!nextOpen; + panel.classList.toggle("is-open", isOpen); + bodyPanel.hidden = !isOpen; + toggle.setAttribute("aria-expanded", String(isOpen)); + trySaveValue(DEBUG_OPEN_STORAGE_KEY, isOpen ? "1" : "0"); + + if (!isOpen) { + stopAutoplay(); + } + + syncShareUrlField(); + } + + function applyPassCount(rawPassCount, persistSelection, source) { + var normalized = Math.round(clamp(Number(rawPassCount), PASS_MIN, PASS_MAX)); + var effectiveSource = source || "panel"; + + if (shouldTrackUndo(effectiveSource, normalized)) { + pushUndo(currentPasses); + } + + currentPasses = runPolishPasses(kind, normalized, effectiveSource); + syncInputs(currentPasses); + syncSourceLabel(effectiveSource); + + if (persistSelection) { + persistPass(currentPasses); + } + + syncUndoState(); + syncShareUrlField(); + } + + toggle.addEventListener("click", function () { + setPanelOpen(!isOpen); + }); + + range.addEventListener("input", function () { + applyPassCount(range.value, true, "slider"); + }); + + number.addEventListener("change", function () { + applyPassCount(number.value, true, "number"); + }); + + speedInput.addEventListener("change", function () { + setAutoplayInterval(speedInput.value, true); + syncSourceLabel("speed"); + }); + + stepButtons.forEach(function (button) { + button.addEventListener("click", function () { + var delta = Number(button.getAttribute("data-step")); + applyPassCount(currentPasses + delta, true, "step"); + }); + }); + + presetButtons.forEach(function (button) { + button.addEventListener("click", function () { + var preset = Number(button.getAttribute("data-preset")); + applyPassCount(preset, true, "preset"); + }); + }); + + resetButton.addEventListener("click", function () { + applyPassCount(PASS_MAX, true, "reset"); + }); + + defaultButton.addEventListener("click", function () { + configuredPasses = getConfiguredPassCount(kind); + applyPassCount(configuredPasses, true, "default"); + }); + + autoplayButton.addEventListener("click", function () { + toggleAutoplay(); + }); + + undoButton.addEventListener("click", function () { + runUndo("undo"); + }); + + redoButton.addEventListener("click", function () { + runRedo("redo"); + }); + + saveAButton.addEventListener("click", function () { + snapshots.a = currentPasses; + persistSnapshots(); + syncSnapshotUI(currentPasses); + syncSourceLabel("save-a"); + }); + + saveBButton.addEventListener("click", function () { + snapshots.b = currentPasses; + persistSnapshots(); + syncSnapshotUI(currentPasses); + syncSourceLabel("save-b"); + }); + + applyAButton.addEventListener("click", function () { + if (Number.isFinite(snapshots.a)) { + applyPassCount(snapshots.a, true, "snapshot-a"); + } + }); + + applyBButton.addEventListener("click", function () { + if (Number.isFinite(snapshots.b)) { + applyPassCount(snapshots.b, true, "snapshot-b"); + } + }); + + toggleAbButton.addEventListener("click", function () { + applySnapshotToggle("snapshot-toggle"); + }); + + copyUrlButton.addEventListener("click", function () { + var shareUrl = buildShareUrl(); + shareInput.value = shareUrl; + + if (navigator.clipboard && typeof navigator.clipboard.writeText === "function") { + navigator.clipboard.writeText(shareUrl) + .then(function () { + syncSourceLabel("share-copied"); + }) + .catch(function () { + syncSourceLabel("share-copy-failed"); + }); + return; + } + + shareInput.focus(); + shareInput.select(); + syncSourceLabel("share-ready"); + }); + + applyUrlButton.addEventListener("click", function () { + var parsed = parsePassStateFromInput(shareInput.value); + var applied = applyStatePayload(parsed, "share-import"); + if (!applied) { + syncSourceLabel("share-invalid"); + } + }); + + pushUrlButton.addEventListener("click", function () { + var shareUrl = buildShareUrl(); + if (window.history && typeof window.history.replaceState === "function") { + window.history.replaceState({}, "", shareUrl); + syncShareUrlField(); + syncSourceLabel("url-pushed"); + return; + } + + shareInput.value = shareUrl; + syncSourceLabel("url-ready"); + }); + + clearHistoryButton.addEventListener("click", function () { + clearHistory("history-clear"); + }); + + shareInput.addEventListener("keydown", function (event) { + if (event.key === "Enter") { + event.preventDefault(); + applyUrlButton.click(); + } + }); + + closeButton.addEventListener("click", function () { + setPanelOpen(false); + }); + + document.addEventListener("site-upgrade:pass-change", syncFromPassEvent); + + document.addEventListener("visibilitychange", function () { + if (document.hidden) { + stopAutoplay(); + } + }); + + window.addEventListener("beforeunload", stopAutoplay); + + document.addEventListener("keydown", function (event) { + if (shortcutOverlay && event.key === "Escape") { + event.preventDefault(); + hideShortcutOverlay(); + return; + } + + if (isEditableTarget(event.target)) { + return; + } + + if (!(event.altKey && event.shiftKey)) { + return; + } + + if (event.key.toLowerCase() === "p") { + event.preventDefault(); + setPanelOpen(!isOpen); + return; + } + + if (event.key.toLowerCase() === "a") { + event.preventDefault(); + toggleAutoplay(); + return; + } + + if (event.key.toLowerCase() === "c") { + event.preventDefault(); + setPanelOpen(false); + return; + } + + if (event.code === "Digit0") { + event.preventDefault(); + applyPassCount(PASS_MAX, true, "shortcut-reset"); + return; + } + + if (event.code === "Digit1" || event.code === "Digit2" || event.code === "Digit3" || event.code === "Digit4") { + event.preventDefault(); + var presetMap = { + Digit1: 25, + Digit2: 50, + Digit3: 75, + Digit4: 100 + }; + applyPassCount(presetMap[event.code], true, "shortcut-preset"); + return; + } + + if (event.code === "Digit5") { + event.preventDefault(); + if (Number.isFinite(snapshots.a)) { + applyPassCount(snapshots.a, true, "shortcut-a"); + } + return; + } + + if (event.code === "Digit6") { + event.preventDefault(); + if (Number.isFinite(snapshots.b)) { + applyPassCount(snapshots.b, true, "shortcut-b"); + } + return; + } + + if (event.code === "Digit7") { + event.preventDefault(); + applySnapshotToggle("shortcut-toggle"); + return; + } + + if (event.key.toLowerCase() === "u") { + event.preventDefault(); + applyUrlButton.click(); + return; + } + + if (event.key.toLowerCase() === "i") { + event.preventDefault(); + shareInput.focus(); + shareInput.select(); + syncSourceLabel("url-focus"); + return; + } + + if (event.key.toLowerCase() === "h") { + event.preventDefault(); + clearHistory("history-clear"); + return; + } + + if (event.key === "/") { + event.preventDefault(); + if (shortcutOverlay) { + hideShortcutOverlay(); + } else { + showShortcutOverlay(); + } + return; + } + + if (event.key.toLowerCase() === "l") { + event.preventDefault(); + pushUrlButton.click(); + return; + } + + if (event.key.toLowerCase() === "z") { + event.preventDefault(); + runUndo("shortcut-undo"); + return; + } + + if (event.key.toLowerCase() === "y") { + event.preventDefault(); + runRedo("shortcut-redo"); + return; + } + + if (event.key === "ArrowUp") { + event.preventDefault(); + applyPassCount(currentPasses + 1, true, "shortcut-up"); + return; + } + + if (event.key === "ArrowDown") { + event.preventDefault(); + applyPassCount(currentPasses - 1, true, "shortcut-down"); + return; + } + + if (event.key === "ArrowRight") { + event.preventDefault(); + applyPassCount(currentPasses + 10, true, "shortcut-right"); + return; + } + + if (event.key === "ArrowLeft") { + event.preventDefault(); + applyPassCount(currentPasses - 10, true, "shortcut-left"); + } + }); + + setAutoplayInterval(autoplayIntervalMs, false); + syncSweepLabel(); + syncInputs(currentPasses); + syncUndoState(); + syncShareUrlField(); + renderHistory(); + + if (queryState && (queryState.snapshotA !== null || queryState.snapshotB !== null)) { + persistSnapshots(); + } + + if (currentPasses !== initialPassCount) { + applyPassCount(currentPasses, true, "state-init"); + } else { + syncSourceLabel("init"); + pushHistory(currentPasses, "init"); + } + + setPanelOpen(isOpen); + + if (shouldAutoStart === true) { + startAutoplay(); + } else if (shouldAutoStart === false) { + stopAutoplay(); + } + + // --- Drag to reposition --- + (function () { + var savedPos = (function () { + var raw = tryLoadValue(getPassPositionStorageKey(kind)); + if (!raw) { return null; } + try { + var p = JSON.parse(raw); + return p && Number.isFinite(p.right) && Number.isFinite(p.bottom) ? p : null; + } catch (e) { return null; } + })(); + + function applyPosition(right, bottom) { + var maxRight = window.innerWidth - 40; + var maxBottom = window.innerHeight - 40; + var r = Math.min(Math.max(right, 0), maxRight); + var b = Math.min(Math.max(bottom, 0), maxBottom); + panel.style.right = r + "px"; + panel.style.bottom = b + "px"; + } + + function resetPosition() { + panel.style.right = ""; + panel.style.bottom = ""; + try { localStorage.removeItem(getPassPositionStorageKey(kind)); } catch (e) { /* */ } + } + + if (savedPos) { + applyPosition(savedPos.right, savedPos.bottom); + } + + var dragState = null; + + dragHead.addEventListener("mousedown", function (evt) { + if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT" || evt.target.tagName === "SUMMARY")) { + return; + } + evt.preventDefault(); + var rect = panel.getBoundingClientRect(); + dragState = { + startX: evt.clientX, + startY: evt.clientY, + startRight: window.innerWidth - rect.right, + startBottom: window.innerHeight - rect.bottom + }; + panel.classList.add("is-dragging"); + }); + + document.addEventListener("mousemove", function (evt) { + if (!dragState) { return; } + var dx = evt.clientX - dragState.startX; + var dy = evt.clientY - dragState.startY; + applyPosition(dragState.startRight - dx, dragState.startBottom - dy); + }); + + document.addEventListener("mouseup", function (evt) { + if (!dragState) { return; } + panel.classList.remove("is-dragging"); + var rect = panel.getBoundingClientRect(); + var finalRight = window.innerWidth - rect.right; + var finalBottom = window.innerHeight - rect.bottom; + trySaveValue(getPassPositionStorageKey(kind), JSON.stringify({ right: finalRight, bottom: finalBottom })); + dragState = null; + }); + + dragHead.addEventListener("touchstart", function (evt) { + if (evt.touches.length !== 1) { return; } + if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT" || evt.target.tagName === "SUMMARY")) { + return; + } + var touch = evt.touches[0]; + var rect = panel.getBoundingClientRect(); + dragState = { + startX: touch.clientX, + startY: touch.clientY, + startRight: window.innerWidth - rect.right, + startBottom: window.innerHeight - rect.bottom + }; + panel.classList.add("is-dragging"); + }, { passive: true }); + + document.addEventListener("touchmove", function (evt) { + if (!dragState) { return; } + if (evt.cancelable) { evt.preventDefault(); } + var touch = evt.touches[0]; + var dx = touch.clientX - dragState.startX; + var dy = touch.clientY - dragState.startY; + applyPosition(dragState.startRight - dx, dragState.startBottom - dy); + }, { passive: false }); + + document.addEventListener("touchend", function () { + if (!dragState) { return; } + panel.classList.remove("is-dragging"); + var rect = panel.getBoundingClientRect(); + var finalRight = window.innerWidth - rect.right; + var finalBottom = window.innerHeight - rect.bottom; + trySaveValue(getPassPositionStorageKey(kind), JSON.stringify({ right: finalRight, bottom: finalBottom })); + dragState = null; + }); + + dragHead.addEventListener("dblclick", function (evt) { + if (evt.target && (evt.target.tagName === "BUTTON" || evt.target.tagName === "INPUT")) { + return; + } + resetPosition(); + }); + })(); + + // --- Shortcut cheat-sheet overlay --- + var shortcutOverlay = null; + + function showShortcutOverlay() { + if (shortcutOverlay) { return; } + shortcutOverlay = document.createElement("div"); + shortcutOverlay.className = "site-pass-shortcut-overlay"; + shortcutOverlay.setAttribute("role", "dialog"); + shortcutOverlay.setAttribute("aria-modal", "true"); + shortcutOverlay.setAttribute("aria-label", "Keyboard shortcuts"); + shortcutOverlay.innerHTML = "" + + "
    " + + "
    " + + "Keyboard Shortcuts" + + "" + + "
    " + + "
    " + + "
    Alt+Shift+P
    Toggle panel
    " + + "
    Alt+Shift+A
    Toggle auto sweep
    " + + "
    Alt+Shift+C
    Close panel
    " + + "
    Alt+Shift+1–4
    Presets 25 / 50 / 75 / 100
    " + + "
    Alt+Shift+0
    Reset to 100
    " + + "
    Alt+Shift+5
    Apply snapshot A
    " + + "
    Alt+Shift+6
    Apply snapshot B
    " + + "
    Alt+Shift+7
    Flip A/B
    " + + "
    Alt+Shift+Z
    Undo
    " + + "
    Alt+Shift+Y
    Redo
    " + + "
    Alt+Shift+U
    Apply URL state
    " + + "
    Alt+Shift+L
    Push URL state
    " + + "
    Alt+Shift+I
    Focus URL input
    " + + "
    Alt+Shift+H
    Clear history log
    " + + "
    Alt+Shift+/
    Show this overlay
    " + + "
    Arrow Up / Right
    +1 / +10 passes
    " + + "
    Arrow Down / Left
    −1 / −10 passes
    " + + "
    " + + "

    Double-click panel header to reset position

    " + + "
    "; + document.body.appendChild(shortcutOverlay); + var closeBtn = shortcutOverlay.querySelector(".site-pass-shortcut-overlay__close"); + if (closeBtn) { closeBtn.focus(); } + + function dismissOverlay(evt) { + if (!shortcutOverlay) { return; } + var box = shortcutOverlay.querySelector(".site-pass-shortcut-overlay__box"); + if (!box || !box.contains(evt.target)) { + hideShortcutOverlay(); + } + } + shortcutOverlay._dismiss = dismissOverlay; + shortcutOverlay.addEventListener("click", dismissOverlay); + if (closeBtn) { + closeBtn.addEventListener("click", function () { hideShortcutOverlay(); }); + } + } + + function hideShortcutOverlay() { + if (!shortcutOverlay) { return; } + shortcutOverlay.remove(); + shortcutOverlay = null; + } + + var sectionEls = panel.querySelectorAll(".site-pass-debug__section"); + var savedSections = (function () { + var raw = tryLoadValue(getPassSectionsStorageKey(kind)); + if (!raw) { return null; } + try { return JSON.parse(raw); } catch (e) { return null; } + })(); + + if (savedSections) { + sectionEls.forEach(function (el) { + var name = el.getAttribute("data-section"); + if (name && Object.prototype.hasOwnProperty.call(savedSections, name)) { + if (savedSections[name]) { + el.setAttribute("open", ""); + } else { + el.removeAttribute("open"); + } + } + }); + } + + // Animated details open/close + function animateDetails(el, opening) { + var inner = el.querySelector("summary + *") || el.querySelector("summary"); + if (!inner) { + return; + } + + // Respect reduced-motion preference — skip animation + var prefersReduced = window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches; + + if (el._animating) { + clearTimeout(el._animating); + el._animating = null; + if (el._innerEl) { el._innerEl.style.height = ""; el._innerEl.style.overflow = ""; } + } + + var wrapper = el._innerEl; + if (!wrapper) { + // Wrap all non-summary children in an animate wrapper once + wrapper = document.createElement("div"); + wrapper.className = "site-pass-debug__section-body"; + var children = Array.prototype.slice.call(el.childNodes); + children.forEach(function (node) { + if (node.nodeType === 1 && node.tagName === "SUMMARY") { return; } + wrapper.appendChild(node); + }); + el.appendChild(wrapper); + el._innerEl = wrapper; + } + + if (prefersReduced) { + wrapper.style.height = ""; + wrapper.style.overflow = ""; + return; + } + + var targetHeight = opening ? wrapper.scrollHeight + "px" : "0px"; + wrapper.style.overflow = "hidden"; + wrapper.style.height = opening ? "0px" : wrapper.scrollHeight + "px"; + + // Force reflow + void wrapper.offsetHeight; + wrapper.style.transition = "height 0.2s ease"; + wrapper.style.height = targetHeight; + + var DURATION = 210; + el._animating = setTimeout(function () { + wrapper.style.transition = ""; + wrapper.style.overflow = ""; + if (opening) { wrapper.style.height = ""; } + el._animating = null; + }, DURATION); + } + + sectionEls.forEach(function (el) { + // Intercept toggle: cancel default, run animation, then commit open state + el.addEventListener("click", function (evt) { + if (evt.target && evt.target.tagName === "SUMMARY") { + evt.preventDefault(); + var willOpen = !el.open; + if (willOpen) { el.setAttribute("open", ""); } + animateDetails(el, willOpen); + if (!willOpen) { + setTimeout(function () { el.removeAttribute("open"); }, 200); + } + var state = {}; + sectionEls.forEach(function (s) { + var sName = s.getAttribute("data-section"); + if (sName) { state[sName] = willOpen ? true : false; } + }); + // Only update the toggled section + var secName = el.getAttribute("data-section"); + var fullState = {}; + sectionEls.forEach(function (s) { + var n = s.getAttribute("data-section"); + if (n) { fullState[n] = n === secName ? willOpen : s.open; } + }); + trySaveValue(getPassSectionsStorageKey(kind), JSON.stringify(fullState)); + } + }); + }); + + // --- Panel width resize handle --- + (function () { + var PANEL_MIN_W = 220; + var PANEL_MAX_W = Math.min(560, window.innerWidth - 28); + var savedWidth = (function () { + var raw = tryLoadValue(getPassWidthStorageKey(kind)); + var v = raw ? parseFloat(raw) : NaN; + return Number.isFinite(v) ? v : null; + })(); + + var innerPanel = panel.querySelector(".site-pass-debug__panel"); + + function applyWidth(w) { + var clamped = Math.min(Math.max(w, PANEL_MIN_W), Math.min(560, window.innerWidth - 28)); + innerPanel.style.width = clamped + "px"; + return clamped; + } + + function resetWidth() { + innerPanel.style.width = ""; + try { localStorage.removeItem(getPassWidthStorageKey(kind)); } catch (e) { /* */ } + } + + if (savedWidth) { + applyWidth(savedWidth); + } + + var resizeDrag = null; + + resizeHandle.addEventListener("mousedown", function (evt) { + evt.preventDefault(); + var rect = innerPanel.getBoundingClientRect(); + resizeDrag = { startX: evt.clientX, startWidth: rect.width }; + panel.classList.add("is-resizing"); + }); + + document.addEventListener("mousemove", function (evt) { + if (!resizeDrag) { return; } + // Handle is on the left edge — drag left = wider, drag right = narrower + var dx = resizeDrag.startX - evt.clientX; + applyWidth(resizeDrag.startWidth + dx); + }); + + document.addEventListener("mouseup", function () { + if (!resizeDrag) { return; } + panel.classList.remove("is-resizing"); + var finalWidth = parseFloat(innerPanel.style.width) || null; + if (finalWidth) { + trySaveValue(getPassWidthStorageKey(kind), String(finalWidth)); + } + resizeDrag = null; + }); + + resizeHandle.addEventListener("dblclick", function () { + resetWidth(); + }); + })(); + + if (exportJsonButton) { + exportJsonButton.addEventListener("click", function () { + var json = JSON.stringify(serializePassState(), null, 2); + if (navigator.clipboard && navigator.clipboard.writeText) { + navigator.clipboard.writeText(json).then(function () { + var orig = exportJsonButton.textContent; + exportJsonButton.textContent = "Copied!"; + setTimeout(function () { exportJsonButton.textContent = orig; }, 1200); + }).catch(function () { + shareInput.value = json; + shareInput.select(); + }); + } else { + shareInput.value = json; + shareInput.select(); + } + }); + } + + // --- Appearance controls (opacity / blur) --- + (function () { + var innerPanel = panel.querySelector(".site-pass-debug__panel"); + var blurEnabled = true; + + var saved = (function () { + var raw = tryLoadValue(getPassPanelStyleStorageKey(kind)); + if (!raw) { return null; } + try { return JSON.parse(raw); } catch (e) { return null; } + })(); + + var currentOpacity = (saved && Number.isFinite(saved.opacity)) ? saved.opacity : 100; + var currentBlur = (saved && typeof saved.blur === "boolean") ? saved.blur : true; + blurEnabled = currentBlur; + + function applyAppearance(opacity, blur) { + innerPanel.style.opacity = (opacity / 100).toFixed(2); + innerPanel.style.backdropFilter = blur ? "blur(10px)" : "none"; + innerPanel.style.webkitBackdropFilter = blur ? "blur(10px)" : "none"; + } + + function syncAppearanceUI() { + if (opacityInput) { opacityInput.value = String(currentOpacity); } + if (toggleBlurButton) { + toggleBlurButton.textContent = blurEnabled ? "Blur On" : "Blur Off"; + toggleBlurButton.classList.toggle("is-active", blurEnabled); + } + } + + function persistAppearance() { + trySaveValue(getPassPanelStyleStorageKey(kind), JSON.stringify({ opacity: currentOpacity, blur: blurEnabled })); + } + + applyAppearance(currentOpacity, blurEnabled); + syncAppearanceUI(); + + if (opacityInput) { + opacityInput.addEventListener("change", function () { + var v = Math.min(100, Math.max(20, Number(opacityInput.value) || 100)); + currentOpacity = v; + opacityInput.value = String(v); + applyAppearance(currentOpacity, blurEnabled); + persistAppearance(); + }); + opacityInput.addEventListener("input", function () { + var v = Math.min(100, Math.max(20, Number(opacityInput.value) || 100)); + applyAppearance(v, blurEnabled); + }); + } + + if (toggleBlurButton) { + toggleBlurButton.addEventListener("click", function () { + blurEnabled = !blurEnabled; + applyAppearance(currentOpacity, blurEnabled); + syncAppearanceUI(); + persistAppearance(); + }); + } + + if (resetAppearanceButton) { + resetAppearanceButton.addEventListener("click", function () { + currentOpacity = 100; + blurEnabled = true; + applyAppearance(currentOpacity, blurEnabled); + syncAppearanceUI(); + try { localStorage.removeItem(getPassPanelStyleStorageKey(kind)); } catch (e) { /* */ } + }); + } + })(); + } + + function ensureSkipLink() { + if (document.querySelector(".site-upgrade-skip")) { + return; + } + + var candidates = ["main", "#main-content", "#mainContent", ".main", ".page", ".container", ".content"]; + var target = null; + + for (var i = 0; i < candidates.length; i += 1) { + var element = document.querySelector(candidates[i]); + if (element) { + target = element; + break; + } + } + + if (!target) { + return; + } + + if (!target.id) { + target.id = "site-upgrade-main"; + } + + var skip = document.createElement("a"); + skip.className = "site-upgrade-skip"; + skip.href = "#" + target.id; + skip.textContent = "Skip to main content"; + document.body.prepend(skip); + } + + function upgradeExternalLinks() { + var links = document.querySelectorAll('a[target="_blank"]'); + links.forEach(function (link) { + var rel = (link.getAttribute("rel") || "").toLowerCase(); + if (!rel.includes("noopener")) { + link.setAttribute("rel", (rel ? rel + " " : "") + "noopener noreferrer"); + } + }); + } + + function applyRevealAnimations() { + if (window.matchMedia && window.matchMedia("(prefers-reduced-motion: reduce)").matches) { + return; + } + + var selectors = [ + "section", + ".card", + ".hero-card", + ".feature-card", + ".system-card", + ".welcome-card", + ".route-card", + ".signal-card", + ".plan-card", + ".link-card", + ".sidebar-card", + ".content-shell", + ".category-block", + ".flow-card", + ".search-panel", + ".resource-group", + ".terminal", + ".run-copy", + ".run-aside", + ".hero-banner", + ".glass-panel", + ".jobs-section", + ".gpu-status", + ".chat-container", + ".stage-container", + ".object-manager", + ".page-link", + ".job-item", + ".metric-card", + ".category-card", + ".product-card" + ]; + + var seen = new Set(); + selectors.forEach(function (selector) { + document.querySelectorAll(selector).forEach(function (node) { + seen.add(node); + }); + }); + + var nodes = Array.from(seen).filter(function (node) { + return node && (node.childElementCount > 0 || (node.textContent || "").trim().length > 0); + }); + + if (!nodes.length) { + return; + } + + nodes.forEach(function (node, index) { + node.classList.add("site-upgrade-reveal"); + node.style.setProperty("--site-upgrade-delay", Math.min(index * 28, 420) + "ms"); + }); + + if (!("IntersectionObserver" in window)) { + nodes.forEach(function (node) { + node.classList.add("is-visible"); + }); + return; + } + + var observer = new IntersectionObserver(function (entries, obs) { + entries.forEach(function (entry) { + if (entry.isIntersecting) { + entry.target.classList.add("is-visible"); + obs.unobserve(entry.target); + } + }); + }, { + threshold: 0.12, + rootMargin: "0px 0px -8% 0px" + }); + + nodes.forEach(function (node) { + observer.observe(node); + }); + } + + function initUpgrade() { + var kind = applySiteKindClass(); + var configuredPasses = getConfiguredPassCount(kind); + var initialPasses = runPolishPasses(kind, configuredPasses); + ensureSkipLink(); + upgradeExternalLinks(); + applyRevealAnimations(); + createPassDebugPanel(kind, initialPasses); + } + + if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", initUpgrade, { once: true }); + } else { + initUpgrade(); + } +})(); diff --git a/docs/guides/ADVANCED_AUTOMATION.md b/docs/guides/ADVANCED_AUTOMATION.md index 1eb7e05b3..12180e920 100644 --- a/docs/guides/ADVANCED_AUTOMATION.md +++ b/docs/guides/ADVANCED_AUTOMATION.md @@ -1,815 +1,815 @@ -# Advanced Automation System - -Comprehensive orchestration and automation layer for QAI workspace with multi-level automation capabilities. - -## Architecture Overview - -``` -┌─────────────────────────────────────────────────────────────┐ -│ Auto-Scheduler (Top Level) │ -│ - Cron-based scheduling │ -│ - Event-based triggers │ -│ - Resource-aware execution │ -└────────────────────┬────────────────────────────────────────┘ - │ -┌────────────────────▼────────────────────────────────────────┐ -│ Master Orchestrator (Workflow Layer) │ -│ - Coordinates sub-orchestrators │ -│ - Dependency management │ -│ - Success/failure handlers │ -└────┬───────────────┬───────────────┬────────────────────────┘ - │ │ │ -┌────▼──────┐ ┌────▼──────┐ ┌────▼──────┐ -│ AutoTrain │ │ Quantum │ │ Evaluation│ -│ │ │ AutoRun │ │ AutoRun │ -└───────────┘ └───────────┘ └───────────┘ -``` - -## Orchestrator Levels - -### Level 1: Domain Orchestrators (Job Execution) -- **autotrain.py** - LoRA fine-tuning jobs -- **quantum_autorun.py** - Quantum training jobs -- **evaluation_autorun.py** - Model evaluation jobs - -### Level 2: Master Orchestrator (Workflow Coordination) -- **master_orchestrator.py** - Coordinates workflows across orchestrators -- Dependencies, priorities, timeouts -- Success/failure handlers -- Resource monitoring - -### Level 3: CI/CD Orchestrator (Quality Gates) -- **ci_orchestrator.py** - Parallel validation -- Unit & integration tests -- Code quality checks -- Deployment preparation - -### Level 4: Auto-Scheduler (Time-Based Automation) -- **auto_scheduler.py** - Cron-based scheduling -- Persistent state -- Auto-retry logic -- Failure notifications - -## Quick Start - -### 1. Run Quick Validation (All Orchestrators) - -```powershell -# Validate all configurations in parallel (fast) -python .\scripts\ci_orchestrator.py --validate-all -``` - -**Output**: Validates autotrain, quantum_autorun, evaluation_autorun in parallel (~1 second) - -### 2. Run Master Orchestrator Workflow - -```powershell -# Run quick validation workflow (dry-run all orchestrators) -python .\scripts\master_orchestrator.py --workflow quick_validation - -# Run daily full pipeline (actual training) -python .\scripts\master_orchestrator.py --workflow daily_full_pipeline -``` - -### 3. Schedule Automated Runs - -```powershell -# Add scheduled job (requires pip install croniter) -python .\scripts\auto_scheduler.py --schedule "daily_training" --workflow "daily_full_pipeline" --cron "0 2 * * *" - -# Start scheduler daemon -python .\scripts\auto_scheduler.py --start - -# Check scheduled jobs -python .\scripts\auto_scheduler.py --list -``` - -### 4. Run Full CI Pipeline - -```powershell -# Complete CI/CD validation + tests + preparation -python .\scripts\ci_orchestrator.py --ci-pipeline -``` - -## VS Code Tasks - -All orchestrators available as VS Code tasks (Ctrl+Shift+P → "Run Task"): - -| Task | Command | -|------|---------| -| **Run: Evaluation AutoRun (dry-run)** | Validate evaluation jobs | -| **Run: Evaluation AutoRun (all)** | Run all evaluation jobs | -| **Run: CI Validate All** | Validate all orchestrators in parallel | -| **Run: CI Pipeline** | Full CI/CD pipeline execution | -| **Run: Master Orchestrator - Quick Validation** | Quick workflow validation | -| **Run: Master Orchestrator - Status** | Show orchestrator status | -| **Run: Model Deployer - Scan** | Scan for deployable models | -| **Run: Model Deployer - Deploy Best** | Auto-deploy best model | -| **Run: Resource Monitor - Snapshot** | Capture resource snapshot | -| **Run: Resource Monitor - Stream** | Real-time resource monitoring | -| **Run: Batch Evaluator - Scan** | Scan and evaluate all models | -| **Run: Results Exporter - Export to Markdown** | Export all results to Markdown | -| **Run: Auto Scheduler - List** | List scheduled jobs | -| **Run: CI Validate All** | Parallel validation of all orchestrators | -| **Run: CI Pipeline** | Full CI/CD pipeline | -| **Run: Master Orchestrator - Quick Validation** | Run quick_validation workflow | -| **Run: Master Orchestrator - Status** | Show master orchestrator status | - -## Configuration Files - -### master_orchestrator.yaml - -Defines workflows and orchestrator coordination: - -```yaml -workflows: - - name: daily_full_pipeline - enabled: true - trigger: schedule - schedule: "0 1 * * *" # Daily at 1 AM - orchestrators: - - autotrain - - quantum_autorun - - evaluation_autorun - on_success: - - notify_slack - - deploy_best_models - on_failure: - - notify_slack - - create_issue -``` - -### Workflow Patterns - -#### Quick Validation (CI/CD) -Dry-run all orchestrators in sequence, fail fast on errors. - -```powershell -python .\scripts\master_orchestrator.py --workflow quick_validation -``` - -#### Daily Full Pipeline -Run training → quantum → evaluation with notifications. - -```powershell -python .\scripts\master_orchestrator.py --workflow daily_full_pipeline -``` - -#### Weekly Comprehensive -Full datasets, all jobs, comprehensive reports. - -```powershell -python .\scripts\master_orchestrator.py --workflow weekly_comprehensive -``` - -## CI/CD Integration - -### GitHub Actions Example - -```yaml -name: QAI CI Pipeline - -on: [push, pull_request] - -jobs: - validate: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 - with: - python-version: '3.11' - - - name: Install dependencies - run: pip install pyyaml pytest - - - name: Validate All Orchestrators - run: python scripts/orchestrators/ci_orchestrator.py --validate-all - - - name: Run Unit Tests - run: python scripts/orchestrators/ci_orchestrator.py --quick-test - - - name: Upload Results - uses: actions/upload-artifact@v3 - with: - name: ci-results - path: data_out/ci_orchestrator/ -``` - -### Azure Pipelines Example - -```yaml -trigger: - - main - -pool: - vmImage: 'windows-latest' - -steps: -- task: UsePythonVersion@0 - inputs: - versionSpec: '3.11' - -- script: pip install pyyaml pytest - displayName: 'Install dependencies' - -- script: python scripts/orchestrators/ci_orchestrator.py --validate-all - displayName: 'Validate orchestrators' - -- script: python scripts/orchestrators/ci_orchestrator.py --ci-pipeline - displayName: 'Run CI pipeline' - -- task: PublishBuildArtifacts@1 - inputs: - pathToPublish: 'data_out/ci_orchestrator' - artifactName: 'ci-results' -``` - -## Auto-Scheduler Commands - -### Schedule a Job - -```powershell -# Daily at 2 AM -python .\scripts\auto_scheduler.py --schedule "daily_training" --workflow "daily_full_pipeline" --cron "0 2 * * *" - -# Every 6 hours -python .\scripts\auto_scheduler.py --schedule "frequent_eval" --workflow "quick_validation" --cron "0 */6 * * *" - -# Weekly on Sundays at midnight -python .\scripts\auto_scheduler.py --schedule "weekly_full" --workflow "weekly_comprehensive" --cron "0 0 * * 0" -``` - -### Manage Scheduled Jobs - -```powershell -# List all jobs -python .\scripts\auto_scheduler.py --list - -# Enable/disable job -python .\scripts\auto_scheduler.py --enable "daily_training" -python .\scripts\auto_scheduler.py --disable "daily_training" - -# Remove job -python .\scripts\auto_scheduler.py --remove "daily_training" - -# Check status -python .\scripts\auto_scheduler.py --status -``` - -### Run as Daemon - -```powershell -# Start in foreground -python .\scripts\auto_scheduler.py --start - -# With custom check interval (default 60 seconds) -python .\scripts\auto_scheduler.py --start --check-interval 30 -``` - -**Note**: Install `croniter` for cron scheduling: `pip install croniter` - -## Master Orchestrator Commands - -### List Available Resources - -```powershell -# List all orchestrators -python .\scripts\master_orchestrator.py --list-orchestrators - -# List all workflows -python .\scripts\master_orchestrator.py --list-workflows - -# Show current status -python .\scripts\master_orchestrator.py --status -``` - -### Run Workflows - -```powershell -# Quick validation (dry-run all) -python .\scripts\master_orchestrator.py --workflow quick_validation - -# Daily full pipeline -python .\scripts\master_orchestrator.py --workflow daily_full_pipeline - -# Weekly comprehensive -python .\scripts\master_orchestrator.py --workflow weekly_comprehensive -``` - -### Run Individual Orchestrators - -```powershell -# Run single orchestrator through master -python .\scripts\master_orchestrator.py --orchestrator autotrain -python .\scripts\master_orchestrator.py --orchestrator quantum_autorun -python .\scripts\master_orchestrator.py --orchestrator evaluation_autorun -``` - -### Daemon Mode - -```powershell -# Run as background service (checks schedules every 60s) -python .\scripts\master_orchestrator.py --daemon - -# Custom check interval -python .\scripts\master_orchestrator.py --daemon --check-interval 120 -``` - -## CI Orchestrator Commands - -### Validation - -```powershell -# Validate all orchestrators (parallel, ~1 second) -python .\scripts\ci_orchestrator.py --validate-all -``` - -### Testing - -```powershell -# Quick test (unit tests only) -python .\scripts\ci_orchestrator.py --quick-test - -# Full test suite (unit + integration) -python .\scripts\ci_orchestrator.py --full-test -``` - -### Deployment - -```powershell -# Prepare deployment artifacts -python .\scripts\ci_orchestrator.py --prepare-deployment -``` - -### Full Pipeline - -```powershell -# Run complete CI/CD pipeline -python .\scripts\ci_orchestrator.py --ci-pipeline -``` - -**Steps**: Validation → Unit Tests → Dataset Validation → Code Quality → Security Scan → Integration Tests → Deployment Preparation - -## Output Structure - -``` -data_out/ -├── master_orchestrator/ -│ ├── status.json # Global status -│ ├── quick_validation_.json # Workflow results -│ └── / -│ └── / -│ └── stdout.log # Execution logs -│ -├── ci_orchestrator/ -│ ├── ci_results.json # CI pipeline results -│ └── deployment_artifacts.json # Deployment manifest -│ -└── auto_scheduler/ - ├── schedule.json # Scheduled jobs config - └── state.json # Scheduler state (last runs, etc.) -``` - -## Status JSON Schema - -### Master Orchestrator Status - -```json -{ - "generated_at": "2025-11-22T17:30:00Z", - "orchestrators": [ - { - "name": "autotrain", - "script": "scripts/training/autotrain.py", - "enabled": true, - "schedule": "0 2 * * *", - "priority": 1, - "dependencies": [], - "last_run": "20251122T173000Z", - "last_status": "succeeded" - } - ], - "workflows": [ - { - "name": "quick_validation", - "enabled": true, - "trigger": "manual", - "orchestrators": ["autotrain", "quantum_autorun", "evaluation_autorun"] - } - ], - "resource_usage": { - "cpu_percent": 25.3, - "memory_percent": 45.8, - "disk_percent": 62.1 - } -} -``` - -### CI Orchestrator Results - -```json -{ - "generated_at": "2025-11-22T17:30:00Z", - "total_steps": 7, - "succeeded": 6, - "failed": 0, - "skipped": 1, - "results": [ - { - "name": "validate_orchestrators", - "status": "succeeded", - "duration_sec": 1.2, - "critical": true - } - ] -} -``` - -### Auto-Scheduler State - -```json -{ - "scheduler_running": true, - "total_jobs": 3, - "enabled_jobs": 2, - "disabled_jobs": 1, - "jobs": [ - { - "name": "daily_training", - "workflow": "daily_full_pipeline", - "cron": "0 2 * * *", - "enabled": true, - "last_run": "2025-11-22T02:00:15Z", - "last_status": "succeeded", - "next_run": "2025-11-23 02:00:00", - "run_count": 15, - "consecutive_failures": 0 - } - ] -} -``` - -## Advanced Features - -### Dependency Management - -Master orchestrator handles dependencies automatically: - -```yaml -orchestrators: - - name: evaluation_autorun - dependencies: [autotrain, quantum_autorun] # Wait for these -``` - -### Resource Limits - -```yaml -resource_limits: - max_concurrent_orchestrators: 2 - max_cpu_percent: 80 - max_memory_gb: 16 - pause_on_resource_exhaustion: true -``` - -### Auto-Retry Logic - -```yaml -orchestrators: - - name: autotrain - retry_on_failure: 3 # Retry up to 3 times - timeout_minutes: 240 # 4 hour timeout -``` - -### Failure Handling - -Auto-scheduler disables jobs after consecutive failures: - -```python -max_consecutive_failures: 3 # Disable after 3 failures -notify_on_failure: true # Send notification -``` - -## Monitoring & Notifications - -### Slack Integration (Placeholder) - -```yaml -notifications: - slack_enabled: true - webhook_url: "${SLACK_WEBHOOK_URL}" - notify_on_failure: true - notify_on_success: false -``` - -### Email Notifications (Placeholder) - -```yaml -notifications: - email_enabled: true - to: "${ADMIN_EMAIL}" - notify_on_failure: true - notify_on_degradation: true -``` - -## Troubleshooting - -### Orchestrator Not Running - -```powershell -# Check master orchestrator status -python .\scripts\master_orchestrator.py --status - -# Check specific orchestrator -python .\scripts\autotrain.py --dry-run -``` - -### Scheduler Not Triggering - -```powershell -# Install croniter -pip install croniter - -# Check schedule syntax -python .\scripts\auto_scheduler.py --list - -# Verify next_run time is set -``` - -### CI Pipeline Failures - -```powershell -# Run individual steps -python .\scripts\ci_orchestrator.py --validate-all -python .\scripts\ci_orchestrator.py --quick-test - -# Check results -type data_out\ci_orchestrator\ci_results.json -``` - -### Resource Exhaustion - -Master orchestrator monitors resources (requires `psutil`): - -```powershell -# Install psutil -pip install psutil - -# Check status with resource usage -python .\scripts\master_orchestrator.py --status -``` - -## Best Practices - -1. **Start with dry-run**: Always validate before execution -2. **Use CI orchestrator**: For pre-commit/pre-push validation -3. **Schedule wisely**: Avoid overlapping resource-intensive jobs -4. **Monitor logs**: Check `data_out/` for execution details -5. **Enable notifications**: Get alerts for failures -6. **Resource limits**: Set appropriate limits for your hardware -7. **Backup regularly**: Automated backups in `master_orchestrator.yaml` - -## Integration Examples - -### Pre-Commit Hook - -```bash -#!/bin/bash -# .git/hooks/pre-commit -python scripts/orchestrators/ci_orchestrator.py --validate-all -exit $? -``` - -### Continuous Deployment - -```powershell -# Run after successful training -python .\scripts\ci_orchestrator.py --prepare-deployment - -# Deploy if validation passes -if ($LASTEXITCODE -eq 0) { - # Deploy to Azure Functions, etc. -} -``` - -### Performance Monitoring - -```powershell -# Scheduled performance check -python .\scripts\auto_scheduler.py --schedule "perf_check" --workflow "quick_validation" --cron "*/30 * * * *" -``` - -## Future Enhancements - -- [ ] Proper cron parser without croniter dependency -- [ ] Slack/Email notification implementation -- [ ] Web dashboard for monitoring -- [ ] Distributed execution across machines -- [ ] GPU resource aware scheduling -- [ ] Model performance tracking -- [ ] Automatic hyperparameter tuning integration -- [ ] Cost estimation and budgeting -- [ ] Rollback on deployment failure - -## New Advanced Features - -### Model Deployment with Quality Gates - -**model_deployer.py** - Automatically deploy trained models with validation: - -```powershell -# Scan for trained models and check quality gates -python .\scripts\model_deployer.py --scan - -# Deploy best model with canary strategy -python .\scripts\model_deployer.py --deploy best --strategy canary - -# Check deployment status -python .\scripts\model_deployer.py --status - -# Rollback to previous version -python .\scripts\model_deployer.py --rollback v1_20251122_123456 -``` - -**Features**: -- Quality gate validation (accuracy > 0.75, loss < 0.5) -- Model scoring and ranking -- Deployment strategies: direct, canary, blue-green -- Version tracking and rollback -- Model registry with metadata - -### Real-Time Resource Monitoring - -**resource_monitor.py** - Monitor system resources with alerts: - -```powershell -# Capture single snapshot -python .\scripts\resource_monitor.py --snapshot - -# Stream real-time monitoring (60s) -python .\scripts\resource_monitor.py --stream --duration 60 - -# View historical data (last 24 hours) -python .\scripts\resource_monitor.py --history --hours 24 - -# Export to CSV -python .\scripts\resource_monitor.py --export csv - -# Set custom alert threshold -python .\scripts\resource_monitor.py --set-threshold cpu_percent 85 -``` - -**Features**: -- CPU, memory, disk, GPU monitoring -- Threshold-based alerts -- Historical data collection (JSONL format) -- Export to CSV/JSON -- Integration with orchestrators - -### Batch Model Evaluation - -**batch_evaluator.py** - Parallel evaluation of multiple models: - -```powershell -# Scan and evaluate all trained models -python .\scripts\batch_evaluator.py --scan-models --evaluate-all - -# Compare specific models -python .\scripts\batch_evaluator.py --compare lora_v1 lora_v2 quantum_v1 - -# Export results to Markdown report -python .\scripts\batch_evaluator.py --export markdown --output report.md - -# Export to JSON -python .\scripts\batch_evaluator.py --export json -``` - -**Features**: -- Parallel evaluation (ThreadPoolExecutor) -- Support for LoRA, Azure, OpenAI, Local, Quantum models -- Result aggregation and ranking -- Multi-format export (JSON, Markdown) -- Side-by-side comparison - -### Multi-Format Results Export - -**results_exporter.py** - Export orchestrator results to multiple formats: - -```powershell -# Export autotrain results to CSV -python .\scripts\results_exporter.py --source autotrain --format csv - -# Export quantum results to Markdown -python .\scripts\results_exporter.py --source quantum_autorun --format markdown - -# Export all orchestrators to Excel -python .\scripts\results_exporter.py --all --format excel - -# Compare orchestrators in HTML -python .\scripts\results_exporter.py --compare autotrain quantum_autorun --format html -``` - -**Supported formats**: -- JSON (machine-readable) -- CSV (spreadsheet import) -- Excel (requires openpyxl) -- Markdown (documentation) -- HTML (web reports) - -### CI/CD Integration - -**GitHub Actions workflow** - Automated CI on every commit: - -`.github/workflows/ci-pipeline.yml` includes: -- Validation on push/PR -- Daily training runs (scheduled) -- Auto-deployment of best models -- Artifact uploads - -**Trigger CI manually**: -```powershell -# Run full CI pipeline locally -python .\scripts\ci_orchestrator.py --ci-pipeline -``` - -**Git hooks** - Pre-commit validation: -```powershell -# Copy sample hook (manual installation) -# .git\hooks\pre-commit.sample contains validation logic -``` - -## Enhanced Orchestrator Integration - -### Chain Orchestrators with Dependencies - -Master orchestrator now supports more complex workflows: - -```yaml -# master_orchestrator.yaml -workflows: - - name: full_ml_pipeline - description: "Complete ML pipeline with deployment" - trigger: manual - orchestrators: - - autotrain # Step 1: Train models - - evaluation_autorun # Step 2: Evaluate models (depends on autotrain) - - model_deploy # Step 3: Deploy best model - on_success: - - deploy_best_model - - send_notification -``` - -### Schedule Workflows with Cron - -```powershell -# Schedule daily training at 2 AM -python .\scripts\auto_scheduler.py --schedule "daily_training" --workflow "daily_full_pipeline" --cron "0 2 * * *" - -# Schedule evaluation every 6 hours -python .\scripts\auto_scheduler.py --schedule "eval_check" --workflow "quick_validation" --cron "0 */6 * * *" - -# Enable/disable jobs -python .\scripts\auto_scheduler.py --disable daily_training -python .\scripts\auto_scheduler.py --enable daily_training - -# Start scheduler daemon -python .\scripts\auto_scheduler.py --start --check-interval 300 -``` - -### Batch Evaluate Multiple Models - -```powershell -# Create batch evaluation config -# batch_eval_config.yaml: -evaluation_tasks: - - model_id: lora_v1 - model_type: lora - model_path: data_out/lora_training/lora_v1 - dataset: datasets/chat/mixed_chat - metrics: [accuracy, perplexity, bleu] - max_samples: 100 - -# Run batch evaluation -python .\scripts\batch_evaluator.py --config batch_eval_config.yaml -``` - -### Export Results to Multiple Formats - -```powershell -# Export single orchestrator -python .\scripts\results_exporter.py --source autotrain --format markdown --output autotrain_report.md - -# Export all orchestrators comparison -python .\scripts\results_exporter.py --all --format html --output comparison.html - -# Filter by status -python .\scripts\results_exporter.py --source quantum_autorun --format csv --filter-status succeeded -``` - -## Related Documentation - -- `AUTOTRAIN_README.md` - LoRA training orchestrator -- `QUANTUM_AUTORUN_README.md` - Quantum training orchestrator -- `EVALUATION_AUTORUN_README.md` - Model evaluation orchestrator -- `QUICK_REFERENCE.md` - Quick command reference -- `PRODUCTION_DEPLOYMENT_PLAN.md` - Deployment guide +# Advanced Automation System + +Comprehensive orchestration and automation layer for QAI workspace with multi-level automation capabilities. + +## Architecture Overview + +``` +┌─────────────────────────────────────────────────────────────┐ +│ Auto-Scheduler (Top Level) │ +│ - Cron-based scheduling │ +│ - Event-based triggers │ +│ - Resource-aware execution │ +└────────────────────┬────────────────────────────────────────┘ + │ +┌────────────────────▼────────────────────────────────────────┐ +│ Master Orchestrator (Workflow Layer) │ +│ - Coordinates sub-orchestrators │ +│ - Dependency management │ +│ - Success/failure handlers │ +└────┬───────────────┬───────────────┬────────────────────────┘ + │ │ │ +┌────▼──────┐ ┌────▼──────┐ ┌────▼──────┐ +│ AutoTrain │ │ Quantum │ │ Evaluation│ +│ │ │ AutoRun │ │ AutoRun │ +└───────────┘ └───────────┘ └───────────┘ +``` + +## Orchestrator Levels + +### Level 1: Domain Orchestrators (Job Execution) +- **autotrain.py** - LoRA fine-tuning jobs +- **quantum_autorun.py** - Quantum training jobs +- **evaluation_autorun.py** - Model evaluation jobs + +### Level 2: Master Orchestrator (Workflow Coordination) +- **master_orchestrator.py** - Coordinates workflows across orchestrators +- Dependencies, priorities, timeouts +- Success/failure handlers +- Resource monitoring + +### Level 3: CI/CD Orchestrator (Quality Gates) +- **ci_orchestrator.py** - Parallel validation +- Unit & integration tests +- Code quality checks +- Deployment preparation + +### Level 4: Auto-Scheduler (Time-Based Automation) +- **auto_scheduler.py** - Cron-based scheduling +- Persistent state +- Auto-retry logic +- Failure notifications + +## Quick Start + +### 1. Run Quick Validation (All Orchestrators) + +```powershell +# Validate all configurations in parallel (fast) +python .\scripts\ci_orchestrator.py --validate-all +``` + +**Output**: Validates autotrain, quantum_autorun, evaluation_autorun in parallel (~1 second) + +### 2. Run Master Orchestrator Workflow + +```powershell +# Run quick validation workflow (dry-run all orchestrators) +python .\scripts\master_orchestrator.py --workflow quick_validation + +# Run daily full pipeline (actual training) +python .\scripts\master_orchestrator.py --workflow daily_full_pipeline +``` + +### 3. Schedule Automated Runs + +```powershell +# Add scheduled job (requires pip install croniter) +python .\scripts\auto_scheduler.py --schedule "daily_training" --workflow "daily_full_pipeline" --cron "0 2 * * *" + +# Start scheduler daemon +python .\scripts\auto_scheduler.py --start + +# Check scheduled jobs +python .\scripts\auto_scheduler.py --list +``` + +### 4. Run Full CI Pipeline + +```powershell +# Complete CI/CD validation + tests + preparation +python .\scripts\ci_orchestrator.py --ci-pipeline +``` + +## VS Code Tasks + +All orchestrators available as VS Code tasks (Ctrl+Shift+P → "Run Task"): + +| Task | Command | +|------|---------| +| **Run: Evaluation AutoRun (dry-run)** | Validate evaluation jobs | +| **Run: Evaluation AutoRun (all)** | Run all evaluation jobs | +| **Run: CI Validate All** | Validate all orchestrators in parallel | +| **Run: CI Pipeline** | Full CI/CD pipeline execution | +| **Run: Master Orchestrator - Quick Validation** | Quick workflow validation | +| **Run: Master Orchestrator - Status** | Show orchestrator status | +| **Run: Model Deployer - Scan** | Scan for deployable models | +| **Run: Model Deployer - Deploy Best** | Auto-deploy best model | +| **Run: Resource Monitor - Snapshot** | Capture resource snapshot | +| **Run: Resource Monitor - Stream** | Real-time resource monitoring | +| **Run: Batch Evaluator - Scan** | Scan and evaluate all models | +| **Run: Results Exporter - Export to Markdown** | Export all results to Markdown | +| **Run: Auto Scheduler - List** | List scheduled jobs | +| **Run: CI Validate All** | Parallel validation of all orchestrators | +| **Run: CI Pipeline** | Full CI/CD pipeline | +| **Run: Master Orchestrator - Quick Validation** | Run quick_validation workflow | +| **Run: Master Orchestrator - Status** | Show master orchestrator status | + +## Configuration Files + +### master_orchestrator.yaml + +Defines workflows and orchestrator coordination: + +```yaml +workflows: + - name: daily_full_pipeline + enabled: true + trigger: schedule + schedule: "0 1 * * *" # Daily at 1 AM + orchestrators: + - autotrain + - quantum_autorun + - evaluation_autorun + on_success: + - notify_slack + - deploy_best_models + on_failure: + - notify_slack + - create_issue +``` + +### Workflow Patterns + +#### Quick Validation (CI/CD) +Dry-run all orchestrators in sequence, fail fast on errors. + +```powershell +python .\scripts\master_orchestrator.py --workflow quick_validation +``` + +#### Daily Full Pipeline +Run training → quantum → evaluation with notifications. + +```powershell +python .\scripts\master_orchestrator.py --workflow daily_full_pipeline +``` + +#### Weekly Comprehensive +Full datasets, all jobs, comprehensive reports. + +```powershell +python .\scripts\master_orchestrator.py --workflow weekly_comprehensive +``` + +## CI/CD Integration + +### GitHub Actions Example + +```yaml +name: QAI CI Pipeline + +on: [push, pull_request] + +jobs: + validate: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: '3.11' + + - name: Install dependencies + run: pip install pyyaml pytest + + - name: Validate All Orchestrators + run: python scripts/orchestrators/ci_orchestrator.py --validate-all + + - name: Run Unit Tests + run: python scripts/orchestrators/ci_orchestrator.py --quick-test + + - name: Upload Results + uses: actions/upload-artifact@v3 + with: + name: ci-results + path: data_out/ci_orchestrator/ +``` + +### Azure Pipelines Example + +```yaml +trigger: + - main + +pool: + vmImage: 'windows-latest' + +steps: +- task: UsePythonVersion@0 + inputs: + versionSpec: '3.11' + +- script: pip install pyyaml pytest + displayName: 'Install dependencies' + +- script: python scripts/orchestrators/ci_orchestrator.py --validate-all + displayName: 'Validate orchestrators' + +- script: python scripts/orchestrators/ci_orchestrator.py --ci-pipeline + displayName: 'Run CI pipeline' + +- task: PublishBuildArtifacts@1 + inputs: + pathToPublish: 'data_out/ci_orchestrator' + artifactName: 'ci-results' +``` + +## Auto-Scheduler Commands + +### Schedule a Job + +```powershell +# Daily at 2 AM +python .\scripts\auto_scheduler.py --schedule "daily_training" --workflow "daily_full_pipeline" --cron "0 2 * * *" + +# Every 6 hours +python .\scripts\auto_scheduler.py --schedule "frequent_eval" --workflow "quick_validation" --cron "0 */6 * * *" + +# Weekly on Sundays at midnight +python .\scripts\auto_scheduler.py --schedule "weekly_full" --workflow "weekly_comprehensive" --cron "0 0 * * 0" +``` + +### Manage Scheduled Jobs + +```powershell +# List all jobs +python .\scripts\auto_scheduler.py --list + +# Enable/disable job +python .\scripts\auto_scheduler.py --enable "daily_training" +python .\scripts\auto_scheduler.py --disable "daily_training" + +# Remove job +python .\scripts\auto_scheduler.py --remove "daily_training" + +# Check status +python .\scripts\auto_scheduler.py --status +``` + +### Run as Daemon + +```powershell +# Start in foreground +python .\scripts\auto_scheduler.py --start + +# With custom check interval (default 60 seconds) +python .\scripts\auto_scheduler.py --start --check-interval 30 +``` + +**Note**: Install `croniter` for cron scheduling: `pip install croniter` + +## Master Orchestrator Commands + +### List Available Resources + +```powershell +# List all orchestrators +python .\scripts\master_orchestrator.py --list-orchestrators + +# List all workflows +python .\scripts\master_orchestrator.py --list-workflows + +# Show current status +python .\scripts\master_orchestrator.py --status +``` + +### Run Workflows + +```powershell +# Quick validation (dry-run all) +python .\scripts\master_orchestrator.py --workflow quick_validation + +# Daily full pipeline +python .\scripts\master_orchestrator.py --workflow daily_full_pipeline + +# Weekly comprehensive +python .\scripts\master_orchestrator.py --workflow weekly_comprehensive +``` + +### Run Individual Orchestrators + +```powershell +# Run single orchestrator through master +python .\scripts\master_orchestrator.py --orchestrator autotrain +python .\scripts\master_orchestrator.py --orchestrator quantum_autorun +python .\scripts\master_orchestrator.py --orchestrator evaluation_autorun +``` + +### Daemon Mode + +```powershell +# Run as background service (checks schedules every 60s) +python .\scripts\master_orchestrator.py --daemon + +# Custom check interval +python .\scripts\master_orchestrator.py --daemon --check-interval 120 +``` + +## CI Orchestrator Commands + +### Validation + +```powershell +# Validate all orchestrators (parallel, ~1 second) +python .\scripts\ci_orchestrator.py --validate-all +``` + +### Testing + +```powershell +# Quick test (unit tests only) +python .\scripts\ci_orchestrator.py --quick-test + +# Full test suite (unit + integration) +python .\scripts\ci_orchestrator.py --full-test +``` + +### Deployment + +```powershell +# Prepare deployment artifacts +python .\scripts\ci_orchestrator.py --prepare-deployment +``` + +### Full Pipeline + +```powershell +# Run complete CI/CD pipeline +python .\scripts\ci_orchestrator.py --ci-pipeline +``` + +**Steps**: Validation → Unit Tests → Dataset Validation → Code Quality → Security Scan → Integration Tests → Deployment Preparation + +## Output Structure + +``` +data_out/ +├── master_orchestrator/ +│ ├── status.json # Global status +│ ├── quick_validation_.json # Workflow results +│ └── / +│ └── / +│ └── stdout.log # Execution logs +│ +├── ci_orchestrator/ +│ ├── ci_results.json # CI pipeline results +│ └── deployment_artifacts.json # Deployment manifest +│ +└── auto_scheduler/ + ├── schedule.json # Scheduled jobs config + └── state.json # Scheduler state (last runs, etc.) +``` + +## Status JSON Schema + +### Master Orchestrator Status + +```json +{ + "generated_at": "2025-11-22T17:30:00Z", + "orchestrators": [ + { + "name": "autotrain", + "script": "scripts/training/autotrain.py", + "enabled": true, + "schedule": "0 2 * * *", + "priority": 1, + "dependencies": [], + "last_run": "20251122T173000Z", + "last_status": "succeeded" + } + ], + "workflows": [ + { + "name": "quick_validation", + "enabled": true, + "trigger": "manual", + "orchestrators": ["autotrain", "quantum_autorun", "evaluation_autorun"] + } + ], + "resource_usage": { + "cpu_percent": 25.3, + "memory_percent": 45.8, + "disk_percent": 62.1 + } +} +``` + +### CI Orchestrator Results + +```json +{ + "generated_at": "2025-11-22T17:30:00Z", + "total_steps": 7, + "succeeded": 6, + "failed": 0, + "skipped": 1, + "results": [ + { + "name": "validate_orchestrators", + "status": "succeeded", + "duration_sec": 1.2, + "critical": true + } + ] +} +``` + +### Auto-Scheduler State + +```json +{ + "scheduler_running": true, + "total_jobs": 3, + "enabled_jobs": 2, + "disabled_jobs": 1, + "jobs": [ + { + "name": "daily_training", + "workflow": "daily_full_pipeline", + "cron": "0 2 * * *", + "enabled": true, + "last_run": "2025-11-22T02:00:15Z", + "last_status": "succeeded", + "next_run": "2025-11-23 02:00:00", + "run_count": 15, + "consecutive_failures": 0 + } + ] +} +``` + +## Advanced Features + +### Dependency Management + +Master orchestrator handles dependencies automatically: + +```yaml +orchestrators: + - name: evaluation_autorun + dependencies: [autotrain, quantum_autorun] # Wait for these +``` + +### Resource Limits + +```yaml +resource_limits: + max_concurrent_orchestrators: 2 + max_cpu_percent: 80 + max_memory_gb: 16 + pause_on_resource_exhaustion: true +``` + +### Auto-Retry Logic + +```yaml +orchestrators: + - name: autotrain + retry_on_failure: 3 # Retry up to 3 times + timeout_minutes: 240 # 4 hour timeout +``` + +### Failure Handling + +Auto-scheduler disables jobs after consecutive failures: + +```python +max_consecutive_failures: 3 # Disable after 3 failures +notify_on_failure: true # Send notification +``` + +## Monitoring & Notifications + +### Slack Integration (Placeholder) + +```yaml +notifications: + slack_enabled: true + webhook_url: "${SLACK_WEBHOOK_URL}" + notify_on_failure: true + notify_on_success: false +``` + +### Email Notifications (Placeholder) + +```yaml +notifications: + email_enabled: true + to: "${ADMIN_EMAIL}" + notify_on_failure: true + notify_on_degradation: true +``` + +## Troubleshooting + +### Orchestrator Not Running + +```powershell +# Check master orchestrator status +python .\scripts\master_orchestrator.py --status + +# Check specific orchestrator +python .\scripts\autotrain.py --dry-run +``` + +### Scheduler Not Triggering + +```powershell +# Install croniter +pip install croniter + +# Check schedule syntax +python .\scripts\auto_scheduler.py --list + +# Verify next_run time is set +``` + +### CI Pipeline Failures + +```powershell +# Run individual steps +python .\scripts\ci_orchestrator.py --validate-all +python .\scripts\ci_orchestrator.py --quick-test + +# Check results +type data_out\ci_orchestrator\ci_results.json +``` + +### Resource Exhaustion + +Master orchestrator monitors resources (requires `psutil`): + +```powershell +# Install psutil +pip install psutil + +# Check status with resource usage +python .\scripts\master_orchestrator.py --status +``` + +## Best Practices + +1. **Start with dry-run**: Always validate before execution +2. **Use CI orchestrator**: For pre-commit/pre-push validation +3. **Schedule wisely**: Avoid overlapping resource-intensive jobs +4. **Monitor logs**: Check `data_out/` for execution details +5. **Enable notifications**: Get alerts for failures +6. **Resource limits**: Set appropriate limits for your hardware +7. **Backup regularly**: Automated backups in `master_orchestrator.yaml` + +## Integration Examples + +### Pre-Commit Hook + +```bash +#!/bin/bash +# .git/hooks/pre-commit +python scripts/orchestrators/ci_orchestrator.py --validate-all +exit $? +``` + +### Continuous Deployment + +```powershell +# Run after successful training +python .\scripts\ci_orchestrator.py --prepare-deployment + +# Deploy if validation passes +if ($LASTEXITCODE -eq 0) { + # Deploy to Azure Functions, etc. +} +``` + +### Performance Monitoring + +```powershell +# Scheduled performance check +python .\scripts\auto_scheduler.py --schedule "perf_check" --workflow "quick_validation" --cron "*/30 * * * *" +``` + +## Future Enhancements + +- [ ] Proper cron parser without croniter dependency +- [ ] Slack/Email notification implementation +- [ ] Web dashboard for monitoring +- [ ] Distributed execution across machines +- [ ] GPU resource aware scheduling +- [ ] Model performance tracking +- [ ] Automatic hyperparameter tuning integration +- [ ] Cost estimation and budgeting +- [ ] Rollback on deployment failure + +## New Advanced Features + +### Model Deployment with Quality Gates + +**model_deployer.py** - Automatically deploy trained models with validation: + +```powershell +# Scan for trained models and check quality gates +python .\scripts\model_deployer.py --scan + +# Deploy best model with canary strategy +python .\scripts\model_deployer.py --deploy best --strategy canary + +# Check deployment status +python .\scripts\model_deployer.py --status + +# Rollback to previous version +python .\scripts\model_deployer.py --rollback v1_20251122_123456 +``` + +**Features**: +- Quality gate validation (accuracy > 0.75, loss < 0.5) +- Model scoring and ranking +- Deployment strategies: direct, canary, blue-green +- Version tracking and rollback +- Model registry with metadata + +### Real-Time Resource Monitoring + +**resource_monitor.py** - Monitor system resources with alerts: + +```powershell +# Capture single snapshot +python .\scripts\resource_monitor.py --snapshot + +# Stream real-time monitoring (60s) +python .\scripts\resource_monitor.py --stream --duration 60 + +# View historical data (last 24 hours) +python .\scripts\resource_monitor.py --history --hours 24 + +# Export to CSV +python .\scripts\resource_monitor.py --export csv + +# Set custom alert threshold +python .\scripts\resource_monitor.py --set-threshold cpu_percent 85 +``` + +**Features**: +- CPU, memory, disk, GPU monitoring +- Threshold-based alerts +- Historical data collection (JSONL format) +- Export to CSV/JSON +- Integration with orchestrators + +### Batch Model Evaluation + +**batch_evaluator.py** - Parallel evaluation of multiple models: + +```powershell +# Scan and evaluate all trained models +python .\scripts\batch_evaluator.py --scan-models --evaluate-all + +# Compare specific models +python .\scripts\batch_evaluator.py --compare lora_v1 lora_v2 quantum_v1 + +# Export results to Markdown report +python .\scripts\batch_evaluator.py --export markdown --output report.md + +# Export to JSON +python .\scripts\batch_evaluator.py --export json +``` + +**Features**: +- Parallel evaluation (ThreadPoolExecutor) +- Support for LoRA, Azure, OpenAI, Local, Quantum models +- Result aggregation and ranking +- Multi-format export (JSON, Markdown) +- Side-by-side comparison + +### Multi-Format Results Export + +**results_exporter.py** - Export orchestrator results to multiple formats: + +```powershell +# Export autotrain results to CSV +python .\scripts\results_exporter.py --source autotrain --format csv + +# Export quantum results to Markdown +python .\scripts\results_exporter.py --source quantum_autorun --format markdown + +# Export all orchestrators to Excel +python .\scripts\results_exporter.py --all --format excel + +# Compare orchestrators in HTML +python .\scripts\results_exporter.py --compare autotrain quantum_autorun --format html +``` + +**Supported formats**: +- JSON (machine-readable) +- CSV (spreadsheet import) +- Excel (requires openpyxl) +- Markdown (documentation) +- HTML (web reports) + +### CI/CD Integration + +**GitHub Actions workflow** - Automated CI on every commit: + +`.github/workflows/ci-pipeline.yml` includes: +- Validation on push/PR +- Daily training runs (scheduled) +- Auto-deployment of best models +- Artifact uploads + +**Trigger CI manually**: +```powershell +# Run full CI pipeline locally +python .\scripts\ci_orchestrator.py --ci-pipeline +``` + +**Git hooks** - Pre-commit validation: +```powershell +# Copy sample hook (manual installation) +# .git\hooks\pre-commit.sample contains validation logic +``` + +## Enhanced Orchestrator Integration + +### Chain Orchestrators with Dependencies + +Master orchestrator now supports more complex workflows: + +```yaml +# master_orchestrator.yaml +workflows: + - name: full_ml_pipeline + description: "Complete ML pipeline with deployment" + trigger: manual + orchestrators: + - autotrain # Step 1: Train models + - evaluation_autorun # Step 2: Evaluate models (depends on autotrain) + - model_deploy # Step 3: Deploy best model + on_success: + - deploy_best_model + - send_notification +``` + +### Schedule Workflows with Cron + +```powershell +# Schedule daily training at 2 AM +python .\scripts\auto_scheduler.py --schedule "daily_training" --workflow "daily_full_pipeline" --cron "0 2 * * *" + +# Schedule evaluation every 6 hours +python .\scripts\auto_scheduler.py --schedule "eval_check" --workflow "quick_validation" --cron "0 */6 * * *" + +# Enable/disable jobs +python .\scripts\auto_scheduler.py --disable daily_training +python .\scripts\auto_scheduler.py --enable daily_training + +# Start scheduler daemon +python .\scripts\auto_scheduler.py --start --check-interval 300 +``` + +### Batch Evaluate Multiple Models + +```powershell +# Create batch evaluation config +# batch_eval_config.yaml: +evaluation_tasks: + - model_id: lora_v1 + model_type: lora + model_path: data_out/lora_training/lora_v1 + dataset: datasets/chat/mixed_chat + metrics: [accuracy, perplexity, bleu] + max_samples: 100 + +# Run batch evaluation +python .\scripts\batch_evaluator.py --config batch_eval_config.yaml +``` + +### Export Results to Multiple Formats + +```powershell +# Export single orchestrator +python .\scripts\results_exporter.py --source autotrain --format markdown --output autotrain_report.md + +# Export all orchestrators comparison +python .\scripts\results_exporter.py --all --format html --output comparison.html + +# Filter by status +python .\scripts\results_exporter.py --source quantum_autorun --format csv --filter-status succeeded +``` + +## Related Documentation + +- `AUTOTRAIN_README.md` - LoRA training orchestrator +- `QUANTUM_AUTORUN_README.md` - Quantum training orchestrator +- `EVALUATION_AUTORUN_README.md` - Model evaluation orchestrator +- `QUICK_REFERENCE.md` - Quick command reference +- `PRODUCTION_DEPLOYMENT_PLAN.md` - Deployment guide diff --git a/docs/guides/ADVANCED_FEATURES_GUIDE.md b/docs/guides/ADVANCED_FEATURES_GUIDE.md index 2ca17467a..0c654456a 100644 --- a/docs/guides/ADVANCED_FEATURES_GUIDE.md +++ b/docs/guides/ADVANCED_FEATURES_GUIDE.md @@ -1,620 +1,620 @@ -# QAI Advanced Features - Complete Guide - -## 🎉 New Features Overview - -This document covers all the advanced features added to the QAI system, including real-time updates, interactive analytics, job queue management, model comparison, and configuration templates. - ---- - -## 📡 1. WebSocket Live Updates - -**File**: `dashboard/websocket_server.py` - -### What It Does -Provides real-time job status updates without polling, reducing server load and providing instant feedback. - -### Features -- **File System Monitoring**: Watches `data_out/` for status file changes -- **Broadcast Updates**: Pushes changes to all connected clients immediately -- **Heartbeat System**: Keeps connections alive with 30-second pings -- **Auto-Reconnect**: Clients automatically reconnect if connection drops - -### How to Start -```powershell -# Install dependencies first -pip install websockets watchdog - -# Start WebSocket server -python .\dashboard\websocket_server.py -``` - -### Connection in Browser -```javascript -const ws = new WebSocket('ws://localhost:8765'); - -ws.onmessage = (event) => { - const data = JSON.parse(event.data); - if (data.type === 'job_update') { - console.log('Job updated:', data.data); - // Update UI with new job status - } -}; -``` - -### Message Types -- `initial_status`: Sent when client first connects -- `job_update`: Sent when job status file changes -- `heartbeat`: Sent every 30 seconds to keep connection alive -- `ping`/`pong`: Client can send ping, server responds with pong - -### Benefits -- ⚡ **Zero Latency**: Updates appear instantly -- 🔋 **Reduced Load**: No more polling every 5-10 seconds -- 📊 **Scalable**: Handles multiple clients efficiently -- 🔄 **Reliable**: Auto-reconnect on disconnection - ---- - -## 📊 2. Interactive Analytics Dashboard - -**File**: `dashboard/analytics.html` - -### What It Does -Comprehensive visualization of training metrics, GPU usage, model performance, and time distribution using Chart.js. - -### Charts Included - -#### **Loss Progression Chart** -- Line chart showing training loss over time -- Tracks improvement across training runs -- Color-coded zones (excellent/good/needs work) - -#### **GPU Utilization History** -- Real-time GPU usage percentage -- Historical tracking -- Warning zones (>80% = critical) - -#### **Model Performance Comparison** -- Bar chart of top 5 models -- Performance score = 1/perplexity × 100 -- Color-coded bars - -#### **Training Time Distribution** -- Doughnut chart showing job duration categories: - - Quick (<10 min) - Green - - Medium (10-60 min) - Yellow - - Long (>60 min) - Red - -### How to Use -```powershell -# Navigate to analytics dashboard -Start-Process "http://localhost:8000/analytics.html" -``` - -### Features -- **Live Updates**: Connects to WebSocket for real-time data -- **Time Range Filter**: Last 24h, 7d, 30d, or all time -- **Model Selection**: Filter by specific model -- **Export Charts**: Download charts as images (coming soon) -- **Comparison Table**: Side-by-side model metrics - -### Keyboard Shortcuts -- `Ctrl+R`: Manual refresh -- `Ctrl+E`: Export charts -- `Ctrl+F`: Toggle filters - -### API Endpoints Used -- `/api/history` - Training history with metrics -- `/api/gpu` - GPU utilization data -- `/api/models` - Model list with performance -- `ws://localhost:8765` - WebSocket for live updates - ---- - -## 📋 3. Job Queue Management System - -**File**: `scripts/job_queue.py` - -### What It Does -Enterprise-grade job scheduling with priorities, dependencies, and automatic retry logic. - -### Features - -#### **Priority Levels** -```python -from scripts.job_queue import JobQueue, JobPriority - -queue = JobQueue() - -# Add high-priority job -queue.add_job( - name="urgent_training", - config={...}, - priority=JobPriority.HIGH -) -``` - -Priority order: `CRITICAL` > `HIGH` > `NORMAL` > `LOW` - -#### **Job Dependencies** -```python -# Job 2 won't start until Job 1 completes -job1 = queue.add_job(name="preprocess", config={...}) -job2 = queue.add_job( - name="train", - config={...}, - dependencies=[job1] -) -``` - -#### **Automatic Retry** -- Failed jobs automatically retry (default: 3 attempts) -- Exponential backoff between retries -- Error logging for troubleshooting - -#### **Job States** -- `PENDING`: Waiting to run -- `RUNNING`: Currently executing -- `COMPLETED`: Successfully finished -- `FAILED`: Error occurred (after all retries) -- `BLOCKED`: Waiting for dependencies -- `CANCELLED`: Manually cancelled - -### API Usage - -**Get Queue Status** -```bash -curl http://localhost:8000/api/job-queue -``` - -Response: -```json -{ - "total_jobs": 15, - "pending": 3, - "running": 1, - "completed": 10, - "failed": 1, - "blocked": 0, - "cancelled": 0, - "queue_length": 3, - "estimated_total_time": 3600 -} -``` - -**View in Hub** -- Click "Job Queue" quick action button -- Or press `Ctrl+Q` keyboard shortcut - -### Example Workflow -```python -from scripts.job_queue import JobQueue, JobPriority - -queue = JobQueue() - -# 1. Preprocess data (high priority) -prep_job = queue.add_job( - name="preprocess_dataset", - config={"action": "clean", "dataset": "mixed_chat"}, - priority=JobPriority.HIGH, - estimated_duration=300 # 5 minutes -) - -# 2. Train model (depends on preprocessing) -train_job = queue.add_job( - name="train_phi35_v2", - config={"epochs": 3, "dataset": "mixed_chat"}, - priority=JobPriority.NORMAL, - dependencies=[prep_job], - estimated_duration=1800 # 30 minutes -) - -# 3. Evaluate model (depends on training) -eval_job = queue.add_job( - name="evaluate_v2", - config={"model": "phi35_v2"}, - priority=JobPriority.NORMAL, - dependencies=[train_job], - estimated_duration=600 # 10 minutes -) - -# Get next job to execute -next_job = queue.get_next_job() -if next_job: - queue.start_job(next_job.id) - # ... execute job ... - queue.complete_job(next_job.id, success=True) -``` - ---- - -## 🔬 4. Model Comparison Feature - -**Location**: Integrated into `dashboard/unified.html` - -### What It Does -Side-by-side comparison of trained models with performance metrics and visual ranking. - -### How to Use - -#### **Method 1: Keyboard Shortcut** -``` -Press Ctrl+M in the unified dashboard -``` - -#### **Method 2: JavaScript Call** -```javascript -compareModels(); -``` - -### Displayed Metrics -- **Model Name**: Identifier -- **Base Model**: Foundation model used -- **LoRA Rank**: Parameter count indicator -- **Size (MB)**: Model file size -- **Performance Score**: Calculated as (1/perplexity × 100) - -### Performance Color Coding -- 🟢 **Excellent** (>80): Green -- 🟡 **Good** (50-80): Yellow -- 🔴 **Needs Work** (<50): Red - -### Example Output -``` -Model Name | Base Model | LoRA Rank | Size (MB) | Performance ---------------------|------------------|-----------|-----------|------------ -phi35_v3_best | microsoft/phi-3 | 64 | 156.2 | 87.5 🟢 -phi35_v2_standard | microsoft/phi-3 | 32 | 98.4 | 72.3 🟡 -phi35_v1_quick | microsoft/phi-3 | 16 | 52.1 | 45.8 🔴 -``` - -### Use Cases -- **Model Selection**: Choose best model for deployment -- **A/B Testing**: Compare different hyperparameters -- **Progress Tracking**: See improvement over iterations -- **Team Collaboration**: Share comparison screenshots - ---- - -## 💾 5. Configuration Templates - -**Location**: Integrated into `dashboard/unified.html` - -### What It Does -Save, load, and share training configurations as reusable templates. - -### Built-in Templates - -#### **Quick Test** -```json -{ - "name": "Quick Test", - "description": "1 epoch, 100 samples - Fast validation", - "config": { - "epochs": 1, - "max_train_samples": 100, - "max_eval_samples": 20, - "batch_size": 4, - "learning_rate": 2e-4, - "lora_rank": 8, - "lora_alpha": 16 - } -} -``` - -#### **Standard Training** -```json -{ - "name": "Standard Training", - "description": "3 epochs, 1000 samples - Production ready", - "config": { - "epochs": 3, - "max_train_samples": 1000, - "max_eval_samples": 200, - "batch_size": 8, - "learning_rate": 5e-5, - "lora_rank": 16, - "lora_alpha": 32 - } -} -``` - -#### **High Quality** -```json -{ - "name": "High Quality", - "description": "5 epochs, all samples - Best results", - "config": { - "epochs": 5, - "max_train_samples": -1, - "max_eval_samples": -1, - "batch_size": 4, - "learning_rate": 3e-5, - "lora_rank": 32, - "lora_alpha": 64, - "warmup_steps": 100 - } -} -``` - -#### **Production** -```json -{ - "name": "Production", - "description": "10 epochs, optimized - Enterprise grade", - "config": { - "epochs": 10, - "max_train_samples": -1, - "max_eval_samples": -1, - "batch_size": 8, - "learning_rate": 2e-5, - "lora_rank": 64, - "lora_alpha": 128, - "warmup_steps": 200, - "weight_decay": 0.01 - } -} -``` - -### How to Use - -#### **Save Current Config** -``` -1. Configure all training parameters in the form -2. Press Ctrl+T or click "Save Template" button -3. Enter a name for your template -4. Template saved to localStorage -``` - -#### **Load Template** -``` -1. Press Ctrl+T or click "Templates" button -2. Select a template (built-in or custom) -3. Click "Load" -4. All form fields populated automatically -``` - -#### **Share Templates** -```javascript -// Export template as JSON -const template = localStorage.getItem('qai_config_templates'); -console.log(template); - -// Import template -const importedTemplates = {...}; -localStorage.setItem('qai_config_templates', JSON.stringify(importedTemplates)); -``` - -### Keyboard Shortcuts -- `Ctrl+T`: Open templates modal -- `Ctrl+S`: Save current config as template - -### Storage -- **Location**: Browser localStorage -- **Format**: JSON -- **Persistence**: Survives browser restarts -- **Export/Import**: Copy/paste JSON for sharing - ---- - -## 🚀 Quick Start Guide - -### 1. Start All Services - -```powershell -# Terminal 1: Main dashboard server -python .\dashboard\serve.py - -# Terminal 2: WebSocket server (optional, for live updates) -pip install websockets watchdog -python .\dashboard\websocket_server.py -``` - -### 2. Access Features - -| Feature | URL | Keyboard Shortcut | -|---------|-----|-------------------| -| **Hub** | http://localhost:8000/ | - | -| **Training Dashboard** | http://localhost:8000/unified.html | - | -| **Analytics** | http://localhost:8000/analytics.html | - | -| **Model Comparison** | In unified.html | `Ctrl+M` | -| **Config Templates** | In unified.html | `Ctrl+T` | -| **Training History** | In hub.html | `Ctrl+H` | -| **Job Queue** | In hub.html | Click "Job Queue" | - -### 3. Test Features - -#### **Test WebSocket** -```javascript -// In browser console -const ws = new WebSocket('ws://localhost:8765'); -ws.onmessage = (e) => console.log(JSON.parse(e.data)); -``` - -#### **Test Job Queue** -```powershell -python .\scripts\job_queue.py -# Should show example jobs -``` - -#### **Test Analytics** -```powershell -# Generate some training data first -python .\scripts\autotrain.py --job phi35_quick_test - -# Then open analytics -Start-Process "http://localhost:8000/analytics.html" -``` - ---- - -## 📈 Performance Improvements - -| Metric | Before | After | Improvement | -|--------|--------|-------|-------------| -| **Real-time Updates** | 5-10s polling | Instant (WebSocket) | 10x faster | -| **Server Load** | High (constant polling) | Low (push notifications) | 80% reduction | -| **Data Visualization** | None | 4 interactive charts | New feature | -| **Job Management** | Manual | Priority queue | Automated | -| **Config Management** | Manual | Templates | 5x faster setup | -| **Model Comparison** | Manual calculation | Automated | Instant | - ---- - -## 🎯 Use Cases - -### Research Team Scenario -``` -1. Create "research_baseline" template (Ctrl+T, Save) -2. Schedule 5 experiments with different hyperparameters (Job Queue) -3. Monitor all jobs in real-time (WebSocket) -4. Compare results (Ctrl+M) -5. Share winning template with team (export JSON) -``` - -### Production Deployment -``` -1. Load "production" template (Ctrl+T, Load "Production") -2. Verify settings in form -3. Submit job (high priority in queue) -4. Monitor GPU usage in Analytics dashboard -5. Compare with previous production model (Ctrl+M) -6. Deploy if performance > 85 -``` - -### Continuous Training -``` -1. Set up job queue with dependencies: - - Preprocess → Train → Evaluate → Deploy -2. Schedule nightly runs -3. Monitor in Analytics dashboard -4. Auto-retry failures (built into job queue) -5. Review results in morning via comparison modal -``` - ---- - -## 🔧 Troubleshooting - -### WebSocket Not Connecting -```powershell -# Check if server is running -netstat -an | Select-String "8765" - -# Start server if not running -python .\dashboard\websocket_server.py -``` - -### Charts Not Loading -``` -1. Check browser console for errors -2. Verify Chart.js CDN is accessible -3. Ensure /api/history endpoint returns data -4. Refresh page (Ctrl+R) -``` - -### Job Queue Empty -```powershell -# Initialize queue with example jobs -python .\scripts\job_queue.py - -# Or add jobs manually via API -curl -X POST http://localhost:8000/api/job-queue/add -d "{...}" -``` - -### Templates Not Saving -``` -1. Check browser localStorage (F12 > Application > Local Storage) -2. Ensure site has storage permissions -3. Try incognito mode to test -4. Export/import manually as workaround -``` - ---- - -## 📚 API Reference - -### New Endpoints - -#### `GET /api/job-queue` -Returns current job queue status. - -**Response:** -```json -{ - "total_jobs": 15, - "pending": 3, - "running": 1, - "completed": 10, - "failed": 1, - "blocked": 0, - "cancelled": 0, - "queue_length": 3, - "estimated_total_time": 3600, - "updated_at": "2025-11-25T10:30:00Z" -} -``` - -#### `WebSocket ws://localhost:8765` -Real-time job updates. - -**Message Format:** -```json -{ - "type": "job_update", - "timestamp": "2025-11-25T10:30:00Z", - "data": { - "jobs": [...], - "active_count": 1 - } -} -``` - ---- - -## 🎓 Best Practices - -### 1. WebSocket Usage -- ✅ Use for real-time monitoring -- ✅ Implement reconnection logic -- ❌ Don't use for initial data load (use REST API) -- ❌ Don't send large payloads over WebSocket - -### 2. Job Queue -- ✅ Set realistic estimated_duration -- ✅ Use priorities wisely (most jobs should be NORMAL) -- ✅ Group related jobs with dependencies -- ❌ Don't create circular dependencies -- ❌ Don't set all jobs to CRITICAL - -### 3. Configuration Templates -- ✅ Save successful configs as templates -- ✅ Document template purpose in description -- ✅ Export templates for team sharing -- ❌ Don't overwrite built-in templates -- ❌ Don't save failed configs - -### 4. Model Comparison -- ✅ Compare models with same base model -- ✅ Use performance score as primary metric -- ✅ Consider other factors (size, speed) -- ❌ Don't compare across different datasets -- ❌ Don't rely solely on perplexity - ---- - -## 🚀 Next Steps - -1. **Try each feature** with the quick start examples -2. **Customize templates** for your specific use cases -3. **Set up job queue** for automated training pipelines -4. **Monitor analytics** to identify optimization opportunities -5. **Share best practices** with your team - ---- - -**Version**: 3.0 -**Last Updated**: November 25, 2025 -**Contributors**: QAI Development Team - -**Happy Training! 🎉🤖📊** +# QAI Advanced Features - Complete Guide + +## 🎉 New Features Overview + +This document covers all the advanced features added to the QAI system, including real-time updates, interactive analytics, job queue management, model comparison, and configuration templates. + +--- + +## 📡 1. WebSocket Live Updates + +**File**: `dashboard/websocket_server.py` + +### What It Does +Provides real-time job status updates without polling, reducing server load and providing instant feedback. + +### Features +- **File System Monitoring**: Watches `data_out/` for status file changes +- **Broadcast Updates**: Pushes changes to all connected clients immediately +- **Heartbeat System**: Keeps connections alive with 30-second pings +- **Auto-Reconnect**: Clients automatically reconnect if connection drops + +### How to Start +```powershell +# Install dependencies first +pip install websockets watchdog + +# Start WebSocket server +python .\dashboard\websocket_server.py +``` + +### Connection in Browser +```javascript +const ws = new WebSocket('ws://localhost:8765'); + +ws.onmessage = (event) => { + const data = JSON.parse(event.data); + if (data.type === 'job_update') { + console.log('Job updated:', data.data); + // Update UI with new job status + } +}; +``` + +### Message Types +- `initial_status`: Sent when client first connects +- `job_update`: Sent when job status file changes +- `heartbeat`: Sent every 30 seconds to keep connection alive +- `ping`/`pong`: Client can send ping, server responds with pong + +### Benefits +- ⚡ **Zero Latency**: Updates appear instantly +- 🔋 **Reduced Load**: No more polling every 5-10 seconds +- 📊 **Scalable**: Handles multiple clients efficiently +- 🔄 **Reliable**: Auto-reconnect on disconnection + +--- + +## 📊 2. Interactive Analytics Dashboard + +**File**: `dashboard/analytics.html` + +### What It Does +Comprehensive visualization of training metrics, GPU usage, model performance, and time distribution using Chart.js. + +### Charts Included + +#### **Loss Progression Chart** +- Line chart showing training loss over time +- Tracks improvement across training runs +- Color-coded zones (excellent/good/needs work) + +#### **GPU Utilization History** +- Real-time GPU usage percentage +- Historical tracking +- Warning zones (>80% = critical) + +#### **Model Performance Comparison** +- Bar chart of top 5 models +- Performance score = 1/perplexity × 100 +- Color-coded bars + +#### **Training Time Distribution** +- Doughnut chart showing job duration categories: + - Quick (<10 min) - Green + - Medium (10-60 min) - Yellow + - Long (>60 min) - Red + +### How to Use +```powershell +# Navigate to analytics dashboard +Start-Process "http://localhost:8000/analytics.html" +``` + +### Features +- **Live Updates**: Connects to WebSocket for real-time data +- **Time Range Filter**: Last 24h, 7d, 30d, or all time +- **Model Selection**: Filter by specific model +- **Export Charts**: Download charts as images (coming soon) +- **Comparison Table**: Side-by-side model metrics + +### Keyboard Shortcuts +- `Ctrl+R`: Manual refresh +- `Ctrl+E`: Export charts +- `Ctrl+F`: Toggle filters + +### API Endpoints Used +- `/api/history` - Training history with metrics +- `/api/gpu` - GPU utilization data +- `/api/models` - Model list with performance +- `ws://localhost:8765` - WebSocket for live updates + +--- + +## 📋 3. Job Queue Management System + +**File**: `scripts/job_queue.py` + +### What It Does +Enterprise-grade job scheduling with priorities, dependencies, and automatic retry logic. + +### Features + +#### **Priority Levels** +```python +from scripts.job_queue import JobQueue, JobPriority + +queue = JobQueue() + +# Add high-priority job +queue.add_job( + name="urgent_training", + config={...}, + priority=JobPriority.HIGH +) +``` + +Priority order: `CRITICAL` > `HIGH` > `NORMAL` > `LOW` + +#### **Job Dependencies** +```python +# Job 2 won't start until Job 1 completes +job1 = queue.add_job(name="preprocess", config={...}) +job2 = queue.add_job( + name="train", + config={...}, + dependencies=[job1] +) +``` + +#### **Automatic Retry** +- Failed jobs automatically retry (default: 3 attempts) +- Exponential backoff between retries +- Error logging for troubleshooting + +#### **Job States** +- `PENDING`: Waiting to run +- `RUNNING`: Currently executing +- `COMPLETED`: Successfully finished +- `FAILED`: Error occurred (after all retries) +- `BLOCKED`: Waiting for dependencies +- `CANCELLED`: Manually cancelled + +### API Usage + +**Get Queue Status** +```bash +curl http://localhost:8000/api/job-queue +``` + +Response: +```json +{ + "total_jobs": 15, + "pending": 3, + "running": 1, + "completed": 10, + "failed": 1, + "blocked": 0, + "cancelled": 0, + "queue_length": 3, + "estimated_total_time": 3600 +} +``` + +**View in Hub** +- Click "Job Queue" quick action button +- Or press `Ctrl+Q` keyboard shortcut + +### Example Workflow +```python +from scripts.job_queue import JobQueue, JobPriority + +queue = JobQueue() + +# 1. Preprocess data (high priority) +prep_job = queue.add_job( + name="preprocess_dataset", + config={"action": "clean", "dataset": "mixed_chat"}, + priority=JobPriority.HIGH, + estimated_duration=300 # 5 minutes +) + +# 2. Train model (depends on preprocessing) +train_job = queue.add_job( + name="train_phi35_v2", + config={"epochs": 3, "dataset": "mixed_chat"}, + priority=JobPriority.NORMAL, + dependencies=[prep_job], + estimated_duration=1800 # 30 minutes +) + +# 3. Evaluate model (depends on training) +eval_job = queue.add_job( + name="evaluate_v2", + config={"model": "phi35_v2"}, + priority=JobPriority.NORMAL, + dependencies=[train_job], + estimated_duration=600 # 10 minutes +) + +# Get next job to execute +next_job = queue.get_next_job() +if next_job: + queue.start_job(next_job.id) + # ... execute job ... + queue.complete_job(next_job.id, success=True) +``` + +--- + +## 🔬 4. Model Comparison Feature + +**Location**: Integrated into `dashboard/unified.html` + +### What It Does +Side-by-side comparison of trained models with performance metrics and visual ranking. + +### How to Use + +#### **Method 1: Keyboard Shortcut** +``` +Press Ctrl+M in the unified dashboard +``` + +#### **Method 2: JavaScript Call** +```javascript +compareModels(); +``` + +### Displayed Metrics +- **Model Name**: Identifier +- **Base Model**: Foundation model used +- **LoRA Rank**: Parameter count indicator +- **Size (MB)**: Model file size +- **Performance Score**: Calculated as (1/perplexity × 100) + +### Performance Color Coding +- 🟢 **Excellent** (>80): Green +- 🟡 **Good** (50-80): Yellow +- 🔴 **Needs Work** (<50): Red + +### Example Output +``` +Model Name | Base Model | LoRA Rank | Size (MB) | Performance +--------------------|------------------|-----------|-----------|------------ +phi35_v3_best | microsoft/phi-3 | 64 | 156.2 | 87.5 🟢 +phi35_v2_standard | microsoft/phi-3 | 32 | 98.4 | 72.3 🟡 +phi35_v1_quick | microsoft/phi-3 | 16 | 52.1 | 45.8 🔴 +``` + +### Use Cases +- **Model Selection**: Choose best model for deployment +- **A/B Testing**: Compare different hyperparameters +- **Progress Tracking**: See improvement over iterations +- **Team Collaboration**: Share comparison screenshots + +--- + +## 💾 5. Configuration Templates + +**Location**: Integrated into `dashboard/unified.html` + +### What It Does +Save, load, and share training configurations as reusable templates. + +### Built-in Templates + +#### **Quick Test** +```json +{ + "name": "Quick Test", + "description": "1 epoch, 100 samples - Fast validation", + "config": { + "epochs": 1, + "max_train_samples": 100, + "max_eval_samples": 20, + "batch_size": 4, + "learning_rate": 2e-4, + "lora_rank": 8, + "lora_alpha": 16 + } +} +``` + +#### **Standard Training** +```json +{ + "name": "Standard Training", + "description": "3 epochs, 1000 samples - Production ready", + "config": { + "epochs": 3, + "max_train_samples": 1000, + "max_eval_samples": 200, + "batch_size": 8, + "learning_rate": 5e-5, + "lora_rank": 16, + "lora_alpha": 32 + } +} +``` + +#### **High Quality** +```json +{ + "name": "High Quality", + "description": "5 epochs, all samples - Best results", + "config": { + "epochs": 5, + "max_train_samples": -1, + "max_eval_samples": -1, + "batch_size": 4, + "learning_rate": 3e-5, + "lora_rank": 32, + "lora_alpha": 64, + "warmup_steps": 100 + } +} +``` + +#### **Production** +```json +{ + "name": "Production", + "description": "10 epochs, optimized - Enterprise grade", + "config": { + "epochs": 10, + "max_train_samples": -1, + "max_eval_samples": -1, + "batch_size": 8, + "learning_rate": 2e-5, + "lora_rank": 64, + "lora_alpha": 128, + "warmup_steps": 200, + "weight_decay": 0.01 + } +} +``` + +### How to Use + +#### **Save Current Config** +``` +1. Configure all training parameters in the form +2. Press Ctrl+T or click "Save Template" button +3. Enter a name for your template +4. Template saved to localStorage +``` + +#### **Load Template** +``` +1. Press Ctrl+T or click "Templates" button +2. Select a template (built-in or custom) +3. Click "Load" +4. All form fields populated automatically +``` + +#### **Share Templates** +```javascript +// Export template as JSON +const template = localStorage.getItem('qai_config_templates'); +console.log(template); + +// Import template +const importedTemplates = {...}; +localStorage.setItem('qai_config_templates', JSON.stringify(importedTemplates)); +``` + +### Keyboard Shortcuts +- `Ctrl+T`: Open templates modal +- `Ctrl+S`: Save current config as template + +### Storage +- **Location**: Browser localStorage +- **Format**: JSON +- **Persistence**: Survives browser restarts +- **Export/Import**: Copy/paste JSON for sharing + +--- + +## 🚀 Quick Start Guide + +### 1. Start All Services + +```powershell +# Terminal 1: Main dashboard server +python .\dashboard\serve.py + +# Terminal 2: WebSocket server (optional, for live updates) +pip install websockets watchdog +python .\dashboard\websocket_server.py +``` + +### 2. Access Features + +| Feature | URL | Keyboard Shortcut | +|---------|-----|-------------------| +| **Hub** | http://localhost:8000/ | - | +| **Training Dashboard** | http://localhost:8000/unified.html | - | +| **Analytics** | http://localhost:8000/analytics.html | - | +| **Model Comparison** | In unified.html | `Ctrl+M` | +| **Config Templates** | In unified.html | `Ctrl+T` | +| **Training History** | In hub.html | `Ctrl+H` | +| **Job Queue** | In hub.html | Click "Job Queue" | + +### 3. Test Features + +#### **Test WebSocket** +```javascript +// In browser console +const ws = new WebSocket('ws://localhost:8765'); +ws.onmessage = (e) => console.log(JSON.parse(e.data)); +``` + +#### **Test Job Queue** +```powershell +python .\scripts\job_queue.py +# Should show example jobs +``` + +#### **Test Analytics** +```powershell +# Generate some training data first +python .\scripts\autotrain.py --job phi35_quick_test + +# Then open analytics +Start-Process "http://localhost:8000/analytics.html" +``` + +--- + +## 📈 Performance Improvements + +| Metric | Before | After | Improvement | +|--------|--------|-------|-------------| +| **Real-time Updates** | 5-10s polling | Instant (WebSocket) | 10x faster | +| **Server Load** | High (constant polling) | Low (push notifications) | 80% reduction | +| **Data Visualization** | None | 4 interactive charts | New feature | +| **Job Management** | Manual | Priority queue | Automated | +| **Config Management** | Manual | Templates | 5x faster setup | +| **Model Comparison** | Manual calculation | Automated | Instant | + +--- + +## 🎯 Use Cases + +### Research Team Scenario +``` +1. Create "research_baseline" template (Ctrl+T, Save) +2. Schedule 5 experiments with different hyperparameters (Job Queue) +3. Monitor all jobs in real-time (WebSocket) +4. Compare results (Ctrl+M) +5. Share winning template with team (export JSON) +``` + +### Production Deployment +``` +1. Load "production" template (Ctrl+T, Load "Production") +2. Verify settings in form +3. Submit job (high priority in queue) +4. Monitor GPU usage in Analytics dashboard +5. Compare with previous production model (Ctrl+M) +6. Deploy if performance > 85 +``` + +### Continuous Training +``` +1. Set up job queue with dependencies: + - Preprocess → Train → Evaluate → Deploy +2. Schedule nightly runs +3. Monitor in Analytics dashboard +4. Auto-retry failures (built into job queue) +5. Review results in morning via comparison modal +``` + +--- + +## 🔧 Troubleshooting + +### WebSocket Not Connecting +```powershell +# Check if server is running +netstat -an | Select-String "8765" + +# Start server if not running +python .\dashboard\websocket_server.py +``` + +### Charts Not Loading +``` +1. Check browser console for errors +2. Verify Chart.js CDN is accessible +3. Ensure /api/history endpoint returns data +4. Refresh page (Ctrl+R) +``` + +### Job Queue Empty +```powershell +# Initialize queue with example jobs +python .\scripts\job_queue.py + +# Or add jobs manually via API +curl -X POST http://localhost:8000/api/job-queue/add -d "{...}" +``` + +### Templates Not Saving +``` +1. Check browser localStorage (F12 > Application > Local Storage) +2. Ensure site has storage permissions +3. Try incognito mode to test +4. Export/import manually as workaround +``` + +--- + +## 📚 API Reference + +### New Endpoints + +#### `GET /api/job-queue` +Returns current job queue status. + +**Response:** +```json +{ + "total_jobs": 15, + "pending": 3, + "running": 1, + "completed": 10, + "failed": 1, + "blocked": 0, + "cancelled": 0, + "queue_length": 3, + "estimated_total_time": 3600, + "updated_at": "2025-11-25T10:30:00Z" +} +``` + +#### `WebSocket ws://localhost:8765` +Real-time job updates. + +**Message Format:** +```json +{ + "type": "job_update", + "timestamp": "2025-11-25T10:30:00Z", + "data": { + "jobs": [...], + "active_count": 1 + } +} +``` + +--- + +## 🎓 Best Practices + +### 1. WebSocket Usage +- ✅ Use for real-time monitoring +- ✅ Implement reconnection logic +- ❌ Don't use for initial data load (use REST API) +- ❌ Don't send large payloads over WebSocket + +### 2. Job Queue +- ✅ Set realistic estimated_duration +- ✅ Use priorities wisely (most jobs should be NORMAL) +- ✅ Group related jobs with dependencies +- ❌ Don't create circular dependencies +- ❌ Don't set all jobs to CRITICAL + +### 3. Configuration Templates +- ✅ Save successful configs as templates +- ✅ Document template purpose in description +- ✅ Export templates for team sharing +- ❌ Don't overwrite built-in templates +- ❌ Don't save failed configs + +### 4. Model Comparison +- ✅ Compare models with same base model +- ✅ Use performance score as primary metric +- ✅ Consider other factors (size, speed) +- ❌ Don't compare across different datasets +- ❌ Don't rely solely on perplexity + +--- + +## 🚀 Next Steps + +1. **Try each feature** with the quick start examples +2. **Customize templates** for your specific use cases +3. **Set up job queue** for automated training pipelines +4. **Monitor analytics** to identify optimization opportunities +5. **Share best practices** with your team + +--- + +**Version**: 3.0 +**Last Updated**: November 25, 2025 +**Contributors**: QAI Development Team + +**Happy Training! 🎉🤖📊** diff --git a/docs/guides/AI_DATASETS_CATALOG.md b/docs/guides/AI_DATASETS_CATALOG.md index 103ed45de..37e919056 100644 --- a/docs/guides/AI_DATASETS_CATALOG.md +++ b/docs/guides/AI_DATASETS_CATALOG.md @@ -1,529 +1,529 @@ -# AI Training Datasets Catalog -**Last Updated:** October 31, 2025 - -A comprehensive guide to datasets for all AI projects in this workspace: -- **Quantum AI**: Quantum machine learning and hybrid classifiers -- **Chat/LLM**: Fine-tuning conversational models (Phi-3.6, GPT, etc.) -- **General ML**: Traditional machine learning tasks - ---- - -## Quick Start - -### 1. Download Datasets -```powershell -# Run the dataset downloader -python .\scripts\download_datasets.py --category all -``` - -### 2. Storage Structure -``` -AI/ -├── datasets/ # Centralized data storage -│ ├── raw/ # Original downloaded data -│ ├── processed/ # Cleaned and preprocessed -│ ├── quantum/ # Quantum ML datasets -│ ├── chat/ # Conversational AI data -│ └── vision/ # Image/video data -├── dataset_index.json # Metadata and inventory -└── scripts/ - ├── download_datasets.py # Automated downloader - └── validate_datasets.py # Data quality checks -``` - ---- - -## 🔬 Quantum AI Datasets - -### Built-in Scikit-Learn Datasets -**Already Available** - No download needed - -| Dataset | Samples | Features | Classes | Use Case | -|---------|---------|----------|---------|----------| -| Iris | 150 | 4 | 3 | Binary/multi-class classification | -| Wine | 178 | 13 | 3 | Quality classification | -| Breast Cancer | 569 | 30 | 2 | Medical diagnosis | -| Digits | 1,797 | 64 | 10 | Image classification | - -**Training Command:** -```powershell -cd quantum-ai -python .\src\quantum_classifier.py --dataset iris -``` - -### UCI Machine Learning Repository -**29 Datasets (27 Working) - Free, High-Quality Datasets for Quantum Experiments** - -#### Medical Domain (8 datasets) -Healthcare applications for quantum machine learning - -**Heart Disease** -- **Size**: 303 samples, 14 features, 2 classes -- **Task**: Binary classification (disease presence/absence) -- **Use**: Quantum medical diagnosis, feature entanglement analysis -- **Training**: `python train_custom_dataset.py --dataset heart_disease --qubits 5 --layers 2` - -**Parkinson's Disease Detection** -- **Size**: 195 samples, 22 features, 2 classes -- **Task**: Binary classification from voice measurements -- **Use**: Quantum voice biomarker analysis, high-dimensional feature processing -- **Training**: `python train_custom_dataset.py --dataset parkinsons --qubits 6 --layers 3` - -**Dermatology** -- **Size**: 366 samples, 34 features, 6 classes -- **Task**: Multi-class skin disease classification -- **Use**: Quantum multi-class medical diagnosis -- **Training**: `python train_custom_dataset.py --dataset dermatology --qubits 6 --layers 3` - -**Liver Disorders** -- **Size**: 345 samples, 6 features, 2 classes -- **Task**: Binary classification from blood tests -- **Use**: Quantum medical screening, low-dimensional quantum circuits -- **Training**: `python train_custom_dataset.py --dataset liver_disorders --qubits 4 --layers 2` - -**Thyroid Conditions** -- **Size**: 215 samples, 5 features, 3 classes -- **Task**: Multi-class endocrine disorder classification -- **Use**: Quantum medical diagnostics with limited features -- **Training**: `python train_custom_dataset.py --dataset thyroid --qubits 4 --layers 2` - -**Statlog Heart Disease** -- **Size**: 270 samples, 13 features, 2 classes -- **Task**: Binary cardiac disease classification -- **Use**: Quantum cardiology models, alternative heart disease dataset -- **Training**: `python train_custom_dataset.py --dataset statlog_heart --qubits 5 --layers 2` - -**Breast Cancer** -- **Size**: 569 samples, 30 features, 2 classes (built-in) -- **Task**: Binary malignant/benign classification -- **Use**: Quantum cancer diagnosis benchmark -- **Training**: `python src/quantum_classifier.py --dataset breast_cancer` - -**Diabetes** -- **Size**: 768 samples, 8 features, 2 classes -- **Task**: Binary diabetes prediction -- **Use**: Quantum metabolic disease screening -- **Training**: `python train_custom_dataset.py --dataset diabetes --qubits 5 --layers 2` - -#### Biology Domain (4 datasets) -Biological sequence and protein classification - -**E. coli Protein Localization** -- **Size**: 336 samples, 7 features, 8 classes -- **Task**: Multi-class protein cellular location prediction -- **Use**: Quantum bioinformatics, protein targeting analysis -- **Training**: `python train_custom_dataset.py --dataset ecoli --qubits 5 --layers 3` - -**Yeast Protein Localization** -- **Size**: 1,484 samples, 8 features, 10 classes -- **Task**: 10-class protein location classification (challenging) -- **Use**: Large-scale quantum multi-class classification -- **Training**: `python train_custom_dataset.py --dataset yeast --qubits 5 --layers 4` - -**Ionosphere** -- **Size**: 351 samples, 34 features, 2 classes -- **Task**: Binary classification of radar returns -- **Use**: Quantum signal processing, feature entanglement -- **Training**: `python train_custom_dataset.py --dataset ionosphere --qubits 6 --layers 2` - -**Sonar** -- **Size**: 208 samples, 60 features, 2 classes -- **Task**: Binary classification (mines vs rocks from sonar signals) -- **Use**: Quantum signal processing, high-dimensional feature space -- **Training**: `python train_custom_dataset.py --dataset sonar --qubits 6 --layers 2` - -#### Image Features Domain (4 datasets) -Computer vision feature-based classification - -**Optical Recognition of Handwritten Digits** -- **Size**: 3,823 samples, 64 features, 10 classes -- **Task**: 10-class digit recognition (0-9) -- **Use**: Large-scale quantum image classification, PCA testing (64→6 qubits) -- **Note**: Requires aggressive dimensionality reduction -- **Training**: `python train_custom_dataset.py --dataset optical_recognition --qubits 6 --layers 4` - -**Pen-Based Handwritten Digits** -- **Size**: 7,494 samples, 16 features, 10 classes (largest dataset) -- **Task**: 10-class digit recognition from pen trajectories -- **Use**: Large-scale quantum ML scalability testing -- **Training**: `python train_custom_dataset.py --dataset pendigits --qubits 6 --layers 4 --batch 32` - -**Iris** -- **Size**: 150 samples, 4 features, 3 classes (built-in) -- **Task**: Multi-class flower species classification -- **Use**: Quantum ML benchmark, classic test case -- **Training**: `python src/quantum_classifier.py --dataset iris` - -**Digits** -- **Size**: 1,797 samples, 64 features, 10 classes (built-in) -- **Task**: 10-class digit image classification -- **Use**: Quantum image classification benchmark -- **Training**: `python src/quantum_classifier.py --dataset digits` - -#### Chemistry/Materials Domain (3 datasets) - -**Wine Quality (Combined Red+White)** -- **Size**: 6,497 samples, 12 features, 7 classes -- **Task**: Multi-class wine quality rating (3-9 scale) -- **Use**: Largest quantum chemistry dataset, quality regression -- **Note**: Combined 1,599 red + 4,898 white wines with wine_type feature -- **Training**: `python train_custom_dataset.py --dataset wine_quality_combined --qubits 5 --layers 3` - -**Wine (Red)** -- **Size**: 1,599 samples, 11 features, 6 classes -- **Task**: Red wine quality classification -- **Use**: Quantum chemistry, quality prediction -- **Training**: `python train_custom_dataset.py --dataset wine_red --qubits 5 --layers 2` - -**Wine (White)** -- **Size**: 4,898 samples, 11 features, 7 classes -- **Task**: White wine quality classification -- **Use**: Large-scale quantum chemistry -- **Training**: `python train_custom_dataset.py --dataset wine_white --qubits 5 --layers 3` - -#### Agriculture Domain (2 datasets) - -**Wheat Seeds** -- **Size**: 210 samples, 7 features, 3 classes -- **Task**: Multi-class wheat variety classification -- **Use**: Quantum agriculture, seed quality analysis -- **Training**: `python train_custom_dataset.py --dataset wheat_seeds --qubits 5 --layers 2` - -**Seeds (Alternative)** -- **Size**: 210 samples, 7 features, 3 classes -- **Task**: Wheat kernel variety classification -- **Use**: Alternative agriculture dataset for cross-validation -- **Training**: `python train_custom_dataset.py --dataset seeds --qubits 5 --layers 2` - -#### Finance Domain (1 dataset) - -**Statlog Australian Credit Approval** -- **Size**: 690 samples, 14 features, 2 classes -- **Task**: Binary credit decision classification -- **Use**: Quantum finance, risk assessment models -- **Training**: `python train_custom_dataset.py --dataset statlog_australian --qubits 5 --layers 2` - -#### Social Science Domain (1 dataset) - -**Contraceptive Method Choice** -- **Size**: 1,473 samples, 9 features, 3 classes -- **Task**: Multi-class contraceptive preference prediction -- **Use**: Quantum social science, demographic analysis -- **Training**: `python train_custom_dataset.py --dataset contraceptive --qubits 5 --layers 2` - -#### Physics Domain (1 dataset) - -**Balance Scale** -- **Size**: 625 samples, 4 features, 3 classes -- **Task**: Multi-class balance tip direction prediction -- **Use**: Quantum physics modeling, equilibrium classification -- **Training**: `python train_custom_dataset.py --dataset balance_scale --qubits 4 --layers 2` - -#### Forensics Domain (1 dataset) - -**Banknote Authentication** -- **Size**: 1,372 samples, 4 features, 2 classes -- **Task**: Binary genuine/counterfeit classification -- **Use**: Quantum security, forensic analysis -- **Training**: `python train_custom_dataset.py --dataset banknote --qubits 4 --layers 2` - -#### Other Domains - -**Glass Identification** -- **Size**: 214 samples, 9 features, 6 classes -- **Task**: Multi-class glass type classification -- **Use**: Quantum forensics, materials analysis -- **Training**: `python train_custom_dataset.py --dataset glass --qubits 5 --layers 2` - -**Blood Transfusion Service** -- **Size**: 748 samples, 4 features, 2 classes -- **Task**: Binary donation prediction -- **Use**: Quantum healthcare logistics -- **Training**: `python train_custom_dataset.py --dataset blood_transfusion --qubits 4 --layers 2` - -**Haberman Survival** -- **Size**: 306 samples, 3 features, 2 classes -- **Task**: Binary cancer survival prediction -- **Use**: Quantum medical prognosis -- **Training**: `python train_custom_dataset.py --dataset haberman --qubits 4 --layers 2` - -**Magic Gamma Telescope** -- **Size**: 19,020 samples, 10 features, 2 classes -- **Task**: Binary particle classification (gamma vs hadron) -- **Use**: Large-scale quantum physics simulation -- **Training**: `python train_custom_dataset.py --dataset magic_gamma --qubits 5 --layers 2` - -**Vertebral Column (CORRUPTED - DO NOT USE)** -- **Status**: File corruption, unable to load -- **Note**: Excluded from all benchmarks - -### Quantum-Specific Datasets - -#### Quantum Many-Body Systems -- **Source**: TensorFlow Quantum datasets -- **URL**: https://www.tensorflow.org/quantum/tutorials/mnist -- **Format**: NumPy arrays -- **Use**: Quantum state preparation, VQE training - -#### QMNIST (Quantum MNIST) -- **Source**: Modified MNIST for quantum circuits -- **Size**: 60,000 train, 10,000 test -- **Format**: 4x4 downsampled images (16 features → 4 qubits) -- **Use**: Quantum image classification - ---- - -## 💬 Chat/LLM Fine-Tuning Datasets - -### High-Quality Conversational Datasets - -#### 1. ShareGPT Conversations -- **Source**: Hugging Face `shareGPT_vicuna_unfiltered` -- **Size**: 90,000+ multi-turn conversations -- **Format**: JSONL -- **Quality**: ⭐⭐⭐⭐⭐ -- **Use**: General chatbot fine-tuning -- **Download**: -```python -from datasets import load_dataset -ds = load_dataset("anon8231489123/ShareGPT_Vicuna_unfiltered") -``` - -#### 2. OpenAssistant Conversations -- **Source**: Hugging Face `OpenAssistant/oasst1` -- **Size**: 161,000 messages across 35,000 conversations -- **Format**: Parquet/JSONL -- **Quality**: ⭐⭐⭐⭐⭐ -- **Languages**: 35 languages -- **Use**: Instruction-following, multi-turn dialogue -- **Download**: -```python -from datasets import load_dataset -ds = load_dataset("OpenAssistant/oasst1") -``` - -#### 3. Alpaca Dataset -- **Source**: Stanford Alpaca -- **Size**: 52,000 instruction-response pairs -- **Format**: JSON -- **Quality**: ⭐⭐⭐⭐ -- **Use**: Instruction tuning -- **URL**: https://github.com/tatsu-lab/stanford_alpaca - -#### 4. Dolly 15k -- **Source**: Databricks -- **Size**: 15,000 instruction-response pairs -- **Format**: JSONL -- **Quality**: ⭐⭐⭐⭐⭐ (Human-generated) -- **License**: CC BY-SA 3.0 (Commercial use allowed) -- **Download**: -```python -from datasets import load_dataset -ds = load_dataset("databricks/databricks-dolly-15k") -``` - -#### 5. WizardLM Evol-Instruct -- **Source**: Microsoft Research -- **Size**: 70,000+ complex instructions -- **Format**: JSONL -- **Quality**: ⭐⭐⭐⭐⭐ -- **Use**: Advanced reasoning, complex tasks -- **Download**: -```python -from datasets import load_dataset -ds = load_dataset("WizardLM/WizardLM_evol_instruct_70k") -``` - -### Domain-Specific Datasets - -#### Medical/Healthcare -- **PubMedQA**: Medical question-answering (1,000 expert-annotated) -- **MedDialog**: Doctor-patient conversations (3.6M+ utterances) -- **MIMIC-III Notes**: Clinical notes (requires credentialing) - -#### Legal -- **LegalBench**: Legal reasoning tasks -- **CaseHOLD**: Legal citation prediction -- **MultiLegalPile**: 689GB legal documents - -#### Code/Programming -- **CodeAlpaca**: 20,000 code instruction pairs -- **Code Contests**: Programming competition problems -- **The Stack**: 3TB of permissively licensed code - -#### Internal Repository Corpus (Synthetic) -- **Source**: Local workspace files (code + markdown docs) -- **Location**: `datasets/chat/app_repo` -- **Format**: `train.json` / `test.json` newline-delimited JSON (each line has `messages` array) -- **Size**: Small (synthetic; generated on demand) -- **License**: Internal use only (do not distribute externally) -- **Use**: Fine-tune Phi-3.6 adapters to improve model awareness of project-specific patterns, configuration philosophy, and extension guidelines. -- **Generation Script**: `scripts/generate_repo_training_dataset.py` -- **Prompt Types**: Summary, functions/classes listing, safe extension guidance. - -**Generate (PowerShell):** -```powershell -python .\scripts\generate_repo_training_dataset.py --max-records 300 -``` - -**Dry-run validate:** -```powershell -python AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py --dataset .\datasets\chat\app_repo --dry-run -``` - -**Smoke-test training (CPU friendly):** -```powershell -python AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py --dataset .\datasets\chat\app_repo --max-train-samples 64 --max-eval-samples 16 -``` - -> Tip: Re-run generation script after significant repository changes to refresh synthetic summaries. Keep max-records modest (≤500) to avoid overfitting and retain generalization from broader public datasets. - -#### Customer Support -- **MultiWOZ**: Multi-domain dialogue (10,000 conversations) -- **Ubuntu IRC**: Technical support logs -- **Coached Conversations**: Customer service training data - ---- - -## 🎯 Dataset Selection Guide - -### For Quantum AI (4-10 qubits): -- **Start with**: Iris, Wine, Breast Cancer (built-in) -- **Next**: UCI datasets (Heart Disease, Sonar) -- **Advanced**: Quantum Many-Body, QMNIST -- **Features**: Keep ≤ 10 features (or use PCA) -- **Samples**: 100-10,000 (quantum simulators are slow) - -### For Phi-3.6 Fine-Tuning: -- **Small tests**: Dolly 15k (fits in memory, high quality) -- **Production**: ShareGPT or OpenAssistant (large, diverse) -- **Specialized**: Domain-specific datasets for targeted use cases -- **Format**: JSONL with `messages` field (Phi-3 chat template) -- **Streaming**: Use `datasets` library for >10GB files - -### For Talk-to-AI Chat: -- **Logs**: Your own conversation history in `ai-projects/chat-cli/logs/*.jsonl` -- **Fine-tune on**: Personal assistant style from your interactions -- **Combine with**: OpenAssistant for general knowledge - ---- - -## 📦 Automated Download Scripts - -### Script 1: Quantum Datasets -**File**: `scripts/download_quantum_datasets.py` -```python -# Downloads UCI and quantum-specific datasets -# Saves to: datasets/quantum/ -# Formats: CSV, NumPy, Parquet -``` - -### Script 2: Chat Datasets -**File**: `scripts/download_chat_datasets.py` -```python -# Downloads ShareGPT, OpenAssistant, Dolly, Alpaca -# Converts to Phi-3 JSONL format -# Saves to: datasets/chat/ -``` - -### Script 3: Dataset Validator -**File**: `scripts/validate_datasets.py` -```python -# Checks file integrity -# Validates JSONL format -# Counts samples and features -# Reports quality metrics -``` - ---- - -## 🔐 License Considerations - -### ✅ Safe for Commercial Use: -- Dolly 15k (CC BY-SA 3.0) -- OpenAssistant (Apache 2.0) -- UCI datasets (Attribution required) -- Scikit-learn datasets (BSD) - -### ⚠️ Research/Non-Commercial Only: -- ShareGPT (No commercial license) -- Alpaca (Based on OpenAI data - terms unclear) - -### 🔒 Requires Credentialing: -- MIMIC-III (Healthcare data - CITI training required) -- Clinical trial data (IRB approval) - ---- - -## 💾 Storage Requirements - -### Current Workspace Storage: -- **Quantum datasets**: ~50 MB (UCI + built-in) -- **Chat datasets (small)**: ~500 MB (Dolly 15k) -- **Chat datasets (large)**: ~5-10 GB (ShareGPT, OpenAssistant) -- **Code datasets**: ~50+ GB (The Stack) - -### Recommended Setup: -```powershell -# Check available space -Get-PSDrive C | Select-Object Used,Free - -# Allocate for datasets -# Minimum: 10 GB for chat fine-tuning -# Recommended: 50 GB for diverse datasets -# Optimal: 500 GB for large-scale training -``` - ---- - -## 🚀 Next Steps - -1. **Run Dataset Downloader** (created below) - ```powershell - python .\scripts\download_datasets.py --category quantum - python .\scripts\download_datasets.py --category chat --dataset dolly - ``` - -2. **Validate Downloads** - ```powershell - python .\scripts\validate_datasets.py - ``` - -3. **Train Models** - ```powershell - # Quantum AI - cd quantum-ai - python .\train_custom_dataset.py - - # Phi-3.6 Fine-tuning - cd AI\microsoft_phi-silica-3.6_v1 - python .\scripts\train_lora.py --dataset ..\..\datasets\chat\dolly15k - ``` - -4. **Monitor Storage** - ```powershell - # Track dataset sizes - Get-ChildItem .\datasets -Recurse | Measure-Object -Property Length -Sum - ``` - ---- - -## 📚 Additional Resources - -### Data Sources: -- **Hugging Face Hub**: https://huggingface.co/datasets -- **Papers with Code**: https://paperswithcode.com/datasets -- **UCI ML Repository**: https://archive.ics.uci.edu/ml/ -- **Kaggle Datasets**: https://www.kaggle.com/datasets -- **Google Dataset Search**: https://datasetsearch.research.google.com/ - -### Tools: -- **Hugging Face `datasets`**: Streaming, caching, auto-download -- **Pandas**: CSV/Excel processing -- **PyArrow**: Fast Parquet reading -- **DVC**: Dataset version control - -### Tutorials: -- Quantum dataset preparation: `ai-projects/quantum-ml/notebooks/` -- JSONL conversion: `AI/microsoft_phi-silica-3.6_v1/scripts/prepare_dataset.py` -- Chat template formatting: Phi-3 documentation +# AI Training Datasets Catalog +**Last Updated:** October 31, 2025 + +A comprehensive guide to datasets for all AI projects in this workspace: +- **Quantum AI**: Quantum machine learning and hybrid classifiers +- **Chat/LLM**: Fine-tuning conversational models (Phi-3.6, GPT, etc.) +- **General ML**: Traditional machine learning tasks + +--- + +## Quick Start + +### 1. Download Datasets +```powershell +# Run the dataset downloader +python .\scripts\download_datasets.py --category all +``` + +### 2. Storage Structure +``` +AI/ +├── datasets/ # Centralized data storage +│ ├── raw/ # Original downloaded data +│ ├── processed/ # Cleaned and preprocessed +│ ├── quantum/ # Quantum ML datasets +│ ├── chat/ # Conversational AI data +│ └── vision/ # Image/video data +├── dataset_index.json # Metadata and inventory +└── scripts/ + ├── download_datasets.py # Automated downloader + └── validate_datasets.py # Data quality checks +``` + +--- + +## 🔬 Quantum AI Datasets + +### Built-in Scikit-Learn Datasets +**Already Available** - No download needed + +| Dataset | Samples | Features | Classes | Use Case | +|---------|---------|----------|---------|----------| +| Iris | 150 | 4 | 3 | Binary/multi-class classification | +| Wine | 178 | 13 | 3 | Quality classification | +| Breast Cancer | 569 | 30 | 2 | Medical diagnosis | +| Digits | 1,797 | 64 | 10 | Image classification | + +**Training Command:** +```powershell +cd quantum-ai +python .\src\quantum_classifier.py --dataset iris +``` + +### UCI Machine Learning Repository +**29 Datasets (27 Working) - Free, High-Quality Datasets for Quantum Experiments** + +#### Medical Domain (8 datasets) +Healthcare applications for quantum machine learning + +**Heart Disease** +- **Size**: 303 samples, 14 features, 2 classes +- **Task**: Binary classification (disease presence/absence) +- **Use**: Quantum medical diagnosis, feature entanglement analysis +- **Training**: `python train_custom_dataset.py --dataset heart_disease --qubits 5 --layers 2` + +**Parkinson's Disease Detection** +- **Size**: 195 samples, 22 features, 2 classes +- **Task**: Binary classification from voice measurements +- **Use**: Quantum voice biomarker analysis, high-dimensional feature processing +- **Training**: `python train_custom_dataset.py --dataset parkinsons --qubits 6 --layers 3` + +**Dermatology** +- **Size**: 366 samples, 34 features, 6 classes +- **Task**: Multi-class skin disease classification +- **Use**: Quantum multi-class medical diagnosis +- **Training**: `python train_custom_dataset.py --dataset dermatology --qubits 6 --layers 3` + +**Liver Disorders** +- **Size**: 345 samples, 6 features, 2 classes +- **Task**: Binary classification from blood tests +- **Use**: Quantum medical screening, low-dimensional quantum circuits +- **Training**: `python train_custom_dataset.py --dataset liver_disorders --qubits 4 --layers 2` + +**Thyroid Conditions** +- **Size**: 215 samples, 5 features, 3 classes +- **Task**: Multi-class endocrine disorder classification +- **Use**: Quantum medical diagnostics with limited features +- **Training**: `python train_custom_dataset.py --dataset thyroid --qubits 4 --layers 2` + +**Statlog Heart Disease** +- **Size**: 270 samples, 13 features, 2 classes +- **Task**: Binary cardiac disease classification +- **Use**: Quantum cardiology models, alternative heart disease dataset +- **Training**: `python train_custom_dataset.py --dataset statlog_heart --qubits 5 --layers 2` + +**Breast Cancer** +- **Size**: 569 samples, 30 features, 2 classes (built-in) +- **Task**: Binary malignant/benign classification +- **Use**: Quantum cancer diagnosis benchmark +- **Training**: `python src/quantum_classifier.py --dataset breast_cancer` + +**Diabetes** +- **Size**: 768 samples, 8 features, 2 classes +- **Task**: Binary diabetes prediction +- **Use**: Quantum metabolic disease screening +- **Training**: `python train_custom_dataset.py --dataset diabetes --qubits 5 --layers 2` + +#### Biology Domain (4 datasets) +Biological sequence and protein classification + +**E. coli Protein Localization** +- **Size**: 336 samples, 7 features, 8 classes +- **Task**: Multi-class protein cellular location prediction +- **Use**: Quantum bioinformatics, protein targeting analysis +- **Training**: `python train_custom_dataset.py --dataset ecoli --qubits 5 --layers 3` + +**Yeast Protein Localization** +- **Size**: 1,484 samples, 8 features, 10 classes +- **Task**: 10-class protein location classification (challenging) +- **Use**: Large-scale quantum multi-class classification +- **Training**: `python train_custom_dataset.py --dataset yeast --qubits 5 --layers 4` + +**Ionosphere** +- **Size**: 351 samples, 34 features, 2 classes +- **Task**: Binary classification of radar returns +- **Use**: Quantum signal processing, feature entanglement +- **Training**: `python train_custom_dataset.py --dataset ionosphere --qubits 6 --layers 2` + +**Sonar** +- **Size**: 208 samples, 60 features, 2 classes +- **Task**: Binary classification (mines vs rocks from sonar signals) +- **Use**: Quantum signal processing, high-dimensional feature space +- **Training**: `python train_custom_dataset.py --dataset sonar --qubits 6 --layers 2` + +#### Image Features Domain (4 datasets) +Computer vision feature-based classification + +**Optical Recognition of Handwritten Digits** +- **Size**: 3,823 samples, 64 features, 10 classes +- **Task**: 10-class digit recognition (0-9) +- **Use**: Large-scale quantum image classification, PCA testing (64→6 qubits) +- **Note**: Requires aggressive dimensionality reduction +- **Training**: `python train_custom_dataset.py --dataset optical_recognition --qubits 6 --layers 4` + +**Pen-Based Handwritten Digits** +- **Size**: 7,494 samples, 16 features, 10 classes (largest dataset) +- **Task**: 10-class digit recognition from pen trajectories +- **Use**: Large-scale quantum ML scalability testing +- **Training**: `python train_custom_dataset.py --dataset pendigits --qubits 6 --layers 4 --batch 32` + +**Iris** +- **Size**: 150 samples, 4 features, 3 classes (built-in) +- **Task**: Multi-class flower species classification +- **Use**: Quantum ML benchmark, classic test case +- **Training**: `python src/quantum_classifier.py --dataset iris` + +**Digits** +- **Size**: 1,797 samples, 64 features, 10 classes (built-in) +- **Task**: 10-class digit image classification +- **Use**: Quantum image classification benchmark +- **Training**: `python src/quantum_classifier.py --dataset digits` + +#### Chemistry/Materials Domain (3 datasets) + +**Wine Quality (Combined Red+White)** +- **Size**: 6,497 samples, 12 features, 7 classes +- **Task**: Multi-class wine quality rating (3-9 scale) +- **Use**: Largest quantum chemistry dataset, quality regression +- **Note**: Combined 1,599 red + 4,898 white wines with wine_type feature +- **Training**: `python train_custom_dataset.py --dataset wine_quality_combined --qubits 5 --layers 3` + +**Wine (Red)** +- **Size**: 1,599 samples, 11 features, 6 classes +- **Task**: Red wine quality classification +- **Use**: Quantum chemistry, quality prediction +- **Training**: `python train_custom_dataset.py --dataset wine_red --qubits 5 --layers 2` + +**Wine (White)** +- **Size**: 4,898 samples, 11 features, 7 classes +- **Task**: White wine quality classification +- **Use**: Large-scale quantum chemistry +- **Training**: `python train_custom_dataset.py --dataset wine_white --qubits 5 --layers 3` + +#### Agriculture Domain (2 datasets) + +**Wheat Seeds** +- **Size**: 210 samples, 7 features, 3 classes +- **Task**: Multi-class wheat variety classification +- **Use**: Quantum agriculture, seed quality analysis +- **Training**: `python train_custom_dataset.py --dataset wheat_seeds --qubits 5 --layers 2` + +**Seeds (Alternative)** +- **Size**: 210 samples, 7 features, 3 classes +- **Task**: Wheat kernel variety classification +- **Use**: Alternative agriculture dataset for cross-validation +- **Training**: `python train_custom_dataset.py --dataset seeds --qubits 5 --layers 2` + +#### Finance Domain (1 dataset) + +**Statlog Australian Credit Approval** +- **Size**: 690 samples, 14 features, 2 classes +- **Task**: Binary credit decision classification +- **Use**: Quantum finance, risk assessment models +- **Training**: `python train_custom_dataset.py --dataset statlog_australian --qubits 5 --layers 2` + +#### Social Science Domain (1 dataset) + +**Contraceptive Method Choice** +- **Size**: 1,473 samples, 9 features, 3 classes +- **Task**: Multi-class contraceptive preference prediction +- **Use**: Quantum social science, demographic analysis +- **Training**: `python train_custom_dataset.py --dataset contraceptive --qubits 5 --layers 2` + +#### Physics Domain (1 dataset) + +**Balance Scale** +- **Size**: 625 samples, 4 features, 3 classes +- **Task**: Multi-class balance tip direction prediction +- **Use**: Quantum physics modeling, equilibrium classification +- **Training**: `python train_custom_dataset.py --dataset balance_scale --qubits 4 --layers 2` + +#### Forensics Domain (1 dataset) + +**Banknote Authentication** +- **Size**: 1,372 samples, 4 features, 2 classes +- **Task**: Binary genuine/counterfeit classification +- **Use**: Quantum security, forensic analysis +- **Training**: `python train_custom_dataset.py --dataset banknote --qubits 4 --layers 2` + +#### Other Domains + +**Glass Identification** +- **Size**: 214 samples, 9 features, 6 classes +- **Task**: Multi-class glass type classification +- **Use**: Quantum forensics, materials analysis +- **Training**: `python train_custom_dataset.py --dataset glass --qubits 5 --layers 2` + +**Blood Transfusion Service** +- **Size**: 748 samples, 4 features, 2 classes +- **Task**: Binary donation prediction +- **Use**: Quantum healthcare logistics +- **Training**: `python train_custom_dataset.py --dataset blood_transfusion --qubits 4 --layers 2` + +**Haberman Survival** +- **Size**: 306 samples, 3 features, 2 classes +- **Task**: Binary cancer survival prediction +- **Use**: Quantum medical prognosis +- **Training**: `python train_custom_dataset.py --dataset haberman --qubits 4 --layers 2` + +**Magic Gamma Telescope** +- **Size**: 19,020 samples, 10 features, 2 classes +- **Task**: Binary particle classification (gamma vs hadron) +- **Use**: Large-scale quantum physics simulation +- **Training**: `python train_custom_dataset.py --dataset magic_gamma --qubits 5 --layers 2` + +**Vertebral Column (CORRUPTED - DO NOT USE)** +- **Status**: File corruption, unable to load +- **Note**: Excluded from all benchmarks + +### Quantum-Specific Datasets + +#### Quantum Many-Body Systems +- **Source**: TensorFlow Quantum datasets +- **URL**: https://www.tensorflow.org/quantum/tutorials/mnist +- **Format**: NumPy arrays +- **Use**: Quantum state preparation, VQE training + +#### QMNIST (Quantum MNIST) +- **Source**: Modified MNIST for quantum circuits +- **Size**: 60,000 train, 10,000 test +- **Format**: 4x4 downsampled images (16 features → 4 qubits) +- **Use**: Quantum image classification + +--- + +## 💬 Chat/LLM Fine-Tuning Datasets + +### High-Quality Conversational Datasets + +#### 1. ShareGPT Conversations +- **Source**: Hugging Face `shareGPT_vicuna_unfiltered` +- **Size**: 90,000+ multi-turn conversations +- **Format**: JSONL +- **Quality**: ⭐⭐⭐⭐⭐ +- **Use**: General chatbot fine-tuning +- **Download**: +```python +from datasets import load_dataset +ds = load_dataset("anon8231489123/ShareGPT_Vicuna_unfiltered") +``` + +#### 2. OpenAssistant Conversations +- **Source**: Hugging Face `OpenAssistant/oasst1` +- **Size**: 161,000 messages across 35,000 conversations +- **Format**: Parquet/JSONL +- **Quality**: ⭐⭐⭐⭐⭐ +- **Languages**: 35 languages +- **Use**: Instruction-following, multi-turn dialogue +- **Download**: +```python +from datasets import load_dataset +ds = load_dataset("OpenAssistant/oasst1") +``` + +#### 3. Alpaca Dataset +- **Source**: Stanford Alpaca +- **Size**: 52,000 instruction-response pairs +- **Format**: JSON +- **Quality**: ⭐⭐⭐⭐ +- **Use**: Instruction tuning +- **URL**: https://github.com/tatsu-lab/stanford_alpaca + +#### 4. Dolly 15k +- **Source**: Databricks +- **Size**: 15,000 instruction-response pairs +- **Format**: JSONL +- **Quality**: ⭐⭐⭐⭐⭐ (Human-generated) +- **License**: CC BY-SA 3.0 (Commercial use allowed) +- **Download**: +```python +from datasets import load_dataset +ds = load_dataset("databricks/databricks-dolly-15k") +``` + +#### 5. WizardLM Evol-Instruct +- **Source**: Microsoft Research +- **Size**: 70,000+ complex instructions +- **Format**: JSONL +- **Quality**: ⭐⭐⭐⭐⭐ +- **Use**: Advanced reasoning, complex tasks +- **Download**: +```python +from datasets import load_dataset +ds = load_dataset("WizardLM/WizardLM_evol_instruct_70k") +``` + +### Domain-Specific Datasets + +#### Medical/Healthcare +- **PubMedQA**: Medical question-answering (1,000 expert-annotated) +- **MedDialog**: Doctor-patient conversations (3.6M+ utterances) +- **MIMIC-III Notes**: Clinical notes (requires credentialing) + +#### Legal +- **LegalBench**: Legal reasoning tasks +- **CaseHOLD**: Legal citation prediction +- **MultiLegalPile**: 689GB legal documents + +#### Code/Programming +- **CodeAlpaca**: 20,000 code instruction pairs +- **Code Contests**: Programming competition problems +- **The Stack**: 3TB of permissively licensed code + +#### Internal Repository Corpus (Synthetic) +- **Source**: Local workspace files (code + markdown docs) +- **Location**: `datasets/chat/app_repo` +- **Format**: `train.json` / `test.json` newline-delimited JSON (each line has `messages` array) +- **Size**: Small (synthetic; generated on demand) +- **License**: Internal use only (do not distribute externally) +- **Use**: Fine-tune Phi-3.6 adapters to improve model awareness of project-specific patterns, configuration philosophy, and extension guidelines. +- **Generation Script**: `scripts/generate_repo_training_dataset.py` +- **Prompt Types**: Summary, functions/classes listing, safe extension guidance. + +**Generate (PowerShell):** +```powershell +python .\scripts\generate_repo_training_dataset.py --max-records 300 +``` + +**Dry-run validate:** +```powershell +python AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py --dataset .\datasets\chat\app_repo --dry-run +``` + +**Smoke-test training (CPU friendly):** +```powershell +python AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py --dataset .\datasets\chat\app_repo --max-train-samples 64 --max-eval-samples 16 +``` + +> Tip: Re-run generation script after significant repository changes to refresh synthetic summaries. Keep max-records modest (≤500) to avoid overfitting and retain generalization from broader public datasets. + +#### Customer Support +- **MultiWOZ**: Multi-domain dialogue (10,000 conversations) +- **Ubuntu IRC**: Technical support logs +- **Coached Conversations**: Customer service training data + +--- + +## 🎯 Dataset Selection Guide + +### For Quantum AI (4-10 qubits): +- **Start with**: Iris, Wine, Breast Cancer (built-in) +- **Next**: UCI datasets (Heart Disease, Sonar) +- **Advanced**: Quantum Many-Body, QMNIST +- **Features**: Keep ≤ 10 features (or use PCA) +- **Samples**: 100-10,000 (quantum simulators are slow) + +### For Phi-3.6 Fine-Tuning: +- **Small tests**: Dolly 15k (fits in memory, high quality) +- **Production**: ShareGPT or OpenAssistant (large, diverse) +- **Specialized**: Domain-specific datasets for targeted use cases +- **Format**: JSONL with `messages` field (Phi-3 chat template) +- **Streaming**: Use `datasets` library for >10GB files + +### For Talk-to-AI Chat: +- **Logs**: Your own conversation history in `ai-projects/chat-cli/logs/*.jsonl` +- **Fine-tune on**: Personal assistant style from your interactions +- **Combine with**: OpenAssistant for general knowledge + +--- + +## 📦 Automated Download Scripts + +### Script 1: Quantum Datasets +**File**: `scripts/download_quantum_datasets.py` +```python +# Downloads UCI and quantum-specific datasets +# Saves to: datasets/quantum/ +# Formats: CSV, NumPy, Parquet +``` + +### Script 2: Chat Datasets +**File**: `scripts/download_chat_datasets.py` +```python +# Downloads ShareGPT, OpenAssistant, Dolly, Alpaca +# Converts to Phi-3 JSONL format +# Saves to: datasets/chat/ +``` + +### Script 3: Dataset Validator +**File**: `scripts/validate_datasets.py` +```python +# Checks file integrity +# Validates JSONL format +# Counts samples and features +# Reports quality metrics +``` + +--- + +## 🔐 License Considerations + +### ✅ Safe for Commercial Use: +- Dolly 15k (CC BY-SA 3.0) +- OpenAssistant (Apache 2.0) +- UCI datasets (Attribution required) +- Scikit-learn datasets (BSD) + +### ⚠️ Research/Non-Commercial Only: +- ShareGPT (No commercial license) +- Alpaca (Based on OpenAI data - terms unclear) + +### 🔒 Requires Credentialing: +- MIMIC-III (Healthcare data - CITI training required) +- Clinical trial data (IRB approval) + +--- + +## 💾 Storage Requirements + +### Current Workspace Storage: +- **Quantum datasets**: ~50 MB (UCI + built-in) +- **Chat datasets (small)**: ~500 MB (Dolly 15k) +- **Chat datasets (large)**: ~5-10 GB (ShareGPT, OpenAssistant) +- **Code datasets**: ~50+ GB (The Stack) + +### Recommended Setup: +```powershell +# Check available space +Get-PSDrive C | Select-Object Used,Free + +# Allocate for datasets +# Minimum: 10 GB for chat fine-tuning +# Recommended: 50 GB for diverse datasets +# Optimal: 500 GB for large-scale training +``` + +--- + +## 🚀 Next Steps + +1. **Run Dataset Downloader** (created below) + ```powershell + python .\scripts\download_datasets.py --category quantum + python .\scripts\download_datasets.py --category chat --dataset dolly + ``` + +2. **Validate Downloads** + ```powershell + python .\scripts\validate_datasets.py + ``` + +3. **Train Models** + ```powershell + # Quantum AI + cd quantum-ai + python .\train_custom_dataset.py + + # Phi-3.6 Fine-tuning + cd AI\microsoft_phi-silica-3.6_v1 + python .\scripts\train_lora.py --dataset ..\..\datasets\chat\dolly15k + ``` + +4. **Monitor Storage** + ```powershell + # Track dataset sizes + Get-ChildItem .\datasets -Recurse | Measure-Object -Property Length -Sum + ``` + +--- + +## 📚 Additional Resources + +### Data Sources: +- **Hugging Face Hub**: https://huggingface.co/datasets +- **Papers with Code**: https://paperswithcode.com/datasets +- **UCI ML Repository**: https://archive.ics.uci.edu/ml/ +- **Kaggle Datasets**: https://www.kaggle.com/datasets +- **Google Dataset Search**: https://datasetsearch.research.google.com/ + +### Tools: +- **Hugging Face `datasets`**: Streaming, caching, auto-download +- **Pandas**: CSV/Excel processing +- **PyArrow**: Fast Parquet reading +- **DVC**: Dataset version control + +### Tutorials: +- Quantum dataset preparation: `ai-projects/quantum-ml/notebooks/` +- JSONL conversion: `AI/microsoft_phi-silica-3.6_v1/scripts/prepare_dataset.py` +- Chat template formatting: Phi-3 documentation diff --git a/docs/guides/AI_IMPROVEMENTS.md b/docs/guides/AI_IMPROVEMENTS.md index 8e1911683..e43055ec2 100644 --- a/docs/guides/AI_IMPROVEMENTS.md +++ b/docs/guides/AI_IMPROVEMENTS.md @@ -1,270 +1,270 @@ -# AI System Improvements - -**Date**: November 21, 2025 -**Status**: ✅ Implemented - -## Overview - -Comprehensive improvements applied across all AI components to enhance performance, stability, and generation quality. - -## 1. Quantum AI Enhancements - -### Hybrid QNN Improvements - -**Residual Connections**: -- Added residual connections in HybridQNN for better gradient flow -- Automatically projects input dimensions when needed -- Prevents degradation in deep networks - -**Advanced Batch Normalization**: -- Optional batch normalization layers for training stability -- Prevents internal covariate shift -- Improves convergence speed - -**Deeper Architecture**: -- Added intermediate hidden layer in decoder (hidden_dim → hidden_dim//2 → output) -- Progressive dropout (higher in early layers, lower in final layers) -- Better feature extraction and representation learning - -**Enhanced Quantum Circuit**: -- **Dual encoding**: RY (amplitude) + RZ (phase) for richer feature representation -- **Final rotation layer**: Additional expressiveness after entanglement layers -- **Improved measurements**: Better quantum-to-classical information transfer - -### Trainer Improvements - -**AdamW Optimizer**: -- Replaced Adam with AdamW for better generalization -- Weight decay (0.01) prevents overfitting -- Optimized beta parameters (0.9, 0.999) - -**Learning Rate Scheduling**: -- ReduceLROnPlateau scheduler adapts to training dynamics -- Automatically reduces LR when validation loss plateaus -- Factor 0.5, patience 5 epochs - -**Gradient Clipping**: -- Clips gradients to max norm of 1.0 -- Prevents exploding gradients in quantum circuits -- Improves training stability - -**Early Stopping**: -- Monitors validation loss for improvements -- Default patience: 10 epochs -- Automatically stops training when model stops improving -- Restores best model weights - -**Best Model Tracking**: -- Automatically saves best model state during training -- Tracks best validation accuracy -- Restores optimal weights at end of training - -**Enhanced Logging**: -- Records learning rate per epoch -- Better progress tracking with batch percentages -- Debug-level logging for detailed analysis -- Error handling with graceful degradation - -## 2. Chat AI Improvements - -### LoRA Provider Enhancements - -**Advanced Generation Parameters**: -```python -top_p: float = 0.9 # Nucleus sampling (90th percentile) -top_k: int = 50 # Top-K sampling -repetition_penalty: 1.1 # Reduces repetitive text -``` - -**Benefits**: -- **Top-P (Nucleus)**: More coherent responses by focusing on high-probability tokens -- **Top-K**: Limits sampling pool for more focused generation -- **Repetition Penalty**: Reduces loops and repetitive patterns -- **Proper EOS Handling**: Clean stopping with pad_token_id and eos_token_id - -**Temperature Control**: -- Default 0.7 for balanced creativity/coherence -- Configurable per-session for different use cases -- Works with nucleus and top-k sampling - -## 3. Training Data Improvements - -### Enhanced Message Formatting - -**End Token Addition**: -- Added `<|end|>` tokens after each message turn -- Helps model learn conversation boundaries -- Improves turn-taking in multi-turn dialogues - -**Content Validation**: -- Skips empty messages automatically -- Strips whitespace for consistency -- Prevents training on malformed data - -**Better Structure**: -```text -Before: <|system|>\n{content}\n -After: <|system|>\n{content}<|end|>\n -``` - -**Benefits**: -- Clearer conversation boundaries -- Better model understanding of turn structure -- Reduced hallucinations at turn boundaries -- Improved instruction following - -## 4. Performance Metrics - -### Expected Improvements - -**Quantum Models**: -- 🔥 **5-10% accuracy improvement** from enhanced circuits -- 🚀 **30% faster convergence** with AdamW + LR scheduling -- 💪 **Better stability** with gradient clipping -- 🎯 **Reduced overfitting** with early stopping + weight decay - -**Chat Models**: -- 📝 **More coherent responses** with nucleus sampling -- 🔄 **Less repetition** with repetition penalty -- 🎯 **Better instruction following** with end tokens -- 💬 **Improved multi-turn conversations** with turn boundaries - -**Training Pipeline**: -- ⚡ **Faster convergence** (20-30% fewer epochs needed) -- 📊 **Better final metrics** (2-5% accuracy gains) -- 🛡️ **Automatic recovery** from poor initialization -- 💾 **Always get best model** with checkpoint management - -## 5. Configuration Changes - -### Quantum Config Additions - -Recommended updates to `quantum_config.yaml`: - -```yaml -ml: - model: - use_residual: true # Enable residual connections - use_batch_norm: true # Enable batch normalization - - training: - use_scheduler: true # Enable LR scheduling - gradient_clip_val: 1.0 # Gradient clipping threshold - early_stopping_patience: 10 # Early stopping patience -``` - -### Chat Provider Config - -```python -# Enhanced LoRA initialization -provider = LoraLocalProvider( - adapter_dir="path/to/adapter", - temperature=0.7, # Creativity level - top_p=0.9, # Nucleus sampling - top_k=50, # Top-K sampling - repetition_penalty=1.1, # Reduce repetition - max_new_tokens=256 # Max response length -) -``` - -## 6. Testing Recommendations - -### Quantum Models - -1. **Compare with baseline**: - ```powershell - # Old model - python ai-projects/quantum-ml/train_custom_dataset.py --preset heart --epochs 50 - - # New model (automatically uses improvements) - python ai-projects/quantum-ml/train_custom_dataset.py --preset heart --epochs 50 - ``` - -2. **Monitor metrics**: - - Check convergence speed (epochs to 90% accuracy) - - Verify early stopping triggers appropriately - - Confirm learning rate reductions - -3. **Validate stability**: - - Run multiple seeds: `--seed 42`, `--seed 123`, `--seed 777` - - Compare variance in final accuracy - - Should see tighter clustering with improvements - -### Chat Models - -1. **A/B testing**: - - Generate 10 responses with old config - - Generate 10 responses with new config (top_p=0.9, rep_penalty=1.1) - - Compare coherence, repetition, instruction following - -2. **Multi-turn validation**: - - Test 5+ turn conversations - - Check for context maintenance - - Verify clean turn boundaries - -## 7. Migration Guide - -### Existing Models - -**Quantum models**: Automatically benefit from trainer improvements when using `train_custom_dataset.py` or `scripts/evaluation/quantum_autorun.py` - -**Chat models**: Update initialization: -```python -# Old -provider = LoraLocalProvider(adapter_dir, temperature=0.7, max_new_tokens=256) - -# New (backward compatible - all parameters optional) -provider = LoraLocalProvider( - adapter_dir, - temperature=0.7, - max_new_tokens=256, - top_p=0.9, # Add these for better quality - top_k=50, - repetition_penalty=1.1 -) -``` - -### Retraining Existing Adapters - -Consider retraining with improved formatting: -```powershell -# LoRA models will benefit from end tokens -python .\scripts\autotrain.py --job phi35_mixed_chat -``` - -## 8. Backward Compatibility - -✅ **All changes are backward compatible**: -- New parameters have sensible defaults -- Existing code continues to work -- Optional features can be disabled -- No breaking changes to APIs - -## 9. Known Limitations - -1. **Batch Normalization**: Requires batch_size > 1. Use `use_batch_norm=False` for batch_size=1 -2. **Residual Connections**: Adds ~5% memory overhead -3. **Early Stopping**: May stop before reaching absolute minimum loss (by design) -4. **Top-K/Top-P**: Increases inference time by ~10-15% vs temperature-only - -## 10. Future Enhancements - -Potential future improvements: -- [ ] Quantum circuit ansatz search (automatic architecture optimization) -- [ ] Multi-task learning for chat models -- [ ] Mixture of Experts for specialized responses -- [ ] Quantum circuit compilation optimizations -- [ ] Adaptive temperature based on uncertainty -- [ ] Beam search for more diverse responses - -## Summary - -These improvements represent state-of-the-art practices in both quantum ML and large language model fine-tuning. Key benefits: - -1. 🎯 **Better Performance**: 5-10% accuracy improvements -2. ⚡ **Faster Training**: 20-30% reduction in epochs needed -3. 💪 **More Stable**: Gradient clipping, early stopping, better optimizers -4. 📝 **Higher Quality**: Better text generation with nucleus sampling -5. 🛡️ **Production Ready**: Automatic checkpointing, error handling, logging - -All changes maintain backward compatibility while providing significant quality and performance improvements. +# AI System Improvements + +**Date**: November 21, 2025 +**Status**: ✅ Implemented + +## Overview + +Comprehensive improvements applied across all AI components to enhance performance, stability, and generation quality. + +## 1. Quantum AI Enhancements + +### Hybrid QNN Improvements + +**Residual Connections**: +- Added residual connections in HybridQNN for better gradient flow +- Automatically projects input dimensions when needed +- Prevents degradation in deep networks + +**Advanced Batch Normalization**: +- Optional batch normalization layers for training stability +- Prevents internal covariate shift +- Improves convergence speed + +**Deeper Architecture**: +- Added intermediate hidden layer in decoder (hidden_dim → hidden_dim//2 → output) +- Progressive dropout (higher in early layers, lower in final layers) +- Better feature extraction and representation learning + +**Enhanced Quantum Circuit**: +- **Dual encoding**: RY (amplitude) + RZ (phase) for richer feature representation +- **Final rotation layer**: Additional expressiveness after entanglement layers +- **Improved measurements**: Better quantum-to-classical information transfer + +### Trainer Improvements + +**AdamW Optimizer**: +- Replaced Adam with AdamW for better generalization +- Weight decay (0.01) prevents overfitting +- Optimized beta parameters (0.9, 0.999) + +**Learning Rate Scheduling**: +- ReduceLROnPlateau scheduler adapts to training dynamics +- Automatically reduces LR when validation loss plateaus +- Factor 0.5, patience 5 epochs + +**Gradient Clipping**: +- Clips gradients to max norm of 1.0 +- Prevents exploding gradients in quantum circuits +- Improves training stability + +**Early Stopping**: +- Monitors validation loss for improvements +- Default patience: 10 epochs +- Automatically stops training when model stops improving +- Restores best model weights + +**Best Model Tracking**: +- Automatically saves best model state during training +- Tracks best validation accuracy +- Restores optimal weights at end of training + +**Enhanced Logging**: +- Records learning rate per epoch +- Better progress tracking with batch percentages +- Debug-level logging for detailed analysis +- Error handling with graceful degradation + +## 2. Chat AI Improvements + +### LoRA Provider Enhancements + +**Advanced Generation Parameters**: +```python +top_p: float = 0.9 # Nucleus sampling (90th percentile) +top_k: int = 50 # Top-K sampling +repetition_penalty: 1.1 # Reduces repetitive text +``` + +**Benefits**: +- **Top-P (Nucleus)**: More coherent responses by focusing on high-probability tokens +- **Top-K**: Limits sampling pool for more focused generation +- **Repetition Penalty**: Reduces loops and repetitive patterns +- **Proper EOS Handling**: Clean stopping with pad_token_id and eos_token_id + +**Temperature Control**: +- Default 0.7 for balanced creativity/coherence +- Configurable per-session for different use cases +- Works with nucleus and top-k sampling + +## 3. Training Data Improvements + +### Enhanced Message Formatting + +**End Token Addition**: +- Added `<|end|>` tokens after each message turn +- Helps model learn conversation boundaries +- Improves turn-taking in multi-turn dialogues + +**Content Validation**: +- Skips empty messages automatically +- Strips whitespace for consistency +- Prevents training on malformed data + +**Better Structure**: +```text +Before: <|system|>\n{content}\n +After: <|system|>\n{content}<|end|>\n +``` + +**Benefits**: +- Clearer conversation boundaries +- Better model understanding of turn structure +- Reduced hallucinations at turn boundaries +- Improved instruction following + +## 4. Performance Metrics + +### Expected Improvements + +**Quantum Models**: +- 🔥 **5-10% accuracy improvement** from enhanced circuits +- 🚀 **30% faster convergence** with AdamW + LR scheduling +- 💪 **Better stability** with gradient clipping +- 🎯 **Reduced overfitting** with early stopping + weight decay + +**Chat Models**: +- 📝 **More coherent responses** with nucleus sampling +- 🔄 **Less repetition** with repetition penalty +- 🎯 **Better instruction following** with end tokens +- 💬 **Improved multi-turn conversations** with turn boundaries + +**Training Pipeline**: +- ⚡ **Faster convergence** (20-30% fewer epochs needed) +- 📊 **Better final metrics** (2-5% accuracy gains) +- 🛡️ **Automatic recovery** from poor initialization +- 💾 **Always get best model** with checkpoint management + +## 5. Configuration Changes + +### Quantum Config Additions + +Recommended updates to `quantum_config.yaml`: + +```yaml +ml: + model: + use_residual: true # Enable residual connections + use_batch_norm: true # Enable batch normalization + + training: + use_scheduler: true # Enable LR scheduling + gradient_clip_val: 1.0 # Gradient clipping threshold + early_stopping_patience: 10 # Early stopping patience +``` + +### Chat Provider Config + +```python +# Enhanced LoRA initialization +provider = LoraLocalProvider( + adapter_dir="path/to/adapter", + temperature=0.7, # Creativity level + top_p=0.9, # Nucleus sampling + top_k=50, # Top-K sampling + repetition_penalty=1.1, # Reduce repetition + max_new_tokens=256 # Max response length +) +``` + +## 6. Testing Recommendations + +### Quantum Models + +1. **Compare with baseline**: + ```powershell + # Old model + python ai-projects/quantum-ml/train_custom_dataset.py --preset heart --epochs 50 + + # New model (automatically uses improvements) + python ai-projects/quantum-ml/train_custom_dataset.py --preset heart --epochs 50 + ``` + +2. **Monitor metrics**: + - Check convergence speed (epochs to 90% accuracy) + - Verify early stopping triggers appropriately + - Confirm learning rate reductions + +3. **Validate stability**: + - Run multiple seeds: `--seed 42`, `--seed 123`, `--seed 777` + - Compare variance in final accuracy + - Should see tighter clustering with improvements + +### Chat Models + +1. **A/B testing**: + - Generate 10 responses with old config + - Generate 10 responses with new config (top_p=0.9, rep_penalty=1.1) + - Compare coherence, repetition, instruction following + +2. **Multi-turn validation**: + - Test 5+ turn conversations + - Check for context maintenance + - Verify clean turn boundaries + +## 7. Migration Guide + +### Existing Models + +**Quantum models**: Automatically benefit from trainer improvements when using `train_custom_dataset.py` or `scripts/evaluation/quantum_autorun.py` + +**Chat models**: Update initialization: +```python +# Old +provider = LoraLocalProvider(adapter_dir, temperature=0.7, max_new_tokens=256) + +# New (backward compatible - all parameters optional) +provider = LoraLocalProvider( + adapter_dir, + temperature=0.7, + max_new_tokens=256, + top_p=0.9, # Add these for better quality + top_k=50, + repetition_penalty=1.1 +) +``` + +### Retraining Existing Adapters + +Consider retraining with improved formatting: +```powershell +# LoRA models will benefit from end tokens +python .\scripts\autotrain.py --job phi35_mixed_chat +``` + +## 8. Backward Compatibility + +✅ **All changes are backward compatible**: +- New parameters have sensible defaults +- Existing code continues to work +- Optional features can be disabled +- No breaking changes to APIs + +## 9. Known Limitations + +1. **Batch Normalization**: Requires batch_size > 1. Use `use_batch_norm=False` for batch_size=1 +2. **Residual Connections**: Adds ~5% memory overhead +3. **Early Stopping**: May stop before reaching absolute minimum loss (by design) +4. **Top-K/Top-P**: Increases inference time by ~10-15% vs temperature-only + +## 10. Future Enhancements + +Potential future improvements: +- [ ] Quantum circuit ansatz search (automatic architecture optimization) +- [ ] Multi-task learning for chat models +- [ ] Mixture of Experts for specialized responses +- [ ] Quantum circuit compilation optimizations +- [ ] Adaptive temperature based on uncertainty +- [ ] Beam search for more diverse responses + +## Summary + +These improvements represent state-of-the-art practices in both quantum ML and large language model fine-tuning. Key benefits: + +1. 🎯 **Better Performance**: 5-10% accuracy improvements +2. ⚡ **Faster Training**: 20-30% reduction in epochs needed +3. 💪 **More Stable**: Gradient clipping, early stopping, better optimizers +4. 📝 **Higher Quality**: Better text generation with nucleus sampling +5. 🛡️ **Production Ready**: Automatic checkpointing, error handling, logging + +All changes maintain backward compatibility while providing significant quality and performance improvements. diff --git a/docs/guides/AI_IMPROVEMENTS_SUMMARY.md b/docs/guides/AI_IMPROVEMENTS_SUMMARY.md index 94e23f846..40dd36306 100644 --- a/docs/guides/AI_IMPROVEMENTS_SUMMARY.md +++ b/docs/guides/AI_IMPROVEMENTS_SUMMARY.md @@ -1,227 +1,227 @@ -# AI Training Improvements - November 27, 2025 - -## Overview -Comprehensive enhancements to the AI training pipeline focused on improving model quality, training efficiency, evaluation metrics, and automated model selection. - -## Key Improvements - -### 1. Training Quality Enhancements - -#### Early Stopping (Prevent Overfitting) -- **File**: `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` -- **Feature**: Added `EarlyStoppingCallback` with configurable patience and threshold -- **Configuration**: - - Patience: 3 evaluation cycles (configurable via YAML) - - Threshold: 0.01 (1% minimum improvement required) -- **Benefit**: Automatically stops training when validation loss plateaus, preventing overfitting and saving compute time - -#### Gradient Checkpointing (Memory Optimization) -- **File**: `AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml` -- **Change**: Enabled by default (`gradient_checkpointing: true`) -- **Benefit**: Reduces memory usage by ~30-40%, allowing larger batch sizes or longer sequences - -#### Gradient Accumulation (Effective Batch Size) -- **Configuration**: Increased from 1 to 4 steps -- **Effect**: Effective batch size = `per_device_batch_size × gradient_accumulation_steps × num_devices` -- **Benefit**: Improves training stability and convergence without requiring more GPU memory - -#### Learning Rate Scheduling -- **Change**: Switched from `linear_with_warmup` to `cosine` annealing -- **Warmup Steps**: 100 (configurable) -- **Benefit**: Better convergence with smooth learning rate decay - -#### Additional Training Optimizations -- **Gradient Clipping**: `max_grad_norm: 1.0` for training stability -- **Weight Decay**: 0.01 for regularization -- **Best Model Loading**: Automatically loads best checkpoint at end of training -- **Metric Tracking**: Monitors `eval_loss` for model selection - -### 2. Evaluation Framework Improvements - -#### Enhanced Diversity Metrics -- **File**: `scripts/evaluate_lora_model.py` -- **New Metrics**: - - **Distinct-1**: Ratio of unique unigrams (measures vocabulary diversity) - - **Distinct-2**: Ratio of unique bigrams (measures phrase diversity) - - **Unique Token Ratio**: Overall unique token percentage - - **Aggregated Diversity**: Average of Distinct-1 and Distinct-2 -- **Example Output**: - ```json - { - "distinct_1": 0.421, - "distinct_2": 0.738, - "unique_token_ratio": 0.421, - "diversity": 0.579 - } - ``` -- **Benefit**: Quantifies model's ability to generate varied responses (higher is better) - -#### Improved Perplexity Computation -- **Fallback Mechanism**: Graceful degradation when model inference fails -- **Pre/Post Training**: Tracks perplexity before and after training -- **Perplexity Improvement**: Calculates relative reduction as ranking metric - -### 3. Automated Model Selection - -#### Multi-Metric Ranking System -- **File**: `scripts/parallel_train.py` -- **Supported Ranking Metrics**: - 1. **`perplexity_improvement`** (default): Relative reduction in perplexity (higher is better) - 2. **`post_perplexity`**: Final perplexity (lower is better) - 3. **`diversity_avg`**: Average of Distinct-1 and Distinct-2 (higher is better) - 4. **`distinct_diversity`**: Alias for diversity_avg - 5. **`combined_improvement`**: Weighted combination (70% perplexity + 30% diversity) - -#### Usage Example -```bash -# Train multiple models and rank by combined quality -python .\scripts\parallel_train.py --config autotrain.yaml --ranking-metric combined_improvement - -# Or prioritize diversity -python .\scripts\parallel_train.py --ranking-metric diversity_avg -``` - -### 4. Configuration Improvements - -#### Updated Default Configuration (`lora.yaml`) -```yaml -# Memory Optimization -gradient_checkpointing: true # Enabled for memory efficiency -gradient_accumulation_steps: 4 # Effective batch size = 4x - -# Training Stability -max_grad_norm: 1 # Gradient clipping -warmup_steps: 100 # Warmup for stable start -lr_scheduler_type: "cosine" # Better convergence - -# Early Stopping -early_stopping_patience: 5 # Stop after 5 cycles without improvement -early_stopping_threshold: 0.01 # Minimum 1% improvement required -``` - -## Performance Metrics - -### Training Efficiency -- **Memory Reduction**: ~30-40% (via gradient checkpointing) -- **Effective Batch Size**: 4x increase (via gradient accumulation) -- **Training Time**: Potentially reduced via early stopping (avg 15-30% savings) - -### Model Quality -- **Perplexity**: Tracked with pre/post comparison -- **Diversity**: Comprehensive metrics (Distinct-1, Distinct-2, unique tokens) -- **Convergence**: Improved via cosine annealing and warmup - -### Evaluation Quality -- **Test Results**: - - Distinct-1: 0.421 (42.1% unique unigrams) - - Distinct-2: 0.738 (73.8% unique bigrams) - - Overall Diversity: 0.579 (high variety in responses) - -## Usage Recommendations - -### Quick Training with New Features -```bash -# Dry-run to validate configuration -python .\AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py --dry-run - -# Quick test run (64 samples, 1 epoch) -python .\AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py \ - --dataset datasets/chat/mixed_chat \ - --max-train-samples 64 \ - --epochs 1 - -# Full training with early stopping -python .\scripts\autotrain.py --job phi35_mixed_chat -``` - -### Evaluation with New Metrics -```bash -# Comprehensive evaluation -python .\scripts\evaluate_lora_model.py \ - --dataset datasets/chat/mixed_chat \ - --model data_out/lora_training/lora_adapter \ - --max-samples 100 \ - --metric perplexity \ - --metric diversity \ - --metric response_length \ - --output-format json -``` - -### Parallel Training with Ranking -```bash -# Train multiple models and rank by quality -python .\scripts\parallel_train.py \ - --config autotrain.yaml \ - --max-parallel 3 \ - --ranking-metric combined_improvement -``` - -## Files Modified - -1. **`AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py`** - - Added early stopping callback - - Enhanced training arguments with gradient accumulation, warmup, and LR scheduling - -2. **`AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml`** - - Updated default configuration with optimized hyperparameters - - Enabled gradient checkpointing and gradient accumulation - -3. **`scripts/evaluate_lora_model.py`** - - Enhanced diversity metrics (Distinct-1, Distinct-2, unique token ratio) - - Improved perplexity computation with fallback mechanism - -4. **`scripts/parallel_train.py`** - - Already had comprehensive ranking system (no changes needed) - - Supports multiple ranking metrics for model selection - -## Next Steps - -### Immediate Actions -1. **Test on Real Workloads**: Run full training jobs with new configurations -2. **Monitor Early Stopping**: Track how often early stopping triggers -3. **Compare Diversity Scores**: Evaluate multiple models using new metrics -4. **Optimize Hyperparameters**: Fine-tune warmup steps and learning rate for specific datasets - -### Future Enhancements -1. **Adaptive Learning Rate**: Implement learning rate finder -2. **Mixed Precision Training**: Optimize for specific hardware (CUDA/MPS/DirectML) -3. **Advanced Scheduling**: Implement warmup + cosine + cooldown phases -4. **Automated HPO**: Grid search or Bayesian optimization for hyperparameters -5. **Multi-Objective Ranking**: Weighted combination of perplexity, diversity, and inference speed - -## Testing Results - -### Dry-Run Validation -- ✅ Configuration parsing successful -- ✅ Dataset validation passed -- ✅ Early stopping callback initialized -- ✅ All hyperparameters loaded correctly - -### Quick Training Test -- ✅ Training completed successfully (64 samples, 1 epoch) -- ✅ Early stopping callback active (patience=3) -- ✅ Gradient checkpointing enabled -- ✅ Metrics logged correctly - -### Evaluation Test -- ✅ Diversity metrics computed successfully - - Distinct-1: 0.421 - - Distinct-2: 0.738 - - Diversity: 0.579 -- ✅ Perplexity computed (with fallback) -- ✅ JSON output generated - -## Conclusion - -These improvements significantly enhance the AI training pipeline across multiple dimensions: -- **Quality**: Early stopping prevents overfitting; improved evaluation metrics quantify model performance -- **Efficiency**: Gradient checkpointing and accumulation enable larger effective batch sizes -- **Automation**: Multi-metric ranking enables automated best model selection -- **Observability**: Comprehensive diversity metrics provide deeper insights into model behavior - -All changes are backward compatible and can be gradually adopted. The default configuration is optimized for immediate use. - ---- -**Date**: November 27, 2025 -**Status**: All improvements validated and tested -**Next Review**: After full-scale training runs complete +# AI Training Improvements - November 27, 2025 + +## Overview +Comprehensive enhancements to the AI training pipeline focused on improving model quality, training efficiency, evaluation metrics, and automated model selection. + +## Key Improvements + +### 1. Training Quality Enhancements + +#### Early Stopping (Prevent Overfitting) +- **File**: `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` +- **Feature**: Added `EarlyStoppingCallback` with configurable patience and threshold +- **Configuration**: + - Patience: 3 evaluation cycles (configurable via YAML) + - Threshold: 0.01 (1% minimum improvement required) +- **Benefit**: Automatically stops training when validation loss plateaus, preventing overfitting and saving compute time + +#### Gradient Checkpointing (Memory Optimization) +- **File**: `AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml` +- **Change**: Enabled by default (`gradient_checkpointing: true`) +- **Benefit**: Reduces memory usage by ~30-40%, allowing larger batch sizes or longer sequences + +#### Gradient Accumulation (Effective Batch Size) +- **Configuration**: Increased from 1 to 4 steps +- **Effect**: Effective batch size = `per_device_batch_size × gradient_accumulation_steps × num_devices` +- **Benefit**: Improves training stability and convergence without requiring more GPU memory + +#### Learning Rate Scheduling +- **Change**: Switched from `linear_with_warmup` to `cosine` annealing +- **Warmup Steps**: 100 (configurable) +- **Benefit**: Better convergence with smooth learning rate decay + +#### Additional Training Optimizations +- **Gradient Clipping**: `max_grad_norm: 1.0` for training stability +- **Weight Decay**: 0.01 for regularization +- **Best Model Loading**: Automatically loads best checkpoint at end of training +- **Metric Tracking**: Monitors `eval_loss` for model selection + +### 2. Evaluation Framework Improvements + +#### Enhanced Diversity Metrics +- **File**: `scripts/evaluate_lora_model.py` +- **New Metrics**: + - **Distinct-1**: Ratio of unique unigrams (measures vocabulary diversity) + - **Distinct-2**: Ratio of unique bigrams (measures phrase diversity) + - **Unique Token Ratio**: Overall unique token percentage + - **Aggregated Diversity**: Average of Distinct-1 and Distinct-2 +- **Example Output**: + ```json + { + "distinct_1": 0.421, + "distinct_2": 0.738, + "unique_token_ratio": 0.421, + "diversity": 0.579 + } + ``` +- **Benefit**: Quantifies model's ability to generate varied responses (higher is better) + +#### Improved Perplexity Computation +- **Fallback Mechanism**: Graceful degradation when model inference fails +- **Pre/Post Training**: Tracks perplexity before and after training +- **Perplexity Improvement**: Calculates relative reduction as ranking metric + +### 3. Automated Model Selection + +#### Multi-Metric Ranking System +- **File**: `scripts/parallel_train.py` +- **Supported Ranking Metrics**: + 1. **`perplexity_improvement`** (default): Relative reduction in perplexity (higher is better) + 2. **`post_perplexity`**: Final perplexity (lower is better) + 3. **`diversity_avg`**: Average of Distinct-1 and Distinct-2 (higher is better) + 4. **`distinct_diversity`**: Alias for diversity_avg + 5. **`combined_improvement`**: Weighted combination (70% perplexity + 30% diversity) + +#### Usage Example +```bash +# Train multiple models and rank by combined quality +python .\scripts\parallel_train.py --config autotrain.yaml --ranking-metric combined_improvement + +# Or prioritize diversity +python .\scripts\parallel_train.py --ranking-metric diversity_avg +``` + +### 4. Configuration Improvements + +#### Updated Default Configuration (`lora.yaml`) +```yaml +# Memory Optimization +gradient_checkpointing: true # Enabled for memory efficiency +gradient_accumulation_steps: 4 # Effective batch size = 4x + +# Training Stability +max_grad_norm: 1 # Gradient clipping +warmup_steps: 100 # Warmup for stable start +lr_scheduler_type: "cosine" # Better convergence + +# Early Stopping +early_stopping_patience: 5 # Stop after 5 cycles without improvement +early_stopping_threshold: 0.01 # Minimum 1% improvement required +``` + +## Performance Metrics + +### Training Efficiency +- **Memory Reduction**: ~30-40% (via gradient checkpointing) +- **Effective Batch Size**: 4x increase (via gradient accumulation) +- **Training Time**: Potentially reduced via early stopping (avg 15-30% savings) + +### Model Quality +- **Perplexity**: Tracked with pre/post comparison +- **Diversity**: Comprehensive metrics (Distinct-1, Distinct-2, unique tokens) +- **Convergence**: Improved via cosine annealing and warmup + +### Evaluation Quality +- **Test Results**: + - Distinct-1: 0.421 (42.1% unique unigrams) + - Distinct-2: 0.738 (73.8% unique bigrams) + - Overall Diversity: 0.579 (high variety in responses) + +## Usage Recommendations + +### Quick Training with New Features +```bash +# Dry-run to validate configuration +python .\AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py --dry-run + +# Quick test run (64 samples, 1 epoch) +python .\AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py \ + --dataset datasets/chat/mixed_chat \ + --max-train-samples 64 \ + --epochs 1 + +# Full training with early stopping +python .\scripts\autotrain.py --job phi35_mixed_chat +``` + +### Evaluation with New Metrics +```bash +# Comprehensive evaluation +python .\scripts\evaluate_lora_model.py \ + --dataset datasets/chat/mixed_chat \ + --model data_out/lora_training/lora_adapter \ + --max-samples 100 \ + --metric perplexity \ + --metric diversity \ + --metric response_length \ + --output-format json +``` + +### Parallel Training with Ranking +```bash +# Train multiple models and rank by quality +python .\scripts\parallel_train.py \ + --config autotrain.yaml \ + --max-parallel 3 \ + --ranking-metric combined_improvement +``` + +## Files Modified + +1. **`AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py`** + - Added early stopping callback + - Enhanced training arguments with gradient accumulation, warmup, and LR scheduling + +2. **`AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml`** + - Updated default configuration with optimized hyperparameters + - Enabled gradient checkpointing and gradient accumulation + +3. **`scripts/evaluate_lora_model.py`** + - Enhanced diversity metrics (Distinct-1, Distinct-2, unique token ratio) + - Improved perplexity computation with fallback mechanism + +4. **`scripts/parallel_train.py`** + - Already had comprehensive ranking system (no changes needed) + - Supports multiple ranking metrics for model selection + +## Next Steps + +### Immediate Actions +1. **Test on Real Workloads**: Run full training jobs with new configurations +2. **Monitor Early Stopping**: Track how often early stopping triggers +3. **Compare Diversity Scores**: Evaluate multiple models using new metrics +4. **Optimize Hyperparameters**: Fine-tune warmup steps and learning rate for specific datasets + +### Future Enhancements +1. **Adaptive Learning Rate**: Implement learning rate finder +2. **Mixed Precision Training**: Optimize for specific hardware (CUDA/MPS/DirectML) +3. **Advanced Scheduling**: Implement warmup + cosine + cooldown phases +4. **Automated HPO**: Grid search or Bayesian optimization for hyperparameters +5. **Multi-Objective Ranking**: Weighted combination of perplexity, diversity, and inference speed + +## Testing Results + +### Dry-Run Validation +- ✅ Configuration parsing successful +- ✅ Dataset validation passed +- ✅ Early stopping callback initialized +- ✅ All hyperparameters loaded correctly + +### Quick Training Test +- ✅ Training completed successfully (64 samples, 1 epoch) +- ✅ Early stopping callback active (patience=3) +- ✅ Gradient checkpointing enabled +- ✅ Metrics logged correctly + +### Evaluation Test +- ✅ Diversity metrics computed successfully + - Distinct-1: 0.421 + - Distinct-2: 0.738 + - Diversity: 0.579 +- ✅ Perplexity computed (with fallback) +- ✅ JSON output generated + +## Conclusion + +These improvements significantly enhance the AI training pipeline across multiple dimensions: +- **Quality**: Early stopping prevents overfitting; improved evaluation metrics quantify model performance +- **Efficiency**: Gradient checkpointing and accumulation enable larger effective batch sizes +- **Automation**: Multi-metric ranking enables automated best model selection +- **Observability**: Comprehensive diversity metrics provide deeper insights into model behavior + +All changes are backward compatible and can be gradually adopted. The default configuration is optimized for immediate use. + +--- +**Date**: November 27, 2025 +**Status**: All improvements validated and tested +**Next Review**: After full-scale training runs complete diff --git a/docs/guides/ARIA_AUTOMATION_GUIDE.md b/docs/guides/ARIA_AUTOMATION_GUIDE.md index edb94f421..1c1a7e9bc 100644 --- a/docs/guides/ARIA_AUTOMATION_GUIDE.md +++ b/docs/guides/ARIA_AUTOMATION_GUIDE.md @@ -1,670 +1,670 @@ -# Aria Automation - Deployment Guide - -Complete guide for automating Aria AI character platform with continuous operation, training, and monitoring. - -## 📋 Table of Contents - -- [Quick Start](#quick-start) -- [Automation Modes](#automation-modes) -- [Background Service Setup](#background-service-setup) -- [Systemd Integration (Linux)](#systemd-integration-linux) -- [Cron Integration](#cron-integration) -- [Master Orchestrator Integration](#master-orchestrator-integration) -- [Monitoring & Health Checks](#monitoring--health-checks) -- [Troubleshooting](#troubleshooting) - ---- - -## 🚀 Quick Start - -### Interactive Mode - -```bash -# Start interactive menu -./scripts/start_aria.sh -``` - -### Command Line Mode - -```bash -# Full stack (server + backend + training + monitoring) -./scripts/start_aria.sh full - -# Server only -./scripts/start_aria.sh server - -# Training only (continuous) -./scripts/start_aria.sh training - -# Single training cycle -./scripts/start_aria.sh training --once - -# Background mode -./scripts/start_aria.sh full --background - -# Check status -./scripts/start_aria.sh status - -# Stop all -./scripts/start_aria.sh stop -``` - -### Direct Python Invocation - -```bash -# Full automation -python3 scripts/aria_automation.py --mode full - -# Server only -python3 scripts/aria_automation.py --mode server - -# Training once -python3 scripts/aria_automation.py --mode training --once - -# Check status -python3 scripts/aria_automation.py --status - -# Stop all processes -python3 scripts/aria_automation.py --stop -``` - ---- - -## 🎯 Automation Modes - -### 1. Full Stack Mode (`--mode full`) - -**Includes:** - -- ✅ Aria web server (port 8080) -- ✅ Azure Functions backend (port 7071) -- ✅ Continuous training (every 30 minutes) -- ✅ Health monitoring (every 60 seconds) -- ✅ Auto-recovery on failures - -**Best for:** Production deployment, 24/7 operation - -**Usage:** - -```bash -python3 scripts/aria_automation.py --mode full -``` - -**Endpoints:** - -- Aria Interface: `http://localhost:8080` -- Auto-Execute: `http://localhost:8080/auto-execute.html` -- Backend API: `http://localhost:7071/api/ai/status` - ---- - -### 2. Server Only Mode (`--mode server`) - -**Includes:** - -- ✅ Aria web server (port 8080) -- ✅ Health monitoring -- ❌ No training -- ❌ No backend - -**Best for:** Development, UI testing, demos - -**Usage:** - -```bash -python3 scripts/aria_automation.py --mode server -``` - ---- - -### 3. Training Only Mode (`--mode training`) - -**Includes:** - -- ✅ Continuous training cycles -- ✅ Dataset monitoring -- ❌ No web server -- ❌ No backend - -**Best for:** Background training, batch processing - -**Usage:** - -```bash -# Continuous training -python3 scripts/aria_automation.py --mode training - -# Single cycle -python3 scripts/aria_automation.py --mode training --once -``` - ---- - -## 🌐 Background Service Setup - -### Using nohup (Simple) - -```bash -# Start in background -nohup python3 scripts/aria_automation.py --mode full \ - > data_out/aria_automation/aria.log 2>&1 & - -# Save PID -echo $! > data_out/aria_automation/aria.pid - -# Check status -python3 scripts/aria_automation.py --status - -# Stop -kill $(cat data_out/aria_automation/aria.pid) -``` - -### Using screen (Recommended for SSH) - -```bash -# Start new screen session -screen -S aria - -# Run automation -python3 scripts/aria_automation.py --mode full - -# Detach: Ctrl+A, then D - -# Reattach -screen -r aria - -# List sessions -screen -ls -``` - -### Using tmux - -```bash -# Start new session -tmux new -s aria - -# Run automation -python3 scripts/aria_automation.py --mode full - -# Detach: Ctrl+B, then D - -# Reattach -tmux attach -t aria - -# List sessions -tmux ls -``` - ---- - -## 🐧 Systemd Integration (Linux) - -### 1. Install Service - -```bash -# Copy service file -sudo cp config/aria_automation.service /etc/systemd/system/ - -# Edit with your username and paths -sudo nano /etc/systemd/system/aria_automation.service - -# Update these lines: -# User=YOUR_USERNAME -# Group=YOUR_USERNAME -# WorkingDirectory=/path/to/Aria - -# Reload systemd -sudo systemctl daemon-reload -``` - -### 2. Configure Service - -Edit `/etc/systemd/system/aria_automation.service`: - -```ini -[Service] -User=bryan -Group=bryan -WorkingDirectory=/workspaces/Aria - -# Choose mode: full, server, or training -ExecStart=/usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --mode full -``` - -### 3. Start Service - -```bash -# Enable on boot -sudo systemctl enable aria_automation - -# Start now -sudo systemctl start aria_automation - -# Check status -sudo systemctl status aria_automation - -# View logs -sudo journalctl -u aria_automation -f - -# Stop service -sudo systemctl stop aria_automation - -# Restart service -sudo systemctl restart aria_automation -``` - -### 4. Service Management - -```bash -# Check if running -sudo systemctl is-active aria_automation - -# Check if enabled on boot -sudo systemctl is-enabled aria_automation - -# Disable on boot -sudo systemctl disable aria_automation - -# View recent logs -sudo journalctl -u aria_automation -n 50 - -# Follow logs in real-time -sudo journalctl -u aria_automation -f -``` - ---- - -## ⏰ Cron Integration - -For periodic training without continuous operation: - -### 1. Edit Crontab - -```bash -crontab -e -``` - -### 2. Add Scheduled Jobs - -```cron -# Run single training cycle every 30 minutes -*/30 * * * * /usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --mode training --once >> /workspaces/Aria/data_out/aria_automation/cron.log 2>&1 - -# Start server at boot -@reboot /usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --mode server >> /workspaces/Aria/data_out/aria_automation/server.log 2>&1 - -# Daily health check at 2 AM -0 2 * * * /usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --status >> /workspaces/Aria/data_out/aria_automation/status.log 2>&1 - -# Weekly full training on Sundays at 3 AM -0 3 * * 0 /workspaces/Aria/scripts/start_aria.sh training --once >> /workspaces/Aria/data_out/aria_automation/weekly.log 2>&1 -``` - -### 3. View Cron Logs - -```bash -# View cron log -tail -f data_out/aria_automation/cron.log - -# List current crontab -crontab -l - -# Remove all cron jobs -crontab -r -``` - ---- - -## 🎼 Master Orchestrator Integration - -Aria automation integrates with the master orchestrator system. - -### Configuration - -Edit `config/master_orchestrator.yaml`: - -```yaml -orchestrators: - - name: aria_automation - script: scripts/aria_automation.py - enabled: true - schedule: "*/30 * * * *" # Every 30 minutes - priority: 5 - retry_on_failure: 3 - timeout_minutes: 15 - dependencies: [] - flags: - mode: training - once: true - -workflows: - # Aria full stack (runs continuously) - - name: aria_full_stack - enabled: false # Enable for 24/7 operation - trigger: "manual" - orchestrators: - - aria_automation - flags: - mode: full - on_success: - - log_result - on_failure: - - restart_aria - - notify_admin -``` - -### Running via Master Orchestrator - -```bash -# Start master orchestrator -python3 scripts/master_orchestrator.py --workflow aria_full_stack - -# Check status -python3 scripts/master_orchestrator.py --status - -# Run as daemon -python3 scripts/master_orchestrator.py --daemon -``` - ---- - -## 📊 Monitoring & Health Checks - -### Status Check - -```bash -# View current status -python3 scripts/aria_automation.py --status -``` - -**Output:** - -``` -================================================================================ -🤖 Aria Automation Status -================================================================================ -Mode: full -Started: 2025-11-29T10:30:00 -Uptime: 2:15:30 -Training Cycles: 4 - -Components: - - Aria Server: ✅ Running - - Functions Backend: ✅ Running - - Training: ✅ Active - -Processes: - - aria_server (PID 12345): running - - functions_backend (PID 12346): running -================================================================================ -``` - -### Status File - -Status is saved to: `data_out/aria_automation/status.json` - -```json -{ - "mode": "full", - "started": "2025-11-29T10:30:00", - "uptime_seconds": 8130, - "server_running": true, - "backend_running": true, - "training_active": true, - "last_health_check": "2025-11-29T12:45:30", - "processes": [...], - "training_cycles": 4, - "errors": [] -} -``` - -### Health Check Endpoints - -**Aria Server:** - -```bash -curl http://localhost:8080 -``` - -**Functions Backend:** - -```bash -curl http://localhost:7071/api/ai/status -``` - -### Monitoring Script - -Create custom monitoring: - -```bash -#!/bin/bash -# Check Aria health every 5 minutes - -while true; do - STATUS=$(python3 scripts/aria_automation.py --status 2>&1) - - if [[ $STATUS == *"Running"* ]]; then - echo "$(date): ✅ Aria healthy" - else - echo "$(date): ❌ Aria unhealthy" - # Send alert (email, Slack, etc.) - fi - - sleep 300 -done -``` - ---- - -## 🔧 Troubleshooting - -### Issue: Port Already in Use - -**Problem:** `Port 8080 already in use` - -**Solution:** - -```bash -# Find process using port -lsof -i :8080 - -# Kill process -kill -9 - -# Or stop via automation -python3 scripts/aria_automation.py --stop -``` - -### Issue: Training Fails - -**Problem:** Training cycles fail repeatedly - -**Solutions:** - -1. Check dataset exists: - - ```bash - ls datasets/chat/aria_movement/ - ``` - -2. Validate dataset: - - ```bash - python3 scripts/validate_datasets.py --category chat - ``` - -3. Check logs: - - ```bash - tail -f data_out/aria_automation/aria_automation.log - ``` - -4. Run single training manually: - - ```bash - python3 scripts/aria_quick_train.py - ``` - -### Issue: Server Won't Start - -**Problem:** Aria server fails to start - -**Solutions:** - -1. Check dependencies: - - ```bash - cd aria_web - pip3 install -r requirements.txt - ``` - -2. Test server manually: - - ```bash - cd aria_web - python3 server.py - ``` - -3. Check for errors: - - ```bash - python3 aria_web/server.py 2>&1 | tee server_debug.log - ``` - -### Issue: Backend Not Starting - -**Problem:** Azure Functions backend won't start - -**Solutions:** - -1. Install Functions Core Tools: - - ```bash - # Windows - npm install -g azure-functions-core-tools@4 - - # Linux - curl https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor > microsoft.gpg - sudo mv microsoft.gpg /etc/apt/trusted.gpg.d/microsoft.gpg - sudo sh -c 'echo "deb [arch=amd64] https://packages.microsoft.com/debian/11/prod bullseye main" > /etc/apt/sources.list.d/dotnetdev.list' - sudo apt update - sudo apt install azure-functions-core-tools-4 - ``` - -2. Check Functions installation: - - ```bash - func --version - ``` - -3. Test Functions manually: - - ```bash - func host start - ``` - -### Issue: Process Zombies - -**Problem:** Stopped processes remain in status - -**Solution:** - -```bash -# Clean PID file -rm data_out/aria_automation/processes.json - -# Force stop all -python3 scripts/aria_automation.py --stop - -# Kill any remaining processes -pkill -f "aria_automation" -pkill -f "aria.*server.py" -``` - -### Issue: High Memory Usage - -**Problem:** Automation consumes too much memory - -**Solutions:** - -1. Reduce training frequency (edit config) -2. Use `--once` flag for training -3. Limit training samples: - - ```bash - # Edit aria_quick_train.py - # Set max_train_samples to lower value - ``` - -### View Logs - -```bash -# Automation logs -tail -f data_out/aria_automation/aria_automation.log - -# Server logs -tail -f data_out/aria_automation/server.log - -# Training logs -tail -f data_out/lora_training/*/last_run.json -``` - ---- - -## 📝 Best Practices - -### 1. Production Deployment - -- ✅ Use systemd for auto-restart -- ✅ Enable monitoring alerts -- ✅ Set resource limits -- ✅ Regular backups -- ✅ Log rotation - -### 2. Development - -- ✅ Use `--mode server` for testing -- ✅ Use `--once` for training tests -- ✅ Monitor logs in real-time -- ✅ Keep test datasets small - -### 3. Resource Management - -- ✅ Limit concurrent processes -- ✅ Schedule training during off-hours -- ✅ Monitor disk space -- ✅ Clean old outputs regularly - -### 4. Security - -- ✅ Run as non-root user -- ✅ Set proper file permissions -- ✅ Firewall rules for ports -- ✅ API key management - ---- - -## 📚 Additional Resources - -- **Main README**: `README.md` -- **Aria Web Docs**: `aria_web/README.md` -- **Auto-Execute Guide**: `aria_web/AUTO-EXECUTE.md` -- **Training Guide**: `scripts/README.md` -- **Master Orchestrator**: `ADVANCED_AUTOMATION.md` - ---- - -## 🆘 Support - -If issues persist: - -1. Check status: `python3 scripts/aria_automation.py --status` -2. View logs: `tail -f data_out/aria_automation/*.log` -3. Test components individually -4. Review error messages in status.json -5. Open GitHub issue with logs - ---- - -**Last Updated:** November 29, 2025 -**Version:** 1.0.0 +# Aria Automation - Deployment Guide + +Complete guide for automating Aria AI character platform with continuous operation, training, and monitoring. + +## 📋 Table of Contents + +- [Quick Start](#quick-start) +- [Automation Modes](#automation-modes) +- [Background Service Setup](#background-service-setup) +- [Systemd Integration (Linux)](#systemd-integration-linux) +- [Cron Integration](#cron-integration) +- [Master Orchestrator Integration](#master-orchestrator-integration) +- [Monitoring & Health Checks](#monitoring--health-checks) +- [Troubleshooting](#troubleshooting) + +--- + +## 🚀 Quick Start + +### Interactive Mode + +```bash +# Start interactive menu +./scripts/start_aria.sh +``` + +### Command Line Mode + +```bash +# Full stack (server + backend + training + monitoring) +./scripts/start_aria.sh full + +# Server only +./scripts/start_aria.sh server + +# Training only (continuous) +./scripts/start_aria.sh training + +# Single training cycle +./scripts/start_aria.sh training --once + +# Background mode +./scripts/start_aria.sh full --background + +# Check status +./scripts/start_aria.sh status + +# Stop all +./scripts/start_aria.sh stop +``` + +### Direct Python Invocation + +```bash +# Full automation +python3 scripts/aria_automation.py --mode full + +# Server only +python3 scripts/aria_automation.py --mode server + +# Training once +python3 scripts/aria_automation.py --mode training --once + +# Check status +python3 scripts/aria_automation.py --status + +# Stop all processes +python3 scripts/aria_automation.py --stop +``` + +--- + +## 🎯 Automation Modes + +### 1. Full Stack Mode (`--mode full`) + +**Includes:** + +- ✅ Aria web server (port 8080) +- ✅ Azure Functions backend (port 7071) +- ✅ Continuous training (every 30 minutes) +- ✅ Health monitoring (every 60 seconds) +- ✅ Auto-recovery on failures + +**Best for:** Production deployment, 24/7 operation + +**Usage:** + +```bash +python3 scripts/aria_automation.py --mode full +``` + +**Endpoints:** + +- Aria Interface: `http://localhost:8080` +- Auto-Execute: `http://localhost:8080/auto-execute.html` +- Backend API: `http://localhost:7071/api/ai/status` + +--- + +### 2. Server Only Mode (`--mode server`) + +**Includes:** + +- ✅ Aria web server (port 8080) +- ✅ Health monitoring +- ❌ No training +- ❌ No backend + +**Best for:** Development, UI testing, demos + +**Usage:** + +```bash +python3 scripts/aria_automation.py --mode server +``` + +--- + +### 3. Training Only Mode (`--mode training`) + +**Includes:** + +- ✅ Continuous training cycles +- ✅ Dataset monitoring +- ❌ No web server +- ❌ No backend + +**Best for:** Background training, batch processing + +**Usage:** + +```bash +# Continuous training +python3 scripts/aria_automation.py --mode training + +# Single cycle +python3 scripts/aria_automation.py --mode training --once +``` + +--- + +## 🌐 Background Service Setup + +### Using nohup (Simple) + +```bash +# Start in background +nohup python3 scripts/aria_automation.py --mode full \ + > data_out/aria_automation/aria.log 2>&1 & + +# Save PID +echo $! > data_out/aria_automation/aria.pid + +# Check status +python3 scripts/aria_automation.py --status + +# Stop +kill $(cat data_out/aria_automation/aria.pid) +``` + +### Using screen (Recommended for SSH) + +```bash +# Start new screen session +screen -S aria + +# Run automation +python3 scripts/aria_automation.py --mode full + +# Detach: Ctrl+A, then D + +# Reattach +screen -r aria + +# List sessions +screen -ls +``` + +### Using tmux + +```bash +# Start new session +tmux new -s aria + +# Run automation +python3 scripts/aria_automation.py --mode full + +# Detach: Ctrl+B, then D + +# Reattach +tmux attach -t aria + +# List sessions +tmux ls +``` + +--- + +## 🐧 Systemd Integration (Linux) + +### 1. Install Service + +```bash +# Copy service file +sudo cp config/aria_automation.service /etc/systemd/system/ + +# Edit with your username and paths +sudo nano /etc/systemd/system/aria_automation.service + +# Update these lines: +# User=YOUR_USERNAME +# Group=YOUR_USERNAME +# WorkingDirectory=/path/to/Aria + +# Reload systemd +sudo systemctl daemon-reload +``` + +### 2. Configure Service + +Edit `/etc/systemd/system/aria_automation.service`: + +```ini +[Service] +User=bryan +Group=bryan +WorkingDirectory=/workspaces/Aria + +# Choose mode: full, server, or training +ExecStart=/usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --mode full +``` + +### 3. Start Service + +```bash +# Enable on boot +sudo systemctl enable aria_automation + +# Start now +sudo systemctl start aria_automation + +# Check status +sudo systemctl status aria_automation + +# View logs +sudo journalctl -u aria_automation -f + +# Stop service +sudo systemctl stop aria_automation + +# Restart service +sudo systemctl restart aria_automation +``` + +### 4. Service Management + +```bash +# Check if running +sudo systemctl is-active aria_automation + +# Check if enabled on boot +sudo systemctl is-enabled aria_automation + +# Disable on boot +sudo systemctl disable aria_automation + +# View recent logs +sudo journalctl -u aria_automation -n 50 + +# Follow logs in real-time +sudo journalctl -u aria_automation -f +``` + +--- + +## ⏰ Cron Integration + +For periodic training without continuous operation: + +### 1. Edit Crontab + +```bash +crontab -e +``` + +### 2. Add Scheduled Jobs + +```cron +# Run single training cycle every 30 minutes +*/30 * * * * /usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --mode training --once >> /workspaces/Aria/data_out/aria_automation/cron.log 2>&1 + +# Start server at boot +@reboot /usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --mode server >> /workspaces/Aria/data_out/aria_automation/server.log 2>&1 + +# Daily health check at 2 AM +0 2 * * * /usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --status >> /workspaces/Aria/data_out/aria_automation/status.log 2>&1 + +# Weekly full training on Sundays at 3 AM +0 3 * * 0 /workspaces/Aria/scripts/start_aria.sh training --once >> /workspaces/Aria/data_out/aria_automation/weekly.log 2>&1 +``` + +### 3. View Cron Logs + +```bash +# View cron log +tail -f data_out/aria_automation/cron.log + +# List current crontab +crontab -l + +# Remove all cron jobs +crontab -r +``` + +--- + +## 🎼 Master Orchestrator Integration + +Aria automation integrates with the master orchestrator system. + +### Configuration + +Edit `config/master_orchestrator.yaml`: + +```yaml +orchestrators: + - name: aria_automation + script: scripts/aria_automation.py + enabled: true + schedule: "*/30 * * * *" # Every 30 minutes + priority: 5 + retry_on_failure: 3 + timeout_minutes: 15 + dependencies: [] + flags: + mode: training + once: true + +workflows: + # Aria full stack (runs continuously) + - name: aria_full_stack + enabled: false # Enable for 24/7 operation + trigger: "manual" + orchestrators: + - aria_automation + flags: + mode: full + on_success: + - log_result + on_failure: + - restart_aria + - notify_admin +``` + +### Running via Master Orchestrator + +```bash +# Start master orchestrator +python3 scripts/master_orchestrator.py --workflow aria_full_stack + +# Check status +python3 scripts/master_orchestrator.py --status + +# Run as daemon +python3 scripts/master_orchestrator.py --daemon +``` + +--- + +## 📊 Monitoring & Health Checks + +### Status Check + +```bash +# View current status +python3 scripts/aria_automation.py --status +``` + +**Output:** + +``` +================================================================================ +🤖 Aria Automation Status +================================================================================ +Mode: full +Started: 2025-11-29T10:30:00 +Uptime: 2:15:30 +Training Cycles: 4 + +Components: + - Aria Server: ✅ Running + - Functions Backend: ✅ Running + - Training: ✅ Active + +Processes: + - aria_server (PID 12345): running + - functions_backend (PID 12346): running +================================================================================ +``` + +### Status File + +Status is saved to: `data_out/aria_automation/status.json` + +```json +{ + "mode": "full", + "started": "2025-11-29T10:30:00", + "uptime_seconds": 8130, + "server_running": true, + "backend_running": true, + "training_active": true, + "last_health_check": "2025-11-29T12:45:30", + "processes": [...], + "training_cycles": 4, + "errors": [] +} +``` + +### Health Check Endpoints + +**Aria Server:** + +```bash +curl http://localhost:8080 +``` + +**Functions Backend:** + +```bash +curl http://localhost:7071/api/ai/status +``` + +### Monitoring Script + +Create custom monitoring: + +```bash +#!/bin/bash +# Check Aria health every 5 minutes + +while true; do + STATUS=$(python3 scripts/aria_automation.py --status 2>&1) + + if [[ $STATUS == *"Running"* ]]; then + echo "$(date): ✅ Aria healthy" + else + echo "$(date): ❌ Aria unhealthy" + # Send alert (email, Slack, etc.) + fi + + sleep 300 +done +``` + +--- + +## 🔧 Troubleshooting + +### Issue: Port Already in Use + +**Problem:** `Port 8080 already in use` + +**Solution:** + +```bash +# Find process using port +lsof -i :8080 + +# Kill process +kill -9 + +# Or stop via automation +python3 scripts/aria_automation.py --stop +``` + +### Issue: Training Fails + +**Problem:** Training cycles fail repeatedly + +**Solutions:** + +1. Check dataset exists: + + ```bash + ls datasets/chat/aria_movement/ + ``` + +2. Validate dataset: + + ```bash + python3 scripts/validate_datasets.py --category chat + ``` + +3. Check logs: + + ```bash + tail -f data_out/aria_automation/aria_automation.log + ``` + +4. Run single training manually: + + ```bash + python3 scripts/aria_quick_train.py + ``` + +### Issue: Server Won't Start + +**Problem:** Aria server fails to start + +**Solutions:** + +1. Check dependencies: + + ```bash + cd aria_web + pip3 install -r requirements.txt + ``` + +2. Test server manually: + + ```bash + cd aria_web + python3 server.py + ``` + +3. Check for errors: + + ```bash + python3 aria_web/server.py 2>&1 | tee server_debug.log + ``` + +### Issue: Backend Not Starting + +**Problem:** Azure Functions backend won't start + +**Solutions:** + +1. Install Functions Core Tools: + + ```bash + # Windows + npm install -g azure-functions-core-tools@4 + + # Linux + curl https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor > microsoft.gpg + sudo mv microsoft.gpg /etc/apt/trusted.gpg.d/microsoft.gpg + sudo sh -c 'echo "deb [arch=amd64] https://packages.microsoft.com/debian/11/prod bullseye main" > /etc/apt/sources.list.d/dotnetdev.list' + sudo apt update + sudo apt install azure-functions-core-tools-4 + ``` + +2. Check Functions installation: + + ```bash + func --version + ``` + +3. Test Functions manually: + + ```bash + func host start + ``` + +### Issue: Process Zombies + +**Problem:** Stopped processes remain in status + +**Solution:** + +```bash +# Clean PID file +rm data_out/aria_automation/processes.json + +# Force stop all +python3 scripts/aria_automation.py --stop + +# Kill any remaining processes +pkill -f "aria_automation" +pkill -f "aria.*server.py" +``` + +### Issue: High Memory Usage + +**Problem:** Automation consumes too much memory + +**Solutions:** + +1. Reduce training frequency (edit config) +2. Use `--once` flag for training +3. Limit training samples: + + ```bash + # Edit aria_quick_train.py + # Set max_train_samples to lower value + ``` + +### View Logs + +```bash +# Automation logs +tail -f data_out/aria_automation/aria_automation.log + +# Server logs +tail -f data_out/aria_automation/server.log + +# Training logs +tail -f data_out/lora_training/*/last_run.json +``` + +--- + +## 📝 Best Practices + +### 1. Production Deployment + +- ✅ Use systemd for auto-restart +- ✅ Enable monitoring alerts +- ✅ Set resource limits +- ✅ Regular backups +- ✅ Log rotation + +### 2. Development + +- ✅ Use `--mode server` for testing +- ✅ Use `--once` for training tests +- ✅ Monitor logs in real-time +- ✅ Keep test datasets small + +### 3. Resource Management + +- ✅ Limit concurrent processes +- ✅ Schedule training during off-hours +- ✅ Monitor disk space +- ✅ Clean old outputs regularly + +### 4. Security + +- ✅ Run as non-root user +- ✅ Set proper file permissions +- ✅ Firewall rules for ports +- ✅ API key management + +--- + +## 📚 Additional Resources + +- **Main README**: `README.md` +- **Aria Web Docs**: `aria_web/README.md` +- **Auto-Execute Guide**: `aria_web/AUTO-EXECUTE.md` +- **Training Guide**: `scripts/README.md` +- **Master Orchestrator**: `ADVANCED_AUTOMATION.md` + +--- + +## 🆘 Support + +If issues persist: + +1. Check status: `python3 scripts/aria_automation.py --status` +2. View logs: `tail -f data_out/aria_automation/*.log` +3. Test components individually +4. Review error messages in status.json +5. Open GitHub issue with logs + +--- + +**Last Updated:** November 29, 2025 +**Version:** 1.0.0 diff --git a/docs/guides/AUTOMATION_FEATURES_SUMMARY.md b/docs/guides/AUTOMATION_FEATURES_SUMMARY.md index c98329c56..300f4bbdd 100644 --- a/docs/guides/AUTOMATION_FEATURES_SUMMARY.md +++ b/docs/guides/AUTOMATION_FEATURES_SUMMARY.md @@ -1,424 +1,424 @@ -# Advanced Automation Features Summary - -**Generated:** November 22, 2025 - -## Overview - -This document summarizes the advanced automation features added to the QAI workspace, including CI/CD integration, auto-deployment, resource monitoring, batch evaluation, and multi-format result export. - -## New Tools Created - -### 1. Model Deployer (`model_deployer.py`) - -**Purpose:** Automatically deploy trained models with quality gates and version control. - -**Key Features:** -- Quality gate validation (configurable thresholds) -- Model scoring based on accuracy, loss, validation metrics -- Deployment strategies: direct, canary, blue-green -- Version tracking and rollback support -- Model registry with metadata - -**Quality Gates (default):** -- `min_accuracy`: 0.75 -- `max_loss`: 0.5 -- `min_f1_score`: 0.70 -- `min_validation_accuracy`: 0.70 - -**Commands:** -```powershell -# Scan for deployable models -python .\scripts\model_deployer.py --scan - -# Deploy best model with canary strategy -python .\scripts\model_deployer.py --deploy best --strategy canary - -# Check deployment status -python .\scripts\model_deployer.py --status - -# Rollback to previous version -python .\scripts\model_deployer.py --rollback v1_20251122_123456 - -# Set custom quality gate -python .\scripts\model_deployer.py --set-quality-gate min_accuracy 0.80 -``` - -### 2. Resource Monitor (`resource_monitor.py`) - -**Purpose:** Real-time system resource monitoring with alerts and historical tracking. - -**Key Features:** -- CPU, memory, disk, GPU monitoring (uses psutil + GPUtil) -- Configurable alert thresholds -- Historical data collection (JSONL format) -- Export to CSV/JSON -- Streaming mode for continuous monitoring - -**Default Thresholds:** -- `cpu_percent`: 90.0% -- `memory_percent`: 90.0% -- `disk_percent`: 90.0% -- `gpu_utilization`: 95.0% -- `gpu_memory_percent`: 95.0% - -**Commands:** -```powershell -# Single snapshot -python .\scripts\resource_monitor.py --snapshot - -# Stream for 60 seconds -python .\scripts\resource_monitor.py --stream --duration 60 - -# View last 24 hours -python .\scripts\resource_monitor.py --history --hours 24 - -# Export to CSV -python .\scripts\resource_monitor.py --export csv - -# Set custom threshold -python .\scripts\resource_monitor.py --set-threshold cpu_percent 85 -``` - -### 3. Batch Evaluator (`batch_evaluator.py`) - -**Purpose:** Parallel evaluation of multiple models with comprehensive aggregation. - -**Key Features:** -- ThreadPoolExecutor for parallel execution -- Support for 5 model types (LoRA, Azure, OpenAI, Local, Quantum) -- Result aggregation and ranking -- Export to JSON/Markdown -- Side-by-side comparison - -**Commands:** -```powershell -# Scan and evaluate all models -python .\scripts\batch_evaluator.py --scan-models --evaluate-all - -# Use config file -python .\scripts\batch_evaluator.py --config batch_eval_config.yaml - -# Compare specific models -python .\scripts\batch_evaluator.py --compare lora_v1 lora_v2 - -# Export results -python .\scripts\batch_evaluator.py --export markdown --output report.md -``` - -### 4. Results Exporter (`results_exporter.py`) - -**Purpose:** Export orchestrator results to multiple formats for reporting and analysis. - -**Supported Formats:** -- JSON (machine-readable) -- CSV (spreadsheet import) -- Excel (requires openpyxl) -- Markdown (documentation) -- HTML (web reports) - -**Commands:** -```powershell -# Export single orchestrator -python .\scripts\results_exporter.py --source autotrain --format csv - -# Export all orchestrators -python .\scripts\results_exporter.py --all --format markdown - -# Compare orchestrators -python .\scripts\results_exporter.py --compare autotrain quantum_autorun --format html - -# Filter by status -python .\scripts\results_exporter.py --source autotrain --format json --filter-status succeeded -``` - -## CI/CD Integration - -### GitHub Actions Workflow - -**File:** `.github/workflows/ci-pipeline.yml` - -**Triggers:** -- Push to main/dev branches -- Pull requests to main -- Daily schedule (2 AM UTC) - -**Jobs:** -1. **Validate:** Run CI validation and unit tests -2. **Train:** Run daily training workflow (scheduled only) -3. **Deploy:** Auto-deploy best model (scheduled only) - -**Artifacts:** -- Validation results -- Training outputs -- Deployment manifest - -### Git Hooks - -**Pre-commit validation** (sample provided): -- `.git/hooks/pre-commit.sample` contains validation logic -- Runs `ci_orchestrator.py --validate-all` before commit -- Prevents commits if validation fails - -## Enhanced Orchestrator Features - -### Dependency Chaining - -Master orchestrator now supports complex dependency chains: - -```yaml -workflows: - - name: full_pipeline - orchestrators: - - autotrain # Step 1 - - evaluation_autorun # Step 2 (depends on autotrain) - - model_deploy # Step 3 (depends on evaluation) -``` - -### Cron Scheduling - -Auto-scheduler supports cron expressions (requires `croniter`): - -```powershell -# Schedule daily training at 2 AM -python .\scripts\auto_scheduler.py --schedule "daily_training" --workflow "daily_full_pipeline" --cron "0 2 * * *" - -# Start scheduler daemon -python .\scripts\auto_scheduler.py --start - -# List scheduled jobs -python .\scripts\auto_scheduler.py --list -``` - -### Resource-Aware Execution - -Master orchestrator monitors resources (requires `psutil`): - -```powershell -# Check status with resource usage -python .\scripts\master_orchestrator.py --status -``` - -Output includes: -- CPU percentage -- Memory percentage -- Disk usage -- Active orchestrators -- Workflow status - -## VS Code Tasks - -**7 new tasks added:** - -| Task | Command | -|------|---------| -| Run: Model Deployer - Scan | `python .\scripts\model_deployer.py --scan` | -| Run: Model Deployer - Deploy Best | `python .\scripts\model_deployer.py --deploy best --strategy canary` | -| Run: Resource Monitor - Snapshot | `python .\scripts\resource_monitor.py --snapshot` | -| Run: Resource Monitor - Stream | `python .\scripts\resource_monitor.py --stream --duration 60` | -| Run: Batch Evaluator - Scan | `python .\scripts\batch_evaluator.py --scan-models --evaluate-all` | -| Run: Results Exporter - Export to Markdown | `python .\scripts\results_exporter.py --all --format markdown` | -| Run: Auto Scheduler - List | `python .\scripts\auto_scheduler.py --list` | - -## Configuration Files - -### Batch Evaluation Config - -**File:** `batch_eval_config.yaml` - -Example structure: -```yaml -evaluation_tasks: - - model_id: lora_phi35_mixed - model_type: lora - model_path: data_out/lora_training/phi35_mixed_chat - dataset: datasets/chat/mixed_chat - metrics: [accuracy, perplexity, bleu] - max_samples: 100 - batch_size: 8 -``` - -## Usage Patterns - -### Complete Training-to-Deployment Pipeline - -```powershell -# 1. Validate all orchestrators -python .\scripts\ci_orchestrator.py --validate-all - -# 2. Run training -python .\scripts\master_orchestrator.py --workflow daily_full_pipeline - -# 3. Monitor resources during training -python .\scripts\resource_monitor.py --stream --duration 3600 - -# 4. Scan and deploy best model -python .\scripts\model_deployer.py --scan -python .\scripts\model_deployer.py --deploy best --strategy canary - -# 5. Export results -python .\scripts\results_exporter.py --all --format html --output report.html -``` - -### Scheduled Automation - -```powershell -# Schedule daily pipeline -python .\scripts\auto_scheduler.py --schedule "daily_full" --workflow "daily_full_pipeline" --cron "0 2 * * *" - -# Schedule hourly validation -python .\scripts\auto_scheduler.py --schedule "hourly_val" --workflow "quick_validation" --cron "0 * * * *" - -# Start scheduler daemon -python .\scripts\auto_scheduler.py --start --check-interval 60 -``` - -### Batch Evaluation Workflow - -```powershell -# 1. Create config with models to evaluate -# (edit batch_eval_config.yaml) - -# 2. Run batch evaluation -python .\scripts\batch_evaluator.py --config batch_eval_config.yaml - -# 3. Export results to multiple formats -python .\scripts\results_exporter.py --source batch_evaluator --format markdown -python .\scripts\results_exporter.py --source batch_evaluator --format excel -``` - -## Dependencies - -**Required (already installed):** -- `pyyaml` - Configuration parsing -- `psutil` - Resource monitoring - -**Optional:** -- `croniter` - Cron expression parsing (for scheduler) -- `GPUtil` - GPU monitoring -- `openpyxl` - Excel export - -**Install optional dependencies:** -```powershell -pip install croniter GPUtil openpyxl -``` - -## Output Locations - -| Tool | Output Directory | -|------|-----------------| -| Model Deployer | `deployed_models/` | -| Resource Monitor | `data_out/resource_monitor/` | -| Batch Evaluator | `data_out/batch_evaluator/` | -| Results Exporter | `exports/` | - -## Integration with Existing Tools - -All new tools integrate seamlessly with existing orchestrators: - -1. **Master Orchestrator** can call model deployer after successful training -2. **CI Orchestrator** can include model deployment in prepare-deployment step -3. **Auto Scheduler** can schedule resource monitoring tasks -4. **Batch Evaluator** uses existing evaluation scripts (when implemented) -5. **Results Exporter** reads status.json from all orchestrators - -## Next Steps - -### Immediate (Ready to Use) -- ✅ Model scanning and quality gate validation -- ✅ Resource monitoring and alerting -- ✅ Results export to multiple formats -- ✅ CI/CD workflow with GitHub Actions -- ✅ Cron-based scheduling - -### Short-term (Requires Implementation) -- ⚠️ Implement actual evaluation scripts for batch evaluator -- ⚠️ Add Slack/email notification handlers -- ⚠️ Implement code quality/security scanning in CI - -### Long-term (Future Enhancements) -- Web dashboard for monitoring -- Distributed execution across machines -- GPU-aware scheduling -- Automatic hyperparameter tuning -- Cost estimation and budgeting - -## Testing Performed - -**Model Deployer:** -- ✅ Scanned data_out/lora_training/ successfully -- ✅ Found 2 LoRA models -- ✅ Quality gates validation working - -**Resource Monitor:** -- ✅ Snapshot captured successfully -- ✅ CPU: 23.7%, Memory: 25.4%, Disk: 61.5% -- ✅ Process count: 372 -- ✅ JSON output formatted correctly - -**Results Exporter:** -- ✅ Exported all orchestrators to JSON -- ✅ Comparison format working -- ✅ File created at exports/all_orchestrators.json - -**CI Pipeline:** -- ✅ GitHub Actions workflow created -- ✅ Validation, training, deployment jobs defined -- ✅ Artifact upload configured - -## Documentation Updates - -**Files Updated:** -- `ADVANCED_AUTOMATION.md` - Added sections for all new tools -- `.vscode/tasks.json` - Added 7 new VS Code tasks -- `batch_eval_config.yaml` - Created sample configuration -- `.github/workflows/ci-pipeline.yml` - Created CI workflow - -**New Files Created:** -- `scripts/model_deployer.py` (470 lines) -- `scripts/resource_monitor.py` (390 lines) -- `scripts/batch_evaluator.py` (340 lines) -- `scripts/results_exporter.py` (420 lines) -- `AUTOMATION_FEATURES_SUMMARY.md` (this file) - -## Command Quick Reference - -```powershell -# Model Deployment -python .\scripts\model_deployer.py --scan -python .\scripts\model_deployer.py --deploy best --strategy canary - -# Resource Monitoring -python .\scripts\resource_monitor.py --snapshot -python .\scripts\resource_monitor.py --stream --duration 60 - -# Batch Evaluation -python .\scripts\batch_evaluator.py --scan-models --evaluate-all -python .\scripts\batch_evaluator.py --config batch_eval_config.yaml - -# Results Export -python .\scripts\results_exporter.py --all --format markdown -python .\scripts\results_exporter.py --compare autotrain quantum_autorun --format html - -# Scheduling -python .\scripts\auto_scheduler.py --schedule "job_name" --workflow "workflow_name" --cron "0 2 * * *" -python .\scripts\auto_scheduler.py --list - -# CI/CD -python .\scripts\ci_orchestrator.py --validate-all -python .\scripts\ci_orchestrator.py --ci-pipeline -``` - -## Related Documentation - -- `ADVANCED_AUTOMATION.md` - Complete automation guide -- `AUTOTRAIN_README.md` - LoRA training orchestrator -- `QUANTUM_AUTORUN_README.md` - Quantum training orchestrator -- `EVALUATION_AUTORUN_README.md` - Model evaluation orchestrator -- `PRODUCTION_DEPLOYMENT_PLAN.md` - Deployment guide -- `QUICK_REFERENCE.md` - Quick command reference - ---- - -**Status:** All features implemented and tested ✅ -**Date:** November 22, 2025 -**Version:** 1.0 +# Advanced Automation Features Summary + +**Generated:** November 22, 2025 + +## Overview + +This document summarizes the advanced automation features added to the QAI workspace, including CI/CD integration, auto-deployment, resource monitoring, batch evaluation, and multi-format result export. + +## New Tools Created + +### 1. Model Deployer (`model_deployer.py`) + +**Purpose:** Automatically deploy trained models with quality gates and version control. + +**Key Features:** +- Quality gate validation (configurable thresholds) +- Model scoring based on accuracy, loss, validation metrics +- Deployment strategies: direct, canary, blue-green +- Version tracking and rollback support +- Model registry with metadata + +**Quality Gates (default):** +- `min_accuracy`: 0.75 +- `max_loss`: 0.5 +- `min_f1_score`: 0.70 +- `min_validation_accuracy`: 0.70 + +**Commands:** +```powershell +# Scan for deployable models +python .\scripts\model_deployer.py --scan + +# Deploy best model with canary strategy +python .\scripts\model_deployer.py --deploy best --strategy canary + +# Check deployment status +python .\scripts\model_deployer.py --status + +# Rollback to previous version +python .\scripts\model_deployer.py --rollback v1_20251122_123456 + +# Set custom quality gate +python .\scripts\model_deployer.py --set-quality-gate min_accuracy 0.80 +``` + +### 2. Resource Monitor (`resource_monitor.py`) + +**Purpose:** Real-time system resource monitoring with alerts and historical tracking. + +**Key Features:** +- CPU, memory, disk, GPU monitoring (uses psutil + GPUtil) +- Configurable alert thresholds +- Historical data collection (JSONL format) +- Export to CSV/JSON +- Streaming mode for continuous monitoring + +**Default Thresholds:** +- `cpu_percent`: 90.0% +- `memory_percent`: 90.0% +- `disk_percent`: 90.0% +- `gpu_utilization`: 95.0% +- `gpu_memory_percent`: 95.0% + +**Commands:** +```powershell +# Single snapshot +python .\scripts\resource_monitor.py --snapshot + +# Stream for 60 seconds +python .\scripts\resource_monitor.py --stream --duration 60 + +# View last 24 hours +python .\scripts\resource_monitor.py --history --hours 24 + +# Export to CSV +python .\scripts\resource_monitor.py --export csv + +# Set custom threshold +python .\scripts\resource_monitor.py --set-threshold cpu_percent 85 +``` + +### 3. Batch Evaluator (`batch_evaluator.py`) + +**Purpose:** Parallel evaluation of multiple models with comprehensive aggregation. + +**Key Features:** +- ThreadPoolExecutor for parallel execution +- Support for 5 model types (LoRA, Azure, OpenAI, Local, Quantum) +- Result aggregation and ranking +- Export to JSON/Markdown +- Side-by-side comparison + +**Commands:** +```powershell +# Scan and evaluate all models +python .\scripts\batch_evaluator.py --scan-models --evaluate-all + +# Use config file +python .\scripts\batch_evaluator.py --config batch_eval_config.yaml + +# Compare specific models +python .\scripts\batch_evaluator.py --compare lora_v1 lora_v2 + +# Export results +python .\scripts\batch_evaluator.py --export markdown --output report.md +``` + +### 4. Results Exporter (`results_exporter.py`) + +**Purpose:** Export orchestrator results to multiple formats for reporting and analysis. + +**Supported Formats:** +- JSON (machine-readable) +- CSV (spreadsheet import) +- Excel (requires openpyxl) +- Markdown (documentation) +- HTML (web reports) + +**Commands:** +```powershell +# Export single orchestrator +python .\scripts\results_exporter.py --source autotrain --format csv + +# Export all orchestrators +python .\scripts\results_exporter.py --all --format markdown + +# Compare orchestrators +python .\scripts\results_exporter.py --compare autotrain quantum_autorun --format html + +# Filter by status +python .\scripts\results_exporter.py --source autotrain --format json --filter-status succeeded +``` + +## CI/CD Integration + +### GitHub Actions Workflow + +**File:** `.github/workflows/ci-pipeline.yml` + +**Triggers:** +- Push to main/dev branches +- Pull requests to main +- Daily schedule (2 AM UTC) + +**Jobs:** +1. **Validate:** Run CI validation and unit tests +2. **Train:** Run daily training workflow (scheduled only) +3. **Deploy:** Auto-deploy best model (scheduled only) + +**Artifacts:** +- Validation results +- Training outputs +- Deployment manifest + +### Git Hooks + +**Pre-commit validation** (sample provided): +- `.git/hooks/pre-commit.sample` contains validation logic +- Runs `ci_orchestrator.py --validate-all` before commit +- Prevents commits if validation fails + +## Enhanced Orchestrator Features + +### Dependency Chaining + +Master orchestrator now supports complex dependency chains: + +```yaml +workflows: + - name: full_pipeline + orchestrators: + - autotrain # Step 1 + - evaluation_autorun # Step 2 (depends on autotrain) + - model_deploy # Step 3 (depends on evaluation) +``` + +### Cron Scheduling + +Auto-scheduler supports cron expressions (requires `croniter`): + +```powershell +# Schedule daily training at 2 AM +python .\scripts\auto_scheduler.py --schedule "daily_training" --workflow "daily_full_pipeline" --cron "0 2 * * *" + +# Start scheduler daemon +python .\scripts\auto_scheduler.py --start + +# List scheduled jobs +python .\scripts\auto_scheduler.py --list +``` + +### Resource-Aware Execution + +Master orchestrator monitors resources (requires `psutil`): + +```powershell +# Check status with resource usage +python .\scripts\master_orchestrator.py --status +``` + +Output includes: +- CPU percentage +- Memory percentage +- Disk usage +- Active orchestrators +- Workflow status + +## VS Code Tasks + +**7 new tasks added:** + +| Task | Command | +|------|---------| +| Run: Model Deployer - Scan | `python .\scripts\model_deployer.py --scan` | +| Run: Model Deployer - Deploy Best | `python .\scripts\model_deployer.py --deploy best --strategy canary` | +| Run: Resource Monitor - Snapshot | `python .\scripts\resource_monitor.py --snapshot` | +| Run: Resource Monitor - Stream | `python .\scripts\resource_monitor.py --stream --duration 60` | +| Run: Batch Evaluator - Scan | `python .\scripts\batch_evaluator.py --scan-models --evaluate-all` | +| Run: Results Exporter - Export to Markdown | `python .\scripts\results_exporter.py --all --format markdown` | +| Run: Auto Scheduler - List | `python .\scripts\auto_scheduler.py --list` | + +## Configuration Files + +### Batch Evaluation Config + +**File:** `batch_eval_config.yaml` + +Example structure: +```yaml +evaluation_tasks: + - model_id: lora_phi35_mixed + model_type: lora + model_path: data_out/lora_training/phi35_mixed_chat + dataset: datasets/chat/mixed_chat + metrics: [accuracy, perplexity, bleu] + max_samples: 100 + batch_size: 8 +``` + +## Usage Patterns + +### Complete Training-to-Deployment Pipeline + +```powershell +# 1. Validate all orchestrators +python .\scripts\ci_orchestrator.py --validate-all + +# 2. Run training +python .\scripts\master_orchestrator.py --workflow daily_full_pipeline + +# 3. Monitor resources during training +python .\scripts\resource_monitor.py --stream --duration 3600 + +# 4. Scan and deploy best model +python .\scripts\model_deployer.py --scan +python .\scripts\model_deployer.py --deploy best --strategy canary + +# 5. Export results +python .\scripts\results_exporter.py --all --format html --output report.html +``` + +### Scheduled Automation + +```powershell +# Schedule daily pipeline +python .\scripts\auto_scheduler.py --schedule "daily_full" --workflow "daily_full_pipeline" --cron "0 2 * * *" + +# Schedule hourly validation +python .\scripts\auto_scheduler.py --schedule "hourly_val" --workflow "quick_validation" --cron "0 * * * *" + +# Start scheduler daemon +python .\scripts\auto_scheduler.py --start --check-interval 60 +``` + +### Batch Evaluation Workflow + +```powershell +# 1. Create config with models to evaluate +# (edit batch_eval_config.yaml) + +# 2. Run batch evaluation +python .\scripts\batch_evaluator.py --config batch_eval_config.yaml + +# 3. Export results to multiple formats +python .\scripts\results_exporter.py --source batch_evaluator --format markdown +python .\scripts\results_exporter.py --source batch_evaluator --format excel +``` + +## Dependencies + +**Required (already installed):** +- `pyyaml` - Configuration parsing +- `psutil` - Resource monitoring + +**Optional:** +- `croniter` - Cron expression parsing (for scheduler) +- `GPUtil` - GPU monitoring +- `openpyxl` - Excel export + +**Install optional dependencies:** +```powershell +pip install croniter GPUtil openpyxl +``` + +## Output Locations + +| Tool | Output Directory | +|------|-----------------| +| Model Deployer | `deployed_models/` | +| Resource Monitor | `data_out/resource_monitor/` | +| Batch Evaluator | `data_out/batch_evaluator/` | +| Results Exporter | `exports/` | + +## Integration with Existing Tools + +All new tools integrate seamlessly with existing orchestrators: + +1. **Master Orchestrator** can call model deployer after successful training +2. **CI Orchestrator** can include model deployment in prepare-deployment step +3. **Auto Scheduler** can schedule resource monitoring tasks +4. **Batch Evaluator** uses existing evaluation scripts (when implemented) +5. **Results Exporter** reads status.json from all orchestrators + +## Next Steps + +### Immediate (Ready to Use) +- ✅ Model scanning and quality gate validation +- ✅ Resource monitoring and alerting +- ✅ Results export to multiple formats +- ✅ CI/CD workflow with GitHub Actions +- ✅ Cron-based scheduling + +### Short-term (Requires Implementation) +- ⚠️ Implement actual evaluation scripts for batch evaluator +- ⚠️ Add Slack/email notification handlers +- ⚠️ Implement code quality/security scanning in CI + +### Long-term (Future Enhancements) +- Web dashboard for monitoring +- Distributed execution across machines +- GPU-aware scheduling +- Automatic hyperparameter tuning +- Cost estimation and budgeting + +## Testing Performed + +**Model Deployer:** +- ✅ Scanned data_out/lora_training/ successfully +- ✅ Found 2 LoRA models +- ✅ Quality gates validation working + +**Resource Monitor:** +- ✅ Snapshot captured successfully +- ✅ CPU: 23.7%, Memory: 25.4%, Disk: 61.5% +- ✅ Process count: 372 +- ✅ JSON output formatted correctly + +**Results Exporter:** +- ✅ Exported all orchestrators to JSON +- ✅ Comparison format working +- ✅ File created at exports/all_orchestrators.json + +**CI Pipeline:** +- ✅ GitHub Actions workflow created +- ✅ Validation, training, deployment jobs defined +- ✅ Artifact upload configured + +## Documentation Updates + +**Files Updated:** +- `ADVANCED_AUTOMATION.md` - Added sections for all new tools +- `.vscode/tasks.json` - Added 7 new VS Code tasks +- `batch_eval_config.yaml` - Created sample configuration +- `.github/workflows/ci-pipeline.yml` - Created CI workflow + +**New Files Created:** +- `scripts/model_deployer.py` (470 lines) +- `scripts/resource_monitor.py` (390 lines) +- `scripts/batch_evaluator.py` (340 lines) +- `scripts/results_exporter.py` (420 lines) +- `AUTOMATION_FEATURES_SUMMARY.md` (this file) + +## Command Quick Reference + +```powershell +# Model Deployment +python .\scripts\model_deployer.py --scan +python .\scripts\model_deployer.py --deploy best --strategy canary + +# Resource Monitoring +python .\scripts\resource_monitor.py --snapshot +python .\scripts\resource_monitor.py --stream --duration 60 + +# Batch Evaluation +python .\scripts\batch_evaluator.py --scan-models --evaluate-all +python .\scripts\batch_evaluator.py --config batch_eval_config.yaml + +# Results Export +python .\scripts\results_exporter.py --all --format markdown +python .\scripts\results_exporter.py --compare autotrain quantum_autorun --format html + +# Scheduling +python .\scripts\auto_scheduler.py --schedule "job_name" --workflow "workflow_name" --cron "0 2 * * *" +python .\scripts\auto_scheduler.py --list + +# CI/CD +python .\scripts\ci_orchestrator.py --validate-all +python .\scripts\ci_orchestrator.py --ci-pipeline +``` + +## Related Documentation + +- `ADVANCED_AUTOMATION.md` - Complete automation guide +- `AUTOTRAIN_README.md` - LoRA training orchestrator +- `QUANTUM_AUTORUN_README.md` - Quantum training orchestrator +- `EVALUATION_AUTORUN_README.md` - Model evaluation orchestrator +- `PRODUCTION_DEPLOYMENT_PLAN.md` - Deployment guide +- `QUICK_REFERENCE.md` - Quick command reference + +--- + +**Status:** All features implemented and tested ✅ +**Date:** November 22, 2025 +**Version:** 1.0 diff --git a/docs/guides/AUTONOMOUS_ORCHESTRATOR_MODEL.md b/docs/guides/AUTONOMOUS_ORCHESTRATOR_MODEL.md index 50243e133..51308dcdf 100644 --- a/docs/guides/AUTONOMOUS_ORCHESTRATOR_MODEL.md +++ b/docs/guides/AUTONOMOUS_ORCHESTRATOR_MODEL.md @@ -1,727 +1,727 @@ -# Autonomous AI Training Orchestrator - Implementation Model - -## Overview - -This model provides a complete blueprint for building production-grade autonomous AI training systems with automatic scaling, resource management, and multi-backend support (classical ML + quantum ML). Use this as a template for your own implementations. - -## Architecture Pattern - -``` -┌─────────────────────────────────────────────────────────────┐ -│ Orchestrator Core (Async) │ -│ - Configuration Management (YAML) │ -│ - Lifecycle Management (Init → Train → Optimize → Deploy) │ -│ - Resource-Aware Scaling (CPU/GPU detection) │ -└─────────────────────────────────────────────────────────────┘ - │ - ┌───────────────────┼───────────────────┐ - ▼ ▼ ▼ -┌──────────────┐ ┌──────────────┐ ┌──────────────┐ -│ Discovery │ │ Training │ │ Optimization │ -│ Engine │ │ Engine │ │ Engine │ -│ │ │ │ │ │ -│ - Dataset │ │ - Classical │ │ - Hyperopt │ -│ Crawling │ │ ML │ │ - AutoML │ -│ - Validation │ │ - Quantum ML │ │ - Pruning │ -│ - Indexing │ │ - Hybrid │ │ - Distill │ -└──────────────┘ └──────────────┘ └──────────────┘ - │ - ┌───────────┴───────────┐ - ▼ ▼ - ┌──────────────┐ ┌──────────────┐ - │ Local │ │ Cloud │ - │ Executor │ │ Executor │ - │ │ │ │ - │ - Multi- │ │ - Azure │ - │ processing │ │ Quantum │ - │ - Ray │ │ - Ray │ - │ (optional) │ │ Cluster │ - └──────────────┘ └──────────────┘ -``` - -## Core Components - -### 1. Configuration Schema (YAML) - -```yaml -# autonomous_training.yaml -discovery: - enabled: true - sources: - - type: "local" - path: "datasets/" - - type: "huggingface" - filter: "classification" - interval_hours: 24 - -training: - workers: 20 # Parallel workers - epochs: 100 - batch_size: 32 - datasets_dir: "datasets/massive_quantum" - output_dir: "data_out/distributed_benchmark" - - # Backend selection - backends: - - type: "classical" - enabled: true - frameworks: ["sklearn", "pytorch"] - - type: "quantum" - enabled: true - frameworks: ["qiskit", "pennylane"] - simulators: ["qiskit_aer", "lightning.qubit"] - -optimization: - enabled: true - methods: - - "hyperparameter_tuning" - - "model_pruning" - - "quantization" - target_metrics: - - "accuracy" - - "inference_time" - - "model_size" - -deployment: - enabled: false - targets: - - type: "azure_ml" - - type: "local_api" - -# SCALING CONFIGURATION (KEY FEATURE) -scaling: - mode: "multiprocessing" # Options: "multiprocessing", "ray", "sequential" - max_workers: null # null = auto-detect CPU count - batch_size: 100 # Process N datasets per batch - resource_limits: - max_cpu_percent: 90 - max_memory_gb: 16 - enable_gpu: false - -# MONITORING -monitoring: - enabled: true - log_level: "INFO" - output_format: "json" - real_time_dashboard: false -``` - -### 2. Orchestrator Core Pattern - -```python -import asyncio -import multiprocessing -import sys -from pathlib import Path -from typing import Dict, List, Optional -import yaml - -# Optional: Ray for distributed execution -try: - import ray - RAY_AVAILABLE = True -except ImportError: - RAY_AVAILABLE = False - -class AutonomousOrchestrator: - """ - Production-grade autonomous training orchestrator with scaling. - - Key Features: - - Async/await for non-blocking operations - - Resource-aware worker allocation - - Multi-backend support (classical + quantum) - - Configurable scaling (multiprocessing or Ray) - - Graceful error handling and recovery - """ - - def __init__(self, config_path: str): - """Initialize with YAML configuration.""" - self.config_path = Path(config_path) - self.config = self._load_config() - - # Scaling configuration - self.scaling_mode = self.config.get("scaling", {}).get("mode", "multiprocessing") - self.max_workers = self.config.get("scaling", {}).get("max_workers", None) - self.batch_size = self.config.get("scaling", {}).get("batch_size", 100) - self.resource_limits = self.config.get("scaling", {}).get("resource_limits", {}) - - # State tracking - self.status = { - "phase": "initialized", - "datasets_discovered": 0, - "datasets_trained": 0, - "models_deployed": 0, - "errors": [] - } - - def _load_config(self) -> Dict: - """Load and validate YAML configuration.""" - with open(self.config_path) as f: - return yaml.safe_load(f) - - async def run_cycle(self, once: bool = False): - """ - Execute complete orchestration cycle. - - Args: - once: Run single cycle and exit (vs continuous loop) - """ - while True: - try: - # Phase 1: Discovery - if self.config["discovery"]["enabled"]: - await self.discover_datasets() - - # Phase 2: Training (RESOURCE-AWARE) - await self.train_models() - - # Phase 3: Optimization - if self.config["optimization"]["enabled"]: - await self.optimize_models() - - # Phase 4: Deployment - if self.config["deployment"]["enabled"]: - await self.deploy_models() - - # Save status - self._save_status() - - if once: - break - - # Wait before next cycle - await asyncio.sleep(3600) # 1 hour - - except Exception as e: - self.status["errors"].append(str(e)) - if once: - raise - - async def train_models(self): - """ - Train models with resource-aware scaling. - - KEY PATTERN: Dynamic worker allocation based on CPU availability - """ - self.status["phase"] = "training" - - # Get CPU count for resource-aware allocation - cpu_count = multiprocessing.cpu_count() - - # Calculate optimal workers - config_workers = self.config["training"]["workers"] - max_workers = self.max_workers or min(cpu_count, config_workers) - - print(f"Training with {max_workers} workers (CPU count: {cpu_count})") - - # Build command for distributed training - benchmark_script = Path(__file__).parent / "distributed_benchmark.py" - datasets_dir = Path(self.config["training"]["datasets_dir"]) - epochs = self.config["training"]["epochs"] - - cmd = [ - sys.executable, - str(benchmark_script), - "--datasets-dir", str(datasets_dir), - "--workers", str(max_workers), - "--epochs", str(epochs) - ] - - # Execute training (async subprocess) - process = await asyncio.create_subprocess_exec( - *cmd, - stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE - ) - - stdout, stderr = await process.communicate() - - if process.returncode == 0: - print("Training completed successfully") - self.status["datasets_trained"] += 1 - else: - error_msg = stderr.decode() - print(f"Training failed: {error_msg}") - self.status["errors"].append(error_msg) - - def _save_status(self): - """Persist orchestrator status to JSON.""" - import json - status_file = Path("data_out/autonomous_training_status.json") - status_file.parent.mkdir(parents=True, exist_ok=True) - - with open(status_file, 'w') as f: - json.dump(self.status, f, indent=2) - -# CLI Entry Point -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser(description="Autonomous Training Orchestrator") - parser.add_argument("--config", default="config/autonomous_training.yaml") - parser.add_argument("--once", action="store_true", help="Run single cycle") - args = parser.parse_args() - - orchestrator = AutonomousOrchestrator(args.config) - asyncio.run(orchestrator.run_cycle(once=args.once)) -``` - -### 3. Distributed Training Worker Pattern - -```python -# distributed_benchmark.py -import multiprocessing as mp -from concurrent.futures import ProcessPoolExecutor -from pathlib import Path -from typing import List, Dict -import pandas as pd -from sklearn.ensemble import RandomForestClassifier -from sklearn.model_selection import train_test_split -from sklearn.metrics import accuracy_score - -def train_single_dataset(args) -> Dict: - """ - Train model on single dataset (worker function). - - Args: - args: Tuple of (dataset_path, epochs, output_dir) - - Returns: - Dict with training results - """ - dataset_path, epochs, output_dir = args - - try: - # Load data - df = pd.read_csv(dataset_path) - X = df.iloc[:, :-1].values - y = df.iloc[:, -1].values - - # Split - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - # Train - model = RandomForestClassifier(n_estimators=100, random_state=42) - model.fit(X_train, y_train) - - # Evaluate - accuracy = accuracy_score(y_test, model.predict(X_test)) - - return { - "dataset": dataset_path.name, - "accuracy": accuracy, - "status": "success", - "samples": len(df) - } - - except Exception as e: - return { - "dataset": dataset_path.name, - "status": "failed", - "error": str(e) - } - -def train_all_datasets( - datasets_dir: Path, - workers: int, - epochs: int, - output_dir: Path -) -> List[Dict]: - """ - Train models on all datasets using multiprocessing. - - KEY PATTERN: ProcessPoolExecutor for CPU-bound ML training - """ - # Discover datasets - dataset_paths = list(datasets_dir.glob("*.csv")) - print(f"Found {len(dataset_paths)} datasets") - - # Prepare arguments for workers - tasks = [(path, epochs, output_dir) for path in dataset_paths] - - # Execute in parallel - results = [] - with ProcessPoolExecutor(max_workers=workers) as executor: - for result in executor.map(train_single_dataset, tasks): - results.append(result) - - # Progress logging - completed = len(results) - if completed % 10 == 0: - print(f"Progress: {completed}/{len(dataset_paths)} datasets") - - return results - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("--datasets-dir", required=True) - parser.add_argument("--workers", type=int, default=4) - parser.add_argument("--epochs", type=int, default=100) - parser.add_argument("--output-dir", default="data_out/benchmark") - args = parser.parse_args() - - results = train_all_datasets( - Path(args.datasets_dir), - args.workers, - args.epochs, - Path(args.output_dir) - ) - - # Save results - import json - output_file = Path(args.output_dir) / "results.json" - output_file.parent.mkdir(parents=True, exist_ok=True) - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) -``` - -### 4. Quantum ML Integration Pattern - -```python -# quantum_integration.py -from qiskit import QuantumCircuit -from qiskit_aer import AerSimulator -from azure.quantum import Workspace - -class QuantumMLBackend: - """ - Unified interface for quantum ML training (local + cloud). - """ - - def __init__(self, backend_type: str = "simulator"): - """ - Args: - backend_type: "simulator" or "azure" - """ - self.backend_type = backend_type - - if backend_type == "azure": - self.workspace = Workspace( - subscription_id="YOUR_SUBSCRIPTION_ID", - resource_group="YOUR_RESOURCE_GROUP", - name="YOUR_WORKSPACE_NAME" - ) - - def create_variational_circuit(self, n_qubits: int, n_layers: int) -> QuantumCircuit: - """Create parameterized quantum circuit for ML.""" - qc = QuantumCircuit(n_qubits) - - for layer in range(n_layers): - # Rotation layer - for qubit in range(n_qubits): - qc.ry(0.5, qubit) # Placeholder, use parameters in production - - # Entanglement layer - for qubit in range(n_qubits - 1): - qc.cx(qubit, qubit + 1) - - qc.measure_all() - return qc - - def train(self, X_train, y_train, n_qubits: int = 4, shots: int = 1024): - """Train quantum classifier.""" - circuit = self.create_variational_circuit(n_qubits, n_layers=2) - - if self.backend_type == "simulator": - backend = AerSimulator() - job = backend.run(circuit, shots=shots) - result = job.result() - return result.get_counts() - - elif self.backend_type == "azure": - target = self.workspace.get_targets("ionq.simulator") - job = target.submit(circuit, shots=shots) - return job.get_results() -``` - -## Production Deployment Checklist - -### 1. Resource Management -- [ ] Implement CPU/GPU detection with `multiprocessing.cpu_count()` -- [ ] Add memory monitoring (use `psutil` library) -- [ ] Set resource limits in config (max CPU %, max memory) -- [ ] Implement graceful degradation when resources are constrained - -### 2. Error Handling -- [ ] Wrap all async operations in try/except blocks -- [ ] Log errors to persistent storage (JSON/database) -- [ ] Implement retry logic with exponential backoff -- [ ] Add health check endpoints for monitoring - -### 3. Monitoring & Observability -- [ ] Real-time dashboard (use `rich` library for CLI) -- [ ] Metrics export (Prometheus format recommended) -- [ ] Progress tracking with detailed status JSON -- [ ] Integration with cloud monitoring (Azure Monitor, CloudWatch) - -### 4. Scalability -- [ ] Test with 100+ datasets -- [ ] Verify worker scaling (test with 1, 4, 8, 16, 32 workers) -- [ ] Benchmark Ray vs multiprocessing for your workload -- [ ] Profile memory usage per worker - -### 5. Configuration Management -- [ ] Validate YAML schema on load -- [ ] Support environment variable overrides -- [ ] Version your configuration files -- [ ] Document all configuration options - -## Usage Examples - -### Basic Usage (Single Cycle) -```powershell -# Run one training cycle with auto-detected workers -python scripts/autonomous_training_orchestrator.py --once - -# Specify custom config -python scripts/autonomous_training_orchestrator.py --config custom.yaml --once -``` - -### Continuous Operation -```powershell -# Run continuously (1 hour interval between cycles) -python scripts/autonomous_training_orchestrator.py - -# Run as background service (Windows) -Start-Process python -ArgumentList "scripts/autonomous_training_orchestrator.py" -WindowStyle Hidden - -# Linux/Mac background -nohup python scripts/autonomous_training_orchestrator.py & -``` - -### Scaling Modes - -**Multiprocessing (Default, Recommended)** -```yaml -scaling: - mode: "multiprocessing" - max_workers: null # Auto-detect -``` - -**Ray Distributed (Advanced)** -```yaml -scaling: - mode: "ray" - max_workers: 64 - ray_config: - num_cpus: 32 - num_gpus: 0 -``` - -## Performance Benchmarks - -Based on our production deployment (552 datasets, 100 epochs): - -| Workers | Datasets/Hour | Accuracy | Memory/Worker | -|---------|---------------|----------|---------------| -| 4 | ~25 | 98-100% | ~500 MB | -| 8 | ~45 | 98-100% | ~500 MB | -| 20 | ~100 | 98-100% | ~500 MB | -| 32 | ~150 | 98-100% | ~500 MB | - -**Key Findings:** -- Linear scaling up to CPU count -- Memory usage stable across worker counts -- 100% accuracy achievable on well-structured datasets -- Overhead: ~5% for orchestration vs direct training - -## Integration Patterns - -### With Azure Quantum -```python -# In config YAML -training: - backends: - - type: "quantum" - enabled: true - azure: - subscription_id: "a07fbd16-xxxx" - resource_group: "rg-quantum-ai" - workspace_name: "quantum-ai-workspace" - targets: - - "ionq.simulator" # FREE - - "rigetti.sim.qvm" # FREE -``` - -### With MLflow Tracking -```python -import mlflow - -class AutonomousOrchestrator: - async def train_models(self): - with mlflow.start_run(): - mlflow.log_param("workers", max_workers) - mlflow.log_param("datasets", len(dataset_paths)) - - # ... training code ... - - mlflow.log_metric("accuracy", avg_accuracy) - mlflow.log_metric("training_time", elapsed_time) -``` - -### With Monitoring Dashboard -```python -# Real-time monitoring -from rich.live import Live -from rich.table import Table - -def create_status_table(orchestrator): - table = Table(title="Training Status") - table.add_column("Metric", style="cyan") - table.add_column("Value", style="green") - - table.add_row("Phase", orchestrator.status["phase"]) - table.add_row("Datasets Trained", str(orchestrator.status["datasets_trained"])) - table.add_row("Models Deployed", str(orchestrator.status["models_deployed"])) - - return table - -# Use in orchestrator -with Live(create_status_table(self), refresh_per_second=1): - await self.train_models() -``` - -## Best Practices - -### 1. Start Small, Scale Gradually -- Test with 10 datasets, 4 workers -- Increase to 100 datasets, 8 workers -- Production: 500+ datasets, CPU-count workers - -### 2. Use Async/Await Correctly -- IO-bound: Use `asyncio` (file I/O, network) -- CPU-bound: Use `ProcessPoolExecutor` (ML training) -- Never block event loop with heavy computation - -### 3. Handle Failures Gracefully -```python -try: - result = await train_model(dataset) -except Exception as e: - log_error(e, dataset) - continue # Don't stop entire pipeline -``` - -### 4. Version Everything -- Configuration files: `config_v1.yaml`, `config_v2.yaml` -- Trained models: Include timestamp and version in filenames -- Results: Save with metadata (date, config hash, git commit) - -### 5. Monitor Resource Usage -```python -import psutil - -def check_resources(): - cpu_percent = psutil.cpu_percent() - memory_percent = psutil.virtual_memory().percent - - if cpu_percent > 90 or memory_percent > 90: - # Reduce workers or pause training - pass -``` - -## Common Issues & Solutions - -### Issue: "unrecognized arguments: --datasets-list" -**Cause:** Argument mismatch between orchestrator and worker script. -**Solution:** Use `--datasets-dir` for directory-based discovery (recommended). - -### Issue: UnicodeEncodeError in logs -**Cause:** Non-ASCII characters (emojis) in output. -**Solution:** Use ASCII alternatives or set encoding: -```python -logging.basicConfig(encoding='utf-8', ...) -``` - -### Issue: Out of memory with many workers -**Cause:** Each worker loads model/data into memory. -**Solution:** -- Reduce worker count -- Use batch processing -- Enable swap/pagefile - -### Issue: Qiskit version conflicts (Azure Quantum) -**Cause:** `azure-quantum` requires old qiskit (0.46), but newer packages need >=1.1. -**Solution:** -- Use separate virtual environments -- Or install azure-quantum in main environment (test first) - -## Repository Structure - -``` -project/ -├── config/ -│ ├── autonomous_training.yaml # Main config -│ └── quantum_config.yaml # Quantum-specific -├── scripts/ -│ ├── autonomous_training_orchestrator.py -│ ├── distributed_benchmark.py -│ ├── quantum_autorun.py -│ └── monitor_autonomous_training.py -├── datasets/ -│ ├── massive_quantum/ # Training data -│ └── dataset_index.json -├── data_out/ -│ ├── autonomous_training_status.json -│ ├── distributed_benchmark/ -│ └── quantum_autorun/ -├── requirements.txt -└── README.md -``` - -## Dependencies - -```txt -# Core -pyyaml>=6.0 -asyncio - -# Classical ML -scikit-learn>=1.3.0 -pandas>=2.0.0 -numpy>=1.24.0 - -# Quantum ML (Optional) -qiskit>=1.0.0 -qiskit-aer>=0.13.0 -azure-quantum>=1.0.0 - -# Distributed (Optional) -ray>=2.0.0 - -# Monitoring -rich>=13.0.0 -psutil>=5.9.0 -``` - -## License & Attribution - -This model is based on the QAI autonomous training orchestrator: -- Repository: Bryan-Roe/QAI -- License: MIT (or your license) -- Author: Bryan Roe - -Feel free to adapt this model to your specific needs. Key patterns to preserve: -1. Async orchestration loop -2. Resource-aware worker allocation -3. YAML-based configuration -4. Graceful error handling -5. Comprehensive monitoring - -## Additional Resources - -- [Azure Quantum Documentation](https://learn.microsoft.com/azure/quantum/) -- [Ray Distributed Computing](https://docs.ray.io/) -- [Qiskit Machine Learning](https://qiskit.org/ecosystem/machine-learning/) -- [Python Multiprocessing Guide](https://docs.python.org/3/library/multiprocessing.html) - ---- - -**Version:** 1.0 -**Last Updated:** November 2025 -**Tested On:** Windows 11, Python 3.11, 552 datasets, 20 workers +# Autonomous AI Training Orchestrator - Implementation Model + +## Overview + +This model provides a complete blueprint for building production-grade autonomous AI training systems with automatic scaling, resource management, and multi-backend support (classical ML + quantum ML). Use this as a template for your own implementations. + +## Architecture Pattern + +``` +┌─────────────────────────────────────────────────────────────┐ +│ Orchestrator Core (Async) │ +│ - Configuration Management (YAML) │ +│ - Lifecycle Management (Init → Train → Optimize → Deploy) │ +│ - Resource-Aware Scaling (CPU/GPU detection) │ +└─────────────────────────────────────────────────────────────┘ + │ + ┌───────────────────┼───────────────────┐ + ▼ ▼ ▼ +┌──────────────┐ ┌──────────────┐ ┌──────────────┐ +│ Discovery │ │ Training │ │ Optimization │ +│ Engine │ │ Engine │ │ Engine │ +│ │ │ │ │ │ +│ - Dataset │ │ - Classical │ │ - Hyperopt │ +│ Crawling │ │ ML │ │ - AutoML │ +│ - Validation │ │ - Quantum ML │ │ - Pruning │ +│ - Indexing │ │ - Hybrid │ │ - Distill │ +└──────────────┘ └──────────────┘ └──────────────┘ + │ + ┌───────────┴───────────┐ + ▼ ▼ + ┌──────────────┐ ┌──────────────┐ + │ Local │ │ Cloud │ + │ Executor │ │ Executor │ + │ │ │ │ + │ - Multi- │ │ - Azure │ + │ processing │ │ Quantum │ + │ - Ray │ │ - Ray │ + │ (optional) │ │ Cluster │ + └──────────────┘ └──────────────┘ +``` + +## Core Components + +### 1. Configuration Schema (YAML) + +```yaml +# autonomous_training.yaml +discovery: + enabled: true + sources: + - type: "local" + path: "datasets/" + - type: "huggingface" + filter: "classification" + interval_hours: 24 + +training: + workers: 20 # Parallel workers + epochs: 100 + batch_size: 32 + datasets_dir: "datasets/massive_quantum" + output_dir: "data_out/distributed_benchmark" + + # Backend selection + backends: + - type: "classical" + enabled: true + frameworks: ["sklearn", "pytorch"] + - type: "quantum" + enabled: true + frameworks: ["qiskit", "pennylane"] + simulators: ["qiskit_aer", "lightning.qubit"] + +optimization: + enabled: true + methods: + - "hyperparameter_tuning" + - "model_pruning" + - "quantization" + target_metrics: + - "accuracy" + - "inference_time" + - "model_size" + +deployment: + enabled: false + targets: + - type: "azure_ml" + - type: "local_api" + +# SCALING CONFIGURATION (KEY FEATURE) +scaling: + mode: "multiprocessing" # Options: "multiprocessing", "ray", "sequential" + max_workers: null # null = auto-detect CPU count + batch_size: 100 # Process N datasets per batch + resource_limits: + max_cpu_percent: 90 + max_memory_gb: 16 + enable_gpu: false + +# MONITORING +monitoring: + enabled: true + log_level: "INFO" + output_format: "json" + real_time_dashboard: false +``` + +### 2. Orchestrator Core Pattern + +```python +import asyncio +import multiprocessing +import sys +from pathlib import Path +from typing import Dict, List, Optional +import yaml + +# Optional: Ray for distributed execution +try: + import ray + RAY_AVAILABLE = True +except ImportError: + RAY_AVAILABLE = False + +class AutonomousOrchestrator: + """ + Production-grade autonomous training orchestrator with scaling. + + Key Features: + - Async/await for non-blocking operations + - Resource-aware worker allocation + - Multi-backend support (classical + quantum) + - Configurable scaling (multiprocessing or Ray) + - Graceful error handling and recovery + """ + + def __init__(self, config_path: str): + """Initialize with YAML configuration.""" + self.config_path = Path(config_path) + self.config = self._load_config() + + # Scaling configuration + self.scaling_mode = self.config.get("scaling", {}).get("mode", "multiprocessing") + self.max_workers = self.config.get("scaling", {}).get("max_workers", None) + self.batch_size = self.config.get("scaling", {}).get("batch_size", 100) + self.resource_limits = self.config.get("scaling", {}).get("resource_limits", {}) + + # State tracking + self.status = { + "phase": "initialized", + "datasets_discovered": 0, + "datasets_trained": 0, + "models_deployed": 0, + "errors": [] + } + + def _load_config(self) -> Dict: + """Load and validate YAML configuration.""" + with open(self.config_path) as f: + return yaml.safe_load(f) + + async def run_cycle(self, once: bool = False): + """ + Execute complete orchestration cycle. + + Args: + once: Run single cycle and exit (vs continuous loop) + """ + while True: + try: + # Phase 1: Discovery + if self.config["discovery"]["enabled"]: + await self.discover_datasets() + + # Phase 2: Training (RESOURCE-AWARE) + await self.train_models() + + # Phase 3: Optimization + if self.config["optimization"]["enabled"]: + await self.optimize_models() + + # Phase 4: Deployment + if self.config["deployment"]["enabled"]: + await self.deploy_models() + + # Save status + self._save_status() + + if once: + break + + # Wait before next cycle + await asyncio.sleep(3600) # 1 hour + + except Exception as e: + self.status["errors"].append(str(e)) + if once: + raise + + async def train_models(self): + """ + Train models with resource-aware scaling. + + KEY PATTERN: Dynamic worker allocation based on CPU availability + """ + self.status["phase"] = "training" + + # Get CPU count for resource-aware allocation + cpu_count = multiprocessing.cpu_count() + + # Calculate optimal workers + config_workers = self.config["training"]["workers"] + max_workers = self.max_workers or min(cpu_count, config_workers) + + print(f"Training with {max_workers} workers (CPU count: {cpu_count})") + + # Build command for distributed training + benchmark_script = Path(__file__).parent / "distributed_benchmark.py" + datasets_dir = Path(self.config["training"]["datasets_dir"]) + epochs = self.config["training"]["epochs"] + + cmd = [ + sys.executable, + str(benchmark_script), + "--datasets-dir", str(datasets_dir), + "--workers", str(max_workers), + "--epochs", str(epochs) + ] + + # Execute training (async subprocess) + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + + stdout, stderr = await process.communicate() + + if process.returncode == 0: + print("Training completed successfully") + self.status["datasets_trained"] += 1 + else: + error_msg = stderr.decode() + print(f"Training failed: {error_msg}") + self.status["errors"].append(error_msg) + + def _save_status(self): + """Persist orchestrator status to JSON.""" + import json + status_file = Path("data_out/autonomous_training_status.json") + status_file.parent.mkdir(parents=True, exist_ok=True) + + with open(status_file, 'w') as f: + json.dump(self.status, f, indent=2) + +# CLI Entry Point +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Autonomous Training Orchestrator") + parser.add_argument("--config", default="config/autonomous_training.yaml") + parser.add_argument("--once", action="store_true", help="Run single cycle") + args = parser.parse_args() + + orchestrator = AutonomousOrchestrator(args.config) + asyncio.run(orchestrator.run_cycle(once=args.once)) +``` + +### 3. Distributed Training Worker Pattern + +```python +# distributed_benchmark.py +import multiprocessing as mp +from concurrent.futures import ProcessPoolExecutor +from pathlib import Path +from typing import List, Dict +import pandas as pd +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score + +def train_single_dataset(args) -> Dict: + """ + Train model on single dataset (worker function). + + Args: + args: Tuple of (dataset_path, epochs, output_dir) + + Returns: + Dict with training results + """ + dataset_path, epochs, output_dir = args + + try: + # Load data + df = pd.read_csv(dataset_path) + X = df.iloc[:, :-1].values + y = df.iloc[:, -1].values + + # Split + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + # Train + model = RandomForestClassifier(n_estimators=100, random_state=42) + model.fit(X_train, y_train) + + # Evaluate + accuracy = accuracy_score(y_test, model.predict(X_test)) + + return { + "dataset": dataset_path.name, + "accuracy": accuracy, + "status": "success", + "samples": len(df) + } + + except Exception as e: + return { + "dataset": dataset_path.name, + "status": "failed", + "error": str(e) + } + +def train_all_datasets( + datasets_dir: Path, + workers: int, + epochs: int, + output_dir: Path +) -> List[Dict]: + """ + Train models on all datasets using multiprocessing. + + KEY PATTERN: ProcessPoolExecutor for CPU-bound ML training + """ + # Discover datasets + dataset_paths = list(datasets_dir.glob("*.csv")) + print(f"Found {len(dataset_paths)} datasets") + + # Prepare arguments for workers + tasks = [(path, epochs, output_dir) for path in dataset_paths] + + # Execute in parallel + results = [] + with ProcessPoolExecutor(max_workers=workers) as executor: + for result in executor.map(train_single_dataset, tasks): + results.append(result) + + # Progress logging + completed = len(results) + if completed % 10 == 0: + print(f"Progress: {completed}/{len(dataset_paths)} datasets") + + return results + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--datasets-dir", required=True) + parser.add_argument("--workers", type=int, default=4) + parser.add_argument("--epochs", type=int, default=100) + parser.add_argument("--output-dir", default="data_out/benchmark") + args = parser.parse_args() + + results = train_all_datasets( + Path(args.datasets_dir), + args.workers, + args.epochs, + Path(args.output_dir) + ) + + # Save results + import json + output_file = Path(args.output_dir) / "results.json" + output_file.parent.mkdir(parents=True, exist_ok=True) + with open(output_file, 'w') as f: + json.dump(results, f, indent=2) +``` + +### 4. Quantum ML Integration Pattern + +```python +# quantum_integration.py +from qiskit import QuantumCircuit +from qiskit_aer import AerSimulator +from azure.quantum import Workspace + +class QuantumMLBackend: + """ + Unified interface for quantum ML training (local + cloud). + """ + + def __init__(self, backend_type: str = "simulator"): + """ + Args: + backend_type: "simulator" or "azure" + """ + self.backend_type = backend_type + + if backend_type == "azure": + self.workspace = Workspace( + subscription_id="YOUR_SUBSCRIPTION_ID", + resource_group="YOUR_RESOURCE_GROUP", + name="YOUR_WORKSPACE_NAME" + ) + + def create_variational_circuit(self, n_qubits: int, n_layers: int) -> QuantumCircuit: + """Create parameterized quantum circuit for ML.""" + qc = QuantumCircuit(n_qubits) + + for layer in range(n_layers): + # Rotation layer + for qubit in range(n_qubits): + qc.ry(0.5, qubit) # Placeholder, use parameters in production + + # Entanglement layer + for qubit in range(n_qubits - 1): + qc.cx(qubit, qubit + 1) + + qc.measure_all() + return qc + + def train(self, X_train, y_train, n_qubits: int = 4, shots: int = 1024): + """Train quantum classifier.""" + circuit = self.create_variational_circuit(n_qubits, n_layers=2) + + if self.backend_type == "simulator": + backend = AerSimulator() + job = backend.run(circuit, shots=shots) + result = job.result() + return result.get_counts() + + elif self.backend_type == "azure": + target = self.workspace.get_targets("ionq.simulator") + job = target.submit(circuit, shots=shots) + return job.get_results() +``` + +## Production Deployment Checklist + +### 1. Resource Management +- [ ] Implement CPU/GPU detection with `multiprocessing.cpu_count()` +- [ ] Add memory monitoring (use `psutil` library) +- [ ] Set resource limits in config (max CPU %, max memory) +- [ ] Implement graceful degradation when resources are constrained + +### 2. Error Handling +- [ ] Wrap all async operations in try/except blocks +- [ ] Log errors to persistent storage (JSON/database) +- [ ] Implement retry logic with exponential backoff +- [ ] Add health check endpoints for monitoring + +### 3. Monitoring & Observability +- [ ] Real-time dashboard (use `rich` library for CLI) +- [ ] Metrics export (Prometheus format recommended) +- [ ] Progress tracking with detailed status JSON +- [ ] Integration with cloud monitoring (Azure Monitor, CloudWatch) + +### 4. Scalability +- [ ] Test with 100+ datasets +- [ ] Verify worker scaling (test with 1, 4, 8, 16, 32 workers) +- [ ] Benchmark Ray vs multiprocessing for your workload +- [ ] Profile memory usage per worker + +### 5. Configuration Management +- [ ] Validate YAML schema on load +- [ ] Support environment variable overrides +- [ ] Version your configuration files +- [ ] Document all configuration options + +## Usage Examples + +### Basic Usage (Single Cycle) +```powershell +# Run one training cycle with auto-detected workers +python scripts/autonomous_training_orchestrator.py --once + +# Specify custom config +python scripts/autonomous_training_orchestrator.py --config custom.yaml --once +``` + +### Continuous Operation +```powershell +# Run continuously (1 hour interval between cycles) +python scripts/autonomous_training_orchestrator.py + +# Run as background service (Windows) +Start-Process python -ArgumentList "scripts/autonomous_training_orchestrator.py" -WindowStyle Hidden + +# Linux/Mac background +nohup python scripts/autonomous_training_orchestrator.py & +``` + +### Scaling Modes + +**Multiprocessing (Default, Recommended)** +```yaml +scaling: + mode: "multiprocessing" + max_workers: null # Auto-detect +``` + +**Ray Distributed (Advanced)** +```yaml +scaling: + mode: "ray" + max_workers: 64 + ray_config: + num_cpus: 32 + num_gpus: 0 +``` + +## Performance Benchmarks + +Based on our production deployment (552 datasets, 100 epochs): + +| Workers | Datasets/Hour | Accuracy | Memory/Worker | +|---------|---------------|----------|---------------| +| 4 | ~25 | 98-100% | ~500 MB | +| 8 | ~45 | 98-100% | ~500 MB | +| 20 | ~100 | 98-100% | ~500 MB | +| 32 | ~150 | 98-100% | ~500 MB | + +**Key Findings:** +- Linear scaling up to CPU count +- Memory usage stable across worker counts +- 100% accuracy achievable on well-structured datasets +- Overhead: ~5% for orchestration vs direct training + +## Integration Patterns + +### With Azure Quantum +```python +# In config YAML +training: + backends: + - type: "quantum" + enabled: true + azure: + subscription_id: "a07fbd16-xxxx" + resource_group: "rg-quantum-ai" + workspace_name: "quantum-ai-workspace" + targets: + - "ionq.simulator" # FREE + - "rigetti.sim.qvm" # FREE +``` + +### With MLflow Tracking +```python +import mlflow + +class AutonomousOrchestrator: + async def train_models(self): + with mlflow.start_run(): + mlflow.log_param("workers", max_workers) + mlflow.log_param("datasets", len(dataset_paths)) + + # ... training code ... + + mlflow.log_metric("accuracy", avg_accuracy) + mlflow.log_metric("training_time", elapsed_time) +``` + +### With Monitoring Dashboard +```python +# Real-time monitoring +from rich.live import Live +from rich.table import Table + +def create_status_table(orchestrator): + table = Table(title="Training Status") + table.add_column("Metric", style="cyan") + table.add_column("Value", style="green") + + table.add_row("Phase", orchestrator.status["phase"]) + table.add_row("Datasets Trained", str(orchestrator.status["datasets_trained"])) + table.add_row("Models Deployed", str(orchestrator.status["models_deployed"])) + + return table + +# Use in orchestrator +with Live(create_status_table(self), refresh_per_second=1): + await self.train_models() +``` + +## Best Practices + +### 1. Start Small, Scale Gradually +- Test with 10 datasets, 4 workers +- Increase to 100 datasets, 8 workers +- Production: 500+ datasets, CPU-count workers + +### 2. Use Async/Await Correctly +- IO-bound: Use `asyncio` (file I/O, network) +- CPU-bound: Use `ProcessPoolExecutor` (ML training) +- Never block event loop with heavy computation + +### 3. Handle Failures Gracefully +```python +try: + result = await train_model(dataset) +except Exception as e: + log_error(e, dataset) + continue # Don't stop entire pipeline +``` + +### 4. Version Everything +- Configuration files: `config_v1.yaml`, `config_v2.yaml` +- Trained models: Include timestamp and version in filenames +- Results: Save with metadata (date, config hash, git commit) + +### 5. Monitor Resource Usage +```python +import psutil + +def check_resources(): + cpu_percent = psutil.cpu_percent() + memory_percent = psutil.virtual_memory().percent + + if cpu_percent > 90 or memory_percent > 90: + # Reduce workers or pause training + pass +``` + +## Common Issues & Solutions + +### Issue: "unrecognized arguments: --datasets-list" +**Cause:** Argument mismatch between orchestrator and worker script. +**Solution:** Use `--datasets-dir` for directory-based discovery (recommended). + +### Issue: UnicodeEncodeError in logs +**Cause:** Non-ASCII characters (emojis) in output. +**Solution:** Use ASCII alternatives or set encoding: +```python +logging.basicConfig(encoding='utf-8', ...) +``` + +### Issue: Out of memory with many workers +**Cause:** Each worker loads model/data into memory. +**Solution:** +- Reduce worker count +- Use batch processing +- Enable swap/pagefile + +### Issue: Qiskit version conflicts (Azure Quantum) +**Cause:** `azure-quantum` requires old qiskit (0.46), but newer packages need >=1.1. +**Solution:** +- Use separate virtual environments +- Or install azure-quantum in main environment (test first) + +## Repository Structure + +``` +project/ +├── config/ +│ ├── autonomous_training.yaml # Main config +│ └── quantum_config.yaml # Quantum-specific +├── scripts/ +│ ├── autonomous_training_orchestrator.py +│ ├── distributed_benchmark.py +│ ├── quantum_autorun.py +│ └── monitor_autonomous_training.py +├── datasets/ +│ ├── massive_quantum/ # Training data +│ └── dataset_index.json +├── data_out/ +│ ├── autonomous_training_status.json +│ ├── distributed_benchmark/ +│ └── quantum_autorun/ +├── requirements.txt +└── README.md +``` + +## Dependencies + +```txt +# Core +pyyaml>=6.0 +asyncio + +# Classical ML +scikit-learn>=1.3.0 +pandas>=2.0.0 +numpy>=1.24.0 + +# Quantum ML (Optional) +qiskit>=1.0.0 +qiskit-aer>=0.13.0 +azure-quantum>=1.0.0 + +# Distributed (Optional) +ray>=2.0.0 + +# Monitoring +rich>=13.0.0 +psutil>=5.9.0 +``` + +## License & Attribution + +This model is based on the QAI autonomous training orchestrator: +- Repository: Bryan-Roe/QAI +- License: MIT (or your license) +- Author: Bryan Roe + +Feel free to adapt this model to your specific needs. Key patterns to preserve: +1. Async orchestration loop +2. Resource-aware worker allocation +3. YAML-based configuration +4. Graceful error handling +5. Comprehensive monitoring + +## Additional Resources + +- [Azure Quantum Documentation](https://learn.microsoft.com/azure/quantum/) +- [Ray Distributed Computing](https://docs.ray.io/) +- [Qiskit Machine Learning](https://qiskit.org/ecosystem/machine-learning/) +- [Python Multiprocessing Guide](https://docs.python.org/3/library/multiprocessing.html) + +--- + +**Version:** 1.0 +**Last Updated:** November 2025 +**Tested On:** Windows 11, Python 3.11, 552 datasets, 20 workers diff --git a/docs/guides/CHAT_IMPROVEMENTS.md b/docs/guides/CHAT_IMPROVEMENTS.md index 94f29109d..27d8743da 100644 --- a/docs/guides/CHAT_IMPROVEMENTS.md +++ b/docs/guides/CHAT_IMPROVEMENTS.md @@ -1,258 +1,258 @@ -# Chat Website Improvements - Complete Enhancement Summary - -## 🎉 Major Enhancements Completed - -Your AI chat website has been significantly upgraded with professional features! - -## ✨ New Features - -### 1. **Markdown Rendering with Code Highlighting** - -- Full markdown support using Marked.js -- Syntax highlighting for code blocks (Highlight.js) -- Supports: **bold**, _italic_, `inline code`, lists, tables, blockquotes -- Beautiful code blocks with language-specific highlighting -- One-click copy buttons on all code blocks - -**Try it:** Ask AI to write code and see formatted syntax highlighting! - -### 2. **Provider Selection** - -- Choose between Local (Free), OpenAI, or Azure OpenAI -- Auto-detect mode finds best available provider -- Provider info displayed in header -- Settings persist across sessions - -### 3. **Enhanced Text Input** - -- Multi-line textarea (auto-expands up to 150px) -- **Enter** to send message -- **Shift+Enter** for new line -- **Ctrl+K** for new chat -- Smart auto-resize as you type - -### 4. **Conversation Persistence** - -- Automatically saves chat history to browser localStorage -- Restores previous conversation on page reload -- Export conversations as JSON files -- Never lose your important chats - -### 5. **Dark Theme** - -- Toggle between light and dark modes -- Modern dark color scheme -- Persists theme preference -- Easy on the eyes for night coding - -### 6. **Statistics & Status Bar** - -- Real-time message counter -- Status updates (Sending, Ready, Error) -- Provider and model information -- Connection status - -### 7. **Enhanced Controls** - -- 🔄 New Chat - Start fresh conversation -- 🗑️ Clear - Clean message display -- 💾 Export - Download chat history as JSON -- 🌓 Theme - Toggle dark/light mode - -### 8. **Better UX** - -- Smooth animations and transitions -- Typing indicators with bouncing dots -- Auto-scroll to latest message -- Responsive design (works on mobile) -- Error handling with friendly messages - -## 🎨 Visual Improvements - -### Typography & Formatting - -- Clean, readable fonts -- Proper line heights and spacing -- Beautiful gradient backgrounds -- Rounded corners and shadows -- Hover effects on interactive elements - -### Code Blocks - -- Dark themed code display -- Copy button on hover -- Syntax highlighting for 180+ languages -- Proper spacing and indentation - -### Message Bubbles - -- User messages: Purple gradient (right-aligned) -- AI messages: White with border (left-aligned) -- System messages: Yellow/centered -- Maximum 70% width for readability - -## 🔧 Technical Enhancements - -### JavaScript Features - -- Async/await for cleaner code -- LocalStorage integration -- Event delegation -- Keyboard shortcut handling -- Error boundaries - -### CSS Improvements - -- Flexbox layouts -- CSS Grid where appropriate -- CSS variables for dark theme -- Media queries for responsiveness -- Smooth transitions - -### Dependencies Added - -- **Marked.js** - Markdown parsing -- **Highlight.js** - Code syntax highlighting -- Both loaded from CDN (no build step needed) - -## 📊 Before vs After - -| Feature | Before | After | -| --------------- | ----------------- | ------------------------------------ | -| **Markdown** | Plain text only | Full markdown with code highlighting | -| **Input** | Single-line input | Multi-line auto-resize textarea | -| **Persistence** | None | LocalStorage + Export | -| **Themes** | Light only | Light + Dark modes | -| **Provider** | Auto-only | Manual selection + Auto | -| **Shortcuts** | Enter only | Enter, Shift+Enter, Ctrl+K | -| **Stats** | None | Message count + Status | -| **Export** | None | JSON export | -| **Code** | Plain text | Syntax highlighted + Copy button | - -## 🚀 How to Use New Features - -### Markdown Examples - -Ask AI: "Show me a Python function" - -```python -def greet(name): - return f"Hello, {name}!" -``` - -Ask: "Create a markdown table" - -| Column 1 | Column 2 | -| -------- | -------- | -| Data 1 | Data 2 | - -### Keyboard Shortcuts - -- `Enter` - Send message -- `Shift + Enter` - New line in message -- `Ctrl + K` - Start new conversation - -### Provider Selection - -1. Click the "Provider" dropdown -2. Select: Auto, Local (Free), OpenAI, or Azure OpenAI -3. Selection saves automatically - -### Export Conversations - -1. Click "💾 Export" button -2. Save JSON file to your computer -3. File includes all messages with timestamps - -### Dark Theme - -1. Click "🌓 Theme" button -2. Instant switch to dark mode -3. Preference saved for next visit - -## 💡 Pro Tips - -1. **Use Markdown**: Ask AI questions like "explain in markdown format" for beautiful formatting -2. **Code Blocks**: Hover over code to reveal copy button -3. **Multi-line Input**: Shift+Enter for complex questions -4. **Keyboard Power**: Use Ctrl+K for quick new chat -5. **Export Important Chats**: Save conversations before clearing -6. **Dark Mode**: Better for long coding sessions -7. **Provider Choice**: Local is free, OpenAI is smarter - -## 🔜 Future Enhancement Ideas - -Potential future additions: - -- Real-time streaming responses (character-by-character) -- Voice input/output -- File upload support -- Image generation -- Chat history sidebar -- Search within conversations -- Multiple conversation tabs -- User authentication -- Cloud sync -- Collaborative chats -- Custom themes/colors -- Plugins/extensions - -## 📈 Performance - -- **Load Time**: < 1s (with CDN caching) -- **Bundle Size**: ~50KB (Marked.js + Highlight.js) -- **Memory**: < 5MB typical usage -- **Responsiveness**: 60 FPS animations - -## 🔒 Privacy - -- All chat history stored **locally** in browser -- No external tracking or analytics -- Export = your data, your control -- Local provider = completely offline capable - -## 🎓 Learning Resources - -### Markdown Syntax - -- Headers: `# H1`, `## H2`, `### H3` -- Bold: `**text**` -- Italic: `*text*` -- Code: `` `code` `` -- Code block: ` `language ... ` ` -- Links: `[text](https://example.com)` -- Lists: `- item` or `1. item` - -### Code Languages Supported - -JavaScript, Python, TypeScript, C#, Java, Go, Rust, Ruby, PHP, HTML, CSS, SQL, Bash, PowerShell, and 165+ more! - -## 📝 Summary - -Your chat website now has: - -- ✅ Professional markdown rendering -- ✅ Beautiful code syntax highlighting -- ✅ Persistent conversation storage -- ✅ Dark theme support -- ✅ Provider selection -- ✅ Export functionality -- ✅ Keyboard shortcuts -- ✅ Enhanced UX/UI -- ✅ Mobile responsiveness -- ✅ Status indicators - -**Result:** A production-ready AI chat interface that rivals commercial products! 🚀 - -## 🌐 Access Your Improved Website - -**URL:** - -**Quick Start:** - -```powershell -cd c:\Users\Bryan\OneDrive\AI -.\start-chat-web.ps1 -``` - -Enjoy your upgraded chat experience! 🎨✨ +# Chat Website Improvements - Complete Enhancement Summary + +## 🎉 Major Enhancements Completed + +Your AI chat website has been significantly upgraded with professional features! + +## ✨ New Features + +### 1. **Markdown Rendering with Code Highlighting** + +- Full markdown support using Marked.js +- Syntax highlighting for code blocks (Highlight.js) +- Supports: **bold**, _italic_, `inline code`, lists, tables, blockquotes +- Beautiful code blocks with language-specific highlighting +- One-click copy buttons on all code blocks + +**Try it:** Ask AI to write code and see formatted syntax highlighting! + +### 2. **Provider Selection** + +- Choose between Local (Free), OpenAI, or Azure OpenAI +- Auto-detect mode finds best available provider +- Provider info displayed in header +- Settings persist across sessions + +### 3. **Enhanced Text Input** + +- Multi-line textarea (auto-expands up to 150px) +- **Enter** to send message +- **Shift+Enter** for new line +- **Ctrl+K** for new chat +- Smart auto-resize as you type + +### 4. **Conversation Persistence** + +- Automatically saves chat history to browser localStorage +- Restores previous conversation on page reload +- Export conversations as JSON files +- Never lose your important chats + +### 5. **Dark Theme** + +- Toggle between light and dark modes +- Modern dark color scheme +- Persists theme preference +- Easy on the eyes for night coding + +### 6. **Statistics & Status Bar** + +- Real-time message counter +- Status updates (Sending, Ready, Error) +- Provider and model information +- Connection status + +### 7. **Enhanced Controls** + +- 🔄 New Chat - Start fresh conversation +- 🗑️ Clear - Clean message display +- 💾 Export - Download chat history as JSON +- 🌓 Theme - Toggle dark/light mode + +### 8. **Better UX** + +- Smooth animations and transitions +- Typing indicators with bouncing dots +- Auto-scroll to latest message +- Responsive design (works on mobile) +- Error handling with friendly messages + +## 🎨 Visual Improvements + +### Typography & Formatting + +- Clean, readable fonts +- Proper line heights and spacing +- Beautiful gradient backgrounds +- Rounded corners and shadows +- Hover effects on interactive elements + +### Code Blocks + +- Dark themed code display +- Copy button on hover +- Syntax highlighting for 180+ languages +- Proper spacing and indentation + +### Message Bubbles + +- User messages: Purple gradient (right-aligned) +- AI messages: White with border (left-aligned) +- System messages: Yellow/centered +- Maximum 70% width for readability + +## 🔧 Technical Enhancements + +### JavaScript Features + +- Async/await for cleaner code +- LocalStorage integration +- Event delegation +- Keyboard shortcut handling +- Error boundaries + +### CSS Improvements + +- Flexbox layouts +- CSS Grid where appropriate +- CSS variables for dark theme +- Media queries for responsiveness +- Smooth transitions + +### Dependencies Added + +- **Marked.js** - Markdown parsing +- **Highlight.js** - Code syntax highlighting +- Both loaded from CDN (no build step needed) + +## 📊 Before vs After + +| Feature | Before | After | +| --------------- | ----------------- | ------------------------------------ | +| **Markdown** | Plain text only | Full markdown with code highlighting | +| **Input** | Single-line input | Multi-line auto-resize textarea | +| **Persistence** | None | LocalStorage + Export | +| **Themes** | Light only | Light + Dark modes | +| **Provider** | Auto-only | Manual selection + Auto | +| **Shortcuts** | Enter only | Enter, Shift+Enter, Ctrl+K | +| **Stats** | None | Message count + Status | +| **Export** | None | JSON export | +| **Code** | Plain text | Syntax highlighted + Copy button | + +## 🚀 How to Use New Features + +### Markdown Examples + +Ask AI: "Show me a Python function" + +```python +def greet(name): + return f"Hello, {name}!" +``` + +Ask: "Create a markdown table" + +| Column 1 | Column 2 | +| -------- | -------- | +| Data 1 | Data 2 | + +### Keyboard Shortcuts + +- `Enter` - Send message +- `Shift + Enter` - New line in message +- `Ctrl + K` - Start new conversation + +### Provider Selection + +1. Click the "Provider" dropdown +2. Select: Auto, Local (Free), OpenAI, or Azure OpenAI +3. Selection saves automatically + +### Export Conversations + +1. Click "💾 Export" button +2. Save JSON file to your computer +3. File includes all messages with timestamps + +### Dark Theme + +1. Click "🌓 Theme" button +2. Instant switch to dark mode +3. Preference saved for next visit + +## 💡 Pro Tips + +1. **Use Markdown**: Ask AI questions like "explain in markdown format" for beautiful formatting +2. **Code Blocks**: Hover over code to reveal copy button +3. **Multi-line Input**: Shift+Enter for complex questions +4. **Keyboard Power**: Use Ctrl+K for quick new chat +5. **Export Important Chats**: Save conversations before clearing +6. **Dark Mode**: Better for long coding sessions +7. **Provider Choice**: Local is free, OpenAI is smarter + +## 🔜 Future Enhancement Ideas + +Potential future additions: + +- Real-time streaming responses (character-by-character) +- Voice input/output +- File upload support +- Image generation +- Chat history sidebar +- Search within conversations +- Multiple conversation tabs +- User authentication +- Cloud sync +- Collaborative chats +- Custom themes/colors +- Plugins/extensions + +## 📈 Performance + +- **Load Time**: < 1s (with CDN caching) +- **Bundle Size**: ~50KB (Marked.js + Highlight.js) +- **Memory**: < 5MB typical usage +- **Responsiveness**: 60 FPS animations + +## 🔒 Privacy + +- All chat history stored **locally** in browser +- No external tracking or analytics +- Export = your data, your control +- Local provider = completely offline capable + +## 🎓 Learning Resources + +### Markdown Syntax + +- Headers: `# H1`, `## H2`, `### H3` +- Bold: `**text**` +- Italic: `*text*` +- Code: `` `code` `` +- Code block: ` `language ... ` ` +- Links: `[text](https://example.com)` +- Lists: `- item` or `1. item` + +### Code Languages Supported + +JavaScript, Python, TypeScript, C#, Java, Go, Rust, Ruby, PHP, HTML, CSS, SQL, Bash, PowerShell, and 165+ more! + +## 📝 Summary + +Your chat website now has: + +- ✅ Professional markdown rendering +- ✅ Beautiful code syntax highlighting +- ✅ Persistent conversation storage +- ✅ Dark theme support +- ✅ Provider selection +- ✅ Export functionality +- ✅ Keyboard shortcuts +- ✅ Enhanced UX/UI +- ✅ Mobile responsiveness +- ✅ Status indicators + +**Result:** A production-ready AI chat interface that rivals commercial products! 🚀 + +## 🌐 Access Your Improved Website + +**URL:** + +**Quick Start:** + +```powershell +cd c:\Users\Bryan\OneDrive\AI +.\start-chat-web.ps1 +``` + +Enjoy your upgraded chat experience! 🎨✨ diff --git a/docs/guides/CHAT_WEBSITE_GUIDE.md b/docs/guides/CHAT_WEBSITE_GUIDE.md index 9db934958..cba068fd2 100644 --- a/docs/guides/CHAT_WEBSITE_GUIDE.md +++ b/docs/guides/CHAT_WEBSITE_GUIDE.md @@ -1,327 +1,327 @@ -# Chat Website - Complete Guide - -## 🎉 Your AI Chat Website is Ready! - -I've created a beautiful, responsive chat website that works with multiple AI providers. - -## 🚀 Quick Start - -The server is already running! Open your browser to: - -**http://localhost:7071/api/chat-web** - -Or run the startup script: -```powershell -.\start-chat-web.ps1 -``` - -## ✨ Features - -- **Beautiful UI**: Gradient design with smooth animations -- **Multiple Providers**: Local (free), OpenAI, Azure OpenAI -- **Real-time Chat**: Instant responses with typing indicators -- **Responsive**: Works on desktop and mobile -- **Zero Config**: Works immediately with free local provider - -## 🏗️ Architecture - -``` -┌─────────────────┐ -│ Browser │ -│ (chat-web/) │ -└────────┬────────┘ - │ HTTP/JSON - ↓ -┌─────────────────┐ -│ Azure Functions │ -│ (function_app.py)│ -└────────┬────────┘ - │ - ↓ -┌─────────────────┐ -│ Chat Providers │ -│(ai-projects/chat-cli/src) │ -└─────────────────┘ -``` - -### Files Created/Modified: - -1. **Frontend** (`chat-web/`) - - `index.html` - Beautiful gradient UI - - `chat.js` - Client-side logic - - `README.md` - Documentation - -2. **Backend** (`function_app.py`) - - Chat API endpoint (`/api/chat`) - - Static file serving (`/api/chat-web`, `/api/chat-web/chat.js`) - - CORS support for local testing - -3. **Utilities** - - `start-chat-web.ps1` - One-command startup script - - `test-chat-web.py` - Functionality tests - -## 💰 Provider Options - -### 1. Local (FREE - Default) ✅ -- **Cost**: $0 -- **Setup**: None required -- **Best for**: Testing, demos, offline use -- Works immediately without any configuration - -### 2. OpenAI -- **Cost**: Pay per token (~$0.002/1K tokens for GPT-4o-mini) -- **Setup**: - ```powershell - $env:OPENAI_API_KEY = "sk-..." - ``` -- **Best for**: Production quality responses - -### 3. Azure OpenAI -- **Cost**: Similar to OpenAI -- **Setup**: - ```powershell - $env:AZURE_OPENAI_API_KEY = "your-key" - $env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/" - $env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini" - ``` -- **Best for**: Enterprise with Azure integration - -## 🔧 API Reference - -### POST /api/chat - -Request: -```json -{ - "messages": [ - {"role": "system", "content": "You are helpful"}, - {"role": "user", "content": "Hello!"} - ], - "provider": "auto", - "model": "gpt-4o-mini" -} -``` - -Response: -```json -{ - "response": "Hi! How can I help you?", - "provider": "local", - "model": "local-echo" -} -``` - -### GET /api/chat-web - -Serves the HTML interface - -### GET /api/chat-web/chat.js - -Serves the JavaScript client - -## 🧪 Testing - -Run the test suite: -```powershell -python .\test-chat-web.py -``` - -Expected output: -``` -================================================== -Chat Web - Functionality Test -================================================== - -Testing local provider... -✓ Provider: local, Model: local-echo -✓ Response: Quick thoughts: Hello Does that help?... -✓ Local provider working! - -Testing provider auto-detection... -✓ Auto-detected: local, Model: local-echo - (Using local fallback - no API keys) - -================================================== -✅ All tests passed! -================================================== -``` - -## 🌐 Deploying to Azure - -### Step 1: Create Function App - -```powershell -# Login -az login - -# Create resource group -az group create --name rg-chat-web --location eastus - -# Create storage account -az storage account create --name chatwebstorage123 --resource-group rg-chat-web --location eastus --sku Standard_LRS - -# Create Function App -az functionapp create ` - --resource-group rg-chat-web ` - --name chat-web-app-unique-123 ` - --storage-account chatwebstorage123 ` - --consumption-plan-location eastus ` - --runtime python ` - --runtime-version 3.11 ` - --functions-version 4 ` - --os-type Linux -``` - -### Step 2: Configure Environment (Optional) - -If using OpenAI or Azure OpenAI: -```powershell -az functionapp config appsettings set ` - --name chat-web-app-unique-123 ` - --resource-group rg-chat-web ` - --settings OPENAI_API_KEY=sk-... -``` - -### Step 3: Deploy - -```powershell -func azure functionapp publish chat-web-app-unique-123 -``` - -### Step 4: Access Your App - -Your chat website will be available at: -``` -https://chat-web-app-unique-123.azurewebsites.net/api/chat-web -``` - -## 🎨 Customization - -### Change Colors - -Edit `chat-web/index.html`, find the gradient definitions: - -```css -background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); -``` - -Replace with your colors: -```css -background: linear-gradient(135deg, #FF6B6B 0%, #4ECDC4 100%); -``` - -### Change System Prompt - -Edit `function_app.py`, add to the messages array: - -```python -messages.insert(0, { - "role": "system", - "content": "You are a friendly cooking assistant." -}) -``` - -### Add Authentication - -Change `auth_level` in `function_app.py`: - -```python -@app.route(route="chat", methods=["POST"], auth_level=func.AuthLevel.FUNCTION) -``` - -Then pass `?code=YOUR_FUNCTION_KEY` in API calls. - -## 🐛 Troubleshooting - -### "Cannot GET /api/chat-web" - -**Problem**: Function not found -**Solution**: Ensure `func start` is running and shows all 4 functions - -### CORS Errors - -**Problem**: Browser blocks API calls -**Solution**: CORS headers are included. Try opening directly at `http://localhost:7071/api/chat-web` - -### "Import could not be resolved" - -**Problem**: Python linting errors -**Solution**: These are false positives. Run: -```powershell -pip install azure-functions colorama -cd talk-to-ai; pip install -r requirements.txt -``` - -### Provider Errors - -**Problem**: "Configuration error" messages -**Solution**: Check environment variables match your provider choice - -### Server Won't Start - -**Problem**: `func start` fails -**Solution**: -1. Check Python version: `python --version` (need 3.8-3.11) -2. Install Core Tools: `npm install -g azure-functions-core-tools@4` -3. Check `local.settings.json` exists - -## 📊 Monitoring - -### Local Development - -Watch the terminal for logs: -``` -[2025-11-08T12:27:53.884Z] Chat function invoked -[2025-11-08T12:27:53.891Z] Using provider: local, model: local-echo -``` - -### Production (Azure) - -View logs in Azure Portal: -1. Navigate to Function App -2. Click "Functions" > "chat" -3. Click "Monitor" -4. View Invocations and Application Insights - -## 🔐 Security Best Practices - -1. **Use Authentication** in production (set `auth_level=FUNCTION` or `ADMIN`) -2. **Set CORS** properly (replace `*` with your domain) -3. **Protect API Keys** (use Azure Key Vault for secrets) -4. **Rate Limiting** (implement in function or use API Management) -5. **Input Validation** (already implemented for message format) - -## 💡 Tips - -- **Free Tier**: Azure Functions has 1M free executions/month -- **Cost Control**: Use `gpt-4o-mini` instead of `gpt-4` for 60x cost reduction -- **Performance**: Enable Application Insights for monitoring -- **Scaling**: Functions auto-scale based on demand - -## 📖 Next Steps - -1. **Try Different Providers**: Set API keys and compare responses -2. **Customize UI**: Change colors, fonts, layout -3. **Add Features**: - - Message history persistence - - User authentication - - File upload support - - Voice input -4. **Deploy to Production**: Follow the Azure deployment guide above - -## 🆘 Support - -- Report issues in the main QAI repo -- Check `ai-projects/chat-cli/README.md` for provider details -- Review `.github/copilot-instructions.md` for architecture - -## 🎉 Congratulations! - -You now have a fully functional AI chat website that: -- ✅ Works locally without any API keys -- ✅ Supports multiple AI providers -- ✅ Has a beautiful, responsive UI -- ✅ Can be deployed to Azure in minutes -- ✅ Costs $0 in free tier mode - -Enjoy chatting with AI! 🤖 +# Chat Website - Complete Guide + +## 🎉 Your AI Chat Website is Ready! + +I've created a beautiful, responsive chat website that works with multiple AI providers. + +## 🚀 Quick Start + +The server is already running! Open your browser to: + +**http://localhost:7071/api/chat-web** + +Or run the startup script: +```powershell +.\start-chat-web.ps1 +``` + +## ✨ Features + +- **Beautiful UI**: Gradient design with smooth animations +- **Multiple Providers**: Local (free), OpenAI, Azure OpenAI +- **Real-time Chat**: Instant responses with typing indicators +- **Responsive**: Works on desktop and mobile +- **Zero Config**: Works immediately with free local provider + +## 🏗️ Architecture + +``` +┌─────────────────┐ +│ Browser │ +│ (chat-web/) │ +└────────┬────────┘ + │ HTTP/JSON + ↓ +┌─────────────────┐ +│ Azure Functions │ +│ (function_app.py)│ +└────────┬────────┘ + │ + ↓ +┌─────────────────┐ +│ Chat Providers │ +│(ai-projects/chat-cli/src) │ +└─────────────────┘ +``` + +### Files Created/Modified: + +1. **Frontend** (`chat-web/`) + - `index.html` - Beautiful gradient UI + - `chat.js` - Client-side logic + - `README.md` - Documentation + +2. **Backend** (`function_app.py`) + - Chat API endpoint (`/api/chat`) + - Static file serving (`/api/chat-web`, `/api/chat-web/chat.js`) + - CORS support for local testing + +3. **Utilities** + - `start-chat-web.ps1` - One-command startup script + - `test-chat-web.py` - Functionality tests + +## 💰 Provider Options + +### 1. Local (FREE - Default) ✅ +- **Cost**: $0 +- **Setup**: None required +- **Best for**: Testing, demos, offline use +- Works immediately without any configuration + +### 2. OpenAI +- **Cost**: Pay per token (~$0.002/1K tokens for GPT-4o-mini) +- **Setup**: + ```powershell + $env:OPENAI_API_KEY = "sk-..." + ``` +- **Best for**: Production quality responses + +### 3. Azure OpenAI +- **Cost**: Similar to OpenAI +- **Setup**: + ```powershell + $env:AZURE_OPENAI_API_KEY = "your-key" + $env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/" + $env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini" + ``` +- **Best for**: Enterprise with Azure integration + +## 🔧 API Reference + +### POST /api/chat + +Request: +```json +{ + "messages": [ + {"role": "system", "content": "You are helpful"}, + {"role": "user", "content": "Hello!"} + ], + "provider": "auto", + "model": "gpt-4o-mini" +} +``` + +Response: +```json +{ + "response": "Hi! How can I help you?", + "provider": "local", + "model": "local-echo" +} +``` + +### GET /api/chat-web + +Serves the HTML interface + +### GET /api/chat-web/chat.js + +Serves the JavaScript client + +## 🧪 Testing + +Run the test suite: +```powershell +python .\test-chat-web.py +``` + +Expected output: +``` +================================================== +Chat Web - Functionality Test +================================================== + +Testing local provider... +✓ Provider: local, Model: local-echo +✓ Response: Quick thoughts: Hello Does that help?... +✓ Local provider working! + +Testing provider auto-detection... +✓ Auto-detected: local, Model: local-echo + (Using local fallback - no API keys) + +================================================== +✅ All tests passed! +================================================== +``` + +## 🌐 Deploying to Azure + +### Step 1: Create Function App + +```powershell +# Login +az login + +# Create resource group +az group create --name rg-chat-web --location eastus + +# Create storage account +az storage account create --name chatwebstorage123 --resource-group rg-chat-web --location eastus --sku Standard_LRS + +# Create Function App +az functionapp create ` + --resource-group rg-chat-web ` + --name chat-web-app-unique-123 ` + --storage-account chatwebstorage123 ` + --consumption-plan-location eastus ` + --runtime python ` + --runtime-version 3.11 ` + --functions-version 4 ` + --os-type Linux +``` + +### Step 2: Configure Environment (Optional) + +If using OpenAI or Azure OpenAI: +```powershell +az functionapp config appsettings set ` + --name chat-web-app-unique-123 ` + --resource-group rg-chat-web ` + --settings OPENAI_API_KEY=sk-... +``` + +### Step 3: Deploy + +```powershell +func azure functionapp publish chat-web-app-unique-123 +``` + +### Step 4: Access Your App + +Your chat website will be available at: +``` +https://chat-web-app-unique-123.azurewebsites.net/api/chat-web +``` + +## 🎨 Customization + +### Change Colors + +Edit `chat-web/index.html`, find the gradient definitions: + +```css +background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); +``` + +Replace with your colors: +```css +background: linear-gradient(135deg, #FF6B6B 0%, #4ECDC4 100%); +``` + +### Change System Prompt + +Edit `function_app.py`, add to the messages array: + +```python +messages.insert(0, { + "role": "system", + "content": "You are a friendly cooking assistant." +}) +``` + +### Add Authentication + +Change `auth_level` in `function_app.py`: + +```python +@app.route(route="chat", methods=["POST"], auth_level=func.AuthLevel.FUNCTION) +``` + +Then pass `?code=YOUR_FUNCTION_KEY` in API calls. + +## 🐛 Troubleshooting + +### "Cannot GET /api/chat-web" + +**Problem**: Function not found +**Solution**: Ensure `func start` is running and shows all 4 functions + +### CORS Errors + +**Problem**: Browser blocks API calls +**Solution**: CORS headers are included. Try opening directly at `http://localhost:7071/api/chat-web` + +### "Import could not be resolved" + +**Problem**: Python linting errors +**Solution**: These are false positives. Run: +```powershell +pip install azure-functions colorama +cd talk-to-ai; pip install -r requirements.txt +``` + +### Provider Errors + +**Problem**: "Configuration error" messages +**Solution**: Check environment variables match your provider choice + +### Server Won't Start + +**Problem**: `func start` fails +**Solution**: +1. Check Python version: `python --version` (need 3.8-3.11) +2. Install Core Tools: `npm install -g azure-functions-core-tools@4` +3. Check `local.settings.json` exists + +## 📊 Monitoring + +### Local Development + +Watch the terminal for logs: +``` +[2025-11-08T12:27:53.884Z] Chat function invoked +[2025-11-08T12:27:53.891Z] Using provider: local, model: local-echo +``` + +### Production (Azure) + +View logs in Azure Portal: +1. Navigate to Function App +2. Click "Functions" > "chat" +3. Click "Monitor" +4. View Invocations and Application Insights + +## 🔐 Security Best Practices + +1. **Use Authentication** in production (set `auth_level=FUNCTION` or `ADMIN`) +2. **Set CORS** properly (replace `*` with your domain) +3. **Protect API Keys** (use Azure Key Vault for secrets) +4. **Rate Limiting** (implement in function or use API Management) +5. **Input Validation** (already implemented for message format) + +## 💡 Tips + +- **Free Tier**: Azure Functions has 1M free executions/month +- **Cost Control**: Use `gpt-4o-mini` instead of `gpt-4` for 60x cost reduction +- **Performance**: Enable Application Insights for monitoring +- **Scaling**: Functions auto-scale based on demand + +## 📖 Next Steps + +1. **Try Different Providers**: Set API keys and compare responses +2. **Customize UI**: Change colors, fonts, layout +3. **Add Features**: + - Message history persistence + - User authentication + - File upload support + - Voice input +4. **Deploy to Production**: Follow the Azure deployment guide above + +## 🆘 Support + +- Report issues in the main QAI repo +- Check `ai-projects/chat-cli/README.md` for provider details +- Review `.github/copilot-instructions.md` for architecture + +## 🎉 Congratulations! + +You now have a fully functional AI chat website that: +- ✅ Works locally without any API keys +- ✅ Supports multiple AI providers +- ✅ Has a beautiful, responsive UI +- ✅ Can be deployed to Azure in minutes +- ✅ Costs $0 in free tier mode + +Enjoy chatting with AI! 🤖 diff --git a/docs/guides/CHAT_WEBSITE_SUCCESS.md b/docs/guides/CHAT_WEBSITE_SUCCESS.md index 4266add71..ad5000c88 100644 --- a/docs/guides/CHAT_WEBSITE_SUCCESS.md +++ b/docs/guides/CHAT_WEBSITE_SUCCESS.md @@ -1,115 +1,115 @@ -# ✅ Chat Website Created Successfully! - -## What Was Built - -I've created a complete, production-ready chat website with: - -### 1. **Frontend** (`chat-web/`) -- Beautiful gradient UI with animations -- Responsive design (works on mobile & desktop) -- Real-time typing indicators -- Message history display -- New chat & clear functions - -### 2. **Backend** (`function_app.py`) -- Azure Functions HTTP endpoints -- Chat API with multiple provider support -- Static file serving -- CORS enabled for local testing -- Error handling & logging - -### 3. **Integration** -- Reuses existing `talk-to-ai` chat logic -- Supports 3 providers: Local (free), OpenAI, Azure OpenAI -- Auto-detects best available provider - -## 🚀 It's Already Running! - -The server is live at: -- **Main Page**: http://localhost:7071/api/chat-web -- **API Endpoint**: http://localhost:7071/api/chat - -## Quick Commands - -```powershell -# Start (if not running) -func start - -# Or use the helper script -.\start-chat-web.ps1 - -# Run tests -python .\test-chat-web.py - -# Stop server -Ctrl+C in the terminal -``` - -## Provider Status - -Currently using: **Local (Free)** -- No API keys required -- Works offline -- Perfect for testing - -To use OpenAI: -```powershell -$env:OPENAI_API_KEY = "sk-..." -# Restart server -``` - -To use Azure OpenAI: -```powershell -$env:AZURE_OPENAI_API_KEY = "your-key" -$env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/" -$env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini" -# Restart server -``` - -## Files Created - -``` -c:\Users\Bryan\OneDrive\AI\ -├── function_app.py # Main backend (consolidated) -├── start-chat-web.ps1 # Startup script -├── test-chat-web.py # Test suite -├── CHAT_WEBSITE_GUIDE.md # Complete documentation -└── chat-web/ - ├── index.html # Beautiful UI - ├── chat.js # Frontend logic - └── README.md # Quick reference -``` - -## Next Steps - -1. **Try it now**: Open http://localhost:7071/api/chat-web in your browser -2. **Test different providers**: Set API keys and restart -3. **Customize**: Edit colors, system prompts, features -4. **Deploy to Azure**: Follow the guide in `CHAT_WEBSITE_GUIDE.md` - -## Key Features - -✅ **Zero configuration** - Works immediately -✅ **Free tier** - No API costs with local provider -✅ **Beautiful UI** - Modern gradient design -✅ **Multiple providers** - Local/OpenAI/Azure -✅ **Production ready** - Error handling, CORS, logging -✅ **Mobile friendly** - Responsive design -✅ **Easy deploy** - Azure Functions ready - -## Documentation - -- **Complete Guide**: `CHAT_WEBSITE_GUIDE.md` -- **Quick Reference**: `chat-web/README.md` -- **Provider Details**: `ai-projects/chat-cli/README.md` -- **Architecture**: `.github/copilot-instructions.md` - -## Cost Summary - -- **Local development**: $0 -- **Local provider**: $0 (unlimited) -- **Azure Functions**: 1M free executions/month -- **OpenAI**: ~$0.002/1K tokens (gpt-4o-mini) -- **Azure OpenAI**: Similar to OpenAI - -Enjoy your new chat website! 🎉 +# ✅ Chat Website Created Successfully! + +## What Was Built + +I've created a complete, production-ready chat website with: + +### 1. **Frontend** (`chat-web/`) +- Beautiful gradient UI with animations +- Responsive design (works on mobile & desktop) +- Real-time typing indicators +- Message history display +- New chat & clear functions + +### 2. **Backend** (`function_app.py`) +- Azure Functions HTTP endpoints +- Chat API with multiple provider support +- Static file serving +- CORS enabled for local testing +- Error handling & logging + +### 3. **Integration** +- Reuses existing `talk-to-ai` chat logic +- Supports 3 providers: Local (free), OpenAI, Azure OpenAI +- Auto-detects best available provider + +## 🚀 It's Already Running! + +The server is live at: +- **Main Page**: http://localhost:7071/api/chat-web +- **API Endpoint**: http://localhost:7071/api/chat + +## Quick Commands + +```powershell +# Start (if not running) +func start + +# Or use the helper script +.\start-chat-web.ps1 + +# Run tests +python .\test-chat-web.py + +# Stop server +Ctrl+C in the terminal +``` + +## Provider Status + +Currently using: **Local (Free)** +- No API keys required +- Works offline +- Perfect for testing + +To use OpenAI: +```powershell +$env:OPENAI_API_KEY = "sk-..." +# Restart server +``` + +To use Azure OpenAI: +```powershell +$env:AZURE_OPENAI_API_KEY = "your-key" +$env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/" +$env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini" +# Restart server +``` + +## Files Created + +``` +c:\Users\Bryan\OneDrive\AI\ +├── function_app.py # Main backend (consolidated) +├── start-chat-web.ps1 # Startup script +├── test-chat-web.py # Test suite +├── CHAT_WEBSITE_GUIDE.md # Complete documentation +└── chat-web/ + ├── index.html # Beautiful UI + ├── chat.js # Frontend logic + └── README.md # Quick reference +``` + +## Next Steps + +1. **Try it now**: Open http://localhost:7071/api/chat-web in your browser +2. **Test different providers**: Set API keys and restart +3. **Customize**: Edit colors, system prompts, features +4. **Deploy to Azure**: Follow the guide in `CHAT_WEBSITE_GUIDE.md` + +## Key Features + +✅ **Zero configuration** - Works immediately +✅ **Free tier** - No API costs with local provider +✅ **Beautiful UI** - Modern gradient design +✅ **Multiple providers** - Local/OpenAI/Azure +✅ **Production ready** - Error handling, CORS, logging +✅ **Mobile friendly** - Responsive design +✅ **Easy deploy** - Azure Functions ready + +## Documentation + +- **Complete Guide**: `CHAT_WEBSITE_GUIDE.md` +- **Quick Reference**: `chat-web/README.md` +- **Provider Details**: `ai-projects/chat-cli/README.md` +- **Architecture**: `.github/copilot-instructions.md` + +## Cost Summary + +- **Local development**: $0 +- **Local provider**: $0 (unlimited) +- **Azure Functions**: 1M free executions/month +- **OpenAI**: ~$0.002/1K tokens (gpt-4o-mini) +- **Azure OpenAI**: Similar to OpenAI + +Enjoy your new chat website! 🎉 diff --git a/docs/guides/DASHBOARD_COMPLETE_GUIDE.md b/docs/guides/DASHBOARD_COMPLETE_GUIDE.md index ebb18b26b..41f3946c2 100644 --- a/docs/guides/DASHBOARD_COMPLETE_GUIDE.md +++ b/docs/guides/DASHBOARD_COMPLETE_GUIDE.md @@ -1,317 +1,317 @@ -# QAI Dashboard Suite - Complete Feature Guide - -## 🎯 Overview - -Three progressive dashboard interfaces for monitoring and managing AI training: - -1. **index.html** - Basic real-time monitoring -2. **enhanced.html** - Full-featured with job controls -3. **advanced.html** - ⭐ Professional-grade with GPU monitoring & charts - -## 🚀 Quick Access - -``` -Basic: http://localhost:8000/index.html -Enhanced: http://localhost:8000/enhanced.html -Advanced: http://localhost:8000/advanced.html ⭐ RECOMMENDED -``` - ---- - -## 🎮 Advanced Dashboard Features - -### **Real-Time GPU Monitoring** -✅ **Live Metrics** (updates every 5 seconds): -- GPU utilization percentage with visual progress bar -- VRAM usage (current / total) -- Core temperature with color-coded warnings: - - 🟢 Green < 70°C (optimal) - - 🟡 Yellow 70-80°C (warm) - - 🔴 Red > 80°C (hot - warning shown) -- Power draw vs power limit -- GPU model name (e.g., RTX 4050 Laptop GPU) - -✅ **Temperature Alerts**: Automatic warning when GPU > 85°C - -✅ **Multiple GPU Support**: Displays all available GPUs - -### **System Resource Monitoring** -✅ **CPU Metrics**: -- Real-time usage percentage -- Core count display -- Clock frequency - -✅ **Memory Metrics**: -- RAM usage (GB used / total GB) -- Usage percentage with progress bar -- Automatic color coding for high usage - -✅ **Disk Metrics**: -- Total storage capacity -- Used space tracking - -### **Interactive Charts** (Chart.js) - -#### 📈 **Perplexity Progress Chart** -- Side-by-side comparison: Pre-training vs Post-training -- Bar chart showing improvement for each model -- Color-coded: Gray (before) → Purple (after) -- Hover tooltips with exact values - -#### ⏱️ **Training Duration Chart** -- Line chart showing time per job -- Smooth curve with gradient fill -- Identifies bottlenecks and optimization opportunities - -### **Smart Notifications** -✅ **Browser Notifications**: Alerts when jobs complete (with permission) -✅ **In-App Toasts**: Success/error messages slide in from top-right -✅ **Sound Alerts**: (Optional) Audio cues for completion -✅ **Visual Indicators**: Pulsing green dot = dashboard live - -### **Export & Reporting** -✅ **One-Click Export**: Download complete training history as JSON -✅ **Report Contents**: -- All job metrics (pre/post loss & perplexity) -- Duration data -- Training timeline -- Model configurations -✅ **Filename**: Auto-dated `training-report-2025-11-25.json` - -### **Job List Views** - -#### 🏃 **Running Jobs Panel** -- Live status with pulsing animation -- Current job name -- Real-time metrics - -#### ✅ **Completed Jobs Panel** -- Last 10 successful jobs -- Final perplexity scores -- Click for full details - -#### ⏳ **Queued Jobs Panel** -- Pending jobs in order -- Estimated start time -- Job configuration preview - -### **Quick Stats Dashboard** -Six key metrics at a glance: -1. ✅ Completed jobs count -2. 🏃 Currently running jobs -3. ⏳ Queued jobs waiting -4. ❌ Failed jobs count -5. ⚡ Average training time -6. 🎯 Best perplexity score achieved - ---- - -## 📡 API Endpoints Reference - -### Core Endpoints -``` -GET /status → Training status JSON -GET /api/datasets → List all datasets with sample counts -GET /api/models → List trained model adapters -GET /api/configs → List training YAML configurations -``` - -### Job Management -``` -GET /api/job/ → Detailed job info + output files -GET /api/logs/ → Last 500 lines of training logs -POST /api/start-training → Launch new training job -``` - -### System Monitoring -``` -GET /api/gpu → Real-time GPU stats (nvidia-smi) -GET /api/gpu-processes → List processes using GPU -GET /api/system → CPU, RAM, disk usage -GET /api/history → Training history for charts -``` - -### Example API Calls -```powershell -# Get GPU status -curl http://localhost:8000/api/gpu | jq - -# View job logs -curl http://localhost:8000/api/logs/phi35_mega_synthetic_full - -# Export training history -Invoke-WebRequest -Uri http://localhost:8000/api/history -OutFile history.json -``` - ---- - -## 🎨 Dashboard Comparison - -| Feature | Basic | Enhanced | Advanced | -|---------|-------|----------|----------| -| Real-time status | ✅ | ✅ | ✅ | -| Job metrics | ✅ | ✅ | ✅ | -| GPU monitoring | ❌ | ❌ | ✅ | -| System resources | ❌ | ❌ | ✅ | -| Interactive charts | ❌ | ❌ | ✅ | -| Job controls | ❌ | ✅ | ✅ | -| Model comparison | ❌ | ✅ | ✅ | -| Log viewer | ❌ | ✅ | ✅ | -| Dataset browser | ❌ | ✅ | ✅ | -| Notifications | ❌ | ❌ | ✅ | -| Export reports | ❌ | ❌ | ✅ | -| Auto-refresh | 10s | 10s | 5s | - ---- - -## 🔧 Usage Scenarios - -### Monitoring Long Training Sessions -1. Open **advanced.html** -2. Enable **Auto-refresh (5s)** checkbox -3. Leave browser tab open -4. Dashboard will: - - Update metrics every 5 seconds - - Show browser notification when jobs complete - - Alert if GPU temperature gets too high - - Track progress with live charts - -### Comparing Model Performance -1. Navigate to **Enhanced** dashboard -2. Go to **🤖 Models** tab -3. See leaderboard ranked by perplexity -4. Best model has **👑** crown badge -5. Click **Deploy** for production use - -### Starting New Training -1. Go to **Enhanced** → **⚡ New Training** tab -2. Select: - - Config: `autotrain_extended_marathon.yaml` - - Dataset: `mega_synthetic` (1,260 samples) - - Model: `Phi-3.5-mini-instruct` -3. Set epochs (3) and learning rate (0.0002) -4. Click **🚀 Start Training** -5. Job appears in **Running Jobs** immediately - -### Troubleshooting Failed Jobs -1. Open **Enhanced** → **🏃 Jobs** tab -2. Find failed job (red border) -3. Click to expand details -4. Click **📜 View Logs** -5. Last 500 lines show error details -6. Fix issue and relaunch - ---- - -## 🎯 Performance Tips - -### Optimal Settings -- **Auto-refresh**: 5-10 seconds (balance between freshness & load) -- **Browser**: Chrome/Edge for best Chart.js performance -- **Multiple tabs**: OK, but increases server load - -### GPU Temperature Management -- **Normal**: < 70°C -- **Acceptable**: 70-80°C -- **Warning**: 80-85°C (dashboard shows yellow) -- **Critical**: > 85°C (dashboard shows red alert) -- **Action**: If consistently > 85°C: - - Check laptop cooling - - Reduce batch size - - Lower max concurrent jobs - -### Memory Optimization -- Running jobs use ~6GB VRAM -- Keep system RAM usage < 80% -- Close unnecessary applications during training -- Dashboard itself uses ~50MB RAM - ---- - -## 📊 Live Data Examples - -### GPU Status (from your system) -```json -{ - "name": "NVIDIA GeForce RTX 4050 Laptop GPU", - "temperature": 48°C, - "utilization": 0%, - "memory_used": 0MB / 6141MB, - "power_draw": 2W -} -``` - -### Completed Job Metrics -```json -{ - "name": "phi35_mega_synthetic_full", - "duration": "4m 5s", - "pre_perplexity": 16.59, - "post_perplexity": 16.16, - "improvement": "2.6%", - "status": "succeeded" -} -``` - ---- - -## 🔮 Keyboard Shortcuts - -- **R** - Refresh all data -- **E** - Export report -- **Space** - Toggle auto-refresh -- **Esc** - Close notifications - ---- - -## 🐛 Troubleshooting - -### Dashboard won't load -```powershell -# Check if server is running -Get-Process python | Where-Object {$_.CommandLine -like '*serve.py*'} - -# Restart server -python .\dashboard\serve.py -``` - -### GPU not showing -- **Issue**: nvidia-smi not found -- **Fix**: Ensure NVIDIA drivers installed -- **Test**: `nvidia-smi` in PowerShell - -### Charts not rendering -- **Issue**: Chart.js CDN blocked -- **Fix**: Check internet connection or download Chart.js locally -- **Fallback**: Use Enhanced dashboard (no charts required) - -### Stale data -- **Issue**: status.json not updating -- **Fix**: Check if training process is running -- **Verify**: `cat data_out\autotrain\status.json` - ---- - -## 🎉 Success Metrics - -Your current training status: -- ✅ **2 jobs completed** successfully -- 🏃 **1 job running** (phi35_comprehensive_marathon) -- ⏳ **10 jobs queued** (3-4 hours remaining) -- 🎯 **Best perplexity**: 16.16 (phi35_mega_synthetic_full) -- 💪 **Improvement**: Up to 46.7% (qwen25_mega_synthetic_full) - -**Dashboard is monitoring all of this in real-time!** 🚀 - ---- - -## 📝 Next Steps - -1. ✅ Open advanced dashboard: http://localhost:8000/advanced.html -2. ✅ Enable auto-refresh for hands-free monitoring -3. ✅ Watch GPU utilization during training -4. ✅ Get notified when marathon completes -5. ✅ Compare models and deploy the winner -6. ✅ Export final report for documentation - -Enjoy your professional AI training dashboard! 🎊 +# QAI Dashboard Suite - Complete Feature Guide + +## 🎯 Overview + +Three progressive dashboard interfaces for monitoring and managing AI training: + +1. **index.html** - Basic real-time monitoring +2. **enhanced.html** - Full-featured with job controls +3. **advanced.html** - ⭐ Professional-grade with GPU monitoring & charts + +## 🚀 Quick Access + +``` +Basic: http://localhost:8000/index.html +Enhanced: http://localhost:8000/enhanced.html +Advanced: http://localhost:8000/advanced.html ⭐ RECOMMENDED +``` + +--- + +## 🎮 Advanced Dashboard Features + +### **Real-Time GPU Monitoring** +✅ **Live Metrics** (updates every 5 seconds): +- GPU utilization percentage with visual progress bar +- VRAM usage (current / total) +- Core temperature with color-coded warnings: + - 🟢 Green < 70°C (optimal) + - 🟡 Yellow 70-80°C (warm) + - 🔴 Red > 80°C (hot - warning shown) +- Power draw vs power limit +- GPU model name (e.g., RTX 4050 Laptop GPU) + +✅ **Temperature Alerts**: Automatic warning when GPU > 85°C + +✅ **Multiple GPU Support**: Displays all available GPUs + +### **System Resource Monitoring** +✅ **CPU Metrics**: +- Real-time usage percentage +- Core count display +- Clock frequency + +✅ **Memory Metrics**: +- RAM usage (GB used / total GB) +- Usage percentage with progress bar +- Automatic color coding for high usage + +✅ **Disk Metrics**: +- Total storage capacity +- Used space tracking + +### **Interactive Charts** (Chart.js) + +#### 📈 **Perplexity Progress Chart** +- Side-by-side comparison: Pre-training vs Post-training +- Bar chart showing improvement for each model +- Color-coded: Gray (before) → Purple (after) +- Hover tooltips with exact values + +#### ⏱️ **Training Duration Chart** +- Line chart showing time per job +- Smooth curve with gradient fill +- Identifies bottlenecks and optimization opportunities + +### **Smart Notifications** +✅ **Browser Notifications**: Alerts when jobs complete (with permission) +✅ **In-App Toasts**: Success/error messages slide in from top-right +✅ **Sound Alerts**: (Optional) Audio cues for completion +✅ **Visual Indicators**: Pulsing green dot = dashboard live + +### **Export & Reporting** +✅ **One-Click Export**: Download complete training history as JSON +✅ **Report Contents**: +- All job metrics (pre/post loss & perplexity) +- Duration data +- Training timeline +- Model configurations +✅ **Filename**: Auto-dated `training-report-2025-11-25.json` + +### **Job List Views** + +#### 🏃 **Running Jobs Panel** +- Live status with pulsing animation +- Current job name +- Real-time metrics + +#### ✅ **Completed Jobs Panel** +- Last 10 successful jobs +- Final perplexity scores +- Click for full details + +#### ⏳ **Queued Jobs Panel** +- Pending jobs in order +- Estimated start time +- Job configuration preview + +### **Quick Stats Dashboard** +Six key metrics at a glance: +1. ✅ Completed jobs count +2. 🏃 Currently running jobs +3. ⏳ Queued jobs waiting +4. ❌ Failed jobs count +5. ⚡ Average training time +6. 🎯 Best perplexity score achieved + +--- + +## 📡 API Endpoints Reference + +### Core Endpoints +``` +GET /status → Training status JSON +GET /api/datasets → List all datasets with sample counts +GET /api/models → List trained model adapters +GET /api/configs → List training YAML configurations +``` + +### Job Management +``` +GET /api/job/ → Detailed job info + output files +GET /api/logs/ → Last 500 lines of training logs +POST /api/start-training → Launch new training job +``` + +### System Monitoring +``` +GET /api/gpu → Real-time GPU stats (nvidia-smi) +GET /api/gpu-processes → List processes using GPU +GET /api/system → CPU, RAM, disk usage +GET /api/history → Training history for charts +``` + +### Example API Calls +```powershell +# Get GPU status +curl http://localhost:8000/api/gpu | jq + +# View job logs +curl http://localhost:8000/api/logs/phi35_mega_synthetic_full + +# Export training history +Invoke-WebRequest -Uri http://localhost:8000/api/history -OutFile history.json +``` + +--- + +## 🎨 Dashboard Comparison + +| Feature | Basic | Enhanced | Advanced | +|---------|-------|----------|----------| +| Real-time status | ✅ | ✅ | ✅ | +| Job metrics | ✅ | ✅ | ✅ | +| GPU monitoring | ❌ | ❌ | ✅ | +| System resources | ❌ | ❌ | ✅ | +| Interactive charts | ❌ | ❌ | ✅ | +| Job controls | ❌ | ✅ | ✅ | +| Model comparison | ❌ | ✅ | ✅ | +| Log viewer | ❌ | ✅ | ✅ | +| Dataset browser | ❌ | ✅ | ✅ | +| Notifications | ❌ | ❌ | ✅ | +| Export reports | ❌ | ❌ | ✅ | +| Auto-refresh | 10s | 10s | 5s | + +--- + +## 🔧 Usage Scenarios + +### Monitoring Long Training Sessions +1. Open **advanced.html** +2. Enable **Auto-refresh (5s)** checkbox +3. Leave browser tab open +4. Dashboard will: + - Update metrics every 5 seconds + - Show browser notification when jobs complete + - Alert if GPU temperature gets too high + - Track progress with live charts + +### Comparing Model Performance +1. Navigate to **Enhanced** dashboard +2. Go to **🤖 Models** tab +3. See leaderboard ranked by perplexity +4. Best model has **👑** crown badge +5. Click **Deploy** for production use + +### Starting New Training +1. Go to **Enhanced** → **⚡ New Training** tab +2. Select: + - Config: `autotrain_extended_marathon.yaml` + - Dataset: `mega_synthetic` (1,260 samples) + - Model: `Phi-3.5-mini-instruct` +3. Set epochs (3) and learning rate (0.0002) +4. Click **🚀 Start Training** +5. Job appears in **Running Jobs** immediately + +### Troubleshooting Failed Jobs +1. Open **Enhanced** → **🏃 Jobs** tab +2. Find failed job (red border) +3. Click to expand details +4. Click **📜 View Logs** +5. Last 500 lines show error details +6. Fix issue and relaunch + +--- + +## 🎯 Performance Tips + +### Optimal Settings +- **Auto-refresh**: 5-10 seconds (balance between freshness & load) +- **Browser**: Chrome/Edge for best Chart.js performance +- **Multiple tabs**: OK, but increases server load + +### GPU Temperature Management +- **Normal**: < 70°C +- **Acceptable**: 70-80°C +- **Warning**: 80-85°C (dashboard shows yellow) +- **Critical**: > 85°C (dashboard shows red alert) +- **Action**: If consistently > 85°C: + - Check laptop cooling + - Reduce batch size + - Lower max concurrent jobs + +### Memory Optimization +- Running jobs use ~6GB VRAM +- Keep system RAM usage < 80% +- Close unnecessary applications during training +- Dashboard itself uses ~50MB RAM + +--- + +## 📊 Live Data Examples + +### GPU Status (from your system) +```json +{ + "name": "NVIDIA GeForce RTX 4050 Laptop GPU", + "temperature": 48°C, + "utilization": 0%, + "memory_used": 0MB / 6141MB, + "power_draw": 2W +} +``` + +### Completed Job Metrics +```json +{ + "name": "phi35_mega_synthetic_full", + "duration": "4m 5s", + "pre_perplexity": 16.59, + "post_perplexity": 16.16, + "improvement": "2.6%", + "status": "succeeded" +} +``` + +--- + +## 🔮 Keyboard Shortcuts + +- **R** - Refresh all data +- **E** - Export report +- **Space** - Toggle auto-refresh +- **Esc** - Close notifications + +--- + +## 🐛 Troubleshooting + +### Dashboard won't load +```powershell +# Check if server is running +Get-Process python | Where-Object {$_.CommandLine -like '*serve.py*'} + +# Restart server +python .\dashboard\serve.py +``` + +### GPU not showing +- **Issue**: nvidia-smi not found +- **Fix**: Ensure NVIDIA drivers installed +- **Test**: `nvidia-smi` in PowerShell + +### Charts not rendering +- **Issue**: Chart.js CDN blocked +- **Fix**: Check internet connection or download Chart.js locally +- **Fallback**: Use Enhanced dashboard (no charts required) + +### Stale data +- **Issue**: status.json not updating +- **Fix**: Check if training process is running +- **Verify**: `cat data_out\autotrain\status.json` + +--- + +## 🎉 Success Metrics + +Your current training status: +- ✅ **2 jobs completed** successfully +- 🏃 **1 job running** (phi35_comprehensive_marathon) +- ⏳ **10 jobs queued** (3-4 hours remaining) +- 🎯 **Best perplexity**: 16.16 (phi35_mega_synthetic_full) +- 💪 **Improvement**: Up to 46.7% (qwen25_mega_synthetic_full) + +**Dashboard is monitoring all of this in real-time!** 🚀 + +--- + +## 📝 Next Steps + +1. ✅ Open advanced dashboard: http://localhost:8000/advanced.html +2. ✅ Enable auto-refresh for hands-free monitoring +3. ✅ Watch GPU utilization during training +4. ✅ Get notified when marathon completes +5. ✅ Compare models and deploy the winner +6. ✅ Export final report for documentation + +Enjoy your professional AI training dashboard! 🎊 diff --git a/docs/guides/DASHBOARD_CONSOLIDATION.md b/docs/guides/DASHBOARD_CONSOLIDATION.md index 6356798d1..78a37cd50 100644 --- a/docs/guides/DASHBOARD_CONSOLIDATION.md +++ b/docs/guides/DASHBOARD_CONSOLIDATION.md @@ -1,246 +1,246 @@ -# Dashboard Consolidation Complete - -## Overview - -Successfully consolidated **6 separate dashboard pages** into a **single unified interface** with tab-based navigation. - -### Before (6 Pages) -1. `hub.html` - Command Center landing page (1036 lines) -2. `unified.html` - Training interface (2991 lines) -3. `analytics.html` - Performance charts (969 lines) -4. `enhanced.html` - Enhanced features -5. `advanced.html` - Advanced dashboard -6. `index.html` - Hub/entry point (484 lines) - -### After (1 Page) -1. **`consolidated.html`** - Complete unified dashboard with 5 tabs - -## Consolidated Dashboard Features - -### 🏠 Overview Tab -- **System Status Cards**: Total jobs, average loss, best model, total training time -- **Loss Progression Chart**: Line chart showing training loss over all jobs -- **Recent Activity Feed**: Last 5 training jobs with status -- **Quick Actions**: Start training, refresh data, system health, tools - -### 🚀 Training Tab -- **Training Form**: Complete job configuration (name, dataset, model, hyperparameters) -- **Quick Presets**: Quick test (2 min), Standard (10 min), Full (60 min) -- **Advanced Settings**: Collapsible section with LoRA rank/alpha, max samples -- **Config Management**: Save/load/reset training configurations -- **Tuning Wizard**: Link to dataset profiler for AI-powered recommendations - -### 📊 Analytics Tab -- **Performance Chart**: Bar chart comparing top 5 models by performance score -- **Time Distribution**: Doughnut chart showing job duration breakdown -- **Model Comparison Table**: Detailed comparison of all jobs (loss, improvement, duration, rank, score) - -### 📜 History Tab -- **Session History Table**: 20 most recent training sessions with details -- **Export Options**: Export to JSON or CSV format -- **Replay Function**: Load previous session configs back into training form -- **Clear History**: Remove all stored sessions - -### 🛠️ Tools Tab -- **Dataset Profiler**: Analyze datasets and get hyperparameter recommendations -- **VRAM Calculator**: Calculate safe batch sizes based on GPU memory -- **Anomaly Detector Status**: Real-time monitoring of training anomalies (spikes, divergences, stagnations) - -## Integrated Phase 26 Features - -All Phase 26 modules are integrated: - -1. **`shared-theme.css`** (450 lines) - - Unified dark mode styling - - Reusable components (cards, buttons, badges, forms, tables) - - CSS variables for consistent theming - -2. **`anomaly-detector.js`** (300 lines) - - Real-time training monitoring - - Desktop notifications for anomalies - - Auto-pause on critical issues - - Statistics tracking (spikes, divergences, stagnations) - -3. **`keyboard-nav.js`** (350 lines) - - Global keyboard shortcuts (Ctrl+H/U/A, Ctrl+S, Ctrl+R) - - Modal shortcuts (Escape to close) - - Hints panel (? key) - - ARIA labels for accessibility - -4. **`session-history.js`** (400 lines) - - localStorage persistence (100 sessions max) - - Filtering by date/status/model/dataset - - Config replay functionality - - CSV/JSON export - - Auto-save every 30 seconds - -## Usage - -### Start Server -```powershell -cd dashboard -python serve.py -``` - -Server starts on `http://localhost:8000` and automatically redirects to `/consolidated.html`. - -### Navigation -- **Mouse**: Click tab buttons at top of page -- **Keyboard**: Ctrl+1/2/3/4/5 for quick tab switching (when keyboard-nav.js is fully integrated) -- **Tab State**: Last active tab is saved to localStorage and restored on page reload - -### Key Workflows - -#### Start Training Job -1. Click **Training** tab (or 🚀 Start New Training button) -2. Fill in job name, select dataset and model -3. Choose preset or customize hyperparameters -4. Expand **Advanced Settings** for LoRA configuration -5. Click **🚀 Start Training** - -#### Analyze Performance -1. Click **Analytics** tab -2. View performance chart comparing models -3. Check time distribution breakdown -4. Review detailed comparison table -5. Export data if needed - -#### Use Tools -1. Click **Tools** tab -2. **Dataset Profiler**: Select dataset → Click Profile → Get AI recommendations -3. **VRAM Calculator**: Configure model/rank → Calculate → Get safe batch size -4. **Anomaly Status**: Monitor real-time anomaly detection statistics - -#### Review History -1. Click **History** tab -2. Browse past training sessions -3. Click **Replay** to load a session's config into training form -4. Export to JSON/CSV for analysis -5. Clear history if needed - -## Technical Details - -### Tab Switching Logic -```javascript -function switchTab(tabName) { - // Hide all tabs - document.querySelectorAll('.tab-content').forEach(tab => { - tab.classList.remove('active'); - }); - - // Remove active from all buttons - document.querySelectorAll('.tab-btn').forEach(btn => { - btn.classList.remove('active'); - }); - - // Show selected tab - document.getElementById(`${tabName}-tab`).classList.add('active'); - - // Set active button - const btnIndex = ['overview', 'training', 'analytics', 'history', 'tools'].indexOf(tabName); - if (btnIndex >= 0) { - document.querySelectorAll('.tab-btn')[btnIndex].classList.add('active'); - } - - // Refresh data for specific tabs - if (tabName === 'analytics') updateCharts(); - else if (tabName === 'history') updateHistoryView(); - else if (tabName === 'tools') updateAnomalyStatus(); - - // Store tab preference - localStorage.setItem('qai-active-tab', tabName); -} -``` - -### Data Refresh -- **Auto-refresh**: Every 5 seconds via `setInterval(refreshData, 5000)` -- **Manual refresh**: Click 🔄 Refresh Data button -- **Chart updates**: Only when Analytics tab is active (performance optimization) -- **Status endpoint**: `/status` (no cache headers for real-time data) - -### Chart Initialization -Three Chart.js charts: -1. **Loss Chart** (line): Shows loss progression across all jobs -2. **Performance Chart** (bar): Compares top 5 models by performance score -3. **Time Distribution** (doughnut): Breaks down jobs by duration (quick/medium/long) - -### Session Persistence -- **Tab state**: `localStorage.setItem('qai-active-tab', tabName)` -- **Training history**: Managed by `session-history.js` (100 session limit) -- **Auto-save**: Training sessions auto-saved every 30 seconds -- **Replay**: Load previous configs with `replaySession(sessionId)` - -## Backups Created - -Original files backed up with `.backup` extension: -- `dashboard/index.html.backup` -- `dashboard/unified.html.backup` -- `dashboard/analytics.html.backup` - -To restore: -```powershell -cd dashboard -Copy-Item index.html.backup index.html -Force -Copy-Item unified.html.backup unified.html -Force -Copy-Item analytics.html.backup analytics.html -Force -``` - -## Files Modified - -1. **`dashboard/consolidated.html`** - NEW: Unified dashboard (1000+ lines) -2. **`dashboard/serve.py`** - Updated: Redirect `/` to `/consolidated.html` instead of `/hub.html` - -## Next Steps - -### Optional Enhancements -1. **Remove old pages**: Delete hub.html, unified.html, analytics.html, enhanced.html, advanced.html after testing -2. **Keyboard shortcuts**: Extend keyboard-nav.js with Ctrl+1/2/3/4/5 for tab switching -3. **Mobile optimization**: Test responsive design on mobile devices -4. **Dashboard themes**: Add light/dark mode toggle -5. **Export all data**: Add "Export All" button on Overview tab - -### Testing Checklist -- [ ] Server starts without errors -- [ ] All tabs switch correctly -- [ ] Training form submits successfully -- [ ] Charts render with data -- [ ] Dataset profiler works -- [ ] Session history persists -- [ ] Anomaly detector initializes -- [ ] Config save/load works -- [ ] Export functions work (JSON/CSV) -- [ ] Mobile responsive design - -## Benefits - -### UX Improvements -- **Single URL**: No more navigating between pages -- **Instant switching**: Tab switching without page reloads -- **Consistent state**: Shared global state across all views -- **Tab memory**: Restores last active tab on reload -- **Keyboard shortcuts**: Quick navigation (when extended) - -### Developer Benefits -- **Easier maintenance**: One file instead of six -- **Shared CSS**: `shared-theme.css` loaded once -- **Shared JS**: Phase 26 modules loaded once -- **Simpler routing**: Only one redirect in serve.py -- **Better state management**: Single page = single state - -### Performance Benefits -- **Fewer HTTP requests**: Load all resources once -- **Faster navigation**: No page reloads -- **Conditional chart updates**: Only update when Analytics tab active -- **Lazy data loading**: Fetch data only when tabs need it -- **Cached resources**: CSS/JS stay in memory - -## Conclusion - -Successfully consolidated 6 separate dashboard pages (6,480+ total lines) into a single unified interface with tab-based navigation. All functionality preserved, Phase 26 features integrated, and UX significantly improved with instant tab switching and persistent state management. - -**Total reduction**: 6 pages → 1 page (~80% reduction in navigation complexity) - ---- - -Last updated: 2025-11-25 -Phase: 27 (Dashboard Consolidation) +# Dashboard Consolidation Complete + +## Overview + +Successfully consolidated **6 separate dashboard pages** into a **single unified interface** with tab-based navigation. + +### Before (6 Pages) +1. `hub.html` - Command Center landing page (1036 lines) +2. `unified.html` - Training interface (2991 lines) +3. `analytics.html` - Performance charts (969 lines) +4. `enhanced.html` - Enhanced features +5. `advanced.html` - Advanced dashboard +6. `index.html` - Hub/entry point (484 lines) + +### After (1 Page) +1. **`consolidated.html`** - Complete unified dashboard with 5 tabs + +## Consolidated Dashboard Features + +### 🏠 Overview Tab +- **System Status Cards**: Total jobs, average loss, best model, total training time +- **Loss Progression Chart**: Line chart showing training loss over all jobs +- **Recent Activity Feed**: Last 5 training jobs with status +- **Quick Actions**: Start training, refresh data, system health, tools + +### 🚀 Training Tab +- **Training Form**: Complete job configuration (name, dataset, model, hyperparameters) +- **Quick Presets**: Quick test (2 min), Standard (10 min), Full (60 min) +- **Advanced Settings**: Collapsible section with LoRA rank/alpha, max samples +- **Config Management**: Save/load/reset training configurations +- **Tuning Wizard**: Link to dataset profiler for AI-powered recommendations + +### 📊 Analytics Tab +- **Performance Chart**: Bar chart comparing top 5 models by performance score +- **Time Distribution**: Doughnut chart showing job duration breakdown +- **Model Comparison Table**: Detailed comparison of all jobs (loss, improvement, duration, rank, score) + +### 📜 History Tab +- **Session History Table**: 20 most recent training sessions with details +- **Export Options**: Export to JSON or CSV format +- **Replay Function**: Load previous session configs back into training form +- **Clear History**: Remove all stored sessions + +### 🛠️ Tools Tab +- **Dataset Profiler**: Analyze datasets and get hyperparameter recommendations +- **VRAM Calculator**: Calculate safe batch sizes based on GPU memory +- **Anomaly Detector Status**: Real-time monitoring of training anomalies (spikes, divergences, stagnations) + +## Integrated Phase 26 Features + +All Phase 26 modules are integrated: + +1. **`shared-theme.css`** (450 lines) + - Unified dark mode styling + - Reusable components (cards, buttons, badges, forms, tables) + - CSS variables for consistent theming + +2. **`anomaly-detector.js`** (300 lines) + - Real-time training monitoring + - Desktop notifications for anomalies + - Auto-pause on critical issues + - Statistics tracking (spikes, divergences, stagnations) + +3. **`keyboard-nav.js`** (350 lines) + - Global keyboard shortcuts (Ctrl+H/U/A, Ctrl+S, Ctrl+R) + - Modal shortcuts (Escape to close) + - Hints panel (? key) + - ARIA labels for accessibility + +4. **`session-history.js`** (400 lines) + - localStorage persistence (100 sessions max) + - Filtering by date/status/model/dataset + - Config replay functionality + - CSV/JSON export + - Auto-save every 30 seconds + +## Usage + +### Start Server +```powershell +cd dashboard +python serve.py +``` + +Server starts on `http://localhost:8000` and automatically redirects to `/consolidated.html`. + +### Navigation +- **Mouse**: Click tab buttons at top of page +- **Keyboard**: Ctrl+1/2/3/4/5 for quick tab switching (when keyboard-nav.js is fully integrated) +- **Tab State**: Last active tab is saved to localStorage and restored on page reload + +### Key Workflows + +#### Start Training Job +1. Click **Training** tab (or 🚀 Start New Training button) +2. Fill in job name, select dataset and model +3. Choose preset or customize hyperparameters +4. Expand **Advanced Settings** for LoRA configuration +5. Click **🚀 Start Training** + +#### Analyze Performance +1. Click **Analytics** tab +2. View performance chart comparing models +3. Check time distribution breakdown +4. Review detailed comparison table +5. Export data if needed + +#### Use Tools +1. Click **Tools** tab +2. **Dataset Profiler**: Select dataset → Click Profile → Get AI recommendations +3. **VRAM Calculator**: Configure model/rank → Calculate → Get safe batch size +4. **Anomaly Status**: Monitor real-time anomaly detection statistics + +#### Review History +1. Click **History** tab +2. Browse past training sessions +3. Click **Replay** to load a session's config into training form +4. Export to JSON/CSV for analysis +5. Clear history if needed + +## Technical Details + +### Tab Switching Logic +```javascript +function switchTab(tabName) { + // Hide all tabs + document.querySelectorAll('.tab-content').forEach(tab => { + tab.classList.remove('active'); + }); + + // Remove active from all buttons + document.querySelectorAll('.tab-btn').forEach(btn => { + btn.classList.remove('active'); + }); + + // Show selected tab + document.getElementById(`${tabName}-tab`).classList.add('active'); + + // Set active button + const btnIndex = ['overview', 'training', 'analytics', 'history', 'tools'].indexOf(tabName); + if (btnIndex >= 0) { + document.querySelectorAll('.tab-btn')[btnIndex].classList.add('active'); + } + + // Refresh data for specific tabs + if (tabName === 'analytics') updateCharts(); + else if (tabName === 'history') updateHistoryView(); + else if (tabName === 'tools') updateAnomalyStatus(); + + // Store tab preference + localStorage.setItem('qai-active-tab', tabName); +} +``` + +### Data Refresh +- **Auto-refresh**: Every 5 seconds via `setInterval(refreshData, 5000)` +- **Manual refresh**: Click 🔄 Refresh Data button +- **Chart updates**: Only when Analytics tab is active (performance optimization) +- **Status endpoint**: `/status` (no cache headers for real-time data) + +### Chart Initialization +Three Chart.js charts: +1. **Loss Chart** (line): Shows loss progression across all jobs +2. **Performance Chart** (bar): Compares top 5 models by performance score +3. **Time Distribution** (doughnut): Breaks down jobs by duration (quick/medium/long) + +### Session Persistence +- **Tab state**: `localStorage.setItem('qai-active-tab', tabName)` +- **Training history**: Managed by `session-history.js` (100 session limit) +- **Auto-save**: Training sessions auto-saved every 30 seconds +- **Replay**: Load previous configs with `replaySession(sessionId)` + +## Backups Created + +Original files backed up with `.backup` extension: +- `dashboard/index.html.backup` +- `dashboard/unified.html.backup` +- `dashboard/analytics.html.backup` + +To restore: +```powershell +cd dashboard +Copy-Item index.html.backup index.html -Force +Copy-Item unified.html.backup unified.html -Force +Copy-Item analytics.html.backup analytics.html -Force +``` + +## Files Modified + +1. **`dashboard/consolidated.html`** - NEW: Unified dashboard (1000+ lines) +2. **`dashboard/serve.py`** - Updated: Redirect `/` to `/consolidated.html` instead of `/hub.html` + +## Next Steps + +### Optional Enhancements +1. **Remove old pages**: Delete hub.html, unified.html, analytics.html, enhanced.html, advanced.html after testing +2. **Keyboard shortcuts**: Extend keyboard-nav.js with Ctrl+1/2/3/4/5 for tab switching +3. **Mobile optimization**: Test responsive design on mobile devices +4. **Dashboard themes**: Add light/dark mode toggle +5. **Export all data**: Add "Export All" button on Overview tab + +### Testing Checklist +- [ ] Server starts without errors +- [ ] All tabs switch correctly +- [ ] Training form submits successfully +- [ ] Charts render with data +- [ ] Dataset profiler works +- [ ] Session history persists +- [ ] Anomaly detector initializes +- [ ] Config save/load works +- [ ] Export functions work (JSON/CSV) +- [ ] Mobile responsive design + +## Benefits + +### UX Improvements +- **Single URL**: No more navigating between pages +- **Instant switching**: Tab switching without page reloads +- **Consistent state**: Shared global state across all views +- **Tab memory**: Restores last active tab on reload +- **Keyboard shortcuts**: Quick navigation (when extended) + +### Developer Benefits +- **Easier maintenance**: One file instead of six +- **Shared CSS**: `shared-theme.css` loaded once +- **Shared JS**: Phase 26 modules loaded once +- **Simpler routing**: Only one redirect in serve.py +- **Better state management**: Single page = single state + +### Performance Benefits +- **Fewer HTTP requests**: Load all resources once +- **Faster navigation**: No page reloads +- **Conditional chart updates**: Only update when Analytics tab active +- **Lazy data loading**: Fetch data only when tabs need it +- **Cached resources**: CSS/JS stay in memory + +## Conclusion + +Successfully consolidated 6 separate dashboard pages (6,480+ total lines) into a single unified interface with tab-based navigation. All functionality preserved, Phase 26 features integrated, and UX significantly improved with instant tab switching and persistent state management. + +**Total reduction**: 6 pages → 1 page (~80% reduction in navigation complexity) + +--- + +Last updated: 2025-11-25 +Phase: 27 (Dashboard Consolidation) diff --git a/docs/guides/DASHBOARD_DEMO.md b/docs/guides/DASHBOARD_DEMO.md index 2cb9cd9ce..94ba6d649 100644 --- a/docs/guides/DASHBOARD_DEMO.md +++ b/docs/guides/DASHBOARD_DEMO.md @@ -1,436 +1,436 @@ -# 🎯 Training Dashboard - Live Demo Guide - -## 🌐 Access Points - -### Primary Dashboard -**URL**: http://localhost:8000/unified.html -**Status**: ✅ Server Running -**File**: `dashboard/unified.html` (2,200+ lines) - -### Server Details -- **Backend**: `dashboard/serve.py` -- **Port**: 8000 -- **Root**: C:\Users\Bryan\OneDrive\AI -- **Status**: 🟢 Operational - ---- - -## 🧪 Live Feature Testing - -### Test 1: Quick Preset (⚡ Fastest) -**Purpose**: Verify preset system and estimate calculations - -#### Steps: -1. Navigate to "Train" tab (or press `6`) -2. Enter job name: `test_quick_demo` -3. Select any dataset from dropdown -4. Click **"⚡ Quick Test"** preset button -5. Observe instant parameter updates: - - Epochs: 1 - - Max Samples: 100 - - LoRA Rank: 4 - - Batch Size: 2 - - Estimated Time: ~2 minutes - - Estimated VRAM: ~3.8 GB - -**Expected Result**: ✅ All fields populate instantly, estimates show ~2 min - ---- - -### Test 2: Advanced Options Toggle -**Purpose**: Verify collapsible section functionality - -#### Steps: -1. Locate **"🔧 Advanced Options"** section -2. Click the header to expand -3. Observe the arrow icon change: ▼ → ▲ -4. Verify 9 additional fields appear: - - Batch Size dropdown - - Gradient Accumulation - - Warmup Steps - - LoRA Rank, Alpha, Dropout - - Weight Decay - - Max Grad Norm - - Random Seed -5. Click header again to collapse - -**Expected Result**: ✅ Smooth toggle animation, all 9 fields visible/hidden - ---- - -### Test 3: Real-Time Estimates -**Purpose**: Verify dynamic calculations update correctly - -#### Steps: -1. Apply Standard preset (📊 button) -2. Note initial estimate: ~10 minutes -3. Change epochs from 3 → 5 -4. Watch estimate update to ~16 minutes -5. Change batch size from 2 → 4 -6. Watch estimate update to ~8 minutes -7. Change LoRA rank from 8 → 16 -8. Watch VRAM estimate update from ~4.0GB → ~4.5GB - -**Expected Result**: ✅ Instant updates, accurate time/VRAM calculations - ---- - -### Test 4: Validation System -**Purpose**: Test form validation and error messages - -#### Test 4a - Invalid Job Name: -1. Enter job name: `My Test Job` (spaces) -2. Click "🚀 Start Training" -3. Expect error: "Job name must be lowercase letters, numbers, and underscores only" - -#### Test 4b - Missing Dataset: -1. Clear job name field -2. Click "🚀 Start Training" -3. Expect error listing: - - "Job name is required" - - "Please select a dataset" - -#### Test 4c - Invalid Range: -1. Set epochs to 25 (exceeds max of 20) -2. Try to submit -3. Expect validation error - -**Expected Result**: ✅ Clear, specific error messages for each violation - ---- - -### Test 5: Config Save/Load -**Purpose**: Verify configuration management - -#### Save Config: -1. Configure a custom training setup: - - Job name: `my_config_test` - - Epochs: 5 - - LoRA Rank: 16 - - Custom learning rate: 1e-4 -2. Click **"💾 Save Config"** button -3. Verify JSON file downloads: `my_config_test.json` -4. Open file, confirm all 19 parameters present - -#### Load Config: -1. Click **"🔄 Reset"** to clear form -2. Click **"📂 Load Config"** button -3. Select the saved JSON file -4. Verify all parameters restore correctly -5. Check estimates recalculate automatically - -**Expected Result**: ✅ Config exports/imports all 19 parameters accurately - ---- - -### Test 6: Tooltips & Help Text -**Purpose**: Verify all fields have helpful guidance - -#### Steps: -1. Hover over "Job Name" label → Tooltip: "Unique identifier for this training job" -2. Hover over "LoRA Rank" → Tooltip: "LoRA rank parameter" -3. Hover over "Batch Size" → Tooltip: "Batch size for training" -4. Check small gray text under each field for hints -5. Verify all 20+ fields have tooltips - -**Expected Result**: ✅ Instant tooltip display, helpful descriptions - ---- - -### Test 7: Dynamic Info Updates -**Purpose**: Verify model and dataset info changes - -#### Model Info: -1. Select "Phi-3.5-mini-instruct" -2. Observe info: "Fast training, good for chat tasks, 3.8B parameters" -3. Switch to "Qwen2.5-3B-Instruct" -4. Observe info: "Efficient architecture, great for reasoning, 3B parameters" - -#### Dataset Info: -1. Select a dataset from dropdown -2. Observe info updates to show selection -3. Note sample count displayed in dropdown options - -**Expected Result**: ✅ Info updates instantly on selection change - ---- - -### Test 8: Preset Comparison -**Purpose**: Compare all 4 presets side-by-side - -| Preset | Epochs | Samples | Rank | Est. Time | Use Case | -|--------|--------|---------|------|-----------|----------| -| ⚡ Quick Test | 1 | 100 | 4 | ~2 min | Pipeline test | -| 📊 Standard | 3 | 1k | 8 | ~10 min | Development | -| 🏆 Full | 5 | -1 (all) | 16 | ~60 min | Thorough | -| 🚀 Production | 10 | -1 (all) | 32 | ~4 hours | Production | - -#### Steps: -1. Click each preset button in sequence -2. Observe parameter changes for each -3. Note estimate scaling with complexity -4. Verify VRAM increases with LoRA rank - -**Expected Result**: ✅ Each preset applies distinct, logical values - ---- - -### Test 9: Evaluation Toggle -**Purpose**: Test conditional field visibility - -#### Steps: -1. Ensure "Enable Evaluation" is checked (default) -2. Verify eval options visible: - - Max Eval Samples (default: 100) - - Eval Steps (default: 50) -3. Uncheck "Enable Evaluation" -4. Verify eval options disappear -5. Re-check to show again - -**Expected Result**: ✅ Smooth show/hide transition, options persist - ---- - -### Test 10: Long-Run Confirmation -**Purpose**: Verify safety dialog for extended jobs - -#### Steps: -1. Apply Production preset (10 epochs, all samples) -2. Ensure estimates show >1 hour -3. Enter valid job name and select dataset -4. Click "🚀 Start Training" -5. Expect confirmation dialog: - - "This training job is estimated to take ~X hours. Continue?" -6. Click "Cancel" to abort -7. Try again, click "OK" to proceed - -**Expected Result**: ✅ Dialog appears for long jobs, prevents accidental runs - ---- - -## 🎨 Visual Features to Observe - -### Gradient Estimate Card -- Beautiful blue gradient background -- Two columns: Time | VRAM -- Large, readable estimates -- Updates in real-time - -### Button States -- Primary button (Start Training): Full-width, prominent green -- Secondary buttons: Gray, compact -- Hover effects on all buttons -- Preset buttons: Colorful, emoji-prefixed - -### Field Organization -- Clean 3-column layouts -- Consistent spacing -- Required fields marked with red asterisk (*) -- Helper text in muted gray - -### Responsive Feedback -- Toast notifications on actions -- Loading states during submission -- Success/error color coding -- Smooth animations - ---- - -## 📊 API Integration Test - -### Datasets Endpoint -```powershell -Invoke-WebRequest -Uri "http://localhost:8000/api/datasets" | ConvertFrom-Json -``` - -**Expected Response**: -```json -{ - "datasets": [ - { - "name": "chat_logs", - "path": "datasets\\chat\\chat_logs", - "train_samples": 3, - "test_samples": 3 - } - ] -} -``` - -### Start Training Endpoint (Dry Run) -```powershell -$body = @{ - name = "api_test_job" - model = "phi35" - dataset = "datasets/chat/chat_logs" - epochs = 1 - max_samples = 10 - learning_rate = "2e-4" - batch_size = 2 - lora_rank = 4 - lora_alpha = 8 -} | ConvertTo-Json - -Invoke-WebRequest -Uri "http://localhost:8000/api/start-training" -Method POST -Body $body -ContentType "application/json" -``` - ---- - -## 🎯 Success Criteria - -### ✅ All Features Working -- [x] 4 presets apply instantly -- [x] Advanced options toggle smoothly -- [x] Estimates update in real-time -- [x] Validation catches all errors -- [x] Config save/load works perfectly -- [x] Tooltips display on hover -- [x] Dynamic info updates correctly -- [x] Long-run confirmation appears -- [x] Eval options toggle visibility -- [x] API endpoints respond correctly - -### ✅ User Experience -- [x] Intuitive layout and flow -- [x] Clear error messages -- [x] Helpful guidance everywhere -- [x] No confusing states -- [x] Fast, responsive interactions -- [x] Professional visual design - -### ✅ Technical Quality -- [x] No console errors -- [x] All validations work -- [x] Proper error handling -- [x] Clean, maintainable code -- [x] Comprehensive documentation -- [x] Production-ready stability - ---- - -## 🚀 Demo Script (2-Minute Tour) - -### Act 1: Quick Start (30 seconds) -1. Open dashboard → Train tab -2. Enter job name: `demo_tour` -3. Click "⚡ Quick Test" preset -4. Show instant estimates: ~2 minutes -5. Click "🚀 Start Training" (don't actually submit) - -### Act 2: Advanced Power (45 seconds) -1. Click "🔧 Advanced Options" to expand -2. Show 9 additional parameters -3. Change LoRA rank 4 → 16 -4. Watch VRAM estimate increase -5. Click "📊 Standard" preset -6. Show all fields update instantly - -### Act 3: Safety Features (45 seconds) -1. Click "🚀 Production" preset -2. Show 4-hour estimate -3. Try invalid job name: `Test Job` -4. Show validation error -5. Fix name: `test_job` -6. Show confirmation dialog for long run -7. Demonstrate config save/load -8. Show successful config export - -**Grand Finale**: "20+ features, zero friction, production-ready!" 🎉 - ---- - -## 📸 Screenshot Checklist - -### Must-Capture Views -1. ✅ Train tab with all basic fields -2. ✅ Advanced options expanded (9 fields visible) -3. ✅ Estimate card showing calculations -4. ✅ All 4 preset buttons in action -5. ✅ Validation error message -6. ✅ Config save dialog -7. ✅ Tooltip hover example -8. ✅ Long-run confirmation dialog - ---- - -## 🎓 Training Scenarios - -### Scenario 1: First-Time User -**Goal**: Test pipeline quickly -- Use "⚡ Quick Test" preset -- 1 epoch, 100 samples -- 2-minute run -- Verify everything works - -### Scenario 2: Iterative Developer -**Goal**: Experiment with hyperparameters -- Start with "📊 Standard" preset -- Adjust LoRA rank (8 → 16) -- Save as `experiment_v1.json` -- Train and evaluate -- Load config, adjust, save as `experiment_v2.json` - -### Scenario 3: Production Team -**Goal**: Deploy best model -- Use "🚀 Production" preset -- Open Advanced Options -- Fine-tune all parameters -- Save as `production_final.json` -- Share with team -- Deploy to production - -### Scenario 4: Budget-Conscious User -**Goal**: Optimize training time -- Monitor estimate card closely -- Balance epochs vs. samples -- Use batch size efficiently -- Aim for <30 minute runs -- Multiple quick iterations - ---- - -## 🔧 Troubleshooting Demo - -### Issue: Dropdown Empty -**Demo**: Show empty dropdown -**Solution**: Navigate to Datasets tab, show datasets loading -**Result**: Dropdown populates automatically - -### Issue: Validation Error -**Demo**: Enter "My Test Job" with spaces -**Solution**: Show error, correct to "my_test_job" -**Result**: Validation passes, ready to submit - -### Issue: Estimates Seem Off -**Demo**: Show default 5-minute estimate -**Solution**: Adjust any parameter to trigger recalculation -**Result**: Accurate time and VRAM shown - ---- - -## 🎉 Final Checklist - -Before considering demo complete: - -- [ ] Server running at http://localhost:8000 -- [ ] Dashboard loads without errors -- [ ] Train tab visible and accessible -- [ ] All 20+ fields present and functional -- [ ] 4 presets working correctly -- [ ] Advanced options toggle works -- [ ] Estimates update in real-time -- [ ] Validation catches errors -- [ ] Config save/load functions -- [ ] Tooltips display on hover -- [ ] API endpoints responding -- [ ] No console errors -- [ ] Professional visual appearance -- [ ] Ready for production use - ---- - -**Demo Status**: ✅ READY TO SHOWCASE -**Server Status**: 🟢 Online at http://localhost:8000 -**Next Step**: Open browser, navigate to Train tab, begin demo! - -*"From zero to advanced training control in 2 minutes!"* 🚀 +# 🎯 Training Dashboard - Live Demo Guide + +## 🌐 Access Points + +### Primary Dashboard +**URL**: http://localhost:8000/unified.html +**Status**: ✅ Server Running +**File**: `dashboard/unified.html` (2,200+ lines) + +### Server Details +- **Backend**: `dashboard/serve.py` +- **Port**: 8000 +- **Root**: C:\Users\Bryan\OneDrive\AI +- **Status**: 🟢 Operational + +--- + +## 🧪 Live Feature Testing + +### Test 1: Quick Preset (⚡ Fastest) +**Purpose**: Verify preset system and estimate calculations + +#### Steps: +1. Navigate to "Train" tab (or press `6`) +2. Enter job name: `test_quick_demo` +3. Select any dataset from dropdown +4. Click **"⚡ Quick Test"** preset button +5. Observe instant parameter updates: + - Epochs: 1 + - Max Samples: 100 + - LoRA Rank: 4 + - Batch Size: 2 + - Estimated Time: ~2 minutes + - Estimated VRAM: ~3.8 GB + +**Expected Result**: ✅ All fields populate instantly, estimates show ~2 min + +--- + +### Test 2: Advanced Options Toggle +**Purpose**: Verify collapsible section functionality + +#### Steps: +1. Locate **"🔧 Advanced Options"** section +2. Click the header to expand +3. Observe the arrow icon change: ▼ → ▲ +4. Verify 9 additional fields appear: + - Batch Size dropdown + - Gradient Accumulation + - Warmup Steps + - LoRA Rank, Alpha, Dropout + - Weight Decay + - Max Grad Norm + - Random Seed +5. Click header again to collapse + +**Expected Result**: ✅ Smooth toggle animation, all 9 fields visible/hidden + +--- + +### Test 3: Real-Time Estimates +**Purpose**: Verify dynamic calculations update correctly + +#### Steps: +1. Apply Standard preset (📊 button) +2. Note initial estimate: ~10 minutes +3. Change epochs from 3 → 5 +4. Watch estimate update to ~16 minutes +5. Change batch size from 2 → 4 +6. Watch estimate update to ~8 minutes +7. Change LoRA rank from 8 → 16 +8. Watch VRAM estimate update from ~4.0GB → ~4.5GB + +**Expected Result**: ✅ Instant updates, accurate time/VRAM calculations + +--- + +### Test 4: Validation System +**Purpose**: Test form validation and error messages + +#### Test 4a - Invalid Job Name: +1. Enter job name: `My Test Job` (spaces) +2. Click "🚀 Start Training" +3. Expect error: "Job name must be lowercase letters, numbers, and underscores only" + +#### Test 4b - Missing Dataset: +1. Clear job name field +2. Click "🚀 Start Training" +3. Expect error listing: + - "Job name is required" + - "Please select a dataset" + +#### Test 4c - Invalid Range: +1. Set epochs to 25 (exceeds max of 20) +2. Try to submit +3. Expect validation error + +**Expected Result**: ✅ Clear, specific error messages for each violation + +--- + +### Test 5: Config Save/Load +**Purpose**: Verify configuration management + +#### Save Config: +1. Configure a custom training setup: + - Job name: `my_config_test` + - Epochs: 5 + - LoRA Rank: 16 + - Custom learning rate: 1e-4 +2. Click **"💾 Save Config"** button +3. Verify JSON file downloads: `my_config_test.json` +4. Open file, confirm all 19 parameters present + +#### Load Config: +1. Click **"🔄 Reset"** to clear form +2. Click **"📂 Load Config"** button +3. Select the saved JSON file +4. Verify all parameters restore correctly +5. Check estimates recalculate automatically + +**Expected Result**: ✅ Config exports/imports all 19 parameters accurately + +--- + +### Test 6: Tooltips & Help Text +**Purpose**: Verify all fields have helpful guidance + +#### Steps: +1. Hover over "Job Name" label → Tooltip: "Unique identifier for this training job" +2. Hover over "LoRA Rank" → Tooltip: "LoRA rank parameter" +3. Hover over "Batch Size" → Tooltip: "Batch size for training" +4. Check small gray text under each field for hints +5. Verify all 20+ fields have tooltips + +**Expected Result**: ✅ Instant tooltip display, helpful descriptions + +--- + +### Test 7: Dynamic Info Updates +**Purpose**: Verify model and dataset info changes + +#### Model Info: +1. Select "Phi-3.5-mini-instruct" +2. Observe info: "Fast training, good for chat tasks, 3.8B parameters" +3. Switch to "Qwen2.5-3B-Instruct" +4. Observe info: "Efficient architecture, great for reasoning, 3B parameters" + +#### Dataset Info: +1. Select a dataset from dropdown +2. Observe info updates to show selection +3. Note sample count displayed in dropdown options + +**Expected Result**: ✅ Info updates instantly on selection change + +--- + +### Test 8: Preset Comparison +**Purpose**: Compare all 4 presets side-by-side + +| Preset | Epochs | Samples | Rank | Est. Time | Use Case | +|--------|--------|---------|------|-----------|----------| +| ⚡ Quick Test | 1 | 100 | 4 | ~2 min | Pipeline test | +| 📊 Standard | 3 | 1k | 8 | ~10 min | Development | +| 🏆 Full | 5 | -1 (all) | 16 | ~60 min | Thorough | +| 🚀 Production | 10 | -1 (all) | 32 | ~4 hours | Production | + +#### Steps: +1. Click each preset button in sequence +2. Observe parameter changes for each +3. Note estimate scaling with complexity +4. Verify VRAM increases with LoRA rank + +**Expected Result**: ✅ Each preset applies distinct, logical values + +--- + +### Test 9: Evaluation Toggle +**Purpose**: Test conditional field visibility + +#### Steps: +1. Ensure "Enable Evaluation" is checked (default) +2. Verify eval options visible: + - Max Eval Samples (default: 100) + - Eval Steps (default: 50) +3. Uncheck "Enable Evaluation" +4. Verify eval options disappear +5. Re-check to show again + +**Expected Result**: ✅ Smooth show/hide transition, options persist + +--- + +### Test 10: Long-Run Confirmation +**Purpose**: Verify safety dialog for extended jobs + +#### Steps: +1. Apply Production preset (10 epochs, all samples) +2. Ensure estimates show >1 hour +3. Enter valid job name and select dataset +4. Click "🚀 Start Training" +5. Expect confirmation dialog: + - "This training job is estimated to take ~X hours. Continue?" +6. Click "Cancel" to abort +7. Try again, click "OK" to proceed + +**Expected Result**: ✅ Dialog appears for long jobs, prevents accidental runs + +--- + +## 🎨 Visual Features to Observe + +### Gradient Estimate Card +- Beautiful blue gradient background +- Two columns: Time | VRAM +- Large, readable estimates +- Updates in real-time + +### Button States +- Primary button (Start Training): Full-width, prominent green +- Secondary buttons: Gray, compact +- Hover effects on all buttons +- Preset buttons: Colorful, emoji-prefixed + +### Field Organization +- Clean 3-column layouts +- Consistent spacing +- Required fields marked with red asterisk (*) +- Helper text in muted gray + +### Responsive Feedback +- Toast notifications on actions +- Loading states during submission +- Success/error color coding +- Smooth animations + +--- + +## 📊 API Integration Test + +### Datasets Endpoint +```powershell +Invoke-WebRequest -Uri "http://localhost:8000/api/datasets" | ConvertFrom-Json +``` + +**Expected Response**: +```json +{ + "datasets": [ + { + "name": "chat_logs", + "path": "datasets\\chat\\chat_logs", + "train_samples": 3, + "test_samples": 3 + } + ] +} +``` + +### Start Training Endpoint (Dry Run) +```powershell +$body = @{ + name = "api_test_job" + model = "phi35" + dataset = "datasets/chat/chat_logs" + epochs = 1 + max_samples = 10 + learning_rate = "2e-4" + batch_size = 2 + lora_rank = 4 + lora_alpha = 8 +} | ConvertTo-Json + +Invoke-WebRequest -Uri "http://localhost:8000/api/start-training" -Method POST -Body $body -ContentType "application/json" +``` + +--- + +## 🎯 Success Criteria + +### ✅ All Features Working +- [x] 4 presets apply instantly +- [x] Advanced options toggle smoothly +- [x] Estimates update in real-time +- [x] Validation catches all errors +- [x] Config save/load works perfectly +- [x] Tooltips display on hover +- [x] Dynamic info updates correctly +- [x] Long-run confirmation appears +- [x] Eval options toggle visibility +- [x] API endpoints respond correctly + +### ✅ User Experience +- [x] Intuitive layout and flow +- [x] Clear error messages +- [x] Helpful guidance everywhere +- [x] No confusing states +- [x] Fast, responsive interactions +- [x] Professional visual design + +### ✅ Technical Quality +- [x] No console errors +- [x] All validations work +- [x] Proper error handling +- [x] Clean, maintainable code +- [x] Comprehensive documentation +- [x] Production-ready stability + +--- + +## 🚀 Demo Script (2-Minute Tour) + +### Act 1: Quick Start (30 seconds) +1. Open dashboard → Train tab +2. Enter job name: `demo_tour` +3. Click "⚡ Quick Test" preset +4. Show instant estimates: ~2 minutes +5. Click "🚀 Start Training" (don't actually submit) + +### Act 2: Advanced Power (45 seconds) +1. Click "🔧 Advanced Options" to expand +2. Show 9 additional parameters +3. Change LoRA rank 4 → 16 +4. Watch VRAM estimate increase +5. Click "📊 Standard" preset +6. Show all fields update instantly + +### Act 3: Safety Features (45 seconds) +1. Click "🚀 Production" preset +2. Show 4-hour estimate +3. Try invalid job name: `Test Job` +4. Show validation error +5. Fix name: `test_job` +6. Show confirmation dialog for long run +7. Demonstrate config save/load +8. Show successful config export + +**Grand Finale**: "20+ features, zero friction, production-ready!" 🎉 + +--- + +## 📸 Screenshot Checklist + +### Must-Capture Views +1. ✅ Train tab with all basic fields +2. ✅ Advanced options expanded (9 fields visible) +3. ✅ Estimate card showing calculations +4. ✅ All 4 preset buttons in action +5. ✅ Validation error message +6. ✅ Config save dialog +7. ✅ Tooltip hover example +8. ✅ Long-run confirmation dialog + +--- + +## 🎓 Training Scenarios + +### Scenario 1: First-Time User +**Goal**: Test pipeline quickly +- Use "⚡ Quick Test" preset +- 1 epoch, 100 samples +- 2-minute run +- Verify everything works + +### Scenario 2: Iterative Developer +**Goal**: Experiment with hyperparameters +- Start with "📊 Standard" preset +- Adjust LoRA rank (8 → 16) +- Save as `experiment_v1.json` +- Train and evaluate +- Load config, adjust, save as `experiment_v2.json` + +### Scenario 3: Production Team +**Goal**: Deploy best model +- Use "🚀 Production" preset +- Open Advanced Options +- Fine-tune all parameters +- Save as `production_final.json` +- Share with team +- Deploy to production + +### Scenario 4: Budget-Conscious User +**Goal**: Optimize training time +- Monitor estimate card closely +- Balance epochs vs. samples +- Use batch size efficiently +- Aim for <30 minute runs +- Multiple quick iterations + +--- + +## 🔧 Troubleshooting Demo + +### Issue: Dropdown Empty +**Demo**: Show empty dropdown +**Solution**: Navigate to Datasets tab, show datasets loading +**Result**: Dropdown populates automatically + +### Issue: Validation Error +**Demo**: Enter "My Test Job" with spaces +**Solution**: Show error, correct to "my_test_job" +**Result**: Validation passes, ready to submit + +### Issue: Estimates Seem Off +**Demo**: Show default 5-minute estimate +**Solution**: Adjust any parameter to trigger recalculation +**Result**: Accurate time and VRAM shown + +--- + +## 🎉 Final Checklist + +Before considering demo complete: + +- [ ] Server running at http://localhost:8000 +- [ ] Dashboard loads without errors +- [ ] Train tab visible and accessible +- [ ] All 20+ fields present and functional +- [ ] 4 presets working correctly +- [ ] Advanced options toggle works +- [ ] Estimates update in real-time +- [ ] Validation catches errors +- [ ] Config save/load functions +- [ ] Tooltips display on hover +- [ ] API endpoints responding +- [ ] No console errors +- [ ] Professional visual appearance +- [ ] Ready for production use + +--- + +**Demo Status**: ✅ READY TO SHOWCASE +**Server Status**: 🟢 Online at http://localhost:8000 +**Next Step**: Open browser, navigate to Train tab, begin demo! + +*"From zero to advanced training control in 2 minutes!"* 🚀 diff --git a/docs/guides/DASHBOARD_ENHANCEMENTS.md b/docs/guides/DASHBOARD_ENHANCEMENTS.md index d0869b620..98a20ef9a 100644 --- a/docs/guides/DASHBOARD_ENHANCEMENTS.md +++ b/docs/guides/DASHBOARD_ENHANCEMENTS.md @@ -1,154 +1,154 @@ -# Dashboard Enhancements Summary - -## New Features Implemented (2025-11-23) - -### 1. Cancel Job Mechanism - -**Backend (`dashboard/app.py`):** -- Added `/api/cancel_job/` POST endpoint -- Reads PID from file-based tracking (`data_out/autotrain/.pid`) -- Terminates process tree using OS-appropriate commands: - - Windows: `taskkill /F /T /PID ` - - Unix/Linux: `os.killpg(os.getpgid(pid), signal.SIGTERM)` -- Updates job status to `cancelled` with timestamp -- Calculates elapsed duration for cancelled jobs - -**Frontend (`dashboard/templates/index.html`):** -- Cancel button appears for `running` and `retry_running` jobs -- Confirmation dialog before cancellation -- Visual feedback: "Cancelling..." → "Cancelled" -- Available in both desktop table and mobile card views - -**Training Script (`scripts/training/autotrain.py`):** -- Writes PID to file after subprocess spawns -- Cleans up PID file automatically on job completion -- PID file location: `data_out/autotrain/.pid` - -### 2. Validated Type Display - -**Data Model:** -- `validated_type` field distinguishes validation phases: - - `"dry-run"`: Job validated during `--dry-run` mode - - `"preflight"`: Job validated before actual execution - -**UI Display:** -- New "Type" column in job tables (desktop and category sections) -- Displayed as small text in validated jobs -- Shows in mobile card view meta info -- Badge styling: Purple badge for `validated` status - -**Use Cases:** -- Distinguish dry-run checks from runtime preflight validation -- Track which jobs passed validation without execution -- Debug configuration issues by validation phase - -### 3. Per-Job ETA Estimates - -**Algorithm:** -- Calculates runner-specific average durations: - - Groups completed jobs by `runner` type ("hf" vs "local") - - Computes mean duration for each runner category -- For running jobs: - - Parses `start_time` to calculate elapsed seconds - - Subtracts elapsed from runner-specific average - - Returns remaining time estimate (rounded to 1 decimal) - -**UI Display:** -- New "ETA (s)" column in desktop tables -- Shows estimated remaining seconds for running jobs -- Displayed in mobile card meta info: "ETA: X.Xs" -- Empty for completed/pending jobs - -**Benefits:** -- More accurate than global average (accounts for runner differences) -- Real-time progress visibility -- Helps users estimate overall pipeline completion - -## Visual Improvements - -### Status Badges -- `succeeded` → Green gradient -- `failed` → Red -- `running` → Blue -- `pending` → Gray -- `validated` → Purple (new) -- `cancelled` → Orange (new) - -### Button Styling -- **Retry**: Gray border, hover brightness -- **Cancel**: Red border/text, hover fills red background - -### Theme System -- Dark/light mode toggle (moon/sun icon in header) -- Persisted via `localStorage` -- CSS variable system for consistent theming -- Responsive design for narrow screens (<640px) - -## API Endpoints - -| Endpoint | Method | Purpose | Returns | -|----------|--------|---------|---------| -| `/api/training_progress` | GET | Current progress snapshot | Full job status with ETA, validated_type, category | -| `/api/retry_job/` | POST | Retry failed/succeeded job | `{accepted: true, retry_count: N}` | -| `/api/cancel_job/` | POST | Cancel running job | `{cancelled: true, pid: N}` | -| `/api/training_progress_history` | GET | Historical snapshots | Array of progress states | -| `/api/training_progress_history_csv` | GET | Export history as CSV | CSV file download | - -## Testing - -### Dry-Run Verification -```powershell -python .\scripts\autotrain.py --dry-run -``` -- Confirms `validated_type: "dry-run"` in output -- All jobs should show `validated` status - -### Dashboard Smoke Test -```powershell -.\venv\Scripts\python.exe -c "from dashboard.app import app; print('OK')" -``` -- Verifies imports and no syntax errors - -### Live Testing -1. Start dashboard: `python dashboard/app.py` or use SocketIO runner -2. Navigate to http://localhost:5000 -3. Click theme toggle (moon icon) to test dark mode -4. Start a training job to see: - - Per-job ETA in running job row - - Cancel button availability - - Validated type display - -### Cancel Testing -1. Start a long-running job: `python .\scripts\autotrain.py --job ` -2. Click "Cancel" button in dashboard -3. Confirm PID file cleanup: `Test-Path data_out/autotrain/.pid` (should be False) -4. Verify status changes to `cancelled` - -## Configuration Example - -All job categories propagate from `autotrain.yaml`: -```yaml -jobs: - - name: phi35_comprehensive_full - runner: hf - category: comprehensive # Shows in collapsible "comprehensive" section - dataset: datasets/chat/comprehensive - ... -``` - -## Backward Compatibility - -- Existing status.json files work without modification -- Jobs without `validated_type` display empty cell (no errors) -- Jobs without `category` grouped under "uncategorized" -- PID files are optional (cancel only works when file present) - -## Future Enhancements (Potential) - -- [ ] Real-time log streaming for running jobs -- [ ] Pause/resume functionality -- [ ] Job queue reordering -- [ ] Email/webhook notifications on completion -- [ ] GPU utilization metrics in dashboard -- [ ] Multi-user authentication and job ownership -- [ ] Historical trend charts (success rate over time) +# Dashboard Enhancements Summary + +## New Features Implemented (2025-11-23) + +### 1. Cancel Job Mechanism + +**Backend (`dashboard/app.py`):** +- Added `/api/cancel_job/` POST endpoint +- Reads PID from file-based tracking (`data_out/autotrain/.pid`) +- Terminates process tree using OS-appropriate commands: + - Windows: `taskkill /F /T /PID ` + - Unix/Linux: `os.killpg(os.getpgid(pid), signal.SIGTERM)` +- Updates job status to `cancelled` with timestamp +- Calculates elapsed duration for cancelled jobs + +**Frontend (`dashboard/templates/index.html`):** +- Cancel button appears for `running` and `retry_running` jobs +- Confirmation dialog before cancellation +- Visual feedback: "Cancelling..." → "Cancelled" +- Available in both desktop table and mobile card views + +**Training Script (`scripts/training/autotrain.py`):** +- Writes PID to file after subprocess spawns +- Cleans up PID file automatically on job completion +- PID file location: `data_out/autotrain/.pid` + +### 2. Validated Type Display + +**Data Model:** +- `validated_type` field distinguishes validation phases: + - `"dry-run"`: Job validated during `--dry-run` mode + - `"preflight"`: Job validated before actual execution + +**UI Display:** +- New "Type" column in job tables (desktop and category sections) +- Displayed as small text in validated jobs +- Shows in mobile card view meta info +- Badge styling: Purple badge for `validated` status + +**Use Cases:** +- Distinguish dry-run checks from runtime preflight validation +- Track which jobs passed validation without execution +- Debug configuration issues by validation phase + +### 3. Per-Job ETA Estimates + +**Algorithm:** +- Calculates runner-specific average durations: + - Groups completed jobs by `runner` type ("hf" vs "local") + - Computes mean duration for each runner category +- For running jobs: + - Parses `start_time` to calculate elapsed seconds + - Subtracts elapsed from runner-specific average + - Returns remaining time estimate (rounded to 1 decimal) + +**UI Display:** +- New "ETA (s)" column in desktop tables +- Shows estimated remaining seconds for running jobs +- Displayed in mobile card meta info: "ETA: X.Xs" +- Empty for completed/pending jobs + +**Benefits:** +- More accurate than global average (accounts for runner differences) +- Real-time progress visibility +- Helps users estimate overall pipeline completion + +## Visual Improvements + +### Status Badges +- `succeeded` → Green gradient +- `failed` → Red +- `running` → Blue +- `pending` → Gray +- `validated` → Purple (new) +- `cancelled` → Orange (new) + +### Button Styling +- **Retry**: Gray border, hover brightness +- **Cancel**: Red border/text, hover fills red background + +### Theme System +- Dark/light mode toggle (moon/sun icon in header) +- Persisted via `localStorage` +- CSS variable system for consistent theming +- Responsive design for narrow screens (<640px) + +## API Endpoints + +| Endpoint | Method | Purpose | Returns | +|----------|--------|---------|---------| +| `/api/training_progress` | GET | Current progress snapshot | Full job status with ETA, validated_type, category | +| `/api/retry_job/` | POST | Retry failed/succeeded job | `{accepted: true, retry_count: N}` | +| `/api/cancel_job/` | POST | Cancel running job | `{cancelled: true, pid: N}` | +| `/api/training_progress_history` | GET | Historical snapshots | Array of progress states | +| `/api/training_progress_history_csv` | GET | Export history as CSV | CSV file download | + +## Testing + +### Dry-Run Verification +```powershell +python .\scripts\autotrain.py --dry-run +``` +- Confirms `validated_type: "dry-run"` in output +- All jobs should show `validated` status + +### Dashboard Smoke Test +```powershell +.\venv\Scripts\python.exe -c "from dashboard.app import app; print('OK')" +``` +- Verifies imports and no syntax errors + +### Live Testing +1. Start dashboard: `python dashboard/app.py` or use SocketIO runner +2. Navigate to http://localhost:5000 +3. Click theme toggle (moon icon) to test dark mode +4. Start a training job to see: + - Per-job ETA in running job row + - Cancel button availability + - Validated type display + +### Cancel Testing +1. Start a long-running job: `python .\scripts\autotrain.py --job ` +2. Click "Cancel" button in dashboard +3. Confirm PID file cleanup: `Test-Path data_out/autotrain/.pid` (should be False) +4. Verify status changes to `cancelled` + +## Configuration Example + +All job categories propagate from `autotrain.yaml`: +```yaml +jobs: + - name: phi35_comprehensive_full + runner: hf + category: comprehensive # Shows in collapsible "comprehensive" section + dataset: datasets/chat/comprehensive + ... +``` + +## Backward Compatibility + +- Existing status.json files work without modification +- Jobs without `validated_type` display empty cell (no errors) +- Jobs without `category` grouped under "uncategorized" +- PID files are optional (cancel only works when file present) + +## Future Enhancements (Potential) + +- [ ] Real-time log streaming for running jobs +- [ ] Pause/resume functionality +- [ ] Job queue reordering +- [ ] Email/webhook notifications on completion +- [ ] GPU utilization metrics in dashboard +- [ ] Multi-user authentication and job ownership +- [ ] Historical trend charts (success rate over time) diff --git a/docs/guides/DASHBOARD_FEATURES.md b/docs/guides/DASHBOARD_FEATURES.md index d8fc2b7c1..bfed0d0b9 100644 --- a/docs/guides/DASHBOARD_FEATURES.md +++ b/docs/guides/DASHBOARD_FEATURES.md @@ -1,180 +1,180 @@ -# QAI Enhanced Dashboard - Feature Guide - -## 🎉 New Features Added! - -### 📊 **5 Interactive Tabs** - -#### 1. **Overview Tab** -- **Live Statistics Cards**: - - ✅ Completed Jobs - - 🏃 Running Jobs - - ⏳ Pending Jobs - - ⚡ Average Duration - - 🎯 Best Model (lowest perplexity) -- **Overall Progress Bar**: Visual completion percentage -- **Recent Jobs**: Quick view of last 5 training runs - -#### 2. **Jobs Tab** -- **Expandable Job Cards**: Click any job to see detailed metrics -- **Status-Based Coloring**: - - 🟢 Green border = Succeeded - - 🟡 Yellow pulsing = Running - - 🔴 Red border = Failed -- **Detailed Metrics Display**: - - Pre/Post training loss - - Pre/Post perplexity - - Training duration - - Improvement percentage -- **📜 View Logs Button**: Real-time log streaming for each job -- **Training Command**: Full command line used for reproduction - -#### 3. **Models Tab** -- **Model Leaderboard**: Ranked by performance (perplexity) -- **👑 Winner Badge**: Best performing model highlighted -- **Side-by-Side Comparison**: - - Final perplexity scores - - Improvement percentages - - Training duration -- **🚀 Quick Deploy**: One-click deployment buttons (future integration) - -#### 4. **Datasets Tab** -- **Dataset Gallery**: Visual cards for all available datasets -- **Sample Counts**: Training & testing splits displayed -- **Quick Stats**: File sizes, creation dates, sample counts -- **Click to Select**: Interactive selection for training - -#### 5. **⚡ New Training Tab** -- **Interactive Training Controls**: - - 📋 Select training configuration (YAML files) - - 📁 Choose dataset from dropdown - - 🤖 Pick model (Phi-3.5, Qwen2.5) - - 🎛️ Configure hyperparameters: - - Job name - - Number of epochs (1-20) - - Learning rate (0.00001-0.001) -- **🚀 Start Training Button**: Launch jobs directly from dashboard -- **Real-time Validation**: Form validation before submission - -### 🔧 **Backend API Endpoints** - -``` -GET /status → Current training status -GET /api/datasets → List all available datasets -GET /api/models → List trained model adapters -GET /api/configs → List training YAML configs -GET /api/job/ → Detailed job information -GET /api/logs/ → Streaming logs for specific job -POST /api/start-training → Launch new training job -``` - -### ✨ **Enhanced Features** - -1. **Real-Time Metrics** - - Live loss curves tracking - - Perplexity improvements calculated automatically - - Duration tracking with human-readable format - -2. **Log Viewer** - - Last 500 lines of training output - - Syntax-highlighted console-style display - - Dark theme for readability - - Auto-scroll to latest entries - -3. **Model Comparison** - - Automatic ranking by performance - - Visual winner highlighting - - Percentage improvement calculations - - Quick comparison cards - -4. **Smart Auto-Refresh** - - Optional 10-second auto-update - - Manual refresh button - - Last update timestamp - - Efficient API calls (only changed data) - -5. **Responsive Design** - - Grid layouts adapt to screen size - - Hover effects for better UX - - Color-coded status indicators - - Smooth animations - -## 🚀 Usage Examples - -### Monitor Active Training -1. Navigate to **📊 Overview** tab -2. Enable **Auto-refresh** checkbox -3. Watch real-time progress updates -4. View **🏃 Running Jobs** stat for active count - -### Compare Model Performance -1. Go to **🤖 Models** tab -2. See all completed models ranked -3. Check perplexity scores and improvements -4. Top model has **👑** crown badge - -### View Detailed Job Info -1. Open **🏃 Jobs** tab -2. Click any job card to expand -3. View pre/post training metrics -4. Click **📜 View Logs** for output - -### Start New Training -1. Switch to **⚡ New Training** tab -2. Select configuration (e.g., `autotrain_extended_marathon`) -3. Choose dataset (e.g., `mega_synthetic`) -4. Set job name and hyperparameters -5. Click **🚀 Start Training** -6. Job appears in jobs list immediately - -## 📡 API Testing - -Test endpoints directly: -```powershell -# Get current status -curl http://localhost:8000/status | jq - -# List datasets -curl http://localhost:8000/api/datasets | jq - -# List trained models -curl http://localhost:8000/api/models | jq - -# Get job details -curl http://localhost:8000/api/job/phi35_mega_synthetic_full | jq - -# View logs -curl http://localhost:8000/api/logs/phi35_mega_synthetic_full -``` - -## 🎨 Visual Indicators - -- **Colors**: - - Purple gradient = Main theme - - Green = Success/Positive - - Yellow = Running/Warning - - Red = Failed/Error - - Gray = Pending/Neutral - -- **Animations**: - - Pulsing = Active job running - - Hover lift = Interactive element - - Smooth progress bars = Live updates - -## 🔮 Future Enhancements - -- Real-time streaming progress bars during training -- TensorBoard integration for loss curves -- Model deployment to Azure ML -- A/B testing framework -- Automated hyperparameter optimization -- Email/Slack notifications on completion -- GPU utilization charts -- Cost tracking per job - -## 📝 Access URLs - -- **Enhanced Dashboard**: http://localhost:8000/enhanced.html -- **Original Dashboard**: http://localhost:8000/index.html -- **API Docs**: Coming soon - -Enjoy your enhanced training dashboard! 🎉 +# QAI Enhanced Dashboard - Feature Guide + +## 🎉 New Features Added! + +### 📊 **5 Interactive Tabs** + +#### 1. **Overview Tab** +- **Live Statistics Cards**: + - ✅ Completed Jobs + - 🏃 Running Jobs + - ⏳ Pending Jobs + - ⚡ Average Duration + - 🎯 Best Model (lowest perplexity) +- **Overall Progress Bar**: Visual completion percentage +- **Recent Jobs**: Quick view of last 5 training runs + +#### 2. **Jobs Tab** +- **Expandable Job Cards**: Click any job to see detailed metrics +- **Status-Based Coloring**: + - 🟢 Green border = Succeeded + - 🟡 Yellow pulsing = Running + - 🔴 Red border = Failed +- **Detailed Metrics Display**: + - Pre/Post training loss + - Pre/Post perplexity + - Training duration + - Improvement percentage +- **📜 View Logs Button**: Real-time log streaming for each job +- **Training Command**: Full command line used for reproduction + +#### 3. **Models Tab** +- **Model Leaderboard**: Ranked by performance (perplexity) +- **👑 Winner Badge**: Best performing model highlighted +- **Side-by-Side Comparison**: + - Final perplexity scores + - Improvement percentages + - Training duration +- **🚀 Quick Deploy**: One-click deployment buttons (future integration) + +#### 4. **Datasets Tab** +- **Dataset Gallery**: Visual cards for all available datasets +- **Sample Counts**: Training & testing splits displayed +- **Quick Stats**: File sizes, creation dates, sample counts +- **Click to Select**: Interactive selection for training + +#### 5. **⚡ New Training Tab** +- **Interactive Training Controls**: + - 📋 Select training configuration (YAML files) + - 📁 Choose dataset from dropdown + - 🤖 Pick model (Phi-3.5, Qwen2.5) + - 🎛️ Configure hyperparameters: + - Job name + - Number of epochs (1-20) + - Learning rate (0.00001-0.001) +- **🚀 Start Training Button**: Launch jobs directly from dashboard +- **Real-time Validation**: Form validation before submission + +### 🔧 **Backend API Endpoints** + +``` +GET /status → Current training status +GET /api/datasets → List all available datasets +GET /api/models → List trained model adapters +GET /api/configs → List training YAML configs +GET /api/job/ → Detailed job information +GET /api/logs/ → Streaming logs for specific job +POST /api/start-training → Launch new training job +``` + +### ✨ **Enhanced Features** + +1. **Real-Time Metrics** + - Live loss curves tracking + - Perplexity improvements calculated automatically + - Duration tracking with human-readable format + +2. **Log Viewer** + - Last 500 lines of training output + - Syntax-highlighted console-style display + - Dark theme for readability + - Auto-scroll to latest entries + +3. **Model Comparison** + - Automatic ranking by performance + - Visual winner highlighting + - Percentage improvement calculations + - Quick comparison cards + +4. **Smart Auto-Refresh** + - Optional 10-second auto-update + - Manual refresh button + - Last update timestamp + - Efficient API calls (only changed data) + +5. **Responsive Design** + - Grid layouts adapt to screen size + - Hover effects for better UX + - Color-coded status indicators + - Smooth animations + +## 🚀 Usage Examples + +### Monitor Active Training +1. Navigate to **📊 Overview** tab +2. Enable **Auto-refresh** checkbox +3. Watch real-time progress updates +4. View **🏃 Running Jobs** stat for active count + +### Compare Model Performance +1. Go to **🤖 Models** tab +2. See all completed models ranked +3. Check perplexity scores and improvements +4. Top model has **👑** crown badge + +### View Detailed Job Info +1. Open **🏃 Jobs** tab +2. Click any job card to expand +3. View pre/post training metrics +4. Click **📜 View Logs** for output + +### Start New Training +1. Switch to **⚡ New Training** tab +2. Select configuration (e.g., `autotrain_extended_marathon`) +3. Choose dataset (e.g., `mega_synthetic`) +4. Set job name and hyperparameters +5. Click **🚀 Start Training** +6. Job appears in jobs list immediately + +## 📡 API Testing + +Test endpoints directly: +```powershell +# Get current status +curl http://localhost:8000/status | jq + +# List datasets +curl http://localhost:8000/api/datasets | jq + +# List trained models +curl http://localhost:8000/api/models | jq + +# Get job details +curl http://localhost:8000/api/job/phi35_mega_synthetic_full | jq + +# View logs +curl http://localhost:8000/api/logs/phi35_mega_synthetic_full +``` + +## 🎨 Visual Indicators + +- **Colors**: + - Purple gradient = Main theme + - Green = Success/Positive + - Yellow = Running/Warning + - Red = Failed/Error + - Gray = Pending/Neutral + +- **Animations**: + - Pulsing = Active job running + - Hover lift = Interactive element + - Smooth progress bars = Live updates + +## 🔮 Future Enhancements + +- Real-time streaming progress bars during training +- TensorBoard integration for loss curves +- Model deployment to Azure ML +- A/B testing framework +- Automated hyperparameter optimization +- Email/Slack notifications on completion +- GPU utilization charts +- Cost tracking per job + +## 📝 Access URLs + +- **Enhanced Dashboard**: http://localhost:8000/enhanced.html +- **Original Dashboard**: http://localhost:8000/index.html +- **API Docs**: Coming soon + +Enjoy your enhanced training dashboard! 🎉 diff --git a/docs/guides/DATASETS_SETUP_COMPLETE.md b/docs/guides/DATASETS_SETUP_COMPLETE.md index ff45e7536..266a3e5c6 100644 --- a/docs/guides/DATASETS_SETUP_COMPLETE.md +++ b/docs/guides/DATASETS_SETUP_COMPLETE.md @@ -1,300 +1,300 @@ -# AI Training Datasets - Setup Complete! ✅ - -**Status**: Ready for AI training -**Date**: October 31, 2025 -**Location**: `C:\Users\Bryan\OneDrive\AI\datasets` - ---- - -## 📦 What's Been Set Up - -### 1. Directory Structure ✅ -``` -AI/ -├── datasets/ # Centralized data storage -│ ├── raw/ # Original downloads -│ ├── processed/ # Cleaned data -│ ├── quantum/ # ✅ 4 datasets downloaded (0.21 MB) -│ │ ├── heart_disease.csv # 303 samples, 14 features -│ │ ├── ionosphere.csv # 351 samples, 35 features -│ │ ├── sonar.csv # 208 samples, 61 features -│ │ └── banknote.csv # 1,372 samples, 5 features -│ ├── chat/ # Ready for LLM datasets -│ ├── vision/ # Ready for image data -│ ├── dataset_index.json # Auto-updated inventory -│ └── README.md # Full usage guide -├── scripts/ # Automation tools -│ ├── download_datasets.py # ✅ Tested & working -│ ├── validate_datasets.py # ✅ Tested & working -│ └── quick_setup_datasets.py # One-command setup -├── AI_DATASETS_CATALOG.md # Comprehensive dataset guide -├── DATASETS_QUICK_REF.md # Quick command reference -└── dataset-requirements.txt # Python dependencies -``` - -### 2. Automated Tools ✅ -- **Downloader**: Downloads from UCI, Hugging Face, Kaggle -- **Validator**: Checks format, integrity, counts samples -- **Quick Setup**: One-command installation -- **Index Manager**: Tracks metadata automatically - -### 3. Documentation ✅ -- **AI_DATASETS_CATALOG.md**: 400+ line comprehensive guide - - Dataset sources (Hugging Face, UCI, Kaggle) - - License information - - Quality ratings - - Usage examples -- **datasets/README.md**: Full usage documentation -- **DATASETS_QUICK_REF.md**: Fast command reference - ---- - -## 🚀 Next Steps - Start Training Now! - -### Option 1: Train Quantum AI (Fastest - 2 minutes) -```powershell -cd quantum-ai -python .\train_custom_dataset.py -``` - -The script already uses the Wine dataset for demo. Modify `load_your_data()` to use downloaded datasets: - -```python -# In train_custom_dataset.py, replace the demo code with: -df = pd.read_csv("../datasets/quantum/heart_disease.csv") -X = df.iloc[:, :-1].values # All features except last -y = df.iloc[:, -1].values # Last column as label -return X, y -``` - -### Option 2: Download Chat Dataset & Fine-tune Phi-3.6 -```powershell -# Step 1: Download Dolly 15k (50 MB, 2 minutes) -python .\scripts\download_datasets.py --category chat --dataset dolly - -# Step 2: Quick test (CPU-friendly, 10 minutes) -cd AI\microsoft_phi-silica-3.6_v1 -python .\scripts\train_lora.py ` - --dataset ..\..\datasets\chat\dolly ` - --config .\lora\lora.yaml ` - --max-train-samples 64 ` - --max-eval-samples 32 ` - --no-stream - -# Step 3: Full training (GPU required, hours) -python .\scripts\train_lora.py ` - --dataset ..\..\datasets\chat\dolly ` - --config .\lora\lora.yaml -``` - -### Option 3: One-Command Setup (Everything) -```powershell -# Downloads quantum + Dolly 15k + validates all -python .\scripts\quick_setup_datasets.py -``` - ---- - -## 📊 Currently Available - -### Quantum Datasets (Downloaded ✅) -| Dataset | Size | Samples | Features | Use Case | -|---------|------|---------|----------|----------| -| Heart Disease | 20 KB | 303 | 14 | Medical diagnosis | -| Ionosphere | 70 KB | 351 | 35 | Radar signal classification | -| Sonar | 80 KB | 208 | 61 | Object detection (mines vs rocks) | -| Banknote | 40 KB | 1,372 | 5 | Authentication (fraud detection) | - -**Total**: 4 datasets, 210 KB, 2,234 samples - -### Built-in Datasets (No Download Needed) -- Iris (150 samples, 4 features) - via scikit-learn -- Wine (178 samples, 13 features) - via scikit-learn -- Breast Cancer (569 samples, 30 features) - via scikit-learn - -### Chat Datasets (Ready to Download) -| Dataset | Size | Quality | License | Command | -|---------|------|---------|---------|---------| -| Dolly 15k | 50 MB | ⭐⭐⭐⭐⭐ | Commercial ✅ | `--dataset dolly` | -| OpenAssistant | 500 MB | ⭐⭐⭐⭐⭐ | Commercial ✅ | `--dataset openassistant` | -| Alpaca | 100 MB | ⭐⭐⭐⭐ | Non-commercial ⚠️ | `--dataset alpaca` | - ---- - -## 🎯 Recommended Workflow - -### For Quantum AI Development: -1. ✅ **Quantum datasets downloaded** - Ready to use! -2. Train on built-in datasets first (Iris, Wine) -3. Switch to UCI datasets for real-world testing -4. Use 4 qubits for Iris, 6-8 qubits for larger datasets -5. PCA recommended for >10 features - -### For LLM Fine-tuning: -1. Download Dolly 15k (commercial-safe, high quality) -2. Test with `--max-train-samples 64` (quick iteration) -3. Scale up to full dataset when ready -4. Monitor GPU memory usage -5. Use streaming for >5GB datasets - -### For Both: -1. Always validate after downloading -2. Check `dataset_index.json` for metadata -3. Monitor disk space (`Get-PSDrive C`) -4. Document your data sources -5. Respect dataset licenses - ---- - -## 🔧 Maintenance Commands - -### Check what's downloaded: -```powershell -python .\scripts\download_datasets.py --list -``` - -### Validate everything: -```powershell -python .\scripts\validate_datasets.py --verbose -``` - -### Download more datasets: -```powershell -# See full catalog -type AI_DATASETS_CATALOG.md - -# Download specific dataset -python .\scripts\download_datasets.py --category chat --dataset dolly -``` - -### Check storage usage: -```powershell -Get-ChildItem .\datasets -Recurse | Measure-Object -Property Length -Sum -``` - ---- - -## 📚 Documentation Index - -1. **AI_DATASETS_CATALOG.md** - Comprehensive guide - - All available datasets - - Sources and licenses - - Quality ratings - - Training examples - -2. **datasets/README.md** - Usage documentation - - Directory structure - - Download instructions - - Training commands - - Troubleshooting - -3. **DATASETS_QUICK_REF.md** - Fast reference - - Common commands - - Size/quality table - - Quick tips - -4. **dataset-requirements.txt** - Dependencies - - `datasets` (Hugging Face) - - `tqdm` (progress bars) - - `pandas`, `numpy`, `scikit-learn` - ---- - -## ✅ Verification Results - -### Downloads: ✅ PASSED -- Heart Disease: 303 samples, 14 features -- Ionosphere: 351 samples, 35 features -- Sonar: 208 samples, 61 features -- Banknote: 1,372 samples, 5 features - -### Validation: ✅ PASSED -- All CSV files valid -- No format errors -- Feature counts consistent -- Ready for training - -### Scripts: ✅ TESTED -- `download_datasets.py --help` ✅ -- `download_datasets.py --category quantum` ✅ -- `validate_datasets.py --category quantum --verbose` ✅ -- All 4 datasets downloaded and validated - ---- - -## 🎓 Learning Resources - -### Dataset Sources: -- **Hugging Face**: 100,000+ datasets for ML/NLP -- **UCI ML Repository**: Classic ML datasets (30+ years) -- **Kaggle**: Competitions and community datasets -- **Papers with Code**: Research-quality datasets - -### Training Tutorials: -- Quantum AI: `ai-projects/quantum-ml/README.md` -- Phi-3.6 Fine-tuning: `AI/microsoft_phi-silica-3.6_v1/README.md` -- Custom datasets: `ai-projects/quantum-ml/train_custom_dataset.py` - -### Tools: -- Hugging Face `datasets` library (streaming, caching) -- Pandas (CSV/Excel processing) -- Scikit-learn (built-in datasets, preprocessing) -- Azure ML (large-scale training) - ---- - -## 🚨 Important Notes - -### Licenses: -- **Commercial OK**: Dolly, OpenAssistant, UCI datasets -- **Non-commercial**: Alpaca, some Kaggle datasets -- Always check license before production use - -### Storage: -- Current: ~0.21 MB (quantum datasets) -- Recommended: 10 GB for diverse datasets -- Large-scale: 50-500 GB for production - -### Dependencies: -Some scripts require packages not in base environment: -```powershell -pip install -r dataset-requirements.txt -``` - -### Git: -Add to `.gitignore`: -``` -datasets/raw/ -datasets/chat/ -datasets/vision/ -*.csv -*.jsonl -``` - -Keep in Git: -``` -datasets/README.md -datasets/dataset_index.json -``` - ---- - -## 🎉 You're All Set! - -Your AI workspace now has: -- ✅ Centralized dataset storage -- ✅ Automated download tools -- ✅ Validation scripts -- ✅ 4 quantum datasets ready -- ✅ Comprehensive documentation -- ✅ Training examples - -**Start training now!** Choose an option from "Next Steps" above. - -For questions or issues, see the documentation files or run: -```powershell -python .\scripts\download_datasets.py --help -python .\scripts\validate_datasets.py --help -``` - -Happy training! 🚀 +# AI Training Datasets - Setup Complete! ✅ + +**Status**: Ready for AI training +**Date**: October 31, 2025 +**Location**: `C:\Users\Bryan\OneDrive\AI\datasets` + +--- + +## 📦 What's Been Set Up + +### 1. Directory Structure ✅ +``` +AI/ +├── datasets/ # Centralized data storage +│ ├── raw/ # Original downloads +│ ├── processed/ # Cleaned data +│ ├── quantum/ # ✅ 4 datasets downloaded (0.21 MB) +│ │ ├── heart_disease.csv # 303 samples, 14 features +│ │ ├── ionosphere.csv # 351 samples, 35 features +│ │ ├── sonar.csv # 208 samples, 61 features +│ │ └── banknote.csv # 1,372 samples, 5 features +│ ├── chat/ # Ready for LLM datasets +│ ├── vision/ # Ready for image data +│ ├── dataset_index.json # Auto-updated inventory +│ └── README.md # Full usage guide +├── scripts/ # Automation tools +│ ├── download_datasets.py # ✅ Tested & working +│ ├── validate_datasets.py # ✅ Tested & working +│ └── quick_setup_datasets.py # One-command setup +├── AI_DATASETS_CATALOG.md # Comprehensive dataset guide +├── DATASETS_QUICK_REF.md # Quick command reference +└── dataset-requirements.txt # Python dependencies +``` + +### 2. Automated Tools ✅ +- **Downloader**: Downloads from UCI, Hugging Face, Kaggle +- **Validator**: Checks format, integrity, counts samples +- **Quick Setup**: One-command installation +- **Index Manager**: Tracks metadata automatically + +### 3. Documentation ✅ +- **AI_DATASETS_CATALOG.md**: 400+ line comprehensive guide + - Dataset sources (Hugging Face, UCI, Kaggle) + - License information + - Quality ratings + - Usage examples +- **datasets/README.md**: Full usage documentation +- **DATASETS_QUICK_REF.md**: Fast command reference + +--- + +## 🚀 Next Steps - Start Training Now! + +### Option 1: Train Quantum AI (Fastest - 2 minutes) +```powershell +cd quantum-ai +python .\train_custom_dataset.py +``` + +The script already uses the Wine dataset for demo. Modify `load_your_data()` to use downloaded datasets: + +```python +# In train_custom_dataset.py, replace the demo code with: +df = pd.read_csv("../datasets/quantum/heart_disease.csv") +X = df.iloc[:, :-1].values # All features except last +y = df.iloc[:, -1].values # Last column as label +return X, y +``` + +### Option 2: Download Chat Dataset & Fine-tune Phi-3.6 +```powershell +# Step 1: Download Dolly 15k (50 MB, 2 minutes) +python .\scripts\download_datasets.py --category chat --dataset dolly + +# Step 2: Quick test (CPU-friendly, 10 minutes) +cd AI\microsoft_phi-silica-3.6_v1 +python .\scripts\train_lora.py ` + --dataset ..\..\datasets\chat\dolly ` + --config .\lora\lora.yaml ` + --max-train-samples 64 ` + --max-eval-samples 32 ` + --no-stream + +# Step 3: Full training (GPU required, hours) +python .\scripts\train_lora.py ` + --dataset ..\..\datasets\chat\dolly ` + --config .\lora\lora.yaml +``` + +### Option 3: One-Command Setup (Everything) +```powershell +# Downloads quantum + Dolly 15k + validates all +python .\scripts\quick_setup_datasets.py +``` + +--- + +## 📊 Currently Available + +### Quantum Datasets (Downloaded ✅) +| Dataset | Size | Samples | Features | Use Case | +|---------|------|---------|----------|----------| +| Heart Disease | 20 KB | 303 | 14 | Medical diagnosis | +| Ionosphere | 70 KB | 351 | 35 | Radar signal classification | +| Sonar | 80 KB | 208 | 61 | Object detection (mines vs rocks) | +| Banknote | 40 KB | 1,372 | 5 | Authentication (fraud detection) | + +**Total**: 4 datasets, 210 KB, 2,234 samples + +### Built-in Datasets (No Download Needed) +- Iris (150 samples, 4 features) - via scikit-learn +- Wine (178 samples, 13 features) - via scikit-learn +- Breast Cancer (569 samples, 30 features) - via scikit-learn + +### Chat Datasets (Ready to Download) +| Dataset | Size | Quality | License | Command | +|---------|------|---------|---------|---------| +| Dolly 15k | 50 MB | ⭐⭐⭐⭐⭐ | Commercial ✅ | `--dataset dolly` | +| OpenAssistant | 500 MB | ⭐⭐⭐⭐⭐ | Commercial ✅ | `--dataset openassistant` | +| Alpaca | 100 MB | ⭐⭐⭐⭐ | Non-commercial ⚠️ | `--dataset alpaca` | + +--- + +## 🎯 Recommended Workflow + +### For Quantum AI Development: +1. ✅ **Quantum datasets downloaded** - Ready to use! +2. Train on built-in datasets first (Iris, Wine) +3. Switch to UCI datasets for real-world testing +4. Use 4 qubits for Iris, 6-8 qubits for larger datasets +5. PCA recommended for >10 features + +### For LLM Fine-tuning: +1. Download Dolly 15k (commercial-safe, high quality) +2. Test with `--max-train-samples 64` (quick iteration) +3. Scale up to full dataset when ready +4. Monitor GPU memory usage +5. Use streaming for >5GB datasets + +### For Both: +1. Always validate after downloading +2. Check `dataset_index.json` for metadata +3. Monitor disk space (`Get-PSDrive C`) +4. Document your data sources +5. Respect dataset licenses + +--- + +## 🔧 Maintenance Commands + +### Check what's downloaded: +```powershell +python .\scripts\download_datasets.py --list +``` + +### Validate everything: +```powershell +python .\scripts\validate_datasets.py --verbose +``` + +### Download more datasets: +```powershell +# See full catalog +type AI_DATASETS_CATALOG.md + +# Download specific dataset +python .\scripts\download_datasets.py --category chat --dataset dolly +``` + +### Check storage usage: +```powershell +Get-ChildItem .\datasets -Recurse | Measure-Object -Property Length -Sum +``` + +--- + +## 📚 Documentation Index + +1. **AI_DATASETS_CATALOG.md** - Comprehensive guide + - All available datasets + - Sources and licenses + - Quality ratings + - Training examples + +2. **datasets/README.md** - Usage documentation + - Directory structure + - Download instructions + - Training commands + - Troubleshooting + +3. **DATASETS_QUICK_REF.md** - Fast reference + - Common commands + - Size/quality table + - Quick tips + +4. **dataset-requirements.txt** - Dependencies + - `datasets` (Hugging Face) + - `tqdm` (progress bars) + - `pandas`, `numpy`, `scikit-learn` + +--- + +## ✅ Verification Results + +### Downloads: ✅ PASSED +- Heart Disease: 303 samples, 14 features +- Ionosphere: 351 samples, 35 features +- Sonar: 208 samples, 61 features +- Banknote: 1,372 samples, 5 features + +### Validation: ✅ PASSED +- All CSV files valid +- No format errors +- Feature counts consistent +- Ready for training + +### Scripts: ✅ TESTED +- `download_datasets.py --help` ✅ +- `download_datasets.py --category quantum` ✅ +- `validate_datasets.py --category quantum --verbose` ✅ +- All 4 datasets downloaded and validated + +--- + +## 🎓 Learning Resources + +### Dataset Sources: +- **Hugging Face**: 100,000+ datasets for ML/NLP +- **UCI ML Repository**: Classic ML datasets (30+ years) +- **Kaggle**: Competitions and community datasets +- **Papers with Code**: Research-quality datasets + +### Training Tutorials: +- Quantum AI: `ai-projects/quantum-ml/README.md` +- Phi-3.6 Fine-tuning: `AI/microsoft_phi-silica-3.6_v1/README.md` +- Custom datasets: `ai-projects/quantum-ml/train_custom_dataset.py` + +### Tools: +- Hugging Face `datasets` library (streaming, caching) +- Pandas (CSV/Excel processing) +- Scikit-learn (built-in datasets, preprocessing) +- Azure ML (large-scale training) + +--- + +## 🚨 Important Notes + +### Licenses: +- **Commercial OK**: Dolly, OpenAssistant, UCI datasets +- **Non-commercial**: Alpaca, some Kaggle datasets +- Always check license before production use + +### Storage: +- Current: ~0.21 MB (quantum datasets) +- Recommended: 10 GB for diverse datasets +- Large-scale: 50-500 GB for production + +### Dependencies: +Some scripts require packages not in base environment: +```powershell +pip install -r dataset-requirements.txt +``` + +### Git: +Add to `.gitignore`: +``` +datasets/raw/ +datasets/chat/ +datasets/vision/ +*.csv +*.jsonl +``` + +Keep in Git: +``` +datasets/README.md +datasets/dataset_index.json +``` + +--- + +## 🎉 You're All Set! + +Your AI workspace now has: +- ✅ Centralized dataset storage +- ✅ Automated download tools +- ✅ Validation scripts +- ✅ 4 quantum datasets ready +- ✅ Comprehensive documentation +- ✅ Training examples + +**Start training now!** Choose an option from "Next Steps" above. + +For questions or issues, see the documentation files or run: +```powershell +python .\scripts\download_datasets.py --help +python .\scripts\validate_datasets.py --help +``` + +Happy training! 🚀 diff --git a/docs/guides/DATASET_EXPANSION_COMPLETE.md b/docs/guides/DATASET_EXPANSION_COMPLETE.md index c7fa39715..3b3e62f22 100644 --- a/docs/guides/DATASET_EXPANSION_COMPLETE.md +++ b/docs/guides/DATASET_EXPANSION_COMPLETE.md @@ -1,312 +1,312 @@ -# Quantum AI Dataset Expansion - Complete Summary -**Date:** November 16, 2025 -**Status:** ✅ Complete - 27 Working Datasets (93% Success Rate) - -## 📊 Executive Summary - -Successfully expanded quantum ML dataset collection from **15 to 27 datasets** (80% increase), adding **14 new datasets** across 8 domain categories. All datasets validated with 1-epoch smoke tests, architecture recommendations generated, and comprehensive 25-epoch benchmark initiated. - -### Key Metrics -- **Total Datasets:** 29 acquired, 27 working (93% success rate) -- **Total Samples:** 54,407 samples (101% increase from 27K baseline) -- **Domain Coverage:** 8 categories (added Finance, Social Science) -- **Quick Test Results:** 27/27 passed (100% success, 31.6s runtime) -- **Architecture Analysis:** 26/27 analyzed with optimal hyperparameters - ---- - -## 🎯 Expansion Details - -### Phase 1: Original Baseline (15 Datasets) -- **Medical:** breast_cancer, diabetes, blood_transfusion, haberman, heart_disease -- **Chemistry:** wine_red, wine_white -- **Physics:** ionosphere, magic_gamma -- **Biology:** iris -- **Agriculture:** wheat_seeds -- **Forensics:** banknote, glass -- **Geophysics:** sonar -- **Corrupted:** vertebral_column (pre-existing) - -### Phase 2: New Additions (14 Datasets) -#### Medical Domain (5 new → 10 total) -- **parkinsons** (195 samples, 22 features, binary) -- **dermatology** (366 samples, 34 features, 6 classes) -- **liver_disorders** (345 samples, 6 features, binary) -- **thyroid** (215 samples, 5 features, 3 classes) -- **statlog_heart** (270 samples, 13 features, binary) - -#### Chemistry Domain (1 new → 3 total) -- **wine_quality_combined** (6497 samples, 12 features, 2 classes with wine_type) - -#### Image Features (NEW - 2 datasets) -- **optical_recognition** (3823 samples, 64 features, 10 digits) - Highest dimensionality -- **pendigits** (7494 samples, 16 features, 10 digits) - Largest dataset - -#### Agriculture (1 new → 2 total) -- **seeds** (210 samples, 7 features, 3 classes) - -#### Finance (NEW - 1 dataset) -- **statlog_australian** (690 samples, 14 features, binary credit approval) - -#### Physics (1 new → 2 total) -- **balance_scale** (625 samples, 4 features, 3 classes) - -#### Social Science (NEW - 1 dataset) -- **contraceptive** (1473 samples, 9 features, 3 methods) - -### Excluded Datasets -- **vertebral_column** - Pre-existing corruption (binary format) -- **ecoli** - Phase 2 download failure (empty file) -- **vehicle** - UCI repository 404 error -- **page_blocks** - UCI repository 404 error - ---- - -## 🏆 Quick Test Results (1-Epoch Validation) - -### Exceptional Performers (>95% Accuracy) -| Dataset | Accuracy | Loss | Samples | Features | -|---------|----------|------|---------|----------| -| wine_white | 99.59% | 0.0778 | 4,898 | 11→4 | -| wine_red | 99.38% | 0.3660 | 1,599 | 11→5 | -| optical_recognition | 98.94% | 0.1321 | 3,823 | 64→6 | -| pendigits | 97.98% | 0.1024 | 7,494 | 16→5 | -| wine_quality_combined | 98.23% | 0.0769 | 6,497 | 12→5 | -| liver_disorders | 95.31% | 0.6090 | 345 | 6→4 | - -### Very Good Performers (75-95%) -| Dataset | Accuracy | Samples | -|---------|----------|---------| -| banknote | 93.38% | 1,372 | -| balance_scale | 91.07% | 625 | -| breast_cancer | 75.89% | 569 | -| statlog_australian | 82.03% | 690 | -| magic_gamma | 77.53% | 19,020 | -| blood_transfusion | 75.00% | 747 | - -### Challenging Datasets (<50%) -| Dataset | Accuracy | Issue | -|---------|----------|-------| -| statlog_heart | 0.00% | Training instability | -| iris | 6.25% | Multi-class complexity | -| wheat_seeds | 40.62% | Limited samples | -| sonar | 40.62% | High dimensionality (60→4) | -| glass | 43.75% | Class imbalance (8.44x) | - ---- - -## 🎨 Architecture Recommendations - -### Qubit Distribution -- **4 qubits:** 15 datasets (58%) - Standard configuration -- **5 qubits:** 5 datasets (19%) - High feature count (11-20) -- **6 qubits:** 6 datasets (23%) - Very high features (>20) - -### Layer Distribution -- **2 layers:** 18 datasets (69%) - Standard/small datasets -- **3 layers:** 3 datasets (12%) - Hard tasks or imbalance -- **4 layers:** 5 datasets (19%) - Multi-class (6-10 classes) - -### Key Recommendations -#### Optical Recognition (Most Complex) -- **Qubits:** 6 (64→6 features, 80% variance) -- **Layers:** 4 (10-class problem) -- **Hidden:** 32 (high-dimensional capacity) -- **Batch:** 16, LR: 0.001 -- **Epochs:** 50 - -#### Pendigits (Largest Dataset) -- **Qubits:** 5 (16→5 features, 85% variance) -- **Layers:** 4 (10-class problem) -- **Hidden:** 32 -- **Batch:** 32 (large dataset efficiency) -- **LR:** 0.001, **Epochs:** 50 - -#### Wine Quality Combined -- **Qubits:** 5 (12→5 features, 85% variance) -- **Layers:** 3 (hard task) -- **Hidden:** 16 -- **Batch:** 32, LR: 0.0005 (imbalanced 3.1x) -- **Epochs:** 50 - ---- - -## 📁 Technical Implementation - -### Dataset Loading Strategies -Implemented 14 dataset-specific loading handlers: -- **Semicolon-delimited:** wine_red, wine_white -- **Comma-delimited:** wine_quality_combined, balance_scale (header) -- **Whitespace-delimited:** wheat_seeds, seeds (no header) -- **Space-delimited:** statlog_australian, statlog_heart (no header) -- **Column skipping:** parkinsons (name), breast_cancer (ID) -- **Row skipping:** blood_transfusion (description line) -- **No header:** optical_recognition, pendigits, contraceptive, dermatology, liver_disorders, thyroid - -### Scripts Updated -1. **quick_test_datasets.py** - 27 datasets, 100% success (31.6s) -2. **dataset_architecture_analyzer.py** - 26/27 analyzed -3. **benchmark_all_datasets.py** - 27 datasets configured, 25-epoch training -4. **expand_quantum_datasets.py** - 16-dataset automated downloader - -### Configuration Files Updated -- **dataset_index.json** - 31 entries (18 baseline + 13 new) -- **AI_DATASETS_CATALOG.md** - Comprehensive 29-dataset documentation -- **DATASET_EXPANSION_PHASE2.md** - Detailed expansion report - ---- - -## 📊 Domain Distribution - -| Domain | Datasets | Samples | Percentage | -|--------|----------|---------|------------| -| **Medical** | 10 | 3,959 | 37% | -| **Chemistry** | 3 | 12,994 | 11% | -| **Image Features** | 2 | 11,317 | 7% | -| **Physics** | 2 | 19,644 | 7% | -| **Agriculture** | 2 | 420 | 7% | -| **Forensics** | 2 | 1,585 | 7% | -| **Finance** | 1 | 690 | 4% | -| **Social Science** | 1 | 1,473 | 4% | -| **Geophysics** | 1 | 208 | 4% | -| **Biology** | 1 | 150 | 4% | - -### Domain Analysis -- **Medical dominance:** 37% of datasets (tripled from 3→10) -- **Large-scale datasets:** magic_gamma (19K), pendigits (7.5K), wine_white (4.9K) -- **Small-scale datasets:** iris (150), parkinsons (195), sonar (208) -- **High-dimensional:** optical_recognition (64), sonar (60), dermatology (34) - ---- - -## 🚀 Comprehensive Benchmark Status - -### Execution Details -- **Command:** `python benchmark_all_datasets.py` -- **Expected Runtime:** 60-90 minutes -- **Datasets:** 26/27 (vertebral_column excluded) -- **Epochs:** 25 per dataset -- **Configuration:** Variable architecture (4-6 qubits, 2-4 layers) - -### Progress Tracking -- ✅ **ionosphere** - 88.52% (best epoch 22) -- ✅ **banknote** - In progress -- ✅ **heart_disease** - 88.52% (85% improvement) -- ✅ **sonar** - 80.95% (2x improvement from quick test) -- ✅ **breast_cancer** - 96.49% (exceptional) -- ✅ **diabetes** - 72.08% -- ❌ **vertebral_column** - Skipped (corrupted) -- ✅ **blood_transfusion** - 80.00% -- ❌ **haberman** - Error (target out of bounds) -- ✅ **wine_red** - 99.38% (perfect convergence) -- ✅ **wine_white** - 99.59% (currently training) -- ⏳ **Remaining 15 datasets** - Pending - -### Expected Outputs -1. **benchmark_results.json** - Complete metrics per dataset per epoch -2. **benchmark_report.md** - Performance analysis with tiers -3. **benchmark_comparison.png** - Visual comparison of all 27 datasets - ---- - -## 📈 Success Metrics - -### Download Success -- **Attempted:** 16 datasets -- **Downloaded:** 14 (87.5%) -- **Failed:** 2 (vehicle, page_blocks - UCI 404) - -### Validation Success -- **Total Acquired:** 29 datasets -- **Working:** 27 (93%) -- **Corrupted:** 2 (vertebral_column, ecoli) - -### Integration Success -- **Quick Test:** 27/27 pass (100%) -- **Architecture Analysis:** 26/27 complete (96%) -- **Benchmark:** 26/27 configured (96%) - ---- - -## 🎓 Lessons Learned - -### Technical Insights -1. **UCI Repository Instability:** 2/16 datasets returned 404 (12% failure rate) -2. **Format Diversity:** 7 different delimiter types (comma, semicolon, space, whitespace, tab) -3. **Header Variations:** 50% have headers, 50% headerless -4. **Column Management:** 3 datasets require column skipping (sequence names, IDs) -5. **Corruption Issues:** Pre-existing vertebral_column unusable, ecoli download corrupted - -### Architecture Patterns -1. **High dimensionality benefits from 6 qubits:** optical_recognition (64→6), sonar (60→6) -2. **Multi-class tasks need 3-4 layers:** Especially 6+ classes -3. **Class imbalance requires lower LR:** 0.0005 vs 0.001 for balanced -4. **Large datasets benefit from batch=32:** pendigits, magic_gamma, wine_quality_combined -5. **Small datasets (<300) need batch=8:** Prevents overfitting - -### Performance Insights -1. **Chemistry datasets exceptional:** Wine datasets 98-99% accuracy -2. **Image features highly learnable:** optical_recognition 98.9%, pendigits 98% -3. **Medical datasets variable:** 0-96% range depending on complexity -4. **Multi-class more challenging:** Average 10-20% lower than binary -5. **Dimensionality reduction critical:** PCA to 4-6 qubits essential - ---- - -## 📋 Next Steps - -### Immediate Actions -1. ✅ **Quick test complete** - All 27 datasets validated -2. ✅ **Architecture recommendations** - 26/27 analyzed -3. ⏳ **Comprehensive benchmark** - Running (ETA 60-90 min) -4. ⏳ **Performance report** - Generate after benchmark - -### Production Deployment -1. **Select top 10 models** for Azure Quantum hardware -2. **Cost analysis** for QPU deployment (IonQ, Quantinuum) -3. **Hyperparameter tuning** for challenging datasets -4. **Ensemble methods** for multi-class problems - -### Future Expansions -1. **Phase 3:** Add 15 more datasets (target: 42 total) -2. **Time series datasets:** UCR Time Series Archive -3. **Computer vision:** MNIST, Fashion-MNIST integration -4. **Text classification:** Sentiment analysis, topic modeling -5. **Hybrid datasets:** Combine quantum + classical features - ---- - -## 📚 Documentation Index - -### Created Files -- `DATASET_EXPANSION_PHASE2.md` - Detailed expansion technical report -- `AI_DATASETS_CATALOG.md` - Complete 29-dataset catalog -- `DATASET_EXPANSION_COMPLETE.md` - This summary (executive overview) -- `results/quick_test_results.json` - 1-epoch validation metrics -- `results/architecture_analysis.json` - Hyperparameter recommendations - -### Updated Files -- `quick_test_datasets.py` - 15→27 datasets, loading strategies -- `dataset_architecture_analyzer.py` - 15→27 dataset analysis -- `benchmark_all_datasets.py` - 15→27 datasets, 14 new entries -- `dataset_index.json` - 18→31 entries - -### Pending Files -- `results/benchmark_results.json` - After 25-epoch training -- `results/benchmark_report.md` - Performance tier analysis -- `results/benchmark_comparison.png` - Visual comparison -- `COMPREHENSIVE_BENCHMARK_SUMMARY_27DATASETS.md` - Final report - ---- - -## 🎯 Conclusion - -Successfully expanded quantum ML dataset portfolio from 15 to 27 working datasets (93% success rate), adding 14 diverse datasets across medical, chemistry, image features, finance, and social science domains. All datasets validated with 1-epoch smoke tests (100% success), architecture recommendations generated for 96%, and comprehensive 25-epoch benchmark initiated. - -**Key Achievement:** Quantum AI system now supports **27 production-ready datasets** spanning **54,407 samples** across **8 domain categories**, with automated loading, validation, and architecture optimization infrastructure. - -**Next Milestone:** Complete comprehensive benchmark, generate performance tiers, and prepare top 10 models for Azure Quantum QPU deployment. - ---- - -**Status:** ✅ Expansion Complete | 🟢 Benchmark Running | 📊 Analysis Pending +# Quantum AI Dataset Expansion - Complete Summary +**Date:** November 16, 2025 +**Status:** ✅ Complete - 27 Working Datasets (93% Success Rate) + +## 📊 Executive Summary + +Successfully expanded quantum ML dataset collection from **15 to 27 datasets** (80% increase), adding **14 new datasets** across 8 domain categories. All datasets validated with 1-epoch smoke tests, architecture recommendations generated, and comprehensive 25-epoch benchmark initiated. + +### Key Metrics +- **Total Datasets:** 29 acquired, 27 working (93% success rate) +- **Total Samples:** 54,407 samples (101% increase from 27K baseline) +- **Domain Coverage:** 8 categories (added Finance, Social Science) +- **Quick Test Results:** 27/27 passed (100% success, 31.6s runtime) +- **Architecture Analysis:** 26/27 analyzed with optimal hyperparameters + +--- + +## 🎯 Expansion Details + +### Phase 1: Original Baseline (15 Datasets) +- **Medical:** breast_cancer, diabetes, blood_transfusion, haberman, heart_disease +- **Chemistry:** wine_red, wine_white +- **Physics:** ionosphere, magic_gamma +- **Biology:** iris +- **Agriculture:** wheat_seeds +- **Forensics:** banknote, glass +- **Geophysics:** sonar +- **Corrupted:** vertebral_column (pre-existing) + +### Phase 2: New Additions (14 Datasets) +#### Medical Domain (5 new → 10 total) +- **parkinsons** (195 samples, 22 features, binary) +- **dermatology** (366 samples, 34 features, 6 classes) +- **liver_disorders** (345 samples, 6 features, binary) +- **thyroid** (215 samples, 5 features, 3 classes) +- **statlog_heart** (270 samples, 13 features, binary) + +#### Chemistry Domain (1 new → 3 total) +- **wine_quality_combined** (6497 samples, 12 features, 2 classes with wine_type) + +#### Image Features (NEW - 2 datasets) +- **optical_recognition** (3823 samples, 64 features, 10 digits) - Highest dimensionality +- **pendigits** (7494 samples, 16 features, 10 digits) - Largest dataset + +#### Agriculture (1 new → 2 total) +- **seeds** (210 samples, 7 features, 3 classes) + +#### Finance (NEW - 1 dataset) +- **statlog_australian** (690 samples, 14 features, binary credit approval) + +#### Physics (1 new → 2 total) +- **balance_scale** (625 samples, 4 features, 3 classes) + +#### Social Science (NEW - 1 dataset) +- **contraceptive** (1473 samples, 9 features, 3 methods) + +### Excluded Datasets +- **vertebral_column** - Pre-existing corruption (binary format) +- **ecoli** - Phase 2 download failure (empty file) +- **vehicle** - UCI repository 404 error +- **page_blocks** - UCI repository 404 error + +--- + +## 🏆 Quick Test Results (1-Epoch Validation) + +### Exceptional Performers (>95% Accuracy) +| Dataset | Accuracy | Loss | Samples | Features | +|---------|----------|------|---------|----------| +| wine_white | 99.59% | 0.0778 | 4,898 | 11→4 | +| wine_red | 99.38% | 0.3660 | 1,599 | 11→5 | +| optical_recognition | 98.94% | 0.1321 | 3,823 | 64→6 | +| pendigits | 97.98% | 0.1024 | 7,494 | 16→5 | +| wine_quality_combined | 98.23% | 0.0769 | 6,497 | 12→5 | +| liver_disorders | 95.31% | 0.6090 | 345 | 6→4 | + +### Very Good Performers (75-95%) +| Dataset | Accuracy | Samples | +|---------|----------|---------| +| banknote | 93.38% | 1,372 | +| balance_scale | 91.07% | 625 | +| breast_cancer | 75.89% | 569 | +| statlog_australian | 82.03% | 690 | +| magic_gamma | 77.53% | 19,020 | +| blood_transfusion | 75.00% | 747 | + +### Challenging Datasets (<50%) +| Dataset | Accuracy | Issue | +|---------|----------|-------| +| statlog_heart | 0.00% | Training instability | +| iris | 6.25% | Multi-class complexity | +| wheat_seeds | 40.62% | Limited samples | +| sonar | 40.62% | High dimensionality (60→4) | +| glass | 43.75% | Class imbalance (8.44x) | + +--- + +## 🎨 Architecture Recommendations + +### Qubit Distribution +- **4 qubits:** 15 datasets (58%) - Standard configuration +- **5 qubits:** 5 datasets (19%) - High feature count (11-20) +- **6 qubits:** 6 datasets (23%) - Very high features (>20) + +### Layer Distribution +- **2 layers:** 18 datasets (69%) - Standard/small datasets +- **3 layers:** 3 datasets (12%) - Hard tasks or imbalance +- **4 layers:** 5 datasets (19%) - Multi-class (6-10 classes) + +### Key Recommendations +#### Optical Recognition (Most Complex) +- **Qubits:** 6 (64→6 features, 80% variance) +- **Layers:** 4 (10-class problem) +- **Hidden:** 32 (high-dimensional capacity) +- **Batch:** 16, LR: 0.001 +- **Epochs:** 50 + +#### Pendigits (Largest Dataset) +- **Qubits:** 5 (16→5 features, 85% variance) +- **Layers:** 4 (10-class problem) +- **Hidden:** 32 +- **Batch:** 32 (large dataset efficiency) +- **LR:** 0.001, **Epochs:** 50 + +#### Wine Quality Combined +- **Qubits:** 5 (12→5 features, 85% variance) +- **Layers:** 3 (hard task) +- **Hidden:** 16 +- **Batch:** 32, LR: 0.0005 (imbalanced 3.1x) +- **Epochs:** 50 + +--- + +## 📁 Technical Implementation + +### Dataset Loading Strategies +Implemented 14 dataset-specific loading handlers: +- **Semicolon-delimited:** wine_red, wine_white +- **Comma-delimited:** wine_quality_combined, balance_scale (header) +- **Whitespace-delimited:** wheat_seeds, seeds (no header) +- **Space-delimited:** statlog_australian, statlog_heart (no header) +- **Column skipping:** parkinsons (name), breast_cancer (ID) +- **Row skipping:** blood_transfusion (description line) +- **No header:** optical_recognition, pendigits, contraceptive, dermatology, liver_disorders, thyroid + +### Scripts Updated +1. **quick_test_datasets.py** - 27 datasets, 100% success (31.6s) +2. **dataset_architecture_analyzer.py** - 26/27 analyzed +3. **benchmark_all_datasets.py** - 27 datasets configured, 25-epoch training +4. **expand_quantum_datasets.py** - 16-dataset automated downloader + +### Configuration Files Updated +- **dataset_index.json** - 31 entries (18 baseline + 13 new) +- **AI_DATASETS_CATALOG.md** - Comprehensive 29-dataset documentation +- **DATASET_EXPANSION_PHASE2.md** - Detailed expansion report + +--- + +## 📊 Domain Distribution + +| Domain | Datasets | Samples | Percentage | +|--------|----------|---------|------------| +| **Medical** | 10 | 3,959 | 37% | +| **Chemistry** | 3 | 12,994 | 11% | +| **Image Features** | 2 | 11,317 | 7% | +| **Physics** | 2 | 19,644 | 7% | +| **Agriculture** | 2 | 420 | 7% | +| **Forensics** | 2 | 1,585 | 7% | +| **Finance** | 1 | 690 | 4% | +| **Social Science** | 1 | 1,473 | 4% | +| **Geophysics** | 1 | 208 | 4% | +| **Biology** | 1 | 150 | 4% | + +### Domain Analysis +- **Medical dominance:** 37% of datasets (tripled from 3→10) +- **Large-scale datasets:** magic_gamma (19K), pendigits (7.5K), wine_white (4.9K) +- **Small-scale datasets:** iris (150), parkinsons (195), sonar (208) +- **High-dimensional:** optical_recognition (64), sonar (60), dermatology (34) + +--- + +## 🚀 Comprehensive Benchmark Status + +### Execution Details +- **Command:** `python benchmark_all_datasets.py` +- **Expected Runtime:** 60-90 minutes +- **Datasets:** 26/27 (vertebral_column excluded) +- **Epochs:** 25 per dataset +- **Configuration:** Variable architecture (4-6 qubits, 2-4 layers) + +### Progress Tracking +- ✅ **ionosphere** - 88.52% (best epoch 22) +- ✅ **banknote** - In progress +- ✅ **heart_disease** - 88.52% (85% improvement) +- ✅ **sonar** - 80.95% (2x improvement from quick test) +- ✅ **breast_cancer** - 96.49% (exceptional) +- ✅ **diabetes** - 72.08% +- ❌ **vertebral_column** - Skipped (corrupted) +- ✅ **blood_transfusion** - 80.00% +- ❌ **haberman** - Error (target out of bounds) +- ✅ **wine_red** - 99.38% (perfect convergence) +- ✅ **wine_white** - 99.59% (currently training) +- ⏳ **Remaining 15 datasets** - Pending + +### Expected Outputs +1. **benchmark_results.json** - Complete metrics per dataset per epoch +2. **benchmark_report.md** - Performance analysis with tiers +3. **benchmark_comparison.png** - Visual comparison of all 27 datasets + +--- + +## 📈 Success Metrics + +### Download Success +- **Attempted:** 16 datasets +- **Downloaded:** 14 (87.5%) +- **Failed:** 2 (vehicle, page_blocks - UCI 404) + +### Validation Success +- **Total Acquired:** 29 datasets +- **Working:** 27 (93%) +- **Corrupted:** 2 (vertebral_column, ecoli) + +### Integration Success +- **Quick Test:** 27/27 pass (100%) +- **Architecture Analysis:** 26/27 complete (96%) +- **Benchmark:** 26/27 configured (96%) + +--- + +## 🎓 Lessons Learned + +### Technical Insights +1. **UCI Repository Instability:** 2/16 datasets returned 404 (12% failure rate) +2. **Format Diversity:** 7 different delimiter types (comma, semicolon, space, whitespace, tab) +3. **Header Variations:** 50% have headers, 50% headerless +4. **Column Management:** 3 datasets require column skipping (sequence names, IDs) +5. **Corruption Issues:** Pre-existing vertebral_column unusable, ecoli download corrupted + +### Architecture Patterns +1. **High dimensionality benefits from 6 qubits:** optical_recognition (64→6), sonar (60→6) +2. **Multi-class tasks need 3-4 layers:** Especially 6+ classes +3. **Class imbalance requires lower LR:** 0.0005 vs 0.001 for balanced +4. **Large datasets benefit from batch=32:** pendigits, magic_gamma, wine_quality_combined +5. **Small datasets (<300) need batch=8:** Prevents overfitting + +### Performance Insights +1. **Chemistry datasets exceptional:** Wine datasets 98-99% accuracy +2. **Image features highly learnable:** optical_recognition 98.9%, pendigits 98% +3. **Medical datasets variable:** 0-96% range depending on complexity +4. **Multi-class more challenging:** Average 10-20% lower than binary +5. **Dimensionality reduction critical:** PCA to 4-6 qubits essential + +--- + +## 📋 Next Steps + +### Immediate Actions +1. ✅ **Quick test complete** - All 27 datasets validated +2. ✅ **Architecture recommendations** - 26/27 analyzed +3. ⏳ **Comprehensive benchmark** - Running (ETA 60-90 min) +4. ⏳ **Performance report** - Generate after benchmark + +### Production Deployment +1. **Select top 10 models** for Azure Quantum hardware +2. **Cost analysis** for QPU deployment (IonQ, Quantinuum) +3. **Hyperparameter tuning** for challenging datasets +4. **Ensemble methods** for multi-class problems + +### Future Expansions +1. **Phase 3:** Add 15 more datasets (target: 42 total) +2. **Time series datasets:** UCR Time Series Archive +3. **Computer vision:** MNIST, Fashion-MNIST integration +4. **Text classification:** Sentiment analysis, topic modeling +5. **Hybrid datasets:** Combine quantum + classical features + +--- + +## 📚 Documentation Index + +### Created Files +- `DATASET_EXPANSION_PHASE2.md` - Detailed expansion technical report +- `AI_DATASETS_CATALOG.md` - Complete 29-dataset catalog +- `DATASET_EXPANSION_COMPLETE.md` - This summary (executive overview) +- `results/quick_test_results.json` - 1-epoch validation metrics +- `results/architecture_analysis.json` - Hyperparameter recommendations + +### Updated Files +- `quick_test_datasets.py` - 15→27 datasets, loading strategies +- `dataset_architecture_analyzer.py` - 15→27 dataset analysis +- `benchmark_all_datasets.py` - 15→27 datasets, 14 new entries +- `dataset_index.json` - 18→31 entries + +### Pending Files +- `results/benchmark_results.json` - After 25-epoch training +- `results/benchmark_report.md` - Performance tier analysis +- `results/benchmark_comparison.png` - Visual comparison +- `COMPREHENSIVE_BENCHMARK_SUMMARY_27DATASETS.md` - Final report + +--- + +## 🎯 Conclusion + +Successfully expanded quantum ML dataset portfolio from 15 to 27 working datasets (93% success rate), adding 14 diverse datasets across medical, chemistry, image features, finance, and social science domains. All datasets validated with 1-epoch smoke tests (100% success), architecture recommendations generated for 96%, and comprehensive 25-epoch benchmark initiated. + +**Key Achievement:** Quantum AI system now supports **27 production-ready datasets** spanning **54,407 samples** across **8 domain categories**, with automated loading, validation, and architecture optimization infrastructure. + +**Next Milestone:** Complete comprehensive benchmark, generate performance tiers, and prepare top 10 models for Azure Quantum QPU deployment. + +--- + +**Status:** ✅ Expansion Complete | 🟢 Benchmark Running | 📊 Analysis Pending diff --git a/docs/guides/DATASET_EXPANSION_PHASE2.md b/docs/guides/DATASET_EXPANSION_PHASE2.md index 8d9d08c2a..e80029919 100644 --- a/docs/guides/DATASET_EXPANSION_PHASE2.md +++ b/docs/guides/DATASET_EXPANSION_PHASE2.md @@ -1,286 +1,286 @@ -# Dataset Expansion Phase 2 - Complete ✅ -**Date:** January 8, 2025 -**Status:** 93% Success (27/29 datasets working) - -## 🎯 Mission: Expand Quantum ML Dataset Collection - -**Objective:** Add 10+ high-quality classification datasets to quantum ML infrastructure -**Result:** Added 14 datasets (87.5% download success, 93% validation success) - -## 📊 Expansion Summary - -### Before -- **Dataset Count:** 15 quantum datasets -- **Total Samples:** ~27,000 -- **Domain Coverage:** 7 categories (medical, biology, chemistry, physics, agriculture, forensics, geophysics) - -### After -- **Dataset Count:** 29 quantum datasets (+93% increase) -- **Total Samples:** 54,407 (+101% increase) -- **Domain Coverage:** 8 categories (added finance, social science) -- **Working Rate:** 93% (27/29 functional) - -## 🆕 New Datasets Added - -### Medical Domain Expansion (5 new datasets) -1. **Parkinsons** (195 samples, 22 features, 2 classes) - Voice biomarker disease detection -2. **Dermatology** (366 samples, 34 features, 6 classes) - Multi-class skin disease diagnosis -3. **Liver Disorders** (345 samples, 6 features, 2 classes) - Blood test screening -4. **Thyroid** (215 samples, 5 features, 3 classes) - Endocrine condition classification -5. **Statlog Heart** (270 samples, 13 features, 2 classes) - Alternative cardiac dataset - -### Biology Domain Expansion (2 new datasets) -6. **Ecoli** (336 samples, 7 features, 8 classes) - E. coli protein localization -7. **Yeast** (1,484 samples, 8 features, 10 classes) - Protein cellular location (10-class challenge) - -### Image Features (2 new datasets) -8. **Optical Recognition** (3,823 samples, 64 features, 10 classes) - Handwritten digits (high-dimensional) -9. **Pendigits** (7,494 samples, 16 features, 10 classes) - Pen-based digits (largest dataset) - -### Chemistry (1 new dataset) -10. **Wine Quality Combined** (6,497 samples, 12 features, 7 classes) - Merged red+white wines with wine_type feature - -### New Domains -11. **Balance Scale** (625 samples, 4 features, 3 classes) - Physics: equilibrium prediction -12. **Statlog Australian** (690 samples, 14 features, 2 classes) - Finance: credit approval -13. **Contraceptive** (1,473 samples, 9 features, 3 classes) - Social Science: demographic choice -14. **Seeds** (210 samples, 7 features, 3 classes) - Agriculture: wheat variety (alternative dataset) - -## 🔧 Technical Implementation - -### Expansion Script: `scripts/expand_quantum_datasets.py` -**500+ lines** with 4 main functions: - -```powershell -# Search candidates -python .\scripts\expand_quantum_datasets.py --search - -# Download all -python .\scripts\expand_quantum_datasets.py --download - -# Validate files -python .\scripts\expand_quantum_datasets.py --validate - -# Update index -python .\scripts\expand_quantum_datasets.py --update-index - -# All-in-one -python .\scripts\expand_quantum_datasets.py --all -``` - -**Features:** -- ✅ 16 manually curated UCI ML Repository datasets -- ✅ SSL context for HTTPS downloads -- ✅ Multiple delimiter support (comma, space, whitespace regex) -- ✅ Column manipulation (skip sequence names, reposition targets) -- ✅ 10% sample count tolerance validation -- ✅ Special dataset combining (wine_quality_combined) -- ✅ Automatic dataset_index.json integration - -### Download Results -- **Attempted:** 16 datasets -- **Successful:** 14 datasets (87.5% success) -- **Failed:** 2 datasets (vehicle, page_blocks - 404 Not Found) -- **Cause:** UCI ML Repository URL migration to archive-beta.ics.uci.edu - -### Validation Results -- **Total Datasets:** 29 (15 previous + 14 new) -- **Working:** 27 datasets (93% success) -- **Failed:** 2 datasets (ecoli validation display bug, vertebral_column pre-existing corruption) -- **Total Samples:** 54,407 -- **Average Features:** 14.6 - -## 📈 Dataset Distribution Analysis - -### By Domain (29 datasets) -- **Medical:** 8 datasets (28%) - Heart disease, parkinsons, dermatology, liver, thyroid, breast cancer, diabetes, statlog_heart -- **Biology:** 4 datasets (14%) - Ecoli, yeast, ionosphere, sonar -- **Chemistry:** 3 datasets (10%) - Wine combined, wine red, wine white -- **Image Features:** 4 datasets (14%) - Optical recognition, pendigits, iris, digits -- **Agriculture:** 2 datasets (7%) - Wheat seeds, seeds -- **Finance:** 1 dataset (3%) - Credit approval -- **Physics:** 1 dataset (3%) - Balance scale -- **Social Science:** 1 dataset (3%) - Contraceptive choice -- **Forensics:** 1 dataset (3%) - Banknote -- **Other:** 3 datasets (10%) - Glass, blood transfusion, haberman -- **Excluded:** 1 dataset (vertebral_column - corrupted) - -### By Size Category -- **Tiny (100-350):** 9 datasets (31%) -- **Small (351-1000):** 11 datasets (38%) -- **Medium (1001-5000):** 7 datasets (24%) -- **Large (5000+):** 2 datasets (7%) - pendigits (7,494), wine_quality_combined (6,497) - -### By Complexity -- **Binary (2 classes):** 14 datasets (48%) -- **Multi-class (3-6 classes):** 10 datasets (34%) -- **Complex (7-10 classes):** 5 datasets (17%) - -### By Feature Count -- **Low-dimensional (3-10):** 19 datasets (66%) -- **Medium-dimensional (11-30):** 6 datasets (21%) -- **High-dimensional (31-64):** 4 datasets (14%) - -## 🎯 Key Achievements - -### Domain Diversity -- ✅ Medical domain now 8 datasets (was 3) - comprehensive healthcare coverage -- ✅ Added finance (credit decisions) and social science (demographics) -- ✅ Biology expanded to 4 datasets with protein localization tasks - -### Scale Testing -- ✅ Largest dataset: Pendigits (7,494 samples) tests quantum simulator scalability -- ✅ Highest dimensional: Optical Recognition (64 features) tests PCA limits -- ✅ Most classes: Yeast (10 classes) and Pendigits (10 classes) for complex classification - -### Special Features -- ✅ Combined dataset creation: wine_quality_combined merges red+white with wine_type feature -- ✅ Alternative datasets: seeds vs wheat_seeds for cross-validation -- ✅ Medical focus: 8 diverse healthcare datasets for quantum medical AI research - -## 📝 Updated Files - -### Automated Updates ✅ -1. `datasets/quantum/*.csv` - 14 new CSV files added -2. `datasets/dataset_index.json` - 13 new entries added (now 31 total) -3. `scripts/expand_quantum_datasets.py` - New 500+ line expansion tool - -### Manual Updates ✅ -4. `AI_DATASETS_CATALOG.md` - Comprehensive 29-dataset documentation with domain grouping - -### Pending Updates 🔄 -5. `ai-projects/quantum-ml/quick_test_datasets.py` - Need to add 14 new datasets to smoke test -6. `ai-projects/quantum-ml/benchmark_all_datasets.py` - Need to add 14 new datasets to DATASETS dict -7. `ai-projects/quantum-ml/dataset_architecture_analyzer.py` - Need to re-run for all 29 datasets - -## 🚀 Next Steps (Priority Order) - -### 1. Quick Validation ⏭️ NEXT -**Update and run quick_test_datasets.py** -- Add 14 new dataset names to validation loop -- Run 1-epoch smoke test (~30 seconds for 29 datasets) -- Identify any loading issues requiring dataset-specific strategies -- Verify 28-29/29 success rate - -### 2. Architecture Analysis -**Run dataset_architecture_analyzer.py** -- Generate qubit/layer recommendations for all 29 datasets -- Special attention to: - - Optical recognition (64 features → 6 qubits with aggressive PCA) - - Pendigits (7,494 samples → batch size 32) - - Yeast (10 classes → 3-4 layers) -- Update `architecture_analysis.json` - -### 3. Benchmark Integration -**Update benchmark_all_datasets.py** -- Add 14 new datasets to DATASETS dict with metadata -- Add dataset-specific loading strategies: - - Delimiter handling (comma, space, whitespace) - - Header flags - - Column skipping (sequence names, IDs) -- Test load_dataset() function with new datasets - -### 4. Comprehensive Benchmark -**Run 25-epoch evaluation on all 29 datasets** -- Expected runtime: 60-90 minutes -- Generate `benchmark_results.json` -- Create `benchmark_report.md` with analysis -- Performance tiers: Exceptional (95-100%), Excellent (85-95%), Very Good (75-85%), Good (70-75%), Challenging (50-70%) -- Domain analysis: Medical avg, Biology avg, Chemistry avg, etc. - -### 5. Documentation -**Create comprehensive expansion report** -- `COMPREHENSIVE_BENCHMARK_SUMMARY_29DATASETS.md` -- Performance tier analysis -- Domain-specific insights -- Production readiness assessment -- Top 10 models for deployment -- Cost analysis for QPU hardware - -## 🐛 Known Issues - -### Download Failures (2 datasets) -1. **Vehicle** (846 samples, 18 features, 4 classes) - 404 Not Found -2. **Page Blocks** (5,473 samples, 10 features, 5 classes) - 404 Not Found - - **Cause:** UCI ML Repository URL migration - - **Alternative:** Available on Kaggle, OpenML, or new UCI archive-beta site - - **Impact:** Low (87.5% download success acceptable) - -### Validation Issues (1 dataset) -1. **Vertebral Column** (pre-existing) - File corruption, excluded from all analysis - - **Status:** CORRUPTED - DO NOT USE - - **Impact:** 27/29 working (93% success rate) - -### Display Bug (non-critical) -1. **Ecoli** shows "-1 features" in download output - - **Cause:** Display bug in feature count calculation (skip_columns=[0] incorrectly subtracted) - - **Actual:** File has 7 features correctly - - **Impact:** None (validation confirms correct feature count) - -## 📊 Success Metrics - -### Expansion Goals ✅ -- [x] Add 10+ new datasets → **Achieved 14 datasets (140%)** -- [x] Maintain >90% success rate → **Achieved 93% (27/29)** -- [x] Double sample count → **Achieved 101% increase (27K→54K)** -- [x] Add new domains → **Achieved finance + social science** - -### Quality Metrics ✅ -- [x] All datasets from trusted source (UCI ML Repository) -- [x] Permissive licenses for research use -- [x] Diverse domain coverage (8 categories) -- [x] Range of complexities (2-10 classes) -- [x] Range of scales (195-7,494 samples) - -### Infrastructure Metrics ✅ -- [x] Reusable expansion script created -- [x] Automated validation pipeline -- [x] Centralized index integration -- [x] Comprehensive documentation - -## 🎓 Lessons Learned - -### Technical Insights -1. **Manual curation superior to API automation** - Specifying delimiter, header, column metadata upfront prevents loading failures -2. **UCI repository evolving** - Need robust URL handling and alternative sources for long-term stability -3. **Special dataset creation valuable** - Combined datasets (wine_quality_combined) test additional features and larger scale -4. **87.5% success rate acceptable** - Given UCI repository URL instability and aging infrastructure - -### Process Improvements -1. **Metadata-driven downloads essential** - Delimiter, header, column skip info prevents runtime loading failures -2. **10% sample count tolerance appropriate** - Accounts for CSV parsing variations -3. **Script-based expansion reusable** - Can easily add more CANDIDATE_DATASETS entries -4. **Validation before index update** - Ensures only working datasets added to official index - -### Dataset Selection Strategy -1. **Prioritize 100-5000 samples** - Quantum simulator constraints -2. **Target 4-60 features** - Qubit count limits (4-6 qubits typical) -3. **Focus on tabular classification** - Quantum circuit suitability -4. **Emphasize medical/biology** - High-value application domains for quantum ML - -## 🔗 Related Documentation - -- **Expansion Script:** `scripts/expand_quantum_datasets.py` -- **Dataset Catalog:** `AI_DATASETS_CATALOG.md` (now 29 datasets) -- **Dataset Index:** `datasets/dataset_index.json` (31 entries) -- **Previous Expansion:** `DATASET_EXPANSION_COMPLETE.md` (Phase 1: 4→15 datasets) -- **Benchmark Results:** `COMPREHENSIVE_BENCHMARK_SUMMARY.md` (15-dataset baseline) - -## 🎉 Conclusion - -**Phase 2 expansion successfully completed:** -- ✅ 93% increase in dataset count (15→29) -- ✅ 101% increase in sample count (27K→54K) -- ✅ 93% validation success rate (27/29) -- ✅ 8 domain categories covered -- ✅ Medical domain tripled (3→8 datasets) -- ✅ Ready for comprehensive 29-dataset benchmark - -**Quantum ML infrastructure now includes:** -- 29 high-quality classification datasets -- 54,407 total samples -- 8 diverse application domains -- Reusable expansion tooling -- Comprehensive documentation - -**Next milestone:** Run comprehensive 25-epoch benchmark on all 29 datasets to establish quantum ML state-of-the-art across diverse classification tasks. +# Dataset Expansion Phase 2 - Complete ✅ +**Date:** January 8, 2025 +**Status:** 93% Success (27/29 datasets working) + +## 🎯 Mission: Expand Quantum ML Dataset Collection + +**Objective:** Add 10+ high-quality classification datasets to quantum ML infrastructure +**Result:** Added 14 datasets (87.5% download success, 93% validation success) + +## 📊 Expansion Summary + +### Before +- **Dataset Count:** 15 quantum datasets +- **Total Samples:** ~27,000 +- **Domain Coverage:** 7 categories (medical, biology, chemistry, physics, agriculture, forensics, geophysics) + +### After +- **Dataset Count:** 29 quantum datasets (+93% increase) +- **Total Samples:** 54,407 (+101% increase) +- **Domain Coverage:** 8 categories (added finance, social science) +- **Working Rate:** 93% (27/29 functional) + +## 🆕 New Datasets Added + +### Medical Domain Expansion (5 new datasets) +1. **Parkinsons** (195 samples, 22 features, 2 classes) - Voice biomarker disease detection +2. **Dermatology** (366 samples, 34 features, 6 classes) - Multi-class skin disease diagnosis +3. **Liver Disorders** (345 samples, 6 features, 2 classes) - Blood test screening +4. **Thyroid** (215 samples, 5 features, 3 classes) - Endocrine condition classification +5. **Statlog Heart** (270 samples, 13 features, 2 classes) - Alternative cardiac dataset + +### Biology Domain Expansion (2 new datasets) +6. **Ecoli** (336 samples, 7 features, 8 classes) - E. coli protein localization +7. **Yeast** (1,484 samples, 8 features, 10 classes) - Protein cellular location (10-class challenge) + +### Image Features (2 new datasets) +8. **Optical Recognition** (3,823 samples, 64 features, 10 classes) - Handwritten digits (high-dimensional) +9. **Pendigits** (7,494 samples, 16 features, 10 classes) - Pen-based digits (largest dataset) + +### Chemistry (1 new dataset) +10. **Wine Quality Combined** (6,497 samples, 12 features, 7 classes) - Merged red+white wines with wine_type feature + +### New Domains +11. **Balance Scale** (625 samples, 4 features, 3 classes) - Physics: equilibrium prediction +12. **Statlog Australian** (690 samples, 14 features, 2 classes) - Finance: credit approval +13. **Contraceptive** (1,473 samples, 9 features, 3 classes) - Social Science: demographic choice +14. **Seeds** (210 samples, 7 features, 3 classes) - Agriculture: wheat variety (alternative dataset) + +## 🔧 Technical Implementation + +### Expansion Script: `scripts/expand_quantum_datasets.py` +**500+ lines** with 4 main functions: + +```powershell +# Search candidates +python .\scripts\expand_quantum_datasets.py --search + +# Download all +python .\scripts\expand_quantum_datasets.py --download + +# Validate files +python .\scripts\expand_quantum_datasets.py --validate + +# Update index +python .\scripts\expand_quantum_datasets.py --update-index + +# All-in-one +python .\scripts\expand_quantum_datasets.py --all +``` + +**Features:** +- ✅ 16 manually curated UCI ML Repository datasets +- ✅ SSL context for HTTPS downloads +- ✅ Multiple delimiter support (comma, space, whitespace regex) +- ✅ Column manipulation (skip sequence names, reposition targets) +- ✅ 10% sample count tolerance validation +- ✅ Special dataset combining (wine_quality_combined) +- ✅ Automatic dataset_index.json integration + +### Download Results +- **Attempted:** 16 datasets +- **Successful:** 14 datasets (87.5% success) +- **Failed:** 2 datasets (vehicle, page_blocks - 404 Not Found) +- **Cause:** UCI ML Repository URL migration to archive-beta.ics.uci.edu + +### Validation Results +- **Total Datasets:** 29 (15 previous + 14 new) +- **Working:** 27 datasets (93% success) +- **Failed:** 2 datasets (ecoli validation display bug, vertebral_column pre-existing corruption) +- **Total Samples:** 54,407 +- **Average Features:** 14.6 + +## 📈 Dataset Distribution Analysis + +### By Domain (29 datasets) +- **Medical:** 8 datasets (28%) - Heart disease, parkinsons, dermatology, liver, thyroid, breast cancer, diabetes, statlog_heart +- **Biology:** 4 datasets (14%) - Ecoli, yeast, ionosphere, sonar +- **Chemistry:** 3 datasets (10%) - Wine combined, wine red, wine white +- **Image Features:** 4 datasets (14%) - Optical recognition, pendigits, iris, digits +- **Agriculture:** 2 datasets (7%) - Wheat seeds, seeds +- **Finance:** 1 dataset (3%) - Credit approval +- **Physics:** 1 dataset (3%) - Balance scale +- **Social Science:** 1 dataset (3%) - Contraceptive choice +- **Forensics:** 1 dataset (3%) - Banknote +- **Other:** 3 datasets (10%) - Glass, blood transfusion, haberman +- **Excluded:** 1 dataset (vertebral_column - corrupted) + +### By Size Category +- **Tiny (100-350):** 9 datasets (31%) +- **Small (351-1000):** 11 datasets (38%) +- **Medium (1001-5000):** 7 datasets (24%) +- **Large (5000+):** 2 datasets (7%) - pendigits (7,494), wine_quality_combined (6,497) + +### By Complexity +- **Binary (2 classes):** 14 datasets (48%) +- **Multi-class (3-6 classes):** 10 datasets (34%) +- **Complex (7-10 classes):** 5 datasets (17%) + +### By Feature Count +- **Low-dimensional (3-10):** 19 datasets (66%) +- **Medium-dimensional (11-30):** 6 datasets (21%) +- **High-dimensional (31-64):** 4 datasets (14%) + +## 🎯 Key Achievements + +### Domain Diversity +- ✅ Medical domain now 8 datasets (was 3) - comprehensive healthcare coverage +- ✅ Added finance (credit decisions) and social science (demographics) +- ✅ Biology expanded to 4 datasets with protein localization tasks + +### Scale Testing +- ✅ Largest dataset: Pendigits (7,494 samples) tests quantum simulator scalability +- ✅ Highest dimensional: Optical Recognition (64 features) tests PCA limits +- ✅ Most classes: Yeast (10 classes) and Pendigits (10 classes) for complex classification + +### Special Features +- ✅ Combined dataset creation: wine_quality_combined merges red+white with wine_type feature +- ✅ Alternative datasets: seeds vs wheat_seeds for cross-validation +- ✅ Medical focus: 8 diverse healthcare datasets for quantum medical AI research + +## 📝 Updated Files + +### Automated Updates ✅ +1. `datasets/quantum/*.csv` - 14 new CSV files added +2. `datasets/dataset_index.json` - 13 new entries added (now 31 total) +3. `scripts/expand_quantum_datasets.py` - New 500+ line expansion tool + +### Manual Updates ✅ +4. `AI_DATASETS_CATALOG.md` - Comprehensive 29-dataset documentation with domain grouping + +### Pending Updates 🔄 +5. `ai-projects/quantum-ml/quick_test_datasets.py` - Need to add 14 new datasets to smoke test +6. `ai-projects/quantum-ml/benchmark_all_datasets.py` - Need to add 14 new datasets to DATASETS dict +7. `ai-projects/quantum-ml/dataset_architecture_analyzer.py` - Need to re-run for all 29 datasets + +## 🚀 Next Steps (Priority Order) + +### 1. Quick Validation ⏭️ NEXT +**Update and run quick_test_datasets.py** +- Add 14 new dataset names to validation loop +- Run 1-epoch smoke test (~30 seconds for 29 datasets) +- Identify any loading issues requiring dataset-specific strategies +- Verify 28-29/29 success rate + +### 2. Architecture Analysis +**Run dataset_architecture_analyzer.py** +- Generate qubit/layer recommendations for all 29 datasets +- Special attention to: + - Optical recognition (64 features → 6 qubits with aggressive PCA) + - Pendigits (7,494 samples → batch size 32) + - Yeast (10 classes → 3-4 layers) +- Update `architecture_analysis.json` + +### 3. Benchmark Integration +**Update benchmark_all_datasets.py** +- Add 14 new datasets to DATASETS dict with metadata +- Add dataset-specific loading strategies: + - Delimiter handling (comma, space, whitespace) + - Header flags + - Column skipping (sequence names, IDs) +- Test load_dataset() function with new datasets + +### 4. Comprehensive Benchmark +**Run 25-epoch evaluation on all 29 datasets** +- Expected runtime: 60-90 minutes +- Generate `benchmark_results.json` +- Create `benchmark_report.md` with analysis +- Performance tiers: Exceptional (95-100%), Excellent (85-95%), Very Good (75-85%), Good (70-75%), Challenging (50-70%) +- Domain analysis: Medical avg, Biology avg, Chemistry avg, etc. + +### 5. Documentation +**Create comprehensive expansion report** +- `COMPREHENSIVE_BENCHMARK_SUMMARY_29DATASETS.md` +- Performance tier analysis +- Domain-specific insights +- Production readiness assessment +- Top 10 models for deployment +- Cost analysis for QPU hardware + +## 🐛 Known Issues + +### Download Failures (2 datasets) +1. **Vehicle** (846 samples, 18 features, 4 classes) - 404 Not Found +2. **Page Blocks** (5,473 samples, 10 features, 5 classes) - 404 Not Found + - **Cause:** UCI ML Repository URL migration + - **Alternative:** Available on Kaggle, OpenML, or new UCI archive-beta site + - **Impact:** Low (87.5% download success acceptable) + +### Validation Issues (1 dataset) +1. **Vertebral Column** (pre-existing) - File corruption, excluded from all analysis + - **Status:** CORRUPTED - DO NOT USE + - **Impact:** 27/29 working (93% success rate) + +### Display Bug (non-critical) +1. **Ecoli** shows "-1 features" in download output + - **Cause:** Display bug in feature count calculation (skip_columns=[0] incorrectly subtracted) + - **Actual:** File has 7 features correctly + - **Impact:** None (validation confirms correct feature count) + +## 📊 Success Metrics + +### Expansion Goals ✅ +- [x] Add 10+ new datasets → **Achieved 14 datasets (140%)** +- [x] Maintain >90% success rate → **Achieved 93% (27/29)** +- [x] Double sample count → **Achieved 101% increase (27K→54K)** +- [x] Add new domains → **Achieved finance + social science** + +### Quality Metrics ✅ +- [x] All datasets from trusted source (UCI ML Repository) +- [x] Permissive licenses for research use +- [x] Diverse domain coverage (8 categories) +- [x] Range of complexities (2-10 classes) +- [x] Range of scales (195-7,494 samples) + +### Infrastructure Metrics ✅ +- [x] Reusable expansion script created +- [x] Automated validation pipeline +- [x] Centralized index integration +- [x] Comprehensive documentation + +## 🎓 Lessons Learned + +### Technical Insights +1. **Manual curation superior to API automation** - Specifying delimiter, header, column metadata upfront prevents loading failures +2. **UCI repository evolving** - Need robust URL handling and alternative sources for long-term stability +3. **Special dataset creation valuable** - Combined datasets (wine_quality_combined) test additional features and larger scale +4. **87.5% success rate acceptable** - Given UCI repository URL instability and aging infrastructure + +### Process Improvements +1. **Metadata-driven downloads essential** - Delimiter, header, column skip info prevents runtime loading failures +2. **10% sample count tolerance appropriate** - Accounts for CSV parsing variations +3. **Script-based expansion reusable** - Can easily add more CANDIDATE_DATASETS entries +4. **Validation before index update** - Ensures only working datasets added to official index + +### Dataset Selection Strategy +1. **Prioritize 100-5000 samples** - Quantum simulator constraints +2. **Target 4-60 features** - Qubit count limits (4-6 qubits typical) +3. **Focus on tabular classification** - Quantum circuit suitability +4. **Emphasize medical/biology** - High-value application domains for quantum ML + +## 🔗 Related Documentation + +- **Expansion Script:** `scripts/expand_quantum_datasets.py` +- **Dataset Catalog:** `AI_DATASETS_CATALOG.md` (now 29 datasets) +- **Dataset Index:** `datasets/dataset_index.json` (31 entries) +- **Previous Expansion:** `DATASET_EXPANSION_COMPLETE.md` (Phase 1: 4→15 datasets) +- **Benchmark Results:** `COMPREHENSIVE_BENCHMARK_SUMMARY.md` (15-dataset baseline) + +## 🎉 Conclusion + +**Phase 2 expansion successfully completed:** +- ✅ 93% increase in dataset count (15→29) +- ✅ 101% increase in sample count (27K→54K) +- ✅ 93% validation success rate (27/29) +- ✅ 8 domain categories covered +- ✅ Medical domain tripled (3→8 datasets) +- ✅ Ready for comprehensive 29-dataset benchmark + +**Quantum ML infrastructure now includes:** +- 29 high-quality classification datasets +- 54,407 total samples +- 8 diverse application domains +- Reusable expansion tooling +- Comprehensive documentation + +**Next milestone:** Run comprehensive 25-epoch benchmark on all 29 datasets to establish quantum ML state-of-the-art across diverse classification tasks. diff --git a/docs/guides/DEVELOPER_TOOLING_SUMMARY.md b/docs/guides/DEVELOPER_TOOLING_SUMMARY.md index 0b23d5490..5234be119 100644 --- a/docs/guides/DEVELOPER_TOOLING_SUMMARY.md +++ b/docs/guides/DEVELOPER_TOOLING_SUMMARY.md @@ -1,403 +1,403 @@ -# Developer Tooling & Test Fixes Summary - -**Date:** November 20, 2025 -**Status:** ✅ Complete - -## Overview - -This iteration focused on building developer experience infrastructure and fixing critical test failures to ensure CI/CD readiness. All enhancements from the previous phase remain intact and functional. - ---- - -## ✅ Completed Work - -### 1. System Health Check Tool (`scripts/system_health_check.py`) - -**Purpose:** Comprehensive diagnostic tool to validate system health across all components. - -**Features:** -- ✅ Validates 4 virtual environments (root, quantum-ai, ML, talk-to-ai) -- ✅ Checks Azure Functions status (running/stopped) -- ✅ Verifies documentation completeness (6 core docs) -- ✅ Counts test files and provides test statistics -- ✅ Validates orchestrator configurations (autotrain, quantum_autorun) -- ✅ Reports dataset inventory (29 quantum + 4 chat = 2.1 GB) -- ✅ Generates actionable recommendations -- ✅ Supports JSON and text output formats - -**Usage:** -```powershell -python .\scripts\system_health_check.py # Text output -python .\scripts\system_health_check.py --json # JSON output -``` - -**Sample Output:** -``` -====================================================================== -QAI SYSTEM HEALTH REPORT -====================================================================== - -Generated: 2025-11-20T21:13:20.414523 -Overall Health: GOOD - -[PYTHON ENVIRONMENTS] - ✓ root: Azure Functions runtime - ✓ quantum-ai: Quantum ML training - ✓ ml: LoRA fine-tuning - ✓ talk-to-ai: Chat CLI - -[AZURE FUNCTIONS] - ✗ Not running (expected at http://localhost:7071) - -[DOCUMENTATION] - Complete: 6/6 - ✓ README.md - ✓ ENHANCEMENTS_SUMMARY.md - ✓ TELEMETRY_COSMOS_ENABLEMENT.md - ✓ QUICK_REFERENCE.md - ✓ QUANTUM_AUTORUN_README.md - ✓ AUTOTRAIN_README.md - -[TESTS] - Test files: 8 - -[ORCHESTRATORS] - ✓ autotrain: 6 jobs configured - ✓ quantum_autorun: 1 jobs configured - -[DATASETS] - Quantum datasets: 29 - Chat datasets: 4 - Total size: 2106.6 MB - -[RECOMMENDATIONS] - 1. Azure Functions not running. Start with: func host start -``` - ---- - -### 2. Pre-Commit Validation Script (`scripts/pre_commit_check.py`) - -**Purpose:** Automated code quality validation for CI/CD pipelines and local development. - -**Features:** -- ✅ **Test runner:** Executes pytest suite with summary reporting -- ✅ **Linter:** Checks code quality (placeholder for ruff/flake8) -- ✅ **Security scanner:** Placeholder for bandit/safety (can be skipped) -- ✅ **Git hygiene:** Warns about large unstaged changes -- ✅ **Documentation validator:** Verifies core docs exist -- ✅ **Colored output:** ANSI terminal colors for readability -- ✅ **Exit codes:** Non-zero on failures (CI/CD compatible) -- ✅ **Configurable checks:** Skip checks via CLI flags - -**Usage:** -```powershell -python .\scripts\pre_commit_check.py # All checks -python .\scripts\pre_commit_check.py --skip security # Skip security scan -python .\scripts\pre_commit_check.py --skip tests lint # Only hygiene + docs -``` - -**Check Types:** -1. **Tests:** Runs `pytest tests/ -q` and verifies exit code -2. **Linting:** Placeholder for ruff/flake8 (currently passes) -3. **Security:** Placeholder for bandit/safety (optional) -4. **Git Hygiene:** Warns if >1000 lines staged -5. **Documentation:** Validates 6 core markdown files exist - -**Integration:** -- Can be added to `.git/hooks/pre-commit` for automatic validation -- Compatible with GitHub Actions/Azure Pipelines -- See `PRE_COMMIT_GUIDE.md` for detailed integration steps - ---- - -### 3. Pre-Commit Guide Documentation (`PRE_COMMIT_GUIDE.md`) - -**Purpose:** Developer guide for using the pre-commit validation system. - -**Contents:** -- ✅ Quick start commands -- ✅ Detailed check descriptions -- ✅ CI/CD integration examples -- ✅ Troubleshooting common issues -- ✅ Configuration options -- ✅ Git hook setup instructions - -**Key Sections:** -- **Overview:** Explains 5-check validation pipeline -- **Quick Start:** Copy-paste commands for immediate use -- **Check Details:** Deep dive into each validator -- **CI/CD Integration:** GitHub Actions YAML example -- **Git Hooks:** Automatic pre-commit validation setup -- **Troubleshooting:** Solutions for common failures - ---- - -### 4. Test Fixes (2 Critical Failures Resolved) - -#### Issue #1: Path Construction in `test_autotrain_integration.py` - -**Problem:** -Line 333 used `glob("*")` which matched both timestamp directories AND the `last_run.json` file. The `max()` call alphabetically picked `last_run.json` (sorts after `20251121T...`), treating it as a directory and causing `stdout.log` path to fail. - -**Root Cause:** -```python -# BROKEN: Matches files + dirs -job_dirs = list((REPO_ROOT / "data_out" / "autotrain" / "fast_fail").glob("*")) -latest = max(job_dirs, key=lambda p: p.name) # Picks "last_run.json" file! -log_file = latest / "stdout.log" # Path: fast_fail/last_run.json/stdout.log (invalid) -``` - -**Fix Applied:** -```python -# FIXED: Filter to directories only -job_dirs = [ - p for p in (REPO_ROOT / "data_out" / "autotrain" / "fast_fail").glob("*") - if p.is_dir() -] -latest = max(job_dirs, key=lambda p: p.name) # Now picks timestamp dir -log_file = latest / "stdout.log" # Path: fast_fail/20251121T051213Z/stdout.log (valid) -``` - -**Impact:** `test_real_run_produces_logs` now passes - -#### Issue #2: Outdated Job Name in `test_autotrain.py` - -**Problem:** -Test referenced `phi36_mixed_chat` job which no longer exists in `autotrain.yaml` (config was updated to use `phi35_mixed_chat`, `mistral_7b_mixed_chat`, etc.). - -**Fix Applied:** -```python -# BEFORE: Nonexistent job -proc = subprocess.run([..., "--job", "phi36_mixed_chat", "--dry-run"], ...) - -# AFTER: Valid job from current config -proc = subprocess.run([..., "--job", "phi35_mixed_chat", "--dry-run"], ...) -``` - -**Impact:** `test_autotrain_dry_run_smoke` now passes - ---- - -## 🧪 Validation Results - -### Final Test Status -``` -=============================== 68 passed in 15.01s ================================ -``` - -**Test Breakdown:** -- `test_autotrain.py`: 1 test (smoke test) -- `test_autotrain_integration.py`: 15 tests (CLI, dry-run, execution, errors) -- `test_autotrain_unit.py`: 24 tests (config parsing, command building) -- `test_chat_endpoint_basic.py`: 1 test (HTTP endpoint) -- `test_database_integration.py`: 3 tests (SQL operations) -- `test_quantum_autorun_integration.py`: 5 tests (orchestrator integration) -- `test_quantum_autorun_unit.py`: 16 tests (quantum config parsing) -- `test_validate_qiskit_env.py`: 3 tests (quantum validation logic) - -### Pre-Commit Check Status -``` -═══════════════════════════════════════════════════════════════════ -RESULT: All checks passed ✓ (4/4) -═══════════════════════════════════════════════════════════════════ - -[1/5] Running unit tests... ✓ 68 tests passed -[2/5] Linting code... ✓ No linting issues -[4/5] Git hygiene... ✓ Git staging area looks clean -[5/5] Checking documentation... ✓ All key documentation present -``` - -### System Health Status -``` -Overall Health: GOOD - -✓ 4 virtual environments validated -✓ 6/6 documentation files present -✓ 2 orchestrators configured (7 total jobs) -✓ 33 datasets available (2.1 GB) -✓ 8 test files with 68 passing tests -``` - ---- - -## 📁 Files Created/Modified - -### New Files -1. `scripts/system_health_check.py` (~380 lines) -2. `scripts/pre_commit_check.py` (~290 lines) -3. `PRE_COMMIT_GUIDE.md` (~200 lines) -4. `DEVELOPER_TOOLING_SUMMARY.md` (this file) - -### Modified Files -1. `tests/test_autotrain_integration.py` (line 333: added `if p.is_dir()` filter) -2. `tests/test_autotrain.py` (line 19, 26, 33: `phi36_mixed_chat` → `phi35_mixed_chat`) - ---- - -## 🚀 Developer Workflow Improvements - -### Before This Iteration -- ❌ No automated validation (manual pytest runs) -- ❌ No system health diagnostics -- ❌ 2 failing integration tests blocking CI/CD -- ❌ No pre-commit best practices documentation - -### After This Iteration -- ✅ One-command validation (`pre_commit_check.py`) -- ✅ Comprehensive health diagnostics (`system_health_check.py`) -- ✅ 100% test pass rate (68/68) -- ✅ CI/CD-ready validation pipeline -- ✅ Developer documentation (`PRE_COMMIT_GUIDE.md`) - ---- - -## 🔧 Usage Examples - -### Quick Validation Before Commit -```powershell -# Run all checks (tests, lint, security, hygiene, docs) -python .\scripts\pre_commit_check.py - -# Skip security scan (faster for local dev) -python .\scripts\pre_commit_check.py --skip security - -# Only run tests and documentation checks -python .\scripts\pre_commit_check.py --skip lint security hygiene -``` - -### System Health Diagnostics -```powershell -# Generate human-readable health report -python .\scripts\system_health_check.py - -# Generate JSON for programmatic parsing -python .\scripts\system_health_check.py --json > health.json -``` - -### CI/CD Integration -```yaml -# GitHub Actions example (.github/workflows/ci.yml) -name: CI -on: [push, pull_request] -jobs: - validate: - runs-on: windows-latest - steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 - with: - python-version: '3.11' - - run: pip install -r requirements.txt - - run: python .\scripts\pre_commit_check.py -``` - ---- - -## 📊 Impact Metrics - -### Code Quality -- **Test Coverage:** 68 tests covering 8 test files -- **Pass Rate:** 100% (was 97% with 2 failures) -- **Validation Time:** ~15s for full test suite -- **Lint Status:** Clean (no issues detected) - -### Developer Experience -- **Validation Automation:** 5 automated checks in one command -- **Diagnostic Capabilities:** 6-category health monitoring -- **Documentation:** 3 new comprehensive guides -- **CI/CD Readiness:** Exit code-based validation for pipelines - -### System Reliability -- **Environment Validation:** 4 venvs actively monitored -- **Orchestrator Status:** 7 configured jobs (2 orchestrators) -- **Dataset Integrity:** 33 datasets (2.1 GB) validated -- **Documentation:** 6/6 core docs present and valid - ---- - -## 🎯 Key Achievements - -1. ✅ **Zero Test Failures:** Fixed 2 critical integration test bugs -2. ✅ **Automated Validation:** Pre-commit check script with 5 validators -3. ✅ **Health Monitoring:** Comprehensive diagnostic tool for system state -4. ✅ **Developer Docs:** Complete guide for pre-commit workflow -5. ✅ **CI/CD Ready:** Exit code-based validation for automated pipelines - ---- - -## 🔍 Technical Details - -### Path Construction Fix -**File:** `tests/test_autotrain_integration.py:333` -**Change:** Added `if p.is_dir()` filter to glob results -**Reason:** Prevent `last_run.json` file from being treated as directory - -### Job Name Update -**File:** `tests/test_autotrain.py:19,26,33` -**Change:** `phi36_mixed_chat` → `phi35_mixed_chat` -**Reason:** Align with current `autotrain.yaml` configuration - -### Health Check Architecture -**File:** `scripts/system_health_check.py` -**Pattern:** `HealthChecker` class with 6 check methods -**Methods:** -- `check_python_environments()` - Validate venvs -- `check_azure_functions()` - Check Functions status -- `check_documentation()` - Verify core docs -- `check_tests()` - Count test files -- `check_orchestrators()` - Parse YAML configs -- `check_datasets()` - Inventory datasets - -### Pre-Commit Check Architecture -**File:** `scripts/pre_commit_check.py` -**Pattern:** Sequential validator execution with colored output -**Validators:** -1. `run_tests()` - Subprocess pytest execution -2. `run_linter()` - Placeholder for ruff/flake8 -3. `run_security_check()` - Placeholder for bandit/safety -4. `check_git_hygiene()` - Warn on large staged changes -5. `check_documentation()` - Verify file existence - ---- - -## 🧭 Next Steps (Recommendations) - -### Immediate (High Priority) -1. ✅ **Add to git hooks:** Install pre-commit script for automatic validation -2. ✅ **CI/CD integration:** Add to GitHub Actions workflow -3. ✅ **Baseline health report:** Run system health check weekly - -### Short-Term (Medium Priority) -1. **Ruff integration:** Replace lint placeholder with actual ruff checks -2. **Security scanning:** Add bandit/safety for dependency vulnerability checks -3. **Coverage reporting:** Add pytest-cov for test coverage metrics -4. **Markdown linting:** Address cosmetic MD031/MD032 warnings (optional) - -### Long-Term (Low Priority) -1. **Type checking:** Add mypy for static type validation -2. **Performance benchmarks:** Track orchestrator execution times -3. **Dataset versioning:** Add checksums to dataset_index.json -4. **Health history:** Store health reports in data_out/health/ - ---- - -## 📖 Related Documentation - -- **PRE_COMMIT_GUIDE.md** - Comprehensive pre-commit validation guide -- **ENHANCEMENTS_SUMMARY.md** - Previous iteration's enhancement details -- **QUICK_REFERENCE.md** - Command reference for all tools -- **TELEMETRY_COSMOS_ENABLEMENT.md** - Telemetry integration guide -- **README.md** - Main project documentation - ---- - -## ✨ Summary - -This iteration successfully built a robust developer tooling ecosystem: -- **3 new tools** (health check, pre-commit validator, documentation) -- **2 critical bugs fixed** (test path construction, outdated job names) -- **100% test pass rate** (68/68 tests passing) -- **CI/CD ready** (automated validation pipeline) - -The system is now production-ready with automated validation, comprehensive diagnostics, and developer-friendly documentation. All previous enhancements remain functional and integrated. +# Developer Tooling & Test Fixes Summary + +**Date:** November 20, 2025 +**Status:** ✅ Complete + +## Overview + +This iteration focused on building developer experience infrastructure and fixing critical test failures to ensure CI/CD readiness. All enhancements from the previous phase remain intact and functional. + +--- + +## ✅ Completed Work + +### 1. System Health Check Tool (`scripts/system_health_check.py`) + +**Purpose:** Comprehensive diagnostic tool to validate system health across all components. + +**Features:** +- ✅ Validates 4 virtual environments (root, quantum-ai, ML, talk-to-ai) +- ✅ Checks Azure Functions status (running/stopped) +- ✅ Verifies documentation completeness (6 core docs) +- ✅ Counts test files and provides test statistics +- ✅ Validates orchestrator configurations (autotrain, quantum_autorun) +- ✅ Reports dataset inventory (29 quantum + 4 chat = 2.1 GB) +- ✅ Generates actionable recommendations +- ✅ Supports JSON and text output formats + +**Usage:** +```powershell +python .\scripts\system_health_check.py # Text output +python .\scripts\system_health_check.py --json # JSON output +``` + +**Sample Output:** +``` +====================================================================== +QAI SYSTEM HEALTH REPORT +====================================================================== + +Generated: 2025-11-20T21:13:20.414523 +Overall Health: GOOD + +[PYTHON ENVIRONMENTS] + ✓ root: Azure Functions runtime + ✓ quantum-ai: Quantum ML training + ✓ ml: LoRA fine-tuning + ✓ talk-to-ai: Chat CLI + +[AZURE FUNCTIONS] + ✗ Not running (expected at http://localhost:7071) + +[DOCUMENTATION] + Complete: 6/6 + ✓ README.md + ✓ ENHANCEMENTS_SUMMARY.md + ✓ TELEMETRY_COSMOS_ENABLEMENT.md + ✓ QUICK_REFERENCE.md + ✓ QUANTUM_AUTORUN_README.md + ✓ AUTOTRAIN_README.md + +[TESTS] + Test files: 8 + +[ORCHESTRATORS] + ✓ autotrain: 6 jobs configured + ✓ quantum_autorun: 1 jobs configured + +[DATASETS] + Quantum datasets: 29 + Chat datasets: 4 + Total size: 2106.6 MB + +[RECOMMENDATIONS] + 1. Azure Functions not running. Start with: func host start +``` + +--- + +### 2. Pre-Commit Validation Script (`scripts/pre_commit_check.py`) + +**Purpose:** Automated code quality validation for CI/CD pipelines and local development. + +**Features:** +- ✅ **Test runner:** Executes pytest suite with summary reporting +- ✅ **Linter:** Checks code quality (placeholder for ruff/flake8) +- ✅ **Security scanner:** Placeholder for bandit/safety (can be skipped) +- ✅ **Git hygiene:** Warns about large unstaged changes +- ✅ **Documentation validator:** Verifies core docs exist +- ✅ **Colored output:** ANSI terminal colors for readability +- ✅ **Exit codes:** Non-zero on failures (CI/CD compatible) +- ✅ **Configurable checks:** Skip checks via CLI flags + +**Usage:** +```powershell +python .\scripts\pre_commit_check.py # All checks +python .\scripts\pre_commit_check.py --skip security # Skip security scan +python .\scripts\pre_commit_check.py --skip tests lint # Only hygiene + docs +``` + +**Check Types:** +1. **Tests:** Runs `pytest tests/ -q` and verifies exit code +2. **Linting:** Placeholder for ruff/flake8 (currently passes) +3. **Security:** Placeholder for bandit/safety (optional) +4. **Git Hygiene:** Warns if >1000 lines staged +5. **Documentation:** Validates 6 core markdown files exist + +**Integration:** +- Can be added to `.git/hooks/pre-commit` for automatic validation +- Compatible with GitHub Actions/Azure Pipelines +- See `PRE_COMMIT_GUIDE.md` for detailed integration steps + +--- + +### 3. Pre-Commit Guide Documentation (`PRE_COMMIT_GUIDE.md`) + +**Purpose:** Developer guide for using the pre-commit validation system. + +**Contents:** +- ✅ Quick start commands +- ✅ Detailed check descriptions +- ✅ CI/CD integration examples +- ✅ Troubleshooting common issues +- ✅ Configuration options +- ✅ Git hook setup instructions + +**Key Sections:** +- **Overview:** Explains 5-check validation pipeline +- **Quick Start:** Copy-paste commands for immediate use +- **Check Details:** Deep dive into each validator +- **CI/CD Integration:** GitHub Actions YAML example +- **Git Hooks:** Automatic pre-commit validation setup +- **Troubleshooting:** Solutions for common failures + +--- + +### 4. Test Fixes (2 Critical Failures Resolved) + +#### Issue #1: Path Construction in `test_autotrain_integration.py` + +**Problem:** +Line 333 used `glob("*")` which matched both timestamp directories AND the `last_run.json` file. The `max()` call alphabetically picked `last_run.json` (sorts after `20251121T...`), treating it as a directory and causing `stdout.log` path to fail. + +**Root Cause:** +```python +# BROKEN: Matches files + dirs +job_dirs = list((REPO_ROOT / "data_out" / "autotrain" / "fast_fail").glob("*")) +latest = max(job_dirs, key=lambda p: p.name) # Picks "last_run.json" file! +log_file = latest / "stdout.log" # Path: fast_fail/last_run.json/stdout.log (invalid) +``` + +**Fix Applied:** +```python +# FIXED: Filter to directories only +job_dirs = [ + p for p in (REPO_ROOT / "data_out" / "autotrain" / "fast_fail").glob("*") + if p.is_dir() +] +latest = max(job_dirs, key=lambda p: p.name) # Now picks timestamp dir +log_file = latest / "stdout.log" # Path: fast_fail/20251121T051213Z/stdout.log (valid) +``` + +**Impact:** `test_real_run_produces_logs` now passes + +#### Issue #2: Outdated Job Name in `test_autotrain.py` + +**Problem:** +Test referenced `phi36_mixed_chat` job which no longer exists in `autotrain.yaml` (config was updated to use `phi35_mixed_chat`, `mistral_7b_mixed_chat`, etc.). + +**Fix Applied:** +```python +# BEFORE: Nonexistent job +proc = subprocess.run([..., "--job", "phi36_mixed_chat", "--dry-run"], ...) + +# AFTER: Valid job from current config +proc = subprocess.run([..., "--job", "phi35_mixed_chat", "--dry-run"], ...) +``` + +**Impact:** `test_autotrain_dry_run_smoke` now passes + +--- + +## 🧪 Validation Results + +### Final Test Status +``` +=============================== 68 passed in 15.01s ================================ +``` + +**Test Breakdown:** +- `test_autotrain.py`: 1 test (smoke test) +- `test_autotrain_integration.py`: 15 tests (CLI, dry-run, execution, errors) +- `test_autotrain_unit.py`: 24 tests (config parsing, command building) +- `test_chat_endpoint_basic.py`: 1 test (HTTP endpoint) +- `test_database_integration.py`: 3 tests (SQL operations) +- `test_quantum_autorun_integration.py`: 5 tests (orchestrator integration) +- `test_quantum_autorun_unit.py`: 16 tests (quantum config parsing) +- `test_validate_qiskit_env.py`: 3 tests (quantum validation logic) + +### Pre-Commit Check Status +``` +═══════════════════════════════════════════════════════════════════ +RESULT: All checks passed ✓ (4/4) +═══════════════════════════════════════════════════════════════════ + +[1/5] Running unit tests... ✓ 68 tests passed +[2/5] Linting code... ✓ No linting issues +[4/5] Git hygiene... ✓ Git staging area looks clean +[5/5] Checking documentation... ✓ All key documentation present +``` + +### System Health Status +``` +Overall Health: GOOD + +✓ 4 virtual environments validated +✓ 6/6 documentation files present +✓ 2 orchestrators configured (7 total jobs) +✓ 33 datasets available (2.1 GB) +✓ 8 test files with 68 passing tests +``` + +--- + +## 📁 Files Created/Modified + +### New Files +1. `scripts/system_health_check.py` (~380 lines) +2. `scripts/pre_commit_check.py` (~290 lines) +3. `PRE_COMMIT_GUIDE.md` (~200 lines) +4. `DEVELOPER_TOOLING_SUMMARY.md` (this file) + +### Modified Files +1. `tests/test_autotrain_integration.py` (line 333: added `if p.is_dir()` filter) +2. `tests/test_autotrain.py` (line 19, 26, 33: `phi36_mixed_chat` → `phi35_mixed_chat`) + +--- + +## 🚀 Developer Workflow Improvements + +### Before This Iteration +- ❌ No automated validation (manual pytest runs) +- ❌ No system health diagnostics +- ❌ 2 failing integration tests blocking CI/CD +- ❌ No pre-commit best practices documentation + +### After This Iteration +- ✅ One-command validation (`pre_commit_check.py`) +- ✅ Comprehensive health diagnostics (`system_health_check.py`) +- ✅ 100% test pass rate (68/68) +- ✅ CI/CD-ready validation pipeline +- ✅ Developer documentation (`PRE_COMMIT_GUIDE.md`) + +--- + +## 🔧 Usage Examples + +### Quick Validation Before Commit +```powershell +# Run all checks (tests, lint, security, hygiene, docs) +python .\scripts\pre_commit_check.py + +# Skip security scan (faster for local dev) +python .\scripts\pre_commit_check.py --skip security + +# Only run tests and documentation checks +python .\scripts\pre_commit_check.py --skip lint security hygiene +``` + +### System Health Diagnostics +```powershell +# Generate human-readable health report +python .\scripts\system_health_check.py + +# Generate JSON for programmatic parsing +python .\scripts\system_health_check.py --json > health.json +``` + +### CI/CD Integration +```yaml +# GitHub Actions example (.github/workflows/ci.yml) +name: CI +on: [push, pull_request] +jobs: + validate: + runs-on: windows-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.11' + - run: pip install -r requirements.txt + - run: python .\scripts\pre_commit_check.py +``` + +--- + +## 📊 Impact Metrics + +### Code Quality +- **Test Coverage:** 68 tests covering 8 test files +- **Pass Rate:** 100% (was 97% with 2 failures) +- **Validation Time:** ~15s for full test suite +- **Lint Status:** Clean (no issues detected) + +### Developer Experience +- **Validation Automation:** 5 automated checks in one command +- **Diagnostic Capabilities:** 6-category health monitoring +- **Documentation:** 3 new comprehensive guides +- **CI/CD Readiness:** Exit code-based validation for pipelines + +### System Reliability +- **Environment Validation:** 4 venvs actively monitored +- **Orchestrator Status:** 7 configured jobs (2 orchestrators) +- **Dataset Integrity:** 33 datasets (2.1 GB) validated +- **Documentation:** 6/6 core docs present and valid + +--- + +## 🎯 Key Achievements + +1. ✅ **Zero Test Failures:** Fixed 2 critical integration test bugs +2. ✅ **Automated Validation:** Pre-commit check script with 5 validators +3. ✅ **Health Monitoring:** Comprehensive diagnostic tool for system state +4. ✅ **Developer Docs:** Complete guide for pre-commit workflow +5. ✅ **CI/CD Ready:** Exit code-based validation for automated pipelines + +--- + +## 🔍 Technical Details + +### Path Construction Fix +**File:** `tests/test_autotrain_integration.py:333` +**Change:** Added `if p.is_dir()` filter to glob results +**Reason:** Prevent `last_run.json` file from being treated as directory + +### Job Name Update +**File:** `tests/test_autotrain.py:19,26,33` +**Change:** `phi36_mixed_chat` → `phi35_mixed_chat` +**Reason:** Align with current `autotrain.yaml` configuration + +### Health Check Architecture +**File:** `scripts/system_health_check.py` +**Pattern:** `HealthChecker` class with 6 check methods +**Methods:** +- `check_python_environments()` - Validate venvs +- `check_azure_functions()` - Check Functions status +- `check_documentation()` - Verify core docs +- `check_tests()` - Count test files +- `check_orchestrators()` - Parse YAML configs +- `check_datasets()` - Inventory datasets + +### Pre-Commit Check Architecture +**File:** `scripts/pre_commit_check.py` +**Pattern:** Sequential validator execution with colored output +**Validators:** +1. `run_tests()` - Subprocess pytest execution +2. `run_linter()` - Placeholder for ruff/flake8 +3. `run_security_check()` - Placeholder for bandit/safety +4. `check_git_hygiene()` - Warn on large staged changes +5. `check_documentation()` - Verify file existence + +--- + +## 🧭 Next Steps (Recommendations) + +### Immediate (High Priority) +1. ✅ **Add to git hooks:** Install pre-commit script for automatic validation +2. ✅ **CI/CD integration:** Add to GitHub Actions workflow +3. ✅ **Baseline health report:** Run system health check weekly + +### Short-Term (Medium Priority) +1. **Ruff integration:** Replace lint placeholder with actual ruff checks +2. **Security scanning:** Add bandit/safety for dependency vulnerability checks +3. **Coverage reporting:** Add pytest-cov for test coverage metrics +4. **Markdown linting:** Address cosmetic MD031/MD032 warnings (optional) + +### Long-Term (Low Priority) +1. **Type checking:** Add mypy for static type validation +2. **Performance benchmarks:** Track orchestrator execution times +3. **Dataset versioning:** Add checksums to dataset_index.json +4. **Health history:** Store health reports in data_out/health/ + +--- + +## 📖 Related Documentation + +- **PRE_COMMIT_GUIDE.md** - Comprehensive pre-commit validation guide +- **ENHANCEMENTS_SUMMARY.md** - Previous iteration's enhancement details +- **QUICK_REFERENCE.md** - Command reference for all tools +- **TELEMETRY_COSMOS_ENABLEMENT.md** - Telemetry integration guide +- **README.md** - Main project documentation + +--- + +## ✨ Summary + +This iteration successfully built a robust developer tooling ecosystem: +- **3 new tools** (health check, pre-commit validator, documentation) +- **2 critical bugs fixed** (test path construction, outdated job names) +- **100% test pass rate** (68/68 tests passing) +- **CI/CD ready** (automated validation pipeline) + +The system is now production-ready with automated validation, comprehensive diagnostics, and developer-friendly documentation. All previous enhancements remain functional and integrated. diff --git a/docs/guides/ENHANCEMENTS_SUMMARY.md b/docs/guides/ENHANCEMENTS_SUMMARY.md index 7bd26b913..974e09cf4 100644 --- a/docs/guides/ENHANCEMENTS_SUMMARY.md +++ b/docs/guides/ENHANCEMENTS_SUMMARY.md @@ -1,409 +1,409 @@ -# QAI Enhancements Summary (December 2024) - -## Overview - -Four major enhancements completed to improve quantum environment management, production observability, and upgrade pathways: - -1. **Quantum Status Integration**: `/api/ai/status` now includes quantum environment health, version info, and conflict detection -2. **Scripted Qiskit 1.x Upgrade Path**: `ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py` provides controlled migration with backup/revert -3. **Telemetry & Cosmos Enablement**: Comprehensive guide for enabling Application Insights tracing and Cosmos DB persistence -4. **Unit Test Coverage**: `tests/test_validate_qiskit_env.py` validates quantum environment conflict detection logic - ---- - -## 1. Quantum Status Integration - -### Changes Made - -**File: `function_app.py` (ai/status endpoint)** -- Added `quantum` section to status JSON payload -- Includes: qiskit version, pennylane version, conflict flag, optional Azure Quantum backend probe -- Gated by `QAI_STATUS_CONNECT_AZURE_QUANTUM` environment variable (defaults to false to avoid latency) - -**File: `ai-projects/quantum-ml/scripts/validate_qiskit_env.py`** -- Extracted `detect_conflict()` function for unit-testable logic -- Refactored `main()` to use structured metadata return - -### Status Endpoint Response - -```json -{ - "quantum": { - "enabled": true, - "qiskit": "0.46.0", - "pennylane": "0.43.0", - "azure_quantum": { - "workspace_connected": false, - "backends": [], - "attempted": false, - "error": null - }, - "conflict": false - } -} -``` - -### Conflict Detection Rules - -- **Pre-1.0 coexistence (OK)**: `qiskit: 0.46.0` + `qiskit_aer: 0.12.2` → `conflict: false` -- **Mixed 1.x + legacy (BAD)**: `qiskit: 1.0.2` + `qiskit_aer: 0.12.2` → `conflict: true` -- **Import errors (BAD)**: Any package import failure → `conflict: true` - -### Usage - -```powershell -# Check quantum status -curl http://localhost:7071/api/ai/status | jq '.quantum' - -# Enable Azure Quantum probing (adds latency) -$env:QAI_STATUS_CONNECT_AZURE_QUANTUM = "true" -# Restart Functions host -``` - -**Production note**: Only enable Azure probing if you need live backend validation. Default (false) minimizes status endpoint latency. - ---- - -## 2. Qiskit 1.x Upgrade Path - -### File Created - -`ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py` - -### Capabilities - -1. **Backup**: Creates timestamped backup (`requirements.backup.TIMESTAMP.txt`) -2. **Dry-run mode**: Preview changes without applying -3. **Upgrade**: Removes legacy pins, adds `qiskit>=1.0.0,<2.0`, bumps machine-learning -4. **Revert**: Restore from backup and reinstall -5. **Environment recreation**: `pip uninstall -y` all quantum packages before install - -### Upgrade Targets - -- `qiskit >= 1.0.0, < 2.0` (flexible minor versions) -- `qiskit-machine-learning >= 0.8.0` (compatible with 1.x) -- Removes explicit `qiskit-aer` pin (becomes transitive dependency or standalone package) - -### Usage - -```powershell -cd quantum-ai - -# Preview changes -python .\scripts\upgrade_qiskit_to_1x.py --dry-run - -# Apply upgrade (creates backup first) -python .\scripts\upgrade_qiskit_to_1x.py --install - -# Revert if issues -python .\scripts\upgrade_qiskit_to_1x.py --revert - -# Validate post-upgrade -python .\scripts\validate_qiskit_env.py -``` - -### Post-Upgrade Checklist - -1. **Smoke test local training**: - ```powershell - python train_custom_dataset.py --preset heart --epochs 1 - ``` - -2. **Verify no conflicts**: - ```powershell - python .\scripts\validate_qiskit_env.py - # Should show: ✓ Environment conflict: False - ``` - -3. **Test Azure Quantum integration** (if using): - ```powershell - python .\src\test_azure_quantum.py - ``` - -4. **Check for deprecated imports**: - - `qiskit.algorithms` → `qiskit_algorithms` (separate package in 1.x) - - `qiskit.providers.aer` → `qiskit_aer` (separate package) - ---- - -## 3. Telemetry & Cosmos DB Enablement - -### Document Created - -`TELEMETRY_COSMOS_ENABLEMENT.md` (comprehensive guide) - -### Key Features Documented - -**Telemetry (Application Insights):** -- Distributed tracing for `/api/chat` and other endpoints -- Custom spans with provider, model, duration attributes -- Exception tracking and dependency instrumentation -- Feature flag: `APPLICATIONINSIGHTS_CONNECTION_STRING` - -**Cosmos DB Persistence:** -- Two strategies: per-message writes or session-level batches -- Feature flags: `QAI_ENABLE_COSMOS`, `QAI_COSMOS_PERSIST_STRATEGY` -- Lazy initialization (no startup failure if disabled) -- Cost optimization guidance (serverless vs provisioned) - -### Quick Enable - -```powershell -# Add to local.settings.json -{ - "Values": { - "APPLICATIONINSIGHTS_CONNECTION_STRING": "InstrumentationKey=...;IngestionEndpoint=...", - "QAI_ENABLE_COSMOS": "true", - "COSMOS_ENDPOINT": "https://qai-cosmos.documents.azure.com:443/", - "COSMOS_KEY": "your_primary_key_here", - "COSMOS_DATABASE": "qai", - "COSMOS_CONTAINER": "chat_sessions", - "QAI_COSMOS_PERSIST_STRATEGY": "messages" - } -} - -# Verify -curl http://localhost:7071/api/ai/status | jq '.telemetry, .cosmos' -``` - -### Status Integration - -```json -{ - "telemetry": { - "enabled": true - }, - "cosmos": { - "enabled": true, - "settings_present": true, - "initialized": true, - "container_id": "chat_sessions", - "database": "qai", - "container": "chat_sessions", - "error": null - } -} -``` - ---- - -## 4. Unit Test Coverage - -### File Created - -`tests/test_validate_qiskit_env.py` - -### Test Cases - -1. **`test_pre_1x_environment_no_conflict`**: - - Scenario: `qiskit: 0.46.0` + `qiskit_aer: 0.12.2` - - Expected: `conflict: false`, recommendation mentions "Pre-1.0" - -2. **`test_mixed_environment_conflict`**: - - Scenario: `qiskit: 1.0.2` + `qiskit_aer: 0.12.2` - - Expected: `conflict: true`, recommendation mentions "mixes Qiskit >=1.x" - -3. **`test_error_import_conflict`**: - - Scenario: `qiskit: 1.0.2` + `qiskit_aer: "error: ImportError..."` - - Expected: `conflict: true`, recommendation mentions "failed to import" - -### Running Tests - -```powershell -# Run all unit tests (including new quantum validation tests) -pytest tests/ - -# Run only quantum validation tests -pytest tests/test_validate_qiskit_env.py -v - -# Expected output: -# tests/test_validate_qiskit_env.py::test_pre_1x_environment_no_conflict PASSED [ 33%] -# tests/test_validate_qiskit_env.py::test_mixed_environment_conflict PASSED [ 66%] -# tests/test_validate_qiskit_env.py::test_error_import_conflict PASSED [100%] -# ========================= 3 passed in 0.04s ========================= -``` - -### Test Architecture - -- **Dynamic import**: Loads validation script without package dependencies -- **Isolated unit tests**: No actual qiskit imports or venv modifications -- **Mock-friendly**: Uses synthetic version dictionaries -- **Fast execution**: ~40ms for all three tests - ---- - -## Current Status Verification - -### All Enhancements Live - -```powershell -# 1. Unit tests passing -pytest tests/test_validate_qiskit_env.py -v -# ✅ 3 passed in 0.04s - -# 2. Status endpoint quantum section present -curl http://localhost:7071/api/ai/status | jq '.quantum' -# ✅ Returns: {"enabled": true, "qiskit": "1.4.5", "conflict": true, ...} - -# 3. Telemetry section present -curl http://localhost:7071/api/ai/status | jq '.telemetry' -# ✅ Returns: {"enabled": false} - -# 4. Upgrade script exists -ls quantum-ai\scripts\upgrade_qiskit_to_1x.py -# ✅ Mode: -a----, Length: ~12 KB -``` - -### Known Observations - -**Root venv shows conflict (expected):** -```json -"quantum": { - "qiskit": "1.4.5", - "conflict": true -} -``` -- **Cause**: Root Functions venv has Qiskit 1.x (possibly from transitive deps or prior install) -- **Impact**: None for quantum-ai training (uses isolated `ai-projects/quantum-ml/venv` with 0.46.0) -- **Resolution**: Either ignore (if quantum endpoints unused) or upgrade root venv using upgrade script - -**Telemetry disabled (expected):** -```json -"telemetry": {"enabled": false} -``` -- **Cause**: `APPLICATIONINSIGHTS_CONNECTION_STRING` not set in `local.settings.json` -- **Impact**: No distributed tracing (development default) -- **Resolution**: See `TELEMETRY_COSMOS_ENABLEMENT.md` to enable - ---- - -## Integration Checklist - -### For Production Deployment - -- [ ] **Enable telemetry** (set `APPLICATIONINSIGHTS_CONNECTION_STRING`) -- [ ] **Test Cosmos persistence** (set `QAI_ENABLE_COSMOS=true` with valid credentials) -- [ ] **Resolve quantum conflict** (upgrade root venv or disable quantum endpoints) -- [ ] **Run full test suite** (`pytest tests/` → all tests should pass) -- [ ] **Document environment variables** (update README with new flags) -- [ ] **Review cost estimates** (Application Insights free tier: 5 GB/month, Cosmos serverless: ~$0.08/1K msgs) - -### For Qiskit 1.x Migration - -- [ ] **Backup current environment** (automatic in upgrade script) -- [ ] **Run dry-run** (`upgrade_qiskit_to_1x.py --dry-run`) -- [ ] **Review changes** (check stdout for removed/added lines) -- [ ] **Apply upgrade** (`--install` flag) -- [ ] **Validate environment** (`validate_qiskit_env.py` → should show `conflict: false`) -- [ ] **Smoke test training** (`train_custom_dataset.py --preset heart --epochs 1`) -- [ ] **Check for deprecated APIs** (qiskit.algorithms, qiskit.providers.aer) -- [ ] **Revert if needed** (`--revert` flag restores backup) - ---- - -## File Inventory - -### New Files - -1. `ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py` (upgrade utility, ~350 lines) -2. `tests/test_validate_qiskit_env.py` (unit tests, ~50 lines) -3. `TELEMETRY_COSMOS_ENABLEMENT.md` (comprehensive guide, ~500 lines) -4. `ENHANCEMENTS_SUMMARY.md` (this document) - -### Modified Files - -1. `function_app.py` (added telemetry/quantum sections to ai/status endpoint) -2. `ai-projects/quantum-ml/scripts/validate_qiskit_env.py` (extracted detect_conflict function) - -### No Changes Required - -- All orchestrators (`autotrain.yaml`, `quantum_autorun.yaml`) work as-is -- Chat providers unaffected (telemetry is optional wrapper) -- Training scripts unchanged (quantum venv isolation maintained) -- Azure deployment configs unchanged (Bicep templates, DEPLOYMENT.md) - ---- - -## Developer Workflow Impact - -### Before Enhancements - -1. **Check quantum status**: Manual `validate_qiskit_env.py` invocation -2. **Upgrade Qiskit**: Manual requirements.txt editing, risky -3. **Enable telemetry**: Unclear which env vars needed -4. **Test conflict logic**: No unit tests, manual validation only - -### After Enhancements - -1. **Check quantum status**: `curl /api/ai/status | jq .quantum` (one command) -2. **Upgrade Qiskit**: `upgrade_qiskit_to_1x.py --dry-run --install` (safe, reversible) -3. **Enable telemetry**: Follow `TELEMETRY_COSMOS_ENABLEMENT.md` step-by-step -4. **Test conflict logic**: `pytest tests/test_validate_qiskit_env.py` (automated) - ---- - -## Next Steps - -### Immediate (Optional) - -1. **Resolve root venv conflict** (if quantum endpoints are used in production): - ```powershell - cd quantum-ai - python .\scripts\upgrade_qiskit_to_1x.py --dry-run # preview - python .\scripts\upgrade_qiskit_to_1x.py --install # apply - ``` - -2. **Enable telemetry for development** (if you want distributed tracing): - - Add `APPLICATIONINSIGHTS_CONNECTION_STRING` to `local.settings.json` - - Restart Functions host (`func host start`) - - Verify: `curl http://localhost:7071/api/ai/status | jq .telemetry.enabled` → `true` - -### Future Enhancements - -1. **Cosmos integration tests**: Add tests in `tests/test_cosmos_integration.py` -2. **Telemetry span validation**: Mock OpenTelemetry spans in chat endpoint tests -3. **Quantum endpoint smoke tests**: Add `/api/quantum/*` endpoint tests -4. **Automated conflict resolution**: Extend upgrade script to auto-detect and propose fixes -5. **Performance profiling**: Use telemetry data to identify slow operations - ---- - -## References - -- **Upgrade Script**: `ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py` -- **Validation Script**: `ai-projects/quantum-ml/scripts/validate_qiskit_env.py` -- **Unit Tests**: `tests/test_validate_qiskit_env.py` -- **Enablement Guide**: `TELEMETRY_COSMOS_ENABLEMENT.md` -- **Status Endpoint**: `GET /api/ai/status` (function_app.py lines ~600-800) -- **Conflict Detection Logic**: `validate_qiskit_env.py::detect_conflict()` (lines ~30-70) - ---- - -## Success Criteria Met - -✅ **Quantum status integrated into /api/ai/status** -- New `quantum` section with versions, conflict flag, optional Azure backends - -✅ **Scripted upgrade path to Qiskit 1.x** -- `upgrade_qiskit_to_1x.py` with dry-run, install, revert modes - -✅ **Telemetry/Cosmos enablement steps** -- Comprehensive `TELEMETRY_COSMOS_ENABLEMENT.md` guide - -✅ **Unit tests for validation logic** -- `tests/test_validate_qiskit_env.py` with 3 scenarios (all passing) - -✅ **All changes non-breaking** -- Existing workflows unchanged (telemetry/Cosmos behind feature flags) -- Quantum-ai venv isolation maintained -- Status endpoint backward-compatible (only additions) - ---- - -## Support - -For questions or issues: -1. Check `TELEMETRY_COSMOS_ENABLEMENT.md` for detailed troubleshooting -2. Run `pytest tests/test_validate_qiskit_env.py -v` to verify test infrastructure -3. Use `upgrade_qiskit_to_1x.py --dry-run` to preview upgrade impact -4. Inspect `/api/ai/status` for live environment diagnostics +# QAI Enhancements Summary (December 2024) + +## Overview + +Four major enhancements completed to improve quantum environment management, production observability, and upgrade pathways: + +1. **Quantum Status Integration**: `/api/ai/status` now includes quantum environment health, version info, and conflict detection +2. **Scripted Qiskit 1.x Upgrade Path**: `ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py` provides controlled migration with backup/revert +3. **Telemetry & Cosmos Enablement**: Comprehensive guide for enabling Application Insights tracing and Cosmos DB persistence +4. **Unit Test Coverage**: `tests/test_validate_qiskit_env.py` validates quantum environment conflict detection logic + +--- + +## 1. Quantum Status Integration + +### Changes Made + +**File: `function_app.py` (ai/status endpoint)** +- Added `quantum` section to status JSON payload +- Includes: qiskit version, pennylane version, conflict flag, optional Azure Quantum backend probe +- Gated by `QAI_STATUS_CONNECT_AZURE_QUANTUM` environment variable (defaults to false to avoid latency) + +**File: `ai-projects/quantum-ml/scripts/validate_qiskit_env.py`** +- Extracted `detect_conflict()` function for unit-testable logic +- Refactored `main()` to use structured metadata return + +### Status Endpoint Response + +```json +{ + "quantum": { + "enabled": true, + "qiskit": "0.46.0", + "pennylane": "0.43.0", + "azure_quantum": { + "workspace_connected": false, + "backends": [], + "attempted": false, + "error": null + }, + "conflict": false + } +} +``` + +### Conflict Detection Rules + +- **Pre-1.0 coexistence (OK)**: `qiskit: 0.46.0` + `qiskit_aer: 0.12.2` → `conflict: false` +- **Mixed 1.x + legacy (BAD)**: `qiskit: 1.0.2` + `qiskit_aer: 0.12.2` → `conflict: true` +- **Import errors (BAD)**: Any package import failure → `conflict: true` + +### Usage + +```powershell +# Check quantum status +curl http://localhost:7071/api/ai/status | jq '.quantum' + +# Enable Azure Quantum probing (adds latency) +$env:QAI_STATUS_CONNECT_AZURE_QUANTUM = "true" +# Restart Functions host +``` + +**Production note**: Only enable Azure probing if you need live backend validation. Default (false) minimizes status endpoint latency. + +--- + +## 2. Qiskit 1.x Upgrade Path + +### File Created + +`ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py` + +### Capabilities + +1. **Backup**: Creates timestamped backup (`requirements.backup.TIMESTAMP.txt`) +2. **Dry-run mode**: Preview changes without applying +3. **Upgrade**: Removes legacy pins, adds `qiskit>=1.0.0,<2.0`, bumps machine-learning +4. **Revert**: Restore from backup and reinstall +5. **Environment recreation**: `pip uninstall -y` all quantum packages before install + +### Upgrade Targets + +- `qiskit >= 1.0.0, < 2.0` (flexible minor versions) +- `qiskit-machine-learning >= 0.8.0` (compatible with 1.x) +- Removes explicit `qiskit-aer` pin (becomes transitive dependency or standalone package) + +### Usage + +```powershell +cd quantum-ai + +# Preview changes +python .\scripts\upgrade_qiskit_to_1x.py --dry-run + +# Apply upgrade (creates backup first) +python .\scripts\upgrade_qiskit_to_1x.py --install + +# Revert if issues +python .\scripts\upgrade_qiskit_to_1x.py --revert + +# Validate post-upgrade +python .\scripts\validate_qiskit_env.py +``` + +### Post-Upgrade Checklist + +1. **Smoke test local training**: + ```powershell + python train_custom_dataset.py --preset heart --epochs 1 + ``` + +2. **Verify no conflicts**: + ```powershell + python .\scripts\validate_qiskit_env.py + # Should show: ✓ Environment conflict: False + ``` + +3. **Test Azure Quantum integration** (if using): + ```powershell + python .\src\test_azure_quantum.py + ``` + +4. **Check for deprecated imports**: + - `qiskit.algorithms` → `qiskit_algorithms` (separate package in 1.x) + - `qiskit.providers.aer` → `qiskit_aer` (separate package) + +--- + +## 3. Telemetry & Cosmos DB Enablement + +### Document Created + +`TELEMETRY_COSMOS_ENABLEMENT.md` (comprehensive guide) + +### Key Features Documented + +**Telemetry (Application Insights):** +- Distributed tracing for `/api/chat` and other endpoints +- Custom spans with provider, model, duration attributes +- Exception tracking and dependency instrumentation +- Feature flag: `APPLICATIONINSIGHTS_CONNECTION_STRING` + +**Cosmos DB Persistence:** +- Two strategies: per-message writes or session-level batches +- Feature flags: `QAI_ENABLE_COSMOS`, `QAI_COSMOS_PERSIST_STRATEGY` +- Lazy initialization (no startup failure if disabled) +- Cost optimization guidance (serverless vs provisioned) + +### Quick Enable + +```powershell +# Add to local.settings.json +{ + "Values": { + "APPLICATIONINSIGHTS_CONNECTION_STRING": "InstrumentationKey=...;IngestionEndpoint=...", + "QAI_ENABLE_COSMOS": "true", + "COSMOS_ENDPOINT": "https://qai-cosmos.documents.azure.com:443/", + "COSMOS_KEY": "your_primary_key_here", + "COSMOS_DATABASE": "qai", + "COSMOS_CONTAINER": "chat_sessions", + "QAI_COSMOS_PERSIST_STRATEGY": "messages" + } +} + +# Verify +curl http://localhost:7071/api/ai/status | jq '.telemetry, .cosmos' +``` + +### Status Integration + +```json +{ + "telemetry": { + "enabled": true + }, + "cosmos": { + "enabled": true, + "settings_present": true, + "initialized": true, + "container_id": "chat_sessions", + "database": "qai", + "container": "chat_sessions", + "error": null + } +} +``` + +--- + +## 4. Unit Test Coverage + +### File Created + +`tests/test_validate_qiskit_env.py` + +### Test Cases + +1. **`test_pre_1x_environment_no_conflict`**: + - Scenario: `qiskit: 0.46.0` + `qiskit_aer: 0.12.2` + - Expected: `conflict: false`, recommendation mentions "Pre-1.0" + +2. **`test_mixed_environment_conflict`**: + - Scenario: `qiskit: 1.0.2` + `qiskit_aer: 0.12.2` + - Expected: `conflict: true`, recommendation mentions "mixes Qiskit >=1.x" + +3. **`test_error_import_conflict`**: + - Scenario: `qiskit: 1.0.2` + `qiskit_aer: "error: ImportError..."` + - Expected: `conflict: true`, recommendation mentions "failed to import" + +### Running Tests + +```powershell +# Run all unit tests (including new quantum validation tests) +pytest tests/ + +# Run only quantum validation tests +pytest tests/test_validate_qiskit_env.py -v + +# Expected output: +# tests/test_validate_qiskit_env.py::test_pre_1x_environment_no_conflict PASSED [ 33%] +# tests/test_validate_qiskit_env.py::test_mixed_environment_conflict PASSED [ 66%] +# tests/test_validate_qiskit_env.py::test_error_import_conflict PASSED [100%] +# ========================= 3 passed in 0.04s ========================= +``` + +### Test Architecture + +- **Dynamic import**: Loads validation script without package dependencies +- **Isolated unit tests**: No actual qiskit imports or venv modifications +- **Mock-friendly**: Uses synthetic version dictionaries +- **Fast execution**: ~40ms for all three tests + +--- + +## Current Status Verification + +### All Enhancements Live + +```powershell +# 1. Unit tests passing +pytest tests/test_validate_qiskit_env.py -v +# ✅ 3 passed in 0.04s + +# 2. Status endpoint quantum section present +curl http://localhost:7071/api/ai/status | jq '.quantum' +# ✅ Returns: {"enabled": true, "qiskit": "1.4.5", "conflict": true, ...} + +# 3. Telemetry section present +curl http://localhost:7071/api/ai/status | jq '.telemetry' +# ✅ Returns: {"enabled": false} + +# 4. Upgrade script exists +ls quantum-ai\scripts\upgrade_qiskit_to_1x.py +# ✅ Mode: -a----, Length: ~12 KB +``` + +### Known Observations + +**Root venv shows conflict (expected):** +```json +"quantum": { + "qiskit": "1.4.5", + "conflict": true +} +``` +- **Cause**: Root Functions venv has Qiskit 1.x (possibly from transitive deps or prior install) +- **Impact**: None for quantum-ai training (uses isolated `ai-projects/quantum-ml/venv` with 0.46.0) +- **Resolution**: Either ignore (if quantum endpoints unused) or upgrade root venv using upgrade script + +**Telemetry disabled (expected):** +```json +"telemetry": {"enabled": false} +``` +- **Cause**: `APPLICATIONINSIGHTS_CONNECTION_STRING` not set in `local.settings.json` +- **Impact**: No distributed tracing (development default) +- **Resolution**: See `TELEMETRY_COSMOS_ENABLEMENT.md` to enable + +--- + +## Integration Checklist + +### For Production Deployment + +- [ ] **Enable telemetry** (set `APPLICATIONINSIGHTS_CONNECTION_STRING`) +- [ ] **Test Cosmos persistence** (set `QAI_ENABLE_COSMOS=true` with valid credentials) +- [ ] **Resolve quantum conflict** (upgrade root venv or disable quantum endpoints) +- [ ] **Run full test suite** (`pytest tests/` → all tests should pass) +- [ ] **Document environment variables** (update README with new flags) +- [ ] **Review cost estimates** (Application Insights free tier: 5 GB/month, Cosmos serverless: ~$0.08/1K msgs) + +### For Qiskit 1.x Migration + +- [ ] **Backup current environment** (automatic in upgrade script) +- [ ] **Run dry-run** (`upgrade_qiskit_to_1x.py --dry-run`) +- [ ] **Review changes** (check stdout for removed/added lines) +- [ ] **Apply upgrade** (`--install` flag) +- [ ] **Validate environment** (`validate_qiskit_env.py` → should show `conflict: false`) +- [ ] **Smoke test training** (`train_custom_dataset.py --preset heart --epochs 1`) +- [ ] **Check for deprecated APIs** (qiskit.algorithms, qiskit.providers.aer) +- [ ] **Revert if needed** (`--revert` flag restores backup) + +--- + +## File Inventory + +### New Files + +1. `ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py` (upgrade utility, ~350 lines) +2. `tests/test_validate_qiskit_env.py` (unit tests, ~50 lines) +3. `TELEMETRY_COSMOS_ENABLEMENT.md` (comprehensive guide, ~500 lines) +4. `ENHANCEMENTS_SUMMARY.md` (this document) + +### Modified Files + +1. `function_app.py` (added telemetry/quantum sections to ai/status endpoint) +2. `ai-projects/quantum-ml/scripts/validate_qiskit_env.py` (extracted detect_conflict function) + +### No Changes Required + +- All orchestrators (`autotrain.yaml`, `quantum_autorun.yaml`) work as-is +- Chat providers unaffected (telemetry is optional wrapper) +- Training scripts unchanged (quantum venv isolation maintained) +- Azure deployment configs unchanged (Bicep templates, DEPLOYMENT.md) + +--- + +## Developer Workflow Impact + +### Before Enhancements + +1. **Check quantum status**: Manual `validate_qiskit_env.py` invocation +2. **Upgrade Qiskit**: Manual requirements.txt editing, risky +3. **Enable telemetry**: Unclear which env vars needed +4. **Test conflict logic**: No unit tests, manual validation only + +### After Enhancements + +1. **Check quantum status**: `curl /api/ai/status | jq .quantum` (one command) +2. **Upgrade Qiskit**: `upgrade_qiskit_to_1x.py --dry-run --install` (safe, reversible) +3. **Enable telemetry**: Follow `TELEMETRY_COSMOS_ENABLEMENT.md` step-by-step +4. **Test conflict logic**: `pytest tests/test_validate_qiskit_env.py` (automated) + +--- + +## Next Steps + +### Immediate (Optional) + +1. **Resolve root venv conflict** (if quantum endpoints are used in production): + ```powershell + cd quantum-ai + python .\scripts\upgrade_qiskit_to_1x.py --dry-run # preview + python .\scripts\upgrade_qiskit_to_1x.py --install # apply + ``` + +2. **Enable telemetry for development** (if you want distributed tracing): + - Add `APPLICATIONINSIGHTS_CONNECTION_STRING` to `local.settings.json` + - Restart Functions host (`func host start`) + - Verify: `curl http://localhost:7071/api/ai/status | jq .telemetry.enabled` → `true` + +### Future Enhancements + +1. **Cosmos integration tests**: Add tests in `tests/test_cosmos_integration.py` +2. **Telemetry span validation**: Mock OpenTelemetry spans in chat endpoint tests +3. **Quantum endpoint smoke tests**: Add `/api/quantum/*` endpoint tests +4. **Automated conflict resolution**: Extend upgrade script to auto-detect and propose fixes +5. **Performance profiling**: Use telemetry data to identify slow operations + +--- + +## References + +- **Upgrade Script**: `ai-projects/quantum-ml/scripts/upgrade_qiskit_to_1x.py` +- **Validation Script**: `ai-projects/quantum-ml/scripts/validate_qiskit_env.py` +- **Unit Tests**: `tests/test_validate_qiskit_env.py` +- **Enablement Guide**: `TELEMETRY_COSMOS_ENABLEMENT.md` +- **Status Endpoint**: `GET /api/ai/status` (function_app.py lines ~600-800) +- **Conflict Detection Logic**: `validate_qiskit_env.py::detect_conflict()` (lines ~30-70) + +--- + +## Success Criteria Met + +✅ **Quantum status integrated into /api/ai/status** +- New `quantum` section with versions, conflict flag, optional Azure backends + +✅ **Scripted upgrade path to Qiskit 1.x** +- `upgrade_qiskit_to_1x.py` with dry-run, install, revert modes + +✅ **Telemetry/Cosmos enablement steps** +- Comprehensive `TELEMETRY_COSMOS_ENABLEMENT.md` guide + +✅ **Unit tests for validation logic** +- `tests/test_validate_qiskit_env.py` with 3 scenarios (all passing) + +✅ **All changes non-breaking** +- Existing workflows unchanged (telemetry/Cosmos behind feature flags) +- Quantum-ai venv isolation maintained +- Status endpoint backward-compatible (only additions) + +--- + +## Support + +For questions or issues: +1. Check `TELEMETRY_COSMOS_ENABLEMENT.md` for detailed troubleshooting +2. Run `pytest tests/test_validate_qiskit_env.py -v` to verify test infrastructure +3. Use `upgrade_qiskit_to_1x.py --dry-run` to preview upgrade impact +4. Inspect `/api/ai/status` for live environment diagnostics diff --git a/docs/guides/MASSIVE_EXPANSION_PROGRESS.md b/docs/guides/MASSIVE_EXPANSION_PROGRESS.md index 56c53ec44..6d629b8d0 100644 --- a/docs/guides/MASSIVE_EXPANSION_PROGRESS.md +++ b/docs/guides/MASSIVE_EXPANSION_PROGRESS.md @@ -1,214 +1,214 @@ -# Massive Dataset Expansion Progress Report - -**Date:** November 16, 2025 -**Goal:** Expand from 27 to 5,000+ quantum ML datasets - ---- - -## ✅ Phase 1: Discovery (COMPLETE) - -**Results:** -- **Discovered:** 1,412 quantum-compatible datasets from OpenML -- **Total available:** 6,369 datasets in OpenML repository -- **Filter criteria:** - - Classification tasks (binary/multi-class) - - 50-50,000 samples - - 2-100 features - - <30% missing values - -**Quality Scoring System (0-100 scale):** -- Sample size (25 pts): Optimal 500-5000 samples -- Feature count (20 pts): Optimal 4-20 features -- Class balance (20 pts): >10% minority class -- Completeness (15 pts): <5% missing values -- Feature quality (10 pts): Numeric ratio -- Domain relevance (10 pts): Medical, physics, biology prioritized - -**Top 20 Candidates (Score ≥94):** -1. vehicle - 846 samples, 19 features (94.5) -2. Apple_Stock_Price_Trends - 2,516 samples, 19 features (94.5) -3. house_16H - 2,000 samples, 17 features (94.4) -4. mental_health_detection - 540 samples, 15 features (94.3) -5. Pumpkin_Seeds - 2,500 samples, 13 features (94.2) -6. FOREX datasets - 1,832 samples, 12 features (94.2) - -**Cache Location:** `datasets/massive_quantum/discovery_cache.json` - ---- - -## 🔄 Phase 2: Download (IN PROGRESS) - -**Status:** -- **Target:** Top 100 datasets (score ≥90) -- **Downloaded:** 3+ datasets (continuing...) -- **Failed:** Some datasets have API issues (handling gracefully) - -**Downloaded So Far:** -1. vehicle_54.csv (63.2 KB) -2. vehicle_994.csv (61.2 KB) -3. vehicle_reproduced_44153.csv (63.2 KB) - -**Improvements Made:** -- ✅ Better error handling (continue on failures) -- ✅ Rate limiting (0.5s delay between downloads) -- ✅ Graceful interrupt handling -- ✅ Skip corrupted/missing datasets - -**Download Command:** -```powershell -python .\scripts\massive_dataset_expansion.py --download --batch-size 100 --min-score 90 -``` - ---- - -## 🚀 Phase 3: Distributed Benchmark System (COMPLETE) - -**Created:** `scripts/distributed_benchmark.py` - -**Features:** -- ✅ Parallel training (configurable workers: 4-20) -- ✅ Hybrid quantum-classical neural network -- ✅ Progress tracking and checkpointing -- ✅ Auto-resume from failures -- ✅ Real-time status updates -- ✅ Automatic resource management -- ✅ Performance tier analysis - -**Quick Test Mode:** -```powershell -# Test on 10 workers, 1 epoch (fast validation) -python .\scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 1 --quick-test -``` - -**Full Benchmark:** -```powershell -# 100 datasets, 10 workers, 25 epochs -python .\scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 25 -``` - -**Architecture:** -- **Model:** HybridQuantumNet (4 qubits, 2 layers) -- **Preprocessing:** StandardScaler + PCA -- **Binary classification:** 2-class output -- **Batch size:** 32 -- **Learning rate:** 0.001 -- **Optimizer:** Adam - -**Output:** -- `data_out/distributed_benchmark/checkpoint.json` (auto-save every 10 datasets) -- `data_out/distributed_benchmark/distributed_results.json` (final results) - ---- - -## 📊 Current Status Summary - -| Phase | Status | Progress | Duration | -|-------|--------|----------|----------| -| Discovery | ✅ Complete | 1,412/5,000 candidates | 10 min | -| Download | 🔄 In Progress | 3/100 datasets | Ongoing | -| Benchmark System | ✅ Complete | System ready | - | -| Validation | ⏳ Pending | 0/100 validated | TBD | -| Full Benchmark | ⏳ Pending | 0/100 trained | TBD | - ---- - -## 🎯 Next Steps - -### Immediate (Today): -1. ✅ Complete download of 100 high-quality datasets -2. ✅ Run validation on all downloads -3. ✅ Execute quick test (1 epoch) to verify system -4. 📊 Run full 25-epoch benchmark on 100 datasets - -### Short-term (This Week): -1. Download next 400 datasets (score 80-90) -2. Benchmark all 500 datasets -3. Analyze performance patterns -4. Identify top 50 production candidates - -### Long-term (Next Month): -1. Download remaining 912 datasets (score 50-80) -2. Complete benchmark of 1,412 total datasets -3. Phase 2: Expand to 5,000 by including lower-quality datasets -4. Build automated quality improvement pipeline - ---- - -## 💡 Key Insights - -**OpenML Advantages:** -- 20,000+ datasets vs UCI's ~500 -- Standardized API (no web scraping) -- Rich metadata (automated scoring) -- Active community (regular updates) - -**Realistic Timeline to 5,000 Datasets:** -- Phase 1 Discovery: 10 minutes (one-time, cached) -- Phase 2 Download: ~50 hours total (100 datasets/hour in batches) -- Phase 3 Validation: ~20 minutes per 1,000 datasets -- Phase 4 Benchmark: ~8-12 hours per 100 datasets (10 workers, 25 epochs) - -**Estimated Total:** 2-3 weeks of continuous operation to reach 5,000 trained models - ---- - -## 🛠️ Technical Specifications - -**System Requirements:** -- **CPU:** Multi-core (8+ cores recommended for 10 workers) -- **RAM:** 16GB minimum, 32GB recommended -- **Storage:** ~10GB for 5,000 datasets + results -- **Network:** Stable connection for OpenML downloads -- **Python:** 3.11+ with openml, scikit-learn, pytorch - -**Dependencies:** -``` -openml>=0.14.0 -scikit-learn>=1.3.0 -pandas>=2.0.0 -numpy>=1.24.0 -torch>=2.0.0 -tqdm>=4.66.0 -joblib>=1.3.0 -``` - ---- - -## 📈 Success Metrics - -**Download Quality:** -- Target: 90% success rate -- Current: 100% (3/3, small sample) - -**Training Performance:** -- Target: 70%+ average accuracy -- Expected: 50-95% range based on dataset difficulty - -**System Reliability:** -- Checkpointing: Every 10 datasets -- Resume capability: 100% -- Error tolerance: Continue on individual failures - ---- - -## 🔗 Related Files - -**Scripts:** -- `scripts/massive_dataset_expansion.py` - Discovery & download system -- `scripts/distributed_benchmark.py` - Parallel training framework -- `scripts/expand_quantum_datasets.py` - Original expansion (27 datasets) - -**Data:** -- `datasets/massive_quantum/` - Downloaded datasets (CSV) -- `datasets/massive_quantum/discovery_cache.json` - 1,412 candidates -- `datasets/quantum/` - Original 27 validated datasets - -**Results:** -- `data_out/distributed_benchmark/` - Training results -- `DATASET_EXPANSION_COMPLETE.md` - Phase 1 (27 datasets) summary -- `DATASET_EXPANSION_PHASE2.md` - Phase 2 technical details - ---- - -**Last Updated:** November 16, 2025 -**Status:** Active Development - Download Phase +# Massive Dataset Expansion Progress Report + +**Date:** November 16, 2025 +**Goal:** Expand from 27 to 5,000+ quantum ML datasets + +--- + +## ✅ Phase 1: Discovery (COMPLETE) + +**Results:** +- **Discovered:** 1,412 quantum-compatible datasets from OpenML +- **Total available:** 6,369 datasets in OpenML repository +- **Filter criteria:** + - Classification tasks (binary/multi-class) + - 50-50,000 samples + - 2-100 features + - <30% missing values + +**Quality Scoring System (0-100 scale):** +- Sample size (25 pts): Optimal 500-5000 samples +- Feature count (20 pts): Optimal 4-20 features +- Class balance (20 pts): >10% minority class +- Completeness (15 pts): <5% missing values +- Feature quality (10 pts): Numeric ratio +- Domain relevance (10 pts): Medical, physics, biology prioritized + +**Top 20 Candidates (Score ≥94):** +1. vehicle - 846 samples, 19 features (94.5) +2. Apple_Stock_Price_Trends - 2,516 samples, 19 features (94.5) +3. house_16H - 2,000 samples, 17 features (94.4) +4. mental_health_detection - 540 samples, 15 features (94.3) +5. Pumpkin_Seeds - 2,500 samples, 13 features (94.2) +6. FOREX datasets - 1,832 samples, 12 features (94.2) + +**Cache Location:** `datasets/massive_quantum/discovery_cache.json` + +--- + +## 🔄 Phase 2: Download (IN PROGRESS) + +**Status:** +- **Target:** Top 100 datasets (score ≥90) +- **Downloaded:** 3+ datasets (continuing...) +- **Failed:** Some datasets have API issues (handling gracefully) + +**Downloaded So Far:** +1. vehicle_54.csv (63.2 KB) +2. vehicle_994.csv (61.2 KB) +3. vehicle_reproduced_44153.csv (63.2 KB) + +**Improvements Made:** +- ✅ Better error handling (continue on failures) +- ✅ Rate limiting (0.5s delay between downloads) +- ✅ Graceful interrupt handling +- ✅ Skip corrupted/missing datasets + +**Download Command:** +```powershell +python .\scripts\massive_dataset_expansion.py --download --batch-size 100 --min-score 90 +``` + +--- + +## 🚀 Phase 3: Distributed Benchmark System (COMPLETE) + +**Created:** `scripts/distributed_benchmark.py` + +**Features:** +- ✅ Parallel training (configurable workers: 4-20) +- ✅ Hybrid quantum-classical neural network +- ✅ Progress tracking and checkpointing +- ✅ Auto-resume from failures +- ✅ Real-time status updates +- ✅ Automatic resource management +- ✅ Performance tier analysis + +**Quick Test Mode:** +```powershell +# Test on 10 workers, 1 epoch (fast validation) +python .\scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 1 --quick-test +``` + +**Full Benchmark:** +```powershell +# 100 datasets, 10 workers, 25 epochs +python .\scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 25 +``` + +**Architecture:** +- **Model:** HybridQuantumNet (4 qubits, 2 layers) +- **Preprocessing:** StandardScaler + PCA +- **Binary classification:** 2-class output +- **Batch size:** 32 +- **Learning rate:** 0.001 +- **Optimizer:** Adam + +**Output:** +- `data_out/distributed_benchmark/checkpoint.json` (auto-save every 10 datasets) +- `data_out/distributed_benchmark/distributed_results.json` (final results) + +--- + +## 📊 Current Status Summary + +| Phase | Status | Progress | Duration | +|-------|--------|----------|----------| +| Discovery | ✅ Complete | 1,412/5,000 candidates | 10 min | +| Download | 🔄 In Progress | 3/100 datasets | Ongoing | +| Benchmark System | ✅ Complete | System ready | - | +| Validation | ⏳ Pending | 0/100 validated | TBD | +| Full Benchmark | ⏳ Pending | 0/100 trained | TBD | + +--- + +## 🎯 Next Steps + +### Immediate (Today): +1. ✅ Complete download of 100 high-quality datasets +2. ✅ Run validation on all downloads +3. ✅ Execute quick test (1 epoch) to verify system +4. 📊 Run full 25-epoch benchmark on 100 datasets + +### Short-term (This Week): +1. Download next 400 datasets (score 80-90) +2. Benchmark all 500 datasets +3. Analyze performance patterns +4. Identify top 50 production candidates + +### Long-term (Next Month): +1. Download remaining 912 datasets (score 50-80) +2. Complete benchmark of 1,412 total datasets +3. Phase 2: Expand to 5,000 by including lower-quality datasets +4. Build automated quality improvement pipeline + +--- + +## 💡 Key Insights + +**OpenML Advantages:** +- 20,000+ datasets vs UCI's ~500 +- Standardized API (no web scraping) +- Rich metadata (automated scoring) +- Active community (regular updates) + +**Realistic Timeline to 5,000 Datasets:** +- Phase 1 Discovery: 10 minutes (one-time, cached) +- Phase 2 Download: ~50 hours total (100 datasets/hour in batches) +- Phase 3 Validation: ~20 minutes per 1,000 datasets +- Phase 4 Benchmark: ~8-12 hours per 100 datasets (10 workers, 25 epochs) + +**Estimated Total:** 2-3 weeks of continuous operation to reach 5,000 trained models + +--- + +## 🛠️ Technical Specifications + +**System Requirements:** +- **CPU:** Multi-core (8+ cores recommended for 10 workers) +- **RAM:** 16GB minimum, 32GB recommended +- **Storage:** ~10GB for 5,000 datasets + results +- **Network:** Stable connection for OpenML downloads +- **Python:** 3.11+ with openml, scikit-learn, pytorch + +**Dependencies:** +``` +openml>=0.14.0 +scikit-learn>=1.3.0 +pandas>=2.0.0 +numpy>=1.24.0 +torch>=2.0.0 +tqdm>=4.66.0 +joblib>=1.3.0 +``` + +--- + +## 📈 Success Metrics + +**Download Quality:** +- Target: 90% success rate +- Current: 100% (3/3, small sample) + +**Training Performance:** +- Target: 70%+ average accuracy +- Expected: 50-95% range based on dataset difficulty + +**System Reliability:** +- Checkpointing: Every 10 datasets +- Resume capability: 100% +- Error tolerance: Continue on individual failures + +--- + +## 🔗 Related Files + +**Scripts:** +- `scripts/massive_dataset_expansion.py` - Discovery & download system +- `scripts/distributed_benchmark.py` - Parallel training framework +- `scripts/expand_quantum_datasets.py` - Original expansion (27 datasets) + +**Data:** +- `datasets/massive_quantum/` - Downloaded datasets (CSV) +- `datasets/massive_quantum/discovery_cache.json` - 1,412 candidates +- `datasets/quantum/` - Original 27 validated datasets + +**Results:** +- `data_out/distributed_benchmark/` - Training results +- `DATASET_EXPANSION_COMPLETE.md` - Phase 1 (27 datasets) summary +- `DATASET_EXPANSION_PHASE2.md` - Phase 2 technical details + +--- + +**Last Updated:** November 16, 2025 +**Status:** Active Development - Download Phase diff --git a/docs/guides/MEDIUM_SCALE_EXPANSION.md b/docs/guides/MEDIUM_SCALE_EXPANSION.md index f5f43f0d3..f1dc6b812 100644 --- a/docs/guides/MEDIUM_SCALE_EXPANSION.md +++ b/docs/guides/MEDIUM_SCALE_EXPANSION.md @@ -1,334 +1,334 @@ -# Medium-Scale Expansion: 100 High-Quality Datasets - -**Started:** November 16, 2025 - 11:00 PM -**Target:** 100 datasets by November 30, 2025 (2 weeks) -**Strategy:** Quality over quantity - Focus on score ≥85 datasets - ---- - -## 🎯 Goals - -### Week 1 (Nov 16-23) -- ✅ Complete 27-dataset benchmark (DONE: 90.55% avg accuracy) -- ✅ Discover 1,412 OpenML candidates (DONE) -- ✅ Test distributed benchmark (DONE: 3 datasets successful) -- 🔄 Download 30-50 datasets overnight (IN PROGRESS) -- ⏳ Validate and quick-test all downloads -- ⏳ Full 25-epoch benchmark on valid datasets -- **Target:** 50 total datasets trained (27 + 23 new) - -### Week 2 (Nov 24-30) -- ⏳ Continue overnight downloads (25-50 more datasets) -- ⏳ Incremental benchmarking as downloads complete -- ⏳ Identify top 20 models for production -- ⏳ Generate comprehensive performance analysis -- **Target:** 100 total datasets trained - ---- - -## 📊 Current Status - -### Existing Infrastructure (Proven) -- **27 UCI datasets:** 90.55% average accuracy -- **Success rate:** 81% (22/27 successful) -- **Top performers:** banknote (100%), iris (100%), dermatology (100%) -- **Architecture:** 4 qubits, 2 layers, 16 hidden nodes - -### OpenML Discovery (Complete) -- **Total candidates:** 1,412 datasets -- **Quality scored:** 0-100 scale (6 factors) -- **Top 100 (score ≥90):** 205 candidates -- **Cache:** `datasets/massive_quantum/discovery_cache.json` - -### Initial OpenML Test (Complete) -- **Downloaded:** 3 vehicle datasets -- **Quick test:** 72% accuracy (1 epoch) -- **Expected full:** 80-90% accuracy (25 epochs) -- **Distributed system:** ✅ Validated (3 parallel workers) - ---- - -## 🚀 Active Operations - -### Overnight Download (IN PROGRESS) - -**Command:** `.\scripts\overnight_download.ps1` -**Started:** November 16, 2025 - 11:00 PM -**Strategy:** 10 batches of 10 datasets each -**Target:** 100 datasets (score ≥90) -**Log:** `data_out/overnight_download.log` - -**Expected Timeline:** -- Batch processing: 30 sec delay between batches -- Download rate: 1-2 datasets/min (OpenML API limit) -- Per-batch time: 5-10 minutes -- Total time: 50-100 minutes per 10 datasets -- **Estimated completion:** 8-12 hours (November 17, 7-11 AM) - -**Expected Outcomes:** -- Best case: 80-100 datasets (80-100% success) -- Realistic: 50-70 datasets (50-70% success) -- Worst case: 30-40 datasets (30-40% success) - -**Monitoring:** -```powershell -# Check progress -Get-Content data_out\overnight_download.log -Wait - -# Count downloads -(Get-ChildItem datasets\massive_quantum\*.csv).Count - -# Check latest batch -Get-Content data_out\overnight_download.log | Select-Object -Last 50 -``` - ---- - -## 📅 Timeline & Milestones - -### Tonight (Nov 16, 11 PM - Nov 17, 8 AM) -- [x] Start overnight download script -- [ ] System runs unattended (8-12 hours) -- [ ] Auto-validation at completion - -### Tomorrow Morning (Nov 17, 8-10 AM) -- [ ] Review overnight log -- [ ] Count successful downloads -- [ ] Run validation: `python .\scripts\massive_dataset_expansion.py --validate` -- [ ] Triage any failures - -### Tomorrow Afternoon (Nov 17, 2-6 PM) -- [ ] Quick test (1 epoch): 15-30 min -- [ ] Full benchmark (25 epochs): 4-8 hours -- [ ] Initial results analysis - -### Rest of Week 1 (Nov 18-23) -- [ ] Continue overnight downloads (2-3 more batches) -- [ ] Incremental benchmarking -- [ ] Reach 50 dataset milestone -- [ ] Mid-point analysis - -### Week 2 (Nov 24-30) -- [ ] Scale to 100 datasets -- [ ] Performance tier analysis -- [ ] Top 20 model selection -- [ ] Production deployment planning - ---- - -## 🎯 Success Metrics - -### Download Phase -- **Target:** 70+ datasets downloaded (70% of 100) -- **Quality:** Average score ≥85 -- **Validation:** 80%+ pass validation checks - -### Training Phase -- **Target:** 60+ datasets successfully trained -- **Performance:** Average accuracy ≥80% (vs 90.55% on UCI) -- **Consistency:** <20% std deviation in accuracy - -### Production Readiness -- **Top tier:** 20+ datasets with ≥90% accuracy -- **Deployment ready:** 10+ models with production specs -- **Azure Quantum:** 5+ models validated on QPU simulators - ---- - -## 📈 Expected Performance - -### Based on Quality Scoring - -| Score Range | Datasets | Expected Accuracy | Use Case | -|-------------|----------|-------------------|----------| -| 90-100 | 100 | 85-95% | Production deployment | -| 85-90 | 50 | 80-90% | Secondary models | -| 80-85 | 55 | 75-85% | Research/testing | - -### Based on 27-Dataset Benchmark - -| Current UCI Avg | OpenML Expected | Delta | -|-----------------|-----------------|-------| -| 90.55% | 85-90% | -0 to -5% | - -**Reasoning for expected drop:** -- OpenML datasets may have more noise -- Different domain distribution -- Less curated than UCI benchmark sets -- But quality scoring should mitigate this - ---- - -## 🔍 Key Questions to Answer - -### After First 30 Datasets -1. Does OpenML quality scoring correlate with actual accuracy? -2. Is 72% (1-epoch) → 85% (25-epoch) improvement realistic? -3. What's the actual download success rate with rate limiting? -4. Are there systematic issues with OpenML format/quality? - -### After 50 Datasets -1. How does OpenML average compare to UCI average (90.55%)? -2. Which domains perform best (medical, finance, etc.)? -3. Do we need to adjust quality scoring algorithm? -4. Is 100 datasets achievable in 2 weeks? - -### After 100 Datasets -1. What's the top 20 model roster? -2. Which models warrant Azure Quantum QPU testing? -3. Should we continue to 500 or stop at 100? -4. What's the cost-benefit of scaling further? - ---- - -## 🛠️ Technical Stack - -### Download System -- **Script:** `scripts/massive_dataset_expansion.py` -- **Orchestrator:** `scripts/overnight_download.ps1` -- **Source:** OpenML Python API -- **Cache:** JSON discovery cache (1,412 datasets) -- **Error handling:** Continue-on-failure, retry logic - -### Training System -- **Script:** `scripts/distributed_benchmark.py` -- **Architecture:** HybridQuantumNet (4 qubits, 2 layers) -- **Parallelization:** multiprocessing.Pool -- **Checkpointing:** Every 10 datasets -- **Resume:** Auto-resume from checkpoint - -### Quality System -- **Scoring:** 6-factor algorithm (0-100 scale) -- **Validation:** Automated format/integrity checks -- **Performance tracking:** JSON results per dataset -- **Analysis:** Automated tier classification - ---- - -## 💰 Resource Estimates - -### Compute -- **Local CPU:** 16 cores recommended -- **RAM:** 16GB minimum -- **Storage:** 50-100GB for datasets + results -- **Duration:** 100-200 hours compute time - -### Network -- **Download:** 5-10GB total datasets -- **API calls:** 1,000-2,000 OpenML queries -- **Rate limiting:** 1-2 datasets/min max - -### Time Investment -- **Overnight runs:** 5-10 nights @ 8-12 hours each -- **Daily monitoring:** 30-60 min/day -- **Analysis:** 2-4 hours/week -- **Total hands-on:** 10-15 hours over 2 weeks - -### Cost -- **Zero-cost option:** All local, no cloud services -- **Optional Azure VM:** $200-400 for acceleration -- **Optional Azure Quantum:** $0 (simulators free) - ---- - -## 🎓 Lessons Learned - -### From 27-Dataset Benchmark -✅ Architecture is solid (90.55% average) -✅ 4 qubits sufficient for most datasets -✅ 25 epochs generally reaches convergence -✅ 81% success rate acceptable - -### From OpenML Discovery -✅ Quality scoring effectively ranks datasets -✅ 1,412 candidates is manageable scale -✅ API rate limiting is primary bottleneck -✅ Batch processing essential for scale - -### From 3-Dataset Quick Test -✅ Distributed system works (parallel processing) -✅ Vehicle datasets perform consistently -✅ 1-epoch tests validate infrastructure quickly -✅ 72% → 85% improvement pattern realistic - ---- - -## 🚨 Risk Mitigation - -### Risk: OpenML API Failures -- **Mitigation:** Batch processing with delays -- **Backup:** Resume capability, skip problematic datasets -- **Monitoring:** Log all errors with dataset IDs - -### Risk: Low Performance on New Datasets -- **Mitigation:** Quality scoring filters low-value datasets -- **Backup:** Focus on score ≥90 first, then expand -- **Decision point:** Stop at 50 if avg <80% - -### Risk: Time Overrun -- **Mitigation:** Realistic 2-week timeline -- **Backup:** Acceptable to finish with 70-80 datasets -- **Flexibility:** Can extend to 3 weeks if needed - -### Risk: Storage Constraints -- **Mitigation:** Compress old results, delete failed datasets -- **Backup:** Azure Blob storage for long-term archive -- **Monitoring:** Check disk space daily - ---- - -## 📞 Next Actions - -### Immediate (Tonight) -✅ Overnight download running -✅ Todo list created -✅ Monitoring plan documented - -### Tomorrow Morning (Priority 1) -1. Check log: `Get-Content data_out\overnight_download.log | Select-Object -Last 100` -2. Count datasets: `(Get-ChildItem datasets\massive_quantum\*.csv).Count` -3. Run validation: `python .\scripts\massive_dataset_expansion.py --validate` - -### Tomorrow Afternoon (Priority 2) -1. Quick test: `python .\scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 1 --quick-test` -2. Review results and adjust strategy -3. Launch full 25-epoch benchmark if quick test successful - -### End of Week 1 (Priority 3) -1. Analyze first batch results -2. Compare OpenML vs UCI performance -3. Decide on continuation strategy -4. Update quality scoring if needed - ---- - -## 📊 Progress Tracking - -### Datasets Downloaded -``` -Current: 3 (vehicle variants) -Target Night 1: 30-50 -Target Week 1: 50-70 -Target Week 2: 100 -``` - -### Datasets Trained -``` -Current: 30 (27 UCI + 3 OpenML quick test) -Target Week 1: 50 -Target Week 2: 100 -``` - -### Performance Metrics -``` -UCI Average: 90.55% -OpenML Target: 85%+ -Combined Target: 87%+ -``` - ---- - -**Status:** 🟢 ACTIVE -**Last Updated:** November 16, 2025 - 11:05 PM -**Next Checkpoint:** November 17, 2025 - 8:00 AM -**Owner:** Quantum AI Expansion Project +# Medium-Scale Expansion: 100 High-Quality Datasets + +**Started:** November 16, 2025 - 11:00 PM +**Target:** 100 datasets by November 30, 2025 (2 weeks) +**Strategy:** Quality over quantity - Focus on score ≥85 datasets + +--- + +## 🎯 Goals + +### Week 1 (Nov 16-23) +- ✅ Complete 27-dataset benchmark (DONE: 90.55% avg accuracy) +- ✅ Discover 1,412 OpenML candidates (DONE) +- ✅ Test distributed benchmark (DONE: 3 datasets successful) +- 🔄 Download 30-50 datasets overnight (IN PROGRESS) +- ⏳ Validate and quick-test all downloads +- ⏳ Full 25-epoch benchmark on valid datasets +- **Target:** 50 total datasets trained (27 + 23 new) + +### Week 2 (Nov 24-30) +- ⏳ Continue overnight downloads (25-50 more datasets) +- ⏳ Incremental benchmarking as downloads complete +- ⏳ Identify top 20 models for production +- ⏳ Generate comprehensive performance analysis +- **Target:** 100 total datasets trained + +--- + +## 📊 Current Status + +### Existing Infrastructure (Proven) +- **27 UCI datasets:** 90.55% average accuracy +- **Success rate:** 81% (22/27 successful) +- **Top performers:** banknote (100%), iris (100%), dermatology (100%) +- **Architecture:** 4 qubits, 2 layers, 16 hidden nodes + +### OpenML Discovery (Complete) +- **Total candidates:** 1,412 datasets +- **Quality scored:** 0-100 scale (6 factors) +- **Top 100 (score ≥90):** 205 candidates +- **Cache:** `datasets/massive_quantum/discovery_cache.json` + +### Initial OpenML Test (Complete) +- **Downloaded:** 3 vehicle datasets +- **Quick test:** 72% accuracy (1 epoch) +- **Expected full:** 80-90% accuracy (25 epochs) +- **Distributed system:** ✅ Validated (3 parallel workers) + +--- + +## 🚀 Active Operations + +### Overnight Download (IN PROGRESS) + +**Command:** `.\scripts\overnight_download.ps1` +**Started:** November 16, 2025 - 11:00 PM +**Strategy:** 10 batches of 10 datasets each +**Target:** 100 datasets (score ≥90) +**Log:** `data_out/overnight_download.log` + +**Expected Timeline:** +- Batch processing: 30 sec delay between batches +- Download rate: 1-2 datasets/min (OpenML API limit) +- Per-batch time: 5-10 minutes +- Total time: 50-100 minutes per 10 datasets +- **Estimated completion:** 8-12 hours (November 17, 7-11 AM) + +**Expected Outcomes:** +- Best case: 80-100 datasets (80-100% success) +- Realistic: 50-70 datasets (50-70% success) +- Worst case: 30-40 datasets (30-40% success) + +**Monitoring:** +```powershell +# Check progress +Get-Content data_out\overnight_download.log -Wait + +# Count downloads +(Get-ChildItem datasets\massive_quantum\*.csv).Count + +# Check latest batch +Get-Content data_out\overnight_download.log | Select-Object -Last 50 +``` + +--- + +## 📅 Timeline & Milestones + +### Tonight (Nov 16, 11 PM - Nov 17, 8 AM) +- [x] Start overnight download script +- [ ] System runs unattended (8-12 hours) +- [ ] Auto-validation at completion + +### Tomorrow Morning (Nov 17, 8-10 AM) +- [ ] Review overnight log +- [ ] Count successful downloads +- [ ] Run validation: `python .\scripts\massive_dataset_expansion.py --validate` +- [ ] Triage any failures + +### Tomorrow Afternoon (Nov 17, 2-6 PM) +- [ ] Quick test (1 epoch): 15-30 min +- [ ] Full benchmark (25 epochs): 4-8 hours +- [ ] Initial results analysis + +### Rest of Week 1 (Nov 18-23) +- [ ] Continue overnight downloads (2-3 more batches) +- [ ] Incremental benchmarking +- [ ] Reach 50 dataset milestone +- [ ] Mid-point analysis + +### Week 2 (Nov 24-30) +- [ ] Scale to 100 datasets +- [ ] Performance tier analysis +- [ ] Top 20 model selection +- [ ] Production deployment planning + +--- + +## 🎯 Success Metrics + +### Download Phase +- **Target:** 70+ datasets downloaded (70% of 100) +- **Quality:** Average score ≥85 +- **Validation:** 80%+ pass validation checks + +### Training Phase +- **Target:** 60+ datasets successfully trained +- **Performance:** Average accuracy ≥80% (vs 90.55% on UCI) +- **Consistency:** <20% std deviation in accuracy + +### Production Readiness +- **Top tier:** 20+ datasets with ≥90% accuracy +- **Deployment ready:** 10+ models with production specs +- **Azure Quantum:** 5+ models validated on QPU simulators + +--- + +## 📈 Expected Performance + +### Based on Quality Scoring + +| Score Range | Datasets | Expected Accuracy | Use Case | +|-------------|----------|-------------------|----------| +| 90-100 | 100 | 85-95% | Production deployment | +| 85-90 | 50 | 80-90% | Secondary models | +| 80-85 | 55 | 75-85% | Research/testing | + +### Based on 27-Dataset Benchmark + +| Current UCI Avg | OpenML Expected | Delta | +|-----------------|-----------------|-------| +| 90.55% | 85-90% | -0 to -5% | + +**Reasoning for expected drop:** +- OpenML datasets may have more noise +- Different domain distribution +- Less curated than UCI benchmark sets +- But quality scoring should mitigate this + +--- + +## 🔍 Key Questions to Answer + +### After First 30 Datasets +1. Does OpenML quality scoring correlate with actual accuracy? +2. Is 72% (1-epoch) → 85% (25-epoch) improvement realistic? +3. What's the actual download success rate with rate limiting? +4. Are there systematic issues with OpenML format/quality? + +### After 50 Datasets +1. How does OpenML average compare to UCI average (90.55%)? +2. Which domains perform best (medical, finance, etc.)? +3. Do we need to adjust quality scoring algorithm? +4. Is 100 datasets achievable in 2 weeks? + +### After 100 Datasets +1. What's the top 20 model roster? +2. Which models warrant Azure Quantum QPU testing? +3. Should we continue to 500 or stop at 100? +4. What's the cost-benefit of scaling further? + +--- + +## 🛠️ Technical Stack + +### Download System +- **Script:** `scripts/massive_dataset_expansion.py` +- **Orchestrator:** `scripts/overnight_download.ps1` +- **Source:** OpenML Python API +- **Cache:** JSON discovery cache (1,412 datasets) +- **Error handling:** Continue-on-failure, retry logic + +### Training System +- **Script:** `scripts/distributed_benchmark.py` +- **Architecture:** HybridQuantumNet (4 qubits, 2 layers) +- **Parallelization:** multiprocessing.Pool +- **Checkpointing:** Every 10 datasets +- **Resume:** Auto-resume from checkpoint + +### Quality System +- **Scoring:** 6-factor algorithm (0-100 scale) +- **Validation:** Automated format/integrity checks +- **Performance tracking:** JSON results per dataset +- **Analysis:** Automated tier classification + +--- + +## 💰 Resource Estimates + +### Compute +- **Local CPU:** 16 cores recommended +- **RAM:** 16GB minimum +- **Storage:** 50-100GB for datasets + results +- **Duration:** 100-200 hours compute time + +### Network +- **Download:** 5-10GB total datasets +- **API calls:** 1,000-2,000 OpenML queries +- **Rate limiting:** 1-2 datasets/min max + +### Time Investment +- **Overnight runs:** 5-10 nights @ 8-12 hours each +- **Daily monitoring:** 30-60 min/day +- **Analysis:** 2-4 hours/week +- **Total hands-on:** 10-15 hours over 2 weeks + +### Cost +- **Zero-cost option:** All local, no cloud services +- **Optional Azure VM:** $200-400 for acceleration +- **Optional Azure Quantum:** $0 (simulators free) + +--- + +## 🎓 Lessons Learned + +### From 27-Dataset Benchmark +✅ Architecture is solid (90.55% average) +✅ 4 qubits sufficient for most datasets +✅ 25 epochs generally reaches convergence +✅ 81% success rate acceptable + +### From OpenML Discovery +✅ Quality scoring effectively ranks datasets +✅ 1,412 candidates is manageable scale +✅ API rate limiting is primary bottleneck +✅ Batch processing essential for scale + +### From 3-Dataset Quick Test +✅ Distributed system works (parallel processing) +✅ Vehicle datasets perform consistently +✅ 1-epoch tests validate infrastructure quickly +✅ 72% → 85% improvement pattern realistic + +--- + +## 🚨 Risk Mitigation + +### Risk: OpenML API Failures +- **Mitigation:** Batch processing with delays +- **Backup:** Resume capability, skip problematic datasets +- **Monitoring:** Log all errors with dataset IDs + +### Risk: Low Performance on New Datasets +- **Mitigation:** Quality scoring filters low-value datasets +- **Backup:** Focus on score ≥90 first, then expand +- **Decision point:** Stop at 50 if avg <80% + +### Risk: Time Overrun +- **Mitigation:** Realistic 2-week timeline +- **Backup:** Acceptable to finish with 70-80 datasets +- **Flexibility:** Can extend to 3 weeks if needed + +### Risk: Storage Constraints +- **Mitigation:** Compress old results, delete failed datasets +- **Backup:** Azure Blob storage for long-term archive +- **Monitoring:** Check disk space daily + +--- + +## 📞 Next Actions + +### Immediate (Tonight) +✅ Overnight download running +✅ Todo list created +✅ Monitoring plan documented + +### Tomorrow Morning (Priority 1) +1. Check log: `Get-Content data_out\overnight_download.log | Select-Object -Last 100` +2. Count datasets: `(Get-ChildItem datasets\massive_quantum\*.csv).Count` +3. Run validation: `python .\scripts\massive_dataset_expansion.py --validate` + +### Tomorrow Afternoon (Priority 2) +1. Quick test: `python .\scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 10 --epochs 1 --quick-test` +2. Review results and adjust strategy +3. Launch full 25-epoch benchmark if quick test successful + +### End of Week 1 (Priority 3) +1. Analyze first batch results +2. Compare OpenML vs UCI performance +3. Decide on continuation strategy +4. Update quality scoring if needed + +--- + +## 📊 Progress Tracking + +### Datasets Downloaded +``` +Current: 3 (vehicle variants) +Target Night 1: 30-50 +Target Week 1: 50-70 +Target Week 2: 100 +``` + +### Datasets Trained +``` +Current: 30 (27 UCI + 3 OpenML quick test) +Target Week 1: 50 +Target Week 2: 100 +``` + +### Performance Metrics +``` +UCI Average: 90.55% +OpenML Target: 85%+ +Combined Target: 87%+ +``` + +--- + +**Status:** 🟢 ACTIVE +**Last Updated:** November 16, 2025 - 11:05 PM +**Next Checkpoint:** November 17, 2025 - 8:00 AM +**Owner:** Quantum AI Expansion Project diff --git a/docs/guides/MONETIZATION_GUIDE.md b/docs/guides/MONETIZATION_GUIDE.md index 6b9b414c6..4ef2f965e 100644 --- a/docs/guides/MONETIZATION_GUIDE.md +++ b/docs/guides/MONETIZATION_GUIDE.md @@ -1,482 +1,482 @@ -# Aria Platform - Monetization & Revenue System - -## Overview - -This document describes the comprehensive monetization system implemented for the Aria platform to generate a sustainable $2,000+ monthly income stream. - -## Revenue Model - -### Target Revenue Breakdown - -To achieve the $2,000 monthly recurring revenue (MRR) target: - -| Tier | Subscribers | Price/Month | Revenue | -|------|-------------|-------------|---------| -| Pro | 5 | $49 | $245 | -| Enterprise | 10 | $199 | $1,990 | -| **Total** | **15** | - | **$2,235** | - -**Annual Recurring Revenue (ARR):** $26,820 - -## Subscription Tiers - -### Free Tier ($0/month) - -**Target Audience:** Trial users, students, hobbyists - -**Features:** -- ✅ Basic Chat (100 messages/month) -- ✅ Aria Character Access (basic) -- ❌ Quantum Computing -- ❌ Advanced Training -- ❌ Website Maker -- ❌ API Access -- ❌ Priority Support -- ❌ Commercial License - -**Conversion Strategy:** -- Upsell to Pro after 100 messages -- Show feature comparisons -- Offer 7-day Pro trial - -### Pro Tier ($49/month) - -**Target Audience:** Professionals, developers, small teams - -**Features:** -- ✅ 10,000 Chat Messages/month -- ✅ Full Aria Character Suite -- ✅ Quantum Computing (50 jobs/month) -- ✅ Advanced Training (20 hours/month) -- ✅ Website Maker (10 sites/month) -- ✅ API Access (10,000 requests/month) -- ✅ Commercial License -- ❌ Custom Models -- ❌ Priority Support - -**Value Proposition:** -- Professional features at affordable price -- Quantum ML experimentation -- Commercial usage rights -- API integration for apps - -### Enterprise Tier ($199/month) - -**Target Audience:** Organizations, agencies, large teams - -**Features:** -- ✅ **Unlimited** Everything -- ✅ Custom Model Training -- ✅ Priority Support (24/7) -- ✅ Dedicated Infrastructure -- ✅ SLA Guarantee (99.9%) -- ✅ White-label Options -- ✅ Dedicated Account Manager - -**Value Proposition:** -- No usage limits -- Enterprise-grade support -- Custom solutions -- Guaranteed uptime - -## Technical Implementation - -### Backend Components - -#### 1. Subscription Manager (`shared/subscription_manager.py`) - -Core features: -- Subscription lifecycle management -- Feature access control -- Usage tracking and limits -- Revenue analytics -- Persistent storage - -```python -from shared.subscription_manager import ( - get_subscription_manager, - SubscriptionTier, - Feature -) - -manager = get_subscription_manager() -subscription = manager.get_subscription(user_id) - -# Check feature access -if subscription.has_feature(Feature.QUANTUM_COMPUTING): - # Allow quantum operations - pass - -# Track usage -if manager.track_usage(user_id, 'quantum_jobs', 1): - # Usage allowed, proceed - pass -else: - # Limit exceeded, upgrade required - pass -``` - -#### 2. API Endpoints (`function_app.py`) - -New endpoints added: - -- **GET /api/subscription/pricing** - Get pricing information -- **GET /api/subscription/status** - Get user subscription status -- **POST /api/subscription/upgrade** - Upgrade subscription -- **GET /api/subscription/revenue** - Get revenue statistics -- **POST /api/subscription/usage** - Track resource usage - -#### 3. Frontend Components - -##### Pricing Page (`pricing.html`) -- Beautiful, responsive design -- Three-tier comparison -- Revenue projection model -- FAQ section -- Call-to-action buttons - -##### Admin Dashboard (`admin_dashboard.html`) -- Real-time revenue metrics -- Subscriber management -- Usage analytics -- Revenue projections -- Export capabilities - -## Usage Tracking - -### Tracked Resources - -| Resource | Free Limit | Pro Limit | Enterprise | -|----------|-----------|-----------|------------| -| Chat Messages | 100/mo | 10,000/mo | Unlimited | -| Quantum Jobs | 0 | 50/mo | Unlimited | -| Training Hours | 0 | 20/mo | Unlimited | -| API Requests | 0 | 10,000/mo | Unlimited | -| Websites Created | 0 | 10/mo | Unlimited | - -### Implementation Example - -```python -# Before executing a quantum job -manager = get_subscription_manager() -if not manager.track_usage(user_id, 'quantum_jobs', 1): - return { - "error": "Quantum job limit exceeded", - "upgrade_url": "/pricing.html", - "current_tier": subscription.tier.value - } - -# Proceed with quantum job -run_quantum_job() -``` - -## Payment Integration - -### Stripe Integration (Ready for Implementation) - -The system is designed to integrate with Stripe for payment processing: - -```python -# Upgrade subscription after successful payment -manager.upgrade_subscription( - user_id=user_id, - tier=SubscriptionTier.PRO, - duration_days=30, - payment_method="stripe", - stripe_subscription_id=stripe_sub_id -) -``` - -**Required Environment Variables:** -- `STRIPE_SECRET_KEY` - Stripe API secret key -- `STRIPE_PUBLISHABLE_KEY` - Stripe publishable key -- `STRIPE_WEBHOOK_SECRET` - Webhook signing secret - -### Payment Flow - -1. User clicks "Upgrade to Pro" on pricing page -2. Redirect to Stripe Checkout -3. User completes payment -4. Stripe webhook confirms payment -5. Backend upgrades subscription -6. User gains immediate access to features - -## Revenue Optimization Strategies - -### 1. Free-to-Paid Conversion - -**Tactics:** -- Show usage progress bars -- "Upgrade to unlock" messaging -- Feature discovery tooltips -- Time-limited trial offers - -**Target Conversion Rate:** 5-10% of free users - -### 2. Pro-to-Enterprise Upsell - -**Triggers:** -- Approaching usage limits -- Team collaboration needs -- Custom model requests -- Support ticket volume - -**Target Conversion Rate:** 20% of Pro users - -### 3. Annual Billing Discount - -Offer 20% discount for annual commitment: -- Pro Annual: $470 (save $118) -- Enterprise Annual: $1,910 (save $478) - -**Benefits:** -- Improved cash flow -- Reduced churn -- Higher customer lifetime value - -### 4. Add-ons (Future Enhancement) - -Additional revenue streams: -- Extra quantum computing hours: $10/10 hours -- Additional API requests: $5/10K requests -- Custom model training: $99/model -- Premium support: $50/month - -## Analytics & Monitoring - -### Key Metrics to Track - -1. **Revenue Metrics** - - Monthly Recurring Revenue (MRR) - - Annual Recurring Revenue (ARR) - - Average Revenue Per User (ARPU) - - Customer Lifetime Value (LTV) - -2. **User Metrics** - - Total subscribers - - Active subscribers - - Subscriber growth rate - - Churn rate - -3. **Conversion Metrics** - - Free-to-Pro conversion rate - - Pro-to-Enterprise conversion rate - - Trial-to-paid conversion rate - -4. **Usage Metrics** - - Features usage by tier - - Limit approaching alerts - - Feature adoption rate - -### Monitoring Dashboard - -Access the admin dashboard at: -``` -http://localhost:8080/admin_dashboard.html -``` - -Features: -- Real-time revenue display -- Subscriber list and management -- Usage analytics -- Revenue projections -- Export capabilities - -## API Examples - -### Check Subscription Status - -```bash -curl http://localhost:7071/api/subscription/status?user_id=demo_user -``` - -Response: -```json -{ - "user_id": "demo_user", - "tier": "pro", - "tier_name": "PRO", - "price": 49, - "is_active": true, - "usage": { - "chat_messages": 150, - "quantum_jobs": 5, - "training_hours": 2, - "api_requests": 1000, - "websites_created": 1 - }, - "limits": { - "chat_messages": 10000, - "quantum_jobs": 50, - "training_hours": 20, - "api_requests": 10000, - "websites_created": 10 - } -} -``` - -### Get Revenue Stats - -```bash -curl http://localhost:7071/api/subscription/revenue -``` - -Response: -```json -{ - "total_subscribers": 15, - "active_subscribers": 15, - "by_tier": { - "free": 0, - "pro": 5, - "enterprise": 10 - }, - "monthly_recurring_revenue": 2235, - "annual_recurring_revenue": 26820 -} -``` - -### Track Usage - -```bash -curl -X POST http://localhost:7071/api/subscription/usage \ - -H "Content-Type: application/json" \ - -d '{ - "user_id": "demo_user", - "resource": "quantum_jobs", - "amount": 1 - }' -``` - -## Testing the System - -### Local Testing - -1. Start Azure Functions: -```bash -func host start -``` - -2. Open pricing page: -```bash -open http://localhost:7071/pricing.html -``` - -3. Open admin dashboard: -```bash -open http://localhost:7071/admin_dashboard.html -``` - -4. Test API endpoints: -```bash -# Get pricing -curl http://localhost:7071/api/subscription/pricing | jq - -# Check subscription -curl http://localhost:7071/api/subscription/status?user_id=test_user | jq - -# Get revenue stats -curl http://localhost:7071/api/subscription/revenue | jq -``` - -### Simulating Target Revenue - -The admin dashboard includes a "Simulate Target Revenue" button that creates demo data showing the $2,000+ MRR scenario with: -- 5 Pro subscribers ($245) -- 10 Enterprise subscribers ($1,990) -- Total: $2,235 MRR - -## Future Enhancements - -### Phase 2 Features - -1. **Payment Gateway Integration** - - Full Stripe integration - - Automated billing - - Invoice generation - - Payment history - -2. **Advanced Analytics** - - Cohort analysis - - Churn prediction - - Revenue forecasting - - A/B testing framework - -3. **Customer Portal** - - Self-service subscription management - - Usage dashboard - - Billing history - - Invoice downloads - -4. **Marketing Automation** - - Email campaigns - - In-app messaging - - Upgrade prompts - - Churn prevention - -5. **API Marketplace** - - Public API marketplace - - Developer documentation - - API key management - - Rate limit customization - -## Support & Documentation - -### For Users -- Pricing page: `/pricing.html` -- API documentation: `/docs/api.md` -- Feature comparison: See pricing page - -### For Administrators -- Admin dashboard: `/admin_dashboard.html` -- This documentation: `MONETIZATION_GUIDE.md` - -### Contact -- Sales: sales@aria-platform.com -- Support: support@aria-platform.com -- Technical: tech@aria-platform.com - -## Compliance & Legal - -### Terms of Service -- Commercial use requires Pro or Enterprise tier -- Free tier for personal/educational use only -- Data retention policies apply -- SLA guarantees for Enterprise only - -### Privacy -- No data shared with third parties -- Usage data for analytics only -- GDPR compliant -- SOC 2 Type II certified (Enterprise) - -### Refund Policy -- 14-day money-back guarantee -- Pro-rated refunds for annual plans -- No questions asked cancellation - -## Success Metrics - -### 30-Day Goals -- [ ] 5 Pro subscribers ($245 MRR) -- [ ] 10 Enterprise subscribers ($1,990 MRR) -- [ ] 5% free-to-paid conversion -- [ ] < 5% monthly churn - -### 90-Day Goals -- [ ] $3,000+ MRR -- [ ] 50+ total subscribers -- [ ] 10% free-to-paid conversion -- [ ] API marketplace launch - -### 12-Month Goals -- [ ] $10,000+ MRR -- [ ] 200+ total subscribers -- [ ] Multiple revenue streams -- [ ] International expansion - -## Conclusion - -This monetization system provides a comprehensive foundation for generating sustainable revenue from the Aria platform. With the target of $2,000 MRR requiring only 5 Pro and 10 Enterprise subscribers, the goal is achievable with focused marketing and customer success efforts. - -The system is designed to scale from the initial $2,000 target to much higher revenue levels as the user base grows and additional revenue streams are added. +# Aria Platform - Monetization & Revenue System + +## Overview + +This document describes the comprehensive monetization system implemented for the Aria platform to generate a sustainable $2,000+ monthly income stream. + +## Revenue Model + +### Target Revenue Breakdown + +To achieve the $2,000 monthly recurring revenue (MRR) target: + +| Tier | Subscribers | Price/Month | Revenue | +|------|-------------|-------------|---------| +| Pro | 5 | $49 | $245 | +| Enterprise | 10 | $199 | $1,990 | +| **Total** | **15** | - | **$2,235** | + +**Annual Recurring Revenue (ARR):** $26,820 + +## Subscription Tiers + +### Free Tier ($0/month) + +**Target Audience:** Trial users, students, hobbyists + +**Features:** +- ✅ Basic Chat (100 messages/month) +- ✅ Aria Character Access (basic) +- ❌ Quantum Computing +- ❌ Advanced Training +- ❌ Website Maker +- ❌ API Access +- ❌ Priority Support +- ❌ Commercial License + +**Conversion Strategy:** +- Upsell to Pro after 100 messages +- Show feature comparisons +- Offer 7-day Pro trial + +### Pro Tier ($49/month) + +**Target Audience:** Professionals, developers, small teams + +**Features:** +- ✅ 10,000 Chat Messages/month +- ✅ Full Aria Character Suite +- ✅ Quantum Computing (50 jobs/month) +- ✅ Advanced Training (20 hours/month) +- ✅ Website Maker (10 sites/month) +- ✅ API Access (10,000 requests/month) +- ✅ Commercial License +- ❌ Custom Models +- ❌ Priority Support + +**Value Proposition:** +- Professional features at affordable price +- Quantum ML experimentation +- Commercial usage rights +- API integration for apps + +### Enterprise Tier ($199/month) + +**Target Audience:** Organizations, agencies, large teams + +**Features:** +- ✅ **Unlimited** Everything +- ✅ Custom Model Training +- ✅ Priority Support (24/7) +- ✅ Dedicated Infrastructure +- ✅ SLA Guarantee (99.9%) +- ✅ White-label Options +- ✅ Dedicated Account Manager + +**Value Proposition:** +- No usage limits +- Enterprise-grade support +- Custom solutions +- Guaranteed uptime + +## Technical Implementation + +### Backend Components + +#### 1. Subscription Manager (`shared/subscription_manager.py`) + +Core features: +- Subscription lifecycle management +- Feature access control +- Usage tracking and limits +- Revenue analytics +- Persistent storage + +```python +from shared.subscription_manager import ( + get_subscription_manager, + SubscriptionTier, + Feature +) + +manager = get_subscription_manager() +subscription = manager.get_subscription(user_id) + +# Check feature access +if subscription.has_feature(Feature.QUANTUM_COMPUTING): + # Allow quantum operations + pass + +# Track usage +if manager.track_usage(user_id, 'quantum_jobs', 1): + # Usage allowed, proceed + pass +else: + # Limit exceeded, upgrade required + pass +``` + +#### 2. API Endpoints (`function_app.py`) + +New endpoints added: + +- **GET /api/subscription/pricing** - Get pricing information +- **GET /api/subscription/status** - Get user subscription status +- **POST /api/subscription/upgrade** - Upgrade subscription +- **GET /api/subscription/revenue** - Get revenue statistics +- **POST /api/subscription/usage** - Track resource usage + +#### 3. Frontend Components + +##### Pricing Page (`pricing.html`) +- Beautiful, responsive design +- Three-tier comparison +- Revenue projection model +- FAQ section +- Call-to-action buttons + +##### Admin Dashboard (`admin_dashboard.html`) +- Real-time revenue metrics +- Subscriber management +- Usage analytics +- Revenue projections +- Export capabilities + +## Usage Tracking + +### Tracked Resources + +| Resource | Free Limit | Pro Limit | Enterprise | +|----------|-----------|-----------|------------| +| Chat Messages | 100/mo | 10,000/mo | Unlimited | +| Quantum Jobs | 0 | 50/mo | Unlimited | +| Training Hours | 0 | 20/mo | Unlimited | +| API Requests | 0 | 10,000/mo | Unlimited | +| Websites Created | 0 | 10/mo | Unlimited | + +### Implementation Example + +```python +# Before executing a quantum job +manager = get_subscription_manager() +if not manager.track_usage(user_id, 'quantum_jobs', 1): + return { + "error": "Quantum job limit exceeded", + "upgrade_url": "/pricing.html", + "current_tier": subscription.tier.value + } + +# Proceed with quantum job +run_quantum_job() +``` + +## Payment Integration + +### Stripe Integration (Ready for Implementation) + +The system is designed to integrate with Stripe for payment processing: + +```python +# Upgrade subscription after successful payment +manager.upgrade_subscription( + user_id=user_id, + tier=SubscriptionTier.PRO, + duration_days=30, + payment_method="stripe", + stripe_subscription_id=stripe_sub_id +) +``` + +**Required Environment Variables:** +- `STRIPE_SECRET_KEY` - Stripe API secret key +- `STRIPE_PUBLISHABLE_KEY` - Stripe publishable key +- `STRIPE_WEBHOOK_SECRET` - Webhook signing secret + +### Payment Flow + +1. User clicks "Upgrade to Pro" on pricing page +2. Redirect to Stripe Checkout +3. User completes payment +4. Stripe webhook confirms payment +5. Backend upgrades subscription +6. User gains immediate access to features + +## Revenue Optimization Strategies + +### 1. Free-to-Paid Conversion + +**Tactics:** +- Show usage progress bars +- "Upgrade to unlock" messaging +- Feature discovery tooltips +- Time-limited trial offers + +**Target Conversion Rate:** 5-10% of free users + +### 2. Pro-to-Enterprise Upsell + +**Triggers:** +- Approaching usage limits +- Team collaboration needs +- Custom model requests +- Support ticket volume + +**Target Conversion Rate:** 20% of Pro users + +### 3. Annual Billing Discount + +Offer 20% discount for annual commitment: +- Pro Annual: $470 (save $118) +- Enterprise Annual: $1,910 (save $478) + +**Benefits:** +- Improved cash flow +- Reduced churn +- Higher customer lifetime value + +### 4. Add-ons (Future Enhancement) + +Additional revenue streams: +- Extra quantum computing hours: $10/10 hours +- Additional API requests: $5/10K requests +- Custom model training: $99/model +- Premium support: $50/month + +## Analytics & Monitoring + +### Key Metrics to Track + +1. **Revenue Metrics** + - Monthly Recurring Revenue (MRR) + - Annual Recurring Revenue (ARR) + - Average Revenue Per User (ARPU) + - Customer Lifetime Value (LTV) + +2. **User Metrics** + - Total subscribers + - Active subscribers + - Subscriber growth rate + - Churn rate + +3. **Conversion Metrics** + - Free-to-Pro conversion rate + - Pro-to-Enterprise conversion rate + - Trial-to-paid conversion rate + +4. **Usage Metrics** + - Features usage by tier + - Limit approaching alerts + - Feature adoption rate + +### Monitoring Dashboard + +Access the admin dashboard at: +``` +http://localhost:8080/admin_dashboard.html +``` + +Features: +- Real-time revenue display +- Subscriber list and management +- Usage analytics +- Revenue projections +- Export capabilities + +## API Examples + +### Check Subscription Status + +```bash +curl http://localhost:7071/api/subscription/status?user_id=demo_user +``` + +Response: +```json +{ + "user_id": "demo_user", + "tier": "pro", + "tier_name": "PRO", + "price": 49, + "is_active": true, + "usage": { + "chat_messages": 150, + "quantum_jobs": 5, + "training_hours": 2, + "api_requests": 1000, + "websites_created": 1 + }, + "limits": { + "chat_messages": 10000, + "quantum_jobs": 50, + "training_hours": 20, + "api_requests": 10000, + "websites_created": 10 + } +} +``` + +### Get Revenue Stats + +```bash +curl http://localhost:7071/api/subscription/revenue +``` + +Response: +```json +{ + "total_subscribers": 15, + "active_subscribers": 15, + "by_tier": { + "free": 0, + "pro": 5, + "enterprise": 10 + }, + "monthly_recurring_revenue": 2235, + "annual_recurring_revenue": 26820 +} +``` + +### Track Usage + +```bash +curl -X POST http://localhost:7071/api/subscription/usage \ + -H "Content-Type: application/json" \ + -d '{ + "user_id": "demo_user", + "resource": "quantum_jobs", + "amount": 1 + }' +``` + +## Testing the System + +### Local Testing + +1. Start Azure Functions: +```bash +func host start +``` + +2. Open pricing page: +```bash +open http://localhost:7071/pricing.html +``` + +3. Open admin dashboard: +```bash +open http://localhost:7071/admin_dashboard.html +``` + +4. Test API endpoints: +```bash +# Get pricing +curl http://localhost:7071/api/subscription/pricing | jq + +# Check subscription +curl http://localhost:7071/api/subscription/status?user_id=test_user | jq + +# Get revenue stats +curl http://localhost:7071/api/subscription/revenue | jq +``` + +### Simulating Target Revenue + +The admin dashboard includes a "Simulate Target Revenue" button that creates demo data showing the $2,000+ MRR scenario with: +- 5 Pro subscribers ($245) +- 10 Enterprise subscribers ($1,990) +- Total: $2,235 MRR + +## Future Enhancements + +### Phase 2 Features + +1. **Payment Gateway Integration** + - Full Stripe integration + - Automated billing + - Invoice generation + - Payment history + +2. **Advanced Analytics** + - Cohort analysis + - Churn prediction + - Revenue forecasting + - A/B testing framework + +3. **Customer Portal** + - Self-service subscription management + - Usage dashboard + - Billing history + - Invoice downloads + +4. **Marketing Automation** + - Email campaigns + - In-app messaging + - Upgrade prompts + - Churn prevention + +5. **API Marketplace** + - Public API marketplace + - Developer documentation + - API key management + - Rate limit customization + +## Support & Documentation + +### For Users +- Pricing page: `/pricing.html` +- API documentation: `/docs/api.md` +- Feature comparison: See pricing page + +### For Administrators +- Admin dashboard: `/admin_dashboard.html` +- This documentation: `MONETIZATION_GUIDE.md` + +### Contact +- Sales: sales@aria-platform.com +- Support: support@aria-platform.com +- Technical: tech@aria-platform.com + +## Compliance & Legal + +### Terms of Service +- Commercial use requires Pro or Enterprise tier +- Free tier for personal/educational use only +- Data retention policies apply +- SLA guarantees for Enterprise only + +### Privacy +- No data shared with third parties +- Usage data for analytics only +- GDPR compliant +- SOC 2 Type II certified (Enterprise) + +### Refund Policy +- 14-day money-back guarantee +- Pro-rated refunds for annual plans +- No questions asked cancellation + +## Success Metrics + +### 30-Day Goals +- [ ] 5 Pro subscribers ($245 MRR) +- [ ] 10 Enterprise subscribers ($1,990 MRR) +- [ ] 5% free-to-paid conversion +- [ ] < 5% monthly churn + +### 90-Day Goals +- [ ] $3,000+ MRR +- [ ] 50+ total subscribers +- [ ] 10% free-to-paid conversion +- [ ] API marketplace launch + +### 12-Month Goals +- [ ] $10,000+ MRR +- [ ] 200+ total subscribers +- [ ] Multiple revenue streams +- [ ] International expansion + +## Conclusion + +This monetization system provides a comprehensive foundation for generating sustainable revenue from the Aria platform. With the target of $2,000 MRR requiring only 5 Pro and 10 Enterprise subscribers, the goal is achievable with focused marketing and customer success efforts. + +The system is designed to scale from the initial $2,000 target to much higher revenue levels as the user base grows and additional revenue streams are added. diff --git a/docs/guides/MONITORING_GUIDE.md b/docs/guides/MONITORING_GUIDE.md index d68cd4cba..de48e5fae 100644 --- a/docs/guides/MONITORING_GUIDE.md +++ b/docs/guides/MONITORING_GUIDE.md @@ -1,536 +1,536 @@ -# Autonomous Training Monitoring Guide - -Complete guide to monitoring and analyzing your autonomous AI training system. - -## Quick Start - -### 1. Real-Time Dashboard (Recommended) -```powershell -# Live monitoring with auto-refresh every 5 seconds -python .\scripts\monitor_autonomous_training.py -``` - -### 2. Quick Status Check -```powershell -# One-time status display -python .\scripts\monitor_autonomous_training.py --once - -# Or compact summary -python .\scripts\monitor_autonomous_training.py --summary -``` - -### 3. Analytics Report -```powershell -# Generate detailed analytics -python .\scripts\training_analytics.py --report - -# View performance chart -python .\scripts\training_analytics.py --chart - -# Export HTML report -python .\scripts\training_analytics.py --html data_out/report.html -``` - -## Monitoring Tools - -### Real-Time Dashboard (`monitor_autonomous_training.py`) - -**Features:** -- 🎯 Live status updates (refreshes every 5 seconds) -- 📊 Performance metrics and trends -- 📝 Recent log activity -- ⚠️ Automatic alerts and warnings -- 🎨 Color-coded output for easy reading - -**Usage:** - -```powershell -# Continuous monitoring (press Ctrl+C to exit) -python .\scripts\monitor_autonomous_training.py - -# One-time display -python .\scripts\monitor_autonomous_training.py --once - -# Compact summary -python .\scripts\monitor_autonomous_training.py --summary - -# Custom refresh interval (10 seconds) -python .\scripts\monitor_autonomous_training.py --refresh 10 - -# Export metrics to CSV -python .\scripts\monitor_autonomous_training.py --export metrics.csv -``` - -**Dashboard Sections:** - -1. **System Overview** - - Current phase (training, optimization, etc.) - - Cycles completed - - Best accuracy achieved - - Last cycle duration - -2. **Dataset Inventory** - - Datasets by category - - Total available datasets - - Distribution breakdown - -3. **Performance Metrics** - - Last 5 cycles summary - - Accuracy trends - - Model counts - - Improvement/decline indicators - -4. **Task Queue** - - Active tasks - - Completed tasks - - Task history - -5. **Recent Activity** - - Last 10 log entries - - Color-coded by severity - - Error/warning highlighting - -6. **Alerts** - - Performance degradation warnings - - Low dataset count alerts - - Error notifications - - System status issues - -### Analytics Tool (`training_analytics.py`) - -**Features:** -- 📈 Performance trend analysis -- 🔮 Accuracy predictions -- 💡 Optimization insights -- 📊 ASCII charts -- 📄 HTML report generation - -**Usage:** - -```powershell -# Text report with recommendations -python .\scripts\training_analytics.py --report - -# ASCII chart of performance -python .\scripts\training_analytics.py --chart - -# Chart specific metric -python .\scripts\training_analytics.py --chart --metric max_accuracy - -# Generate HTML report -python .\scripts\training_analytics.py --html report.html -``` - -**Insights Provided:** - -1. **Performance Trend** - - Initial vs current accuracy - - Total improvement - - Improvement rate per cycle - -2. **Predictions** - - Cycles needed to reach 80%, 85%, 90%, 95% - - Based on current improvement rate - - Helps plan training duration - -3. **Optimization Insights** - - Optimal epoch count - - Plateau detection - - Convergence status - -4. **Model Quality Breakdown** - - Exceptional models (≥95%) - - Excellent models (85-95%) - - Distribution analysis - -5. **Recommendations** - - Action items based on performance - - Training strategy suggestions - - Deployment readiness - -## Monitoring Workflows - -### 1. Active Development Monitoring - -Run dashboard in one terminal while orchestrator runs in another: - -```powershell -# Terminal 1: Start orchestrator -python .\scripts\autonomous_training_orchestrator.py - -# Terminal 2: Monitor in real-time -python .\scripts\monitor_autonomous_training.py -``` - -### 2. Scheduled Status Checks - -Check status periodically without continuous monitoring: - -```powershell -# Check every 10 minutes (Windows Task Scheduler or loop) -while ($true) { - python .\scripts\monitor_autonomous_training.py --summary - Start-Sleep -Seconds 600 -} -``` - -### 3. Performance Analysis Workflow - -After several training cycles: - -```powershell -# 1. Generate text report -python .\scripts\training_analytics.py --report > analysis.txt - -# 2. View performance chart -python .\scripts\training_analytics.py --chart - -# 3. Export detailed HTML report -python .\scripts\training_analytics.py --html report.html - -# 4. Open report in browser -start report.html -``` - -### 4. Data Export for External Analysis - -```powershell -# Export metrics to CSV -python .\scripts\monitor_autonomous_training.py --export metrics.csv - -# Import in Excel, pandas, or other tools -# File includes: timestamp, cycle, epochs, accuracies, model counts -``` - -## Understanding the Dashboard - -### Status Indicators - -**Phase Colors:** -- 🔵 Blue: Data discovery, collection -- 🟡 Yellow: Training in progress -- 🟢 Green: Deployment, success -- 🔴 Red: Error, stopped - -**Accuracy Color Coding:** -- 🟢 Green: ≥90% (Excellent) -- 🔵 Cyan: 75-90% (Good) -- 🟡 Yellow: 60-75% (Fair) -- 🔴 Red: <60% (Needs work) - -**Trend Indicators:** -- ↑ Green: Improving (>1% gain) -- → Yellow: Stable/plateau -- ↓ Red: Declining (>1% loss) - -### Key Metrics Explained - -**Cycles Completed:** -- Number of full training cycles run -- Each cycle = discovery → collection → training → analysis - -**Best Accuracy:** -- Highest mean accuracy achieved across all cycles -- Target: 85-95% for production - -**Mean Accuracy:** -- Average accuracy across all trained datasets -- More realistic than max accuracy - -**Max Accuracy:** -- Best single dataset performance -- Shows model potential - -**Exceptional Models:** -- Models achieving ≥95% accuracy -- Ready for production deployment - -**Successful Count:** -- Number of datasets successfully trained -- Out of total available - -**Improvement Rate:** -- Average accuracy gain per cycle -- Helps predict future performance - -## Alerts & Warnings - -### Performance Degradation -**Trigger:** >5% accuracy drop between cycles - -**Possible Causes:** -- Dataset quality issues -- Overfitting from too many epochs -- Random initialization variance -- System resource constraints - -**Actions:** -1. Check recent logs for errors -2. Verify dataset integrity -3. Consider reducing epochs temporarily -4. Review hyperparameter settings - -### Low Dataset Count -**Trigger:** <100 datasets available - -**Possible Causes:** -- Initial setup incomplete -- Download failures -- Disk space issues - -**Actions:** -1. Check `datasets/` directories -2. Run data collection manually -3. Verify network connectivity -4. Check disk space - -### Plateau Detected -**Trigger:** <0.01% variance in last 3 cycles - -**Possible Causes:** -- Model has converged -- Need more epochs -- Learning rate too low -- Architecture limitations - -**Actions:** -1. Increase epoch count in config -2. Enable hyperparameter tuning -3. Try architecture evolution -4. Consider ensemble methods - -### Training Errors -**Trigger:** Failed training jobs - -**Possible Causes:** -- Categorical data not encoded -- Out of memory -- Corrupt datasets -- Software bugs - -**Actions:** -1. Review error logs -2. Check failed dataset names -3. Preprocess problematic datasets -4. Reduce batch size or workers - -## Advanced Monitoring - -### Custom Metrics Export - -```powershell -# Export and analyze with Python -python .\scripts\monitor_autonomous_training.py --export metrics.csv - -# Load in Python/pandas -import pandas as pd -df = pd.read_csv('metrics.csv') -print(df.describe()) - -# Plot with matplotlib -df.plot(x='cycle', y='mean_accuracy') -``` - -### Log Analysis - -```powershell -# View full logs -Get-Content data_out\autonomous_training.log - -# Filter for errors -Get-Content data_out\autonomous_training.log | Select-String "ERROR" - -# Count warnings -(Get-Content data_out\autonomous_training.log | Select-String "WARNING").Count - -# Tail logs in real-time -Get-Content data_out\autonomous_training.log -Wait -Tail 20 -``` - -### Status File Inspection - -```powershell -# View raw status JSON -Get-Content data_out\autonomous_training_status.json | ConvertFrom-Json - -# Extract specific field -(Get-Content data_out\autonomous_training_status.json | ConvertFrom-Json).best_accuracy - -# Performance history -(Get-Content data_out\autonomous_training_status.json | ConvertFrom-Json).performance_history | ConvertTo-Json -``` - -### Integration with Azure Monitor - -```powershell -# Send metrics to Azure Log Analytics (example) -$status = Get-Content data_out\autonomous_training_status.json | ConvertFrom-Json -$metrics = @{ - Timestamp = Get-Date - BestAccuracy = $status.best_accuracy - CyclesCompleted = $status.cycles_completed - Phase = $status.current_phase -} - -# Post to Azure Monitor REST API -# (Configure workspace ID and shared key) -# Invoke-RestMethod -Uri $uri -Method Post -Body ($metrics | ConvertTo-Json) -``` - -## Monitoring Best Practices - -### 1. Always Monitor During Initial Cycles -- Watch first 3-5 cycles closely -- Verify data collection works -- Confirm training completes successfully -- Check accuracy improvements - -### 2. Set Up Alerting -- Configure email/Slack notifications in config -- Monitor disk space usage -- Watch for repeated errors -- Track memory usage - -### 3. Regular Analysis -- Generate analytics report weekly -- Export metrics for trend analysis -- Review optimization recommendations -- Adjust configuration based on insights - -### 4. Performance Baselines -- Record initial accuracy -- Set target accuracy goals -- Track improvement rate -- Compare against baselines - -### 5. Resource Monitoring -- Monitor CPU usage (should be 80-100% with 20 workers) -- Check memory usage (2-4 GB typical) -- Watch disk I/O -- Ensure adequate disk space (>50 GB free) - -## Troubleshooting - -### Dashboard Shows "Status file not found" -```powershell -# Check if orchestrator is running -Get-Process python | Where-Object {$_.CommandLine -like "*autonomous_training*"} - -# Start orchestrator if not running -python .\scripts\autonomous_training_orchestrator.py --once -``` - -### No Performance Data -```powershell -# Verify status file exists -Test-Path data_out\autonomous_training_status.json - -# Check if orchestrator has completed a cycle -python .\scripts\autonomous_training_orchestrator.py --status -``` - -### Charts Not Displaying Properly -- Ensure terminal supports ANSI colors -- Use Windows Terminal or PowerShell 7+ -- Try `--once` mode for static display - -### Metrics Export Fails -```powershell -# Check data_out directory exists -New-Item -ItemType Directory -Force -Path data_out - -# Verify write permissions -Test-Path data_out -PathType Container -``` - -## Example Monitoring Session - -```powershell -# 1. Start orchestrator -python .\scripts\autonomous_training_orchestrator.py - -# Wait for first cycle to complete (~5-10 minutes) - -# 2. In new terminal, monitor in real-time -python .\scripts\monitor_autonomous_training.py - -# 3. After 3-5 cycles, generate analytics -python .\scripts\training_analytics.py --report - -# 4. Export data for further analysis -python .\scripts\monitor_autonomous_training.py --export metrics.csv -python .\scripts\training_analytics.py --html report.html - -# 5. Open HTML report -start data_out\report.html -``` - -## Integration Examples - -### PowerShell Dashboard Script -```powershell -# monitor.ps1 - Custom monitoring script -while ($true) { - Clear-Host - Write-Host "=== AUTONOMOUS TRAINING MONITOR ===" -ForegroundColor Cyan - Write-Host "" - - python .\scripts\monitor_autonomous_training.py --summary - - Write-Host "`nPress Ctrl+C to exit" - Start-Sleep -Seconds 30 -} -``` - -### Scheduled Report Generation -```powershell -# Create scheduled task to generate reports daily -$action = New-ScheduledTaskAction -Execute "python" -Argument ".\scripts\training_analytics.py --html daily_report.html" -$trigger = New-ScheduledTaskTrigger -Daily -At 6am -Register-ScheduledTask -Action $action -Trigger $trigger -TaskName "AutoTrainingReport" -``` - -### Slack Notification (Future Enhancement) -```python -# In autonomous_training_orchestrator.py -# Add webhook notification on cycle completion -import requests - -def send_slack_notification(status): - webhook_url = "https://hooks.slack.com/services/YOUR/WEBHOOK/URL" - message = { - "text": f"Training Cycle Complete: {status['best_accuracy']:.2%} accuracy" - } - requests.post(webhook_url, json=message) -``` - -## FAQ - -**Q: How often should I check the monitor?** -A: During initial setup, check every cycle. Once stable, periodic checks (hourly/daily) are sufficient. - -**Q: What's a good target accuracy?** -A: 85-90% is excellent for most datasets. 90%+ is production-ready. - -**Q: How long until I see results?** -A: First cycle completes in 5-10 minutes. Significant improvements appear after 3-5 cycles. - -**Q: Can I run monitoring on a different machine?** -A: Yes, if you share the `data_out/` directory or copy status files. - -**Q: How much history is kept?** -A: All history is preserved in status file. Export regularly for long-term analysis. - -## Next Steps - -1. **Set up monitoring**: Start with real-time dashboard -2. **Establish baselines**: Record initial performance -3. **Set goals**: Define target accuracy and timeline -4. **Regular reviews**: Generate analytics reports weekly -5. **Optimize**: Adjust configuration based on insights -6. **Automate**: Set up scheduled exports and reports - -For more information, see: -- `AUTONOMOUS_TRAINING_README.md` - Full orchestrator guide -- `autonomous_training.yaml` - Configuration options -- Logs: `data_out/autonomous_training.log` +# Autonomous Training Monitoring Guide + +Complete guide to monitoring and analyzing your autonomous AI training system. + +## Quick Start + +### 1. Real-Time Dashboard (Recommended) +```powershell +# Live monitoring with auto-refresh every 5 seconds +python .\scripts\monitor_autonomous_training.py +``` + +### 2. Quick Status Check +```powershell +# One-time status display +python .\scripts\monitor_autonomous_training.py --once + +# Or compact summary +python .\scripts\monitor_autonomous_training.py --summary +``` + +### 3. Analytics Report +```powershell +# Generate detailed analytics +python .\scripts\training_analytics.py --report + +# View performance chart +python .\scripts\training_analytics.py --chart + +# Export HTML report +python .\scripts\training_analytics.py --html data_out/report.html +``` + +## Monitoring Tools + +### Real-Time Dashboard (`monitor_autonomous_training.py`) + +**Features:** +- 🎯 Live status updates (refreshes every 5 seconds) +- 📊 Performance metrics and trends +- 📝 Recent log activity +- ⚠️ Automatic alerts and warnings +- 🎨 Color-coded output for easy reading + +**Usage:** + +```powershell +# Continuous monitoring (press Ctrl+C to exit) +python .\scripts\monitor_autonomous_training.py + +# One-time display +python .\scripts\monitor_autonomous_training.py --once + +# Compact summary +python .\scripts\monitor_autonomous_training.py --summary + +# Custom refresh interval (10 seconds) +python .\scripts\monitor_autonomous_training.py --refresh 10 + +# Export metrics to CSV +python .\scripts\monitor_autonomous_training.py --export metrics.csv +``` + +**Dashboard Sections:** + +1. **System Overview** + - Current phase (training, optimization, etc.) + - Cycles completed + - Best accuracy achieved + - Last cycle duration + +2. **Dataset Inventory** + - Datasets by category + - Total available datasets + - Distribution breakdown + +3. **Performance Metrics** + - Last 5 cycles summary + - Accuracy trends + - Model counts + - Improvement/decline indicators + +4. **Task Queue** + - Active tasks + - Completed tasks + - Task history + +5. **Recent Activity** + - Last 10 log entries + - Color-coded by severity + - Error/warning highlighting + +6. **Alerts** + - Performance degradation warnings + - Low dataset count alerts + - Error notifications + - System status issues + +### Analytics Tool (`training_analytics.py`) + +**Features:** +- 📈 Performance trend analysis +- 🔮 Accuracy predictions +- 💡 Optimization insights +- 📊 ASCII charts +- 📄 HTML report generation + +**Usage:** + +```powershell +# Text report with recommendations +python .\scripts\training_analytics.py --report + +# ASCII chart of performance +python .\scripts\training_analytics.py --chart + +# Chart specific metric +python .\scripts\training_analytics.py --chart --metric max_accuracy + +# Generate HTML report +python .\scripts\training_analytics.py --html report.html +``` + +**Insights Provided:** + +1. **Performance Trend** + - Initial vs current accuracy + - Total improvement + - Improvement rate per cycle + +2. **Predictions** + - Cycles needed to reach 80%, 85%, 90%, 95% + - Based on current improvement rate + - Helps plan training duration + +3. **Optimization Insights** + - Optimal epoch count + - Plateau detection + - Convergence status + +4. **Model Quality Breakdown** + - Exceptional models (≥95%) + - Excellent models (85-95%) + - Distribution analysis + +5. **Recommendations** + - Action items based on performance + - Training strategy suggestions + - Deployment readiness + +## Monitoring Workflows + +### 1. Active Development Monitoring + +Run dashboard in one terminal while orchestrator runs in another: + +```powershell +# Terminal 1: Start orchestrator +python .\scripts\autonomous_training_orchestrator.py + +# Terminal 2: Monitor in real-time +python .\scripts\monitor_autonomous_training.py +``` + +### 2. Scheduled Status Checks + +Check status periodically without continuous monitoring: + +```powershell +# Check every 10 minutes (Windows Task Scheduler or loop) +while ($true) { + python .\scripts\monitor_autonomous_training.py --summary + Start-Sleep -Seconds 600 +} +``` + +### 3. Performance Analysis Workflow + +After several training cycles: + +```powershell +# 1. Generate text report +python .\scripts\training_analytics.py --report > analysis.txt + +# 2. View performance chart +python .\scripts\training_analytics.py --chart + +# 3. Export detailed HTML report +python .\scripts\training_analytics.py --html report.html + +# 4. Open report in browser +start report.html +``` + +### 4. Data Export for External Analysis + +```powershell +# Export metrics to CSV +python .\scripts\monitor_autonomous_training.py --export metrics.csv + +# Import in Excel, pandas, or other tools +# File includes: timestamp, cycle, epochs, accuracies, model counts +``` + +## Understanding the Dashboard + +### Status Indicators + +**Phase Colors:** +- 🔵 Blue: Data discovery, collection +- 🟡 Yellow: Training in progress +- 🟢 Green: Deployment, success +- 🔴 Red: Error, stopped + +**Accuracy Color Coding:** +- 🟢 Green: ≥90% (Excellent) +- 🔵 Cyan: 75-90% (Good) +- 🟡 Yellow: 60-75% (Fair) +- 🔴 Red: <60% (Needs work) + +**Trend Indicators:** +- ↑ Green: Improving (>1% gain) +- → Yellow: Stable/plateau +- ↓ Red: Declining (>1% loss) + +### Key Metrics Explained + +**Cycles Completed:** +- Number of full training cycles run +- Each cycle = discovery → collection → training → analysis + +**Best Accuracy:** +- Highest mean accuracy achieved across all cycles +- Target: 85-95% for production + +**Mean Accuracy:** +- Average accuracy across all trained datasets +- More realistic than max accuracy + +**Max Accuracy:** +- Best single dataset performance +- Shows model potential + +**Exceptional Models:** +- Models achieving ≥95% accuracy +- Ready for production deployment + +**Successful Count:** +- Number of datasets successfully trained +- Out of total available + +**Improvement Rate:** +- Average accuracy gain per cycle +- Helps predict future performance + +## Alerts & Warnings + +### Performance Degradation +**Trigger:** >5% accuracy drop between cycles + +**Possible Causes:** +- Dataset quality issues +- Overfitting from too many epochs +- Random initialization variance +- System resource constraints + +**Actions:** +1. Check recent logs for errors +2. Verify dataset integrity +3. Consider reducing epochs temporarily +4. Review hyperparameter settings + +### Low Dataset Count +**Trigger:** <100 datasets available + +**Possible Causes:** +- Initial setup incomplete +- Download failures +- Disk space issues + +**Actions:** +1. Check `datasets/` directories +2. Run data collection manually +3. Verify network connectivity +4. Check disk space + +### Plateau Detected +**Trigger:** <0.01% variance in last 3 cycles + +**Possible Causes:** +- Model has converged +- Need more epochs +- Learning rate too low +- Architecture limitations + +**Actions:** +1. Increase epoch count in config +2. Enable hyperparameter tuning +3. Try architecture evolution +4. Consider ensemble methods + +### Training Errors +**Trigger:** Failed training jobs + +**Possible Causes:** +- Categorical data not encoded +- Out of memory +- Corrupt datasets +- Software bugs + +**Actions:** +1. Review error logs +2. Check failed dataset names +3. Preprocess problematic datasets +4. Reduce batch size or workers + +## Advanced Monitoring + +### Custom Metrics Export + +```powershell +# Export and analyze with Python +python .\scripts\monitor_autonomous_training.py --export metrics.csv + +# Load in Python/pandas +import pandas as pd +df = pd.read_csv('metrics.csv') +print(df.describe()) + +# Plot with matplotlib +df.plot(x='cycle', y='mean_accuracy') +``` + +### Log Analysis + +```powershell +# View full logs +Get-Content data_out\autonomous_training.log + +# Filter for errors +Get-Content data_out\autonomous_training.log | Select-String "ERROR" + +# Count warnings +(Get-Content data_out\autonomous_training.log | Select-String "WARNING").Count + +# Tail logs in real-time +Get-Content data_out\autonomous_training.log -Wait -Tail 20 +``` + +### Status File Inspection + +```powershell +# View raw status JSON +Get-Content data_out\autonomous_training_status.json | ConvertFrom-Json + +# Extract specific field +(Get-Content data_out\autonomous_training_status.json | ConvertFrom-Json).best_accuracy + +# Performance history +(Get-Content data_out\autonomous_training_status.json | ConvertFrom-Json).performance_history | ConvertTo-Json +``` + +### Integration with Azure Monitor + +```powershell +# Send metrics to Azure Log Analytics (example) +$status = Get-Content data_out\autonomous_training_status.json | ConvertFrom-Json +$metrics = @{ + Timestamp = Get-Date + BestAccuracy = $status.best_accuracy + CyclesCompleted = $status.cycles_completed + Phase = $status.current_phase +} + +# Post to Azure Monitor REST API +# (Configure workspace ID and shared key) +# Invoke-RestMethod -Uri $uri -Method Post -Body ($metrics | ConvertTo-Json) +``` + +## Monitoring Best Practices + +### 1. Always Monitor During Initial Cycles +- Watch first 3-5 cycles closely +- Verify data collection works +- Confirm training completes successfully +- Check accuracy improvements + +### 2. Set Up Alerting +- Configure email/Slack notifications in config +- Monitor disk space usage +- Watch for repeated errors +- Track memory usage + +### 3. Regular Analysis +- Generate analytics report weekly +- Export metrics for trend analysis +- Review optimization recommendations +- Adjust configuration based on insights + +### 4. Performance Baselines +- Record initial accuracy +- Set target accuracy goals +- Track improvement rate +- Compare against baselines + +### 5. Resource Monitoring +- Monitor CPU usage (should be 80-100% with 20 workers) +- Check memory usage (2-4 GB typical) +- Watch disk I/O +- Ensure adequate disk space (>50 GB free) + +## Troubleshooting + +### Dashboard Shows "Status file not found" +```powershell +# Check if orchestrator is running +Get-Process python | Where-Object {$_.CommandLine -like "*autonomous_training*"} + +# Start orchestrator if not running +python .\scripts\autonomous_training_orchestrator.py --once +``` + +### No Performance Data +```powershell +# Verify status file exists +Test-Path data_out\autonomous_training_status.json + +# Check if orchestrator has completed a cycle +python .\scripts\autonomous_training_orchestrator.py --status +``` + +### Charts Not Displaying Properly +- Ensure terminal supports ANSI colors +- Use Windows Terminal or PowerShell 7+ +- Try `--once` mode for static display + +### Metrics Export Fails +```powershell +# Check data_out directory exists +New-Item -ItemType Directory -Force -Path data_out + +# Verify write permissions +Test-Path data_out -PathType Container +``` + +## Example Monitoring Session + +```powershell +# 1. Start orchestrator +python .\scripts\autonomous_training_orchestrator.py + +# Wait for first cycle to complete (~5-10 minutes) + +# 2. In new terminal, monitor in real-time +python .\scripts\monitor_autonomous_training.py + +# 3. After 3-5 cycles, generate analytics +python .\scripts\training_analytics.py --report + +# 4. Export data for further analysis +python .\scripts\monitor_autonomous_training.py --export metrics.csv +python .\scripts\training_analytics.py --html report.html + +# 5. Open HTML report +start data_out\report.html +``` + +## Integration Examples + +### PowerShell Dashboard Script +```powershell +# monitor.ps1 - Custom monitoring script +while ($true) { + Clear-Host + Write-Host "=== AUTONOMOUS TRAINING MONITOR ===" -ForegroundColor Cyan + Write-Host "" + + python .\scripts\monitor_autonomous_training.py --summary + + Write-Host "`nPress Ctrl+C to exit" + Start-Sleep -Seconds 30 +} +``` + +### Scheduled Report Generation +```powershell +# Create scheduled task to generate reports daily +$action = New-ScheduledTaskAction -Execute "python" -Argument ".\scripts\training_analytics.py --html daily_report.html" +$trigger = New-ScheduledTaskTrigger -Daily -At 6am +Register-ScheduledTask -Action $action -Trigger $trigger -TaskName "AutoTrainingReport" +``` + +### Slack Notification (Future Enhancement) +```python +# In autonomous_training_orchestrator.py +# Add webhook notification on cycle completion +import requests + +def send_slack_notification(status): + webhook_url = "https://hooks.slack.com/services/YOUR/WEBHOOK/URL" + message = { + "text": f"Training Cycle Complete: {status['best_accuracy']:.2%} accuracy" + } + requests.post(webhook_url, json=message) +``` + +## FAQ + +**Q: How often should I check the monitor?** +A: During initial setup, check every cycle. Once stable, periodic checks (hourly/daily) are sufficient. + +**Q: What's a good target accuracy?** +A: 85-90% is excellent for most datasets. 90%+ is production-ready. + +**Q: How long until I see results?** +A: First cycle completes in 5-10 minutes. Significant improvements appear after 3-5 cycles. + +**Q: Can I run monitoring on a different machine?** +A: Yes, if you share the `data_out/` directory or copy status files. + +**Q: How much history is kept?** +A: All history is preserved in status file. Export regularly for long-term analysis. + +## Next Steps + +1. **Set up monitoring**: Start with real-time dashboard +2. **Establish baselines**: Record initial performance +3. **Set goals**: Define target accuracy and timeline +4. **Regular reviews**: Generate analytics reports weekly +5. **Optimize**: Adjust configuration based on insights +6. **Automate**: Set up scheduled exports and reports + +For more information, see: +- `AUTONOMOUS_TRAINING_README.md` - Full orchestrator guide +- `autonomous_training.yaml` - Configuration options +- Logs: `data_out/autonomous_training.log` diff --git a/docs/guides/NEXT_STEPS_COMPLETED.md b/docs/guides/NEXT_STEPS_COMPLETED.md index f95a23b88..326399954 100644 --- a/docs/guides/NEXT_STEPS_COMPLETED.md +++ b/docs/guides/NEXT_STEPS_COMPLETED.md @@ -1,173 +1,173 @@ -# Next Steps Completion Report - -**Date:** November 17, 2025 -**Status:** ✅ All steps completed - -## Summary - -All four next steps have been successfully executed. The system is now ready for scaled autonomous training with monitoring capabilities. - -## 1. Classical Training Status ✅ - -**Current State:** -- Distributed benchmark has successfully trained 360/552 datasets -- Perfect models achieved: `machine_cpu` (100%), `mfeat-morphological` (100%) -- Exceptional models: `delta_ailerons` (95%), `ionosphere` (94%), `JapaneseVowels` (96%) -- Training configuration: 100 epochs per dataset, 20 workers - -**Outcome:** Previous runs completed successfully with high-quality models - -## 2. Quantum AutoRun Dry-Run ✅ - -**Validation Results:** -All 3 quantum jobs successfully validated: - -1. **heart_quick** (Local simulator) - - Mode: `train_custom_dataset` - - Config: 4 qubits, 1 epoch, FREE local execution - -2. **ionosphere_quick** (Local simulator) - - Mode: `train_custom_dataset` - - Config: 4 qubits, 25 epochs, FREE local execution - -3. **azure_ionq_simulator** (Azure cloud) - - Mode: `azure_hardware` - - Backend: `ionq.simulator` (FREE Azure simulator) - - Config: 3 qubits, 100 shots - -**Outcome:** All quantum job configurations are valid and ready to execute - -## 3. Azure Quantum Integration ⚠️ - -**Achievements:** -- ✅ Azure login completed successfully -- ✅ Azure Quantum SDK installed (`azure-quantum`, `qiskit-ionq`, `pyqir`) -- ✅ Workspace configured: `quantum-ai-workspace` (eastus) - -**Issue Encountered:** -- ⚠️ qiskit dependency version conflicts in `quantum-ai` venv -- `qiskit 0.46.3` installed, but `qiskit>=1.1.0` required by other packages - -**Recommendations:** -1. Use main venv (`C:\Users\Bryan\OneDrive\AI\venv`) for Azure Quantum jobs -2. OR recreate `quantum-ai` venv with compatible qiskit versions -3. OR run jobs from repo root using main venv - -**Next Test:** -```powershell -cd C:\Users\Bryan\OneDrive\AI -python .\quantum-ai\deploy_to_azure_quantum.py --backend ionq.simulator --shots 100 -``` - -## 4. Autonomous Training Monitor ✅ - -**Orchestrator Scaling Improvements:** - -### Fixed Issues: -- ✅ Corrected `--datasets-list` to `--datasets-dir` argument -- ✅ Removed Unicode characters causing logging errors - -### Implemented Features: -1. **Multiprocessing Support** - - CPU count detection via `multiprocessing.cpu_count()` - - Resource-aware worker allocation - - Auto-scaling based on available cores - -2. **Optional Distributed Execution** - - Ray support (optional, fallback to multiprocessing) - - Configurable via `scaling.mode` in YAML config - -3. **Resource Management** - - `max_workers` configuration option - - Dynamic worker allocation: `min(cpu_count, config_workers)` - - Batch size configuration for large datasets - -4. **Configuration Options** (in `autonomous_training.yaml`): -```yaml -scaling: - mode: "multiprocessing" # or "ray" - max_workers: null # null = auto-detect - batch_size: 100 - resource_limits: {} -``` - -**Testing:** -```powershell -# Test single cycle -python .\scripts\autonomous_training_orchestrator.py --once - -# Monitor training -python .\scripts\monitor_autonomous_training.py - -# Check status -python .\scripts\autonomous_training_orchestrator.py --status -``` - -## Files Modified - -1. **`autonomous_training_orchestrator.py`** - - Added multiprocessing support - - Implemented resource-aware worker allocation - - Fixed `--datasets-dir` argument usage - - Removed problematic Unicode logging characters - -2. **`quantum_autorun.yaml`** - - Changed `n_qubits` to `qubits` for Azure jobs - - Aligned with `deploy_to_azure_quantum.py` argument parser - -## Next Actions - -### Immediate: -1. Test scaled orchestrator: - ```powershell - python .\scripts\autonomous_training_orchestrator.py --once - ``` - -2. Monitor training dashboard: - ```powershell - python .\scripts\monitor_autonomous_training.py - ``` - -### Short-term: -1. Fix `quantum-ai` venv qiskit dependencies -2. Run Azure Quantum simulator job successfully -3. Execute full autonomous training cycle with monitoring - -### Long-term: -1. Implement Ray distributed execution for multi-machine scaling -2. Add GPU resource management -3. Integrate Azure Quantum results into autonomous training pipeline -4. Deploy best models to production - -## Performance Metrics - -### Classical ML Training: -- **Datasets processed:** 360/552 (65%) -- **Best accuracy:** 100% (perfect models) -- **Exceptional models:** 4 with >94% accuracy -- **Configuration:** 100 epochs, 20 workers - -### Quantum ML Training: -- **Demo completed:** 47.5% accuracy (4 qubits, 2 layers, 3 epochs) -- **Jobs configured:** 3 (2 local, 1 Azure) -- **Azure backend:** IonQ simulator (FREE) - -### System Scalability: -- **CPU cores detected:** `multiprocessing.cpu_count()` -- **Worker allocation:** Dynamic, resource-aware -- **Batch processing:** Up to 100 datasets per batch -- **Distributed mode:** Optional Ray support - -## Conclusion - -✅ **All next steps completed successfully** - -The system is now equipped with: -- Scaled autonomous training orchestration -- Resource-aware multiprocessing -- Azure Quantum integration (pending venv fix) -- Real-time monitoring capabilities -- High-quality classical ML models -- Validated quantum ML job configurations - -**Ready for production-scale autonomous training with monitoring and cloud quantum backend support.** +# Next Steps Completion Report + +**Date:** November 17, 2025 +**Status:** ✅ All steps completed + +## Summary + +All four next steps have been successfully executed. The system is now ready for scaled autonomous training with monitoring capabilities. + +## 1. Classical Training Status ✅ + +**Current State:** +- Distributed benchmark has successfully trained 360/552 datasets +- Perfect models achieved: `machine_cpu` (100%), `mfeat-morphological` (100%) +- Exceptional models: `delta_ailerons` (95%), `ionosphere` (94%), `JapaneseVowels` (96%) +- Training configuration: 100 epochs per dataset, 20 workers + +**Outcome:** Previous runs completed successfully with high-quality models + +## 2. Quantum AutoRun Dry-Run ✅ + +**Validation Results:** +All 3 quantum jobs successfully validated: + +1. **heart_quick** (Local simulator) + - Mode: `train_custom_dataset` + - Config: 4 qubits, 1 epoch, FREE local execution + +2. **ionosphere_quick** (Local simulator) + - Mode: `train_custom_dataset` + - Config: 4 qubits, 25 epochs, FREE local execution + +3. **azure_ionq_simulator** (Azure cloud) + - Mode: `azure_hardware` + - Backend: `ionq.simulator` (FREE Azure simulator) + - Config: 3 qubits, 100 shots + +**Outcome:** All quantum job configurations are valid and ready to execute + +## 3. Azure Quantum Integration ⚠️ + +**Achievements:** +- ✅ Azure login completed successfully +- ✅ Azure Quantum SDK installed (`azure-quantum`, `qiskit-ionq`, `pyqir`) +- ✅ Workspace configured: `quantum-ai-workspace` (eastus) + +**Issue Encountered:** +- ⚠️ qiskit dependency version conflicts in `quantum-ai` venv +- `qiskit 0.46.3` installed, but `qiskit>=1.1.0` required by other packages + +**Recommendations:** +1. Use main venv (`C:\Users\Bryan\OneDrive\AI\venv`) for Azure Quantum jobs +2. OR recreate `quantum-ai` venv with compatible qiskit versions +3. OR run jobs from repo root using main venv + +**Next Test:** +```powershell +cd C:\Users\Bryan\OneDrive\AI +python .\quantum-ai\deploy_to_azure_quantum.py --backend ionq.simulator --shots 100 +``` + +## 4. Autonomous Training Monitor ✅ + +**Orchestrator Scaling Improvements:** + +### Fixed Issues: +- ✅ Corrected `--datasets-list` to `--datasets-dir` argument +- ✅ Removed Unicode characters causing logging errors + +### Implemented Features: +1. **Multiprocessing Support** + - CPU count detection via `multiprocessing.cpu_count()` + - Resource-aware worker allocation + - Auto-scaling based on available cores + +2. **Optional Distributed Execution** + - Ray support (optional, fallback to multiprocessing) + - Configurable via `scaling.mode` in YAML config + +3. **Resource Management** + - `max_workers` configuration option + - Dynamic worker allocation: `min(cpu_count, config_workers)` + - Batch size configuration for large datasets + +4. **Configuration Options** (in `autonomous_training.yaml`): +```yaml +scaling: + mode: "multiprocessing" # or "ray" + max_workers: null # null = auto-detect + batch_size: 100 + resource_limits: {} +``` + +**Testing:** +```powershell +# Test single cycle +python .\scripts\autonomous_training_orchestrator.py --once + +# Monitor training +python .\scripts\monitor_autonomous_training.py + +# Check status +python .\scripts\autonomous_training_orchestrator.py --status +``` + +## Files Modified + +1. **`autonomous_training_orchestrator.py`** + - Added multiprocessing support + - Implemented resource-aware worker allocation + - Fixed `--datasets-dir` argument usage + - Removed problematic Unicode logging characters + +2. **`quantum_autorun.yaml`** + - Changed `n_qubits` to `qubits` for Azure jobs + - Aligned with `deploy_to_azure_quantum.py` argument parser + +## Next Actions + +### Immediate: +1. Test scaled orchestrator: + ```powershell + python .\scripts\autonomous_training_orchestrator.py --once + ``` + +2. Monitor training dashboard: + ```powershell + python .\scripts\monitor_autonomous_training.py + ``` + +### Short-term: +1. Fix `quantum-ai` venv qiskit dependencies +2. Run Azure Quantum simulator job successfully +3. Execute full autonomous training cycle with monitoring + +### Long-term: +1. Implement Ray distributed execution for multi-machine scaling +2. Add GPU resource management +3. Integrate Azure Quantum results into autonomous training pipeline +4. Deploy best models to production + +## Performance Metrics + +### Classical ML Training: +- **Datasets processed:** 360/552 (65%) +- **Best accuracy:** 100% (perfect models) +- **Exceptional models:** 4 with >94% accuracy +- **Configuration:** 100 epochs, 20 workers + +### Quantum ML Training: +- **Demo completed:** 47.5% accuracy (4 qubits, 2 layers, 3 epochs) +- **Jobs configured:** 3 (2 local, 1 Azure) +- **Azure backend:** IonQ simulator (FREE) + +### System Scalability: +- **CPU cores detected:** `multiprocessing.cpu_count()` +- **Worker allocation:** Dynamic, resource-aware +- **Batch processing:** Up to 100 datasets per batch +- **Distributed mode:** Optional Ray support + +## Conclusion + +✅ **All next steps completed successfully** + +The system is now equipped with: +- Scaled autonomous training orchestration +- Resource-aware multiprocessing +- Azure Quantum integration (pending venv fix) +- Real-time monitoring capabilities +- High-quality classical ML models +- Validated quantum ML job configurations + +**Ready for production-scale autonomous training with monitoring and cloud quantum backend support.** diff --git a/docs/guides/NOTIFICATION_SYSTEM_GUIDE.md b/docs/guides/NOTIFICATION_SYSTEM_GUIDE.md index 4e3cfaee9..ba01941ca 100644 --- a/docs/guides/NOTIFICATION_SYSTEM_GUIDE.md +++ b/docs/guides/NOTIFICATION_SYSTEM_GUIDE.md @@ -1,499 +1,499 @@ -# QAI Notification System Guide - -## Overview - -The QAI platform now includes a comprehensive desktop notification system that alerts users about training events, milestones, and system status - even when the browser window is minimized or in the background. - -**Version**: 1.0 -**Date**: November 2024 -**Status**: Production Ready ✅ - ---- - -## Features - -### 1. **Browser-Based Notifications** 🔔 -- Native desktop notifications using Web Notifications API -- Cross-platform support (Windows, macOS, Linux) -- No external dependencies required -- Works in Chrome, Firefox, Edge, Safari - -### 2. **Training Event Alerts** -- Job started notifications -- Job completed with duration and final loss -- Job failure alerts with error details -- Training milestone notifications (loss thresholds, epoch intervals) - -### 3. **System Monitoring** -- GPU utilization alerts (>95% usage) -- Resource warnings -- Backup completion notifications -- Evaluation completion alerts - -### 4. **User Control** -- Toggle button in QAI Hub header -- Persistent state across sessions -- Permission request on first use -- Visual feedback (🔔 on / 🔕 off) - ---- - -## Installation & Setup - -### Browser Integration (Production) - -**No installation required!** The notification system is built into: -- `dashboard/unified.html` - Training dashboard -- `dashboard/analytics.html` - Analytics dashboard -- `dashboard/hub.html` - QAI Hub with toggle button - -**First-time setup:** -1. Open http://localhost:8000/hub.html -2. Click the notification toggle button (🔕 icon in header) -3. Allow notifications when browser prompts -4. Toggle turns to 🔔 - you're all set! - -### Python CLI Tool (Optional) - -For system-level notifications or integration with external scripts: - -```powershell -# Install dependencies (Windows only) -pip install win10toast - -# Test notification -python scripts/notification_system.py --test - -# Monitor training job -python scripts/notification_system.py --monitor data_out/autotrain/status.json --job-name phi35_test -``` - -**Platform-specific requirements:** -- **Windows**: `pip install win10toast` -- **macOS**: No dependencies (uses `osascript`) -- **Linux**: No dependencies (uses `notify-send`) - ---- - -## Usage Guide - -### Browser Notifications (Recommended) - -#### Enable/Disable Notifications - -1. **Via Hub Toggle**: - - Navigate to http://localhost:8000/hub.html - - Click notification button in header (top right) - - Allow browser permission when prompted - - Toggle anytime to enable/disable - -2. **Automatic Integration**: - - Unified dashboard checks status every 5 seconds - - Auto-notifies on job state changes - - No manual polling required - -#### Notification Events - -**Training Events:** -- 🚀 **Job Started**: "Training Started - Job 'phi35_mixed_chat' has begun training" -- ✅ **Job Complete**: "Training Complete - Job 'phi35_mixed_chat' finished in 45min with loss 0.2341" -- ❌ **Job Failed**: "Training Failed - Job 'phi35_mixed_chat' failed: CUDA out of memory" - -**Milestones:** -- 🎯 **Loss Threshold**: "Milestone Reached - Job 'test': Loss below 0.5 = 0.4523" -- 🎯 **Epoch Milestone**: "Milestone Reached - Job 'test': Epoch 5 complete = 0.3245" - -**System Alerts:** -- ⚠️ **GPU Warning**: "GPU Alert - GPU utilization at 97% (Memory: 10240MB)" -- 💾 **Backup Complete**: "Backup Complete - Created backup 'pre_prod_v1' (342.56 MB)" -- 📊 **Evaluation Done**: "Evaluation Complete - Model 'phi35_lora_v3' - Perplexity: 12.34" - -### Python CLI Tool - -#### Test Notifications - -```powershell -# Send test notification -python scripts/notification_system.py --test -``` - -Output: -``` -Sending test notifications... -✅ Test notification sent -``` - -You should see a desktop notification appear. - -#### Monitor Training Jobs - -```powershell -# Monitor single job -python scripts/notification_system.py \ - --monitor data_out/autotrain/status.json \ - --job-name phi35_mixed_chat -``` - -**Monitoring behavior:** -- Checks status file every 10 seconds -- Sends notification on job start -- Milestone alerts every 5 epochs -- Final notification on completion/failure -- Exits when job finishes - -#### Integration in Scripts - -```python -from scripts.notification_system import NotificationManager - -notifier = NotificationManager() - -# Job events -notifier.notify_job_started("phi35_test") -notifier.notify_job_completed("phi35_test", duration_min=45, final_loss=0.2341) -notifier.notify_job_failed("phi35_test", error="CUDA out of memory") - -# Milestones -notifier.notify_milestone("phi35_test", "Loss below 0.5", 0.4523) - -# System alerts -notifier.notify_gpu_alert(gpu_util=97, memory_used=10240) -notifier.notify_backup_complete("pre_prod_v1", size_mb=342.56) -notifier.notify_evaluation_complete("phi35_lora_v3", perplexity=12.34) -``` - ---- - -## Implementation Details - -### Browser JavaScript (Unified Dashboard) - -Located in `dashboard/unified.html` (lines 2334-2414): - -```javascript -// Initialize notifications -let notificationsEnabled = false; - -function initNotifications() { - if ("Notification" in window) { - if (Notification.permission === "granted") { - notificationsEnabled = true; - } else if (Notification.permission !== "denied") { - Notification.requestPermission().then(permission => { - notificationsEnabled = (permission === "granted"); - }); - } - } -} - -// Send notification -function sendDesktopNotification(title, message, icon = '🔔') { - if (!notificationsEnabled) return; - - const notification = new Notification(title, { - body: message, - icon: 'data:image/svg+xml,...', - requireInteraction: false, - tag: 'qai-training' - }); - - notification.onclick = () => { - window.focus(); - notification.close(); - }; -} - -// Training event helpers -function notifyJobStarted(jobName) { ... } -function notifyJobCompleted(jobName, durationMin, finalLoss) { ... } -function notifyJobFailed(jobName, error) { ... } -function notifyMilestone(jobName, milestone, value) { ... } -function notifyGPUAlert(gpuUtil, memoryUsed) { ... } -``` - -**Integration points:** -- Auto-refresh loop (every 5 seconds) -- Job status comparisons -- GPU monitoring thresholds -- Manual triggers from UI buttons - -### Python CLI Tool - -Located in `scripts/notification_system.py` (415 lines): - -**Core classes:** -1. **NotificationManager**: Cross-platform notification sender - - `send_notification(title, message, icon, duration)` - - `notify_job_started/completed/failed()` - - `notify_milestone()`, `notify_gpu_alert()` - - Platform-specific methods: `_send_windows()`, `_send_macos()`, `_send_linux()` - -2. **TrainingNotifier**: Training-specific monitoring wrapper - - `monitor_training(job_name, status_file)` - Polls status JSON - - Milestone tracking (loss thresholds, epoch intervals) - - Automatic start/complete/fail detection - -**Platform implementations:** -- **Windows**: `win10toast.ToastNotifier` (optional dependency) -- **macOS**: `osascript` shell command (built-in) -- **Linux**: `notify-send` command (built-in) - ---- - -## Configuration - -### Browser Notification Settings - -**Permission levels:** -- `granted`: Notifications enabled -- `denied`: User blocked notifications (requires browser settings change) -- `default`: Not yet requested (will prompt on first toggle) - -**Toggle state persistence:** -- Stored in browser localStorage (future enhancement) -- Currently resets on page reload -- Planned: Remember user preference across sessions - -### Python CLI Milestones - -Edit in `scripts/notification_system.py`: - -```python -class TrainingNotifier: - def __init__(self): - self.milestones = { - 'loss_threshold': 0.5, # Notify when loss drops below this - 'epoch_interval': 5 # Notify every N epochs - } -``` - -**Customization examples:** -```python -# More frequent updates -self.milestones = { - 'loss_threshold': 0.3, # Lower threshold - 'epoch_interval': 1 # Every epoch -} - -# Less frequent (production) -self.milestones = { - 'loss_threshold': 0.1, # Very low threshold - 'epoch_interval': 10 # Every 10 epochs -} -``` - ---- - -## Troubleshooting - -### Browser Notifications Not Working - -**Issue**: Toggle button doesn't show 🔔 - -**Solutions:** -1. Check browser support: - ```javascript - console.log("Notification" in window); // Should be true - console.log(Notification.permission); // Check permission state - ``` - -2. Clear denied permissions: - - Chrome: Settings → Privacy → Site Settings → Notifications - - Firefox: Page Info → Permissions → Show Notifications - - Edge: Settings → Cookies and site permissions → Notifications - -3. Test manually: - ```javascript - new Notification("Test", { body: "This is a test" }); - ``` - -**Issue**: Notifications disappear too quickly - -**Solution**: Increase duration in code: -```javascript -const notification = new Notification(title, { - body: message, - requireInteraction: true // Stays until user dismisses -}); -``` - -### Python CLI Issues - -**Issue**: `ImportError: No module named 'win10toast'` (Windows) - -**Solution**: -```powershell -pip install win10toast -``` - -**Issue**: Notifications fall back to console prints - -**Cause**: Library not installed or platform not detected - -**Check**: -```python -from scripts.notification_system import NotificationManager -notifier = NotificationManager() -print(f"System: {notifier.system}") -print(f"Enabled: {notifier.enabled}") -``` - -**Issue**: Monitor doesn't detect job changes - -**Solutions:** -1. Verify status file exists: - ```powershell - Test-Path data_out/autotrain/status.json - ``` - -2. Check JSON format: - ```powershell - Get-Content data_out/autotrain/status.json | ConvertFrom-Json - ``` - -3. Run with verbose output: - ```python - # Add debug prints in monitor_training() loop - print(f"Current status: {status}") - ``` - ---- - -## Best Practices - -### Development -- Test notifications before long training runs -- Use `--test` flag to verify system works -- Keep milestone intervals reasonable (every 5-10 epochs) -- Don't spam notifications (can be intrusive) - -### Production -- Enable notifications for critical jobs only -- Set higher loss thresholds (e.g., 0.3 instead of 0.5) -- Use GPU alerts for resource monitoring -- Pair with backup notifications for safety - -### User Experience -- Respect browser permission denials -- Provide clear toggle UI (🔔 vs 🔕) -- Include actionable information in messages -- Use appropriate icons for event types - -### Integration -- Call `initNotifications()` on dashboard load -- Check `notificationsEnabled` before sending -- Gracefully handle permission denials -- Combine with WebSocket for real-time updates - ---- - -## API Reference - -### Browser JavaScript Functions - -```javascript -// Initialization -initNotifications() // Request permission if needed - -// Toggle control -toggleNotifications() // Enable/disable notifications - -// Send notification -sendDesktopNotification(title, message, icon) -// title: string - Notification title -// message: string - Body text -// icon: string - Emoji or SVG data URI - -// Training events -notifyJobStarted(jobName) -notifyJobCompleted(jobName, durationMin, finalLoss) -notifyJobFailed(jobName, error) -notifyMilestone(jobName, milestone, value) - -// System alerts -notifyGPUAlert(gpuUtil, memoryUsed) -``` - -### Python CLI - -```python -from scripts.notification_system import NotificationManager, TrainingNotifier - -# Basic usage -notifier = NotificationManager() -notifier.send_notification("Title", "Message", icon="info", duration=10) - -# Training-specific -notifier.notify_job_started("job_name") -notifier.notify_job_completed("job_name", duration_min=45, final_loss=0.234) -notifier.notify_job_failed("job_name", error="Error message") -notifier.notify_milestone("job_name", "Milestone description", value=0.456) - -# System alerts -notifier.notify_gpu_alert(gpu_util=97, memory_used=10240) -notifier.notify_backup_complete("backup_name", size_mb=342.56) -notifier.notify_evaluation_complete("model_name", perplexity=12.34) - -# Monitoring (blocking) -monitor = TrainingNotifier() -monitor.monitor_training("job_name", Path("status.json")) -``` - ---- - -## Future Enhancements - -### Planned Features -- [ ] Persistent toggle state (localStorage) -- [ ] Notification history panel -- [ ] Customizable notification sounds -- [ ] Email/Slack integration -- [ ] Notification filtering (by job type, severity) -- [ ] Grouped notifications (batch updates) -- [ ] Rich notifications with action buttons -- [ ] Mobile push notifications (via PWA) - -### Integration Roadmap -- [ ] WebSocket real-time event streaming -- [ ] Database logging of notification events -- [ ] Admin panel for notification settings -- [ ] Per-user notification preferences -- [ ] Notification analytics (sent/clicked/dismissed) - ---- - -## Related Documentation - -- **QAI Hub Guide**: `QAI_HUB_ENHANCEMENTS_V2.md` - Overview of all hub features -- **Backup System**: `scripts/backup_manager.py` - Triggers backup notifications -- **WebSocket Server**: `dashboard/websocket_server.py` - Real-time job updates -- **Analytics Dashboard**: `dashboard/analytics.html` - Notification integration - ---- - -## Support - -**Issues**: Report notification bugs in project issues -**Feature Requests**: Submit enhancement proposals -**Questions**: Check FAQ section or consult project README - ---- - -## Changelog - -### Version 1.0 (November 2024) -- ✅ Initial browser notification system -- ✅ Python CLI tool for cross-platform notifications -- ✅ Training event notifications (start/complete/fail) -- ✅ Milestone tracking (loss thresholds, epoch intervals) -- ✅ System alerts (GPU, backup, evaluation) -- ✅ Hub integration with toggle button -- ✅ Unified dashboard integration -- ✅ Analytics dashboard integration -- ✅ Documentation and examples - ---- - -**Last Updated**: November 2024 -**Status**: Production Ready ✅ -**Maintainer**: QAI Development Team +# QAI Notification System Guide + +## Overview + +The QAI platform now includes a comprehensive desktop notification system that alerts users about training events, milestones, and system status - even when the browser window is minimized or in the background. + +**Version**: 1.0 +**Date**: November 2024 +**Status**: Production Ready ✅ + +--- + +## Features + +### 1. **Browser-Based Notifications** 🔔 +- Native desktop notifications using Web Notifications API +- Cross-platform support (Windows, macOS, Linux) +- No external dependencies required +- Works in Chrome, Firefox, Edge, Safari + +### 2. **Training Event Alerts** +- Job started notifications +- Job completed with duration and final loss +- Job failure alerts with error details +- Training milestone notifications (loss thresholds, epoch intervals) + +### 3. **System Monitoring** +- GPU utilization alerts (>95% usage) +- Resource warnings +- Backup completion notifications +- Evaluation completion alerts + +### 4. **User Control** +- Toggle button in QAI Hub header +- Persistent state across sessions +- Permission request on first use +- Visual feedback (🔔 on / 🔕 off) + +--- + +## Installation & Setup + +### Browser Integration (Production) + +**No installation required!** The notification system is built into: +- `dashboard/unified.html` - Training dashboard +- `dashboard/analytics.html` - Analytics dashboard +- `dashboard/hub.html` - QAI Hub with toggle button + +**First-time setup:** +1. Open http://localhost:8000/hub.html +2. Click the notification toggle button (🔕 icon in header) +3. Allow notifications when browser prompts +4. Toggle turns to 🔔 - you're all set! + +### Python CLI Tool (Optional) + +For system-level notifications or integration with external scripts: + +```powershell +# Install dependencies (Windows only) +pip install win10toast + +# Test notification +python scripts/notification_system.py --test + +# Monitor training job +python scripts/notification_system.py --monitor data_out/autotrain/status.json --job-name phi35_test +``` + +**Platform-specific requirements:** +- **Windows**: `pip install win10toast` +- **macOS**: No dependencies (uses `osascript`) +- **Linux**: No dependencies (uses `notify-send`) + +--- + +## Usage Guide + +### Browser Notifications (Recommended) + +#### Enable/Disable Notifications + +1. **Via Hub Toggle**: + - Navigate to http://localhost:8000/hub.html + - Click notification button in header (top right) + - Allow browser permission when prompted + - Toggle anytime to enable/disable + +2. **Automatic Integration**: + - Unified dashboard checks status every 5 seconds + - Auto-notifies on job state changes + - No manual polling required + +#### Notification Events + +**Training Events:** +- 🚀 **Job Started**: "Training Started - Job 'phi35_mixed_chat' has begun training" +- ✅ **Job Complete**: "Training Complete - Job 'phi35_mixed_chat' finished in 45min with loss 0.2341" +- ❌ **Job Failed**: "Training Failed - Job 'phi35_mixed_chat' failed: CUDA out of memory" + +**Milestones:** +- 🎯 **Loss Threshold**: "Milestone Reached - Job 'test': Loss below 0.5 = 0.4523" +- 🎯 **Epoch Milestone**: "Milestone Reached - Job 'test': Epoch 5 complete = 0.3245" + +**System Alerts:** +- ⚠️ **GPU Warning**: "GPU Alert - GPU utilization at 97% (Memory: 10240MB)" +- 💾 **Backup Complete**: "Backup Complete - Created backup 'pre_prod_v1' (342.56 MB)" +- 📊 **Evaluation Done**: "Evaluation Complete - Model 'phi35_lora_v3' - Perplexity: 12.34" + +### Python CLI Tool + +#### Test Notifications + +```powershell +# Send test notification +python scripts/notification_system.py --test +``` + +Output: +``` +Sending test notifications... +✅ Test notification sent +``` + +You should see a desktop notification appear. + +#### Monitor Training Jobs + +```powershell +# Monitor single job +python scripts/notification_system.py \ + --monitor data_out/autotrain/status.json \ + --job-name phi35_mixed_chat +``` + +**Monitoring behavior:** +- Checks status file every 10 seconds +- Sends notification on job start +- Milestone alerts every 5 epochs +- Final notification on completion/failure +- Exits when job finishes + +#### Integration in Scripts + +```python +from scripts.notification_system import NotificationManager + +notifier = NotificationManager() + +# Job events +notifier.notify_job_started("phi35_test") +notifier.notify_job_completed("phi35_test", duration_min=45, final_loss=0.2341) +notifier.notify_job_failed("phi35_test", error="CUDA out of memory") + +# Milestones +notifier.notify_milestone("phi35_test", "Loss below 0.5", 0.4523) + +# System alerts +notifier.notify_gpu_alert(gpu_util=97, memory_used=10240) +notifier.notify_backup_complete("pre_prod_v1", size_mb=342.56) +notifier.notify_evaluation_complete("phi35_lora_v3", perplexity=12.34) +``` + +--- + +## Implementation Details + +### Browser JavaScript (Unified Dashboard) + +Located in `dashboard/unified.html` (lines 2334-2414): + +```javascript +// Initialize notifications +let notificationsEnabled = false; + +function initNotifications() { + if ("Notification" in window) { + if (Notification.permission === "granted") { + notificationsEnabled = true; + } else if (Notification.permission !== "denied") { + Notification.requestPermission().then(permission => { + notificationsEnabled = (permission === "granted"); + }); + } + } +} + +// Send notification +function sendDesktopNotification(title, message, icon = '🔔') { + if (!notificationsEnabled) return; + + const notification = new Notification(title, { + body: message, + icon: 'data:image/svg+xml,...', + requireInteraction: false, + tag: 'qai-training' + }); + + notification.onclick = () => { + window.focus(); + notification.close(); + }; +} + +// Training event helpers +function notifyJobStarted(jobName) { ... } +function notifyJobCompleted(jobName, durationMin, finalLoss) { ... } +function notifyJobFailed(jobName, error) { ... } +function notifyMilestone(jobName, milestone, value) { ... } +function notifyGPUAlert(gpuUtil, memoryUsed) { ... } +``` + +**Integration points:** +- Auto-refresh loop (every 5 seconds) +- Job status comparisons +- GPU monitoring thresholds +- Manual triggers from UI buttons + +### Python CLI Tool + +Located in `scripts/notification_system.py` (415 lines): + +**Core classes:** +1. **NotificationManager**: Cross-platform notification sender + - `send_notification(title, message, icon, duration)` + - `notify_job_started/completed/failed()` + - `notify_milestone()`, `notify_gpu_alert()` + - Platform-specific methods: `_send_windows()`, `_send_macos()`, `_send_linux()` + +2. **TrainingNotifier**: Training-specific monitoring wrapper + - `monitor_training(job_name, status_file)` - Polls status JSON + - Milestone tracking (loss thresholds, epoch intervals) + - Automatic start/complete/fail detection + +**Platform implementations:** +- **Windows**: `win10toast.ToastNotifier` (optional dependency) +- **macOS**: `osascript` shell command (built-in) +- **Linux**: `notify-send` command (built-in) + +--- + +## Configuration + +### Browser Notification Settings + +**Permission levels:** +- `granted`: Notifications enabled +- `denied`: User blocked notifications (requires browser settings change) +- `default`: Not yet requested (will prompt on first toggle) + +**Toggle state persistence:** +- Stored in browser localStorage (future enhancement) +- Currently resets on page reload +- Planned: Remember user preference across sessions + +### Python CLI Milestones + +Edit in `scripts/notification_system.py`: + +```python +class TrainingNotifier: + def __init__(self): + self.milestones = { + 'loss_threshold': 0.5, # Notify when loss drops below this + 'epoch_interval': 5 # Notify every N epochs + } +``` + +**Customization examples:** +```python +# More frequent updates +self.milestones = { + 'loss_threshold': 0.3, # Lower threshold + 'epoch_interval': 1 # Every epoch +} + +# Less frequent (production) +self.milestones = { + 'loss_threshold': 0.1, # Very low threshold + 'epoch_interval': 10 # Every 10 epochs +} +``` + +--- + +## Troubleshooting + +### Browser Notifications Not Working + +**Issue**: Toggle button doesn't show 🔔 + +**Solutions:** +1. Check browser support: + ```javascript + console.log("Notification" in window); // Should be true + console.log(Notification.permission); // Check permission state + ``` + +2. Clear denied permissions: + - Chrome: Settings → Privacy → Site Settings → Notifications + - Firefox: Page Info → Permissions → Show Notifications + - Edge: Settings → Cookies and site permissions → Notifications + +3. Test manually: + ```javascript + new Notification("Test", { body: "This is a test" }); + ``` + +**Issue**: Notifications disappear too quickly + +**Solution**: Increase duration in code: +```javascript +const notification = new Notification(title, { + body: message, + requireInteraction: true // Stays until user dismisses +}); +``` + +### Python CLI Issues + +**Issue**: `ImportError: No module named 'win10toast'` (Windows) + +**Solution**: +```powershell +pip install win10toast +``` + +**Issue**: Notifications fall back to console prints + +**Cause**: Library not installed or platform not detected + +**Check**: +```python +from scripts.notification_system import NotificationManager +notifier = NotificationManager() +print(f"System: {notifier.system}") +print(f"Enabled: {notifier.enabled}") +``` + +**Issue**: Monitor doesn't detect job changes + +**Solutions:** +1. Verify status file exists: + ```powershell + Test-Path data_out/autotrain/status.json + ``` + +2. Check JSON format: + ```powershell + Get-Content data_out/autotrain/status.json | ConvertFrom-Json + ``` + +3. Run with verbose output: + ```python + # Add debug prints in monitor_training() loop + print(f"Current status: {status}") + ``` + +--- + +## Best Practices + +### Development +- Test notifications before long training runs +- Use `--test` flag to verify system works +- Keep milestone intervals reasonable (every 5-10 epochs) +- Don't spam notifications (can be intrusive) + +### Production +- Enable notifications for critical jobs only +- Set higher loss thresholds (e.g., 0.3 instead of 0.5) +- Use GPU alerts for resource monitoring +- Pair with backup notifications for safety + +### User Experience +- Respect browser permission denials +- Provide clear toggle UI (🔔 vs 🔕) +- Include actionable information in messages +- Use appropriate icons for event types + +### Integration +- Call `initNotifications()` on dashboard load +- Check `notificationsEnabled` before sending +- Gracefully handle permission denials +- Combine with WebSocket for real-time updates + +--- + +## API Reference + +### Browser JavaScript Functions + +```javascript +// Initialization +initNotifications() // Request permission if needed + +// Toggle control +toggleNotifications() // Enable/disable notifications + +// Send notification +sendDesktopNotification(title, message, icon) +// title: string - Notification title +// message: string - Body text +// icon: string - Emoji or SVG data URI + +// Training events +notifyJobStarted(jobName) +notifyJobCompleted(jobName, durationMin, finalLoss) +notifyJobFailed(jobName, error) +notifyMilestone(jobName, milestone, value) + +// System alerts +notifyGPUAlert(gpuUtil, memoryUsed) +``` + +### Python CLI + +```python +from scripts.notification_system import NotificationManager, TrainingNotifier + +# Basic usage +notifier = NotificationManager() +notifier.send_notification("Title", "Message", icon="info", duration=10) + +# Training-specific +notifier.notify_job_started("job_name") +notifier.notify_job_completed("job_name", duration_min=45, final_loss=0.234) +notifier.notify_job_failed("job_name", error="Error message") +notifier.notify_milestone("job_name", "Milestone description", value=0.456) + +# System alerts +notifier.notify_gpu_alert(gpu_util=97, memory_used=10240) +notifier.notify_backup_complete("backup_name", size_mb=342.56) +notifier.notify_evaluation_complete("model_name", perplexity=12.34) + +# Monitoring (blocking) +monitor = TrainingNotifier() +monitor.monitor_training("job_name", Path("status.json")) +``` + +--- + +## Future Enhancements + +### Planned Features +- [ ] Persistent toggle state (localStorage) +- [ ] Notification history panel +- [ ] Customizable notification sounds +- [ ] Email/Slack integration +- [ ] Notification filtering (by job type, severity) +- [ ] Grouped notifications (batch updates) +- [ ] Rich notifications with action buttons +- [ ] Mobile push notifications (via PWA) + +### Integration Roadmap +- [ ] WebSocket real-time event streaming +- [ ] Database logging of notification events +- [ ] Admin panel for notification settings +- [ ] Per-user notification preferences +- [ ] Notification analytics (sent/clicked/dismissed) + +--- + +## Related Documentation + +- **QAI Hub Guide**: `QAI_HUB_ENHANCEMENTS_V2.md` - Overview of all hub features +- **Backup System**: `scripts/backup_manager.py` - Triggers backup notifications +- **WebSocket Server**: `dashboard/websocket_server.py` - Real-time job updates +- **Analytics Dashboard**: `dashboard/analytics.html` - Notification integration + +--- + +## Support + +**Issues**: Report notification bugs in project issues +**Feature Requests**: Submit enhancement proposals +**Questions**: Check FAQ section or consult project README + +--- + +## Changelog + +### Version 1.0 (November 2024) +- ✅ Initial browser notification system +- ✅ Python CLI tool for cross-platform notifications +- ✅ Training event notifications (start/complete/fail) +- ✅ Milestone tracking (loss thresholds, epoch intervals) +- ✅ System alerts (GPU, backup, evaluation) +- ✅ Hub integration with toggle button +- ✅ Unified dashboard integration +- ✅ Analytics dashboard integration +- ✅ Documentation and examples + +--- + +**Last Updated**: November 2024 +**Status**: Production Ready ✅ +**Maintainer**: QAI Development Team diff --git a/docs/guides/ORCHESTRATORS_VALIDATED.md b/docs/guides/ORCHESTRATORS_VALIDATED.md index 62f8b5ad6..cc8af6609 100644 --- a/docs/guides/ORCHESTRATORS_VALIDATED.md +++ b/docs/guides/ORCHESTRATORS_VALIDATED.md @@ -1,196 +1,196 @@ -# QAI Orchestrators - Validation Complete ✅ - -**Date:** 2025-01-20 -**Status:** ALL SYSTEMS OPERATIONAL - -## Executive Summary - -All three orchestrators in the QAI workspace have been tested, validated, and confirmed operational: -- ✅ **AutoTrain** (LoRA fine-tuning orchestrator) -- ✅ **Quantum AutoRun** (Quantum ML training orchestrator) -- ✅ **Autonomous Training** (Self-optimizing continuous training system) - -## Critical Fixes Applied - -### 1. Virtual Environment Routing (CRITICAL BUG FIX) -**Problem:** Orchestrators were using root venv instead of project-specific venvs, causing "dependencies not installed" errors. - -**Solution:** -- Added `_venv_python_ml()` to `scripts/training/autotrain.py` → routes to `AI/microsoft_phi-silica-3.6_v1/venv/Scripts/python.exe` -- Added `_venv_python_quantum()` to `scripts/evaluation/quantum_autorun.py` → routes to `ai-projects/quantum-ml/venv/Scripts/python.exe` - -**Impact:** Training jobs now execute with correct dependency isolation, preventing cross-contamination between quantum and ML environments. - -### 2. HTML Accessibility Compliance -**Problem:** 11 HTML accessibility violations in `mount/static/index.html`. - -**Solution:** -- Added `for="elementId"` attributes to all form labels -- Added `aria-label` attributes to select elements without visible labels - -**Impact:** Web interface now WCAG-compliant for screen reader users. - -### 3. Error Message Enhancement -**Problem:** Vague error messages didn't indicate which venv to activate. - -**Solution:** -- Enhanced `train_lora.py` error message to show exact venv path and pip install command - -**Impact:** Faster debugging when dependencies are missing. - -## Validation Results - -### AutoTrain (LoRA Fine-Tuning) -**Config:** `autotrain.yaml` -**Jobs Configured:** 6 -**Dry-Run:** ✅ PASSED -**Real Execution:** ✅ CONFIRMED (phi35_mixed_chat running) -**Venv Path:** `C:\Users\Bryan\OneDrive\AI\AI\microsoft_phi-silica-3.6_v1\venv\Scripts\python.exe` - -**Jobs:** -1. `phi35_mixed_chat` - Phi-3.5-mini-instruct on mixed_chat (64 samples, streaming) -2. `phi35_dolly` - Full Dolly-15k dataset -3. `mistral_mixed_chat` - Mistral-7B variant -4. `qwen_mixed_chat` - Qwen2.5 variant -5. `phi35_local_runner` - Uses streamlined local runner -6. `phi35_openassistant` - OpenAssistant dataset - -### Quantum AutoRun (Quantum ML) -**Config:** `quantum_autorun.yaml` -**Jobs Configured:** 3 -**Dry-Run:** ✅ PASSED -**Real Execution:** ✅ STARTED (heart_quick interrupted by user) -**Venv Path:** `C:\Users\Bryan\OneDrive\AI\quantum-ai\venv\Scripts\python.exe` - -**Jobs:** -1. `heart_quick` - Heart disease classification (1 epoch, 128 shots) -2. `ionosphere_full` - Full ionosphere training -3. `azure_ionq_qpu_test` - IonQ QPU hardware submission (PAID, requires confirmation) - -### Autonomous Training (Continuous Optimization) -**Config:** `config/autonomous_training.yaml` -**Status:** ✅ INITIALIZED -**Cycles Completed:** 1 -**Current Phase:** `optimization` -**Dataset Inventory:** Active - -**Features:** -- Continuous training loop with automatic dataset rotation -- Performance history tracking -- Best model checkpointing -- Status JSON at `data_out/autonomous_training_status.json` - -## Command Reference - -```powershell -# AutoTrain -python .\scripts\autotrain.py --dry-run # Validate config -python .\scripts\autotrain.py --list # List jobs -python .\scripts\autotrain.py --job phi35_mixed_chat # Run specific job - -# Quantum AutoRun -python .\scripts\quantum_autorun.py --dry-run # Validate config -python .\scripts\quantum_autorun.py --list # List jobs -python .\scripts\quantum_autorun.py --job heart_quick - -# Autonomous Training -python .\scripts\autonomous_training_orchestrator.py --status # Check status -python .\scripts\autonomous_training_orchestrator.py --once # Single cycle -python .\scripts\autonomous_training_orchestrator.py # Continuous mode -``` - -## Architecture Validation - -### Venv Isolation ✅ -- Root venv: Azure Functions runtime only -- ML venv: transformers, peft, datasets, torch (8GB+ installed) -- Quantum venv: qiskit, pennylane, pytorch (2GB+ installed) - -### YAML Configuration ✅ -- All jobs defined declaratively in YAML -- No hardcoded paths or parameters in Python -- CLI overrides work correctly - -### Status Tracking ✅ -- Machine-readable JSON at `data_out/{autotrain,quantum_autorun}/status.json` -- Per-job timestamped logs in `data_out/autotrain///stdout.log` -- Real-time progress monitoring via status files - -### Error Handling ✅ -- RuntimeError with venv path on missing dependencies -- Validation errors before job execution -- Azure cost confirmation for QPU jobs - -## Active Training Processes - -**Currently Running:** -- `phi35_mixed_chat` - Phi-3.5-mini LoRA training on mixed_chat dataset -- Process using ML venv: `C:\...\AI\microsoft_phi-silica-3.6_v1\venv\Scripts\python.exe` -- Log location: `data_out\autotrain\phi35_mixed_chat\\stdout.log` - -**Log Excerpt (successful model loading):** -``` -Loading checkpoint shards: 100%|##########| 2/2 [00:05<00:00, 2.99s/it] -``` - -## Cost Optimization Notes - -### Free Tier (Unlimited) -- Local quantum simulators: `qiskit_aer`, `lightning.qubit` -- AutoTrain with CPU/local GPU -- Autonomous training on local hardware - -### Paid Tier (Use with Caution) -- Azure Quantum QPU: IonQ ~$0.00003/gate-shot, Quantinuum ~$0.00015/circuit -- **Safety:** Jobs require `azure_confirm_cost: true` flag in YAML - -## Next Steps (Optional) - -1. **Monitor phi35 training:** Check logs in `data_out\autotrain\phi35_mixed_chat\` -2. **Run full quantum suite:** `python .\scripts\quantum_autorun.py` (all jobs, local simulators) -3. **Enable autonomous loop:** `python .\scripts\autonomous_training_orchestrator.py` (continuous optimization) -4. **Azure deployment:** Use Bicep templates in `ai-projects/quantum-ml/azure/` for cloud infrastructure - -## Files Modified in This Session - -### Core Orchestrators -- `scripts/training/autotrain.py` - Added `_venv_python_ml()` function -- `scripts/evaluation/quantum_autorun.py` - Added `_venv_python_quantum()` function -- `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` - Enhanced error message - -### Web Interface -- `mount/static/index.html` - 11 accessibility fixes (for/aria-label attributes) - -### Documentation -- `.github/copilot-instructions.md` - Added automatic venv routing documentation -- `ORCHESTRATORS_VALIDATED.md` - This file (comprehensive validation report) - -## Troubleshooting Reference - -### "Training dependencies not installed" -**Cause:** Wrong venv activated or script not using orchestrator -**Fix:** Run via orchestrator (`autotrain.py`, `quantum_autorun.py`) - they auto-route to correct venv - -### "Dataset not found" -**Cause:** Running from wrong directory -**Fix:** Always run orchestrators from repo root: `c:\Users\Bryan\OneDrive\AI` - -### Quantum backend errors -**Cause:** Azure credentials not configured or workspace doesn't exist -**Fix:** Run `az login` and verify `quantum_config.yaml` matches Portal settings - -### Chat provider not working -**Cause:** Missing API keys or old openai SDK -**Fix:** Set 4 Azure OpenAI env vars + `pip install --upgrade openai` - -## Success Metrics - -- ✅ **82 errors → 0 errors** (all HTML/markdown issues resolved) -- ✅ **0% success rate → 100% dry-run success** (all orchestrators validated) -- ✅ **0 active training jobs → 1 confirmed running** (phi35_mixed_chat executing) -- ✅ **Venv routing:** Manual activation → Automatic project-specific routing -- ✅ **Accessibility:** 11 violations → Full WCAG compliance - ---- - -**Conclusion:** All orchestrators are production-ready. The QAI workspace can now train quantum models, fine-tune LLMs, and run autonomous optimization cycles with proper dependency isolation and accessibility compliance. +# QAI Orchestrators - Validation Complete ✅ + +**Date:** 2025-01-20 +**Status:** ALL SYSTEMS OPERATIONAL + +## Executive Summary + +All three orchestrators in the QAI workspace have been tested, validated, and confirmed operational: +- ✅ **AutoTrain** (LoRA fine-tuning orchestrator) +- ✅ **Quantum AutoRun** (Quantum ML training orchestrator) +- ✅ **Autonomous Training** (Self-optimizing continuous training system) + +## Critical Fixes Applied + +### 1. Virtual Environment Routing (CRITICAL BUG FIX) +**Problem:** Orchestrators were using root venv instead of project-specific venvs, causing "dependencies not installed" errors. + +**Solution:** +- Added `_venv_python_ml()` to `scripts/training/autotrain.py` → routes to `AI/microsoft_phi-silica-3.6_v1/venv/Scripts/python.exe` +- Added `_venv_python_quantum()` to `scripts/evaluation/quantum_autorun.py` → routes to `ai-projects/quantum-ml/venv/Scripts/python.exe` + +**Impact:** Training jobs now execute with correct dependency isolation, preventing cross-contamination between quantum and ML environments. + +### 2. HTML Accessibility Compliance +**Problem:** 11 HTML accessibility violations in `mount/static/index.html`. + +**Solution:** +- Added `for="elementId"` attributes to all form labels +- Added `aria-label` attributes to select elements without visible labels + +**Impact:** Web interface now WCAG-compliant for screen reader users. + +### 3. Error Message Enhancement +**Problem:** Vague error messages didn't indicate which venv to activate. + +**Solution:** +- Enhanced `train_lora.py` error message to show exact venv path and pip install command + +**Impact:** Faster debugging when dependencies are missing. + +## Validation Results + +### AutoTrain (LoRA Fine-Tuning) +**Config:** `autotrain.yaml` +**Jobs Configured:** 6 +**Dry-Run:** ✅ PASSED +**Real Execution:** ✅ CONFIRMED (phi35_mixed_chat running) +**Venv Path:** `C:\Users\Bryan\OneDrive\AI\AI\microsoft_phi-silica-3.6_v1\venv\Scripts\python.exe` + +**Jobs:** +1. `phi35_mixed_chat` - Phi-3.5-mini-instruct on mixed_chat (64 samples, streaming) +2. `phi35_dolly` - Full Dolly-15k dataset +3. `mistral_mixed_chat` - Mistral-7B variant +4. `qwen_mixed_chat` - Qwen2.5 variant +5. `phi35_local_runner` - Uses streamlined local runner +6. `phi35_openassistant` - OpenAssistant dataset + +### Quantum AutoRun (Quantum ML) +**Config:** `quantum_autorun.yaml` +**Jobs Configured:** 3 +**Dry-Run:** ✅ PASSED +**Real Execution:** ✅ STARTED (heart_quick interrupted by user) +**Venv Path:** `C:\Users\Bryan\OneDrive\AI\quantum-ai\venv\Scripts\python.exe` + +**Jobs:** +1. `heart_quick` - Heart disease classification (1 epoch, 128 shots) +2. `ionosphere_full` - Full ionosphere training +3. `azure_ionq_qpu_test` - IonQ QPU hardware submission (PAID, requires confirmation) + +### Autonomous Training (Continuous Optimization) +**Config:** `config/autonomous_training.yaml` +**Status:** ✅ INITIALIZED +**Cycles Completed:** 1 +**Current Phase:** `optimization` +**Dataset Inventory:** Active + +**Features:** +- Continuous training loop with automatic dataset rotation +- Performance history tracking +- Best model checkpointing +- Status JSON at `data_out/autonomous_training_status.json` + +## Command Reference + +```powershell +# AutoTrain +python .\scripts\autotrain.py --dry-run # Validate config +python .\scripts\autotrain.py --list # List jobs +python .\scripts\autotrain.py --job phi35_mixed_chat # Run specific job + +# Quantum AutoRun +python .\scripts\quantum_autorun.py --dry-run # Validate config +python .\scripts\quantum_autorun.py --list # List jobs +python .\scripts\quantum_autorun.py --job heart_quick + +# Autonomous Training +python .\scripts\autonomous_training_orchestrator.py --status # Check status +python .\scripts\autonomous_training_orchestrator.py --once # Single cycle +python .\scripts\autonomous_training_orchestrator.py # Continuous mode +``` + +## Architecture Validation + +### Venv Isolation ✅ +- Root venv: Azure Functions runtime only +- ML venv: transformers, peft, datasets, torch (8GB+ installed) +- Quantum venv: qiskit, pennylane, pytorch (2GB+ installed) + +### YAML Configuration ✅ +- All jobs defined declaratively in YAML +- No hardcoded paths or parameters in Python +- CLI overrides work correctly + +### Status Tracking ✅ +- Machine-readable JSON at `data_out/{autotrain,quantum_autorun}/status.json` +- Per-job timestamped logs in `data_out/autotrain///stdout.log` +- Real-time progress monitoring via status files + +### Error Handling ✅ +- RuntimeError with venv path on missing dependencies +- Validation errors before job execution +- Azure cost confirmation for QPU jobs + +## Active Training Processes + +**Currently Running:** +- `phi35_mixed_chat` - Phi-3.5-mini LoRA training on mixed_chat dataset +- Process using ML venv: `C:\...\AI\microsoft_phi-silica-3.6_v1\venv\Scripts\python.exe` +- Log location: `data_out\autotrain\phi35_mixed_chat\\stdout.log` + +**Log Excerpt (successful model loading):** +``` +Loading checkpoint shards: 100%|##########| 2/2 [00:05<00:00, 2.99s/it] +``` + +## Cost Optimization Notes + +### Free Tier (Unlimited) +- Local quantum simulators: `qiskit_aer`, `lightning.qubit` +- AutoTrain with CPU/local GPU +- Autonomous training on local hardware + +### Paid Tier (Use with Caution) +- Azure Quantum QPU: IonQ ~$0.00003/gate-shot, Quantinuum ~$0.00015/circuit +- **Safety:** Jobs require `azure_confirm_cost: true` flag in YAML + +## Next Steps (Optional) + +1. **Monitor phi35 training:** Check logs in `data_out\autotrain\phi35_mixed_chat\` +2. **Run full quantum suite:** `python .\scripts\quantum_autorun.py` (all jobs, local simulators) +3. **Enable autonomous loop:** `python .\scripts\autonomous_training_orchestrator.py` (continuous optimization) +4. **Azure deployment:** Use Bicep templates in `ai-projects/quantum-ml/azure/` for cloud infrastructure + +## Files Modified in This Session + +### Core Orchestrators +- `scripts/training/autotrain.py` - Added `_venv_python_ml()` function +- `scripts/evaluation/quantum_autorun.py` - Added `_venv_python_quantum()` function +- `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` - Enhanced error message + +### Web Interface +- `mount/static/index.html` - 11 accessibility fixes (for/aria-label attributes) + +### Documentation +- `.github/copilot-instructions.md` - Added automatic venv routing documentation +- `ORCHESTRATORS_VALIDATED.md` - This file (comprehensive validation report) + +## Troubleshooting Reference + +### "Training dependencies not installed" +**Cause:** Wrong venv activated or script not using orchestrator +**Fix:** Run via orchestrator (`autotrain.py`, `quantum_autorun.py`) - they auto-route to correct venv + +### "Dataset not found" +**Cause:** Running from wrong directory +**Fix:** Always run orchestrators from repo root: `c:\Users\Bryan\OneDrive\AI` + +### Quantum backend errors +**Cause:** Azure credentials not configured or workspace doesn't exist +**Fix:** Run `az login` and verify `quantum_config.yaml` matches Portal settings + +### Chat provider not working +**Cause:** Missing API keys or old openai SDK +**Fix:** Set 4 Azure OpenAI env vars + `pip install --upgrade openai` + +## Success Metrics + +- ✅ **82 errors → 0 errors** (all HTML/markdown issues resolved) +- ✅ **0% success rate → 100% dry-run success** (all orchestrators validated) +- ✅ **0 active training jobs → 1 confirmed running** (phi35_mixed_chat executing) +- ✅ **Venv routing:** Manual activation → Automatic project-specific routing +- ✅ **Accessibility:** 11 violations → Full WCAG compliance + +--- + +**Conclusion:** All orchestrators are production-ready. The QAI workspace can now train quantum models, fine-tune LLMs, and run autonomous optimization cycles with proper dependency isolation and accessibility compliance. diff --git a/docs/guides/QAI_FIXED.md b/docs/guides/QAI_FIXED.md index a0d2b2c5e..85647a7d7 100644 --- a/docs/guides/QAI_FIXED.md +++ b/docs/guides/QAI_FIXED.md @@ -1,262 +1,262 @@ -# QAI Workspace - Fixed and Verified ✅ - -**Date:** November 15, 2025 -**Status:** All systems operational - -## Summary of Fixes Applied - -### 1. Virtual Environment Setup -- ✅ **Root venv**: Working (Azure Functions runtime) -- ✅ **quantum-ai venv**: Recreated with all dependencies -- ✅ **talk-to-ai venv**: Recreated with all dependencies - -### 2. Quantum Integration -- ✅ **quantum_provider.py**: Created new quantum-enhanced chat provider -- ✅ **QuantumClassifier**: Working (4 qubits, 2 layers, PennyLane backend) -- ✅ **Quantum endpoints**: Fully implemented in `function_app.py` - - `/api/quantum/classify` - Quantum classification with sentiment analysis - - `/api/quantum/circuit` - Circuit visualization - - `/api/quantum/info` - Capabilities and status - -### 3. Chat Web Interface -- ✅ **Quantum Mode Button**: Toggle quantum enhancements on/off -- ✅ **Quantum Analysis Panel**: Real-time circuit visualization and metrics -- ✅ **Provider Integration**: Auto-detects quantum provider when enabled -- ✅ **Streaming Support**: Full SSE support with quantum analysis - -### 4. Dependencies Installed -**Root venv:** -- azure-functions 1.24.0 -- openai 2.8.0 -- torch 2.9.1+cpu -- pennylane (latest) -- pennylane-lightning (latest) -- numpy (latest) - -**Quantum-AI venv:** -- All requirements from `ai-projects/quantum-ml/requirements.txt` - -**Talk-to-AI venv:** -- All requirements from `ai-projects/chat-cli/requirements.txt` - -## Verification Results - -All components tested and verified: - -``` -✓ PASS: Chat Providers - - Local echo provider working - - Provider completion functional - -✓ PASS: Quantum Classifier - - Initialization successful (4 qubits, 2 layers) - - Forward pass functional with correct output shapes - -✓ PASS: Quantum Provider - - Created successfully with quantum-enhanced-local model - - Quantum classifier available in provider - - Completion works with quantum insights - - Responses contain quantum enhancements (🔬 indicators) - -✓ PASS: Function App Imports - - All chat provider imports OK - - Quantum classifier import OK - - Token utilities import OK -``` - -## New Files Created - -1. **`ai-projects/chat-cli/src/quantum_provider.py`**: Quantum-enhanced chat provider - - Uses variational quantum circuits for sentiment analysis - - Integrates with QuantumClassifier from quantum-ai - - Provides quantum-flavored responses with analysis insights - -2. **`fix-qai.ps1`**: Automated workspace fix script - - Checks/creates virtual environments - - Installs dependencies - - Verifies integrations - - Tests quantum endpoints - -3. **`test-qai.py`**: Comprehensive verification test - - Tests all chat providers - - Validates quantum classifier - - Verifies quantum provider integration - - Checks function app imports - -## Files Modified - -1. **`function_app.py`**: - - Added quantum-ai path to sys.path - - Implemented 3 quantum endpoints (classify, circuit, info) - - Full quantum classification with PennyLane integration - -2. **`ai-projects/chat-cli/src/chat_providers.py`**: - - Added quantum provider detection - - Integrated quantum_provider module - - Updated provider priority (quantum → Azure → OpenAI → local) - -3. **`chat-web/chat.js`**: - - Added quantum mode toggle button - - Implemented quantum analysis panel - - Added quantum circuit visualization - - Integrated quantum API endpoints - -4. **`chat-web/index.html`**: - - Added quantum mode UI components - - Added quantum panel styling - - Added quantum indicator animations - -## Quick Start Commands - -### Test Quantum Provider -```powershell -cd C:\Users\Bryan\OneDrive\AI -.\venv\Scripts\python.exe test-qai.py -``` - -### Run Chat Web with Quantum -```powershell -.\start-chat-web.ps1 -# Then click "🔬 Quantum OFF" button to enable quantum mode -``` - -### Test Quantum CLI -```powershell -cd talk-to-ai -.\venv\Scripts\python.exe src\chat_cli.py --provider quantum --once "What is quantum computing?" -``` - -### Run Quantum Classifier -```powershell -cd quantum-ai -.\venv\Scripts\python.exe src\quantum_classifier.py -``` - -## Architecture Overview - -``` -┌─────────────────────────────────────────────────┐ -│ Azure Functions App │ -│ (function_app.py - Root venv with PennyLane) │ -├─────────────────────────────────────────────────┤ -│ │ -│ ┌────────────────┐ ┌─────────────────┐ │ -│ │ Chat APIs │ │ Quantum APIs │ │ -│ │ │ │ │ │ -│ │ /api/chat │ │ /quantum/ │ │ -│ │ /api/chat/ │ │ classify │ │ -│ │ stream │ │ circuit │ │ -│ │ /api/ai/status │ │ info │ │ -│ └────────┬───────┘ └────────┬────────┘ │ -│ │ │ │ -│ ▼ ▼ │ -│ ┌────────────────────────────────────────┐ │ -│ │ ai-projects/chat-cli/src/ │ │ -│ │ • chat_providers.py (5 providers) │ │ -│ │ • quantum_provider.py (NEW) │ │ -│ │ • token_utils.py │ │ -│ └──────────────┬─────────────────────────┘ │ -│ │ │ -│ ▼ │ -│ ┌────────────────────────────────────────┐ │ -│ │ ai-projects/quantum-ml/src/ │ │ -│ │ • quantum_classifier.py │ │ -│ │ • QuantumClassifier (PennyLane) │ │ -│ │ • Variational circuits (4q, 2l) │ │ -│ └────────────────────────────────────────┘ │ -│ │ -└─────────────────────────────────────────────────┘ - │ - ▼ - ┌──────────────────────┐ - │ chat-web/ │ - │ • index.html │ - │ • chat.js │ - │ - Quantum Mode UI │ - │ - Circuit Viz │ - │ - API Integration │ - └──────────────────────┘ -``` - -## Provider Hierarchy - -When `--provider auto` is used (default): - -1. **Quantum** (if explicitly selected via `--provider quantum`) - - Uses quantum circuits for sentiment analysis - - Provides quantum-enhanced responses - - Requires PennyLane + quantum_classifier - -2. **Azure OpenAI** (if `AZURE_OPENAI_*` env vars set) - - GPT-4o-mini or custom deployment - - Full streaming support - - Production-ready - -3. **OpenAI** (if `OPENAI_API_KEY` set) - - gpt-4o-mini or custom model - - Full streaming support - - Production-ready - -4. **Local Echo** (fallback, always available) - - Zero dependencies - - Offline-capable - - Rule-based responses - -## Cost Optimization - -All components work **100% FREE** by default: - -- ✅ **Quantum computing**: PennyLane simulators (unlimited, free) -- ✅ **Chat**: Local provider (no API keys needed) -- ✅ **Training**: Use `--max-train-samples 64` for CPU -- ✅ **Storage**: Local files + Azurite emulator (free) - -Paid options only if you enable them: -- Azure OpenAI: ~$0.0001/1K tokens (gpt-4o-mini) -- OpenAI: ~$0.00015/1K tokens (gpt-4o-mini) -- Azure Quantum hardware: Only for real quantum processors - -## Next Steps - -1. **Commit Changes**: - ```powershell - git add . - git commit -m "Add quantum-enhanced chat provider and fix workspace" - git push - ``` - -2. **Try Quantum Mode**: - - Start chat web: `.\start-chat-web.ps1` - - Enable quantum mode in UI - - Ask: "What is quantum computing?" - - See quantum analysis panel - -3. **Deploy to Azure** (optional): - - Follow `DEPLOY_CHAT_TO_AZURE.md` - - Quantum endpoints included automatically - -4. **Explore Training**: - - Train quantum models: `quantum-ai\train_custom_dataset.py` - - Fine-tune Phi-3.6: `AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py` - -## Troubleshooting - -If issues occur, run: -```powershell -.\fix-qai.ps1 -``` - -This will: -- Recreate any missing venvs -- Reinstall dependencies -- Verify all integrations -- Test quantum endpoints - -For specific help: -- Quantum issues → See `ai-projects/quantum-ml/README.md` -- Chat issues → See `ai-projects/chat-cli/README.md` -- Deployment → See `DEPLOY_CHAT_TO_AZURE.md` - ---- - -**All systems operational. QAI workspace is ready for quantum-enhanced AI development! 🚀🔬** +# QAI Workspace - Fixed and Verified ✅ + +**Date:** November 15, 2025 +**Status:** All systems operational + +## Summary of Fixes Applied + +### 1. Virtual Environment Setup +- ✅ **Root venv**: Working (Azure Functions runtime) +- ✅ **quantum-ai venv**: Recreated with all dependencies +- ✅ **talk-to-ai venv**: Recreated with all dependencies + +### 2. Quantum Integration +- ✅ **quantum_provider.py**: Created new quantum-enhanced chat provider +- ✅ **QuantumClassifier**: Working (4 qubits, 2 layers, PennyLane backend) +- ✅ **Quantum endpoints**: Fully implemented in `function_app.py` + - `/api/quantum/classify` - Quantum classification with sentiment analysis + - `/api/quantum/circuit` - Circuit visualization + - `/api/quantum/info` - Capabilities and status + +### 3. Chat Web Interface +- ✅ **Quantum Mode Button**: Toggle quantum enhancements on/off +- ✅ **Quantum Analysis Panel**: Real-time circuit visualization and metrics +- ✅ **Provider Integration**: Auto-detects quantum provider when enabled +- ✅ **Streaming Support**: Full SSE support with quantum analysis + +### 4. Dependencies Installed +**Root venv:** +- azure-functions 1.24.0 +- openai 2.8.0 +- torch 2.9.1+cpu +- pennylane (latest) +- pennylane-lightning (latest) +- numpy (latest) + +**Quantum-AI venv:** +- All requirements from `ai-projects/quantum-ml/requirements.txt` + +**Talk-to-AI venv:** +- All requirements from `ai-projects/chat-cli/requirements.txt` + +## Verification Results + +All components tested and verified: + +``` +✓ PASS: Chat Providers + - Local echo provider working + - Provider completion functional + +✓ PASS: Quantum Classifier + - Initialization successful (4 qubits, 2 layers) + - Forward pass functional with correct output shapes + +✓ PASS: Quantum Provider + - Created successfully with quantum-enhanced-local model + - Quantum classifier available in provider + - Completion works with quantum insights + - Responses contain quantum enhancements (🔬 indicators) + +✓ PASS: Function App Imports + - All chat provider imports OK + - Quantum classifier import OK + - Token utilities import OK +``` + +## New Files Created + +1. **`ai-projects/chat-cli/src/quantum_provider.py`**: Quantum-enhanced chat provider + - Uses variational quantum circuits for sentiment analysis + - Integrates with QuantumClassifier from quantum-ai + - Provides quantum-flavored responses with analysis insights + +2. **`fix-qai.ps1`**: Automated workspace fix script + - Checks/creates virtual environments + - Installs dependencies + - Verifies integrations + - Tests quantum endpoints + +3. **`test-qai.py`**: Comprehensive verification test + - Tests all chat providers + - Validates quantum classifier + - Verifies quantum provider integration + - Checks function app imports + +## Files Modified + +1. **`function_app.py`**: + - Added quantum-ai path to sys.path + - Implemented 3 quantum endpoints (classify, circuit, info) + - Full quantum classification with PennyLane integration + +2. **`ai-projects/chat-cli/src/chat_providers.py`**: + - Added quantum provider detection + - Integrated quantum_provider module + - Updated provider priority (quantum → Azure → OpenAI → local) + +3. **`chat-web/chat.js`**: + - Added quantum mode toggle button + - Implemented quantum analysis panel + - Added quantum circuit visualization + - Integrated quantum API endpoints + +4. **`chat-web/index.html`**: + - Added quantum mode UI components + - Added quantum panel styling + - Added quantum indicator animations + +## Quick Start Commands + +### Test Quantum Provider +```powershell +cd C:\Users\Bryan\OneDrive\AI +.\venv\Scripts\python.exe test-qai.py +``` + +### Run Chat Web with Quantum +```powershell +.\start-chat-web.ps1 +# Then click "🔬 Quantum OFF" button to enable quantum mode +``` + +### Test Quantum CLI +```powershell +cd talk-to-ai +.\venv\Scripts\python.exe src\chat_cli.py --provider quantum --once "What is quantum computing?" +``` + +### Run Quantum Classifier +```powershell +cd quantum-ai +.\venv\Scripts\python.exe src\quantum_classifier.py +``` + +## Architecture Overview + +``` +┌─────────────────────────────────────────────────┐ +│ Azure Functions App │ +│ (function_app.py - Root venv with PennyLane) │ +├─────────────────────────────────────────────────┤ +│ │ +│ ┌────────────────┐ ┌─────────────────┐ │ +│ │ Chat APIs │ │ Quantum APIs │ │ +│ │ │ │ │ │ +│ │ /api/chat │ │ /quantum/ │ │ +│ │ /api/chat/ │ │ classify │ │ +│ │ stream │ │ circuit │ │ +│ │ /api/ai/status │ │ info │ │ +│ └────────┬───────┘ └────────┬────────┘ │ +│ │ │ │ +│ ▼ ▼ │ +│ ┌────────────────────────────────────────┐ │ +│ │ ai-projects/chat-cli/src/ │ │ +│ │ • chat_providers.py (5 providers) │ │ +│ │ • quantum_provider.py (NEW) │ │ +│ │ • token_utils.py │ │ +│ └──────────────┬─────────────────────────┘ │ +│ │ │ +│ ▼ │ +│ ┌────────────────────────────────────────┐ │ +│ │ ai-projects/quantum-ml/src/ │ │ +│ │ • quantum_classifier.py │ │ +│ │ • QuantumClassifier (PennyLane) │ │ +│ │ • Variational circuits (4q, 2l) │ │ +│ └────────────────────────────────────────┘ │ +│ │ +└─────────────────────────────────────────────────┘ + │ + ▼ + ┌──────────────────────┐ + │ chat-web/ │ + │ • index.html │ + │ • chat.js │ + │ - Quantum Mode UI │ + │ - Circuit Viz │ + │ - API Integration │ + └──────────────────────┘ +``` + +## Provider Hierarchy + +When `--provider auto` is used (default): + +1. **Quantum** (if explicitly selected via `--provider quantum`) + - Uses quantum circuits for sentiment analysis + - Provides quantum-enhanced responses + - Requires PennyLane + quantum_classifier + +2. **Azure OpenAI** (if `AZURE_OPENAI_*` env vars set) + - GPT-4o-mini or custom deployment + - Full streaming support + - Production-ready + +3. **OpenAI** (if `OPENAI_API_KEY` set) + - gpt-4o-mini or custom model + - Full streaming support + - Production-ready + +4. **Local Echo** (fallback, always available) + - Zero dependencies + - Offline-capable + - Rule-based responses + +## Cost Optimization + +All components work **100% FREE** by default: + +- ✅ **Quantum computing**: PennyLane simulators (unlimited, free) +- ✅ **Chat**: Local provider (no API keys needed) +- ✅ **Training**: Use `--max-train-samples 64` for CPU +- ✅ **Storage**: Local files + Azurite emulator (free) + +Paid options only if you enable them: +- Azure OpenAI: ~$0.0001/1K tokens (gpt-4o-mini) +- OpenAI: ~$0.00015/1K tokens (gpt-4o-mini) +- Azure Quantum hardware: Only for real quantum processors + +## Next Steps + +1. **Commit Changes**: + ```powershell + git add . + git commit -m "Add quantum-enhanced chat provider and fix workspace" + git push + ``` + +2. **Try Quantum Mode**: + - Start chat web: `.\start-chat-web.ps1` + - Enable quantum mode in UI + - Ask: "What is quantum computing?" + - See quantum analysis panel + +3. **Deploy to Azure** (optional): + - Follow `DEPLOY_CHAT_TO_AZURE.md` + - Quantum endpoints included automatically + +4. **Explore Training**: + - Train quantum models: `quantum-ai\train_custom_dataset.py` + - Fine-tune Phi-3.6: `AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py` + +## Troubleshooting + +If issues occur, run: +```powershell +.\fix-qai.ps1 +``` + +This will: +- Recreate any missing venvs +- Reinstall dependencies +- Verify all integrations +- Test quantum endpoints + +For specific help: +- Quantum issues → See `ai-projects/quantum-ml/README.md` +- Chat issues → See `ai-projects/chat-cli/README.md` +- Deployment → See `DEPLOY_CHAT_TO_AZURE.md` + +--- + +**All systems operational. QAI workspace is ready for quantum-enhanced AI development! 🚀🔬** diff --git a/docs/guides/QAI_HUB_ENHANCEMENTS_V2.md b/docs/guides/QAI_HUB_ENHANCEMENTS_V2.md index d8204ca71..90854f573 100644 --- a/docs/guides/QAI_HUB_ENHANCEMENTS_V2.md +++ b/docs/guides/QAI_HUB_ENHANCEMENTS_V2.md @@ -1,404 +1,404 @@ -# QAI Hub - Enhancement Summary v2.1 - -## 🚀 Latest Improvements (November 25, 2025) - -### ✨ Hub Enhancements - -#### **1. Intelligent Stats Monitoring** -- **Color-coded GPU Usage**: Green (<50%), Yellow (50-80%), Red (>80%) -- **Quantum Job Detection**: Auto-updates quantum status badge when quantum jobs are running -- **Error Handling**: Shows "N/A" gracefully when services are unavailable -- **Title Updates**: Browser tab shows "(X) Training Active" when jobs are running - -#### **2. Training History Viewer** -- **Keyboard Shortcut**: `Ctrl+H` to open training history modal -- **Complete Job Analytics**: View all completed jobs with metrics -- **Performance Comparison**: See improvement percentages at a glance -- **Sortable Data**: Duration, Pre/Post Loss, Improvement metrics - -#### **3. Real-Time Job Monitoring** -- **10-Second Polling**: Checks for running jobs every 10 seconds -- **Browser Tab Indicator**: Shows active job count in title -- **Auto-Refresh**: Stats refresh every 30 seconds automatically -- **Status Updates**: All system badges update in real-time - -#### **4. Keyboard Shortcuts** -- `Ctrl+H` - View training history -- `Ctrl+R` - Refresh stats immediately -- `Click Card` - Navigate to system -- `Click Badge` - View system status - ---- - -### 🎯 Training Dashboard Enhancements - -#### **1. Enhanced Validation** -- **Pre-flight Checks**: 5 comprehensive validations before submission -- **Real-time Feedback**: Immediate error messages with guidance -- **Smart Defaults**: Intelligent parameter suggestions -- **Confirmation Dialogs**: For long-running jobs (>1 hour) - -#### **2. Model Comparison Feature** (New!) -- Compare up to 5 models side-by-side -- Performance scoring based on perplexity -- LoRA rank comparison -- Base model identification -- Visual ranking system - -#### **3. Progress Tracking** -- Live job status updates -- Estimated completion time -- VRAM usage monitoring -- CPU/GPU utilization graphs - -#### **4. Advanced Configuration** -- **Config Templates**: Save/load complete configurations -- **Preset System**: Quick Test, Standard, Full, Production -- **Parameter Validation**: Range checks for all 17 parameters -- **Export/Import**: JSON format for reproducibility - ---- - -### 📡 New API Capabilities - -#### **Health Check Endpoint** (`/api/health`) -```json -{ - "status": "healthy|degraded|error", - "timestamp": "2025-11-25T10:30:00Z", - "checks": { - "datasets": {"exists": true, "count": 5}, - "output": {"exists": true, "writable": true}, - "gpu": {"available": true, "count": 1}, - "venvs": { - "quantum_ai": true, - "talk_to_ai": true, - "lora_training": true - } - } -} -``` - -#### **Quick Stats Endpoint** (`/api/stats`) -```json -{ - "training_jobs": 12, - "datasets": 5, - "models": 8, - "gpu_usage": 45, - "active_processes": 2 -} -``` - -#### **Active Processes Endpoint** (`/api/processes`) -```json -{ - "processes": [ - { - "pid": 12345, - "name": "python.exe", - "command": "python autotrain.py...", - "memory_mb": 2048.5, - "cpu_percent": 15.2 - } - ], - "count": 2 -} -``` - ---- - -### 🎨 UI/UX Improvements - -#### **Visual Enhancements** -- **Glass-morphism Design**: Modern frosted glass effect -- **Smooth Animations**: 0.3s-0.4s cubic-bezier transitions -- **Hover Effects**: Cards lift and glow on hover -- **Status Indicators**: Pulsing dots for active systems -- **Color Psychology**: Green=healthy, Yellow=warning, Red=critical - -#### **Responsive Design** -- **Grid System**: Auto-fit, min 350px cards -- **Flexible Stats**: 1-4 columns based on screen width -- **Mobile Friendly**: Touch targets ≥44px -- **Accessible**: ARIA labels, keyboard navigation - -#### **Interactive Elements** -- **Toast Notifications**: 5-second auto-dismiss -- **Modal Dialogs**: Overlay with backdrop blur -- **Loading States**: Skeleton screens while fetching -- **Error Messages**: User-friendly, actionable feedback - ---- - -### 🔧 Technical Improvements - -#### **Performance Optimization** -- **Lazy Loading**: Load stats on demand -- **Debounced Updates**: Prevent excessive API calls -- **Cached Responses**: 30-second client-side cache -- **Batch Requests**: Parallel API calls with Promise.all - -#### **Error Handling** -- **Graceful Degradation**: Shows "N/A" instead of crashes -- **Retry Logic**: Auto-retry failed requests (3 attempts) -- **User Feedback**: Clear error messages with solutions -- **Logging**: Console errors for debugging - -#### **Code Quality** -- **Modular Functions**: Single responsibility principle -- **Async/Await**: Modern promise handling -- **Error Boundaries**: Try-catch blocks on all API calls -- **Type Safety**: Parameter validation before submission - ---- - -### 📊 Monitoring & Analytics - -#### **System Health Dashboard** -- **Datasets**: Count, validation status -- **Output Directory**: Existence, write permissions -- **GPU**: Availability, count, utilization -- **Virtual Environments**: All 3 projects checked - -#### **Job Analytics** -- **Total Jobs**: All-time count -- **Active Jobs**: Currently running -- **Completed**: Success rate -- **Failed**: Error analysis - -#### **Resource Tracking** -- **GPU Usage**: Real-time percentage -- **VRAM**: Allocated vs. total -- **CPU**: Per-process monitoring -- **Memory**: RAM usage by process - ---- - -### 🚀 Performance Metrics - -| Metric | Before | After | Improvement | -|--------|--------|-------|-------------| -| **Page Load** | 2.5s | 1.8s | 28% faster | -| **API Response** | 150ms | 120ms | 20% faster | -| **Stats Refresh** | 60s | 30s | 50% more frequent | -| **Error Recovery** | Manual | Auto | 100% automated | -| **UI Transitions** | 0.2s | 0.4s | Smoother | - ---- - -### 🎓 User Experience Enhancements - -#### **Discoverability** -- **Tooltips**: Hover hints on all elements -- **Help Text**: Contextual guidance -- **Examples**: Sample values for inputs -- **Shortcuts Panel**: `?` key for help - -#### **Workflow Optimization** -- **Quick Actions**: 6 one-click shortcuts -- **Presets**: 4 training configurations -- **Templates**: Save/load complete setups -- **History**: Review past jobs quickly - -#### **Feedback Loop** -- **Immediate Validation**: As you type -- **Progress Indicators**: Know what's happening -- **Completion Notifications**: Toast on success -- **Error Guidance**: Actionable error messages - ---- - -### 🔐 Security & Reliability - -#### **Input Validation** -- **Job Names**: Regex pattern enforcement -- **Numeric Ranges**: Min/max boundaries -- **Required Fields**: Pre-submission checks -- **Injection Prevention**: Sanitized inputs - -#### **Error Recovery** -- **Automatic Retry**: 3 attempts with backoff -- **Fallback Values**: Default to safe options -- **User Notification**: Clear error reporting -- **Graceful Degradation**: Partial functionality maintained - -#### **Rate Limiting** -- **60 requests/minute**: Per IP address -- **429 status**: Clear rate limit errors -- **Retry-After header**: Client respects limits - ---- - -### 📚 Documentation Additions - -#### **New Docs Created** -1. **QAI_HUB_GUIDE.md** (3,200+ lines) - - Complete reference for all 8 systems - - API documentation with examples - - Troubleshooting guide - - Best practices - - Security notes - -2. **QAI_HUB_QUICKREF.md** (600+ lines) - - One-liner commands - - Keyboard shortcuts - - Quick workflows - - Pro tips - - Cheat sheet format - -3. **QAI_HUB_ENHANCEMENTS_V2.md** (This file) - - Latest improvements - - Performance metrics - - Feature comparisons - - Technical details - ---- - -### 🎯 Coming Soon (Roadmap) - -#### **Phase 1: Real-Time Features** -- [ ] WebSocket integration for live updates -- [ ] Job progress bars with percentage -- [ ] Live log streaming -- [ ] Real-time GPU graphs - -#### **Phase 2: Advanced Analytics** -- [ ] Historical charts (Plotly.js) -- [ ] Model performance trending -- [ ] Resource utilization graphs -- [ ] Cost analysis (QPU usage) - -#### **Phase 3: Collaboration** -- [ ] User authentication (OAuth) -- [ ] Team workspaces -- [ ] Shared configurations -- [ ] Job scheduling calendar - -#### **Phase 4: Automation** -- [ ] Auto-training triggers -- [ ] Hyperparameter optimization -- [ ] A/B testing framework -- [ ] CI/CD integration - ---- - -### 🛠️ Developer Notes - -#### **Tech Stack** -- **Frontend**: Vanilla JS (no frameworks for speed) -- **Backend**: Python 3.11 + HTTP server -- **Styling**: CSS3 with custom properties -- **API**: RESTful with JSON responses - -#### **File Structure** -``` -dashboard/ -├── hub.html # Command center (700 lines) -├── unified.html # Training dashboard (2,369 lines) -├── serve.py # HTTP server (450 lines) -├── gpu_monitor.py # GPU utilities (200 lines) -└── static/ # Assets (future) -``` - -#### **Performance Considerations** -- **No frameworks**: 0KB bundle size -- **Inline CSS**: Faster first paint -- **Async loading**: Non-blocking API calls -- **Caching**: Browser cache headers - ---- - -### 📊 Usage Statistics (Hypothetical) - -| Feature | Usage | User Satisfaction | -|---------|-------|-------------------| -| **Quick Actions** | 85% | ⭐⭐⭐⭐⭐ | -| **Training Presets** | 78% | ⭐⭐⭐⭐⭐ | -| **Model Comparison** | 62% | ⭐⭐⭐⭐ | -| **History Viewer** | 55% | ⭐⭐⭐⭐ | -| **API Endpoints** | 45% | ⭐⭐⭐⭐⭐ | - ---- - -### ✅ Testing Coverage - -#### **Unit Tests** (40 total) -- ✅ All API endpoints -- ✅ Parameter validation -- ✅ Error handling -- ✅ Data formatting - -#### **Integration Tests** (30 total) -- ✅ End-to-end workflows -- ✅ Multi-system integration -- ✅ Database operations -- ✅ GPU monitoring - -#### **Manual Testing** -- ✅ All buttons clickable -- ✅ All forms submittable -- ✅ All links navigable -- ✅ Mobile responsive - ---- - -### 🏆 Achievement Milestones - -- ✅ **1,000+ lines** of new code -- ✅ **18 API endpoints** total -- ✅ **8 system cards** integrated -- ✅ **17 training parameters** configurable -- ✅ **4 documentation files** (6,000+ total lines) -- ✅ **Zero critical bugs** in production -- ✅ **100% feature completion** for v2.0 - ---- - -### 🎉 Success Metrics - -| Metric | Target | Achieved | Status | -|--------|--------|----------|--------| -| **Page Load** | <2s | 1.8s | ✅ Beat target | -| **API Response** | <200ms | 120ms | ✅ Beat target | -| **Error Rate** | <1% | 0.2% | ✅ Beat target | -| **User Satisfaction** | >80% | 92% | ✅ Beat target | -| **Documentation** | >2,000 lines | 6,000+ lines | ✅ 3x target | - ---- - -### 💡 Pro Tips for Users - -1. **Use Keyboard Shortcuts**: `Ctrl+H` for history, `Ctrl+R` for refresh -2. **Start with Presets**: Quick Test for validation, then scale up -3. **Save Configurations**: Export JSON for reproducibility -4. **Monitor GPU**: Keep usage <80% for stability -5. **Check Health**: `curl /api/health` before starting jobs -6. **Review History**: Learn from past jobs to optimize -7. **Read Docs**: QAI_HUB_QUICKREF.md has all one-liners -8. **Validate First**: Always dry-run orchestrators before execution - ---- - -### 🔗 Quick Links - -- **Hub**: http://localhost:8000/ -- **Training**: http://localhost:8000/unified.html -- **API Health**: http://localhost:8000/api/health -- **API Stats**: http://localhost:8000/api/stats -- **GitHub**: https://github.com/Bryan-Roe/QAI - ---- - -**Version**: 2.1 -**Last Updated**: November 25, 2025 -**Status**: Production Ready ✅ -**Maintainer**: QAI Development Team - ---- - -## 🎊 Thank You! - -The QAI Hub has evolved from a simple training dashboard into a comprehensive command center for all quantum-AI operations. Your feedback drives continuous improvement! - -**Happy Training! 🚀🤖⚛️** +# QAI Hub - Enhancement Summary v2.1 + +## 🚀 Latest Improvements (November 25, 2025) + +### ✨ Hub Enhancements + +#### **1. Intelligent Stats Monitoring** +- **Color-coded GPU Usage**: Green (<50%), Yellow (50-80%), Red (>80%) +- **Quantum Job Detection**: Auto-updates quantum status badge when quantum jobs are running +- **Error Handling**: Shows "N/A" gracefully when services are unavailable +- **Title Updates**: Browser tab shows "(X) Training Active" when jobs are running + +#### **2. Training History Viewer** +- **Keyboard Shortcut**: `Ctrl+H` to open training history modal +- **Complete Job Analytics**: View all completed jobs with metrics +- **Performance Comparison**: See improvement percentages at a glance +- **Sortable Data**: Duration, Pre/Post Loss, Improvement metrics + +#### **3. Real-Time Job Monitoring** +- **10-Second Polling**: Checks for running jobs every 10 seconds +- **Browser Tab Indicator**: Shows active job count in title +- **Auto-Refresh**: Stats refresh every 30 seconds automatically +- **Status Updates**: All system badges update in real-time + +#### **4. Keyboard Shortcuts** +- `Ctrl+H` - View training history +- `Ctrl+R` - Refresh stats immediately +- `Click Card` - Navigate to system +- `Click Badge` - View system status + +--- + +### 🎯 Training Dashboard Enhancements + +#### **1. Enhanced Validation** +- **Pre-flight Checks**: 5 comprehensive validations before submission +- **Real-time Feedback**: Immediate error messages with guidance +- **Smart Defaults**: Intelligent parameter suggestions +- **Confirmation Dialogs**: For long-running jobs (>1 hour) + +#### **2. Model Comparison Feature** (New!) +- Compare up to 5 models side-by-side +- Performance scoring based on perplexity +- LoRA rank comparison +- Base model identification +- Visual ranking system + +#### **3. Progress Tracking** +- Live job status updates +- Estimated completion time +- VRAM usage monitoring +- CPU/GPU utilization graphs + +#### **4. Advanced Configuration** +- **Config Templates**: Save/load complete configurations +- **Preset System**: Quick Test, Standard, Full, Production +- **Parameter Validation**: Range checks for all 17 parameters +- **Export/Import**: JSON format for reproducibility + +--- + +### 📡 New API Capabilities + +#### **Health Check Endpoint** (`/api/health`) +```json +{ + "status": "healthy|degraded|error", + "timestamp": "2025-11-25T10:30:00Z", + "checks": { + "datasets": {"exists": true, "count": 5}, + "output": {"exists": true, "writable": true}, + "gpu": {"available": true, "count": 1}, + "venvs": { + "quantum_ai": true, + "talk_to_ai": true, + "lora_training": true + } + } +} +``` + +#### **Quick Stats Endpoint** (`/api/stats`) +```json +{ + "training_jobs": 12, + "datasets": 5, + "models": 8, + "gpu_usage": 45, + "active_processes": 2 +} +``` + +#### **Active Processes Endpoint** (`/api/processes`) +```json +{ + "processes": [ + { + "pid": 12345, + "name": "python.exe", + "command": "python autotrain.py...", + "memory_mb": 2048.5, + "cpu_percent": 15.2 + } + ], + "count": 2 +} +``` + +--- + +### 🎨 UI/UX Improvements + +#### **Visual Enhancements** +- **Glass-morphism Design**: Modern frosted glass effect +- **Smooth Animations**: 0.3s-0.4s cubic-bezier transitions +- **Hover Effects**: Cards lift and glow on hover +- **Status Indicators**: Pulsing dots for active systems +- **Color Psychology**: Green=healthy, Yellow=warning, Red=critical + +#### **Responsive Design** +- **Grid System**: Auto-fit, min 350px cards +- **Flexible Stats**: 1-4 columns based on screen width +- **Mobile Friendly**: Touch targets ≥44px +- **Accessible**: ARIA labels, keyboard navigation + +#### **Interactive Elements** +- **Toast Notifications**: 5-second auto-dismiss +- **Modal Dialogs**: Overlay with backdrop blur +- **Loading States**: Skeleton screens while fetching +- **Error Messages**: User-friendly, actionable feedback + +--- + +### 🔧 Technical Improvements + +#### **Performance Optimization** +- **Lazy Loading**: Load stats on demand +- **Debounced Updates**: Prevent excessive API calls +- **Cached Responses**: 30-second client-side cache +- **Batch Requests**: Parallel API calls with Promise.all + +#### **Error Handling** +- **Graceful Degradation**: Shows "N/A" instead of crashes +- **Retry Logic**: Auto-retry failed requests (3 attempts) +- **User Feedback**: Clear error messages with solutions +- **Logging**: Console errors for debugging + +#### **Code Quality** +- **Modular Functions**: Single responsibility principle +- **Async/Await**: Modern promise handling +- **Error Boundaries**: Try-catch blocks on all API calls +- **Type Safety**: Parameter validation before submission + +--- + +### 📊 Monitoring & Analytics + +#### **System Health Dashboard** +- **Datasets**: Count, validation status +- **Output Directory**: Existence, write permissions +- **GPU**: Availability, count, utilization +- **Virtual Environments**: All 3 projects checked + +#### **Job Analytics** +- **Total Jobs**: All-time count +- **Active Jobs**: Currently running +- **Completed**: Success rate +- **Failed**: Error analysis + +#### **Resource Tracking** +- **GPU Usage**: Real-time percentage +- **VRAM**: Allocated vs. total +- **CPU**: Per-process monitoring +- **Memory**: RAM usage by process + +--- + +### 🚀 Performance Metrics + +| Metric | Before | After | Improvement | +|--------|--------|-------|-------------| +| **Page Load** | 2.5s | 1.8s | 28% faster | +| **API Response** | 150ms | 120ms | 20% faster | +| **Stats Refresh** | 60s | 30s | 50% more frequent | +| **Error Recovery** | Manual | Auto | 100% automated | +| **UI Transitions** | 0.2s | 0.4s | Smoother | + +--- + +### 🎓 User Experience Enhancements + +#### **Discoverability** +- **Tooltips**: Hover hints on all elements +- **Help Text**: Contextual guidance +- **Examples**: Sample values for inputs +- **Shortcuts Panel**: `?` key for help + +#### **Workflow Optimization** +- **Quick Actions**: 6 one-click shortcuts +- **Presets**: 4 training configurations +- **Templates**: Save/load complete setups +- **History**: Review past jobs quickly + +#### **Feedback Loop** +- **Immediate Validation**: As you type +- **Progress Indicators**: Know what's happening +- **Completion Notifications**: Toast on success +- **Error Guidance**: Actionable error messages + +--- + +### 🔐 Security & Reliability + +#### **Input Validation** +- **Job Names**: Regex pattern enforcement +- **Numeric Ranges**: Min/max boundaries +- **Required Fields**: Pre-submission checks +- **Injection Prevention**: Sanitized inputs + +#### **Error Recovery** +- **Automatic Retry**: 3 attempts with backoff +- **Fallback Values**: Default to safe options +- **User Notification**: Clear error reporting +- **Graceful Degradation**: Partial functionality maintained + +#### **Rate Limiting** +- **60 requests/minute**: Per IP address +- **429 status**: Clear rate limit errors +- **Retry-After header**: Client respects limits + +--- + +### 📚 Documentation Additions + +#### **New Docs Created** +1. **QAI_HUB_GUIDE.md** (3,200+ lines) + - Complete reference for all 8 systems + - API documentation with examples + - Troubleshooting guide + - Best practices + - Security notes + +2. **QAI_HUB_QUICKREF.md** (600+ lines) + - One-liner commands + - Keyboard shortcuts + - Quick workflows + - Pro tips + - Cheat sheet format + +3. **QAI_HUB_ENHANCEMENTS_V2.md** (This file) + - Latest improvements + - Performance metrics + - Feature comparisons + - Technical details + +--- + +### 🎯 Coming Soon (Roadmap) + +#### **Phase 1: Real-Time Features** +- [ ] WebSocket integration for live updates +- [ ] Job progress bars with percentage +- [ ] Live log streaming +- [ ] Real-time GPU graphs + +#### **Phase 2: Advanced Analytics** +- [ ] Historical charts (Plotly.js) +- [ ] Model performance trending +- [ ] Resource utilization graphs +- [ ] Cost analysis (QPU usage) + +#### **Phase 3: Collaboration** +- [ ] User authentication (OAuth) +- [ ] Team workspaces +- [ ] Shared configurations +- [ ] Job scheduling calendar + +#### **Phase 4: Automation** +- [ ] Auto-training triggers +- [ ] Hyperparameter optimization +- [ ] A/B testing framework +- [ ] CI/CD integration + +--- + +### 🛠️ Developer Notes + +#### **Tech Stack** +- **Frontend**: Vanilla JS (no frameworks for speed) +- **Backend**: Python 3.11 + HTTP server +- **Styling**: CSS3 with custom properties +- **API**: RESTful with JSON responses + +#### **File Structure** +``` +dashboard/ +├── hub.html # Command center (700 lines) +├── unified.html # Training dashboard (2,369 lines) +├── serve.py # HTTP server (450 lines) +├── gpu_monitor.py # GPU utilities (200 lines) +└── static/ # Assets (future) +``` + +#### **Performance Considerations** +- **No frameworks**: 0KB bundle size +- **Inline CSS**: Faster first paint +- **Async loading**: Non-blocking API calls +- **Caching**: Browser cache headers + +--- + +### 📊 Usage Statistics (Hypothetical) + +| Feature | Usage | User Satisfaction | +|---------|-------|-------------------| +| **Quick Actions** | 85% | ⭐⭐⭐⭐⭐ | +| **Training Presets** | 78% | ⭐⭐⭐⭐⭐ | +| **Model Comparison** | 62% | ⭐⭐⭐⭐ | +| **History Viewer** | 55% | ⭐⭐⭐⭐ | +| **API Endpoints** | 45% | ⭐⭐⭐⭐⭐ | + +--- + +### ✅ Testing Coverage + +#### **Unit Tests** (40 total) +- ✅ All API endpoints +- ✅ Parameter validation +- ✅ Error handling +- ✅ Data formatting + +#### **Integration Tests** (30 total) +- ✅ End-to-end workflows +- ✅ Multi-system integration +- ✅ Database operations +- ✅ GPU monitoring + +#### **Manual Testing** +- ✅ All buttons clickable +- ✅ All forms submittable +- ✅ All links navigable +- ✅ Mobile responsive + +--- + +### 🏆 Achievement Milestones + +- ✅ **1,000+ lines** of new code +- ✅ **18 API endpoints** total +- ✅ **8 system cards** integrated +- ✅ **17 training parameters** configurable +- ✅ **4 documentation files** (6,000+ total lines) +- ✅ **Zero critical bugs** in production +- ✅ **100% feature completion** for v2.0 + +--- + +### 🎉 Success Metrics + +| Metric | Target | Achieved | Status | +|--------|--------|----------|--------| +| **Page Load** | <2s | 1.8s | ✅ Beat target | +| **API Response** | <200ms | 120ms | ✅ Beat target | +| **Error Rate** | <1% | 0.2% | ✅ Beat target | +| **User Satisfaction** | >80% | 92% | ✅ Beat target | +| **Documentation** | >2,000 lines | 6,000+ lines | ✅ 3x target | + +--- + +### 💡 Pro Tips for Users + +1. **Use Keyboard Shortcuts**: `Ctrl+H` for history, `Ctrl+R` for refresh +2. **Start with Presets**: Quick Test for validation, then scale up +3. **Save Configurations**: Export JSON for reproducibility +4. **Monitor GPU**: Keep usage <80% for stability +5. **Check Health**: `curl /api/health` before starting jobs +6. **Review History**: Learn from past jobs to optimize +7. **Read Docs**: QAI_HUB_QUICKREF.md has all one-liners +8. **Validate First**: Always dry-run orchestrators before execution + +--- + +### 🔗 Quick Links + +- **Hub**: http://localhost:8000/ +- **Training**: http://localhost:8000/unified.html +- **API Health**: http://localhost:8000/api/health +- **API Stats**: http://localhost:8000/api/stats +- **GitHub**: https://github.com/Bryan-Roe/QAI + +--- + +**Version**: 2.1 +**Last Updated**: November 25, 2025 +**Status**: Production Ready ✅ +**Maintainer**: QAI Development Team + +--- + +## 🎊 Thank You! + +The QAI Hub has evolved from a simple training dashboard into a comprehensive command center for all quantum-AI operations. Your feedback drives continuous improvement! + +**Happy Training! 🚀🤖⚛️** diff --git a/docs/guides/QAI_HUB_GUIDE.md b/docs/guides/QAI_HUB_GUIDE.md index 5c1dda937..a2f51e67c 100644 --- a/docs/guides/QAI_HUB_GUIDE.md +++ b/docs/guides/QAI_HUB_GUIDE.md @@ -1,729 +1,729 @@ -# QAI Hub - Command Center Guide - -## 🌌 Overview - -The **QAI Hub** (http://localhost:8000/) is the central command center that unifies all three QAI projects into a single, cohesive interface. It provides quick access to training dashboards, quantum ML pipelines, chat interfaces, and monitoring tools. - ---- - -## 🚀 Quick Start - -### 1. Start the Dashboard Server - -```powershell -# From repo root -python .\dashboard\serve.py - -# Or use the pre-configured task -# Press Ctrl+Shift+P → "Tasks: Run Task" → "func: host start" -``` - -Server will start at **http://localhost:8000/** - -The root page (/) automatically redirects to **/hub.html** - your command center. - -### 2. Navigate the Hub - -The hub is organized into: -- **Header**: System status badges (Dashboard, Training Pipeline, API, Quantum MCP) -- **Stats Bar**: Real-time counts (Jobs, Datasets, Models, GPU Usage) -- **Quick Actions**: 6 one-click shortcuts -- **System Cards**: 8 detailed system interfaces - ---- - -## 📊 System Cards - -### 1. 🎯 Training Dashboard (`/unified.html`) - -**Primary interface for LoRA fine-tuning** - -Features: -- 20+ training parameters (6 basic + 9 advanced + 4 evaluation) -- Real-time GPU monitoring -- 4 preset configurations (Quick/Standard/Full/Production) -- Config save/load (JSON export/import) -- Live job tracking with auto-refresh -- Comprehensive validation (5 checks) - -**Usage**: -1. Click "Training Dashboard" card -2. Navigate to "Train" tab -3. Fill in job name, select model/dataset -4. Adjust epochs, samples, learning rate -5. (Optional) Expand Advanced Options for fine-grained control -6. Click "Start Training" -7. Monitor progress in "All Jobs" tab - -**Presets**: -- **Quick Test**: 1 epoch, 100 samples (~2 min) - Fast validation -- **Standard**: 3 epochs, 1k samples (~10 min) - Good baseline -- **Full**: 5 epochs, all samples (~60 min) - Production quality -- **Production**: 10 epochs, all samples (~4 hours) - Maximum quality - ---- - -### 2. ⚛️ Quantum ML Pipeline - -**Hybrid quantum-classical machine learning** - -Features: -- Quantum circuit training (PennyLane) -- Azure Quantum integration (ionq.simulator, ionq.qpu) -- 8 MCP server tools (circuit execution, job submission, result retrieval) -- Local & cloud simulators -- Cost estimation before QPU execution - -**Usage**: -```powershell -# Dry-run validation -python .\scripts\quantum_autorun.py --dry-run - -# Run specific job -python .\scripts\quantum_autorun.py --job local_pennylane - -# Azure Quantum submission (requires az login) -python .\scripts\quantum_autorun.py --job azure_ionq_simulator -``` - -**MCP Server** (for AI agents): -```powershell -python .\quantum-ai\quantum_mcp_server.py -``` - -**Cost Gates**: -- Local simulators: FREE -- Azure simulators: FREE -- QPU execution: ~$0.00003-$0.00015 per gate-shot - - Requires `azure_confirm_cost: true` in YAML - ---- - -### 3. 💬 Multi-Provider Chat - -**Unified chat CLI with 4 provider backends** - -Features: -- Azure OpenAI (primary) -- OpenAI (fallback) -- LoRA adapter (custom models) -- Local echo (zero-dependency) -- Automatic provider detection -- Streaming responses -- Memory persistence (SQL/Cosmos) - -**Usage**: -```powershell -# Auto-detect provider -python .\talk-to-ai\src\chat_cli.py - -# Specific provider -python .\talk-to-ai\src\chat_cli.py --provider azure -python .\talk-to-ai\src\chat_cli.py --provider lora --model data_out\lora_training\lora_adapter - -# One-shot mode -python .\talk-to-ai\src\chat_cli.py --provider azure --once "What is quantum computing?" -``` - -**Health Check**: -```powershell -# Check active provider and env vars -curl http://localhost:7071/api/ai/status | ConvertFrom-Json -``` - ---- - -### 4. 📊 Evaluation Suite - -**Automated model assessment** - -Features: -- Perplexity calculation (pre/post training) -- Loss comparison -- Batch evaluation across multiple models -- Model ranking -- Export reports (Markdown/JSON) - -**Usage**: -```powershell -# Dry-run validation -python .\scripts\evaluation_autorun.py --dry-run - -# Run all evaluations -python .\scripts\evaluation_autorun.py - -# Specific evaluation -python .\scripts\evaluation_autorun.py --job perplexity_check -``` - -**Batch Evaluation**: -```powershell -# Scan for models and evaluate all -python .\scripts\batch_evaluator.py --scan-models --evaluate-all - -# Generate comparison report -python .\scripts\results_exporter.py --all --format markdown -``` - ---- - -### 5. 🗃️ Dataset Manager - -**Browse, validate, and manage datasets** - -Features: -- Auto-discovery (datasets/chat/*) -- Sample counting (train/test splits) -- Format validation (JSON schema) -- Dataset generation tools -- Direct access from hub - -**Usage via Dashboard**: -1. Open hub → Quick Actions → "View Datasets" -2. Or Training Dashboard → "Datasets" tab -3. See all datasets with sample counts - -**API Access**: -```powershell -# List all datasets -curl http://localhost:8000/api/datasets | ConvertFrom-Json - -# Response format: -# { -# "datasets": [ -# { -# "name": "chat_logs", -# "path": "datasets\\chat\\chat_logs", -# "train_samples": 1000, -# "test_samples": 200 -# } -# ] -# } -``` - -**Generate Synthetic Data**: -```powershell -# Create 2000 samples -python .\scripts\auto_data_train.py --samples 2000 --output-dir datasets/chat/synthetic_2k -``` - ---- - -### 6. 🔌 API Gateway - -**Azure Functions unified API** - -Endpoints: -- `/api/chat` - Multi-provider chat completion -- `/api/quantum/*` - Quantum circuit execution -- `/api/ai/status` - Health check (provider, env vars, SQL, Cosmos, telemetry) - -**Integration**: -- Dynamic imports from all 3 projects (quantum-ai, talk-to-ai, lora training) -- SQL persistence (unified engine: Azure SQL, PostgreSQL, MySQL, SQLite) -- Cosmos DB persistence (optional, feature-flagged) -- Application Insights telemetry (optional) - -**Usage**: -```powershell -# Start local dev server -func host start - -# Test health endpoint -curl http://localhost:7071/api/ai/status - -# Chat endpoint -curl http://localhost:7071/api/chat ` - -Method POST ` - -Headers @{"Content-Type"="application/json"} ` - -Body '{"messages":[{"role":"user","content":"Hello"}]}' -``` - ---- - -### 7. 📈 Resource Monitor - -**Real-time system monitoring** - -Features: -- GPU utilization & VRAM -- CPU & RAM usage -- Process tracking (PID, memory, CPU%) -- Historical graphs -- Alert system (thresholds) - -**Usage**: -```powershell -# Snapshot (current state) -python .\scripts\resource_monitor.py --snapshot - -# Stream mode (60 seconds) -python .\scripts\resource_monitor.py --stream --duration 60 -``` - -**API Access**: -```powershell -# GPU info -curl http://localhost:8000/api/gpu | ConvertFrom-Json - -# System resources -curl http://localhost:8000/api/system | ConvertFrom-Json - -# Active processes -curl http://localhost:8000/api/processes | ConvertFrom-Json -``` - ---- - -### 8. 🔄 CI/CD Pipeline - -**Automated testing & validation** - -Features: -- 40 unit tests (~0.5s total) -- 30 integration tests (external services) -- Orchestrator validation (YAML configs) -- Artifact packaging -- Deployment automation - -**Usage**: -```powershell -# Run fast unit tests -pytest tests/ -m "not slow and not azure" - -# Or via test runner -python .\scripts\test_runner.py --unit - -# Full CI pipeline -python .\scripts\ci_orchestrator.py --ci-pipeline - -# Master orchestrator (all systems) -python .\scripts\master_orchestrator.py --status -``` - -**VS Code Integration**: -- Native Test Explorer (🧪 sidebar) -- Breakpoint debugging -- Per-test run/debug buttons -- See `VSCODE_TESTING_QUICKREF.md` - ---- - -## ⚡ Quick Actions - -The hub provides 6 one-click shortcuts: - -1. **🚀 Start Training** → `/unified.html#train` -2. **📊 View Datasets** → `/unified.html#datasets` -3. **🤖 Browse Models** → `/unified.html#models` -4. **📈 Monitor GPU** → `/unified.html#monitor` -5. **⚛️ Quantum Jobs** → Toast with CLI command -6. **💬 Chat Interface** → Toast with CLI command - -**Toast Messages**: -- Quick actions that require CLI show instructional toasts -- Example: "To use chat: python ai-projects/chat-cli/src/chat_cli.py --provider azure" - ---- - -## 📡 API Endpoints - -### Training Status -```http -GET /status -``` -Returns training job status (no cache headers). - -### Datasets -```http -GET /api/datasets -``` -Returns all datasets with sample counts. - -### Models -```http -GET /api/models -``` -Returns trained models in `data_out/lora_training/marathon/`. - -### Configs -```http -GET /api/configs -``` -Returns available YAML configs with job counts and estimates. - -### GPU Monitoring -```http -GET /api/gpu -GET /api/gpu-processes -GET /api/system -``` -Returns GPU utilization, VRAM, active processes. - -### Health Check -```http -GET /api/health -``` -Returns comprehensive system health (datasets, output dir, GPU, venvs). - -### Quick Stats -```http -GET /api/stats -``` -Returns summary: jobs, datasets, models, GPU usage, active processes. - -### Active Processes -```http -GET /api/processes -``` -Returns Python processes running training/quantum/chat/serve. - -### Start Training -```http -POST /api/start-training -Content-Type: application/json - -{ - "name": "my_job", - "model": "phi35", - "dataset": "chat_logs", - "epochs": 3, - "max_samples": 1000, - "learning_rate": "2e-4" -} -``` - ---- - -## 🎨 UI Features - -### Dark Theme -- Gradient background (dark blue tones) -- Glass-morphism cards (backdrop blur) -- Smooth animations (hover effects, transitions) -- Color-coded status badges - -### Responsive Design -- Grid layout (auto-fit, min 350px cards) -- Flexbox for stats and actions -- Mobile-friendly (wraps on small screens) - -### Real-Time Updates -- Stats refresh every 30 seconds -- Status badges update on load -- GPU usage live polling -- Toast notifications for actions - -### Accessibility -- Tooltips on all system cards -- Clear action labels -- Keyboard navigation support -- ARIA-compliant badges - ---- - -## 🔧 Configuration - -### Server Settings - -**Port**: 8000 (default) -**Root Redirect**: `/` → `/hub.html` - -To change: -```python -# dashboard/serve.py -PORT = 8080 # Change here -``` - -### Rate Limiting - -**Default**: 60 requests per minute per IP - -To adjust: -```python -# dashboard/serve.py -MAX_REQUESTS_PER_MINUTE = 120 # Increase limit -``` - -### System Status Badges - -Update status in hub.html: -```javascript -// Green = online -
    -
    - System Name -
    - -// Yellow = warning -
    -
    - System Name -
    - -// Red = offline -
    -
    - System Name -
    -``` - ---- - -## 📚 Documentation Files - -- **QAI_HUB_GUIDE.md** (this file) - Hub overview -- **TRAINING_TAB_ENHANCEMENTS.md** - Training dashboard features -- **TRAINING_TAB_QUICKREF.md** - Quick reference for training -- **DASHBOARD_DEMO.md** - Live demo script -- **AUTOTRAIN_README.md** - LoRA training orchestration -- **QUANTUM_AUTORUN_README.md** - Quantum job configuration -- **TELEMETRY_COSMOS_ENABLEMENT.md** - Observability setup -- **VSCODE_TESTING_QUICKREF.md** - Test Explorer shortcuts - ---- - -## 🐛 Troubleshooting - -### Hub Not Loading - -**Symptom**: 404 or blank page at http://localhost:8000/ - -**Fix**: -```powershell -# Check server is running -Get-Process python | Where-Object {$_.CommandLine -like "*serve.py*"} - -# Restart server -Stop-Process -Name python -Force -python .\dashboard\serve.py -``` - -### Stats Showing "--" - -**Symptom**: Stats bar shows dashes instead of numbers - -**Fix**: -```powershell -# Ensure status.json exists -Test-Path .\data_out\autotrain\status.json - -# Run a training job to generate status -python .\scripts\autotrain.py --dry-run -``` - -### System Cards Not Clickable - -**Symptom**: Clicking cards doesn't navigate - -**Fix**: -- Check browser console for JavaScript errors -- Hard refresh: Ctrl+Shift+R -- Clear browser cache - -### API Endpoints 404 - -**Symptom**: /api/* returns 404 - -**Fix**: -```powershell -# Check serve.py is running (not Azure Functions) -# Dashboard server: http://localhost:8000 -# Azure Functions: http://localhost:7071 - -# Restart dashboard server -python .\dashboard\serve.py -``` - ---- - -## 🚀 Advanced Usage - -### Custom System Cards - -Add new systems to hub.html: - -```html -
    -
    🆕
    -

    New System

    -

    Description here...

    -
      -
    • Feature 1
      • -
    • Feature 2
      • -
    -
    - - -
    -
    -``` - -### Custom Quick Actions - -Add to actions-grid: - -```html -
    -
    🔥
    -
    My Action
    -
    -``` - -Update JavaScript: - -```javascript -function quickAction(action) { - const actions = { - // ... existing actions ... - myaction: () => showToast('Custom action triggered!') - }; - // ... rest of function -} -``` - -### Custom API Endpoints - -Add to serve.py: - -```python -# In MyHTTPRequestHandler.do_GET() -elif self.path == '/api/custom': - self.send_json_response(self.get_custom_data()) - return - -# Add handler method -def get_custom_data(self): - return { - 'message': 'Custom API response', - 'data': [1, 2, 3] - } -``` - ---- - -## 🎯 Best Practices - -### 1. Use Hub as Single Entry Point -- Bookmark http://localhost:8000/ (not unified.html) -- All navigation starts from hub -- Consistent UX across systems - -### 2. Monitor Stats Bar -- Quick health check at a glance -- GPU usage = training activity indicator -- Dataset count = new data available - -### 3. Leverage Quick Actions -- Faster than navigating through cards -- Direct deep links to tabs -- Toast messages for CLI commands - -### 4. Check System Status Badges -- Green = fully operational -- Yellow = degraded/optional -- Red = offline/unavailable - -### 5. Use API Endpoints for Automation -- `/api/stats` for dashboard widgets -- `/api/health` for monitoring scripts -- `/api/processes` for job tracking - ---- - -## 📈 Metrics & Monitoring - -### Training Jobs -- Total count (all time) -- Active running jobs -- Completed/failed status -- Average duration - -### Datasets -- Count in `datasets/chat/` -- Train/test sample counts -- Format validation status - -### Models -- Trained adapters in `data_out/lora_training/marathon/` -- Base model references -- LoRA rank configurations - -### GPU Usage -- Utilization percentage (0-100%) -- VRAM allocated vs total -- Active processes on GPU -- Temperature (if available) - ---- - -## 🔐 Security Notes - -### Local Development Only -- Hub is designed for localhost:8000 -- **NOT production-ready** as-is -- No authentication/authorization - -### For Production Deployment -1. Add authentication (OAuth, JWT) -2. Enable HTTPS -3. Implement proper CORS -4. Rate limiting per user (not IP) -5. Input sanitization -6. API key management -7. Secure environment variables - -### Sensitive Data -- SQL connection strings in `.env` or `local.settings.json` -- Azure Quantum credentials via `az login` -- OpenAI/Azure OpenAI keys in environment -- Never commit secrets to git - ---- - -## 🌟 Future Enhancements - -### Planned Features -- [ ] WebSocket support (real-time updates) -- [ ] User authentication -- [ ] Job queue management (pause/resume/cancel) -- [ ] Historical charts (training metrics over time) -- [ ] Model comparison dashboard -- [ ] Dataset visualization (sample previews) -- [ ] Notification system (email/SMS on completion) -- [ ] Remote training submission (from mobile) - -### Suggestions Welcome -Submit ideas via GitHub Issues or contribute PRs! - ---- - -## 📞 Support - -### Documentation -- Main README.md (repo root) -- Individual project READMEs (quantum-ai, talk-to-ai, AI/microsoft_phi-silica-3.6_v1) -- Markdown guides in repo root (20+ files) - -### Testing -- Unit tests: `pytest tests/ -m "not slow and not azure"` -- Integration tests: `pytest tests/ -m integration` -- VS Code Test Explorer: 🧪 sidebar - -### Debugging -- Server logs: Check terminal running serve.py -- Browser console: F12 → Console tab -- API responses: Use curl or Postman -- Process inspection: Task Manager or `Get-Process python` - ---- - -## 📄 License - -Same as main QAI repository (see root LICENSE file). - ---- - -**Last Updated**: 2025-11-25 -**Version**: 2.0 -**Author**: QAI Development Team +# QAI Hub - Command Center Guide + +## 🌌 Overview + +The **QAI Hub** (http://localhost:8000/) is the central command center that unifies all three QAI projects into a single, cohesive interface. It provides quick access to training dashboards, quantum ML pipelines, chat interfaces, and monitoring tools. + +--- + +## 🚀 Quick Start + +### 1. Start the Dashboard Server + +```powershell +# From repo root +python .\dashboard\serve.py + +# Or use the pre-configured task +# Press Ctrl+Shift+P → "Tasks: Run Task" → "func: host start" +``` + +Server will start at **http://localhost:8000/** + +The root page (/) automatically redirects to **/hub.html** - your command center. + +### 2. Navigate the Hub + +The hub is organized into: +- **Header**: System status badges (Dashboard, Training Pipeline, API, Quantum MCP) +- **Stats Bar**: Real-time counts (Jobs, Datasets, Models, GPU Usage) +- **Quick Actions**: 6 one-click shortcuts +- **System Cards**: 8 detailed system interfaces + +--- + +## 📊 System Cards + +### 1. 🎯 Training Dashboard (`/unified.html`) + +**Primary interface for LoRA fine-tuning** + +Features: +- 20+ training parameters (6 basic + 9 advanced + 4 evaluation) +- Real-time GPU monitoring +- 4 preset configurations (Quick/Standard/Full/Production) +- Config save/load (JSON export/import) +- Live job tracking with auto-refresh +- Comprehensive validation (5 checks) + +**Usage**: +1. Click "Training Dashboard" card +2. Navigate to "Train" tab +3. Fill in job name, select model/dataset +4. Adjust epochs, samples, learning rate +5. (Optional) Expand Advanced Options for fine-grained control +6. Click "Start Training" +7. Monitor progress in "All Jobs" tab + +**Presets**: +- **Quick Test**: 1 epoch, 100 samples (~2 min) - Fast validation +- **Standard**: 3 epochs, 1k samples (~10 min) - Good baseline +- **Full**: 5 epochs, all samples (~60 min) - Production quality +- **Production**: 10 epochs, all samples (~4 hours) - Maximum quality + +--- + +### 2. ⚛️ Quantum ML Pipeline + +**Hybrid quantum-classical machine learning** + +Features: +- Quantum circuit training (PennyLane) +- Azure Quantum integration (ionq.simulator, ionq.qpu) +- 8 MCP server tools (circuit execution, job submission, result retrieval) +- Local & cloud simulators +- Cost estimation before QPU execution + +**Usage**: +```powershell +# Dry-run validation +python .\scripts\quantum_autorun.py --dry-run + +# Run specific job +python .\scripts\quantum_autorun.py --job local_pennylane + +# Azure Quantum submission (requires az login) +python .\scripts\quantum_autorun.py --job azure_ionq_simulator +``` + +**MCP Server** (for AI agents): +```powershell +python .\quantum-ai\quantum_mcp_server.py +``` + +**Cost Gates**: +- Local simulators: FREE +- Azure simulators: FREE +- QPU execution: ~$0.00003-$0.00015 per gate-shot + - Requires `azure_confirm_cost: true` in YAML + +--- + +### 3. 💬 Multi-Provider Chat + +**Unified chat CLI with 4 provider backends** + +Features: +- Azure OpenAI (primary) +- OpenAI (fallback) +- LoRA adapter (custom models) +- Local echo (zero-dependency) +- Automatic provider detection +- Streaming responses +- Memory persistence (SQL/Cosmos) + +**Usage**: +```powershell +# Auto-detect provider +python .\talk-to-ai\src\chat_cli.py + +# Specific provider +python .\talk-to-ai\src\chat_cli.py --provider azure +python .\talk-to-ai\src\chat_cli.py --provider lora --model data_out\lora_training\lora_adapter + +# One-shot mode +python .\talk-to-ai\src\chat_cli.py --provider azure --once "What is quantum computing?" +``` + +**Health Check**: +```powershell +# Check active provider and env vars +curl http://localhost:7071/api/ai/status | ConvertFrom-Json +``` + +--- + +### 4. 📊 Evaluation Suite + +**Automated model assessment** + +Features: +- Perplexity calculation (pre/post training) +- Loss comparison +- Batch evaluation across multiple models +- Model ranking +- Export reports (Markdown/JSON) + +**Usage**: +```powershell +# Dry-run validation +python .\scripts\evaluation_autorun.py --dry-run + +# Run all evaluations +python .\scripts\evaluation_autorun.py + +# Specific evaluation +python .\scripts\evaluation_autorun.py --job perplexity_check +``` + +**Batch Evaluation**: +```powershell +# Scan for models and evaluate all +python .\scripts\batch_evaluator.py --scan-models --evaluate-all + +# Generate comparison report +python .\scripts\results_exporter.py --all --format markdown +``` + +--- + +### 5. 🗃️ Dataset Manager + +**Browse, validate, and manage datasets** + +Features: +- Auto-discovery (datasets/chat/*) +- Sample counting (train/test splits) +- Format validation (JSON schema) +- Dataset generation tools +- Direct access from hub + +**Usage via Dashboard**: +1. Open hub → Quick Actions → "View Datasets" +2. Or Training Dashboard → "Datasets" tab +3. See all datasets with sample counts + +**API Access**: +```powershell +# List all datasets +curl http://localhost:8000/api/datasets | ConvertFrom-Json + +# Response format: +# { +# "datasets": [ +# { +# "name": "chat_logs", +# "path": "datasets\\chat\\chat_logs", +# "train_samples": 1000, +# "test_samples": 200 +# } +# ] +# } +``` + +**Generate Synthetic Data**: +```powershell +# Create 2000 samples +python .\scripts\auto_data_train.py --samples 2000 --output-dir datasets/chat/synthetic_2k +``` + +--- + +### 6. 🔌 API Gateway + +**Azure Functions unified API** + +Endpoints: +- `/api/chat` - Multi-provider chat completion +- `/api/quantum/*` - Quantum circuit execution +- `/api/ai/status` - Health check (provider, env vars, SQL, Cosmos, telemetry) + +**Integration**: +- Dynamic imports from all 3 projects (quantum-ai, talk-to-ai, lora training) +- SQL persistence (unified engine: Azure SQL, PostgreSQL, MySQL, SQLite) +- Cosmos DB persistence (optional, feature-flagged) +- Application Insights telemetry (optional) + +**Usage**: +```powershell +# Start local dev server +func host start + +# Test health endpoint +curl http://localhost:7071/api/ai/status + +# Chat endpoint +curl http://localhost:7071/api/chat ` + -Method POST ` + -Headers @{"Content-Type"="application/json"} ` + -Body '{"messages":[{"role":"user","content":"Hello"}]}' +``` + +--- + +### 7. 📈 Resource Monitor + +**Real-time system monitoring** + +Features: +- GPU utilization & VRAM +- CPU & RAM usage +- Process tracking (PID, memory, CPU%) +- Historical graphs +- Alert system (thresholds) + +**Usage**: +```powershell +# Snapshot (current state) +python .\scripts\resource_monitor.py --snapshot + +# Stream mode (60 seconds) +python .\scripts\resource_monitor.py --stream --duration 60 +``` + +**API Access**: +```powershell +# GPU info +curl http://localhost:8000/api/gpu | ConvertFrom-Json + +# System resources +curl http://localhost:8000/api/system | ConvertFrom-Json + +# Active processes +curl http://localhost:8000/api/processes | ConvertFrom-Json +``` + +--- + +### 8. 🔄 CI/CD Pipeline + +**Automated testing & validation** + +Features: +- 40 unit tests (~0.5s total) +- 30 integration tests (external services) +- Orchestrator validation (YAML configs) +- Artifact packaging +- Deployment automation + +**Usage**: +```powershell +# Run fast unit tests +pytest tests/ -m "not slow and not azure" + +# Or via test runner +python .\scripts\test_runner.py --unit + +# Full CI pipeline +python .\scripts\ci_orchestrator.py --ci-pipeline + +# Master orchestrator (all systems) +python .\scripts\master_orchestrator.py --status +``` + +**VS Code Integration**: +- Native Test Explorer (🧪 sidebar) +- Breakpoint debugging +- Per-test run/debug buttons +- See `VSCODE_TESTING_QUICKREF.md` + +--- + +## ⚡ Quick Actions + +The hub provides 6 one-click shortcuts: + +1. **🚀 Start Training** → `/unified.html#train` +2. **📊 View Datasets** → `/unified.html#datasets` +3. **🤖 Browse Models** → `/unified.html#models` +4. **📈 Monitor GPU** → `/unified.html#monitor` +5. **⚛️ Quantum Jobs** → Toast with CLI command +6. **💬 Chat Interface** → Toast with CLI command + +**Toast Messages**: +- Quick actions that require CLI show instructional toasts +- Example: "To use chat: python ai-projects/chat-cli/src/chat_cli.py --provider azure" + +--- + +## 📡 API Endpoints + +### Training Status +```http +GET /status +``` +Returns training job status (no cache headers). + +### Datasets +```http +GET /api/datasets +``` +Returns all datasets with sample counts. + +### Models +```http +GET /api/models +``` +Returns trained models in `data_out/lora_training/marathon/`. + +### Configs +```http +GET /api/configs +``` +Returns available YAML configs with job counts and estimates. + +### GPU Monitoring +```http +GET /api/gpu +GET /api/gpu-processes +GET /api/system +``` +Returns GPU utilization, VRAM, active processes. + +### Health Check +```http +GET /api/health +``` +Returns comprehensive system health (datasets, output dir, GPU, venvs). + +### Quick Stats +```http +GET /api/stats +``` +Returns summary: jobs, datasets, models, GPU usage, active processes. + +### Active Processes +```http +GET /api/processes +``` +Returns Python processes running training/quantum/chat/serve. + +### Start Training +```http +POST /api/start-training +Content-Type: application/json + +{ + "name": "my_job", + "model": "phi35", + "dataset": "chat_logs", + "epochs": 3, + "max_samples": 1000, + "learning_rate": "2e-4" +} +``` + +--- + +## 🎨 UI Features + +### Dark Theme +- Gradient background (dark blue tones) +- Glass-morphism cards (backdrop blur) +- Smooth animations (hover effects, transitions) +- Color-coded status badges + +### Responsive Design +- Grid layout (auto-fit, min 350px cards) +- Flexbox for stats and actions +- Mobile-friendly (wraps on small screens) + +### Real-Time Updates +- Stats refresh every 30 seconds +- Status badges update on load +- GPU usage live polling +- Toast notifications for actions + +### Accessibility +- Tooltips on all system cards +- Clear action labels +- Keyboard navigation support +- ARIA-compliant badges + +--- + +## 🔧 Configuration + +### Server Settings + +**Port**: 8000 (default) +**Root Redirect**: `/` → `/hub.html` + +To change: +```python +# dashboard/serve.py +PORT = 8080 # Change here +``` + +### Rate Limiting + +**Default**: 60 requests per minute per IP + +To adjust: +```python +# dashboard/serve.py +MAX_REQUESTS_PER_MINUTE = 120 # Increase limit +``` + +### System Status Badges + +Update status in hub.html: +```javascript +// Green = online +
    +
    + System Name +
    + +// Yellow = warning +
    +
    + System Name +
    + +// Red = offline +
    +
    + System Name +
    +``` + +--- + +## 📚 Documentation Files + +- **QAI_HUB_GUIDE.md** (this file) - Hub overview +- **TRAINING_TAB_ENHANCEMENTS.md** - Training dashboard features +- **TRAINING_TAB_QUICKREF.md** - Quick reference for training +- **DASHBOARD_DEMO.md** - Live demo script +- **AUTOTRAIN_README.md** - LoRA training orchestration +- **QUANTUM_AUTORUN_README.md** - Quantum job configuration +- **TELEMETRY_COSMOS_ENABLEMENT.md** - Observability setup +- **VSCODE_TESTING_QUICKREF.md** - Test Explorer shortcuts + +--- + +## 🐛 Troubleshooting + +### Hub Not Loading + +**Symptom**: 404 or blank page at http://localhost:8000/ + +**Fix**: +```powershell +# Check server is running +Get-Process python | Where-Object {$_.CommandLine -like "*serve.py*"} + +# Restart server +Stop-Process -Name python -Force +python .\dashboard\serve.py +``` + +### Stats Showing "--" + +**Symptom**: Stats bar shows dashes instead of numbers + +**Fix**: +```powershell +# Ensure status.json exists +Test-Path .\data_out\autotrain\status.json + +# Run a training job to generate status +python .\scripts\autotrain.py --dry-run +``` + +### System Cards Not Clickable + +**Symptom**: Clicking cards doesn't navigate + +**Fix**: +- Check browser console for JavaScript errors +- Hard refresh: Ctrl+Shift+R +- Clear browser cache + +### API Endpoints 404 + +**Symptom**: /api/* returns 404 + +**Fix**: +```powershell +# Check serve.py is running (not Azure Functions) +# Dashboard server: http://localhost:8000 +# Azure Functions: http://localhost:7071 + +# Restart dashboard server +python .\dashboard\serve.py +``` + +--- + +## 🚀 Advanced Usage + +### Custom System Cards + +Add new systems to hub.html: + +```html +
    +
    🆕
    +

    New System

    +

    Description here...

    +
      +
    • Feature 1
      • +
    • Feature 2
      • +
    +
    + + +
    +
    +``` + +### Custom Quick Actions + +Add to actions-grid: + +```html +
    +
    🔥
    +
    My Action
    +
    +``` + +Update JavaScript: + +```javascript +function quickAction(action) { + const actions = { + // ... existing actions ... + myaction: () => showToast('Custom action triggered!') + }; + // ... rest of function +} +``` + +### Custom API Endpoints + +Add to serve.py: + +```python +# In MyHTTPRequestHandler.do_GET() +elif self.path == '/api/custom': + self.send_json_response(self.get_custom_data()) + return + +# Add handler method +def get_custom_data(self): + return { + 'message': 'Custom API response', + 'data': [1, 2, 3] + } +``` + +--- + +## 🎯 Best Practices + +### 1. Use Hub as Single Entry Point +- Bookmark http://localhost:8000/ (not unified.html) +- All navigation starts from hub +- Consistent UX across systems + +### 2. Monitor Stats Bar +- Quick health check at a glance +- GPU usage = training activity indicator +- Dataset count = new data available + +### 3. Leverage Quick Actions +- Faster than navigating through cards +- Direct deep links to tabs +- Toast messages for CLI commands + +### 4. Check System Status Badges +- Green = fully operational +- Yellow = degraded/optional +- Red = offline/unavailable + +### 5. Use API Endpoints for Automation +- `/api/stats` for dashboard widgets +- `/api/health` for monitoring scripts +- `/api/processes` for job tracking + +--- + +## 📈 Metrics & Monitoring + +### Training Jobs +- Total count (all time) +- Active running jobs +- Completed/failed status +- Average duration + +### Datasets +- Count in `datasets/chat/` +- Train/test sample counts +- Format validation status + +### Models +- Trained adapters in `data_out/lora_training/marathon/` +- Base model references +- LoRA rank configurations + +### GPU Usage +- Utilization percentage (0-100%) +- VRAM allocated vs total +- Active processes on GPU +- Temperature (if available) + +--- + +## 🔐 Security Notes + +### Local Development Only +- Hub is designed for localhost:8000 +- **NOT production-ready** as-is +- No authentication/authorization + +### For Production Deployment +1. Add authentication (OAuth, JWT) +2. Enable HTTPS +3. Implement proper CORS +4. Rate limiting per user (not IP) +5. Input sanitization +6. API key management +7. Secure environment variables + +### Sensitive Data +- SQL connection strings in `.env` or `local.settings.json` +- Azure Quantum credentials via `az login` +- OpenAI/Azure OpenAI keys in environment +- Never commit secrets to git + +--- + +## 🌟 Future Enhancements + +### Planned Features +- [ ] WebSocket support (real-time updates) +- [ ] User authentication +- [ ] Job queue management (pause/resume/cancel) +- [ ] Historical charts (training metrics over time) +- [ ] Model comparison dashboard +- [ ] Dataset visualization (sample previews) +- [ ] Notification system (email/SMS on completion) +- [ ] Remote training submission (from mobile) + +### Suggestions Welcome +Submit ideas via GitHub Issues or contribute PRs! + +--- + +## 📞 Support + +### Documentation +- Main README.md (repo root) +- Individual project READMEs (quantum-ai, talk-to-ai, AI/microsoft_phi-silica-3.6_v1) +- Markdown guides in repo root (20+ files) + +### Testing +- Unit tests: `pytest tests/ -m "not slow and not azure"` +- Integration tests: `pytest tests/ -m integration` +- VS Code Test Explorer: 🧪 sidebar + +### Debugging +- Server logs: Check terminal running serve.py +- Browser console: F12 → Console tab +- API responses: Use curl or Postman +- Process inspection: Task Manager or `Get-Process python` + +--- + +## 📄 License + +Same as main QAI repository (see root LICENSE file). + +--- + +**Last Updated**: 2025-11-25 +**Version**: 2.0 +**Author**: QAI Development Team diff --git a/docs/guides/QUANTUM_LLM_TRAINING.md b/docs/guides/QUANTUM_LLM_TRAINING.md index 45df69c99..ce886db8d 100644 --- a/docs/guides/QUANTUM_LLM_TRAINING.md +++ b/docs/guides/QUANTUM_LLM_TRAINING.md @@ -1,430 +1,430 @@ -# Quantum-Enhanced Passive LLM Training - -## Overview - -This feature integrates quantum computing capabilities into passive LLM training, enabling continuous background training with quantum-enhanced optimization. The system combines classical LLM fine-tuning with quantum circuits for improved performance and novel optimization strategies. - -## Key Features - -### 🔬 Quantum Computing Integration -- **Quantum Attention Optimization**: Uses quantum circuits to optimize attention weight distributions in transformer models -- **Quantum Feature Encoding**: Encodes classical features into quantum states for enhanced representation learning -- **Hybrid Quantum-Classical Architecture**: Seamlessly integrates quantum layers with classical neural networks - -### 🤖 Passive Training Mode -- **Continuous Background Training**: Runs autonomously in the background with configurable intervals -- **Integration with Autonomous Orchestrator**: Works seamlessly with the existing autonomous training system -- **Resource-Aware Execution**: Intelligently manages quantum circuit executions to balance performance and cost - -### ⚛️ Quantum Backends -- **Local Simulator**: Fast quantum simulation using PennyLane's default.qubit (free, unlimited use) -- **Azure Quantum**: Production quantum computing via Azure Quantum service - - Free simulators: `ionq.simulator`, `quantinuum.sim.*` - - Paid QPU hardware: `ionq.qpu`, `quantinuum.qpu.*` (requires cost confirmation) - -## Architecture - -``` -┌─────────────────────────────────────────────────────────────┐ -│ Autonomous Training Orchestrator │ -│ ├── Data Discovery & Collection │ -│ ├── Classical Training Cycles │ -│ ├── Quantum-Enhanced LLM Training (NEW) ◄────────────────┐ │ -│ ├── Performance Analysis │ │ -│ └── Optimization & Deployment │ │ -└──────────────────────────────────────────┬──────────────────┘ │ - │ │ - ▼ │ -┌─────────────────────────────────────────────────────────────┴─┐ -│ Quantum-Enhanced LLM Trainer │ -│ ├── Quantum Attention Optimizer │ -│ │ └── Optimizes attention weight distributions │ -│ ├── Quantum Feature Encoder │ -│ │ └── Encodes features into quantum states │ -│ ├── Training Pipeline │ -│ │ ├── Load dataset (JSON/JSONL) │ -│ │ ├── Apply quantum enhancement every N steps │ -│ │ └── Track quantum metrics │ -│ └── Passive Mode Controller │ -│ └── Manages continuous background training │ -└───────────────────────────────────────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────────────────────────────┐ -│ Quantum Computing Layer (ai-projects/quantum-ml/) │ -│ ├── QuantumClassifier (variational circuits) │ -│ ├── HybridQNN (quantum-classical hybrid) │ -│ ├── QuantumLayer (PennyLane integration) │ -│ └── Azure Quantum Integration │ -└─────────────────────────────────────────────────────────────┘ -``` - -## Quick Start - -### 1. Basic Usage (Active Training) - -Train a model with quantum enhancement on a specific dataset: - -```bash -# Using local quantum simulator (free, fast) -python scripts/quantum_llm_trainer.py \ - --dataset datasets/chat/aria_chat \ - --quantum-backend local \ - --n-qubits 4 \ - --epochs 3 - -# Using Azure Quantum simulator (free, requires Azure setup) -python scripts/quantum_llm_trainer.py \ - --dataset datasets/chat/aria_chat \ - --quantum-backend azure \ - --config config/quantum_llm_config.yaml -``` - -### 2. Passive Training Mode - -Enable continuous background training: - -```bash -# Run in passive mode - trains every hour -python scripts/quantum_llm_trainer.py \ - --passive \ - --interval 3600 \ - --config config/quantum_llm_config.yaml -``` - -### 3. Integration with Autonomous Orchestrator - -The quantum LLM training is automatically integrated into the autonomous training orchestrator: - -```bash -# Start autonomous training (includes quantum LLM training) -python scripts/autonomous_training_orchestrator.py - -# Or use the full repo automation -python scripts/repo_automation.py --start -``` - -The orchestrator will automatically run quantum-enhanced LLM training based on the schedule configured in `config/autonomous_training.yaml`. - -## Configuration - -### Main Configuration File: `config/quantum_llm_config.yaml` - -```yaml -quantum_settings: - backend: "local" # or "azure" - n_qubits: 4 - n_quantum_layers: 2 - entanglement: "circular" - -passive_training: - enabled: true - interval_seconds: 3600 # Run every hour - epochs_per_cycle: 1 - -llm_training: - model_name: "microsoft/phi-2" - learning_rate: 0.0001 - batch_size: 4 - - lora: - enabled: true - r: 8 - alpha: 16 - -quantum_enhancement: - optimize_attention: true - attention_optimization_frequency: 10 - quantum_feature_encoding: true -``` - -### Autonomous Integration: `config/autonomous_training.yaml` - -```yaml -# Enable quantum-enhanced LLM training -quantum_llm: - enabled: true - passive_mode: true - backend: "local" - n_qubits: 4 - training_interval_minutes: 60 - optimize_attention: true - quantum_feature_encoding: true -``` - -## How It Works - -### Quantum Attention Optimization - -The quantum attention optimizer uses quantum circuits to enhance attention mechanisms: - -1. **Encoding**: Attention scores are encoded into quantum states -2. **Processing**: Quantum gates apply transformations (superposition, entanglement) -3. **Measurement**: Quantum measurements produce optimized attention weights -4. **Interference**: Quantum interference patterns enhance attention distribution - -**Benefits**: -- More diverse attention patterns -- Better capture of long-range dependencies -- Potential for quantum speedup on quantum hardware - -### Quantum Feature Encoding - -Classical features are encoded into quantum states using amplitude encoding: - -1. **Normalization**: Features are normalized to unit vectors -2. **Amplitude Encoding**: Classical values become quantum amplitudes -3. **Variational Circuit**: Parameterized quantum gates process the encoded state -4. **Measurement**: Results are decoded back to classical features - -**Benefits**: -- Exponential feature space (2^n for n qubits) -- Quantum entanglement captures feature correlations -- Novel feature representations - -### Passive Training Workflow - -``` -┌──────────────────────────────────────────────────────────┐ -│ Passive Training Cycle (runs every interval) │ -├──────────────────────────────────────────────────────────┤ -│ 1. Scan datasets/chat/ for available datasets │ -│ 2. Select random dataset │ -│ 3. Load and prepare data │ -│ 4. Initialize quantum components │ -│ 5. Training loop: │ -│ ├── Forward pass │ -│ ├── Apply quantum optimization (every N steps) │ -│ ├── Backward pass │ -│ └── Update weights │ -│ 6. Save results and metrics │ -│ 7. Update status file │ -│ 8. Sleep until next cycle │ -└──────────────────────────────────────────────────────────┘ -``` - -## Quantum Backends - -### Local Simulator (Recommended for Development) - -- **Backend**: `default.qubit` (PennyLane) -- **Cost**: Free, unlimited -- **Speed**: Fast (CPU-based) -- **Use Case**: Development, testing, experimentation - -```yaml -quantum_settings: - backend: "local" -``` - -### Azure Quantum - -- **Free Simulators**: - - `ionq.simulator` - IonQ quantum simulator - - `quantinuum.sim.h1-1e` - Quantinuum H1 emulator -- **Paid QPU Hardware**: - - `ionq.qpu` - IonQ trapped-ion quantum computer - - `quantinuum.qpu.h1-1` - Quantinuum H1-1 quantum computer - -```yaml -quantum_settings: - backend: "azure" - azure: - resource_id: "your-azure-quantum-workspace-id" - target_backend: "ionq.simulator" - shots: 100 - confirm_cost: false # Must be true for QPU -``` - -## Monitoring and Metrics - -### Status File: `data_out/quantum_llm_training/status.json` - -```json -{ - "status": "success", - "epochs_completed": 3, - "final_loss": 1.234, - "quantum_metrics": { - "circuit_executions": 150, - "optimization_steps": 15, - "quantum_advantage_ratio": 1.05 - }, - "started_at": "2025-12-08T10:00:00", - "completed_at": "2025-12-08T10:15:00" -} -``` - -### Training Results: `data_out/quantum_llm_training/quantum_training_results.json` - -Detailed results including: -- Loss history per epoch -- Quantum circuit execution counts -- Optimization metrics -- Training timestamps - -### Logs - -Logs are written to: -- Console output (INFO level) -- Autonomous training log: `data_out/autonomous_training.log` (when integrated) - -## Performance Considerations - -### Resource Usage - -- **CPU**: Quantum simulation is CPU-intensive (1-4 cores recommended) -- **Memory**: ~2-4 GB per training cycle -- **Disk**: ~100 MB per cycle for checkpoints and results -- **GPU**: Optional (for classical neural network components) - -### Optimization Tips - -1. **Start Small**: Begin with 4 qubits, 2 layers -2. **Tune Frequency**: Adjust `attention_optimization_frequency` (higher = less quantum overhead) -3. **Batch Size**: Use smaller batches (4-8) for faster quantum circuits -4. **Cache Circuits**: Enable circuit caching to avoid recomputation - -### Cost Management (Azure Quantum) - -- Always test with FREE simulators first -- Set `confirm_cost: false` to prevent accidental QPU usage -- Monitor costs in Azure portal -- Start with low shot counts (100-500) - -## Testing - -### Run Unit Tests - -```bash -# Test quantum LLM trainer -pytest tests/test_quantum_llm_trainer.py -v - -# Run all quantum tests -pytest tests/ -k quantum -v - -# Integration tests -pytest tests/test_quantum_llm_trainer.py::TestQuantumLLMIntegration -v -``` - -### Validate Configuration - -```bash -# Test with dry-run -python scripts/quantum_llm_trainer.py \ - --dataset datasets/chat/aria_chat \ - --epochs 1 \ - --quantum-backend local -``` - -## Troubleshooting - -### Quantum Modules Not Available - -If you see warnings about quantum modules: - -```bash -# Install quantum-ai dependencies -cd quantum-ai -pip install -r requirements.txt - -# Or install specific packages -pip install pennylane qiskit torch -``` - -### Azure Quantum Connection Issues - -1. Verify workspace credentials in `config/quantum_llm_config.yaml` -2. Check Azure login: `az login` -3. Verify workspace access: `az quantum workspace list` -4. Try free simulator first: `target_backend: "ionq.simulator"` - -### Performance Issues - -- Reduce `n_qubits` (try 3-4 instead of 8+) -- Reduce `n_quantum_layers` (try 1-2 instead of 3+) -- Increase `attention_optimization_frequency` (apply quantum less often) -- Disable quantum feature encoding temporarily - -### Passive Mode Not Running - -1. Check `config/autonomous_training.yaml`: `quantum_llm.enabled: true` -2. Verify `scripts/quantum_llm_trainer.py` exists -3. Check logs: `tail -f data_out/autonomous_training.log` -4. Verify datasets exist: `ls datasets/chat/*/train.json` - -## Examples - -### Example 1: Quick Test - -```bash -# 1-minute test with local simulator -python scripts/quantum_llm_trainer.py \ - --dataset datasets/chat/aria_chat \ - --quantum-backend local \ - --n-qubits 3 \ - --n-quantum-layers 1 \ - --epochs 1 -``` - -### Example 2: Production Passive Training - -```bash -# Run continuous training in background -nohup python scripts/quantum_llm_trainer.py \ - --passive \ - --interval 3600 \ - --config config/quantum_llm_config.yaml \ - > data_out/quantum_llm_training.log 2>&1 & - -# Monitor -tail -f data_out/quantum_llm_training.log -``` - -### Example 3: Azure Quantum Integration - -```bash -# Configure Azure Quantum -cat > config/quantum_llm_config.yaml << EOF -quantum_settings: - backend: "azure" - n_qubits: 4 - azure: - resource_id: "/subscriptions/.../quantumWorkspaces/my-workspace" - target_backend: "ionq.simulator" - shots: 200 -EOF - -# Run with Azure -python scripts/quantum_llm_trainer.py \ - --dataset datasets/chat/aria_chat \ - --config config/quantum_llm_config.yaml -``` - -## Future Enhancements - -- [ ] Quantum-assisted hyperparameter optimization using QAOA -- [ ] Quantum circuit architecture search (quantum NAS) -- [ ] Multi-qubit entanglement patterns for attention heads -- [ ] Quantum error mitigation for QPU execution -- [ ] Distributed quantum training across multiple backends -- [ ] Quantum-classical co-training strategies - -## References - -- [PennyLane Documentation](https://pennylane.ai/) -- [Azure Quantum Documentation](https://learn.microsoft.com/en-us/azure/quantum/) -- [Quantum Machine Learning Papers](https://arxiv.org/list/quant-ph/recent) -- [Variational Quantum Algorithms](https://arxiv.org/abs/2012.09265) - -## Support - -For issues and questions: -1. Check troubleshooting section above -2. Review logs in `data_out/quantum_llm_training.log` -3. Test with local simulator first -4. Check ai-projects/quantum-ml/ module is properly configured - -## License - -This module is part of the Aria project and follows the same license. +# Quantum-Enhanced Passive LLM Training + +## Overview + +This feature integrates quantum computing capabilities into passive LLM training, enabling continuous background training with quantum-enhanced optimization. The system combines classical LLM fine-tuning with quantum circuits for improved performance and novel optimization strategies. + +## Key Features + +### 🔬 Quantum Computing Integration +- **Quantum Attention Optimization**: Uses quantum circuits to optimize attention weight distributions in transformer models +- **Quantum Feature Encoding**: Encodes classical features into quantum states for enhanced representation learning +- **Hybrid Quantum-Classical Architecture**: Seamlessly integrates quantum layers with classical neural networks + +### 🤖 Passive Training Mode +- **Continuous Background Training**: Runs autonomously in the background with configurable intervals +- **Integration with Autonomous Orchestrator**: Works seamlessly with the existing autonomous training system +- **Resource-Aware Execution**: Intelligently manages quantum circuit executions to balance performance and cost + +### ⚛️ Quantum Backends +- **Local Simulator**: Fast quantum simulation using PennyLane's default.qubit (free, unlimited use) +- **Azure Quantum**: Production quantum computing via Azure Quantum service + - Free simulators: `ionq.simulator`, `quantinuum.sim.*` + - Paid QPU hardware: `ionq.qpu`, `quantinuum.qpu.*` (requires cost confirmation) + +## Architecture + +``` +┌─────────────────────────────────────────────────────────────┐ +│ Autonomous Training Orchestrator │ +│ ├── Data Discovery & Collection │ +│ ├── Classical Training Cycles │ +│ ├── Quantum-Enhanced LLM Training (NEW) ◄────────────────┐ │ +│ ├── Performance Analysis │ │ +│ └── Optimization & Deployment │ │ +└──────────────────────────────────────────┬──────────────────┘ │ + │ │ + ▼ │ +┌─────────────────────────────────────────────────────────────┴─┐ +│ Quantum-Enhanced LLM Trainer │ +│ ├── Quantum Attention Optimizer │ +│ │ └── Optimizes attention weight distributions │ +│ ├── Quantum Feature Encoder │ +│ │ └── Encodes features into quantum states │ +│ ├── Training Pipeline │ +│ │ ├── Load dataset (JSON/JSONL) │ +│ │ ├── Apply quantum enhancement every N steps │ +│ │ └── Track quantum metrics │ +│ └── Passive Mode Controller │ +│ └── Manages continuous background training │ +└───────────────────────────────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────┐ +│ Quantum Computing Layer (ai-projects/quantum-ml/) │ +│ ├── QuantumClassifier (variational circuits) │ +│ ├── HybridQNN (quantum-classical hybrid) │ +│ ├── QuantumLayer (PennyLane integration) │ +│ └── Azure Quantum Integration │ +└─────────────────────────────────────────────────────────────┘ +``` + +## Quick Start + +### 1. Basic Usage (Active Training) + +Train a model with quantum enhancement on a specific dataset: + +```bash +# Using local quantum simulator (free, fast) +python scripts/quantum_llm_trainer.py \ + --dataset datasets/chat/aria_chat \ + --quantum-backend local \ + --n-qubits 4 \ + --epochs 3 + +# Using Azure Quantum simulator (free, requires Azure setup) +python scripts/quantum_llm_trainer.py \ + --dataset datasets/chat/aria_chat \ + --quantum-backend azure \ + --config config/quantum_llm_config.yaml +``` + +### 2. Passive Training Mode + +Enable continuous background training: + +```bash +# Run in passive mode - trains every hour +python scripts/quantum_llm_trainer.py \ + --passive \ + --interval 3600 \ + --config config/quantum_llm_config.yaml +``` + +### 3. Integration with Autonomous Orchestrator + +The quantum LLM training is automatically integrated into the autonomous training orchestrator: + +```bash +# Start autonomous training (includes quantum LLM training) +python scripts/autonomous_training_orchestrator.py + +# Or use the full repo automation +python scripts/repo_automation.py --start +``` + +The orchestrator will automatically run quantum-enhanced LLM training based on the schedule configured in `config/autonomous_training.yaml`. + +## Configuration + +### Main Configuration File: `config/quantum_llm_config.yaml` + +```yaml +quantum_settings: + backend: "local" # or "azure" + n_qubits: 4 + n_quantum_layers: 2 + entanglement: "circular" + +passive_training: + enabled: true + interval_seconds: 3600 # Run every hour + epochs_per_cycle: 1 + +llm_training: + model_name: "microsoft/phi-2" + learning_rate: 0.0001 + batch_size: 4 + + lora: + enabled: true + r: 8 + alpha: 16 + +quantum_enhancement: + optimize_attention: true + attention_optimization_frequency: 10 + quantum_feature_encoding: true +``` + +### Autonomous Integration: `config/autonomous_training.yaml` + +```yaml +# Enable quantum-enhanced LLM training +quantum_llm: + enabled: true + passive_mode: true + backend: "local" + n_qubits: 4 + training_interval_minutes: 60 + optimize_attention: true + quantum_feature_encoding: true +``` + +## How It Works + +### Quantum Attention Optimization + +The quantum attention optimizer uses quantum circuits to enhance attention mechanisms: + +1. **Encoding**: Attention scores are encoded into quantum states +2. **Processing**: Quantum gates apply transformations (superposition, entanglement) +3. **Measurement**: Quantum measurements produce optimized attention weights +4. **Interference**: Quantum interference patterns enhance attention distribution + +**Benefits**: +- More diverse attention patterns +- Better capture of long-range dependencies +- Potential for quantum speedup on quantum hardware + +### Quantum Feature Encoding + +Classical features are encoded into quantum states using amplitude encoding: + +1. **Normalization**: Features are normalized to unit vectors +2. **Amplitude Encoding**: Classical values become quantum amplitudes +3. **Variational Circuit**: Parameterized quantum gates process the encoded state +4. **Measurement**: Results are decoded back to classical features + +**Benefits**: +- Exponential feature space (2^n for n qubits) +- Quantum entanglement captures feature correlations +- Novel feature representations + +### Passive Training Workflow + +``` +┌──────────────────────────────────────────────────────────┐ +│ Passive Training Cycle (runs every interval) │ +├──────────────────────────────────────────────────────────┤ +│ 1. Scan datasets/chat/ for available datasets │ +│ 2. Select random dataset │ +│ 3. Load and prepare data │ +│ 4. Initialize quantum components │ +│ 5. Training loop: │ +│ ├── Forward pass │ +│ ├── Apply quantum optimization (every N steps) │ +│ ├── Backward pass │ +│ └── Update weights │ +│ 6. Save results and metrics │ +│ 7. Update status file │ +│ 8. Sleep until next cycle │ +└──────────────────────────────────────────────────────────┘ +``` + +## Quantum Backends + +### Local Simulator (Recommended for Development) + +- **Backend**: `default.qubit` (PennyLane) +- **Cost**: Free, unlimited +- **Speed**: Fast (CPU-based) +- **Use Case**: Development, testing, experimentation + +```yaml +quantum_settings: + backend: "local" +``` + +### Azure Quantum + +- **Free Simulators**: + - `ionq.simulator` - IonQ quantum simulator + - `quantinuum.sim.h1-1e` - Quantinuum H1 emulator +- **Paid QPU Hardware**: + - `ionq.qpu` - IonQ trapped-ion quantum computer + - `quantinuum.qpu.h1-1` - Quantinuum H1-1 quantum computer + +```yaml +quantum_settings: + backend: "azure" + azure: + resource_id: "your-azure-quantum-workspace-id" + target_backend: "ionq.simulator" + shots: 100 + confirm_cost: false # Must be true for QPU +``` + +## Monitoring and Metrics + +### Status File: `data_out/quantum_llm_training/status.json` + +```json +{ + "status": "success", + "epochs_completed": 3, + "final_loss": 1.234, + "quantum_metrics": { + "circuit_executions": 150, + "optimization_steps": 15, + "quantum_advantage_ratio": 1.05 + }, + "started_at": "2025-12-08T10:00:00", + "completed_at": "2025-12-08T10:15:00" +} +``` + +### Training Results: `data_out/quantum_llm_training/quantum_training_results.json` + +Detailed results including: +- Loss history per epoch +- Quantum circuit execution counts +- Optimization metrics +- Training timestamps + +### Logs + +Logs are written to: +- Console output (INFO level) +- Autonomous training log: `data_out/autonomous_training.log` (when integrated) + +## Performance Considerations + +### Resource Usage + +- **CPU**: Quantum simulation is CPU-intensive (1-4 cores recommended) +- **Memory**: ~2-4 GB per training cycle +- **Disk**: ~100 MB per cycle for checkpoints and results +- **GPU**: Optional (for classical neural network components) + +### Optimization Tips + +1. **Start Small**: Begin with 4 qubits, 2 layers +2. **Tune Frequency**: Adjust `attention_optimization_frequency` (higher = less quantum overhead) +3. **Batch Size**: Use smaller batches (4-8) for faster quantum circuits +4. **Cache Circuits**: Enable circuit caching to avoid recomputation + +### Cost Management (Azure Quantum) + +- Always test with FREE simulators first +- Set `confirm_cost: false` to prevent accidental QPU usage +- Monitor costs in Azure portal +- Start with low shot counts (100-500) + +## Testing + +### Run Unit Tests + +```bash +# Test quantum LLM trainer +pytest tests/test_quantum_llm_trainer.py -v + +# Run all quantum tests +pytest tests/ -k quantum -v + +# Integration tests +pytest tests/test_quantum_llm_trainer.py::TestQuantumLLMIntegration -v +``` + +### Validate Configuration + +```bash +# Test with dry-run +python scripts/quantum_llm_trainer.py \ + --dataset datasets/chat/aria_chat \ + --epochs 1 \ + --quantum-backend local +``` + +## Troubleshooting + +### Quantum Modules Not Available + +If you see warnings about quantum modules: + +```bash +# Install quantum-ai dependencies +cd quantum-ai +pip install -r requirements.txt + +# Or install specific packages +pip install pennylane qiskit torch +``` + +### Azure Quantum Connection Issues + +1. Verify workspace credentials in `config/quantum_llm_config.yaml` +2. Check Azure login: `az login` +3. Verify workspace access: `az quantum workspace list` +4. Try free simulator first: `target_backend: "ionq.simulator"` + +### Performance Issues + +- Reduce `n_qubits` (try 3-4 instead of 8+) +- Reduce `n_quantum_layers` (try 1-2 instead of 3+) +- Increase `attention_optimization_frequency` (apply quantum less often) +- Disable quantum feature encoding temporarily + +### Passive Mode Not Running + +1. Check `config/autonomous_training.yaml`: `quantum_llm.enabled: true` +2. Verify `scripts/quantum_llm_trainer.py` exists +3. Check logs: `tail -f data_out/autonomous_training.log` +4. Verify datasets exist: `ls datasets/chat/*/train.json` + +## Examples + +### Example 1: Quick Test + +```bash +# 1-minute test with local simulator +python scripts/quantum_llm_trainer.py \ + --dataset datasets/chat/aria_chat \ + --quantum-backend local \ + --n-qubits 3 \ + --n-quantum-layers 1 \ + --epochs 1 +``` + +### Example 2: Production Passive Training + +```bash +# Run continuous training in background +nohup python scripts/quantum_llm_trainer.py \ + --passive \ + --interval 3600 \ + --config config/quantum_llm_config.yaml \ + > data_out/quantum_llm_training.log 2>&1 & + +# Monitor +tail -f data_out/quantum_llm_training.log +``` + +### Example 3: Azure Quantum Integration + +```bash +# Configure Azure Quantum +cat > config/quantum_llm_config.yaml << EOF +quantum_settings: + backend: "azure" + n_qubits: 4 + azure: + resource_id: "/subscriptions/.../quantumWorkspaces/my-workspace" + target_backend: "ionq.simulator" + shots: 200 +EOF + +# Run with Azure +python scripts/quantum_llm_trainer.py \ + --dataset datasets/chat/aria_chat \ + --config config/quantum_llm_config.yaml +``` + +## Future Enhancements + +- [ ] Quantum-assisted hyperparameter optimization using QAOA +- [ ] Quantum circuit architecture search (quantum NAS) +- [ ] Multi-qubit entanglement patterns for attention heads +- [ ] Quantum error mitigation for QPU execution +- [ ] Distributed quantum training across multiple backends +- [ ] Quantum-classical co-training strategies + +## References + +- [PennyLane Documentation](https://pennylane.ai/) +- [Azure Quantum Documentation](https://learn.microsoft.com/en-us/azure/quantum/) +- [Quantum Machine Learning Papers](https://arxiv.org/list/quant-ph/recent) +- [Variational Quantum Algorithms](https://arxiv.org/abs/2012.09265) + +## Support + +For issues and questions: +1. Check troubleshooting section above +2. Review logs in `data_out/quantum_llm_training.log` +3. Test with local simulator first +4. Check ai-projects/quantum-ml/ module is properly configured + +## License + +This module is part of the Aria project and follows the same license. diff --git a/docs/guides/QUICK_START_MONETIZATION.md b/docs/guides/QUICK_START_MONETIZATION.md index 9e31697f6..9896cb8a3 100644 --- a/docs/guides/QUICK_START_MONETIZATION.md +++ b/docs/guides/QUICK_START_MONETIZATION.md @@ -1,230 +1,230 @@ -# 🚀 Quick Start: Aria Monetization System - -## ✅ What's Included - -Your Aria platform now has a complete monetization system that generates **$2,235/month** in recurring revenue (exceeding the $2,000 target by 11.8%). - -## 🎯 Revenue Breakdown - -| Tier | Subscribers | Price | Revenue | -|------|-------------|-------|---------| -| Pro | 5 | $49/mo | $245 | -| Enterprise | 10 | $199/mo | $1,990 | -| **Total** | **15** | - | **$2,235/mo** ✅ | - -**Annual Revenue:** $26,820 - -## 🎨 Live Demos - -### View the Pricing Page -```bash -# Option 1: Direct file -open pricing.html - -# Option 2: Via Functions (when running) -open http://localhost:7071/pricing.html -``` - -### View the Admin Dashboard -```bash -# View subscriber stats and revenue -open admin_dashboard.html -``` - -## 🔧 Quick Setup - -### 1. Test the System Locally -```bash -# Import and test subscription manager -python3 << 'PYEOF' -from shared.subscription_manager import get_subscription_manager - -manager = get_subscription_manager() -stats = manager.get_revenue_stats() -print(f"MRR: ${stats['monthly_recurring_revenue']}") -print(f"Target: ACHIEVED!" if stats['monthly_recurring_revenue'] >= 2000 else "Not yet") -PYEOF -``` - -### 2. Start Azure Functions -```bash -func host start -``` - -### 3. Test API Endpoints -```bash -# Get pricing info -curl http://localhost:7071/api/subscription/pricing | jq - -# Check subscription status -curl http://localhost:7071/api/subscription/status?user_id=demo_user | jq - -# Get revenue stats -curl http://localhost:7071/api/subscription/revenue | jq -``` - -## 💳 Enable Payments (Optional) - -### Add Stripe Integration - -1. Get your Stripe API keys from https://stripe.com/dashboard - -2. Set environment variables: -```bash -# In local.settings.json (for local dev) -{ - "Values": { - "STRIPE_SECRET_KEY": "sk_test_...", - "STRIPE_PUBLISHABLE_KEY": "pk_test_...", - "STRIPE_WEBHOOK_SECRET": "whsec_..." - } -} - -# Or in bash/PowerShell -export STRIPE_SECRET_KEY="sk_test_..." -export STRIPE_PUBLISHABLE_KEY="pk_test_..." -``` - -3. Update button click handlers in `pricing.html` to use Stripe Checkout - -## 📊 Monitoring Revenue - -### Check Current Stats -```bash -# Via API -curl http://localhost:7071/api/subscription/revenue | jq - -# Via Dashboard -open admin_dashboard.html -``` - -### Export Data -Open admin dashboard and click "📊 Export Data" button - -## 🎓 Integration Examples - -### Feature Gating -```python -from shared.subscription_manager import get_subscription_manager, Feature - -manager = get_subscription_manager() -subscription = manager.get_subscription(user_id) - -# Check if user can access quantum computing -if subscription.has_feature(Feature.QUANTUM_COMPUTING): - # Allow access - run_quantum_job() -else: - # Show upgrade prompt - return {"error": "Upgrade to Pro to use quantum computing"} -``` - -### Usage Tracking -```python -# Before running expensive operation -if manager.track_usage(user_id, 'quantum_jobs', 1): - # Usage within limits - result = run_quantum_job() -else: - # Limit exceeded - return { - "error": "Quantum job limit exceeded", - "upgrade_url": "/pricing.html" - } -``` - -### Check Remaining Usage -```python -subscription = manager.get_subscription(user_id) -percentage = subscription.get_usage_percentage('quantum_jobs') - -if percentage > 80: - # Send notification: approaching limit - notify_user("You've used 80% of your quantum computing quota") -``` - -## 📁 Key Files - -### Core System -- `shared/subscription_manager.py` - Subscription management logic -- `function_app.py` - API endpoints (5 new endpoints added) - -### UI Components -- `pricing.html` - Beautiful pricing page with 3 tiers -- `admin_dashboard.html` - Revenue analytics dashboard - -### Documentation -- `MONETIZATION_GUIDE.md` - Complete implementation guide -- `INCOME_STREAM_SUMMARY.md` - Executive summary with screenshots -- `QUICK_START_MONETIZATION.md` - This file - -## 🎯 Success Metrics - -``` -✅ Monthly Recurring Revenue: $2,235 (111.8% of target) -✅ Annual Recurring Revenue: $26,820 -✅ Required Subscribers: 15 (5 Pro + 10 Enterprise) -✅ Premium Features: 10 features gated -✅ API Endpoints: 5 new REST endpoints -✅ UI Components: 2 beautiful pages -✅ Status: Production-ready -``` - -## 🚀 Next Steps - -### Immediate (Day 1) -- [ ] Review pricing page and admin dashboard -- [ ] Test API endpoints -- [ ] Configure Stripe API keys - -### Short-term (Week 1) -- [ ] Deploy to production Azure Functions -- [ ] Add pricing link to main navigation -- [ ] Set up email notifications for limits - -### Medium-term (Month 1) -- [ ] Launch marketing campaigns -- [ ] Track conversion metrics -- [ ] Optimize pricing based on data - -## 💡 Marketing Ideas - -### Acquire First Customers -1. **Free Trial Campaign**: Offer Pro tier free for 7 days -2. **Launch Discount**: 20% off first month (limited time) -3. **Educational Content**: Create tutorials showcasing premium features -4. **Social Proof**: Add testimonials section to pricing page -5. **Referral Program**: Give $25 credit for each referral - -### Convert Free to Paid -- Show usage progress bars -- "Unlock feature" messaging when limits hit -- Email reminders at 80% usage -- Highlight most valuable features - -## 📞 Support - -### Questions? -- Technical docs: `MONETIZATION_GUIDE.md` -- API examples: See function_app.py endpoints -- Integration help: Check shared/subscription_manager.py docstrings - -### Need Help? -- Open an issue on GitHub -- Check the comprehensive documentation -- Review the test examples in this file - -## 🎉 Congratulations! - -Your Aria platform now has a complete, production-ready monetization system capable of generating **$2,000+ per month** in recurring revenue! - -**Access your tools:** -- 💰 Pricing Page: `open pricing.html` -- 📊 Admin Dashboard: `open admin_dashboard.html` -- 📚 Full Docs: `MONETIZATION_GUIDE.md` - ---- - -**Target:** $2,000/month -**Achieved:** $2,235/month (111.8%) -**Status:** ✅ Complete & Production-Ready +# 🚀 Quick Start: Aria Monetization System + +## ✅ What's Included + +Your Aria platform now has a complete monetization system that generates **$2,235/month** in recurring revenue (exceeding the $2,000 target by 11.8%). + +## 🎯 Revenue Breakdown + +| Tier | Subscribers | Price | Revenue | +|------|-------------|-------|---------| +| Pro | 5 | $49/mo | $245 | +| Enterprise | 10 | $199/mo | $1,990 | +| **Total** | **15** | - | **$2,235/mo** ✅ | + +**Annual Revenue:** $26,820 + +## 🎨 Live Demos + +### View the Pricing Page +```bash +# Option 1: Direct file +open pricing.html + +# Option 2: Via Functions (when running) +open http://localhost:7071/pricing.html +``` + +### View the Admin Dashboard +```bash +# View subscriber stats and revenue +open admin_dashboard.html +``` + +## 🔧 Quick Setup + +### 1. Test the System Locally +```bash +# Import and test subscription manager +python3 << 'PYEOF' +from shared.subscription_manager import get_subscription_manager + +manager = get_subscription_manager() +stats = manager.get_revenue_stats() +print(f"MRR: ${stats['monthly_recurring_revenue']}") +print(f"Target: ACHIEVED!" if stats['monthly_recurring_revenue'] >= 2000 else "Not yet") +PYEOF +``` + +### 2. Start Azure Functions +```bash +func host start +``` + +### 3. Test API Endpoints +```bash +# Get pricing info +curl http://localhost:7071/api/subscription/pricing | jq + +# Check subscription status +curl http://localhost:7071/api/subscription/status?user_id=demo_user | jq + +# Get revenue stats +curl http://localhost:7071/api/subscription/revenue | jq +``` + +## 💳 Enable Payments (Optional) + +### Add Stripe Integration + +1. Get your Stripe API keys from https://stripe.com/dashboard + +2. Set environment variables: +```bash +# In local.settings.json (for local dev) +{ + "Values": { + "STRIPE_SECRET_KEY": "sk_test_...", + "STRIPE_PUBLISHABLE_KEY": "pk_test_...", + "STRIPE_WEBHOOK_SECRET": "whsec_..." + } +} + +# Or in bash/PowerShell +export STRIPE_SECRET_KEY="sk_test_..." +export STRIPE_PUBLISHABLE_KEY="pk_test_..." +``` + +3. Update button click handlers in `pricing.html` to use Stripe Checkout + +## 📊 Monitoring Revenue + +### Check Current Stats +```bash +# Via API +curl http://localhost:7071/api/subscription/revenue | jq + +# Via Dashboard +open admin_dashboard.html +``` + +### Export Data +Open admin dashboard and click "📊 Export Data" button + +## 🎓 Integration Examples + +### Feature Gating +```python +from shared.subscription_manager import get_subscription_manager, Feature + +manager = get_subscription_manager() +subscription = manager.get_subscription(user_id) + +# Check if user can access quantum computing +if subscription.has_feature(Feature.QUANTUM_COMPUTING): + # Allow access + run_quantum_job() +else: + # Show upgrade prompt + return {"error": "Upgrade to Pro to use quantum computing"} +``` + +### Usage Tracking +```python +# Before running expensive operation +if manager.track_usage(user_id, 'quantum_jobs', 1): + # Usage within limits + result = run_quantum_job() +else: + # Limit exceeded + return { + "error": "Quantum job limit exceeded", + "upgrade_url": "/pricing.html" + } +``` + +### Check Remaining Usage +```python +subscription = manager.get_subscription(user_id) +percentage = subscription.get_usage_percentage('quantum_jobs') + +if percentage > 80: + # Send notification: approaching limit + notify_user("You've used 80% of your quantum computing quota") +``` + +## 📁 Key Files + +### Core System +- `shared/subscription_manager.py` - Subscription management logic +- `function_app.py` - API endpoints (5 new endpoints added) + +### UI Components +- `pricing.html` - Beautiful pricing page with 3 tiers +- `admin_dashboard.html` - Revenue analytics dashboard + +### Documentation +- `MONETIZATION_GUIDE.md` - Complete implementation guide +- `INCOME_STREAM_SUMMARY.md` - Executive summary with screenshots +- `QUICK_START_MONETIZATION.md` - This file + +## 🎯 Success Metrics + +``` +✅ Monthly Recurring Revenue: $2,235 (111.8% of target) +✅ Annual Recurring Revenue: $26,820 +✅ Required Subscribers: 15 (5 Pro + 10 Enterprise) +✅ Premium Features: 10 features gated +✅ API Endpoints: 5 new REST endpoints +✅ UI Components: 2 beautiful pages +✅ Status: Production-ready +``` + +## 🚀 Next Steps + +### Immediate (Day 1) +- [ ] Review pricing page and admin dashboard +- [ ] Test API endpoints +- [ ] Configure Stripe API keys + +### Short-term (Week 1) +- [ ] Deploy to production Azure Functions +- [ ] Add pricing link to main navigation +- [ ] Set up email notifications for limits + +### Medium-term (Month 1) +- [ ] Launch marketing campaigns +- [ ] Track conversion metrics +- [ ] Optimize pricing based on data + +## 💡 Marketing Ideas + +### Acquire First Customers +1. **Free Trial Campaign**: Offer Pro tier free for 7 days +2. **Launch Discount**: 20% off first month (limited time) +3. **Educational Content**: Create tutorials showcasing premium features +4. **Social Proof**: Add testimonials section to pricing page +5. **Referral Program**: Give $25 credit for each referral + +### Convert Free to Paid +- Show usage progress bars +- "Unlock feature" messaging when limits hit +- Email reminders at 80% usage +- Highlight most valuable features + +## 📞 Support + +### Questions? +- Technical docs: `MONETIZATION_GUIDE.md` +- API examples: See function_app.py endpoints +- Integration help: Check shared/subscription_manager.py docstrings + +### Need Help? +- Open an issue on GitHub +- Check the comprehensive documentation +- Review the test examples in this file + +## 🎉 Congratulations! + +Your Aria platform now has a complete, production-ready monetization system capable of generating **$2,000+ per month** in recurring revenue! + +**Access your tools:** +- 💰 Pricing Page: `open pricing.html` +- 📊 Admin Dashboard: `open admin_dashboard.html` +- 📚 Full Docs: `MONETIZATION_GUIDE.md` + +--- + +**Target:** $2,000/month +**Achieved:** $2,235/month (111.8%) +**Status:** ✅ Complete & Production-Ready diff --git a/docs/guides/REPO_AUTOMATION_GUIDE.md b/docs/guides/REPO_AUTOMATION_GUIDE.md index 03c4ae9b4..906efea4d 100644 --- a/docs/guides/REPO_AUTOMATION_GUIDE.md +++ b/docs/guides/REPO_AUTOMATION_GUIDE.md @@ -1,457 +1,457 @@ -# 🚀 Repository-Wide Automation Guide - -Complete automation system for the entire Aria repository. - -## Quick Start - -```bash -# Start everything -./scripts/start_repo_automation.sh full - -# Or use Python directly -python scripts/repo_automation.py --start --daemon - -# Check status -./scripts/start_repo_automation.sh status - -# Stop all -./scripts/start_repo_automation.sh stop -``` - -## Components (Current Architecture) - -The automation system now uses a lightweight component registry with dynamic enabling and automatic dependency resolution. Some legacy scripts listed previously have been consolidated or made optional. - -### 1. Aria Character Automation (`aria`) - -- **What**: Aria web server + Aria-specific training loop + backend (Azure Functions) if available -- **Script**: `scripts/aria_automation.py` -- **Health Check Interval**: 60s -- **Auto-Restart**: Yes -- **Dependencies Auto-Installed**: `psutil` - -### 2. Autonomous Training System (`training`) - -- **What**: Continuous adaptive cycles (dataset discovery + optional download + distributed benchmark training + performance analysis) -- **Script**: `scripts/autonomous_training_orchestrator.py` -- **Health Check Interval**: 5min -- **Auto-Restart**: Yes -- **Integrated**: Dataset auto-discovery (replaces standalone dataset component) -- **Dependencies Auto-Installed**: `pandas`, `torch`, `numpy`, `pyyaml` - -### 3. Quantum Computing Workflows (`quantum`) - -- **What**: Quantum ML / job submission orchestrator -- **Script**: `scripts/quantum_autorun.py` -- **Enabled When**: `config/quantum/quantum_autorun.yaml` (falls back to root `quantum_autorun.yaml`) -- **Auto-Restart**: No (jobs are typically batch / scheduled) -- **Dependencies**: (Add Azure Quantum SDK when environment configured) - -### 4. Model Evaluation System (`evaluation`) - -- **What**: Batch model evaluation against curated test sets -- **Script**: `scripts/evaluation_autorun.py` -- **Enabled When**: `config/evaluation/evaluation_autorun.yaml` (falls back to root `evaluation_autorun.yaml`) -- **Dependencies**: `training` component must be running -- **Auto-Restart**: No (designed for batch cycles) -- **Dependencies Auto-Installed**: `scikit-learn`, `numpy`, `matplotlib`, `seaborn` - -### 5. Status Dashboard (`monitoring` / optional) - -- **What**: Web dashboard for live status (optional) -- **Script**: `scripts/status_dashboard.py` -- **Enabled**: Manually (disabled by default) -- **Dependencies Auto-Installed**: `Flask`, `flask-socketio`, `python-socketio` - -### 6. Backup Manager (`backup` / optional) - -- **What**: Point-in-time backups of adapters, configs, logs -- **Script**: `scripts/backup_manager.py` -- **Enabled**: Manually (disabled by default; invoke on-demand or via cron) -- **Supports**: Incremental backups (hardlinks) + compression + manifest tracking - -### Deprecated / Integrated - -- **Dataset Auto-Discovery**: Integrated into `training` component (no separate process) -- **System Health Monitor**: Basic health checking performed internally; full external monitor can be added if needed. - -Each component’s dependency set is verified just-in-time; missing packages are installed automatically via `pip` before the component starts. Failures are recorded and the component will not start until resolved (preventing restart loops caused purely by missing dependencies). - -## Automatic Dependency Resolution & Conditional Enabling - -### How It Works - -1. On startup or when starting a component, required packages listed in `ComponentConfig.required_packages` are imported. -2. Missing modules trigger automatic `pip install` attempts. -3. Post-install imports are re-validated; any remaining failures are logged and block component start. -4. Dependency health per component appears in `status.json` under `dependency_status` and in `--status` output with an icon: - - `🧩` Dependencies satisfied - - `⚠️` Dependency issue (component may not have started) - -### Conditional Enabling - -Components are auto-enabled based on presence of configuration files: - -- `quantum`: `config/quantum/quantum_autorun.yaml` exists (or root fallback); default disabled. -- `evaluation`: `config/evaluation/evaluation_autorun.yaml` exists (or root fallback); default disabled. -- `training`: always enabled. -- `aria`: always enabled. -- `monitoring`: manual enablement only. -- `backup`: manual enablement only. -- `datasets`: integrated into `training`; no separate component. - -### Adding New Components - -Update `scripts/repo_automation.py` and include a `required_packages` list. Example: - -```python -"my_component": ComponentConfig( - name="My Custom Service", - script="scripts/my_service.py", - command=["python3", "scripts/my_service.py"], - auto_restart=True, - health_check_interval=120, - required_packages=["requests", "rich"], -), -``` - -### Handling Heavy Dependencies (e.g., `torch`) - -- Install attempts are synchronous; large packages may take time. -- To avoid blocking other components, start with smaller ones (`aria`) first or pre-install heavy libs via `pip install -r requirements.txt`. -- If you need GPU variants, pre-install manually (automation will skip if already present). - -### Failure Recovery - -- If dependency installation fails, a concise error is appended to `errors` in `status.json`. -- Fix by manually installing or adjusting network/proxy settings, then restart the component. - -### Security & Safety - -- Only standard `pip install ` commands are executed—no arbitrary shell code. -- Consider pinning versions in `requirements.txt` for reproducibility in production environments. - -## Usage Modes - -### Full Automation (Production) - -```bash -# Interactive -./scripts/start_repo_automation.sh - -# Command line -./scripts/start_repo_automation.sh full - -# Background -./scripts/start_repo_automation.sh full --background -``` - -Starts all 7 components with health monitoring and auto-recovery. - -### Aria Only - -```bash -./scripts/start_repo_automation.sh aria -``` - -Just the Aria character automation (server + training). - -### Training Pipeline - -```bash -./scripts/start_repo_automation.sh training -``` - -Training + evaluation only. - -### Custom Selection - -```bash -# Interactive -./scripts/start_repo_automation.sh -# Choose option 4 - -# Command line -./scripts/start_repo_automation.sh components aria,training,quantum -``` - -## Monitoring - -### Check Status - -```bash -# Using wrapper script -./scripts/start_repo_automation.sh status - -# Using Python directly -python scripts/repo_automation.py --status -``` - -Output shows: - -- Uptime -- Components running/stopped -- Health check count -- Recent errors - -### Status File - -```bash -cat data_out/repo_automation/status.json -``` - -Machine-readable status including: - -- Start time -- Uptime in seconds -- Component states -- Error log - -### Logs - -```bash -# Automation log -tail -f data_out/repo_automation/automation.log - -# Individual component logs -tail -f data_out/aria_automation/*.log -tail -f data_out/autotrain/*.log -``` - -## Operational Smoke Flow - -Use this sequence after config or orchestrator changes. - -```bash -# 1) Repository integration smoke (local fast gate) -python scripts/integration_smoke.py --json - -# 2) CI smoke stage parity check -python scripts/ci_orchestrator.py --integration-smoke - -# 3) Focused integration contract tests -python scripts/ci_orchestrator.py --integration-contract-tests - -# 4) Core orchestrator dry-run checks -python scripts/ci_orchestrator.py --validate-all - -# 5) Unified runtime view (optional continuous watch) -python scripts/status_dashboard.py --watch -``` - -Shortcut in VS Code: run task `integration:contract-gate` from `.vscode/tasks.json` to execute steps 1-4 in one command. - -Shell shortcut: `bash ./scripts/integration_contract_gate.sh` (add `--strict-endpoints` to require local Functions host). - -If step 1 fails, fix integration wiring first. If step 1 passes but step 2 or 3 fails, fix CI parity or contract regressions before merging. - -## Production Deployment - -### Option 1: Systemd Service - -Create `/etc/systemd/system/aria-repo-automation.service`: - -```ini -[Unit] -Description=Aria Repository Automation -After=network.target - -[Service] -Type=simple -User=your_user -WorkingDirectory=/path/to/Aria -ExecStart=/usr/bin/python3 /path/to/Aria/scripts/repo_automation.py --start --daemon -Restart=always -RestartSec=10 - -[Install] -WantedBy=multi-user.target -``` - -Enable and start: - -```bash -sudo systemctl enable aria-repo-automation -sudo systemctl start aria-repo-automation -sudo systemctl status aria-repo-automation -``` - -### Option 2: Cron (Schedule-Based) - -Add to crontab: - -```bash -# Full automation every 30 minutes -*/30 * * * * cd /path/to/Aria && ./scripts/start_repo_automation.sh full --background - -# Or specific components -0 */6 * * * cd /path/to/Aria && ./scripts/start_repo_automation.sh training --background -``` - -### Option 3: Docker/Container - -```dockerfile -FROM python:3.11-slim - -WORKDIR /app -COPY . . - -RUN pip install -r requirements.txt -RUN apt-get update && apt-get install -y func-cli - -CMD ["python3", "scripts/repo_automation.py", "--start", "--daemon"] -``` - -## Configuration - -### Component Settings - -Edit `scripts/repo_automation.py`: - -```python -ComponentConfig( - name="Your Component", - script="scripts/your_script.py", - command=["python3", "scripts/your_script.py", "--continuous"], - auto_restart=True, - health_check_interval=300, # seconds - dependencies=["other_component"], -) -``` - -### Environment Variables - -```bash -# Azure Functions -export AZURE_OPENAI_API_KEY="your-key" -export AZURE_OPENAI_ENDPOINT="your-endpoint" - -# Database -export QAI_DB_CONN="your-connection-string" - -# Cosmos DB -export QAI_ENABLE_COSMOS=true -export COSMOS_ENDPOINT="your-endpoint" -export COSMOS_KEY="your-key" -``` - -## Troubleshooting - -### Component Won't Start - -```bash -# Check dependencies -python scripts/repo_automation.py --status - -# Manual test -python scripts/aria_automation.py --help - -# Check logs -tail -f data_out/repo_automation/automation.log -``` - -### Auto-Restart Loop - -If a component keeps restarting: - -1. Check component-specific logs in `data_out//` -2. Disable auto-restart: Edit `scripts/repo_automation.py`, set `auto_restart=False` -3. Test component manually - -### High Resource Usage - -```bash -# Check monitoring -tail -f data_out/system_health_check/health.log - -# Disable heavy components -./scripts/start_repo_automation.sh components aria,monitoring - -# Adjust health check intervals -# Edit scripts/repo_automation.py, increase health_check_interval -``` - -### Stopping Stuck Processes - -```bash -# Graceful stop -./scripts/start_repo_automation.sh stop - -# Force kill -pkill -f repo_automation.py -pkill -f aria_automation.py -pkill -f autotrain.py -``` - -## Integration with Master Orchestrator - -The repo automation works with the existing master orchestrator: - -```bash -# Master orchestrator runs scheduled workflows -python scripts/master_orchestrator.py --schedule - -# Repo automation provides continuous operation -python scripts/repo_automation.py --start --daemon -``` - -Use both together: - -- **Master Orchestrator**: Scheduled batch jobs (daily training runs, evaluations) -- **Repo Automation**: Continuous services (Aria server, monitoring, backups) - -## Advanced Features - -### Custom Components - -Add to `scripts/repo_automation.py`: - -```python -"your_component": ComponentConfig( - name="Your Custom Component", - script="scripts/your_script.py", - command=["python3", "scripts/your_script.py", "--daemon"], - auto_restart=True, - health_check_interval=300, -), -``` - -### Notification Hooks - -Add notification on failures: - -```python -def on_component_failure(component_name: str): - # Send email/Slack/Teams notification - pass -``` - -### Backup to Cloud - -Edit `scripts/backup_manager.py`: - -```python -def upload_to_cloud(backup_path: Path): - # Upload to Azure Blob Storage / AWS S3 - pass -``` - -## Best Practices - -1. **Start Small**: Test with `aria` component first, then add more -2. **Monitor Logs**: Keep an eye on logs during first 24 hours -3. **Resource Limits**: Set resource limits in systemd service -4. **Backup First**: Create manual backup before enabling automation -5. **Test Recovery**: Manually stop components to test auto-restart -6. **Document Changes**: Update this guide when adding custom components - -## Support - -- **Main Docs**: [README.md](../../README.md) -- **Aria Automation**: [ARIA_AUTOMATION_GUIDE.md](ARIA_AUTOMATION_GUIDE.md) -- **Master Orchestrator**: `scripts/master_orchestrator.py --help` -- **Component Docs**: See individual script help messages - -## License - -Same as repository - see [README.md](../../README.md) +# 🚀 Repository-Wide Automation Guide + +Complete automation system for the entire Aria repository. + +## Quick Start + +```bash +# Start everything +./scripts/start_repo_automation.sh full + +# Or use Python directly +python scripts/repo_automation.py --start --daemon + +# Check status +./scripts/start_repo_automation.sh status + +# Stop all +./scripts/start_repo_automation.sh stop +``` + +## Components (Current Architecture) + +The automation system now uses a lightweight component registry with dynamic enabling and automatic dependency resolution. Some legacy scripts listed previously have been consolidated or made optional. + +### 1. Aria Character Automation (`aria`) + +- **What**: Aria web server + Aria-specific training loop + backend (Azure Functions) if available +- **Script**: `scripts/aria_automation.py` +- **Health Check Interval**: 60s +- **Auto-Restart**: Yes +- **Dependencies Auto-Installed**: `psutil` + +### 2. Autonomous Training System (`training`) + +- **What**: Continuous adaptive cycles (dataset discovery + optional download + distributed benchmark training + performance analysis) +- **Script**: `scripts/autonomous_training_orchestrator.py` +- **Health Check Interval**: 5min +- **Auto-Restart**: Yes +- **Integrated**: Dataset auto-discovery (replaces standalone dataset component) +- **Dependencies Auto-Installed**: `pandas`, `torch`, `numpy`, `pyyaml` + +### 3. Quantum Computing Workflows (`quantum`) + +- **What**: Quantum ML / job submission orchestrator +- **Script**: `scripts/quantum_autorun.py` +- **Enabled When**: `config/quantum/quantum_autorun.yaml` (falls back to root `quantum_autorun.yaml`) +- **Auto-Restart**: No (jobs are typically batch / scheduled) +- **Dependencies**: (Add Azure Quantum SDK when environment configured) + +### 4. Model Evaluation System (`evaluation`) + +- **What**: Batch model evaluation against curated test sets +- **Script**: `scripts/evaluation_autorun.py` +- **Enabled When**: `config/evaluation/evaluation_autorun.yaml` (falls back to root `evaluation_autorun.yaml`) +- **Dependencies**: `training` component must be running +- **Auto-Restart**: No (designed for batch cycles) +- **Dependencies Auto-Installed**: `scikit-learn`, `numpy`, `matplotlib`, `seaborn` + +### 5. Status Dashboard (`monitoring` / optional) + +- **What**: Web dashboard for live status (optional) +- **Script**: `scripts/status_dashboard.py` +- **Enabled**: Manually (disabled by default) +- **Dependencies Auto-Installed**: `Flask`, `flask-socketio`, `python-socketio` + +### 6. Backup Manager (`backup` / optional) + +- **What**: Point-in-time backups of adapters, configs, logs +- **Script**: `scripts/backup_manager.py` +- **Enabled**: Manually (disabled by default; invoke on-demand or via cron) +- **Supports**: Incremental backups (hardlinks) + compression + manifest tracking + +### Deprecated / Integrated + +- **Dataset Auto-Discovery**: Integrated into `training` component (no separate process) +- **System Health Monitor**: Basic health checking performed internally; full external monitor can be added if needed. + +Each component’s dependency set is verified just-in-time; missing packages are installed automatically via `pip` before the component starts. Failures are recorded and the component will not start until resolved (preventing restart loops caused purely by missing dependencies). + +## Automatic Dependency Resolution & Conditional Enabling + +### How It Works + +1. On startup or when starting a component, required packages listed in `ComponentConfig.required_packages` are imported. +2. Missing modules trigger automatic `pip install` attempts. +3. Post-install imports are re-validated; any remaining failures are logged and block component start. +4. Dependency health per component appears in `status.json` under `dependency_status` and in `--status` output with an icon: + - `🧩` Dependencies satisfied + - `⚠️` Dependency issue (component may not have started) + +### Conditional Enabling + +Components are auto-enabled based on presence of configuration files: + +- `quantum`: `config/quantum/quantum_autorun.yaml` exists (or root fallback); default disabled. +- `evaluation`: `config/evaluation/evaluation_autorun.yaml` exists (or root fallback); default disabled. +- `training`: always enabled. +- `aria`: always enabled. +- `monitoring`: manual enablement only. +- `backup`: manual enablement only. +- `datasets`: integrated into `training`; no separate component. + +### Adding New Components + +Update `scripts/repo_automation.py` and include a `required_packages` list. Example: + +```python +"my_component": ComponentConfig( + name="My Custom Service", + script="scripts/my_service.py", + command=["python3", "scripts/my_service.py"], + auto_restart=True, + health_check_interval=120, + required_packages=["requests", "rich"], +), +``` + +### Handling Heavy Dependencies (e.g., `torch`) + +- Install attempts are synchronous; large packages may take time. +- To avoid blocking other components, start with smaller ones (`aria`) first or pre-install heavy libs via `pip install -r requirements.txt`. +- If you need GPU variants, pre-install manually (automation will skip if already present). + +### Failure Recovery + +- If dependency installation fails, a concise error is appended to `errors` in `status.json`. +- Fix by manually installing or adjusting network/proxy settings, then restart the component. + +### Security & Safety + +- Only standard `pip install ` commands are executed—no arbitrary shell code. +- Consider pinning versions in `requirements.txt` for reproducibility in production environments. + +## Usage Modes + +### Full Automation (Production) + +```bash +# Interactive +./scripts/start_repo_automation.sh + +# Command line +./scripts/start_repo_automation.sh full + +# Background +./scripts/start_repo_automation.sh full --background +``` + +Starts all 7 components with health monitoring and auto-recovery. + +### Aria Only + +```bash +./scripts/start_repo_automation.sh aria +``` + +Just the Aria character automation (server + training). + +### Training Pipeline + +```bash +./scripts/start_repo_automation.sh training +``` + +Training + evaluation only. + +### Custom Selection + +```bash +# Interactive +./scripts/start_repo_automation.sh +# Choose option 4 + +# Command line +./scripts/start_repo_automation.sh components aria,training,quantum +``` + +## Monitoring + +### Check Status + +```bash +# Using wrapper script +./scripts/start_repo_automation.sh status + +# Using Python directly +python scripts/repo_automation.py --status +``` + +Output shows: + +- Uptime +- Components running/stopped +- Health check count +- Recent errors + +### Status File + +```bash +cat data_out/repo_automation/status.json +``` + +Machine-readable status including: + +- Start time +- Uptime in seconds +- Component states +- Error log + +### Logs + +```bash +# Automation log +tail -f data_out/repo_automation/automation.log + +# Individual component logs +tail -f data_out/aria_automation/*.log +tail -f data_out/autotrain/*.log +``` + +## Operational Smoke Flow + +Use this sequence after config or orchestrator changes. + +```bash +# 1) Repository integration smoke (local fast gate) +python scripts/integration_smoke.py --json + +# 2) CI smoke stage parity check +python scripts/ci_orchestrator.py --integration-smoke + +# 3) Focused integration contract tests +python scripts/ci_orchestrator.py --integration-contract-tests + +# 4) Core orchestrator dry-run checks +python scripts/ci_orchestrator.py --validate-all + +# 5) Unified runtime view (optional continuous watch) +python scripts/status_dashboard.py --watch +``` + +Shortcut in VS Code: run task `integration:contract-gate` from `.vscode/tasks.json` to execute steps 1-4 in one command. + +Shell shortcut: `bash ./scripts/integration_contract_gate.sh` (add `--strict-endpoints` to require local Functions host). + +If step 1 fails, fix integration wiring first. If step 1 passes but step 2 or 3 fails, fix CI parity or contract regressions before merging. + +## Production Deployment + +### Option 1: Systemd Service + +Create `/etc/systemd/system/aria-repo-automation.service`: + +```ini +[Unit] +Description=Aria Repository Automation +After=network.target + +[Service] +Type=simple +User=your_user +WorkingDirectory=/path/to/Aria +ExecStart=/usr/bin/python3 /path/to/Aria/scripts/repo_automation.py --start --daemon +Restart=always +RestartSec=10 + +[Install] +WantedBy=multi-user.target +``` + +Enable and start: + +```bash +sudo systemctl enable aria-repo-automation +sudo systemctl start aria-repo-automation +sudo systemctl status aria-repo-automation +``` + +### Option 2: Cron (Schedule-Based) + +Add to crontab: + +```bash +# Full automation every 30 minutes +*/30 * * * * cd /path/to/Aria && ./scripts/start_repo_automation.sh full --background + +# Or specific components +0 */6 * * * cd /path/to/Aria && ./scripts/start_repo_automation.sh training --background +``` + +### Option 3: Docker/Container + +```dockerfile +FROM python:3.11-slim + +WORKDIR /app +COPY . . + +RUN pip install -r requirements.txt +RUN apt-get update && apt-get install -y func-cli + +CMD ["python3", "scripts/repo_automation.py", "--start", "--daemon"] +``` + +## Configuration + +### Component Settings + +Edit `scripts/repo_automation.py`: + +```python +ComponentConfig( + name="Your Component", + script="scripts/your_script.py", + command=["python3", "scripts/your_script.py", "--continuous"], + auto_restart=True, + health_check_interval=300, # seconds + dependencies=["other_component"], +) +``` + +### Environment Variables + +```bash +# Azure Functions +export AZURE_OPENAI_API_KEY="your-key" +export AZURE_OPENAI_ENDPOINT="your-endpoint" + +# Database +export QAI_DB_CONN="your-connection-string" + +# Cosmos DB +export QAI_ENABLE_COSMOS=true +export COSMOS_ENDPOINT="your-endpoint" +export COSMOS_KEY="your-key" +``` + +## Troubleshooting + +### Component Won't Start + +```bash +# Check dependencies +python scripts/repo_automation.py --status + +# Manual test +python scripts/aria_automation.py --help + +# Check logs +tail -f data_out/repo_automation/automation.log +``` + +### Auto-Restart Loop + +If a component keeps restarting: + +1. Check component-specific logs in `data_out//` +2. Disable auto-restart: Edit `scripts/repo_automation.py`, set `auto_restart=False` +3. Test component manually + +### High Resource Usage + +```bash +# Check monitoring +tail -f data_out/system_health_check/health.log + +# Disable heavy components +./scripts/start_repo_automation.sh components aria,monitoring + +# Adjust health check intervals +# Edit scripts/repo_automation.py, increase health_check_interval +``` + +### Stopping Stuck Processes + +```bash +# Graceful stop +./scripts/start_repo_automation.sh stop + +# Force kill +pkill -f repo_automation.py +pkill -f aria_automation.py +pkill -f autotrain.py +``` + +## Integration with Master Orchestrator + +The repo automation works with the existing master orchestrator: + +```bash +# Master orchestrator runs scheduled workflows +python scripts/master_orchestrator.py --schedule + +# Repo automation provides continuous operation +python scripts/repo_automation.py --start --daemon +``` + +Use both together: + +- **Master Orchestrator**: Scheduled batch jobs (daily training runs, evaluations) +- **Repo Automation**: Continuous services (Aria server, monitoring, backups) + +## Advanced Features + +### Custom Components + +Add to `scripts/repo_automation.py`: + +```python +"your_component": ComponentConfig( + name="Your Custom Component", + script="scripts/your_script.py", + command=["python3", "scripts/your_script.py", "--daemon"], + auto_restart=True, + health_check_interval=300, +), +``` + +### Notification Hooks + +Add notification on failures: + +```python +def on_component_failure(component_name: str): + # Send email/Slack/Teams notification + pass +``` + +### Backup to Cloud + +Edit `scripts/backup_manager.py`: + +```python +def upload_to_cloud(backup_path: Path): + # Upload to Azure Blob Storage / AWS S3 + pass +``` + +## Best Practices + +1. **Start Small**: Test with `aria` component first, then add more +2. **Monitor Logs**: Keep an eye on logs during first 24 hours +3. **Resource Limits**: Set resource limits in systemd service +4. **Backup First**: Create manual backup before enabling automation +5. **Test Recovery**: Manually stop components to test auto-restart +6. **Document Changes**: Update this guide when adding custom components + +## Support + +- **Main Docs**: [README.md](../../README.md) +- **Aria Automation**: [ARIA_AUTOMATION_GUIDE.md](ARIA_AUTOMATION_GUIDE.md) +- **Master Orchestrator**: `scripts/master_orchestrator.py --help` +- **Component Docs**: See individual script help messages + +## License + +Same as repository - see [README.md](../../README.md) diff --git a/docs/guides/REPO_INTEGRATION_PLAN.md b/docs/guides/REPO_INTEGRATION_PLAN.md index 04524f268..df10d9df4 100644 --- a/docs/guides/REPO_INTEGRATION_PLAN.md +++ b/docs/guides/REPO_INTEGRATION_PLAN.md @@ -1,263 +1,263 @@ -# Aria Repository Integration Plan - -Last updated: 2026-03-13 -Status: Draft for execution - -## Purpose - -This document defines an extensive, execution-ready integration roadmap for the Aria monorepo. It aligns runtime behavior, orchestration, documentation, and operations across the following major areas: - -- Azure Functions API layer (`function_app.py`) -- Aria character web runtime (`aria_web/`, `apps/aria/`) -- Chat provider stack (`ai-projects/chat-cli/`, `shared/chat_providers.py` wrappers) -- Quantum pipeline (`ai-projects/quantum-ml/`, `scripts/quantum_autorun.py`) -- LoRA training and promotion (`ai-projects/lora-training/`, `scripts/train_and_promote.py`) -- Repository automation (`scripts/repo_automation.py`, `scripts/master_orchestrator.py`) - -## Goals - -1. Establish one canonical integration contract for paths, configs, and provider behavior. -2. Preserve backward compatibility for legacy paths while transitioning to canonical locations. -3. Ensure end-to-end workflows are testable with local-first dry-run and health checks. -4. Reduce operational drift between docs and actual runtime logic. -5. Provide a phased rollout with clear validation gates and rollback points. - -## Non-Goals - -1. Replacing all legacy wrappers immediately. -2. Redesigning model architectures or changing provider internals unrelated to integration. -3. Running paid quantum hardware jobs as part of baseline integration validation. - -## Current-State Findings - -1. Config location drift exists between scripts and actual config files. -2. Documentation still references mixed legacy and canonical path conventions. -3. Orchestration and monitoring are useful but split across multiple tools without a single control-plane contract. -4. Provider auto-selection behavior in code is broader than several docs currently describe. - -## Canonical Integration Contract - -### Path Canon - -Automation configs: - -- `config/master_orchestrator.yaml` -- `config/quantum/quantum_autorun.yaml` -- `config/evaluation/evaluation_autorun.yaml` - -Automation status artifacts: - -- `data_out/repo_automation/status.json` -- `data_out/repo_automation/processes.json` -- `data_out/master_orchestrator/status.json` - -Generated outputs: - -- All generated artifacts go under `data_out/**`. -- `datasets/**` remains read-only. - -### Backward Compatibility - -1. Read legacy root-level config/status files when canonical files are absent. -2. Continue writing compatibility status/PID files during migration where needed. -3. Keep import-path wrappers until tests and external scripts are fully migrated. - -### Provider Resolution Contract - -Default auto mode should reflect actual code behavior and docs: - -1. LM Studio (if reachable) -2. Ollama (if reachable) -3. Azure OpenAI (if env complete) -4. OpenAI (if key present) -5. Local fallback - -Explicit provider overrides still take precedence (`agi`, `quantum`, `lora`, `azure`, `openai`, `lmstudio`, `ollama`, `local`). - -## Phased Execution Plan - -## Phase 0: Contract Freeze (Week 1) - -Deliverables: - -1. Finalize this integration contract. -2. Publish path and provider contract in top-level docs. -3. Add a migration note for legacy compatibility behavior. - -Validation: - -1. `python scripts/repo_automation.py --status` -2. `python scripts/master_orchestrator.py --status` -3. `curl http://localhost:7071/api/ai/status` - -Exit Criteria: - -1. No ambiguous path guidance in core docs. - -## Phase 1: Path and Config Normalization (Week 1-2) - -Deliverables: - -1. Normalize default config lookup to canonical `config/**` paths. -2. Keep fallback resolution for legacy root files. -3. Normalize status artifacts to canonical `data_out/**` locations. -4. Keep compatibility reads/writes for legacy status files during transition. - -Validation: - -1. `python scripts/quantum_autorun.py --dry-run` -2. `python scripts/repo_automation.py --status` -3. `python scripts/master_orchestrator.py --status` - -Exit Criteria: - -1. Canonical paths work out-of-the-box. -2. Existing workflows depending on legacy files still function. - -## Phase 2: Provider and API Integration Alignment (Week 2-3) - -Deliverables: - -1. Reconcile docs with actual provider auto-detection behavior. -2. Add provider-resolution smoke tests for explicit and auto modes. -3. Ensure `/api/ai/status` exposes enough readiness diagnostics for troubleshooting. - -Validation: - -1. CLI smoke checks per provider mode. -2. Functions endpoint check for `/api/chat` and `/api/ai/status`. - -Exit Criteria: - -1. Provider docs and runtime behavior are consistent. - -## Phase 3: Orchestration Convergence (Week 3-4) - -Deliverables: - -1. Clarify responsibilities: - -- `repo_automation.py`: process lifecycle and health loops -- `master_orchestrator.py`: workflow dependencies and schedules - -1. Implement robust schedule matching for scheduled workflows. -1. Standardize workflow result metadata and logging layout. - -Validation: - -1. Manual workflow trigger verification. -2. Scheduled trigger verification in daemon mode. - -Exit Criteria: - -1. Scheduled workflows execute reliably with traceable logs. - -## Phase 4: Integration Test Gates (Week 4-5) - -Deliverables: - -1. Add end-to-end smoke matrix: - -- Aria server state endpoint -- Functions status endpoint -- Chat request/response -- Training dry-run -- Quantum dry-run - -1. Wire matrix into CI required checks. - -Validation: - -1. `python scripts/test_runner.py --unit` -2. `pytest -m "not slow and not azure" tests/` -3. CI green for integration matrix. - -Exit Criteria: - -1. Merge gates catch integration drift before release. - -## Phase 5: Observability and Ops Hardening (Week 5-7) - -Deliverables: - -1. Unified health severity model across dashboard and checks. -2. Single runbook for startup, recovery, and rollback. -3. Systemd and cron profiles validated for Linux deployment. - -Validation: - -1. `python scripts/system_health_check.py` -2. `python scripts/status_dashboard.py --watch` - -Exit Criteria: - -1. Fresh environment reaches healthy status via documented runbook. - -## Workstreams - -### Workstream A: Config and Path Governance - -1. Build a shared config-path resolver utility. -2. Adopt it across orchestrators and CI scripts. -3. Add tests for fallback precedence. - -### Workstream B: Runtime Integration - -1. Normalize API endpoint assumptions and response contracts. -2. Keep chat/tts/status endpoint behavior stable. -3. Verify LoRA adapter readiness checks remain intact. - -### Workstream C: Data and Artifact Lineage - -1. Standardize `status.json` schema fields. -2. Attach run IDs and source config references. -3. Ensure outputs are traceable from training to deployment. - -### Workstream D: Documentation and Enablement - -1. Update root README and component READMEs to canonical paths. -2. Keep a migration section with legacy compatibility notes. -3. Provide one quick-start path for local-first integration validation. - -## Risk Register - -1. Risk: Legacy scripts fail after path normalization. -Mitigation: Keep fallback lookup and compatibility file writes. - -2. Risk: Docs and runtime diverge again over time. -Mitigation: CI check for key path strings and provider-order assertions. - -3. Risk: Scheduler changes cause unintentional workflow execution. -Mitigation: Add dry-run schedule simulation before enabling live schedules. - -4. Risk: Optional dependencies fail in clean environments. -Mitigation: Keep dependency checks explicit and fail with actionable errors. - -## Rollout and Rollback - -Rollout: - -1. Merge Phase 1 with compatibility mode enabled. -2. Observe 1-2 release cycles. -3. Promote canonical paths in docs and scripts by default. - -Rollback: - -1. Revert path resolver changes. -2. Continue using legacy root-level configs/status files. -3. Keep compatibility wrappers unchanged. - -## Definition of Done - -1. Canonical config and status paths are used by default. -2. Legacy compatibility remains functional during migration. -3. Provider behavior is documented exactly as implemented. -4. Integration smoke tests run in CI and locally. -5. Runbook enables reproducible startup and diagnosis. - -## Immediate Next Actions - -1. Keep `scripts/config_paths.py` as the single resolver and require `tests/test_config_paths.py` to pass in path-related changes. -2. Standardize `status.json` schema across `repo_automation`, `master_orchestrator`, and `ci_orchestrator` (include `run_id`, `config_path`, and normalized timestamps). -3. Add schedule simulation tests for cron expressions in `master_orchestrator.py` to guard against accidental trigger regressions. -4. Publish a short runbook section that links integration smoke, CI smoke, and health dashboard commands as one operational flow. +# Aria Repository Integration Plan + +Last updated: 2026-03-13 +Status: Draft for execution + +## Purpose + +This document defines an extensive, execution-ready integration roadmap for the Aria monorepo. It aligns runtime behavior, orchestration, documentation, and operations across the following major areas: + +- Azure Functions API layer (`function_app.py`) +- Aria character web runtime (`aria_web/`, `apps/aria/`) +- Chat provider stack (`ai-projects/chat-cli/`, `shared/chat_providers.py` wrappers) +- Quantum pipeline (`ai-projects/quantum-ml/`, `scripts/quantum_autorun.py`) +- LoRA training and promotion (`ai-projects/lora-training/`, `scripts/train_and_promote.py`) +- Repository automation (`scripts/repo_automation.py`, `scripts/master_orchestrator.py`) + +## Goals + +1. Establish one canonical integration contract for paths, configs, and provider behavior. +2. Preserve backward compatibility for legacy paths while transitioning to canonical locations. +3. Ensure end-to-end workflows are testable with local-first dry-run and health checks. +4. Reduce operational drift between docs and actual runtime logic. +5. Provide a phased rollout with clear validation gates and rollback points. + +## Non-Goals + +1. Replacing all legacy wrappers immediately. +2. Redesigning model architectures or changing provider internals unrelated to integration. +3. Running paid quantum hardware jobs as part of baseline integration validation. + +## Current-State Findings + +1. Config location drift exists between scripts and actual config files. +2. Documentation still references mixed legacy and canonical path conventions. +3. Orchestration and monitoring are useful but split across multiple tools without a single control-plane contract. +4. Provider auto-selection behavior in code is broader than several docs currently describe. + +## Canonical Integration Contract + +### Path Canon + +Automation configs: + +- `config/master_orchestrator.yaml` +- `config/quantum/quantum_autorun.yaml` +- `config/evaluation/evaluation_autorun.yaml` + +Automation status artifacts: + +- `data_out/repo_automation/status.json` +- `data_out/repo_automation/processes.json` +- `data_out/master_orchestrator/status.json` + +Generated outputs: + +- All generated artifacts go under `data_out/**`. +- `datasets/**` remains read-only. + +### Backward Compatibility + +1. Read legacy root-level config/status files when canonical files are absent. +2. Continue writing compatibility status/PID files during migration where needed. +3. Keep import-path wrappers until tests and external scripts are fully migrated. + +### Provider Resolution Contract + +Default auto mode should reflect actual code behavior and docs: + +1. LM Studio (if reachable) +2. Ollama (if reachable) +3. Azure OpenAI (if env complete) +4. OpenAI (if key present) +5. Local fallback + +Explicit provider overrides still take precedence (`agi`, `quantum`, `lora`, `azure`, `openai`, `lmstudio`, `ollama`, `local`). + +## Phased Execution Plan + +## Phase 0: Contract Freeze (Week 1) + +Deliverables: + +1. Finalize this integration contract. +2. Publish path and provider contract in top-level docs. +3. Add a migration note for legacy compatibility behavior. + +Validation: + +1. `python scripts/repo_automation.py --status` +2. `python scripts/master_orchestrator.py --status` +3. `curl http://localhost:7071/api/ai/status` + +Exit Criteria: + +1. No ambiguous path guidance in core docs. + +## Phase 1: Path and Config Normalization (Week 1-2) + +Deliverables: + +1. Normalize default config lookup to canonical `config/**` paths. +2. Keep fallback resolution for legacy root files. +3. Normalize status artifacts to canonical `data_out/**` locations. +4. Keep compatibility reads/writes for legacy status files during transition. + +Validation: + +1. `python scripts/quantum_autorun.py --dry-run` +2. `python scripts/repo_automation.py --status` +3. `python scripts/master_orchestrator.py --status` + +Exit Criteria: + +1. Canonical paths work out-of-the-box. +2. Existing workflows depending on legacy files still function. + +## Phase 2: Provider and API Integration Alignment (Week 2-3) + +Deliverables: + +1. Reconcile docs with actual provider auto-detection behavior. +2. Add provider-resolution smoke tests for explicit and auto modes. +3. Ensure `/api/ai/status` exposes enough readiness diagnostics for troubleshooting. + +Validation: + +1. CLI smoke checks per provider mode. +2. Functions endpoint check for `/api/chat` and `/api/ai/status`. + +Exit Criteria: + +1. Provider docs and runtime behavior are consistent. + +## Phase 3: Orchestration Convergence (Week 3-4) + +Deliverables: + +1. Clarify responsibilities: + +- `repo_automation.py`: process lifecycle and health loops +- `master_orchestrator.py`: workflow dependencies and schedules + +1. Implement robust schedule matching for scheduled workflows. +1. Standardize workflow result metadata and logging layout. + +Validation: + +1. Manual workflow trigger verification. +2. Scheduled trigger verification in daemon mode. + +Exit Criteria: + +1. Scheduled workflows execute reliably with traceable logs. + +## Phase 4: Integration Test Gates (Week 4-5) + +Deliverables: + +1. Add end-to-end smoke matrix: + +- Aria server state endpoint +- Functions status endpoint +- Chat request/response +- Training dry-run +- Quantum dry-run + +1. Wire matrix into CI required checks. + +Validation: + +1. `python scripts/test_runner.py --unit` +2. `pytest -m "not slow and not azure" tests/` +3. CI green for integration matrix. + +Exit Criteria: + +1. Merge gates catch integration drift before release. + +## Phase 5: Observability and Ops Hardening (Week 5-7) + +Deliverables: + +1. Unified health severity model across dashboard and checks. +2. Single runbook for startup, recovery, and rollback. +3. Systemd and cron profiles validated for Linux deployment. + +Validation: + +1. `python scripts/system_health_check.py` +2. `python scripts/status_dashboard.py --watch` + +Exit Criteria: + +1. Fresh environment reaches healthy status via documented runbook. + +## Workstreams + +### Workstream A: Config and Path Governance + +1. Build a shared config-path resolver utility. +2. Adopt it across orchestrators and CI scripts. +3. Add tests for fallback precedence. + +### Workstream B: Runtime Integration + +1. Normalize API endpoint assumptions and response contracts. +2. Keep chat/tts/status endpoint behavior stable. +3. Verify LoRA adapter readiness checks remain intact. + +### Workstream C: Data and Artifact Lineage + +1. Standardize `status.json` schema fields. +2. Attach run IDs and source config references. +3. Ensure outputs are traceable from training to deployment. + +### Workstream D: Documentation and Enablement + +1. Update root README and component READMEs to canonical paths. +2. Keep a migration section with legacy compatibility notes. +3. Provide one quick-start path for local-first integration validation. + +## Risk Register + +1. Risk: Legacy scripts fail after path normalization. +Mitigation: Keep fallback lookup and compatibility file writes. + +2. Risk: Docs and runtime diverge again over time. +Mitigation: CI check for key path strings and provider-order assertions. + +3. Risk: Scheduler changes cause unintentional workflow execution. +Mitigation: Add dry-run schedule simulation before enabling live schedules. + +4. Risk: Optional dependencies fail in clean environments. +Mitigation: Keep dependency checks explicit and fail with actionable errors. + +## Rollout and Rollback + +Rollout: + +1. Merge Phase 1 with compatibility mode enabled. +2. Observe 1-2 release cycles. +3. Promote canonical paths in docs and scripts by default. + +Rollback: + +1. Revert path resolver changes. +2. Continue using legacy root-level configs/status files. +3. Keep compatibility wrappers unchanged. + +## Definition of Done + +1. Canonical config and status paths are used by default. +2. Legacy compatibility remains functional during migration. +3. Provider behavior is documented exactly as implemented. +4. Integration smoke tests run in CI and locally. +5. Runbook enables reproducible startup and diagnosis. + +## Immediate Next Actions + +1. Keep `scripts/config_paths.py` as the single resolver and require `tests/test_config_paths.py` to pass in path-related changes. +2. Standardize `status.json` schema across `repo_automation`, `master_orchestrator`, and `ci_orchestrator` (include `run_id`, `config_path`, and normalized timestamps). +3. Add schedule simulation tests for cron expressions in `master_orchestrator.py` to guard against accidental trigger regressions. +4. Publish a short runbook section that links integration smoke, CI smoke, and health dashboard commands as one operational flow. diff --git a/docs/guides/SETUP_MONETIZATION_README.md b/docs/guides/SETUP_MONETIZATION_README.md index 5fa82a793..00a345f51 100644 --- a/docs/guides/SETUP_MONETIZATION_README.md +++ b/docs/guides/SETUP_MONETIZATION_README.md @@ -1,505 +1,505 @@ -# 🎭 Aria Monetization System - Complete Setup Guide - -## 🎉 What's Included - -Your Aria platform now has a **complete, production-ready monetization system** with: - -- ✅ **7 Beautiful Web Pages** covering the entire user journey -- ✅ **Backend Subscription System** with tier management and usage tracking -- ✅ **5 REST API Endpoints** for subscription management -- ✅ **Automated Setup Script** for easy configuration -- ✅ **$2,235/month MRR** achieving 111.8% of $2,000 target -- ✅ **Comprehensive Documentation** with examples and guides - -## 🚀 Quick Start (30 seconds) - -```bash -# 1. Run automated setup -python3 setup_monetization.py - -# 2. Start test server -python3 -m http.server 8000 - -# 3. Open in browser -open http://localhost:8000/monetization-index.html -``` - -## 📱 All Web Pages - -### User-Facing Pages - -| Page | URL | Description | -|------|-----|-------------| -| **Landing** | `monetization-index.html` | Main hub with all links and stats | -| **Pricing** | `pricing.html` | 3-tier comparison with revenue model | -| **Checkout** | `checkout.html?plan=pro` | Stripe-ready payment page | -| **Success** | `subscription-success.html` | Post-payment confirmation | -| **My Subscription** | `my-subscription.html` | User dashboard with usage tracking | -| **Account** | `account.html` | Settings, billing, API keys, security | - -### Admin Pages - -| Page | URL | Description | -|------|-----|-------------| -| **Admin Dashboard** | `admin_dashboard.html` | Revenue analytics and subscriber management | - -## 💰 Subscription Tiers - -### Free Tier - $0/month -- 100 chat messages/month -- Basic Aria character access -- Perfect for trying out - -### Pro Tier - $49/month ⭐ Popular -- 10,000 chat messages/month -- Full Aria character suite -- Quantum computing (50 jobs/mo) -- Advanced training (20 hrs/mo) -- Website maker (10 sites/mo) -- API access (10K requests/mo) -- Commercial license - -### Enterprise Tier - $199/month 👑 -- **Unlimited** everything -- Custom model training -- Priority support (24/7) -- Dedicated infrastructure -- 99.9% SLA guarantee - -## 🎯 Revenue Target Achievement - -``` -Target: $2,000/month MRR -Achieved: $2,235/month MRR (111.8%) - -Revenue Breakdown: -├── 5 Pro @ $49 = $245 -└── 10 Enterprise @ $199 = $1,990 - Total MRR = $2,235 ✅ - Annual Revenue = $26,820 -``` - -## 🌐 Complete User Journey - -```mermaid -graph LR - A[Landing Page] --> B[Pricing Page] - B --> C[Checkout] - C --> D[Success] - D --> E[My Subscription] - E --> F[Account Settings] - F --> E - E --> B -``` - -## 🔧 Setup Script Features - -The `setup_monetization.py` script automatically: - -1. ✅ Checks Python dependencies -2. ✅ Verifies all core files exist -3. ✅ Creates `local.settings.json` -4. ✅ Tests subscription manager -5. ✅ Generates demo data (15 subscribers) -6. ✅ Validates revenue calculations -7. ✅ Offers to start test server - -**Usage:** -```bash -python3 setup_monetization.py -``` - -## 🎨 Page Features - -### 1. Landing Page (`monetization-index.html`) -- Hero section with call-to-action buttons -- Revenue achievement banner -- Platform features showcase -- All pages linked in grid layout -- Quick links to documentation -- API endpoint reference -- Development commands -- Subscription tier preview -- Smooth animations - -### 2. Pricing Page (`pricing.html`) -- 3-tier comparison cards -- Revenue projection model -- Detailed feature comparison table -- FAQ section -- "Popular" badge on Pro tier -- Responsive design -- Upgrade buttons - -### 3. Checkout Page (`checkout.html`) -- Professional payment form -- Card number formatting (4242 4242 4242 4242) -- Expiry date formatting (MM / YY) -- CVV validation -- Country dropdown -- Order summary sidebar -- Plan features list -- Security badges -- Stripe branding -- Terms & conditions checkbox -- Supports URL parameters: `?plan=pro` or `?plan=enterprise` - -### 4. Success Page (`subscription-success.html`) -- Animated checkmark icon -- Celebratory confetti animation -- Subscription confirmation details -- Next steps guide (numbered) -- Support contact information -- Links to dashboard and home -- Plan badge display - -### 5. My Subscription (`my-subscription.html`) -- Current plan display with tier badge -- Real-time usage statistics -- 5 resource types tracked: - - Chat messages - - Quantum jobs - - Training hours - - API requests - - Websites created -- Visual progress bars (green/yellow/red) -- Feature access list -- Billing history table -- Upgrade/cancel buttons -- API integration for live data - -### 6. Account Settings (`account.html`) -- Tabbed interface (5 tabs): - 1. **Profile** - Name, email, company, bio - 2. **Billing** - Payment methods, billing history - 3. **API Keys** - Generate, view, revoke keys - 4. **Notifications** - Email preferences - 5. **Security** - Password, 2FA, sessions -- Form validation -- Danger zone for account deletion -- Active session management - -### 7. Admin Dashboard (`admin_dashboard.html`) -- Real-time MRR/ARR display -- Subscriber count by tier -- Revenue charts -- Subscriber list table -- Export functionality -- Refresh button -- Success alert when target achieved - -## 🔌 API Endpoints - -All pages integrate with these REST endpoints: - -```bash -# Get pricing information -GET /api/subscription/pricing - -# Check user subscription status -GET /api/subscription/status?user_id=demo_user - -# Upgrade subscription -POST /api/subscription/upgrade -{ - "user_id": "demo_user", - "tier": "pro", - "duration_days": 30 -} - -# Get revenue statistics -GET /api/subscription/revenue - -# Track resource usage -POST /api/subscription/usage -{ - "user_id": "demo_user", - "resource": "chat_messages", - "amount": 1 -} -``` - -## 🧪 Testing - -### Test Locally - -```bash -# Start test server -python3 -m http.server 8000 - -# Access pages -open http://localhost:8000/monetization-index.html -open http://localhost:8000/pricing.html -open http://localhost:8000/my-subscription.html -``` - -### Test with Azure Functions - -```bash -# Start Functions host -func host start - -# Test API endpoints -curl http://localhost:7071/api/subscription/pricing | jq -curl http://localhost:7071/api/subscription/status?user_id=demo_user | jq -curl http://localhost:7071/api/subscription/revenue | jq -``` - -### Test User Journey - -1. Start on landing page: `monetization-index.html` -2. Click "View Pricing" → `pricing.html` -3. Click "Upgrade to Pro" → `checkout.html?plan=pro` -4. Fill form and submit → `subscription-success.html?plan=pro` -5. Click "View My Subscription" → `my-subscription.html` -6. Click "Account" → `account.html` - -## 📊 Demo Data - -Generate demo data with 15 subscribers to achieve target revenue: - -```bash -python3 setup_monetization.py -# Select "yes" when prompted to generate demo data -``` - -This creates: -- 5 Pro subscribers @ $49 = $245 -- 10 Enterprise subscribers @ $199 = $1,990 -- Total MRR = $2,235 - -## 🎨 Design Features - -### Colors -- Primary: `#667eea` to `#764ba2` (purple gradient) -- Success: `#4caf50` (green) -- Warning: `#ffa500` (orange) -- Danger: `#f44336` (red) -- Background: White with gradient overlay - -### Typography -- Font: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto -- Headers: Bold, large sizes (2-4em) -- Body: 1em, line-height 1.6 - -### Animations -- Smooth transitions (0.3s ease) -- Hover effects (translateY, scale, shadow) -- Loading states -- Confetti on success page -- Fade-in on page load - -### Responsive Design -- Mobile-first approach -- Breakpoint: 768px -- Grid layouts adapt to screen size -- Touch-friendly buttons (min 44px) - -## 🔒 Security Features - -### Payment Security -- Stripe integration structure (PCI compliant) -- Never store card numbers -- 256-bit SSL encryption -- HTTPS required in production -- Card validation on client side - -### Account Security -- Password requirements -- Two-factor authentication setup -- Session management -- API key rotation -- Account deletion confirmation - -### Data Protection -- Usage data stored locally -- API keys masked -- Secure environment variables -- No hardcoded secrets - -## 📚 Documentation - -| Document | Description | -|----------|-------------| -| `MONETIZATION_GUIDE.md` | Complete technical guide (10,936 chars) | -| `INCOME_STREAM_SUMMARY.md` | Executive summary with screenshots | -| `QUICK_START_MONETIZATION.md` | Quick start guide (5,894 chars) | -| `SETUP_MONETIZATION_README.md` | This file | - -## 🚀 Production Deployment - -### Prerequisites -1. Stripe account and API keys -2. Azure Functions App (or similar hosting) -3. Domain name (optional) -4. SSL certificate (required) - -### Steps - -1. **Configure Stripe** -```bash -# Add to local.settings.json or Azure App Settings -{ - "STRIPE_SECRET_KEY": "sk_live_...", - "STRIPE_PUBLISHABLE_KEY": "pk_live_...", - "STRIPE_WEBHOOK_SECRET": "whsec_..." -} -``` - -2. **Deploy Backend** -```bash -# Deploy Azure Functions -func azure functionapp publish -``` - -3. **Deploy Frontend** -```bash -# Upload HTML files to: -# - Azure Static Web Apps -# - Azure Blob Storage with CDN -# - GitHub Pages -# - Netlify/Vercel -``` - -4. **Configure DNS** -```bash -# Point your domain to hosting -# Enable HTTPS -# Update CORS settings -``` - -5. **Test Production** -```bash -# Test all pages -# Verify API endpoints -# Complete test purchase -# Check admin dashboard -``` - -## 💡 Customization - -### Change Pricing -Edit `shared/subscription_manager.py`: -```python -TIER_PRICING = { - SubscriptionTier.FREE: 0, - SubscriptionTier.PRO: 49, # Change this - SubscriptionTier.ENTERPRISE: 199, # Change this -} -``` - -### Change Features -Edit `TIER_FEATURES` and `TIER_LIMITS` in `subscription_manager.py` - -### Change Design -Edit CSS in each HTML file's ` - - - - -
    -
    - - - - Aria - interactive AI character platform - - - - -
    -
    - -
    -
    -
    -
    -

    Interactive AI character platform

    -

    One website for characters, chat, training, and docs.

    -

    Aria is not a single demo page. It is a stack of public surfaces around an animated character system with natural-language actions, multi-provider chat, autonomous training, quantum ML experiments, and operational dashboards.

    -
    - Launch Aria - Read documentation -
    -
    -
    - 35+ - API routes across chat, vision, subscriptions, and quantum workflows -
    -
    - 6 - provider paths including Azure OpenAI, OpenAI, LMStudio, LoRA, and local fallback -
    -
    - 30m - autonomous training cadence with self-healing orchestration -
    -
    - 80+ - guides, quick references, and architecture notes available in docs -
    -
    -
    - - -
    -
    - -
    -
    -
    -

    Choose a route

    -

    Start from the surface that matches the job.

    -

    The old landing page listed almost everything equally. This route layer creates faster decisions for first-time visitors and repeat operators.

    -
    -
    - - Experience -

    Aria Character

    -

    Open the interactive character stage for movement commands, gestures, object physics, and auto-execute action plans.

    - Open the main demo -     - - Conversation -

    Streaming Chat

    -

    Use the chat interface for provider-backed responses, markdown rendering, code blocks, and backend diagnostics.

    - Enter the chat surface -     - - Operations -

    QAI Command Center

    -

    Inspect orchestrators, model progress, monitoring pages, and system control surfaces from one hub.

    - View the dashboards -     - - Research -

    Quantum ML

    -

    Explore quantum-classical machine learning, circuit submissions, and hybrid workflow demos.

    - Open quantum tools -     - - Reference -

    Documentation

    -

    Browse quick references, deployment guides, training notes, architecture decisions, and performance reports.

    - Read the docs -     - - Business -

    Commerce and Plans

    -

    Navigate storefront, pricing, subscription management, referrals, and revenue analytics without hunting through the repo.

    - View business pages -     -
    -
    -
    - -
    -
    -
    -

    Operating model

    -

    How the web surface maps to the system underneath.

    -

    This gives visitors a mental model of the platform instead of forcing them to infer architecture from filenames.

    -
    -
    -
    -
    01
    -

    Surface

    -

    Character pages, chat, dashboards, docs, quantum tools, and commerce pages are the visible front doors.

    -
    -
    -
    02
    -

    API layer

    -

    Azure Functions, the Aria web server, and MCP tooling expose chat, speech, state, vision, and quantum endpoints.

    -
    -
    -
    03
    -

    AI engines

    -

    Provider routing covers Azure OpenAI, OpenAI, LMStudio, LoRA adapters, AGI reasoning, and a zero-dependency local fallback.

    -
    -
    -
    04
    -

    Training and infra

    -

    Autonomous training, dashboards, Cosmos or SQL persistence, telemetry, and Azure Quantum support long-running workflows.

    -
    -
    -
    -
    - -
    -
    -
    -

    Surface index

    -

    Search every public page and localhost service.

    -

    The index keeps the old site directory value, but it is now grouped, filterable, and easier to scan on both desktop and mobile.

    -
    - -
    -
    - -
    - - - - -
    -
    -

    Showing 34 surfaces across demos, docs, business pages, and localhost services.

    -
    - -
    -
    -
    -

    Experience surfaces

    - 7 pages -
    -
    - -
    - Demo -
    -

    Aria Character

    -

    The main interactive character experience with animated stage controls and command parsing.

    - docs/aria/ -     - -
    - Demo -
    -

    Auto Execute

    -

    Preview and run multi-step action plans for the character with plan and execute modes.

    - docs/aria/auto-execute.html -     - -
    - Demo -
    -

    Chat Interface

    -

    Streaming chat UI with provider information, markdown rendering, and code highlighting.

    - docs/chat/ -     - -
    - Demo -
    -

    Quantum ML

    -

    Quantum-enhanced classification, circuit inspection, and training workflow entrypoint.

    - docs/quantum/ -     - -
    - Demo -
    -

    Dashboard

    -

    High-level overview of the training and monitoring views available in the project.

    - docs/dashboard/ -     - -
    - Demo -
    -

    QAI Command Center

    -

    Unified hub for orchestration status, monitoring shortcuts, and automation control pages.

    - docs/dashboard/hub.html -     - -
    - Docs -
    -

    Documentation Hub

    -

    Searchable reference surface for guides, architecture notes, performance reports, and quick starts.

    - docs/documentation.html -     -
    -
    - -
    -
    -

    Operations and builders

    - 10 pages -
    -
    - -
    Demo
    -

    Unified Dashboard

    -

    Central operations view for metrics, session state, and running workflows.

    - docs/dashboard/unified.html -     - -
    Demo
    -

    Analytics Dashboard

    -

    Performance snapshots, comparison tools, and trend-focused monitoring panels.

    - docs/dashboard/analytics.html -     - -
    Demo
    -

    Advanced Dashboard

    -

    Extra controls and advanced monitoring views for training and experiments.

    - docs/dashboard/advanced.html -     - -
    Demo
    -

    Consolidated Dashboard

    -

    Condensed control surface for operators who want several views in one place.

    - docs/dashboard/consolidated.html -     - -
    Demo
    -

    Enhanced Dashboard

    -

    Extended dashboard variant with upgraded panels and monitoring presentation.

    - docs/dashboard/enhanced.html -     - -
    Demo
    -

    Automation Dashboard

    -

    Template-driven automation controls and generated dashboard components.

    - docs/dashboard/templates/ -     - -
    Demo
    -

    LLM Maker UI

    -

    Tooling surface for model-powered generation workflows and prompt-driven utilities.

    - docs/llm-maker/web_ui.html -     - -
    Demo
    -

    Website Maker

    -

    Interface for website generation flows and template-assisted web output.

    - docs/llm-maker/website_maker_ui.html -     - -
    Demo
    -

    Mount Control

    -

    Supplementary control-center UI for supporting utilities and subsystem access.

    - docs/mount/ -     - -
    Demo
    -

    Quantum Web UI

    -

    Alternate quantum workflow surface focused on circuit interaction and job flows.

    - docs/quantum/web_ui.html -     -
    -
    - -
    -
    -

    Business and subscriptions

    - 9 pages -
    -
    - -
    Business
    -

    Storefront

    -

    Customer-facing store surface for products, features, and packaged offers.

    - docs/store/ -     - -
    Business
    -

    Monetization Portal

    -

    Primary monetization landing surface for tiers, analytics, and account actions.

    - docs/monetization/monetization-index.html -     - -
    Business
    -

    Pricing

    -

    Plan comparison and commercial positioning for productized access.

    - docs/monetization/pricing.html -     - -
    Business
    -

    My Subscription

    -

    Self-serve subscription status and management surface for active customers.

    - docs/monetization/my-subscription.html -     - -
    Business
    -

    Admin Dashboard

    -

    Administrative view for business metrics, subscriber state, and management workflows.

    - docs/monetization/admin_dashboard.html -     - -
    Business
    -

    Revenue Analytics

    -

    Commercial analytics, subscription performance views, and reporting panels.

    - docs/monetization/analytics-dashboard.html -     - -
    Business
    -

    Referrals

    -

    Referral program surface for growth loops, sharing, and incentive tracking.

    - docs/monetization/referrals.html -     - -
    Business
    -

    Account

    -

    Account-level settings, profile details, and billing-adjacent management actions.

    - docs/monetization/account.html -     - -
    Business
    -

    Checkout

    -

    Conversion-focused checkout flow for plan upgrades and commercial transactions.

    - docs/monetization/checkout.html -     -
    -
    - -
    -
    -

    Local development services

    - 8 services -
    -
    - -
    - Local - Checking -
    -

    Aria Live UI

    -

    Full character server with live APIs and synchronized state on port 8080.

    - http://localhost:8080 -     - -
    - Local - Checking -
    -

    Aria Auto Execute

    -

    Local execution surface for multi-step action planning and real API-backed runs.

    - http://localhost:8080/auto-execute.html -     - -
    - Local - Checking -
    -

    Chat Web

    -

    Azure Functions-hosted chat page wired to the live API layer on port 7071.

    - http://localhost:7071/api/chat-web -     - -
    - Local - Checking -
    -

    Health Check

    -

    Provider and subsystem diagnostics, environment visibility, and service readiness check.

    - http://localhost:7071/api/ai/status -     - -
    - Local - Checking -
    -

    QAI Hub

    -

    Locally served operations hub for command-center workflows on port 8000.

    - http://localhost:8000/hub.html -     - -
    - Local - Checking -
    -

    Unified Dashboard

    -

    Local dashboard surface for live metrics, session history, and training monitoring.

    - http://localhost:8000/unified.html -     - -
    - Local - Checking -
    -

    Quantum Dashboard

    -

    Local quantum interface for experiments, jobs, and monitoring on port 5000.

    - http://localhost:5000 -     - -
    - Local - Checking -
    -

    LLM Maker

    -

    Local builder surface for tool and website generation workflows on port 8090.

    - http://localhost:8090 -     -
    -
    -
    -
    -
    - -
    -
    -
    -

    Run the stack

    -

    Local startup should be obvious from the landing page.

    -

    The site now ends with a direct handoff into a practical startup sequence, so visitors can move from browsing to running services without opening multiple docs first.

    -
    -
      -
    • - Character server - Starts the interactive Aria web interface on port 8080 with stage state and character actions. -
      • -
    • - Azure Functions host - Brings chat, text-to-speech, status, subscription, and quantum endpoints online on port 7071. -
      • -
    • - Validation step - Runs a fast repo-wide health check for datasets, scripts, environments, and provider readiness. -
      • -
    -
    -
    - -
    -
    - Quick start sequence -

    Commands lifted from the repo workflow so the landing page stays operationally useful.

    -
    -
    -
    1git clone https://github.com/Bryan-Roe/Aria.git
    -
    2cd Aria
    -
    3pip install -r requirements.txt
    -
    4cd apps/aria && python server.py
    -
    5func host start
    -
    6python scripts/fast_validate.py
    -
    -
    -
    -
    -
    - -
    -
    -

    Aria ties together character interaction, provider-backed chat, autonomous training, quantum ML, and business tooling. © 2026 Bryan Roe.

    -
    - Top - Docs - Aria - Chat - Dashboards - Issues - GitHub -
    -
    -
    - - - - - + + + + + + Aria - Interactive AI Character Platform + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + Aria + interactive AI character platform + + + + +
    +
    + +
    +
    +
    +
    +

    Interactive AI character platform

    +

    One website for characters, chat, training, and docs.

    +

    Aria is not a single demo page. It is a stack of public surfaces around an animated character system with natural-language actions, multi-provider chat, autonomous training, quantum ML experiments, and operational dashboards.

    +
    + Launch Aria + Read documentation +
    +
    +
    + 35+ + API routes across chat, vision, subscriptions, and quantum workflows +
    +
    + 6 + provider paths including Azure OpenAI, OpenAI, LMStudio, LoRA, and local fallback +
    +
    + 30m + autonomous training cadence with self-healing orchestration +
    +
    + 80+ + guides, quick references, and architecture notes available in docs +
    +
    +
    + + +
    +
    + +
    +
    +
    +

    Choose a route

    +

    Start from the surface that matches the job.

    +

    The old landing page listed almost everything equally. This route layer creates faster decisions for first-time visitors and repeat operators.

    +
    +
    + + Experience +

    Aria Character

    +

    Open the interactive character stage for movement commands, gestures, object physics, and auto-execute action plans.

    + Open the main demo +     + + Conversation +

    Streaming Chat

    +

    Use the chat interface for provider-backed responses, markdown rendering, code blocks, and backend diagnostics.

    + Enter the chat surface +     + + Operations +

    QAI Command Center

    +

    Inspect orchestrators, model progress, monitoring pages, and system control surfaces from one hub.

    + View the dashboards +     + + Research +

    Quantum ML

    +

    Explore quantum-classical machine learning, circuit submissions, and hybrid workflow demos.

    + Open quantum tools +     + + Reference +

    Documentation

    +

    Browse quick references, deployment guides, training notes, architecture decisions, and performance reports.

    + Read the docs +     + + Business +

    Commerce and Plans

    +

    Navigate storefront, pricing, subscription management, referrals, and revenue analytics without hunting through the repo.

    + View business pages +     +
    +
    +
    + +
    +
    +
    +

    Operating model

    +

    How the web surface maps to the system underneath.

    +

    This gives visitors a mental model of the platform instead of forcing them to infer architecture from filenames.

    +
    +
    +
    +
    01
    +

    Surface

    +

    Character pages, chat, dashboards, docs, quantum tools, and commerce pages are the visible front doors.

    +
    +
    +
    02
    +

    API layer

    +

    Azure Functions, the Aria web server, and MCP tooling expose chat, speech, state, vision, and quantum endpoints.

    +
    +
    +
    03
    +

    AI engines

    +

    Provider routing covers Azure OpenAI, OpenAI, LMStudio, LoRA adapters, AGI reasoning, and a zero-dependency local fallback.

    +
    +
    +
    04
    +

    Training and infra

    +

    Autonomous training, dashboards, Cosmos or SQL persistence, telemetry, and Azure Quantum support long-running workflows.

    +
    +
    +
    +
    + +
    +
    +
    +

    Surface index

    +

    Search every public page and localhost service.

    +

    The index keeps the old site directory value, but it is now grouped, filterable, and easier to scan on both desktop and mobile.

    +
    + +
    +
    + +
    + + + + +
    +
    +

    Showing 34 surfaces across demos, docs, business pages, and localhost services.

    +
    + +
    +
    +
    +

    Experience surfaces

    + 7 pages +
    +
    + +
    + Demo +
    +

    Aria Character

    +

    The main interactive character experience with animated stage controls and command parsing.

    + docs/aria/ +     + +
    + Demo +
    +

    Auto Execute

    +

    Preview and run multi-step action plans for the character with plan and execute modes.

    + docs/aria/auto-execute.html +     + +
    + Demo +
    +

    Chat Interface

    +

    Streaming chat UI with provider information, markdown rendering, and code highlighting.

    + docs/chat/ +     + +
    + Demo +
    +

    Quantum ML

    +

    Quantum-enhanced classification, circuit inspection, and training workflow entrypoint.

    + docs/quantum/ +     + +
    + Demo +
    +

    Dashboard

    +

    High-level overview of the training and monitoring views available in the project.

    + docs/dashboard/ +     + +
    + Demo +
    +

    QAI Command Center

    +

    Unified hub for orchestration status, monitoring shortcuts, and automation control pages.

    + docs/dashboard/hub.html +     + +
    + Docs +
    +

    Documentation Hub

    +

    Searchable reference surface for guides, architecture notes, performance reports, and quick starts.

    + docs/documentation.html +     +
    +
    + +
    +
    +

    Operations and builders

    + 10 pages +
    +
    + +
    Demo
    +

    Unified Dashboard

    +

    Central operations view for metrics, session state, and running workflows.

    + docs/dashboard/unified.html +     + +
    Demo
    +

    Analytics Dashboard

    +

    Performance snapshots, comparison tools, and trend-focused monitoring panels.

    + docs/dashboard/analytics.html +     + +
    Demo
    +

    Advanced Dashboard

    +

    Extra controls and advanced monitoring views for training and experiments.

    + docs/dashboard/advanced.html +     + +
    Demo
    +

    Consolidated Dashboard

    +

    Condensed control surface for operators who want several views in one place.

    + docs/dashboard/consolidated.html +     + +
    Demo
    +

    Enhanced Dashboard

    +

    Extended dashboard variant with upgraded panels and monitoring presentation.

    + docs/dashboard/enhanced.html +     + +
    Demo
    +

    Automation Dashboard

    +

    Template-driven automation controls and generated dashboard components.

    + docs/dashboard/templates/ +     + +
    Demo
    +

    LLM Maker UI

    +

    Tooling surface for model-powered generation workflows and prompt-driven utilities.

    + docs/llm-maker/web_ui.html +     + +
    Demo
    +

    Website Maker

    +

    Interface for website generation flows and template-assisted web output.

    + docs/llm-maker/website_maker_ui.html +     + +
    Demo
    +

    Mount Control

    +

    Supplementary control-center UI for supporting utilities and subsystem access.

    + docs/mount/ +     + +
    Demo
    +

    Quantum Web UI

    +

    Alternate quantum workflow surface focused on circuit interaction and job flows.

    + docs/quantum/web_ui.html +     +
    +
    + +
    +
    +

    Business and subscriptions

    + 9 pages +
    +
    + +
    Business
    +

    Storefront

    +

    Customer-facing store surface for products, features, and packaged offers.

    + docs/store/ +     + +
    Business
    +

    Monetization Portal

    +

    Primary monetization landing surface for tiers, analytics, and account actions.

    + docs/monetization/monetization-index.html +     + +
    Business
    +

    Pricing

    +

    Plan comparison and commercial positioning for productized access.

    + docs/monetization/pricing.html +     + +
    Business
    +

    My Subscription

    +

    Self-serve subscription status and management surface for active customers.

    + docs/monetization/my-subscription.html +     + +
    Business
    +

    Admin Dashboard

    +

    Administrative view for business metrics, subscriber state, and management workflows.

    + docs/monetization/admin_dashboard.html +     + +
    Business
    +

    Revenue Analytics

    +

    Commercial analytics, subscription performance views, and reporting panels.

    + docs/monetization/analytics-dashboard.html +     + +
    Business
    +

    Referrals

    +

    Referral program surface for growth loops, sharing, and incentive tracking.

    + docs/monetization/referrals.html +     + +
    Business
    +

    Account

    +

    Account-level settings, profile details, and billing-adjacent management actions.

    + docs/monetization/account.html +     + +
    Business
    +

    Checkout

    +

    Conversion-focused checkout flow for plan upgrades and commercial transactions.

    + docs/monetization/checkout.html +     +
    +
    + +
    +
    +

    Local development services

    + 8 services +
    +
    + +
    + Local + Checking +
    +

    Aria Live UI

    +

    Full character server with live APIs and synchronized state on port 8080.

    + http://localhost:8080 +     + +
    + Local + Checking +
    +

    Aria Auto Execute

    +

    Local execution surface for multi-step action planning and real API-backed runs.

    + http://localhost:8080/auto-execute.html +     + +
    + Local + Checking +
    +

    Chat Web

    +

    Azure Functions-hosted chat page wired to the live API layer on port 7071.

    + http://localhost:7071/api/chat-web +     + +
    + Local + Checking +
    +

    Health Check

    +

    Provider and subsystem diagnostics, environment visibility, and service readiness check.

    + http://localhost:7071/api/ai/status +     + +
    + Local + Checking +
    +

    QAI Hub

    +

    Locally served operations hub for command-center workflows on port 8000.

    + http://localhost:8000/hub.html +     + +
    + Local + Checking +
    +

    Unified Dashboard

    +

    Local dashboard surface for live metrics, session history, and training monitoring.

    + http://localhost:8000/unified.html +     + +
    + Local + Checking +
    +

    Quantum Dashboard

    +

    Local quantum interface for experiments, jobs, and monitoring on port 5000.

    + http://localhost:5000 +     + +
    + Local + Checking +
    +

    LLM Maker

    +

    Local builder surface for tool and website generation workflows on port 8090.

    + http://localhost:8090 +     +
    +
    +
    +
    +
    + +
    +
    +
    +

    Run the stack

    +

    Local startup should be obvious from the landing page.

    +

    The site now ends with a direct handoff into a practical startup sequence, so visitors can move from browsing to running services without opening multiple docs first.

    +
    +
      +
    • + Character server + Starts the interactive Aria web interface on port 8080 with stage state and character actions. +
      • +
    • + Azure Functions host + Brings chat, text-to-speech, status, subscription, and quantum endpoints online on port 7071. +
      • +
    • + Validation step + Runs a fast repo-wide health check for datasets, scripts, environments, and provider readiness. +
      • +
    +
    +
    + +
    +
    + Quick start sequence +

    Commands lifted from the repo workflow so the landing page stays operationally useful.

    +
    +
    +
    1git clone https://github.com/Bryan-Roe/Aria.git
    +
    2cd Aria
    +
    3pip install -r requirements.txt
    +
    4cd apps/aria && python server.py
    +
    5func host start
    +
    6python scripts/fast_validate.py
    +
    +
    +
    +
    +
    + +
    +
    +

    Aria ties together character interaction, provider-backed chat, autonomous training, quantum ML, and business tooling. © 2026 Bryan Roe.

    +
    + Top + Docs + Aria + Chat + Dashboards + Issues + GitHub +
    +
    +
    + + + + + diff --git a/docs/llm-maker/web_ui.html b/docs/llm-maker/web_ui.html index e17b5e636..91cf3e8cd 100644 --- a/docs/llm-maker/web_ui.html +++ b/docs/llm-maker/web_ui.html @@ -1,944 +1,944 @@ - - - - - - LLM Maker — AI Tool Creation Studio | Aria - - - - - - - - - - - - - - - - - - - - ← Back to Aria Home -
    -
    -

    🤖 LLM Maker - AI Tool Creation Studio

    -

    Create, test, and manage custom AI tools with natural language

    -
    - - 🌐 Website Maker - - - 📚 Integration Guide - - - 💻 API Docs - - - 💡 Examples - -
    -
    - -
    - -
    -

    ✨ Create New Tool

    -
    -
    - - -
    - 🔢 Fibonacci - 🔄 Palindrome - 🌡️ Temperature -
    -
    - -
    - - -
    - -
    - -
    -
    - - - -
    -
    - -
    - -
    - - -
    - - - -
    -
    -
    - - -
    -

    📊 Statistics

    -
    -
    -
    0
    -
    Total Tools
    -
    -
    -
    0
    -
    Validated
    -
    -
    -
    0
    -
    Executions
    -
    -
    - -
    - -
    - - -
    -

    📚 Quick Integration

    -

    - Use these endpoints to integrate LLM Maker into your apps: -

    - - List all tools: -
    - - GET http://localhost:8090/api/tools -
    - - Create a tool: -
    - - fetch('/api/tools', { - method: 'POST', - body: JSON.stringify({ - name: 'my_tool', - description: 'What it does', - parameters: {'x': 'int'} - }) -}) -
    - - Execute a tool: -
    - - fetch('/api/tools/execute', { - method: 'POST', - body: JSON.stringify({ - tool_id: 'tool_xyz', - arguments: {'x': 5} - }) -}) -
    - -

    - 💡 Tip: Click "Copy" to copy code snippets to clipboard -

    -
    -
    - - -
    -

    🛠️ Your Tools

    -
    -
    - - - - -

    No tools created yet. Start by creating your first tool above!

    -
    -
    -
    -
    -
    - - - - - - + + + + + + LLM Maker — AI Tool Creation Studio | Aria + + + + + + + + + + + + + + + + + + + + ← Back to Aria Home +
    +
    +

    🤖 LLM Maker - AI Tool Creation Studio

    +

    Create, test, and manage custom AI tools with natural language

    +
    + + 🌐 Website Maker + + + 📚 Integration Guide + + + 💻 API Docs + + + 💡 Examples + +
    +
    + +
    + +
    +

    ✨ Create New Tool

    +
    +
    + + +
    + 🔢 Fibonacci + 🔄 Palindrome + 🌡️ Temperature +
    +
    + +
    + + +
    + +
    + +
    +
    + + + +
    +
    + +
    + +
    + + +
    + + + +
    +
    +
    + + +
    +

    📊 Statistics

    +
    +
    +
    0
    +
    Total Tools
    +
    +
    +
    0
    +
    Validated
    +
    +
    +
    0
    +
    Executions
    +
    +
    + +
    + +
    + + +
    +

    📚 Quick Integration

    +

    + Use these endpoints to integrate LLM Maker into your apps: +

    + + List all tools: +
    + + GET http://localhost:8090/api/tools +
    + + Create a tool: +
    + + fetch('/api/tools', { + method: 'POST', + body: JSON.stringify({ + name: 'my_tool', + description: 'What it does', + parameters: {'x': 'int'} + }) +}) +
    + + Execute a tool: +
    + + fetch('/api/tools/execute', { + method: 'POST', + body: JSON.stringify({ + tool_id: 'tool_xyz', + arguments: {'x': 5} + }) +}) +
    + +

    + 💡 Tip: Click "Copy" to copy code snippets to clipboard +

    +
    +
    + + +
    +

    🛠️ Your Tools

    +
    +
    + + + + +

    No tools created yet. Start by creating your first tool above!

    +
    +
    +
    +
    +
    + + + + + + diff --git a/docs/llm-maker/web_ui_api_functions.js b/docs/llm-maker/web_ui_api_functions.js index 7b42a7162..eb027e508 100644 --- a/docs/llm-maker/web_ui_api_functions.js +++ b/docs/llm-maker/web_ui_api_functions.js @@ -1,113 +1,113 @@ -// Replace the mock functions in web_ui.html with these real API functions - -// API Base URL -const API_BASE = ''; // Empty for same-origin requests - -async function createTool() { - const name = document.getElementById('toolName').value.trim(); - const description = document.getElementById('toolDescription').value.trim(); - const parameters = getParameters(); - const returnType = document.getElementById('returnType').value.trim() || 'Any'; - - if (!name || !description) { - showStatus('createStatus', 'Please fill in all required fields', 'error'); - return; - } - - // Show loading - document.getElementById('createBtn').disabled = true; - document.getElementById('createBtnText').style.display = 'none'; - document.getElementById('createLoading').style.display = 'inline-block'; - - try { - const response = await fetch(`${API_BASE}/api/tools`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ - name, - description, - parameters, - return_type: returnType - }) - }); - - const result = await response.json(); - - if (result.success) { - showStatus('createStatus', `Tool "${name}" created successfully!`, 'success'); - document.getElementById('createToolForm').reset(); - // Reset parameters to single row - document.getElementById('parametersContainer').innerHTML = ` -
    - - - -
    - `; - loadTools(); - } else { - showStatus('createStatus', `Error: ${result.error}`, 'error'); - } - } catch (error) { - showStatus('createStatus', `Error: ${error.message}`, 'error'); - } finally { - // Hide loading - document.getElementById('createBtn').disabled = false; - document.getElementById('createBtnText').style.display = 'inline'; - document.getElementById('createLoading').style.display = 'none'; - } -} - -async function loadTools() { - try { - const response = await fetch(`${API_BASE}/api/tools`); - const data = await response.json(); - - tools = data.tools || []; - updateStats(data.stats); - renderTools(); - } catch (error) { - console.error('Error loading tools:', error); - showStatus('createStatus', 'Error loading tools. Is the server running?', 'error'); - } -} - -async function executeTool(toolId, params) { - try { - const response = await fetch(`${API_BASE}/api/tools/execute`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ - tool_id: toolId, - arguments: params - }) - }); - - return await response.json(); - } catch (error) { - return { success: false, error: error.message }; - } -} - -async function deleteTool(toolId) { - try { - const response = await fetch(`${API_BASE}/api/tools/${toolId}`, { - method: 'DELETE' - }); - - const result = await response.json(); - - if (result.success) { - loadTools(); - return true; - } - return false; - } catch (error) { - console.error('Error deleting tool:', error); - return false; - } -} +// Replace the mock functions in web_ui.html with these real API functions + +// API Base URL +const API_BASE = ''; // Empty for same-origin requests + +async function createTool() { + const name = document.getElementById('toolName').value.trim(); + const description = document.getElementById('toolDescription').value.trim(); + const parameters = getParameters(); + const returnType = document.getElementById('returnType').value.trim() || 'Any'; + + if (!name || !description) { + showStatus('createStatus', 'Please fill in all required fields', 'error'); + return; + } + + // Show loading + document.getElementById('createBtn').disabled = true; + document.getElementById('createBtnText').style.display = 'none'; + document.getElementById('createLoading').style.display = 'inline-block'; + + try { + const response = await fetch(`${API_BASE}/api/tools`, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + name, + description, + parameters, + return_type: returnType + }) + }); + + const result = await response.json(); + + if (result.success) { + showStatus('createStatus', `Tool "${name}" created successfully!`, 'success'); + document.getElementById('createToolForm').reset(); + // Reset parameters to single row + document.getElementById('parametersContainer').innerHTML = ` +
    + + + +
    + `; + loadTools(); + } else { + showStatus('createStatus', `Error: ${result.error}`, 'error'); + } + } catch (error) { + showStatus('createStatus', `Error: ${error.message}`, 'error'); + } finally { + // Hide loading + document.getElementById('createBtn').disabled = false; + document.getElementById('createBtnText').style.display = 'inline'; + document.getElementById('createLoading').style.display = 'none'; + } +} + +async function loadTools() { + try { + const response = await fetch(`${API_BASE}/api/tools`); + const data = await response.json(); + + tools = data.tools || []; + updateStats(data.stats); + renderTools(); + } catch (error) { + console.error('Error loading tools:', error); + showStatus('createStatus', 'Error loading tools. Is the server running?', 'error'); + } +} + +async function executeTool(toolId, params) { + try { + const response = await fetch(`${API_BASE}/api/tools/execute`, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + tool_id: toolId, + arguments: params + }) + }); + + return await response.json(); + } catch (error) { + return { success: false, error: error.message }; + } +} + +async function deleteTool(toolId) { + try { + const response = await fetch(`${API_BASE}/api/tools/${toolId}`, { + method: 'DELETE' + }); + + const result = await response.json(); + + if (result.success) { + loadTools(); + return true; + } + return false; + } catch (error) { + console.error('Error deleting tool:', error); + return false; + } +} diff --git a/docs/llm-maker/website_maker_ui.html b/docs/llm-maker/website_maker_ui.html index f9468d5a6..0bde5d148 100644 --- a/docs/llm-maker/website_maker_ui.html +++ b/docs/llm-maker/website_maker_ui.html @@ -1,920 +1,920 @@ - - - - - - AI Website Maker — Aria LLM Tools - - - - - - - - - - - - - - - - - - - - ← Back to Aria Home -
    -
    -

    - - AI Website Maker -

    -

    Automatically generate and update complete websites with AI

    -
    - - Tool Maker - - - -
    -
    - -
    -
    - -
    -

    - - Create New Website -

    - -
    -
    - - -
    - -
    - - -
    - -
    - - -
    - -
    - -
    - index ✕ -
    -
    - - -
    -
    - -
    - -
    - responsive design ✕ - modern styling ✕ -
    -
    - - -
    -
    - - - -
    -
    -
    - - -
    -

    - - Update Existing Website -

    - -
    -
    - - -
    - -
    - - -
    - -
    - - -
    - - - -
    -
    -
    -
    - - -
    -

    - - Your Websites - -

    - -
    -

    - - No websites created yet. Create your first website above! -

    -
    -
    -
    -
    - - - - - - - - - + + + + + + AI Website Maker — Aria LLM Tools + + + + + + + + + + + + + + + + + + + + ← Back to Aria Home +
    +
    +

    + + AI Website Maker +

    +

    Automatically generate and update complete websites with AI

    +
    + + Tool Maker + + + +
    +
    + +
    +
    + +
    +

    + + Create New Website +

    + +
    +
    + + +
    + +
    + + +
    + +
    + + +
    + +
    + +
    + index ✕ +
    +
    + + +
    +
    + +
    + +
    + responsive design ✕ + modern styling ✕ +
    +
    + + +
    +
    + + + +
    +
    +
    + + +
    +

    + + Update Existing Website +

    + +
    +
    + + +
    + +
    + + +
    + +
    + + +
    + + + +
    +
    +
    +
    + + +
    +

    + + Your Websites + +

    + +
    +

    + + No websites created yet. Create your first website above! +

    +
    +
    +
    +
    + + + + + + + + + diff --git a/docs/monetization/account.html b/docs/monetization/account.html index b24a32065..fc4ef39ae 100644 --- a/docs/monetization/account.html +++ b/docs/monetization/account.html @@ -1,557 +1,557 @@ - - - - - - Account Settings — Aria Platform - - - - - - - - - ← Back to Aria Home -
    -
    -

    ⚙️ Account Settings

    -
    - 🏠 Home - 📊 Subscription - 💰 Pricing -
    -
    - -
    -
    👤 Profile
    -
    💳 Billing
    -
    🔑 API Keys
    -
    🔔 Notifications
    -
    🔒 Security
    -
    - - -
    -

    Profile Information

    - -
    -
    -
    - - -
    -
    - - -
    -
    - -
    - - -
    - -
    - - -
    - -
    - - -
    - - - -
    -
    - - -
    -

    Billing & Payment Methods

    - -
    -

    Current Plan

    -

    Free Tier - $0/month

    -

    Upgrade to Pro or Enterprise for more features

    - -
    - -

    Payment Methods

    -
      -
    • -
      - 💳 -
      -
      Visa •••• 4242
      -
      Expires 12/2025
      -
      - Default -
      -
      - - -
      -
      • -
    - - - -

    Billing History

    -

    No billing history yet. Invoices will appear here after your first payment.

    -
    - - -
    -

    API Keys

    - -
    -

    About API Keys

    -

    API keys allow you to integrate Aria with your applications. Keep your keys secure and never share them publicly.

    -

    Note: API access requires a Pro or Enterprise subscription.

    -
    - -

    Your API Keys

    - -
    -
    -
    Production Key
    - sk_live_•••••••••••••••••••••••• -
    -
    - - - -
    -
    - - - -
    - ⚠️ Security Warning -

    Never commit API keys to version control or share them in public forums. Rotate keys regularly for security.

    -
    -
    - - -
    -

    Notification Preferences

    - -

    Email Notifications

    - -
    - -
    - -
    - -
    - -
    - -
    - -
    - -
    - -
    - -
    - - -
    - - -
    -

    Security Settings

    - -

    Change Password

    - -
    -
    - - -
    - -
    - - -
    - -
    - - -
    - - -
    - -

    Two-Factor Authentication

    - -
    -

    Enhance Your Security

    -

    Two-factor authentication adds an extra layer of security to your account.

    - -
    - -

    Active Sessions

    - -
    -
    - 🖥️ -
    -
    Current Session
    -
    Chrome on Windows • Last active: Just now
    -
    -
    - Active -
    - - - -

    Danger Zone

    - -
    -

    Delete Account

    -

    Once you delete your account, there is no going back. Please be certain.

    - -
    -
    -
    - - - - - - + + + + + + Account Settings — Aria Platform + + + + + + + + + ← Back to Aria Home +
    +
    +

    ⚙️ Account Settings

    +
    + 🏠 Home + 📊 Subscription + 💰 Pricing +
    +
    + +
    +
    👤 Profile
    +
    💳 Billing
    +
    🔑 API Keys
    +
    🔔 Notifications
    +
    🔒 Security
    +
    + + +
    +

    Profile Information

    + +
    +
    +
    + + +
    +
    + + +
    +
    + +
    + + +
    + +
    + + +
    + +
    + + +
    + + + +
    +
    + + +
    +

    Billing & Payment Methods

    + +
    +

    Current Plan

    +

    Free Tier - $0/month

    +

    Upgrade to Pro or Enterprise for more features

    + +
    + +

    Payment Methods

    +
      +
    • +
      + 💳 +
      +
      Visa •••• 4242
      +
      Expires 12/2025
      +
      + Default +
      +
      + + +
      +
      • +
    + + + +

    Billing History

    +

    No billing history yet. Invoices will appear here after your first payment.

    +
    + + +
    +

    API Keys

    + +
    +

    About API Keys

    +

    API keys allow you to integrate Aria with your applications. Keep your keys secure and never share them publicly.

    +

    Note: API access requires a Pro or Enterprise subscription.

    +
    + +

    Your API Keys

    + +
    +
    +
    Production Key
    + sk_live_•••••••••••••••••••••••• +
    +
    + + + +
    +
    + + + +
    + ⚠️ Security Warning +

    Never commit API keys to version control or share them in public forums. Rotate keys regularly for security.

    +
    +
    + + +
    +

    Notification Preferences

    + +

    Email Notifications

    + +
    + +
    + +
    + +
    + +
    + +
    + +
    + +
    + +
    + +
    + + +
    + + +
    +

    Security Settings

    + +

    Change Password

    + +
    +
    + + +
    + +
    + + +
    + +
    + + +
    + + +
    + +

    Two-Factor Authentication

    + +
    +

    Enhance Your Security

    +

    Two-factor authentication adds an extra layer of security to your account.

    + +
    + +

    Active Sessions

    + +
    +
    + 🖥️ +
    +
    Current Session
    +
    Chrome on Windows • Last active: Just now
    +
    +
    + Active +
    + + + +

    Danger Zone

    + +
    +

    Delete Account

    +

    Once you delete your account, there is no going back. Please be certain.

    + +
    +
    +
    + + + + + + diff --git a/docs/monetization/admin_dashboard.html b/docs/monetization/admin_dashboard.html index e8b31dadc..cb12512fc 100644 --- a/docs/monetization/admin_dashboard.html +++ b/docs/monetization/admin_dashboard.html @@ -1,569 +1,569 @@ - - - - - - Admin Dashboard — Aria Platform - - - - - - - - - ← Back to Aria Home -
    -

    📊 Aria Platform - Admin Dashboard

    -

    Subscription & Revenue Management

    -
    - -
    - - -
    - ℹ️ Demo Mode: This dashboard shows simulated data. Connect to live subscription manager for real-time statistics. -
    - - -
    -
    -
    Monthly Recurring Revenue
    -
    $0
    -
    Target: $2,000
    -
    -
    -
    -
    - -
    -
    Annual Recurring Revenue
    -
    $0
    -
    Projected annually
    -
    - -
    -
    Total Subscribers
    -
    0
    -
    0 active
    -
    - -
    -
    Free Tier
    -
    0
    -
    Users
    -
    - -
    -
    Pro Tier
    -
    0
    -
    @ $49/mo each
    -
    - -
    -
    Enterprise Tier
    -
    0
    -
    @ $199/mo each
    -
    -
    - - -
    -

    - Revenue by Tier - -

    -
    -
    -
    $0
    -
    Free
    -
    -
    -
    $0
    -
    Pro
    -
    -
    -
    $0
    -
    Enterprise
    -
    -
    -
    - - -
    -

    Active Subscriptions

    -
    - - - - - - - - - - - - - - - - -
    User IDTierStatusStart DateMRR ContributionActions
    - - Loading subscription data... -
    -
    -
    - - -
    -

    Quick Actions

    - - - -
    -
    - - - - - - + + + + + + Admin Dashboard — Aria Platform + + + + + + + + + ← Back to Aria Home +
    +

    📊 Aria Platform - Admin Dashboard

    +

    Subscription & Revenue Management

    +
    + +
    + + +
    + ℹ️ Demo Mode: This dashboard shows simulated data. Connect to live subscription manager for real-time statistics. +
    + + +
    +
    +
    Monthly Recurring Revenue
    +
    $0
    +
    Target: $2,000
    +
    +
    +
    +
    + +
    +
    Annual Recurring Revenue
    +
    $0
    +
    Projected annually
    +
    + +
    +
    Total Subscribers
    +
    0
    +
    0 active
    +
    + +
    +
    Free Tier
    +
    0
    +
    Users
    +
    + +
    +
    Pro Tier
    +
    0
    +
    @ $49/mo each
    +
    + +
    +
    Enterprise Tier
    +
    0
    +
    @ $199/mo each
    +
    +
    + + +
    +

    + Revenue by Tier + +

    +
    +
    +
    $0
    +
    Free
    +
    +
    +
    $0
    +
    Pro
    +
    +
    +
    $0
    +
    Enterprise
    +
    +
    +
    + + +
    +

    Active Subscriptions

    +
    + + + + + + + + + + + + + + + + +
    User IDTierStatusStart DateMRR ContributionActions
    + + Loading subscription data... +
    +
    +
    + + +
    +

    Quick Actions

    + + + +
    +
    + + + + + + diff --git a/docs/monetization/analytics-dashboard.html b/docs/monetization/analytics-dashboard.html index ee2acbb4b..6ae24e3cd 100644 --- a/docs/monetization/analytics-dashboard.html +++ b/docs/monetization/analytics-dashboard.html @@ -1,580 +1,580 @@ - - - - - - Revenue Analytics Dashboard — Aria Platform - - - - - - - - - - - - - - - - - - - ← Back to Aria Home -
    -
    -

    📊 Analytics Dashboard

    -
    - 👑 Admin - 🏠 Home -
    -
    - - -
    -
    -
    💰
    -
    Monthly Revenue
    -
    $2,235
    -
    ↑ 11.8% vs target
    -
    - -
    -
    👥
    -
    Active Subscribers
    -
    15
    -
    ↑ 15 this month
    -
    - -
    -
    📈
    -
    Growth Rate
    -
    +25%
    -
    ↑ Month over month
    -
    - -
    -
    💳
    -
    Conversion Rate
    -
    8.5%
    -
    ↑ 2.5% vs last month
    -
    -
    - - -
    -

    Revenue Trends

    -
    - - -
    -
    - -
    -
    - - -
    -
    -

    Subscribers by Tier

    -
    - -
    -
    - -
    -

    Revenue by Tier

    -
    - -
    -
    -
    - - -
    -

    Usage Analytics

    -
    - -
    -
    - - -
    -

    Top Subscribers by Revenue

    - - - - - - - - - - - - - - -
    RankUser IDTierMRRLifetime ValueStatus
    -
    -
    - - - - - - + + + + + + Revenue Analytics Dashboard — Aria Platform + + + + + + + + + + + + + + + + + + + ← Back to Aria Home +
    +
    +

    📊 Analytics Dashboard

    +
    + 👑 Admin + 🏠 Home +
    +
    + + +
    +
    +
    💰
    +
    Monthly Revenue
    +
    $2,235
    +
    ↑ 11.8% vs target
    +
    + +
    +
    👥
    +
    Active Subscribers
    +
    15
    +
    ↑ 15 this month
    +
    + +
    +
    📈
    +
    Growth Rate
    +
    +25%
    +
    ↑ Month over month
    +
    + +
    +
    💳
    +
    Conversion Rate
    +
    8.5%
    +
    ↑ 2.5% vs last month
    +
    +
    + + +
    +

    Revenue Trends

    +
    + + +
    +
    + +
    +
    + + +
    +
    +

    Subscribers by Tier

    +
    + +
    +
    + +
    +

    Revenue by Tier

    +
    + +
    +
    +
    + + +
    +

    Usage Analytics

    +
    + +
    +
    + + +
    +

    Top Subscribers by Revenue

    + + + + + + + + + + + + + + +
    RankUser IDTierMRRLifetime ValueStatus
    +
    +
    + + + + + + diff --git a/docs/monetization/checkout.html b/docs/monetization/checkout.html index 4a4393dc1..4ccfcbc09 100644 --- a/docs/monetization/checkout.html +++ b/docs/monetization/checkout.html @@ -1,533 +1,533 @@ - - - - - - Checkout — Aria Platform - - - - - - - - - ← Back to Aria Home -
    - ← Back to Pricing - -
    -

    🛒 Complete Your Purchase

    -

    Join thousands of users powering their AI with Aria

    -
    - -
    - ℹ️ Demo Mode: This is a demonstration checkout page. In production, this will integrate with Stripe for secure payment processing. -
    - -
    - -
    -

    Payment Information

    - -
    -
    - - -
    - -
    - - -
    - -
    - - -
    - -
    -
    - - -
    -
    - - -
    -
    - -
    - - -
    - -
    - -
    - - - - -
    - -
    - 🔒 Secured by 256-bit SSL encryption -
    - -
    - 💳 Powered by Stripe
    - In production, payments are processed securely through Stripe.
    - Your card information is never stored on our servers. -
    -
    - - -
    -

    Order Summary

    - -
    Pro Plan
    - -
    -
    - Plan - Pro -
    -
    - Billing - Monthly -
    -
    - Subtotal - $49.00 -
    -
    - Tax - $0.00 -
    -
    - Total - $49.00 -
    -
    - -

    What's Included

    -
      -
    • 10,000 Chat Messages/month
      • -
    • Full Aria Character Suite
      • -
    • Quantum Computing (50 jobs/mo)
      • -
    • Advanced Training (20 hrs/mo)
      • -
    • Website Maker (10 sites/mo)
      • -
    • API Access (10K requests/mo)
      • -
    • Commercial License
      • -
    - -
    - 💰 Save 20% with Annual Billing! -
    - -
    - We accept: - 💳 Visa - 💳 Mastercard - 💳 Amex - 💰 PayPal -
    -
    -
    -
    - - - - - - + + + + + + Checkout — Aria Platform + + + + + + + + + ← Back to Aria Home +
    + ← Back to Pricing + +
    +

    🛒 Complete Your Purchase

    +

    Join thousands of users powering their AI with Aria

    +
    + +
    + ℹ️ Demo Mode: This is a demonstration checkout page. In production, this will integrate with Stripe for secure payment processing. +
    + +
    + +
    +

    Payment Information

    + +
    +
    + + +
    + +
    + + +
    + +
    + + +
    + +
    +
    + + +
    +
    + + +
    +
    + +
    + + +
    + +
    + +
    + + + + +
    + +
    + 🔒 Secured by 256-bit SSL encryption +
    + +
    + 💳 Powered by Stripe
    + In production, payments are processed securely through Stripe.
    + Your card information is never stored on our servers. +
    +
    + + +
    +

    Order Summary

    + +
    Pro Plan
    + +
    +
    + Plan + Pro +
    +
    + Billing + Monthly +
    +
    + Subtotal + $49.00 +
    +
    + Tax + $0.00 +
    +
    + Total + $49.00 +
    +
    + +

    What's Included

    +
      +
    • 10,000 Chat Messages/month
      • +
    • Full Aria Character Suite
      • +
    • Quantum Computing (50 jobs/mo)
      • +
    • Advanced Training (20 hrs/mo)
      • +
    • Website Maker (10 sites/mo)
      • +
    • API Access (10K requests/mo)
      • +
    • Commercial License
      • +
    + +
    + 💰 Save 20% with Annual Billing! +
    + +
    + We accept: + 💳 Visa + 💳 Mastercard + 💳 Amex + 💰 PayPal +
    +
    +
    +
    + + + + + + diff --git a/docs/monetization/monetization-index.html b/docs/monetization/monetization-index.html index 088be5492..2ba2fdd5d 100644 --- a/docs/monetization/monetization-index.html +++ b/docs/monetization/monetization-index.html @@ -1,489 +1,489 @@ - - - - - - - Aria Platform — Monetization Portal - - - - - - - - - - - - - - - - - - - ← - Back to Aria Home -
    -

    🎭 Aria Platform

    -

    AI-Powered Platform with Complete Monetization System

    -
    - 👤 Try Aria - 💰 View Pricing - 📊 My Subscription - 👑 Admin Dashboard -
    -
    - -
    - -
    -

    🎉 $2,000+ Monthly Income Stream Achieved!

    -
    -
    -
    $2,235
    -
    Monthly Recurring Revenue
    -
    -
    -
    $26,820
    -
    Annual Revenue
    -
    -
    -
    15
    -
    Target Subscribers
    -
    -
    -
    111.8%
    -
    Target Achievement
    -
    -
    -
    - - -
    -

    ✨ Platform Features

    -
    -
    -
    🎭
    -

    Aria Character

    -

    Interactive 3D AI character with natural language movement and gestures

    -
    - -
    -
    ⚛️
    -

    Quantum Computing

    -

    Quantum ML integration for advanced computational tasks

    -
    - -
    -
    🤖
    -

    Advanced Training

    -

    LoRA fine-tuning and custom model training capabilities

    -
    - -
    -
    🏗️
    -

    Website Maker

    -

    AI-powered website generation with LLM tool creation

    -
    - -
    -
    🔌
    -

    API Access

    -

    RESTful API for integrating Aria into your applications

    -
    - -
    -
    💼
    -

    Commercial License

    -

    Pro and Enterprise tiers include commercial usage rights

    -
    -
    -
    - - -
    -

    📄 Platform Pages

    -
    - -

    👤 Aria Character

    -

    Interactive 3D AI character with natural language commands. The main platform experience.

    -     - - -

    💰 Pricing Page

    -

    View all subscription tiers and features. Beautiful 3-tier comparison with revenue projections. -

    -     - - -

    👑 Admin Dashboard

    -

    Revenue analytics and subscriber management. Real-time MRR/ARR tracking.

    -     - - -

    📊 My Subscription

    -

    User subscription dashboard with usage tracking and feature access.

    -     - - -

    🛒 Checkout Page

    -

    Secure payment processing interface. Stripe-ready integration.

    -     - - -

    ✅ Success Page

    -

    Post-payment confirmation with onboarding steps.

    -     - - -

    ⚙️ Account Settings

    -

    Profile, billing, API keys, notifications, and security settings.

    -     -
    -
    - - -
    -

    🚀 Getting Started

    - -
    -

    Quick Links

    - -
    - -
    -

    API Endpoints

    -
      -
    • 🔗 GET /api/subscription/pricing - Get pricing tiers
      • -
    • 🔗 GET /api/subscription/status - Check user subscription
      • -
    • 🔗 POST /api/subscription/upgrade - Upgrade subscription
      • -
    • 🔗 GET /api/subscription/revenue - Revenue statistics
      • -
    • 🔗 POST /api/subscription/usage - Track usage
      • -
    -
    - -
    -

    Development Commands

    -
      -
    • 🖥️ python3 setup_monetization.py - Run setup script
      • -
    • 🌐 python3 -m http.server 8000 - Start test server
      • -
    • ⚡ func host start - Start Azure Functions
      • -
    • 🧪 curl http://localhost:7071/api/subscription/revenue | jq - Test API
      • -
    -
    -
    - - -
    -

    💎 Subscription Tiers

    -
    -
    -
    🆓
    -

    Free Tier

    -

    $0/month

    -

    100 chat messages/mo
    Basic Aria character
    Perfect for trying out

    -
    - -
    -
    ⭐
    -

    Pro Tier

    -

    $49/month

    -

    10K messages/mo
    Quantum computing
    Advanced training

    -
    - -
    -
    👑
    -

    Enterprise Tier

    -

    $199/month

    -

    Unlimited everything
    Custom models
    Priority support 24/7

    -
    -
    - -
    -
    - -
    -

    © 2026 Aria Platform | GitHub

    -
    - - - - - - + + + + + + + Aria Platform — Monetization Portal + + + + + + + + + + + + + + + + + + + ← + Back to Aria Home +
    +

    🎭 Aria Platform

    +

    AI-Powered Platform with Complete Monetization System

    +
    + 👤 Try Aria + 💰 View Pricing + 📊 My Subscription + 👑 Admin Dashboard +
    +
    + +
    + +
    +

    🎉 $2,000+ Monthly Income Stream Achieved!

    +
    +
    +
    $2,235
    +
    Monthly Recurring Revenue
    +
    +
    +
    $26,820
    +
    Annual Revenue
    +
    +
    +
    15
    +
    Target Subscribers
    +
    +
    +
    111.8%
    +
    Target Achievement
    +
    +
    +
    + + +
    +

    ✨ Platform Features

    +
    +
    +
    🎭
    +

    Aria Character

    +

    Interactive 3D AI character with natural language movement and gestures

    +
    + +
    +
    ⚛️
    +

    Quantum Computing

    +

    Quantum ML integration for advanced computational tasks

    +
    + +
    +
    🤖
    +

    Advanced Training

    +

    LoRA fine-tuning and custom model training capabilities

    +
    + +
    +
    🏗️
    +

    Website Maker

    +

    AI-powered website generation with LLM tool creation

    +
    + +
    +
    🔌
    +

    API Access

    +

    RESTful API for integrating Aria into your applications

    +
    + +
    +
    💼
    +

    Commercial License

    +

    Pro and Enterprise tiers include commercial usage rights

    +
    +
    +
    + + +
    +

    📄 Platform Pages

    +
    + +

    👤 Aria Character

    +

    Interactive 3D AI character with natural language commands. The main platform experience.

    +     + + +

    💰 Pricing Page

    +

    View all subscription tiers and features. Beautiful 3-tier comparison with revenue projections. +

    +     + + +

    👑 Admin Dashboard

    +

    Revenue analytics and subscriber management. Real-time MRR/ARR tracking.

    +     + + +

    📊 My Subscription

    +

    User subscription dashboard with usage tracking and feature access.

    +     + + +

    🛒 Checkout Page

    +

    Secure payment processing interface. Stripe-ready integration.

    +     + + +

    ✅ Success Page

    +

    Post-payment confirmation with onboarding steps.

    +     + + +

    ⚙️ Account Settings

    +

    Profile, billing, API keys, notifications, and security settings.

    +     +
    +
    + + +
    +

    🚀 Getting Started

    + +
    +

    Quick Links

    + +
    + +
    +

    API Endpoints

    +
      +
    • 🔗 GET /api/subscription/pricing - Get pricing tiers
      • +
    • 🔗 GET /api/subscription/status - Check user subscription
      • +
    • 🔗 POST /api/subscription/upgrade - Upgrade subscription
      • +
    • 🔗 GET /api/subscription/revenue - Revenue statistics
      • +
    • 🔗 POST /api/subscription/usage - Track usage
      • +
    +
    + +
    +

    Development Commands

    +
      +
    • 🖥️ python3 setup_monetization.py - Run setup script
      • +
    • 🌐 python3 -m http.server 8000 - Start test server
      • +
    • ⚡ func host start - Start Azure Functions
      • +
    • 🧪 curl http://localhost:7071/api/subscription/revenue | jq - Test API
      • +
    +
    +
    + + +
    +

    💎 Subscription Tiers

    +
    +
    +
    🆓
    +

    Free Tier

    +

    $0/month

    +

    100 chat messages/mo
    Basic Aria character
    Perfect for trying out

    +
    + +
    +
    ⭐
    +

    Pro Tier

    +

    $49/month

    +

    10K messages/mo
    Quantum computing
    Advanced training

    +
    + +
    +
    👑
    +

    Enterprise Tier

    +

    $199/month

    +

    Unlimited everything
    Custom models
    Priority support 24/7

    +
    +
    + +
    +
    + +
    +

    © 2026 Aria Platform | GitHub

    +
    + + + + + + diff --git a/docs/monetization/my-subscription.html b/docs/monetization/my-subscription.html index 1bbf243b0..ca3b390c7 100644 --- a/docs/monetization/my-subscription.html +++ b/docs/monetization/my-subscription.html @@ -1,672 +1,672 @@ - - - - - - My Subscription — Aria Platform - - - - - - - - - ← Back to Aria Home -
    -
    -

    🎭 My Subscription

    -
    - 🏠 Home - 💰 Pricing - ⚙️ Account -
    -
    - - - -
    - -
    -

    Current Plan

    -
    Free Tier
    - -
    - Status - Active -
    -
    - Monthly Cost - $0 -
    -
    - Billing Cycle - N/A -
    -
    - Next Billing Date - N/A -
    - -
    - - - -
    -
    - - -
    -

    Usage Statistics

    -

    Current billing period usage

    - -
    -
    -
    - 💬 Chat Messages - 0 / 100 -
    -
    -
    -
    -
    - -
    -
    - ⚛️ Quantum Jobs - Not Available -
    -
    -
    -
    -
    - -
    -
    - 🎓 Training Hours - Not Available -
    -
    -
    -
    -
    - -
    -
    - 🌐 API Requests - Not Available -
    -
    -
    -
    -
    - -
    -
    - 🏗️ Websites Created - Not Available -
    -
    -
    -
    -
    -
    -
    - - -
    -

    Plan Features

    -
      -
    • ✓ Basic Chat (100 messages/mo)
      • -
    • ✓ Aria Character Access
      • -
    • ✗ Quantum Computing
      • -
    • ✗ Advanced Training
      • -
    • ✗ Website Maker
      • -
    • ✗ API Access
      • -
    • ✗ Commercial License
      • -
    • ✗ Custom Models
      • -
    • ✗ Priority Support
      • -
    -
    - - -
    -

    Billing History

    -
    - - - - - - - - - - - - - - -
    DateDescriptionAmountStatus
    - No billing history yet -
    -
    -
    -
    -
    - - - - - - + + + + + + My Subscription — Aria Platform + + + + + + + + + ← Back to Aria Home +
    +
    +

    🎭 My Subscription

    +
    + 🏠 Home + 💰 Pricing + ⚙️ Account +
    +
    + + + +
    + +
    +

    Current Plan

    +
    Free Tier
    + +
    + Status + Active +
    +
    + Monthly Cost + $0 +
    +
    + Billing Cycle + N/A +
    +
    + Next Billing Date + N/A +
    + +
    + + + +
    +
    + + +
    +

    Usage Statistics

    +

    Current billing period usage

    + +
    +
    +
    + 💬 Chat Messages + 0 / 100 +
    +
    +
    +
    +
    + +
    +
    + ⚛️ Quantum Jobs + Not Available +
    +
    +
    +
    +
    + +
    +
    + 🎓 Training Hours + Not Available +
    +
    +
    +
    +
    + +
    +
    + 🌐 API Requests + Not Available +
    +
    +
    +
    +
    + +
    +
    + 🏗️ Websites Created + Not Available +
    +
    +
    +
    +
    +
    +
    + + +
    +

    Plan Features

    +
      +
    • ✓ Basic Chat (100 messages/mo)
      • +
    • ✓ Aria Character Access
      • +
    • ✗ Quantum Computing
      • +
    • ✗ Advanced Training
      • +
    • ✗ Website Maker
      • +
    • ✗ API Access
      • +
    • ✗ Commercial License
      • +
    • ✗ Custom Models
      • +
    • ✗ Priority Support
      • +
    +
    + + +
    +

    Billing History

    +
    + + + + + + + + + + + + + + +
    DateDescriptionAmountStatus
    + No billing history yet +
    +
    +
    +
    +
    + + + + + + diff --git a/docs/monetization/pricing.html b/docs/monetization/pricing.html index 7a0374dff..0dc829acf 100644 --- a/docs/monetization/pricing.html +++ b/docs/monetization/pricing.html @@ -1,671 +1,671 @@ - - - - - - Pricing & Plans — Aria Platform - - - - - - - - - - - - - - - - - - ← Back to Aria Home -
    -
    -

    🎭 Aria Platform Pricing

    -

    Choose the perfect plan for your AI-powered journey

    -
    - 💰 Target: $2,000+ Monthly Recurring Revenue -
    -
    - -
    - -
    -
    Free
    -
    - $0 - /month -
    -

    Perfect for trying out Aria

    -
      -
    • - ✓ - Basic Chat (100 messages/mo) -
      • -
    • - ✓ - Aria Character Access -
      • -
    • - ✗ - Quantum Computing -
      • -
    • - ✗ - Advanced Training -
      • -
    • - ✗ - Website Maker -
      • -
    • - ✗ - API Access -
      • -
    - -
    - - -
    -
    POPULAR
    -
    Pro
    -
    - $49 - /month -
    -

    For professionals and small teams

    -
      -
    • - ✓ - 10,000 Chat Messages/mo -
      • -
    • - ✓ - Full Aria Character Suite -
      • -
    • - ✓ - Quantum Computing (50 jobs/mo) -
      • -
    • - ✓ - Advanced Training (20 hrs/mo) -
      • -
    • - ✓ - Website Maker (10 sites/mo) -
      • -
    • - ✓ - API Access (10K requests/mo) -
      • -
    • - ✓ - Commercial License -
      • -
    - -
    - - -
    -
    Enterprise
    -
    - $199 - /month -
    -

    For organizations needing scale

    -
      -
    • - ✓ - Unlimited Chat Messages -
      • -
    • - ✓ - Unlimited Quantum Jobs -
      • -
    • - ✓ - Unlimited Training Hours -
      • -
    • - ✓ - Unlimited API Requests -
      • -
    • - ✓ - Unlimited Websites -
      • -
    • - ✓ - Custom Model Training -
      • -
    • - ✓ - Priority Support (24/7) -
      • -
    • - ✓ - Dedicated Infrastructure -
      • -
    - -
    -
    - - -
    -

    📊 Revenue Projection Model

    -
    -
    -

    Pro Subscribers

    -
    5
    -
    @ $49/month = $245
    -
    -
    -

    Enterprise Subscribers

    -
    10
    -
    @ $199/month = $1,990
    -
    -
    -

    Monthly Revenue

    -
    $2,235
    -
    Exceeds $2,000 target!
    -
    -
    -

    Annual Revenue

    -
    $26,820
    -
    Projected ARR
    -
    -
    -
    - - -
    -

    📋 Detailed Feature Comparison

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    FeatureFreePro ($49/mo)Enterprise ($199/mo)
    Chat Messages100/month10,000/monthUnlimited
    Aria Character✓ Basic✓ Full Suite✓ Custom Animations
    Quantum Computing✗50 jobs/monthUnlimited
    Model Training✗20 hours/monthUnlimited
    Website Generation✗10 sites/monthUnlimited
    API Access✗10K requests/monthUnlimited
    Commercial License✗✓✓
    Custom Models✗✗✓
    Priority Support✗✗✓ 24/7
    SLA Guarantee✗✗✓ 99.9% Uptime
    -
    - - -
    -

    ❓ Frequently Asked Questions

    - -
    -
    How do I upgrade my subscription?
    -
    - Simply click the "Upgrade" button on any plan card. You'll be redirected to our secure payment portal where you can complete the upgrade process. Your new features will be available immediately. -
    -
    - -
    -
    Can I cancel anytime?
    -
    - Yes! You can cancel your subscription at any time. You'll continue to have access to paid features until the end of your current billing period, then your account will automatically revert to the Free tier. -
    -
    - -
    -
    What payment methods do you accept?
    -
    - We accept all major credit cards (Visa, MasterCard, American Express, Discover), PayPal, and wire transfers for Enterprise plans. All payments are processed securely through Stripe. -
    -
    - -
    -
    What happens if I exceed my usage limits?
    -
    - If you approach your usage limits, we'll send you a notification. Once you reach your limit, you'll need to either wait until your monthly reset or upgrade to a higher tier to continue using premium features. -
    -
    - -
    -
    Do you offer discounts for annual billing?
    -
    - Yes! Save 20% by choosing annual billing. A Pro annual subscription is $470 (instead of $588), and Enterprise annual is $1,910 (instead of $2,388). -
    -
    - -
    -
    Can I get a custom plan?
    -
    - Absolutely! For organizations with specific needs, we offer custom Enterprise+ plans with tailored features, dedicated infrastructure, and custom SLAs. Contact our sales team to discuss your requirements. -
    -
    -
    - - -
    - - - - - - + + + + + + Pricing & Plans — Aria Platform + + + + + + + + + + + + + + + + + + ← Back to Aria Home +
    +
    +

    🎭 Aria Platform Pricing

    +

    Choose the perfect plan for your AI-powered journey

    +
    + 💰 Target: $2,000+ Monthly Recurring Revenue +
    +
    + +
    + +
    +
    Free
    +
    + $0 + /month +
    +

    Perfect for trying out Aria

    +
      +
    • + ✓ + Basic Chat (100 messages/mo) +
      • +
    • + ✓ + Aria Character Access +
      • +
    • + ✗ + Quantum Computing +
      • +
    • + ✗ + Advanced Training +
      • +
    • + ✗ + Website Maker +
      • +
    • + ✗ + API Access +
      • +
    + +
    + + +
    +
    POPULAR
    +
    Pro
    +
    + $49 + /month +
    +

    For professionals and small teams

    +
      +
    • + ✓ + 10,000 Chat Messages/mo +
      • +
    • + ✓ + Full Aria Character Suite +
      • +
    • + ✓ + Quantum Computing (50 jobs/mo) +
      • +
    • + ✓ + Advanced Training (20 hrs/mo) +
      • +
    • + ✓ + Website Maker (10 sites/mo) +
      • +
    • + ✓ + API Access (10K requests/mo) +
      • +
    • + ✓ + Commercial License +
      • +
    + +
    + + +
    +
    Enterprise
    +
    + $199 + /month +
    +

    For organizations needing scale

    +
      +
    • + ✓ + Unlimited Chat Messages +
      • +
    • + ✓ + Unlimited Quantum Jobs +
      • +
    • + ✓ + Unlimited Training Hours +
      • +
    • + ✓ + Unlimited API Requests +
      • +
    • + ✓ + Unlimited Websites +
      • +
    • + ✓ + Custom Model Training +
      • +
    • + ✓ + Priority Support (24/7) +
      • +
    • + ✓ + Dedicated Infrastructure +
      • +
    + +
    +
    + + +
    +

    📊 Revenue Projection Model

    +
    +
    +

    Pro Subscribers

    +
    5
    +
    @ $49/month = $245
    +
    +
    +

    Enterprise Subscribers

    +
    10
    +
    @ $199/month = $1,990
    +
    +
    +

    Monthly Revenue

    +
    $2,235
    +
    Exceeds $2,000 target!
    +
    +
    +

    Annual Revenue

    +
    $26,820
    +
    Projected ARR
    +
    +
    +
    + + +
    +

    📋 Detailed Feature Comparison

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    FeatureFreePro ($49/mo)Enterprise ($199/mo)
    Chat Messages100/month10,000/monthUnlimited
    Aria Character✓ Basic✓ Full Suite✓ Custom Animations
    Quantum Computing✗50 jobs/monthUnlimited
    Model Training✗20 hours/monthUnlimited
    Website Generation✗10 sites/monthUnlimited
    API Access✗10K requests/monthUnlimited
    Commercial License✗✓✓
    Custom Models✗✗✓
    Priority Support✗✗✓ 24/7
    SLA Guarantee✗✗✓ 99.9% Uptime
    +
    + + +
    +

    ❓ Frequently Asked Questions

    + +
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    How do I upgrade my subscription?
    +
    + Simply click the "Upgrade" button on any plan card. You'll be redirected to our secure payment portal where you can complete the upgrade process. Your new features will be available immediately. +
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    What payment methods do you accept?
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    What happens if I exceed my usage limits?
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    +
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    Do you offer discounts for annual billing?
    +
    + Yes! Save 20% by choosing annual billing. A Pro annual subscription is $470 (instead of $588), and Enterprise annual is $1,910 (instead of $2,388). +
    +
    + +
    +
    Can I get a custom plan?
    +
    + Absolutely! For organizations with specific needs, we offer custom Enterprise+ plans with tailored features, dedicated infrastructure, and custom SLAs. Contact our sales team to discuss your requirements. +
    +
    +
    + + +
    + + + + + + diff --git a/docs/monetization/referrals.html b/docs/monetization/referrals.html index b89bef121..913e85485 100644 --- a/docs/monetization/referrals.html +++ b/docs/monetization/referrals.html @@ -1,606 +1,606 @@ - - - - - - Referral Program — Aria Platform - - - - - - - - - - - - - - - - - - ← Back to Aria Home -
    -
    -

    🎁 Referral Program

    -
    - 🏠 Home - 📊 Subscription - ⚙️ Account -
    -
    - -
    -

    Earn Money Referring Friends!

    -

    Get 20% commission on every referral that subscribes. Plus bonus rewards!

    - -
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    Your Unique Referral Code

    -
    DEMO1234AB
    - - - -
    -
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    -
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    👥
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    Total Referrals
    -
    0
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    Total Earned
    -
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    $0
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    -
    -
    - - -
    -

    💵 Commission Rates

    - - - - - - - - - - - - - - - - - - - - - - - -
    PlanPriceCommission RateYour Earnings
    Pro$49/month20%$9.80 per referral per month
    Enterprise$199/month20%$39.80 per referral per month
    -
    - - -
    -

    🏆 Milestone Bonuses

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    Earn extra bonuses when you reach these milestones:

    - -
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    🥉
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    5 Referrals
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    Unlock first bonus
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    - -
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    🥈
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    10 Referrals
    -
    Double bonus
    -
    -
    $100
    -
    - -
    -
    🥇
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    25 Referrals
    -
    Gold achievement
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    -
    $300
    -
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    💎
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    50 Referrals
    -
    Diamond status
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    $750
    -
    - -
    -
    👑
    -
    -
    100 Referrals
    -
    Elite referrer
    -
    -
    $2,000
    -
    -
    - - -
    -

    Your Referrals

    - - - - - - - - - - - - - - - -
    DateUserPlanCommissionStatus
    - No referrals yet. Share your code to get started! -
    -
    - - -
    -

    💸 Request Payout

    -

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    - -
    -
    - - - - - - + + + + + + Referral Program — Aria Platform + + + + + + + + + + + + + + + + + + ← Back to Aria Home +
    +
    +

    🎁 Referral Program

    +
    + 🏠 Home + 📊 Subscription + ⚙️ Account +
    +
    + +
    +

    Earn Money Referring Friends!

    +

    Get 20% commission on every referral that subscribes. Plus bonus rewards!

    + +
    +

    Your Unique Referral Code

    +
    DEMO1234AB
    + + + +
    +
    + + +
    +
    +
    👥
    +
    Total Referrals
    +
    0
    +
    + +
    +
    💰
    +
    Total Earned
    +
    $0
    +
    + +
    +
    ⏳
    +
    Pending Payout
    +
    $0
    +
    + +
    +
    ✅
    +
    Paid Out
    +
    $0
    +
    +
    + + +
    +

    💵 Commission Rates

    + + + + + + + + + + + + + + + + + + + + + + + +
    PlanPriceCommission RateYour Earnings
    Pro$49/month20%$9.80 per referral per month
    Enterprise$199/month20%$39.80 per referral per month
    +
    + + +
    +

    🏆 Milestone Bonuses

    +

    Earn extra bonuses when you reach these milestones:

    + +
    +
    🥉
    +
    +
    5 Referrals
    +
    Unlock first bonus
    +
    +
    $50
    +
    + +
    +
    🥈
    +
    +
    10 Referrals
    +
    Double bonus
    +
    +
    $100
    +
    + +
    +
    🥇
    +
    +
    25 Referrals
    +
    Gold achievement
    +
    +
    $300
    +
    + +
    +
    💎
    +
    +
    50 Referrals
    +
    Diamond status
    +
    +
    $750
    +
    + +
    +
    👑
    +
    +
    100 Referrals
    +
    Elite referrer
    +
    +
    $2,000
    +
    +
    + + +
    +

    Your Referrals

    + + + + + + + + + + + + + + + +
    DateUserPlanCommissionStatus
    + No referrals yet. Share your code to get started! +
    +
    + + +
    +

    💸 Request Payout

    +

    + Minimum payout: $25. Payouts are processed within 5 business days. +

    + +
    +
    + + + + + + diff --git a/docs/monetization/subscription-success.html b/docs/monetization/subscription-success.html index a4b579da4..af09891f9 100644 --- a/docs/monetization/subscription-success.html +++ b/docs/monetization/subscription-success.html @@ -1,438 +1,438 @@ - - - - - - Subscription Activated — Welcome to Aria! - - - - - - - - - ← Back to Aria Home -
    -
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    🎉 Welcome to Aria!

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    Your subscription is now active

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    - Order Status - ✓ Confirmed -
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    - Amount - $49.00 -
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    - Next Billing Date - March 4, 2026 -
    -
    - Confirmation Email - Sent -
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    -

    🚀 Next Steps

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    1
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    Explore Your Features

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    Monitor Your Usage

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    - - - - - - + + + + + + Subscription Activated — Welcome to Aria! + + + + + + + + + ← Back to Aria Home +
    +
    +
    + ✓ +
    + +

    🎉 Welcome to Aria!

    +

    Your subscription is now active

    + +
    Pro Plan
    + +
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    + Order Status + ✓ Confirmed +
    +
    + Plan + Pro +
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    +
    + Amount + $49.00 +
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    + Next Billing Date + March 4, 2026 +
    +
    + Confirmation Email + Sent +
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    +

    🚀 Next Steps

    + +
    +
    1
    +
    +

    Explore Your Features

    +

    Check out all the premium features now available in your account

    +
    +
    + +
    +
    2
    +
    +

    Start Building

    +

    Use quantum computing, advanced training, and our website maker

    +
    +
    + +
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    3
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    +

    Monitor Your Usage

    +

    Track your usage and limits in your subscription dashboard

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    + +
    + + 📊 View My Subscription + + + 🏠 Go to Dashboard + +
    + +
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    💡 Need Help Getting Started?

    +

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    +

    + 📧 Email: support@aria-platform.com
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    +
    +
    +
    + + + + + + diff --git a/docs/mount/app.js b/docs/mount/app.js index 14b515e6c..f55c7b141 100644 --- a/docs/mount/app.js +++ b/docs/mount/app.js @@ -1,559 +1,559 @@ -// QAI Control Center JavaScript - -const API_BASE = 'http://localhost:8000'; - -// Global state -let currentProvider = 'auto'; -let chatHistory = []; - -// Initialize app -document.addEventListener('DOMContentLoaded', () => { - initializeTabs(); - initializeForms(); - checkServiceStatus(); - loadDashboard(); - - // Refresh status every 30 seconds - setInterval(checkServiceStatus, 30000); -}); - -// Tab Management -function initializeTabs() { - const tabBtns = document.querySelectorAll('.tab-btn'); - - tabBtns.forEach(btn => { - btn.addEventListener('click', () => { - const tabName = btn.dataset.tab; - switchTab(tabName); - }); - }); -} - -function switchTab(tabName) { - // Update buttons - document.querySelectorAll('.tab-btn').forEach(btn => { - btn.classList.remove('active'); - if (btn.dataset.tab === tabName) { - btn.classList.add('active'); - } - }); - - // Update content - document.querySelectorAll('.tab-content').forEach(content => { - content.classList.remove('active'); - }); - document.getElementById(tabName).classList.add('active'); - - // Load tab-specific data - loadTabData(tabName); -} - -function loadTabData(tabName) { - switch(tabName) { - case 'dashboard': - loadDashboard(); - break; - case 'quantum': - loadQuantumData(); - break; - case 'chat': - loadChatData(); - break; - case 'training': - loadTrainingData(); - break; - } -} - -// Service Status -async function checkServiceStatus() { - try { - const response = await fetch(`${API_BASE}/health`); - const data = await response.json(); - - const indicator = document.getElementById('serviceStatus'); - const dot = indicator.querySelector('.status-dot'); - const text = indicator.querySelector('.status-text'); - - if (data.status === 'healthy') { - dot.classList.add('online'); - dot.classList.remove('offline'); - text.textContent = 'Online'; - } else { - dot.classList.remove('online'); - dot.classList.add('offline'); - text.textContent = 'Error'; - } - } catch (error) { - const indicator = document.getElementById('serviceStatus'); - const dot = indicator.querySelector('.status-dot'); - const text = indicator.querySelector('.status-text'); - dot.classList.remove('online'); - dot.classList.add('offline'); - text.textContent = 'Offline'; - } -} - -// Dashboard -async function loadDashboard() { - try { - const response = await fetch(`${API_BASE}/status`); - const data = await response.json(); - - // Update system status - const statusHtml = ` -
    - Service - ${data.service} -
    -
    - Version - ${data.version} -
    -
    - Quantum Enabled - - ${data.quantum.enabled ? '✓ Yes' : '✗ No'} - -
    -
    - Chat Enabled - - ${data.chat.enabled ? '✓ Yes' : '✗ No'} - -
    -
    - Training Enabled - - ${data.training.enabled ? '✓ Yes' : '✗ No'} - -
    - `; - document.getElementById('systemStatus').innerHTML = statusHtml; - - // Update recent activity - let activityHtml = '
    No recent activity
    '; - - if (data.quantum.recent_results && data.quantum.recent_results.length > 0) { - activityHtml = data.quantum.recent_results.map(result => ` -
    - Quantum: ${result.dataset} - - ${(result.accuracy * 100).toFixed(1)}% accuracy -
    ${result.backend} - ${result.timestamp} -
    - `).join(''); - } - - document.getElementById('recentActivity').innerHTML = activityHtml; - - addLog('Dashboard loaded successfully', 'success'); - } catch (error) { - addLog(`Dashboard load error: ${error.message}`, 'error'); - } -} - -// Quantum AI -async function loadQuantumData() { - try { - // Load datasets - const datasetsResponse = await fetch(`${API_BASE}/quantum/datasets`); - const datasets = await datasetsResponse.json(); - - const datasetSelect = document.getElementById('quantumDataset'); - datasetSelect.innerHTML = '' + - datasets.map(d => ``).join(''); - - // Load status - const statusResponse = await fetch(`${API_BASE}/quantum/status`); - const status = await statusResponse.json(); - - const statusHtml = ` -
    - Backend - ${status.backend} -
    -
    - Azure Connected - - ${status.azure_connected ? '✓ Yes' : '✗ No'} - -
    -
    - Available Backends - ${status.available_backends.length} -
    - `; - document.getElementById('quantumStatus').innerHTML = statusHtml; - - // Load recent results - const resultsHtml = status.recent_results && status.recent_results.length > 0 - ? status.recent_results.map(r => ` -
    - ${r.dataset}
    - Accuracy: ${(r.accuracy * 100).toFixed(1)}%
    - ${r.backend} - ${r.timestamp} -
    - `).join('') - : '
    No results yet
    '; - - document.getElementById('quantumResults').innerHTML = resultsHtml; - - // Load AutoRun jobs (placeholder) - document.getElementById('quantumAutorunJobs').innerHTML = ` -
    - Available jobs will appear here -
    - `; - - addLog('Quantum data loaded', 'info'); - } catch (error) { - addLog(`Quantum load error: ${error.message}`, 'error'); - } -} - -// Chat -async function loadChatData() { - try { - const response = await fetch(`${API_BASE}/chat/status`); - const data = await response.json(); - - // Update provider status - const providersHtml = Object.entries(data.providers).map(([name, info]) => ` -
    - ${name.toUpperCase()} - - ${info.available ? '✓ Available' : '✗ Unavailable'} - ${info.cost ? ` (${info.cost})` : ''} - -
    - `).join(''); - - document.getElementById('chatProviders').innerHTML = providersHtml; - - // Update chat status - const statusHtml = ` -
    - Default Provider - ${data.default_provider} -
    - `; - document.getElementById('chatStatus').innerHTML = statusHtml; - - // Load conversation history - if (data.recent_conversations && data.recent_conversations.length > 0) { - const historyHtml = data.recent_conversations.map(conv => ` -
    - ${conv.file}
    - ${conv.message_count} messages
    - ${conv.preview} -
    - `).join(''); - document.getElementById('chatHistory').innerHTML = historyHtml; - } else { - document.getElementById('chatHistory').innerHTML = - '
    No conversations yet
    '; - } - - addLog('Chat data loaded', 'info'); - } catch (error) { - addLog(`Chat load error: ${error.message}`, 'error'); - } -} - -// Training -async function loadTrainingData() { - try { - // Load datasets - const datasetsResponse = await fetch(`${API_BASE}/training/datasets`); - const datasets = await datasetsResponse.json(); - - // Populate LoRA dataset select - const loraSelect = document.getElementById('loraDataset'); - loraSelect.innerHTML = ''; - - if (datasets.chat && datasets.chat.length > 0) { - datasets.chat.forEach(ds => { - loraSelect.innerHTML += ``; - }); - } - - // Load training status - const statusResponse = await fetch(`${API_BASE}/training/status`); - const status = await statusResponse.json(); - - const statusHtml = ` -
    - System - Ready -
    - `; - document.getElementById('trainingStatus').innerHTML = statusHtml; - - // LoRA adapter status - const adapterHtml = status.lora_adapter.available - ? ` -
    - Status - ✓ Available -
    -
    - Model - ${status.lora_adapter.model || 'N/A'} -
    -
    - Rank - ${status.lora_adapter.rank || 'N/A'} -
    - ` - : '
    No adapter trained yet
    '; - - document.getElementById('loraAdapterStatus').innerHTML = adapterHtml; - - // Load AutoTrain jobs - const jobsResponse = await fetch(`${API_BASE}/training/autotrain/jobs`); - const jobs = await jobsResponse.json(); - - const jobSelect = document.getElementById('autotrainJob'); - jobSelect.innerHTML = '' + - (jobs.jobs || []).map(job => ``).join(''); - - // AutoTrain status - const autotrainHtml = status.orchestrators.autotrain.jobs && - Object.keys(status.orchestrators.autotrain.jobs).length > 0 - ? Object.entries(status.orchestrators.autotrain.jobs).map(([name, job]) => ` -
    - ${name} - ${job.status || 'unknown'} -
    - `).join('') - : '
    No jobs run yet
    '; - - document.getElementById('autotrainStatus').innerHTML = autotrainHtml; - - // Dataset list - const datasetHtml = ` -

    Quantum (${datasets.quantum ? datasets.quantum.length : 0})

    -
    - ${(datasets.quantum || []).map(d => `
    ${d}
    `).join('')} -
    -

    Chat (${datasets.chat ? datasets.chat.length : 0})

    -
    - ${(datasets.chat || []).map(d => `
    ${d}
    `).join('')} -
    -

    Vision (${datasets.vision ? datasets.vision.length : 0})

    -
    - ${(datasets.vision || []).map(d => `
    ${d}
    `).join('')} -
    - `; - document.getElementById('datasetList').innerHTML = datasetHtml; - - addLog('Training data loaded', 'info'); - } catch (error) { - addLog(`Training load error: ${error.message}`, 'error'); - } -} - -// Form Handlers -function initializeForms() { - // Quantum training form - document.getElementById('quantumTrainForm').addEventListener('submit', async (e) => { - e.preventDefault(); - await trainQuantumClassifier(); - }); - - // LoRA training form - document.getElementById('loraTrainForm').addEventListener('submit', async (e) => { - e.preventDefault(); - await trainLoRA(); - }); - - // Chat form - document.getElementById('chatForm').addEventListener('submit', async (e) => { - e.preventDefault(); - await sendChatMessage(); - }); - - // Chat provider change - document.getElementById('chatProvider').addEventListener('change', (e) => { - currentProvider = e.target.value; - }); -} - -async function trainQuantumClassifier() { - const dataset = document.getElementById('quantumDataset').value; - const n_qubits = parseInt(document.getElementById('quantumQubits').value); - const n_layers = parseInt(document.getElementById('quantumLayers').value); - const epochs = parseInt(document.getElementById('quantumEpochs').value); - const backend = document.getElementById('quantumBackend').value; - - if (!dataset) { - addLog('Please select a dataset', 'error'); - return; - } - - addLog(`Starting quantum training: ${dataset}`, 'info'); - - try { - const response = await fetch(`${API_BASE}/quantum/train`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ dataset, n_qubits, n_layers, epochs, backend }) - }); - - const result = await response.json(); - - if (result.success) { - addLog('Quantum training started successfully!', 'success'); - setTimeout(() => loadQuantumData(), 2000); - } else { - addLog(`Training error: ${result.stderr || result.error}`, 'error'); - } - } catch (error) { - addLog(`Request failed: ${error.message}`, 'error'); - } -} - -async function trainLoRA() { - const dataset = document.getElementById('loraDataset').value; - const max_train_samples = parseInt(document.getElementById('loraTrainSamples').value); - const max_eval_samples = parseInt(document.getElementById('loraEvalSamples').value); - const epochs = parseInt(document.getElementById('loraEpochs').value); - - if (!dataset) { - addLog('Please select a dataset', 'error'); - return; - } - - addLog(`Starting LoRA training on ${dataset}`, 'info'); - - try { - const response = await fetch(`${API_BASE}/training/lora`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ dataset, max_train_samples, max_eval_samples, epochs }) - }); - - const result = await response.json(); - - if (result.success) { - addLog('LoRA training started successfully!', 'success'); - setTimeout(() => loadTrainingData(), 2000); - } else { - addLog(`Training error: ${result.stderr || result.error}`, 'error'); - } - } catch (error) { - addLog(`Request failed: ${error.message}`, 'error'); - } -} - -async function sendChatMessage() { - const input = document.getElementById('chatInput'); - const message = input.value.trim(); - - if (!message) return; - - // Add user message to chat - addChatMessage(message, 'user'); - input.value = ''; - - try { - const provider = currentProvider === 'auto' ? null : currentProvider; - - const response = await fetch(`${API_BASE}/chat/message`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ message, provider }) - }); - - const result = await response.json(); - - if (result.success) { - addChatMessage(result.response, 'assistant'); - addLog(`Chat response from ${result.provider}`, 'info'); - } else { - addChatMessage('Error: ' + (result.error || 'Unknown error'), 'system'); - addLog(`Chat error: ${result.error}`, 'error'); - } - } catch (error) { - addChatMessage('Error: ' + error.message, 'system'); - addLog(`Chat request failed: ${error.message}`, 'error'); - } -} - -function addChatMessage(content, role) { - const messagesDiv = document.getElementById('chatMessages'); - const messageDiv = document.createElement('div'); - messageDiv.className = `chat-message ${role}`; - messageDiv.textContent = content; - messagesDiv.appendChild(messageDiv); - messagesDiv.scrollTop = messagesDiv.scrollHeight; -} - -async function runAutoTrain(dryRun) { - const job = document.getElementById('autotrainJob').value; - - if (!job) { - addLog('Please select a job', 'error'); - return; - } - - addLog(`Running AutoTrain job: ${job} ${dryRun ? '(dry run)' : ''}`, 'info'); - - try { - const response = await fetch(`${API_BASE}/training/autotrain`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ job_name: job, dry_run: dryRun }) - }); - - const result = await response.json(); - - if (result.success) { - addLog('AutoTrain job completed successfully!', 'success'); - setTimeout(() => loadTrainingData(), 2000); - } else { - addLog(`AutoTrain error: ${result.stderr || result.error}`, 'error'); - } - } catch (error) { - addLog(`Request failed: ${error.message}`, 'error'); - } -} - -// Quick Actions -function quickAction(action) { - switch(action) { - case 'quantum': - switchTab('quantum'); - break; - case 'chat': - switchTab('chat'); - break; - case 'training': - switchTab('training'); - break; - } -} - -function refreshStatus() { - addLog('Refreshing status...', 'info'); - loadDashboard(); -} - -// Logging -function addLog(message, type = 'info') { - const logOutput = document.getElementById('logOutput'); - const entry = document.createElement('div'); - entry.className = `log-entry ${type}`; - const timestamp = new Date().toLocaleTimeString(); - entry.textContent = `[${timestamp}] ${message}`; - logOutput.appendChild(entry); - logOutput.scrollTop = logOutput.scrollHeight; -} - -function clearLogs() { - document.getElementById('logOutput').innerHTML = ''; - addLog('Logs cleared', 'info'); -} - -function refreshLogs() { - addLog('Logs refreshed', 'info'); -} +// QAI Control Center JavaScript + +const API_BASE = 'http://localhost:8000'; + +// Global state +let currentProvider = 'auto'; +let chatHistory = []; + +// Initialize app +document.addEventListener('DOMContentLoaded', () => { + initializeTabs(); + initializeForms(); + checkServiceStatus(); + loadDashboard(); + + // Refresh status every 30 seconds + setInterval(checkServiceStatus, 30000); +}); + +// Tab Management +function initializeTabs() { + const tabBtns = document.querySelectorAll('.tab-btn'); + + tabBtns.forEach(btn => { + btn.addEventListener('click', () => { + const tabName = btn.dataset.tab; + switchTab(tabName); + }); + }); +} + +function switchTab(tabName) { + // Update buttons + document.querySelectorAll('.tab-btn').forEach(btn => { + btn.classList.remove('active'); + if (btn.dataset.tab === tabName) { + btn.classList.add('active'); + } + }); + + // Update content + document.querySelectorAll('.tab-content').forEach(content => { + content.classList.remove('active'); + }); + document.getElementById(tabName).classList.add('active'); + + // Load tab-specific data + loadTabData(tabName); +} + +function loadTabData(tabName) { + switch(tabName) { + case 'dashboard': + loadDashboard(); + break; + case 'quantum': + loadQuantumData(); + break; + case 'chat': + loadChatData(); + break; + case 'training': + loadTrainingData(); + break; + } +} + +// Service Status +async function checkServiceStatus() { + try { + const response = await fetch(`${API_BASE}/health`); + const data = await response.json(); + + const indicator = document.getElementById('serviceStatus'); + const dot = indicator.querySelector('.status-dot'); + const text = indicator.querySelector('.status-text'); + + if (data.status === 'healthy') { + dot.classList.add('online'); + dot.classList.remove('offline'); + text.textContent = 'Online'; + } else { + dot.classList.remove('online'); + dot.classList.add('offline'); + text.textContent = 'Error'; + } + } catch (error) { + const indicator = document.getElementById('serviceStatus'); + const dot = indicator.querySelector('.status-dot'); + const text = indicator.querySelector('.status-text'); + dot.classList.remove('online'); + dot.classList.add('offline'); + text.textContent = 'Offline'; + } +} + +// Dashboard +async function loadDashboard() { + try { + const response = await fetch(`${API_BASE}/status`); + const data = await response.json(); + + // Update system status + const statusHtml = ` +
    + Service + ${data.service} +
    +
    + Version + ${data.version} +
    +
    + Quantum Enabled + + ${data.quantum.enabled ? '✓ Yes' : '✗ No'} + +
    +
    + Chat Enabled + + ${data.chat.enabled ? '✓ Yes' : '✗ No'} + +
    +
    + Training Enabled + + ${data.training.enabled ? '✓ Yes' : '✗ No'} + +
    + `; + document.getElementById('systemStatus').innerHTML = statusHtml; + + // Update recent activity + let activityHtml = '
    No recent activity
    '; + + if (data.quantum.recent_results && data.quantum.recent_results.length > 0) { + activityHtml = data.quantum.recent_results.map(result => ` +
    + Quantum: ${result.dataset} - + ${(result.accuracy * 100).toFixed(1)}% accuracy +
    ${result.backend} - ${result.timestamp} +
    + `).join(''); + } + + document.getElementById('recentActivity').innerHTML = activityHtml; + + addLog('Dashboard loaded successfully', 'success'); + } catch (error) { + addLog(`Dashboard load error: ${error.message}`, 'error'); + } +} + +// Quantum AI +async function loadQuantumData() { + try { + // Load datasets + const datasetsResponse = await fetch(`${API_BASE}/quantum/datasets`); + const datasets = await datasetsResponse.json(); + + const datasetSelect = document.getElementById('quantumDataset'); + datasetSelect.innerHTML = '' + + datasets.map(d => ``).join(''); + + // Load status + const statusResponse = await fetch(`${API_BASE}/quantum/status`); + const status = await statusResponse.json(); + + const statusHtml = ` +
    + Backend + ${status.backend} +
    +
    + Azure Connected + + ${status.azure_connected ? '✓ Yes' : '✗ No'} + +
    +
    + Available Backends + ${status.available_backends.length} +
    + `; + document.getElementById('quantumStatus').innerHTML = statusHtml; + + // Load recent results + const resultsHtml = status.recent_results && status.recent_results.length > 0 + ? status.recent_results.map(r => ` +
    + ${r.dataset}
    + Accuracy: ${(r.accuracy * 100).toFixed(1)}%
    + ${r.backend} - ${r.timestamp} +
    + `).join('') + : '
    No results yet
    '; + + document.getElementById('quantumResults').innerHTML = resultsHtml; + + // Load AutoRun jobs (placeholder) + document.getElementById('quantumAutorunJobs').innerHTML = ` +
    + Available jobs will appear here +
    + `; + + addLog('Quantum data loaded', 'info'); + } catch (error) { + addLog(`Quantum load error: ${error.message}`, 'error'); + } +} + +// Chat +async function loadChatData() { + try { + const response = await fetch(`${API_BASE}/chat/status`); + const data = await response.json(); + + // Update provider status + const providersHtml = Object.entries(data.providers).map(([name, info]) => ` +
    + ${name.toUpperCase()} + + ${info.available ? '✓ Available' : '✗ Unavailable'} + ${info.cost ? ` (${info.cost})` : ''} + +
    + `).join(''); + + document.getElementById('chatProviders').innerHTML = providersHtml; + + // Update chat status + const statusHtml = ` +
    + Default Provider + ${data.default_provider} +
    + `; + document.getElementById('chatStatus').innerHTML = statusHtml; + + // Load conversation history + if (data.recent_conversations && data.recent_conversations.length > 0) { + const historyHtml = data.recent_conversations.map(conv => ` +
    + ${conv.file}
    + ${conv.message_count} messages
    + ${conv.preview} +
    + `).join(''); + document.getElementById('chatHistory').innerHTML = historyHtml; + } else { + document.getElementById('chatHistory').innerHTML = + '
    No conversations yet
    '; + } + + addLog('Chat data loaded', 'info'); + } catch (error) { + addLog(`Chat load error: ${error.message}`, 'error'); + } +} + +// Training +async function loadTrainingData() { + try { + // Load datasets + const datasetsResponse = await fetch(`${API_BASE}/training/datasets`); + const datasets = await datasetsResponse.json(); + + // Populate LoRA dataset select + const loraSelect = document.getElementById('loraDataset'); + loraSelect.innerHTML = ''; + + if (datasets.chat && datasets.chat.length > 0) { + datasets.chat.forEach(ds => { + loraSelect.innerHTML += ``; + }); + } + + // Load training status + const statusResponse = await fetch(`${API_BASE}/training/status`); + const status = await statusResponse.json(); + + const statusHtml = ` +
    + System + Ready +
    + `; + document.getElementById('trainingStatus').innerHTML = statusHtml; + + // LoRA adapter status + const adapterHtml = status.lora_adapter.available + ? ` +
    + Status + ✓ Available +
    +
    + Model + ${status.lora_adapter.model || 'N/A'} +
    +
    + Rank + ${status.lora_adapter.rank || 'N/A'} +
    + ` + : '
    No adapter trained yet
    '; + + document.getElementById('loraAdapterStatus').innerHTML = adapterHtml; + + // Load AutoTrain jobs + const jobsResponse = await fetch(`${API_BASE}/training/autotrain/jobs`); + const jobs = await jobsResponse.json(); + + const jobSelect = document.getElementById('autotrainJob'); + jobSelect.innerHTML = '' + + (jobs.jobs || []).map(job => ``).join(''); + + // AutoTrain status + const autotrainHtml = status.orchestrators.autotrain.jobs && + Object.keys(status.orchestrators.autotrain.jobs).length > 0 + ? Object.entries(status.orchestrators.autotrain.jobs).map(([name, job]) => ` +
    + ${name} - ${job.status || 'unknown'} +
    + `).join('') + : '
    No jobs run yet
    '; + + document.getElementById('autotrainStatus').innerHTML = autotrainHtml; + + // Dataset list + const datasetHtml = ` +

    Quantum (${datasets.quantum ? datasets.quantum.length : 0})

    +
    + ${(datasets.quantum || []).map(d => `
    ${d}
    `).join('')} +
    +

    Chat (${datasets.chat ? datasets.chat.length : 0})

    +
    + ${(datasets.chat || []).map(d => `
    ${d}
    `).join('')} +
    +

    Vision (${datasets.vision ? datasets.vision.length : 0})

    +
    + ${(datasets.vision || []).map(d => `
    ${d}
    `).join('')} +
    + `; + document.getElementById('datasetList').innerHTML = datasetHtml; + + addLog('Training data loaded', 'info'); + } catch (error) { + addLog(`Training load error: ${error.message}`, 'error'); + } +} + +// Form Handlers +function initializeForms() { + // Quantum training form + document.getElementById('quantumTrainForm').addEventListener('submit', async (e) => { + e.preventDefault(); + await trainQuantumClassifier(); + }); + + // LoRA training form + document.getElementById('loraTrainForm').addEventListener('submit', async (e) => { + e.preventDefault(); + await trainLoRA(); + }); + + // Chat form + document.getElementById('chatForm').addEventListener('submit', async (e) => { + e.preventDefault(); + await sendChatMessage(); + }); + + // Chat provider change + document.getElementById('chatProvider').addEventListener('change', (e) => { + currentProvider = e.target.value; + }); +} + +async function trainQuantumClassifier() { + const dataset = document.getElementById('quantumDataset').value; + const n_qubits = parseInt(document.getElementById('quantumQubits').value); + const n_layers = parseInt(document.getElementById('quantumLayers').value); + const epochs = parseInt(document.getElementById('quantumEpochs').value); + const backend = document.getElementById('quantumBackend').value; + + if (!dataset) { + addLog('Please select a dataset', 'error'); + return; + } + + addLog(`Starting quantum training: ${dataset}`, 'info'); + + try { + const response = await fetch(`${API_BASE}/quantum/train`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ dataset, n_qubits, n_layers, epochs, backend }) + }); + + const result = await response.json(); + + if (result.success) { + addLog('Quantum training started successfully!', 'success'); + setTimeout(() => loadQuantumData(), 2000); + } else { + addLog(`Training error: ${result.stderr || result.error}`, 'error'); + } + } catch (error) { + addLog(`Request failed: ${error.message}`, 'error'); + } +} + +async function trainLoRA() { + const dataset = document.getElementById('loraDataset').value; + const max_train_samples = parseInt(document.getElementById('loraTrainSamples').value); + const max_eval_samples = parseInt(document.getElementById('loraEvalSamples').value); + const epochs = parseInt(document.getElementById('loraEpochs').value); + + if (!dataset) { + addLog('Please select a dataset', 'error'); + return; + } + + addLog(`Starting LoRA training on ${dataset}`, 'info'); + + try { + const response = await fetch(`${API_BASE}/training/lora`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ dataset, max_train_samples, max_eval_samples, epochs }) + }); + + const result = await response.json(); + + if (result.success) { + addLog('LoRA training started successfully!', 'success'); + setTimeout(() => loadTrainingData(), 2000); + } else { + addLog(`Training error: ${result.stderr || result.error}`, 'error'); + } + } catch (error) { + addLog(`Request failed: ${error.message}`, 'error'); + } +} + +async function sendChatMessage() { + const input = document.getElementById('chatInput'); + const message = input.value.trim(); + + if (!message) return; + + // Add user message to chat + addChatMessage(message, 'user'); + input.value = ''; + + try { + const provider = currentProvider === 'auto' ? null : currentProvider; + + const response = await fetch(`${API_BASE}/chat/message`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ message, provider }) + }); + + const result = await response.json(); + + if (result.success) { + addChatMessage(result.response, 'assistant'); + addLog(`Chat response from ${result.provider}`, 'info'); + } else { + addChatMessage('Error: ' + (result.error || 'Unknown error'), 'system'); + addLog(`Chat error: ${result.error}`, 'error'); + } + } catch (error) { + addChatMessage('Error: ' + error.message, 'system'); + addLog(`Chat request failed: ${error.message}`, 'error'); + } +} + +function addChatMessage(content, role) { + const messagesDiv = document.getElementById('chatMessages'); + const messageDiv = document.createElement('div'); + messageDiv.className = `chat-message ${role}`; + messageDiv.textContent = content; + messagesDiv.appendChild(messageDiv); + messagesDiv.scrollTop = messagesDiv.scrollHeight; +} + +async function runAutoTrain(dryRun) { + const job = document.getElementById('autotrainJob').value; + + if (!job) { + addLog('Please select a job', 'error'); + return; + } + + addLog(`Running AutoTrain job: ${job} ${dryRun ? '(dry run)' : ''}`, 'info'); + + try { + const response = await fetch(`${API_BASE}/training/autotrain`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ job_name: job, dry_run: dryRun }) + }); + + const result = await response.json(); + + if (result.success) { + addLog('AutoTrain job completed successfully!', 'success'); + setTimeout(() => loadTrainingData(), 2000); + } else { + addLog(`AutoTrain error: ${result.stderr || result.error}`, 'error'); + } + } catch (error) { + addLog(`Request failed: ${error.message}`, 'error'); + } +} + +// Quick Actions +function quickAction(action) { + switch(action) { + case 'quantum': + switchTab('quantum'); + break; + case 'chat': + switchTab('chat'); + break; + case 'training': + switchTab('training'); + break; + } +} + +function refreshStatus() { + addLog('Refreshing status...', 'info'); + loadDashboard(); +} + +// Logging +function addLog(message, type = 'info') { + const logOutput = document.getElementById('logOutput'); + const entry = document.createElement('div'); + entry.className = `log-entry ${type}`; + const timestamp = new Date().toLocaleTimeString(); + entry.textContent = `[${timestamp}] ${message}`; + logOutput.appendChild(entry); + logOutput.scrollTop = logOutput.scrollHeight; +} + +function clearLogs() { + document.getElementById('logOutput').innerHTML = ''; + addLog('Logs cleared', 'info'); +} + +function refreshLogs() { + addLog('Logs refreshed', 'info'); +} diff --git a/docs/mount/index.html b/docs/mount/index.html index 831815790..817e684de 100644 --- a/docs/mount/index.html +++ b/docs/mount/index.html @@ -1,281 +1,281 @@ - - - - - - QAI Control Center — Aria - - - - - - - - - - - - - - - - - - ← Back to Aria Home -
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    + + + + + + diff --git a/docs/mount/styles.css b/docs/mount/styles.css index 5f08ee9cd..0f525be31 100644 --- a/docs/mount/styles.css +++ b/docs/mount/styles.css @@ -1,524 +1,524 @@ -/* QAI Control Center Styles */ - -:root { - --primary: #667eea; - --primary-dark: #5568d3; - --secondary: #48bb78; - --danger: #f56565; - --warning: #ed8936; - --info: #4299e1; - --dark: #2d3748; - --light: #f7fafc; - --border: #e2e8f0; - --text: #2d3748; - --text-light: #718096; - --success: #48bb78; -} - -* { - margin: 0; - padding: 0; - box-sizing: border-box; -} - -body { - font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); - color: var(--text); - min-height: 100vh; - padding: 20px; -} - -.container { - max-width: 1400px; - margin: 0 auto; - background: white; - border-radius: 12px; - box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3); - overflow: hidden; -} - -/* Header */ -.header { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); - color: white; - padding: 30px; - display: flex; - justify-content: space-between; - align-items: center; -} - -.header h1 { - font-size: 28px; - font-weight: 700; -} - -.status-indicator { - display: flex; - align-items: center; - gap: 10px; - background: rgba(255, 255, 255, 0.2); - padding: 8px 16px; - border-radius: 20px; -} - -.status-dot { - width: 10px; - height: 10px; - border-radius: 50%; - background: var(--warning); - animation: pulse 2s infinite; -} - -.status-dot.online { - background: var(--success); -} - -.status-dot.offline { - background: var(--danger); -} - -@keyframes pulse { - 0%, 100% { opacity: 1; } - 50% { opacity: 0.5; } -} - -/* Tabs */ -.tabs { - display: flex; - background: var(--light); - border-bottom: 2px solid var(--border); - overflow-x: auto; -} - -.tab-btn { - flex: 1; - min-width: 150px; - padding: 16px 20px; - border: none; - background: transparent; - cursor: pointer; - font-size: 16px; - font-weight: 500; - color: var(--text-light); - transition: all 0.3s; - border-bottom: 3px solid transparent; -} - -.tab-btn:hover { - background: white; - color: var(--primary); -} - -.tab-btn.active { - background: white; - color: var(--primary); - border-bottom-color: var(--primary); -} - -/* Tab Content */ -.tab-content { - display: none; - padding: 30px; - animation: fadeIn 0.3s; -} - -.tab-content.active { - display: block; -} - -@keyframes fadeIn { - from { opacity: 0; transform: translateY(10px); } - to { opacity: 1; transform: translateY(0); } -} - -/* Grid Layouts */ -.dashboard-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); - gap: 20px; -} - -.grid-2 { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); - gap: 20px; -} - -.full-width { - grid-column: 1 / -1; -} - -/* Cards */ -.card { - background: white; - border: 1px solid var(--border); - border-radius: 8px; - padding: 24px; - box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05); -} - -.card h2 { - font-size: 20px; - margin-bottom: 16px; - color: var(--dark); -} - -.card h3 { - font-size: 16px; - margin: 16px 0 12px; - color: var(--text); -} - -.card hr { - border: none; - border-top: 1px solid var(--border); - margin: 16px 0; -} - -/* Forms */ -.form-group { - margin-bottom: 16px; -} - -.form-group label { - display: block; - margin-bottom: 6px; - font-weight: 500; - color: var(--text); -} - -.form-group input, -.form-group select, -.form-group textarea { - width: 100%; - padding: 10px 12px; - border: 1px solid var(--border); - border-radius: 6px; - font-size: 14px; - transition: border-color 0.3s; -} - -.form-group input:focus, -.form-group select:focus, -.form-group textarea:focus { - outline: none; - border-color: var(--primary); - box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1); -} - -.form-inline { - display: flex; - gap: 10px; - margin-bottom: 16px; -} - -.form-inline select { - flex: 1; - padding: 10px 12px; - border: 1px solid var(--border); - border-radius: 6px; -} - -/* Buttons */ -.btn { - padding: 10px 20px; - border: none; - border-radius: 6px; - font-size: 14px; - font-weight: 500; - cursor: pointer; - transition: all 0.3s; - display: inline-flex; - align-items: center; - gap: 8px; -} - -.btn-primary { - background: var(--primary); - color: white; -} - -.btn-primary:hover { - background: var(--primary-dark); - transform: translateY(-1px); - box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4); -} - -.btn-secondary { - background: var(--text-light); - color: white; -} - -.btn-secondary:hover { - background: var(--text); -} - -.btn-success { - background: var(--success); - color: white; -} - -.btn-danger { - background: var(--danger); - color: white; -} - -.btn-block { - width: 100%; - justify-content: center; -} - -.btn:disabled { - opacity: 0.5; - cursor: not-allowed; -} - -/* Quick Actions */ -.quick-actions { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); - gap: 12px; -} - -/* Status Items */ -.status-item { - display: flex; - justify-content: space-between; - padding: 12px; - background: var(--light); - border-radius: 6px; - margin-bottom: 8px; -} - -.status-label { - font-weight: 500; - color: var(--text); -} - -.status-value { - color: var(--text-light); -} - -.status-value.success { - color: var(--success); - font-weight: 600; -} - -.status-value.error { - color: var(--danger); - font-weight: 600; -} - -/* Chat Interface */ -.chat-card { - display: flex; - flex-direction: column; - height: 600px; -} - -.chat-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 16px; -} - -.chat-header .form-group { - margin: 0; - flex: 1; - max-width: 200px; -} - -.chat-messages { - flex: 1; - overflow-y: auto; - border: 1px solid var(--border); - border-radius: 6px; - padding: 16px; - background: var(--light); - margin-bottom: 16px; -} - -.chat-message { - margin-bottom: 12px; - padding: 12px; - border-radius: 8px; - max-width: 80%; - word-wrap: break-word; -} - -.chat-message.user { - background: var(--primary); - color: white; - margin-left: auto; -} - -.chat-message.assistant { - background: white; - border: 1px solid var(--border); -} - -.chat-message.system { - background: var(--info); - color: white; - text-align: center; - max-width: 100%; -} - -.chat-input-form { - display: flex; - gap: 10px; -} - -.chat-input-form input { - flex: 1; - padding: 10px 12px; - border: 1px solid var(--border); - border-radius: 6px; -} - -/* Results List */ -.result-item { - padding: 12px; - background: var(--light); - border-radius: 6px; - margin-bottom: 8px; - border-left: 4px solid var(--primary); -} - -.result-item strong { - color: var(--text); -} - -.result-item .accuracy { - color: var(--success); - font-weight: 600; - font-size: 18px; -} - -/* Job List */ -.job-item { - display: flex; - justify-content: space-between; - align-items: center; - padding: 12px; - background: var(--light); - border-radius: 6px; - margin-bottom: 8px; -} - -.job-item button { - margin-left: 8px; -} - -/* Dataset Grid */ -.dataset-grid { - display: grid; - grid-template-columns: repeat(auto-fill, minmax(150px, 1fr)); - gap: 12px; -} - -.dataset-item { - padding: 12px; - background: var(--light); - border: 1px solid var(--border); - border-radius: 6px; - text-align: center; - font-weight: 500; -} - -.dataset-item.quantum { border-left: 4px solid var(--primary); } -.dataset-item.chat { border-left: 4px solid var(--success); } -.dataset-item.vision { border-left: 4px solid var(--warning); } - -/* Logs */ -.log-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 16px; -} - -.log-output { - background: var(--dark); - color: #00ff00; - padding: 16px; - border-radius: 6px; - font-family: 'Courier New', monospace; - font-size: 12px; - max-height: 500px; - overflow-y: auto; -} - -.log-entry { - padding: 4px 0; - border-bottom: 1px solid rgba(255, 255, 255, 0.1); -} - -.log-entry:last-child { - border-bottom: none; -} - -.log-entry.info { color: #00ff00; } -.log-entry.warning { color: #ffa500; } -.log-entry.error { color: #ff0000; } -.log-entry.success { color: #00ffff; } - -/* Loading */ -.loading { - text-align: center; - padding: 20px; - color: var(--text-light); -} - -.loading::after { - content: '...'; - animation: dots 1.5s steps(3, end) infinite; -} - -@keyframes dots { - 0%, 20% { content: '.'; } - 40% { content: '..'; } - 60%, 100% { content: '...'; } -} - -/* Badge */ -.badge { - display: inline-block; - padding: 4px 8px; - border-radius: 12px; - font-size: 12px; - font-weight: 600; -} - -.badge.success { background: var(--success); color: white; } -.badge.error { background: var(--danger); color: white; } -.badge.warning { background: var(--warning); color: white; } -.badge.info { background: var(--info); color: white; } - -/* Responsive */ -@media (max-width: 768px) { - body { - padding: 10px; - } - - .header { - flex-direction: column; - gap: 16px; - text-align: center; - } - - .tabs { - flex-wrap: nowrap; - overflow-x: auto; - } - - .grid-2 { - grid-template-columns: 1fr; - } - - .quick-actions { - grid-template-columns: 1fr; - } -} +/* QAI Control Center Styles */ + +:root { + --primary: #667eea; + --primary-dark: #5568d3; + --secondary: #48bb78; + --danger: #f56565; + --warning: #ed8936; + --info: #4299e1; + --dark: #2d3748; + --light: #f7fafc; + --border: #e2e8f0; + --text: #2d3748; + --text-light: #718096; + --success: #48bb78; +} + +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif; + background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + color: var(--text); + min-height: 100vh; + padding: 20px; +} + +.container { + max-width: 1400px; + margin: 0 auto; + background: white; + border-radius: 12px; + box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3); + overflow: hidden; +} + +/* Header */ +.header { + background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + color: white; + padding: 30px; + display: flex; + justify-content: space-between; + align-items: center; +} + +.header h1 { + font-size: 28px; + font-weight: 700; +} + +.status-indicator { + display: flex; + align-items: center; + gap: 10px; + background: rgba(255, 255, 255, 0.2); + padding: 8px 16px; + border-radius: 20px; +} + +.status-dot { + width: 10px; + height: 10px; + border-radius: 50%; + background: var(--warning); + animation: pulse 2s infinite; +} + +.status-dot.online { + background: var(--success); +} + +.status-dot.offline { + background: var(--danger); +} + +@keyframes pulse { + 0%, 100% { opacity: 1; } + 50% { opacity: 0.5; } +} + +/* Tabs */ +.tabs { + display: flex; + background: var(--light); + border-bottom: 2px solid var(--border); + overflow-x: auto; +} + +.tab-btn { + flex: 1; + min-width: 150px; + padding: 16px 20px; + border: none; + background: transparent; + cursor: pointer; + font-size: 16px; + font-weight: 500; + color: var(--text-light); + transition: all 0.3s; + border-bottom: 3px solid transparent; +} + +.tab-btn:hover { + background: white; + color: var(--primary); +} + +.tab-btn.active { + background: white; + color: var(--primary); + border-bottom-color: var(--primary); +} + +/* Tab Content */ +.tab-content { + display: none; + padding: 30px; + animation: fadeIn 0.3s; +} + +.tab-content.active { + display: block; +} + +@keyframes fadeIn { + from { opacity: 0; transform: translateY(10px); } + to { opacity: 1; transform: translateY(0); } +} + +/* Grid Layouts */ +.dashboard-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); + gap: 20px; +} + +.grid-2 { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); + gap: 20px; +} + +.full-width { + grid-column: 1 / -1; +} + +/* Cards */ +.card { + background: white; + border: 1px solid var(--border); + border-radius: 8px; + padding: 24px; + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05); +} + +.card h2 { + font-size: 20px; + margin-bottom: 16px; + color: var(--dark); +} + +.card h3 { + font-size: 16px; + margin: 16px 0 12px; + color: var(--text); +} + +.card hr { + border: none; + border-top: 1px solid var(--border); + margin: 16px 0; +} + +/* Forms */ +.form-group { + margin-bottom: 16px; +} + +.form-group label { + display: block; + margin-bottom: 6px; + font-weight: 500; + color: var(--text); +} + +.form-group input, +.form-group select, +.form-group textarea { + width: 100%; + padding: 10px 12px; + border: 1px solid var(--border); + border-radius: 6px; + font-size: 14px; + transition: border-color 0.3s; +} + +.form-group input:focus, +.form-group select:focus, +.form-group textarea:focus { + outline: none; + border-color: var(--primary); + box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1); +} + +.form-inline { + display: flex; + gap: 10px; + margin-bottom: 16px; +} + +.form-inline select { + flex: 1; + padding: 10px 12px; + border: 1px solid var(--border); + border-radius: 6px; +} + +/* Buttons */ +.btn { + padding: 10px 20px; + border: none; + border-radius: 6px; + font-size: 14px; + font-weight: 500; + cursor: pointer; + transition: all 0.3s; + display: inline-flex; + align-items: center; + gap: 8px; +} + +.btn-primary { + background: var(--primary); + color: white; +} + +.btn-primary:hover { + background: var(--primary-dark); + transform: translateY(-1px); + box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4); +} + +.btn-secondary { + background: var(--text-light); + color: white; +} + +.btn-secondary:hover { + background: var(--text); +} + +.btn-success { + background: var(--success); + color: white; +} + +.btn-danger { + background: var(--danger); + color: white; +} + +.btn-block { + width: 100%; + justify-content: center; +} + +.btn:disabled { + opacity: 0.5; + cursor: not-allowed; +} + +/* Quick Actions */ +.quick-actions { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); + gap: 12px; +} + +/* Status Items */ +.status-item { + display: flex; + justify-content: space-between; + padding: 12px; + background: var(--light); + border-radius: 6px; + margin-bottom: 8px; +} + +.status-label { + font-weight: 500; + color: var(--text); +} + +.status-value { + color: var(--text-light); +} + +.status-value.success { + color: var(--success); + font-weight: 600; +} + +.status-value.error { + color: var(--danger); + font-weight: 600; +} + +/* Chat Interface */ +.chat-card { + display: flex; + flex-direction: column; + height: 600px; +} + +.chat-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 16px; +} + +.chat-header .form-group { + margin: 0; + flex: 1; + max-width: 200px; +} + +.chat-messages { + flex: 1; + overflow-y: auto; + border: 1px solid var(--border); + border-radius: 6px; + padding: 16px; + background: var(--light); + margin-bottom: 16px; +} + +.chat-message { + margin-bottom: 12px; + padding: 12px; + border-radius: 8px; + max-width: 80%; + word-wrap: break-word; +} + +.chat-message.user { + background: var(--primary); + color: white; + margin-left: auto; +} + +.chat-message.assistant { + background: white; + border: 1px solid var(--border); +} + +.chat-message.system { + background: var(--info); + color: white; + text-align: center; + max-width: 100%; +} + +.chat-input-form { + display: flex; + gap: 10px; +} + +.chat-input-form input { + flex: 1; + padding: 10px 12px; + border: 1px solid var(--border); + border-radius: 6px; +} + +/* Results List */ +.result-item { + padding: 12px; + background: var(--light); + border-radius: 6px; + margin-bottom: 8px; + border-left: 4px solid var(--primary); +} + +.result-item strong { + color: var(--text); +} + +.result-item .accuracy { + color: var(--success); + font-weight: 600; + font-size: 18px; +} + +/* Job List */ +.job-item { + display: flex; + justify-content: space-between; + align-items: center; + padding: 12px; + background: var(--light); + border-radius: 6px; + margin-bottom: 8px; +} + +.job-item button { + margin-left: 8px; +} + +/* Dataset Grid */ +.dataset-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(150px, 1fr)); + gap: 12px; +} + +.dataset-item { + padding: 12px; + background: var(--light); + border: 1px solid var(--border); + border-radius: 6px; + text-align: center; + font-weight: 500; +} + +.dataset-item.quantum { border-left: 4px solid var(--primary); } +.dataset-item.chat { border-left: 4px solid var(--success); } +.dataset-item.vision { border-left: 4px solid var(--warning); } + +/* Logs */ +.log-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 16px; +} + +.log-output { + background: var(--dark); + color: #00ff00; + padding: 16px; + border-radius: 6px; + font-family: 'Courier New', monospace; + font-size: 12px; + max-height: 500px; + overflow-y: auto; +} + +.log-entry { + padding: 4px 0; + border-bottom: 1px solid rgba(255, 255, 255, 0.1); +} + +.log-entry:last-child { + border-bottom: none; +} + +.log-entry.info { color: #00ff00; } +.log-entry.warning { color: #ffa500; } +.log-entry.error { color: #ff0000; } +.log-entry.success { color: #00ffff; } + +/* Loading */ +.loading { + text-align: center; + padding: 20px; + color: var(--text-light); +} + +.loading::after { + content: '...'; + animation: dots 1.5s steps(3, end) infinite; +} + +@keyframes dots { + 0%, 20% { content: '.'; } + 40% { content: '..'; } + 60%, 100% { content: '...'; } +} + +/* Badge */ +.badge { + display: inline-block; + padding: 4px 8px; + border-radius: 12px; + font-size: 12px; + font-weight: 600; +} + +.badge.success { background: var(--success); color: white; } +.badge.error { background: var(--danger); color: white; } +.badge.warning { background: var(--warning); color: white; } +.badge.info { background: var(--info); color: white; } + +/* Responsive */ +@media (max-width: 768px) { + body { + padding: 10px; + } + + .header { + flex-direction: column; + gap: 16px; + text-align: center; + } + + .tabs { + flex-wrap: nowrap; + overflow-x: auto; + } + + .grid-2 { + grid-template-columns: 1fr; + } + + .quick-actions { + grid-template-columns: 1fr; + } +} diff --git a/docs/quantum/AZURE_QUANTUM_SUCCESS.md b/docs/quantum/AZURE_QUANTUM_SUCCESS.md index 67f2f6cd3..c3357a705 100644 --- a/docs/quantum/AZURE_QUANTUM_SUCCESS.md +++ b/docs/quantum/AZURE_QUANTUM_SUCCESS.md @@ -1,199 +1,199 @@ -# Azure Quantum Integration - COMPLETE ✅ - -## 🎉 Mission Accomplished: Made It Rain in Azure (100% FREE!) - -**Date:** November 7, 2025 -**Status:** ✅ OPERATIONAL -**Cost:** $0.00 (all using free simulators) - ---- - -## What We Did - -### 1. ✅ Configured Azure Quantum Workspace -- **Workspace:** `quantum-ai-workspace` -- **Location:** `eastus` -- **Resource Group:** `rg-quantum-ai` -- **Subscription:** `a07fbd16-e722-446d-8efd-0681e85b725c` -- **Status:** Provisioned and fully operational - -### 2. ✅ Verified Azure Quantum Connection -``` -Available Backends (all FREE): -├── rigetti.sim.qvm ⭐ (Used for all experiments) -├── quantinuum.sim.h2-1sc -├── quantinuum.sim.h2-1e -└── 5 total backends available -``` - -### 3. ✅ Ran 4 Quantum Experiments on Azure Cloud - -#### Experiment 1: Bell State (Quantum Entanglement) -- **Purpose:** Demonstrate Einstein's "spooky action" -- **Results:** ✓ 50% |00⟩, 50% |11⟩ (perfect entanglement) -- **Shots:** 1,000 -- **Backend:** rigetti.sim.qvm (FREE) - -#### Experiment 2: Quantum Superposition (3 qubits) -- **Purpose:** Show qubits in multiple states simultaneously -- **Results:** ✓ All 8 states appeared equally (2³ superposition) -- **Shots:** 1,000 -- **Backend:** rigetti.sim.qvm (FREE) - -#### Experiment 3: GHZ State (Triple Entanglement) -- **Purpose:** Maximum 3-qubit entanglement -- **Results:** ✓ Only |000⟩ and |111⟩ states (no mixed states) -- **Shots:** 1,000 -- **Backend:** rigetti.sim.qvm (FREE) - -#### Experiment 4: Quantum Interference -- **Purpose:** Demonstrate wave-like quantum behavior -- **Results:** ✓ Interference patterns matched predictions -- **Shots:** 1,000 -- **Backend:** rigetti.sim.qvm (FREE) - ---- - -## 🏆 Achievements Unlocked - -✅ Azure Quantum workspace deployed and configured -✅ Python SDK integration working (azure-quantum 1.2.1) -✅ Qiskit dependencies resolved (downgraded to 1.4.5 for compatibility) -✅ 4 quantum circuits executed on Azure infrastructure -✅ All results validate quantum mechanics predictions -✅ Zero cloud costs (100% free simulators) -✅ Demonstrated entanglement, superposition, and interference - ---- - -## 📊 Technical Details - -### Infrastructure -```yaml -Platform: Azure Quantum -SDK: azure-quantum 1.2.1 -Provider: Rigetti (rigetti.sim.qvm) -Qiskit: 1.4.5 (downgraded for Azure compatibility) -Provider Packages: qiskit-ionq 1.0.2, qiskit-rigetti 0.4.7 -Authentication: Azure DefaultAzureCredential (az login) -``` - -### Quantum ML Models (Trained Locally) -``` -1. Heart Disease Classifier - 94.64% accuracy ✓ -2. Ionosphere Classifier - 85.71% accuracy ✓ -3. Wine Quality Classifier - 94.44% accuracy ✓ - -Note: Model artifacts (pickles) corrupted during save. -Circuits successfully ran quantum experiments instead. -``` - -### Scripts Created -1. `run_azure_quantum_free.py` - Main demo script (4 experiments) -2. `deploy_quantum_models_azure.py` - Model deployment attempt -3. Azure integration module (`src/azure_quantum_integration.py`) - ---- - -## 💰 Cost Analysis - -**Total Spent: $0.00** - -### Free Tier Benefits: -- ✅ Rigetti simulator: Unlimited shots, no cost -- ✅ Quantinuum simulators: Free tier available -- ✅ Microsoft Quantum simulators: Always free -- ✅ Development/testing: Zero costs - -### Paid Tier (Future): -- IonQ real quantum hardware: ~$0.00003/gate-shot -- Quantinuum H-series: ~$0.00015/circuit -- Only needed for production quantum hardware access - ---- - -## 🚀 Next Steps - -### Immediate (Still Free): -1. **Scale experiments** - Test with 6, 8, 10 qubits -2. **Batch processing** - Submit multiple circuits in parallel -3. **Circuit optimization** - Reduce gate count for efficiency -4. **Export results** - Save to Azure Storage/Blob for analysis - -### Future (Paid Tier): -1. **Real quantum hardware** - Try IonQ or Quantinuum devices -2. **Production deployment** - Integrate with Azure ML pipelines -3. **Hybrid algorithms** - VQE, QAOA for optimization problems -4. **Quantum advantage** - Test problems beyond classical compute - ---- - -## 📝 Lessons Learned - -1. **Version pinning is critical** - Azure Quantum requires Qiskit <2.0 -2. **Free tier is powerful** - All quantum experiments work without cost -3. **Simulator accuracy** - Rigetti simulator matches hardware within 1% -4. **Development workflow** - Always test locally before Azure submission -5. **Pickle corruption** - Model serialization needs better error handling - ---- - -## 🎓 Quantum Concepts Demonstrated - -### Entanglement -- Qubits become correlated beyond classical physics -- Bell state: measuring one instantly affects the other -- Applications: Quantum communication, teleportation - -### Superposition -- Qubits exist in multiple states simultaneously -- Collapses to single state upon measurement -- Applications: Parallel computation, quantum search - -### Interference -- Quantum states can add constructively/destructively -- Enables quantum algorithms to amplify correct answers -- Applications: Grover's search, Shor's factoring - ---- - -## 📊 Job History - -All jobs visible in Azure Portal: -- Navigate to: `quantum-ai-workspace` → Jobs -- Filter by: rigetti.sim.qvm -- Status: All jobs completed successfully ✓ - ---- - -## 🎯 Success Metrics - -| Metric | Target | Actual | Status | -|--------|--------|--------|--------| -| Azure connection | Working | ✅ Connected | PASS | -| Backends available | ≥1 | 5 | PASS | -| Experiments run | 4 | 4 | PASS | -| Success rate | 100% | 100% | PASS | -| Cost | $0 | $0 | PASS | -| Quantum accuracy | Match theory | ✓ Validated | PASS | - ---- - -## 🌟 Conclusion - -**Azure Quantum is fully operational and ready for quantum computing!** - -We successfully: -- Deployed Azure Quantum infrastructure -- Validated quantum mechanics on cloud -- Demonstrated core quantum phenomena -- Maintained zero cloud costs -- Created reusable scripts for future experiments - -**The quantum rain fell successfully! ☁️⚡🌧️** - ---- - -*Generated: November 7, 2025* -*Repository: c:\Users\Bryan\OneDrive\AI* -*Project: QAI - Quantum AI with Azure Integration* +# Azure Quantum Integration - COMPLETE ✅ + +## 🎉 Mission Accomplished: Made It Rain in Azure (100% FREE!) + +**Date:** November 7, 2025 +**Status:** ✅ OPERATIONAL +**Cost:** $0.00 (all using free simulators) + +--- + +## What We Did + +### 1. ✅ Configured Azure Quantum Workspace +- **Workspace:** `quantum-ai-workspace` +- **Location:** `eastus` +- **Resource Group:** `rg-quantum-ai` +- **Subscription:** `a07fbd16-e722-446d-8efd-0681e85b725c` +- **Status:** Provisioned and fully operational + +### 2. ✅ Verified Azure Quantum Connection +``` +Available Backends (all FREE): +├── rigetti.sim.qvm ⭐ (Used for all experiments) +├── quantinuum.sim.h2-1sc +├── quantinuum.sim.h2-1e +└── 5 total backends available +``` + +### 3. ✅ Ran 4 Quantum Experiments on Azure Cloud + +#### Experiment 1: Bell State (Quantum Entanglement) +- **Purpose:** Demonstrate Einstein's "spooky action" +- **Results:** ✓ 50% |00⟩, 50% |11⟩ (perfect entanglement) +- **Shots:** 1,000 +- **Backend:** rigetti.sim.qvm (FREE) + +#### Experiment 2: Quantum Superposition (3 qubits) +- **Purpose:** Show qubits in multiple states simultaneously +- **Results:** ✓ All 8 states appeared equally (2³ superposition) +- **Shots:** 1,000 +- **Backend:** rigetti.sim.qvm (FREE) + +#### Experiment 3: GHZ State (Triple Entanglement) +- **Purpose:** Maximum 3-qubit entanglement +- **Results:** ✓ Only |000⟩ and |111⟩ states (no mixed states) +- **Shots:** 1,000 +- **Backend:** rigetti.sim.qvm (FREE) + +#### Experiment 4: Quantum Interference +- **Purpose:** Demonstrate wave-like quantum behavior +- **Results:** ✓ Interference patterns matched predictions +- **Shots:** 1,000 +- **Backend:** rigetti.sim.qvm (FREE) + +--- + +## 🏆 Achievements Unlocked + +✅ Azure Quantum workspace deployed and configured +✅ Python SDK integration working (azure-quantum 1.2.1) +✅ Qiskit dependencies resolved (downgraded to 1.4.5 for compatibility) +✅ 4 quantum circuits executed on Azure infrastructure +✅ All results validate quantum mechanics predictions +✅ Zero cloud costs (100% free simulators) +✅ Demonstrated entanglement, superposition, and interference + +--- + +## 📊 Technical Details + +### Infrastructure +```yaml +Platform: Azure Quantum +SDK: azure-quantum 1.2.1 +Provider: Rigetti (rigetti.sim.qvm) +Qiskit: 1.4.5 (downgraded for Azure compatibility) +Provider Packages: qiskit-ionq 1.0.2, qiskit-rigetti 0.4.7 +Authentication: Azure DefaultAzureCredential (az login) +``` + +### Quantum ML Models (Trained Locally) +``` +1. Heart Disease Classifier - 94.64% accuracy ✓ +2. Ionosphere Classifier - 85.71% accuracy ✓ +3. Wine Quality Classifier - 94.44% accuracy ✓ + +Note: Model artifacts (pickles) corrupted during save. +Circuits successfully ran quantum experiments instead. +``` + +### Scripts Created +1. `run_azure_quantum_free.py` - Main demo script (4 experiments) +2. `deploy_quantum_models_azure.py` - Model deployment attempt +3. Azure integration module (`src/azure_quantum_integration.py`) + +--- + +## 💰 Cost Analysis + +**Total Spent: $0.00** + +### Free Tier Benefits: +- ✅ Rigetti simulator: Unlimited shots, no cost +- ✅ Quantinuum simulators: Free tier available +- ✅ Microsoft Quantum simulators: Always free +- ✅ Development/testing: Zero costs + +### Paid Tier (Future): +- IonQ real quantum hardware: ~$0.00003/gate-shot +- Quantinuum H-series: ~$0.00015/circuit +- Only needed for production quantum hardware access + +--- + +## 🚀 Next Steps + +### Immediate (Still Free): +1. **Scale experiments** - Test with 6, 8, 10 qubits +2. **Batch processing** - Submit multiple circuits in parallel +3. **Circuit optimization** - Reduce gate count for efficiency +4. **Export results** - Save to Azure Storage/Blob for analysis + +### Future (Paid Tier): +1. **Real quantum hardware** - Try IonQ or Quantinuum devices +2. **Production deployment** - Integrate with Azure ML pipelines +3. **Hybrid algorithms** - VQE, QAOA for optimization problems +4. **Quantum advantage** - Test problems beyond classical compute + +--- + +## 📝 Lessons Learned + +1. **Version pinning is critical** - Azure Quantum requires Qiskit <2.0 +2. **Free tier is powerful** - All quantum experiments work without cost +3. **Simulator accuracy** - Rigetti simulator matches hardware within 1% +4. **Development workflow** - Always test locally before Azure submission +5. **Pickle corruption** - Model serialization needs better error handling + +--- + +## 🎓 Quantum Concepts Demonstrated + +### Entanglement +- Qubits become correlated beyond classical physics +- Bell state: measuring one instantly affects the other +- Applications: Quantum communication, teleportation + +### Superposition +- Qubits exist in multiple states simultaneously +- Collapses to single state upon measurement +- Applications: Parallel computation, quantum search + +### Interference +- Quantum states can add constructively/destructively +- Enables quantum algorithms to amplify correct answers +- Applications: Grover's search, Shor's factoring + +--- + +## 📊 Job History + +All jobs visible in Azure Portal: +- Navigate to: `quantum-ai-workspace` → Jobs +- Filter by: rigetti.sim.qvm +- Status: All jobs completed successfully ✓ + +--- + +## 🎯 Success Metrics + +| Metric | Target | Actual | Status | +|--------|--------|--------|--------| +| Azure connection | Working | ✅ Connected | PASS | +| Backends available | ≥1 | 5 | PASS | +| Experiments run | 4 | 4 | PASS | +| Success rate | 100% | 100% | PASS | +| Cost | $0 | $0 | PASS | +| Quantum accuracy | Match theory | ✓ Validated | PASS | + +--- + +## 🌟 Conclusion + +**Azure Quantum is fully operational and ready for quantum computing!** + +We successfully: +- Deployed Azure Quantum infrastructure +- Validated quantum mechanics on cloud +- Demonstrated core quantum phenomena +- Maintained zero cloud costs +- Created reusable scripts for future experiments + +**The quantum rain fell successfully! ☁️⚡🌧️** + +--- + +*Generated: November 7, 2025* +*Repository: c:\Users\Bryan\OneDrive\AI* +*Project: QAI - Quantum AI with Azure Integration* diff --git a/docs/quantum/QUANTUM_AUTORUN_README.md b/docs/quantum/QUANTUM_AUTORUN_README.md index ba8bc4570..d328134f7 100644 --- a/docs/quantum/QUANTUM_AUTORUN_README.md +++ b/docs/quantum/QUANTUM_AUTORUN_README.md @@ -1,144 +1,144 @@ -# Quantum AutoRun - -A lightweight orchestrator to automate quantum training runs using `ai-projects/quantum-ml/train_custom_dataset.py` (local simulators) and `ai-projects/quantum-ml/deploy_to_azure_quantum.py` (Azure Quantum hardware). It mirrors the AutoTrain pattern used for LoRA fine-tuning and supports both free local execution and cloud hardware submission. - -## What it does - -- Reads jobs from `quantum_autorun.yaml` -- Builds commands for local simulator training OR Azure Quantum hardware submission -- Supports dry-run validation without execution -- Writes per-job run directories and aggregated status JSON -- **Cost safety**: Requires explicit confirmation (`azure_confirm_cost: true`) for paid hardware - -## Files - -- `scripts/evaluation/quantum_autorun.py` – the orchestrator -- `quantum_autorun.yaml` – declarative job definitions -- Outputs under `data_out/quantum_autorun/` - - `//stdout.log` - - `/last_run.json` - - `status.json` with a summary of the last invocation - -## Quick start (PowerShell) - -```powershell -# Validate the default jobs without executing -python .\scripts\quantum_autorun.py --dry-run - -# Run a single job -python .\scripts\quantum_autorun.py --job heart_quick - -# List configured jobs (JSON) -python .\scripts\quantum_autorun.py --list -``` - -You can also use VS Code tasks: - -- Run: Quantum AutoRun (dry-run) -- Run: Quantum AutoRun (all) - -## Config format (quantum_autorun.yaml) - -### Local simulator job (FREE) - -```yaml -version: 1 -jobs: - - name: heart_quick - mode: train_custom_dataset # Local simulator - preset: heart # or use: csv: datasets/quantum/heart_disease.csv - n_qubits: 4 - epochs: 1 - batch_size: 16 - learning_rate: 0.001 - test_size: 0.2 - enabled: true -``` - -### Azure Quantum hardware job - -```yaml - - name: azure_ionq_simulator - mode: azure_hardware # Azure Quantum submission - azure_backend: ionq.simulator # FREE: IonQ cloud simulator - azure_shots: 100 - n_qubits: 3 - enabled: true - - - name: azure_ionq_qpu - mode: azure_hardware - azure_backend: ionq.qpu # PAID: Real quantum hardware - azure_shots: 100 - n_qubits: 3 - azure_confirm_cost: true # REQUIRED for paid hardware - enabled: false - extra_args: - - --circuit-file - - ai-projects/quantum-ml/results/circuit.qasm -``` - -Notes: - -- **Local mode** (`train_custom_dataset`): Choose either `preset` or `csv` (not both). If neither is provided, the script falls back to a small demo dataset. -- **Azure mode** (`azure_hardware`): Requires Azure credentials (`az login`) and a configured quantum workspace. -- **Cost safety**: Jobs targeting paid hardware (e.g., `ionq.qpu`, `rigetti.qpu`) MUST set `azure_confirm_cost: true` or validation will fail. -- Supported presets: `heart`, `ionosphere`, `sonar`, `banknote`. -- Supported Azure backends (examples): `ionq.simulator` (free), `ionq.qpu` (paid), `rigetti.sim.qvm` (free), `quantinuum.sim.h1-1sc` (free). - -## Status endpoint integration - -The `/api/ai/status` Azure Function now includes a `quantum_autorun` field when `data_out/quantum_autorun/status.json` exists. This makes it easy to check the last validation/execution results from the web UI or via curl. - -## Azure Quantum setup - -### Prerequisites - -1. **Azure account** with an active subscription -2. **Azure Quantum workspace** created (see [Azure Quantum quickstart](https://learn.microsoft.com/azure/quantum/)) -3. **Azure CLI** installed and authenticated: `az login` -4. **Config file** updated: `ai-projects/quantum-ml/config/quantum_config.yaml` with your workspace details - -### Quick Azure setup - -```powershell -# Login to Azure -az login - -# Create resource group (if needed) -az group create --name rg-quantum-ai --location eastus - -# Create quantum workspace (via Portal or Bicep) -# See: ai-projects/quantum-ml/azure/DEPLOYMENT.md - -# Update ai-projects/quantum-ml/config/quantum_config.yaml with your: -# - subscription_id -# - resource_group -# - workspace_name -# - location -``` - -### Testing Azure connection - -```powershell -# Dry-run an Azure simulator job (validates config, no execution) -python .\scripts\quantum_autorun.py --job azure_ionq_simulator --dry-run - -# Run Azure simulator job (FREE - no QPU costs) -python .\scripts\quantum_autorun.py --job azure_ionq_simulator -``` - -### Cost management - -- **Always test on simulators first** (`*.simulator` backends are free) -- Set `azure_confirm_cost: true` explicitly for paid hardware jobs -- Check Azure Quantum pricing: ~$0.00003/gate-shot (IonQ), varies by provider -- Monitor costs in Azure Portal → Cost Management -- Dry-run validates cost confirmation before submission - -## Future extensions - -- ~~Azure Quantum submission support~~ ✅ **DONE** -- Parse `results/custom_training_summary.json` after real (non-dry) runs and enrich `status.json` -- Batch experiment grids (varying `n_qubits`, `epochs`, and circuit depth) -- Automatic artifact collation (pull key metrics from Azure job results) -- Cost estimation API integration (show estimated cost during dry-run) +# Quantum AutoRun + +A lightweight orchestrator to automate quantum training runs using `ai-projects/quantum-ml/train_custom_dataset.py` (local simulators) and `ai-projects/quantum-ml/deploy_to_azure_quantum.py` (Azure Quantum hardware). It mirrors the AutoTrain pattern used for LoRA fine-tuning and supports both free local execution and cloud hardware submission. + +## What it does + +- Reads jobs from `quantum_autorun.yaml` +- Builds commands for local simulator training OR Azure Quantum hardware submission +- Supports dry-run validation without execution +- Writes per-job run directories and aggregated status JSON +- **Cost safety**: Requires explicit confirmation (`azure_confirm_cost: true`) for paid hardware + +## Files + +- `scripts/evaluation/quantum_autorun.py` – the orchestrator +- `quantum_autorun.yaml` – declarative job definitions +- Outputs under `data_out/quantum_autorun/` + - `//stdout.log` + - `/last_run.json` + - `status.json` with a summary of the last invocation + +## Quick start (PowerShell) + +```powershell +# Validate the default jobs without executing +python .\scripts\quantum_autorun.py --dry-run + +# Run a single job +python .\scripts\quantum_autorun.py --job heart_quick + +# List configured jobs (JSON) +python .\scripts\quantum_autorun.py --list +``` + +You can also use VS Code tasks: + +- Run: Quantum AutoRun (dry-run) +- Run: Quantum AutoRun (all) + +## Config format (quantum_autorun.yaml) + +### Local simulator job (FREE) + +```yaml +version: 1 +jobs: + - name: heart_quick + mode: train_custom_dataset # Local simulator + preset: heart # or use: csv: datasets/quantum/heart_disease.csv + n_qubits: 4 + epochs: 1 + batch_size: 16 + learning_rate: 0.001 + test_size: 0.2 + enabled: true +``` + +### Azure Quantum hardware job + +```yaml + - name: azure_ionq_simulator + mode: azure_hardware # Azure Quantum submission + azure_backend: ionq.simulator # FREE: IonQ cloud simulator + azure_shots: 100 + n_qubits: 3 + enabled: true + + - name: azure_ionq_qpu + mode: azure_hardware + azure_backend: ionq.qpu # PAID: Real quantum hardware + azure_shots: 100 + n_qubits: 3 + azure_confirm_cost: true # REQUIRED for paid hardware + enabled: false + extra_args: + - --circuit-file + - ai-projects/quantum-ml/results/circuit.qasm +``` + +Notes: + +- **Local mode** (`train_custom_dataset`): Choose either `preset` or `csv` (not both). If neither is provided, the script falls back to a small demo dataset. +- **Azure mode** (`azure_hardware`): Requires Azure credentials (`az login`) and a configured quantum workspace. +- **Cost safety**: Jobs targeting paid hardware (e.g., `ionq.qpu`, `rigetti.qpu`) MUST set `azure_confirm_cost: true` or validation will fail. +- Supported presets: `heart`, `ionosphere`, `sonar`, `banknote`. +- Supported Azure backends (examples): `ionq.simulator` (free), `ionq.qpu` (paid), `rigetti.sim.qvm` (free), `quantinuum.sim.h1-1sc` (free). + +## Status endpoint integration + +The `/api/ai/status` Azure Function now includes a `quantum_autorun` field when `data_out/quantum_autorun/status.json` exists. This makes it easy to check the last validation/execution results from the web UI or via curl. + +## Azure Quantum setup + +### Prerequisites + +1. **Azure account** with an active subscription +2. **Azure Quantum workspace** created (see [Azure Quantum quickstart](https://learn.microsoft.com/azure/quantum/)) +3. **Azure CLI** installed and authenticated: `az login` +4. **Config file** updated: `ai-projects/quantum-ml/config/quantum_config.yaml` with your workspace details + +### Quick Azure setup + +```powershell +# Login to Azure +az login + +# Create resource group (if needed) +az group create --name rg-quantum-ai --location eastus + +# Create quantum workspace (via Portal or Bicep) +# See: ai-projects/quantum-ml/azure/DEPLOYMENT.md + +# Update ai-projects/quantum-ml/config/quantum_config.yaml with your: +# - subscription_id +# - resource_group +# - workspace_name +# - location +``` + +### Testing Azure connection + +```powershell +# Dry-run an Azure simulator job (validates config, no execution) +python .\scripts\quantum_autorun.py --job azure_ionq_simulator --dry-run + +# Run Azure simulator job (FREE - no QPU costs) +python .\scripts\quantum_autorun.py --job azure_ionq_simulator +``` + +### Cost management + +- **Always test on simulators first** (`*.simulator` backends are free) +- Set `azure_confirm_cost: true` explicitly for paid hardware jobs +- Check Azure Quantum pricing: ~$0.00003/gate-shot (IonQ), varies by provider +- Monitor costs in Azure Portal → Cost Management +- Dry-run validates cost confirmation before submission + +## Future extensions + +- ~~Azure Quantum submission support~~ ✅ **DONE** +- Parse `results/custom_training_summary.json` after real (non-dry) runs and enrich `status.json` +- Batch experiment grids (varying `n_qubits`, `epochs`, and circuit depth) +- Automatic artifact collation (pull key metrics from Azure job results) +- Cost estimation API integration (show estimated cost during dry-run) diff --git a/docs/quantum/QUANTUM_AZURE_EXAMPLES.md b/docs/quantum/QUANTUM_AZURE_EXAMPLES.md index 571ab1996..0a0ec9b19 100644 --- a/docs/quantum/QUANTUM_AZURE_EXAMPLES.md +++ b/docs/quantum/QUANTUM_AZURE_EXAMPLES.md @@ -1,289 +1,289 @@ -# Quantum AutoRun: Azure Hardware Examples - -Complete examples for using Quantum AutoRun with Azure Quantum hardware. - -## Prerequisites Checklist - -- [ ] Azure account with active subscription -- [ ] Azure Quantum workspace created and configured -- [ ] Azure CLI installed: `az --version` -- [ ] Authenticated: `az login` -- [ ] Config updated: `ai-projects/quantum-ml/config/quantum_config.yaml` -- [ ] Python environment with quantum-ai dependencies - -## Example 1: Free Azure Simulator (No Costs) - -This example uses IonQ's cloud simulator which is free to use. - -### Config (`quantum_autorun.yaml`) - -```yaml -jobs: - - name: azure_free_test - mode: azure_hardware - azure_backend: ionq.simulator # FREE - azure_shots: 100 - n_qubits: 3 - enabled: true - extra_args: - - --circuit-file - - ai-projects/quantum-ml/results/bell_state.qasm -``` - -### Commands - -```powershell -# Validate without executing -python .\scripts\quantum_autorun.py --job azure_free_test --dry-run - -# Run on Azure simulator (free) -python .\scripts\quantum_autorun.py --job azure_free_test - -# Check results -cat data_out\quantum_autorun\azure_free_test\\stdout.log -``` - -## Example 2: Paid Hardware with Safety Checks - -This example targets real quantum hardware (IonQ QPU) with cost safety enabled. - -### Hardware Config - -```yaml -jobs: - - name: ionq_hardware_run - mode: azure_hardware - azure_backend: ionq.qpu # PAID: Real quantum hardware - azure_shots: 50 # Keep low to minimize cost - n_qubits: 3 - azure_confirm_cost: true # REQUIRED for paid hardware - enabled: false # Keep disabled until ready - extra_args: - - --circuit-file - - ai-projects/quantum-ml/results/optimized_circuit.qasm -``` - -### Safety Validation - -```powershell -# This will FAIL validation (cost not confirmed) -# azure_confirm_cost: false - -# This will PASS validation -# azure_confirm_cost: true - -# Dry-run to verify (no submission) -python .\scripts\quantum_autorun.py --job ionq_hardware_run --dry-run - -# Enable in config and run (charges will apply) -python .\scripts\quantum_autorun.py --job ionq_hardware_run -``` - -### Cost Estimation - -- IonQ QPU: ~$0.00003 per gate-shot -- 50 shots × 10 gates = ~$0.015 -- Always test on simulator first! - -## Example 3: Multi-Backend Comparison - -Compare results across simulators and hardware. - -### Config - -```yaml -jobs: - # Local simulator (baseline) - - name: compare_local - mode: train_custom_dataset - preset: heart - n_qubits: 3 - epochs: 1 - enabled: true - - # Azure IonQ simulator - - name: compare_ionq_sim - mode: azure_hardware - azure_backend: ionq.simulator - azure_shots: 100 - n_qubits: 3 - enabled: true - - # Azure Rigetti simulator - - name: compare_rigetti_sim - mode: azure_hardware - azure_backend: rigetti.sim.qvm - azure_shots: 100 - n_qubits: 3 - enabled: true - - # IonQ hardware (disabled by default) - - name: compare_ionq_hardware - mode: azure_hardware - azure_backend: ionq.qpu - azure_shots: 50 - n_qubits: 3 - azure_confirm_cost: true - enabled: false -``` - -### Batch Execution - -```powershell -# Run all enabled jobs sequentially -python .\scripts\quantum_autorun.py - -# Check aggregated status -cat data_out\quantum_autorun\status.json - -# Compare results programmatically -python .\scripts\analyze_quantum_results.py -``` - -## Example 4: Circuit File Integration - -### Create Circuit - -```python -# ai-projects/quantum-ml/create_test_circuit.py -from qiskit import QuantumCircuit - -def create_bell_state(): - qc = QuantumCircuit(2, 2) - qc.h(0) - qc.cx(0, 1) - qc.measure([0, 1], [0, 1]) - return qc - -if __name__ == "__main__": - circuit = create_bell_state() - circuit.qasm(filename="results/bell_state.qasm") - print("Circuit saved to results/bell_state.qasm") -``` - -### Submit to Azure - -```yaml -jobs: - - name: bell_state_ionq - mode: azure_hardware - azure_backend: ionq.simulator - azure_shots: 1000 - enabled: true - extra_args: - - --circuit-file - - ai-projects/quantum-ml/results/bell_state.qasm -``` - -## Troubleshooting - -### Error: "Not connected to Azure Quantum" - -```powershell -# Check Azure login -az account show - -# Re-login if needed -az login - -# Verify workspace exists -az quantum workspace show --name quantum-ai-workspace --resource-group rg-quantum-ai -``` - -### Error: "COST SAFETY: azure_confirm_cost must be true" - -This is expected! Set `azure_confirm_cost: true` in your config to acknowledge hardware costs. - -### Error: "Backend not found" - -```powershell -# List available backends -az quantum workspace show --name quantum-ai-workspace --resource-group rg-quantum-ai - -# Common backend names: -# - ionq.simulator (free) -# - ionq.qpu (paid) -# - rigetti.sim.qvm (free) -# - quantinuum.sim.h1-1sc (free) -``` - -## Best Practices - -1. **Always test locally first**: Use `mode: train_custom_dataset` to validate circuits -2. **Use simulators for development**: Azure simulators are free and catch errors -3. **Keep shots low initially**: Start with 50-100 shots, increase only if needed -4. **Enable dry-run by default**: Add `--dry-run` to all commands during development -5. **Monitor costs**: Check Azure Portal → Cost Management regularly -6. **Version control circuits**: Keep QASM files in git for reproducibility -7. **Document experiments**: Use descriptive job names and maintain a results log - -## Cost Management Scripts - -### Check Recent Azure Costs - -```powershell -# Last 7 days of Quantum costs -az consumption usage list --start-date (Get-Date).AddDays(-7).ToString("yyyy-MM-dd") | ConvertFrom-Json | Where-Object { $_.instanceName -like "*quantum*" } -``` - -### Set Budget Alert - -```powershell -# Create budget alert for quantum workspace -az consumption budget create --amount 50 --category cost --time-grain monthly --resource-group rg-quantum-ai --budget-name quantum-monthly-budget -``` - -## Integration with CI/CD - -### GitHub Actions Example - -```yaml -name: Quantum Tests - -on: [push] - -jobs: - quantum-validation: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - name: Run quantum autorun dry-run - run: | - python scripts/evaluation/quantum_autorun.py --dry-run - - - name: Run simulator tests only - run: | - python scripts/evaluation/quantum_autorun.py --job azure_ionq_simulator -``` - -## Advanced: Programmatic Job Submission - -```python -# scripts/submit_quantum_batch.py -import subprocess -import json -from pathlib import Path - -jobs = ["azure_sim_1", "azure_sim_2", "azure_sim_3"] - -results = [] -for job in jobs: - cmd = ["python", "scripts/evaluation/quantum_autorun.py", "--job", job] - proc = subprocess.run(cmd, capture_output=True, text=True) - results.append({ - "job": job, - "status": "success" if proc.returncode == 0 else "failed", - "output": proc.stdout - }) - -# Save batch results -with open("data_out/batch_results.json", "w") as f: - json.dump(results, f, indent=2) -``` - -## Support - -- Azure Quantum Docs: -- IonQ Provider: -- Rigetti Provider: -- Cost Calculator: +# Quantum AutoRun: Azure Hardware Examples + +Complete examples for using Quantum AutoRun with Azure Quantum hardware. + +## Prerequisites Checklist + +- [ ] Azure account with active subscription +- [ ] Azure Quantum workspace created and configured +- [ ] Azure CLI installed: `az --version` +- [ ] Authenticated: `az login` +- [ ] Config updated: `ai-projects/quantum-ml/config/quantum_config.yaml` +- [ ] Python environment with quantum-ai dependencies + +## Example 1: Free Azure Simulator (No Costs) + +This example uses IonQ's cloud simulator which is free to use. + +### Config (`quantum_autorun.yaml`) + +```yaml +jobs: + - name: azure_free_test + mode: azure_hardware + azure_backend: ionq.simulator # FREE + azure_shots: 100 + n_qubits: 3 + enabled: true + extra_args: + - --circuit-file + - ai-projects/quantum-ml/results/bell_state.qasm +``` + +### Commands + +```powershell +# Validate without executing +python .\scripts\quantum_autorun.py --job azure_free_test --dry-run + +# Run on Azure simulator (free) +python .\scripts\quantum_autorun.py --job azure_free_test + +# Check results +cat data_out\quantum_autorun\azure_free_test\\stdout.log +``` + +## Example 2: Paid Hardware with Safety Checks + +This example targets real quantum hardware (IonQ QPU) with cost safety enabled. + +### Hardware Config + +```yaml +jobs: + - name: ionq_hardware_run + mode: azure_hardware + azure_backend: ionq.qpu # PAID: Real quantum hardware + azure_shots: 50 # Keep low to minimize cost + n_qubits: 3 + azure_confirm_cost: true # REQUIRED for paid hardware + enabled: false # Keep disabled until ready + extra_args: + - --circuit-file + - ai-projects/quantum-ml/results/optimized_circuit.qasm +``` + +### Safety Validation + +```powershell +# This will FAIL validation (cost not confirmed) +# azure_confirm_cost: false + +# This will PASS validation +# azure_confirm_cost: true + +# Dry-run to verify (no submission) +python .\scripts\quantum_autorun.py --job ionq_hardware_run --dry-run + +# Enable in config and run (charges will apply) +python .\scripts\quantum_autorun.py --job ionq_hardware_run +``` + +### Cost Estimation + +- IonQ QPU: ~$0.00003 per gate-shot +- 50 shots × 10 gates = ~$0.015 +- Always test on simulator first! + +## Example 3: Multi-Backend Comparison + +Compare results across simulators and hardware. + +### Config + +```yaml +jobs: + # Local simulator (baseline) + - name: compare_local + mode: train_custom_dataset + preset: heart + n_qubits: 3 + epochs: 1 + enabled: true + + # Azure IonQ simulator + - name: compare_ionq_sim + mode: azure_hardware + azure_backend: ionq.simulator + azure_shots: 100 + n_qubits: 3 + enabled: true + + # Azure Rigetti simulator + - name: compare_rigetti_sim + mode: azure_hardware + azure_backend: rigetti.sim.qvm + azure_shots: 100 + n_qubits: 3 + enabled: true + + # IonQ hardware (disabled by default) + - name: compare_ionq_hardware + mode: azure_hardware + azure_backend: ionq.qpu + azure_shots: 50 + n_qubits: 3 + azure_confirm_cost: true + enabled: false +``` + +### Batch Execution + +```powershell +# Run all enabled jobs sequentially +python .\scripts\quantum_autorun.py + +# Check aggregated status +cat data_out\quantum_autorun\status.json + +# Compare results programmatically +python .\scripts\analyze_quantum_results.py +``` + +## Example 4: Circuit File Integration + +### Create Circuit + +```python +# ai-projects/quantum-ml/create_test_circuit.py +from qiskit import QuantumCircuit + +def create_bell_state(): + qc = QuantumCircuit(2, 2) + qc.h(0) + qc.cx(0, 1) + qc.measure([0, 1], [0, 1]) + return qc + +if __name__ == "__main__": + circuit = create_bell_state() + circuit.qasm(filename="results/bell_state.qasm") + print("Circuit saved to results/bell_state.qasm") +``` + +### Submit to Azure + +```yaml +jobs: + - name: bell_state_ionq + mode: azure_hardware + azure_backend: ionq.simulator + azure_shots: 1000 + enabled: true + extra_args: + - --circuit-file + - ai-projects/quantum-ml/results/bell_state.qasm +``` + +## Troubleshooting + +### Error: "Not connected to Azure Quantum" + +```powershell +# Check Azure login +az account show + +# Re-login if needed +az login + +# Verify workspace exists +az quantum workspace show --name quantum-ai-workspace --resource-group rg-quantum-ai +``` + +### Error: "COST SAFETY: azure_confirm_cost must be true" + +This is expected! Set `azure_confirm_cost: true` in your config to acknowledge hardware costs. + +### Error: "Backend not found" + +```powershell +# List available backends +az quantum workspace show --name quantum-ai-workspace --resource-group rg-quantum-ai + +# Common backend names: +# - ionq.simulator (free) +# - ionq.qpu (paid) +# - rigetti.sim.qvm (free) +# - quantinuum.sim.h1-1sc (free) +``` + +## Best Practices + +1. **Always test locally first**: Use `mode: train_custom_dataset` to validate circuits +2. **Use simulators for development**: Azure simulators are free and catch errors +3. **Keep shots low initially**: Start with 50-100 shots, increase only if needed +4. **Enable dry-run by default**: Add `--dry-run` to all commands during development +5. **Monitor costs**: Check Azure Portal → Cost Management regularly +6. **Version control circuits**: Keep QASM files in git for reproducibility +7. **Document experiments**: Use descriptive job names and maintain a results log + +## Cost Management Scripts + +### Check Recent Azure Costs + +```powershell +# Last 7 days of Quantum costs +az consumption usage list --start-date (Get-Date).AddDays(-7).ToString("yyyy-MM-dd") | ConvertFrom-Json | Where-Object { $_.instanceName -like "*quantum*" } +``` + +### Set Budget Alert + +```powershell +# Create budget alert for quantum workspace +az consumption budget create --amount 50 --category cost --time-grain monthly --resource-group rg-quantum-ai --budget-name quantum-monthly-budget +``` + +## Integration with CI/CD + +### GitHub Actions Example + +```yaml +name: Quantum Tests + +on: [push] + +jobs: + quantum-validation: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Run quantum autorun dry-run + run: | + python scripts/evaluation/quantum_autorun.py --dry-run + + - name: Run simulator tests only + run: | + python scripts/evaluation/quantum_autorun.py --job azure_ionq_simulator +``` + +## Advanced: Programmatic Job Submission + +```python +# scripts/submit_quantum_batch.py +import subprocess +import json +from pathlib import Path + +jobs = ["azure_sim_1", "azure_sim_2", "azure_sim_3"] + +results = [] +for job in jobs: + cmd = ["python", "scripts/evaluation/quantum_autorun.py", "--job", job] + proc = subprocess.run(cmd, capture_output=True, text=True) + results.append({ + "job": job, + "status": "success" if proc.returncode == 0 else "failed", + "output": proc.stdout + }) + +# Save batch results +with open("data_out/batch_results.json", "w") as f: + json.dump(results, f, indent=2) +``` + +## Support + +- Azure Quantum Docs: +- IonQ Provider: +- Rigetti Provider: +- Cost Calculator: diff --git a/docs/quantum/QUANTUM_WEB_APP_COMPLETE.md b/docs/quantum/QUANTUM_WEB_APP_COMPLETE.md index a293234b0..9c86b0cbd 100644 --- a/docs/quantum/QUANTUM_WEB_APP_COMPLETE.md +++ b/docs/quantum/QUANTUM_WEB_APP_COMPLETE.md @@ -1,349 +1,349 @@ -# 🎉 Repository Enhancement Complete - Interactive Quantum AI Dashboard - -## ✅ What Was Built - -I've created a **production-ready web application** for training and visualizing quantum machine learning models with real-time metrics and an interactive UI. - -## 📦 Deliverables - -### Core Application Files - -1. **`ai-projects/quantum-ml/web_app.py`** (450 lines) - - Flask REST API with 8 endpoints - - Threaded training execution - - Real-time session management - - Automatic result persistence - -2. **`ai-projects/quantum-ml/web_ui/index.html`** (150 lines) - - Modern gradient UI design - - Responsive layout - - Training configuration panel - - Real-time status display - - Interactive charts - -3. **`ai-projects/quantum-ml/web_ui/static/styles.css`** (400 lines) - - Beautiful dark theme with gradients - - Smooth animations and transitions - - Mobile-responsive design - - Professional styling - -4. **`ai-projects/quantum-ml/web_ui/static/app.js`** (350 lines) - - Real-time metric visualization with Chart.js - - 1-second polling for live updates - - Session management logic - - Training history browser - -### Supporting Files - -5. **`ai-projects/quantum-ml/start_dashboard.sh`** - - One-command startup script - - Auto-installs dependencies - - Activates virtual environment - -6. **`ai-projects/quantum-ml/web-requirements.txt`** - - Clean dependency list (Flask, PennyLane, etc.) - -7. **`ai-projects/quantum-ml/demo_dashboard.py`** - - Automated demo script - - Tests all API endpoints - - Runs sample training session - -### Documentation - -8. **`ai-projects/quantum-ml/WEB_DASHBOARD_README.md`** (500+ lines) - - Complete usage guide - - API documentation - - Hyperparameter tuning guide - - Troubleshooting section - - Architecture overview - -9. **`ai-projects/quantum-ml/WEB_DASHBOARD_SUCCESS.md`** - - Setup completion summary - - Quick start guide - - Feature highlights - -10. **Updated `ai-projects/quantum-ml/README.md`** - - Added web dashboard section at top - - Quick start instructions - -11. **Updated `/workspaces/AI/README.md`** - - Highlighted new web dashboard - - Updated project overview - -## 🚀 Key Features Implemented - -### Real-Time Visualization -- ✅ Live training/validation loss charts (updates every 1s) -- ✅ Live accuracy curves with percentage display -- ✅ Progress bar showing elapsed time and % complete -- ✅ Epoch counter and best accuracy tracker -- ✅ Chart.js integration for smooth animations - -### Interactive Training -- ✅ Dataset selection dropdown (4 quantum datasets) -- ✅ Hyperparameter controls (qubits, layers, learning rate, duration, batch size) -- ✅ Start/Stop training buttons with state management -- ✅ Non-blocking threaded execution -- ✅ Session isolation (multiple trainings can run) - -### Session Management -- ✅ Automatic JSON result saving -- ✅ Training history browser -- ✅ Clickable results with detailed metrics -- ✅ Persistent storage across server restarts -- ✅ Thread-safe state management - -### API Architecture -- ✅ RESTful endpoints for all operations -- ✅ Real-time status streaming via polling -- ✅ Error handling and validation -- ✅ CORS support for local development -- ✅ Extensible for future features - -## 🎨 UI/UX Highlights - -- **Modern Design**: Gradient themes, smooth animations -- **Responsive**: Works on desktop, tablet, mobile -- **Intuitive**: No coding required - just select and click -- **Visual Feedback**: Spinning icons, progress bars, live charts -- **Professional**: Production-quality styling and layout - -## 🏗️ Technical Architecture - -### Backend (Python/Flask) -``` -Flask App -├── TrainingSession class (state management) -├── Quantum ML pipeline (PennyLane + scikit-learn) -├── Threading (non-blocking execution) -└── JSON persistence (automatic result saving) -``` - -### Frontend (HTML/CSS/JS) -``` -Single-Page App -├── Configuration panel (dataset + hyperparameters) -├── Status display (real-time metrics) -├── Chart.js visualizations (loss + accuracy) -└── Results browser (training history) -``` - -### Data Flow -``` -User → UI → REST API → Training Thread → Quantum Circuit → Metrics → UI - ↓ - JSON Results -``` - -## 📊 Example Usage - -```bash -# 1. Start the dashboard -cd quantum-ai -./start_dashboard.sh - -# 2. Open browser to http://localhost:5000 - -# 3. Configure training: -# Dataset: heart -# Qubits: 4 -# Layers: 2 -# Learning Rate: 0.01 -# Duration: 10 minutes -# Batch Size: 32 - -# 4. Click "Start Training" - -# 5. Watch real-time charts update every second! -``` - -## 🎯 Success Metrics - -| Metric | Target | Achieved | -|--------|--------|----------| -| Core functionality | Working web app | ✅ Yes | -| Real-time visualization | Live charts | ✅ Yes | -| Session management | Save/load results | ✅ Yes | -| Documentation | Complete guide | ✅ Yes | -| Ease of use | One-command start | ✅ Yes | -| Code quality | Clean, commented | ✅ Yes | -| Performance | Non-blocking training | ✅ Yes | -| UI/UX | Modern, responsive | ✅ Yes | - -## 📈 Performance Characteristics - -- **Frontend**: 65KB total (HTML + CSS + JS) -- **Backend**: ~50MB memory per training session -- **Training Speed**: 1-2 epochs/minute (4 qubits, CPU) -- **Update Frequency**: 1-second polling interval -- **Scalability**: Ready for multi-user deployment - -## 🔗 Integration Points - -### Existing Features -- ✅ Uses existing datasets from `datasets/quantum/*.csv` -- ✅ Compatible with PennyLane training pipeline -- ✅ Integrates with scikit-learn preprocessing -- ✅ Works alongside CLI training scripts - -### Future Enhancements -- 🔮 Azure Quantum hardware integration -- 🔮 Multi-user authentication -- 🔮 Cloud deployment (Azure App Service) -- 🔮 WebSocket streaming (replace polling) -- 🔮 Model comparison tools -- 🔮 Automated hyperparameter tuning - -## 🎓 Learning Resources Created - -### For Users -- Step-by-step quick start guide -- Hyperparameter tuning recommendations -- Example training workflows -- Troubleshooting section - -### For Developers -- API endpoint documentation -- Code architecture overview -- Customization guide (circuits, datasets) -- Extension examples - -## 🚀 Deployment Ready - -### Local Development -```bash -./start_dashboard.sh # One command, fully functional -``` - -### Production Deployment -```bash -# Azure App Service -az webapp up --runtime PYTHON:3.12 --sku B1 --location eastus - -# Docker -docker build -t quantum-ai-dashboard . -docker run -p 5000:5000 quantum-ai-dashboard - -# Heroku -git push heroku main -``` - -## 📝 File Structure Summary - -``` -ai-projects/quantum-ml/ -├── web_app.py # Flask backend (450 lines) -├── start_dashboard.sh # Startup script -├── demo_dashboard.py # Automated demo -├── web-requirements.txt # Dependencies -├── WEB_DASHBOARD_README.md # Complete guide (500+ lines) -├── WEB_DASHBOARD_SUCCESS.md # Setup summary -├── web_ui/ -│ ├── index.html # Main UI (150 lines) -│ └── static/ -│ ├── styles.css # Styling (400 lines) -│ └── app.js # Frontend logic (350 lines) -├── results/ -│ └── training_*.json # Saved sessions -└── ../datasets/quantum/ - ├── heart_disease.csv - ├── ionosphere.csv - ├── sonar.csv - └── banknote.csv -``` - -**Total New Code**: ~1,800 lines across 4 core files + documentation - -## 🎉 Final Status - -### Completed ✅ -1. ✅ Full-featured web application with Flask backend -2. ✅ Beautiful, responsive UI with real-time charts -3. ✅ Threaded training execution (non-blocking) -4. ✅ Session management with persistent storage -5. ✅ One-command startup script -6. ✅ Comprehensive documentation (500+ lines) -7. ✅ Automated demo script -8. ✅ Updated repository READMEs -9. ✅ Dependencies installed and tested -10. ✅ Working demo verified - -### Active -- ⚛️ 45-minute training session still running (from earlier request) -- 📊 Web app ready to launch with `./start_dashboard.sh` - -### Next Steps (Optional) -- 🔮 Deploy to cloud (Azure, Heroku, AWS) -- 🔮 Add WebSocket streaming for even smoother updates -- 🔮 Integrate Azure Quantum hardware backends -- 🔮 Add user authentication for multi-user support -- 🔮 Create automated hyperparameter search - -## 🚀 How to Use Right Now - -```bash -# Terminal 1: Start the dashboard -cd /workspaces/AI/quantum-ai -./start_dashboard.sh - -# Terminal 2: Run the demo (optional) -cd /workspaces/AI/quantum-ai -./venv/bin/python demo_dashboard.py - -# Browser: Open http://localhost:5000 -# Click around, start training, watch real-time charts! -``` - -## 🙏 Technologies Used - -- **Backend**: Flask 3.1.2, Flask-CORS 6.0.1 -- **Quantum ML**: PennyLane 0.43.1 -- **Data Science**: NumPy 2.3.4, Pandas 2.3.3, scikit-learn 1.7.2 -- **Frontend**: Chart.js 4.4.0, vanilla JavaScript -- **Styling**: Custom CSS with gradient themes -- **Threading**: Python stdlib threading module - -## 📊 Repository Structure Improvements - -### Before -``` -ai-projects/quantum-ml/ -├── src/ # Python modules -├── results/ # Training outputs -└── datasets/ # CSV files -``` - -### After (Enhanced) -``` -ai-projects/quantum-ml/ -├── src/ # Python modules -├── results/ # Training outputs -├── datasets/ # CSV files -├── web_app.py # 🆕 Web application -├── web_ui/ # 🆕 Frontend files -│ ├── index.html -│ └── static/ -│ ├── styles.css -│ └── app.js -├── start_dashboard.sh # 🆕 Launch script -├── demo_dashboard.py # 🆕 Demo script -├── WEB_DASHBOARD_README.md # 🆕 Documentation -└── WEB_DASHBOARD_SUCCESS.md # 🆕 Success summary -``` - ---- - -## 🎉 Summary - -You now have a **production-ready, interactive web application** for training quantum AI models with: - -✨ Beautiful UI with real-time visualization -⚡ One-command startup (`./start_dashboard.sh`) -📊 Live charts showing loss and accuracy -🎛️ Interactive hyperparameter tuning -💾 Automatic session saving and history -📚 500+ lines of comprehensive documentation - -**The repository layout has been significantly improved** with a modern web app that makes quantum machine learning accessible, visual, and fun to experiment with! - -🚀 **Ready to explore? Run `./start_dashboard.sh` and open http://localhost:5000!** +# 🎉 Repository Enhancement Complete - Interactive Quantum AI Dashboard + +## ✅ What Was Built + +I've created a **production-ready web application** for training and visualizing quantum machine learning models with real-time metrics and an interactive UI. + +## 📦 Deliverables + +### Core Application Files + +1. **`ai-projects/quantum-ml/web_app.py`** (450 lines) + - Flask REST API with 8 endpoints + - Threaded training execution + - Real-time session management + - Automatic result persistence + +2. **`ai-projects/quantum-ml/web_ui/index.html`** (150 lines) + - Modern gradient UI design + - Responsive layout + - Training configuration panel + - Real-time status display + - Interactive charts + +3. **`ai-projects/quantum-ml/web_ui/static/styles.css`** (400 lines) + - Beautiful dark theme with gradients + - Smooth animations and transitions + - Mobile-responsive design + - Professional styling + +4. **`ai-projects/quantum-ml/web_ui/static/app.js`** (350 lines) + - Real-time metric visualization with Chart.js + - 1-second polling for live updates + - Session management logic + - Training history browser + +### Supporting Files + +5. **`ai-projects/quantum-ml/start_dashboard.sh`** + - One-command startup script + - Auto-installs dependencies + - Activates virtual environment + +6. **`ai-projects/quantum-ml/web-requirements.txt`** + - Clean dependency list (Flask, PennyLane, etc.) + +7. **`ai-projects/quantum-ml/demo_dashboard.py`** + - Automated demo script + - Tests all API endpoints + - Runs sample training session + +### Documentation + +8. **`ai-projects/quantum-ml/WEB_DASHBOARD_README.md`** (500+ lines) + - Complete usage guide + - API documentation + - Hyperparameter tuning guide + - Troubleshooting section + - Architecture overview + +9. **`ai-projects/quantum-ml/WEB_DASHBOARD_SUCCESS.md`** + - Setup completion summary + - Quick start guide + - Feature highlights + +10. **Updated `ai-projects/quantum-ml/README.md`** + - Added web dashboard section at top + - Quick start instructions + +11. **Updated `/workspaces/AI/README.md`** + - Highlighted new web dashboard + - Updated project overview + +## 🚀 Key Features Implemented + +### Real-Time Visualization +- ✅ Live training/validation loss charts (updates every 1s) +- ✅ Live accuracy curves with percentage display +- ✅ Progress bar showing elapsed time and % complete +- ✅ Epoch counter and best accuracy tracker +- ✅ Chart.js integration for smooth animations + +### Interactive Training +- ✅ Dataset selection dropdown (4 quantum datasets) +- ✅ Hyperparameter controls (qubits, layers, learning rate, duration, batch size) +- ✅ Start/Stop training buttons with state management +- ✅ Non-blocking threaded execution +- ✅ Session isolation (multiple trainings can run) + +### Session Management +- ✅ Automatic JSON result saving +- ✅ Training history browser +- ✅ Clickable results with detailed metrics +- ✅ Persistent storage across server restarts +- ✅ Thread-safe state management + +### API Architecture +- ✅ RESTful endpoints for all operations +- ✅ Real-time status streaming via polling +- ✅ Error handling and validation +- ✅ CORS support for local development +- ✅ Extensible for future features + +## 🎨 UI/UX Highlights + +- **Modern Design**: Gradient themes, smooth animations +- **Responsive**: Works on desktop, tablet, mobile +- **Intuitive**: No coding required - just select and click +- **Visual Feedback**: Spinning icons, progress bars, live charts +- **Professional**: Production-quality styling and layout + +## 🏗️ Technical Architecture + +### Backend (Python/Flask) +``` +Flask App +├── TrainingSession class (state management) +├── Quantum ML pipeline (PennyLane + scikit-learn) +├── Threading (non-blocking execution) +└── JSON persistence (automatic result saving) +``` + +### Frontend (HTML/CSS/JS) +``` +Single-Page App +├── Configuration panel (dataset + hyperparameters) +├── Status display (real-time metrics) +├── Chart.js visualizations (loss + accuracy) +└── Results browser (training history) +``` + +### Data Flow +``` +User → UI → REST API → Training Thread → Quantum Circuit → Metrics → UI + ↓ + JSON Results +``` + +## 📊 Example Usage + +```bash +# 1. Start the dashboard +cd quantum-ai +./start_dashboard.sh + +# 2. Open browser to http://localhost:5000 + +# 3. Configure training: +# Dataset: heart +# Qubits: 4 +# Layers: 2 +# Learning Rate: 0.01 +# Duration: 10 minutes +# Batch Size: 32 + +# 4. Click "Start Training" + +# 5. Watch real-time charts update every second! +``` + +## 🎯 Success Metrics + +| Metric | Target | Achieved | +|--------|--------|----------| +| Core functionality | Working web app | ✅ Yes | +| Real-time visualization | Live charts | ✅ Yes | +| Session management | Save/load results | ✅ Yes | +| Documentation | Complete guide | ✅ Yes | +| Ease of use | One-command start | ✅ Yes | +| Code quality | Clean, commented | ✅ Yes | +| Performance | Non-blocking training | ✅ Yes | +| UI/UX | Modern, responsive | ✅ Yes | + +## 📈 Performance Characteristics + +- **Frontend**: 65KB total (HTML + CSS + JS) +- **Backend**: ~50MB memory per training session +- **Training Speed**: 1-2 epochs/minute (4 qubits, CPU) +- **Update Frequency**: 1-second polling interval +- **Scalability**: Ready for multi-user deployment + +## 🔗 Integration Points + +### Existing Features +- ✅ Uses existing datasets from `datasets/quantum/*.csv` +- ✅ Compatible with PennyLane training pipeline +- ✅ Integrates with scikit-learn preprocessing +- ✅ Works alongside CLI training scripts + +### Future Enhancements +- 🔮 Azure Quantum hardware integration +- 🔮 Multi-user authentication +- 🔮 Cloud deployment (Azure App Service) +- 🔮 WebSocket streaming (replace polling) +- 🔮 Model comparison tools +- 🔮 Automated hyperparameter tuning + +## 🎓 Learning Resources Created + +### For Users +- Step-by-step quick start guide +- Hyperparameter tuning recommendations +- Example training workflows +- Troubleshooting section + +### For Developers +- API endpoint documentation +- Code architecture overview +- Customization guide (circuits, datasets) +- Extension examples + +## 🚀 Deployment Ready + +### Local Development +```bash +./start_dashboard.sh # One command, fully functional +``` + +### Production Deployment +```bash +# Azure App Service +az webapp up --runtime PYTHON:3.12 --sku B1 --location eastus + +# Docker +docker build -t quantum-ai-dashboard . +docker run -p 5000:5000 quantum-ai-dashboard + +# Heroku +git push heroku main +``` + +## 📝 File Structure Summary + +``` +ai-projects/quantum-ml/ +├── web_app.py # Flask backend (450 lines) +├── start_dashboard.sh # Startup script +├── demo_dashboard.py # Automated demo +├── web-requirements.txt # Dependencies +├── WEB_DASHBOARD_README.md # Complete guide (500+ lines) +├── WEB_DASHBOARD_SUCCESS.md # Setup summary +├── web_ui/ +│ ├── index.html # Main UI (150 lines) +│ └── static/ +│ ├── styles.css # Styling (400 lines) +│ └── app.js # Frontend logic (350 lines) +├── results/ +│ └── training_*.json # Saved sessions +└── ../datasets/quantum/ + ├── heart_disease.csv + ├── ionosphere.csv + ├── sonar.csv + └── banknote.csv +``` + +**Total New Code**: ~1,800 lines across 4 core files + documentation + +## 🎉 Final Status + +### Completed ✅ +1. ✅ Full-featured web application with Flask backend +2. ✅ Beautiful, responsive UI with real-time charts +3. ✅ Threaded training execution (non-blocking) +4. ✅ Session management with persistent storage +5. ✅ One-command startup script +6. ✅ Comprehensive documentation (500+ lines) +7. ✅ Automated demo script +8. ✅ Updated repository READMEs +9. ✅ Dependencies installed and tested +10. ✅ Working demo verified + +### Active +- ⚛️ 45-minute training session still running (from earlier request) +- 📊 Web app ready to launch with `./start_dashboard.sh` + +### Next Steps (Optional) +- 🔮 Deploy to cloud (Azure, Heroku, AWS) +- 🔮 Add WebSocket streaming for even smoother updates +- 🔮 Integrate Azure Quantum hardware backends +- 🔮 Add user authentication for multi-user support +- 🔮 Create automated hyperparameter search + +## 🚀 How to Use Right Now + +```bash +# Terminal 1: Start the dashboard +cd /workspaces/AI/quantum-ai +./start_dashboard.sh + +# Terminal 2: Run the demo (optional) +cd /workspaces/AI/quantum-ai +./venv/bin/python demo_dashboard.py + +# Browser: Open http://localhost:5000 +# Click around, start training, watch real-time charts! +``` + +## 🙏 Technologies Used + +- **Backend**: Flask 3.1.2, Flask-CORS 6.0.1 +- **Quantum ML**: PennyLane 0.43.1 +- **Data Science**: NumPy 2.3.4, Pandas 2.3.3, scikit-learn 1.7.2 +- **Frontend**: Chart.js 4.4.0, vanilla JavaScript +- **Styling**: Custom CSS with gradient themes +- **Threading**: Python stdlib threading module + +## 📊 Repository Structure Improvements + +### Before +``` +ai-projects/quantum-ml/ +├── src/ # Python modules +├── results/ # Training outputs +└── datasets/ # CSV files +``` + +### After (Enhanced) +``` +ai-projects/quantum-ml/ +├── src/ # Python modules +├── results/ # Training outputs +├── datasets/ # CSV files +├── web_app.py # 🆕 Web application +├── web_ui/ # 🆕 Frontend files +│ ├── index.html +│ └── static/ +│ ├── styles.css +│ └── app.js +├── start_dashboard.sh # 🆕 Launch script +├── demo_dashboard.py # 🆕 Demo script +├── WEB_DASHBOARD_README.md # 🆕 Documentation +└── WEB_DASHBOARD_SUCCESS.md # 🆕 Success summary +``` + +--- + +## 🎉 Summary + +You now have a **production-ready, interactive web application** for training quantum AI models with: + +✨ Beautiful UI with real-time visualization +⚡ One-command startup (`./start_dashboard.sh`) +📊 Live charts showing loss and accuracy +🎛️ Interactive hyperparameter tuning +💾 Automatic session saving and history +📚 500+ lines of comprehensive documentation + +**The repository layout has been significantly improved** with a modern web app that makes quantum machine learning accessible, visual, and fun to experiment with! + +🚀 **Ready to explore? Run `./start_dashboard.sh` and open http://localhost:5000!** diff --git a/docs/quantum/app.js b/docs/quantum/app.js index a904001ae..7f9b6420f 100644 --- a/docs/quantum/app.js +++ b/docs/quantum/app.js @@ -1,793 +1,793 @@ -// Quantum AI Training Dashboard - Frontend Logic - -let currentSessionId = null; -let statusUpdateInterval = null; -let lossChart = null; -let accuracyChart = null; -let circuitCanvas = null; -let circuitCtx = null; -let particleInterval = null; - -// API Base URL -const API_BASE = window.location.origin; - -// Initialize on page load -document.addEventListener('DOMContentLoaded', () => { - console.log('🚀 Quantum AI Dashboard initializing...'); - initializeCircuitCanvas(); - initializeCharts(); - loadDatasets(); - loadResults(); - setupEventListeners(); -}); - -// Setup Event Listeners -function setupEventListeners() { - document.getElementById('start-training-btn').addEventListener('click', startTraining); - document.getElementById('stop-training-btn').addEventListener('click', stopTraining); - document.getElementById('evaluate-btn').addEventListener('click', evaluateNow); - document.getElementById('export-metrics-btn').addEventListener('click', exportMetrics); - document.getElementById('dataset-select').addEventListener('change', updateDatasetInfo); - - // Add input validation - const numericInputs = ['n-qubits', 'n-layers', 'learning-rate', 'duration', 'batch-size', 'early-stopping', 'checkpoint-every', 'warmup-epochs', 'max-grad-norm']; - numericInputs.forEach(id => { - const input = document.getElementById(id); - input.addEventListener('input', () => validateInput(input)); - }); -} - -// Initialize Circuit Canvas -function initializeCircuitCanvas() { - circuitCanvas = document.getElementById('circuit-canvas'); - circuitCtx = circuitCanvas.getContext('2d'); - - // Set canvas size - const rect = circuitCanvas.getBoundingClientRect(); - circuitCanvas.width = rect.width; - circuitCanvas.height = 300; - - drawIdleCircuit(); -} - -// Draw idle circuit state -function drawIdleCircuit() { - if (!circuitCtx) return; - - const ctx = circuitCtx; - const width = circuitCanvas.width; - const height = circuitCanvas.height; - - ctx.clearRect(0, 0, width, height); - - // Draw gradient background - const gradient = ctx.createLinearGradient(0, 0, width, height); - gradient.addColorStop(0, 'rgba(99, 102, 241, 0.1)'); - gradient.addColorStop(1, 'rgba(139, 92, 246, 0.1)'); - ctx.fillStyle = gradient; - ctx.fillRect(0, 0, width, height); - - // Draw placeholder text - ctx.font = 'bold 18px -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto'; - ctx.fillStyle = '#6366f1'; - ctx.textAlign = 'center'; - ctx.fillText('⚛️ Quantum Circuit Visualization', width / 2, height / 2 - 15); - - ctx.font = '14px -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto'; - ctx.fillStyle = '#94a3b8'; - ctx.fillText('Start training to see live circuit diagram', width / 2, height / 2 + 15); - - // Update stats - document.getElementById('gate-count').textContent = '0'; - document.getElementById('circuit-depth').textContent = '0'; -} - -// Draw active quantum circuit -function drawQuantumCircuit(nQubits, nLayers, epoch) { - if (!circuitCtx) return; - - const ctx = circuitCtx; - const width = circuitCanvas.width; - const height = circuitCanvas.height; - - ctx.clearRect(0, 0, width, height); - - // Background - const gradient = ctx.createLinearGradient(0, 0, width, height); - gradient.addColorStop(0, 'rgba(99, 102, 241, 0.05)'); - gradient.addColorStop(1, 'rgba(139, 92, 246, 0.05)'); - ctx.fillStyle = gradient; - ctx.fillRect(0, 0, width, height); - - const padding = 60; - const qubitSpacing = (height - 2 * padding) / (nQubits - 1 || 1); - const layerWidth = (width - 2 * padding) / (nLayers + 2); - - // Draw qubit lines - ctx.strokeStyle = '#475569'; - ctx.lineWidth = 2; - for (let i = 0; i < nQubits; i++) { - const y = padding + i * qubitSpacing; - ctx.beginPath(); - ctx.moveTo(padding, y); - ctx.lineTo(width - padding, y); - ctx.stroke(); - - // Qubit label - ctx.fillStyle = '#cbd5e1'; - ctx.font = '14px monospace'; - ctx.textAlign = 'right'; - ctx.fillText(`|q${i}⟩`, padding - 10, y + 5); - } - - // Animate based on epoch - const pulsePhase = (epoch % 10) / 10; - - // Draw gates with animation - for (let layer = 0; layer < nLayers; layer++) { - const x = padding + (layer + 1) * layerWidth; - - for (let i = 0; i < nQubits; i++) { - const y = padding + i * qubitSpacing; - - // Rotation gate (pulsing) - const gateSize = 25 + Math.sin(pulsePhase * Math.PI * 2) * 3; - ctx.fillStyle = `rgba(239, 68, 68, ${0.7 + Math.sin(pulsePhase * Math.PI * 2) * 0.3})`; - ctx.beginPath(); - ctx.arc(x, y, gateSize / 2, 0, Math.PI * 2); - ctx.fill(); - - // Gate label - ctx.fillStyle = '#fff'; - ctx.font = 'bold 11px monospace'; - ctx.textAlign = 'center'; - ctx.fillText('Ry', x, y + 4); - } - - // CNOT gates (entanglement) - if (nQubits > 1) { - const cnotX = x + layerWidth / 2; - ctx.strokeStyle = `rgba(16, 185, 129, ${0.7 + Math.sin(pulsePhase * Math.PI * 2 + Math.PI) * 0.3})`; - ctx.lineWidth = 3; - - for (let i = 0; i < nQubits - 1; i++) { - const y1 = padding + i * qubitSpacing; - const y2 = padding + (i + 1) * qubitSpacing; - - // Vertical line - ctx.beginPath(); - ctx.moveTo(cnotX, y1); - ctx.lineTo(cnotX, y2); - ctx.stroke(); - - // Control dot - ctx.fillStyle = '#10b981'; - ctx.beginPath(); - ctx.arc(cnotX, y1, 5, 0, Math.PI * 2); - ctx.fill(); - - // Target circle - ctx.strokeStyle = '#10b981'; - ctx.lineWidth = 2; - ctx.beginPath(); - ctx.arc(cnotX, y2, 12, 0, Math.PI * 2); - ctx.stroke(); - ctx.beginPath(); - ctx.moveTo(cnotX - 8, y2); - ctx.lineTo(cnotX + 8, y2); - ctx.stroke(); - ctx.beginPath(); - ctx.moveTo(cnotX, y2 - 8); - ctx.lineTo(cnotX, y2 + 8); - ctx.stroke(); - } - } - } - - // Measurement symbols - const measX = width - padding - 20; - for (let i = 0; i < nQubits; i++) { - const y = padding + i * qubitSpacing; - - // Measurement box - ctx.strokeStyle = '#f59e0b'; - ctx.lineWidth = 2; - ctx.strokeRect(measX - 15, y - 15, 30, 30); - - // Meter symbol - ctx.beginPath(); - ctx.arc(measX, y + 5, 10, Math.PI, 0); - ctx.stroke(); - ctx.beginPath(); - ctx.moveTo(measX, y + 5); - ctx.lineTo(measX + 7, y - 2); - ctx.stroke(); - } - - // Epoch indicator with glow - ctx.save(); - ctx.shadowColor = '#6366f1'; - ctx.shadowBlur = 10; - ctx.fillStyle = '#6366f1'; - ctx.font = 'bold 16px monospace'; - ctx.textAlign = 'left'; - ctx.fillText(`EPOCH ${epoch}`, 15, 25); - ctx.restore(); - - // Update circuit stats - const gateCount = nQubits * nLayers + (nQubits - 1) * nLayers; - const circuitDepth = nLayers * 2; - document.getElementById('gate-count').textContent = gateCount; - document.getElementById('circuit-depth').textContent = circuitDepth; -} - -// Create particle effect -function createParticle(x, y) { - const particle = document.createElement('div'); - particle.className = 'particle'; - particle.style.left = x + 'px'; - particle.style.top = y + 'px'; - particle.style.background = `hsl(${Math.random() * 60 + 200}, 70%, 60%)`; - document.body.appendChild(particle); - - setTimeout(() => particle.remove(), 2000); -} - -// Start particle animation -function startParticleAnimation() { - if (particleInterval) return; - - particleInterval = setInterval(() => { - const canvas = document.getElementById('circuit-canvas'); - if (!canvas) return; - - const rect = canvas.getBoundingClientRect(); - const x = rect.left + Math.random() * rect.width; - const y = rect.top + Math.random() * rect.height; - createParticle(x, y); - }, 300); -} - -// Stop particle animation -function stopParticleAnimation() { - if (particleInterval) { - clearInterval(particleInterval); - particleInterval = null; - } -} - -// Initialize Charts -function initializeCharts() { - const chartOptions = { - responsive: true, - maintainAspectRatio: false, - plugins: { - legend: { - labels: { - color: '#cbd5e1' - } - } - }, - scales: { - x: { - ticks: { color: '#94a3b8' }, - grid: { color: '#334155' } - }, - y: { - ticks: { color: '#94a3b8' }, - grid: { color: '#334155' } - } - } - }; - - // Loss Chart - const lossCtx = document.getElementById('loss-chart').getContext('2d'); - lossChart = new Chart(lossCtx, { - type: 'line', - data: { - labels: [], - datasets: [ - { - label: 'Training Loss', - data: [], - borderColor: '#ef4444', - backgroundColor: 'rgba(239, 68, 68, 0.1)', - tension: 0.4 - }, - { - label: 'Validation Loss', - data: [], - borderColor: '#f59e0b', - backgroundColor: 'rgba(245, 158, 11, 0.1)', - tension: 0.4 - } - ] - }, - options: { - ...chartOptions, - plugins: { - ...chartOptions.plugins, - title: { - display: true, - text: 'Training & Validation Loss', - color: '#cbd5e1' - } - } - } - }); - - // Accuracy Chart - const accCtx = document.getElementById('accuracy-chart').getContext('2d'); - accuracyChart = new Chart(accCtx, { - type: 'line', - data: { - labels: [], - datasets: [ - { - label: 'Validation Accuracy', - data: [], - borderColor: '#10b981', - backgroundColor: 'rgba(16, 185, 129, 0.1)', - tension: 0.4, - fill: true - } - ] - }, - options: { - ...chartOptions, - plugins: { - ...chartOptions.plugins, - title: { - display: true, - text: 'Validation Accuracy', - color: '#cbd5e1' - } - }, - scales: { - ...chartOptions.scales, - y: { - ...chartOptions.scales.y, - min: 0, - max: 1, - ticks: { - ...chartOptions.scales.y.ticks, - callback: function(value) { - return (value * 100).toFixed(0) + '%'; - } - } - } - } - } - }); -} - -// Load Available Datasets -async function loadDatasets() { - try { - const response = await fetch(`${API_BASE}/api/datasets`); - const datasets = await response.json(); - - const select = document.getElementById('dataset-select'); - select.innerHTML = ''; - - datasets.forEach(dataset => { - const option = document.createElement('option'); - option.value = dataset.name; - option.textContent = `${dataset.name} (${dataset.features} features)`; - select.appendChild(option); - }); - - console.log(`✅ Loaded ${datasets.length} datasets`); - } catch (error) { - console.error('Error loading datasets:', error); - showError('Failed to load datasets'); - } -} - -// Update Dataset Info -function updateDatasetInfo() { - const select = document.getElementById('dataset-select'); - const infoDiv = document.getElementById('dataset-info'); - - if (select.value) { - infoDiv.textContent = `Selected: ${select.value}`; - } else { - infoDiv.textContent = ''; - } -} - -// Start Training -async function startTraining() { - const dataset = document.getElementById('dataset-select').value; - - if (!dataset) { - alert('Please select a dataset'); - return; - } - - const config = { - dataset: dataset, - n_qubits: parseInt(document.getElementById('n-qubits').value), - n_layers: parseInt(document.getElementById('n-layers').value), - learning_rate: parseFloat(document.getElementById('learning-rate').value), - duration_minutes: parseInt(document.getElementById('duration').value), - batch_size: parseInt(document.getElementById('batch-size').value), - optimizer: document.getElementById('optimizer-select').value, - early_stopping_patience: parseInt(document.getElementById('early-stopping').value), - checkpoint_every: parseInt(document.getElementById('checkpoint-every').value), - use_parameter_shift: document.getElementById('use-param-shift').checked, - use_warmup: document.getElementById('use-warmup').checked, - warmup_epochs: parseInt(document.getElementById('warmup-epochs').value), - use_lr_decay: document.getElementById('use-lr-decay').checked, - use_gradient_clipping: document.getElementById('use-grad-clip').checked, - max_grad_norm: parseFloat(document.getElementById('max-grad-norm').value) - }; - - console.log('🚀 Starting training with config:', config); - - try { - const response = await fetch(`${API_BASE}/api/train/start`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify(config) - }); - - const result = await response.json(); - currentSessionId = result.session_id; - - // Update UI - document.getElementById('start-training-btn').disabled = true; - document.getElementById('stop-training-btn').disabled = false; - document.getElementById('status-idle').style.display = 'none'; - document.getElementById('status-training').style.display = 'block'; - document.getElementById('progress-container').style.display = 'block'; - document.getElementById('evaluation-section').style.display = 'none'; - document.getElementById('evaluate-btn').disabled = true; - - // Start visual effects - const nQubits = parseInt(document.getElementById('n-qubits').value); - const nLayers = parseInt(document.getElementById('n-layers').value); - drawQuantumCircuit(nQubits, nLayers, 0); - startParticleAnimation(); - - // Start polling for updates - startStatusPolling(); - - console.log(`✅ Training started: ${currentSessionId}`); - } catch (error) { - console.error('Error starting training:', error); - showError('Failed to start training'); - } -} - -// Stop Training -async function stopTraining() { - if (!currentSessionId) return; - - try { - await fetch(`${API_BASE}/api/train/stop/${currentSessionId}`, { - method: 'POST' - }); - - stopStatusPolling(); - console.log('⏹️ Training stopped'); - } catch (error) { - console.error('Error stopping training:', error); - } -} - -// Start Status Polling -function startStatusPolling() { - if (statusUpdateInterval) { - clearInterval(statusUpdateInterval); - } - - statusUpdateInterval = setInterval(updateTrainingStatus, 1000); -} - -// Stop Status Polling -function stopStatusPolling() { - if (statusUpdateInterval) { - clearInterval(statusUpdateInterval); - statusUpdateInterval = null; - } - - // Stop visual effects - stopParticleAnimation(); - drawIdleCircuit(); - - // Reset UI - document.getElementById('start-training-btn').disabled = false; - document.getElementById('stop-training-btn').disabled = true; - document.getElementById('status-idle').style.display = 'block'; - document.getElementById('status-training').style.display = 'none'; - document.getElementById('progress-container').style.display = 'none'; - document.getElementById('evaluate-btn').disabled = true; - document.getElementById('evaluation-section').style.display = 'none'; - - currentSessionId = null; - loadResults(); // Refresh results list -} - -// Update Training Status -async function updateTrainingStatus() { - if (!currentSessionId) return; - - try { - const response = await fetch(`${API_BASE}/api/train/status/${currentSessionId}`); - const status = await response.json(); - - // Update status text - document.getElementById('status-text').textContent = status.status; - document.getElementById('current-epoch').textContent = status.current_epoch; - document.getElementById('current-loss').textContent = status.current_loss.toFixed(4); - document.getElementById('best-val-acc').textContent = (status.best_val_acc * 100).toFixed(2) + '%'; - - // Update performance metrics - if (status.epochs_per_second > 0) { - document.getElementById('training-speed').textContent = status.epochs_per_second.toFixed(2) + ' ep/s'; - } - - if (status.eta_seconds) { - const etaMinutes = Math.floor(status.eta_seconds / 60); - const etaSeconds = Math.floor(status.eta_seconds % 60); - document.getElementById('eta-time').textContent = `${etaMinutes}:${etaSeconds.toString().padStart(2, '0')}`; - } else { - document.getElementById('eta-time').textContent = '-'; - } - - // Enable export button when training has data - if (status.metrics && status.metrics.epochs.length > 0) { - document.getElementById('export-metrics-btn').disabled = false; - } - - // Update elapsed time - if (status.elapsed_time) { - const minutes = Math.floor(status.elapsed_time / 60); - const seconds = Math.floor(status.elapsed_time % 60); - document.getElementById('elapsed-time').textContent = `${minutes}:${seconds.toString().padStart(2, '0')}`; - - // Update progress bar - const configDuration = parseInt(document.getElementById('duration').value) * 60; - const progress = Math.min((status.elapsed_time / configDuration) * 100, 100); - document.getElementById('progress-fill').style.width = progress + '%'; - document.getElementById('progress-text').textContent = progress.toFixed(0) + '% complete'; - } - - // Update visual accuracy bar - const accuracy = status.best_val_acc * 100; - document.getElementById('accuracy-bar').style.width = accuracy + '%'; - document.getElementById('accuracy-percent').textContent = accuracy.toFixed(1) + '%'; - - // Update circuit visualization - const nQubits = parseInt(document.getElementById('n-qubits').value); - const nLayers = parseInt(document.getElementById('n-layers').value); - drawQuantumCircuit(nQubits, nLayers, status.current_epoch); - - // Update charts - if (status.metrics && status.metrics.epochs.length > 0) { - updateCharts(status.metrics); - } - - // Check if completed - if (status.status === 'completed' || status.status === 'early_stopped' || status.status === 'error' || status.status === 'stopped') { - stopStatusPolling(); - - if (status.status === 'completed' || status.status === 'early_stopped') { - showSuccess('Training completed successfully!'); - // Enable evaluation - document.getElementById('evaluate-btn').disabled = false; - // Auto-evaluate if checkpoint exists - if (status.checkpoint_path) { - evaluateNow(); - } - } else if (status.status === 'error') { - showError('Training failed: ' + (status.error_message || 'Unknown error')); - } - } - - } catch (error) { - console.error('Error updating status:', error); - } -} - -// Update Charts with New Data -function updateCharts(metrics) { - // Loss Chart - lossChart.data.labels = metrics.epochs; - lossChart.data.datasets[0].data = metrics.train_loss; - lossChart.data.datasets[1].data = metrics.val_loss; - lossChart.update('none'); // Update without animation for smoothness - - // Accuracy Chart - accuracyChart.data.labels = metrics.epochs; - accuracyChart.data.datasets[0].data = metrics.val_accuracy; - accuracyChart.update('none'); -} - -// Load Training Results -async function loadResults() { - try { - const response = await fetch(`${API_BASE}/api/results`); - const results = await response.json(); - - const resultsDiv = document.getElementById('results-list'); - - if (results.length === 0) { - resultsDiv.innerHTML = '

    No training sessions yet. Start training to see results here.

    '; - return; - } - - resultsDiv.innerHTML = ''; - - results.forEach(result => { - const item = document.createElement('div'); - item.className = 'result-item'; - item.onclick = () => viewResultDetails(result.filename); - - item.innerHTML = ` -
    -
    ${result.dataset}
    -
    ${(result.best_acc * 100).toFixed(2)}% acc
    -
    -
    - 📅 ${result.timestamp.join('_')} - 🔄 ${result.epochs} epochs -
    - `; - - resultsDiv.appendChild(item); - }); - - console.log(`✅ Loaded ${results.length} training results`); - } catch (error) { - console.error('Error loading results:', error); - } -} - -// Input Validation -function validateInput(input) { - const errorDiv = document.getElementById('validation-error'); - const value = parseFloat(input.value); - let error = null; - - switch(input.id) { - case 'n-qubits': - if (value < 1 || value > 10) error = 'Qubits must be between 1 and 10'; - break; - case 'n-layers': - if (value < 1 || value > 20) error = 'Layers must be between 1 and 20'; - break; - case 'learning-rate': - if (value <= 0 || value > 1) error = 'Learning rate must be between 0 and 1'; - break; - case 'duration': - if (value < 1 || value > 120) error = 'Duration must be between 1 and 120 minutes'; - break; - case 'batch-size': - if (value < 8 || value > 128) error = 'Batch size must be between 8 and 128'; - break; - } - - if (error) { - input.style.borderColor = '#ef4444'; - errorDiv.textContent = error; - errorDiv.style.display = 'block'; - document.getElementById('start-training-btn').disabled = true; - } else { - input.style.borderColor = ''; - errorDiv.style.display = 'none'; - document.getElementById('start-training-btn').disabled = false; - } -} - -// Export Metrics -async function exportMetrics() { - if (!currentSessionId) return; - - try { - const response = await fetch(`${API_BASE}/api/export/metrics/${currentSessionId}`); - const blob = await response.blob(); - - // Create download link - const url = window.URL.createObjectURL(blob); - const a = document.createElement('a'); - a.href = url; - a.download = `metrics_${currentSessionId}.csv`; - document.body.appendChild(a); - a.click(); - a.remove(); - window.URL.revokeObjectURL(url); - - showSuccess('Metrics exported successfully!'); - } catch (error) { - console.error('Error exporting metrics:', error); - showError('Failed to export metrics'); - } -} - -// Evaluation -async function evaluateNow() { - if (!currentSessionId) return; - try { - const res = await fetch(`${API_BASE}/api/train/evaluate/${currentSessionId}`); - const data = await res.json(); - if (data.error) { - showError('Evaluation failed: ' + data.error); - return; - } - renderEvaluation(data); - showSuccess('Evaluation complete'); - } catch (e) { - console.error('Evaluation error', e); - showError('Failed to evaluate'); - } -} - -function renderEvaluation(result) { - const section = document.getElementById('evaluation-section'); - const metricsDiv = document.getElementById('eval-metrics'); - const cmDiv = document.getElementById('confusion-matrix'); - section.style.display = 'block'; - - const m = result.metrics; - metricsDiv.innerHTML = ` - Accuracy: ${(m.accuracy * 100).toFixed(2)}% · - Precision: ${(m.precision * 100).toFixed(2)}% · - Recall: ${(m.recall * 100).toFixed(2)}% · - F1: ${(m.f1 * 100).toFixed(2)}% - ${m.roc_auc !== null ? ` · ROC AUC: ${m.roc_auc.toFixed(3)}` : ''} - `; - renderConfusionMatrix(cmDiv, result.confusion_matrix, result.labels); -} - -function renderConfusionMatrix(container, matrix, labels) { - if (!matrix || !matrix.length) { - container.innerHTML = '

    No confusion matrix available.

    '; - return; - } - const n = matrix.length; - let html = ''; - for (let j = 0; j < n; j++) html += ``; - html += ''; - for (let i = 0; i < n; i++) { - html += ``; - for (let j = 0; j < n; j++) html += ``; - html += ''; - } - html += '
    Pred ${labels[j] ?? j}
    True ${labels[i] ?? i}${matrix[i][j]}
    '; - container.innerHTML = html; -} - -// View Result Details -async function viewResultDetails(filename) { - try { - const response = await fetch(`${API_BASE}/api/results/${filename}`); - const data = await response.json(); - - console.log('📊 Result details:', data); - - // Update charts with historical data - if (data.metrics) { - updateCharts(data.metrics); - } - - // Show details (could open a modal or expand in place) - alert(`Training Session Details:\n\nDataset: ${data.config.dataset}\nQubits: ${data.config.n_qubits}\nLayers: ${data.config.n_layers}\nEpochs: ${data.total_epochs}\nBest Accuracy: ${(data.best_val_acc * 100).toFixed(2)}%`); - - } catch (error) { - console.error('Error loading result details:', error); - } -} - -// Show Success Message -function showSuccess(message) { - console.log('✅', message); - // Could add a toast notification here -} - -// Show Error Message -function showError(message) { - console.error('❌', message); - alert('Error: ' + message); -} +// Quantum AI Training Dashboard - Frontend Logic + +let currentSessionId = null; +let statusUpdateInterval = null; +let lossChart = null; +let accuracyChart = null; +let circuitCanvas = null; +let circuitCtx = null; +let particleInterval = null; + +// API Base URL +const API_BASE = window.location.origin; + +// Initialize on page load +document.addEventListener('DOMContentLoaded', () => { + console.log('🚀 Quantum AI Dashboard initializing...'); + initializeCircuitCanvas(); + initializeCharts(); + loadDatasets(); + loadResults(); + setupEventListeners(); +}); + +// Setup Event Listeners +function setupEventListeners() { + document.getElementById('start-training-btn').addEventListener('click', startTraining); + document.getElementById('stop-training-btn').addEventListener('click', stopTraining); + document.getElementById('evaluate-btn').addEventListener('click', evaluateNow); + document.getElementById('export-metrics-btn').addEventListener('click', exportMetrics); + document.getElementById('dataset-select').addEventListener('change', updateDatasetInfo); + + // Add input validation + const numericInputs = ['n-qubits', 'n-layers', 'learning-rate', 'duration', 'batch-size', 'early-stopping', 'checkpoint-every', 'warmup-epochs', 'max-grad-norm']; + numericInputs.forEach(id => { + const input = document.getElementById(id); + input.addEventListener('input', () => validateInput(input)); + }); +} + +// Initialize Circuit Canvas +function initializeCircuitCanvas() { + circuitCanvas = document.getElementById('circuit-canvas'); + circuitCtx = circuitCanvas.getContext('2d'); + + // Set canvas size + const rect = circuitCanvas.getBoundingClientRect(); + circuitCanvas.width = rect.width; + circuitCanvas.height = 300; + + drawIdleCircuit(); +} + +// Draw idle circuit state +function drawIdleCircuit() { + if (!circuitCtx) return; + + const ctx = circuitCtx; + const width = circuitCanvas.width; + const height = circuitCanvas.height; + + ctx.clearRect(0, 0, width, height); + + // Draw gradient background + const gradient = ctx.createLinearGradient(0, 0, width, height); + gradient.addColorStop(0, 'rgba(99, 102, 241, 0.1)'); + gradient.addColorStop(1, 'rgba(139, 92, 246, 0.1)'); + ctx.fillStyle = gradient; + ctx.fillRect(0, 0, width, height); + + // Draw placeholder text + ctx.font = 'bold 18px -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto'; + ctx.fillStyle = '#6366f1'; + ctx.textAlign = 'center'; + ctx.fillText('⚛️ Quantum Circuit Visualization', width / 2, height / 2 - 15); + + ctx.font = '14px -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto'; + ctx.fillStyle = '#94a3b8'; + ctx.fillText('Start training to see live circuit diagram', width / 2, height / 2 + 15); + + // Update stats + document.getElementById('gate-count').textContent = '0'; + document.getElementById('circuit-depth').textContent = '0'; +} + +// Draw active quantum circuit +function drawQuantumCircuit(nQubits, nLayers, epoch) { + if (!circuitCtx) return; + + const ctx = circuitCtx; + const width = circuitCanvas.width; + const height = circuitCanvas.height; + + ctx.clearRect(0, 0, width, height); + + // Background + const gradient = ctx.createLinearGradient(0, 0, width, height); + gradient.addColorStop(0, 'rgba(99, 102, 241, 0.05)'); + gradient.addColorStop(1, 'rgba(139, 92, 246, 0.05)'); + ctx.fillStyle = gradient; + ctx.fillRect(0, 0, width, height); + + const padding = 60; + const qubitSpacing = (height - 2 * padding) / (nQubits - 1 || 1); + const layerWidth = (width - 2 * padding) / (nLayers + 2); + + // Draw qubit lines + ctx.strokeStyle = '#475569'; + ctx.lineWidth = 2; + for (let i = 0; i < nQubits; i++) { + const y = padding + i * qubitSpacing; + ctx.beginPath(); + ctx.moveTo(padding, y); + ctx.lineTo(width - padding, y); + ctx.stroke(); + + // Qubit label + ctx.fillStyle = '#cbd5e1'; + ctx.font = '14px monospace'; + ctx.textAlign = 'right'; + ctx.fillText(`|q${i}⟩`, padding - 10, y + 5); + } + + // Animate based on epoch + const pulsePhase = (epoch % 10) / 10; + + // Draw gates with animation + for (let layer = 0; layer < nLayers; layer++) { + const x = padding + (layer + 1) * layerWidth; + + for (let i = 0; i < nQubits; i++) { + const y = padding + i * qubitSpacing; + + // Rotation gate (pulsing) + const gateSize = 25 + Math.sin(pulsePhase * Math.PI * 2) * 3; + ctx.fillStyle = `rgba(239, 68, 68, ${0.7 + Math.sin(pulsePhase * Math.PI * 2) * 0.3})`; + ctx.beginPath(); + ctx.arc(x, y, gateSize / 2, 0, Math.PI * 2); + ctx.fill(); + + // Gate label + ctx.fillStyle = '#fff'; + ctx.font = 'bold 11px monospace'; + ctx.textAlign = 'center'; + ctx.fillText('Ry', x, y + 4); + } + + // CNOT gates (entanglement) + if (nQubits > 1) { + const cnotX = x + layerWidth / 2; + ctx.strokeStyle = `rgba(16, 185, 129, ${0.7 + Math.sin(pulsePhase * Math.PI * 2 + Math.PI) * 0.3})`; + ctx.lineWidth = 3; + + for (let i = 0; i < nQubits - 1; i++) { + const y1 = padding + i * qubitSpacing; + const y2 = padding + (i + 1) * qubitSpacing; + + // Vertical line + ctx.beginPath(); + ctx.moveTo(cnotX, y1); + ctx.lineTo(cnotX, y2); + ctx.stroke(); + + // Control dot + ctx.fillStyle = '#10b981'; + ctx.beginPath(); + ctx.arc(cnotX, y1, 5, 0, Math.PI * 2); + ctx.fill(); + + // Target circle + ctx.strokeStyle = '#10b981'; + ctx.lineWidth = 2; + ctx.beginPath(); + ctx.arc(cnotX, y2, 12, 0, Math.PI * 2); + ctx.stroke(); + ctx.beginPath(); + ctx.moveTo(cnotX - 8, y2); + ctx.lineTo(cnotX + 8, y2); + ctx.stroke(); + ctx.beginPath(); + ctx.moveTo(cnotX, y2 - 8); + ctx.lineTo(cnotX, y2 + 8); + ctx.stroke(); + } + } + } + + // Measurement symbols + const measX = width - padding - 20; + for (let i = 0; i < nQubits; i++) { + const y = padding + i * qubitSpacing; + + // Measurement box + ctx.strokeStyle = '#f59e0b'; + ctx.lineWidth = 2; + ctx.strokeRect(measX - 15, y - 15, 30, 30); + + // Meter symbol + ctx.beginPath(); + ctx.arc(measX, y + 5, 10, Math.PI, 0); + ctx.stroke(); + ctx.beginPath(); + ctx.moveTo(measX, y + 5); + ctx.lineTo(measX + 7, y - 2); + ctx.stroke(); + } + + // Epoch indicator with glow + ctx.save(); + ctx.shadowColor = '#6366f1'; + ctx.shadowBlur = 10; + ctx.fillStyle = '#6366f1'; + ctx.font = 'bold 16px monospace'; + ctx.textAlign = 'left'; + ctx.fillText(`EPOCH ${epoch}`, 15, 25); + ctx.restore(); + + // Update circuit stats + const gateCount = nQubits * nLayers + (nQubits - 1) * nLayers; + const circuitDepth = nLayers * 2; + document.getElementById('gate-count').textContent = gateCount; + document.getElementById('circuit-depth').textContent = circuitDepth; +} + +// Create particle effect +function createParticle(x, y) { + const particle = document.createElement('div'); + particle.className = 'particle'; + particle.style.left = x + 'px'; + particle.style.top = y + 'px'; + particle.style.background = `hsl(${Math.random() * 60 + 200}, 70%, 60%)`; + document.body.appendChild(particle); + + setTimeout(() => particle.remove(), 2000); +} + +// Start particle animation +function startParticleAnimation() { + if (particleInterval) return; + + particleInterval = setInterval(() => { + const canvas = document.getElementById('circuit-canvas'); + if (!canvas) return; + + const rect = canvas.getBoundingClientRect(); + const x = rect.left + Math.random() * rect.width; + const y = rect.top + Math.random() * rect.height; + createParticle(x, y); + }, 300); +} + +// Stop particle animation +function stopParticleAnimation() { + if (particleInterval) { + clearInterval(particleInterval); + particleInterval = null; + } +} + +// Initialize Charts +function initializeCharts() { + const chartOptions = { + responsive: true, + maintainAspectRatio: false, + plugins: { + legend: { + labels: { + color: '#cbd5e1' + } + } + }, + scales: { + x: { + ticks: { color: '#94a3b8' }, + grid: { color: '#334155' } + }, + y: { + ticks: { color: '#94a3b8' }, + grid: { color: '#334155' } + } + } + }; + + // Loss Chart + const lossCtx = document.getElementById('loss-chart').getContext('2d'); + lossChart = new Chart(lossCtx, { + type: 'line', + data: { + labels: [], + datasets: [ + { + label: 'Training Loss', + data: [], + borderColor: '#ef4444', + backgroundColor: 'rgba(239, 68, 68, 0.1)', + tension: 0.4 + }, + { + label: 'Validation Loss', + data: [], + borderColor: '#f59e0b', + backgroundColor: 'rgba(245, 158, 11, 0.1)', + tension: 0.4 + } + ] + }, + options: { + ...chartOptions, + plugins: { + ...chartOptions.plugins, + title: { + display: true, + text: 'Training & Validation Loss', + color: '#cbd5e1' + } + } + } + }); + + // Accuracy Chart + const accCtx = document.getElementById('accuracy-chart').getContext('2d'); + accuracyChart = new Chart(accCtx, { + type: 'line', + data: { + labels: [], + datasets: [ + { + label: 'Validation Accuracy', + data: [], + borderColor: '#10b981', + backgroundColor: 'rgba(16, 185, 129, 0.1)', + tension: 0.4, + fill: true + } + ] + }, + options: { + ...chartOptions, + plugins: { + ...chartOptions.plugins, + title: { + display: true, + text: 'Validation Accuracy', + color: '#cbd5e1' + } + }, + scales: { + ...chartOptions.scales, + y: { + ...chartOptions.scales.y, + min: 0, + max: 1, + ticks: { + ...chartOptions.scales.y.ticks, + callback: function(value) { + return (value * 100).toFixed(0) + '%'; + } + } + } + } + } + }); +} + +// Load Available Datasets +async function loadDatasets() { + try { + const response = await fetch(`${API_BASE}/api/datasets`); + const datasets = await response.json(); + + const select = document.getElementById('dataset-select'); + select.innerHTML = ''; + + datasets.forEach(dataset => { + const option = document.createElement('option'); + option.value = dataset.name; + option.textContent = `${dataset.name} (${dataset.features} features)`; + select.appendChild(option); + }); + + console.log(`✅ Loaded ${datasets.length} datasets`); + } catch (error) { + console.error('Error loading datasets:', error); + showError('Failed to load datasets'); + } +} + +// Update Dataset Info +function updateDatasetInfo() { + const select = document.getElementById('dataset-select'); + const infoDiv = document.getElementById('dataset-info'); + + if (select.value) { + infoDiv.textContent = `Selected: ${select.value}`; + } else { + infoDiv.textContent = ''; + } +} + +// Start Training +async function startTraining() { + const dataset = document.getElementById('dataset-select').value; + + if (!dataset) { + alert('Please select a dataset'); + return; + } + + const config = { + dataset: dataset, + n_qubits: parseInt(document.getElementById('n-qubits').value), + n_layers: parseInt(document.getElementById('n-layers').value), + learning_rate: parseFloat(document.getElementById('learning-rate').value), + duration_minutes: parseInt(document.getElementById('duration').value), + batch_size: parseInt(document.getElementById('batch-size').value), + optimizer: document.getElementById('optimizer-select').value, + early_stopping_patience: parseInt(document.getElementById('early-stopping').value), + checkpoint_every: parseInt(document.getElementById('checkpoint-every').value), + use_parameter_shift: document.getElementById('use-param-shift').checked, + use_warmup: document.getElementById('use-warmup').checked, + warmup_epochs: parseInt(document.getElementById('warmup-epochs').value), + use_lr_decay: document.getElementById('use-lr-decay').checked, + use_gradient_clipping: document.getElementById('use-grad-clip').checked, + max_grad_norm: parseFloat(document.getElementById('max-grad-norm').value) + }; + + console.log('🚀 Starting training with config:', config); + + try { + const response = await fetch(`${API_BASE}/api/train/start`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(config) + }); + + const result = await response.json(); + currentSessionId = result.session_id; + + // Update UI + document.getElementById('start-training-btn').disabled = true; + document.getElementById('stop-training-btn').disabled = false; + document.getElementById('status-idle').style.display = 'none'; + document.getElementById('status-training').style.display = 'block'; + document.getElementById('progress-container').style.display = 'block'; + document.getElementById('evaluation-section').style.display = 'none'; + document.getElementById('evaluate-btn').disabled = true; + + // Start visual effects + const nQubits = parseInt(document.getElementById('n-qubits').value); + const nLayers = parseInt(document.getElementById('n-layers').value); + drawQuantumCircuit(nQubits, nLayers, 0); + startParticleAnimation(); + + // Start polling for updates + startStatusPolling(); + + console.log(`✅ Training started: ${currentSessionId}`); + } catch (error) { + console.error('Error starting training:', error); + showError('Failed to start training'); + } +} + +// Stop Training +async function stopTraining() { + if (!currentSessionId) return; + + try { + await fetch(`${API_BASE}/api/train/stop/${currentSessionId}`, { + method: 'POST' + }); + + stopStatusPolling(); + console.log('⏹️ Training stopped'); + } catch (error) { + console.error('Error stopping training:', error); + } +} + +// Start Status Polling +function startStatusPolling() { + if (statusUpdateInterval) { + clearInterval(statusUpdateInterval); + } + + statusUpdateInterval = setInterval(updateTrainingStatus, 1000); +} + +// Stop Status Polling +function stopStatusPolling() { + if (statusUpdateInterval) { + clearInterval(statusUpdateInterval); + statusUpdateInterval = null; + } + + // Stop visual effects + stopParticleAnimation(); + drawIdleCircuit(); + + // Reset UI + document.getElementById('start-training-btn').disabled = false; + document.getElementById('stop-training-btn').disabled = true; + document.getElementById('status-idle').style.display = 'block'; + document.getElementById('status-training').style.display = 'none'; + document.getElementById('progress-container').style.display = 'none'; + document.getElementById('evaluate-btn').disabled = true; + document.getElementById('evaluation-section').style.display = 'none'; + + currentSessionId = null; + loadResults(); // Refresh results list +} + +// Update Training Status +async function updateTrainingStatus() { + if (!currentSessionId) return; + + try { + const response = await fetch(`${API_BASE}/api/train/status/${currentSessionId}`); + const status = await response.json(); + + // Update status text + document.getElementById('status-text').textContent = status.status; + document.getElementById('current-epoch').textContent = status.current_epoch; + document.getElementById('current-loss').textContent = status.current_loss.toFixed(4); + document.getElementById('best-val-acc').textContent = (status.best_val_acc * 100).toFixed(2) + '%'; + + // Update performance metrics + if (status.epochs_per_second > 0) { + document.getElementById('training-speed').textContent = status.epochs_per_second.toFixed(2) + ' ep/s'; + } + + if (status.eta_seconds) { + const etaMinutes = Math.floor(status.eta_seconds / 60); + const etaSeconds = Math.floor(status.eta_seconds % 60); + document.getElementById('eta-time').textContent = `${etaMinutes}:${etaSeconds.toString().padStart(2, '0')}`; + } else { + document.getElementById('eta-time').textContent = '-'; + } + + // Enable export button when training has data + if (status.metrics && status.metrics.epochs.length > 0) { + document.getElementById('export-metrics-btn').disabled = false; + } + + // Update elapsed time + if (status.elapsed_time) { + const minutes = Math.floor(status.elapsed_time / 60); + const seconds = Math.floor(status.elapsed_time % 60); + document.getElementById('elapsed-time').textContent = `${minutes}:${seconds.toString().padStart(2, '0')}`; + + // Update progress bar + const configDuration = parseInt(document.getElementById('duration').value) * 60; + const progress = Math.min((status.elapsed_time / configDuration) * 100, 100); + document.getElementById('progress-fill').style.width = progress + '%'; + document.getElementById('progress-text').textContent = progress.toFixed(0) + '% complete'; + } + + // Update visual accuracy bar + const accuracy = status.best_val_acc * 100; + document.getElementById('accuracy-bar').style.width = accuracy + '%'; + document.getElementById('accuracy-percent').textContent = accuracy.toFixed(1) + '%'; + + // Update circuit visualization + const nQubits = parseInt(document.getElementById('n-qubits').value); + const nLayers = parseInt(document.getElementById('n-layers').value); + drawQuantumCircuit(nQubits, nLayers, status.current_epoch); + + // Update charts + if (status.metrics && status.metrics.epochs.length > 0) { + updateCharts(status.metrics); + } + + // Check if completed + if (status.status === 'completed' || status.status === 'early_stopped' || status.status === 'error' || status.status === 'stopped') { + stopStatusPolling(); + + if (status.status === 'completed' || status.status === 'early_stopped') { + showSuccess('Training completed successfully!'); + // Enable evaluation + document.getElementById('evaluate-btn').disabled = false; + // Auto-evaluate if checkpoint exists + if (status.checkpoint_path) { + evaluateNow(); + } + } else if (status.status === 'error') { + showError('Training failed: ' + (status.error_message || 'Unknown error')); + } + } + + } catch (error) { + console.error('Error updating status:', error); + } +} + +// Update Charts with New Data +function updateCharts(metrics) { + // Loss Chart + lossChart.data.labels = metrics.epochs; + lossChart.data.datasets[0].data = metrics.train_loss; + lossChart.data.datasets[1].data = metrics.val_loss; + lossChart.update('none'); // Update without animation for smoothness + + // Accuracy Chart + accuracyChart.data.labels = metrics.epochs; + accuracyChart.data.datasets[0].data = metrics.val_accuracy; + accuracyChart.update('none'); +} + +// Load Training Results +async function loadResults() { + try { + const response = await fetch(`${API_BASE}/api/results`); + const results = await response.json(); + + const resultsDiv = document.getElementById('results-list'); + + if (results.length === 0) { + resultsDiv.innerHTML = '

    No training sessions yet. Start training to see results here.

    '; + return; + } + + resultsDiv.innerHTML = ''; + + results.forEach(result => { + const item = document.createElement('div'); + item.className = 'result-item'; + item.onclick = () => viewResultDetails(result.filename); + + item.innerHTML = ` +
    +
    ${result.dataset}
    +
    ${(result.best_acc * 100).toFixed(2)}% acc
    +
    +
    + 📅 ${result.timestamp.join('_')} + 🔄 ${result.epochs} epochs +
    + `; + + resultsDiv.appendChild(item); + }); + + console.log(`✅ Loaded ${results.length} training results`); + } catch (error) { + console.error('Error loading results:', error); + } +} + +// Input Validation +function validateInput(input) { + const errorDiv = document.getElementById('validation-error'); + const value = parseFloat(input.value); + let error = null; + + switch(input.id) { + case 'n-qubits': + if (value < 1 || value > 10) error = 'Qubits must be between 1 and 10'; + break; + case 'n-layers': + if (value < 1 || value > 20) error = 'Layers must be between 1 and 20'; + break; + case 'learning-rate': + if (value <= 0 || value > 1) error = 'Learning rate must be between 0 and 1'; + break; + case 'duration': + if (value < 1 || value > 120) error = 'Duration must be between 1 and 120 minutes'; + break; + case 'batch-size': + if (value < 8 || value > 128) error = 'Batch size must be between 8 and 128'; + break; + } + + if (error) { + input.style.borderColor = '#ef4444'; + errorDiv.textContent = error; + errorDiv.style.display = 'block'; + document.getElementById('start-training-btn').disabled = true; + } else { + input.style.borderColor = ''; + errorDiv.style.display = 'none'; + document.getElementById('start-training-btn').disabled = false; + } +} + +// Export Metrics +async function exportMetrics() { + if (!currentSessionId) return; + + try { + const response = await fetch(`${API_BASE}/api/export/metrics/${currentSessionId}`); + const blob = await response.blob(); + + // Create download link + const url = window.URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `metrics_${currentSessionId}.csv`; + document.body.appendChild(a); + a.click(); + a.remove(); + window.URL.revokeObjectURL(url); + + showSuccess('Metrics exported successfully!'); + } catch (error) { + console.error('Error exporting metrics:', error); + showError('Failed to export metrics'); + } +} + +// Evaluation +async function evaluateNow() { + if (!currentSessionId) return; + try { + const res = await fetch(`${API_BASE}/api/train/evaluate/${currentSessionId}`); + const data = await res.json(); + if (data.error) { + showError('Evaluation failed: ' + data.error); + return; + } + renderEvaluation(data); + showSuccess('Evaluation complete'); + } catch (e) { + console.error('Evaluation error', e); + showError('Failed to evaluate'); + } +} + +function renderEvaluation(result) { + const section = document.getElementById('evaluation-section'); + const metricsDiv = document.getElementById('eval-metrics'); + const cmDiv = document.getElementById('confusion-matrix'); + section.style.display = 'block'; + + const m = result.metrics; + metricsDiv.innerHTML = ` + Accuracy: ${(m.accuracy * 100).toFixed(2)}% · + Precision: ${(m.precision * 100).toFixed(2)}% · + Recall: ${(m.recall * 100).toFixed(2)}% · + F1: ${(m.f1 * 100).toFixed(2)}% + ${m.roc_auc !== null ? ` · ROC AUC: ${m.roc_auc.toFixed(3)}` : ''} + `; + renderConfusionMatrix(cmDiv, result.confusion_matrix, result.labels); +} + +function renderConfusionMatrix(container, matrix, labels) { + if (!matrix || !matrix.length) { + container.innerHTML = '

    No confusion matrix available.

    '; + return; + } + const n = matrix.length; + let html = ''; + for (let j = 0; j < n; j++) html += ``; + html += ''; + for (let i = 0; i < n; i++) { + html += ``; + for (let j = 0; j < n; j++) html += ``; + html += ''; + } + html += '
    Pred ${labels[j] ?? j}
    True ${labels[i] ?? i}${matrix[i][j]}
    '; + container.innerHTML = html; +} + +// View Result Details +async function viewResultDetails(filename) { + try { + const response = await fetch(`${API_BASE}/api/results/${filename}`); + const data = await response.json(); + + console.log('📊 Result details:', data); + + // Update charts with historical data + if (data.metrics) { + updateCharts(data.metrics); + } + + // Show details (could open a modal or expand in place) + alert(`Training Session Details:\n\nDataset: ${data.config.dataset}\nQubits: ${data.config.n_qubits}\nLayers: ${data.config.n_layers}\nEpochs: ${data.total_epochs}\nBest Accuracy: ${(data.best_val_acc * 100).toFixed(2)}%`); + + } catch (error) { + console.error('Error loading result details:', error); + } +} + +// Show Success Message +function showSuccess(message) { + console.log('✅', message); + // Could add a toast notification here +} + +// Show Error Message +function showError(message) { + console.error('❌', message); + alert('Error: ' + message); +} diff --git a/docs/quantum/index.html b/docs/quantum/index.html index f7851d42b..01d77c966 100644 --- a/docs/quantum/index.html +++ b/docs/quantum/index.html @@ -1,514 +1,514 @@ - - - - - - Quantum ML Dashboard — Aria Demo - - - - - - - - - - - - - - - - - - - -
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    Quantum AI Training Platform

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    Enterprise-grade quantum machine learning with real-time visualization

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    📊 Training Status

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    Ready to train. Configure your model and click "Start Training".

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    📈 Training Metrics

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    - Training Loss - Validation Acc -
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    📚 Training History

    -
    -
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    Iris — 4 qubits, 2 layers
    200 epochs · 4m 32s · Adam
    94.7%
    -
    Moons — 3 qubits, 3 layers
    150 epochs · 3m 18s · Momentum
    91.2%
    -
    Wine — 6 qubits, 2 layers
    300 epochs · 8m 45s · Adam
    87.3%
    -
    Circles — 2 qubits, 4 layers
    100 epochs · 1m 56s · SGD
    82.1%
    -
    -
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    - - -
    -

    Quantum AI Platform v2.0

    -

    Powered by PennyLane · Qiskit · Chart.js

    -
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    - - - - - - + + + + + + Quantum ML Dashboard — Aria Demo + + + + + + + + + + + + + + + + + + + +
    +
    +
    +
    + +
    + ← Back to Aria Home + + +
    +
    + +

    Quantum AI Training Platform

    +

    Enterprise-grade quantum machine learning with real-time visualization

    +
    + + System Online +
    +
    Demo Mode — Simulated Data
    +
    +
    + + +
    + +
    +
    +

    🎛️ Training Configuration

    +
    Advanced
    +
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    ⚛️ Quantum Circuit

    +
    + Gates: 0 + Depth: 0 +
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    Circuit visualization updates during training
    +
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    +

    📊 Training Status

    +
    + + LIVE +
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    💤
    +

    Ready to train. Configure your model and click "Start Training".

    +
    + + + + +
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    +

    📈 Training Metrics

    +
    + Training Loss + Validation Acc +
    +
    +
    +
    +
    + + +
    +
    +

    📚 Training History

    +
    +
    +
    Iris — 4 qubits, 2 layers
    200 epochs · 4m 32s · Adam
    94.7%
    +
    Moons — 3 qubits, 3 layers
    150 epochs · 3m 18s · Momentum
    91.2%
    +
    Wine — 6 qubits, 2 layers
    300 epochs · 8m 45s · Adam
    87.3%
    +
    Circles — 2 qubits, 4 layers
    100 epochs · 1m 56s · SGD
    82.1%
    +
    +
    +
    + + +
    +

    Quantum AI Platform v2.0

    +

    Powered by PennyLane · Qiskit · Chart.js

    +
    +
    + + + + + + diff --git a/docs/quantum/styles.css b/docs/quantum/styles.css index c5c20bb51..e239d5669 100644 --- a/docs/quantum/styles.css +++ b/docs/quantum/styles.css @@ -1,976 +1,976 @@ -:root { - --primary-color: #6366f1; - --primary-dark: #4f46e5; - --primary-light: #818cf8; - --secondary-color: #8b5cf6; - --accent-color: #ec4899; - --success-color: #10b981; - --danger-color: #ef4444; - --warning-color: #f59e0b; - --bg-dark: #0a0e1a; - --bg-darker: #050711; - --bg-card: rgba(30, 41, 59, 0.4); - --bg-card-hover: rgba(51, 65, 85, 0.6); - --text-primary: #f1f5f9; - --text-secondary: #cbd5e1; - --text-muted: #94a3b8; - --border-color: rgba(99, 102, 241, 0.2); - --glass-bg: rgba(30, 41, 59, 0.3); - --glass-border: rgba(148, 163, 184, 0.1); - --gradient-1: linear-gradient(135deg, #667eea 0%, #764ba2 100%); - --gradient-2: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); - --gradient-3: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); - --gradient-text: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%); - --shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.3); - --shadow-lg: 0 20px 40px -10px rgba(0, 0, 0, 0.5); - --shadow-xl: 0 25px 50px -12px rgba(0, 0, 0, 0.7); - --glow: 0 0 20px rgba(99, 102, 241, 0.4); - --glow-intense: 0 0 40px rgba(99, 102, 241, 0.6); -} - -* { - margin: 0; - padding: 0; - box-sizing: border-box; -} - -body { - font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Inter', sans-serif; - background: var(--bg-darker); - color: var(--text-primary); - line-height: 1.6; - min-height: 100vh; - position: relative; - overflow-x: hidden; -} - -/* Background Effects */ -.bg-particles { - position: fixed; - top: 0; - left: 0; - width: 100%; - height: 100%; - background-image: - radial-gradient(circle at 20% 50%, rgba(99, 102, 241, 0.05) 0%, transparent 50%), - radial-gradient(circle at 80% 80%, rgba(139, 92, 246, 0.05) 0%, transparent 50%); - pointer-events: none; - z-index: 0; -} - -.bg-gradient-orb { - position: fixed; - border-radius: 50%; - filter: blur(80px); - opacity: 0.3; - pointer-events: none; - z-index: 0; - animation: float 20s ease-in-out infinite; -} - -.orb-1 { - width: 500px; - height: 500px; - background: radial-gradient(circle, #6366f1, transparent); - top: -250px; - right: -250px; - animation-delay: 0s; -} - -.orb-2 { - width: 400px; - height: 400px; - background: radial-gradient(circle, #8b5cf6, transparent); - bottom: -200px; - left: -200px; - animation-delay: 5s; -} - -.orb-3 { - width: 300px; - height: 300px; - background: radial-gradient(circle, #ec4899, transparent); - top: 50%; - left: 50%; - animation-delay: 10s; -} - -@keyframes float { - 0%, 100% { transform: translate(0, 0) scale(1); } - 25% { transform: translate(50px, -50px) scale(1.1); } - 50% { transform: translate(-30px, 30px) scale(0.9); } - 75% { transform: translate(30px, 50px) scale(1.05); } -} - -.container { - max-width: 1400px; - margin: 0 auto; - padding: 20px; - position: relative; - z-index: 1; -} - -/* Header */ -.header { - background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); - backdrop-filter: blur(20px); - border: 1px solid var(--glass-border); - border-radius: 24px; - padding: 60px 40px; - margin-bottom: 40px; - box-shadow: var(--shadow-xl), var(--glow); - text-align: center; - position: relative; - overflow: hidden; -} - -.header::before { - content: ''; - position: absolute; - top: 0; - left: 0; - right: 0; - height: 1px; - background: linear-gradient(90deg, transparent, rgba(255,255,255,0.3), transparent); -} - -.logo-container { - margin-bottom: 30px; -} - -.quantum-logo { - position: relative; - width: 120px; - height: 120px; - margin: 0 auto; -} - -.logo-ring { - position: absolute; - border-radius: 50%; - border: 2px solid; - opacity: 0.6; -} - -.ring-1 { - width: 120px; - height: 120px; - border-color: #6366f1; - animation: rotate 10s linear infinite; -} - -.ring-2 { - width: 90px; - height: 90px; - top: 15px; - left: 15px; - border-color: #8b5cf6; - animation: rotate 15s linear infinite reverse; -} - -.ring-3 { - width: 60px; - height: 60px; - top: 30px; - left: 30px; - border-color: #ec4899; - animation: rotate 20s linear infinite; -} - -.logo-core { - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%); - font-size: 48px; - font-weight: 900; - background: var(--gradient-text); - -webkit-background-clip: text; - -webkit-text-fill-color: transparent; - background-clip: text; - animation: pulse-glow 2s ease-in-out infinite; -} - -@keyframes rotate { - from { transform: rotate(0deg); } - to { transform: rotate(360deg); } -} - -@keyframes pulse-glow { - 0%, 100% { filter: drop-shadow(0 0 10px rgba(99, 102, 241, 0.8)); } - 50% { filter: drop-shadow(0 0 20px rgba(139, 92, 246, 1)); } -} - -.gradient-text { - font-size: 3rem; - font-weight: 900; - background: var(--gradient-text); - -webkit-background-clip: text; - -webkit-text-fill-color: transparent; - background-clip: text; - margin-bottom: 15px; - letter-spacing: -0.02em; -} - -.subtitle { - font-size: 1.2rem; - opacity: 0.9; - color: var(--text-secondary); - font-weight: 300; - margin-bottom: 20px; -} - -.status-badge { - display: inline-flex; - align-items: center; - gap: 8px; - padding: 8px 20px; - background: rgba(16, 185, 129, 0.1); - border: 1px solid rgba(16, 185, 129, 0.3); - border-radius: 20px; - font-size: 0.9rem; - color: var(--success-color); -} - -.pulse-dot { - width: 8px; - height: 8px; - background: var(--success-color); - border-radius: 50%; - animation: pulse 2s ease-in-out infinite; -} - -@keyframes pulse { - 0%, 100% { opacity: 1; transform: scale(1); } - 50% { opacity: 0.5; transform: scale(1.2); } -} - -/* Main Content */ -.main-content { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); - gap: 24px; - margin-bottom: 40px; -} - -/* Glassmorphism Panel */ -.glass-panel { - background: var(--glass-bg); - backdrop-filter: blur(20px) saturate(180%); - border: 1px solid var(--glass-border); - position: relative; -} - -.glass-panel::before { - content: ''; - position: absolute; - top: 0; - left: 0; - right: 0; - height: 1px; - background: linear-gradient(90deg, transparent, rgba(255,255,255,0.1), transparent); -} - -.panel { - border-radius: 20px; - padding: 30px; - box-shadow: var(--shadow-lg); - transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); -} - -.panel:hover { - transform: translateY(-4px); - box-shadow: var(--shadow-xl), var(--glow); -} - -.panel-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 25px; - padding-bottom: 15px; - border-bottom: 1px solid var(--glass-border); -} - -.panel-header h2 { - font-size: 1.5rem; - font-weight: 700; - color: var(--text-primary); - display: flex; - align-items: center; - gap: 12px; -} - -.icon-gradient { - font-size: 1.8rem; - display: inline-block; - filter: drop-shadow(0 2px 8px rgba(99, 102, 241, 0.6)); -} - -.panel-badge { - padding: 4px 12px; - background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); - border: 1px solid var(--primary-color); - border-radius: 12px; - font-size: 0.75rem; - font-weight: 600; - text-transform: uppercase; - letter-spacing: 0.05em; - color: var(--primary-light); -} - -.live-indicator { - display: flex; - align-items: center; - gap: 8px; - padding: 6px 14px; - background: rgba(239, 68, 68, 0.1); - border: 1px solid var(--danger-color); - border-radius: 12px; - font-size: 0.75rem; - font-weight: 700; - color: var(--danger-color); - text-transform: uppercase; - letter-spacing: 0.05em; -} - -.live-dot { - width: 6px; - height: 6px; - background: var(--danger-color); - border-radius: 50%; - animation: pulse 1.5s ease-in-out infinite; - box-shadow: 0 0 10px var(--danger-color); -} - -/* Form Elements */ -.form-group { - margin-bottom: 20px; -} - -.form-row { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); - gap: 15px; -} - -label { - display: block; - margin-bottom: 8px; - font-weight: 500; - color: var(--text-secondary); - font-size: 0.9rem; -} - -.form-control { - width: 100%; - padding: 14px 18px; - background: rgba(15, 23, 42, 0.6); - border: 2px solid var(--border-color); - border-radius: 12px; - color: var(--text-primary); - font-size: 1rem; - transition: all 0.3s ease; - backdrop-filter: blur(10px); -} - -.form-control:focus { - outline: none; - border-color: var(--primary-color); - box-shadow: 0 0 0 4px rgba(99, 102, 241, 0.1), var(--glow); - background: rgba(15, 23, 42, 0.8); -} - -.form-control:hover { - border-color: var(--primary-light); -} - -.info-text { - margin-top: 8px; - font-size: 0.85rem; - color: var(--text-muted); -} - -/* Buttons */ -.button-group { - display: flex; - gap: 12px; - margin-top: 25px; -} - -.btn { - flex: 1; - padding: 16px 28px; - border: none; - border-radius: 12px; - font-size: 1.05rem; - font-weight: 600; - cursor: pointer; - transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); - display: flex; - align-items: center; - justify-content: center; - gap: 10px; - position: relative; - overflow: hidden; -} - -.btn::before { - content: ''; - position: absolute; - top: 50%; - left: 50%; - width: 0; - height: 0; - border-radius: 50%; - background: rgba(255, 255, 255, 0.2); - transform: translate(-50%, -50%); - transition: width 0.6s, height 0.6s; -} - -.btn:hover::before { - width: 300px; - height: 300px; -} - -.btn:disabled { - opacity: 0.5; - cursor: not-allowed; -} - -.btn-primary { - background: linear-gradient(135deg, var(--primary-color), var(--secondary-color)); - color: white; - box-shadow: 0 4px 15px rgba(99, 102, 241, 0.4); -} - -.btn-primary:hover:not(:disabled) { - transform: translateY(-2px); - box-shadow: 0 8px 25px rgba(99, 102, 241, 0.6); -} - -.btn-primary:active:not(:disabled) { - transform: translateY(0); -} - -.btn-danger { - background: linear-gradient(135deg, var(--danger-color), #dc2626); - color: white; - box-shadow: 0 4px 15px rgba(239, 68, 68, 0.4); -} - -.btn-danger:hover:not(:disabled) { - transform: translateY(-2px); - box-shadow: 0 8px 25px rgba(239, 68, 68, 0.6); -} - -.refresh-btn { - padding: 6px 16px; - background: rgba(99, 102, 241, 0.1); - border: 1px solid var(--primary-color); - border-radius: 8px; - color: var(--primary-color); - font-size: 0.85rem; - cursor: pointer; - transition: all 0.2s; -} - -.refresh-btn:hover { - background: rgba(99, 102, 241, 0.2); - transform: scale(1.05); -} - -.btn-icon { - font-size: 1.2rem; -} - -/* Status Panel */ -.status-message { - text-align: center; - padding: 30px; -} - -.status-icon { - font-size: 4rem; - margin-bottom: 15px; -} - -.status-icon.spinning { - animation: spin 2s linear infinite; -} - -@keyframes spin { - from { transform: rotate(0deg); } - to { transform: rotate(360deg); } -} - -.status-details { - display: grid; - gap: 12px; - margin-top: 20px; -} - -.status-item { - display: flex; - justify-content: space-between; - padding: 12px 16px; - background: var(--bg-dark); - border-radius: 8px; - border-left: 3px solid var(--primary-color); -} - -.status-item .label { - font-weight: 500; - color: var(--text-secondary); -} - -.status-item .value { - font-weight: 600; - color: var(--text-primary); -} - -.status-item.metric-large { - background: linear-gradient(135deg, rgba(99, 102, 241, 0.1), rgba(139, 92, 246, 0.1)); - border-left: 3px solid var(--secondary-color); - padding: 20px; -} - -.accuracy-display { - position: relative; - width: 100%; - height: 60px; - background: var(--bg-dark); - border-radius: 12px; - overflow: hidden; - margin-top: 10px; - box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.3); -} - -.accuracy-bar { - position: absolute; - top: 0; - left: 0; - height: 100%; - width: 0%; - background: linear-gradient(90deg, #10b981, #34d399); - transition: width 0.5s ease; - box-shadow: 0 0 20px rgba(16, 185, 129, 0.5); -} - -.accuracy-percent { - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%); - font-size: 1.8rem; - font-weight: 700; - color: var(--text-primary); - text-shadow: 0 2px 4px rgba(0, 0, 0, 0.5); - z-index: 1; -} - -/* Progress Bar */ -.progress-bar-container { - margin-top: 20px; -} - -.progress-bar { - height: 8px; - background: var(--bg-dark); - border-radius: 4px; - overflow: hidden; -} - -.progress-fill { - height: 100%; - background: var(--gradient-1); - transition: width 0.3s ease; - width: 0%; -} - -.progress-text { - text-align: center; - margin-top: 8px; - font-size: 0.9rem; - color: var(--text-muted); -} - -/* Quantum Circuit Visualization */ -.circuit-panel { - grid-column: 1 / -1; -} - -.canvas-container { - position: relative; - background: rgba(10, 14, 26, 0.6); - border-radius: 16px; - padding: 20px; - border: 2px solid var(--border-color); - box-shadow: inset 0 2px 10px rgba(0, 0, 0, 0.3); -} - -#circuit-canvas { - width: 100%; - height: 300px; - border-radius: 12px; - box-shadow: 0 0 30px rgba(99, 102, 241, 0.2); -} - -.circuit-stats { - display: flex; - gap: 20px; -} - -.stat-item { - display: flex; - align-items: center; - gap: 6px; - font-size: 0.9rem; -} - -.stat-label { - color: var(--text-muted); - font-weight: 500; -} - -.stat-item span:last-child { - color: var(--primary-light); - font-weight: 700; - font-family: 'JetBrains Mono', monospace; -} - -/* Particle Effects */ -@keyframes particle-float { - 0% { - transform: translateY(0) translateX(0) rotate(0deg); - opacity: 1; - } - 100% { - transform: translateY(-100px) translateX(50px) rotate(360deg); - opacity: 0; - } -} - -.particle { - position: absolute; - width: 4px; - height: 4px; - background: var(--primary-color); - border-radius: 50%; - animation: particle-float 2s ease-out forwards; -} - -/* Charts */ -/* Charts */ -.charts-panel { - grid-column: 1 / -1; -} - -.metrics-legend { - display: flex; - gap: 16px; -} - -.legend-item { - display: flex; - align-items: center; - gap: 6px; - font-size: 0.85rem; - color: var(--text-secondary); -} - -.legend-color { - width: 12px; - height: 12px; - border-radius: 3px; - box-shadow: 0 0 8px currentColor; -} - -.chart-container { - position: relative; - height: 320px; - margin-bottom: 30px; - padding: 25px; - background: rgba(10, 14, 26, 0.4); - border-radius: 16px; - border: 1px solid var(--glass-border); - backdrop-filter: blur(10px); -} - -.chart-container:last-child { - margin-bottom: 0; -} - -/* Results List */ -.results-panel { - grid-column: 1 / -1; -} - -.results-list { - display: grid; - gap: 12px; -} - -.result-item { - background: var(--bg-dark); - padding: 16px; - border-radius: 8px; - border-left: 4px solid var(--primary-color); - cursor: pointer; - transition: all 0.2s; -} - -.result-item:hover { - background: var(--bg-card-hover); - transform: translateX(4px); -} - -.result-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 8px; -} - -.result-title { - font-weight: 600; - font-size: 1.1rem; -} - -.result-badge { - padding: 4px 12px; - border-radius: 12px; - font-size: 0.85rem; - font-weight: 600; -} - -.result-badge.success { - background: rgba(16, 185, 129, 0.2); - color: var(--success-color); -} - -.result-meta { - display: flex; - gap: 20px; - font-size: 0.9rem; - color: var(--text-muted); -} - -.result-meta span { - display: flex; - align-items: center; - gap: 6px; -} - -/* Footer */ -.footer { - text-align: center; - padding: 40px 20px; - margin-top: 60px; - border-top: 1px solid var(--glass-border); - background: var(--glass-bg); - backdrop-filter: blur(20px); -} - -.footer-content { - display: flex; - flex-direction: column; - gap: 10px; -} - -.footer-brand { - font-size: 1.1rem; - font-weight: 600; - color: var(--text-primary); -} - -.version { - display: inline-block; - padding: 2px 8px; - background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); - border: 1px solid var(--primary-color); - border-radius: 6px; - font-size: 0.75rem; - margin-left: 8px; -} - -.footer-tech { - color: var(--text-muted); - font-size: 0.9rem; -} - -.footer-copyright { - color: var(--text-muted); - font-size: 0.85rem; -} - -/* Responsive */ -@media (max-width: 768px) { - .main-content { - grid-template-columns: 1fr; - } - - .header h1 { - font-size: 1.8rem; - } - - .button-group { - flex-direction: column; - } - - .chart-container { - height: 250px; - } -} - -/* Animations */ -@keyframes fadeIn { - from { - opacity: 0; - transform: translateY(10px); - } - to { - opacity: 1; - transform: translateY(0); - } -} - -.panel { - animation: fadeIn 0.3s ease; -} - -/* Error and Success Messages */ -.error-message { - background: rgba(239, 68, 68, 0.2); - border: 1px solid var(--danger-color); - border-radius: 8px; - padding: 12px; - margin-top: 12px; - color: var(--danger-color); - font-size: 0.9rem; - display: flex; - align-items: center; - gap: 8px; -} - -.error-message::before { - content: '⚠️'; -} - -.success-message { - background: rgba(16, 185, 129, 0.2); - border: 1px solid var(--success-color); - border-radius: 8px; - padding: 12px; - margin-top: 12px; - color: var(--success-color); - font-size: 0.9rem; - display: flex; - align-items: center; - gap: 8px; -} - -.success-message::before { - content: '✅'; -} - -/* Input validation states */ -.form-control.invalid { - border-color: var(--danger-color) !important; - box-shadow: 0 0 0 3px rgba(239, 68, 68, 0.1); -} - -.form-control:focus:invalid { - box-shadow: 0 0 0 3px rgba(239, 68, 68, 0.2); -} - -/* Secondary button styles */ -.btn-secondary { - background: linear-gradient(135deg, #475569 0%, #334155 100%); - color: var(--text-primary); -} - -.btn-secondary:hover { - background: linear-gradient(135deg, #64748b 0%, #475569 100%); - transform: translateY(-2px); - box-shadow: 0 8px 20px rgba(71, 85, 105, 0.4); -} - -.btn-secondary:disabled { - background: linear-gradient(135deg, #334155 0%, #1e293b 100%); - opacity: 0.4; -} - -/* Advanced settings */ -details.advanced-settings { - margin-top: 12px; - background: rgba(15, 23, 42, 0.3); - border: 1px solid rgba(148, 163, 184, 0.15); - border-radius: 10px; - padding: 10px 12px; -} -details.advanced-settings > summary { - cursor: pointer; - color: var(--text-primary); - font-weight: 600; -} -details.advanced-settings[open] { - backdrop-filter: blur(8px); -} -.advanced-grid { - display: grid; - grid-template-columns: repeat(2, minmax(0, 1fr)); - gap: 12px; - margin-top: 10px; -} -.advanced-grid .toggles { - display: flex; - flex-direction: column; - gap: 8px; -} -.checkbox { - display: flex; - align-items: center; - gap: 8px; - color: var(--text-secondary); -} - -/* Evaluation section */ -.evaluation-section { - margin-top: 16px; - padding-top: 10px; - border-top: 1px dashed rgba(148, 163, 184, 0.2); -} -.panel-header.small h3 { - font-size: 1rem; - margin: 0 0 6px 0; -} -.eval-metrics { - margin-bottom: 10px; -} -.cm-table { - width: 100%; - border-collapse: collapse; - background: rgba(15, 23, 42, 0.3); - border: 1px solid rgba(148, 163, 184, 0.15); - border-radius: 8px; - overflow: hidden; -} -.cm-table th, .cm-table td { - border: 1px solid rgba(148, 163, 184, 0.15); - padding: 8px 10px; - text-align: center; -} -.cm-table th { - background: rgba(99, 102, 241, 0.1); - color: var(--text-primary); -} -.cm-table td { - color: var(--text-secondary); -} - -@media (max-width: 768px) { - .advanced-grid { - grid-template-columns: 1fr; - } -} +:root { + --primary-color: #6366f1; + --primary-dark: #4f46e5; + --primary-light: #818cf8; + --secondary-color: #8b5cf6; + --accent-color: #ec4899; + --success-color: #10b981; + --danger-color: #ef4444; + --warning-color: #f59e0b; + --bg-dark: #0a0e1a; + --bg-darker: #050711; + --bg-card: rgba(30, 41, 59, 0.4); + --bg-card-hover: rgba(51, 65, 85, 0.6); + --text-primary: #f1f5f9; + --text-secondary: #cbd5e1; + --text-muted: #94a3b8; + --border-color: rgba(99, 102, 241, 0.2); + --glass-bg: rgba(30, 41, 59, 0.3); + --glass-border: rgba(148, 163, 184, 0.1); + --gradient-1: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + --gradient-2: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); + --gradient-3: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); + --gradient-text: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%); + --shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.3); + --shadow-lg: 0 20px 40px -10px rgba(0, 0, 0, 0.5); + --shadow-xl: 0 25px 50px -12px rgba(0, 0, 0, 0.7); + --glow: 0 0 20px rgba(99, 102, 241, 0.4); + --glow-intense: 0 0 40px rgba(99, 102, 241, 0.6); +} + +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Inter', sans-serif; + background: var(--bg-darker); + color: var(--text-primary); + line-height: 1.6; + min-height: 100vh; + position: relative; + overflow-x: hidden; +} + +/* Background Effects */ +.bg-particles { + position: fixed; + top: 0; + left: 0; + width: 100%; + height: 100%; + background-image: + radial-gradient(circle at 20% 50%, rgba(99, 102, 241, 0.05) 0%, transparent 50%), + radial-gradient(circle at 80% 80%, rgba(139, 92, 246, 0.05) 0%, transparent 50%); + pointer-events: none; + z-index: 0; +} + +.bg-gradient-orb { + position: fixed; + border-radius: 50%; + filter: blur(80px); + opacity: 0.3; + pointer-events: none; + z-index: 0; + animation: float 20s ease-in-out infinite; +} + +.orb-1 { + width: 500px; + height: 500px; + background: radial-gradient(circle, #6366f1, transparent); + top: -250px; + right: -250px; + animation-delay: 0s; +} + +.orb-2 { + width: 400px; + height: 400px; + background: radial-gradient(circle, #8b5cf6, transparent); + bottom: -200px; + left: -200px; + animation-delay: 5s; +} + +.orb-3 { + width: 300px; + height: 300px; + background: radial-gradient(circle, #ec4899, transparent); + top: 50%; + left: 50%; + animation-delay: 10s; +} + +@keyframes float { + 0%, 100% { transform: translate(0, 0) scale(1); } + 25% { transform: translate(50px, -50px) scale(1.1); } + 50% { transform: translate(-30px, 30px) scale(0.9); } + 75% { transform: translate(30px, 50px) scale(1.05); } +} + +.container { + max-width: 1400px; + margin: 0 auto; + padding: 20px; + position: relative; + z-index: 1; +} + +/* Header */ +.header { + background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); + backdrop-filter: blur(20px); + border: 1px solid var(--glass-border); + border-radius: 24px; + padding: 60px 40px; + margin-bottom: 40px; + box-shadow: var(--shadow-xl), var(--glow); + text-align: center; + position: relative; + overflow: hidden; +} + +.header::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + height: 1px; + background: linear-gradient(90deg, transparent, rgba(255,255,255,0.3), transparent); +} + +.logo-container { + margin-bottom: 30px; +} + +.quantum-logo { + position: relative; + width: 120px; + height: 120px; + margin: 0 auto; +} + +.logo-ring { + position: absolute; + border-radius: 50%; + border: 2px solid; + opacity: 0.6; +} + +.ring-1 { + width: 120px; + height: 120px; + border-color: #6366f1; + animation: rotate 10s linear infinite; +} + +.ring-2 { + width: 90px; + height: 90px; + top: 15px; + left: 15px; + border-color: #8b5cf6; + animation: rotate 15s linear infinite reverse; +} + +.ring-3 { + width: 60px; + height: 60px; + top: 30px; + left: 30px; + border-color: #ec4899; + animation: rotate 20s linear infinite; +} + +.logo-core { + position: absolute; + top: 50%; + left: 50%; + transform: translate(-50%, -50%); + font-size: 48px; + font-weight: 900; + background: var(--gradient-text); + -webkit-background-clip: text; + -webkit-text-fill-color: transparent; + background-clip: text; + animation: pulse-glow 2s ease-in-out infinite; +} + +@keyframes rotate { + from { transform: rotate(0deg); } + to { transform: rotate(360deg); } +} + +@keyframes pulse-glow { + 0%, 100% { filter: drop-shadow(0 0 10px rgba(99, 102, 241, 0.8)); } + 50% { filter: drop-shadow(0 0 20px rgba(139, 92, 246, 1)); } +} + +.gradient-text { + font-size: 3rem; + font-weight: 900; + background: var(--gradient-text); + -webkit-background-clip: text; + -webkit-text-fill-color: transparent; + background-clip: text; + margin-bottom: 15px; + letter-spacing: -0.02em; +} + +.subtitle { + font-size: 1.2rem; + opacity: 0.9; + color: var(--text-secondary); + font-weight: 300; + margin-bottom: 20px; +} + +.status-badge { + display: inline-flex; + align-items: center; + gap: 8px; + padding: 8px 20px; + background: rgba(16, 185, 129, 0.1); + border: 1px solid rgba(16, 185, 129, 0.3); + border-radius: 20px; + font-size: 0.9rem; + color: var(--success-color); +} + +.pulse-dot { + width: 8px; + height: 8px; + background: var(--success-color); + border-radius: 50%; + animation: pulse 2s ease-in-out infinite; +} + +@keyframes pulse { + 0%, 100% { opacity: 1; transform: scale(1); } + 50% { opacity: 0.5; transform: scale(1.2); } +} + +/* Main Content */ +.main-content { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); + gap: 24px; + margin-bottom: 40px; +} + +/* Glassmorphism Panel */ +.glass-panel { + background: var(--glass-bg); + backdrop-filter: blur(20px) saturate(180%); + border: 1px solid var(--glass-border); + position: relative; +} + +.glass-panel::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + height: 1px; + background: linear-gradient(90deg, transparent, rgba(255,255,255,0.1), transparent); +} + +.panel { + border-radius: 20px; + padding: 30px; + box-shadow: var(--shadow-lg); + transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); +} + +.panel:hover { + transform: translateY(-4px); + box-shadow: var(--shadow-xl), var(--glow); +} + +.panel-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 25px; + padding-bottom: 15px; + border-bottom: 1px solid var(--glass-border); +} + +.panel-header h2 { + font-size: 1.5rem; + font-weight: 700; + color: var(--text-primary); + display: flex; + align-items: center; + gap: 12px; +} + +.icon-gradient { + font-size: 1.8rem; + display: inline-block; + filter: drop-shadow(0 2px 8px rgba(99, 102, 241, 0.6)); +} + +.panel-badge { + padding: 4px 12px; + background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); + border: 1px solid var(--primary-color); + border-radius: 12px; + font-size: 0.75rem; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.05em; + color: var(--primary-light); +} + +.live-indicator { + display: flex; + align-items: center; + gap: 8px; + padding: 6px 14px; + background: rgba(239, 68, 68, 0.1); + border: 1px solid var(--danger-color); + border-radius: 12px; + font-size: 0.75rem; + font-weight: 700; + color: var(--danger-color); + text-transform: uppercase; + letter-spacing: 0.05em; +} + +.live-dot { + width: 6px; + height: 6px; + background: var(--danger-color); + border-radius: 50%; + animation: pulse 1.5s ease-in-out infinite; + box-shadow: 0 0 10px var(--danger-color); +} + +/* Form Elements */ +.form-group { + margin-bottom: 20px; +} + +.form-row { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); + gap: 15px; +} + +label { + display: block; + margin-bottom: 8px; + font-weight: 500; + color: var(--text-secondary); + font-size: 0.9rem; +} + +.form-control { + width: 100%; + padding: 14px 18px; + background: rgba(15, 23, 42, 0.6); + border: 2px solid var(--border-color); + border-radius: 12px; + color: var(--text-primary); + font-size: 1rem; + transition: all 0.3s ease; + backdrop-filter: blur(10px); +} + +.form-control:focus { + outline: none; + border-color: var(--primary-color); + box-shadow: 0 0 0 4px rgba(99, 102, 241, 0.1), var(--glow); + background: rgba(15, 23, 42, 0.8); +} + +.form-control:hover { + border-color: var(--primary-light); +} + +.info-text { + margin-top: 8px; + font-size: 0.85rem; + color: var(--text-muted); +} + +/* Buttons */ +.button-group { + display: flex; + gap: 12px; + margin-top: 25px; +} + +.btn { + flex: 1; + padding: 16px 28px; + border: none; + border-radius: 12px; + font-size: 1.05rem; + font-weight: 600; + cursor: pointer; + transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); + display: flex; + align-items: center; + justify-content: center; + gap: 10px; + position: relative; + overflow: hidden; +} + +.btn::before { + content: ''; + position: absolute; + top: 50%; + left: 50%; + width: 0; + height: 0; + border-radius: 50%; + background: rgba(255, 255, 255, 0.2); + transform: translate(-50%, -50%); + transition: width 0.6s, height 0.6s; +} + +.btn:hover::before { + width: 300px; + height: 300px; +} + +.btn:disabled { + opacity: 0.5; + cursor: not-allowed; +} + +.btn-primary { + background: linear-gradient(135deg, var(--primary-color), var(--secondary-color)); + color: white; + box-shadow: 0 4px 15px rgba(99, 102, 241, 0.4); +} + +.btn-primary:hover:not(:disabled) { + transform: translateY(-2px); + box-shadow: 0 8px 25px rgba(99, 102, 241, 0.6); +} + +.btn-primary:active:not(:disabled) { + transform: translateY(0); +} + +.btn-danger { + background: linear-gradient(135deg, var(--danger-color), #dc2626); + color: white; + box-shadow: 0 4px 15px rgba(239, 68, 68, 0.4); +} + +.btn-danger:hover:not(:disabled) { + transform: translateY(-2px); + box-shadow: 0 8px 25px rgba(239, 68, 68, 0.6); +} + +.refresh-btn { + padding: 6px 16px; + background: rgba(99, 102, 241, 0.1); + border: 1px solid var(--primary-color); + border-radius: 8px; + color: var(--primary-color); + font-size: 0.85rem; + cursor: pointer; + transition: all 0.2s; +} + +.refresh-btn:hover { + background: rgba(99, 102, 241, 0.2); + transform: scale(1.05); +} + +.btn-icon { + font-size: 1.2rem; +} + +/* Status Panel */ +.status-message { + text-align: center; + padding: 30px; +} + +.status-icon { + font-size: 4rem; + margin-bottom: 15px; +} + +.status-icon.spinning { + animation: spin 2s linear infinite; +} + +@keyframes spin { + from { transform: rotate(0deg); } + to { transform: rotate(360deg); } +} + +.status-details { + display: grid; + gap: 12px; + margin-top: 20px; +} + +.status-item { + display: flex; + justify-content: space-between; + padding: 12px 16px; + background: var(--bg-dark); + border-radius: 8px; + border-left: 3px solid var(--primary-color); +} + +.status-item .label { + font-weight: 500; + color: var(--text-secondary); +} + +.status-item .value { + font-weight: 600; + color: var(--text-primary); +} + +.status-item.metric-large { + background: linear-gradient(135deg, rgba(99, 102, 241, 0.1), rgba(139, 92, 246, 0.1)); + border-left: 3px solid var(--secondary-color); + padding: 20px; +} + +.accuracy-display { + position: relative; + width: 100%; + height: 60px; + background: var(--bg-dark); + border-radius: 12px; + overflow: hidden; + margin-top: 10px; + box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.3); +} + +.accuracy-bar { + position: absolute; + top: 0; + left: 0; + height: 100%; + width: 0%; + background: linear-gradient(90deg, #10b981, #34d399); + transition: width 0.5s ease; + box-shadow: 0 0 20px rgba(16, 185, 129, 0.5); +} + +.accuracy-percent { + position: absolute; + top: 50%; + left: 50%; + transform: translate(-50%, -50%); + font-size: 1.8rem; + font-weight: 700; + color: var(--text-primary); + text-shadow: 0 2px 4px rgba(0, 0, 0, 0.5); + z-index: 1; +} + +/* Progress Bar */ +.progress-bar-container { + margin-top: 20px; +} + +.progress-bar { + height: 8px; + background: var(--bg-dark); + border-radius: 4px; + overflow: hidden; +} + +.progress-fill { + height: 100%; + background: var(--gradient-1); + transition: width 0.3s ease; + width: 0%; +} + +.progress-text { + text-align: center; + margin-top: 8px; + font-size: 0.9rem; + color: var(--text-muted); +} + +/* Quantum Circuit Visualization */ +.circuit-panel { + grid-column: 1 / -1; +} + +.canvas-container { + position: relative; + background: rgba(10, 14, 26, 0.6); + border-radius: 16px; + padding: 20px; + border: 2px solid var(--border-color); + box-shadow: inset 0 2px 10px rgba(0, 0, 0, 0.3); +} + +#circuit-canvas { + width: 100%; + height: 300px; + border-radius: 12px; + box-shadow: 0 0 30px rgba(99, 102, 241, 0.2); +} + +.circuit-stats { + display: flex; + gap: 20px; +} + +.stat-item { + display: flex; + align-items: center; + gap: 6px; + font-size: 0.9rem; +} + +.stat-label { + color: var(--text-muted); + font-weight: 500; +} + +.stat-item span:last-child { + color: var(--primary-light); + font-weight: 700; + font-family: 'JetBrains Mono', monospace; +} + +/* Particle Effects */ +@keyframes particle-float { + 0% { + transform: translateY(0) translateX(0) rotate(0deg); + opacity: 1; + } + 100% { + transform: translateY(-100px) translateX(50px) rotate(360deg); + opacity: 0; + } +} + +.particle { + position: absolute; + width: 4px; + height: 4px; + background: var(--primary-color); + border-radius: 50%; + animation: particle-float 2s ease-out forwards; +} + +/* Charts */ +/* Charts */ +.charts-panel { + grid-column: 1 / -1; +} + +.metrics-legend { + display: flex; + gap: 16px; +} + +.legend-item { + display: flex; + align-items: center; + gap: 6px; + font-size: 0.85rem; + color: var(--text-secondary); +} + +.legend-color { + width: 12px; + height: 12px; + border-radius: 3px; + box-shadow: 0 0 8px currentColor; +} + +.chart-container { + position: relative; + height: 320px; + margin-bottom: 30px; + padding: 25px; + background: rgba(10, 14, 26, 0.4); + border-radius: 16px; + border: 1px solid var(--glass-border); + backdrop-filter: blur(10px); +} + +.chart-container:last-child { + margin-bottom: 0; +} + +/* Results List */ +.results-panel { + grid-column: 1 / -1; +} + +.results-list { + display: grid; + gap: 12px; +} + +.result-item { + background: var(--bg-dark); + padding: 16px; + border-radius: 8px; + border-left: 4px solid var(--primary-color); + cursor: pointer; + transition: all 0.2s; +} + +.result-item:hover { + background: var(--bg-card-hover); + transform: translateX(4px); +} + +.result-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 8px; +} + +.result-title { + font-weight: 600; + font-size: 1.1rem; +} + +.result-badge { + padding: 4px 12px; + border-radius: 12px; + font-size: 0.85rem; + font-weight: 600; +} + +.result-badge.success { + background: rgba(16, 185, 129, 0.2); + color: var(--success-color); +} + +.result-meta { + display: flex; + gap: 20px; + font-size: 0.9rem; + color: var(--text-muted); +} + +.result-meta span { + display: flex; + align-items: center; + gap: 6px; +} + +/* Footer */ +.footer { + text-align: center; + padding: 40px 20px; + margin-top: 60px; + border-top: 1px solid var(--glass-border); + background: var(--glass-bg); + backdrop-filter: blur(20px); +} + +.footer-content { + display: flex; + flex-direction: column; + gap: 10px; +} + +.footer-brand { + font-size: 1.1rem; + font-weight: 600; + color: var(--text-primary); +} + +.version { + display: inline-block; + padding: 2px 8px; + background: linear-gradient(135deg, rgba(99, 102, 241, 0.2), rgba(139, 92, 246, 0.2)); + border: 1px solid var(--primary-color); + border-radius: 6px; + font-size: 0.75rem; + margin-left: 8px; +} + +.footer-tech { + color: var(--text-muted); + font-size: 0.9rem; +} + +.footer-copyright { + color: var(--text-muted); + font-size: 0.85rem; +} + +/* Responsive */ +@media (max-width: 768px) { + .main-content { + grid-template-columns: 1fr; + } + + .header h1 { + font-size: 1.8rem; + } + + .button-group { + flex-direction: column; + } + + .chart-container { + height: 250px; + } +} + +/* Animations */ +@keyframes fadeIn { + from { + opacity: 0; + transform: translateY(10px); + } + to { + opacity: 1; + transform: translateY(0); + } +} + +.panel { + animation: fadeIn 0.3s ease; +} + +/* Error and Success Messages */ +.error-message { + background: rgba(239, 68, 68, 0.2); + border: 1px solid var(--danger-color); + border-radius: 8px; + padding: 12px; + margin-top: 12px; + color: var(--danger-color); + font-size: 0.9rem; + display: flex; + align-items: center; + gap: 8px; +} + +.error-message::before { + content: '⚠️'; +} + +.success-message { + background: rgba(16, 185, 129, 0.2); + border: 1px solid var(--success-color); + border-radius: 8px; + padding: 12px; + margin-top: 12px; + color: var(--success-color); + font-size: 0.9rem; + display: flex; + align-items: center; + gap: 8px; +} + +.success-message::before { + content: '✅'; +} + +/* Input validation states */ +.form-control.invalid { + border-color: var(--danger-color) !important; + box-shadow: 0 0 0 3px rgba(239, 68, 68, 0.1); +} + +.form-control:focus:invalid { + box-shadow: 0 0 0 3px rgba(239, 68, 68, 0.2); +} + +/* Secondary button styles */ +.btn-secondary { + background: linear-gradient(135deg, #475569 0%, #334155 100%); + color: var(--text-primary); +} + +.btn-secondary:hover { + background: linear-gradient(135deg, #64748b 0%, #475569 100%); + transform: translateY(-2px); + box-shadow: 0 8px 20px rgba(71, 85, 105, 0.4); +} + +.btn-secondary:disabled { + background: linear-gradient(135deg, #334155 0%, #1e293b 100%); + opacity: 0.4; +} + +/* Advanced settings */ +details.advanced-settings { + margin-top: 12px; + background: rgba(15, 23, 42, 0.3); + border: 1px solid rgba(148, 163, 184, 0.15); + border-radius: 10px; + padding: 10px 12px; +} +details.advanced-settings > summary { + cursor: pointer; + color: var(--text-primary); + font-weight: 600; +} +details.advanced-settings[open] { + backdrop-filter: blur(8px); +} +.advanced-grid { + display: grid; + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 12px; + margin-top: 10px; +} +.advanced-grid .toggles { + display: flex; + flex-direction: column; + gap: 8px; +} +.checkbox { + display: flex; + align-items: center; + gap: 8px; + color: var(--text-secondary); +} + +/* Evaluation section */ +.evaluation-section { + margin-top: 16px; + padding-top: 10px; + border-top: 1px dashed rgba(148, 163, 184, 0.2); +} +.panel-header.small h3 { + font-size: 1rem; + margin: 0 0 6px 0; +} +.eval-metrics { + margin-bottom: 10px; +} +.cm-table { + width: 100%; + border-collapse: collapse; + background: rgba(15, 23, 42, 0.3); + border: 1px solid rgba(148, 163, 184, 0.15); + border-radius: 8px; + overflow: hidden; +} +.cm-table th, .cm-table td { + border: 1px solid rgba(148, 163, 184, 0.15); + padding: 8px 10px; + text-align: center; +} +.cm-table th { + background: rgba(99, 102, 241, 0.1); + color: var(--text-primary); +} +.cm-table td { + color: var(--text-secondary); +} + +@media (max-width: 768px) { + .advanced-grid { + grid-template-columns: 1fr; + } +} diff --git a/docs/quantum/web_ui.html b/docs/quantum/web_ui.html index c121b3379..697783a1a 100644 --- a/docs/quantum/web_ui.html +++ b/docs/quantum/web_ui.html @@ -1,286 +1,286 @@ - - - - - - Quantum AI Training Dashboard — Aria - - - - - - - - - - - - - - - - - - - ← Back to Aria Home - -
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    + + + + + + diff --git a/docs/quickref/AUTOMATION_QUICKREF.md b/docs/quickref/AUTOMATION_QUICKREF.md index 96b230c6d..36a3e27ce 100644 --- a/docs/quickref/AUTOMATION_QUICKREF.md +++ b/docs/quickref/AUTOMATION_QUICKREF.md @@ -1,199 +1,199 @@ -# Automation Quick Reference - -## One-Command Training Pipelines - -### Quick Training & Auto-Promote -```powershell -python .\scripts\train_and_promote.py --quick --auto-promote -``` -**What it does**: Train 64 samples, 1 epoch → Evaluate → Promote best model (~7 min) - -### Standard Training & Auto-Promote -```powershell -python .\scripts\train_and_promote.py --standard --auto-promote -``` -**What it does**: Train 500 samples, 3 epochs → Evaluate → Promote (~30 min) - -### Full Training & Auto-Promote -```powershell -python .\scripts\train_and_promote.py --full --auto-promote -``` -**What it does**: Train all samples, 5 epochs → Evaluate → Promote (~2-4 hours) - ---- - -## Scheduled Automation - -### Nightly Training (Runs at 2 AM daily) -```powershell -python .\scripts\training_scheduler.py --start nightly -``` - -### Continuous Training (Retrains when data changes) -```powershell -python .\scripts\training_scheduler.py --start continuous --check-interval 3600 -``` - -### One-Off Scheduled Job -```powershell -python .\scripts\training_scheduler.py --run-once --preset standard -``` - ---- - -## Hyperparameter Optimization - -### Grid Search (Auto-tune hyperparameters) -```powershell -python .\scripts\training_scheduler.py --grid-search -``` - -### Custom Grid Search -```powershell -python .\scripts\training_scheduler.py --grid-search ` - --learning-rates 1e-5 2e-5 5e-5 ` - --batch-sizes 4 8 16 ` - --epochs-list 2 3 5 -``` - ---- - -## VS Code Tasks - -Press `Ctrl+Shift+P` → "Tasks: Run Task" → Select: - -- **Automate: Train & Promote (Quick)** - 64 samples, auto-deploy -- **Automate: Train & Promote (Standard)** - 500 samples, auto-deploy -- **Automate: Grid Search** - Find best hyperparameters -- **Automate: Start Nightly Training** - Background daemon -- **Automate: Full Pipeline** - Complete training + deploy - ---- - -## Output Locations - -### Trained Models -`data_out/lora_training//` - -### Deployed Models -`deployed_models/_/` - -Latest: Read `deployed_models/LATEST.txt` - -### Pipeline Reports -`data_out/train_and_promote/pipeline_.json` - -### Evaluation Results -`data_out/batch_evaluator/results_.json` - -### Scheduler State -`data_out/training_scheduler/scheduler_state.json` - ---- - -## Advanced Usage - -### Custom Training + Promotion -```powershell -python .\scripts\train_and_promote.py ` - --dataset datasets/chat/coding ` - --max-train-samples 200 ` - --epochs 2 ` - --learning-rate 3e-5 ` - --batch-size 4 ` - --auto-promote -``` - -### Dry-Run (Preview without deploying) -```powershell -python .\scripts\train_and_promote.py --quick --auto-promote --dry-run -``` - -### Skip Evaluation (Training only) -```powershell -python .\scripts\train_and_promote.py --standard --skip-eval -``` - -### With Webhook Notification -```powershell -python .\scripts\train_and_promote.py --quick --auto-promote ` - --webhook https://hooks.slack.com/services/YOUR/WEBHOOK/URL -``` - ---- - -## Typical Workflows - -### Development Workflow -```powershell -# 1. Quick validation (7 min) -python .\scripts\train_and_promote.py --quick --auto-promote - -# 2. Use deployed model -$latest = Get-Content .\deployed_models\LATEST.txt -# Test at: .\deployed_models\$latest\ -``` - -### Production Deployment -```powershell -# 1. Full training with grid search -python .\scripts\training_scheduler.py --grid-search - -# 2. Deploy best model -python .\scripts\train_and_promote.py --standard --auto-promote - -# 3. Verify deployment -Get-Content .\deployed_models\LATEST.txt -Get-Content ".\deployed_models\$(Get-Content .\deployed_models\LATEST.txt)\promotion_metadata.json" -``` - -### Continuous Improvement -```powershell -# Start background daemon (retrains when data updated) -python .\scripts\training_scheduler.py --start continuous --check-interval 1800 -``` - ---- - -## Monitoring - -### Check Pipeline Status -```powershell -Get-ChildItem .\data_out\train_and_promote\pipeline_*.json | Sort-Object LastWriteTime -Descending | Select-Object -First 1 | Get-Content | ConvertFrom-Json -``` - -### Check Latest Evaluation -```powershell -Get-ChildItem .\data_out\batch_evaluator\results_*.json | Sort-Object LastWriteTime -Descending | Select-Object -First 1 | Get-Content | ConvertFrom-Json -``` - -### Check Scheduler State -```powershell -Get-Content .\data_out\training_scheduler\scheduler_state.json | ConvertFrom-Json -``` - ---- - -## Troubleshooting - -### Pipeline fails at training -- Check dataset exists: `Test-Path .\datasets\chat\mixed_chat` -- Check venv active: `python --version` -- Check dependencies: `pip list | Select-String "transformers|peft|torch"` - -### Evaluation succeeds but metrics empty -- Check `evaluate_lora_model.py` ran successfully -- Look in `data_out/batch_evaluator//results.json` - -### Promotion fails with permissions error -- Normal on Windows (symlink requires admin) -- Uses `LATEST.txt` fallback automatically - -### Scheduler doesn't run jobs -- Check scheduler state: `Get-Content .\data_out\training_scheduler\scheduler_state.json` -- Verify job schedule matches current time -- Check logs in terminal output - ---- - -**Last Updated**: 2025-11-25 +# Automation Quick Reference + +## One-Command Training Pipelines + +### Quick Training & Auto-Promote +```powershell +python .\scripts\train_and_promote.py --quick --auto-promote +``` +**What it does**: Train 64 samples, 1 epoch → Evaluate → Promote best model (~7 min) + +### Standard Training & Auto-Promote +```powershell +python .\scripts\train_and_promote.py --standard --auto-promote +``` +**What it does**: Train 500 samples, 3 epochs → Evaluate → Promote (~30 min) + +### Full Training & Auto-Promote +```powershell +python .\scripts\train_and_promote.py --full --auto-promote +``` +**What it does**: Train all samples, 5 epochs → Evaluate → Promote (~2-4 hours) + +--- + +## Scheduled Automation + +### Nightly Training (Runs at 2 AM daily) +```powershell +python .\scripts\training_scheduler.py --start nightly +``` + +### Continuous Training (Retrains when data changes) +```powershell +python .\scripts\training_scheduler.py --start continuous --check-interval 3600 +``` + +### One-Off Scheduled Job +```powershell +python .\scripts\training_scheduler.py --run-once --preset standard +``` + +--- + +## Hyperparameter Optimization + +### Grid Search (Auto-tune hyperparameters) +```powershell +python .\scripts\training_scheduler.py --grid-search +``` + +### Custom Grid Search +```powershell +python .\scripts\training_scheduler.py --grid-search ` + --learning-rates 1e-5 2e-5 5e-5 ` + --batch-sizes 4 8 16 ` + --epochs-list 2 3 5 +``` + +--- + +## VS Code Tasks + +Press `Ctrl+Shift+P` → "Tasks: Run Task" → Select: + +- **Automate: Train & Promote (Quick)** - 64 samples, auto-deploy +- **Automate: Train & Promote (Standard)** - 500 samples, auto-deploy +- **Automate: Grid Search** - Find best hyperparameters +- **Automate: Start Nightly Training** - Background daemon +- **Automate: Full Pipeline** - Complete training + deploy + +--- + +## Output Locations + +### Trained Models +`data_out/lora_training//` + +### Deployed Models +`deployed_models/_/` + +Latest: Read `deployed_models/LATEST.txt` + +### Pipeline Reports +`data_out/train_and_promote/pipeline_.json` + +### Evaluation Results +`data_out/batch_evaluator/results_.json` + +### Scheduler State +`data_out/training_scheduler/scheduler_state.json` + +--- + +## Advanced Usage + +### Custom Training + Promotion +```powershell +python .\scripts\train_and_promote.py ` + --dataset datasets/chat/coding ` + --max-train-samples 200 ` + --epochs 2 ` + --learning-rate 3e-5 ` + --batch-size 4 ` + --auto-promote +``` + +### Dry-Run (Preview without deploying) +```powershell +python .\scripts\train_and_promote.py --quick --auto-promote --dry-run +``` + +### Skip Evaluation (Training only) +```powershell +python .\scripts\train_and_promote.py --standard --skip-eval +``` + +### With Webhook Notification +```powershell +python .\scripts\train_and_promote.py --quick --auto-promote ` + --webhook https://hooks.slack.com/services/YOUR/WEBHOOK/URL +``` + +--- + +## Typical Workflows + +### Development Workflow +```powershell +# 1. Quick validation (7 min) +python .\scripts\train_and_promote.py --quick --auto-promote + +# 2. Use deployed model +$latest = Get-Content .\deployed_models\LATEST.txt +# Test at: .\deployed_models\$latest\ +``` + +### Production Deployment +```powershell +# 1. Full training with grid search +python .\scripts\training_scheduler.py --grid-search + +# 2. Deploy best model +python .\scripts\train_and_promote.py --standard --auto-promote + +# 3. Verify deployment +Get-Content .\deployed_models\LATEST.txt +Get-Content ".\deployed_models\$(Get-Content .\deployed_models\LATEST.txt)\promotion_metadata.json" +``` + +### Continuous Improvement +```powershell +# Start background daemon (retrains when data updated) +python .\scripts\training_scheduler.py --start continuous --check-interval 1800 +``` + +--- + +## Monitoring + +### Check Pipeline Status +```powershell +Get-ChildItem .\data_out\train_and_promote\pipeline_*.json | Sort-Object LastWriteTime -Descending | Select-Object -First 1 | Get-Content | ConvertFrom-Json +``` + +### Check Latest Evaluation +```powershell +Get-ChildItem .\data_out\batch_evaluator\results_*.json | Sort-Object LastWriteTime -Descending | Select-Object -First 1 | Get-Content | ConvertFrom-Json +``` + +### Check Scheduler State +```powershell +Get-Content .\data_out\training_scheduler\scheduler_state.json | ConvertFrom-Json +``` + +--- + +## Troubleshooting + +### Pipeline fails at training +- Check dataset exists: `Test-Path .\datasets\chat\mixed_chat` +- Check venv active: `python --version` +- Check dependencies: `pip list | Select-String "transformers|peft|torch"` + +### Evaluation succeeds but metrics empty +- Check `evaluate_lora_model.py` ran successfully +- Look in `data_out/batch_evaluator//results.json` + +### Promotion fails with permissions error +- Normal on Windows (symlink requires admin) +- Uses `LATEST.txt` fallback automatically + +### Scheduler doesn't run jobs +- Check scheduler state: `Get-Content .\data_out\training_scheduler\scheduler_state.json` +- Verify job schedule matches current time +- Check logs in terminal output + +--- + +**Last Updated**: 2025-11-25 diff --git a/docs/quickref/BATCH_EVAL_QUICKREF.md b/docs/quickref/BATCH_EVAL_QUICKREF.md index f786dc181..916cbb391 100644 --- a/docs/quickref/BATCH_EVAL_QUICKREF.md +++ b/docs/quickref/BATCH_EVAL_QUICKREF.md @@ -1,66 +1,66 @@ -# Batch Evaluation & Promotion - Quick Reference - -## Core Commands - -### Evaluate All Models -```powershell -python .\scripts\batch_evaluator.py --scan-models --evaluate-all -``` - -### Evaluate and Promote Best Model -```powershell -python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best -``` - -### Dry-Run Mode -```powershell -python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best --dry-run -``` - ---- - -## What Gets Measured - -- **Perplexity**: Language model quality (lower = better, ~10-100 typical) -- **Diversity**: Unique token ratio (0.0-1.0, higher = better) -- **Response Length**: Avg tokens per response -- **Coherence**: Complete sentence ratio (0.0-1.0) - ---- - -## Promoted Model Location - -After promotion: `deployed_models/_/` - -Contains: -- All adapter files (adapter_model.safetensors, config, etc.) -- `promotion_metadata.json` (metrics, rank, timestamp) -- `../LATEST.txt` points to this directory - ---- - -## Example Workflow - -```powershell -# 1. Train models -python .\scripts\autotrain.py - -# 2. Evaluate and promote best -python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best - -# 3. Use promoted model -$latest = Get-Content .\deployed_models\LATEST.txt -# Now use: .\deployed_models\$latest\ -``` - ---- - -## Troubleshooting - -- **No models found**: Train first with `autotrain.py` or `train_lora.py` -- **Missing transformers**: Run `pip install transformers peft torch` -- **Symlink error (WinError 1314)**: Normal on Windows, uses LATEST.txt fallback - ---- - -**Last Updated**: 2025-11-24 +# Batch Evaluation & Promotion - Quick Reference + +## Core Commands + +### Evaluate All Models +```powershell +python .\scripts\batch_evaluator.py --scan-models --evaluate-all +``` + +### Evaluate and Promote Best Model +```powershell +python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best +``` + +### Dry-Run Mode +```powershell +python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best --dry-run +``` + +--- + +## What Gets Measured + +- **Perplexity**: Language model quality (lower = better, ~10-100 typical) +- **Diversity**: Unique token ratio (0.0-1.0, higher = better) +- **Response Length**: Avg tokens per response +- **Coherence**: Complete sentence ratio (0.0-1.0) + +--- + +## Promoted Model Location + +After promotion: `deployed_models/_/` + +Contains: +- All adapter files (adapter_model.safetensors, config, etc.) +- `promotion_metadata.json` (metrics, rank, timestamp) +- `../LATEST.txt` points to this directory + +--- + +## Example Workflow + +```powershell +# 1. Train models +python .\scripts\autotrain.py + +# 2. Evaluate and promote best +python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best + +# 3. Use promoted model +$latest = Get-Content .\deployed_models\LATEST.txt +# Now use: .\deployed_models\$latest\ +``` + +--- + +## Troubleshooting + +- **No models found**: Train first with `autotrain.py` or `train_lora.py` +- **Missing transformers**: Run `pip install transformers peft torch` +- **Symlink error (WinError 1314)**: Normal on Windows, uses LATEST.txt fallback + +--- + +**Last Updated**: 2025-11-24 diff --git a/docs/quickref/DATASETS_QUICK_REF.md b/docs/quickref/DATASETS_QUICK_REF.md index 6ca912ebe..01a7bef61 100644 --- a/docs/quickref/DATASETS_QUICK_REF.md +++ b/docs/quickref/DATASETS_QUICK_REF.md @@ -1,124 +1,124 @@ -# Quick Reference: AI Training Datasets - -## 🚀 One-Command Setup - -```powershell -python .\scripts\quick_setup_datasets.py -``` - -Downloads quantum datasets + Dolly 15k (~500 MB, 5 minutes) - ---- - -## 📥 Download Commands - -### Quantum ML Datasets -```powershell -python .\scripts\download_datasets.py --category quantum -``` - -### Chat Datasets -```powershell -# Small, high-quality (recommended first) -python .\scripts\download_datasets.py --category chat --dataset dolly - -# Large, multi-turn conversations -python .\scripts\download_datasets.py --category chat --dataset openassistant - -# All datasets under size limit -python .\scripts\download_datasets.py --category chat --dataset all --max-size 5 -``` - ---- - -## ✅ Validate & Check - -```powershell -# Validate all -python .\scripts\validate_datasets.py - -# List downloaded -python .\scripts\download_datasets.py --list - -# Check sizes -Get-ChildItem .\datasets -Recurse | Measure-Object -Property Length -Sum -``` - ---- - -## 🎯 Training Commands - -### Quantum AI -```powershell -cd quantum-ai -python .\train_custom_dataset.py -``` - -### Phi-3.6 Fine-tuning (Small Test) -```powershell -cd AI\microsoft_phi-silica-3.6_v1 -python .\scripts\train_lora.py --dataset ..\..\datasets\chat\dolly --config .\lora\lora.yaml --max-train-samples 64 --no-stream -``` - -### Phi-3.6 Fine-tuning (Full) -```powershell -cd AI\microsoft_phi-silica-3.6_v1 -python .\scripts\train_lora.py --dataset ..\..\datasets\chat\dolly --config .\lora\lora.yaml -``` - ---- - -## 📊 Dataset Sizes & Quality - -| Dataset | Size | Samples | Quality | License | -|---------|------|---------|---------|---------| -| **Quantum** | | | | | -| Heart Disease | <1 MB | 303 | ⭐⭐⭐⭐ | Free | -| Ionosphere | <1 MB | 351 | ⭐⭐⭐⭐ | Free | -| Sonar | <1 MB | 208 | ⭐⭐⭐⭐ | Free | -| **Chat/LLM** | | | | | -| Dolly 15k | 50 MB | 15,000 | ⭐⭐⭐⭐⭐ | Commercial ✅ | -| OpenAssistant | 500 MB | 161,000 | ⭐⭐⭐⭐⭐ | Commercial ✅ | -| Alpaca | 100 MB | 52,000 | ⭐⭐⭐⭐ | Non-commercial ⚠️ | - ---- - -## 📁 Storage Locations - -``` -datasets/ -├── quantum/ # UCI ML datasets (CSV) -├── chat/ # LLM datasets (JSONL) -│ ├── dolly/ # Instruction pairs -│ └── openassistant/ # Multi-turn conversations -├── raw/ # Original downloads -└── processed/ # Cleaned data -``` - ---- - -## 🔧 Install Dependencies - -```powershell -pip install -r dataset-requirements.txt -``` - -Includes: `datasets`, `tqdm`, `pandas`, `numpy`, `scikit-learn` - ---- - -## 📚 Full Documentation - -- **Comprehensive Catalog**: `AI_DATASETS_CATALOG.md` -- **Dataset Directory README**: `datasets/README.md` -- **Scripts Help**: `python .\scripts\download_datasets.py --help` - ---- - -## 💡 Tips - -1. **Start with Dolly 15k** - Small, high-quality, commercial-safe -2. **Validate after download** - Catch format errors early -3. **Test with --max-train-samples 64** - Quick iteration -4. **Monitor disk space** - Large datasets can be 10+ GB -5. **Check licenses** - Some datasets are non-commercial only +# Quick Reference: AI Training Datasets + +## 🚀 One-Command Setup + +```powershell +python .\scripts\quick_setup_datasets.py +``` + +Downloads quantum datasets + Dolly 15k (~500 MB, 5 minutes) + +--- + +## 📥 Download Commands + +### Quantum ML Datasets +```powershell +python .\scripts\download_datasets.py --category quantum +``` + +### Chat Datasets +```powershell +# Small, high-quality (recommended first) +python .\scripts\download_datasets.py --category chat --dataset dolly + +# Large, multi-turn conversations +python .\scripts\download_datasets.py --category chat --dataset openassistant + +# All datasets under size limit +python .\scripts\download_datasets.py --category chat --dataset all --max-size 5 +``` + +--- + +## ✅ Validate & Check + +```powershell +# Validate all +python .\scripts\validate_datasets.py + +# List downloaded +python .\scripts\download_datasets.py --list + +# Check sizes +Get-ChildItem .\datasets -Recurse | Measure-Object -Property Length -Sum +``` + +--- + +## 🎯 Training Commands + +### Quantum AI +```powershell +cd quantum-ai +python .\train_custom_dataset.py +``` + +### Phi-3.6 Fine-tuning (Small Test) +```powershell +cd AI\microsoft_phi-silica-3.6_v1 +python .\scripts\train_lora.py --dataset ..\..\datasets\chat\dolly --config .\lora\lora.yaml --max-train-samples 64 --no-stream +``` + +### Phi-3.6 Fine-tuning (Full) +```powershell +cd AI\microsoft_phi-silica-3.6_v1 +python .\scripts\train_lora.py --dataset ..\..\datasets\chat\dolly --config .\lora\lora.yaml +``` + +--- + +## 📊 Dataset Sizes & Quality + +| Dataset | Size | Samples | Quality | License | +|---------|------|---------|---------|---------| +| **Quantum** | | | | | +| Heart Disease | <1 MB | 303 | ⭐⭐⭐⭐ | Free | +| Ionosphere | <1 MB | 351 | ⭐⭐⭐⭐ | Free | +| Sonar | <1 MB | 208 | ⭐⭐⭐⭐ | Free | +| **Chat/LLM** | | | | | +| Dolly 15k | 50 MB | 15,000 | ⭐⭐⭐⭐⭐ | Commercial ✅ | +| OpenAssistant | 500 MB | 161,000 | ⭐⭐⭐⭐⭐ | Commercial ✅ | +| Alpaca | 100 MB | 52,000 | ⭐⭐⭐⭐ | Non-commercial ⚠️ | + +--- + +## 📁 Storage Locations + +``` +datasets/ +├── quantum/ # UCI ML datasets (CSV) +├── chat/ # LLM datasets (JSONL) +│ ├── dolly/ # Instruction pairs +│ └── openassistant/ # Multi-turn conversations +├── raw/ # Original downloads +└── processed/ # Cleaned data +``` + +--- + +## 🔧 Install Dependencies + +```powershell +pip install -r dataset-requirements.txt +``` + +Includes: `datasets`, `tqdm`, `pandas`, `numpy`, `scikit-learn` + +--- + +## 📚 Full Documentation + +- **Comprehensive Catalog**: `AI_DATASETS_CATALOG.md` +- **Dataset Directory README**: `datasets/README.md` +- **Scripts Help**: `python .\scripts\download_datasets.py --help` + +--- + +## 💡 Tips + +1. **Start with Dolly 15k** - Small, high-quality, commercial-safe +2. **Validate after download** - Catch format errors early +3. **Test with --max-train-samples 64** - Quick iteration +4. **Monitor disk space** - Large datasets can be 10+ GB +5. **Check licenses** - Some datasets are non-commercial only diff --git a/docs/quickref/QAI_HUB_QUICKREF.md b/docs/quickref/QAI_HUB_QUICKREF.md index dbbcf7030..60ca58a5a 100644 --- a/docs/quickref/QAI_HUB_QUICKREF.md +++ b/docs/quickref/QAI_HUB_QUICKREF.md @@ -1,355 +1,355 @@ -# QAI Hub - Quick Reference Card - -## 🚀 One-Liners - -### Start Hub Server -```powershell -python .\dashboard\serve.py -``` -**Hub**: http://localhost:8000/ -**Training Dashboard**: http://localhost:8000/unified.html - ---- - -## 📍 Navigation Map - -``` -http://localhost:8000/ -│ -├─ / (root) → AUTO REDIRECTS TO → /hub.html ✨ -│ -├─ /hub.html (Command Center) -│ ├─ Stats Bar: Jobs, Datasets, Models, GPU Usage -│ ├─ Quick Actions: 6 one-click shortcuts -│ └─ System Cards: 8 major systems -│ -├─ /unified.html (Training Dashboard) -│ ├─ All Jobs: Live tracking -│ ├─ Models: Leaderboard -│ ├─ Datasets: Browse & validate -│ ├─ Configs: YAML management -│ ├─ Train: 20+ parameters + presets -│ └─ Monitor: GPU real-time -│ -└─ API Endpoints (18 total) - ├─ /status - ├─ /api/datasets - ├─ /api/models - ├─ /api/configs - ├─ /api/gpu - ├─ /api/health - ├─ /api/stats - └─ ... (see full list below) -``` - ---- - -## ⚡ 30-Second Workflows - -### 1. Start Training Job -1. Open http://localhost:8000/ → Click "Training Dashboard" -2. Click "Train" tab -3. Fill job name + select dataset -4. Click "Start Training" - -**Or use preset**: -1. Click "Quick Test" preset (1 epoch, 100 samples, ~2 min) -2. Click "Start Training" - -### 2. Check System Health -```powershell -curl http://localhost:8000/api/health | ConvertFrom-Json -``` - -### 3. View All Training Jobs -```powershell -curl http://localhost:8000/status | ConvertFrom-Json -``` - -### 4. Monitor GPU Usage -```powershell -curl http://localhost:8000/api/gpu | ConvertFrom-Json -``` - -### 5. List Datasets -```powershell -curl http://localhost:8000/api/datasets | ConvertFrom-Json -``` - ---- - -## 🎯 Quick Actions (From Hub) - -| Icon | Action | Target | -|------|--------|--------| -| 🚀 | Start Training | `/unified.html#train` | -| 📊 | View Datasets | `/unified.html#datasets` | -| 🤖 | Browse Models | `/unified.html#models` | -| 📈 | Monitor GPU | `/unified.html#monitor` | -| ⚛️ | Quantum Jobs | CLI toast message | -| 💬 | Chat Interface | CLI toast message | - ---- - -## 📡 API Endpoints Reference - -### Training & Jobs -```http -GET /status # Training job status -POST /api/start-training # Create new job -GET /api/history # Historical data -``` - -### Datasets & Models -```http -GET /api/datasets # All datasets + sample counts -GET /api/models # Trained models -GET /api/configs # YAML configs -``` - -### System Monitoring -```http -GET /api/gpu # GPU utilization -GET /api/gpu-processes # GPU process list -GET /api/system # CPU, RAM, disk -GET /api/processes # Active Python processes -``` - -### Health & Stats -```http -GET /api/health # System health check -GET /api/stats # Quick summary -``` - -### Job Details -```http -GET /api/job/:name # Specific job details -GET /api/logs/:name # Training logs -``` - ---- - -## 🎨 Training Presets - -| Preset | Epochs | Samples | Rank | Time | Use Case | -|--------|--------|---------|------|------|----------| -| **Quick Test** | 1 | 100 | 4 | ~2 min | Fast validation | -| **Standard** | 3 | 1,000 | 8 | ~10 min | Good baseline | -| **Full** | 5 | All | 16 | ~60 min | Production quality | -| **Production** | 10 | All | 32 | ~4 hours | Maximum quality | - ---- - -## 🔧 Advanced Training Parameters - -### Basic (Always Visible) -- **Job Name**: Unique identifier (lowercase, underscores) -- **Model**: phi35 or qwen25 -- **Dataset**: From datasets/chat/* -- **Epochs**: 1-20 (default: 3) -- **Max Samples**: 10-N or -1 for all (default: 1000) -- **Learning Rate**: 1e-5 to 5e-4 (default: 2e-4) - -### Advanced (Collapsible) -- **Batch Size**: 1/2/4/8 (default: 2) -- **Gradient Accumulation**: 1-32 (default: 1) -- **Warmup Steps**: 0-N (default: 0) -- **LoRA Rank**: 4-128 (default: 8) -- **LoRA Alpha**: 8-256 (default: 16) -- **LoRA Dropout**: 0.0-0.5 (default: 0.1) -- **Weight Decay**: 0.0-0.5 (default: 0.01) -- **Max Grad Norm**: 0-10 (default: 1.0) -- **Random Seed**: Any integer (default: 42) - -### Evaluation -- **Enable Eval**: Checkbox (default: checked) -- **Max Eval Samples**: 10-N (default: 100) -- **Eval Steps**: 10-N (default: 50) - -**Total**: 17 configurable parameters - ---- - -## 🌟 System Cards Overview - -| System | Icon | Description | Link | -|--------|------|-------------|------| -| **Training Dashboard** | 🎯 | LoRA fine-tuning interface | `/unified.html` | -| **Quantum ML** | ⚛️ | Hybrid quantum-classical | CLI | -| **Chat Interface** | 💬 | Multi-provider chat | CLI | -| **Evaluation Suite** | 📊 | Model assessment | CLI | -| **Dataset Manager** | 🗃️ | Browse & validate | `/unified.html#datasets` | -| **API Gateway** | 🔌 | Azure Functions API | Port 7071 | -| **Resource Monitor** | 📈 | GPU/CPU/RAM tracking | CLI | -| **CI/CD Pipeline** | 🔄 | Testing & deployment | CLI | - ---- - -## 🐛 Quick Troubleshooting - -### Hub Not Loading -```powershell -# Check server -Get-Process python | Where-Object {$_.CommandLine -like "*serve.py*"} - -# Restart -python .\dashboard\serve.py -``` - -### Stats Showing "--" -```powershell -# Generate status.json -python .\scripts\autotrain.py --dry-run -``` - -### API 404 Errors -```powershell -# Verify server is running -curl http://localhost:8000/api/health -``` - -### GPU Not Detected -```powershell -# Check CUDA -python -c "import torch; print(torch.cuda.is_available())" -``` - ---- - -## 📊 Keyboard Shortcuts (In Hub) - -| Key | Action | -|-----|--------| -| **Click Card** | Navigate to system | -| **Click Action** | Execute quick action | -| **F5** | Refresh page (auto-updates stats) | -| **Ctrl+Click** | Open in new tab | - ---- - -## 🎓 Learning Path - -### Beginner (Day 1) -1. Start hub server -2. Explore Quick Actions -3. Run "Quick Test" preset -4. View job in "All Jobs" tab - -### Intermediate (Week 1) -1. Adjust basic parameters (epochs, samples) -2. Try different presets -3. Save/load configurations -4. Monitor GPU usage - -### Advanced (Month 1) -1. Tune advanced parameters -2. Run batch evaluations -3. Use API endpoints -4. Integrate with CI/CD - -### Expert (Ongoing) -1. Quantum ML pipeline -2. Custom provider backends -3. Distributed training -4. Production deployment - ---- - -## 📚 Related Documentation - -| File | Description | -|------|-------------| -| `QAI_HUB_GUIDE.md` | Full hub documentation (this file's parent) | -| `TRAINING_TAB_ENHANCEMENTS.md` | Training features (450 lines) | -| `TRAINING_TAB_QUICKREF.md` | Training quick reference (350 lines) | -| `DASHBOARD_DEMO.md` | Live demo script | -| `AUTOTRAIN_README.md` | LoRA orchestration | -| `QUANTUM_AUTORUN_README.md` | Quantum jobs | - ---- - -## 💡 Pro Tips - -1. **Bookmark the Hub**: http://localhost:8000/ (not unified.html) -2. **Use Presets First**: Validate before custom tuning -3. **Save Configs**: Export JSON for reproducibility -4. **Monitor GPU**: Avoid over-allocation (max 6.1GB VRAM) -5. **Dry-Run Always**: Test orchestrators before real runs -6. **Check Status First**: `curl /api/stats` before starting new jobs -7. **Use Quick Actions**: Faster than navigating cards -8. **Read Tooltips**: Hover over labels for parameter explanations - ---- - -## 🔗 External Resources - -- **PennyLane Docs**: https://pennylane.ai/ -- **Azure Quantum**: https://quantum.microsoft.com/ -- **Hugging Face**: https://huggingface.co/ -- **PyTorch**: https://pytorch.org/ -- **Azure Functions**: https://docs.microsoft.com/azure/azure-functions/ - ---- - -## 📞 Quick Support - -### Server Issues -```powershell -# Check logs -Get-Content .\dashboard\serve.py.log -Tail 50 -``` - -### Training Stuck -```powershell -# Check running jobs -python .\scripts\master_orchestrator.py --status -``` - -### Database Issues -```powershell -# Check SQL connection -curl http://localhost:7071/api/ai/status | ConvertFrom-Json -``` - ---- - -**Cheat Sheet Version**: 1.0 -**Last Updated**: 2025-11-25 -**Print-Friendly**: Yes (A4/Letter) - ---- - -## 🎯 Most Common Commands - -```powershell -# Start everything -python .\dashboard\serve.py # Hub server -func host start # API gateway - -# Training -python .\scripts\autotrain.py --dry-run # Validate -python .\scripts\autotrain.py # Run all jobs - -# Quantum -python .\scripts\quantum_autorun.py --dry-run # Validate -python .\scripts\quantum_autorun.py --job local # Run local - -# Chat -python .\talk-to-ai\src\chat_cli.py --provider azure - -# Testing -pytest tests/ -m "not slow and not azure" # Fast tests -python .\scripts\test_runner.py --all # All tests - -# Monitoring -curl http://localhost:8000/api/stats | ConvertFrom-Json -python .\scripts\resource_monitor.py --snapshot - -# Status -python .\scripts\master_orchestrator.py --status # All systems -curl http://localhost:8000/api/health # Hub health -``` - ---- - -**Quick Access**: Pin this file for instant reference! 📌 +# QAI Hub - Quick Reference Card + +## 🚀 One-Liners + +### Start Hub Server +```powershell +python .\dashboard\serve.py +``` +**Hub**: http://localhost:8000/ +**Training Dashboard**: http://localhost:8000/unified.html + +--- + +## 📍 Navigation Map + +``` +http://localhost:8000/ +│ +├─ / (root) → AUTO REDIRECTS TO → /hub.html ✨ +│ +├─ /hub.html (Command Center) +│ ├─ Stats Bar: Jobs, Datasets, Models, GPU Usage +│ ├─ Quick Actions: 6 one-click shortcuts +│ └─ System Cards: 8 major systems +│ +├─ /unified.html (Training Dashboard) +│ ├─ All Jobs: Live tracking +│ ├─ Models: Leaderboard +│ ├─ Datasets: Browse & validate +│ ├─ Configs: YAML management +│ ├─ Train: 20+ parameters + presets +│ └─ Monitor: GPU real-time +│ +└─ API Endpoints (18 total) + ├─ /status + ├─ /api/datasets + ├─ /api/models + ├─ /api/configs + ├─ /api/gpu + ├─ /api/health + ├─ /api/stats + └─ ... (see full list below) +``` + +--- + +## ⚡ 30-Second Workflows + +### 1. Start Training Job +1. Open http://localhost:8000/ → Click "Training Dashboard" +2. Click "Train" tab +3. Fill job name + select dataset +4. Click "Start Training" + +**Or use preset**: +1. Click "Quick Test" preset (1 epoch, 100 samples, ~2 min) +2. Click "Start Training" + +### 2. Check System Health +```powershell +curl http://localhost:8000/api/health | ConvertFrom-Json +``` + +### 3. View All Training Jobs +```powershell +curl http://localhost:8000/status | ConvertFrom-Json +``` + +### 4. Monitor GPU Usage +```powershell +curl http://localhost:8000/api/gpu | ConvertFrom-Json +``` + +### 5. List Datasets +```powershell +curl http://localhost:8000/api/datasets | ConvertFrom-Json +``` + +--- + +## 🎯 Quick Actions (From Hub) + +| Icon | Action | Target | +|------|--------|--------| +| 🚀 | Start Training | `/unified.html#train` | +| 📊 | View Datasets | `/unified.html#datasets` | +| 🤖 | Browse Models | `/unified.html#models` | +| 📈 | Monitor GPU | `/unified.html#monitor` | +| ⚛️ | Quantum Jobs | CLI toast message | +| 💬 | Chat Interface | CLI toast message | + +--- + +## 📡 API Endpoints Reference + +### Training & Jobs +```http +GET /status # Training job status +POST /api/start-training # Create new job +GET /api/history # Historical data +``` + +### Datasets & Models +```http +GET /api/datasets # All datasets + sample counts +GET /api/models # Trained models +GET /api/configs # YAML configs +``` + +### System Monitoring +```http +GET /api/gpu # GPU utilization +GET /api/gpu-processes # GPU process list +GET /api/system # CPU, RAM, disk +GET /api/processes # Active Python processes +``` + +### Health & Stats +```http +GET /api/health # System health check +GET /api/stats # Quick summary +``` + +### Job Details +```http +GET /api/job/:name # Specific job details +GET /api/logs/:name # Training logs +``` + +--- + +## 🎨 Training Presets + +| Preset | Epochs | Samples | Rank | Time | Use Case | +|--------|--------|---------|------|------|----------| +| **Quick Test** | 1 | 100 | 4 | ~2 min | Fast validation | +| **Standard** | 3 | 1,000 | 8 | ~10 min | Good baseline | +| **Full** | 5 | All | 16 | ~60 min | Production quality | +| **Production** | 10 | All | 32 | ~4 hours | Maximum quality | + +--- + +## 🔧 Advanced Training Parameters + +### Basic (Always Visible) +- **Job Name**: Unique identifier (lowercase, underscores) +- **Model**: phi35 or qwen25 +- **Dataset**: From datasets/chat/* +- **Epochs**: 1-20 (default: 3) +- **Max Samples**: 10-N or -1 for all (default: 1000) +- **Learning Rate**: 1e-5 to 5e-4 (default: 2e-4) + +### Advanced (Collapsible) +- **Batch Size**: 1/2/4/8 (default: 2) +- **Gradient Accumulation**: 1-32 (default: 1) +- **Warmup Steps**: 0-N (default: 0) +- **LoRA Rank**: 4-128 (default: 8) +- **LoRA Alpha**: 8-256 (default: 16) +- **LoRA Dropout**: 0.0-0.5 (default: 0.1) +- **Weight Decay**: 0.0-0.5 (default: 0.01) +- **Max Grad Norm**: 0-10 (default: 1.0) +- **Random Seed**: Any integer (default: 42) + +### Evaluation +- **Enable Eval**: Checkbox (default: checked) +- **Max Eval Samples**: 10-N (default: 100) +- **Eval Steps**: 10-N (default: 50) + +**Total**: 17 configurable parameters + +--- + +## 🌟 System Cards Overview + +| System | Icon | Description | Link | +|--------|------|-------------|------| +| **Training Dashboard** | 🎯 | LoRA fine-tuning interface | `/unified.html` | +| **Quantum ML** | ⚛️ | Hybrid quantum-classical | CLI | +| **Chat Interface** | 💬 | Multi-provider chat | CLI | +| **Evaluation Suite** | 📊 | Model assessment | CLI | +| **Dataset Manager** | 🗃️ | Browse & validate | `/unified.html#datasets` | +| **API Gateway** | 🔌 | Azure Functions API | Port 7071 | +| **Resource Monitor** | 📈 | GPU/CPU/RAM tracking | CLI | +| **CI/CD Pipeline** | 🔄 | Testing & deployment | CLI | + +--- + +## 🐛 Quick Troubleshooting + +### Hub Not Loading +```powershell +# Check server +Get-Process python | Where-Object {$_.CommandLine -like "*serve.py*"} + +# Restart +python .\dashboard\serve.py +``` + +### Stats Showing "--" +```powershell +# Generate status.json +python .\scripts\autotrain.py --dry-run +``` + +### API 404 Errors +```powershell +# Verify server is running +curl http://localhost:8000/api/health +``` + +### GPU Not Detected +```powershell +# Check CUDA +python -c "import torch; print(torch.cuda.is_available())" +``` + +--- + +## 📊 Keyboard Shortcuts (In Hub) + +| Key | Action | +|-----|--------| +| **Click Card** | Navigate to system | +| **Click Action** | Execute quick action | +| **F5** | Refresh page (auto-updates stats) | +| **Ctrl+Click** | Open in new tab | + +--- + +## 🎓 Learning Path + +### Beginner (Day 1) +1. Start hub server +2. Explore Quick Actions +3. Run "Quick Test" preset +4. View job in "All Jobs" tab + +### Intermediate (Week 1) +1. Adjust basic parameters (epochs, samples) +2. Try different presets +3. Save/load configurations +4. Monitor GPU usage + +### Advanced (Month 1) +1. Tune advanced parameters +2. Run batch evaluations +3. Use API endpoints +4. Integrate with CI/CD + +### Expert (Ongoing) +1. Quantum ML pipeline +2. Custom provider backends +3. Distributed training +4. Production deployment + +--- + +## 📚 Related Documentation + +| File | Description | +|------|-------------| +| `QAI_HUB_GUIDE.md` | Full hub documentation (this file's parent) | +| `TRAINING_TAB_ENHANCEMENTS.md` | Training features (450 lines) | +| `TRAINING_TAB_QUICKREF.md` | Training quick reference (350 lines) | +| `DASHBOARD_DEMO.md` | Live demo script | +| `AUTOTRAIN_README.md` | LoRA orchestration | +| `QUANTUM_AUTORUN_README.md` | Quantum jobs | + +--- + +## 💡 Pro Tips + +1. **Bookmark the Hub**: http://localhost:8000/ (not unified.html) +2. **Use Presets First**: Validate before custom tuning +3. **Save Configs**: Export JSON for reproducibility +4. **Monitor GPU**: Avoid over-allocation (max 6.1GB VRAM) +5. **Dry-Run Always**: Test orchestrators before real runs +6. **Check Status First**: `curl /api/stats` before starting new jobs +7. **Use Quick Actions**: Faster than navigating cards +8. **Read Tooltips**: Hover over labels for parameter explanations + +--- + +## 🔗 External Resources + +- **PennyLane Docs**: https://pennylane.ai/ +- **Azure Quantum**: https://quantum.microsoft.com/ +- **Hugging Face**: https://huggingface.co/ +- **PyTorch**: https://pytorch.org/ +- **Azure Functions**: https://docs.microsoft.com/azure/azure-functions/ + +--- + +## 📞 Quick Support + +### Server Issues +```powershell +# Check logs +Get-Content .\dashboard\serve.py.log -Tail 50 +``` + +### Training Stuck +```powershell +# Check running jobs +python .\scripts\master_orchestrator.py --status +``` + +### Database Issues +```powershell +# Check SQL connection +curl http://localhost:7071/api/ai/status | ConvertFrom-Json +``` + +--- + +**Cheat Sheet Version**: 1.0 +**Last Updated**: 2025-11-25 +**Print-Friendly**: Yes (A4/Letter) + +--- + +## 🎯 Most Common Commands + +```powershell +# Start everything +python .\dashboard\serve.py # Hub server +func host start # API gateway + +# Training +python .\scripts\autotrain.py --dry-run # Validate +python .\scripts\autotrain.py # Run all jobs + +# Quantum +python .\scripts\quantum_autorun.py --dry-run # Validate +python .\scripts\quantum_autorun.py --job local # Run local + +# Chat +python .\talk-to-ai\src\chat_cli.py --provider azure + +# Testing +pytest tests/ -m "not slow and not azure" # Fast tests +python .\scripts\test_runner.py --all # All tests + +# Monitoring +curl http://localhost:8000/api/stats | ConvertFrom-Json +python .\scripts\resource_monitor.py --snapshot + +# Status +python .\scripts\master_orchestrator.py --status # All systems +curl http://localhost:8000/api/health # Hub health +``` + +--- + +**Quick Access**: Pin this file for instant reference! 📌 diff --git a/docs/quickref/QUICK_REFERENCE.md b/docs/quickref/QUICK_REFERENCE.md index d956c3f85..bbe74c45e 100644 --- a/docs/quickref/QUICK_REFERENCE.md +++ b/docs/quickref/QUICK_REFERENCE.md @@ -1,170 +1,170 @@ -# 🚀 Aria Chat Quick Reference - -## Start Here (90 seconds) - -### Step 1: Start Backend - -```bash -cd /workspaces/Aria -func host start -# Runs on http://localhost:7071 -``` - -### Step 2: Open Site - -```bash -# Option A: Direct file -open docs/index.html - -# Option B: Web server -python -m http.server 8000 -# Then visit http://localhost:8000/docs/ -``` - -### Step 3: Configure & Chat - -1. Keep server URL: `http://localhost:7071` -2. Click "Test Connection" -3. Type message -4. Press Enter or click Send - -## File Locations - -| File | Purpose | Access | -| ---------------------------- | --------------------- | --------------------------------- | -| `docs/index.html` | Main site | Browser / GitHub Pages | -| `function_app.py` | Backend API | `func host start` | -| `docs/GITHUB_PAGES_SETUP.md` | Full setup guide | Read in editor | -| `docs/README_PAGES.md` | Configuration options | Read in editor | -| `scripts/fast_validate.py` | Verify setup | `python scripts/fast_validate.py` | - -## Common Commands - -```bash -# Verify setup -python scripts/fast_validate.py - -# Run backend -func host start - -# Run static server -python -m http.server 8000 - -# Test API endpoint -curl http://localhost:7071/api/ai/status - -# Check logs (after running) -cat data_out/chat.log -``` - -## Deployment Checklist - -- [ ] **Local Test**: Can chat with function_app on localhost -- [ ] **Deploy Backend**: `func azure functionapp publish ` -- [ ] **Get Azure URL**: Copy from Azure Portal -- [ ] **Update Server URL**: Edit docs/index.html or use param -- [ ] **Enable GitHub Pages**: Settings → Pages → /docs folder -- [ ] **Test Production**: Open GitHub Pages URL -- [ ] **Share URL**: `https://yourusername.github.io/Aria/` - -## Server URLs by Scenario - -| Scenario | Server URL | -| ---------------------- | ------------------------------------ | -| Local development | `http://localhost:7071` | -| Azure Functions | `https://your-app.azurewebsites.net` | -| GitHub Pages parameter | `?server=https://...` | -| Docker container | `http://localhost:8080` | - -## Providers - -| Provider | Setup | Speed | Cost | -| ------------ | ------------------------- | ---------- | ------------ | -| LMStudio | LMSTUDIO_BASE_URL env var | ⚡ Fastest | Free (local) | -| Azure OpenAI | AZURE\_\* env vars | ⚡ Fast | Paid | -| OpenAI | OPENAI_API_KEY env var | ⚡ Fast | Paid | -| Local Echo | None | ⚡ Instant | Free | - -## Troubleshooting 30-Second Fixes - -| Problem | Fix | -| -------------------- | ---------------------------------- | -| "Can't connect" | `func host start` running? | -| "Wrong URL" | Default is `http://localhost:7071` | -| "No response" | Click "Test Connection" | -| "Streaming stops" | Check function_app logs | -| "History disappears" | Disable incognito mode | - -## Performance Tips - -- **Fastest Setup**: LMStudio local mode -- **Best Quality**: Azure OpenAI -- **No Cost**: Local echo mode -- **Responsive UI**: Use temperature 0.5-0.7 - -## URLs to Remember - -```text -Local Dev: http://localhost:7071 -Static Site: http://localhost:8000/docs/ -Production: https://yourusername.github.io/Aria/ -Azure Backend: https://your-app.azurewebsites.net -``` - -## Key Features - -✨ Animated Aria character -💬 Real-time streaming chat -🔌 Multi-provider LLM support -📱 Responsive (desktop & mobile) -💾 Chat history persistence -⚙️ Provider selector -🌡️ Temperature control -🧪 Connection test button - -## Documentation - -| Guide | Purpose | -| ---------------------------------- | -------------------------- | -| GITHUB_PAGES_SETUP.md | Complete setup walkthrough | -| SERVER_CONFIGURATION.md | All deployment scenarios | -| verify_aria_chat.py | Automated verification | -| ARIA_CHAT_GITHUB_PAGES_COMPLETE.md | Full project summary | - -## One-Liners - -```bash -# Verify all components -python verify_aria_chat.py && echo "✓ Ready to go!" - -# Run backend + open site -(func host start &) && sleep 2 && open docs/index.html - -# Deploy to Azure -func azure functionapp publish MyApp && echo "✓ Live!" - -# Test connection -curl -s http://localhost:7071/api/ai/status | python -m json.tool -``` - -## Status Codes - -| Code | Meaning | Action | -| ------------------ | ------------ | ----------------------- | -| 200 | Success | Proceed normally | -| 400 | Bad request | Check message format | -| 404 | Not found | Wrong endpoint/URL | -| 500 | Server error | Check function_app logs | -| Connection refused | Backend down | Run `func host start` | - ---- - -**Quick Links**: - -- 📖 [Full Setup Guide](../GITHUB_PAGES_SETUP.md) -- ⚙️ [Configuration Guide](../README_PAGES.md) -- 🎯 [Project Complete](../summaries/GITHUB_PAGES_SETUP_SUMMARY.md) -- ✅ [Verify Setup](../../scripts/fast_validate.py) - -**Status**: ✅ Ready to Use -**Last Updated**: January 23, 2026 +# 🚀 Aria Chat Quick Reference + +## Start Here (90 seconds) + +### Step 1: Start Backend + +```bash +cd /workspaces/Aria +func host start +# Runs on http://localhost:7071 +``` + +### Step 2: Open Site + +```bash +# Option A: Direct file +open docs/index.html + +# Option B: Web server +python -m http.server 8000 +# Then visit http://localhost:8000/docs/ +``` + +### Step 3: Configure & Chat + +1. Keep server URL: `http://localhost:7071` +2. Click "Test Connection" +3. Type message +4. Press Enter or click Send + +## File Locations + +| File | Purpose | Access | +| ---------------------------- | --------------------- | --------------------------------- | +| `docs/index.html` | Main site | Browser / GitHub Pages | +| `function_app.py` | Backend API | `func host start` | +| `docs/GITHUB_PAGES_SETUP.md` | Full setup guide | Read in editor | +| `docs/README_PAGES.md` | Configuration options | Read in editor | +| `scripts/fast_validate.py` | Verify setup | `python scripts/fast_validate.py` | + +## Common Commands + +```bash +# Verify setup +python scripts/fast_validate.py + +# Run backend +func host start + +# Run static server +python -m http.server 8000 + +# Test API endpoint +curl http://localhost:7071/api/ai/status + +# Check logs (after running) +cat data_out/chat.log +``` + +## Deployment Checklist + +- [ ] **Local Test**: Can chat with function_app on localhost +- [ ] **Deploy Backend**: `func azure functionapp publish ` +- [ ] **Get Azure URL**: Copy from Azure Portal +- [ ] **Update Server URL**: Edit docs/index.html or use param +- [ ] **Enable GitHub Pages**: Settings → Pages → /docs folder +- [ ] **Test Production**: Open GitHub Pages URL +- [ ] **Share URL**: `https://yourusername.github.io/Aria/` + +## Server URLs by Scenario + +| Scenario | Server URL | +| ---------------------- | ------------------------------------ | +| Local development | `http://localhost:7071` | +| Azure Functions | `https://your-app.azurewebsites.net` | +| GitHub Pages parameter | `?server=https://...` | +| Docker container | `http://localhost:8080` | + +## Providers + +| Provider | Setup | Speed | Cost | +| ------------ | ------------------------- | ---------- | ------------ | +| LMStudio | LMSTUDIO_BASE_URL env var | ⚡ Fastest | Free (local) | +| Azure OpenAI | AZURE\_\* env vars | ⚡ Fast | Paid | +| OpenAI | OPENAI_API_KEY env var | ⚡ Fast | Paid | +| Local Echo | None | ⚡ Instant | Free | + +## Troubleshooting 30-Second Fixes + +| Problem | Fix | +| -------------------- | ---------------------------------- | +| "Can't connect" | `func host start` running? | +| "Wrong URL" | Default is `http://localhost:7071` | +| "No response" | Click "Test Connection" | +| "Streaming stops" | Check function_app logs | +| "History disappears" | Disable incognito mode | + +## Performance Tips + +- **Fastest Setup**: LMStudio local mode +- **Best Quality**: Azure OpenAI +- **No Cost**: Local echo mode +- **Responsive UI**: Use temperature 0.5-0.7 + +## URLs to Remember + +```text +Local Dev: http://localhost:7071 +Static Site: http://localhost:8000/docs/ +Production: https://yourusername.github.io/Aria/ +Azure Backend: https://your-app.azurewebsites.net +``` + +## Key Features + +✨ Animated Aria character +💬 Real-time streaming chat +🔌 Multi-provider LLM support +📱 Responsive (desktop & mobile) +💾 Chat history persistence +⚙️ Provider selector +🌡️ Temperature control +🧪 Connection test button + +## Documentation + +| Guide | Purpose | +| ---------------------------------- | -------------------------- | +| GITHUB_PAGES_SETUP.md | Complete setup walkthrough | +| SERVER_CONFIGURATION.md | All deployment scenarios | +| verify_aria_chat.py | Automated verification | +| ARIA_CHAT_GITHUB_PAGES_COMPLETE.md | Full project summary | + +## One-Liners + +```bash +# Verify all components +python verify_aria_chat.py && echo "✓ Ready to go!" + +# Run backend + open site +(func host start &) && sleep 2 && open docs/index.html + +# Deploy to Azure +func azure functionapp publish MyApp && echo "✓ Live!" + +# Test connection +curl -s http://localhost:7071/api/ai/status | python -m json.tool +``` + +## Status Codes + +| Code | Meaning | Action | +| ------------------ | ------------ | ----------------------- | +| 200 | Success | Proceed normally | +| 400 | Bad request | Check message format | +| 404 | Not found | Wrong endpoint/URL | +| 500 | Server error | Check function_app logs | +| Connection refused | Backend down | Run `func host start` | + +--- + +**Quick Links**: + +- 📖 [Full Setup Guide](../GITHUB_PAGES_SETUP.md) +- ⚙️ [Configuration Guide](../README_PAGES.md) +- 🎯 [Project Complete](../summaries/GITHUB_PAGES_SETUP_SUMMARY.md) +- ✅ [Verify Setup](../../scripts/fast_validate.py) + +**Status**: ✅ Ready to Use +**Last Updated**: January 23, 2026 diff --git a/docs/quickref/QUICK_START_CHAT.md b/docs/quickref/QUICK_START_CHAT.md index 973a0aa68..d3478b3fa 100644 --- a/docs/quickref/QUICK_START_CHAT.md +++ b/docs/quickref/QUICK_START_CHAT.md @@ -1,87 +1,87 @@ -# 🚀 Chat Website - Quick Reference - -## Improvements Made ✅ - -### Core Enhancements -1. **Markdown Rendering** - Beautiful formatted text, code blocks, lists, tables -2. **Syntax Highlighting** - 180+ languages with copy buttons -3. **Dark Theme** - Eye-friendly mode toggle -4. **Provider Selection** - Choose Local/OpenAI/Azure -5. **Persistence** - Auto-save & restore conversations -6. **Export** - Download chats as JSON -7. **Better Input** - Multi-line textarea with auto-resize -8. **Keyboard Shortcuts** - Enter, Shift+Enter, Ctrl+K -9. **Stats Bar** - Message count & status -10. **Mobile Responsive** - Works on all devices - -## Access - -**URL:** http://localhost:7071/api/chat-web - -**Start Server:** -```powershell -.\start-chat-web.ps1 -# or -func start -``` - -## Keyboard Shortcuts - -| Key | Action | -|-----|--------| -| `Enter` | Send message | -| `Shift + Enter` | New line | -| `Ctrl + K` | New chat | - -## Controls - -| Button | Function | -|--------|----------| -| 🔄 New Chat | Start fresh | -| 🗑️ Clear | Remove messages | -| 💾 Export | Save as JSON | -| 🌓 Theme | Toggle dark/light | - -## Try These Commands - -**Markdown:** -- "Explain Python decorators with code examples" -- "Create a comparison table of Python vs JavaScript" -- "Show me a REST API example with comments" - -**Code:** -- "Write a quicksort in Python with explanation" -- "Create a React component with TypeScript" -- "Show me async/await examples" - -## Provider Options - -- **Local (Free)** - Works offline, no API key -- **OpenAI** - Set `$env:OPENAI_API_KEY` -- **Azure** - Set `AZURE_OPENAI_*` variables -- **Auto** - Detects best available - -## Features Working - -✅ Markdown rendering with Marked.js -✅ Code syntax highlighting with Highlight.js -✅ Dark/light theme toggle -✅ LocalStorage persistence -✅ JSON export -✅ Provider selection -✅ Multi-line input -✅ Keyboard shortcuts -✅ Message counter -✅ Status bar -✅ Copy code buttons -✅ Auto-scroll -✅ Error handling -✅ Mobile responsive - -## Files Modified - -- `chat-web/index.html` - UI + CSS + CDN imports -- `chat-web/chat.js` - Enhanced logic + persistence -- `function_app.py` - Backend API (unchanged, working) - -**Total**: Production-ready chat interface! 🎉 +# 🚀 Chat Website - Quick Reference + +## Improvements Made ✅ + +### Core Enhancements +1. **Markdown Rendering** - Beautiful formatted text, code blocks, lists, tables +2. **Syntax Highlighting** - 180+ languages with copy buttons +3. **Dark Theme** - Eye-friendly mode toggle +4. **Provider Selection** - Choose Local/OpenAI/Azure +5. **Persistence** - Auto-save & restore conversations +6. **Export** - Download chats as JSON +7. **Better Input** - Multi-line textarea with auto-resize +8. **Keyboard Shortcuts** - Enter, Shift+Enter, Ctrl+K +9. **Stats Bar** - Message count & status +10. **Mobile Responsive** - Works on all devices + +## Access + +**URL:** http://localhost:7071/api/chat-web + +**Start Server:** +```powershell +.\start-chat-web.ps1 +# or +func start +``` + +## Keyboard Shortcuts + +| Key | Action | +|-----|--------| +| `Enter` | Send message | +| `Shift + Enter` | New line | +| `Ctrl + K` | New chat | + +## Controls + +| Button | Function | +|--------|----------| +| 🔄 New Chat | Start fresh | +| 🗑️ Clear | Remove messages | +| 💾 Export | Save as JSON | +| 🌓 Theme | Toggle dark/light | + +## Try These Commands + +**Markdown:** +- "Explain Python decorators with code examples" +- "Create a comparison table of Python vs JavaScript" +- "Show me a REST API example with comments" + +**Code:** +- "Write a quicksort in Python with explanation" +- "Create a React component with TypeScript" +- "Show me async/await examples" + +## Provider Options + +- **Local (Free)** - Works offline, no API key +- **OpenAI** - Set `$env:OPENAI_API_KEY` +- **Azure** - Set `AZURE_OPENAI_*` variables +- **Auto** - Detects best available + +## Features Working + +✅ Markdown rendering with Marked.js +✅ Code syntax highlighting with Highlight.js +✅ Dark/light theme toggle +✅ LocalStorage persistence +✅ JSON export +✅ Provider selection +✅ Multi-line input +✅ Keyboard shortcuts +✅ Message counter +✅ Status bar +✅ Copy code buttons +✅ Auto-scroll +✅ Error handling +✅ Mobile responsive + +## Files Modified + +- `chat-web/index.html` - UI + CSS + CDN imports +- `chat-web/chat.js` - Enhanced logic + persistence +- `function_app.py` - Backend API (unchanged, working) + +**Total**: Production-ready chat interface! 🎉 diff --git a/docs/quickref/SQL_QUICKREF.md b/docs/quickref/SQL_QUICKREF.md index c0e6471c3..597382753 100644 --- a/docs/quickref/SQL_QUICKREF.md +++ b/docs/quickref/SQL_QUICKREF.md @@ -1,193 +1,193 @@ -# SQL Integration - Quick Reference Card - -## 🚀 Quick Start (5 Minutes) - -### Local Development - -```powershell -# 1. Set connection string -$env:QAI_SQL_URL = "sqlite:///./data.sqlite" - -# 2. Run tests -.\venv\Scripts\python.exe -m pytest tests\test_sql_integration.py -v - -# 3. Start Function App -func host start - -# 4. Check status -Invoke-RestMethod http://localhost:7071/api/ai/status | ConvertTo-Json -Depth 5 -``` - -### Azure Production - -```powershell -# 1. Deploy alerts -.\scripts\setup_azure_alerts.ps1 -ResourceGroup "your-rg" -FunctionAppName "your-func" -ActionGroupName "sql-alerts" -EmailRecipient "admin@example.com" - -# 2. Configure connection -az functionapp config appsettings set --name your-func --resource-group your-rg --settings QAI_SQL_URL="mssql+pyodbc://..." - -# 3. Monitor in Application Insights (see AZURE_MONITOR_SQL_SETUP.md for KQL queries) -``` - -## 📊 Key Endpoints - -| Endpoint | Purpose | Example | -| ---------------- | -------------------- | -------------------------------------------------------------------- | -| `/api/ai/status` | Health + SQL metrics | `curl http://localhost:7071/api/ai/status` | -| `engine_stats()` | Pool metrics (code) | `from shared.sql_engine import engine_stats; stats = engine_stats()` | -| `sql_health()` | Connectivity probe | `from shared.sql_engine import sql_health; ok, msg = sql_health()` | - -## 🔧 Environment Variables - -| Variable | Required | Example | -| ----------------------------- | -------- | ------------------------------------------------- | -| `QAI_SQL_URL` | No | `sqlite:///./data.sqlite` or `mssql+pyodbc://...` | -| `QAI_SQL_SLOW_MS` | No | `300` (milliseconds) | -| `QAI_ENABLE_QUERY_TRACKING` | No | `true` (enable persistent metrics) | -| `AZURE_FUNCTIONS_ENVIRONMENT` | No | `development` / `staging` / `production` | - -## 📈 Monitoring Thresholds - -| Metric | Warning | Critical | Action | -| ------------------- | ------- | -------- | ---------------------------------- | -| `saturation_pct` | > 60% | > 80% | Scale pool size | -| `slow_queries_1min` | > 5 | > 10 | Tune threshold or optimize queries | -| `execution_time_ms` | > P95 | > P99 | Add indexes, review query plan | - -## 🛠️ Common Commands - -### Migrations - -```powershell -# Apply all pending migrations -python .\scripts\sql_migrate.py - -# Check migration status (look at output: "2 applied; 1 pending") -python .\scripts\sql_migrate.py -``` - -### Testing - -```powershell -# Run all SQL tests -.\venv\Scripts\python.exe -m pytest tests\test_sql_integration.py -v - -# Run specific test -.\venv\Scripts\python.exe -m pytest tests\test_sql_integration.py::test_saturation_detection -v -``` - -### Pool Scaling - -```powershell -# Increase pool size to 30 connections -$env:QAI_SQL_URL = "postgresql://user:pass@host/db?pool_size=30&max_overflow=10" -``` - -### Alert Deployment - -```powershell -# Dry-run (validate without deploying) -.\scripts\setup_azure_alerts.ps1 -ResourceGroup "rg" -FunctionAppName "func" -DryRun - -# Deploy with new Action Group -.\scripts\setup_azure_alerts.ps1 -ResourceGroup "rg" -FunctionAppName "func" -ActionGroupName "alerts" -EmailRecipient "admin@example.com" - -# Use existing Action Group -.\scripts\setup_azure_alerts.ps1 -ResourceGroup "rg" -FunctionAppName "func" -SkipActionGroup -``` - -## 📝 Key Files - -| File | Purpose | -| ---------------------------------- | ------------------------------------------ | -| `DATABASE_SQL_SETUP.md` | Complete setup & usage guide (20 sections) | -| `AZURE_MONITOR_SQL_SETUP.md` | Azure deployment & KQL queries | -| `SQL_INTEGRATION_COMPLETE.md` | Implementation summary & architecture | -| `shared/sql_engine.py` | Core engine with pool metrics | -| `shared/sql_repository.py` | Key-value CRUD abstraction | -| `scripts/sql_migrate.py` | Migration runner | -| `scripts/setup_azure_alerts.ps1` | Alert deployment automation | -| `config/azure_monitor_alerts.json` | ARM template for alerts | -| `tests/test_sql_integration.py` | Test suite (7 tests) | - -## 🔍 Troubleshooting Quick Hits - -| Issue | Solution | -| ----------------------------- | ---------------------------------------------------------------------------- | -| "No SQL configured" in status | Set `QAI_SQL_URL` or `QAI_DB_CONN` env var | -| Pool saturation > 80% | Increase `pool_size` URL parameter (see DATABASE_SQL_SETUP.md section 18) | -| Too many slow query alerts | Increase `QAI_SQL_SLOW_MS` or optimize queries | -| Migrations not applying | Check `QAI_Migrations` table exists, verify SQL file syntax | -| Tests failing | Ensure SQLAlchemy installed in venv: `.\venv\Scripts\pip install sqlalchemy` | -| Alerts not firing | Verify Application Insights enabled, check KQL query in portal | - -## 📚 KQL Quick Queries - -### Pool Saturation Trend - -```kql -traces | where message has "saturated" | extend saturation_pct = todouble(extract(@"(\d+\.\d+)% saturated", 1, message)) | render timechart -``` - -### Slow Query Frequency - -```kql -traces | where message has "slow query" | summarize count() by bin(timestamp, 5m) | render timechart -``` - -### Top Slow Queries - -```kql -traces | where message has "slow query" | extend sql = extract(@"sql=(.{1,120})", 1, message) | summarize count() by sql | order by count_ desc | take 10 -``` - -## ✅ Production Deployment Checklist - -- [ ] Tests passing locally (7/7) -- [ ] Connection string configured in Azure -- [ ] Alert rules deployed via PowerShell script -- [ ] Action Group configured with team emails -- [ ] Application Insights enabled on Function App -- [ ] Baseline threshold set (`QAI_SQL_SLOW_MS=500`) -- [ ] Pool size calculated based on load (`pool_size=20` for medium traffic) -- [ ] KQL dashboard created in Application Insights -- [ ] Weekly review scheduled for slow query trends -- [ ] Retention cleanup scheduled (if query tracking enabled) - -## 🎯 Performance Targets - -| Environment | Slow Query Threshold | Pool Size | Target P95 | -| ----------- | -------------------- | --------- | ---------- | -| Development | 100ms | 5 | < 150ms | -| Staging | 300ms | 10-20 | < 400ms | -| Production | 500ms | 20-50 | < 600ms | - -## 🚨 Alert Response Playbook - -### Saturation Alert Fires - -1. Check current load in Application Insights -2. Review recent deployments (correlation) -3. Increase `pool_size` by 50% via URL parameter -4. Monitor for 24 hours, iterate - -### Slow Query Alert Fires - -1. Run "Top Slow Queries" KQL in Application Insights -2. Identify frequently-executed queries -3. Add indexes via new migration file -4. Deploy migration, monitor improvement - -### Both Alerts Fire Together - -1. Likely indicates high load or database performance issue -2. Scale pool size immediately -3. Check database server metrics (CPU, I/O, locks) -4. Consider read replicas or query optimization - ---- - -**Full Documentation**: [DATABASE_SQL_SETUP.md](../database/DATABASE_SQL_SETUP.md) | [AZURE_MONITOR_SQL_SETUP.md](../database/AZURE_MONITOR_SQL_SETUP.md) -**Implementation Details**: [SQL_INTEGRATION_COMPLETE.md](../database/SQL_INTEGRATION_COMPLETE.md) -**Tests**: `.\venv\Scripts\python.exe -m pytest tests\test_sql_integration.py -v` +# SQL Integration - Quick Reference Card + +## 🚀 Quick Start (5 Minutes) + +### Local Development + +```powershell +# 1. Set connection string +$env:QAI_SQL_URL = "sqlite:///./data.sqlite" + +# 2. Run tests +.\venv\Scripts\python.exe -m pytest tests\test_sql_integration.py -v + +# 3. Start Function App +func host start + +# 4. Check status +Invoke-RestMethod http://localhost:7071/api/ai/status | ConvertTo-Json -Depth 5 +``` + +### Azure Production + +```powershell +# 1. Deploy alerts +.\scripts\setup_azure_alerts.ps1 -ResourceGroup "your-rg" -FunctionAppName "your-func" -ActionGroupName "sql-alerts" -EmailRecipient "admin@example.com" + +# 2. Configure connection +az functionapp config appsettings set --name your-func --resource-group your-rg --settings QAI_SQL_URL="mssql+pyodbc://..." + +# 3. Monitor in Application Insights (see AZURE_MONITOR_SQL_SETUP.md for KQL queries) +``` + +## 📊 Key Endpoints + +| Endpoint | Purpose | Example | +| ---------------- | -------------------- | -------------------------------------------------------------------- | +| `/api/ai/status` | Health + SQL metrics | `curl http://localhost:7071/api/ai/status` | +| `engine_stats()` | Pool metrics (code) | `from shared.sql_engine import engine_stats; stats = engine_stats()` | +| `sql_health()` | Connectivity probe | `from shared.sql_engine import sql_health; ok, msg = sql_health()` | + +## 🔧 Environment Variables + +| Variable | Required | Example | +| ----------------------------- | -------- | ------------------------------------------------- | +| `QAI_SQL_URL` | No | `sqlite:///./data.sqlite` or `mssql+pyodbc://...` | +| `QAI_SQL_SLOW_MS` | No | `300` (milliseconds) | +| `QAI_ENABLE_QUERY_TRACKING` | No | `true` (enable persistent metrics) | +| `AZURE_FUNCTIONS_ENVIRONMENT` | No | `development` / `staging` / `production` | + +## 📈 Monitoring Thresholds + +| Metric | Warning | Critical | Action | +| ------------------- | ------- | -------- | ---------------------------------- | +| `saturation_pct` | > 60% | > 80% | Scale pool size | +| `slow_queries_1min` | > 5 | > 10 | Tune threshold or optimize queries | +| `execution_time_ms` | > P95 | > P99 | Add indexes, review query plan | + +## 🛠️ Common Commands + +### Migrations + +```powershell +# Apply all pending migrations +python .\scripts\sql_migrate.py + +# Check migration status (look at output: "2 applied; 1 pending") +python .\scripts\sql_migrate.py +``` + +### Testing + +```powershell +# Run all SQL tests +.\venv\Scripts\python.exe -m pytest tests\test_sql_integration.py -v + +# Run specific test +.\venv\Scripts\python.exe -m pytest tests\test_sql_integration.py::test_saturation_detection -v +``` + +### Pool Scaling + +```powershell +# Increase pool size to 30 connections +$env:QAI_SQL_URL = "postgresql://user:pass@host/db?pool_size=30&max_overflow=10" +``` + +### Alert Deployment + +```powershell +# Dry-run (validate without deploying) +.\scripts\setup_azure_alerts.ps1 -ResourceGroup "rg" -FunctionAppName "func" -DryRun + +# Deploy with new Action Group +.\scripts\setup_azure_alerts.ps1 -ResourceGroup "rg" -FunctionAppName "func" -ActionGroupName "alerts" -EmailRecipient "admin@example.com" + +# Use existing Action Group +.\scripts\setup_azure_alerts.ps1 -ResourceGroup "rg" -FunctionAppName "func" -SkipActionGroup +``` + +## 📝 Key Files + +| File | Purpose | +| ---------------------------------- | ------------------------------------------ | +| `DATABASE_SQL_SETUP.md` | Complete setup & usage guide (20 sections) | +| `AZURE_MONITOR_SQL_SETUP.md` | Azure deployment & KQL queries | +| `SQL_INTEGRATION_COMPLETE.md` | Implementation summary & architecture | +| `shared/sql_engine.py` | Core engine with pool metrics | +| `shared/sql_repository.py` | Key-value CRUD abstraction | +| `scripts/sql_migrate.py` | Migration runner | +| `scripts/setup_azure_alerts.ps1` | Alert deployment automation | +| `config/azure_monitor_alerts.json` | ARM template for alerts | +| `tests/test_sql_integration.py` | Test suite (7 tests) | + +## 🔍 Troubleshooting Quick Hits + +| Issue | Solution | +| ----------------------------- | ---------------------------------------------------------------------------- | +| "No SQL configured" in status | Set `QAI_SQL_URL` or `QAI_DB_CONN` env var | +| Pool saturation > 80% | Increase `pool_size` URL parameter (see DATABASE_SQL_SETUP.md section 18) | +| Too many slow query alerts | Increase `QAI_SQL_SLOW_MS` or optimize queries | +| Migrations not applying | Check `QAI_Migrations` table exists, verify SQL file syntax | +| Tests failing | Ensure SQLAlchemy installed in venv: `.\venv\Scripts\pip install sqlalchemy` | +| Alerts not firing | Verify Application Insights enabled, check KQL query in portal | + +## 📚 KQL Quick Queries + +### Pool Saturation Trend + +```kql +traces | where message has "saturated" | extend saturation_pct = todouble(extract(@"(\d+\.\d+)% saturated", 1, message)) | render timechart +``` + +### Slow Query Frequency + +```kql +traces | where message has "slow query" | summarize count() by bin(timestamp, 5m) | render timechart +``` + +### Top Slow Queries + +```kql +traces | where message has "slow query" | extend sql = extract(@"sql=(.{1,120})", 1, message) | summarize count() by sql | order by count_ desc | take 10 +``` + +## ✅ Production Deployment Checklist + +- [ ] Tests passing locally (7/7) +- [ ] Connection string configured in Azure +- [ ] Alert rules deployed via PowerShell script +- [ ] Action Group configured with team emails +- [ ] Application Insights enabled on Function App +- [ ] Baseline threshold set (`QAI_SQL_SLOW_MS=500`) +- [ ] Pool size calculated based on load (`pool_size=20` for medium traffic) +- [ ] KQL dashboard created in Application Insights +- [ ] Weekly review scheduled for slow query trends +- [ ] Retention cleanup scheduled (if query tracking enabled) + +## 🎯 Performance Targets + +| Environment | Slow Query Threshold | Pool Size | Target P95 | +| ----------- | -------------------- | --------- | ---------- | +| Development | 100ms | 5 | < 150ms | +| Staging | 300ms | 10-20 | < 400ms | +| Production | 500ms | 20-50 | < 600ms | + +## 🚨 Alert Response Playbook + +### Saturation Alert Fires + +1. Check current load in Application Insights +2. Review recent deployments (correlation) +3. Increase `pool_size` by 50% via URL parameter +4. Monitor for 24 hours, iterate + +### Slow Query Alert Fires + +1. Run "Top Slow Queries" KQL in Application Insights +2. Identify frequently-executed queries +3. Add indexes via new migration file +4. Deploy migration, monitor improvement + +### Both Alerts Fire Together + +1. Likely indicates high load or database performance issue +2. Scale pool size immediately +3. Check database server metrics (CPU, I/O, locks) +4. Consider read replicas or query optimization + +--- + +**Full Documentation**: [DATABASE_SQL_SETUP.md](../database/DATABASE_SQL_SETUP.md) | [AZURE_MONITOR_SQL_SETUP.md](../database/AZURE_MONITOR_SQL_SETUP.md) +**Implementation Details**: [SQL_INTEGRATION_COMPLETE.md](../database/SQL_INTEGRATION_COMPLETE.md) +**Tests**: `.\venv\Scripts\python.exe -m pytest tests\test_sql_integration.py -v` diff --git a/docs/quickref/TRAINING_COMMANDS_QUICKREF.md b/docs/quickref/TRAINING_COMMANDS_QUICKREF.md index bafb2d1ba..15fff6b6e 100644 --- a/docs/quickref/TRAINING_COMMANDS_QUICKREF.md +++ b/docs/quickref/TRAINING_COMMANDS_QUICKREF.md @@ -1,220 +1,220 @@ -# Training Commands Quick Reference - -Last updated: November 23, 2025 - -## Chat/LLM Training - -### AutoTrain Pipeline (Sequential Jobs) - -```powershell -# Dry-run validation -python .\scripts\autotrain.py --dry-run - -# Run all configured training jobs -python .\scripts\autotrain.py - -# Check status -python .\scripts\autotrain.py --status -``` - -### Smart Orchestrator (Pipeline with Dependencies) - -```powershell -# Run variants pipeline (hyperparameter exploration) -python .\scripts\smart_orchestrator.py --pipeline variants - -# Run full pipeline (train + eval + deploy) -python .\scripts\smart_orchestrator.py --pipeline full - -# Quick validation pipeline -python .\scripts\smart_orchestrator.py --pipeline quick - -# Watch active jobs -python .\scripts\smart_orchestrator.py --watch - -# Dry-run -python .\scripts\smart_orchestrator.py --pipeline variants --dry-run -``` - -### Direct LoRA Training - -```powershell -cd AI\microsoft_phi-silica-3.6_v1 - -# Train on specific dataset -python .\scripts\train_lora.py ` - --dataset ..\..\datasets\chat\comprehensive ` - --hf-model-id microsoft/Phi-3.5-mini-instruct ` - --epochs 3 ` - --learning-rate 0.0002 ` - --device auto - -# Quick smoke test (CPU friendly) -python .\scripts\train_lora.py ` - --dataset ..\..\datasets\chat\mixed_chat ` - --max-train-samples 64 ` - --max-eval-samples 16 ` - --epochs 1 ` - --device cpu -``` - -## Quantum AI Training - -### Correct Arguments - -```powershell -cd quantum-ai - -# Train on custom CSV -python .\train_custom_dataset.py ` - --csv ..\datasets\massive_quantum\synthetic_blobs_1092s_37f_3c_0116.csv ` - --n-qubits 5 ` - --epochs 2 ` - --batch-size 64 - -# Use preset dataset -python .\train_custom_dataset.py ` - --preset heart ` - --n-qubits 5 ` - --epochs 5 ` - --batch-size 16 - -# Other presets: ionosphere, sonar, banknote -``` - -### ❌ WRONG Arguments (Common Mistakes) - -```powershell -# ❌ Don't use --dataset (use --csv) -# ❌ Don't use --qubits (use --n-qubits) -# ❌ Don't use --layers (not supported - layers controlled by config) -``` - -### Quantum AutoRun Pipeline - -```powershell -# Dry-run -python .\scripts\quantum_autorun.py --dry-run - -# Run all quantum jobs -python .\scripts\quantum_autorun.py - -# Check status -python .\scripts\quantum_autorun.py --status -``` - -## Data Generation - -### Synthetic Quantum Datasets - -```powershell -# Generate 200 diverse quantum datasets -python .\scripts\generate_synthetic_datasets.py ` - --count 200 ` - --min-samples 200 ` - --max-samples 10000 ` - --min-features 2 ` - --max-features 50 ` - --min-classes 2 ` - --max-classes 8 -``` - -### Synthetic Chat Datasets - -```powershell -# Generate from repository code -python .\scripts\generate_repo_training_dataset.py --max-records 500 - -# Augment dataset (3x expansion) -python .\AI\microsoft_phi-silica-3.6_v1\scripts\data_augmenter.py ` - --input .\datasets\chat\app_repo\train.json ` - --output .\datasets\chat\app_repo_augmented\train.json ` - --num-aug 2 ` - --prob 0.15 -``` - -### Merge Datasets - -```powershell -# Combine multiple chat datasets -python .\scripts\merge_chat_datasets.py ` - --source .\datasets\chat\mixed_chat .\datasets\chat\dolly .\datasets\chat\app_repo_augmented ` - --out-dir .\datasets\chat\comprehensive ` - --train-ratio 0.9 -``` - -## Testing & Validation - -### Run Tests - -```powershell -# Quick tests (non-slow) -python -m pytest -m "not slow" -q - -# All tests -python -m pytest - -# Specific test file -python -m pytest tests\test_autotrain.py -v - -# With coverage -python -m pytest --cov=. --cov-report=html -``` - -### Environment Health Check - -```powershell -# Check model venv health -python .\scripts\env_autofix.py --dry-run - -# Force rebuild if needed -python .\scripts\env_autofix.py --force -``` - -## Monitoring & Results - -### Check Training Progress - -```powershell -# View autotrain logs -Get-Content .\data_out\autotrain\phi35_comprehensive_full\*\stdout.log -Wait - -# Check orchestrator status -cat .\data_out\smart_orchestrator\variants_summary.json - -# View metrics ranking -cat .\data_out\metrics_ranker\ranking_summary.json -``` - -### Dashboard - -```powershell -# Start status dashboard -python .\scripts\status_dashboard.py - -# Access at http://localhost:5000 -``` - -## Current Training Status - -### Active Jobs (as of last run) - -- **Chat Training**: 11 jobs (comprehensive, baselines, domain-specific, hyperparameters) -- **Quantum Training**: 198 synthetic datasets available -- **Total Training Data**: 15,277 chat samples, 198 quantum datasets - -### Dataset Locations - -- Chat (comprehensive): `datasets/chat/comprehensive/` (15.2K samples) -- Chat (augmented repo): `datasets/chat/app_repo_augmented/` (1.5K samples) -- Quantum (synthetic): `datasets/massive_quantum/` (198 datasets) -- Quantum (UCI): `datasets/quantum/` (29 datasets) - -## Tips - -1. **Always run dry-run first** to validate configuration -2. **Use `--device auto`** for GPU training (falls back to CPU if needed) -3. **Start with small datasets** for quick iteration (max-train-samples) -4. **Monitor GPU usage**: `nvidia-smi -l 1` (if available) -5. **Check logs regularly** to catch issues early -6. **Run tests after major changes** to ensure no regressions +# Training Commands Quick Reference + +Last updated: November 23, 2025 + +## Chat/LLM Training + +### AutoTrain Pipeline (Sequential Jobs) + +```powershell +# Dry-run validation +python .\scripts\autotrain.py --dry-run + +# Run all configured training jobs +python .\scripts\autotrain.py + +# Check status +python .\scripts\autotrain.py --status +``` + +### Smart Orchestrator (Pipeline with Dependencies) + +```powershell +# Run variants pipeline (hyperparameter exploration) +python .\scripts\smart_orchestrator.py --pipeline variants + +# Run full pipeline (train + eval + deploy) +python .\scripts\smart_orchestrator.py --pipeline full + +# Quick validation pipeline +python .\scripts\smart_orchestrator.py --pipeline quick + +# Watch active jobs +python .\scripts\smart_orchestrator.py --watch + +# Dry-run +python .\scripts\smart_orchestrator.py --pipeline variants --dry-run +``` + +### Direct LoRA Training + +```powershell +cd AI\microsoft_phi-silica-3.6_v1 + +# Train on specific dataset +python .\scripts\train_lora.py ` + --dataset ..\..\datasets\chat\comprehensive ` + --hf-model-id microsoft/Phi-3.5-mini-instruct ` + --epochs 3 ` + --learning-rate 0.0002 ` + --device auto + +# Quick smoke test (CPU friendly) +python .\scripts\train_lora.py ` + --dataset ..\..\datasets\chat\mixed_chat ` + --max-train-samples 64 ` + --max-eval-samples 16 ` + --epochs 1 ` + --device cpu +``` + +## Quantum AI Training + +### Correct Arguments + +```powershell +cd quantum-ai + +# Train on custom CSV +python .\train_custom_dataset.py ` + --csv ..\datasets\massive_quantum\synthetic_blobs_1092s_37f_3c_0116.csv ` + --n-qubits 5 ` + --epochs 2 ` + --batch-size 64 + +# Use preset dataset +python .\train_custom_dataset.py ` + --preset heart ` + --n-qubits 5 ` + --epochs 5 ` + --batch-size 16 + +# Other presets: ionosphere, sonar, banknote +``` + +### ❌ WRONG Arguments (Common Mistakes) + +```powershell +# ❌ Don't use --dataset (use --csv) +# ❌ Don't use --qubits (use --n-qubits) +# ❌ Don't use --layers (not supported - layers controlled by config) +``` + +### Quantum AutoRun Pipeline + +```powershell +# Dry-run +python .\scripts\quantum_autorun.py --dry-run + +# Run all quantum jobs +python .\scripts\quantum_autorun.py + +# Check status +python .\scripts\quantum_autorun.py --status +``` + +## Data Generation + +### Synthetic Quantum Datasets + +```powershell +# Generate 200 diverse quantum datasets +python .\scripts\generate_synthetic_datasets.py ` + --count 200 ` + --min-samples 200 ` + --max-samples 10000 ` + --min-features 2 ` + --max-features 50 ` + --min-classes 2 ` + --max-classes 8 +``` + +### Synthetic Chat Datasets + +```powershell +# Generate from repository code +python .\scripts\generate_repo_training_dataset.py --max-records 500 + +# Augment dataset (3x expansion) +python .\AI\microsoft_phi-silica-3.6_v1\scripts\data_augmenter.py ` + --input .\datasets\chat\app_repo\train.json ` + --output .\datasets\chat\app_repo_augmented\train.json ` + --num-aug 2 ` + --prob 0.15 +``` + +### Merge Datasets + +```powershell +# Combine multiple chat datasets +python .\scripts\merge_chat_datasets.py ` + --source .\datasets\chat\mixed_chat .\datasets\chat\dolly .\datasets\chat\app_repo_augmented ` + --out-dir .\datasets\chat\comprehensive ` + --train-ratio 0.9 +``` + +## Testing & Validation + +### Run Tests + +```powershell +# Quick tests (non-slow) +python -m pytest -m "not slow" -q + +# All tests +python -m pytest + +# Specific test file +python -m pytest tests\test_autotrain.py -v + +# With coverage +python -m pytest --cov=. --cov-report=html +``` + +### Environment Health Check + +```powershell +# Check model venv health +python .\scripts\env_autofix.py --dry-run + +# Force rebuild if needed +python .\scripts\env_autofix.py --force +``` + +## Monitoring & Results + +### Check Training Progress + +```powershell +# View autotrain logs +Get-Content .\data_out\autotrain\phi35_comprehensive_full\*\stdout.log -Wait + +# Check orchestrator status +cat .\data_out\smart_orchestrator\variants_summary.json + +# View metrics ranking +cat .\data_out\metrics_ranker\ranking_summary.json +``` + +### Dashboard + +```powershell +# Start status dashboard +python .\scripts\status_dashboard.py + +# Access at http://localhost:5000 +``` + +## Current Training Status + +### Active Jobs (as of last run) + +- **Chat Training**: 11 jobs (comprehensive, baselines, domain-specific, hyperparameters) +- **Quantum Training**: 198 synthetic datasets available +- **Total Training Data**: 15,277 chat samples, 198 quantum datasets + +### Dataset Locations + +- Chat (comprehensive): `datasets/chat/comprehensive/` (15.2K samples) +- Chat (augmented repo): `datasets/chat/app_repo_augmented/` (1.5K samples) +- Quantum (synthetic): `datasets/massive_quantum/` (198 datasets) +- Quantum (UCI): `datasets/quantum/` (29 datasets) + +## Tips + +1. **Always run dry-run first** to validate configuration +2. **Use `--device auto`** for GPU training (falls back to CPU if needed) +3. **Start with small datasets** for quick iteration (max-train-samples) +4. **Monitor GPU usage**: `nvidia-smi -l 1` (if available) +5. **Check logs regularly** to catch issues early +6. **Run tests after major changes** to ensure no regressions diff --git a/docs/quickref/TRAINING_TAB_QUICKREF.md b/docs/quickref/TRAINING_TAB_QUICKREF.md index 92d1aede4..2b592effd 100644 --- a/docs/quickref/TRAINING_TAB_QUICKREF.md +++ b/docs/quickref/TRAINING_TAB_QUICKREF.md @@ -1,253 +1,253 @@ -# Training Tab - Quick Reference Card - -## 🚀 Quick Start (30 seconds) - -### For Testing Pipeline -``` -1. Open: http://localhost:8000/unified.html -2. Click "Train" tab -3. Enter job name: test_run_001 -4. Select dataset from dropdown -5. Click "⚡ Quick Test" preset button -6. Click "🚀 Start Training" -``` - -### For Production Training -``` -1. Open: http://localhost:8000/unified.html -2. Click "Train" tab -3. Enter descriptive job name: prod_chatbot_v2 -4. Select dataset with >1k samples -5. Click "🚀 Production" preset button -6. Review estimates (time & VRAM) -7. Click "🚀 Start Training" -8. Switch to "Jobs" tab to monitor -``` - -## 📋 Quick Presets Comparison - -| Preset | Epochs | Samples | LoRA Rank | Time | Use Case | -|--------|--------|---------|-----------|------|----------| -| ⚡ Quick Test | 1 | 100 | 4 | ~2 min | Pipeline testing | -| 📊 Standard | 3 | 1k | 8 | ~10 min | Iterative dev | -| 🏆 Full | 5 | All | 16 | ~60 min | Thorough training | -| 🚀 Production | 10 | All | 32 | ~4 hours | Production quality | - -## 🎯 Common Workflows - -### Experiment Workflow -``` -1. Load dataset → 2. Quick preset → 3. Train → 4. Evaluate - ↓ -5. Adjust params → 6. Save config → 7. Retrain → 8. Compare -``` - -### Production Workflow -``` -1. Select best dataset → 2. Production preset → 3. Tweak advanced - ↓ -4. Save config → 5. Train → 6. Evaluate → 7. Deploy -``` - -### Hyperparameter Tuning -``` -1. Standard preset → 2. Save as baseline.json - ↓ -3. Toggle "Advanced Options" → 4. Modify LoRA rank - ↓ -5. Save as variant_rank16.json → 6. Train → 7. Compare results -``` - -## ⚙️ Advanced Options Explained - -### When to Adjust - -**Batch Size** ↑ -- More GPU memory available -- Want faster training -- Dataset is large (>10k samples) - -**LoRA Rank** ↑ -- Model underfitting -- Complex task (reasoning, multilingual) -- Have extra training time - -**Learning Rate** ↓ -- Training unstable (loss spikes) -- Fine-tuning pre-trained adapter -- Small dataset (<500 samples) - -**Gradient Accumulation** ↑ -- Batch size limited by memory -- Want larger effective batch -- Training is too noisy - -**Weight Decay** ↑ -- Model overfitting -- Training loss << eval loss -- Dataset has duplicates - -## 🔍 Validation Rules - -### Job Name -- ✅ `my_test_job`, `prod_v2`, `exp_rank_16` -- ❌ `My Test`, `prod-v2`, `exp rank 16` -- Rule: lowercase, numbers, underscores only - -### Epochs -- Range: 1-20 -- Recommended: 3-5 for most tasks -- Warning: >10 shows confirmation dialog - -### Max Samples -- Min: 10 (or -1 for all) -- Recommended: Start with 1000, scale up -- -1 = Use entire dataset - -## 💾 Config Management - -### Save Config -``` -Purpose: Backup successful configs -Format: JSON file with all parameters -Usage: Share with team, version control -Location: Downloads folder -``` - -### Load Config -``` -Purpose: Restore previous settings -Format: .json files exported via "Save" -Usage: Reproduce results, iterate on proven configs -Action: Opens file picker -``` - -## 📊 Estimates Explained - -### Time Estimate -``` -Based on: -- Training samples -- Batch size -- Number of epochs -- ~0.5 seconds per step estimate - -Example: -1000 samples, batch 2, 3 epochs -= (1000/2) × 3 × 0.5s = ~12 minutes -``` - -### VRAM Estimate -``` -Based on: -- Base model size (~3.5 GB) -- LoRA rank (additional 0.5 GB per 8 rank) - -Example: -LoRA rank 16 -= 3.5 + (16/8 × 0.5) = ~4.5 GB -``` - -## ⌨️ Keyboard Shortcuts - -| Key | Action | Tab | -|-----|--------|-----| -| `6` | Switch to Train tab | Any | -| `r` | Refresh data | Any | -| `a` | Toggle auto-refresh | Any | -| `d` | Toggle dark mode | Any | - -## 🐛 Troubleshooting - -### Dataset dropdown empty -- **Check**: Datasets tab loads correctly -- **Fix**: Verify `datasets/chat/` folder has subfolders with train.json/test.json - -### "Failed to start training" error -- **Check**: Job name follows rules (lowercase, underscores) -- **Fix**: Review validation messages, correct highlighted fields - -### Estimates seem wrong -- **Cause**: First-time estimate uses defaults -- **Fix**: Adjust one parameter (epochs) to recalculate - -### Advanced options won't expand -- **Check**: Click the "🔧 Advanced Options" header -- **Visual**: Arrow icon changes ▼ → ▲ - -### Config load doesn't populate fields -- **Check**: File is valid JSON -- **Fix**: Use files exported via "Save Config" button - -## 📈 Performance Tips - -### Faster Training -1. ↑ Batch size (if memory allows) -2. ↓ Max samples (use subset for testing) -3. ↓ LoRA rank (use 4-8 for quick runs) - -### Better Quality -1. ↑ Epochs (5-10 typical) -2. ↑ LoRA rank (16-32 for complex tasks) -3. ↑ Max samples (use full dataset) -4. Enable evaluation (catch overfitting early) - -### Memory Optimization -1. ↓ Batch size to 1 -2. ↑ Gradient accumulation (simulate larger batch) -3. ↓ LoRA rank to minimum (4) - -## 🎓 Learning Path - -### Beginner (Day 1) -1. Use Quick Test preset -2. Observe job progress in Jobs tab -3. Try Standard preset with different datasets - -### Intermediate (Week 1) -1. Open Advanced Options -2. Experiment with LoRA rank (8 → 16) -3. Compare results, save successful configs - -### Advanced (Month 1) -1. Custom parameter combinations -2. Hyperparameter sweeps (save multiple configs) -3. Production deployments with optimized settings - -## 📚 Related Documentation - -- **Full Guide**: `TRAINING_TAB_ENHANCEMENTS.md` -- **Training Orchestration**: `AUTOTRAIN_README.md` -- **Dashboard Overview**: `DASHBOARD_ENHANCEMENTS.md` -- **Server Setup**: `DATABASE_INTEGRATION_GUIDE.md` - -## 🎉 Feature Highlights - -✨ **20+ New Features** -- Advanced options (collapsible) -- Real-time estimates -- 4 quick presets -- Config save/load -- Enhanced validation - -🚀 **Production Ready** -- Comprehensive error handling -- Smart defaults -- Professional UX -- Team-friendly config sharing - -🎯 **User-Centric** -- Tooltips on every field -- Clear validation messages -- Dynamic feedback -- Beginner to expert support - ---- - -**Quick Access**: http://localhost:8000/unified.html → Train Tab - -**Help**: Hover over any field for tooltip guidance - -**Status**: ✅ All systems operational - -*Last Updated: November 25, 2025* +# Training Tab - Quick Reference Card + +## 🚀 Quick Start (30 seconds) + +### For Testing Pipeline +``` +1. Open: http://localhost:8000/unified.html +2. Click "Train" tab +3. Enter job name: test_run_001 +4. Select dataset from dropdown +5. Click "⚡ Quick Test" preset button +6. Click "🚀 Start Training" +``` + +### For Production Training +``` +1. Open: http://localhost:8000/unified.html +2. Click "Train" tab +3. Enter descriptive job name: prod_chatbot_v2 +4. Select dataset with >1k samples +5. Click "🚀 Production" preset button +6. Review estimates (time & VRAM) +7. Click "🚀 Start Training" +8. Switch to "Jobs" tab to monitor +``` + +## 📋 Quick Presets Comparison + +| Preset | Epochs | Samples | LoRA Rank | Time | Use Case | +|--------|--------|---------|-----------|------|----------| +| ⚡ Quick Test | 1 | 100 | 4 | ~2 min | Pipeline testing | +| 📊 Standard | 3 | 1k | 8 | ~10 min | Iterative dev | +| 🏆 Full | 5 | All | 16 | ~60 min | Thorough training | +| 🚀 Production | 10 | All | 32 | ~4 hours | Production quality | + +## 🎯 Common Workflows + +### Experiment Workflow +``` +1. Load dataset → 2. Quick preset → 3. Train → 4. Evaluate + ↓ +5. Adjust params → 6. Save config → 7. Retrain → 8. Compare +``` + +### Production Workflow +``` +1. Select best dataset → 2. Production preset → 3. Tweak advanced + ↓ +4. Save config → 5. Train → 6. Evaluate → 7. Deploy +``` + +### Hyperparameter Tuning +``` +1. Standard preset → 2. Save as baseline.json + ↓ +3. Toggle "Advanced Options" → 4. Modify LoRA rank + ↓ +5. Save as variant_rank16.json → 6. Train → 7. Compare results +``` + +## ⚙️ Advanced Options Explained + +### When to Adjust + +**Batch Size** ↑ +- More GPU memory available +- Want faster training +- Dataset is large (>10k samples) + +**LoRA Rank** ↑ +- Model underfitting +- Complex task (reasoning, multilingual) +- Have extra training time + +**Learning Rate** ↓ +- Training unstable (loss spikes) +- Fine-tuning pre-trained adapter +- Small dataset (<500 samples) + +**Gradient Accumulation** ↑ +- Batch size limited by memory +- Want larger effective batch +- Training is too noisy + +**Weight Decay** ↑ +- Model overfitting +- Training loss << eval loss +- Dataset has duplicates + +## 🔍 Validation Rules + +### Job Name +- ✅ `my_test_job`, `prod_v2`, `exp_rank_16` +- ❌ `My Test`, `prod-v2`, `exp rank 16` +- Rule: lowercase, numbers, underscores only + +### Epochs +- Range: 1-20 +- Recommended: 3-5 for most tasks +- Warning: >10 shows confirmation dialog + +### Max Samples +- Min: 10 (or -1 for all) +- Recommended: Start with 1000, scale up +- -1 = Use entire dataset + +## 💾 Config Management + +### Save Config +``` +Purpose: Backup successful configs +Format: JSON file with all parameters +Usage: Share with team, version control +Location: Downloads folder +``` + +### Load Config +``` +Purpose: Restore previous settings +Format: .json files exported via "Save" +Usage: Reproduce results, iterate on proven configs +Action: Opens file picker +``` + +## 📊 Estimates Explained + +### Time Estimate +``` +Based on: +- Training samples +- Batch size +- Number of epochs +- ~0.5 seconds per step estimate + +Example: +1000 samples, batch 2, 3 epochs += (1000/2) × 3 × 0.5s = ~12 minutes +``` + +### VRAM Estimate +``` +Based on: +- Base model size (~3.5 GB) +- LoRA rank (additional 0.5 GB per 8 rank) + +Example: +LoRA rank 16 += 3.5 + (16/8 × 0.5) = ~4.5 GB +``` + +## ⌨️ Keyboard Shortcuts + +| Key | Action | Tab | +|-----|--------|-----| +| `6` | Switch to Train tab | Any | +| `r` | Refresh data | Any | +| `a` | Toggle auto-refresh | Any | +| `d` | Toggle dark mode | Any | + +## 🐛 Troubleshooting + +### Dataset dropdown empty +- **Check**: Datasets tab loads correctly +- **Fix**: Verify `datasets/chat/` folder has subfolders with train.json/test.json + +### "Failed to start training" error +- **Check**: Job name follows rules (lowercase, underscores) +- **Fix**: Review validation messages, correct highlighted fields + +### Estimates seem wrong +- **Cause**: First-time estimate uses defaults +- **Fix**: Adjust one parameter (epochs) to recalculate + +### Advanced options won't expand +- **Check**: Click the "🔧 Advanced Options" header +- **Visual**: Arrow icon changes ▼ → ▲ + +### Config load doesn't populate fields +- **Check**: File is valid JSON +- **Fix**: Use files exported via "Save Config" button + +## 📈 Performance Tips + +### Faster Training +1. ↑ Batch size (if memory allows) +2. ↓ Max samples (use subset for testing) +3. ↓ LoRA rank (use 4-8 for quick runs) + +### Better Quality +1. ↑ Epochs (5-10 typical) +2. ↑ LoRA rank (16-32 for complex tasks) +3. ↑ Max samples (use full dataset) +4. Enable evaluation (catch overfitting early) + +### Memory Optimization +1. ↓ Batch size to 1 +2. ↑ Gradient accumulation (simulate larger batch) +3. ↓ LoRA rank to minimum (4) + +## 🎓 Learning Path + +### Beginner (Day 1) +1. Use Quick Test preset +2. Observe job progress in Jobs tab +3. Try Standard preset with different datasets + +### Intermediate (Week 1) +1. Open Advanced Options +2. Experiment with LoRA rank (8 → 16) +3. Compare results, save successful configs + +### Advanced (Month 1) +1. Custom parameter combinations +2. Hyperparameter sweeps (save multiple configs) +3. Production deployments with optimized settings + +## 📚 Related Documentation + +- **Full Guide**: `TRAINING_TAB_ENHANCEMENTS.md` +- **Training Orchestration**: `AUTOTRAIN_README.md` +- **Dashboard Overview**: `DASHBOARD_ENHANCEMENTS.md` +- **Server Setup**: `DATABASE_INTEGRATION_GUIDE.md` + +## 🎉 Feature Highlights + +✨ **20+ New Features** +- Advanced options (collapsible) +- Real-time estimates +- 4 quick presets +- Config save/load +- Enhanced validation + +🚀 **Production Ready** +- Comprehensive error handling +- Smart defaults +- Professional UX +- Team-friendly config sharing + +🎯 **User-Centric** +- Tooltips on every field +- Clear validation messages +- Dynamic feedback +- Beginner to expert support + +--- + +**Quick Access**: http://localhost:8000/unified.html → Train Tab + +**Help**: Hover over any field for tooltip guidance + +**Status**: ✅ All systems operational + +*Last Updated: November 25, 2025* diff --git a/docs/quickref/VSCODE_TESTING_QUICKREF.md b/docs/quickref/VSCODE_TESTING_QUICKREF.md index e87dc9c0c..605c1ae83 100644 --- a/docs/quickref/VSCODE_TESTING_QUICKREF.md +++ b/docs/quickref/VSCODE_TESTING_QUICKREF.md @@ -1,98 +1,98 @@ -# VS Code Testing - Quick Reference - -## 🚀 Getting Started - -### Open Test Explorer -1. Click the **beaker icon (🧪)** in the Activity Bar (left sidebar) -2. Or press `Ctrl+Shift+T` -3. Tests auto-discover from `tests/` directory - -### Run Your First Test -- Click the **▶️ play button** next to any test -- Or right-click test → "Run Test" -- Results appear instantly with ✅ or ❌ - -## ⌨️ Keyboard Shortcuts - -| Shortcut | Action | -|----------|--------| -| `Ctrl+; Ctrl+A` | Run all tests | -| `Ctrl+; Ctrl+F` | Run failed tests | -| `Ctrl+; Ctrl+L` | Run last test | -| `Ctrl+; Ctrl+D` | Debug last test | - -## 🎯 Test Profiles - -Quick test configurations accessible from Test Explorer dropdown: - -- **Unit Tests (Fast)** - Quick unit tests only (40 tests in ~0.5s) -- **Integration Tests** - External service tests (30 tests in ~3s) -- **All Fast Tests** - Everything except slow/Azure tests (83 tests in ~10s) -- **AutoTrain Tests** - Training orchestrator tests -- **Quantum Tests** - Quantum-specific test files -- **All Tests** - Complete test suite (84+ tests) -- **All with Coverage** - Full suite + coverage report - -## 🐛 Debugging Tests - -1. Set breakpoints in your code -2. Right-click test → **"Debug Test"** -3. Use Debug toolbar: Step Over (F10), Step Into (F11), Continue (F5) - -**Pro Tip:** Test Explorer shows you exactly which line failed with stack traces! - -## 📊 View Results - -- **Test Output:** Click any test to see stdout/stderr -- **Coverage:** Run with coverage profile, then open `htmlcov/index.html` -- **Filters:** Use Test Explorer search box to find specific tests -- **Test Status:** - - ✅ = Passed - - ❌ = Failed - - ⏭️ = Skipped - - 🔄 = Running - -## 💡 Pro Tips - -1. **Run tests on save:** Tests auto-discover when you save files -2. **Focus on failures:** Click "Run Failed Tests" to re-run only what broke -3. **Use markers:** Filter tests by `unit`, `integration`, `slow`, `azure` -4. **Debug efficiently:** Set breakpoints before debugging tests -5. **Check coverage:** Run coverage profile periodically to find untested code - -## 🔧 Troubleshooting - -### "No tests found" -1. Click refresh button (🔄) in Test Explorer -2. Check Python interpreter: `Ctrl+Shift+P` → "Python: Select Interpreter" -3. Reload window: `Ctrl+Shift+P` → "Developer: Reload Window" - -### Test fails in Test Explorer but passes in terminal -- Verify working directory is set to workspace root -- Check environment variables are exported - -### Import errors -- Ensure virtual environment is activated -- Run: `pip install -r requirements.txt` - -## 📚 More Info - -See **VSCODE_TESTING_GUIDE.md** for comprehensive documentation. - ---- - -**Quick Test Commands:** - -```powershell -# Using test_runner (recommended) -python .\scripts\test_runner.py --all # All fast tests -python .\scripts\test_runner.py --unit # Unit tests only -python .\scripts\test_runner.py --integration # Integration tests -python .\scripts\test_runner.py --coverage # With coverage report - -# Direct pytest commands -python -m pytest tests/ -v -python -m pytest tests/test_autotrain_unit.py -v -python -m pytest tests/ --cov=scripts --cov=shared --cov-report=html -python -m pytest -m "not slow and not azure" tests/ -``` +# VS Code Testing - Quick Reference + +## 🚀 Getting Started + +### Open Test Explorer +1. Click the **beaker icon (🧪)** in the Activity Bar (left sidebar) +2. Or press `Ctrl+Shift+T` +3. Tests auto-discover from `tests/` directory + +### Run Your First Test +- Click the **▶️ play button** next to any test +- Or right-click test → "Run Test" +- Results appear instantly with ✅ or ❌ + +## ⌨️ Keyboard Shortcuts + +| Shortcut | Action | +|----------|--------| +| `Ctrl+; Ctrl+A` | Run all tests | +| `Ctrl+; Ctrl+F` | Run failed tests | +| `Ctrl+; Ctrl+L` | Run last test | +| `Ctrl+; Ctrl+D` | Debug last test | + +## 🎯 Test Profiles + +Quick test configurations accessible from Test Explorer dropdown: + +- **Unit Tests (Fast)** - Quick unit tests only (40 tests in ~0.5s) +- **Integration Tests** - External service tests (30 tests in ~3s) +- **All Fast Tests** - Everything except slow/Azure tests (83 tests in ~10s) +- **AutoTrain Tests** - Training orchestrator tests +- **Quantum Tests** - Quantum-specific test files +- **All Tests** - Complete test suite (84+ tests) +- **All with Coverage** - Full suite + coverage report + +## 🐛 Debugging Tests + +1. Set breakpoints in your code +2. Right-click test → **"Debug Test"** +3. Use Debug toolbar: Step Over (F10), Step Into (F11), Continue (F5) + +**Pro Tip:** Test Explorer shows you exactly which line failed with stack traces! + +## 📊 View Results + +- **Test Output:** Click any test to see stdout/stderr +- **Coverage:** Run with coverage profile, then open `htmlcov/index.html` +- **Filters:** Use Test Explorer search box to find specific tests +- **Test Status:** + - ✅ = Passed + - ❌ = Failed + - ⏭️ = Skipped + - 🔄 = Running + +## 💡 Pro Tips + +1. **Run tests on save:** Tests auto-discover when you save files +2. **Focus on failures:** Click "Run Failed Tests" to re-run only what broke +3. **Use markers:** Filter tests by `unit`, `integration`, `slow`, `azure` +4. **Debug efficiently:** Set breakpoints before debugging tests +5. **Check coverage:** Run coverage profile periodically to find untested code + +## 🔧 Troubleshooting + +### "No tests found" +1. Click refresh button (🔄) in Test Explorer +2. Check Python interpreter: `Ctrl+Shift+P` → "Python: Select Interpreter" +3. Reload window: `Ctrl+Shift+P` → "Developer: Reload Window" + +### Test fails in Test Explorer but passes in terminal +- Verify working directory is set to workspace root +- Check environment variables are exported + +### Import errors +- Ensure virtual environment is activated +- Run: `pip install -r requirements.txt` + +## 📚 More Info + +See **VSCODE_TESTING_GUIDE.md** for comprehensive documentation. + +--- + +**Quick Test Commands:** + +```powershell +# Using test_runner (recommended) +python .\scripts\test_runner.py --all # All fast tests +python .\scripts\test_runner.py --unit # Unit tests only +python .\scripts\test_runner.py --integration # Integration tests +python .\scripts\test_runner.py --coverage # With coverage report + +# Direct pytest commands +python -m pytest tests/ -v +python -m pytest tests/test_autotrain_unit.py -v +python -m pytest tests/ --cov=scripts --cov=shared --cov-report=html +python -m pytest -m "not slow and not azure" tests/ +``` diff --git a/docs/quickrefs/ARIA_QUICKREF.txt b/docs/quickrefs/ARIA_QUICKREF.txt index 11ede9bf6..57632ac83 100644 --- a/docs/quickrefs/ARIA_QUICKREF.txt +++ b/docs/quickrefs/ARIA_QUICKREF.txt @@ -1,259 +1,259 @@ -╔══════════════════════════════════════════════════════════════════════════════╗ -║ 🤖 ARIA AUTOMATION - QUICK REFERENCE 🤖 ║ -╚══════════════════════════════════════════════════════════════════════════════╝ - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ 🚀 QUICK START │ -└──────────────────────────────────────────────────────────────────────────────┘ - - Interactive Menu: - ./scripts/start_aria.sh - - Full Stack (Production): - ./scripts/start_aria.sh full - - Background Mode: - ./scripts/start_aria.sh full --background - - Server Only (Dev): - ./scripts/start_aria.sh server - - Training Once: - ./scripts/start_aria.sh training --once - - Check Status: - ./scripts/start_aria.sh status - - Stop All: - ./scripts/start_aria.sh stop - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ 📊 MONITORING │ -└──────────────────────────────────────────────────────────────────────────────┘ - - Status Check: - python3 scripts/aria_automation.py --status - - View Logs: - tail -f data_out/aria_automation/aria_automation.log - - Service Status (systemd): - sudo systemctl status aria_automation - - Service Logs: - sudo journalctl -u aria_automation -f - - Check Ports: - lsof -i :8080 # Aria server - lsof -i :7071 # Functions backend - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ 🌐 ACCESS POINTS │ -└──────────────────────────────────────────────────────────────────────────────┘ - - Aria Web Interface: - http://localhost:8080 - - Auto-Execute Page: - http://localhost:8080/auto-execute.html - - Backend Health: - http://localhost:7071/api/ai/status - - Status JSON: - cat data_out/aria_automation/status.json - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ 🔧 SYSTEMD SERVICE (Linux) │ -└──────────────────────────────────────────────────────────────────────────────┘ - - Install: - sudo cp config/aria_automation.service /etc/systemd/system/ - sudo systemctl daemon-reload - - Enable & Start: - sudo systemctl enable aria_automation - sudo systemctl start aria_automation - - Control: - sudo systemctl stop aria_automation - sudo systemctl restart aria_automation - sudo systemctl status aria_automation - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ ⏰ CRON SCHEDULING │ -└──────────────────────────────────────────────────────────────────────────────┘ - - Edit Cron: - crontab -e - - Training Every 30 Minutes: - */30 * * * * cd /workspaces/Aria && python3 scripts/aria_automation.py --mode training --once - - Start Server on Boot: - @reboot cd /workspaces/Aria && python3 scripts/aria_automation.py --mode server - - View Cron Jobs: - crontab -l - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ 🎼 MASTER ORCHESTRATOR │ -└──────────────────────────────────────────────────────────────────────────────┘ - - Start Aria Workflow: - python3 scripts/master_orchestrator.py --workflow aria_full_stack - - Check All Status: - python3 scripts/master_orchestrator.py --status - - Run as Daemon: - python3 scripts/master_orchestrator.py --daemon - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ 🆘 TROUBLESHOOTING │ -└──────────────────────────────────────────────────────────────────────────────┘ - - Stop All Processes: - python3 scripts/aria_automation.py --stop - - Kill by Port: - sudo kill $(sudo lsof -t -i:8080) - - Check Dataset: - python3 scripts/validate_datasets.py --category chat - - Test Training: - python3 scripts/aria_quick_train.py - - Clean PIDs: - rm data_out/aria_automation/processes.json - - View Errors: - cat data_out/aria_automation/status.json | grep -A 10 errors - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ 📖 DOCUMENTATION │ -└──────────────────────────────────────────────────────────────────────────────┘ - - Quick Summary: ARIA_AUTOMATION_SUMMARY.md - Complete Guide: ARIA_AUTOMATION_GUIDE.md - Aria Web Docs: aria_web/README.md - Auto-Execute: aria_web/AUTO-EXECUTE.md - Main README: README.md - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ 🎯 AUTOMATION MODES │ -└──────────────────────────────────────────────────────────────────────────────┘ - - full → Server + Backend + Training + Monitoring (Production) - server → Web Interface Only (Development) - training → Background Training Only (Batch Processing) - - ┌──────────────────────────────────────────────────────────────────────────────┐ - │ 🔬 QUANTUM LLM MONITORING │ - └──────────────────────────────────────────────────────────────────────────────┘ - - Check Training Status (Human-Readable): - python3 scripts/quantum_llm_status_check.py - - Check Status (JSON Output): - python3 scripts/quantum_llm_status_check.py --json - - Watch Mode (Auto-Refresh Every 5s): - python3 scripts/quantum_llm_status_check.py --watch - - Custom Output Directory: - python3 scripts/quantum_llm_status_check.py --output /path/to/training - - Analyze Training Metrics: - python3 scripts/quantum_llm_metrics_analyzer.py - - Export Metrics to CSV: - python3 scripts/quantum_llm_metrics_analyzer.py --export metrics.csv - - Check Inference Readiness: - python3 scripts/quantum_llm_status_check.py --json | jq '.inference_ready' - - View Full Status Details: - python3 scripts/quantum_llm_status_check.py --json | jq '.' - - Monitor Training Progress: - watch -n 5 'python3 scripts/quantum_llm_status_check.py' - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ 🦙 LOCAL AI (OLLAMA & LM STUDIO) │ -└──────────────────────────────────────────────────────────────────────────────┘ - - OLLAMA — fully offline, no API key needed: - 1. Install: https://ollama.ai/download - 2. Start: ollama serve (or it auto-starts on most platforms) - 3. Get a model: - ollama pull llama3.2 # 3B, fast & capable - ollama pull mistral # 7B, high quality - ollama pull codellama # 7B, great for code - ollama pull phi3 # 3.8B lightweight - 4. Use with Aria chat CLI: - python3 ai-projects/chat-cli/src/chat_cli.py --provider ollama - python3 ai-projects/chat-cli/src/chat_cli.py --provider ollama --model mistral - 5. Point /api/chat at Ollama: - OLLAMA_BASE_URL=http://127.0.0.1:11434/v1 OLLAMA_MODEL=llama3.2 func host start - 6. Check connection: - curl http://127.0.0.1:11434/api/tags - curl http://localhost:7071/api/ai/status | python3 -m json.tool | grep -A 10 ollama - - LM STUDIO — GUI model runner: - 1. Install: https://lmstudio.ai - 2. Open app → enable "Local Server" in the server tab - 3. Use with Aria: - python3 ai-projects/chat-cli/src/chat_cli.py --provider lmstudio - 4. Custom URL: - LMSTUDIO_BASE_URL=http://127.0.0.1:1234/v1 func host start - - ENV VARS (add to local.settings.json to persist): - OLLAMA_BASE_URL default: http://127.0.0.1:11434/v1 - OLLAMA_MODEL default: llama3.2 - LMSTUDIO_BASE_URL default: http://127.0.0.1:1234/v1 - LMSTUDIO_MODEL default: local-model - - AUTO-DETECTION: if Ollama or LM Studio is running they are picked automatically - before Azure/OpenAI when no explicit --provider is given. - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ ✅ HEALTH CHECKS │ -└──────────────────────────────────────────────────────────────────────────────┘ - - Component Health: - • Aria Server: curl http://localhost:8080 - • Backend API: curl http://localhost:7071/api/ai/status - • Status File: cat data_out/aria_automation/status.json | jq - • Quantum LLM: python3 scripts/quantum_llm_status_check.py - - Process Check: - ps aux | grep aria - - Resource Usage: - htop -p $(pgrep -f aria_automation) - -┌──────────────────────────────────────────────────────────────────────────────┐ -│ 🔐 FILES & PATHS │ -└──────────────────────────────────────────────────────────────────────────────┘ - - Main Script: scripts/aria_automation.py - Start Script: scripts/start_aria.sh - Test Suite: scripts/test_aria_automation.py - Service File: config/aria_automation.service - Config: config/master_orchestrator.yaml - Status JSON: data_out/aria_automation/status.json - PID File: data_out/aria_automation/processes.json - Logs: data_out/aria_automation/*.log - - Quantum LLM Status: data_out/quantum_llm_training/status.json - Quantum Status Script: scripts/quantum_llm_status_check.py - Quantum Metrics Script: scripts/quantum_llm_metrics_analyzer.py - Quantum Trainer: scripts/quantum_llm_trainer.py - -╔══════════════════════════════════════════════════════════════════════════════╗ -║ Need Help? Check ARIA_AUTOMATION_GUIDE.md for detailed instructions ║ -║ Quantum LLM Guide: docs/QUANTUM_LLM_STATUS_TRACKING.md ║ -║ All tests passing ✅ • Ready for production deployment • v1.0.1 ║ -╚══════════════════════════════════════════════════════════════════════════════╝ +╔══════════════════════════════════════════════════════════════════════════════╗ +║ 🤖 ARIA AUTOMATION - QUICK REFERENCE 🤖 ║ +╚══════════════════════════════════════════════════════════════════════════════╝ + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ 🚀 QUICK START │ +└──────────────────────────────────────────────────────────────────────────────┘ + + Interactive Menu: + ./scripts/start_aria.sh + + Full Stack (Production): + ./scripts/start_aria.sh full + + Background Mode: + ./scripts/start_aria.sh full --background + + Server Only (Dev): + ./scripts/start_aria.sh server + + Training Once: + ./scripts/start_aria.sh training --once + + Check Status: + ./scripts/start_aria.sh status + + Stop All: + ./scripts/start_aria.sh stop + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ 📊 MONITORING │ +└──────────────────────────────────────────────────────────────────────────────┘ + + Status Check: + python3 scripts/aria_automation.py --status + + View Logs: + tail -f data_out/aria_automation/aria_automation.log + + Service Status (systemd): + sudo systemctl status aria_automation + + Service Logs: + sudo journalctl -u aria_automation -f + + Check Ports: + lsof -i :8080 # Aria server + lsof -i :7071 # Functions backend + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ 🌐 ACCESS POINTS │ +└──────────────────────────────────────────────────────────────────────────────┘ + + Aria Web Interface: + http://localhost:8080 + + Auto-Execute Page: + http://localhost:8080/auto-execute.html + + Backend Health: + http://localhost:7071/api/ai/status + + Status JSON: + cat data_out/aria_automation/status.json + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ 🔧 SYSTEMD SERVICE (Linux) │ +└──────────────────────────────────────────────────────────────────────────────┘ + + Install: + sudo cp config/aria_automation.service /etc/systemd/system/ + sudo systemctl daemon-reload + + Enable & Start: + sudo systemctl enable aria_automation + sudo systemctl start aria_automation + + Control: + sudo systemctl stop aria_automation + sudo systemctl restart aria_automation + sudo systemctl status aria_automation + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ ⏰ CRON SCHEDULING │ +└──────────────────────────────────────────────────────────────────────────────┘ + + Edit Cron: + crontab -e + + Training Every 30 Minutes: + */30 * * * * cd /workspaces/Aria && python3 scripts/aria_automation.py --mode training --once + + Start Server on Boot: + @reboot cd /workspaces/Aria && python3 scripts/aria_automation.py --mode server + + View Cron Jobs: + crontab -l + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ 🎼 MASTER ORCHESTRATOR │ +└──────────────────────────────────────────────────────────────────────────────┘ + + Start Aria Workflow: + python3 scripts/master_orchestrator.py --workflow aria_full_stack + + Check All Status: + python3 scripts/master_orchestrator.py --status + + Run as Daemon: + python3 scripts/master_orchestrator.py --daemon + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ 🆘 TROUBLESHOOTING │ +└──────────────────────────────────────────────────────────────────────────────┘ + + Stop All Processes: + python3 scripts/aria_automation.py --stop + + Kill by Port: + sudo kill $(sudo lsof -t -i:8080) + + Check Dataset: + python3 scripts/validate_datasets.py --category chat + + Test Training: + python3 scripts/aria_quick_train.py + + Clean PIDs: + rm data_out/aria_automation/processes.json + + View Errors: + cat data_out/aria_automation/status.json | grep -A 10 errors + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ 📖 DOCUMENTATION │ +└──────────────────────────────────────────────────────────────────────────────┘ + + Quick Summary: ARIA_AUTOMATION_SUMMARY.md + Complete Guide: ARIA_AUTOMATION_GUIDE.md + Aria Web Docs: aria_web/README.md + Auto-Execute: aria_web/AUTO-EXECUTE.md + Main README: README.md + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ 🎯 AUTOMATION MODES │ +└──────────────────────────────────────────────────────────────────────────────┘ + + full → Server + Backend + Training + Monitoring (Production) + server → Web Interface Only (Development) + training → Background Training Only (Batch Processing) + + ┌──────────────────────────────────────────────────────────────────────────────┐ + │ 🔬 QUANTUM LLM MONITORING │ + └──────────────────────────────────────────────────────────────────────────────┘ + + Check Training Status (Human-Readable): + python3 scripts/quantum_llm_status_check.py + + Check Status (JSON Output): + python3 scripts/quantum_llm_status_check.py --json + + Watch Mode (Auto-Refresh Every 5s): + python3 scripts/quantum_llm_status_check.py --watch + + Custom Output Directory: + python3 scripts/quantum_llm_status_check.py --output /path/to/training + + Analyze Training Metrics: + python3 scripts/quantum_llm_metrics_analyzer.py + + Export Metrics to CSV: + python3 scripts/quantum_llm_metrics_analyzer.py --export metrics.csv + + Check Inference Readiness: + python3 scripts/quantum_llm_status_check.py --json | jq '.inference_ready' + + View Full Status Details: + python3 scripts/quantum_llm_status_check.py --json | jq '.' + + Monitor Training Progress: + watch -n 5 'python3 scripts/quantum_llm_status_check.py' + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ 🦙 LOCAL AI (OLLAMA & LM STUDIO) │ +└──────────────────────────────────────────────────────────────────────────────┘ + + OLLAMA — fully offline, no API key needed: + 1. Install: https://ollama.ai/download + 2. Start: ollama serve (or it auto-starts on most platforms) + 3. Get a model: + ollama pull llama3.2 # 3B, fast & capable + ollama pull mistral # 7B, high quality + ollama pull codellama # 7B, great for code + ollama pull phi3 # 3.8B lightweight + 4. Use with Aria chat CLI: + python3 ai-projects/chat-cli/src/chat_cli.py --provider ollama + python3 ai-projects/chat-cli/src/chat_cli.py --provider ollama --model mistral + 5. Point /api/chat at Ollama: + OLLAMA_BASE_URL=http://127.0.0.1:11434/v1 OLLAMA_MODEL=llama3.2 func host start + 6. Check connection: + curl http://127.0.0.1:11434/api/tags + curl http://localhost:7071/api/ai/status | python3 -m json.tool | grep -A 10 ollama + + LM STUDIO — GUI model runner: + 1. Install: https://lmstudio.ai + 2. Open app → enable "Local Server" in the server tab + 3. Use with Aria: + python3 ai-projects/chat-cli/src/chat_cli.py --provider lmstudio + 4. Custom URL: + LMSTUDIO_BASE_URL=http://127.0.0.1:1234/v1 func host start + + ENV VARS (add to local.settings.json to persist): + OLLAMA_BASE_URL default: http://127.0.0.1:11434/v1 + OLLAMA_MODEL default: llama3.2 + LMSTUDIO_BASE_URL default: http://127.0.0.1:1234/v1 + LMSTUDIO_MODEL default: local-model + + AUTO-DETECTION: if Ollama or LM Studio is running they are picked automatically + before Azure/OpenAI when no explicit --provider is given. + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ ✅ HEALTH CHECKS │ +└──────────────────────────────────────────────────────────────────────────────┘ + + Component Health: + • Aria Server: curl http://localhost:8080 + • Backend API: curl http://localhost:7071/api/ai/status + • Status File: cat data_out/aria_automation/status.json | jq + • Quantum LLM: python3 scripts/quantum_llm_status_check.py + + Process Check: + ps aux | grep aria + + Resource Usage: + htop -p $(pgrep -f aria_automation) + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ 🔐 FILES & PATHS │ +└──────────────────────────────────────────────────────────────────────────────┘ + + Main Script: scripts/aria_automation.py + Start Script: scripts/start_aria.sh + Test Suite: scripts/test_aria_automation.py + Service File: config/aria_automation.service + Config: config/master_orchestrator.yaml + Status JSON: data_out/aria_automation/status.json + PID File: data_out/aria_automation/processes.json + Logs: data_out/aria_automation/*.log + + Quantum LLM Status: data_out/quantum_llm_training/status.json + Quantum Status Script: scripts/quantum_llm_status_check.py + Quantum Metrics Script: scripts/quantum_llm_metrics_analyzer.py + Quantum Trainer: scripts/quantum_llm_trainer.py + +╔══════════════════════════════════════════════════════════════════════════════╗ +║ Need Help? Check ARIA_AUTOMATION_GUIDE.md for detailed instructions ║ +║ Quantum LLM Guide: docs/QUANTUM_LLM_STATUS_TRACKING.md ║ +║ All tests passing ✅ • Ready for production deployment • v1.0.1 ║ +╚══════════════════════════════════════════════════════════════════════════════╝ diff --git a/docs/quickrefs/QUANTUM_LLM_QUICKREF.md b/docs/quickrefs/QUANTUM_LLM_QUICKREF.md index d48a9ffd2..aee4d08e0 100644 --- a/docs/quickrefs/QUANTUM_LLM_QUICKREF.md +++ b/docs/quickrefs/QUANTUM_LLM_QUICKREF.md @@ -1,257 +1,257 @@ -# Quantum LLM Quick Reference - -## Fast reference for daily quantum LLM development - ---- - -## 🚀 Quick Commands - -```bash -# Test system (5 min) -python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode quick - -# Full training -python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode full - -# With custom config -python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode full --config config/my_config.yaml - -# Monitor training -python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm_quickstart - -# Generate text -python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode generate --model model.pt --prompt "Hello" - -# Validate system -python ai-projects/quantum-ml/validate_quantum_llm.py --full - -# Chat with trained quantum LLM (CLI) -python ai-projects/chat-cli/src/chat_cli.py --provider quantum --model data_out/quantum_llm_chat --once "Hello quantum" - -# Chat via API payload (when function host is running) -# provider=quantum and model points to trained checkpoint directory -# {"messages":[{"role":"user","content":"Hello"}],"provider":"quantum","model":"data_out/quantum_llm_chat"} - -# Compare old vs new -python ai-projects/quantum-ml/quantum_llm_integration.py --mode compare - -# Component tests -python ai-projects/quantum-ml/src/quantum_llm_advanced.py -python ai-projects/quantum-ml/src/quantum_circuit_optimizer.py -python ai-projects/quantum-ml/src/quantum_llm_monitor.py -``` - ---- - -## 📦 Components at a Glance - -| Component | What It Does | Import | -| -------------- | ---------------------------------------------- | ------------------------------------------ | -| **advanced** | Cache, multi-scale attention, error mitigation | `from quantum_llm_advanced import *` | -| **optimizer** | Circuit compilation, batch execution | `from quantum_circuit_optimizer import *` | -| **trainer** | Curriculum learning, orchestration | `from quantum_llm_hybrid_trainer import *` | -| **monitor** | Dashboard, alerts, profiling | `from quantum_llm_monitor import *` | -| **integrated** | Complete system | `from quantum_llm_integrated import *` | -| **datasets** | Tokenizer, data loading | `from quantum_llm_datasets import *` | - ---- - -## ⚙️ Key Configuration Options - -```yaml -# Essential settings -vocab_size: 256 # Character vocab -d_model: 128 # Model dimension -n_qubits: 4 # Qubits per layer -enable_multi_scale_attention: true # Use 2-6 qubits -enable_circuit_caching: true # 2-5x speedup -enable_curriculum: true # Stable training -optimization_level: 2 # 0=none, 3=max -batch_size: 16 # Training batch -learning_rate: 0.0001 # LR -num_epochs: 10 # Training epochs -``` - ---- - -## 🔬 Advanced Features - -### Multi-Scale Attention - -```python -from quantum_llm_advanced import MultiScaleQuantumAttention - -attention = MultiScaleQuantumAttention( - d_model=128, - n_heads=4, - n_qubits_per_head=[2, 3, 4, 6] # Different scales -) -``` - -### Circuit Caching - -```python -from quantum_llm_advanced import QuantumCircuitCache - -cache = QuantumCircuitCache(cache_size=1000) -cache.put("circuit_key", result) -cached_result = cache.get("circuit_key") -``` - -### Curriculum Training - -```python -from quantum_llm_hybrid_trainer import TrainingStage - -stages = [ - TrainingStage("warmup", quantum_ratio=0.0, num_epochs=2), - TrainingStage("transition", quantum_ratio=0.3, num_epochs=3), - TrainingStage("full", quantum_ratio=0.7, num_epochs=10), -] -``` - -### Real-time Monitoring - -```python -from quantum_llm_monitor import TrainingDashboard - -dashboard = TrainingDashboard( - output_dir=Path("data_out/dashboard"), - update_interval=10, - enable_alerts=True -) -``` - ---- - -## 📊 Performance Tips - -1. **Enable caching** for character-level: `enable_circuit_caching: true` -2. **Use curriculum** for stable training: `enable_curriculum: true` -3. **Optimize circuits** for speed: `optimization_level: 2` -4. **Smaller batches** for quantum: `batch_size: 4-8` -5. **Multi-scale** for accuracy: `enable_multi_scale_attention: true` - ---- - -## 🐛 Troubleshooting - -### Import errors - -```bash -cd ai-projects/quantum-ml -export PYTHONPATH="${PYTHONPATH}:$(pwd)/src" -``` - -### Missing dependencies - -```bash -pip install torch pennylane pyyaml numpy -``` - -### Training instability - -- Enable curriculum learning -- Reduce learning rate -- Increase warmup epochs -- Lower quantum ratio initially - -### Slow training - -- Enable circuit caching -- Increase optimization level -- Reduce circuit depth -- Use adaptive entanglement - ---- - -## 📁 Output Structure - -```text -data_out/quantum_llm_quickstart/ -├── final_model.pt # Trained model -├── config.yaml # Used configuration -├── system_report.json # System metrics -├── dashboard/ -│ └── dashboard.json # Live dashboard -├── training/ -│ ├── training_report.json # Training summary -│ └── checkpoint_*.pt # Checkpoints -└── visualizations/ - ├── loss_curve.json # Loss data - └── quantum_metrics.json # Quantum data -``` - ---- - -## 🔗 Documentation Links - -- **Complete Guide:** `ai-projects/quantum-ml/QUANTUM_LLM_README.md` -- **Config Example:** `config/quantum_llm_config_example.yaml` -- **Development Summary:** `docs/QUANTUM_LLM_DEVELOPMENT_SUMMARY.md` -- **Build Complete:** `QUANTUM_LLM_BUILD_COMPLETE.md` - ---- - -## 💡 Common Workflows - -### Quick Test - -```bash -python quantum_llm_quickstart.py --mode quick -# Output: data_out/quantum_llm_quickstart/ -``` - -### Research Experiment - -```bash -# 1. Copy config template -cp config/quantum_llm_config_example.yaml config/my_experiment.yaml - -# 2. Edit configuration -nano config/my_experiment.yaml - -# 3. Run training -python quantum_llm_quickstart.py --mode full --config config/my_experiment.yaml - -# 4. Monitor results -python quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm_full -``` - -### Production Training - -```bash -# With comprehensive monitoring -python quantum_llm_quickstart.py --mode full \ - --config config/production_config.yaml \ - > data_out/training.log 2>&1 & - -# Monitor in real-time -watch -n 5 'cat data_out/quantum_llm_full/dashboard/dashboard.json | python -m json.tool' -``` - ---- - -## 🎯 One-Liners - -```bash -# Quick test -python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode quick - -# Validate -python ai-projects/quantum-ml/validate_quantum_llm.py --full - -# Compare systems -python ai-projects/quantum-ml/quantum_llm_integration.py --mode compare - -# Test tokenizer -python -c "from quantum_llm_datasets import CharacterTokenizer; t=CharacterTokenizer(); print(t.decode(t.encode('Hello')))" - -# Check config -python -c "from quantum_llm_integrated import QuantumLLMConfig; c=QuantumLLMConfig(); print(f\"Model: {c['d_model']}d, {c['n_qubits']}q\")" -``` - ---- - -**Last Updated:** March 9, 2026 -**Version:** 1.0.0 +# Quantum LLM Quick Reference + +## Fast reference for daily quantum LLM development + +--- + +## 🚀 Quick Commands + +```bash +# Test system (5 min) +python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode quick + +# Full training +python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode full + +# With custom config +python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode full --config config/my_config.yaml + +# Monitor training +python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm_quickstart + +# Generate text +python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode generate --model model.pt --prompt "Hello" + +# Validate system +python ai-projects/quantum-ml/validate_quantum_llm.py --full + +# Chat with trained quantum LLM (CLI) +python ai-projects/chat-cli/src/chat_cli.py --provider quantum --model data_out/quantum_llm_chat --once "Hello quantum" + +# Chat via API payload (when function host is running) +# provider=quantum and model points to trained checkpoint directory +# {"messages":[{"role":"user","content":"Hello"}],"provider":"quantum","model":"data_out/quantum_llm_chat"} + +# Compare old vs new +python ai-projects/quantum-ml/quantum_llm_integration.py --mode compare + +# Component tests +python ai-projects/quantum-ml/src/quantum_llm_advanced.py +python ai-projects/quantum-ml/src/quantum_circuit_optimizer.py +python ai-projects/quantum-ml/src/quantum_llm_monitor.py +``` + +--- + +## 📦 Components at a Glance + +| Component | What It Does | Import | +| -------------- | ---------------------------------------------- | ------------------------------------------ | +| **advanced** | Cache, multi-scale attention, error mitigation | `from quantum_llm_advanced import *` | +| **optimizer** | Circuit compilation, batch execution | `from quantum_circuit_optimizer import *` | +| **trainer** | Curriculum learning, orchestration | `from quantum_llm_hybrid_trainer import *` | +| **monitor** | Dashboard, alerts, profiling | `from quantum_llm_monitor import *` | +| **integrated** | Complete system | `from quantum_llm_integrated import *` | +| **datasets** | Tokenizer, data loading | `from quantum_llm_datasets import *` | + +--- + +## ⚙️ Key Configuration Options + +```yaml +# Essential settings +vocab_size: 256 # Character vocab +d_model: 128 # Model dimension +n_qubits: 4 # Qubits per layer +enable_multi_scale_attention: true # Use 2-6 qubits +enable_circuit_caching: true # 2-5x speedup +enable_curriculum: true # Stable training +optimization_level: 2 # 0=none, 3=max +batch_size: 16 # Training batch +learning_rate: 0.0001 # LR +num_epochs: 10 # Training epochs +``` + +--- + +## 🔬 Advanced Features + +### Multi-Scale Attention + +```python +from quantum_llm_advanced import MultiScaleQuantumAttention + +attention = MultiScaleQuantumAttention( + d_model=128, + n_heads=4, + n_qubits_per_head=[2, 3, 4, 6] # Different scales +) +``` + +### Circuit Caching + +```python +from quantum_llm_advanced import QuantumCircuitCache + +cache = QuantumCircuitCache(cache_size=1000) +cache.put("circuit_key", result) +cached_result = cache.get("circuit_key") +``` + +### Curriculum Training + +```python +from quantum_llm_hybrid_trainer import TrainingStage + +stages = [ + TrainingStage("warmup", quantum_ratio=0.0, num_epochs=2), + TrainingStage("transition", quantum_ratio=0.3, num_epochs=3), + TrainingStage("full", quantum_ratio=0.7, num_epochs=10), +] +``` + +### Real-time Monitoring + +```python +from quantum_llm_monitor import TrainingDashboard + +dashboard = TrainingDashboard( + output_dir=Path("data_out/dashboard"), + update_interval=10, + enable_alerts=True +) +``` + +--- + +## 📊 Performance Tips + +1. **Enable caching** for character-level: `enable_circuit_caching: true` +2. **Use curriculum** for stable training: `enable_curriculum: true` +3. **Optimize circuits** for speed: `optimization_level: 2` +4. **Smaller batches** for quantum: `batch_size: 4-8` +5. **Multi-scale** for accuracy: `enable_multi_scale_attention: true` + +--- + +## 🐛 Troubleshooting + +### Import errors + +```bash +cd ai-projects/quantum-ml +export PYTHONPATH="${PYTHONPATH}:$(pwd)/src" +``` + +### Missing dependencies + +```bash +pip install torch pennylane pyyaml numpy +``` + +### Training instability + +- Enable curriculum learning +- Reduce learning rate +- Increase warmup epochs +- Lower quantum ratio initially + +### Slow training + +- Enable circuit caching +- Increase optimization level +- Reduce circuit depth +- Use adaptive entanglement + +--- + +## 📁 Output Structure + +```text +data_out/quantum_llm_quickstart/ +├── final_model.pt # Trained model +├── config.yaml # Used configuration +├── system_report.json # System metrics +├── dashboard/ +│ └── dashboard.json # Live dashboard +├── training/ +│ ├── training_report.json # Training summary +│ └── checkpoint_*.pt # Checkpoints +└── visualizations/ + ├── loss_curve.json # Loss data + └── quantum_metrics.json # Quantum data +``` + +--- + +## 🔗 Documentation Links + +- **Complete Guide:** `ai-projects/quantum-ml/QUANTUM_LLM_README.md` +- **Config Example:** `config/quantum_llm_config_example.yaml` +- **Development Summary:** `docs/QUANTUM_LLM_DEVELOPMENT_SUMMARY.md` +- **Build Complete:** `QUANTUM_LLM_BUILD_COMPLETE.md` + +--- + +## 💡 Common Workflows + +### Quick Test + +```bash +python quantum_llm_quickstart.py --mode quick +# Output: data_out/quantum_llm_quickstart/ +``` + +### Research Experiment + +```bash +# 1. Copy config template +cp config/quantum_llm_config_example.yaml config/my_experiment.yaml + +# 2. Edit configuration +nano config/my_experiment.yaml + +# 3. Run training +python quantum_llm_quickstart.py --mode full --config config/my_experiment.yaml + +# 4. Monitor results +python quantum_llm_quickstart.py --mode monitor --output-dir data_out/quantum_llm_full +``` + +### Production Training + +```bash +# With comprehensive monitoring +python quantum_llm_quickstart.py --mode full \ + --config config/production_config.yaml \ + > data_out/training.log 2>&1 & + +# Monitor in real-time +watch -n 5 'cat data_out/quantum_llm_full/dashboard/dashboard.json | python -m json.tool' +``` + +--- + +## 🎯 One-Liners + +```bash +# Quick test +python ai-projects/quantum-ml/quantum_llm_quickstart.py --mode quick + +# Validate +python ai-projects/quantum-ml/validate_quantum_llm.py --full + +# Compare systems +python ai-projects/quantum-ml/quantum_llm_integration.py --mode compare + +# Test tokenizer +python -c "from quantum_llm_datasets import CharacterTokenizer; t=CharacterTokenizer(); print(t.decode(t.encode('Hello')))" + +# Check config +python -c "from quantum_llm_integrated import QuantumLLMConfig; c=QuantumLLMConfig(); print(f\"Model: {c['d_model']}d, {c['n_qubits']}q\")" +``` + +--- + +**Last Updated:** March 9, 2026 +**Version:** 1.0.0 diff --git a/docs/quickrefs/REPO_AUTOMATION_QUICKREF.txt b/docs/quickrefs/REPO_AUTOMATION_QUICKREF.txt index e03e7632b..6f637053d 100644 --- a/docs/quickrefs/REPO_AUTOMATION_QUICKREF.txt +++ b/docs/quickrefs/REPO_AUTOMATION_QUICKREF.txt @@ -1,162 +1,162 @@ -╔══════════════════════════════════════════════════════════════════════════════╗ -║ 🚀 REPOSITORY AUTOMATION QUICK REFERENCE 🚀 ║ -║ Complete Aria Repository Automation ║ -╚══════════════════════════════════════════════════════════════════════════════╝ - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -⚡ QUICK START COMMANDS -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - Start Everything: - ./scripts/start_repo_automation.sh full - - Start Aria Only: - ./scripts/start_repo_automation.sh aria - - Training Pipeline: - ./scripts/start_repo_automation.sh training - - Custom Selection: - ./scripts/start_repo_automation.sh components aria,training,quantum - - Check Status: - ./scripts/start_repo_automation.sh status - - Stop All: - ./scripts/start_repo_automation.sh stop - - Background Mode: - ./scripts/start_repo_automation.sh full --background - - Interactive Menu: - ./scripts/start_repo_automation.sh - - Run Tests: - python3 scripts/test_repo_automation.py - - Integration Smoke: - python3 scripts/integration_smoke.py - - Demo: - ./scripts/demo_repo_automation.sh - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -🤖 AUTOMATED COMPONENTS (7 Total) -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - 1. aria ✨ Aria Character (server + training) - 2. training 🎓 LoRA Training Pipelines - 3. quantum ⚛️ Quantum Computing Workflows - 4. evaluation 📊 Model Evaluation System - 5. datasets 📦 Auto Dataset Discovery - 6. monitoring 🏥 System Health Monitoring - 7. backup 💾 Automated Backups (daily) - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -📊 MONITORING -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - Status Dashboard: - ./scripts/start_repo_automation.sh status - - Status JSON: - cat data_out/repo_automation/status.json | jq - - Automation Log: - tail -f data_out/repo_automation/automation.log - - Component Logs: - tail -f data_out/aria_automation/*.log - tail -f data_out/autotrain/*.log - tail -f data_out/quantum_autorun/*.log - - Health Check: - python3 scripts/system_health_check.py --once - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -🔧 PYTHON API USAGE -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - python3 scripts/repo_automation.py --start # Start all - python3 scripts/repo_automation.py --start --daemon # Daemon mode - python3 scripts/repo_automation.py --start --components aria,training - python3 scripts/repo_automation.py --status # Check status - python3 scripts/repo_automation.py --stop # Stop all - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -🚀 PRODUCTION DEPLOYMENT -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - Systemd Service: - sudo systemctl enable aria-repo-automation - sudo systemctl start aria-repo-automation - sudo systemctl status aria-repo-automation - sudo systemctl stop aria-repo-automation - - Cron Schedule: - */30 * * * * cd /path/to/Aria && ./scripts/start_repo_automation.sh full -b - - Docker: - docker run -v $(pwd):/app aria-automation python3 scripts/repo_automation.py --start --daemon - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -🔍 TROUBLESHOOTING -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - Component won't start: - - Check: ./scripts/start_repo_automation.sh status - - Test manually: python3 scripts/aria_automation.py --help - - View logs: tail -f data_out/repo_automation/automation.log - - Auto-restart loop: - - Check logs: tail -f data_out// - - Disable: Edit scripts/repo_automation.py, set auto_restart=False - - High resource usage: - - Monitor: tail -f data_out/system_health_check/health.log - - Reduce: Start fewer components - - Force kill stuck processes: - pkill -f repo_automation.py - pkill -f aria_automation.py - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -📚 DOCUMENTATION -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - Full Guide: cat docs/guides/REPO_AUTOMATION_GUIDE.md - Aria Guide: cat ARIA_AUTOMATION_GUIDE.md - Main README: cat README.md - Master Orchestrator: python3 scripts/master_orchestrator.py --help - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -🌐 ACCESS POINTS -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - Aria Web UI: http://localhost:8080 - Azure Functions: http://localhost:7071 - API Status: http://localhost:7071/api/ai/status - Chat API: http://localhost:7071/api/chat - Quantum Dashboard: http://localhost:5000 (if enabled) - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -✨ FEATURES -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - ✅ One-command startup for entire repository - ✅ 7 automated components with health monitoring - ✅ Auto-restart on failure - ✅ Dependency management (components wait for dependencies) - ✅ Background/daemon mode - ✅ Status tracking with JSON output - ✅ Comprehensive logging - ✅ Production-ready (systemd/cron/docker) - ✅ Interactive menu for easy selection - ✅ Integration with master orchestrator - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -For help: ./scripts/start_repo_automation.sh --help -For demo: ./scripts/demo_repo_automation.sh - -Happy Automating! 🚀 +╔══════════════════════════════════════════════════════════════════════════════╗ +║ 🚀 REPOSITORY AUTOMATION QUICK REFERENCE 🚀 ║ +║ Complete Aria Repository Automation ║ +╚══════════════════════════════════════════════════════════════════════════════╝ + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +⚡ QUICK START COMMANDS +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + Start Everything: + ./scripts/start_repo_automation.sh full + + Start Aria Only: + ./scripts/start_repo_automation.sh aria + + Training Pipeline: + ./scripts/start_repo_automation.sh training + + Custom Selection: + ./scripts/start_repo_automation.sh components aria,training,quantum + + Check Status: + ./scripts/start_repo_automation.sh status + + Stop All: + ./scripts/start_repo_automation.sh stop + + Background Mode: + ./scripts/start_repo_automation.sh full --background + + Interactive Menu: + ./scripts/start_repo_automation.sh + + Run Tests: + python3 scripts/test_repo_automation.py + + Integration Smoke: + python3 scripts/integration_smoke.py + + Demo: + ./scripts/demo_repo_automation.sh + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +🤖 AUTOMATED COMPONENTS (7 Total) +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + 1. aria ✨ Aria Character (server + training) + 2. training 🎓 LoRA Training Pipelines + 3. quantum ⚛️ Quantum Computing Workflows + 4. evaluation 📊 Model Evaluation System + 5. datasets 📦 Auto Dataset Discovery + 6. monitoring 🏥 System Health Monitoring + 7. backup 💾 Automated Backups (daily) + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +📊 MONITORING +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + Status Dashboard: + ./scripts/start_repo_automation.sh status + + Status JSON: + cat data_out/repo_automation/status.json | jq + + Automation Log: + tail -f data_out/repo_automation/automation.log + + Component Logs: + tail -f data_out/aria_automation/*.log + tail -f data_out/autotrain/*.log + tail -f data_out/quantum_autorun/*.log + + Health Check: + python3 scripts/system_health_check.py --once + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +🔧 PYTHON API USAGE +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + python3 scripts/repo_automation.py --start # Start all + python3 scripts/repo_automation.py --start --daemon # Daemon mode + python3 scripts/repo_automation.py --start --components aria,training + python3 scripts/repo_automation.py --status # Check status + python3 scripts/repo_automation.py --stop # Stop all + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +🚀 PRODUCTION DEPLOYMENT +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + Systemd Service: + sudo systemctl enable aria-repo-automation + sudo systemctl start aria-repo-automation + sudo systemctl status aria-repo-automation + sudo systemctl stop aria-repo-automation + + Cron Schedule: + */30 * * * * cd /path/to/Aria && ./scripts/start_repo_automation.sh full -b + + Docker: + docker run -v $(pwd):/app aria-automation python3 scripts/repo_automation.py --start --daemon + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +🔍 TROUBLESHOOTING +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + Component won't start: + - Check: ./scripts/start_repo_automation.sh status + - Test manually: python3 scripts/aria_automation.py --help + - View logs: tail -f data_out/repo_automation/automation.log + + Auto-restart loop: + - Check logs: tail -f data_out// + - Disable: Edit scripts/repo_automation.py, set auto_restart=False + + High resource usage: + - Monitor: tail -f data_out/system_health_check/health.log + - Reduce: Start fewer components + + Force kill stuck processes: + pkill -f repo_automation.py + pkill -f aria_automation.py + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +📚 DOCUMENTATION +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + Full Guide: cat docs/guides/REPO_AUTOMATION_GUIDE.md + Aria Guide: cat ARIA_AUTOMATION_GUIDE.md + Main README: cat README.md + Master Orchestrator: python3 scripts/master_orchestrator.py --help + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +🌐 ACCESS POINTS +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + Aria Web UI: http://localhost:8080 + Azure Functions: http://localhost:7071 + API Status: http://localhost:7071/api/ai/status + Chat API: http://localhost:7071/api/chat + Quantum Dashboard: http://localhost:5000 (if enabled) + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +✨ FEATURES +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + + ✅ One-command startup for entire repository + ✅ 7 automated components with health monitoring + ✅ Auto-restart on failure + ✅ Dependency management (components wait for dependencies) + ✅ Background/daemon mode + ✅ Status tracking with JSON output + ✅ Comprehensive logging + ✅ Production-ready (systemd/cron/docker) + ✅ Interactive menu for easy selection + ✅ Integration with master orchestrator + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +For help: ./scripts/start_repo_automation.sh --help +For demo: ./scripts/demo_repo_automation.sh + +Happy Automating! 🚀 diff --git a/docs/store/about.html b/docs/store/about.html index 44eab0ed4..ec7c88fe1 100644 --- a/docs/store/about.html +++ b/docs/store/about.html @@ -1,103 +1,103 @@ - - - - - - About — Aria Store - - - - - - - - - - - - - - - - - - - - - - -
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    About the store

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    Turn raw platform capability into a clear adoption path.

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    Aria Store translates demos, dashboards, training systems, and quantum workflows into offers that operators, buyers, and implementation teams can actually navigate.

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    - Subscriptions - Training - Quantum pilots - Deployment support -
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    Buyer clarity

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    The store should answer what the platform does, which package fits, and what a team should do next without requiring repo archaeology.

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    Every offer is framed around how Aria actually runs: providers, dashboards, training loops, deployment paths, and service ownership.

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    Adoption support

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    The catalog is designed for the handoff between experimentation, onboarding, and scaled deployment.

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    Aria began as an interactive AI character platform with real runtime surfaces: animated character control, multi-provider chat, orchestration dashboards, training pipelines, and quantum ML experiments. The store exists so those capabilities can be packaged into understandable adoption routes.

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    Instead of a generic ecommerce catalog, the store now focuses on platform access, training enablement, pilot engagements, and deployment support. That makes it relevant to teams evaluating Aria for internal tools, public demos, or operator-managed rollouts.

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    The goal is practical clarity. A visitor should be able to tell whether they need a subscription, a training sprint, a quantum pilot, or a broader implementation workshop before they ever reach out.

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    Architecture-aware packaging

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    Offers reflect how the platform is actually structured: surface, API layer, provider routing, training, and infrastructure.

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    Operational readiness

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    Catalog entries point toward dashboards, evaluation loops, rollout planning, and the ongoing work required after the first demo.

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    Deployment-first guidance

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    The store is built to help teams move from exploration to managed use, not just to present feature lists.

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    About the store

    +

    Turn raw platform capability into a clear adoption path.

    +

    Aria Store translates demos, dashboards, training systems, and quantum workflows into offers that operators, buyers, and implementation teams can actually navigate.

    +
    + Subscriptions + Training + Quantum pilots + Deployment support +
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    Buyer clarity

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    The store should answer what the platform does, which package fits, and what a team should do next without requiring repo archaeology.

    +
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    Operator fit

    +

    Every offer is framed around how Aria actually runs: providers, dashboards, training loops, deployment paths, and service ownership.

    +
    +
    + +

    Adoption support

    +

    The catalog is designed for the handoff between experimentation, onboarding, and scaled deployment.

    +
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    + +

    Aria began as an interactive AI character platform with real runtime surfaces: animated character control, multi-provider chat, orchestration dashboards, training pipelines, and quantum ML experiments. The store exists so those capabilities can be packaged into understandable adoption routes.

    +

    Instead of a generic ecommerce catalog, the store now focuses on platform access, training enablement, pilot engagements, and deployment support. That makes it relevant to teams evaluating Aria for internal tools, public demos, or operator-managed rollouts.

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    The goal is practical clarity. A visitor should be able to tell whether they need a subscription, a training sprint, a quantum pilot, or a broader implementation workshop before they ever reach out.

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    Architecture-aware packaging

    +

    Offers reflect how the platform is actually structured: surface, API layer, provider routing, training, and infrastructure.

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    Operational readiness

    +

    Catalog entries point toward dashboards, evaluation loops, rollout planning, and the ongoing work required after the first demo.

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    Deployment-first guidance

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    The store is built to help teams move from exploration to managed use, not just to present feature lists.

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    - -
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    Contact

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    Start the right conversation before rollout.

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    Use this page for subscription questions, implementation scoping, training requests, or platform fit discussions. In this repo version, the form routes to GitHub issues by default.

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    - Sales questions - Support requests - Implementation planning -
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    Open an inquiry

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    - - -
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    This demo routes new requests to the repository issue tracker so the contact flow is usable without a configured sales inbox.

    -
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    Where to send people next

    -
    - -
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    Issues and support

    -

    Use GitHub issues for questions, bugs, or follow-up requests on the public repo surface.

    -
    -
    -
    - -
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    Technical due diligence

    -

    Send evaluators to the docs hub when they need architecture, commands, training notes, or deployment references.

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    Commercial path

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    Use pricing, account, and store catalog pages when the next step is package fit rather than technical exploration.

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    - - - - - - - - - - + + + + + + Contact — Aria Store + + + + + + + + + + + + + + + + + + + + + + +
    + +
    +
    +
    +
    +

    Contact

    +

    Start the right conversation before rollout.

    +

    Use this page for subscription questions, implementation scoping, training requests, or platform fit discussions. In this repo version, the form routes to GitHub issues by default.

    +
    + Sales questions + Support requests + Implementation planning +
    +
    +
    +
    + +
    +
    +
    +
    +
    +

    Open an inquiry

    +
    +
    + + +
    +
    + + +
    +
    + + +
    +
    + + +
    + +
    +

    This demo routes new requests to the repository issue tracker so the contact flow is usable without a configured sales inbox.

    +
    + +
    +

    Where to send people next

    +
    + +
    +

    Issues and support

    +

    Use GitHub issues for questions, bugs, or follow-up requests on the public repo surface.

    +
    +
    +
    + +
    +

    Technical due diligence

    +

    Send evaluators to the docs hub when they need architecture, commands, training notes, or deployment references.

    +
    +
    +
    + +
    +

    Commercial path

    +

    Use pricing, account, and store catalog pages when the next step is package fit rather than technical exploration.

    +
    +
    +
    +
    +
    +
    +
    +
    + + + + + + + + + + diff --git a/docs/store/css/style.css b/docs/store/css/style.css index 82aa000d5..cfa292afe 100644 --- a/docs/store/css/style.css +++ b/docs/store/css/style.css @@ -1,1637 +1,1637 @@ -/* ===== CSS Custom Properties ===== */ -:root { - --color-primary: #2563eb; - --color-primary-dark: #1d4ed8; - --color-secondary: #0f172a; - --color-accent: #f59e0b; - --color-bg: #f8fafc; - --color-white: #ffffff; - --color-text: #1e293b; - --color-text-light: #64748b; - --color-border: #e2e8f0; - --color-success: #22c55e; - --color-danger: #ef4444; - --shadow-sm: 0 1px 2px rgba(0,0,0,0.05); - --shadow-md: 0 4px 6px -1px rgba(0,0,0,0.1), 0 2px 4px -2px rgba(0,0,0,0.1); - --shadow-lg: 0 10px 15px -3px rgba(0,0,0,0.1), 0 4px 6px -4px rgba(0,0,0,0.1); - --radius: 8px; - --radius-lg: 12px; - --max-width: 1200px; - --header-height: 70px; - --transition: 0.2s ease; -} - -/* ===== Reset & Base ===== */ -*, *::before, *::after { - margin: 0; - padding: 0; - box-sizing: border-box; -} - -html { - scroll-behavior: smooth; -} - -body { - font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; - color: var(--color-text); - background: var(--color-bg); - line-height: 1.6; - min-height: 100vh; - display: flex; - flex-direction: column; -} - -a { - text-decoration: none; - color: inherit; -} - -img { - max-width: 100%; - height: auto; - display: block; -} - -ul { list-style: none; } - -button { - cursor: pointer; - font-family: inherit; - border: none; - background: none; -} - -input, select, textarea { - font-family: inherit; - font-size: inherit; -} - -main { - flex: 1; -} - -/* ===== Utility ===== */ -.container { - width: 100%; - max-width: var(--max-width); - margin: 0 auto; - padding: 0 20px; -} - -.btn { - display: inline-flex; - align-items: center; - justify-content: center; - gap: 8px; - padding: 12px 28px; - border-radius: var(--radius); - font-weight: 600; - font-size: 0.95rem; - transition: all var(--transition); - white-space: nowrap; -} - -.btn-primary { - background: var(--color-primary); - color: var(--color-white); -} -.btn-primary:hover { - background: var(--color-primary-dark); - transform: translateY(-1px); - box-shadow: var(--shadow-md); -} - -.btn-outline { - border: 2px solid var(--color-primary); - color: var(--color-primary); - background: transparent; -} -.btn-outline:hover { - background: var(--color-primary); - color: var(--color-white); -} - -.btn-secondary { - background: var(--color-secondary); - color: var(--color-white); -} -.btn-secondary:hover { - opacity: 0.9; - transform: translateY(-1px); -} - -.section-title { - font-size: 1.75rem; - font-weight: 700; - margin-bottom: 8px; -} - -.section-subtitle { - color: var(--color-text-light); - font-size: 1.05rem; - margin-bottom: 40px; -} - -.section { - padding: 80px 0; -} - -/* ===== Header / Navbar ===== */ -.header { - position: sticky; - top: 0; - z-index: 1000; - background: var(--color-white); - border-bottom: 1px solid var(--color-border); - height: var(--header-height); -} - -.header .container { - display: flex; - align-items: center; - justify-content: space-between; - height: 100%; -} - -.logo { - font-size: 1.5rem; - font-weight: 800; - color: var(--color-primary); - display: flex; - align-items: center; - gap: 8px; -} - -.logo i { - font-size: 1.3rem; -} - -.nav-links { - display: flex; - align-items: center; - gap: 32px; -} - -.nav-links a { - font-weight: 500; - color: var(--color-text-light); - transition: color var(--transition); - font-size: 0.95rem; -} - -.nav-links a:hover, -.nav-links a.active { - color: var(--color-primary); -} - -.nav-search { - display: flex; - align-items: center; - gap: 12px; -} - -.nav-search-input { - border: 1px solid var(--color-border); - border-radius: var(--radius); - padding: 8px 14px; - font-size: 0.9rem; - width: 200px; - transition: border-color var(--transition); - outline: none; -} - -.nav-search-input:focus { - border-color: var(--color-primary); -} - -.mobile-menu-btn { - display: none; - font-size: 1.5rem; - color: var(--color-text); -} - -/* ===== Hero Section ===== */ -.hero { - background: linear-gradient(135deg, var(--color-secondary) 0%, #1e3a5f 100%); - color: var(--color-white); - padding: 100px 0; - text-align: center; -} - -.hero h1 { - font-size: 3rem; - font-weight: 800; - margin-bottom: 16px; - line-height: 1.2; -} - -.hero p { - font-size: 1.2rem; - opacity: 0.85; - max-width: 600px; - margin: 0 auto 32px; -} - -.hero .btn { - font-size: 1.05rem; - padding: 14px 36px; -} - -/* ===== Categories ===== */ -.categories-grid { - display: grid; - grid-template-columns: repeat(4, 1fr); - gap: 24px; -} - -.category-card { - background: var(--color-white); - border-radius: var(--radius-lg); - padding: 32px 20px; - text-align: center; - box-shadow: var(--shadow-sm); - border: 1px solid var(--color-border); - transition: all var(--transition); - cursor: pointer; -} - -.category-card:hover { - transform: translateY(-4px); - box-shadow: var(--shadow-lg); - border-color: var(--color-primary); -} - -.category-card i { - font-size: 2.5rem; - color: var(--color-primary); - margin-bottom: 16px; -} - -.category-card h3 { - font-size: 1.05rem; - margin-bottom: 4px; -} - -.category-card p { - font-size: 0.85rem; - color: var(--color-text-light); -} - -/* ===== Product Grid ===== */ -.products-grid { - display: grid; - grid-template-columns: repeat(4, 1fr); - gap: 24px; -} - -.product-card { - background: var(--color-white); - border-radius: var(--radius-lg); - overflow: hidden; - box-shadow: var(--shadow-sm); - border: 1px solid var(--color-border); - transition: all var(--transition); -} - -.product-card:hover { - transform: translateY(-4px); - box-shadow: var(--shadow-lg); -} - -.product-card-image { - width: 100%; - height: 220px; - overflow: hidden; - background: #f1f5f9; - display: flex; - align-items: center; - justify-content: center; -} - -.product-card-image img { - width: 100%; - height: 100%; - object-fit: cover; -} - -.product-card-body { - padding: 16px; -} - -.product-card-category { - font-size: 0.75rem; - text-transform: uppercase; - letter-spacing: 0.5px; - color: var(--color-primary); - font-weight: 600; - margin-bottom: 4px; -} - -.product-card-title { - font-size: 1rem; - font-weight: 600; - margin-bottom: 8px; - line-height: 1.3; -} - -.product-card-price { - font-size: 1.2rem; - font-weight: 700; - color: var(--color-text); - margin-bottom: 12px; -} - -.product-card .btn { - width: 100%; - padding: 10px; - font-size: 0.9rem; -} - -/* ===== Shop Page Layout ===== */ -.shop-layout { - display: grid; - grid-template-columns: 260px 1fr; - gap: 32px; - padding: 40px 0; -} - -.shop-sidebar { - background: var(--color-white); - border-radius: var(--radius-lg); - padding: 24px; - border: 1px solid var(--color-border); - height: fit-content; - position: sticky; - top: calc(var(--header-height) + 20px); -} - -.filter-group { - margin-bottom: 24px; -} - -.filter-group:last-child { - margin-bottom: 0; -} - -.filter-group h4 { - font-size: 0.9rem; - text-transform: uppercase; - letter-spacing: 0.5px; - margin-bottom: 12px; - color: var(--color-text); -} - -.filter-group select, -.filter-group input[type="range"] { - width: 100%; - padding: 8px 12px; - border: 1px solid var(--color-border); - border-radius: var(--radius); - font-size: 0.9rem; - outline: none; -} - -.filter-group select:focus { - border-color: var(--color-primary); -} - -.price-range-labels { - display: flex; - justify-content: space-between; - font-size: 0.85rem; - color: var(--color-text-light); - margin-top: 8px; -} - -.shop-results-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 24px; -} - -.shop-results-header p { - color: var(--color-text-light); - font-size: 0.95rem; -} - -.shop-results-actions { - display: inline-flex; - align-items: center; - gap: 12px; -} - -.shop-results-actions .btn:disabled { - opacity: 0.45; - cursor: not-allowed; - transform: none; -} - -.shop-shortcut-hint { - color: var(--color-text-light); - font-size: 0.82rem; - opacity: 0.85; -} - -.shop-products-grid { - display: grid; - grid-template-columns: repeat(3, 1fr); - gap: 24px; -} - -.no-results { - grid-column: 1 / -1; - text-align: center; - padding: 60px 20px; - color: var(--color-text-light); -} - -.no-results i { - font-size: 3rem; - margin-bottom: 16px; - display: block; -} - -/* ===== Product Detail ===== */ -.product-detail { - padding: 40px 0 80px; -} - -.product-detail-grid { - display: grid; - grid-template-columns: 1fr 1fr; - gap: 48px; - margin-bottom: 60px; -} - -.product-detail-image { - background: var(--color-white); - border-radius: var(--radius-lg); - border: 1px solid var(--color-border); - overflow: hidden; - display: flex; - align-items: center; - justify-content: center; - min-height: 400px; -} - -.product-detail-image img { - width: 100%; - height: 100%; - object-fit: cover; -} - -.product-detail-info h1 { - font-size: 2rem; - margin-bottom: 8px; -} - -.product-detail-category { - color: var(--color-primary); - font-weight: 600; - text-transform: uppercase; - font-size: 0.85rem; - letter-spacing: 0.5px; - margin-bottom: 16px; -} - -.product-detail-price { - font-size: 2rem; - font-weight: 800; - margin-bottom: 20px; -} - -.product-detail-description { - color: var(--color-text-light); - font-size: 1.05rem; - line-height: 1.8; - margin-bottom: 32px; -} - -.product-detail-features { - margin-bottom: 32px; -} - -.product-detail-features h3 { - font-size: 1.1rem; - margin-bottom: 12px; -} - -.product-detail-features ul { - list-style: disc; - padding-left: 20px; -} - -.product-detail-features li { - margin-bottom: 6px; - color: var(--color-text-light); -} - -.product-detail-actions { - display: flex; - gap: 16px; -} - -.product-detail-actions .btn { - flex: 1; - padding: 14px 24px; -} - -.breadcrumb { - display: flex; - align-items: center; - gap: 8px; - font-size: 0.9rem; - color: var(--color-text-light); - margin-bottom: 24px; -} - -.breadcrumb a:hover { - color: var(--color-primary); -} - -.breadcrumb span { - color: var(--color-text); - font-weight: 500; -} - -/* ===== Related Products ===== */ -.related-products { - border-top: 1px solid var(--color-border); - padding-top: 40px; -} - -.related-products h2 { - font-size: 1.5rem; - margin-bottom: 24px; -} - -/* ===== About Page ===== */ -.about-hero { - background: linear-gradient(135deg, var(--color-secondary) 0%, #1e3a5f 100%); - color: var(--color-white); - padding: 80px 0; - text-align: center; -} - -.about-hero h1 { - font-size: 2.5rem; - margin-bottom: 16px; -} - -.about-hero p { - font-size: 1.1rem; - opacity: 0.85; - max-width: 600px; - margin: 0 auto; -} - -.about-content { - padding: 60px 0; -} - -.about-content p { - font-size: 1.05rem; - line-height: 1.8; - color: var(--color-text-light); - max-width: 800px; - margin: 0 auto 24px; -} - -.trust-badges { - display: grid; - grid-template-columns: repeat(3, 1fr); - gap: 32px; - margin-top: 60px; -} - -.trust-badge { - text-align: center; - padding: 32px 20px; - background: var(--color-white); - border-radius: var(--radius-lg); - border: 1px solid var(--color-border); -} - -.trust-badge i { - font-size: 2.5rem; - color: var(--color-primary); - margin-bottom: 16px; -} - -.trust-badge h3 { - margin-bottom: 8px; - font-size: 1.1rem; -} - -.trust-badge p { - font-size: 0.9rem; - color: var(--color-text-light); - margin: 0; -} - -/* ===== Contact Page ===== */ -.contact-section { - padding: 60px 0 80px; -} - -.contact-grid { - display: grid; - grid-template-columns: 1fr 1fr; - gap: 48px; - max-width: 900px; - margin: 0 auto; -} - -.contact-form { - background: var(--color-white); - padding: 32px; - border-radius: var(--radius-lg); - border: 1px solid var(--color-border); -} - -.contact-form h2 { - font-size: 1.5rem; - margin-bottom: 24px; -} - -.form-group { - margin-bottom: 20px; -} - -.form-group label { - display: block; - font-weight: 500; - margin-bottom: 6px; - font-size: 0.9rem; -} - -.form-group input, -.form-group textarea, -.form-group select { - width: 100%; - padding: 10px 14px; - border: 1px solid var(--color-border); - border-radius: var(--radius); - font-size: 0.95rem; - outline: none; - transition: border-color var(--transition); -} - -.form-group input:focus, -.form-group textarea:focus, -.form-group select:focus { - border-color: var(--color-primary); -} - -.form-group textarea { - resize: vertical; - min-height: 120px; -} - -.contact-info { - padding: 32px 0; -} - -.contact-info h2 { - font-size: 1.5rem; - margin-bottom: 24px; -} - -.contact-info-item { - display: flex; - align-items: flex-start; - gap: 16px; - margin-bottom: 24px; -} - -.contact-info-item i { - font-size: 1.3rem; - color: var(--color-primary); - margin-top: 2px; -} - -.contact-info-item h4 { - font-size: 0.95rem; - margin-bottom: 4px; -} - -.contact-info-item p { - color: var(--color-text-light); - font-size: 0.9rem; -} - -/* ===== Page Header (reusable) ===== */ -.page-header { - background: var(--color-secondary); - color: var(--color-white); - padding: 48px 0; - text-align: center; -} - -.page-header h1 { - font-size: 2rem; - margin-bottom: 8px; -} - -.page-header p { - opacity: 0.8; - font-size: 1.05rem; -} - -/* ===== Footer ===== */ -.footer { - background: var(--color-secondary); - color: var(--color-white); - padding: 60px 0 24px; - margin-top: auto; -} - -.footer-grid { - display: grid; - grid-template-columns: 2fr 1fr 1fr 1fr; - gap: 40px; - margin-bottom: 40px; -} - -.footer-about p { - color: rgba(255,255,255,0.6); - font-size: 0.9rem; - line-height: 1.7; - margin-top: 12px; -} - -.footer h4 { - font-size: 0.95rem; - margin-bottom: 16px; - text-transform: uppercase; - letter-spacing: 0.5px; -} - -.footer-links a { - display: block; - color: rgba(255,255,255,0.6); - font-size: 0.9rem; - margin-bottom: 10px; - transition: color var(--transition); -} - -.footer-links a:hover { - color: var(--color-white); -} - -.footer-social { - display: flex; - gap: 12px; - margin-top: 12px; -} - -.footer-social a { - width: 40px; - height: 40px; - border-radius: 50%; - background: rgba(255,255,255,0.1); - display: flex; - align-items: center; - justify-content: center; - color: var(--color-white); - transition: background var(--transition); -} - -.footer-social a:hover { - background: var(--color-primary); -} - -.footer-bottom { - border-top: 1px solid rgba(255,255,255,0.1); - padding-top: 24px; - text-align: center; - color: rgba(255,255,255,0.4); - font-size: 0.85rem; -} - -/* ===== Responsive ===== */ -@media (max-width: 1024px) { - .products-grid { - grid-template-columns: repeat(3, 1fr); - } - .footer-grid { - grid-template-columns: 1fr 1fr; - gap: 32px; - } -} - -@media (max-width: 768px) { - .nav-links, - .nav-search { - display: none; - } - - .nav-links.open { - display: flex; - flex-direction: column; - position: absolute; - top: var(--header-height); - left: 0; - right: 0; - background: var(--color-white); - border-bottom: 1px solid var(--color-border); - padding: 20px; - gap: 16px; - box-shadow: var(--shadow-lg); - } - - .mobile-menu-btn { - display: block; - } - - .hero h1 { - font-size: 2rem; - } - - .hero p { - font-size: 1rem; - } - - .categories-grid { - grid-template-columns: repeat(2, 1fr); - gap: 16px; - } - - .products-grid, - .shop-products-grid { - grid-template-columns: repeat(2, 1fr); - gap: 16px; - } - - .shop-layout { - grid-template-columns: 1fr; - } - - .shop-results-header { - flex-direction: column; - align-items: flex-start; - gap: 10px; - } - - .shop-sidebar { - position: static; - } - - .product-detail-grid { - grid-template-columns: 1fr; - gap: 24px; - } - - .contact-grid { - grid-template-columns: 1fr; - } - - .trust-badges { - grid-template-columns: 1fr; - gap: 16px; - } - - .footer-grid { - grid-template-columns: 1fr; - gap: 24px; - } - - .product-detail-actions { - flex-direction: column; - } - - .section { - padding: 48px 0; - } -} - -@media (max-width: 480px) { - .products-grid, - .shop-products-grid { - grid-template-columns: 1fr; - } - - .categories-grid { - grid-template-columns: 1fr; - } - - .hero { - padding: 60px 0; - } - - .hero h1 { - font-size: 1.75rem; - } -} - -/* ===== Aria Store Refresh ===== */ -body.store-theme { - --store-ink: #122033; - --store-ink-soft: #5f6874; - --store-teal: #0f9d89; - --store-teal-strong: #0a6f63; - --store-coral: #d95b43; - --store-gold: #d3a53e; - --store-slate: #22405c; - --store-line: rgba(18, 32, 51, 0.12); - --store-panel: rgba(255, 255, 255, 0.78); - --store-panel-strong: rgba(255, 255, 255, 0.92); - --store-shadow: 0 24px 64px rgba(18, 32, 51, 0.12); - --store-shadow-soft: 0 16px 36px rgba(18, 32, 51, 0.08); - background: linear-gradient(180deg, #fbf7ef 0%, #f7f2e8 56%, #efe5d6 100%); - color: var(--store-ink); - overflow-x: hidden; -} - -body.store-theme::before { - content: ""; - position: fixed; - inset: 0; - z-index: -2; - background: - radial-gradient(circle at 14% 16%, rgba(15, 157, 137, 0.16), transparent 30%), - radial-gradient(circle at 86% 12%, rgba(217, 91, 67, 0.15), transparent 25%), - radial-gradient(circle at 60% 78%, rgba(211, 165, 62, 0.18), transparent 28%); - pointer-events: none; -} - -body.store-theme::after { - content: ""; - position: fixed; - inset: 0; - z-index: -1; - background-image: - linear-gradient(rgba(18, 32, 51, 0.032) 1px, transparent 1px), - linear-gradient(90deg, rgba(18, 32, 51, 0.032) 1px, transparent 1px); - background-size: 34px 34px; - mask-image: linear-gradient(180deg, rgba(0, 0, 0, 0.82), transparent 92%); - pointer-events: none; -} - -.store-theme a { - color: inherit; -} - -.store-theme .container { - max-width: 1220px; -} - -.store-theme .store-platform-bar { - padding: 10px 0; - background: rgba(18, 32, 51, 0.92); - box-shadow: 0 12px 28px rgba(9, 18, 29, 0.18); -} - -.store-theme .store-platform-inner { - display: flex; - align-items: center; - justify-content: space-between; - gap: 14px; - flex-wrap: wrap; -} - -.store-theme .store-platform-label { - color: rgba(247, 242, 232, 0.74); - font-size: 0.74rem; - font-weight: 700; - letter-spacing: 0.12em; - text-transform: uppercase; -} - -.store-theme .store-platform-links { - display: flex; - align-items: center; - gap: 8px; - flex-wrap: wrap; -} - -.store-theme .store-platform-links a { - padding: 8px 12px; - border-radius: 999px; - color: rgba(247, 242, 232, 0.78); - font-size: 0.84rem; - text-decoration: none; - transition: background var(--transition), color var(--transition), transform var(--transition); -} - -.store-theme .store-platform-links a:hover, -.store-theme .store-platform-links a:focus-visible { - background: rgba(255, 255, 255, 0.12); - color: #fffdf8; - transform: translateY(-1px); - outline: none; -} - -.store-theme .header { - background: rgba(247, 242, 232, 0.8); - backdrop-filter: blur(18px) saturate(1.35); - border-bottom: 1px solid rgba(18, 32, 51, 0.08); - height: auto; - min-height: 78px; - box-shadow: none; -} - -.store-theme .header .container { - gap: 16px; - flex-wrap: wrap; - padding-top: 14px; - padding-bottom: 14px; -} - -.store-theme .logo-store { - color: var(--store-ink); - gap: 12px; - min-width: 0; -} - -.store-theme .logo-mark { - width: 42px; - height: 42px; - border-radius: 14px; - background: - radial-gradient(circle at 50% 34%, var(--store-teal) 0 11px, transparent 12px), - linear-gradient(180deg, var(--store-slate), #16283d); - position: relative; - box-shadow: inset 0 0 0 1px rgba(255, 255, 255, 0.18); - flex-shrink: 0; -} - -.store-theme .logo-mark::after { - content: ""; - position: absolute; - left: 9px; - right: 9px; - bottom: 8px; - height: 11px; - border-radius: 999px 999px 8px 8px; - border: 3px solid rgba(247, 242, 232, 0.96); - border-top: 0; -} - -.store-theme .logo-copy { - display: grid; - gap: 2px; - min-width: 0; -} - -.store-theme .logo-copy strong { - font-family: "Space Grotesk", sans-serif; - font-size: 1.08rem; - line-height: 1; - letter-spacing: -0.03em; -} - -.store-theme .logo-copy span { - font-size: 0.78rem; - color: var(--store-ink-soft); -} - -.store-theme .nav-links { - gap: 8px; -} - -.store-theme .nav-links a { - padding: 10px 14px; - border-radius: 999px; - color: var(--store-ink-soft); - font-size: 0.94rem; - transition: background var(--transition), color var(--transition), transform var(--transition); -} - -.store-theme .nav-links a:hover, -.store-theme .nav-links a.active { - color: var(--store-ink); - background: rgba(255, 255, 255, 0.74); - transform: translateY(-1px); -} - -.store-theme .nav-search { - margin-left: auto; -} - -.store-theme .nav-search-input { - width: min(290px, 100%); - min-height: 44px; - padding: 0 14px; - border-radius: 999px; - border: 1px solid rgba(18, 32, 51, 0.1); - background: rgba(255, 255, 255, 0.8); - color: var(--store-ink); -} - -.store-theme .nav-search-input:focus { - border-color: transparent; - outline: 2px solid rgba(15, 157, 137, 0.22); - outline-offset: 2px; -} - -.store-theme .mobile-menu-btn { - width: 44px; - height: 44px; - border-radius: 14px; - border: 1px solid rgba(18, 32, 51, 0.1); - background: rgba(255, 255, 255, 0.62); - color: var(--store-ink); -} - -.store-theme .mobile-menu-btn:hover { - background: rgba(255, 255, 255, 0.82); -} - -.store-theme .btn { - border-radius: 999px; - font-weight: 700; -} - -.store-theme .btn-primary { - background: linear-gradient(135deg, var(--store-teal), #1bb6a4); - color: #f6fffd; - box-shadow: 0 14px 28px rgba(15, 157, 137, 0.22); -} - -.store-theme .btn-primary:hover { - background: linear-gradient(135deg, var(--store-teal-strong), var(--store-teal)); -} - -.store-theme .btn-outline { - border-color: rgba(18, 32, 51, 0.12); - color: var(--store-teal); -} - -.store-theme .store-subpage-hero { - padding: 34px 0 16px; -} - -.store-theme .page-hero-card, -.store-theme .page-shell, -.store-theme .shop-sidebar, -.store-theme .product-card, -.store-theme .product-detail-image, -.store-theme .contact-form, -.store-theme .trust-badge, -.store-theme .page-intro-card, -.store-theme .contact-info-item, -.store-theme .no-results { - background: var(--store-panel); - border: 1px solid rgba(255, 255, 255, 0.62); - box-shadow: var(--store-shadow-soft); - backdrop-filter: blur(10px); -} - -.store-theme .page-hero-card, -.store-theme .page-shell { - border-radius: 30px; - box-shadow: var(--store-shadow); -} - -.store-theme .page-hero-card { - padding: 30px; -} - -.store-theme .page-shell { - padding: 24px; -} - -.store-theme .store-eyebrow { - display: inline-flex; - align-items: center; - gap: 10px; - margin: 0 0 14px; - font-size: 0.8rem; - font-weight: 700; - text-transform: uppercase; - letter-spacing: 0.14em; - color: var(--store-slate); -} - -.store-theme .store-eyebrow::before { - content: ""; - width: 28px; - height: 2px; - border-radius: 999px; - background: linear-gradient(90deg, var(--store-teal), var(--store-coral)); -} - -.store-theme .page-hero-card h1 { - margin: 0; - font-family: "Space Grotesk", sans-serif; - font-size: clamp(2.4rem, 6vw, 4rem); - line-height: 0.96; - letter-spacing: -0.05em; - max-width: 11ch; -} - -.store-theme .page-hero-card p, -.store-theme .page-intro-card p, -.store-theme .about-content p, -.store-theme .product-detail-description, -.store-theme .product-detail-features li, -.store-theme .contact-info-item p, -.store-theme .shop-results-header p, -.store-theme .form-note { - color: var(--store-ink-soft); - line-height: 1.72; -} - -.store-theme .hero-chip-row { - display: flex; - flex-wrap: wrap; - gap: 10px; - margin-top: 20px; -} - -.store-theme .store-pill { - display: inline-flex; - align-items: center; - gap: 8px; - padding: 7px 10px; - border-radius: 999px; - background: rgba(18, 32, 51, 0.06); - color: var(--store-ink-soft); - font-size: 0.74rem; - font-weight: 700; - text-transform: uppercase; - letter-spacing: 0.1em; -} - -.store-theme .store-section { - padding: 0 0 42px; -} - -.store-theme .page-intro-grid { - display: grid; - grid-template-columns: repeat(3, minmax(0, 1fr)); - gap: 14px; - margin-bottom: 24px; -} - -.store-theme .page-intro-card { - padding: 20px; - border-radius: 20px; -} - -.store-theme .page-intro-card i { - font-size: 1.24rem; - color: var(--store-teal); - margin-bottom: 14px; -} - -.store-theme .page-intro-card h3, -.store-theme .product-detail-features h3, -.store-theme .contact-form h2, -.store-theme .contact-info h2, -.store-theme .related-products h2, -.store-theme .trust-badge h3 { - font-family: "Space Grotesk", sans-serif; - letter-spacing: -0.04em; -} - -.store-theme .about-content p { - max-width: none; - margin: 0 0 18px; -} - -.store-theme .trust-badges { - gap: 16px; - margin-top: 26px; -} - -.store-theme .trust-badge { - text-align: left; - padding: 24px; - border-radius: 20px; -} - -.store-theme .trust-badge i { - color: var(--store-teal); - margin-bottom: 14px; -} - -.store-theme .trust-badge p { - margin: 0; -} - -.store-theme .shop-layout { - gap: 24px; - padding: 0; - align-items: start; -} - -.store-theme .shop-sidebar { - padding: 22px; - border-radius: 24px; - position: sticky; - top: 92px; -} - -.store-theme .filter-group h4 { - color: var(--store-slate); - font-size: 0.78rem; - letter-spacing: 0.08em; -} - -.store-theme .filter-group select, -.store-theme .filter-group input[type="range"] { - border-radius: 14px; - border: 1px solid rgba(18, 32, 51, 0.1); - background: rgba(255, 255, 255, 0.82); -} - -.store-theme .price-range-labels { - color: var(--store-ink-soft); -} - -.store-theme .shop-results-header { - margin-bottom: 18px; -} - -.store-theme .shop-results-actions .btn { - min-height: 38px; - padding: 0 14px; -} - -.store-theme .shop-shortcut-hint { - color: var(--store-ink-soft); -} - -.store-theme .shop-products-grid { - grid-template-columns: repeat(2, minmax(0, 1fr)); - gap: 18px; -} - -.store-theme .product-card { - border-radius: 24px; - overflow: hidden; - border-color: rgba(18, 32, 51, 0.08); -} - -.store-theme .product-card:hover { - transform: translateY(-3px); - background: var(--store-panel-strong); -} - -.store-theme .product-card-image { - height: 220px; - background: linear-gradient(135deg, rgba(15, 157, 137, 0.12), rgba(211, 165, 62, 0.16)); - border-bottom: 1px solid rgba(18, 32, 51, 0.08); -} - -.store-theme .product-card-body { - padding: 18px; -} - -.store-theme .product-card-category, -.store-theme .product-detail-category { - display: inline-flex; - align-items: center; - padding: 6px 10px; - border-radius: 999px; - background: rgba(15, 157, 137, 0.1); - color: var(--store-slate); - font-size: 0.74rem; - font-weight: 700; - text-transform: uppercase; - letter-spacing: 0.08em; -} - -.store-theme .product-card-title { - font-family: "Space Grotesk", sans-serif; - font-size: 1.08rem; - margin: 12px 0 8px; -} - -.store-theme .product-card-title a:hover { - color: var(--store-teal); -} - -.store-theme .product-card-price, -.store-theme .product-detail-price { - font-family: "Space Grotesk", sans-serif; - color: var(--store-ink); -} - -.store-theme .product-card-price { - font-size: 1.38rem; - margin-bottom: 14px; -} - -.store-theme .product-detail { - padding: 0; -} - -.store-theme .breadcrumb { - gap: 6px; - color: var(--store-ink-soft); - margin-bottom: 22px; -} - -.store-theme .breadcrumb a { - color: var(--store-teal); -} - -.store-theme .product-detail-grid { - gap: 32px; - margin-bottom: 42px; -} - -.store-theme .product-detail-image { - min-height: 360px; - border-radius: 24px; -} - -.store-theme .product-detail-info h1 { - font-family: "Space Grotesk", sans-serif; - font-size: clamp(2rem, 5vw, 3rem); - line-height: 1; - letter-spacing: -0.05em; - margin: 14px 0 8px; -} - -.store-theme .product-detail-price { - font-size: 2.2rem; -} - -.store-theme .product-detail-features ul { - padding-left: 0; - list-style: none; - display: grid; - gap: 10px; -} - -.store-theme .product-detail-features li { - position: relative; - padding-left: 20px; -} - -.store-theme .product-detail-features li::before { - content: "•"; - position: absolute; - left: 0; - color: var(--store-teal); - font-weight: 800; -} - -.store-theme .related-products { - border-top: 1px solid rgba(18, 32, 51, 0.12); - padding-top: 30px; -} - -.store-theme .contact-grid { - gap: 24px; - max-width: none; -} - -.store-theme .contact-form { - padding: 28px; - border-radius: 24px; -} - -.store-theme .form-group label { - color: var(--store-ink); - font-weight: 600; -} - -.store-theme .form-group input, -.store-theme .form-group textarea, -.store-theme .form-group select { - border-radius: 14px; - border: 1px solid rgba(18, 32, 51, 0.1); - background: rgba(255, 255, 255, 0.82); -} - -.store-theme .form-group input:focus, -.store-theme .form-group textarea:focus, -.store-theme .form-group select:focus { - border-color: rgba(15, 157, 137, 0.34); - box-shadow: 0 0 0 4px rgba(15, 157, 137, 0.12); -} - -.store-theme .contact-info { - padding: 0; -} - -.store-theme .contact-info-item { - padding: 18px; - border-radius: 20px; - border: 1px solid rgba(255, 255, 255, 0.62); -} - -.store-theme .contact-info-item i { - color: var(--store-teal); -} - -.store-theme .form-note { - margin-top: 14px; - font-size: 0.88rem; -} - -.store-theme .no-results { - padding: 48px 20px; - border-radius: 24px; - border-style: dashed; - border-color: rgba(18, 32, 51, 0.14); -} - -.store-theme .footer { - background: linear-gradient(180deg, #16283d 0%, #122033 100%); - margin-top: 0; - padding: 52px 0 24px; -} - -.store-theme .footer .logo-copy span, -.store-theme .footer-about p, -.store-theme .footer-links a, -.store-theme .footer-bottom { - color: rgba(246, 255, 253, 0.72); -} - -.store-theme .footer-links a:hover { - color: #fff; -} - -.store-theme .footer-social a { - background: rgba(255, 255, 255, 0.08); -} - -.store-theme .footer-social a:hover { - background: rgba(15, 157, 137, 0.82); -} - -.store-theme .footer-bottom { - display: flex; - justify-content: space-between; - gap: 12px; - flex-wrap: wrap; - text-align: left; -} - -.store-theme .footer-meta { - opacity: 0.82; -} - -@media (max-width: 1024px) { - .store-theme .page-intro-grid, - .store-theme .trust-badges, - .store-theme .shop-products-grid { - grid-template-columns: repeat(2, minmax(0, 1fr)); - } - - .store-theme .footer-grid { - grid-template-columns: repeat(2, minmax(0, 1fr)); - } -} - -@media (max-width: 768px) { - .store-theme .store-platform-inner, - .store-theme .store-platform-links, - .store-theme .hero-chip-row, - .store-theme .footer-bottom { - flex-direction: column; - align-items: flex-start; - } - - .store-theme .page-hero-card, - .store-theme .page-shell, - .store-theme .contact-form, - .store-theme .shop-sidebar, - .store-theme .trust-badge, - .store-theme .page-intro-card, - .store-theme .contact-info-item { - padding: 20px; - } - - .store-theme .page-hero-card h1 { - max-width: none; - } - - .store-theme .page-intro-grid, - .store-theme .trust-badges, - .store-theme .shop-products-grid, - .store-theme .footer-grid { - grid-template-columns: 1fr; - } - - .store-theme .shop-results-actions { - flex-wrap: wrap; - } - - .store-theme .nav-search { - width: 100%; - margin-left: 0; - } - - .store-theme .nav-search-input { - width: 100%; - } -} - -@media (max-width: 480px) { - .store-theme .product-detail-actions { - flex-direction: column; - } -} +/* ===== CSS Custom Properties ===== */ +:root { + --color-primary: #2563eb; + --color-primary-dark: #1d4ed8; + --color-secondary: #0f172a; + --color-accent: #f59e0b; + --color-bg: #f8fafc; + --color-white: #ffffff; + --color-text: #1e293b; + --color-text-light: #64748b; + --color-border: #e2e8f0; + --color-success: #22c55e; + --color-danger: #ef4444; + --shadow-sm: 0 1px 2px rgba(0,0,0,0.05); + --shadow-md: 0 4px 6px -1px rgba(0,0,0,0.1), 0 2px 4px -2px rgba(0,0,0,0.1); + --shadow-lg: 0 10px 15px -3px rgba(0,0,0,0.1), 0 4px 6px -4px rgba(0,0,0,0.1); + --radius: 8px; + --radius-lg: 12px; + --max-width: 1200px; + --header-height: 70px; + --transition: 0.2s ease; +} + +/* ===== Reset & Base ===== */ +*, *::before, *::after { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +html { + scroll-behavior: smooth; +} + +body { + font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; + color: var(--color-text); + background: var(--color-bg); + line-height: 1.6; + min-height: 100vh; + display: flex; + flex-direction: column; +} + +a { + text-decoration: none; + color: inherit; +} + +img { + max-width: 100%; + height: auto; + display: block; +} + +ul { list-style: none; } + +button { + cursor: pointer; + font-family: inherit; + border: none; + background: none; +} + +input, select, textarea { + font-family: inherit; + font-size: inherit; +} + +main { + flex: 1; +} + +/* ===== Utility ===== */ +.container { + width: 100%; + max-width: var(--max-width); + margin: 0 auto; + padding: 0 20px; +} + +.btn { + display: inline-flex; + align-items: center; + justify-content: center; + gap: 8px; + padding: 12px 28px; + border-radius: var(--radius); + font-weight: 600; + font-size: 0.95rem; + transition: all var(--transition); + white-space: nowrap; +} + +.btn-primary { + background: var(--color-primary); + color: var(--color-white); +} +.btn-primary:hover { + background: var(--color-primary-dark); + transform: translateY(-1px); + box-shadow: var(--shadow-md); +} + +.btn-outline { + border: 2px solid var(--color-primary); + color: var(--color-primary); + background: transparent; +} +.btn-outline:hover { + background: var(--color-primary); + color: var(--color-white); +} + +.btn-secondary { + background: var(--color-secondary); + color: var(--color-white); +} +.btn-secondary:hover { + opacity: 0.9; + transform: translateY(-1px); +} + +.section-title { + font-size: 1.75rem; + font-weight: 700; + margin-bottom: 8px; +} + +.section-subtitle { + color: var(--color-text-light); + font-size: 1.05rem; + margin-bottom: 40px; +} + +.section { + padding: 80px 0; +} + +/* ===== Header / Navbar ===== */ +.header { + position: sticky; + top: 0; + z-index: 1000; + background: var(--color-white); + border-bottom: 1px solid var(--color-border); + height: var(--header-height); +} + +.header .container { + display: flex; + align-items: center; + justify-content: space-between; + height: 100%; +} + +.logo { + font-size: 1.5rem; + font-weight: 800; + color: var(--color-primary); + display: flex; + align-items: center; + gap: 8px; +} + +.logo i { + font-size: 1.3rem; +} + +.nav-links { + display: flex; + align-items: center; + gap: 32px; +} + +.nav-links a { + font-weight: 500; + color: var(--color-text-light); + transition: color var(--transition); + font-size: 0.95rem; +} + +.nav-links a:hover, +.nav-links a.active { + color: var(--color-primary); +} + +.nav-search { + display: flex; + align-items: center; + gap: 12px; +} + +.nav-search-input { + border: 1px solid var(--color-border); + border-radius: var(--radius); + padding: 8px 14px; + font-size: 0.9rem; + width: 200px; + transition: border-color var(--transition); + outline: none; +} + +.nav-search-input:focus { + border-color: var(--color-primary); +} + +.mobile-menu-btn { + display: none; + font-size: 1.5rem; + color: var(--color-text); +} + +/* ===== Hero Section ===== */ +.hero { + background: linear-gradient(135deg, var(--color-secondary) 0%, #1e3a5f 100%); + color: var(--color-white); + padding: 100px 0; + text-align: center; +} + +.hero h1 { + font-size: 3rem; + font-weight: 800; + margin-bottom: 16px; + line-height: 1.2; +} + +.hero p { + font-size: 1.2rem; + opacity: 0.85; + max-width: 600px; + margin: 0 auto 32px; +} + +.hero .btn { + font-size: 1.05rem; + padding: 14px 36px; +} + +/* ===== Categories ===== */ +.categories-grid { + display: grid; + grid-template-columns: repeat(4, 1fr); + gap: 24px; +} + +.category-card { + background: var(--color-white); + border-radius: var(--radius-lg); + padding: 32px 20px; + text-align: center; + box-shadow: var(--shadow-sm); + border: 1px solid var(--color-border); + transition: all var(--transition); + cursor: pointer; +} + +.category-card:hover { + transform: translateY(-4px); + box-shadow: var(--shadow-lg); + border-color: var(--color-primary); +} + +.category-card i { + font-size: 2.5rem; + color: var(--color-primary); + margin-bottom: 16px; +} + +.category-card h3 { + font-size: 1.05rem; + margin-bottom: 4px; +} + +.category-card p { + font-size: 0.85rem; + color: var(--color-text-light); +} + +/* ===== Product Grid ===== */ +.products-grid { + display: grid; + grid-template-columns: repeat(4, 1fr); + gap: 24px; +} + +.product-card { + background: var(--color-white); + border-radius: var(--radius-lg); + overflow: hidden; + box-shadow: var(--shadow-sm); + border: 1px solid var(--color-border); + transition: all var(--transition); +} + +.product-card:hover { + transform: translateY(-4px); + box-shadow: var(--shadow-lg); +} + +.product-card-image { + width: 100%; + height: 220px; + overflow: hidden; + background: #f1f5f9; + display: flex; + align-items: center; + justify-content: center; +} + +.product-card-image img { + width: 100%; + height: 100%; + object-fit: cover; +} + +.product-card-body { + padding: 16px; +} + +.product-card-category { + font-size: 0.75rem; + text-transform: uppercase; + letter-spacing: 0.5px; + color: var(--color-primary); + font-weight: 600; + margin-bottom: 4px; +} + +.product-card-title { + font-size: 1rem; + font-weight: 600; + margin-bottom: 8px; + line-height: 1.3; +} + +.product-card-price { + font-size: 1.2rem; + font-weight: 700; + color: var(--color-text); + margin-bottom: 12px; +} + +.product-card .btn { + width: 100%; + padding: 10px; + font-size: 0.9rem; +} + +/* ===== Shop Page Layout ===== */ +.shop-layout { + display: grid; + grid-template-columns: 260px 1fr; + gap: 32px; + padding: 40px 0; +} + +.shop-sidebar { + background: var(--color-white); + border-radius: var(--radius-lg); + padding: 24px; + border: 1px solid var(--color-border); + height: fit-content; + position: sticky; + top: calc(var(--header-height) + 20px); +} + +.filter-group { + margin-bottom: 24px; +} + +.filter-group:last-child { + margin-bottom: 0; +} + +.filter-group h4 { + font-size: 0.9rem; + text-transform: uppercase; + letter-spacing: 0.5px; + margin-bottom: 12px; + color: var(--color-text); +} + +.filter-group select, +.filter-group input[type="range"] { + width: 100%; + padding: 8px 12px; + border: 1px solid var(--color-border); + border-radius: var(--radius); + font-size: 0.9rem; + outline: none; +} + +.filter-group select:focus { + border-color: var(--color-primary); +} + +.price-range-labels { + display: flex; + justify-content: space-between; + font-size: 0.85rem; + color: var(--color-text-light); + margin-top: 8px; +} + +.shop-results-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 24px; +} + +.shop-results-header p { + color: var(--color-text-light); + font-size: 0.95rem; +} + +.shop-results-actions { + display: inline-flex; + align-items: center; + gap: 12px; +} + +.shop-results-actions .btn:disabled { + opacity: 0.45; + cursor: not-allowed; + transform: none; +} + +.shop-shortcut-hint { + color: var(--color-text-light); + font-size: 0.82rem; + opacity: 0.85; +} + +.shop-products-grid { + display: grid; + grid-template-columns: repeat(3, 1fr); + gap: 24px; +} + +.no-results { + grid-column: 1 / -1; + text-align: center; + padding: 60px 20px; + color: var(--color-text-light); +} + +.no-results i { + font-size: 3rem; + margin-bottom: 16px; + display: block; +} + +/* ===== Product Detail ===== */ +.product-detail { + padding: 40px 0 80px; +} + +.product-detail-grid { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 48px; + margin-bottom: 60px; +} + +.product-detail-image { + background: var(--color-white); + border-radius: var(--radius-lg); + border: 1px solid var(--color-border); + overflow: hidden; + display: flex; + align-items: center; + justify-content: center; + min-height: 400px; +} + +.product-detail-image img { + width: 100%; + height: 100%; + object-fit: cover; +} + +.product-detail-info h1 { + font-size: 2rem; + margin-bottom: 8px; +} + +.product-detail-category { + color: var(--color-primary); + font-weight: 600; + text-transform: uppercase; + font-size: 0.85rem; + letter-spacing: 0.5px; + margin-bottom: 16px; +} + +.product-detail-price { + font-size: 2rem; + font-weight: 800; + margin-bottom: 20px; +} + +.product-detail-description { + color: var(--color-text-light); + font-size: 1.05rem; + line-height: 1.8; + margin-bottom: 32px; +} + +.product-detail-features { + margin-bottom: 32px; +} + +.product-detail-features h3 { + font-size: 1.1rem; + margin-bottom: 12px; +} + +.product-detail-features ul { + list-style: disc; + padding-left: 20px; +} + +.product-detail-features li { + margin-bottom: 6px; + color: var(--color-text-light); +} + +.product-detail-actions { + display: flex; + gap: 16px; +} + +.product-detail-actions .btn { + flex: 1; + padding: 14px 24px; +} + +.breadcrumb { + display: flex; + align-items: center; + gap: 8px; + font-size: 0.9rem; + color: var(--color-text-light); + margin-bottom: 24px; +} + +.breadcrumb a:hover { + color: var(--color-primary); +} + +.breadcrumb span { + color: var(--color-text); + font-weight: 500; +} + +/* ===== Related Products ===== */ +.related-products { + border-top: 1px solid var(--color-border); + padding-top: 40px; +} + +.related-products h2 { + font-size: 1.5rem; + margin-bottom: 24px; +} + +/* ===== About Page ===== */ +.about-hero { + background: linear-gradient(135deg, var(--color-secondary) 0%, #1e3a5f 100%); + color: var(--color-white); + padding: 80px 0; + text-align: center; +} + +.about-hero h1 { + font-size: 2.5rem; + margin-bottom: 16px; +} + +.about-hero p { + font-size: 1.1rem; + opacity: 0.85; + max-width: 600px; + margin: 0 auto; +} + +.about-content { + padding: 60px 0; +} + +.about-content p { + font-size: 1.05rem; + line-height: 1.8; + color: var(--color-text-light); + max-width: 800px; + margin: 0 auto 24px; +} + +.trust-badges { + display: grid; + grid-template-columns: repeat(3, 1fr); + gap: 32px; + margin-top: 60px; +} + +.trust-badge { + text-align: center; + padding: 32px 20px; + background: var(--color-white); + border-radius: var(--radius-lg); + border: 1px solid var(--color-border); +} + +.trust-badge i { + font-size: 2.5rem; + color: var(--color-primary); + margin-bottom: 16px; +} + +.trust-badge h3 { + margin-bottom: 8px; + font-size: 1.1rem; +} + +.trust-badge p { + font-size: 0.9rem; + color: var(--color-text-light); + margin: 0; +} + +/* ===== Contact Page ===== */ +.contact-section { + padding: 60px 0 80px; +} + +.contact-grid { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 48px; + max-width: 900px; + margin: 0 auto; +} + +.contact-form { + background: var(--color-white); + padding: 32px; + border-radius: var(--radius-lg); + border: 1px solid var(--color-border); +} + +.contact-form h2 { + font-size: 1.5rem; + margin-bottom: 24px; +} + +.form-group { + margin-bottom: 20px; +} + +.form-group label { + display: block; + font-weight: 500; + margin-bottom: 6px; + font-size: 0.9rem; +} + +.form-group input, +.form-group textarea, +.form-group select { + width: 100%; + padding: 10px 14px; + border: 1px solid var(--color-border); + border-radius: var(--radius); + font-size: 0.95rem; + outline: none; + transition: border-color var(--transition); +} + +.form-group input:focus, +.form-group textarea:focus, +.form-group select:focus { + border-color: var(--color-primary); +} + +.form-group textarea { + resize: vertical; + min-height: 120px; +} + +.contact-info { + padding: 32px 0; +} + +.contact-info h2 { + font-size: 1.5rem; + margin-bottom: 24px; +} + +.contact-info-item { + display: flex; + align-items: flex-start; + gap: 16px; + margin-bottom: 24px; +} + +.contact-info-item i { + font-size: 1.3rem; + color: var(--color-primary); + margin-top: 2px; +} + +.contact-info-item h4 { + font-size: 0.95rem; + margin-bottom: 4px; +} + +.contact-info-item p { + color: var(--color-text-light); + font-size: 0.9rem; +} + +/* ===== Page Header (reusable) ===== */ +.page-header { + background: var(--color-secondary); + color: var(--color-white); + padding: 48px 0; + text-align: center; +} + +.page-header h1 { + font-size: 2rem; + margin-bottom: 8px; +} + +.page-header p { + opacity: 0.8; + font-size: 1.05rem; +} + +/* ===== Footer ===== */ +.footer { + background: var(--color-secondary); + color: var(--color-white); + padding: 60px 0 24px; + margin-top: auto; +} + +.footer-grid { + display: grid; + grid-template-columns: 2fr 1fr 1fr 1fr; + gap: 40px; + margin-bottom: 40px; +} + +.footer-about p { + color: rgba(255,255,255,0.6); + font-size: 0.9rem; + line-height: 1.7; + margin-top: 12px; +} + +.footer h4 { + font-size: 0.95rem; + margin-bottom: 16px; + text-transform: uppercase; + letter-spacing: 0.5px; +} + +.footer-links a { + display: block; + color: rgba(255,255,255,0.6); + font-size: 0.9rem; + margin-bottom: 10px; + transition: color var(--transition); +} + +.footer-links a:hover { + color: var(--color-white); +} + +.footer-social { + display: flex; + gap: 12px; + margin-top: 12px; +} + +.footer-social a { + width: 40px; + height: 40px; + border-radius: 50%; + background: rgba(255,255,255,0.1); + display: flex; + align-items: center; + justify-content: center; + color: var(--color-white); + transition: background var(--transition); +} + +.footer-social a:hover { + background: var(--color-primary); +} + +.footer-bottom { + border-top: 1px solid rgba(255,255,255,0.1); + padding-top: 24px; + text-align: center; + color: rgba(255,255,255,0.4); + font-size: 0.85rem; +} + +/* ===== Responsive ===== */ +@media (max-width: 1024px) { + .products-grid { + grid-template-columns: repeat(3, 1fr); + } + .footer-grid { + grid-template-columns: 1fr 1fr; + gap: 32px; + } +} + +@media (max-width: 768px) { + .nav-links, + .nav-search { + display: none; + } + + .nav-links.open { + display: flex; + flex-direction: column; + position: absolute; + top: var(--header-height); + left: 0; + right: 0; + background: var(--color-white); + border-bottom: 1px solid var(--color-border); + padding: 20px; + gap: 16px; + box-shadow: var(--shadow-lg); + } + + .mobile-menu-btn { + display: block; + } + + .hero h1 { + font-size: 2rem; + } + + .hero p { + font-size: 1rem; + } + + .categories-grid { + grid-template-columns: repeat(2, 1fr); + gap: 16px; + } + + .products-grid, + .shop-products-grid { + grid-template-columns: repeat(2, 1fr); + gap: 16px; + } + + .shop-layout { + grid-template-columns: 1fr; + } + + .shop-results-header { + flex-direction: column; + align-items: flex-start; + gap: 10px; + } + + .shop-sidebar { + position: static; + } + + .product-detail-grid { + grid-template-columns: 1fr; + gap: 24px; + } + + .contact-grid { + grid-template-columns: 1fr; + } + + .trust-badges { + grid-template-columns: 1fr; + gap: 16px; + } + + .footer-grid { + grid-template-columns: 1fr; + gap: 24px; + } + + .product-detail-actions { + flex-direction: column; + } + + .section { + padding: 48px 0; + } +} + +@media (max-width: 480px) { + .products-grid, + .shop-products-grid { + grid-template-columns: 1fr; + } + + .categories-grid { + grid-template-columns: 1fr; + } + + .hero { + padding: 60px 0; + } + + .hero h1 { + font-size: 1.75rem; + } +} + +/* ===== Aria Store Refresh ===== */ +body.store-theme { + --store-ink: #122033; + --store-ink-soft: #5f6874; + --store-teal: #0f9d89; + --store-teal-strong: #0a6f63; + --store-coral: #d95b43; + --store-gold: #d3a53e; + --store-slate: #22405c; + --store-line: rgba(18, 32, 51, 0.12); + --store-panel: rgba(255, 255, 255, 0.78); + --store-panel-strong: rgba(255, 255, 255, 0.92); + --store-shadow: 0 24px 64px rgba(18, 32, 51, 0.12); + --store-shadow-soft: 0 16px 36px rgba(18, 32, 51, 0.08); + background: linear-gradient(180deg, #fbf7ef 0%, #f7f2e8 56%, #efe5d6 100%); + color: var(--store-ink); + overflow-x: hidden; +} + +body.store-theme::before { + content: ""; + position: fixed; + inset: 0; + z-index: -2; + background: + radial-gradient(circle at 14% 16%, rgba(15, 157, 137, 0.16), transparent 30%), + radial-gradient(circle at 86% 12%, rgba(217, 91, 67, 0.15), transparent 25%), + radial-gradient(circle at 60% 78%, rgba(211, 165, 62, 0.18), transparent 28%); + pointer-events: none; +} + +body.store-theme::after { + content: ""; + position: fixed; + inset: 0; + z-index: -1; + background-image: + linear-gradient(rgba(18, 32, 51, 0.032) 1px, transparent 1px), + linear-gradient(90deg, rgba(18, 32, 51, 0.032) 1px, transparent 1px); + background-size: 34px 34px; + mask-image: linear-gradient(180deg, rgba(0, 0, 0, 0.82), transparent 92%); + pointer-events: none; +} + +.store-theme a { + color: inherit; +} + +.store-theme .container { + max-width: 1220px; +} + +.store-theme .store-platform-bar { + padding: 10px 0; + background: rgba(18, 32, 51, 0.92); + box-shadow: 0 12px 28px rgba(9, 18, 29, 0.18); +} + +.store-theme .store-platform-inner { + display: flex; + align-items: center; + justify-content: space-between; + gap: 14px; + flex-wrap: wrap; +} + +.store-theme .store-platform-label { + color: rgba(247, 242, 232, 0.74); + font-size: 0.74rem; + font-weight: 700; + letter-spacing: 0.12em; + text-transform: uppercase; +} + +.store-theme .store-platform-links { + display: flex; + align-items: center; + gap: 8px; + flex-wrap: wrap; +} + +.store-theme .store-platform-links a { + padding: 8px 12px; + border-radius: 999px; + color: rgba(247, 242, 232, 0.78); + font-size: 0.84rem; + text-decoration: none; + transition: background var(--transition), color var(--transition), transform var(--transition); +} + +.store-theme .store-platform-links a:hover, +.store-theme .store-platform-links a:focus-visible { + background: rgba(255, 255, 255, 0.12); + color: #fffdf8; + transform: translateY(-1px); + outline: none; +} + +.store-theme .header { + background: rgba(247, 242, 232, 0.8); + backdrop-filter: blur(18px) saturate(1.35); + border-bottom: 1px solid rgba(18, 32, 51, 0.08); + height: auto; + min-height: 78px; + box-shadow: none; +} + +.store-theme .header .container { + gap: 16px; + flex-wrap: wrap; + padding-top: 14px; + padding-bottom: 14px; +} + +.store-theme .logo-store { + color: var(--store-ink); + gap: 12px; + min-width: 0; +} + +.store-theme .logo-mark { + width: 42px; + height: 42px; + border-radius: 14px; + background: + radial-gradient(circle at 50% 34%, var(--store-teal) 0 11px, transparent 12px), + linear-gradient(180deg, var(--store-slate), #16283d); + position: relative; + box-shadow: inset 0 0 0 1px rgba(255, 255, 255, 0.18); + flex-shrink: 0; +} + +.store-theme .logo-mark::after { + content: ""; + position: absolute; + left: 9px; + right: 9px; + bottom: 8px; + height: 11px; + border-radius: 999px 999px 8px 8px; + border: 3px solid rgba(247, 242, 232, 0.96); + border-top: 0; +} + +.store-theme .logo-copy { + display: grid; + gap: 2px; + min-width: 0; +} + +.store-theme .logo-copy strong { + font-family: "Space Grotesk", sans-serif; + font-size: 1.08rem; + line-height: 1; + letter-spacing: -0.03em; +} + +.store-theme .logo-copy span { + font-size: 0.78rem; + color: var(--store-ink-soft); +} + +.store-theme .nav-links { + gap: 8px; +} + +.store-theme .nav-links a { + padding: 10px 14px; + border-radius: 999px; + color: var(--store-ink-soft); + font-size: 0.94rem; + transition: background var(--transition), color var(--transition), transform var(--transition); +} + +.store-theme .nav-links a:hover, +.store-theme .nav-links a.active { + color: var(--store-ink); + background: rgba(255, 255, 255, 0.74); + transform: translateY(-1px); +} + +.store-theme .nav-search { + margin-left: auto; +} + +.store-theme .nav-search-input { + width: min(290px, 100%); + min-height: 44px; + padding: 0 14px; + border-radius: 999px; + border: 1px solid rgba(18, 32, 51, 0.1); + background: rgba(255, 255, 255, 0.8); + color: var(--store-ink); +} + +.store-theme .nav-search-input:focus { + border-color: transparent; + outline: 2px solid rgba(15, 157, 137, 0.22); + outline-offset: 2px; +} + +.store-theme .mobile-menu-btn { + width: 44px; + height: 44px; + border-radius: 14px; + border: 1px solid rgba(18, 32, 51, 0.1); + background: rgba(255, 255, 255, 0.62); + color: var(--store-ink); +} + +.store-theme .mobile-menu-btn:hover { + background: rgba(255, 255, 255, 0.82); +} + +.store-theme .btn { + border-radius: 999px; + font-weight: 700; +} + +.store-theme .btn-primary { + background: linear-gradient(135deg, var(--store-teal), #1bb6a4); + color: #f6fffd; + box-shadow: 0 14px 28px rgba(15, 157, 137, 0.22); +} + +.store-theme .btn-primary:hover { + background: linear-gradient(135deg, var(--store-teal-strong), var(--store-teal)); +} + +.store-theme .btn-outline { + border-color: rgba(18, 32, 51, 0.12); + color: var(--store-teal); +} + +.store-theme .store-subpage-hero { + padding: 34px 0 16px; +} + +.store-theme .page-hero-card, +.store-theme .page-shell, +.store-theme .shop-sidebar, +.store-theme .product-card, +.store-theme .product-detail-image, +.store-theme .contact-form, +.store-theme .trust-badge, +.store-theme .page-intro-card, +.store-theme .contact-info-item, +.store-theme .no-results { + background: var(--store-panel); + border: 1px solid rgba(255, 255, 255, 0.62); + box-shadow: var(--store-shadow-soft); + backdrop-filter: blur(10px); +} + +.store-theme .page-hero-card, +.store-theme .page-shell { + border-radius: 30px; + box-shadow: var(--store-shadow); +} + +.store-theme .page-hero-card { + padding: 30px; +} + +.store-theme .page-shell { + padding: 24px; +} + +.store-theme .store-eyebrow { + display: inline-flex; + align-items: center; + gap: 10px; + margin: 0 0 14px; + font-size: 0.8rem; + font-weight: 700; + text-transform: uppercase; + letter-spacing: 0.14em; + color: var(--store-slate); +} + +.store-theme .store-eyebrow::before { + content: ""; + width: 28px; + height: 2px; + border-radius: 999px; + background: linear-gradient(90deg, var(--store-teal), var(--store-coral)); +} + +.store-theme .page-hero-card h1 { + margin: 0; + font-family: "Space Grotesk", sans-serif; + font-size: clamp(2.4rem, 6vw, 4rem); + line-height: 0.96; + letter-spacing: -0.05em; + max-width: 11ch; +} + +.store-theme .page-hero-card p, +.store-theme .page-intro-card p, +.store-theme .about-content p, +.store-theme .product-detail-description, +.store-theme .product-detail-features li, +.store-theme .contact-info-item p, +.store-theme .shop-results-header p, +.store-theme .form-note { + color: var(--store-ink-soft); + line-height: 1.72; +} + +.store-theme .hero-chip-row { + display: flex; + flex-wrap: wrap; + gap: 10px; + margin-top: 20px; +} + +.store-theme .store-pill { + display: inline-flex; + align-items: center; + gap: 8px; + padding: 7px 10px; + border-radius: 999px; + background: rgba(18, 32, 51, 0.06); + color: var(--store-ink-soft); + font-size: 0.74rem; + font-weight: 700; + text-transform: uppercase; + letter-spacing: 0.1em; +} + +.store-theme .store-section { + padding: 0 0 42px; +} + +.store-theme .page-intro-grid { + display: grid; + grid-template-columns: repeat(3, minmax(0, 1fr)); + gap: 14px; + margin-bottom: 24px; +} + +.store-theme .page-intro-card { + padding: 20px; + border-radius: 20px; +} + +.store-theme .page-intro-card i { + font-size: 1.24rem; + color: var(--store-teal); + margin-bottom: 14px; +} + +.store-theme .page-intro-card h3, +.store-theme .product-detail-features h3, +.store-theme .contact-form h2, +.store-theme .contact-info h2, +.store-theme .related-products h2, +.store-theme .trust-badge h3 { + font-family: "Space Grotesk", sans-serif; + letter-spacing: -0.04em; +} + +.store-theme .about-content p { + max-width: none; + margin: 0 0 18px; +} + +.store-theme .trust-badges { + gap: 16px; + margin-top: 26px; +} + +.store-theme .trust-badge { + text-align: left; + padding: 24px; + border-radius: 20px; +} + +.store-theme .trust-badge i { + color: var(--store-teal); + margin-bottom: 14px; +} + +.store-theme .trust-badge p { + margin: 0; +} + +.store-theme .shop-layout { + gap: 24px; + padding: 0; + align-items: start; +} + +.store-theme .shop-sidebar { + padding: 22px; + border-radius: 24px; + position: sticky; + top: 92px; +} + +.store-theme .filter-group h4 { + color: var(--store-slate); + font-size: 0.78rem; + letter-spacing: 0.08em; +} + +.store-theme .filter-group select, +.store-theme .filter-group input[type="range"] { + border-radius: 14px; + border: 1px solid rgba(18, 32, 51, 0.1); + background: rgba(255, 255, 255, 0.82); +} + +.store-theme .price-range-labels { + color: var(--store-ink-soft); +} + +.store-theme .shop-results-header { + margin-bottom: 18px; +} + +.store-theme .shop-results-actions .btn { + min-height: 38px; + padding: 0 14px; +} + +.store-theme .shop-shortcut-hint { + color: var(--store-ink-soft); +} + +.store-theme .shop-products-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 18px; +} + +.store-theme .product-card { + border-radius: 24px; + overflow: hidden; + border-color: rgba(18, 32, 51, 0.08); +} + +.store-theme .product-card:hover { + transform: translateY(-3px); + background: var(--store-panel-strong); +} + +.store-theme .product-card-image { + height: 220px; + background: linear-gradient(135deg, rgba(15, 157, 137, 0.12), rgba(211, 165, 62, 0.16)); + border-bottom: 1px solid rgba(18, 32, 51, 0.08); +} + +.store-theme .product-card-body { + padding: 18px; +} + +.store-theme .product-card-category, +.store-theme .product-detail-category { + display: inline-flex; + align-items: center; + padding: 6px 10px; + border-radius: 999px; + background: rgba(15, 157, 137, 0.1); + color: var(--store-slate); + font-size: 0.74rem; + font-weight: 700; + text-transform: uppercase; + letter-spacing: 0.08em; +} + +.store-theme .product-card-title { + font-family: "Space Grotesk", sans-serif; + font-size: 1.08rem; + margin: 12px 0 8px; +} + +.store-theme .product-card-title a:hover { + color: var(--store-teal); +} + +.store-theme .product-card-price, +.store-theme .product-detail-price { + font-family: "Space Grotesk", sans-serif; + color: var(--store-ink); +} + +.store-theme .product-card-price { + font-size: 1.38rem; + margin-bottom: 14px; +} + +.store-theme .product-detail { + padding: 0; +} + +.store-theme .breadcrumb { + gap: 6px; + color: var(--store-ink-soft); + margin-bottom: 22px; +} + +.store-theme .breadcrumb a { + color: var(--store-teal); +} + +.store-theme .product-detail-grid { + gap: 32px; + margin-bottom: 42px; +} + +.store-theme .product-detail-image { + min-height: 360px; + border-radius: 24px; +} + +.store-theme .product-detail-info h1 { + font-family: "Space Grotesk", sans-serif; + font-size: clamp(2rem, 5vw, 3rem); + line-height: 1; + letter-spacing: -0.05em; + margin: 14px 0 8px; +} + +.store-theme .product-detail-price { + font-size: 2.2rem; +} + +.store-theme .product-detail-features ul { + padding-left: 0; + list-style: none; + display: grid; + gap: 10px; +} + +.store-theme .product-detail-features li { + position: relative; + padding-left: 20px; +} + +.store-theme .product-detail-features li::before { + content: "•"; + position: absolute; + left: 0; + color: var(--store-teal); + font-weight: 800; +} + +.store-theme .related-products { + border-top: 1px solid rgba(18, 32, 51, 0.12); + padding-top: 30px; +} + +.store-theme .contact-grid { + gap: 24px; + max-width: none; +} + +.store-theme .contact-form { + padding: 28px; + border-radius: 24px; +} + +.store-theme .form-group label { + color: var(--store-ink); + font-weight: 600; +} + +.store-theme .form-group input, +.store-theme .form-group textarea, +.store-theme .form-group select { + border-radius: 14px; + border: 1px solid rgba(18, 32, 51, 0.1); + background: rgba(255, 255, 255, 0.82); +} + +.store-theme .form-group input:focus, +.store-theme .form-group textarea:focus, +.store-theme .form-group select:focus { + border-color: rgba(15, 157, 137, 0.34); + box-shadow: 0 0 0 4px rgba(15, 157, 137, 0.12); +} + +.store-theme .contact-info { + padding: 0; +} + +.store-theme .contact-info-item { + padding: 18px; + border-radius: 20px; + border: 1px solid rgba(255, 255, 255, 0.62); +} + +.store-theme .contact-info-item i { + color: var(--store-teal); +} + +.store-theme .form-note { + margin-top: 14px; + font-size: 0.88rem; +} + +.store-theme .no-results { + padding: 48px 20px; + border-radius: 24px; + border-style: dashed; + border-color: rgba(18, 32, 51, 0.14); +} + +.store-theme .footer { + background: linear-gradient(180deg, #16283d 0%, #122033 100%); + margin-top: 0; + padding: 52px 0 24px; +} + +.store-theme .footer .logo-copy span, +.store-theme .footer-about p, +.store-theme .footer-links a, +.store-theme .footer-bottom { + color: rgba(246, 255, 253, 0.72); +} + +.store-theme .footer-links a:hover { + color: #fff; +} + +.store-theme .footer-social a { + background: rgba(255, 255, 255, 0.08); +} + +.store-theme .footer-social a:hover { + background: rgba(15, 157, 137, 0.82); +} + +.store-theme .footer-bottom { + display: flex; + justify-content: space-between; + gap: 12px; + flex-wrap: wrap; + text-align: left; +} + +.store-theme .footer-meta { + opacity: 0.82; +} + +@media (max-width: 1024px) { + .store-theme .page-intro-grid, + .store-theme .trust-badges, + .store-theme .shop-products-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .store-theme .footer-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } +} + +@media (max-width: 768px) { + .store-theme .store-platform-inner, + .store-theme .store-platform-links, + .store-theme .hero-chip-row, + .store-theme .footer-bottom { + flex-direction: column; + align-items: flex-start; + } + + .store-theme .page-hero-card, + .store-theme .page-shell, + .store-theme .contact-form, + .store-theme .shop-sidebar, + .store-theme .trust-badge, + .store-theme .page-intro-card, + .store-theme .contact-info-item { + padding: 20px; + } + + .store-theme .page-hero-card h1 { + max-width: none; + } + + .store-theme .page-intro-grid, + .store-theme .trust-badges, + .store-theme .shop-products-grid, + .store-theme .footer-grid { + grid-template-columns: 1fr; + } + + .store-theme .shop-results-actions { + flex-wrap: wrap; + } + + .store-theme .nav-search { + width: 100%; + margin-left: 0; + } + + .store-theme .nav-search-input { + width: 100%; + } +} + +@media (max-width: 480px) { + .store-theme .product-detail-actions { + flex-direction: column; + } +} /* ===== Accessibility: skip-to-content ===== */ diff --git a/docs/store/index.html b/docs/store/index.html index b72e02f7c..372f31316 100644 --- a/docs/store/index.html +++ b/docs/store/index.html @@ -1,917 +1,917 @@ - - - - - - Aria Store - Plans, Access, and Adoption - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - Aria - interactive AI character platform - - - - -
    -
    - -
    -
    -
    -

    Storefront and pricing

    -

    Decide how to adopt Aria without decoding the repo.

    -

    The store should answer three questions immediately: what the platform unlocks, which plan fits the workload, and where a buyer or operator should go next. This page now routes all three cleanly.

    -
    - See pricing - Browse catalog - Talk to sales -
    -
    -
    3Adoption paths from free exploration to enterprise deployment.
    -
    35+APIs available across chat, vision, subscriptions, and quantum workflows.
    -
    6Provider routes including Azure OpenAI, OpenAI, LMStudio, LoRA, and local fallback.
    -
    1Commercial entry point that links pricing, checkout, account, and product pages.
    -
    -
    - - -
    - -
    - - Evaluate -

    Start with docs and local usage

    -

    Use the docs hub when you need startup commands, architecture notes, training guides, and repo-wide navigation before buying anything.

    - Open documentation -     - - Buy -

    Compare plans and upgrade paths

    -

    Go straight to pricing when the question is plan fit, commercial packaging, or feature access by tier.

    - Open pricing -     - - Operate -

    Manage active subscriptions

    -

    Use the account and subscription surfaces for renewals, plan changes, referrals, and operational customer actions.

    - Manage account -     -
    - -
    -
    - Explorer -

    Free

    -
    $0 / month
    -

    Use Aria locally, explore the character interface, and test provider fallbacks without commercial commitment.

    -
      -
    • Local fallback chat and core character controls.
      • -
    • Source access and self-hosted experimentation.
      • -
    • Good fit for evaluation, demos, and developer onboarding.
      • -
    - Compare free tier -
    - -
    - Builder -

    Pro

    -
    $29 / month
    -

    For teams that need managed providers, full character workflows, dashboards, and richer model-backed features.

    -
      -
    • Azure OpenAI and OpenAI provider routes.
      • -
    • LoRA fine-tuning allowance and world generation access.
      • -
    • Dashboard visibility with higher API throughput.
      • -
    - Start Pro -
    - -
    - Operator -

    Enterprise

    -
    Custom engagement
    -

    For organizations that need managed infrastructure, real quantum workflows, custom deployment, or procurement support.

    -
      -
    • Dedicated capacity and managed cloud resources.
      • -
    • Advanced training, deployment, and operator support paths.
      • -
    • Best fit for production rollout and platform integration.
      • -
    - Talk to sales -
    -
    - -
    -
    -

    Decision support

    -

    What changes as you move up the stack.

    -

    The store should show the operational consequences of each tier, not just a price. This comparison keeps the differences practical.

    -
      -
    • Free is for local evaluation and source-first exploration.
      • -
    • Pro is for provider-backed usage, richer runtime features, and faster execution paths.
      • -
    • Enterprise is for managed deployment, special access, and organizational onboarding.
      • -
    -
    - -
    - - - - - - - - - - - - - - - - - -
    CapabilityFreeProEnterprise
    Local character demoYesYesYes
    Managed AI providersNoYesYes
    LoRA fine-tuning accessNoYesYes
    Quantum simulator accessNoYesYes
    Real quantum workloadsNoNoYes
    Managed onboardingNoNoYes
    -
    -
    - -
    - - Catalog -

    Browse products

    -

    Open the catalog surfaces when you want to view packaged product pages and storefront content.

    - Open products -     - - Context -

    Read about the platform

    -

    Use the about page for positioning, background, and broader product framing.

    - Open about -     - - Growth -

    Referral and incentive flows

    -

    Jump directly to referral pages when the next step is growth, sharing, or campaign work.

    - Open referrals -     - - Contact -

    Talk to a human

    -

    Use the contact page when the buyer needs answers before selecting a tier or moving to checkout.

    - Open contact -     -
    -
    - -
    -
    -

    The store now routes evaluation, pricing, account management, and enterprise contact as one coherent buying surface.

    -
    - Home - Docs - Pricing - Account - Issues -
    -
    -
    - - - - + + + + + + Aria Store - Plans, Access, and Adoption + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + Aria + interactive AI character platform + + + + +
    +
    + +
    +
    +
    +

    Storefront and pricing

    +

    Decide how to adopt Aria without decoding the repo.

    +

    The store should answer three questions immediately: what the platform unlocks, which plan fits the workload, and where a buyer or operator should go next. This page now routes all three cleanly.

    +
    + See pricing + Browse catalog + Talk to sales +
    +
    +
    3Adoption paths from free exploration to enterprise deployment.
    +
    35+APIs available across chat, vision, subscriptions, and quantum workflows.
    +
    6Provider routes including Azure OpenAI, OpenAI, LMStudio, LoRA, and local fallback.
    +
    1Commercial entry point that links pricing, checkout, account, and product pages.
    +
    +
    + + +
    + +
    + + Evaluate +

    Start with docs and local usage

    +

    Use the docs hub when you need startup commands, architecture notes, training guides, and repo-wide navigation before buying anything.

    + Open documentation +     + + Buy +

    Compare plans and upgrade paths

    +

    Go straight to pricing when the question is plan fit, commercial packaging, or feature access by tier.

    + Open pricing +     + + Operate +

    Manage active subscriptions

    +

    Use the account and subscription surfaces for renewals, plan changes, referrals, and operational customer actions.

    + Manage account +     +
    + +
    +
    + Explorer +

    Free

    +
    $0 / month
    +

    Use Aria locally, explore the character interface, and test provider fallbacks without commercial commitment.

    +
      +
    • Local fallback chat and core character controls.
      • +
    • Source access and self-hosted experimentation.
      • +
    • Good fit for evaluation, demos, and developer onboarding.
      • +
    + Compare free tier +
    + +
    + Builder +

    Pro

    +
    $29 / month
    +

    For teams that need managed providers, full character workflows, dashboards, and richer model-backed features.

    +
      +
    • Azure OpenAI and OpenAI provider routes.
      • +
    • LoRA fine-tuning allowance and world generation access.
      • +
    • Dashboard visibility with higher API throughput.
      • +
    + Start Pro +
    + +
    + Operator +

    Enterprise

    +
    Custom engagement
    +

    For organizations that need managed infrastructure, real quantum workflows, custom deployment, or procurement support.

    +
      +
    • Dedicated capacity and managed cloud resources.
      • +
    • Advanced training, deployment, and operator support paths.
      • +
    • Best fit for production rollout and platform integration.
      • +
    + Talk to sales +
    +
    + +
    +
    +

    Decision support

    +

    What changes as you move up the stack.

    +

    The store should show the operational consequences of each tier, not just a price. This comparison keeps the differences practical.

    +
      +
    • Free is for local evaluation and source-first exploration.
      • +
    • Pro is for provider-backed usage, richer runtime features, and faster execution paths.
      • +
    • Enterprise is for managed deployment, special access, and organizational onboarding.
      • +
    +
    + +
    + + + + + + + + + + + + + + + + + +
    CapabilityFreeProEnterprise
    Local character demoYesYesYes
    Managed AI providersNoYesYes
    LoRA fine-tuning accessNoYesYes
    Quantum simulator accessNoYesYes
    Real quantum workloadsNoNoYes
    Managed onboardingNoNoYes
    +
    +
    + +
    + + Catalog +

    Browse products

    +

    Open the catalog surfaces when you want to view packaged product pages and storefront content.

    + Open products +     + + Context +

    Read about the platform

    +

    Use the about page for positioning, background, and broader product framing.

    + Open about +     + + Growth +

    Referral and incentive flows

    +

    Jump directly to referral pages when the next step is growth, sharing, or campaign work.

    + Open referrals +     + + Contact +

    Talk to a human

    +

    Use the contact page when the buyer needs answers before selecting a tier or moving to checkout.

    + Open contact +     +
    +
    + +
    +
    +

    The store now routes evaluation, pricing, account management, and enterprise contact as one coherent buying surface.

    +
    + Home + Docs + Pricing + Account + Issues +
    +
    +
    + + + + diff --git a/docs/store/js/app.js b/docs/store/js/app.js index 6f195213d..1c3b0388e 100644 --- a/docs/store/js/app.js +++ b/docs/store/js/app.js @@ -1,415 +1,415 @@ -// Main application logic: product filtering, searching, and page-specific init. - -// ===== Utility ===== -function getQueryParam(key) { - const params = new URLSearchParams(window.location.search); - return params.get(key); -} - -function getProductById(id) { - return PRODUCTS.find(function (p) { return p.id === parseInt(id); }); -} - -function getCategoryName(categoryId) { - var category = typeof getCategoryById === 'function' ? getCategoryById(categoryId) : null; - return category ? category.name : categoryId; -} - -// ===== Homepage ===== -function initHomePage() { - initComponents('home'); - - // Render category cards - var catGrid = document.getElementById('categoriesGrid'); - if (catGrid) { - catGrid.innerHTML = CATEGORIES.map(function (cat) { - return '' + - '' + - '

    ' + cat.name + '

    ' + - '

    Browse ' + cat.name + '

    ' + - '    '; - }).join(''); - } - - // Render featured products - var featuredGrid = document.getElementById('featuredGrid'); - if (featuredGrid) { - var featured = PRODUCTS.filter(function (p) { return p.featured; }); - if (featured.length === 0) featured = PRODUCTS.slice(0, 4); - featuredGrid.innerHTML = featured.map(renderProductCard).join(''); - } -} - -// ===== Shop / Products Listing ===== -function initShopPage() { - initComponents('shop'); - - var categoryFilter = document.getElementById('categoryFilter'); - var sortFilter = document.getElementById('sortFilter'); - var priceRange = document.getElementById('priceRange'); - var priceLabel = document.getElementById('priceLabel'); - var grid = document.getElementById('shopProductsGrid'); - var resultsCount = document.getElementById('resultsCount'); - var clearFiltersBtn = document.getElementById('clearFiltersBtn'); - var navSearch = document.getElementById('navSearchInput'); - - // Find max price for range slider first so URL state can clamp correctly. - var maxPrice = Math.max.apply(null, PRODUCTS.map(function (p) { return p.price; })); - if (priceRange) { - priceRange.max = Math.ceil(maxPrice / 10) * 10 + 50; - priceRange.value = priceRange.max; - } - - function hasOption(selectEl, value) { - if (!selectEl) return false; - return Array.prototype.some.call(selectEl.options, function (opt) { - return opt.value === value; - }); - } - - function clampNumber(value, min, max) { - if (!isFinite(value)) return max; - return Math.max(min, Math.min(max, value)); - } - - function parseUrlState() { - var params = new URLSearchParams(window.location.search); - var category = params.get('category') || 'all'; - var sort = params.get('sort') || 'default'; - var search = (params.get('search') || '').trim(); - var maxP = parseFloat(params.get('maxPrice')); - - if (!hasOption(categoryFilter, category)) category = 'all'; - if (!hasOption(sortFilter, sort)) sort = 'default'; - - if (priceRange) { - var sliderMax = parseFloat(priceRange.max); - maxP = clampNumber(maxP, 0, sliderMax); - } - - return { - category: category, - sort: sort, - search: search, - maxP: maxP - }; - } - - function applyState(state) { - if (categoryFilter) categoryFilter.value = state.category; - if (sortFilter) sortFilter.value = state.sort; - - if (priceRange) { - var sliderMax = parseFloat(priceRange.max); - var nextMax = isFinite(state.maxP) ? state.maxP : sliderMax; - priceRange.value = String(clampNumber(nextMax, 0, sliderMax)); - } - - if (navSearch) { - navSearch.value = state.search || ''; - } - } - - function getCurrentState() { - return { - category: categoryFilter ? categoryFilter.value : 'all', - sort: sortFilter ? sortFilter.value : 'default', - search: navSearch ? navSearch.value.trim() : '', - maxP: priceRange ? parseFloat(priceRange.value) : Infinity - }; - } - - function updateUrlFromState(state) { - var params = new URLSearchParams(); - - if (state.category && state.category !== 'all') params.set('category', state.category); - if (state.search) params.set('search', state.search); - if (state.sort && state.sort !== 'default') params.set('sort', state.sort); - - if (priceRange) { - var sliderMax = parseFloat(priceRange.max); - if (state.maxP < sliderMax) { - params.set('maxPrice', String(Math.round(state.maxP))); - } - } - - var query = params.toString(); - var newPath = 'products.html' + (query ? '?' + query : ''); - var currentPath = window.location.pathname.split('/').pop() + window.location.search; - - if (newPath !== currentPath) { - history.replaceState({ filters: state }, '', newPath); - } - } - - function buildResultSummary(count, state) { - var summary = count + ' offer' + (count !== 1 ? 's' : '') + ' found'; - var details = []; - - if (state.category !== 'all') { - details.push(getCategoryName(state.category)); - } - - if (state.search) { - details.push('search: "' + state.search + '"'); - } - - if (priceRange) { - var sliderMax = parseFloat(priceRange.max); - if (state.maxP < sliderMax) { - details.push('max $' + state.maxP.toFixed(0)); - } - } - - if (details.length) { - summary += ' • ' + details.join(' • '); - } - - return summary; - } - - function hasActiveFilters(state) { - if (!state) return false; - if (state.category !== 'all') return true; - if (state.sort !== 'default') return true; - if (state.search) return true; - if (priceRange) { - var sliderMax = parseFloat(priceRange.max); - if (state.maxP < sliderMax) return true; - } - return false; - } - - function clearAllFilters() { - if (categoryFilter) categoryFilter.value = 'all'; - if (sortFilter) sortFilter.value = 'default'; - if (priceRange) priceRange.value = priceRange.max; - if (navSearch) navSearch.value = ''; - renderProducts(); - } - - function renderProducts(options) { - options = options || {}; - var state = getCurrentState(); - var category = state.category; - var sort = state.sort; - var maxP = state.maxP; - var search = state.search.toLowerCase(); - - var filtered = PRODUCTS.filter(function (p) { - var catMatch = category === 'all' || p.category === category; - var priceMatch = p.price <= maxP; - var categoryName = getCategoryName(p.category).toLowerCase(); - var searchMatch = !search || - p.name.toLowerCase().indexOf(search) !== -1 || - p.description.toLowerCase().indexOf(search) !== -1 || - categoryName.indexOf(search) !== -1; - return catMatch && priceMatch && searchMatch; - }); - - // Sort - if (sort === 'price-low') { - filtered.sort(function (a, b) { return a.price - b.price; }); - } else if (sort === 'price-high') { - filtered.sort(function (a, b) { return b.price - a.price; }); - } else if (sort === 'name') { - filtered.sort(function (a, b) { return a.name.localeCompare(b.name); }); - } - - if (resultsCount) { - resultsCount.textContent = buildResultSummary(filtered.length, state); - } - - if (clearFiltersBtn) { - clearFiltersBtn.disabled = !hasActiveFilters(state); - } - - if (priceLabel) { - priceLabel.textContent = '$' + maxP.toFixed(0); - } - - if (grid) { - if (filtered.length === 0) { - grid.innerHTML = '

    No offers match your current filters.

    '; - } else { - grid.innerHTML = filtered.map(renderProductCard).join(''); - } - } - - if (!options.skipUrlSync) { - updateUrlFromState(state); - } - } - - function applyUrlStateAndRender() { - var state = parseUrlState(); - applyState(state); - renderProducts({ skipUrlSync: true }); - } - - // Event listeners - if (categoryFilter) categoryFilter.addEventListener('change', renderProducts); - if (sortFilter) sortFilter.addEventListener('change', renderProducts); - if (priceRange) priceRange.addEventListener('input', renderProducts); - if (clearFiltersBtn) clearFiltersBtn.addEventListener('click', clearAllFilters); - - // Re-filter on nav search input - if (navSearch) { - navSearch.addEventListener('input', renderProducts); - } - - window.addEventListener('store:nav-search-submit', function (event) { - if (!navSearch) return; - if (event && event.detail && typeof event.detail.query === 'string') { - navSearch.value = event.detail.query; - renderProducts(); - } - }); - - document.addEventListener('keydown', function (event) { - if (!navSearch) return; - - var activeEl = document.activeElement; - var tag = activeEl && activeEl.tagName ? activeEl.tagName.toLowerCase() : ''; - var isTyping = tag === 'input' || tag === 'textarea' || (activeEl && activeEl.isContentEditable); - - if ((event.ctrlKey || event.metaKey) && event.key.toLowerCase() === 'k') { - event.preventDefault(); - navSearch.focus(); - navSearch.select(); - return; - } - - if (event.key === '/' && !isTyping) { - event.preventDefault(); - navSearch.focus(); - return; - } - - if (event.key === 'Escape' && hasActiveFilters(getCurrentState())) { - event.preventDefault(); - clearAllFilters(); - } - }); - - window.addEventListener('popstate', function () { - applyUrlStateAndRender(); - }); - - applyUrlStateAndRender(); -} - -// ===== Product Detail ===== -function initProductPage() { - initComponents('shop'); - - var productId = getQueryParam('id'); - var product = productId ? getProductById(productId) : null; - var container = document.getElementById('productDetail'); - var relatedGrid = document.getElementById('relatedGrid'); - - if (!product || !container) { - if (container) { - container.innerHTML = '
    ' + - '

    Offer Not Found

    ' + - '

    The offer you are looking for does not exist.

    ' + - 'Browse Catalog
    '; - } - return; - } - - // Set page title - document.title = product.name + ' - Aria Store'; - - // Render breadcrumb + detail - var featuresHtml = ''; - if (product.features && product.features.length > 0) { - featuresHtml = '
    ' + - '

    Key Features

      ' + - product.features.map(function (f) { return '
    • ' + f + '
      • '; }).join('') + - '
    '; - } - - container.innerHTML = - '
    ' + - 'Home / ' + - 'Catalog / ' + - '' + product.name + '' + - '
    ' + - '
    ' + - '
    ' + - '' + product.name + '' + - '
    ' + - '
    ' + - '
    ' + getCategoryName(product.category) + '
    ' + - '

    ' + product.name + '

    ' + - '
    $' + product.price.toFixed(2) + '
    ' + - '

    ' + product.description + '

    ' + - featuresHtml + - '
    ' + - '' + - ' Talk to the Team' + - '' + - '
    ' + - '
    ' + - '
    '; - - // Related products (same category, different id) - if (relatedGrid) { - var related = PRODUCTS.filter(function (p) { - return p.category === product.category && p.id !== product.id; - }).slice(0, 4); - - // If not enough in same category, fill with others - if (related.length < 4) { - var others = PRODUCTS.filter(function (p) { - return p.id !== product.id && related.indexOf(p) === -1; - }); - related = related.concat(others).slice(0, 4); - } - - if (related.length > 0) { - relatedGrid.innerHTML = related.map(renderProductCard).join(''); - } - } -} - -// ===== About Page ===== -function initAboutPage() { - initComponents('about'); -} - -// ===== Contact Page ===== -function initContactPage() { - initComponents('contact'); - - // Pre-fill product subject if coming from product detail page - var productName = getQueryParam('product'); - var subjectInput = document.getElementById('contactSubject'); - if (productName && subjectInput) { - subjectInput.value = 'Inquiry about: ' + productName; - } - - // Handle form submission (mailto fallback) - var form = document.getElementById('contactForm'); - if (form) { - form.addEventListener('submit', function (e) { - e.preventDefault(); - var name = document.getElementById('contactName').value; - var email = document.getElementById('contactEmail').value; - var subject = document.getElementById('contactSubject').value; - var message = document.getElementById('contactMessage').value; - - var title = subject || 'Store inquiry'; - var body = '### Store inquiry\n' + - '- Name: ' + name + '\n' + - '- Email: ' + email + '\n' + - '- Page: docs/store/contact.html\n\n' + - message; - var issueUrl = 'https://github.com/Bryan-Roe/Aria/issues/new?title=' + - encodeURIComponent(title) + '&body=' + encodeURIComponent(body); - - window.location.href = issueUrl; - }); - } -} +// Main application logic: product filtering, searching, and page-specific init. + +// ===== Utility ===== +function getQueryParam(key) { + const params = new URLSearchParams(window.location.search); + return params.get(key); +} + +function getProductById(id) { + return PRODUCTS.find(function (p) { return p.id === parseInt(id); }); +} + +function getCategoryName(categoryId) { + var category = typeof getCategoryById === 'function' ? getCategoryById(categoryId) : null; + return category ? category.name : categoryId; +} + +// ===== Homepage ===== +function initHomePage() { + initComponents('home'); + + // Render category cards + var catGrid = document.getElementById('categoriesGrid'); + if (catGrid) { + catGrid.innerHTML = CATEGORIES.map(function (cat) { + return '' + + '' + + '

    ' + cat.name + '

    ' + + '

    Browse ' + cat.name + '

    ' + + '    '; + }).join(''); + } + + // Render featured products + var featuredGrid = document.getElementById('featuredGrid'); + if (featuredGrid) { + var featured = PRODUCTS.filter(function (p) { return p.featured; }); + if (featured.length === 0) featured = PRODUCTS.slice(0, 4); + featuredGrid.innerHTML = featured.map(renderProductCard).join(''); + } +} + +// ===== Shop / Products Listing ===== +function initShopPage() { + initComponents('shop'); + + var categoryFilter = document.getElementById('categoryFilter'); + var sortFilter = document.getElementById('sortFilter'); + var priceRange = document.getElementById('priceRange'); + var priceLabel = document.getElementById('priceLabel'); + var grid = document.getElementById('shopProductsGrid'); + var resultsCount = document.getElementById('resultsCount'); + var clearFiltersBtn = document.getElementById('clearFiltersBtn'); + var navSearch = document.getElementById('navSearchInput'); + + // Find max price for range slider first so URL state can clamp correctly. + var maxPrice = Math.max.apply(null, PRODUCTS.map(function (p) { return p.price; })); + if (priceRange) { + priceRange.max = Math.ceil(maxPrice / 10) * 10 + 50; + priceRange.value = priceRange.max; + } + + function hasOption(selectEl, value) { + if (!selectEl) return false; + return Array.prototype.some.call(selectEl.options, function (opt) { + return opt.value === value; + }); + } + + function clampNumber(value, min, max) { + if (!isFinite(value)) return max; + return Math.max(min, Math.min(max, value)); + } + + function parseUrlState() { + var params = new URLSearchParams(window.location.search); + var category = params.get('category') || 'all'; + var sort = params.get('sort') || 'default'; + var search = (params.get('search') || '').trim(); + var maxP = parseFloat(params.get('maxPrice')); + + if (!hasOption(categoryFilter, category)) category = 'all'; + if (!hasOption(sortFilter, sort)) sort = 'default'; + + if (priceRange) { + var sliderMax = parseFloat(priceRange.max); + maxP = clampNumber(maxP, 0, sliderMax); + } + + return { + category: category, + sort: sort, + search: search, + maxP: maxP + }; + } + + function applyState(state) { + if (categoryFilter) categoryFilter.value = state.category; + if (sortFilter) sortFilter.value = state.sort; + + if (priceRange) { + var sliderMax = parseFloat(priceRange.max); + var nextMax = isFinite(state.maxP) ? state.maxP : sliderMax; + priceRange.value = String(clampNumber(nextMax, 0, sliderMax)); + } + + if (navSearch) { + navSearch.value = state.search || ''; + } + } + + function getCurrentState() { + return { + category: categoryFilter ? categoryFilter.value : 'all', + sort: sortFilter ? sortFilter.value : 'default', + search: navSearch ? navSearch.value.trim() : '', + maxP: priceRange ? parseFloat(priceRange.value) : Infinity + }; + } + + function updateUrlFromState(state) { + var params = new URLSearchParams(); + + if (state.category && state.category !== 'all') params.set('category', state.category); + if (state.search) params.set('search', state.search); + if (state.sort && state.sort !== 'default') params.set('sort', state.sort); + + if (priceRange) { + var sliderMax = parseFloat(priceRange.max); + if (state.maxP < sliderMax) { + params.set('maxPrice', String(Math.round(state.maxP))); + } + } + + var query = params.toString(); + var newPath = 'products.html' + (query ? '?' + query : ''); + var currentPath = window.location.pathname.split('/').pop() + window.location.search; + + if (newPath !== currentPath) { + history.replaceState({ filters: state }, '', newPath); + } + } + + function buildResultSummary(count, state) { + var summary = count + ' offer' + (count !== 1 ? 's' : '') + ' found'; + var details = []; + + if (state.category !== 'all') { + details.push(getCategoryName(state.category)); + } + + if (state.search) { + details.push('search: "' + state.search + '"'); + } + + if (priceRange) { + var sliderMax = parseFloat(priceRange.max); + if (state.maxP < sliderMax) { + details.push('max $' + state.maxP.toFixed(0)); + } + } + + if (details.length) { + summary += ' • ' + details.join(' • '); + } + + return summary; + } + + function hasActiveFilters(state) { + if (!state) return false; + if (state.category !== 'all') return true; + if (state.sort !== 'default') return true; + if (state.search) return true; + if (priceRange) { + var sliderMax = parseFloat(priceRange.max); + if (state.maxP < sliderMax) return true; + } + return false; + } + + function clearAllFilters() { + if (categoryFilter) categoryFilter.value = 'all'; + if (sortFilter) sortFilter.value = 'default'; + if (priceRange) priceRange.value = priceRange.max; + if (navSearch) navSearch.value = ''; + renderProducts(); + } + + function renderProducts(options) { + options = options || {}; + var state = getCurrentState(); + var category = state.category; + var sort = state.sort; + var maxP = state.maxP; + var search = state.search.toLowerCase(); + + var filtered = PRODUCTS.filter(function (p) { + var catMatch = category === 'all' || p.category === category; + var priceMatch = p.price <= maxP; + var categoryName = getCategoryName(p.category).toLowerCase(); + var searchMatch = !search || + p.name.toLowerCase().indexOf(search) !== -1 || + p.description.toLowerCase().indexOf(search) !== -1 || + categoryName.indexOf(search) !== -1; + return catMatch && priceMatch && searchMatch; + }); + + // Sort + if (sort === 'price-low') { + filtered.sort(function (a, b) { return a.price - b.price; }); + } else if (sort === 'price-high') { + filtered.sort(function (a, b) { return b.price - a.price; }); + } else if (sort === 'name') { + filtered.sort(function (a, b) { return a.name.localeCompare(b.name); }); + } + + if (resultsCount) { + resultsCount.textContent = buildResultSummary(filtered.length, state); + } + + if (clearFiltersBtn) { + clearFiltersBtn.disabled = !hasActiveFilters(state); + } + + if (priceLabel) { + priceLabel.textContent = '$' + maxP.toFixed(0); + } + + if (grid) { + if (filtered.length === 0) { + grid.innerHTML = '

    No offers match your current filters.

    '; + } else { + grid.innerHTML = filtered.map(renderProductCard).join(''); + } + } + + if (!options.skipUrlSync) { + updateUrlFromState(state); + } + } + + function applyUrlStateAndRender() { + var state = parseUrlState(); + applyState(state); + renderProducts({ skipUrlSync: true }); + } + + // Event listeners + if (categoryFilter) categoryFilter.addEventListener('change', renderProducts); + if (sortFilter) sortFilter.addEventListener('change', renderProducts); + if (priceRange) priceRange.addEventListener('input', renderProducts); + if (clearFiltersBtn) clearFiltersBtn.addEventListener('click', clearAllFilters); + + // Re-filter on nav search input + if (navSearch) { + navSearch.addEventListener('input', renderProducts); + } + + window.addEventListener('store:nav-search-submit', function (event) { + if (!navSearch) return; + if (event && event.detail && typeof event.detail.query === 'string') { + navSearch.value = event.detail.query; + renderProducts(); + } + }); + + document.addEventListener('keydown', function (event) { + if (!navSearch) return; + + var activeEl = document.activeElement; + var tag = activeEl && activeEl.tagName ? activeEl.tagName.toLowerCase() : ''; + var isTyping = tag === 'input' || tag === 'textarea' || (activeEl && activeEl.isContentEditable); + + if ((event.ctrlKey || event.metaKey) && event.key.toLowerCase() === 'k') { + event.preventDefault(); + navSearch.focus(); + navSearch.select(); + return; + } + + if (event.key === '/' && !isTyping) { + event.preventDefault(); + navSearch.focus(); + return; + } + + if (event.key === 'Escape' && hasActiveFilters(getCurrentState())) { + event.preventDefault(); + clearAllFilters(); + } + }); + + window.addEventListener('popstate', function () { + applyUrlStateAndRender(); + }); + + applyUrlStateAndRender(); +} + +// ===== Product Detail ===== +function initProductPage() { + initComponents('shop'); + + var productId = getQueryParam('id'); + var product = productId ? getProductById(productId) : null; + var container = document.getElementById('productDetail'); + var relatedGrid = document.getElementById('relatedGrid'); + + if (!product || !container) { + if (container) { + container.innerHTML = '
    ' + + '

    Offer Not Found

    ' + + '

    The offer you are looking for does not exist.

    ' + + 'Browse Catalog
    '; + } + return; + } + + // Set page title + document.title = product.name + ' - Aria Store'; + + // Render breadcrumb + detail + var featuresHtml = ''; + if (product.features && product.features.length > 0) { + featuresHtml = '
    ' + + '

    Key Features

      ' + + product.features.map(function (f) { return '
    • ' + f + '
      • '; }).join('') + + '
    '; + } + + container.innerHTML = + '
    ' + + 'Home / ' + + 'Catalog / ' + + '' + product.name + '' + + '
    ' + + '
    ' + + '
    ' + + '' + product.name + '' + + '
    ' + + '
    ' + + '
    ' + getCategoryName(product.category) + '
    ' + + '

    ' + product.name + '

    ' + + '
    $' + product.price.toFixed(2) + '
    ' + + '

    ' + product.description + '

    ' + + featuresHtml + + '
    ' + + '' + + ' Talk to the Team' + + '' + + '
    ' + + '
    ' + + '
    '; + + // Related products (same category, different id) + if (relatedGrid) { + var related = PRODUCTS.filter(function (p) { + return p.category === product.category && p.id !== product.id; + }).slice(0, 4); + + // If not enough in same category, fill with others + if (related.length < 4) { + var others = PRODUCTS.filter(function (p) { + return p.id !== product.id && related.indexOf(p) === -1; + }); + related = related.concat(others).slice(0, 4); + } + + if (related.length > 0) { + relatedGrid.innerHTML = related.map(renderProductCard).join(''); + } + } +} + +// ===== About Page ===== +function initAboutPage() { + initComponents('about'); +} + +// ===== Contact Page ===== +function initContactPage() { + initComponents('contact'); + + // Pre-fill product subject if coming from product detail page + var productName = getQueryParam('product'); + var subjectInput = document.getElementById('contactSubject'); + if (productName && subjectInput) { + subjectInput.value = 'Inquiry about: ' + productName; + } + + // Handle form submission (mailto fallback) + var form = document.getElementById('contactForm'); + if (form) { + form.addEventListener('submit', function (e) { + e.preventDefault(); + var name = document.getElementById('contactName').value; + var email = document.getElementById('contactEmail').value; + var subject = document.getElementById('contactSubject').value; + var message = document.getElementById('contactMessage').value; + + var title = subject || 'Store inquiry'; + var body = '### Store inquiry\n' + + '- Name: ' + name + '\n' + + '- Email: ' + email + '\n' + + '- Page: docs/store/contact.html\n\n' + + message; + var issueUrl = 'https://github.com/Bryan-Roe/Aria/issues/new?title=' + + encodeURIComponent(title) + '&body=' + encodeURIComponent(body); + + window.location.href = issueUrl; + }); + } +} diff --git a/docs/store/js/components.js b/docs/store/js/components.js index c1c10f30b..37ce44218 100644 --- a/docs/store/js/components.js +++ b/docs/store/js/components.js @@ -1,191 +1,191 @@ -// Shared UI components rendered via JavaScript. -// These keep the navbar and footer consistent across all pages. - -function getNavbar(activePage) { - return ` - -
    -
    - Aria Platform -
    - Home - Docs - Aria - Chat - Dashboards - Pricing - ★ Star on GitHub -
    -
    -
    -
    -
    - - -
    - -
    - -
    -
    `; -} - -function getFooter() { - const year = new Date().getFullYear(); - return ` - `; -} - -function renderProductCard(product) { - var category = typeof getCategoryById === 'function' ? getCategoryById(product.category) : null; - var categoryLabel = category ? category.name : product.category; - return ` -
    - -
    - ${product.name} -
    -     -
    -
    ${categoryLabel}
    -

    - ${product.name} -

    -
    $${product.price.toFixed(2)}
    - View Offer -
    -
    `; -} - -// Initialize shared components -function initComponents(activePage) { - // Insert navbar - const headerSlot = document.getElementById('header-slot'); - if (headerSlot) { - headerSlot.innerHTML = getNavbar(activePage); - } - - // Insert footer - const footerSlot = document.getElementById('footer-slot'); - if (footerSlot) { - footerSlot.innerHTML = getFooter(); - } - - // Mobile menu toggle - document.addEventListener('click', function (e) { - if (e.target.closest('#mobileMenuBtn')) { - const nav = document.getElementById('navLinks'); - if (nav) nav.classList.toggle('open'); - return; - } - - const nav = document.getElementById('navLinks'); - const toggle = document.getElementById('mobileMenuBtn'); - if (nav && nav.classList.contains('open')) { - const clickedNav = nav.contains(e.target); - const clickedToggle = toggle && toggle.contains(e.target); - if (!clickedNav && !clickedToggle) { - nav.classList.remove('open'); - } - } - }); - - // Nav search (redirect to products page with query) - document.addEventListener('keydown', function (e) { - if (e.key === 'Enter' && e.target.id === 'navSearchInput') { - const query = e.target.value.trim(); - if (query) { - const onCatalogPage = /\/products\.html$/i.test(window.location.pathname || ''); - if (onCatalogPage) { - window.dispatchEvent(new CustomEvent('store:nav-search-submit', { detail: { query: query } })); - } else { - window.location.href = `products.html?search=${encodeURIComponent(query)}`; - } - } - } - }); - - if (!window.__storeSearchHotkeysBound) { - window.__storeSearchHotkeysBound = true; - document.addEventListener('keydown', function (e) { - const navSearch = document.getElementById('navSearchInput'); - if (!navSearch) return; - - const activeEl = document.activeElement; - const tag = activeEl && activeEl.tagName ? activeEl.tagName.toLowerCase() : ''; - const isTyping = tag === 'input' || tag === 'textarea' || (activeEl && activeEl.isContentEditable); - - if ((e.ctrlKey || e.metaKey) && e.key.toLowerCase() === 'k') { - e.preventDefault(); - navSearch.focus(); - navSearch.select(); - return; - } - - if (e.key === '/' && !isTyping) { - e.preventDefault(); - navSearch.focus(); - } - }); - } -} +// Shared UI components rendered via JavaScript. +// These keep the navbar and footer consistent across all pages. + +function getNavbar(activePage) { + return ` + +
    +
    + Aria Platform +
    + Home + Docs + Aria + Chat + Dashboards + Pricing + ★ Star on GitHub +
    +
    +
    +
    +
    + + +
    + +
    + +
    +
    `; +} + +function getFooter() { + const year = new Date().getFullYear(); + return ` + `; +} + +function renderProductCard(product) { + var category = typeof getCategoryById === 'function' ? getCategoryById(product.category) : null; + var categoryLabel = category ? category.name : product.category; + return ` +
    + +
    + ${product.name} +
    +     +
    +
    ${categoryLabel}
    +

    + ${product.name} +

    +
    $${product.price.toFixed(2)}
    + View Offer +
    +
    `; +} + +// Initialize shared components +function initComponents(activePage) { + // Insert navbar + const headerSlot = document.getElementById('header-slot'); + if (headerSlot) { + headerSlot.innerHTML = getNavbar(activePage); + } + + // Insert footer + const footerSlot = document.getElementById('footer-slot'); + if (footerSlot) { + footerSlot.innerHTML = getFooter(); + } + + // Mobile menu toggle + document.addEventListener('click', function (e) { + if (e.target.closest('#mobileMenuBtn')) { + const nav = document.getElementById('navLinks'); + if (nav) nav.classList.toggle('open'); + return; + } + + const nav = document.getElementById('navLinks'); + const toggle = document.getElementById('mobileMenuBtn'); + if (nav && nav.classList.contains('open')) { + const clickedNav = nav.contains(e.target); + const clickedToggle = toggle && toggle.contains(e.target); + if (!clickedNav && !clickedToggle) { + nav.classList.remove('open'); + } + } + }); + + // Nav search (redirect to products page with query) + document.addEventListener('keydown', function (e) { + if (e.key === 'Enter' && e.target.id === 'navSearchInput') { + const query = e.target.value.trim(); + if (query) { + const onCatalogPage = /\/products\.html$/i.test(window.location.pathname || ''); + if (onCatalogPage) { + window.dispatchEvent(new CustomEvent('store:nav-search-submit', { detail: { query: query } })); + } else { + window.location.href = `products.html?search=${encodeURIComponent(query)}`; + } + } + } + }); + + if (!window.__storeSearchHotkeysBound) { + window.__storeSearchHotkeysBound = true; + document.addEventListener('keydown', function (e) { + const navSearch = document.getElementById('navSearchInput'); + if (!navSearch) return; + + const activeEl = document.activeElement; + const tag = activeEl && activeEl.tagName ? activeEl.tagName.toLowerCase() : ''; + const isTyping = tag === 'input' || tag === 'textarea' || (activeEl && activeEl.isContentEditable); + + if ((e.ctrlKey || e.metaKey) && e.key.toLowerCase() === 'k') { + e.preventDefault(); + navSearch.focus(); + navSearch.select(); + return; + } + + if (e.key === '/' && !isTyping) { + e.preventDefault(); + navSearch.focus(); + } + }); + } +} diff --git a/docs/store/js/products.js b/docs/store/js/products.js index 843b59ba1..2de137a2a 100644 --- a/docs/store/js/products.js +++ b/docs/store/js/products.js @@ -1,78 +1,78 @@ -// Product data - Edit this file to add, remove, or update products. -// Each product needs: id, name, price, category, image, description. -// Optional: featured (boolean), features (array of strings). - -const PRODUCTS = [ - { - id: 1, - name: "Aria Pro Access", - price: 29.00, - category: "subscriptions", - image: "images/placeholder.svg", - description: "Monthly access package for teams that want provider-backed chat, richer character workflows, dashboards, and production-grade runtime features without a custom engagement.", - featured: true, - features: [ - "Managed provider access for Azure OpenAI and OpenAI routes", - "Full character actions, world generation, and dashboard surfaces", - "Priority access to training and evaluation workflows", - "Fits teams moving from local demos to cloud-backed usage" - ] - }, - { - id: 2, - name: "Autonomous Training Sprint", - price: 399.00, - category: "training", - image: "images/placeholder.svg", - description: "A focused enablement package for teams adopting LoRA fine-tuning, evaluation loops, and autonomous training cycles across the Aria stack.", - featured: true, - features: [ - "Training pipeline review and dataset readiness guidance", - "Evaluation setup for batch runs, analytics, and promotion gates", - "Operator walkthrough for dashboards and performance tracking", - "Best fit before turning on continuous or scheduled retraining" - ] - }, - { - id: 3, - name: "Quantum Pilot Package", - price: 1299.00, - category: "quantum", - image: "images/placeholder.svg", - description: "Hybrid quantum-classical pilot planning for teams exploring simulators, Azure Quantum workflows, and proof-of-value experiments before committing to larger workloads.", - featured: true, - features: [ - "Circuit design and simulation workflow review", - "Azure Quantum integration planning and guardrail setup", - "Pilot architecture guidance for hybrid quantum ML experiments", - "Intended for research or enterprise readiness conversations" - ] - }, - { - id: 4, - name: "Enterprise Deployment Workshop", - price: 799.00, - category: "services", - image: "images/placeholder.svg", - description: "Hands-on deployment scoping for organizations packaging Aria for internal rollout, managed infrastructure, and operator onboarding.", - featured: false, - features: [ - "Deployment path review across docs, APIs, and automation surfaces", - "Recommendations for monitoring, subscriptions, and service ownership", - "Rollout plan for customer-facing or internal operator teams", - "Ideal precursor to a broader enterprise implementation" - ] - } -]; - -// Categories used for filtering -const CATEGORIES = [ - { id: "subscriptions", name: "Subscriptions", icon: "fa-layer-group" }, - { id: "training", name: "Training", icon: "fa-brain" }, - { id: "quantum", name: "Quantum Programs", icon: "fa-atom" }, - { id: "services", name: "Deployment Services", icon: "fa-server" } -]; - -function getCategoryById(id) { - return CATEGORIES.find(function (category) { return category.id === id; }) || null; -} +// Product data - Edit this file to add, remove, or update products. +// Each product needs: id, name, price, category, image, description. +// Optional: featured (boolean), features (array of strings). + +const PRODUCTS = [ + { + id: 1, + name: "Aria Pro Access", + price: 29.00, + category: "subscriptions", + image: "images/placeholder.svg", + description: "Monthly access package for teams that want provider-backed chat, richer character workflows, dashboards, and production-grade runtime features without a custom engagement.", + featured: true, + features: [ + "Managed provider access for Azure OpenAI and OpenAI routes", + "Full character actions, world generation, and dashboard surfaces", + "Priority access to training and evaluation workflows", + "Fits teams moving from local demos to cloud-backed usage" + ] + }, + { + id: 2, + name: "Autonomous Training Sprint", + price: 399.00, + category: "training", + image: "images/placeholder.svg", + description: "A focused enablement package for teams adopting LoRA fine-tuning, evaluation loops, and autonomous training cycles across the Aria stack.", + featured: true, + features: [ + "Training pipeline review and dataset readiness guidance", + "Evaluation setup for batch runs, analytics, and promotion gates", + "Operator walkthrough for dashboards and performance tracking", + "Best fit before turning on continuous or scheduled retraining" + ] + }, + { + id: 3, + name: "Quantum Pilot Package", + price: 1299.00, + category: "quantum", + image: "images/placeholder.svg", + description: "Hybrid quantum-classical pilot planning for teams exploring simulators, Azure Quantum workflows, and proof-of-value experiments before committing to larger workloads.", + featured: true, + features: [ + "Circuit design and simulation workflow review", + "Azure Quantum integration planning and guardrail setup", + "Pilot architecture guidance for hybrid quantum ML experiments", + "Intended for research or enterprise readiness conversations" + ] + }, + { + id: 4, + name: "Enterprise Deployment Workshop", + price: 799.00, + category: "services", + image: "images/placeholder.svg", + description: "Hands-on deployment scoping for organizations packaging Aria for internal rollout, managed infrastructure, and operator onboarding.", + featured: false, + features: [ + "Deployment path review across docs, APIs, and automation surfaces", + "Recommendations for monitoring, subscriptions, and service ownership", + "Rollout plan for customer-facing or internal operator teams", + "Ideal precursor to a broader enterprise implementation" + ] + } +]; + +// Categories used for filtering +const CATEGORIES = [ + { id: "subscriptions", name: "Subscriptions", icon: "fa-layer-group" }, + { id: "training", name: "Training", icon: "fa-brain" }, + { id: "quantum", name: "Quantum Programs", icon: "fa-atom" }, + { id: "services", name: "Deployment Services", icon: "fa-server" } +]; + +function getCategoryById(id) { + return CATEGORIES.find(function (category) { return category.id === id; }) || null; +} diff --git a/docs/store/product.html b/docs/store/product.html index 125e0cf3a..df6c06d9e 100644 --- a/docs/store/product.html +++ b/docs/store/product.html @@ -1,67 +1,67 @@ - - - - - - Offer — Aria Store - - - - - - - - - - - - - - - - - - - - - -
    - -
    -
    -
    -
    -

    Offer detail

    -

    Review scope before you commit.

    -

    Each offer page summarizes what the package is for, what it includes, and which related packages a team should compare before moving forward.

    -
    -
    -
    - -
    -
    -
    -
    - -
    - -
    -

    You May Also Like

    -
    - -
    -
    -
    -
    -
    -
    - - - - - - - - - - + + + + + + Offer — Aria Store + + + + + + + + + + + + + + + + + + + + + +
    + +
    +
    +
    +
    +

    Offer detail

    +

    Review scope before you commit.

    +

    Each offer page summarizes what the package is for, what it includes, and which related packages a team should compare before moving forward.

    +
    +
    +
    + +
    +
    +
    +
    + +
    + +
    +

    You May Also Like

    +
    + +
    +
    +
    +
    +
    +
    + + + + + + + + + + diff --git a/docs/store/products.html b/docs/store/products.html index ff17e07b3..a5fde7bdb 100644 --- a/docs/store/products.html +++ b/docs/store/products.html @@ -1,108 +1,108 @@ - - - - - - Catalog — Aria Store - - - - - - - - - - - - - - - - - - - - - - -
    - -
    -
    -
    -
    -

    Catalog

    -

    Browse subscriptions, enablement, and rollout support.

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AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py -**Issue**: Missing `pass` statement in exception handler (line 681-683) -```python -# Before (BROKEN): -except Exception: - # Swallow unexpected callback errors -is_streaming = is_iterable_dataset(train_ds) - -# After (FIXED): -except Exception: - # Swallow unexpected callback errors - pass - -is_streaming = is_iterable_dataset(train_ds) -``` -**Impact**: Training pipeline could not be imported or executed - -### 2. scripts/validate_datasets.py -**Issue**: Empty exception handler body (line 152-155) -```python -# Before (BROKEN): -except json.JSONDecodeError as e: - -# Check if file was empty... - -# After (FIXED): -except json.JSONDecodeError as e: - stats["format_errors"].append(f"Line {i}: Invalid JSON - {str(e)}") - -# Check if file was empty... -``` -**Impact**: Dataset validation script failed to run - -### 3. scripts/repo_automation.py -**Issue 1**: Extra malformed docstring (line 345) -```python -# Before (BROKEN): -def stop_component(self, name: str): - """Stop a single component""" - """ - return - -# After (FIXED): -def stop_component(self, name: str): - """Stop a single component""" - if name not in self.processes: - return -``` - -**Issue 2**: For loop incorrectly placed inside function call (line 601-610) -```python -# Before (BROKEN): -dep_ok = (status.get("dependency_status", {}).get( - # Fallback: if PID not recorded, try discovering existing processes - for name, component in self.components.items(): - if name not in dynamic_running: - # ... more code ... - name, True) if status else True) - -# After (FIXED): -dep_ok = (status.get("dependency_status", {}).get( - name, True) if status else True) -``` -**Impact**: Repository automation system could not start or check status - -### 4. ai-projects/quantum-ml/production/test_api.py -**Issues**: -- Missing opening `"""` for module docstring (line 1) -- Duplicate code section (lines 293-578 - entire file duplicated) -- Duplicate `timeout` parameter in requests calls (line 28, 241) - -**Changes**: -- Added opening `"""` to module docstring -- Removed duplicate code (kept only first 292 lines) -- Fixed duplicate timeout parameters - -**Impact**: Production API tests could not run - -### 5. ai-projects/quantum-ml/production/banknote_api.py -**Issues**: -- Missing opening `"""` for module docstring (line 1) -- Duplicate code section (lines 310-618 - entire file duplicated) - -**Changes**: -- Added opening `"""` to module docstring -- Removed duplicate code (kept only first 309 lines) - -**Impact**: Production API server could not start - -### 6. AI/microsoft_phi-silica-3.6_v1/python mcp.py -**Issue**: Duplicate `api_version` parameter (line 34-35) -```python -# Before (BROKEN): -self.azureai = ChatCompletionsClient( - endpoint = "https://models.github.ai/inference", - credential = AzureKeyCredential(os.environ["GITHUB_TOKEN"]), - api_version = "2024-12-01-preview", - api_version = "2024-08-01-preview", # DUPLICATE -) - -# After (FIXED): -self.azureai = ChatCompletionsClient( - endpoint = "https://models.github.ai/inference", - credential = AzureKeyCredential(os.environ["GITHUB_TOKEN"]), - api_version = "2024-12-01-preview", -) -``` -**Impact**: MCP client initialization failed - -## Validation Results - -### Before Fixes -``` -❌ IndentationError in train_lora.py (line 683) -❌ IndentationError in validate_datasets.py (line 155) -❌ IndentationError in repo_automation.py (line 345) -❌ SyntaxError in repo_automation.py (line 603) -❌ SyntaxError in test_api.py (unterminated string) -❌ SyntaxError in test_api.py (duplicate keyword argument) -❌ SyntaxError in banknote_api.py (unterminated string) -❌ SyntaxError in python mcp.py (duplicate keyword argument) -``` - -### After Fixes -``` -✅ AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py -✅ scripts/validate_datasets.py -✅ scripts/repo_automation.py -✅ ai-projects/quantum-ml/production/test_api.py -✅ ai-projects/quantum-ml/production/banknote_api.py -✅ AI/microsoft_phi-silica-3.6_v1/python mcp.py -``` - -## Test Results - -### AI Improvements Test Suite -```bash -python scripts/test_ai_improvements.py -``` - -**Results**: -- ✅ **Chat improvements**: All tests passing - - top_p parameter (nucleus sampling) - - top_k parameter (top-k sampling) - - repetition_penalty parameter - - Proper EOS token handling -- ⚠️ **Quantum improvements**: Requires `pennylane` dependency (expected in CI environment) -- ⚠️ **Training improvements**: Requires `torch` dependency (expected in CI environment) - -### Smoke Test -```bash -cd ai-projects/chat-cli/src && python _smoke_test.py -``` - -**Result**: ✅ Chat provider working correctly - -## Impact Assessment - -### Critical Components Fixed -1. **Training Pipeline**: LoRA fine-tuning can now proceed without syntax errors -2. **Repository Automation**: Full automation system can now start and monitor components -3. **Dataset Validation**: Datasets can be properly validated before training -4. **Production APIs**: Quantum-powered banknote fraud detector APIs operational -5. **MCP Integration**: GitHub AI model integration functional - -### Breaking Changes -None - all changes are bug fixes that restore intended functionality - -### Dependencies -No new dependencies added. Existing optional dependencies remain optional: -- `pennylane`: Required for quantum ML features -- `torch`: Required for deep learning training -- `psutil`: Optional for enhanced process monitoring - -## How to Verify - -Run the validation script: -```bash -python scripts/test_ai_improvements.py -``` - -Check specific components: -```bash -# Training pipeline -python -m py_compile AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py - -# Automation -python -m py_compile scripts/repo_automation.py - -# Dataset validation -python -m py_compile scripts/validate_datasets.py - -# Production APIs -python -m py_compile ai-projects/quantum-ml/production/test_api.py -python -m py_compile ai-projects/quantum-ml/production/banknote_api.py - -# MCP integration -python -m py_compile "AI/microsoft_phi-silica-3.6_v1/python mcp.py" -``` - -## Next Steps - -With syntax errors resolved, the following components are now ready for use: - -1. **Autonomous Training**: Can be started with proper Python syntax - ```bash - nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 & - ``` - -2. **Repository Automation**: Full system automation available - ```bash - ./scripts/start_repo_automation.sh full - ``` - -3. **Dataset Validation**: Pre-training validation functional - ```bash - python scripts/validate_datasets.py --category chat - ``` - -4. **Production APIs**: Quantum ML APIs deployable - ```bash - python ai-projects/quantum-ml/production/banknote_api.py - python ai-projects/quantum-ml/production/test_api.py - ``` - -## Conclusion - -All identified syntax errors have been resolved. The AI training, automation, and production systems are now syntactically correct and ready for use. Remaining test failures are due to optional dependencies not present in the CI environment, which is expected behavior. +# AI Syntax Fixes Summary + +## Overview +Fixed multiple Python syntax errors across the Aria repository that were preventing AI training, automation, and production components from functioning correctly. + +## Files Fixed + +### 1. AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py +**Issue**: Missing `pass` statement in exception handler (line 681-683) +```python +# Before (BROKEN): +except Exception: + # Swallow unexpected callback errors +is_streaming = is_iterable_dataset(train_ds) + +# After (FIXED): +except Exception: + # Swallow unexpected callback errors + pass + +is_streaming = is_iterable_dataset(train_ds) +``` +**Impact**: Training pipeline could not be imported or executed + +### 2. scripts/validate_datasets.py +**Issue**: Empty exception handler body (line 152-155) +```python +# Before (BROKEN): +except json.JSONDecodeError as e: + +# Check if file was empty... + +# After (FIXED): +except json.JSONDecodeError as e: + stats["format_errors"].append(f"Line {i}: Invalid JSON - {str(e)}") + +# Check if file was empty... +``` +**Impact**: Dataset validation script failed to run + +### 3. scripts/repo_automation.py +**Issue 1**: Extra malformed docstring (line 345) +```python +# Before (BROKEN): +def stop_component(self, name: str): + """Stop a single component""" + """ + return + +# After (FIXED): +def stop_component(self, name: str): + """Stop a single component""" + if name not in self.processes: + return +``` + +**Issue 2**: For loop incorrectly placed inside function call (line 601-610) +```python +# Before (BROKEN): +dep_ok = (status.get("dependency_status", {}).get( + # Fallback: if PID not recorded, try discovering existing processes + for name, component in self.components.items(): + if name not in dynamic_running: + # ... more code ... + name, True) if status else True) + +# After (FIXED): +dep_ok = (status.get("dependency_status", {}).get( + name, True) if status else True) +``` +**Impact**: Repository automation system could not start or check status + +### 4. ai-projects/quantum-ml/production/test_api.py +**Issues**: +- Missing opening `"""` for module docstring (line 1) +- Duplicate code section (lines 293-578 - entire file duplicated) +- Duplicate `timeout` parameter in requests calls (line 28, 241) + +**Changes**: +- Added opening `"""` to module docstring +- Removed duplicate code (kept only first 292 lines) +- Fixed duplicate timeout parameters + +**Impact**: Production API tests could not run + +### 5. ai-projects/quantum-ml/production/banknote_api.py +**Issues**: +- Missing opening `"""` for module docstring (line 1) +- Duplicate code section (lines 310-618 - entire file duplicated) + +**Changes**: +- Added opening `"""` to module docstring +- Removed duplicate code (kept only first 309 lines) + +**Impact**: Production API server could not start + +### 6. AI/microsoft_phi-silica-3.6_v1/python mcp.py +**Issue**: Duplicate `api_version` parameter (line 34-35) +```python +# Before (BROKEN): +self.azureai = ChatCompletionsClient( + endpoint = "https://models.github.ai/inference", + credential = AzureKeyCredential(os.environ["GITHUB_TOKEN"]), + api_version = "2024-12-01-preview", + api_version = "2024-08-01-preview", # DUPLICATE +) + +# After (FIXED): +self.azureai = ChatCompletionsClient( + endpoint = "https://models.github.ai/inference", + credential = AzureKeyCredential(os.environ["GITHUB_TOKEN"]), + api_version = "2024-12-01-preview", +) +``` +**Impact**: MCP client initialization failed + +## Validation Results + +### Before Fixes +``` +❌ IndentationError in train_lora.py (line 683) +❌ IndentationError in validate_datasets.py (line 155) +❌ IndentationError in repo_automation.py (line 345) +❌ SyntaxError in repo_automation.py (line 603) +❌ SyntaxError in test_api.py (unterminated string) +❌ SyntaxError in test_api.py (duplicate keyword argument) +❌ SyntaxError in banknote_api.py (unterminated string) +❌ SyntaxError in python mcp.py (duplicate keyword argument) +``` + +### After Fixes +``` +✅ AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py +✅ scripts/validate_datasets.py +✅ scripts/repo_automation.py +✅ ai-projects/quantum-ml/production/test_api.py +✅ ai-projects/quantum-ml/production/banknote_api.py +✅ AI/microsoft_phi-silica-3.6_v1/python mcp.py +``` + +## Test Results + +### AI Improvements Test Suite +```bash +python scripts/test_ai_improvements.py +``` + +**Results**: +- ✅ **Chat improvements**: All tests passing + - top_p parameter (nucleus sampling) + - top_k parameter (top-k sampling) + - repetition_penalty parameter + - Proper EOS token handling +- ⚠️ **Quantum improvements**: Requires `pennylane` dependency (expected in CI environment) +- ⚠️ **Training improvements**: Requires `torch` dependency (expected in CI environment) + +### Smoke Test +```bash +cd ai-projects/chat-cli/src && python _smoke_test.py +``` + +**Result**: ✅ Chat provider working correctly + +## Impact Assessment + +### Critical Components Fixed +1. **Training Pipeline**: LoRA fine-tuning can now proceed without syntax errors +2. **Repository Automation**: Full automation system can now start and monitor components +3. **Dataset Validation**: Datasets can be properly validated before training +4. **Production APIs**: Quantum-powered banknote fraud detector APIs operational +5. **MCP Integration**: GitHub AI model integration functional + +### Breaking Changes +None - all changes are bug fixes that restore intended functionality + +### Dependencies +No new dependencies added. Existing optional dependencies remain optional: +- `pennylane`: Required for quantum ML features +- `torch`: Required for deep learning training +- `psutil`: Optional for enhanced process monitoring + +## How to Verify + +Run the validation script: +```bash +python scripts/test_ai_improvements.py +``` + +Check specific components: +```bash +# Training pipeline +python -m py_compile AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py + +# Automation +python -m py_compile scripts/repo_automation.py + +# Dataset validation +python -m py_compile scripts/validate_datasets.py + +# Production APIs +python -m py_compile ai-projects/quantum-ml/production/test_api.py +python -m py_compile ai-projects/quantum-ml/production/banknote_api.py + +# MCP integration +python -m py_compile "AI/microsoft_phi-silica-3.6_v1/python mcp.py" +``` + +## Next Steps + +With syntax errors resolved, the following components are now ready for use: + +1. **Autonomous Training**: Can be started with proper Python syntax + ```bash + nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 & + ``` + +2. **Repository Automation**: Full system automation available + ```bash + ./scripts/start_repo_automation.sh full + ``` + +3. **Dataset Validation**: Pre-training validation functional + ```bash + python scripts/validate_datasets.py --category chat + ``` + +4. **Production APIs**: Quantum ML APIs deployable + ```bash + python ai-projects/quantum-ml/production/banknote_api.py + python ai-projects/quantum-ml/production/test_api.py + ``` + +## Conclusion + +All identified syntax errors have been resolved. The AI training, automation, and production systems are now syntactically correct and ready for use. Remaining test failures are due to optional dependencies not present in the CI environment, which is expected behavior. diff --git a/docs/summaries/ARIA_AUTOMATION_SUMMARY.md b/docs/summaries/ARIA_AUTOMATION_SUMMARY.md index 487e2b32f..7f01623cd 100644 --- a/docs/summaries/ARIA_AUTOMATION_SUMMARY.md +++ b/docs/summaries/ARIA_AUTOMATION_SUMMARY.md @@ -1,481 +1,481 @@ -# 🤖 Aria Automation - Complete Setup Summary - -**Created:** November 29, 2025 -**Status:** ✅ Fully Functional & Tested - -## 🎯 What Was Automated - -Aria is now fully automated with: - -1. **Unified Automation Orchestrator** - Single script to control everything -2. **Auto-Start Service** - Interactive and background service modes -3. **Continuous Training** - Automatic model improvement cycles -4. **Health Monitoring** - Auto-recovery and status tracking -5. **Master Orchestrator Integration** - Scheduled workflows -6. **Production Deployment** - Systemd service & cron scheduling - ---- - -## 📁 Files Created - -### Core Automation - -- ✅ `scripts/aria_automation.py` - Main automation orchestrator (550+ lines) -- ✅ `scripts/start_aria.sh` - Interactive startup script -- ✅ `scripts/test_aria_automation.py` - Automated test suite - -### Configuration - -- ✅ `config/aria_automation.service` - Systemd service file -- ✅ `config/master_orchestrator.yaml` - Updated with Aria workflows - -### Documentation - -- ✅ `ARIA_AUTOMATION_GUIDE.md` - Complete deployment guide (400+ lines) - ---- - -## 🚀 Quick Start Commands - -### Interactive Menu - -```bash -./scripts/start_aria.sh -# Select from menu: -# 1) Full Stack - Server + Backend + Training -# 2) Server Only - Web interface only -# 3) Training Only - Continuous training -# 4) Single Train - One cycle and exit -# 5) Status - Check current status -# 6) Stop All - Stop all processes -``` - -### Command Line (Recommended) - -```bash -# Full automation (server + backend + training + monitoring) -./scripts/start_aria.sh full - -# Background mode -./scripts/start_aria.sh full --background - -# Check status -./scripts/start_aria.sh status - -# Stop all -./scripts/start_aria.sh stop -``` - -### Direct Python - -```bash -# Full automation -python3 scripts/aria_automation.py --mode full - -# Server only -python3 scripts/aria_automation.py --mode server - -# Training once -python3 scripts/aria_automation.py --mode training --once - -# Check status -python3 scripts/aria_automation.py --status -``` - ---- - -## 🎯 Automation Modes Explained - -### Mode: `full` (Production) - -**What runs:** - -- ✅ Aria web server (port 8080) -- ✅ Azure Functions backend (port 7071) -- ✅ Continuous training (every 30 min) -- ✅ Health monitoring (every 60 sec) -- ✅ Auto-recovery on failures - -**When to use:** - -- Production deployment -- 24/7 operation -- Complete automation - -**Access:** - -- Aria UI: -- Auto-Execute: -- API: - -### Mode: `server` (Development) - -**What runs:** - -- ✅ Aria web server (port 8080) -- ✅ Health monitoring -- ❌ No training -- ❌ No backend - -**When to use:** - -- UI development -- Testing changes -- Demos without training - -### Mode: `training` (Background) - -**What runs:** - -- ✅ Continuous training cycles -- ✅ Dataset monitoring -- ❌ No web server - -**When to use:** - -- Dedicated training runs -- Batch processing -- Resource optimization - ---- - -## 📊 Monitoring & Status - -### Check Status - -```bash -python3 scripts/aria_automation.py --status -``` - -**Output:** - -``` -================================================================================ -🤖 Aria Automation Status -================================================================================ -Mode: full -Started: 2025-11-29T10:30:00 -Uptime: 2:15:30 -Training Cycles: 4 - -Components: - - Aria Server: ✅ Running - - Functions Backend: ✅ Running - - Training: ✅ Active -================================================================================ -``` - -### Status File - -Real-time status saved to: `data_out/aria_automation/status.json` - -### Health Endpoints - -```bash -# Aria server -curl http://localhost:8080 - -# Backend API -curl http://localhost:7071/api/ai/status -``` - ---- - -## 🔄 Background Services - -### Using systemd (Linux - Recommended) - -1. **Install Service:** - -```bash -sudo cp config/aria_automation.service /etc/systemd/system/ -sudo nano /etc/systemd/system/aria_automation.service -# Update User, Group, WorkingDirectory -sudo systemctl daemon-reload -``` - -2. **Start Service:** - -```bash -sudo systemctl enable aria_automation # Start on boot -sudo systemctl start aria_automation # Start now -sudo systemctl status aria_automation # Check status -``` - -3. **View Logs:** - -```bash -sudo journalctl -u aria_automation -f -``` - -### Using screen (SSH Sessions) - -```bash -screen -S aria -python3 scripts/aria_automation.py --mode full -# Detach: Ctrl+A, then D -# Reattach: screen -r aria -``` - -### Using nohup (Simple) - -```bash -nohup python3 scripts/aria_automation.py --mode full > aria.log 2>&1 & -``` - -### Using Cron (Scheduled) - -```bash -crontab -e - -# Add lines: -# Training every 30 minutes -*/30 * * * * /usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --mode training --once - -# Start server on boot -@reboot /usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --mode server -``` - ---- - -## 🎼 Master Orchestrator Integration - -Aria is now integrated with the master orchestrator system. - -### Configuration Added - -In `config/master_orchestrator.yaml`: - -```yaml -orchestrators: - - name: aria_automation - script: scripts/aria_automation.py - enabled: true - schedule: "*/30 * * * *" # Every 30 minutes - priority: 5 - retry_on_failure: 3 - timeout_minutes: 15 - flags: - mode: training - once: true - -workflows: - - name: aria_full_stack - enabled: false # Enable for 24/7 - trigger: "manual" - orchestrators: - - aria_automation - flags: - mode: full -``` - -### Running via Master Orchestrator - -```bash -# Start Aria workflow -python3 scripts/master_orchestrator.py --workflow aria_full_stack - -# Check all orchestrators -python3 scripts/master_orchestrator.py --status - -# Run as daemon -python3 scripts/master_orchestrator.py --daemon -``` - ---- - -## ✅ Testing Results - -All automation components tested and verified: - -``` -================================================================================ -Test Summary -================================================================================ -Tests Passed: 14/14 - -✅ All tests passed! Automation is ready to use. -``` - -**Tests Validated:** - -- ✅ File structure (7/7 files present) -- ✅ Python dependencies (4/4 core modules) -- ✅ Script functionality (help, status) -- ✅ Executable permissions - ---- - -## 🔧 Features & Capabilities - -### Auto-Start - -- Detects if services already running -- Graceful startup with health checks -- Port availability validation - -### Auto-Recovery - -- Health checks every 60 seconds -- Automatic restart on failure -- Process monitoring with psutil - -### Training Automation - -- Continuous training cycles -- Configurable intervals -- Dataset validation -- Error handling & retries - -### Process Management - -- PID tracking & cleanup -- Signal handling (SIGINT, SIGTERM) -- Graceful shutdown -- Zombie process prevention - -### Status Tracking - -- Real-time status JSON -- Component health monitoring -- Training cycle counting -- Error history - ---- - -## 📚 Documentation - -All documentation is comprehensive and complete: - -1. **Quick Start:** This file (ARIA_AUTOMATION_SUMMARY.md) -2. **Full Guide:** ARIA_AUTOMATION_GUIDE.md (400+ lines) -3. **Aria Web:** aria_web/README.md -4. **Auto-Execute:** aria_web/AUTO-EXECUTE.md -5. **Training:** scripts/README.md - ---- - -## 🎯 Next Steps - -### Immediate Actions - -1. Test the automation: - - ```bash - ./scripts/start_aria.sh full - ``` - -2. Access Aria web interface: - - ``` - http://localhost:8080 - ``` - -3. Try auto-execute: - - ``` - http://localhost:8080/auto-execute.html - ``` - -### Production Setup - -1. Configure systemd service -2. Enable on boot: `sudo systemctl enable aria_automation` -3. Setup monitoring alerts -4. Configure backups - -### Optional Enhancements - -1. Enable master orchestrator workflows -2. Setup notification webhooks -3. Configure resource limits -4. Add custom training schedules - ---- - -## 🆘 Troubleshooting - -### Port Already in Use - -```bash -# Check what's using the port -lsof -i :8080 -lsof -i :7071 - -# Stop Aria processes -python3 scripts/aria_automation.py --stop -``` - -### Training Fails - -```bash -# Validate dataset -python3 scripts/validate_datasets.py --category chat - -# Test training manually -python3 scripts/aria_quick_train.py -``` - -### View Logs - -```bash -# Automation logs -tail -f data_out/aria_automation/aria_automation.log - -# Service logs (systemd) -sudo journalctl -u aria_automation -f -``` - ---- - -## 📈 Performance & Resource Usage - -### Typical Resource Usage (Full Mode) - -- **Memory:** ~500MB-1GB (server + backend) -- **CPU:** 5-10% idle, 50-80% during training -- **Disk:** ~100MB per training cycle -- **Network:** Minimal (local only) - -### Optimization Tips - -1. Adjust training interval (default: 30 min) -2. Use `--once` for manual control -3. Run training-only mode overnight -4. Clean old outputs regularly - ---- - -## 🎉 Summary - -**Aria is now fully automated!** - -✅ **One-command startup** - `./scripts/start_aria.sh full` -✅ **Auto-recovery** - Restarts on failures -✅ **Continuous training** - Learns automatically -✅ **Production-ready** - Systemd service included -✅ **Well-documented** - Complete guides provided -✅ **Fully tested** - All tests passing - -**Key Files:** - -- 🚀 Start: `./scripts/start_aria.sh` -- 📊 Status: `python3 scripts/aria_automation.py --status` -- 🛑 Stop: `python3 scripts/aria_automation.py --stop` -- 📖 Docs: `ARIA_AUTOMATION_GUIDE.md` - ---- - -## 🔗 Related Documentation - -- **Main README:** `README.md` -- **Automation Guide:** `ARIA_AUTOMATION_GUIDE.md` -- **Aria Web:** `aria_web/README.md` -- **Auto-Execute:** `aria_web/AUTO-EXECUTE.md` -- **Master Orchestrator:** `ADVANCED_AUTOMATION.md` - ---- - -**Happy Automating! 🤖✨** - -*For support or issues, check the logs in `data_out/aria_automation/` or review `ARIA_AUTOMATION_GUIDE.md`* +# 🤖 Aria Automation - Complete Setup Summary + +**Created:** November 29, 2025 +**Status:** ✅ Fully Functional & Tested + +## 🎯 What Was Automated + +Aria is now fully automated with: + +1. **Unified Automation Orchestrator** - Single script to control everything +2. **Auto-Start Service** - Interactive and background service modes +3. **Continuous Training** - Automatic model improvement cycles +4. **Health Monitoring** - Auto-recovery and status tracking +5. **Master Orchestrator Integration** - Scheduled workflows +6. **Production Deployment** - Systemd service & cron scheduling + +--- + +## 📁 Files Created + +### Core Automation + +- ✅ `scripts/aria_automation.py` - Main automation orchestrator (550+ lines) +- ✅ `scripts/start_aria.sh` - Interactive startup script +- ✅ `scripts/test_aria_automation.py` - Automated test suite + +### Configuration + +- ✅ `config/aria_automation.service` - Systemd service file +- ✅ `config/master_orchestrator.yaml` - Updated with Aria workflows + +### Documentation + +- ✅ `ARIA_AUTOMATION_GUIDE.md` - Complete deployment guide (400+ lines) + +--- + +## 🚀 Quick Start Commands + +### Interactive Menu + +```bash +./scripts/start_aria.sh +# Select from menu: +# 1) Full Stack - Server + Backend + Training +# 2) Server Only - Web interface only +# 3) Training Only - Continuous training +# 4) Single Train - One cycle and exit +# 5) Status - Check current status +# 6) Stop All - Stop all processes +``` + +### Command Line (Recommended) + +```bash +# Full automation (server + backend + training + monitoring) +./scripts/start_aria.sh full + +# Background mode +./scripts/start_aria.sh full --background + +# Check status +./scripts/start_aria.sh status + +# Stop all +./scripts/start_aria.sh stop +``` + +### Direct Python + +```bash +# Full automation +python3 scripts/aria_automation.py --mode full + +# Server only +python3 scripts/aria_automation.py --mode server + +# Training once +python3 scripts/aria_automation.py --mode training --once + +# Check status +python3 scripts/aria_automation.py --status +``` + +--- + +## 🎯 Automation Modes Explained + +### Mode: `full` (Production) + +**What runs:** + +- ✅ Aria web server (port 8080) +- ✅ Azure Functions backend (port 7071) +- ✅ Continuous training (every 30 min) +- ✅ Health monitoring (every 60 sec) +- ✅ Auto-recovery on failures + +**When to use:** + +- Production deployment +- 24/7 operation +- Complete automation + +**Access:** + +- Aria UI: +- Auto-Execute: +- API: + +### Mode: `server` (Development) + +**What runs:** + +- ✅ Aria web server (port 8080) +- ✅ Health monitoring +- ❌ No training +- ❌ No backend + +**When to use:** + +- UI development +- Testing changes +- Demos without training + +### Mode: `training` (Background) + +**What runs:** + +- ✅ Continuous training cycles +- ✅ Dataset monitoring +- ❌ No web server + +**When to use:** + +- Dedicated training runs +- Batch processing +- Resource optimization + +--- + +## 📊 Monitoring & Status + +### Check Status + +```bash +python3 scripts/aria_automation.py --status +``` + +**Output:** + +``` +================================================================================ +🤖 Aria Automation Status +================================================================================ +Mode: full +Started: 2025-11-29T10:30:00 +Uptime: 2:15:30 +Training Cycles: 4 + +Components: + - Aria Server: ✅ Running + - Functions Backend: ✅ Running + - Training: ✅ Active +================================================================================ +``` + +### Status File + +Real-time status saved to: `data_out/aria_automation/status.json` + +### Health Endpoints + +```bash +# Aria server +curl http://localhost:8080 + +# Backend API +curl http://localhost:7071/api/ai/status +``` + +--- + +## 🔄 Background Services + +### Using systemd (Linux - Recommended) + +1. **Install Service:** + +```bash +sudo cp config/aria_automation.service /etc/systemd/system/ +sudo nano /etc/systemd/system/aria_automation.service +# Update User, Group, WorkingDirectory +sudo systemctl daemon-reload +``` + +2. **Start Service:** + +```bash +sudo systemctl enable aria_automation # Start on boot +sudo systemctl start aria_automation # Start now +sudo systemctl status aria_automation # Check status +``` + +3. **View Logs:** + +```bash +sudo journalctl -u aria_automation -f +``` + +### Using screen (SSH Sessions) + +```bash +screen -S aria +python3 scripts/aria_automation.py --mode full +# Detach: Ctrl+A, then D +# Reattach: screen -r aria +``` + +### Using nohup (Simple) + +```bash +nohup python3 scripts/aria_automation.py --mode full > aria.log 2>&1 & +``` + +### Using Cron (Scheduled) + +```bash +crontab -e + +# Add lines: +# Training every 30 minutes +*/30 * * * * /usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --mode training --once + +# Start server on boot +@reboot /usr/bin/python3 /workspaces/Aria/scripts/aria_automation.py --mode server +``` + +--- + +## 🎼 Master Orchestrator Integration + +Aria is now integrated with the master orchestrator system. + +### Configuration Added + +In `config/master_orchestrator.yaml`: + +```yaml +orchestrators: + - name: aria_automation + script: scripts/aria_automation.py + enabled: true + schedule: "*/30 * * * *" # Every 30 minutes + priority: 5 + retry_on_failure: 3 + timeout_minutes: 15 + flags: + mode: training + once: true + +workflows: + - name: aria_full_stack + enabled: false # Enable for 24/7 + trigger: "manual" + orchestrators: + - aria_automation + flags: + mode: full +``` + +### Running via Master Orchestrator + +```bash +# Start Aria workflow +python3 scripts/master_orchestrator.py --workflow aria_full_stack + +# Check all orchestrators +python3 scripts/master_orchestrator.py --status + +# Run as daemon +python3 scripts/master_orchestrator.py --daemon +``` + +--- + +## ✅ Testing Results + +All automation components tested and verified: + +``` +================================================================================ +Test Summary +================================================================================ +Tests Passed: 14/14 + +✅ All tests passed! Automation is ready to use. +``` + +**Tests Validated:** + +- ✅ File structure (7/7 files present) +- ✅ Python dependencies (4/4 core modules) +- ✅ Script functionality (help, status) +- ✅ Executable permissions + +--- + +## 🔧 Features & Capabilities + +### Auto-Start + +- Detects if services already running +- Graceful startup with health checks +- Port availability validation + +### Auto-Recovery + +- Health checks every 60 seconds +- Automatic restart on failure +- Process monitoring with psutil + +### Training Automation + +- Continuous training cycles +- Configurable intervals +- Dataset validation +- Error handling & retries + +### Process Management + +- PID tracking & cleanup +- Signal handling (SIGINT, SIGTERM) +- Graceful shutdown +- Zombie process prevention + +### Status Tracking + +- Real-time status JSON +- Component health monitoring +- Training cycle counting +- Error history + +--- + +## 📚 Documentation + +All documentation is comprehensive and complete: + +1. **Quick Start:** This file (ARIA_AUTOMATION_SUMMARY.md) +2. **Full Guide:** ARIA_AUTOMATION_GUIDE.md (400+ lines) +3. **Aria Web:** aria_web/README.md +4. **Auto-Execute:** aria_web/AUTO-EXECUTE.md +5. **Training:** scripts/README.md + +--- + +## 🎯 Next Steps + +### Immediate Actions + +1. Test the automation: + + ```bash + ./scripts/start_aria.sh full + ``` + +2. Access Aria web interface: + + ``` + http://localhost:8080 + ``` + +3. Try auto-execute: + + ``` + http://localhost:8080/auto-execute.html + ``` + +### Production Setup + +1. Configure systemd service +2. Enable on boot: `sudo systemctl enable aria_automation` +3. Setup monitoring alerts +4. Configure backups + +### Optional Enhancements + +1. Enable master orchestrator workflows +2. Setup notification webhooks +3. Configure resource limits +4. Add custom training schedules + +--- + +## 🆘 Troubleshooting + +### Port Already in Use + +```bash +# Check what's using the port +lsof -i :8080 +lsof -i :7071 + +# Stop Aria processes +python3 scripts/aria_automation.py --stop +``` + +### Training Fails + +```bash +# Validate dataset +python3 scripts/validate_datasets.py --category chat + +# Test training manually +python3 scripts/aria_quick_train.py +``` + +### View Logs + +```bash +# Automation logs +tail -f data_out/aria_automation/aria_automation.log + +# Service logs (systemd) +sudo journalctl -u aria_automation -f +``` + +--- + +## 📈 Performance & Resource Usage + +### Typical Resource Usage (Full Mode) + +- **Memory:** ~500MB-1GB (server + backend) +- **CPU:** 5-10% idle, 50-80% during training +- **Disk:** ~100MB per training cycle +- **Network:** Minimal (local only) + +### Optimization Tips + +1. Adjust training interval (default: 30 min) +2. Use `--once` for manual control +3. Run training-only mode overnight +4. Clean old outputs regularly + +--- + +## 🎉 Summary + +**Aria is now fully automated!** + +✅ **One-command startup** - `./scripts/start_aria.sh full` +✅ **Auto-recovery** - Restarts on failures +✅ **Continuous training** - Learns automatically +✅ **Production-ready** - Systemd service included +✅ **Well-documented** - Complete guides provided +✅ **Fully tested** - All tests passing + +**Key Files:** + +- 🚀 Start: `./scripts/start_aria.sh` +- 📊 Status: `python3 scripts/aria_automation.py --status` +- 🛑 Stop: `python3 scripts/aria_automation.py --stop` +- 📖 Docs: `ARIA_AUTOMATION_GUIDE.md` + +--- + +## 🔗 Related Documentation + +- **Main README:** `README.md` +- **Automation Guide:** `ARIA_AUTOMATION_GUIDE.md` +- **Aria Web:** `aria_web/README.md` +- **Auto-Execute:** `aria_web/AUTO-EXECUTE.md` +- **Master Orchestrator:** `ADVANCED_AUTOMATION.md` + +--- + +**Happy Automating! 🤖✨** + +*For support or issues, check the logs in `data_out/aria_automation/` or review `ARIA_AUTOMATION_GUIDE.md`* diff --git a/docs/summaries/GITHUB_PAGES_SETUP_SUMMARY.md b/docs/summaries/GITHUB_PAGES_SETUP_SUMMARY.md index ce2313517..2e5b8c682 100644 --- a/docs/summaries/GITHUB_PAGES_SETUP_SUMMARY.md +++ b/docs/summaries/GITHUB_PAGES_SETUP_SUMMARY.md @@ -1,273 +1,273 @@ -# GitHub Pages Setup Summary - -## ✅ Implementation Complete - -The Aria 3D character interface is now ready for deployment to GitHub Pages! - -## 📦 What Was Created - -### Core Files (in `docs/` directory) - -1. **`index.html`** (41KB) - - Main interface with 3D CSS character - - Purple gradient banner explaining static mode - - Links to full project repository - - Object manager panel - - Command input and chat interface - - Status/log display - -2. **`aria_controller.js`** (73KB) - - Complete client-side logic - - Animation system (jump, dance, wave, spin) - - 3D waypoint navigation - - Object interaction (pickup, drop, throw) - - Expression system - - Graceful API fallback (already present) - - Eye tracking and idle animations - -3. **`README.md`** (3.9KB) - - User-facing documentation - - Feature overview - - Quick command examples - - Usage tips - - Links to full project - -4. **`DEPLOYMENT_GUIDE.md`** (5.7KB) - - Complete deployment instructions - - Both automatic and manual methods - - Troubleshooting guide - - Verification checklist - - Customization options - -5. **`QAI_DOCS_INDEX.md`** (5.0KB) - - Preserved technical documentation - - Links to training guides - - Quantum computing docs - - Database setup guides - -### Deployment Infrastructure - -6. **`.github/workflows/pages.yml`** - - GitHub Actions workflow - - Automatic deployment on push - - Proper permissions configured - - Artifact upload and deployment - -## 🎯 Key Features - -### Static Mode Capabilities - -✅ **Works Completely Offline** -- No backend server required -- All processing client-side -- Graceful API fallback built-in - -✅ **Full Character Control** -- Jump, dance, wave, spin animations -- Movement commands (left, right, up, down) -- 3D positioning with depth -- Expression changes (smile, sad, surprised, etc.) - -✅ **Object Interactions** -- Drag and drop objects -- Pickup and hold objects -- Drop objects -- Throw objects with trajectories -- Toggle object visibility - -✅ **3D Navigation System** -- 10 predefined waypoints -- Circle and spiral movements -- Chat-based navigation -- Smooth animated transitions - -✅ **Visual Feedback** -- Real-time command log -- Status messages -- Animation feedback -- Error handling - -## 🚀 Deployment Instructions - -### Quick Start - -1. **Merge this PR to `main`** - ```bash - git checkout main - git merge copilot/setup-git-pages-3d-world - git push origin main - ``` - -2. **Enable GitHub Pages** - - Go to repository Settings → Pages - - Set Source to "GitHub Actions" - - Wait for workflow to complete - -3. **Visit Your Site** - - URL: https://bryan-roe.github.io/Aria/ - - Should be live within 1-2 minutes - -### Verification Checklist - -After deployment: -- [ ] Site loads at https://bryan-roe.github.io/Aria/ -- [ ] Purple banner appears at top -- [ ] Character is visible on stage -- [ ] Objects (apple, book, cup, ball, flower) are visible -- [ ] Command input accepts text -- [ ] "jump" command makes character jump -- [ ] "wave" command makes character wave -- [ ] Objects can be dragged with mouse -- [ ] Chat commands work (/goto, /waypoints, /circle) -- [ ] No critical console errors (network fallbacks are OK) - -## 🎨 What Users Will Experience - -### First Impression -- Prominent banner explaining this is a static demo -- Link to full project repository -- Clean, modern gradient interface -- 3D character with smooth animations - -### Interaction Flow -1. User types a command (e.g., "jump") -2. System logs the command -3. Character performs animation -4. Feedback shows in both UI and logs -5. Network error logged (expected in static mode) -6. Local fallback executes successfully - -### Command Examples -``` -jump → Character jumps -wave → Character waves -dance → Dance animation -move left → Character moves left -smile → Change to happy expression -pick up apple → Pickup apple object -/goto center → Navigate to center waypoint -/circle → Circular 3D movement -/spiral → Spiral 3D movement -``` - -## 📊 Technical Details - -### Architecture -- **Pure Static Site**: HTML + CSS + JavaScript -- **No Build Step**: Direct deployment of source files -- **No Dependencies**: No npm packages or frameworks -- **Progressive Enhancement**: Works on all modern browsers - -### Browser Compatibility -- ✅ Chrome 90+ (full support) -- ✅ Firefox 88+ (full support) -- ✅ Safari 14+ (full support) -- ✅ Edge 90+ (full support) -- ⚠️ IE 11 (limited support, basic functionality) - -### Performance -- **Initial Load**: ~120KB (HTML + JS) -- **Render Time**: < 100ms -- **Animation FPS**: 60fps -- **Memory Usage**: < 50MB -- **CPU Usage**: < 5% idle, < 15% during animations - -## 🔍 Differences from Full Version - -### What Works (Static Mode) -- ✅ All character animations -- ✅ Object interactions -- ✅ 3D navigation -- ✅ Local command processing -- ✅ Expression system -- ✅ Gesture system - -### What Requires Backend (Not in Static Mode) -- ❌ AI-powered natural language processing -- ❌ LLM-based command interpretation -- ❌ Server-side state persistence -- ❌ Multi-user synchronization -- ❌ Advanced world generation -- ❌ Real-time backend API features - -## 📚 Documentation Structure - -``` -docs/ -├── index.html ← Main demo page -├── aria_controller.js ← Client-side logic -├── README.md ← User guide -├── DEPLOYMENT_GUIDE.md ← Deployment instructions -├── QAI_DOCS_INDEX.md ← Technical docs index -└── [subdirectories]/ ← Additional documentation -``` - -## 🎓 Learning Resources - -For users exploring the demo: -1. Start with README.md for quick overview -2. Try basic commands (jump, wave, dance) -3. Explore object interactions -4. Test waypoint navigation -5. Read full project docs for backend features - -For developers setting up deployment: -1. Read DEPLOYMENT_GUIDE.md thoroughly -2. Choose deployment method (Actions vs Branch) -3. Follow verification checklist -4. Troubleshoot using provided guide -5. Customize as needed - -## �� Known Limitations - -1. **Network Errors in Console** - - Expected behavior in static mode - - Gracefully falls back to local processing - - No impact on functionality - -2. **No AI Processing** - - Commands parsed locally with simple rules - - Complex natural language may not work - - Use specific commands for best results - -3. **No State Persistence** - - Page refresh resets everything - - No save/load functionality - - Objects return to default positions - -4. **Limited Multi-Command** - - Complex sequences may need manual chaining - - No backend coordination - -## 🎉 Success Metrics - -After deployment, the demo successfully: -- ✅ Showcases Aria's 3D character system -- ✅ Demonstrates animation capabilities -- ✅ Provides interactive experience -- ✅ Works 100% offline -- ✅ Requires zero backend infrastructure -- ✅ Links to full project for deeper exploration - -## 📞 Support - -If issues arise: -1. Check DEPLOYMENT_GUIDE.md troubleshooting section -2. Verify GitHub Actions workflow logs -3. Check browser console for errors -4. Open issue in main repository -5. Reference this summary document - -## 🔗 Important Links - -- **Live Demo** (after deployment): https://bryan-roe.github.io/Aria/ -- **Main Repository**: https://github.com/Bryan-Roe/Aria -- **Deployment Workflow**: `.github/workflows/pages.yml` -- **User Guide**: `docs/README.md` -- **Deployment Guide**: `docs/DEPLOYMENT_GUIDE.md` - ---- - -**Status**: ✅ Ready for deployment -**Last Updated**: January 19, 2026 -**Created By**: GitHub Copilot Agent +# GitHub Pages Setup Summary + +## ✅ Implementation Complete + +The Aria 3D character interface is now ready for deployment to GitHub Pages! + +## 📦 What Was Created + +### Core Files (in `docs/` directory) + +1. **`index.html`** (41KB) + - Main interface with 3D CSS character + - Purple gradient banner explaining static mode + - Links to full project repository + - Object manager panel + - Command input and chat interface + - Status/log display + +2. **`aria_controller.js`** (73KB) + - Complete client-side logic + - Animation system (jump, dance, wave, spin) + - 3D waypoint navigation + - Object interaction (pickup, drop, throw) + - Expression system + - Graceful API fallback (already present) + - Eye tracking and idle animations + +3. **`README.md`** (3.9KB) + - User-facing documentation + - Feature overview + - Quick command examples + - Usage tips + - Links to full project + +4. **`DEPLOYMENT_GUIDE.md`** (5.7KB) + - Complete deployment instructions + - Both automatic and manual methods + - Troubleshooting guide + - Verification checklist + - Customization options + +5. **`QAI_DOCS_INDEX.md`** (5.0KB) + - Preserved technical documentation + - Links to training guides + - Quantum computing docs + - Database setup guides + +### Deployment Infrastructure + +6. **`.github/workflows/pages.yml`** + - GitHub Actions workflow + - Automatic deployment on push + - Proper permissions configured + - Artifact upload and deployment + +## 🎯 Key Features + +### Static Mode Capabilities + +✅ **Works Completely Offline** +- No backend server required +- All processing client-side +- Graceful API fallback built-in + +✅ **Full Character Control** +- Jump, dance, wave, spin animations +- Movement commands (left, right, up, down) +- 3D positioning with depth +- Expression changes (smile, sad, surprised, etc.) + +✅ **Object Interactions** +- Drag and drop objects +- Pickup and hold objects +- Drop objects +- Throw objects with trajectories +- Toggle object visibility + +✅ **3D Navigation System** +- 10 predefined waypoints +- Circle and spiral movements +- Chat-based navigation +- Smooth animated transitions + +✅ **Visual Feedback** +- Real-time command log +- Status messages +- Animation feedback +- Error handling + +## 🚀 Deployment Instructions + +### Quick Start + +1. **Merge this PR to `main`** + ```bash + git checkout main + git merge copilot/setup-git-pages-3d-world + git push origin main + ``` + +2. **Enable GitHub Pages** + - Go to repository Settings → Pages + - Set Source to "GitHub Actions" + - Wait for workflow to complete + +3. **Visit Your Site** + - URL: https://bryan-roe.github.io/Aria/ + - Should be live within 1-2 minutes + +### Verification Checklist + +After deployment: +- [ ] Site loads at https://bryan-roe.github.io/Aria/ +- [ ] Purple banner appears at top +- [ ] Character is visible on stage +- [ ] Objects (apple, book, cup, ball, flower) are visible +- [ ] Command input accepts text +- [ ] "jump" command makes character jump +- [ ] "wave" command makes character wave +- [ ] Objects can be dragged with mouse +- [ ] Chat commands work (/goto, /waypoints, /circle) +- [ ] No critical console errors (network fallbacks are OK) + +## 🎨 What Users Will Experience + +### First Impression +- Prominent banner explaining this is a static demo +- Link to full project repository +- Clean, modern gradient interface +- 3D character with smooth animations + +### Interaction Flow +1. User types a command (e.g., "jump") +2. System logs the command +3. Character performs animation +4. Feedback shows in both UI and logs +5. Network error logged (expected in static mode) +6. Local fallback executes successfully + +### Command Examples +``` +jump → Character jumps +wave → Character waves +dance → Dance animation +move left → Character moves left +smile → Change to happy expression +pick up apple → Pickup apple object +/goto center → Navigate to center waypoint +/circle → Circular 3D movement +/spiral → Spiral 3D movement +``` + +## 📊 Technical Details + +### Architecture +- **Pure Static Site**: HTML + CSS + JavaScript +- **No Build Step**: Direct deployment of source files +- **No Dependencies**: No npm packages or frameworks +- **Progressive Enhancement**: Works on all modern browsers + +### Browser Compatibility +- ✅ Chrome 90+ (full support) +- ✅ Firefox 88+ (full support) +- ✅ Safari 14+ (full support) +- ✅ Edge 90+ (full support) +- ⚠️ IE 11 (limited support, basic functionality) + +### Performance +- **Initial Load**: ~120KB (HTML + JS) +- **Render Time**: < 100ms +- **Animation FPS**: 60fps +- **Memory Usage**: < 50MB +- **CPU Usage**: < 5% idle, < 15% during animations + +## 🔍 Differences from Full Version + +### What Works (Static Mode) +- ✅ All character animations +- ✅ Object interactions +- ✅ 3D navigation +- ✅ Local command processing +- ✅ Expression system +- ✅ Gesture system + +### What Requires Backend (Not in Static Mode) +- ❌ AI-powered natural language processing +- ❌ LLM-based command interpretation +- ❌ Server-side state persistence +- ❌ Multi-user synchronization +- ❌ Advanced world generation +- ❌ Real-time backend API features + +## 📚 Documentation Structure + +``` +docs/ +├── index.html ← Main demo page +├── aria_controller.js ← Client-side logic +├── README.md ← User guide +├── DEPLOYMENT_GUIDE.md ← Deployment instructions +├── QAI_DOCS_INDEX.md ← Technical docs index +└── [subdirectories]/ ← Additional documentation +``` + +## 🎓 Learning Resources + +For users exploring the demo: +1. Start with README.md for quick overview +2. Try basic commands (jump, wave, dance) +3. Explore object interactions +4. Test waypoint navigation +5. Read full project docs for backend features + +For developers setting up deployment: +1. Read DEPLOYMENT_GUIDE.md thoroughly +2. Choose deployment method (Actions vs Branch) +3. Follow verification checklist +4. Troubleshoot using provided guide +5. Customize as needed + +## �� Known Limitations + +1. **Network Errors in Console** + - Expected behavior in static mode + - Gracefully falls back to local processing + - No impact on functionality + +2. **No AI Processing** + - Commands parsed locally with simple rules + - Complex natural language may not work + - Use specific commands for best results + +3. **No State Persistence** + - Page refresh resets everything + - No save/load functionality + - Objects return to default positions + +4. **Limited Multi-Command** + - Complex sequences may need manual chaining + - No backend coordination + +## 🎉 Success Metrics + +After deployment, the demo successfully: +- ✅ Showcases Aria's 3D character system +- ✅ Demonstrates animation capabilities +- ✅ Provides interactive experience +- ✅ Works 100% offline +- ✅ Requires zero backend infrastructure +- ✅ Links to full project for deeper exploration + +## 📞 Support + +If issues arise: +1. Check DEPLOYMENT_GUIDE.md troubleshooting section +2. Verify GitHub Actions workflow logs +3. Check browser console for errors +4. Open issue in main repository +5. Reference this summary document + +## 🔗 Important Links + +- **Live Demo** (after deployment): https://bryan-roe.github.io/Aria/ +- **Main Repository**: https://github.com/Bryan-Roe/Aria +- **Deployment Workflow**: `.github/workflows/pages.yml` +- **User Guide**: `docs/README.md` +- **Deployment Guide**: `docs/DEPLOYMENT_GUIDE.md` + +--- + +**Status**: ✅ Ready for deployment +**Last Updated**: January 19, 2026 +**Created By**: GitHub Copilot Agent diff --git a/docs/summaries/IMPLEMENTATION_COMPLETE.txt b/docs/summaries/IMPLEMENTATION_COMPLETE.txt index ae5aa6d54..dec800a18 100644 --- a/docs/summaries/IMPLEMENTATION_COMPLETE.txt +++ b/docs/summaries/IMPLEMENTATION_COMPLETE.txt @@ -1,300 +1,300 @@ -================================================================================ - ARIA MONETIZATION SYSTEM - IMPLEMENTATION COMPLETE ✅ -================================================================================ - -Date: February 4, 2026 -Status: Production Ready -Target: $2,000/month MRR -Achievement: $2,235/month MRR (111.8%) - -================================================================================ - FILES CREATED/MODIFIED -================================================================================ - -WEB PAGES (7): - ✓ monetization-index.html - Main landing page - ✓ pricing.html - Pricing comparison - ✓ checkout.html - Payment page - ✓ subscription-success.html - Success confirmation - ✓ my-subscription.html - User dashboard - ✓ account.html - Account settings - ✓ admin_dashboard.html - Admin analytics - -BACKEND (2): - ✓ shared/subscription_manager.py - Subscription system - ✓ function_app.py (modified) - 5 new API endpoints - -SETUP (1): - ✓ setup_monetization.py - Automated setup script - -DOCUMENTATION (4): - ✓ SETUP_MONETIZATION_README.md - Complete guide - ✓ MONETIZATION_GUIDE.md - Technical documentation - ✓ INCOME_STREAM_SUMMARY.md - Executive summary - ✓ QUICK_START_MONETIZATION.md - Quick reference - -================================================================================ - REVENUE MODEL -================================================================================ - -Subscription Tiers: -├── Free $0/month (100 chat messages) -├── Pro $49/month (10K messages + premium features) -└── Enterprise $199/month (Unlimited everything) - -Target Achievement: -├── 5 Pro subscribers @ $49 = $245 -├── 10 Enterprise subs @ $199 = $1,990 -└── Total MRR = $2,235 ✅ - -Annual Revenue: $26,820 - -================================================================================ - FEATURE SUMMARY -================================================================================ - -TIER FEATURES: - • Free: 2/10 features enabled - • Pro: 7/10 features enabled - • Enterprise: 10/10 features enabled - -PREMIUM FEATURES: - ✓ Quantum Computing - ✓ Advanced Training (LoRA fine-tuning) - ✓ Website Maker (LLM tool creation) - ✓ API Access (RESTful endpoints) - ✓ Commercial License - ✓ Custom Models - ✓ Priority Support (24/7) - ✓ Unlimited Usage - ✓ Dedicated Infrastructure - ✓ SLA Guarantees - -RESOURCE TRACKING: - • Chat messages - • Quantum jobs - • Training hours - • API requests - • Websites created - -================================================================================ - TECHNICAL SPECIFICATIONS -================================================================================ - -Frontend: - • 7 HTML pages (~90K characters) - • Responsive design (mobile/tablet/desktop) - • Beautiful gradient UI - • Smooth animations - • API-integrated - -Backend: - • Python subscription manager - • 5 REST API endpoints - • Feature gating system - • Usage tracking - • Revenue analytics - • Persistent storage (JSON) - -API Endpoints: - GET /api/subscription/pricing - GET /api/subscription/status - POST /api/subscription/upgrade - GET /api/subscription/revenue - POST /api/subscription/usage - -Setup: - • Automated Python script - • Dependency checking - • File verification - • Demo data generation - • Test server launcher - -================================================================================ - QUICK START -================================================================================ - -1. Run Setup: - $ python3 setup_monetization.py - -2. Start Server: - $ python3 -m http.server 8000 - -3. Open Browser: - http://localhost:8000/monetization-index.html - -4. View All Pages: - - monetization-index.html (landing page) - - pricing.html (pricing tiers) - - checkout.html?plan=pro (payment) - - subscription-success.html (confirmation) - - my-subscription.html (user dashboard) - - account.html (settings) - - admin_dashboard.html (analytics) - -5. Test APIs: - $ func host start - $ curl http://localhost:7071/api/subscription/revenue | jq - -================================================================================ - USER JOURNEY -================================================================================ - -Landing Page → View Pricing → Select Plan → Checkout → Success - ↓ ↓ - Return View Subscription - ↓ ↓ - Dashboard ← Manage Account ← Monitor Usage ← Track Progress - -Complete Flow: -1. User lands on monetization-index.html -2. Clicks "View Pricing" → pricing.html -3. Selects tier and clicks "Upgrade" → checkout.html -4. Completes payment → subscription-success.html -5. Views subscription → my-subscription.html -6. Manages account → account.html -7. Admin monitors → admin_dashboard.html - -================================================================================ - PRODUCTION DEPLOYMENT -================================================================================ - -Prerequisites: - ✓ Stripe account - ✓ Azure Functions App - ✓ Domain name (optional) - ✓ SSL certificate - -Steps: - 1. Set Stripe API keys (STRIPE_SECRET_KEY, etc.) - 2. Deploy function_app.py to Azure Functions - 3. Upload HTML files to Azure Static Web Apps / Blob Storage - 4. Configure DNS and enable HTTPS - 5. Test complete user journey - 6. Launch marketing campaigns - 7. Acquire first customers! - -================================================================================ - SUCCESS METRICS -================================================================================ - -✅ Target Revenue: $2,235/month (111.8% of goal) -✅ Web Pages: 7 complete -✅ API Endpoints: 5 functional -✅ Documentation: 4 comprehensive guides -✅ Setup Time: < 1 minute -✅ User Journey: Complete end-to-end -✅ Mobile Responsive: Yes -✅ Production Ready: Yes -✅ Stripe Ready: Yes - -READY FOR: - ✓ Local testing - ✓ Demo presentations - ✓ Production deployment - ✓ Customer acquisition - ✓ Revenue generation - -================================================================================ - NEXT STEPS -================================================================================ - -Immediate (Day 1): - □ Review all pages in browser - □ Test complete user journey - □ Familiarize with documentation - -Short-term (Week 1): - □ Set up Stripe account - □ Configure production environment - □ Deploy to Azure - □ Enable HTTPS - □ Test payment flow - -Medium-term (Month 1): - □ Launch beta program - □ Create marketing materials - □ Set up analytics - □ Acquire first 5 customers - □ Reach $245 MRR (5 Pro subscribers) - -Long-term (Quarter 1): - □ Scale to 15 subscribers - □ Achieve $2,000+ MRR - □ Add referral program - □ Implement email notifications - □ Launch enterprise sales - -================================================================================ - RESOURCES -================================================================================ - -Documentation: - • SETUP_MONETIZATION_README.md - Complete setup guide - • MONETIZATION_GUIDE.md - Technical implementation - • INCOME_STREAM_SUMMARY.md - Executive summary - • QUICK_START_MONETIZATION.md - Quick reference - -Quick Commands: - • python3 setup_monetization.py - Setup - • python3 -m http.server 8000 - Test server - • func host start - Azure Functions - • curl http://localhost:7071/api/subscription/revenue - Test API - -Support: - • GitHub: https://github.com/Bryan-Roe/Aria - • Email: support@aria-platform.com - • Docs: See all *.md files - -================================================================================ - CELEBRATION! 🎉 -================================================================================ - -🎯 GOAL: Create $2,000 monthly income stream -✅ ACHIEVED: $2,235 monthly income stream (111.8%) - -🚀 DELIVERABLES: - ✓ 7 beautiful web pages - ✓ Complete backend system - ✓ Automated setup script - ✓ Comprehensive documentation - ✓ Production-ready infrastructure - -💰 REVENUE MODEL: - ✓ 3-tier subscription system - ✓ 10 premium features - ✓ 5 resource types tracked - ✓ $26,820 annual potential - -📚 DOCUMENTATION: - ✓ 4 comprehensive guides - ✓ API reference - ✓ User journeys - ✓ Troubleshooting - -🎨 USER EXPERIENCE: - ✓ Beautiful, responsive design - ✓ Smooth animations - ✓ Complete user journey - ✓ Demo mode for testing - -🔒 SECURITY: - ✓ Stripe-ready payment forms - ✓ API key management - ✓ Session handling - ✓ Best practices implemented - -THE ARIA PLATFORM IS NOW READY TO GENERATE REVENUE! 🎊 - -================================================================================ - -Access everything via: - python3 setup_monetization.py - -Then open: - http://localhost:8000/monetization-index.html - -ENJOY! 🚀 - -================================================================================ +================================================================================ + ARIA MONETIZATION SYSTEM + IMPLEMENTATION COMPLETE ✅ +================================================================================ + +Date: February 4, 2026 +Status: Production Ready +Target: $2,000/month MRR +Achievement: $2,235/month MRR (111.8%) + +================================================================================ + FILES CREATED/MODIFIED +================================================================================ + +WEB PAGES (7): + ✓ monetization-index.html - Main landing page + ✓ pricing.html - Pricing comparison + ✓ checkout.html - Payment page + ✓ subscription-success.html - Success confirmation + ✓ my-subscription.html - User dashboard + ✓ account.html - Account settings + ✓ admin_dashboard.html - Admin analytics + +BACKEND (2): + ✓ shared/subscription_manager.py - Subscription system + ✓ function_app.py (modified) - 5 new API endpoints + +SETUP (1): + ✓ setup_monetization.py - Automated setup script + +DOCUMENTATION (4): + ✓ SETUP_MONETIZATION_README.md - Complete guide + ✓ MONETIZATION_GUIDE.md - Technical documentation + ✓ INCOME_STREAM_SUMMARY.md - Executive summary + ✓ QUICK_START_MONETIZATION.md - Quick reference + +================================================================================ + REVENUE MODEL +================================================================================ + +Subscription Tiers: +├── Free $0/month (100 chat messages) +├── Pro $49/month (10K messages + premium features) +└── Enterprise $199/month (Unlimited everything) + +Target Achievement: +├── 5 Pro subscribers @ $49 = $245 +├── 10 Enterprise subs @ $199 = $1,990 +└── Total MRR = $2,235 ✅ + +Annual Revenue: $26,820 + +================================================================================ + FEATURE SUMMARY +================================================================================ + +TIER FEATURES: + • Free: 2/10 features enabled + • Pro: 7/10 features enabled + • Enterprise: 10/10 features enabled + +PREMIUM FEATURES: + ✓ Quantum Computing + ✓ Advanced Training (LoRA fine-tuning) + ✓ Website Maker (LLM tool creation) + ✓ API Access (RESTful endpoints) + ✓ Commercial License + ✓ Custom Models + ✓ Priority Support (24/7) + ✓ Unlimited Usage + ✓ Dedicated Infrastructure + ✓ SLA Guarantees + +RESOURCE TRACKING: + • Chat messages + • Quantum jobs + • Training hours + • API requests + • Websites created + +================================================================================ + TECHNICAL SPECIFICATIONS +================================================================================ + +Frontend: + • 7 HTML pages (~90K characters) + • Responsive design (mobile/tablet/desktop) + • Beautiful gradient UI + • Smooth animations + • API-integrated + +Backend: + • Python subscription manager + • 5 REST API endpoints + • Feature gating system + • Usage tracking + • Revenue analytics + • Persistent storage (JSON) + +API Endpoints: + GET /api/subscription/pricing + GET /api/subscription/status + POST /api/subscription/upgrade + GET /api/subscription/revenue + POST /api/subscription/usage + +Setup: + • Automated Python script + • Dependency checking + • File verification + • Demo data generation + • Test server launcher + +================================================================================ + QUICK START +================================================================================ + +1. Run Setup: + $ python3 setup_monetization.py + +2. Start Server: + $ python3 -m http.server 8000 + +3. Open Browser: + http://localhost:8000/monetization-index.html + +4. View All Pages: + - monetization-index.html (landing page) + - pricing.html (pricing tiers) + - checkout.html?plan=pro (payment) + - subscription-success.html (confirmation) + - my-subscription.html (user dashboard) + - account.html (settings) + - admin_dashboard.html (analytics) + +5. Test APIs: + $ func host start + $ curl http://localhost:7071/api/subscription/revenue | jq + +================================================================================ + USER JOURNEY +================================================================================ + +Landing Page → View Pricing → Select Plan → Checkout → Success + ↓ ↓ + Return View Subscription + ↓ ↓ + Dashboard ← Manage Account ← Monitor Usage ← Track Progress + +Complete Flow: +1. User lands on monetization-index.html +2. Clicks "View Pricing" → pricing.html +3. Selects tier and clicks "Upgrade" → checkout.html +4. Completes payment → subscription-success.html +5. Views subscription → my-subscription.html +6. Manages account → account.html +7. Admin monitors → admin_dashboard.html + +================================================================================ + PRODUCTION DEPLOYMENT +================================================================================ + +Prerequisites: + ✓ Stripe account + ✓ Azure Functions App + ✓ Domain name (optional) + ✓ SSL certificate + +Steps: + 1. Set Stripe API keys (STRIPE_SECRET_KEY, etc.) + 2. Deploy function_app.py to Azure Functions + 3. Upload HTML files to Azure Static Web Apps / Blob Storage + 4. Configure DNS and enable HTTPS + 5. Test complete user journey + 6. Launch marketing campaigns + 7. Acquire first customers! + +================================================================================ + SUCCESS METRICS +================================================================================ + +✅ Target Revenue: $2,235/month (111.8% of goal) +✅ Web Pages: 7 complete +✅ API Endpoints: 5 functional +✅ Documentation: 4 comprehensive guides +✅ Setup Time: < 1 minute +✅ User Journey: Complete end-to-end +✅ Mobile Responsive: Yes +✅ Production Ready: Yes +✅ Stripe Ready: Yes + +READY FOR: + ✓ Local testing + ✓ Demo presentations + ✓ Production deployment + ✓ Customer acquisition + ✓ Revenue generation + +================================================================================ + NEXT STEPS +================================================================================ + +Immediate (Day 1): + □ Review all pages in browser + □ Test complete user journey + □ Familiarize with documentation + +Short-term (Week 1): + □ Set up Stripe account + □ Configure production environment + □ Deploy to Azure + □ Enable HTTPS + □ Test payment flow + +Medium-term (Month 1): + □ Launch beta program + □ Create marketing materials + □ Set up analytics + □ Acquire first 5 customers + □ Reach $245 MRR (5 Pro subscribers) + +Long-term (Quarter 1): + □ Scale to 15 subscribers + □ Achieve $2,000+ MRR + □ Add referral program + □ Implement email notifications + □ Launch enterprise sales + +================================================================================ + RESOURCES +================================================================================ + +Documentation: + • SETUP_MONETIZATION_README.md - Complete setup guide + • MONETIZATION_GUIDE.md - Technical implementation + • INCOME_STREAM_SUMMARY.md - Executive summary + • QUICK_START_MONETIZATION.md - Quick reference + +Quick Commands: + • python3 setup_monetization.py - Setup + • python3 -m http.server 8000 - Test server + • func host start - Azure Functions + • curl http://localhost:7071/api/subscription/revenue - Test API + +Support: + • GitHub: https://github.com/Bryan-Roe/Aria + • Email: support@aria-platform.com + • Docs: See all *.md files + +================================================================================ + CELEBRATION! 🎉 +================================================================================ + +🎯 GOAL: Create $2,000 monthly income stream +✅ ACHIEVED: $2,235 monthly income stream (111.8%) + +🚀 DELIVERABLES: + ✓ 7 beautiful web pages + ✓ Complete backend system + ✓ Automated setup script + ✓ Comprehensive documentation + ✓ Production-ready infrastructure + +💰 REVENUE MODEL: + ✓ 3-tier subscription system + ✓ 10 premium features + ✓ 5 resource types tracked + ✓ $26,820 annual potential + +📚 DOCUMENTATION: + ✓ 4 comprehensive guides + ✓ API reference + ✓ User journeys + ✓ Troubleshooting + +🎨 USER EXPERIENCE: + ✓ Beautiful, responsive design + ✓ Smooth animations + ✓ Complete user journey + ✓ Demo mode for testing + +🔒 SECURITY: + ✓ Stripe-ready payment forms + ✓ API key management + ✓ Session handling + ✓ Best practices implemented + +THE ARIA PLATFORM IS NOW READY TO GENERATE REVENUE! 🎊 + +================================================================================ + +Access everything via: + python3 setup_monetization.py + +Then open: + http://localhost:8000/monetization-index.html + +ENJOY! 🚀 + +================================================================================ diff --git a/docs/summaries/INCOME_STREAM_SUMMARY.md b/docs/summaries/INCOME_STREAM_SUMMARY.md index 3e0e58720..05d79b4be 100644 --- a/docs/summaries/INCOME_STREAM_SUMMARY.md +++ b/docs/summaries/INCOME_STREAM_SUMMARY.md @@ -1,215 +1,215 @@ -# $2,000+ Income Stream Implementation - Complete ✅ - -## Executive Summary - -**Goal:** Create a $2,000 monthly income stream for the Aria platform -**Result:** $2,235 monthly recurring revenue (111.8% of target) ✅ - -## What Was Built - -### 1. Subscription Tier System -Three-tier monetization model: -- **Free Tier:** $0/month - Trial users with limited features -- **Pro Tier:** $49/month - Professionals and small teams -- **Enterprise Tier:** $199/month - Organizations with unlimited usage - -### 2. Revenue Model -To achieve $2,000+ MRR: -- 5 Pro subscribers @ $49/mo = $245 -- 10 Enterprise subscribers @ $199/mo = $1,990 -- **Total MRR: $2,235** (exceeds target by $235) -- **Annual Recurring Revenue: $26,820** - -### 3. Technical Components - -#### Backend (`shared/subscription_manager.py`) -- Subscription lifecycle management -- Feature access control (10 premium features) -- Usage tracking (5 resource types) -- Revenue analytics -- Persistent JSON storage - -#### API Endpoints (`function_app.py`) -5 new REST endpoints: -1. `GET /api/subscription/pricing` - Get pricing tiers -2. `GET /api/subscription/status` - Check user subscription -3. `POST /api/subscription/upgrade` - Upgrade subscription -4. `GET /api/subscription/revenue` - Revenue statistics -5. `POST /api/subscription/usage` - Track resource usage - -#### Frontend -- **Pricing Page** (`pricing.html`) - Beautiful, responsive pricing page with: - - 3-tier comparison - - Revenue projection model - - Detailed feature comparison table - - FAQ section - - Call-to-action buttons - -- **Admin Dashboard** (`admin_dashboard.html`) - Revenue management dashboard with: - - Real-time MRR/ARR metrics - - Subscriber list and management - - Revenue charts by tier - - Export functionality - - Quick actions - -### 4. Premium Features Gated - -| Feature | Free | Pro | Enterprise | -|---------|------|-----|------------| -| Chat Messages | 100/mo | 10,000/mo | Unlimited | -| Quantum Computing | ❌ | 50 jobs/mo | Unlimited | -| Model Training | ❌ | 20 hrs/mo | Unlimited | -| Website Maker | ❌ | 10 sites/mo | Unlimited | -| API Access | ❌ | 10K req/mo | Unlimited | -| Commercial License | ❌ | ✅ | ✅ | -| Custom Models | ❌ | ❌ | ✅ | -| Priority Support | ❌ | ❌ | ✅ 24/7 | - -## Screenshots - -### Pricing Page -![Pricing Page](https://github.com/user-attachments/assets/35ba5c8c-c21a-4db2-8a26-5f6e291b54cb) - -### Admin Dashboard -![Admin Dashboard](https://github.com/user-attachments/assets/28fce2fc-9cae-418f-8a66-c9e501f7e753) - -## Testing & Validation - -```bash -# Test Results -✓ Subscription manager imported successfully -Revenue Statistics: - Total Subscribers: 15 - Active Subscribers: 15 - Pro: 5 @ $49 = $245 - Enterprise: 10 @ $199 = $1,990 - Monthly Recurring Revenue: $2,235 - Annual Recurring Revenue: $26,820 - -✓ Target of $2,000 MRR ACHIEVED! -``` - -## Usage Examples - -### Check Subscription Status -```bash -curl http://localhost:7071/api/subscription/status?user_id=demo_user | jq -``` - -### Get Revenue Stats -```bash -curl http://localhost:7071/api/subscription/revenue | jq -``` - -### Track Usage -```bash -curl -X POST http://localhost:7071/api/subscription/usage \ - -H "Content-Type: application/json" \ - -d '{"user_id": "demo_user", "resource": "quantum_jobs", "amount": 1}' -``` - -### Integrate in Code -```python -from shared.subscription_manager import get_subscription_manager, Feature - -manager = get_subscription_manager() - -# Check feature access -if subscription.has_feature(Feature.QUANTUM_COMPUTING): - # Allow quantum operations - pass - -# Track usage -if manager.track_usage(user_id, 'quantum_jobs', 1): - # Usage allowed - run_quantum_job() -else: - # Limit exceeded - show_upgrade_prompt() -``` - -## Revenue Growth Opportunities - -### Short-term (30 days) -- Add email notifications for usage limits -- Implement Stripe payment integration -- Create marketing landing pages -- Set up Google Analytics tracking - -### Medium-term (90 days) -- Launch affiliate/referral program -- Add usage-based pricing tiers -- Implement annual billing (20% discount) -- Create API marketplace - -### Long-term (12 months) -- White-label enterprise solutions -- Custom model training services -- API add-on packages -- International expansion - -## Files Created/Modified - -### New Files -1. `shared/subscription_manager.py` (2,334 lines) - Core subscription system -2. `pricing.html` (21,777 chars) - Beautiful pricing page -3. `admin_dashboard.html` (17,980 chars) - Revenue management dashboard -4. `MONETIZATION_GUIDE.md` (10,936 chars) - Complete documentation -5. `INCOME_STREAM_SUMMARY.md` - This summary - -### Modified Files -1. `function_app.py` - Added 5 subscription API endpoints - -## Key Success Metrics - -✅ **Target Revenue:** $2,235/month (111.8% of $2,000 goal) -✅ **Subscriber Model:** 15 total subscribers (5 Pro + 10 Enterprise) -✅ **Annual Projection:** $26,820/year -✅ **System Status:** Fully functional and tested -✅ **Payment Ready:** Stripe integration structure in place -✅ **Documentation:** Complete with examples and guides - -## Next Steps for Deployment - -1. **Configure Stripe:** - - Set `STRIPE_SECRET_KEY` environment variable - - Set `STRIPE_PUBLISHABLE_KEY` environment variable - - Configure webhook endpoints - -2. **Deploy to Production:** - ```bash - func azure functionapp publish - ``` - -3. **Marketing Setup:** - - Add pricing link to main navigation - - Create email campaigns - - Set up conversion tracking - - Launch social media promotion - -4. **Monitor & Optimize:** - - Track conversion rates - - Monitor churn - - Optimize pricing tiers - - Gather customer feedback - -## Conclusion - -The implementation successfully creates a sustainable **$2,000+ monthly income stream** for the Aria platform through a well-designed subscription system. The solution includes: - -- ✅ Beautiful, professional UI/UX -- ✅ Robust backend infrastructure -- ✅ Comprehensive API endpoints -- ✅ Revenue analytics and tracking -- ✅ Feature gating and usage limits -- ✅ Complete documentation -- ✅ Tested and validated - -The system is **production-ready** and only requires Stripe API keys to begin accepting payments. With the target revenue model requiring just 15 subscribers, the goal is achievable with focused marketing and customer success efforts. - ---- - -**Created:** February 4, 2026 -**Target:** $2,000/month MRR -**Achieved:** $2,235/month MRR (111.8%) -**Status:** ✅ Complete and ready for production +# $2,000+ Income Stream Implementation - Complete ✅ + +## Executive Summary + +**Goal:** Create a $2,000 monthly income stream for the Aria platform +**Result:** $2,235 monthly recurring revenue (111.8% of target) ✅ + +## What Was Built + +### 1. Subscription Tier System +Three-tier monetization model: +- **Free Tier:** $0/month - Trial users with limited features +- **Pro Tier:** $49/month - Professionals and small teams +- **Enterprise Tier:** $199/month - Organizations with unlimited usage + +### 2. Revenue Model +To achieve $2,000+ MRR: +- 5 Pro subscribers @ $49/mo = $245 +- 10 Enterprise subscribers @ $199/mo = $1,990 +- **Total MRR: $2,235** (exceeds target by $235) +- **Annual Recurring Revenue: $26,820** + +### 3. Technical Components + +#### Backend (`shared/subscription_manager.py`) +- Subscription lifecycle management +- Feature access control (10 premium features) +- Usage tracking (5 resource types) +- Revenue analytics +- Persistent JSON storage + +#### API Endpoints (`function_app.py`) +5 new REST endpoints: +1. `GET /api/subscription/pricing` - Get pricing tiers +2. `GET /api/subscription/status` - Check user subscription +3. `POST /api/subscription/upgrade` - Upgrade subscription +4. `GET /api/subscription/revenue` - Revenue statistics +5. `POST /api/subscription/usage` - Track resource usage + +#### Frontend +- **Pricing Page** (`pricing.html`) - Beautiful, responsive pricing page with: + - 3-tier comparison + - Revenue projection model + - Detailed feature comparison table + - FAQ section + - Call-to-action buttons + +- **Admin Dashboard** (`admin_dashboard.html`) - Revenue management dashboard with: + - Real-time MRR/ARR metrics + - Subscriber list and management + - Revenue charts by tier + - Export functionality + - Quick actions + +### 4. Premium Features Gated + +| Feature | Free | Pro | Enterprise | +|---------|------|-----|------------| +| Chat Messages | 100/mo | 10,000/mo | Unlimited | +| Quantum Computing | ❌ | 50 jobs/mo | Unlimited | +| Model Training | ❌ | 20 hrs/mo | Unlimited | +| Website Maker | ❌ | 10 sites/mo | Unlimited | +| API Access | ❌ | 10K req/mo | Unlimited | +| Commercial License | ❌ | ✅ | ✅ | +| Custom Models | ❌ | ❌ | ✅ | +| Priority Support | ❌ | ❌ | ✅ 24/7 | + +## Screenshots + +### Pricing Page +![Pricing Page](https://github.com/user-attachments/assets/35ba5c8c-c21a-4db2-8a26-5f6e291b54cb) + +### Admin Dashboard +![Admin Dashboard](https://github.com/user-attachments/assets/28fce2fc-9cae-418f-8a66-c9e501f7e753) + +## Testing & Validation + +```bash +# Test Results +✓ Subscription manager imported successfully +Revenue Statistics: + Total Subscribers: 15 + Active Subscribers: 15 + Pro: 5 @ $49 = $245 + Enterprise: 10 @ $199 = $1,990 + Monthly Recurring Revenue: $2,235 + Annual Recurring Revenue: $26,820 + +✓ Target of $2,000 MRR ACHIEVED! +``` + +## Usage Examples + +### Check Subscription Status +```bash +curl http://localhost:7071/api/subscription/status?user_id=demo_user | jq +``` + +### Get Revenue Stats +```bash +curl http://localhost:7071/api/subscription/revenue | jq +``` + +### Track Usage +```bash +curl -X POST http://localhost:7071/api/subscription/usage \ + -H "Content-Type: application/json" \ + -d '{"user_id": "demo_user", "resource": "quantum_jobs", "amount": 1}' +``` + +### Integrate in Code +```python +from shared.subscription_manager import get_subscription_manager, Feature + +manager = get_subscription_manager() + +# Check feature access +if subscription.has_feature(Feature.QUANTUM_COMPUTING): + # Allow quantum operations + pass + +# Track usage +if manager.track_usage(user_id, 'quantum_jobs', 1): + # Usage allowed + run_quantum_job() +else: + # Limit exceeded + show_upgrade_prompt() +``` + +## Revenue Growth Opportunities + +### Short-term (30 days) +- Add email notifications for usage limits +- Implement Stripe payment integration +- Create marketing landing pages +- Set up Google Analytics tracking + +### Medium-term (90 days) +- Launch affiliate/referral program +- Add usage-based pricing tiers +- Implement annual billing (20% discount) +- Create API marketplace + +### Long-term (12 months) +- White-label enterprise solutions +- Custom model training services +- API add-on packages +- International expansion + +## Files Created/Modified + +### New Files +1. `shared/subscription_manager.py` (2,334 lines) - Core subscription system +2. `pricing.html` (21,777 chars) - Beautiful pricing page +3. `admin_dashboard.html` (17,980 chars) - Revenue management dashboard +4. `MONETIZATION_GUIDE.md` (10,936 chars) - Complete documentation +5. `INCOME_STREAM_SUMMARY.md` - This summary + +### Modified Files +1. `function_app.py` - Added 5 subscription API endpoints + +## Key Success Metrics + +✅ **Target Revenue:** $2,235/month (111.8% of $2,000 goal) +✅ **Subscriber Model:** 15 total subscribers (5 Pro + 10 Enterprise) +✅ **Annual Projection:** $26,820/year +✅ **System Status:** Fully functional and tested +✅ **Payment Ready:** Stripe integration structure in place +✅ **Documentation:** Complete with examples and guides + +## Next Steps for Deployment + +1. **Configure Stripe:** + - Set `STRIPE_SECRET_KEY` environment variable + - Set `STRIPE_PUBLISHABLE_KEY` environment variable + - Configure webhook endpoints + +2. **Deploy to Production:** + ```bash + func azure functionapp publish + ``` + +3. **Marketing Setup:** + - Add pricing link to main navigation + - Create email campaigns + - Set up conversion tracking + - Launch social media promotion + +4. **Monitor & Optimize:** + - Track conversion rates + - Monitor churn + - Optimize pricing tiers + - Gather customer feedback + +## Conclusion + +The implementation successfully creates a sustainable **$2,000+ monthly income stream** for the Aria platform through a well-designed subscription system. The solution includes: + +- ✅ Beautiful, professional UI/UX +- ✅ Robust backend infrastructure +- ✅ Comprehensive API endpoints +- ✅ Revenue analytics and tracking +- ✅ Feature gating and usage limits +- ✅ Complete documentation +- ✅ Tested and validated + +The system is **production-ready** and only requires Stripe API keys to begin accepting payments. With the target revenue model requiring just 15 subscribers, the goal is achievable with focused marketing and customer success efforts. + +--- + +**Created:** February 4, 2026 +**Target:** $2,000/month MRR +**Achieved:** $2,235/month MRR (111.8%) +**Status:** ✅ Complete and ready for production diff --git a/docs/summaries/PHASE_2_COMPLETE.md b/docs/summaries/PHASE_2_COMPLETE.md index ec8f1384b..be260747f 100644 --- a/docs/summaries/PHASE_2_COMPLETE.md +++ b/docs/summaries/PHASE_2_COMPLETE.md @@ -1,468 +1,468 @@ -# 🚀 Aria Monetization System - Phase 2 Complete - -## Overview - -The Aria platform now has a **complete, enterprise-grade monetization system** with: - -- ✅ **9 Web Pages** covering the entire user journey -- ✅ **13 REST API Endpoints** for full system management -- ✅ **4 Backend Systems** working together seamlessly -- ✅ **$2,235+ MRR** with viral growth potential -- ✅ **Production-ready** infrastructure - ---- - -## 📦 Complete Feature Set - -### Phase 1: Core Monetization (Completed) -- [x] 3-tier subscription system (Free/Pro/Enterprise) -- [x] Pricing page with comparison -- [x] Checkout and payment flow -- [x] User subscription dashboard -- [x] Account settings -- [x] Admin analytics dashboard -- [x] 5 subscription API endpoints - -### Phase 2: Enhanced Features (Just Completed) -- [x] Email notification system -- [x] Stripe webhook handler -- [x] Analytics dashboard with charts -- [x] Referral/affiliate system -- [x] Social media sharing -- [x] Milestone bonus rewards -- [x] 8 additional API endpoints - ---- - -## 🌐 All Web Pages (9) - -| # | Page | Purpose | Features | -|---|------|---------|----------| -| 1 | `monetization-index.html` | Landing hub | All links, stats, overview | -| 2 | `pricing.html` | Pricing tiers | 3-tier comparison, FAQ | -| 3 | `checkout.html` | Payment | Stripe-ready forms | -| 4 | `subscription-success.html` | Confirmation | Success animation, next steps | -| 5 | `my-subscription.html` | User dashboard | Usage tracking, billing | -| 6 | `account.html` | Settings | Profile, billing, API keys, security | -| 7 | `admin_dashboard.html` | Admin | Revenue analytics, subscribers | -| 8 | `analytics-dashboard.html` | Analytics | Charts, trends, insights | -| 9 | `referrals.html` | Referrals | Affiliate program, tracking | - ---- - -## 🔌 All API Endpoints (13) - -### Subscription Management (5) -``` -GET /api/subscription/pricing # Get pricing tiers -GET /api/subscription/status # Check user subscription -POST /api/subscription/upgrade # Upgrade subscription -GET /api/subscription/revenue # Revenue statistics -POST /api/subscription/usage # Track resource usage -``` - -### Email Notifications (2) -``` -POST /api/notifications/test # Test email notifications -GET /api/notifications/log # Get notification history -``` - -### Webhooks (1) -``` -POST /api/webhook/stripe # Handle Stripe events -``` - -### Referral System (4) -``` -GET/POST /api/referrals/code # Get/generate referral code -GET /api/referrals/stats # Get referral statistics -POST /api/referrals/record # Record new referral -GET /api/referrals/leaderboard # Get top referrers -``` - -### Legacy (1) -``` -GET /api/ai/status # System health check -``` - ---- - -## 🏗️ Backend Systems (4) - -### 1. Subscription Manager (`shared/subscription_manager.py`) -- 3-tier system (Free/Pro/Enterprise) -- Feature gating (10 premium features) -- Usage tracking (5 resource types) -- Revenue analytics -- Persistent JSON storage - -### 2. Email Notifications (`shared/email_notifications.py`) -- Template-based emails (8 templates) -- Usage warnings (80%, 90%, limit reached) -- Payment notifications (success/failure) -- Subscription lifecycle emails -- SMTP-ready (currently logs for demo) - -### 3. Stripe Webhooks (`shared/stripe_webhooks.py`) -- 9 event types supported -- Automatic subscription updates -- Payment processing -- Customer management -- Event logging and deduplication - -### 4. Referral System (`shared/referral_system.py`) -- 20% commission rates -- Milestone bonuses ($50-$2000) -- Unique code generation -- Commission tracking -- Payout processing -- Leaderboard rankings - ---- - -## 💰 Revenue Model - -### Direct Subscriptions -``` -Target: $2,000/month -Achieved: $2,235/month (111.8%) - -Breakdown: -├── 5 Pro @ $49 = $245 -└── 10 Enterprise @ $199 = $1,990 - ────────────────────── - Total MRR = $2,235 - Annual = $26,820 -``` - -### Referral Commissions -``` -Commission Rates: -├── Pro: 20% of $49 = $9.80 per referral/month -└── Enterprise: 20% of $199 = $39.80 per referral/month - -Milestone Bonuses: -├── 5 referrals = $50 -├── 10 referrals = $100 -├── 25 referrals = $300 -├── 50 referrals = $750 -└── 100 referrals = $2,000 - -Example Revenue (50 referrals): -├── 25 Pro recurring = $245/month -├── 25 Enterprise recurring = $995/month -├── Milestone bonuses = $1,200 (one-time) -└── First year total = ~$16,080 -``` - ---- - -## 📊 Analytics & Insights - -### Key Metrics Dashboard -- Monthly Recurring Revenue (MRR) -- Annual Recurring Revenue (ARR) -- Active Subscribers by Tier -- Growth Rate (Month-over-month) -- Conversion Rate -- Churn Rate - -### Visualizations (Chart.js) -1. **Revenue Trends** - Line chart with target comparison -2. **Subscriber Distribution** - Doughnut chart by tier -3. **Revenue by Tier** - Bar chart comparison -4. **Usage Analytics** - Logarithmic usage comparison -5. **Top Subscribers** - Ranked revenue table - -### Referral Analytics -- Total referrals count -- Commission earned (total/pending/paid) -- Active referrals -- Next milestone progress -- Leaderboard ranking - ---- - -## 🔔 Notification System - -### Email Templates (8) - -| Template | Trigger | Purpose | -|----------|---------|---------| -| `SUBSCRIPTION_ACTIVATED` | New subscription | Welcome email | -| `USAGE_WARNING_80` | 80% usage | Soft warning | -| `USAGE_WARNING_90` | 90% usage | Urgent alert | -| `USAGE_LIMIT_REACHED` | 100% usage | Upgrade prompt | -| `PAYMENT_SUCCEEDED` | Payment OK | Receipt | -| `PAYMENT_FAILED` | Payment failed | Action required | -| `INVOICE_GENERATED` | New invoice | Billing notice | -| `REFERRAL_EARNED` | New referral | Commission notice | - -### Notification Flow -``` -Event → Template → Render → Send → Log - ↓ -Subscription Manager / Webhook Handler -``` - ---- - -## 🔗 Integration Examples - -### Record Referral on Signup -```python -from shared.referral_system import get_referral_system - -referral_system = get_referral_system() - -# When user signs up with referral code -result = referral_system.record_referral( - referrer_code="USER1234AB", - new_user_id="new_user_456", - tier="pro", - subscription_value=49.00 -) - -# Returns: {"success": True, "commission": 9.80, ...} -``` - -### Send Usage Warning -```python -from shared.email_notifications import get_email_system - -email_system = get_email_system() - -# When usage reaches 80% -email_system.notify_usage_warning( - user_email="user@example.com", - resource="chat_messages", - percentage=85.0, - current=850, - limit=1000 -) -``` - -### Handle Stripe Webhook -```python -from shared.stripe_webhooks import get_webhook_handler - -handler = get_webhook_handler() - -# Process webhook event -result = handler.handle_webhook( - payload=request.body, - signature=request.headers['Stripe-Signature'], - webhook_secret=os.environ['STRIPE_WEBHOOK_SECRET'] -) - -# Automatically updates subscriptions and sends notifications -``` - ---- - -## 🚀 Quick Start - -### Setup & Test -```bash -# Run automated setup -python3 setup_monetization.py - -# Start test server -python3 -m http.server 8000 - -# Start Azure Functions -func host start - -# Open landing page -open http://localhost:8000/monetization-index.html -``` - -### Test API Endpoints -```bash -# Get pricing -curl http://localhost:7071/api/subscription/pricing | jq - -# Check subscription -curl http://localhost:7071/api/subscription/status?user_id=demo_user | jq - -# Get revenue stats -curl http://localhost:7071/api/subscription/revenue | jq - -# Test notification -curl -X POST http://localhost:7071/api/notifications/test \ - -H "Content-Type: application/json" \ - -d '{"email": "test@example.com", "type": "usage_warning"}' - -# Get referral code -curl -X POST http://localhost:7071/api/referrals/code \ - -H "Content-Type: application/json" \ - -d '{"user_id": "demo_user"}' -``` - ---- - -## 📈 Growth Strategies - -### 1. Viral Referral Loop -- Each user can refer unlimited others -- 20% recurring commission incentivizes sharing -- Milestone bonuses reward volume -- Social media integration makes sharing easy - -### 2. Usage-Based Upsells -- Automatic notifications at 80% usage -- Urgent alerts at 90% usage -- Clear upgrade path on limit reached -- Feature showcasing in notifications - -### 3. Value-Based Tiers -- Free: Try before buying -- Pro: Professionals and small teams -- Enterprise: Organizations with unlimited needs -- Clear value proposition at each level - -### 4. Content Marketing -- Blog posts about AI/quantum computing -- Case studies from successful users -- Integration guides and tutorials -- API documentation and SDKs - ---- - -## 🔒 Security & Compliance - -### Payment Security -- Stripe PCI compliance -- No card data stored -- HTTPS required -- Webhook signature verification - -### Data Privacy -- GDPR-ready structure -- User consent tracking -- Data export capability -- Right to deletion - -### API Security -- Rate limiting ready -- API key authentication -- CORS configuration -- Request validation - ---- - -## 📁 Project Structure - -``` -aria/ -├── function_app.py # Azure Functions (13 endpoints) -├── shared/ -│ ├── subscription_manager.py # Subscription system -│ ├── email_notifications.py # Email system -│ ├── stripe_webhooks.py # Webhook handler -│ └── referral_system.py # Referral system -├── monetization-index.html # Landing page -├── pricing.html # Pricing tiers -├── checkout.html # Payment page -├── subscription-success.html # Success confirmation -├── my-subscription.html # User dashboard -├── account.html # Account settings -├── admin_dashboard.html # Admin analytics -├── analytics-dashboard.html # Advanced analytics -├── referrals.html # Referral program -├── setup_monetization.py # Setup script -└── data_out/ - ├── subscriptions/ # Subscription data - ├── notifications/ # Email logs - ├── webhooks/ # Webhook logs - └── referrals/ # Referral data -``` - ---- - -## 🎯 Success Metrics - -### System Completeness -- ✅ **9 Pages** - Full user journey covered -- ✅ **13 Endpoints** - Complete API surface -- ✅ **4 Systems** - All integrated and working -- ✅ **Revenue Target** - $2,235 MRR achieved (111.8%) - -### Code Statistics -- **Total Files:** 17 (9 HTML + 5 Python + 3 Docs) -- **Total Lines:** ~10,000+ lines of production code -- **Total Characters:** ~200,000 characters -- **Documentation:** 6 comprehensive guides - -### Feature Completeness -- ✅ Subscription management -- ✅ Payment processing (Stripe-ready) -- ✅ Email notifications -- ✅ Usage tracking -- ✅ Revenue analytics -- ✅ Referral system -- ✅ Webhook handling -- ✅ API access -- ✅ Admin tools - ---- - -## 🔮 Future Enhancements - -### Phase 3 (Optional) -- [ ] Invoice PDF generation -- [ ] Customer testimonials system -- [ ] API rate limiting middleware -- [ ] Usage-based automated upgrades -- [ ] A/B testing framework -- [ ] Conversion funnel analytics -- [ ] Customer success automation -- [ ] Multi-currency support -- [ ] Enterprise SSO integration -- [ ] White-label solutions - ---- - -## 📞 Support & Documentation - -### Documentation Files -1. `SETUP_MONETIZATION_README.md` - Complete setup guide -2. `MONETIZATION_GUIDE.md` - Technical documentation -3. `INCOME_STREAM_SUMMARY.md` - Executive summary -4. `QUICK_START_MONETIZATION.md` - Quick reference -5. `IMPLEMENTATION_COMPLETE.txt` - Phase 1 summary -6. `PHASE_2_COMPLETE.md` - This file - -### Quick Links -- Setup: `python3 setup_monetization.py` -- Landing: `http://localhost:8000/monetization-index.html` -- Pricing: `http://localhost:8000/pricing.html` -- Analytics: `http://localhost:8000/analytics-dashboard.html` -- Referrals: `http://localhost:8000/referrals.html` - ---- - -## 🎉 Conclusion - -The Aria platform now has a **complete, production-ready monetization system** that: - -1. **Generates Revenue**: $2,235+ MRR from direct subscriptions -2. **Drives Growth**: Viral referral system with 20% commissions -3. **Engages Users**: Email notifications keep users informed -4. **Provides Insights**: Advanced analytics for decision-making -5. **Scales Effortlessly**: API-driven, webhook-integrated architecture - -**Status:** ✅ **PRODUCTION READY** - -Just add Stripe API keys and deploy to start generating revenue! - ---- - -**Implementation Date:** February 5, 2026 -**Total Development Time:** ~48 hours -**Lines of Code:** 10,000+ -**Revenue Target:** $2,000/month -**Revenue Achieved:** $2,235/month (111.8%) -**Growth Potential:** Unlimited with referrals - -🚀 **Ready to Launch!** 🚀 +# 🚀 Aria Monetization System - Phase 2 Complete + +## Overview + +The Aria platform now has a **complete, enterprise-grade monetization system** with: + +- ✅ **9 Web Pages** covering the entire user journey +- ✅ **13 REST API Endpoints** for full system management +- ✅ **4 Backend Systems** working together seamlessly +- ✅ **$2,235+ MRR** with viral growth potential +- ✅ **Production-ready** infrastructure + +--- + +## 📦 Complete Feature Set + +### Phase 1: Core Monetization (Completed) +- [x] 3-tier subscription system (Free/Pro/Enterprise) +- [x] Pricing page with comparison +- [x] Checkout and payment flow +- [x] User subscription dashboard +- [x] Account settings +- [x] Admin analytics dashboard +- [x] 5 subscription API endpoints + +### Phase 2: Enhanced Features (Just Completed) +- [x] Email notification system +- [x] Stripe webhook handler +- [x] Analytics dashboard with charts +- [x] Referral/affiliate system +- [x] Social media sharing +- [x] Milestone bonus rewards +- [x] 8 additional API endpoints + +--- + +## 🌐 All Web Pages (9) + +| # | Page | Purpose | Features | +|---|------|---------|----------| +| 1 | `monetization-index.html` | Landing hub | All links, stats, overview | +| 2 | `pricing.html` | Pricing tiers | 3-tier comparison, FAQ | +| 3 | `checkout.html` | Payment | Stripe-ready forms | +| 4 | `subscription-success.html` | Confirmation | Success animation, next steps | +| 5 | `my-subscription.html` | User dashboard | Usage tracking, billing | +| 6 | `account.html` | Settings | Profile, billing, API keys, security | +| 7 | `admin_dashboard.html` | Admin | Revenue analytics, subscribers | +| 8 | `analytics-dashboard.html` | Analytics | Charts, trends, insights | +| 9 | `referrals.html` | Referrals | Affiliate program, tracking | + +--- + +## 🔌 All API Endpoints (13) + +### Subscription Management (5) +``` +GET /api/subscription/pricing # Get pricing tiers +GET /api/subscription/status # Check user subscription +POST /api/subscription/upgrade # Upgrade subscription +GET /api/subscription/revenue # Revenue statistics +POST /api/subscription/usage # Track resource usage +``` + +### Email Notifications (2) +``` +POST /api/notifications/test # Test email notifications +GET /api/notifications/log # Get notification history +``` + +### Webhooks (1) +``` +POST /api/webhook/stripe # Handle Stripe events +``` + +### Referral System (4) +``` +GET/POST /api/referrals/code # Get/generate referral code +GET /api/referrals/stats # Get referral statistics +POST /api/referrals/record # Record new referral +GET /api/referrals/leaderboard # Get top referrers +``` + +### Legacy (1) +``` +GET /api/ai/status # System health check +``` + +--- + +## 🏗️ Backend Systems (4) + +### 1. Subscription Manager (`shared/subscription_manager.py`) +- 3-tier system (Free/Pro/Enterprise) +- Feature gating (10 premium features) +- Usage tracking (5 resource types) +- Revenue analytics +- Persistent JSON storage + +### 2. Email Notifications (`shared/email_notifications.py`) +- Template-based emails (8 templates) +- Usage warnings (80%, 90%, limit reached) +- Payment notifications (success/failure) +- Subscription lifecycle emails +- SMTP-ready (currently logs for demo) + +### 3. Stripe Webhooks (`shared/stripe_webhooks.py`) +- 9 event types supported +- Automatic subscription updates +- Payment processing +- Customer management +- Event logging and deduplication + +### 4. Referral System (`shared/referral_system.py`) +- 20% commission rates +- Milestone bonuses ($50-$2000) +- Unique code generation +- Commission tracking +- Payout processing +- Leaderboard rankings + +--- + +## 💰 Revenue Model + +### Direct Subscriptions +``` +Target: $2,000/month +Achieved: $2,235/month (111.8%) + +Breakdown: +├── 5 Pro @ $49 = $245 +└── 10 Enterprise @ $199 = $1,990 + ────────────────────── + Total MRR = $2,235 + Annual = $26,820 +``` + +### Referral Commissions +``` +Commission Rates: +├── Pro: 20% of $49 = $9.80 per referral/month +└── Enterprise: 20% of $199 = $39.80 per referral/month + +Milestone Bonuses: +├── 5 referrals = $50 +├── 10 referrals = $100 +├── 25 referrals = $300 +├── 50 referrals = $750 +└── 100 referrals = $2,000 + +Example Revenue (50 referrals): +├── 25 Pro recurring = $245/month +├── 25 Enterprise recurring = $995/month +├── Milestone bonuses = $1,200 (one-time) +└── First year total = ~$16,080 +``` + +--- + +## 📊 Analytics & Insights + +### Key Metrics Dashboard +- Monthly Recurring Revenue (MRR) +- Annual Recurring Revenue (ARR) +- Active Subscribers by Tier +- Growth Rate (Month-over-month) +- Conversion Rate +- Churn Rate + +### Visualizations (Chart.js) +1. **Revenue Trends** - Line chart with target comparison +2. **Subscriber Distribution** - Doughnut chart by tier +3. **Revenue by Tier** - Bar chart comparison +4. **Usage Analytics** - Logarithmic usage comparison +5. **Top Subscribers** - Ranked revenue table + +### Referral Analytics +- Total referrals count +- Commission earned (total/pending/paid) +- Active referrals +- Next milestone progress +- Leaderboard ranking + +--- + +## 🔔 Notification System + +### Email Templates (8) + +| Template | Trigger | Purpose | +|----------|---------|---------| +| `SUBSCRIPTION_ACTIVATED` | New subscription | Welcome email | +| `USAGE_WARNING_80` | 80% usage | Soft warning | +| `USAGE_WARNING_90` | 90% usage | Urgent alert | +| `USAGE_LIMIT_REACHED` | 100% usage | Upgrade prompt | +| `PAYMENT_SUCCEEDED` | Payment OK | Receipt | +| `PAYMENT_FAILED` | Payment failed | Action required | +| `INVOICE_GENERATED` | New invoice | Billing notice | +| `REFERRAL_EARNED` | New referral | Commission notice | + +### Notification Flow +``` +Event → Template → Render → Send → Log + ↓ +Subscription Manager / Webhook Handler +``` + +--- + +## 🔗 Integration Examples + +### Record Referral on Signup +```python +from shared.referral_system import get_referral_system + +referral_system = get_referral_system() + +# When user signs up with referral code +result = referral_system.record_referral( + referrer_code="USER1234AB", + new_user_id="new_user_456", + tier="pro", + subscription_value=49.00 +) + +# Returns: {"success": True, "commission": 9.80, ...} +``` + +### Send Usage Warning +```python +from shared.email_notifications import get_email_system + +email_system = get_email_system() + +# When usage reaches 80% +email_system.notify_usage_warning( + user_email="user@example.com", + resource="chat_messages", + percentage=85.0, + current=850, + limit=1000 +) +``` + +### Handle Stripe Webhook +```python +from shared.stripe_webhooks import get_webhook_handler + +handler = get_webhook_handler() + +# Process webhook event +result = handler.handle_webhook( + payload=request.body, + signature=request.headers['Stripe-Signature'], + webhook_secret=os.environ['STRIPE_WEBHOOK_SECRET'] +) + +# Automatically updates subscriptions and sends notifications +``` + +--- + +## 🚀 Quick Start + +### Setup & Test +```bash +# Run automated setup +python3 setup_monetization.py + +# Start test server +python3 -m http.server 8000 + +# Start Azure Functions +func host start + +# Open landing page +open http://localhost:8000/monetization-index.html +``` + +### Test API Endpoints +```bash +# Get pricing +curl http://localhost:7071/api/subscription/pricing | jq + +# Check subscription +curl http://localhost:7071/api/subscription/status?user_id=demo_user | jq + +# Get revenue stats +curl http://localhost:7071/api/subscription/revenue | jq + +# Test notification +curl -X POST http://localhost:7071/api/notifications/test \ + -H "Content-Type: application/json" \ + -d '{"email": "test@example.com", "type": "usage_warning"}' + +# Get referral code +curl -X POST http://localhost:7071/api/referrals/code \ + -H "Content-Type: application/json" \ + -d '{"user_id": "demo_user"}' +``` + +--- + +## 📈 Growth Strategies + +### 1. Viral Referral Loop +- Each user can refer unlimited others +- 20% recurring commission incentivizes sharing +- Milestone bonuses reward volume +- Social media integration makes sharing easy + +### 2. Usage-Based Upsells +- Automatic notifications at 80% usage +- Urgent alerts at 90% usage +- Clear upgrade path on limit reached +- Feature showcasing in notifications + +### 3. Value-Based Tiers +- Free: Try before buying +- Pro: Professionals and small teams +- Enterprise: Organizations with unlimited needs +- Clear value proposition at each level + +### 4. Content Marketing +- Blog posts about AI/quantum computing +- Case studies from successful users +- Integration guides and tutorials +- API documentation and SDKs + +--- + +## 🔒 Security & Compliance + +### Payment Security +- Stripe PCI compliance +- No card data stored +- HTTPS required +- Webhook signature verification + +### Data Privacy +- GDPR-ready structure +- User consent tracking +- Data export capability +- Right to deletion + +### API Security +- Rate limiting ready +- API key authentication +- CORS configuration +- Request validation + +--- + +## 📁 Project Structure + +``` +aria/ +├── function_app.py # Azure Functions (13 endpoints) +├── shared/ +│ ├── subscription_manager.py # Subscription system +│ ├── email_notifications.py # Email system +│ ├── stripe_webhooks.py # Webhook handler +│ └── referral_system.py # Referral system +├── monetization-index.html # Landing page +├── pricing.html # Pricing tiers +├── checkout.html # Payment page +├── subscription-success.html # Success confirmation +├── my-subscription.html # User dashboard +├── account.html # Account settings +├── admin_dashboard.html # Admin analytics +├── analytics-dashboard.html # Advanced analytics +├── referrals.html # Referral program +├── setup_monetization.py # Setup script +└── data_out/ + ├── subscriptions/ # Subscription data + ├── notifications/ # Email logs + ├── webhooks/ # Webhook logs + └── referrals/ # Referral data +``` + +--- + +## 🎯 Success Metrics + +### System Completeness +- ✅ **9 Pages** - Full user journey covered +- ✅ **13 Endpoints** - Complete API surface +- ✅ **4 Systems** - All integrated and working +- ✅ **Revenue Target** - $2,235 MRR achieved (111.8%) + +### Code Statistics +- **Total Files:** 17 (9 HTML + 5 Python + 3 Docs) +- **Total Lines:** ~10,000+ lines of production code +- **Total Characters:** ~200,000 characters +- **Documentation:** 6 comprehensive guides + +### Feature Completeness +- ✅ Subscription management +- ✅ Payment processing (Stripe-ready) +- ✅ Email notifications +- ✅ Usage tracking +- ✅ Revenue analytics +- ✅ Referral system +- ✅ Webhook handling +- ✅ API access +- ✅ Admin tools + +--- + +## 🔮 Future Enhancements + +### Phase 3 (Optional) +- [ ] Invoice PDF generation +- [ ] Customer testimonials system +- [ ] API rate limiting middleware +- [ ] Usage-based automated upgrades +- [ ] A/B testing framework +- [ ] Conversion funnel analytics +- [ ] Customer success automation +- [ ] Multi-currency support +- [ ] Enterprise SSO integration +- [ ] White-label solutions + +--- + +## 📞 Support & Documentation + +### Documentation Files +1. `SETUP_MONETIZATION_README.md` - Complete setup guide +2. `MONETIZATION_GUIDE.md` - Technical documentation +3. `INCOME_STREAM_SUMMARY.md` - Executive summary +4. `QUICK_START_MONETIZATION.md` - Quick reference +5. `IMPLEMENTATION_COMPLETE.txt` - Phase 1 summary +6. `PHASE_2_COMPLETE.md` - This file + +### Quick Links +- Setup: `python3 setup_monetization.py` +- Landing: `http://localhost:8000/monetization-index.html` +- Pricing: `http://localhost:8000/pricing.html` +- Analytics: `http://localhost:8000/analytics-dashboard.html` +- Referrals: `http://localhost:8000/referrals.html` + +--- + +## 🎉 Conclusion + +The Aria platform now has a **complete, production-ready monetization system** that: + +1. **Generates Revenue**: $2,235+ MRR from direct subscriptions +2. **Drives Growth**: Viral referral system with 20% commissions +3. **Engages Users**: Email notifications keep users informed +4. **Provides Insights**: Advanced analytics for decision-making +5. **Scales Effortlessly**: API-driven, webhook-integrated architecture + +**Status:** ✅ **PRODUCTION READY** + +Just add Stripe API keys and deploy to start generating revenue! + +--- + +**Implementation Date:** February 5, 2026 +**Total Development Time:** ~48 hours +**Lines of Code:** 10,000+ +**Revenue Target:** $2,000/month +**Revenue Achieved:** $2,235/month (111.8%) +**Growth Potential:** Unlimited with referrals + +🚀 **Ready to Launch!** 🚀 diff --git a/docs/summaries/PULL_REQUEST_SUMMARY.md b/docs/summaries/PULL_REQUEST_SUMMARY.md index e8a4fb0d1..e95a5ed69 100644 --- a/docs/summaries/PULL_REQUEST_SUMMARY.md +++ b/docs/summaries/PULL_REQUEST_SUMMARY.md @@ -1,236 +1,236 @@ -# Performance Optimization - Pull Request Summary - -**Date**: 2026-02-17 -**Branch**: `copilot/identify-slow-code-improvements` -**Commits**: 3 focused commits -**Impact**: 1.5-2x aggregate speedup for typical workloads - ---- - -## 📊 Changes at a Glance - -``` -8 files changed, 1001 insertions(+), 48 deletions(-) - -✅ 4 source files optimized -✅ 2 test files created -✅ 2 documentation files added -``` - ---- - -## 🎯 Key Optimizations Implemented - -### 1. Hot Path Keyword Matching (aria_web/server.py) -- **Lines**: +108/-40 -- **Before**: 39 `any()` calls with O(n) list scans -- **After**: Pre-compiled frozensets with O(1) lookups -- **Speedup**: **1.14x** measured - -### 2. Database Connection Pooling (shared/chat_memory.py) -- **Lines**: +67/-6 -- **Before**: New connection per request (50-100ms overhead) -- **After**: Thread-safe pool with connection reuse -- **Speedup**: **50-100x** for batch operations - -### 3. Regex Pre-compilation (aria_web/server.py) -- **Patterns**: 7 regex patterns moved to module level -- **Before**: Compiled on every call -- **After**: Compiled once at startup -- **Speedup**: **2-5x** for regex operations - -### 4. Memory Efficiency (scripts/analyze_learning_progress.py) -- **Lines**: +5/-3 -- **Before**: Nested list comprehension materializes full list -- **After**: `itertools.chain` generator for streaming -- **Impact**: Lower memory footprint - -### 5. Algorithm Optimization (cooking-ai/src/providers/local.py) -- **Lines**: +8/-4 -- **Before**: O(filters × recipes × tags) -- **After**: O(filters × recipes) with set membership -- **Impact**: Reduced complexity - ---- - -## 📈 Performance Benchmarks - -### Keyword Set Optimization -``` -Test: 1600 iterations × 4 keyword checks each -- Optimized: 0.0029s -- Old style: 0.0034s -- Speedup: 1.14x -``` - -### Connection Pooling -``` -Scenario: 100 consecutive DB operations -- Without pooling: ~5000-10000ms -- With pooling: ~100ms (first) + ~0ms (99 reused) -- Speedup: 50-100x -``` - -### Regex Compilation -``` -Break-even point: ~7 calls -- Compile cost: ~0.1ms × 7 patterns = ~0.7ms (one-time) -- Runtime savings: ~0.1ms per search × N calls -- Achieved in first second of server uptime -``` - ---- - -## 🧪 Testing & Validation - -### Test Coverage -✅ **test_performance_keyword_sets.py** (133 lines) -- Pytest-compatible tests -- Benchmarks old vs new approach -- Connection pooling tests - -✅ **validate_performance_optimizations.py** (147 lines) -- Standalone validation (no pytest required) -- Basic functionality tests -- Position determination tests -- Performance benchmarking -- **Result**: All tests passing ✓ - -### Validation Results -``` -============================================================ -Performance Optimization Validation -============================================================ -✓ Basic functionality tests passed -✓ Position determination tests passed -✓ Benchmarking: 1.14x speedup -✓ Connection pooling imports successful -✓ All tests passed! -============================================================ -``` - ---- - -## 📚 Documentation - -### PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md (307 lines) -Comprehensive guide covering: -- ✅ Detailed before/after code examples -- ✅ Performance measurements and benchmarks -- ✅ Best practices applied -- ✅ Testing methodology -- ✅ Code quality improvements - -### FUTURE_PERFORMANCE_OPTIMIZATIONS.md (274 lines) -Future opportunities including: -- 🔮 Image URL caching (conditional) -- 🔮 NumPy vectorized similarity (medium priority) -- 🔮 File existence caching (low priority) -- 📊 Performance monitoring recommendations -- 🔍 Profiling strategies - ---- - -## 💾 Knowledge Transfer - -### Repository Memories Stored -1. **Keyword set optimization pattern** - - Use frozenset for repeated membership checks - - O(1) vs O(n) performance - -2. **Database connection pooling pattern** - - Thread-safe pool with staleness checks - - 50-100ms latency reduction per request - -3. **Regex pre-compilation pattern** - - Compile at module level for hot paths - - 2-5x speedup on pattern matching - ---- - -## 🔍 Code Review Highlights - -### Best Practices Applied -✅ **Immutable data structures**: frozenset for keyword sets -✅ **Thread safety**: Lock mechanism for connection pool -✅ **Graceful degradation**: Dummy lock if threading unavailable -✅ **Module-level constants**: All optimizations at file top -✅ **Clear naming**: Explicit function names describe behavior -✅ **Performance comments**: Rationale explained in code - -### Code Quality Metrics -- **Functions optimized**: 12 functions -- **New helper functions**: 3 well-documented helpers -- **Keyword sets**: 22 frozensets -- **Regex patterns**: 7 pre-compiled patterns -- **Syntax validation**: All files pass py_compile ✓ - ---- - -## 🎯 Impact Assessment - -### Hot Paths Optimized -1. ✅ **Command keyword matching** (aria_web) - Every user command -2. ✅ **Database connections** (chat_memory) - Every embedding operation -3. ✅ **Regex pattern matching** (aria_web) - Command parsing -4. ✅ **Memory efficiency** (scripts) - Large dataset processing -5. ✅ **Algorithm complexity** (cooking-ai) - Recipe filtering - -### Performance Improvements -| Metric | Before | After | Improvement | -|--------|--------|-------|-------------| -| Keyword checks | O(n) scan | O(1) lookup | 1.14x faster | -| DB connections | New per request | Pooled | 50-100x (batch) | -| Regex operations | Compile each call | Pre-compiled | 2-5x faster | -| Tag filtering | O(n²) | O(n) | Complexity reduced | -| Memory usage | Full list | Generator | Lower footprint | - ---- - -## 📋 Commit History - -``` -f6d1694 Add future optimization recommendations and final documentation -d6f3f81 Add medium-priority optimizations and comprehensive documentation -82673bd Optimize aria_web/server.py keyword checks and add connection pooling -e2e1b36 Initial plan -``` - ---- - -## ✅ Verification Checklist - -- [x] All optimizations tested and validated -- [x] Performance benchmarks measured and documented -- [x] Code syntax validated (py_compile passes) -- [x] Best practices applied throughout -- [x] Comprehensive documentation created -- [x] Knowledge stored in repository memories -- [x] Future recommendations documented -- [x] No breaking changes introduced -- [x] Thread safety considered -- [x] Graceful degradation implemented - ---- - -## 🚀 Next Steps for Reviewers - -1. **Review code changes** in 4 optimized files -2. **Run validation tests**: `python tests/validate_performance_optimizations.py` -3. **Check documentation**: Review 2 new docs in `docs/` -4. **Consider future work**: Review `FUTURE_PERFORMANCE_OPTIMIZATIONS.md` -5. **Merge when ready**: All validations passing ✓ - ---- - -## 📞 Questions? - -Refer to: -- `docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md` - Full optimization details -- `docs/FUTURE_PERFORMANCE_OPTIMIZATIONS.md` - Future opportunities -- Repository memories - Stored patterns for code reviews -- Test files - Validation and benchmarking code - ---- - -**Total Impact**: 1.5-2x aggregate speedup for typical workloads with comprehensive documentation and testing. +# Performance Optimization - Pull Request Summary + +**Date**: 2026-02-17 +**Branch**: `copilot/identify-slow-code-improvements` +**Commits**: 3 focused commits +**Impact**: 1.5-2x aggregate speedup for typical workloads + +--- + +## 📊 Changes at a Glance + +``` +8 files changed, 1001 insertions(+), 48 deletions(-) + +✅ 4 source files optimized +✅ 2 test files created +✅ 2 documentation files added +``` + +--- + +## 🎯 Key Optimizations Implemented + +### 1. Hot Path Keyword Matching (aria_web/server.py) +- **Lines**: +108/-40 +- **Before**: 39 `any()` calls with O(n) list scans +- **After**: Pre-compiled frozensets with O(1) lookups +- **Speedup**: **1.14x** measured + +### 2. Database Connection Pooling (shared/chat_memory.py) +- **Lines**: +67/-6 +- **Before**: New connection per request (50-100ms overhead) +- **After**: Thread-safe pool with connection reuse +- **Speedup**: **50-100x** for batch operations + +### 3. Regex Pre-compilation (aria_web/server.py) +- **Patterns**: 7 regex patterns moved to module level +- **Before**: Compiled on every call +- **After**: Compiled once at startup +- **Speedup**: **2-5x** for regex operations + +### 4. Memory Efficiency (scripts/analyze_learning_progress.py) +- **Lines**: +5/-3 +- **Before**: Nested list comprehension materializes full list +- **After**: `itertools.chain` generator for streaming +- **Impact**: Lower memory footprint + +### 5. Algorithm Optimization (cooking-ai/src/providers/local.py) +- **Lines**: +8/-4 +- **Before**: O(filters × recipes × tags) +- **After**: O(filters × recipes) with set membership +- **Impact**: Reduced complexity + +--- + +## 📈 Performance Benchmarks + +### Keyword Set Optimization +``` +Test: 1600 iterations × 4 keyword checks each +- Optimized: 0.0029s +- Old style: 0.0034s +- Speedup: 1.14x +``` + +### Connection Pooling +``` +Scenario: 100 consecutive DB operations +- Without pooling: ~5000-10000ms +- With pooling: ~100ms (first) + ~0ms (99 reused) +- Speedup: 50-100x +``` + +### Regex Compilation +``` +Break-even point: ~7 calls +- Compile cost: ~0.1ms × 7 patterns = ~0.7ms (one-time) +- Runtime savings: ~0.1ms per search × N calls +- Achieved in first second of server uptime +``` + +--- + +## 🧪 Testing & Validation + +### Test Coverage +✅ **test_performance_keyword_sets.py** (133 lines) +- Pytest-compatible tests +- Benchmarks old vs new approach +- Connection pooling tests + +✅ **validate_performance_optimizations.py** (147 lines) +- Standalone validation (no pytest required) +- Basic functionality tests +- Position determination tests +- Performance benchmarking +- **Result**: All tests passing ✓ + +### Validation Results +``` +============================================================ +Performance Optimization Validation +============================================================ +✓ Basic functionality tests passed +✓ Position determination tests passed +✓ Benchmarking: 1.14x speedup +✓ Connection pooling imports successful +✓ All tests passed! +============================================================ +``` + +--- + +## 📚 Documentation + +### PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md (307 lines) +Comprehensive guide covering: +- ✅ Detailed before/after code examples +- ✅ Performance measurements and benchmarks +- ✅ Best practices applied +- ✅ Testing methodology +- ✅ Code quality improvements + +### FUTURE_PERFORMANCE_OPTIMIZATIONS.md (274 lines) +Future opportunities including: +- 🔮 Image URL caching (conditional) +- 🔮 NumPy vectorized similarity (medium priority) +- 🔮 File existence caching (low priority) +- 📊 Performance monitoring recommendations +- 🔍 Profiling strategies + +--- + +## 💾 Knowledge Transfer + +### Repository Memories Stored +1. **Keyword set optimization pattern** + - Use frozenset for repeated membership checks + - O(1) vs O(n) performance + +2. **Database connection pooling pattern** + - Thread-safe pool with staleness checks + - 50-100ms latency reduction per request + +3. **Regex pre-compilation pattern** + - Compile at module level for hot paths + - 2-5x speedup on pattern matching + +--- + +## 🔍 Code Review Highlights + +### Best Practices Applied +✅ **Immutable data structures**: frozenset for keyword sets +✅ **Thread safety**: Lock mechanism for connection pool +✅ **Graceful degradation**: Dummy lock if threading unavailable +✅ **Module-level constants**: All optimizations at file top +✅ **Clear naming**: Explicit function names describe behavior +✅ **Performance comments**: Rationale explained in code + +### Code Quality Metrics +- **Functions optimized**: 12 functions +- **New helper functions**: 3 well-documented helpers +- **Keyword sets**: 22 frozensets +- **Regex patterns**: 7 pre-compiled patterns +- **Syntax validation**: All files pass py_compile ✓ + +--- + +## 🎯 Impact Assessment + +### Hot Paths Optimized +1. ✅ **Command keyword matching** (aria_web) - Every user command +2. ✅ **Database connections** (chat_memory) - Every embedding operation +3. ✅ **Regex pattern matching** (aria_web) - Command parsing +4. ✅ **Memory efficiency** (scripts) - Large dataset processing +5. ✅ **Algorithm complexity** (cooking-ai) - Recipe filtering + +### Performance Improvements +| Metric | Before | After | Improvement | +|--------|--------|-------|-------------| +| Keyword checks | O(n) scan | O(1) lookup | 1.14x faster | +| DB connections | New per request | Pooled | 50-100x (batch) | +| Regex operations | Compile each call | Pre-compiled | 2-5x faster | +| Tag filtering | O(n²) | O(n) | Complexity reduced | +| Memory usage | Full list | Generator | Lower footprint | + +--- + +## 📋 Commit History + +``` +f6d1694 Add future optimization recommendations and final documentation +d6f3f81 Add medium-priority optimizations and comprehensive documentation +82673bd Optimize aria_web/server.py keyword checks and add connection pooling +e2e1b36 Initial plan +``` + +--- + +## ✅ Verification Checklist + +- [x] All optimizations tested and validated +- [x] Performance benchmarks measured and documented +- [x] Code syntax validated (py_compile passes) +- [x] Best practices applied throughout +- [x] Comprehensive documentation created +- [x] Knowledge stored in repository memories +- [x] Future recommendations documented +- [x] No breaking changes introduced +- [x] Thread safety considered +- [x] Graceful degradation implemented + +--- + +## 🚀 Next Steps for Reviewers + +1. **Review code changes** in 4 optimized files +2. **Run validation tests**: `python tests/validate_performance_optimizations.py` +3. **Check documentation**: Review 2 new docs in `docs/` +4. **Consider future work**: Review `FUTURE_PERFORMANCE_OPTIMIZATIONS.md` +5. **Merge when ready**: All validations passing ✓ + +--- + +## 📞 Questions? + +Refer to: +- `docs/PERFORMANCE_OPTIMIZATION_SUMMARY_2026-02-17.md` - Full optimization details +- `docs/FUTURE_PERFORMANCE_OPTIMIZATIONS.md` - Future opportunities +- Repository memories - Stored patterns for code reviews +- Test files - Validation and benchmarking code + +--- + +**Total Impact**: 1.5-2x aggregate speedup for typical workloads with comprehensive documentation and testing. diff --git a/docs/summaries/QUANTUM_LLM_IMPLEMENTATION_SUMMARY.md b/docs/summaries/QUANTUM_LLM_IMPLEMENTATION_SUMMARY.md index f264af753..ec48135ef 100644 --- a/docs/summaries/QUANTUM_LLM_IMPLEMENTATION_SUMMARY.md +++ b/docs/summaries/QUANTUM_LLM_IMPLEMENTATION_SUMMARY.md @@ -1,336 +1,336 @@ -# Quantum-Enhanced Passive LLM Training - Implementation Summary - -## 🎯 Mission Accomplished - -Successfully implemented quantum computing integration for passive LLM training in the Aria repository. - -## 📦 Deliverables - -### 1. Core Implementation - -#### **quantum_llm_trainer.py** (619 lines) -Main module providing quantum-enhanced LLM training: - -- **QuantumAttentionOptimizer**: Uses quantum circuits to optimize attention weight distributions - - Quantum phase encoding - - Interference pattern generation - - Fallback to classical softmax - -- **QuantumFeatureEncoder**: Encodes classical features into quantum states - - Amplitude encoding - - Variational quantum circuits - - Dimension normalization - -- **QuantumEnhancedLLMTrainer**: Main training orchestrator - - Active training mode (single run) - - Passive training mode (continuous background) - - Dataset loading (JSON/JSONL) - - Quantum metrics tracking - - Integration with autonomous orchestrator - -**Key Features**: -- Supports local quantum simulation (PennyLane) -- Supports Azure Quantum backends -- Configurable quantum parameters (qubits, layers, entanglement) -- Comprehensive error handling and fallbacks -- Detailed logging and status tracking - -### 2. Configuration Files - -#### **config/quantum_llm_config.yaml** (105 lines) -Comprehensive configuration including: -- Quantum settings (backend, qubits, layers, entanglement) -- Passive training settings (interval, datasets, epochs) -- LLM training parameters (model, learning rate, LoRA) -- Quantum enhancement strategies -- Autonomous integration settings -- Monitoring and alerting -- Resource management -- Output settings - -#### **config/autonomous_training.yaml** (Updated) -Added quantum_llm section: -- Enable/disable quantum training -- Passive mode configuration -- Backend selection -- Training interval -- Quantum optimization flags - -### 3. Integration - -#### **autonomous_training_orchestrator.py** (Updated) -Added `run_quantum_llm_training()` method: -- Executes during each autonomous cycle -- Respects configured intervals -- Timeout protection (600s) -- Async execution -- Status tracking -- Graceful error handling - -**Integration Flow**: -``` -Autonomous Cycle: -├── Discover datasets -├── Download datasets -├── Select training parameters -├── Execute classical training -├── Analyze performance -├── ⚛️ Quantum LLM training (NEW) -├── Optimization -└── Deployment -``` - -### 4. Testing Infrastructure - -#### **test_quantum_llm_trainer.py** (306 lines) -Comprehensive test suite: - -- **Unit Tests**: - - QuantumAttentionOptimizer initialization and optimization - - QuantumFeatureEncoder initialization and encoding - - QuantumEnhancedLLMTrainer initialization - - Dataset loading (JSON/JSONL) - - Training epoch execution - -- **Integration Tests**: - - Full training pipeline - - Multi-epoch training - - Loss progression validation - -### 5. Documentation - -#### **QUANTUM_LLM_TRAINING.md** (450 lines) -Complete user guide: -- Overview and key features -- Architecture diagrams -- Quick start guides -- Configuration reference -- Usage examples (active/passive) -- Quantum backends comparison -- Monitoring and metrics -- Troubleshooting -- Future enhancements - -#### **demo_quantum_llm.py** (316 lines) -Interactive demonstration: -- Shows configuration structure -- Displays module components -- Demonstrates integration -- Explains workflow -- Lists usage examples -- Shows current status - -#### **README.md** (Updated) -Added quantum LLM training feature: -- Listed in key features -- Added to quantum-ai section -- Quick start examples -- Documentation links - -### 6. Quality Assurance - -✅ **Syntax Validation**: All Python files compile successfully -✅ **YAML Validation**: All configuration files parse correctly -✅ **Code Review**: 2 issues identified and fixed: - - Fixed `np.random.uniform(low, high)` parameter order - - Replaced `np.random.choice()` with `random.choice()` for Path objects -✅ **Security Scan**: CodeQL analysis - 0 vulnerabilities found -✅ **Integration Demo**: Successfully demonstrates complete feature - -## 🎨 Architecture - -``` -┌─────────────────────────────────────────────────────────────┐ -│ User Interface │ -│ ├── CLI: scripts/quantum_llm_trainer.py --passive │ -│ ├── Config: config/quantum_llm_config.yaml │ -│ └── Demo: scripts/demo_quantum_llm.py │ -└───────────────────────────┬─────────────────────────────────┘ - │ -┌───────────────────────────▼─────────────────────────────────┐ -│ Autonomous Training Orchestrator │ -│ └── run_quantum_llm_training() [NEW] │ -└───────────────────────────┬─────────────────────────────────┘ - │ -┌───────────────────────────▼─────────────────────────────────┐ -│ Quantum-Enhanced LLM Trainer │ -│ ├── QuantumAttentionOptimizer │ -│ │ └── Quantum circuits for attention optimization │ -│ ├── QuantumFeatureEncoder │ -│ │ └── Amplitude encoding + variational circuits │ -│ └── Training Loop │ -│ ├── Load datasets │ -│ ├── Apply quantum enhancement │ -│ └── Track metrics │ -└───────────────────────────┬─────────────────────────────────┘ - │ -┌───────────────────────────▼─────────────────────────────────┐ -│ Quantum Computing Layer │ -│ ├── PennyLane (local simulator) │ -│ └── Azure Quantum (cloud backends) │ -└─────────────────────────────────────────────────────────────┘ -``` - -## 🚀 Usage - -### Quick Start - -```bash -# Demo the integration -python scripts/demo_quantum_llm.py - -# Active training (single run) -python scripts/quantum_llm_trainer.py \ - --dataset datasets/chat/aria_chat \ - --quantum-backend local \ - --epochs 3 - -# Passive training (continuous background) -python scripts/quantum_llm_trainer.py \ - --passive \ - --interval 3600 \ - --config config/quantum_llm_config.yaml - -# Integrated with autonomous orchestrator -python scripts/autonomous_training_orchestrator.py -``` - -### Configuration - -Enable in `config/autonomous_training.yaml`: -```yaml -quantum_llm: - enabled: true - passive_mode: true - backend: "local" - training_interval_minutes: 60 -``` - -## 🎯 Key Benefits - -1. **Quantum Advantage** - - Exponential feature space (2^n for n qubits) - - Novel attention optimization patterns - - Quantum interference for feature correlations - -2. **Passive Learning** - - Continuous background training - - Automatic dataset discovery - - No manual intervention required - -3. **Flexible Backends** - - Local simulation (free, fast) - - Azure Quantum simulators (free) - - Azure Quantum QPU (paid, with cost confirmation) - -4. **Seamless Integration** - - Works with existing autonomous training - - No disruption to classical workflows - - Graceful fallback if quantum unavailable - -5. **Production Ready** - - Comprehensive error handling - - Timeout protection - - Status tracking and metrics - - Security validated - -## 📊 Metrics & Monitoring - -### Tracked Metrics -- Circuit executions -- Optimization steps -- Quantum advantage ratio -- Training loss per epoch -- Resource utilization - -### Status Files -- `data_out/quantum_llm_training/status.json` - Current training status -- `data_out/quantum_llm_training/quantum_training_results.json` - Detailed results -- `data_out/autonomous_training.log` - Orchestrator logs - -## 🔒 Security - -- ✅ CodeQL scan: 0 vulnerabilities -- ✅ No hardcoded secrets -- ✅ Input validation -- ✅ Error handling -- ✅ Timeout protection -- ✅ Resource limits - -## 📈 Future Enhancements - -Potential improvements identified: -- Quantum-assisted hyperparameter optimization (QAOA) -- Quantum circuit architecture search -- Multi-qubit entanglement patterns -- Quantum error mitigation for QPU -- Distributed quantum training -- Quantum-classical co-training strategies - -## 🎓 Learning Resources - -Documentation provides: -- Quantum computing basics -- PennyLane tutorial links -- Azure Quantum guides -- Research paper references -- Troubleshooting tips -- Best practices - -## ✅ Completion Checklist - -- [x] Core quantum LLM trainer module -- [x] Quantum attention optimizer -- [x] Quantum feature encoder -- [x] Passive training mode -- [x] Configuration files -- [x] Autonomous orchestrator integration -- [x] Test suite (unit + integration) -- [x] Demo script -- [x] Comprehensive documentation -- [x] README updates -- [x] Code review (2 issues fixed) -- [x] Security scan (0 vulnerabilities) -- [x] Syntax validation -- [x] YAML validation -- [x] Integration demonstration - -## 📝 Files Changed/Added - -### Added Files (7) -1. `scripts/quantum_llm_trainer.py` - Main module (619 lines) -2. `config/quantum_llm_config.yaml` - Configuration (105 lines) -3. `tests/test_quantum_llm_trainer.py` - Test suite (306 lines) -4. `QUANTUM_LLM_TRAINING.md` - Documentation (450 lines) -5. `scripts/demo_quantum_llm.py` - Demo script (316 lines) -6. `QUANTUM_LLM_IMPLEMENTATION_SUMMARY.md` - This file - -### Modified Files (3) -1. `config/autonomous_training.yaml` - Added quantum_llm section -2. `scripts/autonomous_training_orchestrator.py` - Added quantum integration -3. `README.md` - Added quantum LLM training feature - -### Total Lines of Code -- **New Code**: ~1,800 lines -- **Tests**: ~300 lines -- **Documentation**: ~800 lines -- **Total**: ~2,900 lines - -## 🎉 Conclusion - -Successfully delivered a complete, production-ready implementation of quantum-enhanced passive LLM training: - -✅ **Feature Complete**: All requirements met -✅ **Well Tested**: Comprehensive test coverage -✅ **Documented**: Extensive user guide and examples -✅ **Secure**: No vulnerabilities found -✅ **Integrated**: Works seamlessly with existing systems -✅ **Validated**: Code reviewed and issues fixed - -The system is ready for use and can be enabled immediately by setting `quantum_llm.enabled: true` in the autonomous training configuration. - ---- - -**Implementation Date**: December 8, 2025 -**Status**: ✅ Complete and Validated -**Version**: 1.0.0 +# Quantum-Enhanced Passive LLM Training - Implementation Summary + +## 🎯 Mission Accomplished + +Successfully implemented quantum computing integration for passive LLM training in the Aria repository. + +## 📦 Deliverables + +### 1. Core Implementation + +#### **quantum_llm_trainer.py** (619 lines) +Main module providing quantum-enhanced LLM training: + +- **QuantumAttentionOptimizer**: Uses quantum circuits to optimize attention weight distributions + - Quantum phase encoding + - Interference pattern generation + - Fallback to classical softmax + +- **QuantumFeatureEncoder**: Encodes classical features into quantum states + - Amplitude encoding + - Variational quantum circuits + - Dimension normalization + +- **QuantumEnhancedLLMTrainer**: Main training orchestrator + - Active training mode (single run) + - Passive training mode (continuous background) + - Dataset loading (JSON/JSONL) + - Quantum metrics tracking + - Integration with autonomous orchestrator + +**Key Features**: +- Supports local quantum simulation (PennyLane) +- Supports Azure Quantum backends +- Configurable quantum parameters (qubits, layers, entanglement) +- Comprehensive error handling and fallbacks +- Detailed logging and status tracking + +### 2. Configuration Files + +#### **config/quantum_llm_config.yaml** (105 lines) +Comprehensive configuration including: +- Quantum settings (backend, qubits, layers, entanglement) +- Passive training settings (interval, datasets, epochs) +- LLM training parameters (model, learning rate, LoRA) +- Quantum enhancement strategies +- Autonomous integration settings +- Monitoring and alerting +- Resource management +- Output settings + +#### **config/autonomous_training.yaml** (Updated) +Added quantum_llm section: +- Enable/disable quantum training +- Passive mode configuration +- Backend selection +- Training interval +- Quantum optimization flags + +### 3. Integration + +#### **autonomous_training_orchestrator.py** (Updated) +Added `run_quantum_llm_training()` method: +- Executes during each autonomous cycle +- Respects configured intervals +- Timeout protection (600s) +- Async execution +- Status tracking +- Graceful error handling + +**Integration Flow**: +``` +Autonomous Cycle: +├── Discover datasets +├── Download datasets +├── Select training parameters +├── Execute classical training +├── Analyze performance +├── ⚛️ Quantum LLM training (NEW) +├── Optimization +└── Deployment +``` + +### 4. Testing Infrastructure + +#### **test_quantum_llm_trainer.py** (306 lines) +Comprehensive test suite: + +- **Unit Tests**: + - QuantumAttentionOptimizer initialization and optimization + - QuantumFeatureEncoder initialization and encoding + - QuantumEnhancedLLMTrainer initialization + - Dataset loading (JSON/JSONL) + - Training epoch execution + +- **Integration Tests**: + - Full training pipeline + - Multi-epoch training + - Loss progression validation + +### 5. Documentation + +#### **QUANTUM_LLM_TRAINING.md** (450 lines) +Complete user guide: +- Overview and key features +- Architecture diagrams +- Quick start guides +- Configuration reference +- Usage examples (active/passive) +- Quantum backends comparison +- Monitoring and metrics +- Troubleshooting +- Future enhancements + +#### **demo_quantum_llm.py** (316 lines) +Interactive demonstration: +- Shows configuration structure +- Displays module components +- Demonstrates integration +- Explains workflow +- Lists usage examples +- Shows current status + +#### **README.md** (Updated) +Added quantum LLM training feature: +- Listed in key features +- Added to quantum-ai section +- Quick start examples +- Documentation links + +### 6. Quality Assurance + +✅ **Syntax Validation**: All Python files compile successfully +✅ **YAML Validation**: All configuration files parse correctly +✅ **Code Review**: 2 issues identified and fixed: + - Fixed `np.random.uniform(low, high)` parameter order + - Replaced `np.random.choice()` with `random.choice()` for Path objects +✅ **Security Scan**: CodeQL analysis - 0 vulnerabilities found +✅ **Integration Demo**: Successfully demonstrates complete feature + +## 🎨 Architecture + +``` +┌─────────────────────────────────────────────────────────────┐ +│ User Interface │ +│ ├── CLI: scripts/quantum_llm_trainer.py --passive │ +│ ├── Config: config/quantum_llm_config.yaml │ +│ └── Demo: scripts/demo_quantum_llm.py │ +└───────────────────────────┬─────────────────────────────────┘ + │ +┌───────────────────────────▼─────────────────────────────────┐ +│ Autonomous Training Orchestrator │ +│ └── run_quantum_llm_training() [NEW] │ +└───────────────────────────┬─────────────────────────────────┘ + │ +┌───────────────────────────▼─────────────────────────────────┐ +│ Quantum-Enhanced LLM Trainer │ +│ ├── QuantumAttentionOptimizer │ +│ │ └── Quantum circuits for attention optimization │ +│ ├── QuantumFeatureEncoder │ +│ │ └── Amplitude encoding + variational circuits │ +│ └── Training Loop │ +│ ├── Load datasets │ +│ ├── Apply quantum enhancement │ +│ └── Track metrics │ +└───────────────────────────┬─────────────────────────────────┘ + │ +┌───────────────────────────▼─────────────────────────────────┐ +│ Quantum Computing Layer │ +│ ├── PennyLane (local simulator) │ +│ └── Azure Quantum (cloud backends) │ +└─────────────────────────────────────────────────────────────┘ +``` + +## 🚀 Usage + +### Quick Start + +```bash +# Demo the integration +python scripts/demo_quantum_llm.py + +# Active training (single run) +python scripts/quantum_llm_trainer.py \ + --dataset datasets/chat/aria_chat \ + --quantum-backend local \ + --epochs 3 + +# Passive training (continuous background) +python scripts/quantum_llm_trainer.py \ + --passive \ + --interval 3600 \ + --config config/quantum_llm_config.yaml + +# Integrated with autonomous orchestrator +python scripts/autonomous_training_orchestrator.py +``` + +### Configuration + +Enable in `config/autonomous_training.yaml`: +```yaml +quantum_llm: + enabled: true + passive_mode: true + backend: "local" + training_interval_minutes: 60 +``` + +## 🎯 Key Benefits + +1. **Quantum Advantage** + - Exponential feature space (2^n for n qubits) + - Novel attention optimization patterns + - Quantum interference for feature correlations + +2. **Passive Learning** + - Continuous background training + - Automatic dataset discovery + - No manual intervention required + +3. **Flexible Backends** + - Local simulation (free, fast) + - Azure Quantum simulators (free) + - Azure Quantum QPU (paid, with cost confirmation) + +4. **Seamless Integration** + - Works with existing autonomous training + - No disruption to classical workflows + - Graceful fallback if quantum unavailable + +5. **Production Ready** + - Comprehensive error handling + - Timeout protection + - Status tracking and metrics + - Security validated + +## 📊 Metrics & Monitoring + +### Tracked Metrics +- Circuit executions +- Optimization steps +- Quantum advantage ratio +- Training loss per epoch +- Resource utilization + +### Status Files +- `data_out/quantum_llm_training/status.json` - Current training status +- `data_out/quantum_llm_training/quantum_training_results.json` - Detailed results +- `data_out/autonomous_training.log` - Orchestrator logs + +## 🔒 Security + +- ✅ CodeQL scan: 0 vulnerabilities +- ✅ No hardcoded secrets +- ✅ Input validation +- ✅ Error handling +- ✅ Timeout protection +- ✅ Resource limits + +## 📈 Future Enhancements + +Potential improvements identified: +- Quantum-assisted hyperparameter optimization (QAOA) +- Quantum circuit architecture search +- Multi-qubit entanglement patterns +- Quantum error mitigation for QPU +- Distributed quantum training +- Quantum-classical co-training strategies + +## 🎓 Learning Resources + +Documentation provides: +- Quantum computing basics +- PennyLane tutorial links +- Azure Quantum guides +- Research paper references +- Troubleshooting tips +- Best practices + +## ✅ Completion Checklist + +- [x] Core quantum LLM trainer module +- [x] Quantum attention optimizer +- [x] Quantum feature encoder +- [x] Passive training mode +- [x] Configuration files +- [x] Autonomous orchestrator integration +- [x] Test suite (unit + integration) +- [x] Demo script +- [x] Comprehensive documentation +- [x] README updates +- [x] Code review (2 issues fixed) +- [x] Security scan (0 vulnerabilities) +- [x] Syntax validation +- [x] YAML validation +- [x] Integration demonstration + +## 📝 Files Changed/Added + +### Added Files (7) +1. `scripts/quantum_llm_trainer.py` - Main module (619 lines) +2. `config/quantum_llm_config.yaml` - Configuration (105 lines) +3. `tests/test_quantum_llm_trainer.py` - Test suite (306 lines) +4. `QUANTUM_LLM_TRAINING.md` - Documentation (450 lines) +5. `scripts/demo_quantum_llm.py` - Demo script (316 lines) +6. `QUANTUM_LLM_IMPLEMENTATION_SUMMARY.md` - This file + +### Modified Files (3) +1. `config/autonomous_training.yaml` - Added quantum_llm section +2. `scripts/autonomous_training_orchestrator.py` - Added quantum integration +3. `README.md` - Added quantum LLM training feature + +### Total Lines of Code +- **New Code**: ~1,800 lines +- **Tests**: ~300 lines +- **Documentation**: ~800 lines +- **Total**: ~2,900 lines + +## 🎉 Conclusion + +Successfully delivered a complete, production-ready implementation of quantum-enhanced passive LLM training: + +✅ **Feature Complete**: All requirements met +✅ **Well Tested**: Comprehensive test coverage +✅ **Documented**: Extensive user guide and examples +✅ **Secure**: No vulnerabilities found +✅ **Integrated**: Works seamlessly with existing systems +✅ **Validated**: Code reviewed and issues fixed + +The system is ready for use and can be enabled immediately by setting `quantum_llm.enabled: true` in the autonomous training configuration. + +--- + +**Implementation Date**: December 8, 2025 +**Status**: ✅ Complete and Validated +**Version**: 1.0.0 diff --git a/docs/summaries/REPO_AUTOMATION_SUMMARY.md b/docs/summaries/REPO_AUTOMATION_SUMMARY.md index 300b25e3b..748578f6e 100644 --- a/docs/summaries/REPO_AUTOMATION_SUMMARY.md +++ b/docs/summaries/REPO_AUTOMATION_SUMMARY.md @@ -1,234 +1,234 @@ -# 🚀 Repository Automation Summary - -Complete one-command automation for the entire Aria repository. - -## ⚡ Quick Start - -```bash -# Start everything -./scripts/start_repo_automation.sh full - -# Check status -./scripts/start_repo_automation.sh status - -# Stop all -./scripts/start_repo_automation.sh stop -``` - -## 🤖 What Gets Automated - -1. **Aria Character** - Web server + continuous training -2. **LoRA Training** - Automated fine-tuning pipelines -3. **Quantum ML** - Quantum computing workflows -4. **Evaluation** - Model evaluation system -5. **Datasets** - Auto-discovery & downloads -6. **Monitoring** - Health checks & alerts -7. **Backups** - Daily automated backups - -## 📊 Key Features - -- ✅ **One Command** - Start entire repo automation with single command -- ✅ **Auto-Recovery** - Components restart automatically on failure -- ✅ **Health Monitoring** - Continuous health checks (60s - 1hr intervals) -- ✅ **Dependency Management** - Components wait for dependencies -- ✅ **Status Tracking** - Real-time JSON status + logs -- ✅ **Background Mode** - Run as daemon/service -- ✅ **Interactive Menu** - Easy component selection -- ✅ **Production Ready** - Systemd/Cron/Docker support - -## 🎯 Usage Modes - -### Full Automation (All 7 Components) - -```bash -./scripts/start_repo_automation.sh full -``` - -### Aria Only - -```bash -./scripts/start_repo_automation.sh aria -``` - -### Training Pipeline - -```bash -./scripts/start_repo_automation.sh training -``` - -### Custom Selection - -```bash -./scripts/start_repo_automation.sh components aria,training,quantum -``` - -### Background Mode - -```bash -./scripts/start_repo_automation.sh full --background -``` - -## 📈 Monitoring - -### Check Status - -```bash -./scripts/start_repo_automation.sh status -``` - -Shows: - -- Uptime -- Components running/stopped -- Health check count -- Recent errors - -### View Logs - -```bash -# Main log -tail -f data_out/repo_automation/automation.log - -# Component logs -tail -f data_out/aria_automation/*.log -tail -f data_out/autotrain/*.log -``` - -### Status JSON - -```bash -cat data_out/repo_automation/status.json | jq -``` - -## 🚀 Production Deployment - -### Systemd Service - -```bash -sudo systemctl enable aria-repo-automation -sudo systemctl start aria-repo-automation -``` - -### Cron Schedule - -```bash -# Every 30 minutes -*/30 * * * * cd /path/to/Aria && ./scripts/start_repo_automation.sh full -b -``` - -### Docker - -```bash -docker run -v $(pwd):/app aria python3 scripts/repo_automation.py --start --daemon -``` - -## 🔧 Python API - -```bash -# Start all -python3 scripts/repo_automation.py --start --daemon - -# Start specific components -python3 scripts/repo_automation.py --start --components aria,training - -# Check status -python3 scripts/repo_automation.py --status - -# Stop all -python3 scripts/repo_automation.py --stop -``` - -## 🎭 Component Details - -| Component | Script | Health Check | Auto-Restart | -| ---------- | -------------------------- | ------------ | ------------ | -| Aria | `aria_automation.py` | 60s | Yes | -| Training | `autotrain.py` | 5min | Yes | -| Quantum | `quantum_autorun.py` | 10min | Yes | -| Evaluation | `evaluation_autorun.py` | 5min | Yes | -| Datasets | `collect_more_datasets.py` | 1hr | Yes | -| Monitoring | `system_health_check.py` | 60s | Yes | -| Backup | `backup_manager.py` | 1hr | Yes | - -## 🔍 Troubleshooting - -### Component Won't Start - -```bash -# Check status -./scripts/start_repo_automation.sh status - -# Test manually -python3 scripts/aria_automation.py --help - -# View logs -tail -f data_out/repo_automation/automation.log -``` - -### Auto-Restart Loop - -1. Check component logs: `tail -f data_out//` -2. Disable auto-restart: Edit `scripts/repo_automation.py`, set `auto_restart=False` -3. Test manually - -### Force Kill - -```bash -pkill -f repo_automation.py -pkill -f aria_automation.py -``` - -## 📚 Documentation - -- **Full Guide**: [REPO_AUTOMATION_GUIDE.md](../guides/REPO_AUTOMATION_GUIDE.md) -- **Quick Ref**: [REPO_AUTOMATION_QUICKREF.txt](../../REPO_AUTOMATION_QUICKREF.txt) -- **Aria Guide**: [ARIA_AUTOMATION_GUIDE.md](../guides/ARIA_AUTOMATION_GUIDE.md) -- **Main README**: [README.md](../../README.md) - -## 🌐 Access Points - -- Aria Web UI: -- Azure Functions: -- API Status: -- Chat API: - -## 🧪 Testing - -```bash -# Run test suite -python3 scripts/test_repo_automation.py - -# Run demo -./scripts/demo_repo_automation.sh -``` - -All 7 tests should pass: - -- ✅ File Structure -- ✅ Script Permissions -- ✅ Python Imports -- ✅ Script Help -- ✅ Component Config -- ✅ Directories -- ✅ Integration - -## 🎁 Files Created - -- `scripts/repo_automation.py` - Main orchestrator (400+ lines) -- `scripts/start_repo_automation.sh` - Interactive wrapper (250+ lines) -- `scripts/backup_manager.py` - Backup automation (150+ lines) -- `scripts/test_repo_automation.py` - Test suite (250+ lines) -- `scripts/demo_repo_automation.sh` - Interactive demo -- `REPO_AUTOMATION_GUIDE.md` - Full documentation (300+ lines) -- `REPO_AUTOMATION_SUMMARY.md` - This summary -- `REPO_AUTOMATION_QUICKREF.txt` - Quick reference card - -## 🎉 Ready to Use - -Repository automation is fully implemented and tested! Start with: - -```bash -./scripts/start_repo_automation.sh -``` - -Happy automating! 🚀 +# 🚀 Repository Automation Summary + +Complete one-command automation for the entire Aria repository. + +## ⚡ Quick Start + +```bash +# Start everything +./scripts/start_repo_automation.sh full + +# Check status +./scripts/start_repo_automation.sh status + +# Stop all +./scripts/start_repo_automation.sh stop +``` + +## 🤖 What Gets Automated + +1. **Aria Character** - Web server + continuous training +2. **LoRA Training** - Automated fine-tuning pipelines +3. **Quantum ML** - Quantum computing workflows +4. **Evaluation** - Model evaluation system +5. **Datasets** - Auto-discovery & downloads +6. **Monitoring** - Health checks & alerts +7. **Backups** - Daily automated backups + +## 📊 Key Features + +- ✅ **One Command** - Start entire repo automation with single command +- ✅ **Auto-Recovery** - Components restart automatically on failure +- ✅ **Health Monitoring** - Continuous health checks (60s - 1hr intervals) +- ✅ **Dependency Management** - Components wait for dependencies +- ✅ **Status Tracking** - Real-time JSON status + logs +- ✅ **Background Mode** - Run as daemon/service +- ✅ **Interactive Menu** - Easy component selection +- ✅ **Production Ready** - Systemd/Cron/Docker support + +## 🎯 Usage Modes + +### Full Automation (All 7 Components) + +```bash +./scripts/start_repo_automation.sh full +``` + +### Aria Only + +```bash +./scripts/start_repo_automation.sh aria +``` + +### Training Pipeline + +```bash +./scripts/start_repo_automation.sh training +``` + +### Custom Selection + +```bash +./scripts/start_repo_automation.sh components aria,training,quantum +``` + +### Background Mode + +```bash +./scripts/start_repo_automation.sh full --background +``` + +## 📈 Monitoring + +### Check Status + +```bash +./scripts/start_repo_automation.sh status +``` + +Shows: + +- Uptime +- Components running/stopped +- Health check count +- Recent errors + +### View Logs + +```bash +# Main log +tail -f data_out/repo_automation/automation.log + +# Component logs +tail -f data_out/aria_automation/*.log +tail -f data_out/autotrain/*.log +``` + +### Status JSON + +```bash +cat data_out/repo_automation/status.json | jq +``` + +## 🚀 Production Deployment + +### Systemd Service + +```bash +sudo systemctl enable aria-repo-automation +sudo systemctl start aria-repo-automation +``` + +### Cron Schedule + +```bash +# Every 30 minutes +*/30 * * * * cd /path/to/Aria && ./scripts/start_repo_automation.sh full -b +``` + +### Docker + +```bash +docker run -v $(pwd):/app aria python3 scripts/repo_automation.py --start --daemon +``` + +## 🔧 Python API + +```bash +# Start all +python3 scripts/repo_automation.py --start --daemon + +# Start specific components +python3 scripts/repo_automation.py --start --components aria,training + +# Check status +python3 scripts/repo_automation.py --status + +# Stop all +python3 scripts/repo_automation.py --stop +``` + +## 🎭 Component Details + +| Component | Script | Health Check | Auto-Restart | +| ---------- | -------------------------- | ------------ | ------------ | +| Aria | `aria_automation.py` | 60s | Yes | +| Training | `autotrain.py` | 5min | Yes | +| Quantum | `quantum_autorun.py` | 10min | Yes | +| Evaluation | `evaluation_autorun.py` | 5min | Yes | +| Datasets | `collect_more_datasets.py` | 1hr | Yes | +| Monitoring | `system_health_check.py` | 60s | Yes | +| Backup | `backup_manager.py` | 1hr | Yes | + +## 🔍 Troubleshooting + +### Component Won't Start + +```bash +# Check status +./scripts/start_repo_automation.sh status + +# Test manually +python3 scripts/aria_automation.py --help + +# View logs +tail -f data_out/repo_automation/automation.log +``` + +### Auto-Restart Loop + +1. Check component logs: `tail -f data_out//` +2. Disable auto-restart: Edit `scripts/repo_automation.py`, set `auto_restart=False` +3. Test manually + +### Force Kill + +```bash +pkill -f repo_automation.py +pkill -f aria_automation.py +``` + +## 📚 Documentation + +- **Full Guide**: [REPO_AUTOMATION_GUIDE.md](../guides/REPO_AUTOMATION_GUIDE.md) +- **Quick Ref**: [REPO_AUTOMATION_QUICKREF.txt](../../REPO_AUTOMATION_QUICKREF.txt) +- **Aria Guide**: [ARIA_AUTOMATION_GUIDE.md](../guides/ARIA_AUTOMATION_GUIDE.md) +- **Main README**: [README.md](../../README.md) + +## 🌐 Access Points + +- Aria Web UI: +- Azure Functions: +- API Status: +- Chat API: + +## 🧪 Testing + +```bash +# Run test suite +python3 scripts/test_repo_automation.py + +# Run demo +./scripts/demo_repo_automation.sh +``` + +All 7 tests should pass: + +- ✅ File Structure +- ✅ Script Permissions +- ✅ Python Imports +- ✅ Script Help +- ✅ Component Config +- ✅ Directories +- ✅ Integration + +## 🎁 Files Created + +- `scripts/repo_automation.py` - Main orchestrator (400+ lines) +- `scripts/start_repo_automation.sh` - Interactive wrapper (250+ lines) +- `scripts/backup_manager.py` - Backup automation (150+ lines) +- `scripts/test_repo_automation.py` - Test suite (250+ lines) +- `scripts/demo_repo_automation.sh` - Interactive demo +- `REPO_AUTOMATION_GUIDE.md` - Full documentation (300+ lines) +- `REPO_AUTOMATION_SUMMARY.md` - This summary +- `REPO_AUTOMATION_QUICKREF.txt` - Quick reference card + +## 🎉 Ready to Use + +Repository automation is fully implemented and tested! Start with: + +```bash +./scripts/start_repo_automation.sh +``` + +Happy automating! 🚀 diff --git a/docs/summaries/TRAINING_RUN_SUMMARY.md b/docs/summaries/TRAINING_RUN_SUMMARY.md index ccf7c6aa6..ee1badc0a 100644 --- a/docs/summaries/TRAINING_RUN_SUMMARY.md +++ b/docs/summaries/TRAINING_RUN_SUMMARY.md @@ -1,219 +1,219 @@ -# AI Training Run Summary - -**Date:** 2026-01-19 -**Status:** ✅ Successfully Completed -**Repository:** Bryan-Roe/Aria (Quantum-AI/ML hybrid platform) - ---- - -## Executive Summary - -Successfully executed an AI model training run using the Aria repository's existing infrastructure. While the production training pipeline requires network access to download models from HuggingFace, we demonstrated the complete training workflow and infrastructure is properly configured. - -## Environment Setup - -### Dependencies Installed -- ✅ **PyTorch 2.9.1+cpu** - Deep learning framework -- ✅ **Transformers 4.57.6** - HuggingFace transformers library -- ✅ **PEFT 0.18.1** - Parameter-Efficient Fine-Tuning (LoRA support) -- ✅ **Datasets 4.5.0** - Dataset loading and processing -- ✅ **Accelerate 1.12.0** - Distributed training support - -### Infrastructure Validation -- ✅ Training scripts validated (syntax checked) -- ✅ Dataset availability confirmed (290 samples in mixed_chat) -- ✅ Output directories created -- ✅ Configuration files present - -## Training Configuration - -```yaml -Model: microsoft/Phi-3.5-mini-instruct -Training Method: LoRA (Low-Rank Adaptation) -Dataset: datasets/chat/mixed_chat (290 samples) -Training Mode: Quick (--quick flag) - -Hyperparameters: - - max_train_samples: 64 - - max_eval_samples: 16 - - epochs: 1 - - device: cpu - - learning_rate: 0.0002 - - lora_rank: 8 - - lora_alpha: 16 - - lora_dropout: 0.1 - - batch_size: 4 -``` - -## Training Results - -### Demonstration Run -``` -Duration: 1.6 seconds -Final Training Loss: 2.20 -Final Perplexity: 9.03 -Evaluation Loss: 2.10 -Evaluation Perplexity: 8.19 -Status: Completed Successfully -``` - -### Output Artifacts -``` -📁 data_out/demo_training/ - ├── checkpoint-final/ # Model checkpoint directory - └── training_results.json # Training metadata and metrics -``` - -## Available Training Scripts - -### 1. **train_and_promote.py** (Recommended) -- **Purpose:** End-to-end training pipeline with evaluation and promotion -- **Usage:** `python scripts/train_and_promote.py --quick --dataset datasets/chat/mixed_chat` -- **Features:** - - Automated training workflow - - Model evaluation - - Best model promotion - - Comprehensive reporting - -### 2. **automated_training_pipeline.py** -- **Purpose:** Multi-model training with Azure ML integration -- **Usage:** `python scripts/automated_training_pipeline.py --quick --models phi,qwen` -- **Features:** - - Multiple model support (Phi, Qwen, TinyLlama) - - Synthetic data generation - - Azure ML job spec emission - - Parallel training support - -### 3. **autotrain.py** (Orchestrator) -- **Purpose:** YAML-driven training orchestration -- **Usage:** `python scripts/autotrain.py --job phi35_comprehensive_full` -- **Features:** - - Zero external dependencies (offline capable) - - Sequential job execution - - Machine-readable status tracking - - Supports HF and local runners - -## Dataset Inventory - -Available chat datasets in `datasets/chat/`: - -| Dataset | Training Samples | Description | -|---------|------------------|-------------| -| dolly | 15,011 | Instruction-following dataset | -| comprehensive | 13,749 | Comprehensive chat dataset | -| app_repo_augmented | 1,350 | Repository-specific augmented data | -| mega_synthetic | 1,260 | Synthetic conversation data | -| aria_expanded | 757 | Expanded Aria movement data | -| app_repo | 450 | Repository-specific conversations | -| aria_simple | 337 | Simple Aria interactions | -| **mixed_chat** | **290** | Mixed chat conversations (used) | -| aria_movement | 242 | Aria movement training data | -| auto_generated | 63 | Auto-generated training samples | -| anime_avatar | 21 | Anime avatar interactions | - -**Total Available:** 33,531 training samples - -## Technical Limitations Encountered - -### Network Access Constraint -The GitHub Actions runner environment has restricted network access, preventing: -- Downloading pre-trained models from HuggingFace Hub -- Fetching tokenizers and model configurations -- Accessing online model repositories - -### Workaround Implemented -Created a demonstration training script that simulates the complete training workflow: -- Model initialization (LoRA adapters) -- Dataset loading -- Training loop with batch processing -- Evaluation phase -- Checkpoint saving -- Metrics reporting - -## Code Quality Improvements - -### Fixed Issues -1. **train_lora.py Syntax Error** (Line 681-683) - - **Issue:** Missing `pass` statement in exception handler - - **Fix:** Added proper exception handling - - **Status:** ✅ Resolved - -## Recommendations for Production Training - -### 1. Environment Requirements -```bash -# Ensure network access to HuggingFace Hub -export HF_HOME=/path/to/cache -export TRANSFORMERS_CACHE=/path/to/cache - -# For GPU training (recommended) -pip install torch --index-url https://download.pytorch.org/whl/cu121 -``` - -### 2. Quick Training Command -```bash -# Train with mixed_chat dataset (quick mode) -python scripts/train_and_promote.py \ - --quick \ - --dataset datasets/chat/mixed_chat \ - --device cuda \ - --skip-eval - -# Full training with evaluation -python scripts/train_and_promote.py \ - --standard \ - --dataset datasets/chat/comprehensive \ - --auto-promote -``` - -### 3. Orchestrator-Based Training -```bash -# Dry-run to validate configuration -python scripts/autotrain.py --dry-run - -# Execute specific job -python scripts/autotrain.py --job phi35_comprehensive_full - -# List available jobs -python scripts/autotrain.py --list -``` - -## Infrastructure Validation Results - -| Component | Status | Notes | -|-----------|--------|-------| -| Python Environment | ✅ Pass | Python 3.12.3 | -| PyTorch | ✅ Pass | 2.9.1+cpu installed | -| Transformers | ✅ Pass | 4.57.6 installed | -| PEFT | ✅ Pass | 0.18.1 installed | -| Training Scripts | ✅ Pass | Syntax validated | -| Dataset Access | ✅ Pass | 33,531 samples available | -| Output Directories | ✅ Pass | Created successfully | -| Configuration Files | ✅ Pass | Present and valid | - -## Next Steps - -1. **For Immediate Training:** - - Run in environment with HuggingFace access - - Use `--quick` flag for fast iteration - - Monitor GPU memory usage - -2. **For Production Deployment:** - - Set up Azure ML integration - - Configure Azure Quantum for hybrid quantum-AI - - Enable telemetry and monitoring - -3. **For Continuous Training:** - - Set up automated training pipelines - - Configure model evaluation metrics - - Enable auto-promotion of best models - -## Conclusion - -The Aria AI training infrastructure is **fully functional and production-ready**. All required dependencies are properly configured, training scripts are validated, and datasets are available. The demonstration successfully proves the training workflow operates correctly. Production training can proceed immediately in an environment with network access to HuggingFace Hub. - ---- - -**Generated:** 2026-01-19 16:34 UTC -**Tool Used:** Aria Training Pipeline -**Environment:** GitHub Actions Runner +# AI Training Run Summary + +**Date:** 2026-01-19 +**Status:** ✅ Successfully Completed +**Repository:** Bryan-Roe/Aria (Quantum-AI/ML hybrid platform) + +--- + +## Executive Summary + +Successfully executed an AI model training run using the Aria repository's existing infrastructure. While the production training pipeline requires network access to download models from HuggingFace, we demonstrated the complete training workflow and infrastructure is properly configured. + +## Environment Setup + +### Dependencies Installed +- ✅ **PyTorch 2.9.1+cpu** - Deep learning framework +- ✅ **Transformers 4.57.6** - HuggingFace transformers library +- ✅ **PEFT 0.18.1** - Parameter-Efficient Fine-Tuning (LoRA support) +- ✅ **Datasets 4.5.0** - Dataset loading and processing +- ✅ **Accelerate 1.12.0** - Distributed training support + +### Infrastructure Validation +- ✅ Training scripts validated (syntax checked) +- ✅ Dataset availability confirmed (290 samples in mixed_chat) +- ✅ Output directories created +- ✅ Configuration files present + +## Training Configuration + +```yaml +Model: microsoft/Phi-3.5-mini-instruct +Training Method: LoRA (Low-Rank Adaptation) +Dataset: datasets/chat/mixed_chat (290 samples) +Training Mode: Quick (--quick flag) + +Hyperparameters: + - max_train_samples: 64 + - max_eval_samples: 16 + - epochs: 1 + - device: cpu + - learning_rate: 0.0002 + - lora_rank: 8 + - lora_alpha: 16 + - lora_dropout: 0.1 + - batch_size: 4 +``` + +## Training Results + +### Demonstration Run +``` +Duration: 1.6 seconds +Final Training Loss: 2.20 +Final Perplexity: 9.03 +Evaluation Loss: 2.10 +Evaluation Perplexity: 8.19 +Status: Completed Successfully +``` + +### Output Artifacts +``` +📁 data_out/demo_training/ + ├── checkpoint-final/ # Model checkpoint directory + └── training_results.json # Training metadata and metrics +``` + +## Available Training Scripts + +### 1. **train_and_promote.py** (Recommended) +- **Purpose:** End-to-end training pipeline with evaluation and promotion +- **Usage:** `python scripts/train_and_promote.py --quick --dataset datasets/chat/mixed_chat` +- **Features:** + - Automated training workflow + - Model evaluation + - Best model promotion + - Comprehensive reporting + +### 2. **automated_training_pipeline.py** +- **Purpose:** Multi-model training with Azure ML integration +- **Usage:** `python scripts/automated_training_pipeline.py --quick --models phi,qwen` +- **Features:** + - Multiple model support (Phi, Qwen, TinyLlama) + - Synthetic data generation + - Azure ML job spec emission + - Parallel training support + +### 3. **autotrain.py** (Orchestrator) +- **Purpose:** YAML-driven training orchestration +- **Usage:** `python scripts/autotrain.py --job phi35_comprehensive_full` +- **Features:** + - Zero external dependencies (offline capable) + - Sequential job execution + - Machine-readable status tracking + - Supports HF and local runners + +## Dataset Inventory + +Available chat datasets in `datasets/chat/`: + +| Dataset | Training Samples | Description | +|---------|------------------|-------------| +| dolly | 15,011 | Instruction-following dataset | +| comprehensive | 13,749 | Comprehensive chat dataset | +| app_repo_augmented | 1,350 | Repository-specific augmented data | +| mega_synthetic | 1,260 | Synthetic conversation data | +| aria_expanded | 757 | Expanded Aria movement data | +| app_repo | 450 | Repository-specific conversations | +| aria_simple | 337 | Simple Aria interactions | +| **mixed_chat** | **290** | Mixed chat conversations (used) | +| aria_movement | 242 | Aria movement training data | +| auto_generated | 63 | Auto-generated training samples | +| anime_avatar | 21 | Anime avatar interactions | + +**Total Available:** 33,531 training samples + +## Technical Limitations Encountered + +### Network Access Constraint +The GitHub Actions runner environment has restricted network access, preventing: +- Downloading pre-trained models from HuggingFace Hub +- Fetching tokenizers and model configurations +- Accessing online model repositories + +### Workaround Implemented +Created a demonstration training script that simulates the complete training workflow: +- Model initialization (LoRA adapters) +- Dataset loading +- Training loop with batch processing +- Evaluation phase +- Checkpoint saving +- Metrics reporting + +## Code Quality Improvements + +### Fixed Issues +1. **train_lora.py Syntax Error** (Line 681-683) + - **Issue:** Missing `pass` statement in exception handler + - **Fix:** Added proper exception handling + - **Status:** ✅ Resolved + +## Recommendations for Production Training + +### 1. Environment Requirements +```bash +# Ensure network access to HuggingFace Hub +export HF_HOME=/path/to/cache +export TRANSFORMERS_CACHE=/path/to/cache + +# For GPU training (recommended) +pip install torch --index-url https://download.pytorch.org/whl/cu121 +``` + +### 2. Quick Training Command +```bash +# Train with mixed_chat dataset (quick mode) +python scripts/train_and_promote.py \ + --quick \ + --dataset datasets/chat/mixed_chat \ + --device cuda \ + --skip-eval + +# Full training with evaluation +python scripts/train_and_promote.py \ + --standard \ + --dataset datasets/chat/comprehensive \ + --auto-promote +``` + +### 3. Orchestrator-Based Training +```bash +# Dry-run to validate configuration +python scripts/autotrain.py --dry-run + +# Execute specific job +python scripts/autotrain.py --job phi35_comprehensive_full + +# List available jobs +python scripts/autotrain.py --list +``` + +## Infrastructure Validation Results + +| Component | Status | Notes | +|-----------|--------|-------| +| Python Environment | ✅ Pass | Python 3.12.3 | +| PyTorch | ✅ Pass | 2.9.1+cpu installed | +| Transformers | ✅ Pass | 4.57.6 installed | +| PEFT | ✅ Pass | 0.18.1 installed | +| Training Scripts | ✅ Pass | Syntax validated | +| Dataset Access | ✅ Pass | 33,531 samples available | +| Output Directories | ✅ Pass | Created successfully | +| Configuration Files | ✅ Pass | Present and valid | + +## Next Steps + +1. **For Immediate Training:** + - Run in environment with HuggingFace access + - Use `--quick` flag for fast iteration + - Monitor GPU memory usage + +2. **For Production Deployment:** + - Set up Azure ML integration + - Configure Azure Quantum for hybrid quantum-AI + - Enable telemetry and monitoring + +3. **For Continuous Training:** + - Set up automated training pipelines + - Configure model evaluation metrics + - Enable auto-promotion of best models + +## Conclusion + +The Aria AI training infrastructure is **fully functional and production-ready**. All required dependencies are properly configured, training scripts are validated, and datasets are available. The demonstration successfully proves the training workflow operates correctly. Production training can proceed immediately in an environment with network access to HuggingFace Hub. + +--- + +**Generated:** 2026-01-19 16:34 UTC +**Tool Used:** Aria Training Pipeline +**Environment:** GitHub Actions Runner diff --git a/docs/training/AUTOMATED_TRAINING_PIPELINE.md b/docs/training/AUTOMATED_TRAINING_PIPELINE.md index 3bb89a096..be85d8b58 100644 --- a/docs/training/AUTOMATED_TRAINING_PIPELINE.md +++ b/docs/training/AUTOMATED_TRAINING_PIPELINE.md @@ -1,270 +1,270 @@ -# Automated Multi-Model Training Pipeline - -This document explains the usage of `scripts/automated_training_pipeline.py`, a high-level orchestration wrapper for rapid LoRA experimentation across supported base models (currently `phi`, `qwen`, and `tinyllama`). It unifies synthetic dataset generation, conditional training, ranking, cleanup, and Azure ML job spec emission. - ---- - -## Why Use This Wrapper? - -* One command drives multi-model synthetic data generation + (optional) LoRA training. -* Consistent summary artifacts for downstream dashboards & CI. -* Seamless edge cases: generate-only, cleanup, evaluation disable. -* Turn-key Azure ML remote execution spec without manual YAML authoring. -* Append-only historical lineage maintained separately by `parallel_train.py` (`data_out/parallel_training/status.json`). - ---- - -## Core Outputs - -| Artifact | Location | Purpose | -|----------|----------|---------| -| Summary JSON | `data_out/automated_training/summary_.json` | Aggregated results for this wrapper invocation (per model). | -| Azure ML Job Spec (optional) | `.azureml/job_.yaml` | Ready for `az ml job create --file` remote submission. | -| Conda Environment Definition | `.azureml/environment.yml` | Base environment for Azure ML job. Generated if missing or forced. | -| Synthetic Dataset | `datasets/chat/auto_generated/` | Train/Test JSON/JSONL for quick experiments. | -| Status History | `data_out/parallel_training/status.json` | Long-term cumulative log from underlying training script(s). | - ---- - -## Key Flags - -| Flag | Description | Default | -|------|-------------|---------| -| `--models phi,qwen,tinyllama` | Comma list of models to process. | `phi,qwen` | -| `--quick` | Generate ~100 samples (fast dev mode). Ignored if `--samples` specified. | Off | -| `--samples ` | Override synthetic sample count. | None | -| `--generate-only` | Create synthetic dataset but skip training entirely. | Off | -| `--no-eval` | Skip evaluation & sample generation in underlying training. | Off | -| `--cleanup` | Remove intermediate checkpoints after successful training. | Off | -| `--ranking-metric perplexity_improvement / post_perplexity / diversity_avg / combined_improvement / distinct_diversity` | Controls ranking metric selection (distinct_diversity is an alias of diversity_avg). | `perplexity_improvement` | -| `--min-train-samples ` | Skip training if train sample count below threshold. | 50 | -| `--output-name ` | Custom label replacing timestamp-based run label. | Auto timestamp | -| `--azure-ml-spec` | Emit Azure ML job spec + environment file. | Off | -| `--azure-ml-compute ` | Target compute cluster name for AML job. | `cpu-cluster` | -| `--azure-ml-experiment ` | AML experiment name. | `lora-autotrain` | -| `--azure-ml-env-name ` | Conda environment logical name. | `auto-training-env` | -| `--azure-ml-image ` | Base container image for AML. | `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04` | -| `--force-azure-ml` | Overwrite existing environment.yml even if present. | Off | - ---- - -## Typical Workflows - -### 1. Quick Multi-Model Training (default models) - -```powershell -python .\scripts\automated_training_pipeline.py --quick -``` - -Produces synthetic data (~100 requested → final ~63 train, 7 test) and trains both models (unless below `--min-train-samples`). Summary generated. - -### 2. Generate Data Only (No Training) - -```powershell -python .\scripts\automated_training_pipeline.py --generate-only --quick --models phi,qwen -``` - -Creates synthetic dataset. Summary file marks each model with `training_skipped: true`. - -### 3. Targeted Single Model With Cleanup - -```powershell -python .\scripts\automated_training_pipeline.py --models phi --samples 300 --cleanup -``` - -Generates 300 samples, trains phi LoRA, evaluates (unless `--no-eval`), ranks job(s), then removes intermediate checkpoints preserving adapter weights & metrics. - -### 4. Ranking by Post-Perplexity - -```powershell -python .\scripts\automated_training_pipeline.py --ranking-metric post_perplexity --quick -``` - -Ranking uses absolute post-training perplexity (lower is better) instead of improvement delta. - -### 5. Skip Evaluation Entirely - -```powershell -python .\scripts\automated_training_pipeline.py --no-eval --quick -``` - -Training still occurs; evaluation block omitted from status entry. - -### 6. Emit Azure ML Spec (Generate Only) - -```powershell -python .\scripts\automated_training_pipeline.py --azure-ml-spec --generate-only --quick --models phi,qwen -``` - -Generates synthetic data, writes summary, emits: - -* `.azureml/environment.yml` -* `.azureml/job_.yaml` - -Command inside job YAML includes `--generate-only` (training will be skipped remotely as well). - -### 7. Full Remote Spec (Train + Cleanup) - -```powershell -python .\scripts\automated_training_pipeline.py --azure-ml-spec --cleanup --quick --models phi -``` - -Job spec command will run training (no `--generate-only` flag unless specified) and perform cleanup stage. - -### 8. TinyLlama Ultrafast Single Model Run - -```powershell -python .\scripts\automated_training_pipeline.py --models tinyllama --quick --ranking-metric diversity_avg -``` - -Runs synthetic generation & ultrafast TinyLlama LoRA config path defined in `auto_data_train.py` (config: `AI/microsoft_phi-silica-3.6_v1/lora/lora_tinyllama_ultrafast.yaml`). Ranking uses average diversity (Distinct-1/2) for experimentation focused on response variety. - ---- - -## Supported Models - -| Key | Base HF Model ID | Config (ultrafast) | Notes | -|-----|------------------|--------------------|-------| -| phi | `microsoft/Phi-3.5-mini-instruct` | `AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml` | General baseline, medium size | -| qwen | `Qwen/Qwen2.5-3B-Instruct` | `AI/microsoft_phi-silica-3.6_v1/lora/lora_qwen_ultrafast.yaml` (example, if present) | Higher capacity, slower | -| tinyllama | `TinyLlama/TinyLlama-1.1B-Chat-v1.0` | `AI/microsoft_phi-silica-3.6_v1/lora/lora_tinyllama_ultrafast.yaml` | Small, very fast experimentation | - -> If a config file is missing for a model key, add one under the LoRA directory following parameter patterns of existing ultrafast configs. - ---- - -## Ranking Metrics - -The wrapper propagates the ranking metric to the underlying parallel trainer: - -| Metric | Definition | Direction | When Useful | -|--------|------------|-----------|-------------| -| `perplexity_improvement` | Relative drop: (pre - post) / pre | Higher better | General quality gains | -| `post_perplexity` | Final perplexity (stored negative internally for sorting) | Lower better | Absolute model quality target | -| `diversity_avg` | (Distinct-1 + Distinct-2) / 2 from sample generations | Higher better | Variety / reduced repetition | -| `distinct_diversity` | Alias of `diversity_avg` | Higher better | Convenience naming | -| `combined_improvement` | `0.7 * perplexity_improvement + 0.3 * diversity_avg` | Higher better | Balanced quality vs variety | - -Sample diversity and echo ratio are computed only when evaluation & sample generation are enabled (i.e., not using `--no-eval`). Generate-only runs do not produce ranking data. - ---- - -## Azure ML Submission Guide (Manual) - -* Ensure Azure CLI & ML extension installed: - -```powershell -az extension add -n ml -``` - -* Fill in Azure ML placeholders in `.env` (added automatically if missing): - * `AZURE_ML_SUBSCRIPTION_ID` - * `AZURE_ML_RESOURCE_GROUP` - * `AZURE_ML_WORKSPACE` - * Confirm `AZURE_ML_COMPUTE_TARGET` matches existing cluster. - -* Log in & set defaults: - -```powershell -az login -az account set --subscription -az configure --defaults group= workspace= -``` - -* (Optional) Create or update environment: - -```powershell -az ml environment create --file .azureml/environment.yml -``` - -* Submit job: - -```powershell -az ml job create --file .azureml/job_.yaml -``` - -* Monitor: - -```powershell -az ml job show --name automated-training- -az ml job stream --name automated-training- -``` - -> Note: The generated job YAML intentionally omits `--azure-ml-spec` to prevent nested spec emission during remote execution. - ---- - -## Summary JSON Structure - -Minimal example (generate-only, truncated): - -```json -{ - "run_label": "multi_", - "models": ["phi", "qwen"], - "generate_only": true, - "runs": [ - { - "model": "phi", - "run_id": null, - "training_skipped": true, - "jobs": [{"status": "skipped", "dataset_train_samples": 63}] - } - ] -} -``` - -For training runs `run_id` links to the underlying last entry in `data_out/parallel_training/status.json` with detailed evaluation + ranking info. - ---- - -## Internal Mechanics - -* Synthetic dataset creation delegates to `scripts/auto_data_train.py` with `--train-mode none` when generate-only. -* Training & evaluation wrapper logic lives in `scripts/parallel_train.py`; wrapper only reads latest status entry. -* Ranking metrics implemented in `parallel_train.py` include improvement, absolute post perplexity, diversity average + alias, and combined weighting. -* Cleanup removes large checkpoint directories while preserving adapter artifacts. - ---- - -## Extending - -1. Add new base model key (e.g. `mistral`) to `valid_models` set and implement branch logic in `auto_data_train.py` including: HF model ID, ultrafast config path, default learning rate, naming prefix. -2. Introduce additional ranking metric → update `parallel_train.py` ranking computation and CLI choices. -3. Additional cloud target (e.g. Azure Batch) → new spec emission function following pattern of `emit_azure_ml_spec`. -4. Add new evaluation component → augment `_perform_evaluation()` in `parallel_train.py` and include metric in ranking logic if needed. - ---- - -## Troubleshooting - -| Symptom | Cause | Resolution | -|---------|-------|-----------| -| No job YAML produced | Forgot `--azure-ml-spec` flag | Re-run with flag. | -| Job spec command trains unexpectedly | Omitted `--generate-only` in original invocation | Add `--generate-only` and regenerate spec. | -| AML job fails environment solve | Missing dependency versions | Add pinned versions to `environment.yml` & re-run with `--force-azure-ml`. | -| Skipped training due to samples | `--min-train-samples` threshold | Lower threshold or increase `--samples`. | -| Ranking field null | Generate-only or evaluation disabled | Perform real training with evaluation enabled. | -| Azure ML validation skipped | Azure CLI / ML extension absent | Install CLI + `az extension add -n ml` and re-run validation. | - ---- - -## Recommended Next Steps - -* CI step to validate generated YAML schema with `az ml job validate` (implemented – use `scripts/azureml_ci_validate.py` or `ci_orchestrator.py --ci-pipeline`). -* Integrate summary ingestion into dashboard view. -* Expand synthetic recipe diversity for robustness. -* Add combined diversity / length regularization metric. -* Add automatic canary deployment via `model_deployer.py --deploy best --strategy canary` post-validation. - ---- - -## Version & Maintenance - -Last updated: 2025-11-25 (tinyllama + extended ranking metrics) -Script path: `scripts/automated_training_pipeline.py` -Maintain consistency with project instructions in `copilot-instructions.md` for dataset immutability & output conventions. - ---- - -Happy automating! ⚙️ +# Automated Multi-Model Training Pipeline + +This document explains the usage of `scripts/automated_training_pipeline.py`, a high-level orchestration wrapper for rapid LoRA experimentation across supported base models (currently `phi`, `qwen`, and `tinyllama`). It unifies synthetic dataset generation, conditional training, ranking, cleanup, and Azure ML job spec emission. + +--- + +## Why Use This Wrapper? + +* One command drives multi-model synthetic data generation + (optional) LoRA training. +* Consistent summary artifacts for downstream dashboards & CI. +* Seamless edge cases: generate-only, cleanup, evaluation disable. +* Turn-key Azure ML remote execution spec without manual YAML authoring. +* Append-only historical lineage maintained separately by `parallel_train.py` (`data_out/parallel_training/status.json`). + +--- + +## Core Outputs + +| Artifact | Location | Purpose | +|----------|----------|---------| +| Summary JSON | `data_out/automated_training/summary_.json` | Aggregated results for this wrapper invocation (per model). | +| Azure ML Job Spec (optional) | `.azureml/job_.yaml` | Ready for `az ml job create --file` remote submission. | +| Conda Environment Definition | `.azureml/environment.yml` | Base environment for Azure ML job. Generated if missing or forced. | +| Synthetic Dataset | `datasets/chat/auto_generated/` | Train/Test JSON/JSONL for quick experiments. | +| Status History | `data_out/parallel_training/status.json` | Long-term cumulative log from underlying training script(s). | + +--- + +## Key Flags + +| Flag | Description | Default | +|------|-------------|---------| +| `--models phi,qwen,tinyllama` | Comma list of models to process. | `phi,qwen` | +| `--quick` | Generate ~100 samples (fast dev mode). Ignored if `--samples` specified. | Off | +| `--samples ` | Override synthetic sample count. | None | +| `--generate-only` | Create synthetic dataset but skip training entirely. | Off | +| `--no-eval` | Skip evaluation & sample generation in underlying training. | Off | +| `--cleanup` | Remove intermediate checkpoints after successful training. | Off | +| `--ranking-metric perplexity_improvement / post_perplexity / diversity_avg / combined_improvement / distinct_diversity` | Controls ranking metric selection (distinct_diversity is an alias of diversity_avg). | `perplexity_improvement` | +| `--min-train-samples ` | Skip training if train sample count below threshold. | 50 | +| `--output-name ` | Custom label replacing timestamp-based run label. | Auto timestamp | +| `--azure-ml-spec` | Emit Azure ML job spec + environment file. | Off | +| `--azure-ml-compute ` | Target compute cluster name for AML job. | `cpu-cluster` | +| `--azure-ml-experiment ` | AML experiment name. | `lora-autotrain` | +| `--azure-ml-env-name ` | Conda environment logical name. | `auto-training-env` | +| `--azure-ml-image ` | Base container image for AML. | `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04` | +| `--force-azure-ml` | Overwrite existing environment.yml even if present. | Off | + +--- + +## Typical Workflows + +### 1. Quick Multi-Model Training (default models) + +```powershell +python .\scripts\automated_training_pipeline.py --quick +``` + +Produces synthetic data (~100 requested → final ~63 train, 7 test) and trains both models (unless below `--min-train-samples`). Summary generated. + +### 2. Generate Data Only (No Training) + +```powershell +python .\scripts\automated_training_pipeline.py --generate-only --quick --models phi,qwen +``` + +Creates synthetic dataset. Summary file marks each model with `training_skipped: true`. + +### 3. Targeted Single Model With Cleanup + +```powershell +python .\scripts\automated_training_pipeline.py --models phi --samples 300 --cleanup +``` + +Generates 300 samples, trains phi LoRA, evaluates (unless `--no-eval`), ranks job(s), then removes intermediate checkpoints preserving adapter weights & metrics. + +### 4. Ranking by Post-Perplexity + +```powershell +python .\scripts\automated_training_pipeline.py --ranking-metric post_perplexity --quick +``` + +Ranking uses absolute post-training perplexity (lower is better) instead of improvement delta. + +### 5. Skip Evaluation Entirely + +```powershell +python .\scripts\automated_training_pipeline.py --no-eval --quick +``` + +Training still occurs; evaluation block omitted from status entry. + +### 6. Emit Azure ML Spec (Generate Only) + +```powershell +python .\scripts\automated_training_pipeline.py --azure-ml-spec --generate-only --quick --models phi,qwen +``` + +Generates synthetic data, writes summary, emits: + +* `.azureml/environment.yml` +* `.azureml/job_.yaml` + +Command inside job YAML includes `--generate-only` (training will be skipped remotely as well). + +### 7. Full Remote Spec (Train + Cleanup) + +```powershell +python .\scripts\automated_training_pipeline.py --azure-ml-spec --cleanup --quick --models phi +``` + +Job spec command will run training (no `--generate-only` flag unless specified) and perform cleanup stage. + +### 8. TinyLlama Ultrafast Single Model Run + +```powershell +python .\scripts\automated_training_pipeline.py --models tinyllama --quick --ranking-metric diversity_avg +``` + +Runs synthetic generation & ultrafast TinyLlama LoRA config path defined in `auto_data_train.py` (config: `AI/microsoft_phi-silica-3.6_v1/lora/lora_tinyllama_ultrafast.yaml`). Ranking uses average diversity (Distinct-1/2) for experimentation focused on response variety. + +--- + +## Supported Models + +| Key | Base HF Model ID | Config (ultrafast) | Notes | +|-----|------------------|--------------------|-------| +| phi | `microsoft/Phi-3.5-mini-instruct` | `AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml` | General baseline, medium size | +| qwen | `Qwen/Qwen2.5-3B-Instruct` | `AI/microsoft_phi-silica-3.6_v1/lora/lora_qwen_ultrafast.yaml` (example, if present) | Higher capacity, slower | +| tinyllama | `TinyLlama/TinyLlama-1.1B-Chat-v1.0` | `AI/microsoft_phi-silica-3.6_v1/lora/lora_tinyllama_ultrafast.yaml` | Small, very fast experimentation | + +> If a config file is missing for a model key, add one under the LoRA directory following parameter patterns of existing ultrafast configs. + +--- + +## Ranking Metrics + +The wrapper propagates the ranking metric to the underlying parallel trainer: + +| Metric | Definition | Direction | When Useful | +|--------|------------|-----------|-------------| +| `perplexity_improvement` | Relative drop: (pre - post) / pre | Higher better | General quality gains | +| `post_perplexity` | Final perplexity (stored negative internally for sorting) | Lower better | Absolute model quality target | +| `diversity_avg` | (Distinct-1 + Distinct-2) / 2 from sample generations | Higher better | Variety / reduced repetition | +| `distinct_diversity` | Alias of `diversity_avg` | Higher better | Convenience naming | +| `combined_improvement` | `0.7 * perplexity_improvement + 0.3 * diversity_avg` | Higher better | Balanced quality vs variety | + +Sample diversity and echo ratio are computed only when evaluation & sample generation are enabled (i.e., not using `--no-eval`). Generate-only runs do not produce ranking data. + +--- + +## Azure ML Submission Guide (Manual) + +* Ensure Azure CLI & ML extension installed: + +```powershell +az extension add -n ml +``` + +* Fill in Azure ML placeholders in `.env` (added automatically if missing): + * `AZURE_ML_SUBSCRIPTION_ID` + * `AZURE_ML_RESOURCE_GROUP` + * `AZURE_ML_WORKSPACE` + * Confirm `AZURE_ML_COMPUTE_TARGET` matches existing cluster. + +* Log in & set defaults: + +```powershell +az login +az account set --subscription +az configure --defaults group= workspace= +``` + +* (Optional) Create or update environment: + +```powershell +az ml environment create --file .azureml/environment.yml +``` + +* Submit job: + +```powershell +az ml job create --file .azureml/job_.yaml +``` + +* Monitor: + +```powershell +az ml job show --name automated-training- +az ml job stream --name automated-training- +``` + +> Note: The generated job YAML intentionally omits `--azure-ml-spec` to prevent nested spec emission during remote execution. + +--- + +## Summary JSON Structure + +Minimal example (generate-only, truncated): + +```json +{ + "run_label": "multi_", + "models": ["phi", "qwen"], + "generate_only": true, + "runs": [ + { + "model": "phi", + "run_id": null, + "training_skipped": true, + "jobs": [{"status": "skipped", "dataset_train_samples": 63}] + } + ] +} +``` + +For training runs `run_id` links to the underlying last entry in `data_out/parallel_training/status.json` with detailed evaluation + ranking info. + +--- + +## Internal Mechanics + +* Synthetic dataset creation delegates to `scripts/auto_data_train.py` with `--train-mode none` when generate-only. +* Training & evaluation wrapper logic lives in `scripts/parallel_train.py`; wrapper only reads latest status entry. +* Ranking metrics implemented in `parallel_train.py` include improvement, absolute post perplexity, diversity average + alias, and combined weighting. +* Cleanup removes large checkpoint directories while preserving adapter artifacts. + +--- + +## Extending + +1. Add new base model key (e.g. `mistral`) to `valid_models` set and implement branch logic in `auto_data_train.py` including: HF model ID, ultrafast config path, default learning rate, naming prefix. +2. Introduce additional ranking metric → update `parallel_train.py` ranking computation and CLI choices. +3. Additional cloud target (e.g. Azure Batch) → new spec emission function following pattern of `emit_azure_ml_spec`. +4. Add new evaluation component → augment `_perform_evaluation()` in `parallel_train.py` and include metric in ranking logic if needed. + +--- + +## Troubleshooting + +| Symptom | Cause | Resolution | +|---------|-------|-----------| +| No job YAML produced | Forgot `--azure-ml-spec` flag | Re-run with flag. | +| Job spec command trains unexpectedly | Omitted `--generate-only` in original invocation | Add `--generate-only` and regenerate spec. | +| AML job fails environment solve | Missing dependency versions | Add pinned versions to `environment.yml` & re-run with `--force-azure-ml`. | +| Skipped training due to samples | `--min-train-samples` threshold | Lower threshold or increase `--samples`. | +| Ranking field null | Generate-only or evaluation disabled | Perform real training with evaluation enabled. | +| Azure ML validation skipped | Azure CLI / ML extension absent | Install CLI + `az extension add -n ml` and re-run validation. | + +--- + +## Recommended Next Steps + +* CI step to validate generated YAML schema with `az ml job validate` (implemented – use `scripts/azureml_ci_validate.py` or `ci_orchestrator.py --ci-pipeline`). +* Integrate summary ingestion into dashboard view. +* Expand synthetic recipe diversity for robustness. +* Add combined diversity / length regularization metric. +* Add automatic canary deployment via `model_deployer.py --deploy best --strategy canary` post-validation. + +--- + +## Version & Maintenance + +Last updated: 2025-11-25 (tinyllama + extended ranking metrics) +Script path: `scripts/automated_training_pipeline.py` +Maintain consistency with project instructions in `copilot-instructions.md` for dataset immutability & output conventions. + +--- + +Happy automating! ⚙️ diff --git a/docs/training/AUTONOMOUS_TRAINING_README.md b/docs/training/AUTONOMOUS_TRAINING_README.md index b2eea6c74..f717c1ea5 100644 --- a/docs/training/AUTONOMOUS_TRAINING_README.md +++ b/docs/training/AUTONOMOUS_TRAINING_README.md @@ -1,378 +1,378 @@ -# Autonomous AI Training System - -## Overview - -The Autonomous Training Orchestrator automatically manages the complete AI training lifecycle without manual intervention: - -- 🔍 **Auto-Discovery**: Continuously scans for new datasets -- 📥 **Data Collection**: Downloads datasets when inventory is low -- 🚀 **Intelligent Training**: Progressively trains models with adaptive epochs -- 📊 **Performance Monitoring**: Tracks accuracy and alerts on degradation -- ⚙️ **Optimization**: Automatically tunes hyperparameters -- 🎯 **Deployment**: Deploys best models when criteria are met - -## Quick Start - -### 1. Single Training Cycle (Test Mode) - -```powershell -python .\scripts\autonomous_training_orchestrator.py --once -``` - -### 2. Continuous Autonomous Mode - -```powershell -python .\scripts\autonomous_training_orchestrator.py -``` - -### 3. Check Status - -```powershell -python .\scripts\autonomous_training_orchestrator.py --status -``` - -## Configuration - -Edit `config/autonomous_training.yaml` to customize behavior: - -```yaml -autonomous_mode: - continuous: true # Run continuously - cycle_interval_minutes: 30 # Time between cycles - -training: - epochs_progression: # Progressive training - - 25 - - 50 - - 100 - - 200 - workers: 20 # Parallel workers - adaptive_epochs: true # Auto-adjust epochs -``` - -## How It Works - -### Autonomous Cycle - -Each cycle consists of: - -1. **Discovery Phase** (2-5 min) - - Scans local dataset directories - - Catalogs available datasets - - Checks dataset counts against thresholds - -2. **Collection Phase** (10-30 min, if needed) - - Downloads new datasets if below minimum - - Validates and preprocesses data - - Updates dataset inventory - -3. **Training Phase** (5-10 min per cycle) - - Selects optimal epoch count based on history - - Trains quantum models on all datasets - - Uses 20 parallel workers for speed - - Checkpoints every 10 datasets - -4. **Analysis Phase** (1-2 min) - - Evaluates model performance - - Tracks accuracy trends - - Identifies best performers - - Alerts on degradation - -5. **Optimization Phase** (5-10 min) - - Tunes hyperparameters - - Explores architecture variations - - Prunes underperforming models - -6. **Deployment Phase** (2-5 min) - - Deploys models meeting accuracy threshold - - Updates production endpoints - - Creates model artifacts - -### Progressive Training Strategy - -The system intelligently increases training epochs across cycles: - -- **Cycle 1**: 25 epochs (quick validation) -- **Cycle 2**: 50 epochs (improved convergence) -- **Cycle 3**: 100 epochs (deep learning) -- **Cycle 4+**: 200 epochs (maximum performance) - -If accuracy plateaus or degrades, the system automatically adjusts: - -- Low accuracy → increase epochs -- Plateau detected → boost epochs further -- Degradation → alert and investigate - -## Monitoring - -### Status File - -Real-time status: `data_out/autonomous_training_status.json` - -```json -{ - "started_at": "2025-11-17T10:00:00", - "cycles_completed": 5, - "best_accuracy": 0.8245, - "current_phase": "training", - "total_datasets_available": 552, - "performance_history": [...] -} -``` - -### Logs - -Detailed logs: `data_out/autonomous_training.log` - -### Results - -Training results: `data_out/autonomous_results/` - -## Use Cases - -### 1. Continuous Improvement - -Run 24/7 to continuously improve models: - -```powershell -# Terminal 1: Start orchestrator -python .\scripts\autonomous_training_orchestrator.py - -# Terminal 2: Monitor status -while ($true) { - python .\scripts\autonomous_training_orchestrator.py --status - Start-Sleep -Seconds 300 -} -``` - -### 2. Daily Training Cycles - -Run scheduled training sessions: - -```yaml -# config/autonomous_training.yaml -autonomous_mode: - continuous: true - cycle_interval_minutes: 1440 # Once per day - max_cycles: 7 # One week -``` - -### 3. Dataset-Driven Training - -Automatically train when new data arrives: - -```yaml -data_collection: - auto_discover: true - min_datasets: 1000 # Will download if below - quality_threshold: 70 # Higher quality only -``` - -### 4. Production Deployment Pipeline - -Automatically deploy best models: - -```yaml -deployment: - auto_deploy_best: true - min_accuracy_for_deployment: 0.92 - azure_quantum_enabled: true -``` - -## Advanced Features - -### Adaptive Learning - -- **Curriculum Learning**: Start with easier datasets -- **Active Learning**: Focus on uncertain predictions -- **Transfer Learning**: Use pre-trained models - -### Resource Management - -- Automatic GPU/CPU allocation -- Disk space monitoring -- Memory optimization - -### Notifications - -- Email alerts on completion/errors -- Slack integration -- Performance degradation warnings - -## Example Workflow - -```powershell -# 1. Initial setup (one-time) -python .\scripts\autonomous_training_orchestrator.py --once - -# 2. Review configuration -code config\autonomous_training.yaml - -# 3. Start continuous mode -python .\scripts\autonomous_training_orchestrator.py - -# Output: -# 🤖 Starting Autonomous Training Orchestrator (Continuous Mode) -# Configuration: config/autonomous_training.yaml -# Status file: data_out/autonomous_training_status.json -# Cycle interval: 30 minutes -# -# ================================================================================ -# 🔄 AUTONOMOUS CYCLE #1 -# ================================================================================ -# -# 🔍 Starting autonomous dataset discovery... -# Found 552 datasets in massive_quantum -# ✅ Total datasets discovered: 552 -# -# 🚀 Starting training cycle with 25 epochs... -# Executing: python .\scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 20 --epochs 25 -# ✅ Training cycle completed successfully -# Mean accuracy: 75.28% -# Datasets trained: 414 -# -# 📊 Analyzing performance... -# 🏆 New best accuracy: 75.28% -# -# ✅ Cycle #1 completed in 156.8s -# Best accuracy so far: 75.28% -# Total datasets: 552 -# -# ⏳ Waiting 30 minutes until next cycle... -``` - -## Integration with Existing Systems - -### Talk-to-AI Chat - -```python -# Chat provider can query training status -from scripts.autonomous_training_orchestrator import AutonomousTrainingOrchestrator - -orchestrator = AutonomousTrainingOrchestrator() -status = orchestrator.status -print(f"Current best model: {status['best_accuracy']:.2%}") -``` - -### Azure Functions - -```python -# Function can trigger training cycles -import azure.functions as func -from scripts.autonomous_training_orchestrator import AutonomousTrainingOrchestrator - -async def main(req: func.HttpRequest): - orchestrator = AutonomousTrainingOrchestrator() - await orchestrator.run_once() - return func.HttpResponse("Training cycle completed") -``` - -### Quantum AI - -```python -# Use best trained models for quantum classification -from scripts.autonomous_training_orchestrator import AutonomousTrainingOrchestrator - -orchestrator = AutonomousTrainingOrchestrator() -best_accuracy = orchestrator.status['best_accuracy'] -# Deploy to quantum hardware if threshold met -``` - -## Troubleshooting - -### Orchestrator Won't Start - -```powershell -# Check Python environment -python --version # Should be 3.8+ - -# Install dependencies -pip install pyyaml - -# Check config file -python -c "import yaml; yaml.safe_load(open('config/autonomous_training.yaml'))" -``` - -### Training Fails - -```powershell -# Check datasets exist -Get-ChildItem datasets\massive_quantum\*.csv | Measure-Object | Select-Object Count - -# Verify benchmark script -python .\scripts\distributed_benchmark.py --help - -# Check logs -Get-Content data_out\autonomous_training.log -Tail 50 -``` - -### Performance Degradation - -```powershell -# Check status history -python .\scripts\autonomous_training_orchestrator.py --status - -# Review performance trends -python -c "import json; data=json.load(open('data_out/autonomous_training_status.json')); [print(f\"{h['timestamp']}: {h['mean_accuracy']:.2%}\") for h in data['performance_history']]" -``` - -## Safety & Best Practices - -1. **Start with Test Mode**: Run `--once` before continuous mode -2. **Monitor First Cycles**: Watch first 3-5 cycles for issues -3. **Set Reasonable Intervals**: 30-60 minutes prevents resource exhaustion -4. **Limit Max Cycles**: Use `max_cycles` during testing -5. **Backup Status Files**: Preserve training history -6. **Review Logs Regularly**: Check `autonomous_training.log` -7. **Gradual Deployment**: Test `auto_deploy_best: false` first - -## Performance Metrics - -Typical performance on 552 datasets: - -| Metric | Value | -|--------|-------| -| Cycle Duration | 5-10 minutes | -| Datasets/Second | 1-2 datasets | -| Memory Usage | 2-4 GB | -| CPU Usage | 80-100% (20 workers) | -| Disk I/O | Medium | -| Accuracy Improvement | 1-3% per cycle | - -## Future Enhancements - -- [ ] Neural architecture search -- [ ] Automated feature engineering -- [ ] Distributed training across nodes -- [ ] Real-time model serving -- [ ] A/B testing framework -- [ ] Model versioning and rollback -- [ ] Cost optimization for cloud resources - -## Contributing - -### Note on CLI scripts - -Training orchestration and helper scripts are often executed from CI or as subprocesses. When creating new CLI scripts under `scripts/`, add the repository root to `sys.path` at the top of the file to ensure imports from `shared/` work regardless of CWD. Example: - -```python -from pathlib import Path -import sys - -REPO_ROOT = Path(__file__).resolve().parent.parent -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from shared.json_utils import load_status_json -``` - -To extend the autonomous training system: - -1. Add new optimization strategies in `optimization_cycle()` -2. Implement custom deployment targets in `deployment_cycle()` -3. Create new data sources in `download_new_datasets()` -4. Add monitoring integrations in `analyze_performance()` - -## License - -Part of the QAI project - see main repository LICENSE +# Autonomous AI Training System + +## Overview + +The Autonomous Training Orchestrator automatically manages the complete AI training lifecycle without manual intervention: + +- 🔍 **Auto-Discovery**: Continuously scans for new datasets +- 📥 **Data Collection**: Downloads datasets when inventory is low +- 🚀 **Intelligent Training**: Progressively trains models with adaptive epochs +- 📊 **Performance Monitoring**: Tracks accuracy and alerts on degradation +- ⚙️ **Optimization**: Automatically tunes hyperparameters +- 🎯 **Deployment**: Deploys best models when criteria are met + +## Quick Start + +### 1. Single Training Cycle (Test Mode) + +```powershell +python .\scripts\autonomous_training_orchestrator.py --once +``` + +### 2. Continuous Autonomous Mode + +```powershell +python .\scripts\autonomous_training_orchestrator.py +``` + +### 3. Check Status + +```powershell +python .\scripts\autonomous_training_orchestrator.py --status +``` + +## Configuration + +Edit `config/autonomous_training.yaml` to customize behavior: + +```yaml +autonomous_mode: + continuous: true # Run continuously + cycle_interval_minutes: 30 # Time between cycles + +training: + epochs_progression: # Progressive training + - 25 + - 50 + - 100 + - 200 + workers: 20 # Parallel workers + adaptive_epochs: true # Auto-adjust epochs +``` + +## How It Works + +### Autonomous Cycle + +Each cycle consists of: + +1. **Discovery Phase** (2-5 min) + - Scans local dataset directories + - Catalogs available datasets + - Checks dataset counts against thresholds + +2. **Collection Phase** (10-30 min, if needed) + - Downloads new datasets if below minimum + - Validates and preprocesses data + - Updates dataset inventory + +3. **Training Phase** (5-10 min per cycle) + - Selects optimal epoch count based on history + - Trains quantum models on all datasets + - Uses 20 parallel workers for speed + - Checkpoints every 10 datasets + +4. **Analysis Phase** (1-2 min) + - Evaluates model performance + - Tracks accuracy trends + - Identifies best performers + - Alerts on degradation + +5. **Optimization Phase** (5-10 min) + - Tunes hyperparameters + - Explores architecture variations + - Prunes underperforming models + +6. **Deployment Phase** (2-5 min) + - Deploys models meeting accuracy threshold + - Updates production endpoints + - Creates model artifacts + +### Progressive Training Strategy + +The system intelligently increases training epochs across cycles: + +- **Cycle 1**: 25 epochs (quick validation) +- **Cycle 2**: 50 epochs (improved convergence) +- **Cycle 3**: 100 epochs (deep learning) +- **Cycle 4+**: 200 epochs (maximum performance) + +If accuracy plateaus or degrades, the system automatically adjusts: + +- Low accuracy → increase epochs +- Plateau detected → boost epochs further +- Degradation → alert and investigate + +## Monitoring + +### Status File + +Real-time status: `data_out/autonomous_training_status.json` + +```json +{ + "started_at": "2025-11-17T10:00:00", + "cycles_completed": 5, + "best_accuracy": 0.8245, + "current_phase": "training", + "total_datasets_available": 552, + "performance_history": [...] +} +``` + +### Logs + +Detailed logs: `data_out/autonomous_training.log` + +### Results + +Training results: `data_out/autonomous_results/` + +## Use Cases + +### 1. Continuous Improvement + +Run 24/7 to continuously improve models: + +```powershell +# Terminal 1: Start orchestrator +python .\scripts\autonomous_training_orchestrator.py + +# Terminal 2: Monitor status +while ($true) { + python .\scripts\autonomous_training_orchestrator.py --status + Start-Sleep -Seconds 300 +} +``` + +### 2. Daily Training Cycles + +Run scheduled training sessions: + +```yaml +# config/autonomous_training.yaml +autonomous_mode: + continuous: true + cycle_interval_minutes: 1440 # Once per day + max_cycles: 7 # One week +``` + +### 3. Dataset-Driven Training + +Automatically train when new data arrives: + +```yaml +data_collection: + auto_discover: true + min_datasets: 1000 # Will download if below + quality_threshold: 70 # Higher quality only +``` + +### 4. Production Deployment Pipeline + +Automatically deploy best models: + +```yaml +deployment: + auto_deploy_best: true + min_accuracy_for_deployment: 0.92 + azure_quantum_enabled: true +``` + +## Advanced Features + +### Adaptive Learning + +- **Curriculum Learning**: Start with easier datasets +- **Active Learning**: Focus on uncertain predictions +- **Transfer Learning**: Use pre-trained models + +### Resource Management + +- Automatic GPU/CPU allocation +- Disk space monitoring +- Memory optimization + +### Notifications + +- Email alerts on completion/errors +- Slack integration +- Performance degradation warnings + +## Example Workflow + +```powershell +# 1. Initial setup (one-time) +python .\scripts\autonomous_training_orchestrator.py --once + +# 2. Review configuration +code config\autonomous_training.yaml + +# 3. Start continuous mode +python .\scripts\autonomous_training_orchestrator.py + +# Output: +# 🤖 Starting Autonomous Training Orchestrator (Continuous Mode) +# Configuration: config/autonomous_training.yaml +# Status file: data_out/autonomous_training_status.json +# Cycle interval: 30 minutes +# +# ================================================================================ +# 🔄 AUTONOMOUS CYCLE #1 +# ================================================================================ +# +# 🔍 Starting autonomous dataset discovery... +# Found 552 datasets in massive_quantum +# ✅ Total datasets discovered: 552 +# +# 🚀 Starting training cycle with 25 epochs... +# Executing: python .\scripts\distributed_benchmark.py --datasets-dir datasets/massive_quantum --workers 20 --epochs 25 +# ✅ Training cycle completed successfully +# Mean accuracy: 75.28% +# Datasets trained: 414 +# +# 📊 Analyzing performance... +# 🏆 New best accuracy: 75.28% +# +# ✅ Cycle #1 completed in 156.8s +# Best accuracy so far: 75.28% +# Total datasets: 552 +# +# ⏳ Waiting 30 minutes until next cycle... +``` + +## Integration with Existing Systems + +### Talk-to-AI Chat + +```python +# Chat provider can query training status +from scripts.autonomous_training_orchestrator import AutonomousTrainingOrchestrator + +orchestrator = AutonomousTrainingOrchestrator() +status = orchestrator.status +print(f"Current best model: {status['best_accuracy']:.2%}") +``` + +### Azure Functions + +```python +# Function can trigger training cycles +import azure.functions as func +from scripts.autonomous_training_orchestrator import AutonomousTrainingOrchestrator + +async def main(req: func.HttpRequest): + orchestrator = AutonomousTrainingOrchestrator() + await orchestrator.run_once() + return func.HttpResponse("Training cycle completed") +``` + +### Quantum AI + +```python +# Use best trained models for quantum classification +from scripts.autonomous_training_orchestrator import AutonomousTrainingOrchestrator + +orchestrator = AutonomousTrainingOrchestrator() +best_accuracy = orchestrator.status['best_accuracy'] +# Deploy to quantum hardware if threshold met +``` + +## Troubleshooting + +### Orchestrator Won't Start + +```powershell +# Check Python environment +python --version # Should be 3.8+ + +# Install dependencies +pip install pyyaml + +# Check config file +python -c "import yaml; yaml.safe_load(open('config/autonomous_training.yaml'))" +``` + +### Training Fails + +```powershell +# Check datasets exist +Get-ChildItem datasets\massive_quantum\*.csv | Measure-Object | Select-Object Count + +# Verify benchmark script +python .\scripts\distributed_benchmark.py --help + +# Check logs +Get-Content data_out\autonomous_training.log -Tail 50 +``` + +### Performance Degradation + +```powershell +# Check status history +python .\scripts\autonomous_training_orchestrator.py --status + +# Review performance trends +python -c "import json; data=json.load(open('data_out/autonomous_training_status.json')); [print(f\"{h['timestamp']}: {h['mean_accuracy']:.2%}\") for h in data['performance_history']]" +``` + +## Safety & Best Practices + +1. **Start with Test Mode**: Run `--once` before continuous mode +2. **Monitor First Cycles**: Watch first 3-5 cycles for issues +3. **Set Reasonable Intervals**: 30-60 minutes prevents resource exhaustion +4. **Limit Max Cycles**: Use `max_cycles` during testing +5. **Backup Status Files**: Preserve training history +6. **Review Logs Regularly**: Check `autonomous_training.log` +7. **Gradual Deployment**: Test `auto_deploy_best: false` first + +## Performance Metrics + +Typical performance on 552 datasets: + +| Metric | Value | +|--------|-------| +| Cycle Duration | 5-10 minutes | +| Datasets/Second | 1-2 datasets | +| Memory Usage | 2-4 GB | +| CPU Usage | 80-100% (20 workers) | +| Disk I/O | Medium | +| Accuracy Improvement | 1-3% per cycle | + +## Future Enhancements + +- [ ] Neural architecture search +- [ ] Automated feature engineering +- [ ] Distributed training across nodes +- [ ] Real-time model serving +- [ ] A/B testing framework +- [ ] Model versioning and rollback +- [ ] Cost optimization for cloud resources + +## Contributing + +### Note on CLI scripts + +Training orchestration and helper scripts are often executed from CI or as subprocesses. When creating new CLI scripts under `scripts/`, add the repository root to `sys.path` at the top of the file to ensure imports from `shared/` work regardless of CWD. Example: + +```python +from pathlib import Path +import sys + +REPO_ROOT = Path(__file__).resolve().parent.parent +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from shared.json_utils import load_status_json +``` + +To extend the autonomous training system: + +1. Add new optimization strategies in `optimization_cycle()` +2. Implement custom deployment targets in `deployment_cycle()` +3. Create new data sources in `download_new_datasets()` +4. Add monitoring integrations in `analyze_performance()` + +## License + +Part of the QAI project - see main repository LICENSE diff --git a/docs/training/AUTOTRAIN_README.md b/docs/training/AUTOTRAIN_README.md index dc825a49d..4f70aa317 100644 --- a/docs/training/AUTOTRAIN_README.md +++ b/docs/training/AUTOTRAIN_README.md @@ -1,379 +1,379 @@ -# AutoTrain – Declarative Fine-Tuning Orchestration - -**AutoTrain** is a zero-dependency orchestrator for running LoRA fine-tuning jobs defined in YAML. It runs fully offline (after initial model download), supports both **HuggingFace** and **local** runners, and produces machine-readable status files for monitoring. - ---- - -## Features - -- **Declarative config** – Define jobs once in `autotrain.yaml`; run repeatedly without boilerplate. -- **Two runners** – Choose between the full HF stack (`train_lora.py`) or the streamlined local runner (`run_local_lora_training.py`). -- **Dry-run mode** – Validate configs, check file paths, and build commands without executing expensive GPU work. -- **Status tracking** – Every run writes JSON logs (`data_out/autotrain//last_run.json` and `data_out/autotrain/status.json`). -- **VS Code tasks** – Pre-configured tasks for quick access (dry-run and full run). -- **Azure Functions integration** – The `/api/ai/status` endpoint now surfaces the latest AutoTrain summary. - ---- - -## Quick Start - -### 1. Define Jobs in `autotrain.yaml` - -Edit the top-level `autotrain.yaml` to declare one or more fine-tuning jobs: - -```yaml -version: 1 -jobs: - - name: phi36_mixed_chat - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/mixed_chat - save_dir: data_out/lora_training - epochs: 1 - max_train_samples: 64 - max_eval_samples: 16 - learning_rate: 0.0002 - lora_dropout: 0.1 -``` - -**Key fields:** - -- `name` (required) – Unique job identifier. -- `runner` (required) – `"hf"` or `"local"`. -- `dataset` (optional for local) – Path to dataset directory (train.json/test.json). -- `config` (optional) – Path to YAML config (lora.yaml or local_config.yaml). -- `save_dir` (optional) – Where to write checkpoints/adapters. -- `epochs`, `learning_rate`, `lora_dropout`, `max_train_samples`, etc. – Overrides from config. - -### 2. Validate with Dry-Run - -Check that paths exist, commands are valid, and dataset metadata is correct without starting training: - -```powershell -python .\scripts\autotrain.py --dry-run -``` - -**Output:** JSON for each job with `"status": "validated"` or `"status": "missing"` (if files are absent). - -### 3. Run a Single Job - -Execute just one named job: - -```powershell -python .\scripts\autotrain.py --job phi36_mixed_chat -``` - -**Result:** Logs to `data_out/autotrain/phi36_mixed_chat//stdout.log` and `last_run.json`. - -### 4. Run All Jobs - -If you have multiple jobs defined, run them sequentially: - -```powershell -python .\scripts\autotrain.py -``` - -**Result:** Each job executes in order. Non-zero exit if any fail. - ---- - -## Job Runners - -### HuggingFace Runner (`hf`) - -- **Script:** `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` -- **Features:** Full HF Trainer stack, DeepSpeed support, streaming datasets, Azure Blob manifests, metrics logging. -- **When to use:** Production-scale runs, multi-GPU, cloud storage integrations. -- **Example job:** - - ```yaml - - name: phi36_dolly_full - runner: hf - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - dataset: datasets/chat/dolly - epochs: 3 - hf_model_id: microsoft/Phi-3.5-mini-instruct - ``` - -### Local Runner (`local`) - -- **Script:** `scripts/run_local_lora_training.py` -- **Features:** Lightweight, QLoRA-friendly, offline-first, simple YAML config (`local_config.yaml`). -- **When to use:** CPU-only or consumer GPU (RTX 3060, M1/M2), quick experiments, no Azure deps. -- **Example job:** - - ```yaml - - name: local_quick_test - runner: local - config: AI/microsoft_phi-silica-3.6_v1/local_train/local_config.yaml - epochs: 1 - max_train_samples: 10 - ``` - ---- - -## CLI Reference - -```bash -python scripts/training/autotrain.py [OPTIONS] - -Options: - --config PATH Path to autotrain.yaml (default: autotrain.yaml at repo root) - --job NAME Run only the named job - --dry-run Validate and print commands; do not execute - --list List configured jobs and exit (JSON) - --reinstall Force reinstall for local runner jobs (sets job.reinstall=true) -``` - -**Examples:** - -```powershell -# Validate all jobs -python .\scripts\autotrain.py --dry-run - -# List jobs -python .\scripts\autotrain.py --list - -# Run a specific job -python .\scripts\autotrain.py --job phi36_mixed_chat - -# Force venv reinstall for local runner jobs -python .\scripts\autotrain.py --job local_quick_test --reinstall - -# Run all jobs -python .\scripts\autotrain.py -``` - ---- - -## Output Structure - -```bash -data_out/ - autotrain/ - / - / - stdout.log # Verbatim subprocess output - last_run.json # Latest run metadata - status.json # Summary of all recent runs -``` - -**`last_run.json` schema:** - -```json -{ - "name": "phi36_mixed_chat", - "runner": "hf", - "cmd": ["python", "AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py", ...], - "start_time": "20251116T120530Z", - "status": "succeeded", - "return_code": 0, - "duration_sec": 183.45, - "log": "data_out/autotrain/phi36_mixed_chat/20251116T120530Z/stdout.log", - "output_dir": "data_out/lora_training" -} -``` - -**`status.json` schema:** - -```json -{ - "generated_at": "2025-11-16T12:08:53Z", - "jobs": [ ... ] // Array of last_run objects for each job -} -``` - ---- - -## VS Code Tasks - -Pre-configured tasks in `.vscode/tasks.json`: - -- **Run: AutoTrain (dry-run)** – Validate configs without running training. -- **Run: AutoTrain (all)** – Execute all configured jobs sequentially. - -**Access via:** `Terminal > Run Task...` → select the desired AutoTrain task. - ---- - -## Azure Functions Integration - -The HTTP status endpoint (`/api/ai/status`) now includes the latest AutoTrain summary: - -```json -{ - "active_provider": "local", - "model": "fallback", - "lora": { ... }, - "autotrain": { - "generated_at": "2025-11-16T12:08:53Z", - "jobs": [ ... ] - }, - "status": "ok" -} -``` - -**Use cases:** - -- Monitor last job status from CI/CD. -- Surface training state in web UI. -- Track multi-job pipelines. - ---- - -## Common Workflows - -### Quick Smoke Test (10 samples, 1 epoch) - -```yaml -- name: smoke_test - runner: hf - dataset: datasets/chat/mixed_chat - config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - epochs: 1 - max_train_samples: 10 - max_eval_samples: 4 -``` - -```powershell -python .\scripts\autotrain.py --job smoke_test -``` - -### CPU-Only Local Training (QLoRA-friendly) - -```yaml -- name: cpu_local - runner: local - config: AI/microsoft_phi-silica-3.6_v1/local_train/local_config.yaml - epochs: 1 - max_train_samples: 50 -``` - -Make sure `local_config.yaml` has: - -```yaml -use_4bit: true -max_seq_length: 256 -gradient_checkpointing: true -batch_size: 1 -gradient_accumulation_steps: 8 -``` - -### Multi-Job Pipeline - -```yaml -jobs: - - name: baseline - runner: hf - dataset: datasets/chat/mixed_chat - epochs: 1 - max_train_samples: 64 - - - name: extended - runner: hf - dataset: datasets/chat/dolly - epochs: 3 - max_train_samples: 500 -``` - -```powershell -python .\scripts\autotrain.py -``` - -Jobs run sequentially. If `baseline` fails, `extended` is skipped. Check `status.json` for details. - ---- - -## Troubleshooting - -### Missing Config File - -**Error:** `Config not found: autotrain.yaml` -**Fix:** Ensure you're running from the repo root or specify `--config` with the full path. - -### Job Not Found - -**Error:** `Job not found in config: my_job` -**Fix:** Verify the job name in `autotrain.yaml` matches exactly (case-sensitive). - -### Dry-Run Shows Missing Files - -**Output:** `"status": "missing"`, `"missing": ["datasets/chat/missing_data"]` -**Fix:** Update dataset paths to point to existing directories or create placeholder data. - -### Training Fails with Return Code 1 - -**Output:** `"status": "failed"`, `"return_code": 1` -**Fix:** Open the stdout.log at `data_out/autotrain///stdout.log` for detailed error traces (OOM, missing deps, etc.). - -### Local Runner Venv Issues - -**Symptom:** `ModuleNotFoundError` after updating dependencies. -**Fix:** Add `reinstall: true` to the job config or use `--reinstall` flag: - -```powershell -python .\scripts\autotrain.py --job my_local_job --reinstall -``` - ---- - -## Design Rationale - -**Why YAML?** Declarative configs reduce boilerplate and improve reproducibility. A single file defines all experiments. - -**Why sequential execution?** Simplicity. Parallel multi-GPU jobs are better handled by dedicated schedulers (Kubernetes, Azure Container Apps Jobs, etc.). AutoTrain is for local/dev workflows. - -**Why two runners?** The HF runner (`train_lora.py`) offers production-scale features (DeepSpeed, Azure Blob, observability). The local runner (`run_local_lora_training.py`) is streamlined for quick iteration on consumer hardware. - -**Status JSON format?** Machine-readable logs enable CI/CD integration, web UIs, and progress monitoring without parsing unstructured logs. - ---- - -## Next Steps - -- **Hyperparameter sweeps:** Use `extra_args` to pass per-job custom flags. -- **Multi-GPU local:** Invoke `accelerate launch` directly in `extra_args` for the HF runner. -- **Cloud integration:** Adapt `autotrain.py` to submit jobs to Azure Container Apps Jobs or AWS Batch. -- **Web UI:** Build a simple dashboard that reads `status.json` and displays job history, perplexity trends, and artifact links. - ---- - -## Related Docs - -- **Training scripts:** - - `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` (HF runner) - - `scripts/run_local_lora_training.py` (local runner) - - `AI/microsoft_phi-silica-3.6_v1/local_train/README.md` (local setup details) -- **Azure Functions:** - - `http_ai_status/__init__.py` (status endpoint integration) - - `function_app.py` (root function handler) -- **Datasets:** - - `datasets/chat/mixed_chat/` (example chat dataset) - - `DATASETS_QUICK_REF.md` (catalog) - ---- - -## FAQ - -**Q: Can I use multiple configs in one job?** -A: No. Each job uses one config file. For different hyperparams, define separate jobs. - -**Q: Can I run AutoTrain in CI/CD?** -A: Yes. Use `--dry-run` in CI to validate configs. For training, run in GitHub Actions with self-hosted runners or cloud VMs with GPUs. - -**Q: How do I resume a failed job?** -A: The HF runner supports `--resume-from `. Add it to `extra_args`: - -```yaml -extra_args: ["--resume-from", "data_out/lora_training/checkpoint-500"] -``` - -**Q: Can I schedule jobs to run automatically?** -A: Not yet. Use OS-level schedulers (Windows Task Scheduler, cron) or integrate AutoTrain into Azure Container Apps Jobs with a timer trigger. - -**Q: Does AutoTrain support other models (GPT-2, Llama)?** -A: Yes. Update `hf_model_id` and ensure the training script supports the model architecture. The HF runner is model-agnostic. - ---- - -**AutoTrain is designed for simplicity, reproducibility, and offline-first workflows. Feedback and contributions welcome!** +# AutoTrain – Declarative Fine-Tuning Orchestration + +**AutoTrain** is a zero-dependency orchestrator for running LoRA fine-tuning jobs defined in YAML. It runs fully offline (after initial model download), supports both **HuggingFace** and **local** runners, and produces machine-readable status files for monitoring. + +--- + +## Features + +- **Declarative config** – Define jobs once in `autotrain.yaml`; run repeatedly without boilerplate. +- **Two runners** – Choose between the full HF stack (`train_lora.py`) or the streamlined local runner (`run_local_lora_training.py`). +- **Dry-run mode** – Validate configs, check file paths, and build commands without executing expensive GPU work. +- **Status tracking** – Every run writes JSON logs (`data_out/autotrain//last_run.json` and `data_out/autotrain/status.json`). +- **VS Code tasks** – Pre-configured tasks for quick access (dry-run and full run). +- **Azure Functions integration** – The `/api/ai/status` endpoint now surfaces the latest AutoTrain summary. + +--- + +## Quick Start + +### 1. Define Jobs in `autotrain.yaml` + +Edit the top-level `autotrain.yaml` to declare one or more fine-tuning jobs: + +```yaml +version: 1 +jobs: + - name: phi36_mixed_chat + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/mixed_chat + save_dir: data_out/lora_training + epochs: 1 + max_train_samples: 64 + max_eval_samples: 16 + learning_rate: 0.0002 + lora_dropout: 0.1 +``` + +**Key fields:** + +- `name` (required) – Unique job identifier. +- `runner` (required) – `"hf"` or `"local"`. +- `dataset` (optional for local) – Path to dataset directory (train.json/test.json). +- `config` (optional) – Path to YAML config (lora.yaml or local_config.yaml). +- `save_dir` (optional) – Where to write checkpoints/adapters. +- `epochs`, `learning_rate`, `lora_dropout`, `max_train_samples`, etc. – Overrides from config. + +### 2. Validate with Dry-Run + +Check that paths exist, commands are valid, and dataset metadata is correct without starting training: + +```powershell +python .\scripts\autotrain.py --dry-run +``` + +**Output:** JSON for each job with `"status": "validated"` or `"status": "missing"` (if files are absent). + +### 3. Run a Single Job + +Execute just one named job: + +```powershell +python .\scripts\autotrain.py --job phi36_mixed_chat +``` + +**Result:** Logs to `data_out/autotrain/phi36_mixed_chat//stdout.log` and `last_run.json`. + +### 4. Run All Jobs + +If you have multiple jobs defined, run them sequentially: + +```powershell +python .\scripts\autotrain.py +``` + +**Result:** Each job executes in order. Non-zero exit if any fail. + +--- + +## Job Runners + +### HuggingFace Runner (`hf`) + +- **Script:** `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` +- **Features:** Full HF Trainer stack, DeepSpeed support, streaming datasets, Azure Blob manifests, metrics logging. +- **When to use:** Production-scale runs, multi-GPU, cloud storage integrations. +- **Example job:** + + ```yaml + - name: phi36_dolly_full + runner: hf + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + dataset: datasets/chat/dolly + epochs: 3 + hf_model_id: microsoft/Phi-3.5-mini-instruct + ``` + +### Local Runner (`local`) + +- **Script:** `scripts/run_local_lora_training.py` +- **Features:** Lightweight, QLoRA-friendly, offline-first, simple YAML config (`local_config.yaml`). +- **When to use:** CPU-only or consumer GPU (RTX 3060, M1/M2), quick experiments, no Azure deps. +- **Example job:** + + ```yaml + - name: local_quick_test + runner: local + config: AI/microsoft_phi-silica-3.6_v1/local_train/local_config.yaml + epochs: 1 + max_train_samples: 10 + ``` + +--- + +## CLI Reference + +```bash +python scripts/training/autotrain.py [OPTIONS] + +Options: + --config PATH Path to autotrain.yaml (default: autotrain.yaml at repo root) + --job NAME Run only the named job + --dry-run Validate and print commands; do not execute + --list List configured jobs and exit (JSON) + --reinstall Force reinstall for local runner jobs (sets job.reinstall=true) +``` + +**Examples:** + +```powershell +# Validate all jobs +python .\scripts\autotrain.py --dry-run + +# List jobs +python .\scripts\autotrain.py --list + +# Run a specific job +python .\scripts\autotrain.py --job phi36_mixed_chat + +# Force venv reinstall for local runner jobs +python .\scripts\autotrain.py --job local_quick_test --reinstall + +# Run all jobs +python .\scripts\autotrain.py +``` + +--- + +## Output Structure + +```bash +data_out/ + autotrain/ + / + / + stdout.log # Verbatim subprocess output + last_run.json # Latest run metadata + status.json # Summary of all recent runs +``` + +**`last_run.json` schema:** + +```json +{ + "name": "phi36_mixed_chat", + "runner": "hf", + "cmd": ["python", "AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py", ...], + "start_time": "20251116T120530Z", + "status": "succeeded", + "return_code": 0, + "duration_sec": 183.45, + "log": "data_out/autotrain/phi36_mixed_chat/20251116T120530Z/stdout.log", + "output_dir": "data_out/lora_training" +} +``` + +**`status.json` schema:** + +```json +{ + "generated_at": "2025-11-16T12:08:53Z", + "jobs": [ ... ] // Array of last_run objects for each job +} +``` + +--- + +## VS Code Tasks + +Pre-configured tasks in `.vscode/tasks.json`: + +- **Run: AutoTrain (dry-run)** – Validate configs without running training. +- **Run: AutoTrain (all)** – Execute all configured jobs sequentially. + +**Access via:** `Terminal > Run Task...` → select the desired AutoTrain task. + +--- + +## Azure Functions Integration + +The HTTP status endpoint (`/api/ai/status`) now includes the latest AutoTrain summary: + +```json +{ + "active_provider": "local", + "model": "fallback", + "lora": { ... }, + "autotrain": { + "generated_at": "2025-11-16T12:08:53Z", + "jobs": [ ... ] + }, + "status": "ok" +} +``` + +**Use cases:** + +- Monitor last job status from CI/CD. +- Surface training state in web UI. +- Track multi-job pipelines. + +--- + +## Common Workflows + +### Quick Smoke Test (10 samples, 1 epoch) + +```yaml +- name: smoke_test + runner: hf + dataset: datasets/chat/mixed_chat + config: AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + epochs: 1 + max_train_samples: 10 + max_eval_samples: 4 +``` + +```powershell +python .\scripts\autotrain.py --job smoke_test +``` + +### CPU-Only Local Training (QLoRA-friendly) + +```yaml +- name: cpu_local + runner: local + config: AI/microsoft_phi-silica-3.6_v1/local_train/local_config.yaml + epochs: 1 + max_train_samples: 50 +``` + +Make sure `local_config.yaml` has: + +```yaml +use_4bit: true +max_seq_length: 256 +gradient_checkpointing: true +batch_size: 1 +gradient_accumulation_steps: 8 +``` + +### Multi-Job Pipeline + +```yaml +jobs: + - name: baseline + runner: hf + dataset: datasets/chat/mixed_chat + epochs: 1 + max_train_samples: 64 + + - name: extended + runner: hf + dataset: datasets/chat/dolly + epochs: 3 + max_train_samples: 500 +``` + +```powershell +python .\scripts\autotrain.py +``` + +Jobs run sequentially. If `baseline` fails, `extended` is skipped. Check `status.json` for details. + +--- + +## Troubleshooting + +### Missing Config File + +**Error:** `Config not found: autotrain.yaml` +**Fix:** Ensure you're running from the repo root or specify `--config` with the full path. + +### Job Not Found + +**Error:** `Job not found in config: my_job` +**Fix:** Verify the job name in `autotrain.yaml` matches exactly (case-sensitive). + +### Dry-Run Shows Missing Files + +**Output:** `"status": "missing"`, `"missing": ["datasets/chat/missing_data"]` +**Fix:** Update dataset paths to point to existing directories or create placeholder data. + +### Training Fails with Return Code 1 + +**Output:** `"status": "failed"`, `"return_code": 1` +**Fix:** Open the stdout.log at `data_out/autotrain///stdout.log` for detailed error traces (OOM, missing deps, etc.). + +### Local Runner Venv Issues + +**Symptom:** `ModuleNotFoundError` after updating dependencies. +**Fix:** Add `reinstall: true` to the job config or use `--reinstall` flag: + +```powershell +python .\scripts\autotrain.py --job my_local_job --reinstall +``` + +--- + +## Design Rationale + +**Why YAML?** Declarative configs reduce boilerplate and improve reproducibility. A single file defines all experiments. + +**Why sequential execution?** Simplicity. Parallel multi-GPU jobs are better handled by dedicated schedulers (Kubernetes, Azure Container Apps Jobs, etc.). AutoTrain is for local/dev workflows. + +**Why two runners?** The HF runner (`train_lora.py`) offers production-scale features (DeepSpeed, Azure Blob, observability). The local runner (`run_local_lora_training.py`) is streamlined for quick iteration on consumer hardware. + +**Status JSON format?** Machine-readable logs enable CI/CD integration, web UIs, and progress monitoring without parsing unstructured logs. + +--- + +## Next Steps + +- **Hyperparameter sweeps:** Use `extra_args` to pass per-job custom flags. +- **Multi-GPU local:** Invoke `accelerate launch` directly in `extra_args` for the HF runner. +- **Cloud integration:** Adapt `autotrain.py` to submit jobs to Azure Container Apps Jobs or AWS Batch. +- **Web UI:** Build a simple dashboard that reads `status.json` and displays job history, perplexity trends, and artifact links. + +--- + +## Related Docs + +- **Training scripts:** + - `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` (HF runner) + - `scripts/run_local_lora_training.py` (local runner) + - `AI/microsoft_phi-silica-3.6_v1/local_train/README.md` (local setup details) +- **Azure Functions:** + - `http_ai_status/__init__.py` (status endpoint integration) + - `function_app.py` (root function handler) +- **Datasets:** + - `datasets/chat/mixed_chat/` (example chat dataset) + - `DATASETS_QUICK_REF.md` (catalog) + +--- + +## FAQ + +**Q: Can I use multiple configs in one job?** +A: No. Each job uses one config file. For different hyperparams, define separate jobs. + +**Q: Can I run AutoTrain in CI/CD?** +A: Yes. Use `--dry-run` in CI to validate configs. For training, run in GitHub Actions with self-hosted runners or cloud VMs with GPUs. + +**Q: How do I resume a failed job?** +A: The HF runner supports `--resume-from `. Add it to `extra_args`: + +```yaml +extra_args: ["--resume-from", "data_out/lora_training/checkpoint-500"] +``` + +**Q: Can I schedule jobs to run automatically?** +A: Not yet. Use OS-level schedulers (Windows Task Scheduler, cron) or integrate AutoTrain into Azure Container Apps Jobs with a timer trigger. + +**Q: Does AutoTrain support other models (GPT-2, Llama)?** +A: Yes. Update `hf_model_id` and ensure the training script supports the model architecture. The HF runner is model-agnostic. + +--- + +**AutoTrain is designed for simplicity, reproducibility, and offline-first workflows. Feedback and contributions welcome!** diff --git a/docs/training/BATCH_EVALUATION_GUIDE.md b/docs/training/BATCH_EVALUATION_GUIDE.md index b75b18631..3429b19b1 100644 --- a/docs/training/BATCH_EVALUATION_GUIDE.md +++ b/docs/training/BATCH_EVALUATION_GUIDE.md @@ -1,298 +1,298 @@ -# Batch Evaluation & Model Promotion Guide - -## Overview - -The batch evaluation system provides: - -- **Parallel model evaluation** with real metrics -- **Automatic best-model promotion** to `deployed_models/` -- **Comprehensive reporting** (JSON, Markdown) - ---- - -## Quick Start - -### 1. Evaluate All Trained Models - -```powershell -python .\scripts\batch_evaluator.py --scan-models --evaluate-all -``` - -This will: -- Scan `data_out/lora_training/` for LoRA adapters -- Evaluate each model with default metrics -- Save results to `data_out/batch_evaluator/results_.json` - -### 2. Evaluate and Promote Best Model - -```powershell -python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best -``` - -This will: -- Evaluate all models -- Promote the best model to `deployed_models/_/` -- Create `deployed_models/LATEST.txt` pointing to the promoted model -- Save promotion metadata with metrics and timestamp - -### 3. Dry-Run Mode - -```powershell -python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best --dry-run -``` - -Preview what would be promoted without making changes. - ---- - -## Metrics - -The evaluation system computes the following metrics: - -### Perplexity -- **Description**: Language model quality (lower is better) -- **Range**: Typically 1-100 for fine-tuned models -- **Interpretation**: Measures how "surprised" the model is by the test data -- **Note**: Uses fallback heuristic for compatibility with some model versions - -### Diversity -- **Description**: Unique token ratio in responses -- **Range**: 0.0-1.0 (higher is better) -- **Interpretation**: Measures vocabulary richness and variation - -### Response Length -- **Description**: Average response length in tokens -- **Range**: Variable (depends on dataset) -- **Interpretation**: Helps ensure responses are appropriately sized - -### Coherence -- **Description**: Ratio of complete sentences -- **Range**: 0.0-1.0 (higher is better) -- **Interpretation**: Simple heuristic for response completeness - ---- - -## Command Reference - -### Scan and Evaluate - -```powershell -# Evaluate all models with default settings -python .\scripts\batch_evaluator.py --scan-models --evaluate-all - -# Control parallelism (default: 3 workers) -python .\scripts\batch_evaluator.py --scan-models --evaluate-all --max-workers 1 - -# Load tasks from config file -python .\scripts\batch_evaluator.py --config batch_eval_config.yaml -``` - -### Promotion - -```powershell -# Promote best model (default target: deployed_models/) -python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best - -# Custom promotion target -python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best --promote-target c:\my\models - -# Dry-run (show what would be done) -python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best --dry-run -``` - -### Reporting - -```powershell -# Export results as JSON -python .\scripts\batch_evaluator.py --export json --output report.json - -# Export results as Markdown -python .\scripts\batch_evaluator.py --export markdown --output report.md - -# Export both formats -python .\scripts\batch_evaluator.py --export both --output report -``` - -### Comparison - -```powershell -# Compare specific models -python .\scripts\batch_evaluator.py --compare checkpoint-64 lora_adapter -``` - ---- - -## Promoted Model Structure - -After promotion, the `deployed_models/` directory contains: - -``` -deployed_models/ -├── checkpoint-64_20251124_234342/ # Promoted model directory -│ ├── adapter_config.json -│ ├── adapter_model.safetensors # LoRA weights -│ ├── promotion_metadata.json # Metrics, rank, timestamp -│ ├── tokenizer.json -│ ├── tokenizer_config.json -│ └── ... (all adapter files) -└── LATEST.txt # Points to latest promotion -``` - -### promotion_metadata.json - -```json -{ - "model_id": "checkpoint-64", - "source_path": "C:\\...\\data_out\\lora_training\\checkpoint-64", - "deployment_name": "checkpoint-64_20251124_234342", - "deployment_path": "C:\\...\\deployed_models\\checkpoint-64_20251124_234342", - "metrics": { - "perplexity": 10.188461538461539 - }, - "promoted_at": "2025-11-25T07:43:42.329423Z", - "rank": 1 -} -``` - ---- - -## Integration with Training - -### Automated Workflow - -1. **Train models** with `autotrain.py` or `train_lora.py` -2. **Evaluate all adapters**: - ```powershell - python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best - ``` -3. **Use promoted model** from `deployed_models/latest/` (or read `LATEST.txt`) - -### Example CI/CD Pipeline - -```powershell -# 1. Train -python .\scripts\autotrain.py - -# 2. Evaluate and promote -python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best - -# 3. Read promoted model path -$latestModel = Get-Content .\deployed_models\LATEST.txt - -# 4. Deploy to production (example) -Copy-Item ".\deployed_models\$latestModel" -Destination "C:\production\models\" -Recurse -``` - ---- - -## Configuration File Format - -`batch_eval_config.yaml`: - -```yaml -evaluation_tasks: - - model_id: phi35_mixed_chat - model_type: lora - model_path: data_out/lora_training/phi35_mixed_chat - dataset: datasets/chat/mixed_chat - metrics: - - perplexity - - diversity - - response_length - - coherence - max_samples: 100 - - - model_id: phi35_coding - model_type: lora - model_path: data_out/lora_training/phi35_coding - dataset: datasets/chat/coding - metrics: - - perplexity - - diversity - max_samples: 50 -``` - ---- - -## Ranking Logic - -Models are ranked by: -1. **Perplexity** (lower is better) - if available -2. **Accuracy** (higher is better) - if available -3. **First evaluated** - fallback if no metrics - -The top-ranked model is promoted when `--promote-best` is used. - ---- - -## Troubleshooting - -### No models found during scan - -**Cause**: No LoRA adapters in `data_out/lora_training/` - -**Solution**: Train models first: -```powershell -python .\scripts\autotrain.py -``` - -### Evaluation fails with "transformers not found" - -**Cause**: Missing dependencies - -**Solution**: Install evaluation dependencies: -```powershell -pip install transformers peft torch -``` - -### Promotion fails with "WinError 1314" - -**Cause**: Insufficient privileges to create symlinks on Windows - -**Behavior**: Automatically falls back to creating `LATEST.txt` instead - -**No action needed** - the fallback works identically for reading the latest model. - ---- - -## Advanced Usage - -### Custom Metrics - -Extend `evaluate_lora_model.py` to add new metrics: - -```python -def compute_custom_metric(texts: List[str]) -> float: - # Your metric logic here - return score - -# Add to evaluation: -if "custom" in metrics: - results["custom"] = compute_custom_metric(texts) -``` - -### Parallel Evaluation Tuning - -```powershell -# Low parallelism for limited CPU/GPU -python .\scripts\batch_evaluator.py --max-workers 1 - -# High parallelism for powerful machines -python .\scripts\batch_evaluator.py --max-workers 8 -``` - -### Filter Models by Type - -Modify `scan_models()` in `batch_evaluator.py` to filter by directory name patterns. - ---- - -## Next Steps - -- **Automate evaluations** with `evaluation_autorun.py` (scheduled runs) -- **Compare training configs** by evaluating multiple hyperparameter variants -- **Monitor model quality** over time by tracking promoted model metrics - ---- - -**Last Updated**: 2025-11-24 +# Batch Evaluation & Model Promotion Guide + +## Overview + +The batch evaluation system provides: + +- **Parallel model evaluation** with real metrics +- **Automatic best-model promotion** to `deployed_models/` +- **Comprehensive reporting** (JSON, Markdown) + +--- + +## Quick Start + +### 1. Evaluate All Trained Models + +```powershell +python .\scripts\batch_evaluator.py --scan-models --evaluate-all +``` + +This will: +- Scan `data_out/lora_training/` for LoRA adapters +- Evaluate each model with default metrics +- Save results to `data_out/batch_evaluator/results_.json` + +### 2. Evaluate and Promote Best Model + +```powershell +python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best +``` + +This will: +- Evaluate all models +- Promote the best model to `deployed_models/_/` +- Create `deployed_models/LATEST.txt` pointing to the promoted model +- Save promotion metadata with metrics and timestamp + +### 3. Dry-Run Mode + +```powershell +python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best --dry-run +``` + +Preview what would be promoted without making changes. + +--- + +## Metrics + +The evaluation system computes the following metrics: + +### Perplexity +- **Description**: Language model quality (lower is better) +- **Range**: Typically 1-100 for fine-tuned models +- **Interpretation**: Measures how "surprised" the model is by the test data +- **Note**: Uses fallback heuristic for compatibility with some model versions + +### Diversity +- **Description**: Unique token ratio in responses +- **Range**: 0.0-1.0 (higher is better) +- **Interpretation**: Measures vocabulary richness and variation + +### Response Length +- **Description**: Average response length in tokens +- **Range**: Variable (depends on dataset) +- **Interpretation**: Helps ensure responses are appropriately sized + +### Coherence +- **Description**: Ratio of complete sentences +- **Range**: 0.0-1.0 (higher is better) +- **Interpretation**: Simple heuristic for response completeness + +--- + +## Command Reference + +### Scan and Evaluate + +```powershell +# Evaluate all models with default settings +python .\scripts\batch_evaluator.py --scan-models --evaluate-all + +# Control parallelism (default: 3 workers) +python .\scripts\batch_evaluator.py --scan-models --evaluate-all --max-workers 1 + +# Load tasks from config file +python .\scripts\batch_evaluator.py --config batch_eval_config.yaml +``` + +### Promotion + +```powershell +# Promote best model (default target: deployed_models/) +python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best + +# Custom promotion target +python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best --promote-target c:\my\models + +# Dry-run (show what would be done) +python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best --dry-run +``` + +### Reporting + +```powershell +# Export results as JSON +python .\scripts\batch_evaluator.py --export json --output report.json + +# Export results as Markdown +python .\scripts\batch_evaluator.py --export markdown --output report.md + +# Export both formats +python .\scripts\batch_evaluator.py --export both --output report +``` + +### Comparison + +```powershell +# Compare specific models +python .\scripts\batch_evaluator.py --compare checkpoint-64 lora_adapter +``` + +--- + +## Promoted Model Structure + +After promotion, the `deployed_models/` directory contains: + +``` +deployed_models/ +├── checkpoint-64_20251124_234342/ # Promoted model directory +│ ├── adapter_config.json +│ ├── adapter_model.safetensors # LoRA weights +│ ├── promotion_metadata.json # Metrics, rank, timestamp +│ ├── tokenizer.json +│ ├── tokenizer_config.json +│ └── ... (all adapter files) +└── LATEST.txt # Points to latest promotion +``` + +### promotion_metadata.json + +```json +{ + "model_id": "checkpoint-64", + "source_path": "C:\\...\\data_out\\lora_training\\checkpoint-64", + "deployment_name": "checkpoint-64_20251124_234342", + "deployment_path": "C:\\...\\deployed_models\\checkpoint-64_20251124_234342", + "metrics": { + "perplexity": 10.188461538461539 + }, + "promoted_at": "2025-11-25T07:43:42.329423Z", + "rank": 1 +} +``` + +--- + +## Integration with Training + +### Automated Workflow + +1. **Train models** with `autotrain.py` or `train_lora.py` +2. **Evaluate all adapters**: + ```powershell + python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best + ``` +3. **Use promoted model** from `deployed_models/latest/` (or read `LATEST.txt`) + +### Example CI/CD Pipeline + +```powershell +# 1. Train +python .\scripts\autotrain.py + +# 2. Evaluate and promote +python .\scripts\batch_evaluator.py --scan-models --evaluate-all --promote-best + +# 3. Read promoted model path +$latestModel = Get-Content .\deployed_models\LATEST.txt + +# 4. Deploy to production (example) +Copy-Item ".\deployed_models\$latestModel" -Destination "C:\production\models\" -Recurse +``` + +--- + +## Configuration File Format + +`batch_eval_config.yaml`: + +```yaml +evaluation_tasks: + - model_id: phi35_mixed_chat + model_type: lora + model_path: data_out/lora_training/phi35_mixed_chat + dataset: datasets/chat/mixed_chat + metrics: + - perplexity + - diversity + - response_length + - coherence + max_samples: 100 + + - model_id: phi35_coding + model_type: lora + model_path: data_out/lora_training/phi35_coding + dataset: datasets/chat/coding + metrics: + - perplexity + - diversity + max_samples: 50 +``` + +--- + +## Ranking Logic + +Models are ranked by: +1. **Perplexity** (lower is better) - if available +2. **Accuracy** (higher is better) - if available +3. **First evaluated** - fallback if no metrics + +The top-ranked model is promoted when `--promote-best` is used. + +--- + +## Troubleshooting + +### No models found during scan + +**Cause**: No LoRA adapters in `data_out/lora_training/` + +**Solution**: Train models first: +```powershell +python .\scripts\autotrain.py +``` + +### Evaluation fails with "transformers not found" + +**Cause**: Missing dependencies + +**Solution**: Install evaluation dependencies: +```powershell +pip install transformers peft torch +``` + +### Promotion fails with "WinError 1314" + +**Cause**: Insufficient privileges to create symlinks on Windows + +**Behavior**: Automatically falls back to creating `LATEST.txt` instead + +**No action needed** - the fallback works identically for reading the latest model. + +--- + +## Advanced Usage + +### Custom Metrics + +Extend `evaluate_lora_model.py` to add new metrics: + +```python +def compute_custom_metric(texts: List[str]) -> float: + # Your metric logic here + return score + +# Add to evaluation: +if "custom" in metrics: + results["custom"] = compute_custom_metric(texts) +``` + +### Parallel Evaluation Tuning + +```powershell +# Low parallelism for limited CPU/GPU +python .\scripts\batch_evaluator.py --max-workers 1 + +# High parallelism for powerful machines +python .\scripts\batch_evaluator.py --max-workers 8 +``` + +### Filter Models by Type + +Modify `scan_models()` in `batch_evaluator.py` to filter by directory name patterns. + +--- + +## Next Steps + +- **Automate evaluations** with `evaluation_autorun.py` (scheduled runs) +- **Compare training configs** by evaluating multiple hyperparameter variants +- **Monitor model quality** over time by tracking promoted model metrics + +--- + +**Last Updated**: 2025-11-24 diff --git a/docs/training/EVALUATION_AUTORUN_README.md b/docs/training/EVALUATION_AUTORUN_README.md index 9544de162..912dc0918 100644 --- a/docs/training/EVALUATION_AUTORUN_README.md +++ b/docs/training/EVALUATION_AUTORUN_README.md @@ -1,337 +1,337 @@ -# Evaluation AutoRun Orchestrator - -**Automated AI model evaluation** following the same orchestration pattern as `autotrain.py` and `quantum_autorun.py`. - -## Quick Start - -```powershell -# Validate configuration (dry-run) -python .\scripts\evaluation_autorun.py --dry-run - -# List all configured jobs -python .\scripts\evaluation_autorun.py --list - -# Run a specific evaluation job -python .\scripts\evaluation_autorun.py --job eval_smoke_test - -# Run all enabled evaluation jobs -python .\scripts\evaluation_autorun.py -``` - -## Architecture - -### YAML-Driven Configuration (`evaluation_autorun.yaml`) - -Define evaluation jobs with: -- **Model types**: `lora`, `azure`, `openai`, `local`, `quantum` -- **Datasets**: Path to test dataset -- **Metrics**: Configurable evaluation metrics per model type -- **Output formats**: JSON, CSV, or Markdown - -### Supported Model Types & Metrics - -| Model Type | Metrics | Requirements | -|------------|---------|--------------| -| `lora` | accuracy, bleu, rouge, response_time, token_efficiency | LoRA adapter path | -| `azure` | accuracy, bleu, rouge, response_time, cost_per_token | Azure OpenAI credentials | -| `openai` | accuracy, bleu, rouge, response_time, cost_per_token | OpenAI API key | -| `local` | response_time, determinism, rule_coverage | None (offline) | -| `quantum` | accuracy, precision, recall, f1_score, circuit_depth | Quantum model results | - -### Output Structure - -``` -data_out/evaluation_autorun/ -├── status.json # Global summary -├── / -│ ├── last_run.json # Latest run metadata -│ └── / -│ ├── results.json # Evaluation results -│ └── stdout.log # Full execution log -``` - -## Configuration Examples - -### LoRA Model Evaluation - -```yaml -- name: eval_lora_phi35 - enabled: true - model_type: lora - model_path: data_out/lora_training/phi35 - dataset: datasets/chat/dolly - max_samples: 100 - metrics: - - accuracy - - bleu - - rouge - - response_time - output_format: json - save_results: true - batch_size: 4 -``` - -### Azure OpenAI Baseline - -```yaml -- name: eval_azure_baseline - enabled: true - model_type: azure - azure_deployment: gpt-4o-mini - dataset: datasets/chat/mixed_chat - max_samples: 50 - metrics: - - accuracy - - response_time - - cost_per_token - output_format: json -``` - -### Quantum Classifier Evaluation - -```yaml -- name: eval_quantum_heart - enabled: true - model_type: quantum - model_path: ai-projects/quantum-ml/results/heart_disease_model.json - dataset: datasets/quantum/heart_disease.csv - max_samples: null # Full test set - metrics: - - accuracy - - precision - - recall - - f1_score - output_format: json -``` - -### Local Provider (Free/Offline) - -```yaml -- name: eval_local_baseline - enabled: true - model_type: local - dataset: datasets/chat/mixed_chat - max_samples: 20 - metrics: - - response_time - - determinism - output_format: json -``` - -## Execution Modes - -### Dry-Run (Validation Only) - -```powershell -python .\scripts\evaluation_autorun.py --dry-run -``` - -**Output**: Validates configuration, checks paths, prints commands without execution. - -### Single Job Execution - -```powershell -python .\scripts\evaluation_autorun.py --job eval_smoke_test -``` - -**Output**: Runs only the specified job, writes results and logs. - -### All Jobs - -```powershell -python .\scripts\evaluation_autorun.py -``` - -**Output**: Runs all enabled jobs sequentially, aggregates status. - -### List Jobs - -```powershell -python .\scripts\evaluation_autorun.py --list -``` - -**Output**: JSON array of all configured jobs with their parameters. - -## Status JSON Schema - -```json -{ - "generated_at": "2025-11-22T17:30:00Z", - "total_jobs": 5, - "succeeded": 3, - "failed": 0, - "validated": 2, - "missing": 0, - "jobs": [ - { - "name": "eval_smoke_test", - "model_type": "lora", - "cmd": ["python", "..."], - "start_time": "20251122T173000Z", - "status": "succeeded", - "return_code": 0, - "duration_sec": 12.5, - "log": "data_out/.../stdout.log", - "results_file": "data_out/.../results.json", - "metrics_computed": ["accuracy", "response_time"], - "evaluation_summary": { - "accuracy": 0.85, - "avg_response_time_ms": 234 - } - } - ] -} -``` - -## VS Code Tasks - -Add to `.vscode/tasks.json`: - -```json -{ - "label": "Run: Evaluation AutoRun (dry-run)", - "type": "shell", - "options": { "cwd": "${workspaceFolder}" }, - "command": "python", - "args": [".\\scripts\\evaluation_autorun.py", "--dry-run"], - "isBackground": false -}, -{ - "label": "Run: Evaluation AutoRun (all)", - "type": "shell", - "options": { "cwd": "${workspaceFolder}" }, - "command": "python", - "args": [".\\scripts\\evaluation_autorun.py"], - "isBackground": false -} -``` - -## Environment Variables - -### Azure OpenAI (for `model_type: azure`) - -```powershell -$env:AZURE_OPENAI_API_KEY = "your-key" -$env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/" -$env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini" -$env:AZURE_OPENAI_API_VERSION = "2024-08-01-preview" -``` - -### OpenAI (for `model_type: openai`) - -```powershell -$env:OPENAI_API_KEY = "your-key" -$env:OPENAI_MODEL = "gpt-4o-mini" # Optional -``` - -## Integration with CI/CD - -### GitHub Actions Example - -```yaml -- name: Run evaluation suite - run: | - python scripts/evaluation/evaluation_autorun.py --dry-run - python scripts/evaluation/evaluation_autorun.py --job eval_smoke_test - -- name: Upload evaluation results - uses: actions/upload-artifact@v3 - with: - name: evaluation-results - path: data_out/evaluation_autorun/ -``` - -### Azure Pipelines Example - -```yaml -- script: python scripts/evaluation/evaluation_autorun.py --dry-run - displayName: 'Validate evaluation config' - -- script: python scripts/evaluation/evaluation_autorun.py - displayName: 'Run evaluation suite' - -- task: PublishBuildArtifacts@1 - inputs: - pathToPublish: 'data_out/evaluation_autorun' - artifactName: 'evaluation-results' -``` - -## Design Patterns Used - -✅ **YAML-driven declarative config** - Single source of truth for evaluation jobs -✅ **Sequential execution** - Jobs run in order with clear progress tracking -✅ **Dry-run validation** - Check paths and config before execution -✅ **Machine-readable status** - JSON output for automation -✅ **Timestamped runs** - No overwrites, full audit trail -✅ **Project venv isolation** - Uses correct Python environment per domain -✅ **Modular evaluation scripts** - One script per model type -✅ **Flexible metrics** - Configurable per job and model type - -## Extending the Orchestrator - -### Adding a New Model Type - -1. **Create evaluation script**: `scripts/evaluate__model.py` -2. **Add to `EVAL_SCRIPTS` dict** in `evaluation_autorun.py` -3. **Define supported metrics** in `SUPPORTED_METRICS` dict -4. **Update YAML examples** in this README - -### Adding a New Metric - -1. **Implement in evaluation script** for relevant model types -2. **Add to `SUPPORTED_METRICS`** for those model types -3. **Document in README** with usage examples - -### Custom Output Formats - -Evaluation scripts should support: -- `--output-format json` (default) -- `--output-format csv` (for spreadsheet analysis) -- `--output-format markdown` (for reports) - -## Troubleshooting - -### "Config not found" Error - -```powershell -# Specify config path explicitly -python .\scripts\evaluation_autorun.py --config .\path\to\evaluation_autorun.yaml -``` - -### Missing Dependencies - -```powershell -# Install evaluation dependencies -pip install -r requirements.txt -pip install nltk rouge-score # For NLP metrics -``` - -### Azure Authentication Failures - -```powershell -# Verify environment variables -echo $env:AZURE_OPENAI_API_KEY -echo $env:AZURE_OPENAI_ENDPOINT - -# Check endpoint format (must include https://) -$env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/" -``` - -### Evaluation Script Not Found - -Current limitation: Evaluation scripts need to be created per model type. The orchestrator validates their existence during dry-run. If scripts don't exist yet, you'll see warnings but the orchestrator will still validate the configuration. - -## Next Steps - -1. **Implement evaluation scripts** in `scripts/evaluate_*_model.py` -2. **Add VS Code tasks** for quick evaluation runs -3. **Integrate with CI/CD** for automated regression testing -4. **Create comparison reports** across model types -5. **Add visualization** for evaluation results - -## Related Documentation - -- `AUTOTRAIN_README.md` - ML training orchestrator -- `QUANTUM_AUTORUN_README.md` - Quantum training orchestrator -- `AI_DATASETS_CATALOG.md` - Dataset reference -- `QUICK_REFERENCE.md` - All orchestrators overview +# Evaluation AutoRun Orchestrator + +**Automated AI model evaluation** following the same orchestration pattern as `autotrain.py` and `quantum_autorun.py`. + +## Quick Start + +```powershell +# Validate configuration (dry-run) +python .\scripts\evaluation_autorun.py --dry-run + +# List all configured jobs +python .\scripts\evaluation_autorun.py --list + +# Run a specific evaluation job +python .\scripts\evaluation_autorun.py --job eval_smoke_test + +# Run all enabled evaluation jobs +python .\scripts\evaluation_autorun.py +``` + +## Architecture + +### YAML-Driven Configuration (`evaluation_autorun.yaml`) + +Define evaluation jobs with: +- **Model types**: `lora`, `azure`, `openai`, `local`, `quantum` +- **Datasets**: Path to test dataset +- **Metrics**: Configurable evaluation metrics per model type +- **Output formats**: JSON, CSV, or Markdown + +### Supported Model Types & Metrics + +| Model Type | Metrics | Requirements | +|------------|---------|--------------| +| `lora` | accuracy, bleu, rouge, response_time, token_efficiency | LoRA adapter path | +| `azure` | accuracy, bleu, rouge, response_time, cost_per_token | Azure OpenAI credentials | +| `openai` | accuracy, bleu, rouge, response_time, cost_per_token | OpenAI API key | +| `local` | response_time, determinism, rule_coverage | None (offline) | +| `quantum` | accuracy, precision, recall, f1_score, circuit_depth | Quantum model results | + +### Output Structure + +``` +data_out/evaluation_autorun/ +├── status.json # Global summary +├── / +│ ├── last_run.json # Latest run metadata +│ └── / +│ ├── results.json # Evaluation results +│ └── stdout.log # Full execution log +``` + +## Configuration Examples + +### LoRA Model Evaluation + +```yaml +- name: eval_lora_phi35 + enabled: true + model_type: lora + model_path: data_out/lora_training/phi35 + dataset: datasets/chat/dolly + max_samples: 100 + metrics: + - accuracy + - bleu + - rouge + - response_time + output_format: json + save_results: true + batch_size: 4 +``` + +### Azure OpenAI Baseline + +```yaml +- name: eval_azure_baseline + enabled: true + model_type: azure + azure_deployment: gpt-4o-mini + dataset: datasets/chat/mixed_chat + max_samples: 50 + metrics: + - accuracy + - response_time + - cost_per_token + output_format: json +``` + +### Quantum Classifier Evaluation + +```yaml +- name: eval_quantum_heart + enabled: true + model_type: quantum + model_path: ai-projects/quantum-ml/results/heart_disease_model.json + dataset: datasets/quantum/heart_disease.csv + max_samples: null # Full test set + metrics: + - accuracy + - precision + - recall + - f1_score + output_format: json +``` + +### Local Provider (Free/Offline) + +```yaml +- name: eval_local_baseline + enabled: true + model_type: local + dataset: datasets/chat/mixed_chat + max_samples: 20 + metrics: + - response_time + - determinism + output_format: json +``` + +## Execution Modes + +### Dry-Run (Validation Only) + +```powershell +python .\scripts\evaluation_autorun.py --dry-run +``` + +**Output**: Validates configuration, checks paths, prints commands without execution. + +### Single Job Execution + +```powershell +python .\scripts\evaluation_autorun.py --job eval_smoke_test +``` + +**Output**: Runs only the specified job, writes results and logs. + +### All Jobs + +```powershell +python .\scripts\evaluation_autorun.py +``` + +**Output**: Runs all enabled jobs sequentially, aggregates status. + +### List Jobs + +```powershell +python .\scripts\evaluation_autorun.py --list +``` + +**Output**: JSON array of all configured jobs with their parameters. + +## Status JSON Schema + +```json +{ + "generated_at": "2025-11-22T17:30:00Z", + "total_jobs": 5, + "succeeded": 3, + "failed": 0, + "validated": 2, + "missing": 0, + "jobs": [ + { + "name": "eval_smoke_test", + "model_type": "lora", + "cmd": ["python", "..."], + "start_time": "20251122T173000Z", + "status": "succeeded", + "return_code": 0, + "duration_sec": 12.5, + "log": "data_out/.../stdout.log", + "results_file": "data_out/.../results.json", + "metrics_computed": ["accuracy", "response_time"], + "evaluation_summary": { + "accuracy": 0.85, + "avg_response_time_ms": 234 + } + } + ] +} +``` + +## VS Code Tasks + +Add to `.vscode/tasks.json`: + +```json +{ + "label": "Run: Evaluation AutoRun (dry-run)", + "type": "shell", + "options": { "cwd": "${workspaceFolder}" }, + "command": "python", + "args": [".\\scripts\\evaluation_autorun.py", "--dry-run"], + "isBackground": false +}, +{ + "label": "Run: Evaluation AutoRun (all)", + "type": "shell", + "options": { "cwd": "${workspaceFolder}" }, + "command": "python", + "args": [".\\scripts\\evaluation_autorun.py"], + "isBackground": false +} +``` + +## Environment Variables + +### Azure OpenAI (for `model_type: azure`) + +```powershell +$env:AZURE_OPENAI_API_KEY = "your-key" +$env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/" +$env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini" +$env:AZURE_OPENAI_API_VERSION = "2024-08-01-preview" +``` + +### OpenAI (for `model_type: openai`) + +```powershell +$env:OPENAI_API_KEY = "your-key" +$env:OPENAI_MODEL = "gpt-4o-mini" # Optional +``` + +## Integration with CI/CD + +### GitHub Actions Example + +```yaml +- name: Run evaluation suite + run: | + python scripts/evaluation/evaluation_autorun.py --dry-run + python scripts/evaluation/evaluation_autorun.py --job eval_smoke_test + +- name: Upload evaluation results + uses: actions/upload-artifact@v3 + with: + name: evaluation-results + path: data_out/evaluation_autorun/ +``` + +### Azure Pipelines Example + +```yaml +- script: python scripts/evaluation/evaluation_autorun.py --dry-run + displayName: 'Validate evaluation config' + +- script: python scripts/evaluation/evaluation_autorun.py + displayName: 'Run evaluation suite' + +- task: PublishBuildArtifacts@1 + inputs: + pathToPublish: 'data_out/evaluation_autorun' + artifactName: 'evaluation-results' +``` + +## Design Patterns Used + +✅ **YAML-driven declarative config** - Single source of truth for evaluation jobs +✅ **Sequential execution** - Jobs run in order with clear progress tracking +✅ **Dry-run validation** - Check paths and config before execution +✅ **Machine-readable status** - JSON output for automation +✅ **Timestamped runs** - No overwrites, full audit trail +✅ **Project venv isolation** - Uses correct Python environment per domain +✅ **Modular evaluation scripts** - One script per model type +✅ **Flexible metrics** - Configurable per job and model type + +## Extending the Orchestrator + +### Adding a New Model Type + +1. **Create evaluation script**: `scripts/evaluate__model.py` +2. **Add to `EVAL_SCRIPTS` dict** in `evaluation_autorun.py` +3. **Define supported metrics** in `SUPPORTED_METRICS` dict +4. **Update YAML examples** in this README + +### Adding a New Metric + +1. **Implement in evaluation script** for relevant model types +2. **Add to `SUPPORTED_METRICS`** for those model types +3. **Document in README** with usage examples + +### Custom Output Formats + +Evaluation scripts should support: +- `--output-format json` (default) +- `--output-format csv` (for spreadsheet analysis) +- `--output-format markdown` (for reports) + +## Troubleshooting + +### "Config not found" Error + +```powershell +# Specify config path explicitly +python .\scripts\evaluation_autorun.py --config .\path\to\evaluation_autorun.yaml +``` + +### Missing Dependencies + +```powershell +# Install evaluation dependencies +pip install -r requirements.txt +pip install nltk rouge-score # For NLP metrics +``` + +### Azure Authentication Failures + +```powershell +# Verify environment variables +echo $env:AZURE_OPENAI_API_KEY +echo $env:AZURE_OPENAI_ENDPOINT + +# Check endpoint format (must include https://) +$env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/" +``` + +### Evaluation Script Not Found + +Current limitation: Evaluation scripts need to be created per model type. The orchestrator validates their existence during dry-run. If scripts don't exist yet, you'll see warnings but the orchestrator will still validate the configuration. + +## Next Steps + +1. **Implement evaluation scripts** in `scripts/evaluate_*_model.py` +2. **Add VS Code tasks** for quick evaluation runs +3. **Integrate with CI/CD** for automated regression testing +4. **Create comparison reports** across model types +5. **Add visualization** for evaluation results + +## Related Documentation + +- `AUTOTRAIN_README.md` - ML training orchestrator +- `QUANTUM_AUTORUN_README.md` - Quantum training orchestrator +- `AI_DATASETS_CATALOG.md` - Dataset reference +- `QUICK_REFERENCE.md` - All orchestrators overview diff --git a/docs/training/EXTENDED_MARATHON_TRAINING.md b/docs/training/EXTENDED_MARATHON_TRAINING.md index ec9054ccd..962db82fe 100644 --- a/docs/training/EXTENDED_MARATHON_TRAINING.md +++ b/docs/training/EXTENDED_MARATHON_TRAINING.md @@ -1,163 +1,163 @@ -# Extended Marathon Training Session -# ==================================== -# Multi-hour comprehensive AI training with automatic data generation -# -# Created: 2025-11-25 -# Estimated Runtime: 4-6 hours -# Expected Output: 13 trained LoRA adapters - -## Overview - -This configuration enables extended AI training across multiple datasets and models: - -### 1. Data Generation (Completed) -- **2,000 synthetic chat samples** generated via `auto_data_train.py` -- Sources: Template-based + Repository Q&A + Augmentation -- Location: `datasets/chat/mega_synthetic/` - -### 2. Training Jobs (13 total) - -#### Synthetic Data Training (2 jobs, ~30-45 min each) -- `phi35_mega_synthetic_full` - Phi-3.5 on new synthetic data (3 epochs, all samples) -- `qwen25_mega_synthetic_full` - Qwen2.5 on new synthetic data (3 epochs, all samples) - -#### Comprehensive Dataset Marathon (2 jobs, ~45-60 min each) -- `phi35_comprehensive_marathon` - 5 epochs, 3000 samples -- `qwen25_comprehensive_marathon` - 5 epochs, 3000 samples - -#### Dolly Full Dataset (2 jobs, ~60-90 min each) -- `phi35_dolly_marathon` - 5 epochs, 5000 samples -- `qwen25_dolly_marathon` - 5 epochs, 5000 samples - -#### Mixed Chat Extended (2 jobs, ~30-40 min each) -- `phi35_mixed_extended` - 5 epochs, 2000 samples -- `qwen25_mixed_extended` - 5 epochs, 2000 samples - -#### Repository-Augmented Extended (2 jobs, ~25-35 min each) -- `phi35_repo_marathon` - 4 epochs, 1500 samples -- `qwen25_repo_marathon` - 4 epochs, 1500 samples - -#### Hyperparameter Exploration (3 jobs, ~30-40 min each) -- `phi35_comprehensive_lr_low_extended` - Low LR (0.0001), 5 epochs -- `phi35_comprehensive_lr_high_extended` - High LR (0.0004), 4 epochs -- `qwen25_comprehensive_dropout_test` - Higher dropout (0.15), 4 epochs - -### 3. Expected Outputs - -Each job produces: -- `data_out/lora_training/marathon//` - Trained adapter -- `data_out/autotrain///stdout.log` - Training logs -- `data_out/autotrain//last_run.json` - Job metadata -- `data_out/autotrain/status.json` - Overall progress - -### 4. Launch Commands - -```powershell -# Dry-run to validate all jobs (recommended first) -python .\scripts\autotrain.py --config autotrain_extended_marathon.yaml --dry-run - -# Launch full marathon training -python .\scripts\autotrain.py --config autotrain_extended_marathon.yaml - -# Resume if interrupted (skip completed jobs) -python .\scripts\autotrain.py --config autotrain_extended_marathon.yaml --resume - -# Run specific job only -python .\scripts\autotrain.py --config autotrain_extended_marathon.yaml --job phi35_mega_synthetic_full -``` - -### 5. Monitoring Progress - -```powershell -# Check overall status -python .\scripts\master_orchestrator.py --status - -# Watch training logs (during job execution) -Get-Content data_out\autotrain\\\stdout.log -Wait - -# View status JSON (programmatic) -Get-Content data_out\autotrain\status.json | ConvertFrom-Json | Format-List -``` - -### 6. Resource Requirements - -- **GPU**: CUDA-compatible GPU recommended (10x+ faster) - - Without GPU: Expect 2-3x longer runtime (10-15 hours total) -- **RAM**: 8GB minimum, 16GB recommended -- **Disk**: ~10GB for all outputs -- **Active monitoring**: Optional but recommended for first hour - -### 7. Safety Features - -- **Incremental status updates**: `status.json` updated after each job -- **Resume capability**: `--resume` flag skips succeeded jobs -- **PID tracking**: Each job's PID saved for cancellation support -- **DB logging**: Successful runs logged to SQL (if configured) - -### 8. Post-Training Analysis - -After completion, use these tools: - -```powershell -# Evaluate all trained models -python .\scripts\batch_evaluator.py --scan-models --evaluate-all - -# Rank and promote best model -python .\scripts\batch_evaluator.py --promote-best - -# Export results to markdown -python .\scripts\results_exporter.py --all --format markdown - -# Compare metrics -python .\scripts\training_analytics.py --compare-all -``` - -### 9. Expected Timeline - -| Phase | Duration | Jobs | -|-------|----------|------| -| Synthetic Data Training | 1-1.5 hrs | 2 | -| Comprehensive Marathon | 1.5-2 hrs | 2 | -| Dolly Full Dataset | 2-3 hrs | 2 | -| Mixed Chat Extended | 1-1.5 hrs | 2 | -| Repo Extended | 1 hr | 2 | -| HPO Variations | 1.5-2 hrs | 3 | -| **Total** | **4-6 hrs** | **13** | - -*Times assume GPU acceleration. CPU-only systems: multiply by 2-3x* - -### 10. Troubleshooting - -**Out of Memory**: -```yaml -# Reduce max_train_samples in YAML (e.g., 3000 → 1500) -# Or add to job config: -no_stream: true # Disable streaming for stability -``` - -**Training stuck**: -```powershell -# Find running job PID -Get-Content data_out\autotrain\*.pid - -# Kill if needed -Stop-Process -Id - -# Resume from last completed -python .\scripts\autotrain.py --config autotrain_extended_marathon.yaml --resume -``` - -**Low GPU utilization**: -- Check `device: auto` is set (not `cpu`) -- Verify CUDA: `python -c "import torch; print(torch.cuda.is_available())"` -- Install GPU torch: `pip install torch --index-url https://download.pytorch.org/whl/cu121` - -## Next Steps - -1. ✅ Data generation complete (`datasets/chat/mega_synthetic/`) -2. ✅ Configuration created (`autotrain_extended_marathon.yaml`) -3. ⏳ **Ready to launch marathon training** -4. ⏳ Post-training evaluation & promotion - ---- -*For questions or issues, check `AUTOTRAIN_README.md` and `.github/copilot-instructions.md`* +# Extended Marathon Training Session +# ==================================== +# Multi-hour comprehensive AI training with automatic data generation +# +# Created: 2025-11-25 +# Estimated Runtime: 4-6 hours +# Expected Output: 13 trained LoRA adapters + +## Overview + +This configuration enables extended AI training across multiple datasets and models: + +### 1. Data Generation (Completed) +- **2,000 synthetic chat samples** generated via `auto_data_train.py` +- Sources: Template-based + Repository Q&A + Augmentation +- Location: `datasets/chat/mega_synthetic/` + +### 2. Training Jobs (13 total) + +#### Synthetic Data Training (2 jobs, ~30-45 min each) +- `phi35_mega_synthetic_full` - Phi-3.5 on new synthetic data (3 epochs, all samples) +- `qwen25_mega_synthetic_full` - Qwen2.5 on new synthetic data (3 epochs, all samples) + +#### Comprehensive Dataset Marathon (2 jobs, ~45-60 min each) +- `phi35_comprehensive_marathon` - 5 epochs, 3000 samples +- `qwen25_comprehensive_marathon` - 5 epochs, 3000 samples + +#### Dolly Full Dataset (2 jobs, ~60-90 min each) +- `phi35_dolly_marathon` - 5 epochs, 5000 samples +- `qwen25_dolly_marathon` - 5 epochs, 5000 samples + +#### Mixed Chat Extended (2 jobs, ~30-40 min each) +- `phi35_mixed_extended` - 5 epochs, 2000 samples +- `qwen25_mixed_extended` - 5 epochs, 2000 samples + +#### Repository-Augmented Extended (2 jobs, ~25-35 min each) +- `phi35_repo_marathon` - 4 epochs, 1500 samples +- `qwen25_repo_marathon` - 4 epochs, 1500 samples + +#### Hyperparameter Exploration (3 jobs, ~30-40 min each) +- `phi35_comprehensive_lr_low_extended` - Low LR (0.0001), 5 epochs +- `phi35_comprehensive_lr_high_extended` - High LR (0.0004), 4 epochs +- `qwen25_comprehensive_dropout_test` - Higher dropout (0.15), 4 epochs + +### 3. Expected Outputs + +Each job produces: +- `data_out/lora_training/marathon//` - Trained adapter +- `data_out/autotrain///stdout.log` - Training logs +- `data_out/autotrain//last_run.json` - Job metadata +- `data_out/autotrain/status.json` - Overall progress + +### 4. Launch Commands + +```powershell +# Dry-run to validate all jobs (recommended first) +python .\scripts\autotrain.py --config autotrain_extended_marathon.yaml --dry-run + +# Launch full marathon training +python .\scripts\autotrain.py --config autotrain_extended_marathon.yaml + +# Resume if interrupted (skip completed jobs) +python .\scripts\autotrain.py --config autotrain_extended_marathon.yaml --resume + +# Run specific job only +python .\scripts\autotrain.py --config autotrain_extended_marathon.yaml --job phi35_mega_synthetic_full +``` + +### 5. Monitoring Progress + +```powershell +# Check overall status +python .\scripts\master_orchestrator.py --status + +# Watch training logs (during job execution) +Get-Content data_out\autotrain\\\stdout.log -Wait + +# View status JSON (programmatic) +Get-Content data_out\autotrain\status.json | ConvertFrom-Json | Format-List +``` + +### 6. Resource Requirements + +- **GPU**: CUDA-compatible GPU recommended (10x+ faster) + - Without GPU: Expect 2-3x longer runtime (10-15 hours total) +- **RAM**: 8GB minimum, 16GB recommended +- **Disk**: ~10GB for all outputs +- **Active monitoring**: Optional but recommended for first hour + +### 7. Safety Features + +- **Incremental status updates**: `status.json` updated after each job +- **Resume capability**: `--resume` flag skips succeeded jobs +- **PID tracking**: Each job's PID saved for cancellation support +- **DB logging**: Successful runs logged to SQL (if configured) + +### 8. Post-Training Analysis + +After completion, use these tools: + +```powershell +# Evaluate all trained models +python .\scripts\batch_evaluator.py --scan-models --evaluate-all + +# Rank and promote best model +python .\scripts\batch_evaluator.py --promote-best + +# Export results to markdown +python .\scripts\results_exporter.py --all --format markdown + +# Compare metrics +python .\scripts\training_analytics.py --compare-all +``` + +### 9. Expected Timeline + +| Phase | Duration | Jobs | +|-------|----------|------| +| Synthetic Data Training | 1-1.5 hrs | 2 | +| Comprehensive Marathon | 1.5-2 hrs | 2 | +| Dolly Full Dataset | 2-3 hrs | 2 | +| Mixed Chat Extended | 1-1.5 hrs | 2 | +| Repo Extended | 1 hr | 2 | +| HPO Variations | 1.5-2 hrs | 3 | +| **Total** | **4-6 hrs** | **13** | + +*Times assume GPU acceleration. CPU-only systems: multiply by 2-3x* + +### 10. Troubleshooting + +**Out of Memory**: +```yaml +# Reduce max_train_samples in YAML (e.g., 3000 → 1500) +# Or add to job config: +no_stream: true # Disable streaming for stability +``` + +**Training stuck**: +```powershell +# Find running job PID +Get-Content data_out\autotrain\*.pid + +# Kill if needed +Stop-Process -Id + +# Resume from last completed +python .\scripts\autotrain.py --config autotrain_extended_marathon.yaml --resume +``` + +**Low GPU utilization**: +- Check `device: auto` is set (not `cpu`) +- Verify CUDA: `python -c "import torch; print(torch.cuda.is_available())"` +- Install GPU torch: `pip install torch --index-url https://download.pytorch.org/whl/cu121` + +## Next Steps + +1. ✅ Data generation complete (`datasets/chat/mega_synthetic/`) +2. ✅ Configuration created (`autotrain_extended_marathon.yaml`) +3. ⏳ **Ready to launch marathon training** +4. ⏳ Post-training evaluation & promotion + +--- +*For questions or issues, check `AUTOTRAIN_README.md` and `.github/copilot-instructions.md`* diff --git a/docs/training/FAST_TRAINING_GUIDE.md b/docs/training/FAST_TRAINING_GUIDE.md index 14164b708..477405599 100644 --- a/docs/training/FAST_TRAINING_GUIDE.md +++ b/docs/training/FAST_TRAINING_GUIDE.md @@ -1,165 +1,165 @@ -# Fast Training Guide - -## Quick Start - -Run optimized training in under 10 minutes: - -```powershell -.\scripts\fast_train.ps1 -Mode quick -``` - -## Training Modes - -### Quick Mode (5-10 minutes) -- 64 samples per model -- 2 models: Phi-3.5 + Qwen2.5-3B -- Perfect for testing and validation -```powershell -.\scripts\fast_train.ps1 -Mode quick -``` - -### Medium Mode (20-30 minutes) -- 256 samples per model -- Better model quality -- Good balance of speed/performance -```powershell -.\scripts\fast_train.ps1 -Mode medium -``` - -### Focused Mode (40-60 minutes) -- 500 samples per model -- Comprehensive dataset -- Production-ready models -```powershell -.\scripts\fast_train.ps1 -Mode focused -``` - -## Parallel Training - -Run multiple jobs simultaneously (requires adequate RAM/GPU): - -```powershell -# 2 concurrent jobs (default) -.\scripts\fast_train.ps1 -Mode quick -Parallel 2 - -# 4 concurrent jobs (if you have 4 GPUs or 32GB+ RAM) -.\scripts\fast_train.ps1 -Mode medium -Parallel 4 -``` - -## Model Selection - -Train specific models only: - -```powershell -# Only Phi-3.5 models -.\scripts\fast_train.ps1 -Mode quick -Model phi35 - -# Only Qwen2.5 models -.\scripts\fast_train.ps1 -Mode quick -Model qwen25 -``` - -## Speed Optimizations - -The fast training configuration includes: - -1. **Reduced Sample Sizes**: 64-500 vs 1000-15000 in full training -2. **Smaller LoRA Rank**: 4-8 vs 16 (50% faster, minimal quality loss) -3. **Optimized Batch Sizes**: 2-16 vs 1 (better GPU utilization) -4. **Shorter Sequences**: 384 vs 512 tokens (20% speedup) -5. **Parallel Execution**: 2-4 jobs concurrently -6. **Gradient Checkpointing**: Enabled for memory efficiency -7. **Fast Attention**: Enabled if hardware supports it - -## Azure ML Training - -For even faster training with dedicated GPU: - -```powershell -# Deploy to Azure ML with V100 GPU -.\scripts\deploy_training_to_azure.ps1 ` - -SubscriptionId "your-subscription-id" ` - -JobFilter "phi35_quick*" - -# Monitor at: https://ml.azure.com/ -``` - -**Benefits:** -- V100 GPU (3-5x faster than CPU) -- Auto-scales to $0 when idle -- Parallel job execution across multiple nodes -- ~$3/hour when running - -## Monitor Progress - -Real-time monitoring: - -```powershell -# Check status once -python .\scripts\parallel_train.py --list - -# Continuous monitoring -Get-Content .\data_out\parallel_training\status.json -``` - -## Expected Training Times - -| Mode | Samples | Local CPU | Local GPU | Azure V100 | -|------|---------|-----------|-----------|------------| -| Quick | 64 | 5-10 min | 2-3 min | 1-2 min | -| Medium | 256 | 20-30 min | 8-12 min | 4-6 min | -| Focused | 500 | 40-60 min | 15-20 min | 8-12 min | - -## Results Location - -- **Models**: `data_out/lora_training//` -- **Logs**: `data_out/parallel_training//*/stdout.log` -- **Status**: `data_out/parallel_training/status.json` - -## Comparison: Full vs Fast Training - -| Metric | Full Training | Fast Training | Speedup | -|--------|--------------|---------------|---------| -| Samples | 1000-15000 | 64-500 | 3-20x fewer | -| Time per job | 2-8 hours | 5-60 min | 3-10x faster | -| Total time (11 jobs) | 20-80 hours | 1-6 hours | 10-20x faster | -| Model quality | 100% | 90-95% | Minimal loss | -| Cost (Azure) | $60-240 | $3-18 | 10-20x cheaper | - -## Tips - -1. **Start with quick mode** to validate everything works -2. **Use parallel=2** unless you have 4+ GPUs or 64GB+ RAM -3. **Monitor GPU memory** - reduce parallel count if OOM errors occur -4. **Azure ML recommended** for production training (much faster) -5. **Fast training good for**: rapid iteration, hyperparameter tuning, CI/CD -6. **Full training needed for**: production deployment, maximum quality - -## Troubleshooting - -**Out of Memory**: -```powershell -# Reduce parallel jobs -.\scripts\fast_train.ps1 -Mode quick -Parallel 1 - -# Or reduce batch size in lora_fast.yaml -``` - -**Slow on CPU**: -```powershell -# Use Azure ML for GPU acceleration -.\scripts\deploy_training_to_azure.ps1 -SubscriptionId "..." -``` - -**Jobs failing**: -```powershell -# Check logs -Get-Content data_out\parallel_training\\*\stdout.log -``` - -## Next Steps - -After fast training completes: -1. Test models with `ai-projects/chat-cli/src/chat_cli.py` -2. Deploy best model to Azure Functions -3. Run evaluation suite for quality metrics -4. If quality insufficient, run focused or full training +# Fast Training Guide + +## Quick Start + +Run optimized training in under 10 minutes: + +```powershell +.\scripts\fast_train.ps1 -Mode quick +``` + +## Training Modes + +### Quick Mode (5-10 minutes) +- 64 samples per model +- 2 models: Phi-3.5 + Qwen2.5-3B +- Perfect for testing and validation +```powershell +.\scripts\fast_train.ps1 -Mode quick +``` + +### Medium Mode (20-30 minutes) +- 256 samples per model +- Better model quality +- Good balance of speed/performance +```powershell +.\scripts\fast_train.ps1 -Mode medium +``` + +### Focused Mode (40-60 minutes) +- 500 samples per model +- Comprehensive dataset +- Production-ready models +```powershell +.\scripts\fast_train.ps1 -Mode focused +``` + +## Parallel Training + +Run multiple jobs simultaneously (requires adequate RAM/GPU): + +```powershell +# 2 concurrent jobs (default) +.\scripts\fast_train.ps1 -Mode quick -Parallel 2 + +# 4 concurrent jobs (if you have 4 GPUs or 32GB+ RAM) +.\scripts\fast_train.ps1 -Mode medium -Parallel 4 +``` + +## Model Selection + +Train specific models only: + +```powershell +# Only Phi-3.5 models +.\scripts\fast_train.ps1 -Mode quick -Model phi35 + +# Only Qwen2.5 models +.\scripts\fast_train.ps1 -Mode quick -Model qwen25 +``` + +## Speed Optimizations + +The fast training configuration includes: + +1. **Reduced Sample Sizes**: 64-500 vs 1000-15000 in full training +2. **Smaller LoRA Rank**: 4-8 vs 16 (50% faster, minimal quality loss) +3. **Optimized Batch Sizes**: 2-16 vs 1 (better GPU utilization) +4. **Shorter Sequences**: 384 vs 512 tokens (20% speedup) +5. **Parallel Execution**: 2-4 jobs concurrently +6. **Gradient Checkpointing**: Enabled for memory efficiency +7. **Fast Attention**: Enabled if hardware supports it + +## Azure ML Training + +For even faster training with dedicated GPU: + +```powershell +# Deploy to Azure ML with V100 GPU +.\scripts\deploy_training_to_azure.ps1 ` + -SubscriptionId "your-subscription-id" ` + -JobFilter "phi35_quick*" + +# Monitor at: https://ml.azure.com/ +``` + +**Benefits:** +- V100 GPU (3-5x faster than CPU) +- Auto-scales to $0 when idle +- Parallel job execution across multiple nodes +- ~$3/hour when running + +## Monitor Progress + +Real-time monitoring: + +```powershell +# Check status once +python .\scripts\parallel_train.py --list + +# Continuous monitoring +Get-Content .\data_out\parallel_training\status.json +``` + +## Expected Training Times + +| Mode | Samples | Local CPU | Local GPU | Azure V100 | +|------|---------|-----------|-----------|------------| +| Quick | 64 | 5-10 min | 2-3 min | 1-2 min | +| Medium | 256 | 20-30 min | 8-12 min | 4-6 min | +| Focused | 500 | 40-60 min | 15-20 min | 8-12 min | + +## Results Location + +- **Models**: `data_out/lora_training//` +- **Logs**: `data_out/parallel_training//*/stdout.log` +- **Status**: `data_out/parallel_training/status.json` + +## Comparison: Full vs Fast Training + +| Metric | Full Training | Fast Training | Speedup | +|--------|--------------|---------------|---------| +| Samples | 1000-15000 | 64-500 | 3-20x fewer | +| Time per job | 2-8 hours | 5-60 min | 3-10x faster | +| Total time (11 jobs) | 20-80 hours | 1-6 hours | 10-20x faster | +| Model quality | 100% | 90-95% | Minimal loss | +| Cost (Azure) | $60-240 | $3-18 | 10-20x cheaper | + +## Tips + +1. **Start with quick mode** to validate everything works +2. **Use parallel=2** unless you have 4+ GPUs or 64GB+ RAM +3. **Monitor GPU memory** - reduce parallel count if OOM errors occur +4. **Azure ML recommended** for production training (much faster) +5. **Fast training good for**: rapid iteration, hyperparameter tuning, CI/CD +6. **Full training needed for**: production deployment, maximum quality + +## Troubleshooting + +**Out of Memory**: +```powershell +# Reduce parallel jobs +.\scripts\fast_train.ps1 -Mode quick -Parallel 1 + +# Or reduce batch size in lora_fast.yaml +``` + +**Slow on CPU**: +```powershell +# Use Azure ML for GPU acceleration +.\scripts\deploy_training_to_azure.ps1 -SubscriptionId "..." +``` + +**Jobs failing**: +```powershell +# Check logs +Get-Content data_out\parallel_training\\*\stdout.log +``` + +## Next Steps + +After fast training completes: +1. Test models with `ai-projects/chat-cli/src/chat_cli.py` +2. Deploy best model to Azure Functions +3. Run evaluation suite for quality metrics +4. If quality insufficient, run focused or full training diff --git a/docs/training/TRAINING_STARTED.md b/docs/training/TRAINING_STARTED.md index 05c993593..ed4ae62bc 100644 --- a/docs/training/TRAINING_STARTED.md +++ b/docs/training/TRAINING_STARTED.md @@ -1,157 +1,157 @@ -# 🚀 Comprehensive Training Started - -**Status:** Active -**Started:** 2025-11-21 15:02 UTC -**Estimated Completion:** 6-12 hours - -## Training Jobs Running - -### LoRA Fine-Tuning (3 models) -- **Phi-3.5 Mini** (microsoft/Phi-3.5-mini-instruct) - - Dataset: mixed_chat (full) - - Epochs: 3 - - Output: `data_out/lora_training/phi35/` - -- **Qwen2.5 3B** (Qwen/Qwen2.5-3B-Instruct) - - Dataset: mixed_chat (full) - - Epochs: 3 - - Output: `data_out/lora_training/qwen25_3b/` - -- **Phi-3.5 MAX** (microsoft/Phi-3.5-mini-instruct) - - Dataset: Dolly 15k (full) - - Epochs: 3 - - Output: `data_out/lora_training/phi35_max_performance/` - -### Quantum Training (2 models) -- **Heart Disease Classifier** - - Dataset: heart_disease.csv - - Qubits: 4 - - Epochs: 50 - - Output: `ai-projects/quantum-ml/results/` - -- **Ionosphere Classifier** - - Dataset: ionosphere.csv - - Qubits: 4 - - Epochs: 100 - - Output: `ai-projects/quantum-ml/results/` - -## Monitoring - -**Quick Status:** -```powershell -.\scripts\monitor_training.ps1 -``` - -**Continuous Monitoring:** -```powershell -.\scripts\monitor_training.ps1 -Continuous -``` - -**Check Processes:** -```powershell -Get-Process -Name python -``` - -**View Logs:** -```powershell -# LoRA logs -Get-Content data_out\autotrain\status.json | ConvertFrom-Json - -# Quantum logs -Get-Content data_out\quantum_autorun\status.json | ConvertFrom-Json -``` - -## What Was Fixed - -1. **Removed memory-heavy models** (Mistral 7B, Mixtral 8x7B, Qwen 7B) -2. **Fixed quantum venv** - Recreated with proper dependencies -3. **Fixed emoji encoding** - Added UTF-8 reconfiguration for Windows -4. **Installed sentencepiece** - Required for Mistral/Qwen tokenizers -5. **Optimized configuration** - Focused on models that fit in available memory - -## Expected Outputs - -### LoRA Adapters -Each model will generate: -- `adapter_config.json` - LoRA configuration -- `adapter_model.safetensors` - Trained adapter weights -- `training_args.bin` - Training configuration -- Checkpoints in timestamped directories - -### Quantum Models -Each quantum job will generate: -- `results/_quantum_model.pth` - Trained model -- `results/_scaler.pkl` - Data scaler -- `results/_pca.pkl` - PCA transformer (if needed) -- `results/_training.png` - Training curves -- `results/_summary.json` - Training metrics - -## Using Trained Models - -### LoRA Chat -```powershell -cd talk-to-ai -python .\src\chat_cli.py --provider lora --model ..\data_out\lora_training\phi35 -``` - -### Azure Functions Integration -The latest trained adapter will be auto-detected at: -``` -/api/ai/status -``` - -### Quantum Inference -```powershell -cd quantum-ai -python .\src\quantum_classifier.py --load results/heart_disease_quantum_model.pth -``` - -## Next Steps After Training - -1. **Validate Models** - ```powershell - # Test LoRA adapter - python .\AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py --dry-run - - # Test quantum model - cd quantum-ai; python .\train_custom_dataset.py --preset heart --epochs 1 - ``` - -2. **Deploy to Azure Functions** - ```powershell - func host start # Test locally first - .\deploy-chat-to-azure.ps1 # Deploy to Azure - ``` - -3. **Compare Performance** - - Check training logs for accuracy metrics - - Compare model sizes and inference speeds - - Select best-performing model for production - -## Troubleshooting - -**If training stops:** -```powershell -# Check if processes are still running -Get-Process -Name python - -# Restart LoRA training -python .\scripts\autotrain.py - -# Restart quantum training -python .\scripts\quantum_autorun.py -``` - -**Out of memory:** -- Reduce `max_train_samples` in autotrain.yaml -- Reduce `batch_size` in quantum_autorun.yaml -- Close other applications - -**Training too slow:** -- Consider using Google Colab free GPU -- Reduce `epochs` in configuration files -- Use smaller datasets for testing - ---- - -**All training is fully automated and will complete without user intervention!** 🎉 +# 🚀 Comprehensive Training Started + +**Status:** Active +**Started:** 2025-11-21 15:02 UTC +**Estimated Completion:** 6-12 hours + +## Training Jobs Running + +### LoRA Fine-Tuning (3 models) +- **Phi-3.5 Mini** (microsoft/Phi-3.5-mini-instruct) + - Dataset: mixed_chat (full) + - Epochs: 3 + - Output: `data_out/lora_training/phi35/` + +- **Qwen2.5 3B** (Qwen/Qwen2.5-3B-Instruct) + - Dataset: mixed_chat (full) + - Epochs: 3 + - Output: `data_out/lora_training/qwen25_3b/` + +- **Phi-3.5 MAX** (microsoft/Phi-3.5-mini-instruct) + - Dataset: Dolly 15k (full) + - Epochs: 3 + - Output: `data_out/lora_training/phi35_max_performance/` + +### Quantum Training (2 models) +- **Heart Disease Classifier** + - Dataset: heart_disease.csv + - Qubits: 4 + - Epochs: 50 + - Output: `ai-projects/quantum-ml/results/` + +- **Ionosphere Classifier** + - Dataset: ionosphere.csv + - Qubits: 4 + - Epochs: 100 + - Output: `ai-projects/quantum-ml/results/` + +## Monitoring + +**Quick Status:** +```powershell +.\scripts\monitor_training.ps1 +``` + +**Continuous Monitoring:** +```powershell +.\scripts\monitor_training.ps1 -Continuous +``` + +**Check Processes:** +```powershell +Get-Process -Name python +``` + +**View Logs:** +```powershell +# LoRA logs +Get-Content data_out\autotrain\status.json | ConvertFrom-Json + +# Quantum logs +Get-Content data_out\quantum_autorun\status.json | ConvertFrom-Json +``` + +## What Was Fixed + +1. **Removed memory-heavy models** (Mistral 7B, Mixtral 8x7B, Qwen 7B) +2. **Fixed quantum venv** - Recreated with proper dependencies +3. **Fixed emoji encoding** - Added UTF-8 reconfiguration for Windows +4. **Installed sentencepiece** - Required for Mistral/Qwen tokenizers +5. **Optimized configuration** - Focused on models that fit in available memory + +## Expected Outputs + +### LoRA Adapters +Each model will generate: +- `adapter_config.json` - LoRA configuration +- `adapter_model.safetensors` - Trained adapter weights +- `training_args.bin` - Training configuration +- Checkpoints in timestamped directories + +### Quantum Models +Each quantum job will generate: +- `results/_quantum_model.pth` - Trained model +- `results/_scaler.pkl` - Data scaler +- `results/_pca.pkl` - PCA transformer (if needed) +- `results/_training.png` - Training curves +- `results/_summary.json` - Training metrics + +## Using Trained Models + +### LoRA Chat +```powershell +cd talk-to-ai +python .\src\chat_cli.py --provider lora --model ..\data_out\lora_training\phi35 +``` + +### Azure Functions Integration +The latest trained adapter will be auto-detected at: +``` +/api/ai/status +``` + +### Quantum Inference +```powershell +cd quantum-ai +python .\src\quantum_classifier.py --load results/heart_disease_quantum_model.pth +``` + +## Next Steps After Training + +1. **Validate Models** + ```powershell + # Test LoRA adapter + python .\AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py --dry-run + + # Test quantum model + cd quantum-ai; python .\train_custom_dataset.py --preset heart --epochs 1 + ``` + +2. **Deploy to Azure Functions** + ```powershell + func host start # Test locally first + .\deploy-chat-to-azure.ps1 # Deploy to Azure + ``` + +3. **Compare Performance** + - Check training logs for accuracy metrics + - Compare model sizes and inference speeds + - Select best-performing model for production + +## Troubleshooting + +**If training stops:** +```powershell +# Check if processes are still running +Get-Process -Name python + +# Restart LoRA training +python .\scripts\autotrain.py + +# Restart quantum training +python .\scripts\quantum_autorun.py +``` + +**Out of memory:** +- Reduce `max_train_samples` in autotrain.yaml +- Reduce `batch_size` in quantum_autorun.yaml +- Close other applications + +**Training too slow:** +- Consider using Google Colab free GPU +- Reduce `epochs` in configuration files +- Use smaller datasets for testing + +--- + +**All training is fully automated and will complete without user intervention!** 🎉 diff --git a/docs/training/TRAINING_TAB_ENHANCEMENTS.md b/docs/training/TRAINING_TAB_ENHANCEMENTS.md index 83ddac715..39652a28c 100644 --- a/docs/training/TRAINING_TAB_ENHANCEMENTS.md +++ b/docs/training/TRAINING_TAB_ENHANCEMENTS.md @@ -1,327 +1,327 @@ -# Training Tab Enhancements - Complete Summary - -## 🎯 Overview -The training tab has been significantly enhanced with advanced features, better validation, real-time estimates, and a professional UI that serves both beginners and power users. - -## ✨ New Features - -### 1. **Enhanced Input Fields** -- ✅ **Job Name** - Required field with validation (lowercase, underscores, no spaces) -- ✅ **Model Selection** - Dynamic info cards showing parameters and use cases -- ✅ **Dataset Dropdown** - Auto-populated from API with sample counts -- ✅ **Epochs** (1-20 range with validation) -- ✅ **Max Train Samples** (-1 for all samples) -- ✅ **Learning Rate** with pattern validation - -### 2. **Collapsible Advanced Options Section** 🔧 -Click to reveal/hide advanced parameters: - -#### Training Parameters: -- **Batch Size** - Dropdown (1/2/4/8) with memory hints -- **Gradient Accumulation** - Simulate larger batches (1-32) -- **Warmup Steps** - Learning rate warmup period - -#### LoRA Configuration: -- **LoRA Rank** (4-128) - Controls adapter size -- **LoRA Alpha** (8-256) - Scaling parameter -- **LoRA Dropout** (0-0.5) - Regularization - -#### Optimization: -- **Weight Decay** (0-0.5) - L2 regularization -- **Max Grad Norm** (0-10) - Gradient clipping -- **Random Seed** - For reproducibility - -### 3. **Evaluation Options** ✓ -- ✅ Enable/disable evaluation toggle -- ✅ Max eval samples input -- ✅ Eval steps frequency control -- ✅ Conditional display (only shows when enabled) - -### 4. **Real-Time Estimates** ⏱️ -Beautiful gradient card showing: -- **Estimated Training Time** - Calculated from epochs, samples, and batch size -- **VRAM Usage** - Based on LoRA rank and model size -- Updates dynamically as parameters change - -### 5. **Quick Presets** ⚡ -One-click configuration templates: - -#### ⚡ Quick Test -- 1 epoch, 100 samples -- LoRA rank 4, batch size 2 -- ~1-2 minutes -- Perfect for testing pipeline - -#### 📊 Standard -- 3 epochs, 1k samples -- LoRA rank 8, batch size 2 -- ~5-10 minutes -- Good for iterative development - -#### 🏆 Full Training -- 5 epochs, all samples -- LoRA rank 16, batch size 4 -- ~30-60 minutes -- Thorough training run - -#### 🚀 Production -- 10 epochs, all samples -- LoRA rank 32, batch size 4 -- ~2-4 hours -- Production-quality training - -### 6. **Smart Validation** 🛡️ -Comprehensive pre-submission checks: -- ✅ Job name required and format validation -- ✅ Dataset selection required -- ✅ Epoch range validation (1-20) -- ✅ Sample count validation (≥10 or -1) -- ✅ Learning rate pattern check -- ✅ Clear error messages with all issues listed - -### 7. **Configuration Management** 💾 - -#### Save Config -- Export current settings as JSON -- Auto-named based on job name -- Includes all parameters (basic + advanced) -- Easy backup and sharing - -#### Load Config -- Import previously saved configs -- File picker with .json filter -- Validates JSON structure -- Applies all parameters automatically -- Success/error notifications - -#### Reset Form -- Return to smart defaults -- Confirmation toast -- Updates all estimates -- Preserves dataset list - -### 8. **Enhanced User Experience** 🎨 - -#### Tooltips -Every field has helpful tooltips explaining: -- What the parameter does -- Typical/recommended values -- Performance implications - -#### Helper Text -Small gray text under inputs providing: -- Value ranges -- Default recommendations -- Performance hints - -#### Visual Feedback -- Required fields marked with red asterisks -- Model info updates on selection -- Dataset info updates on selection -- Progress indication during submission - -#### Confirmation Dialogs -- Warns for training jobs >1 hour -- Prevents accidental long runs -- Shows estimated duration - -## 🔧 JavaScript Functions Added - -### Core Functions -```javascript -startTraining() // Enhanced with validation and confirmation -updateModelInfo() // Dynamic model descriptions -updateDatasetInfo() // Dataset selection feedback -updateEstimate() // Real-time time/VRAM calculations -calculateEstimatedTime() // Helper for time estimation -``` - -### Advanced Features -```javascript -toggleAdvancedOptions() // Show/hide advanced section -toggleEvalOptions() // Show/hide eval settings -validateTrainingParams() // Pre-submission validation -``` - -### Presets & Config -```javascript -applyPreset() // Apply quick preset (quick/standard/full/production) -saveAsConfig() // Export config as JSON -loadConfigFile() // Import config from JSON -resetForm() // Reset to defaults -``` - -## 📊 Technical Details - -### Estimation Algorithms - -#### Time Estimate -``` -steps_per_epoch = ceil(samples / batch_size) -total_steps = steps_per_epoch × epochs -estimated_minutes = ceil((total_steps × 0.5) / 60) -``` - -#### VRAM Estimate -``` -base_vram = 3.5 GB (base model) -lora_vram = (rank / 8) × 0.5 GB -total_vram = base_vram + lora_vram -``` - -### Parameter Ranges -| Parameter | Min | Max | Default | Step | -|-----------|-----|-----|---------|------| -| Epochs | 1 | 20 | 3 | 1 | -| Max Samples | 10 | ∞ | 1000 | 10 | -| Batch Size | 1 | 8 | 2 | - | -| LoRA Rank | 4 | 128 | 8 | 4 | -| LoRA Alpha | 8 | 256 | 16 | 8 | -| LoRA Dropout | 0 | 0.5 | 0.1 | 0.05 | -| Gradient Accum | 1 | 32 | 1 | 1 | -| Warmup Steps | 0 | ∞ | 0 | 10 | -| Weight Decay | 0 | 0.5 | 0.01 | 0.01 | -| Max Grad Norm | 0 | 10 | 1.0 | 0.1 | - -## 🚀 Usage Guide - -### For Beginners -1. Enter a **job name** (e.g., `my_first_job`) -2. Select **dataset** from dropdown -3. Click a **Quick Preset** button (⚡ Quick Test recommended) -4. Click **🚀 Start Training** -5. Switch to Jobs tab to monitor progress - -### For Power Users -1. Configure **basic parameters** (name, dataset, epochs) -2. Click **🔧 Advanced Options** to expand -3. Fine-tune **LoRA settings** (rank, alpha, dropout) -4. Adjust **optimization parameters** (batch size, gradient accumulation) -5. Set **evaluation frequency** and sample counts -6. Review **estimates** (time and VRAM) -7. **Save config** for future use -8. Click **🚀 Start Training** - -### Configuration Workflow -``` -Create config → Save as JSON → Share/backup - ↓ -Load config → Modify → Train → Evaluate - ↓ -Compare results → Iterate → Production -``` - -## 📝 API Integration - -### Training Submission Payload -```json -{ - "name": "my_training_job", - "model": "phi35", - "dataset": "datasets/chat/mixed_chat", - "epochs": 3, - "max_samples": 1000, - "learning_rate": "2e-4", - "batch_size": 2, - "lora_rank": 8, - "lora_alpha": 16, - "lora_dropout": 0.1, - "gradient_accumulation": 1, - "warmup_steps": 0, - "weight_decay": 0.01, - "max_grad_norm": 1.0, - "random_seed": 42, - "enable_eval": true, - "max_eval_samples": 100, - "eval_steps": 50 -} -``` - -### Server Endpoints Used -- `GET /api/datasets` - Populate dataset dropdown -- `POST /api/start-training` - Submit training job - -## 🎯 Benefits - -### For Users -- ✅ **Faster setup** with presets -- ✅ **Better visibility** with estimates -- ✅ **Fewer errors** with validation -- ✅ **Easier experimentation** with config save/load -- ✅ **Professional UX** with tooltips and feedback - -### For Developers -- ✅ **Maintainable code** with clear functions -- ✅ **Extensible** - easy to add new parameters -- ✅ **Well-documented** with inline comments -- ✅ **Type-safe** with proper parsing (parseInt, parseFloat) -- ✅ **Error handling** at every level - -## 🔮 Future Enhancements (Ready to Add) - -### Potential Additions -1. **Template Library** - Pre-built configs for common tasks -2. **Historical Configs** - Recently used settings dropdown -3. **A/B Testing** - Queue multiple variations -4. **Cost Estimator** - Show GPU hours and estimated cost -5. **Dataset Preview** - Sample data before training -6. **Auto-tuning** - Suggest optimal hyperparameters -7. **Progress Bars** - Real-time training progress on form -8. **Model Comparison** - Side-by-side parameter analysis -9. **Resource Checker** - Verify GPU availability before submission -10. **Scheduling** - Queue jobs for later execution - -## 📊 Testing Checklist - -### Manual Tests Performed ✅ -- [x] Server starts successfully -- [x] Datasets API endpoint returns data -- [x] Dataset dropdown populates correctly -- [x] All input fields accept valid values -- [x] Validation catches invalid inputs -- [x] Presets apply correct values -- [x] Estimates update in real-time -- [x] Save config downloads JSON -- [x] Load config applies values -- [x] Advanced options toggle works -- [x] Eval options toggle works -- [x] Form reset returns to defaults -- [x] Tooltips display on hover -- [x] Submit shows loading state -- [x] Error messages are clear - -### Browser Compatibility -- ✅ Modern browsers (Chrome, Edge, Firefox, Safari) -- ✅ Responsive layout (works on tablets) -- ✅ Keyboard navigation support -- ✅ Screen reader friendly (with proper labels) - -## 📚 Documentation - -### File Locations -- **Dashboard HTML**: `dashboard/unified.html` -- **Server**: `dashboard/serve.py` -- **This Guide**: `TRAINING_TAB_ENHANCEMENTS.md` - -### Related Documentation -- `DASHBOARD_ENHANCEMENTS.md` - Previous dashboard improvements -- `AUTOTRAIN_README.md` - Training orchestration details -- `DATABASE_INTEGRATION_GUIDE.md` - SQL logging integration - -## 🎉 Summary - -The training tab is now a **production-ready, feature-rich interface** that: -- Serves beginners with presets and validation -- Empowers experts with advanced controls -- Provides transparency with real-time estimates -- Ensures reliability with comprehensive validation -- Enhances productivity with config management - -**Total Enhancements**: 20+ new features, 10+ new functions, 15+ validation checks - -**Ready for**: Production use, team collaboration, experimentation, CI/CD integration - ---- -*Last Updated: November 25, 2025* -*Version: 2.0* -*Status: ✅ Production Ready* +# Training Tab Enhancements - Complete Summary + +## 🎯 Overview +The training tab has been significantly enhanced with advanced features, better validation, real-time estimates, and a professional UI that serves both beginners and power users. + +## ✨ New Features + +### 1. **Enhanced Input Fields** +- ✅ **Job Name** - Required field with validation (lowercase, underscores, no spaces) +- ✅ **Model Selection** - Dynamic info cards showing parameters and use cases +- ✅ **Dataset Dropdown** - Auto-populated from API with sample counts +- ✅ **Epochs** (1-20 range with validation) +- ✅ **Max Train Samples** (-1 for all samples) +- ✅ **Learning Rate** with pattern validation + +### 2. **Collapsible Advanced Options Section** 🔧 +Click to reveal/hide advanced parameters: + +#### Training Parameters: +- **Batch Size** - Dropdown (1/2/4/8) with memory hints +- **Gradient Accumulation** - Simulate larger batches (1-32) +- **Warmup Steps** - Learning rate warmup period + +#### LoRA Configuration: +- **LoRA Rank** (4-128) - Controls adapter size +- **LoRA Alpha** (8-256) - Scaling parameter +- **LoRA Dropout** (0-0.5) - Regularization + +#### Optimization: +- **Weight Decay** (0-0.5) - L2 regularization +- **Max Grad Norm** (0-10) - Gradient clipping +- **Random Seed** - For reproducibility + +### 3. **Evaluation Options** ✓ +- ✅ Enable/disable evaluation toggle +- ✅ Max eval samples input +- ✅ Eval steps frequency control +- ✅ Conditional display (only shows when enabled) + +### 4. **Real-Time Estimates** ⏱️ +Beautiful gradient card showing: +- **Estimated Training Time** - Calculated from epochs, samples, and batch size +- **VRAM Usage** - Based on LoRA rank and model size +- Updates dynamically as parameters change + +### 5. **Quick Presets** ⚡ +One-click configuration templates: + +#### ⚡ Quick Test +- 1 epoch, 100 samples +- LoRA rank 4, batch size 2 +- ~1-2 minutes +- Perfect for testing pipeline + +#### 📊 Standard +- 3 epochs, 1k samples +- LoRA rank 8, batch size 2 +- ~5-10 minutes +- Good for iterative development + +#### 🏆 Full Training +- 5 epochs, all samples +- LoRA rank 16, batch size 4 +- ~30-60 minutes +- Thorough training run + +#### 🚀 Production +- 10 epochs, all samples +- LoRA rank 32, batch size 4 +- ~2-4 hours +- Production-quality training + +### 6. **Smart Validation** 🛡️ +Comprehensive pre-submission checks: +- ✅ Job name required and format validation +- ✅ Dataset selection required +- ✅ Epoch range validation (1-20) +- ✅ Sample count validation (≥10 or -1) +- ✅ Learning rate pattern check +- ✅ Clear error messages with all issues listed + +### 7. **Configuration Management** 💾 + +#### Save Config +- Export current settings as JSON +- Auto-named based on job name +- Includes all parameters (basic + advanced) +- Easy backup and sharing + +#### Load Config +- Import previously saved configs +- File picker with .json filter +- Validates JSON structure +- Applies all parameters automatically +- Success/error notifications + +#### Reset Form +- Return to smart defaults +- Confirmation toast +- Updates all estimates +- Preserves dataset list + +### 8. **Enhanced User Experience** 🎨 + +#### Tooltips +Every field has helpful tooltips explaining: +- What the parameter does +- Typical/recommended values +- Performance implications + +#### Helper Text +Small gray text under inputs providing: +- Value ranges +- Default recommendations +- Performance hints + +#### Visual Feedback +- Required fields marked with red asterisks +- Model info updates on selection +- Dataset info updates on selection +- Progress indication during submission + +#### Confirmation Dialogs +- Warns for training jobs >1 hour +- Prevents accidental long runs +- Shows estimated duration + +## 🔧 JavaScript Functions Added + +### Core Functions +```javascript +startTraining() // Enhanced with validation and confirmation +updateModelInfo() // Dynamic model descriptions +updateDatasetInfo() // Dataset selection feedback +updateEstimate() // Real-time time/VRAM calculations +calculateEstimatedTime() // Helper for time estimation +``` + +### Advanced Features +```javascript +toggleAdvancedOptions() // Show/hide advanced section +toggleEvalOptions() // Show/hide eval settings +validateTrainingParams() // Pre-submission validation +``` + +### Presets & Config +```javascript +applyPreset() // Apply quick preset (quick/standard/full/production) +saveAsConfig() // Export config as JSON +loadConfigFile() // Import config from JSON +resetForm() // Reset to defaults +``` + +## 📊 Technical Details + +### Estimation Algorithms + +#### Time Estimate +``` +steps_per_epoch = ceil(samples / batch_size) +total_steps = steps_per_epoch × epochs +estimated_minutes = ceil((total_steps × 0.5) / 60) +``` + +#### VRAM Estimate +``` +base_vram = 3.5 GB (base model) +lora_vram = (rank / 8) × 0.5 GB +total_vram = base_vram + lora_vram +``` + +### Parameter Ranges +| Parameter | Min | Max | Default | Step | +|-----------|-----|-----|---------|------| +| Epochs | 1 | 20 | 3 | 1 | +| Max Samples | 10 | ∞ | 1000 | 10 | +| Batch Size | 1 | 8 | 2 | - | +| LoRA Rank | 4 | 128 | 8 | 4 | +| LoRA Alpha | 8 | 256 | 16 | 8 | +| LoRA Dropout | 0 | 0.5 | 0.1 | 0.05 | +| Gradient Accum | 1 | 32 | 1 | 1 | +| Warmup Steps | 0 | ∞ | 0 | 10 | +| Weight Decay | 0 | 0.5 | 0.01 | 0.01 | +| Max Grad Norm | 0 | 10 | 1.0 | 0.1 | + +## 🚀 Usage Guide + +### For Beginners +1. Enter a **job name** (e.g., `my_first_job`) +2. Select **dataset** from dropdown +3. Click a **Quick Preset** button (⚡ Quick Test recommended) +4. Click **🚀 Start Training** +5. Switch to Jobs tab to monitor progress + +### For Power Users +1. Configure **basic parameters** (name, dataset, epochs) +2. Click **🔧 Advanced Options** to expand +3. Fine-tune **LoRA settings** (rank, alpha, dropout) +4. Adjust **optimization parameters** (batch size, gradient accumulation) +5. Set **evaluation frequency** and sample counts +6. Review **estimates** (time and VRAM) +7. **Save config** for future use +8. Click **🚀 Start Training** + +### Configuration Workflow +``` +Create config → Save as JSON → Share/backup + ↓ +Load config → Modify → Train → Evaluate + ↓ +Compare results → Iterate → Production +``` + +## 📝 API Integration + +### Training Submission Payload +```json +{ + "name": "my_training_job", + "model": "phi35", + "dataset": "datasets/chat/mixed_chat", + "epochs": 3, + "max_samples": 1000, + "learning_rate": "2e-4", + "batch_size": 2, + "lora_rank": 8, + "lora_alpha": 16, + "lora_dropout": 0.1, + "gradient_accumulation": 1, + "warmup_steps": 0, + "weight_decay": 0.01, + "max_grad_norm": 1.0, + "random_seed": 42, + "enable_eval": true, + "max_eval_samples": 100, + "eval_steps": 50 +} +``` + +### Server Endpoints Used +- `GET /api/datasets` - Populate dataset dropdown +- `POST /api/start-training` - Submit training job + +## 🎯 Benefits + +### For Users +- ✅ **Faster setup** with presets +- ✅ **Better visibility** with estimates +- ✅ **Fewer errors** with validation +- ✅ **Easier experimentation** with config save/load +- ✅ **Professional UX** with tooltips and feedback + +### For Developers +- ✅ **Maintainable code** with clear functions +- ✅ **Extensible** - easy to add new parameters +- ✅ **Well-documented** with inline comments +- ✅ **Type-safe** with proper parsing (parseInt, parseFloat) +- ✅ **Error handling** at every level + +## 🔮 Future Enhancements (Ready to Add) + +### Potential Additions +1. **Template Library** - Pre-built configs for common tasks +2. **Historical Configs** - Recently used settings dropdown +3. **A/B Testing** - Queue multiple variations +4. **Cost Estimator** - Show GPU hours and estimated cost +5. **Dataset Preview** - Sample data before training +6. **Auto-tuning** - Suggest optimal hyperparameters +7. **Progress Bars** - Real-time training progress on form +8. **Model Comparison** - Side-by-side parameter analysis +9. **Resource Checker** - Verify GPU availability before submission +10. **Scheduling** - Queue jobs for later execution + +## 📊 Testing Checklist + +### Manual Tests Performed ✅ +- [x] Server starts successfully +- [x] Datasets API endpoint returns data +- [x] Dataset dropdown populates correctly +- [x] All input fields accept valid values +- [x] Validation catches invalid inputs +- [x] Presets apply correct values +- [x] Estimates update in real-time +- [x] Save config downloads JSON +- [x] Load config applies values +- [x] Advanced options toggle works +- [x] Eval options toggle works +- [x] Form reset returns to defaults +- [x] Tooltips display on hover +- [x] Submit shows loading state +- [x] Error messages are clear + +### Browser Compatibility +- ✅ Modern browsers (Chrome, Edge, Firefox, Safari) +- ✅ Responsive layout (works on tablets) +- ✅ Keyboard navigation support +- ✅ Screen reader friendly (with proper labels) + +## 📚 Documentation + +### File Locations +- **Dashboard HTML**: `dashboard/unified.html` +- **Server**: `dashboard/serve.py` +- **This Guide**: `TRAINING_TAB_ENHANCEMENTS.md` + +### Related Documentation +- `DASHBOARD_ENHANCEMENTS.md` - Previous dashboard improvements +- `AUTOTRAIN_README.md` - Training orchestration details +- `DATABASE_INTEGRATION_GUIDE.md` - SQL logging integration + +## 🎉 Summary + +The training tab is now a **production-ready, feature-rich interface** that: +- Serves beginners with presets and validation +- Empowers experts with advanced controls +- Provides transparency with real-time estimates +- Ensures reliability with comprehensive validation +- Enhances productivity with config management + +**Total Enhancements**: 20+ new features, 10+ new functions, 15+ validation checks + +**Ready for**: Production use, team collaboration, experimentation, CI/CD integration + +--- +*Last Updated: November 25, 2025* +*Version: 2.0* +*Status: ✅ Production Ready* diff --git a/docs/training/TRAINING_VALIDATION_RESULTS.md b/docs/training/TRAINING_VALIDATION_RESULTS.md index 876259cc3..28dd378d3 100644 --- a/docs/training/TRAINING_VALIDATION_RESULTS.md +++ b/docs/training/TRAINING_VALIDATION_RESULTS.md @@ -1,223 +1,223 @@ -# AI Training Validation Results - November 27, 2025 - -## Executive Summary - -Successfully validated all AI training improvements through: -1. ✅ **Model Comparison** - Analyzed historical evaluation data from 12 parallel training runs -2. ⚙️ **Parallel Training** - Currently executing 2 concurrent jobs with automated ranking -3. ⚙️ **Full-Scale Training** - Training with 512 samples and 3 epochs (in progress) - -## Key Findings from Historical Data - -### Best Performing Models (by Perplexity Improvement) - -| Rank | Model | Pre-Perplexity | Post-Perplexity | Improvement | Distinct-1 | Distinct-2 | Diversity | -|------|-------|----------------|-----------------|-------------|------------|------------|-----------| -| 1 | qwen_ultra (171830) | 73.56 | 47.37 | **35.6%** | 0.623 | 0.890 | 0.815 | -| 2 | qwen_ultra (171042) | 73.56 | 48.47 | **34.1%** | 0.662 | 0.920 | - | -| 3 | tinyllama_ultra (200414) | 7.15 | 6.35 | **11.2%** | 0.714 | 0.975 | 0.845 | -| 4 | phi35_ultra (172043) | 24.56 | 23.56 | **4.08%** | 0.707 | 0.957 | - | - -### Best Performing Models (by Diversity) - -| Rank | Model | Diversity Avg | Distinct-1 | Distinct-2 | Perplexity Improvement | -|------|-------|---------------|------------|------------|------------------------| -| 1 | tinyllama_ultra (200414) | **0.845** | 0.714 | 0.975 | 11.2% | -| 2 | tinyllama_ultra (200606) | **0.815** | 0.720 | 0.911 | 11.2% | -| 3 | qwen_ultra (171830) | **0.756** | 0.623 | 0.890 | 35.6% | - -### Combined Score Rankings (70% Perplexity + 30% Diversity) - -Using the formula: `combined_score = (1/post_perplexity × 0.7) + (diversity_avg × 0.3)` - -| Rank | Model | Combined Score | Post-Perplexity | Diversity | Notes | -|------|-------|----------------|-----------------|-----------|-------| -| 1 | tinyllama_ultra (200414) | **0.264** | 6.35 | 0.845 | Best overall balance | -| 2 | qwen_ultra (171830) | **0.242** | 47.37 | 0.815 | Strong perplexity + diversity | -| 3 | phi35_ultra (172043) | **0.054** | 23.56 | - | Good perplexity baseline | - -## Diversity Metrics Analysis - -### What the Metrics Mean - -- **Distinct-1**: Ratio of unique unigrams (individual words). Higher = more vocabulary variety -- **Distinct-2**: Ratio of unique bigrams (word pairs). Higher = more phrase diversity -- **Diversity Avg**: Average of Distinct-1 and Distinct-2. Overall diversity measure (0-1 scale) - -### Key Insights - -1. **TinyLlama** models show exceptional diversity (0.84-0.85) but started with low perplexity -2. **Qwen 2.5** models achieved the best perplexity improvements (34-36%) with good diversity (0.76-0.82) -3. **Phi-3.5** models showed moderate improvements (3-4%) but excellent diversity when measured (0.71-0.96) - -## Training Configuration Impact - -All models trained with optimized configurations: -- **Gradient Checkpointing**: Enabled (memory efficiency) -- **Gradient Accumulation**: 4 steps (effective 4x batch size) -- **Learning Rate Schedule**: Cosine annealing with warmup -- **Early Stopping**: Patience=3, threshold=1% - -### Dataset Statistics - -- **Total Runs**: 12 parallel training sessions -- **Cumulative Train Samples**: 756 samples across all runs -- **Cumulative Test Samples**: 84 samples -- **Average Training Time**: 20-40 seconds per job (64 samples) - -## Parallel Training Capabilities - -The automated training orchestrator successfully demonstrated: - -### Multi-Metric Ranking - -The system supports 5 ranking strategies: -1. `perplexity_improvement` - Relative reduction (default) -2. `post_perplexity` - Final perplexity (lower is better) -3. `diversity_avg` - Average Distinct-1 & Distinct-2 (higher is better) -4. `distinct_diversity` - Alias for diversity_avg -5. `combined_improvement` - Weighted 70/30 perplexity/diversity - -### Automated Best Model Selection - -Example from historical data: -```json -{ - "job_ranking": [ - { - "name": "qwen_ultra_autogen_20251124_171830", - "score": 0.3560657185839326, - "metric": "perplexity_improvement", - "pre_perplexity": 73.55645926592268, - "post_perplexity": 47.365525740912155, - "status": "succeeded" - } - ] -} -``` - -### Safety Guards Demonstrated - -- **Min Train Samples**: Jobs automatically skipped if below threshold -- **Historical Tracking**: All runs append to status.json with full provenance -- **Cleanup**: Optional checkpoint removal after successful training - -## Current Active Tasks - -### Task 1: Full-Scale Training (In Progress) -```bash -python .\AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py \ - --dataset datasets/chat/mixed_chat \ - --max-train-samples 512 \ - --max-eval-samples 128 \ - --epochs 3 \ - --save-dir data_out/lora_training/full_scale_test -``` -**Status**: Model checkpoint loading in progress -**Expected Outcome**: Observe early stopping behavior with larger dataset - -### Task 2: Parallel Training with Ranking (In Progress) -```bash -python .\scripts\parallel_train.py \ - --config autotrain.yaml \ - --filter "phi35_mixed_chat_lr*" \ - --ranking-metric combined_improvement \ - --max-parallel 2 \ - --generate-samples 5 -``` -**Status**: 2 jobs running concurrently (phi35_mixed_chat_lr_low, phi35_mixed_chat_lr_high) -**Expected Outcome**: Automated ranking by combined perplexity + diversity score - -## Recommendations Based on Findings - -### For Maximum Perplexity Improvement -- **Use Qwen 2.5-3B** models with cosine LR scheduling -- Expected improvement: 30-35% reduction in perplexity -- Training time: ~20-25 seconds for 64 samples - -### For Best Diversity -- **Use TinyLlama** for high-diversity responses -- Distinct-1: 0.71-0.72, Distinct-2: 0.91-0.98 -- Ideal for creative text generation - -### For Balanced Quality (Recommended) -- **Use Qwen 2.5-3B** with combined ranking metric -- Achieves 30%+ perplexity improvement + 0.75-0.82 diversity -- Best overall performance across both metrics - -### Training Strategy -1. Start with **quick validation** (64 samples, 1 epoch) to test hyperparameters -2. Use **parallel training** with multiple configurations to find best settings -3. Run **full training** (500-1000 samples, 3+ epochs) with winning config -4. Enable **early stopping** to prevent overfitting and save compute - -## Evidence of Improvement System Working - -### Automated Evaluation -Every training run automatically generates: -- Pre-training perplexity baseline -- Post-training perplexity measurement -- Sample generations with prompts -- Diversity metrics (Distinct-1, Distinct-2) -- Average response length and echo ratio - -### Historical Tracking -Status file maintains complete audit trail: -- Run ID with timestamp -- Job configurations -- Dataset statistics -- Timing information -- Full evaluation results -- Ranking by chosen metric - -### Example Output -```json -{ - "evaluation": { - "pre_eval_loss": 4.298, - "pre_eval_perplexity": 73.556, - "post_eval_loss": 3.858, - "post_eval_perplexity": 47.365, - "diversity": { - "distinct_1": 0.623, - "distinct_2": 0.890, - "avg_response_tokens": 70.67, - "avg_echo_ratio": 0.222 - } - } -} -``` - -## Next Steps - -### Immediate Actions -1. ✅ Monitor full-scale training for early stopping trigger -2. ✅ Wait for parallel training to complete and review ranking results -3. ✅ Compare hyperparameter variants (lr_low vs lr_high) - -### Future Optimizations -1. **Grid Search**: Systematically test LR × dropout × batch size combinations -2. **Dataset Scaling**: Test with comprehensive dataset (15K+ samples) -3. **Multi-Model Ranking**: Parallel train Phi-3.5, Qwen-2.5, and TinyLlama simultaneously -4. **Automated Deployment**: Use best model from ranking for production deployment - -## Conclusion - -The enhanced AI training system successfully demonstrates: - -✅ **Automated Model Selection** - Parallel training with multi-metric ranking -✅ **Comprehensive Evaluation** - Perplexity + diversity metrics for quality assessment -✅ **Training Efficiency** - Gradient checkpointing, accumulation, early stopping -✅ **Historical Tracking** - Complete audit trail of all training runs -✅ **Safety Guards** - Min sample thresholds, graceful skipping, cleanup options - -**Best Overall Model**: Qwen 2.5-3B with 35.6% perplexity improvement + 0.815 diversity score - -The system is production-ready and capable of autonomous hyperparameter optimization with human-interpretable quality metrics. - ---- - -**Generated**: November 27, 2025 -**Data Source**: 12 parallel training runs (November 24-27, 2025) -**Total Models Evaluated**: 6 unique model variants -**Training Framework**: HuggingFace + PEFT + LoRA +# AI Training Validation Results - November 27, 2025 + +## Executive Summary + +Successfully validated all AI training improvements through: +1. ✅ **Model Comparison** - Analyzed historical evaluation data from 12 parallel training runs +2. ⚙️ **Parallel Training** - Currently executing 2 concurrent jobs with automated ranking +3. ⚙️ **Full-Scale Training** - Training with 512 samples and 3 epochs (in progress) + +## Key Findings from Historical Data + +### Best Performing Models (by Perplexity Improvement) + +| Rank | Model | Pre-Perplexity | Post-Perplexity | Improvement | Distinct-1 | Distinct-2 | Diversity | +|------|-------|----------------|-----------------|-------------|------------|------------|-----------| +| 1 | qwen_ultra (171830) | 73.56 | 47.37 | **35.6%** | 0.623 | 0.890 | 0.815 | +| 2 | qwen_ultra (171042) | 73.56 | 48.47 | **34.1%** | 0.662 | 0.920 | - | +| 3 | tinyllama_ultra (200414) | 7.15 | 6.35 | **11.2%** | 0.714 | 0.975 | 0.845 | +| 4 | phi35_ultra (172043) | 24.56 | 23.56 | **4.08%** | 0.707 | 0.957 | - | + +### Best Performing Models (by Diversity) + +| Rank | Model | Diversity Avg | Distinct-1 | Distinct-2 | Perplexity Improvement | +|------|-------|---------------|------------|------------|------------------------| +| 1 | tinyllama_ultra (200414) | **0.845** | 0.714 | 0.975 | 11.2% | +| 2 | tinyllama_ultra (200606) | **0.815** | 0.720 | 0.911 | 11.2% | +| 3 | qwen_ultra (171830) | **0.756** | 0.623 | 0.890 | 35.6% | + +### Combined Score Rankings (70% Perplexity + 30% Diversity) + +Using the formula: `combined_score = (1/post_perplexity × 0.7) + (diversity_avg × 0.3)` + +| Rank | Model | Combined Score | Post-Perplexity | Diversity | Notes | +|------|-------|----------------|-----------------|-----------|-------| +| 1 | tinyllama_ultra (200414) | **0.264** | 6.35 | 0.845 | Best overall balance | +| 2 | qwen_ultra (171830) | **0.242** | 47.37 | 0.815 | Strong perplexity + diversity | +| 3 | phi35_ultra (172043) | **0.054** | 23.56 | - | Good perplexity baseline | + +## Diversity Metrics Analysis + +### What the Metrics Mean + +- **Distinct-1**: Ratio of unique unigrams (individual words). Higher = more vocabulary variety +- **Distinct-2**: Ratio of unique bigrams (word pairs). Higher = more phrase diversity +- **Diversity Avg**: Average of Distinct-1 and Distinct-2. Overall diversity measure (0-1 scale) + +### Key Insights + +1. **TinyLlama** models show exceptional diversity (0.84-0.85) but started with low perplexity +2. **Qwen 2.5** models achieved the best perplexity improvements (34-36%) with good diversity (0.76-0.82) +3. **Phi-3.5** models showed moderate improvements (3-4%) but excellent diversity when measured (0.71-0.96) + +## Training Configuration Impact + +All models trained with optimized configurations: +- **Gradient Checkpointing**: Enabled (memory efficiency) +- **Gradient Accumulation**: 4 steps (effective 4x batch size) +- **Learning Rate Schedule**: Cosine annealing with warmup +- **Early Stopping**: Patience=3, threshold=1% + +### Dataset Statistics + +- **Total Runs**: 12 parallel training sessions +- **Cumulative Train Samples**: 756 samples across all runs +- **Cumulative Test Samples**: 84 samples +- **Average Training Time**: 20-40 seconds per job (64 samples) + +## Parallel Training Capabilities + +The automated training orchestrator successfully demonstrated: + +### Multi-Metric Ranking + +The system supports 5 ranking strategies: +1. `perplexity_improvement` - Relative reduction (default) +2. `post_perplexity` - Final perplexity (lower is better) +3. `diversity_avg` - Average Distinct-1 & Distinct-2 (higher is better) +4. `distinct_diversity` - Alias for diversity_avg +5. `combined_improvement` - Weighted 70/30 perplexity/diversity + +### Automated Best Model Selection + +Example from historical data: +```json +{ + "job_ranking": [ + { + "name": "qwen_ultra_autogen_20251124_171830", + "score": 0.3560657185839326, + "metric": "perplexity_improvement", + "pre_perplexity": 73.55645926592268, + "post_perplexity": 47.365525740912155, + "status": "succeeded" + } + ] +} +``` + +### Safety Guards Demonstrated + +- **Min Train Samples**: Jobs automatically skipped if below threshold +- **Historical Tracking**: All runs append to status.json with full provenance +- **Cleanup**: Optional checkpoint removal after successful training + +## Current Active Tasks + +### Task 1: Full-Scale Training (In Progress) +```bash +python .\AI\microsoft_phi-silica-3.6_v1\scripts\train_lora.py \ + --dataset datasets/chat/mixed_chat \ + --max-train-samples 512 \ + --max-eval-samples 128 \ + --epochs 3 \ + --save-dir data_out/lora_training/full_scale_test +``` +**Status**: Model checkpoint loading in progress +**Expected Outcome**: Observe early stopping behavior with larger dataset + +### Task 2: Parallel Training with Ranking (In Progress) +```bash +python .\scripts\parallel_train.py \ + --config autotrain.yaml \ + --filter "phi35_mixed_chat_lr*" \ + --ranking-metric combined_improvement \ + --max-parallel 2 \ + --generate-samples 5 +``` +**Status**: 2 jobs running concurrently (phi35_mixed_chat_lr_low, phi35_mixed_chat_lr_high) +**Expected Outcome**: Automated ranking by combined perplexity + diversity score + +## Recommendations Based on Findings + +### For Maximum Perplexity Improvement +- **Use Qwen 2.5-3B** models with cosine LR scheduling +- Expected improvement: 30-35% reduction in perplexity +- Training time: ~20-25 seconds for 64 samples + +### For Best Diversity +- **Use TinyLlama** for high-diversity responses +- Distinct-1: 0.71-0.72, Distinct-2: 0.91-0.98 +- Ideal for creative text generation + +### For Balanced Quality (Recommended) +- **Use Qwen 2.5-3B** with combined ranking metric +- Achieves 30%+ perplexity improvement + 0.75-0.82 diversity +- Best overall performance across both metrics + +### Training Strategy +1. Start with **quick validation** (64 samples, 1 epoch) to test hyperparameters +2. Use **parallel training** with multiple configurations to find best settings +3. Run **full training** (500-1000 samples, 3+ epochs) with winning config +4. Enable **early stopping** to prevent overfitting and save compute + +## Evidence of Improvement System Working + +### Automated Evaluation +Every training run automatically generates: +- Pre-training perplexity baseline +- Post-training perplexity measurement +- Sample generations with prompts +- Diversity metrics (Distinct-1, Distinct-2) +- Average response length and echo ratio + +### Historical Tracking +Status file maintains complete audit trail: +- Run ID with timestamp +- Job configurations +- Dataset statistics +- Timing information +- Full evaluation results +- Ranking by chosen metric + +### Example Output +```json +{ + "evaluation": { + "pre_eval_loss": 4.298, + "pre_eval_perplexity": 73.556, + "post_eval_loss": 3.858, + "post_eval_perplexity": 47.365, + "diversity": { + "distinct_1": 0.623, + "distinct_2": 0.890, + "avg_response_tokens": 70.67, + "avg_echo_ratio": 0.222 + } + } +} +``` + +## Next Steps + +### Immediate Actions +1. ✅ Monitor full-scale training for early stopping trigger +2. ✅ Wait for parallel training to complete and review ranking results +3. ✅ Compare hyperparameter variants (lr_low vs lr_high) + +### Future Optimizations +1. **Grid Search**: Systematically test LR × dropout × batch size combinations +2. **Dataset Scaling**: Test with comprehensive dataset (15K+ samples) +3. **Multi-Model Ranking**: Parallel train Phi-3.5, Qwen-2.5, and TinyLlama simultaneously +4. **Automated Deployment**: Use best model from ranking for production deployment + +## Conclusion + +The enhanced AI training system successfully demonstrates: + +✅ **Automated Model Selection** - Parallel training with multi-metric ranking +✅ **Comprehensive Evaluation** - Perplexity + diversity metrics for quality assessment +✅ **Training Efficiency** - Gradient checkpointing, accumulation, early stopping +✅ **Historical Tracking** - Complete audit trail of all training runs +✅ **Safety Guards** - Min sample thresholds, graceful skipping, cleanup options + +**Best Overall Model**: Qwen 2.5-3B with 35.6% perplexity improvement + 0.815 diversity score + +The system is production-ready and capable of autonomous hyperparameter optimization with human-interpretable quality metrics. + +--- + +**Generated**: November 27, 2025 +**Data Source**: 12 parallel training runs (November 24-27, 2025) +**Total Models Evaluated**: 6 unique model variants +**Training Framework**: HuggingFace + PEFT + LoRA diff --git a/function_app.py b/function_app.py index 3e7b8b9dd..a7f491824 100644 --- a/function_app.py +++ b/function_app.py @@ -1,3795 +1,3795 @@ -# ============================================================================= -# QAI Azure Functions Application -# ============================================================================= -import importlib.util as _iu -import json -import logging -import os -import re -import subprocess -import sys -import time -from datetime import datetime, timezone -from pathlib import Path - -import azure.functions as func - -# Import AI projects via centralized registry (replaced scattered sys.path manipulation) -from shared.core.module_registry import AIProjectsRegistry -from shared.import_helpers import create_stub_function, safe_import -from shared.json_utils import load_status_json - -# Initialize registry and get chat providers API -_ai_registry = AIProjectsRegistry() -try: - _chat_cli_api = _ai_registry.chat_cli() - detect_provider = _chat_cli_api.detect_provider - prune_messages = _chat_cli_api.token_utils.prune_messages -except Exception as _registry_err: - logging.warning(f"[startup] AI projects registry failed: {_registry_err}") - detect_provider = None - prune_messages = None - -# Pre-compiled word split regex used in token/word counting hot paths. -_RE_WORD_SPLIT = re.compile(r"\S+") - -# Import defensive import helper - -# ----------------------------------------------------------------------------- -# Optional unified SQL engine health + pool metrics (multi-database support) -# ----------------------------------------------------------------------------- -sql_funcs = safe_import( - "shared.sql_engine", - import_names=("sql_health", "engine_stats"), - fallback_factory=create_stub_function, -) -sql_health = sql_funcs["sql_health"] -engine_stats = sql_funcs["engine_stats"] - -# ----------------------------------------------------------------------------- -# Early Telemetry Initialization (non-fatal if unavailable) -# ----------------------------------------------------------------------------- -telemetry_module = safe_import("shared.telemetry", log_failure=False) -if telemetry_module and hasattr(telemetry_module, "init_telemetry"): - try: - telemetry_module.init_telemetry() - except Exception as _telemetry_err: # noqa: BLE001 - logging.warning(f"[startup] Telemetry init skipped: {_telemetry_err}") -else: - logging.warning("[startup] Telemetry init skipped: module unavailable") - -# Try to initialize generic OpenTelemetry tracing (best-effort) -tracing_module = safe_import("shared.tracing", log_failure=False) -if tracing_module and hasattr(tracing_module, "init_tracing"): - try: - tracing_module.init_tracing(service_name="qai.functions") - except Exception as _trace_err: # noqa: BLE001 - don't fail on missing libs - logging.debug(f"[startup] Tracing init skipped: {_trace_err}") -else: - logging.debug("[startup] Tracing init skipped: module unavailable") - -# ----------------------------------------------------------------------------- -# Optional Cosmos Client import (lazy health + persistence) -# ----------------------------------------------------------------------------- -cosmos_client = safe_import("shared.cosmos_client", log_failure=True) -if not cosmos_client: - logging.info("[startup] Cosmos client unavailable") - -# Memory / DB logging utilities (fault-tolerant) -db_logging = safe_import( - "shared.db_logging", - import_names=("log_chat_message_safe",), - fallback_factory=lambda name: None, -) -log_chat_message_safe = db_logging["log_chat_message_safe"] - -# Chat memory functions with graceful degradation -chat_memory_funcs = safe_import( - "shared.chat_memory", - import_names=("generate_embedding", "fetch_similar_messages", "store_embedding"), - fallback_factory=lambda name: { - "generate_embedding": lambda text: [], - "fetch_similar_messages": lambda query_emb, top_k=5, session_id=None: [], - "store_embedding": lambda message_id, embedding, model: False, - }.get(name, lambda *args, **kwargs: None), -) -generate_embedding = chat_memory_funcs["generate_embedding"] -fetch_similar_messages = chat_memory_funcs["fetch_similar_messages"] -store_embedding = chat_memory_funcs["store_embedding"] -try: - from shared.db_logging import log_chat_message_safe -except Exception: # pragma: no cover - if shared not on path - log_chat_message_safe = None # type: ignore -try: - from shared.chat_memory import (fetch_similar_messages, generate_embedding, - store_embedding) -except Exception: - # Provide graceful degradations so endpoint still works - def generate_embedding(text: str): # type: ignore - return [] - - def fetch_similar_messages(query_emb, top_k=5, session_id=None): # type: ignore - return [] - - def store_embedding(message_id, embedding, model): # type: ignore - pass - - -# File caching for repeated JSON reads -try: - from shared.file_cache import read_json_cached -except Exception: # pragma: no cover - # Fallback if file_cache not available - def read_json_cached(file_path, ttl_seconds=60): # type: ignore - import json - - with open(file_path, "r") as f: - return json.load(f) - return False - - -# Add scripts to path for vision inference (kept for legacy support) -scripts_path = Path(__file__).resolve().parent / "scripts" -if str(scripts_path) not in sys.path: - sys.path.insert(0, str(scripts_path)) - -# ----------------------------------------------------------------------------- -# Subscription Manager (optional) -# ----------------------------------------------------------------------------- -try: # pragma: no cover - defensive import - from shared.subscription_manager import (SubscriptionTier, - get_subscription_manager) - - subscription_manager_available = True -except Exception as _sub_err: # noqa: BLE001 - logging.info(f"[startup] Subscription manager unavailable: {_sub_err}") - subscription_manager_available = False - get_subscription_manager = None # type: ignore - - -# OpenTelemetry tracer (optional) -try: # pragma: no cover - from opentelemetry import trace # type: ignore - - _tracer = trace.get_tracer("qai.functions") -except Exception: # pragma: no cover - library optional - _tracer = None - -app = func.FunctionApp() - - -# ============================================================================= -# Chat Web Interface - Serves the HTML/JS frontend -# ============================================================================= - - -@app.route(route="chat-web", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) -def serve_chat_web(req: func.HttpRequest) -> func.HttpResponse: - """Serve the chat web interface HTML""" - try: - html_path = Path(__file__).resolve().parent / "apps" / "chat" / "index.html" - - if html_path.exists(): - with open(html_path, "r", encoding="utf-8") as f: - html_content = f.read() - - return func.HttpResponse( - html_content, - status_code=200, - mimetype="text/html", - headers={ - "Cache-Control": "no-store, no-cache, must-revalidate, max-age=0", - "Pragma": "no-cache", - "Expires": "0", - }, - ) - else: - return func.HttpResponse( - f"

    Error

    Chat interface not found at {html_path}

    ", - status_code=404, - mimetype="text/html", - ) - except Exception as e: - logging.error(f"Error serving chat web: {str(e)}") - return func.HttpResponse( - f"

    Error

    {str(e)}

    ", status_code=500, mimetype="text/html" - ) - - -@app.route( - route="chat-web/chat.js", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def serve_chat_js(req: func.HttpRequest) -> func.HttpResponse: - """Serve the chat JavaScript file""" - try: - js_path = Path(__file__).resolve().parent / "apps" / "chat" / "chat.js" - - if js_path.exists(): - with open(js_path, "r", encoding="utf-8") as f: - js_content = f.read() - - return func.HttpResponse( - js_content, - status_code=200, - mimetype="application/javascript", - headers={ - "Cache-Control": "no-store, no-cache, must-revalidate, max-age=0", - "Pragma": "no-cache", - "Expires": "0", - }, - ) - else: - return func.HttpResponse( - f"// Error: JavaScript file not found at {js_path}", - status_code=404, - mimetype="application/javascript", - ) - except Exception as e: - logging.error(f"Error serving chat.js: {str(e)}") - return func.HttpResponse( - f"// Error: {str(e)}", status_code=500, mimetype="application/javascript" - ) - - -# ============================================================================= -# Chat API - Backend for AI interactions -# ============================================================================= - - -def _extract_text_content(content) -> str: - """Extract user-visible text from a message content payload. - - Supports both plain string content and OpenAI-style content blocks. - """ - if isinstance(content, str): - return content.strip() - - def _is_text_like_block_type(block_type: object) -> bool: - if not isinstance(block_type, str): - return False - normalized = block_type.strip().lower() - return normalized == "text" or normalized.endswith("_text") - - if isinstance(content, list): - parts: list[str] = [] - for block in content: - if not isinstance(block, dict): - continue - if not _is_text_like_block_type(block.get("type")): - continue - text_value = block.get("text") - if isinstance(text_value, str): - trimmed = text_value.strip() - if trimmed: - parts.append(trimmed) - return "\n".join(parts).strip() - if content is None: - return "" - return str(content).strip() - - -def _is_compaction_placeholder_message(content: str) -> bool: - """Return True for synthetic chat-compaction placeholder messages. - - Some chat clients or upstream conversation-compaction layers can inject - assistant placeholders such as ``Compacted conversation`` into history. - Those markers are not useful prompt content and can cause later turns to - orbit around the placeholder instead of the real user request. - """ - if not isinstance(content, str): - return False - - normalized_lines = [ - line.strip().lower() for line in content.splitlines() if line.strip() - ] - if not normalized_lines: - return False - - placeholder_lines = { - "compacted conversation", - "conversation compacted", - } - return all(line in placeholder_lines for line in normalized_lines) - - -def _sanitize_chat_messages(messages) -> list[dict]: - """Normalize incoming chat messages and reject empty content. - - This prevents upstream provider 400s like: - "messages: text content blocks must contain non-whitespace text". - """ - if not isinstance(messages, list) or not messages: - raise ValueError("No messages provided") - - sanitized: list[dict] = [] - for idx, msg in enumerate(messages): - if not isinstance(msg, dict) or "role" not in msg or "content" not in msg: - raise ValueError( - f"Invalid message format at index {idx}. Expected {{role, content}}" - ) - - content = msg.get("content") - normalized_content = None - - if isinstance(content, str): - text_content = content.strip() - if text_content: - normalized_content = text_content - elif isinstance(content, list): - # Current chat/token pipeline is text-centric; normalize block payloads - # to plain text to avoid downstream `.strip()` failures. - text_content = _extract_text_content(content) - if text_content: - normalized_content = text_content - elif content is not None: - text_content = str(content).strip() - if text_content: - normalized_content = text_content - - if normalized_content is None: - continue - - if _is_compaction_placeholder_message(normalized_content): - logging.info( - "Dropping synthetic compaction placeholder from chat history at index %d", - idx, - ) - continue - - msg_copy = dict(msg) - msg_copy["content"] = normalized_content - sanitized.append(msg_copy) - - if not sanitized: - raise ValueError("No non-empty message content provided") - - return sanitized - - -def _detect_provider_with_runtime_fallback( - *, - explicit: str | None = None, - model_override: str | None = None, - temperature: float | None = None, - max_output_tokens: int | None = None, -): - """Detect provider with graceful runtime fallback to local echo. - - In constrained test/runtime environments the optional ``openai`` package may - be unavailable while env vars still point to OpenAI/Azure/LMStudio/Ollama. - In those cases, degrade to ``local`` provider instead of returning HTTP 500 - from status/chat endpoints. - """ - - try: - return detect_provider( - explicit=explicit, - model_override=model_override, - temperature=temperature, - max_output_tokens=max_output_tokens, - ) - except RuntimeError as provider_error: - error_text = str(provider_error).lower() - if "openai package not installed" not in error_text: - raise - - logging.warning( - "Provider detection failed due to missing optional openai package; " - "falling back to local provider. explicit=%s model_override=%s error=%s", - explicit, - model_override, - provider_error, - ) - return detect_provider(explicit="local", model_override="local-echo") - - -@app.route(route="chat", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) -def chat(req: func.HttpRequest) -> func.HttpResponse: - """ - HTTP endpoint for chat interactions. - - POST /api/chat - Body: { - "messages": [{"role": "user|assistant|system", "content": "..."}], - "provider": "auto|openai|azure|lmstudio|ollama|agi|quantum|local" (optional), - "model": "model-name" (optional), - "stream": false (optional, streaming not implemented in HTTP yet) - } - - Response: { - "response": "assistant's reply", - "provider": "azure|openai|lmstudio|ollama|agi|quantum-llm|local", - "model": "model-name" - } - """ - logging.info("Chat function invoked") - - # Telemetry span setup (optional) - span_ctx = ( - _tracer.start_as_current_span("chat_request") if _tracer is not None else None - ) - try: - if span_ctx: - span_ctx.__enter__() - # Parse request - req_body = req.get_json() - messages = _sanitize_chat_messages(req_body.get("messages", [])) - # Optional client-provided session identifier - session_id = req_body.get("session_id") - provider_choice = req_body.get("provider", os.getenv("QAI_PROVIDER", "auto")) - model_override = req_body.get("model", os.getenv("QAI_LORA_MODEL")) - temperature = req_body.get("temperature") - max_output_tokens = req_body.get("max_output_tokens") - max_context_tokens = req_body.get("max_context_tokens") - system_prompt = req_body.get("system_prompt") - - # ============================= - # Memory Retrieval (SQL-backed) - # ============================= - user_message_content = next( - ( - _extract_text_content(m.get("content")) - for m in reversed(messages) - if m.get("role") == "user" - ), - None, - ) - memory_messages: list[dict] = [] - user_embedding = None - if user_message_content: - try: - user_embedding = generate_embedding(user_message_content) - similar = fetch_similar_messages( - user_embedding, top_k=5, session_id=session_id - ) - for idx, sm in enumerate(similar): - # Inject prior memory as system messages (helps provider summarize past context) - memory_content = sm.get("content") - # Validate non-empty - if memory_content and str(memory_content).strip(): - memory_messages.append( - { - "role": "system", - "content": f"[Memory #{idx+1} | similarity={sm.get('similarity'):.3f}] {memory_content}", - } - ) - except Exception as mem_err: # noqa: BLE001 - logging.warning(f"Memory retrieval failed: {mem_err}") - - # Compose final message list with memory injected before existing system/user messages - if memory_messages: - messages = memory_messages + messages - - # Get provider (with overrides) AFTER memory injection so pruning sees augmented context - provider, info = _detect_provider_with_runtime_fallback( - explicit=provider_choice, - model_override=model_override, - temperature=temperature, - max_output_tokens=max_output_tokens, - ) - logging.info(f"Using provider: {info.name}, model: {info.model}") - - start_time = time.perf_counter() - pruned_messages, stats, system_msg = prune_messages( - messages=messages, - provider=info.name, - model=info.model, - max_context_tokens=max_context_tokens, - reserve_output_tokens=int(max_output_tokens) if max_output_tokens else 1024, - system_prompt=system_prompt, - ) - # Completion (non-streaming for HTTP simplicity) - result = provider.complete(pruned_messages, stream=False) - duration_ms = int((time.perf_counter() - start_time) * 1000) - - # If result is still a generator, consume it - if hasattr(result, "__iter__") and not isinstance(result, str): - result = "".join(result) - - # ============================= - # Self-Learning: Log conversation for training - # ============================= - try: - logs_dir = ( - Path(__file__).resolve().parent / "ai-projects" / "chat-cli" / "logs" - ) - logs_dir.mkdir(parents=True, exist_ok=True) - - # Create timestamped log file - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - log_file = logs_dir / f"chat_{timestamp}_{session_id or 'anonymous'}.jsonl" - - # Append conversation to log - with open(log_file, "a", encoding="utf-8") as f: - # Log user message - if user_message_content: - f.write( - json.dumps( - { - "role": "user", - "content": user_message_content, - "timestamp": datetime.now().isoformat(), - "provider": info.name, - "model": info.model, - } - ) - + "\n" - ) - # Log assistant response - f.write( - json.dumps( - { - "role": "assistant", - "content": str(result), - "timestamp": datetime.now().isoformat(), - "provider": info.name, - "model": info.model, - } - ) - + "\n" - ) - except Exception as log_err: - logging.warning(f"Self-learning conversation logging failed: {log_err}") - - # ============================= - # Logging + Embedding Storage - # ============================= - if log_chat_message_safe: - try: - # Log user message first (so conversation exists), then assistant reply - if user_message_content: - user_log = log_chat_message_safe( - session_id=session_id, - provider=info.name, - model=info.model, - role="user", - content=user_message_content, - execution_time_ms=None, - finish_reason=None, - ) - if user_log.get("success") and user_embedding: - try: - store_embedding( - user_log.get("message_id"), - user_embedding, - model=info.model, - ) - except Exception as se: # noqa: BLE001 - logging.warning(f"Store embedding failed: {se}") - # Log assistant message - log_chat_message_safe( - session_id=session_id, - provider=info.name, - model=info.model, - role="assistant", - content=str(result), - execution_time_ms=duration_ms, - finish_reason="stop", - ) - except Exception as log_err: # noqa: BLE001 - logging.warning(f"Chat DB logging failed: {log_err}") - - # Cosmos persistence (feature-flagged) - cosmos_written = False - user_id = session_id or "anonymous" - if cosmos_client and os.getenv("QAI_ENABLE_COSMOS", "false").lower() == "true": - try: - if os.getenv("QAI_COSMOS_PERSIST_STRATEGY", "messages") == "messages": - # Persist user and assistant messages separately - last_user_msg = next( - (m for m in reversed(messages) if m.get("role") == "user"), None - ) - if last_user_msg: - cosmos_client.record_chat_message( - user_id, - { - "role": "user", - "content": user_message_content, - "timestamp": time.time(), - }, - provider=info.name, - model=info.model, - ) - cosmos_client.record_chat_message( - user_id, - { - "role": "assistant", - "content": str(result), - "timestamp": time.time(), - }, - provider=info.name, - model=info.model, - ) - cosmos_written = True - else: - # Session-level persistence - cosmos_client.record_chat_session( - user_id, messages, provider=info.name, model=info.model - ) - cosmos_written = True - except Exception as c_err: # noqa: BLE001 - logging.warning(f"[cosmos] Persistence failed: {c_err}") - - response_data = { - "response": result, - "provider": info.name, - "model": info.model, - "memory_injected": len(memory_messages), - "pruning": { - "original_tokens": stats.original_tokens, - "pruned_tokens": stats.pruned_tokens, - "removed_count": stats.removed_count, - "budget": stats.budget, - "reserve_output_tokens": stats.reserve_output_tokens, - }, - "telemetry_span": bool(_tracer), - "duration_ms": duration_ms, - "cosmos_persisted": cosmos_written, - } - - if span_ctx and hasattr(span_ctx, "__exit__"): - try: - # Annotate span - span = trace.get_current_span() if _tracer else None # type: ignore - if span: - span.set_attribute("provider", info.name) - span.set_attribute("model", info.model) - span.set_attribute("duration_ms", duration_ms) - span.set_attribute("memory_injected", len(memory_messages)) - span.set_attribute("cosmos_persisted", cosmos_written) - finally: - span_ctx.__exit__(None, None, None) - - return func.HttpResponse( - json.dumps(response_data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except ValueError as ve: - logging.error(f"Validation error: {str(ve)}") - return func.HttpResponse( - json.dumps({"error": f"Validation error: {str(ve)}"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - except RuntimeError as re: - logging.error(f"Runtime error: {str(re)}") - return func.HttpResponse( - json.dumps({"error": f"Configuration error: {str(re)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - except Exception as e: - logging.error(f"Unexpected error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Internal server error: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route(route="chat", methods=["OPTIONS"], auth_level=func.AuthLevel.ANONYMOUS) -def chat_options(req: func.HttpRequest) -> func.HttpResponse: - """Handle CORS preflight requests""" - return func.HttpResponse( - "", status_code=200, headers=create_cors_response_headers() - ) - - -def create_cors_response_headers(): - """Create common CORS headers for all responses.""" - return { - "Access-Control-Allow-Origin": "*", - "Access-Control-Allow-Methods": "POST, GET, OPTIONS", - "Access-Control-Allow-Headers": "Content-Type", - } - - -# ============================================================================= -# Automation Tool Endpoints: Resource Monitor, Model Deployer, Results Exporter, Evaluation -# ============================================================================= - - -@app.route( - route="resource-monitor", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def resource_monitor_status(req: func.HttpRequest) -> func.HttpResponse: - """Return latest resource monitor snapshot.""" - try: - snap_path = ( - Path(__file__).resolve().parent - / "data_out" - / "resource_monitor_snapshot.json" - ) - if snap_path.exists(): - # Use cached read with 60s TTL (resource snapshots change infrequently) - data = read_json_cached(snap_path, ttl_seconds=60) - if data: - return func.HttpResponse( - json.dumps(data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - else: - return func.HttpResponse( - json.dumps({"error": "Failed to load snapshot"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - else: - return func.HttpResponse( - json.dumps({"error": "No snapshot found"}), - status_code=404, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - except Exception as e: - logging.error(f"Error reading resource snapshot: {e}") - return func.HttpResponse( - json.dumps({"error": str(e)}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="model-deployer/status", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def model_deployer_status(req: func.HttpRequest) -> func.HttpResponse: - """Return model deployer registry status.""" - try: - reg_path = ( - Path(__file__).resolve().parent / "deployed_models" / "model_registry.json" - ) - if reg_path.exists(): - with open(reg_path, "r") as f: - data = json.load(f) - return func.HttpResponse( - json.dumps(data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - else: - return func.HttpResponse( - json.dumps({"error": "No registry found"}), - status_code=404, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - except Exception as e: - return func.HttpResponse( - json.dumps({"error": str(e)}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route(route="results-export", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) -def results_export(req: func.HttpRequest) -> func.HttpResponse: - """Return latest results export (all orchestrators).""" - try: - res_path = ( - Path(__file__).resolve().parent / "exports" / "all_orchestrators.json" - ) - if res_path.exists(): - with open(res_path, "r") as f: - data = json.load(f) - return func.HttpResponse( - json.dumps(data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - else: - return func.HttpResponse( - json.dumps({"error": "No results found"}), - status_code=404, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - except Exception as e: - return func.HttpResponse( - json.dumps({"error": str(e)}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="evaluation-results", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def evaluation_results(req: func.HttpRequest) -> func.HttpResponse: - """Return latest batch evaluation results.""" - try: - eval_path = ( - Path(__file__).resolve().parent / "data_out" / "evaluation_results.json" - ) - if eval_path.exists(): - with open(eval_path, "r") as f: - data = json.load(f) - return func.HttpResponse( - json.dumps(data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - else: - return func.HttpResponse( - json.dumps({"error": "No evaluation results found"}), - status_code=404, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - except Exception as e: - return func.HttpResponse( - json.dumps({"error": str(e)}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -# ============================================================================= -# Streaming Chat API (Server-Sent Events compatible) -# ============================================================================= - -# Movement command patterns - optimized with frozensets for O(1) lookups -_WALK_LEFT = frozenset(["[aria:walk:left]", "walk left"]) -_WALK_RIGHT = frozenset(["[aria:walk:right]", "walk right"]) -_WALK_UP = frozenset(["[aria:walk:up]", "walk up"]) -_WALK_DOWN = frozenset(["[aria:walk:down]", "walk down"]) -_MOVE_LEFT = frozenset(["[aria:move:left]", "aria move left"]) -_MOVE_RIGHT = frozenset(["[aria:move:right]", "aria move right"]) -_MOVE_UP = frozenset(["[aria:move:up]", "aria move up"]) -_MOVE_DOWN = frozenset(["[aria:move:down]", "aria move down"]) -_CENTER = frozenset(["[aria:center]", "go to center", "move to center"]) -_WAVE = frozenset(["[aria:wave]", "aria wave"]) -_JUMP = frozenset(["[aria:jump]", "aria jump"]) -_DANCE = frozenset(["[aria:dance]", "aria dance"]) - -# Distance constants for movement commands -WALK_DISTANCE = 200 # pixels -MOVE_DISTANCE = 100 # pixels - - -def parse_movement_commands(text: str) -> dict: - """Parse movement commands from AI response text. - - Uses pre-compiled frozensets for O(1) keyword matching. - - Args: - text: AI response text to parse - - Returns: - dict with 'commands' list, or empty dict if no commands found - """ - lower_text = text.lower() - commands = [] - - # Movement commands - using frozenset intersection for fast matching - if any(cmd in lower_text for cmd in _WALK_LEFT): - commands.append( - {"action": "walk", "direction": "left", "distance": WALK_DISTANCE} - ) - if any(cmd in lower_text for cmd in _WALK_RIGHT): - commands.append( - {"action": "walk", "direction": "right", "distance": WALK_DISTANCE} - ) - if any(cmd in lower_text for cmd in _WALK_UP): - commands.append( - {"action": "walk", "direction": "up", "distance": WALK_DISTANCE} - ) - if any(cmd in lower_text for cmd in _WALK_DOWN): - commands.append( - {"action": "walk", "direction": "down", "distance": WALK_DISTANCE} - ) - - if any(cmd in lower_text for cmd in _MOVE_LEFT): - commands.append( - {"action": "move", "direction": "left", "distance": MOVE_DISTANCE} - ) - if any(cmd in lower_text for cmd in _MOVE_RIGHT): - commands.append( - {"action": "move", "direction": "right", "distance": MOVE_DISTANCE} - ) - if any(cmd in lower_text for cmd in _MOVE_UP): - commands.append( - {"action": "move", "direction": "up", "distance": MOVE_DISTANCE} - ) - if any(cmd in lower_text for cmd in _MOVE_DOWN): - commands.append( - {"action": "move", "direction": "down", "distance": MOVE_DISTANCE} - ) - - # Position commands - if any(cmd in lower_text for cmd in _CENTER): - commands.append({"action": "center"}) - - # Action commands - if any(cmd in lower_text for cmd in _WAVE): - commands.append({"action": "wave"}) - if any(cmd in lower_text for cmd in _JUMP): - commands.append({"action": "jump"}) - if any(cmd in lower_text for cmd in _DANCE): - commands.append({"action": "dance"}) - - return {"commands": commands} if commands else {} - - -@app.route(route="chat/stream", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) -def chat_stream(req: func.HttpRequest) -> func.HttpResponse: - """ - POST /api/chat/stream with JSON body similar to /api/chat. - Returns text/event-stream; each event is a JSON object with a 'delta' field. - """ - logging.info("Chat stream function invoked") - try: - body = req.get_json() - messages = _sanitize_chat_messages(body.get("messages", [])) - provider_choice = body.get("provider", "auto") - model_override = body.get("model") - temperature = body.get("temperature") - max_output_tokens = body.get("max_output_tokens") - max_context_tokens = body.get("max_context_tokens") - system_prompt = body.get("system_prompt") - - # ============================= - # Memory Retrieval — mirrors /api/chat behavior - # ============================= - stream_user_content = next( - ( - _extract_text_content(m.get("content")) - for m in reversed(messages) - if m.get("role") == "user" - ), - None, - ) - stream_memory_messages: list[dict] = [] - if stream_user_content: - try: - stream_embedding = generate_embedding(stream_user_content) - similar_msgs = fetch_similar_messages( - stream_embedding, top_k=5, session_id=body.get("session_id") - ) - for idx, sm in enumerate(similar_msgs): - memory_content = sm.get("content") - # Validate non-empty - if memory_content and str(memory_content).strip(): - stream_memory_messages.append( - { - "role": "system", - "content": f"[Memory #{idx+1} | similarity={sm.get('similarity'):.3f}] {memory_content}", - } - ) - except Exception as _mem_err: # noqa: BLE001 - logging.warning(f"Stream memory retrieval failed: {_mem_err}") - if stream_memory_messages: - messages = stream_memory_messages + messages - - provider, info = _detect_provider_with_runtime_fallback( - explicit=provider_choice, - model_override=model_override, - temperature=temperature, - max_output_tokens=max_output_tokens, - ) - - pruned_messages, stats, _ = prune_messages( - messages=messages, - provider=info.name, - model=info.model, - max_context_tokens=max_context_tokens, - reserve_output_tokens=int(max_output_tokens) if max_output_tokens else 1024, - system_prompt=system_prompt, - ) - - gen = provider.complete(pruned_messages, stream=True) - - def sse_iterable(): # generator yielding bytes - try: - # Send a prelude event with meta - pre = { - "provider": info.name, - "model": info.model, - "memory_messages": len(stream_memory_messages), - "pruning": { - "original_tokens": stats.original_tokens, - "pruned_tokens": stats.pruned_tokens, - "removed_count": stats.removed_count, - "budget": stats.budget, - "reserve_output_tokens": stats.reserve_output_tokens, - }, - } - yield (f"event: meta\n" f"data: {json.dumps(pre)}\n\n").encode("utf-8") - - # We'll stream both textual deltas and token-level events when possible - import re - - # Try to use tiktoken for token-level tokenization when available - enc = None - try: - import tiktoken as _tt - - try: - from tiktoken import encoding_for_model - - enc = encoding_for_model(info.model or "gpt-4o-mini") - except Exception: - enc = _tt.get_encoding("cl100k_base") - except Exception: - enc = None - - cumulative_text = "" - prev_token_count = 0 - prev_word_count = 0 - token_index = 0 - movement_commands_sent = False - - for chunk in gen: - if not chunk: - continue - - # Raw textual delta (keep for compatibility) - payload = json.dumps({"delta": chunk}) - yield (f"data: {payload}\n\n").encode("utf-8") - - # Accumulate for tokenization; note: chunk may be partial - cumulative_text += chunk - - # Check for movement commands periodically - if not movement_commands_sent and len(cumulative_text) > 20: - movement_data = parse_movement_commands(cumulative_text) - if movement_data.get("commands"): - movement_event = json.dumps(movement_data) - yield ( - f"event: movement\ndata: {movement_event}\n\n" - ).encode("utf-8") - movement_commands_sent = True - - # Token-level events: prefer byte tokenization (tiktoken) when available - if enc is not None: - try: - tok_ids = enc.encode(cumulative_text) - new_ids = tok_ids[prev_token_count:] - if new_ids: - for tid in new_ids: - try: - txt = enc.decode([tid]) - except Exception: - txt = "" - evt = json.dumps( - { - "token_index": token_index, - "token": txt, - "cumulative": cumulative_text, - } - ) - yield (f"event: token\n" f"data: {evt}\n\n").encode( - "utf-8" - ) - token_index += 1 - prev_token_count = len(tok_ids) - except Exception: - # degrade to word-level if full tokenization fails - enc = None - - if enc is None: - # fallback: emit word-level token events (split by whitespace) - words = list(re.finditer(r"\S+", cumulative_text)) - if len(words) > prev_word_count: - for w in words[prev_word_count:]: - token_text = w.group(0) - evt = json.dumps( - { - "token_index": token_index, - "token": token_text, - "cumulative": cumulative_text, - } - ) - yield (f"event: token\n" f"data: {evt}\n\n").encode( - "utf-8" - ) - token_index += 1 - prev_word_count = len(words) - - yield b"event: done\ndata: {}\n\n" - except Exception as e: - err = json.dumps({"error": str(e)}) - yield (f"event: error\n" f"data: {err}\n\n").encode("utf-8") - - return func.HttpResponse( - body=sse_iterable(), - status_code=200, - mimetype="text/event-stream", - headers={**create_cors_response_headers(), "Cache-Control": "no-cache"}, - ) - - except ValueError as ve: - logging.error(f"chat/stream validation error: {ve}") - return func.HttpResponse( - json.dumps({"error": f"Validation error: {str(ve)}"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - except Exception as e: # noqa: BLE001 - logging.error(f"chat/stream error: {e}") - return func.HttpResponse( - json.dumps({"error": str(e)}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route(route="tts", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) -def tts(req: func.HttpRequest) -> func.HttpResponse: - """Synthesize text to audio using a remote TTS provider (Azure Speech preferred). - - POST /api/tts - Body: { "text": "...", "voice": "Name", "rate": 1.0, "pitch": 1.0, "format": "wav" } - - Response: { "audio_base64": "...", "format": "wav", "timepoints": [{"word":"...","start_ms":0,"end_ms":123}, ...] } - If remote TTS provider isn't available, returns 501 with explanation. - """ - try: - body = req.get_json() or {} - text = (body.get("text") or "").strip() - if not text: - return func.HttpResponse( - json.dumps({"error": "No text provided"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Optional voice/rate/pitch params - voice = body.get("voice") - rate = float(body.get("rate") or 1.0) - _pitch = float(body.get("pitch") or 1.0) - _out_format = (body.get("format") or "wav").lower() - - # Prefer Azure Speech if configured - az_key = ( - os.getenv("AZURE_SPEECH_KEY") - or os.getenv("AZURE_SPEECH_API_KEY") - or os.getenv("AZURE_SPEECH_SUBSCRIPTION") - ) - az_region = os.getenv("AZURE_SPEECH_REGION") or os.getenv("AZURE_REGION") - - if az_key and az_region: - try: - import base64 - import io - import re - import wave - - try: - import azure.cognitiveservices.speech as speechsdk - except Exception: - return func.HttpResponse( - json.dumps( - { - "error": ( - "Azure Speech SDK not available on server " - "(install azure-cognitiveservices-speech)" - ) - } - ), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Configure speech - scfg = speechsdk.SpeechConfig(subscription=az_key, region=az_region) - # force WAV output for simpler handling - scfg.set_speech_synthesis_output_format( - speechsdk.SpeechSynthesisOutputFormat.Riff16Khz16BitMonoPcm - ) - if voice: - try: - scfg.speech_synthesis_voice_name = voice - except Exception: - pass - - synthesizer = speechsdk.SpeechSynthesizer( - speech_config=scfg, audio_config=None - ) - - # Do the synthesis - result = synthesizer.speak_text_async(text).get() - - if result.reason != speechsdk.ResultReason.SynthesizingAudioCompleted: - # Could be 'Canceled' with details - detail = getattr(result, "error_details", None) or str( - result.reason - ) - return func.HttpResponse( - json.dumps( - {"error": "Synthesis failed", "detail": str(detail)} - ), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Extract audio bytes - stream = speechsdk.AudioDataStream(result) - audio_bytes = stream.readall() - - # Compute approximate word timings by splitting text and sizing by character counts - try: - f = io.BytesIO(audio_bytes) - with wave.open(f, "rb") as wr: - framerate = wr.getframerate() - frames = wr.getnframes() - duration_s = ( - frames / float(framerate) - if framerate and frames - else max(0.2, len(text) * 0.02) - ) - except Exception: - duration_s = max(0.2, len(text) * 0.02) - - words = re.findall(r"\S+", text) - total_chars = sum(len(w) for w in words) or 1 - timepoints = [] - cursor = 0.0 - for w in words: - proportion = len(w) / total_chars - dur = duration_s * proportion - start_ms = int(cursor * 1000) - end_ms = int((cursor + dur) * 1000) - timepoints.append( - {"word": w, "start_ms": start_ms, "end_ms": end_ms} - ) - cursor += dur - - import base64 as _b64 - - audio_b64 = _b64.b64encode(audio_bytes).decode("ascii") - - return func.HttpResponse( - json.dumps( - { - "audio_base64": audio_b64, - "format": "wav", - "timepoints": timepoints, - } - ), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - except Exception as e: - logging.exception(f"TTS (Azure) synth failed: {e}") - return func.HttpResponse( - json.dumps({"error": f"TTS provider error: {e}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # No remote TTS provider is configured. Attempt optional local fallbacks if enabled. - enable_local = os.getenv("QAI_ENABLE_LOCAL_TTS", "true").lower() in ( - "true", - "1", - "yes", - "y", - ) - - if enable_local: - # Try pyttsx3 (offline, best on Windows) first - try: - try: - import base64 - import io - import re - import tempfile - import wave - - import pyttsx3 - except Exception: # pyttsx3 not available - pyttsx3 = None - - if pyttsx3 is not None: - tmp = None - try: - tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".wav") - tmp_path = tmp.name - tmp.close() - - engine = pyttsx3.init() - # Try to set rate (pyttsx3 rate is an int; we scale from given rate) - try: - engine.setProperty("rate", int(200 * (rate or 1.0))) - except Exception: - pass - # Try to select voice by name if provided - try: - if voice: - voices = engine.getProperty("voices") or [] - for v in voices: - try: - if voice.lower() in (v.name or "").lower(): - engine.setProperty("voice", v.id) - break - except Exception: - continue - except Exception: - pass - - engine.save_to_file(text, tmp_path) - engine.runAndWait() - - with open(tmp_path, "rb") as fh: - audio_bytes = fh.read() - - # compute approximate duration using wave reader - try: - f = io.BytesIO(audio_bytes) - with wave.open(f, "rb") as wr: - framerate = wr.getframerate() - frames = wr.getnframes() - duration_s = ( - frames / float(framerate) - if framerate and frames - else max(0.2, len(text) * 0.02) - ) - except Exception: - duration_s = max(0.2, len(text) * 0.02) - - words = re.findall(r"\S+", text) - total_chars = sum(len(w) for w in words) or 1 - timepoints = [] - cursor = 0.0 - for w in words: - proportion = len(w) / total_chars - dur = duration_s * proportion - start_ms = int(cursor * 1000) - end_ms = int((cursor + dur) * 1000) - timepoints.append( - {"word": w, "start_ms": start_ms, "end_ms": end_ms} - ) - cursor += dur - - audio_b64 = base64.b64encode(audio_bytes).decode("ascii") - return func.HttpResponse( - json.dumps( - { - "audio_base64": audio_b64, - "format": "wav", - "timepoints": timepoints, - } - ), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - finally: - try: - if ( - tmp is not None - and tmp_path - and os.path.exists(tmp_path) - ): - os.unlink(tmp_path) - except Exception: - pass - - # If pyttsx3 not available or failed, try gTTS (mp3 output) - try: - import base64 - import re - import tempfile - - from gtts import gTTS - except Exception: - gTTS = None - - if gTTS is not None: - tmp = None - try: - tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") - tmp_path = tmp.name - tmp.close() - - tts_obj = gTTS(text=text) - tts_obj.save(tmp_path) - - with open(tmp_path, "rb") as fh: - audio_bytes = fh.read() - - # approximate duration: fallback to char-count based estimate - duration_s = max(0.2, len(text) * 0.02) - words = re.findall(r"\S+", text) - total_chars = sum(len(w) for w in words) or 1 - timepoints = [] - cursor = 0.0 - for w in words: - proportion = len(w) / total_chars - dur = duration_s * proportion - start_ms = int(cursor * 1000) - end_ms = int((cursor + dur) * 1000) - timepoints.append( - {"word": w, "start_ms": start_ms, "end_ms": end_ms} - ) - cursor += dur - - audio_b64 = base64.b64encode(audio_bytes).decode("ascii") - return func.HttpResponse( - json.dumps( - { - "audio_base64": audio_b64, - "format": "mp3", - "timepoints": timepoints, - } - ), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - finally: - try: - if ( - tmp is not None - and tmp_path - and os.path.exists(tmp_path) - ): - os.unlink(tmp_path) - except Exception: - pass - - except Exception as e: - logging.exception(f"Local fallback TTS failed: {e}") - return func.HttpResponse( - json.dumps({"error": f"Local TTS provider failed: {e}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # If we reach here remote + local TTS are unavailable - return func.HttpResponse( - json.dumps( - { - "error": "No remote TTS provider configured and no local fallback available.", - "help": ( - "Set AZURE_SPEECH_KEY and AZURE_SPEECH_REGION to enable " - "Azure speech, or install pyttsx3 or gTTS and set " - "QAI_ENABLE_LOCAL_TTS=true in local.settings.json/.env " - "to enable local fallback. See local.settings.json.example " - "and .env.example in the repo for templates." - ), - } - ), - status_code=501, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: # noqa: BLE001 - logging.exception(f"/tts error: {e}") - return func.HttpResponse( - json.dumps({"error": str(e)}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -# ============================================================================= -# Backend Control - Start/Status -# ============================================================================= - - -@app.route(route="start-backend", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) -def start_backend(req: func.HttpRequest) -> func.HttpResponse: - """Start the Azure Functions backend (already running if this endpoint responds)""" - logging.info("Backend start request received") - - # If this endpoint responds, the backend is already running - return func.HttpResponse( - json.dumps( - { - "status": "already_running", - "message": "Backend is already running (this endpoint is responding)", - } - ), - mimetype="application/json", - status_code=200, - ) - - -# ============================================================================= -# Status API - Health and environment diagnostics -# ============================================================================= - - -@app.route(route="ai/status", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) -def ai_status(req: func.HttpRequest) -> func.HttpResponse: - """Health / status endpoint for provider readiness and environment diagnostics. - - GET /api/ai/status - - Returns JSON describing: - - active_provider: which provider auto-detect selects (azure|openai|local|lora) - - model: resolved model/deployment or LoRA adapter path - - env: presence of key environment variables for Azure/OpenAI - - ml_inprocess: whether ML libraries are importable in-process - - venv: path to local venv python and whether key ML libs are installed there - - lora: default adapter path readiness indicators - - assets and known endpoints - """ - try: - - def _load_status_payload( - status_path: Path, *, require_clean: bool = False - ) -> dict: - loaded = load_status_json(status_path) - if loaded.get("_status_file_error"): - if require_clean and loaded.get("_status_file_exists"): - raise ValueError(loaded["_status_file_error"]) - return {} - return { - k: v for k, v in loaded.items() if not k.startswith("_status_file_") - } - - # Environment flags - azure_env = { - "AZURE_OPENAI_API_KEY": bool(os.getenv("AZURE_OPENAI_API_KEY")), - "AZURE_OPENAI_ENDPOINT": bool(os.getenv("AZURE_OPENAI_ENDPOINT")), - "AZURE_OPENAI_DEPLOYMENT": bool(os.getenv("AZURE_OPENAI_DEPLOYMENT")), - "AZURE_OPENAI_API_VERSION": bool(os.getenv("AZURE_OPENAI_API_VERSION")), - } - openai_env = { - "OPENAI_API_KEY": bool(os.getenv("OPENAI_API_KEY")), - "OPENAI_MODEL": bool(os.getenv("OPENAI_MODEL")), - } - - # Local AI provider config (Ollama + LM Studio) - ollama_base_url = os.getenv("OLLAMA_BASE_URL", "http://127.0.0.1:11434/v1") - lmstudio_base_url = os.getenv("LMSTUDIO_BASE_URL", "http://127.0.0.1:1234/v1") - try: - from chat_providers import ( # type: ignore - _check_lm_studio_available, _check_ollama_available) - - ollama_reachable = _check_ollama_available(ollama_base_url) - lmstudio_reachable = _check_lm_studio_available(lmstudio_base_url) - except Exception: - ollama_reachable = False - lmstudio_reachable = False - local_providers_env = { - "ollama": { - "base_url": ollama_base_url, - "model": os.getenv("OLLAMA_MODEL", "llama3.2"), - "reachable": ollama_reachable, - "OLLAMA_BASE_URL_set": bool(os.getenv("OLLAMA_BASE_URL")), - "OLLAMA_MODEL_set": bool(os.getenv("OLLAMA_MODEL")), - "install_hint": "https://ollama.ai — run: ollama serve && ollama pull llama3.2", - }, - "lmstudio": { - "base_url": lmstudio_base_url, - "model": os.getenv("LMSTUDIO_MODEL", "local-model"), - "reachable": lmstudio_reachable, - "LMSTUDIO_BASE_URL_set": bool(os.getenv("LMSTUDIO_BASE_URL")), - "LMSTUDIO_MODEL_set": bool(os.getenv("LMSTUDIO_MODEL")), - "install_hint": "https://lmstudio.ai — open app and enable Local Server", - }, - } - - # ML availability in-process - inproc_ml = { - "torch": _iu.find_spec("torch") is not None, - "transformers": _iu.find_spec("transformers") is not None, - "peft": _iu.find_spec("peft") is not None, - } - - repo_root = Path(__file__).resolve().parent - venv_python = repo_root / "venv" / "Scripts" / "python.exe" - venv_info = { - "path": str(venv_python), - "exists": venv_python.exists(), - "packages": {}, - "error": None, - } - - if venv_info["exists"]: - try: - code = ( - "import json, importlib.util, importlib.metadata as md;" - "mods=['torch','transformers','peft'];" - "avail={m:(importlib.util.find_spec(m) is not None) for m in mods};" - "vers={};" - "\nfor m in mods:\n\t" - "\n\ttry:\n\t\tvers[m]=md.version(m)\n\texcept Exception:\n\t\tvers[m]=None;" - "print(json.dumps({'available':avail,'versions':vers}))" - ) - proc = subprocess.run( - [str(venv_python), "-c", code], - capture_output=True, - text=True, - timeout=12, - ) - if proc.returncode == 0: - data = json.loads(proc.stdout.strip() or "{}") - venv_info["packages"] = data - else: - venv_info["error"] = ( - proc.stderr.strip() or f"exit {proc.returncode}" - ) - except Exception as e: # noqa: BLE001 - venv_info["error"] = str(e) - - # LoRA adapter defaults - lora_default = repo_root / "data_out" / "lora_training" / "lora_adapter" - adapter_cfg = lora_default / "adapter_config.json" - tokenizer_dir = lora_default.parent / "tokenizer" - lora_info = { - "default_adapter_path": str(lora_default), - "exists": lora_default.exists(), - "adapter_config_exists": adapter_cfg.exists(), - "tokenizer_dir_exists": tokenizer_dir.exists(), - "base_model": None, - "inproc_ready": all(inproc_ml.values()), - "subprocess_ready": ( - venv_info.get("exists") - and bool( - venv_info.get("packages", {}).get("available", {}).get("torch") - ) - and bool( - venv_info.get("packages", {}) - .get("available", {}) - .get("transformers") - ) - and bool(venv_info.get("packages", {}).get("available", {}).get("peft")) - ), - } - if lora_info["adapter_config_exists"]: - try: - with open(adapter_cfg, "r", encoding="utf-8") as f: - cfg = json.load(f) - lora_info["base_model"] = cfg.get("base_model_name_or_path") - except Exception: - pass - - # Detect active provider - provider, info = _detect_provider_with_runtime_fallback(explicit="auto") - - # Assets - chat_web_html = (repo_root / "apps" / "chat" / "index.html").exists() - chat_web_js = (repo_root / "apps" / "chat" / "chat.js").exists() - - # Cosmos status (lazy health) - cosmos_status = None - if cosmos_client: - try: - cosmos_status = cosmos_client.health() - except Exception as cs_err: # noqa: BLE001 - cosmos_status = {"enabled": False, "error": str(cs_err)} - - # Unified SQL status (may reflect Azure SQL, PostgreSQL, MySQL, SQLite) - sql_info = None - try: - sql_info = sql_health() - try: # augment with pool metrics + saturation alerts - pool_info = engine_stats() - sql_info["pool"] = pool_info - # Surface critical alerts at top level for visibility - if pool_info.get("saturation_alert"): - sql_info["alert"] = pool_info["saturation_alert"] - if pool_info.get("slow_queries_1min", 0) > 10: - freq_alert = ( - f"{pool_info['slow_queries_1min']} slow queries in last 60s " - f"(threshold={pool_info.get('slow_query_threshold_ms')}ms)" - ) - sql_info["slow_query_alert"] = freq_alert - logging.warning(f"[ai_status] {freq_alert}") - except Exception as _ps: # noqa: BLE001 - sql_info["pool"] = {"enabled": False, "error": str(_ps)} - except Exception as _se: # noqa: BLE001 - sql_info = {"enabled": False, "error": str(_se)} - - # Telemetry status - try: - from shared.telemetry import \ - is_enabled as _telemetry_is_enabled # type: ignore - - telemetry_info = {"enabled": _telemetry_is_enabled()} - except Exception: - telemetry_info = {"enabled": False} - - # Quantum environment status (non-blocking, gated by optional env var) - quantum_info = { - "enabled": False, - "qiskit": None, - "pennylane": None, - "llm_model_available": False, - "llm_checkpoint_path": None, - "inference_ready": False, - "status_file": None, - "trainer_status": "not_started", - "azure_quantum": { - "workspace_connected": False, - "backends": [], - "attempted": False, - "error": None, - }, - "conflict": None, - } - try: # gather local versions - import qiskit # type: ignore - - quantum_info["qiskit"] = getattr(qiskit, "__version__", None) - quantum_info["enabled"] = True - except Exception as _qe: - quantum_info["qiskit"] = f"error: {_qe}" # noqa: BLE001 - try: - import pennylane # type: ignore - - quantum_info["pennylane"] = getattr(pennylane, "__version__", None) - except Exception: - pass - try: - from quantum_llm_trainer import \ - get_quantum_llm_status # type: ignore - - quantum_llm_status = get_quantum_llm_status( - output_dir=repo_root / "data_out" / "quantum_llm_training" - ) - quantum_info.update( - { - "llm_model_available": bool( - quantum_llm_status.get("checkpoint_exists") - ), - "llm_checkpoint_path": quantum_llm_status.get("checkpoint_path"), - "inference_ready": bool(quantum_llm_status.get("inference_ready")), - "status_file": quantum_llm_status.get("status_file"), - "trainer_status": quantum_llm_status.get("status"), - } - ) - except Exception: - pass - # Conflict detection using validate script (import functions defensively) - try: - from quantum_ai.scripts.validate_qiskit_env import \ - detect_conflict # type: ignore - except Exception: - # Fallback manual conflict heuristic - def detect_conflict(versions): - if ( - versions.get("qiskit") - and str(versions.get("qiskit")).startswith("1.") - and versions.get("qiskit_aer") - ): - return {"conflict": True} - return {"conflict": False} - - try: - # Build synthetic versions map for conflict check - versions_map = {} - for name in ["qiskit", "qiskit_aer", "qiskit_machine_learning"]: - try: - mod = __import__(name) - versions_map[name] = getattr(mod, "__version__", "unknown") - except Exception as ie: # noqa: BLE001 - versions_map[name] = f"error: {ie}" - conflict_meta = detect_conflict(versions_map) - quantum_info["conflict"] = conflict_meta.get("conflict") - except Exception as _ce: # noqa: BLE001 - quantum_info["conflict"] = f"error: {_ce}" - - # Optional Azure Quantum backend probing (requires env flag to avoid latency) - if os.getenv("QAI_STATUS_CONNECT_AZURE_QUANTUM", "false").lower() == "true": - quantum_info["azure_quantum"]["attempted"] = True - try: - from quantum_ai.src.azure_quantum_integration import \ - AzureQuantumIntegration # type: ignore - - cfg_path = ( - Path(__file__).resolve().parent - / "ai-projects" - / "quantum-ml" - / "config" - / "quantum_config.yaml" - ) - if cfg_path.exists(): - aq = AzureQuantumIntegration(str(cfg_path)) - aq.connect() - bnames = aq.list_backends()[:8] - quantum_info["azure_quantum"].update( - { - "workspace_connected": True, - "backends": bnames, - } - ) - else: - quantum_info["azure_quantum"][ - "error" - ] = "quantum_config.yaml missing" - except Exception as aq_err: # noqa: BLE001 - quantum_info["azure_quantum"]["error"] = str(aq_err) - - # Self-Learning System Status - learning_info = { - "enabled": False, - "training_cycles": 0, - "total_conversations": 0, - "new_conversations": 0, - "last_training": None, - "best_model_path": None, - "model_history": [], - } - try: - learning_status_file = ( - Path(__file__).resolve().parent - / "data_out" - / "self_learning" - / "status.json" - ) - loaded_learning_status = load_status_json(learning_status_file) - if not loaded_learning_status.get("_status_file_error"): - learning_status = { - k: v - for k, v in loaded_learning_status.items() - if not k.startswith("_status_file_") - } - learning_info["enabled"] = learning_status.get("learning_enabled", True) - learning_info["training_cycles"] = learning_status.get( - "training_cycles", 0 - ) - learning_info["total_conversations"] = learning_status.get( - "total_conversations", 0 - ) - learning_info["new_conversations"] = learning_status.get( - "conversations_since_last_train", 0 - ) - learning_info["last_training"] = learning_status.get("last_training") - learning_info["best_model_path"] = learning_status.get( - "best_model_path" - ) - learning_info["model_history"] = learning_status.get( - "model_history", [] - )[ - -3: - ] # Last 3 - elif loaded_learning_status.get("_status_file_exists"): - learning_info["error"] = loaded_learning_status.get( - "_status_file_error" - ) - except Exception as _le: # noqa: BLE001 - learning_info["error"] = str(_le) - - # Orchestrator Health Aggregation - orchestrator_health = { - "enabled": True, - "orchestrators": {}, - "overall_status": "unknown", - "last_checked": datetime.now(timezone.utc) - .isoformat() - .replace("+00:00", "Z"), - "active_count": 0, - "failed_count": 0, - } - try: - data_out_dir = Path(__file__).resolve().parent / "data_out" - - # Autonomous training (uses top-level status + heartbeat) - try: - autotrain_status_file = data_out_dir / "autonomous_training_status.json" - at_status = _load_status_payload( - autotrain_status_file, require_clean=True - ) - if at_status: - heartbeat_file = data_out_dir / "autonomous_training_heartbeat.json" - heartbeat_running = False - heartbeat = _load_status_payload(heartbeat_file) - if heartbeat: - try: - heartbeat_running = heartbeat.get( - "state" - ) == "completed" or heartbeat.get("pid") - except Exception: - pass - - orchestrator_health["orchestrators"]["autonomous_training"] = { - "name": "autonomous_training", - "status": ( - "ok" if at_status.get("cycles_completed", 0) > 0 else "idle" - ), - "cycles_completed": at_status.get("cycles_completed", 0), - "best_accuracy": at_status.get("best_accuracy"), - "last_updated": at_status.get("last_updated"), - "heartbeat_running": heartbeat_running, - "performance_trend": ( - "improving" - if len(at_status.get("performance_history", [])) > 1 - and at_status["performance_history"][-1].get("accuracy", 0) - > at_status["performance_history"][0].get("accuracy", 0) - else "unknown" - ), - } - if heartbeat_running: - orchestrator_health["active_count"] += 1 - except Exception as _ate: # noqa: BLE001 - orchestrator_health["orchestrators"]["autonomous_training"] = { - "status": "error", - "error": str(_ate), - } - orchestrator_health["failed_count"] += 1 - - # Standard orchestrators (autotrain, quantum_autorun, evaluation_autorun, etc.) - standard_names = [ - "autotrain", - "quantum_autorun", - "evaluation_autorun", - "integration_smoke", - "autonomous_agent", - ] - for name in standard_names: - try: - status_file = data_out_dir / name / "status.json" - orch_status = _load_status_payload(status_file, require_clean=True) - if orch_status: - - # Normalize to common schema - total = orch_status.get("total_jobs", 0) - succeeded = orch_status.get("succeeded", 0) - failed = orch_status.get("failed", 0) - - if total == 0: - health_status = "idle" - elif failed > 0: - health_status = "degraded" - else: - health_status = "ok" - - orchestrator_health["orchestrators"][name] = { - "name": name, - "status": health_status, - "total_jobs": total, - "succeeded": succeeded, - "failed": failed, - "running": orch_status.get("running", 0), - "last_updated": orch_status.get( - "last_updated", orch_status.get("generated_at") - ), - "success_rate": ( - (succeeded / total * 100) if total > 0 else 100.0 - ), - } - - if health_status == "ok": - orchestrator_health["active_count"] += 1 - elif health_status == "degraded": - orchestrator_health["failed_count"] += 1 - except Exception as _ose: # noqa: BLE001 - logging.debug( - f"[ai_status] Orchestrator {name} health check failed: {_ose}" - ) - # Only track as failed if file exists but is malformed - if (data_out_dir / name / "status.json").exists(): - orchestrator_health["orchestrators"][name] = { - "status": "error", - "error": str(_ose), - } - orchestrator_health["failed_count"] += 1 - - # Determine overall platform health - if orchestrator_health["failed_count"] > 0: - orchestrator_health["overall_status"] = "degraded" - elif orchestrator_health["active_count"] > 0: - orchestrator_health["overall_status"] = "healthy" - else: - orchestrator_health["overall_status"] = "idle" - - except Exception as _oh: # noqa: BLE001 - logging.warning( - f"[ai_status] Orchestrator health aggregation failed: {_oh}" - ) - orchestrator_health["overall_status"] = "error" - orchestrator_health["error"] = str(_oh) - - payload = { - "active_provider": info.name, - "model": info.model, - "env": { - "azure_openai": azure_env, - "openai": openai_env, - "local_fallback": True, - "local_providers": local_providers_env, - }, - "ml_inprocess": inproc_ml, - "lora": lora_info, - "venv": venv_info, - "cosmos": cosmos_status, - "sql": sql_info, - "telemetry": telemetry_info, - "quantum": quantum_info, - "self_learning": learning_info, - "orchestrator_health": orchestrator_health, - "temperature": float(os.getenv("CHAT_TEMPERATURE", "0.7")), - "server": { - "executable": sys.executable, - "python_version": sys.version, - "cwd": os.getcwd(), - }, - "assets": { - "chat_web_html": chat_web_html, - "chat_web_js": chat_web_js, - }, - "endpoints": [ - "/api/chat-web", - "/api/chat-web/chat.js", - "/api/chat", - "/api/chat/stream", - "/api/ai/status", - "/api/vision/infer", - "/api/vision/batch-infer", - "/api/image/generate", - ], - "status": "ok", - } - - return func.HttpResponse( - json.dumps(payload), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: # noqa: BLE001 - logging.error(f"ai/status error: {e}") - return func.HttpResponse( - json.dumps({"status": "error", "error": str(e)}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -# ============================================================================= -# Vision AI Endpoints - Expression/emotion classification -# ============================================================================= - - -@app.route(route="vision/infer", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) -def vision_infer(req: func.HttpRequest) -> func.HttpResponse: - """ - Vision inference endpoint for expression/emotion classification. - - POST /api/vision/infer - Body (option 1 - base64): - { - "image": "base64_encoded_image_string", - "format": "base64" - } - - Body (option 2 - URL): - { - "image_url": "https://example.com/image.jpg", - "format": "url" - } - - Response: - { - "label": "happy", - "confidence": 0.92, - "scores": { - "happy": 0.92, - "sad": 0.05, - "neutral": 0.03 - }, - "model_info": { - "checkpoint": "...", - "classes": ["happy", "sad", "neutral"], - "device": "cpu" - } - } - """ - logging.info("Vision infer endpoint invoked") - - try: - # Lazy import vision inference (only loaded when needed) - try: - from vision_inference import VisionInference - except ImportError as e: - return func.HttpResponse( - json.dumps({"error": f"Vision inference not available: {e}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Parse request - req_body = req.get_json() - image_data = req_body.get("image") - image_url = req_body.get("image_url") - format_type = req_body.get("format", "base64") - - if not image_data and not image_url: - return func.HttpResponse( - json.dumps( - { - "error": "No image provided. Include 'image' (base64) or 'image_url' in request body." - } - ), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Initialize vision inference (loads latest checkpoint) - # Cache the instance for performance (singleton pattern) - if not hasattr(vision_infer, "_vision_model"): - logging.info("Initializing vision model (first request)...") - try: - vision_infer._vision_model = VisionInference() - except FileNotFoundError as e: - return func.HttpResponse( - json.dumps( - { - "error": "No trained model found", - "detail": str(e), - "help": "Train a model first using: python scripts/train_vision.py", - } - ), - status_code=404, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - vi = vision_infer._vision_model - - # Run inference based on input format - if image_url: - # Fetch image from URL - try: - import io - - import requests - from PIL import Image - - response = requests.get(image_url, timeout=10) - response.raise_for_status() - img = Image.open(io.BytesIO(response.content)) - result = vi.predict(img) - except Exception as e: - return func.HttpResponse( - json.dumps({"error": f"Failed to fetch image from URL: {e}"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - elif format_type == "base64": - # Decode base64 image - try: - result = vi.predict_base64(image_data) - except Exception as e: - return func.HttpResponse( - json.dumps({"error": f"Failed to decode base64 image: {e}"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - else: - return func.HttpResponse( - json.dumps( - { - "error": f"Unsupported format: {format_type}. Use 'base64' or provide 'image_url'." - } - ), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Add model metadata to response - response_data = {**result, "model_info": vi.get_model_info()} - - return func.HttpResponse( - json.dumps(response_data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Vision infer error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Vision inference failed: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="vision/infer", methods=["OPTIONS"], auth_level=func.AuthLevel.ANONYMOUS -) -def vision_infer_options(req: func.HttpRequest) -> func.HttpResponse: - """Handle CORS preflight for vision inference""" - return func.HttpResponse( - "", status_code=200, headers=create_cors_response_headers() - ) - - -@app.route( - route="vision/batch-infer", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS -) -def vision_batch_infer(req: func.HttpRequest) -> func.HttpResponse: - """ - Batch vision inference endpoint for multiple images. - - POST /api/vision/batch-infer - Body: - { - "images": [ - {"data": "base64_1", "id": "img1"}, - {"data": "base64_2", "id": "img2"}, - ... - ] - } - - Response: - { - "results": [ - {"id": "img1", "label": "happy", "confidence": 0.92, ...}, - {"id": "img2", "label": "sad", "confidence": 0.85, ...} - ], - "total": 2, - "model_info": {...} - } - """ - logging.info("Vision batch infer endpoint invoked") - - try: - import base64 - import io - - from PIL import Image - from vision_inference import VisionInference - except ImportError as e: - return func.HttpResponse( - json.dumps({"error": f"Vision inference not available: {e}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - try: - req_body = req.get_json() - images_data = req_body.get("images", []) - - if not images_data: - return func.HttpResponse( - json.dumps({"error": "No images provided"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Limit batch size to prevent overload - max_batch_size = 50 - if len(images_data) > max_batch_size: - return func.HttpResponse( - json.dumps( - {"error": f"Batch size exceeds limit of {max_batch_size} images"} - ), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Initialize vision model - if not hasattr(vision_batch_infer, "_vision_model"): - try: - vision_batch_infer._vision_model = VisionInference() - except FileNotFoundError as e: - return func.HttpResponse( - json.dumps({"error": "No trained model found", "detail": str(e)}), - status_code=404, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - vi = vision_batch_infer._vision_model - - # Decode all images - pil_images = [] - image_ids = [] - for idx, img_data in enumerate(images_data): - try: - img_id = img_data.get("id", f"image_{idx}") - b64_data = img_data.get("data") - - img_bytes = base64.b64decode(b64_data) - pil_img = Image.open(io.BytesIO(img_bytes)) - - pil_images.append(pil_img) - image_ids.append(img_id) - except Exception as e: - logging.warning(f"Failed to decode image {idx}: {e}") - continue - - if not pil_images: - return func.HttpResponse( - json.dumps({"error": "No valid images could be decoded"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Run batch inference - predictions = vi.predict_batch(pil_images) - - # Combine predictions with IDs - results = [] - for img_id, pred in zip(image_ids, predictions): - results.append({"id": img_id, **pred}) - - response_data = { - "results": results, - "total": len(results), - "model_info": vi.get_model_info(), - } - - return func.HttpResponse( - json.dumps(response_data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Vision batch infer error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Batch inference failed: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="image/generate", - methods=["POST", "OPTIONS"], - auth_level=func.AuthLevel.ANONYMOUS, -) -def image_generate(req: func.HttpRequest) -> func.HttpResponse: - """ - AI Image generation endpoint using OpenAI DALL-E. - - POST /api/image/generate - Body: - { - "prompt": "description of image to generate", - "size": "512x512", - "style": "anime" - } - - Response: - { - "image_url": "https://...", - "image_data": "base64_encoded_image", - "prompt": "original prompt", - "model": "dall-e-2" - } - """ - if req.method == "OPTIONS": - return func.HttpResponse( - status_code=200, headers=create_cors_response_headers() - ) - - logging.info("Image generation endpoint invoked") - - try: - req_body = req.get_json() - prompt = req_body.get("prompt", "") - size = req_body.get("size", "512x512") - style_hint = req_body.get("style", "") - - if not prompt: - return func.HttpResponse( - json.dumps({"error": "Prompt is required"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - if style_hint: - prompt = f"{prompt}, {style_hint} style" - - try: - import os - - from openai import OpenAI - - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: - api_key = os.getenv("AZURE_OPENAI_API_KEY") - endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") - - if api_key and endpoint: - client = OpenAI( - api_key=api_key, base_url=f"{endpoint}/openai/deployments" - ) - else: - raise ValueError("No OpenAI API key configured") - else: - client = OpenAI(api_key=api_key) - - response = client.images.generate( - model="dall-e-2", - prompt=prompt, - size=size if size in ["256x256", "512x512", "1024x1024"] else "512x512", - n=1, - response_format="url", - ) - - image_url = response.data[0].url - - response_data = { - "image_url": image_url, - "prompt": prompt, - "model": "dall-e-2", - "size": size, - } - - return func.HttpResponse( - json.dumps(response_data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as openai_error: - logging.warning(f"OpenAI image generation failed: {openai_error}") - # Detect Azure/OpenAI quota/premium allowance errors and provide - # a clearer fallback message for users. - try: - from shared.azure_utils import (format_quota_message, - is_quota_error) - except Exception: - is_quota_error = None - format_quota_message = None - - placeholder_svg = "\n".join( - [ - '', - " ", - ' ', - ' ', - ' ', - ' ', - " ", - " ", - ' ', - ' ✨', - ( - ' ' - "Aria" - ), - ( - ' ' - "AI Assistant" - ), - ( - ' ' - "Image generation unavailable" - ), - ( - ' {openai_error.__class__.__name__}' - ), - "", - ] - ) - - import base64 - - svg_base64 = base64.b64encode(placeholder_svg.encode()).decode() - - # Prefer a helpful quota message when available - err_text = str(openai_error) - if is_quota_error is not None and is_quota_error(openai_error): - if format_quota_message is not None: - err_text = format_quota_message( - openai_error, service_name="OpenAI / Azure Images API" - ) - - response_data = { - "image_data": svg_base64, - "prompt": prompt, - "model": "fallback-svg", - "size": "512x512", - "fallback": True, - "error": err_text, - } - - return func.HttpResponse( - json.dumps(response_data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Image generation error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Image generation failed: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -# ============================================================================= -# Quantum AI Endpoints - Advanced quantum computing features -# ============================================================================= - - -@app.route( - route="quantum/classify", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS -) -def quantum_classify(req: func.HttpRequest) -> func.HttpResponse: - """ - Quantum classification endpoint. - - POST /api/quantum/classify - Body: { - "features": [0.1, 0.5, 0.3, ...], // Feature vector - "n_qubits": 4, // Optional - "n_layers": 2 // Optional - } - - Response: { - "classification": "positive|negative|neutral", - "confidence": 0.85, - "quantum_state": {...} - } - """ - logging.info("Quantum classify endpoint invoked") - - try: - # Import quantum modules - try: - import numpy as np - import torch - from quantum_classifier import QuantumClassifier - except ImportError as e: - return func.HttpResponse( - json.dumps({"error": f"Quantum dependencies not available: {e}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Parse request - req_body = req.get_json() - features = req_body.get("features", []) - n_qubits = req_body.get("n_qubits", 4) - n_layers = req_body.get("n_layers", 2) - - if not features: - return func.HttpResponse( - json.dumps({"error": "No features provided"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - # Initialize quantum classifier - classifier = QuantumClassifier() - - # Prepare features - feature_array = np.array(features[:n_qubits]) - if len(feature_array) < n_qubits: - feature_array = np.pad(feature_array, (0, n_qubits - len(feature_array))) - - # Convert to torch tensor and scale to [0, 2π] - inputs = torch.tensor(feature_array, dtype=torch.float32) * 2 * np.pi - - # Create random weights (in production, use trained weights) - weights = torch.randn(n_layers, n_qubits, 2, dtype=torch.float32) * 0.1 - - # Run quantum circuit - output = classifier.forward(inputs.unsqueeze(0), weights) - - # Interpret results - avg_value = float(output.mean()) - confidence = abs(avg_value) - - if avg_value > 0.3: - classification = "positive" - elif avg_value < -0.3: - classification = "negative" - else: - classification = "neutral" - - response_data = { - "classification": classification, - "confidence": confidence, - "quantum_state": { - "expectation_values": output.tolist(), - "average": avg_value, - "n_qubits": n_qubits, - "n_layers": n_layers, - }, - } - - return func.HttpResponse( - json.dumps(response_data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Quantum classify error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Quantum classification failed: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="quantum/circuit", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS -) -def quantum_circuit(req: func.HttpRequest) -> func.HttpResponse: - """ - Create and visualize a quantum circuit. - - POST /api/quantum/circuit - Body: { - "n_qubits": 4, - "n_layers": 2, - "entanglement": "linear|circular|full" - } - - Response: { - "circuit_info": {...}, - "gates": [...], - "visualization": "text representation" - } - """ - logging.info("Quantum circuit endpoint invoked") - - try: - req_body = req.get_json() - n_qubits = req_body.get("n_qubits", 4) - n_layers = req_body.get("n_layers", 2) - entanglement = req_body.get("entanglement", "linear") - - # Create circuit description - gates = [] - - # Input encoding layer - for i in range(n_qubits): - gates.append( - {"type": "RY", "qubit": i, "layer": 0, "parameter": "input[i]"} - ) - - # Variational layers - for layer in range(n_layers): - # Rotation gates - for i in range(n_qubits): - gates.append( - { - "type": "RY", - "qubit": i, - "layer": layer + 1, - "parameter": f"θ_{layer}_{i}_0", - } - ) - gates.append( - { - "type": "RZ", - "qubit": i, - "layer": layer + 1, - "parameter": f"θ_{layer}_{i}_1", - } - ) - - # Entanglement gates - if entanglement == "linear": - for i in range(n_qubits - 1): - gates.append( - { - "type": "CNOT", - "control": i, - "target": i + 1, - "layer": layer + 1, - } - ) - elif entanglement == "circular": - for i in range(n_qubits): - gates.append( - { - "type": "CNOT", - "control": i, - "target": (i + 1) % n_qubits, - "layer": layer + 1, - } - ) - elif entanglement == "full": - for i in range(n_qubits): - for j in range(i + 1, n_qubits): - gates.append( - { - "type": "CNOT", - "control": i, - "target": j, - "layer": layer + 1, - } - ) - - # Measurements - for i in range(n_qubits): - gates.append( - { - "type": "Measure", - "qubit": i, - "layer": n_layers + 1, - "observable": "PauliZ", - } - ) - - # Create text visualization using list for efficiency (avoids O(n²) string concatenation) - viz_parts = [ - f"Quantum Circuit ({n_qubits} qubits, {n_layers} layers, {entanglement} entanglement)\n", - "=" * 60 + "\n\n", - ] - - for layer in range(n_layers + 2): - viz_parts.append(f"Layer {layer}:\n") - layer_gates = [g for g in gates if g.get("layer") == layer] - for gate in layer_gates: - if gate["type"] in ["RY", "RZ"]: - viz_parts.append( - f" {gate['type']}({gate['parameter']}) on qubit {gate['qubit']}\n" - ) - elif gate["type"] == "CNOT": - viz_parts.append( - f" CNOT: control={gate['control']}, target={gate['target']}\n" - ) - elif gate["type"] == "Measure": - viz_parts.append( - f" Measure qubit {gate['qubit']} ({gate['observable']})\n" - ) - viz_parts.append("\n") - - visualization = "".join(viz_parts) - - response_data = { - "circuit_info": { - "n_qubits": n_qubits, - "n_layers": n_layers, - "entanglement": entanglement, - "total_gates": len(gates), - "depth": n_layers + 2, - }, - "gates": gates, - "visualization": visualization, - } - - return func.HttpResponse( - json.dumps(response_data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Quantum circuit error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Circuit creation failed: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="quantum/llm", methods=["POST", "GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def quantum_llm(req: func.HttpRequest) -> func.HttpResponse: - """ - Quantum LLM inference and training endpoint. - - GET /api/quantum/llm → return model status and capabilities - POST /api/quantum/llm → generate text or trigger a training cycle - - POST body (generate): - {"action": "generate", "prompt": "Quantum computing", "max_tokens": 50} - - POST body (train): - {"action": "train", "dataset_path": "datasets/chat/...", "epochs": 1} - """ - logging.info("Quantum LLM endpoint invoked: %s", req.method) - - try: - # Lazy import to avoid hard dependency at startup - repo_root = Path(__file__).resolve().parent - quantum_ml_src = ( - Path(__file__).resolve().parent / "ai-projects" / "quantum-ml" / "src" - ) - scripts_dir = Path(__file__).resolve().parent / "scripts" - for p in [str(quantum_ml_src), str(scripts_dir)]: - if p not in sys.path: - sys.path.insert(0, p) - - try: - from quantum_llm_trainer import (QUANTUM_AVAILABLE, - QuantumEnhancedLLMTrainer, - get_quantum_llm_status) - - trainer_available = True - except ImportError as ie: - trainer_available = False - QUANTUM_AVAILABLE = False - _trainer_import_err = str(ie) - get_quantum_llm_status = None - - if req.method == "GET": - readiness = None - if trainer_available and get_quantum_llm_status is not None: - readiness = get_quantum_llm_status( - output_dir=Path(__file__).resolve().parent - / "data_out" - / "quantum_llm_training" - ) - return func.HttpResponse( - json.dumps( - { - "available": trainer_available, - "quantum_circuits": QUANTUM_AVAILABLE, - "model": "QuantumLLM (hybrid quantum-classical transformer)", - "capabilities": { - "generate": trainer_available, - "train": trainer_available, - "n_qubits": 4, - "backends": ["default.qubit", "lightning.qubit"], - }, - "readiness": readiness, - "import_error": ( - None if trainer_available else _trainer_import_err - ), - } - ), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - if not trainer_available: - return func.HttpResponse( - json.dumps( - { - "error": "Quantum LLM trainer not available", - "details": _trainer_import_err, - } - ), - status_code=503, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - try: - body = req.get_json() if req.get_body() else {} - except ValueError: - return func.HttpResponse( - json.dumps({"error": "Invalid JSON body"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - action = body.get("action", "generate") - - if action == "generate": - prompt = str(body.get("prompt", "Quantum")).strip()[:256] - if not prompt: - prompt = "Quantum" - max_tokens = min(int(body.get("max_tokens", 50)), 200) - - config = { - "n_qubits": 4, - "n_quantum_layers": 2, - "d_model": 64, - "max_seq_len": 32, - } - trainer = QuantumEnhancedLLMTrainer(config) - - prompt_token_ids = [ - ord(c) % trainer.model_config["vocab_size"] for c in prompt[:32] - ] - try: - import torch - - prompt_ids = torch.tensor([prompt_token_ids], dtype=torch.long) - except Exception: - # Keep endpoint usable in lightweight environments where torch is - # intentionally absent; fake/alternate trainer implementations can - # still accept a nested token list. - prompt_ids = [prompt_token_ids] - - generated = trainer.model.generate( - prompt_ids, max_new_tokens=max_tokens, temperature=0.8, top_k=20 - ) - # Decode back to text using the simple char mapping - generated_row = generated[0] - tokens = ( - generated_row.tolist() - if hasattr(generated_row, "tolist") - else list(generated_row) - ) - text = "".join( - chr(t % 128) if 32 <= (t % 128) < 127 else "?" for t in tokens - ) - - return func.HttpResponse( - json.dumps( - { - "action": "generate", - "prompt": prompt, - "generated": text, - "tokens": len(tokens), - "quantum_available": QUANTUM_AVAILABLE, - "readiness": ( - get_quantum_llm_status( - output_dir=repo_root - / "data_out" - / "quantum_llm_training" - ) - if get_quantum_llm_status is not None - else None - ), - } - ), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - elif action == "train": - dataset_path = body.get("dataset_path", "datasets/chat") - dataset_path_obj = Path(dataset_path) - if not dataset_path_obj.is_absolute(): - dataset_path_obj = repo_root / dataset_path_obj - dataset_path_obj = dataset_path_obj.resolve(strict=False) - - # Basic path traversal protection: keep training datasets in-repo. - try: - dataset_path_obj.relative_to(repo_root.resolve()) - except ValueError: - return func.HttpResponse( - json.dumps( - { - "error": "dataset_path must point to a location inside the repository" - } - ), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - epochs = min(int(body.get("epochs", 1)), 5) - output_dir = repo_root / "data_out" / "quantum_llm_api" - - config = {"n_qubits": 4, "n_quantum_layers": 2, "d_model": 64} - trainer = QuantumEnhancedLLMTrainer(config) - results = trainer.train_with_quantum_enhancement( - dataset_path=dataset_path_obj, - output_dir=output_dir, - epochs=epochs, - model=None, - ) - - return func.HttpResponse( - json.dumps( - { - "action": "train", - "status": results["status"], - "epochs_completed": results["epochs_completed"], - "final_loss": results["final_loss"], - "circuit_executions": results["quantum_metrics"][ - "circuit_executions" - ], - "checkpoint_path": results.get("checkpoint_path"), - "readiness": ( - get_quantum_llm_status(output_dir=output_dir) - if get_quantum_llm_status is not None - else None - ), - } - ), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - else: - return func.HttpResponse( - json.dumps( - {"error": f"Unknown action: {action!r}. Use 'generate' or 'train'."} - ), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Quantum LLM error: {e}", exc_info=True) - return func.HttpResponse( - json.dumps({"error": f"Quantum LLM request failed: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route(route="quantum/info", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) -def quantum_info(req: func.HttpRequest) -> func.HttpResponse: - """ - Get quantum computing capabilities and status. - - GET /api/quantum/info - - Response: { - "available": true, - "backends": [...], - "capabilities": {...} - } - """ - logging.info("Quantum info endpoint invoked") - - try: - # Check if quantum modules are available - try: - import pennylane # noqa: F401 - import quantum_classifier # noqa: F401 - - quantum_available = True - - # Get available backends - backends = [ - { - "name": "default.qubit", - "description": "PennyLane default simulator", - "type": "simulator", - }, - { - "name": "lightning.qubit", - "description": "Fast C++ simulator", - "type": "simulator", - }, - { - "name": "qiskit.aer", - "description": "Qiskit Aer simulator", - "type": "simulator", - }, - ] - - capabilities = { - "max_qubits": 20, - "supports_gpu": False, - "variational_circuits": True, - "hybrid_models": True, - "azure_quantum_ready": True, - } - - except ImportError: - quantum_available = False - backends = [] - capabilities = {} - - response_data = { - "available": quantum_available, - "backends": backends, - "capabilities": capabilities, - "quantum_provider": "quantum-enhanced-local", - "version": "1.0.0", - } - - return func.HttpResponse( - json.dumps(response_data), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Quantum info error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to get quantum info: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -# ============================================================================= -# SUBSCRIPTION & MONETIZATION ENDPOINTS -# ============================================================================= - - -@app.route( - route="subscription/pricing", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def subscription_pricing(req: func.HttpRequest) -> func.HttpResponse: - """ - Get pricing information for all subscription tiers. - - GET /api/subscription/pricing - - Response: { - "tiers": { - "free": {...}, - "pro": {...}, - "enterprise": {...} - } - } - """ - logging.info("Pricing endpoint invoked") - - try: - from shared.subscription_manager import (TIER_FEATURES, TIER_LIMITS, - TIER_PRICING, - SubscriptionTier) - - pricing_info = {"tiers": {}} - - for tier in SubscriptionTier: - pricing_info["tiers"][tier.value] = { - "name": tier.name, - "price": TIER_PRICING[tier], - "currency": "USD", - "billing_period": "monthly", - "features": { - f.value: enabled for f, enabled in TIER_FEATURES[tier].items() - }, - "limits": TIER_LIMITS[tier], - } - - return func.HttpResponse( - json.dumps(pricing_info), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Pricing endpoint error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to get pricing: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="subscription/status", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def subscription_status(req: func.HttpRequest) -> func.HttpResponse: - """ - Get subscription status for a user. - - GET /api/subscription/status?user_id= - - Response: { - "user_id": "...", - "tier": "pro", - "is_active": true, - "usage": {...}, - "limits": {...} - } - """ - logging.info("Subscription status endpoint invoked") - - try: - if not subscription_manager_available: - return func.HttpResponse( - json.dumps({"error": "Subscription manager not available"}), - status_code=503, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - user_id = req.params.get("user_id", "demo_user") - - manager = get_subscription_manager() - subscription = manager.get_subscription(user_id) - - return func.HttpResponse( - json.dumps(subscription.to_dict()), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Subscription status error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to get subscription status: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="subscription/upgrade", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS -) -def subscription_upgrade(req: func.HttpRequest) -> func.HttpResponse: - """ - Upgrade a user's subscription. - - POST /api/subscription/upgrade - Body: { - "user_id": "...", - "tier": "pro" | "enterprise", - "duration_days": 30, - "payment_method": "stripe", - "stripe_subscription_id": "..." - } - - Response: { - "success": true, - "subscription": {...} - } - """ - logging.info("Subscription upgrade endpoint invoked") - - try: - if not subscription_manager_available: - return func.HttpResponse( - json.dumps({"error": "Subscription manager not available"}), - status_code=503, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - body = json.loads(req.get_body().decode("utf-8")) - user_id = body.get("user_id", "demo_user") - tier_str = body.get("tier", "pro") - duration_days = body.get("duration_days", 30) - payment_method = body.get("payment_method") - stripe_subscription_id = body.get("stripe_subscription_id") - - tier = SubscriptionTier(tier_str) - - manager = get_subscription_manager() - subscription = manager.upgrade_subscription( - user_id=user_id, - tier=tier, - duration_days=duration_days, - payment_method=payment_method, - stripe_subscription_id=stripe_subscription_id, - ) - - return func.HttpResponse( - json.dumps({"success": True, "subscription": subscription.to_dict()}), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Subscription upgrade error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to upgrade subscription: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="subscription/revenue", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def subscription_revenue(req: func.HttpRequest) -> func.HttpResponse: - """ - Get revenue statistics and projections. - - GET /api/subscription/revenue - - Response: { - "total_subscribers": 15, - "active_subscribers": 15, - "by_tier": {...}, - "monthly_recurring_revenue": 2235, - "annual_recurring_revenue": 26820 - } - """ - logging.info("Revenue stats endpoint invoked") - - try: - if not subscription_manager_available: - return func.HttpResponse( - json.dumps({"error": "Subscription manager not available"}), - status_code=503, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - manager = get_subscription_manager() - stats = manager.get_revenue_stats() - - return func.HttpResponse( - json.dumps(stats), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Revenue stats error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to get revenue stats: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="subscription/usage", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS -) -def subscription_track_usage(req: func.HttpRequest) -> func.HttpResponse: - """ - Track resource usage for a user. - - POST /api/subscription/usage - Body: { - "user_id": "...", - "resource": "chat_messages" | "quantum_jobs" | "training_hours" | "api_requests" | "websites_created", - "amount": 1 - } - - Response: { - "success": true, - "allowed": true, - "current_usage": {...} - } - """ - logging.info("Usage tracking endpoint invoked") - - try: - if not subscription_manager_available: - return func.HttpResponse( - json.dumps({"error": "Subscription manager not available"}), - status_code=503, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - body = json.loads(req.get_body().decode("utf-8")) - user_id = body.get("user_id", "demo_user") - resource = body.get("resource", "api_requests") - amount = body.get("amount", 1) - - manager = get_subscription_manager() - allowed = manager.track_usage(user_id, resource, amount) - - subscription = manager.get_subscription(user_id) - - return func.HttpResponse( - json.dumps( - { - "success": True, - "allowed": allowed, - "current_usage": subscription.usage, - "limits": subscription.to_dict()["limits"], - } - ), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Usage tracking error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to track usage: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -# ----------------------------------------------------------------------------- -# Stripe Webhook Handler -# ----------------------------------------------------------------------------- -@app.route( - route="webhook/stripe", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS -) -def stripe_webhook(req: func.HttpRequest) -> func.HttpResponse: - """ - Handle Stripe webhook events. - - POST /api/webhook/stripe - Headers: Stripe-Signature - Body: Stripe event payload - - Response: { - "status": "success" | "error", - "message": "..." - } - """ - logging.info("Stripe webhook endpoint invoked") - - try: - from shared.stripe_webhooks import get_webhook_handler - - payload = req.get_body().decode("utf-8") - signature = req.headers.get("Stripe-Signature", "") - webhook_secret = os.environ.get("STRIPE_WEBHOOK_SECRET") - - handler = get_webhook_handler() - result = handler.handle_webhook(payload, signature, webhook_secret) - - status_code = 200 if result["status"] in ["success", "ignored"] else 500 - - return func.HttpResponse( - json.dumps(result), - status_code=status_code, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Stripe webhook error: {str(e)}") - return func.HttpResponse( - json.dumps({"status": "error", "message": str(e)}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -# ----------------------------------------------------------------------------- -# Email Notifications Test Endpoint -# ----------------------------------------------------------------------------- -@app.route( - route="notifications/test", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS -) -def test_notifications(req: func.HttpRequest) -> func.HttpResponse: - """ - Test email notification system. - - POST /api/notifications/test - Body: { - "email": "user@example.com", - "type": "usage_warning" | "payment_succeeded" | "subscription_activated" - } - - Response: { - "success": true, - "message": "Notification sent" - } - """ - logging.info("Test notification endpoint invoked") - - try: - from shared.email_notifications import get_email_system - - body = json.loads(req.get_body().decode("utf-8")) - email = body.get("email", "test@example.com") - notification_type = body.get("type", "usage_warning") - - email_system = get_email_system() - - # Send test notification based on type - if notification_type == "usage_warning": - success = email_system.notify_usage_warning( - user_email=email, - resource="chat_messages", - percentage=85.0, - current=850, - limit=1000, - ) - elif notification_type == "payment_succeeded": - success = email_system.notify_payment_succeeded( - user_email=email, amount=49.00, invoice_id="inv_test123" - ) - elif notification_type == "subscription_activated": - success = email_system.notify_subscription_activated( - user_email=email, tier="Pro", price=49.00 - ) - else: - return func.HttpResponse( - json.dumps( - {"error": f"Unknown notification type: {notification_type}"} - ), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - return func.HttpResponse( - json.dumps( - { - "success": success, - "message": f"Test notification sent to {email}", - "type": notification_type, - } - ), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Test notification error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to send test notification: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -# ----------------------------------------------------------------------------- -# Notifications Log Endpoint -# ----------------------------------------------------------------------------- -@app.route( - route="notifications/log", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def notifications_log(req: func.HttpRequest) -> func.HttpResponse: - """ - Get email notification log. - - GET /api/notifications/log?user_email=user@example.com - - Response: { - "notifications": [...] - } - """ - logging.info("Notifications log endpoint invoked") - - try: - from shared.email_notifications import get_email_system - - user_email = req.params.get("user_email") - - email_system = get_email_system() - notifications = email_system.get_sent_emails(user_email) - - return func.HttpResponse( - json.dumps({"notifications": notifications, "count": len(notifications)}), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Notifications log error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to get notifications log: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -# ----------------------------------------------------------------------------- -# Referral System Endpoints -# ----------------------------------------------------------------------------- -@app.route( - route="referrals/code", methods=["GET", "POST"], auth_level=func.AuthLevel.ANONYMOUS -) -def referral_code(req: func.HttpRequest) -> func.HttpResponse: - """ - Get or generate referral code for a user. - - GET /api/referrals/code?user_id=... - POST /api/referrals/code with {"user_id": "..."} - - Response: { - "referral_code": "ABC123DEF", - "user_id": "..." - } - """ - logging.info("Referral code endpoint invoked") - - try: - from shared.referral_system import get_referral_system - - if req.method == "GET": - user_id = req.params.get("user_id", "demo_user") - else: - body = json.loads(req.get_body().decode("utf-8")) - user_id = body.get("user_id", "demo_user") - - referral_system = get_referral_system() - - # Get existing or generate new code - code = referral_system.get_referral_code(user_id) - if not code: - code = referral_system.generate_referral_code(user_id) - - return func.HttpResponse( - json.dumps({"referral_code": code, "user_id": user_id}), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Referral code error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to get referral code: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="referrals/stats", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def referral_stats(req: func.HttpRequest) -> func.HttpResponse: - """ - Get referral statistics for a user. - - GET /api/referrals/stats?user_id=... - - Response: { - "referral_code": "...", - "referral_count": 5, - "total_commission": 100.00, - "pending_commission": 50.00, - "paid_commission": 50.00, - "referrals": [...] - } - """ - logging.info("Referral stats endpoint invoked") - - try: - from shared.referral_system import get_referral_system - - user_id = req.params.get("user_id", "demo_user") - - referral_system = get_referral_system() - stats = referral_system.get_referral_stats(user_id) - - return func.HttpResponse( - json.dumps(stats), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Referral stats error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to get referral stats: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="referrals/record", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS -) -def record_referral(req: func.HttpRequest) -> func.HttpResponse: - """ - Record a new referral. - - POST /api/referrals/record - Body: { - "referrer_code": "ABC123", - "new_user_id": "user123", - "tier": "pro", - "subscription_value": 49.00 - } - - Response: { - "success": true, - "commission": 9.80, - "referral_count": 5 - } - """ - logging.info("Record referral endpoint invoked") - - try: - from shared.referral_system import get_referral_system - - body = json.loads(req.get_body().decode("utf-8")) - referrer_code = body.get("referrer_code") - new_user_id = body.get("new_user_id") - tier = body.get("tier") - subscription_value = body.get("subscription_value") - - if not all([referrer_code, new_user_id, tier, subscription_value]): - return func.HttpResponse( - json.dumps({"error": "Missing required fields"}), - status_code=400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - referral_system = get_referral_system() - result = referral_system.record_referral( - referrer_code=referrer_code, - new_user_id=new_user_id, - tier=tier, - subscription_value=subscription_value, - ) - - return func.HttpResponse( - json.dumps(result), - status_code=200 if result.get("success") else 400, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Record referral error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to record referral: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - -@app.route( - route="referrals/leaderboard", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def referral_leaderboard(req: func.HttpRequest) -> func.HttpResponse: - """ - Get referral leaderboard. - - GET /api/referrals/leaderboard?limit=10 - - Response: { - "leaderboard": [ - {"rank": 1, "user_id": "...", "referral_count": 50, "total_commission": 500} - ] - } - """ - logging.info("Referral leaderboard endpoint invoked") - - try: - from shared.referral_system import get_referral_system - - limit = int(req.params.get("limit", "10")) - - referral_system = get_referral_system() - leaderboard = referral_system.get_leaderboard(limit) - - return func.HttpResponse( - json.dumps({"leaderboard": leaderboard}), - status_code=200, - mimetype="application/json", - headers=create_cors_response_headers(), - ) - - except Exception as e: - logging.error(f"Referral leaderboard error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Failed to get leaderboard: {str(e)}"}), - status_code=500, - mimetype="application/json", - headers=create_cors_response_headers(), - ) +# ============================================================================= +# QAI Azure Functions Application +# ============================================================================= +import importlib.util as _iu +import json +import logging +import os +import re +import subprocess +import sys +import time +from datetime import datetime, timezone +from pathlib import Path + +import azure.functions as func + +# Import AI projects via centralized registry (replaced scattered sys.path manipulation) +from shared.core.module_registry import AIProjectsRegistry +from shared.import_helpers import create_stub_function, safe_import +from shared.json_utils import load_status_json + +# Initialize registry and get chat providers API +_ai_registry = AIProjectsRegistry() +try: + _chat_cli_api = _ai_registry.chat_cli() + detect_provider = _chat_cli_api.detect_provider + prune_messages = _chat_cli_api.token_utils.prune_messages +except Exception as _registry_err: + logging.warning(f"[startup] AI projects registry failed: {_registry_err}") + detect_provider = None + prune_messages = None + +# Pre-compiled word split regex used in token/word counting hot paths. +_RE_WORD_SPLIT = re.compile(r"\S+") + +# Import defensive import helper + +# ----------------------------------------------------------------------------- +# Optional unified SQL engine health + pool metrics (multi-database support) +# ----------------------------------------------------------------------------- +sql_funcs = safe_import( + "shared.sql_engine", + import_names=("sql_health", "engine_stats"), + fallback_factory=create_stub_function, +) +sql_health = sql_funcs["sql_health"] +engine_stats = sql_funcs["engine_stats"] + +# ----------------------------------------------------------------------------- +# Early Telemetry Initialization (non-fatal if unavailable) +# ----------------------------------------------------------------------------- +telemetry_module = safe_import("shared.telemetry", log_failure=False) +if telemetry_module and hasattr(telemetry_module, "init_telemetry"): + try: + telemetry_module.init_telemetry() + except Exception as _telemetry_err: # noqa: BLE001 + logging.warning(f"[startup] Telemetry init skipped: {_telemetry_err}") +else: + logging.warning("[startup] Telemetry init skipped: module unavailable") + +# Try to initialize generic OpenTelemetry tracing (best-effort) +tracing_module = safe_import("shared.tracing", log_failure=False) +if tracing_module and hasattr(tracing_module, "init_tracing"): + try: + tracing_module.init_tracing(service_name="qai.functions") + except Exception as _trace_err: # noqa: BLE001 - don't fail on missing libs + logging.debug(f"[startup] Tracing init skipped: {_trace_err}") +else: + logging.debug("[startup] Tracing init skipped: module unavailable") + +# ----------------------------------------------------------------------------- +# Optional Cosmos Client import (lazy health + persistence) +# ----------------------------------------------------------------------------- +cosmos_client = safe_import("shared.cosmos_client", log_failure=True) +if not cosmos_client: + logging.info("[startup] Cosmos client unavailable") + +# Memory / DB logging utilities (fault-tolerant) +db_logging = safe_import( + "shared.db_logging", + import_names=("log_chat_message_safe",), + fallback_factory=lambda name: None, +) +log_chat_message_safe = db_logging["log_chat_message_safe"] + +# Chat memory functions with graceful degradation +chat_memory_funcs = safe_import( + "shared.chat_memory", + import_names=("generate_embedding", "fetch_similar_messages", "store_embedding"), + fallback_factory=lambda name: { + "generate_embedding": lambda text: [], + "fetch_similar_messages": lambda query_emb, top_k=5, session_id=None: [], + "store_embedding": lambda message_id, embedding, model: False, + }.get(name, lambda *args, **kwargs: None), +) +generate_embedding = chat_memory_funcs["generate_embedding"] +fetch_similar_messages = chat_memory_funcs["fetch_similar_messages"] +store_embedding = chat_memory_funcs["store_embedding"] +try: + from shared.db_logging import log_chat_message_safe +except Exception: # pragma: no cover - if shared not on path + log_chat_message_safe = None # type: ignore +try: + from shared.chat_memory import (fetch_similar_messages, generate_embedding, + store_embedding) +except Exception: + # Provide graceful degradations so endpoint still works + def generate_embedding(text: str): # type: ignore + return [] + + def fetch_similar_messages(query_emb, top_k=5, session_id=None): # type: ignore + return [] + + def store_embedding(message_id, embedding, model): # type: ignore + pass + + +# File caching for repeated JSON reads +try: + from shared.file_cache import read_json_cached +except Exception: # pragma: no cover + # Fallback if file_cache not available + def read_json_cached(file_path, ttl_seconds=60): # type: ignore + import json + + with open(file_path, "r") as f: + return json.load(f) + return False + + +# Add scripts to path for vision inference (kept for legacy support) +scripts_path = Path(__file__).resolve().parent / "scripts" +if str(scripts_path) not in sys.path: + sys.path.insert(0, str(scripts_path)) + +# ----------------------------------------------------------------------------- +# Subscription Manager (optional) +# ----------------------------------------------------------------------------- +try: # pragma: no cover - defensive import + from shared.subscription_manager import (SubscriptionTier, + get_subscription_manager) + + subscription_manager_available = True +except Exception as _sub_err: # noqa: BLE001 + logging.info(f"[startup] Subscription manager unavailable: {_sub_err}") + subscription_manager_available = False + get_subscription_manager = None # type: ignore + + +# OpenTelemetry tracer (optional) +try: # pragma: no cover + from opentelemetry import trace # type: ignore + + _tracer = trace.get_tracer("qai.functions") +except Exception: # pragma: no cover - library optional + _tracer = None + +app = func.FunctionApp() + + +# ============================================================================= +# Chat Web Interface - Serves the HTML/JS frontend +# ============================================================================= + + +@app.route(route="chat-web", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) +def serve_chat_web(req: func.HttpRequest) -> func.HttpResponse: + """Serve the chat web interface HTML""" + try: + html_path = Path(__file__).resolve().parent / "apps" / "chat" / "index.html" + + if html_path.exists(): + with open(html_path, "r", encoding="utf-8") as f: + html_content = f.read() + + return func.HttpResponse( + html_content, + status_code=200, + mimetype="text/html", + headers={ + "Cache-Control": "no-store, no-cache, must-revalidate, max-age=0", + "Pragma": "no-cache", + "Expires": "0", + }, + ) + else: + return func.HttpResponse( + f"

    Error

    Chat interface not found at {html_path}

    ", + status_code=404, + mimetype="text/html", + ) + except Exception as e: + logging.error(f"Error serving chat web: {str(e)}") + return func.HttpResponse( + f"

    Error

    {str(e)}

    ", status_code=500, mimetype="text/html" + ) + + +@app.route( + route="chat-web/chat.js", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def serve_chat_js(req: func.HttpRequest) -> func.HttpResponse: + """Serve the chat JavaScript file""" + try: + js_path = Path(__file__).resolve().parent / "apps" / "chat" / "chat.js" + + if js_path.exists(): + with open(js_path, "r", encoding="utf-8") as f: + js_content = f.read() + + return func.HttpResponse( + js_content, + status_code=200, + mimetype="application/javascript", + headers={ + "Cache-Control": "no-store, no-cache, must-revalidate, max-age=0", + "Pragma": "no-cache", + "Expires": "0", + }, + ) + else: + return func.HttpResponse( + f"// Error: JavaScript file not found at {js_path}", + status_code=404, + mimetype="application/javascript", + ) + except Exception as e: + logging.error(f"Error serving chat.js: {str(e)}") + return func.HttpResponse( + f"// Error: {str(e)}", status_code=500, mimetype="application/javascript" + ) + + +# ============================================================================= +# Chat API - Backend for AI interactions +# ============================================================================= + + +def _extract_text_content(content) -> str: + """Extract user-visible text from a message content payload. + + Supports both plain string content and OpenAI-style content blocks. + """ + if isinstance(content, str): + return content.strip() + + def _is_text_like_block_type(block_type: object) -> bool: + if not isinstance(block_type, str): + return False + normalized = block_type.strip().lower() + return normalized == "text" or normalized.endswith("_text") + + if isinstance(content, list): + parts: list[str] = [] + for block in content: + if not isinstance(block, dict): + continue + if not _is_text_like_block_type(block.get("type")): + continue + text_value = block.get("text") + if isinstance(text_value, str): + trimmed = text_value.strip() + if trimmed: + parts.append(trimmed) + return "\n".join(parts).strip() + if content is None: + return "" + return str(content).strip() + + +def _is_compaction_placeholder_message(content: str) -> bool: + """Return True for synthetic chat-compaction placeholder messages. + + Some chat clients or upstream conversation-compaction layers can inject + assistant placeholders such as ``Compacted conversation`` into history. + Those markers are not useful prompt content and can cause later turns to + orbit around the placeholder instead of the real user request. + """ + if not isinstance(content, str): + return False + + normalized_lines = [ + line.strip().lower() for line in content.splitlines() if line.strip() + ] + if not normalized_lines: + return False + + placeholder_lines = { + "compacted conversation", + "conversation compacted", + } + return all(line in placeholder_lines for line in normalized_lines) + + +def _sanitize_chat_messages(messages) -> list[dict]: + """Normalize incoming chat messages and reject empty content. + + This prevents upstream provider 400s like: + "messages: text content blocks must contain non-whitespace text". + """ + if not isinstance(messages, list) or not messages: + raise ValueError("No messages provided") + + sanitized: list[dict] = [] + for idx, msg in enumerate(messages): + if not isinstance(msg, dict) or "role" not in msg or "content" not in msg: + raise ValueError( + f"Invalid message format at index {idx}. Expected {{role, content}}" + ) + + content = msg.get("content") + normalized_content = None + + if isinstance(content, str): + text_content = content.strip() + if text_content: + normalized_content = text_content + elif isinstance(content, list): + # Current chat/token pipeline is text-centric; normalize block payloads + # to plain text to avoid downstream `.strip()` failures. + text_content = _extract_text_content(content) + if text_content: + normalized_content = text_content + elif content is not None: + text_content = str(content).strip() + if text_content: + normalized_content = text_content + + if normalized_content is None: + continue + + if _is_compaction_placeholder_message(normalized_content): + logging.info( + "Dropping synthetic compaction placeholder from chat history at index %d", + idx, + ) + continue + + msg_copy = dict(msg) + msg_copy["content"] = normalized_content + sanitized.append(msg_copy) + + if not sanitized: + raise ValueError("No non-empty message content provided") + + return sanitized + + +def _detect_provider_with_runtime_fallback( + *, + explicit: str | None = None, + model_override: str | None = None, + temperature: float | None = None, + max_output_tokens: int | None = None, +): + """Detect provider with graceful runtime fallback to local echo. + + In constrained test/runtime environments the optional ``openai`` package may + be unavailable while env vars still point to OpenAI/Azure/LMStudio/Ollama. + In those cases, degrade to ``local`` provider instead of returning HTTP 500 + from status/chat endpoints. + """ + + try: + return detect_provider( + explicit=explicit, + model_override=model_override, + temperature=temperature, + max_output_tokens=max_output_tokens, + ) + except RuntimeError as provider_error: + error_text = str(provider_error).lower() + if "openai package not installed" not in error_text: + raise + + logging.warning( + "Provider detection failed due to missing optional openai package; " + "falling back to local provider. explicit=%s model_override=%s error=%s", + explicit, + model_override, + provider_error, + ) + return detect_provider(explicit="local", model_override="local-echo") + + +@app.route(route="chat", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) +def chat(req: func.HttpRequest) -> func.HttpResponse: + """ + HTTP endpoint for chat interactions. + + POST /api/chat + Body: { + "messages": [{"role": "user|assistant|system", "content": "..."}], + "provider": "auto|openai|azure|lmstudio|ollama|agi|quantum|local" (optional), + "model": "model-name" (optional), + "stream": false (optional, streaming not implemented in HTTP yet) + } + + Response: { + "response": "assistant's reply", + "provider": "azure|openai|lmstudio|ollama|agi|quantum-llm|local", + "model": "model-name" + } + """ + logging.info("Chat function invoked") + + # Telemetry span setup (optional) + span_ctx = ( + _tracer.start_as_current_span("chat_request") if _tracer is not None else None + ) + try: + if span_ctx: + span_ctx.__enter__() + # Parse request + req_body = req.get_json() + messages = _sanitize_chat_messages(req_body.get("messages", [])) + # Optional client-provided session identifier + session_id = req_body.get("session_id") + provider_choice = req_body.get("provider", os.getenv("QAI_PROVIDER", "auto")) + model_override = req_body.get("model", os.getenv("QAI_LORA_MODEL")) + temperature = req_body.get("temperature") + max_output_tokens = req_body.get("max_output_tokens") + max_context_tokens = req_body.get("max_context_tokens") + system_prompt = req_body.get("system_prompt") + + # ============================= + # Memory Retrieval (SQL-backed) + # ============================= + user_message_content = next( + ( + _extract_text_content(m.get("content")) + for m in reversed(messages) + if m.get("role") == "user" + ), + None, + ) + memory_messages: list[dict] = [] + user_embedding = None + if user_message_content: + try: + user_embedding = generate_embedding(user_message_content) + similar = fetch_similar_messages( + user_embedding, top_k=5, session_id=session_id + ) + for idx, sm in enumerate(similar): + # Inject prior memory as system messages (helps provider summarize past context) + memory_content = sm.get("content") + # Validate non-empty + if memory_content and str(memory_content).strip(): + memory_messages.append( + { + "role": "system", + "content": f"[Memory #{idx+1} | similarity={sm.get('similarity'):.3f}] {memory_content}", + } + ) + except Exception as mem_err: # noqa: BLE001 + logging.warning(f"Memory retrieval failed: {mem_err}") + + # Compose final message list with memory injected before existing system/user messages + if memory_messages: + messages = memory_messages + messages + + # Get provider (with overrides) AFTER memory injection so pruning sees augmented context + provider, info = _detect_provider_with_runtime_fallback( + explicit=provider_choice, + model_override=model_override, + temperature=temperature, + max_output_tokens=max_output_tokens, + ) + logging.info(f"Using provider: {info.name}, model: {info.model}") + + start_time = time.perf_counter() + pruned_messages, stats, system_msg = prune_messages( + messages=messages, + provider=info.name, + model=info.model, + max_context_tokens=max_context_tokens, + reserve_output_tokens=int(max_output_tokens) if max_output_tokens else 1024, + system_prompt=system_prompt, + ) + # Completion (non-streaming for HTTP simplicity) + result = provider.complete(pruned_messages, stream=False) + duration_ms = int((time.perf_counter() - start_time) * 1000) + + # If result is still a generator, consume it + if hasattr(result, "__iter__") and not isinstance(result, str): + result = "".join(result) + + # ============================= + # Self-Learning: Log conversation for training + # ============================= + try: + logs_dir = ( + Path(__file__).resolve().parent / "ai-projects" / "chat-cli" / "logs" + ) + logs_dir.mkdir(parents=True, exist_ok=True) + + # Create timestamped log file + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + log_file = logs_dir / f"chat_{timestamp}_{session_id or 'anonymous'}.jsonl" + + # Append conversation to log + with open(log_file, "a", encoding="utf-8") as f: + # Log user message + if user_message_content: + f.write( + json.dumps( + { + "role": "user", + "content": user_message_content, + "timestamp": datetime.now().isoformat(), + "provider": info.name, + "model": info.model, + } + ) + + "\n" + ) + # Log assistant response + f.write( + json.dumps( + { + "role": "assistant", + "content": str(result), + "timestamp": datetime.now().isoformat(), + "provider": info.name, + "model": info.model, + } + ) + + "\n" + ) + except Exception as log_err: + logging.warning(f"Self-learning conversation logging failed: {log_err}") + + # ============================= + # Logging + Embedding Storage + # ============================= + if log_chat_message_safe: + try: + # Log user message first (so conversation exists), then assistant reply + if user_message_content: + user_log = log_chat_message_safe( + session_id=session_id, + provider=info.name, + model=info.model, + role="user", + content=user_message_content, + execution_time_ms=None, + finish_reason=None, + ) + if user_log.get("success") and user_embedding: + try: + store_embedding( + user_log.get("message_id"), + user_embedding, + model=info.model, + ) + except Exception as se: # noqa: BLE001 + logging.warning(f"Store embedding failed: {se}") + # Log assistant message + log_chat_message_safe( + session_id=session_id, + provider=info.name, + model=info.model, + role="assistant", + content=str(result), + execution_time_ms=duration_ms, + finish_reason="stop", + ) + except Exception as log_err: # noqa: BLE001 + logging.warning(f"Chat DB logging failed: {log_err}") + + # Cosmos persistence (feature-flagged) + cosmos_written = False + user_id = session_id or "anonymous" + if cosmos_client and os.getenv("QAI_ENABLE_COSMOS", "false").lower() == "true": + try: + if os.getenv("QAI_COSMOS_PERSIST_STRATEGY", "messages") == "messages": + # Persist user and assistant messages separately + last_user_msg = next( + (m for m in reversed(messages) if m.get("role") == "user"), None + ) + if last_user_msg: + cosmos_client.record_chat_message( + user_id, + { + "role": "user", + "content": user_message_content, + "timestamp": time.time(), + }, + provider=info.name, + model=info.model, + ) + cosmos_client.record_chat_message( + user_id, + { + "role": "assistant", + "content": str(result), + "timestamp": time.time(), + }, + provider=info.name, + model=info.model, + ) + cosmos_written = True + else: + # Session-level persistence + cosmos_client.record_chat_session( + user_id, messages, provider=info.name, model=info.model + ) + cosmos_written = True + except Exception as c_err: # noqa: BLE001 + logging.warning(f"[cosmos] Persistence failed: {c_err}") + + response_data = { + "response": result, + "provider": info.name, + "model": info.model, + "memory_injected": len(memory_messages), + "pruning": { + "original_tokens": stats.original_tokens, + "pruned_tokens": stats.pruned_tokens, + "removed_count": stats.removed_count, + "budget": stats.budget, + "reserve_output_tokens": stats.reserve_output_tokens, + }, + "telemetry_span": bool(_tracer), + "duration_ms": duration_ms, + "cosmos_persisted": cosmos_written, + } + + if span_ctx and hasattr(span_ctx, "__exit__"): + try: + # Annotate span + span = trace.get_current_span() if _tracer else None # type: ignore + if span: + span.set_attribute("provider", info.name) + span.set_attribute("model", info.model) + span.set_attribute("duration_ms", duration_ms) + span.set_attribute("memory_injected", len(memory_messages)) + span.set_attribute("cosmos_persisted", cosmos_written) + finally: + span_ctx.__exit__(None, None, None) + + return func.HttpResponse( + json.dumps(response_data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except ValueError as ve: + logging.error(f"Validation error: {str(ve)}") + return func.HttpResponse( + json.dumps({"error": f"Validation error: {str(ve)}"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except RuntimeError as re: + logging.error(f"Runtime error: {str(re)}") + return func.HttpResponse( + json.dumps({"error": f"Configuration error: {str(re)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except Exception as e: + logging.error(f"Unexpected error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Internal server error: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route(route="chat", methods=["OPTIONS"], auth_level=func.AuthLevel.ANONYMOUS) +def chat_options(req: func.HttpRequest) -> func.HttpResponse: + """Handle CORS preflight requests""" + return func.HttpResponse( + "", status_code=200, headers=create_cors_response_headers() + ) + + +def create_cors_response_headers(): + """Create common CORS headers for all responses.""" + return { + "Access-Control-Allow-Origin": "*", + "Access-Control-Allow-Methods": "POST, GET, OPTIONS", + "Access-Control-Allow-Headers": "Content-Type", + } + + +# ============================================================================= +# Automation Tool Endpoints: Resource Monitor, Model Deployer, Results Exporter, Evaluation +# ============================================================================= + + +@app.route( + route="resource-monitor", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def resource_monitor_status(req: func.HttpRequest) -> func.HttpResponse: + """Return latest resource monitor snapshot.""" + try: + snap_path = ( + Path(__file__).resolve().parent + / "data_out" + / "resource_monitor_snapshot.json" + ) + if snap_path.exists(): + # Use cached read with 60s TTL (resource snapshots change infrequently) + data = read_json_cached(snap_path, ttl_seconds=60) + if data: + return func.HttpResponse( + json.dumps(data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + else: + return func.HttpResponse( + json.dumps({"error": "Failed to load snapshot"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + else: + return func.HttpResponse( + json.dumps({"error": "No snapshot found"}), + status_code=404, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except Exception as e: + logging.error(f"Error reading resource snapshot: {e}") + return func.HttpResponse( + json.dumps({"error": str(e)}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="model-deployer/status", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def model_deployer_status(req: func.HttpRequest) -> func.HttpResponse: + """Return model deployer registry status.""" + try: + reg_path = ( + Path(__file__).resolve().parent / "deployed_models" / "model_registry.json" + ) + if reg_path.exists(): + with open(reg_path, "r") as f: + data = json.load(f) + return func.HttpResponse( + json.dumps(data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + else: + return func.HttpResponse( + json.dumps({"error": "No registry found"}), + status_code=404, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except Exception as e: + return func.HttpResponse( + json.dumps({"error": str(e)}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route(route="results-export", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) +def results_export(req: func.HttpRequest) -> func.HttpResponse: + """Return latest results export (all orchestrators).""" + try: + res_path = ( + Path(__file__).resolve().parent / "exports" / "all_orchestrators.json" + ) + if res_path.exists(): + with open(res_path, "r") as f: + data = json.load(f) + return func.HttpResponse( + json.dumps(data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + else: + return func.HttpResponse( + json.dumps({"error": "No results found"}), + status_code=404, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except Exception as e: + return func.HttpResponse( + json.dumps({"error": str(e)}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="evaluation-results", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def evaluation_results(req: func.HttpRequest) -> func.HttpResponse: + """Return latest batch evaluation results.""" + try: + eval_path = ( + Path(__file__).resolve().parent / "data_out" / "evaluation_results.json" + ) + if eval_path.exists(): + with open(eval_path, "r") as f: + data = json.load(f) + return func.HttpResponse( + json.dumps(data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + else: + return func.HttpResponse( + json.dumps({"error": "No evaluation results found"}), + status_code=404, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except Exception as e: + return func.HttpResponse( + json.dumps({"error": str(e)}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +# ============================================================================= +# Streaming Chat API (Server-Sent Events compatible) +# ============================================================================= + +# Movement command patterns - optimized with frozensets for O(1) lookups +_WALK_LEFT = frozenset(["[aria:walk:left]", "walk left"]) +_WALK_RIGHT = frozenset(["[aria:walk:right]", "walk right"]) +_WALK_UP = frozenset(["[aria:walk:up]", "walk up"]) +_WALK_DOWN = frozenset(["[aria:walk:down]", "walk down"]) +_MOVE_LEFT = frozenset(["[aria:move:left]", "aria move left"]) +_MOVE_RIGHT = frozenset(["[aria:move:right]", "aria move right"]) +_MOVE_UP = frozenset(["[aria:move:up]", "aria move up"]) +_MOVE_DOWN = frozenset(["[aria:move:down]", "aria move down"]) +_CENTER = frozenset(["[aria:center]", "go to center", "move to center"]) +_WAVE = frozenset(["[aria:wave]", "aria wave"]) +_JUMP = frozenset(["[aria:jump]", "aria jump"]) +_DANCE = frozenset(["[aria:dance]", "aria dance"]) + +# Distance constants for movement commands +WALK_DISTANCE = 200 # pixels +MOVE_DISTANCE = 100 # pixels + + +def parse_movement_commands(text: str) -> dict: + """Parse movement commands from AI response text. + + Uses pre-compiled frozensets for O(1) keyword matching. + + Args: + text: AI response text to parse + + Returns: + dict with 'commands' list, or empty dict if no commands found + """ + lower_text = text.lower() + commands = [] + + # Movement commands - using frozenset intersection for fast matching + if any(cmd in lower_text for cmd in _WALK_LEFT): + commands.append( + {"action": "walk", "direction": "left", "distance": WALK_DISTANCE} + ) + if any(cmd in lower_text for cmd in _WALK_RIGHT): + commands.append( + {"action": "walk", "direction": "right", "distance": WALK_DISTANCE} + ) + if any(cmd in lower_text for cmd in _WALK_UP): + commands.append( + {"action": "walk", "direction": "up", "distance": WALK_DISTANCE} + ) + if any(cmd in lower_text for cmd in _WALK_DOWN): + commands.append( + {"action": "walk", "direction": "down", "distance": WALK_DISTANCE} + ) + + if any(cmd in lower_text for cmd in _MOVE_LEFT): + commands.append( + {"action": "move", "direction": "left", "distance": MOVE_DISTANCE} + ) + if any(cmd in lower_text for cmd in _MOVE_RIGHT): + commands.append( + {"action": "move", "direction": "right", "distance": MOVE_DISTANCE} + ) + if any(cmd in lower_text for cmd in _MOVE_UP): + commands.append( + {"action": "move", "direction": "up", "distance": MOVE_DISTANCE} + ) + if any(cmd in lower_text for cmd in _MOVE_DOWN): + commands.append( + {"action": "move", "direction": "down", "distance": MOVE_DISTANCE} + ) + + # Position commands + if any(cmd in lower_text for cmd in _CENTER): + commands.append({"action": "center"}) + + # Action commands + if any(cmd in lower_text for cmd in _WAVE): + commands.append({"action": "wave"}) + if any(cmd in lower_text for cmd in _JUMP): + commands.append({"action": "jump"}) + if any(cmd in lower_text for cmd in _DANCE): + commands.append({"action": "dance"}) + + return {"commands": commands} if commands else {} + + +@app.route(route="chat/stream", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) +def chat_stream(req: func.HttpRequest) -> func.HttpResponse: + """ + POST /api/chat/stream with JSON body similar to /api/chat. + Returns text/event-stream; each event is a JSON object with a 'delta' field. + """ + logging.info("Chat stream function invoked") + try: + body = req.get_json() + messages = _sanitize_chat_messages(body.get("messages", [])) + provider_choice = body.get("provider", "auto") + model_override = body.get("model") + temperature = body.get("temperature") + max_output_tokens = body.get("max_output_tokens") + max_context_tokens = body.get("max_context_tokens") + system_prompt = body.get("system_prompt") + + # ============================= + # Memory Retrieval — mirrors /api/chat behavior + # ============================= + stream_user_content = next( + ( + _extract_text_content(m.get("content")) + for m in reversed(messages) + if m.get("role") == "user" + ), + None, + ) + stream_memory_messages: list[dict] = [] + if stream_user_content: + try: + stream_embedding = generate_embedding(stream_user_content) + similar_msgs = fetch_similar_messages( + stream_embedding, top_k=5, session_id=body.get("session_id") + ) + for idx, sm in enumerate(similar_msgs): + memory_content = sm.get("content") + # Validate non-empty + if memory_content and str(memory_content).strip(): + stream_memory_messages.append( + { + "role": "system", + "content": f"[Memory #{idx+1} | similarity={sm.get('similarity'):.3f}] {memory_content}", + } + ) + except Exception as _mem_err: # noqa: BLE001 + logging.warning(f"Stream memory retrieval failed: {_mem_err}") + if stream_memory_messages: + messages = stream_memory_messages + messages + + provider, info = _detect_provider_with_runtime_fallback( + explicit=provider_choice, + model_override=model_override, + temperature=temperature, + max_output_tokens=max_output_tokens, + ) + + pruned_messages, stats, _ = prune_messages( + messages=messages, + provider=info.name, + model=info.model, + max_context_tokens=max_context_tokens, + reserve_output_tokens=int(max_output_tokens) if max_output_tokens else 1024, + system_prompt=system_prompt, + ) + + gen = provider.complete(pruned_messages, stream=True) + + def sse_iterable(): # generator yielding bytes + try: + # Send a prelude event with meta + pre = { + "provider": info.name, + "model": info.model, + "memory_messages": len(stream_memory_messages), + "pruning": { + "original_tokens": stats.original_tokens, + "pruned_tokens": stats.pruned_tokens, + "removed_count": stats.removed_count, + "budget": stats.budget, + "reserve_output_tokens": stats.reserve_output_tokens, + }, + } + yield (f"event: meta\n" f"data: {json.dumps(pre)}\n\n").encode("utf-8") + + # We'll stream both textual deltas and token-level events when possible + import re + + # Try to use tiktoken for token-level tokenization when available + enc = None + try: + import tiktoken as _tt + + try: + from tiktoken import encoding_for_model + + enc = encoding_for_model(info.model or "gpt-4o-mini") + except Exception: + enc = _tt.get_encoding("cl100k_base") + except Exception: + enc = None + + cumulative_text = "" + prev_token_count = 0 + prev_word_count = 0 + token_index = 0 + movement_commands_sent = False + + for chunk in gen: + if not chunk: + continue + + # Raw textual delta (keep for compatibility) + payload = json.dumps({"delta": chunk}) + yield (f"data: {payload}\n\n").encode("utf-8") + + # Accumulate for tokenization; note: chunk may be partial + cumulative_text += chunk + + # Check for movement commands periodically + if not movement_commands_sent and len(cumulative_text) > 20: + movement_data = parse_movement_commands(cumulative_text) + if movement_data.get("commands"): + movement_event = json.dumps(movement_data) + yield ( + f"event: movement\ndata: {movement_event}\n\n" + ).encode("utf-8") + movement_commands_sent = True + + # Token-level events: prefer byte tokenization (tiktoken) when available + if enc is not None: + try: + tok_ids = enc.encode(cumulative_text) + new_ids = tok_ids[prev_token_count:] + if new_ids: + for tid in new_ids: + try: + txt = enc.decode([tid]) + except Exception: + txt = "" + evt = json.dumps( + { + "token_index": token_index, + "token": txt, + "cumulative": cumulative_text, + } + ) + yield (f"event: token\n" f"data: {evt}\n\n").encode( + "utf-8" + ) + token_index += 1 + prev_token_count = len(tok_ids) + except Exception: + # degrade to word-level if full tokenization fails + enc = None + + if enc is None: + # fallback: emit word-level token events (split by whitespace) + words = list(re.finditer(r"\S+", cumulative_text)) + if len(words) > prev_word_count: + for w in words[prev_word_count:]: + token_text = w.group(0) + evt = json.dumps( + { + "token_index": token_index, + "token": token_text, + "cumulative": cumulative_text, + } + ) + yield (f"event: token\n" f"data: {evt}\n\n").encode( + "utf-8" + ) + token_index += 1 + prev_word_count = len(words) + + yield b"event: done\ndata: {}\n\n" + except Exception as e: + err = json.dumps({"error": str(e)}) + yield (f"event: error\n" f"data: {err}\n\n").encode("utf-8") + + return func.HttpResponse( + body=sse_iterable(), + status_code=200, + mimetype="text/event-stream", + headers={**create_cors_response_headers(), "Cache-Control": "no-cache"}, + ) + + except ValueError as ve: + logging.error(f"chat/stream validation error: {ve}") + return func.HttpResponse( + json.dumps({"error": f"Validation error: {str(ve)}"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except Exception as e: # noqa: BLE001 + logging.error(f"chat/stream error: {e}") + return func.HttpResponse( + json.dumps({"error": str(e)}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route(route="tts", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) +def tts(req: func.HttpRequest) -> func.HttpResponse: + """Synthesize text to audio using a remote TTS provider (Azure Speech preferred). + + POST /api/tts + Body: { "text": "...", "voice": "Name", "rate": 1.0, "pitch": 1.0, "format": "wav" } + + Response: { "audio_base64": "...", "format": "wav", "timepoints": [{"word":"...","start_ms":0,"end_ms":123}, ...] } + If remote TTS provider isn't available, returns 501 with explanation. + """ + try: + body = req.get_json() or {} + text = (body.get("text") or "").strip() + if not text: + return func.HttpResponse( + json.dumps({"error": "No text provided"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Optional voice/rate/pitch params + voice = body.get("voice") + rate = float(body.get("rate") or 1.0) + _pitch = float(body.get("pitch") or 1.0) + _out_format = (body.get("format") or "wav").lower() + + # Prefer Azure Speech if configured + az_key = ( + os.getenv("AZURE_SPEECH_KEY") + or os.getenv("AZURE_SPEECH_API_KEY") + or os.getenv("AZURE_SPEECH_SUBSCRIPTION") + ) + az_region = os.getenv("AZURE_SPEECH_REGION") or os.getenv("AZURE_REGION") + + if az_key and az_region: + try: + import base64 + import io + import re + import wave + + try: + import azure.cognitiveservices.speech as speechsdk + except Exception: + return func.HttpResponse( + json.dumps( + { + "error": ( + "Azure Speech SDK not available on server " + "(install azure-cognitiveservices-speech)" + ) + } + ), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Configure speech + scfg = speechsdk.SpeechConfig(subscription=az_key, region=az_region) + # force WAV output for simpler handling + scfg.set_speech_synthesis_output_format( + speechsdk.SpeechSynthesisOutputFormat.Riff16Khz16BitMonoPcm + ) + if voice: + try: + scfg.speech_synthesis_voice_name = voice + except Exception: + pass + + synthesizer = speechsdk.SpeechSynthesizer( + speech_config=scfg, audio_config=None + ) + + # Do the synthesis + result = synthesizer.speak_text_async(text).get() + + if result.reason != speechsdk.ResultReason.SynthesizingAudioCompleted: + # Could be 'Canceled' with details + detail = getattr(result, "error_details", None) or str( + result.reason + ) + return func.HttpResponse( + json.dumps( + {"error": "Synthesis failed", "detail": str(detail)} + ), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Extract audio bytes + stream = speechsdk.AudioDataStream(result) + audio_bytes = stream.readall() + + # Compute approximate word timings by splitting text and sizing by character counts + try: + f = io.BytesIO(audio_bytes) + with wave.open(f, "rb") as wr: + framerate = wr.getframerate() + frames = wr.getnframes() + duration_s = ( + frames / float(framerate) + if framerate and frames + else max(0.2, len(text) * 0.02) + ) + except Exception: + duration_s = max(0.2, len(text) * 0.02) + + words = re.findall(r"\S+", text) + total_chars = sum(len(w) for w in words) or 1 + timepoints = [] + cursor = 0.0 + for w in words: + proportion = len(w) / total_chars + dur = duration_s * proportion + start_ms = int(cursor * 1000) + end_ms = int((cursor + dur) * 1000) + timepoints.append( + {"word": w, "start_ms": start_ms, "end_ms": end_ms} + ) + cursor += dur + + import base64 as _b64 + + audio_b64 = _b64.b64encode(audio_bytes).decode("ascii") + + return func.HttpResponse( + json.dumps( + { + "audio_base64": audio_b64, + "format": "wav", + "timepoints": timepoints, + } + ), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except Exception as e: + logging.exception(f"TTS (Azure) synth failed: {e}") + return func.HttpResponse( + json.dumps({"error": f"TTS provider error: {e}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # No remote TTS provider is configured. Attempt optional local fallbacks if enabled. + enable_local = os.getenv("QAI_ENABLE_LOCAL_TTS", "true").lower() in ( + "true", + "1", + "yes", + "y", + ) + + if enable_local: + # Try pyttsx3 (offline, best on Windows) first + try: + try: + import base64 + import io + import re + import tempfile + import wave + + import pyttsx3 + except Exception: # pyttsx3 not available + pyttsx3 = None + + if pyttsx3 is not None: + tmp = None + try: + tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".wav") + tmp_path = tmp.name + tmp.close() + + engine = pyttsx3.init() + # Try to set rate (pyttsx3 rate is an int; we scale from given rate) + try: + engine.setProperty("rate", int(200 * (rate or 1.0))) + except Exception: + pass + # Try to select voice by name if provided + try: + if voice: + voices = engine.getProperty("voices") or [] + for v in voices: + try: + if voice.lower() in (v.name or "").lower(): + engine.setProperty("voice", v.id) + break + except Exception: + continue + except Exception: + pass + + engine.save_to_file(text, tmp_path) + engine.runAndWait() + + with open(tmp_path, "rb") as fh: + audio_bytes = fh.read() + + # compute approximate duration using wave reader + try: + f = io.BytesIO(audio_bytes) + with wave.open(f, "rb") as wr: + framerate = wr.getframerate() + frames = wr.getnframes() + duration_s = ( + frames / float(framerate) + if framerate and frames + else max(0.2, len(text) * 0.02) + ) + except Exception: + duration_s = max(0.2, len(text) * 0.02) + + words = re.findall(r"\S+", text) + total_chars = sum(len(w) for w in words) or 1 + timepoints = [] + cursor = 0.0 + for w in words: + proportion = len(w) / total_chars + dur = duration_s * proportion + start_ms = int(cursor * 1000) + end_ms = int((cursor + dur) * 1000) + timepoints.append( + {"word": w, "start_ms": start_ms, "end_ms": end_ms} + ) + cursor += dur + + audio_b64 = base64.b64encode(audio_bytes).decode("ascii") + return func.HttpResponse( + json.dumps( + { + "audio_base64": audio_b64, + "format": "wav", + "timepoints": timepoints, + } + ), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + finally: + try: + if ( + tmp is not None + and tmp_path + and os.path.exists(tmp_path) + ): + os.unlink(tmp_path) + except Exception: + pass + + # If pyttsx3 not available or failed, try gTTS (mp3 output) + try: + import base64 + import re + import tempfile + + from gtts import gTTS + except Exception: + gTTS = None + + if gTTS is not None: + tmp = None + try: + tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") + tmp_path = tmp.name + tmp.close() + + tts_obj = gTTS(text=text) + tts_obj.save(tmp_path) + + with open(tmp_path, "rb") as fh: + audio_bytes = fh.read() + + # approximate duration: fallback to char-count based estimate + duration_s = max(0.2, len(text) * 0.02) + words = re.findall(r"\S+", text) + total_chars = sum(len(w) for w in words) or 1 + timepoints = [] + cursor = 0.0 + for w in words: + proportion = len(w) / total_chars + dur = duration_s * proportion + start_ms = int(cursor * 1000) + end_ms = int((cursor + dur) * 1000) + timepoints.append( + {"word": w, "start_ms": start_ms, "end_ms": end_ms} + ) + cursor += dur + + audio_b64 = base64.b64encode(audio_bytes).decode("ascii") + return func.HttpResponse( + json.dumps( + { + "audio_base64": audio_b64, + "format": "mp3", + "timepoints": timepoints, + } + ), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + finally: + try: + if ( + tmp is not None + and tmp_path + and os.path.exists(tmp_path) + ): + os.unlink(tmp_path) + except Exception: + pass + + except Exception as e: + logging.exception(f"Local fallback TTS failed: {e}") + return func.HttpResponse( + json.dumps({"error": f"Local TTS provider failed: {e}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # If we reach here remote + local TTS are unavailable + return func.HttpResponse( + json.dumps( + { + "error": "No remote TTS provider configured and no local fallback available.", + "help": ( + "Set AZURE_SPEECH_KEY and AZURE_SPEECH_REGION to enable " + "Azure speech, or install pyttsx3 or gTTS and set " + "QAI_ENABLE_LOCAL_TTS=true in local.settings.json/.env " + "to enable local fallback. See local.settings.json.example " + "and .env.example in the repo for templates." + ), + } + ), + status_code=501, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: # noqa: BLE001 + logging.exception(f"/tts error: {e}") + return func.HttpResponse( + json.dumps({"error": str(e)}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +# ============================================================================= +# Backend Control - Start/Status +# ============================================================================= + + +@app.route(route="start-backend", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) +def start_backend(req: func.HttpRequest) -> func.HttpResponse: + """Start the Azure Functions backend (already running if this endpoint responds)""" + logging.info("Backend start request received") + + # If this endpoint responds, the backend is already running + return func.HttpResponse( + json.dumps( + { + "status": "already_running", + "message": "Backend is already running (this endpoint is responding)", + } + ), + mimetype="application/json", + status_code=200, + ) + + +# ============================================================================= +# Status API - Health and environment diagnostics +# ============================================================================= + + +@app.route(route="ai/status", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) +def ai_status(req: func.HttpRequest) -> func.HttpResponse: + """Health / status endpoint for provider readiness and environment diagnostics. + + GET /api/ai/status + + Returns JSON describing: + - active_provider: which provider auto-detect selects (azure|openai|local|lora) + - model: resolved model/deployment or LoRA adapter path + - env: presence of key environment variables for Azure/OpenAI + - ml_inprocess: whether ML libraries are importable in-process + - venv: path to local venv python and whether key ML libs are installed there + - lora: default adapter path readiness indicators + - assets and known endpoints + """ + try: + + def _load_status_payload( + status_path: Path, *, require_clean: bool = False + ) -> dict: + loaded = load_status_json(status_path) + if loaded.get("_status_file_error"): + if require_clean and loaded.get("_status_file_exists"): + raise ValueError(loaded["_status_file_error"]) + return {} + return { + k: v for k, v in loaded.items() if not k.startswith("_status_file_") + } + + # Environment flags + azure_env = { + "AZURE_OPENAI_API_KEY": bool(os.getenv("AZURE_OPENAI_API_KEY")), + "AZURE_OPENAI_ENDPOINT": bool(os.getenv("AZURE_OPENAI_ENDPOINT")), + "AZURE_OPENAI_DEPLOYMENT": bool(os.getenv("AZURE_OPENAI_DEPLOYMENT")), + "AZURE_OPENAI_API_VERSION": bool(os.getenv("AZURE_OPENAI_API_VERSION")), + } + openai_env = { + "OPENAI_API_KEY": bool(os.getenv("OPENAI_API_KEY")), + "OPENAI_MODEL": bool(os.getenv("OPENAI_MODEL")), + } + + # Local AI provider config (Ollama + LM Studio) + ollama_base_url = os.getenv("OLLAMA_BASE_URL", "http://127.0.0.1:11434/v1") + lmstudio_base_url = os.getenv("LMSTUDIO_BASE_URL", "http://127.0.0.1:1234/v1") + try: + from chat_providers import ( # type: ignore + _check_lm_studio_available, _check_ollama_available) + + ollama_reachable = _check_ollama_available(ollama_base_url) + lmstudio_reachable = _check_lm_studio_available(lmstudio_base_url) + except Exception: + ollama_reachable = False + lmstudio_reachable = False + local_providers_env = { + "ollama": { + "base_url": ollama_base_url, + "model": os.getenv("OLLAMA_MODEL", "llama3.2"), + "reachable": ollama_reachable, + "OLLAMA_BASE_URL_set": bool(os.getenv("OLLAMA_BASE_URL")), + "OLLAMA_MODEL_set": bool(os.getenv("OLLAMA_MODEL")), + "install_hint": "https://ollama.ai — run: ollama serve && ollama pull llama3.2", + }, + "lmstudio": { + "base_url": lmstudio_base_url, + "model": os.getenv("LMSTUDIO_MODEL", "local-model"), + "reachable": lmstudio_reachable, + "LMSTUDIO_BASE_URL_set": bool(os.getenv("LMSTUDIO_BASE_URL")), + "LMSTUDIO_MODEL_set": bool(os.getenv("LMSTUDIO_MODEL")), + "install_hint": "https://lmstudio.ai — open app and enable Local Server", + }, + } + + # ML availability in-process + inproc_ml = { + "torch": _iu.find_spec("torch") is not None, + "transformers": _iu.find_spec("transformers") is not None, + "peft": _iu.find_spec("peft") is not None, + } + + repo_root = Path(__file__).resolve().parent + venv_python = repo_root / "venv" / "Scripts" / "python.exe" + venv_info = { + "path": str(venv_python), + "exists": venv_python.exists(), + "packages": {}, + "error": None, + } + + if venv_info["exists"]: + try: + code = ( + "import json, importlib.util, importlib.metadata as md;" + "mods=['torch','transformers','peft'];" + "avail={m:(importlib.util.find_spec(m) is not None) for m in mods};" + "vers={};" + "\nfor m in mods:\n\t" + "\n\ttry:\n\t\tvers[m]=md.version(m)\n\texcept Exception:\n\t\tvers[m]=None;" + "print(json.dumps({'available':avail,'versions':vers}))" + ) + proc = subprocess.run( + [str(venv_python), "-c", code], + capture_output=True, + text=True, + timeout=12, + ) + if proc.returncode == 0: + data = json.loads(proc.stdout.strip() or "{}") + venv_info["packages"] = data + else: + venv_info["error"] = ( + proc.stderr.strip() or f"exit {proc.returncode}" + ) + except Exception as e: # noqa: BLE001 + venv_info["error"] = str(e) + + # LoRA adapter defaults + lora_default = repo_root / "data_out" / "lora_training" / "lora_adapter" + adapter_cfg = lora_default / "adapter_config.json" + tokenizer_dir = lora_default.parent / "tokenizer" + lora_info = { + "default_adapter_path": str(lora_default), + "exists": lora_default.exists(), + "adapter_config_exists": adapter_cfg.exists(), + "tokenizer_dir_exists": tokenizer_dir.exists(), + "base_model": None, + "inproc_ready": all(inproc_ml.values()), + "subprocess_ready": ( + venv_info.get("exists") + and bool( + venv_info.get("packages", {}).get("available", {}).get("torch") + ) + and bool( + venv_info.get("packages", {}) + .get("available", {}) + .get("transformers") + ) + and bool(venv_info.get("packages", {}).get("available", {}).get("peft")) + ), + } + if lora_info["adapter_config_exists"]: + try: + with open(adapter_cfg, "r", encoding="utf-8") as f: + cfg = json.load(f) + lora_info["base_model"] = cfg.get("base_model_name_or_path") + except Exception: + pass + + # Detect active provider + provider, info = _detect_provider_with_runtime_fallback(explicit="auto") + + # Assets + chat_web_html = (repo_root / "apps" / "chat" / "index.html").exists() + chat_web_js = (repo_root / "apps" / "chat" / "chat.js").exists() + + # Cosmos status (lazy health) + cosmos_status = None + if cosmos_client: + try: + cosmos_status = cosmos_client.health() + except Exception as cs_err: # noqa: BLE001 + cosmos_status = {"enabled": False, "error": str(cs_err)} + + # Unified SQL status (may reflect Azure SQL, PostgreSQL, MySQL, SQLite) + sql_info = None + try: + sql_info = sql_health() + try: # augment with pool metrics + saturation alerts + pool_info = engine_stats() + sql_info["pool"] = pool_info + # Surface critical alerts at top level for visibility + if pool_info.get("saturation_alert"): + sql_info["alert"] = pool_info["saturation_alert"] + if pool_info.get("slow_queries_1min", 0) > 10: + freq_alert = ( + f"{pool_info['slow_queries_1min']} slow queries in last 60s " + f"(threshold={pool_info.get('slow_query_threshold_ms')}ms)" + ) + sql_info["slow_query_alert"] = freq_alert + logging.warning(f"[ai_status] {freq_alert}") + except Exception as _ps: # noqa: BLE001 + sql_info["pool"] = {"enabled": False, "error": str(_ps)} + except Exception as _se: # noqa: BLE001 + sql_info = {"enabled": False, "error": str(_se)} + + # Telemetry status + try: + from shared.telemetry import \ + is_enabled as _telemetry_is_enabled # type: ignore + + telemetry_info = {"enabled": _telemetry_is_enabled()} + except Exception: + telemetry_info = {"enabled": False} + + # Quantum environment status (non-blocking, gated by optional env var) + quantum_info = { + "enabled": False, + "qiskit": None, + "pennylane": None, + "llm_model_available": False, + "llm_checkpoint_path": None, + "inference_ready": False, + "status_file": None, + "trainer_status": "not_started", + "azure_quantum": { + "workspace_connected": False, + "backends": [], + "attempted": False, + "error": None, + }, + "conflict": None, + } + try: # gather local versions + import qiskit # type: ignore + + quantum_info["qiskit"] = getattr(qiskit, "__version__", None) + quantum_info["enabled"] = True + except Exception as _qe: + quantum_info["qiskit"] = f"error: {_qe}" # noqa: BLE001 + try: + import pennylane # type: ignore + + quantum_info["pennylane"] = getattr(pennylane, "__version__", None) + except Exception: + pass + try: + from quantum_llm_trainer import \ + get_quantum_llm_status # type: ignore + + quantum_llm_status = get_quantum_llm_status( + output_dir=repo_root / "data_out" / "quantum_llm_training" + ) + quantum_info.update( + { + "llm_model_available": bool( + quantum_llm_status.get("checkpoint_exists") + ), + "llm_checkpoint_path": quantum_llm_status.get("checkpoint_path"), + "inference_ready": bool(quantum_llm_status.get("inference_ready")), + "status_file": quantum_llm_status.get("status_file"), + "trainer_status": quantum_llm_status.get("status"), + } + ) + except Exception: + pass + # Conflict detection using validate script (import functions defensively) + try: + from quantum_ai.scripts.validate_qiskit_env import \ + detect_conflict # type: ignore + except Exception: + # Fallback manual conflict heuristic + def detect_conflict(versions): + if ( + versions.get("qiskit") + and str(versions.get("qiskit")).startswith("1.") + and versions.get("qiskit_aer") + ): + return {"conflict": True} + return {"conflict": False} + + try: + # Build synthetic versions map for conflict check + versions_map = {} + for name in ["qiskit", "qiskit_aer", "qiskit_machine_learning"]: + try: + mod = __import__(name) + versions_map[name] = getattr(mod, "__version__", "unknown") + except Exception as ie: # noqa: BLE001 + versions_map[name] = f"error: {ie}" + conflict_meta = detect_conflict(versions_map) + quantum_info["conflict"] = conflict_meta.get("conflict") + except Exception as _ce: # noqa: BLE001 + quantum_info["conflict"] = f"error: {_ce}" + + # Optional Azure Quantum backend probing (requires env flag to avoid latency) + if os.getenv("QAI_STATUS_CONNECT_AZURE_QUANTUM", "false").lower() == "true": + quantum_info["azure_quantum"]["attempted"] = True + try: + from quantum_ai.src.azure_quantum_integration import \ + AzureQuantumIntegration # type: ignore + + cfg_path = ( + Path(__file__).resolve().parent + / "ai-projects" + / "quantum-ml" + / "config" + / "quantum_config.yaml" + ) + if cfg_path.exists(): + aq = AzureQuantumIntegration(str(cfg_path)) + aq.connect() + bnames = aq.list_backends()[:8] + quantum_info["azure_quantum"].update( + { + "workspace_connected": True, + "backends": bnames, + } + ) + else: + quantum_info["azure_quantum"][ + "error" + ] = "quantum_config.yaml missing" + except Exception as aq_err: # noqa: BLE001 + quantum_info["azure_quantum"]["error"] = str(aq_err) + + # Self-Learning System Status + learning_info = { + "enabled": False, + "training_cycles": 0, + "total_conversations": 0, + "new_conversations": 0, + "last_training": None, + "best_model_path": None, + "model_history": [], + } + try: + learning_status_file = ( + Path(__file__).resolve().parent + / "data_out" + / "self_learning" + / "status.json" + ) + loaded_learning_status = load_status_json(learning_status_file) + if not loaded_learning_status.get("_status_file_error"): + learning_status = { + k: v + for k, v in loaded_learning_status.items() + if not k.startswith("_status_file_") + } + learning_info["enabled"] = learning_status.get("learning_enabled", True) + learning_info["training_cycles"] = learning_status.get( + "training_cycles", 0 + ) + learning_info["total_conversations"] = learning_status.get( + "total_conversations", 0 + ) + learning_info["new_conversations"] = learning_status.get( + "conversations_since_last_train", 0 + ) + learning_info["last_training"] = learning_status.get("last_training") + learning_info["best_model_path"] = learning_status.get( + "best_model_path" + ) + learning_info["model_history"] = learning_status.get( + "model_history", [] + )[ + -3: + ] # Last 3 + elif loaded_learning_status.get("_status_file_exists"): + learning_info["error"] = loaded_learning_status.get( + "_status_file_error" + ) + except Exception as _le: # noqa: BLE001 + learning_info["error"] = str(_le) + + # Orchestrator Health Aggregation + orchestrator_health = { + "enabled": True, + "orchestrators": {}, + "overall_status": "unknown", + "last_checked": datetime.now(timezone.utc) + .isoformat() + .replace("+00:00", "Z"), + "active_count": 0, + "failed_count": 0, + } + try: + data_out_dir = Path(__file__).resolve().parent / "data_out" + + # Autonomous training (uses top-level status + heartbeat) + try: + autotrain_status_file = data_out_dir / "autonomous_training_status.json" + at_status = _load_status_payload( + autotrain_status_file, require_clean=True + ) + if at_status: + heartbeat_file = data_out_dir / "autonomous_training_heartbeat.json" + heartbeat_running = False + heartbeat = _load_status_payload(heartbeat_file) + if heartbeat: + try: + heartbeat_running = heartbeat.get( + "state" + ) == "completed" or heartbeat.get("pid") + except Exception: + pass + + orchestrator_health["orchestrators"]["autonomous_training"] = { + "name": "autonomous_training", + "status": ( + "ok" if at_status.get("cycles_completed", 0) > 0 else "idle" + ), + "cycles_completed": at_status.get("cycles_completed", 0), + "best_accuracy": at_status.get("best_accuracy"), + "last_updated": at_status.get("last_updated"), + "heartbeat_running": heartbeat_running, + "performance_trend": ( + "improving" + if len(at_status.get("performance_history", [])) > 1 + and at_status["performance_history"][-1].get("accuracy", 0) + > at_status["performance_history"][0].get("accuracy", 0) + else "unknown" + ), + } + if heartbeat_running: + orchestrator_health["active_count"] += 1 + except Exception as _ate: # noqa: BLE001 + orchestrator_health["orchestrators"]["autonomous_training"] = { + "status": "error", + "error": str(_ate), + } + orchestrator_health["failed_count"] += 1 + + # Standard orchestrators (autotrain, quantum_autorun, evaluation_autorun, etc.) + standard_names = [ + "autotrain", + "quantum_autorun", + "evaluation_autorun", + "integration_smoke", + "autonomous_agent", + ] + for name in standard_names: + try: + status_file = data_out_dir / name / "status.json" + orch_status = _load_status_payload(status_file, require_clean=True) + if orch_status: + + # Normalize to common schema + total = orch_status.get("total_jobs", 0) + succeeded = orch_status.get("succeeded", 0) + failed = orch_status.get("failed", 0) + + if total == 0: + health_status = "idle" + elif failed > 0: + health_status = "degraded" + else: + health_status = "ok" + + orchestrator_health["orchestrators"][name] = { + "name": name, + "status": health_status, + "total_jobs": total, + "succeeded": succeeded, + "failed": failed, + "running": orch_status.get("running", 0), + "last_updated": orch_status.get( + "last_updated", orch_status.get("generated_at") + ), + "success_rate": ( + (succeeded / total * 100) if total > 0 else 100.0 + ), + } + + if health_status == "ok": + orchestrator_health["active_count"] += 1 + elif health_status == "degraded": + orchestrator_health["failed_count"] += 1 + except Exception as _ose: # noqa: BLE001 + logging.debug( + f"[ai_status] Orchestrator {name} health check failed: {_ose}" + ) + # Only track as failed if file exists but is malformed + if (data_out_dir / name / "status.json").exists(): + orchestrator_health["orchestrators"][name] = { + "status": "error", + "error": str(_ose), + } + orchestrator_health["failed_count"] += 1 + + # Determine overall platform health + if orchestrator_health["failed_count"] > 0: + orchestrator_health["overall_status"] = "degraded" + elif orchestrator_health["active_count"] > 0: + orchestrator_health["overall_status"] = "healthy" + else: + orchestrator_health["overall_status"] = "idle" + + except Exception as _oh: # noqa: BLE001 + logging.warning( + f"[ai_status] Orchestrator health aggregation failed: {_oh}" + ) + orchestrator_health["overall_status"] = "error" + orchestrator_health["error"] = str(_oh) + + payload = { + "active_provider": info.name, + "model": info.model, + "env": { + "azure_openai": azure_env, + "openai": openai_env, + "local_fallback": True, + "local_providers": local_providers_env, + }, + "ml_inprocess": inproc_ml, + "lora": lora_info, + "venv": venv_info, + "cosmos": cosmos_status, + "sql": sql_info, + "telemetry": telemetry_info, + "quantum": quantum_info, + "self_learning": learning_info, + "orchestrator_health": orchestrator_health, + "temperature": float(os.getenv("CHAT_TEMPERATURE", "0.7")), + "server": { + "executable": sys.executable, + "python_version": sys.version, + "cwd": os.getcwd(), + }, + "assets": { + "chat_web_html": chat_web_html, + "chat_web_js": chat_web_js, + }, + "endpoints": [ + "/api/chat-web", + "/api/chat-web/chat.js", + "/api/chat", + "/api/chat/stream", + "/api/ai/status", + "/api/vision/infer", + "/api/vision/batch-infer", + "/api/image/generate", + ], + "status": "ok", + } + + return func.HttpResponse( + json.dumps(payload), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: # noqa: BLE001 + logging.error(f"ai/status error: {e}") + return func.HttpResponse( + json.dumps({"status": "error", "error": str(e)}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +# ============================================================================= +# Vision AI Endpoints - Expression/emotion classification +# ============================================================================= + + +@app.route(route="vision/infer", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) +def vision_infer(req: func.HttpRequest) -> func.HttpResponse: + """ + Vision inference endpoint for expression/emotion classification. + + POST /api/vision/infer + Body (option 1 - base64): + { + "image": "base64_encoded_image_string", + "format": "base64" + } + + Body (option 2 - URL): + { + "image_url": "https://example.com/image.jpg", + "format": "url" + } + + Response: + { + "label": "happy", + "confidence": 0.92, + "scores": { + "happy": 0.92, + "sad": 0.05, + "neutral": 0.03 + }, + "model_info": { + "checkpoint": "...", + "classes": ["happy", "sad", "neutral"], + "device": "cpu" + } + } + """ + logging.info("Vision infer endpoint invoked") + + try: + # Lazy import vision inference (only loaded when needed) + try: + from vision_inference import VisionInference + except ImportError as e: + return func.HttpResponse( + json.dumps({"error": f"Vision inference not available: {e}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Parse request + req_body = req.get_json() + image_data = req_body.get("image") + image_url = req_body.get("image_url") + format_type = req_body.get("format", "base64") + + if not image_data and not image_url: + return func.HttpResponse( + json.dumps( + { + "error": "No image provided. Include 'image' (base64) or 'image_url' in request body." + } + ), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Initialize vision inference (loads latest checkpoint) + # Cache the instance for performance (singleton pattern) + if not hasattr(vision_infer, "_vision_model"): + logging.info("Initializing vision model (first request)...") + try: + vision_infer._vision_model = VisionInference() + except FileNotFoundError as e: + return func.HttpResponse( + json.dumps( + { + "error": "No trained model found", + "detail": str(e), + "help": "Train a model first using: python scripts/train_vision.py", + } + ), + status_code=404, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + vi = vision_infer._vision_model + + # Run inference based on input format + if image_url: + # Fetch image from URL + try: + import io + + import requests + from PIL import Image + + response = requests.get(image_url, timeout=10) + response.raise_for_status() + img = Image.open(io.BytesIO(response.content)) + result = vi.predict(img) + except Exception as e: + return func.HttpResponse( + json.dumps({"error": f"Failed to fetch image from URL: {e}"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + elif format_type == "base64": + # Decode base64 image + try: + result = vi.predict_base64(image_data) + except Exception as e: + return func.HttpResponse( + json.dumps({"error": f"Failed to decode base64 image: {e}"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + else: + return func.HttpResponse( + json.dumps( + { + "error": f"Unsupported format: {format_type}. Use 'base64' or provide 'image_url'." + } + ), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Add model metadata to response + response_data = {**result, "model_info": vi.get_model_info()} + + return func.HttpResponse( + json.dumps(response_data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Vision infer error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Vision inference failed: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="vision/infer", methods=["OPTIONS"], auth_level=func.AuthLevel.ANONYMOUS +) +def vision_infer_options(req: func.HttpRequest) -> func.HttpResponse: + """Handle CORS preflight for vision inference""" + return func.HttpResponse( + "", status_code=200, headers=create_cors_response_headers() + ) + + +@app.route( + route="vision/batch-infer", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS +) +def vision_batch_infer(req: func.HttpRequest) -> func.HttpResponse: + """ + Batch vision inference endpoint for multiple images. + + POST /api/vision/batch-infer + Body: + { + "images": [ + {"data": "base64_1", "id": "img1"}, + {"data": "base64_2", "id": "img2"}, + ... + ] + } + + Response: + { + "results": [ + {"id": "img1", "label": "happy", "confidence": 0.92, ...}, + {"id": "img2", "label": "sad", "confidence": 0.85, ...} + ], + "total": 2, + "model_info": {...} + } + """ + logging.info("Vision batch infer endpoint invoked") + + try: + import base64 + import io + + from PIL import Image + from vision_inference import VisionInference + except ImportError as e: + return func.HttpResponse( + json.dumps({"error": f"Vision inference not available: {e}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + try: + req_body = req.get_json() + images_data = req_body.get("images", []) + + if not images_data: + return func.HttpResponse( + json.dumps({"error": "No images provided"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Limit batch size to prevent overload + max_batch_size = 50 + if len(images_data) > max_batch_size: + return func.HttpResponse( + json.dumps( + {"error": f"Batch size exceeds limit of {max_batch_size} images"} + ), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Initialize vision model + if not hasattr(vision_batch_infer, "_vision_model"): + try: + vision_batch_infer._vision_model = VisionInference() + except FileNotFoundError as e: + return func.HttpResponse( + json.dumps({"error": "No trained model found", "detail": str(e)}), + status_code=404, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + vi = vision_batch_infer._vision_model + + # Decode all images + pil_images = [] + image_ids = [] + for idx, img_data in enumerate(images_data): + try: + img_id = img_data.get("id", f"image_{idx}") + b64_data = img_data.get("data") + + img_bytes = base64.b64decode(b64_data) + pil_img = Image.open(io.BytesIO(img_bytes)) + + pil_images.append(pil_img) + image_ids.append(img_id) + except Exception as e: + logging.warning(f"Failed to decode image {idx}: {e}") + continue + + if not pil_images: + return func.HttpResponse( + json.dumps({"error": "No valid images could be decoded"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Run batch inference + predictions = vi.predict_batch(pil_images) + + # Combine predictions with IDs + results = [] + for img_id, pred in zip(image_ids, predictions): + results.append({"id": img_id, **pred}) + + response_data = { + "results": results, + "total": len(results), + "model_info": vi.get_model_info(), + } + + return func.HttpResponse( + json.dumps(response_data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Vision batch infer error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Batch inference failed: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="image/generate", + methods=["POST", "OPTIONS"], + auth_level=func.AuthLevel.ANONYMOUS, +) +def image_generate(req: func.HttpRequest) -> func.HttpResponse: + """ + AI Image generation endpoint using OpenAI DALL-E. + + POST /api/image/generate + Body: + { + "prompt": "description of image to generate", + "size": "512x512", + "style": "anime" + } + + Response: + { + "image_url": "https://...", + "image_data": "base64_encoded_image", + "prompt": "original prompt", + "model": "dall-e-2" + } + """ + if req.method == "OPTIONS": + return func.HttpResponse( + status_code=200, headers=create_cors_response_headers() + ) + + logging.info("Image generation endpoint invoked") + + try: + req_body = req.get_json() + prompt = req_body.get("prompt", "") + size = req_body.get("size", "512x512") + style_hint = req_body.get("style", "") + + if not prompt: + return func.HttpResponse( + json.dumps({"error": "Prompt is required"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + if style_hint: + prompt = f"{prompt}, {style_hint} style" + + try: + import os + + from openai import OpenAI + + api_key = os.getenv("OPENAI_API_KEY") + if not api_key: + api_key = os.getenv("AZURE_OPENAI_API_KEY") + endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") + + if api_key and endpoint: + client = OpenAI( + api_key=api_key, base_url=f"{endpoint}/openai/deployments" + ) + else: + raise ValueError("No OpenAI API key configured") + else: + client = OpenAI(api_key=api_key) + + response = client.images.generate( + model="dall-e-2", + prompt=prompt, + size=size if size in ["256x256", "512x512", "1024x1024"] else "512x512", + n=1, + response_format="url", + ) + + image_url = response.data[0].url + + response_data = { + "image_url": image_url, + "prompt": prompt, + "model": "dall-e-2", + "size": size, + } + + return func.HttpResponse( + json.dumps(response_data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as openai_error: + logging.warning(f"OpenAI image generation failed: {openai_error}") + # Detect Azure/OpenAI quota/premium allowance errors and provide + # a clearer fallback message for users. + try: + from shared.azure_utils import (format_quota_message, + is_quota_error) + except Exception: + is_quota_error = None + format_quota_message = None + + placeholder_svg = "\n".join( + [ + '', + " ", + ' ', + ' ', + ' ', + ' ', + " ", + " ", + ' ', + ' ✨', + ( + ' ' + "Aria" + ), + ( + ' ' + "AI Assistant" + ), + ( + ' ' + "Image generation unavailable" + ), + ( + ' {openai_error.__class__.__name__}' + ), + "", + ] + ) + + import base64 + + svg_base64 = base64.b64encode(placeholder_svg.encode()).decode() + + # Prefer a helpful quota message when available + err_text = str(openai_error) + if is_quota_error is not None and is_quota_error(openai_error): + if format_quota_message is not None: + err_text = format_quota_message( + openai_error, service_name="OpenAI / Azure Images API" + ) + + response_data = { + "image_data": svg_base64, + "prompt": prompt, + "model": "fallback-svg", + "size": "512x512", + "fallback": True, + "error": err_text, + } + + return func.HttpResponse( + json.dumps(response_data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Image generation error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Image generation failed: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +# ============================================================================= +# Quantum AI Endpoints - Advanced quantum computing features +# ============================================================================= + + +@app.route( + route="quantum/classify", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS +) +def quantum_classify(req: func.HttpRequest) -> func.HttpResponse: + """ + Quantum classification endpoint. + + POST /api/quantum/classify + Body: { + "features": [0.1, 0.5, 0.3, ...], // Feature vector + "n_qubits": 4, // Optional + "n_layers": 2 // Optional + } + + Response: { + "classification": "positive|negative|neutral", + "confidence": 0.85, + "quantum_state": {...} + } + """ + logging.info("Quantum classify endpoint invoked") + + try: + # Import quantum modules + try: + import numpy as np + import torch + from quantum_classifier import QuantumClassifier + except ImportError as e: + return func.HttpResponse( + json.dumps({"error": f"Quantum dependencies not available: {e}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Parse request + req_body = req.get_json() + features = req_body.get("features", []) + n_qubits = req_body.get("n_qubits", 4) + n_layers = req_body.get("n_layers", 2) + + if not features: + return func.HttpResponse( + json.dumps({"error": "No features provided"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Initialize quantum classifier + classifier = QuantumClassifier() + + # Prepare features + feature_array = np.array(features[:n_qubits]) + if len(feature_array) < n_qubits: + feature_array = np.pad(feature_array, (0, n_qubits - len(feature_array))) + + # Convert to torch tensor and scale to [0, 2π] + inputs = torch.tensor(feature_array, dtype=torch.float32) * 2 * np.pi + + # Create random weights (in production, use trained weights) + weights = torch.randn(n_layers, n_qubits, 2, dtype=torch.float32) * 0.1 + + # Run quantum circuit + output = classifier.forward(inputs.unsqueeze(0), weights) + + # Interpret results + avg_value = float(output.mean()) + confidence = abs(avg_value) + + if avg_value > 0.3: + classification = "positive" + elif avg_value < -0.3: + classification = "negative" + else: + classification = "neutral" + + response_data = { + "classification": classification, + "confidence": confidence, + "quantum_state": { + "expectation_values": output.tolist(), + "average": avg_value, + "n_qubits": n_qubits, + "n_layers": n_layers, + }, + } + + return func.HttpResponse( + json.dumps(response_data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Quantum classify error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Quantum classification failed: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="quantum/circuit", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS +) +def quantum_circuit(req: func.HttpRequest) -> func.HttpResponse: + """ + Create and visualize a quantum circuit. + + POST /api/quantum/circuit + Body: { + "n_qubits": 4, + "n_layers": 2, + "entanglement": "linear|circular|full" + } + + Response: { + "circuit_info": {...}, + "gates": [...], + "visualization": "text representation" + } + """ + logging.info("Quantum circuit endpoint invoked") + + try: + req_body = req.get_json() + n_qubits = req_body.get("n_qubits", 4) + n_layers = req_body.get("n_layers", 2) + entanglement = req_body.get("entanglement", "linear") + + # Create circuit description + gates = [] + + # Input encoding layer + for i in range(n_qubits): + gates.append( + {"type": "RY", "qubit": i, "layer": 0, "parameter": "input[i]"} + ) + + # Variational layers + for layer in range(n_layers): + # Rotation gates + for i in range(n_qubits): + gates.append( + { + "type": "RY", + "qubit": i, + "layer": layer + 1, + "parameter": f"θ_{layer}_{i}_0", + } + ) + gates.append( + { + "type": "RZ", + "qubit": i, + "layer": layer + 1, + "parameter": f"θ_{layer}_{i}_1", + } + ) + + # Entanglement gates + if entanglement == "linear": + for i in range(n_qubits - 1): + gates.append( + { + "type": "CNOT", + "control": i, + "target": i + 1, + "layer": layer + 1, + } + ) + elif entanglement == "circular": + for i in range(n_qubits): + gates.append( + { + "type": "CNOT", + "control": i, + "target": (i + 1) % n_qubits, + "layer": layer + 1, + } + ) + elif entanglement == "full": + for i in range(n_qubits): + for j in range(i + 1, n_qubits): + gates.append( + { + "type": "CNOT", + "control": i, + "target": j, + "layer": layer + 1, + } + ) + + # Measurements + for i in range(n_qubits): + gates.append( + { + "type": "Measure", + "qubit": i, + "layer": n_layers + 1, + "observable": "PauliZ", + } + ) + + # Create text visualization using list for efficiency (avoids O(n²) string concatenation) + viz_parts = [ + f"Quantum Circuit ({n_qubits} qubits, {n_layers} layers, {entanglement} entanglement)\n", + "=" * 60 + "\n\n", + ] + + for layer in range(n_layers + 2): + viz_parts.append(f"Layer {layer}:\n") + layer_gates = [g for g in gates if g.get("layer") == layer] + for gate in layer_gates: + if gate["type"] in ["RY", "RZ"]: + viz_parts.append( + f" {gate['type']}({gate['parameter']}) on qubit {gate['qubit']}\n" + ) + elif gate["type"] == "CNOT": + viz_parts.append( + f" CNOT: control={gate['control']}, target={gate['target']}\n" + ) + elif gate["type"] == "Measure": + viz_parts.append( + f" Measure qubit {gate['qubit']} ({gate['observable']})\n" + ) + viz_parts.append("\n") + + visualization = "".join(viz_parts) + + response_data = { + "circuit_info": { + "n_qubits": n_qubits, + "n_layers": n_layers, + "entanglement": entanglement, + "total_gates": len(gates), + "depth": n_layers + 2, + }, + "gates": gates, + "visualization": visualization, + } + + return func.HttpResponse( + json.dumps(response_data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Quantum circuit error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Circuit creation failed: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="quantum/llm", methods=["POST", "GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def quantum_llm(req: func.HttpRequest) -> func.HttpResponse: + """ + Quantum LLM inference and training endpoint. + + GET /api/quantum/llm → return model status and capabilities + POST /api/quantum/llm → generate text or trigger a training cycle + + POST body (generate): + {"action": "generate", "prompt": "Quantum computing", "max_tokens": 50} + + POST body (train): + {"action": "train", "dataset_path": "datasets/chat/...", "epochs": 1} + """ + logging.info("Quantum LLM endpoint invoked: %s", req.method) + + try: + # Lazy import to avoid hard dependency at startup + repo_root = Path(__file__).resolve().parent + quantum_ml_src = ( + Path(__file__).resolve().parent / "ai-projects" / "quantum-ml" / "src" + ) + scripts_dir = Path(__file__).resolve().parent / "scripts" + for p in [str(quantum_ml_src), str(scripts_dir)]: + if p not in sys.path: + sys.path.insert(0, p) + + try: + from quantum_llm_trainer import (QUANTUM_AVAILABLE, + QuantumEnhancedLLMTrainer, + get_quantum_llm_status) + + trainer_available = True + except ImportError as ie: + trainer_available = False + QUANTUM_AVAILABLE = False + _trainer_import_err = str(ie) + get_quantum_llm_status = None + + if req.method == "GET": + readiness = None + if trainer_available and get_quantum_llm_status is not None: + readiness = get_quantum_llm_status( + output_dir=Path(__file__).resolve().parent + / "data_out" + / "quantum_llm_training" + ) + return func.HttpResponse( + json.dumps( + { + "available": trainer_available, + "quantum_circuits": QUANTUM_AVAILABLE, + "model": "QuantumLLM (hybrid quantum-classical transformer)", + "capabilities": { + "generate": trainer_available, + "train": trainer_available, + "n_qubits": 4, + "backends": ["default.qubit", "lightning.qubit"], + }, + "readiness": readiness, + "import_error": ( + None if trainer_available else _trainer_import_err + ), + } + ), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + if not trainer_available: + return func.HttpResponse( + json.dumps( + { + "error": "Quantum LLM trainer not available", + "details": _trainer_import_err, + } + ), + status_code=503, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + try: + body = req.get_json() if req.get_body() else {} + except ValueError: + return func.HttpResponse( + json.dumps({"error": "Invalid JSON body"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + action = body.get("action", "generate") + + if action == "generate": + prompt = str(body.get("prompt", "Quantum")).strip()[:256] + if not prompt: + prompt = "Quantum" + max_tokens = min(int(body.get("max_tokens", 50)), 200) + + config = { + "n_qubits": 4, + "n_quantum_layers": 2, + "d_model": 64, + "max_seq_len": 32, + } + trainer = QuantumEnhancedLLMTrainer(config) + + prompt_token_ids = [ + ord(c) % trainer.model_config["vocab_size"] for c in prompt[:32] + ] + try: + import torch + + prompt_ids = torch.tensor([prompt_token_ids], dtype=torch.long) + except Exception: + # Keep endpoint usable in lightweight environments where torch is + # intentionally absent; fake/alternate trainer implementations can + # still accept a nested token list. + prompt_ids = [prompt_token_ids] + + generated = trainer.model.generate( + prompt_ids, max_new_tokens=max_tokens, temperature=0.8, top_k=20 + ) + # Decode back to text using the simple char mapping + generated_row = generated[0] + tokens = ( + generated_row.tolist() + if hasattr(generated_row, "tolist") + else list(generated_row) + ) + text = "".join( + chr(t % 128) if 32 <= (t % 128) < 127 else "?" for t in tokens + ) + + return func.HttpResponse( + json.dumps( + { + "action": "generate", + "prompt": prompt, + "generated": text, + "tokens": len(tokens), + "quantum_available": QUANTUM_AVAILABLE, + "readiness": ( + get_quantum_llm_status( + output_dir=repo_root + / "data_out" + / "quantum_llm_training" + ) + if get_quantum_llm_status is not None + else None + ), + } + ), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + elif action == "train": + dataset_path = body.get("dataset_path", "datasets/chat") + dataset_path_obj = Path(dataset_path) + if not dataset_path_obj.is_absolute(): + dataset_path_obj = repo_root / dataset_path_obj + dataset_path_obj = dataset_path_obj.resolve(strict=False) + + # Basic path traversal protection: keep training datasets in-repo. + try: + dataset_path_obj.relative_to(repo_root.resolve()) + except ValueError: + return func.HttpResponse( + json.dumps( + { + "error": "dataset_path must point to a location inside the repository" + } + ), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + epochs = min(int(body.get("epochs", 1)), 5) + output_dir = repo_root / "data_out" / "quantum_llm_api" + + config = {"n_qubits": 4, "n_quantum_layers": 2, "d_model": 64} + trainer = QuantumEnhancedLLMTrainer(config) + results = trainer.train_with_quantum_enhancement( + dataset_path=dataset_path_obj, + output_dir=output_dir, + epochs=epochs, + model=None, + ) + + return func.HttpResponse( + json.dumps( + { + "action": "train", + "status": results["status"], + "epochs_completed": results["epochs_completed"], + "final_loss": results["final_loss"], + "circuit_executions": results["quantum_metrics"][ + "circuit_executions" + ], + "checkpoint_path": results.get("checkpoint_path"), + "readiness": ( + get_quantum_llm_status(output_dir=output_dir) + if get_quantum_llm_status is not None + else None + ), + } + ), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + else: + return func.HttpResponse( + json.dumps( + {"error": f"Unknown action: {action!r}. Use 'generate' or 'train'."} + ), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Quantum LLM error: {e}", exc_info=True) + return func.HttpResponse( + json.dumps({"error": f"Quantum LLM request failed: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route(route="quantum/info", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) +def quantum_info(req: func.HttpRequest) -> func.HttpResponse: + """ + Get quantum computing capabilities and status. + + GET /api/quantum/info + + Response: { + "available": true, + "backends": [...], + "capabilities": {...} + } + """ + logging.info("Quantum info endpoint invoked") + + try: + # Check if quantum modules are available + try: + import pennylane # noqa: F401 + import quantum_classifier # noqa: F401 + + quantum_available = True + + # Get available backends + backends = [ + { + "name": "default.qubit", + "description": "PennyLane default simulator", + "type": "simulator", + }, + { + "name": "lightning.qubit", + "description": "Fast C++ simulator", + "type": "simulator", + }, + { + "name": "qiskit.aer", + "description": "Qiskit Aer simulator", + "type": "simulator", + }, + ] + + capabilities = { + "max_qubits": 20, + "supports_gpu": False, + "variational_circuits": True, + "hybrid_models": True, + "azure_quantum_ready": True, + } + + except ImportError: + quantum_available = False + backends = [] + capabilities = {} + + response_data = { + "available": quantum_available, + "backends": backends, + "capabilities": capabilities, + "quantum_provider": "quantum-enhanced-local", + "version": "1.0.0", + } + + return func.HttpResponse( + json.dumps(response_data), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Quantum info error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to get quantum info: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +# ============================================================================= +# SUBSCRIPTION & MONETIZATION ENDPOINTS +# ============================================================================= + + +@app.route( + route="subscription/pricing", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def subscription_pricing(req: func.HttpRequest) -> func.HttpResponse: + """ + Get pricing information for all subscription tiers. + + GET /api/subscription/pricing + + Response: { + "tiers": { + "free": {...}, + "pro": {...}, + "enterprise": {...} + } + } + """ + logging.info("Pricing endpoint invoked") + + try: + from shared.subscription_manager import (TIER_FEATURES, TIER_LIMITS, + TIER_PRICING, + SubscriptionTier) + + pricing_info = {"tiers": {}} + + for tier in SubscriptionTier: + pricing_info["tiers"][tier.value] = { + "name": tier.name, + "price": TIER_PRICING[tier], + "currency": "USD", + "billing_period": "monthly", + "features": { + f.value: enabled for f, enabled in TIER_FEATURES[tier].items() + }, + "limits": TIER_LIMITS[tier], + } + + return func.HttpResponse( + json.dumps(pricing_info), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Pricing endpoint error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to get pricing: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="subscription/status", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def subscription_status(req: func.HttpRequest) -> func.HttpResponse: + """ + Get subscription status for a user. + + GET /api/subscription/status?user_id= + + Response: { + "user_id": "...", + "tier": "pro", + "is_active": true, + "usage": {...}, + "limits": {...} + } + """ + logging.info("Subscription status endpoint invoked") + + try: + if not subscription_manager_available: + return func.HttpResponse( + json.dumps({"error": "Subscription manager not available"}), + status_code=503, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + user_id = req.params.get("user_id", "demo_user") + + manager = get_subscription_manager() + subscription = manager.get_subscription(user_id) + + return func.HttpResponse( + json.dumps(subscription.to_dict()), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Subscription status error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to get subscription status: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="subscription/upgrade", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS +) +def subscription_upgrade(req: func.HttpRequest) -> func.HttpResponse: + """ + Upgrade a user's subscription. + + POST /api/subscription/upgrade + Body: { + "user_id": "...", + "tier": "pro" | "enterprise", + "duration_days": 30, + "payment_method": "stripe", + "stripe_subscription_id": "..." + } + + Response: { + "success": true, + "subscription": {...} + } + """ + logging.info("Subscription upgrade endpoint invoked") + + try: + if not subscription_manager_available: + return func.HttpResponse( + json.dumps({"error": "Subscription manager not available"}), + status_code=503, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + body = json.loads(req.get_body().decode("utf-8")) + user_id = body.get("user_id", "demo_user") + tier_str = body.get("tier", "pro") + duration_days = body.get("duration_days", 30) + payment_method = body.get("payment_method") + stripe_subscription_id = body.get("stripe_subscription_id") + + tier = SubscriptionTier(tier_str) + + manager = get_subscription_manager() + subscription = manager.upgrade_subscription( + user_id=user_id, + tier=tier, + duration_days=duration_days, + payment_method=payment_method, + stripe_subscription_id=stripe_subscription_id, + ) + + return func.HttpResponse( + json.dumps({"success": True, "subscription": subscription.to_dict()}), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Subscription upgrade error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to upgrade subscription: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="subscription/revenue", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def subscription_revenue(req: func.HttpRequest) -> func.HttpResponse: + """ + Get revenue statistics and projections. + + GET /api/subscription/revenue + + Response: { + "total_subscribers": 15, + "active_subscribers": 15, + "by_tier": {...}, + "monthly_recurring_revenue": 2235, + "annual_recurring_revenue": 26820 + } + """ + logging.info("Revenue stats endpoint invoked") + + try: + if not subscription_manager_available: + return func.HttpResponse( + json.dumps({"error": "Subscription manager not available"}), + status_code=503, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + manager = get_subscription_manager() + stats = manager.get_revenue_stats() + + return func.HttpResponse( + json.dumps(stats), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Revenue stats error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to get revenue stats: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="subscription/usage", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS +) +def subscription_track_usage(req: func.HttpRequest) -> func.HttpResponse: + """ + Track resource usage for a user. + + POST /api/subscription/usage + Body: { + "user_id": "...", + "resource": "chat_messages" | "quantum_jobs" | "training_hours" | "api_requests" | "websites_created", + "amount": 1 + } + + Response: { + "success": true, + "allowed": true, + "current_usage": {...} + } + """ + logging.info("Usage tracking endpoint invoked") + + try: + if not subscription_manager_available: + return func.HttpResponse( + json.dumps({"error": "Subscription manager not available"}), + status_code=503, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + body = json.loads(req.get_body().decode("utf-8")) + user_id = body.get("user_id", "demo_user") + resource = body.get("resource", "api_requests") + amount = body.get("amount", 1) + + manager = get_subscription_manager() + allowed = manager.track_usage(user_id, resource, amount) + + subscription = manager.get_subscription(user_id) + + return func.HttpResponse( + json.dumps( + { + "success": True, + "allowed": allowed, + "current_usage": subscription.usage, + "limits": subscription.to_dict()["limits"], + } + ), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Usage tracking error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to track usage: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +# ----------------------------------------------------------------------------- +# Stripe Webhook Handler +# ----------------------------------------------------------------------------- +@app.route( + route="webhook/stripe", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS +) +def stripe_webhook(req: func.HttpRequest) -> func.HttpResponse: + """ + Handle Stripe webhook events. + + POST /api/webhook/stripe + Headers: Stripe-Signature + Body: Stripe event payload + + Response: { + "status": "success" | "error", + "message": "..." + } + """ + logging.info("Stripe webhook endpoint invoked") + + try: + from shared.stripe_webhooks import get_webhook_handler + + payload = req.get_body().decode("utf-8") + signature = req.headers.get("Stripe-Signature", "") + webhook_secret = os.environ.get("STRIPE_WEBHOOK_SECRET") + + handler = get_webhook_handler() + result = handler.handle_webhook(payload, signature, webhook_secret) + + status_code = 200 if result["status"] in ["success", "ignored"] else 500 + + return func.HttpResponse( + json.dumps(result), + status_code=status_code, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Stripe webhook error: {str(e)}") + return func.HttpResponse( + json.dumps({"status": "error", "message": str(e)}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +# ----------------------------------------------------------------------------- +# Email Notifications Test Endpoint +# ----------------------------------------------------------------------------- +@app.route( + route="notifications/test", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS +) +def test_notifications(req: func.HttpRequest) -> func.HttpResponse: + """ + Test email notification system. + + POST /api/notifications/test + Body: { + "email": "user@example.com", + "type": "usage_warning" | "payment_succeeded" | "subscription_activated" + } + + Response: { + "success": true, + "message": "Notification sent" + } + """ + logging.info("Test notification endpoint invoked") + + try: + from shared.email_notifications import get_email_system + + body = json.loads(req.get_body().decode("utf-8")) + email = body.get("email", "test@example.com") + notification_type = body.get("type", "usage_warning") + + email_system = get_email_system() + + # Send test notification based on type + if notification_type == "usage_warning": + success = email_system.notify_usage_warning( + user_email=email, + resource="chat_messages", + percentage=85.0, + current=850, + limit=1000, + ) + elif notification_type == "payment_succeeded": + success = email_system.notify_payment_succeeded( + user_email=email, amount=49.00, invoice_id="inv_test123" + ) + elif notification_type == "subscription_activated": + success = email_system.notify_subscription_activated( + user_email=email, tier="Pro", price=49.00 + ) + else: + return func.HttpResponse( + json.dumps( + {"error": f"Unknown notification type: {notification_type}"} + ), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + return func.HttpResponse( + json.dumps( + { + "success": success, + "message": f"Test notification sent to {email}", + "type": notification_type, + } + ), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Test notification error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to send test notification: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +# ----------------------------------------------------------------------------- +# Notifications Log Endpoint +# ----------------------------------------------------------------------------- +@app.route( + route="notifications/log", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def notifications_log(req: func.HttpRequest) -> func.HttpResponse: + """ + Get email notification log. + + GET /api/notifications/log?user_email=user@example.com + + Response: { + "notifications": [...] + } + """ + logging.info("Notifications log endpoint invoked") + + try: + from shared.email_notifications import get_email_system + + user_email = req.params.get("user_email") + + email_system = get_email_system() + notifications = email_system.get_sent_emails(user_email) + + return func.HttpResponse( + json.dumps({"notifications": notifications, "count": len(notifications)}), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Notifications log error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to get notifications log: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +# ----------------------------------------------------------------------------- +# Referral System Endpoints +# ----------------------------------------------------------------------------- +@app.route( + route="referrals/code", methods=["GET", "POST"], auth_level=func.AuthLevel.ANONYMOUS +) +def referral_code(req: func.HttpRequest) -> func.HttpResponse: + """ + Get or generate referral code for a user. + + GET /api/referrals/code?user_id=... + POST /api/referrals/code with {"user_id": "..."} + + Response: { + "referral_code": "ABC123DEF", + "user_id": "..." + } + """ + logging.info("Referral code endpoint invoked") + + try: + from shared.referral_system import get_referral_system + + if req.method == "GET": + user_id = req.params.get("user_id", "demo_user") + else: + body = json.loads(req.get_body().decode("utf-8")) + user_id = body.get("user_id", "demo_user") + + referral_system = get_referral_system() + + # Get existing or generate new code + code = referral_system.get_referral_code(user_id) + if not code: + code = referral_system.generate_referral_code(user_id) + + return func.HttpResponse( + json.dumps({"referral_code": code, "user_id": user_id}), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Referral code error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to get referral code: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="referrals/stats", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def referral_stats(req: func.HttpRequest) -> func.HttpResponse: + """ + Get referral statistics for a user. + + GET /api/referrals/stats?user_id=... + + Response: { + "referral_code": "...", + "referral_count": 5, + "total_commission": 100.00, + "pending_commission": 50.00, + "paid_commission": 50.00, + "referrals": [...] + } + """ + logging.info("Referral stats endpoint invoked") + + try: + from shared.referral_system import get_referral_system + + user_id = req.params.get("user_id", "demo_user") + + referral_system = get_referral_system() + stats = referral_system.get_referral_stats(user_id) + + return func.HttpResponse( + json.dumps(stats), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Referral stats error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to get referral stats: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="referrals/record", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS +) +def record_referral(req: func.HttpRequest) -> func.HttpResponse: + """ + Record a new referral. + + POST /api/referrals/record + Body: { + "referrer_code": "ABC123", + "new_user_id": "user123", + "tier": "pro", + "subscription_value": 49.00 + } + + Response: { + "success": true, + "commission": 9.80, + "referral_count": 5 + } + """ + logging.info("Record referral endpoint invoked") + + try: + from shared.referral_system import get_referral_system + + body = json.loads(req.get_body().decode("utf-8")) + referrer_code = body.get("referrer_code") + new_user_id = body.get("new_user_id") + tier = body.get("tier") + subscription_value = body.get("subscription_value") + + if not all([referrer_code, new_user_id, tier, subscription_value]): + return func.HttpResponse( + json.dumps({"error": "Missing required fields"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + referral_system = get_referral_system() + result = referral_system.record_referral( + referrer_code=referrer_code, + new_user_id=new_user_id, + tier=tier, + subscription_value=subscription_value, + ) + + return func.HttpResponse( + json.dumps(result), + status_code=200 if result.get("success") else 400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Record referral error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to record referral: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route( + route="referrals/leaderboard", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def referral_leaderboard(req: func.HttpRequest) -> func.HttpResponse: + """ + Get referral leaderboard. + + GET /api/referrals/leaderboard?limit=10 + + Response: { + "leaderboard": [ + {"rank": 1, "user_id": "...", "referral_count": 50, "total_commission": 500} + ] + } + """ + logging.info("Referral leaderboard endpoint invoked") + + try: + from shared.referral_system import get_referral_system + + limit = int(req.params.get("limit", "10")) + + referral_system = get_referral_system() + leaderboard = referral_system.get_leaderboard(limit) + + return func.HttpResponse( + json.dumps({"leaderboard": leaderboard}), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + except Exception as e: + logging.error(f"Referral leaderboard error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Failed to get leaderboard: {str(e)}"}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) diff --git a/functions/http_ai_runner/__init__.py b/functions/http_ai_runner/__init__.py index 96a1e0cb1..f8e2ec52b 100644 --- a/functions/http_ai_runner/__init__.py +++ b/functions/http_ai_runner/__init__.py @@ -1,67 +1,67 @@ -import json -import logging -import os -import sys -from pathlib import Path -from typing import Any, Dict - -import azure.functions as func - -# Add shared folder to path -sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "shared")) -from chat_providers import detect_provider # noqa: E402 - - -def _parse_body(req: func.HttpRequest) -> Dict[str, Any]: - try: - return req.get_json() or {} - except ValueError: - return {} - - -def main(req: func.HttpRequest) -> func.HttpResponse: - """HTTP trigger to run AI one-shot. - - Query/body params: - prompt: User prompt (required). - provider: Provider ('local', 'openai', 'azure', 'auto'). - model: Model/deployment override. - """ - params = {k.lower(): v for k, v in req.params.items()} - body = _parse_body(req) - - prompt = params.get("prompt") or body.get("prompt") - provider_choice = ( - params.get("provider") - or body.get("provider") - or os.getenv("DEFAULT_AI_PROVIDER", "local") - ) - model = params.get("model") or body.get("model") - - if not prompt: - return func.HttpResponse( - json.dumps({"error": "Missing 'prompt'"}), - status_code=400, - mimetype="application/json", - ) - - try: - provider, info = detect_provider(explicit=provider_choice, model_override=model) - messages = [{"role": "user", "content": prompt}] - reply = provider.complete(messages, stream=False) - payload = { - "prompt": prompt, - "reply": reply, - "provider": info.name, - "model": info.model, - } - return func.HttpResponse( - json.dumps(payload, ensure_ascii=False), - status_code=200, - mimetype="application/json", - ) - except Exception as e: # noqa: BLE001 - logging.exception("AI HTTP run failed") - return func.HttpResponse( - json.dumps({"error": str(e)}), status_code=500, mimetype="application/json" - ) +import json +import logging +import os +import sys +from pathlib import Path +from typing import Any, Dict + +import azure.functions as func + +# Add shared folder to path +sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "shared")) +from chat_providers import detect_provider # noqa: E402 + + +def _parse_body(req: func.HttpRequest) -> Dict[str, Any]: + try: + return req.get_json() or {} + except ValueError: + return {} + + +def main(req: func.HttpRequest) -> func.HttpResponse: + """HTTP trigger to run AI one-shot. + + Query/body params: + prompt: User prompt (required). + provider: Provider ('local', 'openai', 'azure', 'auto'). + model: Model/deployment override. + """ + params = {k.lower(): v for k, v in req.params.items()} + body = _parse_body(req) + + prompt = params.get("prompt") or body.get("prompt") + provider_choice = ( + params.get("provider") + or body.get("provider") + or os.getenv("DEFAULT_AI_PROVIDER", "local") + ) + model = params.get("model") or body.get("model") + + if not prompt: + return func.HttpResponse( + json.dumps({"error": "Missing 'prompt'"}), + status_code=400, + mimetype="application/json", + ) + + try: + provider, info = detect_provider(explicit=provider_choice, model_override=model) + messages = [{"role": "user", "content": prompt}] + reply = provider.complete(messages, stream=False) + payload = { + "prompt": prompt, + "reply": reply, + "provider": info.name, + "model": info.model, + } + return func.HttpResponse( + json.dumps(payload, ensure_ascii=False), + status_code=200, + mimetype="application/json", + ) + except Exception as e: # noqa: BLE001 + logging.exception("AI HTTP run failed") + return func.HttpResponse( + json.dumps({"error": str(e)}), status_code=500, mimetype="application/json" + ) diff --git a/functions/http_ai_runner/function.json b/functions/http_ai_runner/function.json index 9f38d2127..ecf4ec621 100644 --- a/functions/http_ai_runner/function.json +++ b/functions/http_ai_runner/function.json @@ -1,17 +1,17 @@ -{ - "bindings": [ - { - "authLevel": "Anonymous", - "type": "httpTrigger", - "direction": "in", - "name": "req", - "methods": ["get", "post"], - "route": "ai/run" - }, - { - "type": "http", - "direction": "out", - "name": "$return" - } - ] +{ + "bindings": [ + { + "authLevel": "Anonymous", + "type": "httpTrigger", + "direction": "in", + "name": "req", + "methods": ["get", "post"], + "route": "ai/run" + }, + { + "type": "http", + "direction": "out", + "name": "$return" + } + ] } diff --git a/functions/http_ai_status/__init__.py b/functions/http_ai_status/__init__.py index 5e704eda0..f9e5a30f3 100644 --- a/functions/http_ai_status/__init__.py +++ b/functions/http_ai_status/__init__.py @@ -1,248 +1,248 @@ -import importlib.util as _iu -import json -import os -import subprocess -import sys -from pathlib import Path - -import azure.functions as func -import yaml - -# Reuse shared chat providers (already copied for performance) -shared_path = Path(__file__).resolve().parent.parent / "shared" -if str(shared_path) not in sys.path: - sys.path.insert(0, str(shared_path)) - -from chat_providers import detect_provider # noqa: E402 - - -def main(req: func.HttpRequest) -> func.HttpResponse: - """Health / status endpoint for AI provider readiness. - - Returns JSON describing: - - active_provider: which provider auto-detect selects (azure|openai|local) - - model: resolved model/deployment name - - env: boolean flags indicating if required env vars are present for each cloud provider - - temperature: current CHAT_TEMPERATURE setting - - This helps verify cloud configuration after deploying to Azure. - """ - # Collect environment info - azure_env = { - "AZURE_OPENAI_API_KEY": bool(os.getenv("AZURE_OPENAI_API_KEY")), - "AZURE_OPENAI_ENDPOINT": bool(os.getenv("AZURE_OPENAI_ENDPOINT")), - "AZURE_OPENAI_DEPLOYMENT": bool(os.getenv("AZURE_OPENAI_DEPLOYMENT")), - "AZURE_OPENAI_API_VERSION": bool(os.getenv("AZURE_OPENAI_API_VERSION")), - } - openai_env = { - "OPENAI_API_KEY": bool(os.getenv("OPENAI_API_KEY")), - "OPENAI_MODEL": bool(os.getenv("OPENAI_MODEL")), - } - - # In-process ML deps availability - inproc_ml = { - "torch": _iu.find_spec("torch") is not None, - "transformers": _iu.find_spec("transformers") is not None, - "peft": _iu.find_spec("peft") is not None, - } - - # Repo root and venv python - repo_root = Path(__file__).resolve().parents[1] - venv_python = repo_root / "venv" / "Scripts" / "python.exe" - venv_info = { - "path": str(venv_python), - "exists": venv_python.exists(), - "packages": {}, - "error": None, - } - if venv_info["exists"]: - try: - code = ( - "import json, importlib.util, importlib.metadata as md;" - "mods=['torch','transformers','peft'];" - "avail={m:(importlib.util.find_spec(m) is not None) for m in mods};" - "vers={};" - "\nfor m in mods:\n\t" - "\n\ttry:\n\t\tvers[m]=md.version(m)\n\texcept Exception:\n\t\tvers[m]=None;" - "print(json.dumps({'available':avail,'versions':vers}))" - ) - proc = subprocess.run( - [str(venv_python), "-c", code], - capture_output=True, - text=True, - timeout=10, - ) - if proc.returncode == 0: - data = json.loads(proc.stdout.strip() or "{}") - venv_info["packages"] = data - else: - venv_info["error"] = proc.stderr.strip() or f"exit {proc.returncode}" - except Exception as e: # noqa: BLE001 - venv_info["error"] = str(e) - - # LoRA default adapter location and readiness - lora_default = repo_root / "data_out" / "lora_training" / "lora_adapter" - adapter_cfg = lora_default / "adapter_config.json" - tokenizer_dir = lora_default.parent / "tokenizer" - lora_info = { - "default_adapter_path": str(lora_default), - "exists": lora_default.exists(), - "adapter_config_exists": adapter_cfg.exists(), - "tokenizer_dir_exists": tokenizer_dir.exists(), - "base_model": None, - "inproc_ready": all(inproc_ml.values()), - "subprocess_ready": venv_info["exists"] - and bool(venv_info.get("packages", {}).get("available", {}).get("torch")) - and bool(venv_info.get("packages", {}).get("available", {}).get("transformers")) - and bool(venv_info.get("packages", {}).get("available", {}).get("peft")), - } - if lora_info["adapter_config_exists"]: - try: - with open(adapter_cfg, "r", encoding="utf-8") as f: - cfg = json.load(f) - lora_info["base_model"] = cfg.get("base_model_name_or_path") - except Exception: - pass - - try: - provider, info = detect_provider(explicit="auto") - temperature = os.getenv("CHAT_TEMPERATURE", "0.7") - # Known endpoints and assets - chat_web_html = (repo_root / "chat-web" / "index.html").exists() - chat_web_js = (repo_root / "chat-web" / "chat.js").exists() - - # AutoTrain status if present - autotrain_dir = repo_root / "data_out" / "autotrain" - autotrain_status_path = autotrain_dir / "status.json" - autotrain_last: dict | None = None - if autotrain_status_path.exists(): - try: - with autotrain_status_path.open("r", encoding="utf-8") as f: - autotrain_last = json.load(f) - except Exception: - autotrain_last = {"error": "failed to parse status.json"} - - # Quantum AutoRun status if present - qautorun_dir = repo_root / "data_out" / "quantum_autorun" - qautorun_status_path = qautorun_dir / "status.json" - qautorun_last: dict | None = None - quantum_azure: dict | None = None - if qautorun_status_path.exists(): - try: - with qautorun_status_path.open("r", encoding="utf-8") as f: - qautorun_last = json.load(f) - except Exception: - qautorun_last = {"error": "failed to parse status.json"} - - # Build Azure Quantum context and job list if metadata present - try: - cfg_path = ( - repo_root - / "ai-projects" - / "quantum-ml" - / "config" - / "quantum_config.yaml" - ) - azure_ctx = None - workspace_url = None - if cfg_path.exists(): - with cfg_path.open("r", encoding="utf-8") as f: - cfg = yaml.safe_load(f) or {} - az = cfg.get("azure", {}) - sub = az.get("subscription_id") - rg = az.get("resource_group") - ws = az.get("workspace_name") - loc = az.get("location") - azure_ctx = { - "subscription_id": sub, - "resource_group": rg, - "workspace_name": ws, - "location": loc, - } - if sub and rg and ws: - workspace_url = ( - f"https://portal.azure.com/#resource/subscriptions/{sub}/resourceGroups/{rg}" - f"/providers/Microsoft.Quantum/Workspaces/{ws}/overview" - ) - # Extract azure job metadata from autorun status - azure_jobs = [] - jobs = (qautorun_last or {}).get("jobs", []) - for j in jobs: - meta = j.get("meta", {}) if isinstance(j, dict) else {} - job_id = meta.get("azure_job_id") - if job_id: - azure_jobs.append( - { - "name": j.get("name"), - "mode": j.get("mode"), - "job_id": job_id, - "backend": meta.get("azure_backend") - or meta.get("backend") - or j.get("mode"), - "success": meta.get("azure_success"), - "counts": meta.get("azure_counts"), - "results_file": meta.get("azure_results_file"), - } - ) - if azure_ctx or azure_jobs: - quantum_azure = { - "workspace": azure_ctx, - "workspace_portal_url": workspace_url, - "jobs": azure_jobs, - "portal_job_url_template": ( - "https://portal.azure.com/#view/Microsoft_Azure_Quantum/JobDetailsBlade?" - "jobId={job_id}&subscriptionId={subscription_id}&resourceGroup={resource_group}" - "&workspaceName={workspace_name}&location={location}" - ), - } - except Exception: - # ignore enrichment failures; keep core payload intact - pass - - payload = { - "active_provider": info.name, - "model": info.model, - "env": { - "azure_openai": azure_env, - "openai": openai_env, - "local_fallback": True, - }, - "ml_inprocess": inproc_ml, - "lora": lora_info, - "venv": venv_info, - "temperature": temperature, - "server": { - "executable": sys.executable, - "python_version": sys.version, - "cwd": os.getcwd(), - }, - "assets": { - "chat_web_html": chat_web_html, - "chat_web_js": chat_web_js, - }, - "autotrain": autotrain_last, - "quantum_autorun": qautorun_last, - "quantum_azure": quantum_azure, - "endpoints": [ - "/api/chat-web", - "/api/chat-web/chat.js", - "/api/chat", - "/api/ai/status", - ], - "status": "ok", - } - return func.HttpResponse( - json.dumps(payload), status_code=200, mimetype="application/json" - ) - except Exception as e: # noqa: BLE001 - payload = { - "status": "error", - "error": str(e), - "env": { - "azure_openai": azure_env, - "openai": openai_env, - }, - } - return func.HttpResponse( - json.dumps(payload), status_code=500, mimetype="application/json" - ) +import importlib.util as _iu +import json +import os +import subprocess +import sys +from pathlib import Path + +import azure.functions as func +import yaml + +# Reuse shared chat providers (already copied for performance) +shared_path = Path(__file__).resolve().parent.parent / "shared" +if str(shared_path) not in sys.path: + sys.path.insert(0, str(shared_path)) + +from chat_providers import detect_provider # noqa: E402 + + +def main(req: func.HttpRequest) -> func.HttpResponse: + """Health / status endpoint for AI provider readiness. + + Returns JSON describing: + - active_provider: which provider auto-detect selects (azure|openai|local) + - model: resolved model/deployment name + - env: boolean flags indicating if required env vars are present for each cloud provider + - temperature: current CHAT_TEMPERATURE setting + + This helps verify cloud configuration after deploying to Azure. + """ + # Collect environment info + azure_env = { + "AZURE_OPENAI_API_KEY": bool(os.getenv("AZURE_OPENAI_API_KEY")), + "AZURE_OPENAI_ENDPOINT": bool(os.getenv("AZURE_OPENAI_ENDPOINT")), + "AZURE_OPENAI_DEPLOYMENT": bool(os.getenv("AZURE_OPENAI_DEPLOYMENT")), + "AZURE_OPENAI_API_VERSION": bool(os.getenv("AZURE_OPENAI_API_VERSION")), + } + openai_env = { + "OPENAI_API_KEY": bool(os.getenv("OPENAI_API_KEY")), + "OPENAI_MODEL": bool(os.getenv("OPENAI_MODEL")), + } + + # In-process ML deps availability + inproc_ml = { + "torch": _iu.find_spec("torch") is not None, + "transformers": _iu.find_spec("transformers") is not None, + "peft": _iu.find_spec("peft") is not None, + } + + # Repo root and venv python + repo_root = Path(__file__).resolve().parents[1] + venv_python = repo_root / "venv" / "Scripts" / "python.exe" + venv_info = { + "path": str(venv_python), + "exists": venv_python.exists(), + "packages": {}, + "error": None, + } + if venv_info["exists"]: + try: + code = ( + "import json, importlib.util, importlib.metadata as md;" + "mods=['torch','transformers','peft'];" + "avail={m:(importlib.util.find_spec(m) is not None) for m in mods};" + "vers={};" + "\nfor m in mods:\n\t" + "\n\ttry:\n\t\tvers[m]=md.version(m)\n\texcept Exception:\n\t\tvers[m]=None;" + "print(json.dumps({'available':avail,'versions':vers}))" + ) + proc = subprocess.run( + [str(venv_python), "-c", code], + capture_output=True, + text=True, + timeout=10, + ) + if proc.returncode == 0: + data = json.loads(proc.stdout.strip() or "{}") + venv_info["packages"] = data + else: + venv_info["error"] = proc.stderr.strip() or f"exit {proc.returncode}" + except Exception as e: # noqa: BLE001 + venv_info["error"] = str(e) + + # LoRA default adapter location and readiness + lora_default = repo_root / "data_out" / "lora_training" / "lora_adapter" + adapter_cfg = lora_default / "adapter_config.json" + tokenizer_dir = lora_default.parent / "tokenizer" + lora_info = { + "default_adapter_path": str(lora_default), + "exists": lora_default.exists(), + "adapter_config_exists": adapter_cfg.exists(), + "tokenizer_dir_exists": tokenizer_dir.exists(), + "base_model": None, + "inproc_ready": all(inproc_ml.values()), + "subprocess_ready": venv_info["exists"] + and bool(venv_info.get("packages", {}).get("available", {}).get("torch")) + and bool(venv_info.get("packages", {}).get("available", {}).get("transformers")) + and bool(venv_info.get("packages", {}).get("available", {}).get("peft")), + } + if lora_info["adapter_config_exists"]: + try: + with open(adapter_cfg, "r", encoding="utf-8") as f: + cfg = json.load(f) + lora_info["base_model"] = cfg.get("base_model_name_or_path") + except Exception: + pass + + try: + provider, info = detect_provider(explicit="auto") + temperature = os.getenv("CHAT_TEMPERATURE", "0.7") + # Known endpoints and assets + chat_web_html = (repo_root / "chat-web" / "index.html").exists() + chat_web_js = (repo_root / "chat-web" / "chat.js").exists() + + # AutoTrain status if present + autotrain_dir = repo_root / "data_out" / "autotrain" + autotrain_status_path = autotrain_dir / "status.json" + autotrain_last: dict | None = None + if autotrain_status_path.exists(): + try: + with autotrain_status_path.open("r", encoding="utf-8") as f: + autotrain_last = json.load(f) + except Exception: + autotrain_last = {"error": "failed to parse status.json"} + + # Quantum AutoRun status if present + qautorun_dir = repo_root / "data_out" / "quantum_autorun" + qautorun_status_path = qautorun_dir / "status.json" + qautorun_last: dict | None = None + quantum_azure: dict | None = None + if qautorun_status_path.exists(): + try: + with qautorun_status_path.open("r", encoding="utf-8") as f: + qautorun_last = json.load(f) + except Exception: + qautorun_last = {"error": "failed to parse status.json"} + + # Build Azure Quantum context and job list if metadata present + try: + cfg_path = ( + repo_root + / "ai-projects" + / "quantum-ml" + / "config" + / "quantum_config.yaml" + ) + azure_ctx = None + workspace_url = None + if cfg_path.exists(): + with cfg_path.open("r", encoding="utf-8") as f: + cfg = yaml.safe_load(f) or {} + az = cfg.get("azure", {}) + sub = az.get("subscription_id") + rg = az.get("resource_group") + ws = az.get("workspace_name") + loc = az.get("location") + azure_ctx = { + "subscription_id": sub, + "resource_group": rg, + "workspace_name": ws, + "location": loc, + } + if sub and rg and ws: + workspace_url = ( + f"https://portal.azure.com/#resource/subscriptions/{sub}/resourceGroups/{rg}" + f"/providers/Microsoft.Quantum/Workspaces/{ws}/overview" + ) + # Extract azure job metadata from autorun status + azure_jobs = [] + jobs = (qautorun_last or {}).get("jobs", []) + for j in jobs: + meta = j.get("meta", {}) if isinstance(j, dict) else {} + job_id = meta.get("azure_job_id") + if job_id: + azure_jobs.append( + { + "name": j.get("name"), + "mode": j.get("mode"), + "job_id": job_id, + "backend": meta.get("azure_backend") + or meta.get("backend") + or j.get("mode"), + "success": meta.get("azure_success"), + "counts": meta.get("azure_counts"), + "results_file": meta.get("azure_results_file"), + } + ) + if azure_ctx or azure_jobs: + quantum_azure = { + "workspace": azure_ctx, + "workspace_portal_url": workspace_url, + "jobs": azure_jobs, + "portal_job_url_template": ( + "https://portal.azure.com/#view/Microsoft_Azure_Quantum/JobDetailsBlade?" + "jobId={job_id}&subscriptionId={subscription_id}&resourceGroup={resource_group}" + "&workspaceName={workspace_name}&location={location}" + ), + } + except Exception: + # ignore enrichment failures; keep core payload intact + pass + + payload = { + "active_provider": info.name, + "model": info.model, + "env": { + "azure_openai": azure_env, + "openai": openai_env, + "local_fallback": True, + }, + "ml_inprocess": inproc_ml, + "lora": lora_info, + "venv": venv_info, + "temperature": temperature, + "server": { + "executable": sys.executable, + "python_version": sys.version, + "cwd": os.getcwd(), + }, + "assets": { + "chat_web_html": chat_web_html, + "chat_web_js": chat_web_js, + }, + "autotrain": autotrain_last, + "quantum_autorun": qautorun_last, + "quantum_azure": quantum_azure, + "endpoints": [ + "/api/chat-web", + "/api/chat-web/chat.js", + "/api/chat", + "/api/ai/status", + ], + "status": "ok", + } + return func.HttpResponse( + json.dumps(payload), status_code=200, mimetype="application/json" + ) + except Exception as e: # noqa: BLE001 + payload = { + "status": "error", + "error": str(e), + "env": { + "azure_openai": azure_env, + "openai": openai_env, + }, + } + return func.HttpResponse( + json.dumps(payload), status_code=500, mimetype="application/json" + ) diff --git a/functions/http_ai_status/function.json b/functions/http_ai_status/function.json index 74a5a435c..0f78424fb 100644 --- a/functions/http_ai_status/function.json +++ b/functions/http_ai_status/function.json @@ -1,17 +1,17 @@ -{ - "bindings": [ - { - "authLevel": "Anonymous", - "type": "httpTrigger", - "direction": "in", - "name": "req", - "methods": ["get"], - "route": "ai/status" - }, - { - "type": "http", - "direction": "out", - "name": "$return" - } - ] -} +{ + "bindings": [ + { + "authLevel": "Anonymous", + "type": "httpTrigger", + "direction": "in", + "name": "req", + "methods": ["get"], + "route": "ai/status" + }, + { + "type": "http", + "direction": "out", + "name": "$return" + } + ] +} diff --git a/functions/http_chat/function_app.py b/functions/http_chat/function_app.py index f35015f93..3bf909147 100644 --- a/functions/http_chat/function_app.py +++ b/functions/http_chat/function_app.py @@ -1,141 +1,141 @@ -import json -import logging -import sys -from pathlib import Path - -import azure.functions as func - -from chat_providers import detect_provider -from shared.http_utils import create_cors_headers, validate_messages - -# Add chat-cli to path so we can import chat_providers -talk_to_ai_path = ( - Path(__file__).resolve().parent.parent / "ai-projects" / "chat-cli" / "src" -) -sys.path.insert(0, str(talk_to_ai_path)) - -# Add repo root to path so we can import shared utilities as a package -repo_root = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(repo_root)) - - -app = func.FunctionApp() - - -@app.route(route="chat", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) -def chat(req: func.HttpRequest) -> func.HttpResponse: - """ - HTTP endpoint for chat interactions. - - POST /api/chat - Body: { - "messages": [{"role": "user|assistant|system", "content": "..."}], - "provider": "auto|openai|azure|local" (optional), - "model": "model-name" (optional), - "stream": false (optional, streaming not implemented in HTTP yet) - } - - Response: { - "response": "assistant's reply", - "provider": "azure|openai|local", - "model": "model-name" - } - """ - logging.info("Chat function invoked") - - try: - # Parse request - req_body = req.get_json() - messages = req_body.get("messages", []) - provider_choice = req_body.get("provider", "auto") - model_override = req_body.get("model") - - # If LoRA provider selected without a model path, try default path - if (provider_choice or "").lower() == "lora" and not model_override: - default_adapter = ( - Path(__file__).resolve().parent.parent - / "data_out" - / "lora_training" - / "lora_adapter" - ) - if default_adapter.exists(): - model_override = str(default_adapter) - else: - return func.HttpResponse( - json.dumps( - { - "error": "LoRA provider selected but no adapter path provided and default path not found.", - "hint": "Provide 'model' in request body (e.g., data_out/lora_training/lora_adapter) or create the default adapter directory.", - "defaultTried": str(default_adapter), - } - ), - status_code=400, - mimetype="application/json", - ) - - if not messages: - return func.HttpResponse( - json.dumps({"error": "No messages provided"}), - status_code=400, - mimetype="application/json", - ) - - # Validate messages format - is_valid, error_msg = validate_messages(messages) - if not is_valid: - return func.HttpResponse( - json.dumps({"error": error_msg}), - status_code=400, - mimetype="application/json", - ) - - # Get provider - provider, info = detect_provider( - explicit=provider_choice, model_override=model_override - ) - - logging.info(f"Using provider: {info.name}, model: {info.model}") - - # Get completion (non-streaming for HTTP simplicity) - result = provider.complete(messages, stream=False) - - # If result is still a generator, consume it - if hasattr(result, "__iter__") and not isinstance(result, str): - result = "".join(result) - - response_data = {"response": result, "provider": info.name, "model": info.model} - - return func.HttpResponse( - json.dumps(response_data), - status_code=200, - mimetype="application/json", - headers=create_cors_headers(), - ) - - except ValueError as ve: - logging.error(f"Validation error: {str(ve)}") - return func.HttpResponse( - json.dumps({"error": f"Validation error: {str(ve)}"}), - status_code=400, - mimetype="application/json", - ) - except RuntimeError as re: - logging.error(f"Runtime error: {str(re)}") - return func.HttpResponse( - json.dumps({"error": f"Configuration error: {str(re)}"}), - status_code=500, - mimetype="application/json", - ) - except Exception as e: - logging.error(f"Unexpected error: {str(e)}") - return func.HttpResponse( - json.dumps({"error": f"Internal server error: {str(e)}"}), - status_code=500, - mimetype="application/json", - ) - - -@app.route(route="chat", methods=["OPTIONS"], auth_level=func.AuthLevel.ANONYMOUS) -def chat_options(req: func.HttpRequest) -> func.HttpResponse: - """Handle CORS preflight requests""" - return func.HttpResponse("", status_code=200, headers=create_cors_headers()) +import json +import logging +import sys +from pathlib import Path + +import azure.functions as func + +from chat_providers import detect_provider +from shared.http_utils import create_cors_headers, validate_messages + +# Add chat-cli to path so we can import chat_providers +talk_to_ai_path = ( + Path(__file__).resolve().parent.parent / "ai-projects" / "chat-cli" / "src" +) +sys.path.insert(0, str(talk_to_ai_path)) + +# Add repo root to path so we can import shared utilities as a package +repo_root = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(repo_root)) + + +app = func.FunctionApp() + + +@app.route(route="chat", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) +def chat(req: func.HttpRequest) -> func.HttpResponse: + """ + HTTP endpoint for chat interactions. + + POST /api/chat + Body: { + "messages": [{"role": "user|assistant|system", "content": "..."}], + "provider": "auto|openai|azure|local" (optional), + "model": "model-name" (optional), + "stream": false (optional, streaming not implemented in HTTP yet) + } + + Response: { + "response": "assistant's reply", + "provider": "azure|openai|local", + "model": "model-name" + } + """ + logging.info("Chat function invoked") + + try: + # Parse request + req_body = req.get_json() + messages = req_body.get("messages", []) + provider_choice = req_body.get("provider", "auto") + model_override = req_body.get("model") + + # If LoRA provider selected without a model path, try default path + if (provider_choice or "").lower() == "lora" and not model_override: + default_adapter = ( + Path(__file__).resolve().parent.parent + / "data_out" + / "lora_training" + / "lora_adapter" + ) + if default_adapter.exists(): + model_override = str(default_adapter) + else: + return func.HttpResponse( + json.dumps( + { + "error": "LoRA provider selected but no adapter path provided and default path not found.", + "hint": "Provide 'model' in request body (e.g., data_out/lora_training/lora_adapter) or create the default adapter directory.", + "defaultTried": str(default_adapter), + } + ), + status_code=400, + mimetype="application/json", + ) + + if not messages: + return func.HttpResponse( + json.dumps({"error": "No messages provided"}), + status_code=400, + mimetype="application/json", + ) + + # Validate messages format + is_valid, error_msg = validate_messages(messages) + if not is_valid: + return func.HttpResponse( + json.dumps({"error": error_msg}), + status_code=400, + mimetype="application/json", + ) + + # Get provider + provider, info = detect_provider( + explicit=provider_choice, model_override=model_override + ) + + logging.info(f"Using provider: {info.name}, model: {info.model}") + + # Get completion (non-streaming for HTTP simplicity) + result = provider.complete(messages, stream=False) + + # If result is still a generator, consume it + if hasattr(result, "__iter__") and not isinstance(result, str): + result = "".join(result) + + response_data = {"response": result, "provider": info.name, "model": info.model} + + return func.HttpResponse( + json.dumps(response_data), + status_code=200, + mimetype="application/json", + headers=create_cors_headers(), + ) + + except ValueError as ve: + logging.error(f"Validation error: {str(ve)}") + return func.HttpResponse( + json.dumps({"error": f"Validation error: {str(ve)}"}), + status_code=400, + mimetype="application/json", + ) + except RuntimeError as re: + logging.error(f"Runtime error: {str(re)}") + return func.HttpResponse( + json.dumps({"error": f"Configuration error: {str(re)}"}), + status_code=500, + mimetype="application/json", + ) + except Exception as e: + logging.error(f"Unexpected error: {str(e)}") + return func.HttpResponse( + json.dumps({"error": f"Internal server error: {str(e)}"}), + status_code=500, + mimetype="application/json", + ) + + +@app.route(route="chat", methods=["OPTIONS"], auth_level=func.AuthLevel.ANONYMOUS) +def chat_options(req: func.HttpRequest) -> func.HttpResponse: + """Handle CORS preflight requests""" + return func.HttpResponse("", status_code=200, headers=create_cors_headers()) diff --git a/functions/http_chat_web/function_app.py b/functions/http_chat_web/function_app.py index 053d83cc2..3b67ba6a3 100644 --- a/functions/http_chat_web/function_app.py +++ b/functions/http_chat_web/function_app.py @@ -1,44 +1,44 @@ -import sys -from pathlib import Path - -import azure.functions as func - -from shared.http_utils import serve_static_file - -# Ensure repository root is on sys.path so shared utilities can be imported as a package -repo_root = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(repo_root)) - - -app = func.FunctionApp() - - -@app.route(route="chat-web", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) -def serve_chat_web(req: func.HttpRequest) -> func.HttpResponse: - """Serve the chat web interface""" - html_path = Path(__file__).resolve().parent.parent / "apps" / "chat" / "index.html" - content, status_code, headers = serve_static_file( - html_path, "text/html", use_cache_headers=True - ) - - return func.HttpResponse( - content, status_code=status_code, mimetype="text/html", headers=headers - ) - - -@app.route( - route="chat-web/chat.js", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS -) -def serve_chat_js(req: func.HttpRequest) -> func.HttpResponse: - """Serve the chat JavaScript file""" - js_path = Path(__file__).resolve().parent.parent / "apps" / "chat" / "chat.js" - content, status_code, headers = serve_static_file( - js_path, "application/javascript", use_cache_headers=True - ) - - return func.HttpResponse( - content, - status_code=status_code, - mimetype="application/javascript", - headers=headers, - ) +import sys +from pathlib import Path + +import azure.functions as func + +from shared.http_utils import serve_static_file + +# Ensure repository root is on sys.path so shared utilities can be imported as a package +repo_root = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(repo_root)) + + +app = func.FunctionApp() + + +@app.route(route="chat-web", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) +def serve_chat_web(req: func.HttpRequest) -> func.HttpResponse: + """Serve the chat web interface""" + html_path = Path(__file__).resolve().parent.parent / "apps" / "chat" / "index.html" + content, status_code, headers = serve_static_file( + html_path, "text/html", use_cache_headers=True + ) + + return func.HttpResponse( + content, status_code=status_code, mimetype="text/html", headers=headers + ) + + +@app.route( + route="chat-web/chat.js", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS +) +def serve_chat_js(req: func.HttpRequest) -> func.HttpResponse: + """Serve the chat JavaScript file""" + js_path = Path(__file__).resolve().parent.parent / "apps" / "chat" / "chat.js" + content, status_code, headers = serve_static_file( + js_path, "application/javascript", use_cache_headers=True + ) + + return func.HttpResponse( + content, + status_code=status_code, + mimetype="application/javascript", + headers=headers, + ) diff --git a/functions/timer_ai_runner/__init__.py b/functions/timer_ai_runner/__init__.py index 42444123d..4d31e562c 100644 --- a/functions/timer_ai_runner/__init__.py +++ b/functions/timer_ai_runner/__init__.py @@ -1,37 +1,37 @@ -import datetime -import logging -import os -import sys -from datetime import timezone -from pathlib import Path - -import azure.functions as func - -# Add shared folder to path -sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "shared")) -from chat_providers import detect_provider # noqa: E402 - - -def main(myTimer: func.TimerRequest) -> None: - utc_timestamp = datetime.datetime.now(timezone.utc).isoformat() - if myTimer.past_due: - logging.warning("The timer is past due!") - - provider_choice = os.getenv("DEFAULT_AI_PROVIDER", "local") - prompt = os.getenv("AI_DEFAULT_PROMPT", f"Automated check-in at {utc_timestamp}") - model = os.getenv("AI_MODEL") - - try: - provider, info = detect_provider(explicit=provider_choice, model_override=model) - messages = [{"role": "user", "content": prompt}] - reply = provider.complete(messages, stream=False) - logging.info( - "AI timer run succeeded. Provider=%s, Model=%s, Reply=%s", - info.name, - info.model, - reply, - ) - except Exception as e: # noqa: BLE001 - logging.exception("AI timer run failed: %s", e) - - logging.info("Python timer trigger function ran at %s", utc_timestamp) +import datetime +import logging +import os +import sys +from datetime import timezone +from pathlib import Path + +import azure.functions as func + +# Add shared folder to path +sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "shared")) +from chat_providers import detect_provider # noqa: E402 + + +def main(myTimer: func.TimerRequest) -> None: + utc_timestamp = datetime.datetime.now(timezone.utc).isoformat() + if myTimer.past_due: + logging.warning("The timer is past due!") + + provider_choice = os.getenv("DEFAULT_AI_PROVIDER", "local") + prompt = os.getenv("AI_DEFAULT_PROMPT", f"Automated check-in at {utc_timestamp}") + model = os.getenv("AI_MODEL") + + try: + provider, info = detect_provider(explicit=provider_choice, model_override=model) + messages = [{"role": "user", "content": prompt}] + reply = provider.complete(messages, stream=False) + logging.info( + "AI timer run succeeded. Provider=%s, Model=%s, Reply=%s", + info.name, + info.model, + reply, + ) + except Exception as e: # noqa: BLE001 + logging.exception("AI timer run failed: %s", e) + + logging.info("Python timer trigger function ran at %s", utc_timestamp) diff --git a/functions/timer_ai_runner/function.json b/functions/timer_ai_runner/function.json index 6a6e5aa22..4ea65a7ad 100644 --- a/functions/timer_ai_runner/function.json +++ b/functions/timer_ai_runner/function.json @@ -1,10 +1,10 @@ -{ - "bindings": [ - { - "name": "myTimer", - "type": "timerTrigger", - "direction": "in", - "schedule": "0 */1 * * * *" - } - ] +{ + "bindings": [ + { + "name": "myTimer", + "type": "timerTrigger", + "direction": "in", + "schedule": "0 */1 * * * *" + } + ] } diff --git a/host.json b/host.json index d0d92fc89..3851686fc 100644 --- a/host.json +++ b/host.json @@ -1,20 +1,20 @@ -{ - "version": "2.0", - "logging": { - "applicationInsights": { - "samplingSettings": { - "isEnabled": true, - "excludedTypes": "Request" - } - } - }, - "extensionBundle": { - "id": "Microsoft.Azure.Functions.ExtensionBundle", - "version": "[4.*, 5.0.0)" - }, - "languageWorkers": { - "python": { - "defaultExecutablePath": ".\\venv\\Scripts\\python.exe" - } - } +{ + "version": "2.0", + "logging": { + "applicationInsights": { + "samplingSettings": { + "isEnabled": true, + "excludedTypes": "Request" + } + } + }, + "extensionBundle": { + "id": "Microsoft.Azure.Functions.ExtensionBundle", + "version": "[4.*, 5.0.0)" + }, + "languageWorkers": { + "python": { + "defaultExecutablePath": ".\\venv\\Scripts\\python.exe" + } + } } diff --git a/local.settings.json b/local.settings.json index abda17d23..718051bcb 100644 --- a/local.settings.json +++ b/local.settings.json @@ -1,19 +1,19 @@ -{ - "IsEncrypted": false, - "Values": { - "AzureWebJobsStorage": "UseDevelopmentStorage=true", - "FUNCTIONS_WORKER_RUNTIME": "python", - "QAI_DB_CONN": "sqlite:///data_out/qai.db", - "QAI_SQL_POOL_SIZE": "10", - "OLLAMA_BASE_URL": "http://127.0.0.1:11434/v1", - "OLLAMA_MODEL": "tinyllama", - "LMSTUDIO_BASE_URL": "http://192.168.1.153:1234/v1", - "LMSTUDIO_MODEL": "nvidia/nemotron-3-nano-4b", - "CHAT_TEMPERATURE": "0.7", - "CHAT_MAX_TOKENS": "2048", - "QAI_ENABLE_LOCAL_TTS": "true", - "QAI_ENABLE_COSMOS": "false", - "ARIA_PORT": "8080", - "ARIA_RENDER_MODE": "ue5" - } -} +{ + "IsEncrypted": false, + "Values": { + "AzureWebJobsStorage": "UseDevelopmentStorage=true", + "FUNCTIONS_WORKER_RUNTIME": "python", + "QAI_DB_CONN": "sqlite:///data_out/qai.db", + "QAI_SQL_POOL_SIZE": "10", + "OLLAMA_BASE_URL": "http://127.0.0.1:11434/v1", + "OLLAMA_MODEL": "tinyllama", + "LMSTUDIO_BASE_URL": "http://192.168.1.153:1234/v1", + "LMSTUDIO_MODEL": "nvidia/nemotron-3-nano-4b", + "CHAT_TEMPERATURE": "0.7", + "CHAT_MAX_TOKENS": "2048", + "QAI_ENABLE_LOCAL_TTS": "true", + "QAI_ENABLE_COSMOS": "false", + "ARIA_PORT": "8080", + "ARIA_RENDER_MODE": "ue5" + } +} diff --git a/local_dev_adapter.py b/local_dev_adapter.py index 829875da7..3355a6910 100644 --- a/local_dev_adapter.py +++ b/local_dev_adapter.py @@ -1,351 +1,351 @@ -""" -Local Developer Adapter for Azure Functions endpoints - -This tiny adapter lets you run selected Azure Functions handlers locally without -needing the Azure Functions Core Tools host. It's intentionally small and only -exposes the `/api/ai/status` endpoint used by the repo for health checks. - -Usage: - # Run server on port 7071 (default) - python local_dev_adapter.py - -Design notes: -- Imports the `function_app` module and calls `ai_status()` directly. -- Uses Flask when available, but falls back to a stdlib HTTP server so strict - integration smoke checks can run in minimal Python environments. -""" - -from __future__ import annotations - -import json -import logging -import sys -import types -from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer -from pathlib import Path -from typing import Any, Dict, Optional, Tuple - -try: - from flask import Flask, Response, make_response - - HAS_FLASK = True -except ModuleNotFoundError: # pragma: no cover - exercised in minimal envs - Flask = None # type: ignore[assignment] - Response = Any # type: ignore[assignment] - make_response = None # type: ignore[assignment] - HAS_FLASK = False - -# Ensure repo modules are importable when running from the repo root -repo_root = Path(__file__).resolve().parent -# Make sure common src paths are on sys.path BEFORE importing function_app -# function_app imports modules like token_utils at module-import time, so we -# must ensure those directories are available. -sys.path.insert(0, str(repo_root / "ai-projects" / "chat-cli" / "src")) -sys.path.insert(0, str(repo_root / "ai-projects" / "quantum-ml" / "src")) -sys.path.insert(0, str(repo_root / "scripts")) -sys.path.insert(0, str(repo_root)) - -logger = logging.getLogger(__name__) - -# Attempt to import the real function_app and the azure.functions HttpResponse -# type. If azure.functions isn't available in the current dev/test environment -# (common in lightweight test containers), create a minimal local shim so that -# function_app can import and its ai_status handler can be invoked. The shim -# implements only what the local adapter needs: FunctionApp decorator, simple -# HttpRequest/HttpResponse types and an AuthLevel constant. -try: - from azure.functions import HttpResponse as AzureHttpResponse - - import function_app -except ModuleNotFoundError as e: - # Provide a lightweight shim for azure.functions when it's not installed. - if "azure.functions" in str(e): - logger.debug( - "azure.functions not found; installing lightweight shim for local dev adapter" - ) - fake_mod = types.ModuleType("azure.functions") - - class AuthLevel: - ANONYMOUS = "ANONYMOUS" - - class HttpRequest: # minimal request placeholder with helpful helpers - def __init__( - self, - method: str = "GET", - url: str = "/", - params: dict | None = None, - headers: dict | None = None, - body: Any = None, - route_params: dict | None = None, - ): - self.method = method - self.url = url - self.params = params or {} - self.route_params = route_params or {} - # Normalize headers to lowercase keys for convenience - self.headers = {k.lower(): v for k, v in (headers or {}).items()} - # Normalize body to bytes internally - if isinstance(body, bytes): - self._body = body - elif isinstance(body, str): - self._body = body.encode("utf-8") - elif body is None: - self._body = b"" - else: - try: - self._body = json.dumps(body).encode("utf-8") - except Exception: - self._body = str(body).encode("utf-8") - self._json_cache = None - - def get_body(self) -> bytes: - return self._body - - def get_json(self, force: bool = False): - """Parse and return JSON body. Raises ValueError on parse failure. - - Args: - force: If True, re-parse even if cached. - """ - if self._json_cache is not None and not force: - return self._json_cache - try: - text = self._body.decode("utf-8") - parsed = json.loads(text) if text else {} - self._json_cache = parsed - return parsed - except Exception as e: - logger.debug("HttpRequest.get_json failed: %s", e) - raise ValueError("Failed to parse JSON body") from e - - class HttpResponse: - def __init__( - self, - body=b"", - status_code: int = 200, - mimetype: str | None = None, - headers: dict | None = None, - ): - # Normalize to bytes to match real azure HttpResponse.get_body() - if isinstance(body, str): - self._body = body.encode("utf-8") - elif isinstance(body, bytes): - self._body = body - else: - try: - self._body = json.dumps(body).encode("utf-8") - except Exception: - self._body = str(body).encode("utf-8") - self.status_code = status_code - self.mimetype = mimetype - self.headers = headers or {} - - def get_body(self): - return self._body - - class FunctionApp: - def __init__(self): - self._routes = [] - - def route(self, *args, **kwargs): - def decorator(fn): - # attach route metadata but otherwise return the original function - try: - fn.__qai_route__ = {"args": args, "kwargs": kwargs} - except Exception: - pass - return fn - - return decorator - - fake_mod.AuthLevel = AuthLevel - fake_mod.HttpRequest = HttpRequest - fake_mod.HttpResponse = HttpResponse - fake_mod.FunctionApp = FunctionApp - - # Insert into sys.modules so import statements in function_app succeed - sys.modules.setdefault("azure.functions", fake_mod) - - # Now import function_app and reference the shim's HttpResponse - import function_app - - AzureHttpResponse = fake_mod.HttpResponse - else: - raise - - -def _azure_response_parts( - resp: AzureHttpResponse, -) -> Tuple[bytes, int, Optional[str], Dict[str, Any]]: - """Extract body/status/mimetype/headers from azure.functions.HttpResponse.""" - body_bytes = resp.get_body() - # Ensure bytes - if not isinstance(body_bytes, (bytes, bytearray)): - try: - body_bytes = str(body_bytes).encode("utf-8") - except Exception: - body_bytes = b"" - - mimetype = getattr(resp, "mimetype", None) - headers = dict(getattr(resp, "headers", None) or {}) - if not mimetype: - content_type = headers.get("Content-Type") or headers.get("content-type") - if content_type: - mimetype = content_type - else: - # Heuristic: if body decodes to JSON, set application/json - try: - json.loads(body_bytes.decode("utf-8")) - mimetype = "application/json" - except Exception: - mimetype = None - - status_code = int(getattr(resp, "status_code", 200)) - return bytes(body_bytes), status_code, mimetype, headers - - -def _azure_to_flask(resp: AzureHttpResponse) -> Response: - """Convert an azure.functions.HttpResponse to a Flask Response.""" - body_bytes, status_code, mimetype, headers = _azure_response_parts(resp) - - flask_resp = make_response(body_bytes, status_code) - if mimetype: - flask_resp.mimetype = mimetype - - # Copy headers - try: - for k, v in headers.items(): - flask_resp.headers[k] = v - except Exception: - # best-effort fallback for unexpected header shapes - logger.debug( - "Unexpected header shape when converting azure HttpResponse to Flask Response" - ) - - return flask_resp - - -def get_ai_status_response() -> Tuple[Response, int]: - """Call the function_app.ai_status handler and return a Flask response. - - ai_status() does not depend on incoming request data so we just call it - with a lightweight HttpRequest and adapt the returned azure.functions.HttpResponse. - """ - # Provide a minimal HttpRequest instance for greater compatibility with - # handlers that expect `req` to be an azure.functions.HttpRequest. - try: - req = getattr(function_app, "HttpRequest", None) - except Exception: - req = None - - if req is None or not hasattr(req, "get_body"): - # Use shim's HttpRequest if available in sys.modules - try: - from azure.functions import \ - HttpRequest as ShimHttpRequest # type: ignore - - fake_req = ShimHttpRequest(method="GET", url="/api/ai/status") - except Exception: - fake_req = None - else: - fake_req = req(method="GET", url="/api/ai/status") - - azure_resp = function_app.ai_status(fake_req) - flask_resp = _azure_to_flask(azure_resp) - return flask_resp - - -def get_ai_status_parts() -> Tuple[bytes, int, Optional[str], Dict[str, Any]]: - """Return endpoint response components for non-Flask fallback servers.""" - try: - req = getattr(function_app, "HttpRequest", None) - except Exception: - req = None - - if req is None or not hasattr(req, "get_body"): - try: - from azure.functions import \ - HttpRequest as ShimHttpRequest # type: ignore - - fake_req = ShimHttpRequest(method="GET", url="/api/ai/status") - except Exception: - fake_req = None - else: - fake_req = req(method="GET", url="/api/ai/status") - - azure_resp = function_app.ai_status(fake_req) - return _azure_response_parts(azure_resp) - - -def create_app() -> Flask: - if not HAS_FLASK: - raise RuntimeError("Flask is not installed") - - app = Flask(__name__) - - @app.get("/api/ai/status") - def ai_status_route(): - return get_ai_status_response() - - return app - - -def run_stdlib_server(host: str = "0.0.0.0", port: int = 7071) -> None: - """Serve /api/ai/status using stdlib HTTP server (no Flask dependency).""" - - class _Handler(BaseHTTPRequestHandler): - def do_GET(self) -> None: # noqa: N802 - if self.path.split("?", 1)[0] != "/api/ai/status": - self.send_response(404) - self.send_header("Content-Type", "application/json") - self.end_headers() - self.wfile.write(b'{"error":"not found"}') - return - - try: - body, status_code, mimetype, headers = get_ai_status_parts() - except Exception as exc: # noqa: BLE001 - logger.exception("Failed to build /api/ai/status response: %s", exc) - body = json.dumps({"error": str(exc)}).encode("utf-8") - status_code = 500 - mimetype = "application/json" - headers = {} - - self.send_response(status_code) - sent_content_type = False - if mimetype: - self.send_header("Content-Type", str(mimetype)) - sent_content_type = True - - for key, value in headers.items(): - key_str = str(key) - if key_str.lower() == "content-type": - if sent_content_type: - continue - sent_content_type = True - self.send_header(key_str, str(value)) - - if not sent_content_type: - self.send_header("Content-Type", "application/json") - self.end_headers() - self.wfile.write(body) - - def log_message(self, _fmt: str, *_args: Any) -> None: - return - - server = ThreadingHTTPServer((host, port), _Handler) - logger.info("Starting stdlib local dev adapter on http://%s:%s", host, port) - try: - server.serve_forever() - finally: - server.server_close() - - -if __name__ == "__main__": - print("Starting local dev adapter for /api/ai/status on http://0.0.0.0:7071") - # Use port 7071 to match Functions local host default - if HAS_FLASK: - app = create_app() - app.run(host="0.0.0.0", port=7071, debug=False) - else: - run_stdlib_server(host="0.0.0.0", port=7071) +""" +Local Developer Adapter for Azure Functions endpoints + +This tiny adapter lets you run selected Azure Functions handlers locally without +needing the Azure Functions Core Tools host. It's intentionally small and only +exposes the `/api/ai/status` endpoint used by the repo for health checks. + +Usage: + # Run server on port 7071 (default) + python local_dev_adapter.py + +Design notes: +- Imports the `function_app` module and calls `ai_status()` directly. +- Uses Flask when available, but falls back to a stdlib HTTP server so strict + integration smoke checks can run in minimal Python environments. +""" + +from __future__ import annotations + +import json +import logging +import sys +import types +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from pathlib import Path +from typing import Any, Dict, Optional, Tuple + +try: + from flask import Flask, Response, make_response + + HAS_FLASK = True +except ModuleNotFoundError: # pragma: no cover - exercised in minimal envs + Flask = None # type: ignore[assignment] + Response = Any # type: ignore[assignment] + make_response = None # type: ignore[assignment] + HAS_FLASK = False + +# Ensure repo modules are importable when running from the repo root +repo_root = Path(__file__).resolve().parent +# Make sure common src paths are on sys.path BEFORE importing function_app +# function_app imports modules like token_utils at module-import time, so we +# must ensure those directories are available. +sys.path.insert(0, str(repo_root / "ai-projects" / "chat-cli" / "src")) +sys.path.insert(0, str(repo_root / "ai-projects" / "quantum-ml" / "src")) +sys.path.insert(0, str(repo_root / "scripts")) +sys.path.insert(0, str(repo_root)) + +logger = logging.getLogger(__name__) + +# Attempt to import the real function_app and the azure.functions HttpResponse +# type. If azure.functions isn't available in the current dev/test environment +# (common in lightweight test containers), create a minimal local shim so that +# function_app can import and its ai_status handler can be invoked. The shim +# implements only what the local adapter needs: FunctionApp decorator, simple +# HttpRequest/HttpResponse types and an AuthLevel constant. +try: + from azure.functions import HttpResponse as AzureHttpResponse + + import function_app +except ModuleNotFoundError as e: + # Provide a lightweight shim for azure.functions when it's not installed. + if "azure.functions" in str(e): + logger.debug( + "azure.functions not found; installing lightweight shim for local dev adapter" + ) + fake_mod = types.ModuleType("azure.functions") + + class AuthLevel: + ANONYMOUS = "ANONYMOUS" + + class HttpRequest: # minimal request placeholder with helpful helpers + def __init__( + self, + method: str = "GET", + url: str = "/", + params: dict | None = None, + headers: dict | None = None, + body: Any = None, + route_params: dict | None = None, + ): + self.method = method + self.url = url + self.params = params or {} + self.route_params = route_params or {} + # Normalize headers to lowercase keys for convenience + self.headers = {k.lower(): v for k, v in (headers or {}).items()} + # Normalize body to bytes internally + if isinstance(body, bytes): + self._body = body + elif isinstance(body, str): + self._body = body.encode("utf-8") + elif body is None: + self._body = b"" + else: + try: + self._body = json.dumps(body).encode("utf-8") + except Exception: + self._body = str(body).encode("utf-8") + self._json_cache = None + + def get_body(self) -> bytes: + return self._body + + def get_json(self, force: bool = False): + """Parse and return JSON body. Raises ValueError on parse failure. + + Args: + force: If True, re-parse even if cached. + """ + if self._json_cache is not None and not force: + return self._json_cache + try: + text = self._body.decode("utf-8") + parsed = json.loads(text) if text else {} + self._json_cache = parsed + return parsed + except Exception as e: + logger.debug("HttpRequest.get_json failed: %s", e) + raise ValueError("Failed to parse JSON body") from e + + class HttpResponse: + def __init__( + self, + body=b"", + status_code: int = 200, + mimetype: str | None = None, + headers: dict | None = None, + ): + # Normalize to bytes to match real azure HttpResponse.get_body() + if isinstance(body, str): + self._body = body.encode("utf-8") + elif isinstance(body, bytes): + self._body = body + else: + try: + self._body = json.dumps(body).encode("utf-8") + except Exception: + self._body = str(body).encode("utf-8") + self.status_code = status_code + self.mimetype = mimetype + self.headers = headers or {} + + def get_body(self): + return self._body + + class FunctionApp: + def __init__(self): + self._routes = [] + + def route(self, *args, **kwargs): + def decorator(fn): + # attach route metadata but otherwise return the original function + try: + fn.__qai_route__ = {"args": args, "kwargs": kwargs} + except Exception: + pass + return fn + + return decorator + + fake_mod.AuthLevel = AuthLevel + fake_mod.HttpRequest = HttpRequest + fake_mod.HttpResponse = HttpResponse + fake_mod.FunctionApp = FunctionApp + + # Insert into sys.modules so import statements in function_app succeed + sys.modules.setdefault("azure.functions", fake_mod) + + # Now import function_app and reference the shim's HttpResponse + import function_app + + AzureHttpResponse = fake_mod.HttpResponse + else: + raise + + +def _azure_response_parts( + resp: AzureHttpResponse, +) -> Tuple[bytes, int, Optional[str], Dict[str, Any]]: + """Extract body/status/mimetype/headers from azure.functions.HttpResponse.""" + body_bytes = resp.get_body() + # Ensure bytes + if not isinstance(body_bytes, (bytes, bytearray)): + try: + body_bytes = str(body_bytes).encode("utf-8") + except Exception: + body_bytes = b"" + + mimetype = getattr(resp, "mimetype", None) + headers = dict(getattr(resp, "headers", None) or {}) + if not mimetype: + content_type = headers.get("Content-Type") or headers.get("content-type") + if content_type: + mimetype = content_type + else: + # Heuristic: if body decodes to JSON, set application/json + try: + json.loads(body_bytes.decode("utf-8")) + mimetype = "application/json" + except Exception: + mimetype = None + + status_code = int(getattr(resp, "status_code", 200)) + return bytes(body_bytes), status_code, mimetype, headers + + +def _azure_to_flask(resp: AzureHttpResponse) -> Response: + """Convert an azure.functions.HttpResponse to a Flask Response.""" + body_bytes, status_code, mimetype, headers = _azure_response_parts(resp) + + flask_resp = make_response(body_bytes, status_code) + if mimetype: + flask_resp.mimetype = mimetype + + # Copy headers + try: + for k, v in headers.items(): + flask_resp.headers[k] = v + except Exception: + # best-effort fallback for unexpected header shapes + logger.debug( + "Unexpected header shape when converting azure HttpResponse to Flask Response" + ) + + return flask_resp + + +def get_ai_status_response() -> Tuple[Response, int]: + """Call the function_app.ai_status handler and return a Flask response. + + ai_status() does not depend on incoming request data so we just call it + with a lightweight HttpRequest and adapt the returned azure.functions.HttpResponse. + """ + # Provide a minimal HttpRequest instance for greater compatibility with + # handlers that expect `req` to be an azure.functions.HttpRequest. + try: + req = getattr(function_app, "HttpRequest", None) + except Exception: + req = None + + if req is None or not hasattr(req, "get_body"): + # Use shim's HttpRequest if available in sys.modules + try: + from azure.functions import \ + HttpRequest as ShimHttpRequest # type: ignore + + fake_req = ShimHttpRequest(method="GET", url="/api/ai/status") + except Exception: + fake_req = None + else: + fake_req = req(method="GET", url="/api/ai/status") + + azure_resp = function_app.ai_status(fake_req) + flask_resp = _azure_to_flask(azure_resp) + return flask_resp + + +def get_ai_status_parts() -> Tuple[bytes, int, Optional[str], Dict[str, Any]]: + """Return endpoint response components for non-Flask fallback servers.""" + try: + req = getattr(function_app, "HttpRequest", None) + except Exception: + req = None + + if req is None or not hasattr(req, "get_body"): + try: + from azure.functions import \ + HttpRequest as ShimHttpRequest # type: ignore + + fake_req = ShimHttpRequest(method="GET", url="/api/ai/status") + except Exception: + fake_req = None + else: + fake_req = req(method="GET", url="/api/ai/status") + + azure_resp = function_app.ai_status(fake_req) + return _azure_response_parts(azure_resp) + + +def create_app() -> Flask: + if not HAS_FLASK: + raise RuntimeError("Flask is not installed") + + app = Flask(__name__) + + @app.get("/api/ai/status") + def ai_status_route(): + return get_ai_status_response() + + return app + + +def run_stdlib_server(host: str = "0.0.0.0", port: int = 7071) -> None: + """Serve /api/ai/status using stdlib HTTP server (no Flask dependency).""" + + class _Handler(BaseHTTPRequestHandler): + def do_GET(self) -> None: # noqa: N802 + if self.path.split("?", 1)[0] != "/api/ai/status": + self.send_response(404) + self.send_header("Content-Type", "application/json") + self.end_headers() + self.wfile.write(b'{"error":"not found"}') + return + + try: + body, status_code, mimetype, headers = get_ai_status_parts() + except Exception as exc: # noqa: BLE001 + logger.exception("Failed to build /api/ai/status response: %s", exc) + body = json.dumps({"error": str(exc)}).encode("utf-8") + status_code = 500 + mimetype = "application/json" + headers = {} + + self.send_response(status_code) + sent_content_type = False + if mimetype: + self.send_header("Content-Type", str(mimetype)) + sent_content_type = True + + for key, value in headers.items(): + key_str = str(key) + if key_str.lower() == "content-type": + if sent_content_type: + continue + sent_content_type = True + self.send_header(key_str, str(value)) + + if not sent_content_type: + self.send_header("Content-Type", "application/json") + self.end_headers() + self.wfile.write(body) + + def log_message(self, _fmt: str, *_args: Any) -> None: + return + + server = ThreadingHTTPServer((host, port), _Handler) + logger.info("Starting stdlib local dev adapter on http://%s:%s", host, port) + try: + server.serve_forever() + finally: + server.server_close() + + +if __name__ == "__main__": + print("Starting local dev adapter for /api/ai/status on http://0.0.0.0:7071") + # Use port 7071 to match Functions local host default + if HAS_FLASK: + app = create_app() + app.run(host="0.0.0.0", port=7071, debug=False) + else: + run_stdlib_server(host="0.0.0.0", port=7071) diff --git a/lora_infer_bridge.py b/lora_infer_bridge.py index 6c3dc4b95..6d21886dc 100644 --- a/lora_infer_bridge.py +++ b/lora_infer_bridge.py @@ -1,39 +1,39 @@ -"""Compatibility shim for LoRA inference bridge imports. - -Re-exports the canonical bridge implementation from -``ai-projects/chat-cli/src/lora_infer_bridge.py``. -""" - -from __future__ import annotations - -import importlib.util -import sys -from pathlib import Path - -_CANONICAL = ( - Path(__file__).resolve().parent - / "ai-projects" - / "chat-cli" - / "src" - / "lora_infer_bridge.py" -) - -_spec = importlib.util.spec_from_file_location( - "_canonical_lora_infer_bridge_root", _CANONICAL -) -if _spec is None or _spec.loader is None: - raise ImportError(f"Unable to load canonical bridge: {_CANONICAL}") - -_mod = importlib.util.module_from_spec(_spec) -sys.modules[_spec.name] = _mod -_spec.loader.exec_module(_mod) - -for _name, _value in _mod.__dict__.items(): - if _name.startswith("__"): - continue - globals()[_name] = _value - -if hasattr(_mod, "__all__"): - __all__ = list(_mod.__all__) # type: ignore[attr-defined] -else: - __all__ = [k for k in globals() if not k.startswith("__")] +"""Compatibility shim for LoRA inference bridge imports. + +Re-exports the canonical bridge implementation from +``ai-projects/chat-cli/src/lora_infer_bridge.py``. +""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path + +_CANONICAL = ( + Path(__file__).resolve().parent + / "ai-projects" + / "chat-cli" + / "src" + / "lora_infer_bridge.py" +) + +_spec = importlib.util.spec_from_file_location( + "_canonical_lora_infer_bridge_root", _CANONICAL +) +if _spec is None or _spec.loader is None: + raise ImportError(f"Unable to load canonical bridge: {_CANONICAL}") + +_mod = importlib.util.module_from_spec(_spec) +sys.modules[_spec.name] = _mod +_spec.loader.exec_module(_mod) + +for _name, _value in _mod.__dict__.items(): + if _name.startswith("__"): + continue + globals()[_name] = _value + +if hasattr(_mod, "__all__"): + __all__ = list(_mod.__all__) # type: ignore[attr-defined] +else: + __all__ = [k for k in globals() if not k.startswith("__")] diff --git a/mount/README.md b/mount/README.md index 612b490f1..304c9a7e9 100644 --- a/mount/README.md +++ b/mount/README.md @@ -1,360 +1,360 @@ -# QAI Integration Service - -A unified microservice that integrates and orchestrates all QAI workspace components: Quantum AI, Chat systems, and Training pipelines. - -**✨ NEW: Beautiful Web UI included!** See [WEB_UI_GUIDE.md](WEB_UI_GUIDE.md) for details. - -## 🎯 Purpose - -This service provides a single REST API to: -- **Quantum AI**: Train quantum classifiers, manage backends, run autorun jobs -- **Chat**: Interface with multiple providers (local, Azure OpenAI, LoRA), manage conversations -- **Training**: Run LoRA training, orchestrate autotrain jobs, monitor training runs - -## 🚀 Quick Start - -### Installation - -```powershell -cd mount -python -m venv venv -.\venv\Scripts\Activate.ps1 -pip install -r requirements.txt -``` - -### Configuration - -Edit `config.yaml` to customize: -- Service settings (host, port, debug mode) -- Paths to other QAI components -- Enable/disable specific integrations -- API settings (CORS, rate limiting) - -### Run the Service - -```powershell -# Quick start with web UI -.\start.ps1 - -# Or manual start -python app.py - -# Production mode -uvicorn app:app --host 0.0.0.0 --port 8000 -``` - -**Access the Web UI**: Open http://localhost:8000 in your browser - -**Access the API docs**: http://localhost:8000/docs - -## 📚 API Documentation - -Once running, visit: -- **🎨 Web UI**: (Main interface) -- **Interactive API docs**: -- **ReDoc**: -- **OpenAPI JSON**: - -**Prefer the Web UI?** See the complete [Web UI Guide](WEB_UI_GUIDE.md) - -## 🔌 Endpoints - -### Root & Health - -- `GET /` - Service information -- `GET /health` - Health check -- `GET /status` - Comprehensive system status - -### Quantum AI (`/quantum/*`) - -- `GET /quantum/status` - Quantum system status -- `GET /quantum/datasets` - List quantum datasets -- `GET /quantum/backends` - List available backends -- `GET /quantum/circuit-info` - Circuit type information -- `POST /quantum/train` - Train quantum classifier -- `POST /quantum/autorun` - Run quantum autorun job - -Example: -```bash -curl -X POST http://localhost:8000/quantum/train \ - -H "Content-Type: application/json" \ - -d '{ - "dataset": "heart", - "n_qubits": 4, - "epochs": 10, - "backend": "qiskit_aer" - }' -``` - -### Chat (`/chat/*`) - -- `GET /chat/status` - Chat system status -- `GET /chat/providers` - Available providers and their status -- `GET /chat/detect-provider` - Auto-detect best provider -- `POST /chat/message` - Send message and get response -- `GET /chat/conversations` - List saved conversations -- `GET /chat/conversations/{filename}` - Get specific conversation - -Example: -```bash -curl -X POST http://localhost:8000/chat/message \ - -H "Content-Type: application/json" \ - -d '{ - "message": "Hello, how are you?", - "provider": "local" - }' -``` - -### Training (`/training/*`) - -- `GET /training/status` - Training system status -- `GET /training/datasets` - List available datasets -- `GET /training/lora-adapter` - LoRA adapter info -- `GET /training/runs` - List training runs -- `GET /training/runs/{run_name}` - Get specific run metrics -- `GET /training/autotrain/jobs` - List autotrain jobs -- `POST /training/lora` - Train LoRA adapter -- `POST /training/autotrain` - Run autotrain orchestrator - -Example: -```bash -curl -X POST http://localhost:8000/training/lora \ - -H "Content-Type: application/json" \ - -d '{ - "dataset": "../../datasets/chat/dolly", - "max_train_samples": 64, - "epochs": 1 - }' -``` - -## 🏗️ Architecture - -``` -mount/ -├── app.py # FastAPI application -├── config.yaml # Configuration file -├── quantum_integration.py # Quantum AI integration -├── chat_integration.py # Chat integration -├── training_integration.py # Training integration -└── requirements.txt # Python dependencies -``` - -### Integration Modules - -Each integration module provides: -- **Status checking**: Current state and availability -- **Operation execution**: Run jobs, train models, send messages -- **Data retrieval**: List datasets, conversations, results -- **Configuration**: Load and validate settings - -### Design Patterns - -- **Async/await**: Non-blocking operations -- **Background tasks**: Long-running operations (training, etc.) -- **Subprocess execution**: Isolated execution of existing scripts -- **CORS support**: Cross-origin requests for web frontends -- **Pydantic validation**: Type-safe request/response models - -## 🔧 Configuration - -### Service Settings - -```yaml -service: - name: qai-integration-service - version: 0.1.0 - host: 0.0.0.0 - port: 8000 - debug: true -``` - -### Path Configuration - -All paths are relative to the workspace root: - -```yaml -paths: - workspace_root: .. - quantum_ai: ../quantum-ai - talk_to_ai: ../talk-to-ai - phi_training: ../AI/microsoft_phi-silica-3.6_v1 - datasets: ../datasets - data_out: ../data_out -``` - -### Feature Flags - -Enable/disable integrations: - -```yaml -quantum: - enabled: true -chat: - enabled: true -training: - enabled: true -``` - -## 💡 Usage Examples - -### Check Overall Status - -```powershell -curl http://localhost:8000/status -``` - -### Train Quantum Classifier - -```powershell -$body = @{ - dataset = "heart" - n_qubits = 4 - epochs = 10 - backend = "qiskit_aer" -} | ConvertTo-Json - -Invoke-RestMethod -Uri http://localhost:8000/quantum/train -Method Post -Body $body -ContentType "application/json" -``` - -### Chat with Local Provider - -```powershell -$body = @{ - message = "What is quantum computing?" - provider = "local" -} | ConvertTo-Json - -Invoke-RestMethod -Uri http://localhost:8000/chat/message -Method Post -Body $body -ContentType "application/json" -``` - -### Run AutoTrain Job - -```powershell -$body = @{ - job_name = "phi36_mixed_chat" - dry_run = $false -} | ConvertTo-Json - -Invoke-RestMethod -Uri http://localhost:8000/training/autotrain -Method Post -Body $body -ContentType "application/json" -``` - -## 🐛 Debugging - -### Enable Debug Logging - -Set `debug: true` in `config.yaml` or run with: - -```powershell -$env:LOG_LEVEL = "DEBUG" -python app.py -``` - -### Check Integration Status - -Each integration can be checked individually: - -```powershell -curl http://localhost:8000/quantum/status -curl http://localhost:8000/chat/status -curl http://localhost:8000/training/status -``` - -## 🔐 Security Notes - -- **Local development**: Default configuration is for local development only -- **Production**: Update CORS origins, enable rate limiting, add authentication -- **Environment variables**: Store API keys in environment, not config files -- **Network exposure**: Bind to `127.0.0.1` instead of `0.0.0.0` for local-only access - -## 🚦 Testing - -### Manual Testing - -Use the interactive Swagger UI at http://localhost:8000/docs - -### Automated Testing - -```powershell -# Create test script -pytest tests/test_integration_service.py -``` - -### Health Check - -```powershell -# Simple health check -curl http://localhost:8000/health - -# Should return: {"status": "healthy", ...} -``` - -## 📈 Monitoring - -### Status Endpoint - -The `/status` endpoint provides comprehensive information: - -```json -{ - "service": "qai-integration-service", - "version": "0.1.0", - "quantum": { - "enabled": true, - "backend": "qiskit_aer", - "azure_connected": false, - "available_backends": ["qiskit_aer", "lightning.qubit"], - "recent_results": [...] - }, - "chat": { - "enabled": true, - "default_provider": "local", - "providers": {...} - }, - "training": { - "enabled": true, - "orchestrators": {...}, - "lora_adapter": {...} - } -} -``` - -## 🛠️ Development - -### Adding New Endpoints - -1. Add method to appropriate integration module -2. Create Pydantic models for request/response -3. Add route to `app.py` -4. Update this README - -### Adding New Integration - -1. Create new `*_integration.py` module -2. Implement `get_status()` method -3. Add to `config.yaml` -4. Import and initialize in `app.py` -5. Add routes - -## 📝 Future Enhancements - -- [ ] WebSocket support for streaming responses -- [ ] Authentication and authorization -- [ ] Rate limiting per endpoint -- [ ] Metrics collection (Prometheus) -- [ ] Job queuing system (Celery/Redis) -- [ ] Docker containerization -- [ ] Azure deployment templates -- [ ] Integration tests - -## 🤝 Contributing - -Follow the QAI workspace patterns: -- YAML-driven configuration -- Async operations where possible -- Comprehensive error handling -- Clear logging -- Type hints and Pydantic models - -## 📄 License - -Part of the QAI workspace. See root LICENSE file. +# QAI Integration Service + +A unified microservice that integrates and orchestrates all QAI workspace components: Quantum AI, Chat systems, and Training pipelines. + +**✨ NEW: Beautiful Web UI included!** See [WEB_UI_GUIDE.md](WEB_UI_GUIDE.md) for details. + +## 🎯 Purpose + +This service provides a single REST API to: +- **Quantum AI**: Train quantum classifiers, manage backends, run autorun jobs +- **Chat**: Interface with multiple providers (local, Azure OpenAI, LoRA), manage conversations +- **Training**: Run LoRA training, orchestrate autotrain jobs, monitor training runs + +## 🚀 Quick Start + +### Installation + +```powershell +cd mount +python -m venv venv +.\venv\Scripts\Activate.ps1 +pip install -r requirements.txt +``` + +### Configuration + +Edit `config.yaml` to customize: +- Service settings (host, port, debug mode) +- Paths to other QAI components +- Enable/disable specific integrations +- API settings (CORS, rate limiting) + +### Run the Service + +```powershell +# Quick start with web UI +.\start.ps1 + +# Or manual start +python app.py + +# Production mode +uvicorn app:app --host 0.0.0.0 --port 8000 +``` + +**Access the Web UI**: Open http://localhost:8000 in your browser + +**Access the API docs**: http://localhost:8000/docs + +## 📚 API Documentation + +Once running, visit: +- **🎨 Web UI**: (Main interface) +- **Interactive API docs**: +- **ReDoc**: +- **OpenAPI JSON**: + +**Prefer the Web UI?** See the complete [Web UI Guide](WEB_UI_GUIDE.md) + +## 🔌 Endpoints + +### Root & Health + +- `GET /` - Service information +- `GET /health` - Health check +- `GET /status` - Comprehensive system status + +### Quantum AI (`/quantum/*`) + +- `GET /quantum/status` - Quantum system status +- `GET /quantum/datasets` - List quantum datasets +- `GET /quantum/backends` - List available backends +- `GET /quantum/circuit-info` - Circuit type information +- `POST /quantum/train` - Train quantum classifier +- `POST /quantum/autorun` - Run quantum autorun job + +Example: +```bash +curl -X POST http://localhost:8000/quantum/train \ + -H "Content-Type: application/json" \ + -d '{ + "dataset": "heart", + "n_qubits": 4, + "epochs": 10, + "backend": "qiskit_aer" + }' +``` + +### Chat (`/chat/*`) + +- `GET /chat/status` - Chat system status +- `GET /chat/providers` - Available providers and their status +- `GET /chat/detect-provider` - Auto-detect best provider +- `POST /chat/message` - Send message and get response +- `GET /chat/conversations` - List saved conversations +- `GET /chat/conversations/{filename}` - Get specific conversation + +Example: +```bash +curl -X POST http://localhost:8000/chat/message \ + -H "Content-Type: application/json" \ + -d '{ + "message": "Hello, how are you?", + "provider": "local" + }' +``` + +### Training (`/training/*`) + +- `GET /training/status` - Training system status +- `GET /training/datasets` - List available datasets +- `GET /training/lora-adapter` - LoRA adapter info +- `GET /training/runs` - List training runs +- `GET /training/runs/{run_name}` - Get specific run metrics +- `GET /training/autotrain/jobs` - List autotrain jobs +- `POST /training/lora` - Train LoRA adapter +- `POST /training/autotrain` - Run autotrain orchestrator + +Example: +```bash +curl -X POST http://localhost:8000/training/lora \ + -H "Content-Type: application/json" \ + -d '{ + "dataset": "../../datasets/chat/dolly", + "max_train_samples": 64, + "epochs": 1 + }' +``` + +## 🏗️ Architecture + +``` +mount/ +├── app.py # FastAPI application +├── config.yaml # Configuration file +├── quantum_integration.py # Quantum AI integration +├── chat_integration.py # Chat integration +├── training_integration.py # Training integration +└── requirements.txt # Python dependencies +``` + +### Integration Modules + +Each integration module provides: +- **Status checking**: Current state and availability +- **Operation execution**: Run jobs, train models, send messages +- **Data retrieval**: List datasets, conversations, results +- **Configuration**: Load and validate settings + +### Design Patterns + +- **Async/await**: Non-blocking operations +- **Background tasks**: Long-running operations (training, etc.) +- **Subprocess execution**: Isolated execution of existing scripts +- **CORS support**: Cross-origin requests for web frontends +- **Pydantic validation**: Type-safe request/response models + +## 🔧 Configuration + +### Service Settings + +```yaml +service: + name: qai-integration-service + version: 0.1.0 + host: 0.0.0.0 + port: 8000 + debug: true +``` + +### Path Configuration + +All paths are relative to the workspace root: + +```yaml +paths: + workspace_root: .. + quantum_ai: ../quantum-ai + talk_to_ai: ../talk-to-ai + phi_training: ../AI/microsoft_phi-silica-3.6_v1 + datasets: ../datasets + data_out: ../data_out +``` + +### Feature Flags + +Enable/disable integrations: + +```yaml +quantum: + enabled: true +chat: + enabled: true +training: + enabled: true +``` + +## 💡 Usage Examples + +### Check Overall Status + +```powershell +curl http://localhost:8000/status +``` + +### Train Quantum Classifier + +```powershell +$body = @{ + dataset = "heart" + n_qubits = 4 + epochs = 10 + backend = "qiskit_aer" +} | ConvertTo-Json + +Invoke-RestMethod -Uri http://localhost:8000/quantum/train -Method Post -Body $body -ContentType "application/json" +``` + +### Chat with Local Provider + +```powershell +$body = @{ + message = "What is quantum computing?" + provider = "local" +} | ConvertTo-Json + +Invoke-RestMethod -Uri http://localhost:8000/chat/message -Method Post -Body $body -ContentType "application/json" +``` + +### Run AutoTrain Job + +```powershell +$body = @{ + job_name = "phi36_mixed_chat" + dry_run = $false +} | ConvertTo-Json + +Invoke-RestMethod -Uri http://localhost:8000/training/autotrain -Method Post -Body $body -ContentType "application/json" +``` + +## 🐛 Debugging + +### Enable Debug Logging + +Set `debug: true` in `config.yaml` or run with: + +```powershell +$env:LOG_LEVEL = "DEBUG" +python app.py +``` + +### Check Integration Status + +Each integration can be checked individually: + +```powershell +curl http://localhost:8000/quantum/status +curl http://localhost:8000/chat/status +curl http://localhost:8000/training/status +``` + +## 🔐 Security Notes + +- **Local development**: Default configuration is for local development only +- **Production**: Update CORS origins, enable rate limiting, add authentication +- **Environment variables**: Store API keys in environment, not config files +- **Network exposure**: Bind to `127.0.0.1` instead of `0.0.0.0` for local-only access + +## 🚦 Testing + +### Manual Testing + +Use the interactive Swagger UI at http://localhost:8000/docs + +### Automated Testing + +```powershell +# Create test script +pytest tests/test_integration_service.py +``` + +### Health Check + +```powershell +# Simple health check +curl http://localhost:8000/health + +# Should return: {"status": "healthy", ...} +``` + +## 📈 Monitoring + +### Status Endpoint + +The `/status` endpoint provides comprehensive information: + +```json +{ + "service": "qai-integration-service", + "version": "0.1.0", + "quantum": { + "enabled": true, + "backend": "qiskit_aer", + "azure_connected": false, + "available_backends": ["qiskit_aer", "lightning.qubit"], + "recent_results": [...] + }, + "chat": { + "enabled": true, + "default_provider": "local", + "providers": {...} + }, + "training": { + "enabled": true, + "orchestrators": {...}, + "lora_adapter": {...} + } +} +``` + +## 🛠️ Development + +### Adding New Endpoints + +1. Add method to appropriate integration module +2. Create Pydantic models for request/response +3. Add route to `app.py` +4. Update this README + +### Adding New Integration + +1. Create new `*_integration.py` module +2. Implement `get_status()` method +3. Add to `config.yaml` +4. Import and initialize in `app.py` +5. Add routes + +## 📝 Future Enhancements + +- [ ] WebSocket support for streaming responses +- [ ] Authentication and authorization +- [ ] Rate limiting per endpoint +- [ ] Metrics collection (Prometheus) +- [ ] Job queuing system (Celery/Redis) +- [ ] Docker containerization +- [ ] Azure deployment templates +- [ ] Integration tests + +## 🤝 Contributing + +Follow the QAI workspace patterns: +- YAML-driven configuration +- Async operations where possible +- Comprehensive error handling +- Clear logging +- Type hints and Pydantic models + +## 📄 License + +Part of the QAI workspace. See root LICENSE file. diff --git a/mount/SETUP_COMPLETE.md b/mount/SETUP_COMPLETE.md index 5e2286fb9..bcbfdde06 100644 --- a/mount/SETUP_COMPLETE.md +++ b/mount/SETUP_COMPLETE.md @@ -1,229 +1,229 @@ -# 🎉 QAI Web UI - Complete! - -## ✨ What We Built - -A beautiful, full-featured web application to control your entire QAI workspace! - -### 📁 Structure - -``` -mount/ -├── app.py # FastAPI backend with web UI support -├── config.yaml # Configuration -├── requirements.txt # Python dependencies -│ -├── quantum_integration.py # Quantum AI module -├── chat_integration.py # Chat systems module -├── training_integration.py # Training module -│ -├── static/ # Web UI files -│ ├── index.html # Main interface (5 tabs) -│ ├── styles.css # Beautiful styling -│ └── app.js # Frontend logic -│ -├── start.ps1 # Quick start script -├── start.bat # Windows batch start -├── launch.ps1 # Auto-open browser -├── test-service.ps1 # API test script -│ -├── README.md # Full documentation -├── WEB_UI_GUIDE.md # Web UI guide -└── SETUP_COMPLETE.md # This file! -``` - -## 🚀 Quick Start - -### Option 1: Full Launch (Recommended) -```powershell -cd mount -.\launch.ps1 -``` -This will: -- ✓ Check if service is running -- ✓ Start service if needed -- ✓ Wait for it to be ready -- ✓ Open your browser automatically - -### Option 2: Manual Start -```powershell -cd mount -.\start.ps1 -# Then open http://localhost:8000 -``` - -### Option 3: Development Mode -```powershell -cd mount -python app.py -``` - -## 🎨 Features - -### Dashboard Tab 📊 -- System health overview -- Quick action buttons -- Recent activity feed -- Status indicators - -### Quantum AI Tab ⚛️ -- Train quantum classifiers -- Select datasets (heart, ionosphere, sonar, banknote) -- Configure qubits, layers, epochs -- Choose backend (Qiskit Aer, Lightning, Azure) -- View training results -- Run AutoRun jobs - -### Chat Tab 💬 -- Interactive chat interface -- Multiple providers: - - 🆓 Local (always available) - - ☁️ Azure OpenAI - - 🤖 OpenAI - - 🎯 LoRA (your trained model) -- Auto-detect best provider -- Save conversations - -### Training Tab 🎓 -- Train LoRA adapters -- Select chat datasets -- Configure training parameters -- Run AutoTrain orchestrator -- View training status -- Monitor LoRA adapter - -### Logs Tab 📝 -- Real-time system logs -- Color-coded by level -- Clear and refresh controls - -## 🧪 Test It - -```powershell -cd mount -.\test-service.ps1 -``` - -This tests all API endpoints to ensure everything works. - -## 📚 Documentation - -- **Web UI Guide**: See `WEB_UI_GUIDE.md` -- **API Documentation**: See `README.md` -- **Interactive Docs**: http://localhost:8000/docs (when running) - -## 🎯 Example Workflows - -### Train a Quantum Model -1. Launch: `.\launch.ps1` -2. Go to **⚛️ Quantum AI** tab -3. Select "heart" dataset -4. Keep defaults (4 qubits, 2 layers, 10 epochs) -5. Click **🚀 Start Training** -6. Watch logs for progress - -### Chat with Local AI -1. Launch: `.\launch.ps1` -2. Go to **💬 Chat** tab -3. Provider is already "Local" (free!) -4. Type: "What is quantum computing?" -5. Press Enter -6. Get instant response! - -### Train a LoRA Adapter -1. Launch: `.\launch.ps1` -2. Go to **🎓 Training** tab -3. Select "dolly" dataset -4. Set samples to 64 (quick test) -5. Click **🚀 Start Training** -6. Check logs for progress -7. Use in Chat tab when done! - -## 🎨 UI Highlights - -- **Modern Design**: Gradient purple theme -- **Responsive**: Works on mobile/tablet/desktop -- **Smooth Animations**: Tab transitions, button effects -- **Visual Feedback**: Color-coded status, loading states -- **Auto-refresh**: Status updates every 30s -- **Real-time Logs**: See everything that happens - -## 🔧 Configuration - -Edit `config.yaml` to customize: -- Server host/port -- Enable/disable features -- CORS settings -- Paths to components - -## 💡 Pro Tips - -1. **Keep Logs Open**: Open in another window to monitor -2. **Use Quick Actions**: Dashboard buttons jump to right tab -3. **Auto-detect Provider**: Let system choose best chat provider -4. **Dry Run First**: Test orchestrator jobs before running -5. **Start Small**: Use 64 samples for quick training tests - -## 🌟 What Makes It Special - -### For Quantum AI: -- Visual interface for complex quantum operations -- Easy dataset selection and parameter tuning -- Real-time result monitoring -- No command-line needed! - -### For Chat: -- Beautiful chat interface like modern apps -- Seamless provider switching -- Works offline (local provider) -- Conversation history - -### For Training: -- Simplified LoRA training workflow -- Orchestrator job management -- Dataset browsing -- Progress monitoring - -## 🚧 Next Steps - -1. **Try It Out**: Run `.\launch.ps1` and explore! -2. **Train Something**: Start with quantum heart dataset -3. **Chat**: Try the local provider (always works) -4. **Customize**: Edit colors in `styles.css` -5. **Extend**: Add custom endpoints to `app.py` - -## 📖 Learn More - -- **Backend API**: Full REST API at `/docs` -- **Integration Modules**: See Python files -- **Configuration**: Check `config.yaml` -- **Troubleshooting**: See `WEB_UI_GUIDE.md` - -## 🎉 You're Ready! - -Everything is set up and ready to use. Just run: - -```powershell -cd mount -.\launch.ps1 -``` - -And your browser will open to the QAI Control Center! - ---- - -**Built with**: -- FastAPI (Backend) -- Vanilla JavaScript (Frontend) -- CSS3 (Styling) -- Python Integration Modules - -**Features**: -- 20+ API endpoints -- 5 interactive tabs -- Real-time updates -- Beautiful UI -- Complete integration - -**Status**: ✅ Production Ready - -Enjoy your QAI Control Center! 🚀 +# 🎉 QAI Web UI - Complete! + +## ✨ What We Built + +A beautiful, full-featured web application to control your entire QAI workspace! + +### 📁 Structure + +``` +mount/ +├── app.py # FastAPI backend with web UI support +├── config.yaml # Configuration +├── requirements.txt # Python dependencies +│ +├── quantum_integration.py # Quantum AI module +├── chat_integration.py # Chat systems module +├── training_integration.py # Training module +│ +├── static/ # Web UI files +│ ├── index.html # Main interface (5 tabs) +│ ├── styles.css # Beautiful styling +│ └── app.js # Frontend logic +│ +├── start.ps1 # Quick start script +├── start.bat # Windows batch start +├── launch.ps1 # Auto-open browser +├── test-service.ps1 # API test script +│ +├── README.md # Full documentation +├── WEB_UI_GUIDE.md # Web UI guide +└── SETUP_COMPLETE.md # This file! +``` + +## 🚀 Quick Start + +### Option 1: Full Launch (Recommended) +```powershell +cd mount +.\launch.ps1 +``` +This will: +- ✓ Check if service is running +- ✓ Start service if needed +- ✓ Wait for it to be ready +- ✓ Open your browser automatically + +### Option 2: Manual Start +```powershell +cd mount +.\start.ps1 +# Then open http://localhost:8000 +``` + +### Option 3: Development Mode +```powershell +cd mount +python app.py +``` + +## 🎨 Features + +### Dashboard Tab 📊 +- System health overview +- Quick action buttons +- Recent activity feed +- Status indicators + +### Quantum AI Tab ⚛️ +- Train quantum classifiers +- Select datasets (heart, ionosphere, sonar, banknote) +- Configure qubits, layers, epochs +- Choose backend (Qiskit Aer, Lightning, Azure) +- View training results +- Run AutoRun jobs + +### Chat Tab 💬 +- Interactive chat interface +- Multiple providers: + - 🆓 Local (always available) + - ☁️ Azure OpenAI + - 🤖 OpenAI + - 🎯 LoRA (your trained model) +- Auto-detect best provider +- Save conversations + +### Training Tab 🎓 +- Train LoRA adapters +- Select chat datasets +- Configure training parameters +- Run AutoTrain orchestrator +- View training status +- Monitor LoRA adapter + +### Logs Tab 📝 +- Real-time system logs +- Color-coded by level +- Clear and refresh controls + +## 🧪 Test It + +```powershell +cd mount +.\test-service.ps1 +``` + +This tests all API endpoints to ensure everything works. + +## 📚 Documentation + +- **Web UI Guide**: See `WEB_UI_GUIDE.md` +- **API Documentation**: See `README.md` +- **Interactive Docs**: http://localhost:8000/docs (when running) + +## 🎯 Example Workflows + +### Train a Quantum Model +1. Launch: `.\launch.ps1` +2. Go to **⚛️ Quantum AI** tab +3. Select "heart" dataset +4. Keep defaults (4 qubits, 2 layers, 10 epochs) +5. Click **🚀 Start Training** +6. Watch logs for progress + +### Chat with Local AI +1. Launch: `.\launch.ps1` +2. Go to **💬 Chat** tab +3. Provider is already "Local" (free!) +4. Type: "What is quantum computing?" +5. Press Enter +6. Get instant response! + +### Train a LoRA Adapter +1. Launch: `.\launch.ps1` +2. Go to **🎓 Training** tab +3. Select "dolly" dataset +4. Set samples to 64 (quick test) +5. Click **🚀 Start Training** +6. Check logs for progress +7. Use in Chat tab when done! + +## 🎨 UI Highlights + +- **Modern Design**: Gradient purple theme +- **Responsive**: Works on mobile/tablet/desktop +- **Smooth Animations**: Tab transitions, button effects +- **Visual Feedback**: Color-coded status, loading states +- **Auto-refresh**: Status updates every 30s +- **Real-time Logs**: See everything that happens + +## 🔧 Configuration + +Edit `config.yaml` to customize: +- Server host/port +- Enable/disable features +- CORS settings +- Paths to components + +## 💡 Pro Tips + +1. **Keep Logs Open**: Open in another window to monitor +2. **Use Quick Actions**: Dashboard buttons jump to right tab +3. **Auto-detect Provider**: Let system choose best chat provider +4. **Dry Run First**: Test orchestrator jobs before running +5. **Start Small**: Use 64 samples for quick training tests + +## 🌟 What Makes It Special + +### For Quantum AI: +- Visual interface for complex quantum operations +- Easy dataset selection and parameter tuning +- Real-time result monitoring +- No command-line needed! + +### For Chat: +- Beautiful chat interface like modern apps +- Seamless provider switching +- Works offline (local provider) +- Conversation history + +### For Training: +- Simplified LoRA training workflow +- Orchestrator job management +- Dataset browsing +- Progress monitoring + +## 🚧 Next Steps + +1. **Try It Out**: Run `.\launch.ps1` and explore! +2. **Train Something**: Start with quantum heart dataset +3. **Chat**: Try the local provider (always works) +4. **Customize**: Edit colors in `styles.css` +5. **Extend**: Add custom endpoints to `app.py` + +## 📖 Learn More + +- **Backend API**: Full REST API at `/docs` +- **Integration Modules**: See Python files +- **Configuration**: Check `config.yaml` +- **Troubleshooting**: See `WEB_UI_GUIDE.md` + +## 🎉 You're Ready! + +Everything is set up and ready to use. Just run: + +```powershell +cd mount +.\launch.ps1 +``` + +And your browser will open to the QAI Control Center! + +--- + +**Built with**: +- FastAPI (Backend) +- Vanilla JavaScript (Frontend) +- CSS3 (Styling) +- Python Integration Modules + +**Features**: +- 20+ API endpoints +- 5 interactive tabs +- Real-time updates +- Beautiful UI +- Complete integration + +**Status**: ✅ Production Ready + +Enjoy your QAI Control Center! 🚀 diff --git a/mount/START_HERE.md b/mount/START_HERE.md index 525588d6a..f009ac9a3 100644 --- a/mount/START_HERE.md +++ b/mount/START_HERE.md @@ -1,222 +1,222 @@ -# 🎉 QAI Web UI Setup Complete! - -## What You Got - -A **complete web-based control center** for your entire QAI workspace! - -### 📦 Package Contents - -✅ **Backend API** (FastAPI) -- 20+ REST endpoints -- Integration with quantum, chat, and training systems -- Auto-serves web UI - -✅ **Beautiful Web Interface** -- 5 interactive tabs (Dashboard, Quantum, Chat, Training, Logs) -- Real-time status updates -- Modern gradient design -- Responsive (works on all devices) - -✅ **Integration Modules** -- `quantum_integration.py` - Quantum AI operations -- `chat_integration.py` - Multi-provider chat -- `training_integration.py` - LoRA training & orchestration - -✅ **Helper Scripts** -- `launch.ps1` - One-click start + browser open -- `start.ps1` - Quick start -- `test-service.ps1` - API tests - -✅ **Documentation** -- `README.md` - Complete API docs -- `WEB_UI_GUIDE.md` - Web UI guide -- `SETUP_COMPLETE.md` - Feature overview - -## 🚀 How to Launch - -**Super simple - one command:** - -```powershell -cd mount -.\launch.ps1 -``` - -That's it! Your browser opens automatically to the control center. - -## 🎯 What You Can Do - -### 1️⃣ Train Quantum Models -- Pick a dataset (heart, ionosphere, sonar, banknote) -- Configure qubits and layers -- Watch training in real-time -- See accuracy results - -### 2️⃣ Chat with AI -- Use local provider (free, always works) -- Or use Azure/OpenAI (with API keys) -- Or use your trained LoRA model -- Save conversation history - -### 3️⃣ Train LoRA Adapters -- Select chat datasets -- Configure training parameters -- Monitor progress -- Use trained model in chat - -### 4️⃣ Run Orchestrators -- Quantum AutoRun jobs -- AutoTrain jobs -- Dry-run validation -- Status monitoring - -### 5️⃣ Monitor Everything -- System health dashboard -- Real-time logs -- Recent activity feed -- Provider status - -## 📁 File Structure - -``` -mount/ -├── 🚀 launch.ps1 ← Start here! -├── ⚙️ app.py (FastAPI backend) -├── 📝 config.yaml (Configuration) -├── 📦 requirements.txt (Dependencies) -│ -├── 🧩 Integration Modules: -│ ├── quantum_integration.py -│ ├── chat_integration.py -│ └── training_integration.py -│ -├── 🎨 Web UI: -│ └── static/ -│ ├── index.html -│ ├── styles.css -│ └── app.js -│ -└── 📚 Documentation: - ├── README.md - ├── WEB_UI_GUIDE.md - └── SETUP_COMPLETE.md -``` - -## 🎨 UI Preview - -**Dashboard**: System overview + quick actions -**Quantum AI**: Train models visually -**Chat**: Beautiful chat interface -**Training**: LoRA training + orchestration -**Logs**: Real-time activity monitoring - -All with a modern purple gradient theme! - -## 💡 First Steps - -1. **Launch it**: `cd mount; .\launch.ps1` -2. **Try Quantum**: Go to Quantum tab, select "heart" dataset, train -3. **Try Chat**: Go to Chat tab, ask anything (uses free local provider) -4. **Explore**: Click around, everything is documented in tooltips - -## 🔧 Tech Stack - -- **Backend**: FastAPI + Python 3.10+ -- **Frontend**: Vanilla JavaScript + CSS3 -- **Integration**: Direct imports from quantum-ai, talk-to-ai, phi-training -- **API**: REST with CORS support -- **UI**: Responsive, mobile-friendly - -## 📊 Capabilities - -| Feature | Status | Notes | -|---------|--------|-------| -| Quantum Training | ✅ Ready | All datasets available | -| Chat (Local) | ✅ Ready | Always available, free | -| Chat (Cloud) | ✅ Ready | Requires API keys | -| LoRA Training | ✅ Ready | CPU/GPU supported | -| AutoRun Jobs | ✅ Ready | Quantum orchestration | -| AutoTrain Jobs | ✅ Ready | Training orchestration | -| Web UI | ✅ Ready | 5 full-featured tabs | -| API Docs | ✅ Ready | Swagger + ReDoc | -| Real-time Logs | ✅ Ready | Color-coded | - -## 🎓 Learning Path - -**Beginner** (5 minutes): -1. Launch the app -2. Browse the dashboard -3. Try local chat - -**Intermediate** (15 minutes): -1. Train a quantum model (heart dataset, default settings) -2. Check results -3. Try different datasets - -**Advanced** (30+ minutes): -1. Train a LoRA adapter -2. Use it in chat -3. Run orchestrator jobs -4. Customize configuration - -## 🌟 Highlights - -**🆓 Free Local Operations**: -- Local chat provider (offline-capable) -- Quantum training on simulators -- All features work without cloud services - -**⚡ Fast & Responsive**: -- Real-time updates -- Auto-refresh (30s intervals) -- Instant feedback - -**🎨 Beautiful Design**: -- Modern gradient interface -- Smooth animations -- Color-coded status -- Mobile-friendly - -**🔧 Fully Integrated**: -- Direct access to all QAI components -- Unified API -- Consistent experience - -## 📚 Resources - -- **Quick Start**: See `WEB_UI_GUIDE.md` -- **API Reference**: See `README.md` -- **API Browser**: http://localhost:8000/docs (when running) -- **Main Workspace Guide**: See `../copilot-instructions.md` - -## 🎯 Next Actions - -1. **Launch**: Run `.\launch.ps1` -2. **Explore**: Try all 5 tabs -3. **Train**: Run a quick quantum training -4. **Chat**: Test the local provider -5. **Customize**: Edit `config.yaml` or `static/styles.css` - -## 💬 Support - -All features are documented: -- Hover over UI elements for tooltips -- Check the Logs tab for detailed output -- See WEB_UI_GUIDE.md for troubleshooting -- Use /docs endpoint for API reference - -## ✅ Ready to Go! - -Everything is set up and tested. Just run: - -```powershell -cd C:\Users\Bryan\OneDrive\AI\mount -.\launch.ps1 -``` - -Your QAI Control Center will open in your browser! - ---- - -**🎉 Congratulations! You now have a full-featured web UI for your AI workspace!** - -Enjoy exploring all the capabilities! 🚀 +# 🎉 QAI Web UI Setup Complete! + +## What You Got + +A **complete web-based control center** for your entire QAI workspace! + +### 📦 Package Contents + +✅ **Backend API** (FastAPI) +- 20+ REST endpoints +- Integration with quantum, chat, and training systems +- Auto-serves web UI + +✅ **Beautiful Web Interface** +- 5 interactive tabs (Dashboard, Quantum, Chat, Training, Logs) +- Real-time status updates +- Modern gradient design +- Responsive (works on all devices) + +✅ **Integration Modules** +- `quantum_integration.py` - Quantum AI operations +- `chat_integration.py` - Multi-provider chat +- `training_integration.py` - LoRA training & orchestration + +✅ **Helper Scripts** +- `launch.ps1` - One-click start + browser open +- `start.ps1` - Quick start +- `test-service.ps1` - API tests + +✅ **Documentation** +- `README.md` - Complete API docs +- `WEB_UI_GUIDE.md` - Web UI guide +- `SETUP_COMPLETE.md` - Feature overview + +## 🚀 How to Launch + +**Super simple - one command:** + +```powershell +cd mount +.\launch.ps1 +``` + +That's it! Your browser opens automatically to the control center. + +## 🎯 What You Can Do + +### 1️⃣ Train Quantum Models +- Pick a dataset (heart, ionosphere, sonar, banknote) +- Configure qubits and layers +- Watch training in real-time +- See accuracy results + +### 2️⃣ Chat with AI +- Use local provider (free, always works) +- Or use Azure/OpenAI (with API keys) +- Or use your trained LoRA model +- Save conversation history + +### 3️⃣ Train LoRA Adapters +- Select chat datasets +- Configure training parameters +- Monitor progress +- Use trained model in chat + +### 4️⃣ Run Orchestrators +- Quantum AutoRun jobs +- AutoTrain jobs +- Dry-run validation +- Status monitoring + +### 5️⃣ Monitor Everything +- System health dashboard +- Real-time logs +- Recent activity feed +- Provider status + +## 📁 File Structure + +``` +mount/ +├── 🚀 launch.ps1 ← Start here! +├── ⚙️ app.py (FastAPI backend) +├── 📝 config.yaml (Configuration) +├── 📦 requirements.txt (Dependencies) +│ +├── 🧩 Integration Modules: +│ ├── quantum_integration.py +│ ├── chat_integration.py +│ └── training_integration.py +│ +├── 🎨 Web UI: +│ └── static/ +│ ├── index.html +│ ├── styles.css +│ └── app.js +│ +└── 📚 Documentation: + ├── README.md + ├── WEB_UI_GUIDE.md + └── SETUP_COMPLETE.md +``` + +## 🎨 UI Preview + +**Dashboard**: System overview + quick actions +**Quantum AI**: Train models visually +**Chat**: Beautiful chat interface +**Training**: LoRA training + orchestration +**Logs**: Real-time activity monitoring + +All with a modern purple gradient theme! + +## 💡 First Steps + +1. **Launch it**: `cd mount; .\launch.ps1` +2. **Try Quantum**: Go to Quantum tab, select "heart" dataset, train +3. **Try Chat**: Go to Chat tab, ask anything (uses free local provider) +4. **Explore**: Click around, everything is documented in tooltips + +## 🔧 Tech Stack + +- **Backend**: FastAPI + Python 3.10+ +- **Frontend**: Vanilla JavaScript + CSS3 +- **Integration**: Direct imports from quantum-ai, talk-to-ai, phi-training +- **API**: REST with CORS support +- **UI**: Responsive, mobile-friendly + +## 📊 Capabilities + +| Feature | Status | Notes | +|---------|--------|-------| +| Quantum Training | ✅ Ready | All datasets available | +| Chat (Local) | ✅ Ready | Always available, free | +| Chat (Cloud) | ✅ Ready | Requires API keys | +| LoRA Training | ✅ Ready | CPU/GPU supported | +| AutoRun Jobs | ✅ Ready | Quantum orchestration | +| AutoTrain Jobs | ✅ Ready | Training orchestration | +| Web UI | ✅ Ready | 5 full-featured tabs | +| API Docs | ✅ Ready | Swagger + ReDoc | +| Real-time Logs | ✅ Ready | Color-coded | + +## 🎓 Learning Path + +**Beginner** (5 minutes): +1. Launch the app +2. Browse the dashboard +3. Try local chat + +**Intermediate** (15 minutes): +1. Train a quantum model (heart dataset, default settings) +2. Check results +3. Try different datasets + +**Advanced** (30+ minutes): +1. Train a LoRA adapter +2. Use it in chat +3. Run orchestrator jobs +4. Customize configuration + +## 🌟 Highlights + +**🆓 Free Local Operations**: +- Local chat provider (offline-capable) +- Quantum training on simulators +- All features work without cloud services + +**⚡ Fast & Responsive**: +- Real-time updates +- Auto-refresh (30s intervals) +- Instant feedback + +**🎨 Beautiful Design**: +- Modern gradient interface +- Smooth animations +- Color-coded status +- Mobile-friendly + +**🔧 Fully Integrated**: +- Direct access to all QAI components +- Unified API +- Consistent experience + +## 📚 Resources + +- **Quick Start**: See `WEB_UI_GUIDE.md` +- **API Reference**: See `README.md` +- **API Browser**: http://localhost:8000/docs (when running) +- **Main Workspace Guide**: See `../copilot-instructions.md` + +## 🎯 Next Actions + +1. **Launch**: Run `.\launch.ps1` +2. **Explore**: Try all 5 tabs +3. **Train**: Run a quick quantum training +4. **Chat**: Test the local provider +5. **Customize**: Edit `config.yaml` or `static/styles.css` + +## 💬 Support + +All features are documented: +- Hover over UI elements for tooltips +- Check the Logs tab for detailed output +- See WEB_UI_GUIDE.md for troubleshooting +- Use /docs endpoint for API reference + +## ✅ Ready to Go! + +Everything is set up and tested. Just run: + +```powershell +cd C:\Users\Bryan\OneDrive\AI\mount +.\launch.ps1 +``` + +Your QAI Control Center will open in your browser! + +--- + +**🎉 Congratulations! You now have a full-featured web UI for your AI workspace!** + +Enjoy exploring all the capabilities! 🚀 diff --git a/mount/WEB_UI_GUIDE.md b/mount/WEB_UI_GUIDE.md index 429397994..45b0424b7 100644 --- a/mount/WEB_UI_GUIDE.md +++ b/mount/WEB_UI_GUIDE.md @@ -1,323 +1,323 @@ -# QAI Web UI - Quick Start Guide - -Beautiful web interface to control all your QAI operations! - -## 🚀 Super Quick Start - -```powershell -cd mount -.\start.ps1 -``` - -Then open your browser to: **http://localhost:8000** - -## 📱 Features - -### 📊 Dashboard -- Real-time system status -- Quick action buttons -- Recent activity feed -- Health monitoring - -### ⚛️ Quantum AI Tab -- Train quantum classifiers with custom parameters -- View available backends (Qiskit Aer, Lightning, Azure) -- Monitor training results -- Run AutoRun orchestrator jobs - -### 💬 Chat Tab -- Interactive chat interface -- Multiple provider support: - - 🆓 Local (Free, offline) - - ☁️ Azure OpenAI - - 🤖 OpenAI - - 🎯 LoRA Adapter (if trained) -- Auto-detect best available provider -- Conversation history - -### 🎓 Training Tab -- Train LoRA adapters on custom datasets -- Run AutoTrain orchestrator jobs -- Monitor training status -- View all available datasets (quantum/chat/vision) -- Check LoRA adapter status - -### 📝 Logs Tab -- Real-time system logs -- Color-coded by severity (info/warning/error/success) -- Clear and refresh controls - -## 🎨 UI Features - -- **Modern Design**: Beautiful gradient interface with smooth animations -- **Responsive**: Works on desktop, tablet, and mobile -- **Real-time Updates**: Auto-refresh status every 30 seconds -- **Easy Navigation**: Tab-based interface -- **Visual Feedback**: Color-coded status indicators - -## 🔧 How to Use - -### Starting the Service - -**Option 1: PowerShell Script** -```powershell -cd mount -.\start.ps1 -``` - -**Option 2: Manual Start** -```powershell -cd mount -python -m venv venv -.\venv\Scripts\Activate.ps1 -pip install -r requirements.txt -python app.py -``` - -**Option 3: Production Mode** -```powershell -uvicorn app:app --host 0.0.0.0 --port 8000 -``` - -### Training a Quantum Classifier - -1. Go to **⚛️ Quantum AI** tab -2. Select a dataset (heart, ionosphere, sonar, etc.) -3. Configure parameters: - - Number of qubits (2-20) - - Number of layers (1-10) - - Training epochs - - Backend (Qiskit Aer recommended for local) -4. Click **🚀 Start Training** -5. Monitor progress in the logs - -### Using Chat - -1. Go to **💬 Chat** tab -2. Select provider (or use auto-detect) -3. Type your message -4. Press **Send** or hit Enter -5. See response instantly - -Provider recommendations: -- **Local**: Always available, free, offline -- **Azure/OpenAI**: Best quality, requires API keys -- **LoRA**: Use your trained model - -### Training a LoRA Adapter - -1. Go to **🎓 Training** tab -2. Select a chat dataset -3. Configure: - - Max training samples (64 for quick test) - - Max eval samples (16 for quick test) - - Epochs (1 for testing) -4. Click **🚀 Start Training** -5. Monitor in logs tab - -### Running Orchestrator Jobs - -**Quantum AutoRun:** -1. Go to **⚛️ Quantum AI** tab -2. Scroll to "Quantum AutoRun Jobs" section -3. Select a predefined job -4. Run directly or do a dry-run first - -**AutoTrain:** -1. Go to **🎓 Training** tab -2. Select an AutoTrain job -3. Click **Dry Run** to validate -4. Click **Run Job** to execute - -## 🎯 Quick Examples - -### Example 1: Quick Quantum Test -1. Dashboard → Click "⚛️ Train Quantum Model" -2. Select "heart" dataset -3. Keep default parameters -4. Start training -5. Check results in Dashboard after completion - -### Example 2: Free Local Chat -1. Dashboard → Click "💬 Start Chat" -2. Provider is already on "Local" (free!) -3. Ask: "What is quantum computing?" -4. Get instant response - -### Example 3: Train a Quick LoRA -1. Dashboard → Click "🎓 Train LoRA" -2. Select "dolly" dataset -3. Set samples to 64 (fast test) -4. Start training -5. Use trained adapter in Chat tab - -## 🔍 Monitoring - -### Dashboard Indicators - -**Status Dot Colors:** -- 🟢 Green = Online and healthy -- 🟡 Yellow = Checking... -- 🔴 Red = Offline or error - -**System Status:** -- ✓ Green checkmark = Enabled and working -- ✗ Red X = Disabled or error - -### Logs Tab - -Watch real-time activity: -- 🟢 **Info**: Normal operations -- 🟡 **Warning**: Attention needed -- 🔴 **Error**: Something failed -- 🔵 **Success**: Operation completed - -## ⚙️ Configuration - -Edit `config.yaml` to customize: - -```yaml -service: - host: 0.0.0.0 # Change to 127.0.0.1 for localhost only - port: 8000 # Change port if needed - debug: true # Set false for production - -api: - cors_enabled: true - cors_origins: - - http://localhost:3000 - - http://localhost:7071 -``` - -## 🐛 Troubleshooting - -### "Service Offline" Status -**Solution:** Check if the backend is running -```powershell -cd mount -python app.py -``` - -### CORS Errors -**Solution:** Add your URL to `config.yaml` under `cors_origins` - -### Can't Load Data -**Solution:** Make sure all paths in `config.yaml` point to correct locations - -### Training Errors -**Solution:** Check the Logs tab for detailed error messages - -### Chat Not Working -**Solution:** -- Local provider always works (no setup needed) -- For Azure/OpenAI: Set environment variables -- For LoRA: Train an adapter first - -## 🚀 Advanced Usage - -### Custom API Base URL - -If running the backend on a different host/port, edit `app.js`: - -```javascript -const API_BASE = 'http://your-server:8000'; -``` - -### Running on Network - -To access from other devices: - -```powershell -# In config.yaml, set: -service: - host: 0.0.0.0 # Listen on all interfaces - -# Then access from other devices: -# http://YOUR-IP:8000 -``` - -### Production Deployment - -```powershell -# Install production dependencies -pip install gunicorn - -# Run with Gunicorn -gunicorn app:app --workers 4 --bind 0.0.0.0:8000 -``` - -## 📊 Performance Tips - -1. **Quantum Training**: Start with small epochs (10) for testing -2. **LoRA Training**: Use 64 samples for quick validation -3. **Chat**: Local provider is instant, cloud providers have latency -4. **Refresh Rate**: Increase interval in app.js if using on slow connection - -## 🎨 Customization - -### Changing Colors - -Edit `static/styles.css` variables: - -```css -:root { - --primary: #667eea; /* Main brand color */ - --secondary: #48bb78; /* Secondary color */ - --success: #48bb78; /* Success green */ - --danger: #f56565; /* Error red */ -} -``` - -### Adding Custom Sections - -1. Add HTML section in `static/index.html` -2. Add styles in `static/styles.css` -3. Add JavaScript logic in `static/app.js` -4. Connect to backend API endpoints - -## 🔐 Security Notes - -**Development Mode:** -- CORS is wide open -- Debug mode enabled -- No authentication - -**Production Recommendations:** -- Set `debug: false` in config -- Restrict CORS origins -- Add authentication middleware -- Use HTTPS -- Set secure cookie flags - -## 📖 API Documentation - -While the UI is running, you can also access: -- **Swagger UI**: http://localhost:8000/docs -- **ReDoc**: http://localhost:8000/redoc - -These provide full API documentation for programmatic access. - -## 💡 Tips & Tricks - -1. **Use Keyboard Shortcuts**: Enter to send chat messages -2. **Quick Navigation**: Click dashboard quick actions to jump to tabs -3. **Monitor Everything**: Keep Logs tab open in another window -4. **Save Conversations**: Chat history auto-saves to JSONL files -5. **Dry Run First**: Always test orchestrator jobs with dry-run - -## 🤝 Need Help? - -Check the main README.md for: -- Detailed system architecture -- Backend API documentation -- Integration module details -- Configuration options -- Troubleshooting guides - -## 🎉 You're Ready! - -Open http://localhost:8000 and start exploring your AI control center! - ---- - -**Made with ❤️ for the QAI Workspace** +# QAI Web UI - Quick Start Guide + +Beautiful web interface to control all your QAI operations! + +## 🚀 Super Quick Start + +```powershell +cd mount +.\start.ps1 +``` + +Then open your browser to: **http://localhost:8000** + +## 📱 Features + +### 📊 Dashboard +- Real-time system status +- Quick action buttons +- Recent activity feed +- Health monitoring + +### ⚛️ Quantum AI Tab +- Train quantum classifiers with custom parameters +- View available backends (Qiskit Aer, Lightning, Azure) +- Monitor training results +- Run AutoRun orchestrator jobs + +### 💬 Chat Tab +- Interactive chat interface +- Multiple provider support: + - 🆓 Local (Free, offline) + - ☁️ Azure OpenAI + - 🤖 OpenAI + - 🎯 LoRA Adapter (if trained) +- Auto-detect best available provider +- Conversation history + +### 🎓 Training Tab +- Train LoRA adapters on custom datasets +- Run AutoTrain orchestrator jobs +- Monitor training status +- View all available datasets (quantum/chat/vision) +- Check LoRA adapter status + +### 📝 Logs Tab +- Real-time system logs +- Color-coded by severity (info/warning/error/success) +- Clear and refresh controls + +## 🎨 UI Features + +- **Modern Design**: Beautiful gradient interface with smooth animations +- **Responsive**: Works on desktop, tablet, and mobile +- **Real-time Updates**: Auto-refresh status every 30 seconds +- **Easy Navigation**: Tab-based interface +- **Visual Feedback**: Color-coded status indicators + +## 🔧 How to Use + +### Starting the Service + +**Option 1: PowerShell Script** +```powershell +cd mount +.\start.ps1 +``` + +**Option 2: Manual Start** +```powershell +cd mount +python -m venv venv +.\venv\Scripts\Activate.ps1 +pip install -r requirements.txt +python app.py +``` + +**Option 3: Production Mode** +```powershell +uvicorn app:app --host 0.0.0.0 --port 8000 +``` + +### Training a Quantum Classifier + +1. Go to **⚛️ Quantum AI** tab +2. Select a dataset (heart, ionosphere, sonar, etc.) +3. Configure parameters: + - Number of qubits (2-20) + - Number of layers (1-10) + - Training epochs + - Backend (Qiskit Aer recommended for local) +4. Click **🚀 Start Training** +5. Monitor progress in the logs + +### Using Chat + +1. Go to **💬 Chat** tab +2. Select provider (or use auto-detect) +3. Type your message +4. Press **Send** or hit Enter +5. See response instantly + +Provider recommendations: +- **Local**: Always available, free, offline +- **Azure/OpenAI**: Best quality, requires API keys +- **LoRA**: Use your trained model + +### Training a LoRA Adapter + +1. Go to **🎓 Training** tab +2. Select a chat dataset +3. Configure: + - Max training samples (64 for quick test) + - Max eval samples (16 for quick test) + - Epochs (1 for testing) +4. Click **🚀 Start Training** +5. Monitor in logs tab + +### Running Orchestrator Jobs + +**Quantum AutoRun:** +1. Go to **⚛️ Quantum AI** tab +2. Scroll to "Quantum AutoRun Jobs" section +3. Select a predefined job +4. Run directly or do a dry-run first + +**AutoTrain:** +1. Go to **🎓 Training** tab +2. Select an AutoTrain job +3. Click **Dry Run** to validate +4. Click **Run Job** to execute + +## 🎯 Quick Examples + +### Example 1: Quick Quantum Test +1. Dashboard → Click "⚛️ Train Quantum Model" +2. Select "heart" dataset +3. Keep default parameters +4. Start training +5. Check results in Dashboard after completion + +### Example 2: Free Local Chat +1. Dashboard → Click "💬 Start Chat" +2. Provider is already on "Local" (free!) +3. Ask: "What is quantum computing?" +4. Get instant response + +### Example 3: Train a Quick LoRA +1. Dashboard → Click "🎓 Train LoRA" +2. Select "dolly" dataset +3. Set samples to 64 (fast test) +4. Start training +5. Use trained adapter in Chat tab + +## 🔍 Monitoring + +### Dashboard Indicators + +**Status Dot Colors:** +- 🟢 Green = Online and healthy +- 🟡 Yellow = Checking... +- 🔴 Red = Offline or error + +**System Status:** +- ✓ Green checkmark = Enabled and working +- ✗ Red X = Disabled or error + +### Logs Tab + +Watch real-time activity: +- 🟢 **Info**: Normal operations +- 🟡 **Warning**: Attention needed +- 🔴 **Error**: Something failed +- 🔵 **Success**: Operation completed + +## ⚙️ Configuration + +Edit `config.yaml` to customize: + +```yaml +service: + host: 0.0.0.0 # Change to 127.0.0.1 for localhost only + port: 8000 # Change port if needed + debug: true # Set false for production + +api: + cors_enabled: true + cors_origins: + - http://localhost:3000 + - http://localhost:7071 +``` + +## 🐛 Troubleshooting + +### "Service Offline" Status +**Solution:** Check if the backend is running +```powershell +cd mount +python app.py +``` + +### CORS Errors +**Solution:** Add your URL to `config.yaml` under `cors_origins` + +### Can't Load Data +**Solution:** Make sure all paths in `config.yaml` point to correct locations + +### Training Errors +**Solution:** Check the Logs tab for detailed error messages + +### Chat Not Working +**Solution:** +- Local provider always works (no setup needed) +- For Azure/OpenAI: Set environment variables +- For LoRA: Train an adapter first + +## 🚀 Advanced Usage + +### Custom API Base URL + +If running the backend on a different host/port, edit `app.js`: + +```javascript +const API_BASE = 'http://your-server:8000'; +``` + +### Running on Network + +To access from other devices: + +```powershell +# In config.yaml, set: +service: + host: 0.0.0.0 # Listen on all interfaces + +# Then access from other devices: +# http://YOUR-IP:8000 +``` + +### Production Deployment + +```powershell +# Install production dependencies +pip install gunicorn + +# Run with Gunicorn +gunicorn app:app --workers 4 --bind 0.0.0.0:8000 +``` + +## 📊 Performance Tips + +1. **Quantum Training**: Start with small epochs (10) for testing +2. **LoRA Training**: Use 64 samples for quick validation +3. **Chat**: Local provider is instant, cloud providers have latency +4. **Refresh Rate**: Increase interval in app.js if using on slow connection + +## 🎨 Customization + +### Changing Colors + +Edit `static/styles.css` variables: + +```css +:root { + --primary: #667eea; /* Main brand color */ + --secondary: #48bb78; /* Secondary color */ + --success: #48bb78; /* Success green */ + --danger: #f56565; /* Error red */ +} +``` + +### Adding Custom Sections + +1. Add HTML section in `static/index.html` +2. Add styles in `static/styles.css` +3. Add JavaScript logic in `static/app.js` +4. Connect to backend API endpoints + +## 🔐 Security Notes + +**Development Mode:** +- CORS is wide open +- Debug mode enabled +- No authentication + +**Production Recommendations:** +- Set `debug: false` in config +- Restrict CORS origins +- Add authentication middleware +- Use HTTPS +- Set secure cookie flags + +## 📖 API Documentation + +While the UI is running, you can also access: +- **Swagger UI**: http://localhost:8000/docs +- **ReDoc**: http://localhost:8000/redoc + +These provide full API documentation for programmatic access. + +## 💡 Tips & Tricks + +1. **Use Keyboard Shortcuts**: Enter to send chat messages +2. **Quick Navigation**: Click dashboard quick actions to jump to tabs +3. **Monitor Everything**: Keep Logs tab open in another window +4. **Save Conversations**: Chat history auto-saves to JSONL files +5. **Dry Run First**: Always test orchestrator jobs with dry-run + +## 🤝 Need Help? + +Check the main README.md for: +- Detailed system architecture +- Backend API documentation +- Integration module details +- Configuration options +- Troubleshooting guides + +## 🎉 You're Ready! + +Open http://localhost:8000 and start exploring your AI control center! + +--- + +**Made with ❤️ for the QAI Workspace** diff --git a/mount/__init__.py b/mount/__init__.py index ee230c276..17bd69ba4 100644 --- a/mount/__init__.py +++ b/mount/__init__.py @@ -1,7 +1,7 @@ -""" -QAI Integration Service -Unified microservice for quantum AI, chat, and training orchestration -""" - -__version__ = "0.1.0" -__author__ = "QAI Team" +""" +QAI Integration Service +Unified microservice for quantum AI, chat, and training orchestration +""" + +__version__ = "0.1.0" +__author__ = "QAI Team" diff --git a/mount/app.py b/mount/app.py index 0944382bc..790e24ca4 100644 --- a/mount/app.py +++ b/mount/app.py @@ -1,358 +1,358 @@ -""" -QAI Integration Service - FastAPI Application -Unified API for quantum AI, chat, and training operations -""" - -import sys -from contextlib import asynccontextmanager -from pathlib import Path -from typing import Optional - -import yaml -from fastapi import BackgroundTasks, FastAPI, HTTPException -from fastapi.middleware.cors import CORSMiddleware -from fastapi.responses import FileResponse -from fastapi.staticfiles import StaticFiles -from pydantic import BaseModel, Field - -# Add parent directory to path for imports -sys.path.insert(0, str(Path(__file__).parent)) - -from chat_integration import ChatIntegration -from quantum_integration import QuantumIntegration -from training_integration import TrainingIntegration - - -# Pydantic models for request/response -class ChatRequest(BaseModel): - message: str - provider: Optional[str] = None - stream: bool = False - conversation_id: Optional[str] = None - - -class ChatResponse(BaseModel): - success: bool - provider: Optional[str] = None - message: Optional[str] = None - response: Optional[str] = None - conversation_id: Optional[str] = None - timestamp: Optional[str] = None - error: Optional[str] = None - - -class TrainQuantumRequest(BaseModel): - dataset: str - n_qubits: int = Field(default=4, ge=2, le=20) - n_layers: int = Field(default=2, ge=1, le=10) - epochs: int = Field(default=10, ge=1, le=1000) - backend: str = "qiskit_aer" - - -class TrainLoRARequest(BaseModel): - dataset: str - max_train_samples: int = Field(default=64, ge=1) - max_eval_samples: int = Field(default=16, ge=1) - epochs: int = Field(default=1, ge=1) - - -class OrchestratorRequest(BaseModel): - job_name: Optional[str] = None - dry_run: bool = False - - -# Load configuration -config_path = Path(__file__).parent / "config.yaml" -with open(config_path) as f: - config = yaml.safe_load(f) - - -# Initialize integration modules -quantum_integration = QuantumIntegration(config) -chat_integration = ChatIntegration(config) -training_integration = TrainingIntegration(config) - - -@asynccontextmanager -async def lifespan(app: FastAPI): - """Lifespan context manager for startup/shutdown""" - # Startup - print("🚀 QAI Integration Service starting...") - print(f"📊 Quantum enabled: {config['quantum']['enabled']}") - print(f"💬 Chat enabled: {config['chat']['enabled']}") - print(f"🎓 Training enabled: {config['training']['enabled']}") - yield - # Shutdown - print("🛑 QAI Integration Service shutting down...") - - -# Create FastAPI app -app = FastAPI( - title="QAI Integration Service", - description="Unified API for Quantum AI, Chat, and Training operations", - version=config["service"]["version"], - lifespan=lifespan, -) - -# CORS configuration -if config["api"]["cors_enabled"]: - app.add_middleware( - CORSMiddleware, - allow_origins=config["api"]["cors_origins"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], - ) - -# Mount static files -static_path = Path(__file__).parent / "static" -if static_path.exists(): - app.mount("/static", StaticFiles(directory=str(static_path)), name="static") - - -# ============================================================================ -# Root & Health Endpoints -# ============================================================================ - - -@app.get("/") -async def root(): - """Serve the web UI""" - static_index = Path(__file__).parent / "static" / "index.html" - if static_index.exists(): - return FileResponse(str(static_index)) - - # Fallback to API info if no UI - return { - "service": config["service"]["name"], - "version": config["service"]["version"], - "status": "operational", - "endpoints": { - "health": "/health", - "status": "/status", - "quantum": "/quantum/*", - "chat": "/chat/*", - "training": "/training/*", - }, - } - - -@app.get("/health") -async def health_check(): - """Health check endpoint""" - return { - "status": "healthy", - "service": config["service"]["name"], - "version": config["service"]["version"], - } - - -@app.get("/status") -async def get_full_status(): - """Get comprehensive system status""" - quantum_status = await quantum_integration.get_status() - chat_status = await chat_integration.get_status() - training_status = await training_integration.get_status() - - return { - "service": config["service"]["name"], - "version": config["service"]["version"], - "quantum": quantum_status, - "chat": chat_status, - "training": training_status, - } - - -# ============================================================================ -# Quantum Endpoints -# ============================================================================ - - -@app.get("/quantum/status") -async def get_quantum_status(): - """Get quantum system status""" - return await quantum_integration.get_status() - - -@app.get("/quantum/datasets") -async def list_quantum_datasets(): - """List available quantum datasets""" - return await quantum_integration.list_datasets() - - -@app.get("/quantum/backends") -async def list_quantum_backends(): - """List available quantum backends""" - status = await quantum_integration.get_status() - return { - "backends": status["available_backends"], - "azure_connected": status["azure_connected"], - } - - -@app.post("/quantum/train") -async def train_quantum_classifier( - request: TrainQuantumRequest, background_tasks: BackgroundTasks -): - """Train a quantum classifier""" - result = await quantum_integration.train_classifier( - dataset=request.dataset, - n_qubits=request.n_qubits, - n_layers=request.n_layers, - epochs=request.epochs, - backend=request.backend, - ) - return result - - -@app.post("/quantum/autorun") -async def run_quantum_autorun(request: OrchestratorRequest): - """Run a quantum autorun job""" - if not request.job_name: - raise HTTPException(status_code=400, detail="job_name is required") - - result = await quantum_integration.run_autorun_job( - job_name=request.job_name, dry_run=request.dry_run - ) - return result - - -@app.get("/quantum/circuit-info") -async def get_circuit_info(circuit_type: str = "variational"): - """Get quantum circuit information""" - return await quantum_integration.get_circuit_info(circuit_type) - - -# ============================================================================ -# Chat Endpoints -# ============================================================================ - - -@app.get("/chat/status") -async def get_chat_status(): - """Get chat system status""" - return await chat_integration.get_status() - - -@app.post("/chat/message", response_model=ChatResponse) -async def send_chat_message(request: ChatRequest): - """Send a chat message and get response""" - result = await chat_integration.chat( - message=request.message, - provider=request.provider, - stream=request.stream, - conversation_id=request.conversation_id, - ) - return ChatResponse(**result) - - -@app.get("/chat/providers") -async def get_chat_providers(): - """Get available chat providers""" - status = await chat_integration.get_status() - return status["providers"] - - -@app.get("/chat/detect-provider") -async def detect_best_provider(): - """Auto-detect best available chat provider""" - provider = await chat_integration.detect_provider() - return {"provider": provider} - - -@app.get("/chat/conversations") -async def list_conversations(): - """List all saved conversations""" - return await chat_integration.list_conversations() - - -@app.get("/chat/conversations/{filename}") -async def get_conversation(filename: str): - """Get a specific conversation""" - messages = await chat_integration.get_conversation(filename) - if not messages: - raise HTTPException(status_code=404, detail="Conversation not found") - return {"filename": filename, "messages": messages} - - -# ============================================================================ -# Training Endpoints -# ============================================================================ - - -@app.get("/training/status") -async def get_training_status(): - """Get training system status""" - return await training_integration.get_status() - - -@app.get("/training/datasets") -async def list_training_datasets(): - """List available training datasets""" - return await training_integration.list_datasets() - - -@app.post("/training/lora") -async def train_lora(request: TrainLoRARequest, background_tasks: BackgroundTasks): - """Train a LoRA adapter""" - result = await training_integration.train_lora( - dataset=request.dataset, - max_train_samples=request.max_train_samples, - max_eval_samples=request.max_eval_samples, - epochs=request.epochs, - ) - return result - - -@app.post("/training/autotrain") -async def run_autotrain(request: OrchestratorRequest): - """Run AutoTrain orchestrator""" - result = await training_integration.run_autotrain( - job_name=request.job_name, dry_run=request.dry_run - ) - return result - - -@app.get("/training/autotrain/jobs") -async def list_autotrain_jobs(): - """List all configured AutoTrain jobs""" - jobs = await training_integration.list_autotrain_jobs() - return {"jobs": jobs} - - -@app.get("/training/lora-adapter") -async def get_lora_adapter_info(): - """Get LoRA adapter information""" - status = await training_integration.get_status() - return status["lora_adapter"] - - -@app.get("/training/runs") -async def list_training_runs(): - """List recent training runs""" - status = await training_integration.get_status() - return status["recent_trainings"] - - -@app.get("/training/runs/{run_name}") -async def get_training_metrics(run_name: str): - """Get metrics for a specific training run""" - metrics = await training_integration.get_training_metrics(run_name) - if "error" in metrics: - raise HTTPException(status_code=404, detail=metrics["error"]) - return metrics - - -# ============================================================================ -# Main entry point -# ============================================================================ - -if __name__ == "__main__": - import uvicorn - - uvicorn.run( - "app:app", - host=config["service"]["host"], - port=config["service"]["port"], - reload=config["service"]["debug"], - ) +""" +QAI Integration Service - FastAPI Application +Unified API for quantum AI, chat, and training operations +""" + +import sys +from contextlib import asynccontextmanager +from pathlib import Path +from typing import Optional + +import yaml +from fastapi import BackgroundTasks, FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import FileResponse +from fastapi.staticfiles import StaticFiles +from pydantic import BaseModel, Field + +# Add parent directory to path for imports +sys.path.insert(0, str(Path(__file__).parent)) + +from chat_integration import ChatIntegration +from quantum_integration import QuantumIntegration +from training_integration import TrainingIntegration + + +# Pydantic models for request/response +class ChatRequest(BaseModel): + message: str + provider: Optional[str] = None + stream: bool = False + conversation_id: Optional[str] = None + + +class ChatResponse(BaseModel): + success: bool + provider: Optional[str] = None + message: Optional[str] = None + response: Optional[str] = None + conversation_id: Optional[str] = None + timestamp: Optional[str] = None + error: Optional[str] = None + + +class TrainQuantumRequest(BaseModel): + dataset: str + n_qubits: int = Field(default=4, ge=2, le=20) + n_layers: int = Field(default=2, ge=1, le=10) + epochs: int = Field(default=10, ge=1, le=1000) + backend: str = "qiskit_aer" + + +class TrainLoRARequest(BaseModel): + dataset: str + max_train_samples: int = Field(default=64, ge=1) + max_eval_samples: int = Field(default=16, ge=1) + epochs: int = Field(default=1, ge=1) + + +class OrchestratorRequest(BaseModel): + job_name: Optional[str] = None + dry_run: bool = False + + +# Load configuration +config_path = Path(__file__).parent / "config.yaml" +with open(config_path) as f: + config = yaml.safe_load(f) + + +# Initialize integration modules +quantum_integration = QuantumIntegration(config) +chat_integration = ChatIntegration(config) +training_integration = TrainingIntegration(config) + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Lifespan context manager for startup/shutdown""" + # Startup + print("🚀 QAI Integration Service starting...") + print(f"📊 Quantum enabled: {config['quantum']['enabled']}") + print(f"💬 Chat enabled: {config['chat']['enabled']}") + print(f"🎓 Training enabled: {config['training']['enabled']}") + yield + # Shutdown + print("🛑 QAI Integration Service shutting down...") + + +# Create FastAPI app +app = FastAPI( + title="QAI Integration Service", + description="Unified API for Quantum AI, Chat, and Training operations", + version=config["service"]["version"], + lifespan=lifespan, +) + +# CORS configuration +if config["api"]["cors_enabled"]: + app.add_middleware( + CORSMiddleware, + allow_origins=config["api"]["cors_origins"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + +# Mount static files +static_path = Path(__file__).parent / "static" +if static_path.exists(): + app.mount("/static", StaticFiles(directory=str(static_path)), name="static") + + +# ============================================================================ +# Root & Health Endpoints +# ============================================================================ + + +@app.get("/") +async def root(): + """Serve the web UI""" + static_index = Path(__file__).parent / "static" / "index.html" + if static_index.exists(): + return FileResponse(str(static_index)) + + # Fallback to API info if no UI + return { + "service": config["service"]["name"], + "version": config["service"]["version"], + "status": "operational", + "endpoints": { + "health": "/health", + "status": "/status", + "quantum": "/quantum/*", + "chat": "/chat/*", + "training": "/training/*", + }, + } + + +@app.get("/health") +async def health_check(): + """Health check endpoint""" + return { + "status": "healthy", + "service": config["service"]["name"], + "version": config["service"]["version"], + } + + +@app.get("/status") +async def get_full_status(): + """Get comprehensive system status""" + quantum_status = await quantum_integration.get_status() + chat_status = await chat_integration.get_status() + training_status = await training_integration.get_status() + + return { + "service": config["service"]["name"], + "version": config["service"]["version"], + "quantum": quantum_status, + "chat": chat_status, + "training": training_status, + } + + +# ============================================================================ +# Quantum Endpoints +# ============================================================================ + + +@app.get("/quantum/status") +async def get_quantum_status(): + """Get quantum system status""" + return await quantum_integration.get_status() + + +@app.get("/quantum/datasets") +async def list_quantum_datasets(): + """List available quantum datasets""" + return await quantum_integration.list_datasets() + + +@app.get("/quantum/backends") +async def list_quantum_backends(): + """List available quantum backends""" + status = await quantum_integration.get_status() + return { + "backends": status["available_backends"], + "azure_connected": status["azure_connected"], + } + + +@app.post("/quantum/train") +async def train_quantum_classifier( + request: TrainQuantumRequest, background_tasks: BackgroundTasks +): + """Train a quantum classifier""" + result = await quantum_integration.train_classifier( + dataset=request.dataset, + n_qubits=request.n_qubits, + n_layers=request.n_layers, + epochs=request.epochs, + backend=request.backend, + ) + return result + + +@app.post("/quantum/autorun") +async def run_quantum_autorun(request: OrchestratorRequest): + """Run a quantum autorun job""" + if not request.job_name: + raise HTTPException(status_code=400, detail="job_name is required") + + result = await quantum_integration.run_autorun_job( + job_name=request.job_name, dry_run=request.dry_run + ) + return result + + +@app.get("/quantum/circuit-info") +async def get_circuit_info(circuit_type: str = "variational"): + """Get quantum circuit information""" + return await quantum_integration.get_circuit_info(circuit_type) + + +# ============================================================================ +# Chat Endpoints +# ============================================================================ + + +@app.get("/chat/status") +async def get_chat_status(): + """Get chat system status""" + return await chat_integration.get_status() + + +@app.post("/chat/message", response_model=ChatResponse) +async def send_chat_message(request: ChatRequest): + """Send a chat message and get response""" + result = await chat_integration.chat( + message=request.message, + provider=request.provider, + stream=request.stream, + conversation_id=request.conversation_id, + ) + return ChatResponse(**result) + + +@app.get("/chat/providers") +async def get_chat_providers(): + """Get available chat providers""" + status = await chat_integration.get_status() + return status["providers"] + + +@app.get("/chat/detect-provider") +async def detect_best_provider(): + """Auto-detect best available chat provider""" + provider = await chat_integration.detect_provider() + return {"provider": provider} + + +@app.get("/chat/conversations") +async def list_conversations(): + """List all saved conversations""" + return await chat_integration.list_conversations() + + +@app.get("/chat/conversations/{filename}") +async def get_conversation(filename: str): + """Get a specific conversation""" + messages = await chat_integration.get_conversation(filename) + if not messages: + raise HTTPException(status_code=404, detail="Conversation not found") + return {"filename": filename, "messages": messages} + + +# ============================================================================ +# Training Endpoints +# ============================================================================ + + +@app.get("/training/status") +async def get_training_status(): + """Get training system status""" + return await training_integration.get_status() + + +@app.get("/training/datasets") +async def list_training_datasets(): + """List available training datasets""" + return await training_integration.list_datasets() + + +@app.post("/training/lora") +async def train_lora(request: TrainLoRARequest, background_tasks: BackgroundTasks): + """Train a LoRA adapter""" + result = await training_integration.train_lora( + dataset=request.dataset, + max_train_samples=request.max_train_samples, + max_eval_samples=request.max_eval_samples, + epochs=request.epochs, + ) + return result + + +@app.post("/training/autotrain") +async def run_autotrain(request: OrchestratorRequest): + """Run AutoTrain orchestrator""" + result = await training_integration.run_autotrain( + job_name=request.job_name, dry_run=request.dry_run + ) + return result + + +@app.get("/training/autotrain/jobs") +async def list_autotrain_jobs(): + """List all configured AutoTrain jobs""" + jobs = await training_integration.list_autotrain_jobs() + return {"jobs": jobs} + + +@app.get("/training/lora-adapter") +async def get_lora_adapter_info(): + """Get LoRA adapter information""" + status = await training_integration.get_status() + return status["lora_adapter"] + + +@app.get("/training/runs") +async def list_training_runs(): + """List recent training runs""" + status = await training_integration.get_status() + return status["recent_trainings"] + + +@app.get("/training/runs/{run_name}") +async def get_training_metrics(run_name: str): + """Get metrics for a specific training run""" + metrics = await training_integration.get_training_metrics(run_name) + if "error" in metrics: + raise HTTPException(status_code=404, detail=metrics["error"]) + return metrics + + +# ============================================================================ +# Main entry point +# ============================================================================ + +if __name__ == "__main__": + import uvicorn + + uvicorn.run( + "app:app", + host=config["service"]["host"], + port=config["service"]["port"], + reload=config["service"]["debug"], + ) diff --git a/mount/chat_integration.py b/mount/chat_integration.py index 02431f49d..27ce036c4 100644 --- a/mount/chat_integration.py +++ b/mount/chat_integration.py @@ -1,213 +1,213 @@ -""" -Chat Integration Module -Interfaces with talk-to-ai chat providers and conversation management -""" - -import json -import subprocess -import sys -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List, Optional - - -class ChatIntegration: - """Integration layer for chat operations""" - - def __init__(self, config: Dict[str, Any]): - self.config = config - self.workspace_root = Path(config["paths"]["workspace_root"]).resolve() - self.chat_path = Path(config["paths"]["talk_to_ai"]).resolve() - self.logs_dir = self.chat_path / "logs" - self.logs_dir.mkdir(exist_ok=True) - - async def get_status(self) -> Dict[str, Any]: - """Get current chat system status""" - return { - "enabled": self.config["chat"]["enabled"], - "default_provider": self.config["chat"].get("default_provider", "local"), - "providers": self._get_provider_status(), - "recent_conversations": self._get_recent_conversations(), - } - - def _get_provider_status(self) -> Dict[str, Dict[str, Any]]: - """Check status of all chat providers""" - import os - - providers = {} - - # Local provider (always available) - providers["local"] = { - "enabled": True, - "available": True, - "offline": True, - "cost": "free", - } - - # Azure OpenAI - azure_keys = [ - "AZURE_OPENAI_API_KEY", - "AZURE_OPENAI_ENDPOINT", - "AZURE_OPENAI_DEPLOYMENT", - "AZURE_OPENAI_API_VERSION", - ] - providers["azure"] = { - "enabled": self.config["chat"]["providers"]["azure"]["enabled"], - "available": all(os.getenv(key) for key in azure_keys), - "configured": all(os.getenv(key) for key in azure_keys), - "cost": "paid", - } - - # OpenAI - providers["openai"] = { - "enabled": True, - "available": bool(os.getenv("OPENAI_API_KEY")), - "configured": bool(os.getenv("OPENAI_API_KEY")), - "cost": "paid", - } - - # LoRA adapter - lora_path = Path(self.config["chat"]["providers"]["lora"]["adapter_path"]) - lora_available = (lora_path / "adapter_config.json").exists() - - providers["lora"] = { - "enabled": self.config["chat"]["providers"]["lora"]["enabled"], - "available": lora_available, - "adapter_path": str(lora_path) if lora_available else None, - "cost": "free", - } - - return providers - - def _get_recent_conversations(self, limit: int = 5) -> List[Dict[str, Any]]: - """Get recent conversation logs""" - conversations = [] - - if not self.logs_dir.exists(): - return conversations - - # Find all JSONL conversation files - jsonl_files = sorted( - self.logs_dir.glob("chat_*.jsonl"), - key=lambda p: p.stat().st_mtime, - reverse=True, - )[:limit] - - for jsonl_file in jsonl_files: - try: - messages = [] - with open(jsonl_file) as f: - for line in f: - if line.strip(): - messages.append(json.loads(line)) - - conversations.append( - { - "file": jsonl_file.name, - "timestamp": messages[0].get("timestamp") if messages else None, - "message_count": len(messages), - "preview": ( - messages[0].get("content", "")[:100] if messages else "" - ), - } - ) - except Exception: - continue - - return conversations - - async def chat( - self, - message: str, - provider: Optional[str] = None, - stream: bool = False, - conversation_id: Optional[str] = None, - ) -> Dict[str, Any]: - """Send a chat message and get response""" - try: - provider = provider or self.config["chat"]["default_provider"] - - # For now, we'll use subprocess to call the chat CLI - # In production, you'd import the provider classes directly - chat_script = self.chat_path / "src" / "chat_cli.py" - - cmd = [ - sys.executable, - str(chat_script), - "--provider", - provider, - "--once", - message, - ] - - result = subprocess.run( - cmd, capture_output=True, text=True, cwd=str(self.chat_path) - ) - - return { - "success": result.returncode == 0, - "provider": provider, - "message": message, - "response": result.stdout.strip(), - "conversation_id": conversation_id - or f"conv_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - } - except Exception as e: - return {"success": False, "error": str(e)} - - async def list_conversations(self) -> List[Dict[str, Any]]: - """List all saved conversations""" - return self._get_recent_conversations(limit=50) - - async def get_conversation(self, filename: str) -> List[Dict[str, Any]]: - """Get full conversation from a log file""" - file_path = self.logs_dir / filename - - if not file_path.exists(): - return [] - - messages = [] - with open(file_path) as f: - for line in f: - if line.strip(): - messages.append(json.loads(line)) - - return messages - - async def save_conversation( - self, messages: List[Dict[str, Any]], filename: Optional[str] = None - ) -> Dict[str, Any]: - """Save conversation to JSONL file""" - try: - if not filename: - filename = f"chat_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jsonl" - - file_path = self.logs_dir / filename - - with open(file_path, "w") as f: - for msg in messages: - f.write(json.dumps(msg) + "\n") - - return { - "success": True, - "filename": filename, - "message_count": len(messages), - "path": str(file_path), - } - except Exception as e: - return {"success": False, "error": str(e)} - - async def detect_provider(self) -> str: - """Auto-detect best available chat provider""" - providers = self._get_provider_status() - - # Priority: Azure > OpenAI > LoRA > Local - if providers["azure"]["available"]: - return "azure" - elif providers["openai"]["available"]: - return "openai" - elif providers["lora"]["available"]: - return "lora" - else: - return "local" +""" +Chat Integration Module +Interfaces with talk-to-ai chat providers and conversation management +""" + +import json +import subprocess +import sys +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional + + +class ChatIntegration: + """Integration layer for chat operations""" + + def __init__(self, config: Dict[str, Any]): + self.config = config + self.workspace_root = Path(config["paths"]["workspace_root"]).resolve() + self.chat_path = Path(config["paths"]["talk_to_ai"]).resolve() + self.logs_dir = self.chat_path / "logs" + self.logs_dir.mkdir(exist_ok=True) + + async def get_status(self) -> Dict[str, Any]: + """Get current chat system status""" + return { + "enabled": self.config["chat"]["enabled"], + "default_provider": self.config["chat"].get("default_provider", "local"), + "providers": self._get_provider_status(), + "recent_conversations": self._get_recent_conversations(), + } + + def _get_provider_status(self) -> Dict[str, Dict[str, Any]]: + """Check status of all chat providers""" + import os + + providers = {} + + # Local provider (always available) + providers["local"] = { + "enabled": True, + "available": True, + "offline": True, + "cost": "free", + } + + # Azure OpenAI + azure_keys = [ + "AZURE_OPENAI_API_KEY", + "AZURE_OPENAI_ENDPOINT", + "AZURE_OPENAI_DEPLOYMENT", + "AZURE_OPENAI_API_VERSION", + ] + providers["azure"] = { + "enabled": self.config["chat"]["providers"]["azure"]["enabled"], + "available": all(os.getenv(key) for key in azure_keys), + "configured": all(os.getenv(key) for key in azure_keys), + "cost": "paid", + } + + # OpenAI + providers["openai"] = { + "enabled": True, + "available": bool(os.getenv("OPENAI_API_KEY")), + "configured": bool(os.getenv("OPENAI_API_KEY")), + "cost": "paid", + } + + # LoRA adapter + lora_path = Path(self.config["chat"]["providers"]["lora"]["adapter_path"]) + lora_available = (lora_path / "adapter_config.json").exists() + + providers["lora"] = { + "enabled": self.config["chat"]["providers"]["lora"]["enabled"], + "available": lora_available, + "adapter_path": str(lora_path) if lora_available else None, + "cost": "free", + } + + return providers + + def _get_recent_conversations(self, limit: int = 5) -> List[Dict[str, Any]]: + """Get recent conversation logs""" + conversations = [] + + if not self.logs_dir.exists(): + return conversations + + # Find all JSONL conversation files + jsonl_files = sorted( + self.logs_dir.glob("chat_*.jsonl"), + key=lambda p: p.stat().st_mtime, + reverse=True, + )[:limit] + + for jsonl_file in jsonl_files: + try: + messages = [] + with open(jsonl_file) as f: + for line in f: + if line.strip(): + messages.append(json.loads(line)) + + conversations.append( + { + "file": jsonl_file.name, + "timestamp": messages[0].get("timestamp") if messages else None, + "message_count": len(messages), + "preview": ( + messages[0].get("content", "")[:100] if messages else "" + ), + } + ) + except Exception: + continue + + return conversations + + async def chat( + self, + message: str, + provider: Optional[str] = None, + stream: bool = False, + conversation_id: Optional[str] = None, + ) -> Dict[str, Any]: + """Send a chat message and get response""" + try: + provider = provider or self.config["chat"]["default_provider"] + + # For now, we'll use subprocess to call the chat CLI + # In production, you'd import the provider classes directly + chat_script = self.chat_path / "src" / "chat_cli.py" + + cmd = [ + sys.executable, + str(chat_script), + "--provider", + provider, + "--once", + message, + ] + + result = subprocess.run( + cmd, capture_output=True, text=True, cwd=str(self.chat_path) + ) + + return { + "success": result.returncode == 0, + "provider": provider, + "message": message, + "response": result.stdout.strip(), + "conversation_id": conversation_id + or f"conv_{datetime.now().strftime('%Y%m%d_%H%M%S')}", + "timestamp": datetime.now().isoformat(), + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def list_conversations(self) -> List[Dict[str, Any]]: + """List all saved conversations""" + return self._get_recent_conversations(limit=50) + + async def get_conversation(self, filename: str) -> List[Dict[str, Any]]: + """Get full conversation from a log file""" + file_path = self.logs_dir / filename + + if not file_path.exists(): + return [] + + messages = [] + with open(file_path) as f: + for line in f: + if line.strip(): + messages.append(json.loads(line)) + + return messages + + async def save_conversation( + self, messages: List[Dict[str, Any]], filename: Optional[str] = None + ) -> Dict[str, Any]: + """Save conversation to JSONL file""" + try: + if not filename: + filename = f"chat_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jsonl" + + file_path = self.logs_dir / filename + + with open(file_path, "w") as f: + for msg in messages: + f.write(json.dumps(msg) + "\n") + + return { + "success": True, + "filename": filename, + "message_count": len(messages), + "path": str(file_path), + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def detect_provider(self) -> str: + """Auto-detect best available chat provider""" + providers = self._get_provider_status() + + # Priority: Azure > OpenAI > LoRA > Local + if providers["azure"]["available"]: + return "azure" + elif providers["openai"]["available"]: + return "openai" + elif providers["lora"]["available"]: + return "lora" + else: + return "local" diff --git a/mount/config.yaml b/mount/config.yaml index c858a3b58..d77f90cf5 100644 --- a/mount/config.yaml +++ b/mount/config.yaml @@ -1,71 +1,71 @@ -# QAI Integration Service Configuration - -service: - name: qai-integration-service - version: 0.1.0 - host: 0.0.0.0 - port: 8000 - debug: true - -# Paths (relative to workspace root) -paths: - workspace_root: .. - quantum_ai: ../quantum-ai - talk_to_ai: ../talk-to-ai - phi_training: ../AI/microsoft_phi-silica-3.6_v1 - datasets: ../datasets - data_out: ../data_out - scripts: ../scripts - -# Quantum AI Configuration -quantum: - enabled: true - config_file: ../ai-projects/quantum-ml/config/quantum_config.yaml - mcp_server_enabled: true - default_backend: qiskit_aer - results_dir: ../ai-projects/quantum-ml/results - -# Chat Configuration -chat: - enabled: true - default_provider: local # local, azure, openai, lora - providers: - local: - enabled: true - offline: true - azure: - enabled: true - requires_env: true - lora: - enabled: true - adapter_path: ../data_out/lora_training/lora_adapter - -# Training Configuration -training: - enabled: true - orchestrators: - autotrain: - config_file: ../autotrain.yaml - status_file: ../data_out/autotrain/status.json - quantum_autorun: - config_file: ../quantum_autorun.yaml - status_file: ../data_out/quantum_autorun/status.json - output_dir: ../data_out - -# Monitoring -monitoring: - status_check_interval: 60 # seconds - log_level: INFO - metrics_enabled: true - -# API Configuration -api: - cors_enabled: true - cors_origins: - - http://localhost:3000 - - http://localhost:7071 - - http://127.0.0.1:3000 - - http://127.0.0.1:7071 - rate_limiting: - enabled: false - requests_per_minute: 60 +# QAI Integration Service Configuration + +service: + name: qai-integration-service + version: 0.1.0 + host: 0.0.0.0 + port: 8000 + debug: true + +# Paths (relative to workspace root) +paths: + workspace_root: .. + quantum_ai: ../quantum-ai + talk_to_ai: ../talk-to-ai + phi_training: ../AI/microsoft_phi-silica-3.6_v1 + datasets: ../datasets + data_out: ../data_out + scripts: ../scripts + +# Quantum AI Configuration +quantum: + enabled: true + config_file: ../ai-projects/quantum-ml/config/quantum_config.yaml + mcp_server_enabled: true + default_backend: qiskit_aer + results_dir: ../ai-projects/quantum-ml/results + +# Chat Configuration +chat: + enabled: true + default_provider: local # local, azure, openai, lora + providers: + local: + enabled: true + offline: true + azure: + enabled: true + requires_env: true + lora: + enabled: true + adapter_path: ../data_out/lora_training/lora_adapter + +# Training Configuration +training: + enabled: true + orchestrators: + autotrain: + config_file: ../autotrain.yaml + status_file: ../data_out/autotrain/status.json + quantum_autorun: + config_file: ../quantum_autorun.yaml + status_file: ../data_out/quantum_autorun/status.json + output_dir: ../data_out + +# Monitoring +monitoring: + status_check_interval: 60 # seconds + log_level: INFO + metrics_enabled: true + +# API Configuration +api: + cors_enabled: true + cors_origins: + - http://localhost:3000 + - http://localhost:7071 + - http://127.0.0.1:3000 + - http://127.0.0.1:7071 + rate_limiting: + enabled: false + requests_per_minute: 60 diff --git a/mount/max_training_config.yaml b/mount/max_training_config.yaml index cc94621ee..f55eb6ea1 100644 --- a/mount/max_training_config.yaml +++ b/mount/max_training_config.yaml @@ -1,65 +1,65 @@ -# Max Performance Training Configuration -# This config pushes your hardware to maximum utilization - -training: - # Aggressive batch sizes (adjust down if OOM) - batch_size: 8 # Increase if you have good GPU - gradient_accumulation_steps: 4 # Effective batch = 8 * 4 = 32 - - # Use all available CPU cores - dataloader_num_workers: 8 # Adjust based on your CPU cores - - # Memory optimization - use_fp16: true # Mixed precision for speed - gradient_checkpointing: true # Save memory, slight speed cost - - # Training intensity - max_train_samples: null # Use full dataset - max_eval_samples: 500 - epochs: 3 - - # Learning rate - learning_rate: 0.0003 - warmup_steps: 500 - - # Optimizer settings (aggressive) - adam_beta1: 0.9 - adam_beta2: 0.999 - weight_decay: 0.01 - max_grad_norm: 1.0 - - # Logging (frequent for monitoring) - logging_steps: 10 - eval_steps: 100 - save_steps: 500 - - # LoRA config (larger for more capacity) - lora_r: 16 # Rank (8-64, higher = more parameters) - lora_alpha: 32 # Scaling factor - lora_dropout: 0.1 - target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"] - -# Multi-dataset training (train on multiple datasets) -datasets: - - name: dolly - path: ../../datasets/chat/dolly - weight: 1.0 - - name: openassistant - path: ../../datasets/chat/openassistant - weight: 0.5 - - name: mixed_chat - path: ../../datasets/chat/mixed_chat - weight: 0.3 - -# Hardware optimization -hardware: - use_cuda: true # Auto-detect GPU - use_mps: false # Mac M1/M2 - pin_memory: true - non_blocking: true - -# Output -output: - save_dir: ../../data_out/lora_training/max_performance - checkpoint_dir: ../../data_out/lora_training/max_performance/checkpoints - log_dir: ../../data_out/lora_training/max_performance/logs +# Max Performance Training Configuration +# This config pushes your hardware to maximum utilization + +training: + # Aggressive batch sizes (adjust down if OOM) + batch_size: 8 # Increase if you have good GPU + gradient_accumulation_steps: 4 # Effective batch = 8 * 4 = 32 + + # Use all available CPU cores + dataloader_num_workers: 8 # Adjust based on your CPU cores + + # Memory optimization + use_fp16: true # Mixed precision for speed + gradient_checkpointing: true # Save memory, slight speed cost + + # Training intensity + max_train_samples: null # Use full dataset + max_eval_samples: 500 + epochs: 3 + + # Learning rate + learning_rate: 0.0003 + warmup_steps: 500 + + # Optimizer settings (aggressive) + adam_beta1: 0.9 + adam_beta2: 0.999 + weight_decay: 0.01 + max_grad_norm: 1.0 + + # Logging (frequent for monitoring) + logging_steps: 10 + eval_steps: 100 + save_steps: 500 + + # LoRA config (larger for more capacity) + lora_r: 16 # Rank (8-64, higher = more parameters) + lora_alpha: 32 # Scaling factor + lora_dropout: 0.1 + target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"] + +# Multi-dataset training (train on multiple datasets) +datasets: + - name: dolly + path: ../../datasets/chat/dolly + weight: 1.0 + - name: openassistant + path: ../../datasets/chat/openassistant + weight: 0.5 + - name: mixed_chat + path: ../../datasets/chat/mixed_chat + weight: 0.3 + +# Hardware optimization +hardware: + use_cuda: true # Auto-detect GPU + use_mps: false # Mac M1/M2 + pin_memory: true + non_blocking: true + +# Output +output: + save_dir: ../../data_out/lora_training/max_performance + checkpoint_dir: ../../data_out/lora_training/max_performance/checkpoints + log_dir: ../../data_out/lora_training/max_performance/logs diff --git a/mount/quantum_integration.py b/mount/quantum_integration.py index a3265f647..f48232658 100644 --- a/mount/quantum_integration.py +++ b/mount/quantum_integration.py @@ -1,199 +1,199 @@ -""" -Quantum AI Integration Module -Interfaces with quantum-ai training, circuits, and Azure Quantum -""" - -import json -import subprocess -import sys -from pathlib import Path -from typing import Any, Dict, List - -import yaml - - -class QuantumIntegration: - """Integration layer for quantum AI operations""" - - def __init__(self, config: Dict[str, Any]): - self.config = config - self.workspace_root = Path(config["paths"]["workspace_root"]).resolve() - self.quantum_path = Path(config["paths"]["quantum_ai"]).resolve() - self.results_dir = ( - Path(config["paths"].get("quantum_ai", "../quantum-ai")) / "results" - ) - self.quantum_config = self._load_quantum_config() - - def _load_quantum_config(self) -> Dict[str, Any]: - """Load quantum configuration from YAML""" - config_path = ( - self.workspace_root / "quantum-ai" / "config" / "quantum_config.yaml" - ) - if config_path.exists(): - with open(config_path) as f: - return yaml.safe_load(f) - return {} - - async def get_status(self) -> Dict[str, Any]: - """Get current quantum system status""" - return { - "enabled": self.config["quantum"]["enabled"], - "backend": self.config["quantum"].get("default_backend", "qiskit_aer"), - "mcp_server": self.config["quantum"].get("mcp_server_enabled", False), - "azure_connected": self._check_azure_connection(), - "available_backends": self._get_available_backends(), - "recent_results": self._get_recent_results(), - } - - def _check_azure_connection(self) -> bool: - """Check if Azure Quantum is configured and connected""" - azure_config = self.quantum_config.get("azure", {}) - return bool( - azure_config.get("subscription_id") - and azure_config.get("resource_group") - and azure_config.get("workspace_name") - ) - - def _get_available_backends(self) -> List[str]: - """Get list of available quantum backends""" - backends = ["qiskit_aer", "lightning.qubit"] - if self._check_azure_connection(): - backends.extend(["rigetti.sim.qvm", "ionq.simulator"]) - return backends - - def _get_recent_results(self, limit: int = 5) -> List[Dict[str, Any]]: - """Get recent quantum training results""" - results = [] - results_path = self.workspace_root / "quantum-ai" / "results" - - if not results_path.exists(): - return results - - # Find all JSON result files - json_files = sorted( - results_path.glob("*.json"), key=lambda p: p.stat().st_mtime, reverse=True - )[:limit] - - for json_file in json_files: - try: - with open(json_file) as f: - data = json.load(f) - results.append( - { - "file": json_file.name, - "timestamp": data.get("timestamp"), - "dataset": data.get("dataset"), - "accuracy": data.get("test_accuracy"), - "backend": data.get("backend"), - } - ) - except Exception: - continue - - return results - - async def train_classifier( - self, - dataset: str, - n_qubits: int = 4, - n_layers: int = 2, - epochs: int = 10, - backend: str = "qiskit_aer", - ) -> Dict[str, Any]: - """Train a quantum classifier""" - try: - train_script = self.quantum_path / "train_custom_dataset.py" - - cmd = [ - sys.executable, - str(train_script), - "--preset", - dataset, - "--epochs", - str(epochs), - "--backend", - backend, - ] - - result = subprocess.run( - cmd, capture_output=True, text=True, cwd=str(self.quantum_path) - ) - - return { - "success": result.returncode == 0, - "stdout": result.stdout, - "stderr": result.stderr, - "return_code": result.returncode, - } - except Exception as e: - return {"success": False, "error": str(e)} - - async def list_datasets(self) -> List[Dict[str, Any]]: - """List available quantum datasets""" - datasets_path = self.workspace_root / "datasets" / "quantum" - - if not datasets_path.exists(): - return [] - - datasets = [] - for csv_file in datasets_path.glob("*.csv"): - datasets.append( - { - "name": csv_file.stem, - "path": str(csv_file), - "size_mb": csv_file.stat().st_size / (1024 * 1024), - } - ) - - return datasets - - async def get_circuit_info( - self, circuit_type: str = "variational" - ) -> Dict[str, Any]: - """Get information about quantum circuit types""" - return { - "circuit_type": circuit_type, - "entanglement_modes": ["linear", "circular", "full"], - "supported_gates": ["RY", "RZ", "CNOT", "H", "X"], - "max_qubits": 20, # Local simulator limit - "layer_structure": { - "input": "RY encoding", - "variational": "RY/RZ + entanglement", - "measurement": "PauliZ", - }, - } - - async def run_autorun_job( - self, job_name: str, dry_run: bool = False - ) -> Dict[str, Any]: - """Run a quantum autorun job""" - try: - autorun_script = self.workspace_root / "scripts" / "quantum_autorun.py" - - cmd = [sys.executable, str(autorun_script), "--job", job_name] - if dry_run: - cmd.append("--dry-run") - - result = subprocess.run( - cmd, capture_output=True, text=True, cwd=str(self.workspace_root) - ) - - # Load status file if it exists - status_file = ( - self.workspace_root / "data_out" / "quantum_autorun" / "status.json" - ) - status_data = {} - if status_file.exists(): - with open(status_file) as f: - status_data = json.load(f) - - return { - "success": result.returncode == 0, - "job_name": job_name, - "dry_run": dry_run, - "stdout": result.stdout, - "stderr": result.stderr, - "status": status_data.get("jobs", {}).get(job_name, {}), - } - except Exception as e: - return {"success": False, "error": str(e)} +""" +Quantum AI Integration Module +Interfaces with quantum-ai training, circuits, and Azure Quantum +""" + +import json +import subprocess +import sys +from pathlib import Path +from typing import Any, Dict, List + +import yaml + + +class QuantumIntegration: + """Integration layer for quantum AI operations""" + + def __init__(self, config: Dict[str, Any]): + self.config = config + self.workspace_root = Path(config["paths"]["workspace_root"]).resolve() + self.quantum_path = Path(config["paths"]["quantum_ai"]).resolve() + self.results_dir = ( + Path(config["paths"].get("quantum_ai", "../quantum-ai")) / "results" + ) + self.quantum_config = self._load_quantum_config() + + def _load_quantum_config(self) -> Dict[str, Any]: + """Load quantum configuration from YAML""" + config_path = ( + self.workspace_root / "quantum-ai" / "config" / "quantum_config.yaml" + ) + if config_path.exists(): + with open(config_path) as f: + return yaml.safe_load(f) + return {} + + async def get_status(self) -> Dict[str, Any]: + """Get current quantum system status""" + return { + "enabled": self.config["quantum"]["enabled"], + "backend": self.config["quantum"].get("default_backend", "qiskit_aer"), + "mcp_server": self.config["quantum"].get("mcp_server_enabled", False), + "azure_connected": self._check_azure_connection(), + "available_backends": self._get_available_backends(), + "recent_results": self._get_recent_results(), + } + + def _check_azure_connection(self) -> bool: + """Check if Azure Quantum is configured and connected""" + azure_config = self.quantum_config.get("azure", {}) + return bool( + azure_config.get("subscription_id") + and azure_config.get("resource_group") + and azure_config.get("workspace_name") + ) + + def _get_available_backends(self) -> List[str]: + """Get list of available quantum backends""" + backends = ["qiskit_aer", "lightning.qubit"] + if self._check_azure_connection(): + backends.extend(["rigetti.sim.qvm", "ionq.simulator"]) + return backends + + def _get_recent_results(self, limit: int = 5) -> List[Dict[str, Any]]: + """Get recent quantum training results""" + results = [] + results_path = self.workspace_root / "quantum-ai" / "results" + + if not results_path.exists(): + return results + + # Find all JSON result files + json_files = sorted( + results_path.glob("*.json"), key=lambda p: p.stat().st_mtime, reverse=True + )[:limit] + + for json_file in json_files: + try: + with open(json_file) as f: + data = json.load(f) + results.append( + { + "file": json_file.name, + "timestamp": data.get("timestamp"), + "dataset": data.get("dataset"), + "accuracy": data.get("test_accuracy"), + "backend": data.get("backend"), + } + ) + except Exception: + continue + + return results + + async def train_classifier( + self, + dataset: str, + n_qubits: int = 4, + n_layers: int = 2, + epochs: int = 10, + backend: str = "qiskit_aer", + ) -> Dict[str, Any]: + """Train a quantum classifier""" + try: + train_script = self.quantum_path / "train_custom_dataset.py" + + cmd = [ + sys.executable, + str(train_script), + "--preset", + dataset, + "--epochs", + str(epochs), + "--backend", + backend, + ] + + result = subprocess.run( + cmd, capture_output=True, text=True, cwd=str(self.quantum_path) + ) + + return { + "success": result.returncode == 0, + "stdout": result.stdout, + "stderr": result.stderr, + "return_code": result.returncode, + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def list_datasets(self) -> List[Dict[str, Any]]: + """List available quantum datasets""" + datasets_path = self.workspace_root / "datasets" / "quantum" + + if not datasets_path.exists(): + return [] + + datasets = [] + for csv_file in datasets_path.glob("*.csv"): + datasets.append( + { + "name": csv_file.stem, + "path": str(csv_file), + "size_mb": csv_file.stat().st_size / (1024 * 1024), + } + ) + + return datasets + + async def get_circuit_info( + self, circuit_type: str = "variational" + ) -> Dict[str, Any]: + """Get information about quantum circuit types""" + return { + "circuit_type": circuit_type, + "entanglement_modes": ["linear", "circular", "full"], + "supported_gates": ["RY", "RZ", "CNOT", "H", "X"], + "max_qubits": 20, # Local simulator limit + "layer_structure": { + "input": "RY encoding", + "variational": "RY/RZ + entanglement", + "measurement": "PauliZ", + }, + } + + async def run_autorun_job( + self, job_name: str, dry_run: bool = False + ) -> Dict[str, Any]: + """Run a quantum autorun job""" + try: + autorun_script = self.workspace_root / "scripts" / "quantum_autorun.py" + + cmd = [sys.executable, str(autorun_script), "--job", job_name] + if dry_run: + cmd.append("--dry-run") + + result = subprocess.run( + cmd, capture_output=True, text=True, cwd=str(self.workspace_root) + ) + + # Load status file if it exists + status_file = ( + self.workspace_root / "data_out" / "quantum_autorun" / "status.json" + ) + status_data = {} + if status_file.exists(): + with open(status_file) as f: + status_data = json.load(f) + + return { + "success": result.returncode == 0, + "job_name": job_name, + "dry_run": dry_run, + "stdout": result.stdout, + "stderr": result.stderr, + "status": status_data.get("jobs", {}).get(job_name, {}), + } + except Exception as e: + return {"success": False, "error": str(e)} diff --git a/mount/requirements.txt b/mount/requirements.txt index d0ddb3370..25839775d 100644 --- a/mount/requirements.txt +++ b/mount/requirements.txt @@ -1,26 +1,26 @@ -# QAI Integration Service Requirements - -# Web Framework -fastapi>=0.115.0 -uvicorn[standard]>=0.32.0 -pydantic>=2.10.0 -pydantic-settings>=2.7.0 - -# Async Support -aiofiles>=23.2.1 -httpx>=0.28.0 - -# Configuration -pyyaml>=6.0.1 -python-dotenv>=1.0.0 - -# Monitoring & Logging -python-json-logger>=2.0.7 - -# Data Processing -pandas>=2.2.0 -numpy>=1.26.4 - -# Optional: Add if you want direct imports from other modules -# -e ../quantum-ai -# -e ../talk-to-ai +# QAI Integration Service Requirements + +# Web Framework +fastapi>=0.115.0 +uvicorn[standard]>=0.32.0 +pydantic>=2.10.0 +pydantic-settings>=2.7.0 + +# Async Support +aiofiles>=23.2.1 +httpx>=0.28.0 + +# Configuration +pyyaml>=6.0.1 +python-dotenv>=1.0.0 + +# Monitoring & Logging +python-json-logger>=2.0.7 + +# Data Processing +pandas>=2.2.0 +numpy>=1.26.4 + +# Optional: Add if you want direct imports from other modules +# -e ../quantum-ai +# -e ../talk-to-ai diff --git a/mount/static/app.js b/mount/static/app.js index 14b515e6c..f55c7b141 100644 --- a/mount/static/app.js +++ b/mount/static/app.js @@ -1,559 +1,559 @@ -// QAI Control Center JavaScript - -const API_BASE = 'http://localhost:8000'; - -// Global state -let currentProvider = 'auto'; -let chatHistory = []; - -// Initialize app -document.addEventListener('DOMContentLoaded', () => { - initializeTabs(); - initializeForms(); - checkServiceStatus(); - loadDashboard(); - - // Refresh status every 30 seconds - setInterval(checkServiceStatus, 30000); -}); - -// Tab Management -function initializeTabs() { - const tabBtns = document.querySelectorAll('.tab-btn'); - - tabBtns.forEach(btn => { - btn.addEventListener('click', () => { - const tabName = btn.dataset.tab; - switchTab(tabName); - }); - }); -} - -function switchTab(tabName) { - // Update buttons - document.querySelectorAll('.tab-btn').forEach(btn => { - btn.classList.remove('active'); - if (btn.dataset.tab === tabName) { - btn.classList.add('active'); - } - }); - - // Update content - document.querySelectorAll('.tab-content').forEach(content => { - content.classList.remove('active'); - }); - document.getElementById(tabName).classList.add('active'); - - // Load tab-specific data - loadTabData(tabName); -} - -function loadTabData(tabName) { - switch(tabName) { - case 'dashboard': - loadDashboard(); - break; - case 'quantum': - loadQuantumData(); - break; - case 'chat': - loadChatData(); - break; - case 'training': - loadTrainingData(); - break; - } -} - -// Service Status -async function checkServiceStatus() { - try { - const response = await fetch(`${API_BASE}/health`); - const data = await response.json(); - - const indicator = document.getElementById('serviceStatus'); - const dot = indicator.querySelector('.status-dot'); - const text = indicator.querySelector('.status-text'); - - if (data.status === 'healthy') { - dot.classList.add('online'); - dot.classList.remove('offline'); - text.textContent = 'Online'; - } else { - dot.classList.remove('online'); - dot.classList.add('offline'); - text.textContent = 'Error'; - } - } catch (error) { - const indicator = document.getElementById('serviceStatus'); - const dot = indicator.querySelector('.status-dot'); - const text = indicator.querySelector('.status-text'); - dot.classList.remove('online'); - dot.classList.add('offline'); - text.textContent = 'Offline'; - } -} - -// Dashboard -async function loadDashboard() { - try { - const response = await fetch(`${API_BASE}/status`); - const data = await response.json(); - - // Update system status - const statusHtml = ` -
    - Service - ${data.service} -
    -
    - Version - ${data.version} -
    -
    - Quantum Enabled - - ${data.quantum.enabled ? '✓ Yes' : '✗ No'} - -
    -
    - Chat Enabled - - ${data.chat.enabled ? '✓ Yes' : '✗ No'} - -
    -
    - Training Enabled - - ${data.training.enabled ? '✓ Yes' : '✗ No'} - -
    - `; - document.getElementById('systemStatus').innerHTML = statusHtml; - - // Update recent activity - let activityHtml = '
    No recent activity
    '; - - if (data.quantum.recent_results && data.quantum.recent_results.length > 0) { - activityHtml = data.quantum.recent_results.map(result => ` -
    - Quantum: ${result.dataset} - - ${(result.accuracy * 100).toFixed(1)}% accuracy -
    ${result.backend} - ${result.timestamp} -
    - `).join(''); - } - - document.getElementById('recentActivity').innerHTML = activityHtml; - - addLog('Dashboard loaded successfully', 'success'); - } catch (error) { - addLog(`Dashboard load error: ${error.message}`, 'error'); - } -} - -// Quantum AI -async function loadQuantumData() { - try { - // Load datasets - const datasetsResponse = await fetch(`${API_BASE}/quantum/datasets`); - const datasets = await datasetsResponse.json(); - - const datasetSelect = document.getElementById('quantumDataset'); - datasetSelect.innerHTML = '' + - datasets.map(d => ``).join(''); - - // Load status - const statusResponse = await fetch(`${API_BASE}/quantum/status`); - const status = await statusResponse.json(); - - const statusHtml = ` -
    - Backend - ${status.backend} -
    -
    - Azure Connected - - ${status.azure_connected ? '✓ Yes' : '✗ No'} - -
    -
    - Available Backends - ${status.available_backends.length} -
    - `; - document.getElementById('quantumStatus').innerHTML = statusHtml; - - // Load recent results - const resultsHtml = status.recent_results && status.recent_results.length > 0 - ? status.recent_results.map(r => ` -
    - ${r.dataset}
    - Accuracy: ${(r.accuracy * 100).toFixed(1)}%
    - ${r.backend} - ${r.timestamp} -
    - `).join('') - : '
    No results yet
    '; - - document.getElementById('quantumResults').innerHTML = resultsHtml; - - // Load AutoRun jobs (placeholder) - document.getElementById('quantumAutorunJobs').innerHTML = ` -
    - Available jobs will appear here -
    - `; - - addLog('Quantum data loaded', 'info'); - } catch (error) { - addLog(`Quantum load error: ${error.message}`, 'error'); - } -} - -// Chat -async function loadChatData() { - try { - const response = await fetch(`${API_BASE}/chat/status`); - const data = await response.json(); - - // Update provider status - const providersHtml = Object.entries(data.providers).map(([name, info]) => ` -
    - ${name.toUpperCase()} - - ${info.available ? '✓ Available' : '✗ Unavailable'} - ${info.cost ? ` (${info.cost})` : ''} - -
    - `).join(''); - - document.getElementById('chatProviders').innerHTML = providersHtml; - - // Update chat status - const statusHtml = ` -
    - Default Provider - ${data.default_provider} -
    - `; - document.getElementById('chatStatus').innerHTML = statusHtml; - - // Load conversation history - if (data.recent_conversations && data.recent_conversations.length > 0) { - const historyHtml = data.recent_conversations.map(conv => ` -
    - ${conv.file}
    - ${conv.message_count} messages
    - ${conv.preview} -
    - `).join(''); - document.getElementById('chatHistory').innerHTML = historyHtml; - } else { - document.getElementById('chatHistory').innerHTML = - '
    No conversations yet
    '; - } - - addLog('Chat data loaded', 'info'); - } catch (error) { - addLog(`Chat load error: ${error.message}`, 'error'); - } -} - -// Training -async function loadTrainingData() { - try { - // Load datasets - const datasetsResponse = await fetch(`${API_BASE}/training/datasets`); - const datasets = await datasetsResponse.json(); - - // Populate LoRA dataset select - const loraSelect = document.getElementById('loraDataset'); - loraSelect.innerHTML = ''; - - if (datasets.chat && datasets.chat.length > 0) { - datasets.chat.forEach(ds => { - loraSelect.innerHTML += ``; - }); - } - - // Load training status - const statusResponse = await fetch(`${API_BASE}/training/status`); - const status = await statusResponse.json(); - - const statusHtml = ` -
    - System - Ready -
    - `; - document.getElementById('trainingStatus').innerHTML = statusHtml; - - // LoRA adapter status - const adapterHtml = status.lora_adapter.available - ? ` -
    - Status - ✓ Available -
    -
    - Model - ${status.lora_adapter.model || 'N/A'} -
    -
    - Rank - ${status.lora_adapter.rank || 'N/A'} -
    - ` - : '
    No adapter trained yet
    '; - - document.getElementById('loraAdapterStatus').innerHTML = adapterHtml; - - // Load AutoTrain jobs - const jobsResponse = await fetch(`${API_BASE}/training/autotrain/jobs`); - const jobs = await jobsResponse.json(); - - const jobSelect = document.getElementById('autotrainJob'); - jobSelect.innerHTML = '' + - (jobs.jobs || []).map(job => ``).join(''); - - // AutoTrain status - const autotrainHtml = status.orchestrators.autotrain.jobs && - Object.keys(status.orchestrators.autotrain.jobs).length > 0 - ? Object.entries(status.orchestrators.autotrain.jobs).map(([name, job]) => ` -
    - ${name} - ${job.status || 'unknown'} -
    - `).join('') - : '
    No jobs run yet
    '; - - document.getElementById('autotrainStatus').innerHTML = autotrainHtml; - - // Dataset list - const datasetHtml = ` -

    Quantum (${datasets.quantum ? datasets.quantum.length : 0})

    -
    - ${(datasets.quantum || []).map(d => `
    ${d}
    `).join('')} -
    -

    Chat (${datasets.chat ? datasets.chat.length : 0})

    -
    - ${(datasets.chat || []).map(d => `
    ${d}
    `).join('')} -
    -

    Vision (${datasets.vision ? datasets.vision.length : 0})

    -
    - ${(datasets.vision || []).map(d => `
    ${d}
    `).join('')} -
    - `; - document.getElementById('datasetList').innerHTML = datasetHtml; - - addLog('Training data loaded', 'info'); - } catch (error) { - addLog(`Training load error: ${error.message}`, 'error'); - } -} - -// Form Handlers -function initializeForms() { - // Quantum training form - document.getElementById('quantumTrainForm').addEventListener('submit', async (e) => { - e.preventDefault(); - await trainQuantumClassifier(); - }); - - // LoRA training form - document.getElementById('loraTrainForm').addEventListener('submit', async (e) => { - e.preventDefault(); - await trainLoRA(); - }); - - // Chat form - document.getElementById('chatForm').addEventListener('submit', async (e) => { - e.preventDefault(); - await sendChatMessage(); - }); - - // Chat provider change - document.getElementById('chatProvider').addEventListener('change', (e) => { - currentProvider = e.target.value; - }); -} - -async function trainQuantumClassifier() { - const dataset = document.getElementById('quantumDataset').value; - const n_qubits = parseInt(document.getElementById('quantumQubits').value); - const n_layers = parseInt(document.getElementById('quantumLayers').value); - const epochs = parseInt(document.getElementById('quantumEpochs').value); - const backend = document.getElementById('quantumBackend').value; - - if (!dataset) { - addLog('Please select a dataset', 'error'); - return; - } - - addLog(`Starting quantum training: ${dataset}`, 'info'); - - try { - const response = await fetch(`${API_BASE}/quantum/train`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ dataset, n_qubits, n_layers, epochs, backend }) - }); - - const result = await response.json(); - - if (result.success) { - addLog('Quantum training started successfully!', 'success'); - setTimeout(() => loadQuantumData(), 2000); - } else { - addLog(`Training error: ${result.stderr || result.error}`, 'error'); - } - } catch (error) { - addLog(`Request failed: ${error.message}`, 'error'); - } -} - -async function trainLoRA() { - const dataset = document.getElementById('loraDataset').value; - const max_train_samples = parseInt(document.getElementById('loraTrainSamples').value); - const max_eval_samples = parseInt(document.getElementById('loraEvalSamples').value); - const epochs = parseInt(document.getElementById('loraEpochs').value); - - if (!dataset) { - addLog('Please select a dataset', 'error'); - return; - } - - addLog(`Starting LoRA training on ${dataset}`, 'info'); - - try { - const response = await fetch(`${API_BASE}/training/lora`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ dataset, max_train_samples, max_eval_samples, epochs }) - }); - - const result = await response.json(); - - if (result.success) { - addLog('LoRA training started successfully!', 'success'); - setTimeout(() => loadTrainingData(), 2000); - } else { - addLog(`Training error: ${result.stderr || result.error}`, 'error'); - } - } catch (error) { - addLog(`Request failed: ${error.message}`, 'error'); - } -} - -async function sendChatMessage() { - const input = document.getElementById('chatInput'); - const message = input.value.trim(); - - if (!message) return; - - // Add user message to chat - addChatMessage(message, 'user'); - input.value = ''; - - try { - const provider = currentProvider === 'auto' ? null : currentProvider; - - const response = await fetch(`${API_BASE}/chat/message`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ message, provider }) - }); - - const result = await response.json(); - - if (result.success) { - addChatMessage(result.response, 'assistant'); - addLog(`Chat response from ${result.provider}`, 'info'); - } else { - addChatMessage('Error: ' + (result.error || 'Unknown error'), 'system'); - addLog(`Chat error: ${result.error}`, 'error'); - } - } catch (error) { - addChatMessage('Error: ' + error.message, 'system'); - addLog(`Chat request failed: ${error.message}`, 'error'); - } -} - -function addChatMessage(content, role) { - const messagesDiv = document.getElementById('chatMessages'); - const messageDiv = document.createElement('div'); - messageDiv.className = `chat-message ${role}`; - messageDiv.textContent = content; - messagesDiv.appendChild(messageDiv); - messagesDiv.scrollTop = messagesDiv.scrollHeight; -} - -async function runAutoTrain(dryRun) { - const job = document.getElementById('autotrainJob').value; - - if (!job) { - addLog('Please select a job', 'error'); - return; - } - - addLog(`Running AutoTrain job: ${job} ${dryRun ? '(dry run)' : ''}`, 'info'); - - try { - const response = await fetch(`${API_BASE}/training/autotrain`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ job_name: job, dry_run: dryRun }) - }); - - const result = await response.json(); - - if (result.success) { - addLog('AutoTrain job completed successfully!', 'success'); - setTimeout(() => loadTrainingData(), 2000); - } else { - addLog(`AutoTrain error: ${result.stderr || result.error}`, 'error'); - } - } catch (error) { - addLog(`Request failed: ${error.message}`, 'error'); - } -} - -// Quick Actions -function quickAction(action) { - switch(action) { - case 'quantum': - switchTab('quantum'); - break; - case 'chat': - switchTab('chat'); - break; - case 'training': - switchTab('training'); - break; - } -} - -function refreshStatus() { - addLog('Refreshing status...', 'info'); - loadDashboard(); -} - -// Logging -function addLog(message, type = 'info') { - const logOutput = document.getElementById('logOutput'); - const entry = document.createElement('div'); - entry.className = `log-entry ${type}`; - const timestamp = new Date().toLocaleTimeString(); - entry.textContent = `[${timestamp}] ${message}`; - logOutput.appendChild(entry); - logOutput.scrollTop = logOutput.scrollHeight; -} - -function clearLogs() { - document.getElementById('logOutput').innerHTML = ''; - addLog('Logs cleared', 'info'); -} - -function refreshLogs() { - addLog('Logs refreshed', 'info'); -} +// QAI Control Center JavaScript + +const API_BASE = 'http://localhost:8000'; + +// Global state +let currentProvider = 'auto'; +let chatHistory = []; + +// Initialize app +document.addEventListener('DOMContentLoaded', () => { + initializeTabs(); + initializeForms(); + checkServiceStatus(); + loadDashboard(); + + // Refresh status every 30 seconds + setInterval(checkServiceStatus, 30000); +}); + +// Tab Management +function initializeTabs() { + const tabBtns = document.querySelectorAll('.tab-btn'); + + tabBtns.forEach(btn => { + btn.addEventListener('click', () => { + const tabName = btn.dataset.tab; + switchTab(tabName); + }); + }); +} + +function switchTab(tabName) { + // Update buttons + document.querySelectorAll('.tab-btn').forEach(btn => { + btn.classList.remove('active'); + if (btn.dataset.tab === tabName) { + btn.classList.add('active'); + } + }); + + // Update content + document.querySelectorAll('.tab-content').forEach(content => { + content.classList.remove('active'); + }); + document.getElementById(tabName).classList.add('active'); + + // Load tab-specific data + loadTabData(tabName); +} + +function loadTabData(tabName) { + switch(tabName) { + case 'dashboard': + loadDashboard(); + break; + case 'quantum': + loadQuantumData(); + break; + case 'chat': + loadChatData(); + break; + case 'training': + loadTrainingData(); + break; + } +} + +// Service Status +async function checkServiceStatus() { + try { + const response = await fetch(`${API_BASE}/health`); + const data = await response.json(); + + const indicator = document.getElementById('serviceStatus'); + const dot = indicator.querySelector('.status-dot'); + const text = indicator.querySelector('.status-text'); + + if (data.status === 'healthy') { + dot.classList.add('online'); + dot.classList.remove('offline'); + text.textContent = 'Online'; + } else { + dot.classList.remove('online'); + dot.classList.add('offline'); + text.textContent = 'Error'; + } + } catch (error) { + const indicator = document.getElementById('serviceStatus'); + const dot = indicator.querySelector('.status-dot'); + const text = indicator.querySelector('.status-text'); + dot.classList.remove('online'); + dot.classList.add('offline'); + text.textContent = 'Offline'; + } +} + +// Dashboard +async function loadDashboard() { + try { + const response = await fetch(`${API_BASE}/status`); + const data = await response.json(); + + // Update system status + const statusHtml = ` +
    + Service + ${data.service} +
    +
    + Version + ${data.version} +
    +
    + Quantum Enabled + + ${data.quantum.enabled ? '✓ Yes' : '✗ No'} + +
    +
    + Chat Enabled + + ${data.chat.enabled ? '✓ Yes' : '✗ No'} + +
    +
    + Training Enabled + + ${data.training.enabled ? '✓ Yes' : '✗ No'} + +
    + `; + document.getElementById('systemStatus').innerHTML = statusHtml; + + // Update recent activity + let activityHtml = '
    No recent activity
    '; + + if (data.quantum.recent_results && data.quantum.recent_results.length > 0) { + activityHtml = data.quantum.recent_results.map(result => ` +
    + Quantum: ${result.dataset} - + ${(result.accuracy * 100).toFixed(1)}% accuracy +
    ${result.backend} - ${result.timestamp} +
    + `).join(''); + } + + document.getElementById('recentActivity').innerHTML = activityHtml; + + addLog('Dashboard loaded successfully', 'success'); + } catch (error) { + addLog(`Dashboard load error: ${error.message}`, 'error'); + } +} + +// Quantum AI +async function loadQuantumData() { + try { + // Load datasets + const datasetsResponse = await fetch(`${API_BASE}/quantum/datasets`); + const datasets = await datasetsResponse.json(); + + const datasetSelect = document.getElementById('quantumDataset'); + datasetSelect.innerHTML = '' + + datasets.map(d => ``).join(''); + + // Load status + const statusResponse = await fetch(`${API_BASE}/quantum/status`); + const status = await statusResponse.json(); + + const statusHtml = ` +
    + Backend + ${status.backend} +
    +
    + Azure Connected + + ${status.azure_connected ? '✓ Yes' : '✗ No'} + +
    +
    + Available Backends + ${status.available_backends.length} +
    + `; + document.getElementById('quantumStatus').innerHTML = statusHtml; + + // Load recent results + const resultsHtml = status.recent_results && status.recent_results.length > 0 + ? status.recent_results.map(r => ` +
    + ${r.dataset}
    + Accuracy: ${(r.accuracy * 100).toFixed(1)}%
    + ${r.backend} - ${r.timestamp} +
    + `).join('') + : '
    No results yet
    '; + + document.getElementById('quantumResults').innerHTML = resultsHtml; + + // Load AutoRun jobs (placeholder) + document.getElementById('quantumAutorunJobs').innerHTML = ` +
    + Available jobs will appear here +
    + `; + + addLog('Quantum data loaded', 'info'); + } catch (error) { + addLog(`Quantum load error: ${error.message}`, 'error'); + } +} + +// Chat +async function loadChatData() { + try { + const response = await fetch(`${API_BASE}/chat/status`); + const data = await response.json(); + + // Update provider status + const providersHtml = Object.entries(data.providers).map(([name, info]) => ` +
    + ${name.toUpperCase()} + + ${info.available ? '✓ Available' : '✗ Unavailable'} + ${info.cost ? ` (${info.cost})` : ''} + +
    + `).join(''); + + document.getElementById('chatProviders').innerHTML = providersHtml; + + // Update chat status + const statusHtml = ` +
    + Default Provider + ${data.default_provider} +
    + `; + document.getElementById('chatStatus').innerHTML = statusHtml; + + // Load conversation history + if (data.recent_conversations && data.recent_conversations.length > 0) { + const historyHtml = data.recent_conversations.map(conv => ` +
    + ${conv.file}
    + ${conv.message_count} messages
    + ${conv.preview} +
    + `).join(''); + document.getElementById('chatHistory').innerHTML = historyHtml; + } else { + document.getElementById('chatHistory').innerHTML = + '
    No conversations yet
    '; + } + + addLog('Chat data loaded', 'info'); + } catch (error) { + addLog(`Chat load error: ${error.message}`, 'error'); + } +} + +// Training +async function loadTrainingData() { + try { + // Load datasets + const datasetsResponse = await fetch(`${API_BASE}/training/datasets`); + const datasets = await datasetsResponse.json(); + + // Populate LoRA dataset select + const loraSelect = document.getElementById('loraDataset'); + loraSelect.innerHTML = ''; + + if (datasets.chat && datasets.chat.length > 0) { + datasets.chat.forEach(ds => { + loraSelect.innerHTML += ``; + }); + } + + // Load training status + const statusResponse = await fetch(`${API_BASE}/training/status`); + const status = await statusResponse.json(); + + const statusHtml = ` +
    + System + Ready +
    + `; + document.getElementById('trainingStatus').innerHTML = statusHtml; + + // LoRA adapter status + const adapterHtml = status.lora_adapter.available + ? ` +
    + Status + ✓ Available +
    +
    + Model + ${status.lora_adapter.model || 'N/A'} +
    +
    + Rank + ${status.lora_adapter.rank || 'N/A'} +
    + ` + : '
    No adapter trained yet
    '; + + document.getElementById('loraAdapterStatus').innerHTML = adapterHtml; + + // Load AutoTrain jobs + const jobsResponse = await fetch(`${API_BASE}/training/autotrain/jobs`); + const jobs = await jobsResponse.json(); + + const jobSelect = document.getElementById('autotrainJob'); + jobSelect.innerHTML = '' + + (jobs.jobs || []).map(job => ``).join(''); + + // AutoTrain status + const autotrainHtml = status.orchestrators.autotrain.jobs && + Object.keys(status.orchestrators.autotrain.jobs).length > 0 + ? Object.entries(status.orchestrators.autotrain.jobs).map(([name, job]) => ` +
    + ${name} - ${job.status || 'unknown'} +
    + `).join('') + : '
    No jobs run yet
    '; + + document.getElementById('autotrainStatus').innerHTML = autotrainHtml; + + // Dataset list + const datasetHtml = ` +

    Quantum (${datasets.quantum ? datasets.quantum.length : 0})

    +
    + ${(datasets.quantum || []).map(d => `
    ${d}
    `).join('')} +
    +

    Chat (${datasets.chat ? datasets.chat.length : 0})

    +
    + ${(datasets.chat || []).map(d => `
    ${d}
    `).join('')} +
    +

    Vision (${datasets.vision ? datasets.vision.length : 0})

    +
    + ${(datasets.vision || []).map(d => `
    ${d}
    `).join('')} +
    + `; + document.getElementById('datasetList').innerHTML = datasetHtml; + + addLog('Training data loaded', 'info'); + } catch (error) { + addLog(`Training load error: ${error.message}`, 'error'); + } +} + +// Form Handlers +function initializeForms() { + // Quantum training form + document.getElementById('quantumTrainForm').addEventListener('submit', async (e) => { + e.preventDefault(); + await trainQuantumClassifier(); + }); + + // LoRA training form + document.getElementById('loraTrainForm').addEventListener('submit', async (e) => { + e.preventDefault(); + await trainLoRA(); + }); + + // Chat form + document.getElementById('chatForm').addEventListener('submit', async (e) => { + e.preventDefault(); + await sendChatMessage(); + }); + + // Chat provider change + document.getElementById('chatProvider').addEventListener('change', (e) => { + currentProvider = e.target.value; + }); +} + +async function trainQuantumClassifier() { + const dataset = document.getElementById('quantumDataset').value; + const n_qubits = parseInt(document.getElementById('quantumQubits').value); + const n_layers = parseInt(document.getElementById('quantumLayers').value); + const epochs = parseInt(document.getElementById('quantumEpochs').value); + const backend = document.getElementById('quantumBackend').value; + + if (!dataset) { + addLog('Please select a dataset', 'error'); + return; + } + + addLog(`Starting quantum training: ${dataset}`, 'info'); + + try { + const response = await fetch(`${API_BASE}/quantum/train`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ dataset, n_qubits, n_layers, epochs, backend }) + }); + + const result = await response.json(); + + if (result.success) { + addLog('Quantum training started successfully!', 'success'); + setTimeout(() => loadQuantumData(), 2000); + } else { + addLog(`Training error: ${result.stderr || result.error}`, 'error'); + } + } catch (error) { + addLog(`Request failed: ${error.message}`, 'error'); + } +} + +async function trainLoRA() { + const dataset = document.getElementById('loraDataset').value; + const max_train_samples = parseInt(document.getElementById('loraTrainSamples').value); + const max_eval_samples = parseInt(document.getElementById('loraEvalSamples').value); + const epochs = parseInt(document.getElementById('loraEpochs').value); + + if (!dataset) { + addLog('Please select a dataset', 'error'); + return; + } + + addLog(`Starting LoRA training on ${dataset}`, 'info'); + + try { + const response = await fetch(`${API_BASE}/training/lora`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ dataset, max_train_samples, max_eval_samples, epochs }) + }); + + const result = await response.json(); + + if (result.success) { + addLog('LoRA training started successfully!', 'success'); + setTimeout(() => loadTrainingData(), 2000); + } else { + addLog(`Training error: ${result.stderr || result.error}`, 'error'); + } + } catch (error) { + addLog(`Request failed: ${error.message}`, 'error'); + } +} + +async function sendChatMessage() { + const input = document.getElementById('chatInput'); + const message = input.value.trim(); + + if (!message) return; + + // Add user message to chat + addChatMessage(message, 'user'); + input.value = ''; + + try { + const provider = currentProvider === 'auto' ? null : currentProvider; + + const response = await fetch(`${API_BASE}/chat/message`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ message, provider }) + }); + + const result = await response.json(); + + if (result.success) { + addChatMessage(result.response, 'assistant'); + addLog(`Chat response from ${result.provider}`, 'info'); + } else { + addChatMessage('Error: ' + (result.error || 'Unknown error'), 'system'); + addLog(`Chat error: ${result.error}`, 'error'); + } + } catch (error) { + addChatMessage('Error: ' + error.message, 'system'); + addLog(`Chat request failed: ${error.message}`, 'error'); + } +} + +function addChatMessage(content, role) { + const messagesDiv = document.getElementById('chatMessages'); + const messageDiv = document.createElement('div'); + messageDiv.className = `chat-message ${role}`; + messageDiv.textContent = content; + messagesDiv.appendChild(messageDiv); + messagesDiv.scrollTop = messagesDiv.scrollHeight; +} + +async function runAutoTrain(dryRun) { + const job = document.getElementById('autotrainJob').value; + + if (!job) { + addLog('Please select a job', 'error'); + return; + } + + addLog(`Running AutoTrain job: ${job} ${dryRun ? '(dry run)' : ''}`, 'info'); + + try { + const response = await fetch(`${API_BASE}/training/autotrain`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ job_name: job, dry_run: dryRun }) + }); + + const result = await response.json(); + + if (result.success) { + addLog('AutoTrain job completed successfully!', 'success'); + setTimeout(() => loadTrainingData(), 2000); + } else { + addLog(`AutoTrain error: ${result.stderr || result.error}`, 'error'); + } + } catch (error) { + addLog(`Request failed: ${error.message}`, 'error'); + } +} + +// Quick Actions +function quickAction(action) { + switch(action) { + case 'quantum': + switchTab('quantum'); + break; + case 'chat': + switchTab('chat'); + break; + case 'training': + switchTab('training'); + break; + } +} + +function refreshStatus() { + addLog('Refreshing status...', 'info'); + loadDashboard(); +} + +// Logging +function addLog(message, type = 'info') { + const logOutput = document.getElementById('logOutput'); + const entry = document.createElement('div'); + entry.className = `log-entry ${type}`; + const timestamp = new Date().toLocaleTimeString(); + entry.textContent = `[${timestamp}] ${message}`; + logOutput.appendChild(entry); + logOutput.scrollTop = logOutput.scrollHeight; +} + +function clearLogs() { + document.getElementById('logOutput').innerHTML = ''; + addLog('Logs cleared', 'info'); +} + +function refreshLogs() { + addLog('Logs refreshed', 'info'); +} diff --git a/mount/static/index.html b/mount/static/index.html index e62d72ffd..509065799 100644 --- a/mount/static/index.html +++ b/mount/static/index.html @@ -1,264 +1,264 @@ - - - - - - QAI Control Center - - - -
    - -
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    🧠 QAI Control Center

    -
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    + + + + diff --git a/mount/static/styles.css b/mount/static/styles.css index 5f08ee9cd..0f525be31 100644 --- a/mount/static/styles.css +++ b/mount/static/styles.css @@ -1,524 +1,524 @@ -/* QAI Control Center Styles */ - -:root { - --primary: #667eea; - --primary-dark: #5568d3; - --secondary: #48bb78; - --danger: #f56565; - --warning: #ed8936; - --info: #4299e1; - --dark: #2d3748; - --light: #f7fafc; - --border: #e2e8f0; - --text: #2d3748; - --text-light: #718096; - --success: #48bb78; -} - -* { - margin: 0; - padding: 0; - box-sizing: border-box; -} - -body { - font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); - color: var(--text); - min-height: 100vh; - padding: 20px; -} - -.container { - max-width: 1400px; - margin: 0 auto; - background: white; - border-radius: 12px; - box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3); - overflow: hidden; -} - -/* Header */ -.header { 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16px; - font-weight: 500; - color: var(--text-light); - transition: all 0.3s; - border-bottom: 3px solid transparent; -} - -.tab-btn:hover { - background: white; - color: var(--primary); -} - -.tab-btn.active { - background: white; - color: var(--primary); - border-bottom-color: var(--primary); -} - -/* Tab Content */ -.tab-content { - display: none; - padding: 30px; - animation: fadeIn 0.3s; -} - -.tab-content.active { - display: block; -} - -@keyframes fadeIn { - from { opacity: 0; transform: translateY(10px); } - to { opacity: 1; transform: translateY(0); } -} - -/* Grid Layouts */ -.dashboard-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); - gap: 20px; -} - -.grid-2 { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); - gap: 20px; -} - -.full-width { - grid-column: 1 / -1; -} - -/* Cards */ -.card { - background: white; - border: 1px solid var(--border); - border-radius: 8px; - padding: 24px; - box-shadow: 0 2px 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6px; - margin-bottom: 8px; -} - -.job-item button { - margin-left: 8px; -} - -/* Dataset Grid */ -.dataset-grid { - display: grid; - grid-template-columns: repeat(auto-fill, minmax(150px, 1fr)); - gap: 12px; -} - -.dataset-item { - padding: 12px; - background: var(--light); - border: 1px solid var(--border); - border-radius: 6px; - text-align: center; - font-weight: 500; -} - -.dataset-item.quantum { border-left: 4px solid var(--primary); } -.dataset-item.chat { border-left: 4px solid var(--success); } -.dataset-item.vision { border-left: 4px solid var(--warning); } - -/* Logs */ -.log-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 16px; -} - -.log-output { - background: var(--dark); - color: #00ff00; - padding: 16px; - border-radius: 6px; - font-family: 'Courier New', monospace; - font-size: 12px; - max-height: 500px; - overflow-y: auto; -} - -.log-entry { - padding: 4px 0; - border-bottom: 1px solid rgba(255, 255, 255, 0.1); -} - -.log-entry:last-child { - border-bottom: none; -} - -.log-entry.info { color: #00ff00; } -.log-entry.warning { color: #ffa500; } -.log-entry.error { color: #ff0000; } -.log-entry.success { color: #00ffff; } - -/* Loading */ -.loading { - text-align: center; - padding: 20px; - color: var(--text-light); -} - -.loading::after { - content: '...'; - animation: dots 1.5s steps(3, end) infinite; -} - -@keyframes dots { - 0%, 20% { content: '.'; } - 40% { content: '..'; } - 60%, 100% { content: '...'; } -} - -/* Badge */ -.badge { - display: inline-block; - padding: 4px 8px; - border-radius: 12px; - font-size: 12px; - font-weight: 600; -} - -.badge.success { background: var(--success); color: white; } -.badge.error { background: var(--danger); color: white; } -.badge.warning { background: var(--warning); color: white; } -.badge.info { background: var(--info); color: white; } - -/* Responsive */ -@media (max-width: 768px) { - body { - padding: 10px; - } - - .header { - flex-direction: column; - gap: 16px; - text-align: center; - } - - .tabs { - flex-wrap: nowrap; - overflow-x: auto; - } - - .grid-2 { - grid-template-columns: 1fr; - } - - .quick-actions { - grid-template-columns: 1fr; - } -} +/* QAI Control Center Styles */ + +:root { + --primary: #667eea; + --primary-dark: #5568d3; + --secondary: #48bb78; + --danger: #f56565; + --warning: #ed8936; + --info: #4299e1; + --dark: #2d3748; + --light: #f7fafc; + --border: #e2e8f0; + --text: #2d3748; + --text-light: #718096; + --success: #48bb78; +} + +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif; + background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + color: var(--text); + min-height: 100vh; + padding: 20px; +} + +.container { + max-width: 1400px; + margin: 0 auto; + background: white; + border-radius: 12px; + box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3); + overflow: hidden; +} + +/* Header */ +.header { + background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + color: white; + padding: 30px; + display: flex; + justify-content: space-between; + align-items: center; +} + +.header h1 { + font-size: 28px; + font-weight: 700; +} + +.status-indicator { + display: flex; + align-items: center; + gap: 10px; + background: rgba(255, 255, 255, 0.2); + padding: 8px 16px; + border-radius: 20px; +} + +.status-dot { + width: 10px; + height: 10px; + border-radius: 50%; + background: var(--warning); + animation: pulse 2s infinite; +} + +.status-dot.online { + background: var(--success); +} + +.status-dot.offline { + background: var(--danger); +} + +@keyframes pulse { + 0%, 100% { opacity: 1; } + 50% { opacity: 0.5; } +} + +/* Tabs */ +.tabs { + display: flex; + background: var(--light); + border-bottom: 2px solid var(--border); + overflow-x: auto; +} + +.tab-btn { + flex: 1; + min-width: 150px; + padding: 16px 20px; + border: none; + background: transparent; + cursor: pointer; + font-size: 16px; + font-weight: 500; + color: var(--text-light); + transition: all 0.3s; + border-bottom: 3px solid transparent; +} + +.tab-btn:hover { + background: white; + color: var(--primary); +} + +.tab-btn.active { + background: white; + color: var(--primary); + border-bottom-color: var(--primary); +} + +/* Tab Content */ +.tab-content { + display: none; + padding: 30px; + animation: fadeIn 0.3s; +} + +.tab-content.active { + display: block; +} + +@keyframes fadeIn { + from { opacity: 0; transform: translateY(10px); } + to { opacity: 1; transform: translateY(0); } +} + +/* Grid Layouts */ +.dashboard-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); + gap: 20px; +} + +.grid-2 { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); + gap: 20px; +} + +.full-width { + grid-column: 1 / -1; +} + +/* Cards */ +.card { + background: white; + border: 1px solid var(--border); + border-radius: 8px; + padding: 24px; + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05); +} + +.card h2 { + font-size: 20px; + margin-bottom: 16px; + color: var(--dark); +} + +.card h3 { + font-size: 16px; + margin: 16px 0 12px; + color: var(--text); +} + +.card hr { + border: none; + border-top: 1px solid var(--border); + margin: 16px 0; +} + +/* Forms */ +.form-group { + margin-bottom: 16px; +} + +.form-group label { + display: block; + margin-bottom: 6px; + font-weight: 500; + color: var(--text); +} + +.form-group input, +.form-group select, +.form-group textarea { + width: 100%; + padding: 10px 12px; + border: 1px solid var(--border); + border-radius: 6px; + font-size: 14px; + transition: border-color 0.3s; +} + +.form-group input:focus, +.form-group select:focus, +.form-group textarea:focus { + outline: none; + border-color: var(--primary); + box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1); +} + +.form-inline { + display: flex; + gap: 10px; + margin-bottom: 16px; +} + +.form-inline select { + flex: 1; + padding: 10px 12px; + border: 1px solid var(--border); + border-radius: 6px; +} + +/* Buttons */ +.btn { + padding: 10px 20px; + border: none; + border-radius: 6px; + font-size: 14px; + font-weight: 500; + cursor: pointer; + transition: all 0.3s; + display: inline-flex; + align-items: center; + gap: 8px; +} + +.btn-primary { + background: var(--primary); + color: white; +} + +.btn-primary:hover { + background: var(--primary-dark); + transform: translateY(-1px); + box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4); +} + +.btn-secondary { + background: var(--text-light); + color: white; +} + +.btn-secondary:hover { + background: var(--text); +} + +.btn-success { + background: var(--success); + color: white; +} + +.btn-danger { + background: var(--danger); + color: white; +} + +.btn-block { + width: 100%; + justify-content: center; +} + +.btn:disabled { + opacity: 0.5; + cursor: not-allowed; +} + +/* Quick Actions */ +.quick-actions { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); + gap: 12px; +} + +/* Status Items */ +.status-item { + display: flex; + justify-content: space-between; + padding: 12px; + background: var(--light); + border-radius: 6px; + margin-bottom: 8px; +} + +.status-label { + font-weight: 500; + color: var(--text); +} + +.status-value { + color: var(--text-light); +} + +.status-value.success { + color: var(--success); + font-weight: 600; +} + +.status-value.error { + color: var(--danger); + font-weight: 600; +} + +/* Chat Interface */ +.chat-card { + display: flex; + flex-direction: column; + height: 600px; +} + +.chat-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 16px; +} + +.chat-header .form-group { + margin: 0; + flex: 1; + max-width: 200px; +} + +.chat-messages { + flex: 1; + overflow-y: auto; + border: 1px solid var(--border); + border-radius: 6px; + padding: 16px; + background: var(--light); + margin-bottom: 16px; +} + +.chat-message { + margin-bottom: 12px; + padding: 12px; + border-radius: 8px; + max-width: 80%; + word-wrap: break-word; +} + +.chat-message.user { + background: var(--primary); + color: white; + margin-left: auto; +} + +.chat-message.assistant { + background: white; + border: 1px solid var(--border); +} + +.chat-message.system { + background: var(--info); + color: white; + text-align: center; + max-width: 100%; +} + +.chat-input-form { + display: flex; + gap: 10px; +} + +.chat-input-form input { + flex: 1; + padding: 10px 12px; + border: 1px solid var(--border); + border-radius: 6px; +} + +/* Results List */ +.result-item { + padding: 12px; + background: var(--light); + border-radius: 6px; + margin-bottom: 8px; + border-left: 4px solid var(--primary); +} + +.result-item strong { + color: var(--text); +} + +.result-item .accuracy { + color: var(--success); + font-weight: 600; + font-size: 18px; +} + +/* Job List */ +.job-item { + display: flex; + justify-content: space-between; + align-items: center; + padding: 12px; + background: var(--light); + border-radius: 6px; + margin-bottom: 8px; +} + +.job-item button { + margin-left: 8px; +} + +/* Dataset Grid */ +.dataset-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(150px, 1fr)); + gap: 12px; +} + +.dataset-item { + padding: 12px; + background: var(--light); + border: 1px solid var(--border); + border-radius: 6px; + text-align: center; + font-weight: 500; +} + +.dataset-item.quantum { border-left: 4px solid var(--primary); } +.dataset-item.chat { border-left: 4px solid var(--success); } +.dataset-item.vision { border-left: 4px solid var(--warning); } + +/* Logs */ +.log-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 16px; +} + +.log-output { + background: var(--dark); + color: #00ff00; + padding: 16px; + border-radius: 6px; + font-family: 'Courier New', monospace; + font-size: 12px; + max-height: 500px; + overflow-y: auto; +} + +.log-entry { + padding: 4px 0; + border-bottom: 1px solid rgba(255, 255, 255, 0.1); +} + +.log-entry:last-child { + border-bottom: none; +} + +.log-entry.info { color: #00ff00; } +.log-entry.warning { color: #ffa500; } +.log-entry.error { color: #ff0000; } +.log-entry.success { color: #00ffff; } + +/* Loading */ +.loading { + text-align: center; + padding: 20px; + color: var(--text-light); +} + +.loading::after { + content: '...'; + animation: dots 1.5s steps(3, end) infinite; +} + +@keyframes dots { + 0%, 20% { content: '.'; } + 40% { content: '..'; } + 60%, 100% { content: '...'; } +} + +/* Badge */ +.badge { + display: inline-block; + padding: 4px 8px; + border-radius: 12px; + font-size: 12px; + font-weight: 600; +} + +.badge.success { background: var(--success); color: white; } +.badge.error { background: var(--danger); color: white; } +.badge.warning { background: var(--warning); color: white; } +.badge.info { background: var(--info); color: white; } + +/* Responsive */ +@media (max-width: 768px) { + body { + padding: 10px; + } + + .header { + flex-direction: column; + gap: 16px; + text-align: center; + } + + .tabs { + flex-wrap: nowrap; + overflow-x: auto; + } + + .grid-2 { + grid-template-columns: 1fr; + } + + .quick-actions { + grid-template-columns: 1fr; + } +} diff --git a/mount/train_max_performance.py b/mount/train_max_performance.py index c9fdbf273..9c19d4026 100644 --- a/mount/train_max_performance.py +++ b/mount/train_max_performance.py @@ -1,280 +1,280 @@ -""" -Maximum Performance AI Training Script -Utilizes all available CPU/GPU resources for intensive training -""" - -import os -import sys -import time -from pathlib import Path -from typing import Any, Dict - -import psutil -import torch - - -def check_system_resources(): - """Check and display available system resources""" - print("=" * 70) - print("SYSTEM RESOURCES CHECK") - print("=" * 70) - - # CPU info - cpu_count = psutil.cpu_count(logical=True) - cpu_physical = psutil.cpu_count(logical=False) - cpu_percent = psutil.cpu_percent(interval=1) - - print("\n🖥️ CPU:") - print(f" Logical cores: {cpu_count}") - print(f" Physical cores: {cpu_physical}") - print(f" Current usage: {cpu_percent}%") - - # Memory info - memory = psutil.virtual_memory() - print("\n💾 RAM:") - print(f" Total: {memory.total / (1024**3):.1f} GB") - print(f" Available: {memory.available / (1024**3):.1f} GB") - print(f" Used: {memory.percent}%") - - # GPU info - if torch.cuda.is_available(): - print("\n🎮 GPU:") - for i in range(torch.cuda.device_count()): - gpu_name = torch.cuda.get_device_name(i) - gpu_memory = torch.cuda.get_device_properties(i).total_memory / (1024**3) - print(f" GPU {i}: {gpu_name}") - print(f" Memory: {gpu_memory:.1f} GB") - device = "cuda" - compute_capability = torch.cuda.get_device_capability() - print(f" CUDA Version: {torch.version.cuda}") - print(f" Compute Capability: {compute_capability}") - else: - print("\n⚠️ No GPU detected - will use CPU only") - device = "cpu" - - # Disk info - disk = psutil.disk_usage("C:\\") - print("\n💿 Disk (C:):") - print(f" Total: {disk.total / (1024**3):.1f} GB") - print(f" Free: {disk.free / (1024**3):.1f} GB") - - print("\n" + "=" * 70) - return device, cpu_count - - -def get_optimal_settings( - device: str, cpu_count: int, memory_gb: float -) -> Dict[str, Any]: - """Calculate optimal training settings based on hardware""" - settings = {} - - if device == "cuda": - gpu_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3) - - # Aggressive settings for GPU - if gpu_memory >= 16: - settings["batch_size"] = 16 - settings["gradient_accumulation"] = 2 - settings["workers"] = min(8, cpu_count // 2) - elif gpu_memory >= 8: - settings["batch_size"] = 8 - settings["gradient_accumulation"] = 4 - settings["workers"] = min(6, cpu_count // 2) - else: - settings["batch_size"] = 4 - settings["gradient_accumulation"] = 8 - settings["workers"] = min(4, cpu_count // 2) - else: - # CPU-only settings - settings["batch_size"] = 2 - settings["gradient_accumulation"] = 16 - settings["workers"] = max(1, cpu_count - 2) # Leave 2 cores free - - return settings - - -def monitor_resources(): - """Monitor system resources during training""" - cpu_percent = psutil.cpu_percent(interval=0.1) - memory = psutil.virtual_memory() - - status = f"CPU: {cpu_percent:.1f}% | RAM: {memory.percent:.1f}% ({memory.used / (1024**3):.1f}/{memory.total / (1024**3):.1f} GB)" - - if torch.cuda.is_available(): - gpu_memory = torch.cuda.memory_allocated(0) / (1024**3) - gpu_total = torch.cuda.get_device_properties(0).total_memory / (1024**3) - gpu_percent = (gpu_memory / gpu_total) * 100 - status += f" | GPU: {gpu_percent:.1f}% ({gpu_memory:.1f}/{gpu_total:.1f} GB)" - - return status - - -def setup_environment_variables(settings: Dict[str, Any]): - """Set environment variables for maximum performance""" - - # PyTorch optimizations - os.environ["OMP_NUM_THREADS"] = str(settings["workers"]) - os.environ["MKL_NUM_THREADS"] = str(settings["workers"]) - os.environ["NUMEXPR_NUM_THREADS"] = str(settings["workers"]) - - # CUDA optimizations (if available) - if torch.cuda.is_available(): - os.environ["CUDA_LAUNCH_BLOCKING"] = "0" - torch.backends.cudnn.benchmark = True - torch.backends.cudnn.deterministic = False - - # Memory optimizations - os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:512" - - print("\n✓ Environment optimized for maximum performance") - - -def run_max_training( - dataset: str = "../../datasets/chat/dolly", - model: str = "microsoft/Phi-3.5-mini-instruct", - epochs: int = 3, -): - """Run training with maximum hardware utilization""" - - print("\n" + "=" * 70) - print("MAXIMUM PERFORMANCE AI TRAINING") - print("=" * 70) - - # Check system resources - device, cpu_count = check_system_resources() - memory = psutil.virtual_memory() - memory_gb = memory.total / (1024**3) - - # Get optimal settings - settings = get_optimal_settings(device, cpu_count, memory_gb) - - print("\n📊 OPTIMAL SETTINGS:") - print(f" Batch size: {settings['batch_size']}") - print(f" Gradient accumulation: {settings['gradient_accumulation']}") - print( - f" Effective batch size: {settings['batch_size'] * settings['gradient_accumulation']}" - ) - print(f" DataLoader workers: {settings['workers']}") - print(f" Device: {device.upper()}") - - # Setup environment - setup_environment_variables(settings) - - # Build training command - script_path = ( - Path(__file__).parent.parent - / "AI" - / "microsoft_phi-silica-3.6_v1" - / "scripts" - / "train_lora.py" - ) - config_path = ( - Path(__file__).parent.parent - / "AI" - / "microsoft_phi-silica-3.6_v1" - / "lora" - / "lora.yaml" - ) - - # Convert dataset path to absolute - dataset_abs = Path(dataset) - if not dataset_abs.is_absolute(): - dataset_abs = Path(__file__).parent / dataset - - save_dir = ( - Path(__file__).parent.parent / "data_out" / "lora_training" / "max_performance" - ) - - cmd = [ - sys.executable, - str(script_path), - "--dataset", - str(dataset_abs.resolve()), - "--config", - str(config_path), - "--epochs", - str(epochs), - "--save-dir", - str(save_dir), - ] - - print("\n🚀 STARTING TRAINING...") - print(f" Dataset: {dataset}") - print(f" Model: {model}") - print(f" Epochs: {epochs}") - print(" Output: data_out/lora_training/max_performance") - print("\n" + "=" * 70) - - # Confirmation - print("\n⚠️ WARNING: This will use maximum system resources!") - print(" - High CPU/GPU usage") - print(" - Significant RAM consumption") - print(" - May slow down other applications") - print("\nPress Ctrl+C within 5 seconds to cancel...") - - try: - time.sleep(5) - except KeyboardInterrupt: - print("\n\n❌ Training cancelled by user") - return - - print("\n▶️ Training started! Monitor resource usage below:\n") - - # Start training with resource monitoring - import subprocess - - process = subprocess.Popen( - cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1 - ) - - last_update = time.time() - - try: - for line in process.stdout: - print(line, end="") - - # Update resource monitor every 5 seconds - if time.time() - last_update > 5: - print(f"\n📊 Resources: {monitor_resources()}\n") - last_update = time.time() - - process.wait() - - if process.returncode == 0: - print("\n" + "=" * 70) - print("✅ TRAINING COMPLETED SUCCESSFULLY!") - print("=" * 70) - else: - print("\n" + "=" * 70) - print("❌ TRAINING FAILED") - print("=" * 70) - - except KeyboardInterrupt: - print("\n\n⚠️ Training interrupted by user") - process.terminate() - process.wait() - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser(description="Max Performance AI Training") - parser.add_argument( - "--dataset", default="../../datasets/chat/dolly", help="Dataset path" - ) - parser.add_argument( - "--model", default="microsoft/Phi-3.5-mini-instruct", help="Model to train" - ) - parser.add_argument("--epochs", type=int, default=3, help="Number of epochs") - parser.add_argument( - "--check-only", - action="store_true", - help="Only check system resources, do not train", - ) - - args = parser.parse_args() - - if args.check_only: - check_system_resources() - else: - run_max_training(args.dataset, args.model, args.epochs) +""" +Maximum Performance AI Training Script +Utilizes all available CPU/GPU resources for intensive training +""" + +import os +import sys +import time +from pathlib import Path +from typing import Any, Dict + +import psutil +import torch + + +def check_system_resources(): + """Check and display available system resources""" + print("=" * 70) + print("SYSTEM RESOURCES CHECK") + print("=" * 70) + + # CPU info + cpu_count = psutil.cpu_count(logical=True) + cpu_physical = psutil.cpu_count(logical=False) + cpu_percent = psutil.cpu_percent(interval=1) + + print("\n🖥️ CPU:") + print(f" Logical cores: {cpu_count}") + print(f" Physical cores: {cpu_physical}") + print(f" Current usage: {cpu_percent}%") + + # Memory info + memory = psutil.virtual_memory() + print("\n💾 RAM:") + print(f" Total: {memory.total / (1024**3):.1f} GB") + print(f" Available: {memory.available / (1024**3):.1f} GB") + print(f" Used: {memory.percent}%") + + # GPU info + if torch.cuda.is_available(): + print("\n🎮 GPU:") + for i in range(torch.cuda.device_count()): + gpu_name = torch.cuda.get_device_name(i) + gpu_memory = torch.cuda.get_device_properties(i).total_memory / (1024**3) + print(f" GPU {i}: {gpu_name}") + print(f" Memory: {gpu_memory:.1f} GB") + device = "cuda" + compute_capability = torch.cuda.get_device_capability() + print(f" CUDA Version: {torch.version.cuda}") + print(f" Compute Capability: {compute_capability}") + else: + print("\n⚠️ No GPU detected - will use CPU only") + device = "cpu" + + # Disk info + disk = psutil.disk_usage("C:\\") + print("\n💿 Disk (C:):") + print(f" Total: {disk.total / (1024**3):.1f} GB") + print(f" Free: {disk.free / (1024**3):.1f} GB") + + print("\n" + "=" * 70) + return device, cpu_count + + +def get_optimal_settings( + device: str, cpu_count: int, memory_gb: float +) -> Dict[str, Any]: + """Calculate optimal training settings based on hardware""" + settings = {} + + if device == "cuda": + gpu_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3) + + # Aggressive settings for GPU + if gpu_memory >= 16: + settings["batch_size"] = 16 + settings["gradient_accumulation"] = 2 + settings["workers"] = min(8, cpu_count // 2) + elif gpu_memory >= 8: + settings["batch_size"] = 8 + settings["gradient_accumulation"] = 4 + settings["workers"] = min(6, cpu_count // 2) + else: + settings["batch_size"] = 4 + settings["gradient_accumulation"] = 8 + settings["workers"] = min(4, cpu_count // 2) + else: + # CPU-only settings + settings["batch_size"] = 2 + settings["gradient_accumulation"] = 16 + settings["workers"] = max(1, cpu_count - 2) # Leave 2 cores free + + return settings + + +def monitor_resources(): + """Monitor system resources during training""" + cpu_percent = psutil.cpu_percent(interval=0.1) + memory = psutil.virtual_memory() + + status = f"CPU: {cpu_percent:.1f}% | RAM: {memory.percent:.1f}% ({memory.used / (1024**3):.1f}/{memory.total / (1024**3):.1f} GB)" + + if torch.cuda.is_available(): + gpu_memory = torch.cuda.memory_allocated(0) / (1024**3) + gpu_total = torch.cuda.get_device_properties(0).total_memory / (1024**3) + gpu_percent = (gpu_memory / gpu_total) * 100 + status += f" | GPU: {gpu_percent:.1f}% ({gpu_memory:.1f}/{gpu_total:.1f} GB)" + + return status + + +def setup_environment_variables(settings: Dict[str, Any]): + """Set environment variables for maximum performance""" + + # PyTorch optimizations + os.environ["OMP_NUM_THREADS"] = str(settings["workers"]) + os.environ["MKL_NUM_THREADS"] = str(settings["workers"]) + os.environ["NUMEXPR_NUM_THREADS"] = str(settings["workers"]) + + # CUDA optimizations (if available) + if torch.cuda.is_available(): + os.environ["CUDA_LAUNCH_BLOCKING"] = "0" + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.deterministic = False + + # Memory optimizations + os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:512" + + print("\n✓ Environment optimized for maximum performance") + + +def run_max_training( + dataset: str = "../../datasets/chat/dolly", + model: str = "microsoft/Phi-3.5-mini-instruct", + epochs: int = 3, +): + """Run training with maximum hardware utilization""" + + print("\n" + "=" * 70) + print("MAXIMUM PERFORMANCE AI TRAINING") + print("=" * 70) + + # Check system resources + device, cpu_count = check_system_resources() + memory = psutil.virtual_memory() + memory_gb = memory.total / (1024**3) + + # Get optimal settings + settings = get_optimal_settings(device, cpu_count, memory_gb) + + print("\n📊 OPTIMAL SETTINGS:") + print(f" Batch size: {settings['batch_size']}") + print(f" Gradient accumulation: {settings['gradient_accumulation']}") + print( + f" Effective batch size: {settings['batch_size'] * settings['gradient_accumulation']}" + ) + print(f" DataLoader workers: {settings['workers']}") + print(f" Device: {device.upper()}") + + # Setup environment + setup_environment_variables(settings) + + # Build training command + script_path = ( + Path(__file__).parent.parent + / "AI" + / "microsoft_phi-silica-3.6_v1" + / "scripts" + / "train_lora.py" + ) + config_path = ( + Path(__file__).parent.parent + / "AI" + / "microsoft_phi-silica-3.6_v1" + / "lora" + / "lora.yaml" + ) + + # Convert dataset path to absolute + dataset_abs = Path(dataset) + if not dataset_abs.is_absolute(): + dataset_abs = Path(__file__).parent / dataset + + save_dir = ( + Path(__file__).parent.parent / "data_out" / "lora_training" / "max_performance" + ) + + cmd = [ + sys.executable, + str(script_path), + "--dataset", + str(dataset_abs.resolve()), + "--config", + str(config_path), + "--epochs", + str(epochs), + "--save-dir", + str(save_dir), + ] + + print("\n🚀 STARTING TRAINING...") + print(f" Dataset: {dataset}") + print(f" Model: {model}") + print(f" Epochs: {epochs}") + print(" Output: data_out/lora_training/max_performance") + print("\n" + "=" * 70) + + # Confirmation + print("\n⚠️ WARNING: This will use maximum system resources!") + print(" - High CPU/GPU usage") + print(" - Significant RAM consumption") + print(" - May slow down other applications") + print("\nPress Ctrl+C within 5 seconds to cancel...") + + try: + time.sleep(5) + except KeyboardInterrupt: + print("\n\n❌ Training cancelled by user") + return + + print("\n▶️ Training started! Monitor resource usage below:\n") + + # Start training with resource monitoring + import subprocess + + process = subprocess.Popen( + cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1 + ) + + last_update = time.time() + + try: + for line in process.stdout: + print(line, end="") + + # Update resource monitor every 5 seconds + if time.time() - last_update > 5: + print(f"\n📊 Resources: {monitor_resources()}\n") + last_update = time.time() + + process.wait() + + if process.returncode == 0: + print("\n" + "=" * 70) + print("✅ TRAINING COMPLETED SUCCESSFULLY!") + print("=" * 70) + else: + print("\n" + "=" * 70) + print("❌ TRAINING FAILED") + print("=" * 70) + + except KeyboardInterrupt: + print("\n\n⚠️ Training interrupted by user") + process.terminate() + process.wait() + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Max Performance AI Training") + parser.add_argument( + "--dataset", default="../../datasets/chat/dolly", help="Dataset path" + ) + parser.add_argument( + "--model", default="microsoft/Phi-3.5-mini-instruct", help="Model to train" + ) + parser.add_argument("--epochs", type=int, default=3, help="Number of epochs") + parser.add_argument( + "--check-only", + action="store_true", + help="Only check system resources, do not train", + ) + + args = parser.parse_args() + + if args.check_only: + check_system_resources() + else: + run_max_training(args.dataset, args.model, args.epochs) diff --git a/mount/training_integration.py b/mount/training_integration.py index 4e1a46053..df76ee759 100644 --- a/mount/training_integration.py +++ b/mount/training_integration.py @@ -1,263 +1,263 @@ -""" -Training Integration Module -Interfaces with LoRA training and orchestration systems -""" - -import json -import subprocess -import sys -from pathlib import Path -from typing import Any, Dict, List, Optional - - -class TrainingIntegration: - """Integration layer for training operations""" - - def __init__(self, config: Dict[str, Any]): - self.config = config - self.workspace_root = Path(config["paths"]["workspace_root"]).resolve() - self.phi_path = Path(config["paths"]["phi_training"]).resolve() - self.scripts_path = Path(config["paths"]["scripts"]).resolve() - self.output_dir = Path(config["paths"]["data_out"]).resolve() - - async def get_status(self) -> Dict[str, Any]: - """Get current training system status""" - return { - "enabled": self.config["training"]["enabled"], - "orchestrators": { - "autotrain": self._get_autotrain_status(), - "quantum_autorun": self._get_quantum_autorun_status(), - }, - "lora_adapter": self._check_lora_adapter(), - "recent_trainings": self._get_recent_trainings(), - } - - def _get_autotrain_status(self) -> Dict[str, Any]: - """Get AutoTrain orchestrator status""" - config_file = self.workspace_root / "autotrain.yaml" - status_file = self.output_dir / "autotrain" / "status.json" - - status = { - "config_exists": config_file.exists(), - "status_file": None, - "jobs": [], - } - - if status_file.exists(): - try: - with open(status_file) as f: - status_data = json.load(f) - status["status_file"] = str(status_file) - status["jobs"] = status_data.get("jobs", {}) - status["last_run"] = status_data.get("timestamp") - except Exception: - pass - - return status - - def _get_quantum_autorun_status(self) -> Dict[str, Any]: - """Get Quantum AutoRun orchestrator status""" - config_file = self.workspace_root / "quantum_autorun.yaml" - status_file = self.output_dir / "quantum_autorun" / "status.json" - - status = { - "config_exists": config_file.exists(), - "status_file": None, - "jobs": [], - } - - if status_file.exists(): - try: - with open(status_file) as f: - status_data = json.load(f) - status["status_file"] = str(status_file) - status["jobs"] = status_data.get("jobs", {}) - status["last_run"] = status_data.get("timestamp") - except Exception: - pass - - return status - - def _check_lora_adapter(self) -> Dict[str, Any]: - """Check if LoRA adapter is available""" - adapter_dir = self.output_dir / "lora_training" / "lora_adapter" - config_file = adapter_dir / "adapter_config.json" - - adapter_info = { - "available": config_file.exists(), - "path": str(adapter_dir) if config_file.exists() else None, - } - - if config_file.exists(): - try: - with open(config_file) as f: - config = json.load(f) - adapter_info["rank"] = config.get("r") - adapter_info["target_modules"] = config.get("target_modules") - adapter_info["model"] = config.get("base_model_name_or_path") - except Exception: - pass - - return adapter_info - - def _get_recent_trainings(self, limit: int = 5) -> List[Dict[str, Any]]: - """Get recent training runs""" - trainings = [] - lora_output = self.output_dir / "lora_training" - - if not lora_output.exists(): - return trainings - - # Look for training logs and checkpoints - for run_dir in sorted( - lora_output.iterdir(), key=lambda p: p.stat().st_mtime, reverse=True - )[:limit]: - if run_dir.is_dir(): - trainings.append( - { - "name": run_dir.name, - "path": str(run_dir), - "timestamp": run_dir.stat().st_mtime, - "has_adapter": (run_dir / "adapter_config.json").exists(), - } - ) - - return trainings - - async def run_autotrain( - self, job_name: Optional[str] = None, dry_run: bool = False - ) -> Dict[str, Any]: - """Run AutoTrain orchestrator""" - try: - autotrain_script = self.scripts_path / "autotrain.py" - - cmd = [sys.executable, str(autotrain_script)] - if dry_run: - cmd.append("--dry-run") - if job_name: - cmd.extend(["--job", job_name]) - - result = subprocess.run( - cmd, capture_output=True, text=True, cwd=str(self.workspace_root) - ) - - return { - "success": result.returncode == 0, - "job_name": job_name or "all", - "dry_run": dry_run, - "stdout": result.stdout, - "stderr": result.stderr, - } - except Exception as e: - return {"success": False, "error": str(e)} - - async def list_autotrain_jobs(self) -> List[str]: - """List all configured AutoTrain jobs""" - try: - autotrain_script = self.scripts_path / "autotrain.py" - - result = subprocess.run( - [sys.executable, str(autotrain_script), "--list"], - capture_output=True, - text=True, - cwd=str(self.workspace_root), - ) - - if result.returncode == 0: - # Parse job names from output - lines = result.stdout.strip().split("\n") - jobs = [ - line.strip() - for line in lines - if line.strip() and not line.startswith("-") - ] - return jobs - return [] - except Exception: - return [] - - async def train_lora( - self, - dataset: str, - max_train_samples: int = 64, - max_eval_samples: int = 16, - epochs: int = 1, - ) -> Dict[str, Any]: - """Run LoRA training directly""" - try: - train_script = self.phi_path / "scripts" / "train_lora.py" - config_file = self.phi_path / "lora" / "lora.yaml" - - cmd = [ - sys.executable, - str(train_script), - "--dataset", - dataset, - "--config", - str(config_file), - "--max-train-samples", - str(max_train_samples), - "--max-eval-samples", - str(max_eval_samples), - "--epochs", - str(epochs), - ] - - result = subprocess.run( - cmd, capture_output=True, text=True, cwd=str(self.phi_path) - ) - - return { - "success": result.returncode == 0, - "dataset": dataset, - "samples": max_train_samples, - "epochs": epochs, - "stdout": result.stdout, - "stderr": result.stderr, - } - except Exception as e: - return {"success": False, "error": str(e)} - - async def list_datasets(self) -> Dict[str, List[str]]: - """List available training datasets""" - datasets_root = self.workspace_root / "datasets" - - result = {"quantum": [], "chat": [], "vision": []} - - # Quantum datasets (CSV) - quantum_dir = datasets_root / "quantum" - if quantum_dir.exists(): - result["quantum"] = [f.stem for f in quantum_dir.glob("*.csv")] - - # Chat datasets (JSONL directories) - chat_dir = datasets_root / "chat" - if chat_dir.exists(): - result["chat"] = [d.name for d in chat_dir.iterdir() if d.is_dir()] - - # Vision datasets - vision_dir = datasets_root / "vision" - if vision_dir.exists(): - result["vision"] = [d.name for d in vision_dir.iterdir() if d.is_dir()] - - return result - - async def get_training_metrics(self, run_name: str) -> Dict[str, Any]: - """Get metrics for a specific training run""" - run_dir = self.output_dir / "lora_training" / run_name - - if not run_dir.exists(): - return {"error": "Training run not found"} - - metrics = { - "run_name": run_name, - "path": str(run_dir), - "has_adapter": (run_dir / "adapter_config.json").exists(), - "checkpoints": [], - } - - # Look for checkpoint directories - for checkpoint in run_dir.glob("checkpoint-*"): - if checkpoint.is_dir(): - metrics["checkpoints"].append(checkpoint.name) - - return metrics +""" +Training Integration Module +Interfaces with LoRA training and orchestration systems +""" + +import json +import subprocess +import sys +from pathlib import Path +from typing import Any, Dict, List, Optional + + +class TrainingIntegration: + """Integration layer for training operations""" + + def __init__(self, config: Dict[str, Any]): + self.config = config + self.workspace_root = Path(config["paths"]["workspace_root"]).resolve() + self.phi_path = Path(config["paths"]["phi_training"]).resolve() + self.scripts_path = Path(config["paths"]["scripts"]).resolve() + self.output_dir = Path(config["paths"]["data_out"]).resolve() + + async def get_status(self) -> Dict[str, Any]: + """Get current training system status""" + return { + "enabled": self.config["training"]["enabled"], + "orchestrators": { + "autotrain": self._get_autotrain_status(), + "quantum_autorun": self._get_quantum_autorun_status(), + }, + "lora_adapter": self._check_lora_adapter(), + "recent_trainings": self._get_recent_trainings(), + } + + def _get_autotrain_status(self) -> Dict[str, Any]: + """Get AutoTrain orchestrator status""" + config_file = self.workspace_root / "autotrain.yaml" + status_file = self.output_dir / "autotrain" / "status.json" + + status = { + "config_exists": config_file.exists(), + "status_file": None, + "jobs": [], + } + + if status_file.exists(): + try: + with open(status_file) as f: + status_data = json.load(f) + status["status_file"] = str(status_file) + status["jobs"] = status_data.get("jobs", {}) + status["last_run"] = status_data.get("timestamp") + except Exception: + pass + + return status + + def _get_quantum_autorun_status(self) -> Dict[str, Any]: + """Get Quantum AutoRun orchestrator status""" + config_file = self.workspace_root / "quantum_autorun.yaml" + status_file = self.output_dir / "quantum_autorun" / "status.json" + + status = { + "config_exists": config_file.exists(), + "status_file": None, + "jobs": [], + } + + if status_file.exists(): + try: + with open(status_file) as f: + status_data = json.load(f) + status["status_file"] = str(status_file) + status["jobs"] = status_data.get("jobs", {}) + status["last_run"] = status_data.get("timestamp") + except Exception: + pass + + return status + + def _check_lora_adapter(self) -> Dict[str, Any]: + """Check if LoRA adapter is available""" + adapter_dir = self.output_dir / "lora_training" / "lora_adapter" + config_file = adapter_dir / "adapter_config.json" + + adapter_info = { + "available": config_file.exists(), + "path": str(adapter_dir) if config_file.exists() else None, + } + + if config_file.exists(): + try: + with open(config_file) as f: + config = json.load(f) + adapter_info["rank"] = config.get("r") + adapter_info["target_modules"] = config.get("target_modules") + adapter_info["model"] = config.get("base_model_name_or_path") + except Exception: + pass + + return adapter_info + + def _get_recent_trainings(self, limit: int = 5) -> List[Dict[str, Any]]: + """Get recent training runs""" + trainings = [] + lora_output = self.output_dir / "lora_training" + + if not lora_output.exists(): + return trainings + + # Look for training logs and checkpoints + for run_dir in sorted( + lora_output.iterdir(), key=lambda p: p.stat().st_mtime, reverse=True + )[:limit]: + if run_dir.is_dir(): + trainings.append( + { + "name": run_dir.name, + "path": str(run_dir), + "timestamp": run_dir.stat().st_mtime, + "has_adapter": (run_dir / "adapter_config.json").exists(), + } + ) + + return trainings + + async def run_autotrain( + self, job_name: Optional[str] = None, dry_run: bool = False + ) -> Dict[str, Any]: + """Run AutoTrain orchestrator""" + try: + autotrain_script = self.scripts_path / "autotrain.py" + + cmd = [sys.executable, str(autotrain_script)] + if dry_run: + cmd.append("--dry-run") + if job_name: + cmd.extend(["--job", job_name]) + + result = subprocess.run( + cmd, capture_output=True, text=True, cwd=str(self.workspace_root) + ) + + return { + "success": result.returncode == 0, + "job_name": job_name or "all", + "dry_run": dry_run, + "stdout": result.stdout, + "stderr": result.stderr, + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def list_autotrain_jobs(self) -> List[str]: + """List all configured AutoTrain jobs""" + try: + autotrain_script = self.scripts_path / "autotrain.py" + + result = subprocess.run( + [sys.executable, str(autotrain_script), "--list"], + capture_output=True, + text=True, + cwd=str(self.workspace_root), + ) + + if result.returncode == 0: + # Parse job names from output + lines = result.stdout.strip().split("\n") + jobs = [ + line.strip() + for line in lines + if line.strip() and not line.startswith("-") + ] + return jobs + return [] + except Exception: + return [] + + async def train_lora( + self, + dataset: str, + max_train_samples: int = 64, + max_eval_samples: int = 16, + epochs: int = 1, + ) -> Dict[str, Any]: + """Run LoRA training directly""" + try: + train_script = self.phi_path / "scripts" / "train_lora.py" + config_file = self.phi_path / "lora" / "lora.yaml" + + cmd = [ + sys.executable, + str(train_script), + "--dataset", + dataset, + "--config", + str(config_file), + "--max-train-samples", + str(max_train_samples), + "--max-eval-samples", + str(max_eval_samples), + "--epochs", + str(epochs), + ] + + result = subprocess.run( + cmd, capture_output=True, text=True, cwd=str(self.phi_path) + ) + + return { + "success": result.returncode == 0, + "dataset": dataset, + "samples": max_train_samples, + "epochs": epochs, + "stdout": result.stdout, + "stderr": result.stderr, + } + except Exception as e: + return {"success": False, "error": str(e)} + + async def list_datasets(self) -> Dict[str, List[str]]: + """List available training datasets""" + datasets_root = self.workspace_root / "datasets" + + result = {"quantum": [], "chat": [], "vision": []} + + # Quantum datasets (CSV) + quantum_dir = datasets_root / "quantum" + if quantum_dir.exists(): + result["quantum"] = [f.stem for f in quantum_dir.glob("*.csv")] + + # Chat datasets (JSONL directories) + chat_dir = datasets_root / "chat" + if chat_dir.exists(): + result["chat"] = [d.name for d in chat_dir.iterdir() if d.is_dir()] + + # Vision datasets + vision_dir = datasets_root / "vision" + if vision_dir.exists(): + result["vision"] = [d.name for d in vision_dir.iterdir() if d.is_dir()] + + return result + + async def get_training_metrics(self, run_name: str) -> Dict[str, Any]: + """Get metrics for a specific training run""" + run_dir = self.output_dir / "lora_training" / run_name + + if not run_dir.exists(): + return {"error": "Training run not found"} + + metrics = { + "run_name": run_name, + "path": str(run_dir), + "has_adapter": (run_dir / "adapter_config.json").exists(), + "checkpoints": [], + } + + # Look for checkpoint directories + for checkpoint in run_dir.glob("checkpoint-*"): + if checkpoint.is_dir(): + metrics["checkpoints"].append(checkpoint.name) + + return metrics diff --git a/quantum-ai/README.md b/quantum-ai/README.md index 04b38f6f4..1683962cc 100644 --- a/quantum-ai/README.md +++ b/quantum-ai/README.md @@ -1,123 +1,123 @@ -# Quantum Code LLM - -Self-contained quantum-classical transformer language model for code generation. - -## Architecture - -``` -Tokens → Embedding + Positional Encoding - → N × QuantumTransformerBlock - ├── QuantumKernelAttention - │ Q, K projected through variational quantum circuit - │ scores = φ(Q) · φ(K)ᵀ / √n_qubits - └── QuantumFFN - Linear → quantum variational circuit → Linear - → Output head (weight-tied to embedding) -``` - -## Quantum Backend Auto-Detection - -| Priority | Backend | Requires | -| -------- | ------- | --------- | -| 1st | `qiskit.aer` | `pennylane-qiskit` + `qiskit-aer` | -| 2nd | `default.qubit` | `pennylane` (already in repo) | -| 3rd | classical MLP | nothing (always available) | - -## Quick Start - -```python -import sys -from pathlib import Path - -sys.path.insert(0, str(Path("quantum-ai/src").resolve())) -from api import generate, train - -# Train on built-in Python code snippets -model, tokenizer = train( - model_cfg={"n_qubits": 4, "d_model": 64, "n_layers": 2}, - train_cfg={"n_epochs": 5, "lr": 3e-3}, -) - -# Generate code -print(generate(model, tokenizer, "def factorial(n):")) -``` - -## Run Demo - -```bash -python quantum-ai/examples/quantum_code_llm_demo.py - -# Override settings via env vars -QLCM_EPOCHS=10 QLCM_QUBITS=6 python quantum-ai/examples/quantum_code_llm_demo.py - -# Force classical backend -QLCM_BACKEND=classical python quantum-ai/examples/quantum_code_llm_demo.py -``` - -## Fast Smoke Check (Local/CI-Friendly) - -```bash -# Fast deterministic path (recommended for CI/local sanity checks) -python3 quantum-ai/scripts/smoke_quantum_code_llm.py - -# Override backend/epochs if needed -python3 quantum-ai/scripts/smoke_quantum_code_llm.py --backend auto --epochs 1 -``` - -If dependencies are missing, install minimum runtime packages first: - -```bash -python3 -m pip install -r quantum-ai/requirements-smoke.txt -``` - -## Config Reference - -| Parameter | Default | Description | -|-----------|---------|-------------| -| `n_qubits` | 4 | qubits per quantum layer | -| `d_model` | 64 | model hidden dimension | -| `n_heads` | 4 | attention heads | -| `n_layers` | 2 | transformer blocks | -| `n_var_layers` | 2 | variational layers inside each circuit | -| `max_seq_len` | 128 | maximum context length | -| `backend` | `"auto"` | `"auto"` \| `"qiskit.aer"` \| `"default.qubit"` \| `"classical"` | - -## Train with Custom Code - -```python -import sys -from pathlib import Path - -sys.path.insert(0, str(Path("quantum-ai/src").resolve())) -from api import generate, train - -MY_CODE = [ - "def hello(name):\n print('Hi ' + name)\n", - "def bye(name):\n print('Bye ' + name)\n", -] - -model, tok = train(extra_snippets=MY_CODE) -print(generate(model, tok, "def greet(")) -``` - -## Checkpoints - -```python -import sys -from pathlib import Path - -sys.path.insert(0, str(Path("quantum-ai/src").resolve())) -from api import load_checkpoint, save_checkpoint - -checkpoint = Path("data_out/quantum_code_llm/checkpoint.pt") -save_checkpoint(model, tok, checkpoint, extra={"run": "quickstart"}) -restored_model, restored_tok, metadata = load_checkpoint(checkpoint) -print(metadata["path"], metadata["backend"]) -``` - -## Files - -| File | Description | -|------|-------------| -| `src/quantum_code_llm.py` | Self-contained module (~700 lines) | -| `examples/quantum_code_llm_demo.py` | Training + generation demo | +# Quantum Code LLM + +Self-contained quantum-classical transformer language model for code generation. + +## Architecture + +``` +Tokens → Embedding + Positional Encoding + → N × QuantumTransformerBlock + ├── QuantumKernelAttention + │ Q, K projected through variational quantum circuit + │ scores = φ(Q) · φ(K)ᵀ / √n_qubits + └── QuantumFFN + Linear → quantum variational circuit → Linear + → Output head (weight-tied to embedding) +``` + +## Quantum Backend Auto-Detection + +| Priority | Backend | Requires | +| -------- | ------- | --------- | +| 1st | `qiskit.aer` | `pennylane-qiskit` + `qiskit-aer` | +| 2nd | `default.qubit` | `pennylane` (already in repo) | +| 3rd | classical MLP | nothing (always available) | + +## Quick Start + +```python +import sys +from pathlib import Path + +sys.path.insert(0, str(Path("quantum-ai/src").resolve())) +from api import generate, train + +# Train on built-in Python code snippets +model, tokenizer = train( + model_cfg={"n_qubits": 4, "d_model": 64, "n_layers": 2}, + train_cfg={"n_epochs": 5, "lr": 3e-3}, +) + +# Generate code +print(generate(model, tokenizer, "def factorial(n):")) +``` + +## Run Demo + +```bash +python quantum-ai/examples/quantum_code_llm_demo.py + +# Override settings via env vars +QLCM_EPOCHS=10 QLCM_QUBITS=6 python quantum-ai/examples/quantum_code_llm_demo.py + +# Force classical backend +QLCM_BACKEND=classical python quantum-ai/examples/quantum_code_llm_demo.py +``` + +## Fast Smoke Check (Local/CI-Friendly) + +```bash +# Fast deterministic path (recommended for CI/local sanity checks) +python3 quantum-ai/scripts/smoke_quantum_code_llm.py + +# Override backend/epochs if needed +python3 quantum-ai/scripts/smoke_quantum_code_llm.py --backend auto --epochs 1 +``` + +If dependencies are missing, install minimum runtime packages first: + +```bash +python3 -m pip install -r quantum-ai/requirements-smoke.txt +``` + +## Config Reference + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `n_qubits` | 4 | qubits per quantum layer | +| `d_model` | 64 | model hidden dimension | +| `n_heads` | 4 | attention heads | +| `n_layers` | 2 | transformer blocks | +| `n_var_layers` | 2 | variational layers inside each circuit | +| `max_seq_len` | 128 | maximum context length | +| `backend` | `"auto"` | `"auto"` \| `"qiskit.aer"` \| `"default.qubit"` \| `"classical"` | + +## Train with Custom Code + +```python +import sys +from pathlib import Path + +sys.path.insert(0, str(Path("quantum-ai/src").resolve())) +from api import generate, train + +MY_CODE = [ + "def hello(name):\n print('Hi ' + name)\n", + "def bye(name):\n print('Bye ' + name)\n", +] + +model, tok = train(extra_snippets=MY_CODE) +print(generate(model, tok, "def greet(")) +``` + +## Checkpoints + +```python +import sys +from pathlib import Path + +sys.path.insert(0, str(Path("quantum-ai/src").resolve())) +from api import load_checkpoint, save_checkpoint + +checkpoint = Path("data_out/quantum_code_llm/checkpoint.pt") +save_checkpoint(model, tok, checkpoint, extra={"run": "quickstart"}) +restored_model, restored_tok, metadata = load_checkpoint(checkpoint) +print(metadata["path"], metadata["backend"]) +``` + +## Files + +| File | Description | +|------|-------------| +| `src/quantum_code_llm.py` | Self-contained module (~700 lines) | +| `examples/quantum_code_llm_demo.py` | Training + generation demo | diff --git a/quantum-ai/examples/quantum_code_chat.py b/quantum-ai/examples/quantum_code_chat.py index b1f52ddbb..e26e19469 100644 --- a/quantum-ai/examples/quantum_code_chat.py +++ b/quantum-ai/examples/quantum_code_chat.py @@ -1,221 +1,221 @@ -#!/usr/bin/env python3 -"""quantum_code_chat.py — Interactive REPL for the Quantum Code LLM. - -Trains (or loads a checkpoint), then lets you type code prompts and watch -the model complete them. - -Usage ------ - # Train fresh (10 epochs) then chat - python ai-projects/quantum-ml/examples/quantum_code_chat.py - - # Load existing checkpoint and chat immediately - python ai-projects/quantum-ml/examples/quantum_code_chat.py --load - - # Train more before chatting - python ai-projects/quantum-ml/examples/quantum_code_chat.py --epochs 20 - -Commands inside the REPL ------------------------- - — complete that code prompt - /temp 0.6 — set temperature (default 0.8, lower = more focused) - /topk 20 — set top-k (default 40) - /tokens 100 — set max new tokens (default 80) - /retrain — run another 5 epochs of training then return to chat - /save — save checkpoint to data_out/quantum_code_llm/checkpoint.pt - /load — load checkpoint from data_out/quantum_code_llm/checkpoint.pt - /stats — show model info and backend - /help — show this list - /quit — exit -""" - -from __future__ import annotations - -import argparse -import os -import sys - -# Add src directory to path before importing quantum_code_llm -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) - -try: - import quantum_code_llm -except ImportError: - print( - f"Error: Could not import quantum_code_llm. Make sure it's installed in {sys.path[0]}" - ) - raise - -CHECKPOINT = "data_out/quantum_code_llm/checkpoint.pt" - -MODEL_CFG = { - "n_qubits": 4, - "d_model": 64, - "n_heads": 4, - "n_layers": 2, - "n_var_layers": 2, - "max_seq_len": 128, - "dropout": 0.1, - "backend": "auto", -} - - -def save_checkpoint( - model: quantum_code_llm.QuantumCodeLLM, tokenizer: quantum_code_llm.CodeTokenizer -) -> None: - quantum_code_llm.save_checkpoint(model, tokenizer, CHECKPOINT) - print(f" ✓ Saved -> {CHECKPOINT}") - - -def load_checkpoint() -> ( - tuple[quantum_code_llm.QuantumCodeLLM, quantum_code_llm.CodeTokenizer] -): - model, tokenizer, _metadata = quantum_code_llm.load_checkpoint( - CHECKPOINT, map_location="cpu" - ) - print(f" ✓ Loaded <- {CHECKPOINT}") - return model, tokenizer - - -def retrain( - model: quantum_code_llm.QuantumCodeLLM, - tokenizer: quantum_code_llm.CodeTokenizer, - n_epochs: int = 5, -) -> None: - tcfg = quantum_code_llm.TrainConfig( - n_epochs=n_epochs, batch_size=8, lr=1e-3, log_every=40 - ) - trainer = quantum_code_llm.QuantumCodeTrainer(model, tokenizer, tcfg) - trainer.train() - - -def chat_loop( - model: quantum_code_llm.QuantumCodeLLM, tokenizer: quantum_code_llm.CodeTokenizer -) -> None: - temperature = 0.8 - top_k = 40 - max_new = 80 - - print() - print("━" * 60) - print(" Quantum Code LLM — Chat (type /help for commands)") - print( - f" backend: {model.backend} | params: {model.parameter_count()['total']:,}" - ) - print("━" * 60) - print() - - while True: - try: - prompt = input("▶ ").strip() - except (EOFError, KeyboardInterrupt): - print("\nBye!") - break - - if not prompt: - continue - - # ── built-in commands ────────────────────────────────────────── - if prompt == "/quit" or prompt == "/exit": - print("Bye!") - break - - elif prompt == "/help": - print(__doc__) - - elif prompt == "/stats": - p = model.parameter_count() - print(f" backend : {model.backend}") - print(f" params : {p['total']:,} total {p['trainable']:,} trainable") - print(f" vocab : {tokenizer.vocab_size}") - cfg = model.config - print( - f" d_model : {cfg.d_model} n_layers: {cfg.n_layers} n_qubits: {cfg.n_qubits}" - ) - print(f" temp : {temperature} top_k: {top_k} max_tokens: {max_new}") - - elif prompt.startswith("/temp "): - try: - temperature = float(prompt.split()[1]) - print(f" temperature → {temperature}") - except ValueError: - print(" usage: /temp 0.7") - - elif prompt.startswith("/topk "): - try: - top_k = int(prompt.split()[1]) - print(f" top_k → {top_k}") - except ValueError: - print(" usage: /topk 30") - - elif prompt.startswith("/tokens "): - try: - max_new = int(prompt.split()[1]) - print(f" max_new_tokens → {max_new}") - except ValueError: - print(" usage: /tokens 120") - - elif prompt == "/save": - save_checkpoint(model, tokenizer) - - elif prompt == "/load": - if os.path.exists(CHECKPOINT): - model, tokenizer = load_checkpoint() - else: - print(f" No checkpoint at {CHECKPOINT}") - - elif prompt.startswith("/retrain"): - parts = prompt.split() - n = int(parts[1]) if len(parts) > 1 else 5 - print(f" Running {n} more epochs …") - retrain(model, tokenizer, n_epochs=n) - save_checkpoint(model, tokenizer) - - # ── generation ──────────────────────────────────────────────── - else: - result = quantum_code_llm.generate( - model, - tokenizer, - prompt=prompt, - max_new_tokens=max_new, - temperature=temperature, - top_k=top_k, - ) - print() - print(result) - print() - - -def main() -> None: - parser = argparse.ArgumentParser(description="Quantum Code LLM chat REPL") - parser.add_argument( - "--load", action="store_true", help="load checkpoint instead of training" - ) - parser.add_argument( - "--epochs", type=int, default=15, help="epochs to train (default 15)" - ) - args = parser.parse_args() - - tokenizer = quantum_code_llm.CodeTokenizer() - - if args.load and os.path.exists(CHECKPOINT): - model, tokenizer = load_checkpoint() - else: - if args.load: - print(f"No checkpoint found at {CHECKPOINT}, training fresh ...\n") - model, tokenizer = quantum_code_llm.train( - model_cfg=MODEL_CFG, - train_cfg={ - "n_epochs": args.epochs, - "batch_size": 8, - "lr": 3e-3, - "log_every": 50, - }, - ) - save_checkpoint(model, tokenizer) - - chat_loop(model, tokenizer) - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +"""quantum_code_chat.py — Interactive REPL for the Quantum Code LLM. + +Trains (or loads a checkpoint), then lets you type code prompts and watch +the model complete them. + +Usage +----- + # Train fresh (10 epochs) then chat + python ai-projects/quantum-ml/examples/quantum_code_chat.py + + # Load existing checkpoint and chat immediately + python ai-projects/quantum-ml/examples/quantum_code_chat.py --load + + # Train more before chatting + python ai-projects/quantum-ml/examples/quantum_code_chat.py --epochs 20 + +Commands inside the REPL +------------------------ + — complete that code prompt + /temp 0.6 — set temperature (default 0.8, lower = more focused) + /topk 20 — set top-k (default 40) + /tokens 100 — set max new tokens (default 80) + /retrain — run another 5 epochs of training then return to chat + /save — save checkpoint to data_out/quantum_code_llm/checkpoint.pt + /load — load checkpoint from data_out/quantum_code_llm/checkpoint.pt + /stats — show model info and backend + /help — show this list + /quit — exit +""" + +from __future__ import annotations + +import argparse +import os +import sys + +# Add src directory to path before importing quantum_code_llm +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) + +try: + import quantum_code_llm +except ImportError: + print( + f"Error: Could not import quantum_code_llm. Make sure it's installed in {sys.path[0]}" + ) + raise + +CHECKPOINT = "data_out/quantum_code_llm/checkpoint.pt" + +MODEL_CFG = { + "n_qubits": 4, + "d_model": 64, + "n_heads": 4, + "n_layers": 2, + "n_var_layers": 2, + "max_seq_len": 128, + "dropout": 0.1, + "backend": "auto", +} + + +def save_checkpoint( + model: quantum_code_llm.QuantumCodeLLM, tokenizer: quantum_code_llm.CodeTokenizer +) -> None: + quantum_code_llm.save_checkpoint(model, tokenizer, CHECKPOINT) + print(f" ✓ Saved -> {CHECKPOINT}") + + +def load_checkpoint() -> ( + tuple[quantum_code_llm.QuantumCodeLLM, quantum_code_llm.CodeTokenizer] +): + model, tokenizer, _metadata = quantum_code_llm.load_checkpoint( + CHECKPOINT, map_location="cpu" + ) + print(f" ✓ Loaded <- {CHECKPOINT}") + return model, tokenizer + + +def retrain( + model: quantum_code_llm.QuantumCodeLLM, + tokenizer: quantum_code_llm.CodeTokenizer, + n_epochs: int = 5, +) -> None: + tcfg = quantum_code_llm.TrainConfig( + n_epochs=n_epochs, batch_size=8, lr=1e-3, log_every=40 + ) + trainer = quantum_code_llm.QuantumCodeTrainer(model, tokenizer, tcfg) + trainer.train() + + +def chat_loop( + model: quantum_code_llm.QuantumCodeLLM, tokenizer: quantum_code_llm.CodeTokenizer +) -> None: + temperature = 0.8 + top_k = 40 + max_new = 80 + + print() + print("━" * 60) + print(" Quantum Code LLM — Chat (type /help for commands)") + print( + f" backend: {model.backend} | params: {model.parameter_count()['total']:,}" + ) + print("━" * 60) + print() + + while True: + try: + prompt = input("▶ ").strip() + except (EOFError, KeyboardInterrupt): + print("\nBye!") + break + + if not prompt: + continue + + # ── built-in commands ────────────────────────────────────────── + if prompt == "/quit" or prompt == "/exit": + print("Bye!") + break + + elif prompt == "/help": + print(__doc__) + + elif prompt == "/stats": + p = model.parameter_count() + print(f" backend : {model.backend}") + print(f" params : {p['total']:,} total {p['trainable']:,} trainable") + print(f" vocab : {tokenizer.vocab_size}") + cfg = model.config + print( + f" d_model : {cfg.d_model} n_layers: {cfg.n_layers} n_qubits: {cfg.n_qubits}" + ) + print(f" temp : {temperature} top_k: {top_k} max_tokens: {max_new}") + + elif prompt.startswith("/temp "): + try: + temperature = float(prompt.split()[1]) + print(f" temperature → {temperature}") + except ValueError: + print(" usage: /temp 0.7") + + elif prompt.startswith("/topk "): + try: + top_k = int(prompt.split()[1]) + print(f" top_k → {top_k}") + except ValueError: + print(" usage: /topk 30") + + elif prompt.startswith("/tokens "): + try: + max_new = int(prompt.split()[1]) + print(f" max_new_tokens → {max_new}") + except ValueError: + print(" usage: /tokens 120") + + elif prompt == "/save": + save_checkpoint(model, tokenizer) + + elif prompt == "/load": + if os.path.exists(CHECKPOINT): + model, tokenizer = load_checkpoint() + else: + print(f" No checkpoint at {CHECKPOINT}") + + elif prompt.startswith("/retrain"): + parts = prompt.split() + n = int(parts[1]) if len(parts) > 1 else 5 + print(f" Running {n} more epochs …") + retrain(model, tokenizer, n_epochs=n) + save_checkpoint(model, tokenizer) + + # ── generation ──────────────────────────────────────────────── + else: + result = quantum_code_llm.generate( + model, + tokenizer, + prompt=prompt, + max_new_tokens=max_new, + temperature=temperature, + top_k=top_k, + ) + print() + print(result) + print() + + +def main() -> None: + parser = argparse.ArgumentParser(description="Quantum Code LLM chat REPL") + parser.add_argument( + "--load", action="store_true", help="load checkpoint instead of training" + ) + parser.add_argument( + "--epochs", type=int, default=15, help="epochs to train (default 15)" + ) + args = parser.parse_args() + + tokenizer = quantum_code_llm.CodeTokenizer() + + if args.load and os.path.exists(CHECKPOINT): + model, tokenizer = load_checkpoint() + else: + if args.load: + print(f"No checkpoint found at {CHECKPOINT}, training fresh ...\n") + model, tokenizer = quantum_code_llm.train( + model_cfg=MODEL_CFG, + train_cfg={ + "n_epochs": args.epochs, + "batch_size": 8, + "lr": 3e-3, + "log_every": 50, + }, + ) + save_checkpoint(model, tokenizer) + + chat_loop(model, tokenizer) + + +if __name__ == "__main__": + main() diff --git a/quantum-ai/examples/quantum_code_llm_demo.py b/quantum-ai/examples/quantum_code_llm_demo.py index da280fa21..d0e0019f0 100644 --- a/quantum-ai/examples/quantum_code_llm_demo.py +++ b/quantum-ai/examples/quantum_code_llm_demo.py @@ -1,96 +1,96 @@ -#!/usr/bin/env python3 -"""quantum_code_llm_demo.py — Quick demo: train the Quantum Code LLM and generate code. - -Usage ------ - python ai-projects/quantum-ml/examples/quantum_code_llm_demo.py - -Options (set via env vars or edit the CONFIG dicts below): - QLCM_EPOCHS number of training epochs (default 3) - QLCM_QUBITS number of qubits (default 4) - QLCM_DMODEL model hidden dim (default 64) - QLCM_BACKEND quantum backend (default auto) -""" - -from __future__ import annotations - -import os -import sys - -# Allow running from repo root as well as from examples/ -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) - -from quantum_code_llm import generate, train # noqa: E402 - -# ── Configuration ────────────────────────────────────────────────────────── -MODEL_CFG = { - "n_qubits": int(os.environ.get("QLCM_QUBITS", "4")), - "d_model": int(os.environ.get("QLCM_DMODEL", "64")), - "n_heads": 4, - "n_layers": 2, - "n_var_layers": 2, - "max_seq_len": 64, - "dropout": 0.1, - "backend": os.environ.get("QLCM_BACKEND", "auto"), -} - -TRAIN_CFG = { - "n_epochs": int(os.environ.get("QLCM_EPOCHS", "3")), - "batch_size": 8, - "lr": 3e-3, - "seq_len": 64, - "log_every": 30, -} - -# Optional extra code snippets to add to the training corpus -EXTRA_SNIPPETS = [ - "def power(base, exp):\n return base ** exp\n", - "def absolute(n):\n return abs(n)\n", - "def concat(a, b):\n return str(a) + str(b)\n", -] - -# Prompts used for generation after training -PROMPTS = [ - "def ", - "class ", - "def factorial(n):", - "def is_prime(n):", - "class Stack:", -] - -# ── Main ─────────────────────────────────────────────────────────────────── - - -def main() -> None: - print("=" * 60) - print(" Quantum Code LLM — Demo") - print("=" * 60) - - model, tokenizer = train( - model_cfg=MODEL_CFG, - train_cfg=TRAIN_CFG, - extra_snippets=EXTRA_SNIPPETS, - ) - - print("\n" + "=" * 60) - print(" Code Generation Samples") - print("=" * 60) - - for prompt in PROMPTS: - print(f"\nPrompt: {prompt!r}") - print("-" * 40) - result = generate( - model, - tokenizer, - prompt=prompt, - max_new_tokens=80, - temperature=0.8, - top_k=40, - ) - print(result) - - print("\n✓ Demo complete.") - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +"""quantum_code_llm_demo.py — Quick demo: train the Quantum Code LLM and generate code. + +Usage +----- + python ai-projects/quantum-ml/examples/quantum_code_llm_demo.py + +Options (set via env vars or edit the CONFIG dicts below): + QLCM_EPOCHS number of training epochs (default 3) + QLCM_QUBITS number of qubits (default 4) + QLCM_DMODEL model hidden dim (default 64) + QLCM_BACKEND quantum backend (default auto) +""" + +from __future__ import annotations + +import os +import sys + +# Allow running from repo root as well as from examples/ +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) + +from quantum_code_llm import generate, train # noqa: E402 + +# ── Configuration ────────────────────────────────────────────────────────── +MODEL_CFG = { + "n_qubits": int(os.environ.get("QLCM_QUBITS", "4")), + "d_model": int(os.environ.get("QLCM_DMODEL", "64")), + "n_heads": 4, + "n_layers": 2, + "n_var_layers": 2, + "max_seq_len": 64, + "dropout": 0.1, + "backend": os.environ.get("QLCM_BACKEND", "auto"), +} + +TRAIN_CFG = { + "n_epochs": int(os.environ.get("QLCM_EPOCHS", "3")), + "batch_size": 8, + "lr": 3e-3, + "seq_len": 64, + "log_every": 30, +} + +# Optional extra code snippets to add to the training corpus +EXTRA_SNIPPETS = [ + "def power(base, exp):\n return base ** exp\n", + "def absolute(n):\n return abs(n)\n", + "def concat(a, b):\n return str(a) + str(b)\n", +] + +# Prompts used for generation after training +PROMPTS = [ + "def ", + "class ", + "def factorial(n):", + "def is_prime(n):", + "class Stack:", +] + +# ── Main ─────────────────────────────────────────────────────────────────── + + +def main() -> None: + print("=" * 60) + print(" Quantum Code LLM — Demo") + print("=" * 60) + + model, tokenizer = train( + model_cfg=MODEL_CFG, + train_cfg=TRAIN_CFG, + extra_snippets=EXTRA_SNIPPETS, + ) + + print("\n" + "=" * 60) + print(" Code Generation Samples") + print("=" * 60) + + for prompt in PROMPTS: + print(f"\nPrompt: {prompt!r}") + print("-" * 40) + result = generate( + model, + tokenizer, + prompt=prompt, + max_new_tokens=80, + temperature=0.8, + top_k=40, + ) + print(result) + + print("\n✓ Demo complete.") + + +if __name__ == "__main__": + main() diff --git a/quantum-ai/results/architecture_analysis.json b/quantum-ai/results/architecture_analysis.json index 972778fbd..46a0fb9e0 100644 --- a/quantum-ai/results/architecture_analysis.json +++ b/quantum-ai/results/architecture_analysis.json @@ -1,2614 +1,2614 @@ -{ - "timestamp": "2026-03-08T22:58:00.593082", - "total_datasets": 26, - "analyses": [ - { - "dataset": "balance_scale", - "statistics": { - "samples": 624, - "features": 4, - "classes": 3, - "task_type": "multiclass", - "difficulty": "easy", - "imbalance_ratio": 6.0, - "missing_ratio": 0.0, - "class_distribution": [ - 48, - 288, - 288 - ] - }, - "recommended_architecture": { - "n_qubits": 4, - "n_quantum_layers": 2, - "hidden_dim": 16, - "learning_rate": 0.0005, - "batch_size": 16, - "epochs": 50, - "expected_pca_variance": 1.0 - }, - "reasoning": { - "qubits": "Low feature count (\u22644) - use 4 qubits minimum", - "layers": "Standard task - use 2 layers (efficient baseline)", - "hidden_dim": "Standard configuration - use hidden_dim=16", - "learning_rate": "Imbalanced classes (ratio: 6.0) - lower LR for stability", - "batch_size": "Medium dataset - standard batch_size=16", - "epochs": "Complex task - train for 50 epochs" - } - }, - { - "dataset": "banknote", - "statistics": { - "samples": 1371, - 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use hidden_dim=16 to reduce parameters", + "learning_rate": "Small dataset - lower LR to avoid overfitting", + "batch_size": "Small dataset - use batch_size=8 for more gradient updates", + "epochs": "Complex task - train for 50 epochs" + } + }, + { + "dataset": "statlog_australian", + "statistics": { + "samples": 690, + "features": 0, + "classes": 690, + "task_type": "binary", + "difficulty": "medium", + "imbalance_ratio": 1.0, + "missing_ratio": 0.0, + "class_distribution": [ + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ] + }, + "recommended_architecture": { + "n_qubits": 4, + "n_quantum_layers": 2, + "hidden_dim": 16, + "learning_rate": 0.001, + "batch_size": 16, + "epochs": 40, + "expected_pca_variance": 1.0 + }, + "reasoning": { + "qubits": "Low feature count (\u22644) - use 4 qubits minimum", + "layers": "Standard task - use 2 layers (efficient baseline)", + "hidden_dim": "Standard configuration - use hidden_dim=16", + "learning_rate": "Balanced dataset - standard LR for faster convergence", + "batch_size": "Medium dataset - standard batch_size=16", + "epochs": "Standard task - 40 epochs for convergence" + } + }, + { + "dataset": "statlog_heart", + "statistics": { + "samples": 270, + "features": 0, + "classes": 270, + "task_type": "binary", + "difficulty": "medium", + "imbalance_ratio": 1.0, + "missing_ratio": 0.0, + "class_distribution": [ + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ] + }, + "recommended_architecture": { + "n_qubits": 4, + "n_quantum_layers": 2, + "hidden_dim": 16, + "learning_rate": 0.0005, + "batch_size": 8, + "epochs": 30, + "expected_pca_variance": 1.0 + }, + "reasoning": { + "qubits": "Low feature count (\u22644) - use 4 qubits minimum", + "layers": "Small dataset (<300 samples) - use 2 layers to avoid overfitting", + "hidden_dim": "Small dataset - use hidden_dim=16 to reduce parameters", + "learning_rate": "Small dataset - lower LR to avoid overfitting", + "batch_size": "Small dataset - use batch_size=8 for more gradient updates", + "epochs": "Small dataset - 30 epochs to avoid overfitting" + } + }, + { + "dataset": "thyroid", + "statistics": { + "samples": 215, + "features": 5, + "classes": 3, + "task_type": "multiclass", + "difficulty": "medium", + "imbalance_ratio": 5.0, + "missing_ratio": 0.0, + "class_distribution": [ + 150, + 35, + 30 + ] + }, + "recommended_architecture": { + "n_qubits": 4, + "n_quantum_layers": 2, + "hidden_dim": 16, + "learning_rate": 0.0005, + "batch_size": 8, + "epochs": 50, + "expected_pca_variance": 0.9 + }, + "reasoning": { + "qubits": "Medium feature count (5-10) - standard 4 qubits", + "layers": "Small dataset (<300 samples) - use 2 layers to avoid overfitting", + "hidden_dim": "Small dataset - use hidden_dim=16 to reduce parameters", + "learning_rate": "Imbalanced classes (ratio: 5.0) - lower LR for stability", + "batch_size": "Small dataset - use batch_size=8 for more gradient updates", + "epochs": "Complex task - train for 50 epochs" + } + }, + { + "dataset": "wheat_seeds", + "statistics": { + "samples": 210, + "features": 7, + "classes": 3, + "task_type": "multiclass", + "difficulty": "medium", + "imbalance_ratio": 1.0, + "missing_ratio": 0.0, + "class_distribution": [ + 70, + 70, + 70 + ] + }, + "recommended_architecture": { + "n_qubits": 4, + "n_quantum_layers": 2, + "hidden_dim": 16, + "learning_rate": 0.0005, + "batch_size": 8, + "epochs": 50, + "expected_pca_variance": 0.9 + }, + "reasoning": { + "qubits": "Medium feature count (5-10) - standard 4 qubits", + "layers": "Small dataset (<300 samples) - use 2 layers to avoid overfitting", + "hidden_dim": "Small dataset - use hidden_dim=16 to reduce parameters", + "learning_rate": "Small dataset - lower LR to avoid overfitting", + "batch_size": "Small dataset - use batch_size=8 for more gradient updates", + "epochs": "Complex task - train for 50 epochs" + } + }, + { + "dataset": "wine_quality_combined", + "statistics": { + "samples": 6497, + "features": 12, + "classes": 2, + "task_type": "multiclass", + "difficulty": "hard", + "imbalance_ratio": 3.063164477798624, + "missing_ratio": 0.0, + "class_distribution": [ + 4898, + 1599 + ] + }, + "recommended_architecture": { + "n_qubits": 5, + "n_quantum_layers": 3, + "hidden_dim": 16, + "learning_rate": 0.0005, + "batch_size": 32, + "epochs": 50, + "expected_pca_variance": 0.85 + }, + "reasoning": { + "qubits": "High feature count (11-20) - use 5 qubits for better representation", + "layers": "Hard task - use 3 layers for learning capacity", + "hidden_dim": "Standard configuration - use hidden_dim=16", + "learning_rate": "Imbalanced classes (ratio: 3.1) - lower LR for stability", + "batch_size": "Large dataset - use batch_size=32 for efficiency", + "epochs": "Complex task - train for 50 epochs" + } + }, + { + "dataset": "wine_red", + "statistics": { + "samples": 1599, + "features": 11, + "classes": 6, + "task_type": "multiclass", + "difficulty": "hard", + "imbalance_ratio": 68.1, + "missing_ratio": 0.0, + "class_distribution": [ + 681, + 638, + 199, + 53, + 18, + 10 + ] + }, + "recommended_architecture": { + "n_qubits": 5, + "n_quantum_layers": 4, + "hidden_dim": 16, + "learning_rate": 0.0005, + "batch_size": 16, + "epochs": 50, + "expected_pca_variance": 0.85 + }, + "reasoning": { + "qubits": "High feature count (11-20) - use 5 qubits for better representation", + "layers": "Multi-class (6 classes) - use 4 layers for complexity", + "hidden_dim": "Standard configuration - use hidden_dim=16", + "learning_rate": "Imbalanced classes (ratio: 68.1) - lower LR for stability", + "batch_size": "Medium dataset - standard batch_size=16", + "epochs": "Complex task - train for 50 epochs" + } + }, + { + "dataset": "wine_white", + "statistics": { + "samples": 4898, + "features": 11, + "classes": 7, + "task_type": "multiclass", + "difficulty": "hard", + "imbalance_ratio": 439.6, + "missing_ratio": 0.0, + "class_distribution": [ + 2198, + 1457, + 880, + 175, + 163, + 20, + 5 + ] + }, + "recommended_architecture": { + "n_qubits": 5, + "n_quantum_layers": 4, + "hidden_dim": 16, + "learning_rate": 0.0005, + "batch_size": 16, + "epochs": 50, + "expected_pca_variance": 0.85 + }, + "reasoning": { + "qubits": "High feature count (11-20) - use 5 qubits for better representation", + "layers": "Multi-class (7 classes) - use 4 layers for complexity", + "hidden_dim": "Standard configuration - use hidden_dim=16", + "learning_rate": "Imbalanced classes (ratio: 439.6) - lower LR for stability", + "batch_size": "Medium dataset - standard batch_size=16", + "epochs": "Complex task - train for 50 epochs" + } + } + ], + "distributions": { + "qubits": { + "4": 15, + "6": 6, + "5": 5 + }, + "layers": { + "2": 18, + "3": 3, + "4": 5 + } + } } diff --git a/quantum-ai/scripts/validate_qiskit_env.py b/quantum-ai/scripts/validate_qiskit_env.py index 4162666bf..2cdd3f453 100644 --- a/quantum-ai/scripts/validate_qiskit_env.py +++ b/quantum-ai/scripts/validate_qiskit_env.py @@ -1,42 +1,42 @@ -"""Compatibility wrapper for Qiskit environment validation. - -The canonical script lives in: - ai-projects/quantum-ml/scripts/validate_qiskit_env.py - -This wrapper preserves legacy path expectations used by tests and tooling. -""" - -from __future__ import annotations - -import importlib.util -from pathlib import Path - -_CANONICAL = ( - Path(__file__).resolve().parents[2] - / "ai-projects" - / "quantum-ml" - / "scripts" - / "validate_qiskit_env.py" -) - -if not _CANONICAL.exists(): - raise FileNotFoundError(f"Canonical validator not found: {_CANONICAL}") - -_spec = importlib.util.spec_from_file_location( - "_validate_qiskit_env_canonical", _CANONICAL -) -if _spec is None or _spec.loader is None: - raise ImportError(f"Unable to load spec for {_CANONICAL}") - -_mod = importlib.util.module_from_spec(_spec) -_spec.loader.exec_module(_mod) - -# Re-export callable surface used by tests and scripts. -find_distributions = _mod.find_distributions -classify = _mod.classify -detect_conflict = _mod.detect_conflict -main = _mod.main - - -if __name__ == "__main__": - main() +"""Compatibility wrapper for Qiskit environment validation. + +The canonical script lives in: + ai-projects/quantum-ml/scripts/validate_qiskit_env.py + +This wrapper preserves legacy path expectations used by tests and tooling. +""" + +from __future__ import annotations + +import importlib.util +from pathlib import Path + +_CANONICAL = ( + Path(__file__).resolve().parents[2] + / "ai-projects" + / "quantum-ml" + / "scripts" + / "validate_qiskit_env.py" +) + +if not _CANONICAL.exists(): + raise FileNotFoundError(f"Canonical validator not found: {_CANONICAL}") + +_spec = importlib.util.spec_from_file_location( + "_validate_qiskit_env_canonical", _CANONICAL +) +if _spec is None or _spec.loader is None: + raise ImportError(f"Unable to load spec for {_CANONICAL}") + +_mod = importlib.util.module_from_spec(_spec) +_spec.loader.exec_module(_mod) + +# Re-export callable surface used by tests and scripts. +find_distributions = _mod.find_distributions +classify = _mod.classify +detect_conflict = _mod.detect_conflict +main = _mod.main + + +if __name__ == "__main__": + main() diff --git a/quantum-ai/src/quantum_code_llm.py b/quantum-ai/src/quantum_code_llm.py index 251a64d56..6daddcd30 100644 --- a/quantum-ai/src/quantum_code_llm.py +++ b/quantum-ai/src/quantum_code_llm.py @@ -1,896 +1,896 @@ -"""quantum_code_llm.py — Self-contained Quantum LLM for Code Generation -======================================================================== -Architecture: Hybrid quantum-classical transformer - - Quantum kernel attention : angle encoding + variational circuit as feature map - - Quantum FFN middle layer : variational quantum circuit between linear projections - - Code-aware tokenizer : character-level + keyword special tokens - - Auto-detect backend : Qiskit Aer (via pennylane-qiskit) → PennyLane - default.qubit → classical MLP fallback - -Quick start ------------ - from quantum_code_llm import train, generate - - model, tokenizer = train() # train on built-in Python snippets - print(generate(model, tokenizer, "def factorial(n):")) -""" - -from __future__ import annotations - -import math -import random -import time -from dataclasses import asdict, dataclass, field -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import DataLoader, Dataset - -# ───────────────────────────────────────────────────────────────────────────── -# 1. QUANTUM BACKEND DETECTION -# ───────────────────────────────────────────────────────────────────────────── - -QUANTUM_BACKEND: str = "classical" # resolved at module load - -try: - import pennylane as qml # type: ignore - - _PENNYLANE_AVAILABLE = True -except ImportError: - _PENNYLANE_AVAILABLE = False - -_QISKIT_AER_AVAILABLE = False -if _PENNYLANE_AVAILABLE: - try: - import pennylane_qiskit # type: ignore # noqa: F401 - from qiskit_aer import AerSimulator # type: ignore # noqa: F401 - - _QISKIT_AER_AVAILABLE = True - except ImportError: - pass - - -def _make_device(n_qubits: int): - """Return (backend_label, qml.device) using the best available backend.""" - if _QISKIT_AER_AVAILABLE: - try: - dev = qml.device( - "qiskit.aer", wires=n_qubits, backend="statevector_simulator" - ) - return "qiskit.aer", dev - except Exception: - pass - if _PENNYLANE_AVAILABLE: - dev = qml.device("default.qubit", wires=n_qubits) - return "default.qubit", dev - return "classical", None - - -# ───────────────────────────────────────────────────────────────────────────── -# 2. CODE TOKENIZER -# ───────────────────────────────────────────────────────────────────────────── - -_CODE_KEYWORDS = [ - "def ", - "class ", - "return ", - "import ", - "from ", - "with ", - " ", # 4-space indent block - "if ", - "else:", - "elif ", - "for ", - "while ", - "try:", - "except ", - "pass", - "None", - "True", - "False", - "self.", - "print(", -] - - -class CodeTokenizer: - """Character-level tokenizer with code-aware multi-char special tokens. - - Token priority: special tokens → individual printable ASCII characters. - """ - - PAD, BOS, EOS, UNK = 0, 1, 2, 3 - - def __init__(self, keywords: List[str] = _CODE_KEYWORDS) -> None: - # Special single-char controls - self._special = ["", "", "", ""] - # Multi-char keyword tokens - self._keywords: List[str] = sorted(keywords, key=len, reverse=True) - # All printable ASCII characters - self._chars: List[str] = [chr(c) for c in range(32, 127)] - - self._tok2id: dict[str, int] = {} - idx = len(self._special) - for kw in self._keywords: - self._tok2id[kw] = idx - idx += 1 - for ch in self._chars: - if ch not in self._tok2id: - self._tok2id[ch] = idx - idx += 1 - - self._id2tok: dict[int, str] = {v: k for k, v in self._tok2id.items()} - self._id2tok[self.PAD] = "" - self._id2tok[self.BOS] = "" - self._id2tok[self.EOS] = "" - self._id2tok[self.UNK] = "?" - - self.vocab_size: int = idx - - def encode( - self, text: str, add_bos: bool = True, add_eos: bool = True - ) -> List[int]: - ids: List[int] = [] - if add_bos: - ids.append(self.BOS) - i = 0 - while i < len(text): - matched = False - for kw in self._keywords: - if text[i : i + len(kw)] == kw: - ids.append(self._tok2id[kw]) - i += len(kw) - matched = True - break - if not matched: - ch = text[i] - ids.append(self._tok2id.get(ch, self.UNK)) - i += 1 - if add_eos: - ids.append(self.EOS) - return ids - - def decode(self, ids: List[int], skip_special: bool = True) -> str: - parts: List[str] = [] - for i in ids: - tok = self._id2tok.get(i, "") - if skip_special and i in (self.PAD, self.BOS, self.EOS, self.UNK): - continue - parts.append(tok) - return "".join(parts) - - @property - def keywords(self) -> List[str]: - return list(self._keywords) - - def to_dict(self) -> Dict[str, Any]: - return {"keywords": self.keywords} - - @classmethod - def from_dict(cls, payload: Optional[Dict[str, Any]]) -> "CodeTokenizer": - if not payload: - return cls() - keywords = payload.get("keywords", _CODE_KEYWORDS) - if not isinstance(keywords, list): - keywords = list(_CODE_KEYWORDS) - return cls(keywords=keywords) - - -# ───────────────────────────────────────────────────────────────────────────── -# 3. QUANTUM FEATURE MAP LAYER -# ───────────────────────────────────────────────────────────────────────────── - - -class QuantumFeatureMapLayer(nn.Module): - """Variational quantum circuit used as a learnable feature map. - - Input : (batch, n_qubits) — values normalised to [-π, π] - Output : (batch, n_qubits) — Pauli-Z expectation values ∈ [-1, 1] - - When quantum is unavailable, a classical Tanh-bounded MLP is used. - """ - - def __init__(self, n_qubits: int, n_var_layers: int, device) -> None: - super().__init__() - self.n_qubits = n_qubits - self.quantum = device is not None - - if self.quantum: - - @qml.qnode(device, interface="torch", diff_method="best") - def _circuit(inputs, weights): - qml.AngleEmbedding(inputs, wires=range(n_qubits), rotation="Y") - qml.StronglyEntanglingLayers(weights, wires=range(n_qubits)) - return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] - - weight_shapes = {"weights": (n_var_layers, n_qubits, 3)} - self.qlayer = qml.qnn.TorchLayer(_circuit, weight_shapes) - else: - # Classical fallback: MLP that mimics bounded quantum outputs - self.qlayer = nn.Sequential( - nn.Linear(n_qubits, n_qubits * 4), - nn.Tanh(), - nn.Linear(n_qubits * 4, n_qubits), - nn.Tanh(), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - # x: (..., n_qubits) - shape = x.shape - x_flat = x.reshape(-1, self.n_qubits) - out = self.qlayer(x_flat) - if isinstance(out, list): - out = torch.stack(out, dim=-1) - return out.reshape(*shape[:-1], self.n_qubits) - - -# ───────────────────────────────────────────────────────────────────────────── -# 4. QUANTUM KERNEL ATTENTION -# ───────────────────────────────────────────────────────────────────────────── - - -class QuantumKernelAttention(nn.Module): - """Multi-head attention where Q and K are projected through a quantum - feature map before computing scaled dot-product attention. - - Q_quantum = φ(W_q · x) - K_quantum = φ(W_k · x) - scores = Q_quantum · K_quantum^T / sqrt(n_qubits) - - φ is the QuantumFeatureMapLayer — a variational quantum circuit. - V is projected classically as in standard attention. - """ - - def __init__( - self, - d_model: int, - n_heads: int, - n_qubits: int, - n_var_layers: int, - device, - dropout: float = 0.1, - ) -> None: - super().__init__() - assert d_model % n_heads == 0 - self.n_heads = n_heads - self.n_qubits = n_qubits - self.head_dim = d_model // n_heads - - # Classical projections - self.w_q = nn.Linear(d_model, n_heads * n_qubits, bias=False) - self.w_k = nn.Linear(d_model, n_heads * n_qubits, bias=False) - self.w_v = nn.Linear(d_model, d_model, bias=False) - self.w_o = nn.Linear(d_model, d_model, bias=False) - - # Shared quantum feature map for Q and K - self.q_map = QuantumFeatureMapLayer(n_qubits, n_var_layers, device) - self.k_map = QuantumFeatureMapLayer(n_qubits, n_var_layers, device) - - self.drop = nn.Dropout(dropout) - self.scale = math.sqrt(n_qubits) - - def forward( - self, - x: torch.Tensor, - mask: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - B, T, _ = x.shape - - # Project and reshape for multi-head: (B, T, n_heads, n_qubits) - q = self.w_q(x).view(B, T, self.n_heads, self.n_qubits) - k = self.w_k(x).view(B, T, self.n_heads, self.n_qubits) - v = self.w_v(x).view(B, T, self.n_heads, self.head_dim) - - # Normalise input to quantum circuit range [-π, π] - q_norm = torch.tanh(q) * math.pi - k_norm = torch.tanh(k) * math.pi - - # Apply quantum feature map (batch over heads) - q_q = self.q_map(q_norm) # (B, T, n_heads, n_qubits) - k_q = self.k_map(k_norm) # (B, T, n_heads, n_qubits) - - # Transpose to (B, n_heads, T, n_qubits) for bmm - q_q = q_q.transpose(1, 2) - k_q = k_q.transpose(1, 2) - v = v.transpose(1, 2) # (B, n_heads, T, head_dim) - - # Scaled dot-product attention with quantum features - scores = torch.matmul(q_q, k_q.transpose(-2, -1)) / self.scale # (B, H, T, T) - - # Causal mask: prevent attending to future tokens - causal = torch.triu( - torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1 - ) - scores = scores.masked_fill(causal.unsqueeze(0).unsqueeze(0), float("-inf")) - if mask is not None: - scores = scores.masked_fill(mask, float("-inf")) - - attn = self.drop(F.softmax(scores, dim=-1)) - out = torch.matmul(attn, v) # (B, H, T, head_dim) - - # Recombine heads - out = out.transpose(1, 2).contiguous().view(B, T, -1) - return self.w_o(out) - - -# ───────────────────────────────────────────────────────────────────────────── -# 5. QUANTUM FEED-FORWARD NETWORK -# ───────────────────────────────────────────────────────────────────────────── - - -class QuantumFFN(nn.Module): - """Feed-forward block with a quantum variational circuit in the middle. - - x → Linear(d_model → n_qubits) → quantum_circuit → Linear(n_qubits → d_model) - - The quantum circuit is a variational layer that can learn non-linear - quantum feature transformations. Classical paths are used when no - quantum backend is available. - """ - - def __init__( - self, - d_model: int, - n_qubits: int, - n_var_layers: int, - device, - dropout: float = 0.1, - ) -> None: - super().__init__() - self.down = nn.Linear(d_model, n_qubits) - self.quantum = QuantumFeatureMapLayer(n_qubits, n_var_layers, device) - self.up = nn.Linear(n_qubits, d_model) - self.gate = nn.Linear(d_model, d_model) # classical gating - self.norm_inner = nn.LayerNorm(n_qubits) - self.drop = nn.Dropout(dropout) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - # Quantum path: project → quantum feature map → project back - h = F.gelu(self.down(x)) - h = self.norm_inner(h) - h_norm = torch.tanh(h) * math.pi # normalise to [-π, π] - h_q = self.quantum(h_norm) - out = self.drop(self.up(h_q)) - # Gating: blend quantum output with classical gate - gate = torch.sigmoid(self.gate(x)) - return gate * out + (1 - gate) * x - - -# ───────────────────────────────────────────────────────────────────────────── -# 6. QUANTUM TRANSFORMER BLOCK -# ───────────────────────────────────────────────────────────────────────────── - - -class QuantumTransformerBlock(nn.Module): - """Pre-norm transformer block with quantum attention and quantum FFN.""" - - def __init__( - self, - d_model: int, - n_heads: int, - n_qubits: int, - n_var_layers: int, - device, - dropout: float = 0.1, - ) -> None: - super().__init__() - self.norm1 = nn.LayerNorm(d_model) - self.attn = QuantumKernelAttention( - d_model, n_heads, n_qubits, n_var_layers, device, dropout - ) - self.norm2 = nn.LayerNorm(d_model) - self.ffn = QuantumFFN(d_model, n_qubits, n_var_layers, device, dropout) - - def forward( - self, x: torch.Tensor, mask: Optional[torch.Tensor] = None - ) -> torch.Tensor: - x = x + self.attn(self.norm1(x), mask) - x = x + self.ffn(self.norm2(x)) - return x - - -# ───────────────────────────────────────────────────────────────────────────── -# 7. QUANTUM CODE LLM (MAIN MODEL) -# ───────────────────────────────────────────────────────────────────────────── - - -@dataclass -class QuantumCodeLLMConfig: - vocab_size: int = 120 - d_model: int = 64 - n_heads: int = 4 - n_layers: int = 2 - n_qubits: int = 4 - n_var_layers: int = 2 # variational layers inside each quantum circuit - max_seq_len: int = 128 - dropout: float = 0.1 - backend: str = "auto" # "auto" | "qiskit.aer" | "default.qubit" | "classical" - - -class QuantumCodeLLM(nn.Module): - """Quantum-enhanced language model for code generation. - - Embedding → Positional Encoding → N × QuantumTransformerBlock → Output head - """ - - def __init__(self, config: QuantumCodeLLMConfig) -> None: - super().__init__() - self.config = config - - # Resolve quantum backend - if config.backend == "auto": - self._backend_label, self._qdevice = _make_device(config.n_qubits) - elif config.backend == "classical": - self._backend_label, self._qdevice = "classical", None - else: - if not _PENNYLANE_AVAILABLE: - raise RuntimeError( - "A non-classical backend was requested but PennyLane is not installed" - ) - self._backend_label = config.backend - self._qdevice = qml.device(config.backend, wires=config.n_qubits) - - # Embeddings - self.token_emb = nn.Embedding(config.vocab_size, config.d_model, padding_idx=0) - self.pos_emb = nn.Embedding(config.max_seq_len, config.d_model) - self.emb_drop = nn.Dropout(config.dropout) - - # Transformer blocks - self.blocks = nn.ModuleList( - [ - QuantumTransformerBlock( - config.d_model, - config.n_heads, - config.n_qubits, - config.n_var_layers, - self._qdevice, - config.dropout, - ) - for _ in range(config.n_layers) - ] - ) - - self.norm = nn.LayerNorm(config.d_model) - self.head = nn.Linear(config.d_model, config.vocab_size, bias=False) - - # Weight tying: output head shares weights with token embedding - self.head.weight = self.token_emb.weight - - self._init_weights() - - def _init_weights(self) -> None: - for m in self.modules(): - if isinstance(m, nn.Linear): - nn.init.xavier_uniform_(m.weight) - if m.bias is not None: - nn.init.zeros_(m.bias) - elif isinstance(m, nn.Embedding): - nn.init.normal_(m.weight, std=0.02) - - def forward( - self, input_ids: torch.Tensor, mask: Optional[torch.Tensor] = None - ) -> torch.Tensor: - """ - Args: - input_ids : (B, T) token indices - mask : (B, 1, T, T) optional padding mask - - Returns: - logits : (B, T, vocab_size) - """ - B, T = input_ids.shape - if T > self.config.max_seq_len: - raise ValueError( - f"input length {T} exceeds max_seq_len={self.config.max_seq_len}; " - "truncate input_ids before calling forward" - ) - tok = self.token_emb(input_ids) - pos = self.pos_emb(torch.arange(T, device=input_ids.device).unsqueeze(0)) - x = self.emb_drop(tok + pos) - - for block in self.blocks: - x = block(x, mask) - - x = self.norm(x) - return self.head(x) - - @torch.no_grad() - def generate( - self, - prompt_ids: torch.Tensor, - max_new_tokens: int = 64, - temperature: float = 0.8, - top_k: int = 40, - eos_id: int = 2, - ) -> torch.Tensor: - """Autoregressively generate tokens using temperature + top-k sampling.""" - if prompt_ids.ndim != 2 or prompt_ids.shape[0] != 1: - raise ValueError("prompt_ids must have shape (1, T)") - if temperature <= 0: - raise ValueError("temperature must be > 0") - if top_k < 0: - raise ValueError("top_k must be >= 0") - - self.eval() - ids = prompt_ids.clone() # (1, T) - for _ in range(max_new_tokens): - # Truncate to max_seq_len - ids_cond = ids[:, -self.config.max_seq_len :] - logits = self.forward(ids_cond) # (1, T, vocab) - next_logits = logits[:, -1, :] / temperature # (1, vocab) - - # Top-k filtering - if top_k > 0: - k = min(top_k, next_logits.shape[-1]) - kth_val = torch.topk(next_logits, k).values[:, -1, None] - next_logits = next_logits.masked_fill( - next_logits < kth_val, float("-inf") - ) - - probs = F.softmax(next_logits, dim=-1) - next_id = torch.multinomial(probs, num_samples=1) # (1, 1) - ids = torch.cat([ids, next_id], dim=1) - if next_id.item() == eos_id: - break - return ids - - @property - def backend(self) -> str: - return self._backend_label - - def parameter_count(self) -> dict: - total = sum(p.numel() for p in self.parameters()) - trainable = sum(p.numel() for p in self.parameters() if p.requires_grad) - return {"total": total, "trainable": trainable} - - -# ───────────────────────────────────────────────────────────────────────────── -# 8. BUILT-IN CODE DATASET -# ───────────────────────────────────────────────────────────────────────────── - -_PYTHON_SNIPPETS = [ - "def add(a, b):\n return a + b\n", - "def subtract(a, b):\n return a - b\n", - "def multiply(x, y):\n return x * y\n", - "def divide(x, y):\n if y == 0:\n return None\n return x / y\n", - "def square(n):\n return n * n\n", - "def cube(n):\n return n * n * n\n", - "def is_even(n):\n return n % 2 == 0\n", - "def is_odd(n):\n return n % 2 != 0\n", - "def factorial(n):\n if n <= 1:\n return 1\n return n * factorial(n - 1)\n", - "def fibonacci(n):\n if n <= 1:\n return n\n return fibonacci(n - 1) + fibonacci(n - 2)\n", - "def max_value(lst):\n return max(lst)\n", - "def min_value(lst):\n return min(lst)\n", - "def sum_list(lst):\n return sum(lst)\n", - "def reverse_string(s):\n return s[::-1]\n", - "def to_upper(s):\n return s.upper()\n", - "def to_lower(s):\n return s.lower()\n", - "def count_chars(s):\n return len(s)\n", - "def greet(name):\n return 'Hello, ' + name + '!'\n", - "class Counter:\n def __init__(self):\n self.count = 0\n def increment(self):\n self.count += 1\n def get(self):\n return self.count\n", - "class Stack:\n def __init__(self):\n self.items = []\n def push(self, item):\n self.items.append(item)\n def pop(self):\n return self.items.pop()\n def is_empty(self):\n return len(self.items) == 0\n", - "def bubble_sort(lst):\n n = len(lst)\n for i in range(n):\n for j in range(n - i - 1):\n if lst[j] > lst[j + 1]:\n lst[j], lst[j + 1] = lst[j + 1], lst[j]\n return lst\n", - "def binary_search(lst, target):\n low, high = 0, len(lst) - 1\n while low <= high:\n mid = (low + high) // 2\n if lst[mid] == target:\n return mid\n elif lst[mid] < target:\n low = mid + 1\n else:\n high = mid - 1\n return -1\n", - "def flatten(lst):\n result = []\n for item in lst:\n if isinstance(item, list):\n result.extend(flatten(item))\n else:\n result.append(item)\n return result\n", - "def gcd(a, b):\n while b:\n a, b = b, a % b\n return a\n", - "def lcm(a, b):\n return a * b // gcd(a, b)\n", - "def is_prime(n):\n if n < 2:\n return False\n for i in range(2, int(n ** 0.5) + 1):\n if n % i == 0:\n return False\n return True\n", - "def clamp(value, lo, hi):\n return max(lo, min(hi, value))\n", - "def average(lst):\n return sum(lst) / len(lst)\n", - "def unique(lst):\n return list(set(lst))\n", - "def zip_dicts(keys, values):\n return dict(zip(keys, values))\n", -] - - -class CodeDataset(Dataset): - """Next-token prediction dataset from code snippets. - - Each sample is a sequence of max_seq_len tokens. The target is the - input shifted by one position. - """ - - def __init__( - self, - tokenizer: CodeTokenizer, - snippets: List[str] = _PYTHON_SNIPPETS, - seq_len: int = 64, - ) -> None: - self.tokenizer = tokenizer - self.seq_len = seq_len - # Concatenate all snippets into one long token stream - full_text = "\n".join(snippets) + "\n" - self.tokens = tokenizer.encode(full_text, add_bos=False, add_eos=False) - - def __len__(self) -> int: - return max(0, len(self.tokens) - self.seq_len - 1) - - def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: - chunk = self.tokens[idx : idx + self.seq_len + 1] - # Pad if needed (last chunk) - while len(chunk) < self.seq_len + 1: - chunk.append(CodeTokenizer.PAD) - x = torch.tensor(chunk[:-1], dtype=torch.long) - y = torch.tensor(chunk[1:], dtype=torch.long) - return x, y - - -# ───────────────────────────────────────────────────────────────────────────── -# 9. TRAINER -# ───────────────────────────────────────────────────────────────────────────── - - -@dataclass -class TrainConfig: - n_epochs: int = 5 - batch_size: int = 8 - lr: float = 3e-3 - weight_decay: float = 1e-4 - warmup_steps: int = 50 - seq_len: int = 64 - grad_clip: float = 1.0 - log_every: int = 20 - device: str = "cpu" - seed: int = 42 - extra_snippets: List[str] = field(default_factory=list) - - -class QuantumCodeTrainer: - """Training loop for QuantumCodeLLM.""" - - def __init__( - self, - model: QuantumCodeLLM, - tokenizer: CodeTokenizer, - train_cfg: TrainConfig, - ) -> None: - self.model = model.to(train_cfg.device) - self.tokenizer = tokenizer - self.cfg = train_cfg - self._device = torch.device(train_cfg.device) - - snippets = _PYTHON_SNIPPETS + train_cfg.extra_snippets - dataset = CodeDataset(tokenizer, snippets, seq_len=train_cfg.seq_len) - self.loader = DataLoader( - dataset, - batch_size=train_cfg.batch_size, - shuffle=True, - drop_last=True, - ) - - self.optimizer = torch.optim.AdamW( - model.parameters(), - lr=train_cfg.lr, - weight_decay=train_cfg.weight_decay, - ) - total_steps = len(self.loader) * train_cfg.n_epochs - self.scheduler = torch.optim.lr_scheduler.OneCycleLR( - self.optimizer, - max_lr=train_cfg.lr, - total_steps=max(1, total_steps), - pct_start=0.1, - anneal_strategy="cos", - ) - - def train(self) -> List[dict]: - """Run the training loop. Returns per-epoch metric dicts.""" - torch.manual_seed(self.cfg.seed) - random.seed(self.cfg.seed) - np.random.seed(self.cfg.seed) - - history: List[dict] = [] - step = 0 - - for epoch in range(1, self.cfg.n_epochs + 1): - self.model.train() - epoch_loss = 0.0 - t0 = time.time() - - for batch_idx, (x, y) in enumerate(self.loader): - x, y = x.to(self._device), y.to(self._device) - - logits = self.model(x) # (B, T, vocab) - B, T, V = logits.shape - loss = F.cross_entropy( - logits.view(B * T, V), - y.view(B * T), - ignore_index=CodeTokenizer.PAD, - ) - - self.optimizer.zero_grad() - loss.backward() - nn.utils.clip_grad_norm_(self.model.parameters(), self.cfg.grad_clip) - self.optimizer.step() - self.scheduler.step() - - epoch_loss += loss.item() - step += 1 - - if step % self.cfg.log_every == 0: - lr_now = self.scheduler.get_last_lr()[0] - perp = math.exp(min(loss.item(), 20)) - print( - f" step {step:>5d} | loss {loss.item():.4f} " - f"| ppl {perp:.1f} | lr {lr_now:.2e}" - ) - - avg_loss = epoch_loss / max(1, len(self.loader)) - elapsed = time.time() - t0 - print( - f"Epoch {epoch}/{self.cfg.n_epochs} — " - f"avg loss {avg_loss:.4f} | ppl {math.exp(min(avg_loss, 20)):.1f} " - f"| {elapsed:.1f}s" - ) - history.append( - {"epoch": epoch, "loss": avg_loss, "ppl": math.exp(min(avg_loss, 20))} - ) - - return history - - -# ───────────────────────────────────────────────────────────────────────────── -# 10. PUBLIC ENTRY POINTS -# ───────────────────────────────────────────────────────────────────────────── - - -def train( - model_cfg: Optional[dict] = None, - train_cfg: Optional[dict] = None, - extra_snippets: Optional[List[str]] = None, -) -> Tuple[QuantumCodeLLM, CodeTokenizer]: - """Train a QuantumCodeLLM and return (model, tokenizer). - - Args: - model_cfg : kwargs for QuantumCodeLLMConfig (e.g. n_qubits, d_model) - train_cfg : kwargs for TrainConfig (e.g. n_epochs, lr) - extra_snippets : additional Python code strings to train on - - Example:: - - model, tok = train({"n_qubits": 4, "d_model": 64}, {"n_epochs": 3}) - print(generate(model, tok, "def hello(")) - """ - model_cfg = model_cfg or {} - train_cfg = train_cfg or {} - extra_snippets = extra_snippets or [] - - tokenizer = CodeTokenizer() - mcfg = QuantumCodeLLMConfig(vocab_size=tokenizer.vocab_size, **model_cfg) - model = QuantumCodeLLM(mcfg) - - print(f"QuantumCodeLLM ready — backend: {model.backend}") - params = model.parameter_count() - print(f"Parameters: {params['total']:,} total, {params['trainable']:,} trainable") - print(f"Vocab size: {tokenizer.vocab_size}") - - tcfg = TrainConfig(extra_snippets=extra_snippets, **train_cfg) - trainer = QuantumCodeTrainer(model, tokenizer, tcfg) - - print(f"\nStarting training ({tcfg.n_epochs} epochs) ...") - trainer.train() - return model, tokenizer - - -def save_checkpoint( - model: QuantumCodeLLM, - tokenizer: CodeTokenizer, - checkpoint_path: str | Path, - extra: Optional[Dict[str, Any]] = None, -) -> Path: - """Persist model + config + tokenizer metadata to a checkpoint file.""" - path = Path(checkpoint_path) - path.parent.mkdir(parents=True, exist_ok=True) - - payload = { - "model_state": model.state_dict(), - "config": asdict(model.config), - "tokenizer": tokenizer.to_dict(), - "backend": model.backend, - "saved_at": time.time(), - "extra": extra or {}, - } - torch.save(payload, path) - return path - - -def load_checkpoint( - checkpoint_path: str | Path, - map_location: str | torch.device = "cpu", - backend_override: Optional[str] = None, -) -> Tuple[QuantumCodeLLM, CodeTokenizer, Dict[str, Any]]: - """Load a checkpoint created by save_checkpoint. - - Also supports legacy payloads where config was serialized as QuantumCodeLLMConfig. - """ - path = Path(checkpoint_path) - if not path.exists(): - raise FileNotFoundError(f"Checkpoint not found: {path}") - - payload = torch.load(path, map_location=map_location, weights_only=False) - if "model_state" not in payload: - raise ValueError(f"Invalid checkpoint payload: missing model_state in {path}") - - tokenizer = CodeTokenizer.from_dict(payload.get("tokenizer")) - - raw_config = payload.get("config") - if isinstance(raw_config, QuantumCodeLLMConfig): - config = raw_config - elif isinstance(raw_config, dict): - config = QuantumCodeLLMConfig(**raw_config) - else: - raise ValueError(f"Invalid checkpoint payload: missing valid config in {path}") - - config.vocab_size = tokenizer.vocab_size - if backend_override is not None: - config.backend = backend_override - - model = QuantumCodeLLM(config) - model.load_state_dict(payload["model_state"]) - model.eval() - - metadata = { - "path": str(path), - "backend": payload.get("backend", model.backend), - "saved_at": payload.get("saved_at"), - "extra": payload.get("extra", {}), - } - return model, tokenizer, metadata - - -def generate( - model: QuantumCodeLLM, - tokenizer: CodeTokenizer, - prompt: str = "def ", - max_new_tokens: int = 80, - temperature: float = 0.8, - top_k: int = 40, - device: str = "cpu", -) -> str: - """Generate code continuation from a text prompt. - - Example:: - - code = generate(model, tokenizer, "def factorial(n):", max_new_tokens=60) - print(code) - """ - if temperature <= 0: - raise ValueError("temperature must be > 0") - if top_k < 0: - raise ValueError("top_k must be >= 0") - - model.eval() - ids = tokenizer.encode(prompt, add_bos=True, add_eos=False) - input_tensor = torch.tensor([ids], dtype=torch.long, device=device) - with torch.no_grad(): - out_ids = model.generate( - input_tensor, - max_new_tokens=max_new_tokens, - temperature=temperature, - top_k=top_k, - eos_id=tokenizer.EOS, - ) - # Strip the prompt prefix, decode only new tokens - new_ids = out_ids[0, len(ids) :].tolist() - return prompt + tokenizer.decode(new_ids) - - -__all__ = [ - "CodeTokenizer", - "CodeDataset", - "QuantumFeatureMapLayer", - "QuantumKernelAttention", - "QuantumFFN", - "QuantumTransformerBlock", - "QuantumCodeLLMConfig", - "QuantumCodeLLM", - "TrainConfig", - "QuantumCodeTrainer", - "train", - "generate", - "save_checkpoint", - "load_checkpoint", -] +"""quantum_code_llm.py — Self-contained Quantum LLM for Code Generation +======================================================================== +Architecture: Hybrid quantum-classical transformer + - Quantum kernel attention : angle encoding + variational circuit as feature map + - Quantum FFN middle layer : variational quantum circuit between linear projections + - Code-aware tokenizer : character-level + keyword special tokens + - Auto-detect backend : Qiskit Aer (via pennylane-qiskit) → PennyLane + default.qubit → classical MLP fallback + +Quick start +----------- + from quantum_code_llm import train, generate + + model, tokenizer = train() # train on built-in Python snippets + print(generate(model, tokenizer, "def factorial(n):")) +""" + +from __future__ import annotations + +import math +import random +import time +from dataclasses import asdict, dataclass, field +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader, Dataset + +# ───────────────────────────────────────────────────────────────────────────── +# 1. QUANTUM BACKEND DETECTION +# ───────────────────────────────────────────────────────────────────────────── + +QUANTUM_BACKEND: str = "classical" # resolved at module load + +try: + import pennylane as qml # type: ignore + + _PENNYLANE_AVAILABLE = True +except ImportError: + _PENNYLANE_AVAILABLE = False + +_QISKIT_AER_AVAILABLE = False +if _PENNYLANE_AVAILABLE: + try: + import pennylane_qiskit # type: ignore # noqa: F401 + from qiskit_aer import AerSimulator # type: ignore # noqa: F401 + + _QISKIT_AER_AVAILABLE = True + except ImportError: + pass + + +def _make_device(n_qubits: int): + """Return (backend_label, qml.device) using the best available backend.""" + if _QISKIT_AER_AVAILABLE: + try: + dev = qml.device( + "qiskit.aer", wires=n_qubits, backend="statevector_simulator" + ) + return "qiskit.aer", dev + except Exception: + pass + if _PENNYLANE_AVAILABLE: + dev = qml.device("default.qubit", wires=n_qubits) + return "default.qubit", dev + return "classical", None + + +# ───────────────────────────────────────────────────────────────────────────── +# 2. CODE TOKENIZER +# ───────────────────────────────────────────────────────────────────────────── + +_CODE_KEYWORDS = [ + "def ", + "class ", + "return ", + "import ", + "from ", + "with ", + " ", # 4-space indent block + "if ", + "else:", + "elif ", + "for ", + "while ", + "try:", + "except ", + "pass", + "None", + "True", + "False", + "self.", + "print(", +] + + +class CodeTokenizer: + """Character-level tokenizer with code-aware multi-char special tokens. + + Token priority: special tokens → individual printable ASCII characters. + """ + + PAD, BOS, EOS, UNK = 0, 1, 2, 3 + + def __init__(self, keywords: List[str] = _CODE_KEYWORDS) -> None: + # Special single-char controls + self._special = ["", "", "", ""] + # Multi-char keyword tokens + self._keywords: List[str] = sorted(keywords, key=len, reverse=True) + # All printable ASCII characters + self._chars: List[str] = [chr(c) for c in range(32, 127)] + + self._tok2id: dict[str, int] = {} + idx = len(self._special) + for kw in self._keywords: + self._tok2id[kw] = idx + idx += 1 + for ch in self._chars: + if ch not in self._tok2id: + self._tok2id[ch] = idx + idx += 1 + + self._id2tok: dict[int, str] = {v: k for k, v in self._tok2id.items()} + self._id2tok[self.PAD] = "" + self._id2tok[self.BOS] = "" + self._id2tok[self.EOS] = "" + self._id2tok[self.UNK] = "?" + + self.vocab_size: int = idx + + def encode( + self, text: str, add_bos: bool = True, add_eos: bool = True + ) -> List[int]: + ids: List[int] = [] + if add_bos: + ids.append(self.BOS) + i = 0 + while i < len(text): + matched = False + for kw in self._keywords: + if text[i : i + len(kw)] == kw: + ids.append(self._tok2id[kw]) + i += len(kw) + matched = True + break + if not matched: + ch = text[i] + ids.append(self._tok2id.get(ch, self.UNK)) + i += 1 + if add_eos: + ids.append(self.EOS) + return ids + + def decode(self, ids: List[int], skip_special: bool = True) -> str: + parts: List[str] = [] + for i in ids: + tok = self._id2tok.get(i, "") + if skip_special and i in (self.PAD, self.BOS, self.EOS, self.UNK): + continue + parts.append(tok) + return "".join(parts) + + @property + def keywords(self) -> List[str]: + return list(self._keywords) + + def to_dict(self) -> Dict[str, Any]: + return {"keywords": self.keywords} + + @classmethod + def from_dict(cls, payload: Optional[Dict[str, Any]]) -> "CodeTokenizer": + if not payload: + return cls() + keywords = payload.get("keywords", _CODE_KEYWORDS) + if not isinstance(keywords, list): + keywords = list(_CODE_KEYWORDS) + return cls(keywords=keywords) + + +# ───────────────────────────────────────────────────────────────────────────── +# 3. QUANTUM FEATURE MAP LAYER +# ───────────────────────────────────────────────────────────────────────────── + + +class QuantumFeatureMapLayer(nn.Module): + """Variational quantum circuit used as a learnable feature map. + + Input : (batch, n_qubits) — values normalised to [-π, π] + Output : (batch, n_qubits) — Pauli-Z expectation values ∈ [-1, 1] + + When quantum is unavailable, a classical Tanh-bounded MLP is used. + """ + + def __init__(self, n_qubits: int, n_var_layers: int, device) -> None: + super().__init__() + self.n_qubits = n_qubits + self.quantum = device is not None + + if self.quantum: + + @qml.qnode(device, interface="torch", diff_method="best") + def _circuit(inputs, weights): + qml.AngleEmbedding(inputs, wires=range(n_qubits), rotation="Y") + qml.StronglyEntanglingLayers(weights, wires=range(n_qubits)) + return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] + + weight_shapes = {"weights": (n_var_layers, n_qubits, 3)} + self.qlayer = qml.qnn.TorchLayer(_circuit, weight_shapes) + else: + # Classical fallback: MLP that mimics bounded quantum outputs + self.qlayer = nn.Sequential( + nn.Linear(n_qubits, n_qubits * 4), + nn.Tanh(), + nn.Linear(n_qubits * 4, n_qubits), + nn.Tanh(), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # x: (..., n_qubits) + shape = x.shape + x_flat = x.reshape(-1, self.n_qubits) + out = self.qlayer(x_flat) + if isinstance(out, list): + out = torch.stack(out, dim=-1) + return out.reshape(*shape[:-1], self.n_qubits) + + +# ───────────────────────────────────────────────────────────────────────────── +# 4. QUANTUM KERNEL ATTENTION +# ───────────────────────────────────────────────────────────────────────────── + + +class QuantumKernelAttention(nn.Module): + """Multi-head attention where Q and K are projected through a quantum + feature map before computing scaled dot-product attention. + + Q_quantum = φ(W_q · x) + K_quantum = φ(W_k · x) + scores = Q_quantum · K_quantum^T / sqrt(n_qubits) + + φ is the QuantumFeatureMapLayer — a variational quantum circuit. + V is projected classically as in standard attention. + """ + + def __init__( + self, + d_model: int, + n_heads: int, + n_qubits: int, + n_var_layers: int, + device, + dropout: float = 0.1, + ) -> None: + super().__init__() + assert d_model % n_heads == 0 + self.n_heads = n_heads + self.n_qubits = n_qubits + self.head_dim = d_model // n_heads + + # Classical projections + self.w_q = nn.Linear(d_model, n_heads * n_qubits, bias=False) + self.w_k = nn.Linear(d_model, n_heads * n_qubits, bias=False) + self.w_v = nn.Linear(d_model, d_model, bias=False) + self.w_o = nn.Linear(d_model, d_model, bias=False) + + # Shared quantum feature map for Q and K + self.q_map = QuantumFeatureMapLayer(n_qubits, n_var_layers, device) + self.k_map = QuantumFeatureMapLayer(n_qubits, n_var_layers, device) + + self.drop = nn.Dropout(dropout) + self.scale = math.sqrt(n_qubits) + + def forward( + self, + x: torch.Tensor, + mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + B, T, _ = x.shape + + # Project and reshape for multi-head: (B, T, n_heads, n_qubits) + q = self.w_q(x).view(B, T, self.n_heads, self.n_qubits) + k = self.w_k(x).view(B, T, self.n_heads, self.n_qubits) + v = self.w_v(x).view(B, T, self.n_heads, self.head_dim) + + # Normalise input to quantum circuit range [-π, π] + q_norm = torch.tanh(q) * math.pi + k_norm = torch.tanh(k) * math.pi + + # Apply quantum feature map (batch over heads) + q_q = self.q_map(q_norm) # (B, T, n_heads, n_qubits) + k_q = self.k_map(k_norm) # (B, T, n_heads, n_qubits) + + # Transpose to (B, n_heads, T, n_qubits) for bmm + q_q = q_q.transpose(1, 2) + k_q = k_q.transpose(1, 2) + v = v.transpose(1, 2) # (B, n_heads, T, head_dim) + + # Scaled dot-product attention with quantum features + scores = torch.matmul(q_q, k_q.transpose(-2, -1)) / self.scale # (B, H, T, T) + + # Causal mask: prevent attending to future tokens + causal = torch.triu( + torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1 + ) + scores = scores.masked_fill(causal.unsqueeze(0).unsqueeze(0), float("-inf")) + if mask is not None: + scores = scores.masked_fill(mask, float("-inf")) + + attn = self.drop(F.softmax(scores, dim=-1)) + out = torch.matmul(attn, v) # (B, H, T, head_dim) + + # Recombine heads + out = out.transpose(1, 2).contiguous().view(B, T, -1) + return self.w_o(out) + + +# ───────────────────────────────────────────────────────────────────────────── +# 5. QUANTUM FEED-FORWARD NETWORK +# ───────────────────────────────────────────────────────────────────────────── + + +class QuantumFFN(nn.Module): + """Feed-forward block with a quantum variational circuit in the middle. + + x → Linear(d_model → n_qubits) → quantum_circuit → Linear(n_qubits → d_model) + + The quantum circuit is a variational layer that can learn non-linear + quantum feature transformations. Classical paths are used when no + quantum backend is available. + """ + + def __init__( + self, + d_model: int, + n_qubits: int, + n_var_layers: int, + device, + dropout: float = 0.1, + ) -> None: + super().__init__() + self.down = nn.Linear(d_model, n_qubits) + self.quantum = QuantumFeatureMapLayer(n_qubits, n_var_layers, device) + self.up = nn.Linear(n_qubits, d_model) + self.gate = nn.Linear(d_model, d_model) # classical gating + self.norm_inner = nn.LayerNorm(n_qubits) + self.drop = nn.Dropout(dropout) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # Quantum path: project → quantum feature map → project back + h = F.gelu(self.down(x)) + h = self.norm_inner(h) + h_norm = torch.tanh(h) * math.pi # normalise to [-π, π] + h_q = self.quantum(h_norm) + out = self.drop(self.up(h_q)) + # Gating: blend quantum output with classical gate + gate = torch.sigmoid(self.gate(x)) + return gate * out + (1 - gate) * x + + +# ───────────────────────────────────────────────────────────────────────────── +# 6. QUANTUM TRANSFORMER BLOCK +# ───────────────────────────────────────────────────────────────────────────── + + +class QuantumTransformerBlock(nn.Module): + """Pre-norm transformer block with quantum attention and quantum FFN.""" + + def __init__( + self, + d_model: int, + n_heads: int, + n_qubits: int, + n_var_layers: int, + device, + dropout: float = 0.1, + ) -> None: + super().__init__() + self.norm1 = nn.LayerNorm(d_model) + self.attn = QuantumKernelAttention( + d_model, n_heads, n_qubits, n_var_layers, device, dropout + ) + self.norm2 = nn.LayerNorm(d_model) + self.ffn = QuantumFFN(d_model, n_qubits, n_var_layers, device, dropout) + + def forward( + self, x: torch.Tensor, mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + x = x + self.attn(self.norm1(x), mask) + x = x + self.ffn(self.norm2(x)) + return x + + +# ───────────────────────────────────────────────────────────────────────────── +# 7. QUANTUM CODE LLM (MAIN MODEL) +# ───────────────────────────────────────────────────────────────────────────── + + +@dataclass +class QuantumCodeLLMConfig: + vocab_size: int = 120 + d_model: int = 64 + n_heads: int = 4 + n_layers: int = 2 + n_qubits: int = 4 + n_var_layers: int = 2 # variational layers inside each quantum circuit + max_seq_len: int = 128 + dropout: float = 0.1 + backend: str = "auto" # "auto" | "qiskit.aer" | "default.qubit" | "classical" + + +class QuantumCodeLLM(nn.Module): + """Quantum-enhanced language model for code generation. + + Embedding → Positional Encoding → N × QuantumTransformerBlock → Output head + """ + + def __init__(self, config: QuantumCodeLLMConfig) -> None: + super().__init__() + self.config = config + + # Resolve quantum backend + if config.backend == "auto": + self._backend_label, self._qdevice = _make_device(config.n_qubits) + elif config.backend == "classical": + self._backend_label, self._qdevice = "classical", None + else: + if not _PENNYLANE_AVAILABLE: + raise RuntimeError( + "A non-classical backend was requested but PennyLane is not installed" + ) + self._backend_label = config.backend + self._qdevice = qml.device(config.backend, wires=config.n_qubits) + + # Embeddings + self.token_emb = nn.Embedding(config.vocab_size, config.d_model, padding_idx=0) + self.pos_emb = nn.Embedding(config.max_seq_len, config.d_model) + self.emb_drop = nn.Dropout(config.dropout) + + # Transformer blocks + self.blocks = nn.ModuleList( + [ + QuantumTransformerBlock( + config.d_model, + config.n_heads, + config.n_qubits, + config.n_var_layers, + self._qdevice, + config.dropout, + ) + for _ in range(config.n_layers) + ] + ) + + self.norm = nn.LayerNorm(config.d_model) + self.head = nn.Linear(config.d_model, config.vocab_size, bias=False) + + # Weight tying: output head shares weights with token embedding + self.head.weight = self.token_emb.weight + + self._init_weights() + + def _init_weights(self) -> None: + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Embedding): + nn.init.normal_(m.weight, std=0.02) + + def forward( + self, input_ids: torch.Tensor, mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """ + Args: + input_ids : (B, T) token indices + mask : (B, 1, T, T) optional padding mask + + Returns: + logits : (B, T, vocab_size) + """ + B, T = input_ids.shape + if T > self.config.max_seq_len: + raise ValueError( + f"input length {T} exceeds max_seq_len={self.config.max_seq_len}; " + "truncate input_ids before calling forward" + ) + tok = self.token_emb(input_ids) + pos = self.pos_emb(torch.arange(T, device=input_ids.device).unsqueeze(0)) + x = self.emb_drop(tok + pos) + + for block in self.blocks: + x = block(x, mask) + + x = self.norm(x) + return self.head(x) + + @torch.no_grad() + def generate( + self, + prompt_ids: torch.Tensor, + max_new_tokens: int = 64, + temperature: float = 0.8, + top_k: int = 40, + eos_id: int = 2, + ) -> torch.Tensor: + """Autoregressively generate tokens using temperature + top-k sampling.""" + if prompt_ids.ndim != 2 or prompt_ids.shape[0] != 1: + raise ValueError("prompt_ids must have shape (1, T)") + if temperature <= 0: + raise ValueError("temperature must be > 0") + if top_k < 0: + raise ValueError("top_k must be >= 0") + + self.eval() + ids = prompt_ids.clone() # (1, T) + for _ in range(max_new_tokens): + # Truncate to max_seq_len + ids_cond = ids[:, -self.config.max_seq_len :] + logits = self.forward(ids_cond) # (1, T, vocab) + next_logits = logits[:, -1, :] / temperature # (1, vocab) + + # Top-k filtering + if top_k > 0: + k = min(top_k, next_logits.shape[-1]) + kth_val = torch.topk(next_logits, k).values[:, -1, None] + next_logits = next_logits.masked_fill( + next_logits < kth_val, float("-inf") + ) + + probs = F.softmax(next_logits, dim=-1) + next_id = torch.multinomial(probs, num_samples=1) # (1, 1) + ids = torch.cat([ids, next_id], dim=1) + if next_id.item() == eos_id: + break + return ids + + @property + def backend(self) -> str: + return self._backend_label + + def parameter_count(self) -> dict: + total = sum(p.numel() for p in self.parameters()) + trainable = sum(p.numel() for p in self.parameters() if p.requires_grad) + return {"total": total, "trainable": trainable} + + +# ───────────────────────────────────────────────────────────────────────────── +# 8. BUILT-IN CODE DATASET +# ───────────────────────────────────────────────────────────────────────────── + +_PYTHON_SNIPPETS = [ + "def add(a, b):\n return a + b\n", + "def subtract(a, b):\n return a - b\n", + "def multiply(x, y):\n return x * y\n", + "def divide(x, y):\n if y == 0:\n return None\n return x / y\n", + "def square(n):\n return n * n\n", + "def cube(n):\n return n * n * n\n", + "def is_even(n):\n return n % 2 == 0\n", + "def is_odd(n):\n return n % 2 != 0\n", + "def factorial(n):\n if n <= 1:\n return 1\n return n * factorial(n - 1)\n", + "def fibonacci(n):\n if n <= 1:\n return n\n return fibonacci(n - 1) + fibonacci(n - 2)\n", + "def max_value(lst):\n return max(lst)\n", + "def min_value(lst):\n return min(lst)\n", + "def sum_list(lst):\n return sum(lst)\n", + "def reverse_string(s):\n return s[::-1]\n", + "def to_upper(s):\n return s.upper()\n", + "def to_lower(s):\n return s.lower()\n", + "def count_chars(s):\n return len(s)\n", + "def greet(name):\n return 'Hello, ' + name + '!'\n", + "class Counter:\n def __init__(self):\n self.count = 0\n def increment(self):\n self.count += 1\n def get(self):\n return self.count\n", + "class Stack:\n def __init__(self):\n self.items = []\n def push(self, item):\n self.items.append(item)\n def pop(self):\n return self.items.pop()\n def is_empty(self):\n return len(self.items) == 0\n", + "def bubble_sort(lst):\n n = len(lst)\n for i in range(n):\n for j in range(n - i - 1):\n if lst[j] > lst[j + 1]:\n lst[j], lst[j + 1] = lst[j + 1], lst[j]\n return lst\n", + "def binary_search(lst, target):\n low, high = 0, len(lst) - 1\n while low <= high:\n mid = (low + high) // 2\n if lst[mid] == target:\n return mid\n elif lst[mid] < target:\n low = mid + 1\n else:\n high = mid - 1\n return -1\n", + "def flatten(lst):\n result = []\n for item in lst:\n if isinstance(item, list):\n result.extend(flatten(item))\n else:\n result.append(item)\n return result\n", + "def gcd(a, b):\n while b:\n a, b = b, a % b\n return a\n", + "def lcm(a, b):\n return a * b // gcd(a, b)\n", + "def is_prime(n):\n if n < 2:\n return False\n for i in range(2, int(n ** 0.5) + 1):\n if n % i == 0:\n return False\n return True\n", + "def clamp(value, lo, hi):\n return max(lo, min(hi, value))\n", + "def average(lst):\n return sum(lst) / len(lst)\n", + "def unique(lst):\n return list(set(lst))\n", + "def zip_dicts(keys, values):\n return dict(zip(keys, values))\n", +] + + +class CodeDataset(Dataset): + """Next-token prediction dataset from code snippets. + + Each sample is a sequence of max_seq_len tokens. The target is the + input shifted by one position. + """ + + def __init__( + self, + tokenizer: CodeTokenizer, + snippets: List[str] = _PYTHON_SNIPPETS, + seq_len: int = 64, + ) -> None: + self.tokenizer = tokenizer + self.seq_len = seq_len + # Concatenate all snippets into one long token stream + full_text = "\n".join(snippets) + "\n" + self.tokens = tokenizer.encode(full_text, add_bos=False, add_eos=False) + + def __len__(self) -> int: + return max(0, len(self.tokens) - self.seq_len - 1) + + def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: + chunk = self.tokens[idx : idx + self.seq_len + 1] + # Pad if needed (last chunk) + while len(chunk) < self.seq_len + 1: + chunk.append(CodeTokenizer.PAD) + x = torch.tensor(chunk[:-1], dtype=torch.long) + y = torch.tensor(chunk[1:], dtype=torch.long) + return x, y + + +# ───────────────────────────────────────────────────────────────────────────── +# 9. TRAINER +# ───────────────────────────────────────────────────────────────────────────── + + +@dataclass +class TrainConfig: + n_epochs: int = 5 + batch_size: int = 8 + lr: float = 3e-3 + weight_decay: float = 1e-4 + warmup_steps: int = 50 + seq_len: int = 64 + grad_clip: float = 1.0 + log_every: int = 20 + device: str = "cpu" + seed: int = 42 + extra_snippets: List[str] = field(default_factory=list) + + +class QuantumCodeTrainer: + """Training loop for QuantumCodeLLM.""" + + def __init__( + self, + model: QuantumCodeLLM, + tokenizer: CodeTokenizer, + train_cfg: TrainConfig, + ) -> None: + self.model = model.to(train_cfg.device) + self.tokenizer = tokenizer + self.cfg = train_cfg + self._device = torch.device(train_cfg.device) + + snippets = _PYTHON_SNIPPETS + train_cfg.extra_snippets + dataset = CodeDataset(tokenizer, snippets, seq_len=train_cfg.seq_len) + self.loader = DataLoader( + dataset, + batch_size=train_cfg.batch_size, + shuffle=True, + drop_last=True, + ) + + self.optimizer = torch.optim.AdamW( + model.parameters(), + lr=train_cfg.lr, + weight_decay=train_cfg.weight_decay, + ) + total_steps = len(self.loader) * train_cfg.n_epochs + self.scheduler = torch.optim.lr_scheduler.OneCycleLR( + self.optimizer, + max_lr=train_cfg.lr, + total_steps=max(1, total_steps), + pct_start=0.1, + anneal_strategy="cos", + ) + + def train(self) -> List[dict]: + """Run the training loop. Returns per-epoch metric dicts.""" + torch.manual_seed(self.cfg.seed) + random.seed(self.cfg.seed) + np.random.seed(self.cfg.seed) + + history: List[dict] = [] + step = 0 + + for epoch in range(1, self.cfg.n_epochs + 1): + self.model.train() + epoch_loss = 0.0 + t0 = time.time() + + for batch_idx, (x, y) in enumerate(self.loader): + x, y = x.to(self._device), y.to(self._device) + + logits = self.model(x) # (B, T, vocab) + B, T, V = logits.shape + loss = F.cross_entropy( + logits.view(B * T, V), + y.view(B * T), + ignore_index=CodeTokenizer.PAD, + ) + + self.optimizer.zero_grad() + loss.backward() + nn.utils.clip_grad_norm_(self.model.parameters(), self.cfg.grad_clip) + self.optimizer.step() + self.scheduler.step() + + epoch_loss += loss.item() + step += 1 + + if step % self.cfg.log_every == 0: + lr_now = self.scheduler.get_last_lr()[0] + perp = math.exp(min(loss.item(), 20)) + print( + f" step {step:>5d} | loss {loss.item():.4f} " + f"| ppl {perp:.1f} | lr {lr_now:.2e}" + ) + + avg_loss = epoch_loss / max(1, len(self.loader)) + elapsed = time.time() - t0 + print( + f"Epoch {epoch}/{self.cfg.n_epochs} — " + f"avg loss {avg_loss:.4f} | ppl {math.exp(min(avg_loss, 20)):.1f} " + f"| {elapsed:.1f}s" + ) + history.append( + {"epoch": epoch, "loss": avg_loss, "ppl": math.exp(min(avg_loss, 20))} + ) + + return history + + +# ───────────────────────────────────────────────────────────────────────────── +# 10. PUBLIC ENTRY POINTS +# ───────────────────────────────────────────────────────────────────────────── + + +def train( + model_cfg: Optional[dict] = None, + train_cfg: Optional[dict] = None, + extra_snippets: Optional[List[str]] = None, +) -> Tuple[QuantumCodeLLM, CodeTokenizer]: + """Train a QuantumCodeLLM and return (model, tokenizer). + + Args: + model_cfg : kwargs for QuantumCodeLLMConfig (e.g. n_qubits, d_model) + train_cfg : kwargs for TrainConfig (e.g. n_epochs, lr) + extra_snippets : additional Python code strings to train on + + Example:: + + model, tok = train({"n_qubits": 4, "d_model": 64}, {"n_epochs": 3}) + print(generate(model, tok, "def hello(")) + """ + model_cfg = model_cfg or {} + train_cfg = train_cfg or {} + extra_snippets = extra_snippets or [] + + tokenizer = CodeTokenizer() + mcfg = QuantumCodeLLMConfig(vocab_size=tokenizer.vocab_size, **model_cfg) + model = QuantumCodeLLM(mcfg) + + print(f"QuantumCodeLLM ready — backend: {model.backend}") + params = model.parameter_count() + print(f"Parameters: {params['total']:,} total, {params['trainable']:,} trainable") + print(f"Vocab size: {tokenizer.vocab_size}") + + tcfg = TrainConfig(extra_snippets=extra_snippets, **train_cfg) + trainer = QuantumCodeTrainer(model, tokenizer, tcfg) + + print(f"\nStarting training ({tcfg.n_epochs} epochs) ...") + trainer.train() + return model, tokenizer + + +def save_checkpoint( + model: QuantumCodeLLM, + tokenizer: CodeTokenizer, + checkpoint_path: str | Path, + extra: Optional[Dict[str, Any]] = None, +) -> Path: + """Persist model + config + tokenizer metadata to a checkpoint file.""" + path = Path(checkpoint_path) + path.parent.mkdir(parents=True, exist_ok=True) + + payload = { + "model_state": model.state_dict(), + "config": asdict(model.config), + "tokenizer": tokenizer.to_dict(), + "backend": model.backend, + "saved_at": time.time(), + "extra": extra or {}, + } + torch.save(payload, path) + return path + + +def load_checkpoint( + checkpoint_path: str | Path, + map_location: str | torch.device = "cpu", + backend_override: Optional[str] = None, +) -> Tuple[QuantumCodeLLM, CodeTokenizer, Dict[str, Any]]: + """Load a checkpoint created by save_checkpoint. + + Also supports legacy payloads where config was serialized as QuantumCodeLLMConfig. + """ + path = Path(checkpoint_path) + if not path.exists(): + raise FileNotFoundError(f"Checkpoint not found: {path}") + + payload = torch.load(path, map_location=map_location, weights_only=False) + if "model_state" not in payload: + raise ValueError(f"Invalid checkpoint payload: missing model_state in {path}") + + tokenizer = CodeTokenizer.from_dict(payload.get("tokenizer")) + + raw_config = payload.get("config") + if isinstance(raw_config, QuantumCodeLLMConfig): + config = raw_config + elif isinstance(raw_config, dict): + config = QuantumCodeLLMConfig(**raw_config) + else: + raise ValueError(f"Invalid checkpoint payload: missing valid config in {path}") + + config.vocab_size = tokenizer.vocab_size + if backend_override is not None: + config.backend = backend_override + + model = QuantumCodeLLM(config) + model.load_state_dict(payload["model_state"]) + model.eval() + + metadata = { + "path": str(path), + "backend": payload.get("backend", model.backend), + "saved_at": payload.get("saved_at"), + "extra": payload.get("extra", {}), + } + return model, tokenizer, metadata + + +def generate( + model: QuantumCodeLLM, + tokenizer: CodeTokenizer, + prompt: str = "def ", + max_new_tokens: int = 80, + temperature: float = 0.8, + top_k: int = 40, + device: str = "cpu", +) -> str: + """Generate code continuation from a text prompt. + + Example:: + + code = generate(model, tokenizer, "def factorial(n):", max_new_tokens=60) + print(code) + """ + if temperature <= 0: + raise ValueError("temperature must be > 0") + if top_k < 0: + raise ValueError("top_k must be >= 0") + + model.eval() + ids = tokenizer.encode(prompt, add_bos=True, add_eos=False) + input_tensor = torch.tensor([ids], dtype=torch.long, device=device) + with torch.no_grad(): + out_ids = model.generate( + input_tensor, + max_new_tokens=max_new_tokens, + temperature=temperature, + top_k=top_k, + eos_id=tokenizer.EOS, + ) + # Strip the prompt prefix, decode only new tokens + new_ids = out_ids[0, len(ids) :].tolist() + return prompt + tokenizer.decode(new_ids) + + +__all__ = [ + "CodeTokenizer", + "CodeDataset", + "QuantumFeatureMapLayer", + "QuantumKernelAttention", + "QuantumFFN", + "QuantumTransformerBlock", + "QuantumCodeLLMConfig", + "QuantumCodeLLM", + "TrainConfig", + "QuantumCodeTrainer", + "train", + "generate", + "save_checkpoint", + "load_checkpoint", +] diff --git a/quantum-ai/web_app.py b/quantum-ai/web_app.py index 03e232e1d..badb9ca9a 100644 --- a/quantum-ai/web_app.py +++ b/quantum-ai/web_app.py @@ -1,106 +1,106 @@ -"""Compatibility wrapper for the quantum web app module. - -The canonical implementation lives in: - ai-projects/quantum-ml/web_app.py - -This wrapper preserves legacy import paths used by tests and scripts. -""" - -from __future__ import annotations - -import importlib.util -import sys -from pathlib import Path - -_CANONICAL = ( - Path(__file__).resolve().parents[1] / "ai-projects" / "quantum-ml" / "web_app.py" -) - -if not _CANONICAL.exists(): - raise FileNotFoundError(f"Canonical web app not found: {_CANONICAL}") - -_spec = importlib.util.spec_from_file_location("_canonical_quantum_web_app", _CANONICAL) -if _spec is None or _spec.loader is None: - raise ImportError(f"Unable to load spec for {_CANONICAL}") - -_mod = importlib.util.module_from_spec(_spec) -sys.modules[_spec.name] = _mod -_spec.loader.exec_module(_mod) - - -def _compat_load_checkpoint(): - """Compatibility endpoint using the legacy quantum-ai checkpoints directory.""" - payload = _mod.request.json or {} - checkpoint_path = payload.get("checkpoint_path") - - if not checkpoint_path: - return _mod.jsonify({"error": "No checkpoint path provided"}), 400 - - try: - requested_path = Path(checkpoint_path) - checkpoint_dir = Path(__file__).resolve().parent / "checkpoints" - - try: - resolved_path = requested_path.resolve() - allowed_dir = checkpoint_dir.resolve() - except (OSError, RuntimeError): - return _mod.jsonify({"error": "Invalid checkpoint path"}), 400 - - if hasattr(resolved_path, "is_relative_to"): - if not resolved_path.is_relative_to(allowed_dir): - return ( - _mod.jsonify( - { - "error": "Invalid checkpoint path: must be within checkpoints directory" - } - ), - 403, - ) - else: - try: - resolved_path.relative_to(allowed_dir) - except ValueError: - return ( - _mod.jsonify( - { - "error": "Invalid checkpoint path: must be within checkpoints directory" - } - ), - 403, - ) - - if not resolved_path.exists(): - return _mod.jsonify({"error": "Checkpoint file not found"}), 404 - - checkpoint = _mod.np.load(str(resolved_path), allow_pickle=True) - weights = checkpoint["weights"] - epoch = int(checkpoint["epoch"]) - config = ( - checkpoint["config"].item() - if isinstance(checkpoint["config"], _mod.np.ndarray) - else checkpoint["config"] - ) - - return _mod.jsonify( - { - "success": True, - "weights_shape": list(weights.shape), - "epoch": epoch, - "config": config, - "message": f"Checkpoint loaded from epoch {epoch}", - } - ) - except Exception as exc: - return _mod.jsonify({"error": str(exc)}), 500 - - -# Keep the route path and endpoint name stable while swapping the handler logic -# to use the legacy checkpoint root expected by callers of ai-projects/quantum-ml/web_app.py. -_mod.app.view_functions["load_checkpoint"] = _compat_load_checkpoint - -# Re-export public symbols for compatibility. -for _name, _value in _mod.__dict__.items(): - if not _name.startswith("__"): - globals()[_name] = _value - -load_checkpoint = _compat_load_checkpoint +"""Compatibility wrapper for the quantum web app module. + +The canonical implementation lives in: + ai-projects/quantum-ml/web_app.py + +This wrapper preserves legacy import paths used by tests and scripts. +""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path + +_CANONICAL = ( + Path(__file__).resolve().parents[1] / "ai-projects" / "quantum-ml" / "web_app.py" +) + +if not _CANONICAL.exists(): + raise FileNotFoundError(f"Canonical web app not found: {_CANONICAL}") + +_spec = importlib.util.spec_from_file_location("_canonical_quantum_web_app", _CANONICAL) +if _spec is None or _spec.loader is None: + raise ImportError(f"Unable to load spec for {_CANONICAL}") + +_mod = importlib.util.module_from_spec(_spec) +sys.modules[_spec.name] = _mod +_spec.loader.exec_module(_mod) + + +def _compat_load_checkpoint(): + """Compatibility endpoint using the legacy quantum-ai checkpoints directory.""" + payload = _mod.request.json or {} + checkpoint_path = payload.get("checkpoint_path") + + if not checkpoint_path: + return _mod.jsonify({"error": "No checkpoint path provided"}), 400 + + try: + requested_path = Path(checkpoint_path) + checkpoint_dir = Path(__file__).resolve().parent / "checkpoints" + + try: + resolved_path = requested_path.resolve() + allowed_dir = checkpoint_dir.resolve() + except (OSError, RuntimeError): + return _mod.jsonify({"error": "Invalid checkpoint path"}), 400 + + if hasattr(resolved_path, "is_relative_to"): + if not resolved_path.is_relative_to(allowed_dir): + return ( + _mod.jsonify( + { + "error": "Invalid checkpoint path: must be within checkpoints directory" + } + ), + 403, + ) + else: + try: + resolved_path.relative_to(allowed_dir) + except ValueError: + return ( + _mod.jsonify( + { + "error": "Invalid checkpoint path: must be within checkpoints directory" + } + ), + 403, + ) + + if not resolved_path.exists(): + return _mod.jsonify({"error": "Checkpoint file not found"}), 404 + + checkpoint = _mod.np.load(str(resolved_path), allow_pickle=True) + weights = checkpoint["weights"] + epoch = int(checkpoint["epoch"]) + config = ( + checkpoint["config"].item() + if isinstance(checkpoint["config"], _mod.np.ndarray) + else checkpoint["config"] + ) + + return _mod.jsonify( + { + "success": True, + "weights_shape": list(weights.shape), + "epoch": epoch, + "config": config, + "message": f"Checkpoint loaded from epoch {epoch}", + } + ) + except Exception as exc: + return _mod.jsonify({"error": str(exc)}), 500 + + +# Keep the route path and endpoint name stable while swapping the handler logic +# to use the legacy checkpoint root expected by callers of ai-projects/quantum-ml/web_app.py. +_mod.app.view_functions["load_checkpoint"] = _compat_load_checkpoint + +# Re-export public symbols for compatibility. +for _name, _value in _mod.__dict__.items(): + if not _name.startswith("__"): + globals()[_name] = _value + +load_checkpoint = _compat_load_checkpoint diff --git a/requirements.txt b/requirements.txt index 4a3d9a46f..93ea7edb0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,48 +1,48 @@ -azure-monitor-opentelemetry>=1.0.0,<2.0.0 # Application Insights telemetry -opentelemetry-api>=1.23.0,<2.0.0 -opentelemetry-sdk>=1.23.0,<2.0.0 -azure-cosmos>=4.7.0,<5.0.0 # Cosmos DB client - -azure-functions -openai>=1.58.0 -colorama>=0.4.6 -pytest>=8.0.0 -pytest-asyncio>=0.24.0 -tiktoken>=0.8.0 -pyyaml>=6.0.1 -pyodbc>=5.0.1 # Database logging (Azure SQL / SQL Server) -sqlalchemy>=2.0.36 # Optional higher-level DB access (future use) - -# Optional relational drivers (uncomment if using these backends) -# psycopg2-binary>=2.9.9 # PostgreSQL support -# mysqlclient>=2.2.4 # MySQL/MariaDB support - -# Dashboard / WebSocket -Flask>=3.1.3 -flask-socketio>=5.4.0 -python-socketio>=5.11.0 -eventlet>=0.37.0 # Optional async server for SocketIO (can fallback to threading) -gradio>=5.24.0 - -# Optional TTS fallbacks for local development: pyttsx3 (offline, Windows-friendly) and gTTS -# If you want server-side TTS without Azure credentials, enable QAI_ENABLE_LOCAL_TTS and -# install these packages. -pyttsx3>=2.90 -gTTS>=2.3.0 - -# Optional evaluation & visualization (confusion-matrix, plots) -# Install these when you want to produce evaluation artifacts: -# scikit-learn, matplotlib, seaborn -scikit-learn>=1.6.0 -matplotlib>=3.9.0 -seaborn>=0.13.0 - -# Vision inference dependencies -Pillow>=11.1.0 -torch>=2.8.0 -numpy>=1.26.4 - -# JSON schema validation library used by tests and cooking-ai utilities -jsonschema>=4.23.0 - -# NOTE: After adding telemetry & cosmos, run task: pip install (functions) +azure-monitor-opentelemetry>=1.0.0,<2.0.0 # Application Insights telemetry +opentelemetry-api>=1.23.0,<2.0.0 +opentelemetry-sdk>=1.23.0,<2.0.0 +azure-cosmos>=4.7.0,<5.0.0 # Cosmos DB client + +azure-functions +openai>=1.58.0 +colorama>=0.4.6 +pytest>=8.0.0 +pytest-asyncio>=0.24.0 +tiktoken>=0.8.0 +pyyaml>=6.0.1 +pyodbc>=5.0.1 # Database logging (Azure SQL / SQL Server) +sqlalchemy>=2.0.36 # Optional higher-level DB access (future use) + +# Optional relational drivers (uncomment if using these backends) +# psycopg2-binary>=2.9.9 # PostgreSQL support +# mysqlclient>=2.2.4 # MySQL/MariaDB support + +# Dashboard / WebSocket +Flask>=3.1.3 +flask-socketio>=5.4.0 +python-socketio>=5.11.0 +eventlet>=0.37.0 # Optional async server for SocketIO (can fallback to threading) +gradio>=5.24.0 + +# Optional TTS fallbacks for local development: pyttsx3 (offline, Windows-friendly) and gTTS +# If you want server-side TTS without Azure credentials, enable QAI_ENABLE_LOCAL_TTS and +# install these packages. +pyttsx3>=2.90 +gTTS>=2.3.0 + +# Optional evaluation & visualization (confusion-matrix, plots) +# Install these when you want to produce evaluation artifacts: +# scikit-learn, matplotlib, seaborn +scikit-learn>=1.6.0 +matplotlib>=3.9.0 +seaborn>=0.13.0 + +# Vision inference dependencies +Pillow>=11.1.0 +torch>=2.8.0 +numpy>=1.26.4 + +# JSON schema validation library used by tests and cooking-ai utilities +jsonschema>=4.23.0 + +# NOTE: After adding telemetry & cosmos, run task: pip install (functions) diff --git a/scripts/README.md b/scripts/README.md index f9ad4cca3..041fca861 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -1,516 +1,516 @@ -# Scripts - -This folder contains automation utilities, orchestrators, and tools for the QAI workspace. - -## 🎯 Quick Start Commands - -### Directory Layout (CLI grouping) - -- Training scripts: [training/](training/) (train_vision) -- Monitoring utilities: [`status_dashboard.py`](status_dashboard.py), [`resource_monitor.py`](resource_monitor.py), [`system_health_check.py`](system_health_check.py) -- Shims: legacy paths in [scripts/](.) still dispatch to the moved files for backwards compatibility. - -### Training Automation - -```powershell -# Quick multi-model training with evaluation -python .\scripts\automated_training_pipeline.py --quick - -# TinyLlama ultrafast iteration -python .\scripts\automated_training_pipeline.py --models tinyllama --quick - -# Parallel training (Phi + Qwen) -python .\scripts\parallel_train.py --models phi,qwen --quick - -# Single model training -python .\scripts\auto_data_train.py --model phi --quick -``` - -### Testing & Validation - -```powershell -# Run all fast tests -python .\scripts\test_runner.py --all - -# Unit tests only -python .\scripts\test_runner.py --unit - -# Full CI pipeline -python .\scripts\ci_orchestrator.py --ci-pipeline - -# Validate orchestrators -python .\scripts\ci_orchestrator.py --validate-all - -# Fast integration smoke checks -python .\scripts\integration_smoke.py - -# Focused integration contract tests -python .\scripts\ci_orchestrator.py --integration-contract-tests - -# One-command local gate script -bash ./scripts/integration_contract_gate.sh - -# One-command local gate (VS Code task label) -# integration:contract-gate - -# Automate the repeated local fix/validate loop (one-shot) -python .\scripts\repo_health_automation.py --once --strict-endpoints - -# Same automation, but also run full pytest smoke -python .\scripts\repo_health_automation.py --once --strict-endpoints --full-pytest - -# Continuous unattended loop every 5 minutes -python .\scripts\repo_health_automation.py --watch --interval 300 --strict-endpoints - -# Shell wrapper (friendlier command UX) -./scripts/start_repo_health_automation.sh once --strict -./scripts/start_repo_health_automation.sh watch --strict --interval 300 -./scripts/start_repo_health_automation.sh status - -# VS Code task labels: -# - repo-health:once-strict -# - repo-health:once-strict-full-pytest -# - repo-health:watch-strict -``` - -#### `repo_health_automation.py` - -**Purpose:** Automate repeated repo-health cycles (pre-commit + integration contract gate + optional full pytest) so fix/validation can run unattended. - -**Key options:** - -- `--once` / `--watch` — run one cycle or continuous loop -- `--strict-endpoints` — run integration gate in strict endpoint mode -- `--full-pytest` — include `pytest tests -q --maxfail=1 --tb=short` -- `--auto-fix-ruff` — run `ruff check --fix` on changed `.py` files before checks -- `--continue-on-fail` — continue all steps even after a failed step - -**Status output:** - -- `data_out/repo_health_automation/status.json` - -### Quantum Operations - -```powershell -# Run quantum training jobs -python .\scripts\quantum_autorun.py --dry-run - -# Validate quantum environment -python -m pytest tests\test_validate_qiskit_env.py -v -``` - -## 📚 Core Automation Tools - -### Training Orchestrators - -#### `automated_training_pipeline.py` - -**Purpose:** Single entry point for multi-model LoRA training with data generation, evaluation, and Azure ML spec emission. - -**Key Features:** - -- Generate synthetic data (phi, qwen, tinyllama) -- Sequential or parallel model training -- Evaluation with perplexity & diversity metrics -- Multiple ranking strategies (improvement, diversity, combined) -- Azure ML job spec emission -- Checkpoint cleanup - -**Usage:** - -```powershell -# Quick both models with evaluation -python .\scripts\automated_training_pipeline.py --quick - -# Custom samples and ranking -python .\scripts\automated_training_pipeline.py --models phi --samples 300 --ranking-metric diversity_avg - -# Generate data only -python .\scripts\automated_training_pipeline.py --generate-only --samples 200 - -# Emit Azure ML spec -python .\scripts\automated_training_pipeline.py --azure-ml-spec --quick --models phi,qwen -``` - -#### `parallel_train.py` - -**Purpose:** Parallel execution of multiple LoRA training jobs with shared evaluation and ranking. - -**Key Features:** - -- Concurrent training via ThreadPoolExecutor -- Shared evaluation step (perplexity, diversity, echo ratio) -- Configurable ranking metrics -- Append-only status history -- Supports 5+ ranking strategies - -**Usage:** - -```powershell -# Train all models in parallel -python .\scripts\parallel_train.py --models phi,qwen,tinyllama --quick - -# Custom ranking metric -python .\scripts\parallel_train.py --models phi,qwen --ranking-metric combined_improvement -``` - -#### `auto_data_train.py` - -**Purpose:** Generate synthetic training data + single model LoRA training. - -**Usage:** - -```powershell -# Phi model with synthetic data -python .\scripts\auto_data_train.py --model phi --quick - -# TinyLlama with custom samples -python .\scripts\auto_data_train.py --model tinyllama --samples 300 -``` - -### Master Orchestrators - -#### `master_orchestrator.py` - -**Purpose:** High-level workflow coordination for complex training pipelines. - -**Workflows:** - -- `quick_validation`: Validate all configs -- `full_training`: Complete training cycle -- `evaluation_only`: Run evaluations on existing models - -**Usage:** - -```powershell -# Quick validation -python .\scripts\master_orchestrator.py --workflow quick_validation - -# Check status -python .\scripts\master_orchestrator.py --status -``` - -#### `autotrain.py` - -**Purpose:** HuggingFace AutoTrain wrapper with multi-job support. - -**Usage:** - -```powershell -# Dry run validation -python .\scripts\autotrain.py --dry-run - -# Run all jobs -python .\scripts\autotrain.py - -# Single job -python .\scripts\autotrain.py --job phi36_mixed_chat -``` - -### Testing Infrastructure - -#### `test_runner.py` ⭐ **Recommended** - -**Purpose:** Centralized test orchestrator with intelligent filtering and result aggregation. - -**Test Suites:** - -- `unit`: 40 fast tests (~0.5s) -- `integration`: 30 external service tests (~3s) -- `all_fast`: 83 tests excluding slow/azure (~10s) -- `autotrain`, `quantum`, `database`, `chat`: Focused suites - -**Usage:** - -```powershell -# Run all fast tests -python .\scripts\test_runner.py --all - -# Unit tests with coverage -python .\scripts\test_runner.py --unit --coverage - -# Watch mode (re-run on change) -python .\scripts\test_runner.py --unit --integration --watch - -# List available suites -python .\scripts\test_runner.py --list-suites -``` - -**Features:** - -- ANSI escape code handling -- Regex-based pytest output parsing -- Marker filtering (`not slow and not azure`) -- JSON + Markdown result reports -- Parallel suite execution -- Coverage integration - -#### `ci_orchestrator.py` - -**Purpose:** Continuous integration pipeline with staged validation gates. - -**Steps:** - -1. Orchestrator validations (autotrain, quantum, evaluation) -2. Integration smoke checks (cross-component) -3. Integration contract unit tests (resolver, scheduler, status schemas) -4. Unit tests (via test_runner) -5. Dataset validation -6. Integration tests -7. Code quality checks -8. Security scanning -9. Deployment preparation -10. Azure ML validation - -**Usage:** - -```powershell -# Full CI pipeline -python .\scripts\ci_orchestrator.py --ci-pipeline - -# Validate orchestrators only -python .\scripts\ci_orchestrator.py --validate-all - -# Run focused integration contract tests only -python .\scripts\ci_orchestrator.py --integration-contract-tests - -# Run full contract gate sequence (smoke + contract tests + validate-all) -bash ./scripts/integration_contract_gate.sh - -# Or run the VS Code task: integration:contract-gate -``` - -**Current Status:** 5/10 passing (all critical steps ✅) - -### Evaluation & Analysis - -#### `batch_evaluator.py` - -**Purpose:** Parallel evaluation of multiple models with comprehensive result aggregation. - -**Features:** - -- Parallel model evaluation (ThreadPoolExecutor) -- Support for LoRA, Azure, OpenAI, Local, Quantum models -- Configurable metrics per model type -- Result ranking and comparison -- Export to JSON/Markdown/CSV - -**Usage:** - -```powershell -# Scan and evaluate all models -python .\scripts\batch_evaluator.py --scan-models --evaluate-all - -# Compare specific models -python .\scripts\batch_evaluator.py --compare lora azure openai - -# Export results -python .\scripts\batch_evaluator.py --export markdown --output report.md -``` - -#### `training_analytics.py` - -**Purpose:** Analyze training metrics and generate performance reports. - -**Usage:** - -```powershell -# Analyze recent training runs -python .\scripts\training_analytics.py --recent 5 - -# Generate comparison report -python .\scripts\training_analytics.py --compare phi qwen tinyllama -``` - -### Azure ML Integration - -#### `azureml_ci_validate.py` - -**Purpose:** Validate and optionally submit Azure ML job specs. - -**Features:** - -- YAML schema validation -- Environment spec checking -- `.env` placeholder gating (prevents submission with unresolved credentials) -- Graceful fallback when Azure CLI not installed - -**Usage:** - -```powershell -# Validate latest job spec -python .\scripts\azureml_ci_validate.py - -# Validate and submit -python .\scripts\azureml_ci_validate.py --submit - -# Force submit (bypass gating) -python .\scripts\azureml_ci_validate.py --submit --force-submit -``` - -### Dataset Management - -#### `validate_datasets.py` - -**Purpose:** Validate dataset integrity across all categories. - -**Categories:** `quantum`, `chat`, `all` - -**Usage:** - -```powershell -# Validate chat datasets -python .\scripts\validate_datasets.py --category chat - -# Validate all -python .\scripts\validate_datasets.py --category all -``` - -#### `download_datasets.py` - -**Purpose:** Download and organize datasets from HuggingFace. - -**Usage:** - -```powershell -# Download all configured datasets -python .\scripts\download_datasets.py - -# Download specific dataset -python .\scripts\download_datasets.py --dataset dolly-15k -``` - -### Monitoring & Diagnostics - -#### `resource_monitor.py` - -**Purpose:** Monitor system resources during training. - -**Usage:** - -```powershell -# Single snapshot -python .\scripts\resource_monitor.py --snapshot - -# Stream for 60 seconds -python .\scripts\resource_monitor.py --stream --duration 60 -``` - -#### `system_health_check.py` - -**Purpose:** Comprehensive system health validation. - -**Checks:** - -- Python environment -- Required packages -- GPU availability -- Disk space -- Dataset accessibility - -**Usage:** - -```powershell -python .\scripts\system_health_check.py -``` - -### SQL Integration - -#### `sql_migrate.py` - -**Purpose:** Database migration tool for chat/telemetry tables. - -**Usage:** - -```powershell -# Run migrations -python .\scripts\sql_migrate.py -``` - -#### `sql_health_monitor.py` - -**Purpose:** Monitor SQL connection pool and query performance. - -**Usage:** - -```powershell -python .\scripts\sql_health_monitor.py -``` - -## 🔧 Utility Scripts - -### PowerShell Scripts - -- `Start-LocalLoraTraining.ps1`: Task Scheduler-friendly training wrapper -- `Analyze-TrainingLogs.ps1`: Parse and analyze training logs -- `quick_status.ps1`: Quick system status check -- `fast_train.ps1`: Rapid training shortcut - -### Python Utilities - -- `env_autofix.py`: Auto-fix environment issues -- `pre_commit_check.py`: Pre-commit validation hooks -- `metrics_ranker.py`: Rank models by custom metrics -- `results_exporter.py`: Export results to multiple formats - -## 📖 Legacy Scripts (Reference) - -### run_local_lora_training.py - -One-command offline LoRA training for TinyLlama on CPU. It auto-creates a venv under `AI/microsoft_phi-silica-3.6_v1/local_train`, installs requirements, and runs the training script with a small dataset and minimal epochs so it completes quickly on Windows. - -Usage (PowerShell): - -```powershell -# From repo root -python .\scripts\run_local_lora_training.py - -# Customize -python .\scripts\run_local_lora_training.py --max-samples 50 --epochs 2 --config local_config.yaml - -# Force reinstall deps in the venv -python .\scripts\run_local_lora_training.py --reinstall - -# Preview without training -python .\scripts\run_local_lora_training.py --dry-run -``` - -Outputs are written to `AI/microsoft_phi-silica-3.6_v1/local_train/outputs/final` (LoRA adapter + tokenizer files). - -Notes: - -- 4-bit quantization is disabled by default for Windows/CPU; the tiny training still runs fast with TinyLlama. -- The runner sets `HF_HUB_DISABLE_SYMLINKS_WARNING=1` to reduce warnings on Windows filesystems. - -## Start-LocalLoraTraining.ps1 (Windows Task Scheduler friendly) - -PowerShell wrapper that resolves paths, sets env, logs to a timestamped file, and runs the Python runner. - -Usage (PowerShell): - -```powershell -# From repo root (or any folder) -powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\Start-LocalLoraTraining.ps1 -MaxSamples 10 -Epochs 1 -Config local_config.yaml - -# Optional flags -powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\Start-LocalLoraTraining.ps1 -Reinstall -powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\Start-LocalLoraTraining.ps1 -DryRun -``` - -Logs: - -- Default log directory: `AI/microsoft_phi-silica-3.6_v1/local_train/logs` -- File name: `train_YYYYMMDD_HHMMSS.log` - -Task Scheduler tip: - -- Action: `Start a program` -- Program/script: `powershell.exe` -- Add arguments: - `-NoProfile -ExecutionPolicy Bypass -File "C:\Users\Bryan\OneDrive\AI\scripts\Start-LocalLoraTraining.ps1" -MaxSamples 10 -Epochs 1` -- Start in: `C:\Users\Bryan\OneDrive\AI` - -## VS Code task: one-click run - -A task labeled `Run: Local LoRA training` is available. Open the Command Palette → “Run Task…” → select it to run. +# Scripts + +This folder contains automation utilities, orchestrators, and tools for the QAI workspace. + +## 🎯 Quick Start Commands + +### Directory Layout (CLI grouping) + +- Training scripts: [training/](training/) (train_vision) +- Monitoring utilities: [`status_dashboard.py`](status_dashboard.py), [`resource_monitor.py`](resource_monitor.py), [`system_health_check.py`](system_health_check.py) +- Shims: legacy paths in [scripts/](.) still dispatch to the moved files for backwards compatibility. + +### Training Automation + +```powershell +# Quick multi-model training with evaluation +python .\scripts\automated_training_pipeline.py --quick + +# TinyLlama ultrafast iteration +python .\scripts\automated_training_pipeline.py --models tinyllama --quick + +# Parallel training (Phi + Qwen) +python .\scripts\parallel_train.py --models phi,qwen --quick + +# Single model training +python .\scripts\auto_data_train.py --model phi --quick +``` + +### Testing & Validation + +```powershell +# Run all fast tests +python .\scripts\test_runner.py --all + +# Unit tests only +python .\scripts\test_runner.py --unit + +# Full CI pipeline +python .\scripts\ci_orchestrator.py --ci-pipeline + +# Validate orchestrators +python .\scripts\ci_orchestrator.py --validate-all + +# Fast integration smoke checks +python .\scripts\integration_smoke.py + +# Focused integration contract tests +python .\scripts\ci_orchestrator.py --integration-contract-tests + +# One-command local gate script +bash ./scripts/integration_contract_gate.sh + +# One-command local gate (VS Code task label) +# integration:contract-gate + +# Automate the repeated local fix/validate loop (one-shot) +python .\scripts\repo_health_automation.py --once --strict-endpoints + +# Same automation, but also run full pytest smoke +python .\scripts\repo_health_automation.py --once --strict-endpoints --full-pytest + +# Continuous unattended loop every 5 minutes +python .\scripts\repo_health_automation.py --watch --interval 300 --strict-endpoints + +# Shell wrapper (friendlier command UX) +./scripts/start_repo_health_automation.sh once --strict +./scripts/start_repo_health_automation.sh watch --strict --interval 300 +./scripts/start_repo_health_automation.sh status + +# VS Code task labels: +# - repo-health:once-strict +# - repo-health:once-strict-full-pytest +# - repo-health:watch-strict +``` + +#### `repo_health_automation.py` + +**Purpose:** Automate repeated repo-health cycles (pre-commit + integration contract gate + optional full pytest) so fix/validation can run unattended. + +**Key options:** + +- `--once` / `--watch` — run one cycle or continuous loop +- `--strict-endpoints` — run integration gate in strict endpoint mode +- `--full-pytest` — include `pytest tests -q --maxfail=1 --tb=short` +- `--auto-fix-ruff` — run `ruff check --fix` on changed `.py` files before checks +- `--continue-on-fail` — continue all steps even after a failed step + +**Status output:** + +- `data_out/repo_health_automation/status.json` + +### Quantum Operations + +```powershell +# Run quantum training jobs +python .\scripts\quantum_autorun.py --dry-run + +# Validate quantum environment +python -m pytest tests\test_validate_qiskit_env.py -v +``` + +## 📚 Core Automation Tools + +### Training Orchestrators + +#### `automated_training_pipeline.py` + +**Purpose:** Single entry point for multi-model LoRA training with data generation, evaluation, and Azure ML spec emission. + +**Key Features:** + +- Generate synthetic data (phi, qwen, tinyllama) +- Sequential or parallel model training +- Evaluation with perplexity & diversity metrics +- Multiple ranking strategies (improvement, diversity, combined) +- Azure ML job spec emission +- Checkpoint cleanup + +**Usage:** + +```powershell +# Quick both models with evaluation +python .\scripts\automated_training_pipeline.py --quick + +# Custom samples and ranking +python .\scripts\automated_training_pipeline.py --models phi --samples 300 --ranking-metric diversity_avg + +# Generate data only +python .\scripts\automated_training_pipeline.py --generate-only --samples 200 + +# Emit Azure ML spec +python .\scripts\automated_training_pipeline.py --azure-ml-spec --quick --models phi,qwen +``` + +#### `parallel_train.py` + +**Purpose:** Parallel execution of multiple LoRA training jobs with shared evaluation and ranking. + +**Key Features:** + +- Concurrent training via ThreadPoolExecutor +- Shared evaluation step (perplexity, diversity, echo ratio) +- Configurable ranking metrics +- Append-only status history +- Supports 5+ ranking strategies + +**Usage:** + +```powershell +# Train all models in parallel +python .\scripts\parallel_train.py --models phi,qwen,tinyllama --quick + +# Custom ranking metric +python .\scripts\parallel_train.py --models phi,qwen --ranking-metric combined_improvement +``` + +#### `auto_data_train.py` + +**Purpose:** Generate synthetic training data + single model LoRA training. + +**Usage:** + +```powershell +# Phi model with synthetic data +python .\scripts\auto_data_train.py --model phi --quick + +# TinyLlama with custom samples +python .\scripts\auto_data_train.py --model tinyllama --samples 300 +``` + +### Master Orchestrators + +#### `master_orchestrator.py` + +**Purpose:** High-level workflow coordination for complex training pipelines. + +**Workflows:** + +- `quick_validation`: Validate all configs +- `full_training`: Complete training cycle +- `evaluation_only`: Run evaluations on existing models + +**Usage:** + +```powershell +# Quick validation +python .\scripts\master_orchestrator.py --workflow quick_validation + +# Check status +python .\scripts\master_orchestrator.py --status +``` + +#### `autotrain.py` + +**Purpose:** HuggingFace AutoTrain wrapper with multi-job support. + +**Usage:** + +```powershell +# Dry run validation +python .\scripts\autotrain.py --dry-run + +# Run all jobs +python .\scripts\autotrain.py + +# Single job +python .\scripts\autotrain.py --job phi36_mixed_chat +``` + +### Testing Infrastructure + +#### `test_runner.py` ⭐ **Recommended** + +**Purpose:** Centralized test orchestrator with intelligent filtering and result aggregation. + +**Test Suites:** + +- `unit`: 40 fast tests (~0.5s) +- `integration`: 30 external service tests (~3s) +- `all_fast`: 83 tests excluding slow/azure (~10s) +- `autotrain`, `quantum`, `database`, `chat`: Focused suites + +**Usage:** + +```powershell +# Run all fast tests +python .\scripts\test_runner.py --all + +# Unit tests with coverage +python .\scripts\test_runner.py --unit --coverage + +# Watch mode (re-run on change) +python .\scripts\test_runner.py --unit --integration --watch + +# List available suites +python .\scripts\test_runner.py --list-suites +``` + +**Features:** + +- ANSI escape code handling +- Regex-based pytest output parsing +- Marker filtering (`not slow and not azure`) +- JSON + Markdown result reports +- Parallel suite execution +- Coverage integration + +#### `ci_orchestrator.py` + +**Purpose:** Continuous integration pipeline with staged validation gates. + +**Steps:** + +1. Orchestrator validations (autotrain, quantum, evaluation) +2. Integration smoke checks (cross-component) +3. Integration contract unit tests (resolver, scheduler, status schemas) +4. Unit tests (via test_runner) +5. Dataset validation +6. Integration tests +7. Code quality checks +8. Security scanning +9. Deployment preparation +10. Azure ML validation + +**Usage:** + +```powershell +# Full CI pipeline +python .\scripts\ci_orchestrator.py --ci-pipeline + +# Validate orchestrators only +python .\scripts\ci_orchestrator.py --validate-all + +# Run focused integration contract tests only +python .\scripts\ci_orchestrator.py --integration-contract-tests + +# Run full contract gate sequence (smoke + contract tests + validate-all) +bash ./scripts/integration_contract_gate.sh + +# Or run the VS Code task: integration:contract-gate +``` + +**Current Status:** 5/10 passing (all critical steps ✅) + +### Evaluation & Analysis + +#### `batch_evaluator.py` + +**Purpose:** Parallel evaluation of multiple models with comprehensive result aggregation. + +**Features:** + +- Parallel model evaluation (ThreadPoolExecutor) +- Support for LoRA, Azure, OpenAI, Local, Quantum models +- Configurable metrics per model type +- Result ranking and comparison +- Export to JSON/Markdown/CSV + +**Usage:** + +```powershell +# Scan and evaluate all models +python .\scripts\batch_evaluator.py --scan-models --evaluate-all + +# Compare specific models +python .\scripts\batch_evaluator.py --compare lora azure openai + +# Export results +python .\scripts\batch_evaluator.py --export markdown --output report.md +``` + +#### `training_analytics.py` + +**Purpose:** Analyze training metrics and generate performance reports. + +**Usage:** + +```powershell +# Analyze recent training runs +python .\scripts\training_analytics.py --recent 5 + +# Generate comparison report +python .\scripts\training_analytics.py --compare phi qwen tinyllama +``` + +### Azure ML Integration + +#### `azureml_ci_validate.py` + +**Purpose:** Validate and optionally submit Azure ML job specs. + +**Features:** + +- YAML schema validation +- Environment spec checking +- `.env` placeholder gating (prevents submission with unresolved credentials) +- Graceful fallback when Azure CLI not installed + +**Usage:** + +```powershell +# Validate latest job spec +python .\scripts\azureml_ci_validate.py + +# Validate and submit +python .\scripts\azureml_ci_validate.py --submit + +# Force submit (bypass gating) +python .\scripts\azureml_ci_validate.py --submit --force-submit +``` + +### Dataset Management + +#### `validate_datasets.py` + +**Purpose:** Validate dataset integrity across all categories. + +**Categories:** `quantum`, `chat`, `all` + +**Usage:** + +```powershell +# Validate chat datasets +python .\scripts\validate_datasets.py --category chat + +# Validate all +python .\scripts\validate_datasets.py --category all +``` + +#### `download_datasets.py` + +**Purpose:** Download and organize datasets from HuggingFace. + +**Usage:** + +```powershell +# Download all configured datasets +python .\scripts\download_datasets.py + +# Download specific dataset +python .\scripts\download_datasets.py --dataset dolly-15k +``` + +### Monitoring & Diagnostics + +#### `resource_monitor.py` + +**Purpose:** Monitor system resources during training. + +**Usage:** + +```powershell +# Single snapshot +python .\scripts\resource_monitor.py --snapshot + +# Stream for 60 seconds +python .\scripts\resource_monitor.py --stream --duration 60 +``` + +#### `system_health_check.py` + +**Purpose:** Comprehensive system health validation. + +**Checks:** + +- Python environment +- Required packages +- GPU availability +- Disk space +- Dataset accessibility + +**Usage:** + +```powershell +python .\scripts\system_health_check.py +``` + +### SQL Integration + +#### `sql_migrate.py` + +**Purpose:** Database migration tool for chat/telemetry tables. + +**Usage:** + +```powershell +# Run migrations +python .\scripts\sql_migrate.py +``` + +#### `sql_health_monitor.py` + +**Purpose:** Monitor SQL connection pool and query performance. + +**Usage:** + +```powershell +python .\scripts\sql_health_monitor.py +``` + +## 🔧 Utility Scripts + +### PowerShell Scripts + +- `Start-LocalLoraTraining.ps1`: Task Scheduler-friendly training wrapper +- `Analyze-TrainingLogs.ps1`: Parse and analyze training logs +- `quick_status.ps1`: Quick system status check +- `fast_train.ps1`: Rapid training shortcut + +### Python Utilities + +- `env_autofix.py`: Auto-fix environment issues +- `pre_commit_check.py`: Pre-commit validation hooks +- `metrics_ranker.py`: Rank models by custom metrics +- `results_exporter.py`: Export results to multiple formats + +## 📖 Legacy Scripts (Reference) + +### run_local_lora_training.py + +One-command offline LoRA training for TinyLlama on CPU. It auto-creates a venv under `AI/microsoft_phi-silica-3.6_v1/local_train`, installs requirements, and runs the training script with a small dataset and minimal epochs so it completes quickly on Windows. + +Usage (PowerShell): + +```powershell +# From repo root +python .\scripts\run_local_lora_training.py + +# Customize +python .\scripts\run_local_lora_training.py --max-samples 50 --epochs 2 --config local_config.yaml + +# Force reinstall deps in the venv +python .\scripts\run_local_lora_training.py --reinstall + +# Preview without training +python .\scripts\run_local_lora_training.py --dry-run +``` + +Outputs are written to `AI/microsoft_phi-silica-3.6_v1/local_train/outputs/final` (LoRA adapter + tokenizer files). + +Notes: + +- 4-bit quantization is disabled by default for Windows/CPU; the tiny training still runs fast with TinyLlama. +- The runner sets `HF_HUB_DISABLE_SYMLINKS_WARNING=1` to reduce warnings on Windows filesystems. + +## Start-LocalLoraTraining.ps1 (Windows Task Scheduler friendly) + +PowerShell wrapper that resolves paths, sets env, logs to a timestamped file, and runs the Python runner. + +Usage (PowerShell): + +```powershell +# From repo root (or any folder) +powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\Start-LocalLoraTraining.ps1 -MaxSamples 10 -Epochs 1 -Config local_config.yaml + +# Optional flags +powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\Start-LocalLoraTraining.ps1 -Reinstall +powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\Start-LocalLoraTraining.ps1 -DryRun +``` + +Logs: + +- Default log directory: `AI/microsoft_phi-silica-3.6_v1/local_train/logs` +- File name: `train_YYYYMMDD_HHMMSS.log` + +Task Scheduler tip: + +- Action: `Start a program` +- Program/script: `powershell.exe` +- Add arguments: + `-NoProfile -ExecutionPolicy Bypass -File "C:\Users\Bryan\OneDrive\AI\scripts\Start-LocalLoraTraining.ps1" -MaxSamples 10 -Epochs 1` +- Start in: `C:\Users\Bryan\OneDrive\AI` + +## VS Code task: one-click run + +A task labeled `Run: Local LoRA training` is available. Open the Command Palette → “Run Task…” → select it to run. diff --git a/scripts/__init__.py b/scripts/__init__.py index e573721f0..afb88c399 100644 --- a/scripts/__init__.py +++ b/scripts/__init__.py @@ -1,6 +1,6 @@ -""" -QAI Scripts Module - -This package contains orchestration scripts, training utilities, and automation tools -for the QAI (Quantum AI) project. -""" +""" +QAI Scripts Module + +This package contains orchestration scripts, training utilities, and automation tools +for the QAI (Quantum AI) project. +""" diff --git a/scripts/aria_automation.py b/scripts/aria_automation.py index d3675b927..985e655ad 100644 --- a/scripts/aria_automation.py +++ b/scripts/aria_automation.py @@ -1,647 +1,647 @@ -#!/usr/bin/env python3 -""" -Aria Automation Orchestrator - -Comprehensive automation system for Aria AI character platform: -- Auto-start web server and backend -- Continuous training with Aria datasets -- Auto-execution of LLM commands -- Health monitoring and recovery -- Integration with master orchestrator - -Usage: - # Start full automation (server + training + monitoring) - python scripts/aria_automation.py --mode full - - # Server only (no training) - python scripts/aria_automation.py --mode server - - # Training only (no server) - python scripts/aria_automation.py --mode training - - # Single training cycle - python scripts/aria_automation.py --mode training --once - - # Check status - python scripts/aria_automation.py --status - - # Stop all Aria processes - python scripts/aria_automation.py --stop - -Features: - ✅ Auto-start Aria web server on port 8080 - ✅ Auto-start Azure Functions backend - ✅ Continuous training with dataset monitoring - ✅ Health checks and auto-recovery - ✅ LLM-powered auto-execution - ✅ Process management and cleanup -""" - -import argparse -import json -import signal -import subprocess -import sys -import threading -import time -from dataclasses import dataclass, field -from datetime import datetime, timedelta -from pathlib import Path -from typing import Any, Dict, List, Optional - -import psutil - -REPO_ROOT = Path(__file__).resolve().parents[1] -ARIA_WEB_DIR = REPO_ROOT / "aria_web" -DATA_OUT = REPO_ROOT / "data_out" / "aria_automation" -STATUS_FILE = DATA_OUT / "status.json" -PID_FILE = DATA_OUT / "processes.json" - -# Ensure output directory exists -DATA_OUT.mkdir(parents=True, exist_ok=True) - - -@dataclass -class ProcessInfo: - """Track running processes""" - - name: str - pid: int - command: str - started: str - status: str = "running" - port: Optional[int] = None - health_url: Optional[str] = None - - -@dataclass -class AriaAutomationStatus: - """Overall automation status""" - - mode: str - started: str - uptime_seconds: float = 0 - server_running: bool = False - backend_running: bool = False - training_active: bool = False - last_health_check: Optional[str] = None - processes: List[Dict[str, Any]] = field(default_factory=list) - training_cycles: int = 0 - errors: List[str] = field(default_factory=list) - - -class AriaAutomation: - """Main automation orchestrator for Aria""" - - def __init__(self, mode: str = "full"): - self.mode = mode - self.processes: Dict[str, ProcessInfo] = {} - self.running = True - self.start_time = datetime.now() - self.training_cycles = 0 - self.errors: List[str] = [] - - # Performance optimization: cache port checks - self._port_cache: Dict[int, tuple[bool, float]] = {} - self._port_cache_ttl = 5.0 # Cache port checks for 5 seconds - - # Performance optimization: cache process listings - self._process_cache: Optional[List[psutil.Process]] = None - self._process_cache_time = 0 - self._process_cache_ttl = 10.0 # Cache process list for 10 seconds - self._status_dirty = True # Track if status needs saving - - # Setup signal handlers - signal.signal(signal.SIGINT, self._signal_handler) - signal.signal(signal.SIGTERM, self._signal_handler) - - def _signal_handler(self, signum, frame): - """Handle shutdown signals gracefully""" - print(f"\n⚠️ Received signal {signum}, shutting down gracefully...") - self.running = False - self.stop_all() - sys.exit(0) - - def save_status(self, force: bool = False): - """Save current status to JSON (only if changed or forced)""" - # Skip if status hasn't changed and not forced - if not self._status_dirty and not force: - return - - status = AriaAutomationStatus( - mode=self.mode, - started=self.start_time.isoformat(), - uptime_seconds=(datetime.now() - self.start_time).total_seconds(), - server_running=self._is_process_running("aria_server"), - backend_running=self._is_process_running("functions_backend"), - training_active=self._is_process_running("training"), - last_health_check=datetime.now().isoformat(), - processes=[vars(p) for p in self.processes.values()], - training_cycles=self.training_cycles, - errors=self.errors[-10:], # Last 10 errors - ) - - with open(STATUS_FILE, "w") as f: - json.dump(vars(status), f, indent=2) - - self._status_dirty = False - - def load_pids(self) -> Dict[str, int]: - """Load PIDs from previous run""" - if not PID_FILE.exists(): - return {} - - try: - with open(PID_FILE, "r") as f: - return json.load(f) - except Exception as e: - print(f"⚠️ Could not load PIDs: {e}") - return {} - - def save_pids(self): - """Save current process PIDs""" - pids = {name: p.pid for name, p in self.processes.items()} - with open(PID_FILE, "w") as f: - json.dump(pids, f, indent=2) - - def _is_process_running(self, name: str) -> bool: - """Check if named process is running""" - if name not in self.processes: - return False - - try: - process = psutil.Process(self.processes[name].pid) - return process.is_running() and process.status() != psutil.STATUS_ZOMBIE - except (psutil.NoSuchProcess, psutil.AccessDenied): - return False - - def _check_port(self, port: int) -> bool: - """Check if port is in use with caching""" - import socket - - current_time = time.time() - - # Check cache - if port in self._port_cache: - cached_result, cache_time = self._port_cache[port] - if current_time - cache_time < self._port_cache_ttl: - return cached_result - - # Cache miss or expired - check port - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: - result = sock.connect_ex(("localhost", port)) == 0 - - # Update cache - self._port_cache[port] = (result, current_time) - return result - - def _get_process_list(self) -> List[psutil.Process]: - """Get cached process listing to avoid expensive psutil.process_iter() calls""" - current_time = time.time() - - # Check cache - if self._process_cache is not None: - if current_time - self._process_cache_time < self._process_cache_ttl: - return self._process_cache - - # Cache miss or expired - get process list - self._process_cache = list(psutil.process_iter(["pid", "name", "cmdline"])) - self._process_cache_time = current_time - return self._process_cache - - def start_aria_server(self) -> bool: - """Start Aria web server""" - print("\n🚀 Starting Aria web server...") - - # Check if already running - if self._check_port(8080): - print("⚠️ Port 8080 already in use") - # Try to find existing process using cached process list - for proc in self._get_process_list(): - try: - cmdline = proc.info["cmdline"] - if cmdline: - cmdline_str = " ".join(cmdline) - if "server.py" in cmdline_str: - print(f"ℹ️ Found existing server (PID {proc.info['pid']})") - self.processes["aria_server"] = ProcessInfo( - name="aria_server", - pid=proc.info["pid"], - command="python server.py", - started=datetime.now().isoformat(), - port=8080, - health_url="http://localhost:8080", - ) - return True - except (psutil.AccessDenied, psutil.NoSuchProcess): - continue - return False - - try: - # Start server process - proc = subprocess.Popen( - [sys.executable, "server.py"], - cwd=ARIA_WEB_DIR, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, - ) - - # Wait for server to start with exponential backoff - print("⏳ Waiting for server to start...") - max_wait = 10 # seconds - check_interval = 0.1 # Start with 100ms checks - elapsed = 0 - - while elapsed < max_wait: - if self._check_port(8080): - print( - f"✅ Aria server started on http://localhost:8080 (PID {proc.pid})" - ) - self.processes["aria_server"] = ProcessInfo( - name="aria_server", - pid=proc.pid, - command="python server.py", - started=datetime.now().isoformat(), - port=8080, - health_url="http://localhost:8080", - ) - self.save_pids() - return True - - # Exponential backoff: 0.1s, 0.2s, 0.4s, 0.8s, then cap at 1s - time.sleep(check_interval) - elapsed += check_interval - check_interval = min(check_interval * 2, 1.0) - - print(f"❌ Server failed to start within {max_wait} seconds") - proc.terminate() - return False - - except Exception as e: - error = f"Failed to start Aria server: {e}" - print(f"❌ {error}") - self.errors.append(error) - self._status_dirty = True - return False - - def start_functions_backend(self) -> bool: - """Start Azure Functions backend""" - print("\n🚀 Starting Azure Functions backend...") - - # Check if already running - if self._check_port(7071): - print("⚠️ Port 7071 already in use") - return True - - try: - # Check if func is available - result = subprocess.run( - ["func", "--version"], capture_output=True, text=True, timeout=5 - ) - - if result.returncode != 0: - print("⚠️ Azure Functions Core Tools not installed") - print( - " Install from: https://docs.microsoft.com/azure/azure-functions/functions-run-local" - ) - return False - - # Start Functions host - proc = subprocess.Popen( - ["func", "host", "start"], - cwd=REPO_ROOT, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, - ) - - # Wait for backend to start - print("⏳ Waiting for Functions backend to start...") - for i in range(15): - time.sleep(1) - if self._check_port(7071): - print( - f"✅ Functions backend started on http://localhost:7071 (PID {proc.pid})" - ) - self.processes["functions_backend"] = ProcessInfo( - name="functions_backend", - pid=proc.pid, - command="func host start", - started=datetime.now().isoformat(), - port=7071, - health_url="http://localhost:7071/api/ai/status", - ) - self.save_pids() - return True - - print("❌ Backend failed to start within 15 seconds") - proc.terminate() - return False - - except FileNotFoundError: - print("⚠️ Azure Functions Core Tools not found in PATH") - return False - except Exception as e: - error = f"Failed to start Functions backend: {e}" - print(f"❌ {error}") - self.errors.append(error) - return False - - def run_training_cycle(self, quick: bool = True) -> bool: - """Run single training cycle""" - print(f"\n📚 Running training cycle #{self.training_cycles + 1}") - - try: - # Use aria_quick_train for fast iterations - script = REPO_ROOT / "scripts" / "aria_quick_train.py" - - if not script.exists(): - print( - "⚠️ aria_quick_train.py not found, using automate_aria_movement.py" - ) - script = REPO_ROOT / "scripts" / "automate_aria_movement.py" - - cmd = [sys.executable, str(script)] - if quick: - cmd.append("--quick") - - print(f"Command: {' '.join(cmd)}") - - result = subprocess.run( - cmd, - cwd=REPO_ROOT, - capture_output=True, - text=True, - timeout=600, # 10 minute timeout - ) - - if result.returncode == 0: - self.training_cycles += 1 - self._status_dirty = True - print(f"✅ Training cycle #{self.training_cycles} completed") - return True - else: - error = f"Training cycle failed: {result.stderr[-200:]}" - print(f"❌ {error}") - self.errors.append(error) - self._status_dirty = True - return False - - except subprocess.TimeoutExpired: - error = "Training cycle timed out after 10 minutes" - print(f"❌ {error}") - self.errors.append(error) - self._status_dirty = True - return False - except Exception as e: - error = f"Training cycle error: {e}" - print(f"❌ {error}") - self.errors.append(error) - self._status_dirty = True - return False - - def health_check(self) -> Dict[str, bool]: - """Check health of all components""" - health = {"aria_server": False, "functions_backend": False, "training": False} - - # Check Aria server - if self._is_process_running("aria_server"): - if self._check_port(8080): - health["aria_server"] = True - - # Check Functions backend - if self._is_process_running("functions_backend"): - if self._check_port(7071): - health["functions_backend"] = True - - # Check if training is active - health["training"] = self._is_process_running("training") - - return health - - def auto_recovery(self, health: Dict[str, bool]): - """Attempt to recover failed components""" - if not health["aria_server"] and self.mode in ["full", "server"]: - print("\n🔄 Attempting to recover Aria server...") - self.start_aria_server() - - if not health["functions_backend"] and self.mode == "full": - print("\n🔄 Attempting to recover Functions backend...") - self.start_functions_backend() - - def monitoring_loop(self, interval: int = 60): - """Continuous monitoring with auto-recovery""" - print(f"\n👁️ Starting monitoring loop (interval: {interval}s)") - - while self.running: - time.sleep(interval) - - health = self.health_check() - print(f"\n🔍 Health check: {json.dumps(health, indent=2)}") - - # Auto-recovery - self.auto_recovery(health) - - # Save status - self.save_status() - - def training_loop(self, interval: int = 1800, once: bool = False): - """Continuous training loop""" - print(f"\n🎓 Starting training loop (interval: {interval}s, once: {once})") - - while self.running: - success = self.run_training_cycle(quick=True) - - if once: - print(f"\n✅ Single training cycle completed (success: {success})") - break - - if success: - print(f"\n⏳ Waiting {interval}s until next training cycle...") - time.sleep(interval) - else: - print("\n⚠️ Training failed, waiting 5 minutes before retry...") - time.sleep(300) - - def start(self, once: bool = False): - """Start automation based on mode""" - print(f"\n{'='*80}") - print(f"🤖 Aria Automation Starting - Mode: {self.mode}") - print(f"{'='*80}\n") - - if self.mode in ["full", "server"]: - # Start Aria server - if not self.start_aria_server(): - print("❌ Failed to start Aria server") - if self.mode == "server": - return - - if self.mode == "full": - # Start Functions backend - if not self.start_functions_backend(): - print("⚠️ Functions backend not started (continuing anyway)") - - if self.mode in ["full", "training"]: - # Start training loop in separate thread - training_thread = threading.Thread( - target=self.training_loop, - args=(1800, once), # 30 minute interval - daemon=True, - ) - training_thread.start() - - if self.mode in ["full", "server"]: - # Start monitoring loop - try: - self.monitoring_loop(interval=60) # Check every minute - except KeyboardInterrupt: - print("\n⚠️ Monitoring stopped by user") - elif self.mode == "training": - # Wait for training to complete - if once: - training_thread.join() - else: - try: - while self.running: - time.sleep(10) - self.save_status() - except KeyboardInterrupt: - print("\n⚠️ Training stopped by user") - - def stop_all(self): - """Stop all managed processes""" - print("\n🛑 Stopping all Aria processes...") - - for name, proc_info in self.processes.items(): - try: - process = psutil.Process(proc_info.pid) - print(f" Stopping {name} (PID {proc_info.pid})...") - process.terminate() - - # Wait for graceful shutdown - try: - process.wait(timeout=5) - except psutil.TimeoutExpired: - print(f" Force killing {name}...") - process.kill() - - print(f" ✅ {name} stopped") - except (psutil.NoSuchProcess, psutil.AccessDenied): - print(f" ⚠️ {name} already stopped") - - # Clear PID file - if PID_FILE.exists(): - PID_FILE.unlink() - - print("✅ All processes stopped") - - @staticmethod - def show_status(): - """Display current automation status""" - if not STATUS_FILE.exists(): - print("❌ No automation currently running") - return - - try: - with open(STATUS_FILE, "r") as f: - status = json.load(f) - - print("\n" + "=" * 80) - print("🤖 Aria Automation Status") - print("=" * 80) - print(f"Mode: {status['mode']}") - print(f"Started: {status['started']}") - print(f"Uptime: {timedelta(seconds=int(status['uptime_seconds']))}") - print(f"Training Cycles: {status['training_cycles']}") - print("\nComponents:") - print( - f" - Aria Server: {'✅ Running' if status['server_running'] else '❌ Stopped'}" - ) - print( - f" - Functions Backend: {'✅ Running' if status['backend_running'] else '❌ Stopped'}" - ) - print( - f" - Training: {'✅ Active' if status['training_active'] else '❌ Inactive'}" - ) - - if status["errors"]: - print(f"\nRecent Errors ({len(status['errors'])}):") - for error in status["errors"][-5:]: - print(f" - {error}") - - print("\nProcesses:") - for proc in status["processes"]: - print(f" - {proc['name']} (PID {proc['pid']}): {proc['status']}") - - print("=" * 80 + "\n") - - except Exception as e: - print(f"❌ Error reading status: {e}") - - -def main(): - parser = argparse.ArgumentParser( - description="Aria Automation Orchestrator", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # Start full automation (recommended) - python scripts/aria_automation.py --mode full - - # Server only (no training) - python scripts/aria_automation.py --mode server - - # Single training run - python scripts/aria_automation.py --mode training --once - - # Check status - python scripts/aria_automation.py --status - - # Stop all - python scripts/aria_automation.py --stop - """, - ) - - parser.add_argument( - "--mode", - choices=["full", "server", "training"], - default="full", - help="Automation mode: full (server+training), server only, or training only", - ) - parser.add_argument( - "--once", - action="store_true", - help="Run training once and exit (only applies to training mode)", - ) - parser.add_argument( - "--status", action="store_true", help="Show current automation status" - ) - parser.add_argument( - "--stop", action="store_true", help="Stop all Aria automation processes" - ) - - args = parser.parse_args() - - # Handle status check - if args.status: - AriaAutomation.show_status() - return - - # Handle stop - if args.stop: - automation = AriaAutomation() - automation.processes = { - name: ProcessInfo(name=name, pid=pid, command="", started="") - for name, pid in automation.load_pids().items() - } - automation.stop_all() - return - - # Start automation - automation = AriaAutomation(mode=args.mode) - automation.start(once=args.once) - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +""" +Aria Automation Orchestrator + +Comprehensive automation system for Aria AI character platform: +- Auto-start web server and backend +- Continuous training with Aria datasets +- Auto-execution of LLM commands +- Health monitoring and recovery +- Integration with master orchestrator + +Usage: + # Start full automation (server + training + monitoring) + python scripts/aria_automation.py --mode full + + # Server only (no training) + python scripts/aria_automation.py --mode server + + # Training only (no server) + python scripts/aria_automation.py --mode training + + # Single training cycle + python scripts/aria_automation.py --mode training --once + + # Check status + python scripts/aria_automation.py --status + + # Stop all Aria processes + python scripts/aria_automation.py --stop + +Features: + ✅ Auto-start Aria web server on port 8080 + ✅ Auto-start Azure Functions backend + ✅ Continuous training with dataset monitoring + ✅ Health checks and auto-recovery + ✅ LLM-powered auto-execution + ✅ Process management and cleanup +""" + +import argparse +import json +import signal +import subprocess +import sys +import threading +import time +from dataclasses import dataclass, field +from datetime import datetime, timedelta +from pathlib import Path +from typing import Any, Dict, List, Optional + +import psutil + +REPO_ROOT = Path(__file__).resolve().parents[1] +ARIA_WEB_DIR = REPO_ROOT / "aria_web" +DATA_OUT = REPO_ROOT / "data_out" / "aria_automation" +STATUS_FILE = DATA_OUT / "status.json" +PID_FILE = DATA_OUT / "processes.json" + +# Ensure output directory exists +DATA_OUT.mkdir(parents=True, exist_ok=True) + + +@dataclass +class ProcessInfo: + """Track running processes""" + + name: str + pid: int + command: str + started: str + status: str = "running" + port: Optional[int] = None + health_url: Optional[str] = None + + +@dataclass +class AriaAutomationStatus: + """Overall automation status""" + + mode: str + started: str + uptime_seconds: float = 0 + server_running: bool = False + backend_running: bool = False + training_active: bool = False + last_health_check: Optional[str] = None + processes: List[Dict[str, Any]] = field(default_factory=list) + training_cycles: int = 0 + errors: List[str] = field(default_factory=list) + + +class AriaAutomation: + """Main automation orchestrator for Aria""" + + def __init__(self, mode: str = "full"): + self.mode = mode + self.processes: Dict[str, ProcessInfo] = {} + self.running = True + self.start_time = datetime.now() + self.training_cycles = 0 + self.errors: List[str] = [] + + # Performance optimization: cache port checks + self._port_cache: Dict[int, tuple[bool, float]] = {} + self._port_cache_ttl = 5.0 # Cache port checks for 5 seconds + + # Performance optimization: cache process listings + self._process_cache: Optional[List[psutil.Process]] = None + self._process_cache_time = 0 + self._process_cache_ttl = 10.0 # Cache process list for 10 seconds + self._status_dirty = True # Track if status needs saving + + # Setup signal handlers + signal.signal(signal.SIGINT, self._signal_handler) + signal.signal(signal.SIGTERM, self._signal_handler) + + def _signal_handler(self, signum, frame): + """Handle shutdown signals gracefully""" + print(f"\n⚠️ Received signal {signum}, shutting down gracefully...") + self.running = False + self.stop_all() + sys.exit(0) + + def save_status(self, force: bool = False): + """Save current status to JSON (only if changed or forced)""" + # Skip if status hasn't changed and not forced + if not self._status_dirty and not force: + return + + status = AriaAutomationStatus( + mode=self.mode, + started=self.start_time.isoformat(), + uptime_seconds=(datetime.now() - self.start_time).total_seconds(), + server_running=self._is_process_running("aria_server"), + backend_running=self._is_process_running("functions_backend"), + training_active=self._is_process_running("training"), + last_health_check=datetime.now().isoformat(), + processes=[vars(p) for p in self.processes.values()], + training_cycles=self.training_cycles, + errors=self.errors[-10:], # Last 10 errors + ) + + with open(STATUS_FILE, "w") as f: + json.dump(vars(status), f, indent=2) + + self._status_dirty = False + + def load_pids(self) -> Dict[str, int]: + """Load PIDs from previous run""" + if not PID_FILE.exists(): + return {} + + try: + with open(PID_FILE, "r") as f: + return json.load(f) + except Exception as e: + print(f"⚠️ Could not load PIDs: {e}") + return {} + + def save_pids(self): + """Save current process PIDs""" + pids = {name: p.pid for name, p in self.processes.items()} + with open(PID_FILE, "w") as f: + json.dump(pids, f, indent=2) + + def _is_process_running(self, name: str) -> bool: + """Check if named process is running""" + if name not in self.processes: + return False + + try: + process = psutil.Process(self.processes[name].pid) + return process.is_running() and process.status() != psutil.STATUS_ZOMBIE + except (psutil.NoSuchProcess, psutil.AccessDenied): + return False + + def _check_port(self, port: int) -> bool: + """Check if port is in use with caching""" + import socket + + current_time = time.time() + + # Check cache + if port in self._port_cache: + cached_result, cache_time = self._port_cache[port] + if current_time - cache_time < self._port_cache_ttl: + return cached_result + + # Cache miss or expired - check port + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: + result = sock.connect_ex(("localhost", port)) == 0 + + # Update cache + self._port_cache[port] = (result, current_time) + return result + + def _get_process_list(self) -> List[psutil.Process]: + """Get cached process listing to avoid expensive psutil.process_iter() calls""" + current_time = time.time() + + # Check cache + if self._process_cache is not None: + if current_time - self._process_cache_time < self._process_cache_ttl: + return self._process_cache + + # Cache miss or expired - get process list + self._process_cache = list(psutil.process_iter(["pid", "name", "cmdline"])) + self._process_cache_time = current_time + return self._process_cache + + def start_aria_server(self) -> bool: + """Start Aria web server""" + print("\n🚀 Starting Aria web server...") + + # Check if already running + if self._check_port(8080): + print("⚠️ Port 8080 already in use") + # Try to find existing process using cached process list + for proc in self._get_process_list(): + try: + cmdline = proc.info["cmdline"] + if cmdline: + cmdline_str = " ".join(cmdline) + if "server.py" in cmdline_str: + print(f"ℹ️ Found existing server (PID {proc.info['pid']})") + self.processes["aria_server"] = ProcessInfo( + name="aria_server", + pid=proc.info["pid"], + command="python server.py", + started=datetime.now().isoformat(), + port=8080, + health_url="http://localhost:8080", + ) + return True + except (psutil.AccessDenied, psutil.NoSuchProcess): + continue + return False + + try: + # Start server process + proc = subprocess.Popen( + [sys.executable, "server.py"], + cwd=ARIA_WEB_DIR, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Wait for server to start with exponential backoff + print("⏳ Waiting for server to start...") + max_wait = 10 # seconds + check_interval = 0.1 # Start with 100ms checks + elapsed = 0 + + while elapsed < max_wait: + if self._check_port(8080): + print( + f"✅ Aria server started on http://localhost:8080 (PID {proc.pid})" + ) + self.processes["aria_server"] = ProcessInfo( + name="aria_server", + pid=proc.pid, + command="python server.py", + started=datetime.now().isoformat(), + port=8080, + health_url="http://localhost:8080", + ) + self.save_pids() + return True + + # Exponential backoff: 0.1s, 0.2s, 0.4s, 0.8s, then cap at 1s + time.sleep(check_interval) + elapsed += check_interval + check_interval = min(check_interval * 2, 1.0) + + print(f"❌ Server failed to start within {max_wait} seconds") + proc.terminate() + return False + + except Exception as e: + error = f"Failed to start Aria server: {e}" + print(f"❌ {error}") + self.errors.append(error) + self._status_dirty = True + return False + + def start_functions_backend(self) -> bool: + """Start Azure Functions backend""" + print("\n🚀 Starting Azure Functions backend...") + + # Check if already running + if self._check_port(7071): + print("⚠️ Port 7071 already in use") + return True + + try: + # Check if func is available + result = subprocess.run( + ["func", "--version"], capture_output=True, text=True, timeout=5 + ) + + if result.returncode != 0: + print("⚠️ Azure Functions Core Tools not installed") + print( + " Install from: https://docs.microsoft.com/azure/azure-functions/functions-run-local" + ) + return False + + # Start Functions host + proc = subprocess.Popen( + ["func", "host", "start"], + cwd=REPO_ROOT, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Wait for backend to start + print("⏳ Waiting for Functions backend to start...") + for i in range(15): + time.sleep(1) + if self._check_port(7071): + print( + f"✅ Functions backend started on http://localhost:7071 (PID {proc.pid})" + ) + self.processes["functions_backend"] = ProcessInfo( + name="functions_backend", + pid=proc.pid, + command="func host start", + started=datetime.now().isoformat(), + port=7071, + health_url="http://localhost:7071/api/ai/status", + ) + self.save_pids() + return True + + print("❌ Backend failed to start within 15 seconds") + proc.terminate() + return False + + except FileNotFoundError: + print("⚠️ Azure Functions Core Tools not found in PATH") + return False + except Exception as e: + error = f"Failed to start Functions backend: {e}" + print(f"❌ {error}") + self.errors.append(error) + return False + + def run_training_cycle(self, quick: bool = True) -> bool: + """Run single training cycle""" + print(f"\n📚 Running training cycle #{self.training_cycles + 1}") + + try: + # Use aria_quick_train for fast iterations + script = REPO_ROOT / "scripts" / "aria_quick_train.py" + + if not script.exists(): + print( + "⚠️ aria_quick_train.py not found, using automate_aria_movement.py" + ) + script = REPO_ROOT / "scripts" / "automate_aria_movement.py" + + cmd = [sys.executable, str(script)] + if quick: + cmd.append("--quick") + + print(f"Command: {' '.join(cmd)}") + + result = subprocess.run( + cmd, + cwd=REPO_ROOT, + capture_output=True, + text=True, + timeout=600, # 10 minute timeout + ) + + if result.returncode == 0: + self.training_cycles += 1 + self._status_dirty = True + print(f"✅ Training cycle #{self.training_cycles} completed") + return True + else: + error = f"Training cycle failed: {result.stderr[-200:]}" + print(f"❌ {error}") + self.errors.append(error) + self._status_dirty = True + return False + + except subprocess.TimeoutExpired: + error = "Training cycle timed out after 10 minutes" + print(f"❌ {error}") + self.errors.append(error) + self._status_dirty = True + return False + except Exception as e: + error = f"Training cycle error: {e}" + print(f"❌ {error}") + self.errors.append(error) + self._status_dirty = True + return False + + def health_check(self) -> Dict[str, bool]: + """Check health of all components""" + health = {"aria_server": False, "functions_backend": False, "training": False} + + # Check Aria server + if self._is_process_running("aria_server"): + if self._check_port(8080): + health["aria_server"] = True + + # Check Functions backend + if self._is_process_running("functions_backend"): + if self._check_port(7071): + health["functions_backend"] = True + + # Check if training is active + health["training"] = self._is_process_running("training") + + return health + + def auto_recovery(self, health: Dict[str, bool]): + """Attempt to recover failed components""" + if not health["aria_server"] and self.mode in ["full", "server"]: + print("\n🔄 Attempting to recover Aria server...") + self.start_aria_server() + + if not health["functions_backend"] and self.mode == "full": + print("\n🔄 Attempting to recover Functions backend...") + self.start_functions_backend() + + def monitoring_loop(self, interval: int = 60): + """Continuous monitoring with auto-recovery""" + print(f"\n👁️ Starting monitoring loop (interval: {interval}s)") + + while self.running: + time.sleep(interval) + + health = self.health_check() + print(f"\n🔍 Health check: {json.dumps(health, indent=2)}") + + # Auto-recovery + self.auto_recovery(health) + + # Save status + self.save_status() + + def training_loop(self, interval: int = 1800, once: bool = False): + """Continuous training loop""" + print(f"\n🎓 Starting training loop (interval: {interval}s, once: {once})") + + while self.running: + success = self.run_training_cycle(quick=True) + + if once: + print(f"\n✅ Single training cycle completed (success: {success})") + break + + if success: + print(f"\n⏳ Waiting {interval}s until next training cycle...") + time.sleep(interval) + else: + print("\n⚠️ Training failed, waiting 5 minutes before retry...") + time.sleep(300) + + def start(self, once: bool = False): + """Start automation based on mode""" + print(f"\n{'='*80}") + print(f"🤖 Aria Automation Starting - Mode: {self.mode}") + print(f"{'='*80}\n") + + if self.mode in ["full", "server"]: + # Start Aria server + if not self.start_aria_server(): + print("❌ Failed to start Aria server") + if self.mode == "server": + return + + if self.mode == "full": + # Start Functions backend + if not self.start_functions_backend(): + print("⚠️ Functions backend not started (continuing anyway)") + + if self.mode in ["full", "training"]: + # Start training loop in separate thread + training_thread = threading.Thread( + target=self.training_loop, + args=(1800, once), # 30 minute interval + daemon=True, + ) + training_thread.start() + + if self.mode in ["full", "server"]: + # Start monitoring loop + try: + self.monitoring_loop(interval=60) # Check every minute + except KeyboardInterrupt: + print("\n⚠️ Monitoring stopped by user") + elif self.mode == "training": + # Wait for training to complete + if once: + training_thread.join() + else: + try: + while self.running: + time.sleep(10) + self.save_status() + except KeyboardInterrupt: + print("\n⚠️ Training stopped by user") + + def stop_all(self): + """Stop all managed processes""" + print("\n🛑 Stopping all Aria processes...") + + for name, proc_info in self.processes.items(): + try: + process = psutil.Process(proc_info.pid) + print(f" Stopping {name} (PID {proc_info.pid})...") + process.terminate() + + # Wait for graceful shutdown + try: + process.wait(timeout=5) + except psutil.TimeoutExpired: + print(f" Force killing {name}...") + process.kill() + + print(f" ✅ {name} stopped") + except (psutil.NoSuchProcess, psutil.AccessDenied): + print(f" ⚠️ {name} already stopped") + + # Clear PID file + if PID_FILE.exists(): + PID_FILE.unlink() + + print("✅ All processes stopped") + + @staticmethod + def show_status(): + """Display current automation status""" + if not STATUS_FILE.exists(): + print("❌ No automation currently running") + return + + try: + with open(STATUS_FILE, "r") as f: + status = json.load(f) + + print("\n" + "=" * 80) + print("🤖 Aria Automation Status") + print("=" * 80) + print(f"Mode: {status['mode']}") + print(f"Started: {status['started']}") + print(f"Uptime: {timedelta(seconds=int(status['uptime_seconds']))}") + print(f"Training Cycles: {status['training_cycles']}") + print("\nComponents:") + print( + f" - Aria Server: {'✅ Running' if status['server_running'] else '❌ Stopped'}" + ) + print( + f" - Functions Backend: {'✅ Running' if status['backend_running'] else '❌ Stopped'}" + ) + print( + f" - Training: {'✅ Active' if status['training_active'] else '❌ Inactive'}" + ) + + if status["errors"]: + print(f"\nRecent Errors ({len(status['errors'])}):") + for error in status["errors"][-5:]: + print(f" - {error}") + + print("\nProcesses:") + for proc in status["processes"]: + print(f" - {proc['name']} (PID {proc['pid']}): {proc['status']}") + + print("=" * 80 + "\n") + + except Exception as e: + print(f"❌ Error reading status: {e}") + + +def main(): + parser = argparse.ArgumentParser( + description="Aria Automation Orchestrator", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Start full automation (recommended) + python scripts/aria_automation.py --mode full + + # Server only (no training) + python scripts/aria_automation.py --mode server + + # Single training run + python scripts/aria_automation.py --mode training --once + + # Check status + python scripts/aria_automation.py --status + + # Stop all + python scripts/aria_automation.py --stop + """, + ) + + parser.add_argument( + "--mode", + choices=["full", "server", "training"], + default="full", + help="Automation mode: full (server+training), server only, or training only", + ) + parser.add_argument( + "--once", + action="store_true", + help="Run training once and exit (only applies to training mode)", + ) + parser.add_argument( + "--status", action="store_true", help="Show current automation status" + ) + parser.add_argument( + "--stop", action="store_true", help="Stop all Aria automation processes" + ) + + args = parser.parse_args() + + # Handle status check + if args.status: + AriaAutomation.show_status() + return + + # Handle stop + if args.stop: + automation = AriaAutomation() + automation.processes = { + name: ProcessInfo(name=name, pid=pid, command="", started="") + for name, pid in automation.load_pids().items() + } + automation.stop_all() + return + + # Start automation + automation = AriaAutomation(mode=args.mode) + automation.start(once=args.once) + + +if __name__ == "__main__": + main() diff --git a/scripts/aria_demo.py b/scripts/aria_demo.py index 0b63fc70e..8e15e9601 100644 --- a/scripts/aria_demo.py +++ b/scripts/aria_demo.py @@ -1,116 +1,116 @@ -#!/usr/bin/env python -"""Interactive Aria Visual Command Demo""" -import sys -from pathlib import Path - -REPO_ROOT = Path(__file__).resolve().parents[1] -sys.path.insert( - 0, str(REPO_ROOT / "ai-projects" / "lora-training" / "microsoft_phi-silica-3.6_v1") -) - -import re - -import torch -from peft import PeftModel -from transformers import AutoModelForCausalLM, AutoTokenizer - - -class AriaCommandGenerator: - def __init__(self, adapter_path: str): - base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" - print("🎨 Loading Aria Visual Model...") - - self.tokenizer = AutoTokenizer.from_pretrained(base_model) - model = AutoModelForCausalLM.from_pretrained( - base_model, torch_dtype=torch.float16, device_map="auto" - ) - self.model = PeftModel.from_pretrained(model, adapter_path) - print("✅ Model loaded!\n") - - def generate_command(self, user_input: str) -> list[str]: - """Generate Aria command tags from natural language""" - input_text = f"<|user|>\n{user_input}\n<|assistant|>\n" - inputs = self.tokenizer(input_text, return_tensors="pt").to(self.model.device) - - with torch.no_grad(): - outputs = self.model.generate( - **inputs, - max_new_tokens=30, - temperature=0.1, - do_sample=True, - top_p=0.9, - repetition_penalty=1.5, - pad_token_id=self.tokenizer.pad_token_id or self.tokenizer.eos_token_id, - ) - - response = self.tokenizer.decode( - outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True - ) - tags = re.findall(r"\[aria:[^\]]+\]", response) - return tags if tags else [] - - def show_categories(self): - """Display available command categories""" - categories = { - "🚶 Movement": ["move left", "walk right", "run up"], - "😊 Expressions": ["smile", "look happy", "surprised face", "wink"], - "👋 Gestures": ["wave", "thumbs up", "point left", "clap"], - "💃 Animations": ["jump", "dance", "spin", "bow", "backflip"], - "🧍 Poses": ["sit down", "stand up", "crouch"], - "📷 Camera": ["center", "zoom in", "face left"], - "✨ Effects": ["sparkle", "glow", "hearts"], - "🎭 Combos": ["dance with sparkles", "jump and smile"], - } - - print("=" * 80) - print("🎨 ARIA VISUAL COMMAND CATEGORIES") - print("=" * 80) - for category, examples in categories.items(): - print(f"\n{category}") - for ex in examples[:3]: - print(f" • {ex}") - print("\n" + "=" * 80) - - -def main(): - adapter = ( - REPO_ROOT / "data_out" / "aria_models" / "aria_expanded_v2" / "lora_adapter" - ) - - if not adapter.exists(): - print(f"❌ Model not found: {adapter}") - return - - aria = AriaCommandGenerator(str(adapter)) - aria.show_categories() - - print("\n🎮 Interactive Mode - Type commands or 'quit' to exit") - print("💡 Try: 'aria smile', 'jump', 'dance with sparkles'\n") - - while True: - try: - user_input = input("👤 You: ").strip() - - if user_input.lower() in ["quit", "exit", "q"]: - print("👋 Goodbye!") - break - - if not user_input: - continue - - tags = aria.generate_command(user_input) - - if tags: - print(f"🎨 Aria: {' '.join(tags[:2])}\n") # Show first 2 tags - else: - print("❓ No command tags generated. Try a different phrase.\n") - - except KeyboardInterrupt: - print("\n👋 Goodbye!") - break - except Exception as e: - print(f"❌ Error: {e}\n") - - -if __name__ == "__main__": - main() +#!/usr/bin/env python +"""Interactive Aria Visual Command Demo""" +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert( + 0, str(REPO_ROOT / "ai-projects" / "lora-training" / "microsoft_phi-silica-3.6_v1") +) + +import re + +import torch +from peft import PeftModel +from transformers import AutoModelForCausalLM, AutoTokenizer + + +class AriaCommandGenerator: + def __init__(self, adapter_path: str): + base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" + print("🎨 Loading Aria Visual Model...") + + self.tokenizer = AutoTokenizer.from_pretrained(base_model) + model = AutoModelForCausalLM.from_pretrained( + base_model, torch_dtype=torch.float16, device_map="auto" + ) + self.model = PeftModel.from_pretrained(model, adapter_path) + print("✅ Model loaded!\n") + + def generate_command(self, user_input: str) -> list[str]: + """Generate Aria command tags from natural language""" + input_text = f"<|user|>\n{user_input}\n<|assistant|>\n" + inputs = self.tokenizer(input_text, return_tensors="pt").to(self.model.device) + + with torch.no_grad(): + outputs = self.model.generate( + **inputs, + max_new_tokens=30, + temperature=0.1, + do_sample=True, + top_p=0.9, + repetition_penalty=1.5, + pad_token_id=self.tokenizer.pad_token_id or self.tokenizer.eos_token_id, + ) + + response = self.tokenizer.decode( + outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True + ) + tags = re.findall(r"\[aria:[^\]]+\]", response) + return tags if tags else [] + + def show_categories(self): + """Display available command categories""" + categories = { + "🚶 Movement": ["move left", "walk right", "run up"], + "😊 Expressions": ["smile", "look happy", "surprised face", "wink"], + "👋 Gestures": ["wave", "thumbs up", "point left", "clap"], + "💃 Animations": ["jump", "dance", "spin", "bow", "backflip"], + "🧍 Poses": ["sit down", "stand up", "crouch"], + "📷 Camera": ["center", "zoom in", "face left"], + "✨ Effects": ["sparkle", "glow", "hearts"], + "🎭 Combos": ["dance with sparkles", "jump and smile"], + } + + print("=" * 80) + print("🎨 ARIA VISUAL COMMAND CATEGORIES") + print("=" * 80) + for category, examples in categories.items(): + print(f"\n{category}") + for ex in examples[:3]: + print(f" • {ex}") + print("\n" + "=" * 80) + + +def main(): + adapter = ( + REPO_ROOT / "data_out" / "aria_models" / "aria_expanded_v2" / "lora_adapter" + ) + + if not adapter.exists(): + print(f"❌ Model not found: {adapter}") + return + + aria = AriaCommandGenerator(str(adapter)) + aria.show_categories() + + print("\n🎮 Interactive Mode - Type commands or 'quit' to exit") + print("💡 Try: 'aria smile', 'jump', 'dance with sparkles'\n") + + while True: + try: + user_input = input("👤 You: ").strip() + + if user_input.lower() in ["quit", "exit", "q"]: + print("👋 Goodbye!") + break + + if not user_input: + continue + + tags = aria.generate_command(user_input) + + if tags: + print(f"🎨 Aria: {' '.join(tags[:2])}\n") # Show first 2 tags + else: + print("❓ No command tags generated. Try a different phrase.\n") + + except KeyboardInterrupt: + print("\n👋 Goodbye!") + break + except Exception as e: + print(f"❌ Error: {e}\n") + + +if __name__ == "__main__": + main() diff --git a/scripts/aria_test.py b/scripts/aria_test.py index 3f5a77e98..174fa9913 100644 --- a/scripts/aria_test.py +++ b/scripts/aria_test.py @@ -1,104 +1,104 @@ -#!/usr/bin/env python -"""Test trained Aria model - instant feedback""" -import sys -from pathlib import Path -from typing import Any - -__test__ = False - -# Add paths -REPO_ROOT = Path(__file__).resolve().parents[1] -sys.path.insert(0, str(REPO_ROOT)) -sys.path.insert( - 0, str(REPO_ROOT / "ai-projects" / "lora-training" / "microsoft_phi-silica-3.6_v1") -) - - -def _load_optional_dependencies() -> tuple[Any, Any, Any, Any]: - """Load heavyweight optional ML dependencies only when the script is executed.""" - try: - import torch - from peft import PeftModel - from transformers import AutoModelForCausalLM, AutoTokenizer - except ImportError as e: - raise RuntimeError(f"Missing dependencies: {e}") from e - - return AutoTokenizer, AutoModelForCausalLM, PeftModel, torch - - -def run_aria_model_test(adapter_path: str): - """Quick test of Aria movement model.""" - AutoTokenizer, AutoModelForCausalLM, PeftModel, torch = ( - _load_optional_dependencies() - ) - adapter_path = Path(adapter_path) - if not adapter_path.exists(): - print(f"❌ Adapter not found: {adapter_path}") - return False - - print(f"🔍 Loading model from: {adapter_path}") - - # Load base model - base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" - tokenizer = AutoTokenizer.from_pretrained(base_model) - model = AutoModelForCausalLM.from_pretrained( - base_model, torch_dtype=torch.float16, device_map="auto" - ) - - # Load LoRA adapter - model = PeftModel.from_pretrained(model, str(adapter_path)) - model.eval() - - # Test commands - expanded visual features - test_prompts = [ - "Move Aria left", - "Make Aria smile", - "Aria jump", - "Thumbs up", - "Aria dance with sparkles", - "Look surprised", - "Wave hello", - "Spin around", - ] - - print("\n" + "=" * 70) - print("🎭 Testing Aria Commands:") - print("=" * 70) - - for prompt in test_prompts: - messages = [{"role": "user", "content": prompt}] - text = tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True - ) - inputs = tokenizer(text, return_tensors="pt").to(model.device) - - with torch.no_grad(): - outputs = model.generate(**inputs, max_new_tokens=50, do_sample=False) - - response = tokenizer.decode(outputs[0], skip_special_tokens=True) - # Extract just the assistant's response - if "<|assistant|>" in response: - response = response.split("<|assistant|>")[-1].strip() - - print(f"\n👤 User: {prompt}") - print(f"🤖 Aria: {response}") - - # Check for command tags - if "[aria:" in response.lower(): - print(" ✅ Command detected!") - else: - print(" ⚠️ No command tag found") - - print("\n" + "=" * 70) - return True - - -if __name__ == "__main__": - adapter = ( - REPO_ROOT / "data_out" / "aria_models" / "aria_expanded_v2" / "lora_adapter" - ) - try: - run_aria_model_test(str(adapter)) - except RuntimeError as exc: - print(f"❌ {exc}") - sys.exit(1) +#!/usr/bin/env python +"""Test trained Aria model - instant feedback""" +import sys +from pathlib import Path +from typing import Any + +__test__ = False + +# Add paths +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) +sys.path.insert( + 0, str(REPO_ROOT / "ai-projects" / "lora-training" / "microsoft_phi-silica-3.6_v1") +) + + +def _load_optional_dependencies() -> tuple[Any, Any, Any, Any]: + """Load heavyweight optional ML dependencies only when the script is executed.""" + try: + import torch + from peft import PeftModel + from transformers import AutoModelForCausalLM, AutoTokenizer + except ImportError as e: + raise RuntimeError(f"Missing dependencies: {e}") from e + + return AutoTokenizer, AutoModelForCausalLM, PeftModel, torch + + +def run_aria_model_test(adapter_path: str): + """Quick test of Aria movement model.""" + AutoTokenizer, AutoModelForCausalLM, PeftModel, torch = ( + _load_optional_dependencies() + ) + adapter_path = Path(adapter_path) + if not adapter_path.exists(): + print(f"❌ Adapter not found: {adapter_path}") + return False + + print(f"🔍 Loading model from: {adapter_path}") + + # Load base model + base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" + tokenizer = AutoTokenizer.from_pretrained(base_model) + model = AutoModelForCausalLM.from_pretrained( + base_model, torch_dtype=torch.float16, device_map="auto" + ) + + # Load LoRA adapter + model = PeftModel.from_pretrained(model, str(adapter_path)) + model.eval() + + # Test commands - expanded visual features + test_prompts = [ + "Move Aria left", + "Make Aria smile", + "Aria jump", + "Thumbs up", + "Aria dance with sparkles", + "Look surprised", + "Wave hello", + "Spin around", + ] + + print("\n" + "=" * 70) + print("🎭 Testing Aria Commands:") + print("=" * 70) + + for prompt in test_prompts: + messages = [{"role": "user", "content": prompt}] + text = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + inputs = tokenizer(text, return_tensors="pt").to(model.device) + + with torch.no_grad(): + outputs = model.generate(**inputs, max_new_tokens=50, do_sample=False) + + response = tokenizer.decode(outputs[0], skip_special_tokens=True) + # Extract just the assistant's response + if "<|assistant|>" in response: + response = response.split("<|assistant|>")[-1].strip() + + print(f"\n👤 User: {prompt}") + print(f"🤖 Aria: {response}") + + # Check for command tags + if "[aria:" in response.lower(): + print(" ✅ Command detected!") + else: + print(" ⚠️ No command tag found") + + print("\n" + "=" * 70) + return True + + +if __name__ == "__main__": + adapter = ( + REPO_ROOT / "data_out" / "aria_models" / "aria_expanded_v2" / "lora_adapter" + ) + try: + run_aria_model_test(str(adapter)) + except RuntimeError as exc: + print(f"❌ {exc}") + sys.exit(1) diff --git a/scripts/aria_test_debug.py b/scripts/aria_test_debug.py index 55ca2836f..304f023f5 100644 --- a/scripts/aria_test_debug.py +++ b/scripts/aria_test_debug.py @@ -1,61 +1,61 @@ -#!/usr/bin/env python -"""Debug Aria model output to see what it's generating""" -import sys -from pathlib import Path - -REPO_ROOT = Path(__file__).resolve().parents[1] -sys.path.insert( - 0, str(REPO_ROOT / "ai-projects" / "lora-training" / "microsoft_phi-silica-3.6_v1") -) - -import torch -from peft import PeftModel -from transformers import AutoModelForCausalLM, AutoTokenizer - - -def test_aria_debug(adapter_path: str): - base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" - - print(f"🔍 Loading base model: {base_model}") - tokenizer = AutoTokenizer.from_pretrained(base_model) - model = AutoModelForCausalLM.from_pretrained( - base_model, torch_dtype=torch.float16, device_map="auto" - ) - - print(f"🔍 Loading adapter: {adapter_path}") - model = PeftModel.from_pretrained(model, adapter_path) - - # Test with raw prompt - test_prompts = ["move left", "aria smile", "jump", "wave hello"] - - print("\n" + "=" * 80) - print("🧪 RAW MODEL OUTPUT DEBUG") - print("=" * 80) - - for prompt in test_prompts: - # Try simple format - input_text = f"<|user|>\n{prompt}\n<|assistant|>\n" - inputs = tokenizer(input_text, return_tensors="pt").to(model.device) - - with torch.no_grad(): - outputs = model.generate( - **inputs, - max_new_tokens=50, - do_sample=False, - pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id, - ) - - response = tokenizer.decode(outputs[0], skip_special_tokens=False) - clean_response = tokenizer.decode(outputs[0], skip_special_tokens=True) - - print(f"\n📝 Prompt: {prompt}") - print(f"📤 Full output:\n{response}") - print(f"🧹 Clean output:\n{clean_response}") - print("-" * 80) - - -if __name__ == "__main__": - adapter = ( - REPO_ROOT / "data_out" / "aria_models" / "aria_expanded_v2" / "lora_adapter" - ) - test_aria_debug(str(adapter)) +#!/usr/bin/env python +"""Debug Aria model output to see what it's generating""" +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert( + 0, str(REPO_ROOT / "ai-projects" / "lora-training" / "microsoft_phi-silica-3.6_v1") +) + +import torch +from peft import PeftModel +from transformers import AutoModelForCausalLM, AutoTokenizer + + +def test_aria_debug(adapter_path: str): + base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" + + print(f"🔍 Loading base model: {base_model}") + tokenizer = AutoTokenizer.from_pretrained(base_model) + model = AutoModelForCausalLM.from_pretrained( + base_model, torch_dtype=torch.float16, device_map="auto" + ) + + print(f"🔍 Loading adapter: {adapter_path}") + model = PeftModel.from_pretrained(model, adapter_path) + + # Test with raw prompt + test_prompts = ["move left", "aria smile", "jump", "wave hello"] + + print("\n" + "=" * 80) + print("🧪 RAW MODEL OUTPUT DEBUG") + print("=" * 80) + + for prompt in test_prompts: + # Try simple format + input_text = f"<|user|>\n{prompt}\n<|assistant|>\n" + inputs = tokenizer(input_text, return_tensors="pt").to(model.device) + + with torch.no_grad(): + outputs = model.generate( + **inputs, + max_new_tokens=50, + do_sample=False, + pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id, + ) + + response = tokenizer.decode(outputs[0], skip_special_tokens=False) + clean_response = tokenizer.decode(outputs[0], skip_special_tokens=True) + + print(f"\n📝 Prompt: {prompt}") + print(f"📤 Full output:\n{response}") + print(f"🧹 Clean output:\n{clean_response}") + print("-" * 80) + + +if __name__ == "__main__": + adapter = ( + REPO_ROOT / "data_out" / "aria_models" / "aria_expanded_v2" / "lora_adapter" + ) + test_aria_debug(str(adapter)) diff --git a/scripts/aria_test_final.py b/scripts/aria_test_final.py index 1b7273e93..6ffe8fdb0 100644 --- a/scripts/aria_test_final.py +++ b/scripts/aria_test_final.py @@ -1,79 +1,79 @@ -#!/usr/bin/env python -"""Test Aria model with proper generation constraints""" -import sys -from pathlib import Path - -REPO_ROOT = Path(__file__).resolve().parents[1] -sys.path.insert( - 0, str(REPO_ROOT / "ai-projects" / "lora-training" / "microsoft_phi-silica-3.6_v1") -) - -import torch -from peft import PeftModel -from transformers import AutoModelForCausalLM, AutoTokenizer - - -def test_aria_final(adapter_path: str): - base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" - - print(f"🔍 Loading model with adapter: {Path(adapter_path).name}") - tokenizer = AutoTokenizer.from_pretrained(base_model) - model = AutoModelForCausalLM.from_pretrained( - base_model, torch_dtype=torch.float16, device_map="auto" - ) - model = PeftModel.from_pretrained(model, adapter_path) - - test_commands = [ - "move left", - "aria smile", - "jump", - "wave hello", - "look surprised", - "dance with sparkles", - "thumbs up", - "spin around", - ] - - print("\n" + "=" * 80) - print("🎨 ARIA VISUAL COMMANDS TEST") - print("=" * 80) - - for prompt in test_commands: - input_text = f"<|user|>\n{prompt}\n<|assistant|>\n" - inputs = tokenizer(input_text, return_tensors="pt").to(model.device) - - with torch.no_grad(): - outputs = model.generate( - **inputs, - max_new_tokens=20, # Short - just need the tag - temperature=0.1, # Low temperature = more deterministic - do_sample=True, - top_p=0.9, - repetition_penalty=1.5, # Penalize repeating tokens - pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id, - eos_token_id=tokenizer.eos_token_id, - ) - - response = tokenizer.decode( - outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True - ) - - # Extract first command tag - import re - - tags = re.findall(r"\[aria:[^\]]+\]", response) - - print(f"\n📝 Command: {prompt}") - print(f" Raw output: {response[:100]}") - if tags: - print(f" ✅ Tags found: {' '.join(tags[:2])}") # Show first 2 tags - else: - print(" ❌ No command tags detected") - print("-" * 80) - - -if __name__ == "__main__": - adapter = ( - REPO_ROOT / "data_out" / "aria_models" / "aria_expanded_v2" / "lora_adapter" - ) - test_aria_final(str(adapter)) +#!/usr/bin/env python +"""Test Aria model with proper generation constraints""" +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert( + 0, str(REPO_ROOT / "ai-projects" / "lora-training" / "microsoft_phi-silica-3.6_v1") +) + +import torch +from peft import PeftModel +from transformers import AutoModelForCausalLM, AutoTokenizer + + +def test_aria_final(adapter_path: str): + base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" + + print(f"🔍 Loading model with adapter: {Path(adapter_path).name}") + tokenizer = AutoTokenizer.from_pretrained(base_model) + model = AutoModelForCausalLM.from_pretrained( + base_model, torch_dtype=torch.float16, device_map="auto" + ) + model = PeftModel.from_pretrained(model, adapter_path) + + test_commands = [ + "move left", + "aria smile", + "jump", + "wave hello", + "look surprised", + "dance with sparkles", + "thumbs up", + "spin around", + ] + + print("\n" + "=" * 80) + print("🎨 ARIA VISUAL COMMANDS TEST") + print("=" * 80) + + for prompt in test_commands: + input_text = f"<|user|>\n{prompt}\n<|assistant|>\n" + inputs = tokenizer(input_text, return_tensors="pt").to(model.device) + + with torch.no_grad(): + outputs = model.generate( + **inputs, + max_new_tokens=20, # Short - just need the tag + temperature=0.1, # Low temperature = more deterministic + do_sample=True, + top_p=0.9, + repetition_penalty=1.5, # Penalize repeating tokens + pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id, + eos_token_id=tokenizer.eos_token_id, + ) + + response = tokenizer.decode( + outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True + ) + + # Extract first command tag + import re + + tags = re.findall(r"\[aria:[^\]]+\]", response) + + print(f"\n📝 Command: {prompt}") + print(f" Raw output: {response[:100]}") + if tags: + print(f" ✅ Tags found: {' '.join(tags[:2])}") # Show first 2 tags + else: + print(" ❌ No command tags detected") + print("-" * 80) + + +if __name__ == "__main__": + adapter = ( + REPO_ROOT / "data_out" / "aria_models" / "aria_expanded_v2" / "lora_adapter" + ) + test_aria_final(str(adapter)) diff --git a/scripts/backup_manager.py b/scripts/backup_manager.py index b9d44d82e..a49233cf3 100644 --- a/scripts/backup_manager.py +++ b/scripts/backup_manager.py @@ -1,481 +1,481 @@ -"""Automated Backup System for QAI Models, Configs, and Data""" - -import hashlib -import json -import os -import shutil -import tarfile -from datetime import datetime -from pathlib import Path -from typing import Dict, List - - -class BackupManager: - """Manages automated backups of training artifacts""" - - def __init__(self, backup_dir: str = "backups"): - self.backup_dir = Path(backup_dir) - self.backup_dir.mkdir(parents=True, exist_ok=True) - self.manifest_file = self.backup_dir / "backup_manifest.json" - self.load_manifest() - - def load_manifest(self): - """Load backup manifest""" - if self.manifest_file.exists(): - with open(self.manifest_file, "r") as f: - self.manifest = json.load(f) - else: - self.manifest = {"backups": [], "last_backup": None} - - def save_manifest(self): - """Save backup manifest""" - with open(self.manifest_file, "w") as f: - json.dump(self.manifest, f, indent=2) - - def calculate_checksum(self, file_path: Path) -> str: - """Calculate SHA256 checksum of file""" - sha256 = hashlib.sha256() - with open(file_path, "rb") as f: - for chunk in iter(lambda: f.read(4096), b""): - sha256.update(chunk) - return sha256.hexdigest() - - def create_backup( - self, - include_models: bool = True, - include_configs: bool = True, - include_datasets: bool = False, - include_logs: bool = True, - compress: bool = True, - description: str = "", - incremental: bool = False, - ) -> Dict: - """Create comprehensive backup""" - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - backup_name = f"qai_backup_{timestamp}" - # Ensure uniqueness if multiple backups created within same second - # Build set of existing backup names for O(1) lookup - existing_names = {b.get("name") for b in self.manifest.get("backups", [])} - suffix_counter = 2 - while backup_name in existing_names: - backup_name = f"qai_backup_{timestamp}_{suffix_counter}" - suffix_counter += 1 - backup_path = self.backup_dir / backup_name - backup_path.mkdir(parents=True, exist_ok=True) - - # Build map of previous file checksums if incremental requested - previous_checksums: Dict[str, str] = {} - if incremental and self.manifest["backups"]: - # Use most recent backup entry (last in list) – supports either compressed or directory backups - last_backup = self.manifest["backups"][-1] - for entry in last_backup.get("files", []): - # Legacy entries may be strings (no checksum) - if isinstance(entry, dict): - previous_checksums[entry["path"]] = entry.get("checksum", "") - elif isinstance(entry, str): - # Cannot compute checksum for legacy compressed backup – treat as changed - continue - - backup_info = { - "name": backup_name, - "timestamp": datetime.now().isoformat(), - "description": description, - "includes": { - "models": include_models, - "configs": include_configs, - "datasets": include_datasets, - "logs": include_logs, - }, - "files": [], # list[{'path': str, 'checksum': str, 'unchanged': bool}] - "size_bytes": 0, - "checksum": None, - "incremental": incremental, - "unchanged_files": 0, - "changed_files": 0, - } - - print(f"Creating backup: {backup_name}") - - # Backup models - if include_models: - models_src = Path("data_out/lora_training/lora_adapter") - if models_src.exists(): - models_dst = backup_path / "models" - models_dst.mkdir(parents=True, exist_ok=True) - self._copy_directory( - models_src, models_dst, backup_info, previous_checksums, incremental - ) - print(" ✓ Backed up models") - - # Backup configs - if include_configs: - configs = [ - "autotrain.yaml", - "quantum_autorun.yaml", - "evaluation_autorun.yaml", - "batch_eval_config.yaml", - ] - configs_dst = backup_path / "configs" - configs_dst.mkdir(parents=True, exist_ok=True) - - for config_file in configs: - config_path = Path(config_file) - if config_path.exists(): - dst = configs_dst / config_file - dst.parent.mkdir(parents=True, exist_ok=True) - # Use unified copy/link logic for incremental detection - self._copy_or_link( - config_path, dst, backup_info, previous_checksums, incremental - ) - - print(" ✓ Backed up configs") - - # Backup datasets (optional - can be large) - if include_datasets: - datasets_src = Path("datasets") - if datasets_src.exists(): - datasets_dst = backup_path / "datasets" - datasets_dst.mkdir(parents=True, exist_ok=True) - self._copy_directory( - datasets_src, - datasets_dst, - backup_info, - previous_checksums, - incremental, - ) - print(" ✓ Backed up datasets") - - # Backup training logs - if include_logs: - logs_src = Path("data_out/autotrain") - if logs_src.exists(): - logs_dst = backup_path / "logs" - logs_dst.mkdir(parents=True, exist_ok=True) - - # Only backup JSON files (not large model files) - for json_file in logs_src.rglob("*.json"): - rel_path = json_file.relative_to(logs_src) - dst = logs_dst / rel_path - dst.parent.mkdir(parents=True, exist_ok=True) - self._copy_or_link( - json_file, dst, backup_info, previous_checksums, incremental - ) - - print(" ✓ Backed up logs") - - # Create backup metadata - metadata = { - "backup_info": backup_info, - "system_info": { - "python_version": self._get_python_version(), - "pytorch_version": self._get_pytorch_version(), - "cuda_available": self._check_cuda(), - }, - } - - metadata_file = backup_path / "backup_metadata.json" - with open(metadata_file, "w") as f: - json.dump(metadata, f, indent=2) - - # Compress if requested - if compress: - archive_path = self.backup_dir / f"{backup_name}.tar.gz" - print(" Compressing backup...") - - with tarfile.open(archive_path, "w:gz") as tar: - tar.add(backup_path, arcname=backup_name) - - # Calculate archive checksum - backup_info["checksum"] = self.calculate_checksum(archive_path) - backup_info["compressed_path"] = str(archive_path) - backup_info["compressed_size"] = archive_path.stat().st_size - - # Remove uncompressed directory - shutil.rmtree(backup_path) - - print(f" ✓ Compressed to {archive_path.name}") - print(f" Size: {backup_info['compressed_size'] / 1024 / 1024:.2f} MB") - else: - backup_info["path"] = str(backup_path) - - # Update manifest - self.manifest["backups"].append(backup_info) - self.manifest["last_backup"] = backup_info["timestamp"] - self.save_manifest() - - print(f"✅ Backup complete: {backup_name}") - return backup_info - - def _copy_directory( - self, - src: Path, - dst: Path, - backup_info: Dict, - previous_checksums: Dict[str, str], - incremental: bool, - ): - """Recursively copy directory and track files (supports incremental)""" - for item in src.rglob("*"): - if item.is_file(): - rel_path = item.relative_to(src) - dst_path = dst / rel_path - dst_path.parent.mkdir(parents=True, exist_ok=True) - self._copy_or_link( - item, dst_path, backup_info, previous_checksums, incremental - ) - - def _copy_or_link( - self, - src_file: Path, - dst_path: Path, - backup_info: Dict, - previous_checksums: Dict[str, str], - incremental: bool, - ): - """Copy file or create hardlink if unchanged in incremental mode""" - checksum = self.calculate_checksum(src_file) - file_path_str = str(src_file) - unchanged = False - if incremental and previous_checksums.get(file_path_str) == checksum: - # Attempt hardlink for efficiency - try: - os.link(src_file, dst_path) - unchanged = True - except Exception: - shutil.copy2(src_file, dst_path) # Fallback - else: - shutil.copy2(src_file, dst_path) - backup_info["files"].append( - {"path": file_path_str, "checksum": checksum, "unchanged": unchanged} - ) - size = src_file.stat().st_size - backup_info["size_bytes"] += size - if unchanged: - backup_info["unchanged_files"] += 1 - else: - backup_info["changed_files"] += 1 - - def _get_python_version(self) -> str: - """Get Python version""" - import sys - - return f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}" - - def _get_pytorch_version(self) -> str: - """Get PyTorch version""" - try: - import torch - - return torch.__version__ - except ImportError: - return "Not installed" - - def _check_cuda(self) -> bool: - """Check CUDA availability""" - try: - import torch - - return torch.cuda.is_available() - except ImportError: - return False - - def list_backups(self) -> List[Dict]: - """List all backups""" - return self.manifest["backups"] - - def restore_backup(self, backup_name: str, target_dir: str = "."): - """Restore backup to target directory""" - backup_info = None - for backup in self.manifest["backups"]: - if backup["name"] == backup_name: - backup_info = backup - break - - if not backup_info: - raise ValueError(f"Backup '{backup_name}' not found") - - print(f"Restoring backup: {backup_name}") - - if "compressed_path" in backup_info: - archive_path = Path(backup_info["compressed_path"]) - - if not archive_path.exists(): - raise FileNotFoundError(f"Backup archive not found: {archive_path}") - - # Verify checksum - if backup_info.get("checksum"): - current_checksum = self.calculate_checksum(archive_path) - if current_checksum != backup_info["checksum"]: - raise ValueError("Backup checksum mismatch! File may be corrupted.") - - # Extract archive safely - filter to prevent path traversal attacks - with tarfile.open(archive_path, "r:gz") as tar: - # Python 3.12+ has built-in filter, for older versions we validate manually - target_path = Path(target_dir).resolve() - safe_members = [] - for member in tar.getmembers(): - member_path = (target_path / member.name).resolve() - # Ensure extraction stays within target directory - if ( - not str(member_path).startswith(str(target_path) + os.sep) - and member_path != target_path - ): - raise ValueError( - f"Attempted path traversal in tarfile: {member.name}" - ) - safe_members.append(member) - # Extract only validated members - tar.extractall( - target_dir, members=safe_members - ) # nosec B202 - members validated above - - print(f"✅ Backup restored to: {target_dir}") - else: - backup_path = Path(backup_info["path"]) - if backup_path.exists(): - shutil.copytree(backup_path, Path(target_dir) / backup_name) - print(f"✅ Backup restored to: {target_dir}/{backup_name}") - else: - raise FileNotFoundError(f"Backup directory not found: {backup_path}") - - def delete_backup(self, backup_name: str): - """Delete a backup""" - backup_info = None - backup_index = None - - for idx, backup in enumerate(self.manifest["backups"]): - if backup["name"] == backup_name: - backup_info = backup - backup_index = idx - break - - if not backup_info: - raise ValueError(f"Backup '{backup_name}' not found") - - # Delete archive or directory - if "compressed_path" in backup_info: - archive_path = Path(backup_info["compressed_path"]) - if archive_path.exists(): - archive_path.unlink() - elif "path" in backup_info: - backup_path = Path(backup_info["path"]) - if backup_path.exists(): - shutil.rmtree(backup_path) - - # Remove from manifest - self.manifest["backups"].pop(backup_index) - self.save_manifest() - - print(f"✅ Deleted backup: {backup_name}") - - def cleanup_old_backups(self, keep_count: int = 5): - """Keep only the most recent N backups""" - if len(self.manifest["backups"]) <= keep_count: - print( - f"Only {len(self.manifest['backups'])} backups exist, no cleanup needed" - ) - return - - # Sort by timestamp - sorted_backups = sorted( - self.manifest["backups"], key=lambda x: x["timestamp"], reverse=True - ) - - # Delete old backups - to_delete = sorted_backups[keep_count:] - for backup in to_delete: - try: - self.delete_backup(backup["name"]) - print(f" Cleaned up: {backup['name']}") - except Exception as e: - print(f" Failed to delete {backup['name']}: {e}") - - print(f"✅ Cleanup complete. Kept {keep_count} most recent backups") - - -# CLI Interface -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser(description="QAI Backup Manager") - parser.add_argument( - "action", - choices=["create", "list", "restore", "delete", "cleanup"], - help="Action to perform", - ) - parser.add_argument("--name", help="Backup name (for restore/delete)") - parser.add_argument("--no-models", action="store_true", help="Exclude models") - parser.add_argument("--no-configs", action="store_true", help="Exclude configs") - parser.add_argument( - "--include-datasets", action="store_true", help="Include datasets (large)" - ) - parser.add_argument("--no-logs", action="store_true", help="Exclude logs") - parser.add_argument("--no-compress", action="store_true", help="Skip compression") - parser.add_argument("--description", default="", help="Backup description") - parser.add_argument( - "--incremental", - action="store_true", - help="Perform incremental backup (hardlink unchanged files)", - ) - parser.add_argument( - "--target-dir", default=".", help="Target directory for restore" - ) - parser.add_argument( - "--keep", type=int, default=5, help="Number of backups to keep (cleanup)" - ) - - args = parser.parse_args() - - manager = BackupManager() - - if args.action == "create": - backup_info = manager.create_backup( - include_models=not args.no_models, - include_configs=not args.no_configs, - include_datasets=args.include_datasets, - include_logs=not args.no_logs, - compress=not args.no_compress, - description=args.description, - incremental=args.incremental, - ) - print(f"\n📦 Backup ID: {backup_info['name']}") - print( - f"📁 Files: {len(backup_info['files'])} (changed: {backup_info['changed_files']}, unchanged: {backup_info['unchanged_files']})" - ) - print( - f"💾 Size: {backup_info.get('compressed_size', backup_info['size_bytes']) / 1024 / 1024:.2f} MB" - ) - - elif args.action == "list": - backups = manager.list_backups() - if not backups: - print("No backups found") - else: - print(f"\n📋 Available Backups ({len(backups)}):\n") - for backup in reversed(backups): - size = backup.get("compressed_size", backup["size_bytes"]) / 1024 / 1024 - timestamp = datetime.fromisoformat(backup["timestamp"]).strftime( - "%Y-%m-%d %H:%M:%S" - ) - print(f" {backup['name']}") - print(f" Time: {timestamp}") - print(f" Size: {size:.2f} MB") - print(f" Files: {len(backup['files'])}") - if backup["description"]: - print(f" Desc: {backup['description']}") - print() - - elif args.action == "restore": - if not args.name: - print("Error: --name required for restore") - else: - manager.restore_backup(args.name, args.target_dir) - - elif args.action == "delete": - if not args.name: - print("Error: --name required for delete") - else: - manager.delete_backup(args.name) - - elif args.action == "cleanup": - manager.cleanup_old_backups(args.keep) +"""Automated Backup System for QAI Models, Configs, and Data""" + +import hashlib +import json +import os +import shutil +import tarfile +from datetime import datetime +from pathlib import Path +from typing import Dict, List + + +class BackupManager: + """Manages automated backups of training artifacts""" + + def __init__(self, backup_dir: str = "backups"): + self.backup_dir = Path(backup_dir) + self.backup_dir.mkdir(parents=True, exist_ok=True) + self.manifest_file = self.backup_dir / "backup_manifest.json" + self.load_manifest() + + def load_manifest(self): + """Load backup manifest""" + if self.manifest_file.exists(): + with open(self.manifest_file, "r") as f: + self.manifest = json.load(f) + else: + self.manifest = {"backups": [], "last_backup": None} + + def save_manifest(self): + """Save backup manifest""" + with open(self.manifest_file, "w") as f: + json.dump(self.manifest, f, indent=2) + + def calculate_checksum(self, file_path: Path) -> str: + """Calculate SHA256 checksum of file""" + sha256 = hashlib.sha256() + with open(file_path, "rb") as f: + for chunk in iter(lambda: f.read(4096), b""): + sha256.update(chunk) + return sha256.hexdigest() + + def create_backup( + self, + include_models: bool = True, + include_configs: bool = True, + include_datasets: bool = False, + include_logs: bool = True, + compress: bool = True, + description: str = "", + incremental: bool = False, + ) -> Dict: + """Create comprehensive backup""" + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + backup_name = f"qai_backup_{timestamp}" + # Ensure uniqueness if multiple backups created within same second + # Build set of existing backup names for O(1) lookup + existing_names = {b.get("name") for b in self.manifest.get("backups", [])} + suffix_counter = 2 + while backup_name in existing_names: + backup_name = f"qai_backup_{timestamp}_{suffix_counter}" + suffix_counter += 1 + backup_path = self.backup_dir / backup_name + backup_path.mkdir(parents=True, exist_ok=True) + + # Build map of previous file checksums if incremental requested + previous_checksums: Dict[str, str] = {} + if incremental and self.manifest["backups"]: + # Use most recent backup entry (last in list) – supports either compressed or directory backups + last_backup = self.manifest["backups"][-1] + for entry in last_backup.get("files", []): + # Legacy entries may be strings (no checksum) + if isinstance(entry, dict): + previous_checksums[entry["path"]] = entry.get("checksum", "") + elif isinstance(entry, str): + # Cannot compute checksum for legacy compressed backup – treat as changed + continue + + backup_info = { + "name": backup_name, + "timestamp": datetime.now().isoformat(), + "description": description, + "includes": { + "models": include_models, + "configs": include_configs, + "datasets": include_datasets, + "logs": include_logs, + }, + "files": [], # list[{'path': str, 'checksum': str, 'unchanged': bool}] + "size_bytes": 0, + "checksum": None, + "incremental": incremental, + "unchanged_files": 0, + "changed_files": 0, + } + + print(f"Creating backup: {backup_name}") + + # Backup models + if include_models: + models_src = Path("data_out/lora_training/lora_adapter") + if models_src.exists(): + models_dst = backup_path / "models" + models_dst.mkdir(parents=True, exist_ok=True) + self._copy_directory( + models_src, models_dst, backup_info, previous_checksums, incremental + ) + print(" ✓ Backed up models") + + # Backup configs + if include_configs: + configs = [ + "autotrain.yaml", + "quantum_autorun.yaml", + "evaluation_autorun.yaml", + "batch_eval_config.yaml", + ] + configs_dst = backup_path / "configs" + configs_dst.mkdir(parents=True, exist_ok=True) + + for config_file in configs: + config_path = Path(config_file) + if config_path.exists(): + dst = configs_dst / config_file + dst.parent.mkdir(parents=True, exist_ok=True) + # Use unified copy/link logic for incremental detection + self._copy_or_link( + config_path, dst, backup_info, previous_checksums, incremental + ) + + print(" ✓ Backed up configs") + + # Backup datasets (optional - can be large) + if include_datasets: + datasets_src = Path("datasets") + if datasets_src.exists(): + datasets_dst = backup_path / "datasets" + datasets_dst.mkdir(parents=True, exist_ok=True) + self._copy_directory( + datasets_src, + datasets_dst, + backup_info, + previous_checksums, + incremental, + ) + print(" ✓ Backed up datasets") + + # Backup training logs + if include_logs: + logs_src = Path("data_out/autotrain") + if logs_src.exists(): + logs_dst = backup_path / "logs" + logs_dst.mkdir(parents=True, exist_ok=True) + + # Only backup JSON files (not large model files) + for json_file in logs_src.rglob("*.json"): + rel_path = json_file.relative_to(logs_src) + dst = logs_dst / rel_path + dst.parent.mkdir(parents=True, exist_ok=True) + self._copy_or_link( + json_file, dst, backup_info, previous_checksums, incremental + ) + + print(" ✓ Backed up logs") + + # Create backup metadata + metadata = { + "backup_info": backup_info, + "system_info": { + "python_version": self._get_python_version(), + "pytorch_version": self._get_pytorch_version(), + "cuda_available": self._check_cuda(), + }, + } + + metadata_file = backup_path / "backup_metadata.json" + with open(metadata_file, "w") as f: + json.dump(metadata, f, indent=2) + + # Compress if requested + if compress: + archive_path = self.backup_dir / f"{backup_name}.tar.gz" + print(" Compressing backup...") + + with tarfile.open(archive_path, "w:gz") as tar: + tar.add(backup_path, arcname=backup_name) + + # Calculate archive checksum + backup_info["checksum"] = self.calculate_checksum(archive_path) + backup_info["compressed_path"] = str(archive_path) + backup_info["compressed_size"] = archive_path.stat().st_size + + # Remove uncompressed directory + shutil.rmtree(backup_path) + + print(f" ✓ Compressed to {archive_path.name}") + print(f" Size: {backup_info['compressed_size'] / 1024 / 1024:.2f} MB") + else: + backup_info["path"] = str(backup_path) + + # Update manifest + self.manifest["backups"].append(backup_info) + self.manifest["last_backup"] = backup_info["timestamp"] + self.save_manifest() + + print(f"✅ Backup complete: {backup_name}") + return backup_info + + def _copy_directory( + self, + src: Path, + dst: Path, + backup_info: Dict, + previous_checksums: Dict[str, str], + incremental: bool, + ): + """Recursively copy directory and track files (supports incremental)""" + for item in src.rglob("*"): + if item.is_file(): + rel_path = item.relative_to(src) + dst_path = dst / rel_path + dst_path.parent.mkdir(parents=True, exist_ok=True) + self._copy_or_link( + item, dst_path, backup_info, previous_checksums, incremental + ) + + def _copy_or_link( + self, + src_file: Path, + dst_path: Path, + backup_info: Dict, + previous_checksums: Dict[str, str], + incremental: bool, + ): + """Copy file or create hardlink if unchanged in incremental mode""" + checksum = self.calculate_checksum(src_file) + file_path_str = str(src_file) + unchanged = False + if incremental and previous_checksums.get(file_path_str) == checksum: + # Attempt hardlink for efficiency + try: + os.link(src_file, dst_path) + unchanged = True + except Exception: + shutil.copy2(src_file, dst_path) # Fallback + else: + shutil.copy2(src_file, dst_path) + backup_info["files"].append( + {"path": file_path_str, "checksum": checksum, "unchanged": unchanged} + ) + size = src_file.stat().st_size + backup_info["size_bytes"] += size + if unchanged: + backup_info["unchanged_files"] += 1 + else: + backup_info["changed_files"] += 1 + + def _get_python_version(self) -> str: + """Get Python version""" + import sys + + return f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}" + + def _get_pytorch_version(self) -> str: + """Get PyTorch version""" + try: + import torch + + return torch.__version__ + except ImportError: + return "Not installed" + + def _check_cuda(self) -> bool: + """Check CUDA availability""" + try: + import torch + + return torch.cuda.is_available() + except ImportError: + return False + + def list_backups(self) -> List[Dict]: + """List all backups""" + return self.manifest["backups"] + + def restore_backup(self, backup_name: str, target_dir: str = "."): + """Restore backup to target directory""" + backup_info = None + for backup in self.manifest["backups"]: + if backup["name"] == backup_name: + backup_info = backup + break + + if not backup_info: + raise ValueError(f"Backup '{backup_name}' not found") + + print(f"Restoring backup: {backup_name}") + + if "compressed_path" in backup_info: + archive_path = Path(backup_info["compressed_path"]) + + if not archive_path.exists(): + raise FileNotFoundError(f"Backup archive not found: {archive_path}") + + # Verify checksum + if backup_info.get("checksum"): + current_checksum = self.calculate_checksum(archive_path) + if current_checksum != backup_info["checksum"]: + raise ValueError("Backup checksum mismatch! File may be corrupted.") + + # Extract archive safely - filter to prevent path traversal attacks + with tarfile.open(archive_path, "r:gz") as tar: + # Python 3.12+ has built-in filter, for older versions we validate manually + target_path = Path(target_dir).resolve() + safe_members = [] + for member in tar.getmembers(): + member_path = (target_path / member.name).resolve() + # Ensure extraction stays within target directory + if ( + not str(member_path).startswith(str(target_path) + os.sep) + and member_path != target_path + ): + raise ValueError( + f"Attempted path traversal in tarfile: {member.name}" + ) + safe_members.append(member) + # Extract only validated members + tar.extractall( + target_dir, members=safe_members + ) # nosec B202 - members validated above + + print(f"✅ Backup restored to: {target_dir}") + else: + backup_path = Path(backup_info["path"]) + if backup_path.exists(): + shutil.copytree(backup_path, Path(target_dir) / backup_name) + print(f"✅ Backup restored to: {target_dir}/{backup_name}") + else: + raise FileNotFoundError(f"Backup directory not found: {backup_path}") + + def delete_backup(self, backup_name: str): + """Delete a backup""" + backup_info = None + backup_index = None + + for idx, backup in enumerate(self.manifest["backups"]): + if backup["name"] == backup_name: + backup_info = backup + backup_index = idx + break + + if not backup_info: + raise ValueError(f"Backup '{backup_name}' not found") + + # Delete archive or directory + if "compressed_path" in backup_info: + archive_path = Path(backup_info["compressed_path"]) + if archive_path.exists(): + archive_path.unlink() + elif "path" in backup_info: + backup_path = Path(backup_info["path"]) + if backup_path.exists(): + shutil.rmtree(backup_path) + + # Remove from manifest + self.manifest["backups"].pop(backup_index) + self.save_manifest() + + print(f"✅ Deleted backup: {backup_name}") + + def cleanup_old_backups(self, keep_count: int = 5): + """Keep only the most recent N backups""" + if len(self.manifest["backups"]) <= keep_count: + print( + f"Only {len(self.manifest['backups'])} backups exist, no cleanup needed" + ) + return + + # Sort by timestamp + sorted_backups = sorted( + self.manifest["backups"], key=lambda x: x["timestamp"], reverse=True + ) + + # Delete old backups + to_delete = sorted_backups[keep_count:] + for backup in to_delete: + try: + self.delete_backup(backup["name"]) + print(f" Cleaned up: {backup['name']}") + except Exception as e: + print(f" Failed to delete {backup['name']}: {e}") + + print(f"✅ Cleanup complete. Kept {keep_count} most recent backups") + + +# CLI Interface +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="QAI Backup Manager") + parser.add_argument( + "action", + choices=["create", "list", "restore", "delete", "cleanup"], + help="Action to perform", + ) + parser.add_argument("--name", help="Backup name (for restore/delete)") + parser.add_argument("--no-models", action="store_true", help="Exclude models") + parser.add_argument("--no-configs", action="store_true", help="Exclude configs") + parser.add_argument( + "--include-datasets", action="store_true", help="Include datasets (large)" + ) + parser.add_argument("--no-logs", action="store_true", help="Exclude logs") + parser.add_argument("--no-compress", action="store_true", help="Skip compression") + parser.add_argument("--description", default="", help="Backup description") + parser.add_argument( + "--incremental", + action="store_true", + help="Perform incremental backup (hardlink unchanged files)", + ) + parser.add_argument( + "--target-dir", default=".", help="Target directory for restore" + ) + parser.add_argument( + "--keep", type=int, default=5, help="Number of backups to keep (cleanup)" + ) + + args = parser.parse_args() + + manager = BackupManager() + + if args.action == "create": + backup_info = manager.create_backup( + include_models=not args.no_models, + include_configs=not args.no_configs, + include_datasets=args.include_datasets, + include_logs=not args.no_logs, + compress=not args.no_compress, + description=args.description, + incremental=args.incremental, + ) + print(f"\n📦 Backup ID: {backup_info['name']}") + print( + f"📁 Files: {len(backup_info['files'])} (changed: {backup_info['changed_files']}, unchanged: {backup_info['unchanged_files']})" + ) + print( + f"💾 Size: {backup_info.get('compressed_size', backup_info['size_bytes']) / 1024 / 1024:.2f} MB" + ) + + elif args.action == "list": + backups = manager.list_backups() + if not backups: + print("No backups found") + else: + print(f"\n📋 Available Backups ({len(backups)}):\n") + for backup in reversed(backups): + size = backup.get("compressed_size", backup["size_bytes"]) / 1024 / 1024 + timestamp = datetime.fromisoformat(backup["timestamp"]).strftime( + "%Y-%m-%d %H:%M:%S" + ) + print(f" {backup['name']}") + print(f" Time: {timestamp}") + print(f" Size: {size:.2f} MB") + print(f" Files: {len(backup['files'])}") + if backup["description"]: + print(f" Desc: {backup['description']}") + print() + + elif args.action == "restore": + if not args.name: + print("Error: --name required for restore") + else: + manager.restore_backup(args.name, args.target_dir) + + elif args.action == "delete": + if not args.name: + print("Error: --name required for delete") + else: + manager.delete_backup(args.name) + + elif args.action == "cleanup": + manager.cleanup_old_backups(args.keep) diff --git a/scripts/batch_evaluator.py b/scripts/batch_evaluator.py index dd1396c7b..1cd170710 100644 --- a/scripts/batch_evaluator.py +++ b/scripts/batch_evaluator.py @@ -1,573 +1,573 @@ -#!/usr/bin/env python -""" -Batch Evaluator - -Parallel evaluation of multiple models with comprehensive result aggregation. - -Features: -- Parallel model evaluation (ThreadPoolExecutor) -- Support for multiple model types (LoRA, Azure, OpenAI, Local, Quantum) -- Configurable metrics per model type -- Result aggregation and ranking -- Export to multiple formats -- Comparison reports - -Usage examples (PowerShell): - python .\\scripts\\batch_evaluator.py --config batch_eval_config.yaml - python .\\scripts\\batch_evaluator.py --scan-models --evaluate-all - python .\\scripts\\batch_evaluator.py --compare lora azure openai - python .\\scripts\\batch_evaluator.py --export markdown --output report.md -""" - -from __future__ import annotations - -import argparse -import json -import subprocess -import sys -import time -from concurrent.futures import ThreadPoolExecutor, as_completed -from dataclasses import dataclass, field -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, List, Optional - -import yaml - -REPO_ROOT = Path(__file__).resolve().parents[1] -DATA_OUT = REPO_ROOT / "data_out" / "batch_evaluator" - -# Add shared directory to path for performance utilities -sys.path.insert(0, str(REPO_ROOT / "shared")) -from performance_utils import find_json_in_output - - -@dataclass -class EvaluationTask: - """A single model evaluation task.""" - - model_id: str - model_type: str - model_path: str - dataset: str - metrics: List[str] - max_samples: Optional[int] = None - batch_size: int = 8 - - -@dataclass -class EvaluationResult: - """Result from evaluating a model.""" - - model_id: str - model_type: str - dataset: str = "" - status: str = "succeeded" # succeeded, failed, timeout - duration: float = 0.0 - model_path: str = "" # Path to the evaluated model - metrics: Dict[str, float] = field(default_factory=dict) - error: Optional[str] = None - - -# Backward-compatible alias used by older tests and scripts. -EvalResult = EvaluationResult - - -class BatchEvaluator: - """Orchestrates parallel model evaluation.""" - - def __init__(self, max_workers: int = 3): - self.data_out = DATA_OUT - self.data_out.mkdir(parents=True, exist_ok=True) - self.max_workers = max_workers - self.tasks: List[EvaluationTask] = [] - self.results: List[EvaluationResult] = [] - # Performance optimization: cache results lookup by model_id - self._results_cache: Dict[str, EvaluationResult] = {} - - def load_config(self, config_file: Path): - """Load evaluation tasks from config file.""" - with config_file.open("r") as f: - config = yaml.safe_load(f) - - # Use list comprehension for better performance - self.tasks.extend( - [ - EvaluationTask(**task_data) - for task_data in config.get("evaluation_tasks", []) - ] - ) - - print(f"[batch_eval] Loaded {len(self.tasks)} evaluation tasks") - - def scan_models(self) -> List[EvaluationTask]: - """Scan for trained models and create evaluation tasks.""" - # Scan LoRA models - use list comprehension - lora_dir = DATA_OUT.parent / "lora_training" - tasks = [] - if lora_dir.exists(): - tasks = [ - EvaluationTask( - model_id=model_dir.name, - model_type="lora", - model_path=str(model_dir), - dataset="datasets/chat/mixed_chat", - metrics=["accuracy", "perplexity", "bleu"], - max_samples=100, - ) - for model_dir in lora_dir.iterdir() - if model_dir.is_dir() and (model_dir / "adapter_config.json").exists() - ] - - print(f"[batch_eval] Found {len(tasks)} models to evaluate") - return tasks - - def evaluate_model(self, task: EvaluationTask) -> EvaluationResult: - """Evaluate a single model.""" - print(f"[batch_eval] Evaluating: {task.model_id} ({task.model_type})") - - # Build evaluation command - cmd = [ - sys.executable, - str(REPO_ROOT / "scripts" / "evaluate_lora_model.py"), - "--model", - task.model_path, - "--dataset", - task.dataset, - "--max-samples", - str(task.max_samples or 1000), - "--output-format", - "json", - "--save-dir", - str(self.data_out / task.model_id), - ] - - # Add metrics (each as separate --metric flag) - for metric in task.metrics: - cmd.extend(["--metric", metric]) - - t0 = time.time() - try: - result = subprocess.run( - cmd, - cwd=str(REPO_ROOT), - capture_output=True, - text=True, - timeout=1800, # 30 minutes - ) - - duration = time.time() - t0 - - # Parse results - result_obj = EvaluationResult( - model_id=task.model_id, - model_type=task.model_type, - dataset=task.dataset, - model_path=task.model_path, - status="succeeded" if result.returncode == 0 else "failed", - duration=duration, - ) - - # Try to extract metrics from output using optimized utility - if result.returncode == 0: - data = find_json_in_output( - result.stdout, key="metrics", search_from_end=True, max_lines=50 - ) - if data and "metrics" in data: - result_obj.metrics = data["metrics"] - else: - result_obj.error = result.stderr - - return result_obj - - except subprocess.TimeoutExpired: - return EvaluationResult( - model_id=task.model_id, - model_type=task.model_type, - dataset=task.dataset, - model_path=task.model_path, - status="timeout", - duration=1800, - error="Evaluation timed out after 30 minutes", - ) - except Exception as e: - return EvaluationResult( - model_id=task.model_id, - model_type=task.model_type, - dataset=task.dataset, - model_path=task.model_path, - status="failed", - duration=0, - error=str(e), - ) - - def run_parallel(self): - """Run all evaluation tasks in parallel.""" - print("\n[batch_eval] Starting parallel evaluation") - print(f"[batch_eval] Tasks: {len(self.tasks)}") - print(f"[batch_eval] Workers: {self.max_workers}\n") - - with ThreadPoolExecutor(max_workers=self.max_workers) as executor: - futures = { - executor.submit(self.evaluate_model, task): task for task in self.tasks - } - - for future in as_completed(futures): - task = futures[future] - try: - result = future.result() - self.results.append(result) - # Update cache for O(1) lookups - self._results_cache[result.model_id] = result - - # Use ASCII-safe status indicators - status_icon = "[OK]" if result.status == "succeeded" else "[FAIL]" - print( - f"{status_icon} {result.model_id}: {result.status} ({result.duration:.1f}s)" - ) - - if result.metrics: - for metric, value in result.metrics.items(): - print(f" {metric}: {value:.4f}") - - except Exception as e: - print(f"[ERROR] {task.model_id}: Exception - {e}") - - print("\n[batch_eval] Evaluation complete") - # Use already classified results from aggregate to avoid redundant passes - succeeded_count = sum(1 for r in self.results if r.status == "succeeded") - failed_count = len(self.results) - succeeded_count - print(f"[batch_eval] Succeeded: {succeeded_count}") - print(f"[batch_eval] Failed: {failed_count}") - - def aggregate_results(self) -> Dict: - """Aggregate all evaluation results. - - Optimized to iterate results only once for classification and metrics. - """ - succeeded = [] - failed = [] - total_duration = 0.0 - - # Single pass through results for classification and duration sum - for r in self.results: - total_duration += r.duration - if r.status == "succeeded": - succeeded.append(r) - else: - failed.append(r) - - # Rank by primary metric (accuracy if available) - ranked = sorted( - succeeded, - key=lambda r: r.metrics.get("accuracy", r.metrics.get("perplexity", 0)), - reverse=True, - ) - - # Pre-compute ranking list - ranking = [ - {"rank": i + 1, "model_id": r.model_id, "metrics": r.metrics} - for i, r in enumerate(ranked) - ] - - return { - "evaluated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), - "total_models": len(self.results), - "succeeded": len(succeeded), - "failed": len(failed), - "total_duration": total_duration, - "best_model": ranked[0].model_id if ranked else None, - "results": [r.__dict__ for r in self.results], - "ranking": ranking, - } - - def export_markdown(self, output_file: Path): - """Export results as Markdown report.""" - aggregated = self.aggregate_results() - - with output_file.open("w") as f: - f.write("# Model Evaluation Report\n\n") - f.write(f"**Generated:** {aggregated['evaluated_at']}\n\n") - f.write(f"**Total Models:** {aggregated['total_models']}\n") - f.write(f"**Succeeded:** {aggregated['succeeded']}\n") - f.write(f"**Failed:** {aggregated['failed']}\n") - f.write(f"**Total Duration:** {aggregated['total_duration']:.1f}s\n\n") - - if aggregated["best_model"]: - f.write(f"**Best Model:** {aggregated['best_model']}\n\n") - - # Results table - f.write("## Evaluation Results\n\n") - f.write("| Rank | Model | Type | Status | Duration | Metrics |\n") - f.write("|------|-------|------|--------|----------|----------|\n") - - for item in aggregated["ranking"]: - metrics_str = ", ".join( - f"{k}={v:.3f}" for k, v in item["metrics"].items() - ) - f.write( - f"| {item['rank']} | {item['model_id']} | - | ✓ | - | {metrics_str} |\n" - ) - - # Failed models - failed = [r for r in self.results if r.status != "succeeded"] - if failed: - f.write("\n## Failed Evaluations\n\n") - for result in failed: - f.write(f"- **{result.model_id}**: {result.status}") - if result.error: - f.write(f" - {result.error}") - f.write("\n") - - print(f"[batch_eval] Exported Markdown report to: {output_file}") - - def export_json(self, output_file: Path): - """Export results as JSON.""" - aggregated = self.aggregate_results() - with output_file.open("w") as f: - json.dump(aggregated, f, indent=2) - print(f"[batch_eval] Exported JSON to: {output_file}") - - def compare_models(self, model_ids: List[str]) -> Dict: - """Compare specific models side-by-side using fast lookups with fallback.""" - comparison: List[EvaluationResult] = [] - - # Primary path: O(1) cache lookup. - for model_id in model_ids: - result = self._results_cache.get(model_id) - if result is not None: - comparison.append(result) - - # Fallback path for tests/callers that set `self.results` directly. - if len(comparison) != len(model_ids): - model_ids_set = set(model_ids) - seen = {r.model_id for r in comparison} - for result in self.results: - if result.model_id in model_ids_set and result.model_id not in seen: - comparison.append(result) - seen.add(result.model_id) - - # Preserve requested ordering. - comparison_by_id = {r.model_id: r for r in comparison} - comparison = [ - comparison_by_id[mid] for mid in model_ids if mid in comparison_by_id - ] - - return { - "models": [r.model_id for r in comparison], - "comparison": [ - { - "model_id": r.model_id, - "model_type": r.model_type, - "status": r.status, - "duration": r.duration, - "metrics": r.metrics, - } - for r in comparison - ], - } - - def promote_best_model( - self, target_dir: Path | None = None, dry_run: bool = False - ) -> Dict: - """ - Promote the best-ranked model to deployed_models/. - - Args: - target_dir: Target directory (default: deployed_models/) - dry_run: If True, only show what would be done - - Returns: - Dict with promotion details (model_id, source, destination, metrics, timestamp) - """ - if not self.results: - raise ValueError("No evaluation results available. Run evaluation first.") - - # Get best model from aggregated results - aggregated = self.aggregate_results() - best_model_id = aggregated.get("best_model") - - if not best_model_id: - raise ValueError("No best model found (all evaluations may have failed)") - - # Prefer O(1) cache lookup instead of O(n) linear search, but fall back if needed - best_result = self._results_cache.get(best_model_id) - if best_result is None: - # Fallback to linear search to tolerate transient cache inconsistencies - best_result = next( - (r for r in self.results if r.model_id == best_model_id), - None, - ) - if best_result is None: - raise ValueError( - f"Best model {best_model_id} not found in evaluation results; " - "this indicates an internal consistency error." - ) - - # Determine target directory - if target_dir is None: - target_dir = REPO_ROOT / "deployed_models" - - # Create deployment name with timestamp - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - deployment_name = f"{best_model_id}_{timestamp}" - deployment_path = target_dir / deployment_name - - promotion_info = { - "model_id": best_model_id, - "source_path": best_result.model_path, - "deployment_name": deployment_name, - "deployment_path": str(deployment_path), - "metrics": best_result.metrics, - "promoted_at": datetime.now(timezone.utc).isoformat() + "Z", - "rank": 1, - } - - if dry_run: - print(f"[promote] DRY-RUN: Would promote {best_model_id}") - print(f"[promote] Source: {best_result.model_path}") - print(f"[promote] Target: {deployment_path}") - print(f"[promote] Metrics: {json.dumps(best_result.metrics, indent=2)}") - return promotion_info - - # Create deployment directory - deployment_path.mkdir(parents=True, exist_ok=True) - - # Copy adapter files - source_path = Path(best_result.model_path) - import shutil - - for file in source_path.iterdir(): - if file.is_file(): - shutil.copy2(file, deployment_path / file.name) - print(f"[promote] Copied: {file.name}") - - # Create metadata file - metadata_file = deployment_path / "promotion_metadata.json" - with metadata_file.open("w") as f: - json.dump(promotion_info, f, indent=2) - print(f"[promote] Created metadata: {metadata_file}") - - # Create symlink to latest - latest_link = target_dir / "latest" - if latest_link.exists(): - if latest_link.is_symlink(): - latest_link.unlink() - else: - print("[promote] Warning: 'latest' exists but is not a symlink") - - # Create relative symlink on Windows (requires admin or developer mode) - try: - latest_link.symlink_to(deployment_name, target_is_directory=True) - print(f"[promote] Updated symlink: {latest_link} -> {deployment_name}") - except (OSError, NotImplementedError) as e: - print(f"[promote] Warning: Could not create symlink ({e})") - # Fallback: write a text file pointing to latest - with (target_dir / "LATEST.txt").open("w") as f: - f.write(deployment_name) - print("[promote] Created LATEST.txt instead") - - print(f"[promote] ✓ Promoted {best_model_id} to {deployment_path}") - return promotion_info - - -def main(): - ap = argparse.ArgumentParser(description="Batch Model Evaluator") - ap.add_argument("--config", type=Path, help="Load evaluation tasks from config") - ap.add_argument( - "--scan-models", action="store_true", help="Scan for trained models" - ) - ap.add_argument( - "--evaluate-all", action="store_true", help="Evaluate all scanned models" - ) - ap.add_argument("--compare", nargs="+", help="Compare specific models") - ap.add_argument( - "--export", choices=["json", "markdown", "both"], help="Export results" - ) - ap.add_argument("--output", type=Path, help="Output file for export") - ap.add_argument( - "--max-workers", type=int, default=3, help="Number of parallel workers" - ) - ap.add_argument( - "--promote-best", - action="store_true", - help="Promote best model to deployed_models/", - ) - ap.add_argument( - "--promote-target", - type=Path, - help="Target directory for promotion (default: deployed_models/)", - ) - ap.add_argument( - "--dry-run", - action="store_true", - help="Dry-run mode (show actions without executing)", - ) - args = ap.parse_args() - - evaluator = BatchEvaluator(max_workers=args.max_workers) - - if args.config: - evaluator.load_config(args.config) - - if args.scan_models: - tasks = evaluator.scan_models() - if args.evaluate_all: - evaluator.tasks = tasks - - if evaluator.tasks and args.evaluate_all: - evaluator.run_parallel() - - # Auto-save results - results_file = ( - evaluator.data_out - / f"results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - ) - evaluator.export_json(results_file) - - # Auto-promote if requested - if args.promote_best: - try: - promotion_info = evaluator.promote_best_model( - target_dir=args.promote_target, dry_run=args.dry_run - ) - if not args.dry_run: - # Save promotion info - promo_file = ( - evaluator.data_out - / f"promotion_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - ) - with promo_file.open("w") as f: - json.dump(promotion_info, f, indent=2) - print(f"[promote] Saved promotion info to: {promo_file}") - except Exception as e: - print(f"[promote] Error: {e}", file=sys.stderr) - - if args.compare: - comparison = evaluator.compare_models(args.compare) - print(json.dumps(comparison, indent=2)) - - if args.export: - if not args.output: - args.output = ( - evaluator.data_out - / f"report_{datetime.now().strftime('%Y%m%d_%H%M%S')}" - ) - - if args.export in ["json", "both"]: - json_file = args.output.with_suffix(".json") - evaluator.export_json(json_file) - - if args.export in ["markdown", "both"]: - md_file = args.output.with_suffix(".md") - evaluator.export_markdown(md_file) - - return - - # Default: show help - if not any([args.config, args.scan_models, args.compare, args.export]): - ap.print_help() - - -if __name__ == "__main__": - main() +#!/usr/bin/env python +""" +Batch Evaluator + +Parallel evaluation of multiple models with comprehensive result aggregation. + +Features: +- Parallel model evaluation (ThreadPoolExecutor) +- Support for multiple model types (LoRA, Azure, OpenAI, Local, Quantum) +- Configurable metrics per model type +- Result aggregation and ranking +- Export to multiple formats +- Comparison reports + +Usage examples (PowerShell): + python .\\scripts\\batch_evaluator.py --config batch_eval_config.yaml + python .\\scripts\\batch_evaluator.py --scan-models --evaluate-all + python .\\scripts\\batch_evaluator.py --compare lora azure openai + python .\\scripts\\batch_evaluator.py --export markdown --output report.md +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import dataclass, field +from datetime import datetime, timezone +from pathlib import Path +from typing import Dict, List, Optional + +import yaml + +REPO_ROOT = Path(__file__).resolve().parents[1] +DATA_OUT = REPO_ROOT / "data_out" / "batch_evaluator" + +# Add shared directory to path for performance utilities +sys.path.insert(0, str(REPO_ROOT / "shared")) +from performance_utils import find_json_in_output + + +@dataclass +class EvaluationTask: + """A single model evaluation task.""" + + model_id: str + model_type: str + model_path: str + dataset: str + metrics: List[str] + max_samples: Optional[int] = None + batch_size: int = 8 + + +@dataclass +class EvaluationResult: + """Result from evaluating a model.""" + + model_id: str + model_type: str + dataset: str = "" + status: str = "succeeded" # succeeded, failed, timeout + duration: float = 0.0 + model_path: str = "" # Path to the evaluated model + metrics: Dict[str, float] = field(default_factory=dict) + error: Optional[str] = None + + +# Backward-compatible alias used by older tests and scripts. +EvalResult = EvaluationResult + + +class BatchEvaluator: + """Orchestrates parallel model evaluation.""" + + def __init__(self, max_workers: int = 3): + self.data_out = DATA_OUT + self.data_out.mkdir(parents=True, exist_ok=True) + self.max_workers = max_workers + self.tasks: List[EvaluationTask] = [] + self.results: List[EvaluationResult] = [] + # Performance optimization: cache results lookup by model_id + self._results_cache: Dict[str, EvaluationResult] = {} + + def load_config(self, config_file: Path): + """Load evaluation tasks from config file.""" + with config_file.open("r") as f: + config = yaml.safe_load(f) + + # Use list comprehension for better performance + self.tasks.extend( + [ + EvaluationTask(**task_data) + for task_data in config.get("evaluation_tasks", []) + ] + ) + + print(f"[batch_eval] Loaded {len(self.tasks)} evaluation tasks") + + def scan_models(self) -> List[EvaluationTask]: + """Scan for trained models and create evaluation tasks.""" + # Scan LoRA models - use list comprehension + lora_dir = DATA_OUT.parent / "lora_training" + tasks = [] + if lora_dir.exists(): + tasks = [ + EvaluationTask( + model_id=model_dir.name, + model_type="lora", + model_path=str(model_dir), + dataset="datasets/chat/mixed_chat", + metrics=["accuracy", "perplexity", "bleu"], + max_samples=100, + ) + for model_dir in lora_dir.iterdir() + if model_dir.is_dir() and (model_dir / "adapter_config.json").exists() + ] + + print(f"[batch_eval] Found {len(tasks)} models to evaluate") + return tasks + + def evaluate_model(self, task: EvaluationTask) -> EvaluationResult: + """Evaluate a single model.""" + print(f"[batch_eval] Evaluating: {task.model_id} ({task.model_type})") + + # Build evaluation command + cmd = [ + sys.executable, + str(REPO_ROOT / "scripts" / "evaluate_lora_model.py"), + "--model", + task.model_path, + "--dataset", + task.dataset, + "--max-samples", + str(task.max_samples or 1000), + "--output-format", + "json", + "--save-dir", + str(self.data_out / task.model_id), + ] + + # Add metrics (each as separate --metric flag) + for metric in task.metrics: + cmd.extend(["--metric", metric]) + + t0 = time.time() + try: + result = subprocess.run( + cmd, + cwd=str(REPO_ROOT), + capture_output=True, + text=True, + timeout=1800, # 30 minutes + ) + + duration = time.time() - t0 + + # Parse results + result_obj = EvaluationResult( + model_id=task.model_id, + model_type=task.model_type, + dataset=task.dataset, + model_path=task.model_path, + status="succeeded" if result.returncode == 0 else "failed", + duration=duration, + ) + + # Try to extract metrics from output using optimized utility + if result.returncode == 0: + data = find_json_in_output( + result.stdout, key="metrics", search_from_end=True, max_lines=50 + ) + if data and "metrics" in data: + result_obj.metrics = data["metrics"] + else: + result_obj.error = result.stderr + + return result_obj + + except subprocess.TimeoutExpired: + return EvaluationResult( + model_id=task.model_id, + model_type=task.model_type, + dataset=task.dataset, + model_path=task.model_path, + status="timeout", + duration=1800, + error="Evaluation timed out after 30 minutes", + ) + except Exception as e: + return EvaluationResult( + model_id=task.model_id, + model_type=task.model_type, + dataset=task.dataset, + model_path=task.model_path, + status="failed", + duration=0, + error=str(e), + ) + + def run_parallel(self): + """Run all evaluation tasks in parallel.""" + print("\n[batch_eval] Starting parallel evaluation") + print(f"[batch_eval] Tasks: {len(self.tasks)}") + print(f"[batch_eval] Workers: {self.max_workers}\n") + + with ThreadPoolExecutor(max_workers=self.max_workers) as executor: + futures = { + executor.submit(self.evaluate_model, task): task for task in self.tasks + } + + for future in as_completed(futures): + task = futures[future] + try: + result = future.result() + self.results.append(result) + # Update cache for O(1) lookups + self._results_cache[result.model_id] = result + + # Use ASCII-safe status indicators + status_icon = "[OK]" if result.status == "succeeded" else "[FAIL]" + print( + f"{status_icon} {result.model_id}: {result.status} ({result.duration:.1f}s)" + ) + + if result.metrics: + for metric, value in result.metrics.items(): + print(f" {metric}: {value:.4f}") + + except Exception as e: + print(f"[ERROR] {task.model_id}: Exception - {e}") + + print("\n[batch_eval] Evaluation complete") + # Use already classified results from aggregate to avoid redundant passes + succeeded_count = sum(1 for r in self.results if r.status == "succeeded") + failed_count = len(self.results) - succeeded_count + print(f"[batch_eval] Succeeded: {succeeded_count}") + print(f"[batch_eval] Failed: {failed_count}") + + def aggregate_results(self) -> Dict: + """Aggregate all evaluation results. + + Optimized to iterate results only once for classification and metrics. + """ + succeeded = [] + failed = [] + total_duration = 0.0 + + # Single pass through results for classification and duration sum + for r in self.results: + total_duration += r.duration + if r.status == "succeeded": + succeeded.append(r) + else: + failed.append(r) + + # Rank by primary metric (accuracy if available) + ranked = sorted( + succeeded, + key=lambda r: r.metrics.get("accuracy", r.metrics.get("perplexity", 0)), + reverse=True, + ) + + # Pre-compute ranking list + ranking = [ + {"rank": i + 1, "model_id": r.model_id, "metrics": r.metrics} + for i, r in enumerate(ranked) + ] + + return { + "evaluated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), + "total_models": len(self.results), + "succeeded": len(succeeded), + "failed": len(failed), + "total_duration": total_duration, + "best_model": ranked[0].model_id if ranked else None, + "results": [r.__dict__ for r in self.results], + "ranking": ranking, + } + + def export_markdown(self, output_file: Path): + """Export results as Markdown report.""" + aggregated = self.aggregate_results() + + with output_file.open("w") as f: + f.write("# Model Evaluation Report\n\n") + f.write(f"**Generated:** {aggregated['evaluated_at']}\n\n") + f.write(f"**Total Models:** {aggregated['total_models']}\n") + f.write(f"**Succeeded:** {aggregated['succeeded']}\n") + f.write(f"**Failed:** {aggregated['failed']}\n") + f.write(f"**Total Duration:** {aggregated['total_duration']:.1f}s\n\n") + + if aggregated["best_model"]: + f.write(f"**Best Model:** {aggregated['best_model']}\n\n") + + # Results table + f.write("## Evaluation Results\n\n") + f.write("| Rank | Model | Type | Status | Duration | Metrics |\n") + f.write("|------|-------|------|--------|----------|----------|\n") + + for item in aggregated["ranking"]: + metrics_str = ", ".join( + f"{k}={v:.3f}" for k, v in item["metrics"].items() + ) + f.write( + f"| {item['rank']} | {item['model_id']} | - | ✓ | - | {metrics_str} |\n" + ) + + # Failed models + failed = [r for r in self.results if r.status != "succeeded"] + if failed: + f.write("\n## Failed Evaluations\n\n") + for result in failed: + f.write(f"- **{result.model_id}**: {result.status}") + if result.error: + f.write(f" - {result.error}") + f.write("\n") + + print(f"[batch_eval] Exported Markdown report to: {output_file}") + + def export_json(self, output_file: Path): + """Export results as JSON.""" + aggregated = self.aggregate_results() + with output_file.open("w") as f: + json.dump(aggregated, f, indent=2) + print(f"[batch_eval] Exported JSON to: {output_file}") + + def compare_models(self, model_ids: List[str]) -> Dict: + """Compare specific models side-by-side using fast lookups with fallback.""" + comparison: List[EvaluationResult] = [] + + # Primary path: O(1) cache lookup. + for model_id in model_ids: + result = self._results_cache.get(model_id) + if result is not None: + comparison.append(result) + + # Fallback path for tests/callers that set `self.results` directly. + if len(comparison) != len(model_ids): + model_ids_set = set(model_ids) + seen = {r.model_id for r in comparison} + for result in self.results: + if result.model_id in model_ids_set and result.model_id not in seen: + comparison.append(result) + seen.add(result.model_id) + + # Preserve requested ordering. + comparison_by_id = {r.model_id: r for r in comparison} + comparison = [ + comparison_by_id[mid] for mid in model_ids if mid in comparison_by_id + ] + + return { + "models": [r.model_id for r in comparison], + "comparison": [ + { + "model_id": r.model_id, + "model_type": r.model_type, + "status": r.status, + "duration": r.duration, + "metrics": r.metrics, + } + for r in comparison + ], + } + + def promote_best_model( + self, target_dir: Path | None = None, dry_run: bool = False + ) -> Dict: + """ + Promote the best-ranked model to deployed_models/. + + Args: + target_dir: Target directory (default: deployed_models/) + dry_run: If True, only show what would be done + + Returns: + Dict with promotion details (model_id, source, destination, metrics, timestamp) + """ + if not self.results: + raise ValueError("No evaluation results available. Run evaluation first.") + + # Get best model from aggregated results + aggregated = self.aggregate_results() + best_model_id = aggregated.get("best_model") + + if not best_model_id: + raise ValueError("No best model found (all evaluations may have failed)") + + # Prefer O(1) cache lookup instead of O(n) linear search, but fall back if needed + best_result = self._results_cache.get(best_model_id) + if best_result is None: + # Fallback to linear search to tolerate transient cache inconsistencies + best_result = next( + (r for r in self.results if r.model_id == best_model_id), + None, + ) + if best_result is None: + raise ValueError( + f"Best model {best_model_id} not found in evaluation results; " + "this indicates an internal consistency error." + ) + + # Determine target directory + if target_dir is None: + target_dir = REPO_ROOT / "deployed_models" + + # Create deployment name with timestamp + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + deployment_name = f"{best_model_id}_{timestamp}" + deployment_path = target_dir / deployment_name + + promotion_info = { + "model_id": best_model_id, + "source_path": best_result.model_path, + "deployment_name": deployment_name, + "deployment_path": str(deployment_path), + "metrics": best_result.metrics, + "promoted_at": datetime.now(timezone.utc).isoformat() + "Z", + "rank": 1, + } + + if dry_run: + print(f"[promote] DRY-RUN: Would promote {best_model_id}") + print(f"[promote] Source: {best_result.model_path}") + print(f"[promote] Target: {deployment_path}") + print(f"[promote] Metrics: {json.dumps(best_result.metrics, indent=2)}") + return promotion_info + + # Create deployment directory + deployment_path.mkdir(parents=True, exist_ok=True) + + # Copy adapter files + source_path = Path(best_result.model_path) + import shutil + + for file in source_path.iterdir(): + if file.is_file(): + shutil.copy2(file, deployment_path / file.name) + print(f"[promote] Copied: {file.name}") + + # Create metadata file + metadata_file = deployment_path / "promotion_metadata.json" + with metadata_file.open("w") as f: + json.dump(promotion_info, f, indent=2) + print(f"[promote] Created metadata: {metadata_file}") + + # Create symlink to latest + latest_link = target_dir / "latest" + if latest_link.exists(): + if latest_link.is_symlink(): + latest_link.unlink() + else: + print("[promote] Warning: 'latest' exists but is not a symlink") + + # Create relative symlink on Windows (requires admin or developer mode) + try: + latest_link.symlink_to(deployment_name, target_is_directory=True) + print(f"[promote] Updated symlink: {latest_link} -> {deployment_name}") + except (OSError, NotImplementedError) as e: + print(f"[promote] Warning: Could not create symlink ({e})") + # Fallback: write a text file pointing to latest + with (target_dir / "LATEST.txt").open("w") as f: + f.write(deployment_name) + print("[promote] Created LATEST.txt instead") + + print(f"[promote] ✓ Promoted {best_model_id} to {deployment_path}") + return promotion_info + + +def main(): + ap = argparse.ArgumentParser(description="Batch Model Evaluator") + ap.add_argument("--config", type=Path, help="Load evaluation tasks from config") + ap.add_argument( + "--scan-models", action="store_true", help="Scan for trained models" + ) + ap.add_argument( + "--evaluate-all", action="store_true", help="Evaluate all scanned models" + ) + ap.add_argument("--compare", nargs="+", help="Compare specific models") + ap.add_argument( + "--export", choices=["json", "markdown", "both"], help="Export results" + ) + ap.add_argument("--output", type=Path, help="Output file for export") + ap.add_argument( + "--max-workers", type=int, default=3, help="Number of parallel workers" + ) + ap.add_argument( + "--promote-best", + action="store_true", + help="Promote best model to deployed_models/", + ) + ap.add_argument( + "--promote-target", + type=Path, + help="Target directory for promotion (default: deployed_models/)", + ) + ap.add_argument( + "--dry-run", + action="store_true", + help="Dry-run mode (show actions without executing)", + ) + args = ap.parse_args() + + evaluator = BatchEvaluator(max_workers=args.max_workers) + + if args.config: + evaluator.load_config(args.config) + + if args.scan_models: + tasks = evaluator.scan_models() + if args.evaluate_all: + evaluator.tasks = tasks + + if evaluator.tasks and args.evaluate_all: + evaluator.run_parallel() + + # Auto-save results + results_file = ( + evaluator.data_out + / f"results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + ) + evaluator.export_json(results_file) + + # Auto-promote if requested + if args.promote_best: + try: + promotion_info = evaluator.promote_best_model( + target_dir=args.promote_target, dry_run=args.dry_run + ) + if not args.dry_run: + # Save promotion info + promo_file = ( + evaluator.data_out + / f"promotion_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + ) + with promo_file.open("w") as f: + json.dump(promotion_info, f, indent=2) + print(f"[promote] Saved promotion info to: {promo_file}") + except Exception as e: + print(f"[promote] Error: {e}", file=sys.stderr) + + if args.compare: + comparison = evaluator.compare_models(args.compare) + print(json.dumps(comparison, indent=2)) + + if args.export: + if not args.output: + args.output = ( + evaluator.data_out + / f"report_{datetime.now().strftime('%Y%m%d_%H%M%S')}" + ) + + if args.export in ["json", "both"]: + json_file = args.output.with_suffix(".json") + evaluator.export_json(json_file) + + if args.export in ["markdown", "both"]: + md_file = args.output.with_suffix(".md") + evaluator.export_markdown(md_file) + + return + + # Default: show help + if not any([args.config, args.scan_models, args.compare, args.export]): + ap.print_help() + + +if __name__ == "__main__": + main() diff --git a/scripts/benchmark_performance.py b/scripts/benchmark_performance.py index 11232d35d..22accac6a 100644 --- a/scripts/benchmark_performance.py +++ b/scripts/benchmark_performance.py @@ -1,178 +1,178 @@ -#!/usr/bin/env python -""" -Performance Benchmark Script - -Demonstrates the performance improvements from the optimization work. -Compares old patterns vs new optimized patterns. -""" - -import json -# Add shared to path -import sys -import tempfile -import time -from pathlib import Path - -REPO_ROOT = Path(__file__).resolve().parents[1] -sys.path.insert(0, str(REPO_ROOT / "shared")) - -from performance_utils import find_json_in_output, stream_jsonl, tail_file - - -def benchmark_tail_file(): - """Benchmark: Old readlines() vs new deque approach""" - print("\n" + "=" * 80) - print("BENCHMARK 1: File Tailing") - print("=" * 80) - - # Create large test file - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".log") as f: - temp_path = Path(f.name) - for i in range(100000): # 100K lines - f.write(f"Log line {i} with some content to make it realistic\n") - - size_mb = temp_path.stat().st_size / (1024 * 1024) - print(f"Test file: {size_mb:.1f} MB, 100,000 lines") - print("Task: Get last 20 lines\n") - - # Old method: readlines() - t0 = time.time() - with open(temp_path, "r") as f: - lines = f.readlines() - result1 = lines[-20:] - t_old = time.time() - t0 - - # New method: deque - t0 = time.time() - result2 = tail_file(temp_path, max_lines=20) - t_new = time.time() - t0 - - # Verify results match - assert [line.strip() for line in result1] == [line.strip() for line in result2] - - print(f"Old method (readlines): {t_old*1000:.2f}ms") - print(f"New method (deque): {t_new*1000:.2f}ms") - print(f"Speedup: {t_old/t_new:.1f}x faster") - print(f"Memory savings: ~{size_mb:.1f} MB (entire file vs 20 lines)") - - temp_path.unlink() - return t_old / t_new - - -def benchmark_json_parsing(): - """Benchmark: Old splitlines() vs new rsplit() with reverse search""" - print("\n" + "=" * 80) - print("BENCHMARK 2: JSON Parsing from Command Output") - print("=" * 80) - - # Create realistic command output with JSON at the end - output_lines = [] - output_lines.append("Starting process...") - for i in range(1000): - output_lines.append(f"Processing item {i}...") - output_lines.append('{"metrics": {"accuracy": 0.95, "loss": 0.05}}') - output_lines.append("Complete.") - - output = "\n".join(output_lines) - print(f"Output size: {len(output):,} chars, {len(output_lines):,} lines") - print("Task: Extract JSON metrics from end\n") - - # Old method: splitlines() and forward search - t0 = time.time() - result1 = None - for line in output.splitlines(): - if line.strip().startswith("{"): - try: - data = json.loads(line) - if "metrics" in data: - result1 = data - break - except: - pass - t_old = time.time() - t0 - - # New method: rsplit() and reverse search - t0 = time.time() - result2 = find_json_in_output( - output, key="metrics", search_from_end=True, max_lines=50 - ) - t_new = time.time() - t0 - - # Verify results match - assert result1 == result2 - - print(f"Old method (forward): {t_old*1000:.3f}ms") - print(f"New method (reverse): {t_new*1000:.3f}ms") - print(f"Speedup: {t_old/t_new:.1f}x faster") - - return t_old / t_new - - -def benchmark_jsonl_streaming(): - """Benchmark: Load all vs streaming JSONL""" - print("\n" + "=" * 80) - print("BENCHMARK 3: JSONL Processing") - print("=" * 80) - - # Create test JSONL file - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".jsonl") as f: - temp_path = Path(f.name) - for i in range(10000): - f.write(json.dumps({"id": i, "data": "x" * 100}) + "\n") - - size_mb = temp_path.stat().st_size / (1024 * 1024) - print(f"Test file: {size_mb:.1f} MB, 10,000 records") - print("Task: Process records with filtering\n") - - # Old method: Load all into list - t0 = time.time() - with open(temp_path, "r") as f: - all_records = [json.loads(line) for line in f if line.strip()] - count1 = sum(1 for r in all_records if r["id"] % 2 == 0) - t_old = time.time() - t0 - - # New method: Stream with generator - t0 = time.time() - count2 = sum( - 1 for r in stream_jsonl(temp_path, filter_fn=lambda x: x["id"] % 2 == 0) - ) - t_new = time.time() - t0 - - # Verify results match - assert count1 == count2 == 5000 - - print(f"Old method (load all): {t_old*1000:.2f}ms") - print(f"New method (streaming): {t_new*1000:.2f}ms") - print(f"Speedup: {t_old/t_new:.1f}x faster") - print(f"Memory savings: ~{size_mb:.1f} MB (streaming vs full load)") - - temp_path.unlink() - return t_old / t_new - - -def main(): - print("\n" + "=" * 80) - print("🚀 ARIA PERFORMANCE OPTIMIZATION BENCHMARKS") - print("=" * 80) - print("\nThese benchmarks demonstrate the performance improvements from") - print("the optimization work completed in this PR.\n") - - speedups = [] - - speedups.append(benchmark_tail_file()) - speedups.append(benchmark_json_parsing()) - speedups.append(benchmark_jsonl_streaming()) - - print("\n" + "=" * 80) - print("SUMMARY") - print("=" * 80) - print(f"Average speedup: {sum(speedups)/len(speedups):.1f}x") - print(f"Total time saved: {sum(speedups)/len(speedups) - 1:.1%} faster") - print("\nThese optimizations are now available in shared/performance_utils.py") - print("and have been integrated into monitoring and evaluation scripts.") - print("\nFor more details, see docs/PERFORMANCE_OPTIMIZATION_GUIDE.md") - print() - - -if __name__ == "__main__": - main() +#!/usr/bin/env python +""" +Performance Benchmark Script + +Demonstrates the performance improvements from the optimization work. +Compares old patterns vs new optimized patterns. +""" + +import json +# Add shared to path +import sys +import tempfile +import time +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT / "shared")) + +from performance_utils import find_json_in_output, stream_jsonl, tail_file + + +def benchmark_tail_file(): + """Benchmark: Old readlines() vs new deque approach""" + print("\n" + "=" * 80) + print("BENCHMARK 1: File Tailing") + print("=" * 80) + + # Create large test file + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".log") as f: + temp_path = Path(f.name) + for i in range(100000): # 100K lines + f.write(f"Log line {i} with some content to make it realistic\n") + + size_mb = temp_path.stat().st_size / (1024 * 1024) + print(f"Test file: {size_mb:.1f} MB, 100,000 lines") + print("Task: Get last 20 lines\n") + + # Old method: readlines() + t0 = time.time() + with open(temp_path, "r") as f: + lines = f.readlines() + result1 = lines[-20:] + t_old = time.time() - t0 + + # New method: deque + t0 = time.time() + result2 = tail_file(temp_path, max_lines=20) + t_new = time.time() - t0 + + # Verify results match + assert [line.strip() for line in result1] == [line.strip() for line in result2] + + print(f"Old method (readlines): {t_old*1000:.2f}ms") + print(f"New method (deque): {t_new*1000:.2f}ms") + print(f"Speedup: {t_old/t_new:.1f}x faster") + print(f"Memory savings: ~{size_mb:.1f} MB (entire file vs 20 lines)") + + temp_path.unlink() + return t_old / t_new + + +def benchmark_json_parsing(): + """Benchmark: Old splitlines() vs new rsplit() with reverse search""" + print("\n" + "=" * 80) + print("BENCHMARK 2: JSON Parsing from Command Output") + print("=" * 80) + + # Create realistic command output with JSON at the end + output_lines = [] + output_lines.append("Starting process...") + for i in range(1000): + output_lines.append(f"Processing item {i}...") + output_lines.append('{"metrics": {"accuracy": 0.95, "loss": 0.05}}') + output_lines.append("Complete.") + + output = "\n".join(output_lines) + print(f"Output size: {len(output):,} chars, {len(output_lines):,} lines") + print("Task: Extract JSON metrics from end\n") + + # Old method: splitlines() and forward search + t0 = time.time() + result1 = None + for line in output.splitlines(): + if line.strip().startswith("{"): + try: + data = json.loads(line) + if "metrics" in data: + result1 = data + break + except: + pass + t_old = time.time() - t0 + + # New method: rsplit() and reverse search + t0 = time.time() + result2 = find_json_in_output( + output, key="metrics", search_from_end=True, max_lines=50 + ) + t_new = time.time() - t0 + + # Verify results match + assert result1 == result2 + + print(f"Old method (forward): {t_old*1000:.3f}ms") + print(f"New method (reverse): {t_new*1000:.3f}ms") + print(f"Speedup: {t_old/t_new:.1f}x faster") + + return t_old / t_new + + +def benchmark_jsonl_streaming(): + """Benchmark: Load all vs streaming JSONL""" + print("\n" + "=" * 80) + print("BENCHMARK 3: JSONL Processing") + print("=" * 80) + + # Create test JSONL file + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".jsonl") as f: + temp_path = Path(f.name) + for i in range(10000): + f.write(json.dumps({"id": i, "data": "x" * 100}) + "\n") + + size_mb = temp_path.stat().st_size / (1024 * 1024) + print(f"Test file: {size_mb:.1f} MB, 10,000 records") + print("Task: Process records with filtering\n") + + # Old method: Load all into list + t0 = time.time() + with open(temp_path, "r") as f: + all_records = [json.loads(line) for line in f if line.strip()] + count1 = sum(1 for r in all_records if r["id"] % 2 == 0) + t_old = time.time() - t0 + + # New method: Stream with generator + t0 = time.time() + count2 = sum( + 1 for r in stream_jsonl(temp_path, filter_fn=lambda x: x["id"] % 2 == 0) + ) + t_new = time.time() - t0 + + # Verify results match + assert count1 == count2 == 5000 + + print(f"Old method (load all): {t_old*1000:.2f}ms") + print(f"New method (streaming): {t_new*1000:.2f}ms") + print(f"Speedup: {t_old/t_new:.1f}x faster") + print(f"Memory savings: ~{size_mb:.1f} MB (streaming vs full load)") + + temp_path.unlink() + return t_old / t_new + + +def main(): + print("\n" + "=" * 80) + print("🚀 ARIA PERFORMANCE OPTIMIZATION BENCHMARKS") + print("=" * 80) + print("\nThese benchmarks demonstrate the performance improvements from") + print("the optimization work completed in this PR.\n") + + speedups = [] + + speedups.append(benchmark_tail_file()) + speedups.append(benchmark_json_parsing()) + speedups.append(benchmark_jsonl_streaming()) + + print("\n" + "=" * 80) + print("SUMMARY") + print("=" * 80) + print(f"Average speedup: {sum(speedups)/len(speedups):.1f}x") + print(f"Total time saved: {sum(speedups)/len(speedups) - 1:.1%} faster") + print("\nThese optimizations are now available in shared/performance_utils.py") + print("and have been integrated into monitoring and evaluation scripts.") + print("\nFor more details, see docs/PERFORMANCE_OPTIMIZATION_GUIDE.md") + print() + + +if __name__ == "__main__": + main() diff --git a/scripts/ci_orchestrator.py b/scripts/ci_orchestrator.py index ef590c044..63ddcc994 100644 --- a/scripts/ci_orchestrator.py +++ b/scripts/ci_orchestrator.py @@ -1,575 +1,575 @@ -#!/usr/bin/env python -""" -CI/CD Orchestrator - -Specialized orchestrator for continuous integration and deployment workflows. -Validates all configurations, runs quick tests, and prepares deployment artifacts. - -Features: -- Fast validation (all --dry-run checks in parallel) -- Unit test execution -- Integration test execution -- Code quality checks -- Security scanning -- Deployment artifact preparation -- GitHub Actions / Azure Pipelines integration - -Usage examples (PowerShell): - python .\\scripts\\ci_orchestrator.py --validate-all - python .\\scripts\\ci_orchestrator.py --quick-test - python .\\scripts\\ci_orchestrator.py --full-test - python .\\scripts\\ci_orchestrator.py --prepare-deployment - python .\\scripts\\ci_orchestrator.py --ci-pipeline # Run full CI pipeline -""" - -from __future__ import annotations - -import argparse -import json -import subprocess -import sys -import time -from concurrent.futures import ThreadPoolExecutor, as_completed -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Dict, List, Optional - -try: - from .config_paths import resolve_existing_config_path -except ImportError: - from config_paths import resolve_existing_config_path - -REPO_ROOT = Path(__file__).resolve().parents[1] -DATA_OUT = REPO_ROOT / "data_out" / "ci_orchestrator" - - -@dataclass -class ValidationJob: - name: str - cmd: List[str] - critical: bool = True # If True, failure blocks deployment - - -class CIOrchestrator: - def __init__(self): - self.repo_root = REPO_ROOT - self.data_out = DATA_OUT - self.data_out.mkdir(parents=True, exist_ok=True) - self.run_id = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - self.results: List[Dict[str, Any]] = [] - - def _resolved_config_paths(self) -> Dict[str, Optional[str]]: - """Resolve key orchestrator config paths for status metadata.""" - config_keys = [ - "autotrain", - "quantum_autorun", - "evaluation_autorun", - "master_orchestrator", - ] - resolved: Dict[str, Optional[str]] = {} - for key in config_keys: - selected = resolve_existing_config_path(self.repo_root, key) - resolved[key] = ( - str(selected.relative_to(self.repo_root)) if selected else None - ) - return resolved - - def validate_all_orchestrators(self) -> bool: - """Run --dry-run on all orchestrators in parallel.""" - print("\n[ci] ========================================") - print("[ci] Validating All Orchestrators") - print("[ci] ========================================\n") - - jobs = [ - ValidationJob( - "autotrain", [sys.executable, "scripts/autotrain.py", "--dry-run"] - ), - ValidationJob( - "quantum_autorun", - [sys.executable, "scripts/quantum_autorun.py", "--dry-run"], - ), - ValidationJob( - "evaluation_autorun", - [sys.executable, "scripts/evaluation_autorun.py", "--dry-run"], - ), - ] - - return self._run_parallel_jobs(jobs) - - def run_unit_tests(self) -> bool: - """Run all unit tests using test_runner.""" - print("\n[ci] Running Unit Tests") - cmd = [sys.executable, "scripts/test_runner.py", "--unit"] - return self._run_command("unit_tests", cmd) - - def run_integration_tests(self) -> bool: - """Run integration tests using test_runner.""" - print("\n[ci] Running Integration Tests") - cmd = [sys.executable, "scripts/test_runner.py", "--integration"] - return self._run_command("integration_tests", cmd, critical=False) - - def run_integration_smoke(self) -> bool: - """Run fast cross-component integration smoke checks.""" - print("\n[ci] Running Integration Smoke Checks") - cmd = [sys.executable, "scripts/integration_smoke.py"] - return self._run_command("integration_smoke", cmd) - - def run_integration_contract_tests(self) -> bool: - """Run focused integration contract unit tests.""" - print("\n[ci] Running Integration Contract Unit Tests") - cmd = [ - sys.executable, - "-m", - "pytest", - "-q", - "tests/test_config_paths.py", - "tests/test_master_orchestrator_schedule.py", - "tests/test_status_schema.py", - "tests/test_integration_smoke_schema.py", - "tests/test_status_schema_fixtures.py", - "tests/test_integration_contract_gate_script.py", - "tests/test_agent_mode_delegation_contracts.py", - ] - return self._run_command("integration_contract_tests", cmd) - - def validate_datasets(self) -> bool: - """Validate dataset integrity.""" - print("\n[ci] Validating Datasets") - cmd = [sys.executable, "scripts/validate_datasets.py", "--category", "chat"] - return self._run_command("validate_datasets", cmd, critical=False) - - def check_code_quality(self) -> bool: - """Run code quality checks.""" - print("\n[ci] Checking Code Quality") - # This would run tools like pylint, flake8, black, mypy - # For now, just a placeholder - result = { - "name": "code_quality", - "status": "skipped", - "message": "Code quality tools not configured", - } - self.results.append(result) - return True - - def security_scan(self) -> bool: - """Run security vulnerability scanning.""" - print("\n[ci] Security Scanning") - # This would run tools like bandit, safety - result = { - "name": "security_scan", - "status": "skipped", - "message": "Security scanning not configured", - } - self.results.append(result) - return True - - def prepare_deployment(self) -> bool: - """Prepare deployment artifacts.""" - print("\n[ci] Preparing Deployment Artifacts") - - artifacts = { - "timestamp": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), - "configurations": [], - "models": [], - "scripts": [], - } - - # Check for trained models - lora_dir = self.repo_root / "data_out" / "lora_training" - if lora_dir.exists(): - for adapter_dir in lora_dir.iterdir(): - if ( - adapter_dir.is_dir() - and (adapter_dir / "adapter_config.json").exists() - ): - artifacts["models"].append( - { - "type": "lora", - "path": str(adapter_dir.relative_to(self.repo_root)), - "size_mb": sum( - f.stat().st_size - for f in adapter_dir.rglob("*") - if f.is_file() - ) - / (1024 * 1024), - } - ) - - # List key configuration files - config_candidates = { - "autotrain", - "quantum_autorun", - "evaluation_autorun", - "master_orchestrator", - } - for config_key in sorted(config_candidates): - selected = resolve_existing_config_path(self.repo_root, config_key) - if selected is not None: - artifacts["configurations"].append( - str(selected.relative_to(self.repo_root)) - ) - - local_settings = self.repo_root / "local.settings.json" - if local_settings.exists(): - artifacts["configurations"].append( - str(local_settings.relative_to(self.repo_root)) - ) - - # Save artifacts manifest - manifest_file = self.data_out / "deployment_artifacts.json" - with manifest_file.open("w") as f: - json.dump(artifacts, f, indent=2) - - print(f"[ci] Deployment artifacts manifest: {manifest_file}") - print(f"[ci] Found {len(artifacts['models'])} trained models") - - result = { - "name": "prepare_deployment", - "status": "succeeded", - "artifacts": artifacts, - } - self.results.append(result) - return True - - def azureml_validate(self) -> bool: - """Validate latest Azure ML job spec if available using 'az ml job validate'. - - Returns True if validation succeeded or was skipped gracefully (no spec / CLI missing). - """ - print("\n[ci] Azure ML Job Spec Validation") - aml_dir = self.repo_root / ".azureml" - if not aml_dir.exists(): - self.results.append( - { - "name": "azureml_validate", - "status": "skipped", - "message": ".azureml directory missing", - } - ) - return True - job_specs = sorted( - aml_dir.glob("job_*.yaml"), key=lambda p: p.stat().st_mtime, reverse=True - ) - if not job_specs: - self.results.append( - { - "name": "azureml_validate", - "status": "skipped", - "message": "No job_*.yaml files found", - } - ) - return True - latest = job_specs[0] - # Check az CLI presence - try: - az_check = subprocess.run( - ["az", "version"], capture_output=True, text=True, timeout=30 - ) - except Exception as e: - self.results.append( - { - "name": "azureml_validate", - "status": "skipped", - "message": f"Azure CLI not available: {e}", - } - ) - return True - if az_check.returncode != 0: - self.results.append( - { - "name": "azureml_validate", - "status": "skipped", - "message": "Azure CLI not installed or not in PATH", - } - ) - return True - # Perform validation - try: - val_proc = subprocess.run( - ["az", "ml", "job", "validate", "--file", str(latest)], - capture_output=True, - text=True, - timeout=120, - ) - status = "succeeded" if val_proc.returncode == 0 else "failed" - self.results.append( - { - "name": "azureml_validate", - "status": status, - "job_file": str(latest.relative_to(self.repo_root)), - "return_code": val_proc.returncode, - "stdout_tail": val_proc.stdout[-500:] if val_proc.stdout else "", - "stderr_tail": val_proc.stderr[-500:] if val_proc.stderr else "", - } - ) - if status == "failed": - print(f"[ci] [FAIL] Azure ML validation failed for {latest}") - if val_proc.stderr: - print(val_proc.stderr) - else: - print(f"[ci] [OK] Azure ML validation passed: {latest}") - return status == "succeeded" - except subprocess.TimeoutExpired: - self.results.append( - { - "name": "azureml_validate", - "status": "timeout", - "job_file": str(latest.relative_to(self.repo_root)), - } - ) - return False - except Exception as e: - self.results.append( - {"name": "azureml_validate", "status": "error", "message": str(e)} - ) - return False - - def run_ci_pipeline(self) -> bool: - """Run the full CI pipeline.""" - print("\n[ci] ========================================") - print("[ci] Starting Full CI Pipeline") - print("[ci] ========================================\n") - - pipeline_steps = [ - ("Validate Orchestrators", self.validate_all_orchestrators), - ("Integration Smoke", self.run_integration_smoke), - ("Integration Contract Tests", self.run_integration_contract_tests), - ("Unit Tests", self.run_unit_tests), - ("Validate Datasets", self.validate_datasets), - ("Code Quality", self.check_code_quality), - ("Security Scan", self.security_scan), - ("Integration Tests", self.run_integration_tests), - ("Prepare Deployment", self.prepare_deployment), - ("Azure ML Validate", self.azureml_validate), - ] - - all_passed = True - for step_name, step_func in pipeline_steps: - print(f"\n[ci] Step: {step_name}") - if not step_func(): - all_passed = False - print(f"[ci] [FAIL] Step failed: {step_name}") - # Continue with remaining steps even on failure - else: - print(f"[ci] [OK] Step passed: {step_name}") - - self._save_results() - return all_passed - - def _run_parallel_jobs(self, jobs: List[ValidationJob]) -> bool: - """Run multiple jobs in parallel.""" - all_passed = True - - with ThreadPoolExecutor(max_workers=len(jobs)) as executor: - futures = { - executor.submit(self._run_validation_job, job): job for job in jobs - } - - for future in as_completed(futures): - job = futures[future] - try: - result = future.result() - self.results.append(result) - if result["status"] != "succeeded" and job.critical: - all_passed = False - except Exception as e: - print(f"[ci] Exception in job {job.name}: {e}") - all_passed = False - - return all_passed - - def _run_validation_job(self, job: ValidationJob) -> Dict[str, Any]: - """Run a single validation job.""" - print(f"[ci] Validating: {job.name}") - t0 = time.perf_counter() - - try: - result = subprocess.run( - job.cmd, - cwd=str(self.repo_root), - capture_output=True, - text=True, - timeout=300, # 5 minute timeout - ) - - duration = time.perf_counter() - t0 - status = "succeeded" if result.returncode == 0 else "failed" - - return { - "name": job.name, - "cmd": job.cmd, - "status": status, - "return_code": result.returncode, - "duration_sec": round(duration, 2), - "critical": job.critical, - } - except subprocess.TimeoutExpired: - return { - "name": job.name, - "status": "timeout", - "critical": job.critical, - } - except Exception as e: - return { - "name": job.name, - "status": "error", - "message": str(e), - "critical": job.critical, - } - - def _run_command(self, name: str, cmd: List[str], critical: bool = True) -> bool: - """Run a single command and track result.""" - t0 = time.perf_counter() - - try: - result = subprocess.run( - cmd, - cwd=str(self.repo_root), - capture_output=True, - text=True, - timeout=600, # 10 minute timeout - ) - - duration = time.perf_counter() - t0 - status = "succeeded" if result.returncode == 0 else "failed" - - self.results.append( - { - "name": name, - "cmd": cmd, - "status": status, - "return_code": result.returncode, - "duration_sec": round(duration, 2), - "critical": critical, - } - ) - - if status != "succeeded": - print(f"[ci] Failed: {name}") - if result.stdout: - print(f"[ci] stdout: {result.stdout[-500:]}") # Last 500 chars - if result.stderr: - print(f"[ci] stderr: {result.stderr[-500:]}") - - return status == "succeeded" - - except subprocess.TimeoutExpired: - self.results.append( - { - "name": name, - "status": "timeout", - "critical": critical, - } - ) - return False - except Exception as e: - self.results.append( - { - "name": name, - "status": "error", - "message": str(e), - "critical": critical, - } - ) - return False - - def _save_results(self): - """Save CI results to disk.""" - summary = { - "generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), - "run_id": self.run_id, - "config_path": None, - "config_paths": self._resolved_config_paths(), - "total_steps": len(self.results), - "succeeded": sum(1 for r in self.results if r["status"] == "succeeded"), - "failed": sum(1 for r in self.results if r["status"] == "failed"), - "skipped": sum(1 for r in self.results if r["status"] == "skipped"), - "results": self.results, - } - - results_file = self.data_out / "ci_results.json" - with results_file.open("w") as f: - json.dump(summary, f, indent=2, default=str) - - print(f"\n[ci] Results saved: {results_file}") - print(f"[ci] Summary: {summary['succeeded']}/{summary['total_steps']} passed") - - -def main(): - ap = argparse.ArgumentParser(description="CI/CD Orchestrator") - ap.add_argument( - "--validate-all", action="store_true", help="Validate all orchestrators" - ) - ap.add_argument( - "--quick-test", action="store_true", help="Run quick tests (unit only)" - ) - ap.add_argument("--full-test", action="store_true", help="Run all tests") - ap.add_argument( - "--prepare-deployment", action="store_true", help="Prepare deployment artifacts" - ) - ap.add_argument("--ci-pipeline", action="store_true", help="Run full CI pipeline") - ap.add_argument( - "--validate-azureml", - action="store_true", - help="Validate latest Azure ML job spec and schema", - ) - ap.add_argument( - "--integration-smoke", - action="store_true", - help="Run fast integration smoke checks", - ) - ap.add_argument( - "--integration-contract-tests", - action="store_true", - help="Run focused integration contract unit tests", - ) - args = ap.parse_args() - - ci = CIOrchestrator() - - if args.validate_all: - success = ci.validate_all_orchestrators() - ci._save_results() - sys.exit(0 if success else 1) - - if args.quick_test: - success = ci.run_unit_tests() - ci._save_results() - sys.exit(0 if success else 1) - - if args.full_test: - success = ci.run_unit_tests() and ci.run_integration_tests() - ci._save_results() - sys.exit(0 if success else 1) - - if args.prepare_deployment: - success = ci.prepare_deployment() - ci._save_results() - sys.exit(0 if success else 1) - - if args.integration_smoke: - success = ci.run_integration_smoke() - ci._save_results() - sys.exit(0 if success else 1) - - if args.integration_contract_tests: - success = ci.run_integration_contract_tests() - ci._save_results() - sys.exit(0 if success else 1) - - if args.ci_pipeline: - success = ci.run_ci_pipeline() - sys.exit(0 if success else 1) - - if args.validate_azureml: - success = ci.azureml_validate() - ci._save_results() - sys.exit(0 if success else 1) - - # Default: show help - ap.print_help() - - -if __name__ == "__main__": - main() +#!/usr/bin/env python +""" +CI/CD Orchestrator + +Specialized orchestrator for continuous integration and deployment workflows. +Validates all configurations, runs quick tests, and prepares deployment artifacts. + +Features: +- Fast validation (all --dry-run checks in parallel) +- Unit test execution +- Integration test execution +- Code quality checks +- Security scanning +- Deployment artifact preparation +- GitHub Actions / Azure Pipelines integration + +Usage examples (PowerShell): + python .\\scripts\\ci_orchestrator.py --validate-all + python .\\scripts\\ci_orchestrator.py --quick-test + python .\\scripts\\ci_orchestrator.py --full-test + python .\\scripts\\ci_orchestrator.py --prepare-deployment + python .\\scripts\\ci_orchestrator.py --ci-pipeline # Run full CI pipeline +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Dict, List, Optional + +try: + from .config_paths import resolve_existing_config_path +except ImportError: + from config_paths import resolve_existing_config_path + +REPO_ROOT = Path(__file__).resolve().parents[1] +DATA_OUT = REPO_ROOT / "data_out" / "ci_orchestrator" + + +@dataclass +class ValidationJob: + name: str + cmd: List[str] + critical: bool = True # If True, failure blocks deployment + + +class CIOrchestrator: + def __init__(self): + self.repo_root = REPO_ROOT + self.data_out = DATA_OUT + self.data_out.mkdir(parents=True, exist_ok=True) + self.run_id = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") + self.results: List[Dict[str, Any]] = [] + + def _resolved_config_paths(self) -> Dict[str, Optional[str]]: + """Resolve key orchestrator config paths for status metadata.""" + config_keys = [ + "autotrain", + "quantum_autorun", + "evaluation_autorun", + "master_orchestrator", + ] + resolved: Dict[str, Optional[str]] = {} + for key in config_keys: + selected = resolve_existing_config_path(self.repo_root, key) + resolved[key] = ( + str(selected.relative_to(self.repo_root)) if selected else None + ) + return resolved + + def validate_all_orchestrators(self) -> bool: + """Run --dry-run on all orchestrators in parallel.""" + print("\n[ci] ========================================") + print("[ci] Validating All Orchestrators") + print("[ci] ========================================\n") + + jobs = [ + ValidationJob( + "autotrain", [sys.executable, "scripts/autotrain.py", "--dry-run"] + ), + ValidationJob( + "quantum_autorun", + [sys.executable, "scripts/quantum_autorun.py", "--dry-run"], + ), + ValidationJob( + "evaluation_autorun", + [sys.executable, "scripts/evaluation_autorun.py", "--dry-run"], + ), + ] + + return self._run_parallel_jobs(jobs) + + def run_unit_tests(self) -> bool: + """Run all unit tests using test_runner.""" + print("\n[ci] Running Unit Tests") + cmd = [sys.executable, "scripts/test_runner.py", "--unit"] + return self._run_command("unit_tests", cmd) + + def run_integration_tests(self) -> bool: + """Run integration tests using test_runner.""" + print("\n[ci] Running Integration Tests") + cmd = [sys.executable, "scripts/test_runner.py", "--integration"] + return self._run_command("integration_tests", cmd, critical=False) + + def run_integration_smoke(self) -> bool: + """Run fast cross-component integration smoke checks.""" + print("\n[ci] Running Integration Smoke Checks") + cmd = [sys.executable, "scripts/integration_smoke.py"] + return self._run_command("integration_smoke", cmd) + + def run_integration_contract_tests(self) -> bool: + """Run focused integration contract unit tests.""" + print("\n[ci] Running Integration Contract Unit Tests") + cmd = [ + sys.executable, + "-m", + "pytest", + "-q", + "tests/test_config_paths.py", + "tests/test_master_orchestrator_schedule.py", + "tests/test_status_schema.py", + "tests/test_integration_smoke_schema.py", + "tests/test_status_schema_fixtures.py", + "tests/test_integration_contract_gate_script.py", + "tests/test_agent_mode_delegation_contracts.py", + ] + return self._run_command("integration_contract_tests", cmd) + + def validate_datasets(self) -> bool: + """Validate dataset integrity.""" + print("\n[ci] Validating Datasets") + cmd = [sys.executable, "scripts/validate_datasets.py", "--category", "chat"] + return self._run_command("validate_datasets", cmd, critical=False) + + def check_code_quality(self) -> bool: + """Run code quality checks.""" + print("\n[ci] Checking Code Quality") + # This would run tools like pylint, flake8, black, mypy + # For now, just a placeholder + result = { + "name": "code_quality", + "status": "skipped", + "message": "Code quality tools not configured", + } + self.results.append(result) + return True + + def security_scan(self) -> bool: + """Run security vulnerability scanning.""" + print("\n[ci] Security Scanning") + # This would run tools like bandit, safety + result = { + "name": "security_scan", + "status": "skipped", + "message": "Security scanning not configured", + } + self.results.append(result) + return True + + def prepare_deployment(self) -> bool: + """Prepare deployment artifacts.""" + print("\n[ci] Preparing Deployment Artifacts") + + artifacts = { + "timestamp": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), + "configurations": [], + "models": [], + "scripts": [], + } + + # Check for trained models + lora_dir = self.repo_root / "data_out" / "lora_training" + if lora_dir.exists(): + for adapter_dir in lora_dir.iterdir(): + if ( + adapter_dir.is_dir() + and (adapter_dir / "adapter_config.json").exists() + ): + artifacts["models"].append( + { + "type": "lora", + "path": str(adapter_dir.relative_to(self.repo_root)), + "size_mb": sum( + f.stat().st_size + for f in adapter_dir.rglob("*") + if f.is_file() + ) + / (1024 * 1024), + } + ) + + # List key configuration files + config_candidates = { + "autotrain", + "quantum_autorun", + "evaluation_autorun", + "master_orchestrator", + } + for config_key in sorted(config_candidates): + selected = resolve_existing_config_path(self.repo_root, config_key) + if selected is not None: + artifacts["configurations"].append( + str(selected.relative_to(self.repo_root)) + ) + + local_settings = self.repo_root / "local.settings.json" + if local_settings.exists(): + artifacts["configurations"].append( + str(local_settings.relative_to(self.repo_root)) + ) + + # Save artifacts manifest + manifest_file = self.data_out / "deployment_artifacts.json" + with manifest_file.open("w") as f: + json.dump(artifacts, f, indent=2) + + print(f"[ci] Deployment artifacts manifest: {manifest_file}") + print(f"[ci] Found {len(artifacts['models'])} trained models") + + result = { + "name": "prepare_deployment", + "status": "succeeded", + "artifacts": artifacts, + } + self.results.append(result) + return True + + def azureml_validate(self) -> bool: + """Validate latest Azure ML job spec if available using 'az ml job validate'. + + Returns True if validation succeeded or was skipped gracefully (no spec / CLI missing). + """ + print("\n[ci] Azure ML Job Spec Validation") + aml_dir = self.repo_root / ".azureml" + if not aml_dir.exists(): + self.results.append( + { + "name": "azureml_validate", + "status": "skipped", + "message": ".azureml directory missing", + } + ) + return True + job_specs = sorted( + aml_dir.glob("job_*.yaml"), key=lambda p: p.stat().st_mtime, reverse=True + ) + if not job_specs: + self.results.append( + { + "name": "azureml_validate", + "status": "skipped", + "message": "No job_*.yaml files found", + } + ) + return True + latest = job_specs[0] + # Check az CLI presence + try: + az_check = subprocess.run( + ["az", "version"], capture_output=True, text=True, timeout=30 + ) + except Exception as e: + self.results.append( + { + "name": "azureml_validate", + "status": "skipped", + "message": f"Azure CLI not available: {e}", + } + ) + return True + if az_check.returncode != 0: + self.results.append( + { + "name": "azureml_validate", + "status": "skipped", + "message": "Azure CLI not installed or not in PATH", + } + ) + return True + # Perform validation + try: + val_proc = subprocess.run( + ["az", "ml", "job", "validate", "--file", str(latest)], + capture_output=True, + text=True, + timeout=120, + ) + status = "succeeded" if val_proc.returncode == 0 else "failed" + self.results.append( + { + "name": "azureml_validate", + "status": status, + "job_file": str(latest.relative_to(self.repo_root)), + "return_code": val_proc.returncode, + "stdout_tail": val_proc.stdout[-500:] if val_proc.stdout else "", + "stderr_tail": val_proc.stderr[-500:] if val_proc.stderr else "", + } + ) + if status == "failed": + print(f"[ci] [FAIL] Azure ML validation failed for {latest}") + if val_proc.stderr: + print(val_proc.stderr) + else: + print(f"[ci] [OK] Azure ML validation passed: {latest}") + return status == "succeeded" + except subprocess.TimeoutExpired: + self.results.append( + { + "name": "azureml_validate", + "status": "timeout", + "job_file": str(latest.relative_to(self.repo_root)), + } + ) + return False + except Exception as e: + self.results.append( + {"name": "azureml_validate", "status": "error", "message": str(e)} + ) + return False + + def run_ci_pipeline(self) -> bool: + """Run the full CI pipeline.""" + print("\n[ci] ========================================") + print("[ci] Starting Full CI Pipeline") + print("[ci] ========================================\n") + + pipeline_steps = [ + ("Validate Orchestrators", self.validate_all_orchestrators), + ("Integration Smoke", self.run_integration_smoke), + ("Integration Contract Tests", self.run_integration_contract_tests), + ("Unit Tests", self.run_unit_tests), + ("Validate Datasets", self.validate_datasets), + ("Code Quality", self.check_code_quality), + ("Security Scan", self.security_scan), + ("Integration Tests", self.run_integration_tests), + ("Prepare Deployment", self.prepare_deployment), + ("Azure ML Validate", self.azureml_validate), + ] + + all_passed = True + for step_name, step_func in pipeline_steps: + print(f"\n[ci] Step: {step_name}") + if not step_func(): + all_passed = False + print(f"[ci] [FAIL] Step failed: {step_name}") + # Continue with remaining steps even on failure + else: + print(f"[ci] [OK] Step passed: {step_name}") + + self._save_results() + return all_passed + + def _run_parallel_jobs(self, jobs: List[ValidationJob]) -> bool: + """Run multiple jobs in parallel.""" + all_passed = True + + with ThreadPoolExecutor(max_workers=len(jobs)) as executor: + futures = { + executor.submit(self._run_validation_job, job): job for job in jobs + } + + for future in as_completed(futures): + job = futures[future] + try: + result = future.result() + self.results.append(result) + if result["status"] != "succeeded" and job.critical: + all_passed = False + except Exception as e: + print(f"[ci] Exception in job {job.name}: {e}") + all_passed = False + + return all_passed + + def _run_validation_job(self, job: ValidationJob) -> Dict[str, Any]: + """Run a single validation job.""" + print(f"[ci] Validating: {job.name}") + t0 = time.perf_counter() + + try: + result = subprocess.run( + job.cmd, + cwd=str(self.repo_root), + capture_output=True, + text=True, + timeout=300, # 5 minute timeout + ) + + duration = time.perf_counter() - t0 + status = "succeeded" if result.returncode == 0 else "failed" + + return { + "name": job.name, + "cmd": job.cmd, + "status": status, + "return_code": result.returncode, + "duration_sec": round(duration, 2), + "critical": job.critical, + } + except subprocess.TimeoutExpired: + return { + "name": job.name, + "status": "timeout", + "critical": job.critical, + } + except Exception as e: + return { + "name": job.name, + "status": "error", + "message": str(e), + "critical": job.critical, + } + + def _run_command(self, name: str, cmd: List[str], critical: bool = True) -> bool: + """Run a single command and track result.""" + t0 = time.perf_counter() + + try: + result = subprocess.run( + cmd, + cwd=str(self.repo_root), + capture_output=True, + text=True, + timeout=600, # 10 minute timeout + ) + + duration = time.perf_counter() - t0 + status = "succeeded" if result.returncode == 0 else "failed" + + self.results.append( + { + "name": name, + "cmd": cmd, + "status": status, + "return_code": result.returncode, + "duration_sec": round(duration, 2), + "critical": critical, + } + ) + + if status != "succeeded": + print(f"[ci] Failed: {name}") + if result.stdout: + print(f"[ci] stdout: {result.stdout[-500:]}") # Last 500 chars + if result.stderr: + print(f"[ci] stderr: {result.stderr[-500:]}") + + return status == "succeeded" + + except subprocess.TimeoutExpired: + self.results.append( + { + "name": name, + "status": "timeout", + "critical": critical, + } + ) + return False + except Exception as e: + self.results.append( + { + "name": name, + "status": "error", + "message": str(e), + "critical": critical, + } + ) + return False + + def _save_results(self): + """Save CI results to disk.""" + summary = { + "generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), + "run_id": self.run_id, + "config_path": None, + "config_paths": self._resolved_config_paths(), + "total_steps": len(self.results), + "succeeded": sum(1 for r in self.results if r["status"] == "succeeded"), + "failed": sum(1 for r in self.results if r["status"] == "failed"), + "skipped": sum(1 for r in self.results if r["status"] == "skipped"), + "results": self.results, + } + + results_file = self.data_out / "ci_results.json" + with results_file.open("w") as f: + json.dump(summary, f, indent=2, default=str) + + print(f"\n[ci] Results saved: {results_file}") + print(f"[ci] Summary: {summary['succeeded']}/{summary['total_steps']} passed") + + +def main(): + ap = argparse.ArgumentParser(description="CI/CD Orchestrator") + ap.add_argument( + "--validate-all", action="store_true", help="Validate all orchestrators" + ) + ap.add_argument( + "--quick-test", action="store_true", help="Run quick tests (unit only)" + ) + ap.add_argument("--full-test", action="store_true", help="Run all tests") + ap.add_argument( + "--prepare-deployment", action="store_true", help="Prepare deployment artifacts" + ) + ap.add_argument("--ci-pipeline", action="store_true", help="Run full CI pipeline") + ap.add_argument( + "--validate-azureml", + action="store_true", + help="Validate latest Azure ML job spec and schema", + ) + ap.add_argument( + "--integration-smoke", + action="store_true", + help="Run fast integration smoke checks", + ) + ap.add_argument( + "--integration-contract-tests", + action="store_true", + help="Run focused integration contract unit tests", + ) + args = ap.parse_args() + + ci = CIOrchestrator() + + if args.validate_all: + success = ci.validate_all_orchestrators() + ci._save_results() + sys.exit(0 if success else 1) + + if args.quick_test: + success = ci.run_unit_tests() + ci._save_results() + sys.exit(0 if success else 1) + + if args.full_test: + success = ci.run_unit_tests() and ci.run_integration_tests() + ci._save_results() + sys.exit(0 if success else 1) + + if args.prepare_deployment: + success = ci.prepare_deployment() + ci._save_results() + sys.exit(0 if success else 1) + + if args.integration_smoke: + success = ci.run_integration_smoke() + ci._save_results() + sys.exit(0 if success else 1) + + if args.integration_contract_tests: + success = ci.run_integration_contract_tests() + ci._save_results() + sys.exit(0 if success else 1) + + if args.ci_pipeline: + success = ci.run_ci_pipeline() + sys.exit(0 if success else 1) + + if args.validate_azureml: + success = ci.azureml_validate() + ci._save_results() + sys.exit(0 if success else 1) + + # Default: show help + ap.print_help() + + +if __name__ == "__main__": + main() diff --git a/scripts/cleanup_artifacts.py b/scripts/cleanup_artifacts.py index 503381dfa..d9526688d 100644 --- a/scripts/cleanup_artifacts.py +++ b/scripts/cleanup_artifacts.py @@ -1,127 +1,127 @@ -#!/usr/bin/env python -""" -Artifact cleanup for data_out/. - -Applies retention policies (max age, max count) to old training artifacts, -status JSONs, and log files. Safe by default — use --apply to actually delete. - -Usage: - python scripts/cleanup_artifacts.py # Dry-run (preview) - python scripts/cleanup_artifacts.py --apply # Actually delete - python scripts/cleanup_artifacts.py --max-age 14 # Keep last 14 days - python scripts/cleanup_artifacts.py --max-count 20 # Keep last 20 per dir -""" -import argparse -import json -import time -from datetime import datetime, timezone -from pathlib import Path - -REPO_ROOT = Path(__file__).resolve().parents[1] -DATA_OUT = REPO_ROOT / "data_out" - -# Directories to clean (relative to data_out/) -CLEANABLE = [ - "autotrain", - "quantum_autorun", - "evaluation", - "test_runner", - "autonomous_training", - "aria_automation", -] - -# File patterns eligible for cleanup -PATTERNS = ["*.json", "*.log", "*.md", "*.csv"] - -# Files to always keep -KEEP_FILES = {"status.json", "latest_results.json"} - - -def _file_age_days(path: Path) -> float: - return (time.time() - path.stat().st_mtime) / 86400 - - -def find_candidates( - max_age_days: int = 30, max_count: int = 50 -) -> list[tuple[Path, str]]: - """Return (path, reason) tuples for files eligible for deletion.""" - candidates: list[tuple[Path, str]] = [] - - for dirname in CLEANABLE: - dirpath = DATA_OUT / dirname - if not dirpath.is_dir(): - continue - - # Gather all matching files, newest first - files: list[Path] = [] - for pattern in PATTERNS: - files.extend(dirpath.glob(pattern)) - files.sort(key=lambda p: p.stat().st_mtime, reverse=True) - - for idx, f in enumerate(files): - if f.name in KEEP_FILES: - continue - - age = _file_age_days(f) - if age > max_age_days: - candidates.append((f, f"age={int(age)}d > {max_age_days}d")) - elif idx >= max_count: - candidates.append((f, f"count={idx + 1} > {max_count}")) - - return candidates - - -def main() -> None: - ap = argparse.ArgumentParser(description="Clean old artifacts from data_out/") - ap.add_argument( - "--apply", action="store_true", help="Actually delete (default: dry-run)" - ) - ap.add_argument( - "--max-age", type=int, default=30, help="Max age in days (default: 30)" - ) - ap.add_argument( - "--max-count", - type=int, - default=50, - help="Max files per directory (default: 50)", - ) - args = ap.parse_args() - - candidates = find_candidates(max_age_days=args.max_age, max_count=args.max_count) - - if not candidates: - print("✅ No artifacts to clean up.") - return - - total_bytes = 0 - print(f"{'🗑️ CLEANUP' if args.apply else '👀 DRY RUN'} — {len(candidates)} files\n") - - for path, reason in candidates: - size = path.stat().st_size - total_bytes += size - rel = path.relative_to(REPO_ROOT) - print(f" {'DEL' if args.apply else ' '} {rel} ({size:,} bytes, {reason})") - if args.apply: - path.unlink() - - mb = total_bytes / (1024 * 1024) - print( - f"\n{'Deleted' if args.apply else 'Would delete'}: {len(candidates)} files, {mb:.2f} MB" - ) - - # Write summary - summary = { - "timestamp": datetime.now(timezone.utc).isoformat(), - "applied": args.apply, - "files_count": len(candidates), - "total_bytes": total_bytes, - "max_age_days": args.max_age, - "max_count": args.max_count, - } - summary_path = DATA_OUT / "cleanup_summary.json" - summary_path.parent.mkdir(parents=True, exist_ok=True) - summary_path.write_text(json.dumps(summary, indent=2), encoding="utf-8") - - -if __name__ == "__main__": - main() +#!/usr/bin/env python +""" +Artifact cleanup for data_out/. + +Applies retention policies (max age, max count) to old training artifacts, +status JSONs, and log files. Safe by default — use --apply to actually delete. + +Usage: + python scripts/cleanup_artifacts.py # Dry-run (preview) + python scripts/cleanup_artifacts.py --apply # Actually delete + python scripts/cleanup_artifacts.py --max-age 14 # Keep last 14 days + python scripts/cleanup_artifacts.py --max-count 20 # Keep last 20 per dir +""" +import argparse +import json +import time +from datetime import datetime, timezone +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +DATA_OUT = REPO_ROOT / "data_out" + +# Directories to clean (relative to data_out/) +CLEANABLE = [ + "autotrain", + "quantum_autorun", + "evaluation", + "test_runner", + "autonomous_training", + "aria_automation", +] + +# File patterns eligible for cleanup +PATTERNS = ["*.json", "*.log", "*.md", "*.csv"] + +# Files to always keep +KEEP_FILES = {"status.json", "latest_results.json"} + + +def _file_age_days(path: Path) -> float: + return (time.time() - path.stat().st_mtime) / 86400 + + +def find_candidates( + max_age_days: int = 30, max_count: int = 50 +) -> list[tuple[Path, str]]: + """Return (path, reason) tuples for files eligible for deletion.""" + candidates: list[tuple[Path, str]] = [] + + for dirname in CLEANABLE: + dirpath = DATA_OUT / dirname + if not dirpath.is_dir(): + continue + + # Gather all matching files, newest first + files: list[Path] = [] + for pattern in PATTERNS: + files.extend(dirpath.glob(pattern)) + files.sort(key=lambda p: p.stat().st_mtime, reverse=True) + + for idx, f in enumerate(files): + if f.name in KEEP_FILES: + continue + + age = _file_age_days(f) + if age > max_age_days: + candidates.append((f, f"age={int(age)}d > {max_age_days}d")) + elif idx >= max_count: + candidates.append((f, f"count={idx + 1} > {max_count}")) + + return candidates + + +def main() -> None: + ap = argparse.ArgumentParser(description="Clean old artifacts from data_out/") + ap.add_argument( + "--apply", action="store_true", help="Actually delete (default: dry-run)" + ) + ap.add_argument( + "--max-age", type=int, default=30, help="Max age in days (default: 30)" + ) + ap.add_argument( + "--max-count", + type=int, + default=50, + help="Max files per directory (default: 50)", + ) + args = ap.parse_args() + + candidates = find_candidates(max_age_days=args.max_age, max_count=args.max_count) + + if not candidates: + print("✅ No artifacts to clean up.") + return + + total_bytes = 0 + print(f"{'🗑️ CLEANUP' if args.apply else '👀 DRY RUN'} — {len(candidates)} files\n") + + for path, reason in candidates: + size = path.stat().st_size + total_bytes += size + rel = path.relative_to(REPO_ROOT) + print(f" {'DEL' if args.apply else ' '} {rel} ({size:,} bytes, {reason})") + if args.apply: + path.unlink() + + mb = total_bytes / (1024 * 1024) + print( + f"\n{'Deleted' if args.apply else 'Would delete'}: {len(candidates)} files, {mb:.2f} MB" + ) + + # Write summary + summary = { + "timestamp": datetime.now(timezone.utc).isoformat(), + "applied": args.apply, + "files_count": len(candidates), + "total_bytes": total_bytes, + "max_age_days": args.max_age, + "max_count": args.max_count, + } + summary_path = DATA_OUT / "cleanup_summary.json" + summary_path.parent.mkdir(parents=True, exist_ok=True) + summary_path.write_text(json.dumps(summary, indent=2), encoding="utf-8") + + +if __name__ == "__main__": + main() diff --git a/scripts/config_paths.py b/scripts/config_paths.py index 266f9422d..3cb53d2d4 100644 --- a/scripts/config_paths.py +++ b/scripts/config_paths.py @@ -1,60 +1,60 @@ -"""Shared config path resolution helpers for orchestrators and CI scripts.""" - -from __future__ import annotations - -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -# Canonical path first, legacy fallback second when present. -_CONFIG_CANDIDATES: Dict[str, Tuple[str, ...]] = { - "master_orchestrator": ( - "config/master_orchestrator.yaml", - "master_orchestrator.yaml", - ), - "quantum_autorun": ( - "config/quantum/quantum_autorun.yaml", - "quantum_autorun.yaml", - ), - "evaluation_autorun": ( - "config/evaluation/evaluation_autorun.yaml", - "evaluation_autorun.yaml", - ), - "autotrain": ( - "config/training/autotrain.yaml", - "autotrain.yaml", - ), -} - - -def known_config_keys() -> List[str]: - """Return supported config keys in stable sorted order.""" - return sorted(_CONFIG_CANDIDATES.keys()) - - -def get_config_candidates(repo_root: Path, key: str) -> List[Path]: - """Return candidate config paths for a key, canonical-first.""" - if key not in _CONFIG_CANDIDATES: - raise KeyError(f"Unknown config key: {key}") - return [repo_root / rel for rel in _CONFIG_CANDIDATES[key]] - - -def canonical_config_path(repo_root: Path, key: str) -> Path: - """Return canonical (preferred) config path for a key.""" - return get_config_candidates(repo_root, key)[0] - - -def resolve_existing_config_path(repo_root: Path, key: str) -> Optional[Path]: - """Return first existing config path for key, or None if none exist.""" - candidates = get_config_candidates(repo_root, key) - return next((path for path in candidates if path.exists()), None) - - -def resolve_config_path(repo_root: Path, key: str) -> Path: - """Resolve config path for runtime use. - - Returns first existing candidate; if none exist, returns canonical path. - """ - existing = resolve_existing_config_path(repo_root, key) - if existing is not None: - return existing - return canonical_config_path(repo_root, key) +"""Shared config path resolution helpers for orchestrators and CI scripts.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +# Canonical path first, legacy fallback second when present. +_CONFIG_CANDIDATES: Dict[str, Tuple[str, ...]] = { + "master_orchestrator": ( + "config/master_orchestrator.yaml", + "master_orchestrator.yaml", + ), + "quantum_autorun": ( + "config/quantum/quantum_autorun.yaml", + "quantum_autorun.yaml", + ), + "evaluation_autorun": ( + "config/evaluation/evaluation_autorun.yaml", + "evaluation_autorun.yaml", + ), + "autotrain": ( + "config/training/autotrain.yaml", + "autotrain.yaml", + ), +} + + +def known_config_keys() -> List[str]: + """Return supported config keys in stable sorted order.""" + return sorted(_CONFIG_CANDIDATES.keys()) + + +def get_config_candidates(repo_root: Path, key: str) -> List[Path]: + """Return candidate config paths for a key, canonical-first.""" + if key not in _CONFIG_CANDIDATES: + raise KeyError(f"Unknown config key: {key}") + return [repo_root / rel for rel in _CONFIG_CANDIDATES[key]] + + +def canonical_config_path(repo_root: Path, key: str) -> Path: + """Return canonical (preferred) config path for a key.""" + return get_config_candidates(repo_root, key)[0] + + +def resolve_existing_config_path(repo_root: Path, key: str) -> Optional[Path]: + """Return first existing config path for key, or None if none exist.""" + candidates = get_config_candidates(repo_root, key) + return next((path for path in candidates if path.exists()), None) + + +def resolve_config_path(repo_root: Path, key: str) -> Path: + """Resolve config path for runtime use. + + Returns first existing candidate; if none exist, returns canonical path. + """ + existing = resolve_existing_config_path(repo_root, key) + if existing is not None: + return existing + return canonical_config_path(repo_root, key) diff --git a/scripts/demo_automation.sh b/scripts/demo_automation.sh index 9389071ce..99b850af3 100644 --- a/scripts/demo_automation.sh +++ b/scripts/demo_automation.sh @@ -1,164 +1,164 @@ -#!/bin/bash -# Quick demo of Aria automation capabilities -# Shows all major features in action - -set -e - -BLUE='\033[0;34m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -NC='\033[0m' - -echo -e "${BLUE}" -cat << "EOF" -╔══════════════════════════════════════════════════════════════════════════════╗ -║ 🤖 Aria Automation Demo 🤖 ║ -║ Demonstrating Full Automation Capabilities ║ -╚══════════════════════════════════════════════════════════════════════════════╝ -EOF -echo -e "${NC}" - -echo "" -echo -e "${YELLOW}This demo will showcase:${NC}" -echo " 1. Automation test suite" -echo " 2. Status checking" -echo " 3. Available commands" -echo " 4. Configuration validation" -echo "" -read -p "Press Enter to continue..." - -echo "" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo -e "${GREEN}STEP 1: Running Automation Test Suite${NC}" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo "" -python3 scripts/test_aria_automation.py - -echo "" -read -p "Press Enter to continue..." - -echo "" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo -e "${GREEN}STEP 2: Checking Current Status${NC}" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo "" -python3 scripts/aria_automation.py --status || echo "No automation currently running (this is normal)" - -echo "" -read -p "Press Enter to continue..." - -echo "" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo -e "${GREEN}STEP 3: Available Automation Commands${NC}" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo "" -echo -e "${YELLOW}Start Commands:${NC}" -echo " ./scripts/start_aria.sh # Interactive menu" -echo " ./scripts/start_aria.sh full # Full stack" -echo " ./scripts/start_aria.sh full --background # Background mode" -echo " ./scripts/start_aria.sh server # Server only" -echo " ./scripts/start_aria.sh training --once # Single training" -echo "" -echo -e "${YELLOW}Management Commands:${NC}" -echo " ./scripts/start_aria.sh status # Check status" -echo " ./scripts/start_aria.sh stop # Stop all" -echo "" -echo -e "${YELLOW}Direct Python Commands:${NC}" -echo " python3 scripts/aria_automation.py --mode full # Full automation" -echo " python3 scripts/aria_automation.py --mode server # Server only" -echo " python3 scripts/aria_automation.py --mode training # Training only" -echo " python3 scripts/aria_automation.py --status # Status check" -echo " python3 scripts/aria_automation.py --stop # Stop all" -echo "" - -read -p "Press Enter to continue..." - -echo "" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo -e "${GREEN}STEP 4: Configuration Files${NC}" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo "" -echo -e "${YELLOW}Automation Scripts:${NC}" -ls -lh scripts/aria_automation.py scripts/start_aria.sh scripts/test_aria_automation.py 2>/dev/null || echo "Scripts directory not accessible" -echo "" -echo -e "${YELLOW}Configuration Files:${NC}" -ls -lh config/aria_automation.service config/master_orchestrator.yaml 2>/dev/null || echo "Config directory not accessible" -echo "" -echo -e "${YELLOW}Documentation:${NC}" -ls -lh ARIA_AUTOMATION_GUIDE.md ARIA_AUTOMATION_SUMMARY.md ARIA_QUICKREF.txt 2>/dev/null || echo "Documentation not accessible" -echo "" - -read -p "Press Enter to continue..." - -echo "" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo -e "${GREEN}STEP 5: Quick Reference${NC}" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo "" -cat ARIA_QUICKREF.txt -echo "" - -read -p "Press Enter to continue..." - -echo "" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo -e "${GREEN}STEP 6: Integration Options${NC}" -echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" -echo "" -echo -e "${YELLOW}Background Service Options:${NC}" -echo "" -echo "1. Systemd (Linux - Recommended for Production)" -echo " sudo cp config/aria_automation.service /etc/systemd/system/" -echo " sudo systemctl enable aria_automation" -echo " sudo systemctl start aria_automation" -echo "" -echo "2. Screen (SSH Sessions)" -echo " screen -S aria" -echo " python3 scripts/aria_automation.py --mode full" -echo " # Detach: Ctrl+A, then D" -echo "" -echo "3. Nohup (Simple Background)" -echo " nohup python3 scripts/aria_automation.py --mode full > aria.log 2>&1 &" -echo "" -echo "4. Cron (Scheduled Training)" -echo " crontab -e" -echo " # Add: */30 * * * * cd /workspaces/Aria && python3 scripts/aria_automation.py --mode training --once" -echo "" -echo "5. Master Orchestrator (Advanced)" -echo " python3 scripts/master_orchestrator.py --workflow aria_full_stack" -echo "" - -read -p "Press Enter to finish..." - -echo "" -echo -e "${GREEN}╔═══════════════════════════════════════════════════════════════════════════════╗${NC}" -echo -e "${GREEN}║ 🎉 Demo Complete! 🎉 ║${NC}" -echo -e "${GREEN}╠═══════════════════════════════════════════════════════════════════════════════╣${NC}" -echo -e "${GREEN}║ Aria automation is fully configured and ready to use! ║${NC}" -echo -e "${GREEN}║ ║${NC}" -echo -e "${GREEN}║ Quick Start: ║${NC}" -echo -e "${GREEN}║ ./scripts/start_aria.sh # Interactive menu ║${NC}" -echo -e "${GREEN}║ ./scripts/start_aria.sh full # Start full automation ║${NC}" -echo -e "${GREEN}║ ║${NC}" -echo -e "${GREEN}║ Documentation: ║${NC}" -echo -e "${GREEN}║ ARIA_AUTOMATION_GUIDE.md - Complete guide ║${NC}" -echo -e "${GREEN}║ ARIA_AUTOMATION_SUMMARY.md - Quick summary ║${NC}" -echo -e "${GREEN}║ ARIA_QUICKREF.txt - Command reference ║${NC}" -echo -e "${GREEN}║ ║${NC}" -echo -e "${GREEN}║ Access Points: ║${NC}" -echo -e "${GREEN}║ http://localhost:8080 - Aria web interface ║${NC}" -echo -e "${GREEN}║ http://localhost:8080/auto-execute.html - Auto-execute page ║${NC}" -echo -e "${GREEN}║ http://localhost:7071/api/ai/status - Backend API ║${NC}" -echo -e "${GREEN}╚═══════════════════════════════════════════════════════════════════════════════╝${NC}" -echo "" -echo -e "${BLUE}Would you like to start Aria automation now?${NC}" -read -p "Start automation? (y/N): " -n 1 -r -echo -if [[ $REPLY =~ ^[Yy]$ ]]; then - echo "" - echo -e "${GREEN}Starting Aria automation...${NC}" - ./scripts/start_aria.sh -else - echo "" - echo -e "${YELLOW}To start later, run: ./scripts/start_aria.sh${NC}" -fi +#!/bin/bash +# Quick demo of Aria automation capabilities +# Shows all major features in action + +set -e + +BLUE='\033[0;34m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +NC='\033[0m' + +echo -e "${BLUE}" +cat << "EOF" +╔══════════════════════════════════════════════════════════════════════════════╗ +║ 🤖 Aria Automation Demo 🤖 ║ +║ Demonstrating Full Automation Capabilities ║ +╚══════════════════════════════════════════════════════════════════════════════╝ +EOF +echo -e "${NC}" + +echo "" +echo -e "${YELLOW}This demo will showcase:${NC}" +echo " 1. Automation test suite" +echo " 2. Status checking" +echo " 3. Available commands" +echo " 4. Configuration validation" +echo "" +read -p "Press Enter to continue..." + +echo "" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo -e "${GREEN}STEP 1: Running Automation Test Suite${NC}" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo "" +python3 scripts/test_aria_automation.py + +echo "" +read -p "Press Enter to continue..." + +echo "" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo -e "${GREEN}STEP 2: Checking Current Status${NC}" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo "" +python3 scripts/aria_automation.py --status || echo "No automation currently running (this is normal)" + +echo "" +read -p "Press Enter to continue..." + +echo "" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo -e "${GREEN}STEP 3: Available Automation Commands${NC}" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo "" +echo -e "${YELLOW}Start Commands:${NC}" +echo " ./scripts/start_aria.sh # Interactive menu" +echo " ./scripts/start_aria.sh full # Full stack" +echo " ./scripts/start_aria.sh full --background # Background mode" +echo " ./scripts/start_aria.sh server # Server only" +echo " ./scripts/start_aria.sh training --once # Single training" +echo "" +echo -e "${YELLOW}Management Commands:${NC}" +echo " ./scripts/start_aria.sh status # Check status" +echo " ./scripts/start_aria.sh stop # Stop all" +echo "" +echo -e "${YELLOW}Direct Python Commands:${NC}" +echo " python3 scripts/aria_automation.py --mode full # Full automation" +echo " python3 scripts/aria_automation.py --mode server # Server only" +echo " python3 scripts/aria_automation.py --mode training # Training only" +echo " python3 scripts/aria_automation.py --status # Status check" +echo " python3 scripts/aria_automation.py --stop # Stop all" +echo "" + +read -p "Press Enter to continue..." + +echo "" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo -e "${GREEN}STEP 4: Configuration Files${NC}" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo "" +echo -e "${YELLOW}Automation Scripts:${NC}" +ls -lh scripts/aria_automation.py scripts/start_aria.sh scripts/test_aria_automation.py 2>/dev/null || echo "Scripts directory not accessible" +echo "" +echo -e "${YELLOW}Configuration Files:${NC}" +ls -lh config/aria_automation.service config/master_orchestrator.yaml 2>/dev/null || echo "Config directory not accessible" +echo "" +echo -e "${YELLOW}Documentation:${NC}" +ls -lh ARIA_AUTOMATION_GUIDE.md ARIA_AUTOMATION_SUMMARY.md ARIA_QUICKREF.txt 2>/dev/null || echo "Documentation not accessible" +echo "" + +read -p "Press Enter to continue..." + +echo "" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo -e "${GREEN}STEP 5: Quick Reference${NC}" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo "" +cat ARIA_QUICKREF.txt +echo "" + +read -p "Press Enter to continue..." + +echo "" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo -e "${GREEN}STEP 6: Integration Options${NC}" +echo -e "${BLUE}═══════════════════════════════════════════════════════════════════════════${NC}" +echo "" +echo -e "${YELLOW}Background Service Options:${NC}" +echo "" +echo "1. Systemd (Linux - Recommended for Production)" +echo " sudo cp config/aria_automation.service /etc/systemd/system/" +echo " sudo systemctl enable aria_automation" +echo " sudo systemctl start aria_automation" +echo "" +echo "2. Screen (SSH Sessions)" +echo " screen -S aria" +echo " python3 scripts/aria_automation.py --mode full" +echo " # Detach: Ctrl+A, then D" +echo "" +echo "3. Nohup (Simple Background)" +echo " nohup python3 scripts/aria_automation.py --mode full > aria.log 2>&1 &" +echo "" +echo "4. Cron (Scheduled Training)" +echo " crontab -e" +echo " # Add: */30 * * * * cd /workspaces/Aria && python3 scripts/aria_automation.py --mode training --once" +echo "" +echo "5. Master Orchestrator (Advanced)" +echo " python3 scripts/master_orchestrator.py --workflow aria_full_stack" +echo "" + +read -p "Press Enter to finish..." + +echo "" +echo -e "${GREEN}╔═══════════════════════════════════════════════════════════════════════════════╗${NC}" +echo -e "${GREEN}║ 🎉 Demo Complete! 🎉 ║${NC}" +echo -e "${GREEN}╠═══════════════════════════════════════════════════════════════════════════════╣${NC}" +echo -e "${GREEN}║ Aria automation is fully configured and ready to use! ║${NC}" +echo -e "${GREEN}║ ║${NC}" +echo -e "${GREEN}║ Quick Start: ║${NC}" +echo -e "${GREEN}║ ./scripts/start_aria.sh # Interactive menu ║${NC}" +echo -e "${GREEN}║ ./scripts/start_aria.sh full # Start full automation ║${NC}" +echo -e "${GREEN}║ ║${NC}" +echo -e "${GREEN}║ Documentation: ║${NC}" +echo -e "${GREEN}║ ARIA_AUTOMATION_GUIDE.md - Complete guide ║${NC}" +echo -e "${GREEN}║ ARIA_AUTOMATION_SUMMARY.md - Quick summary ║${NC}" +echo -e "${GREEN}║ ARIA_QUICKREF.txt - Command reference ║${NC}" +echo -e "${GREEN}║ ║${NC}" +echo -e "${GREEN}║ Access Points: ║${NC}" +echo -e "${GREEN}║ http://localhost:8080 - Aria web interface ║${NC}" +echo -e "${GREEN}║ http://localhost:8080/auto-execute.html - Auto-execute page ║${NC}" +echo -e "${GREEN}║ http://localhost:7071/api/ai/status - Backend API ║${NC}" +echo -e "${GREEN}╚═══════════════════════════════════════════════════════════════════════════════╝${NC}" +echo "" +echo -e "${BLUE}Would you like to start Aria automation now?${NC}" +read -p "Start automation? (y/N): " -n 1 -r +echo +if [[ $REPLY =~ ^[Yy]$ ]]; then + echo "" + echo -e "${GREEN}Starting Aria automation...${NC}" + ./scripts/start_aria.sh +else + echo "" + echo -e "${YELLOW}To start later, run: ./scripts/start_aria.sh${NC}" +fi diff --git a/scripts/demo_quantum_llm.py b/scripts/demo_quantum_llm.py index 8bd7ebc86..10c831700 100644 --- a/scripts/demo_quantum_llm.py +++ b/scripts/demo_quantum_llm.py @@ -1,270 +1,270 @@ -#!/usr/bin/env python3 -""" -Demo script for Quantum-Enhanced Passive LLM Training -Shows the integration working without requiring full dependencies -""" - -import json -import sys -from pathlib import Path - - -def demonstrate_quantum_llm_integration(): - """Demonstrate the quantum LLM training integration""" - - print("=" * 80) - print("Quantum-Enhanced Passive LLM Training - Integration Demo") - print("=" * 80) - print() - - # 1. Show configuration structure - print("📋 Configuration Structure:") - print("-" * 80) - - try: - import yaml - - # Load quantum LLM config - config_path = Path("config/quantum_llm_config.yaml") - if config_path.exists(): - with open(config_path) as f: - quantum_config = yaml.safe_load(f) - - print("\n✅ Quantum LLM Configuration (config/quantum_llm_config.yaml):") - print(f" Backend: {quantum_config['quantum_settings']['backend']}") - print(f" Qubits: {quantum_config['quantum_settings']['n_qubits']}") - print( - f" Quantum Layers: {quantum_config['quantum_settings']['n_quantum_layers']}" - ) - print( - f" Passive Training: {quantum_config['passive_training']['enabled']}" - ) - print( - f" Training Interval: {quantum_config['passive_training']['interval_seconds']}s" - ) - print( - f" Optimize Attention: {quantum_config['quantum_enhancement']['optimize_attention']}" - ) - print( - f" Quantum Feature Encoding: {quantum_config['quantum_enhancement']['quantum_feature_encoding']}" - ) - - # Load autonomous training config - auto_config_path = Path("config/autonomous_training.yaml") - if auto_config_path.exists(): - with open(auto_config_path) as f: - auto_config = yaml.safe_load(f) - - if "quantum_llm" in auto_config: - print( - "\n✅ Autonomous Training Integration (config/autonomous_training.yaml):" - ) - print( - f" Quantum LLM Enabled: {auto_config['quantum_llm']['enabled']}" - ) - print(f" Passive Mode: {auto_config['quantum_llm']['passive_mode']}") - print(f" Backend: {auto_config['quantum_llm']['backend']}") - print( - f" Training Interval: {auto_config['quantum_llm']['training_interval_minutes']} minutes" - ) - - except Exception as e: - print(f"⚠️ Could not load config: {e}") - - # 2. Show module structure - print("\n\n📦 Module Structure:") - print("-" * 80) - - module_path = Path("scripts/quantum_llm_trainer.py") - if module_path.exists(): - print(f"✅ Quantum LLM Trainer: {module_path}") - print(" Components:") - print( - " - QuantumAttentionOptimizer: Optimizes attention weights using quantum circuits" - ) - print(" - QuantumFeatureEncoder: Encodes features into quantum states") - print(" - QuantumEnhancedLLMTrainer: Main training orchestrator") - print(" Modes:") - print(" - Active: Train on specific dataset with quantum enhancement") - print(" - Passive: Continuous background training at intervals") - else: - print("❌ Quantum LLM Trainer not found") - - # 3. Show integration points - print("\n\n🔗 Integration Points:") - print("-" * 80) - - orchestrator_path = Path("scripts/autonomous_training_orchestrator.py") - if orchestrator_path.exists(): - print(f"✅ Autonomous Training Orchestrator: {orchestrator_path}") - - # Check for quantum integration - with open(orchestrator_path) as f: - content = f.read() - - if "run_quantum_llm_training" in content: - print(" ✅ Quantum LLM training method integrated") - print(" - Called during each autonomous training cycle") - print(" - Respects configured training intervals") - print(" - Runs asynchronously with timeout protection") - else: - print(" ❌ Quantum LLM training method not found") - - # 4. Show workflow - print("\n\n🔄 Training Workflow:") - print("-" * 80) - print( - """ - Autonomous Training Cycle (every 30 minutes): - ├── 1. Discover datasets (quantum, chat, vision) - ├── 2. Download new datasets if needed - ├── 3. Select optimal training parameters - ├── 4. Execute classical training - ├── 5. Analyze performance - ├── 6. ⚛️ Quantum-Enhanced LLM Training (NEW) - │ ├── Load chat dataset - │ ├── Initialize quantum components - │ │ ├── QuantumAttentionOptimizer (4 qubits, 2 layers) - │ │ └── QuantumFeatureEncoder (amplitude encoding) - │ ├── Training loop - │ │ ├── Forward pass through LLM - │ │ ├── Apply quantum optimization every N steps - │ │ ├── Backward pass and weight update - │ │ └── Track quantum metrics - │ └── Save results and metrics - ├── 7. Run optimization (hyperparameter tuning) - └── 8. Deploy if ready - - Quantum Enhancement Features: - - Attention weight optimization via quantum circuits - - Quantum feature encoding for richer representations - - Hybrid quantum-classical architecture - - Cost-aware execution (local simulator vs Azure Quantum) - """ - ) - - # 5. Show command examples - print("\n📝 Usage Examples:") - print("-" * 80) - print( - """ - # Active Training (single run) - python scripts/quantum_llm_trainer.py \\ - --dataset datasets/chat/aria_chat \\ - --quantum-backend local \\ - --n-qubits 4 \\ - --epochs 3 - - # Passive Training (continuous background) - python scripts/quantum_llm_trainer.py \\ - --passive \\ - --interval 3600 \\ - --config config/quantum_llm_config.yaml - - # Integrated with Autonomous Orchestrator - python scripts/autonomous_training_orchestrator.py - # (Quantum LLM training runs automatically every 60 minutes) - - # Full Repository Automation - python scripts/repo_automation.py --start - # (Includes quantum LLM training in the full automation suite) - """ - ) - - # 6. Show benefits - print("\n\n✨ Key Benefits:") - print("-" * 80) - print( - """ - 1. Quantum Advantage: - - Exponential feature space (2^n for n qubits) - - Novel attention optimization patterns - - Quantum interference for better feature correlations - - 2. Passive Learning: - - Continuous background training without manual intervention - - Automatic dataset discovery and selection - - Resource-aware execution - - 3. Integration: - - Seamless integration with existing autonomous training - - No disruption to classical training workflows - - Fallback to classical methods if quantum unavailable - - 4. Cost Management: - - Free local quantum simulation for development - - Free Azure Quantum simulators for validation - - Paid QPU access with explicit cost confirmation - - 5. Monitoring: - - Track quantum circuit executions - - Monitor quantum advantage ratio - - Detailed metrics and logging - """ - ) - - # 7. Show status - print("\n\n📊 Current Status:") - print("-" * 80) - - # Check if training has run - status_file = Path("data_out/quantum_llm_training/status.json") - if status_file.exists(): - try: - with open(status_file) as f: - status = json.load(f) - print("✅ Training status found:") - print(f" Status: {status.get('status', 'unknown')}") - print(f" Epochs Completed: {status.get('epochs_completed', 0)}") - print(f" Final Loss: {status.get('final_loss', 'N/A')}") - if "quantum_metrics" in status: - print( - f" Quantum Executions: {status['quantum_metrics'].get('circuit_executions', 0)}" - ) - except Exception as e: - print(f"⚠️ Could not read status: {e}") - else: - print("ℹ️ No training runs yet") - print(" Run 'python scripts/quantum_llm_trainer.py --help' to get started") - - # 8. Documentation - print("\n\n📚 Documentation:") - print("-" * 80) - - doc_file = Path("QUANTUM_LLM_TRAINING.md") - if doc_file.exists(): - print(f"✅ Comprehensive documentation: {doc_file}") - print(" Sections:") - print(" - Overview and key features") - print(" - Architecture and workflow") - print(" - Configuration guide") - print(" - Quick start and examples") - print(" - Quantum backends (local/Azure)") - print(" - Monitoring and metrics") - print(" - Troubleshooting") - - print("\n\n" + "=" * 80) - print("✅ Quantum-Enhanced Passive LLM Training is ready!") - print("=" * 80) - print() - print("Next Steps:") - print("1. Review documentation: cat QUANTUM_LLM_TRAINING.md") - print("2. Test active training: python scripts/quantum_llm_trainer.py --help") - print("3. Enable passive training: Edit config/autonomous_training.yaml") - print( - "4. Start autonomous orchestrator: python scripts/autonomous_training_orchestrator.py" - ) - print() - - -if __name__ == "__main__": - try: - demonstrate_quantum_llm_integration() - except KeyboardInterrupt: - print("\n\nInterrupted by user") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - - traceback.print_exc() - sys.exit(1) +#!/usr/bin/env python3 +""" +Demo script for Quantum-Enhanced Passive LLM Training +Shows the integration working without requiring full dependencies +""" + +import json +import sys +from pathlib import Path + + +def demonstrate_quantum_llm_integration(): + """Demonstrate the quantum LLM training integration""" + + print("=" * 80) + print("Quantum-Enhanced Passive LLM Training - Integration Demo") + print("=" * 80) + print() + + # 1. Show configuration structure + print("📋 Configuration Structure:") + print("-" * 80) + + try: + import yaml + + # Load quantum LLM config + config_path = Path("config/quantum_llm_config.yaml") + if config_path.exists(): + with open(config_path) as f: + quantum_config = yaml.safe_load(f) + + print("\n✅ Quantum LLM Configuration (config/quantum_llm_config.yaml):") + print(f" Backend: {quantum_config['quantum_settings']['backend']}") + print(f" Qubits: {quantum_config['quantum_settings']['n_qubits']}") + print( + f" Quantum Layers: {quantum_config['quantum_settings']['n_quantum_layers']}" + ) + print( + f" Passive Training: {quantum_config['passive_training']['enabled']}" + ) + print( + f" Training Interval: {quantum_config['passive_training']['interval_seconds']}s" + ) + print( + f" Optimize Attention: {quantum_config['quantum_enhancement']['optimize_attention']}" + ) + print( + f" Quantum Feature Encoding: {quantum_config['quantum_enhancement']['quantum_feature_encoding']}" + ) + + # Load autonomous training config + auto_config_path = Path("config/autonomous_training.yaml") + if auto_config_path.exists(): + with open(auto_config_path) as f: + auto_config = yaml.safe_load(f) + + if "quantum_llm" in auto_config: + print( + "\n✅ Autonomous Training Integration (config/autonomous_training.yaml):" + ) + print( + f" Quantum LLM Enabled: {auto_config['quantum_llm']['enabled']}" + ) + print(f" Passive Mode: {auto_config['quantum_llm']['passive_mode']}") + print(f" Backend: {auto_config['quantum_llm']['backend']}") + print( + f" Training Interval: {auto_config['quantum_llm']['training_interval_minutes']} minutes" + ) + + except Exception as e: + print(f"⚠️ Could not load config: {e}") + + # 2. Show module structure + print("\n\n📦 Module Structure:") + print("-" * 80) + + module_path = Path("scripts/quantum_llm_trainer.py") + if module_path.exists(): + print(f"✅ Quantum LLM Trainer: {module_path}") + print(" Components:") + print( + " - QuantumAttentionOptimizer: Optimizes attention weights using quantum circuits" + ) + print(" - QuantumFeatureEncoder: Encodes features into quantum states") + print(" - QuantumEnhancedLLMTrainer: Main training orchestrator") + print(" Modes:") + print(" - Active: Train on specific dataset with quantum enhancement") + print(" - Passive: Continuous background training at intervals") + else: + print("❌ Quantum LLM Trainer not found") + + # 3. Show integration points + print("\n\n🔗 Integration Points:") + print("-" * 80) + + orchestrator_path = Path("scripts/autonomous_training_orchestrator.py") + if orchestrator_path.exists(): + print(f"✅ Autonomous Training Orchestrator: {orchestrator_path}") + + # Check for quantum integration + with open(orchestrator_path) as f: + content = f.read() + + if "run_quantum_llm_training" in content: + print(" ✅ Quantum LLM training method integrated") + print(" - Called during each autonomous training cycle") + print(" - Respects configured training intervals") + print(" - Runs asynchronously with timeout protection") + else: + print(" ❌ Quantum LLM training method not found") + + # 4. Show workflow + print("\n\n🔄 Training Workflow:") + print("-" * 80) + print( + """ + Autonomous Training Cycle (every 30 minutes): + ├── 1. Discover datasets (quantum, chat, vision) + ├── 2. Download new datasets if needed + ├── 3. Select optimal training parameters + ├── 4. Execute classical training + ├── 5. Analyze performance + ├── 6. ⚛️ Quantum-Enhanced LLM Training (NEW) + │ ├── Load chat dataset + │ ├── Initialize quantum components + │ │ ├── QuantumAttentionOptimizer (4 qubits, 2 layers) + │ │ └── QuantumFeatureEncoder (amplitude encoding) + │ ├── Training loop + │ │ ├── Forward pass through LLM + │ │ ├── Apply quantum optimization every N steps + │ │ ├── Backward pass and weight update + │ │ └── Track quantum metrics + │ └── Save results and metrics + ├── 7. Run optimization (hyperparameter tuning) + └── 8. Deploy if ready + + Quantum Enhancement Features: + - Attention weight optimization via quantum circuits + - Quantum feature encoding for richer representations + - Hybrid quantum-classical architecture + - Cost-aware execution (local simulator vs Azure Quantum) + """ + ) + + # 5. Show command examples + print("\n📝 Usage Examples:") + print("-" * 80) + print( + """ + # Active Training (single run) + python scripts/quantum_llm_trainer.py \\ + --dataset datasets/chat/aria_chat \\ + --quantum-backend local \\ + --n-qubits 4 \\ + --epochs 3 + + # Passive Training (continuous background) + python scripts/quantum_llm_trainer.py \\ + --passive \\ + --interval 3600 \\ + --config config/quantum_llm_config.yaml + + # Integrated with Autonomous Orchestrator + python scripts/autonomous_training_orchestrator.py + # (Quantum LLM training runs automatically every 60 minutes) + + # Full Repository Automation + python scripts/repo_automation.py --start + # (Includes quantum LLM training in the full automation suite) + """ + ) + + # 6. Show benefits + print("\n\n✨ Key Benefits:") + print("-" * 80) + print( + """ + 1. Quantum Advantage: + - Exponential feature space (2^n for n qubits) + - Novel attention optimization patterns + - Quantum interference for better feature correlations + + 2. Passive Learning: + - Continuous background training without manual intervention + - Automatic dataset discovery and selection + - Resource-aware execution + + 3. Integration: + - Seamless integration with existing autonomous training + - No disruption to classical training workflows + - Fallback to classical methods if quantum unavailable + + 4. Cost Management: + - Free local quantum simulation for development + - Free Azure Quantum simulators for validation + - Paid QPU access with explicit cost confirmation + + 5. Monitoring: + - Track quantum circuit executions + - Monitor quantum advantage ratio + - Detailed metrics and logging + """ + ) + + # 7. Show status + print("\n\n📊 Current Status:") + print("-" * 80) + + # Check if training has run + status_file = Path("data_out/quantum_llm_training/status.json") + if status_file.exists(): + try: + with open(status_file) as f: + status = json.load(f) + print("✅ Training status found:") + print(f" Status: {status.get('status', 'unknown')}") + print(f" Epochs Completed: {status.get('epochs_completed', 0)}") + print(f" Final Loss: {status.get('final_loss', 'N/A')}") + if "quantum_metrics" in status: + print( + f" Quantum Executions: {status['quantum_metrics'].get('circuit_executions', 0)}" + ) + except Exception as e: + print(f"⚠️ Could not read status: {e}") + else: + print("ℹ️ No training runs yet") + print(" Run 'python scripts/quantum_llm_trainer.py --help' to get started") + + # 8. Documentation + print("\n\n📚 Documentation:") + print("-" * 80) + + doc_file = Path("QUANTUM_LLM_TRAINING.md") + if doc_file.exists(): + print(f"✅ Comprehensive documentation: {doc_file}") + print(" Sections:") + print(" - Overview and key features") + print(" - Architecture and workflow") + print(" - Configuration guide") + print(" - Quick start and examples") + print(" - Quantum backends (local/Azure)") + print(" - Monitoring and metrics") + print(" - Troubleshooting") + + print("\n\n" + "=" * 80) + print("✅ Quantum-Enhanced Passive LLM Training is ready!") + print("=" * 80) + print() + print("Next Steps:") + print("1. Review documentation: cat QUANTUM_LLM_TRAINING.md") + print("2. Test active training: python scripts/quantum_llm_trainer.py --help") + print("3. Enable passive training: Edit config/autonomous_training.yaml") + print( + "4. Start autonomous orchestrator: python scripts/autonomous_training_orchestrator.py" + ) + print() + + +if __name__ == "__main__": + try: + demonstrate_quantum_llm_integration() + except KeyboardInterrupt: + print("\n\nInterrupted by user") + except Exception as e: + print(f"\n❌ Error: {e}") + import traceback + + traceback.print_exc() + sys.exit(1) diff --git a/scripts/demo_repo_automation.sh b/scripts/demo_repo_automation.sh index af2a743cd..61283d47f 100644 --- a/scripts/demo_repo_automation.sh +++ b/scripts/demo_repo_automation.sh @@ -1,100 +1,100 @@ -#!/bin/bash -# Quick Demo of Repository Automation System - -set -e - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -REPO_ROOT="$(dirname "$SCRIPT_DIR")" - -# Colors -GREEN='\033[0;32m' -BLUE='\033[0;34m' -CYAN='\033[0;36m' -YELLOW='\033[1;33m' -NC='\033[0m' - -echo -e "${BLUE}" -cat << "EOF" -╔══════════════════════════════════════════════════════════════════════════════╗ -║ 🚀 REPOSITORY AUTOMATION SYSTEM DEMO 🚀 ║ -║ Complete Automation for Aria Repository ║ -╚══════════════════════════════════════════════════════════════════════════════╝ -EOF -echo -e "${NC}" - -echo -e "${CYAN}This demo will show you:${NC}" -echo " 1. Test suite validation" -echo " 2. Component overview" -echo " 3. Usage examples" -echo " 4. Status checking" -echo "" -read -p "Press Enter to continue..." - -# Step 1: Run tests -echo -e "\n${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" -echo -e "${GREEN}Step 1: Running Test Suite${NC}" -echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" -python3 "$REPO_ROOT/scripts/test_repo_automation.py" - -read -p "Press Enter to continue..." - -# Step 2: Show components -echo -e "\n${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" -echo -e "${GREEN}Step 2: Available Components${NC}" -echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" -echo "" -echo -e "${CYAN}✨ aria${NC} - Aria character (server + training)" -echo -e "${CYAN}🎓 training${NC} - LoRA training pipelines" -echo -e "${CYAN}⚛️ quantum${NC} - Quantum computing workflows" -echo -e "${CYAN}📊 evaluation${NC} - Model evaluation system" -echo -e "${CYAN}📦 datasets${NC} - Auto dataset discovery" -echo -e "${CYAN}🏥 monitoring${NC} - System health monitoring" -echo -e "${CYAN}💾 backup${NC} - Automated backups" -echo "" - -read -p "Press Enter to continue..." - -# Step 3: Usage examples -echo -e "\n${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" -echo -e "${GREEN}Step 3: Usage Examples${NC}" -echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" -echo "" -echo -e "${YELLOW}🚀 Start everything:${NC}" -echo " ./scripts/start_repo_automation.sh full" -echo "" -echo -e "${YELLOW}🎭 Start Aria only:${NC}" -echo " ./scripts/start_repo_automation.sh aria" -echo "" -echo -e "${YELLOW}🎓 Start training pipeline:${NC}" -echo " ./scripts/start_repo_automation.sh training" -echo "" -echo -e "${YELLOW}🎯 Custom selection:${NC}" -echo " ./scripts/start_repo_automation.sh components aria,training,quantum" -echo "" -echo -e "${YELLOW}📊 Check status:${NC}" -echo " ./scripts/start_repo_automation.sh status" -echo "" -echo -e "${YELLOW}🛑 Stop all:${NC}" -echo " ./scripts/start_repo_automation.sh stop" -echo "" -echo -e "${YELLOW}🌙 Background mode:${NC}" -echo " ./scripts/start_repo_automation.sh full --background" -echo "" - -read -p "Press Enter to continue..." - -# Step 4: Check current status -echo -e "\n${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" -echo -e "${GREEN}Step 4: Current Status${NC}" -echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" -python3 "$REPO_ROOT/scripts/repo_automation.py" --status - -echo "" -echo -e "${GREEN}✅ Demo complete!${NC}" -echo "" -echo -e "${CYAN}Next steps:${NC}" -echo " 1. Start automation: ./scripts/start_repo_automation.sh" -echo " 2. Read the guide: cat REPO_AUTOMATION_GUIDE.md" -echo " 3. Monitor status: ./scripts/start_repo_automation.sh status" -echo "" -echo -e "${YELLOW}Happy automating! 🚀${NC}" +#!/bin/bash +# Quick Demo of Repository Automation System + +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(dirname "$SCRIPT_DIR")" + +# Colors +GREEN='\033[0;32m' +BLUE='\033[0;34m' +CYAN='\033[0;36m' +YELLOW='\033[1;33m' +NC='\033[0m' + +echo -e "${BLUE}" +cat << "EOF" +╔══════════════════════════════════════════════════════════════════════════════╗ +║ 🚀 REPOSITORY AUTOMATION SYSTEM DEMO 🚀 ║ +║ Complete Automation for Aria Repository ║ +╚══════════════════════════════════════════════════════════════════════════════╝ +EOF +echo -e "${NC}" + +echo -e "${CYAN}This demo will show you:${NC}" +echo " 1. Test suite validation" +echo " 2. Component overview" +echo " 3. Usage examples" +echo " 4. Status checking" +echo "" +read -p "Press Enter to continue..." + +# Step 1: Run tests +echo -e "\n${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +echo -e "${GREEN}Step 1: Running Test Suite${NC}" +echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +python3 "$REPO_ROOT/scripts/test_repo_automation.py" + +read -p "Press Enter to continue..." + +# Step 2: Show components +echo -e "\n${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +echo -e "${GREEN}Step 2: Available Components${NC}" +echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +echo "" +echo -e "${CYAN}✨ aria${NC} - Aria character (server + training)" +echo -e "${CYAN}🎓 training${NC} - LoRA training pipelines" +echo -e "${CYAN}⚛️ quantum${NC} - Quantum computing workflows" +echo -e "${CYAN}📊 evaluation${NC} - Model evaluation system" +echo -e "${CYAN}📦 datasets${NC} - Auto dataset discovery" +echo -e "${CYAN}🏥 monitoring${NC} - System health monitoring" +echo -e "${CYAN}💾 backup${NC} - Automated backups" +echo "" + +read -p "Press Enter to continue..." + +# Step 3: Usage examples +echo -e "\n${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +echo -e "${GREEN}Step 3: Usage Examples${NC}" +echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +echo "" +echo -e "${YELLOW}🚀 Start everything:${NC}" +echo " ./scripts/start_repo_automation.sh full" +echo "" +echo -e "${YELLOW}🎭 Start Aria only:${NC}" +echo " ./scripts/start_repo_automation.sh aria" +echo "" +echo -e "${YELLOW}🎓 Start training pipeline:${NC}" +echo " ./scripts/start_repo_automation.sh training" +echo "" +echo -e "${YELLOW}🎯 Custom selection:${NC}" +echo " ./scripts/start_repo_automation.sh components aria,training,quantum" +echo "" +echo -e "${YELLOW}📊 Check status:${NC}" +echo " ./scripts/start_repo_automation.sh status" +echo "" +echo -e "${YELLOW}🛑 Stop all:${NC}" +echo " ./scripts/start_repo_automation.sh stop" +echo "" +echo -e "${YELLOW}🌙 Background mode:${NC}" +echo " ./scripts/start_repo_automation.sh full --background" +echo "" + +read -p "Press Enter to continue..." + +# Step 4: Check current status +echo -e "\n${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +echo -e "${GREEN}Step 4: Current Status${NC}" +echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +python3 "$REPO_ROOT/scripts/repo_automation.py" --status + +echo "" +echo -e "${GREEN}✅ Demo complete!${NC}" +echo "" +echo -e "${CYAN}Next steps:${NC}" +echo " 1. Start automation: ./scripts/start_repo_automation.sh" +echo " 2. Read the guide: cat REPO_AUTOMATION_GUIDE.md" +echo " 3. Monitor status: ./scripts/start_repo_automation.sh status" +echo "" +echo -e "${YELLOW}Happy automating! 🚀${NC}" diff --git a/scripts/distributed_benchmark.py b/scripts/distributed_benchmark.py index 4867831ff..ca50440d8 100644 --- a/scripts/distributed_benchmark.py +++ b/scripts/distributed_benchmark.py @@ -1,539 +1,539 @@ -""" -Distributed Quantum ML Benchmark System -======================================== - -Train 100+ datasets simultaneously using parallel processing. -Optimized for multi-core CPUs with intelligent workload distribution. - -Features: -- Parallel training (configurable workers) -- Progress tracking and checkpointing -- Real-time status dashboard -- Automatic resource management -- GPU support (if available) -- Fault tolerance (continue on errors) - -Usage: - # Benchmark 100 datasets with 10 parallel workers - python distributed_benchmark.py --datasets-dir massive_quantum --workers 10 --epochs 25 - - # Quick test (1 epoch, 4 workers) - python distributed_benchmark.py --datasets-dir massive_quantum --workers 4 --epochs 1 --quick-test - - # Resume from checkpoint - python distributed_benchmark.py --datasets-dir massive_quantum --workers 10 --resume - -Architecture: -- Master process: Coordinates workers, aggregates results -- Worker processes: Train individual datasets independently -- Shared memory: Progress tracking and result collection -- Checkpoints: Auto-save every 10 datasets - -Author: Quantum AI Workspace -Date: November 16, 2025 -""" - -import argparse -import json -import multiprocessing as mp -import time -import warnings -from datetime import datetime -from functools import partial -from pathlib import Path -from typing import Dict, List - -import numpy as np -import pandas as pd -import torch -import torch.nn as nn -import torch.optim as optim - -warnings.filterwarnings("ignore") - -# Import quantum ML components (reuse from existing codebase) -import sys - -sys.path.insert(0, str(Path(__file__).parent.parent / "ai-projects" / "quantum-ml")) - -try: - from sklearn.decomposition import PCA - from sklearn.model_selection import train_test_split - from sklearn.preprocessing import StandardScaler -except ImportError: - print("❌ sklearn required: pip install scikit-learn") - sys.exit(1) - - -class HybridQuantumNet(nn.Module): - """Simplified hybrid quantum-classical network for parallel training.""" - - def __init__(self, n_qubits: int = 4, n_layers: int = 2, hidden_dim: int = 16): - super().__init__() - self.n_qubits = n_qubits - self.n_layers = n_layers - - # Classical preprocessing - self.fc1 = nn.Linear(n_qubits, hidden_dim) - self.relu = nn.ReLU() - self.dropout = nn.Dropout(0.2) - - # Quantum-inspired layer (simplified for speed) - self.quantum_weight = nn.Parameter(torch.randn(hidden_dim, hidden_dim)) - - # Classical postprocessing - self.fc2 = nn.Linear(hidden_dim, 2) # Binary classification - - def forward(self, input_features): - features = self.fc1(input_features) - features = self.relu(features) - features = self.dropout(features) - - # Quantum-inspired transformation - features = torch.matmul(features, self.quantum_weight) - features = torch.tanh(features) # Bounded activation (quantum-like) - - output = self.fc2(features) - return output - - -def train_single_dataset( - dataset_path: Path, - n_qubits: int = 4, - n_layers: int = 2, - epochs: int = 25, - batch_size: int = 32, - learning_rate: float = 0.001, - quick_test: bool = False, -) -> Dict: - """ - Train on a single dataset (worker function). - - Args: - dataset_path: Path to CSV file - n_qubits: Number of qubits (features after PCA) - n_layers: Number of quantum layers - epochs: Training epochs - batch_size: Batch size - learning_rate: Learning rate - quick_test: Fast mode (1 epoch, small samples) - - Returns: - Results dictionary - """ - start_time = time.time() - dataset_name = dataset_path.stem - - try: - # Load dataset - df = pd.read_csv(dataset_path) - - if "target" not in df.columns: - return { - "dataset": dataset_name, - "status": "error", - "error": "Missing target column", - "duration": time.time() - start_time, - } - - # Prepare data - feature_matrix = df.drop("target", axis=1).values - target_labels = df["target"].values - - # Handle categorical targets - from sklearn.preprocessing import LabelEncoder - - le = LabelEncoder() - target_labels = le.fit_transform(target_labels) - - # Binary classification only for now - if len(np.unique(target_labels)) > 2: - # Convert to binary (largest class vs rest) - majority_class = np.bincount(target_labels).argmax() - target_labels = (target_labels == majority_class).astype(int) - - num_samples = len(feature_matrix) - num_features = feature_matrix.shape[1] - num_classes = len(np.unique(target_labels)) - - # Quick test mode - if quick_test: - epochs = 1 - if num_samples > 500: - indices = np.random.choice(num_samples, 500, replace=False) - feature_matrix = feature_matrix[indices] - target_labels = target_labels[indices] - num_samples = 500 - - # Feature preprocessing - scaler = StandardScaler() - scaled_features = scaler.fit_transform(feature_matrix) - - # PCA to n_qubits features - if num_features > n_qubits: - pca = PCA(n_components=n_qubits) - reduced_features = pca.fit_transform(scaled_features) - variance_explained = pca.explained_variance_ratio_.sum() - else: - reduced_features = scaled_features - # Pad if needed - if num_features < n_qubits: - padding = np.zeros((num_samples, n_qubits - num_features)) - reduced_features = np.hstack([reduced_features, padding]) - variance_explained = 1.0 - - # Train/test split - X_train, X_test, y_train, y_test = train_test_split( - reduced_features, - target_labels, - test_size=0.2, - random_state=42, - stratify=target_labels, - ) - - # Convert to tensors - X_train_t = torch.FloatTensor(X_train) - y_train_t = torch.LongTensor(y_train) - X_test_t = torch.FloatTensor(X_test) - y_test_t = torch.LongTensor(y_test) - - # Initialize model - model = HybridQuantumNet(n_qubits=n_qubits, n_layers=n_layers, hidden_dim=16) - criterion = nn.CrossEntropyLoss() - optimizer = optim.Adam(model.parameters(), lr=learning_rate) - - # Training loop - best_acc = 0.0 - best_epoch = 0 - training_history = [] - - for epoch in range(epochs): - model.train() - - # Mini-batch training - n_batches = (len(X_train) + batch_size - 1) // batch_size - epoch_loss = 0.0 - - for i in range(n_batches): - start_idx = i * batch_size - end_idx = min((i + 1) * batch_size, len(X_train)) - - batch_X = X_train_t[start_idx:end_idx] - batch_y = y_train_t[start_idx:end_idx] - - optimizer.zero_grad() - outputs = model(batch_X) - loss = criterion(outputs, batch_y) - loss.backward() - optimizer.step() - - epoch_loss += loss.item() - - avg_loss = epoch_loss / n_batches - - # Validation - model.eval() - with torch.no_grad(): - outputs = model(X_test_t) - _, predicted = torch.max(outputs, 1) - accuracy = (predicted == y_test_t).float().mean().item() - - training_history.append( - {"epoch": epoch + 1, "train_loss": avg_loss, "val_acc": accuracy} - ) - - if accuracy > best_acc: - best_acc = accuracy - best_epoch = epoch + 1 - - duration = time.time() - start_time - - # Final results - final_acc = training_history[-1]["val_acc"] - - return { - "dataset": dataset_name, - "status": "success", - "samples": num_samples, - "features": num_features, - "classes": num_classes, - "variance_explained": variance_explained, - "best_accuracy": best_acc, - "best_epoch": best_epoch, - "final_accuracy": final_acc, - "epochs_trained": epochs, - "duration_seconds": duration, - "training_history": training_history, - } - - except Exception as e: - return { - "dataset": dataset_name, - "status": "error", - "error": str(e), - "duration": time.time() - start_time, - } - - -def worker_init(): - """Initialize worker process.""" - # Set random seeds for reproducibility - np.random.seed(int(time.time() * 1000) % 2**32) - torch.manual_seed(int(time.time() * 1000) % 2**32) - - -class DistributedBenchmark: - """Manages distributed training across multiple datasets.""" - - def __init__( - self, - datasets_dir: Path, - output_dir: Path, - n_workers: int = 4, - epochs: int = 25, - quick_test: bool = False, - ): - self.datasets_dir = datasets_dir - self.output_dir = output_dir - self.n_workers = n_workers - self.epochs = epochs - self.quick_test = quick_test - - self.output_dir.mkdir(parents=True, exist_ok=True) - self.checkpoint_file = self.output_dir / "checkpoint.json" - self.results_file = self.output_dir / "distributed_results.json" - - def load_checkpoint(self) -> Dict: - """Load checkpoint if exists.""" - if self.checkpoint_file.exists(): - with open(self.checkpoint_file, "r") as f: - return json.load(f) - return {"completed": [], "results": []} - - def save_checkpoint(self, checkpoint: Dict): - """Save checkpoint.""" - with open(self.checkpoint_file, "w") as f: - json.dump(checkpoint, f, indent=2) - - def run_benchmark(self, resume: bool = False): - """ - Run distributed benchmark on all datasets. - - Args: - resume: Resume from checkpoint if available - """ - print("=" * 70) - print("🚀 DISTRIBUTED QUANTUM ML BENCHMARK") - print("=" * 70) - - # Find all CSV files - csv_files = list(self.datasets_dir.glob("*.csv")) - - if not csv_files: - print(f"❌ No CSV files found in {self.datasets_dir}") - return - - print("\n📊 Configuration:") - print(f" Datasets: {len(csv_files)}") - print(f" Workers: {self.n_workers}") - print(f" Epochs: {self.epochs}") - print(f" Quick test: {self.quick_test}") - print(f" Output: {self.output_dir}") - - # Load checkpoint - checkpoint = ( - self.load_checkpoint() if resume else {"completed": [], "results": []} - ) - completed_names = set(checkpoint["completed"]) - - # Filter remaining datasets - remaining = [f for f in csv_files if f.stem not in completed_names] - - if resume and completed_names: - print("\n♻️ Resuming from checkpoint:") - print(f" Completed: {len(completed_names)}") - print(f" Remaining: {len(remaining)}") - - if not remaining: - print("\n✅ All datasets already completed!") - return - - print( - f"\n🔄 Processing {len(remaining)} datasets with {self.n_workers} workers..." - ) - - # Create partial function with fixed parameters - worker_func = partial( - train_single_dataset, - n_qubits=4, - n_layers=2, - epochs=self.epochs, - batch_size=32, - learning_rate=0.001, - quick_test=self.quick_test, - ) - - # Start parallel training - start_time = time.time() - - with mp.Pool(processes=self.n_workers, initializer=worker_init) as pool: - # Track progress - results = [] - completed_count = len(completed_names) - - for i, result in enumerate(pool.imap_unordered(worker_func, remaining), 1): - completed_count += 1 - results.append(result) - checkpoint["results"].append(result) - checkpoint["completed"].append(result["dataset"]) - - # Progress update - status_symbol = "✓" if result["status"] == "success" else "✗" - if result["status"] == "success": - print( - f"\n[{completed_count}/{len(csv_files)}] {status_symbol} {result['dataset']}" - ) - print( - f" Accuracy: {result['best_accuracy']:.2%} (epoch {result['best_epoch']})" - ) - print(f" Duration: {result['duration_seconds']:.1f}s") - else: - print( - f"\n[{completed_count}/{len(csv_files)}] {status_symbol} {result['dataset']}" - ) - print(f" Error: {result.get('error', 'Unknown')}") - - # Save checkpoint every 10 datasets - if i % 10 == 0: - self.save_checkpoint(checkpoint) - print(f"\n💾 Checkpoint saved ({i}/{len(remaining)} processed)") - - # Final save - self.save_checkpoint(checkpoint) - - total_time = time.time() - start_time - - # Generate summary - self.generate_summary(checkpoint["results"], total_time) - - def generate_summary(self, results: List[Dict], total_time: float): - """Generate benchmark summary report.""" - print("\n" + "=" * 70) - print("📊 BENCHMARK SUMMARY") - print("=" * 70) - - successful = [r for r in results if r["status"] == "success"] - failed = [r for r in results if r["status"] == "error"] - - print(f"\n✅ Completed: {len(successful)}/{len(results)}") - print(f"❌ Failed: {len(failed)}") - print(f"⏱️ Total time: {total_time/60:.1f} minutes") - print(f"⚡ Avg time per dataset: {total_time/len(results):.1f} seconds") - - if successful: - accuracies = [r["best_accuracy"] for r in successful] - - print("\n📈 Accuracy Statistics:") - print(f" Mean: {np.mean(accuracies):.2%}") - print(f" Median: {np.median(accuracies):.2%}") - print(f" Std Dev: {np.std(accuracies):.2%}") - print(f" Min: {np.min(accuracies):.2%}") - print(f" Max: {np.max(accuracies):.2%}") - - # Performance tiers - exceptional = [r for r in successful if r["best_accuracy"] >= 0.95] - excellent = [r for r in successful if 0.85 <= r["best_accuracy"] < 0.95] - good = [r for r in successful if 0.75 <= r["best_accuracy"] < 0.85] - challenging = [r for r in successful if r["best_accuracy"] < 0.75] - - print("\n🏆 Performance Tiers:") - print(f" Exceptional (≥95%): {len(exceptional)}") - print(f" Excellent (85-95%): {len(excellent)}") - print(f" Good (75-85%): {len(good)}") - print(f" Challenging (<75%): {len(challenging)}") - - # Top 10 - top_datasets = sorted( - successful, key=lambda x: x["best_accuracy"], reverse=True - )[:10] - print("\n🥇 TOP 10 DATASETS:") - for rank, result in enumerate(top_datasets, 1): - print( - f" {rank}. {result['dataset']}: {result['best_accuracy']:.2%} " - f"({result['samples']} samples, {result['features']} features)" - ) - - # Save detailed results - summary = { - "timestamp": datetime.now().isoformat(), - "total_datasets": len(results), - "successful": len(successful), - "failed": len(failed), - "total_time_seconds": total_time, - "epochs": self.epochs, - "workers": self.n_workers, - "results": results, - } - - with open(self.results_file, "w") as f: - json.dump(summary, f, indent=2) - - print(f"\n✓ Detailed results saved to: {self.results_file}") - - -def main(): - parser = argparse.ArgumentParser( - description="Distributed Quantum ML Benchmark System", - formatter_class=argparse.RawDescriptionHelpFormatter, - ) - - parser.add_argument( - "--datasets-dir", - type=str, - default="datasets/massive_quantum", - help="Directory containing CSV datasets", - ) - parser.add_argument( - "--output-dir", - type=str, - default="data_out/distributed_benchmark", - help="Output directory for results", - ) - parser.add_argument( - "--workers", type=int, default=4, help="Number of parallel workers" - ) - parser.add_argument( - "--epochs", type=int, default=25, help="Training epochs per dataset" - ) - parser.add_argument( - "--quick-test", - action="store_true", - help="Quick test mode (1 epoch, 500 samples max)", - ) - parser.add_argument("--resume", action="store_true", help="Resume from checkpoint") - - args = parser.parse_args() - - datasets_dir = Path(args.datasets_dir) - output_dir = Path(args.output_dir) - - if not datasets_dir.exists(): - print(f"❌ Datasets directory not found: {datasets_dir}") - print(" Run massive_dataset_expansion.py --download first") - return - - benchmark = DistributedBenchmark( - datasets_dir=datasets_dir, - output_dir=output_dir, - n_workers=args.workers, - epochs=args.epochs, - quick_test=args.quick_test, - ) - - benchmark.run_benchmark(resume=args.resume) - - -if __name__ == "__main__": - # Set multiprocessing start method - mp.set_start_method("spawn", force=True) - main() +""" +Distributed Quantum ML Benchmark System +======================================== + +Train 100+ datasets simultaneously using parallel processing. +Optimized for multi-core CPUs with intelligent workload distribution. + +Features: +- Parallel training (configurable workers) +- Progress tracking and checkpointing +- Real-time status dashboard +- Automatic resource management +- GPU support (if available) +- Fault tolerance (continue on errors) + +Usage: + # Benchmark 100 datasets with 10 parallel workers + python distributed_benchmark.py --datasets-dir massive_quantum --workers 10 --epochs 25 + + # Quick test (1 epoch, 4 workers) + python distributed_benchmark.py --datasets-dir massive_quantum --workers 4 --epochs 1 --quick-test + + # Resume from checkpoint + python distributed_benchmark.py --datasets-dir massive_quantum --workers 10 --resume + +Architecture: +- Master process: Coordinates workers, aggregates results +- Worker processes: Train individual datasets independently +- Shared memory: Progress tracking and result collection +- Checkpoints: Auto-save every 10 datasets + +Author: Quantum AI Workspace +Date: November 16, 2025 +""" + +import argparse +import json +import multiprocessing as mp +import time +import warnings +from datetime import datetime +from functools import partial +from pathlib import Path +from typing import Dict, List + +import numpy as np +import pandas as pd +import torch +import torch.nn as nn +import torch.optim as optim + +warnings.filterwarnings("ignore") + +# Import quantum ML components (reuse from existing codebase) +import sys + +sys.path.insert(0, str(Path(__file__).parent.parent / "ai-projects" / "quantum-ml")) + +try: + from sklearn.decomposition import PCA + from sklearn.model_selection import train_test_split + from sklearn.preprocessing import StandardScaler +except ImportError: + print("❌ sklearn required: pip install scikit-learn") + sys.exit(1) + + +class HybridQuantumNet(nn.Module): + """Simplified hybrid quantum-classical network for parallel training.""" + + def __init__(self, n_qubits: int = 4, n_layers: int = 2, hidden_dim: int = 16): + super().__init__() + self.n_qubits = n_qubits + self.n_layers = n_layers + + # Classical preprocessing + self.fc1 = nn.Linear(n_qubits, hidden_dim) + self.relu = nn.ReLU() + self.dropout = nn.Dropout(0.2) + + # Quantum-inspired layer (simplified for speed) + self.quantum_weight = nn.Parameter(torch.randn(hidden_dim, hidden_dim)) + + # Classical postprocessing + self.fc2 = nn.Linear(hidden_dim, 2) # Binary classification + + def forward(self, input_features): + features = self.fc1(input_features) + features = self.relu(features) + features = self.dropout(features) + + # Quantum-inspired transformation + features = torch.matmul(features, self.quantum_weight) + features = torch.tanh(features) # Bounded activation (quantum-like) + + output = self.fc2(features) + return output + + +def train_single_dataset( + dataset_path: Path, + n_qubits: int = 4, + n_layers: int = 2, + epochs: int = 25, + batch_size: int = 32, + learning_rate: float = 0.001, + quick_test: bool = False, +) -> Dict: + """ + Train on a single dataset (worker function). + + Args: + dataset_path: Path to CSV file + n_qubits: Number of qubits (features after PCA) + n_layers: Number of quantum layers + epochs: Training epochs + batch_size: Batch size + learning_rate: Learning rate + quick_test: Fast mode (1 epoch, small samples) + + Returns: + Results dictionary + """ + start_time = time.time() + dataset_name = dataset_path.stem + + try: + # Load dataset + df = pd.read_csv(dataset_path) + + if "target" not in df.columns: + return { + "dataset": dataset_name, + "status": "error", + "error": "Missing target column", + "duration": time.time() - start_time, + } + + # Prepare data + feature_matrix = df.drop("target", axis=1).values + target_labels = df["target"].values + + # Handle categorical targets + from sklearn.preprocessing import LabelEncoder + + le = LabelEncoder() + target_labels = le.fit_transform(target_labels) + + # Binary classification only for now + if len(np.unique(target_labels)) > 2: + # Convert to binary (largest class vs rest) + majority_class = np.bincount(target_labels).argmax() + target_labels = (target_labels == majority_class).astype(int) + + num_samples = len(feature_matrix) + num_features = feature_matrix.shape[1] + num_classes = len(np.unique(target_labels)) + + # Quick test mode + if quick_test: + epochs = 1 + if num_samples > 500: + indices = np.random.choice(num_samples, 500, replace=False) + feature_matrix = feature_matrix[indices] + target_labels = target_labels[indices] + num_samples = 500 + + # Feature preprocessing + scaler = StandardScaler() + scaled_features = scaler.fit_transform(feature_matrix) + + # PCA to n_qubits features + if num_features > n_qubits: + pca = PCA(n_components=n_qubits) + reduced_features = pca.fit_transform(scaled_features) + variance_explained = pca.explained_variance_ratio_.sum() + else: + reduced_features = scaled_features + # Pad if needed + if num_features < n_qubits: + padding = np.zeros((num_samples, n_qubits - num_features)) + reduced_features = np.hstack([reduced_features, padding]) + variance_explained = 1.0 + + # Train/test split + X_train, X_test, y_train, y_test = train_test_split( + reduced_features, + target_labels, + test_size=0.2, + random_state=42, + stratify=target_labels, + ) + + # Convert to tensors + X_train_t = torch.FloatTensor(X_train) + y_train_t = torch.LongTensor(y_train) + X_test_t = torch.FloatTensor(X_test) + y_test_t = torch.LongTensor(y_test) + + # Initialize model + model = HybridQuantumNet(n_qubits=n_qubits, n_layers=n_layers, hidden_dim=16) + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + + # Training loop + best_acc = 0.0 + best_epoch = 0 + training_history = [] + + for epoch in range(epochs): + model.train() + + # Mini-batch training + n_batches = (len(X_train) + batch_size - 1) // batch_size + epoch_loss = 0.0 + + for i in range(n_batches): + start_idx = i * batch_size + end_idx = min((i + 1) * batch_size, len(X_train)) + + batch_X = X_train_t[start_idx:end_idx] + batch_y = y_train_t[start_idx:end_idx] + + optimizer.zero_grad() + outputs = model(batch_X) + loss = criterion(outputs, batch_y) + loss.backward() + optimizer.step() + + epoch_loss += loss.item() + + avg_loss = epoch_loss / n_batches + + # Validation + model.eval() + with torch.no_grad(): + outputs = model(X_test_t) + _, predicted = torch.max(outputs, 1) + accuracy = (predicted == y_test_t).float().mean().item() + + training_history.append( + {"epoch": epoch + 1, "train_loss": avg_loss, "val_acc": accuracy} + ) + + if accuracy > best_acc: + best_acc = accuracy + best_epoch = epoch + 1 + + duration = time.time() - start_time + + # Final results + final_acc = training_history[-1]["val_acc"] + + return { + "dataset": dataset_name, + "status": "success", + "samples": num_samples, + "features": num_features, + "classes": num_classes, + "variance_explained": variance_explained, + "best_accuracy": best_acc, + "best_epoch": best_epoch, + "final_accuracy": final_acc, + "epochs_trained": epochs, + "duration_seconds": duration, + "training_history": training_history, + } + + except Exception as e: + return { + "dataset": dataset_name, + "status": "error", + "error": str(e), + "duration": time.time() - start_time, + } + + +def worker_init(): + """Initialize worker process.""" + # Set random seeds for reproducibility + np.random.seed(int(time.time() * 1000) % 2**32) + torch.manual_seed(int(time.time() * 1000) % 2**32) + + +class DistributedBenchmark: + """Manages distributed training across multiple datasets.""" + + def __init__( + self, + datasets_dir: Path, + output_dir: Path, + n_workers: int = 4, + epochs: int = 25, + quick_test: bool = False, + ): + self.datasets_dir = datasets_dir + self.output_dir = output_dir + self.n_workers = n_workers + self.epochs = epochs + self.quick_test = quick_test + + self.output_dir.mkdir(parents=True, exist_ok=True) + self.checkpoint_file = self.output_dir / "checkpoint.json" + self.results_file = self.output_dir / "distributed_results.json" + + def load_checkpoint(self) -> Dict: + """Load checkpoint if exists.""" + if self.checkpoint_file.exists(): + with open(self.checkpoint_file, "r") as f: + return json.load(f) + return {"completed": [], "results": []} + + def save_checkpoint(self, checkpoint: Dict): + """Save checkpoint.""" + with open(self.checkpoint_file, "w") as f: + json.dump(checkpoint, f, indent=2) + + def run_benchmark(self, resume: bool = False): + """ + Run distributed benchmark on all datasets. + + Args: + resume: Resume from checkpoint if available + """ + print("=" * 70) + print("🚀 DISTRIBUTED QUANTUM ML BENCHMARK") + print("=" * 70) + + # Find all CSV files + csv_files = list(self.datasets_dir.glob("*.csv")) + + if not csv_files: + print(f"❌ No CSV files found in {self.datasets_dir}") + return + + print("\n📊 Configuration:") + print(f" Datasets: {len(csv_files)}") + print(f" Workers: {self.n_workers}") + print(f" Epochs: {self.epochs}") + print(f" Quick test: {self.quick_test}") + print(f" Output: {self.output_dir}") + + # Load checkpoint + checkpoint = ( + self.load_checkpoint() if resume else {"completed": [], "results": []} + ) + completed_names = set(checkpoint["completed"]) + + # Filter remaining datasets + remaining = [f for f in csv_files if f.stem not in completed_names] + + if resume and completed_names: + print("\n♻️ Resuming from checkpoint:") + print(f" Completed: {len(completed_names)}") + print(f" Remaining: {len(remaining)}") + + if not remaining: + print("\n✅ All datasets already completed!") + return + + print( + f"\n🔄 Processing {len(remaining)} datasets with {self.n_workers} workers..." + ) + + # Create partial function with fixed parameters + worker_func = partial( + train_single_dataset, + n_qubits=4, + n_layers=2, + epochs=self.epochs, + batch_size=32, + learning_rate=0.001, + quick_test=self.quick_test, + ) + + # Start parallel training + start_time = time.time() + + with mp.Pool(processes=self.n_workers, initializer=worker_init) as pool: + # Track progress + results = [] + completed_count = len(completed_names) + + for i, result in enumerate(pool.imap_unordered(worker_func, remaining), 1): + completed_count += 1 + results.append(result) + checkpoint["results"].append(result) + checkpoint["completed"].append(result["dataset"]) + + # Progress update + status_symbol = "✓" if result["status"] == "success" else "✗" + if result["status"] == "success": + print( + f"\n[{completed_count}/{len(csv_files)}] {status_symbol} {result['dataset']}" + ) + print( + f" Accuracy: {result['best_accuracy']:.2%} (epoch {result['best_epoch']})" + ) + print(f" Duration: {result['duration_seconds']:.1f}s") + else: + print( + f"\n[{completed_count}/{len(csv_files)}] {status_symbol} {result['dataset']}" + ) + print(f" Error: {result.get('error', 'Unknown')}") + + # Save checkpoint every 10 datasets + if i % 10 == 0: + self.save_checkpoint(checkpoint) + print(f"\n💾 Checkpoint saved ({i}/{len(remaining)} processed)") + + # Final save + self.save_checkpoint(checkpoint) + + total_time = time.time() - start_time + + # Generate summary + self.generate_summary(checkpoint["results"], total_time) + + def generate_summary(self, results: List[Dict], total_time: float): + """Generate benchmark summary report.""" + print("\n" + "=" * 70) + print("📊 BENCHMARK SUMMARY") + print("=" * 70) + + successful = [r for r in results if r["status"] == "success"] + failed = [r for r in results if r["status"] == "error"] + + print(f"\n✅ Completed: {len(successful)}/{len(results)}") + print(f"❌ Failed: {len(failed)}") + print(f"⏱️ Total time: {total_time/60:.1f} minutes") + print(f"⚡ Avg time per dataset: {total_time/len(results):.1f} seconds") + + if successful: + accuracies = [r["best_accuracy"] for r in successful] + + print("\n📈 Accuracy Statistics:") + print(f" Mean: {np.mean(accuracies):.2%}") + print(f" Median: {np.median(accuracies):.2%}") + print(f" Std Dev: {np.std(accuracies):.2%}") + print(f" Min: {np.min(accuracies):.2%}") + print(f" Max: {np.max(accuracies):.2%}") + + # Performance tiers + exceptional = [r for r in successful if r["best_accuracy"] >= 0.95] + excellent = [r for r in successful if 0.85 <= r["best_accuracy"] < 0.95] + good = [r for r in successful if 0.75 <= r["best_accuracy"] < 0.85] + challenging = [r for r in successful if r["best_accuracy"] < 0.75] + + print("\n🏆 Performance Tiers:") + print(f" Exceptional (≥95%): {len(exceptional)}") + print(f" Excellent (85-95%): {len(excellent)}") + print(f" Good (75-85%): {len(good)}") + print(f" Challenging (<75%): {len(challenging)}") + + # Top 10 + top_datasets = sorted( + successful, key=lambda x: x["best_accuracy"], reverse=True + )[:10] + print("\n🥇 TOP 10 DATASETS:") + for rank, result in enumerate(top_datasets, 1): + print( + f" {rank}. {result['dataset']}: {result['best_accuracy']:.2%} " + f"({result['samples']} samples, {result['features']} features)" + ) + + # Save detailed results + summary = { + "timestamp": datetime.now().isoformat(), + "total_datasets": len(results), + "successful": len(successful), + "failed": len(failed), + "total_time_seconds": total_time, + "epochs": self.epochs, + "workers": self.n_workers, + "results": results, + } + + with open(self.results_file, "w") as f: + json.dump(summary, f, indent=2) + + print(f"\n✓ Detailed results saved to: {self.results_file}") + + +def main(): + parser = argparse.ArgumentParser( + description="Distributed Quantum ML Benchmark System", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + parser.add_argument( + "--datasets-dir", + type=str, + default="datasets/massive_quantum", + help="Directory containing CSV datasets", + ) + parser.add_argument( + "--output-dir", + type=str, + default="data_out/distributed_benchmark", + help="Output directory for results", + ) + parser.add_argument( + "--workers", type=int, default=4, help="Number of parallel workers" + ) + parser.add_argument( + "--epochs", type=int, default=25, help="Training epochs per dataset" + ) + parser.add_argument( + "--quick-test", + action="store_true", + help="Quick test mode (1 epoch, 500 samples max)", + ) + parser.add_argument("--resume", action="store_true", help="Resume from checkpoint") + + args = parser.parse_args() + + datasets_dir = Path(args.datasets_dir) + output_dir = Path(args.output_dir) + + if not datasets_dir.exists(): + print(f"❌ Datasets directory not found: {datasets_dir}") + print(" Run massive_dataset_expansion.py --download first") + return + + benchmark = DistributedBenchmark( + datasets_dir=datasets_dir, + output_dir=output_dir, + n_workers=args.workers, + epochs=args.epochs, + quick_test=args.quick_test, + ) + + benchmark.run_benchmark(resume=args.resume) + + +if __name__ == "__main__": + # Set multiprocessing start method + mp.set_start_method("spawn", force=True) + main() diff --git a/scripts/evaluate_azure_model.py b/scripts/evaluate_azure_model.py index 0310258be..b18d0bce1 100644 --- a/scripts/evaluate_azure_model.py +++ b/scripts/evaluate_azure_model.py @@ -1,134 +1,134 @@ -#!/usr/bin/env python -""" -Evaluation script for Azure OpenAI deployments with a local offline fallback. - -Works similarly to evaluate_openai_model.py but accepts --deployment to -choose the Azure deployment name. If Azure credentials are missing the script -falls back to a deterministic local predictor (echo) and still produces -machine-readable evaluation outputs which are useful for CI smoke tests. -""" - -from __future__ import annotations - -import argparse -import json -import os -import sys -import time -from pathlib import Path -from typing import Any, Dict, List - -# Add shared directory to path for imports -REPO_ROOT = Path(__file__).resolve().parent.parent -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from shared.evaluation_utils import compute_accuracy, load_jsonl, naive_predict - -try: - import openai # type: ignore - - HAS_AZURE_OPENAI = True -except Exception: - openai = None - HAS_AZURE_OPENAI = False - - -def azure_call(example: Dict[str, Any], deployment: str | None) -> str: - # If Azure OpenAI isn't configured, fall back to naive predictor - if not HAS_AZURE_OPENAI or not os.getenv("AZURE_OPENAI_API_KEY"): - return naive_predict(example) - - prompt = example.get("input") or " ".join( - [m.get("content", "") for m in (example.get("messages") or [])] - ) - try: - # Minimal safe attempt – real deployments may differ in invocation - resp = openai.ChatCompletion.create( - deployment_id=deployment, - messages=[{"role": "user", "content": prompt}], - max_tokens=200, - ) - return resp.choices[0].message.content.strip() - except Exception: - return naive_predict(example) - - -def run( - dataset: Path, - max_samples: int | None, - metrics: List[str], - deployment: str | None, - save_dir: Path | None, -) -> Dict[str, Any]: - data = load_jsonl(dataset, max_samples) - preds: List[str] = [] - expects: List[str | None] = [] - timings: List[float] = [] - - use_azure = ( - HAS_AZURE_OPENAI - and os.getenv("AZURE_OPENAI_API_KEY") - and os.getenv("AZURE_OPENAI_ENDPOINT") - ) - - for ex in data: - t0 = time.perf_counter() - if use_azure: - p = azure_call(ex, deployment) - else: - p = naive_predict(ex) - timings.append((time.perf_counter() - t0) * 1000.0) - preds.append(p) - expects.append(ex.get("expected") or ex.get("label")) - - summary: Dict[str, Any] = {"samples": len(preds)} - if "response_time" in metrics: - summary["response_time_ms"] = ( - round(sum(timings) / len(timings), 3) if timings else 0.0 - ) - if "accuracy" in metrics: - summary["accuracy"] = round(compute_accuracy(preds, expects), 4) - - if save_dir: - save_dir.mkdir(parents=True, exist_ok=True) - out = { - "summary": summary, - "predictions": [{"pred": p, "expected": e} for p, e in zip(preds, expects)], - } - (save_dir / "results.json").write_text( - json.dumps(out, indent=2), encoding="utf-8" - ) - - return summary - - -def parse_args(): - ap = argparse.ArgumentParser( - description="Evaluate Azure OpenAI deployment (offline fallback for CI)" - ) - ap.add_argument("--dataset", required=True) - ap.add_argument("--max-samples", type=int, default=None) - ap.add_argument("--metric", action="append", dest="metrics") - ap.add_argument("--deployment", default=None) - ap.add_argument("--save-dir", default=None) - return ap.parse_args() - - -def main(): - args = parse_args() - metrics = args.metrics or ["accuracy", "response_time"] - dataset = Path(args.dataset) - save = Path(args.save_dir) if args.save_dir else None - - try: - summary = run(dataset, args.max_samples, metrics, args.deployment, save) - print(json.dumps({"summary": summary})) - return 0 - except Exception as e: - print(f"[error] {e}") - return 1 - - -if __name__ == "__main__": - raise SystemExit(main()) +#!/usr/bin/env python +""" +Evaluation script for Azure OpenAI deployments with a local offline fallback. + +Works similarly to evaluate_openai_model.py but accepts --deployment to +choose the Azure deployment name. If Azure credentials are missing the script +falls back to a deterministic local predictor (echo) and still produces +machine-readable evaluation outputs which are useful for CI smoke tests. +""" + +from __future__ import annotations + +import argparse +import json +import os +import sys +import time +from pathlib import Path +from typing import Any, Dict, List + +# Add shared directory to path for imports +REPO_ROOT = Path(__file__).resolve().parent.parent +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from shared.evaluation_utils import compute_accuracy, load_jsonl, naive_predict + +try: + import openai # type: ignore + + HAS_AZURE_OPENAI = True +except Exception: + openai = None + HAS_AZURE_OPENAI = False + + +def azure_call(example: Dict[str, Any], deployment: str | None) -> str: + # If Azure OpenAI isn't configured, fall back to naive predictor + if not HAS_AZURE_OPENAI or not os.getenv("AZURE_OPENAI_API_KEY"): + return naive_predict(example) + + prompt = example.get("input") or " ".join( + [m.get("content", "") for m in (example.get("messages") or [])] + ) + try: + # Minimal safe attempt – real deployments may differ in invocation + resp = openai.ChatCompletion.create( + deployment_id=deployment, + messages=[{"role": "user", "content": prompt}], + max_tokens=200, + ) + return resp.choices[0].message.content.strip() + except Exception: + return naive_predict(example) + + +def run( + dataset: Path, + max_samples: int | None, + metrics: List[str], + deployment: str | None, + save_dir: Path | None, +) -> Dict[str, Any]: + data = load_jsonl(dataset, max_samples) + preds: List[str] = [] + expects: List[str | None] = [] + timings: List[float] = [] + + use_azure = ( + HAS_AZURE_OPENAI + and os.getenv("AZURE_OPENAI_API_KEY") + and os.getenv("AZURE_OPENAI_ENDPOINT") + ) + + for ex in data: + t0 = time.perf_counter() + if use_azure: + p = azure_call(ex, deployment) + else: + p = naive_predict(ex) + timings.append((time.perf_counter() - t0) * 1000.0) + preds.append(p) + expects.append(ex.get("expected") or ex.get("label")) + + summary: Dict[str, Any] = {"samples": len(preds)} + if "response_time" in metrics: + summary["response_time_ms"] = ( + round(sum(timings) / len(timings), 3) if timings else 0.0 + ) + if "accuracy" in metrics: + summary["accuracy"] = round(compute_accuracy(preds, expects), 4) + + if save_dir: + save_dir.mkdir(parents=True, exist_ok=True) + out = { + "summary": summary, + "predictions": [{"pred": p, "expected": e} for p, e in zip(preds, expects)], + } + (save_dir / "results.json").write_text( + json.dumps(out, indent=2), encoding="utf-8" + ) + + return summary + + +def parse_args(): + ap = argparse.ArgumentParser( + description="Evaluate Azure OpenAI deployment (offline fallback for CI)" + ) + ap.add_argument("--dataset", required=True) + ap.add_argument("--max-samples", type=int, default=None) + ap.add_argument("--metric", action="append", dest="metrics") + ap.add_argument("--deployment", default=None) + ap.add_argument("--save-dir", default=None) + return ap.parse_args() + + +def main(): + args = parse_args() + metrics = args.metrics or ["accuracy", "response_time"] + dataset = Path(args.dataset) + save = Path(args.save_dir) if args.save_dir else None + + try: + summary = run(dataset, args.max_samples, metrics, args.deployment, save) + print(json.dumps({"summary": summary})) + return 0 + except Exception as e: + print(f"[error] {e}") + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/evaluate_local_model.py b/scripts/evaluate_local_model.py index 07ea02550..2851f8fc1 100644 --- a/scripts/evaluate_local_model.py +++ b/scripts/evaluate_local_model.py @@ -1,169 +1,169 @@ -#!/usr/bin/env python -""" -Lightweight local evaluation script for offline testing and CI. - -This evaluator is intentionally conservative and has zero external -dependencies so it can be used in smoke tests and CI environments. - -Supported dataset formats: -- JSONL (one object per line) -- JSON array -- simple CSV (first column is input, second column is label) - -Supported metrics (minimal, approximate): -- accuracy: exact string match against `expected` or `label` -- response_time: measured time to run the predictor (avg ms) -- determinism: fraction of identical predictions when invoking predictor twice -- basic_bleu: naive unigram overlap score (0..1) - -The default predictor is a simple deterministic "echo" function which -returns "echo: ". This keeps tests fast and offline. -""" - -from __future__ import annotations - -import argparse -import json -import sys -import time -from pathlib import Path -from typing import Any, Dict, List - -# Add shared module to path for imports -sys.path.insert(0, str(Path(__file__).parent.parent / "shared")) -from evaluation_utils import load_dataset, naive_predict - - -def compute_accuracy(preds: List[str], expects: List[str]) -> float: - if not preds: - return 0.0 - match = 0 - for p, e in zip(preds, expects): - if e is None: - continue - if p.strip() == e.strip(): - match += 1 - return match / len(preds) - - -def basic_bleu(preds: List[str], expects: List[str]) -> float: - # Simple unigram overlap score averaged across samples - def score_one(p: str, e: str) -> float: - p_tokens = p.split() - e_tokens = e.split() - if not p_tokens or not e_tokens: - return 0.0 - overlap = sum(1 for t in p_tokens if t in e_tokens) - return overlap / len(p_tokens) - - if not preds: - return 0.0 - total = 0.0 - count = 0 - for p, e in zip(preds, expects): - if e is None: - continue - total += score_one(p, e) - count += 1 - return total / count if count else 0.0 - - -def run_evaluation( - dataset_path: Path, - max_samples: int | None, - metrics: List[str], - save_dir: Path | None, -) -> Dict[str, Any]: - data = load_dataset(dataset_path, max_samples) - if not data: - raise ValueError("No data found for evaluation") - - preds: List[str] = [] - expects: List[str | None] = [] - - # Warm run - for ex in data: - preds.append(naive_predict(ex)) - if "expected" in ex: - expects.append(ex["expected"]) - elif "label" in ex: - expects.append(str(ex["label"])) - else: - expects.append(None) - - results: Dict[str, Any] = {"samples": len(preds)} - - if "determinism" in metrics: - # run predictor twice and compare - preds2 = [naive_predict(ex) for ex in data] - identical = sum(1 for a, b in zip(preds, preds2) if a == b) - results["determinism"] = identical / len(preds) - - if "response_time" in metrics: - times: List[float] = [] - for ex in data: - t0 = time.perf_counter() - _ = naive_predict(ex) - times.append((time.perf_counter() - t0) * 1000.0) - avg_ms = sum(times) / len(times) - results["response_time_ms"] = round(avg_ms, 3) - - if "accuracy" in metrics: - results["accuracy"] = round(compute_accuracy(preds, expects), 4) - - if "basic_bleu" in metrics or "bleu" in metrics: - results["basic_bleu"] = round(basic_bleu(preds, expects), 4) - - # Save results - if save_dir: - save_dir.mkdir(parents=True, exist_ok=True) - out = { - "summary": results, - "predictions": [{"pred": p, "expected": e} for p, e in zip(preds, expects)], - } - path = save_dir / "results.json" - path.write_text(json.dumps(out, indent=2), encoding="utf-8") - - return results - - -def parse_args(): - ap = argparse.ArgumentParser( - description="Evaluate a local model (lightweight, offline)" - ) - ap.add_argument("--dataset", required=True, help="Path to dataset (json/jsonl/csv)") - ap.add_argument( - "--max-samples", - type=int, - default=None, - help="Limit number of evaluation samples", - ) - ap.add_argument( - "--metric", dest="metrics", action="append", help="Metric to compute (repeat)" - ) - ap.add_argument("--output-format", default="json", choices=["json", "text"]) - ap.add_argument("--save-dir", help="Directory to save results", default=None) - return ap.parse_args() - - -def main(): - args = parse_args() - metrics = args.metrics or ["accuracy", "determinism", "response_time"] - dataset = Path(args.dataset) - save = Path(args.save_dir) if args.save_dir else None - - try: - results = run_evaluation(dataset, args.max_samples, metrics, save) - if args.output_format == "json": - print(json.dumps({"summary": results})) - else: - for k, v in results.items(): - print(f"{k}: {v}") - return 0 - except Exception as e: - print(f"[error] Evaluation failed: {e}") - return 1 - - -if __name__ == "__main__": - raise SystemExit(main()) +#!/usr/bin/env python +""" +Lightweight local evaluation script for offline testing and CI. + +This evaluator is intentionally conservative and has zero external +dependencies so it can be used in smoke tests and CI environments. + +Supported dataset formats: +- JSONL (one object per line) +- JSON array +- simple CSV (first column is input, second column is label) + +Supported metrics (minimal, approximate): +- accuracy: exact string match against `expected` or `label` +- response_time: measured time to run the predictor (avg ms) +- determinism: fraction of identical predictions when invoking predictor twice +- basic_bleu: naive unigram overlap score (0..1) + +The default predictor is a simple deterministic "echo" function which +returns "echo: ". This keeps tests fast and offline. +""" + +from __future__ import annotations + +import argparse +import json +import sys +import time +from pathlib import Path +from typing import Any, Dict, List + +# Add shared module to path for imports +sys.path.insert(0, str(Path(__file__).parent.parent / "shared")) +from evaluation_utils import load_dataset, naive_predict + + +def compute_accuracy(preds: List[str], expects: List[str]) -> float: + if not preds: + return 0.0 + match = 0 + for p, e in zip(preds, expects): + if e is None: + continue + if p.strip() == e.strip(): + match += 1 + return match / len(preds) + + +def basic_bleu(preds: List[str], expects: List[str]) -> float: + # Simple unigram overlap score averaged across samples + def score_one(p: str, e: str) -> float: + p_tokens = p.split() + e_tokens = e.split() + if not p_tokens or not e_tokens: + return 0.0 + overlap = sum(1 for t in p_tokens if t in e_tokens) + return overlap / len(p_tokens) + + if not preds: + return 0.0 + total = 0.0 + count = 0 + for p, e in zip(preds, expects): + if e is None: + continue + total += score_one(p, e) + count += 1 + return total / count if count else 0.0 + + +def run_evaluation( + dataset_path: Path, + max_samples: int | None, + metrics: List[str], + save_dir: Path | None, +) -> Dict[str, Any]: + data = load_dataset(dataset_path, max_samples) + if not data: + raise ValueError("No data found for evaluation") + + preds: List[str] = [] + expects: List[str | None] = [] + + # Warm run + for ex in data: + preds.append(naive_predict(ex)) + if "expected" in ex: + expects.append(ex["expected"]) + elif "label" in ex: + expects.append(str(ex["label"])) + else: + expects.append(None) + + results: Dict[str, Any] = {"samples": len(preds)} + + if "determinism" in metrics: + # run predictor twice and compare + preds2 = [naive_predict(ex) for ex in data] + identical = sum(1 for a, b in zip(preds, preds2) if a == b) + results["determinism"] = identical / len(preds) + + if "response_time" in metrics: + times: List[float] = [] + for ex in data: + t0 = time.perf_counter() + _ = naive_predict(ex) + times.append((time.perf_counter() - t0) * 1000.0) + avg_ms = sum(times) / len(times) + results["response_time_ms"] = round(avg_ms, 3) + + if "accuracy" in metrics: + results["accuracy"] = round(compute_accuracy(preds, expects), 4) + + if "basic_bleu" in metrics or "bleu" in metrics: + results["basic_bleu"] = round(basic_bleu(preds, expects), 4) + + # Save results + if save_dir: + save_dir.mkdir(parents=True, exist_ok=True) + out = { + "summary": results, + "predictions": [{"pred": p, "expected": e} for p, e in zip(preds, expects)], + } + path = save_dir / "results.json" + path.write_text(json.dumps(out, indent=2), encoding="utf-8") + + return results + + +def parse_args(): + ap = argparse.ArgumentParser( + description="Evaluate a local model (lightweight, offline)" + ) + ap.add_argument("--dataset", required=True, help="Path to dataset (json/jsonl/csv)") + ap.add_argument( + "--max-samples", + type=int, + default=None, + help="Limit number of evaluation samples", + ) + ap.add_argument( + "--metric", dest="metrics", action="append", help="Metric to compute (repeat)" + ) + ap.add_argument("--output-format", default="json", choices=["json", "text"]) + ap.add_argument("--save-dir", help="Directory to save results", default=None) + return ap.parse_args() + + +def main(): + args = parse_args() + metrics = args.metrics or ["accuracy", "determinism", "response_time"] + dataset = Path(args.dataset) + save = Path(args.save_dir) if args.save_dir else None + + try: + results = run_evaluation(dataset, args.max_samples, metrics, save) + if args.output_format == "json": + print(json.dumps({"summary": results})) + else: + for k, v in results.items(): + print(f"{k}: {v}") + return 0 + except Exception as e: + print(f"[error] Evaluation failed: {e}") + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/evaluate_model.py b/scripts/evaluate_model.py index 241bf5cf8..6b432d86a 100644 --- a/scripts/evaluate_model.py +++ b/scripts/evaluate_model.py @@ -1,163 +1,163 @@ -""" -Model Evaluation Script - -Delegates to evaluate_lora_model.py when available, otherwise provides a -lightweight fallback that can validate dataset format, compute simple text -metrics (BLEU, exact-match), and report structured JSON results. - -Usage: - python scripts/evaluate_model.py --model path/to/model --dataset path/to/data.jsonl - python scripts/evaluate_model.py --model path/to/model --dataset path/to/data.jsonl --output results.json - python scripts/evaluate_model.py --model path/to/model --dataset path/to/data.jsonl --metrics accuracy bleu -""" - -import sys -from pathlib import Path - -# Import the real evaluation logic from evaluate_lora_model -sys.path.insert(0, str(Path(__file__).parent)) - -try: - from evaluate_lora_model import main as lora_main - - def main(): - """Delegate to evaluate_lora_model for actual implementation.""" - print("Note: Delegating to evaluate_lora_model.py for evaluation") - print("For full control, use evaluate_lora_model.py directly\n") - lora_main() - -except ImportError: - # Fallback with lightweight metrics — - # no transformers/torch required - import argparse - import json - from collections import Counter - from datetime import datetime, timezone - - def _load_dataset(path: str) -> list[dict]: - """Load JSONL or JSON dataset.""" - p = Path(path) - if not p.exists(): - raise FileNotFoundError(f"Dataset not found: {path}") - - text = p.read_text(encoding="utf-8").strip() - if text.startswith("["): - return json.loads(text) - - # JSONL - return [json.loads(line) for line in text.splitlines() if line.strip()] - - def _extract_pairs(records: list[dict]) -> list[tuple[str, str]]: - """Extract (reference, candidate) pairs from chat-formatted records.""" - pairs: list[tuple[str, str]] = [] - for rec in records: - msgs = rec.get("messages", []) - for i, msg in enumerate(msgs): - if msg.get("role") == "assistant": - # Use previous user message as input context - ref = msg.get("content", "") - pairs.append((ref, "")) # candidate empty until model runs - return pairs - - def _bleu_score(reference: str, candidate: str) -> float: - """Simplified unigram BLEU (no external deps).""" - ref_tokens = reference.lower().split() - cand_tokens = candidate.lower().split() - if not ref_tokens or not cand_tokens: - return 0.0 - ref_counts = Counter(ref_tokens) - match = sum(min(ref_counts[t], 1) for t in cand_tokens if t in ref_counts) - precision = match / len(cand_tokens) if cand_tokens else 0.0 - brevity = min(1.0, len(cand_tokens) / len(ref_tokens)) if ref_tokens else 0.0 - return precision * brevity - - def evaluate(model_path: str, dataset_path: str, metrics: list[str]) -> dict: - """Lightweight evaluation: validate dataset + compute format metrics.""" - results: dict = {} - - # Validate model path - model_p = Path(model_path) - results["model_exists"] = model_p.exists() - results["model_path"] = str(model_p) - - if model_p.is_dir(): - has_config = (model_p / "adapter_config.json").exists() - has_weights = (model_p / "adapter_model.safetensors").exists() or ( - model_p / "adapter_model.bin" - ).exists() - results["adapter_valid"] = has_config and has_weights - else: - results["adapter_valid"] = False - - # Load and validate dataset - try: - records = _load_dataset(dataset_path) - results["dataset_records"] = len(records) - results["dataset_valid"] = True - except Exception as exc: - results["dataset_records"] = 0 - results["dataset_valid"] = False - results["dataset_error"] = str(exc) - # Return early — can't compute actual metrics - for m in metrics: - results[m] = 0.0 - results["note"] = "Fallback evaluation — dataset load failed" - return results - - # Validate chat format - valid_format = 0 - for rec in records: - msgs = rec.get("messages", []) - if isinstance(msgs, list) and len(msgs) >= 2: - roles = {m.get("role") for m in msgs} - if "user" in roles and "assistant" in roles: - valid_format += 1 - results["valid_chat_format"] = valid_format - results["format_rate"] = round(valid_format / max(len(records), 1), 4) - - # Compute requested metrics (stub values since we can't run the model) - for m in metrics: - if m == "format_rate": - results[m] = results["format_rate"] - elif m == "dataset_size": - results[m] = len(records) - else: - # Mark as needing real inference - results[m] = None - - results["note"] = ( - "Fallback evaluation — install transformers+torch for real metrics. " - "Dataset format and adapter structure validated." - ) - results["timestamp"] = datetime.now(timezone.utc).isoformat() - return results - - def main(): - ap = argparse.ArgumentParser(description="Evaluate a trained model.") - ap.add_argument( - "--model", required=True, help="Path to trained model or adapter" - ) - ap.add_argument( - "--dataset", required=True, help="Path to evaluation dataset (JSONL/JSON)" - ) - ap.add_argument( - "--metrics", - nargs="+", - default=["accuracy", "format_rate"], - help="Metrics to compute (accuracy, bleu, format_rate, dataset_size)", - ) - ap.add_argument("--output", help="Path to write results JSON") - args = ap.parse_args() - - results = evaluate(args.model, args.dataset, args.metrics) - print(json.dumps(results, indent=2)) - - if args.output: - out = Path(args.output) - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(results, indent=2), encoding="utf-8") - print(f"\n📋 Results written to {args.output}") - - -if __name__ == "__main__": - main() +""" +Model Evaluation Script + +Delegates to evaluate_lora_model.py when available, otherwise provides a +lightweight fallback that can validate dataset format, compute simple text +metrics (BLEU, exact-match), and report structured JSON results. + +Usage: + python scripts/evaluate_model.py --model path/to/model --dataset path/to/data.jsonl + python scripts/evaluate_model.py --model path/to/model --dataset path/to/data.jsonl --output results.json + python scripts/evaluate_model.py --model path/to/model --dataset path/to/data.jsonl --metrics accuracy bleu +""" + +import sys +from pathlib import Path + +# Import the real evaluation logic from evaluate_lora_model +sys.path.insert(0, str(Path(__file__).parent)) + +try: + from evaluate_lora_model import main as lora_main + + def main(): + """Delegate to evaluate_lora_model for actual implementation.""" + print("Note: Delegating to evaluate_lora_model.py for evaluation") + print("For full control, use evaluate_lora_model.py directly\n") + lora_main() + +except ImportError: + # Fallback with lightweight metrics — + # no transformers/torch required + import argparse + import json + from collections import Counter + from datetime import datetime, timezone + + def _load_dataset(path: str) -> list[dict]: + """Load JSONL or JSON dataset.""" + p = Path(path) + if not p.exists(): + raise FileNotFoundError(f"Dataset not found: {path}") + + text = p.read_text(encoding="utf-8").strip() + if text.startswith("["): + return json.loads(text) + + # JSONL + return [json.loads(line) for line in text.splitlines() if line.strip()] + + def _extract_pairs(records: list[dict]) -> list[tuple[str, str]]: + """Extract (reference, candidate) pairs from chat-formatted records.""" + pairs: list[tuple[str, str]] = [] + for rec in records: + msgs = rec.get("messages", []) + for i, msg in enumerate(msgs): + if msg.get("role") == "assistant": + # Use previous user message as input context + ref = msg.get("content", "") + pairs.append((ref, "")) # candidate empty until model runs + return pairs + + def _bleu_score(reference: str, candidate: str) -> float: + """Simplified unigram BLEU (no external deps).""" + ref_tokens = reference.lower().split() + cand_tokens = candidate.lower().split() + if not ref_tokens or not cand_tokens: + return 0.0 + ref_counts = Counter(ref_tokens) + match = sum(min(ref_counts[t], 1) for t in cand_tokens if t in ref_counts) + precision = match / len(cand_tokens) if cand_tokens else 0.0 + brevity = min(1.0, len(cand_tokens) / len(ref_tokens)) if ref_tokens else 0.0 + return precision * brevity + + def evaluate(model_path: str, dataset_path: str, metrics: list[str]) -> dict: + """Lightweight evaluation: validate dataset + compute format metrics.""" + results: dict = {} + + # Validate model path + model_p = Path(model_path) + results["model_exists"] = model_p.exists() + results["model_path"] = str(model_p) + + if model_p.is_dir(): + has_config = (model_p / "adapter_config.json").exists() + has_weights = (model_p / "adapter_model.safetensors").exists() or ( + model_p / "adapter_model.bin" + ).exists() + results["adapter_valid"] = has_config and has_weights + else: + results["adapter_valid"] = False + + # Load and validate dataset + try: + records = _load_dataset(dataset_path) + results["dataset_records"] = len(records) + results["dataset_valid"] = True + except Exception as exc: + results["dataset_records"] = 0 + results["dataset_valid"] = False + results["dataset_error"] = str(exc) + # Return early — can't compute actual metrics + for m in metrics: + results[m] = 0.0 + results["note"] = "Fallback evaluation — dataset load failed" + return results + + # Validate chat format + valid_format = 0 + for rec in records: + msgs = rec.get("messages", []) + if isinstance(msgs, list) and len(msgs) >= 2: + roles = {m.get("role") for m in msgs} + if "user" in roles and "assistant" in roles: + valid_format += 1 + results["valid_chat_format"] = valid_format + results["format_rate"] = round(valid_format / max(len(records), 1), 4) + + # Compute requested metrics (stub values since we can't run the model) + for m in metrics: + if m == "format_rate": + results[m] = results["format_rate"] + elif m == "dataset_size": + results[m] = len(records) + else: + # Mark as needing real inference + results[m] = None + + results["note"] = ( + "Fallback evaluation — install transformers+torch for real metrics. " + "Dataset format and adapter structure validated." + ) + results["timestamp"] = datetime.now(timezone.utc).isoformat() + return results + + def main(): + ap = argparse.ArgumentParser(description="Evaluate a trained model.") + ap.add_argument( + "--model", required=True, help="Path to trained model or adapter" + ) + ap.add_argument( + "--dataset", required=True, help="Path to evaluation dataset (JSONL/JSON)" + ) + ap.add_argument( + "--metrics", + nargs="+", + default=["accuracy", "format_rate"], + help="Metrics to compute (accuracy, bleu, format_rate, dataset_size)", + ) + ap.add_argument("--output", help="Path to write results JSON") + args = ap.parse_args() + + results = evaluate(args.model, args.dataset, args.metrics) + print(json.dumps(results, indent=2)) + + if args.output: + out = Path(args.output) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(results, indent=2), encoding="utf-8") + print(f"\n📋 Results written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/evaluate_openai_model.py b/scripts/evaluate_openai_model.py index 5e8cb20c7..c055e7268 100644 --- a/scripts/evaluate_openai_model.py +++ b/scripts/evaluate_openai_model.py @@ -1,154 +1,154 @@ -#!/usr/bin/env python -""" -Evaluation harness for OpenAI / remote API-backed models. - -This script will try to call the OpenAI API if credentials are available. -When running in CI or offline, the script falls back to a local deterministic -predictor so it remains useful for smoke tests. - -Supported metrics: accuracy, response_time, basic_bleu -""" - -from __future__ import annotations - -import argparse -import json -import os -import sys -import time -from pathlib import Path -from typing import Any, Dict, List - -# Ensure repository root is on sys.path before importing local shared modules. -REPO_ROOT = Path(__file__).resolve().parent.parent -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from shared.evaluation_utils import compute_metrics, load_jsonl, naive_predict - -try: - import openai # type: ignore - - HAS_OPENAI = True -except Exception: - openai = None - HAS_OPENAI = False - - -def call_openai_completion( - example: Dict[str, Any], deployment: str | None = None -) -> str: - # Minimal safe wrapper; default to naive_predict when API unavailable - if not HAS_OPENAI or not os.getenv("OPENAI_API_KEY"): - return naive_predict(example) - - prompt = None - if "input" in example: - prompt = example["input"] - else: - msgs = example.get("messages") or [] - # join user messages - prompt = ( - " ".join([m.get("content", "") for m in msgs if m.get("role") == "user"]) - or "" - ) - - if not prompt: - return naive_predict(example) - - # Use chat completion if available or fallback to completion - try: - if deployment: - resp = openai.ChatCompletion.create( - model=deployment, - messages=[{"role": "user", "content": prompt}], - max_tokens=200, - temperature=0.0, - ) - return resp.choices[0].message.content.strip() - else: - # best-effort fallback when only older API present - resp = openai.Completion.create( - model="text-davinci-003", prompt=prompt, max_tokens=200, temperature=0.0 - ) - return str(resp.choices[0].text).strip() - except Exception: - # When the API call fails, fall back (useful for CI) - return naive_predict(example) - - -def run( - dataset: Path, - max_samples: int | None, - metrics: List[str], - deployment: str | None, - save_dir: Path | None, -) -> Dict[str, Any]: - data = load_jsonl(dataset, max_samples) - preds = [] - expects = [] - - # prefer OpenAI call when available - use_openai = HAS_OPENAI and os.getenv("OPENAI_API_KEY") - - timings: List[float] = [] - for ex in data: - t0 = time.perf_counter() - if use_openai: - p = call_openai_completion(ex, deployment) - else: - p = naive_predict(ex) - timings.append((time.perf_counter() - t0) * 1000.0) - preds.append(p) - expects.append(ex.get("expected") or ex.get("label")) - - summary: Dict[str, Any] = {"samples": len(preds)} - if "response_time" in metrics: - summary["response_time_ms"] = ( - round(sum(timings) / len(timings), 3) if timings else 0.0 - ) - if "accuracy" in metrics: - summary.update(compute_metrics(preds, expects)) - - if save_dir: - save_dir.mkdir(parents=True, exist_ok=True) - out = { - "summary": summary, - "predictions": [{"pred": p, "expected": e} for p, e in zip(preds, expects)], - } - (save_dir / "results.json").write_text( - json.dumps(out, indent=2), encoding="utf-8" - ) - - return summary - - -def parse_args(): - ap = argparse.ArgumentParser( - description="Evaluate via OpenAI API (with local fallback for CI)" - ) - ap.add_argument("--dataset", required=True) - ap.add_argument("--max-samples", type=int, default=None) - ap.add_argument("--metric", action="append", dest="metrics") - ap.add_argument("--deployment", default=None) - ap.add_argument("--save-dir", default=None) - return ap.parse_args() - - -def main(): - args = parse_args() - metrics = args.metrics or ["accuracy", "response_time"] - dataset = Path(args.dataset) - save = Path(args.save_dir) if args.save_dir else None - - try: - summary = run(dataset, args.max_samples, metrics, args.deployment, save) - print(json.dumps({"summary": summary})) - return 0 - except Exception as e: - print(f"[error] {e}") - return 1 - - -if __name__ == "__main__": - raise SystemExit(main()) +#!/usr/bin/env python +""" +Evaluation harness for OpenAI / remote API-backed models. + +This script will try to call the OpenAI API if credentials are available. +When running in CI or offline, the script falls back to a local deterministic +predictor so it remains useful for smoke tests. + +Supported metrics: accuracy, response_time, basic_bleu +""" + +from __future__ import annotations + +import argparse +import json +import os +import sys +import time +from pathlib import Path +from typing import Any, Dict, List + +# Ensure repository root is on sys.path before importing local shared modules. +REPO_ROOT = Path(__file__).resolve().parent.parent +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from shared.evaluation_utils import compute_metrics, load_jsonl, naive_predict + +try: + import openai # type: ignore + + HAS_OPENAI = True +except Exception: + openai = None + HAS_OPENAI = False + + +def call_openai_completion( + example: Dict[str, Any], deployment: str | None = None +) -> str: + # Minimal safe wrapper; default to naive_predict when API unavailable + if not HAS_OPENAI or not os.getenv("OPENAI_API_KEY"): + return naive_predict(example) + + prompt = None + if "input" in example: + prompt = example["input"] + else: + msgs = example.get("messages") or [] + # join user messages + prompt = ( + " ".join([m.get("content", "") for m in msgs if m.get("role") == "user"]) + or "" + ) + + if not prompt: + return naive_predict(example) + + # Use chat completion if available or fallback to completion + try: + if deployment: + resp = openai.ChatCompletion.create( + model=deployment, + messages=[{"role": "user", "content": prompt}], + max_tokens=200, + temperature=0.0, + ) + return resp.choices[0].message.content.strip() + else: + # best-effort fallback when only older API present + resp = openai.Completion.create( + model="text-davinci-003", prompt=prompt, max_tokens=200, temperature=0.0 + ) + return str(resp.choices[0].text).strip() + except Exception: + # When the API call fails, fall back (useful for CI) + return naive_predict(example) + + +def run( + dataset: Path, + max_samples: int | None, + metrics: List[str], + deployment: str | None, + save_dir: Path | None, +) -> Dict[str, Any]: + data = load_jsonl(dataset, max_samples) + preds = [] + expects = [] + + # prefer OpenAI call when available + use_openai = HAS_OPENAI and os.getenv("OPENAI_API_KEY") + + timings: List[float] = [] + for ex in data: + t0 = time.perf_counter() + if use_openai: + p = call_openai_completion(ex, deployment) + else: + p = naive_predict(ex) + timings.append((time.perf_counter() - t0) * 1000.0) + preds.append(p) + expects.append(ex.get("expected") or ex.get("label")) + + summary: Dict[str, Any] = {"samples": len(preds)} + if "response_time" in metrics: + summary["response_time_ms"] = ( + round(sum(timings) / len(timings), 3) if timings else 0.0 + ) + if "accuracy" in metrics: + summary.update(compute_metrics(preds, expects)) + + if save_dir: + save_dir.mkdir(parents=True, exist_ok=True) + out = { + "summary": summary, + "predictions": [{"pred": p, "expected": e} for p, e in zip(preds, expects)], + } + (save_dir / "results.json").write_text( + json.dumps(out, indent=2), encoding="utf-8" + ) + + return summary + + +def parse_args(): + ap = argparse.ArgumentParser( + description="Evaluate via OpenAI API (with local fallback for CI)" + ) + ap.add_argument("--dataset", required=True) + ap.add_argument("--max-samples", type=int, default=None) + ap.add_argument("--metric", action="append", dest="metrics") + ap.add_argument("--deployment", default=None) + ap.add_argument("--save-dir", default=None) + return ap.parse_args() + + +def main(): + args = parse_args() + metrics = args.metrics or ["accuracy", "response_time"] + dataset = Path(args.dataset) + save = Path(args.save_dir) if args.save_dir else None + + try: + summary = run(dataset, args.max_samples, metrics, args.deployment, save) + print(json.dumps({"summary": summary})) + return 0 + except Exception as e: + print(f"[error] {e}") + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/evaluate_quantum_model.py b/scripts/evaluate_quantum_model.py index cdfdcceed..d527c2197 100644 --- a/scripts/evaluate_quantum_model.py +++ b/scripts/evaluate_quantum_model.py @@ -1,148 +1,148 @@ -#!/usr/bin/env python -""" -Simple evaluation wrapper for quantum model artifacts (lightweight). - -This script is designed for CI-friendly evaluation of precomputed quantum model -results. It expects a model JSON file (model_path) containing a `predictions` -array aligned with the dataset entries, or a mapping object. The dataset may be -CSV or JSONL containing labels for evaluation. - -Supported metrics: accuracy, precision, recall, f1_score -""" - -from __future__ import annotations - -import argparse -import json -import sys -from pathlib import Path -from typing import Any, Dict, List, Optional - -# Ensure repository root is on sys.path before importing local shared modules. -REPO_ROOT = Path(__file__).resolve().parent.parent -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from shared.evaluation_utils import load_labels_from_dataset - - -def load_model_predictions( - model_path: Path, max_samples: Optional[int] = None -) -> List[Any]: - if not model_path.exists(): - raise FileNotFoundError(model_path) - with model_path.open("r", encoding="utf-8") as f: - obj = json.load(f) - preds = obj.get("predictions") if isinstance(obj, dict) else None - if preds is None: - # If the model file contains a single scalar or mapping return a best-effort - # representation: try 'labels' or 'prediction' keys - preds = obj.get("labels") or obj.get("prediction") or [] - if max_samples is not None: - preds = preds[:max_samples] - return preds - - -def compute_binary_metrics(y_true: List[Any], y_pred: List[Any]) -> Dict[str, float]: - # Convert to strings for stable comparison - paired = list(zip(y_true, y_pred)) - if not paired: - return {"accuracy": 0.0, "precision": 0.0, "recall": 0.0, "f1_score": 0.0} - - tp = fp = tn = fn = 0 - for t, p in paired: - if t is None: - continue - t_s = str(t).strip() - p_s = str(p).strip() - # assume binary labels '1'/'0' or 'true'/'false' - if t_s == p_s: - if t_s.lower() in ("1", "true", "yes"): - tp += 1 - else: - tn += 1 - else: - if p_s.lower() in ("1", "true", "yes"): - fp += 1 - else: - fn += 1 - - total = tp + tn + fp + fn - accuracy = (tp + tn) / total if total else 0.0 - precision = tp / (tp + fp) if (tp + fp) else 0.0 - recall = tp / (tp + fn) if (tp + fn) else 0.0 - f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0 - return { - "accuracy": round(accuracy, 4), - "precision": round(precision, 4), - "recall": round(recall, 4), - "f1_score": round(f1, 4), - } - - -def run( - dataset: Path, - model: Path, - max_samples: Optional[int], - metrics: List[str], - save_dir: Optional[Path], -) -> Dict[str, Any]: - y_true = load_labels_from_dataset(dataset, max_samples) - y_pred = load_model_predictions(model, max_samples) - - if len(y_pred) < len(y_true): - # pad with None - y_pred += [None] * (len(y_true) - len(y_pred)) - - # For now support binary metrics via compute_binary_metrics - summary: Dict[str, float] = {} - if any(m in ("accuracy", "precision", "recall", "f1_score") for m in metrics): - mvals = compute_binary_metrics(y_true, y_pred) - # include requested metrics - for m in ("accuracy", "precision", "recall", "f1_score"): - if m in metrics: - summary[m] = mvals.get(m, 0.0) - - if save_dir: - save_dir.mkdir(parents=True, exist_ok=True) - out = { - "summary": summary, - "predictions": [{"pred": p, "expected": t} for p, t in zip(y_pred, y_true)], - } - (save_dir / "results.json").write_text( - json.dumps(out, indent=2), encoding="utf-8" - ) - - return summary - - -def parse_args(): - ap = argparse.ArgumentParser( - description="Evaluate lightweight quantum model artifacts" - ) - ap.add_argument("--dataset", required=True) - ap.add_argument("--model", required=True) - ap.add_argument("--max-samples", type=int, default=None) - ap.add_argument("--metric", action="append", dest="metrics") - ap.add_argument("--save-dir", default=None) - return ap.parse_args() - - -def main(): - args = parse_args() - metrics = args.metrics or ["accuracy"] - dataset = Path(args.dataset) - model = Path(args.model) - savedir = Path(args.save_dir) if args.save_dir else None - - try: - summary = run(dataset, model, args.max_samples, metrics, savedir) - print(json.dumps({"summary": summary})) - return 0 - except Exception as e: - print(f"[error] {e}") - return 1 - - -if __name__ == "__main__": - raise SystemExit(main()) +#!/usr/bin/env python +""" +Simple evaluation wrapper for quantum model artifacts (lightweight). + +This script is designed for CI-friendly evaluation of precomputed quantum model +results. It expects a model JSON file (model_path) containing a `predictions` +array aligned with the dataset entries, or a mapping object. The dataset may be +CSV or JSONL containing labels for evaluation. + +Supported metrics: accuracy, precision, recall, f1_score +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path +from typing import Any, Dict, List, Optional + +# Ensure repository root is on sys.path before importing local shared modules. +REPO_ROOT = Path(__file__).resolve().parent.parent +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from shared.evaluation_utils import load_labels_from_dataset + + +def load_model_predictions( + model_path: Path, max_samples: Optional[int] = None +) -> List[Any]: + if not model_path.exists(): + raise FileNotFoundError(model_path) + with model_path.open("r", encoding="utf-8") as f: + obj = json.load(f) + preds = obj.get("predictions") if isinstance(obj, dict) else None + if preds is None: + # If the model file contains a single scalar or mapping return a best-effort + # representation: try 'labels' or 'prediction' keys + preds = obj.get("labels") or obj.get("prediction") or [] + if max_samples is not None: + preds = preds[:max_samples] + return preds + + +def compute_binary_metrics(y_true: List[Any], y_pred: List[Any]) -> Dict[str, float]: + # Convert to strings for stable comparison + paired = list(zip(y_true, y_pred)) + if not paired: + return {"accuracy": 0.0, "precision": 0.0, "recall": 0.0, "f1_score": 0.0} + + tp = fp = tn = fn = 0 + for t, p in paired: + if t is None: + continue + t_s = str(t).strip() + p_s = str(p).strip() + # assume binary labels '1'/'0' or 'true'/'false' + if t_s == p_s: + if t_s.lower() in ("1", "true", "yes"): + tp += 1 + else: + tn += 1 + else: + if p_s.lower() in ("1", "true", "yes"): + fp += 1 + else: + fn += 1 + + total = tp + tn + fp + fn + accuracy = (tp + tn) / total if total else 0.0 + precision = tp / (tp + fp) if (tp + fp) else 0.0 + recall = tp / (tp + fn) if (tp + fn) else 0.0 + f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0 + return { + "accuracy": round(accuracy, 4), + "precision": round(precision, 4), + "recall": round(recall, 4), + "f1_score": round(f1, 4), + } + + +def run( + dataset: Path, + model: Path, + max_samples: Optional[int], + metrics: List[str], + save_dir: Optional[Path], +) -> Dict[str, Any]: + y_true = load_labels_from_dataset(dataset, max_samples) + y_pred = load_model_predictions(model, max_samples) + + if len(y_pred) < len(y_true): + # pad with None + y_pred += [None] * (len(y_true) - len(y_pred)) + + # For now support binary metrics via compute_binary_metrics + summary: Dict[str, float] = {} + if any(m in ("accuracy", "precision", "recall", "f1_score") for m in metrics): + mvals = compute_binary_metrics(y_true, y_pred) + # include requested metrics + for m in ("accuracy", "precision", "recall", "f1_score"): + if m in metrics: + summary[m] = mvals.get(m, 0.0) + + if save_dir: + save_dir.mkdir(parents=True, exist_ok=True) + out = { + "summary": summary, + "predictions": [{"pred": p, "expected": t} for p, t in zip(y_pred, y_true)], + } + (save_dir / "results.json").write_text( + json.dumps(out, indent=2), encoding="utf-8" + ) + + return summary + + +def parse_args(): + ap = argparse.ArgumentParser( + description="Evaluate lightweight quantum model artifacts" + ) + ap.add_argument("--dataset", required=True) + ap.add_argument("--model", required=True) + ap.add_argument("--max-samples", type=int, default=None) + ap.add_argument("--metric", action="append", dest="metrics") + ap.add_argument("--save-dir", default=None) + return ap.parse_args() + + +def main(): + args = parse_args() + metrics = args.metrics or ["accuracy"] + dataset = Path(args.dataset) + model = Path(args.model) + savedir = Path(args.save_dir) if args.save_dir else None + + try: + summary = run(dataset, model, args.max_samples, metrics, savedir) + print(json.dumps({"summary": summary})) + return 0 + except Exception as e: + print(f"[error] {e}") + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/extract_chat_logs_dataset.py b/scripts/extract_chat_logs_dataset.py index 6b738334c..4794c3bc6 100644 --- a/scripts/extract_chat_logs_dataset.py +++ b/scripts/extract_chat_logs_dataset.py @@ -1,201 +1,201 @@ -"""Extract chat conversation logs from ai-projects/chat-cli/logs into Phi-3 style dataset. - -Each log file is JSONL with entries: {"role": "user|assistant", "content": "...", "timestamp": "..."} -We transform these into training records with a messages list. - -Strategy: -- For each assistant reply, pair it with the most recent user message (basic turn pair dataset) -- Additionally build rolling window examples (up to --context-window messages) when available -- Deduplicate by hash of concatenated message contents -- Provide fallback synthetic record if no logs are found (to keep structure testable) - -Outputs: - datasets/chat/chat_logs/train.json - datasets/chat/chat_logs/test.json - datasets/chat/chat_logs/metadata.json - -Usage (PowerShell): - python .\\scripts\\extract_chat_logs_dataset.py --max-records 500 - python AI\\microsoft_phi-silica-3.6_v1\\scripts\\train_lora.py --dataset .\\datasets\\chat\\chat_logs --dry-run -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import random -from pathlib import Path -from typing import Dict, List - -LOGS_DIR = Path("ai-projects/chat-cli/logs") -OUTPUT_DIR = Path("datasets/chat/chat_logs") - - -def iter_logs() -> List[Path]: - if not LOGS_DIR.exists(): - return [] - return sorted([p for p in LOGS_DIR.glob("*.jsonl") if p.is_file()]) - - -def read_jsonl(path: Path) -> List[Dict]: - records: List[Dict] = [] - with path.open("r", encoding="utf-8", errors="ignore") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - obj = json.loads(line) - if isinstance(obj, dict) and "role" in obj and "content" in obj: - records.append(obj) - except Exception: - continue - return records - - -def build_examples(messages: List[Dict], context_window: int) -> List[Dict]: - # Simple user->assistant pairs - build list directly - pairs = [] - last_user = None - for m in messages: - if m.get("role") == "user": - last_user = m - elif m.get("role") == "assistant" and last_user: - pairs.append({"messages": [last_user, m]}) - - # Rolling windows - use list comprehension for better performance - windows = [] - if context_window > 2: - for i in range(len(messages)): - if messages[i].get("role") == "assistant": - start = max(0, i - context_window + 1) - window = messages[start : i + 1] - # Must contain at least one user+assistant - if any(x.get("role") == "user" for x in window) and any( - x.get("role") == "assistant" for x in window - ): - windows.append({"messages": window}) - - # Combine lists efficiently with extend - pairs.extend(windows) - return pairs - - -def hash_example(example: Dict) -> str: - concat = "\n".join( - [ - f"{m.get('role', '')}: {m.get('content', '')[:400]}" - for m in example.get("messages", []) - ] - ) - return hashlib.sha256(concat.encode("utf-8")).hexdigest()[:24] - - -def main(): - ap = argparse.ArgumentParser(description="Extract chat logs into training dataset") - ap.add_argument( - "--max-records", type=int, default=1000, help="Maximum examples to output" - ) - ap.add_argument("--train-ratio", type=float, default=0.9, help="Train split ratio") - ap.add_argument( - "--context-window", - type=int, - default=6, - help="Max messages in rolling window examples", - ) - ap.add_argument("--seed", type=int, default=42, help="RNG seed") - args = ap.parse_args() - - random.seed(args.seed) - - OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - - log_files = iter_logs() - all_examples: List[Dict] = [] - for lf in log_files: - msgs = read_jsonl(lf) - if not msgs: - continue - exs = build_examples(msgs, args.context_window) - # Add metadata and hash in batch using list comprehension - enriched_exs = [ - {**e, "source_file": lf.name, "hash": hash_example(e)} for e in exs - ] - all_examples.extend(enriched_exs) - - # Deduplicate by hash - keeps first occurrence (Python 3.7+ dict is ordered) - # Explicit iteration ensures first occurrence is preserved - uniq = {} - for e in all_examples: - h = e["hash"] - if h not in uniq: - uniq[h] = e - examples = list(uniq.values()) - - if not examples: - # Fallback synthetic example - fallback = { - "messages": [ - { - "role": "user", - "content": "How does the local chat provider respond to a greeting?", - }, - { - "role": "assistant", - "content": "The local provider generates a concise heuristic reply; this synthetic example exists because no real logs were found.", - }, - ], - "hash": hash_example( - { - "messages": [ - {"role": "user", "content": "fallback"}, - {"role": "assistant", "content": "fallback"}, - ] - } - ), - "source_file": "", - } - examples = [fallback] - - # Limit - if len(examples) > args.max_records: - examples = examples[: args.max_records] - - random.shuffle(examples) - n_train = int(len(examples) * args.train_ratio) - # Ensure at least one training example if any examples exist - if n_train == 0 and examples: - n_train = 1 - train = examples[:n_train] - test = examples[n_train:] or examples[:1] - - def write(path: Path, recs: List[Dict]): - with path.open("w", encoding="utf-8") as f: - for r in recs: - out = { - "messages": r["messages"], - "hash": r["hash"], - "source_file": r.get("source_file"), - } - f.write(json.dumps(out, ensure_ascii=False) + "\n") - - write(OUTPUT_DIR / "train.json", train) - write(OUTPUT_DIR / "test.json", test) - meta = { - "log_files": [lf.name for lf in log_files], - "total_examples": len(examples), - "train_examples": len(train), - "test_examples": len(test), - "seed": args.seed, - "context_window": args.context_window, - } - with (OUTPUT_DIR / "metadata.json").open("w", encoding="utf-8") as f: - json.dump(meta, f, indent=2) - - print(json.dumps(meta, indent=2)) - print(f"Dataset written to {OUTPUT_DIR}") - - -if __name__ == "__main__": - main() +"""Extract chat conversation logs from ai-projects/chat-cli/logs into Phi-3 style dataset. + +Each log file is JSONL with entries: {"role": "user|assistant", "content": "...", "timestamp": "..."} +We transform these into training records with a messages list. + +Strategy: +- For each assistant reply, pair it with the most recent user message (basic turn pair dataset) +- Additionally build rolling window examples (up to --context-window messages) when available +- Deduplicate by hash of concatenated message contents +- Provide fallback synthetic record if no logs are found (to keep structure testable) + +Outputs: + datasets/chat/chat_logs/train.json + datasets/chat/chat_logs/test.json + datasets/chat/chat_logs/metadata.json + +Usage (PowerShell): + python .\\scripts\\extract_chat_logs_dataset.py --max-records 500 + python AI\\microsoft_phi-silica-3.6_v1\\scripts\\train_lora.py --dataset .\\datasets\\chat\\chat_logs --dry-run +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import random +from pathlib import Path +from typing import Dict, List + +LOGS_DIR = Path("ai-projects/chat-cli/logs") +OUTPUT_DIR = Path("datasets/chat/chat_logs") + + +def iter_logs() -> List[Path]: + if not LOGS_DIR.exists(): + return [] + return sorted([p for p in LOGS_DIR.glob("*.jsonl") if p.is_file()]) + + +def read_jsonl(path: Path) -> List[Dict]: + records: List[Dict] = [] + with path.open("r", encoding="utf-8", errors="ignore") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + obj = json.loads(line) + if isinstance(obj, dict) and "role" in obj and "content" in obj: + records.append(obj) + except Exception: + continue + return records + + +def build_examples(messages: List[Dict], context_window: int) -> List[Dict]: + # Simple user->assistant pairs - build list directly + pairs = [] + last_user = None + for m in messages: + if m.get("role") == "user": + last_user = m + elif m.get("role") == "assistant" and last_user: + pairs.append({"messages": [last_user, m]}) + + # Rolling windows - use list comprehension for better performance + windows = [] + if context_window > 2: + for i in range(len(messages)): + if messages[i].get("role") == "assistant": + start = max(0, i - context_window + 1) + window = messages[start : i + 1] + # Must contain at least one user+assistant + if any(x.get("role") == "user" for x in window) and any( + x.get("role") == "assistant" for x in window + ): + windows.append({"messages": window}) + + # Combine lists efficiently with extend + pairs.extend(windows) + return pairs + + +def hash_example(example: Dict) -> str: + concat = "\n".join( + [ + f"{m.get('role', '')}: {m.get('content', '')[:400]}" + for m in example.get("messages", []) + ] + ) + return hashlib.sha256(concat.encode("utf-8")).hexdigest()[:24] + + +def main(): + ap = argparse.ArgumentParser(description="Extract chat logs into training dataset") + ap.add_argument( + "--max-records", type=int, default=1000, help="Maximum examples to output" + ) + ap.add_argument("--train-ratio", type=float, default=0.9, help="Train split ratio") + ap.add_argument( + "--context-window", + type=int, + default=6, + help="Max messages in rolling window examples", + ) + ap.add_argument("--seed", type=int, default=42, help="RNG seed") + args = ap.parse_args() + + random.seed(args.seed) + + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + + log_files = iter_logs() + all_examples: List[Dict] = [] + for lf in log_files: + msgs = read_jsonl(lf) + if not msgs: + continue + exs = build_examples(msgs, args.context_window) + # Add metadata and hash in batch using list comprehension + enriched_exs = [ + {**e, "source_file": lf.name, "hash": hash_example(e)} for e in exs + ] + all_examples.extend(enriched_exs) + + # Deduplicate by hash - keeps first occurrence (Python 3.7+ dict is ordered) + # Explicit iteration ensures first occurrence is preserved + uniq = {} + for e in all_examples: + h = e["hash"] + if h not in uniq: + uniq[h] = e + examples = list(uniq.values()) + + if not examples: + # Fallback synthetic example + fallback = { + "messages": [ + { + "role": "user", + "content": "How does the local chat provider respond to a greeting?", + }, + { + "role": "assistant", + "content": "The local provider generates a concise heuristic reply; this synthetic example exists because no real logs were found.", + }, + ], + "hash": hash_example( + { + "messages": [ + {"role": "user", "content": "fallback"}, + {"role": "assistant", "content": "fallback"}, + ] + } + ), + "source_file": "", + } + examples = [fallback] + + # Limit + if len(examples) > args.max_records: + examples = examples[: args.max_records] + + random.shuffle(examples) + n_train = int(len(examples) * args.train_ratio) + # Ensure at least one training example if any examples exist + if n_train == 0 and examples: + n_train = 1 + train = examples[:n_train] + test = examples[n_train:] or examples[:1] + + def write(path: Path, recs: List[Dict]): + with path.open("w", encoding="utf-8") as f: + for r in recs: + out = { + "messages": r["messages"], + "hash": r["hash"], + "source_file": r.get("source_file"), + } + f.write(json.dumps(out, ensure_ascii=False) + "\n") + + write(OUTPUT_DIR / "train.json", train) + write(OUTPUT_DIR / "test.json", test) + meta = { + "log_files": [lf.name for lf in log_files], + "total_examples": len(examples), + "train_examples": len(train), + "test_examples": len(test), + "seed": args.seed, + "context_window": args.context_window, + } + with (OUTPUT_DIR / "metadata.json").open("w", encoding="utf-8") as f: + json.dump(meta, f, indent=2) + + print(json.dumps(meta, indent=2)) + print(f"Dataset written to {OUTPUT_DIR}") + + +if __name__ == "__main__": + main() diff --git a/scripts/final_validation.py b/scripts/final_validation.py index c41d6fa3b..a658130d6 100644 --- a/scripts/final_validation.py +++ b/scripts/final_validation.py @@ -1,115 +1,115 @@ -import re -from functools import lru_cache -from pathlib import Path - -# Pre-compile regex patterns for performance -_RE_ONCLICK = re.compile(r'onclick=["\']([^"\']+)["\']') -_RE_FUNC_NAMES = re.compile(r"([a-zA-Z_][a-zA-Z0-9_]*)\s*\(") -_RE_ELEMENT_IDS = re.compile(r'id=["\']([^"\']+)["\']') -_RE_GET_BY_ID = re.compile(r"getElementById\(['\"]([^'\"]+)['\"]\)") -_RE_FETCH_CALLS = re.compile(r"fetch\(['\"]([^'\"]+)['\"]\)") - - -@lru_cache(maxsize=128) -def _compile_function_patterns(func_name: str): - """Compile and cache function definition patterns for a given function name.""" - escaped = re.escape(func_name) - return [ - re.compile(rf"function\s+{escaped}\s*\("), - re.compile(rf"const\s+{escaped}\s*="), - re.compile(rf"let\s+{escaped}\s*="), - re.compile(rf"var\s+{escaped}\s*="), - ] - - -def _resolve_dashboard_path() -> Path: - """Resolve dashboard HTML path across legacy and current layouts.""" - repo_root = Path(__file__).resolve().parents[1] - candidates = [ - repo_root / "dashboard" / "unified.html", - repo_root / "apps" / "dashboard" / "unified.html", - ] - for path in candidates: - if path.exists(): - return path - return candidates[0] - - -def run_validation() -> bool: - html_file = _resolve_dashboard_path() - if not html_file.exists(): - print(f"Dashboard file not found: {html_file}") - return False - - content = html_file.read_text(encoding="utf-8") - - print("=== Final Dashboard Validation ===\n") - - # 1. Button functions - onclick_handlers = _RE_ONCLICK.findall(content) - function_calls = set() - for match in onclick_handlers: - func_names = _RE_FUNC_NAMES.findall(match) - function_calls.update(func_names) - - built_in_methods = {"stopPropagation", "preventDefault"} - function_calls -= built_in_methods - - defined_count = 0 - for func_name in function_calls: - patterns = _compile_function_patterns(func_name) - if any(p.search(content) for p in patterns): - defined_count += 1 - - print(f" Button Functions: {defined_count}/{len(function_calls)} defined") - - # 2. Element ID references - element_ids = set(_RE_ELEMENT_IDS.findall(content)) - get_by_id_refs = set(_RE_GET_BY_ID.findall(content)) - - missing_ids = get_by_id_refs - element_ids - if missing_ids: - print(f' Missing Element IDs: {", ".join(sorted(missing_ids))}') - else: - print(f" Element IDs: All {len(get_by_id_refs)} references valid") - - # 3. API endpoints - fetch_calls = set(_RE_FETCH_CALLS.findall(content)) - print(f" API Endpoints: {len(fetch_calls)} endpoints defined") - - # 4. Critical features check - features = { - "Dark Mode": "toggleDarkMode" in content, - "Search/Filter": "filterJobs" in content, - "Keyboard Shortcuts": "toggleShortcuts" in content, - "GPU Monitoring": "/api/gpu" in content, - "Connection Status": "updateConnectionStatus" in content, - "Performance Badges": "addPerformanceBadge" in content, - "Chart.js": "Chart.js" in content or "chartjs" in content.lower(), - } - - print("\n Feature Completeness:") - for feature, present in features.items(): - status = "OK" if present else "MISSING" - print(f"{status} {feature}") - - # 5. Summary - all_valid = ( - defined_count == len(function_calls) - and not missing_ids - and all(features.values()) - ) - - print(f'\n{"=" * 50}') - if all_valid: - print("ALL CHECKS PASSED") - print("Dashboard is fully functional") - print(f"Ready at: {html_file}") - else: - print("Some issues detected - see details above") - - return all_valid - - -if __name__ == "__main__": - run_validation() +import re +from functools import lru_cache +from pathlib import Path + +# Pre-compile regex patterns for performance +_RE_ONCLICK = re.compile(r'onclick=["\']([^"\']+)["\']') +_RE_FUNC_NAMES = re.compile(r"([a-zA-Z_][a-zA-Z0-9_]*)\s*\(") +_RE_ELEMENT_IDS = re.compile(r'id=["\']([^"\']+)["\']') +_RE_GET_BY_ID = re.compile(r"getElementById\(['\"]([^'\"]+)['\"]\)") +_RE_FETCH_CALLS = re.compile(r"fetch\(['\"]([^'\"]+)['\"]\)") + + +@lru_cache(maxsize=128) +def _compile_function_patterns(func_name: str): + """Compile and cache function definition patterns for a given function name.""" + escaped = re.escape(func_name) + return [ + re.compile(rf"function\s+{escaped}\s*\("), + re.compile(rf"const\s+{escaped}\s*="), + re.compile(rf"let\s+{escaped}\s*="), + re.compile(rf"var\s+{escaped}\s*="), + ] + + +def _resolve_dashboard_path() -> Path: + """Resolve dashboard HTML path across legacy and current layouts.""" + repo_root = Path(__file__).resolve().parents[1] + candidates = [ + repo_root / "dashboard" / "unified.html", + repo_root / "apps" / "dashboard" / "unified.html", + ] + for path in candidates: + if path.exists(): + return path + return candidates[0] + + +def run_validation() -> bool: + html_file = _resolve_dashboard_path() + if not html_file.exists(): + print(f"Dashboard file not found: {html_file}") + return False + + content = html_file.read_text(encoding="utf-8") + + print("=== Final Dashboard Validation ===\n") + + # 1. Button functions + onclick_handlers = _RE_ONCLICK.findall(content) + function_calls = set() + for match in onclick_handlers: + func_names = _RE_FUNC_NAMES.findall(match) + function_calls.update(func_names) + + built_in_methods = {"stopPropagation", "preventDefault"} + function_calls -= built_in_methods + + defined_count = 0 + for func_name in function_calls: + patterns = _compile_function_patterns(func_name) + if any(p.search(content) for p in patterns): + defined_count += 1 + + print(f" Button Functions: {defined_count}/{len(function_calls)} defined") + + # 2. Element ID references + element_ids = set(_RE_ELEMENT_IDS.findall(content)) + get_by_id_refs = set(_RE_GET_BY_ID.findall(content)) + + missing_ids = get_by_id_refs - element_ids + if missing_ids: + print(f' Missing Element IDs: {", ".join(sorted(missing_ids))}') + else: + print(f" Element IDs: All {len(get_by_id_refs)} references valid") + + # 3. API endpoints + fetch_calls = set(_RE_FETCH_CALLS.findall(content)) + print(f" API Endpoints: {len(fetch_calls)} endpoints defined") + + # 4. Critical features check + features = { + "Dark Mode": "toggleDarkMode" in content, + "Search/Filter": "filterJobs" in content, + "Keyboard Shortcuts": "toggleShortcuts" in content, + "GPU Monitoring": "/api/gpu" in content, + "Connection Status": "updateConnectionStatus" in content, + "Performance Badges": "addPerformanceBadge" in content, + "Chart.js": "Chart.js" in content or "chartjs" in content.lower(), + } + + print("\n Feature Completeness:") + for feature, present in features.items(): + status = "OK" if present else "MISSING" + print(f"{status} {feature}") + + # 5. Summary + all_valid = ( + defined_count == len(function_calls) + and not missing_ids + and all(features.values()) + ) + + print(f'\n{"=" * 50}') + if all_valid: + print("ALL CHECKS PASSED") + print("Dashboard is fully functional") + print(f"Ready at: {html_file}") + else: + print("Some issues detected - see details above") + + return all_valid + + +if __name__ == "__main__": + run_validation() diff --git a/scripts/generate_github_actions_dataset.py b/scripts/generate_github_actions_dataset.py index 6f4631d75..bbdc82ed6 100644 --- a/scripts/generate_github_actions_dataset.py +++ b/scripts/generate_github_actions_dataset.py @@ -1,630 +1,630 @@ -"""Generate synthetic GitHub Actions training dataset from workflow files. - -Creates a dataset suitable for chat fine-tuning about GitHub Actions, CI/CD, and workflows. -Output format: - datasets/chat/github_actions/train.json (newline-delimited JSON objects) - datasets/chat/github_actions/test.json (newline-delimited JSON objects) -Each line: {"messages": [ {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."} ]} - -Design principles: -- Generate Q&A pairs about workflow structure, triggers, jobs, steps -- Include CI/CD best practices and explanations -- Cover common GitHub Actions patterns and use cases -- Deterministic with --seed for reproducibility - -Usage: - python scripts/generate_github_actions_dataset.py --max-records 200 -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import random -from dataclasses import dataclass -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List - -import yaml - -DEFAULT_OUTPUT = Path("datasets/chat/github_actions") - - -@dataclass -class WorkflowInfo: - name: str - path: str - content: str - parsed_yaml: Dict[str, Any] - triggers: List[str] - jobs: List[str] - uses_actions: List[str] - - -def parse_workflow(path: Path) -> WorkflowInfo | None: - """Parse a GitHub Actions workflow file""" - try: - content = path.read_text(encoding="utf-8") - parsed = yaml.safe_load(content) - - if not isinstance(parsed, dict): - return None - - # Extract key information - name = parsed.get("name", path.stem) - - # Extract triggers - triggers = [] - if "on" in parsed: - on_config = parsed["on"] - if isinstance(on_config, str): - triggers.append(on_config) - elif isinstance(on_config, list): - triggers.extend(on_config) - elif isinstance(on_config, dict): - triggers.extend(on_config.keys()) - - # Extract jobs - jobs = [] - if "jobs" in parsed and isinstance(parsed["jobs"], dict): - jobs = list(parsed["jobs"].keys()) - - # Extract used actions - uses_actions = [] - if "jobs" in parsed and isinstance(parsed["jobs"], dict): - for job_name, job_config in parsed["jobs"].items(): - if isinstance(job_config, dict) and "steps" in job_config: - for step in job_config.get("steps", []): - if isinstance(step, dict) and "uses" in step: - uses_actions.append(step["uses"]) - - return WorkflowInfo( - name=name, - path=str(path.relative_to(path.parents[2])), # relative to repo root - content=content, - parsed_yaml=parsed, - triggers=triggers, - jobs=jobs, - uses_actions=list(set(uses_actions)), - ) - except Exception as e: - print(f"Warning: Could not parse {path}: {e}") - return None - - -def generate_workflow_overview_qa(workflow: WorkflowInfo) -> tuple[str, str]: - """Generate Q&A about workflow overview""" - user = f"What does the GitHub Actions workflow '{workflow.name}' do?" - - assistant_parts = [f"The workflow '{workflow.name}' is a CI/CD pipeline that:"] - - if workflow.triggers: - trigger_desc = ", ".join(workflow.triggers) - assistant_parts.append(f"\n- Triggers on: {trigger_desc}") - - if workflow.jobs: - job_desc = ", ".join(workflow.jobs) - assistant_parts.append(f"- Contains {len(workflow.jobs)} job(s): {job_desc}") - - if workflow.uses_actions: - assistant_parts.append( - f"- Uses {len(workflow.uses_actions)} different GitHub Actions" - ) - - # Add purpose based on name/jobs - purpose_hints = { - "test": "running automated tests", - "ci": "continuous integration checks", - "deploy": "deploying code to production or staging", - "train": "training machine learning models", - "validate": "validating code quality and correctness", - "build": "building and compiling the application", - "lint": "checking code style and quality", - } - - for keyword, purpose in purpose_hints.items(): - if keyword in workflow.name.lower() or any( - keyword in job.lower() for job in workflow.jobs - ): - assistant_parts.append( - f"\nThis workflow is primarily focused on {purpose}." - ) - break - - return user, "\n".join(assistant_parts) - - -def generate_trigger_qa(workflow: WorkflowInfo) -> tuple[str, str]: - """Generate Q&A about workflow triggers""" - user = f"When does the '{workflow.name}' workflow run?" - - if not workflow.triggers: - return user, "This workflow has no explicit triggers defined." - - assistant_parts = [f"The '{workflow.name}' workflow is triggered by:"] - - trigger_explanations = { - "push": "code pushes to specified branches", - "pull_request": "pull request events (opened, synchronized, etc.)", - "workflow_dispatch": "manual workflow runs via GitHub UI or API", - "schedule": "scheduled cron jobs at specific times", - "release": "release events (published, created, etc.)", - "issues": "issue events (opened, labeled, etc.)", - "issue_comment": "comments on issues or pull requests", - } - - for trigger in workflow.triggers: - explanation = trigger_explanations.get(trigger, f"{trigger} events") - assistant_parts.append(f"\n- {trigger}: {explanation}") - - # Add schedule details if present - if "schedule" in workflow.triggers and "on" in workflow.parsed_yaml: - on_config = workflow.parsed_yaml["on"] - if isinstance(on_config, dict) and "schedule" in on_config: - schedule_list = on_config["schedule"] - if schedule_list and len(schedule_list) > 0: - cron = schedule_list[0].get("cron", "") - if cron: - assistant_parts.append(f"\nSchedule: {cron}") - - return user, "\n".join(assistant_parts) - - -def generate_jobs_qa(workflow: WorkflowInfo) -> tuple[str, str]: - """Generate Q&A about workflow jobs""" - user = f"What jobs are defined in the '{workflow.name}' workflow?" - - if not workflow.jobs: - return user, "This workflow has no jobs defined." - - assistant_parts = [f"The workflow defines {len(workflow.jobs)} job(s):\n"] - - jobs_config = workflow.parsed_yaml.get("jobs", {}) - for job_name in workflow.jobs: - job_config = jobs_config.get(job_name, {}) - assistant_parts.append(f"\n**{job_name}**:") - - # Runner - runs_on = job_config.get("runs-on", "unknown") - assistant_parts.append(f" - Runs on: {runs_on}") - - # Dependencies - if "needs" in job_config: - needs = job_config["needs"] - if isinstance(needs, str): - assistant_parts.append(f" - Depends on: {needs}") - elif isinstance(needs, list): - assistant_parts.append(f" - Depends on: {', '.join(needs)}") - - # Steps count - steps = job_config.get("steps", []) - assistant_parts.append(f" - Contains {len(steps)} step(s)") - - # Conditions - if "if" in job_config: - assistant_parts.append(f" - Conditional: {job_config['if']}") - - return user, "\n".join(assistant_parts) - - -def generate_actions_used_qa(workflow: WorkflowInfo) -> tuple[str, str]: - """Generate Q&A about actions used in workflow""" - user = f"What GitHub Actions are used in the '{workflow.name}' workflow?" - - if not workflow.uses_actions: - return user, "This workflow doesn't use any external GitHub Actions." - - assistant_parts = [ - f"The workflow uses {len(workflow.uses_actions)} different actions:\n" - ] - - action_descriptions = { - "actions/checkout": "Checks out the repository code", - "actions/setup-python": "Sets up a Python environment", - "actions/setup-node": "Sets up a Node.js environment", - "actions/upload-artifact": "Uploads build artifacts for later use", - "actions/download-artifact": "Downloads previously uploaded artifacts", - "actions/cache": "Caches dependencies to speed up workflows", - "azure/login": "Authenticates with Azure", - "azure/cli": "Runs Azure CLI commands", - } - - for action in sorted(workflow.uses_actions): - # Extract action name without version - action_name = action.split("@")[0] if "@" in action else action - description = action_descriptions.get( - action_name, f"Performs {action_name} functionality" - ) - assistant_parts.append(f"- **{action}**: {description}") - - return user, "\n".join(assistant_parts) - - -def generate_best_practices_qa(workflow: WorkflowInfo) -> tuple[str, str]: - """Generate Q&A about best practices in the workflow""" - user = ( - f"What are the best practices demonstrated in the '{workflow.name}' workflow?" - ) - - practices = [] - - # Check for specific patterns - if "actions/checkout" in " ".join(workflow.uses_actions): - practices.append("- Uses actions/checkout to ensure clean code access") - - if any("cache" in action.lower() for action in workflow.uses_actions): - practices.append("- Implements caching to improve workflow performance") - - if any("upload-artifact" in action.lower() for action in workflow.uses_actions): - practices.append("- Preserves build artifacts for debugging and deployment") - - jobs_config = workflow.parsed_yaml.get("jobs", {}) - for job_name, job_config in jobs_config.items(): - if "needs" in job_config: - practices.append("- Uses job dependencies to create a proper pipeline flow") - break - - # Check for conditional execution - for job_name, job_config in jobs_config.items(): - if "if" in job_config: - practices.append( - "- Uses conditional job execution to optimize resource usage" - ) - break - - if "schedule" in workflow.triggers: - practices.append("- Implements scheduled runs for regular automated tasks") - - if "workflow_dispatch" in workflow.triggers: - practices.append("- Allows manual workflow triggering for flexibility") - - # Check for matrix strategy - for job_name, job_config in jobs_config.items(): - if "strategy" in job_config and "matrix" in job_config["strategy"]: - practices.append( - "- Uses matrix strategy for testing across multiple configurations" - ) - break - - if not practices: - practices.append( - "- Follows standard GitHub Actions structure with jobs and steps" - ) - - assistant = ( - f"The '{workflow.name}' workflow demonstrates several best practices:\n\n" - + "\n".join(practices) - ) - return user, assistant - - -def generate_modification_qa(workflow: WorkflowInfo) -> tuple[str, str]: - """Generate Q&A about how to modify the workflow""" - user = f"How would I modify the '{workflow.name}' workflow to add a new step?" - - assistant = f"""To add a new step to the '{workflow.name}' workflow: - -1. Choose which job to modify (available: {', '.join(workflow.jobs) if workflow.jobs else 'none'}) -2. Locate the `steps` section within that job -3. Add a new step with: - - `name`: A descriptive name for the step - - `run`: Command(s) to execute, or - - `uses`: An action to use (e.g., actions/checkout@v4) -4. Optionally add conditions with `if:` to control when the step runs - -Example step structure: -```yaml -- name: My new step - run: | - echo "Running my command" - python my_script.py -``` - -Or using an action: -```yaml -- name: Upload results - uses: actions/upload-artifact@v4 - with: - name: my-artifact - path: results/ -``` - -Remember to test your changes in a feature branch before merging to main.""" - - return user, assistant - - -def generate_troubleshooting_qa(workflow: WorkflowInfo) -> tuple[str, str]: - """Generate Q&A about troubleshooting workflows""" - user = f"How do I debug failures in the '{workflow.name}' workflow?" - - assistant = f"""To debug failures in the '{workflow.name}' workflow: - -1. **Check the Actions tab**: Go to GitHub Actions tab to see run history -2. **Review logs**: Click on the failed run and expand failed steps to see detailed logs -3. **Look for error messages**: Red text usually indicates the failure point -4. **Verify triggers**: Ensure the workflow triggered as expected (check triggers: {', '.join(workflow.triggers) if workflow.triggers else 'none'}) -5. **Test locally**: Try running the commands from failed steps on your local machine -6. **Check dependencies**: Ensure all required dependencies are installed -7. **Review permissions**: Verify the workflow has necessary permissions -8. **Use workflow_dispatch**: Add manual trigger for easier testing - -Common issues: -- Missing environment variables or secrets -- Incorrect file paths -- Dependency version conflicts -- Insufficient permissions -- Timeout issues (increase with `timeout-minutes`) - -For persistent issues, you can enable debug logging by setting these secrets: -- ACTIONS_STEP_DEBUG: true -- ACTIONS_RUNNER_DEBUG: true""" - - return user, assistant - - -# Template functions for generating Q&A pairs -PROMPT_TEMPLATES = { - "overview": generate_workflow_overview_qa, - "triggers": generate_trigger_qa, - "jobs": generate_jobs_qa, - "actions": generate_actions_used_qa, - "best_practices": generate_best_practices_qa, - "modification": generate_modification_qa, - "troubleshooting": generate_troubleshooting_qa, -} - - -def generate_general_qa_pairs() -> List[Dict[str, Any]]: - """Generate general GitHub Actions Q&A pairs""" - general_qa = [ - { - "messages": [ - {"role": "user", "content": "What is GitHub Actions?"}, - { - "role": "assistant", - "content": "GitHub Actions is a CI/CD (Continuous Integration/Continuous Deployment) platform built into GitHub that allows you to automate your software development workflows. You can:\n\n- Run tests automatically on every push or pull request\n- Build and deploy applications\n- Automate code quality checks and linting\n- Schedule regular tasks\n- Respond to GitHub events (issues, releases, etc.)\n\nWorkflows are defined in YAML files stored in `.github/workflows/` directory.", - }, - ], - "source": "general_knowledge", - "template": "concept", - }, - { - "messages": [ - { - "role": "user", - "content": "What is the structure of a GitHub Actions workflow file?", - }, - { - "role": "assistant", - "content": "A GitHub Actions workflow file is a YAML file with the following main sections:\n\n1. **name**: Optional workflow name displayed in the Actions tab\n2. **on**: Defines triggers (push, pull_request, schedule, etc.)\n3. **jobs**: Contains one or more jobs to run\n - Each job has:\n - **runs-on**: Runner type (ubuntu-latest, windows-latest, etc.)\n - **steps**: Sequential actions or commands to execute\n - **needs**: Optional dependencies on other jobs\n4. **env**: Optional environment variables\n5. **permissions**: Optional permissions settings\n\nExample:\n```yaml\nname: CI\non: [push, pull_request]\njobs:\n test:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - run: npm test\n```", - }, - ], - "source": "general_knowledge", - "template": "structure", - }, - { - "messages": [ - { - "role": "user", - "content": "What are the most common GitHub Actions triggers?", - }, - { - "role": "assistant", - "content": "The most common GitHub Actions triggers are:\n\n1. **push**: Triggers on code pushes to specified branches\n ```yaml\n on:\n push:\n branches: [main, dev]\n ```\n\n2. **pull_request**: Triggers on PR events (opened, synchronized, etc.)\n ```yaml\n on:\n pull_request:\n branches: [main]\n ```\n\n3. **schedule**: Triggers on a cron schedule\n ```yaml\n on:\n schedule:\n - cron: '0 2 * * *' # Daily at 2 AM UTC\n ```\n\n4. **workflow_dispatch**: Allows manual workflow runs\n ```yaml\n on:\n workflow_dispatch:\n inputs:\n environment:\n description: 'Environment to deploy to'\n required: true\n ```\n\n5. **release**: Triggers on release events\n ```yaml\n on:\n release:\n types: [published]\n ```", - }, - ], - "source": "general_knowledge", - "template": "triggers", - }, - { - "messages": [ - {"role": "user", "content": "How do I use secrets in GitHub Actions?"}, - { - "role": "assistant", - "content": "To use secrets in GitHub Actions:\n\n1. **Add secrets**: Go to repository Settings > Secrets and variables > Actions\n2. **Click 'New repository secret'** and add your secret (e.g., API keys, passwords)\n3. **Reference in workflow**: Use `${{ secrets.SECRET_NAME }}` syntax\n\nExample:\n```yaml\nsteps:\n - name: Deploy\n env:\n API_KEY: ${{ secrets.API_KEY }}\n DATABASE_URL: ${{ secrets.DATABASE_URL }}\n run: |\n echo \"Deploying with credentials...\"\n ./deploy.sh\n```\n\n**Best practices**:\n- Never commit secrets to code\n- Use environment-specific secrets for dev/staging/prod\n- Secrets are masked in logs\n- Use GitHub's OIDC for cloud provider authentication when possible\n- Consider using environment protection rules for sensitive deployments", - }, - ], - "source": "general_knowledge", - "template": "secrets", - }, - { - "messages": [ - { - "role": "user", - "content": "What is the difference between 'run' and 'uses' in a workflow step?", - }, - { - "role": "assistant", - "content": "In GitHub Actions workflow steps:\n\n**run**: Executes shell commands directly\n```yaml\n- name: Install dependencies\n run: |\n npm install\n npm run build\n```\n- Runs arbitrary commands in the runner's shell\n- Can use multiple lines with `|` or `>`\n- Good for simple commands or scripts\n- Has access to environment variables\n\n**uses**: Invokes a reusable action\n```yaml\n- name: Checkout code\n uses: actions/checkout@v4\n with:\n fetch-depth: 0\n```\n- References a pre-built action from GitHub Marketplace or your repository\n- Actions are versioned (using @v4, @main, or commit SHA)\n- Can accept parameters via `with:`\n- Promotes code reuse and maintainability\n- Examples: actions/checkout, actions/setup-python, azure/login\n\n**When to use**:\n- Use `run` for simple commands and scripts\n- Use `uses` for complex operations, third-party integrations, or reusable logic", - }, - ], - "source": "general_knowledge", - "template": "concepts", - }, - { - "messages": [ - { - "role": "user", - "content": "How do I run jobs in parallel vs sequentially in GitHub Actions?", - }, - { - "role": "assistant", - "content": "GitHub Actions supports both parallel and sequential job execution:\n\n**Parallel (default)**: Jobs run simultaneously by default\n```yaml\njobs:\n test:\n runs-on: ubuntu-latest\n steps:\n - run: npm test\n \n lint:\n runs-on: ubuntu-latest\n steps:\n - run: npm run lint\n```\nBoth `test` and `lint` run at the same time.\n\n**Sequential**: Use `needs` to create dependencies\n```yaml\njobs:\n build:\n runs-on: ubuntu-latest\n steps:\n - run: npm build\n \n test:\n needs: build # Waits for build to complete\n runs-on: ubuntu-latest\n steps:\n - run: npm test\n \n deploy:\n needs: [build, test] # Waits for both\n runs-on: ubuntu-latest\n steps:\n - run: ./deploy.sh\n```\n\n**Benefits**:\n- Parallel: Faster total execution time\n- Sequential: Ensures correct order (e.g., build before test, test before deploy)\n- Mix both: Some jobs parallel, others sequential", - }, - ], - "source": "general_knowledge", - "template": "parallelism", - }, - { - "messages": [ - { - "role": "user", - "content": "What is a matrix strategy in GitHub Actions?", - }, - { - "role": "assistant", - "content": "A matrix strategy in GitHub Actions allows you to run a job with multiple configurations automatically:\n\n```yaml\njobs:\n test:\n runs-on: ubuntu-latest\n strategy:\n matrix:\n python-version: ['3.9', '3.10', '3.11', '3.12']\n os: [ubuntu-latest, windows-latest, macos-latest]\n steps:\n - uses: actions/setup-python@v5\n with:\n python-version: ${{ matrix.python-version }}\n - run: python --version\n - run: pytest\n```\n\nThis creates 12 jobs (4 Python versions × 3 OS) automatically.\n\n**Key features**:\n- `${{ matrix.variable }}` accesses matrix values\n- `fail-fast: false` continues other jobs if one fails\n- `max-parallel` limits concurrent jobs\n- `include` adds specific combinations\n- `exclude` removes specific combinations\n\n**Use cases**:\n- Testing across multiple Python/Node versions\n- Cross-platform testing (Linux, Windows, macOS)\n- Testing with different dependency versions\n- Different configuration combinations", - }, - ], - "source": "general_knowledge", - "template": "matrix", - }, - { - "messages": [ - { - "role": "user", - "content": "How do I cache dependencies in GitHub Actions?", - }, - { - "role": "assistant", - "content": "Caching dependencies speeds up workflow runs by reusing previously downloaded packages:\n\n**Using actions/cache**:\n```yaml\nsteps:\n - uses: actions/checkout@v4\n \n - uses: actions/cache@v4\n with:\n path: ~/.npm\n key: ${{ runner.os }}-node-${{ hashFiles('**/package-lock.json') }}\n restore-keys: |\n ${{ runner.os }}-node-\n \n - run: npm install\n```\n\n**Language-specific setup actions** with built-in caching:\n```yaml\n# Python with pip cache\n- uses: actions/setup-python@v5\n with:\n python-version: '3.11'\n cache: 'pip'\n\n# Node.js with npm cache\n- uses: actions/setup-node@v4\n with:\n node-version: '20'\n cache: 'npm'\n```\n\n**Benefits**:\n- Reduces workflow time by 30-70%\n- Decreases network load\n- Cache expires after 7 days of no access\n- Maximum cache size: 10 GB per repository\n\n**Best practices**:\n- Use hash of lock files in cache key for version tracking\n- Include OS in key if caching OS-specific files\n- Use restore-keys for partial cache hits", - }, - ], - "source": "general_knowledge", - "template": "caching", - }, - ] - - return general_qa - - -def build_records( - workflows: List[WorkflowInfo], max_records: int, seed: int -) -> List[Dict[str, Any]]: - """Build Q&A records from workflows""" - random.seed(seed) - records: List[Dict[str, Any]] = [] - - # Add general Q&A pairs - general_records = generate_general_qa_pairs() - records.extend(general_records) - - # Generate workflow-specific Q&A - for workflow in workflows: - # Randomize template order per workflow - template_keys = list(PROMPT_TEMPLATES.keys()) - random.shuffle(template_keys) - - for template_key in template_keys: - if len(records) >= max_records: - break - - try: - template_func = PROMPT_TEMPLATES[template_key] - user_prompt, assistant_answer = template_func(workflow) - - h = hashlib.sha256( - (workflow.name + template_key + assistant_answer[:100]).encode( - "utf-8" - ) - ).hexdigest()[:16] - rec = { - "messages": [ - {"role": "user", "content": user_prompt}, - {"role": "assistant", "content": assistant_answer}, - ], - "source_workflow": workflow.name, - "source_path": workflow.path, - "template": template_key, - "hash": h, - } - records.append(rec) - except Exception as e: - print( - f"Warning: Could not generate {template_key} for {workflow.name}: {e}" - ) - - if len(records) >= max_records: - break - - return records - - -def write_jsonl(path: Path, records: List[Dict[str, Any]]): - """Write records to JSONL file""" - with path.open("w", encoding="utf-8") as f: - for rec in records: - f.write(json.dumps(rec, ensure_ascii=False) + "\n") - - -def main(): - ap = argparse.ArgumentParser(description="Generate GitHub Actions training dataset") - ap.add_argument( - "--workflow-dir", - default=".github/workflows", - help="Directory containing workflow files", - ) - ap.add_argument( - "--output-dir", default=str(DEFAULT_OUTPUT), help="Output dataset directory" - ) - ap.add_argument( - "--max-records", - type=int, - default=200, - help="Maximum total records (train+test)", - ) - ap.add_argument("--seed", type=int, default=42, help="Random seed") - ap.add_argument("--train-ratio", type=float, default=0.85, help="Train split ratio") - args = ap.parse_args() - - # Find repository root (go up from scripts dir) - repo_root = Path(__file__).resolve().parents[1] - workflow_dir = repo_root / args.workflow_dir - out_dir = repo_root / args.output_dir - out_dir.mkdir(parents=True, exist_ok=True) - - # Parse all workflow files - workflows: List[WorkflowInfo] = [] - if workflow_dir.exists(): - for pattern in ("*.yml", "*.yaml"): - for workflow_file in workflow_dir.glob(pattern): - workflow_info = parse_workflow(workflow_file) - if workflow_info: - workflows.append(workflow_info) - print(f"Parsed workflow: {workflow_info.name}") - - if not workflows: - print(f"Warning: No workflow files found in {workflow_dir}") - print("Generating dataset with only general GitHub Actions Q&A...") - - # Generate Q&A records - records = build_records(workflows, max_records=args.max_records, seed=args.seed) - - # Train/test split - random.seed(args.seed) - random.shuffle(records) - n_train = int(len(records) * args.train_ratio) - train_recs = records[:n_train] - test_recs = records[n_train:] or records[: max(1, len(records) // 10)] - - # Write datasets - write_jsonl(out_dir / "train.json", train_recs) - write_jsonl(out_dir / "test.json", test_recs) - - # Write metadata - meta = { - "total_records": len(records), - "train_records": len(train_recs), - "test_records": len(test_recs), - "generation_seed": args.seed, - "workflows_parsed": len(workflows), - "workflow_names": [w.name for w in workflows], - "generation_date": datetime.now().isoformat(), - } - with (out_dir / "metadata.json").open("w", encoding="utf-8") as f: - json.dump(meta, f, indent=2) - - print(f"\n{'='*60}") - print("Dataset generated successfully!") - print(f"{'='*60}") - print(json.dumps(meta, indent=2)) - print(f"\nOutput directory: {out_dir}") - print(f"Train dataset: {out_dir / 'train.json'}") - print(f"Test dataset: {out_dir / 'test.json'}") - - -if __name__ == "__main__": - main() +"""Generate synthetic GitHub Actions training dataset from workflow files. + +Creates a dataset suitable for chat fine-tuning about GitHub Actions, CI/CD, and workflows. +Output format: + datasets/chat/github_actions/train.json (newline-delimited JSON objects) + datasets/chat/github_actions/test.json (newline-delimited JSON objects) +Each line: {"messages": [ {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."} ]} + +Design principles: +- Generate Q&A pairs about workflow structure, triggers, jobs, steps +- Include CI/CD best practices and explanations +- Cover common GitHub Actions patterns and use cases +- Deterministic with --seed for reproducibility + +Usage: + python scripts/generate_github_actions_dataset.py --max-records 200 +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import random +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List + +import yaml + +DEFAULT_OUTPUT = Path("datasets/chat/github_actions") + + +@dataclass +class WorkflowInfo: + name: str + path: str + content: str + parsed_yaml: Dict[str, Any] + triggers: List[str] + jobs: List[str] + uses_actions: List[str] + + +def parse_workflow(path: Path) -> WorkflowInfo | None: + """Parse a GitHub Actions workflow file""" + try: + content = path.read_text(encoding="utf-8") + parsed = yaml.safe_load(content) + + if not isinstance(parsed, dict): + return None + + # Extract key information + name = parsed.get("name", path.stem) + + # Extract triggers + triggers = [] + if "on" in parsed: + on_config = parsed["on"] + if isinstance(on_config, str): + triggers.append(on_config) + elif isinstance(on_config, list): + triggers.extend(on_config) + elif isinstance(on_config, dict): + triggers.extend(on_config.keys()) + + # Extract jobs + jobs = [] + if "jobs" in parsed and isinstance(parsed["jobs"], dict): + jobs = list(parsed["jobs"].keys()) + + # Extract used actions + uses_actions = [] + if "jobs" in parsed and isinstance(parsed["jobs"], dict): + for job_name, job_config in parsed["jobs"].items(): + if isinstance(job_config, dict) and "steps" in job_config: + for step in job_config.get("steps", []): + if isinstance(step, dict) and "uses" in step: + uses_actions.append(step["uses"]) + + return WorkflowInfo( + name=name, + path=str(path.relative_to(path.parents[2])), # relative to repo root + content=content, + parsed_yaml=parsed, + triggers=triggers, + jobs=jobs, + uses_actions=list(set(uses_actions)), + ) + except Exception as e: + print(f"Warning: Could not parse {path}: {e}") + return None + + +def generate_workflow_overview_qa(workflow: WorkflowInfo) -> tuple[str, str]: + """Generate Q&A about workflow overview""" + user = f"What does the GitHub Actions workflow '{workflow.name}' do?" + + assistant_parts = [f"The workflow '{workflow.name}' is a CI/CD pipeline that:"] + + if workflow.triggers: + trigger_desc = ", ".join(workflow.triggers) + assistant_parts.append(f"\n- Triggers on: {trigger_desc}") + + if workflow.jobs: + job_desc = ", ".join(workflow.jobs) + assistant_parts.append(f"- Contains {len(workflow.jobs)} job(s): {job_desc}") + + if workflow.uses_actions: + assistant_parts.append( + f"- Uses {len(workflow.uses_actions)} different GitHub Actions" + ) + + # Add purpose based on name/jobs + purpose_hints = { + "test": "running automated tests", + "ci": "continuous integration checks", + "deploy": "deploying code to production or staging", + "train": "training machine learning models", + "validate": "validating code quality and correctness", + "build": "building and compiling the application", + "lint": "checking code style and quality", + } + + for keyword, purpose in purpose_hints.items(): + if keyword in workflow.name.lower() or any( + keyword in job.lower() for job in workflow.jobs + ): + assistant_parts.append( + f"\nThis workflow is primarily focused on {purpose}." + ) + break + + return user, "\n".join(assistant_parts) + + +def generate_trigger_qa(workflow: WorkflowInfo) -> tuple[str, str]: + """Generate Q&A about workflow triggers""" + user = f"When does the '{workflow.name}' workflow run?" + + if not workflow.triggers: + return user, "This workflow has no explicit triggers defined." + + assistant_parts = [f"The '{workflow.name}' workflow is triggered by:"] + + trigger_explanations = { + "push": "code pushes to specified branches", + "pull_request": "pull request events (opened, synchronized, etc.)", + "workflow_dispatch": "manual workflow runs via GitHub UI or API", + "schedule": "scheduled cron jobs at specific times", + "release": "release events (published, created, etc.)", + "issues": "issue events (opened, labeled, etc.)", + "issue_comment": "comments on issues or pull requests", + } + + for trigger in workflow.triggers: + explanation = trigger_explanations.get(trigger, f"{trigger} events") + assistant_parts.append(f"\n- {trigger}: {explanation}") + + # Add schedule details if present + if "schedule" in workflow.triggers and "on" in workflow.parsed_yaml: + on_config = workflow.parsed_yaml["on"] + if isinstance(on_config, dict) and "schedule" in on_config: + schedule_list = on_config["schedule"] + if schedule_list and len(schedule_list) > 0: + cron = schedule_list[0].get("cron", "") + if cron: + assistant_parts.append(f"\nSchedule: {cron}") + + return user, "\n".join(assistant_parts) + + +def generate_jobs_qa(workflow: WorkflowInfo) -> tuple[str, str]: + """Generate Q&A about workflow jobs""" + user = f"What jobs are defined in the '{workflow.name}' workflow?" + + if not workflow.jobs: + return user, "This workflow has no jobs defined." + + assistant_parts = [f"The workflow defines {len(workflow.jobs)} job(s):\n"] + + jobs_config = workflow.parsed_yaml.get("jobs", {}) + for job_name in workflow.jobs: + job_config = jobs_config.get(job_name, {}) + assistant_parts.append(f"\n**{job_name}**:") + + # Runner + runs_on = job_config.get("runs-on", "unknown") + assistant_parts.append(f" - Runs on: {runs_on}") + + # Dependencies + if "needs" in job_config: + needs = job_config["needs"] + if isinstance(needs, str): + assistant_parts.append(f" - Depends on: {needs}") + elif isinstance(needs, list): + assistant_parts.append(f" - Depends on: {', '.join(needs)}") + + # Steps count + steps = job_config.get("steps", []) + assistant_parts.append(f" - Contains {len(steps)} step(s)") + + # Conditions + if "if" in job_config: + assistant_parts.append(f" - Conditional: {job_config['if']}") + + return user, "\n".join(assistant_parts) + + +def generate_actions_used_qa(workflow: WorkflowInfo) -> tuple[str, str]: + """Generate Q&A about actions used in workflow""" + user = f"What GitHub Actions are used in the '{workflow.name}' workflow?" + + if not workflow.uses_actions: + return user, "This workflow doesn't use any external GitHub Actions." + + assistant_parts = [ + f"The workflow uses {len(workflow.uses_actions)} different actions:\n" + ] + + action_descriptions = { + "actions/checkout": "Checks out the repository code", + "actions/setup-python": "Sets up a Python environment", + "actions/setup-node": "Sets up a Node.js environment", + "actions/upload-artifact": "Uploads build artifacts for later use", + "actions/download-artifact": "Downloads previously uploaded artifacts", + "actions/cache": "Caches dependencies to speed up workflows", + "azure/login": "Authenticates with Azure", + "azure/cli": "Runs Azure CLI commands", + } + + for action in sorted(workflow.uses_actions): + # Extract action name without version + action_name = action.split("@")[0] if "@" in action else action + description = action_descriptions.get( + action_name, f"Performs {action_name} functionality" + ) + assistant_parts.append(f"- **{action}**: {description}") + + return user, "\n".join(assistant_parts) + + +def generate_best_practices_qa(workflow: WorkflowInfo) -> tuple[str, str]: + """Generate Q&A about best practices in the workflow""" + user = ( + f"What are the best practices demonstrated in the '{workflow.name}' workflow?" + ) + + practices = [] + + # Check for specific patterns + if "actions/checkout" in " ".join(workflow.uses_actions): + practices.append("- Uses actions/checkout to ensure clean code access") + + if any("cache" in action.lower() for action in workflow.uses_actions): + practices.append("- Implements caching to improve workflow performance") + + if any("upload-artifact" in action.lower() for action in workflow.uses_actions): + practices.append("- Preserves build artifacts for debugging and deployment") + + jobs_config = workflow.parsed_yaml.get("jobs", {}) + for job_name, job_config in jobs_config.items(): + if "needs" in job_config: + practices.append("- Uses job dependencies to create a proper pipeline flow") + break + + # Check for conditional execution + for job_name, job_config in jobs_config.items(): + if "if" in job_config: + practices.append( + "- Uses conditional job execution to optimize resource usage" + ) + break + + if "schedule" in workflow.triggers: + practices.append("- Implements scheduled runs for regular automated tasks") + + if "workflow_dispatch" in workflow.triggers: + practices.append("- Allows manual workflow triggering for flexibility") + + # Check for matrix strategy + for job_name, job_config in jobs_config.items(): + if "strategy" in job_config and "matrix" in job_config["strategy"]: + practices.append( + "- Uses matrix strategy for testing across multiple configurations" + ) + break + + if not practices: + practices.append( + "- Follows standard GitHub Actions structure with jobs and steps" + ) + + assistant = ( + f"The '{workflow.name}' workflow demonstrates several best practices:\n\n" + + "\n".join(practices) + ) + return user, assistant + + +def generate_modification_qa(workflow: WorkflowInfo) -> tuple[str, str]: + """Generate Q&A about how to modify the workflow""" + user = f"How would I modify the '{workflow.name}' workflow to add a new step?" + + assistant = f"""To add a new step to the '{workflow.name}' workflow: + +1. Choose which job to modify (available: {', '.join(workflow.jobs) if workflow.jobs else 'none'}) +2. Locate the `steps` section within that job +3. Add a new step with: + - `name`: A descriptive name for the step + - `run`: Command(s) to execute, or + - `uses`: An action to use (e.g., actions/checkout@v4) +4. Optionally add conditions with `if:` to control when the step runs + +Example step structure: +```yaml +- name: My new step + run: | + echo "Running my command" + python my_script.py +``` + +Or using an action: +```yaml +- name: Upload results + uses: actions/upload-artifact@v4 + with: + name: my-artifact + path: results/ +``` + +Remember to test your changes in a feature branch before merging to main.""" + + return user, assistant + + +def generate_troubleshooting_qa(workflow: WorkflowInfo) -> tuple[str, str]: + """Generate Q&A about troubleshooting workflows""" + user = f"How do I debug failures in the '{workflow.name}' workflow?" + + assistant = f"""To debug failures in the '{workflow.name}' workflow: + +1. **Check the Actions tab**: Go to GitHub Actions tab to see run history +2. **Review logs**: Click on the failed run and expand failed steps to see detailed logs +3. **Look for error messages**: Red text usually indicates the failure point +4. **Verify triggers**: Ensure the workflow triggered as expected (check triggers: {', '.join(workflow.triggers) if workflow.triggers else 'none'}) +5. **Test locally**: Try running the commands from failed steps on your local machine +6. **Check dependencies**: Ensure all required dependencies are installed +7. **Review permissions**: Verify the workflow has necessary permissions +8. **Use workflow_dispatch**: Add manual trigger for easier testing + +Common issues: +- Missing environment variables or secrets +- Incorrect file paths +- Dependency version conflicts +- Insufficient permissions +- Timeout issues (increase with `timeout-minutes`) + +For persistent issues, you can enable debug logging by setting these secrets: +- ACTIONS_STEP_DEBUG: true +- ACTIONS_RUNNER_DEBUG: true""" + + return user, assistant + + +# Template functions for generating Q&A pairs +PROMPT_TEMPLATES = { + "overview": generate_workflow_overview_qa, + "triggers": generate_trigger_qa, + "jobs": generate_jobs_qa, + "actions": generate_actions_used_qa, + "best_practices": generate_best_practices_qa, + "modification": generate_modification_qa, + "troubleshooting": generate_troubleshooting_qa, +} + + +def generate_general_qa_pairs() -> List[Dict[str, Any]]: + """Generate general GitHub Actions Q&A pairs""" + general_qa = [ + { + "messages": [ + {"role": "user", "content": "What is GitHub Actions?"}, + { + "role": "assistant", + "content": "GitHub Actions is a CI/CD (Continuous Integration/Continuous Deployment) platform built into GitHub that allows you to automate your software development workflows. You can:\n\n- Run tests automatically on every push or pull request\n- Build and deploy applications\n- Automate code quality checks and linting\n- Schedule regular tasks\n- Respond to GitHub events (issues, releases, etc.)\n\nWorkflows are defined in YAML files stored in `.github/workflows/` directory.", + }, + ], + "source": "general_knowledge", + "template": "concept", + }, + { + "messages": [ + { + "role": "user", + "content": "What is the structure of a GitHub Actions workflow file?", + }, + { + "role": "assistant", + "content": "A GitHub Actions workflow file is a YAML file with the following main sections:\n\n1. **name**: Optional workflow name displayed in the Actions tab\n2. **on**: Defines triggers (push, pull_request, schedule, etc.)\n3. **jobs**: Contains one or more jobs to run\n - Each job has:\n - **runs-on**: Runner type (ubuntu-latest, windows-latest, etc.)\n - **steps**: Sequential actions or commands to execute\n - **needs**: Optional dependencies on other jobs\n4. **env**: Optional environment variables\n5. **permissions**: Optional permissions settings\n\nExample:\n```yaml\nname: CI\non: [push, pull_request]\njobs:\n test:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - run: npm test\n```", + }, + ], + "source": "general_knowledge", + "template": "structure", + }, + { + "messages": [ + { + "role": "user", + "content": "What are the most common GitHub Actions triggers?", + }, + { + "role": "assistant", + "content": "The most common GitHub Actions triggers are:\n\n1. **push**: Triggers on code pushes to specified branches\n ```yaml\n on:\n push:\n branches: [main, dev]\n ```\n\n2. **pull_request**: Triggers on PR events (opened, synchronized, etc.)\n ```yaml\n on:\n pull_request:\n branches: [main]\n ```\n\n3. **schedule**: Triggers on a cron schedule\n ```yaml\n on:\n schedule:\n - cron: '0 2 * * *' # Daily at 2 AM UTC\n ```\n\n4. **workflow_dispatch**: Allows manual workflow runs\n ```yaml\n on:\n workflow_dispatch:\n inputs:\n environment:\n description: 'Environment to deploy to'\n required: true\n ```\n\n5. **release**: Triggers on release events\n ```yaml\n on:\n release:\n types: [published]\n ```", + }, + ], + "source": "general_knowledge", + "template": "triggers", + }, + { + "messages": [ + {"role": "user", "content": "How do I use secrets in GitHub Actions?"}, + { + "role": "assistant", + "content": "To use secrets in GitHub Actions:\n\n1. **Add secrets**: Go to repository Settings > Secrets and variables > Actions\n2. **Click 'New repository secret'** and add your secret (e.g., API keys, passwords)\n3. **Reference in workflow**: Use `${{ secrets.SECRET_NAME }}` syntax\n\nExample:\n```yaml\nsteps:\n - name: Deploy\n env:\n API_KEY: ${{ secrets.API_KEY }}\n DATABASE_URL: ${{ secrets.DATABASE_URL }}\n run: |\n echo \"Deploying with credentials...\"\n ./deploy.sh\n```\n\n**Best practices**:\n- Never commit secrets to code\n- Use environment-specific secrets for dev/staging/prod\n- Secrets are masked in logs\n- Use GitHub's OIDC for cloud provider authentication when possible\n- Consider using environment protection rules for sensitive deployments", + }, + ], + "source": "general_knowledge", + "template": "secrets", + }, + { + "messages": [ + { + "role": "user", + "content": "What is the difference between 'run' and 'uses' in a workflow step?", + }, + { + "role": "assistant", + "content": "In GitHub Actions workflow steps:\n\n**run**: Executes shell commands directly\n```yaml\n- name: Install dependencies\n run: |\n npm install\n npm run build\n```\n- Runs arbitrary commands in the runner's shell\n- Can use multiple lines with `|` or `>`\n- Good for simple commands or scripts\n- Has access to environment variables\n\n**uses**: Invokes a reusable action\n```yaml\n- name: Checkout code\n uses: actions/checkout@v4\n with:\n fetch-depth: 0\n```\n- References a pre-built action from GitHub Marketplace or your repository\n- Actions are versioned (using @v4, @main, or commit SHA)\n- Can accept parameters via `with:`\n- Promotes code reuse and maintainability\n- Examples: actions/checkout, actions/setup-python, azure/login\n\n**When to use**:\n- Use `run` for simple commands and scripts\n- Use `uses` for complex operations, third-party integrations, or reusable logic", + }, + ], + "source": "general_knowledge", + "template": "concepts", + }, + { + "messages": [ + { + "role": "user", + "content": "How do I run jobs in parallel vs sequentially in GitHub Actions?", + }, + { + "role": "assistant", + "content": "GitHub Actions supports both parallel and sequential job execution:\n\n**Parallel (default)**: Jobs run simultaneously by default\n```yaml\njobs:\n test:\n runs-on: ubuntu-latest\n steps:\n - run: npm test\n \n lint:\n runs-on: ubuntu-latest\n steps:\n - run: npm run lint\n```\nBoth `test` and `lint` run at the same time.\n\n**Sequential**: Use `needs` to create dependencies\n```yaml\njobs:\n build:\n runs-on: ubuntu-latest\n steps:\n - run: npm build\n \n test:\n needs: build # Waits for build to complete\n runs-on: ubuntu-latest\n steps:\n - run: npm test\n \n deploy:\n needs: [build, test] # Waits for both\n runs-on: ubuntu-latest\n steps:\n - run: ./deploy.sh\n```\n\n**Benefits**:\n- Parallel: Faster total execution time\n- Sequential: Ensures correct order (e.g., build before test, test before deploy)\n- Mix both: Some jobs parallel, others sequential", + }, + ], + "source": "general_knowledge", + "template": "parallelism", + }, + { + "messages": [ + { + "role": "user", + "content": "What is a matrix strategy in GitHub Actions?", + }, + { + "role": "assistant", + "content": "A matrix strategy in GitHub Actions allows you to run a job with multiple configurations automatically:\n\n```yaml\njobs:\n test:\n runs-on: ubuntu-latest\n strategy:\n matrix:\n python-version: ['3.9', '3.10', '3.11', '3.12']\n os: [ubuntu-latest, windows-latest, macos-latest]\n steps:\n - uses: actions/setup-python@v5\n with:\n python-version: ${{ matrix.python-version }}\n - run: python --version\n - run: pytest\n```\n\nThis creates 12 jobs (4 Python versions × 3 OS) automatically.\n\n**Key features**:\n- `${{ matrix.variable }}` accesses matrix values\n- `fail-fast: false` continues other jobs if one fails\n- `max-parallel` limits concurrent jobs\n- `include` adds specific combinations\n- `exclude` removes specific combinations\n\n**Use cases**:\n- Testing across multiple Python/Node versions\n- Cross-platform testing (Linux, Windows, macOS)\n- Testing with different dependency versions\n- Different configuration combinations", + }, + ], + "source": "general_knowledge", + "template": "matrix", + }, + { + "messages": [ + { + "role": "user", + "content": "How do I cache dependencies in GitHub Actions?", + }, + { + "role": "assistant", + "content": "Caching dependencies speeds up workflow runs by reusing previously downloaded packages:\n\n**Using actions/cache**:\n```yaml\nsteps:\n - uses: actions/checkout@v4\n \n - uses: actions/cache@v4\n with:\n path: ~/.npm\n key: ${{ runner.os }}-node-${{ hashFiles('**/package-lock.json') }}\n restore-keys: |\n ${{ runner.os }}-node-\n \n - run: npm install\n```\n\n**Language-specific setup actions** with built-in caching:\n```yaml\n# Python with pip cache\n- uses: actions/setup-python@v5\n with:\n python-version: '3.11'\n cache: 'pip'\n\n# Node.js with npm cache\n- uses: actions/setup-node@v4\n with:\n node-version: '20'\n cache: 'npm'\n```\n\n**Benefits**:\n- Reduces workflow time by 30-70%\n- Decreases network load\n- Cache expires after 7 days of no access\n- Maximum cache size: 10 GB per repository\n\n**Best practices**:\n- Use hash of lock files in cache key for version tracking\n- Include OS in key if caching OS-specific files\n- Use restore-keys for partial cache hits", + }, + ], + "source": "general_knowledge", + "template": "caching", + }, + ] + + return general_qa + + +def build_records( + workflows: List[WorkflowInfo], max_records: int, seed: int +) -> List[Dict[str, Any]]: + """Build Q&A records from workflows""" + random.seed(seed) + records: List[Dict[str, Any]] = [] + + # Add general Q&A pairs + general_records = generate_general_qa_pairs() + records.extend(general_records) + + # Generate workflow-specific Q&A + for workflow in workflows: + # Randomize template order per workflow + template_keys = list(PROMPT_TEMPLATES.keys()) + random.shuffle(template_keys) + + for template_key in template_keys: + if len(records) >= max_records: + break + + try: + template_func = PROMPT_TEMPLATES[template_key] + user_prompt, assistant_answer = template_func(workflow) + + h = hashlib.sha256( + (workflow.name + template_key + assistant_answer[:100]).encode( + "utf-8" + ) + ).hexdigest()[:16] + rec = { + "messages": [ + {"role": "user", "content": user_prompt}, + {"role": "assistant", "content": assistant_answer}, + ], + "source_workflow": workflow.name, + "source_path": workflow.path, + "template": template_key, + "hash": h, + } + records.append(rec) + except Exception as e: + print( + f"Warning: Could not generate {template_key} for {workflow.name}: {e}" + ) + + if len(records) >= max_records: + break + + return records + + +def write_jsonl(path: Path, records: List[Dict[str, Any]]): + """Write records to JSONL file""" + with path.open("w", encoding="utf-8") as f: + for rec in records: + f.write(json.dumps(rec, ensure_ascii=False) + "\n") + + +def main(): + ap = argparse.ArgumentParser(description="Generate GitHub Actions training dataset") + ap.add_argument( + "--workflow-dir", + default=".github/workflows", + help="Directory containing workflow files", + ) + ap.add_argument( + "--output-dir", default=str(DEFAULT_OUTPUT), help="Output dataset directory" + ) + ap.add_argument( + "--max-records", + type=int, + default=200, + help="Maximum total records (train+test)", + ) + ap.add_argument("--seed", type=int, default=42, help="Random seed") + ap.add_argument("--train-ratio", type=float, default=0.85, help="Train split ratio") + args = ap.parse_args() + + # Find repository root (go up from scripts dir) + repo_root = Path(__file__).resolve().parents[1] + workflow_dir = repo_root / args.workflow_dir + out_dir = repo_root / args.output_dir + out_dir.mkdir(parents=True, exist_ok=True) + + # Parse all workflow files + workflows: List[WorkflowInfo] = [] + if workflow_dir.exists(): + for pattern in ("*.yml", "*.yaml"): + for workflow_file in workflow_dir.glob(pattern): + workflow_info = parse_workflow(workflow_file) + if workflow_info: + workflows.append(workflow_info) + print(f"Parsed workflow: {workflow_info.name}") + + if not workflows: + print(f"Warning: No workflow files found in {workflow_dir}") + print("Generating dataset with only general GitHub Actions Q&A...") + + # Generate Q&A records + records = build_records(workflows, max_records=args.max_records, seed=args.seed) + + # Train/test split + random.seed(args.seed) + random.shuffle(records) + n_train = int(len(records) * args.train_ratio) + train_recs = records[:n_train] + test_recs = records[n_train:] or records[: max(1, len(records) // 10)] + + # Write datasets + write_jsonl(out_dir / "train.json", train_recs) + write_jsonl(out_dir / "test.json", test_recs) + + # Write metadata + meta = { + "total_records": len(records), + "train_records": len(train_recs), + "test_records": len(test_recs), + "generation_seed": args.seed, + "workflows_parsed": len(workflows), + "workflow_names": [w.name for w in workflows], + "generation_date": datetime.now().isoformat(), + } + with (out_dir / "metadata.json").open("w", encoding="utf-8") as f: + json.dump(meta, f, indent=2) + + print(f"\n{'='*60}") + print("Dataset generated successfully!") + print(f"{'='*60}") + print(json.dumps(meta, indent=2)) + print(f"\nOutput directory: {out_dir}") + print(f"Train dataset: {out_dir / 'train.json'}") + print(f"Test dataset: {out_dir / 'test.json'}") + + +if __name__ == "__main__": + main() diff --git a/scripts/integration_smoke.py b/scripts/integration_smoke.py index 44b8587c1..8f020611b 100644 --- a/scripts/integration_smoke.py +++ b/scripts/integration_smoke.py @@ -1,407 +1,407 @@ -#!/usr/bin/env python3 -"""Fast integration smoke checks for the Aria repository. - -This script validates critical integration wiring without running full test suites. -It is safe for local development and CI gates. -""" - -from __future__ import annotations - -import argparse -import json -import subprocess -import sys -import time -from dataclasses import asdict, dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional -from urllib.error import URLError -from urllib.request import urlopen - -try: - from .config_paths import (canonical_config_path, get_config_candidates, - resolve_existing_config_path) -except ImportError: - from config_paths import (canonical_config_path, get_config_candidates, - resolve_existing_config_path) - -REPO_ROOT = Path(__file__).resolve().parents[1] -DATA_OUT = REPO_ROOT / "data_out" / "integration_smoke" -# Adapter startup can be slow in cold/containerized environments because -# importing `function_app` initializes multiple subsystems. Keep this timeout -# comfortably above typical observed startup (~13s) to avoid false negatives. -LOCAL_DEV_ADAPTER_PROBE_TIMEOUT_SEC = 25.0 -LOCAL_DEV_ADAPTER_REQUEST_TIMEOUT_SEC = 10.0 - - -@dataclass -class StepResult: - name: str - status: str - critical: bool - duration_sec: float - detail: str = "" - - -def _run_command( - name: str, - cmd: List[str], - *, - critical: bool = True, - timeout: int = 180, -) -> StepResult: - start = time.perf_counter() - try: - proc = subprocess.run( - cmd, - cwd=str(REPO_ROOT), - capture_output=True, - text=True, - timeout=timeout, - check=False, - ) - duration = round(time.perf_counter() - start, 2) - status = "succeeded" if proc.returncode == 0 else "failed" - - tail_stdout = (proc.stdout or "").strip()[-400:] - tail_stderr = (proc.stderr or "").strip()[-400:] - detail_parts = [f"rc={proc.returncode}"] - if tail_stdout: - detail_parts.append(f"stdout={tail_stdout}") - if tail_stderr: - detail_parts.append(f"stderr={tail_stderr}") - - return StepResult( - name=name, - status=status, - critical=critical, - duration_sec=duration, - detail=" | ".join(detail_parts), - ) - except subprocess.TimeoutExpired: - duration = round(time.perf_counter() - start, 2) - return StepResult( - name=name, - status="timeout", - critical=critical, - duration_sec=duration, - detail=f"timeout={timeout}s", - ) - except Exception as exc: # noqa: BLE001 - duration = round(time.perf_counter() - start, 2) - return StepResult( - name=name, - status="error", - critical=critical, - duration_sec=duration, - detail=str(exc), - ) - - -def _check_config_paths() -> List[StepResult]: - checks = { - "master_orchestrator_config": "master_orchestrator", - "quantum_autorun_config": "quantum_autorun", - "evaluation_autorun_config": "evaluation_autorun", - } - - results: List[StepResult] = [] - for name, config_key in checks.items(): - candidates = get_config_candidates(REPO_ROOT, config_key) - canonical = canonical_config_path(REPO_ROOT, config_key) - start = time.perf_counter() - found: Optional[Path] = next((p for p in candidates if p.exists()), None) - duration = round(time.perf_counter() - start, 2) - - if found is None: - results.append( - StepResult( - name=name, - status="failed", - critical=True, - duration_sec=duration, - detail="missing canonical and legacy config", - ) - ) - continue - - if found == canonical: - results.append( - StepResult( - name=name, - status="succeeded", - critical=True, - duration_sec=duration, - detail=f"resolved={found.relative_to(REPO_ROOT)}", - ) - ) - else: - results.append( - StepResult( - name=name, - status="warning", - critical=False, - duration_sec=duration, - detail=( - "using legacy path; prefer " - f"{canonical.relative_to(REPO_ROOT)}" - ), - ) - ) - - return results - - -def _fetch_local_functions_payload(url: str, timeout: int = 2) -> Dict[str, Any]: - """Fetch and parse the local Functions status payload.""" - with urlopen(url, timeout=timeout) as resp: # noqa: S310 - local probe - return json.loads(resp.read().decode("utf-8")) - - -def _probe_with_local_dev_adapter(url: str) -> Optional[Dict[str, Any]]: - """Best-effort fallback: start local adapter and retry endpoint probe.""" - proc: Optional[subprocess.Popen[str]] = None - deadline = time.time() + LOCAL_DEV_ADAPTER_PROBE_TIMEOUT_SEC - - try: - proc = subprocess.Popen( - [sys.executable, "local_dev_adapter.py"], - cwd=str(REPO_ROOT), - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - text=True, - ) - - while time.time() < deadline: - try: - remaining = max(1.0, deadline - time.time()) - request_timeout = min( - LOCAL_DEV_ADAPTER_REQUEST_TIMEOUT_SEC, - remaining, - ) - return _fetch_local_functions_payload(url, timeout=request_timeout) - except (URLError, TimeoutError, OSError, json.JSONDecodeError, ValueError): - time.sleep(0.25) - - return None - except OSError: - return None - finally: - if proc is not None and proc.poll() is None: - proc.terminate() - try: - proc.wait(timeout=2) - except subprocess.TimeoutExpired: - proc.kill() - - -def _probe_functions_endpoint(strict: bool) -> StepResult: - name = "functions_ai_status_endpoint" - start = time.perf_counter() - url = "http://localhost:7071/api/ai/status" - try: - payload = _fetch_local_functions_payload(url) - provider = payload.get("active_provider", "unknown") - duration = round(time.perf_counter() - start, 2) - return StepResult( - name=name, - status="succeeded", - critical=strict, - duration_sec=duration, - detail=f"provider={provider}", - ) - except (URLError, TimeoutError, OSError): - duration = round(time.perf_counter() - start, 2) - if strict: - # First retry directly to absorb transient startup races. - for _ in range(2): - time.sleep(0.25) - try: - payload = _fetch_local_functions_payload(url) - provider = payload.get("active_provider", "unknown") - duration = round(time.perf_counter() - start, 2) - return StepResult( - name=name, - status="succeeded", - critical=True, - duration_sec=duration, - detail=f"provider={provider} | via=direct_retry", - ) - except (URLError, TimeoutError, OSError): - continue - - fallback_payload = _probe_with_local_dev_adapter(url) - if fallback_payload is not None: - provider = fallback_payload.get("active_provider", "unknown") - duration = round(time.perf_counter() - start, 2) - return StepResult( - name=name, - status="succeeded", - critical=True, - duration_sec=duration, - detail=f"provider={provider} | via=local_dev_adapter", - ) - - # Final direct retry covers the case where adapter startup fails - # because another process is already bound to :7071. - try: - payload = _fetch_local_functions_payload(url) - provider = payload.get("active_provider", "unknown") - duration = round(time.perf_counter() - start, 2) - return StepResult( - name=name, - status="succeeded", - critical=True, - duration_sec=duration, - detail=f"provider={provider} | via=final_direct_retry", - ) - except (URLError, TimeoutError, OSError): - return StepResult( - name=name, - status="failed", - critical=True, - duration_sec=duration, - detail=f"endpoint_unreachable={url}", - ) - return StepResult( - name=name, - status="skipped", - critical=False, - duration_sec=duration, - detail="functions host not running (non-strict mode)", - ) - except Exception as exc: # noqa: BLE001 - duration = round(time.perf_counter() - start, 2) - return StepResult( - name=name, - status="error", - critical=strict, - duration_sec=duration, - detail=str(exc), - ) - - -def _resolved_config_paths() -> Dict[str, Optional[str]]: - """Resolve key config paths for summary metadata.""" - config_keys = [ - "master_orchestrator", - "quantum_autorun", - "evaluation_autorun", - ] - resolved: Dict[str, Optional[str]] = {} - for key in config_keys: - selected = resolve_existing_config_path(REPO_ROOT, key) - resolved[key] = str(selected.relative_to(REPO_ROOT)) if selected else None - return resolved - - -def run_smoke(strict_endpoints: bool) -> Dict[str, Any]: - steps: List[StepResult] = [] - - steps.extend(_check_config_paths()) - - steps.append( - _run_command( - "master_orchestrator_status", - [sys.executable, "scripts/master_orchestrator.py", "--status"], - critical=True, - ) - ) - steps.append( - _run_command( - "quantum_autorun_dry_run", - [sys.executable, "scripts/quantum_autorun.py", "--dry-run"], - critical=True, - ) - ) - steps.append( - _run_command( - "repo_automation_status", - [sys.executable, "scripts/repo_automation.py", "--status"], - critical=True, - ) - ) - steps.append( - _run_command( - "chat_cli_local_once", - [ - sys.executable, - "ai-projects/chat-cli/src/chat_cli.py", - "--provider", - "local", - "--once", - "integration smoke ping", - ], - critical=True, - ) - ) - - steps.append(_probe_functions_endpoint(strict_endpoints)) - - total = len(steps) - succeeded = sum(1 for s in steps if s.status == "succeeded") - failed_critical = [ - s for s in steps if s.critical and s.status not in {"succeeded", "warning"} - ] - generated_at = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) - run_id = time.strftime("%Y%m%dT%H%M%SZ", time.gmtime()) - - summary = { - "generated_at": generated_at, - "run_id": run_id, - "config_path": None, - "config_paths": _resolved_config_paths(), - "strict_endpoints": strict_endpoints, - "total_steps": total, - "succeeded": succeeded, - "warnings": sum(1 for s in steps if s.status == "warning"), - "failed": sum(1 for s in steps if s.status == "failed"), - "skipped": sum(1 for s in steps if s.status == "skipped"), - "errors": sum(1 for s in steps if s.status == "error"), - "passed": len(failed_critical) == 0, - "results": [asdict(s) for s in steps], - } - return summary - - -def main() -> int: - ap = argparse.ArgumentParser(description="Run Aria integration smoke checks") - ap.add_argument( - "--strict-endpoints", - action="store_true", - help="Fail if local Functions endpoint is unavailable", - ) - ap.add_argument( - "--output", - default=str(DATA_OUT / "status.json"), - help="Path to write JSON summary", - ) - ap.add_argument( - "--json", - action="store_true", - help="Print JSON summary to stdout", - ) - args = ap.parse_args() - - summary = run_smoke(strict_endpoints=args.strict_endpoints) - - output_path = Path(args.output) - output_path.parent.mkdir(parents=True, exist_ok=True) - with output_path.open("w", encoding="utf-8") as f: - json.dump(summary, f, indent=2) - - if args.json: - print(json.dumps(summary, indent=2)) - else: - print("[integration_smoke] Summary") - print( - f"passed={summary['passed']} | succeeded={summary['succeeded']}/{summary['total_steps']} " - f"| warnings={summary['warnings']} | failed={summary['failed']} | errors={summary['errors']}" - ) - print(f"output={output_path}") - - return 0 if summary["passed"] else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) +#!/usr/bin/env python3 +"""Fast integration smoke checks for the Aria repository. + +This script validates critical integration wiring without running full test suites. +It is safe for local development and CI gates. +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +import time +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional +from urllib.error import URLError +from urllib.request import urlopen + +try: + from .config_paths import (canonical_config_path, get_config_candidates, + resolve_existing_config_path) +except ImportError: + from config_paths import (canonical_config_path, get_config_candidates, + resolve_existing_config_path) + +REPO_ROOT = Path(__file__).resolve().parents[1] +DATA_OUT = REPO_ROOT / "data_out" / "integration_smoke" +# Adapter startup can be slow in cold/containerized environments because +# importing `function_app` initializes multiple subsystems. Keep this timeout +# comfortably above typical observed startup (~13s) to avoid false negatives. +LOCAL_DEV_ADAPTER_PROBE_TIMEOUT_SEC = 25.0 +LOCAL_DEV_ADAPTER_REQUEST_TIMEOUT_SEC = 10.0 + + +@dataclass +class StepResult: + name: str + status: str + critical: bool + duration_sec: float + detail: str = "" + + +def _run_command( + name: str, + cmd: List[str], + *, + critical: bool = True, + timeout: int = 180, +) -> StepResult: + start = time.perf_counter() + try: + proc = subprocess.run( + cmd, + cwd=str(REPO_ROOT), + capture_output=True, + text=True, + timeout=timeout, + check=False, + ) + duration = round(time.perf_counter() - start, 2) + status = "succeeded" if proc.returncode == 0 else "failed" + + tail_stdout = (proc.stdout or "").strip()[-400:] + tail_stderr = (proc.stderr or "").strip()[-400:] + detail_parts = [f"rc={proc.returncode}"] + if tail_stdout: + detail_parts.append(f"stdout={tail_stdout}") + if tail_stderr: + detail_parts.append(f"stderr={tail_stderr}") + + return StepResult( + name=name, + status=status, + critical=critical, + duration_sec=duration, + detail=" | ".join(detail_parts), + ) + except subprocess.TimeoutExpired: + duration = round(time.perf_counter() - start, 2) + return StepResult( + name=name, + status="timeout", + critical=critical, + duration_sec=duration, + detail=f"timeout={timeout}s", + ) + except Exception as exc: # noqa: BLE001 + duration = round(time.perf_counter() - start, 2) + return StepResult( + name=name, + status="error", + critical=critical, + duration_sec=duration, + detail=str(exc), + ) + + +def _check_config_paths() -> List[StepResult]: + checks = { + "master_orchestrator_config": "master_orchestrator", + "quantum_autorun_config": "quantum_autorun", + "evaluation_autorun_config": "evaluation_autorun", + } + + results: List[StepResult] = [] + for name, config_key in checks.items(): + candidates = get_config_candidates(REPO_ROOT, config_key) + canonical = canonical_config_path(REPO_ROOT, config_key) + start = time.perf_counter() + found: Optional[Path] = next((p for p in candidates if p.exists()), None) + duration = round(time.perf_counter() - start, 2) + + if found is None: + results.append( + StepResult( + name=name, + status="failed", + critical=True, + duration_sec=duration, + detail="missing canonical and legacy config", + ) + ) + continue + + if found == canonical: + results.append( + StepResult( + name=name, + status="succeeded", + critical=True, + duration_sec=duration, + detail=f"resolved={found.relative_to(REPO_ROOT)}", + ) + ) + else: + results.append( + StepResult( + name=name, + status="warning", + critical=False, + duration_sec=duration, + detail=( + "using legacy path; prefer " + f"{canonical.relative_to(REPO_ROOT)}" + ), + ) + ) + + return results + + +def _fetch_local_functions_payload(url: str, timeout: int = 2) -> Dict[str, Any]: + """Fetch and parse the local Functions status payload.""" + with urlopen(url, timeout=timeout) as resp: # noqa: S310 - local probe + return json.loads(resp.read().decode("utf-8")) + + +def _probe_with_local_dev_adapter(url: str) -> Optional[Dict[str, Any]]: + """Best-effort fallback: start local adapter and retry endpoint probe.""" + proc: Optional[subprocess.Popen[str]] = None + deadline = time.time() + LOCAL_DEV_ADAPTER_PROBE_TIMEOUT_SEC + + try: + proc = subprocess.Popen( + [sys.executable, "local_dev_adapter.py"], + cwd=str(REPO_ROOT), + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + text=True, + ) + + while time.time() < deadline: + try: + remaining = max(1.0, deadline - time.time()) + request_timeout = min( + LOCAL_DEV_ADAPTER_REQUEST_TIMEOUT_SEC, + remaining, + ) + return _fetch_local_functions_payload(url, timeout=request_timeout) + except (URLError, TimeoutError, OSError, json.JSONDecodeError, ValueError): + time.sleep(0.25) + + return None + except OSError: + return None + finally: + if proc is not None and proc.poll() is None: + proc.terminate() + try: + proc.wait(timeout=2) + except subprocess.TimeoutExpired: + proc.kill() + + +def _probe_functions_endpoint(strict: bool) -> StepResult: + name = "functions_ai_status_endpoint" + start = time.perf_counter() + url = "http://localhost:7071/api/ai/status" + try: + payload = _fetch_local_functions_payload(url) + provider = payload.get("active_provider", "unknown") + duration = round(time.perf_counter() - start, 2) + return StepResult( + name=name, + status="succeeded", + critical=strict, + duration_sec=duration, + detail=f"provider={provider}", + ) + except (URLError, TimeoutError, OSError): + duration = round(time.perf_counter() - start, 2) + if strict: + # First retry directly to absorb transient startup races. + for _ in range(2): + time.sleep(0.25) + try: + payload = _fetch_local_functions_payload(url) + provider = payload.get("active_provider", "unknown") + duration = round(time.perf_counter() - start, 2) + return StepResult( + name=name, + status="succeeded", + critical=True, + duration_sec=duration, + detail=f"provider={provider} | via=direct_retry", + ) + except (URLError, TimeoutError, OSError): + continue + + fallback_payload = _probe_with_local_dev_adapter(url) + if fallback_payload is not None: + provider = fallback_payload.get("active_provider", "unknown") + duration = round(time.perf_counter() - start, 2) + return StepResult( + name=name, + status="succeeded", + critical=True, + duration_sec=duration, + detail=f"provider={provider} | via=local_dev_adapter", + ) + + # Final direct retry covers the case where adapter startup fails + # because another process is already bound to :7071. + try: + payload = _fetch_local_functions_payload(url) + provider = payload.get("active_provider", "unknown") + duration = round(time.perf_counter() - start, 2) + return StepResult( + name=name, + status="succeeded", + critical=True, + duration_sec=duration, + detail=f"provider={provider} | via=final_direct_retry", + ) + except (URLError, TimeoutError, OSError): + return StepResult( + name=name, + status="failed", + critical=True, + duration_sec=duration, + detail=f"endpoint_unreachable={url}", + ) + return StepResult( + name=name, + status="skipped", + critical=False, + duration_sec=duration, + detail="functions host not running (non-strict mode)", + ) + except Exception as exc: # noqa: BLE001 + duration = round(time.perf_counter() - start, 2) + return StepResult( + name=name, + status="error", + critical=strict, + duration_sec=duration, + detail=str(exc), + ) + + +def _resolved_config_paths() -> Dict[str, Optional[str]]: + """Resolve key config paths for summary metadata.""" + config_keys = [ + "master_orchestrator", + "quantum_autorun", + "evaluation_autorun", + ] + resolved: Dict[str, Optional[str]] = {} + for key in config_keys: + selected = resolve_existing_config_path(REPO_ROOT, key) + resolved[key] = str(selected.relative_to(REPO_ROOT)) if selected else None + return resolved + + +def run_smoke(strict_endpoints: bool) -> Dict[str, Any]: + steps: List[StepResult] = [] + + steps.extend(_check_config_paths()) + + steps.append( + _run_command( + "master_orchestrator_status", + [sys.executable, "scripts/master_orchestrator.py", "--status"], + critical=True, + ) + ) + steps.append( + _run_command( + "quantum_autorun_dry_run", + [sys.executable, "scripts/quantum_autorun.py", "--dry-run"], + critical=True, + ) + ) + steps.append( + _run_command( + "repo_automation_status", + [sys.executable, "scripts/repo_automation.py", "--status"], + critical=True, + ) + ) + steps.append( + _run_command( + "chat_cli_local_once", + [ + sys.executable, + "ai-projects/chat-cli/src/chat_cli.py", + "--provider", + "local", + "--once", + "integration smoke ping", + ], + critical=True, + ) + ) + + steps.append(_probe_functions_endpoint(strict_endpoints)) + + total = len(steps) + succeeded = sum(1 for s in steps if s.status == "succeeded") + failed_critical = [ + s for s in steps if s.critical and s.status not in {"succeeded", "warning"} + ] + generated_at = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) + run_id = time.strftime("%Y%m%dT%H%M%SZ", time.gmtime()) + + summary = { + "generated_at": generated_at, + "run_id": run_id, + "config_path": None, + "config_paths": _resolved_config_paths(), + "strict_endpoints": strict_endpoints, + "total_steps": total, + "succeeded": succeeded, + "warnings": sum(1 for s in steps if s.status == "warning"), + "failed": sum(1 for s in steps if s.status == "failed"), + "skipped": sum(1 for s in steps if s.status == "skipped"), + "errors": sum(1 for s in steps if s.status == "error"), + "passed": len(failed_critical) == 0, + "results": [asdict(s) for s in steps], + } + return summary + + +def main() -> int: + ap = argparse.ArgumentParser(description="Run Aria integration smoke checks") + ap.add_argument( + "--strict-endpoints", + action="store_true", + help="Fail if local Functions endpoint is unavailable", + ) + ap.add_argument( + "--output", + default=str(DATA_OUT / "status.json"), + help="Path to write JSON summary", + ) + ap.add_argument( + "--json", + action="store_true", + help="Print JSON summary to stdout", + ) + args = ap.parse_args() + + summary = run_smoke(strict_endpoints=args.strict_endpoints) + + output_path = Path(args.output) + output_path.parent.mkdir(parents=True, exist_ok=True) + with output_path.open("w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + + if args.json: + print(json.dumps(summary, indent=2)) + else: + print("[integration_smoke] Summary") + print( + f"passed={summary['passed']} | succeeded={summary['succeeded']}/{summary['total_steps']} " + f"| warnings={summary['warnings']} | failed={summary['failed']} | errors={summary['errors']}" + ) + print(f"output={output_path}") + + return 0 if summary["passed"] else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/job_queue.py b/scripts/job_queue.py index 8d1cb1eeb..805cf1363 100644 --- a/scripts/job_queue.py +++ b/scripts/job_queue.py @@ -1,395 +1,395 @@ -"""Job Queue Management System for QAI Training""" - -import heapq -import json -import uuid -from dataclasses import asdict, dataclass -from datetime import datetime -from enum import Enum -from pathlib import Path -from typing import Dict, List, Optional - - -class JobPriority(Enum): - """Job priority levels""" - - LOW = 1 - NORMAL = 2 - HIGH = 3 - CRITICAL = 4 - - -class JobStatus(Enum): - """Job execution status""" - - PENDING = "pending" - RUNNING = "running" - COMPLETED = "completed" - FAILED = "failed" - CANCELLED = "cancelled" - BLOCKED = "blocked" - - -@dataclass -class QueuedJob: - """Represents a job in the queue""" - - id: str - name: str - config: Dict - priority: JobPriority - status: JobStatus - created_at: str - started_at: Optional[str] = None - completed_at: Optional[str] = None - dependencies: List[str] = None - tags: List[str] = None - estimated_duration: int = 0 # seconds - retry_count: int = 0 - max_retries: int = 3 - error_message: Optional[str] = None - - def __post_init__(self): - if self.dependencies is None: - self.dependencies = [] - if self.tags is None: - self.tags = [] - - def __lt__(self, other): - """For priority queue ordering (higher priority first)""" - if self.priority.value != other.priority.value: - return self.priority.value > other.priority.value - return self.created_at < other.created_at - - -class JobQueue: - """Manages training job queue with priorities and dependencies""" - - def __init__(self, queue_file: str = "data_out/job_queue.json"): - self.queue_file = Path(queue_file) - self.queue_file.parent.mkdir(parents=True, exist_ok=True) - - self.jobs: Dict[str, QueuedJob] = {} - self.priority_queue = [] - - self.load_queue() - - def load_queue(self): - """Load queue from disk""" - if self.queue_file.exists(): - try: - with open(self.queue_file, "r") as f: - data = json.load(f) - - for job_data in data.get("jobs", []): - job = QueuedJob( - id=job_data["id"], - name=job_data["name"], - config=job_data["config"], - priority=JobPriority[job_data["priority"]], - status=JobStatus(job_data["status"]), - created_at=job_data["created_at"], - started_at=job_data.get("started_at"), - completed_at=job_data.get("completed_at"), - dependencies=job_data.get("dependencies", []), - tags=job_data.get("tags", []), - estimated_duration=job_data.get("estimated_duration", 0), - retry_count=job_data.get("retry_count", 0), - max_retries=job_data.get("max_retries", 3), - error_message=job_data.get("error_message"), - ) - self.jobs[job.id] = job - - if job.status == JobStatus.PENDING: - heapq.heappush(self.priority_queue, job) - - print(f"Loaded {len(self.jobs)} jobs from queue") - except Exception as e: - print(f"Error loading queue: {e}") - - def save_queue(self): - """Save queue to disk""" - try: - data = { - "jobs": [asdict(job) for job in self.jobs.values()], - "updated_at": datetime.now().isoformat(), - } - - # Convert enums to strings for JSON - for job_data in data["jobs"]: - job_data["priority"] = job_data["priority"].name - job_data["status"] = job_data["status"].value - - with open(self.queue_file, "w") as f: - json.dump(data, f, indent=2) - - except Exception as e: - print(f"Error saving queue: {e}") - - def add_job( - self, - name: str, - config: Dict, - priority: JobPriority = JobPriority.NORMAL, - dependencies: List[str] = None, - tags: List[str] = None, - estimated_duration: int = 0, - ) -> str: - """Add a new job to the queue""" - job_id = f"job_{uuid.uuid4().hex[:16]}" - - job = QueuedJob( - id=job_id, - name=name, - config=config, - priority=priority, - status=JobStatus.PENDING, - created_at=datetime.now().isoformat(), - dependencies=dependencies or [], - tags=tags or [], - estimated_duration=estimated_duration, - ) - - self.jobs[job_id] = job - heapq.heappush(self.priority_queue, job) - - self.save_queue() - - print(f"Added job {job_id}: {name} (priority: {priority.name})") - return job_id - - def get_next_job(self) -> Optional[QueuedJob]: - """Get the next job to execute (considering dependencies)""" - while self.priority_queue: - job = heapq.heappop(self.priority_queue) - - # Check if job still exists and is pending - if job.id not in self.jobs or self.jobs[job.id].status != JobStatus.PENDING: - continue - - # Check dependencies - if self.check_dependencies(job): - return job - else: - # Dependencies not met, mark as blocked - job.status = JobStatus.BLOCKED - self.save_queue() - - return None - - def check_dependencies(self, job: QueuedJob) -> bool: - """Check if all job dependencies are completed""" - if not job.dependencies: - return True - - for dep_id in job.dependencies: - if dep_id not in self.jobs: - return False - - dep_job = self.jobs[dep_id] - if dep_job.status != JobStatus.COMPLETED: - return False - - return True - - def start_job(self, job_id: str): - """Mark job as running""" - if job_id in self.jobs: - self.jobs[job_id].status = JobStatus.RUNNING - self.jobs[job_id].started_at = datetime.now().isoformat() - self.save_queue() - print(f"Started job {job_id}") - - def complete_job(self, job_id: str, success: bool = True, error: str = None): - """Mark job as completed or failed""" - if job_id not in self.jobs: - return - - job = self.jobs[job_id] - - if success: - job.status = JobStatus.COMPLETED - job.completed_at = datetime.now().isoformat() - print(f"Completed job {job_id}") - - # Unblock dependent jobs - self.unblock_dependent_jobs(job_id) - else: - job.retry_count += 1 - - if job.retry_count < job.max_retries: - job.status = JobStatus.PENDING - job.error_message = error - heapq.heappush(self.priority_queue, job) - print( - f"Retrying job {job_id} (attempt {job.retry_count + 1}/{job.max_retries})" - ) - else: - job.status = JobStatus.FAILED - job.error_message = error - print(f"Failed job {job_id}: {error}") - - self.save_queue() - - def unblock_dependent_jobs(self, completed_job_id: str): - """Unblock jobs that were waiting for this job""" - for job in self.jobs.values(): - if ( - job.status == JobStatus.BLOCKED - and completed_job_id in job.dependencies - and self.check_dependencies(job) - ): - - job.status = JobStatus.PENDING - heapq.heappush(self.priority_queue, job) - print(f"Unblocked job {job.id}") - - def cancel_job(self, job_id: str): - """Cancel a pending or blocked job""" - CANCELLABLE_STATUSES = {JobStatus.PENDING, JobStatus.BLOCKED} # O(1) set lookup - if job_id in self.jobs: - job = self.jobs[job_id] - if job.status in CANCELLABLE_STATUSES: - job.status = JobStatus.CANCELLED - self.save_queue() - print(f"Cancelled job {job_id}") - return True - return False - - def get_queue_status(self) -> Dict: - """Get current queue status""" - # Single-pass aggregation for efficiency - ACTIVE_STATUSES = {JobStatus.PENDING, JobStatus.BLOCKED} - counts = { - "total_jobs": len(self.jobs), - "pending": 0, - "running": 0, - "completed": 0, - "failed": 0, - "blocked": 0, - "cancelled": 0, - "estimated_total_time": 0, - } - - for job in self.jobs.values(): - # Count by status - if job.status == JobStatus.PENDING: - counts["pending"] += 1 - elif job.status == JobStatus.RUNNING: - counts["running"] += 1 - elif job.status == JobStatus.COMPLETED: - counts["completed"] += 1 - elif job.status == JobStatus.FAILED: - counts["failed"] += 1 - elif job.status == JobStatus.BLOCKED: - counts["blocked"] += 1 - elif job.status == JobStatus.CANCELLED: - counts["cancelled"] += 1 - - # Sum estimated time for active jobs - if job.status in ACTIVE_STATUSES: - counts["estimated_total_time"] += job.estimated_duration - - counts["queue_length"] = len(self.priority_queue) - return counts - - def get_job_details(self, job_id: str) -> Optional[Dict]: - """Get detailed information about a job""" - if job_id in self.jobs: - job = self.jobs[job_id] - return { - "id": job.id, - "name": job.name, - "config": job.config, - "priority": job.priority.name, - "status": job.status.value, - "created_at": job.created_at, - "started_at": job.started_at, - "completed_at": job.completed_at, - "dependencies": job.dependencies, - "tags": job.tags, - "estimated_duration": job.estimated_duration, - "retry_count": job.retry_count, - "max_retries": job.max_retries, - "error_message": job.error_message, - } - return None - - def list_jobs( - self, status: Optional[JobStatus] = None, tags: List[str] = None - ) -> List[Dict]: - """List all jobs, optionally filtered by status or tags""" - jobs = list(self.jobs.values()) - - if status: - jobs = [j for j in jobs if j.status == status] - - if tags: - # Set intersection optimization: convert to sets for O(n) instead of O(n²) lookup - tags_set = set(tags) - jobs = [j for j in jobs if set(j.tags) & tags_set] - - return [self.get_job_details(j.id) for j in jobs] - - def clear_completed(self): - """Remove completed and cancelled jobs from queue""" - REMOVABLE_STATUSES = { - JobStatus.COMPLETED, - JobStatus.CANCELLED, - } # O(1) set lookup - to_remove = [ - job_id - for job_id, job in self.jobs.items() - if job.status in REMOVABLE_STATUSES - ] - - for job_id in to_remove: - del self.jobs[job_id] - - self.save_queue() - print(f"Cleared {len(to_remove)} completed/cancelled jobs") - - -# Example usage -if __name__ == "__main__": - queue = JobQueue() - - # Example: Add jobs with dependencies - job1 = queue.add_job( - name="preprocess_data", - config={"dataset": "mixed_chat", "action": "preprocess"}, - priority=JobPriority.HIGH, - tags=["preprocessing"], - estimated_duration=300, # 5 minutes - ) - - job2 = queue.add_job( - name="train_model_v1", - config={"epochs": 3, "dataset": "mixed_chat"}, - priority=JobPriority.NORMAL, - dependencies=[job1], # Depends on preprocessing - tags=["training", "v1"], - estimated_duration=1800, # 30 minutes - ) - - job3 = queue.add_job( - name="evaluate_model", - config={"model": "v1", "test_set": "test.json"}, - priority=JobPriority.NORMAL, - dependencies=[job2], # Depends on training - tags=["evaluation"], - estimated_duration=600, # 10 minutes - ) - - # Print queue status - status = queue.get_queue_status() - print("\nQueue Status:") - for key, value in status.items(): - print(f" {key}: {value}") - - # List pending jobs - print("\nPending Jobs:") - for job in queue.list_jobs(status=JobStatus.PENDING): - print( - f" - {job['name']} (priority: {job['priority']}, deps: {job['dependencies']})" - ) +"""Job Queue Management System for QAI Training""" + +import heapq +import json +import uuid +from dataclasses import asdict, dataclass +from datetime import datetime +from enum import Enum +from pathlib import Path +from typing import Dict, List, Optional + + +class JobPriority(Enum): + """Job priority levels""" + + LOW = 1 + NORMAL = 2 + HIGH = 3 + CRITICAL = 4 + + +class JobStatus(Enum): + """Job execution status""" + + PENDING = "pending" + RUNNING = "running" + COMPLETED = "completed" + FAILED = "failed" + CANCELLED = "cancelled" + BLOCKED = "blocked" + + +@dataclass +class QueuedJob: + """Represents a job in the queue""" + + id: str + name: str + config: Dict + priority: JobPriority + status: JobStatus + created_at: str + started_at: Optional[str] = None + completed_at: Optional[str] = None + dependencies: List[str] = None + tags: List[str] = None + estimated_duration: int = 0 # seconds + retry_count: int = 0 + max_retries: int = 3 + error_message: Optional[str] = None + + def __post_init__(self): + if self.dependencies is None: + self.dependencies = [] + if self.tags is None: + self.tags = [] + + def __lt__(self, other): + """For priority queue ordering (higher priority first)""" + if self.priority.value != other.priority.value: + return self.priority.value > other.priority.value + return self.created_at < other.created_at + + +class JobQueue: + """Manages training job queue with priorities and dependencies""" + + def __init__(self, queue_file: str = "data_out/job_queue.json"): + self.queue_file = Path(queue_file) + self.queue_file.parent.mkdir(parents=True, exist_ok=True) + + self.jobs: Dict[str, QueuedJob] = {} + self.priority_queue = [] + + self.load_queue() + + def load_queue(self): + """Load queue from disk""" + if self.queue_file.exists(): + try: + with open(self.queue_file, "r") as f: + data = json.load(f) + + for job_data in data.get("jobs", []): + job = QueuedJob( + id=job_data["id"], + name=job_data["name"], + config=job_data["config"], + priority=JobPriority[job_data["priority"]], + status=JobStatus(job_data["status"]), + created_at=job_data["created_at"], + started_at=job_data.get("started_at"), + completed_at=job_data.get("completed_at"), + dependencies=job_data.get("dependencies", []), + tags=job_data.get("tags", []), + estimated_duration=job_data.get("estimated_duration", 0), + retry_count=job_data.get("retry_count", 0), + max_retries=job_data.get("max_retries", 3), + error_message=job_data.get("error_message"), + ) + self.jobs[job.id] = job + + if job.status == JobStatus.PENDING: + heapq.heappush(self.priority_queue, job) + + print(f"Loaded {len(self.jobs)} jobs from queue") + except Exception as e: + print(f"Error loading queue: {e}") + + def save_queue(self): + """Save queue to disk""" + try: + data = { + "jobs": [asdict(job) for job in self.jobs.values()], + "updated_at": datetime.now().isoformat(), + } + + # Convert enums to strings for JSON + for job_data in data["jobs"]: + job_data["priority"] = job_data["priority"].name + job_data["status"] = job_data["status"].value + + with open(self.queue_file, "w") as f: + json.dump(data, f, indent=2) + + except Exception as e: + print(f"Error saving queue: {e}") + + def add_job( + self, + name: str, + config: Dict, + priority: JobPriority = JobPriority.NORMAL, + dependencies: List[str] = None, + tags: List[str] = None, + estimated_duration: int = 0, + ) -> str: + """Add a new job to the queue""" + job_id = f"job_{uuid.uuid4().hex[:16]}" + + job = QueuedJob( + id=job_id, + name=name, + config=config, + priority=priority, + status=JobStatus.PENDING, + created_at=datetime.now().isoformat(), + dependencies=dependencies or [], + tags=tags or [], + estimated_duration=estimated_duration, + ) + + self.jobs[job_id] = job + heapq.heappush(self.priority_queue, job) + + self.save_queue() + + print(f"Added job {job_id}: {name} (priority: {priority.name})") + return job_id + + def get_next_job(self) -> Optional[QueuedJob]: + """Get the next job to execute (considering dependencies)""" + while self.priority_queue: + job = heapq.heappop(self.priority_queue) + + # Check if job still exists and is pending + if job.id not in self.jobs or self.jobs[job.id].status != JobStatus.PENDING: + continue + + # Check dependencies + if self.check_dependencies(job): + return job + else: + # Dependencies not met, mark as blocked + job.status = JobStatus.BLOCKED + self.save_queue() + + return None + + def check_dependencies(self, job: QueuedJob) -> bool: + """Check if all job dependencies are completed""" + if not job.dependencies: + return True + + for dep_id in job.dependencies: + if dep_id not in self.jobs: + return False + + dep_job = self.jobs[dep_id] + if dep_job.status != JobStatus.COMPLETED: + return False + + return True + + def start_job(self, job_id: str): + """Mark job as running""" + if job_id in self.jobs: + self.jobs[job_id].status = JobStatus.RUNNING + self.jobs[job_id].started_at = datetime.now().isoformat() + self.save_queue() + print(f"Started job {job_id}") + + def complete_job(self, job_id: str, success: bool = True, error: str = None): + """Mark job as completed or failed""" + if job_id not in self.jobs: + return + + job = self.jobs[job_id] + + if success: + job.status = JobStatus.COMPLETED + job.completed_at = datetime.now().isoformat() + print(f"Completed job {job_id}") + + # Unblock dependent jobs + self.unblock_dependent_jobs(job_id) + else: + job.retry_count += 1 + + if job.retry_count < job.max_retries: + job.status = JobStatus.PENDING + job.error_message = error + heapq.heappush(self.priority_queue, job) + print( + f"Retrying job {job_id} (attempt {job.retry_count + 1}/{job.max_retries})" + ) + else: + job.status = JobStatus.FAILED + job.error_message = error + print(f"Failed job {job_id}: {error}") + + self.save_queue() + + def unblock_dependent_jobs(self, completed_job_id: str): + """Unblock jobs that were waiting for this job""" + for job in self.jobs.values(): + if ( + job.status == JobStatus.BLOCKED + and completed_job_id in job.dependencies + and self.check_dependencies(job) + ): + + job.status = JobStatus.PENDING + heapq.heappush(self.priority_queue, job) + print(f"Unblocked job {job.id}") + + def cancel_job(self, job_id: str): + """Cancel a pending or blocked job""" + CANCELLABLE_STATUSES = {JobStatus.PENDING, JobStatus.BLOCKED} # O(1) set lookup + if job_id in self.jobs: + job = self.jobs[job_id] + if job.status in CANCELLABLE_STATUSES: + job.status = JobStatus.CANCELLED + self.save_queue() + print(f"Cancelled job {job_id}") + return True + return False + + def get_queue_status(self) -> Dict: + """Get current queue status""" + # Single-pass aggregation for efficiency + ACTIVE_STATUSES = {JobStatus.PENDING, JobStatus.BLOCKED} + counts = { + "total_jobs": len(self.jobs), + "pending": 0, + "running": 0, + "completed": 0, + "failed": 0, + "blocked": 0, + "cancelled": 0, + "estimated_total_time": 0, + } + + for job in self.jobs.values(): + # Count by status + if job.status == JobStatus.PENDING: + counts["pending"] += 1 + elif job.status == JobStatus.RUNNING: + counts["running"] += 1 + elif job.status == JobStatus.COMPLETED: + counts["completed"] += 1 + elif job.status == JobStatus.FAILED: + counts["failed"] += 1 + elif job.status == JobStatus.BLOCKED: + counts["blocked"] += 1 + elif job.status == JobStatus.CANCELLED: + counts["cancelled"] += 1 + + # Sum estimated time for active jobs + if job.status in ACTIVE_STATUSES: + counts["estimated_total_time"] += job.estimated_duration + + counts["queue_length"] = len(self.priority_queue) + return counts + + def get_job_details(self, job_id: str) -> Optional[Dict]: + """Get detailed information about a job""" + if job_id in self.jobs: + job = self.jobs[job_id] + return { + "id": job.id, + "name": job.name, + "config": job.config, + "priority": job.priority.name, + "status": job.status.value, + "created_at": job.created_at, + "started_at": job.started_at, + "completed_at": job.completed_at, + "dependencies": job.dependencies, + "tags": job.tags, + "estimated_duration": job.estimated_duration, + "retry_count": job.retry_count, + "max_retries": job.max_retries, + "error_message": job.error_message, + } + return None + + def list_jobs( + self, status: Optional[JobStatus] = None, tags: List[str] = None + ) -> List[Dict]: + """List all jobs, optionally filtered by status or tags""" + jobs = list(self.jobs.values()) + + if status: + jobs = [j for j in jobs if j.status == status] + + if tags: + # Set intersection optimization: convert to sets for O(n) instead of O(n²) lookup + tags_set = set(tags) + jobs = [j for j in jobs if set(j.tags) & tags_set] + + return [self.get_job_details(j.id) for j in jobs] + + def clear_completed(self): + """Remove completed and cancelled jobs from queue""" + REMOVABLE_STATUSES = { + JobStatus.COMPLETED, + JobStatus.CANCELLED, + } # O(1) set lookup + to_remove = [ + job_id + for job_id, job in self.jobs.items() + if job.status in REMOVABLE_STATUSES + ] + + for job_id in to_remove: + del self.jobs[job_id] + + self.save_queue() + print(f"Cleared {len(to_remove)} completed/cancelled jobs") + + +# Example usage +if __name__ == "__main__": + queue = JobQueue() + + # Example: Add jobs with dependencies + job1 = queue.add_job( + name="preprocess_data", + config={"dataset": "mixed_chat", "action": "preprocess"}, + priority=JobPriority.HIGH, + tags=["preprocessing"], + estimated_duration=300, # 5 minutes + ) + + job2 = queue.add_job( + name="train_model_v1", + config={"epochs": 3, "dataset": "mixed_chat"}, + priority=JobPriority.NORMAL, + dependencies=[job1], # Depends on preprocessing + tags=["training", "v1"], + estimated_duration=1800, # 30 minutes + ) + + job3 = queue.add_job( + name="evaluate_model", + config={"model": "v1", "test_set": "test.json"}, + priority=JobPriority.NORMAL, + dependencies=[job2], # Depends on training + tags=["evaluation"], + estimated_duration=600, # 10 minutes + ) + + # Print queue status + status = queue.get_queue_status() + print("\nQueue Status:") + for key, value in status.items(): + print(f" {key}: {value}") + + # List pending jobs + print("\nPending Jobs:") + for job in queue.list_jobs(status=JobStatus.PENDING): + print( + f" - {job['name']} (priority: {job['priority']}, deps: {job['dependencies']})" + ) diff --git a/scripts/master_orchestrator.py b/scripts/master_orchestrator.py index 36cd20a8b..009b9d084 100644 --- a/scripts/master_orchestrator.py +++ b/scripts/master_orchestrator.py @@ -1,615 +1,615 @@ -#!/usr/bin/env python -""" -Master Orchestrator - -Coordinates all sub-orchestrators (autotrain, quantum_autorun, evaluation_autorun) -with scheduling, dependency management, and workflow automation. - -Features: -- Schedule-based execution (cron-like syntax) -- Dependency management between orchestrators -- Workflow pipelines with success/failure handlers -- Resource monitoring and limits -- Automatic retry on failure -- Health checks and metrics -- Backup and cleanup automation - -Usage examples (PowerShell): - python .\\scripts\\master_orchestrator.py --workflow daily_full_pipeline - python .\\scripts\\master_orchestrator.py --workflow quick_validation - python .\\scripts\\master_orchestrator.py --orchestrator autotrain - python .\\scripts\\master_orchestrator.py --daemon # Run as background service - python .\\scripts\\master_orchestrator.py --status # Check status -""" - -from __future__ import annotations - -import argparse -import json -import signal -import subprocess -import sys -import time -from dataclasses import dataclass, field -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Dict, List, Optional - -try: - from .config_paths import resolve_config_path -except ImportError: - from config_paths import resolve_config_path - -try: - import yaml -except Exception: - raise SystemExit("pyyaml is required. Install with: pip install pyyaml") from None - -try: - import psutil -except Exception: - psutil = None # Optional, for resource monitoring - -REPO_ROOT = Path(__file__).resolve().parent.parent -# Ensure repository root is on sys.path before importing local shared modules. -if str(REPO_ROOT / "shared") not in sys.path: - sys.path.insert(0, str(REPO_ROOT / "shared")) -DATA_OUT = REPO_ROOT / "data_out" / "master_orchestrator" -CONFIG_FILE = resolve_config_path(REPO_ROOT, "master_orchestrator") - - -@dataclass -class OrchestratorConfig: - name: str - script: str - enabled: bool = True - schedule: Optional[str] = None - priority: int = 1 - retry_on_failure: int = 0 - timeout_minutes: int = 0 - dependencies: List[str] = field(default_factory=list) - last_run: Optional[str] = None - last_status: Optional[str] = None - - -@dataclass -class WorkflowConfig: - name: str - enabled: bool = True - trigger: str = "manual" # schedule | manual | webhook - schedule: Optional[str] = None - orchestrators: List[str] = field(default_factory=list) - flags: Dict[str, Any] = field(default_factory=dict) - on_success: List[str] = field(default_factory=list) - on_failure: List[str] = field(default_factory=list) - - -class MasterOrchestrator: - def __init__(self, config_path: Path): - self.config_path = config_path - self.run_id = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - self.config = self._load_config() - self.orchestrators: Dict[str, OrchestratorConfig] = {} - self.workflows: Dict[str, WorkflowConfig] = {} - self.running = True - self.status_file = DATA_OUT / "status.json" - self._last_schedule_run: Dict[str, str] = {} - self._parse_config() - self._ensure_dirs() - - def _load_config(self) -> Dict[str, Any]: - if not self.config_path.exists(): - raise SystemExit(f"Config not found: {self.config_path}") - with self.config_path.open("r") as f: - return yaml.safe_load(f) or {} - - def _parse_config(self): - # Parse orchestrators - for item in self.config.get("orchestrators", []): - orc = OrchestratorConfig( - name=item["name"], - script=item["script"], - enabled=item.get("enabled", True), - schedule=item.get("schedule"), - priority=item.get("priority", 1), - retry_on_failure=item.get("retry_on_failure", 0), - timeout_minutes=item.get("timeout_minutes", 0), - dependencies=item.get("dependencies", []), - ) - self.orchestrators[orc.name] = orc - - # Parse workflows - for item in self.config.get("workflows", []): - wf = WorkflowConfig( - name=item["name"], - enabled=item.get("enabled", True), - trigger=item.get("trigger", "manual"), - schedule=item.get("schedule"), - orchestrators=item.get("orchestrators", []), - flags=item.get("flags", {}), - on_success=item.get("on_success", []), - on_failure=item.get("on_failure", []), - ) - self.workflows[wf.name] = wf - - def _ensure_dirs(self): - DATA_OUT.mkdir(parents=True, exist_ok=True) - - def run_orchestrator( - self, name: str, flags: Dict[str, Any] = None - ) -> Dict[str, Any]: - """Run a single orchestrator.""" - if name not in self.orchestrators: - return {"status": "error", "message": f"Unknown orchestrator: {name}"} - - orc = self.orchestrators[name] - if not orc.enabled: - return {"status": "skipped", "message": f"Orchestrator disabled: {name}"} - - # Check dependencies - for dep in orc.dependencies: - if dep in self.orchestrators: - dep_orc = self.orchestrators[dep] - if dep_orc.last_status != "succeeded": - return { - "status": "blocked", - "message": f"Dependency not met: {dep} (status: {dep_orc.last_status})", - } - - script_path = REPO_ROOT / orc.script - if not script_path.exists(): - return {"status": "error", "message": f"Script not found: {script_path}"} - - # Build command - cmd = [sys.executable, str(script_path)] - - # Apply flags - if flags: - if flags.get("dry_run"): - cmd.append("--dry-run") - if "max_train_samples" in flags and flags["max_train_samples"] is not None: - cmd.extend(["--max-train-samples", str(flags["max_train_samples"])]) - - print(f"[master] Running orchestrator: {name}") - print(f"[master] Command: {' '.join(cmd)}") - - ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - log_dir = DATA_OUT / name / ts - log_dir.mkdir(parents=True, exist_ok=True) - log_file = log_dir / "stdout.log" - - t0 = time.time() - result = { - "name": name, - "cmd": cmd, - "start_time": ts, - "status": "running", - } - - try: - with log_file.open("w") as logf: - proc = subprocess.Popen( - cmd, - cwd=str(REPO_ROOT), - stdout=logf, - stderr=subprocess.STDOUT, - text=True, - ) - - # Wait with timeout - timeout = orc.timeout_minutes * 60 if orc.timeout_minutes > 0 else None - rc = proc.wait(timeout=timeout) - - duration = time.time() - t0 - result["return_code"] = rc - result["duration_sec"] = round(duration, 2) - result["status"] = "succeeded" if rc == 0 else "failed" - result["log"] = str(log_file) - - # Update orchestrator state - orc.last_run = ts - orc.last_status = result["status"] - - except subprocess.TimeoutExpired: - proc.kill() - result["status"] = "timeout" - result["message"] = f"Exceeded timeout of {orc.timeout_minutes} minutes" - except Exception as e: - result["status"] = "error" - result["message"] = str(e) - - return result - - def run_workflow(self, name: str) -> Dict[str, Any]: - """Run a workflow pipeline.""" - if name not in self.workflows: - return {"status": "error", "message": f"Unknown workflow: {name}"} - - wf = self.workflows[name] - if not wf.enabled: - return {"status": "skipped", "message": f"Workflow disabled: {name}"} - - print("\n[master] ========================================") - print(f"[master] Starting workflow: {name}") - print(f"[master] Orchestrators: {', '.join(wf.orchestrators)}") - print("[master] ========================================\n") - - results = [] - all_succeeded = True - - for orc_name in wf.orchestrators: - result = self.run_orchestrator(orc_name, flags=wf.flags) - results.append(result) - print(json.dumps(result, indent=2, default=str)) - - SUCCESS_STATUSES = {"succeeded", "skipped"} # O(1) set lookup - if result["status"] not in SUCCESS_STATUSES: - all_succeeded = False - # Stop on first failure unless configured otherwise - break - - workflow_result = { - "workflow": name, - "start_time": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), - "run_id": self.run_id, - "config_path": str(self.config_path.relative_to(REPO_ROOT)), - "status": "succeeded" if all_succeeded else "failed", - "orchestrators": results, - } - - # Execute handlers - handlers = wf.on_success if all_succeeded else wf.on_failure - for handler in handlers: - print(f"[master] Executing handler: {handler}") - self._execute_handler(handler, workflow_result) - - # Save workflow result - self._save_workflow_result(workflow_result) - - return workflow_result - - def _execute_handler(self, handler: str, result: Dict[str, Any]): - """Execute a success/failure handler.""" - if handler == "notify_slack": - print("[master] Would notify Slack (not implemented)") - elif handler == "update_dashboard": - print("[master] Would update dashboard (not implemented)") - elif handler == "deploy_best_models": - print("[master] Would deploy best models (not implemented)") - elif handler == "create_issue": - print("[master] Would create GitHub issue (not implemented)") - elif handler == "fail_build": - sys.exit(1) - elif handler == "log_result": - print(f"[master] Logged result: {result['status']}") - elif handler == "generate_report": - print("[master] Would generate comprehensive report (not implemented)") - elif handler == "backup_models": - print("[master] Would backup models (not implemented)") - elif handler == "create_incident": - print("[master] Would create incident ticket (not implemented)") - else: - print(f"[master] Unknown handler: {handler}") - - def _save_workflow_result(self, result: Dict[str, Any]): - """Save workflow result to disk.""" - wf_name = result["workflow"] - ts = result["start_time"].replace(":", "").replace("-", "") - result_file = DATA_OUT / f"{wf_name}_{ts}.json" - with result_file.open("w") as f: - json.dump(result, f, indent=2, default=str) - print(f"[master] Workflow result saved: {result_file}") - - def list_orchestrators(self) -> List[Dict[str, Any]]: - """List all configured orchestrators.""" - return [ - { - "name": orc.name, - "script": orc.script, - "enabled": orc.enabled, - "schedule": orc.schedule, - "priority": orc.priority, - "dependencies": orc.dependencies, - "last_run": orc.last_run, - "last_status": orc.last_status, - } - for orc in sorted(self.orchestrators.values(), key=lambda x: x.priority) - ] - - def list_workflows(self) -> List[Dict[str, Any]]: - """List all configured workflows.""" - return [ - { - "name": wf.name, - "enabled": wf.enabled, - "trigger": wf.trigger, - "schedule": wf.schedule, - "orchestrators": wf.orchestrators, - } - for wf in self.workflows.values() - ] - - def get_status(self) -> Dict[str, Any]: - """Get overall status.""" - return { - "generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), - "run_id": self.run_id, - "config_path": str(self.config_path.relative_to(REPO_ROOT)), - "orchestrators": self.list_orchestrators(), - "workflows": self.list_workflows(), - "resource_usage": self._get_resource_usage(), - } - - def _get_resource_usage(self) -> Dict[str, Any]: - """Get current resource usage.""" - if not psutil: - return {"available": False} - - return { - "available": True, - "cpu_percent": psutil.cpu_percent(interval=1), - "memory_percent": psutil.virtual_memory().percent, - "disk_percent": psutil.disk_usage(str(REPO_ROOT)).percent, - } - - def daemon_mode(self, check_interval: int = 60): - """Run in daemon mode, checking schedules periodically.""" - print("[master] Starting daemon mode...") - print(f"[master] Check interval: {check_interval} seconds") - - def signal_handler(sig, frame): - print("\n[master] Received shutdown signal") - self.running = False - - signal.signal(signal.SIGINT, signal_handler) - signal.signal(signal.SIGTERM, signal_handler) - - while self.running: - print( - f"[master] Health check at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" - ) - - # Check scheduled workflows - for wf_name, wf in self.workflows.items(): - if wf.enabled and wf.trigger == "schedule" and wf.schedule: - if self._should_run_now(wf_name, wf.schedule): - print(f"[master] Triggering scheduled workflow: {wf_name}") - self.run_workflow(wf_name) - - time.sleep(check_interval) - - print("[master] Daemon stopped") - - @staticmethod - def _match_cron_field( - field: str, - current: int, - min_value: int, - max_value: int, - *, - is_day_of_week: bool = False, - ) -> bool: - """Match a single cron field. - - Supported syntax: - - `*` - - `*/n` - - `a` - - `a,b,c` - - `a-b` - - `a-b/n` - """ - field = (field or "").strip() - if not field: - return False - if field == "*": - return True - - for part in field.split(","): - part = part.strip() - if not part: - continue - - # Handle step expressions (e.g., */15, 1-10/2) - if "/" in part: - base, step_str = part.split("/", 1) - try: - step = int(step_str) - except ValueError: - continue - if step <= 0: - continue - - if base == "*": - start, end = min_value, max_value - elif "-" in base: - try: - start, end = [int(x) for x in base.split("-", 1)] - except ValueError: - continue - else: - try: - start = end = int(base) - except ValueError: - continue - - if is_day_of_week: - if start == 7: - start = 0 - if end == 7: - end = 0 - - if start > end or start < min_value or end > max_value: - continue - if start <= current <= end and (current - start) % step == 0: - return True - continue - - # Handle ranges (e.g., 10-20) - if "-" in part: - try: - start, end = [int(x) for x in part.split("-", 1)] - except ValueError: - continue - - if is_day_of_week: - if start == 7: - start = 0 - if end == 7: - end = 0 - - if start <= current <= end: - return True - continue - - # Handle single values - try: - value = int(part) - except ValueError: - continue - - if is_day_of_week and value == 7: - value = 0 - if value == current: - return True - - return False - - def _should_run_now(self, workflow_name: str, schedule: str) -> bool: - """Check if a cron-like schedule matches current UTC time. - - Uses standard 5-field cron expressions: minute hour day month day_of_week. - Day-of-month and day-of-week follow OR semantics when both are constrained. - """ - schedule = (schedule or "").strip() - if not schedule or schedule.lower() == "continuous": - return False - - parts = schedule.split() - if len(parts) != 5: - return False - - minute_f, hour_f, dom_f, month_f, dow_f = parts - now = datetime.now(timezone.utc) - - minute_ok = self._match_cron_field(minute_f, now.minute, 0, 59) - hour_ok = self._match_cron_field(hour_f, now.hour, 0, 23) - month_ok = self._match_cron_field(month_f, now.month, 1, 12) - if not (minute_ok and hour_ok and month_ok): - return False - - # Cron day-of-week convention: 0 or 7 = Sunday, 1 = Monday, ... 6 = Saturday - current_dow = (now.weekday() + 1) % 7 - dom_ok = self._match_cron_field(dom_f, now.day, 1, 31) - dow_ok = self._match_cron_field( - dow_f, - current_dow, - 0, - 7, - is_day_of_week=True, - ) - - dom_any = dom_f == "*" - dow_any = dow_f == "*" - if dom_any and dow_any: - day_ok = True - elif dom_any: - day_ok = dow_ok - elif dow_any: - day_ok = dom_ok - else: - day_ok = dom_ok or dow_ok - - if not day_ok: - return False - - # Avoid duplicate triggers in the same minute when check_interval < 60. - dedupe_key = f"{workflow_name}:{schedule}" - minute_key = now.strftime("%Y%m%d%H%M") - if self._last_schedule_run.get(dedupe_key) == minute_key: - return False - - self._last_schedule_run[dedupe_key] = minute_key - return True - - -def main(): - ap = argparse.ArgumentParser(description="Master Orchestrator") - ap.add_argument("--config", default=str(CONFIG_FILE), help="Config file path") - ap.add_argument("--workflow", help="Run a specific workflow") - ap.add_argument("--orchestrator", help="Run a specific orchestrator") - ap.add_argument( - "--list-orchestrators", action="store_true", help="List all orchestrators" - ) - ap.add_argument("--list-workflows", action="store_true", help="List all workflows") - ap.add_argument("--status", action="store_true", help="Show current status") - ap.add_argument("--daemon", action="store_true", help="Run in daemon mode") - ap.add_argument( - "--check-interval", type=int, default=60, help="Daemon check interval (seconds)" - ) - ap.add_argument( - "--skip-validation", action="store_true", help="Skip config validation (unsafe)" - ) - args = ap.parse_args() - - config_path = Path(args.config) - - # Validate configuration unless --skip-validation is set - if not args.skip_validation: - try: - from config_validator import ConfigValidator - except ImportError: - try: - from shared.config_validator import ConfigValidator - except ImportError: - print("⚠️ config_validator not available, skipping validation") - ConfigValidator = None - - if ConfigValidator: - validator = ConfigValidator(REPO_ROOT) - result = validator.validate_file(config_path) - if not result.valid: - print("\n❌ Configuration validation failed:") - print(result.report(verbose=True)) - print("\nUse --skip-validation to bypass (not recommended).") - sys.exit(1) - print(f"\n✅ Config validation passed: {config_path}") - - master = MasterOrchestrator(config_path) - - if args.list_orchestrators: - print(json.dumps(master.list_orchestrators(), indent=2)) - return - - if args.list_workflows: - print(json.dumps(master.list_workflows(), indent=2)) - return - - if args.status: - status = master.get_status() - print(json.dumps(status, indent=2, default=str)) - - # Save status - with master.status_file.open("w") as f: - json.dump(status, f, indent=2, default=str) - return - - if args.daemon: - master.daemon_mode(check_interval=args.check_interval) - return - - if args.workflow: - result = master.run_workflow(args.workflow) - sys.exit(0 if result["status"] == "succeeded" else 1) - - if args.orchestrator: - result = master.run_orchestrator(args.orchestrator) - print(json.dumps(result, indent=2, default=str)) - sys.exit(0 if result["status"] == "succeeded" else 1) - - # No action specified, show help - ap.print_help() - - -if __name__ == "__main__": - main() +#!/usr/bin/env python +""" +Master Orchestrator + +Coordinates all sub-orchestrators (autotrain, quantum_autorun, evaluation_autorun) +with scheduling, dependency management, and workflow automation. + +Features: +- Schedule-based execution (cron-like syntax) +- Dependency management between orchestrators +- Workflow pipelines with success/failure handlers +- Resource monitoring and limits +- Automatic retry on failure +- Health checks and metrics +- Backup and cleanup automation + +Usage examples (PowerShell): + python .\\scripts\\master_orchestrator.py --workflow daily_full_pipeline + python .\\scripts\\master_orchestrator.py --workflow quick_validation + python .\\scripts\\master_orchestrator.py --orchestrator autotrain + python .\\scripts\\master_orchestrator.py --daemon # Run as background service + python .\\scripts\\master_orchestrator.py --status # Check status +""" + +from __future__ import annotations + +import argparse +import json +import signal +import subprocess +import sys +import time +from dataclasses import dataclass, field +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Dict, List, Optional + +try: + from .config_paths import resolve_config_path +except ImportError: + from config_paths import resolve_config_path + +try: + import yaml +except Exception: + raise SystemExit("pyyaml is required. Install with: pip install pyyaml") from None + +try: + import psutil +except Exception: + psutil = None # Optional, for resource monitoring + +REPO_ROOT = Path(__file__).resolve().parent.parent +# Ensure repository root is on sys.path before importing local shared modules. +if str(REPO_ROOT / "shared") not in sys.path: + sys.path.insert(0, str(REPO_ROOT / "shared")) +DATA_OUT = REPO_ROOT / "data_out" / "master_orchestrator" +CONFIG_FILE = resolve_config_path(REPO_ROOT, "master_orchestrator") + + +@dataclass +class OrchestratorConfig: + name: str + script: str + enabled: bool = True + schedule: Optional[str] = None + priority: int = 1 + retry_on_failure: int = 0 + timeout_minutes: int = 0 + dependencies: List[str] = field(default_factory=list) + last_run: Optional[str] = None + last_status: Optional[str] = None + + +@dataclass +class WorkflowConfig: + name: str + enabled: bool = True + trigger: str = "manual" # schedule | manual | webhook + schedule: Optional[str] = None + orchestrators: List[str] = field(default_factory=list) + flags: Dict[str, Any] = field(default_factory=dict) + on_success: List[str] = field(default_factory=list) + on_failure: List[str] = field(default_factory=list) + + +class MasterOrchestrator: + def __init__(self, config_path: Path): + self.config_path = config_path + self.run_id = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") + self.config = self._load_config() + self.orchestrators: Dict[str, OrchestratorConfig] = {} + self.workflows: Dict[str, WorkflowConfig] = {} + self.running = True + self.status_file = DATA_OUT / "status.json" + self._last_schedule_run: Dict[str, str] = {} + self._parse_config() + self._ensure_dirs() + + def _load_config(self) -> Dict[str, Any]: + if not self.config_path.exists(): + raise SystemExit(f"Config not found: {self.config_path}") + with self.config_path.open("r") as f: + return yaml.safe_load(f) or {} + + def _parse_config(self): + # Parse orchestrators + for item in self.config.get("orchestrators", []): + orc = OrchestratorConfig( + name=item["name"], + script=item["script"], + enabled=item.get("enabled", True), + schedule=item.get("schedule"), + priority=item.get("priority", 1), + retry_on_failure=item.get("retry_on_failure", 0), + timeout_minutes=item.get("timeout_minutes", 0), + dependencies=item.get("dependencies", []), + ) + self.orchestrators[orc.name] = orc + + # Parse workflows + for item in self.config.get("workflows", []): + wf = WorkflowConfig( + name=item["name"], + enabled=item.get("enabled", True), + trigger=item.get("trigger", "manual"), + schedule=item.get("schedule"), + orchestrators=item.get("orchestrators", []), + flags=item.get("flags", {}), + on_success=item.get("on_success", []), + on_failure=item.get("on_failure", []), + ) + self.workflows[wf.name] = wf + + def _ensure_dirs(self): + DATA_OUT.mkdir(parents=True, exist_ok=True) + + def run_orchestrator( + self, name: str, flags: Dict[str, Any] = None + ) -> Dict[str, Any]: + """Run a single orchestrator.""" + if name not in self.orchestrators: + return {"status": "error", "message": f"Unknown orchestrator: {name}"} + + orc = self.orchestrators[name] + if not orc.enabled: + return {"status": "skipped", "message": f"Orchestrator disabled: {name}"} + + # Check dependencies + for dep in orc.dependencies: + if dep in self.orchestrators: + dep_orc = self.orchestrators[dep] + if dep_orc.last_status != "succeeded": + return { + "status": "blocked", + "message": f"Dependency not met: {dep} (status: {dep_orc.last_status})", + } + + script_path = REPO_ROOT / orc.script + if not script_path.exists(): + return {"status": "error", "message": f"Script not found: {script_path}"} + + # Build command + cmd = [sys.executable, str(script_path)] + + # Apply flags + if flags: + if flags.get("dry_run"): + cmd.append("--dry-run") + if "max_train_samples" in flags and flags["max_train_samples"] is not None: + cmd.extend(["--max-train-samples", str(flags["max_train_samples"])]) + + print(f"[master] Running orchestrator: {name}") + print(f"[master] Command: {' '.join(cmd)}") + + ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") + log_dir = DATA_OUT / name / ts + log_dir.mkdir(parents=True, exist_ok=True) + log_file = log_dir / "stdout.log" + + t0 = time.time() + result = { + "name": name, + "cmd": cmd, + "start_time": ts, + "status": "running", + } + + try: + with log_file.open("w") as logf: + proc = subprocess.Popen( + cmd, + cwd=str(REPO_ROOT), + stdout=logf, + stderr=subprocess.STDOUT, + text=True, + ) + + # Wait with timeout + timeout = orc.timeout_minutes * 60 if orc.timeout_minutes > 0 else None + rc = proc.wait(timeout=timeout) + + duration = time.time() - t0 + result["return_code"] = rc + result["duration_sec"] = round(duration, 2) + result["status"] = "succeeded" if rc == 0 else "failed" + result["log"] = str(log_file) + + # Update orchestrator state + orc.last_run = ts + orc.last_status = result["status"] + + except subprocess.TimeoutExpired: + proc.kill() + result["status"] = "timeout" + result["message"] = f"Exceeded timeout of {orc.timeout_minutes} minutes" + except Exception as e: + result["status"] = "error" + result["message"] = str(e) + + return result + + def run_workflow(self, name: str) -> Dict[str, Any]: + """Run a workflow pipeline.""" + if name not in self.workflows: + return {"status": "error", "message": f"Unknown workflow: {name}"} + + wf = self.workflows[name] + if not wf.enabled: + return {"status": "skipped", "message": f"Workflow disabled: {name}"} + + print("\n[master] ========================================") + print(f"[master] Starting workflow: {name}") + print(f"[master] Orchestrators: {', '.join(wf.orchestrators)}") + print("[master] ========================================\n") + + results = [] + all_succeeded = True + + for orc_name in wf.orchestrators: + result = self.run_orchestrator(orc_name, flags=wf.flags) + results.append(result) + print(json.dumps(result, indent=2, default=str)) + + SUCCESS_STATUSES = {"succeeded", "skipped"} # O(1) set lookup + if result["status"] not in SUCCESS_STATUSES: + all_succeeded = False + # Stop on first failure unless configured otherwise + break + + workflow_result = { + "workflow": name, + "start_time": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), + "run_id": self.run_id, + "config_path": str(self.config_path.relative_to(REPO_ROOT)), + "status": "succeeded" if all_succeeded else "failed", + "orchestrators": results, + } + + # Execute handlers + handlers = wf.on_success if all_succeeded else wf.on_failure + for handler in handlers: + print(f"[master] Executing handler: {handler}") + self._execute_handler(handler, workflow_result) + + # Save workflow result + self._save_workflow_result(workflow_result) + + return workflow_result + + def _execute_handler(self, handler: str, result: Dict[str, Any]): + """Execute a success/failure handler.""" + if handler == "notify_slack": + print("[master] Would notify Slack (not implemented)") + elif handler == "update_dashboard": + print("[master] Would update dashboard (not implemented)") + elif handler == "deploy_best_models": + print("[master] Would deploy best models (not implemented)") + elif handler == "create_issue": + print("[master] Would create GitHub issue (not implemented)") + elif handler == "fail_build": + sys.exit(1) + elif handler == "log_result": + print(f"[master] Logged result: {result['status']}") + elif handler == "generate_report": + print("[master] Would generate comprehensive report (not implemented)") + elif handler == "backup_models": + print("[master] Would backup models (not implemented)") + elif handler == "create_incident": + print("[master] Would create incident ticket (not implemented)") + else: + print(f"[master] Unknown handler: {handler}") + + def _save_workflow_result(self, result: Dict[str, Any]): + """Save workflow result to disk.""" + wf_name = result["workflow"] + ts = result["start_time"].replace(":", "").replace("-", "") + result_file = DATA_OUT / f"{wf_name}_{ts}.json" + with result_file.open("w") as f: + json.dump(result, f, indent=2, default=str) + print(f"[master] Workflow result saved: {result_file}") + + def list_orchestrators(self) -> List[Dict[str, Any]]: + """List all configured orchestrators.""" + return [ + { + "name": orc.name, + "script": orc.script, + "enabled": orc.enabled, + "schedule": orc.schedule, + "priority": orc.priority, + "dependencies": orc.dependencies, + "last_run": orc.last_run, + "last_status": orc.last_status, + } + for orc in sorted(self.orchestrators.values(), key=lambda x: x.priority) + ] + + def list_workflows(self) -> List[Dict[str, Any]]: + """List all configured workflows.""" + return [ + { + "name": wf.name, + "enabled": wf.enabled, + "trigger": wf.trigger, + "schedule": wf.schedule, + "orchestrators": wf.orchestrators, + } + for wf in self.workflows.values() + ] + + def get_status(self) -> Dict[str, Any]: + """Get overall status.""" + return { + "generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), + "run_id": self.run_id, + "config_path": str(self.config_path.relative_to(REPO_ROOT)), + "orchestrators": self.list_orchestrators(), + "workflows": self.list_workflows(), + "resource_usage": self._get_resource_usage(), + } + + def _get_resource_usage(self) -> Dict[str, Any]: + """Get current resource usage.""" + if not psutil: + return {"available": False} + + return { + "available": True, + "cpu_percent": psutil.cpu_percent(interval=1), + "memory_percent": psutil.virtual_memory().percent, + "disk_percent": psutil.disk_usage(str(REPO_ROOT)).percent, + } + + def daemon_mode(self, check_interval: int = 60): + """Run in daemon mode, checking schedules periodically.""" + print("[master] Starting daemon mode...") + print(f"[master] Check interval: {check_interval} seconds") + + def signal_handler(sig, frame): + print("\n[master] Received shutdown signal") + self.running = False + + signal.signal(signal.SIGINT, signal_handler) + signal.signal(signal.SIGTERM, signal_handler) + + while self.running: + print( + f"[master] Health check at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" + ) + + # Check scheduled workflows + for wf_name, wf in self.workflows.items(): + if wf.enabled and wf.trigger == "schedule" and wf.schedule: + if self._should_run_now(wf_name, wf.schedule): + print(f"[master] Triggering scheduled workflow: {wf_name}") + self.run_workflow(wf_name) + + time.sleep(check_interval) + + print("[master] Daemon stopped") + + @staticmethod + def _match_cron_field( + field: str, + current: int, + min_value: int, + max_value: int, + *, + is_day_of_week: bool = False, + ) -> bool: + """Match a single cron field. + + Supported syntax: + - `*` + - `*/n` + - `a` + - `a,b,c` + - `a-b` + - `a-b/n` + """ + field = (field or "").strip() + if not field: + return False + if field == "*": + return True + + for part in field.split(","): + part = part.strip() + if not part: + continue + + # Handle step expressions (e.g., */15, 1-10/2) + if "/" in part: + base, step_str = part.split("/", 1) + try: + step = int(step_str) + except ValueError: + continue + if step <= 0: + continue + + if base == "*": + start, end = min_value, max_value + elif "-" in base: + try: + start, end = [int(x) for x in base.split("-", 1)] + except ValueError: + continue + else: + try: + start = end = int(base) + except ValueError: + continue + + if is_day_of_week: + if start == 7: + start = 0 + if end == 7: + end = 0 + + if start > end or start < min_value or end > max_value: + continue + if start <= current <= end and (current - start) % step == 0: + return True + continue + + # Handle ranges (e.g., 10-20) + if "-" in part: + try: + start, end = [int(x) for x in part.split("-", 1)] + except ValueError: + continue + + if is_day_of_week: + if start == 7: + start = 0 + if end == 7: + end = 0 + + if start <= current <= end: + return True + continue + + # Handle single values + try: + value = int(part) + except ValueError: + continue + + if is_day_of_week and value == 7: + value = 0 + if value == current: + return True + + return False + + def _should_run_now(self, workflow_name: str, schedule: str) -> bool: + """Check if a cron-like schedule matches current UTC time. + + Uses standard 5-field cron expressions: minute hour day month day_of_week. + Day-of-month and day-of-week follow OR semantics when both are constrained. + """ + schedule = (schedule or "").strip() + if not schedule or schedule.lower() == "continuous": + return False + + parts = schedule.split() + if len(parts) != 5: + return False + + minute_f, hour_f, dom_f, month_f, dow_f = parts + now = datetime.now(timezone.utc) + + minute_ok = self._match_cron_field(minute_f, now.minute, 0, 59) + hour_ok = self._match_cron_field(hour_f, now.hour, 0, 23) + month_ok = self._match_cron_field(month_f, now.month, 1, 12) + if not (minute_ok and hour_ok and month_ok): + return False + + # Cron day-of-week convention: 0 or 7 = Sunday, 1 = Monday, ... 6 = Saturday + current_dow = (now.weekday() + 1) % 7 + dom_ok = self._match_cron_field(dom_f, now.day, 1, 31) + dow_ok = self._match_cron_field( + dow_f, + current_dow, + 0, + 7, + is_day_of_week=True, + ) + + dom_any = dom_f == "*" + dow_any = dow_f == "*" + if dom_any and dow_any: + day_ok = True + elif dom_any: + day_ok = dow_ok + elif dow_any: + day_ok = dom_ok + else: + day_ok = dom_ok or dow_ok + + if not day_ok: + return False + + # Avoid duplicate triggers in the same minute when check_interval < 60. + dedupe_key = f"{workflow_name}:{schedule}" + minute_key = now.strftime("%Y%m%d%H%M") + if self._last_schedule_run.get(dedupe_key) == minute_key: + return False + + self._last_schedule_run[dedupe_key] = minute_key + return True + + +def main(): + ap = argparse.ArgumentParser(description="Master Orchestrator") + ap.add_argument("--config", default=str(CONFIG_FILE), help="Config file path") + ap.add_argument("--workflow", help="Run a specific workflow") + ap.add_argument("--orchestrator", help="Run a specific orchestrator") + ap.add_argument( + "--list-orchestrators", action="store_true", help="List all orchestrators" + ) + ap.add_argument("--list-workflows", action="store_true", help="List all workflows") + ap.add_argument("--status", action="store_true", help="Show current status") + ap.add_argument("--daemon", action="store_true", help="Run in daemon mode") + ap.add_argument( + "--check-interval", type=int, default=60, help="Daemon check interval (seconds)" + ) + ap.add_argument( + "--skip-validation", action="store_true", help="Skip config validation (unsafe)" + ) + args = ap.parse_args() + + config_path = Path(args.config) + + # Validate configuration unless --skip-validation is set + if not args.skip_validation: + try: + from config_validator import ConfigValidator + except ImportError: + try: + from shared.config_validator import ConfigValidator + except ImportError: + print("⚠️ config_validator not available, skipping validation") + ConfigValidator = None + + if ConfigValidator: + validator = ConfigValidator(REPO_ROOT) + result = validator.validate_file(config_path) + if not result.valid: + print("\n❌ Configuration validation failed:") + print(result.report(verbose=True)) + print("\nUse --skip-validation to bypass (not recommended).") + sys.exit(1) + print(f"\n✅ Config validation passed: {config_path}") + + master = MasterOrchestrator(config_path) + + if args.list_orchestrators: + print(json.dumps(master.list_orchestrators(), indent=2)) + return + + if args.list_workflows: + print(json.dumps(master.list_workflows(), indent=2)) + return + + if args.status: + status = master.get_status() + print(json.dumps(status, indent=2, default=str)) + + # Save status + with master.status_file.open("w") as f: + json.dump(status, f, indent=2, default=str) + return + + if args.daemon: + master.daemon_mode(check_interval=args.check_interval) + return + + if args.workflow: + result = master.run_workflow(args.workflow) + sys.exit(0 if result["status"] == "succeeded" else 1) + + if args.orchestrator: + result = master.run_orchestrator(args.orchestrator) + print(json.dumps(result, indent=2, default=str)) + sys.exit(0 if result["status"] == "succeeded" else 1) + + # No action specified, show help + ap.print_help() + + +if __name__ == "__main__": + main() diff --git a/scripts/monitor_autonomous_training.py b/scripts/monitor_autonomous_training.py index 3df8618a8..9c9a5c812 100644 --- a/scripts/monitor_autonomous_training.py +++ b/scripts/monitor_autonomous_training.py @@ -1,581 +1,581 @@ -""" -Real-time Monitoring Dashboard for Autonomous AI Training -Provides live status, metrics, and alerts -""" - -import argparse -import json -import os -import subprocess -import time -from datetime import datetime -from pathlib import Path -from typing import Dict, List, Optional - -# Color codes for terminal output - - -class Colors: - HEADER = "\033[95m" - OKBLUE = "\033[94m" - OKCYAN = "\033[96m" - OKGREEN = "\033[92m" - WARNING = "\033[93m" - FAIL = "\033[91m" - ENDC = "\033[0m" - BOLD = "\033[1m" - UNDERLINE = "\033[4m" - - -class TrainingMonitor: - """Monitor autonomous training in real-time""" - - def __init__( - self, - status_file: str = "data_out/autonomous_training_status.json", - log_file: str = "data_out/autonomous_training.log", - ): - self.status_file = Path(status_file) - self.log_file = Path(log_file) - self.heartbeat_file = ( - self.status_file.parent / "autonomous_training_heartbeat.json" - ) - self.last_status = None - self.last_log_position = 0 - - def clear_screen(self): - """Clear terminal screen""" - try: - if os.name == "nt": - subprocess.run(["cmd", "/c", "cls"], check=False) - else: - subprocess.run(["clear"], check=False) - except Exception: - # If clearing fails, just print newlines - print("\n" * 50) - - def load_status(self) -> Optional[Dict]: - """Load current status from file""" - if not self.status_file.exists(): - return None - - try: - with open(self.status_file) as f: - return json.load(f) - except Exception as e: - return {"error": str(e)} - - def load_heartbeat(self) -> Optional[Dict]: - """Load lightweight heartbeat metadata if present.""" - if not self.heartbeat_file.exists(): - return None - try: - with open(self.heartbeat_file) as f: - return json.load(f) - except Exception: - return None - - def get_recent_logs(self, lines: int = 20) -> List[str]: - """Get recent log entries using streaming to avoid memory issues""" - if not self.log_file.exists(): - return [] - - try: - # Stream log file with rolling buffer instead of loading entire file - buffer = [] - with open(self.log_file, "r") as f: - for line in f: - buffer.append(line) - if len(buffer) > lines: - buffer.pop(0) # Keep only last N lines - return buffer - except Exception: - return [] - - def format_duration(self, seconds: float) -> str: - """Format duration in human-readable format""" - if seconds < 60: - return f"{seconds:.1f}s" - elif seconds < 3600: - return f"{seconds/60:.1f}m" - else: - hours = seconds / 3600 - return f"{hours:.1f}h" - - def format_percentage(self, value: float) -> str: - """Format percentage with color coding""" - pct = value * 100 - if pct >= 90: - return f"{Colors.OKGREEN}{pct:.2f}%{Colors.ENDC}" - elif pct >= 75: - return f"{Colors.OKCYAN}{pct:.2f}%{Colors.ENDC}" - elif pct >= 60: - return f"{Colors.WARNING}{pct:.2f}%{Colors.ENDC}" - else: - return f"{Colors.FAIL}{pct:.2f}%{Colors.ENDC}" - - def print_header(self): - """Print dashboard header""" - print(f"\n{Colors.BOLD}{Colors.HEADER}{'='*80}{Colors.ENDC}") - print( - f"{Colors.BOLD}{Colors.HEADER}🤖 AUTONOMOUS AI TRAINING MONITOR{Colors.ENDC}" - ) - print(f"{Colors.BOLD}{Colors.HEADER}{'='*80}{Colors.ENDC}\n") - print(f"📊 Status File: {self.status_file}") - print(f"📝 Log File: {self.log_file}") - print(f"🕐 Updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n") - - def print_overview(self, status: Dict): - """Print system overview""" - print(f"{Colors.BOLD}SYSTEM OVERVIEW{Colors.ENDC}") - print("─" * 80) - - if "error" in status: - print( - f"{Colors.FAIL}❌ Error loading status: {status['error']}{Colors.ENDC}" - ) - return - - # Basic info - started = status.get("started_at", status.get("last_updated", "Unknown")) - phase = status.get("current_phase", status.get("status", "unknown")) - cycles = status.get("cycles_completed", 0) - - # Phase with color - phase_colors = { - "initialization": Colors.OKCYAN, - "data_discovery": Colors.OKBLUE, - "data_collection": Colors.OKBLUE, - "training": Colors.WARNING, - "optimization": Colors.OKCYAN, - "deployment": Colors.OKGREEN, - "stopped": Colors.FAIL, - "error": Colors.FAIL, - } - phase_color = phase_colors.get(phase, Colors.ENDC) - - print(f" Status: {phase_color}{phase.upper()}{Colors.ENDC}") - print(f" Started: {started}") - print(f" Cycles Completed: {Colors.BOLD}{cycles}{Colors.ENDC}") - - # Best accuracy - best_acc = status.get("best_accuracy", 0) - print(f" Best Accuracy: {self.format_percentage(best_acc)}") - - # Last cycle - if "last_cycle_completed_at" in status: - last_cycle = status["last_cycle_completed_at"] - duration = status.get("last_cycle_duration_seconds", 0) - print(f" Last Cycle: {last_cycle} ({self.format_duration(duration)})") - elif status.get("last_updated"): - print(f" Last Cycle: {status.get('last_updated')}") - - print() - - def print_heartbeat(self): - """Print orchestrator heartbeat state and freshness.""" - hb = self.load_heartbeat() - if not hb: - return - - print(f"{Colors.BOLD}HEARTBEAT{Colors.ENDC}") - print("─" * 80) - ts = hb.get("timestamp") - state = hb.get("state", "unknown") - pid = hb.get("pid", "-") - next_eta = hb.get("next_cycle_eta") - - freshness = "unknown" - freshness_state = "stale" - if ts: - try: - hb_dt = datetime.fromisoformat(ts) - age = datetime.now() - hb_dt - freshness = f"{int(age.total_seconds())}s ago" - age_sec = age.total_seconds() - if age_sec <= 15: - freshness_state = "fresh" - elif age_sec <= 60: - freshness_state = "warming" - else: - freshness_state = "stale" - except Exception: - freshness = ts - - freshness_label = { - "fresh": f"{Colors.OKGREEN}fresh{Colors.ENDC}", - "warming": f"{Colors.WARNING}aging{Colors.ENDC}", - "stale": f"{Colors.FAIL}stale{Colors.ENDC}", - }.get(freshness_state, freshness_state) - - print(f" State: {state.upper()}") - print(f" PID: {pid}") - print(f" Last Beat: {ts} ({freshness}, {freshness_label})") - if next_eta: - print(f" Next Cycle ETA: {next_eta}") - print() - - def print_datasets(self, status: Dict): - """Print dataset information""" - print(f"{Colors.BOLD}DATASET INVENTORY{Colors.ENDC}") - print("─" * 80) - - inventory = status.get("dataset_inventory", {}) - total = status.get("total_datasets_available") - if total is None: - total = sum( - v.get("count", 0) if isinstance(v, dict) else int(v) - for v in inventory.values() - ) - - if inventory: - for category, item in inventory.items(): - count = item.get("count", 0) if isinstance(item, dict) else int(item) - print( - f" {category:20s}: {Colors.BOLD}{count:4d}{Colors.ENDC} datasets" - ) - - print(f"\n {Colors.BOLD}Total Available: {total}{Colors.ENDC}") - print() - - def print_performance(self, status: Dict): - """Print performance metrics""" - print(f"{Colors.BOLD}PERFORMANCE METRICS{Colors.ENDC}") - print("─" * 80) - - history = status.get("performance_history", []) - - if not history: - print(" No performance data yet") - print() - return - - # Show last 5 cycles - recent = history[-5:] - - print( - f" {'Cycle':<8} {'Epochs':<8} {'Mean Acc':<12} {'Max Acc':<12} {'Models':<10}" - ) - print(" " + "─" * 70) - - for i, perf in enumerate(recent, start=len(history) - len(recent) + 1): - mean_acc = perf.get("mean_accuracy", perf.get("accuracy", 0)) - max_acc = perf.get("max_accuracy", perf.get("accuracy", mean_acc)) - epochs = perf.get("epochs", "-") - successful = perf.get("successful_count", perf.get("datasets_trained", 0)) - cycle_display = perf.get("cycle", i) - - mean_str = f"{mean_acc*100:.2f}%" - max_str = f"{max_acc*100:.2f}%" - - print( - f" #{cycle_display:<7} {str(epochs):<8} {mean_str:<12} {max_str:<12} {successful:<10}" - ) - - # Trend analysis - if len(history) >= 2: - prev_acc = history[-2].get("mean_accuracy", history[-2].get("accuracy", 0)) - curr_acc = history[-1].get("mean_accuracy", history[-1].get("accuracy", 0)) - diff = curr_acc - prev_acc - - if diff > 0.01: - trend = f"{Colors.OKGREEN}↑ +{diff*100:.2f}% (Improving){Colors.ENDC}" - elif diff < -0.01: - trend = f"{Colors.FAIL}↓ {diff*100:.2f}% (Declining){Colors.ENDC}" - else: - trend = f"{Colors.WARNING}→ Stable{Colors.ENDC}" - - print(f"\n Trend: {trend}") - - plateau_cycles = status.get("plateau_cycles", 0) - promotions = status.get("promotions", []) - if plateau_cycles: - print(f" Plateau Cycles at Peak: {plateau_cycles}") - if promotions: - latest = promotions[-1] - print( - f" Promotions: {len(promotions)} " - f"(latest v{latest.get('version', '?')} @ cycle {latest.get('cycle', '?')})" - ) - - print() - - def print_active_tasks(self, status: Dict): - """Print active and completed tasks""" - print(f"{Colors.BOLD}TASK QUEUE{Colors.ENDC}") - print("─" * 80) - - active = status.get("active_tasks", []) - completed = status.get("completed_tasks", []) - - if active: - print(f" {Colors.WARNING}Active Tasks:{Colors.ENDC}") - for task in active[-5:]: # Last 5 - task_type = task.get("type", "unknown") - started = task.get("started_at", "") - epochs = task.get("epochs", "") - print(f" • {task_type} (epochs: {epochs}) - started {started}") - else: - print(f" {Colors.OKGREEN}No active tasks{Colors.ENDC}") - - if completed: - print(f"\n Completed: {len(completed)} tasks") - - print() - - def print_recent_logs(self): - """Print recent log entries""" - print(f"{Colors.BOLD}RECENT ACTIVITY{Colors.ENDC}") - print("─" * 80) - - logs = self.get_recent_logs(10) - - if not logs: - print(" No log entries") - print() - return - - for log in logs: - log = log.strip() - if not log: - continue - - # Color code by log level - if "ERROR" in log or "FAIL" in log: - print(f" {Colors.FAIL}{log}{Colors.ENDC}") - elif "WARNING" in log or "WARN" in log: - print(f" {Colors.WARNING}{log}{Colors.ENDC}") - elif "SUCCESS" in log or "✅" in log: - print(f" {Colors.OKGREEN}{log}{Colors.ENDC}") - else: - print(f" {log}") - - print() - - def print_alerts(self, status: Dict): - """Print any alerts or warnings""" - alerts = [] - - # Check for errors - phase = status.get("current_phase", status.get("status", "unknown")) - if phase == "error": - alerts.append(("ERROR", status.get("error", "Unknown error"))) - - # Check for stopped state - if phase == "stopped": - alerts.append(("WARNING", "Training orchestrator is stopped")) - - # Check for performance degradation - history = status.get("performance_history", []) - if len(history) >= 2: - prev = history[-2].get("mean_accuracy", history[-2].get("accuracy", 0)) - curr = history[-1].get("mean_accuracy", history[-1].get("accuracy", 0)) - if curr < prev - 0.05: - alerts.append( - ("WARNING", f"Performance degradation: {prev:.2%} → {curr:.2%}") - ) - - # Check dataset count - total = status.get("total_datasets_available") - if total is None: - inventory = status.get("dataset_inventory", {}) - total = sum( - v.get("count", 0) if isinstance(v, dict) else int(v) - for v in inventory.values() - ) - if total < 100: - alerts.append(("WARNING", f"Low dataset count: {total}")) - - if alerts: - print(f"{Colors.BOLD}ALERTS{Colors.ENDC}") - print("─" * 80) - for level, message in alerts: - if level == "ERROR": - print(f" {Colors.FAIL}❌ {message}{Colors.ENDC}") - else: - print(f" {Colors.WARNING}⚠️ {message}{Colors.ENDC}") - print() - - def print_dashboard(self): - """Print complete monitoring dashboard""" - self.clear_screen() - self.print_header() - - status = self.load_status() - - if status is None: - print( - f"{Colors.WARNING}⚠️ Status file not found. Is the orchestrator running?{Colors.ENDC}" - ) - print( - "\nStart it with: python ./scripts/autonomous_training_demo.py --cycles 3 --interval 5" - ) - return - - self.print_overview(status) - self.print_heartbeat() - self.print_alerts(status) - self.print_datasets(status) - self.print_performance(status) - self.print_active_tasks(status) - self.print_recent_logs() - - print(f"{Colors.BOLD}{'─'*80}{Colors.ENDC}") - print("Press Ctrl+C to exit | Refreshing every 5 seconds...") - - def print_summary(self): - """Print compact summary""" - status = self.load_status() - heartbeat = self.load_heartbeat() - - if status is None: - print(f"{Colors.WARNING}Status file not found{Colors.ENDC}") - return - - phase = status.get("current_phase", status.get("status", "unknown")) - cycles = status.get("cycles_completed", 0) - best_acc = status.get("best_accuracy", 0) - total = status.get("total_datasets_available") - if total is None: - inventory = status.get("dataset_inventory", {}) - total = sum( - v.get("count", 0) if isinstance(v, dict) else int(v) - for v in inventory.values() - ) - - print(f"\n{'='*80}") - print("AUTONOMOUS TRAINING STATUS") - print(f"{'='*80}") - print(f"Phase: {phase.upper()}") - print(f"Cycles: {cycles}") - print(f"Best Accuracy: {best_acc:.2%}") - print(f"Datasets: {total}") - print(f"Plateau Cycles: {status.get('plateau_cycles', 0)}") - promotions = status.get("promotions", []) - print(f"Promotions: {len(promotions)}") - if heartbeat: - print( - f"Heartbeat: {heartbeat.get('state', 'unknown').upper()} @ {heartbeat.get('timestamp', '-')}" - ) - if heartbeat.get("next_cycle_eta"): - print(f"Next Cycle ETA: {heartbeat.get('next_cycle_eta')}") - - history = status.get("performance_history", []) - if history: - latest = history[-1] - print("\nLatest Results:") - print(f" Epochs: {latest.get('epochs', '-')}") - print( - f" Mean Accuracy: {latest.get('mean_accuracy', latest.get('accuracy', 0)):.2%}" - ) - print( - f" Max Accuracy: {latest.get('max_accuracy', latest.get('accuracy', 0)):.2%}" - ) - print( - f" Successful: {latest.get('successful_count', latest.get('datasets_trained', 0))}" - ) - print(f" Exceptional: {latest.get('exceptional_models', 0)}") - - print(f"{'='*80}\n") - - def monitor_continuous(self, refresh_seconds: int = 5): - """Monitor in continuous mode with auto-refresh""" - try: - while True: - self.print_dashboard() - time.sleep(refresh_seconds) - except KeyboardInterrupt: - print(f"\n\n{Colors.OKGREEN}Monitoring stopped{Colors.ENDC}\n") - - def export_metrics(self, output_file: str = "data_out/training_metrics.csv"): - """Export metrics to CSV for analysis""" - status = self.load_status() - - if not status or "performance_history" not in status: - print("No metrics to export") - return - - import csv - - output_path = Path(output_file) - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, "w", newline="") as f: - writer = csv.DictWriter( - f, - fieldnames=[ - "timestamp", - "cycle", - "epochs", - "mean_accuracy", - "median_accuracy", - "max_accuracy", - "successful_count", - "failed_count", - "exceptional_models", - "excellent_models", - ], - ) - writer.writeheader() - - for i, perf in enumerate(status["performance_history"], start=1): - mean_acc = perf.get("mean_accuracy", perf.get("accuracy", 0)) - max_acc = perf.get("max_accuracy", perf.get("accuracy", mean_acc)) - writer.writerow( - { - "timestamp": perf.get("timestamp", ""), - "cycle": perf.get("cycle", i), - "epochs": perf.get("epochs", ""), - "mean_accuracy": mean_acc, - "median_accuracy": perf.get("median_accuracy", 0), - "max_accuracy": max_acc, - "successful_count": perf.get( - "successful_count", perf.get("datasets_trained", 0) - ), - "failed_count": perf.get("failed_count", 0), - "exceptional_models": perf.get("exceptional_models", 0), - "excellent_models": perf.get("excellent_models", 0), - } - ) - - print(f"{Colors.OKGREEN}✅ Metrics exported to {output_path}{Colors.ENDC}") - - -def main(): - parser = argparse.ArgumentParser(description="Monitor Autonomous AI Training") - parser.add_argument( - "--status-file", - default="data_out/autonomous_training_status.json", - help="Path to status file", - ) - parser.add_argument( - "--log-file", - default="data_out/autonomous_training.log", - help="Path to log file", - ) - parser.add_argument("--summary", action="store_true", help="Print summary and exit") - parser.add_argument("--export", metavar="FILE", help="Export metrics to CSV file") - parser.add_argument( - "--refresh", - type=int, - default=5, - help="Refresh interval in seconds (default: 5)", - ) - parser.add_argument("--once", action="store_true", help="Display once and exit") - - args = parser.parse_args() - - monitor = TrainingMonitor(args.status_file, args.log_file) - - if args.summary: - monitor.print_summary() - elif args.export: - monitor.export_metrics(args.export) - elif args.once: - monitor.print_dashboard() - else: - monitor.monitor_continuous(args.refresh) - - -if __name__ == "__main__": - main() +""" +Real-time Monitoring Dashboard for Autonomous AI Training +Provides live status, metrics, and alerts +""" + +import argparse +import json +import os +import subprocess +import time +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Optional + +# Color codes for terminal output + + +class Colors: + HEADER = "\033[95m" + OKBLUE = "\033[94m" + OKCYAN = "\033[96m" + OKGREEN = "\033[92m" + WARNING = "\033[93m" + FAIL = "\033[91m" + ENDC = "\033[0m" + BOLD = "\033[1m" + UNDERLINE = "\033[4m" + + +class TrainingMonitor: + """Monitor autonomous training in real-time""" + + def __init__( + self, + status_file: str = "data_out/autonomous_training_status.json", + log_file: str = "data_out/autonomous_training.log", + ): + self.status_file = Path(status_file) + self.log_file = Path(log_file) + self.heartbeat_file = ( + self.status_file.parent / "autonomous_training_heartbeat.json" + ) + self.last_status = None + self.last_log_position = 0 + + def clear_screen(self): + """Clear terminal screen""" + try: + if os.name == "nt": + subprocess.run(["cmd", "/c", "cls"], check=False) + else: + subprocess.run(["clear"], check=False) + except Exception: + # If clearing fails, just print newlines + print("\n" * 50) + + def load_status(self) -> Optional[Dict]: + """Load current status from file""" + if not self.status_file.exists(): + return None + + try: + with open(self.status_file) as f: + return json.load(f) + except Exception as e: + return {"error": str(e)} + + def load_heartbeat(self) -> Optional[Dict]: + """Load lightweight heartbeat metadata if present.""" + if not self.heartbeat_file.exists(): + return None + try: + with open(self.heartbeat_file) as f: + return json.load(f) + except Exception: + return None + + def get_recent_logs(self, lines: int = 20) -> List[str]: + """Get recent log entries using streaming to avoid memory issues""" + if not self.log_file.exists(): + return [] + + try: + # Stream log file with rolling buffer instead of loading entire file + buffer = [] + with open(self.log_file, "r") as f: + for line in f: + buffer.append(line) + if len(buffer) > lines: + buffer.pop(0) # Keep only last N lines + return buffer + except Exception: + return [] + + def format_duration(self, seconds: float) -> str: + """Format duration in human-readable format""" + if seconds < 60: + return f"{seconds:.1f}s" + elif seconds < 3600: + return f"{seconds/60:.1f}m" + else: + hours = seconds / 3600 + return f"{hours:.1f}h" + + def format_percentage(self, value: float) -> str: + """Format percentage with color coding""" + pct = value * 100 + if pct >= 90: + return f"{Colors.OKGREEN}{pct:.2f}%{Colors.ENDC}" + elif pct >= 75: + return f"{Colors.OKCYAN}{pct:.2f}%{Colors.ENDC}" + elif pct >= 60: + return f"{Colors.WARNING}{pct:.2f}%{Colors.ENDC}" + else: + return f"{Colors.FAIL}{pct:.2f}%{Colors.ENDC}" + + def print_header(self): + """Print dashboard header""" + print(f"\n{Colors.BOLD}{Colors.HEADER}{'='*80}{Colors.ENDC}") + print( + f"{Colors.BOLD}{Colors.HEADER}🤖 AUTONOMOUS AI TRAINING MONITOR{Colors.ENDC}" + ) + print(f"{Colors.BOLD}{Colors.HEADER}{'='*80}{Colors.ENDC}\n") + print(f"📊 Status File: {self.status_file}") + print(f"📝 Log File: {self.log_file}") + print(f"🕐 Updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n") + + def print_overview(self, status: Dict): + """Print system overview""" + print(f"{Colors.BOLD}SYSTEM OVERVIEW{Colors.ENDC}") + print("─" * 80) + + if "error" in status: + print( + f"{Colors.FAIL}❌ Error loading status: {status['error']}{Colors.ENDC}" + ) + return + + # Basic info + started = status.get("started_at", status.get("last_updated", "Unknown")) + phase = status.get("current_phase", status.get("status", "unknown")) + cycles = status.get("cycles_completed", 0) + + # Phase with color + phase_colors = { + "initialization": Colors.OKCYAN, + "data_discovery": Colors.OKBLUE, + "data_collection": Colors.OKBLUE, + "training": Colors.WARNING, + "optimization": Colors.OKCYAN, + "deployment": Colors.OKGREEN, + "stopped": Colors.FAIL, + "error": Colors.FAIL, + } + phase_color = phase_colors.get(phase, Colors.ENDC) + + print(f" Status: {phase_color}{phase.upper()}{Colors.ENDC}") + print(f" Started: {started}") + print(f" Cycles Completed: {Colors.BOLD}{cycles}{Colors.ENDC}") + + # Best accuracy + best_acc = status.get("best_accuracy", 0) + print(f" Best Accuracy: {self.format_percentage(best_acc)}") + + # Last cycle + if "last_cycle_completed_at" in status: + last_cycle = status["last_cycle_completed_at"] + duration = status.get("last_cycle_duration_seconds", 0) + print(f" Last Cycle: {last_cycle} ({self.format_duration(duration)})") + elif status.get("last_updated"): + print(f" Last Cycle: {status.get('last_updated')}") + + print() + + def print_heartbeat(self): + """Print orchestrator heartbeat state and freshness.""" + hb = self.load_heartbeat() + if not hb: + return + + print(f"{Colors.BOLD}HEARTBEAT{Colors.ENDC}") + print("─" * 80) + ts = hb.get("timestamp") + state = hb.get("state", "unknown") + pid = hb.get("pid", "-") + next_eta = hb.get("next_cycle_eta") + + freshness = "unknown" + freshness_state = "stale" + if ts: + try: + hb_dt = datetime.fromisoformat(ts) + age = datetime.now() - hb_dt + freshness = f"{int(age.total_seconds())}s ago" + age_sec = age.total_seconds() + if age_sec <= 15: + freshness_state = "fresh" + elif age_sec <= 60: + freshness_state = "warming" + else: + freshness_state = "stale" + except Exception: + freshness = ts + + freshness_label = { + "fresh": f"{Colors.OKGREEN}fresh{Colors.ENDC}", + "warming": f"{Colors.WARNING}aging{Colors.ENDC}", + "stale": f"{Colors.FAIL}stale{Colors.ENDC}", + }.get(freshness_state, freshness_state) + + print(f" State: {state.upper()}") + print(f" PID: {pid}") + print(f" Last Beat: {ts} ({freshness}, {freshness_label})") + if next_eta: + print(f" Next Cycle ETA: {next_eta}") + print() + + def print_datasets(self, status: Dict): + """Print dataset information""" + print(f"{Colors.BOLD}DATASET INVENTORY{Colors.ENDC}") + print("─" * 80) + + inventory = status.get("dataset_inventory", {}) + total = status.get("total_datasets_available") + if total is None: + total = sum( + v.get("count", 0) if isinstance(v, dict) else int(v) + for v in inventory.values() + ) + + if inventory: + for category, item in inventory.items(): + count = item.get("count", 0) if isinstance(item, dict) else int(item) + print( + f" {category:20s}: {Colors.BOLD}{count:4d}{Colors.ENDC} datasets" + ) + + print(f"\n {Colors.BOLD}Total Available: {total}{Colors.ENDC}") + print() + + def print_performance(self, status: Dict): + """Print performance metrics""" + print(f"{Colors.BOLD}PERFORMANCE METRICS{Colors.ENDC}") + print("─" * 80) + + history = status.get("performance_history", []) + + if not history: + print(" No performance data yet") + print() + return + + # Show last 5 cycles + recent = history[-5:] + + print( + f" {'Cycle':<8} {'Epochs':<8} {'Mean Acc':<12} {'Max Acc':<12} {'Models':<10}" + ) + print(" " + "─" * 70) + + for i, perf in enumerate(recent, start=len(history) - len(recent) + 1): + mean_acc = perf.get("mean_accuracy", perf.get("accuracy", 0)) + max_acc = perf.get("max_accuracy", perf.get("accuracy", mean_acc)) + epochs = perf.get("epochs", "-") + successful = perf.get("successful_count", perf.get("datasets_trained", 0)) + cycle_display = perf.get("cycle", i) + + mean_str = f"{mean_acc*100:.2f}%" + max_str = f"{max_acc*100:.2f}%" + + print( + f" #{cycle_display:<7} {str(epochs):<8} {mean_str:<12} {max_str:<12} {successful:<10}" + ) + + # Trend analysis + if len(history) >= 2: + prev_acc = history[-2].get("mean_accuracy", history[-2].get("accuracy", 0)) + curr_acc = history[-1].get("mean_accuracy", history[-1].get("accuracy", 0)) + diff = curr_acc - prev_acc + + if diff > 0.01: + trend = f"{Colors.OKGREEN}↑ +{diff*100:.2f}% (Improving){Colors.ENDC}" + elif diff < -0.01: + trend = f"{Colors.FAIL}↓ {diff*100:.2f}% (Declining){Colors.ENDC}" + else: + trend = f"{Colors.WARNING}→ Stable{Colors.ENDC}" + + print(f"\n Trend: {trend}") + + plateau_cycles = status.get("plateau_cycles", 0) + promotions = status.get("promotions", []) + if plateau_cycles: + print(f" Plateau Cycles at Peak: {plateau_cycles}") + if promotions: + latest = promotions[-1] + print( + f" Promotions: {len(promotions)} " + f"(latest v{latest.get('version', '?')} @ cycle {latest.get('cycle', '?')})" + ) + + print() + + def print_active_tasks(self, status: Dict): + """Print active and completed tasks""" + print(f"{Colors.BOLD}TASK QUEUE{Colors.ENDC}") + print("─" * 80) + + active = status.get("active_tasks", []) + completed = status.get("completed_tasks", []) + + if active: + print(f" {Colors.WARNING}Active Tasks:{Colors.ENDC}") + for task in active[-5:]: # Last 5 + task_type = task.get("type", "unknown") + started = task.get("started_at", "") + epochs = task.get("epochs", "") + print(f" • {task_type} (epochs: {epochs}) - started {started}") + else: + print(f" {Colors.OKGREEN}No active tasks{Colors.ENDC}") + + if completed: + print(f"\n Completed: {len(completed)} tasks") + + print() + + def print_recent_logs(self): + """Print recent log entries""" + print(f"{Colors.BOLD}RECENT ACTIVITY{Colors.ENDC}") + print("─" * 80) + + logs = self.get_recent_logs(10) + + if not logs: + print(" No log entries") + print() + return + + for log in logs: + log = log.strip() + if not log: + continue + + # Color code by log level + if "ERROR" in log or "FAIL" in log: + print(f" {Colors.FAIL}{log}{Colors.ENDC}") + elif "WARNING" in log or "WARN" in log: + print(f" {Colors.WARNING}{log}{Colors.ENDC}") + elif "SUCCESS" in log or "✅" in log: + print(f" {Colors.OKGREEN}{log}{Colors.ENDC}") + else: + print(f" {log}") + + print() + + def print_alerts(self, status: Dict): + """Print any alerts or warnings""" + alerts = [] + + # Check for errors + phase = status.get("current_phase", status.get("status", "unknown")) + if phase == "error": + alerts.append(("ERROR", status.get("error", "Unknown error"))) + + # Check for stopped state + if phase == "stopped": + alerts.append(("WARNING", "Training orchestrator is stopped")) + + # Check for performance degradation + history = status.get("performance_history", []) + if len(history) >= 2: + prev = history[-2].get("mean_accuracy", history[-2].get("accuracy", 0)) + curr = history[-1].get("mean_accuracy", history[-1].get("accuracy", 0)) + if curr < prev - 0.05: + alerts.append( + ("WARNING", f"Performance degradation: {prev:.2%} → {curr:.2%}") + ) + + # Check dataset count + total = status.get("total_datasets_available") + if total is None: + inventory = status.get("dataset_inventory", {}) + total = sum( + v.get("count", 0) if isinstance(v, dict) else int(v) + for v in inventory.values() + ) + if total < 100: + alerts.append(("WARNING", f"Low dataset count: {total}")) + + if alerts: + print(f"{Colors.BOLD}ALERTS{Colors.ENDC}") + print("─" * 80) + for level, message in alerts: + if level == "ERROR": + print(f" {Colors.FAIL}❌ {message}{Colors.ENDC}") + else: + print(f" {Colors.WARNING}⚠️ {message}{Colors.ENDC}") + print() + + def print_dashboard(self): + """Print complete monitoring dashboard""" + self.clear_screen() + self.print_header() + + status = self.load_status() + + if status is None: + print( + f"{Colors.WARNING}⚠️ Status file not found. Is the orchestrator running?{Colors.ENDC}" + ) + print( + "\nStart it with: python ./scripts/autonomous_training_demo.py --cycles 3 --interval 5" + ) + return + + self.print_overview(status) + self.print_heartbeat() + self.print_alerts(status) + self.print_datasets(status) + self.print_performance(status) + self.print_active_tasks(status) + self.print_recent_logs() + + print(f"{Colors.BOLD}{'─'*80}{Colors.ENDC}") + print("Press Ctrl+C to exit | Refreshing every 5 seconds...") + + def print_summary(self): + """Print compact summary""" + status = self.load_status() + heartbeat = self.load_heartbeat() + + if status is None: + print(f"{Colors.WARNING}Status file not found{Colors.ENDC}") + return + + phase = status.get("current_phase", status.get("status", "unknown")) + cycles = status.get("cycles_completed", 0) + best_acc = status.get("best_accuracy", 0) + total = status.get("total_datasets_available") + if total is None: + inventory = status.get("dataset_inventory", {}) + total = sum( + v.get("count", 0) if isinstance(v, dict) else int(v) + for v in inventory.values() + ) + + print(f"\n{'='*80}") + print("AUTONOMOUS TRAINING STATUS") + print(f"{'='*80}") + print(f"Phase: {phase.upper()}") + print(f"Cycles: {cycles}") + print(f"Best Accuracy: {best_acc:.2%}") + print(f"Datasets: {total}") + print(f"Plateau Cycles: {status.get('plateau_cycles', 0)}") + promotions = status.get("promotions", []) + print(f"Promotions: {len(promotions)}") + if heartbeat: + print( + f"Heartbeat: {heartbeat.get('state', 'unknown').upper()} @ {heartbeat.get('timestamp', '-')}" + ) + if heartbeat.get("next_cycle_eta"): + print(f"Next Cycle ETA: {heartbeat.get('next_cycle_eta')}") + + history = status.get("performance_history", []) + if history: + latest = history[-1] + print("\nLatest Results:") + print(f" Epochs: {latest.get('epochs', '-')}") + print( + f" Mean Accuracy: {latest.get('mean_accuracy', latest.get('accuracy', 0)):.2%}" + ) + print( + f" Max Accuracy: {latest.get('max_accuracy', latest.get('accuracy', 0)):.2%}" + ) + print( + f" Successful: {latest.get('successful_count', latest.get('datasets_trained', 0))}" + ) + print(f" Exceptional: {latest.get('exceptional_models', 0)}") + + print(f"{'='*80}\n") + + def monitor_continuous(self, refresh_seconds: int = 5): + """Monitor in continuous mode with auto-refresh""" + try: + while True: + self.print_dashboard() + time.sleep(refresh_seconds) + except KeyboardInterrupt: + print(f"\n\n{Colors.OKGREEN}Monitoring stopped{Colors.ENDC}\n") + + def export_metrics(self, output_file: str = "data_out/training_metrics.csv"): + """Export metrics to CSV for analysis""" + status = self.load_status() + + if not status or "performance_history" not in status: + print("No metrics to export") + return + + import csv + + output_path = Path(output_file) + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter( + f, + fieldnames=[ + "timestamp", + "cycle", + "epochs", + "mean_accuracy", + "median_accuracy", + "max_accuracy", + "successful_count", + "failed_count", + "exceptional_models", + "excellent_models", + ], + ) + writer.writeheader() + + for i, perf in enumerate(status["performance_history"], start=1): + mean_acc = perf.get("mean_accuracy", perf.get("accuracy", 0)) + max_acc = perf.get("max_accuracy", perf.get("accuracy", mean_acc)) + writer.writerow( + { + "timestamp": perf.get("timestamp", ""), + "cycle": perf.get("cycle", i), + "epochs": perf.get("epochs", ""), + "mean_accuracy": mean_acc, + "median_accuracy": perf.get("median_accuracy", 0), + "max_accuracy": max_acc, + "successful_count": perf.get( + "successful_count", perf.get("datasets_trained", 0) + ), + "failed_count": perf.get("failed_count", 0), + "exceptional_models": perf.get("exceptional_models", 0), + "excellent_models": perf.get("excellent_models", 0), + } + ) + + print(f"{Colors.OKGREEN}✅ Metrics exported to {output_path}{Colors.ENDC}") + + +def main(): + parser = argparse.ArgumentParser(description="Monitor Autonomous AI Training") + parser.add_argument( + "--status-file", + default="data_out/autonomous_training_status.json", + help="Path to status file", + ) + parser.add_argument( + "--log-file", + default="data_out/autonomous_training.log", + help="Path to log file", + ) + parser.add_argument("--summary", action="store_true", help="Print summary and exit") + parser.add_argument("--export", metavar="FILE", help="Export metrics to CSV file") + parser.add_argument( + "--refresh", + type=int, + default=5, + help="Refresh interval in seconds (default: 5)", + ) + parser.add_argument("--once", action="store_true", help="Display once and exit") + + args = parser.parse_args() + + monitor = TrainingMonitor(args.status_file, args.log_file) + + if args.summary: + monitor.print_summary() + elif args.export: + monitor.export_metrics(args.export) + elif args.once: + monitor.print_dashboard() + else: + monitor.monitor_continuous(args.refresh) + + +if __name__ == "__main__": + main() diff --git a/scripts/notification_system.py b/scripts/notification_system.py index 638e626c7..c97d32481 100644 --- a/scripts/notification_system.py +++ b/scripts/notification_system.py @@ -1,306 +1,306 @@ -"""Desktop Notification System for QAI Training Events""" - -import platform -import subprocess # nosec B404 - subprocess used safely with list arguments, no shell=True -from pathlib import Path - - -class NotificationManager: - """Cross-platform desktop notifications for training events""" - - def __init__(self): - self.system = platform.system() - self.enabled = self._check_notification_support() - - def _check_notification_support(self) -> bool: - """Check if notifications are supported on this platform""" - if self.system == "Windows": - try: - from win10toast import ToastNotifier - - self.toaster = ToastNotifier() - return True - except ImportError: - print( - "Warning: win10toast not installed. Install with: pip install win10toast" - ) - return False - elif self.system == "Darwin": # macOS - return True # Uses osascript - elif self.system == "Linux": - return True # Uses notify-send - return False - - def send_notification( - self, title: str, message: str, icon: str = "info", duration: int = 10 - ): - """Send cross-platform desktop notification""" - if not self.enabled: - print(f"[Notification] {title}: {message}") - return - - try: - if self.system == "Windows": - self._send_windows(title, message, icon, duration) - elif self.system == "Darwin": - self._send_macos(title, message) - elif self.system == "Linux": - self._send_linux(title, message, icon) - except Exception as e: - print(f"Notification error: {e}") - - def _send_windows(self, title: str, message: str, icon: str, duration: int): - """Send Windows 10 toast notification""" - icon_path = None - if icon == "success": - # Use default Windows icon or provide custom path - pass - - self.toaster.show_toast( - title=title, - msg=message, - icon_path=icon_path, - duration=duration, - threaded=True, - ) - - def _send_macos(self, title: str, message: str): - """Send macOS notification using osascript""" - # For AppleScript strings, we need to escape backslashes and double quotes - # Also replace newlines (\n and \r) with spaces to prevent script injection - safe_title = ( - title.replace("\\", "\\\\") - .replace('"', '\\"') - .replace("\n", " ") - .replace("\r", " ") - ) - safe_message = ( - message.replace("\\", "\\\\") - .replace('"', '\\"') - .replace("\n", " ") - .replace("\r", " ") - ) - - script = f'display notification "{safe_message}" with title "{safe_title}"' - try: - # Using subprocess with list arguments prevents shell injection - # The script is passed as a single argument to osascript -e - result = subprocess.run( - ["osascript", "-e", script], capture_output=True, text=True, timeout=5 - ) - if result.returncode != 0: - print( - f"macOS notification warning: osascript returned {result.returncode}, stderr: {result.stderr}" - ) - except subprocess.TimeoutExpired: - print("macOS notification error: osascript timed out") - except FileNotFoundError: - print("macOS notification error: osascript not found") - except OSError as e: - print(f"macOS notification error: {e}") - - def _send_linux(self, title: str, message: str, icon: str): - """Send Linux notification using notify-send""" - import subprocess - - icon_name = { - "info": "dialog-information", - "success": "dialog-ok", - "warning": "dialog-warning", - "error": "dialog-error", - }.get(icon, "dialog-information") - - # Use subprocess with list arguments to prevent command injection - try: - result = subprocess.run( - ["notify-send", "-i", icon_name, title, message], - capture_output=True, - text=True, - timeout=5, - ) - if result.returncode != 0: - print( - f"Linux notification warning: notify-send returned {result.returncode}, stderr: {result.stderr}" - ) - except subprocess.TimeoutExpired: - print("Linux notification error: notify-send timed out") - except FileNotFoundError: - print("Linux notification error: notify-send not found") - except OSError as e: - print(f"Linux notification error: {e}") - - def notify_job_started(self, job_name: str): - """Notify when training job starts""" - self.send_notification( - title="🚀 Training Started", - message=f"Job '{job_name}' has begun training", - icon="info", - ) - - def notify_job_completed(self, job_name: str, duration_min: int, final_loss: float): - """Notify when training job completes successfully""" - self.send_notification( - title="✅ Training Complete", - message=f"Job '{job_name}' finished in {duration_min}min with loss {final_loss:.4f}", - icon="success", - duration=15, - ) - - def notify_job_failed(self, job_name: str, error: str): - """Notify when training job fails""" - self.send_notification( - title="❌ Training Failed", - message=f"Job '{job_name}' failed: {error[:100]}", - icon="error", - duration=20, - ) - - def notify_milestone(self, job_name: str, milestone: str, value: float): - """Notify when training reaches a milestone""" - self.send_notification( - title="🎯 Milestone Reached", - message=f"Job '{job_name}': {milestone} = {value:.4f}", - icon="success", - ) - - def notify_gpu_alert(self, gpu_util: int, memory_used: int): - """Notify about GPU resource alerts""" - if gpu_util > 95: - self.send_notification( - title="⚠️ GPU Alert", - message=f"GPU utilization at {gpu_util}% (Memory: {memory_used}MB)", - icon="warning", - ) - - def notify_backup_complete(self, backup_name: str, size_mb: float): - """Notify when backup completes""" - self.send_notification( - title="💾 Backup Complete", - message=f"Created backup '{backup_name}' ({size_mb:.2f} MB)", - icon="success", - ) - - def notify_evaluation_complete(self, model_name: str, perplexity: float): - """Notify when model evaluation completes""" - self.send_notification( - title="📊 Evaluation Complete", - message=f"Model '{model_name}' - Perplexity: {perplexity:.2f}", - icon="info", - ) - - -# Integration with training orchestrators - - -class TrainingNotifier: - """Wrapper for training-specific notifications""" - - def __init__(self): - self.notifier = NotificationManager() - self.milestones = { - "loss_threshold": 0.5, # Notify when loss drops below this - "epoch_interval": 5, # Notify every N epochs - } - - def monitor_training(self, job_name: str, status_file: Path): - """Monitor training progress and send notifications""" - import json - import time - - last_epoch = 0 - job_started = False - - while True: - if not status_file.exists(): - time.sleep(5) - continue - - try: - with open(status_file, "r") as f: - status = json.load(f) - - if not job_started and status.get("status") == "running": - self.notifier.notify_job_started(job_name) - job_started = True - - # Check for milestones - current_loss = status.get("current_loss", float("inf")) - current_epoch = status.get("current_epoch", 0) - - if current_loss < self.milestones["loss_threshold"] and last_epoch == 0: - self.notifier.notify_milestone( - job_name, - f"Loss below {self.milestones['loss_threshold']}", - current_loss, - ) - - if ( - current_epoch > 0 - and current_epoch % self.milestones["epoch_interval"] == 0 - ): - if current_epoch != last_epoch: - self.notifier.notify_milestone( - job_name, f"Epoch {current_epoch} complete", current_loss - ) - last_epoch = current_epoch - - # Check if completed - if status.get("status") == "completed": - duration_min = status.get("duration_sec", 0) // 60 - final_loss = status.get("final_loss", 0) - self.notifier.notify_job_completed( - job_name, duration_min, final_loss - ) - break - - elif status.get("status") == "failed": - error = status.get("error", "Unknown error") - self.notifier.notify_job_failed(job_name, error) - break - - except Exception as e: - print(f"Error monitoring training: {e}") - - time.sleep(10) # Check every 10 seconds - - -# CLI for testing notifications -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser(description="QAI Notification System") - parser.add_argument("--test", action="store_true", help="Send test notification") - parser.add_argument("--monitor", help="Monitor job status file") - parser.add_argument( - "--job-name", default="Test Job", help="Job name for monitoring" - ) - - args = parser.parse_args() - - notifier = NotificationManager() - - if args.test: - print("Sending test notifications...") - notifier.send_notification( - title="🧪 QAI Test Notification", - message="Notification system is working correctly!", - icon="success", - ) - print("✅ Test notification sent") - - elif args.monitor: - print(f"Monitoring job: {args.job_name}") - print(f"Status file: {args.monitor}") - - monitor = TrainingNotifier() - monitor.monitor_training(args.job_name, Path(args.monitor)) - - else: - print("QAI Notification System") - print(f"Platform: {notifier.system}") - print(f"Enabled: {notifier.enabled}") - print("\nExamples:") - print(" Test notification: python notification_system.py --test") - print( - " Monitor training: python notification_system.py --monitor data_out/autotrain/status.json --job-name my_job" - ) +"""Desktop Notification System for QAI Training Events""" + +import platform +import subprocess # nosec B404 - subprocess used safely with list arguments, no shell=True +from pathlib import Path + + +class NotificationManager: + """Cross-platform desktop notifications for training events""" + + def __init__(self): + self.system = platform.system() + self.enabled = self._check_notification_support() + + def _check_notification_support(self) -> bool: + """Check if notifications are supported on this platform""" + if self.system == "Windows": + try: + from win10toast import ToastNotifier + + self.toaster = ToastNotifier() + return True + except ImportError: + print( + "Warning: win10toast not installed. Install with: pip install win10toast" + ) + return False + elif self.system == "Darwin": # macOS + return True # Uses osascript + elif self.system == "Linux": + return True # Uses notify-send + return False + + def send_notification( + self, title: str, message: str, icon: str = "info", duration: int = 10 + ): + """Send cross-platform desktop notification""" + if not self.enabled: + print(f"[Notification] {title}: {message}") + return + + try: + if self.system == "Windows": + self._send_windows(title, message, icon, duration) + elif self.system == "Darwin": + self._send_macos(title, message) + elif self.system == "Linux": + self._send_linux(title, message, icon) + except Exception as e: + print(f"Notification error: {e}") + + def _send_windows(self, title: str, message: str, icon: str, duration: int): + """Send Windows 10 toast notification""" + icon_path = None + if icon == "success": + # Use default Windows icon or provide custom path + pass + + self.toaster.show_toast( + title=title, + msg=message, + icon_path=icon_path, + duration=duration, + threaded=True, + ) + + def _send_macos(self, title: str, message: str): + """Send macOS notification using osascript""" + # For AppleScript strings, we need to escape backslashes and double quotes + # Also replace newlines (\n and \r) with spaces to prevent script injection + safe_title = ( + title.replace("\\", "\\\\") + .replace('"', '\\"') + .replace("\n", " ") + .replace("\r", " ") + ) + safe_message = ( + message.replace("\\", "\\\\") + .replace('"', '\\"') + .replace("\n", " ") + .replace("\r", " ") + ) + + script = f'display notification "{safe_message}" with title "{safe_title}"' + try: + # Using subprocess with list arguments prevents shell injection + # The script is passed as a single argument to osascript -e + result = subprocess.run( + ["osascript", "-e", script], capture_output=True, text=True, timeout=5 + ) + if result.returncode != 0: + print( + f"macOS notification warning: osascript returned {result.returncode}, stderr: {result.stderr}" + ) + except subprocess.TimeoutExpired: + print("macOS notification error: osascript timed out") + except FileNotFoundError: + print("macOS notification error: osascript not found") + except OSError as e: + print(f"macOS notification error: {e}") + + def _send_linux(self, title: str, message: str, icon: str): + """Send Linux notification using notify-send""" + import subprocess + + icon_name = { + "info": "dialog-information", + "success": "dialog-ok", + "warning": "dialog-warning", + "error": "dialog-error", + }.get(icon, "dialog-information") + + # Use subprocess with list arguments to prevent command injection + try: + result = subprocess.run( + ["notify-send", "-i", icon_name, title, message], + capture_output=True, + text=True, + timeout=5, + ) + if result.returncode != 0: + print( + f"Linux notification warning: notify-send returned {result.returncode}, stderr: {result.stderr}" + ) + except subprocess.TimeoutExpired: + print("Linux notification error: notify-send timed out") + except FileNotFoundError: + print("Linux notification error: notify-send not found") + except OSError as e: + print(f"Linux notification error: {e}") + + def notify_job_started(self, job_name: str): + """Notify when training job starts""" + self.send_notification( + title="🚀 Training Started", + message=f"Job '{job_name}' has begun training", + icon="info", + ) + + def notify_job_completed(self, job_name: str, duration_min: int, final_loss: float): + """Notify when training job completes successfully""" + self.send_notification( + title="✅ Training Complete", + message=f"Job '{job_name}' finished in {duration_min}min with loss {final_loss:.4f}", + icon="success", + duration=15, + ) + + def notify_job_failed(self, job_name: str, error: str): + """Notify when training job fails""" + self.send_notification( + title="❌ Training Failed", + message=f"Job '{job_name}' failed: {error[:100]}", + icon="error", + duration=20, + ) + + def notify_milestone(self, job_name: str, milestone: str, value: float): + """Notify when training reaches a milestone""" + self.send_notification( + title="🎯 Milestone Reached", + message=f"Job '{job_name}': {milestone} = {value:.4f}", + icon="success", + ) + + def notify_gpu_alert(self, gpu_util: int, memory_used: int): + """Notify about GPU resource alerts""" + if gpu_util > 95: + self.send_notification( + title="⚠️ GPU Alert", + message=f"GPU utilization at {gpu_util}% (Memory: {memory_used}MB)", + icon="warning", + ) + + def notify_backup_complete(self, backup_name: str, size_mb: float): + """Notify when backup completes""" + self.send_notification( + title="💾 Backup Complete", + message=f"Created backup '{backup_name}' ({size_mb:.2f} MB)", + icon="success", + ) + + def notify_evaluation_complete(self, model_name: str, perplexity: float): + """Notify when model evaluation completes""" + self.send_notification( + title="📊 Evaluation Complete", + message=f"Model '{model_name}' - Perplexity: {perplexity:.2f}", + icon="info", + ) + + +# Integration with training orchestrators + + +class TrainingNotifier: + """Wrapper for training-specific notifications""" + + def __init__(self): + self.notifier = NotificationManager() + self.milestones = { + "loss_threshold": 0.5, # Notify when loss drops below this + "epoch_interval": 5, # Notify every N epochs + } + + def monitor_training(self, job_name: str, status_file: Path): + """Monitor training progress and send notifications""" + import json + import time + + last_epoch = 0 + job_started = False + + while True: + if not status_file.exists(): + time.sleep(5) + continue + + try: + with open(status_file, "r") as f: + status = json.load(f) + + if not job_started and status.get("status") == "running": + self.notifier.notify_job_started(job_name) + job_started = True + + # Check for milestones + current_loss = status.get("current_loss", float("inf")) + current_epoch = status.get("current_epoch", 0) + + if current_loss < self.milestones["loss_threshold"] and last_epoch == 0: + self.notifier.notify_milestone( + job_name, + f"Loss below {self.milestones['loss_threshold']}", + current_loss, + ) + + if ( + current_epoch > 0 + and current_epoch % self.milestones["epoch_interval"] == 0 + ): + if current_epoch != last_epoch: + self.notifier.notify_milestone( + job_name, f"Epoch {current_epoch} complete", current_loss + ) + last_epoch = current_epoch + + # Check if completed + if status.get("status") == "completed": + duration_min = status.get("duration_sec", 0) // 60 + final_loss = status.get("final_loss", 0) + self.notifier.notify_job_completed( + job_name, duration_min, final_loss + ) + break + + elif status.get("status") == "failed": + error = status.get("error", "Unknown error") + self.notifier.notify_job_failed(job_name, error) + break + + except Exception as e: + print(f"Error monitoring training: {e}") + + time.sleep(10) # Check every 10 seconds + + +# CLI for testing notifications +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="QAI Notification System") + parser.add_argument("--test", action="store_true", help="Send test notification") + parser.add_argument("--monitor", help="Monitor job status file") + parser.add_argument( + "--job-name", default="Test Job", help="Job name for monitoring" + ) + + args = parser.parse_args() + + notifier = NotificationManager() + + if args.test: + print("Sending test notifications...") + notifier.send_notification( + title="🧪 QAI Test Notification", + message="Notification system is working correctly!", + icon="success", + ) + print("✅ Test notification sent") + + elif args.monitor: + print(f"Monitoring job: {args.job_name}") + print(f"Status file: {args.monitor}") + + monitor = TrainingNotifier() + monitor.monitor_training(args.job_name, Path(args.monitor)) + + else: + print("QAI Notification System") + print(f"Platform: {notifier.system}") + print(f"Enabled: {notifier.enabled}") + print("\nExamples:") + print(" Test notification: python notification_system.py --test") + print( + " Monitor training: python notification_system.py --monitor data_out/autotrain/status.json --job-name my_job" + ) diff --git a/scripts/parallel_train.py b/scripts/parallel_train.py index dd98b6af6..d61c12b04 100644 --- a/scripts/parallel_train.py +++ b/scripts/parallel_train.py @@ -1,766 +1,766 @@ -""" -Parallel training launcher - runs multiple training jobs concurrently -Optimized for maximum throughput on multi-GPU or multi-CPU systems -""" - -import argparse -import asyncio -import json -import math -import os -import shutil -import sys -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List, Optional - -import yaml - -############################################### -# Parallel Training Orchestrator Enhancements # -# - Historical run append mode for status.json # -# - Min train samples safety guard # -# - Post-training evaluation (perplexity + gen)# -# - Qwen compatibility via config HF id # -############################################### - - -class ParallelTrainer: - """Manages parallel execution of training jobs with evaluation & safety guards.""" - - def __init__( - self, - config_path: str, - max_parallel: int = 3, - min_train_samples: Optional[int] = None, - generate_samples: int = 3, - perform_evaluation: bool = True, - cleanup: bool = False, - ranking_metric: str = "perplexity_improvement", - ): - """Initialize parallel trainer. - - Args: - config_path: Path to autotrain YAML config - max_parallel: Maximum concurrent jobs (default: 3) - min_train_samples: Skip training if counted train samples below this threshold - generate_samples: Number of sample generations for post-training evaluation - """ - self.config_path = Path(config_path) - self.max_parallel = max_parallel - self.min_train_samples = min_train_samples - self.generate_samples = generate_samples - self.root = Path(__file__).parent.parent - self.venv_python = ( - self.root / "AI/microsoft_phi-silica-3.6_v1/venv/Scripts/python.exe" - ) - self.train_script = ( - self.root / "AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py" - ) - self.perform_evaluation = perform_evaluation - self.cleanup = cleanup - self.ranking_metric = ranking_metric - - # Load config - with open(self.config_path, "r", encoding="utf-8") as f: - self.config = yaml.safe_load(f) or {} - - self.jobs = self.config.get("jobs", []) - self.results: List[Dict[str, Any]] = [] - - async def run_job(self, job: Dict[str, Any], device_id: int) -> Dict[str, Any]: - """ - Run a single training job. - - Args: - job: Job configuration - device_id: GPU/CPU device ID for this job - - Returns: - Job result with status and timing - """ - job_name = job["name"] - start_time = datetime.now() - - print(f"\n[{job_name}] Starting on device {device_id}") - - # Resolve dataset path & gather dataset stats (best effort) - raw_dataset_path = job["dataset"] - ds_path = Path(raw_dataset_path) - if not ds_path.is_absolute(): - ds_path = self.root / ds_path - - train_count = None - test_count = None - try: - if ds_path.is_dir(): - # Look for train/test files - use efficient binary read for line counting - for candidate, attr in [ - ("train.json", "train"), - ("train.jsonl", "train"), - ("test.json", "test"), - ("test.jsonl", "test"), - ]: - fpath = ds_path / candidate - if fpath.exists(): - # Efficient line counting using binary mode and buffer - line_count = 0 - with open(fpath, "rb") as f: - # Read in 64KB chunks for better I/O performance - buf_size = 65536 - read_f = f.read - buf = read_f(buf_size) - while buf: - # Count non-empty lines by checking for content - line_count += buf.count(b"\n") - buf = read_f(buf_size) - if attr == "train": - train_count = (train_count or 0) + line_count - else: - test_count = (test_count or 0) + line_count - elif ds_path.is_file(): - # Efficient line counting for single files - line_count = 0 - with open(ds_path, "rb") as f: - buf_size = 65536 - read_f = f.read - buf = read_f(buf_size) - while buf: - line_count += buf.count(b"\n") - buf = read_f(buf_size) - train_count = line_count - except Exception as ds_err: - print(f"[{job_name}] Warning: failed counting dataset samples: {ds_err}") - - # Safety guard: skip if insufficient train samples - if ( - self.min_train_samples is not None - and train_count is not None - and train_count < self.min_train_samples - ): - end_time = datetime.now() - duration = (end_time - start_time).total_seconds() - result = { - "name": job_name, - "status": "skipped", - "reason": f"insufficient_train_samples (<{self.min_train_samples})", - "device": device_id, - "start_time": start_time.isoformat(), - "end_time": end_time.isoformat(), - "duration_seconds": duration, - "log_file": None, - "output_dir": job["save_dir"], - "dataset_path": str(ds_path), - "dataset_train_samples": train_count, - "dataset_test_samples": test_count, - } - print( - f"[{job_name}] ⚠ Skipping training: train_samples={train_count} < {self.min_train_samples}" - ) - return result - - # Build command for actual training - cmd = [ - str(self.venv_python), - str(self.train_script), - "--config", - job.get("config", "AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml"), - "--dataset", - job["dataset"], - "--save-dir", - job["save_dir"], - "--epochs", - str(job.get("epochs", 1)), - "--learning-rate", - str(job.get("learning_rate", 0.0002)), - "--lora-dropout", - str(job.get("lora_dropout", 0.1)), - "--hf-model-id", - job["hf_model_id"], - "--device", - "auto", # Use auto device selection - ] - - # Set CUDA_VISIBLE_DEVICES for device isolation - env = dict(os.environ) - if device_id >= 0: - env["CUDA_VISIBLE_DEVICES"] = str(device_id) - - if job.get("max_train_samples"): - cmd.extend(["--max-train-samples", str(job["max_train_samples"])]) - if job.get("max_eval_samples"): - cmd.extend(["--max-eval-samples", str(job["max_eval_samples"])]) - # Note: lora_rank should be set in config YAML, not via CLI - if job.get("no_stream"): - cmd.append("--no-stream") - - # Create output directory - output_dir = ( - self.root - / "data_out/parallel_training" - / job_name - / start_time.strftime("%Y%m%dT%H%M%SZ") - ) - output_dir.mkdir(parents=True, exist_ok=True) - - log_file = output_dir / "stdout.log" - - # Run job - try: - with open(log_file, "w", encoding="utf-8") as f: - process = await asyncio.create_subprocess_exec( - *cmd, - stdout=f, - stderr=asyncio.subprocess.STDOUT, - cwd=str(self.root), - env=env, - ) - returncode = await process.wait() - - end_time = datetime.now() - duration = (end_time - start_time).total_seconds() - - result: Dict[str, Any] = { - "name": job_name, - "status": "succeeded" if returncode == 0 else "failed", - "return_code": returncode, - "device": device_id, - "start_time": start_time.isoformat(), - "end_time": end_time.isoformat(), - "duration_seconds": duration, - "log_file": str(log_file), - "output_dir": job["save_dir"], - "dataset_path": str(ds_path), - "dataset_train_samples": train_count, - "dataset_test_samples": test_count, - } - - print( - f"[{job_name}] {'✓' if returncode == 0 else '✗'} Completed in {duration:.1f}s" - ) - - # Post-training evaluation (only if succeeded) - if returncode == 0: - # Artifact paths - adapter_dir = self.root / job["save_dir"] / "lora_adapter" - tokenizer_dir = self.root / job["save_dir"] / "tokenizer" - result["adapter_dir"] = str(adapter_dir) - result["tokenizer_dir"] = str(tokenizer_dir) - if self.perform_evaluation: - eval_data = self._perform_evaluation(job, result) - if eval_data: - result["evaluation"] = eval_data - else: - result["evaluation"] = None - if self.cleanup: - self._perform_cleanup(job, result) - - except Exception as e: - result = { - "name": job_name, - "status": "error", - "error": str(e), - "device": device_id, - "start_time": start_time.isoformat(), - "log_file": str(log_file), - "dataset_path": str(ds_path), - "dataset_train_samples": train_count, - "dataset_test_samples": test_count, - } - print(f"[{job_name}] ✗ Error: {e}") - - return result - - def _perform_evaluation( - self, job: Dict[str, Any], result: Dict[str, Any] - ) -> Optional[Dict[str, Any]]: - """Quick evaluation: parse metrics.jsonl & generate sample outputs. - - Returns evaluation dict or None if unavailable. - """ - save_dir = self.root / job["save_dir"] - metrics_file = save_dir / "metrics.jsonl" - eval_info: Dict[str, Any] = {} - - # Parse metrics file for pre/post perplexity - try: - if metrics_file.exists(): - pre_ppl = post_ppl = None - pre_loss = post_loss = None - with metrics_file.open("r", encoding="utf-8") as mf: - for line in mf: - line = line.strip() - if not line: - continue - rec = json.loads(line) - phase = rec.get("phase") - if phase == "pre": - pre_loss = rec.get("eval_loss") - pre_ppl = rec.get("eval_perplexity") or ( - math.e ** rec.get("eval_loss", 0) - ) - elif phase == "post": - post_loss = rec.get("eval_loss") - post_ppl = rec.get("eval_perplexity") or ( - math.e ** rec.get("eval_loss", 0) - ) - # Early exit once we have both phases - if pre_ppl is not None and post_ppl is not None: - break - if pre_loss is not None: - eval_info["pre_eval_loss"] = pre_loss - if pre_ppl is not None: - eval_info["pre_eval_perplexity"] = pre_ppl - if post_loss is not None: - eval_info["post_eval_loss"] = post_loss - if post_ppl is not None: - eval_info["post_eval_perplexity"] = post_ppl - except Exception as m_err: - eval_info["metrics_error"] = f"metrics_parse_failed: {m_err}" - - # Sample generations (best effort) - prompts = [ - "Hello! Provide a concise helpful assistant greeting.", - "Explain one optimization used in ultrafast fine-tuning.", - "Summarize the purpose of this training run in one sentence.", - "List two potential risks in rapid fine-tuning.", - "Give one improvement suggestion for dataset quality.", - ] - samples: List[Dict[str, Any]] = [] - if self.generate_samples > 0: - try: - from peft import PeftModel # type: ignore - from transformers import AutoModelForCausalLM # type: ignore - from transformers import AutoTokenizer - - base_model_id = job.get("hf_model_id") - adapter_dir = save_dir / "lora_adapter" - if adapter_dir.exists(): - tokenizer = AutoTokenizer.from_pretrained( - base_model_id, use_fast=True - ) - if tokenizer.pad_token is None: - tokenizer.pad_token = tokenizer.eos_token - base_model = AutoModelForCausalLM.from_pretrained(base_model_id) - model = PeftModel.from_pretrained(base_model, str(adapter_dir)) - model.eval() - import torch - - for p in prompts[: self.generate_samples]: - try: - inputs = tokenizer(p, return_tensors="pt") - with torch.no_grad(): - output_ids = model.generate( - **inputs, - max_new_tokens=80, - do_sample=True, - temperature=0.8, - top_p=0.9, - ) - gen_text = tokenizer.decode( - output_ids[0], skip_special_tokens=True - ) - samples.append({"prompt": p, "response": gen_text}) - except Exception as gen_err: - samples.append({"prompt": p, "error": str(gen_err)}) - else: - eval_info["generation_note"] = ( - "adapter_dir_missing; skipping sample generation" - ) - except Exception as g_err: - eval_info["generation_error"] = f"generation_failed: {g_err}" - if samples: - # Diversity metrics (Distinct-1/2) & echo ratio - def _distinct_1_2(texts: List[str]) -> Dict[str, float]: - unigrams_total = 0 - unigrams_set = set() - bigrams_total = 0 - bigrams_set = set() - for t in texts: - toks = t.split() - unigrams_total += len(toks) - unigrams_set.update(toks) - bgs = list(zip(toks, toks[1:])) - bigrams_total += len(bgs) - bigrams_set.update(bgs) - d1 = len(unigrams_set) / unigrams_total if unigrams_total else 0.0 - d2 = len(bigrams_set) / bigrams_total if bigrams_total else 0.0 - return {"distinct_1": d1, "distinct_2": d2} - - def _echo_ratio(prompt: str, response: str) -> float: - p_tokens = prompt.lower().split() - r_tokens = response.lower().split() - if not r_tokens: - return 0.0 - overlap = sum(1 for t in r_tokens if t in p_tokens) - return overlap / len(r_tokens) - - responses = [s.get("response", "") for s in samples] - diversity = _distinct_1_2(responses) - avg_len = ( - sum(len(r.split()) for r in responses) / len(responses) - if responses - else 0.0 - ) - echo_scores = [ - _echo_ratio(s["prompt"], s.get("response", "")) for s in samples - ] - eval_info["samples"] = samples - eval_info["diversity"] = { - "distinct_1": diversity["distinct_1"], - "distinct_2": diversity["distinct_2"], - "avg_response_tokens": avg_len, - "avg_echo_ratio": ( - sum(echo_scores) / len(echo_scores) if echo_scores else 0.0 - ), - } - return eval_info or None - - def _perform_cleanup(self, job: Dict[str, Any], result: Dict[str, Any]) -> None: - """Remove intermediate checkpoints to slim output directory.""" - save_dir = self.root / job["save_dir"] - if not save_dir.exists(): - return - preserved = {"lora_adapter", "tokenizer", "metrics.jsonl"} - try: - for item in save_dir.iterdir(): - name = item.name - if item.is_dir(): - if name not in preserved and name.startswith("checkpoint"): - shutil.rmtree(item, ignore_errors=True) - elif item.is_file() and name.startswith("checkpoint"): - try: - item.unlink() - except Exception: - pass - result["cleanup"] = "completed" - except Exception as c_err: - result["cleanup"] = f"error: {c_err}" - - def _compute_ranking(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """Compute ranking of jobs based on selected metric. - - Supported metrics: - - perplexity_improvement: relative reduction (higher better) - - post_perplexity: final perplexity (lower better; stored negative for sorting) - - diversity_avg: average of distinct-1 and distinct-2 (higher better) - - distinct_diversity: alias of diversity_avg - - combined_improvement: weighted combo of perplexity_improvement (70%) + diversity_avg (30%) - """ - ranked: List[Dict[str, Any]] = [] - for r in results: - ev = r.get("evaluation") or {} - pre_ppl = ev.get("pre_eval_perplexity") - post_ppl = ev.get("post_eval_perplexity") - diversity = ev.get("diversity") or {} - d1 = diversity.get("distinct_1") - d2 = diversity.get("distinct_2") - diversity_avg = ( - (d1 + d2) / 2 if (d1 is not None and d2 is not None) else None - ) - # Skip jobs without post perplexity for metrics relying on perplexity - if ( - self.ranking_metric - in ("perplexity_improvement", "post_perplexity", "combined_improvement") - and post_ppl is None - ): - continue - if ( - self.ranking_metric == "perplexity_improvement" - and pre_ppl is not None - and post_ppl is not None - ): - ppl_improvement = ( - (pre_ppl - post_ppl) / pre_ppl if pre_ppl and pre_ppl > 0 else 0.0 - ) - score = ppl_improvement - elif self.ranking_metric == "post_perplexity" and post_ppl is not None: - ppl_improvement = ( - (pre_ppl - post_ppl) / pre_ppl - if (pre_ppl and post_ppl and pre_ppl > 0) - else None - ) - score = -post_ppl # lower is better - elif self.ranking_metric in ("diversity_avg", "distinct_diversity"): - if diversity_avg is None: - # Cannot rank this job on diversity; skip it - continue - ppl_improvement = ( - (pre_ppl - post_ppl) / pre_ppl - if (pre_ppl and post_ppl and pre_ppl > 0) - else None - ) - score = diversity_avg - elif self.ranking_metric == "combined_improvement": - ppl_improvement = ( - (pre_ppl - post_ppl) / pre_ppl - if (pre_ppl and post_ppl and pre_ppl > 0) - else 0.0 - ) - div_component = diversity_avg if diversity_avg is not None else 0.0 - score = 0.7 * ppl_improvement + 0.3 * div_component - else: - # Fallback to post perplexity if unknown metric - ppl_improvement = ( - (pre_ppl - post_ppl) / pre_ppl - if (pre_ppl and post_ppl and pre_ppl > 0) - else None - ) - score = -post_ppl if post_ppl is not None else 0.0 - ranked.append( - { - "name": r.get("name"), - "score": score, - "metric": self.ranking_metric, - "pre_perplexity": pre_ppl, - "post_perplexity": post_ppl, - "perplexity_improvement": ppl_improvement, - "distinct_1": d1, - "distinct_2": d2, - "diversity_avg": diversity_avg, - "status": r.get("status"), - } - ) - ranked.sort(key=lambda x: x["score"], reverse=True) - return ranked - - async def run_all_parallel(self, job_filter: str = "*"): - """ - Run all jobs in parallel with concurrency limit. - - Args: - job_filter: Glob pattern to filter job names - """ - import fnmatch - - # Filter jobs - filtered_jobs = [j for j in self.jobs if fnmatch.fnmatch(j["name"], job_filter)] - - if not filtered_jobs: - print(f"No jobs match filter: {job_filter}") - return - - print( - f"Running {len(filtered_jobs)} jobs with max {self.max_parallel} parallel" - ) - - # Create semaphore for concurrency control - semaphore = asyncio.Semaphore(self.max_parallel) - - async def run_with_semaphore(job, device_id): - async with semaphore: - return await self.run_job(job, device_id) - - # Assign device IDs cyclically - tasks = [ - run_with_semaphore(job, i % self.max_parallel) - for i, job in enumerate(filtered_jobs) - ] - - # Run all tasks - start_time = datetime.now() - self.results = await asyncio.gather(*tasks) - end_time = datetime.now() - - # Prepare run entry - status_file = self.root / "data_out/parallel_training/status.json" - status_file.parent.mkdir(parents=True, exist_ok=True) - agg_train = sum( - r.get("dataset_train_samples") or 0 - for r in self.results - if r.get("dataset_train_samples") is not None - ) - agg_test = sum( - r.get("dataset_test_samples") or 0 - for r in self.results - if r.get("dataset_test_samples") is not None - ) - - run_entry = { - "run_id": end_time.strftime("%Y%m%dT%H%M%S"), - "total_jobs": len(filtered_jobs), - "max_parallel": self.max_parallel, - "total_duration_seconds": (end_time - start_time).total_seconds(), - "timestamp": end_time.isoformat(), - "aggregate_train_samples": agg_train if agg_train else None, - "aggregate_test_samples": agg_test if agg_test else None, - "jobs": self.results, - } - - # Ranking computation - ranking = self._compute_ranking(self.results) - if ranking: - run_entry["job_ranking"] = ranking - # Append mode historical persistence (correctly placed inside run_all_parallel) - if status_file.exists(): - try: - with status_file.open("r", encoding="utf-8") as f: - existing = json.load(f) - except Exception: - existing = {} - else: - existing = {} - - # Normalize legacy format - runs = [] - if "runs" in existing and isinstance(existing["runs"], list): - runs = existing["runs"] - elif "jobs" in existing: - legacy = dict(existing) - runs = [legacy] - runs.append(run_entry) - new_status = { - "runs": runs, - "total_runs": len(runs), - "last_updated": end_time.isoformat(), - } - try: - new_status["cumulative_train_samples"] = ( - sum((r.get("aggregate_train_samples") or 0) for r in runs) or None - ) - new_status["cumulative_test_samples"] = ( - sum((r.get("aggregate_test_samples") or 0) for r in runs) or None - ) - except Exception: - pass - with status_file.open("w", encoding="utf-8") as f: - json.dump(new_status, f, indent=2) - - # Print summary - print("\n" + "=" * 70) - print("Parallel Training Summary") - print("=" * 70) - - # Single pass to classify results and collect failed/skipped for display - succeeded = 0 - skipped = 0 - failed = 0 - failed_jobs = [] - skipped_jobs = [] - - for r in self.results: - status = r.get("status") - if status == "succeeded": - succeeded += 1 - elif status == "skipped": - skipped += 1 - skipped_jobs.append(r) - else: - failed += 1 - failed_jobs.append(r) - - print(f"\nTotal Jobs: {len(self.results)}") - print(f"Succeeded: {succeeded}") - print(f"Skipped: {skipped}") - print(f"Failed: {failed}") - print(f"Total Time: {(end_time - start_time).total_seconds():.1f}s") - print(f"\nStatus file: {status_file}") - - # Show failed jobs if any - if failed > 0: - print("\nFailed Jobs:") - for r in failed_jobs: - print( - f" - {r['name']}: {r.get('error', 'return code ' + str(r.get('return_code')))}" - ) - if skipped > 0: - print("\nSkipped Jobs:") - for r in skipped_jobs: - print( - f" - {r['name']}: {r.get('reason', 'min_train_samples threshold')} train_samples={r.get('dataset_train_samples')}" - ) - - -def main(): - parser = argparse.ArgumentParser( - description="Parallel training launcher with historical status & evaluation" - ) - parser.add_argument( - "--config", - default="autotrain_fast.yaml", - help="Training config YAML (default: autotrain_fast.yaml)", - ) - parser.add_argument( - "--max-parallel", type=int, default=3, help="Max concurrent jobs (default: 3)" - ) - parser.add_argument( - "--filter", - default="*", - help="Job name filter pattern (e.g., 'phi35*' or 'quick*')", - ) - parser.add_argument("--list", action="store_true", help="List jobs without running") - parser.add_argument( - "--min-train-samples", - type=int, - default=None, - help="Skip jobs whose counted train samples are below this threshold", - ) - parser.add_argument( - "--generate-samples", - type=int, - default=3, - help="Number of sample generations to produce after successful training", - ) - parser.add_argument( - "--no-eval", - action="store_true", - help="Disable post-training evaluation and sample generation entirely", - ) - parser.add_argument( - "--cleanup", - action="store_true", - help="Remove intermediate checkpoint artifacts after successful training", - ) - parser.add_argument( - "--ranking-metric", - choices=[ - "perplexity_improvement", - "post_perplexity", - "diversity_avg", - "combined_improvement", - "distinct_diversity", - ], - default="perplexity_improvement", - help="Metric used to rank jobs in status history (distinct_diversity alias of diversity_avg)", - ) - - args = parser.parse_args() - - trainer = ParallelTrainer( - args.config, - max_parallel=args.max_parallel, - min_train_samples=args.min_train_samples, - generate_samples=args.generate_samples, - perform_evaluation=not args.no_eval, - cleanup=args.cleanup, - ranking_metric=args.ranking_metric, - ) - - if args.list: - print(f"Jobs in {args.config}:") - for i, job in enumerate(trainer.jobs, 1): - print(f"{i}. {job['name']}") - print(f" Dataset: {job['dataset']}") - print(f" Samples: {job.get('max_train_samples', 'all')}") - print(f" Model: {job['hf_model_id']}") - return - - # Run parallel training - asyncio.run(trainer.run_all_parallel(args.filter)) - - # Determine exit status (skipped jobs are not failures) - failures = [ - r for r in trainer.results if r.get("status") not in ("succeeded", "skipped") - ] - if failures: - print("\n[parallel_train] One or more jobs failed. Exiting with code 1.") - sys.exit(1) - else: - print( - "\n[parallel_train] All jobs completed (including skips). Exiting with code 0." - ) - sys.exit(0) - - -if __name__ == "__main__": - main() +""" +Parallel training launcher - runs multiple training jobs concurrently +Optimized for maximum throughput on multi-GPU or multi-CPU systems +""" + +import argparse +import asyncio +import json +import math +import os +import shutil +import sys +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional + +import yaml + +############################################### +# Parallel Training Orchestrator Enhancements # +# - Historical run append mode for status.json # +# - Min train samples safety guard # +# - Post-training evaluation (perplexity + gen)# +# - Qwen compatibility via config HF id # +############################################### + + +class ParallelTrainer: + """Manages parallel execution of training jobs with evaluation & safety guards.""" + + def __init__( + self, + config_path: str, + max_parallel: int = 3, + min_train_samples: Optional[int] = None, + generate_samples: int = 3, + perform_evaluation: bool = True, + cleanup: bool = False, + ranking_metric: str = "perplexity_improvement", + ): + """Initialize parallel trainer. + + Args: + config_path: Path to autotrain YAML config + max_parallel: Maximum concurrent jobs (default: 3) + min_train_samples: Skip training if counted train samples below this threshold + generate_samples: Number of sample generations for post-training evaluation + """ + self.config_path = Path(config_path) + self.max_parallel = max_parallel + self.min_train_samples = min_train_samples + self.generate_samples = generate_samples + self.root = Path(__file__).parent.parent + self.venv_python = ( + self.root / "AI/microsoft_phi-silica-3.6_v1/venv/Scripts/python.exe" + ) + self.train_script = ( + self.root / "AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py" + ) + self.perform_evaluation = perform_evaluation + self.cleanup = cleanup + self.ranking_metric = ranking_metric + + # Load config + with open(self.config_path, "r", encoding="utf-8") as f: + self.config = yaml.safe_load(f) or {} + + self.jobs = self.config.get("jobs", []) + self.results: List[Dict[str, Any]] = [] + + async def run_job(self, job: Dict[str, Any], device_id: int) -> Dict[str, Any]: + """ + Run a single training job. + + Args: + job: Job configuration + device_id: GPU/CPU device ID for this job + + Returns: + Job result with status and timing + """ + job_name = job["name"] + start_time = datetime.now() + + print(f"\n[{job_name}] Starting on device {device_id}") + + # Resolve dataset path & gather dataset stats (best effort) + raw_dataset_path = job["dataset"] + ds_path = Path(raw_dataset_path) + if not ds_path.is_absolute(): + ds_path = self.root / ds_path + + train_count = None + test_count = None + try: + if ds_path.is_dir(): + # Look for train/test files - use efficient binary read for line counting + for candidate, attr in [ + ("train.json", "train"), + ("train.jsonl", "train"), + ("test.json", "test"), + ("test.jsonl", "test"), + ]: + fpath = ds_path / candidate + if fpath.exists(): + # Efficient line counting using binary mode and buffer + line_count = 0 + with open(fpath, "rb") as f: + # Read in 64KB chunks for better I/O performance + buf_size = 65536 + read_f = f.read + buf = read_f(buf_size) + while buf: + # Count non-empty lines by checking for content + line_count += buf.count(b"\n") + buf = read_f(buf_size) + if attr == "train": + train_count = (train_count or 0) + line_count + else: + test_count = (test_count or 0) + line_count + elif ds_path.is_file(): + # Efficient line counting for single files + line_count = 0 + with open(ds_path, "rb") as f: + buf_size = 65536 + read_f = f.read + buf = read_f(buf_size) + while buf: + line_count += buf.count(b"\n") + buf = read_f(buf_size) + train_count = line_count + except Exception as ds_err: + print(f"[{job_name}] Warning: failed counting dataset samples: {ds_err}") + + # Safety guard: skip if insufficient train samples + if ( + self.min_train_samples is not None + and train_count is not None + and train_count < self.min_train_samples + ): + end_time = datetime.now() + duration = (end_time - start_time).total_seconds() + result = { + "name": job_name, + "status": "skipped", + "reason": f"insufficient_train_samples (<{self.min_train_samples})", + "device": device_id, + "start_time": start_time.isoformat(), + "end_time": end_time.isoformat(), + "duration_seconds": duration, + "log_file": None, + "output_dir": job["save_dir"], + "dataset_path": str(ds_path), + "dataset_train_samples": train_count, + "dataset_test_samples": test_count, + } + print( + f"[{job_name}] ⚠ Skipping training: train_samples={train_count} < {self.min_train_samples}" + ) + return result + + # Build command for actual training + cmd = [ + str(self.venv_python), + str(self.train_script), + "--config", + job.get("config", "AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml"), + "--dataset", + job["dataset"], + "--save-dir", + job["save_dir"], + "--epochs", + str(job.get("epochs", 1)), + "--learning-rate", + str(job.get("learning_rate", 0.0002)), + "--lora-dropout", + str(job.get("lora_dropout", 0.1)), + "--hf-model-id", + job["hf_model_id"], + "--device", + "auto", # Use auto device selection + ] + + # Set CUDA_VISIBLE_DEVICES for device isolation + env = dict(os.environ) + if device_id >= 0: + env["CUDA_VISIBLE_DEVICES"] = str(device_id) + + if job.get("max_train_samples"): + cmd.extend(["--max-train-samples", str(job["max_train_samples"])]) + if job.get("max_eval_samples"): + cmd.extend(["--max-eval-samples", str(job["max_eval_samples"])]) + # Note: lora_rank should be set in config YAML, not via CLI + if job.get("no_stream"): + cmd.append("--no-stream") + + # Create output directory + output_dir = ( + self.root + / "data_out/parallel_training" + / job_name + / start_time.strftime("%Y%m%dT%H%M%SZ") + ) + output_dir.mkdir(parents=True, exist_ok=True) + + log_file = output_dir / "stdout.log" + + # Run job + try: + with open(log_file, "w", encoding="utf-8") as f: + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=f, + stderr=asyncio.subprocess.STDOUT, + cwd=str(self.root), + env=env, + ) + returncode = await process.wait() + + end_time = datetime.now() + duration = (end_time - start_time).total_seconds() + + result: Dict[str, Any] = { + "name": job_name, + "status": "succeeded" if returncode == 0 else "failed", + "return_code": returncode, + "device": device_id, + "start_time": start_time.isoformat(), + "end_time": end_time.isoformat(), + "duration_seconds": duration, + "log_file": str(log_file), + "output_dir": job["save_dir"], + "dataset_path": str(ds_path), + "dataset_train_samples": train_count, + "dataset_test_samples": test_count, + } + + print( + f"[{job_name}] {'✓' if returncode == 0 else '✗'} Completed in {duration:.1f}s" + ) + + # Post-training evaluation (only if succeeded) + if returncode == 0: + # Artifact paths + adapter_dir = self.root / job["save_dir"] / "lora_adapter" + tokenizer_dir = self.root / job["save_dir"] / "tokenizer" + result["adapter_dir"] = str(adapter_dir) + result["tokenizer_dir"] = str(tokenizer_dir) + if self.perform_evaluation: + eval_data = self._perform_evaluation(job, result) + if eval_data: + result["evaluation"] = eval_data + else: + result["evaluation"] = None + if self.cleanup: + self._perform_cleanup(job, result) + + except Exception as e: + result = { + "name": job_name, + "status": "error", + "error": str(e), + "device": device_id, + "start_time": start_time.isoformat(), + "log_file": str(log_file), + "dataset_path": str(ds_path), + "dataset_train_samples": train_count, + "dataset_test_samples": test_count, + } + print(f"[{job_name}] ✗ Error: {e}") + + return result + + def _perform_evaluation( + self, job: Dict[str, Any], result: Dict[str, Any] + ) -> Optional[Dict[str, Any]]: + """Quick evaluation: parse metrics.jsonl & generate sample outputs. + + Returns evaluation dict or None if unavailable. + """ + save_dir = self.root / job["save_dir"] + metrics_file = save_dir / "metrics.jsonl" + eval_info: Dict[str, Any] = {} + + # Parse metrics file for pre/post perplexity + try: + if metrics_file.exists(): + pre_ppl = post_ppl = None + pre_loss = post_loss = None + with metrics_file.open("r", encoding="utf-8") as mf: + for line in mf: + line = line.strip() + if not line: + continue + rec = json.loads(line) + phase = rec.get("phase") + if phase == "pre": + pre_loss = rec.get("eval_loss") + pre_ppl = rec.get("eval_perplexity") or ( + math.e ** rec.get("eval_loss", 0) + ) + elif phase == "post": + post_loss = rec.get("eval_loss") + post_ppl = rec.get("eval_perplexity") or ( + math.e ** rec.get("eval_loss", 0) + ) + # Early exit once we have both phases + if pre_ppl is not None and post_ppl is not None: + break + if pre_loss is not None: + eval_info["pre_eval_loss"] = pre_loss + if pre_ppl is not None: + eval_info["pre_eval_perplexity"] = pre_ppl + if post_loss is not None: + eval_info["post_eval_loss"] = post_loss + if post_ppl is not None: + eval_info["post_eval_perplexity"] = post_ppl + except Exception as m_err: + eval_info["metrics_error"] = f"metrics_parse_failed: {m_err}" + + # Sample generations (best effort) + prompts = [ + "Hello! Provide a concise helpful assistant greeting.", + "Explain one optimization used in ultrafast fine-tuning.", + "Summarize the purpose of this training run in one sentence.", + "List two potential risks in rapid fine-tuning.", + "Give one improvement suggestion for dataset quality.", + ] + samples: List[Dict[str, Any]] = [] + if self.generate_samples > 0: + try: + from peft import PeftModel # type: ignore + from transformers import AutoModelForCausalLM # type: ignore + from transformers import AutoTokenizer + + base_model_id = job.get("hf_model_id") + adapter_dir = save_dir / "lora_adapter" + if adapter_dir.exists(): + tokenizer = AutoTokenizer.from_pretrained( + base_model_id, use_fast=True + ) + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + base_model = AutoModelForCausalLM.from_pretrained(base_model_id) + model = PeftModel.from_pretrained(base_model, str(adapter_dir)) + model.eval() + import torch + + for p in prompts[: self.generate_samples]: + try: + inputs = tokenizer(p, return_tensors="pt") + with torch.no_grad(): + output_ids = model.generate( + **inputs, + max_new_tokens=80, + do_sample=True, + temperature=0.8, + top_p=0.9, + ) + gen_text = tokenizer.decode( + output_ids[0], skip_special_tokens=True + ) + samples.append({"prompt": p, "response": gen_text}) + except Exception as gen_err: + samples.append({"prompt": p, "error": str(gen_err)}) + else: + eval_info["generation_note"] = ( + "adapter_dir_missing; skipping sample generation" + ) + except Exception as g_err: + eval_info["generation_error"] = f"generation_failed: {g_err}" + if samples: + # Diversity metrics (Distinct-1/2) & echo ratio + def _distinct_1_2(texts: List[str]) -> Dict[str, float]: + unigrams_total = 0 + unigrams_set = set() + bigrams_total = 0 + bigrams_set = set() + for t in texts: + toks = t.split() + unigrams_total += len(toks) + unigrams_set.update(toks) + bgs = list(zip(toks, toks[1:])) + bigrams_total += len(bgs) + bigrams_set.update(bgs) + d1 = len(unigrams_set) / unigrams_total if unigrams_total else 0.0 + d2 = len(bigrams_set) / bigrams_total if bigrams_total else 0.0 + return {"distinct_1": d1, "distinct_2": d2} + + def _echo_ratio(prompt: str, response: str) -> float: + p_tokens = prompt.lower().split() + r_tokens = response.lower().split() + if not r_tokens: + return 0.0 + overlap = sum(1 for t in r_tokens if t in p_tokens) + return overlap / len(r_tokens) + + responses = [s.get("response", "") for s in samples] + diversity = _distinct_1_2(responses) + avg_len = ( + sum(len(r.split()) for r in responses) / len(responses) + if responses + else 0.0 + ) + echo_scores = [ + _echo_ratio(s["prompt"], s.get("response", "")) for s in samples + ] + eval_info["samples"] = samples + eval_info["diversity"] = { + "distinct_1": diversity["distinct_1"], + "distinct_2": diversity["distinct_2"], + "avg_response_tokens": avg_len, + "avg_echo_ratio": ( + sum(echo_scores) / len(echo_scores) if echo_scores else 0.0 + ), + } + return eval_info or None + + def _perform_cleanup(self, job: Dict[str, Any], result: Dict[str, Any]) -> None: + """Remove intermediate checkpoints to slim output directory.""" + save_dir = self.root / job["save_dir"] + if not save_dir.exists(): + return + preserved = {"lora_adapter", "tokenizer", "metrics.jsonl"} + try: + for item in save_dir.iterdir(): + name = item.name + if item.is_dir(): + if name not in preserved and name.startswith("checkpoint"): + shutil.rmtree(item, ignore_errors=True) + elif item.is_file() and name.startswith("checkpoint"): + try: + item.unlink() + except Exception: + pass + result["cleanup"] = "completed" + except Exception as c_err: + result["cleanup"] = f"error: {c_err}" + + def _compute_ranking(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Compute ranking of jobs based on selected metric. + + Supported metrics: + - perplexity_improvement: relative reduction (higher better) + - post_perplexity: final perplexity (lower better; stored negative for sorting) + - diversity_avg: average of distinct-1 and distinct-2 (higher better) + - distinct_diversity: alias of diversity_avg + - combined_improvement: weighted combo of perplexity_improvement (70%) + diversity_avg (30%) + """ + ranked: List[Dict[str, Any]] = [] + for r in results: + ev = r.get("evaluation") or {} + pre_ppl = ev.get("pre_eval_perplexity") + post_ppl = ev.get("post_eval_perplexity") + diversity = ev.get("diversity") or {} + d1 = diversity.get("distinct_1") + d2 = diversity.get("distinct_2") + diversity_avg = ( + (d1 + d2) / 2 if (d1 is not None and d2 is not None) else None + ) + # Skip jobs without post perplexity for metrics relying on perplexity + if ( + self.ranking_metric + in ("perplexity_improvement", "post_perplexity", "combined_improvement") + and post_ppl is None + ): + continue + if ( + self.ranking_metric == "perplexity_improvement" + and pre_ppl is not None + and post_ppl is not None + ): + ppl_improvement = ( + (pre_ppl - post_ppl) / pre_ppl if pre_ppl and pre_ppl > 0 else 0.0 + ) + score = ppl_improvement + elif self.ranking_metric == "post_perplexity" and post_ppl is not None: + ppl_improvement = ( + (pre_ppl - post_ppl) / pre_ppl + if (pre_ppl and post_ppl and pre_ppl > 0) + else None + ) + score = -post_ppl # lower is better + elif self.ranking_metric in ("diversity_avg", "distinct_diversity"): + if diversity_avg is None: + # Cannot rank this job on diversity; skip it + continue + ppl_improvement = ( + (pre_ppl - post_ppl) / pre_ppl + if (pre_ppl and post_ppl and pre_ppl > 0) + else None + ) + score = diversity_avg + elif self.ranking_metric == "combined_improvement": + ppl_improvement = ( + (pre_ppl - post_ppl) / pre_ppl + if (pre_ppl and post_ppl and pre_ppl > 0) + else 0.0 + ) + div_component = diversity_avg if diversity_avg is not None else 0.0 + score = 0.7 * ppl_improvement + 0.3 * div_component + else: + # Fallback to post perplexity if unknown metric + ppl_improvement = ( + (pre_ppl - post_ppl) / pre_ppl + if (pre_ppl and post_ppl and pre_ppl > 0) + else None + ) + score = -post_ppl if post_ppl is not None else 0.0 + ranked.append( + { + "name": r.get("name"), + "score": score, + "metric": self.ranking_metric, + "pre_perplexity": pre_ppl, + "post_perplexity": post_ppl, + "perplexity_improvement": ppl_improvement, + "distinct_1": d1, + "distinct_2": d2, + "diversity_avg": diversity_avg, + "status": r.get("status"), + } + ) + ranked.sort(key=lambda x: x["score"], reverse=True) + return ranked + + async def run_all_parallel(self, job_filter: str = "*"): + """ + Run all jobs in parallel with concurrency limit. + + Args: + job_filter: Glob pattern to filter job names + """ + import fnmatch + + # Filter jobs + filtered_jobs = [j for j in self.jobs if fnmatch.fnmatch(j["name"], job_filter)] + + if not filtered_jobs: + print(f"No jobs match filter: {job_filter}") + return + + print( + f"Running {len(filtered_jobs)} jobs with max {self.max_parallel} parallel" + ) + + # Create semaphore for concurrency control + semaphore = asyncio.Semaphore(self.max_parallel) + + async def run_with_semaphore(job, device_id): + async with semaphore: + return await self.run_job(job, device_id) + + # Assign device IDs cyclically + tasks = [ + run_with_semaphore(job, i % self.max_parallel) + for i, job in enumerate(filtered_jobs) + ] + + # Run all tasks + start_time = datetime.now() + self.results = await asyncio.gather(*tasks) + end_time = datetime.now() + + # Prepare run entry + status_file = self.root / "data_out/parallel_training/status.json" + status_file.parent.mkdir(parents=True, exist_ok=True) + agg_train = sum( + r.get("dataset_train_samples") or 0 + for r in self.results + if r.get("dataset_train_samples") is not None + ) + agg_test = sum( + r.get("dataset_test_samples") or 0 + for r in self.results + if r.get("dataset_test_samples") is not None + ) + + run_entry = { + "run_id": end_time.strftime("%Y%m%dT%H%M%S"), + "total_jobs": len(filtered_jobs), + "max_parallel": self.max_parallel, + "total_duration_seconds": (end_time - start_time).total_seconds(), + "timestamp": end_time.isoformat(), + "aggregate_train_samples": agg_train if agg_train else None, + "aggregate_test_samples": agg_test if agg_test else None, + "jobs": self.results, + } + + # Ranking computation + ranking = self._compute_ranking(self.results) + if ranking: + run_entry["job_ranking"] = ranking + # Append mode historical persistence (correctly placed inside run_all_parallel) + if status_file.exists(): + try: + with status_file.open("r", encoding="utf-8") as f: + existing = json.load(f) + except Exception: + existing = {} + else: + existing = {} + + # Normalize legacy format + runs = [] + if "runs" in existing and isinstance(existing["runs"], list): + runs = existing["runs"] + elif "jobs" in existing: + legacy = dict(existing) + runs = [legacy] + runs.append(run_entry) + new_status = { + "runs": runs, + "total_runs": len(runs), + "last_updated": end_time.isoformat(), + } + try: + new_status["cumulative_train_samples"] = ( + sum((r.get("aggregate_train_samples") or 0) for r in runs) or None + ) + new_status["cumulative_test_samples"] = ( + sum((r.get("aggregate_test_samples") or 0) for r in runs) or None + ) + except Exception: + pass + with status_file.open("w", encoding="utf-8") as f: + json.dump(new_status, f, indent=2) + + # Print summary + print("\n" + "=" * 70) + print("Parallel Training Summary") + print("=" * 70) + + # Single pass to classify results and collect failed/skipped for display + succeeded = 0 + skipped = 0 + failed = 0 + failed_jobs = [] + skipped_jobs = [] + + for r in self.results: + status = r.get("status") + if status == "succeeded": + succeeded += 1 + elif status == "skipped": + skipped += 1 + skipped_jobs.append(r) + else: + failed += 1 + failed_jobs.append(r) + + print(f"\nTotal Jobs: {len(self.results)}") + print(f"Succeeded: {succeeded}") + print(f"Skipped: {skipped}") + print(f"Failed: {failed}") + print(f"Total Time: {(end_time - start_time).total_seconds():.1f}s") + print(f"\nStatus file: {status_file}") + + # Show failed jobs if any + if failed > 0: + print("\nFailed Jobs:") + for r in failed_jobs: + print( + f" - {r['name']}: {r.get('error', 'return code ' + str(r.get('return_code')))}" + ) + if skipped > 0: + print("\nSkipped Jobs:") + for r in skipped_jobs: + print( + f" - {r['name']}: {r.get('reason', 'min_train_samples threshold')} train_samples={r.get('dataset_train_samples')}" + ) + + +def main(): + parser = argparse.ArgumentParser( + description="Parallel training launcher with historical status & evaluation" + ) + parser.add_argument( + "--config", + default="autotrain_fast.yaml", + help="Training config YAML (default: autotrain_fast.yaml)", + ) + parser.add_argument( + "--max-parallel", type=int, default=3, help="Max concurrent jobs (default: 3)" + ) + parser.add_argument( + "--filter", + default="*", + help="Job name filter pattern (e.g., 'phi35*' or 'quick*')", + ) + parser.add_argument("--list", action="store_true", help="List jobs without running") + parser.add_argument( + "--min-train-samples", + type=int, + default=None, + help="Skip jobs whose counted train samples are below this threshold", + ) + parser.add_argument( + "--generate-samples", + type=int, + default=3, + help="Number of sample generations to produce after successful training", + ) + parser.add_argument( + "--no-eval", + action="store_true", + help="Disable post-training evaluation and sample generation entirely", + ) + parser.add_argument( + "--cleanup", + action="store_true", + help="Remove intermediate checkpoint artifacts after successful training", + ) + parser.add_argument( + "--ranking-metric", + choices=[ + "perplexity_improvement", + "post_perplexity", + "diversity_avg", + "combined_improvement", + "distinct_diversity", + ], + default="perplexity_improvement", + help="Metric used to rank jobs in status history (distinct_diversity alias of diversity_avg)", + ) + + args = parser.parse_args() + + trainer = ParallelTrainer( + args.config, + max_parallel=args.max_parallel, + min_train_samples=args.min_train_samples, + generate_samples=args.generate_samples, + perform_evaluation=not args.no_eval, + cleanup=args.cleanup, + ranking_metric=args.ranking_metric, + ) + + if args.list: + print(f"Jobs in {args.config}:") + for i, job in enumerate(trainer.jobs, 1): + print(f"{i}. {job['name']}") + print(f" Dataset: {job['dataset']}") + print(f" Samples: {job.get('max_train_samples', 'all')}") + print(f" Model: {job['hf_model_id']}") + return + + # Run parallel training + asyncio.run(trainer.run_all_parallel(args.filter)) + + # Determine exit status (skipped jobs are not failures) + failures = [ + r for r in trainer.results if r.get("status") not in ("succeeded", "skipped") + ] + if failures: + print("\n[parallel_train] One or more jobs failed. Exiting with code 1.") + sys.exit(1) + else: + print( + "\n[parallel_train] All jobs completed (including skips). Exiting with code 0." + ) + sys.exit(0) + + +if __name__ == "__main__": + main() diff --git a/scripts/pre_commit_check.py b/scripts/pre_commit_check.py index 654c1cdd1..c8f12e9f1 100644 --- a/scripts/pre_commit_check.py +++ b/scripts/pre_commit_check.py @@ -1,356 +1,356 @@ -#!/usr/bin/env python3 -"""Pre-commit validation script for QAI workspace. - -Runs automated checks before committing code changes: -- Unit tests (pytest) -- Linting (ruff/pycodestyle) -- Security scan (secrets detection) -- Git hygiene (file sizes, unwanted files) - -Usage: - python scripts/pre_commit_check.py - python scripts/pre_commit_check.py --checks tests,lint - python scripts/pre_commit_check.py --skip security -""" -import argparse -import os -import re -import subprocess -import sys -from pathlib import Path -from typing import List, Tuple - -# ANSI color codes -GREEN = "\033[92m" -YELLOW = "\033[93m" -RED = "\033[91m" -CYAN = "\033[96m" -RESET = "\033[0m" -BOLD = "\033[1m" - -REPO_ROOT = Path(__file__).resolve().parent.parent - - -def _staged_files(*pathspecs: str) -> List[str]: - """Return staged files filtered by optional git pathspecs.""" - cmd = ["git", "diff", "--cached", "--name-only"] - if pathspecs: - cmd.extend(["--", *pathspecs]) - code, stdout, _ = run_command(cmd) - if code != 0: - return [] - return [line.strip() for line in stdout.splitlines() if line.strip()] - - -def _find_repo_doc(doc_name: str) -> Path | None: - """Find a documentation file by basename anywhere in the repo.""" - direct = REPO_ROOT / doc_name - if direct.exists(): - return direct - - for path in REPO_ROOT.rglob(doc_name): - text = str(path) - if any(part in text for part in ["venv/", ".venv/", "__pycache__/"]): - continue - return path - return None - - -def print_header(text: str): - print(f"\n{CYAN}{BOLD}{text}{RESET}") - - -def print_success(text: str): - print(f"{GREEN}✓ {text}{RESET}") - - -def print_warning(text: str): - print(f"{YELLOW}⚠ {text}{RESET}") - - -def print_error(text: str): - print(f"{RED}✗ {text}{RESET}") - - -def run_command( - cmd: List[str], cwd: Path = REPO_ROOT, timeout_seconds: int = 120 -) -> Tuple[int, str, str]: - """Run a command and return (exit_code, stdout, stderr).""" - try: - result = subprocess.run( - cmd, - cwd=cwd, - capture_output=True, - text=True, - timeout=timeout_seconds, - ) - return result.returncode, result.stdout, result.stderr - except subprocess.TimeoutExpired: - return 1, "", f"Command timed out after {timeout_seconds}s" - except Exception as e: - return 1, "", str(e) - - -def check_unit_tests() -> bool: - """Run pytest on all test files.""" - print_header("[1/5] Running unit tests...") - - default_timeout = 600 - raw_timeout = os.environ.get("PRE_COMMIT_TEST_TIMEOUT", str(default_timeout)) - try: - test_timeout = max(60, int(raw_timeout)) - except ValueError: - test_timeout = default_timeout - - pytest_exe = REPO_ROOT / "venv" / "Scripts" / "python.exe" - if not pytest_exe.exists(): - pytest_exe = Path(sys.executable) - - code, stdout, stderr = run_command( - [str(pytest_exe), "-m", "pytest", "tests/", "-v", "--tb=short"], - timeout_seconds=test_timeout, - ) - - if code == 0: - # Parse test count from output - match = re.search(r"(\d+) passed", stdout) - if match: - print_success(f"{match.group(1)} tests passed") - else: - print_success("All tests passed") - return True - else: - print_error("Tests failed") - print(stdout[-500:] if len(stdout) > 500 else stdout) # Show last 500 chars - if stderr: - print(stderr[-300:] if len(stderr) > 300 else stderr) - return False - - -def check_linting() -> bool: - """Run linting checks on Python files.""" - print_header("[2/5] Linting code...") - - staged_python_files = _staged_files("*.py") - if not staged_python_files: - print_success("No staged Python files to lint") - return True - - # Try ruff first (fast, modern linter). - # We intentionally gate on high-signal, correctness/safety-focused rules - # to avoid blocking commits on large-scale formatting debt in unrelated - # staged files. - critical_rules = ["E9", "F63", "F7", "F82", "B904"] - code, stdout, stderr = run_command( - [ - sys.executable, - "-m", - "ruff", - "check", - *staged_python_files, - "--select", - ",".join(critical_rules), - ] - ) - - if code == 5: # ruff not installed - print_warning( - "ruff not installed, skipping linting (install with: pip install ruff)" - ) - return True - - if code == 0: - print_success("No linting issues found") - return True - else: - issues = [line for line in stdout.splitlines() if line.strip()] - print_error( - "Found critical linting issues " - f"({','.join(critical_rules)}) in {len(staged_python_files)} staged file(s)" - ) - for issue in issues[:10]: - print(f" {issue}") - return False - - -def check_security() -> bool: - """Scan for hardcoded secrets and security issues.""" - print_header("[3/5] Security scan...") - - issues = [] - - # Check for common secret patterns - secret_patterns = [ - (r'api[_-]?key\s*=\s*["\'][^"\']{20,}["\']', "Potential API key"), - (r'password\s*=\s*["\'][^"\']+["\']', "Hardcoded password"), - (r"sk-[a-zA-Z0-9]{20,}", "OpenAI API key"), - (r"AKIA[0-9A-Z]{16}", "AWS Access Key"), - ] - - py_files = list(REPO_ROOT.glob("**/*.py")) - py_files = [ - f for f in py_files if "venv" not in str(f) and "__pycache__" not in str(f) - ] - - for py_file in py_files[:50]: # Limit to first 50 files for speed - try: - content = py_file.read_text(encoding="utf-8", errors="ignore") - for pattern, desc in secret_patterns: - if re.search(pattern, content, re.IGNORECASE): - # Exclude test files and env templates - if ( - "test_" not in py_file.name - and "example" not in py_file.name.lower() - ): - issues.append(f"{py_file.name}: {desc}") - except Exception: - continue - - # Check if .env is being committed - code, stdout, _ = run_command(["git", "diff", "--cached", "--name-only"]) - if code == 0: - staged_files = stdout.split("\n") - if any(".env" in f and "example" not in f for f in staged_files): - issues.append(".env file in staging (should be in .gitignore)") - - if issues: - print_error(f"Found {len(issues)} potential security issues:") - for issue in issues[:5]: - print(f" {issue}") - return False - else: - print_success("No security issues detected") - return True - - -def check_git_hygiene() -> bool: - """Check for large files and unwanted files in staging.""" - print_header("[4/5] Git hygiene...") - - code, stdout, _ = run_command(["git", "diff", "--cached", "--name-only"]) - - if code != 0: - print_warning("Not in a git repository or no staged files") - return True - - staged_files = stdout.split("\n") - issues = [] - - for file_path in staged_files: - if not file_path.strip(): - continue - - full_path = REPO_ROOT / file_path - - # Check for unwanted files - if any( - pattern in file_path - for pattern in ["__pycache__", ".pyc", "venv/", ".venv/", "__azurite_db"] - ): - issues.append(f"Unwanted file: {file_path}") - - # Check file size (warn if >10MB) - if full_path.exists() and full_path.is_file(): - size_mb = full_path.stat().st_size / (1024 * 1024) - if size_mb > 10: - issues.append(f"Large file ({size_mb:.1f}MB): {file_path}") - - if issues: - print_warning(f"Found {len(issues)} git hygiene issues:") - for issue in issues[:5]: - print(f" {issue}") - # Don't fail, just warn - return True - else: - print_success("Git staging area looks clean") - return True - - -def check_documentation() -> bool: - """Verify documentation is up-to-date.""" - print_header("[5/5] Checking documentation...") - - # Check if README exists and is non-empty - readme = REPO_ROOT / "README.md" - if not readme.exists(): - print_error("README.md not found") - return False - - if readme.stat().st_size < 100: - print_error("README.md is suspiciously small") - return False - - # Check for recent enhancements docs - required_docs = [ - "ENHANCEMENTS_SUMMARY.md", - "TELEMETRY_COSMOS_ENABLEMENT.md", - "QUICK_REFERENCE.md", - ] - - missing = [doc for doc in required_docs if _find_repo_doc(doc) is None] - - if missing: - print_warning(f"Missing documentation: {', '.join(missing)}") - # Don't fail, just warn - else: - print_success("All key documentation present") - - return True - - -def main(): - parser = argparse.ArgumentParser(description="Pre-commit validation for QAI") - parser.add_argument( - "--checks", - help="Comma-separated list of checks to run (tests,lint,security,git,docs)", - default="tests,lint,security,git,docs", - ) - parser.add_argument( - "--skip", - help="Comma-separated list of checks to skip", - default="", - ) - args = parser.parse_args() - - enabled_checks = set(args.checks.split(",")) - skipped_checks = set(args.skip.split(",")) if args.skip else set() - enabled_checks -= skipped_checks - - print(f"\n{BOLD}{'═' * 67}{RESET}") - print(f"{BOLD}{CYAN}QAI PRE-COMMIT VALIDATION{RESET}") - print(f"{BOLD}{'═' * 67}{RESET}") - - results = {} - - if "tests" in enabled_checks: - results["tests"] = check_unit_tests() - - if "lint" in enabled_checks: - results["lint"] = check_linting() - - if "security" in enabled_checks: - results["security"] = check_security() - - if "git" in enabled_checks: - results["git"] = check_git_hygiene() - - if "docs" in enabled_checks: - results["docs"] = check_documentation() - - # Summary - print(f"\n{BOLD}{'═' * 67}{RESET}") - passed = sum(1 for v in results.values() if v) - total = len(results) - - if passed == total: - print(f"{GREEN}{BOLD}RESULT: All checks passed ✓ ({passed}/{total}){RESET}") - print(f"{BOLD}{'═' * 67}{RESET}\n") - return 0 - else: - print(f"{RED}{BOLD}RESULT: Some checks failed ({passed}/{total} passed){RESET}") - print(f"{BOLD}{'═' * 67}{RESET}\n") - return 1 - - -if __name__ == "__main__": - sys.exit(main()) +#!/usr/bin/env python3 +"""Pre-commit validation script for QAI workspace. + +Runs automated checks before committing code changes: +- Unit tests (pytest) +- Linting (ruff/pycodestyle) +- Security scan (secrets detection) +- Git hygiene (file sizes, unwanted files) + +Usage: + python scripts/pre_commit_check.py + python scripts/pre_commit_check.py --checks tests,lint + python scripts/pre_commit_check.py --skip security +""" +import argparse +import os +import re +import subprocess +import sys +from pathlib import Path +from typing import List, Tuple + +# ANSI color codes +GREEN = "\033[92m" +YELLOW = "\033[93m" +RED = "\033[91m" +CYAN = "\033[96m" +RESET = "\033[0m" +BOLD = "\033[1m" + +REPO_ROOT = Path(__file__).resolve().parent.parent + + +def _staged_files(*pathspecs: str) -> List[str]: + """Return staged files filtered by optional git pathspecs.""" + cmd = ["git", "diff", "--cached", "--name-only"] + if pathspecs: + cmd.extend(["--", *pathspecs]) + code, stdout, _ = run_command(cmd) + if code != 0: + return [] + return [line.strip() for line in stdout.splitlines() if line.strip()] + + +def _find_repo_doc(doc_name: str) -> Path | None: + """Find a documentation file by basename anywhere in the repo.""" + direct = REPO_ROOT / doc_name + if direct.exists(): + return direct + + for path in REPO_ROOT.rglob(doc_name): + text = str(path) + if any(part in text for part in ["venv/", ".venv/", "__pycache__/"]): + continue + return path + return None + + +def print_header(text: str): + print(f"\n{CYAN}{BOLD}{text}{RESET}") + + +def print_success(text: str): + print(f"{GREEN}✓ {text}{RESET}") + + +def print_warning(text: str): + print(f"{YELLOW}⚠ {text}{RESET}") + + +def print_error(text: str): + print(f"{RED}✗ {text}{RESET}") + + +def run_command( + cmd: List[str], cwd: Path = REPO_ROOT, timeout_seconds: int = 120 +) -> Tuple[int, str, str]: + """Run a command and return (exit_code, stdout, stderr).""" + try: + result = subprocess.run( + cmd, + cwd=cwd, + capture_output=True, + text=True, + timeout=timeout_seconds, + ) + return result.returncode, result.stdout, result.stderr + except subprocess.TimeoutExpired: + return 1, "", f"Command timed out after {timeout_seconds}s" + except Exception as e: + return 1, "", str(e) + + +def check_unit_tests() -> bool: + """Run pytest on all test files.""" + print_header("[1/5] Running unit tests...") + + default_timeout = 600 + raw_timeout = os.environ.get("PRE_COMMIT_TEST_TIMEOUT", str(default_timeout)) + try: + test_timeout = max(60, int(raw_timeout)) + except ValueError: + test_timeout = default_timeout + + pytest_exe = REPO_ROOT / "venv" / "Scripts" / "python.exe" + if not pytest_exe.exists(): + pytest_exe = Path(sys.executable) + + code, stdout, stderr = run_command( + [str(pytest_exe), "-m", "pytest", "tests/", "-v", "--tb=short"], + timeout_seconds=test_timeout, + ) + + if code == 0: + # Parse test count from output + match = re.search(r"(\d+) passed", stdout) + if match: + print_success(f"{match.group(1)} tests passed") + else: + print_success("All tests passed") + return True + else: + print_error("Tests failed") + print(stdout[-500:] if len(stdout) > 500 else stdout) # Show last 500 chars + if stderr: + print(stderr[-300:] if len(stderr) > 300 else stderr) + return False + + +def check_linting() -> bool: + """Run linting checks on Python files.""" + print_header("[2/5] Linting code...") + + staged_python_files = _staged_files("*.py") + if not staged_python_files: + print_success("No staged Python files to lint") + return True + + # Try ruff first (fast, modern linter). + # We intentionally gate on high-signal, correctness/safety-focused rules + # to avoid blocking commits on large-scale formatting debt in unrelated + # staged files. + critical_rules = ["E9", "F63", "F7", "F82", "B904"] + code, stdout, stderr = run_command( + [ + sys.executable, + "-m", + "ruff", + "check", + *staged_python_files, + "--select", + ",".join(critical_rules), + ] + ) + + if code == 5: # ruff not installed + print_warning( + "ruff not installed, skipping linting (install with: pip install ruff)" + ) + return True + + if code == 0: + print_success("No linting issues found") + return True + else: + issues = [line for line in stdout.splitlines() if line.strip()] + print_error( + "Found critical linting issues " + f"({','.join(critical_rules)}) in {len(staged_python_files)} staged file(s)" + ) + for issue in issues[:10]: + print(f" {issue}") + return False + + +def check_security() -> bool: + """Scan for hardcoded secrets and security issues.""" + print_header("[3/5] Security scan...") + + issues = [] + + # Check for common secret patterns + secret_patterns = [ + (r'api[_-]?key\s*=\s*["\'][^"\']{20,}["\']', "Potential API key"), + (r'password\s*=\s*["\'][^"\']+["\']', "Hardcoded password"), + (r"sk-[a-zA-Z0-9]{20,}", "OpenAI API key"), + (r"AKIA[0-9A-Z]{16}", "AWS Access Key"), + ] + + py_files = list(REPO_ROOT.glob("**/*.py")) + py_files = [ + f for f in py_files if "venv" not in str(f) and "__pycache__" not in str(f) + ] + + for py_file in py_files[:50]: # Limit to first 50 files for speed + try: + content = py_file.read_text(encoding="utf-8", errors="ignore") + for pattern, desc in secret_patterns: + if re.search(pattern, content, re.IGNORECASE): + # Exclude test files and env templates + if ( + "test_" not in py_file.name + and "example" not in py_file.name.lower() + ): + issues.append(f"{py_file.name}: {desc}") + except Exception: + continue + + # Check if .env is being committed + code, stdout, _ = run_command(["git", "diff", "--cached", "--name-only"]) + if code == 0: + staged_files = stdout.split("\n") + if any(".env" in f and "example" not in f for f in staged_files): + issues.append(".env file in staging (should be in .gitignore)") + + if issues: + print_error(f"Found {len(issues)} potential security issues:") + for issue in issues[:5]: + print(f" {issue}") + return False + else: + print_success("No security issues detected") + return True + + +def check_git_hygiene() -> bool: + """Check for large files and unwanted files in staging.""" + print_header("[4/5] Git hygiene...") + + code, stdout, _ = run_command(["git", "diff", "--cached", "--name-only"]) + + if code != 0: + print_warning("Not in a git repository or no staged files") + return True + + staged_files = stdout.split("\n") + issues = [] + + for file_path in staged_files: + if not file_path.strip(): + continue + + full_path = REPO_ROOT / file_path + + # Check for unwanted files + if any( + pattern in file_path + for pattern in ["__pycache__", ".pyc", "venv/", ".venv/", "__azurite_db"] + ): + issues.append(f"Unwanted file: {file_path}") + + # Check file size (warn if >10MB) + if full_path.exists() and full_path.is_file(): + size_mb = full_path.stat().st_size / (1024 * 1024) + if size_mb > 10: + issues.append(f"Large file ({size_mb:.1f}MB): {file_path}") + + if issues: + print_warning(f"Found {len(issues)} git hygiene issues:") + for issue in issues[:5]: + print(f" {issue}") + # Don't fail, just warn + return True + else: + print_success("Git staging area looks clean") + return True + + +def check_documentation() -> bool: + """Verify documentation is up-to-date.""" + print_header("[5/5] Checking documentation...") + + # Check if README exists and is non-empty + readme = REPO_ROOT / "README.md" + if not readme.exists(): + print_error("README.md not found") + return False + + if readme.stat().st_size < 100: + print_error("README.md is suspiciously small") + return False + + # Check for recent enhancements docs + required_docs = [ + "ENHANCEMENTS_SUMMARY.md", + "TELEMETRY_COSMOS_ENABLEMENT.md", + "QUICK_REFERENCE.md", + ] + + missing = [doc for doc in required_docs if _find_repo_doc(doc) is None] + + if missing: + print_warning(f"Missing documentation: {', '.join(missing)}") + # Don't fail, just warn + else: + print_success("All key documentation present") + + return True + + +def main(): + parser = argparse.ArgumentParser(description="Pre-commit validation for QAI") + parser.add_argument( + "--checks", + help="Comma-separated list of checks to run (tests,lint,security,git,docs)", + default="tests,lint,security,git,docs", + ) + parser.add_argument( + "--skip", + help="Comma-separated list of checks to skip", + default="", + ) + args = parser.parse_args() + + enabled_checks = set(args.checks.split(",")) + skipped_checks = set(args.skip.split(",")) if args.skip else set() + enabled_checks -= skipped_checks + + print(f"\n{BOLD}{'═' * 67}{RESET}") + print(f"{BOLD}{CYAN}QAI PRE-COMMIT VALIDATION{RESET}") + print(f"{BOLD}{'═' * 67}{RESET}") + + results = {} + + if "tests" in enabled_checks: + results["tests"] = check_unit_tests() + + if "lint" in enabled_checks: + results["lint"] = check_linting() + + if "security" in enabled_checks: + results["security"] = check_security() + + if "git" in enabled_checks: + results["git"] = check_git_hygiene() + + if "docs" in enabled_checks: + results["docs"] = check_documentation() + + # Summary + print(f"\n{BOLD}{'═' * 67}{RESET}") + passed = sum(1 for v in results.values() if v) + total = len(results) + + if passed == total: + print(f"{GREEN}{BOLD}RESULT: All checks passed ✓ ({passed}/{total}){RESET}") + print(f"{BOLD}{'═' * 67}{RESET}\n") + return 0 + else: + print(f"{RED}{BOLD}RESULT: Some checks failed ({passed}/{total} passed){RESET}") + print(f"{BOLD}{'═' * 67}{RESET}\n") + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/quantum_autorun.py b/scripts/quantum_autorun.py index c191f1543..51a4fdc90 100644 --- a/scripts/quantum_autorun.py +++ b/scripts/quantum_autorun.py @@ -1,352 +1,352 @@ -"""Quantum AutoRun Orchestrator CLI - -Minimal CLI for validating and listing quantum jobs from a YAML config. -Implements: -- --help: prints usage and description -- --config: path to YAML config (defaults to config/quantum/quantum_autorun.yaml) -- --list: prints jobs as JSON to stdout -- --dry-run: validates config, writes data_out/quantum_autorun/status.json, prints summary -- --job NAME: filters to a specific job (non-zero exit if not found) - -Config schema (YAML): -jobs: - - name: heart_quick - preset: heart - epochs: 1 - n_qubits: 4 -""" - -from __future__ import annotations - -import argparse -import json -import sys -from dataclasses import dataclass, field -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List - -try: - from .config_paths import resolve_config_path -except ImportError: - from config_paths import resolve_config_path - -try: - import yaml # type: ignore -except Exception: # pragma: no cover - yaml = None # Tests will provide simple configs; fail gracefully if missing - - -REPO_ROOT = Path(__file__).resolve().parents[1] -DEFAULT_CONFIG = resolve_config_path(REPO_ROOT, "quantum_autorun") -STATUS_DIR = REPO_ROOT / "data_out" / "quantum_autorun" -STATUS_FILE = STATUS_DIR / "status.json" -DATA_OUT = REPO_ROOT / "data_out" - -# Paths for helper scripts used by jobs -TRAIN_SCRIPT = REPO_ROOT / "ai-projects" / "quantum-ml" / "train_custom_dataset.py" -AZURE_SUBMIT_SCRIPT = ( - REPO_ROOT / "ai-projects" / "quantum-ml" / "deploy_to_azure_quantum.py" -) - -# Known preset datasets -PRESETS = ("heart", "ionosphere", "sonar", "banknote") - - -@dataclass -class QJob: - """Dataclass representing a single quantum_autorun job definition. - - Tests expect a minimal set of attributes and stable defaults so this - class mirrors the structure used across the repo. - """ - - name: str - mode: str = "train_custom_dataset" - enabled: bool = True - - # Dataset / training args - preset: str | None = None - csv: str | None = None - label_col: str | None = None - drop_cols: str | None = None - epochs: int | None = None - batch_size: int | None = None - learning_rate: float | None = None - test_size: float | None = None - n_qubits: int | None = None - - # Extra arguments for CLI - extra_args: list[str] = field(default_factory=list) - - # Azure-specific - azure_backend: str | None = None - azure_shots: int | None = None - azure_confirm_cost: bool = False - - -# Backwards compatible alias expected by tests -def read_yaml(path: Path) -> Dict[str, Any]: - return load_config(path) - - -def load_jobs(path: Path) -> List[QJob]: - """Load jobs from a YAML path and return a list of QJob objects. - - This function accepts the same input used by the CLI and normalises - values into typed QJob instances. It handles both proper YAML parsing - (pyyaml) and the simple fallback loader used when pyyaml is missing. - """ - data = read_yaml(path) - jobs: List[QJob] = [] - for raw in data.get("jobs", []): - # Raw loader may return dicts with string values when PyYAML is not - # available; attempt sensible conversions. - def _get(key, default=None): - v = raw.get(key, default) - return v - - # Convert basic numeric types conservatively - def _int(v): - if v is None: - return None - try: - return int(v) - except Exception: - return None - - def _float(v): - if v is None: - return None - try: - return float(v) - except Exception: - return None - - j = QJob( - name=str(_get("name")), - mode=str(_get("mode", "train_custom_dataset")), - enabled=bool(_get("enabled", True)), - preset=_get("preset"), - csv=_get("csv"), - label_col=_get("label_col"), - drop_cols=_get("drop_cols"), - epochs=_int(_get("epochs")), - batch_size=_int(_get("batch_size")), - learning_rate=_float(_get("learning_rate")), - test_size=_float(_get("test_size")), - n_qubits=_int(_get("n_qubits")), - extra_args=_get("extra_args") or [], - azure_backend=_get("azure_backend"), - azure_shots=_int(_get("azure_shots")), - azure_confirm_cost=bool(_get("azure_confirm_cost", False)), - ) - jobs.append(j) - return jobs - - -def _python_executable() -> str: - """Return a usable python executable for subprocess commands.""" - return sys.executable or "python" - - -def build_command(job: QJob) -> List[str]: - """Build the command line (list form) to execute a QJob. - - The function returns an argument list appropriate for subprocess.run. - """ - py = _python_executable() - - if job.mode == "azure_hardware": - cmd = [py, str(AZURE_SUBMIT_SCRIPT)] - if job.azure_backend: - cmd.extend(["--backend", str(job.azure_backend)]) - if job.azure_shots is not None: - cmd.extend(["--shots", str(job.azure_shots)]) - # Tests check for this flag name specifically - if job.n_qubits is not None: - cmd.extend(["--n-qubits", str(job.n_qubits)]) - if job.extra_args: - cmd.extend(job.extra_args) - return cmd - - # Default: local training run - cmd = [py, str(TRAIN_SCRIPT)] - if job.preset: - cmd.extend(["--preset", str(job.preset)]) - if job.csv: - cmd.extend(["--csv", str(job.csv)]) - if job.label_col: - cmd.extend(["--label-col", str(job.label_col)]) - if job.drop_cols: - cmd.extend(["--drop-cols", str(job.drop_cols)]) - if job.epochs is not None: - cmd.extend(["--epochs", str(job.epochs)]) - if job.batch_size is not None: - cmd.extend(["--batch-size", str(job.batch_size)]) - if job.learning_rate is not None: - cmd.extend(["--learning-rate", str(job.learning_rate)]) - if job.test_size is not None: - cmd.extend(["--test-size", str(job.test_size)]) - if job.n_qubits is not None: - cmd.extend(["--n-qubits", str(job.n_qubits)]) - if job.extra_args: - cmd.extend(job.extra_args) - - return cmd - - -def validate_job(job: QJob) -> Dict[str, Any]: - """Validate a QJob definition and return a dict with status and missing items. - - The returned dict contains keys: status (ok/missing), missing (list). - """ - missing = [] - # Mode-specific checks - if job.mode == "train_custom_dataset": - if not TRAIN_SCRIPT.exists(): - missing.append(str(TRAIN_SCRIPT.name)) - - # Preset or CSV must be provided for training - if not job.preset and not job.csv: - missing.append("preset or csv") - - # If preset present, ensure it's known - if job.preset and job.preset not in PRESETS: - missing.append("Unknown preset: " + str(job.preset)) - - if job.csv: - p = Path(job.csv) - if not p.exists(): - missing.append(f"Missing CSV: {job.csv}") - - elif job.mode == "azure_hardware": - # Azure deployment script required - if not AZURE_SUBMIT_SCRIPT.exists(): - missing.append(str(AZURE_SUBMIT_SCRIPT.name)) - - # QPU requires explicit cost confirmation - if ( - job.azure_backend - and ".qpu" in str(job.azure_backend) - and not job.azure_confirm_cost - ): - missing.append("azure_confirm_cost") - - status = "ok" if not missing else "missing" - return {"status": status, "missing": missing} - - -def collect_status(jobs: List[Dict[str, Any]]) -> Dict[str, Any]: - """Construct status payload (in-memory) used by the CLI and tests.""" - return { - "generated_at": datetime.now().isoformat() + "Z", - "jobs": jobs, - "last_updated": None, - "succeeded": 0, - "failed": 0, - "running": 0, - "avg_duration": None, - } - - -def load_config(path: Path) -> Dict[str, Any]: - if not path.exists(): - return {"jobs": []} - if yaml is None: - # Minimal YAML loader fallback: handle a very small subset for tests - # Prefer PyYAML when available. - text = path.read_text(encoding="utf-8") - # Extremely simple parse: find lines under jobs: and collect name/preset/etc - jobs: List[Dict[str, Any]] = [] - current: Dict[str, Any] = {} - in_jobs = False - for line in text.splitlines(): - s = line.strip() - if s.startswith("jobs:"): - in_jobs = True - continue - if in_jobs and s.startswith("-"): - if current: - jobs.append(current) - current = {} - continue - if in_jobs and ":" in s: - k, v = s.split(":", 1) - current[k.strip()] = v.strip() - if current: - jobs.append(current) - return {"jobs": jobs} - # Normal path: use safe_load - data = yaml.safe_load(path.read_text(encoding="utf-8")) or {} - if not isinstance(data, dict): - return {"jobs": []} - data.setdefault("jobs", []) - return data - - -def filter_jobs(jobs: List[Dict[str, Any]], name: str | None) -> List[Dict[str, Any]]: - if not name: - return jobs - filtered = [j for j in jobs if j.get("name") == name] - return filtered - - -def write_status(jobs: List[Dict[str, Any]]) -> None: - STATUS_DIR.mkdir(parents=True, exist_ok=True) - payload = { - "total_jobs": len(jobs), - "jobs": jobs, - "last_updated": None, - "succeeded": 0, - "failed": 0, - "running": 0, - "avg_duration": None, - } - STATUS_FILE.write_text(json.dumps(payload, indent=2), encoding="utf-8") - - -def main(argv: List[str] | None = None) -> int: - parser = argparse.ArgumentParser(description="Quantum AutoRun Orchestrator") - parser.add_argument( - "--config", type=str, default=str(DEFAULT_CONFIG), help="Path to YAML config" - ) - parser.add_argument("--list", action="store_true", help="List jobs as JSON") - parser.add_argument( - "--dry-run", action="store_true", help="Validate config and write status.json" - ) - parser.add_argument( - "--job", type=str, default=None, help="Filter to a specific job by name" - ) - args = parser.parse_args(argv) - - cfg_path = Path(args.config) - cfg = load_config(cfg_path) - jobs: List[Dict[str, Any]] = cfg.get("jobs", []) - - if args.job: - jobs = filter_jobs(jobs, args.job) - if not jobs: - msg = f"Job '{args.job}' not found" - # Print to stderr to satisfy test expectations - print(msg, file=sys.stderr) - return 1 - - if args.list: - print(json.dumps(jobs, indent=2)) - return 0 - - if args.dry_run: - write_status(jobs) - # Print summary to stdout for tests - names = ", ".join([j.get("name", "") for j in jobs]) - print(f"Validated {len(jobs)} job(s): {names}") - return 0 - - # No operation requested; show help and exit 0 - parser.print_help() - return 0 - - -if __name__ == "__main__": - sys.exit(main()) +"""Quantum AutoRun Orchestrator CLI + +Minimal CLI for validating and listing quantum jobs from a YAML config. +Implements: +- --help: prints usage and description +- --config: path to YAML config (defaults to config/quantum/quantum_autorun.yaml) +- --list: prints jobs as JSON to stdout +- --dry-run: validates config, writes data_out/quantum_autorun/status.json, prints summary +- --job NAME: filters to a specific job (non-zero exit if not found) + +Config schema (YAML): +jobs: + - name: heart_quick + preset: heart + epochs: 1 + n_qubits: 4 +""" + +from __future__ import annotations + +import argparse +import json +import sys +from dataclasses import dataclass, field +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List + +try: + from .config_paths import resolve_config_path +except ImportError: + from config_paths import resolve_config_path + +try: + import yaml # type: ignore +except Exception: # pragma: no cover + yaml = None # Tests will provide simple configs; fail gracefully if missing + + +REPO_ROOT = Path(__file__).resolve().parents[1] +DEFAULT_CONFIG = resolve_config_path(REPO_ROOT, "quantum_autorun") +STATUS_DIR = REPO_ROOT / "data_out" / "quantum_autorun" +STATUS_FILE = STATUS_DIR / "status.json" +DATA_OUT = REPO_ROOT / "data_out" + +# Paths for helper scripts used by jobs +TRAIN_SCRIPT = REPO_ROOT / "ai-projects" / "quantum-ml" / "train_custom_dataset.py" +AZURE_SUBMIT_SCRIPT = ( + REPO_ROOT / "ai-projects" / "quantum-ml" / "deploy_to_azure_quantum.py" +) + +# Known preset datasets +PRESETS = ("heart", "ionosphere", "sonar", "banknote") + + +@dataclass +class QJob: + """Dataclass representing a single quantum_autorun job definition. + + Tests expect a minimal set of attributes and stable defaults so this + class mirrors the structure used across the repo. + """ + + name: str + mode: str = "train_custom_dataset" + enabled: bool = True + + # Dataset / training args + preset: str | None = None + csv: str | None = None + label_col: str | None = None + drop_cols: str | None = None + epochs: int | None = None + batch_size: int | None = None + learning_rate: float | None = None + test_size: float | None = None + n_qubits: int | None = None + + # Extra arguments for CLI + extra_args: list[str] = field(default_factory=list) + + # Azure-specific + azure_backend: str | None = None + azure_shots: int | None = None + azure_confirm_cost: bool = False + + +# Backwards compatible alias expected by tests +def read_yaml(path: Path) -> Dict[str, Any]: + return load_config(path) + + +def load_jobs(path: Path) -> List[QJob]: + """Load jobs from a YAML path and return a list of QJob objects. + + This function accepts the same input used by the CLI and normalises + values into typed QJob instances. It handles both proper YAML parsing + (pyyaml) and the simple fallback loader used when pyyaml is missing. + """ + data = read_yaml(path) + jobs: List[QJob] = [] + for raw in data.get("jobs", []): + # Raw loader may return dicts with string values when PyYAML is not + # available; attempt sensible conversions. + def _get(key, default=None): + v = raw.get(key, default) + return v + + # Convert basic numeric types conservatively + def _int(v): + if v is None: + return None + try: + return int(v) + except Exception: + return None + + def _float(v): + if v is None: + return None + try: + return float(v) + except Exception: + return None + + j = QJob( + name=str(_get("name")), + mode=str(_get("mode", "train_custom_dataset")), + enabled=bool(_get("enabled", True)), + preset=_get("preset"), + csv=_get("csv"), + label_col=_get("label_col"), + drop_cols=_get("drop_cols"), + epochs=_int(_get("epochs")), + batch_size=_int(_get("batch_size")), + learning_rate=_float(_get("learning_rate")), + test_size=_float(_get("test_size")), + n_qubits=_int(_get("n_qubits")), + extra_args=_get("extra_args") or [], + azure_backend=_get("azure_backend"), + azure_shots=_int(_get("azure_shots")), + azure_confirm_cost=bool(_get("azure_confirm_cost", False)), + ) + jobs.append(j) + return jobs + + +def _python_executable() -> str: + """Return a usable python executable for subprocess commands.""" + return sys.executable or "python" + + +def build_command(job: QJob) -> List[str]: + """Build the command line (list form) to execute a QJob. + + The function returns an argument list appropriate for subprocess.run. + """ + py = _python_executable() + + if job.mode == "azure_hardware": + cmd = [py, str(AZURE_SUBMIT_SCRIPT)] + if job.azure_backend: + cmd.extend(["--backend", str(job.azure_backend)]) + if job.azure_shots is not None: + cmd.extend(["--shots", str(job.azure_shots)]) + # Tests check for this flag name specifically + if job.n_qubits is not None: + cmd.extend(["--n-qubits", str(job.n_qubits)]) + if job.extra_args: + cmd.extend(job.extra_args) + return cmd + + # Default: local training run + cmd = [py, str(TRAIN_SCRIPT)] + if job.preset: + cmd.extend(["--preset", str(job.preset)]) + if job.csv: + cmd.extend(["--csv", str(job.csv)]) + if job.label_col: + cmd.extend(["--label-col", str(job.label_col)]) + if job.drop_cols: + cmd.extend(["--drop-cols", str(job.drop_cols)]) + if job.epochs is not None: + cmd.extend(["--epochs", str(job.epochs)]) + if job.batch_size is not None: + cmd.extend(["--batch-size", str(job.batch_size)]) + if job.learning_rate is not None: + cmd.extend(["--learning-rate", str(job.learning_rate)]) + if job.test_size is not None: + cmd.extend(["--test-size", str(job.test_size)]) + if job.n_qubits is not None: + cmd.extend(["--n-qubits", str(job.n_qubits)]) + if job.extra_args: + cmd.extend(job.extra_args) + + return cmd + + +def validate_job(job: QJob) -> Dict[str, Any]: + """Validate a QJob definition and return a dict with status and missing items. + + The returned dict contains keys: status (ok/missing), missing (list). + """ + missing = [] + # Mode-specific checks + if job.mode == "train_custom_dataset": + if not TRAIN_SCRIPT.exists(): + missing.append(str(TRAIN_SCRIPT.name)) + + # Preset or CSV must be provided for training + if not job.preset and not job.csv: + missing.append("preset or csv") + + # If preset present, ensure it's known + if job.preset and job.preset not in PRESETS: + missing.append("Unknown preset: " + str(job.preset)) + + if job.csv: + p = Path(job.csv) + if not p.exists(): + missing.append(f"Missing CSV: {job.csv}") + + elif job.mode == "azure_hardware": + # Azure deployment script required + if not AZURE_SUBMIT_SCRIPT.exists(): + missing.append(str(AZURE_SUBMIT_SCRIPT.name)) + + # QPU requires explicit cost confirmation + if ( + job.azure_backend + and ".qpu" in str(job.azure_backend) + and not job.azure_confirm_cost + ): + missing.append("azure_confirm_cost") + + status = "ok" if not missing else "missing" + return {"status": status, "missing": missing} + + +def collect_status(jobs: List[Dict[str, Any]]) -> Dict[str, Any]: + """Construct status payload (in-memory) used by the CLI and tests.""" + return { + "generated_at": datetime.now().isoformat() + "Z", + "jobs": jobs, + "last_updated": None, + "succeeded": 0, + "failed": 0, + "running": 0, + "avg_duration": None, + } + + +def load_config(path: Path) -> Dict[str, Any]: + if not path.exists(): + return {"jobs": []} + if yaml is None: + # Minimal YAML loader fallback: handle a very small subset for tests + # Prefer PyYAML when available. + text = path.read_text(encoding="utf-8") + # Extremely simple parse: find lines under jobs: and collect name/preset/etc + jobs: List[Dict[str, Any]] = [] + current: Dict[str, Any] = {} + in_jobs = False + for line in text.splitlines(): + s = line.strip() + if s.startswith("jobs:"): + in_jobs = True + continue + if in_jobs and s.startswith("-"): + if current: + jobs.append(current) + current = {} + continue + if in_jobs and ":" in s: + k, v = s.split(":", 1) + current[k.strip()] = v.strip() + if current: + jobs.append(current) + return {"jobs": jobs} + # Normal path: use safe_load + data = yaml.safe_load(path.read_text(encoding="utf-8")) or {} + if not isinstance(data, dict): + return {"jobs": []} + data.setdefault("jobs", []) + return data + + +def filter_jobs(jobs: List[Dict[str, Any]], name: str | None) -> List[Dict[str, Any]]: + if not name: + return jobs + filtered = [j for j in jobs if j.get("name") == name] + return filtered + + +def write_status(jobs: List[Dict[str, Any]]) -> None: + STATUS_DIR.mkdir(parents=True, exist_ok=True) + payload = { + "total_jobs": len(jobs), + "jobs": jobs, + "last_updated": None, + "succeeded": 0, + "failed": 0, + "running": 0, + "avg_duration": None, + } + STATUS_FILE.write_text(json.dumps(payload, indent=2), encoding="utf-8") + + +def main(argv: List[str] | None = None) -> int: + parser = argparse.ArgumentParser(description="Quantum AutoRun Orchestrator") + parser.add_argument( + "--config", type=str, default=str(DEFAULT_CONFIG), help="Path to YAML config" + ) + parser.add_argument("--list", action="store_true", help="List jobs as JSON") + parser.add_argument( + "--dry-run", action="store_true", help="Validate config and write status.json" + ) + parser.add_argument( + "--job", type=str, default=None, help="Filter to a specific job by name" + ) + args = parser.parse_args(argv) + + cfg_path = Path(args.config) + cfg = load_config(cfg_path) + jobs: List[Dict[str, Any]] = cfg.get("jobs", []) + + if args.job: + jobs = filter_jobs(jobs, args.job) + if not jobs: + msg = f"Job '{args.job}' not found" + # Print to stderr to satisfy test expectations + print(msg, file=sys.stderr) + return 1 + + if args.list: + print(json.dumps(jobs, indent=2)) + return 0 + + if args.dry_run: + write_status(jobs) + # Print summary to stdout for tests + names = ", ".join([j.get("name", "") for j in jobs]) + print(f"Validated {len(jobs)} job(s): {names}") + return 0 + + # No operation requested; show help and exit 0 + parser.print_help() + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/quantum_llm_trainer.py b/scripts/quantum_llm_trainer.py index f532070ce..126167e50 100644 --- a/scripts/quantum_llm_trainer.py +++ b/scripts/quantum_llm_trainer.py @@ -1,1341 +1,1341 @@ -""" -Quantum-Enhanced LLM Training Module -===================================== - -Trains a QuantumLLM (transformer with real quantum circuits in attention and -feed-forward layers) on character-level language modeling tasks. - -Features: -- Real quantum circuits via PennyLane in attention and FFN layers -- Character-level dataset for proof-of-concept training -- Gradient backpropagation through quantum circuits -- Passive background training mode -- Classical fallback when quantum libraries unavailable - -Usage: - # Train on a text file - python quantum_llm_trainer.py --dataset path/to/text_or_json - - # Train with custom architecture - python quantum_llm_trainer.py --dataset path/to/data --n-qubits 4 --d-model 64 - - # Passive mode (background training) - python quantum_llm_trainer.py --passive --interval 3600 - -Author: Quantum AI Workspace -""" - -import argparse -import json -import logging -import sys -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List, Optional - -import numpy as np -import torch -import torch.nn as nn -import yaml -from torch.utils.data import DataLoader, Dataset - -REPO_ROOT = Path(__file__).resolve().parent.parent -DEFAULT_OUTPUT_DIR = REPO_ROOT / "data_out" / "quantum_llm_training" -DEFAULT_STATUS_FILE = DEFAULT_OUTPUT_DIR / "status.json" -CHECKPOINT_FILENAMES = ( - "best_quantum_llm.pt", - "quantum_llm_checkpoint.pt", - "final_model.pt", -) - -# Add ai-projects/quantum-ml to path -quantum_ml_path = Path(__file__).parent.parent / "ai-projects" / "quantum-ml" -quantum_ml_src = quantum_ml_path / "src" -for p in [str(quantum_ml_path), str(quantum_ml_src)]: - if p not in sys.path: - sys.path.insert(0, p) - -try: - from quantum_transformer import QUANTUM_AVAILABLE, QuantumLLM -except ImportError as e: - logging.warning(f"QuantumLLM not available: {e}") - QUANTUM_AVAILABLE = False - -try: - from hybrid_qnn import QuantumLayer - - QUANTUM_LAYER_AVAILABLE = True -except ImportError: - QUANTUM_LAYER_AVAILABLE = False - -# Setup logging -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" -) -logger = logging.getLogger(__name__) - - -def _resolve_repo_path(path_value: str | Path | None, *, default: Path) -> Path: - """Resolve repo-relative paths deterministically.""" - if path_value is None: - return default - - path = Path(path_value) - if not path.is_absolute(): - path = REPO_ROOT / path - return path - - -def _repo_relative_str(path_value: str | Path | None) -> str | None: - """Return a repo-relative string when possible to keep status JSON portable.""" - if path_value is None: - return None - - path = Path(path_value) - try: - return str(path.resolve().relative_to(REPO_ROOT.resolve())) - except Exception: - return str(path) - - -def _normalise_checkpoint_reference( - base_dir: Path, checkpoint_ref: str | Path | None -) -> Path | None: - """Resolve a checkpoint path from status metadata or direct references.""" - if not checkpoint_ref: - return None - - candidate = Path(checkpoint_ref) - if not candidate.is_absolute(): - direct_candidate = base_dir / candidate - if direct_candidate.exists(): - candidate = direct_candidate - else: - candidate = REPO_ROOT / candidate - return candidate - - -def get_quantum_llm_status( - *, - status_file: str | Path | None = None, - output_dir: str | Path | None = None, -) -> Dict[str, Any]: - """Load Quantum LLM runtime status and checkpoint readiness metadata.""" - resolved_output_dir = _resolve_repo_path(output_dir, default=DEFAULT_OUTPUT_DIR) - resolved_status_file = _resolve_repo_path( - status_file, - default=resolved_output_dir / "status.json", - ) - - payload: Dict[str, Any] = { - "available": False, - "status": "not_started", - "output_dir": _repo_relative_str(resolved_output_dir), - "status_file": _repo_relative_str(resolved_status_file), - "checkpoint_path": None, - "best_checkpoint_path": None, - "checkpoint_exists": False, - "inference_ready": False, - "quantum_available": QUANTUM_AVAILABLE, - "epochs_completed": 0, - "best_loss": None, - "final_loss": None, - "last_updated": None, - "last_error": None, - "passive_mode": False, - "mode": None, - } - - if resolved_status_file.exists(): - try: - with open(resolved_status_file, "r", encoding="utf-8") as status_handle: - existing = json.load(status_handle) - if isinstance(existing, dict): - payload.update(existing) - payload["available"] = True - except Exception as exc: # noqa: BLE001 - payload.update( - { - "status": "error", - "available": False, - "last_error": f"Failed to read status file: {exc}", - } - ) - - checkpoint_ref = ( - payload.get("best_checkpoint_path") - or payload.get("checkpoint_path") - or payload.get("last_checkpoint_path") - ) - checkpoint_path = _normalise_checkpoint_reference( - resolved_output_dir, checkpoint_ref - ) - if checkpoint_path is None: - for checkpoint_name in CHECKPOINT_FILENAMES: - candidate = resolved_output_dir / checkpoint_name - if candidate.exists(): - checkpoint_path = candidate - break - - if checkpoint_path is not None: - payload["checkpoint_path"] = _repo_relative_str(checkpoint_path) - payload["checkpoint_exists"] = checkpoint_path.exists() - payload["inference_ready"] = bool( - checkpoint_path.exists() - and payload.get("status") in {"completed", "running", "idle"} - ) - - payload["status_file_exists"] = resolved_status_file.exists() - return payload - - -def write_quantum_llm_status( - data: Dict[str, Any], - *, - status_file: str | Path | None = None, - output_dir: str | Path | None = None, -) -> Dict[str, Any]: - """Persist Quantum LLM status metadata in a repo-consistent JSON artifact.""" - resolved_output_dir = _resolve_repo_path(output_dir, default=DEFAULT_OUTPUT_DIR) - resolved_status_file = _resolve_repo_path( - status_file, - default=resolved_output_dir / "status.json", - ) - resolved_status_file.parent.mkdir(parents=True, exist_ok=True) - - payload = { - "output_dir": _repo_relative_str(resolved_output_dir), - "status_file": _repo_relative_str(resolved_status_file), - "last_updated": datetime.now().isoformat(), - } - payload.update(data) - - for path_key in ( - "dataset_path", - "checkpoint_path", - "best_checkpoint_path", - "last_checkpoint_path", - "results_file", - "last_cycle_output_dir", - ): - if payload.get(path_key): - payload[path_key] = _repo_relative_str(payload[path_key]) - - with open(resolved_status_file, "w", encoding="utf-8") as status_handle: - json.dump(payload, status_handle, indent=2) - - return payload - - -# --------------------------------------------------------------------------- -# Character-level dataset -# --------------------------------------------------------------------------- - - -class CharacterDataset(Dataset): - """Character-level dataset for quantum LLM training. - - Reads text, builds a character vocabulary, and produces overlapping - windows of (input_ids, target_ids) where target is shifted by one - position (standard next-token prediction). - """ - - def __init__(self, text: str, seq_len: int = 32, vocab_size: int = 1000): - self.seq_len = seq_len - - # Build vocabulary from the text (up to vocab_size unique chars) - unique_chars = sorted(set(text)) - if len(unique_chars) > vocab_size - 1: - unique_chars = unique_chars[: vocab_size - 1] - - self.char_to_id = {c: i + 1 for i, c in enumerate(unique_chars)} - self.char_to_id["\x00"] = 0 # padding / unknown - self.id_to_char = {v: k for k, v in self.char_to_id.items()} - self.actual_vocab_size = len(self.char_to_id) - - # Encode entire text - self.encoded = [self.char_to_id.get(c, 0) for c in text] - - # We need at least seq_len + 1 characters for one sample - if len(self.encoded) < seq_len + 1: - # Pad with zeros if text is too short - self.encoded = self.encoded + [0] * (seq_len + 1 - len(self.encoded)) - - logger.info( - f"CharacterDataset: {len(text)} chars, " - f"{self.actual_vocab_size} unique tokens, " - f"{len(self)} samples (seq_len={seq_len})" - ) - - def __len__(self) -> int: - return max(1, len(self.encoded) - self.seq_len) - - def __getitem__(self, idx: int): - chunk = self.encoded[idx : idx + self.seq_len + 1] - input_ids = torch.tensor(chunk[:-1], dtype=torch.long) - target_ids = torch.tensor(chunk[1:], dtype=torch.long) - return input_ids, target_ids - - def decode(self, ids) -> str: - """Convert token ids back to text.""" - if isinstance(ids, torch.Tensor): - ids = ids.tolist() - return "".join(self.id_to_char.get(i, "?") for i in ids) - - -# --------------------------------------------------------------------------- -# Feature encoder (kept for auxiliary use, uses real QuantumLayer) -# --------------------------------------------------------------------------- - - -class QuantumFeatureEncoder: - """Encodes classical features into quantum-enhanced representations.""" - - def __init__(self, n_qubits: int = 4, n_layers: int = 2): - self.n_qubits = n_qubits - self.n_layers = n_layers - self.quantum_layer = None - - if QUANTUM_LAYER_AVAILABLE: - try: - self.quantum_layer = QuantumLayer( - n_qubits=n_qubits, - n_layers=n_layers, - device="default.qubit", - entanglement="circular", - ) - logger.info("Initialized quantum feature encoder") - except Exception as e: - logger.warning(f"Failed to initialize quantum layer: {e}") - - def encode(self, features: torch.Tensor) -> torch.Tensor: - if self.quantum_layer is None: - return torch.tanh(features) - - try: - batch_size, feature_dim = features.shape - quantum_dim = 2**self.n_qubits - if feature_dim < quantum_dim: - padded = torch.zeros(batch_size, quantum_dim, device=features.device) - padded[:, :feature_dim] = features - features = padded - elif feature_dim > quantum_dim: - features = features[:, :quantum_dim] - - features_norm = features / ( - torch.norm(features, dim=1, keepdim=True) + 1e-8 - ) - return self.quantum_layer(features_norm) - except Exception as e: - logger.warning(f"Quantum encoding failed: {e}, using classical fallback") - return torch.tanh(features) - - -class QuantumAttentionOptimizer: - """Optimizes transformer attention weights using quantum circuits. - - When PennyLane is available, attention scores are processed through a - variational quantum circuit that can learn non-linear feature interactions. - Falls back to classical softmax normalization when quantum libs are absent. - """ - - def __init__(self, n_qubits: int = 4, n_layers: int = 2): - self.n_qubits = n_qubits - self.n_layers = n_layers - self._quantum_layer = None - - if QUANTUM_LAYER_AVAILABLE: - try: - self._quantum_layer = QuantumLayer( - n_qubits=n_qubits, - n_layers=n_layers, - device="default.qubit", - entanglement="circular", - ) - logger.info("Initialized QuantumAttentionOptimizer") - except Exception as e: - logger.warning(f"Failed to initialize quantum attention optimizer: {e}") - - @staticmethod - def _resize_quantum_output( - quantum_output: torch.Tensor, - target_len: int, - *, - dtype: torch.dtype, - device: torch.device, - ) -> torch.Tensor: - """Resize quantum output back to the chunk length. - - QuantumLayer returns expectation values per qubit, which is often much - shorter than the flattened attention chunk fed into amplitude encoding. - Expand the returned vector deterministically so the original attention - tensor shape is always preserved. - """ - flat = quantum_output.reshape(-1).to(device=device, dtype=dtype) - if target_len <= 0: - return flat[:0] - if flat.numel() == 0: - return torch.zeros(target_len, dtype=dtype, device=device) - if flat.numel() == target_len: - return flat - - repeats = (target_len + flat.numel() - 1) // flat.numel() - return flat.repeat(repeats)[:target_len] - - def optimize_attention_weights( - self, attention_scores: torch.Tensor - ) -> torch.Tensor: - """Apply quantum optimization to attention scores. - - Args: - attention_scores: Tensor of any shape containing attention logits. - - Returns: - Tensor with the same shape, quantum-processed or softmax-normalised. - """ - if self._quantum_layer is None: - return torch.softmax(attention_scores.float(), dim=-1) - - try: - orig_shape = attention_scores.shape - flat = attention_scores.reshape(-1).float().detach() - q_dim = 2**self.n_qubits - - chunks = flat.split(q_dim) - processed = [] - for chunk in chunks: - chunk_len = chunk.shape[0] - padded = torch.zeros(q_dim, dtype=flat.dtype, device=flat.device) - padded[:chunk_len] = chunk - normed = padded / (padded.norm() + 1e-8) - out = self._quantum_layer(normed.unsqueeze(0)).squeeze(0) - processed.append( - self._resize_quantum_output( - out, - chunk_len, - dtype=flat.dtype, - device=flat.device, - ) - ) - - result = torch.cat(processed).reshape(orig_shape) - return result - except Exception as e: - logger.warning( - f"Quantum attention optimization failed: {e}, using classical fallback" - ) - return torch.softmax(attention_scores.float(), dim=-1) - - -# --------------------------------------------------------------------------- -# Main trainer -# --------------------------------------------------------------------------- - - -class QuantumEnhancedLLMTrainer: - """ - Trains a QuantumLLM on character-level language modeling. - - The QuantumLLM uses real PennyLane quantum circuits inside its - transformer blocks (QuantumSelfAttention and QuantumFeedForward). - Gradients flow through the quantum circuits via PennyLane's - torch interface. - """ - - def __init__(self, config: Dict[str, Any]): - self.config = config - self.passive_mode = config.get("passive", False) - self.interval = config.get("interval", 3600) - output_config = ( - config.get("output", {}) if isinstance(config.get("output"), dict) else {} - ) - integration_config = ( - config.get("autonomous_integration", {}) - if isinstance(config.get("autonomous_integration"), dict) - else {} - ) - self.default_output_dir = _resolve_repo_path( - output_config.get("save_dir") or config.get("output_dir"), - default=DEFAULT_OUTPUT_DIR, - ) - self.status_file = _resolve_repo_path( - integration_config.get("status_file") or config.get("status_file"), - default=self.default_output_dir / "status.json", - ) - - # Top-level attributes expected by tests and external callers - self.quantum_backend = config.get("quantum_backend", "local") - self.n_qubits = config.get("n_qubits", 4) - self.n_layers = config.get("n_quantum_layers", 2) - - # Device - use_gpu = config.get("use_gpu", True) - self.device = torch.device( - "cuda" if torch.cuda.is_available() and use_gpu else "cpu" - ) - - # Build quantum transformer config (supports both flat and nested layouts) - qt_config = config.get("quantum_transformer", {}) - self.model_config = { - "vocab_size": qt_config.get("vocab_size", config.get("vocab_size", 256)), - "d_model": qt_config.get("d_model", config.get("d_model", 64)), - "n_heads": qt_config.get("n_heads", config.get("n_heads", 4)), - "n_transformer_layers": qt_config.get( - "n_transformer_layers", config.get("n_transformer_layers", 2) - ), - "n_qubits": qt_config.get("n_qubits", config.get("n_qubits", 4)), - "n_quantum_layers": qt_config.get( - "n_quantum_layers", config.get("n_quantum_layers", 2) - ), - "max_seq_len": qt_config.get("max_seq_len", config.get("max_seq_len", 32)), - "entanglement": qt_config.get( - "entanglement", config.get("entanglement", "circular") - ), - "dropout": qt_config.get("dropout", config.get("dropout", 0.1)), - "use_quantum_attention": qt_config.get("use_quantum_attention", True), - "use_quantum_ffn": qt_config.get("use_quantum_ffn", True), - "tie_embeddings": qt_config.get("tie_embeddings", True), - } - - # Create model - self.model = QuantumLLM.from_config({"quantum_transformer": self.model_config}) - self.model = self.model.to(self.device) - - # Optimizer - lr = qt_config.get("learning_rate", config.get("learning_rate", 0.001)) - wd = qt_config.get("weight_decay", config.get("weight_decay", 0.01)) - self.optimizer = torch.optim.AdamW( - self.model.parameters(), lr=lr, weight_decay=wd - ) - - # Loss and gradient clipping - self.criterion = nn.CrossEntropyLoss() - self.grad_clip = qt_config.get( - "gradient_clip", config.get("gradient_clip", 1.0) - ) - self.batch_size = qt_config.get("batch_size", config.get("batch_size", 4)) - - # Learning rate scheduler - self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - self.optimizer, mode="min", factor=0.5, patience=3 - ) - - # Feature encoder (auxiliary) - self.feature_encoder = QuantumFeatureEncoder( - n_qubits=self.model_config["n_qubits"], - n_layers=self.model_config["n_quantum_layers"], - ) - - # Quantum attention optimizer (used by _train_epoch_with_quantum) - self.attention_optimizer = QuantumAttentionOptimizer( - n_qubits=self.model_config["n_qubits"], - n_layers=self.model_config["n_quantum_layers"], - ) - - # Metrics - self.training_history: List[Dict] = [] - self.quantum_metrics = { - "circuit_executions": 0, - "optimization_steps": 0, - "quantum_available": QUANTUM_AVAILABLE, - } - - n_params = sum(p.numel() for p in self.model.parameters()) - logger.info("Initialized QuantumEnhancedLLMTrainer") - logger.info(f" Device: {self.device}") - logger.info(f" Parameters: {n_params:,}") - logger.info(f" Quantum available: {QUANTUM_AVAILABLE}") - logger.info(f" Model config: {self.model_config}") - - def _save_checkpoint( - self, - checkpoint_path: Path, - *, - epoch: int, - loss: float, - training_mode: str, - ) -> Path: - """Persist a Quantum LLM checkpoint with enough metadata for inference.""" - checkpoint_path.parent.mkdir(parents=True, exist_ok=True) - torch.save( - { - "model_state_dict": self.model.state_dict(), - "optimizer_state_dict": self.optimizer.state_dict(), - "epoch": epoch, - "loss": loss, - "model_config": self.model_config, - "training_mode": training_mode, - "quantum_metrics": self.quantum_metrics, - "training_history": self.training_history, - "saved_at": datetime.now().isoformat(), - }, - checkpoint_path, - ) - return checkpoint_path - - # ------------------------------------------------------------------ - # Dataset loading - # ------------------------------------------------------------------ - - def _extract_text(self, dataset_path: Path) -> str: - """Extract raw text from various file formats.""" - text_parts = [] - - if dataset_path.is_file(): - if dataset_path.suffix == ".txt": - text_parts.append(dataset_path.read_text(errors="replace")) - - elif dataset_path.suffix == ".jsonl": - with open(dataset_path) as f: - for line in f: - line = line.strip() - if not line: - continue - record = json.loads(line) - for key in ("text", "content", "message", "input", "output"): - if key in record: - text_parts.append(str(record[key])) - - elif dataset_path.suffix == ".json": - with open(dataset_path) as f: - try: - data = json.load(f) - records = data if isinstance(data, list) else [data] - except json.JSONDecodeError: - # File may be JSONL-formatted despite .json extension - f.seek(0) - records = [json.loads(ln) for ln in f if ln.strip()] - for record in records: - if isinstance(record, dict): - for key in ( - "text", - "content", - "message", - "input", - "output", - ): - if key in record: - text_parts.append(str(record[key])) - elif isinstance(record, str): - text_parts.append(record) - - elif dataset_path.is_dir(): - for pattern in ["*.txt", "*.json", "*.jsonl"]: - for fp in sorted(dataset_path.glob(pattern)): - text_parts.append(self._extract_text(fp)) - for subdir_name in ["train.json", "train.jsonl"]: - sub = dataset_path / subdir_name - if sub.exists(): - text_parts.append(self._extract_text(sub)) - - combined = "\n".join(text_parts) - if not combined.strip(): - # Generate a small synthetic corpus so training can still run - logger.warning( - "No text extracted from dataset -- using synthetic placeholder text" - ) - combined = ( - "The quick brown fox jumps over the lazy dog. " - "Pack my box with five dozen liquor jugs. " - "How vexingly quick daft zebras jump. " - ) * 50 - - return combined - - def _make_dataloader(self, dataset_path: Path) -> DataLoader: - """Build a DataLoader from a dataset path.""" - text = self._extract_text(dataset_path) - dataset = CharacterDataset( - text=text, - seq_len=self.model_config["max_seq_len"], - vocab_size=self.model_config["vocab_size"], - ) - return DataLoader( - dataset, - batch_size=self.batch_size, - shuffle=True, - drop_last=True, - ) - - # ------------------------------------------------------------------ - # Training loop - # ------------------------------------------------------------------ - - def _estimate_circuit_evals(self, batch_size: int, seq_len: int) -> int: - """Estimate the number of quantum circuit evaluations per batch.""" - if not QUANTUM_AVAILABLE: - return 0 - n_heads = self.model_config["n_heads"] - n_blocks = self.model_config["n_transformer_layers"] - # Attention: 2 maps (Q, K) * batch*seq per head per block - attn_evals = 2 * batch_size * seq_len * n_heads * n_blocks - # FFN: 1 map * batch*seq per block - ffn_evals = batch_size * seq_len * n_blocks - return attn_evals + ffn_evals - - def _train_epoch(self, dataloader: DataLoader, epoch: int) -> float: - """Train one epoch with real forward/backward through quantum circuits.""" - self.model.train() - total_loss = 0.0 - num_batches = 0 - - for batch_idx, (input_ids, targets) in enumerate(dataloader): - input_ids = input_ids.to(self.device) - targets = targets.to(self.device) - - # Forward pass (gradients flow through quantum circuits) - logits = self.model(input_ids) - - # Cross-entropy loss - loss = self.criterion( - logits.view(-1, self.model.vocab_size), - targets.view(-1), - ) - - # Backward pass - self.optimizer.zero_grad() - loss.backward() - - # Gradient clipping - if self.grad_clip > 0: - torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_clip) - - self.optimizer.step() - - batch_loss = loss.item() - total_loss += batch_loss - num_batches += 1 - - # Track quantum circuit evaluations - evals = self._estimate_circuit_evals(input_ids.shape[0], input_ids.shape[1]) - self.quantum_metrics["circuit_executions"] += evals - self.quantum_metrics["optimization_steps"] += 1 - - if batch_idx % 5 == 0: - logger.info( - f" Epoch {epoch+1} | Batch {batch_idx}/{len(dataloader)} | " - f"Loss: {batch_loss:.4f} | " - f"Circuit evals: {evals}" - ) - - avg_loss = total_loss / max(num_batches, 1) - self.training_history.append( - { - "epoch": epoch, - "loss": avg_loss, - "circuit_executions": self.quantum_metrics["circuit_executions"], - "lr": self.optimizer.param_groups[0]["lr"], - } - ) - - # Update scheduler - self.scheduler.step(avg_loss) - - return avg_loss - - def train_with_quantum_enhancement( - self, - dataset_path: Path, - output_dir: Path, - epochs: int = 3, - model: Optional[Any] = None, - ) -> Dict[str, Any]: - """ - Train the QuantumLLM on a dataset. - - Args: - dataset_path: Path to training data (text, json, or jsonl) - output_dir: Directory for results and checkpoints - epochs: Number of training epochs - - Returns: - Training results and quantum metrics - """ - logger.info("Starting quantum-enhanced LLM training") - logger.info(f" Dataset: {dataset_path}") - logger.info(f" Output: {output_dir}") - logger.info(f" Epochs: {epochs}") - logger.info(f" Mode: {'simulated' if model is None else 'real'}") - - output_dir = Path(output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - status_file = output_dir / "status.json" - dataset_path = Path(dataset_path) - training_mode = "simulated" if model is None else "real" - - results = { - "status": "success", - "epochs_completed": 0, - "final_loss": 0.0, - "quantum_metrics": self.quantum_metrics, - "model_config": self.model_config, - "started_at": datetime.now().isoformat(), - } - - best_loss = float("inf") - best_checkpoint_path: Path | None = None - final_checkpoint_path: Path | None = None - - write_quantum_llm_status( - { - "available": True, - "status": "running", - "mode": training_mode, - "passive_mode": self.passive_mode, - "dataset_path": dataset_path, - "epochs_requested": epochs, - "epochs_completed": 0, - "quantum_available": QUANTUM_AVAILABLE, - "best_loss": None, - "final_loss": None, - "last_error": None, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - "started_at": results["started_at"], - }, - status_file=status_file, - output_dir=output_dir, - ) - - try: - if model is None: - # Simulated quantum training path -- uses _train_epoch_with_quantum - # which applies the quantum attention optimizer each step. - dataset = self._load_dataset(dataset_path) - for epoch in range(epochs): - logger.info(f"\n--- Epoch {epoch + 1}/{epochs} (simulated) ---") - epoch_loss = self._train_epoch_with_quantum(None, dataset, epoch) - results["epochs_completed"] = epoch + 1 - results["final_loss"] = epoch_loss - if epoch_loss < best_loss: - best_loss = epoch_loss - best_checkpoint_path = self._save_checkpoint( - output_dir / "best_quantum_llm.pt", - epoch=epoch, - loss=epoch_loss, - training_mode=training_mode, - ) - logger.info( - f" Epoch {epoch+1} complete | Avg Loss: {epoch_loss:.4f}" - ) - write_quantum_llm_status( - { - "available": True, - "status": "running", - "mode": training_mode, - "passive_mode": self.passive_mode, - "dataset_path": dataset_path, - "epochs_requested": epochs, - "epochs_completed": results["epochs_completed"], - "best_loss": ( - None if best_loss == float("inf") else best_loss - ), - "final_loss": epoch_loss, - "best_checkpoint_path": best_checkpoint_path, - "quantum_available": QUANTUM_AVAILABLE, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - "started_at": results["started_at"], - }, - status_file=status_file, - output_dir=output_dir, - ) - else: - # Real model training path -- builds DataLoader and runs full backprop - dataloader = self._make_dataloader(dataset_path) - logger.info(f" Batches per epoch: {len(dataloader)}") - for epoch in range(epochs): - logger.info(f"\n--- Epoch {epoch + 1}/{epochs} ---") - epoch_loss = self._train_epoch(dataloader, epoch) - results["epochs_completed"] = epoch + 1 - results["final_loss"] = epoch_loss - logger.info( - f" Epoch {epoch+1} complete | Avg Loss: {epoch_loss:.4f} | " - f"LR: {self.optimizer.param_groups[0]['lr']:.6f}" - ) - if epoch_loss < best_loss: - best_loss = epoch_loss - best_checkpoint_path = self._save_checkpoint( - output_dir / "best_quantum_llm.pt", - epoch=epoch, - loss=epoch_loss, - training_mode=training_mode, - ) - logger.info(f" Saved best checkpoint: {best_checkpoint_path}") - write_quantum_llm_status( - { - "available": True, - "status": "running", - "mode": training_mode, - "passive_mode": self.passive_mode, - "dataset_path": dataset_path, - "epochs_requested": epochs, - "epochs_completed": results["epochs_completed"], - "best_loss": ( - None if best_loss == float("inf") else best_loss - ), - "final_loss": epoch_loss, - "best_checkpoint_path": best_checkpoint_path, - "quantum_available": QUANTUM_AVAILABLE, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - "started_at": results["started_at"], - }, - status_file=status_file, - output_dir=output_dir, - ) - self._generate_sample(output_dir, dataloader.dataset) - - final_checkpoint_path = self._save_checkpoint( - output_dir / "final_model.pt", - epoch=max(results["epochs_completed"] - 1, 0), - loss=float(results["final_loss"]), - training_mode=training_mode, - ) - - results["completed_at"] = datetime.now().isoformat() - results["best_loss"] = best_loss - results["checkpoint_path"] = str( - best_checkpoint_path or final_checkpoint_path - ) - - # Save results JSON - results_file = output_dir / "quantum_training_results.json" - with open(results_file, "w", encoding="utf-8") as f: - json.dump(results, f, indent=2) - logger.info(f"\nTraining complete! Results saved to: {results_file}") - - write_quantum_llm_status( - { - "available": True, - "status": "completed", - "mode": training_mode, - "passive_mode": self.passive_mode, - "dataset_path": dataset_path, - "epochs_requested": epochs, - "epochs_completed": results["epochs_completed"], - "best_loss": best_loss, - "final_loss": results["final_loss"], - "checkpoint_path": best_checkpoint_path or final_checkpoint_path, - "best_checkpoint_path": best_checkpoint_path - or final_checkpoint_path, - "last_checkpoint_path": final_checkpoint_path, - "quantum_available": QUANTUM_AVAILABLE, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - "results_file": results_file, - "started_at": results["started_at"], - "completed_at": results["completed_at"], - }, - status_file=status_file, - output_dir=output_dir, - ) - - return results - except Exception as exc: - write_quantum_llm_status( - { - "available": True, - "status": "failed", - "mode": training_mode, - "passive_mode": self.passive_mode, - "dataset_path": dataset_path, - "epochs_requested": epochs, - "epochs_completed": results["epochs_completed"], - "best_loss": None if best_loss == float("inf") else best_loss, - "final_loss": results["final_loss"], - "best_checkpoint_path": best_checkpoint_path, - "last_checkpoint_path": final_checkpoint_path, - "quantum_available": QUANTUM_AVAILABLE, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - "started_at": results["started_at"], - "last_error": str(exc), - }, - status_file=status_file, - output_dir=output_dir, - ) - raise - - def _load_dataset(self, dataset_path: Path) -> List[Dict[str, Any]]: - """Load training dataset from JSONL or JSON format.""" - dataset = [] - - if dataset_path.is_file(): - if dataset_path.suffix == ".jsonl": - with open(dataset_path) as f: - for line in f: - if line.strip(): - dataset.append(json.loads(line)) - elif dataset_path.suffix == ".json": - with open(dataset_path) as f: - try: - data = json.load(f) - dataset = data if isinstance(data, list) else [data] - except json.JSONDecodeError: - # File may be JSONL-formatted despite .json extension - f.seek(0) - dataset = [json.loads(ln) for ln in f if ln.strip()] - elif dataset_path.is_dir(): - # Look for train files using glob for efficiency - train_files = list(dataset_path.glob("train.json")) + list( - dataset_path.glob("train.jsonl") - ) - if train_files: - return self._load_dataset(train_files[0]) - - return dataset - - def _train_epoch_with_quantum( - self, model: Optional[Any], dataset: List[Dict[str, Any]], epoch: int - ) -> float: - """ - Train one epoch with quantum enhancement. - - Returns: - Average loss for the epoch - """ - total_loss = 0.0 - num_batches = max(1, len(dataset) // 32) - - # Base loss decreases each epoch to simulate convergence - base_loss = max(0.3, 1.0 - epoch * 0.15) - - for batch_idx in range(num_batches): - # Simulate forward pass with deterministic downward trend per epoch - batch_loss = base_loss + np.random.uniform(-0.1, 0.1) - - # Apply quantum optimization every N steps - if batch_idx % 10 == 0: - # Quantum-enhanced optimization step - mock_attention = torch.randn(1, 8, 8) - optimized = self.attention_optimizer.optimize_attention_weights( - mock_attention - ) - self.quantum_metrics["circuit_executions"] += 1 - self.quantum_metrics["optimization_steps"] += 1 - - # Simulate quantum advantage (small improvement) - batch_loss *= 0.98 - - total_loss += batch_loss - - avg_loss = total_loss / num_batches - self.training_history.append( - { - "epoch": epoch, - "loss": avg_loss, - "quantum_executions": self.quantum_metrics["circuit_executions"], - } - ) - - return avg_loss - - def _generate_sample(self, output_dir: Path, dataset: CharacterDataset): - """Generate a sample from the trained model.""" - try: - prompt_ids = torch.tensor( - [[1, 2, 3, 4]], dtype=torch.long, device=self.device - ) - generated = self.model.generate( - prompt_ids, max_new_tokens=50, temperature=0.8, top_k=20 - ) - text = dataset.decode(generated[0]) - logger.info(f" Sample generation: {text[:100]}...") - - sample_path = output_dir / "generated_sample.txt" - sample_path.write_text(text) - except Exception as e: - logger.warning(f"Sample generation failed: {e}") - - # ------------------------------------------------------------------ - # Passive training - # ------------------------------------------------------------------ - - def run_passive_training(self): - """Run in passive mode -- continuous background training cycles.""" - logger.info("Starting passive quantum-enhanced LLM training") - logger.info(f" Interval: {self.interval} seconds") - - import signal - import time - - self.running = True - - def signal_handler(sig, frame): - logger.info("Received shutdown signal") - self.running = False - - signal.signal(signal.SIGINT, signal_handler) - signal.signal(signal.SIGTERM, signal_handler) - - cycle_count = 0 - write_quantum_llm_status( - { - "available": True, - "status": "idle", - "mode": "passive", - "passive_mode": True, - "current_cycle": cycle_count, - "last_error": None, - "quantum_available": QUANTUM_AVAILABLE, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - }, - status_file=self.status_file, - output_dir=self.default_output_dir, - ) - - while self.running: - cycle_count += 1 - logger.info(f"\n=== Passive Training Cycle {cycle_count} ===") - - try: - write_quantum_llm_status( - { - "available": True, - "status": "running", - "mode": "passive", - "passive_mode": True, - "current_cycle": cycle_count, - "last_error": None, - "quantum_available": QUANTUM_AVAILABLE, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - }, - status_file=self.status_file, - output_dir=self.default_output_dir, - ) - - # Look for available datasets using combined glob pattern - datasets_dir = Path("datasets/chat") - if datasets_dir.exists(): - # Use explicit patterns to match only train.json and train.jsonl - dataset_files = list(datasets_dir.glob("*/train.json")) + list( - datasets_dir.glob("*/train.jsonl") - ) - - dataset_files = ( - list(datasets_dir.glob("*/train.json")) - + list(datasets_dir.glob("*/train.jsonl")) - + list(datasets_dir.glob("*.txt")) - ) - - if dataset_files: - import random - - dataset_path = random.choice(dataset_files) - logger.info(f"Selected dataset: {dataset_path}") - - output_dir = ( - Path("data_out/quantum_llm_training") - / f"cycle_{cycle_count}" - ) - - results = self.train_with_quantum_enhancement( - dataset_path=dataset_path, - output_dir=output_dir, - epochs=1, - ) - logger.info( - f"Cycle {cycle_count} complete: " - f"Loss={results['final_loss']:.4f}" - ) - write_quantum_llm_status( - { - "available": True, - "status": "idle", - "mode": "passive", - "passive_mode": True, - "current_cycle": cycle_count, - "dataset_path": dataset_path, - "epochs_completed": results.get("epochs_completed", 0), - "best_loss": results.get("best_loss"), - "final_loss": results.get("final_loss"), - "checkpoint_path": results.get("checkpoint_path"), - "best_checkpoint_path": results.get("checkpoint_path"), - "last_cycle_output_dir": output_dir, - "last_error": None, - "quantum_available": QUANTUM_AVAILABLE, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - "completed_at": results.get("completed_at"), - }, - status_file=self.status_file, - output_dir=self.default_output_dir, - ) - else: - logger.warning("No datasets found for passive training") - write_quantum_llm_status( - { - "available": True, - "status": "idle", - "mode": "passive", - "passive_mode": True, - "current_cycle": cycle_count, - "last_error": "No datasets found for passive training", - "quantum_available": QUANTUM_AVAILABLE, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - }, - status_file=self.status_file, - output_dir=self.default_output_dir, - ) - else: - logger.warning(f"Datasets directory not found: {datasets_dir}") - write_quantum_llm_status( - { - "available": True, - "status": "idle", - "mode": "passive", - "passive_mode": True, - "current_cycle": cycle_count, - "last_error": f"Datasets directory not found: {datasets_dir}", - "quantum_available": QUANTUM_AVAILABLE, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - }, - status_file=self.status_file, - output_dir=self.default_output_dir, - ) - - except Exception as e: - logger.error(f"Error in passive training cycle: {e}", exc_info=True) - write_quantum_llm_status( - { - "available": True, - "status": "failed", - "mode": "passive", - "passive_mode": True, - "current_cycle": cycle_count, - "last_error": str(e), - "quantum_available": QUANTUM_AVAILABLE, - "training_history": self.training_history, - "quantum_metrics": self.quantum_metrics, - }, - status_file=self.status_file, - output_dir=self.default_output_dir, - ) - - if self.running: - if self.interval == 0: - logger.info( - "Interval is 0; completed single passive training cycle, exiting." - ) - break - logger.info(f"Waiting {self.interval} seconds until next cycle...") - time.sleep(self.interval) - - logger.info("Passive training stopped") - - -# --------------------------------------------------------------------------- -# CLI -# --------------------------------------------------------------------------- - - -def main(): - parser = argparse.ArgumentParser( - description="Quantum-Enhanced LLM Training with Real Quantum Circuits", - formatter_class=argparse.RawDescriptionHelpFormatter, - ) - - # Dataset / output - parser.add_argument("--dataset", type=str, help="Path to training data") - parser.add_argument( - "--output-dir", - type=str, - default="data_out/quantum_llm_training", - help="Output directory for results", - ) - - # Model architecture - parser.add_argument("--vocab-size", type=int, default=256) - parser.add_argument("--d-model", type=int, default=64) - parser.add_argument("--n-heads", type=int, default=4) - parser.add_argument("--n-transformer-layers", type=int, default=2) - parser.add_argument("--max-seq-len", type=int, default=32) - - # Quantum circuit settings - parser.add_argument("--n-qubits", type=int, default=4) - parser.add_argument("--n-quantum-layers", type=int, default=2) - parser.add_argument( - "--entanglement", - type=str, - default="circular", - choices=["linear", "circular", "full"], - ) - - # Training - parser.add_argument("--epochs", type=int, default=3) - parser.add_argument("--batch-size", type=int, default=4) - parser.add_argument("--learning-rate", type=float, default=0.001) - parser.add_argument("--gradient-clip", type=float, default=1.0) - - # Passive mode - parser.add_argument("--passive", action="store_true") - parser.add_argument("--interval", type=int, default=3600) - - # Config file - parser.add_argument("--config", type=str, help="Path to YAML config file") - - args = parser.parse_args() - - # Build config from CLI args - config = { - "quantum_transformer": { - "vocab_size": args.vocab_size, - "d_model": args.d_model, - "n_heads": args.n_heads, - "n_transformer_layers": args.n_transformer_layers, - "max_seq_len": args.max_seq_len, - "n_qubits": args.n_qubits, - "n_quantum_layers": args.n_quantum_layers, - "entanglement": args.entanglement, - "learning_rate": args.learning_rate, - "batch_size": args.batch_size, - "gradient_clip": args.gradient_clip, - }, - "passive": args.passive, - "interval": args.interval, - } - - # Merge config file if provided - if args.config: - config_path = Path(args.config) - if config_path.exists(): - with open(config_path) as f: - file_config = yaml.safe_load(f) - config.update(file_config) - - # Create trainer - trainer = QuantumEnhancedLLMTrainer(config) - - if args.passive: - trainer.run_passive_training() - else: - if not args.dataset: - logger.error("--dataset is required for active training mode") - return 1 - - results = trainer.train_with_quantum_enhancement( - dataset_path=Path(args.dataset), - output_dir=Path(args.output_dir), - epochs=args.epochs, - ) - - logger.info("\nTraining Summary:") - logger.info(f" Status: {results['status']}") - logger.info(f" Epochs: {results['epochs_completed']}") - logger.info(f" Final Loss: {results['final_loss']:.4f}") - logger.info(f" Best Loss: {results.get('best_loss', 'N/A')}") - logger.info( - f" Circuit Executions: " - f"{results['quantum_metrics']['circuit_executions']}" - ) - - return 0 - - -if __name__ == "__main__": - sys.exit(main()) +""" +Quantum-Enhanced LLM Training Module +===================================== + +Trains a QuantumLLM (transformer with real quantum circuits in attention and +feed-forward layers) on character-level language modeling tasks. + +Features: +- Real quantum circuits via PennyLane in attention and FFN layers +- Character-level dataset for proof-of-concept training +- Gradient backpropagation through quantum circuits +- Passive background training mode +- Classical fallback when quantum libraries unavailable + +Usage: + # Train on a text file + python quantum_llm_trainer.py --dataset path/to/text_or_json + + # Train with custom architecture + python quantum_llm_trainer.py --dataset path/to/data --n-qubits 4 --d-model 64 + + # Passive mode (background training) + python quantum_llm_trainer.py --passive --interval 3600 + +Author: Quantum AI Workspace +""" + +import argparse +import json +import logging +import sys +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional + +import numpy as np +import torch +import torch.nn as nn +import yaml +from torch.utils.data import DataLoader, Dataset + +REPO_ROOT = Path(__file__).resolve().parent.parent +DEFAULT_OUTPUT_DIR = REPO_ROOT / "data_out" / "quantum_llm_training" +DEFAULT_STATUS_FILE = DEFAULT_OUTPUT_DIR / "status.json" +CHECKPOINT_FILENAMES = ( + "best_quantum_llm.pt", + "quantum_llm_checkpoint.pt", + "final_model.pt", +) + +# Add ai-projects/quantum-ml to path +quantum_ml_path = Path(__file__).parent.parent / "ai-projects" / "quantum-ml" +quantum_ml_src = quantum_ml_path / "src" +for p in [str(quantum_ml_path), str(quantum_ml_src)]: + if p not in sys.path: + sys.path.insert(0, p) + +try: + from quantum_transformer import QUANTUM_AVAILABLE, QuantumLLM +except ImportError as e: + logging.warning(f"QuantumLLM not available: {e}") + QUANTUM_AVAILABLE = False + +try: + from hybrid_qnn import QuantumLayer + + QUANTUM_LAYER_AVAILABLE = True +except ImportError: + QUANTUM_LAYER_AVAILABLE = False + +# Setup logging +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + + +def _resolve_repo_path(path_value: str | Path | None, *, default: Path) -> Path: + """Resolve repo-relative paths deterministically.""" + if path_value is None: + return default + + path = Path(path_value) + if not path.is_absolute(): + path = REPO_ROOT / path + return path + + +def _repo_relative_str(path_value: str | Path | None) -> str | None: + """Return a repo-relative string when possible to keep status JSON portable.""" + if path_value is None: + return None + + path = Path(path_value) + try: + return str(path.resolve().relative_to(REPO_ROOT.resolve())) + except Exception: + return str(path) + + +def _normalise_checkpoint_reference( + base_dir: Path, checkpoint_ref: str | Path | None +) -> Path | None: + """Resolve a checkpoint path from status metadata or direct references.""" + if not checkpoint_ref: + return None + + candidate = Path(checkpoint_ref) + if not candidate.is_absolute(): + direct_candidate = base_dir / candidate + if direct_candidate.exists(): + candidate = direct_candidate + else: + candidate = REPO_ROOT / candidate + return candidate + + +def get_quantum_llm_status( + *, + status_file: str | Path | None = None, + output_dir: str | Path | None = None, +) -> Dict[str, Any]: + """Load Quantum LLM runtime status and checkpoint readiness metadata.""" + resolved_output_dir = _resolve_repo_path(output_dir, default=DEFAULT_OUTPUT_DIR) + resolved_status_file = _resolve_repo_path( + status_file, + default=resolved_output_dir / "status.json", + ) + + payload: Dict[str, Any] = { + "available": False, + "status": "not_started", + "output_dir": _repo_relative_str(resolved_output_dir), + "status_file": _repo_relative_str(resolved_status_file), + "checkpoint_path": None, + "best_checkpoint_path": None, + "checkpoint_exists": False, + "inference_ready": False, + "quantum_available": QUANTUM_AVAILABLE, + "epochs_completed": 0, + "best_loss": None, + "final_loss": None, + "last_updated": None, + "last_error": None, + "passive_mode": False, + "mode": None, + } + + if resolved_status_file.exists(): + try: + with open(resolved_status_file, "r", encoding="utf-8") as status_handle: + existing = json.load(status_handle) + if isinstance(existing, dict): + payload.update(existing) + payload["available"] = True + except Exception as exc: # noqa: BLE001 + payload.update( + { + "status": "error", + "available": False, + "last_error": f"Failed to read status file: {exc}", + } + ) + + checkpoint_ref = ( + payload.get("best_checkpoint_path") + or payload.get("checkpoint_path") + or payload.get("last_checkpoint_path") + ) + checkpoint_path = _normalise_checkpoint_reference( + resolved_output_dir, checkpoint_ref + ) + if checkpoint_path is None: + for checkpoint_name in CHECKPOINT_FILENAMES: + candidate = resolved_output_dir / checkpoint_name + if candidate.exists(): + checkpoint_path = candidate + break + + if checkpoint_path is not None: + payload["checkpoint_path"] = _repo_relative_str(checkpoint_path) + payload["checkpoint_exists"] = checkpoint_path.exists() + payload["inference_ready"] = bool( + checkpoint_path.exists() + and payload.get("status") in {"completed", "running", "idle"} + ) + + payload["status_file_exists"] = resolved_status_file.exists() + return payload + + +def write_quantum_llm_status( + data: Dict[str, Any], + *, + status_file: str | Path | None = None, + output_dir: str | Path | None = None, +) -> Dict[str, Any]: + """Persist Quantum LLM status metadata in a repo-consistent JSON artifact.""" + resolved_output_dir = _resolve_repo_path(output_dir, default=DEFAULT_OUTPUT_DIR) + resolved_status_file = _resolve_repo_path( + status_file, + default=resolved_output_dir / "status.json", + ) + resolved_status_file.parent.mkdir(parents=True, exist_ok=True) + + payload = { + "output_dir": _repo_relative_str(resolved_output_dir), + "status_file": _repo_relative_str(resolved_status_file), + "last_updated": datetime.now().isoformat(), + } + payload.update(data) + + for path_key in ( + "dataset_path", + "checkpoint_path", + "best_checkpoint_path", + "last_checkpoint_path", + "results_file", + "last_cycle_output_dir", + ): + if payload.get(path_key): + payload[path_key] = _repo_relative_str(payload[path_key]) + + with open(resolved_status_file, "w", encoding="utf-8") as status_handle: + json.dump(payload, status_handle, indent=2) + + return payload + + +# --------------------------------------------------------------------------- +# Character-level dataset +# --------------------------------------------------------------------------- + + +class CharacterDataset(Dataset): + """Character-level dataset for quantum LLM training. + + Reads text, builds a character vocabulary, and produces overlapping + windows of (input_ids, target_ids) where target is shifted by one + position (standard next-token prediction). + """ + + def __init__(self, text: str, seq_len: int = 32, vocab_size: int = 1000): + self.seq_len = seq_len + + # Build vocabulary from the text (up to vocab_size unique chars) + unique_chars = sorted(set(text)) + if len(unique_chars) > vocab_size - 1: + unique_chars = unique_chars[: vocab_size - 1] + + self.char_to_id = {c: i + 1 for i, c in enumerate(unique_chars)} + self.char_to_id["\x00"] = 0 # padding / unknown + self.id_to_char = {v: k for k, v in self.char_to_id.items()} + self.actual_vocab_size = len(self.char_to_id) + + # Encode entire text + self.encoded = [self.char_to_id.get(c, 0) for c in text] + + # We need at least seq_len + 1 characters for one sample + if len(self.encoded) < seq_len + 1: + # Pad with zeros if text is too short + self.encoded = self.encoded + [0] * (seq_len + 1 - len(self.encoded)) + + logger.info( + f"CharacterDataset: {len(text)} chars, " + f"{self.actual_vocab_size} unique tokens, " + f"{len(self)} samples (seq_len={seq_len})" + ) + + def __len__(self) -> int: + return max(1, len(self.encoded) - self.seq_len) + + def __getitem__(self, idx: int): + chunk = self.encoded[idx : idx + self.seq_len + 1] + input_ids = torch.tensor(chunk[:-1], dtype=torch.long) + target_ids = torch.tensor(chunk[1:], dtype=torch.long) + return input_ids, target_ids + + def decode(self, ids) -> str: + """Convert token ids back to text.""" + if isinstance(ids, torch.Tensor): + ids = ids.tolist() + return "".join(self.id_to_char.get(i, "?") for i in ids) + + +# --------------------------------------------------------------------------- +# Feature encoder (kept for auxiliary use, uses real QuantumLayer) +# --------------------------------------------------------------------------- + + +class QuantumFeatureEncoder: + """Encodes classical features into quantum-enhanced representations.""" + + def __init__(self, n_qubits: int = 4, n_layers: int = 2): + self.n_qubits = n_qubits + self.n_layers = n_layers + self.quantum_layer = None + + if QUANTUM_LAYER_AVAILABLE: + try: + self.quantum_layer = QuantumLayer( + n_qubits=n_qubits, + n_layers=n_layers, + device="default.qubit", + entanglement="circular", + ) + logger.info("Initialized quantum feature encoder") + except Exception as e: + logger.warning(f"Failed to initialize quantum layer: {e}") + + def encode(self, features: torch.Tensor) -> torch.Tensor: + if self.quantum_layer is None: + return torch.tanh(features) + + try: + batch_size, feature_dim = features.shape + quantum_dim = 2**self.n_qubits + if feature_dim < quantum_dim: + padded = torch.zeros(batch_size, quantum_dim, device=features.device) + padded[:, :feature_dim] = features + features = padded + elif feature_dim > quantum_dim: + features = features[:, :quantum_dim] + + features_norm = features / ( + torch.norm(features, dim=1, keepdim=True) + 1e-8 + ) + return self.quantum_layer(features_norm) + except Exception as e: + logger.warning(f"Quantum encoding failed: {e}, using classical fallback") + return torch.tanh(features) + + +class QuantumAttentionOptimizer: + """Optimizes transformer attention weights using quantum circuits. + + When PennyLane is available, attention scores are processed through a + variational quantum circuit that can learn non-linear feature interactions. + Falls back to classical softmax normalization when quantum libs are absent. + """ + + def __init__(self, n_qubits: int = 4, n_layers: int = 2): + self.n_qubits = n_qubits + self.n_layers = n_layers + self._quantum_layer = None + + if QUANTUM_LAYER_AVAILABLE: + try: + self._quantum_layer = QuantumLayer( + n_qubits=n_qubits, + n_layers=n_layers, + device="default.qubit", + entanglement="circular", + ) + logger.info("Initialized QuantumAttentionOptimizer") + except Exception as e: + logger.warning(f"Failed to initialize quantum attention optimizer: {e}") + + @staticmethod + def _resize_quantum_output( + quantum_output: torch.Tensor, + target_len: int, + *, + dtype: torch.dtype, + device: torch.device, + ) -> torch.Tensor: + """Resize quantum output back to the chunk length. + + QuantumLayer returns expectation values per qubit, which is often much + shorter than the flattened attention chunk fed into amplitude encoding. + Expand the returned vector deterministically so the original attention + tensor shape is always preserved. + """ + flat = quantum_output.reshape(-1).to(device=device, dtype=dtype) + if target_len <= 0: + return flat[:0] + if flat.numel() == 0: + return torch.zeros(target_len, dtype=dtype, device=device) + if flat.numel() == target_len: + return flat + + repeats = (target_len + flat.numel() - 1) // flat.numel() + return flat.repeat(repeats)[:target_len] + + def optimize_attention_weights( + self, attention_scores: torch.Tensor + ) -> torch.Tensor: + """Apply quantum optimization to attention scores. + + Args: + attention_scores: Tensor of any shape containing attention logits. + + Returns: + Tensor with the same shape, quantum-processed or softmax-normalised. + """ + if self._quantum_layer is None: + return torch.softmax(attention_scores.float(), dim=-1) + + try: + orig_shape = attention_scores.shape + flat = attention_scores.reshape(-1).float().detach() + q_dim = 2**self.n_qubits + + chunks = flat.split(q_dim) + processed = [] + for chunk in chunks: + chunk_len = chunk.shape[0] + padded = torch.zeros(q_dim, dtype=flat.dtype, device=flat.device) + padded[:chunk_len] = chunk + normed = padded / (padded.norm() + 1e-8) + out = self._quantum_layer(normed.unsqueeze(0)).squeeze(0) + processed.append( + self._resize_quantum_output( + out, + chunk_len, + dtype=flat.dtype, + device=flat.device, + ) + ) + + result = torch.cat(processed).reshape(orig_shape) + return result + except Exception as e: + logger.warning( + f"Quantum attention optimization failed: {e}, using classical fallback" + ) + return torch.softmax(attention_scores.float(), dim=-1) + + +# --------------------------------------------------------------------------- +# Main trainer +# --------------------------------------------------------------------------- + + +class QuantumEnhancedLLMTrainer: + """ + Trains a QuantumLLM on character-level language modeling. + + The QuantumLLM uses real PennyLane quantum circuits inside its + transformer blocks (QuantumSelfAttention and QuantumFeedForward). + Gradients flow through the quantum circuits via PennyLane's + torch interface. + """ + + def __init__(self, config: Dict[str, Any]): + self.config = config + self.passive_mode = config.get("passive", False) + self.interval = config.get("interval", 3600) + output_config = ( + config.get("output", {}) if isinstance(config.get("output"), dict) else {} + ) + integration_config = ( + config.get("autonomous_integration", {}) + if isinstance(config.get("autonomous_integration"), dict) + else {} + ) + self.default_output_dir = _resolve_repo_path( + output_config.get("save_dir") or config.get("output_dir"), + default=DEFAULT_OUTPUT_DIR, + ) + self.status_file = _resolve_repo_path( + integration_config.get("status_file") or config.get("status_file"), + default=self.default_output_dir / "status.json", + ) + + # Top-level attributes expected by tests and external callers + self.quantum_backend = config.get("quantum_backend", "local") + self.n_qubits = config.get("n_qubits", 4) + self.n_layers = config.get("n_quantum_layers", 2) + + # Device + use_gpu = config.get("use_gpu", True) + self.device = torch.device( + "cuda" if torch.cuda.is_available() and use_gpu else "cpu" + ) + + # Build quantum transformer config (supports both flat and nested layouts) + qt_config = config.get("quantum_transformer", {}) + self.model_config = { + "vocab_size": qt_config.get("vocab_size", config.get("vocab_size", 256)), + "d_model": qt_config.get("d_model", config.get("d_model", 64)), + "n_heads": qt_config.get("n_heads", config.get("n_heads", 4)), + "n_transformer_layers": qt_config.get( + "n_transformer_layers", config.get("n_transformer_layers", 2) + ), + "n_qubits": qt_config.get("n_qubits", config.get("n_qubits", 4)), + "n_quantum_layers": qt_config.get( + "n_quantum_layers", config.get("n_quantum_layers", 2) + ), + "max_seq_len": qt_config.get("max_seq_len", config.get("max_seq_len", 32)), + "entanglement": qt_config.get( + "entanglement", config.get("entanglement", "circular") + ), + "dropout": qt_config.get("dropout", config.get("dropout", 0.1)), + "use_quantum_attention": qt_config.get("use_quantum_attention", True), + "use_quantum_ffn": qt_config.get("use_quantum_ffn", True), + "tie_embeddings": qt_config.get("tie_embeddings", True), + } + + # Create model + self.model = QuantumLLM.from_config({"quantum_transformer": self.model_config}) + self.model = self.model.to(self.device) + + # Optimizer + lr = qt_config.get("learning_rate", config.get("learning_rate", 0.001)) + wd = qt_config.get("weight_decay", config.get("weight_decay", 0.01)) + self.optimizer = torch.optim.AdamW( + self.model.parameters(), lr=lr, weight_decay=wd + ) + + # Loss and gradient clipping + self.criterion = nn.CrossEntropyLoss() + self.grad_clip = qt_config.get( + "gradient_clip", config.get("gradient_clip", 1.0) + ) + self.batch_size = qt_config.get("batch_size", config.get("batch_size", 4)) + + # Learning rate scheduler + self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + self.optimizer, mode="min", factor=0.5, patience=3 + ) + + # Feature encoder (auxiliary) + self.feature_encoder = QuantumFeatureEncoder( + n_qubits=self.model_config["n_qubits"], + n_layers=self.model_config["n_quantum_layers"], + ) + + # Quantum attention optimizer (used by _train_epoch_with_quantum) + self.attention_optimizer = QuantumAttentionOptimizer( + n_qubits=self.model_config["n_qubits"], + n_layers=self.model_config["n_quantum_layers"], + ) + + # Metrics + self.training_history: List[Dict] = [] + self.quantum_metrics = { + "circuit_executions": 0, + "optimization_steps": 0, + "quantum_available": QUANTUM_AVAILABLE, + } + + n_params = sum(p.numel() for p in self.model.parameters()) + logger.info("Initialized QuantumEnhancedLLMTrainer") + logger.info(f" Device: {self.device}") + logger.info(f" Parameters: {n_params:,}") + logger.info(f" Quantum available: {QUANTUM_AVAILABLE}") + logger.info(f" Model config: {self.model_config}") + + def _save_checkpoint( + self, + checkpoint_path: Path, + *, + epoch: int, + loss: float, + training_mode: str, + ) -> Path: + """Persist a Quantum LLM checkpoint with enough metadata for inference.""" + checkpoint_path.parent.mkdir(parents=True, exist_ok=True) + torch.save( + { + "model_state_dict": self.model.state_dict(), + "optimizer_state_dict": self.optimizer.state_dict(), + "epoch": epoch, + "loss": loss, + "model_config": self.model_config, + "training_mode": training_mode, + "quantum_metrics": self.quantum_metrics, + "training_history": self.training_history, + "saved_at": datetime.now().isoformat(), + }, + checkpoint_path, + ) + return checkpoint_path + + # ------------------------------------------------------------------ + # Dataset loading + # ------------------------------------------------------------------ + + def _extract_text(self, dataset_path: Path) -> str: + """Extract raw text from various file formats.""" + text_parts = [] + + if dataset_path.is_file(): + if dataset_path.suffix == ".txt": + text_parts.append(dataset_path.read_text(errors="replace")) + + elif dataset_path.suffix == ".jsonl": + with open(dataset_path) as f: + for line in f: + line = line.strip() + if not line: + continue + record = json.loads(line) + for key in ("text", "content", "message", "input", "output"): + if key in record: + text_parts.append(str(record[key])) + + elif dataset_path.suffix == ".json": + with open(dataset_path) as f: + try: + data = json.load(f) + records = data if isinstance(data, list) else [data] + except json.JSONDecodeError: + # File may be JSONL-formatted despite .json extension + f.seek(0) + records = [json.loads(ln) for ln in f if ln.strip()] + for record in records: + if isinstance(record, dict): + for key in ( + "text", + "content", + "message", + "input", + "output", + ): + if key in record: + text_parts.append(str(record[key])) + elif isinstance(record, str): + text_parts.append(record) + + elif dataset_path.is_dir(): + for pattern in ["*.txt", "*.json", "*.jsonl"]: + for fp in sorted(dataset_path.glob(pattern)): + text_parts.append(self._extract_text(fp)) + for subdir_name in ["train.json", "train.jsonl"]: + sub = dataset_path / subdir_name + if sub.exists(): + text_parts.append(self._extract_text(sub)) + + combined = "\n".join(text_parts) + if not combined.strip(): + # Generate a small synthetic corpus so training can still run + logger.warning( + "No text extracted from dataset -- using synthetic placeholder text" + ) + combined = ( + "The quick brown fox jumps over the lazy dog. " + "Pack my box with five dozen liquor jugs. " + "How vexingly quick daft zebras jump. " + ) * 50 + + return combined + + def _make_dataloader(self, dataset_path: Path) -> DataLoader: + """Build a DataLoader from a dataset path.""" + text = self._extract_text(dataset_path) + dataset = CharacterDataset( + text=text, + seq_len=self.model_config["max_seq_len"], + vocab_size=self.model_config["vocab_size"], + ) + return DataLoader( + dataset, + batch_size=self.batch_size, + shuffle=True, + drop_last=True, + ) + + # ------------------------------------------------------------------ + # Training loop + # ------------------------------------------------------------------ + + def _estimate_circuit_evals(self, batch_size: int, seq_len: int) -> int: + """Estimate the number of quantum circuit evaluations per batch.""" + if not QUANTUM_AVAILABLE: + return 0 + n_heads = self.model_config["n_heads"] + n_blocks = self.model_config["n_transformer_layers"] + # Attention: 2 maps (Q, K) * batch*seq per head per block + attn_evals = 2 * batch_size * seq_len * n_heads * n_blocks + # FFN: 1 map * batch*seq per block + ffn_evals = batch_size * seq_len * n_blocks + return attn_evals + ffn_evals + + def _train_epoch(self, dataloader: DataLoader, epoch: int) -> float: + """Train one epoch with real forward/backward through quantum circuits.""" + self.model.train() + total_loss = 0.0 + num_batches = 0 + + for batch_idx, (input_ids, targets) in enumerate(dataloader): + input_ids = input_ids.to(self.device) + targets = targets.to(self.device) + + # Forward pass (gradients flow through quantum circuits) + logits = self.model(input_ids) + + # Cross-entropy loss + loss = self.criterion( + logits.view(-1, self.model.vocab_size), + targets.view(-1), + ) + + # Backward pass + self.optimizer.zero_grad() + loss.backward() + + # Gradient clipping + if self.grad_clip > 0: + torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_clip) + + self.optimizer.step() + + batch_loss = loss.item() + total_loss += batch_loss + num_batches += 1 + + # Track quantum circuit evaluations + evals = self._estimate_circuit_evals(input_ids.shape[0], input_ids.shape[1]) + self.quantum_metrics["circuit_executions"] += evals + self.quantum_metrics["optimization_steps"] += 1 + + if batch_idx % 5 == 0: + logger.info( + f" Epoch {epoch+1} | Batch {batch_idx}/{len(dataloader)} | " + f"Loss: {batch_loss:.4f} | " + f"Circuit evals: {evals}" + ) + + avg_loss = total_loss / max(num_batches, 1) + self.training_history.append( + { + "epoch": epoch, + "loss": avg_loss, + "circuit_executions": self.quantum_metrics["circuit_executions"], + "lr": self.optimizer.param_groups[0]["lr"], + } + ) + + # Update scheduler + self.scheduler.step(avg_loss) + + return avg_loss + + def train_with_quantum_enhancement( + self, + dataset_path: Path, + output_dir: Path, + epochs: int = 3, + model: Optional[Any] = None, + ) -> Dict[str, Any]: + """ + Train the QuantumLLM on a dataset. + + Args: + dataset_path: Path to training data (text, json, or jsonl) + output_dir: Directory for results and checkpoints + epochs: Number of training epochs + + Returns: + Training results and quantum metrics + """ + logger.info("Starting quantum-enhanced LLM training") + logger.info(f" Dataset: {dataset_path}") + logger.info(f" Output: {output_dir}") + logger.info(f" Epochs: {epochs}") + logger.info(f" Mode: {'simulated' if model is None else 'real'}") + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + status_file = output_dir / "status.json" + dataset_path = Path(dataset_path) + training_mode = "simulated" if model is None else "real" + + results = { + "status": "success", + "epochs_completed": 0, + "final_loss": 0.0, + "quantum_metrics": self.quantum_metrics, + "model_config": self.model_config, + "started_at": datetime.now().isoformat(), + } + + best_loss = float("inf") + best_checkpoint_path: Path | None = None + final_checkpoint_path: Path | None = None + + write_quantum_llm_status( + { + "available": True, + "status": "running", + "mode": training_mode, + "passive_mode": self.passive_mode, + "dataset_path": dataset_path, + "epochs_requested": epochs, + "epochs_completed": 0, + "quantum_available": QUANTUM_AVAILABLE, + "best_loss": None, + "final_loss": None, + "last_error": None, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + "started_at": results["started_at"], + }, + status_file=status_file, + output_dir=output_dir, + ) + + try: + if model is None: + # Simulated quantum training path -- uses _train_epoch_with_quantum + # which applies the quantum attention optimizer each step. + dataset = self._load_dataset(dataset_path) + for epoch in range(epochs): + logger.info(f"\n--- Epoch {epoch + 1}/{epochs} (simulated) ---") + epoch_loss = self._train_epoch_with_quantum(None, dataset, epoch) + results["epochs_completed"] = epoch + 1 + results["final_loss"] = epoch_loss + if epoch_loss < best_loss: + best_loss = epoch_loss + best_checkpoint_path = self._save_checkpoint( + output_dir / "best_quantum_llm.pt", + epoch=epoch, + loss=epoch_loss, + training_mode=training_mode, + ) + logger.info( + f" Epoch {epoch+1} complete | Avg Loss: {epoch_loss:.4f}" + ) + write_quantum_llm_status( + { + "available": True, + "status": "running", + "mode": training_mode, + "passive_mode": self.passive_mode, + "dataset_path": dataset_path, + "epochs_requested": epochs, + "epochs_completed": results["epochs_completed"], + "best_loss": ( + None if best_loss == float("inf") else best_loss + ), + "final_loss": epoch_loss, + "best_checkpoint_path": best_checkpoint_path, + "quantum_available": QUANTUM_AVAILABLE, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + "started_at": results["started_at"], + }, + status_file=status_file, + output_dir=output_dir, + ) + else: + # Real model training path -- builds DataLoader and runs full backprop + dataloader = self._make_dataloader(dataset_path) + logger.info(f" Batches per epoch: {len(dataloader)}") + for epoch in range(epochs): + logger.info(f"\n--- Epoch {epoch + 1}/{epochs} ---") + epoch_loss = self._train_epoch(dataloader, epoch) + results["epochs_completed"] = epoch + 1 + results["final_loss"] = epoch_loss + logger.info( + f" Epoch {epoch+1} complete | Avg Loss: {epoch_loss:.4f} | " + f"LR: {self.optimizer.param_groups[0]['lr']:.6f}" + ) + if epoch_loss < best_loss: + best_loss = epoch_loss + best_checkpoint_path = self._save_checkpoint( + output_dir / "best_quantum_llm.pt", + epoch=epoch, + loss=epoch_loss, + training_mode=training_mode, + ) + logger.info(f" Saved best checkpoint: {best_checkpoint_path}") + write_quantum_llm_status( + { + "available": True, + "status": "running", + "mode": training_mode, + "passive_mode": self.passive_mode, + "dataset_path": dataset_path, + "epochs_requested": epochs, + "epochs_completed": results["epochs_completed"], + "best_loss": ( + None if best_loss == float("inf") else best_loss + ), + "final_loss": epoch_loss, + "best_checkpoint_path": best_checkpoint_path, + "quantum_available": QUANTUM_AVAILABLE, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + "started_at": results["started_at"], + }, + status_file=status_file, + output_dir=output_dir, + ) + self._generate_sample(output_dir, dataloader.dataset) + + final_checkpoint_path = self._save_checkpoint( + output_dir / "final_model.pt", + epoch=max(results["epochs_completed"] - 1, 0), + loss=float(results["final_loss"]), + training_mode=training_mode, + ) + + results["completed_at"] = datetime.now().isoformat() + results["best_loss"] = best_loss + results["checkpoint_path"] = str( + best_checkpoint_path or final_checkpoint_path + ) + + # Save results JSON + results_file = output_dir / "quantum_training_results.json" + with open(results_file, "w", encoding="utf-8") as f: + json.dump(results, f, indent=2) + logger.info(f"\nTraining complete! Results saved to: {results_file}") + + write_quantum_llm_status( + { + "available": True, + "status": "completed", + "mode": training_mode, + "passive_mode": self.passive_mode, + "dataset_path": dataset_path, + "epochs_requested": epochs, + "epochs_completed": results["epochs_completed"], + "best_loss": best_loss, + "final_loss": results["final_loss"], + "checkpoint_path": best_checkpoint_path or final_checkpoint_path, + "best_checkpoint_path": best_checkpoint_path + or final_checkpoint_path, + "last_checkpoint_path": final_checkpoint_path, + "quantum_available": QUANTUM_AVAILABLE, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + "results_file": results_file, + "started_at": results["started_at"], + "completed_at": results["completed_at"], + }, + status_file=status_file, + output_dir=output_dir, + ) + + return results + except Exception as exc: + write_quantum_llm_status( + { + "available": True, + "status": "failed", + "mode": training_mode, + "passive_mode": self.passive_mode, + "dataset_path": dataset_path, + "epochs_requested": epochs, + "epochs_completed": results["epochs_completed"], + "best_loss": None if best_loss == float("inf") else best_loss, + "final_loss": results["final_loss"], + "best_checkpoint_path": best_checkpoint_path, + "last_checkpoint_path": final_checkpoint_path, + "quantum_available": QUANTUM_AVAILABLE, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + "started_at": results["started_at"], + "last_error": str(exc), + }, + status_file=status_file, + output_dir=output_dir, + ) + raise + + def _load_dataset(self, dataset_path: Path) -> List[Dict[str, Any]]: + """Load training dataset from JSONL or JSON format.""" + dataset = [] + + if dataset_path.is_file(): + if dataset_path.suffix == ".jsonl": + with open(dataset_path) as f: + for line in f: + if line.strip(): + dataset.append(json.loads(line)) + elif dataset_path.suffix == ".json": + with open(dataset_path) as f: + try: + data = json.load(f) + dataset = data if isinstance(data, list) else [data] + except json.JSONDecodeError: + # File may be JSONL-formatted despite .json extension + f.seek(0) + dataset = [json.loads(ln) for ln in f if ln.strip()] + elif dataset_path.is_dir(): + # Look for train files using glob for efficiency + train_files = list(dataset_path.glob("train.json")) + list( + dataset_path.glob("train.jsonl") + ) + if train_files: + return self._load_dataset(train_files[0]) + + return dataset + + def _train_epoch_with_quantum( + self, model: Optional[Any], dataset: List[Dict[str, Any]], epoch: int + ) -> float: + """ + Train one epoch with quantum enhancement. + + Returns: + Average loss for the epoch + """ + total_loss = 0.0 + num_batches = max(1, len(dataset) // 32) + + # Base loss decreases each epoch to simulate convergence + base_loss = max(0.3, 1.0 - epoch * 0.15) + + for batch_idx in range(num_batches): + # Simulate forward pass with deterministic downward trend per epoch + batch_loss = base_loss + np.random.uniform(-0.1, 0.1) + + # Apply quantum optimization every N steps + if batch_idx % 10 == 0: + # Quantum-enhanced optimization step + mock_attention = torch.randn(1, 8, 8) + optimized = self.attention_optimizer.optimize_attention_weights( + mock_attention + ) + self.quantum_metrics["circuit_executions"] += 1 + self.quantum_metrics["optimization_steps"] += 1 + + # Simulate quantum advantage (small improvement) + batch_loss *= 0.98 + + total_loss += batch_loss + + avg_loss = total_loss / num_batches + self.training_history.append( + { + "epoch": epoch, + "loss": avg_loss, + "quantum_executions": self.quantum_metrics["circuit_executions"], + } + ) + + return avg_loss + + def _generate_sample(self, output_dir: Path, dataset: CharacterDataset): + """Generate a sample from the trained model.""" + try: + prompt_ids = torch.tensor( + [[1, 2, 3, 4]], dtype=torch.long, device=self.device + ) + generated = self.model.generate( + prompt_ids, max_new_tokens=50, temperature=0.8, top_k=20 + ) + text = dataset.decode(generated[0]) + logger.info(f" Sample generation: {text[:100]}...") + + sample_path = output_dir / "generated_sample.txt" + sample_path.write_text(text) + except Exception as e: + logger.warning(f"Sample generation failed: {e}") + + # ------------------------------------------------------------------ + # Passive training + # ------------------------------------------------------------------ + + def run_passive_training(self): + """Run in passive mode -- continuous background training cycles.""" + logger.info("Starting passive quantum-enhanced LLM training") + logger.info(f" Interval: {self.interval} seconds") + + import signal + import time + + self.running = True + + def signal_handler(sig, frame): + logger.info("Received shutdown signal") + self.running = False + + signal.signal(signal.SIGINT, signal_handler) + signal.signal(signal.SIGTERM, signal_handler) + + cycle_count = 0 + write_quantum_llm_status( + { + "available": True, + "status": "idle", + "mode": "passive", + "passive_mode": True, + "current_cycle": cycle_count, + "last_error": None, + "quantum_available": QUANTUM_AVAILABLE, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + }, + status_file=self.status_file, + output_dir=self.default_output_dir, + ) + + while self.running: + cycle_count += 1 + logger.info(f"\n=== Passive Training Cycle {cycle_count} ===") + + try: + write_quantum_llm_status( + { + "available": True, + "status": "running", + "mode": "passive", + "passive_mode": True, + "current_cycle": cycle_count, + "last_error": None, + "quantum_available": QUANTUM_AVAILABLE, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + }, + status_file=self.status_file, + output_dir=self.default_output_dir, + ) + + # Look for available datasets using combined glob pattern + datasets_dir = Path("datasets/chat") + if datasets_dir.exists(): + # Use explicit patterns to match only train.json and train.jsonl + dataset_files = list(datasets_dir.glob("*/train.json")) + list( + datasets_dir.glob("*/train.jsonl") + ) + + dataset_files = ( + list(datasets_dir.glob("*/train.json")) + + list(datasets_dir.glob("*/train.jsonl")) + + list(datasets_dir.glob("*.txt")) + ) + + if dataset_files: + import random + + dataset_path = random.choice(dataset_files) + logger.info(f"Selected dataset: {dataset_path}") + + output_dir = ( + Path("data_out/quantum_llm_training") + / f"cycle_{cycle_count}" + ) + + results = self.train_with_quantum_enhancement( + dataset_path=dataset_path, + output_dir=output_dir, + epochs=1, + ) + logger.info( + f"Cycle {cycle_count} complete: " + f"Loss={results['final_loss']:.4f}" + ) + write_quantum_llm_status( + { + "available": True, + "status": "idle", + "mode": "passive", + "passive_mode": True, + "current_cycle": cycle_count, + "dataset_path": dataset_path, + "epochs_completed": results.get("epochs_completed", 0), + "best_loss": results.get("best_loss"), + "final_loss": results.get("final_loss"), + "checkpoint_path": results.get("checkpoint_path"), + "best_checkpoint_path": results.get("checkpoint_path"), + "last_cycle_output_dir": output_dir, + "last_error": None, + "quantum_available": QUANTUM_AVAILABLE, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + "completed_at": results.get("completed_at"), + }, + status_file=self.status_file, + output_dir=self.default_output_dir, + ) + else: + logger.warning("No datasets found for passive training") + write_quantum_llm_status( + { + "available": True, + "status": "idle", + "mode": "passive", + "passive_mode": True, + "current_cycle": cycle_count, + "last_error": "No datasets found for passive training", + "quantum_available": QUANTUM_AVAILABLE, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + }, + status_file=self.status_file, + output_dir=self.default_output_dir, + ) + else: + logger.warning(f"Datasets directory not found: {datasets_dir}") + write_quantum_llm_status( + { + "available": True, + "status": "idle", + "mode": "passive", + "passive_mode": True, + "current_cycle": cycle_count, + "last_error": f"Datasets directory not found: {datasets_dir}", + "quantum_available": QUANTUM_AVAILABLE, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + }, + status_file=self.status_file, + output_dir=self.default_output_dir, + ) + + except Exception as e: + logger.error(f"Error in passive training cycle: {e}", exc_info=True) + write_quantum_llm_status( + { + "available": True, + "status": "failed", + "mode": "passive", + "passive_mode": True, + "current_cycle": cycle_count, + "last_error": str(e), + "quantum_available": QUANTUM_AVAILABLE, + "training_history": self.training_history, + "quantum_metrics": self.quantum_metrics, + }, + status_file=self.status_file, + output_dir=self.default_output_dir, + ) + + if self.running: + if self.interval == 0: + logger.info( + "Interval is 0; completed single passive training cycle, exiting." + ) + break + logger.info(f"Waiting {self.interval} seconds until next cycle...") + time.sleep(self.interval) + + logger.info("Passive training stopped") + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + + +def main(): + parser = argparse.ArgumentParser( + description="Quantum-Enhanced LLM Training with Real Quantum Circuits", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + # Dataset / output + parser.add_argument("--dataset", type=str, help="Path to training data") + parser.add_argument( + "--output-dir", + type=str, + default="data_out/quantum_llm_training", + help="Output directory for results", + ) + + # Model architecture + parser.add_argument("--vocab-size", type=int, default=256) + parser.add_argument("--d-model", type=int, default=64) + parser.add_argument("--n-heads", type=int, default=4) + parser.add_argument("--n-transformer-layers", type=int, default=2) + parser.add_argument("--max-seq-len", type=int, default=32) + + # Quantum circuit settings + parser.add_argument("--n-qubits", type=int, default=4) + parser.add_argument("--n-quantum-layers", type=int, default=2) + parser.add_argument( + "--entanglement", + type=str, + default="circular", + choices=["linear", "circular", "full"], + ) + + # Training + parser.add_argument("--epochs", type=int, default=3) + parser.add_argument("--batch-size", type=int, default=4) + parser.add_argument("--learning-rate", type=float, default=0.001) + parser.add_argument("--gradient-clip", type=float, default=1.0) + + # Passive mode + parser.add_argument("--passive", action="store_true") + parser.add_argument("--interval", type=int, default=3600) + + # Config file + parser.add_argument("--config", type=str, help="Path to YAML config file") + + args = parser.parse_args() + + # Build config from CLI args + config = { + "quantum_transformer": { + "vocab_size": args.vocab_size, + "d_model": args.d_model, + "n_heads": args.n_heads, + "n_transformer_layers": args.n_transformer_layers, + "max_seq_len": args.max_seq_len, + "n_qubits": args.n_qubits, + "n_quantum_layers": args.n_quantum_layers, + "entanglement": args.entanglement, + "learning_rate": args.learning_rate, + "batch_size": args.batch_size, + "gradient_clip": args.gradient_clip, + }, + "passive": args.passive, + "interval": args.interval, + } + + # Merge config file if provided + if args.config: + config_path = Path(args.config) + if config_path.exists(): + with open(config_path) as f: + file_config = yaml.safe_load(f) + config.update(file_config) + + # Create trainer + trainer = QuantumEnhancedLLMTrainer(config) + + if args.passive: + trainer.run_passive_training() + else: + if not args.dataset: + logger.error("--dataset is required for active training mode") + return 1 + + results = trainer.train_with_quantum_enhancement( + dataset_path=Path(args.dataset), + output_dir=Path(args.output_dir), + epochs=args.epochs, + ) + + logger.info("\nTraining Summary:") + logger.info(f" Status: {results['status']}") + logger.info(f" Epochs: {results['epochs_completed']}") + logger.info(f" Final Loss: {results['final_loss']:.4f}") + logger.info(f" Best Loss: {results.get('best_loss', 'N/A')}") + logger.info( + f" Circuit Executions: " + f"{results['quantum_metrics']['circuit_executions']}" + ) + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/repo_automation.py b/scripts/repo_automation.py index 2945291e7..27f6165f9 100644 --- a/scripts/repo_automation.py +++ b/scripts/repo_automation.py @@ -1,862 +1,862 @@ -#!/usr/bin/env python3 -"""Repository-Wide Automation System - -This script provides lightweight management for repository-level -automation components: starting/stopping processes, tracking PIDs and -status, performing health checks and automatically attempting to -install Python package dependencies when needed. -""" - -import argparse -import importlib -import json -import signal -import subprocess -import sys -import threading -import time -from dataclasses import dataclass, field -from datetime import datetime, timedelta, timezone -from pathlib import Path -from typing import Any, Dict, List, Optional - -try: - from .config_paths import resolve_existing_config_path -except ImportError: - from config_paths import resolve_existing_config_path - -try: - import psutil -except Exception: # pragma: no cover - optional dependency - psutil = None - -REPO_ROOT = Path(__file__).resolve().parent.parent -# Add shared dir for config_validator import -sys.path.insert(0, str(REPO_ROOT / "shared")) -AUTOMATION_DIR = REPO_ROOT / "data_out" / "repo_automation" -PID_FILE = AUTOMATION_DIR / "processes.json" -STATUS_FILE = AUTOMATION_DIR / "status.json" - -# Backward-compatible legacy locations. -LEGACY_PID_FILE = REPO_ROOT / "processes.json" -LEGACY_STATUS_FILE = REPO_ROOT / "automation_status.json" - -AUTOMATION_DIR.mkdir(parents=True, exist_ok=True) - - -class _ExistingProcessWrapper: - - def __init__(self, pid: int): - self.pid = pid - - def poll(self): # Returns None if running, non-zero if not - try: - if psutil is None: - # Without psutil we cannot confirm; assume running - return None - proc = psutil.Process(self.pid) - if proc.is_running() and proc.status() != psutil.STATUS_ZOMBIE: - return None - return 1 - except Exception: - return 1 - - -@dataclass -class ComponentConfig: - # Configuration for automated component - # required_packages: list of importable module names (pip names assumed identical unless - # a tuple 'pip_name:import_name' is provided). These will be verified/installed prior - # to component start to achieve zero manual intervention. - name: str - enabled: bool = True - script: Optional[str] = None - command: Optional[List[str]] = None - auto_restart: bool = True - health_check_interval: int = 300 # 5 minutes - dependencies: List[str] = field(default_factory=list) - required_packages: List[str] = field(default_factory=list) - - -@dataclass -class AutomationStatus: - # Overall automation status - generated_at: str - run_id: str - started: str - config_path: Optional[str] = None - config_paths: Dict[str, Optional[str]] = field(default_factory=dict) - uptime_seconds: float = 0 - components_running: Dict[str, bool] = field(default_factory=dict) - dependency_status: Dict[str, bool] = field(default_factory=dict) - last_health_check: Optional[str] = None - total_cycles: int = 0 - errors: List[str] = field(default_factory=list) - - -class RepoAutomation: - """ """ - - def __init__(self): - self.components: Dict[str, ComponentConfig] = self._init_components() - self.processes: Dict[str, Any] = {} - self.running = True - self.start_time = datetime.now(timezone.utc) - self.run_id = self.start_time.strftime("%Y%m%dT%H%M%SZ") - self.total_cycles = 0 - self.errors: List[str] = [] - self.dependency_status: Dict[str, bool] = {} - - # Attempt to auto-enable components based on config presence - self._auto_enable_components() - - # Attach to any existing processes from previous run (if processes.json exists) - self._attach_existing_from_pidfile() - - # Setup signal handlers - signal.signal(signal.SIGINT, self._signal_handler) - signal.signal(signal.SIGTERM, self._signal_handler) - - def _init_components(self) -> Dict[str, ComponentConfig]: - """Initialize all automation components with dependency metadata""" - return { - "aria": ComponentConfig( - name="Aria Character Automation", - script="scripts/aria_automation.py", - command=["python3", "scripts/aria_automation.py", "--mode", "full"], - auto_restart=True, - health_check_interval=60, - required_packages=["psutil"], - ), - "training": ComponentConfig( - name="Autonomous Training System", - script="scripts/autonomous_training_orchestrator.py", - command=["python3", "scripts/autonomous_training_orchestrator.py"], - auto_restart=True, - health_check_interval=300, - # Use proper pip/import mapping for PyYAML - required_packages=[ - "pandas", - "torch", - "numpy", - "PyYAML:yaml", - ], - ), - "quantum": ComponentConfig( - name="Quantum Computing Workflows", - script="scripts/quantum_autorun.py", - command=["python3", "scripts/quantum_autorun.py"], - auto_restart=False, - health_check_interval=600, - enabled=False, # Will be enabled if quantum_autorun.yaml exists - required_packages=[], # Add azure quantum SDK here when environment ready - ), - "evaluation": ComponentConfig( - name="Model Evaluation System", - script="scripts/evaluation_autorun.py", - command=["python3", "scripts/evaluation_autorun.py"], - auto_restart=False, - health_check_interval=300, - dependencies=["training"], - enabled=False, # Enabled if evaluation_autorun.yaml exists - required_packages=["scikit-learn", "numpy", "matplotlib", "seaborn"], - ), - "datasets": ComponentConfig( - name="Dataset Auto-Discovery (Integrated in training)", - script="scripts/autonomous_training_orchestrator.py", - command=["python3", "scripts/autonomous_training_orchestrator.py"], - auto_restart=False, - health_check_interval=3600, - enabled=False, # Included in training component - ), - "monitoring": ComponentConfig( - name="Status Dashboard", - script="scripts/status_dashboard.py", - command=["python3", "scripts/status_dashboard.py"], - auto_restart=False, - health_check_interval=60, - enabled=False, - # Correct pip/import names - required_packages=[ - "Flask", - "Flask-SocketIO:flask_socketio", - "python-socketio:socketio", - ], - ), - "backup": ComponentConfig( - name="Backup Manager", - script="scripts/backup_manager.py", - command=["python3", "scripts/backup_manager.py"], - auto_restart=False, - health_check_interval=3600, - enabled=False, - ), - } - - def _auto_enable_components(self): - """Enable optional components based on presence of their config files""" - config_keys = { - "quantum": "quantum_autorun", - "evaluation": "evaluation_autorun", - } - for name, config_key in config_keys.items(): - if name in self.components and resolve_existing_config_path( - REPO_ROOT, config_key - ): - self.components[name].enabled = True - - @staticmethod - def _read_json(path: Path) -> Optional[Dict[str, Any]]: - """Read JSON file safely and return None on parse/read errors.""" - try: - with open(path, "r") as f: - data = json.load(f) - return data if isinstance(data, dict) else None - except Exception: - return None - - def _pid_files(self) -> List[Path]: - """PID file candidates in canonical-first order.""" - return [PID_FILE, LEGACY_PID_FILE] - - def _status_files(self) -> List[Path]: - """Status file candidates in canonical-first order.""" - return [STATUS_FILE, LEGACY_STATUS_FILE] - - @staticmethod - def _utc_now_str() -> str: - """UTC timestamp in stable ISO-like format.""" - return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - - def _resolved_optional_config_paths(self) -> Dict[str, Optional[str]]: - """Resolve optional orchestrator config paths for status metadata.""" - config_keys = { - "quantum": "quantum_autorun", - "evaluation": "evaluation_autorun", - } - resolved: Dict[str, Optional[str]] = {} - for name, key in config_keys.items(): - path = resolve_existing_config_path(REPO_ROOT, key) - resolved[name] = ( - str(path.relative_to(REPO_ROOT)) if path is not None else None - ) - return resolved - - def _attach_existing_from_pidfile(self): - """Attach to previously recorded processes if still running""" - if psutil is None: - return - - # Read both canonical and legacy PID files; canonical entries win. - mapping: Dict[str, int] = {} - for pid_file in reversed(self._pid_files()): - if not pid_file.exists(): - continue - data = self._read_json(pid_file) - if not data: - continue - for name, pid in data.items(): - try: - mapping[name] = int(pid) - except Exception: - continue - - for name, pid in mapping.items(): - if name in self.components: - try: - proc = psutil.Process(pid) - if proc.is_running() and proc.status() != psutil.STATUS_ZOMBIE: - self.processes[name] = _ExistingProcessWrapper(pid) - except Exception: - continue - - def _signal_handler(self, signum, frame): - """Handle shutdown signals gracefully""" - print(f"\n⚠️ Received signal {signum}, shutting down...") - self.running = False - self.stop_all() - sys.exit(0) - - def _get_pid_map(self) -> Dict[str, int]: - """Load PID mapping from file if present""" - merged: Dict[str, int] = {} - # Legacy first, canonical last so canonical values win. - for pid_file in reversed(self._pid_files()): - if not pid_file.exists(): - continue - data = self._read_json(pid_file) - if not data: - continue - for key, value in data.items(): - if value is None: - continue - try: - merged[key] = int(value) - except Exception: - continue - return merged - - def _remove_pid_entry(self, name: str): - """Remove a single component entry from PID file""" - for pid_file in self._pid_files(): - if not pid_file.exists(): - continue - try: - data = self._read_json(pid_file) or {} - if name in data: - data.pop(name, None) - with open(pid_file, "w") as f: - json.dump(data, f, indent=2) - except Exception: - continue - - def save_status(self): - """Save current status to JSON""" - status = AutomationStatus( - generated_at=self._utc_now_str(), - run_id=self.run_id, - started=self.start_time.strftime("%Y-%m-%dT%H:%M:%SZ"), - config_path=None, - config_paths=self._resolved_optional_config_paths(), - uptime_seconds=( - datetime.now(timezone.utc) - self.start_time - ).total_seconds(), - components_running={ - name: self._is_component_running(name) - for name in self.components.keys() - }, - dependency_status=self.dependency_status, - last_health_check=self._utc_now_str(), - total_cycles=self.total_cycles, - errors=self.errors[-20:], # Last 20 errors - ) - - for status_file in self._status_files(): - try: - with open(status_file, "w") as f: - json.dump(vars(status), f, indent=2) - except Exception: - continue - - def _is_component_running(self, name: str) -> bool: - """Check if component is running""" - if name not in self.processes: - return False - - proc = self.processes[name] - if proc is None: - return False - - try: - return proc.poll() is None - except Exception: - return False - - def start_component(self, name: str) -> bool: - """Start a single component""" - if name not in self.components: - print(f"❌ Unknown component: {name}") - return False - - component = self.components[name] - - if not component.enabled: - print(f"⚠️ Component '{component.name}' is disabled") - return False - - # Check dependencies - for dep in component.dependencies: - if not self._is_component_running(dep): - print(f"⚠️ Dependency '{dep}' not running, starting it first...") - if not self.start_component(dep): - print(f"❌ Failed to start dependency '{dep}'") - return False - - # Detect and attach to existing process (prevents duplicates) - existing = self._find_existing_process(component) - if existing is not None: - print( - f"\n🔗 Found existing process for {component.name} (PID {existing.pid}), attaching instead of starting new instance" - ) - self.processes[name] = _ExistingProcessWrapper(existing.pid) - # Assume satisfied if already running - self.dependency_status[name] = True - self.save_status() - self._save_process_pids() - # Enforce single-instance: terminate any duplicates beyond the attached one - self._enforce_single_instance(component, keep_pid=existing.pid) - return True - - # Ensure dependencies (auto-install if missing) - if not self._ensure_dependencies(name, component.required_packages): - print( - f"❌ Cannot start {component.name} due to dependency installation failure" - ) - return False - - print(f"\n🚀 Starting {component.name}...") - - try: - # Start process - proc = subprocess.Popen( - component.command, - cwd=REPO_ROOT, - stdout=subprocess.DEVNULL, # Avoid pipe blocking - stderr=subprocess.DEVNULL, - ) - - self.processes[name] = proc - - # Wait with short polling interval instead of fixed sleep - max_wait = 2.0 - check_interval = 0.2 - elapsed = 0 - - while elapsed < max_wait: - if self._is_component_running(name): - print(f"✅ {component.name} started (PID {proc.pid})") - self._save_process_pids() - # Enforce single-instance: ensure no stray duplicates remain - self._enforce_single_instance(component, keep_pid=proc.pid) - return True - time.sleep(check_interval) - elapsed += check_interval - - if not self._is_component_running(name): - print(f"❌ {component.name} failed to start") - return False - else: - # Started but took the full wait time - print(f"✅ {component.name} started (PID {proc.pid})") - self._save_process_pids() - self._enforce_single_instance(component, keep_pid=proc.pid) - return True - - except Exception as e: - error = f"Failed to start {component.name}: {e}" - print(f"❌ {error}") - self.errors.append(error) - return False - - def stop_component(self, name: str): - """Stop a single component""" - if name not in self.components or name not in self.processes: - return - - component = self.components[name] - proc = self.processes[name] - - print(f"🛑 Stopping {component.name}...") - - try: - # Handle both subprocess.Popen and _ExistingProcessWrapper - if hasattr(proc, "terminate"): - proc.terminate() - proc.wait(timeout=10) - elif psutil is not None and hasattr(proc, "pid"): - p = psutil.Process(proc.pid) - p.terminate() - try: - p.wait(timeout=10) - except Exception: - pass - print(f"✅ {component.name} stopped") - except subprocess.TimeoutExpired: - print(f"⚠️ Force killing {component.name}...") - try: - if hasattr(proc, "kill"): - proc.kill() - proc.wait() - elif psutil is not None and hasattr(proc, "pid"): - p = psutil.Process(proc.pid) - p.kill() - except Exception: - pass - except Exception as e: - print(f"⚠️ Error stopping {component.name}: {e}") - - self.processes[name] = None - # Update PID file to reflect stop - self._remove_pid_entry(name) - self._save_process_pids() - - def start_all(self, components: Optional[List[str]] = None): - """Start all or specified components""" - print("\n" + "=" * 80) - print("🤖 Repository-Wide Automation Starting") - print("=" * 80) - - components_to_start = components or list(self.components.keys()) - - for name in components_to_start: - if name in self.components: - self.start_component(name) - else: - print(f"⚠️ Unknown component: {name}") - - self.save_status() - - def stop_all(self): - """Stop all components""" - print("\n🛑 Stopping all components...") - - for name in self.components.keys(): - self.stop_component(name) - - self.save_status() - # Clear PID file - for pid_file in self._pid_files(): - if pid_file.exists(): - try: - pid_file.unlink() - except Exception: - continue - print("✅ All components stopped") - - def health_check(self) -> Dict[str, bool]: - """Check health of all components""" - health = {} - - for name, component in self.components.items(): - is_running = self._is_component_running(name) - health[name] = is_running - - if component.enabled and not is_running and component.auto_restart: - print(f"\n🔄 Auto-restarting {component.name}...") - self.start_component(name) - - return health - - def _enforce_single_instance( - self, component: ComponentConfig, keep_pid: Optional[int] = None - ): - # Ensure only one process for the given component's script is running. - # Terminates any extra instances beyond keep_pid. - if psutil is None or not component.script: - return - script_name = Path(component.script).name - duplicates = [] - try: - for proc in psutil.process_iter(["pid", "cmdline", "create_time"]): - cmd = proc.info.get("cmdline") or [] - if any(script_name in part for part in cmd): - if keep_pid is not None and proc.pid == keep_pid: - continue - duplicates.append(proc) - except Exception: - return - if len(duplicates) > 0: - print( - f"\n⚖️ Enforcing single-instance for {component.name}: {len(duplicates)} duplicate(s) found" - ) - for p in duplicates: - try: - p.terminate() - p.wait(timeout=5) - print(f" • Terminated duplicate PID {p.pid}") - except Exception: - try: - p.kill() - print(f" • Killed duplicate PID {p.pid}") - except Exception: - print(f" • Unable to terminate duplicate PID {p.pid}") - - def _find_existing_process(self, component: ComponentConfig): - """Attempt to find an already-running process for the component's script""" - if psutil is None or not component.script: - return None - script_name = Path(component.script).name - try: - for proc in psutil.process_iter(["pid", "cmdline"]): - cmd = proc.info.get("cmdline") or [] - if any(script_name in part for part in cmd): - return proc - except Exception: - return None - return None - - def _ensure_dependencies(self, name: str, required: List[str]) -> bool: - """Ensure required Python packages are installed. Returns True if all satisfied.""" - if not required: - self.dependency_status[name] = True - return True - missing: List[str] = [] - for spec in required: - pip_name, import_name = ( - (spec.split(":", 1) + [spec])[:2] if ":" in spec else (spec, spec) - ) - try: - importlib.import_module(import_name) - except Exception: - missing.append(pip_name) - if not missing: - self.dependency_status[name] = True - return True - print(f"🔧 Installing missing dependencies for {name}: {', '.join(missing)}") - for pkg in missing: - try: - result = subprocess.run( - [sys.executable, "-m", "pip", "install", pkg], - capture_output=True, - text=True, - ) - if result.returncode != 0: - err = ( - result.stderr.strip().splitlines()[-1] - if result.stderr - else f"Unknown error installing {pkg}" - ) - self.errors.append( - f"Dependency install failed ({name}): {pkg} -> {err}" - ) - self.dependency_status[name] = False - return False - except Exception as e: - self.errors.append( - f"Dependency install exception ({name}): {pkg} -> {e}" - ) - self.dependency_status[name] = False - return False - # Verify imports post-install - post_missing = [] - for spec in required: - pip_name, import_name = ( - (spec.split(":", 1) + [spec])[:2] if ":" in spec else (spec, spec) - ) - try: - importlib.import_module(import_name) - except Exception: - post_missing.append(pip_name) - if post_missing: - self.errors.append( - f"Dependencies still missing after install ({name}): {', '.join(post_missing)}" - ) - self.dependency_status[name] = False - return False - self.dependency_status[name] = True - return True - - def _save_process_pids(self): - """Persist current process PIDs for continuity""" - mapping = { - name: getattr(proc, "pid", None) - for name, proc in self.processes.items() - if proc is not None - } - for pid_file in self._pid_files(): - try: - with open(pid_file, "w") as f: - json.dump(mapping, f, indent=2) - except Exception: - continue - - def monitoring_loop(self, interval: int = 60): - """Continuous monitoring with auto-recovery""" - print(f"\n👁️ Starting monitoring loop (interval: {interval}s)") - - while self.running: - time.sleep(interval) - - self.total_cycles += 1 - health = self.health_check() - - print( - f"\n🔍 Health check #{self.total_cycles}: " - f"{sum(health.values())}/{len(health)} components running" - ) - - self.save_status() - - def show_status(self): - """Display current status""" - pid_map = self._get_pid_map() - status = None - for status_file in self._status_files(): - if not status_file.exists(): - continue - data = self._read_json(status_file) - if data: - status = data - break - - print("\n" + "=" * 80) - print("🤖 Repository Automation Status") - print("=" * 80) - - if status: - print(f"Started: {status.get('started', 'unknown')}") - print(f"Uptime: {timedelta(seconds=int(status.get('uptime_seconds', 0)))}") - print(f"Health Checks: {status.get('total_cycles', 0)}") - else: - print("Started: unknown (status file missing)") - print("Uptime: n/a") - print("Health Checks: n/a") - - print("\n📊 Components:") - # Build a dynamic running map using PID file and psutil - dynamic_running: Dict[str, bool] = {} - if psutil is not None: - for name, pid in pid_map.items(): - try: - p = psutil.Process(pid) - dynamic_running[name] = ( - p.is_running() and p.status() != psutil.STATUS_ZOMBIE - ) - except Exception: - dynamic_running[name] = False - - # Fallback: if PID not recorded, try discovering existing processes - if psutil is not None: - for name, component in self.components.items(): - if name not in dynamic_running: - try: - proc = self._find_existing_process(component) - dynamic_running[name] = proc is not None - except Exception: - dynamic_running[name] = False - - # Prefer dynamic running info; fall back to status file content - components_running = status.get("components_running", {}) if status else {} - - for name in self.components.keys(): - running = dynamic_running.get(name, components_running.get(name, False)) - component = self.components.get(name) - if component: - status_icon = "✅" if running else "❌" - dep_ok = ( - status.get("dependency_status", {}).get(name, True) - if status - else True - ) - dep_icon = "🧩" if dep_ok else "⚠️" - pid_info = f" (PID {pid_map.get(name)})" if name in pid_map else "" - print(f" {status_icon} {component.name}{pid_info} ({dep_icon} deps)") - - # Recent errors - if status and status.get("errors"): - print(f"\n⚠️ Recent Errors ({len(status['errors'])}):") - for error in status["errors"][-5:]: - print(f" - {error}") - - if not status and not pid_map: - print( - "\nℹ️ No status or PID information found. If automation is running in another session, it may not have written status yet." - ) - - print("=" * 80 + "\n") - - def run_daemon(self): - """Run as background daemon""" - print("\n🌙 Running in daemon mode...") - - # Start monitoring in background thread - monitor_thread = threading.Thread(target=self.monitoring_loop, daemon=True) - monitor_thread.start() - - try: - while self.running: - time.sleep(10) - self.save_status() - except KeyboardInterrupt: - print("\n⚠️ Daemon stopped by user") - - -def main(): - parser = argparse.ArgumentParser( - description="Repository-Wide Automation System", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # Start all automation - python scripts/repo_automation.py --start - - # Start specific components - python scripts/repo_automation.py --start --components aria,training - - # Check status - python scripts/repo_automation.py --status - - # Stop all - python scripts/repo_automation.py --stop - - # Run as daemon - python scripts/repo_automation.py --daemon - """, - ) - - parser.add_argument( - "--start", action="store_true", help="Start automation components" - ) - parser.add_argument( - "--stop", action="store_true", help="Stop all automation components" - ) - parser.add_argument("--status", action="store_true", help="Show current status") - parser.add_argument( - "--daemon", action="store_true", help="Run as background daemon" - ) - parser.add_argument( - "--components", - help="Comma-separated list of components to start (aria,training,quantum,etc.)", - ) - parser.add_argument( - "--validate", - action="store_true", - help="Validate all orchestrator configs before starting", - ) - - args = parser.parse_args() - - # Validate configurations if requested or before daemon start - if args.validate or args.start: - try: - from shared.config_validator import validate_configs_before_daemon - except ImportError: - from config_validator import validate_configs_before_daemon - - print("\n🔍 Pre-flight validation...") - all_valid, results = validate_configs_before_daemon( - repo_root=REPO_ROOT, exit_on_error=False, verbose=False - ) - - if args.validate: - # Exit after validation if --validate flag was used - sys.exit(0 if all_valid else 1) - - if not all_valid: - print("\n⚠️ Configuration issues detected. Proceeding anyway...") - - automation = RepoAutomation() - - if args.status: - automation.show_status() - return - - if args.stop: - # Load existing processes if any - automation.stop_all() - return - - if args.start: - components = None - if args.components: - components = [c.strip() for c in args.components.split(",")] - - automation.start_all(components) - - if args.daemon: - automation.run_daemon() - else: - # Keep running with monitoring - try: - automation.monitoring_loop() - except KeyboardInterrupt: - print("\n⚠️ Stopped by user") - automation.stop_all() - return - - # Default: show help - parser.print_help() - - -if __name__ == "__main__": - main() +#!/usr/bin/env python3 +"""Repository-Wide Automation System + +This script provides lightweight management for repository-level +automation components: starting/stopping processes, tracking PIDs and +status, performing health checks and automatically attempting to +install Python package dependencies when needed. +""" + +import argparse +import importlib +import json +import signal +import subprocess +import sys +import threading +import time +from dataclasses import dataclass, field +from datetime import datetime, timedelta, timezone +from pathlib import Path +from typing import Any, Dict, List, Optional + +try: + from .config_paths import resolve_existing_config_path +except ImportError: + from config_paths import resolve_existing_config_path + +try: + import psutil +except Exception: # pragma: no cover - optional dependency + psutil = None + +REPO_ROOT = Path(__file__).resolve().parent.parent +# Add shared dir for config_validator import +sys.path.insert(0, str(REPO_ROOT / "shared")) +AUTOMATION_DIR = REPO_ROOT / "data_out" / "repo_automation" +PID_FILE = AUTOMATION_DIR / "processes.json" +STATUS_FILE = AUTOMATION_DIR / "status.json" + +# Backward-compatible legacy locations. +LEGACY_PID_FILE = REPO_ROOT / "processes.json" +LEGACY_STATUS_FILE = REPO_ROOT / "automation_status.json" + +AUTOMATION_DIR.mkdir(parents=True, exist_ok=True) + + +class _ExistingProcessWrapper: + + def __init__(self, pid: int): + self.pid = pid + + def poll(self): # Returns None if running, non-zero if not + try: + if psutil is None: + # Without psutil we cannot confirm; assume running + return None + proc = psutil.Process(self.pid) + if proc.is_running() and proc.status() != psutil.STATUS_ZOMBIE: + return None + return 1 + except Exception: + return 1 + + +@dataclass +class ComponentConfig: + # Configuration for automated component + # required_packages: list of importable module names (pip names assumed identical unless + # a tuple 'pip_name:import_name' is provided). These will be verified/installed prior + # to component start to achieve zero manual intervention. + name: str + enabled: bool = True + script: Optional[str] = None + command: Optional[List[str]] = None + auto_restart: bool = True + health_check_interval: int = 300 # 5 minutes + dependencies: List[str] = field(default_factory=list) + required_packages: List[str] = field(default_factory=list) + + +@dataclass +class AutomationStatus: + # Overall automation status + generated_at: str + run_id: str + started: str + config_path: Optional[str] = None + config_paths: Dict[str, Optional[str]] = field(default_factory=dict) + uptime_seconds: float = 0 + components_running: Dict[str, bool] = field(default_factory=dict) + dependency_status: Dict[str, bool] = field(default_factory=dict) + last_health_check: Optional[str] = None + total_cycles: int = 0 + errors: List[str] = field(default_factory=list) + + +class RepoAutomation: + """ """ + + def __init__(self): + self.components: Dict[str, ComponentConfig] = self._init_components() + self.processes: Dict[str, Any] = {} + self.running = True + self.start_time = datetime.now(timezone.utc) + self.run_id = self.start_time.strftime("%Y%m%dT%H%M%SZ") + self.total_cycles = 0 + self.errors: List[str] = [] + self.dependency_status: Dict[str, bool] = {} + + # Attempt to auto-enable components based on config presence + self._auto_enable_components() + + # Attach to any existing processes from previous run (if processes.json exists) + self._attach_existing_from_pidfile() + + # Setup signal handlers + signal.signal(signal.SIGINT, self._signal_handler) + signal.signal(signal.SIGTERM, self._signal_handler) + + def _init_components(self) -> Dict[str, ComponentConfig]: + """Initialize all automation components with dependency metadata""" + return { + "aria": ComponentConfig( + name="Aria Character Automation", + script="scripts/aria_automation.py", + command=["python3", "scripts/aria_automation.py", "--mode", "full"], + auto_restart=True, + health_check_interval=60, + required_packages=["psutil"], + ), + "training": ComponentConfig( + name="Autonomous Training System", + script="scripts/autonomous_training_orchestrator.py", + command=["python3", "scripts/autonomous_training_orchestrator.py"], + auto_restart=True, + health_check_interval=300, + # Use proper pip/import mapping for PyYAML + required_packages=[ + "pandas", + "torch", + "numpy", + "PyYAML:yaml", + ], + ), + "quantum": ComponentConfig( + name="Quantum Computing Workflows", + script="scripts/quantum_autorun.py", + command=["python3", "scripts/quantum_autorun.py"], + auto_restart=False, + health_check_interval=600, + enabled=False, # Will be enabled if quantum_autorun.yaml exists + required_packages=[], # Add azure quantum SDK here when environment ready + ), + "evaluation": ComponentConfig( + name="Model Evaluation System", + script="scripts/evaluation_autorun.py", + command=["python3", "scripts/evaluation_autorun.py"], + auto_restart=False, + health_check_interval=300, + dependencies=["training"], + enabled=False, # Enabled if evaluation_autorun.yaml exists + required_packages=["scikit-learn", "numpy", "matplotlib", "seaborn"], + ), + "datasets": ComponentConfig( + name="Dataset Auto-Discovery (Integrated in training)", + script="scripts/autonomous_training_orchestrator.py", + command=["python3", "scripts/autonomous_training_orchestrator.py"], + auto_restart=False, + health_check_interval=3600, + enabled=False, # Included in training component + ), + "monitoring": ComponentConfig( + name="Status Dashboard", + script="scripts/status_dashboard.py", + command=["python3", "scripts/status_dashboard.py"], + auto_restart=False, + health_check_interval=60, + enabled=False, + # Correct pip/import names + required_packages=[ + "Flask", + "Flask-SocketIO:flask_socketio", + "python-socketio:socketio", + ], + ), + "backup": ComponentConfig( + name="Backup Manager", + script="scripts/backup_manager.py", + command=["python3", "scripts/backup_manager.py"], + auto_restart=False, + health_check_interval=3600, + enabled=False, + ), + } + + def _auto_enable_components(self): + """Enable optional components based on presence of their config files""" + config_keys = { + "quantum": "quantum_autorun", + "evaluation": "evaluation_autorun", + } + for name, config_key in config_keys.items(): + if name in self.components and resolve_existing_config_path( + REPO_ROOT, config_key + ): + self.components[name].enabled = True + + @staticmethod + def _read_json(path: Path) -> Optional[Dict[str, Any]]: + """Read JSON file safely and return None on parse/read errors.""" + try: + with open(path, "r") as f: + data = json.load(f) + return data if isinstance(data, dict) else None + except Exception: + return None + + def _pid_files(self) -> List[Path]: + """PID file candidates in canonical-first order.""" + return [PID_FILE, LEGACY_PID_FILE] + + def _status_files(self) -> List[Path]: + """Status file candidates in canonical-first order.""" + return [STATUS_FILE, LEGACY_STATUS_FILE] + + @staticmethod + def _utc_now_str() -> str: + """UTC timestamp in stable ISO-like format.""" + return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") + + def _resolved_optional_config_paths(self) -> Dict[str, Optional[str]]: + """Resolve optional orchestrator config paths for status metadata.""" + config_keys = { + "quantum": "quantum_autorun", + "evaluation": "evaluation_autorun", + } + resolved: Dict[str, Optional[str]] = {} + for name, key in config_keys.items(): + path = resolve_existing_config_path(REPO_ROOT, key) + resolved[name] = ( + str(path.relative_to(REPO_ROOT)) if path is not None else None + ) + return resolved + + def _attach_existing_from_pidfile(self): + """Attach to previously recorded processes if still running""" + if psutil is None: + return + + # Read both canonical and legacy PID files; canonical entries win. + mapping: Dict[str, int] = {} + for pid_file in reversed(self._pid_files()): + if not pid_file.exists(): + continue + data = self._read_json(pid_file) + if not data: + continue + for name, pid in data.items(): + try: + mapping[name] = int(pid) + except Exception: + continue + + for name, pid in mapping.items(): + if name in self.components: + try: + proc = psutil.Process(pid) + if proc.is_running() and proc.status() != psutil.STATUS_ZOMBIE: + self.processes[name] = _ExistingProcessWrapper(pid) + except Exception: + continue + + def _signal_handler(self, signum, frame): + """Handle shutdown signals gracefully""" + print(f"\n⚠️ Received signal {signum}, shutting down...") + self.running = False + self.stop_all() + sys.exit(0) + + def _get_pid_map(self) -> Dict[str, int]: + """Load PID mapping from file if present""" + merged: Dict[str, int] = {} + # Legacy first, canonical last so canonical values win. + for pid_file in reversed(self._pid_files()): + if not pid_file.exists(): + continue + data = self._read_json(pid_file) + if not data: + continue + for key, value in data.items(): + if value is None: + continue + try: + merged[key] = int(value) + except Exception: + continue + return merged + + def _remove_pid_entry(self, name: str): + """Remove a single component entry from PID file""" + for pid_file in self._pid_files(): + if not pid_file.exists(): + continue + try: + data = self._read_json(pid_file) or {} + if name in data: + data.pop(name, None) + with open(pid_file, "w") as f: + json.dump(data, f, indent=2) + except Exception: + continue + + def save_status(self): + """Save current status to JSON""" + status = AutomationStatus( + generated_at=self._utc_now_str(), + run_id=self.run_id, + started=self.start_time.strftime("%Y-%m-%dT%H:%M:%SZ"), + config_path=None, + config_paths=self._resolved_optional_config_paths(), + uptime_seconds=( + datetime.now(timezone.utc) - self.start_time + ).total_seconds(), + components_running={ + name: self._is_component_running(name) + for name in self.components.keys() + }, + dependency_status=self.dependency_status, + last_health_check=self._utc_now_str(), + total_cycles=self.total_cycles, + errors=self.errors[-20:], # Last 20 errors + ) + + for status_file in self._status_files(): + try: + with open(status_file, "w") as f: + json.dump(vars(status), f, indent=2) + except Exception: + continue + + def _is_component_running(self, name: str) -> bool: + """Check if component is running""" + if name not in self.processes: + return False + + proc = self.processes[name] + if proc is None: + return False + + try: + return proc.poll() is None + except Exception: + return False + + def start_component(self, name: str) -> bool: + """Start a single component""" + if name not in self.components: + print(f"❌ Unknown component: {name}") + return False + + component = self.components[name] + + if not component.enabled: + print(f"⚠️ Component '{component.name}' is disabled") + return False + + # Check dependencies + for dep in component.dependencies: + if not self._is_component_running(dep): + print(f"⚠️ Dependency '{dep}' not running, starting it first...") + if not self.start_component(dep): + print(f"❌ Failed to start dependency '{dep}'") + return False + + # Detect and attach to existing process (prevents duplicates) + existing = self._find_existing_process(component) + if existing is not None: + print( + f"\n🔗 Found existing process for {component.name} (PID {existing.pid}), attaching instead of starting new instance" + ) + self.processes[name] = _ExistingProcessWrapper(existing.pid) + # Assume satisfied if already running + self.dependency_status[name] = True + self.save_status() + self._save_process_pids() + # Enforce single-instance: terminate any duplicates beyond the attached one + self._enforce_single_instance(component, keep_pid=existing.pid) + return True + + # Ensure dependencies (auto-install if missing) + if not self._ensure_dependencies(name, component.required_packages): + print( + f"❌ Cannot start {component.name} due to dependency installation failure" + ) + return False + + print(f"\n🚀 Starting {component.name}...") + + try: + # Start process + proc = subprocess.Popen( + component.command, + cwd=REPO_ROOT, + stdout=subprocess.DEVNULL, # Avoid pipe blocking + stderr=subprocess.DEVNULL, + ) + + self.processes[name] = proc + + # Wait with short polling interval instead of fixed sleep + max_wait = 2.0 + check_interval = 0.2 + elapsed = 0 + + while elapsed < max_wait: + if self._is_component_running(name): + print(f"✅ {component.name} started (PID {proc.pid})") + self._save_process_pids() + # Enforce single-instance: ensure no stray duplicates remain + self._enforce_single_instance(component, keep_pid=proc.pid) + return True + time.sleep(check_interval) + elapsed += check_interval + + if not self._is_component_running(name): + print(f"❌ {component.name} failed to start") + return False + else: + # Started but took the full wait time + print(f"✅ {component.name} started (PID {proc.pid})") + self._save_process_pids() + self._enforce_single_instance(component, keep_pid=proc.pid) + return True + + except Exception as e: + error = f"Failed to start {component.name}: {e}" + print(f"❌ {error}") + self.errors.append(error) + return False + + def stop_component(self, name: str): + """Stop a single component""" + if name not in self.components or name not in self.processes: + return + + component = self.components[name] + proc = self.processes[name] + + print(f"🛑 Stopping {component.name}...") + + try: + # Handle both subprocess.Popen and _ExistingProcessWrapper + if hasattr(proc, "terminate"): + proc.terminate() + proc.wait(timeout=10) + elif psutil is not None and hasattr(proc, "pid"): + p = psutil.Process(proc.pid) + p.terminate() + try: + p.wait(timeout=10) + except Exception: + pass + print(f"✅ {component.name} stopped") + except subprocess.TimeoutExpired: + print(f"⚠️ Force killing {component.name}...") + try: + if hasattr(proc, "kill"): + proc.kill() + proc.wait() + elif psutil is not None and hasattr(proc, "pid"): + p = psutil.Process(proc.pid) + p.kill() + except Exception: + pass + except Exception as e: + print(f"⚠️ Error stopping {component.name}: {e}") + + self.processes[name] = None + # Update PID file to reflect stop + self._remove_pid_entry(name) + self._save_process_pids() + + def start_all(self, components: Optional[List[str]] = None): + """Start all or specified components""" + print("\n" + "=" * 80) + print("🤖 Repository-Wide Automation Starting") + print("=" * 80) + + components_to_start = components or list(self.components.keys()) + + for name in components_to_start: + if name in self.components: + self.start_component(name) + else: + print(f"⚠️ Unknown component: {name}") + + self.save_status() + + def stop_all(self): + """Stop all components""" + print("\n🛑 Stopping all components...") + + for name in self.components.keys(): + self.stop_component(name) + + self.save_status() + # Clear PID file + for pid_file in self._pid_files(): + if pid_file.exists(): + try: + pid_file.unlink() + except Exception: + continue + print("✅ All components stopped") + + def health_check(self) -> Dict[str, bool]: + """Check health of all components""" + health = {} + + for name, component in self.components.items(): + is_running = self._is_component_running(name) + health[name] = is_running + + if component.enabled and not is_running and component.auto_restart: + print(f"\n🔄 Auto-restarting {component.name}...") + self.start_component(name) + + return health + + def _enforce_single_instance( + self, component: ComponentConfig, keep_pid: Optional[int] = None + ): + # Ensure only one process for the given component's script is running. + # Terminates any extra instances beyond keep_pid. + if psutil is None or not component.script: + return + script_name = Path(component.script).name + duplicates = [] + try: + for proc in psutil.process_iter(["pid", "cmdline", "create_time"]): + cmd = proc.info.get("cmdline") or [] + if any(script_name in part for part in cmd): + if keep_pid is not None and proc.pid == keep_pid: + continue + duplicates.append(proc) + except Exception: + return + if len(duplicates) > 0: + print( + f"\n⚖️ Enforcing single-instance for {component.name}: {len(duplicates)} duplicate(s) found" + ) + for p in duplicates: + try: + p.terminate() + p.wait(timeout=5) + print(f" • Terminated duplicate PID {p.pid}") + except Exception: + try: + p.kill() + print(f" • Killed duplicate PID {p.pid}") + except Exception: + print(f" • Unable to terminate duplicate PID {p.pid}") + + def _find_existing_process(self, component: ComponentConfig): + """Attempt to find an already-running process for the component's script""" + if psutil is None or not component.script: + return None + script_name = Path(component.script).name + try: + for proc in psutil.process_iter(["pid", "cmdline"]): + cmd = proc.info.get("cmdline") or [] + if any(script_name in part for part in cmd): + return proc + except Exception: + return None + return None + + def _ensure_dependencies(self, name: str, required: List[str]) -> bool: + """Ensure required Python packages are installed. Returns True if all satisfied.""" + if not required: + self.dependency_status[name] = True + return True + missing: List[str] = [] + for spec in required: + pip_name, import_name = ( + (spec.split(":", 1) + [spec])[:2] if ":" in spec else (spec, spec) + ) + try: + importlib.import_module(import_name) + except Exception: + missing.append(pip_name) + if not missing: + self.dependency_status[name] = True + return True + print(f"🔧 Installing missing dependencies for {name}: {', '.join(missing)}") + for pkg in missing: + try: + result = subprocess.run( + [sys.executable, "-m", "pip", "install", pkg], + capture_output=True, + text=True, + ) + if result.returncode != 0: + err = ( + result.stderr.strip().splitlines()[-1] + if result.stderr + else f"Unknown error installing {pkg}" + ) + self.errors.append( + f"Dependency install failed ({name}): {pkg} -> {err}" + ) + self.dependency_status[name] = False + return False + except Exception as e: + self.errors.append( + f"Dependency install exception ({name}): {pkg} -> {e}" + ) + self.dependency_status[name] = False + return False + # Verify imports post-install + post_missing = [] + for spec in required: + pip_name, import_name = ( + (spec.split(":", 1) + [spec])[:2] if ":" in spec else (spec, spec) + ) + try: + importlib.import_module(import_name) + except Exception: + post_missing.append(pip_name) + if post_missing: + self.errors.append( + f"Dependencies still missing after install ({name}): {', '.join(post_missing)}" + ) + self.dependency_status[name] = False + return False + self.dependency_status[name] = True + return True + + def _save_process_pids(self): + """Persist current process PIDs for continuity""" + mapping = { + name: getattr(proc, "pid", None) + for name, proc in self.processes.items() + if proc is not None + } + for pid_file in self._pid_files(): + try: + with open(pid_file, "w") as f: + json.dump(mapping, f, indent=2) + except Exception: + continue + + def monitoring_loop(self, interval: int = 60): + """Continuous monitoring with auto-recovery""" + print(f"\n👁️ Starting monitoring loop (interval: {interval}s)") + + while self.running: + time.sleep(interval) + + self.total_cycles += 1 + health = self.health_check() + + print( + f"\n🔍 Health check #{self.total_cycles}: " + f"{sum(health.values())}/{len(health)} components running" + ) + + self.save_status() + + def show_status(self): + """Display current status""" + pid_map = self._get_pid_map() + status = None + for status_file in self._status_files(): + if not status_file.exists(): + continue + data = self._read_json(status_file) + if data: + status = data + break + + print("\n" + "=" * 80) + print("🤖 Repository Automation Status") + print("=" * 80) + + if status: + print(f"Started: {status.get('started', 'unknown')}") + print(f"Uptime: {timedelta(seconds=int(status.get('uptime_seconds', 0)))}") + print(f"Health Checks: {status.get('total_cycles', 0)}") + else: + print("Started: unknown (status file missing)") + print("Uptime: n/a") + print("Health Checks: n/a") + + print("\n📊 Components:") + # Build a dynamic running map using PID file and psutil + dynamic_running: Dict[str, bool] = {} + if psutil is not None: + for name, pid in pid_map.items(): + try: + p = psutil.Process(pid) + dynamic_running[name] = ( + p.is_running() and p.status() != psutil.STATUS_ZOMBIE + ) + except Exception: + dynamic_running[name] = False + + # Fallback: if PID not recorded, try discovering existing processes + if psutil is not None: + for name, component in self.components.items(): + if name not in dynamic_running: + try: + proc = self._find_existing_process(component) + dynamic_running[name] = proc is not None + except Exception: + dynamic_running[name] = False + + # Prefer dynamic running info; fall back to status file content + components_running = status.get("components_running", {}) if status else {} + + for name in self.components.keys(): + running = dynamic_running.get(name, components_running.get(name, False)) + component = self.components.get(name) + if component: + status_icon = "✅" if running else "❌" + dep_ok = ( + status.get("dependency_status", {}).get(name, True) + if status + else True + ) + dep_icon = "🧩" if dep_ok else "⚠️" + pid_info = f" (PID {pid_map.get(name)})" if name in pid_map else "" + print(f" {status_icon} {component.name}{pid_info} ({dep_icon} deps)") + + # Recent errors + if status and status.get("errors"): + print(f"\n⚠️ Recent Errors ({len(status['errors'])}):") + for error in status["errors"][-5:]: + print(f" - {error}") + + if not status and not pid_map: + print( + "\nℹ️ No status or PID information found. If automation is running in another session, it may not have written status yet." + ) + + print("=" * 80 + "\n") + + def run_daemon(self): + """Run as background daemon""" + print("\n🌙 Running in daemon mode...") + + # Start monitoring in background thread + monitor_thread = threading.Thread(target=self.monitoring_loop, daemon=True) + monitor_thread.start() + + try: + while self.running: + time.sleep(10) + self.save_status() + except KeyboardInterrupt: + print("\n⚠️ Daemon stopped by user") + + +def main(): + parser = argparse.ArgumentParser( + description="Repository-Wide Automation System", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Start all automation + python scripts/repo_automation.py --start + + # Start specific components + python scripts/repo_automation.py --start --components aria,training + + # Check status + python scripts/repo_automation.py --status + + # Stop all + python scripts/repo_automation.py --stop + + # Run as daemon + python scripts/repo_automation.py --daemon + """, + ) + + parser.add_argument( + "--start", action="store_true", help="Start automation components" + ) + parser.add_argument( + "--stop", action="store_true", help="Stop all automation components" + ) + parser.add_argument("--status", action="store_true", help="Show current status") + parser.add_argument( + "--daemon", action="store_true", help="Run as background daemon" + ) + parser.add_argument( + "--components", + help="Comma-separated list of components to start (aria,training,quantum,etc.)", + ) + parser.add_argument( + "--validate", + action="store_true", + help="Validate all orchestrator configs before starting", + ) + + args = parser.parse_args() + + # Validate configurations if requested or before daemon start + if args.validate or args.start: + try: + from shared.config_validator import validate_configs_before_daemon + except ImportError: + from config_validator import validate_configs_before_daemon + + print("\n🔍 Pre-flight validation...") + all_valid, results = validate_configs_before_daemon( + repo_root=REPO_ROOT, exit_on_error=False, verbose=False + ) + + if args.validate: + # Exit after validation if --validate flag was used + sys.exit(0 if all_valid else 1) + + if not all_valid: + print("\n⚠️ Configuration issues detected. Proceeding anyway...") + + automation = RepoAutomation() + + if args.status: + automation.show_status() + return + + if args.stop: + # Load existing processes if any + automation.stop_all() + return + + if args.start: + components = None + if args.components: + components = [c.strip() for c in args.components.split(",")] + + automation.start_all(components) + + if args.daemon: + automation.run_daemon() + else: + # Keep running with monitoring + try: + automation.monitoring_loop() + except KeyboardInterrupt: + print("\n⚠️ Stopped by user") + automation.stop_all() + return + + # Default: show help + parser.print_help() + + +if __name__ == "__main__": + main() diff --git a/scripts/self_learning_chat.py b/scripts/self_learning_chat.py index 0ece86886..db5d76ae4 100644 --- a/scripts/self_learning_chat.py +++ b/scripts/self_learning_chat.py @@ -1,6 +1,6 @@ -#!/usr/bin/env python -"""Compatibility shim. Actual implementation in scripts/training/cli/self_learning_chat.py""" -from scripts.training.cli.self_learning_chat import main - -if __name__ == "__main__": - raise SystemExit(main()) +#!/usr/bin/env python +"""Compatibility shim. Actual implementation in scripts/training/cli/self_learning_chat.py""" +from scripts.training.cli.self_learning_chat import main + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/start_repo_automation.sh b/scripts/start_repo_automation.sh index 36d587a24..c86dccd1c 100644 --- a/scripts/start_repo_automation.sh +++ b/scripts/start_repo_automation.sh @@ -1,268 +1,268 @@ -#!/bin/bash -# Complete Repository Automation Startup Script -# One-command automation for the entire Aria repository - -set -e - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -REPO_ROOT="$(dirname "$SCRIPT_DIR")" - -# Colors -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -BLUE='\033[0;34m' -CYAN='\033[0;36m' -MAGENTA='\033[0;35m' -NC='\033[0m' - -function print_banner() { - echo -e "${BLUE}" - cat << "EOF" -╔══════════════════════════════════════════════════════════════════════════════╗ -║ 🚀 ARIA REPOSITORY AUTOMATION SYSTEM 🚀 ║ -║ Complete Automation for the Entire Repo ║ -╚══════════════════════════════════════════════════════════════════════════════╝ -EOF - echo -e "${NC}" -} - -function show_components() { - echo -e "${CYAN}Available Components:${NC}" - echo "" - echo " 1. ${GREEN}aria${NC} - Aria character (server + training)" - echo " 2. ${GREEN}training${NC} - LoRA training pipelines" - echo " 3. ${GREEN}quantum${NC} - Quantum computing workflows" - echo " 4. ${GREEN}evaluation${NC} - Model evaluation system" - echo " 5. ${GREEN}datasets${NC} - Auto dataset discovery" - echo " 6. ${GREEN}monitoring${NC} - System health monitoring" - echo " 7. ${GREEN}backup${NC} - Automated backups" - echo "" - echo " ${YELLOW}ALL${NC} - Start all components" - echo "" -} - -function show_menu() { - echo -e "${BLUE}Select automation mode:${NC}" - echo "" - echo " ${GREEN}1)${NC} Full Automation - Start ALL components (production)" - echo " ${GREEN}2)${NC} Aria Only - Just Aria character automation" - echo " ${GREEN}3)${NC} Training Pipeline - Training + evaluation only" - echo " ${GREEN}4)${NC} Custom Selection - Choose specific components" - echo " ${GREEN}5)${NC} Status - Check current automation status" - echo " ${GREEN}6)${NC} Stop All - Stop all automation" - echo " ${GREEN}0)${NC} Exit" - echo "" - read -p "Enter choice [0-6]: " choice -} - -function check_dependencies() { - echo -e "${YELLOW}Checking dependencies...${NC}" - - if ! command -v python3 &> /dev/null; then - echo -e "${RED}❌ Python 3 not found${NC}" - exit 1 - fi - echo -e "${GREEN}✅ Python 3: $(python3 --version)${NC}" - - if ! python3 -c "import psutil" 2>/dev/null; then - echo -e "${YELLOW}⚠️ psutil not installed, installing...${NC}" - pip3 install psutil - fi - echo -e "${GREEN}✅ psutil installed${NC}" -} - -function start_full_automation() { - echo -e "${GREEN}🚀 Starting Full Repository Automation...${NC}" - echo "" - python3 "$REPO_ROOT/scripts/repo_automation.py" --start --daemon -} - -function start_aria_only() { - echo -e "${GREEN}🚀 Starting Aria Automation Only...${NC}" - echo "" - python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components aria --daemon -} - -function start_training_pipeline() { - echo -e "${GREEN}🚀 Starting Training Pipeline...${NC}" - echo "" - python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components training,evaluation --daemon -} - -function start_custom() { - echo "" - show_components - read -p "Enter components (comma-separated, e.g., aria,training,quantum): " components - - if [ -z "$components" ]; then - echo -e "${RED}❌ No components specified${NC}" - return - fi - - echo -e "${GREEN}🚀 Starting selected components...${NC}" - echo "" - python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components "$components" --daemon -} - -function show_status() { - python3 "$REPO_ROOT/scripts/repo_automation.py" --status -} - -function stop_all() { - echo -e "${YELLOW}🛑 Stopping all automation...${NC}" - python3 "$REPO_ROOT/scripts/repo_automation.py" --stop - echo -e "${GREEN}✅ All automation stopped${NC}" -} - -function run_background() { - local mode=$1 - echo -e "${GREEN}🚀 Starting in background mode...${NC}" - - case "$mode" in - full) - nohup python3 "$REPO_ROOT/scripts/repo_automation.py" --start --daemon \ - > "$REPO_ROOT/data_out/repo_automation/automation.log" 2>&1 & - ;; - aria) - nohup python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components aria --daemon \ - > "$REPO_ROOT/data_out/repo_automation/automation.log" 2>&1 & - ;; - training) - nohup python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components training,evaluation --daemon \ - > "$REPO_ROOT/data_out/repo_automation/automation.log" 2>&1 & - ;; - *) - nohup python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components "$mode" --daemon \ - > "$REPO_ROOT/data_out/repo_automation/automation.log" 2>&1 & - ;; - esac - - local pid=$! - echo -e "${GREEN}✅ Started with PID $pid${NC}" - echo -e "${BLUE}ℹ️ View logs: tail -f $REPO_ROOT/data_out/repo_automation/automation.log${NC}" - echo -e "${BLUE}ℹ️ Check status: $0 status${NC}" -} - -# Main script -print_banner -check_dependencies - -# Check for command line arguments -if [ $# -gt 0 ]; then - case "$1" in - full|all) - if [ "$2" == "--background" ] || [ "$2" == "-b" ]; then - run_background "full" - else - start_full_automation - fi - exit 0 - ;; - aria) - if [ "$2" == "--background" ] || [ "$2" == "-b" ]; then - run_background "aria" - else - start_aria_only - fi - exit 0 - ;; - training) - if [ "$2" == "--background" ] || [ "$2" == "-b" ]; then - run_background "training" - else - start_training_pipeline - fi - exit 0 - ;; - status) - show_status - exit 0 - ;; - stop) - stop_all - exit 0 - ;; - components) - if [ -z "$2" ]; then - show_components - exit 0 - else - if [ "$3" == "--background" ] || [ "$3" == "-b" ]; then - run_background "$2" - else - python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components "$2" --daemon - fi - exit 0 - fi - ;; - --help|-h) - echo "Usage: $0 [mode] [options]" - echo "" - echo "Modes:" - echo " full, all Full automation (all components)" - echo " aria Aria character only" - echo " training Training pipeline only" - echo " components List available components" - echo " status Check automation status" - echo " stop Stop all automation" - echo "" - echo "Options:" - echo " --background, -b Run in background" - echo "" - echo "Examples:" - echo " $0 full # Start everything" - echo " $0 aria -b # Start Aria in background" - echo " $0 components aria,training # Custom selection" - echo " $0 status # Check status" - echo " $0 stop # Stop all" - exit 0 - ;; - *) - echo -e "${RED}❌ Invalid mode: $1${NC}" - echo "Run '$0 --help' for usage" - exit 1 - ;; - esac -fi - -# Interactive menu -while true; do - show_menu - - case $choice in - 1) - start_full_automation - break - ;; - 2) - start_aria_only - break - ;; - 3) - start_training_pipeline - break - ;; - 4) - start_custom - break - ;; - 5) - show_status - ;; - 6) - stop_all - ;; - 0) - echo -e "${GREEN}👋 Goodbye!${NC}" - exit 0 - ;; - *) - echo -e "${RED}❌ Invalid choice${NC}" - ;; - esac - - echo "" - read -p "Press Enter to continue..." - echo "" -done +#!/bin/bash +# Complete Repository Automation Startup Script +# One-command automation for the entire Aria repository + +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(dirname "$SCRIPT_DIR")" + +# Colors +RED='\033[0;31m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +BLUE='\033[0;34m' +CYAN='\033[0;36m' +MAGENTA='\033[0;35m' +NC='\033[0m' + +function print_banner() { + echo -e "${BLUE}" + cat << "EOF" +╔══════════════════════════════════════════════════════════════════════════════╗ +║ 🚀 ARIA REPOSITORY AUTOMATION SYSTEM 🚀 ║ +║ Complete Automation for the Entire Repo ║ +╚══════════════════════════════════════════════════════════════════════════════╝ +EOF + echo -e "${NC}" +} + +function show_components() { + echo -e "${CYAN}Available Components:${NC}" + echo "" + echo " 1. ${GREEN}aria${NC} - Aria character (server + training)" + echo " 2. ${GREEN}training${NC} - LoRA training pipelines" + echo " 3. ${GREEN}quantum${NC} - Quantum computing workflows" + echo " 4. ${GREEN}evaluation${NC} - Model evaluation system" + echo " 5. ${GREEN}datasets${NC} - Auto dataset discovery" + echo " 6. ${GREEN}monitoring${NC} - System health monitoring" + echo " 7. ${GREEN}backup${NC} - Automated backups" + echo "" + echo " ${YELLOW}ALL${NC} - Start all components" + echo "" +} + +function show_menu() { + echo -e "${BLUE}Select automation mode:${NC}" + echo "" + echo " ${GREEN}1)${NC} Full Automation - Start ALL components (production)" + echo " ${GREEN}2)${NC} Aria Only - Just Aria character automation" + echo " ${GREEN}3)${NC} Training Pipeline - Training + evaluation only" + echo " ${GREEN}4)${NC} Custom Selection - Choose specific components" + echo " ${GREEN}5)${NC} Status - Check current automation status" + echo " ${GREEN}6)${NC} Stop All - Stop all automation" + echo " ${GREEN}0)${NC} Exit" + echo "" + read -p "Enter choice [0-6]: " choice +} + +function check_dependencies() { + echo -e "${YELLOW}Checking dependencies...${NC}" + + if ! command -v python3 &> /dev/null; then + echo -e "${RED}❌ Python 3 not found${NC}" + exit 1 + fi + echo -e "${GREEN}✅ Python 3: $(python3 --version)${NC}" + + if ! python3 -c "import psutil" 2>/dev/null; then + echo -e "${YELLOW}⚠️ psutil not installed, installing...${NC}" + pip3 install psutil + fi + echo -e "${GREEN}✅ psutil installed${NC}" +} + +function start_full_automation() { + echo -e "${GREEN}🚀 Starting Full Repository Automation...${NC}" + echo "" + python3 "$REPO_ROOT/scripts/repo_automation.py" --start --daemon +} + +function start_aria_only() { + echo -e "${GREEN}🚀 Starting Aria Automation Only...${NC}" + echo "" + python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components aria --daemon +} + +function start_training_pipeline() { + echo -e "${GREEN}🚀 Starting Training Pipeline...${NC}" + echo "" + python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components training,evaluation --daemon +} + +function start_custom() { + echo "" + show_components + read -p "Enter components (comma-separated, e.g., aria,training,quantum): " components + + if [ -z "$components" ]; then + echo -e "${RED}❌ No components specified${NC}" + return + fi + + echo -e "${GREEN}🚀 Starting selected components...${NC}" + echo "" + python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components "$components" --daemon +} + +function show_status() { + python3 "$REPO_ROOT/scripts/repo_automation.py" --status +} + +function stop_all() { + echo -e "${YELLOW}🛑 Stopping all automation...${NC}" + python3 "$REPO_ROOT/scripts/repo_automation.py" --stop + echo -e "${GREEN}✅ All automation stopped${NC}" +} + +function run_background() { + local mode=$1 + echo -e "${GREEN}🚀 Starting in background mode...${NC}" + + case "$mode" in + full) + nohup python3 "$REPO_ROOT/scripts/repo_automation.py" --start --daemon \ + > "$REPO_ROOT/data_out/repo_automation/automation.log" 2>&1 & + ;; + aria) + nohup python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components aria --daemon \ + > "$REPO_ROOT/data_out/repo_automation/automation.log" 2>&1 & + ;; + training) + nohup python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components training,evaluation --daemon \ + > "$REPO_ROOT/data_out/repo_automation/automation.log" 2>&1 & + ;; + *) + nohup python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components "$mode" --daemon \ + > "$REPO_ROOT/data_out/repo_automation/automation.log" 2>&1 & + ;; + esac + + local pid=$! + echo -e "${GREEN}✅ Started with PID $pid${NC}" + echo -e "${BLUE}ℹ️ View logs: tail -f $REPO_ROOT/data_out/repo_automation/automation.log${NC}" + echo -e "${BLUE}ℹ️ Check status: $0 status${NC}" +} + +# Main script +print_banner +check_dependencies + +# Check for command line arguments +if [ $# -gt 0 ]; then + case "$1" in + full|all) + if [ "$2" == "--background" ] || [ "$2" == "-b" ]; then + run_background "full" + else + start_full_automation + fi + exit 0 + ;; + aria) + if [ "$2" == "--background" ] || [ "$2" == "-b" ]; then + run_background "aria" + else + start_aria_only + fi + exit 0 + ;; + training) + if [ "$2" == "--background" ] || [ "$2" == "-b" ]; then + run_background "training" + else + start_training_pipeline + fi + exit 0 + ;; + status) + show_status + exit 0 + ;; + stop) + stop_all + exit 0 + ;; + components) + if [ -z "$2" ]; then + show_components + exit 0 + else + if [ "$3" == "--background" ] || [ "$3" == "-b" ]; then + run_background "$2" + else + python3 "$REPO_ROOT/scripts/repo_automation.py" --start --components "$2" --daemon + fi + exit 0 + fi + ;; + --help|-h) + echo "Usage: $0 [mode] [options]" + echo "" + echo "Modes:" + echo " full, all Full automation (all components)" + echo " aria Aria character only" + echo " training Training pipeline only" + echo " components List available components" + echo " status Check automation status" + echo " stop Stop all automation" + echo "" + echo "Options:" + echo " --background, -b Run in background" + echo "" + echo "Examples:" + echo " $0 full # Start everything" + echo " $0 aria -b # Start Aria in background" + echo " $0 components aria,training # Custom selection" + echo " $0 status # Check status" + echo " $0 stop # Stop all" + exit 0 + ;; + *) + echo -e "${RED}❌ Invalid mode: $1${NC}" + echo "Run '$0 --help' for usage" + exit 1 + ;; + esac +fi + +# Interactive menu +while true; do + show_menu + + case $choice in + 1) + start_full_automation + break + ;; + 2) + start_aria_only + break + ;; + 3) + start_training_pipeline + break + ;; + 4) + start_custom + break + ;; + 5) + show_status + ;; + 6) + stop_all + ;; + 0) + echo -e "${GREEN}👋 Goodbye!${NC}" + exit 0 + ;; + *) + echo -e "${RED}❌ Invalid choice${NC}" + ;; + esac + + echo "" + read -p "Press Enter to continue..." + echo "" +done diff --git a/scripts/test_ai_improvements.py b/scripts/test_ai_improvements.py index 3e454e446..e77764f3a 100644 --- a/scripts/test_ai_improvements.py +++ b/scripts/test_ai_improvements.py @@ -1,223 +1,223 @@ -""" -Test script to demonstrate AI improvements -""" - -import sys -from pathlib import Path - -if "pytest" in sys.modules: - import pytest - - pytestmark = pytest.mark.skip( - reason="script-style improvement demo is environment-dependent" - ) - -# Add project paths -sys.path.insert( - 0, str(Path(__file__).parent.parent / "ai-projects" / "quantum-ml" / "src") -) -sys.path.insert( - 0, str(Path(__file__).parent.parent / "ai-projects" / "chat-cli" / "src") -) - - -def test_quantum_improvements(): - """Test quantum AI improvements""" - print("\n" + "=" * 60) - print("TESTING QUANTUM AI IMPROVEMENTS") - print("=" * 60) - - try: - import torch - from hybrid_qnn import HybridQNN, QuantumClassicalTrainer - - print("\n✅ Imports successful") - - # Test enhanced model with new features - print("\n1️⃣ Testing Enhanced HybridQNN...") - model = HybridQNN( - input_dim=10, - hidden_dim=16, - n_qubits=4, - n_quantum_layers=2, - entanglement="circular", - output_dim=2, - dropout=0.2, - use_batch_norm=True, # New feature - use_residual=True, # New feature - ) - print(" ✅ Model created with residual connections and batch norm") - - # Test forward pass - x = torch.randn(8, 10) - output = model(x) - print(f" ✅ Forward pass successful: {x.shape} → {output.shape}") - - # Test enhanced trainer - print("\n2️⃣ Testing Enhanced Trainer...") - trainer = QuantumClassicalTrainer( - model, - learning_rate=0.001, - device="cpu", - use_scheduler=True, # New feature - gradient_clip_val=1.0, # New feature - ) - print(" ✅ Trainer created with LR scheduling and gradient clipping") - print(f" ✅ Best model tracking: initialized at {trainer.best_val_acc:.4f}") - print(f" ✅ Learning rates tracked: {len(trainer.learning_rates)} epochs") - - # Test circuit improvements - print("\n3️⃣ Testing Enhanced Quantum Circuit...") - print(" ✅ Dual encoding (RY + RZ) implemented") - print(" ✅ Final rotation layer added") - print(" ✅ Enhanced measurement strategy") - - print("\n🎉 All quantum improvements working correctly!") - return True - - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - - traceback.print_exc() - return False - - -def test_chat_improvements(): - """Test chat AI improvements""" - print("\n" + "=" * 60) - print("TESTING CHAT AI IMPROVEMENTS") - print("=" * 60) - - try: - - print("\n✅ Imports successful") - - # Test enhanced parameters (even if model not available) - print("\n1️⃣ Testing Enhanced Generation Parameters...") - print(" ✅ top_p parameter added (nucleus sampling)") - print(" ✅ top_k parameter added (top-k sampling)") - print(" ✅ repetition_penalty parameter added") - print(" ✅ Proper EOS token handling implemented") - - # Test parameter initialization - print("\n2️⃣ Testing Parameter Defaults...") - test_params = { - "temperature": 0.7, - "top_p": 0.9, - "top_k": 50, - "repetition_penalty": 1.1, - "max_new_tokens": 256, - } - - for param, value in test_params.items(): - print(f" ✅ {param}: {value}") - - print("\n🎉 All chat improvements configured correctly!") - return True - - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - - traceback.print_exc() - return False - - -def test_training_improvements(): - """Test training data improvements""" - print("\n" + "=" * 60) - print("TESTING TRAINING DATA IMPROVEMENTS") - print("=" * 60) - - try: - # Import the improved function - sys.path.insert( - 0, - str( - Path(__file__).parent.parent - / "AI" - / "microsoft_phi-silica-3.6_v1" - / "scripts" - ), - ) - from train_lora import build_text_from_messages - - print("\n✅ Imports successful") - - # Test with sample messages - print("\n1️⃣ Testing Enhanced Message Formatting...") - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Hello!"}, - {"role": "assistant", "content": "Hi there! How can I help?"}, - # Empty message (should be skipped) - {"role": "user", "content": ""}, - ] - - formatted = build_text_from_messages(messages) - - # Check for improvements - has_end_tokens = "<|end|>" in formatted - skips_empty = len(formatted.split("<|user|>")) == 2 # Only 1 user message - - print(f" ✅ End tokens added: {has_end_tokens}") - print(f" ✅ Empty messages skipped: {skips_empty}") - print(f" ✅ Content stripped and cleaned: {True}") - - print("\n2️⃣ Sample Formatted Output:") - print(" " + "\n ".join(formatted[:200].split("\n")[:5])) - print(" ...") - - print("\n🎉 All training improvements working correctly!") - return True - - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - - traceback.print_exc() - return False - - -def main(): - """Run all improvement tests""" - print("\n╔════════════════════════════════════════════════════════════════╗") - print("║ AI IMPROVEMENTS VALIDATION TEST SUITE ║") - print("╚════════════════════════════════════════════════════════════════╝") - - results = {} - - # Run tests - results["quantum"] = test_quantum_improvements() - results["chat"] = test_chat_improvements() - results["training"] = test_training_improvements() - - # Summary - print("\n" + "=" * 60) - print("TEST SUMMARY") - print("=" * 60) - - for component, passed in results.items(): - status = "✅ PASSED" if passed else "❌ FAILED" - print(f"{component.upper()}: {status}") - - all_passed = all(results.values()) - - if all_passed: - print("\n🎉 ALL IMPROVEMENTS VALIDATED SUCCESSFULLY! 🎉") - print("\n📊 Expected Performance Gains:") - print(" • 5-10% accuracy improvement (quantum models)") - print(" • 20-30% faster convergence") - print(" • More coherent chat responses") - print(" • Less repetitive outputs") - print(" • Better training stability") - print("\n📄 See AI_IMPROVEMENTS.md for full details") - else: - print("\n⚠️ Some tests failed. Check error messages above.") - - return 0 if all_passed else 1 - - -if __name__ == "__main__": - sys.exit(main()) +""" +Test script to demonstrate AI improvements +""" + +import sys +from pathlib import Path + +if "pytest" in sys.modules: + import pytest + + pytestmark = pytest.mark.skip( + reason="script-style improvement demo is environment-dependent" + ) + +# Add project paths +sys.path.insert( + 0, str(Path(__file__).parent.parent / "ai-projects" / "quantum-ml" / "src") +) +sys.path.insert( + 0, str(Path(__file__).parent.parent / "ai-projects" / "chat-cli" / "src") +) + + +def test_quantum_improvements(): + """Test quantum AI improvements""" + print("\n" + "=" * 60) + print("TESTING QUANTUM AI IMPROVEMENTS") + print("=" * 60) + + try: + import torch + from hybrid_qnn import HybridQNN, QuantumClassicalTrainer + + print("\n✅ Imports successful") + + # Test enhanced model with new features + print("\n1️⃣ Testing Enhanced HybridQNN...") + model = HybridQNN( + input_dim=10, + hidden_dim=16, + n_qubits=4, + n_quantum_layers=2, + entanglement="circular", + output_dim=2, + dropout=0.2, + use_batch_norm=True, # New feature + use_residual=True, # New feature + ) + print(" ✅ Model created with residual connections and batch norm") + + # Test forward pass + x = torch.randn(8, 10) + output = model(x) + print(f" ✅ Forward pass successful: {x.shape} → {output.shape}") + + # Test enhanced trainer + print("\n2️⃣ Testing Enhanced Trainer...") + trainer = QuantumClassicalTrainer( + model, + learning_rate=0.001, + device="cpu", + use_scheduler=True, # New feature + gradient_clip_val=1.0, # New feature + ) + print(" ✅ Trainer created with LR scheduling and gradient clipping") + print(f" ✅ Best model tracking: initialized at {trainer.best_val_acc:.4f}") + print(f" ✅ Learning rates tracked: {len(trainer.learning_rates)} epochs") + + # Test circuit improvements + print("\n3️⃣ Testing Enhanced Quantum Circuit...") + print(" ✅ Dual encoding (RY + RZ) implemented") + print(" ✅ Final rotation layer added") + print(" ✅ Enhanced measurement strategy") + + print("\n🎉 All quantum improvements working correctly!") + return True + + except Exception as e: + print(f"\n❌ Error: {e}") + import traceback + + traceback.print_exc() + return False + + +def test_chat_improvements(): + """Test chat AI improvements""" + print("\n" + "=" * 60) + print("TESTING CHAT AI IMPROVEMENTS") + print("=" * 60) + + try: + + print("\n✅ Imports successful") + + # Test enhanced parameters (even if model not available) + print("\n1️⃣ Testing Enhanced Generation Parameters...") + print(" ✅ top_p parameter added (nucleus sampling)") + print(" ✅ top_k parameter added (top-k sampling)") + print(" ✅ repetition_penalty parameter added") + print(" ✅ Proper EOS token handling implemented") + + # Test parameter initialization + print("\n2️⃣ Testing Parameter Defaults...") + test_params = { + "temperature": 0.7, + "top_p": 0.9, + "top_k": 50, + "repetition_penalty": 1.1, + "max_new_tokens": 256, + } + + for param, value in test_params.items(): + print(f" ✅ {param}: {value}") + + print("\n🎉 All chat improvements configured correctly!") + return True + + except Exception as e: + print(f"\n❌ Error: {e}") + import traceback + + traceback.print_exc() + return False + + +def test_training_improvements(): + """Test training data improvements""" + print("\n" + "=" * 60) + print("TESTING TRAINING DATA IMPROVEMENTS") + print("=" * 60) + + try: + # Import the improved function + sys.path.insert( + 0, + str( + Path(__file__).parent.parent + / "AI" + / "microsoft_phi-silica-3.6_v1" + / "scripts" + ), + ) + from train_lora import build_text_from_messages + + print("\n✅ Imports successful") + + # Test with sample messages + print("\n1️⃣ Testing Enhanced Message Formatting...") + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello!"}, + {"role": "assistant", "content": "Hi there! How can I help?"}, + # Empty message (should be skipped) + {"role": "user", "content": ""}, + ] + + formatted = build_text_from_messages(messages) + + # Check for improvements + has_end_tokens = "<|end|>" in formatted + skips_empty = len(formatted.split("<|user|>")) == 2 # Only 1 user message + + print(f" ✅ End tokens added: {has_end_tokens}") + print(f" ✅ Empty messages skipped: {skips_empty}") + print(f" ✅ Content stripped and cleaned: {True}") + + print("\n2️⃣ Sample Formatted Output:") + print(" " + "\n ".join(formatted[:200].split("\n")[:5])) + print(" ...") + + print("\n🎉 All training improvements working correctly!") + return True + + except Exception as e: + print(f"\n❌ Error: {e}") + import traceback + + traceback.print_exc() + return False + + +def main(): + """Run all improvement tests""" + print("\n╔════════════════════════════════════════════════════════════════╗") + print("║ AI IMPROVEMENTS VALIDATION TEST SUITE ║") + print("╚════════════════════════════════════════════════════════════════╝") + + results = {} + + # Run tests + results["quantum"] = test_quantum_improvements() + results["chat"] = test_chat_improvements() + results["training"] = test_training_improvements() + + # Summary + print("\n" + "=" * 60) + print("TEST SUMMARY") + print("=" * 60) + + for component, passed in results.items(): + status = "✅ PASSED" if passed else "❌ FAILED" + print(f"{component.upper()}: {status}") + + all_passed = all(results.values()) + + if all_passed: + print("\n🎉 ALL IMPROVEMENTS VALIDATED SUCCESSFULLY! 🎉") + print("\n📊 Expected Performance Gains:") + print(" • 5-10% accuracy improvement (quantum models)") + print(" • 20-30% faster convergence") + print(" • More coherent chat responses") + print(" • Less repetitive outputs") + print(" • Better training stability") + print("\n📄 See AI_IMPROVEMENTS.md for full details") + else: + print("\n⚠️ Some tests failed. Check error messages above.") + + return 0 if all_passed else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/test_aria_automation.py b/scripts/test_aria_automation.py index 727034056..43593644b 100644 --- a/scripts/test_aria_automation.py +++ b/scripts/test_aria_automation.py @@ -1,214 +1,214 @@ -#!/usr/bin/env python3 -""" -Quick test of Aria automation components -Validates that all automation pieces work together -""" -import subprocess -import sys -from pathlib import Path - -if "pytest" in sys.modules: - import pytest - - pytestmark = pytest.mark.skip( - reason="script-style automation smoke checks are environment-dependent" - ) - -REPO_ROOT = Path(__file__).resolve().parents[1] -GREEN = "\033[92m" -RED = "\033[91m" -YELLOW = "\033[93m" -BLUE = "\033[94m" -RESET = "\033[0m" - - -def print_test(msg): - print(f"{BLUE}[TEST]{RESET} {msg}") - - -def print_pass(msg): - print(f"{GREEN}[PASS]{RESET} {msg}") - - -def print_fail(msg): - print(f"{RED}[FAIL]{RESET} {msg}") - - -def print_warn(msg): - print(f"{YELLOW}[WARN]{RESET} {msg}") - - -def check_file_exists(path): - """Test if required file exists""" - test_path = REPO_ROOT / path - print_test(f"Checking {path}...") - if test_path.exists(): - print_pass(f"{path} exists") - return True - else: - print_fail(f"{path} not found") - return False - - -def check_import(module_name): - """Test if Python module can be imported""" - print_test(f"Importing {module_name}...") - try: - result = subprocess.run( - [sys.executable, "-c", f"import {module_name}"], - capture_output=True, - timeout=5, - ) - if result.returncode == 0: - print_pass(f"{module_name} imported successfully") - return True - else: - print_fail(f"Failed to import {module_name}") - return False - except Exception as e: - print_fail(f"Error importing {module_name}: {e}") - return False - - -def check_script_help(script_path): - """Test if script shows help""" - test_path = REPO_ROOT / script_path - print_test(f"Testing {script_path} --help...") - try: - result = subprocess.run( - [sys.executable, str(test_path), "--help"], - capture_output=True, - timeout=5, - text=True, - ) - if result.returncode == 0 and ( - "usage" in result.stdout.lower() or "help" in result.stdout.lower() - ): - print_pass(f"{script_path} help works") - return True - else: - print_warn(f"{script_path} help may not work properly") - return True # Not critical - except Exception as e: - print_fail(f"Error testing {script_path}: {e}") - return False - - -def test_status_check(): - """Test status check functionality""" - print_test("Testing status check...") - try: - result = subprocess.run( - [ - sys.executable, - str(REPO_ROOT / "scripts" / "aria_automation.py"), - "--status", - ], - capture_output=True, - timeout=10, - text=True, - ) - # Status check should work even if nothing is running - if "Automation" in result.stdout or "No automation" in result.stdout: - print_pass("Status check works") - return True - else: - print_warn("Status check output unexpected") - print(f"Output: {result.stdout[:200]}") - return True # Not critical - except Exception as e: - print_fail(f"Status check failed: {e}") - return False - - -def main(): - print("\n" + "=" * 80) - print(f"{BLUE}Aria Automation Test Suite{RESET}") - print("=" * 80 + "\n") - - tests_passed = 0 - tests_total = 0 - - # Test 1: Check required files - print(f"\n{BLUE}=== File Structure Tests ==={RESET}\n") - files_to_check = [ - "scripts/aria_automation.py", - "scripts/start_aria.sh", - "config/aria_automation.service", - "config/master_orchestrator.yaml", - "ARIA_AUTOMATION_GUIDE.md", - "aria_web/server.py", - "aria_web/index.html", - ] - - for file_path in files_to_check: - tests_total += 1 - if check_file_exists(file_path): - tests_passed += 1 - - # Test 2: Check Python dependencies - print(f"\n{BLUE}=== Dependency Tests ==={RESET}\n") - modules_to_test = ["json", "subprocess", "pathlib", "threading"] - - for module in modules_to_test: - tests_total += 1 - if check_import(module): - tests_passed += 1 - - # Test optional psutil - print_test("Checking optional dependency: psutil...") - if check_import("psutil"): - print_pass("psutil available (recommended)") - else: - print_warn("psutil not available (will be installed on first run)") - - # Test 3: Script functionality - print(f"\n{BLUE}=== Script Tests ==={RESET}\n") - - tests_total += 1 - if check_script_help("scripts/aria_automation.py"): - tests_passed += 1 - - tests_total += 1 - if test_status_check(): - tests_passed += 1 - - # Test 4: Check start script is executable - print_test("Checking start script permissions...") - start_script = REPO_ROOT / "scripts" / "start_aria.sh" - if start_script.exists(): - import os - - if os.access(start_script, os.X_OK): - print_pass("start_aria.sh is executable") - tests_total += 1 - tests_passed += 1 - else: - print_warn( - "start_aria.sh not executable (run: chmod +x scripts/start_aria.sh)" - ) - tests_total += 1 - - # Summary - print("\n" + "=" * 80) - print(f"{BLUE}Test Summary{RESET}") - print("=" * 80) - print(f"Tests Passed: {tests_passed}/{tests_total}") - - if tests_passed == tests_total: - print(f"\n{GREEN}✅ All tests passed! Automation is ready to use.{RESET}\n") - print(f"{BLUE}Quick Start:{RESET}") - print(" ./scripts/start_aria.sh # Interactive menu") - print(" ./scripts/start_aria.sh full # Start full stack") - print(" ./scripts/start_aria.sh status # Check status") - return 0 - elif tests_passed >= tests_total * 0.8: - print(f"\n{YELLOW}⚠️ Most tests passed. Review warnings above.{RESET}\n") - return 0 - else: - print(f"\n{RED}❌ Some tests failed. Review errors above.{RESET}\n") - return 1 - - -if __name__ == "__main__": - sys.exit(main()) +#!/usr/bin/env python3 +""" +Quick test of Aria automation components +Validates that all automation pieces work together +""" +import subprocess +import sys +from pathlib import Path + +if "pytest" in sys.modules: + import pytest + + pytestmark = pytest.mark.skip( + reason="script-style automation smoke checks are environment-dependent" + ) + +REPO_ROOT = Path(__file__).resolve().parents[1] +GREEN = "\033[92m" +RED = "\033[91m" +YELLOW = "\033[93m" +BLUE = "\033[94m" +RESET = "\033[0m" + + +def print_test(msg): + print(f"{BLUE}[TEST]{RESET} {msg}") + + +def print_pass(msg): + print(f"{GREEN}[PASS]{RESET} {msg}") + + +def print_fail(msg): + print(f"{RED}[FAIL]{RESET} {msg}") + + +def print_warn(msg): + print(f"{YELLOW}[WARN]{RESET} {msg}") + + +def check_file_exists(path): + """Test if required file exists""" + test_path = REPO_ROOT / path + print_test(f"Checking {path}...") + if test_path.exists(): + print_pass(f"{path} exists") + return True + else: + print_fail(f"{path} not found") + return False + + +def check_import(module_name): + """Test if Python module can be imported""" + print_test(f"Importing {module_name}...") + try: + result = subprocess.run( + [sys.executable, "-c", f"import {module_name}"], + capture_output=True, + timeout=5, + ) + if result.returncode == 0: + print_pass(f"{module_name} imported successfully") + return True + else: + print_fail(f"Failed to import {module_name}") + return False + except Exception as e: + print_fail(f"Error importing {module_name}: {e}") + return False + + +def check_script_help(script_path): + """Test if script shows help""" + test_path = REPO_ROOT / script_path + print_test(f"Testing {script_path} --help...") + try: + result = subprocess.run( + [sys.executable, str(test_path), "--help"], + capture_output=True, + timeout=5, + text=True, + ) + if result.returncode == 0 and ( + "usage" in result.stdout.lower() or "help" in result.stdout.lower() + ): + print_pass(f"{script_path} help works") + return True + else: + print_warn(f"{script_path} help may not work properly") + return True # Not critical + except Exception as e: + print_fail(f"Error testing {script_path}: {e}") + return False + + +def test_status_check(): + """Test status check functionality""" + print_test("Testing status check...") + try: + result = subprocess.run( + [ + sys.executable, + str(REPO_ROOT / "scripts" / "aria_automation.py"), + "--status", + ], + capture_output=True, + timeout=10, + text=True, + ) + # Status check should work even if nothing is running + if "Automation" in result.stdout or "No automation" in result.stdout: + print_pass("Status check works") + return True + else: + print_warn("Status check output unexpected") + print(f"Output: {result.stdout[:200]}") + return True # Not critical + except Exception as e: + print_fail(f"Status check failed: {e}") + return False + + +def main(): + print("\n" + "=" * 80) + print(f"{BLUE}Aria Automation Test Suite{RESET}") + print("=" * 80 + "\n") + + tests_passed = 0 + tests_total = 0 + + # Test 1: Check required files + print(f"\n{BLUE}=== File Structure Tests ==={RESET}\n") + files_to_check = [ + "scripts/aria_automation.py", + "scripts/start_aria.sh", + "config/aria_automation.service", + "config/master_orchestrator.yaml", + "ARIA_AUTOMATION_GUIDE.md", + "aria_web/server.py", + "aria_web/index.html", + ] + + for file_path in files_to_check: + tests_total += 1 + if check_file_exists(file_path): + tests_passed += 1 + + # Test 2: Check Python dependencies + print(f"\n{BLUE}=== Dependency Tests ==={RESET}\n") + modules_to_test = ["json", "subprocess", "pathlib", "threading"] + + for module in modules_to_test: + tests_total += 1 + if check_import(module): + tests_passed += 1 + + # Test optional psutil + print_test("Checking optional dependency: psutil...") + if check_import("psutil"): + print_pass("psutil available (recommended)") + else: + print_warn("psutil not available (will be installed on first run)") + + # Test 3: Script functionality + print(f"\n{BLUE}=== Script Tests ==={RESET}\n") + + tests_total += 1 + if check_script_help("scripts/aria_automation.py"): + tests_passed += 1 + + tests_total += 1 + if test_status_check(): + tests_passed += 1 + + # Test 4: Check start script is executable + print_test("Checking start script permissions...") + start_script = REPO_ROOT / "scripts" / "start_aria.sh" + if start_script.exists(): + import os + + if os.access(start_script, os.X_OK): + print_pass("start_aria.sh is executable") + tests_total += 1 + tests_passed += 1 + else: + print_warn( + "start_aria.sh not executable (run: chmod +x scripts/start_aria.sh)" + ) + tests_total += 1 + + # Summary + print("\n" + "=" * 80) + print(f"{BLUE}Test Summary{RESET}") + print("=" * 80) + print(f"Tests Passed: {tests_passed}/{tests_total}") + + if tests_passed == tests_total: + print(f"\n{GREEN}✅ All tests passed! Automation is ready to use.{RESET}\n") + print(f"{BLUE}Quick Start:{RESET}") + print(" ./scripts/start_aria.sh # Interactive menu") + print(" ./scripts/start_aria.sh full # Start full stack") + print(" ./scripts/start_aria.sh status # Check status") + return 0 + elif tests_passed >= tests_total * 0.8: + print(f"\n{YELLOW}⚠️ Most tests passed. Review warnings above.{RESET}\n") + return 0 + else: + print(f"\n{RED}❌ Some tests failed. Review errors above.{RESET}\n") + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/test_repo_automation.py b/scripts/test_repo_automation.py index 8bafc0863..93dce17bb 100644 --- a/scripts/test_repo_automation.py +++ b/scripts/test_repo_automation.py @@ -1,282 +1,282 @@ -#!/usr/bin/env python3 -""" -Test Suite for Repository Automation System - -Validates: -- File structure -- Component configuration -- Script functionality -- Dependencies -- Integration points -""" - -import subprocess -import sys -from pathlib import Path - -if "pytest" in sys.modules: - import pytest - - pytestmark = pytest.mark.skip( - reason="script-style repository audit checks are environment-dependent" - ) - -REPO_ROOT = Path(__file__).resolve().parents[1] - - -def check_file_exists(path: Path, description: str) -> bool: - """Check if file exists""" - if path.exists(): - print(f"✅ {description}: {path}") - return True - else: - print(f"❌ {description} missing: {path}") - return False - - -def test_file_structure(): - """Test required files exist""" - print("\n📁 Testing file structure...") - - files = [ - (REPO_ROOT / "scripts/repo_automation.py", "Main automation script"), - (REPO_ROOT / "scripts/start_repo_automation.sh", "Startup wrapper"), - (REPO_ROOT / "scripts/backup_manager.py", "Backup manager"), - (REPO_ROOT / "scripts/aria_automation.py", "Aria automation"), - (REPO_ROOT / "scripts/autotrain.py", "Training orchestrator"), - (REPO_ROOT / "scripts/quantum_autorun.py", "Quantum orchestrator"), - (REPO_ROOT / "scripts/evaluation_autorun.py", "Evaluation orchestrator"), - (REPO_ROOT / "REPO_AUTOMATION_GUIDE.md", "Documentation"), - ] - - result = all(check_file_exists(path, desc) for path, desc in files) - if "pytest" in sys.modules: - assert result - return - return result - - -def test_scripts_executable(): - """Test scripts are executable""" - print("\n🔐 Testing script permissions...") - - scripts = [ - REPO_ROOT / "scripts/start_repo_automation.sh", - REPO_ROOT / "scripts/start_aria.sh", - ] - - all_executable = True - for script in scripts: - if script.exists() and script.stat().st_mode & 0o111: - print(f"✅ Executable: {script.name}") - else: - print(f"❌ Not executable: {script.name}") - all_executable = False - - if "pytest" in sys.modules: - assert all_executable - return - return all_executable - - -def test_imports(): - """Test Python imports""" - print("\n📦 Testing Python imports...") - - ok = True - try: - import psutil - - print("✅ psutil installed") - except ImportError: - print("❌ psutil not installed") - ok = False - - if "pytest" in sys.modules: - assert ok - return - return ok - - -def test_script_help(): - """Test scripts respond to --help""" - print("\n❓ Testing script help messages...") - - scripts = [ - "scripts/repo_automation.py", - "scripts/aria_automation.py", - "scripts/backup_manager.py", - ] - - all_ok = True - for script in scripts: - try: - result = subprocess.run( - ["python3", str(REPO_ROOT / script), "--help"], - capture_output=True, - text=True, - timeout=5, - ) - if result.returncode == 0: - print(f"✅ {script} --help works") - else: - print(f"❌ {script} --help failed") - all_ok = False - except Exception as e: - print(f"❌ {script} error: {e}") - all_ok = False - - if "pytest" in sys.modules: - assert all_ok - return - return all_ok - - -def test_component_config(): - """Test component configuration""" - print("\n⚙️ Testing component configuration...") - - ok = True - sys.path.insert(0, str(REPO_ROOT / "scripts")) - try: - from repo_automation import RepoAutomation - - automation = RepoAutomation() - - expected_components = [ - "aria", - "training", - "quantum", - "evaluation", - "datasets", - "monitoring", - "backup", - ] - - for component in expected_components: - if component in automation.components: - print(f"✅ Component configured: {component}") - else: - print(f"❌ Component missing: {component}") - ok = False - - except Exception as e: - print(f"❌ Configuration error: {e}") - ok = False - - if "pytest" in sys.modules: - assert ok - return - return ok - - -def test_directories(): - """Test required directories exist or can be created""" - print("\n📂 Testing directories...") - - dirs = [ - REPO_ROOT / "data_out", - REPO_ROOT / "data_out/repo_automation", - REPO_ROOT / "backups", - ] - - ok = True - for directory in dirs: - directory.mkdir(parents=True, exist_ok=True) - if directory.exists(): - print(f"✅ Directory: {directory}") - else: - print(f"❌ Cannot create: {directory}") - ok = False - - if "pytest" in sys.modules: - assert ok - return - return ok - - -def test_integration(): - """Test integration with existing systems""" - print("\n🔗 Testing integration points...") - - # Check master orchestrator integration - master_config = REPO_ROOT / "config/master_orchestrator.yaml" - if master_config.exists(): - print("✅ Master orchestrator config exists") - else: - print("⚠️ Master orchestrator config not found (optional)") - - # Check component scripts exist - component_scripts = [ - "scripts/aria_automation.py", - "scripts/autotrain.py", - "scripts/quantum_autorun.py", - "scripts/evaluation_autorun.py", - ] - - all_exist = True - for script in component_scripts: - path = REPO_ROOT / script - if path.exists(): - print(f"✅ Component script: {script}") - else: - print(f"❌ Missing component: {script}") - all_exist = False - - if "pytest" in sys.modules: - assert all_exist - return - return all_exist - - -def main(): - """Run all tests""" - print("=" * 80) - print("🧪 Repository Automation Test Suite") - print("=" * 80) - - tests = [ - ("File Structure", test_file_structure), - ("Script Permissions", test_scripts_executable), - ("Python Imports", test_imports), - ("Script Help", test_script_help), - ("Component Config", test_component_config), - ("Directories", test_directories), - ("Integration", test_integration), - ] - - results = [] - for name, test_func in tests: - try: - result = test_func() - results.append((name, result)) - except Exception as e: - print(f"\n❌ {name} error: {e}") - results.append((name, False)) - - # Summary - print("\n" + "=" * 80) - print("📊 Test Results Summary") - print("=" * 80) - - passed = sum(1 for _, result in results if result) - total = len(results) - - for name, result in results: - status = "✅ PASS" if result else "❌ FAIL" - print(f"{status}: {name}") - - print("\n" + "=" * 80) - print(f"Total: {passed}/{total} tests passed") - print("=" * 80 + "\n") - - if passed == total: - print("🎉 All tests passed! Repository automation ready to use.") - return 0 - else: - print("⚠️ Some tests failed. Please fix issues before using automation.") - return 1 - - -if __name__ == "__main__": - sys.exit(main()) +#!/usr/bin/env python3 +""" +Test Suite for Repository Automation System + +Validates: +- File structure +- Component configuration +- Script functionality +- Dependencies +- Integration points +""" + +import subprocess +import sys +from pathlib import Path + +if "pytest" in sys.modules: + import pytest + + pytestmark = pytest.mark.skip( + reason="script-style repository audit checks are environment-dependent" + ) + +REPO_ROOT = Path(__file__).resolve().parents[1] + + +def check_file_exists(path: Path, description: str) -> bool: + """Check if file exists""" + if path.exists(): + print(f"✅ {description}: {path}") + return True + else: + print(f"❌ {description} missing: {path}") + return False + + +def test_file_structure(): + """Test required files exist""" + print("\n📁 Testing file structure...") + + files = [ + (REPO_ROOT / "scripts/repo_automation.py", "Main automation script"), + (REPO_ROOT / "scripts/start_repo_automation.sh", "Startup wrapper"), + (REPO_ROOT / "scripts/backup_manager.py", "Backup manager"), + (REPO_ROOT / "scripts/aria_automation.py", "Aria automation"), + (REPO_ROOT / "scripts/autotrain.py", "Training orchestrator"), + (REPO_ROOT / "scripts/quantum_autorun.py", "Quantum orchestrator"), + (REPO_ROOT / "scripts/evaluation_autorun.py", "Evaluation orchestrator"), + (REPO_ROOT / "REPO_AUTOMATION_GUIDE.md", "Documentation"), + ] + + result = all(check_file_exists(path, desc) for path, desc in files) + if "pytest" in sys.modules: + assert result + return + return result + + +def test_scripts_executable(): + """Test scripts are executable""" + print("\n🔐 Testing script permissions...") + + scripts = [ + REPO_ROOT / "scripts/start_repo_automation.sh", + REPO_ROOT / "scripts/start_aria.sh", + ] + + all_executable = True + for script in scripts: + if script.exists() and script.stat().st_mode & 0o111: + print(f"✅ Executable: {script.name}") + else: + print(f"❌ Not executable: {script.name}") + all_executable = False + + if "pytest" in sys.modules: + assert all_executable + return + return all_executable + + +def test_imports(): + """Test Python imports""" + print("\n📦 Testing Python imports...") + + ok = True + try: + import psutil + + print("✅ psutil installed") + except ImportError: + print("❌ psutil not installed") + ok = False + + if "pytest" in sys.modules: + assert ok + return + return ok + + +def test_script_help(): + """Test scripts respond to --help""" + print("\n❓ Testing script help messages...") + + scripts = [ + "scripts/repo_automation.py", + "scripts/aria_automation.py", + "scripts/backup_manager.py", + ] + + all_ok = True + for script in scripts: + try: + result = subprocess.run( + ["python3", str(REPO_ROOT / script), "--help"], + capture_output=True, + text=True, + timeout=5, + ) + if result.returncode == 0: + print(f"✅ {script} --help works") + else: + print(f"❌ {script} --help failed") + all_ok = False + except Exception as e: + print(f"❌ {script} error: {e}") + all_ok = False + + if "pytest" in sys.modules: + assert all_ok + return + return all_ok + + +def test_component_config(): + """Test component configuration""" + print("\n⚙️ Testing component configuration...") + + ok = True + sys.path.insert(0, str(REPO_ROOT / "scripts")) + try: + from repo_automation import RepoAutomation + + automation = RepoAutomation() + + expected_components = [ + "aria", + "training", + "quantum", + "evaluation", + "datasets", + "monitoring", + "backup", + ] + + for component in expected_components: + if component in automation.components: + print(f"✅ Component configured: {component}") + else: + print(f"❌ Component missing: {component}") + ok = False + + except Exception as e: + print(f"❌ Configuration error: {e}") + ok = False + + if "pytest" in sys.modules: + assert ok + return + return ok + + +def test_directories(): + """Test required directories exist or can be created""" + print("\n📂 Testing directories...") + + dirs = [ + REPO_ROOT / "data_out", + REPO_ROOT / "data_out/repo_automation", + REPO_ROOT / "backups", + ] + + ok = True + for directory in dirs: + directory.mkdir(parents=True, exist_ok=True) + if directory.exists(): + print(f"✅ Directory: {directory}") + else: + print(f"❌ Cannot create: {directory}") + ok = False + + if "pytest" in sys.modules: + assert ok + return + return ok + + +def test_integration(): + """Test integration with existing systems""" + print("\n🔗 Testing integration points...") + + # Check master orchestrator integration + master_config = REPO_ROOT / "config/master_orchestrator.yaml" + if master_config.exists(): + print("✅ Master orchestrator config exists") + else: + print("⚠️ Master orchestrator config not found (optional)") + + # Check component scripts exist + component_scripts = [ + "scripts/aria_automation.py", + "scripts/autotrain.py", + "scripts/quantum_autorun.py", + "scripts/evaluation_autorun.py", + ] + + all_exist = True + for script in component_scripts: + path = REPO_ROOT / script + if path.exists(): + print(f"✅ Component script: {script}") + else: + print(f"❌ Missing component: {script}") + all_exist = False + + if "pytest" in sys.modules: + assert all_exist + return + return all_exist + + +def main(): + """Run all tests""" + print("=" * 80) + print("🧪 Repository Automation Test Suite") + print("=" * 80) + + tests = [ + ("File Structure", test_file_structure), + ("Script Permissions", test_scripts_executable), + ("Python Imports", test_imports), + ("Script Help", test_script_help), + ("Component Config", test_component_config), + ("Directories", test_directories), + ("Integration", test_integration), + ] + + results = [] + for name, test_func in tests: + try: + result = test_func() + results.append((name, result)) + except Exception as e: + print(f"\n❌ {name} error: {e}") + results.append((name, False)) + + # Summary + print("\n" + "=" * 80) + print("📊 Test Results Summary") + print("=" * 80) + + passed = sum(1 for _, result in results if result) + total = len(results) + + for name, result in results: + status = "✅ PASS" if result else "❌ FAIL" + print(f"{status}: {name}") + + print("\n" + "=" * 80) + print(f"Total: {passed}/{total} tests passed") + print("=" * 80 + "\n") + + if passed == total: + print("🎉 All tests passed! Repository automation ready to use.") + return 0 + else: + print("⚠️ Some tests failed. Please fix issues before using automation.") + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/test_runner.py b/scripts/test_runner.py index b304ce515..5087526da 100644 --- a/scripts/test_runner.py +++ b/scripts/test_runner.py @@ -1,311 +1,311 @@ -#!/usr/bin/env python -""" -Centralized test orchestrator with intelligent filtering and result aggregation. - -Test Suites: - unit — Fast unit tests (excludes slow, azure, integration, quantum, gpu) - integration — Integration tests only - all_fast — All tests except slow and azure - all — Every test - autotrain — Autotrain-related tests - quantum — Quantum-related tests - database — Database integration tests - chat — Chat provider tests - -Usage: - python scripts/test_runner.py --unit - python scripts/test_runner.py --unit --coverage - python scripts/test_runner.py --all - python scripts/test_runner.py --integration - python scripts/test_runner.py --list-suites - python scripts/test_runner.py --unit --integration --watch -""" -import argparse -import json -import re -import subprocess -import sys -import time -from datetime import datetime, timezone -from pathlib import Path - -REPO_ROOT = Path(__file__).resolve().parents[1] -TESTS_DIR = REPO_ROOT / "tests" -DATA_OUT = REPO_ROOT / "data_out" - -# --------------------------------------------------------------------------- -# Suite Definitions -# --------------------------------------------------------------------------- -SUITES = { - "unit": { - "description": "Fast unit tests (~0.5s)", - "pytest_args": [ - "-m", - "not slow and not azure and not integration and not quantum and not gpu", - ], - }, - "integration": { - "description": "Integration / external-service tests (~3s)", - "pytest_args": ["-m", "integration"], - }, - "all_fast": { - "description": "All tests except slow & azure (~10s)", - "pytest_args": ["-m", "not slow and not azure"], - }, - "all": { - "description": "Full test suite (may be slow)", - "pytest_args": [], - }, - "autotrain": { - "description": "Autotrain-related tests", - "pytest_args": ["-k", "autotrain or train"], - }, - "quantum": { - "description": "Quantum ML tests", - "pytest_args": ["-m", "quantum"], - }, - "database": { - "description": "Database integration tests", - "pytest_args": ["-k", "database or sql"], - }, - "chat": { - "description": "Chat provider tests", - "pytest_args": ["-k", "provider or chat or lmstudio"], - }, -} - -# --------------------------------------------------------------------------- -# Result Parsing -# --------------------------------------------------------------------------- -_RESULT_RE = re.compile( - r"=+ (?:(\d+) passed)?" - r"(?:,? ?(\d+) failed)?" - r"(?:,? ?(\d+) error)?" - r"(?:,? ?(\d+) skipped)?" - r"(?:,? ?(\d+) warning)?" - r".*=+", -) - - -def _parse_pytest_summary(output: str) -> dict: - """Extract counts from pytest's one-line summary.""" - m = _RESULT_RE.search(output) - if not m: - return {"passed": 0, "failed": 0, "errors": 0, "skipped": 0} - return { - "passed": int(m.group(1) or 0), - "failed": int(m.group(2) or 0), - "errors": int(m.group(3) or 0), - "skipped": int(m.group(4) or 0), - } - - -# --------------------------------------------------------------------------- -# Runner -# --------------------------------------------------------------------------- -def run_suite(name: str, *, coverage: bool = False, verbose: int = 1) -> dict: - """Run a single pytest suite and return structured results.""" - suite = SUITES[name] - cmd = [sys.executable, "-m", "pytest", str(TESTS_DIR)] - cmd.extend(suite["pytest_args"]) - - if coverage: - cmd.extend(["--cov=shared", "--cov=scripts", "--cov-report=term-missing"]) - - if verbose == 0: - cmd.append("-q") - elif verbose >= 2: - cmd.append("-v") - - # Strip ANSI codes for parsing - cmd.extend(["--tb=short", "--no-header"]) - - start = time.monotonic() - result = subprocess.run(cmd, capture_output=True, text=True, cwd=str(REPO_ROOT)) - elapsed = round(time.monotonic() - start, 2) - - # Remove ANSI escape sequences for reliable regex - clean = re.sub(r"\x1b\[[0-9;]*m", "", result.stdout + result.stderr) - - summary = _parse_pytest_summary(clean) - summary.update( - { - "suite": name, - "returncode": result.returncode, - "duration_s": elapsed, - "success": result.returncode == 0, - } - ) - - return summary, result.stdout, result.stderr - - -def list_suites() -> None: - """Print available test suites.""" - print("Available test suites:\n") - for name, info in SUITES.items(): - print(f" {name:15s} {info['description']}") - print(f"\nUsage: python {Path(__file__).name} -- [--coverage]") - - -# --------------------------------------------------------------------------- -# Report -# --------------------------------------------------------------------------- -def write_report(results: list[dict]) -> Path: - """Write JSON + Markdown reports to data_out/.""" - out_dir = DATA_OUT / "test_runner" - out_dir.mkdir(parents=True, exist_ok=True) - ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - - report = { - "timestamp": ts, - "suites": results, - "overall_success": all(r["success"] for r in results), - } - - json_path = out_dir / "latest_results.json" - json_path.write_text(json.dumps(report, indent=2), encoding="utf-8") - - # Markdown summary - md_lines = [f"# Test Runner Results — {ts}\n"] - for r in results: - icon = "✅" if r["success"] else "❌" - md_lines.append( - f"| {icon} | **{r['suite']}** | passed={r['passed']} failed={r['failed']} " - f"errors={r['errors']} skipped={r['skipped']} | {r['duration_s']}s |" - ) - md_path = out_dir / "latest_results.md" - md_path.write_text("\n".join(md_lines), encoding="utf-8") - - return json_path - - -# --------------------------------------------------------------------------- -# Watch Mode -# --------------------------------------------------------------------------- -def _watch_loop(suites: list[str], coverage: bool, verbose: int) -> None: - """Re-run suites when Python files change (poll-based, 2 s interval).""" - print("👀 Watch mode — press Ctrl+C to stop\n") - last_mtime: float = 0.0 - - while True: - current = max( - ( - p.stat().st_mtime - for p in REPO_ROOT.rglob("*.py") - if "venv" not in str(p) - ), - default=0.0, - ) - if current > last_mtime: - last_mtime = current - _run_selected(suites, coverage=coverage, verbose=verbose) - time.sleep(2) - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- -def _run_selected(suites: list[str], *, coverage: bool, verbose: int) -> bool: - """Run requested suites, print output, return overall success.""" - results = [] - overall = True - - for name in suites: - print(f"\n{'='*60}") - print(f" Running suite: {name} ({SUITES[name]['description']})") - print(f"{'='*60}\n") - - summary, stdout, stderr = run_suite(name, coverage=coverage, verbose=verbose) - results.append(summary) - - # Print pytest output (preserving colours in terminal) - if stdout: - print(stdout) - if stderr: - print(stderr, file=sys.stderr) - - icon = "✅" if summary["success"] else "❌" - print( - f"\n{icon} {name}: passed={summary['passed']} failed={summary['failed']} " - f"errors={summary['errors']} skipped={summary['skipped']} " - f"({summary['duration_s']}s)" - ) - if not summary["success"]: - overall = False - - report_path = write_report(results) - print(f"\n📋 Report written to {report_path.relative_to(REPO_ROOT)}") - - if overall: - print("\n✅ All suites passed!") - else: - print("\n❌ Some suites failed — see output above.") - - return overall - - -def main() -> None: - ap = argparse.ArgumentParser( - description="QAI centralized test runner", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=__doc__, - ) - ap.add_argument("--unit", action="store_true", help="Run unit tests") - ap.add_argument("--integration", action="store_true", help="Run integration tests") - ap.add_argument("--all", action="store_true", help="Run all tests") - ap.add_argument("--all-fast", action="store_true", help="All except slow & azure") - ap.add_argument("--autotrain", action="store_true", help="Autotrain tests") - ap.add_argument("--quantum", action="store_true", help="Quantum tests") - ap.add_argument("--database", action="store_true", help="Database tests") - ap.add_argument("--chat", action="store_true", help="Chat tests") - ap.add_argument("--coverage", action="store_true", help="Enable coverage reporting") - ap.add_argument("--watch", action="store_true", help="Re-run on file changes") - ap.add_argument("--list-suites", action="store_true", help="List available suites") - ap.add_argument( - "--verbose", - type=int, - default=1, - help="Verbosity (0=quiet, 1=normal, 2=verbose)", - ) - args = ap.parse_args() - - if args.list_suites: - list_suites() - return - - # Collect requested suites - selected: list[str] = [] - if args.unit: - selected.append("unit") - if args.integration: - selected.append("integration") - if args.all: - selected.append("all") - if args.all_fast: - selected.append("all_fast") - if args.autotrain: - selected.append("autotrain") - if args.quantum: - selected.append("quantum") - if args.database: - selected.append("database") - if args.chat: - selected.append("chat") - - if not selected: - selected = ["unit"] # Default to unit tests - - if args.watch: - try: - _watch_loop(selected, coverage=args.coverage, verbose=args.verbose) - except KeyboardInterrupt: - print("\n⏹ Watch mode stopped.") - return - - success = _run_selected(selected, coverage=args.coverage, verbose=args.verbose) - sys.exit(0 if success else 1) - - -if __name__ == "__main__": - main() +#!/usr/bin/env python +""" +Centralized test orchestrator with intelligent filtering and result aggregation. + +Test Suites: + unit — Fast unit tests (excludes slow, azure, integration, quantum, gpu) + integration — Integration tests only + all_fast — All tests except slow and azure + all — Every test + autotrain — Autotrain-related tests + quantum — Quantum-related tests + database — Database integration tests + chat — Chat provider tests + +Usage: + python scripts/test_runner.py --unit + python scripts/test_runner.py --unit --coverage + python scripts/test_runner.py --all + python scripts/test_runner.py --integration + python scripts/test_runner.py --list-suites + python scripts/test_runner.py --unit --integration --watch +""" +import argparse +import json +import re +import subprocess +import sys +import time +from datetime import datetime, timezone +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +TESTS_DIR = REPO_ROOT / "tests" +DATA_OUT = REPO_ROOT / "data_out" + +# --------------------------------------------------------------------------- +# Suite Definitions +# --------------------------------------------------------------------------- +SUITES = { + "unit": { + "description": "Fast unit tests (~0.5s)", + "pytest_args": [ + "-m", + "not slow and not azure and not integration and not quantum and not gpu", + ], + }, + "integration": { + "description": "Integration / external-service tests (~3s)", + "pytest_args": ["-m", "integration"], + }, + "all_fast": { + "description": "All tests except slow & azure (~10s)", + "pytest_args": ["-m", "not slow and not azure"], + }, + "all": { + "description": "Full test suite (may be slow)", + "pytest_args": [], + }, + "autotrain": { + "description": "Autotrain-related tests", + "pytest_args": ["-k", "autotrain or train"], + }, + "quantum": { + "description": "Quantum ML tests", + "pytest_args": ["-m", "quantum"], + }, + "database": { + "description": "Database integration tests", + "pytest_args": ["-k", "database or sql"], + }, + "chat": { + "description": "Chat provider tests", + "pytest_args": ["-k", "provider or chat or lmstudio"], + }, +} + +# --------------------------------------------------------------------------- +# Result Parsing +# --------------------------------------------------------------------------- +_RESULT_RE = re.compile( + r"=+ (?:(\d+) passed)?" + r"(?:,? ?(\d+) failed)?" + r"(?:,? ?(\d+) error)?" + r"(?:,? ?(\d+) skipped)?" + r"(?:,? ?(\d+) warning)?" + r".*=+", +) + + +def _parse_pytest_summary(output: str) -> dict: + """Extract counts from pytest's one-line summary.""" + m = _RESULT_RE.search(output) + if not m: + return {"passed": 0, "failed": 0, "errors": 0, "skipped": 0} + return { + "passed": int(m.group(1) or 0), + "failed": int(m.group(2) or 0), + "errors": int(m.group(3) or 0), + "skipped": int(m.group(4) or 0), + } + + +# --------------------------------------------------------------------------- +# Runner +# --------------------------------------------------------------------------- +def run_suite(name: str, *, coverage: bool = False, verbose: int = 1) -> dict: + """Run a single pytest suite and return structured results.""" + suite = SUITES[name] + cmd = [sys.executable, "-m", "pytest", str(TESTS_DIR)] + cmd.extend(suite["pytest_args"]) + + if coverage: + cmd.extend(["--cov=shared", "--cov=scripts", "--cov-report=term-missing"]) + + if verbose == 0: + cmd.append("-q") + elif verbose >= 2: + cmd.append("-v") + + # Strip ANSI codes for parsing + cmd.extend(["--tb=short", "--no-header"]) + + start = time.monotonic() + result = subprocess.run(cmd, capture_output=True, text=True, cwd=str(REPO_ROOT)) + elapsed = round(time.monotonic() - start, 2) + + # Remove ANSI escape sequences for reliable regex + clean = re.sub(r"\x1b\[[0-9;]*m", "", result.stdout + result.stderr) + + summary = _parse_pytest_summary(clean) + summary.update( + { + "suite": name, + "returncode": result.returncode, + "duration_s": elapsed, + "success": result.returncode == 0, + } + ) + + return summary, result.stdout, result.stderr + + +def list_suites() -> None: + """Print available test suites.""" + print("Available test suites:\n") + for name, info in SUITES.items(): + print(f" {name:15s} {info['description']}") + print(f"\nUsage: python {Path(__file__).name} -- [--coverage]") + + +# --------------------------------------------------------------------------- +# Report +# --------------------------------------------------------------------------- +def write_report(results: list[dict]) -> Path: + """Write JSON + Markdown reports to data_out/.""" + out_dir = DATA_OUT / "test_runner" + out_dir.mkdir(parents=True, exist_ok=True) + ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") + + report = { + "timestamp": ts, + "suites": results, + "overall_success": all(r["success"] for r in results), + } + + json_path = out_dir / "latest_results.json" + json_path.write_text(json.dumps(report, indent=2), encoding="utf-8") + + # Markdown summary + md_lines = [f"# Test Runner Results — {ts}\n"] + for r in results: + icon = "✅" if r["success"] else "❌" + md_lines.append( + f"| {icon} | **{r['suite']}** | passed={r['passed']} failed={r['failed']} " + f"errors={r['errors']} skipped={r['skipped']} | {r['duration_s']}s |" + ) + md_path = out_dir / "latest_results.md" + md_path.write_text("\n".join(md_lines), encoding="utf-8") + + return json_path + + +# --------------------------------------------------------------------------- +# Watch Mode +# --------------------------------------------------------------------------- +def _watch_loop(suites: list[str], coverage: bool, verbose: int) -> None: + """Re-run suites when Python files change (poll-based, 2 s interval).""" + print("👀 Watch mode — press Ctrl+C to stop\n") + last_mtime: float = 0.0 + + while True: + current = max( + ( + p.stat().st_mtime + for p in REPO_ROOT.rglob("*.py") + if "venv" not in str(p) + ), + default=0.0, + ) + if current > last_mtime: + last_mtime = current + _run_selected(suites, coverage=coverage, verbose=verbose) + time.sleep(2) + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- +def _run_selected(suites: list[str], *, coverage: bool, verbose: int) -> bool: + """Run requested suites, print output, return overall success.""" + results = [] + overall = True + + for name in suites: + print(f"\n{'='*60}") + print(f" Running suite: {name} ({SUITES[name]['description']})") + print(f"{'='*60}\n") + + summary, stdout, stderr = run_suite(name, coverage=coverage, verbose=verbose) + results.append(summary) + + # Print pytest output (preserving colours in terminal) + if stdout: + print(stdout) + if stderr: + print(stderr, file=sys.stderr) + + icon = "✅" if summary["success"] else "❌" + print( + f"\n{icon} {name}: passed={summary['passed']} failed={summary['failed']} " + f"errors={summary['errors']} skipped={summary['skipped']} " + f"({summary['duration_s']}s)" + ) + if not summary["success"]: + overall = False + + report_path = write_report(results) + print(f"\n📋 Report written to {report_path.relative_to(REPO_ROOT)}") + + if overall: + print("\n✅ All suites passed!") + else: + print("\n❌ Some suites failed — see output above.") + + return overall + + +def main() -> None: + ap = argparse.ArgumentParser( + description="QAI centralized test runner", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=__doc__, + ) + ap.add_argument("--unit", action="store_true", help="Run unit tests") + ap.add_argument("--integration", action="store_true", help="Run integration tests") + ap.add_argument("--all", action="store_true", help="Run all tests") + ap.add_argument("--all-fast", action="store_true", help="All except slow & azure") + ap.add_argument("--autotrain", action="store_true", help="Autotrain tests") + ap.add_argument("--quantum", action="store_true", help="Quantum tests") + ap.add_argument("--database", action="store_true", help="Database tests") + ap.add_argument("--chat", action="store_true", help="Chat tests") + ap.add_argument("--coverage", action="store_true", help="Enable coverage reporting") + ap.add_argument("--watch", action="store_true", help="Re-run on file changes") + ap.add_argument("--list-suites", action="store_true", help="List available suites") + ap.add_argument( + "--verbose", + type=int, + default=1, + help="Verbosity (0=quiet, 1=normal, 2=verbose)", + ) + args = ap.parse_args() + + if args.list_suites: + list_suites() + return + + # Collect requested suites + selected: list[str] = [] + if args.unit: + selected.append("unit") + if args.integration: + selected.append("integration") + if args.all: + selected.append("all") + if args.all_fast: + selected.append("all_fast") + if args.autotrain: + selected.append("autotrain") + if args.quantum: + selected.append("quantum") + if args.database: + selected.append("database") + if args.chat: + selected.append("chat") + + if not selected: + selected = ["unit"] # Default to unit tests + + if args.watch: + try: + _watch_loop(selected, coverage=args.coverage, verbose=args.verbose) + except KeyboardInterrupt: + print("\n⏹ Watch mode stopped.") + return + + success = _run_selected(selected, coverage=args.coverage, verbose=args.verbose) + sys.exit(0 if success else 1) + + +if __name__ == "__main__": + main() diff --git a/scripts/train_quantum_llm_chat.py b/scripts/train_quantum_llm_chat.py index 424acf5da..ddae10ded 100644 --- a/scripts/train_quantum_llm_chat.py +++ b/scripts/train_quantum_llm_chat.py @@ -1,366 +1,366 @@ -#!/usr/bin/env python3 -""" -Train a Quantum-Enhanced LLM for Chat -====================================== - -Trains a small quantum LLM model that can be used for interactive chat. -Integrates quantum circuits into the attention mechanism. - -Usage: - python scripts/train_quantum_llm_chat.py --quick - python scripts/train_quantum_llm_chat.py --epochs 5 --output my_model -""" - -import argparse -import inspect -import json -import logging -import sys -from datetime import datetime -from pathlib import Path - -try: - import torch - import torch.nn as nn - from torch.utils.data import DataLoader, Dataset -except Exception: # pragma: no cover - optional dependency in lightweight envs - torch = None - nn = None - - class Dataset: # type: ignore[override] - """Fallback placeholder when torch is unavailable.""" - - DataLoader = None # type: ignore[assignment] - -# Add paths -repo_root = Path(__file__).resolve().parent.parent -quantum_ml_path = repo_root / "ai-projects" / "quantum-ml" -quantum_ml_src = quantum_ml_path / "src" -for p in [str(quantum_ml_path), str(quantum_ml_src)]: - if p not in sys.path: - sys.path.insert(0, p) - -try: - from quantum_transformer import QUANTUM_AVAILABLE, QuantumLLM -except ImportError as e: - logging.warning(f"QuantumLLM unavailable at import time: {e}") - QUANTUM_AVAILABLE = False - QuantumLLM = None - -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" -) -logger = logging.getLogger(__name__) - - -class SimpleCharDataset(Dataset): - """Character-level dataset for language modeling.""" - - def __init__(self, text: str, seq_len: int = 64): - self.seq_len = seq_len - - # Build character vocabulary - chars = sorted(set(text)) - self.vocab_size = len(chars) + 1 # +1 for padding - self.char_to_idx = {c: i + 1 for i, c in enumerate(chars)} - self.char_to_idx[""] = 0 - self.idx_to_char = {i: c for c, i in self.char_to_idx.items()} - - # Encode text - self.data = [self.char_to_idx.get(c, 0) for c in text] - - # Pad if needed - if len(self.data) < seq_len + 1: - self.data = self.data + [0] * (seq_len + 1 - len(self.data)) - - def __len__(self): - return max(0, len(self.data) - self.seq_len) - - def __getitem__(self, idx): - chunk = self.data[idx : idx + self.seq_len + 1] - x = torch.tensor(chunk[:-1], dtype=torch.long) - y = torch.tensor(chunk[1:], dtype=torch.long) - return x, y - - -def get_training_text(): - """Get sample training text about quantum computing and AI.""" - return ( - """ -Quantum computing uses quantum mechanics to process information in fundamentally new ways. -Unlike classical computers that use bits, quantum computers use qubits that can exist in superposition. -This means a qubit can be both zero and one simultaneously, enabling parallel computation. -Entanglement is another key quantum property where qubits become correlated. -When qubits are entangled, measuring one instantly affects the others regardless of distance. -Quantum gates manipulate qubits through unitary transformations to perform computations. -Variational quantum circuits are hybrid quantum-classical algorithms useful for machine learning. -They combine parameterized quantum circuits with classical optimization to solve problems. -Language models learn patterns in text by predicting the next token given previous context. -Transformers use attention mechanisms to weigh the importance of different input tokens. -Self-attention allows each position to attend to all positions in the previous layer. -Quantum-enhanced transformers integrate quantum circuits into the attention computation. -This creates quantum attention patterns that can capture complex relationships in data. -Training neural networks involves computing gradients and updating parameters via backpropagation. -Quantum circuits are differentiable, allowing gradients to flow through quantum layers. -This enables end-to-end training of hybrid quantum-classical neural networks. -Machine learning on quantum computers is an emerging field with great potential. -Quantum machine learning algorithms may provide advantages for certain tasks. -Current quantum computers are noisy and limited in scale, making practical applications challenging. -However, quantum simulators allow us to prototype and test quantum algorithms efficiently. -The future of AI may involve hybrid systems combining classical and quantum processing. -Quantum attention mechanisms can potentially discover patterns classical systems cannot find. -Research continues to explore the boundaries of what quantum computing can achieve. -As quantum hardware improves, practical quantum machine learning applications will emerge. -The combination of quantum physics and artificial intelligence opens exciting new possibilities. -""" - * 20 - ) # Repeat for more training data - - -def _build_quantum_llm(model_cls, args, vocab_size: int): - """Instantiate QuantumLLM using compatible layer-count kwarg. - - Supports both constructor styles: - - n_transformer_layers (current) - - n_layers (legacy) - """ - init_params = inspect.signature(model_cls.__init__).parameters - if "n_transformer_layers" in init_params: - layer_kwarg = "n_transformer_layers" - elif "n_layers" in init_params: - layer_kwarg = "n_layers" - else: - # Conservative default for modern implementation - layer_kwarg = "n_transformer_layers" - - model_kwargs = { - "vocab_size": vocab_size, - "d_model": args.d_model, - "n_heads": args.n_heads, - "max_seq_len": args.seq_len, - "n_qubits": args.n_qubits, - "n_quantum_layers": 2, - "dropout": 0.1, - "use_quantum_attention": True, - "use_quantum_ffn": True, - layer_kwarg: args.n_layers, - } - return model_cls(**model_kwargs), layer_kwarg - - -def _write_json_atomic(path: Path, payload: dict): - """Atomically write JSON content to avoid partial/truncated files.""" - tmp_path = path.with_suffix(path.suffix + ".tmp") - with open(tmp_path, "w", encoding="utf-8") as f: - json.dump(payload, f, indent=2) - tmp_path.replace(path) - - -def train_quantum_llm(args): - """Train a quantum LLM model.""" - - logger.info("=" * 80) - logger.info("QUANTUM LLM TRAINING") - logger.info("=" * 80) - - if not QUANTUM_AVAILABLE: - logger.error( - "Quantum layers not available. Install pennylane: pip install pennylane" - ) - return 1 - - if torch is None or nn is None or DataLoader is None: - logger.error( - "PyTorch is not installed. Install torch to run training: pip install torch" - ) - return 1 - - # Setup - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - logger.info(f"Device: {device}") - - # Create dataset - logger.info("Creating dataset...") - text = get_training_text() - if args.quick: - # Keep quick mode truly fast for local smoke runs. - text = text[: max(args.seq_len * 80, 4000)] - dataset = SimpleCharDataset(text, seq_len=args.seq_len) - vocab_size = dataset.vocab_size - logger.info(f"Vocabulary size: {vocab_size}") - logger.info(f"Dataset size: {len(dataset)} sequences") - - dataloader = DataLoader( - dataset, batch_size=args.batch_size, shuffle=True, num_workers=0 - ) - - # Create model - logger.info("Creating quantum LLM...") - logger.info(f" d_model: {args.d_model}") - logger.info(f" n_layers: {args.n_layers}") - logger.info(f" n_heads: {args.n_heads}") - logger.info(f" n_qubits: {args.n_qubits}") - - model, layer_kwarg = _build_quantum_llm(QuantumLLM, args, vocab_size) - logger.info(f" constructor layer kwarg: {layer_kwarg}") - model = model.to(device) - - n_params = sum(p.numel() for p in model.parameters()) - logger.info(f"Model parameters: {n_params:,}") - - # Training setup - optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr) - criterion = nn.CrossEntropyLoss(ignore_index=0) # Ignore padding - - # Training loop - logger.info(f"\nStarting training for {args.epochs} epochs...") - model.train() - - max_batches = args.max_batches_per_epoch if args.max_batches_per_epoch > 0 else None - total_batches = len(dataloader) - display_total_batches = ( - min(total_batches, max_batches) if max_batches else total_batches - ) - - for epoch in range(args.epochs): - total_loss = 0 - n_batches = 0 - - for batch_idx, (x, y) in enumerate(dataloader): - if max_batches is not None and batch_idx >= max_batches: - break - - x, y = x.to(device), y.to(device) - - optimizer.zero_grad() - - # Forward pass - logits = model(x) # [batch, seq_len, vocab_size] - - # Compute loss - loss = criterion(logits.view(-1, vocab_size), y.view(-1)) - - # Backward pass - loss.backward() - torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) - optimizer.step() - - total_loss += loss.item() - n_batches += 1 - - if (batch_idx + 1) % 10 == 0: - avg_loss = total_loss / n_batches - logger.info( - f" Epoch {epoch+1}/{args.epochs} | Batch {batch_idx+1}/{display_total_batches} | Loss: {avg_loss:.4f}" - ) - - avg_epoch_loss = total_loss / max(n_batches, 1) - logger.info( - f"Epoch {epoch+1}/{args.epochs} completed | Avg Loss: {avg_epoch_loss:.4f}" - ) - - # Save model - output_dir = Path("data_out") / args.output - output_dir.mkdir(parents=True, exist_ok=True) - - # Save model checkpoint - checkpoint_path = output_dir / "quantum_llm_checkpoint.pt" - torch.save( - { - "model_state_dict": model.state_dict(), - "vocab_size": vocab_size, - "d_model": args.d_model, - "n_heads": args.n_heads, - "n_transformer_layers": args.n_layers, - "n_layers": args.n_layers, - "n_qubits": args.n_qubits, - "n_quantum_layers": 2, - "max_seq_len": args.seq_len, - "max_seq_length": args.seq_len, - "char_to_idx": dataset.char_to_idx, - "idx_to_char": dataset.idx_to_char, - }, - checkpoint_path, - ) - - logger.info(f"\nModel saved to: {checkpoint_path}") - - # Save config - config_path = output_dir / "config.json" - config = { - "vocab_size": vocab_size, - "d_model": args.d_model, - "n_heads": args.n_heads, - "n_transformer_layers": args.n_layers, - "n_layers": args.n_layers, - "n_qubits": args.n_qubits, - "n_quantum_layers": 2, - "max_seq_len": args.seq_len, - "max_seq_length": args.seq_len, - "trained_at": datetime.now().isoformat(), - "quantum_available": QUANTUM_AVAILABLE, - } - - _write_json_atomic(config_path, config) - - logger.info(f"Config saved to: {config_path}") - logger.info("\n" + "=" * 80) - logger.info("TRAINING COMPLETE!") - logger.info("=" * 80) - logger.info("\nTo chat with this model, run:") - logger.info( - f" python ai-projects/chat-cli/src/chat_cli.py --provider quantum --model {output_dir}" - ) - - return 0 - - -def main(): - parser = argparse.ArgumentParser( - description="Train a quantum-enhanced LLM for chat" - ) - parser.add_argument( - "--epochs", type=int, default=3, help="Number of training epochs" - ) - parser.add_argument("--batch-size", type=int, default=8, help="Batch size") - parser.add_argument("--seq-len", type=int, default=64, help="Sequence length") - parser.add_argument("--d-model", type=int, default=64, help="Model dimension") - parser.add_argument("--n-layers", type=int, default=2, help="Number of layers") - parser.add_argument( - "--n-heads", type=int, default=2, help="Number of attention heads" - ) - parser.add_argument( - "--n-qubits", type=int, default=2, help="Number of qubits per quantum layer" - ) - parser.add_argument("--lr", type=float, default=1e-3, help="Learning rate") - parser.add_argument( - "--output", type=str, default="quantum_llm_chat", help="Output directory name" - ) - parser.add_argument( - "--quick", action="store_true", help="Quick training (2 epochs, small model)" - ) - parser.add_argument( - "--max-batches-per-epoch", - type=int, - default=0, - help="Optional cap on batches per epoch (0 = all batches)", - ) - - args = parser.parse_args() - - # Quick mode overrides - if args.quick: - args.epochs = 2 - args.d_model = 32 - args.n_layers = 2 - args.n_heads = 2 - args.n_qubits = 2 - args.seq_len = 32 - args.max_batches_per_epoch = 20 - logger.info("Quick mode enabled: using minimal settings for fast training") - - return train_quantum_llm(args) - - -if __name__ == "__main__": - sys.exit(main()) +#!/usr/bin/env python3 +""" +Train a Quantum-Enhanced LLM for Chat +====================================== + +Trains a small quantum LLM model that can be used for interactive chat. +Integrates quantum circuits into the attention mechanism. + +Usage: + python scripts/train_quantum_llm_chat.py --quick + python scripts/train_quantum_llm_chat.py --epochs 5 --output my_model +""" + +import argparse +import inspect +import json +import logging +import sys +from datetime import datetime +from pathlib import Path + +try: + import torch + import torch.nn as nn + from torch.utils.data import DataLoader, Dataset +except Exception: # pragma: no cover - optional dependency in lightweight envs + torch = None + nn = None + + class Dataset: # type: ignore[override] + """Fallback placeholder when torch is unavailable.""" + + DataLoader = None # type: ignore[assignment] + +# Add paths +repo_root = Path(__file__).resolve().parent.parent +quantum_ml_path = repo_root / "ai-projects" / "quantum-ml" +quantum_ml_src = quantum_ml_path / "src" +for p in [str(quantum_ml_path), str(quantum_ml_src)]: + if p not in sys.path: + sys.path.insert(0, p) + +try: + from quantum_transformer import QUANTUM_AVAILABLE, QuantumLLM +except ImportError as e: + logging.warning(f"QuantumLLM unavailable at import time: {e}") + QUANTUM_AVAILABLE = False + QuantumLLM = None + +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + + +class SimpleCharDataset(Dataset): + """Character-level dataset for language modeling.""" + + def __init__(self, text: str, seq_len: int = 64): + self.seq_len = seq_len + + # Build character vocabulary + chars = sorted(set(text)) + self.vocab_size = len(chars) + 1 # +1 for padding + self.char_to_idx = {c: i + 1 for i, c in enumerate(chars)} + self.char_to_idx[""] = 0 + self.idx_to_char = {i: c for c, i in self.char_to_idx.items()} + + # Encode text + self.data = [self.char_to_idx.get(c, 0) for c in text] + + # Pad if needed + if len(self.data) < seq_len + 1: + self.data = self.data + [0] * (seq_len + 1 - len(self.data)) + + def __len__(self): + return max(0, len(self.data) - self.seq_len) + + def __getitem__(self, idx): + chunk = self.data[idx : idx + self.seq_len + 1] + x = torch.tensor(chunk[:-1], dtype=torch.long) + y = torch.tensor(chunk[1:], dtype=torch.long) + return x, y + + +def get_training_text(): + """Get sample training text about quantum computing and AI.""" + return ( + """ +Quantum computing uses quantum mechanics to process information in fundamentally new ways. +Unlike classical computers that use bits, quantum computers use qubits that can exist in superposition. +This means a qubit can be both zero and one simultaneously, enabling parallel computation. +Entanglement is another key quantum property where qubits become correlated. +When qubits are entangled, measuring one instantly affects the others regardless of distance. +Quantum gates manipulate qubits through unitary transformations to perform computations. +Variational quantum circuits are hybrid quantum-classical algorithms useful for machine learning. +They combine parameterized quantum circuits with classical optimization to solve problems. +Language models learn patterns in text by predicting the next token given previous context. +Transformers use attention mechanisms to weigh the importance of different input tokens. +Self-attention allows each position to attend to all positions in the previous layer. +Quantum-enhanced transformers integrate quantum circuits into the attention computation. +This creates quantum attention patterns that can capture complex relationships in data. +Training neural networks involves computing gradients and updating parameters via backpropagation. +Quantum circuits are differentiable, allowing gradients to flow through quantum layers. +This enables end-to-end training of hybrid quantum-classical neural networks. +Machine learning on quantum computers is an emerging field with great potential. +Quantum machine learning algorithms may provide advantages for certain tasks. +Current quantum computers are noisy and limited in scale, making practical applications challenging. +However, quantum simulators allow us to prototype and test quantum algorithms efficiently. +The future of AI may involve hybrid systems combining classical and quantum processing. +Quantum attention mechanisms can potentially discover patterns classical systems cannot find. +Research continues to explore the boundaries of what quantum computing can achieve. +As quantum hardware improves, practical quantum machine learning applications will emerge. +The combination of quantum physics and artificial intelligence opens exciting new possibilities. +""" + * 20 + ) # Repeat for more training data + + +def _build_quantum_llm(model_cls, args, vocab_size: int): + """Instantiate QuantumLLM using compatible layer-count kwarg. + + Supports both constructor styles: + - n_transformer_layers (current) + - n_layers (legacy) + """ + init_params = inspect.signature(model_cls.__init__).parameters + if "n_transformer_layers" in init_params: + layer_kwarg = "n_transformer_layers" + elif "n_layers" in init_params: + layer_kwarg = "n_layers" + else: + # Conservative default for modern implementation + layer_kwarg = "n_transformer_layers" + + model_kwargs = { + "vocab_size": vocab_size, + "d_model": args.d_model, + "n_heads": args.n_heads, + "max_seq_len": args.seq_len, + "n_qubits": args.n_qubits, + "n_quantum_layers": 2, + "dropout": 0.1, + "use_quantum_attention": True, + "use_quantum_ffn": True, + layer_kwarg: args.n_layers, + } + return model_cls(**model_kwargs), layer_kwarg + + +def _write_json_atomic(path: Path, payload: dict): + """Atomically write JSON content to avoid partial/truncated files.""" + tmp_path = path.with_suffix(path.suffix + ".tmp") + with open(tmp_path, "w", encoding="utf-8") as f: + json.dump(payload, f, indent=2) + tmp_path.replace(path) + + +def train_quantum_llm(args): + """Train a quantum LLM model.""" + + logger.info("=" * 80) + logger.info("QUANTUM LLM TRAINING") + logger.info("=" * 80) + + if not QUANTUM_AVAILABLE: + logger.error( + "Quantum layers not available. Install pennylane: pip install pennylane" + ) + return 1 + + if torch is None or nn is None or DataLoader is None: + logger.error( + "PyTorch is not installed. Install torch to run training: pip install torch" + ) + return 1 + + # Setup + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + logger.info(f"Device: {device}") + + # Create dataset + logger.info("Creating dataset...") + text = get_training_text() + if args.quick: + # Keep quick mode truly fast for local smoke runs. + text = text[: max(args.seq_len * 80, 4000)] + dataset = SimpleCharDataset(text, seq_len=args.seq_len) + vocab_size = dataset.vocab_size + logger.info(f"Vocabulary size: {vocab_size}") + logger.info(f"Dataset size: {len(dataset)} sequences") + + dataloader = DataLoader( + dataset, batch_size=args.batch_size, shuffle=True, num_workers=0 + ) + + # Create model + logger.info("Creating quantum LLM...") + logger.info(f" d_model: {args.d_model}") + logger.info(f" n_layers: {args.n_layers}") + logger.info(f" n_heads: {args.n_heads}") + logger.info(f" n_qubits: {args.n_qubits}") + + model, layer_kwarg = _build_quantum_llm(QuantumLLM, args, vocab_size) + logger.info(f" constructor layer kwarg: {layer_kwarg}") + model = model.to(device) + + n_params = sum(p.numel() for p in model.parameters()) + logger.info(f"Model parameters: {n_params:,}") + + # Training setup + optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr) + criterion = nn.CrossEntropyLoss(ignore_index=0) # Ignore padding + + # Training loop + logger.info(f"\nStarting training for {args.epochs} epochs...") + model.train() + + max_batches = args.max_batches_per_epoch if args.max_batches_per_epoch > 0 else None + total_batches = len(dataloader) + display_total_batches = ( + min(total_batches, max_batches) if max_batches else total_batches + ) + + for epoch in range(args.epochs): + total_loss = 0 + n_batches = 0 + + for batch_idx, (x, y) in enumerate(dataloader): + if max_batches is not None and batch_idx >= max_batches: + break + + x, y = x.to(device), y.to(device) + + optimizer.zero_grad() + + # Forward pass + logits = model(x) # [batch, seq_len, vocab_size] + + # Compute loss + loss = criterion(logits.view(-1, vocab_size), y.view(-1)) + + # Backward pass + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + + total_loss += loss.item() + n_batches += 1 + + if (batch_idx + 1) % 10 == 0: + avg_loss = total_loss / n_batches + logger.info( + f" Epoch {epoch+1}/{args.epochs} | Batch {batch_idx+1}/{display_total_batches} | Loss: {avg_loss:.4f}" + ) + + avg_epoch_loss = total_loss / max(n_batches, 1) + logger.info( + f"Epoch {epoch+1}/{args.epochs} completed | Avg Loss: {avg_epoch_loss:.4f}" + ) + + # Save model + output_dir = Path("data_out") / args.output + output_dir.mkdir(parents=True, exist_ok=True) + + # Save model checkpoint + checkpoint_path = output_dir / "quantum_llm_checkpoint.pt" + torch.save( + { + "model_state_dict": model.state_dict(), + "vocab_size": vocab_size, + "d_model": args.d_model, + "n_heads": args.n_heads, + "n_transformer_layers": args.n_layers, + "n_layers": args.n_layers, + "n_qubits": args.n_qubits, + "n_quantum_layers": 2, + "max_seq_len": args.seq_len, + "max_seq_length": args.seq_len, + "char_to_idx": dataset.char_to_idx, + "idx_to_char": dataset.idx_to_char, + }, + checkpoint_path, + ) + + logger.info(f"\nModel saved to: {checkpoint_path}") + + # Save config + config_path = output_dir / "config.json" + config = { + "vocab_size": vocab_size, + "d_model": args.d_model, + "n_heads": args.n_heads, + "n_transformer_layers": args.n_layers, + "n_layers": args.n_layers, + "n_qubits": args.n_qubits, + "n_quantum_layers": 2, + "max_seq_len": args.seq_len, + "max_seq_length": args.seq_len, + "trained_at": datetime.now().isoformat(), + "quantum_available": QUANTUM_AVAILABLE, + } + + _write_json_atomic(config_path, config) + + logger.info(f"Config saved to: {config_path}") + logger.info("\n" + "=" * 80) + logger.info("TRAINING COMPLETE!") + logger.info("=" * 80) + logger.info("\nTo chat with this model, run:") + logger.info( + f" python ai-projects/chat-cli/src/chat_cli.py --provider quantum --model {output_dir}" + ) + + return 0 + + +def main(): + parser = argparse.ArgumentParser( + description="Train a quantum-enhanced LLM for chat" + ) + parser.add_argument( + "--epochs", type=int, default=3, help="Number of training epochs" + ) + parser.add_argument("--batch-size", type=int, default=8, help="Batch size") + parser.add_argument("--seq-len", type=int, default=64, help="Sequence length") + parser.add_argument("--d-model", type=int, default=64, help="Model dimension") + parser.add_argument("--n-layers", type=int, default=2, help="Number of layers") + parser.add_argument( + "--n-heads", type=int, default=2, help="Number of attention heads" + ) + parser.add_argument( + "--n-qubits", type=int, default=2, help="Number of qubits per quantum layer" + ) + parser.add_argument("--lr", type=float, default=1e-3, help="Learning rate") + parser.add_argument( + "--output", type=str, default="quantum_llm_chat", help="Output directory name" + ) + parser.add_argument( + "--quick", action="store_true", help="Quick training (2 epochs, small model)" + ) + parser.add_argument( + "--max-batches-per-epoch", + type=int, + default=0, + help="Optional cap on batches per epoch (0 = all batches)", + ) + + args = parser.parse_args() + + # Quick mode overrides + if args.quick: + args.epochs = 2 + args.d_model = 32 + args.n_layers = 2 + args.n_heads = 2 + args.n_qubits = 2 + args.seq_len = 32 + args.max_batches_per_epoch = 20 + logger.info("Quick mode enabled: using minimal settings for fast training") + + return train_quantum_llm(args) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/training_analytics.py b/scripts/training_analytics.py index 400bbad54..6cba78c3e 100644 --- a/scripts/training_analytics.py +++ b/scripts/training_analytics.py @@ -1,414 +1,414 @@ -""" -Advanced Analytics for Autonomous Training -Generates charts, trends, and insights -""" - -import argparse -import os -import statistics -import sys -from datetime import datetime -from pathlib import Path -from typing import Dict - -# Ensure repository root is on sys.path as early as possible so subprocess -# invocations and test runners can import local packages reliably. -REPO_ROOT = Path(__file__).resolve().parent.parent -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from shared.json_utils import load_status_json # noqa: E402 - - -class TrainingAnalytics: - """Analyze training performance and generate insights""" - - def __init__(self, status_file: str = "data_out/autonomous_training_status.json"): - self.status_file = Path(status_file) - self.status = self.load_status() - - def load_status(self) -> Dict: - """Load status from file""" - loaded = load_status_json(self.status_file) - if loaded.get("_status_file_error"): - return {} - return {k: v for k, v in loaded.items() if not k.startswith("_status_file_")} - - @staticmethod - def _get_accuracy(perf: Dict) -> float: - """Get accuracy from either modern or legacy status schema.""" - return perf.get("mean_accuracy", perf.get("accuracy", 0.0)) - - def calculate_improvement_rate(self) -> float: - """Calculate average improvement rate per cycle""" - history = self.status.get("performance_history", []) - - if len(history) < 2: - return 0.0 - - first_acc = self._get_accuracy(history[0]) - last_acc = self._get_accuracy(history[-1]) - cycles = len(history) - - if cycles > 1: - return (last_acc - first_acc) / (cycles - 1) - return 0.0 - - def predict_target_accuracy(self, target: float = 0.90) -> int: - """Predict cycles needed to reach target accuracy""" - history = self.status.get("performance_history", []) - - if not history: - return 0 - - current = self._get_accuracy(history[-1]) - improvement_rate = self.calculate_improvement_rate() - - if improvement_rate <= 0 or current >= target: - return 0 - - cycles_needed = (target - current) / improvement_rate - return int(cycles_needed) + 1 - - def identify_best_epoch_count(self) -> int: - """Identify optimal epoch count based on accuracy/time tradeoff""" - history = self.status.get("performance_history", []) - - if not history: - return 100 - - # Group by epoch count and calculate average accuracy - epoch_performance = {} - - for perf in history: - epochs = perf.get("epochs") - if not epochs: - continue - accuracy = self._get_accuracy(perf) - - if epochs not in epoch_performance: - epoch_performance[epochs] = [] - epoch_performance[epochs].append(accuracy) - - if not epoch_performance: - return 100 - - best_epochs = 100 - best_avg = 0.0 - - for epochs, accuracies in epoch_performance.items(): - avg = statistics.fmean(accuracies) - if avg > best_avg: - best_avg = avg - best_epochs = epochs - - return best_epochs - - def detect_plateau(self, window: int = 3) -> bool: - """Detect if performance has plateaued""" - history = self.status.get("performance_history", []) - - if len(history) < window: - return False - - recent = history[-window:] - accuracies = [self._get_accuracy(p) for p in recent] - - # Check if variance is very low - variance = statistics.pvariance(accuracies) - - return variance < 0.0001 # Less than 0.01% variance - - def generate_report(self) -> str: - """Generate comprehensive analytics report""" - report = [] - improvement_rate = 0.0 - report.append("\n" + "=" * 80) - report.append("AUTONOMOUS TRAINING ANALYTICS REPORT") - report.append("=" * 80 + "\n") - - # Overview - cycles = self.status.get("cycles_completed", 0) - best_acc = self.status.get("best_accuracy", 0) - plateau_cycles = self.status.get("plateau_cycles", 0) - promotions = self.status.get("promotions", []) - total_datasets = self.status.get("total_datasets_available") - if total_datasets is None: - total_datasets = len(self.status.get("dataset_inventory", {})) - - report.append("OVERVIEW") - report.append("-" * 80) - report.append(f"Total Cycles: {cycles}") - report.append(f"Best Accuracy: {best_acc:.2%}") - report.append(f"Total Datasets: {total_datasets}") - report.append(f"Plateau Cycles at Peak: {plateau_cycles}") - report.append(f"Promotions Completed: {len(promotions)}") - if promotions: - p = promotions[-1] - report.append( - ( - f"Latest Promotion: v{p.get('version', '?')} at cycle " - f"{p.get('cycle', '?')} ({p.get('accuracy', 0):.2%})" - ) - ) - report.append("") - - # Performance trend - history = self.status.get("performance_history", []) - if history: - report.append("PERFORMANCE TREND") - report.append("-" * 80) - - first = self._get_accuracy(history[0]) - last = self._get_accuracy(history[-1]) - improvement = last - first - - report.append(f"Initial Accuracy: {first:.2%}") - report.append(f"Current Accuracy: {last:.2%}") - report.append( - f"Total Improvement: {improvement:.2%} (+{improvement*100:.2f} percentage points)" - ) - - improvement_rate = self.calculate_improvement_rate() - report.append(f"Improvement Rate: {improvement_rate*100:.3f}% per cycle") - report.append("") - - # Predictions - report.append("PREDICTIONS") - report.append("-" * 80) - - for target in [0.80, 0.85, 0.90, 0.95]: - cycles_needed = self.predict_target_accuracy(target) - if cycles_needed > 0: - report.append(f"Cycles to reach {target:.0%}: ~{cycles_needed}") - report.append("") - - # Epoch analysis - best_epochs = self.identify_best_epoch_count() - report.append("OPTIMIZATION INSIGHTS") - report.append("-" * 80) - report.append(f"Optimal Epoch Count: {best_epochs}") - - plateau = self.detect_plateau() - if plateau: - report.append( - "Status: ⚠️ PLATEAU DETECTED - Consider increasing epochs or tuning hyperparameters" - ) - else: - report.append("Status: ✅ Model is still improving") - report.append("") - - # Model quality breakdown - if history: - latest = history[-1] - exceptional = latest.get("exceptional_models", 0) - excellent = latest.get("excellent_models", 0) - successful = latest.get("successful_count", 0) - - report.append("MODEL QUALITY BREAKDOWN (Latest Cycle)") - report.append("-" * 80) - denom = successful if successful > 0 else 1 - report.append( - f"Exceptional (≥95%): {exceptional} ({exceptional/denom*100:.1f}%)" - ) - report.append( - f"Excellent (85-95%): {excellent} ({excellent/denom*100:.1f}%)" - ) - report.append(f"Total Successful: {successful}") - report.append("") - - # Recommendations - report.append("RECOMMENDATIONS") - report.append("-" * 80) - - if plateau: - report.append("• Increase epoch count to 200+") - report.append("• Enable hyperparameter tuning") - report.append("• Try architecture evolution") - elif improvement_rate < 0.001: - report.append("• Progress is slow - consider boosting epochs") - else: - report.append("• Continue current training strategy") - report.append("• Performance is improving steadily") - - if plateau_cycles >= 5: - report.append( - "• Plateau stable for 5+ cycles — promotion cadence is active" - ) - if promotions: - report.append("• Model promotion history available in status['promotions']") - - if best_acc >= 0.90: - report.append("• Ready for production deployment") - report.append("• Enable auto_deploy_best in config") - - report.append("\n" + "=" * 80 + "\n") - - return "\n".join(report) - - def generate_ascii_chart(self, metric: str = "mean_accuracy") -> str: - """Generate ASCII chart of performance over time""" - history = self.status.get("performance_history", []) - - if not history: - return "No data available" - - # Extract values - values = [p.get(metric, p.get("accuracy", 0)) for p in history] - - # Scale to chart height (20 rows) - chart_height = 20 - min_val = min(values) - max_val = max(values) - - if max_val == min_val: - return "All values are equal" - - # Normalize values - scaled = [] - for v in values: - normalized = (v - min_val) / (max_val - min_val) - scaled.append(int(normalized * (chart_height - 1))) - - # Build chart - chart = [] - chart.append(f"\n{metric.upper()} OVER TIME") - chart.append("─" * 80) - chart.append(f"Max: {max_val:.2%} │") - - for row in range(chart_height - 1, -1, -1): - chars = [] - for value in scaled: - if value >= row: - chars.append("█") - else: - chars.append(" ") - chart.append(" │" + "".join(chars)) - - chart.append(f"Min: {min_val:.2%} └" + "─" * len(scaled)) - chart.append(f" Cycle: 1{' ' * (len(scaled) - 5)}{len(scaled)}") - chart.append("") - - return "\n".join(chart) - - def export_html_report(self, output_file: str = "data_out/training_report.html"): - """Export analytics as HTML report""" - output_path = Path(output_file) - output_path.parent.mkdir(parents=True, exist_ok=True) - - html = f""" - - - - Autonomous Training Report - - - -

    Autonomous Training Analytics Report

    -

    Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}

    -

    Overview

    -
      -
    • Cycles completed: {self.status.get('cycles_completed', 0)}
      • -
    • Best accuracy: {self.status.get('best_accuracy', 0):.2%}
      • -
    • Total datasets: {self.status.get( - 'total_datasets_available', - len(self.status.get('dataset_inventory', {})), - )}
      • -
    -

    Report

    - 
    {self.generate_report()}
    - - -""" - - history = self.status.get("performance_history", []) - for i, perf in enumerate(history, start=1): - row_mean = perf.get("mean_accuracy", perf.get("accuracy", 0)) - row_max = perf.get("max_accuracy", perf.get("accuracy", row_mean)) - html += f""" - - #{i} - {perf.get('epochs', '-')} - {row_mean:.2%} - {row_max:.2%} - {perf.get('exceptional_models', 0)} - {perf.get('successful_count', perf.get('datasets_trained', 0))} - -""" - - html += f""" - - -

    Analysis & Recommendations

    -
    - 
    {self.generate_report()}
    -
    - - - -""" - - with open(output_path, "w") as f: - f.write(html) - - print(f"✅ HTML report exported to {output_path}") - - -def main(): - parser = argparse.ArgumentParser(description="Autonomous Training Analytics") - parser.add_argument( - "--status-file", - default="data_out/autonomous_training_status.json", - help="Path to status file", - ) - parser.add_argument("--report", action="store_true", help="Generate text report") - parser.add_argument("--chart", action="store_true", help="Display ASCII chart") - parser.add_argument("--html", metavar="FILE", help="Export HTML report") - parser.add_argument( - "--metric", - default="mean_accuracy", - help="Metric to chart (default: mean_accuracy)", - ) - - args = parser.parse_args() - - analytics = TrainingAnalytics(args.status_file) - - try: - if args.report: - print(analytics.generate_report()) - elif args.chart: - print(analytics.generate_ascii_chart(args.metric)) - elif args.html: - analytics.export_html_report(args.html) - else: - # Default: show report and chart - print(analytics.generate_report()) - print(analytics.generate_ascii_chart()) - except BrokenPipeError: - # Handle broken pipe when output is piped to commands like head - _squelch_stdout_after_broken_pipe() - return - - -def _squelch_stdout_after_broken_pipe() -> None: - """Redirect stdout to /dev/null to prevent shutdown-time flush errors.""" - try: - devnull = os.open(os.devnull, os.O_WRONLY) - os.dup2(devnull, sys.stdout.fileno()) - os.close(devnull) - except Exception: - pass - - -if __name__ == "__main__": - try: - main() - try: - sys.stdout.flush() - except BrokenPipeError: - _squelch_stdout_after_broken_pipe() - sys.exit(0) - except BrokenPipeError: - _squelch_stdout_after_broken_pipe() - sys.exit(0) +""" +Advanced Analytics for Autonomous Training +Generates charts, trends, and insights +""" + +import argparse +import os +import statistics +import sys +from datetime import datetime +from pathlib import Path +from typing import Dict + +# Ensure repository root is on sys.path as early as possible so subprocess +# invocations and test runners can import local packages reliably. +REPO_ROOT = Path(__file__).resolve().parent.parent +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from shared.json_utils import load_status_json # noqa: E402 + + +class TrainingAnalytics: + """Analyze training performance and generate insights""" + + def __init__(self, status_file: str = "data_out/autonomous_training_status.json"): + self.status_file = Path(status_file) + self.status = self.load_status() + + def load_status(self) -> Dict: + """Load status from file""" + loaded = load_status_json(self.status_file) + if loaded.get("_status_file_error"): + return {} + return {k: v for k, v in loaded.items() if not k.startswith("_status_file_")} + + @staticmethod + def _get_accuracy(perf: Dict) -> float: + """Get accuracy from either modern or legacy status schema.""" + return perf.get("mean_accuracy", perf.get("accuracy", 0.0)) + + def calculate_improvement_rate(self) -> float: + """Calculate average improvement rate per cycle""" + history = self.status.get("performance_history", []) + + if len(history) < 2: + return 0.0 + + first_acc = self._get_accuracy(history[0]) + last_acc = self._get_accuracy(history[-1]) + cycles = len(history) + + if cycles > 1: + return (last_acc - first_acc) / (cycles - 1) + return 0.0 + + def predict_target_accuracy(self, target: float = 0.90) -> int: + """Predict cycles needed to reach target accuracy""" + history = self.status.get("performance_history", []) + + if not history: + return 0 + + current = self._get_accuracy(history[-1]) + improvement_rate = self.calculate_improvement_rate() + + if improvement_rate <= 0 or current >= target: + return 0 + + cycles_needed = (target - current) / improvement_rate + return int(cycles_needed) + 1 + + def identify_best_epoch_count(self) -> int: + """Identify optimal epoch count based on accuracy/time tradeoff""" + history = self.status.get("performance_history", []) + + if not history: + return 100 + + # Group by epoch count and calculate average accuracy + epoch_performance = {} + + for perf in history: + epochs = perf.get("epochs") + if not epochs: + continue + accuracy = self._get_accuracy(perf) + + if epochs not in epoch_performance: + epoch_performance[epochs] = [] + epoch_performance[epochs].append(accuracy) + + if not epoch_performance: + return 100 + + best_epochs = 100 + best_avg = 0.0 + + for epochs, accuracies in epoch_performance.items(): + avg = statistics.fmean(accuracies) + if avg > best_avg: + best_avg = avg + best_epochs = epochs + + return best_epochs + + def detect_plateau(self, window: int = 3) -> bool: + """Detect if performance has plateaued""" + history = self.status.get("performance_history", []) + + if len(history) < window: + return False + + recent = history[-window:] + accuracies = [self._get_accuracy(p) for p in recent] + + # Check if variance is very low + variance = statistics.pvariance(accuracies) + + return variance < 0.0001 # Less than 0.01% variance + + def generate_report(self) -> str: + """Generate comprehensive analytics report""" + report = [] + improvement_rate = 0.0 + report.append("\n" + "=" * 80) + report.append("AUTONOMOUS TRAINING ANALYTICS REPORT") + report.append("=" * 80 + "\n") + + # Overview + cycles = self.status.get("cycles_completed", 0) + best_acc = self.status.get("best_accuracy", 0) + plateau_cycles = self.status.get("plateau_cycles", 0) + promotions = self.status.get("promotions", []) + total_datasets = self.status.get("total_datasets_available") + if total_datasets is None: + total_datasets = len(self.status.get("dataset_inventory", {})) + + report.append("OVERVIEW") + report.append("-" * 80) + report.append(f"Total Cycles: {cycles}") + report.append(f"Best Accuracy: {best_acc:.2%}") + report.append(f"Total Datasets: {total_datasets}") + report.append(f"Plateau Cycles at Peak: {plateau_cycles}") + report.append(f"Promotions Completed: {len(promotions)}") + if promotions: + p = promotions[-1] + report.append( + ( + f"Latest Promotion: v{p.get('version', '?')} at cycle " + f"{p.get('cycle', '?')} ({p.get('accuracy', 0):.2%})" + ) + ) + report.append("") + + # Performance trend + history = self.status.get("performance_history", []) + if history: + report.append("PERFORMANCE TREND") + report.append("-" * 80) + + first = self._get_accuracy(history[0]) + last = self._get_accuracy(history[-1]) + improvement = last - first + + report.append(f"Initial Accuracy: {first:.2%}") + report.append(f"Current Accuracy: {last:.2%}") + report.append( + f"Total Improvement: {improvement:.2%} (+{improvement*100:.2f} percentage points)" + ) + + improvement_rate = self.calculate_improvement_rate() + report.append(f"Improvement Rate: {improvement_rate*100:.3f}% per cycle") + report.append("") + + # Predictions + report.append("PREDICTIONS") + report.append("-" * 80) + + for target in [0.80, 0.85, 0.90, 0.95]: + cycles_needed = self.predict_target_accuracy(target) + if cycles_needed > 0: + report.append(f"Cycles to reach {target:.0%}: ~{cycles_needed}") + report.append("") + + # Epoch analysis + best_epochs = self.identify_best_epoch_count() + report.append("OPTIMIZATION INSIGHTS") + report.append("-" * 80) + report.append(f"Optimal Epoch Count: {best_epochs}") + + plateau = self.detect_plateau() + if plateau: + report.append( + "Status: ⚠️ PLATEAU DETECTED - Consider increasing epochs or tuning hyperparameters" + ) + else: + report.append("Status: ✅ Model is still improving") + report.append("") + + # Model quality breakdown + if history: + latest = history[-1] + exceptional = latest.get("exceptional_models", 0) + excellent = latest.get("excellent_models", 0) + successful = latest.get("successful_count", 0) + + report.append("MODEL QUALITY BREAKDOWN (Latest Cycle)") + report.append("-" * 80) + denom = successful if successful > 0 else 1 + report.append( + f"Exceptional (≥95%): {exceptional} ({exceptional/denom*100:.1f}%)" + ) + report.append( + f"Excellent (85-95%): {excellent} ({excellent/denom*100:.1f}%)" + ) + report.append(f"Total Successful: {successful}") + report.append("") + + # Recommendations + report.append("RECOMMENDATIONS") + report.append("-" * 80) + + if plateau: + report.append("• Increase epoch count to 200+") + report.append("• Enable hyperparameter tuning") + report.append("• Try architecture evolution") + elif improvement_rate < 0.001: + report.append("• Progress is slow - consider boosting epochs") + else: + report.append("• Continue current training strategy") + report.append("• Performance is improving steadily") + + if plateau_cycles >= 5: + report.append( + "• Plateau stable for 5+ cycles — promotion cadence is active" + ) + if promotions: + report.append("• Model promotion history available in status['promotions']") + + if best_acc >= 0.90: + report.append("• Ready for production deployment") + report.append("• Enable auto_deploy_best in config") + + report.append("\n" + "=" * 80 + "\n") + + return "\n".join(report) + + def generate_ascii_chart(self, metric: str = "mean_accuracy") -> str: + """Generate ASCII chart of performance over time""" + history = self.status.get("performance_history", []) + + if not history: + return "No data available" + + # Extract values + values = [p.get(metric, p.get("accuracy", 0)) for p in history] + + # Scale to chart height (20 rows) + chart_height = 20 + min_val = min(values) + max_val = max(values) + + if max_val == min_val: + return "All values are equal" + + # Normalize values + scaled = [] + for v in values: + normalized = (v - min_val) / (max_val - min_val) + scaled.append(int(normalized * (chart_height - 1))) + + # Build chart + chart = [] + chart.append(f"\n{metric.upper()} OVER TIME") + chart.append("─" * 80) + chart.append(f"Max: {max_val:.2%} │") + + for row in range(chart_height - 1, -1, -1): + chars = [] + for value in scaled: + if value >= row: + chars.append("█") + else: + chars.append(" ") + chart.append(" │" + "".join(chars)) + + chart.append(f"Min: {min_val:.2%} └" + "─" * len(scaled)) + chart.append(f" Cycle: 1{' ' * (len(scaled) - 5)}{len(scaled)}") + chart.append("") + + return "\n".join(chart) + + def export_html_report(self, output_file: str = "data_out/training_report.html"): + """Export analytics as HTML report""" + output_path = Path(output_file) + output_path.parent.mkdir(parents=True, exist_ok=True) + + html = f""" + + + + Autonomous Training Report + + + +

    Autonomous Training Analytics Report

    +

    Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}

    +

    Overview

    +
      +
    • Cycles completed: {self.status.get('cycles_completed', 0)}
      • +
    • Best accuracy: {self.status.get('best_accuracy', 0):.2%}
      • +
    • Total datasets: {self.status.get( + 'total_datasets_available', + len(self.status.get('dataset_inventory', {})), + )}
      • +
    +

    Report

    + 
    {self.generate_report()}
    + + +""" + + history = self.status.get("performance_history", []) + for i, perf in enumerate(history, start=1): + row_mean = perf.get("mean_accuracy", perf.get("accuracy", 0)) + row_max = perf.get("max_accuracy", perf.get("accuracy", row_mean)) + html += f""" + + #{i} + {perf.get('epochs', '-')} + {row_mean:.2%} + {row_max:.2%} + {perf.get('exceptional_models', 0)} + {perf.get('successful_count', perf.get('datasets_trained', 0))} + +""" + + html += f""" + + +

    Analysis & Recommendations

    +
    + 
    {self.generate_report()}
    +
    + + + +""" + + with open(output_path, "w") as f: + f.write(html) + + print(f"✅ HTML report exported to {output_path}") + + +def main(): + parser = argparse.ArgumentParser(description="Autonomous Training Analytics") + parser.add_argument( + "--status-file", + default="data_out/autonomous_training_status.json", + help="Path to status file", + ) + parser.add_argument("--report", action="store_true", help="Generate text report") + parser.add_argument("--chart", action="store_true", help="Display ASCII chart") + parser.add_argument("--html", metavar="FILE", help="Export HTML report") + parser.add_argument( + "--metric", + default="mean_accuracy", + help="Metric to chart (default: mean_accuracy)", + ) + + args = parser.parse_args() + + analytics = TrainingAnalytics(args.status_file) + + try: + if args.report: + print(analytics.generate_report()) + elif args.chart: + print(analytics.generate_ascii_chart(args.metric)) + elif args.html: + analytics.export_html_report(args.html) + else: + # Default: show report and chart + print(analytics.generate_report()) + print(analytics.generate_ascii_chart()) + except BrokenPipeError: + # Handle broken pipe when output is piped to commands like head + _squelch_stdout_after_broken_pipe() + return + + +def _squelch_stdout_after_broken_pipe() -> None: + """Redirect stdout to /dev/null to prevent shutdown-time flush errors.""" + try: + devnull = os.open(os.devnull, os.O_WRONLY) + os.dup2(devnull, sys.stdout.fileno()) + os.close(devnull) + except Exception: + pass + + +if __name__ == "__main__": + try: + main() + try: + sys.stdout.flush() + except BrokenPipeError: + _squelch_stdout_after_broken_pipe() + sys.exit(0) + except BrokenPipeError: + _squelch_stdout_after_broken_pipe() + sys.exit(0) diff --git a/scripts/validate_dashboard.py b/scripts/validate_dashboard.py index dbb925d93..2c69a027d 100644 --- a/scripts/validate_dashboard.py +++ b/scripts/validate_dashboard.py @@ -1,131 +1,131 @@ -import re -from pathlib import Path - -# Pre-compile regex patterns for performance -_RE_CONSOLE_LOG = re.compile(r"console\.log\([^)]+\)") -_RE_GET_BY_ID = re.compile(r"getElementById\(['\"]([^'\"]+)['\"]\)") -_RE_QUERY_SELECTOR = re.compile(r"querySelector\(['\"]([^'\"]+)['\"]\)") -_RE_ELEMENT_IDS = re.compile(r'id=["\']([^"\']+)["\']') -_RE_ASYNC_FUNCTION = re.compile(r"async\s+function") -_RE_AWAIT = re.compile(r"\bawait\s+") -_RE_EVENT_LISTENER = re.compile(r"addEventListener\s*\(") -_RE_FETCH_CALLS = re.compile(r"fetch\(['\"]([^'\"]+)['\"]\)") -_RE_LOCALSTORAGE = re.compile( - r"localStorage\.(getItem|setItem|removeItem)\(['\"]([^'\"]+)['\"]\)" -) -_RE_ONCLICK = re.compile(r'onclick=["\']([^"\']+)["\']') - - -def _resolve_dashboard_path() -> Path: - """Resolve dashboard HTML path across legacy and current layouts.""" - repo_root = Path(__file__).resolve().parents[1] - candidates = [ - repo_root / "dashboard" / "unified.html", - repo_root / "apps" / "dashboard" / "unified.html", - ] - for path in candidates: - if path.exists(): - return path - return candidates[0] - - -def run_validation() -> bool: - html_file = _resolve_dashboard_path() - if not html_file.exists(): - print(f"Dashboard file not found: {html_file}") - return False - - content = html_file.read_text(encoding="utf-8") - - print("=== Extended Dashboard Validation ===\n") - - issues_found = [] - - # 1. Check for console.log statements (potential debugging artifacts) - console_logs = _RE_CONSOLE_LOG.findall(content) - if console_logs: - print( - f" Found {len(console_logs)} console.log statements (consider removing for production)" - ) - else: - print(" No console.log statements") - - # 2. Check for undefined variable references in common patterns - print("\n Checking variable references...") - potential_issues = { - "getElementById": _RE_GET_BY_ID.findall(content), - "querySelector": _RE_QUERY_SELECTOR.findall(content), - } - - element_ids = _RE_ELEMENT_IDS.findall(content) - print(f" Found {len(element_ids)} element IDs defined") - - missing_ids = [] - for method, ids in potential_issues.items(): - for id_ref in ids: - if "#" not in id_ref and "." not in id_ref and "[" not in id_ref: - if id_ref not in element_ids: - missing_ids.append((method, id_ref)) - - if missing_ids: - print("\n Potential missing element IDs:") - for method, id_ref in missing_ids: - print(f' - {method}("{id_ref}")') - else: - print(" All element ID references appear valid") - - # 3. Check for async/await patterns - async_functions = len(_RE_ASYNC_FUNCTION.findall(content)) - await_calls = len(_RE_AWAIT.findall(content)) - print(f"\n Async functions: {async_functions}, await calls: {await_calls}") - - # 4. Check for event listeners - event_listeners = len(_RE_EVENT_LISTENER.findall(content)) - print(f" Event listeners: {event_listeners}") - - # 5. Check for fetch calls (API endpoints) - fetch_calls = _RE_FETCH_CALLS.findall(content) - print(f"\n API endpoints used: {len(set(fetch_calls))}") - for endpoint in sorted(set(fetch_calls)): - print(f" - {endpoint}") - - # 6. Check for localStorage usage - localstorage_keys = _RE_LOCALSTORAGE.findall(content) - if localstorage_keys: - print(f"\n LocalStorage keys: {len(set([k[1] for k in localstorage_keys]))}") - for method, key in sorted(set(localstorage_keys)): - print(f" - {key} ({method})") - - # 7. Check for potential syntax errors in inline onclick handlers - print("\n Checking inline onclick syntax...") - onclick_handlers = _RE_ONCLICK.findall(content) - syntax_ok = True - for i, handler in enumerate(onclick_handlers, 1): - if handler.count("(") != handler.count(")"): - print(f" Unbalanced parentheses in onclick #{i}: {handler[:50]}...") - syntax_ok = False - issues_found.append(f"onclick_{i}") - - if syntax_ok: - print(" All onclick handlers have valid syntax") - - # 8. Check for Chart.js dependency - if "Chart.js" in content or "chartjs" in content.lower(): - print("\n Chart.js dependency detected") - if "cdn.jsdelivr.net/npm/chart.js" in content: - print(" Chart.js loaded from CDN") - - print(f'\n{"=" * 50}') - if not issues_found: - print(" Dashboard validation complete - No critical issues found!") - print(f"\n Dashboard is ready for use at {html_file}") - else: - print(f" Found {len(issues_found)} potential issues") - for issue in issues_found: - print(f" - {issue}") - - return not issues_found - - -if __name__ == "__main__": - run_validation() +import re +from pathlib import Path + +# Pre-compile regex patterns for performance +_RE_CONSOLE_LOG = re.compile(r"console\.log\([^)]+\)") +_RE_GET_BY_ID = re.compile(r"getElementById\(['\"]([^'\"]+)['\"]\)") +_RE_QUERY_SELECTOR = re.compile(r"querySelector\(['\"]([^'\"]+)['\"]\)") +_RE_ELEMENT_IDS = re.compile(r'id=["\']([^"\']+)["\']') +_RE_ASYNC_FUNCTION = re.compile(r"async\s+function") +_RE_AWAIT = re.compile(r"\bawait\s+") +_RE_EVENT_LISTENER = re.compile(r"addEventListener\s*\(") +_RE_FETCH_CALLS = re.compile(r"fetch\(['\"]([^'\"]+)['\"]\)") +_RE_LOCALSTORAGE = re.compile( + r"localStorage\.(getItem|setItem|removeItem)\(['\"]([^'\"]+)['\"]\)" +) +_RE_ONCLICK = re.compile(r'onclick=["\']([^"\']+)["\']') + + +def _resolve_dashboard_path() -> Path: + """Resolve dashboard HTML path across legacy and current layouts.""" + repo_root = Path(__file__).resolve().parents[1] + candidates = [ + repo_root / "dashboard" / "unified.html", + repo_root / "apps" / "dashboard" / "unified.html", + ] + for path in candidates: + if path.exists(): + return path + return candidates[0] + + +def run_validation() -> bool: + html_file = _resolve_dashboard_path() + if not html_file.exists(): + print(f"Dashboard file not found: {html_file}") + return False + + content = html_file.read_text(encoding="utf-8") + + print("=== Extended Dashboard Validation ===\n") + + issues_found = [] + + # 1. Check for console.log statements (potential debugging artifacts) + console_logs = _RE_CONSOLE_LOG.findall(content) + if console_logs: + print( + f" Found {len(console_logs)} console.log statements (consider removing for production)" + ) + else: + print(" No console.log statements") + + # 2. Check for undefined variable references in common patterns + print("\n Checking variable references...") + potential_issues = { + "getElementById": _RE_GET_BY_ID.findall(content), + "querySelector": _RE_QUERY_SELECTOR.findall(content), + } + + element_ids = _RE_ELEMENT_IDS.findall(content) + print(f" Found {len(element_ids)} element IDs defined") + + missing_ids = [] + for method, ids in potential_issues.items(): + for id_ref in ids: + if "#" not in id_ref and "." not in id_ref and "[" not in id_ref: + if id_ref not in element_ids: + missing_ids.append((method, id_ref)) + + if missing_ids: + print("\n Potential missing element IDs:") + for method, id_ref in missing_ids: + print(f' - {method}("{id_ref}")') + else: + print(" All element ID references appear valid") + + # 3. Check for async/await patterns + async_functions = len(_RE_ASYNC_FUNCTION.findall(content)) + await_calls = len(_RE_AWAIT.findall(content)) + print(f"\n Async functions: {async_functions}, await calls: {await_calls}") + + # 4. Check for event listeners + event_listeners = len(_RE_EVENT_LISTENER.findall(content)) + print(f" Event listeners: {event_listeners}") + + # 5. Check for fetch calls (API endpoints) + fetch_calls = _RE_FETCH_CALLS.findall(content) + print(f"\n API endpoints used: {len(set(fetch_calls))}") + for endpoint in sorted(set(fetch_calls)): + print(f" - {endpoint}") + + # 6. Check for localStorage usage + localstorage_keys = _RE_LOCALSTORAGE.findall(content) + if localstorage_keys: + print(f"\n LocalStorage keys: {len(set([k[1] for k in localstorage_keys]))}") + for method, key in sorted(set(localstorage_keys)): + print(f" - {key} ({method})") + + # 7. Check for potential syntax errors in inline onclick handlers + print("\n Checking inline onclick syntax...") + onclick_handlers = _RE_ONCLICK.findall(content) + syntax_ok = True + for i, handler in enumerate(onclick_handlers, 1): + if handler.count("(") != handler.count(")"): + print(f" Unbalanced parentheses in onclick #{i}: {handler[:50]}...") + syntax_ok = False + issues_found.append(f"onclick_{i}") + + if syntax_ok: + print(" All onclick handlers have valid syntax") + + # 8. Check for Chart.js dependency + if "Chart.js" in content or "chartjs" in content.lower(): + print("\n Chart.js dependency detected") + if "cdn.jsdelivr.net/npm/chart.js" in content: + print(" Chart.js loaded from CDN") + + print(f'\n{"=" * 50}') + if not issues_found: + print(" Dashboard validation complete - No critical issues found!") + print(f"\n Dashboard is ready for use at {html_file}") + else: + print(f" Found {len(issues_found)} potential issues") + for issue in issues_found: + print(f" - {issue}") + + return not issues_found + + +if __name__ == "__main__": + run_validation() diff --git a/scripts/validate_optimizations.py b/scripts/validate_optimizations.py index 51a73b79b..0ad635a45 100755 --- a/scripts/validate_optimizations.py +++ b/scripts/validate_optimizations.py @@ -1,205 +1,205 @@ -#!/usr/bin/env python3 -""" -Quick validation script for Phase 1 & 2 optimizations. -Runs basic checks without requiring pytest. -""" -import sys -import time -from pathlib import Path - -# Ensure repository root is on sys.path before importing local shared modules. -REPO_ROOT = Path(__file__).resolve().parent.parent -if str(REPO_ROOT / "aria_web") not in sys.path: - sys.path.insert(0, str(REPO_ROOT / "aria_web")) -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - - -def test_aria_keyword_sets(): - """Test that keyword sets are properly defined.""" - print("Testing Aria web server keyword sets...") - try: - from aria_web.server import (_MOVE_KEYWORDS, _SAY_KEYWORDS, - _any_word_in_text) - - # Check they're frozensets - assert isinstance(_MOVE_KEYWORDS, frozenset), "Keywords should be frozensets" - assert isinstance(_SAY_KEYWORDS, frozenset), "Keywords should be frozensets" - - # Check function works - assert _any_word_in_text(_MOVE_KEYWORDS, "move left"), "Should match 'move'" - assert not _any_word_in_text( - _MOVE_KEYWORDS, "dance" - ), "Should not match 'dance'" - - print(" ✅ Aria web server optimizations validated") - return True - except Exception as e: - print(f" ❌ Aria web server test failed: {e}") - return False - - -def test_chat_memory_pooling(): - """Test that connection pooling functions exist.""" - print("Testing chat memory connection pooling...") - try: - import shared.chat_memory as cm - - # Check functions exist - assert hasattr(cm, "_get_conn"), "Should have _get_conn function" - assert hasattr(cm, "_return_conn"), "Should have _return_conn function" - - # Check pool exists (may be empty) - if not hasattr(cm, "_connection_pool"): - cm._connection_pool = [] - - print(" ✅ Chat memory pooling functions validated") - return True - except Exception as e: - print(f" ❌ Chat memory test failed: {e}") - return False - - -def test_batch_evaluator_optimization(): - """Test that batch evaluator compare_models is optimized.""" - print("Testing batch evaluator optimizations...") - try: - sys.path.insert(0, str(REPO_ROOT / "scripts")) - from batch_evaluator import BatchEvaluator, EvaluationResult - - # Create test evaluator - evaluator = BatchEvaluator() - - # Add test results - for i in range(10): - result = EvaluationResult( - model_id=f"model_{i}", - model_type="test", - dataset="test_data", - status="completed", - duration=10.0, - metrics={"accuracy": 0.8}, - ) - evaluator.results.append(result) - - # Test comparison - comparison = evaluator.compare_models(["model_5", "model_7"]) - assert len(comparison["models"]) == 2, "Should return 2 models" - assert "model_5" in comparison["models"], "Should include model_5" - - print(" ✅ Batch evaluator optimizations validated") - return True - except Exception as e: - print(f" ❌ Batch evaluator test failed: {e}") - return False - - -def test_file_streaming(): - """Test file streaming optimization logic.""" - print("Testing file streaming optimizations...") - try: - import tempfile - - # Create test file - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".log") as f: - test_file = Path(f.name) - # Write 1000 lines - for i in range(1000): - f.write(f"Line {i}\n") - - try: - # Test small file path (< 64KB) - size = test_file.stat().st_size - - if size <= 65536: - with open(test_file, "r") as f: - lines = f.readlines() - last_100 = lines[-100:] - assert len(last_100) == 100, "Should get last 100 lines" - - print(" ✅ File streaming logic validated") - return True - finally: - test_file.unlink() - - except Exception as e: - print(f" ❌ File streaming test failed: {e}") - return False - - -def test_dict_iteration(): - """Test dictionary iteration patterns.""" - print("Testing dictionary iteration patterns...") - try: - test_dict = {"key_1": "val1", "key_2": "val2", "key_3": "val3"} - - # Old way (still works but less Pythonic) - keys_old = [k for k in test_dict.keys()] - - # New way (optimized) - keys_new = [k for k in test_dict] - - assert keys_old == keys_new, "Results should be identical" - - print(" ✅ Dictionary iteration patterns validated") - return True - except Exception as e: - print(f" ❌ Dictionary iteration test failed: {e}") - return False - - -def performance_benchmark(): - """Quick performance benchmark of optimizations.""" - print("\nPerformance Benchmark:") - - try: - from aria_web.server import _MOVE_KEYWORDS, _any_word_in_text - - # Test keyword matching - test_commands = ["move left", "go right", "walk forward"] * 100 - - start = time.perf_counter() - for cmd in test_commands: - _any_word_in_text(_MOVE_KEYWORDS, cmd) - elapsed = time.perf_counter() - start - - print(f" Keyword matching (300 calls): {elapsed*1000:.2f}ms") - - except Exception as e: - print(f" ⚠️ Benchmark skipped: {e}") - - -def main(): - print("=" * 60) - print("Performance Optimization Validation") - print("=" * 60) - - results = [] - - # Run tests - results.append(("Aria Web Keywords", test_aria_keyword_sets())) - results.append(("Chat Memory Pooling", test_chat_memory_pooling())) - results.append(("Batch Evaluator", test_batch_evaluator_optimization())) - results.append(("File Streaming", test_file_streaming())) - results.append(("Dict Iteration", test_dict_iteration())) - - # Performance benchmark - performance_benchmark() - - # Summary - print("\n" + "=" * 60) - print("Summary:") - passed = sum(1 for _, result in results if result) - total = len(results) - print(f" {passed}/{total} tests passed") - - if passed == total: - print(" ✅ All optimizations validated successfully!") - return 0 - else: - print(" ⚠️ Some tests failed - review output above") - return 1 - - -if __name__ == "__main__": - sys.exit(main()) +#!/usr/bin/env python3 +""" +Quick validation script for Phase 1 & 2 optimizations. +Runs basic checks without requiring pytest. +""" +import sys +import time +from pathlib import Path + +# Ensure repository root is on sys.path before importing local shared modules. +REPO_ROOT = Path(__file__).resolve().parent.parent +if str(REPO_ROOT / "aria_web") not in sys.path: + sys.path.insert(0, str(REPO_ROOT / "aria_web")) +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + + +def test_aria_keyword_sets(): + """Test that keyword sets are properly defined.""" + print("Testing Aria web server keyword sets...") + try: + from aria_web.server import (_MOVE_KEYWORDS, _SAY_KEYWORDS, + _any_word_in_text) + + # Check they're frozensets + assert isinstance(_MOVE_KEYWORDS, frozenset), "Keywords should be frozensets" + assert isinstance(_SAY_KEYWORDS, frozenset), "Keywords should be frozensets" + + # Check function works + assert _any_word_in_text(_MOVE_KEYWORDS, "move left"), "Should match 'move'" + assert not _any_word_in_text( + _MOVE_KEYWORDS, "dance" + ), "Should not match 'dance'" + + print(" ✅ Aria web server optimizations validated") + return True + except Exception as e: + print(f" ❌ Aria web server test failed: {e}") + return False + + +def test_chat_memory_pooling(): + """Test that connection pooling functions exist.""" + print("Testing chat memory connection pooling...") + try: + import shared.chat_memory as cm + + # Check functions exist + assert hasattr(cm, "_get_conn"), "Should have _get_conn function" + assert hasattr(cm, "_return_conn"), "Should have _return_conn function" + + # Check pool exists (may be empty) + if not hasattr(cm, "_connection_pool"): + cm._connection_pool = [] + + print(" ✅ Chat memory pooling functions validated") + return True + except Exception as e: + print(f" ❌ Chat memory test failed: {e}") + return False + + +def test_batch_evaluator_optimization(): + """Test that batch evaluator compare_models is optimized.""" + print("Testing batch evaluator optimizations...") + try: + sys.path.insert(0, str(REPO_ROOT / "scripts")) + from batch_evaluator import BatchEvaluator, EvaluationResult + + # Create test evaluator + evaluator = BatchEvaluator() + + # Add test results + for i in range(10): + result = EvaluationResult( + model_id=f"model_{i}", + model_type="test", + dataset="test_data", + status="completed", + duration=10.0, + metrics={"accuracy": 0.8}, + ) + evaluator.results.append(result) + + # Test comparison + comparison = evaluator.compare_models(["model_5", "model_7"]) + assert len(comparison["models"]) == 2, "Should return 2 models" + assert "model_5" in comparison["models"], "Should include model_5" + + print(" ✅ Batch evaluator optimizations validated") + return True + except Exception as e: + print(f" ❌ Batch evaluator test failed: {e}") + return False + + +def test_file_streaming(): + """Test file streaming optimization logic.""" + print("Testing file streaming optimizations...") + try: + import tempfile + + # Create test file + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".log") as f: + test_file = Path(f.name) + # Write 1000 lines + for i in range(1000): + f.write(f"Line {i}\n") + + try: + # Test small file path (< 64KB) + size = test_file.stat().st_size + + if size <= 65536: + with open(test_file, "r") as f: + lines = f.readlines() + last_100 = lines[-100:] + assert len(last_100) == 100, "Should get last 100 lines" + + print(" ✅ File streaming logic validated") + return True + finally: + test_file.unlink() + + except Exception as e: + print(f" ❌ File streaming test failed: {e}") + return False + + +def test_dict_iteration(): + """Test dictionary iteration patterns.""" + print("Testing dictionary iteration patterns...") + try: + test_dict = {"key_1": "val1", "key_2": "val2", "key_3": "val3"} + + # Old way (still works but less Pythonic) + keys_old = [k for k in test_dict.keys()] + + # New way (optimized) + keys_new = [k for k in test_dict] + + assert keys_old == keys_new, "Results should be identical" + + print(" ✅ Dictionary iteration patterns validated") + return True + except Exception as e: + print(f" ❌ Dictionary iteration test failed: {e}") + return False + + +def performance_benchmark(): + """Quick performance benchmark of optimizations.""" + print("\nPerformance Benchmark:") + + try: + from aria_web.server import _MOVE_KEYWORDS, _any_word_in_text + + # Test keyword matching + test_commands = ["move left", "go right", "walk forward"] * 100 + + start = time.perf_counter() + for cmd in test_commands: + _any_word_in_text(_MOVE_KEYWORDS, cmd) + elapsed = time.perf_counter() - start + + print(f" Keyword matching (300 calls): {elapsed*1000:.2f}ms") + + except Exception as e: + print(f" ⚠️ Benchmark skipped: {e}") + + +def main(): + print("=" * 60) + print("Performance Optimization Validation") + print("=" * 60) + + results = [] + + # Run tests + results.append(("Aria Web Keywords", test_aria_keyword_sets())) + results.append(("Chat Memory Pooling", test_chat_memory_pooling())) + results.append(("Batch Evaluator", test_batch_evaluator_optimization())) + results.append(("File Streaming", test_file_streaming())) + results.append(("Dict Iteration", test_dict_iteration())) + + # Performance benchmark + performance_benchmark() + + # Summary + print("\n" + "=" * 60) + print("Summary:") + passed = sum(1 for _, result in results if result) + total = len(results) + print(f" {passed}/{total} tests passed") + + if passed == total: + print(" ✅ All optimizations validated successfully!") + return 0 + else: + print(" ⚠️ Some tests failed - review output above") + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/vision_inference.py b/scripts/vision_inference.py index 453be0dad..7afa721b2 100644 --- a/scripts/vision_inference.py +++ b/scripts/vision_inference.py @@ -1,321 +1,321 @@ -"""Vision inference utilities for expression/emotion classification. - -This module provides model loading, preprocessing, and inference functions -that can be used by the Azure Functions endpoint, CLI tools, or other consumers. - -Usage: - from scripts.vision_inference import VisionInference - - # Initialize (loads latest checkpoint by default) - vi = VisionInference() - - # Infer from PIL Image - result = vi.predict(pil_image) - # {'label': 'happy', 'confidence': 0.92, 'scores': {'happy': 0.92, 'sad': 0.05, ...}} - - # Infer from base64 - result = vi.predict_base64(base64_str) - - # Infer from file path - result = vi.predict_file('path/to/image.jpg') -""" - -from __future__ import annotations - -import base64 -import io -import json -import logging -from pathlib import Path -from typing import Dict, List, Optional, Union - -import numpy as np -from PIL import Image - -try: - import torch - from torch import nn -except ImportError as err: - raise ImportError( - "PyTorch is required for vision inference. Install with: pip install torch" - ) from err - - -# Default checkpoint locations (prioritize latest training output) -DEFAULT_CHECKPOINT_DIRS = [ - "data_out/vision_training", - "scripts/checkpoints", - "checkpoints", -] - - -class TinyConvNet(nn.Module): - """Minimal CNN architecture matching train_vision.py. - - This must stay in sync with the training script's model definition. - """ - - def __init__(self, in_channels=3, num_classes=2): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(in_channels, 16, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=True), - nn.MaxPool2d(2), - nn.Conv2d(16, 32, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.AdaptiveAvgPool2d(1), - nn.Flatten(), - nn.Linear(32, num_classes), - ) - - def forward(self, x): - return self.net(x) - - -class VisionInference: - """High-level interface for vision model inference.""" - - def __init__( - self, - checkpoint_path: Optional[str] = None, - device: Optional[str] = None, - img_size: int = 64, - ): - """Initialize vision inference. - - Args: - checkpoint_path: Path to .pt checkpoint. If None, searches default locations. - device: 'cpu', 'cuda', or None (auto-detect). - img_size: Input image size (square). Default 64x64. - """ - self.img_size = img_size - - # Auto-detect device - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - self.device = torch.device(device) - - # Find checkpoint - if checkpoint_path is None: - checkpoint_path = self._find_latest_checkpoint() - - if checkpoint_path is None: - raise FileNotFoundError( - "No checkpoint found. Train a model first using scripts/train_vision.py" - ) - - self.checkpoint_path = Path(checkpoint_path) - logging.info(f"Loading vision model from {self.checkpoint_path}") - - # Load checkpoint - ckpt = torch.load( - self.checkpoint_path, map_location=self.device, weights_only=True - ) - self.classes = ckpt.get("classes", ["class_0", "class_1"]) - - # Initialize model - self.model = TinyConvNet(num_classes=len(self.classes)).to(self.device) - self.model.load_state_dict(ckpt["model"]) - self.model.eval() - - logging.info(f"Model loaded successfully. Classes: {self.classes}") - - def _find_latest_checkpoint(self) -> Optional[Path]: - """Search default locations for the most recent checkpoint.""" - repo_root = Path(__file__).resolve().parent.parent - - candidates = [] - for dir_rel in DEFAULT_CHECKPOINT_DIRS: - dir_path = repo_root / dir_rel - if dir_path.exists(): - for ckpt in dir_path.glob("*.pt"): - candidates.append(ckpt) - - if not candidates: - return None - - # Sort by modification time, newest first - candidates.sort(key=lambda p: p.stat().st_mtime, reverse=True) - return candidates[0] - - def preprocess(self, img: Image.Image) -> torch.Tensor: - """Convert PIL Image to normalized tensor (1, C, H, W).""" - # Ensure RGB - if img.mode != "RGB": - img = img.convert("RGB") - - # Resize to expected input size - img = img.resize((self.img_size, self.img_size), Image.BILINEAR) - - # Convert to numpy array and normalize to [0, 1] - arr = np.asarray(img, dtype=np.float32) / 255.0 - - # Convert to tensor (C, H, W) and add batch dimension - tensor = torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0) - - return tensor.to(self.device) - - def predict( - self, img: Image.Image - ) -> Dict[str, Union[str, float, Dict[str, float]]]: - """Run inference on a PIL Image. - - Args: - img: PIL Image object - - Returns: - Dictionary with: - - label: predicted class name - - confidence: confidence score for predicted class (0-1) - - scores: dict mapping all class names to their scores - """ - tensor = self.preprocess(img) - - with torch.no_grad(): - logits = self.model(tensor) - probs = torch.softmax(logits, dim=1).squeeze(0).cpu().numpy() - - # Get top prediction - pred_idx = int(np.argmax(probs)) - pred_label = self.classes[pred_idx] - pred_conf = float(probs[pred_idx]) - - # Build scores dict - scores = {self.classes[i]: float(probs[i]) for i in range(len(self.classes))} - - return { - "label": pred_label, - "confidence": pred_conf, - "scores": scores, - } - - def predict_base64( - self, b64_str: str - ) -> Dict[str, Union[str, float, Dict[str, float]]]: - """Run inference on a base64-encoded image. - - Args: - b64_str: Base64-encoded image string (PNG, JPG, etc.) - - Returns: - Same as predict() - """ - # Decode base64 to bytes - img_bytes = base64.b64decode(b64_str) - - # Open as PIL Image - img = Image.open(io.BytesIO(img_bytes)) - - return self.predict(img) - - def predict_file( - self, file_path: str - ) -> Dict[str, Union[str, float, Dict[str, float]]]: - """Run inference on an image file. - - Args: - file_path: Path to image file - - Returns: - Same as predict() - """ - img = Image.open(file_path) - return self.predict(img) - - def predict_batch( - self, images: List[Image.Image] - ) -> List[Dict[str, Union[str, float, Dict[str, float]]]]: - """Run inference on a batch of PIL Images. - - Args: - images: List of PIL Image objects - - Returns: - List of prediction dictionaries (one per image) - """ - # Stack preprocessed images into batch - tensors = [self.preprocess(img) for img in images] - batch = torch.cat(tensors, dim=0) - - with torch.no_grad(): - logits = self.model(batch) - probs = torch.softmax(logits, dim=1).cpu().numpy() - - # Build results - results = [] - for i in range(len(images)): - pred_idx = int(np.argmax(probs[i])) - pred_label = self.classes[pred_idx] - pred_conf = float(probs[i][pred_idx]) - scores = { - self.classes[j]: float(probs[i][j]) for j in range(len(self.classes)) - } - - results.append( - { - "label": pred_label, - "confidence": pred_conf, - "scores": scores, - } - ) - - return results - - def get_model_info(self) -> Dict[str, Union[str, List[str], int]]: - """Get metadata about the loaded model. - - Returns: - Dictionary with checkpoint path, classes, device, etc. - """ - return { - "checkpoint_path": str(self.checkpoint_path), - "classes": self.classes, - "num_classes": len(self.classes), - "device": str(self.device), - "img_size": self.img_size, - } - - -def main(): - """CLI tool for testing vision inference.""" - import argparse - - parser = argparse.ArgumentParser(description="Vision inference CLI") - parser.add_argument("image", type=str, help="Path to image file") - parser.add_argument("--checkpoint", type=str, help="Path to checkpoint (optional)") - parser.add_argument( - "--device", - type=str, - choices=["cpu", "cuda"], - help="Device (auto-detect if not specified)", - ) - parser.add_argument("--img-size", type=int, default=64, help="Input image size") - parser.add_argument("--json", action="store_true", help="Output as JSON") - - args = parser.parse_args() - - # Configure logging - logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") - - # Initialize inference - vi = VisionInference( - checkpoint_path=args.checkpoint, - device=args.device, - img_size=args.img_size, - ) - - # Run prediction - result = vi.predict_file(args.image) - - if args.json: - print(json.dumps(result, indent=2)) - else: - print(f"Prediction: {result['label']}") - print(f"Confidence: {result['confidence']:.4f}") - print("\nAll scores:") - for label, score in result["scores"].items(): - print(f" {label}: {score:.4f}") - - -if __name__ == "__main__": - main() +"""Vision inference utilities for expression/emotion classification. + +This module provides model loading, preprocessing, and inference functions +that can be used by the Azure Functions endpoint, CLI tools, or other consumers. + +Usage: + from scripts.vision_inference import VisionInference + + # Initialize (loads latest checkpoint by default) + vi = VisionInference() + + # Infer from PIL Image + result = vi.predict(pil_image) + # {'label': 'happy', 'confidence': 0.92, 'scores': {'happy': 0.92, 'sad': 0.05, ...}} + + # Infer from base64 + result = vi.predict_base64(base64_str) + + # Infer from file path + result = vi.predict_file('path/to/image.jpg') +""" + +from __future__ import annotations + +import base64 +import io +import json +import logging +from pathlib import Path +from typing import Dict, List, Optional, Union + +import numpy as np +from PIL import Image + +try: + import torch + from torch import nn +except ImportError as err: + raise ImportError( + "PyTorch is required for vision inference. Install with: pip install torch" + ) from err + + +# Default checkpoint locations (prioritize latest training output) +DEFAULT_CHECKPOINT_DIRS = [ + "data_out/vision_training", + "scripts/checkpoints", + "checkpoints", +] + + +class TinyConvNet(nn.Module): + """Minimal CNN architecture matching train_vision.py. + + This must stay in sync with the training script's model definition. + """ + + def __init__(self, in_channels=3, num_classes=2): + super().__init__() + self.net = nn.Sequential( + nn.Conv2d(in_channels, 16, kernel_size=3, stride=1, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(2), + nn.Conv2d(16, 32, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.AdaptiveAvgPool2d(1), + nn.Flatten(), + nn.Linear(32, num_classes), + ) + + def forward(self, x): + return self.net(x) + + +class VisionInference: + """High-level interface for vision model inference.""" + + def __init__( + self, + checkpoint_path: Optional[str] = None, + device: Optional[str] = None, + img_size: int = 64, + ): + """Initialize vision inference. + + Args: + checkpoint_path: Path to .pt checkpoint. If None, searches default locations. + device: 'cpu', 'cuda', or None (auto-detect). + img_size: Input image size (square). Default 64x64. + """ + self.img_size = img_size + + # Auto-detect device + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + self.device = torch.device(device) + + # Find checkpoint + if checkpoint_path is None: + checkpoint_path = self._find_latest_checkpoint() + + if checkpoint_path is None: + raise FileNotFoundError( + "No checkpoint found. Train a model first using scripts/train_vision.py" + ) + + self.checkpoint_path = Path(checkpoint_path) + logging.info(f"Loading vision model from {self.checkpoint_path}") + + # Load checkpoint + ckpt = torch.load( + self.checkpoint_path, map_location=self.device, weights_only=True + ) + self.classes = ckpt.get("classes", ["class_0", "class_1"]) + + # Initialize model + self.model = TinyConvNet(num_classes=len(self.classes)).to(self.device) + self.model.load_state_dict(ckpt["model"]) + self.model.eval() + + logging.info(f"Model loaded successfully. Classes: {self.classes}") + + def _find_latest_checkpoint(self) -> Optional[Path]: + """Search default locations for the most recent checkpoint.""" + repo_root = Path(__file__).resolve().parent.parent + + candidates = [] + for dir_rel in DEFAULT_CHECKPOINT_DIRS: + dir_path = repo_root / dir_rel + if dir_path.exists(): + for ckpt in dir_path.glob("*.pt"): + candidates.append(ckpt) + + if not candidates: + return None + + # Sort by modification time, newest first + candidates.sort(key=lambda p: p.stat().st_mtime, reverse=True) + return candidates[0] + + def preprocess(self, img: Image.Image) -> torch.Tensor: + """Convert PIL Image to normalized tensor (1, C, H, W).""" + # Ensure RGB + if img.mode != "RGB": + img = img.convert("RGB") + + # Resize to expected input size + img = img.resize((self.img_size, self.img_size), Image.BILINEAR) + + # Convert to numpy array and normalize to [0, 1] + arr = np.asarray(img, dtype=np.float32) / 255.0 + + # Convert to tensor (C, H, W) and add batch dimension + tensor = torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0) + + return tensor.to(self.device) + + def predict( + self, img: Image.Image + ) -> Dict[str, Union[str, float, Dict[str, float]]]: + """Run inference on a PIL Image. + + Args: + img: PIL Image object + + Returns: + Dictionary with: + - label: predicted class name + - confidence: confidence score for predicted class (0-1) + - scores: dict mapping all class names to their scores + """ + tensor = self.preprocess(img) + + with torch.no_grad(): + logits = self.model(tensor) + probs = torch.softmax(logits, dim=1).squeeze(0).cpu().numpy() + + # Get top prediction + pred_idx = int(np.argmax(probs)) + pred_label = self.classes[pred_idx] + pred_conf = float(probs[pred_idx]) + + # Build scores dict + scores = {self.classes[i]: float(probs[i]) for i in range(len(self.classes))} + + return { + "label": pred_label, + "confidence": pred_conf, + "scores": scores, + } + + def predict_base64( + self, b64_str: str + ) -> Dict[str, Union[str, float, Dict[str, float]]]: + """Run inference on a base64-encoded image. + + Args: + b64_str: Base64-encoded image string (PNG, JPG, etc.) + + Returns: + Same as predict() + """ + # Decode base64 to bytes + img_bytes = base64.b64decode(b64_str) + + # Open as PIL Image + img = Image.open(io.BytesIO(img_bytes)) + + return self.predict(img) + + def predict_file( + self, file_path: str + ) -> Dict[str, Union[str, float, Dict[str, float]]]: + """Run inference on an image file. + + Args: + file_path: Path to image file + + Returns: + Same as predict() + """ + img = Image.open(file_path) + return self.predict(img) + + def predict_batch( + self, images: List[Image.Image] + ) -> List[Dict[str, Union[str, float, Dict[str, float]]]]: + """Run inference on a batch of PIL Images. + + Args: + images: List of PIL Image objects + + Returns: + List of prediction dictionaries (one per image) + """ + # Stack preprocessed images into batch + tensors = [self.preprocess(img) for img in images] + batch = torch.cat(tensors, dim=0) + + with torch.no_grad(): + logits = self.model(batch) + probs = torch.softmax(logits, dim=1).cpu().numpy() + + # Build results + results = [] + for i in range(len(images)): + pred_idx = int(np.argmax(probs[i])) + pred_label = self.classes[pred_idx] + pred_conf = float(probs[i][pred_idx]) + scores = { + self.classes[j]: float(probs[i][j]) for j in range(len(self.classes)) + } + + results.append( + { + "label": pred_label, + "confidence": pred_conf, + "scores": scores, + } + ) + + return results + + def get_model_info(self) -> Dict[str, Union[str, List[str], int]]: + """Get metadata about the loaded model. + + Returns: + Dictionary with checkpoint path, classes, device, etc. + """ + return { + "checkpoint_path": str(self.checkpoint_path), + "classes": self.classes, + "num_classes": len(self.classes), + "device": str(self.device), + "img_size": self.img_size, + } + + +def main(): + """CLI tool for testing vision inference.""" + import argparse + + parser = argparse.ArgumentParser(description="Vision inference CLI") + parser.add_argument("image", type=str, help="Path to image file") + parser.add_argument("--checkpoint", type=str, help="Path to checkpoint (optional)") + parser.add_argument( + "--device", + type=str, + choices=["cpu", "cuda"], + help="Device (auto-detect if not specified)", + ) + parser.add_argument("--img-size", type=int, default=64, help="Input image size") + parser.add_argument("--json", action="store_true", help="Output as JSON") + + args = parser.parse_args() + + # Configure logging + logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") + + # Initialize inference + vi = VisionInference( + checkpoint_path=args.checkpoint, + device=args.device, + img_size=args.img_size, + ) + + # Run prediction + result = vi.predict_file(args.image) + + if args.json: + print(json.dumps(result, indent=2)) + else: + print(f"Prediction: {result['label']}") + print(f"Confidence: {result['confidence']:.4f}") + print("\nAll scores:") + for label, score in result["scores"].items(): + print(f" {label}: {score:.4f}") + + +if __name__ == "__main__": + main() diff --git a/setup_monetization.py b/setup_monetization.py index 85ce63789..29ebeafd7 100755 --- a/setup_monetization.py +++ b/setup_monetization.py @@ -1,305 +1,305 @@ -#!/usr/bin/env python3 -""" -Aria Monetization System Setup Script -Automatically configures and tests the monetization system -""" - -import json -import subprocess -import sys -from pathlib import Path - - -# Color codes for terminal output -class Colors: - HEADER = "\033[95m" - BLUE = "\033[94m" - GREEN = "\033[92m" - YELLOW = "\033[93m" - RED = "\033[91m" - END = "\033[0m" - BOLD = "\033[1m" - - -def print_header(text): - print(f"\n{Colors.HEADER}{Colors.BOLD}{'='*80}{Colors.END}") - print(f"{Colors.HEADER}{Colors.BOLD}{text:^80}{Colors.END}") - print(f"{Colors.HEADER}{Colors.BOLD}{'='*80}{Colors.END}\n") - - -def print_success(text): - print(f"{Colors.GREEN}✓ {text}{Colors.END}") - - -def print_error(text): - print(f"{Colors.RED}✗ {text}{Colors.END}") - - -def print_info(text): - print(f"{Colors.BLUE}ℹ {text}{Colors.END}") - - -def print_warning(text): - print(f"{Colors.YELLOW}⚠ {text}{Colors.END}") - - -def check_file_exists(filepath): - """Check if a file exists""" - return Path(filepath).exists() - - -def check_python_imports(): - """Check if required Python packages are available""" - print_info("Checking Python dependencies...") - - required = ["json", "datetime", "enum", "pathlib"] - missing = [] - - for module in required: - try: - __import__(module) - print_success(f" {module} available") - except ImportError: - missing.append(module) - print_error(f" {module} missing") - - return len(missing) == 0 - - -def verify_core_files(): - """Verify all core monetization files exist""" - print_info("Verifying core files...") - - files = { - "Backend": ["shared/subscription_manager.py", "function_app.py"], - "Frontend": [ - "web/monetization/pricing.html", - "web/monetization/admin_dashboard.html", - "web/monetization/my-subscription.html", - "web/monetization/checkout.html", - "web/monetization/subscription-success.html", - "web/monetization/account.html", - ], - "Documentation": [ - "docs/guides/MONETIZATION_GUIDE.md", - "docs/summaries/INCOME_STREAM_SUMMARY.md", - "docs/guides/QUICK_START_MONETIZATION.md", - ], - } - - all_exist = True - for category, file_list in files.items(): - print(f"\n {category}:") - for file in file_list: - if check_file_exists(file): - print_success(f" {file}") - else: - print_error(f" {file} - MISSING") - all_exist = False - - return all_exist - - -def test_subscription_manager(): - """Test the subscription manager""" - print_info("Testing subscription manager...") - - try: - # Import the subscription manager - sys.path.insert(0, "shared") - from subscription_manager import (SubscriptionTier, - get_subscription_manager) - - # Create manager instance - manager = get_subscription_manager() - print_success(" Subscription manager imported") - - # Test creating subscriptions - manager.upgrade_subscription("test_user_1", SubscriptionTier.PRO, 30, "test") - print_success(" Pro subscription created") - - manager.upgrade_subscription( - "test_user_2", SubscriptionTier.ENTERPRISE, 30, "test" - ) - print_success(" Enterprise subscription created") - - # Get revenue stats - stats = manager.get_revenue_stats() - print_success( - f" Revenue stats retrieved: ${stats['monthly_recurring_revenue']} MRR" - ) - - return True - except Exception as e: - print_error(f" Error: {str(e)}") - return False - - -def generate_demo_data(): - """Generate demo subscription data for testing""" - print_info("Generating demo data...") - - try: - sys.path.insert(0, "shared") - from subscription_manager import (SubscriptionTier, - get_subscription_manager) - - manager = get_subscription_manager() - - # Create 5 Pro subscribers - for i in range(5): - manager.upgrade_subscription( - f"pro_user_{i+1}", SubscriptionTier.PRO, 30, "demo" - ) - print_success(" Created 5 Pro subscribers") - - # Create 10 Enterprise subscribers - for i in range(10): - manager.upgrade_subscription( - f"ent_user_{i+1}", SubscriptionTier.ENTERPRISE, 30, "demo" - ) - print_success(" Created 10 Enterprise subscribers") - - # Get final stats - stats = manager.get_revenue_stats() - print_success(f" Total MRR: ${stats['monthly_recurring_revenue']}") - print_success(f" Total ARR: ${stats['annual_recurring_revenue']}") - - if stats["monthly_recurring_revenue"] >= 2000: - print_success( - f" 🎉 TARGET ACHIEVED! ({stats['monthly_recurring_revenue']/2000*100:.1f}% of $2,000 goal)" - ) - - return True - except Exception as e: - print_error(f" Error: {str(e)}") - return False - - -def start_test_server(): - """Start a simple HTTP server for testing""" - print_info("Starting test server...") - print_info(" Open http://localhost:8000/pricing.html in your browser") - print_info(" Press Ctrl+C to stop the server") - - try: - subprocess.run(["python3", "-m", "http.server", "8000"]) - except KeyboardInterrupt: - print_success("\n Server stopped") - except Exception as e: - print_error(f" Error starting server: {str(e)}") - - -def create_local_settings(): - """Create local.settings.json if it doesn't exist""" - if not check_file_exists("local.settings.json"): - print_info("Creating local.settings.json...") - - settings = { - "IsEncrypted": False, - "Values": { - "AzureWebJobsStorage": "UseDevelopmentStorage=true", - "FUNCTIONS_WORKER_RUNTIME": "python", - "QAI_DB_CONN": "sqlite:///data_out/qai.db", - "QAI_SQL_POOL_SIZE": "10", - }, - } - - try: - with open("local.settings.json", "w") as f: - json.dump(settings, f, indent=2) - print_success(" local.settings.json created") - return True - except Exception as e: - print_error(f" Error: {str(e)}") - return False - else: - print_success(" local.settings.json already exists") - return True - - -def print_next_steps(): - """Print next steps for the user""" - print_header("SETUP COMPLETE! 🎉") - - print(f"{Colors.BOLD}Next Steps:{Colors.END}\n") - - print(f"{Colors.GREEN}1. View the Pricing Page:{Colors.END}") - print(" python3 -m http.server 8000") - print(" Then open: http://localhost:8000/web/monetization/pricing.html\n") - - print(f"{Colors.GREEN}2. View the Admin Dashboard:{Colors.END}") - print(" Open: http://localhost:8000/web/monetization/admin_dashboard.html\n") - - print(f"{Colors.GREEN}3. Test the APIs (optional):{Colors.END}") - print(" func host start") - print(" curl http://localhost:7071/api/subscription/revenue | jq\n") - - print(f"{Colors.GREEN}4. View Documentation:{Colors.END}") - print(" cat docs/guides/QUICK_START_MONETIZATION.md\n") - - print(f"{Colors.GREEN}5. Access All Pages:{Colors.END}") - print(" - web/monetization/pricing.html - Pricing tiers") - print(" - web/monetization/admin_dashboard.html - Revenue dashboard") - print(" - web/monetization/my-subscription.html - User subscription") - print(" - web/monetization/checkout.html - Payment page") - print(" - web/monetization/subscription-success.html - Success page") - print(" - web/monetization/account.html - Account settings\n") - - -def main(): - """Main setup function""" - print_header("ARIA MONETIZATION SYSTEM - SETUP") - - # Step 1: Check Python environment - if not check_python_imports(): - print_error("Missing required Python packages") - return False - print() - - # Step 2: Verify core files - if not verify_core_files(): - print_error("Missing core files - please ensure all files are present") - return False - print() - - # Step 3: Create local settings - create_local_settings() - print() - - # Step 4: Test subscription manager - if not test_subscription_manager(): - print_warning("Subscription manager test failed - continuing anyway") - print() - - # Step 5: Generate demo data - generate_demo = input( - f"{Colors.BLUE}Generate demo data (5 Pro + 10 Enterprise subscribers)? (y/n): {Colors.END}" - ).lower() - if generate_demo == "y": - generate_demo_data() - print() - - # Step 6: Print next steps - print_next_steps() - - # Step 7: Offer to start server - start_server = input( - f"{Colors.BLUE}Start test server now? (y/n): {Colors.END}" - ).lower() - if start_server == "y": - start_test_server() - - return True - - -if __name__ == "__main__": - try: - success = main() - sys.exit(0 if success else 1) - except KeyboardInterrupt: - print(f"\n{Colors.YELLOW}Setup interrupted{Colors.END}") - sys.exit(1) - except Exception as e: - print_error(f"Unexpected error: {str(e)}") - sys.exit(1) +#!/usr/bin/env python3 +""" +Aria Monetization System Setup Script +Automatically configures and tests the monetization system +""" + +import json +import subprocess +import sys +from pathlib import Path + + +# Color codes for terminal output +class Colors: + HEADER = "\033[95m" + BLUE = "\033[94m" + GREEN = "\033[92m" + YELLOW = "\033[93m" + RED = "\033[91m" + END = "\033[0m" + BOLD = "\033[1m" + + +def print_header(text): + print(f"\n{Colors.HEADER}{Colors.BOLD}{'='*80}{Colors.END}") + print(f"{Colors.HEADER}{Colors.BOLD}{text:^80}{Colors.END}") + print(f"{Colors.HEADER}{Colors.BOLD}{'='*80}{Colors.END}\n") + + +def print_success(text): + print(f"{Colors.GREEN}✓ {text}{Colors.END}") + + +def print_error(text): + print(f"{Colors.RED}✗ {text}{Colors.END}") + + +def print_info(text): + print(f"{Colors.BLUE}ℹ {text}{Colors.END}") + + +def print_warning(text): + print(f"{Colors.YELLOW}⚠ {text}{Colors.END}") + + +def check_file_exists(filepath): + """Check if a file exists""" + return Path(filepath).exists() + + +def check_python_imports(): + """Check if required Python packages are available""" + print_info("Checking Python dependencies...") + + required = ["json", "datetime", "enum", "pathlib"] + missing = [] + + for module in required: + try: + __import__(module) + print_success(f" {module} available") + except ImportError: + missing.append(module) + print_error(f" {module} missing") + + return len(missing) == 0 + + +def verify_core_files(): + """Verify all core monetization files exist""" + print_info("Verifying core files...") + + files = { + "Backend": ["shared/subscription_manager.py", "function_app.py"], + "Frontend": [ + "web/monetization/pricing.html", + "web/monetization/admin_dashboard.html", + "web/monetization/my-subscription.html", + "web/monetization/checkout.html", + "web/monetization/subscription-success.html", + "web/monetization/account.html", + ], + "Documentation": [ + "docs/guides/MONETIZATION_GUIDE.md", + "docs/summaries/INCOME_STREAM_SUMMARY.md", + "docs/guides/QUICK_START_MONETIZATION.md", + ], + } + + all_exist = True + for category, file_list in files.items(): + print(f"\n {category}:") + for file in file_list: + if check_file_exists(file): + print_success(f" {file}") + else: + print_error(f" {file} - MISSING") + all_exist = False + + return all_exist + + +def test_subscription_manager(): + """Test the subscription manager""" + print_info("Testing subscription manager...") + + try: + # Import the subscription manager + sys.path.insert(0, "shared") + from subscription_manager import (SubscriptionTier, + get_subscription_manager) + + # Create manager instance + manager = get_subscription_manager() + print_success(" Subscription manager imported") + + # Test creating subscriptions + manager.upgrade_subscription("test_user_1", SubscriptionTier.PRO, 30, "test") + print_success(" Pro subscription created") + + manager.upgrade_subscription( + "test_user_2", SubscriptionTier.ENTERPRISE, 30, "test" + ) + print_success(" Enterprise subscription created") + + # Get revenue stats + stats = manager.get_revenue_stats() + print_success( + f" Revenue stats retrieved: ${stats['monthly_recurring_revenue']} MRR" + ) + + return True + except Exception as e: + print_error(f" Error: {str(e)}") + return False + + +def generate_demo_data(): + """Generate demo subscription data for testing""" + print_info("Generating demo data...") + + try: + sys.path.insert(0, "shared") + from subscription_manager import (SubscriptionTier, + get_subscription_manager) + + manager = get_subscription_manager() + + # Create 5 Pro subscribers + for i in range(5): + manager.upgrade_subscription( + f"pro_user_{i+1}", SubscriptionTier.PRO, 30, "demo" + ) + print_success(" Created 5 Pro subscribers") + + # Create 10 Enterprise subscribers + for i in range(10): + manager.upgrade_subscription( + f"ent_user_{i+1}", SubscriptionTier.ENTERPRISE, 30, "demo" + ) + print_success(" Created 10 Enterprise subscribers") + + # Get final stats + stats = manager.get_revenue_stats() + print_success(f" Total MRR: ${stats['monthly_recurring_revenue']}") + print_success(f" Total ARR: ${stats['annual_recurring_revenue']}") + + if stats["monthly_recurring_revenue"] >= 2000: + print_success( + f" 🎉 TARGET ACHIEVED! ({stats['monthly_recurring_revenue']/2000*100:.1f}% of $2,000 goal)" + ) + + return True + except Exception as e: + print_error(f" Error: {str(e)}") + return False + + +def start_test_server(): + """Start a simple HTTP server for testing""" + print_info("Starting test server...") + print_info(" Open http://localhost:8000/pricing.html in your browser") + print_info(" Press Ctrl+C to stop the server") + + try: + subprocess.run(["python3", "-m", "http.server", "8000"]) + except KeyboardInterrupt: + print_success("\n Server stopped") + except Exception as e: + print_error(f" Error starting server: {str(e)}") + + +def create_local_settings(): + """Create local.settings.json if it doesn't exist""" + if not check_file_exists("local.settings.json"): + print_info("Creating local.settings.json...") + + settings = { + "IsEncrypted": False, + "Values": { + "AzureWebJobsStorage": "UseDevelopmentStorage=true", + "FUNCTIONS_WORKER_RUNTIME": "python", + "QAI_DB_CONN": "sqlite:///data_out/qai.db", + "QAI_SQL_POOL_SIZE": "10", + }, + } + + try: + with open("local.settings.json", "w") as f: + json.dump(settings, f, indent=2) + print_success(" local.settings.json created") + return True + except Exception as e: + print_error(f" Error: {str(e)}") + return False + else: + print_success(" local.settings.json already exists") + return True + + +def print_next_steps(): + """Print next steps for the user""" + print_header("SETUP COMPLETE! 🎉") + + print(f"{Colors.BOLD}Next Steps:{Colors.END}\n") + + print(f"{Colors.GREEN}1. View the Pricing Page:{Colors.END}") + print(" python3 -m http.server 8000") + print(" Then open: http://localhost:8000/web/monetization/pricing.html\n") + + print(f"{Colors.GREEN}2. View the Admin Dashboard:{Colors.END}") + print(" Open: http://localhost:8000/web/monetization/admin_dashboard.html\n") + + print(f"{Colors.GREEN}3. Test the APIs (optional):{Colors.END}") + print(" func host start") + print(" curl http://localhost:7071/api/subscription/revenue | jq\n") + + print(f"{Colors.GREEN}4. View Documentation:{Colors.END}") + print(" cat docs/guides/QUICK_START_MONETIZATION.md\n") + + print(f"{Colors.GREEN}5. Access All Pages:{Colors.END}") + print(" - web/monetization/pricing.html - Pricing tiers") + print(" - web/monetization/admin_dashboard.html - Revenue dashboard") + print(" - web/monetization/my-subscription.html - User subscription") + print(" - web/monetization/checkout.html - Payment page") + print(" - web/monetization/subscription-success.html - Success page") + print(" - web/monetization/account.html - Account settings\n") + + +def main(): + """Main setup function""" + print_header("ARIA MONETIZATION SYSTEM - SETUP") + + # Step 1: Check Python environment + if not check_python_imports(): + print_error("Missing required Python packages") + return False + print() + + # Step 2: Verify core files + if not verify_core_files(): + print_error("Missing core files - please ensure all files are present") + return False + print() + + # Step 3: Create local settings + create_local_settings() + print() + + # Step 4: Test subscription manager + if not test_subscription_manager(): + print_warning("Subscription manager test failed - continuing anyway") + print() + + # Step 5: Generate demo data + generate_demo = input( + f"{Colors.BLUE}Generate demo data (5 Pro + 10 Enterprise subscribers)? (y/n): {Colors.END}" + ).lower() + if generate_demo == "y": + generate_demo_data() + print() + + # Step 6: Print next steps + print_next_steps() + + # Step 7: Offer to start server + start_server = input( + f"{Colors.BLUE}Start test server now? (y/n): {Colors.END}" + ).lower() + if start_server == "y": + start_test_server() + + return True + + +if __name__ == "__main__": + try: + success = main() + sys.exit(0 if success else 1) + except KeyboardInterrupt: + print(f"\n{Colors.YELLOW}Setup interrupted{Colors.END}") + sys.exit(1) + except Exception as e: + print_error(f"Unexpected error: {str(e)}") + sys.exit(1) diff --git a/shared/__init__.py b/shared/__init__.py index 1e0e5742d..6dbd8d291 100644 --- a/shared/__init__.py +++ b/shared/__init__.py @@ -1,35 +1,35 @@ -""" -Shared infrastructure for the Aria / QAI platform. - -Re-exports key utilities so callers can write: - from shared import load_json, validate_messages, safe_import - from shared.chat_providers import BaseChatProvider - -Heavy or optional modules (cosmos, telemetry, db_logging, sql_engine) are NOT -eagerly imported here — import them directly when needed so startup stays fast. -""" - -from shared.file_cache import DEFAULT_TTL_SECONDS # noqa: F401 -from shared.file_cache import read_json_cached -from shared.http_utils import validate_messages # noqa: F401 -from shared.import_helpers import safe_import # noqa: F401 -# --- Lightweight, no-external-dependency helpers --- -from shared.json_utils import load_json # noqa: F401 -from shared.performance_utils import tail_file # noqa: F401 -from shared.script_utils import get_repo_root # noqa: F401 - -__all__ = [ - # json_utils - "load_json", - # http_utils - "validate_messages", - # file_cache - "read_json_cached", - "DEFAULT_TTL_SECONDS", - # script_utils - "get_repo_root", - # import_helpers - "safe_import", - # performance_utils - "tail_file", -] +""" +Shared infrastructure for the Aria / QAI platform. + +Re-exports key utilities so callers can write: + from shared import load_json, validate_messages, safe_import + from shared.chat_providers import BaseChatProvider + +Heavy or optional modules (cosmos, telemetry, db_logging, sql_engine) are NOT +eagerly imported here — import them directly when needed so startup stays fast. +""" + +from shared.file_cache import DEFAULT_TTL_SECONDS # noqa: F401 +from shared.file_cache import read_json_cached +from shared.http_utils import validate_messages # noqa: F401 +from shared.import_helpers import safe_import # noqa: F401 +# --- Lightweight, no-external-dependency helpers --- +from shared.json_utils import load_json # noqa: F401 +from shared.performance_utils import tail_file # noqa: F401 +from shared.script_utils import get_repo_root # noqa: F401 + +__all__ = [ + # json_utils + "load_json", + # http_utils + "validate_messages", + # file_cache + "read_json_cached", + "DEFAULT_TTL_SECONDS", + # script_utils + "get_repo_root", + # import_helpers + "safe_import", + # performance_utils + "tail_file", +] diff --git a/shared/ai_runner.py b/shared/ai_runner.py index 61770d95c..91a3090ff 100644 --- a/shared/ai_runner.py +++ b/shared/ai_runner.py @@ -1,101 +1,101 @@ -"""Shared AI runner module for Azure Functions. - -Provides a simple helper to invoke the existing talk-to-ai CLI (`chat_cli.py`) in one-shot -mode so we can reuse the provider auto-detection logic without refactoring. - -Environment variables influencing behavior: - DEFAULT_AI_PROVIDER -> provider passed when caller does not supply one (default: 'local') - WRITE_AI_RUN_LOG -> if '1' (default), write output to ai-projects/chat-cli/logs/auto_run_.txt - SYSTEM_PROMPT -> optional system prompt override forwarded to CLI via --system - -The runner returns the raw assistant output as a string plus a metadata dict. -""" - -from __future__ import annotations - -import logging -import os -import re -import subprocess -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, Optional, Tuple - -ROOT_DIR = Path(__file__).resolve().parent.parent -CHAT_CLI = ROOT_DIR / "ai-projects" / "chat-cli" / "src" / "chat_cli.py" -LOG_DIR = ROOT_DIR / "ai-projects" / "chat-cli" / "logs" - -# Cached ANSI escape regex for performance across imports -_ANSI_ESCAPE_RE = re.compile(r"\x1B\[[0-?]*[ -/]*[@-~]") - - -def run_chat_once( - prompt: str, - provider: Optional[str] = None, - model: Optional[str] = None, - system: Optional[str] = None, - timeout: int = 120, -) -> Tuple[str, Dict[str, str]]: - """Run the chat CLI in one-shot mode and capture its stdout. - - Parameters - ---------- - prompt: User prompt to send. - provider: Provider name ('local', 'openai', 'azure', or 'auto'). Defaults to env DEFAULT_AI_PROVIDER or 'local'. - model: Optional model override forwarded via --model. - system: Optional system prompt override forwarded via --system. - timeout: Seconds before aborting the subprocess. - """ - if not CHAT_CLI.exists(): - raise FileNotFoundError(f"chat_cli.py not found at {CHAT_CLI}") - - provider = provider or os.getenv("DEFAULT_AI_PROVIDER", "local") - system = system or os.getenv("SYSTEM_PROMPT") - - cmd = [sys.executable, str(CHAT_CLI), "--provider", provider, "--once", prompt] - if model: - cmd.extend(["--model", model]) - if system: - cmd.extend(["--system", system]) - - logging.info("Running chat CLI: %s", " ".join(cmd)) - proc = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout) - if proc.returncode != 0: - raise RuntimeError( - f"chat_cli failed (exit {proc.returncode}): {proc.stderr.strip() or 'no stderr'}" - ) - - raw_output = proc.stdout - - # Strip ANSI color codes for easier consumption (module-level cached regex) - output = _ANSI_ESCAPE_RE.sub("", raw_output).strip() - - # Try to extract only the assistant content after the 'assistant> ' prompt - reply = output - marker = "assistant> " - idx = output.rfind(marker) - if idx != -1: - reply = output[idx + len(marker) :].rstrip() - - metadata = {"provider": provider} - if model: - metadata["model"] = model - - # Optional logging of output to file for later review - if os.getenv("WRITE_AI_RUN_LOG", "1") == "1": - try: - LOG_DIR.mkdir(parents=True, exist_ok=True) - ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") - log_path = LOG_DIR / f"auto_run_{ts}.txt" - with log_path.open("w", encoding="utf-8") as f: - f.write(f"PROMPT: {prompt}\n") - f.write("OUTPUT (clean):\n") - f.write(output + "\n\n") - f.write("REPLY ONLY:\n") - f.write(reply + "\n") - logging.info("Wrote AI run log to %s", log_path) - except Exception as e: # noqa: BLE001 - logging.warning("Failed to write AI run log: %s", e) - - return reply, metadata +"""Shared AI runner module for Azure Functions. + +Provides a simple helper to invoke the existing talk-to-ai CLI (`chat_cli.py`) in one-shot +mode so we can reuse the provider auto-detection logic without refactoring. + +Environment variables influencing behavior: + DEFAULT_AI_PROVIDER -> provider passed when caller does not supply one (default: 'local') + WRITE_AI_RUN_LOG -> if '1' (default), write output to ai-projects/chat-cli/logs/auto_run_.txt + SYSTEM_PROMPT -> optional system prompt override forwarded to CLI via --system + +The runner returns the raw assistant output as a string plus a metadata dict. +""" + +from __future__ import annotations + +import logging +import os +import re +import subprocess +import sys +from datetime import datetime, timezone +from pathlib import Path +from typing import Dict, Optional, Tuple + +ROOT_DIR = Path(__file__).resolve().parent.parent +CHAT_CLI = ROOT_DIR / "ai-projects" / "chat-cli" / "src" / "chat_cli.py" +LOG_DIR = ROOT_DIR / "ai-projects" / "chat-cli" / "logs" + +# Cached ANSI escape regex for performance across imports +_ANSI_ESCAPE_RE = re.compile(r"\x1B\[[0-?]*[ -/]*[@-~]") + + +def run_chat_once( + prompt: str, + provider: Optional[str] = None, + model: Optional[str] = None, + system: Optional[str] = None, + timeout: int = 120, +) -> Tuple[str, Dict[str, str]]: + """Run the chat CLI in one-shot mode and capture its stdout. + + Parameters + ---------- + prompt: User prompt to send. + provider: Provider name ('local', 'openai', 'azure', or 'auto'). Defaults to env DEFAULT_AI_PROVIDER or 'local'. + model: Optional model override forwarded via --model. + system: Optional system prompt override forwarded via --system. + timeout: Seconds before aborting the subprocess. + """ + if not CHAT_CLI.exists(): + raise FileNotFoundError(f"chat_cli.py not found at {CHAT_CLI}") + + provider = provider or os.getenv("DEFAULT_AI_PROVIDER", "local") + system = system or os.getenv("SYSTEM_PROMPT") + + cmd = [sys.executable, str(CHAT_CLI), "--provider", provider, "--once", prompt] + if model: + cmd.extend(["--model", model]) + if system: + cmd.extend(["--system", system]) + + logging.info("Running chat CLI: %s", " ".join(cmd)) + proc = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout) + if proc.returncode != 0: + raise RuntimeError( + f"chat_cli failed (exit {proc.returncode}): {proc.stderr.strip() or 'no stderr'}" + ) + + raw_output = proc.stdout + + # Strip ANSI color codes for easier consumption (module-level cached regex) + output = _ANSI_ESCAPE_RE.sub("", raw_output).strip() + + # Try to extract only the assistant content after the 'assistant> ' prompt + reply = output + marker = "assistant> " + idx = output.rfind(marker) + if idx != -1: + reply = output[idx + len(marker) :].rstrip() + + metadata = {"provider": provider} + if model: + metadata["model"] = model + + # Optional logging of output to file for later review + if os.getenv("WRITE_AI_RUN_LOG", "1") == "1": + try: + LOG_DIR.mkdir(parents=True, exist_ok=True) + ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") + log_path = LOG_DIR / f"auto_run_{ts}.txt" + with log_path.open("w", encoding="utf-8") as f: + f.write(f"PROMPT: {prompt}\n") + f.write("OUTPUT (clean):\n") + f.write(output + "\n\n") + f.write("REPLY ONLY:\n") + f.write(reply + "\n") + logging.info("Wrote AI run log to %s", log_path) + except Exception as e: # noqa: BLE001 + logging.warning("Failed to write AI run log: %s", e) + + return reply, metadata diff --git a/shared/chat_memory.py b/shared/chat_memory.py index a78a9f59e..6bac7f678 100644 --- a/shared/chat_memory.py +++ b/shared/chat_memory.py @@ -1,378 +1,378 @@ -"""Semantic chat memory backed by SQL embeddings. - -Functions are fault-tolerant and degrade gracefully when the database -or embedding APIs are unavailable. - -Design: - - generate_embedding(text): attempts Azure OpenAI embeddings, then OpenAI, - then falls back to a lightweight local hashing embedding (fixed dim=256). - - store_embedding(message_id, embedding, model): persists embedding bytes - to [dbo].[ChatMessageEmbeddings]. Float32 little-endian layout. - - fetch_similar_messages(query_embedding, top_k=5, session_id=None): loads - recent embeddings (optionally scoped to a session) and computes cosine - similarity in Python, returning the top-k matches with message content. - -Environment variables: - QAI_DB_CONN: SQL connection string (ODBC Driver 18 for SQL Server recommended) - AZURE_OPENAI_API_KEY / AZURE_OPENAI_ENDPOINT / AZURE_OPENAI_EMBEDDING_DEPLOYMENT - OPENAI_API_KEY (for public OpenAI embedding fallback) - -Table schema created in database/Tables/ChatMessageEmbeddings.sql -""" - -from __future__ import annotations - -import hashlib -import heapq -import math -import os -import struct -from threading import RLock -from typing import List, Optional, Sequence - -try: - import pyodbc # type: ignore -except Exception: # pragma: no cover - pyodbc = None # type: ignore - -try: # OpenAI unified SDK - from openai import AzureOpenAI, OpenAI # type: ignore -except Exception: # pragma: no cover - OpenAI = None # type: ignore - AzureOpenAI = None # type: ignore - -try: - from shared.azure_utils import format_quota_message, is_quota_error -except Exception: # pragma: no cover - best effort import - # Provide simple fallbacks if helper isn't available - def is_quota_error(e: Exception) -> bool: # noqa: D401 - if e is None: - return False - txt = str(e).lower() - return any( - k in txt - for k in ( - "quota", - "premium", - "exceed", - "allowance", - "insufficient", - "billing", - ) - ) - - def format_quota_message( - e: Exception, service_name: str = "Azure OpenAI" - ) -> str: # noqa: D401 - return f"{service_name} quota/premium limit reached. Details: {str(e)}" - - -# ------------------------- DB Helpers with Connection Pooling ------------------------- - -# Connection pool for embedding operations (reduces connection overhead) -_connection_pool = [] -_MAX_POOL_SIZE = 5 - -# Per-thread connection cache for fast reuse without reconnecting. -_thread_connections = {} - -# Backward-compatible aliases expected by existing tests and scripts. -_conn_cache = _thread_connections -_conn_lock = RLock() - - -def _get_conn(): # noqa: ANN001 - """Get a database connection from pool/cache or create a new one. - - Priority: - 1. Shared pool connection (allows dead-connection replacement tests) - 2. Per-thread cached connection - 3. New connection - """ - conn_str = os.getenv("QAI_DB_CONN") - if not conn_str or not pyodbc: - return None - - thread_id = __import__("threading").get_ident() - - # 1) Try shared pool first. - attempted_pool = False - while True: - with _conn_lock: - if not _connection_pool: - break - attempted_pool = True - conn = _connection_pool.pop() - try: - conn.cursor().execute("SELECT 1") - with _conn_lock: - _thread_connections[thread_id] = conn - return conn - except Exception: - try: - conn.close() - except Exception: - pass - - # 2) Fast path: per-thread cached connection (only if pool wasn't consulted). - # If pooled connections were attempted and found dead, create a fresh - # connection below to avoid returning potentially stale thread cache. - if not attempted_pool: - with _conn_lock: - cached = _thread_connections.get(thread_id) - if cached is not None: - try: - cached.cursor().execute("SELECT 1") - return cached - except Exception: - try: - cached.close() - except Exception: - pass - with _conn_lock: - _thread_connections.pop(thread_id, None) - - # 3) No valid pooled/cached connections, create a new one - try: - conn = pyodbc.connect(conn_str, timeout=4) - with _conn_lock: - _thread_connections[thread_id] = conn - return conn - except Exception: - return None - - -def _return_conn(conn): # noqa: ANN001 - """Return a connection to the pool for reuse when not thread-cached.""" - if not conn: - return - - thread_id = __import__("threading").get_ident() - with _conn_lock: - # Keep current-thread cached connection hot for immediate reuse. - if _thread_connections.get(thread_id) is conn: - return - - if len(_connection_pool) < _MAX_POOL_SIZE: - _connection_pool.append(conn) - return - - try: - conn.close() - except Exception: - pass - - -# ------------------------- Embedding Generation ------------------------- - - -_LOCAL_DIM = 256 # dimension for lightweight local fallback - - -def _hash_embedding(text: str, dim: int = _LOCAL_DIM) -> List[float]: - """Very lightweight deterministic hashing embedding. - - Not semantically rich but provides some signal for similarity - within the same workspace when no embedding API is configured. - - Optimized: Uses module-level hashlib import and single-pass norm calculation. - """ - tokens = [t for t in text.lower().split() if t] - vec = [0.0] * dim - if not tokens: - return vec - - # Build vector with hash-based indices - for tok in tokens: - h = int(hashlib.sha256(tok.encode("utf-8")).hexdigest(), 16) - idx = h % dim - vec[idx] += 1.0 - - # L2 normalize in single pass - sum_sq = sum(v * v for v in vec) - if sum_sq > 0: - norm = math.sqrt(sum_sq) - return [v / norm for v in vec] - return vec - - -def generate_embedding(text: str) -> List[float]: # noqa: ANN001 - """Generate an embedding for text using Azure OpenAI > OpenAI > local hash. - - Returns a list[float]; errors fall back to hash embedding. - """ - text = text or "" - # Azure first - az_key = os.getenv("AZURE_OPENAI_API_KEY") - az_ep = os.getenv("AZURE_OPENAI_ENDPOINT") - az_emb = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT") - if az_key and az_ep and az_emb and AzureOpenAI is not None: - try: - client = AzureOpenAI(api_key=az_key, azure_endpoint=az_ep) - resp = client.embeddings.create(model=az_emb, input=[text]) - return resp.data[0].embedding # type: ignore[attr-defined] - except Exception as e: - # If this looks like a quota/premium issue, log and fall back to - # the lightweight local hash embedding so the app remains usable. - if is_quota_error(e): - try: - import logging - - logging.getLogger(__name__).warning( - "Azure embedding call detected quota/premium error: %s", str(e) - ) - except Exception: - pass - return _hash_embedding(text) - # Otherwise continue to try public OpenAI or local fallback - pass - # Public OpenAI - oi_key = os.getenv("OPENAI_API_KEY") - if oi_key and OpenAI is not None: - try: - client = OpenAI(api_key=oi_key) - resp = client.embeddings.create( - model="text-embedding-3-small", input=[text] - ) - return resp.data[0].embedding # type: ignore[attr-defined] - except Exception: - pass - # Fallback - return _hash_embedding(text) - - -# ------------------------- Embedding Persistence ------------------------- - - -def _serialize_f32(vec: Sequence[float]) -> bytes: - return struct.pack(f"<{len(vec)}f", *[float(v) for v in vec]) - - -def store_embedding( - message_id: Optional[str], embedding: Sequence[float], model: str -) -> bool: # noqa: ANN001 - if not message_id or not embedding: - return False - conn = _get_conn() - if not conn: - return False - try: - cursor = conn.cursor() - blob = _serialize_f32(embedding) - cursor.execute( - "INSERT INTO dbo.ChatMessageEmbeddings (MessageId, EmbeddingModel, EmbeddingDim, EmbeddingVector) VALUES (?,?,?,?)", - message_id, - model or "unknown-model", - len(embedding), - blob, - ) - conn.commit() - return True - except Exception: - return False - finally: - # Return connection to pool instead of closing - _return_conn(conn) - - -# ------------------------- Similarity Search ------------------------- - - -def _deserialize_f32(blob: bytes, dim: int) -> List[float]: - if not blob: - return [0.0] * dim - # Expect exact length = dim * 4 - try: - return list(struct.unpack(f"<{dim}f", blob[: dim * 4])) - except Exception: - # Fallback slice-based - out = [] - for i in range(dim): - chunk = blob[i * 4 : (i + 1) * 4] - if len(chunk) == 4: - out.append(struct.unpack(" float: - if not a or not b or len(a) != len(b): - return 0.0 - dot = sum(x * y for x, y in zip(a, b)) - na = math.sqrt(sum(x * x for x in a)) or 1.0 - nb = math.sqrt(sum(y * y for y in b)) or 1.0 - return dot / (na * nb) - - -def fetch_similar_messages( - query_embedding: Sequence[float], top_k: int = 5, session_id: Optional[str] = None -) -> List[dict]: # noqa: ANN001 - """Return top_k similar past messages using Python-side cosine similarity. - - If session_id is provided, restrict search to that session's conversation(s). - For performance we limit to the most recent 500 embeddings. - - Optimization: Uses heapq.nlargest for O(n log k) top-k selection instead of - O(n log n) full sort when top_k is small relative to result set. - Uses connection pooling to avoid creating new connections for every query. - """ - if not query_embedding: - return [] - conn = _get_conn() - if not conn: - return [] - try: - cursor = conn.cursor() - if session_id: - cursor.execute( - "SELECT TOP 500 e.MessageId, e.EmbeddingModel, e.EmbeddingDim, e.EmbeddingVector, m.Content " - "FROM dbo.ChatMessageEmbeddings e JOIN dbo.ChatMessages m ON e.MessageId=m.MessageId " - "JOIN dbo.ChatConversations c ON m.ConversationId=c.ConversationId " - "WHERE c.SessionId=? ORDER BY e.CreatedAt DESC", - session_id, - ) - else: - cursor.execute( - "SELECT TOP 500 e.MessageId, e.EmbeddingModel, e.EmbeddingDim, e.EmbeddingVector, m.Content " - "FROM dbo.ChatMessageEmbeddings e JOIN dbo.ChatMessages m ON e.MessageId=m.MessageId " - "ORDER BY e.CreatedAt DESC", - ) - rows = cursor.fetchall() - - # Build scored list with only positive similarities - scored = [] - for r in rows: - dim = r.EmbeddingDim - emb = _deserialize_f32(r.EmbeddingVector, dim) - sim = _cosine(query_embedding, emb) - if sim > 0: - scored.append( - { - "message_id": r.MessageId, - "content": r.Content, - "similarity": sim, - "embedding_model": r.EmbeddingModel, - } - ) - - # Use heapq.nlargest for efficient top-k selection (O(n log k) vs O(n log n)) - # This is more efficient when top_k << len(scored) - return heapq.nlargest(top_k, scored, key=lambda x: x["similarity"]) - except Exception: - return [] - finally: - # Return connection to pool instead of closing - _return_conn(conn) - - -__all__ = [ - "generate_embedding", - "store_embedding", - "fetch_similar_messages", - "_get_conn", - "_return_conn", - "_conn_cache", - "_conn_lock", - "_connection_pool", -] +"""Semantic chat memory backed by SQL embeddings. + +Functions are fault-tolerant and degrade gracefully when the database +or embedding APIs are unavailable. + +Design: + - generate_embedding(text): attempts Azure OpenAI embeddings, then OpenAI, + then falls back to a lightweight local hashing embedding (fixed dim=256). + - store_embedding(message_id, embedding, model): persists embedding bytes + to [dbo].[ChatMessageEmbeddings]. Float32 little-endian layout. + - fetch_similar_messages(query_embedding, top_k=5, session_id=None): loads + recent embeddings (optionally scoped to a session) and computes cosine + similarity in Python, returning the top-k matches with message content. + +Environment variables: + QAI_DB_CONN: SQL connection string (ODBC Driver 18 for SQL Server recommended) + AZURE_OPENAI_API_KEY / AZURE_OPENAI_ENDPOINT / AZURE_OPENAI_EMBEDDING_DEPLOYMENT + OPENAI_API_KEY (for public OpenAI embedding fallback) + +Table schema created in database/Tables/ChatMessageEmbeddings.sql +""" + +from __future__ import annotations + +import hashlib +import heapq +import math +import os +import struct +from threading import RLock +from typing import List, Optional, Sequence + +try: + import pyodbc # type: ignore +except Exception: # pragma: no cover + pyodbc = None # type: ignore + +try: # OpenAI unified SDK + from openai import AzureOpenAI, OpenAI # type: ignore +except Exception: # pragma: no cover + OpenAI = None # type: ignore + AzureOpenAI = None # type: ignore + +try: + from shared.azure_utils import format_quota_message, is_quota_error +except Exception: # pragma: no cover - best effort import + # Provide simple fallbacks if helper isn't available + def is_quota_error(e: Exception) -> bool: # noqa: D401 + if e is None: + return False + txt = str(e).lower() + return any( + k in txt + for k in ( + "quota", + "premium", + "exceed", + "allowance", + "insufficient", + "billing", + ) + ) + + def format_quota_message( + e: Exception, service_name: str = "Azure OpenAI" + ) -> str: # noqa: D401 + return f"{service_name} quota/premium limit reached. Details: {str(e)}" + + +# ------------------------- DB Helpers with Connection Pooling ------------------------- + +# Connection pool for embedding operations (reduces connection overhead) +_connection_pool = [] +_MAX_POOL_SIZE = 5 + +# Per-thread connection cache for fast reuse without reconnecting. +_thread_connections = {} + +# Backward-compatible aliases expected by existing tests and scripts. +_conn_cache = _thread_connections +_conn_lock = RLock() + + +def _get_conn(): # noqa: ANN001 + """Get a database connection from pool/cache or create a new one. + + Priority: + 1. Shared pool connection (allows dead-connection replacement tests) + 2. Per-thread cached connection + 3. New connection + """ + conn_str = os.getenv("QAI_DB_CONN") + if not conn_str or not pyodbc: + return None + + thread_id = __import__("threading").get_ident() + + # 1) Try shared pool first. + attempted_pool = False + while True: + with _conn_lock: + if not _connection_pool: + break + attempted_pool = True + conn = _connection_pool.pop() + try: + conn.cursor().execute("SELECT 1") + with _conn_lock: + _thread_connections[thread_id] = conn + return conn + except Exception: + try: + conn.close() + except Exception: + pass + + # 2) Fast path: per-thread cached connection (only if pool wasn't consulted). + # If pooled connections were attempted and found dead, create a fresh + # connection below to avoid returning potentially stale thread cache. + if not attempted_pool: + with _conn_lock: + cached = _thread_connections.get(thread_id) + if cached is not None: + try: + cached.cursor().execute("SELECT 1") + return cached + except Exception: + try: + cached.close() + except Exception: + pass + with _conn_lock: + _thread_connections.pop(thread_id, None) + + # 3) No valid pooled/cached connections, create a new one + try: + conn = pyodbc.connect(conn_str, timeout=4) + with _conn_lock: + _thread_connections[thread_id] = conn + return conn + except Exception: + return None + + +def _return_conn(conn): # noqa: ANN001 + """Return a connection to the pool for reuse when not thread-cached.""" + if not conn: + return + + thread_id = __import__("threading").get_ident() + with _conn_lock: + # Keep current-thread cached connection hot for immediate reuse. + if _thread_connections.get(thread_id) is conn: + return + + if len(_connection_pool) < _MAX_POOL_SIZE: + _connection_pool.append(conn) + return + + try: + conn.close() + except Exception: + pass + + +# ------------------------- Embedding Generation ------------------------- + + +_LOCAL_DIM = 256 # dimension for lightweight local fallback + + +def _hash_embedding(text: str, dim: int = _LOCAL_DIM) -> List[float]: + """Very lightweight deterministic hashing embedding. + + Not semantically rich but provides some signal for similarity + within the same workspace when no embedding API is configured. + + Optimized: Uses module-level hashlib import and single-pass norm calculation. + """ + tokens = [t for t in text.lower().split() if t] + vec = [0.0] * dim + if not tokens: + return vec + + # Build vector with hash-based indices + for tok in tokens: + h = int(hashlib.sha256(tok.encode("utf-8")).hexdigest(), 16) + idx = h % dim + vec[idx] += 1.0 + + # L2 normalize in single pass + sum_sq = sum(v * v for v in vec) + if sum_sq > 0: + norm = math.sqrt(sum_sq) + return [v / norm for v in vec] + return vec + + +def generate_embedding(text: str) -> List[float]: # noqa: ANN001 + """Generate an embedding for text using Azure OpenAI > OpenAI > local hash. + + Returns a list[float]; errors fall back to hash embedding. + """ + text = text or "" + # Azure first + az_key = os.getenv("AZURE_OPENAI_API_KEY") + az_ep = os.getenv("AZURE_OPENAI_ENDPOINT") + az_emb = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT") + if az_key and az_ep and az_emb and AzureOpenAI is not None: + try: + client = AzureOpenAI(api_key=az_key, azure_endpoint=az_ep) + resp = client.embeddings.create(model=az_emb, input=[text]) + return resp.data[0].embedding # type: ignore[attr-defined] + except Exception as e: + # If this looks like a quota/premium issue, log and fall back to + # the lightweight local hash embedding so the app remains usable. + if is_quota_error(e): + try: + import logging + + logging.getLogger(__name__).warning( + "Azure embedding call detected quota/premium error: %s", str(e) + ) + except Exception: + pass + return _hash_embedding(text) + # Otherwise continue to try public OpenAI or local fallback + pass + # Public OpenAI + oi_key = os.getenv("OPENAI_API_KEY") + if oi_key and OpenAI is not None: + try: + client = OpenAI(api_key=oi_key) + resp = client.embeddings.create( + model="text-embedding-3-small", input=[text] + ) + return resp.data[0].embedding # type: ignore[attr-defined] + except Exception: + pass + # Fallback + return _hash_embedding(text) + + +# ------------------------- Embedding Persistence ------------------------- + + +def _serialize_f32(vec: Sequence[float]) -> bytes: + return struct.pack(f"<{len(vec)}f", *[float(v) for v in vec]) + + +def store_embedding( + message_id: Optional[str], embedding: Sequence[float], model: str +) -> bool: # noqa: ANN001 + if not message_id or not embedding: + return False + conn = _get_conn() + if not conn: + return False + try: + cursor = conn.cursor() + blob = _serialize_f32(embedding) + cursor.execute( + "INSERT INTO dbo.ChatMessageEmbeddings (MessageId, EmbeddingModel, EmbeddingDim, EmbeddingVector) VALUES (?,?,?,?)", + message_id, + model or "unknown-model", + len(embedding), + blob, + ) + conn.commit() + return True + except Exception: + return False + finally: + # Return connection to pool instead of closing + _return_conn(conn) + + +# ------------------------- Similarity Search ------------------------- + + +def _deserialize_f32(blob: bytes, dim: int) -> List[float]: + if not blob: + return [0.0] * dim + # Expect exact length = dim * 4 + try: + return list(struct.unpack(f"<{dim}f", blob[: dim * 4])) + except Exception: + # Fallback slice-based + out = [] + for i in range(dim): + chunk = blob[i * 4 : (i + 1) * 4] + if len(chunk) == 4: + out.append(struct.unpack(" float: + if not a or not b or len(a) != len(b): + return 0.0 + dot = sum(x * y for x, y in zip(a, b)) + na = math.sqrt(sum(x * x for x in a)) or 1.0 + nb = math.sqrt(sum(y * y for y in b)) or 1.0 + return dot / (na * nb) + + +def fetch_similar_messages( + query_embedding: Sequence[float], top_k: int = 5, session_id: Optional[str] = None +) -> List[dict]: # noqa: ANN001 + """Return top_k similar past messages using Python-side cosine similarity. + + If session_id is provided, restrict search to that session's conversation(s). + For performance we limit to the most recent 500 embeddings. + + Optimization: Uses heapq.nlargest for O(n log k) top-k selection instead of + O(n log n) full sort when top_k is small relative to result set. + Uses connection pooling to avoid creating new connections for every query. + """ + if not query_embedding: + return [] + conn = _get_conn() + if not conn: + return [] + try: + cursor = conn.cursor() + if session_id: + cursor.execute( + "SELECT TOP 500 e.MessageId, e.EmbeddingModel, e.EmbeddingDim, e.EmbeddingVector, m.Content " + "FROM dbo.ChatMessageEmbeddings e JOIN dbo.ChatMessages m ON e.MessageId=m.MessageId " + "JOIN dbo.ChatConversations c ON m.ConversationId=c.ConversationId " + "WHERE c.SessionId=? ORDER BY e.CreatedAt DESC", + session_id, + ) + else: + cursor.execute( + "SELECT TOP 500 e.MessageId, e.EmbeddingModel, e.EmbeddingDim, e.EmbeddingVector, m.Content " + "FROM dbo.ChatMessageEmbeddings e JOIN dbo.ChatMessages m ON e.MessageId=m.MessageId " + "ORDER BY e.CreatedAt DESC", + ) + rows = cursor.fetchall() + + # Build scored list with only positive similarities + scored = [] + for r in rows: + dim = r.EmbeddingDim + emb = _deserialize_f32(r.EmbeddingVector, dim) + sim = _cosine(query_embedding, emb) + if sim > 0: + scored.append( + { + "message_id": r.MessageId, + "content": r.Content, + "similarity": sim, + "embedding_model": r.EmbeddingModel, + } + ) + + # Use heapq.nlargest for efficient top-k selection (O(n log k) vs O(n log n)) + # This is more efficient when top_k << len(scored) + return heapq.nlargest(top_k, scored, key=lambda x: x["similarity"]) + except Exception: + return [] + finally: + # Return connection to pool instead of closing + _return_conn(conn) + + +__all__ = [ + "generate_embedding", + "store_embedding", + "fetch_similar_messages", + "_get_conn", + "_return_conn", + "_conn_cache", + "_conn_lock", + "_connection_pool", +] diff --git a/shared/chat_providers.py b/shared/chat_providers.py index 3309b64dd..425d9f405 100644 --- a/shared/chat_providers.py +++ b/shared/chat_providers.py @@ -1,92 +1,92 @@ -"""Chat providers re-export module for shared infrastructure. - -This module re-exports chat providers from the canonical source at -ai-projects/chat-cli/src/chat_providers.py to avoid code duplication while -maintaining backward compatibility for imports from shared/. - -Usage: - from shared.chat_providers import detect_provider, RoleMessage - # or after adding shared/ to sys.path: - from chat_providers import detect_provider -""" - -from __future__ import annotations - -import importlib.util -import sys -from pathlib import Path - -# Load the canonical chat_providers module directly from ai-projects/chat-cli/src -_canonical_path = ( - Path(__file__).resolve().parent.parent - / "ai-projects" - / "chat-cli" - / "src" - / "chat_providers.py" -) -_spec = importlib.util.spec_from_file_location( - "_canonical_chat_providers", _canonical_path -) -_canonical_module = importlib.util.module_from_spec(_spec) -sys.modules["_canonical_chat_providers"] = _canonical_module -_spec.loader.exec_module(_canonical_module) - -# Re-export all public symbols from canonical chat_providers -RoleMessage = _canonical_module.RoleMessage -ProviderChoice = _canonical_module.ProviderChoice -BaseChatProvider = _canonical_module.BaseChatProvider -LocalEchoProvider = _canonical_module.LocalEchoProvider -OpenAIProvider = _canonical_module.OpenAIProvider -AzureOpenAIProvider = _canonical_module.AzureOpenAIProvider -detect_provider = _canonical_module.detect_provider - -# Build __all__ dynamically based on what's exported and conditionally add optional providers -__all__ = [ - "RoleMessage", - "ProviderChoice", - "BaseChatProvider", - "LocalEchoProvider", - "OpenAIProvider", - "AzureOpenAIProvider", - "detect_provider", -] - -# Conditionally export LoraLocalProvider if available -try: - LoraLocalProvider = _canonical_module.LoraLocalProvider - __all__.append("LoraLocalProvider") -except AttributeError: - pass - -# Conditionally export LMStudioProvider if available -try: - LMStudioProvider = _canonical_module.LMStudioProvider - __all__.append("LMStudioProvider") -except AttributeError: - pass - -# Conditionally export AGI provider using the same dynamic import pattern -try: - _agi_path = ( - Path(__file__).resolve().parent.parent - / "ai-projects" - / "chat-cli" - / "src" - / "agi_provider.py" - ) - if _agi_path.exists(): - _agi_spec = importlib.util.spec_from_file_location( - "_agi_provider_module", _agi_path - ) - _agi_module = importlib.util.module_from_spec(_agi_spec) - sys.modules["_agi_provider_module"] = _agi_module - _agi_spec.loader.exec_module(_agi_module) - AGIProvider = _agi_module.AGIProvider - AGIContext = _agi_module.AGIContext - ReasoningStep = _agi_module.ReasoningStep - create_agi_provider = _agi_module.create_agi_provider - __all__.extend( - ["AGIProvider", "AGIContext", "ReasoningStep", "create_agi_provider"] - ) -except (ImportError, AttributeError): - pass +"""Chat providers re-export module for shared infrastructure. + +This module re-exports chat providers from the canonical source at +ai-projects/chat-cli/src/chat_providers.py to avoid code duplication while +maintaining backward compatibility for imports from shared/. + +Usage: + from shared.chat_providers import detect_provider, RoleMessage + # or after adding shared/ to sys.path: + from chat_providers import detect_provider +""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path + +# Load the canonical chat_providers module directly from ai-projects/chat-cli/src +_canonical_path = ( + Path(__file__).resolve().parent.parent + / "ai-projects" + / "chat-cli" + / "src" + / "chat_providers.py" +) +_spec = importlib.util.spec_from_file_location( + "_canonical_chat_providers", _canonical_path +) +_canonical_module = importlib.util.module_from_spec(_spec) +sys.modules["_canonical_chat_providers"] = _canonical_module +_spec.loader.exec_module(_canonical_module) + +# Re-export all public symbols from canonical chat_providers +RoleMessage = _canonical_module.RoleMessage +ProviderChoice = _canonical_module.ProviderChoice +BaseChatProvider = _canonical_module.BaseChatProvider +LocalEchoProvider = _canonical_module.LocalEchoProvider +OpenAIProvider = _canonical_module.OpenAIProvider +AzureOpenAIProvider = _canonical_module.AzureOpenAIProvider +detect_provider = _canonical_module.detect_provider + +# Build __all__ dynamically based on what's exported and conditionally add optional providers +__all__ = [ + "RoleMessage", + "ProviderChoice", + "BaseChatProvider", + "LocalEchoProvider", + "OpenAIProvider", + "AzureOpenAIProvider", + "detect_provider", +] + +# Conditionally export LoraLocalProvider if available +try: + LoraLocalProvider = _canonical_module.LoraLocalProvider + __all__.append("LoraLocalProvider") +except AttributeError: + pass + +# Conditionally export LMStudioProvider if available +try: + LMStudioProvider = _canonical_module.LMStudioProvider + __all__.append("LMStudioProvider") +except AttributeError: + pass + +# Conditionally export AGI provider using the same dynamic import pattern +try: + _agi_path = ( + Path(__file__).resolve().parent.parent + / "ai-projects" + / "chat-cli" + / "src" + / "agi_provider.py" + ) + if _agi_path.exists(): + _agi_spec = importlib.util.spec_from_file_location( + "_agi_provider_module", _agi_path + ) + _agi_module = importlib.util.module_from_spec(_agi_spec) + sys.modules["_agi_provider_module"] = _agi_module + _agi_spec.loader.exec_module(_agi_module) + AGIProvider = _agi_module.AGIProvider + AGIContext = _agi_module.AGIContext + ReasoningStep = _agi_module.ReasoningStep + create_agi_provider = _agi_module.create_agi_provider + __all__.extend( + ["AGIProvider", "AGIContext", "ReasoningStep", "create_agi_provider"] + ) +except (ImportError, AttributeError): + pass diff --git a/shared/cosmos_client.py b/shared/cosmos_client.py index 53a4a495d..e0f8f0044 100644 --- a/shared/cosmos_client.py +++ b/shared/cosmos_client.py @@ -1,296 +1,296 @@ -"""Cosmos DB client scaffold for QAI. - -Provides lazy singleton client & container access for chat sessions and quantum job metadata. -Initialization is gated by env var QAI_ENABLE_COSMOS=true and required Cosmos settings. - -Env variables required: - COSMOS_ENDPOINT - COSMOS_KEY - COSMOS_DATABASE (default: qai) - COSMOS_CONTAINER (default: chat_sessions) - -Partition key strategy: - /userId for chat sessions (high cardinality recommended) - For quantum jobs, you may create a separate container with /jobGroup or HPK later. -""" - -from __future__ import annotations - -import logging -import os -from typing import Any, Dict, Optional, Union - -try: - from azure.cosmos import CosmosClient, PartitionKey # type: ignore -except Exception: # pragma: no cover - dependency optional until installed - CosmosClient = None # type: ignore - -_CLIENT: Optional[CosmosClient] = None -_CONTAINER: Optional[Any] = None # Container proxy object when initialized - - -def _enabled() -> bool: - return os.getenv("QAI_ENABLE_COSMOS", "false").lower() == "true" - - -def _settings_present() -> bool: - return all(os.getenv(k) for k in ["COSMOS_ENDPOINT", "COSMOS_KEY"]) - - -def init() -> bool: - global _CLIENT, _CONTAINER - if not _enabled(): - logging.info("[cosmos] Disabled via QAI_ENABLE_COSMOS flag.") - return False - if _CLIENT is not None: - return True - if CosmosClient is None: - logging.warning( - "[cosmos] azure-cosmos package not available; cannot initialize." - ) - return False - if not _settings_present(): - logging.warning( - "[cosmos] Missing required settings (COSMOS_ENDPOINT / COSMOS_KEY)." - ) - return False - - endpoint = os.getenv("COSMOS_ENDPOINT") - key = os.getenv("COSMOS_KEY") - database_name = os.getenv("COSMOS_DATABASE", "qai") - container_name = os.getenv("COSMOS_CONTAINER", "chat_sessions") - - try: - _CLIENT = CosmosClient(endpoint, credential=key) # type: ignore - db = None - # Create DB if not exists - try: - db = _CLIENT.create_database_if_not_exists(id=database_name) - except Exception as e: - logging.error(f"[cosmos] Failed creating database {database_name}: {e}") - return False - # Create container if not exists - try: - _CONTAINER = db.create_container_if_not_exists( - id=container_name, - partition_key=PartitionKey(path="/userId"), - offer_throughput=400, - ) - except Exception as e: - logging.error(f"[cosmos] Failed creating container {container_name}: {e}") - return False - logging.info( - f"[cosmos] Initialized container {container_name} in database {database_name}." - ) - return True - except Exception as e: - logging.error(f"[cosmos] Initialization error: {e}") - return False - - -def container(): - if _CLIENT is None or _CONTAINER is None: - return None - return _CONTAINER - - -def health() -> Dict[str, Any]: - """Return Cosmos client health/status details without raising. - - Attempts lazy init if enabled to surface container readiness. - """ - status: Dict[str, Any] = { - "enabled": _enabled(), - "settings_present": _settings_present(), - "initialized": False, - "container_id": None, - "database": os.getenv("COSMOS_DATABASE", "qai"), - "container": os.getenv("COSMOS_CONTAINER", "chat_sessions"), - "error": None, - } - if not status["enabled"]: - return status - try: - if init(): - status["initialized"] = _CLIENT is not None and _CONTAINER is not None - if _CONTAINER is not None: - try: - status["container_id"] = getattr(_CONTAINER, "id", None) - except Exception: # pragma: no cover - defensive - pass - except Exception as e: # pragma: no cover - defensive - status["error"] = str(e) - return status - - -def record_chat_message( - user_id: str, message: Dict[str, Any], provider: str, model: str -) -> bool: - """Persist a single chat message. user_id may be 'anonymous' if not provided.""" - if not init(): # ensures initialization or early exit - return False - c = container() - if c is None: - return False - try: - import uuid - - # Validate content is non-empty before storing (vulnerability fix) - content = message.get("content", "") - if not content or not str(content).strip(): - logging.warning( - f"[cosmos] Skipping empty message content for user {user_id}" - ) - return False - - # Use UUID to prevent ID collisions (CRITICAL FIX: data loss prevention) - doc_id = f"{user_id}-{uuid.uuid4().hex}" - doc = { - "id": doc_id, - "userId": user_id, - "role": message.get("role"), - "content": content, - "provider": provider, - "model": model, - "timestamp": message.get("timestamp"), # Keep for querying - } - c.upsert_item(doc) - return True - except Exception as e: - logging.warning(f"[cosmos] Failed to upsert chat message: {e}") - return False - - -def record_chat_session( - user_id: str, messages: list[Dict[str, Any]], provider: str, model: str -) -> bool: - """Persist entire chat session as one document (alternative strategy).""" - if not init(): - return False - c = container() - if c is None: - return False - try: - import time - import uuid - - # Use UUID to prevent ID collisions - doc_id = f"session-{user_id}-{uuid.uuid4().hex}" - doc = { - "id": doc_id, - "userId": user_id, - "messages": messages, - "provider": provider, - "model": model, - "messageCount": len(messages), - "timestamp": time.time(), # For querying/sorting - } - c.upsert_item(doc) - return True - except Exception as e: - logging.warning(f"[cosmos] Failed to upsert chat session: {e}") - return False - - -# ---------------- Worlds Container & Helpers ----------------- -_WORLDS_CONTAINER = None # lazy-created container for aria worlds - - -def worlds_container(): - """Return (and lazily create) the dedicated worlds container. - - Partition key: /theme_seed for high-cardinality and point lookups. - Container name configurable via COSMOS_WORLDS_CONTAINER (default: aria_worlds). - Gracefully returns None if Cosmos disabled or sdk missing. - """ - global _WORLDS_CONTAINER - if not init(): # ensures base client init - return None - if CosmosClient is None: # sdk missing - return None - if _WORLDS_CONTAINER is not None: - return _WORLDS_CONTAINER - try: - db = _CLIENT.get_database_client( - os.getenv("COSMOS_DATABASE", "qai") - ) # type: ignore - worlds_name = os.getenv("COSMOS_WORLDS_CONTAINER", "aria_worlds") - # Create if not exists with partition key /theme_seed - from azure.cosmos import PartitionKey # type: ignore - - _WORLDS_CONTAINER = db.create_container_if_not_exists( - id=worlds_name, - partition_key=PartitionKey(path="/theme_seed"), - offer_throughput=400, - ) - return _WORLDS_CONTAINER - except Exception as e: - logging.warning(f"[cosmos] Failed to get/create worlds container: {e}") - return None - - -def record_world(doc: Dict[str, Any]) -> bool: - """Upsert a world document into the dedicated worlds container. - - Expected doc schema (minimum): - id: stable id (e.g., world--) - theme_seed: _ - theme, seed, objects, environment, createdUtc, generationMethod, objectCount - Additional metadata allowed. TTL may be applied externally at container level. - """ - c = worlds_container() - if c is None: - return False - try: - c.upsert_item(doc) - return True - except Exception as e: - logging.warning(f"[cosmos] Failed to upsert world doc: {e}") - return False - - -def get_world(theme: str, seed: Union[str, int]) -> Optional[Dict[str, Any]]: - """Fetch a single world by theme + seed. Returns None if not found or unavailable.""" - c = worlds_container() - if c is None: - return None - try: - seed_str = str(seed) - theme_seed = f"{theme}_{seed_str}" - query = f"SELECT * FROM c WHERE c.theme_seed = '{theme_seed}'" - items = list(c.query_items(query=query, enable_cross_partition_query=True)) - return items[0] if items else None - except Exception as e: - logging.warning(f"[cosmos] get_world error: {e}") - return None - - -def list_worlds(limit: int = 100) -> list[Dict[str, Any]]: - """List world documents (lightweight metadata). Returns empty list if unavailable.""" - c = worlds_container() - if c is None: - return [] - try: - query = "SELECT c.id, c.theme, c.seed, c.objectCount, c.generationMethod, c.createdUtc FROM c" - items = [] - for item in c.query_items(query=query, enable_cross_partition_query=True): - items.append(item) - if len(items) >= limit: - break - return items - except Exception as e: - logging.warning(f"[cosmos] list_worlds error: {e}") - return [] - - -__all__ = [ - "init", - "record_chat_message", - "record_chat_session", - "container", - "health", - "worlds_container", - "record_world", - "get_world", - "list_worlds", -] +"""Cosmos DB client scaffold for QAI. + +Provides lazy singleton client & container access for chat sessions and quantum job metadata. +Initialization is gated by env var QAI_ENABLE_COSMOS=true and required Cosmos settings. + +Env variables required: + COSMOS_ENDPOINT + COSMOS_KEY + COSMOS_DATABASE (default: qai) + COSMOS_CONTAINER (default: chat_sessions) + +Partition key strategy: + /userId for chat sessions (high cardinality recommended) + For quantum jobs, you may create a separate container with /jobGroup or HPK later. +""" + +from __future__ import annotations + +import logging +import os +from typing import Any, Dict, Optional, Union + +try: + from azure.cosmos import CosmosClient, PartitionKey # type: ignore +except Exception: # pragma: no cover - dependency optional until installed + CosmosClient = None # type: ignore + +_CLIENT: Optional[CosmosClient] = None +_CONTAINER: Optional[Any] = None # Container proxy object when initialized + + +def _enabled() -> bool: + return os.getenv("QAI_ENABLE_COSMOS", "false").lower() == "true" + + +def _settings_present() -> bool: + return all(os.getenv(k) for k in ["COSMOS_ENDPOINT", "COSMOS_KEY"]) + + +def init() -> bool: + global _CLIENT, _CONTAINER + if not _enabled(): + logging.info("[cosmos] Disabled via QAI_ENABLE_COSMOS flag.") + return False + if _CLIENT is not None: + return True + if CosmosClient is None: + logging.warning( + "[cosmos] azure-cosmos package not available; cannot initialize." + ) + return False + if not _settings_present(): + logging.warning( + "[cosmos] Missing required settings (COSMOS_ENDPOINT / COSMOS_KEY)." + ) + return False + + endpoint = os.getenv("COSMOS_ENDPOINT") + key = os.getenv("COSMOS_KEY") + database_name = os.getenv("COSMOS_DATABASE", "qai") + container_name = os.getenv("COSMOS_CONTAINER", "chat_sessions") + + try: + _CLIENT = CosmosClient(endpoint, credential=key) # type: ignore + db = None + # Create DB if not exists + try: + db = _CLIENT.create_database_if_not_exists(id=database_name) + except Exception as e: + logging.error(f"[cosmos] Failed creating database {database_name}: {e}") + return False + # Create container if not exists + try: + _CONTAINER = db.create_container_if_not_exists( + id=container_name, + partition_key=PartitionKey(path="/userId"), + offer_throughput=400, + ) + except Exception as e: + logging.error(f"[cosmos] Failed creating container {container_name}: {e}") + return False + logging.info( + f"[cosmos] Initialized container {container_name} in database {database_name}." + ) + return True + except Exception as e: + logging.error(f"[cosmos] Initialization error: {e}") + return False + + +def container(): + if _CLIENT is None or _CONTAINER is None: + return None + return _CONTAINER + + +def health() -> Dict[str, Any]: + """Return Cosmos client health/status details without raising. + + Attempts lazy init if enabled to surface container readiness. + """ + status: Dict[str, Any] = { + "enabled": _enabled(), + "settings_present": _settings_present(), + "initialized": False, + "container_id": None, + "database": os.getenv("COSMOS_DATABASE", "qai"), + "container": os.getenv("COSMOS_CONTAINER", "chat_sessions"), + "error": None, + } + if not status["enabled"]: + return status + try: + if init(): + status["initialized"] = _CLIENT is not None and _CONTAINER is not None + if _CONTAINER is not None: + try: + status["container_id"] = getattr(_CONTAINER, "id", None) + except Exception: # pragma: no cover - defensive + pass + except Exception as e: # pragma: no cover - defensive + status["error"] = str(e) + return status + + +def record_chat_message( + user_id: str, message: Dict[str, Any], provider: str, model: str +) -> bool: + """Persist a single chat message. user_id may be 'anonymous' if not provided.""" + if not init(): # ensures initialization or early exit + return False + c = container() + if c is None: + return False + try: + import uuid + + # Validate content is non-empty before storing (vulnerability fix) + content = message.get("content", "") + if not content or not str(content).strip(): + logging.warning( + f"[cosmos] Skipping empty message content for user {user_id}" + ) + return False + + # Use UUID to prevent ID collisions (CRITICAL FIX: data loss prevention) + doc_id = f"{user_id}-{uuid.uuid4().hex}" + doc = { + "id": doc_id, + "userId": user_id, + "role": message.get("role"), + "content": content, + "provider": provider, + "model": model, + "timestamp": message.get("timestamp"), # Keep for querying + } + c.upsert_item(doc) + return True + except Exception as e: + logging.warning(f"[cosmos] Failed to upsert chat message: {e}") + return False + + +def record_chat_session( + user_id: str, messages: list[Dict[str, Any]], provider: str, model: str +) -> bool: + """Persist entire chat session as one document (alternative strategy).""" + if not init(): + return False + c = container() + if c is None: + return False + try: + import time + import uuid + + # Use UUID to prevent ID collisions + doc_id = f"session-{user_id}-{uuid.uuid4().hex}" + doc = { + "id": doc_id, + "userId": user_id, + "messages": messages, + "provider": provider, + "model": model, + "messageCount": len(messages), + "timestamp": time.time(), # For querying/sorting + } + c.upsert_item(doc) + return True + except Exception as e: + logging.warning(f"[cosmos] Failed to upsert chat session: {e}") + return False + + +# ---------------- Worlds Container & Helpers ----------------- +_WORLDS_CONTAINER = None # lazy-created container for aria worlds + + +def worlds_container(): + """Return (and lazily create) the dedicated worlds container. + + Partition key: /theme_seed for high-cardinality and point lookups. + Container name configurable via COSMOS_WORLDS_CONTAINER (default: aria_worlds). + Gracefully returns None if Cosmos disabled or sdk missing. + """ + global _WORLDS_CONTAINER + if not init(): # ensures base client init + return None + if CosmosClient is None: # sdk missing + return None + if _WORLDS_CONTAINER is not None: + return _WORLDS_CONTAINER + try: + db = _CLIENT.get_database_client( + os.getenv("COSMOS_DATABASE", "qai") + ) # type: ignore + worlds_name = os.getenv("COSMOS_WORLDS_CONTAINER", "aria_worlds") + # Create if not exists with partition key /theme_seed + from azure.cosmos import PartitionKey # type: ignore + + _WORLDS_CONTAINER = db.create_container_if_not_exists( + id=worlds_name, + partition_key=PartitionKey(path="/theme_seed"), + offer_throughput=400, + ) + return _WORLDS_CONTAINER + except Exception as e: + logging.warning(f"[cosmos] Failed to get/create worlds container: {e}") + return None + + +def record_world(doc: Dict[str, Any]) -> bool: + """Upsert a world document into the dedicated worlds container. + + Expected doc schema (minimum): + id: stable id (e.g., world--) + theme_seed: _ + theme, seed, objects, environment, createdUtc, generationMethod, objectCount + Additional metadata allowed. TTL may be applied externally at container level. + """ + c = worlds_container() + if c is None: + return False + try: + c.upsert_item(doc) + return True + except Exception as e: + logging.warning(f"[cosmos] Failed to upsert world doc: {e}") + return False + + +def get_world(theme: str, seed: Union[str, int]) -> Optional[Dict[str, Any]]: + """Fetch a single world by theme + seed. Returns None if not found or unavailable.""" + c = worlds_container() + if c is None: + return None + try: + seed_str = str(seed) + theme_seed = f"{theme}_{seed_str}" + query = f"SELECT * FROM c WHERE c.theme_seed = '{theme_seed}'" + items = list(c.query_items(query=query, enable_cross_partition_query=True)) + return items[0] if items else None + except Exception as e: + logging.warning(f"[cosmos] get_world error: {e}") + return None + + +def list_worlds(limit: int = 100) -> list[Dict[str, Any]]: + """List world documents (lightweight metadata). Returns empty list if unavailable.""" + c = worlds_container() + if c is None: + return [] + try: + query = "SELECT c.id, c.theme, c.seed, c.objectCount, c.generationMethod, c.createdUtc FROM c" + items = [] + for item in c.query_items(query=query, enable_cross_partition_query=True): + items.append(item) + if len(items) >= limit: + break + return items + except Exception as e: + logging.warning(f"[cosmos] list_worlds error: {e}") + return [] + + +__all__ = [ + "init", + "record_chat_message", + "record_chat_session", + "container", + "health", + "worlds_container", + "record_world", + "get_world", + "list_worlds", +] diff --git a/shared/db_logging.py b/shared/db_logging.py index 24aedbd9a..c3cc64f14 100644 --- a/shared/db_logging.py +++ b/shared/db_logging.py @@ -1,308 +1,308 @@ -"""Database logging helpers for QAI. - -Centralized, fault-tolerant wrappers around stored procedures: - - sp_LogQuantumTrainingRun - - sp_LogLoRATrainingRun - - sp_LogChatConversation - - sp_RegisterDataset (used opportunistically for datasets) - -Behavior: - - If env var QAI_DB_CONN is missing, all functions become NO-OP. - - If pyodbc or driver not available, errors are swallowed and a warning emitted once. - - Each log_* function returns a dict with {success: bool, error: Optional[str], ids: {...}}. -""" - -from __future__ import annotations - -import json -import os -from pathlib import Path -from typing import Any, Dict, List, Optional - -_WARNED = False # Emit import/connection warnings only once -REPO_ROOT = Path(__file__).resolve().parents[1] - - -def _warn_once(msg: str) -> None: - global _WARNED - if not _WARNED: - print(f"[db_logging] WARN: {msg}") - _WARNED = True - - -def _get_conn(): - """Return a pyodbc connection or None if unavailable/not configured.""" - conn_str = os.getenv("QAI_DB_CONN") - if not conn_str: - return None - try: - import pyodbc # noqa: WPS433 - - return pyodbc.connect(conn_str, timeout=5) - except Exception as e: # noqa: BLE001 - _warn_once(f"DB unavailable: {e}") - return None - - -def _safe_exec(cursor, sql: str, params: List[Any]) -> bool: - try: - cursor.execute(sql, params) - return True - except Exception as e: # noqa: BLE001 - _warn_once(f"Exec failed: {e}") - return False - - -def log_chat_message_safe( - session_id: Optional[str], - provider: str, - model: Optional[str], - role: str, - content: str, - token_count: Optional[int] = None, - prompt_tokens: Optional[int] = None, - completion_tokens: Optional[int] = None, - total_tokens: Optional[int] = None, - execution_time_ms: Optional[int] = None, - finish_reason: Optional[str] = None, - log_file_path: Optional[str] = None, -) -> Dict[str, Any]: - conn = _get_conn() - if not conn: - return {"success": False, "skipped": True} - try: - cursor = conn.cursor() - sql = ( - "DECLARE @ConvId UNIQUEIDENTIFIER, @MsgId UNIQUEIDENTIFIER; " - "EXEC sp_LogChatConversation " - "@SessionId=?, @Provider=?, @Model=?, @Title=?, @Role=?, @Content=?, " - "@TokenCount=?, @PromptTokens=?, @CompletionTokens=?, @TotalTokens=?, " - "@ExecutionTimeMs=?, @FinishReason=?, @LogFilePath=?, " - "@ConversationId=@ConvId OUTPUT, @MessageId=@MsgId OUTPUT; " - "SELECT @ConvId AS ConversationId, @MsgId AS MessageId;" - ) - params = [ - session_id, - provider, - model, - None, # Title (optional auto-title not implemented yet) - role, - content, - token_count, - prompt_tokens, - completion_tokens, - total_tokens, - execution_time_ms, - finish_reason, - log_file_path, - ] - ok = _safe_exec(cursor, sql, params) - conv_id = msg_id = None - if ok: - row = cursor.fetchone() - if row: - conv_id, msg_id = row.ConversationId, row.MessageId - conn.commit() - return {"success": ok, "conversation_id": conv_id, "message_id": msg_id} - except Exception as e: # noqa: BLE001 - _warn_once(f"Chat log failed: {e}") - return {"success": False, "error": str(e)} - finally: - try: - conn.close() - except Exception: - pass - - -def _parse_quantum_summary() -> Dict[str, Any]: - summary_path = ( - REPO_ROOT - / "ai-projects" - / "quantum-ml" - / "results" - / "custom_training_summary.json" - ) - if not summary_path.exists(): - return {} - try: - data = json.loads(summary_path.read_text(encoding="utf-8")) - return { - "train_loss_last": data.get("metrics", {}).get("train_loss_last"), - "val_loss_last": data.get("metrics", {}).get("val_loss_last"), - "val_acc_last": data.get("metrics", {}).get("val_acc_last"), - "val_acc_best": data.get("metrics", {}).get("val_acc_best"), - } - except Exception as e: # noqa: BLE001 - _warn_once(f"Parse quantum summary failed: {e}") - return {} - - -def log_quantum_run_safe( - job, result: Dict[str, Any], dataset_name: str, log_path: str -) -> Dict[str, Any]: # noqa: ANN001 - conn = _get_conn() - if not conn: - return {"success": False, "skipped": True} - try: - metrics = _parse_quantum_summary() - cursor = conn.cursor() - # Stored procedure requires many params; pass only the essentials, rely on defaults - sql = ( - "DECLARE @RunId UNIQUEIDENTIFIER; EXEC sp_LogQuantumTrainingRun " - "@JobName=?, @DatasetName=?, @Backend=?, @NumQubits=?, @NumLayers=?, @Entanglement=?, " - "@LearningRate=?, @Epochs=?, @BatchSize=?, @TrainAccuracy=?, @ValAccuracy=?, @TestAccuracy=?, " - "@TrainLoss=?, @ValLoss=?, @TestLoss=?, @TotalShots=?, @ExecutionTimeSeconds=?, @IsAzureHardware=?, " - "@AzureJobId=?, @AzureProvider=?, @EstimatedCostUSD=?, @CircuitDepth=?, @NumParameters=?, " - "@StatusJsonPath=?, @ResultsJsonPath=?, @Status=?, @ErrorMessage=?, @RunId=@RunId OUTPUT; " - "SELECT @RunId AS RunId;" - ) - backend = ( - job.azure_backend - if getattr(job, "mode", "") == "azure_hardware" - else "qiskit_aer" - ) - params = [ - job.name, - dataset_name, - backend, - getattr(job, "n_qubits", None) or 4, - 2, # NumLayers (fixed in current HybridQNN) - "linear", # Entanglement (current circuit pattern) - getattr(job, "learning_rate", None) or 0.001, - getattr(job, "epochs", None) or 1, - getattr(job, "batch_size", None) or 16, - None, # TrainAccuracy (not explicitly tracked) - metrics.get("val_acc_last"), # ValAccuracy - # TestAccuracy (reuse best val acc as proxy) - metrics.get("val_acc_best"), - metrics.get("train_loss_last"), # TrainLoss - metrics.get("val_loss_last"), # ValLoss - None, # TestLoss - getattr(job, "azure_shots", None), # TotalShots for hardware - result.get("duration_sec"), - 1 if getattr(job, "mode", "") == "azure_hardware" else 0, - result.get("meta", {}).get("azure_job_id"), - job.azure_backend if getattr(job, "mode", "") == "azure_hardware" else None, - None, # EstimatedCostUSD (could be added later) - None, # CircuitDepth - None, # NumParameters - str(Path(log_path).parent / "status.json"), - log_path, - result.get("status"), - None if result.get("status") == "succeeded" else "Non-zero return code", - ] - ok = _safe_exec(cursor, sql, params) - run_id = None - if ok: - row = cursor.fetchone() - if row: - run_id = row.RunId - conn.commit() - return {"success": ok, "run_id": run_id} - except Exception as e: # noqa: BLE001 - _warn_once(f"Quantum run log failed: {e}") - return {"success": False, "error": str(e)} - finally: - try: - conn.close() - except Exception: - pass - - -def _load_yaml(path: Path) -> Dict[str, Any]: - try: - import yaml # noqa: WPS433 - - return yaml.safe_load(path.read_text(encoding="utf-8")) or {} - except Exception: - return {} - - -def log_lora_run_safe(job, result: Dict[str, Any]) -> Dict[str, Any]: # noqa: ANN001 - conn = _get_conn() - if not conn: - return {"success": False, "skipped": True} - try: - cursor = conn.cursor() - cfg_path = ( - Path(job.config) - if getattr(job, "config", None) - else REPO_ROOT / "AI" / "microsoft_phi-silica-3.6_v1" / "lora" / "lora.yaml" - ) - cfg = _load_yaml(cfg_path) - # Extract values with fallbacks - lora_rank = cfg.get("lora_rank", 8) # Not present -> placeholder - lora_alpha = cfg.get("lora_alpha", 16) - lora_dropout = job.lora_dropout or cfg.get("lora_dropout", 0.1) - sequence_len = cfg.get("finetune_train_seqlen", 512) - learning_rate = job.learning_rate or cfg.get("learning_rate", 2e-4) - target_modules = ( - cfg.get("lora_target_modules") - or cfg.get("target_modules") - or ["q_proj", "v_proj"] - ) - dataset_path = job.dataset or cfg.get("finetune_dataset", "data") - dataset_name = Path(str(dataset_path)).name - sql = ( - "DECLARE @RunId UNIQUEIDENTIFIER; EXEC sp_LogLoRATrainingRun " - "@JobName=?, @Model=?, @DatasetName=?, @DatasetPath=?, @MaxTrainSamples=?, @MaxEvalSamples=?, " - "@Epochs=?, @BatchSize=?, @SequenceLength=?, @LearningRate=?, @LoraRank=?, @LoraAlpha=?, @LoraDropout=?, " - "@TargetModules=?, @TrainLoss=?, @EvalLoss=?, @TrainPerplexity=?, @EvalPerplexity=?, @TotalSteps=?, @ActualEpochs=?, " - "@ExecutionTimeSeconds=?, @GpuMemoryPeakGB=?, @AdapterSavePath=?, @ConfigYamlPath=?, @LogsPath=?, @IsStreaming=?, @Runner=?, @Status=?, " - "@ErrorMessage=?, @RunId=@RunId OUTPUT; SELECT @RunId AS RunId;" - ) - params = [ - job.name, - job.hf_model_id or "Phi-3.6-mini-instruct", - dataset_name, - str(dataset_path), - job.max_train_samples, - job.max_eval_samples, - job.epochs or cfg.get("epochs", 1), - cfg.get("finetune_train_batch_size", 1), - sequence_len, - learning_rate, - lora_rank, - lora_alpha, - lora_dropout, - json.dumps(target_modules), - # TrainLoss (available inside training script; could parse later) - None, - None, # EvalLoss - None, # TrainPerplexity - None, # EvalPerplexity - None, # TotalSteps - job.epochs or cfg.get("epochs", 1), - result.get("duration_sec"), - None, # GpuMemoryPeakGB - None, # AdapterSavePath - str(cfg_path), - result.get("log"), - 1, # IsStreaming (default behavior) - job.runner, - result.get("status"), - None if result.get("status") == "succeeded" else "Non-zero return code", - ] - ok = _safe_exec(cursor, sql, params) - run_id = None - if ok: - row = cursor.fetchone() - if row: - run_id = row.RunId - conn.commit() - return {"success": ok, "run_id": run_id} - except Exception as e: # noqa: BLE001 - _warn_once(f"LoRA run log failed: {e}") - return {"success": False, "error": str(e)} - finally: - try: - conn.close() - except Exception: - pass - - -__all__ = [ - "log_chat_message_safe", - "log_quantum_run_safe", - "log_lora_run_safe", -] +"""Database logging helpers for QAI. + +Centralized, fault-tolerant wrappers around stored procedures: + - sp_LogQuantumTrainingRun + - sp_LogLoRATrainingRun + - sp_LogChatConversation + - sp_RegisterDataset (used opportunistically for datasets) + +Behavior: + - If env var QAI_DB_CONN is missing, all functions become NO-OP. + - If pyodbc or driver not available, errors are swallowed and a warning emitted once. + - Each log_* function returns a dict with {success: bool, error: Optional[str], ids: {...}}. +""" + +from __future__ import annotations + +import json +import os +from pathlib import Path +from typing import Any, Dict, List, Optional + +_WARNED = False # Emit import/connection warnings only once +REPO_ROOT = Path(__file__).resolve().parents[1] + + +def _warn_once(msg: str) -> None: + global _WARNED + if not _WARNED: + print(f"[db_logging] WARN: {msg}") + _WARNED = True + + +def _get_conn(): + """Return a pyodbc connection or None if unavailable/not configured.""" + conn_str = os.getenv("QAI_DB_CONN") + if not conn_str: + return None + try: + import pyodbc # noqa: WPS433 + + return pyodbc.connect(conn_str, timeout=5) + except Exception as e: # noqa: BLE001 + _warn_once(f"DB unavailable: {e}") + return None + + +def _safe_exec(cursor, sql: str, params: List[Any]) -> bool: + try: + cursor.execute(sql, params) + return True + except Exception as e: # noqa: BLE001 + _warn_once(f"Exec failed: {e}") + return False + + +def log_chat_message_safe( + session_id: Optional[str], + provider: str, + model: Optional[str], + role: str, + content: str, + token_count: Optional[int] = None, + prompt_tokens: Optional[int] = None, + completion_tokens: Optional[int] = None, + total_tokens: Optional[int] = None, + execution_time_ms: Optional[int] = None, + finish_reason: Optional[str] = None, + log_file_path: Optional[str] = None, +) -> Dict[str, Any]: + conn = _get_conn() + if not conn: + return {"success": False, "skipped": True} + try: + cursor = conn.cursor() + sql = ( + "DECLARE @ConvId UNIQUEIDENTIFIER, @MsgId UNIQUEIDENTIFIER; " + "EXEC sp_LogChatConversation " + "@SessionId=?, @Provider=?, @Model=?, @Title=?, @Role=?, @Content=?, " + "@TokenCount=?, @PromptTokens=?, @CompletionTokens=?, @TotalTokens=?, " + "@ExecutionTimeMs=?, @FinishReason=?, @LogFilePath=?, " + "@ConversationId=@ConvId OUTPUT, @MessageId=@MsgId OUTPUT; " + "SELECT @ConvId AS ConversationId, @MsgId AS MessageId;" + ) + params = [ + session_id, + provider, + model, + None, # Title (optional auto-title not implemented yet) + role, + content, + token_count, + prompt_tokens, + completion_tokens, + total_tokens, + execution_time_ms, + finish_reason, + log_file_path, + ] + ok = _safe_exec(cursor, sql, params) + conv_id = msg_id = None + if ok: + row = cursor.fetchone() + if row: + conv_id, msg_id = row.ConversationId, row.MessageId + conn.commit() + return {"success": ok, "conversation_id": conv_id, "message_id": msg_id} + except Exception as e: # noqa: BLE001 + _warn_once(f"Chat log failed: {e}") + return {"success": False, "error": str(e)} + finally: + try: + conn.close() + except Exception: + pass + + +def _parse_quantum_summary() -> Dict[str, Any]: + summary_path = ( + REPO_ROOT + / "ai-projects" + / "quantum-ml" + / "results" + / "custom_training_summary.json" + ) + if not summary_path.exists(): + return {} + try: + data = json.loads(summary_path.read_text(encoding="utf-8")) + return { + "train_loss_last": data.get("metrics", {}).get("train_loss_last"), + "val_loss_last": data.get("metrics", {}).get("val_loss_last"), + "val_acc_last": data.get("metrics", {}).get("val_acc_last"), + "val_acc_best": data.get("metrics", {}).get("val_acc_best"), + } + except Exception as e: # noqa: BLE001 + _warn_once(f"Parse quantum summary failed: {e}") + return {} + + +def log_quantum_run_safe( + job, result: Dict[str, Any], dataset_name: str, log_path: str +) -> Dict[str, Any]: # noqa: ANN001 + conn = _get_conn() + if not conn: + return {"success": False, "skipped": True} + try: + metrics = _parse_quantum_summary() + cursor = conn.cursor() + # Stored procedure requires many params; pass only the essentials, rely on defaults + sql = ( + "DECLARE @RunId UNIQUEIDENTIFIER; EXEC sp_LogQuantumTrainingRun " + "@JobName=?, @DatasetName=?, @Backend=?, @NumQubits=?, @NumLayers=?, @Entanglement=?, " + "@LearningRate=?, @Epochs=?, @BatchSize=?, @TrainAccuracy=?, @ValAccuracy=?, @TestAccuracy=?, " + "@TrainLoss=?, @ValLoss=?, @TestLoss=?, @TotalShots=?, @ExecutionTimeSeconds=?, @IsAzureHardware=?, " + "@AzureJobId=?, @AzureProvider=?, @EstimatedCostUSD=?, @CircuitDepth=?, @NumParameters=?, " + "@StatusJsonPath=?, @ResultsJsonPath=?, @Status=?, @ErrorMessage=?, @RunId=@RunId OUTPUT; " + "SELECT @RunId AS RunId;" + ) + backend = ( + job.azure_backend + if getattr(job, "mode", "") == "azure_hardware" + else "qiskit_aer" + ) + params = [ + job.name, + dataset_name, + backend, + getattr(job, "n_qubits", None) or 4, + 2, # NumLayers (fixed in current HybridQNN) + "linear", # Entanglement (current circuit pattern) + getattr(job, "learning_rate", None) or 0.001, + getattr(job, "epochs", None) or 1, + getattr(job, "batch_size", None) or 16, + None, # TrainAccuracy (not explicitly tracked) + metrics.get("val_acc_last"), # ValAccuracy + # TestAccuracy (reuse best val acc as proxy) + metrics.get("val_acc_best"), + metrics.get("train_loss_last"), # TrainLoss + metrics.get("val_loss_last"), # ValLoss + None, # TestLoss + getattr(job, "azure_shots", None), # TotalShots for hardware + result.get("duration_sec"), + 1 if getattr(job, "mode", "") == "azure_hardware" else 0, + result.get("meta", {}).get("azure_job_id"), + job.azure_backend if getattr(job, "mode", "") == "azure_hardware" else None, + None, # EstimatedCostUSD (could be added later) + None, # CircuitDepth + None, # NumParameters + str(Path(log_path).parent / "status.json"), + log_path, + result.get("status"), + None if result.get("status") == "succeeded" else "Non-zero return code", + ] + ok = _safe_exec(cursor, sql, params) + run_id = None + if ok: + row = cursor.fetchone() + if row: + run_id = row.RunId + conn.commit() + return {"success": ok, "run_id": run_id} + except Exception as e: # noqa: BLE001 + _warn_once(f"Quantum run log failed: {e}") + return {"success": False, "error": str(e)} + finally: + try: + conn.close() + except Exception: + pass + + +def _load_yaml(path: Path) -> Dict[str, Any]: + try: + import yaml # noqa: WPS433 + + return yaml.safe_load(path.read_text(encoding="utf-8")) or {} + except Exception: + return {} + + +def log_lora_run_safe(job, result: Dict[str, Any]) -> Dict[str, Any]: # noqa: ANN001 + conn = _get_conn() + if not conn: + return {"success": False, "skipped": True} + try: + cursor = conn.cursor() + cfg_path = ( + Path(job.config) + if getattr(job, "config", None) + else REPO_ROOT / "AI" / "microsoft_phi-silica-3.6_v1" / "lora" / "lora.yaml" + ) + cfg = _load_yaml(cfg_path) + # Extract values with fallbacks + lora_rank = cfg.get("lora_rank", 8) # Not present -> placeholder + lora_alpha = cfg.get("lora_alpha", 16) + lora_dropout = job.lora_dropout or cfg.get("lora_dropout", 0.1) + sequence_len = cfg.get("finetune_train_seqlen", 512) + learning_rate = job.learning_rate or cfg.get("learning_rate", 2e-4) + target_modules = ( + cfg.get("lora_target_modules") + or cfg.get("target_modules") + or ["q_proj", "v_proj"] + ) + dataset_path = job.dataset or cfg.get("finetune_dataset", "data") + dataset_name = Path(str(dataset_path)).name + sql = ( + "DECLARE @RunId UNIQUEIDENTIFIER; EXEC sp_LogLoRATrainingRun " + "@JobName=?, @Model=?, @DatasetName=?, @DatasetPath=?, @MaxTrainSamples=?, @MaxEvalSamples=?, " + "@Epochs=?, @BatchSize=?, @SequenceLength=?, @LearningRate=?, @LoraRank=?, @LoraAlpha=?, @LoraDropout=?, " + "@TargetModules=?, @TrainLoss=?, @EvalLoss=?, @TrainPerplexity=?, @EvalPerplexity=?, @TotalSteps=?, @ActualEpochs=?, " + "@ExecutionTimeSeconds=?, @GpuMemoryPeakGB=?, @AdapterSavePath=?, @ConfigYamlPath=?, @LogsPath=?, @IsStreaming=?, @Runner=?, @Status=?, " + "@ErrorMessage=?, @RunId=@RunId OUTPUT; SELECT @RunId AS RunId;" + ) + params = [ + job.name, + job.hf_model_id or "Phi-3.6-mini-instruct", + dataset_name, + str(dataset_path), + job.max_train_samples, + job.max_eval_samples, + job.epochs or cfg.get("epochs", 1), + cfg.get("finetune_train_batch_size", 1), + sequence_len, + learning_rate, + lora_rank, + lora_alpha, + lora_dropout, + json.dumps(target_modules), + # TrainLoss (available inside training script; could parse later) + None, + None, # EvalLoss + None, # TrainPerplexity + None, # EvalPerplexity + None, # TotalSteps + job.epochs or cfg.get("epochs", 1), + result.get("duration_sec"), + None, # GpuMemoryPeakGB + None, # AdapterSavePath + str(cfg_path), + result.get("log"), + 1, # IsStreaming (default behavior) + job.runner, + result.get("status"), + None if result.get("status") == "succeeded" else "Non-zero return code", + ] + ok = _safe_exec(cursor, sql, params) + run_id = None + if ok: + row = cursor.fetchone() + if row: + run_id = row.RunId + conn.commit() + return {"success": ok, "run_id": run_id} + except Exception as e: # noqa: BLE001 + _warn_once(f"LoRA run log failed: {e}") + return {"success": False, "error": str(e)} + finally: + try: + conn.close() + except Exception: + pass + + +__all__ = [ + "log_chat_message_safe", + "log_quantum_run_safe", + "log_lora_run_safe", +] diff --git a/shared/email_notifications.py b/shared/email_notifications.py index 611c6474f..bdd2633ae 100644 --- a/shared/email_notifications.py +++ b/shared/email_notifications.py @@ -1,406 +1,406 @@ -""" -Email notification system for Aria monetization -Handles subscription events, usage alerts, and billing notifications -""" - -import json -import logging -import re -from datetime import datetime -from enum import Enum -from pathlib import Path -from typing import Any, Dict, List, Optional - -logger = logging.getLogger(__name__) - -# Pre-compile regex patterns for performance -_RE_HTML_TAGS = re.compile(r"<[^<]+?>") -_RE_WHITESPACE = re.compile(r"\s+") - - -class EmailTemplate(Enum): - """Email template types""" - - WELCOME = "welcome" - SUBSCRIPTION_ACTIVATED = "subscription_activated" - SUBSCRIPTION_CANCELLED = "subscription_cancelled" - SUBSCRIPTION_EXPIRED = "subscription_expired" - USAGE_WARNING_80 = "usage_warning_80" - USAGE_WARNING_90 = "usage_warning_90" - USAGE_LIMIT_REACHED = "usage_limit_reached" - PAYMENT_SUCCEEDED = "payment_succeeded" - PAYMENT_FAILED = "payment_failed" - INVOICE_GENERATED = "invoice_generated" - TRIAL_ENDING = "trial_ending" - UPGRADE_REMINDER = "upgrade_reminder" - - -class EmailNotificationSystem: - """Handles email notifications for subscription events""" - - def __init__(self, smtp_host: Optional[str] = None, smtp_port: int = 587): - self.smtp_host = smtp_host or "localhost" - self.smtp_port = smtp_port - self.from_email = "noreply@aria-platform.com" - self.template_dir = Path("templates/emails") - self.template_dir.mkdir(parents=True, exist_ok=True) - - # Store sent emails for testing/demo - self.sent_emails: List[Dict[str, Any]] = [] - self.notification_log = Path("data_out/notifications/email_log.json") - self.notification_log.parent.mkdir(parents=True, exist_ok=True) - - def send_email( - self, - to_email: str, - subject: str, - body_html: str, - body_text: Optional[str] = None, - ) -> bool: - """ - Send an email notification - - Args: - to_email: Recipient email address - subject: Email subject line - body_html: HTML email body - body_text: Plain text email body (optional) - - Returns: - True if sent successfully, False otherwise - """ - try: - email_data = { - "to": to_email, - "from": self.from_email, - "subject": subject, - "body_html": body_html, - "body_text": body_text or self._strip_html(body_html), - "timestamp": datetime.now().isoformat(), - "status": "sent", - } - - # In production, use actual SMTP library - # import smtplib - # from email.mime.multipart import MIMEMultipart - # from email.mime.text import MIMEText - # ... send via SMTP - - # For demo/testing, log the email - self.sent_emails.append(email_data) - self._log_notification(email_data) - - logger.info(f"Email sent to {to_email}: {subject}") - return True - - except Exception as e: - logger.error(f"Failed to send email to {to_email}: {str(e)}") - return False - - def send_template_email( - self, to_email: str, template: EmailTemplate, context: Dict[str, Any] - ) -> bool: - """ - Send an email using a template - - Args: - to_email: Recipient email address - template: Email template to use - context: Template context variables - - Returns: - True if sent successfully - """ - template_data = self._get_template(template) - subject = self._render_template(template_data["subject"], context) - body_html = self._render_template(template_data["body_html"], context) - body_text = self._render_template(template_data["body_text"], context) - - return self.send_email(to_email, subject, body_html, body_text) - - def notify_subscription_activated( - self, user_email: str, tier: str, price: float - ) -> bool: - """Notify user that subscription is activated""" - context = { - "tier": tier, - "price": price, - "date": datetime.now().strftime("%B %d, %Y"), - "dashboard_url": "https://aria-platform.com/my-subscription.html", - } - return self.send_template_email( - user_email, EmailTemplate.SUBSCRIPTION_ACTIVATED, context - ) - - def notify_usage_warning( - self, - user_email: str, - resource: str, - percentage: float, - current: int, - limit: int, - ) -> bool: - """Notify user about approaching usage limits""" - template = ( - EmailTemplate.USAGE_WARNING_90 - if percentage >= 90 - else EmailTemplate.USAGE_WARNING_80 - ) - - context = { - "resource": resource, - "percentage": int(percentage), - "current": current, - "limit": limit, - "remaining": limit - current, - "upgrade_url": "https://aria-platform.com/pricing.html", - } - - return self.send_template_email(user_email, template, context) - - def notify_usage_limit_reached( - self, user_email: str, resource: str, limit: int - ) -> bool: - """Notify user that usage limit has been reached""" - context = { - "resource": resource, - "limit": limit, - "upgrade_url": "https://aria-platform.com/pricing.html", - "reset_date": self._get_next_reset_date(), - } - - return self.send_template_email( - user_email, EmailTemplate.USAGE_LIMIT_REACHED, context - ) - - def notify_payment_succeeded( - self, user_email: str, amount: float, invoice_id: str - ) -> bool: - """Notify user of successful payment""" - context = { - "amount": amount, - "invoice_id": invoice_id, - "date": datetime.now().strftime("%B %d, %Y"), - "invoice_url": f"https://aria-platform.com/invoices/{invoice_id}", - } - - return self.send_template_email( - user_email, EmailTemplate.PAYMENT_SUCCEEDED, context - ) - - def notify_payment_failed( - self, user_email: str, amount: float, reason: str - ) -> bool: - """Notify user of failed payment""" - context = { - "amount": amount, - "reason": reason, - "date": datetime.now().strftime("%B %d, %Y"), - "billing_url": "https://aria-platform.com/account.html", - } - - return self.send_template_email( - user_email, EmailTemplate.PAYMENT_FAILED, context - ) - - def _get_template(self, template: EmailTemplate) -> Dict[str, str]: - """Get email template data""" - templates = { - EmailTemplate.SUBSCRIPTION_ACTIVATED: { - "subject": "Welcome to Aria {tier} Plan! 🎉", - "body_html": """ -

    Welcome to Aria {tier}!

    -

    Your subscription has been activated successfully.

    -

    Plan: {tier}
    - Price: ${price}/month
    - Date: {date}

    -

    View Your Subscription Dashboard

    -

    Thank you for choosing Aria!

    - """, - "body_text": """ - Welcome to Aria {tier}! - - Your subscription has been activated successfully. - - Plan: {tier} - Price: ${price}/month - Date: {date} - - View your dashboard: {dashboard_url} - - Thank you for choosing Aria! - """, - }, - EmailTemplate.USAGE_WARNING_80: { - "subject": "⚠️ Usage Alert: {percentage}% of {resource} limit used", - "body_html": """ -

    Usage Alert

    -

    You've used {percentage}% of your {resource} limit.

    -

    Current usage: {current} / {limit}
    - Remaining: {remaining}

    -

    Consider upgrading your plan for higher limits.

    - """, - "body_text": """ - Usage Alert - - You've used {percentage}% of your {resource} limit. - - Current usage: {current} / {limit} - Remaining: {remaining} - - Consider upgrading: {upgrade_url} - """, - }, - EmailTemplate.USAGE_WARNING_90: { - "subject": "🚨 Urgent: {percentage}% of {resource} limit used", - "body_html": """ -

    Urgent Usage Alert

    -

    You've used {percentage}% of your {resource} limit!

    -

    Current usage: {current} / {limit}
    - Remaining: {remaining}

    -

    Action needed: Upgrade now to avoid service interruption.

    - """, - "body_text": """ - Urgent Usage Alert - - You've used {percentage}% of your {resource} limit! - - Current usage: {current} / {limit} - Remaining: {remaining} - - Action needed: Upgrade now to avoid service interruption. - {upgrade_url} - """, - }, - EmailTemplate.USAGE_LIMIT_REACHED: { - "subject": "🛑 {resource} limit reached", - "body_html": """ -

    Usage Limit Reached

    -

    You've reached your {resource} limit of {limit}.

    -

    Your limit will reset on {reset_date}.

    -

    To continue using this feature, upgrade your plan now.

    - """, - "body_text": """ - Usage Limit Reached - - You've reached your {resource} limit of {limit}. - - Your limit will reset on {reset_date}. - - To continue, upgrade your plan: {upgrade_url} - """, - }, - EmailTemplate.PAYMENT_SUCCEEDED: { - "subject": "✅ Payment Received - Invoice #{invoice_id}", - "body_html": """ -

    Payment Successful

    -

    We've received your payment of ${amount}.

    -

    Invoice: #{invoice_id}
    - Date: {date}

    -

    View Invoice

    -

    Thank you for your business!

    - """, - "body_text": """ - Payment Successful - - We've received your payment of ${amount}. - - Invoice: #{invoice_id} - Date: {date} - - View invoice: {invoice_url} - - Thank you for your business! - """, - }, - EmailTemplate.PAYMENT_FAILED: { - "subject": "❌ Payment Failed - Action Required", - "body_html": """ -

    Payment Failed

    -

    We were unable to process your payment of ${amount}.

    -

    Reason: {reason}
    - Date: {date}

    -

    Please update your payment method to avoid service interruption.

    - """, - "body_text": """ - Payment Failed - Action Required - - We were unable to process your payment of ${amount}. - - Reason: {reason} - Date: {date} - - Please update your payment method: {billing_url} - """, - }, - } - - return templates.get( - template, - { - "subject": "Notification from Aria", - "body_html": "

    Notification

    ", - "body_text": "Notification", - }, - ) - - def _render_template(self, template: str, context: Dict[str, Any]) -> str: - """Simple template rendering""" - result = template - for key, value in context.items(): - result = result.replace(f"{{{key}}}", str(value)) - return result - - def _strip_html(self, html: str) -> str: - """Strip HTML tags for plain text version""" - text = _RE_HTML_TAGS.sub("", html) - text = _RE_WHITESPACE.sub(" ", text) - return text.strip() - - def _get_next_reset_date(self) -> str: - """Get next monthly reset date""" - from datetime import timedelta - - next_month = datetime.now() + timedelta(days=30) - return next_month.strftime("%B %d, %Y") - - def _log_notification(self, email_data: Dict[str, Any]) -> None: - """Log notification to file""" - try: - # Load existing log - log_data = [] - if self.notification_log.exists(): - with open(self.notification_log, "r") as f: - log_data = json.load(f) - - # Append new notification - log_data.append(email_data) - - # Keep only last 1000 notifications - if len(log_data) > 1000: - log_data = log_data[-1000:] - - # Save log - with open(self.notification_log, "w") as f: - json.dump(log_data, f, indent=2) - - except Exception as e: - logger.error(f"Failed to log notification: {str(e)}") - - def get_sent_emails(self, user_email: Optional[str] = None) -> List[Dict[str, Any]]: - """Get sent emails (for testing/demo)""" - if user_email: - return [e for e in self.sent_emails if e["to"] == user_email] - return self.sent_emails - - -# Global instance -_email_system: Optional[EmailNotificationSystem] = None - - -def get_email_system() -> EmailNotificationSystem: - """Get global email notification system instance""" - global _email_system - if _email_system is None: - _email_system = EmailNotificationSystem() - return _email_system +""" +Email notification system for Aria monetization +Handles subscription events, usage alerts, and billing notifications +""" + +import json +import logging +import re +from datetime import datetime +from enum import Enum +from pathlib import Path +from typing import Any, Dict, List, Optional + +logger = logging.getLogger(__name__) + +# Pre-compile regex patterns for performance +_RE_HTML_TAGS = re.compile(r"<[^<]+?>") +_RE_WHITESPACE = re.compile(r"\s+") + + +class EmailTemplate(Enum): + """Email template types""" + + WELCOME = "welcome" + SUBSCRIPTION_ACTIVATED = "subscription_activated" + SUBSCRIPTION_CANCELLED = "subscription_cancelled" + SUBSCRIPTION_EXPIRED = "subscription_expired" + USAGE_WARNING_80 = "usage_warning_80" + USAGE_WARNING_90 = "usage_warning_90" + USAGE_LIMIT_REACHED = "usage_limit_reached" + PAYMENT_SUCCEEDED = "payment_succeeded" + PAYMENT_FAILED = "payment_failed" + INVOICE_GENERATED = "invoice_generated" + TRIAL_ENDING = "trial_ending" + UPGRADE_REMINDER = "upgrade_reminder" + + +class EmailNotificationSystem: + """Handles email notifications for subscription events""" + + def __init__(self, smtp_host: Optional[str] = None, smtp_port: int = 587): + self.smtp_host = smtp_host or "localhost" + self.smtp_port = smtp_port + self.from_email = "noreply@aria-platform.com" + self.template_dir = Path("templates/emails") + self.template_dir.mkdir(parents=True, exist_ok=True) + + # Store sent emails for testing/demo + self.sent_emails: List[Dict[str, Any]] = [] + self.notification_log = Path("data_out/notifications/email_log.json") + self.notification_log.parent.mkdir(parents=True, exist_ok=True) + + def send_email( + self, + to_email: str, + subject: str, + body_html: str, + body_text: Optional[str] = None, + ) -> bool: + """ + Send an email notification + + Args: + to_email: Recipient email address + subject: Email subject line + body_html: HTML email body + body_text: Plain text email body (optional) + + Returns: + True if sent successfully, False otherwise + """ + try: + email_data = { + "to": to_email, + "from": self.from_email, + "subject": subject, + "body_html": body_html, + "body_text": body_text or self._strip_html(body_html), + "timestamp": datetime.now().isoformat(), + "status": "sent", + } + + # In production, use actual SMTP library + # import smtplib + # from email.mime.multipart import MIMEMultipart + # from email.mime.text import MIMEText + # ... send via SMTP + + # For demo/testing, log the email + self.sent_emails.append(email_data) + self._log_notification(email_data) + + logger.info(f"Email sent to {to_email}: {subject}") + return True + + except Exception as e: + logger.error(f"Failed to send email to {to_email}: {str(e)}") + return False + + def send_template_email( + self, to_email: str, template: EmailTemplate, context: Dict[str, Any] + ) -> bool: + """ + Send an email using a template + + Args: + to_email: Recipient email address + template: Email template to use + context: Template context variables + + Returns: + True if sent successfully + """ + template_data = self._get_template(template) + subject = self._render_template(template_data["subject"], context) + body_html = self._render_template(template_data["body_html"], context) + body_text = self._render_template(template_data["body_text"], context) + + return self.send_email(to_email, subject, body_html, body_text) + + def notify_subscription_activated( + self, user_email: str, tier: str, price: float + ) -> bool: + """Notify user that subscription is activated""" + context = { + "tier": tier, + "price": price, + "date": datetime.now().strftime("%B %d, %Y"), + "dashboard_url": "https://aria-platform.com/my-subscription.html", + } + return self.send_template_email( + user_email, EmailTemplate.SUBSCRIPTION_ACTIVATED, context + ) + + def notify_usage_warning( + self, + user_email: str, + resource: str, + percentage: float, + current: int, + limit: int, + ) -> bool: + """Notify user about approaching usage limits""" + template = ( + EmailTemplate.USAGE_WARNING_90 + if percentage >= 90 + else EmailTemplate.USAGE_WARNING_80 + ) + + context = { + "resource": resource, + "percentage": int(percentage), + "current": current, + "limit": limit, + "remaining": limit - current, + "upgrade_url": "https://aria-platform.com/pricing.html", + } + + return self.send_template_email(user_email, template, context) + + def notify_usage_limit_reached( + self, user_email: str, resource: str, limit: int + ) -> bool: + """Notify user that usage limit has been reached""" + context = { + "resource": resource, + "limit": limit, + "upgrade_url": "https://aria-platform.com/pricing.html", + "reset_date": self._get_next_reset_date(), + } + + return self.send_template_email( + user_email, EmailTemplate.USAGE_LIMIT_REACHED, context + ) + + def notify_payment_succeeded( + self, user_email: str, amount: float, invoice_id: str + ) -> bool: + """Notify user of successful payment""" + context = { + "amount": amount, + "invoice_id": invoice_id, + "date": datetime.now().strftime("%B %d, %Y"), + "invoice_url": f"https://aria-platform.com/invoices/{invoice_id}", + } + + return self.send_template_email( + user_email, EmailTemplate.PAYMENT_SUCCEEDED, context + ) + + def notify_payment_failed( + self, user_email: str, amount: float, reason: str + ) -> bool: + """Notify user of failed payment""" + context = { + "amount": amount, + "reason": reason, + "date": datetime.now().strftime("%B %d, %Y"), + "billing_url": "https://aria-platform.com/account.html", + } + + return self.send_template_email( + user_email, EmailTemplate.PAYMENT_FAILED, context + ) + + def _get_template(self, template: EmailTemplate) -> Dict[str, str]: + """Get email template data""" + templates = { + EmailTemplate.SUBSCRIPTION_ACTIVATED: { + "subject": "Welcome to Aria {tier} Plan! 🎉", + "body_html": """ +

    Welcome to Aria {tier}!

    +

    Your subscription has been activated successfully.

    +

    Plan: {tier}
    + Price: ${price}/month
    + Date: {date}

    +

    View Your Subscription Dashboard

    +

    Thank you for choosing Aria!

    + """, + "body_text": """ + Welcome to Aria {tier}! + + Your subscription has been activated successfully. + + Plan: {tier} + Price: ${price}/month + Date: {date} + + View your dashboard: {dashboard_url} + + Thank you for choosing Aria! + """, + }, + EmailTemplate.USAGE_WARNING_80: { + "subject": "⚠️ Usage Alert: {percentage}% of {resource} limit used", + "body_html": """ +

    Usage Alert

    +

    You've used {percentage}% of your {resource} limit.

    +

    Current usage: {current} / {limit}
    + Remaining: {remaining}

    +

    Consider upgrading your plan for higher limits.

    + """, + "body_text": """ + Usage Alert + + You've used {percentage}% of your {resource} limit. + + Current usage: {current} / {limit} + Remaining: {remaining} + + Consider upgrading: {upgrade_url} + """, + }, + EmailTemplate.USAGE_WARNING_90: { + "subject": "🚨 Urgent: {percentage}% of {resource} limit used", + "body_html": """ +

    Urgent Usage Alert

    +

    You've used {percentage}% of your {resource} limit!

    +

    Current usage: {current} / {limit}
    + Remaining: {remaining}

    +

    Action needed: Upgrade now to avoid service interruption.

    + """, + "body_text": """ + Urgent Usage Alert + + You've used {percentage}% of your {resource} limit! + + Current usage: {current} / {limit} + Remaining: {remaining} + + Action needed: Upgrade now to avoid service interruption. + {upgrade_url} + """, + }, + EmailTemplate.USAGE_LIMIT_REACHED: { + "subject": "🛑 {resource} limit reached", + "body_html": """ +

    Usage Limit Reached

    +

    You've reached your {resource} limit of {limit}.

    +

    Your limit will reset on {reset_date}.

    +

    To continue using this feature, upgrade your plan now.

    + """, + "body_text": """ + Usage Limit Reached + + You've reached your {resource} limit of {limit}. + + Your limit will reset on {reset_date}. + + To continue, upgrade your plan: {upgrade_url} + """, + }, + EmailTemplate.PAYMENT_SUCCEEDED: { + "subject": "✅ Payment Received - Invoice #{invoice_id}", + "body_html": """ +

    Payment Successful

    +

    We've received your payment of ${amount}.

    +

    Invoice: #{invoice_id}
    + Date: {date}

    +

    View Invoice

    +

    Thank you for your business!

    + """, + "body_text": """ + Payment Successful + + We've received your payment of ${amount}. + + Invoice: #{invoice_id} + Date: {date} + + View invoice: {invoice_url} + + Thank you for your business! + """, + }, + EmailTemplate.PAYMENT_FAILED: { + "subject": "❌ Payment Failed - Action Required", + "body_html": """ +

    Payment Failed

    +

    We were unable to process your payment of ${amount}.

    +

    Reason: {reason}
    + Date: {date}

    +

    Please update your payment method to avoid service interruption.

    + """, + "body_text": """ + Payment Failed - Action Required + + We were unable to process your payment of ${amount}. + + Reason: {reason} + Date: {date} + + Please update your payment method: {billing_url} + """, + }, + } + + return templates.get( + template, + { + "subject": "Notification from Aria", + "body_html": "

    Notification

    ", + "body_text": "Notification", + }, + ) + + def _render_template(self, template: str, context: Dict[str, Any]) -> str: + """Simple template rendering""" + result = template + for key, value in context.items(): + result = result.replace(f"{{{key}}}", str(value)) + return result + + def _strip_html(self, html: str) -> str: + """Strip HTML tags for plain text version""" + text = _RE_HTML_TAGS.sub("", html) + text = _RE_WHITESPACE.sub(" ", text) + return text.strip() + + def _get_next_reset_date(self) -> str: + """Get next monthly reset date""" + from datetime import timedelta + + next_month = datetime.now() + timedelta(days=30) + return next_month.strftime("%B %d, %Y") + + def _log_notification(self, email_data: Dict[str, Any]) -> None: + """Log notification to file""" + try: + # Load existing log + log_data = [] + if self.notification_log.exists(): + with open(self.notification_log, "r") as f: + log_data = json.load(f) + + # Append new notification + log_data.append(email_data) + + # Keep only last 1000 notifications + if len(log_data) > 1000: + log_data = log_data[-1000:] + + # Save log + with open(self.notification_log, "w") as f: + json.dump(log_data, f, indent=2) + + except Exception as e: + logger.error(f"Failed to log notification: {str(e)}") + + def get_sent_emails(self, user_email: Optional[str] = None) -> List[Dict[str, Any]]: + """Get sent emails (for testing/demo)""" + if user_email: + return [e for e in self.sent_emails if e["to"] == user_email] + return self.sent_emails + + +# Global instance +_email_system: Optional[EmailNotificationSystem] = None + + +def get_email_system() -> EmailNotificationSystem: + """Get global email notification system instance""" + global _email_system + if _email_system is None: + _email_system = EmailNotificationSystem() + return _email_system diff --git a/shared/http_utils.py b/shared/http_utils.py index a7585f0e8..e2934d405 100644 --- a/shared/http_utils.py +++ b/shared/http_utils.py @@ -1,236 +1,236 @@ -"""Common validation and serving utilities for HTTP endpoints. - -Provides reusable validation functions and file serving utilities to reduce -duplication across Azure Functions endpoints. -""" - -import logging -from pathlib import Path -from typing import Any, Dict, Optional, Tuple - -_LOGGER = logging.getLogger(__name__) - - -def _is_text_like_block_type(block_type: Any) -> bool: - """Return True for text block types used by OpenAI-compatible APIs.""" - if not isinstance(block_type, str): - return False - normalized = block_type.strip().lower() - return normalized == "text" or normalized.endswith("_text") - - -def _has_non_whitespace_text_content(content: Any) -> bool: - """Return True when content includes valid, non-whitespace text. - - Supports plain string content and OpenAI-style block content lists. - Non-text blocks are ignored for this specific validation check. - """ - if isinstance(content, str): - return bool(content.strip()) - - if isinstance(content, list): - has_text_block = False - for block in content: - if not isinstance(block, dict): - continue - if not _is_text_like_block_type(block.get("type")): - continue - has_text_block = True - block_text = block.get("text") - if isinstance(block_text, str) and block_text.strip(): - return True - # If there were text blocks but none had non-whitespace text, reject. - if has_text_block: - return False - # For block-based content with no text blocks, treat as valid (e.g. image-only). - return True - - if content is None: - return False - - return bool(str(content).strip()) - - -def validate_messages(messages: Any) -> Tuple[bool, Optional[str]]: - """Validate chat messages format. - - Args: - messages: The messages to validate (should be list of dicts) - - Returns: - Tuple of (is_valid, error_message) - - If valid: (True, None) - - If invalid: (False, "error description") - - Expected format: - [{"role": "user|assistant|system", "content": "..."}] - """ - if not messages: - return False, "No messages provided" - - if not isinstance(messages, list): - return False, "Messages must be a list" - - for idx, msg in enumerate(messages): - if not isinstance(msg, dict): - return False, f"Message {idx} must be a dict" - - if "role" not in msg: - return False, f"Message {idx} missing 'role' field" - - if "content" not in msg: - return False, f"Message {idx} missing 'content' field" - - if not _has_non_whitespace_text_content(msg.get("content")): - return False, ( - f"Message {idx} has empty or whitespace-only content. " - "Text content must contain non-whitespace text." - ) - - # Validate role is one of the expected values - valid_roles = {"user", "assistant", "system"} - roles_str = ", ".join(sorted(valid_roles)) - if msg["role"] not in valid_roles: - return ( - False, - f"Message {idx} has invalid role '{msg['role']}'. Expected one of: {roles_str}", - ) - - return True, None - - -def create_cors_headers( - allow_origin: str = "*", - allow_methods: str = "POST, GET, OPTIONS", - allow_headers: str = "Content-Type", -) -> Dict[str, str]: - """Create standard CORS headers for HTTP responses. - - Args: - allow_origin: Allowed origins (default: "*") - allow_methods: Allowed HTTP methods (default: "POST, GET, OPTIONS") - allow_headers: Allowed headers (default: "Content-Type") - - Returns: - Dict of CORS headers - """ - return { - "Access-Control-Allow-Origin": allow_origin, - "Access-Control-Allow-Methods": allow_methods, - "Access-Control-Allow-Headers": allow_headers, - } - - -def create_no_cache_headers() -> Dict[str, str]: - """Create headers that prevent caching. - - Useful for serving dynamic content that should always be fresh. - - Returns: - Dict of cache-control headers - """ - return { - "Cache-Control": "no-store, no-cache, must-revalidate, max-age=0", - "Pragma": "no-cache", - "Expires": "0", - } - - -def validate_provider_choice( - provider_choice: Optional[str], model_override: Optional[str] = None -) -> Tuple[bool, Optional[str], Optional[Dict[str, Any]]]: - """Validate provider choice and model override. - - Args: - provider_choice: The requested provider ('auto', 'openai', 'azure', 'local', 'lora') - model_override: Optional model path/name - - Returns: - Tuple of (is_valid, error_message, hints) - - If valid: (True, None, None) - - If invalid: (False, "error description", {"hint": "...", "key": "value"}) - """ - if not provider_choice: - return True, None, None - - provider_lower = provider_choice.lower() - valid_providers = {"auto", "openai", "azure", "local", "lora", "lmstudio"} - - if provider_lower not in valid_providers: - return ( - False, - f"Invalid provider '{provider_choice}'", - { - "hint": f"Valid providers: {', '.join(sorted(valid_providers))}", - "requested": provider_choice, - }, - ) - - # LoRA provider requires model path - if provider_lower == "lora" and not model_override: - return ( - False, - "LoRA provider requires model path", - { - "hint": "Provide 'model' in request body (e.g., data_out/lora_training/lora_adapter)", - "provider": provider_choice, - }, - ) - - return True, None, None - - -def serve_static_file( - file_path: Path, mimetype: str, use_cache_headers: bool = False -) -> Tuple[Optional[str], int, Dict[str, str]]: - """Serve a static file with appropriate headers. - - Args: - file_path: Path to the file to serve - mimetype: MIME type for the response (e.g., 'text/html', 'application/javascript') - use_cache_headers: Whether to add no-cache headers (default: False for better caching) - - Returns: - Tuple of (content, status_code, headers) - - On success: (file_content, 200, headers_dict) - - On error: (error_message, error_code, {}) - - Example: - content, status, headers = serve_static_file( - Path("chat-web/index.html"), - "text/html", - use_cache_headers=True - ) - return func.HttpResponse(content, status_code=status, mimetype=mimetype, headers=headers) - """ - try: - if not file_path.exists(): - error_msg = f"File not found: {file_path}" - if mimetype.startswith("text/html"): - error_msg = f"

    Error

    {error_msg}

    " - elif mimetype.startswith("application/javascript"): - error_msg = f"// Error: {error_msg}" - else: - error_msg = f"Error: {error_msg}" - - return error_msg, 404, {} - - with open(file_path, "r", encoding="utf-8") as f: - content = f.read() - - headers = {} - if use_cache_headers: - headers.update(create_no_cache_headers()) - - return content, 200, headers - - except Exception as e: - _LOGGER.error(f"Error serving file {file_path}: {e}") - - error_msg = f"Internal error: {str(e)}" - if mimetype.startswith("text/html"): - error_msg = f"

    Error

    {error_msg}

    " - elif mimetype.startswith("application/javascript"): - error_msg = f"// Error: {error_msg}" - - return error_msg, 500, {} +"""Common validation and serving utilities for HTTP endpoints. + +Provides reusable validation functions and file serving utilities to reduce +duplication across Azure Functions endpoints. +""" + +import logging +from pathlib import Path +from typing import Any, Dict, Optional, Tuple + +_LOGGER = logging.getLogger(__name__) + + +def _is_text_like_block_type(block_type: Any) -> bool: + """Return True for text block types used by OpenAI-compatible APIs.""" + if not isinstance(block_type, str): + return False + normalized = block_type.strip().lower() + return normalized == "text" or normalized.endswith("_text") + + +def _has_non_whitespace_text_content(content: Any) -> bool: + """Return True when content includes valid, non-whitespace text. + + Supports plain string content and OpenAI-style block content lists. + Non-text blocks are ignored for this specific validation check. + """ + if isinstance(content, str): + return bool(content.strip()) + + if isinstance(content, list): + has_text_block = False + for block in content: + if not isinstance(block, dict): + continue + if not _is_text_like_block_type(block.get("type")): + continue + has_text_block = True + block_text = block.get("text") + if isinstance(block_text, str) and block_text.strip(): + return True + # If there were text blocks but none had non-whitespace text, reject. + if has_text_block: + return False + # For block-based content with no text blocks, treat as valid (e.g. image-only). + return True + + if content is None: + return False + + return bool(str(content).strip()) + + +def validate_messages(messages: Any) -> Tuple[bool, Optional[str]]: + """Validate chat messages format. + + Args: + messages: The messages to validate (should be list of dicts) + + Returns: + Tuple of (is_valid, error_message) + - If valid: (True, None) + - If invalid: (False, "error description") + + Expected format: + [{"role": "user|assistant|system", "content": "..."}] + """ + if not messages: + return False, "No messages provided" + + if not isinstance(messages, list): + return False, "Messages must be a list" + + for idx, msg in enumerate(messages): + if not isinstance(msg, dict): + return False, f"Message {idx} must be a dict" + + if "role" not in msg: + return False, f"Message {idx} missing 'role' field" + + if "content" not in msg: + return False, f"Message {idx} missing 'content' field" + + if not _has_non_whitespace_text_content(msg.get("content")): + return False, ( + f"Message {idx} has empty or whitespace-only content. " + "Text content must contain non-whitespace text." + ) + + # Validate role is one of the expected values + valid_roles = {"user", "assistant", "system"} + roles_str = ", ".join(sorted(valid_roles)) + if msg["role"] not in valid_roles: + return ( + False, + f"Message {idx} has invalid role '{msg['role']}'. Expected one of: {roles_str}", + ) + + return True, None + + +def create_cors_headers( + allow_origin: str = "*", + allow_methods: str = "POST, GET, OPTIONS", + allow_headers: str = "Content-Type", +) -> Dict[str, str]: + """Create standard CORS headers for HTTP responses. + + Args: + allow_origin: Allowed origins (default: "*") + allow_methods: Allowed HTTP methods (default: "POST, GET, OPTIONS") + allow_headers: Allowed headers (default: "Content-Type") + + Returns: + Dict of CORS headers + """ + return { + "Access-Control-Allow-Origin": allow_origin, + "Access-Control-Allow-Methods": allow_methods, + "Access-Control-Allow-Headers": allow_headers, + } + + +def create_no_cache_headers() -> Dict[str, str]: + """Create headers that prevent caching. + + Useful for serving dynamic content that should always be fresh. + + Returns: + Dict of cache-control headers + """ + return { + "Cache-Control": "no-store, no-cache, must-revalidate, max-age=0", + "Pragma": "no-cache", + "Expires": "0", + } + + +def validate_provider_choice( + provider_choice: Optional[str], model_override: Optional[str] = None +) -> Tuple[bool, Optional[str], Optional[Dict[str, Any]]]: + """Validate provider choice and model override. + + Args: + provider_choice: The requested provider ('auto', 'openai', 'azure', 'local', 'lora') + model_override: Optional model path/name + + Returns: + Tuple of (is_valid, error_message, hints) + - If valid: (True, None, None) + - If invalid: (False, "error description", {"hint": "...", "key": "value"}) + """ + if not provider_choice: + return True, None, None + + provider_lower = provider_choice.lower() + valid_providers = {"auto", "openai", "azure", "local", "lora", "lmstudio"} + + if provider_lower not in valid_providers: + return ( + False, + f"Invalid provider '{provider_choice}'", + { + "hint": f"Valid providers: {', '.join(sorted(valid_providers))}", + "requested": provider_choice, + }, + ) + + # LoRA provider requires model path + if provider_lower == "lora" and not model_override: + return ( + False, + "LoRA provider requires model path", + { + "hint": "Provide 'model' in request body (e.g., data_out/lora_training/lora_adapter)", + "provider": provider_choice, + }, + ) + + return True, None, None + + +def serve_static_file( + file_path: Path, mimetype: str, use_cache_headers: bool = False +) -> Tuple[Optional[str], int, Dict[str, str]]: + """Serve a static file with appropriate headers. + + Args: + file_path: Path to the file to serve + mimetype: MIME type for the response (e.g., 'text/html', 'application/javascript') + use_cache_headers: Whether to add no-cache headers (default: False for better caching) + + Returns: + Tuple of (content, status_code, headers) + - On success: (file_content, 200, headers_dict) + - On error: (error_message, error_code, {}) + + Example: + content, status, headers = serve_static_file( + Path("chat-web/index.html"), + "text/html", + use_cache_headers=True + ) + return func.HttpResponse(content, status_code=status, mimetype=mimetype, headers=headers) + """ + try: + if not file_path.exists(): + error_msg = f"File not found: {file_path}" + if mimetype.startswith("text/html"): + error_msg = f"

    Error

    {error_msg}

    " + elif mimetype.startswith("application/javascript"): + error_msg = f"// Error: {error_msg}" + else: + error_msg = f"Error: {error_msg}" + + return error_msg, 404, {} + + with open(file_path, "r", encoding="utf-8") as f: + content = f.read() + + headers = {} + if use_cache_headers: + headers.update(create_no_cache_headers()) + + return content, 200, headers + + except Exception as e: + _LOGGER.error(f"Error serving file {file_path}: {e}") + + error_msg = f"Internal error: {str(e)}" + if mimetype.startswith("text/html"): + error_msg = f"

    Error

    {error_msg}

    " + elif mimetype.startswith("application/javascript"): + error_msg = f"// Error: {error_msg}" + + return error_msg, 500, {} diff --git a/shared/import_helpers.py b/shared/import_helpers.py index 8befeba16..035e4248a 100644 --- a/shared/import_helpers.py +++ b/shared/import_helpers.py @@ -1,108 +1,108 @@ -"""Utilities for defensive imports with graceful fallbacks. - -This module provides helpers to reduce boilerplate when importing optional -dependencies that may not be available in all runtime environments. -""" - -import logging -from typing import Any, Callable, Dict, Optional, Tuple - -_LOGGER = logging.getLogger(__name__) - - -def safe_import( - module_path: str, - *, - import_names: Optional[Tuple[str, ...]] = None, - fallback_factory: Optional[Callable[[str], Any]] = None, - log_failure: bool = True, -) -> Any: - """Safely import a module or specific names, with optional fallbacks. - - Args: - module_path: Full module path (e.g., 'shared.sql_engine') - import_names: Optional tuple of specific names to import from the module - (e.g., ('sql_health', 'engine_stats')). If None, returns the module. - fallback_factory: Optional callable that receives the import name and returns - a fallback function/object. Called once per import_name on failure. - log_failure: Whether to log import failures (default: True) - - Returns: - - If import_names is None: the imported module, or None on failure - - If import_names is provided: dict mapping names to imported values or fallbacks - - Examples: - # Import entire module - sql_engine = safe_import('shared.sql_engine') - - # Import specific functions with fallbacks - funcs = safe_import( - 'shared.sql_engine', - import_names=('sql_health', 'engine_stats'), - fallback_factory=lambda name: lambda: {"enabled": False, "error": f"{name}_import_failed"} - ) - sql_health = funcs['sql_health'] - engine_stats = funcs['engine_stats'] - """ - try: - # Import the module - parts = module_path.split(".") - module = __import__(module_path, fromlist=parts[-1:] if len(parts) > 1 else []) - - if import_names is None: - # Return the whole module - return module - - # Extract specific names - result: Dict[str, Any] = {} - for name in import_names: - if hasattr(module, name): - result[name] = getattr(module, name) - elif fallback_factory: - result[name] = fallback_factory(name) - else: - result[name] = None - - return result - - except Exception as e: - if log_failure: - _LOGGER.info(f"[safe_import] Failed to import {module_path}: {e}") - - if import_names is None: - # No specific names requested, return None - return None - - # Build fallback dict - result: Dict[str, Any] = {} - for name in import_names: - if fallback_factory: - result[name] = fallback_factory(name) - else: - result[name] = None - - return result - - -def create_stub_function( - name: str, error_key: str = "error" -) -> Callable[..., Dict[str, Any]]: - """Create a stub function that returns a dict indicating unavailability. - - Args: - name: The function name (used in error message) - error_key: Key name for the error field (default: 'error') - - Returns: - A function that accepts any args/kwargs and returns an error dict - - Example: - sql_health = create_stub_function('sql_health') - # sql_health() returns {"enabled": False, "error": "sql_health_unavailable"} - """ - - def stub(*args, **kwargs) -> Dict[str, Any]: - return {"enabled": False, error_key: f"{name}_unavailable"} - - stub.__name__ = name - return stub +"""Utilities for defensive imports with graceful fallbacks. + +This module provides helpers to reduce boilerplate when importing optional +dependencies that may not be available in all runtime environments. +""" + +import logging +from typing import Any, Callable, Dict, Optional, Tuple + +_LOGGER = logging.getLogger(__name__) + + +def safe_import( + module_path: str, + *, + import_names: Optional[Tuple[str, ...]] = None, + fallback_factory: Optional[Callable[[str], Any]] = None, + log_failure: bool = True, +) -> Any: + """Safely import a module or specific names, with optional fallbacks. + + Args: + module_path: Full module path (e.g., 'shared.sql_engine') + import_names: Optional tuple of specific names to import from the module + (e.g., ('sql_health', 'engine_stats')). If None, returns the module. + fallback_factory: Optional callable that receives the import name and returns + a fallback function/object. Called once per import_name on failure. + log_failure: Whether to log import failures (default: True) + + Returns: + - If import_names is None: the imported module, or None on failure + - If import_names is provided: dict mapping names to imported values or fallbacks + + Examples: + # Import entire module + sql_engine = safe_import('shared.sql_engine') + + # Import specific functions with fallbacks + funcs = safe_import( + 'shared.sql_engine', + import_names=('sql_health', 'engine_stats'), + fallback_factory=lambda name: lambda: {"enabled": False, "error": f"{name}_import_failed"} + ) + sql_health = funcs['sql_health'] + engine_stats = funcs['engine_stats'] + """ + try: + # Import the module + parts = module_path.split(".") + module = __import__(module_path, fromlist=parts[-1:] if len(parts) > 1 else []) + + if import_names is None: + # Return the whole module + return module + + # Extract specific names + result: Dict[str, Any] = {} + for name in import_names: + if hasattr(module, name): + result[name] = getattr(module, name) + elif fallback_factory: + result[name] = fallback_factory(name) + else: + result[name] = None + + return result + + except Exception as e: + if log_failure: + _LOGGER.info(f"[safe_import] Failed to import {module_path}: {e}") + + if import_names is None: + # No specific names requested, return None + return None + + # Build fallback dict + result: Dict[str, Any] = {} + for name in import_names: + if fallback_factory: + result[name] = fallback_factory(name) + else: + result[name] = None + + return result + + +def create_stub_function( + name: str, error_key: str = "error" +) -> Callable[..., Dict[str, Any]]: + """Create a stub function that returns a dict indicating unavailability. + + Args: + name: The function name (used in error message) + error_key: Key name for the error field (default: 'error') + + Returns: + A function that accepts any args/kwargs and returns an error dict + + Example: + sql_health = create_stub_function('sql_health') + # sql_health() returns {"enabled": False, "error": "sql_health_unavailable"} + """ + + def stub(*args, **kwargs) -> Dict[str, Any]: + return {"enabled": False, error_key: f"{name}_unavailable"} + + stub.__name__ = name + return stub diff --git a/shared/json_utils.py b/shared/json_utils.py index 635a03447..3d5857404 100644 --- a/shared/json_utils.py +++ b/shared/json_utils.py @@ -1,256 +1,256 @@ -#!/usr/bin/env python -""" -Shared JSON I/O utilities. - -This module provides standardized JSON reading and writing functions -to reduce code duplication and ensure consistent formatting. -""" - -from __future__ import annotations - -import json -import time -from pathlib import Path -from typing import Any, Dict, List, Optional, Union - - -def load_json(path: Union[str, Path], default: Optional[Any] = None) -> Any: - """ - Load JSON data from a file with optional default fallback. - - Args: - path: Path to the JSON file - default: Optional default value if file doesn't exist or is invalid. - If None, exceptions are raised normally. - - Returns: - Parsed JSON data (dict, list, or other JSON type) - - Raises: - FileNotFoundError: If file doesn't exist and no default provided - JSONDecodeError: If file is not valid JSON and no default provided - - Examples: - # Load JSON file - data = load_json("config.json") - - # Load with default fallback - data = load_json("config.json", default={}) - """ - path = Path(path) - - try: - with path.open("r", encoding="utf-8") as f: - return json.load(f) - except (FileNotFoundError, json.JSONDecodeError): - if default is not None: - return default - raise - - -def load_status_json( - path: Union[str, Path], - *, - max_age_seconds: Optional[int] = None, - default: Optional[Dict[str, Any]] = None, -) -> Dict[str, Any]: - """ - Load a status JSON file safely with optional staleness metadata. - - Always returns a dictionary and appends helper metadata keys: - - _status_file_exists: bool - - _status_file_age_seconds: Optional[float] - - _status_file_stale: Optional[bool] - - _status_file_error: Optional[str] - - Args: - path: Path to status JSON file - max_age_seconds: Optional staleness threshold - default: Optional dict merged as fallback base - - Returns: - Dictionary containing parsed JSON object (if valid) plus metadata. - """ - file_path = Path(path) - result: Dict[str, Any] = dict(default or {}) - - exists = file_path.exists() - age_seconds: Optional[float] = None - stale: Optional[bool] = None - error: Optional[str] = None - - if exists: - try: - age_seconds = max(0.0, time.time() - file_path.stat().st_mtime) - if max_age_seconds is not None: - stale = age_seconds > max_age_seconds - - with file_path.open("r", encoding="utf-8") as f: - payload = json.load(f) - - if isinstance(payload, dict): - result.update(payload) - else: - error = "JSON root is not an object" - except Exception as exc: - error = f"{type(exc).__name__}: {exc}" - else: - error = "File not found" - - result["_status_file_exists"] = exists - result["_status_file_age_seconds"] = age_seconds - result["_status_file_stale"] = stale - result["_status_file_error"] = error - return result - - -def save_json( - data: Any, - path: Union[str, Path], - indent: int = 2, - ensure_ascii: bool = False, - create_parents: bool = True, -) -> None: - """ - Save data to a JSON file with consistent formatting. - - Args: - data: Data to serialize (must be JSON-serializable) - path: Path where to save the JSON file - indent: Number of spaces for indentation (default: 2) - ensure_ascii: If True, escape non-ASCII characters (default: False) - create_parents: If True, create parent directories (default: True) - - Examples: - # Save dict to JSON - save_json({"key": "value"}, "output.json") - - # Save without indentation (compact) - save_json(data, "compact.json", indent=None) - """ - path = Path(path) - - if create_parents: - path.parent.mkdir(parents=True, exist_ok=True) - - with path.open("w", encoding="utf-8") as f: - json.dump(data, f, indent=indent, ensure_ascii=ensure_ascii) - - -def load_jsonl( - path: Union[str, Path], max_lines: Optional[int] = None, skip_empty: bool = True -) -> List[Dict[str, Any]]: - """ - Load JSONL file (one JSON object per line). - - Args: - path: Path to the JSONL file - max_lines: Optional limit on number of lines to read - skip_empty: If True, skip empty lines (default: True) - - Returns: - List of dictionaries parsed from the JSONL file - - Raises: - FileNotFoundError: If the file doesn't exist - JSONDecodeError: If a line is not valid JSON - - Examples: - # Load all lines - data = load_jsonl("data.jsonl") - - # Load first 100 lines - data = load_jsonl("data.jsonl", max_lines=100) - """ - path = Path(path) - - if not path.exists(): - raise FileNotFoundError(f"File not found: {path}") - - data: List[Dict[str, Any]] = [] - with path.open("r", encoding="utf-8") as f: - for i, line in enumerate(f): - if max_lines is not None and i >= max_lines: - break - - line = line.strip() - if skip_empty and not line: - continue - - data.append(json.loads(line)) - - return data - - -def save_jsonl( - data: List[Any], - path: Union[str, Path], - create_parents: bool = True, - ensure_ascii: bool = False, -) -> None: - """ - Save data to JSONL file (one JSON object per line). - - Args: - data: List of objects to serialize (each must be JSON-serializable) - path: Path where to save the JSONL file - create_parents: If True, create parent directories (default: True) - ensure_ascii: If True, escape non-ASCII characters (default: False) - - Examples: - # Save list of dicts to JSONL - data = [{"id": 1}, {"id": 2}] - save_jsonl(data, "output.jsonl") - """ - path = Path(path) - - if create_parents: - path.parent.mkdir(parents=True, exist_ok=True) - - with path.open("w", encoding="utf-8") as f: - for obj in data: - f.write(json.dumps(obj, ensure_ascii=ensure_ascii) + "\n") - - -def merge_json_files( - input_paths: List[Union[str, Path]], - output_path: Union[str, Path], - merge_strategy: str = "extend", -) -> None: - """ - Merge multiple JSON files into one. - - Args: - input_paths: List of paths to JSON files to merge - output_path: Path where to save the merged JSON - merge_strategy: How to merge files: - - "extend": Expect lists and concatenate them - - "update": Expect dicts and merge them (later files override) - - Examples: - # Merge multiple JSON array files - merge_json_files(["file1.json", "file2.json"], "merged.json") - - # Merge dict files with update strategy - merge_json_files(["base.json", "override.json"], "final.json", merge_strategy="update") - """ - if merge_strategy == "extend": - merged: List[Any] = [] - for path in input_paths: - data = load_json(path) - if isinstance(data, list): - merged.extend(data) - else: - merged.append(data) - elif merge_strategy == "update": - merged: Dict[str, Any] = {} - for path in input_paths: - data = load_json(path) - if isinstance(data, dict): - merged.update(data) - else: - raise ValueError(f"Expected dict in {path} for update strategy") - else: - raise ValueError(f"Unknown merge strategy: {merge_strategy}") - - save_json(merged, output_path) +#!/usr/bin/env python +""" +Shared JSON I/O utilities. + +This module provides standardized JSON reading and writing functions +to reduce code duplication and ensure consistent formatting. +""" + +from __future__ import annotations + +import json +import time +from pathlib import Path +from typing import Any, Dict, List, Optional, Union + + +def load_json(path: Union[str, Path], default: Optional[Any] = None) -> Any: + """ + Load JSON data from a file with optional default fallback. + + Args: + path: Path to the JSON file + default: Optional default value if file doesn't exist or is invalid. + If None, exceptions are raised normally. + + Returns: + Parsed JSON data (dict, list, or other JSON type) + + Raises: + FileNotFoundError: If file doesn't exist and no default provided + JSONDecodeError: If file is not valid JSON and no default provided + + Examples: + # Load JSON file + data = load_json("config.json") + + # Load with default fallback + data = load_json("config.json", default={}) + """ + path = Path(path) + + try: + with path.open("r", encoding="utf-8") as f: + return json.load(f) + except (FileNotFoundError, json.JSONDecodeError): + if default is not None: + return default + raise + + +def load_status_json( + path: Union[str, Path], + *, + max_age_seconds: Optional[int] = None, + default: Optional[Dict[str, Any]] = None, +) -> Dict[str, Any]: + """ + Load a status JSON file safely with optional staleness metadata. + + Always returns a dictionary and appends helper metadata keys: + - _status_file_exists: bool + - _status_file_age_seconds: Optional[float] + - _status_file_stale: Optional[bool] + - _status_file_error: Optional[str] + + Args: + path: Path to status JSON file + max_age_seconds: Optional staleness threshold + default: Optional dict merged as fallback base + + Returns: + Dictionary containing parsed JSON object (if valid) plus metadata. + """ + file_path = Path(path) + result: Dict[str, Any] = dict(default or {}) + + exists = file_path.exists() + age_seconds: Optional[float] = None + stale: Optional[bool] = None + error: Optional[str] = None + + if exists: + try: + age_seconds = max(0.0, time.time() - file_path.stat().st_mtime) + if max_age_seconds is not None: + stale = age_seconds > max_age_seconds + + with file_path.open("r", encoding="utf-8") as f: + payload = json.load(f) + + if isinstance(payload, dict): + result.update(payload) + else: + error = "JSON root is not an object" + except Exception as exc: + error = f"{type(exc).__name__}: {exc}" + else: + error = "File not found" + + result["_status_file_exists"] = exists + result["_status_file_age_seconds"] = age_seconds + result["_status_file_stale"] = stale + result["_status_file_error"] = error + return result + + +def save_json( + data: Any, + path: Union[str, Path], + indent: int = 2, + ensure_ascii: bool = False, + create_parents: bool = True, +) -> None: + """ + Save data to a JSON file with consistent formatting. + + Args: + data: Data to serialize (must be JSON-serializable) + path: Path where to save the JSON file + indent: Number of spaces for indentation (default: 2) + ensure_ascii: If True, escape non-ASCII characters (default: False) + create_parents: If True, create parent directories (default: True) + + Examples: + # Save dict to JSON + save_json({"key": "value"}, "output.json") + + # Save without indentation (compact) + save_json(data, "compact.json", indent=None) + """ + path = Path(path) + + if create_parents: + path.parent.mkdir(parents=True, exist_ok=True) + + with path.open("w", encoding="utf-8") as f: + json.dump(data, f, indent=indent, ensure_ascii=ensure_ascii) + + +def load_jsonl( + path: Union[str, Path], max_lines: Optional[int] = None, skip_empty: bool = True +) -> List[Dict[str, Any]]: + """ + Load JSONL file (one JSON object per line). + + Args: + path: Path to the JSONL file + max_lines: Optional limit on number of lines to read + skip_empty: If True, skip empty lines (default: True) + + Returns: + List of dictionaries parsed from the JSONL file + + Raises: + FileNotFoundError: If the file doesn't exist + JSONDecodeError: If a line is not valid JSON + + Examples: + # Load all lines + data = load_jsonl("data.jsonl") + + # Load first 100 lines + data = load_jsonl("data.jsonl", max_lines=100) + """ + path = Path(path) + + if not path.exists(): + raise FileNotFoundError(f"File not found: {path}") + + data: List[Dict[str, Any]] = [] + with path.open("r", encoding="utf-8") as f: + for i, line in enumerate(f): + if max_lines is not None and i >= max_lines: + break + + line = line.strip() + if skip_empty and not line: + continue + + data.append(json.loads(line)) + + return data + + +def save_jsonl( + data: List[Any], + path: Union[str, Path], + create_parents: bool = True, + ensure_ascii: bool = False, +) -> None: + """ + Save data to JSONL file (one JSON object per line). + + Args: + data: List of objects to serialize (each must be JSON-serializable) + path: Path where to save the JSONL file + create_parents: If True, create parent directories (default: True) + ensure_ascii: If True, escape non-ASCII characters (default: False) + + Examples: + # Save list of dicts to JSONL + data = [{"id": 1}, {"id": 2}] + save_jsonl(data, "output.jsonl") + """ + path = Path(path) + + if create_parents: + path.parent.mkdir(parents=True, exist_ok=True) + + with path.open("w", encoding="utf-8") as f: + for obj in data: + f.write(json.dumps(obj, ensure_ascii=ensure_ascii) + "\n") + + +def merge_json_files( + input_paths: List[Union[str, Path]], + output_path: Union[str, Path], + merge_strategy: str = "extend", +) -> None: + """ + Merge multiple JSON files into one. + + Args: + input_paths: List of paths to JSON files to merge + output_path: Path where to save the merged JSON + merge_strategy: How to merge files: + - "extend": Expect lists and concatenate them + - "update": Expect dicts and merge them (later files override) + + Examples: + # Merge multiple JSON array files + merge_json_files(["file1.json", "file2.json"], "merged.json") + + # Merge dict files with update strategy + merge_json_files(["base.json", "override.json"], "final.json", merge_strategy="update") + """ + if merge_strategy == "extend": + merged: List[Any] = [] + for path in input_paths: + data = load_json(path) + if isinstance(data, list): + merged.extend(data) + else: + merged.append(data) + elif merge_strategy == "update": + merged: Dict[str, Any] = {} + for path in input_paths: + data = load_json(path) + if isinstance(data, dict): + merged.update(data) + else: + raise ValueError(f"Expected dict in {path} for update strategy") + else: + raise ValueError(f"Unknown merge strategy: {merge_strategy}") + + save_json(merged, output_path) diff --git a/shared/performance_utils.py b/shared/performance_utils.py index 0eae0dc09..a6495de3a 100644 --- a/shared/performance_utils.py +++ b/shared/performance_utils.py @@ -1,490 +1,490 @@ -""" -Performance Utilities - -Common performance-optimized patterns for the Aria codebase. -Provides reusable functions that follow best practices. -""" - -import hashlib -import json -import time -from collections import deque -from functools import wraps -from pathlib import Path -from typing import Any, Callable, Iterator, List, Optional - - -def tail_file(file_path: Path, max_lines: int = 20) -> List[str]: - """ - Memory-efficient tail operation for log files. - - Uses collections.deque to keep only the last N lines in memory, - instead of loading the entire file. - - Args: - file_path: Path to the file to read - max_lines: Number of lines to return from the end - - Returns: - List of the last max_lines from the file - - Example: - >>> logs = tail_file(Path("data_out/training.log"), max_lines=50) - >>> for log in logs: - ... print(log.strip()) - """ - if not file_path.exists(): - return [] - - try: - with open(file_path, "r", encoding="utf-8", errors="ignore") as f: - return list(deque(f, maxlen=max_lines)) - except Exception: - return [] - - -def tail_file_smart( - file_path: Path, max_lines: int = 20, small_file_threshold: int = 65536 -) -> List[str]: - """ - Smart tail operation that adapts to file size. - - For small files (< threshold), reads entire file. - For large files, reads backwards in blocks for efficiency. - - Args: - file_path: Path to the file to read - max_lines: Number of lines to return from the end - small_file_threshold: Size in bytes below which to use simple read - - Returns: - List of the last max_lines from the file - """ - if not file_path.exists(): - return [] - - try: - size = file_path.stat().st_size - - # Small file: simple read - if size <= small_file_threshold: - with file_path.open("r", encoding="utf-8", errors="ignore") as f: - lines = f.readlines() - return lines[-max_lines:] - - # Large file: read backwards in blocks - block_size = 8192 - with file_path.open("rb") as f: - pos = max(0, size - block_size) - f.seek(pos) - buf = f.read(block_size) - - while True: - decoded = buf.decode("utf-8", errors="ignore") - lines = decoded.splitlines() - - if len(lines) >= max_lines or pos == 0: - return lines[-max_lines:] - - # Move further back - new_pos = max(0, pos - block_size) - read_size = pos - new_pos - f.seek(new_pos) - more = f.read(read_size) - buf = more + buf - pos = new_pos - except Exception: - return [] - - -def stream_jsonl( - file_path: Path, filter_fn: Optional[Callable[[dict], bool]] = None -) -> Iterator[dict]: - """ - Memory-efficient streaming of JSONL files. - - Yields one JSON object at a time instead of loading the entire file. - - Args: - file_path: Path to JSONL file - filter_fn: Optional function to filter records (return True to include) - - Yields: - Parsed JSON objects from the file - - Example: - >>> for record in stream_jsonl(Path("dataset.jsonl")): - ... process(record) - - >>> # With filtering - >>> valid_records = stream_jsonl( - ... Path("dataset.jsonl"), - ... filter_fn=lambda r: r.get('valid', False) - ... ) - >>> for record in valid_records: - ... process(record) - """ - if not file_path.exists(): - return - - with open(file_path, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - - try: - obj = json.loads(line) - if filter_fn is None or filter_fn(obj): - yield obj - except json.JSONDecodeError: - continue - - -def batch_process( - items: List[Any], batch_size: int, process_fn: Callable[[List[Any]], None] -) -> None: - """ - Process items in batches to reduce memory pressure. - - Args: - items: List of items to process - batch_size: Number of items per batch - process_fn: Function that takes a batch of items - - Example: - >>> def save_batch(batch): - ... with open('output.json', 'a') as f: - ... for item in batch: - ... f.write(json.dumps(item) + '\\n') - >>> - >>> large_dataset = load_data() - >>> batch_process(large_dataset, batch_size=100, process_fn=save_batch) - """ - for i in range(0, len(items), batch_size): - batch = items[i : i + batch_size] - process_fn(batch) - - -def find_json_in_output( - output: str, - key: Optional[str] = None, - search_from_end: bool = True, - max_lines: int = 50, -) -> Optional[dict]: - """ - Efficiently find JSON object in command output. - - Searches from the end by default since metrics/results are typically - at the bottom of the output. - - Args: - output: String output to search - key: Optional key that must be present in the JSON object - search_from_end: If True, search from the end of output - max_lines: Maximum number of lines to search - - Returns: - First matching JSON object, or None if not found - - Example: - >>> output = subprocess.run(['./script.sh'], capture_output=True, text=True) - >>> metrics = find_json_in_output(output.stdout, key='metrics') - >>> if metrics: - ... print(f"Accuracy: {metrics['metrics']['accuracy']}") - """ - # Split and optionally reverse for end-first search - lines = ( - output.rsplit("\n", max_lines) - if search_from_end - else output.split("\n", max_lines) - ) - line_iter = reversed(lines) if search_from_end else lines - - for line in line_iter: - line = line.strip() - if line.startswith("{") and line.endswith("}"): - try: - obj = json.loads(line) - if key is None or key in obj: - return obj - except json.JSONDecodeError: - continue - - return None - - -class FileCache: - """ - Simple in-memory cache for file contents with size limits. - - Use for files that are read multiple times but don't change often. - - Example: - >>> cache = FileCache(max_size_mb=10) - >>> - >>> # First read - from disk - >>> data1 = cache.read(Path('config.yaml')) - >>> - >>> # Second read - from cache (fast) - >>> data2 = cache.read(Path('config.yaml')) - >>> - >>> # Clear cache if needed - >>> cache.clear() - """ - - def __init__(self, max_size_mb: float = 10.0): - self._cache: dict[Path, bytes] = {} - self._sizes: dict[Path, int] = {} - self.max_size_bytes = int(max_size_mb * 1024 * 1024) - self.current_size = 0 - - def read(self, file_path: Path, encoding: str = "utf-8") -> str: - """Read file from cache or disk.""" - if file_path in self._cache: - return self._cache[file_path].decode(encoding) - - # Read from disk - data = file_path.read_bytes() - size = len(data) - - # Only cache if it fits - if self.current_size + size <= self.max_size_bytes: - self._cache[file_path] = data - self._sizes[file_path] = size - self.current_size += size - - return data.decode(encoding) - - def read_bytes(self, file_path: Path) -> bytes: - """Read file bytes from cache or disk.""" - if file_path in self._cache: - return self._cache[file_path] - - data = file_path.read_bytes() - size = len(data) - - if self.current_size + size <= self.max_size_bytes: - self._cache[file_path] = data - self._sizes[file_path] = size - self.current_size += size - - return data - - def invalidate(self, file_path: Path) -> None: - """Remove a file from cache.""" - if file_path in self._cache: - size = self._sizes[file_path] - del self._cache[file_path] - del self._sizes[file_path] - self.current_size -= size - - def clear(self) -> None: - """Clear entire cache.""" - self._cache.clear() - self._sizes.clear() - self.current_size = 0 - - def stats(self) -> dict: - """Get cache statistics.""" - return { - "entries": len(self._cache), - "current_size_mb": self.current_size / (1024 * 1024), - "max_size_mb": self.max_size_bytes / (1024 * 1024), - "utilization": ( - (self.current_size / self.max_size_bytes) * 100 - if self.max_size_bytes > 0 - else 0 - ), - } - - -def timeit(func: Callable) -> Callable: - """ - Decorator to measure function execution time. - - Prints timing information when the function completes. - Useful for identifying performance bottlenecks. - - Example: - >>> @timeit - ... def expensive_operation(): - ... # Do work - ... pass - >>> - >>> expensive_operation() - # Output: expensive_operation took 1.23s - """ - - @wraps(func) - def wrapper(*args, **kwargs): - t0 = time.time() - result = func(*args, **kwargs) - duration = time.time() - t0 - print(f"{func.__name__} took {duration:.2f}s") - return result - - return wrapper - - -def memoize_with_ttl(ttl_seconds: float = 60.0): - """ - Memoization decorator with time-to-live (TTL) for cache entries. - - Unlike functools.lru_cache, this expires entries after a time period. - Useful for caching API responses or file reads that may change. - - Args: - ttl_seconds: How long to cache results (default: 60 seconds) - - Example: - >>> @memoize_with_ttl(ttl_seconds=300) # 5 minutes - ... def fetch_config(): - ... return load_expensive_config() - >>> - >>> # First call - reads from disk - >>> config1 = fetch_config() - >>> - >>> # Second call within 5 min - returns cached - >>> config2 = fetch_config() - """ - - def decorator(func: Callable) -> Callable: - cache: dict = {} - cache_times: dict = {} - - @wraps(func) - def wrapper(*args, **kwargs): - # Create cache key from args/kwargs - key_data = (args, tuple(sorted(kwargs.items()))) - cache_key = hashlib.md5(str(key_data).encode()).hexdigest() - - # Check if cached and not expired - if cache_key in cache: - age = time.time() - cache_times[cache_key] - if age < ttl_seconds: - return cache[cache_key] - - # Call function and cache result - result = func(*args, **kwargs) - cache[cache_key] = result - cache_times[cache_key] = time.time() - - return result - - # Add cache management methods - wrapper.cache_clear = lambda: (cache.clear(), cache_times.clear()) - wrapper.cache_info = lambda: {"size": len(cache), "ttl": ttl_seconds} - - return wrapper - - return decorator - - -# Example usage and tests -if __name__ == "__main__": - import tempfile - - print("Performance Utilities - Example Usage\n") - - # Test tail_file - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".log") as f: - temp_path = Path(f.name) - for i in range(100): - f.write(f"Log line {i}\n") - - print("1. tail_file() - Last 5 lines:") - lines = tail_file(temp_path, max_lines=5) - for line in lines: - print(f" {line.strip()}") - - # Test stream_jsonl - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".jsonl") as f: - temp_jsonl = Path(f.name) - for i in range(10): - f.write(json.dumps({"id": i, "valid": i % 2 == 0}) + "\n") - - print("\n2. stream_jsonl() - Valid records only:") - for obj in stream_jsonl(temp_jsonl, filter_fn=lambda x: x.get("valid")): - print(f" ID: {obj['id']}") - - # Test find_json_in_output - output = """ - Starting process... - Processing data... - {"metrics": {"accuracy": 0.95, "loss": 0.05}} - Complete. - """ - - print("\n3. find_json_in_output() - Extract metrics:") - metrics = find_json_in_output(output, key="metrics") - if metrics: - print(f" Found: {metrics}") - - # Test FileCache - print("\n4. FileCache() - Caching demo:") - cache = FileCache(max_size_mb=1) - - # First read - t0 = time.time() - content1 = cache.read(temp_path) - t1 = time.time() - t0 - print(f" First read (disk): {t1*1000:.2f}ms") - - # Second read - t0 = time.time() - content2 = cache.read(temp_path) - t2 = time.time() - t0 - print(f" Second read (cache): {t2*1000:.2f}ms") - print(f" Speedup: {t1/t2:.1f}x") - print(f" Cache stats: {cache.stats()}") - - # Test timeit decorator - print("\n5. @timeit decorator - Timing functions:") - - @timeit - def slow_function(): - time.sleep(0.1) - return "done" - - result = slow_function() - - # Test memoize_with_ttl - print("\n6. @memoize_with_ttl - Caching with expiration:") - - call_count = 0 - - @memoize_with_ttl(ttl_seconds=1.0) - def expensive_computation(x): - global call_count - call_count += 1 - time.sleep(0.05) - return x * 2 - - # First call - t0 = time.time() - r1 = expensive_computation(5) - t1 = time.time() - t0 - print(f" First call: {t1*1000:.2f}ms, result={r1}, calls={call_count}") - - # Second call (cached) - t0 = time.time() - r2 = expensive_computation(5) - t2 = time.time() - t0 - print(f" Cached call: {t2*1000:.2f}ms, result={r2}, calls={call_count}") - print(f" Speedup: {t1/t2:.1f}x") - - # Wait for TTL expiration - time.sleep(1.1) - - # Third call (expired, recalculated) - t0 = time.time() - r3 = expensive_computation(5) - t3 = time.time() - t0 - print(f" After TTL: {t3*1000:.2f}ms, result={r3}, calls={call_count}") - - # Cleanup - temp_path.unlink() - temp_jsonl.unlink() - - print("\n✅ All examples completed successfully!") +""" +Performance Utilities + +Common performance-optimized patterns for the Aria codebase. +Provides reusable functions that follow best practices. +""" + +import hashlib +import json +import time +from collections import deque +from functools import wraps +from pathlib import Path +from typing import Any, Callable, Iterator, List, Optional + + +def tail_file(file_path: Path, max_lines: int = 20) -> List[str]: + """ + Memory-efficient tail operation for log files. + + Uses collections.deque to keep only the last N lines in memory, + instead of loading the entire file. + + Args: + file_path: Path to the file to read + max_lines: Number of lines to return from the end + + Returns: + List of the last max_lines from the file + + Example: + >>> logs = tail_file(Path("data_out/training.log"), max_lines=50) + >>> for log in logs: + ... print(log.strip()) + """ + if not file_path.exists(): + return [] + + try: + with open(file_path, "r", encoding="utf-8", errors="ignore") as f: + return list(deque(f, maxlen=max_lines)) + except Exception: + return [] + + +def tail_file_smart( + file_path: Path, max_lines: int = 20, small_file_threshold: int = 65536 +) -> List[str]: + """ + Smart tail operation that adapts to file size. + + For small files (< threshold), reads entire file. + For large files, reads backwards in blocks for efficiency. + + Args: + file_path: Path to the file to read + max_lines: Number of lines to return from the end + small_file_threshold: Size in bytes below which to use simple read + + Returns: + List of the last max_lines from the file + """ + if not file_path.exists(): + return [] + + try: + size = file_path.stat().st_size + + # Small file: simple read + if size <= small_file_threshold: + with file_path.open("r", encoding="utf-8", errors="ignore") as f: + lines = f.readlines() + return lines[-max_lines:] + + # Large file: read backwards in blocks + block_size = 8192 + with file_path.open("rb") as f: + pos = max(0, size - block_size) + f.seek(pos) + buf = f.read(block_size) + + while True: + decoded = buf.decode("utf-8", errors="ignore") + lines = decoded.splitlines() + + if len(lines) >= max_lines or pos == 0: + return lines[-max_lines:] + + # Move further back + new_pos = max(0, pos - block_size) + read_size = pos - new_pos + f.seek(new_pos) + more = f.read(read_size) + buf = more + buf + pos = new_pos + except Exception: + return [] + + +def stream_jsonl( + file_path: Path, filter_fn: Optional[Callable[[dict], bool]] = None +) -> Iterator[dict]: + """ + Memory-efficient streaming of JSONL files. + + Yields one JSON object at a time instead of loading the entire file. + + Args: + file_path: Path to JSONL file + filter_fn: Optional function to filter records (return True to include) + + Yields: + Parsed JSON objects from the file + + Example: + >>> for record in stream_jsonl(Path("dataset.jsonl")): + ... process(record) + + >>> # With filtering + >>> valid_records = stream_jsonl( + ... Path("dataset.jsonl"), + ... filter_fn=lambda r: r.get('valid', False) + ... ) + >>> for record in valid_records: + ... process(record) + """ + if not file_path.exists(): + return + + with open(file_path, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + + try: + obj = json.loads(line) + if filter_fn is None or filter_fn(obj): + yield obj + except json.JSONDecodeError: + continue + + +def batch_process( + items: List[Any], batch_size: int, process_fn: Callable[[List[Any]], None] +) -> None: + """ + Process items in batches to reduce memory pressure. + + Args: + items: List of items to process + batch_size: Number of items per batch + process_fn: Function that takes a batch of items + + Example: + >>> def save_batch(batch): + ... with open('output.json', 'a') as f: + ... for item in batch: + ... f.write(json.dumps(item) + '\\n') + >>> + >>> large_dataset = load_data() + >>> batch_process(large_dataset, batch_size=100, process_fn=save_batch) + """ + for i in range(0, len(items), batch_size): + batch = items[i : i + batch_size] + process_fn(batch) + + +def find_json_in_output( + output: str, + key: Optional[str] = None, + search_from_end: bool = True, + max_lines: int = 50, +) -> Optional[dict]: + """ + Efficiently find JSON object in command output. + + Searches from the end by default since metrics/results are typically + at the bottom of the output. + + Args: + output: String output to search + key: Optional key that must be present in the JSON object + search_from_end: If True, search from the end of output + max_lines: Maximum number of lines to search + + Returns: + First matching JSON object, or None if not found + + Example: + >>> output = subprocess.run(['./script.sh'], capture_output=True, text=True) + >>> metrics = find_json_in_output(output.stdout, key='metrics') + >>> if metrics: + ... print(f"Accuracy: {metrics['metrics']['accuracy']}") + """ + # Split and optionally reverse for end-first search + lines = ( + output.rsplit("\n", max_lines) + if search_from_end + else output.split("\n", max_lines) + ) + line_iter = reversed(lines) if search_from_end else lines + + for line in line_iter: + line = line.strip() + if line.startswith("{") and line.endswith("}"): + try: + obj = json.loads(line) + if key is None or key in obj: + return obj + except json.JSONDecodeError: + continue + + return None + + +class FileCache: + """ + Simple in-memory cache for file contents with size limits. + + Use for files that are read multiple times but don't change often. + + Example: + >>> cache = FileCache(max_size_mb=10) + >>> + >>> # First read - from disk + >>> data1 = cache.read(Path('config.yaml')) + >>> + >>> # Second read - from cache (fast) + >>> data2 = cache.read(Path('config.yaml')) + >>> + >>> # Clear cache if needed + >>> cache.clear() + """ + + def __init__(self, max_size_mb: float = 10.0): + self._cache: dict[Path, bytes] = {} + self._sizes: dict[Path, int] = {} + self.max_size_bytes = int(max_size_mb * 1024 * 1024) + self.current_size = 0 + + def read(self, file_path: Path, encoding: str = "utf-8") -> str: + """Read file from cache or disk.""" + if file_path in self._cache: + return self._cache[file_path].decode(encoding) + + # Read from disk + data = file_path.read_bytes() + size = len(data) + + # Only cache if it fits + if self.current_size + size <= self.max_size_bytes: + self._cache[file_path] = data + self._sizes[file_path] = size + self.current_size += size + + return data.decode(encoding) + + def read_bytes(self, file_path: Path) -> bytes: + """Read file bytes from cache or disk.""" + if file_path in self._cache: + return self._cache[file_path] + + data = file_path.read_bytes() + size = len(data) + + if self.current_size + size <= self.max_size_bytes: + self._cache[file_path] = data + self._sizes[file_path] = size + self.current_size += size + + return data + + def invalidate(self, file_path: Path) -> None: + """Remove a file from cache.""" + if file_path in self._cache: + size = self._sizes[file_path] + del self._cache[file_path] + del self._sizes[file_path] + self.current_size -= size + + def clear(self) -> None: + """Clear entire cache.""" + self._cache.clear() + self._sizes.clear() + self.current_size = 0 + + def stats(self) -> dict: + """Get cache statistics.""" + return { + "entries": len(self._cache), + "current_size_mb": self.current_size / (1024 * 1024), + "max_size_mb": self.max_size_bytes / (1024 * 1024), + "utilization": ( + (self.current_size / self.max_size_bytes) * 100 + if self.max_size_bytes > 0 + else 0 + ), + } + + +def timeit(func: Callable) -> Callable: + """ + Decorator to measure function execution time. + + Prints timing information when the function completes. + Useful for identifying performance bottlenecks. + + Example: + >>> @timeit + ... def expensive_operation(): + ... # Do work + ... pass + >>> + >>> expensive_operation() + # Output: expensive_operation took 1.23s + """ + + @wraps(func) + def wrapper(*args, **kwargs): + t0 = time.time() + result = func(*args, **kwargs) + duration = time.time() - t0 + print(f"{func.__name__} took {duration:.2f}s") + return result + + return wrapper + + +def memoize_with_ttl(ttl_seconds: float = 60.0): + """ + Memoization decorator with time-to-live (TTL) for cache entries. + + Unlike functools.lru_cache, this expires entries after a time period. + Useful for caching API responses or file reads that may change. + + Args: + ttl_seconds: How long to cache results (default: 60 seconds) + + Example: + >>> @memoize_with_ttl(ttl_seconds=300) # 5 minutes + ... def fetch_config(): + ... return load_expensive_config() + >>> + >>> # First call - reads from disk + >>> config1 = fetch_config() + >>> + >>> # Second call within 5 min - returns cached + >>> config2 = fetch_config() + """ + + def decorator(func: Callable) -> Callable: + cache: dict = {} + cache_times: dict = {} + + @wraps(func) + def wrapper(*args, **kwargs): + # Create cache key from args/kwargs + key_data = (args, tuple(sorted(kwargs.items()))) + cache_key = hashlib.md5(str(key_data).encode()).hexdigest() + + # Check if cached and not expired + if cache_key in cache: + age = time.time() - cache_times[cache_key] + if age < ttl_seconds: + return cache[cache_key] + + # Call function and cache result + result = func(*args, **kwargs) + cache[cache_key] = result + cache_times[cache_key] = time.time() + + return result + + # Add cache management methods + wrapper.cache_clear = lambda: (cache.clear(), cache_times.clear()) + wrapper.cache_info = lambda: {"size": len(cache), "ttl": ttl_seconds} + + return wrapper + + return decorator + + +# Example usage and tests +if __name__ == "__main__": + import tempfile + + print("Performance Utilities - Example Usage\n") + + # Test tail_file + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".log") as f: + temp_path = Path(f.name) + for i in range(100): + f.write(f"Log line {i}\n") + + print("1. tail_file() - Last 5 lines:") + lines = tail_file(temp_path, max_lines=5) + for line in lines: + print(f" {line.strip()}") + + # Test stream_jsonl + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".jsonl") as f: + temp_jsonl = Path(f.name) + for i in range(10): + f.write(json.dumps({"id": i, "valid": i % 2 == 0}) + "\n") + + print("\n2. stream_jsonl() - Valid records only:") + for obj in stream_jsonl(temp_jsonl, filter_fn=lambda x: x.get("valid")): + print(f" ID: {obj['id']}") + + # Test find_json_in_output + output = """ + Starting process... + Processing data... + {"metrics": {"accuracy": 0.95, "loss": 0.05}} + Complete. + """ + + print("\n3. find_json_in_output() - Extract metrics:") + metrics = find_json_in_output(output, key="metrics") + if metrics: + print(f" Found: {metrics}") + + # Test FileCache + print("\n4. FileCache() - Caching demo:") + cache = FileCache(max_size_mb=1) + + # First read + t0 = time.time() + content1 = cache.read(temp_path) + t1 = time.time() - t0 + print(f" First read (disk): {t1*1000:.2f}ms") + + # Second read + t0 = time.time() + content2 = cache.read(temp_path) + t2 = time.time() - t0 + print(f" Second read (cache): {t2*1000:.2f}ms") + print(f" Speedup: {t1/t2:.1f}x") + print(f" Cache stats: {cache.stats()}") + + # Test timeit decorator + print("\n5. @timeit decorator - Timing functions:") + + @timeit + def slow_function(): + time.sleep(0.1) + return "done" + + result = slow_function() + + # Test memoize_with_ttl + print("\n6. @memoize_with_ttl - Caching with expiration:") + + call_count = 0 + + @memoize_with_ttl(ttl_seconds=1.0) + def expensive_computation(x): + global call_count + call_count += 1 + time.sleep(0.05) + return x * 2 + + # First call + t0 = time.time() + r1 = expensive_computation(5) + t1 = time.time() - t0 + print(f" First call: {t1*1000:.2f}ms, result={r1}, calls={call_count}") + + # Second call (cached) + t0 = time.time() + r2 = expensive_computation(5) + t2 = time.time() - t0 + print(f" Cached call: {t2*1000:.2f}ms, result={r2}, calls={call_count}") + print(f" Speedup: {t1/t2:.1f}x") + + # Wait for TTL expiration + time.sleep(1.1) + + # Third call (expired, recalculated) + t0 = time.time() + r3 = expensive_computation(5) + t3 = time.time() - t0 + print(f" After TTL: {t3*1000:.2f}ms, result={r3}, calls={call_count}") + + # Cleanup + temp_path.unlink() + temp_jsonl.unlink() + + print("\n✅ All examples completed successfully!") diff --git a/shared/referral_system.py b/shared/referral_system.py index b07ddba11..3cee67172 100644 --- a/shared/referral_system.py +++ b/shared/referral_system.py @@ -1,328 +1,328 @@ -""" -Referral and Affiliate System for Aria monetization -Tracks referrals, calculates commissions, and manages payouts -""" - -import json -import logging -import secrets -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List, Optional - -logger = logging.getLogger(__name__) - - -class ReferralSystem: - """Manages referral and affiliate program""" - - def __init__(self): - self.referrals_file = Path("data_out/referrals/referrals.json") - self.referrals_file.parent.mkdir(parents=True, exist_ok=True) - self.referrals_data = self._load_referrals() - - # Commission rates (percentage of revenue) - self.commission_rates = { - "pro": 0.20, # 20% of $49 = $9.80 per referral - "enterprise": 0.20, # 20% of $199 = $39.80 per referral - } - - # Bonus for reaching milestones - self.milestone_bonuses = { - 5: 50, # $50 bonus at 5 referrals - 10: 100, # $100 bonus at 10 referrals - 25: 300, # $300 bonus at 25 referrals - 50: 750, # $750 bonus at 50 referrals - 100: 2000, # $2,000 bonus at 100 referrals - } - - def generate_referral_code(self, user_id: str) -> str: - """Generate unique referral code for user""" - # Create short, memorable code - code = f"{user_id[:4].upper()}{secrets.token_hex(3).upper()}" - - # Store in user's referral data - if user_id not in self.referrals_data: - self.referrals_data[user_id] = { - "user_id": user_id, - "referral_code": code, - "referrals": [], - "total_commission": 0.0, - "pending_commission": 0.0, - "paid_commission": 0.0, - "created_at": datetime.now().isoformat(), - "referral_count": 0, - "milestone_bonuses_earned": [], - } - else: - self.referrals_data[user_id]["referral_code"] = code - - self._save_referrals() - return code - - def get_referral_code(self, user_id: str) -> Optional[str]: - """Get user's referral code""" - if user_id in self.referrals_data: - return self.referrals_data[user_id].get("referral_code") - return None - - def record_referral( - self, referrer_code: str, new_user_id: str, tier: str, subscription_value: float - ) -> Dict[str, Any]: - """ - Record a new referral - - Args: - referrer_code: Referral code used - new_user_id: ID of new user who signed up - tier: Subscription tier (pro/enterprise) - subscription_value: Monthly subscription value - - Returns: - Dict with referral details and commission - """ - # Find referrer by code - referrer_id = None - for user_id, data in self.referrals_data.items(): - if data.get("referral_code") == referrer_code: - referrer_id = user_id - break - - if not referrer_id: - logger.error(f"Invalid referral code: {referrer_code}") - return {"success": False, "error": "Invalid referral code"} - - # Calculate commission - commission_rate = self.commission_rates.get(tier.lower(), 0.15) - commission = subscription_value * commission_rate - - # Create referral record - referral = { - "referred_user_id": new_user_id, - "tier": tier, - "subscription_value": subscription_value, - "commission": commission, - "status": "active", - "referred_at": datetime.now().isoformat(), - "last_payment": datetime.now().isoformat(), - } - - # Update referrer's data - referrer_data = self.referrals_data[referrer_id] - referrer_data["referrals"].append(referral) - referrer_data["referral_count"] += 1 - referrer_data["pending_commission"] += commission - referrer_data["total_commission"] += commission - - # Check for milestone bonuses - count = referrer_data["referral_count"] - if count in self.milestone_bonuses: - bonus = self.milestone_bonuses[count] - if count not in referrer_data.get("milestone_bonuses_earned", []): - referrer_data["pending_commission"] += bonus - referrer_data["total_commission"] += bonus - referrer_data.setdefault("milestone_bonuses_earned", []).append(count) - logger.info(f"Milestone bonus earned: {count} referrals = ${bonus}") - - self._save_referrals() - - # Send notification - self._notify_referral(referrer_id, new_user_id, commission) - - return { - "success": True, - "referrer_id": referrer_id, - "commission": commission, - "total_commission": referrer_data["total_commission"], - "referral_count": count, - } - - def get_referral_stats(self, user_id: str) -> Dict[str, Any]: - """Get referral statistics for user""" - if user_id not in self.referrals_data: - return { - "referral_code": None, - "referral_count": 0, - "total_commission": 0.0, - "pending_commission": 0.0, - "paid_commission": 0.0, - "referrals": [], - } - - data = self.referrals_data[user_id] - - # Calculate active referrals - active_referrals = [ - r for r in data.get("referrals", []) if r["status"] == "active" - ] - - # Next milestone - next_milestone = None - current_count = data["referral_count"] - for milestone in sorted(self.milestone_bonuses.keys()): - if current_count < milestone: - next_milestone = { - "count": milestone, - "bonus": self.milestone_bonuses[milestone], - "remaining": milestone - current_count, - } - break - - return { - "referral_code": data.get("referral_code"), - "referral_count": data["referral_count"], - "active_referral_count": len(active_referrals), - "total_commission": data["total_commission"], - "pending_commission": data["pending_commission"], - "paid_commission": data["paid_commission"], - "referrals": data.get("referrals", []), - "next_milestone": next_milestone, - "milestone_bonuses_earned": data.get("milestone_bonuses_earned", []), - } - - def process_payout(self, user_id: str) -> Dict[str, Any]: - """ - Process payout for user's pending commissions - - Args: - user_id: User ID to process payout for - - Returns: - Dict with payout details - """ - if user_id not in self.referrals_data: - return {"success": False, "error": "User not found"} - - data = self.referrals_data[user_id] - pending = data["pending_commission"] - - if pending < 25.0: # Minimum payout threshold - return { - "success": False, - "error": f"Minimum payout is $25. Current: ${pending:.2f}", - } - - # Record payout - payout = { - "amount": pending, - "date": datetime.now().isoformat(), - "method": "bank_transfer", # or PayPal, Stripe, etc. - "status": "processed", - } - - data["paid_commission"] += pending - data["pending_commission"] = 0.0 - data.setdefault("payouts", []).append(payout) - - self._save_referrals() - - logger.info(f"Payout processed for {user_id}: ${pending:.2f}") - - return { - "success": True, - "amount": pending, - "method": "bank_transfer", - "processed_at": payout["date"], - } - - def get_leaderboard(self, limit: int = 10) -> List[Dict[str, Any]]: - """Get top referrers leaderboard""" - leaderboard = [] - - for user_id, data in self.referrals_data.items(): - leaderboard.append( - { - "user_id": user_id, - "referral_count": data["referral_count"], - "total_commission": data["total_commission"], - "rank": 0, # Will be set after sorting - } - ) - - # Sort by total commission - leaderboard.sort(key=lambda x: x["total_commission"], reverse=True) - - # Set ranks - for i, entry in enumerate(leaderboard[:limit]): - entry["rank"] = i + 1 - - return leaderboard[:limit] - - def cancel_referral(self, referrer_id: str, referred_user_id: str) -> bool: - """Cancel a referral (e.g., if referred user cancels)""" - if referrer_id not in self.referrals_data: - return False - - data = self.referrals_data[referrer_id] - - for referral in data.get("referrals", []): - if referral["referred_user_id"] == referred_user_id: - if referral["status"] == "active": - referral["status"] = "cancelled" - referral["cancelled_at"] = datetime.now().isoformat() - - # Deduct commission if not yet paid - commission = referral["commission"] - if data["pending_commission"] >= commission: - data["pending_commission"] -= commission - - self._save_referrals() - logger.info(f"Referral cancelled: {referred_user_id}") - return True - - return False - - def _notify_referral(self, referrer_id: str, new_user_id: str, commission: float): - """Send referral notification email""" - try: - from shared.email_notifications import get_email_system - - email_system = get_email_system() - - # In production, get actual email from user_id - referrer_email = f"{referrer_id}@example.com" - - subject = f"🎉 New Referral! You earned ${commission:.2f}" - body_html = f""" -

    New Referral!

    -

    Congratulations! Someone signed up using your referral code.

    -

    Commission Earned: ${commission:.2f}

    -

    Referred User: {new_user_id}

    -

    View Your Referrals

    - """ - - email_system.send_email(referrer_email, subject, body_html) - - except Exception as e: - logger.error(f"Failed to send referral notification: {str(e)}") - - def _load_referrals(self) -> Dict[str, Any]: - """Load referrals from file""" - if self.referrals_file.exists(): - try: - with open(self.referrals_file, "r") as f: - return json.load(f) - except Exception as e: - logger.error(f"Failed to load referrals: {str(e)}") - - return {} - - def _save_referrals(self): - """Save referrals to file""" - try: - with open(self.referrals_file, "w") as f: - json.dump(self.referrals_data, f, indent=2) - except Exception as e: - logger.error(f"Failed to save referrals: {str(e)}") - - -# Global instance -_referral_system: Optional[ReferralSystem] = None - - -def get_referral_system() -> ReferralSystem: - """Get global referral system instance""" - global _referral_system - if _referral_system is None: - _referral_system = ReferralSystem() - return _referral_system +""" +Referral and Affiliate System for Aria monetization +Tracks referrals, calculates commissions, and manages payouts +""" + +import json +import logging +import secrets +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional + +logger = logging.getLogger(__name__) + + +class ReferralSystem: + """Manages referral and affiliate program""" + + def __init__(self): + self.referrals_file = Path("data_out/referrals/referrals.json") + self.referrals_file.parent.mkdir(parents=True, exist_ok=True) + self.referrals_data = self._load_referrals() + + # Commission rates (percentage of revenue) + self.commission_rates = { + "pro": 0.20, # 20% of $49 = $9.80 per referral + "enterprise": 0.20, # 20% of $199 = $39.80 per referral + } + + # Bonus for reaching milestones + self.milestone_bonuses = { + 5: 50, # $50 bonus at 5 referrals + 10: 100, # $100 bonus at 10 referrals + 25: 300, # $300 bonus at 25 referrals + 50: 750, # $750 bonus at 50 referrals + 100: 2000, # $2,000 bonus at 100 referrals + } + + def generate_referral_code(self, user_id: str) -> str: + """Generate unique referral code for user""" + # Create short, memorable code + code = f"{user_id[:4].upper()}{secrets.token_hex(3).upper()}" + + # Store in user's referral data + if user_id not in self.referrals_data: + self.referrals_data[user_id] = { + "user_id": user_id, + "referral_code": code, + "referrals": [], + "total_commission": 0.0, + "pending_commission": 0.0, + "paid_commission": 0.0, + "created_at": datetime.now().isoformat(), + "referral_count": 0, + "milestone_bonuses_earned": [], + } + else: + self.referrals_data[user_id]["referral_code"] = code + + self._save_referrals() + return code + + def get_referral_code(self, user_id: str) -> Optional[str]: + """Get user's referral code""" + if user_id in self.referrals_data: + return self.referrals_data[user_id].get("referral_code") + return None + + def record_referral( + self, referrer_code: str, new_user_id: str, tier: str, subscription_value: float + ) -> Dict[str, Any]: + """ + Record a new referral + + Args: + referrer_code: Referral code used + new_user_id: ID of new user who signed up + tier: Subscription tier (pro/enterprise) + subscription_value: Monthly subscription value + + Returns: + Dict with referral details and commission + """ + # Find referrer by code + referrer_id = None + for user_id, data in self.referrals_data.items(): + if data.get("referral_code") == referrer_code: + referrer_id = user_id + break + + if not referrer_id: + logger.error(f"Invalid referral code: {referrer_code}") + return {"success": False, "error": "Invalid referral code"} + + # Calculate commission + commission_rate = self.commission_rates.get(tier.lower(), 0.15) + commission = subscription_value * commission_rate + + # Create referral record + referral = { + "referred_user_id": new_user_id, + "tier": tier, + "subscription_value": subscription_value, + "commission": commission, + "status": "active", + "referred_at": datetime.now().isoformat(), + "last_payment": datetime.now().isoformat(), + } + + # Update referrer's data + referrer_data = self.referrals_data[referrer_id] + referrer_data["referrals"].append(referral) + referrer_data["referral_count"] += 1 + referrer_data["pending_commission"] += commission + referrer_data["total_commission"] += commission + + # Check for milestone bonuses + count = referrer_data["referral_count"] + if count in self.milestone_bonuses: + bonus = self.milestone_bonuses[count] + if count not in referrer_data.get("milestone_bonuses_earned", []): + referrer_data["pending_commission"] += bonus + referrer_data["total_commission"] += bonus + referrer_data.setdefault("milestone_bonuses_earned", []).append(count) + logger.info(f"Milestone bonus earned: {count} referrals = ${bonus}") + + self._save_referrals() + + # Send notification + self._notify_referral(referrer_id, new_user_id, commission) + + return { + "success": True, + "referrer_id": referrer_id, + "commission": commission, + "total_commission": referrer_data["total_commission"], + "referral_count": count, + } + + def get_referral_stats(self, user_id: str) -> Dict[str, Any]: + """Get referral statistics for user""" + if user_id not in self.referrals_data: + return { + "referral_code": None, + "referral_count": 0, + "total_commission": 0.0, + "pending_commission": 0.0, + "paid_commission": 0.0, + "referrals": [], + } + + data = self.referrals_data[user_id] + + # Calculate active referrals + active_referrals = [ + r for r in data.get("referrals", []) if r["status"] == "active" + ] + + # Next milestone + next_milestone = None + current_count = data["referral_count"] + for milestone in sorted(self.milestone_bonuses.keys()): + if current_count < milestone: + next_milestone = { + "count": milestone, + "bonus": self.milestone_bonuses[milestone], + "remaining": milestone - current_count, + } + break + + return { + "referral_code": data.get("referral_code"), + "referral_count": data["referral_count"], + "active_referral_count": len(active_referrals), + "total_commission": data["total_commission"], + "pending_commission": data["pending_commission"], + "paid_commission": data["paid_commission"], + "referrals": data.get("referrals", []), + "next_milestone": next_milestone, + "milestone_bonuses_earned": data.get("milestone_bonuses_earned", []), + } + + def process_payout(self, user_id: str) -> Dict[str, Any]: + """ + Process payout for user's pending commissions + + Args: + user_id: User ID to process payout for + + Returns: + Dict with payout details + """ + if user_id not in self.referrals_data: + return {"success": False, "error": "User not found"} + + data = self.referrals_data[user_id] + pending = data["pending_commission"] + + if pending < 25.0: # Minimum payout threshold + return { + "success": False, + "error": f"Minimum payout is $25. Current: ${pending:.2f}", + } + + # Record payout + payout = { + "amount": pending, + "date": datetime.now().isoformat(), + "method": "bank_transfer", # or PayPal, Stripe, etc. + "status": "processed", + } + + data["paid_commission"] += pending + data["pending_commission"] = 0.0 + data.setdefault("payouts", []).append(payout) + + self._save_referrals() + + logger.info(f"Payout processed for {user_id}: ${pending:.2f}") + + return { + "success": True, + "amount": pending, + "method": "bank_transfer", + "processed_at": payout["date"], + } + + def get_leaderboard(self, limit: int = 10) -> List[Dict[str, Any]]: + """Get top referrers leaderboard""" + leaderboard = [] + + for user_id, data in self.referrals_data.items(): + leaderboard.append( + { + "user_id": user_id, + "referral_count": data["referral_count"], + "total_commission": data["total_commission"], + "rank": 0, # Will be set after sorting + } + ) + + # Sort by total commission + leaderboard.sort(key=lambda x: x["total_commission"], reverse=True) + + # Set ranks + for i, entry in enumerate(leaderboard[:limit]): + entry["rank"] = i + 1 + + return leaderboard[:limit] + + def cancel_referral(self, referrer_id: str, referred_user_id: str) -> bool: + """Cancel a referral (e.g., if referred user cancels)""" + if referrer_id not in self.referrals_data: + return False + + data = self.referrals_data[referrer_id] + + for referral in data.get("referrals", []): + if referral["referred_user_id"] == referred_user_id: + if referral["status"] == "active": + referral["status"] = "cancelled" + referral["cancelled_at"] = datetime.now().isoformat() + + # Deduct commission if not yet paid + commission = referral["commission"] + if data["pending_commission"] >= commission: + data["pending_commission"] -= commission + + self._save_referrals() + logger.info(f"Referral cancelled: {referred_user_id}") + return True + + return False + + def _notify_referral(self, referrer_id: str, new_user_id: str, commission: float): + """Send referral notification email""" + try: + from shared.email_notifications import get_email_system + + email_system = get_email_system() + + # In production, get actual email from user_id + referrer_email = f"{referrer_id}@example.com" + + subject = f"🎉 New Referral! You earned ${commission:.2f}" + body_html = f""" +

    New Referral!

    +

    Congratulations! Someone signed up using your referral code.

    +

    Commission Earned: ${commission:.2f}

    +

    Referred User: {new_user_id}

    +

    View Your Referrals

    + """ + + email_system.send_email(referrer_email, subject, body_html) + + except Exception as e: + logger.error(f"Failed to send referral notification: {str(e)}") + + def _load_referrals(self) -> Dict[str, Any]: + """Load referrals from file""" + if self.referrals_file.exists(): + try: + with open(self.referrals_file, "r") as f: + return json.load(f) + except Exception as e: + logger.error(f"Failed to load referrals: {str(e)}") + + return {} + + def _save_referrals(self): + """Save referrals to file""" + try: + with open(self.referrals_file, "w") as f: + json.dump(self.referrals_data, f, indent=2) + except Exception as e: + logger.error(f"Failed to save referrals: {str(e)}") + + +# Global instance +_referral_system: Optional[ReferralSystem] = None + + +def get_referral_system() -> ReferralSystem: + """Get global referral system instance""" + global _referral_system + if _referral_system is None: + _referral_system = ReferralSystem() + return _referral_system diff --git a/shared/request_validator.py b/shared/request_validator.py index 4378a8b29..671bbd57e 100644 --- a/shared/request_validator.py +++ b/shared/request_validator.py @@ -1,176 +1,176 @@ -""" -Centralized request validation helpers for function_app.py endpoints. - -Usage: - from shared.request_validator import validate_request, ValidationError - - body, err = validate_request(req, schema={ - "messages": {"type": list, "required": True, "min_length": 1}, - "provider": {"type": str}, - "temperature": {"type": (int, float), "min": 0, "max": 2}, - }) - if err: - return func.HttpResponse(json.dumps({"error": err}), status_code=400, ...) -""" - -from __future__ import annotations - -import json -import logging - -logger = logging.getLogger(__name__) - - -class ValidationError(Exception): - """Raised when request validation fails.""" - - def __init__(self, message: str) -> None: - self.message = message - super().__init__(message) - - -def parse_json_body(req) -> tuple[dict | None, str | None]: - """Safely parse JSON from an Azure Functions HttpRequest. - - Returns (body_dict, error_message). On success error_message is None. - """ - try: - body = req.get_json() - if not isinstance(body, dict): - return None, "Request body must be a JSON object" - return body, None - except ValueError: - # Try raw body fallback - try: - raw = req.get_body().decode("utf-8") - if not raw.strip(): - return None, "Empty request body" - body = json.loads(raw) - if not isinstance(body, dict): - return None, "Request body must be a JSON object" - return body, None - except (json.JSONDecodeError, UnicodeDecodeError) as exc: - return None, f"Invalid JSON: {exc}" - - -def validate_fields(body: dict, schema: dict[str, dict]) -> str | None: - """Validate fields in *body* against *schema*. - - Schema format per field: - "field_name": { - "type": str | list | int | (int, float), # expected type(s) - "required": True, # field must be present and non-None - "min_length": 1, # for str/list — minimum length - "max_length": 1000, # for str/list — maximum length - "min": 0, # for numbers — minimum value - "max": 100, # for numbers — maximum value - "allowed": ["a", "b"], # allowlist of values - } - - Returns an error string or None if valid. - """ - for field, rules in schema.items(): - value = body.get(field) - - # Required check - if rules.get("required") and value is None: - return f"Missing required field: {field}" - - if value is None: - continue # optional and absent — skip remaining checks - - # Type check - expected = rules.get("type") - if expected and not isinstance(value, expected): - type_name = ( - expected.__name__ - if isinstance(expected, type) - else " | ".join(t.__name__ for t in expected) - ) - return f"Field '{field}' must be {type_name}" - - # Length checks (str / list) - if isinstance(value, (str, list)): - min_len = rules.get("min_length") - if min_len is not None and len(value) < min_len: - return f"Field '{field}' must have at least {min_len} item(s)" - - max_len = rules.get("max_length") - if max_len is not None and len(value) > max_len: - return f"Field '{field}' exceeds max length {max_len}" - - # Numeric range - if isinstance(value, (int, float)): - min_val = rules.get("min") - if min_val is not None and value < min_val: - return f"Field '{field}' must be >= {min_val}" - - max_val = rules.get("max") - if max_val is not None and value > max_val: - return f"Field '{field}' must be <= {max_val}" - - # Allowlist - allowed = rules.get("allowed") - if allowed is not None and value not in allowed: - return f"Field '{field}' must be one of {allowed}" - - return None - - -def validate_request(req, schema: dict[str, dict]) -> tuple[dict | None, str | None]: - """Full request validation: parse JSON + field checks. - - Returns (body, error). If error is not None, body may be None. - """ - body, parse_err = parse_json_body(req) - if parse_err: - return None, parse_err - - field_err = validate_fields(body, schema) # type: ignore[arg-type] - if field_err: - return body, field_err - - return body, None - - -# --- Pre-built schemas for common endpoints --- - -CHAT_SCHEMA = { - "messages": {"type": list, "required": True, "min_length": 1, "max_length": 500}, - "provider": { - "type": str, - "allowed": [ - "auto", - "azure", - "azure_openai", - "openai", - "lmstudio", - "ollama", - "lora", - "agi", - "quantum", - "local", - ], - }, - "temperature": {"type": (int, float), "min": 0, "max": 2}, - "max_output_tokens": {"type": int, "min": 1, "max": 128000}, - "max_context_tokens": {"type": int, "min": 1, "max": 128000}, - "system_prompt": {"type": str, "max_length": 10000}, -} - -TTS_SCHEMA = { - "text": {"type": str, "required": True, "min_length": 1, "max_length": 5000}, - "voice": {"type": str}, - "rate": {"type": str}, -} - -QUANTUM_JOB_SCHEMA = { - "circuit_type": {"type": str, "required": True}, - "backend": {"type": str}, - "shots": {"type": int, "min": 1, "max": 100000}, -} - -SUBSCRIPTION_SCHEMA = { - "user_id": {"type": str, "required": True, "min_length": 1}, - "tier": {"type": str, "allowed": ["FREE", "PRO", "ENTERPRISE"]}, -} +""" +Centralized request validation helpers for function_app.py endpoints. + +Usage: + from shared.request_validator import validate_request, ValidationError + + body, err = validate_request(req, schema={ + "messages": {"type": list, "required": True, "min_length": 1}, + "provider": {"type": str}, + "temperature": {"type": (int, float), "min": 0, "max": 2}, + }) + if err: + return func.HttpResponse(json.dumps({"error": err}), status_code=400, ...) +""" + +from __future__ import annotations + +import json +import logging + +logger = logging.getLogger(__name__) + + +class ValidationError(Exception): + """Raised when request validation fails.""" + + def __init__(self, message: str) -> None: + self.message = message + super().__init__(message) + + +def parse_json_body(req) -> tuple[dict | None, str | None]: + """Safely parse JSON from an Azure Functions HttpRequest. + + Returns (body_dict, error_message). On success error_message is None. + """ + try: + body = req.get_json() + if not isinstance(body, dict): + return None, "Request body must be a JSON object" + return body, None + except ValueError: + # Try raw body fallback + try: + raw = req.get_body().decode("utf-8") + if not raw.strip(): + return None, "Empty request body" + body = json.loads(raw) + if not isinstance(body, dict): + return None, "Request body must be a JSON object" + return body, None + except (json.JSONDecodeError, UnicodeDecodeError) as exc: + return None, f"Invalid JSON: {exc}" + + +def validate_fields(body: dict, schema: dict[str, dict]) -> str | None: + """Validate fields in *body* against *schema*. + + Schema format per field: + "field_name": { + "type": str | list | int | (int, float), # expected type(s) + "required": True, # field must be present and non-None + "min_length": 1, # for str/list — minimum length + "max_length": 1000, # for str/list — maximum length + "min": 0, # for numbers — minimum value + "max": 100, # for numbers — maximum value + "allowed": ["a", "b"], # allowlist of values + } + + Returns an error string or None if valid. + """ + for field, rules in schema.items(): + value = body.get(field) + + # Required check + if rules.get("required") and value is None: + return f"Missing required field: {field}" + + if value is None: + continue # optional and absent — skip remaining checks + + # Type check + expected = rules.get("type") + if expected and not isinstance(value, expected): + type_name = ( + expected.__name__ + if isinstance(expected, type) + else " | ".join(t.__name__ for t in expected) + ) + return f"Field '{field}' must be {type_name}" + + # Length checks (str / list) + if isinstance(value, (str, list)): + min_len = rules.get("min_length") + if min_len is not None and len(value) < min_len: + return f"Field '{field}' must have at least {min_len} item(s)" + + max_len = rules.get("max_length") + if max_len is not None and len(value) > max_len: + return f"Field '{field}' exceeds max length {max_len}" + + # Numeric range + if isinstance(value, (int, float)): + min_val = rules.get("min") + if min_val is not None and value < min_val: + return f"Field '{field}' must be >= {min_val}" + + max_val = rules.get("max") + if max_val is not None and value > max_val: + return f"Field '{field}' must be <= {max_val}" + + # Allowlist + allowed = rules.get("allowed") + if allowed is not None and value not in allowed: + return f"Field '{field}' must be one of {allowed}" + + return None + + +def validate_request(req, schema: dict[str, dict]) -> tuple[dict | None, str | None]: + """Full request validation: parse JSON + field checks. + + Returns (body, error). If error is not None, body may be None. + """ + body, parse_err = parse_json_body(req) + if parse_err: + return None, parse_err + + field_err = validate_fields(body, schema) # type: ignore[arg-type] + if field_err: + return body, field_err + + return body, None + + +# --- Pre-built schemas for common endpoints --- + +CHAT_SCHEMA = { + "messages": {"type": list, "required": True, "min_length": 1, "max_length": 500}, + "provider": { + "type": str, + "allowed": [ + "auto", + "azure", + "azure_openai", + "openai", + "lmstudio", + "ollama", + "lora", + "agi", + "quantum", + "local", + ], + }, + "temperature": {"type": (int, float), "min": 0, "max": 2}, + "max_output_tokens": {"type": int, "min": 1, "max": 128000}, + "max_context_tokens": {"type": int, "min": 1, "max": 128000}, + "system_prompt": {"type": str, "max_length": 10000}, +} + +TTS_SCHEMA = { + "text": {"type": str, "required": True, "min_length": 1, "max_length": 5000}, + "voice": {"type": str}, + "rate": {"type": str}, +} + +QUANTUM_JOB_SCHEMA = { + "circuit_type": {"type": str, "required": True}, + "backend": {"type": str}, + "shots": {"type": int, "min": 1, "max": 100000}, +} + +SUBSCRIPTION_SCHEMA = { + "user_id": {"type": str, "required": True, "min_length": 1}, + "tier": {"type": str, "allowed": ["FREE", "PRO", "ENTERPRISE"]}, +} diff --git a/shared/script_utils.py b/shared/script_utils.py index 315f37fa0..d2ccbccda 100644 --- a/shared/script_utils.py +++ b/shared/script_utils.py @@ -1,110 +1,110 @@ -#!/usr/bin/env python -""" -Shared utilities for script setup and common patterns. - -This module provides common functionality used across multiple scripts -to reduce code duplication: -- Repository path detection and setup -- Argument parsing helpers -- Common script patterns -""" - -from __future__ import annotations - -import sys -from pathlib import Path -from typing import Optional - - -def get_repo_root(script_file: Optional[str] = None) -> Path: - """ - Get the repository root directory. - - Args: - script_file: Optional __file__ value from the calling script. - If not provided, uses this module's location. - - Returns: - Path to the repository root directory - - Examples: - # From a script in scripts/ directory - repo_root = get_repo_root(__file__) - - # Auto-detect from this module - repo_root = get_repo_root() - """ - if script_file: - # Script is in scripts/ subdirectory, parent is repo root - script_path = Path(script_file).resolve() - return script_path.parent.parent - else: - # This file is in shared/ subdirectory, parent is repo root - return Path(__file__).resolve().parent.parent - - -def setup_path(script_file: Optional[str] = None, *additional_paths: str) -> Path: - """ - Setup sys.path to include repository root and optional additional paths. - - This ensures imports from shared/ and other top-level modules work correctly. - - Args: - script_file: Optional __file__ value from the calling script - *additional_paths: Additional relative paths to add (from repo root) - - Returns: - Path to the repository root directory - - Examples: - # Add repo root to path - repo_root = setup_path(__file__) - - # Add repo root and specific subdirectory - repo_root = setup_path(__file__, "ai-projects/quantum-ml/src") - - # Then import from shared - from shared.evaluation_utils import load_jsonl - """ - repo_root = get_repo_root(script_file) - - # Add repo root if not already in path - repo_root_str = str(repo_root) - if repo_root_str not in sys.path: - sys.path.insert(0, repo_root_str) - - # Add additional paths - for rel_path in additional_paths: - full_path = str(repo_root / rel_path) - if full_path not in sys.path: - sys.path.insert(0, full_path) - - return repo_root - - -def get_data_out_dir(script_file: str, subdir: Optional[str] = None) -> Path: - """ - Get the standard data_out directory for a script's outputs. - - Args: - script_file: __file__ value from the calling script - subdir: Optional subdirectory name (defaults to script name without .py) - - Returns: - Path to the data_out directory for this script - - Examples: - # Creates data_out/my_script/ - output_dir = get_data_out_dir(__file__) - - # Creates data_out/custom_name/ - output_dir = get_data_out_dir(__file__, "custom_name") - """ - repo_root = get_repo_root(script_file) - script_name = Path(script_file).stem - - if subdir is None: - subdir = script_name - - output_dir = repo_root / "data_out" / subdir - return output_dir +#!/usr/bin/env python +""" +Shared utilities for script setup and common patterns. + +This module provides common functionality used across multiple scripts +to reduce code duplication: +- Repository path detection and setup +- Argument parsing helpers +- Common script patterns +""" + +from __future__ import annotations + +import sys +from pathlib import Path +from typing import Optional + + +def get_repo_root(script_file: Optional[str] = None) -> Path: + """ + Get the repository root directory. + + Args: + script_file: Optional __file__ value from the calling script. + If not provided, uses this module's location. + + Returns: + Path to the repository root directory + + Examples: + # From a script in scripts/ directory + repo_root = get_repo_root(__file__) + + # Auto-detect from this module + repo_root = get_repo_root() + """ + if script_file: + # Script is in scripts/ subdirectory, parent is repo root + script_path = Path(script_file).resolve() + return script_path.parent.parent + else: + # This file is in shared/ subdirectory, parent is repo root + return Path(__file__).resolve().parent.parent + + +def setup_path(script_file: Optional[str] = None, *additional_paths: str) -> Path: + """ + Setup sys.path to include repository root and optional additional paths. + + This ensures imports from shared/ and other top-level modules work correctly. + + Args: + script_file: Optional __file__ value from the calling script + *additional_paths: Additional relative paths to add (from repo root) + + Returns: + Path to the repository root directory + + Examples: + # Add repo root to path + repo_root = setup_path(__file__) + + # Add repo root and specific subdirectory + repo_root = setup_path(__file__, "ai-projects/quantum-ml/src") + + # Then import from shared + from shared.evaluation_utils import load_jsonl + """ + repo_root = get_repo_root(script_file) + + # Add repo root if not already in path + repo_root_str = str(repo_root) + if repo_root_str not in sys.path: + sys.path.insert(0, repo_root_str) + + # Add additional paths + for rel_path in additional_paths: + full_path = str(repo_root / rel_path) + if full_path not in sys.path: + sys.path.insert(0, full_path) + + return repo_root + + +def get_data_out_dir(script_file: str, subdir: Optional[str] = None) -> Path: + """ + Get the standard data_out directory for a script's outputs. + + Args: + script_file: __file__ value from the calling script + subdir: Optional subdirectory name (defaults to script name without .py) + + Returns: + Path to the data_out directory for this script + + Examples: + # Creates data_out/my_script/ + output_dir = get_data_out_dir(__file__) + + # Creates data_out/custom_name/ + output_dir = get_data_out_dir(__file__, "custom_name") + """ + repo_root = get_repo_root(script_file) + script_name = Path(script_file).stem + + if subdir is None: + subdir = script_name + + output_dir = repo_root / "data_out" / subdir + return output_dir diff --git a/shared/sql_engine.py b/shared/sql_engine.py index f766660fc..733675299 100644 --- a/shared/sql_engine.py +++ b/shared/sql_engine.py @@ -1,388 +1,388 @@ -"""Unified SQL engine helper for QAI. - -Purpose: - - Provide a lazy, pooled SQLAlchemy Engine built from either QAI_SQL_URL - or (fallback) QAI_DB_CONN ODBC connection string. - - Support multiple RDBMS vendors (SQL Server, PostgreSQL, MySQL, SQLite). - - Offer a simple health probe used by /api/ai/status. - - Provide lightweight pool statistics for diagnostics. - - Slow query logging with configurable threshold. - - Gracefully degrade when no URL or driver is available. - -Environment variables: - QAI_SQL_URL -> Preferred full SQLAlchemy URL (e.g. postgresql+psycopg://user:pass@host/db) - QAI_DB_CONN -> Legacy ODBC string for SQL Server (used with pyodbc fallback) - QAI_SQL_SLOW_MS -> (optional) slow query threshold in milliseconds (default: 500) - -Usage: - from shared.sql_engine import sql_health, engine_stats, quick_query - -Design notes: - - Engine cached globally; pool_pre_ping=True to evict dead connections. - - For SQL Server ODBC fallback we URL-encode the entire connection string. - - Deliberately lightweight (no ORM Session dependency) but future-ready. - - Stats function is defensive; returns None values if pool does not expose metrics. -""" - -from __future__ import annotations - -import hashlib -import logging -import os -import time -import urllib.parse -from collections import deque -from typing import Any, Dict, Optional - -# Configuration constants -SQL_POOL_RECYCLE_SECONDS = 1800 # Refresh idle connections every 30 minutes -SLOW_QUERY_THRESHOLD_MS = 500 # Default slow query threshold - -# Attempt to import SQLAlchemy; provide graceful fallback if unavailable -_SQLALCHEMY_AVAILABLE = True -try: - from sqlalchemy import create_engine, text -except Exception: # pragma: no cover - _SQLALCHEMY_AVAILABLE = False - create_engine = None # type: ignore - text = None # type: ignore - -_ENGINE = None # cached engine instance -_LAST_URL = None - -# Slow query frequency tracking (in-memory, last 60 seconds) -# Use collections.deque for O(1) append and efficient pruning from left -_recent_slow_queries: deque[tuple[float, float]] = deque() # (timestamp, duration_ms) -_SLOW_QUERY_CACHE_MAX_SIZE = 1000 # Maximum entries to prevent unbounded growth - - -def _prune_recent_slow_queries() -> None: - """Remove slow query entries older than 60 seconds. - - Uses efficient deque operations - pops from left since entries are - chronologically ordered. - """ - current_time = time.time() - cutoff_time = current_time - 60 - # Pop old entries from the left (oldest first) - while _recent_slow_queries and _recent_slow_queries[0][0] < cutoff_time: - _recent_slow_queries.popleft() - - # Also enforce max size to prevent memory growth - while len(_recent_slow_queries) > _SLOW_QUERY_CACHE_MAX_SIZE: - _recent_slow_queries.popleft() - - -def _compute_query_hash(sql: str) -> str: - """Compute SHA256 hash of normalized SQL for tracking.""" - # Use faster string operations - avoid multiple replace calls - normalized_sql = " ".join(sql.split()) - return hashlib.sha256(normalized_sql.encode("utf-8")).hexdigest()[:16] - - -def _track_query_metrics(sql: str, duration_ms: float, vendor: str) -> None: - """Persist query metrics to QAI_QueryMetrics table if tracking enabled.""" - if os.getenv("QAI_ENABLE_QUERY_TRACKING", "false").lower() != "true": - return - - engine = get_engine() - if not engine: - return - - try: - query_hash = _compute_query_hash(sql) - sql_snippet = sql[:500] - - insert_sql = text( - "INSERT INTO QAI_QueryMetrics (query_hash, sql_snippet, vendor, execution_time_ms, executed_at) " - "VALUES (:hash, :snippet, :vendor, :duration, :ts)" - ) - - with engine.begin() as conn: - conn.execute( - insert_sql, - { - "hash": query_hash, - "snippet": sql_snippet, - "vendor": vendor, - "duration": duration_ms, - "ts": time.time(), - }, - ) - except Exception as e: # noqa: BLE001 - # Silent degradation - don't fail queries due to tracking issues - logging.debug(f"[sql_engine] Query tracking failed: {e}") - - -# ---------------------------------------------------------------------------- -# URL Resolution -# ---------------------------------------------------------------------------- - - -def _build_url_from_odbc(conn_str: str) -> str: - """Convert raw ODBC connection string into SQLAlchemy pyodbc URL. - - Example input: - Driver={ODBC Driver 18 for SQL Server};Server=tcp:host.database.windows.net,1433;Database=db;Uid=user;Pwd=pw;Encrypt=yes;TrustServerCertificate=no; - - Returns: - mssql+pyodbc:///?odbc_connect= - """ - return f"mssql+pyodbc:///?odbc_connect={urllib.parse.quote_plus(conn_str)}" - - -def resolve_sql_url() -> Optional[str]: - url = os.getenv("QAI_SQL_URL") - if url: - return url.strip() or None - odbc = os.getenv("QAI_DB_CONN") - if odbc: - return _build_url_from_odbc(odbc) - return None - - -def resolve_slow_query_threshold() -> float: - """Determine slow query threshold in milliseconds with environment awareness. - - Priority: QAI_SQL_SLOW_MS env var > environment profile > default 500ms - Profiles: dev=100ms, staging=300ms, production=500ms - """ - explicit = os.getenv("QAI_SQL_SLOW_MS") - if explicit: - try: - return float(explicit) - except ValueError: - pass - - env_profile = os.getenv("AZURE_FUNCTIONS_ENVIRONMENT", "development").lower() - if "dev" in env_profile or "local" in env_profile: - return 100.0 - elif "stag" in env_profile or "test" in env_profile: - return 300.0 - else: # production or unknown - return 500.0 - - -# ---------------------------------------------------------------------------- -# Engine Accessors -# ---------------------------------------------------------------------------- - - -def get_engine(): # noqa: ANN001 - global _ENGINE, _LAST_URL - url = resolve_sql_url() - if not url: - return None - if not _SQLALCHEMY_AVAILABLE: - # Fallback: no SQLAlchemy installed - # Return None to allow sqlite3 fallback in calling code - # Only SQLite URLs are supported in fallback mode - if str(url).startswith("sqlite"): - _ENGINE = None - _LAST_URL = url - return None - # Unsupported vendor without SQLAlchemy - return None - if _ENGINE is None or _LAST_URL != url: - try: - _ENGINE = create_engine( - url, - pool_pre_ping=True, - pool_recycle=SQL_POOL_RECYCLE_SECONDS, # refresh idle conns every 30m - future=True, - ) - _LAST_URL = url - except Exception as e: # noqa: BLE001 - logging.warning(f"[sql_engine] Engine creation failed: {e}") - _ENGINE = None - return None - return _ENGINE - - -# ---------------------------------------------------------------------------- -# Health Probe -# ---------------------------------------------------------------------------- - - -def sql_health() -> dict: - engine = get_engine() - if not engine: - return {"enabled": False, "url": None} - info = { - "enabled": True, - "url": str(getattr(engine, "url", "")), - "vendor": getattr(engine.dialect, "name", "unknown"), - "connectivity": False, - "error": None, - } - try: - if _SQLALCHEMY_AVAILABLE: - with engine.connect() as conn: - val = conn.execute(text("SELECT 1")).scalar() - info["connectivity"] = bool(val == 1) - else: - # Fallback: direct sqlite3 connectivity check - import sqlite3 - - # Use in-memory DB for health probe - with sqlite3.connect(":memory:") as conn: - cur = conn.execute("SELECT 1") - row = cur.fetchone() - info["connectivity"] = bool(row and row[0] == 1) - except Exception as e: # noqa: BLE001 - info["error"] = str(e) - return info - - -# ---------------------------------------------------------------------------- -# Pool Statistics (best effort) -# ---------------------------------------------------------------------------- - - -def _safe_call(obj: Any, name: str) -> Any: - try: - attr = getattr(obj, name, None) - if callable(attr): - return attr() - return attr - except Exception: - return None - - -def engine_stats() -> Dict[str, Any]: - engine = get_engine() - if not engine: - return {"enabled": False} - pool = getattr(engine, "pool", None) - stats: Dict[str, Any] = { - "enabled": True, - "type": pool.__class__.__name__ if pool else None, - "vendor": getattr(engine.dialect, "name", "unknown"), - "size": None, - "checkedout": None, - "overflow": None, - "recycle": None, - "timeout": None, - "status": None, - "saturation_alert": None, - "saturation_pct": None, - "slow_queries_1min": 0, - "slow_query_threshold_ms": resolve_slow_query_threshold(), - } - if not _SQLALCHEMY_AVAILABLE: - # Minimal stats for fallback - stats.update( - { - "type": "DirectSQLite", - "vendor": "sqlite", - "status": "ok", - } - ) - _prune_recent_slow_queries() - stats["slow_queries_1min"] = len(_recent_slow_queries) - return stats - if pool: - for attr in ["size", "checkedout", "overflow"]: - stats[attr] = _safe_call(pool, attr) - # Internal attributes (QueuePool only) - stats["recycle"] = getattr(pool, "_recycle", None) - stats["timeout"] = getattr(pool, "_timeout", None) - stats["status"] = _safe_call(pool, "status") - - # Saturation detection - pool_size = stats["size"] - checked_out_connections = stats["checkedout"] - if pool_size and checked_out_connections is not None: - saturation_percentage = (checked_out_connections / pool_size) * 100 - stats["saturation_pct"] = round(saturation_percentage, 1) - if saturation_percentage > 80: - stats["saturation_alert"] = ( - f"Pool {saturation_percentage:.1f}% saturated ({checked_out_connections}/{pool_size})" - ) - logging.warning( - f"[sql_engine] {stats['saturation_alert']} vendor={stats['vendor']}" - ) - - # Slow query frequency - _prune_recent_slow_queries() - stats["slow_queries_1min"] = len(_recent_slow_queries) - - return stats - - -# ---------------------------------------------------------------------------- -# Convenience Exec (read-only quick queries) with slow query logging -# ---------------------------------------------------------------------------- - - -def quick_query(sql: str, **kwargs) -> list[dict]: # noqa: ANN001 - """Execute a read-only query and return list of row dicts. - - Optional kwargs: - simulate_delay (float) -> seconds to sleep before executing (test hook) - """ - engine = get_engine() - if not engine: - return [] - simulated_delay_seconds = float(kwargs.get("simulate_delay", 0) or 0) - if simulated_delay_seconds > 0: - time.sleep(simulated_delay_seconds) - start_time = time.perf_counter() - result_rows: list[dict] = [] - if _SQLALCHEMY_AVAILABLE: - try: - with engine.connect() as connection: - query_result = connection.execute(text(sql)) - column_names = query_result.keys() - result_rows = [ - dict(zip(column_names, row)) for row in query_result.fetchall() - ] - except Exception as execution_error: # noqa: BLE001 - logging.warning(f"[sql_engine] quick_query failed: {execution_error}") - return [] - else: - # Fallback: execute via sqlite3 - import sqlite3 - - try: - with sqlite3.connect(":memory:") as connection: - cursor = connection.execute(sql) - # SQLite cursor.description provides columns - column_names = [ - description[0] for description in (cursor.description or []) - ] - data = cursor.fetchall() - if column_names: - result_rows = [dict(zip(column_names, row)) for row in data] - else: - result_rows = [] - except Exception as execution_error: # noqa: BLE001 - logging.warning( - f"[sql_engine] quick_query fallback failed: {execution_error}" - ) - return [] - execution_duration_ms = (time.perf_counter() - start_time) * 1000.0 - slow_query_threshold_ms = resolve_slow_query_threshold() - database_vendor = getattr(engine.dialect, "name", "unknown") - - # Track all queries if enabled (not just slow ones) - _track_query_metrics(sql, execution_duration_ms, database_vendor) - - if execution_duration_ms > slow_query_threshold_ms: - truncated_sql = sql[:120].replace("\n", " ") - logging.warning( - f"[sql_engine] slow query ({execution_duration_ms:.1f} ms > {slow_query_threshold_ms} ms) vendor={database_vendor} sql={truncated_sql}" - ) - # Track slow query frequency - _recent_slow_queries.append((time.time(), execution_duration_ms)) - return result_rows - - -__all__ = [ - "resolve_sql_url", - "get_engine", - "sql_health", - "quick_query", - "engine_stats", - "resolve_slow_query_threshold", -] +"""Unified SQL engine helper for QAI. + +Purpose: + - Provide a lazy, pooled SQLAlchemy Engine built from either QAI_SQL_URL + or (fallback) QAI_DB_CONN ODBC connection string. + - Support multiple RDBMS vendors (SQL Server, PostgreSQL, MySQL, SQLite). + - Offer a simple health probe used by /api/ai/status. + - Provide lightweight pool statistics for diagnostics. + - Slow query logging with configurable threshold. + - Gracefully degrade when no URL or driver is available. + +Environment variables: + QAI_SQL_URL -> Preferred full SQLAlchemy URL (e.g. postgresql+psycopg://user:pass@host/db) + QAI_DB_CONN -> Legacy ODBC string for SQL Server (used with pyodbc fallback) + QAI_SQL_SLOW_MS -> (optional) slow query threshold in milliseconds (default: 500) + +Usage: + from shared.sql_engine import sql_health, engine_stats, quick_query + +Design notes: + - Engine cached globally; pool_pre_ping=True to evict dead connections. + - For SQL Server ODBC fallback we URL-encode the entire connection string. + - Deliberately lightweight (no ORM Session dependency) but future-ready. + - Stats function is defensive; returns None values if pool does not expose metrics. +""" + +from __future__ import annotations + +import hashlib +import logging +import os +import time +import urllib.parse +from collections import deque +from typing import Any, Dict, Optional + +# Configuration constants +SQL_POOL_RECYCLE_SECONDS = 1800 # Refresh idle connections every 30 minutes +SLOW_QUERY_THRESHOLD_MS = 500 # Default slow query threshold + +# Attempt to import SQLAlchemy; provide graceful fallback if unavailable +_SQLALCHEMY_AVAILABLE = True +try: + from sqlalchemy import create_engine, text +except Exception: # pragma: no cover + _SQLALCHEMY_AVAILABLE = False + create_engine = None # type: ignore + text = None # type: ignore + +_ENGINE = None # cached engine instance +_LAST_URL = None + +# Slow query frequency tracking (in-memory, last 60 seconds) +# Use collections.deque for O(1) append and efficient pruning from left +_recent_slow_queries: deque[tuple[float, float]] = deque() # (timestamp, duration_ms) +_SLOW_QUERY_CACHE_MAX_SIZE = 1000 # Maximum entries to prevent unbounded growth + + +def _prune_recent_slow_queries() -> None: + """Remove slow query entries older than 60 seconds. + + Uses efficient deque operations - pops from left since entries are + chronologically ordered. + """ + current_time = time.time() + cutoff_time = current_time - 60 + # Pop old entries from the left (oldest first) + while _recent_slow_queries and _recent_slow_queries[0][0] < cutoff_time: + _recent_slow_queries.popleft() + + # Also enforce max size to prevent memory growth + while len(_recent_slow_queries) > _SLOW_QUERY_CACHE_MAX_SIZE: + _recent_slow_queries.popleft() + + +def _compute_query_hash(sql: str) -> str: + """Compute SHA256 hash of normalized SQL for tracking.""" + # Use faster string operations - avoid multiple replace calls + normalized_sql = " ".join(sql.split()) + return hashlib.sha256(normalized_sql.encode("utf-8")).hexdigest()[:16] + + +def _track_query_metrics(sql: str, duration_ms: float, vendor: str) -> None: + """Persist query metrics to QAI_QueryMetrics table if tracking enabled.""" + if os.getenv("QAI_ENABLE_QUERY_TRACKING", "false").lower() != "true": + return + + engine = get_engine() + if not engine: + return + + try: + query_hash = _compute_query_hash(sql) + sql_snippet = sql[:500] + + insert_sql = text( + "INSERT INTO QAI_QueryMetrics (query_hash, sql_snippet, vendor, execution_time_ms, executed_at) " + "VALUES (:hash, :snippet, :vendor, :duration, :ts)" + ) + + with engine.begin() as conn: + conn.execute( + insert_sql, + { + "hash": query_hash, + "snippet": sql_snippet, + "vendor": vendor, + "duration": duration_ms, + "ts": time.time(), + }, + ) + except Exception as e: # noqa: BLE001 + # Silent degradation - don't fail queries due to tracking issues + logging.debug(f"[sql_engine] Query tracking failed: {e}") + + +# ---------------------------------------------------------------------------- +# URL Resolution +# ---------------------------------------------------------------------------- + + +def _build_url_from_odbc(conn_str: str) -> str: + """Convert raw ODBC connection string into SQLAlchemy pyodbc URL. + + Example input: + Driver={ODBC Driver 18 for SQL Server};Server=tcp:host.database.windows.net,1433;Database=db;Uid=user;Pwd=pw;Encrypt=yes;TrustServerCertificate=no; + + Returns: + mssql+pyodbc:///?odbc_connect= + """ + return f"mssql+pyodbc:///?odbc_connect={urllib.parse.quote_plus(conn_str)}" + + +def resolve_sql_url() -> Optional[str]: + url = os.getenv("QAI_SQL_URL") + if url: + return url.strip() or None + odbc = os.getenv("QAI_DB_CONN") + if odbc: + return _build_url_from_odbc(odbc) + return None + + +def resolve_slow_query_threshold() -> float: + """Determine slow query threshold in milliseconds with environment awareness. + + Priority: QAI_SQL_SLOW_MS env var > environment profile > default 500ms + Profiles: dev=100ms, staging=300ms, production=500ms + """ + explicit = os.getenv("QAI_SQL_SLOW_MS") + if explicit: + try: + return float(explicit) + except ValueError: + pass + + env_profile = os.getenv("AZURE_FUNCTIONS_ENVIRONMENT", "development").lower() + if "dev" in env_profile or "local" in env_profile: + return 100.0 + elif "stag" in env_profile or "test" in env_profile: + return 300.0 + else: # production or unknown + return 500.0 + + +# ---------------------------------------------------------------------------- +# Engine Accessors +# ---------------------------------------------------------------------------- + + +def get_engine(): # noqa: ANN001 + global _ENGINE, _LAST_URL + url = resolve_sql_url() + if not url: + return None + if not _SQLALCHEMY_AVAILABLE: + # Fallback: no SQLAlchemy installed + # Return None to allow sqlite3 fallback in calling code + # Only SQLite URLs are supported in fallback mode + if str(url).startswith("sqlite"): + _ENGINE = None + _LAST_URL = url + return None + # Unsupported vendor without SQLAlchemy + return None + if _ENGINE is None or _LAST_URL != url: + try: + _ENGINE = create_engine( + url, + pool_pre_ping=True, + pool_recycle=SQL_POOL_RECYCLE_SECONDS, # refresh idle conns every 30m + future=True, + ) + _LAST_URL = url + except Exception as e: # noqa: BLE001 + logging.warning(f"[sql_engine] Engine creation failed: {e}") + _ENGINE = None + return None + return _ENGINE + + +# ---------------------------------------------------------------------------- +# Health Probe +# ---------------------------------------------------------------------------- + + +def sql_health() -> dict: + engine = get_engine() + if not engine: + return {"enabled": False, "url": None} + info = { + "enabled": True, + "url": str(getattr(engine, "url", "")), + "vendor": getattr(engine.dialect, "name", "unknown"), + "connectivity": False, + "error": None, + } + try: + if _SQLALCHEMY_AVAILABLE: + with engine.connect() as conn: + val = conn.execute(text("SELECT 1")).scalar() + info["connectivity"] = bool(val == 1) + else: + # Fallback: direct sqlite3 connectivity check + import sqlite3 + + # Use in-memory DB for health probe + with sqlite3.connect(":memory:") as conn: + cur = conn.execute("SELECT 1") + row = cur.fetchone() + info["connectivity"] = bool(row and row[0] == 1) + except Exception as e: # noqa: BLE001 + info["error"] = str(e) + return info + + +# ---------------------------------------------------------------------------- +# Pool Statistics (best effort) +# ---------------------------------------------------------------------------- + + +def _safe_call(obj: Any, name: str) -> Any: + try: + attr = getattr(obj, name, None) + if callable(attr): + return attr() + return attr + except Exception: + return None + + +def engine_stats() -> Dict[str, Any]: + engine = get_engine() + if not engine: + return {"enabled": False} + pool = getattr(engine, "pool", None) + stats: Dict[str, Any] = { + "enabled": True, + "type": pool.__class__.__name__ if pool else None, + "vendor": getattr(engine.dialect, "name", "unknown"), + "size": None, + "checkedout": None, + "overflow": None, + "recycle": None, + "timeout": None, + "status": None, + "saturation_alert": None, + "saturation_pct": None, + "slow_queries_1min": 0, + "slow_query_threshold_ms": resolve_slow_query_threshold(), + } + if not _SQLALCHEMY_AVAILABLE: + # Minimal stats for fallback + stats.update( + { + "type": "DirectSQLite", + "vendor": "sqlite", + "status": "ok", + } + ) + _prune_recent_slow_queries() + stats["slow_queries_1min"] = len(_recent_slow_queries) + return stats + if pool: + for attr in ["size", "checkedout", "overflow"]: + stats[attr] = _safe_call(pool, attr) + # Internal attributes (QueuePool only) + stats["recycle"] = getattr(pool, "_recycle", None) + stats["timeout"] = getattr(pool, "_timeout", None) + stats["status"] = _safe_call(pool, "status") + + # Saturation detection + pool_size = stats["size"] + checked_out_connections = stats["checkedout"] + if pool_size and checked_out_connections is not None: + saturation_percentage = (checked_out_connections / pool_size) * 100 + stats["saturation_pct"] = round(saturation_percentage, 1) + if saturation_percentage > 80: + stats["saturation_alert"] = ( + f"Pool {saturation_percentage:.1f}% saturated ({checked_out_connections}/{pool_size})" + ) + logging.warning( + f"[sql_engine] {stats['saturation_alert']} vendor={stats['vendor']}" + ) + + # Slow query frequency + _prune_recent_slow_queries() + stats["slow_queries_1min"] = len(_recent_slow_queries) + + return stats + + +# ---------------------------------------------------------------------------- +# Convenience Exec (read-only quick queries) with slow query logging +# ---------------------------------------------------------------------------- + + +def quick_query(sql: str, **kwargs) -> list[dict]: # noqa: ANN001 + """Execute a read-only query and return list of row dicts. + + Optional kwargs: + simulate_delay (float) -> seconds to sleep before executing (test hook) + """ + engine = get_engine() + if not engine: + return [] + simulated_delay_seconds = float(kwargs.get("simulate_delay", 0) or 0) + if simulated_delay_seconds > 0: + time.sleep(simulated_delay_seconds) + start_time = time.perf_counter() + result_rows: list[dict] = [] + if _SQLALCHEMY_AVAILABLE: + try: + with engine.connect() as connection: + query_result = connection.execute(text(sql)) + column_names = query_result.keys() + result_rows = [ + dict(zip(column_names, row)) for row in query_result.fetchall() + ] + except Exception as execution_error: # noqa: BLE001 + logging.warning(f"[sql_engine] quick_query failed: {execution_error}") + return [] + else: + # Fallback: execute via sqlite3 + import sqlite3 + + try: + with sqlite3.connect(":memory:") as connection: + cursor = connection.execute(sql) + # SQLite cursor.description provides columns + column_names = [ + description[0] for description in (cursor.description or []) + ] + data = cursor.fetchall() + if column_names: + result_rows = [dict(zip(column_names, row)) for row in data] + else: + result_rows = [] + except Exception as execution_error: # noqa: BLE001 + logging.warning( + f"[sql_engine] quick_query fallback failed: {execution_error}" + ) + return [] + execution_duration_ms = (time.perf_counter() - start_time) * 1000.0 + slow_query_threshold_ms = resolve_slow_query_threshold() + database_vendor = getattr(engine.dialect, "name", "unknown") + + # Track all queries if enabled (not just slow ones) + _track_query_metrics(sql, execution_duration_ms, database_vendor) + + if execution_duration_ms > slow_query_threshold_ms: + truncated_sql = sql[:120].replace("\n", " ") + logging.warning( + f"[sql_engine] slow query ({execution_duration_ms:.1f} ms > {slow_query_threshold_ms} ms) vendor={database_vendor} sql={truncated_sql}" + ) + # Track slow query frequency + _recent_slow_queries.append((time.time(), execution_duration_ms)) + return result_rows + + +__all__ = [ + "resolve_sql_url", + "get_engine", + "sql_health", + "quick_query", + "engine_stats", + "resolve_slow_query_threshold", +] diff --git a/shared/sql_repository.py b/shared/sql_repository.py index 3e8761968..a412c60e8 100644 --- a/shared/sql_repository.py +++ b/shared/sql_repository.py @@ -1,309 +1,309 @@ -"""Generic lightweight SQL repository utilities. - -Provides a key-value store abstraction for multi-database support using -SQLAlchemy core when available, and a sqlite3-based fallback when SQLAlchemy -is not installed. Table auto-creation is vendor-aware and idempotent. - -Table name: QAI_KeyValue -Columns: - key_name (primary key), value_data (text/blob), updated_at (timestamp) - -Graceful degradation: If an engine or driver is unavailable, operations return -fallback values instead of raising. -""" - -from __future__ import annotations - -import logging -import os -import sqlite3 -from datetime import datetime, timezone -from typing import Optional - -# Conditional SQLAlchemy import -_SQLALCHEMY_AVAILABLE = True -try: - from sqlalchemy import text # type: ignore -except Exception: # pragma: no cover - _SQLALCHEMY_AVAILABLE = False - text = None # type: ignore - -from .sql_engine import get_engine, resolve_sql_url - -_TABLE_CREATED = False -_SQLITE_CONN: Optional[sqlite3.Connection] = None - -# ---------------------------------------------------------------------------- -# Helpers (fallback) -# ---------------------------------------------------------------------------- - - -def _sqlite_path_from_url(url: str) -> str: - """Resolve sqlite database path from SQLAlchemy-style URL. - Supports file paths (sqlite:///path/to.db) and in-memory (:memory:). - """ - if url.endswith(":memory:"): - return ":memory:" - if url.startswith("sqlite:///"): - path = url[len("sqlite:///") :] - return os.path.normpath(path) - # Default to in-memory if unrecognized format - return ":memory:" - - -def _get_sqlite_conn() -> sqlite3.Connection: - global _SQLITE_CONN - if _SQLITE_CONN is not None: - return _SQLITE_CONN - url = resolve_sql_url() or "sqlite:///:memory:" - db_path = _sqlite_path_from_url(url) - # Ensure directory exists for file-based DBs - if db_path != ":memory:": - try: - os.makedirs(os.path.dirname(db_path) or ".", exist_ok=True) - except Exception: # pragma: no cover - pass - _SQLITE_CONN = sqlite3.connect(db_path, check_same_thread=False) - return _SQLITE_CONN - - -# ---------------------------------------------------------------------------- -# Table Creation (idempotent) -# ---------------------------------------------------------------------------- - - -def _ensure_table(): - global _TABLE_CREATED - if _TABLE_CREATED: - return True - - engine = get_engine() - if not engine and not _SQLALCHEMY_AVAILABLE: - # Fallback path: sqlite3 direct - try: - conn = _get_sqlite_conn() - conn.execute( - "CREATE TABLE IF NOT EXISTS QAI_KeyValue (" - "key_name TEXT PRIMARY KEY, value_data TEXT, updated_at TEXT)" - ) - conn.commit() - _TABLE_CREATED = True - return True - except Exception as e: # noqa: BLE001 - logging.warning( - f"[sql_repository] sqlite fallback table create failed: {e}" - ) - return False - - if not engine: - return False - - vendor = getattr(engine.dialect, "name", "unknown") - try: - if vendor == "sqlite": - ddl = "CREATE TABLE IF NOT EXISTS QAI_KeyValue (key_name TEXT PRIMARY KEY, value_data TEXT, updated_at TEXT)" - elif vendor in {"postgresql", "postgres"}: - ddl = ( - "CREATE TABLE IF NOT EXISTS QAI_KeyValue (" - "key_name VARCHAR(200) PRIMARY KEY, " - "value_data TEXT, " - "updated_at TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP)" - ) - elif vendor in {"mysql"}: - ddl = ( - "CREATE TABLE IF NOT EXISTS QAI_KeyValue (" - "key_name VARCHAR(200) PRIMARY KEY, " - "value_data TEXT, " - "updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP)" - ) - else: # mssql & fallback - # SQL Server: need IF NOT EXISTS pattern - ddl = ( - "IF NOT EXISTS (SELECT 1 FROM sys.tables WHERE name='QAI_KeyValue') " - "BEGIN CREATE TABLE dbo.QAI_KeyValue (" - "key_name NVARCHAR(200) NOT NULL PRIMARY KEY, " - "value_data NVARCHAR(MAX) NULL, " - "updated_at DATETIME2 DEFAULT SYSUTCDATETIME()) END" - ) - with engine.begin() as conn: - conn.execute(text(ddl)) - _TABLE_CREATED = True - return True - except Exception as e: # noqa: BLE001 - logging.warning(f"[sql_repository] table create failed: {e}") - return False - - -# ---------------------------------------------------------------------------- -# CRUD Operations -# ---------------------------------------------------------------------------- - - -def put_value(key: str, value: str) -> bool: - if not _ensure_table(): - return False - - engine = get_engine() - if not engine and not _SQLALCHEMY_AVAILABLE: - # Fallback: sqlite3 direct - try: - conn = _get_sqlite_conn() - conn.execute( - "INSERT OR REPLACE INTO QAI_KeyValue (key_name, value_data, updated_at) VALUES (?, ?, ?)", - (key, value, datetime.now(timezone.utc).isoformat()), - ) - conn.commit() - return True - except Exception as e: # noqa: BLE001 - logging.warning(f"[sql_repository] sqlite fallback put_value failed: {e}") - return False - - if not engine: - return False - - vendor = getattr(engine.dialect, "name", "unknown") - try: - with engine.begin() as conn: - if vendor == "sqlite": - conn.execute( - text( - "REPLACE INTO QAI_KeyValue (key_name,value_data,updated_at) VALUES (:key_name,:value_data,:ts)" - ), - { - "key_name": key, - "value_data": value, - "ts": datetime.now(timezone.utc).isoformat(), - }, - ) - elif vendor in {"postgresql", "postgres"}: - conn.execute( - text( - "INSERT INTO QAI_KeyValue (key_name,value_data) VALUES (:key_name,:value_data) ON CONFLICT (key_name) DO UPDATE SET value_data=EXCLUDED.value_data, updated_at=CURRENT_TIMESTAMP" - ), - {"key_name": key, "value_data": value}, - ) - elif vendor == "mysql": - conn.execute( - text( - "INSERT INTO QAI_KeyValue (key_name,value_data) VALUES (:key_name,:value_data) ON DUPLICATE KEY UPDATE value_data=VALUES(value_data)" - ), - {"key_name": key, "value_data": value}, - ) - else: # SQL Server - conn.execute( - text( - "MERGE dbo.QAI_KeyValue AS tgt USING (SELECT :key_name AS key_name, :value_data AS value_data) AS src ON tgt.key_name=src.key_name WHEN MATCHED THEN UPDATE SET value_data=src.value_data, updated_at=SYSUTCDATETIME() WHEN NOT MATCHED THEN INSERT (key_name,value_data,updated_at) VALUES (src.key_name, src.value_data, SYSUTCDATETIME());" - ), - {"key_name": key, "value_data": value}, - ) - return True - except Exception as e: # noqa: BLE001 - logging.warning(f"[sql_repository] put_value failed: {e}") - return False - - -def get_value(key: str) -> Optional[str]: - if not _ensure_table(): - return None - - engine = get_engine() - if not engine and not _SQLALCHEMY_AVAILABLE: - try: - conn = _get_sqlite_conn() - cur = conn.execute( - "SELECT value_data FROM QAI_KeyValue WHERE key_name=?", (key,) - ) - row = cur.fetchone() - return None if not row else row[0] - except Exception as e: # noqa: BLE001 - logging.warning(f"[sql_repository] sqlite fallback get_value failed: {e}") - return None - - if not engine: - return None - - try: - with engine.connect() as conn: - res = conn.execute( - text("SELECT value_data FROM QAI_KeyValue WHERE key_name=:key_name"), - {"key_name": key}, - ).fetchone() - return None if not res else res[0] - except Exception as e: # noqa: BLE001 - logging.warning(f"[sql_repository] get_value failed: {e}") - return None - - -def delete_value(key: str) -> bool: - if not _ensure_table(): - return False - - engine = get_engine() - if not engine and not _SQLALCHEMY_AVAILABLE: - try: - conn = _get_sqlite_conn() - conn.execute("DELETE FROM QAI_KeyValue WHERE key_name=?", (key,)) - conn.commit() - return True - except Exception as e: # noqa: BLE001 - logging.warning( - f"[sql_repository] sqlite fallback delete_value failed: {e}" - ) - return False - - if not engine: - return False - - try: - with engine.begin() as conn: - conn.execute( - text("DELETE FROM QAI_KeyValue WHERE key_name=:key_name"), - {"key_name": key}, - ) - return True - except Exception as e: # noqa: BLE001 - logging.warning(f"[sql_repository] delete_value failed: {e}") - return False - - -def list_values(limit: int = 100) -> list[dict]: # noqa: ANN001 - if not _ensure_table(): - return [] - - engine = get_engine() - if not engine and not _SQLALCHEMY_AVAILABLE: - try: - conn = _get_sqlite_conn() - cur = conn.execute( - "SELECT key_name, value_data, updated_at FROM QAI_KeyValue ORDER BY updated_at DESC LIMIT ?", - (limit,), - ) - return [ - {"key_name": row[0], "value_data": row[1], "updated_at": row[2]} - for row in cur.fetchall() - ] - except Exception as e: # noqa: BLE001 - logging.warning(f"[sql_repository] sqlite fallback list_values failed: {e}") - return [] - - if not engine: - return [] - - try: - with engine.connect() as conn: - res = conn.execute( - text( - "SELECT key_name, value_data, updated_at FROM QAI_KeyValue ORDER BY updated_at DESC LIMIT :limit" - ), - {"limit": limit}, - ) - return [ - {"key_name": row[0], "value_data": row[1], "updated_at": row[2]} - for row in res.fetchall() - ] - except Exception as e: # noqa: BLE001 - logging.warning(f"[sql_repository] list_values failed: {e}") - return [] - - -__all__ = ["put_value", "get_value", "delete_value", "list_values"] +"""Generic lightweight SQL repository utilities. + +Provides a key-value store abstraction for multi-database support using +SQLAlchemy core when available, and a sqlite3-based fallback when SQLAlchemy +is not installed. Table auto-creation is vendor-aware and idempotent. + +Table name: QAI_KeyValue +Columns: + key_name (primary key), value_data (text/blob), updated_at (timestamp) + +Graceful degradation: If an engine or driver is unavailable, operations return +fallback values instead of raising. +""" + +from __future__ import annotations + +import logging +import os +import sqlite3 +from datetime import datetime, timezone +from typing import Optional + +# Conditional SQLAlchemy import +_SQLALCHEMY_AVAILABLE = True +try: + from sqlalchemy import text # type: ignore +except Exception: # pragma: no cover + _SQLALCHEMY_AVAILABLE = False + text = None # type: ignore + +from .sql_engine import get_engine, resolve_sql_url + +_TABLE_CREATED = False +_SQLITE_CONN: Optional[sqlite3.Connection] = None + +# ---------------------------------------------------------------------------- +# Helpers (fallback) +# ---------------------------------------------------------------------------- + + +def _sqlite_path_from_url(url: str) -> str: + """Resolve sqlite database path from SQLAlchemy-style URL. + Supports file paths (sqlite:///path/to.db) and in-memory (:memory:). + """ + if url.endswith(":memory:"): + return ":memory:" + if url.startswith("sqlite:///"): + path = url[len("sqlite:///") :] + return os.path.normpath(path) + # Default to in-memory if unrecognized format + return ":memory:" + + +def _get_sqlite_conn() -> sqlite3.Connection: + global _SQLITE_CONN + if _SQLITE_CONN is not None: + return _SQLITE_CONN + url = resolve_sql_url() or "sqlite:///:memory:" + db_path = _sqlite_path_from_url(url) + # Ensure directory exists for file-based DBs + if db_path != ":memory:": + try: + os.makedirs(os.path.dirname(db_path) or ".", exist_ok=True) + except Exception: # pragma: no cover + pass + _SQLITE_CONN = sqlite3.connect(db_path, check_same_thread=False) + return _SQLITE_CONN + + +# ---------------------------------------------------------------------------- +# Table Creation (idempotent) +# ---------------------------------------------------------------------------- + + +def _ensure_table(): + global _TABLE_CREATED + if _TABLE_CREATED: + return True + + engine = get_engine() + if not engine and not _SQLALCHEMY_AVAILABLE: + # Fallback path: sqlite3 direct + try: + conn = _get_sqlite_conn() + conn.execute( + "CREATE TABLE IF NOT EXISTS QAI_KeyValue (" + "key_name TEXT PRIMARY KEY, value_data TEXT, updated_at TEXT)" + ) + conn.commit() + _TABLE_CREATED = True + return True + except Exception as e: # noqa: BLE001 + logging.warning( + f"[sql_repository] sqlite fallback table create failed: {e}" + ) + return False + + if not engine: + return False + + vendor = getattr(engine.dialect, "name", "unknown") + try: + if vendor == "sqlite": + ddl = "CREATE TABLE IF NOT EXISTS QAI_KeyValue (key_name TEXT PRIMARY KEY, value_data TEXT, updated_at TEXT)" + elif vendor in {"postgresql", "postgres"}: + ddl = ( + "CREATE TABLE IF NOT EXISTS QAI_KeyValue (" + "key_name VARCHAR(200) PRIMARY KEY, " + "value_data TEXT, " + "updated_at TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP)" + ) + elif vendor in {"mysql"}: + ddl = ( + "CREATE TABLE IF NOT EXISTS QAI_KeyValue (" + "key_name VARCHAR(200) PRIMARY KEY, " + "value_data TEXT, " + "updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP)" + ) + else: # mssql & fallback + # SQL Server: need IF NOT EXISTS pattern + ddl = ( + "IF NOT EXISTS (SELECT 1 FROM sys.tables WHERE name='QAI_KeyValue') " + "BEGIN CREATE TABLE dbo.QAI_KeyValue (" + "key_name NVARCHAR(200) NOT NULL PRIMARY KEY, " + "value_data NVARCHAR(MAX) NULL, " + "updated_at DATETIME2 DEFAULT SYSUTCDATETIME()) END" + ) + with engine.begin() as conn: + conn.execute(text(ddl)) + _TABLE_CREATED = True + return True + except Exception as e: # noqa: BLE001 + logging.warning(f"[sql_repository] table create failed: {e}") + return False + + +# ---------------------------------------------------------------------------- +# CRUD Operations +# ---------------------------------------------------------------------------- + + +def put_value(key: str, value: str) -> bool: + if not _ensure_table(): + return False + + engine = get_engine() + if not engine and not _SQLALCHEMY_AVAILABLE: + # Fallback: sqlite3 direct + try: + conn = _get_sqlite_conn() + conn.execute( + "INSERT OR REPLACE INTO QAI_KeyValue (key_name, value_data, updated_at) VALUES (?, ?, ?)", + (key, value, datetime.now(timezone.utc).isoformat()), + ) + conn.commit() + return True + except Exception as e: # noqa: BLE001 + logging.warning(f"[sql_repository] sqlite fallback put_value failed: {e}") + return False + + if not engine: + return False + + vendor = getattr(engine.dialect, "name", "unknown") + try: + with engine.begin() as conn: + if vendor == "sqlite": + conn.execute( + text( + "REPLACE INTO QAI_KeyValue (key_name,value_data,updated_at) VALUES (:key_name,:value_data,:ts)" + ), + { + "key_name": key, + "value_data": value, + "ts": datetime.now(timezone.utc).isoformat(), + }, + ) + elif vendor in {"postgresql", "postgres"}: + conn.execute( + text( + "INSERT INTO QAI_KeyValue (key_name,value_data) VALUES (:key_name,:value_data) ON CONFLICT (key_name) DO UPDATE SET value_data=EXCLUDED.value_data, updated_at=CURRENT_TIMESTAMP" + ), + {"key_name": key, "value_data": value}, + ) + elif vendor == "mysql": + conn.execute( + text( + "INSERT INTO QAI_KeyValue (key_name,value_data) VALUES (:key_name,:value_data) ON DUPLICATE KEY UPDATE value_data=VALUES(value_data)" + ), + {"key_name": key, "value_data": value}, + ) + else: # SQL Server + conn.execute( + text( + "MERGE dbo.QAI_KeyValue AS tgt USING (SELECT :key_name AS key_name, :value_data AS value_data) AS src ON tgt.key_name=src.key_name WHEN MATCHED THEN UPDATE SET value_data=src.value_data, updated_at=SYSUTCDATETIME() WHEN NOT MATCHED THEN INSERT (key_name,value_data,updated_at) VALUES (src.key_name, src.value_data, SYSUTCDATETIME());" + ), + {"key_name": key, "value_data": value}, + ) + return True + except Exception as e: # noqa: BLE001 + logging.warning(f"[sql_repository] put_value failed: {e}") + return False + + +def get_value(key: str) -> Optional[str]: + if not _ensure_table(): + return None + + engine = get_engine() + if not engine and not _SQLALCHEMY_AVAILABLE: + try: + conn = _get_sqlite_conn() + cur = conn.execute( + "SELECT value_data FROM QAI_KeyValue WHERE key_name=?", (key,) + ) + row = cur.fetchone() + return None if not row else row[0] + except Exception as e: # noqa: BLE001 + logging.warning(f"[sql_repository] sqlite fallback get_value failed: {e}") + return None + + if not engine: + return None + + try: + with engine.connect() as conn: + res = conn.execute( + text("SELECT value_data FROM QAI_KeyValue WHERE key_name=:key_name"), + {"key_name": key}, + ).fetchone() + return None if not res else res[0] + except Exception as e: # noqa: BLE001 + logging.warning(f"[sql_repository] get_value failed: {e}") + return None + + +def delete_value(key: str) -> bool: + if not _ensure_table(): + return False + + engine = get_engine() + if not engine and not _SQLALCHEMY_AVAILABLE: + try: + conn = _get_sqlite_conn() + conn.execute("DELETE FROM QAI_KeyValue WHERE key_name=?", (key,)) + conn.commit() + return True + except Exception as e: # noqa: BLE001 + logging.warning( + f"[sql_repository] sqlite fallback delete_value failed: {e}" + ) + return False + + if not engine: + return False + + try: + with engine.begin() as conn: + conn.execute( + text("DELETE FROM QAI_KeyValue WHERE key_name=:key_name"), + {"key_name": key}, + ) + return True + except Exception as e: # noqa: BLE001 + logging.warning(f"[sql_repository] delete_value failed: {e}") + return False + + +def list_values(limit: int = 100) -> list[dict]: # noqa: ANN001 + if not _ensure_table(): + return [] + + engine = get_engine() + if not engine and not _SQLALCHEMY_AVAILABLE: + try: + conn = _get_sqlite_conn() + cur = conn.execute( + "SELECT key_name, value_data, updated_at FROM QAI_KeyValue ORDER BY updated_at DESC LIMIT ?", + (limit,), + ) + return [ + {"key_name": row[0], "value_data": row[1], "updated_at": row[2]} + for row in cur.fetchall() + ] + except Exception as e: # noqa: BLE001 + logging.warning(f"[sql_repository] sqlite fallback list_values failed: {e}") + return [] + + if not engine: + return [] + + try: + with engine.connect() as conn: + res = conn.execute( + text( + "SELECT key_name, value_data, updated_at FROM QAI_KeyValue ORDER BY updated_at DESC LIMIT :limit" + ), + {"limit": limit}, + ) + return [ + {"key_name": row[0], "value_data": row[1], "updated_at": row[2]} + for row in res.fetchall() + ] + except Exception as e: # noqa: BLE001 + logging.warning(f"[sql_repository] list_values failed: {e}") + return [] + + +__all__ = ["put_value", "get_value", "delete_value", "list_values"] diff --git a/shared/stripe_webhooks.py b/shared/stripe_webhooks.py index 5d4376d4f..3ff8e28aa 100644 --- a/shared/stripe_webhooks.py +++ b/shared/stripe_webhooks.py @@ -1,344 +1,344 @@ -""" -Stripe webhook handler for Aria monetization -Processes Stripe payment events and updates subscriptions -""" - -import json -import logging -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, Optional - -logger = logging.getLogger(__name__) - - -class StripeWebhookHandler: - """Handles Stripe webhook events""" - - def __init__(self): - self.webhook_log = Path("data_out/webhooks/stripe_events.json") - self.webhook_log.parent.mkdir(parents=True, exist_ok=True) - self.processed_events = set() - - def handle_webhook( - self, payload: str, signature: str, webhook_secret: Optional[str] = None - ) -> Dict[str, Any]: - """ - Handle incoming Stripe webhook - - Args: - payload: Raw webhook payload - signature: Stripe signature header - webhook_secret: Webhook signing secret - - Returns: - Response dict with status and message - """ - try: - # In production, verify signature with Stripe - # import stripe - # event = stripe.Webhook.construct_event( - # payload, signature, webhook_secret - # ) - - # For demo, parse JSON directly - event = json.loads(payload) - - # Check for duplicate events - event_id = event.get("id") - if event_id in self.processed_events: - logger.info(f"Duplicate event {event_id}, skipping") - return {"status": "success", "message": "Duplicate event"} - - # Log the event - self._log_event(event) - - # Route to appropriate handler - event_type = event.get("type") - handler = self._get_event_handler(event_type) - - if handler: - result = handler(event) - self.processed_events.add(event_id) - return {"status": "success", "result": result} - else: - logger.warning(f"No handler for event type: {event_type}") - return {"status": "ignored", "message": f"No handler for {event_type}"} - - except Exception as e: - logger.error(f"Webhook handling failed: {str(e)}") - return {"status": "error", "message": str(e)} - - def _get_event_handler(self, event_type: str): - """Get handler function for event type""" - handlers = { - "customer.subscription.created": self._handle_subscription_created, - "customer.subscription.updated": self._handle_subscription_updated, - "customer.subscription.deleted": self._handle_subscription_deleted, - "invoice.payment_succeeded": self._handle_payment_succeeded, - "invoice.payment_failed": self._handle_payment_failed, - "charge.succeeded": self._handle_charge_succeeded, - "charge.failed": self._handle_charge_failed, - "customer.created": self._handle_customer_created, - "customer.updated": self._handle_customer_updated, - } - return handlers.get(event_type) - - def _handle_subscription_created(self, event: Dict[str, Any]) -> Dict[str, Any]: - """Handle subscription.created event""" - subscription = event["data"]["object"] - - # Extract subscription details - customer_id = subscription.get("customer") - subscription_id = subscription.get("id") - status = subscription.get("status") - plan_id = subscription.get("plan", {}).get("id") - amount = subscription.get("plan", {}).get("amount", 0) / 100 - - logger.info( - f"Subscription created: {subscription_id} for customer {customer_id}" - ) - - # Update subscription in database - from shared.subscription_manager import get_subscription_manager - - manager = get_subscription_manager() - - # Map Stripe plan to tier - tier = self._map_plan_to_tier(plan_id, amount) - - # Create/update subscription - manager.upgrade_subscription( - user_id=customer_id, - tier=tier, - duration_days=30, - payment_method="stripe", - stripe_subscription_id=subscription_id, - ) - - # Send notification - from shared.email_notifications import get_email_system - - email_system = get_email_system() - email_system.notify_subscription_activated( - user_email=self._get_customer_email(customer_id), - tier=tier.value.upper(), - price=amount, - ) - - return { - "subscription_id": subscription_id, - "customer_id": customer_id, - "tier": tier.value, - "status": status, - } - - def _handle_subscription_updated(self, event: Dict[str, Any]) -> Dict[str, Any]: - """Handle subscription.updated event""" - subscription = event["data"]["object"] - - customer_id = subscription.get("customer") - subscription_id = subscription.get("id") - status = subscription.get("status") - - logger.info(f"Subscription updated: {subscription_id}, status: {status}") - - # Handle status changes - if status == "canceled": - return self._handle_subscription_deleted(event) - elif status == "past_due": - # Send payment reminder - from shared.email_notifications import get_email_system - - email_system = get_email_system() - email_system.notify_payment_failed( - user_email=self._get_customer_email(customer_id), - amount=subscription.get("plan", {}).get("amount", 0) / 100, - reason="Payment past due", - ) - - return {"subscription_id": subscription_id, "status": status} - - def _handle_subscription_deleted(self, event: Dict[str, Any]) -> Dict[str, Any]: - """Handle subscription.deleted event""" - subscription = event["data"]["object"] - - customer_id = subscription.get("customer") - subscription_id = subscription.get("id") - - logger.info(f"Subscription deleted: {subscription_id}") - - # Downgrade to free tier - from shared.subscription_manager import get_subscription_manager - - manager = get_subscription_manager() - - # Get user_id from customer_id mapping - # In production, maintain a customer_id -> user_id mapping - user_id = customer_id - - # Cancel subscription (keeps access until period end) - subscription_obj = manager.get_subscription(user_id) - if subscription_obj: - subscription_obj.is_active = False - manager._save_subscriptions() - - return { - "subscription_id": subscription_id, - "customer_id": customer_id, - "action": "cancelled", - } - - def _handle_payment_succeeded(self, event: Dict[str, Any]) -> Dict[str, Any]: - """Handle invoice.payment_succeeded event""" - invoice = event["data"]["object"] - - customer_id = invoice.get("customer") - invoice_id = invoice.get("id") - amount = invoice.get("amount_paid", 0) / 100 - - logger.info(f"Payment succeeded: {invoice_id}, amount: ${amount}") - - # Send receipt - from shared.email_notifications import get_email_system - - email_system = get_email_system() - email_system.notify_payment_succeeded( - user_email=self._get_customer_email(customer_id), - amount=amount, - invoice_id=invoice_id, - ) - - return {"invoice_id": invoice_id, "amount": amount, "status": "paid"} - - def _handle_payment_failed(self, event: Dict[str, Any]) -> Dict[str, Any]: - """Handle invoice.payment_failed event""" - invoice = event["data"]["object"] - - customer_id = invoice.get("customer") - invoice_id = invoice.get("id") - amount = invoice.get("amount_due", 0) / 100 - - logger.info(f"Payment failed: {invoice_id}, amount: ${amount}") - - # Send notification - from shared.email_notifications import get_email_system - - email_system = get_email_system() - email_system.notify_payment_failed( - user_email=self._get_customer_email(customer_id), - amount=amount, - reason="Payment declined", - ) - - return {"invoice_id": invoice_id, "amount": amount, "status": "failed"} - - def _handle_charge_succeeded(self, event: Dict[str, Any]) -> Dict[str, Any]: - """Handle charge.succeeded event""" - charge = event["data"]["object"] - - charge_id = charge.get("id") - amount = charge.get("amount", 0) / 100 - - logger.info(f"Charge succeeded: {charge_id}, amount: ${amount}") - - return {"charge_id": charge_id, "amount": amount, "status": "succeeded"} - - def _handle_charge_failed(self, event: Dict[str, Any]) -> Dict[str, Any]: - """Handle charge.failed event""" - charge = event["data"]["object"] - - charge_id = charge.get("id") - amount = charge.get("amount", 0) / 100 - failure_message = charge.get("failure_message", "Unknown error") - - logger.info(f"Charge failed: {charge_id}, reason: {failure_message}") - - return { - "charge_id": charge_id, - "amount": amount, - "status": "failed", - "reason": failure_message, - } - - def _handle_customer_created(self, event: Dict[str, Any]) -> Dict[str, Any]: - """Handle customer.created event""" - customer = event["data"]["object"] - - customer_id = customer.get("id") - email = customer.get("email") - - logger.info(f"Customer created: {customer_id}, email: {email}") - - return {"customer_id": customer_id, "email": email} - - def _handle_customer_updated(self, event: Dict[str, Any]) -> Dict[str, Any]: - """Handle customer.updated event""" - customer = event["data"]["object"] - - customer_id = customer.get("id") - - logger.info(f"Customer updated: {customer_id}") - - return {"customer_id": customer_id} - - def _map_plan_to_tier(self, plan_id: str, amount: float): - """Map Stripe plan to subscription tier""" - from shared.subscription_manager import SubscriptionTier - - # Map based on amount - if amount >= 199: - return SubscriptionTier.ENTERPRISE - elif amount >= 49: - return SubscriptionTier.PRO - else: - return SubscriptionTier.FREE - - def _get_customer_email(self, customer_id: str) -> str: - """Get customer email from Stripe customer ID""" - # In production, query Stripe API or database - # For demo, return placeholder - return f"{customer_id}@example.com" - - def _log_event(self, event: Dict[str, Any]) -> None: - """Log webhook event to file""" - try: - # Load existing log - log_data = [] - if self.webhook_log.exists(): - with open(self.webhook_log, "r") as f: - log_data = json.load(f) - - # Append new event - log_data.append( - { - "event_id": event.get("id"), - "type": event.get("type"), - "created": event.get("created"), - "processed_at": datetime.now().isoformat(), - "data": event.get("data", {}), - } - ) - - # Keep only last 500 events - if len(log_data) > 500: - log_data = log_data[-500:] - - # Save log - with open(self.webhook_log, "w") as f: - json.dump(log_data, f, indent=2) - - except Exception as e: - logger.error(f"Failed to log webhook event: {str(e)}") - - -# Global instance -_webhook_handler: Optional[StripeWebhookHandler] = None - - -def get_webhook_handler() -> StripeWebhookHandler: - """Get global webhook handler instance""" - global _webhook_handler - if _webhook_handler is None: - _webhook_handler = StripeWebhookHandler() - return _webhook_handler +""" +Stripe webhook handler for Aria monetization +Processes Stripe payment events and updates subscriptions +""" + +import json +import logging +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, Optional + +logger = logging.getLogger(__name__) + + +class StripeWebhookHandler: + """Handles Stripe webhook events""" + + def __init__(self): + self.webhook_log = Path("data_out/webhooks/stripe_events.json") + self.webhook_log.parent.mkdir(parents=True, exist_ok=True) + self.processed_events = set() + + def handle_webhook( + self, payload: str, signature: str, webhook_secret: Optional[str] = None + ) -> Dict[str, Any]: + """ + Handle incoming Stripe webhook + + Args: + payload: Raw webhook payload + signature: Stripe signature header + webhook_secret: Webhook signing secret + + Returns: + Response dict with status and message + """ + try: + # In production, verify signature with Stripe + # import stripe + # event = stripe.Webhook.construct_event( + # payload, signature, webhook_secret + # ) + + # For demo, parse JSON directly + event = json.loads(payload) + + # Check for duplicate events + event_id = event.get("id") + if event_id in self.processed_events: + logger.info(f"Duplicate event {event_id}, skipping") + return {"status": "success", "message": "Duplicate event"} + + # Log the event + self._log_event(event) + + # Route to appropriate handler + event_type = event.get("type") + handler = self._get_event_handler(event_type) + + if handler: + result = handler(event) + self.processed_events.add(event_id) + return {"status": "success", "result": result} + else: + logger.warning(f"No handler for event type: {event_type}") + return {"status": "ignored", "message": f"No handler for {event_type}"} + + except Exception as e: + logger.error(f"Webhook handling failed: {str(e)}") + return {"status": "error", "message": str(e)} + + def _get_event_handler(self, event_type: str): + """Get handler function for event type""" + handlers = { + "customer.subscription.created": self._handle_subscription_created, + "customer.subscription.updated": self._handle_subscription_updated, + "customer.subscription.deleted": self._handle_subscription_deleted, + "invoice.payment_succeeded": self._handle_payment_succeeded, + "invoice.payment_failed": self._handle_payment_failed, + "charge.succeeded": self._handle_charge_succeeded, + "charge.failed": self._handle_charge_failed, + "customer.created": self._handle_customer_created, + "customer.updated": self._handle_customer_updated, + } + return handlers.get(event_type) + + def _handle_subscription_created(self, event: Dict[str, Any]) -> Dict[str, Any]: + """Handle subscription.created event""" + subscription = event["data"]["object"] + + # Extract subscription details + customer_id = subscription.get("customer") + subscription_id = subscription.get("id") + status = subscription.get("status") + plan_id = subscription.get("plan", {}).get("id") + amount = subscription.get("plan", {}).get("amount", 0) / 100 + + logger.info( + f"Subscription created: {subscription_id} for customer {customer_id}" + ) + + # Update subscription in database + from shared.subscription_manager import get_subscription_manager + + manager = get_subscription_manager() + + # Map Stripe plan to tier + tier = self._map_plan_to_tier(plan_id, amount) + + # Create/update subscription + manager.upgrade_subscription( + user_id=customer_id, + tier=tier, + duration_days=30, + payment_method="stripe", + stripe_subscription_id=subscription_id, + ) + + # Send notification + from shared.email_notifications import get_email_system + + email_system = get_email_system() + email_system.notify_subscription_activated( + user_email=self._get_customer_email(customer_id), + tier=tier.value.upper(), + price=amount, + ) + + return { + "subscription_id": subscription_id, + "customer_id": customer_id, + "tier": tier.value, + "status": status, + } + + def _handle_subscription_updated(self, event: Dict[str, Any]) -> Dict[str, Any]: + """Handle subscription.updated event""" + subscription = event["data"]["object"] + + customer_id = subscription.get("customer") + subscription_id = subscription.get("id") + status = subscription.get("status") + + logger.info(f"Subscription updated: {subscription_id}, status: {status}") + + # Handle status changes + if status == "canceled": + return self._handle_subscription_deleted(event) + elif status == "past_due": + # Send payment reminder + from shared.email_notifications import get_email_system + + email_system = get_email_system() + email_system.notify_payment_failed( + user_email=self._get_customer_email(customer_id), + amount=subscription.get("plan", {}).get("amount", 0) / 100, + reason="Payment past due", + ) + + return {"subscription_id": subscription_id, "status": status} + + def _handle_subscription_deleted(self, event: Dict[str, Any]) -> Dict[str, Any]: + """Handle subscription.deleted event""" + subscription = event["data"]["object"] + + customer_id = subscription.get("customer") + subscription_id = subscription.get("id") + + logger.info(f"Subscription deleted: {subscription_id}") + + # Downgrade to free tier + from shared.subscription_manager import get_subscription_manager + + manager = get_subscription_manager() + + # Get user_id from customer_id mapping + # In production, maintain a customer_id -> user_id mapping + user_id = customer_id + + # Cancel subscription (keeps access until period end) + subscription_obj = manager.get_subscription(user_id) + if subscription_obj: + subscription_obj.is_active = False + manager._save_subscriptions() + + return { + "subscription_id": subscription_id, + "customer_id": customer_id, + "action": "cancelled", + } + + def _handle_payment_succeeded(self, event: Dict[str, Any]) -> Dict[str, Any]: + """Handle invoice.payment_succeeded event""" + invoice = event["data"]["object"] + + customer_id = invoice.get("customer") + invoice_id = invoice.get("id") + amount = invoice.get("amount_paid", 0) / 100 + + logger.info(f"Payment succeeded: {invoice_id}, amount: ${amount}") + + # Send receipt + from shared.email_notifications import get_email_system + + email_system = get_email_system() + email_system.notify_payment_succeeded( + user_email=self._get_customer_email(customer_id), + amount=amount, + invoice_id=invoice_id, + ) + + return {"invoice_id": invoice_id, "amount": amount, "status": "paid"} + + def _handle_payment_failed(self, event: Dict[str, Any]) -> Dict[str, Any]: + """Handle invoice.payment_failed event""" + invoice = event["data"]["object"] + + customer_id = invoice.get("customer") + invoice_id = invoice.get("id") + amount = invoice.get("amount_due", 0) / 100 + + logger.info(f"Payment failed: {invoice_id}, amount: ${amount}") + + # Send notification + from shared.email_notifications import get_email_system + + email_system = get_email_system() + email_system.notify_payment_failed( + user_email=self._get_customer_email(customer_id), + amount=amount, + reason="Payment declined", + ) + + return {"invoice_id": invoice_id, "amount": amount, "status": "failed"} + + def _handle_charge_succeeded(self, event: Dict[str, Any]) -> Dict[str, Any]: + """Handle charge.succeeded event""" + charge = event["data"]["object"] + + charge_id = charge.get("id") + amount = charge.get("amount", 0) / 100 + + logger.info(f"Charge succeeded: {charge_id}, amount: ${amount}") + + return {"charge_id": charge_id, "amount": amount, "status": "succeeded"} + + def _handle_charge_failed(self, event: Dict[str, Any]) -> Dict[str, Any]: + """Handle charge.failed event""" + charge = event["data"]["object"] + + charge_id = charge.get("id") + amount = charge.get("amount", 0) / 100 + failure_message = charge.get("failure_message", "Unknown error") + + logger.info(f"Charge failed: {charge_id}, reason: {failure_message}") + + return { + "charge_id": charge_id, + "amount": amount, + "status": "failed", + "reason": failure_message, + } + + def _handle_customer_created(self, event: Dict[str, Any]) -> Dict[str, Any]: + """Handle customer.created event""" + customer = event["data"]["object"] + + customer_id = customer.get("id") + email = customer.get("email") + + logger.info(f"Customer created: {customer_id}, email: {email}") + + return {"customer_id": customer_id, "email": email} + + def _handle_customer_updated(self, event: Dict[str, Any]) -> Dict[str, Any]: + """Handle customer.updated event""" + customer = event["data"]["object"] + + customer_id = customer.get("id") + + logger.info(f"Customer updated: {customer_id}") + + return {"customer_id": customer_id} + + def _map_plan_to_tier(self, plan_id: str, amount: float): + """Map Stripe plan to subscription tier""" + from shared.subscription_manager import SubscriptionTier + + # Map based on amount + if amount >= 199: + return SubscriptionTier.ENTERPRISE + elif amount >= 49: + return SubscriptionTier.PRO + else: + return SubscriptionTier.FREE + + def _get_customer_email(self, customer_id: str) -> str: + """Get customer email from Stripe customer ID""" + # In production, query Stripe API or database + # For demo, return placeholder + return f"{customer_id}@example.com" + + def _log_event(self, event: Dict[str, Any]) -> None: + """Log webhook event to file""" + try: + # Load existing log + log_data = [] + if self.webhook_log.exists(): + with open(self.webhook_log, "r") as f: + log_data = json.load(f) + + # Append new event + log_data.append( + { + "event_id": event.get("id"), + "type": event.get("type"), + "created": event.get("created"), + "processed_at": datetime.now().isoformat(), + "data": event.get("data", {}), + } + ) + + # Keep only last 500 events + if len(log_data) > 500: + log_data = log_data[-500:] + + # Save log + with open(self.webhook_log, "w") as f: + json.dump(log_data, f, indent=2) + + except Exception as e: + logger.error(f"Failed to log webhook event: {str(e)}") + + +# Global instance +_webhook_handler: Optional[StripeWebhookHandler] = None + + +def get_webhook_handler() -> StripeWebhookHandler: + """Get global webhook handler instance""" + global _webhook_handler + if _webhook_handler is None: + _webhook_handler = StripeWebhookHandler() + return _webhook_handler diff --git a/shared/subscription_manager.py b/shared/subscription_manager.py index 26ad74624..6b2f2f9e3 100644 --- a/shared/subscription_manager.py +++ b/shared/subscription_manager.py @@ -1,377 +1,377 @@ -""" -Subscription and monetization management for Aria platform. -Handles subscription tiers, usage tracking, and feature gating. -""" - -import json -import logging -from datetime import datetime, timedelta -from enum import Enum -from pathlib import Path -from typing import Any, Dict, List, Optional - -logger = logging.getLogger(__name__) - - -class SubscriptionTier(Enum): - """Subscription tier levels""" - - FREE = "free" - PRO = "pro" - ENTERPRISE = "enterprise" - - -class Feature(Enum): - """Platform features that can be gated""" - - BASIC_CHAT = "basic_chat" - ARIA_CHARACTER = "aria_character" - QUANTUM_COMPUTING = "quantum_computing" - ADVANCED_TRAINING = "advanced_training" - WEBSITE_MAKER = "website_maker" - API_ACCESS = "api_access" - PRIORITY_SUPPORT = "priority_support" - CUSTOM_MODELS = "custom_models" - UNLIMITED_REQUESTS = "unlimited_requests" - COMMERCIAL_LICENSE = "commercial_license" - - -# Subscription tier pricing (monthly in USD) -TIER_PRICING = { - SubscriptionTier.FREE: 0, - SubscriptionTier.PRO: 49, - SubscriptionTier.ENTERPRISE: 199, -} - -# Feature access matrix -TIER_FEATURES = { - SubscriptionTier.FREE: { - Feature.BASIC_CHAT: True, - Feature.ARIA_CHARACTER: True, - Feature.QUANTUM_COMPUTING: False, - Feature.ADVANCED_TRAINING: False, - Feature.WEBSITE_MAKER: False, - Feature.API_ACCESS: False, - Feature.PRIORITY_SUPPORT: False, - Feature.CUSTOM_MODELS: False, - Feature.UNLIMITED_REQUESTS: False, - Feature.COMMERCIAL_LICENSE: False, - }, - SubscriptionTier.PRO: { - Feature.BASIC_CHAT: True, - Feature.ARIA_CHARACTER: True, - Feature.QUANTUM_COMPUTING: True, - Feature.ADVANCED_TRAINING: True, - Feature.WEBSITE_MAKER: True, - Feature.API_ACCESS: True, - Feature.PRIORITY_SUPPORT: False, - Feature.CUSTOM_MODELS: False, - Feature.UNLIMITED_REQUESTS: False, - Feature.COMMERCIAL_LICENSE: True, - }, - SubscriptionTier.ENTERPRISE: { - Feature.BASIC_CHAT: True, - Feature.ARIA_CHARACTER: True, - Feature.QUANTUM_COMPUTING: True, - Feature.ADVANCED_TRAINING: True, - Feature.WEBSITE_MAKER: True, - Feature.API_ACCESS: True, - Feature.PRIORITY_SUPPORT: True, - Feature.CUSTOM_MODELS: True, - Feature.UNLIMITED_REQUESTS: True, - Feature.COMMERCIAL_LICENSE: True, - }, -} - -# Usage limits per tier (monthly) -TIER_LIMITS = { - SubscriptionTier.FREE: { - "chat_messages": 100, - "quantum_jobs": 0, - "training_hours": 0, - "api_requests": 0, - "websites_created": 0, - }, - SubscriptionTier.PRO: { - "chat_messages": 10000, - "quantum_jobs": 50, - "training_hours": 20, - "api_requests": 10000, - "websites_created": 10, - }, - SubscriptionTier.ENTERPRISE: { - "chat_messages": -1, # unlimited - "quantum_jobs": -1, - "training_hours": -1, - "api_requests": -1, - "websites_created": -1, - }, -} - - -class Subscription: - """Represents a user subscription""" - - def __init__( - self, - user_id: str, - tier: SubscriptionTier = SubscriptionTier.FREE, - start_date: Optional[datetime] = None, - end_date: Optional[datetime] = None, - payment_method: Optional[str] = None, - stripe_subscription_id: Optional[str] = None, - ): - self.user_id = user_id - self._tier = tier # Use private attribute - self.start_date = start_date or datetime.now() - self.end_date = end_date - self.payment_method = payment_method - self.stripe_subscription_id = stripe_subscription_id - self.usage = { - "chat_messages": 0, - "quantum_jobs": 0, - "training_hours": 0, - "api_requests": 0, - "websites_created": 0, - } - self.usage_reset_date = datetime.now() + timedelta(days=30) - # Cache tier limits to avoid repeated dictionary lookups - self._tier_limits = TIER_LIMITS.get(self._tier, {}) - if not self._tier_limits: - logger.warning(f"Unknown tier {self._tier} - using empty limits") - - @property - def tier(self) -> SubscriptionTier: - """Get subscription tier""" - return self._tier - - @tier.setter - def tier(self, value: SubscriptionTier): - """Set subscription tier and update cached limits""" - self._tier = value - # Update cached tier limits whenever tier changes - self._tier_limits = TIER_LIMITS.get(value, {}) - if not self._tier_limits: - logger.warning(f"Unknown tier {value} - using empty limits") - - def is_active(self) -> bool: - """Check if subscription is currently active""" - if self.tier == SubscriptionTier.FREE: - return True - if self.end_date is None: - return True - return datetime.now() < self.end_date - - def has_feature(self, feature: Feature) -> bool: - """Check if subscription has access to a feature""" - if not self.is_active(): - return False - return TIER_FEATURES.get(self.tier, {}).get(feature, False) - - def check_limit(self, resource: str, amount: int = 1) -> bool: - """Check if usage is within limits - optimized with cached tier limits""" - if not self.is_active(): - return False - - # Reset usage if period expired - if datetime.now() > self.usage_reset_date: - self.reset_usage() - - # Use cached tier limits - limit = self._tier_limits.get(resource, 0) - if limit == -1: # unlimited - return True - - current_usage = self.usage.get(resource, 0) - return (current_usage + amount) <= limit - - def increment_usage(self, resource: str, amount: int = 1) -> bool: - """Increment usage counter if within limits""" - if self.check_limit(resource, amount): - self.usage[resource] = self.usage.get(resource, 0) + amount - return True - return False - - def reset_usage(self): - """Reset monthly usage counters""" - self.usage = { - "chat_messages": 0, - "quantum_jobs": 0, - "training_hours": 0, - "api_requests": 0, - "websites_created": 0, - } - self.usage_reset_date = datetime.now() + timedelta(days=30) - - def get_usage_percentage(self, resource: str) -> float: - """Get usage as percentage of limit - optimized with cached tier limits""" - # Use cached tier limits - limit = self._tier_limits.get(resource, 0) - if limit == -1: - return 0.0 # unlimited - if limit == 0: - return 100.0 - current = self.usage.get(resource, 0) - return (current / limit) * 100 - - def to_dict(self) -> Dict[str, Any]: - """Convert subscription to dictionary""" - return { - "user_id": self.user_id, - "tier": self.tier.value, - "tier_name": self.tier.name, - "price": TIER_PRICING[self.tier], - "is_active": self.is_active(), - "start_date": self.start_date.isoformat() if self.start_date else None, - "end_date": self.end_date.isoformat() if self.end_date else None, - "payment_method": self.payment_method, - "stripe_subscription_id": self.stripe_subscription_id, - "usage": self.usage, - "usage_reset_date": self.usage_reset_date.isoformat(), - "limits": TIER_LIMITS[self.tier], - "features": {f.value: self.has_feature(f) for f in Feature}, - } - - -class SubscriptionManager: - """Manages subscriptions and feature access""" - - def __init__(self, storage_path: Optional[Path] = None): - self.storage_path = storage_path or Path("data_out/subscriptions") - self.storage_path.mkdir(parents=True, exist_ok=True) - self.subscriptions: Dict[str, Subscription] = {} - self._load_subscriptions() - - def _load_subscriptions(self): - """Load subscriptions from storage""" - try: - subscription_file = self.storage_path / "subscriptions.json" - if subscription_file.exists(): - with open(subscription_file, "r") as f: - data = json.load(f) - for user_id, sub_data in data.items(): - tier = SubscriptionTier(sub_data.get("tier", "free")) - sub = Subscription( - user_id=user_id, - tier=tier, - start_date=( - datetime.fromisoformat(sub_data["start_date"]) - if sub_data.get("start_date") - else None - ), - end_date=( - datetime.fromisoformat(sub_data["end_date"]) - if sub_data.get("end_date") - else None - ), - payment_method=sub_data.get("payment_method"), - stripe_subscription_id=sub_data.get( - "stripe_subscription_id" - ), - ) - sub.usage = sub_data.get("usage", sub.usage) - if sub_data.get("usage_reset_date"): - sub.usage_reset_date = datetime.fromisoformat( - sub_data["usage_reset_date"] - ) - self.subscriptions[user_id] = sub - except Exception as e: - logger.error(f"Failed to load subscriptions: {e}") - - def _save_subscriptions(self): - """Save subscriptions to storage""" - try: - subscription_file = self.storage_path / "subscriptions.json" - data = { - user_id: sub.to_dict() for user_id, sub in self.subscriptions.items() - } - with open(subscription_file, "w") as f: - json.dump(data, f, indent=2) - except Exception as e: - logger.error(f"Failed to save subscriptions: {e}") - - def get_subscription(self, user_id: str) -> Subscription: - """Get or create subscription for user""" - if user_id not in self.subscriptions: - self.subscriptions[user_id] = Subscription(user_id) - self._save_subscriptions() - return self.subscriptions[user_id] - - def upgrade_subscription( - self, - user_id: str, - tier: SubscriptionTier, - duration_days: int = 30, - payment_method: Optional[str] = None, - stripe_subscription_id: Optional[str] = None, - ) -> Subscription: - """Upgrade user subscription""" - sub = self.get_subscription(user_id) - sub.tier = tier # Property setter automatically updates _tier_limits - sub.start_date = datetime.now() - sub.end_date = datetime.now() + timedelta(days=duration_days) - sub.payment_method = payment_method - sub.stripe_subscription_id = stripe_subscription_id - self._save_subscriptions() - logger.info(f"Upgraded {user_id} to {tier.value}") - return sub - - def cancel_subscription(self, user_id: str) -> bool: - """Cancel user subscription (downgrade to free at end of period)""" - if user_id in self.subscriptions: - sub = self.subscriptions[user_id] - # Set to expire but don't immediate downgrade (grace period) - logger.info(f"Cancelled subscription for {user_id}, expires {sub.end_date}") - self._save_subscriptions() - return True - return False - - def check_access(self, user_id: str, feature: Feature) -> bool: - """Check if user has access to a feature""" - sub = self.get_subscription(user_id) - return sub.has_feature(feature) - - def track_usage(self, user_id: str, resource: str, amount: int = 1) -> bool: - """Track resource usage, returns False if limit exceeded""" - sub = self.get_subscription(user_id) - result = sub.increment_usage(resource, amount) - if result: - self._save_subscriptions() - return result - - def get_revenue_stats(self) -> Dict[str, Any]: - """Calculate revenue statistics""" - stats = { - "total_subscribers": len(self.subscriptions), - "active_subscribers": 0, - "by_tier": {tier.value: 0 for tier in SubscriptionTier}, - "monthly_recurring_revenue": 0, - "annual_recurring_revenue": 0, - } - - for sub in self.subscriptions.values(): - if sub.is_active(): - stats["active_subscribers"] += 1 - stats["by_tier"][sub.tier.value] += 1 - price = TIER_PRICING[sub.tier] - stats["monthly_recurring_revenue"] += price - - stats["annual_recurring_revenue"] = stats["monthly_recurring_revenue"] * 12 - - return stats - - def get_all_subscriptions(self) -> List[Dict[str, Any]]: - """Get all subscriptions as dictionaries""" - return [sub.to_dict() for sub in self.subscriptions.values()] - - -# Global instance -_subscription_manager: Optional[SubscriptionManager] = None - - -def get_subscription_manager() -> SubscriptionManager: - """Get global subscription manager instance""" - global _subscription_manager - if _subscription_manager is None: - _subscription_manager = SubscriptionManager() - return _subscription_manager +""" +Subscription and monetization management for Aria platform. +Handles subscription tiers, usage tracking, and feature gating. +""" + +import json +import logging +from datetime import datetime, timedelta +from enum import Enum +from pathlib import Path +from typing import Any, Dict, List, Optional + +logger = logging.getLogger(__name__) + + +class SubscriptionTier(Enum): + """Subscription tier levels""" + + FREE = "free" + PRO = "pro" + ENTERPRISE = "enterprise" + + +class Feature(Enum): + """Platform features that can be gated""" + + BASIC_CHAT = "basic_chat" + ARIA_CHARACTER = "aria_character" + QUANTUM_COMPUTING = "quantum_computing" + ADVANCED_TRAINING = "advanced_training" + WEBSITE_MAKER = "website_maker" + API_ACCESS = "api_access" + PRIORITY_SUPPORT = "priority_support" + CUSTOM_MODELS = "custom_models" + UNLIMITED_REQUESTS = "unlimited_requests" + COMMERCIAL_LICENSE = "commercial_license" + + +# Subscription tier pricing (monthly in USD) +TIER_PRICING = { + SubscriptionTier.FREE: 0, + SubscriptionTier.PRO: 49, + SubscriptionTier.ENTERPRISE: 199, +} + +# Feature access matrix +TIER_FEATURES = { + SubscriptionTier.FREE: { + Feature.BASIC_CHAT: True, + Feature.ARIA_CHARACTER: True, + Feature.QUANTUM_COMPUTING: False, + Feature.ADVANCED_TRAINING: False, + Feature.WEBSITE_MAKER: False, + Feature.API_ACCESS: False, + Feature.PRIORITY_SUPPORT: False, + Feature.CUSTOM_MODELS: False, + Feature.UNLIMITED_REQUESTS: False, + Feature.COMMERCIAL_LICENSE: False, + }, + SubscriptionTier.PRO: { + Feature.BASIC_CHAT: True, + Feature.ARIA_CHARACTER: True, + Feature.QUANTUM_COMPUTING: True, + Feature.ADVANCED_TRAINING: True, + Feature.WEBSITE_MAKER: True, + Feature.API_ACCESS: True, + Feature.PRIORITY_SUPPORT: False, + Feature.CUSTOM_MODELS: False, + Feature.UNLIMITED_REQUESTS: False, + Feature.COMMERCIAL_LICENSE: True, + }, + SubscriptionTier.ENTERPRISE: { + Feature.BASIC_CHAT: True, + Feature.ARIA_CHARACTER: True, + Feature.QUANTUM_COMPUTING: True, + Feature.ADVANCED_TRAINING: True, + Feature.WEBSITE_MAKER: True, + Feature.API_ACCESS: True, + Feature.PRIORITY_SUPPORT: True, + Feature.CUSTOM_MODELS: True, + Feature.UNLIMITED_REQUESTS: True, + Feature.COMMERCIAL_LICENSE: True, + }, +} + +# Usage limits per tier (monthly) +TIER_LIMITS = { + SubscriptionTier.FREE: { + "chat_messages": 100, + "quantum_jobs": 0, + "training_hours": 0, + "api_requests": 0, + "websites_created": 0, + }, + SubscriptionTier.PRO: { + "chat_messages": 10000, + "quantum_jobs": 50, + "training_hours": 20, + "api_requests": 10000, + "websites_created": 10, + }, + SubscriptionTier.ENTERPRISE: { + "chat_messages": -1, # unlimited + "quantum_jobs": -1, + "training_hours": -1, + "api_requests": -1, + "websites_created": -1, + }, +} + + +class Subscription: + """Represents a user subscription""" + + def __init__( + self, + user_id: str, + tier: SubscriptionTier = SubscriptionTier.FREE, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + payment_method: Optional[str] = None, + stripe_subscription_id: Optional[str] = None, + ): + self.user_id = user_id + self._tier = tier # Use private attribute + self.start_date = start_date or datetime.now() + self.end_date = end_date + self.payment_method = payment_method + self.stripe_subscription_id = stripe_subscription_id + self.usage = { + "chat_messages": 0, + "quantum_jobs": 0, + "training_hours": 0, + "api_requests": 0, + "websites_created": 0, + } + self.usage_reset_date = datetime.now() + timedelta(days=30) + # Cache tier limits to avoid repeated dictionary lookups + self._tier_limits = TIER_LIMITS.get(self._tier, {}) + if not self._tier_limits: + logger.warning(f"Unknown tier {self._tier} - using empty limits") + + @property + def tier(self) -> SubscriptionTier: + """Get subscription tier""" + return self._tier + + @tier.setter + def tier(self, value: SubscriptionTier): + """Set subscription tier and update cached limits""" + self._tier = value + # Update cached tier limits whenever tier changes + self._tier_limits = TIER_LIMITS.get(value, {}) + if not self._tier_limits: + logger.warning(f"Unknown tier {value} - using empty limits") + + def is_active(self) -> bool: + """Check if subscription is currently active""" + if self.tier == SubscriptionTier.FREE: + return True + if self.end_date is None: + return True + return datetime.now() < self.end_date + + def has_feature(self, feature: Feature) -> bool: + """Check if subscription has access to a feature""" + if not self.is_active(): + return False + return TIER_FEATURES.get(self.tier, {}).get(feature, False) + + def check_limit(self, resource: str, amount: int = 1) -> bool: + """Check if usage is within limits - optimized with cached tier limits""" + if not self.is_active(): + return False + + # Reset usage if period expired + if datetime.now() > self.usage_reset_date: + self.reset_usage() + + # Use cached tier limits + limit = self._tier_limits.get(resource, 0) + if limit == -1: # unlimited + return True + + current_usage = self.usage.get(resource, 0) + return (current_usage + amount) <= limit + + def increment_usage(self, resource: str, amount: int = 1) -> bool: + """Increment usage counter if within limits""" + if self.check_limit(resource, amount): + self.usage[resource] = self.usage.get(resource, 0) + amount + return True + return False + + def reset_usage(self): + """Reset monthly usage counters""" + self.usage = { + "chat_messages": 0, + "quantum_jobs": 0, + "training_hours": 0, + "api_requests": 0, + "websites_created": 0, + } + self.usage_reset_date = datetime.now() + timedelta(days=30) + + def get_usage_percentage(self, resource: str) -> float: + """Get usage as percentage of limit - optimized with cached tier limits""" + # Use cached tier limits + limit = self._tier_limits.get(resource, 0) + if limit == -1: + return 0.0 # unlimited + if limit == 0: + return 100.0 + current = self.usage.get(resource, 0) + return (current / limit) * 100 + + def to_dict(self) -> Dict[str, Any]: + """Convert subscription to dictionary""" + return { + "user_id": self.user_id, + "tier": self.tier.value, + "tier_name": self.tier.name, + "price": TIER_PRICING[self.tier], + "is_active": self.is_active(), + "start_date": self.start_date.isoformat() if self.start_date else None, + "end_date": self.end_date.isoformat() if self.end_date else None, + "payment_method": self.payment_method, + "stripe_subscription_id": self.stripe_subscription_id, + "usage": self.usage, + "usage_reset_date": self.usage_reset_date.isoformat(), + "limits": TIER_LIMITS[self.tier], + "features": {f.value: self.has_feature(f) for f in Feature}, + } + + +class SubscriptionManager: + """Manages subscriptions and feature access""" + + def __init__(self, storage_path: Optional[Path] = None): + self.storage_path = storage_path or Path("data_out/subscriptions") + self.storage_path.mkdir(parents=True, exist_ok=True) + self.subscriptions: Dict[str, Subscription] = {} + self._load_subscriptions() + + def _load_subscriptions(self): + """Load subscriptions from storage""" + try: + subscription_file = self.storage_path / "subscriptions.json" + if subscription_file.exists(): + with open(subscription_file, "r") as f: + data = json.load(f) + for user_id, sub_data in data.items(): + tier = SubscriptionTier(sub_data.get("tier", "free")) + sub = Subscription( + user_id=user_id, + tier=tier, + start_date=( + datetime.fromisoformat(sub_data["start_date"]) + if sub_data.get("start_date") + else None + ), + end_date=( + datetime.fromisoformat(sub_data["end_date"]) + if sub_data.get("end_date") + else None + ), + payment_method=sub_data.get("payment_method"), + stripe_subscription_id=sub_data.get( + "stripe_subscription_id" + ), + ) + sub.usage = sub_data.get("usage", sub.usage) + if sub_data.get("usage_reset_date"): + sub.usage_reset_date = datetime.fromisoformat( + sub_data["usage_reset_date"] + ) + self.subscriptions[user_id] = sub + except Exception as e: + logger.error(f"Failed to load subscriptions: {e}") + + def _save_subscriptions(self): + """Save subscriptions to storage""" + try: + subscription_file = self.storage_path / "subscriptions.json" + data = { + user_id: sub.to_dict() for user_id, sub in self.subscriptions.items() + } + with open(subscription_file, "w") as f: + json.dump(data, f, indent=2) + except Exception as e: + logger.error(f"Failed to save subscriptions: {e}") + + def get_subscription(self, user_id: str) -> Subscription: + """Get or create subscription for user""" + if user_id not in self.subscriptions: + self.subscriptions[user_id] = Subscription(user_id) + self._save_subscriptions() + return self.subscriptions[user_id] + + def upgrade_subscription( + self, + user_id: str, + tier: SubscriptionTier, + duration_days: int = 30, + payment_method: Optional[str] = None, + stripe_subscription_id: Optional[str] = None, + ) -> Subscription: + """Upgrade user subscription""" + sub = self.get_subscription(user_id) + sub.tier = tier # Property setter automatically updates _tier_limits + sub.start_date = datetime.now() + sub.end_date = datetime.now() + timedelta(days=duration_days) + sub.payment_method = payment_method + sub.stripe_subscription_id = stripe_subscription_id + self._save_subscriptions() + logger.info(f"Upgraded {user_id} to {tier.value}") + return sub + + def cancel_subscription(self, user_id: str) -> bool: + """Cancel user subscription (downgrade to free at end of period)""" + if user_id in self.subscriptions: + sub = self.subscriptions[user_id] + # Set to expire but don't immediate downgrade (grace period) + logger.info(f"Cancelled subscription for {user_id}, expires {sub.end_date}") + self._save_subscriptions() + return True + return False + + def check_access(self, user_id: str, feature: Feature) -> bool: + """Check if user has access to a feature""" + sub = self.get_subscription(user_id) + return sub.has_feature(feature) + + def track_usage(self, user_id: str, resource: str, amount: int = 1) -> bool: + """Track resource usage, returns False if limit exceeded""" + sub = self.get_subscription(user_id) + result = sub.increment_usage(resource, amount) + if result: + self._save_subscriptions() + return result + + def get_revenue_stats(self) -> Dict[str, Any]: + """Calculate revenue statistics""" + stats = { + "total_subscribers": len(self.subscriptions), + "active_subscribers": 0, + "by_tier": {tier.value: 0 for tier in SubscriptionTier}, + "monthly_recurring_revenue": 0, + "annual_recurring_revenue": 0, + } + + for sub in self.subscriptions.values(): + if sub.is_active(): + stats["active_subscribers"] += 1 + stats["by_tier"][sub.tier.value] += 1 + price = TIER_PRICING[sub.tier] + stats["monthly_recurring_revenue"] += price + + stats["annual_recurring_revenue"] = stats["monthly_recurring_revenue"] * 12 + + return stats + + def get_all_subscriptions(self) -> List[Dict[str, Any]]: + """Get all subscriptions as dictionaries""" + return [sub.to_dict() for sub in self.subscriptions.values()] + + +# Global instance +_subscription_manager: Optional[SubscriptionManager] = None + + +def get_subscription_manager() -> SubscriptionManager: + """Get global subscription manager instance""" + global _subscription_manager + if _subscription_manager is None: + _subscription_manager = SubscriptionManager() + return _subscription_manager diff --git a/shared/telemetry.py b/shared/telemetry.py index 8acc8e56b..3b779267a 100644 --- a/shared/telemetry.py +++ b/shared/telemetry.py @@ -1,51 +1,51 @@ -"""Telemetry initialization for QAI Functions. - -Sets up Azure Monitor OpenTelemetry if APPLICATIONINSIGHTS_CONNECTION_STRING is present. -Falls back gracefully if dependencies are missing. - -Usage: - from shared.telemetry import init_telemetry - init_telemetry() -""" - -from __future__ import annotations - -import logging -import os - -_INITIALIZED = False - - -def init_telemetry() -> bool: - global _INITIALIZED - if _INITIALIZED: - return True - - conn = os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING") - if not conn: - logging.info( - "[telemetry] No Application Insights connection string; telemetry disabled." - ) - return False - - try: - # Azure Monitor OpenTelemetry configuration. - from azure.monitor.opentelemetry import \ - configure_azure_monitor # type: ignore - - configure_azure_monitor(connection_string=conn) - _INITIALIZED = True - logging.info("[telemetry] Azure Monitor OpenTelemetry configured.") - return True - except Exception as e: # pragma: no cover - defensive - logging.warning( - f"[telemetry] Failed to initialize Azure Monitor instrumentation: {e}" - ) - return False - - -def is_enabled() -> bool: - return _INITIALIZED - - -__all__ = ["init_telemetry", "is_enabled"] +"""Telemetry initialization for QAI Functions. + +Sets up Azure Monitor OpenTelemetry if APPLICATIONINSIGHTS_CONNECTION_STRING is present. +Falls back gracefully if dependencies are missing. + +Usage: + from shared.telemetry import init_telemetry + init_telemetry() +""" + +from __future__ import annotations + +import logging +import os + +_INITIALIZED = False + + +def init_telemetry() -> bool: + global _INITIALIZED + if _INITIALIZED: + return True + + conn = os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING") + if not conn: + logging.info( + "[telemetry] No Application Insights connection string; telemetry disabled." + ) + return False + + try: + # Azure Monitor OpenTelemetry configuration. + from azure.monitor.opentelemetry import \ + configure_azure_monitor # type: ignore + + configure_azure_monitor(connection_string=conn) + _INITIALIZED = True + logging.info("[telemetry] Azure Monitor OpenTelemetry configured.") + return True + except Exception as e: # pragma: no cover - defensive + logging.warning( + f"[telemetry] Failed to initialize Azure Monitor instrumentation: {e}" + ) + return False + + +def is_enabled() -> bool: + return _INITIALIZED + + +__all__ = ["init_telemetry", "is_enabled"] diff --git a/shared/token_utils.py b/shared/token_utils.py index e698620d8..cb691b5a0 100644 --- a/shared/token_utils.py +++ b/shared/token_utils.py @@ -1,53 +1,53 @@ -"""Token utilities re-export module for shared infrastructure. - -This module re-exports token utilities from the canonical source at -ai-projects/chat-cli/src/token_utils.py to avoid code duplication while -maintaining backward compatibility for imports from shared/. - -Usage: - from shared.token_utils import prune_messages, count_messages_tokens - # or after adding shared/ to sys.path: - from token_utils import prune_messages -""" - -from __future__ import annotations - -import importlib.util -import sys -from pathlib import Path - -# Load canonical token utils from current chat-cli location, with legacy fallback. -_repo_root = Path(__file__).resolve().parent.parent -_canonical_candidates = [ - _repo_root / "ai-projects" / "chat-cli" / "src" / "token_utils.py", - # Legacy fallback path removed — talk-to-ai migrated to ai-projects/chat-cli -] - -_canonical_path = next((p for p in _canonical_candidates if p.exists()), None) -if _canonical_path is None: - raise FileNotFoundError("token_utils canonical file not found in known locations") - -_spec = importlib.util.spec_from_file_location( - "_canonical_token_utils", _canonical_path -) -if _spec is None or _spec.loader is None: - raise ImportError(f"Unable to load canonical token utils: {_canonical_path}") - -_canonical_module = importlib.util.module_from_spec(_spec) -sys.modules[_spec.name] = _canonical_module -_spec.loader.exec_module(_canonical_module) - -# Re-export all public symbols from canonical token_utils -RoleMessage = _canonical_module.RoleMessage -MODEL_CONTEXT_DEFAULTS = _canonical_module.MODEL_CONTEXT_DEFAULTS -PruneStats = _canonical_module.PruneStats -count_messages_tokens = _canonical_module.count_messages_tokens -prune_messages = _canonical_module.prune_messages - -__all__ = [ - "RoleMessage", - "MODEL_CONTEXT_DEFAULTS", - "PruneStats", - "count_messages_tokens", - "prune_messages", -] +"""Token utilities re-export module for shared infrastructure. + +This module re-exports token utilities from the canonical source at +ai-projects/chat-cli/src/token_utils.py to avoid code duplication while +maintaining backward compatibility for imports from shared/. + +Usage: + from shared.token_utils import prune_messages, count_messages_tokens + # or after adding shared/ to sys.path: + from token_utils import prune_messages +""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path + +# Load canonical token utils from current chat-cli location, with legacy fallback. +_repo_root = Path(__file__).resolve().parent.parent +_canonical_candidates = [ + _repo_root / "ai-projects" / "chat-cli" / "src" / "token_utils.py", + # Legacy fallback path removed — talk-to-ai migrated to ai-projects/chat-cli +] + +_canonical_path = next((p for p in _canonical_candidates if p.exists()), None) +if _canonical_path is None: + raise FileNotFoundError("token_utils canonical file not found in known locations") + +_spec = importlib.util.spec_from_file_location( + "_canonical_token_utils", _canonical_path +) +if _spec is None or _spec.loader is None: + raise ImportError(f"Unable to load canonical token utils: {_canonical_path}") + +_canonical_module = importlib.util.module_from_spec(_spec) +sys.modules[_spec.name] = _canonical_module +_spec.loader.exec_module(_canonical_module) + +# Re-export all public symbols from canonical token_utils +RoleMessage = _canonical_module.RoleMessage +MODEL_CONTEXT_DEFAULTS = _canonical_module.MODEL_CONTEXT_DEFAULTS +PruneStats = _canonical_module.PruneStats +count_messages_tokens = _canonical_module.count_messages_tokens +prune_messages = _canonical_module.prune_messages + +__all__ = [ + "RoleMessage", + "MODEL_CONTEXT_DEFAULTS", + "PruneStats", + "count_messages_tokens", + "prune_messages", +] diff --git a/tests/AUTOTRAIN_TEST_COVERAGE.md b/tests/AUTOTRAIN_TEST_COVERAGE.md index 7303b30c8..80d845347 100644 --- a/tests/AUTOTRAIN_TEST_COVERAGE.md +++ b/tests/AUTOTRAIN_TEST_COVERAGE.md @@ -1,163 +1,163 @@ -# AutoTrain Test Coverage - -Comprehensive test suite for the AutoTrain orchestration system. - -## Test Files - -### 1. `test_autotrain.py` (Smoke Test) -- **Purpose:** Quick end-to-end validation -- **Coverage:** Default config dry-run, status JSON generation -- **Run time:** < 1 second - -### 2. `test_autotrain_unit.py` (Unit Tests) -**24 test cases covering:** - -#### Job Dataclass (2 tests) -- Minimal job creation with defaults -- Full job with all parameters - -#### YAML Parsing (4 tests) -- YAML file reading -- Single job loading -- Multiple job loading -- Edge case: missing name field - -#### HF Command Builder (4 tests) -- Minimal command structure -- Dataset and config parameters -- All override parameters (learning rate, dropout, epochs, samples, seed, model ID, save dir) -- Extra args pass-through - -#### Local Command Builder (5 tests) -- Minimal command structure -- Config path handling (basename extraction) -- Samples and epochs parameters -- Reinstall flag -- Ignoring HF-specific parameters - -#### Validation (2 tests) -- Dry-run detects missing training scripts -- Dry-run detects missing dataset paths - -#### Status JSON (1 test) -- Status collection and structure verification - -#### CLI Parsing (1 test) -- List option JSON output - -#### Edge Cases (5 tests) -- Empty jobs list -- No jobs key in config -- Null/None values in job config -- Empty extra_args list -- Paths with spaces - -### 3. `test_autotrain_integration.py` (Integration Tests) -**14 test cases (excluding 1 slow test):** - -#### CLI Invocation (3 tests) -- `--help` option -- `--list` option with JSON validation -- Missing config file error handling - -#### Dry Run Mode (3 tests) -- Valid config validation -- Status JSON creation -- Invalid dataset path detection - -#### Single Job Execution (2 tests) -- `--job` filter to single job -- Nonexistent job name error - -#### Output Structure (2 tests) -- status.json schema validation -- Timestamped run directory creation - -#### Multi-Job Execution (1 test) -- Sequential execution of multiple jobs -- Order preservation - -#### Error Handling (2 tests) -- Malformed YAML detection -- Missing job name handling - -#### Reinstall Flag (1 test) -- `--reinstall` flag passed to local runner - -#### Slow Tests (1 test, excluded by default) -- Real execution with log creation - -## Running Tests - -### All AutoTrain tests (fast) -```powershell -.\venv\Scripts\python.exe -m pytest tests\ -k "autotrain" -v -m "not slow" -``` - -### Unit tests only -```powershell -.\venv\Scripts\python.exe -m pytest tests\test_autotrain_unit.py -v -``` - -### Integration tests only -```powershell -.\venv\Scripts\python.exe -m pytest tests\test_autotrain_integration.py -v -m "not slow" -``` - -### Include slow tests (actual training execution) -```powershell -.\venv\Scripts\python.exe -m pytest tests\ -k "autotrain" -v -``` - -### Quick smoke test -```powershell -.\venv\Scripts\python.exe -m pytest tests\test_autotrain.py -v -``` - -## Coverage Summary - -| Component | Coverage | -|-----------|----------| -| Job dataclass | ✅ Full | -| YAML parsing | ✅ Full | -| HF command builder | ✅ Full | -| Local command builder | ✅ Full | -| Validation logic | ✅ Core paths | -| Status JSON generation | ✅ Full | -| CLI argument parsing | ✅ Key options | -| Dry-run mode | ✅ Full | -| Multi-job execution | ✅ Sequential flow | -| Error handling | ✅ Common cases | -| Output structure | ✅ Directories and files | - -## Test Execution Time - -- **Unit tests:** ~0.2s -- **Integration tests (no slow):** ~1.5s -- **Total (excluding slow):** ~1.7s -- **With slow tests:** ~60s (adds real subprocess execution) - -## CI/CD Integration - -These tests are suitable for: -- Pre-commit hooks (unit tests only) -- Pull request validation (all non-slow tests) -- Nightly builds (all tests including slow) - -Example GitHub Actions workflow: -```yaml -- name: Test AutoTrain - run: | - python -m pytest tests/ -k "autotrain" -v -m "not slow" --junitxml=test-results.xml -``` - -## Future Test Improvements - -- [ ] Test Azure Blob manifest parsing (requires mock or fixture) -- [ ] Test DeepSpeed config integration -- [ ] Test last_run.json persistence (currently only validates on non-dry-run) -- [ ] Test concurrent job safety (file locking) -- [ ] Test very long job names (path length limits) -- [ ] Test Unicode in job names and dataset paths -- [ ] Performance tests for large job lists -- [ ] Test resume-from checkpoint handling +# AutoTrain Test Coverage + +Comprehensive test suite for the AutoTrain orchestration system. + +## Test Files + +### 1. `test_autotrain.py` (Smoke Test) +- **Purpose:** Quick end-to-end validation +- **Coverage:** Default config dry-run, status JSON generation +- **Run time:** < 1 second + +### 2. `test_autotrain_unit.py` (Unit Tests) +**24 test cases covering:** + +#### Job Dataclass (2 tests) +- Minimal job creation with defaults +- Full job with all parameters + +#### YAML Parsing (4 tests) +- YAML file reading +- Single job loading +- Multiple job loading +- Edge case: missing name field + +#### HF Command Builder (4 tests) +- Minimal command structure +- Dataset and config parameters +- All override parameters (learning rate, dropout, epochs, samples, seed, model ID, save dir) +- Extra args pass-through + +#### Local Command Builder (5 tests) +- Minimal command structure +- Config path handling (basename extraction) +- Samples and epochs parameters +- Reinstall flag +- Ignoring HF-specific parameters + +#### Validation (2 tests) +- Dry-run detects missing training scripts +- Dry-run detects missing dataset paths + +#### Status JSON (1 test) +- Status collection and structure verification + +#### CLI Parsing (1 test) +- List option JSON output + +#### Edge Cases (5 tests) +- Empty jobs list +- No jobs key in config +- Null/None values in job config +- Empty extra_args list +- Paths with spaces + +### 3. `test_autotrain_integration.py` (Integration Tests) +**14 test cases (excluding 1 slow test):** + +#### CLI Invocation (3 tests) +- `--help` option +- `--list` option with JSON validation +- Missing config file error handling + +#### Dry Run Mode (3 tests) +- Valid config validation +- Status JSON creation +- Invalid dataset path detection + +#### Single Job Execution (2 tests) +- `--job` filter to single job +- Nonexistent job name error + +#### Output Structure (2 tests) +- status.json schema validation +- Timestamped run directory creation + +#### Multi-Job Execution (1 test) +- Sequential execution of multiple jobs +- Order preservation + +#### Error Handling (2 tests) +- Malformed YAML detection +- Missing job name handling + +#### Reinstall Flag (1 test) +- `--reinstall` flag passed to local runner + +#### Slow Tests (1 test, excluded by default) +- Real execution with log creation + +## Running Tests + +### All AutoTrain tests (fast) +```powershell +.\venv\Scripts\python.exe -m pytest tests\ -k "autotrain" -v -m "not slow" +``` + +### Unit tests only +```powershell +.\venv\Scripts\python.exe -m pytest tests\test_autotrain_unit.py -v +``` + +### Integration tests only +```powershell +.\venv\Scripts\python.exe -m pytest tests\test_autotrain_integration.py -v -m "not slow" +``` + +### Include slow tests (actual training execution) +```powershell +.\venv\Scripts\python.exe -m pytest tests\ -k "autotrain" -v +``` + +### Quick smoke test +```powershell +.\venv\Scripts\python.exe -m pytest tests\test_autotrain.py -v +``` + +## Coverage Summary + +| Component | Coverage | +|-----------|----------| +| Job dataclass | ✅ Full | +| YAML parsing | ✅ Full | +| HF command builder | ✅ Full | +| Local command builder | ✅ Full | +| Validation logic | ✅ Core paths | +| Status JSON generation | ✅ Full | +| CLI argument parsing | ✅ Key options | +| Dry-run mode | ✅ Full | +| Multi-job execution | ✅ Sequential flow | +| Error handling | ✅ Common cases | +| Output structure | ✅ Directories and files | + +## Test Execution Time + +- **Unit tests:** ~0.2s +- **Integration tests (no slow):** ~1.5s +- **Total (excluding slow):** ~1.7s +- **With slow tests:** ~60s (adds real subprocess execution) + +## CI/CD Integration + +These tests are suitable for: +- Pre-commit hooks (unit tests only) +- Pull request validation (all non-slow tests) +- Nightly builds (all tests including slow) + +Example GitHub Actions workflow: +```yaml +- name: Test AutoTrain + run: | + python -m pytest tests/ -k "autotrain" -v -m "not slow" --junitxml=test-results.xml +``` + +## Future Test Improvements + +- [ ] Test Azure Blob manifest parsing (requires mock or fixture) +- [ ] Test DeepSpeed config integration +- [ ] Test last_run.json persistence (currently only validates on non-dry-run) +- [ ] Test concurrent job safety (file locking) +- [ ] Test very long job names (path length limits) +- [ ] Test Unicode in job names and dataset paths +- [ ] Performance tests for large job lists +- [ ] Test resume-from checkpoint handling diff --git a/tests/conftest.py b/tests/conftest.py index 8bbd7de12..2c2cd9905 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,146 +1,146 @@ -"""Pytest configuration for QAI test suite. - -This conftest ensures that the scripts package is importable during tests. -""" - -import json -import sys -from pathlib import Path -from unittest.mock import Mock - -import pytest - -# Add project root to Python path for importing scripts -# Ensure websockets.client is attached to the websockets namespace. -# In Python 3.14, submodules are not auto-attached on parent import; pyppeteer -# requires websockets.client to be accessible as an attribute. -try: - import websockets - import websockets.client # noqa: F401 — forces attachment to websockets namespace -except ImportError: - pass - -REPO_ROOT = Path(__file__).parent.parent -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -# Make apps/aria importable so tests can do `from server import ...` at the top -_ARIA_APP_DIR = str(REPO_ROOT / "apps" / "aria") -if _ARIA_APP_DIR not in sys.path: - sys.path.insert(0, _ARIA_APP_DIR) - -# ==================== FIXTURES ==================== - - -@pytest.fixture -def temp_data_dir(tmp_path): - """Create temporary data directory for testing""" - data_dir = tmp_path / "data" - data_dir.mkdir() - return data_dir - - -@pytest.fixture -def sample_json_data(): - """Sample JSON data for testing""" - return { - "id": 1, - "name": "test", - "value": 123.45, - "items": [1, 2, 3], - "nested": {"key": "value"}, - } - - -@pytest.fixture -def sample_chat_messages(): - """Sample chat messages for testing""" - return [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - {"role": "user", "content": "How are you?"}, - {"role": "assistant", "content": "I'm doing well, thank you!"}, - ] - - -@pytest.fixture -def mock_openai_client(): - """Mock OpenAI client""" - mock_client = Mock() - mock_response = Mock() - mock_response.choices = [Mock(message=Mock(content="Test response"))] - mock_client.chat.completions.create.return_value = mock_response - return mock_client - - -@pytest.fixture -def sample_training_config(): - """Sample training configuration""" - return { - "model": "TinyLlama/TinyLlama-1.1B", - "dataset": "test_dataset", - "epochs": 10, - "batch_size": 8, - "learning_rate": 1e-4, - "max_seq_length": 512, - } - - -@pytest.fixture -def sample_aria_action(): - """Sample Aria action""" - return {"action": "move", "direction": "left", "distance": 50} - - -@pytest.fixture -def sample_aria_world_state(): - """Sample Aria world state""" - return { - "position": (0, 0), - "holding": None, - "expression": "neutral", - "objects": [], - "world_theme": "default", - } - - -# ==================== HELPER FUNCTIONS ==================== - - -def assert_valid_json(json_str): - """Assert that a string is valid JSON""" - try: - json.loads(json_str) - return True - except json.JSONDecodeError: - return False - - -def assert_dict_keys_exist(data_dict, required_keys): - """Assert that all required keys exist in a dictionary""" - for key in required_keys: - assert key in data_dict, f"Missing required key: {key}" - - -def assert_valid_provider(provider_name): - """Assert that provider name is valid""" - valid_providers = ["azure_openai", "openai", "lmstudio", "local"] - assert provider_name in valid_providers, f"Invalid provider: {provider_name}" - - -# ==================== PYTEST HOOKS ==================== - - -def pytest_configure(config): - """Configure pytest with custom markers""" - config.addinivalue_line( - "markers", "slow: marks tests as slow (deselect with '-m \"not slow\"')" - ) - config.addinivalue_line( - "markers", "azure: marks tests that require Azure credentials" - ) - config.addinivalue_line("markers", "integration: marks tests as integration tests") - config.addinivalue_line( - "markers", "quantum: marks tests that require quantum backends" - ) - config.addinivalue_line("markers", "gpu: marks tests that require GPU") +"""Pytest configuration for QAI test suite. + +This conftest ensures that the scripts package is importable during tests. +""" + +import json +import sys +from pathlib import Path +from unittest.mock import Mock + +import pytest + +# Add project root to Python path for importing scripts +# Ensure websockets.client is attached to the websockets namespace. +# In Python 3.14, submodules are not auto-attached on parent import; pyppeteer +# requires websockets.client to be accessible as an attribute. +try: + import websockets + import websockets.client # noqa: F401 — forces attachment to websockets namespace +except ImportError: + pass + +REPO_ROOT = Path(__file__).parent.parent +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +# Make apps/aria importable so tests can do `from server import ...` at the top +_ARIA_APP_DIR = str(REPO_ROOT / "apps" / "aria") +if _ARIA_APP_DIR not in sys.path: + sys.path.insert(0, _ARIA_APP_DIR) + +# ==================== FIXTURES ==================== + + +@pytest.fixture +def temp_data_dir(tmp_path): + """Create temporary data directory for testing""" + data_dir = tmp_path / "data" + data_dir.mkdir() + return data_dir + + +@pytest.fixture +def sample_json_data(): + """Sample JSON data for testing""" + return { + "id": 1, + "name": "test", + "value": 123.45, + "items": [1, 2, 3], + "nested": {"key": "value"}, + } + + +@pytest.fixture +def sample_chat_messages(): + """Sample chat messages for testing""" + return [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + {"role": "user", "content": "How are you?"}, + {"role": "assistant", "content": "I'm doing well, thank you!"}, + ] + + +@pytest.fixture +def mock_openai_client(): + """Mock OpenAI client""" + mock_client = Mock() + mock_response = Mock() + mock_response.choices = [Mock(message=Mock(content="Test response"))] + mock_client.chat.completions.create.return_value = mock_response + return mock_client + + +@pytest.fixture +def sample_training_config(): + """Sample training configuration""" + return { + "model": "TinyLlama/TinyLlama-1.1B", + "dataset": "test_dataset", + "epochs": 10, + "batch_size": 8, + "learning_rate": 1e-4, + "max_seq_length": 512, + } + + +@pytest.fixture +def sample_aria_action(): + """Sample Aria action""" + return {"action": "move", "direction": "left", "distance": 50} + + +@pytest.fixture +def sample_aria_world_state(): + """Sample Aria world state""" + return { + "position": (0, 0), + "holding": None, + "expression": "neutral", + "objects": [], + "world_theme": "default", + } + + +# ==================== HELPER FUNCTIONS ==================== + + +def assert_valid_json(json_str): + """Assert that a string is valid JSON""" + try: + json.loads(json_str) + return True + except json.JSONDecodeError: + return False + + +def assert_dict_keys_exist(data_dict, required_keys): + """Assert that all required keys exist in a dictionary""" + for key in required_keys: + assert key in data_dict, f"Missing required key: {key}" + + +def assert_valid_provider(provider_name): + """Assert that provider name is valid""" + valid_providers = ["azure_openai", "openai", "lmstudio", "local"] + assert provider_name in valid_providers, f"Invalid provider: {provider_name}" + + +# ==================== PYTEST HOOKS ==================== + + +def pytest_configure(config): + """Configure pytest with custom markers""" + config.addinivalue_line( + "markers", "slow: marks tests as slow (deselect with '-m \"not slow\"')" + ) + config.addinivalue_line( + "markers", "azure: marks tests that require Azure credentials" + ) + config.addinivalue_line("markers", "integration: marks tests as integration tests") + config.addinivalue_line( + "markers", "quantum: marks tests that require quantum backends" + ) + config.addinivalue_line("markers", "gpu: marks tests that require GPU") diff --git a/tests/fixtures/status_schema/ci_results.required.json b/tests/fixtures/status_schema/ci_results.required.json index a3c679b0b..a30abed0f 100644 --- a/tests/fixtures/status_schema/ci_results.required.json +++ b/tests/fixtures/status_schema/ci_results.required.json @@ -1,23 +1,23 @@ -{ - "top_level_required": [ - "generated_at", - "run_id", - "config_path", - "config_paths", - "total_steps", - "succeeded", - "failed", - "skipped", - "results" - ], - "config_paths_required": [ - "autotrain", - "quantum_autorun", - "evaluation_autorun", - "master_orchestrator" - ], - "result_required": [ - "name", - "status" - ] -} +{ + "top_level_required": [ + "generated_at", + "run_id", + "config_path", + "config_paths", + "total_steps", + "succeeded", + "failed", + "skipped", + "results" + ], + "config_paths_required": [ + "autotrain", + "quantum_autorun", + "evaluation_autorun", + "master_orchestrator" + ], + "result_required": [ + "name", + "status" + ] +} diff --git a/tests/fixtures/status_schema/integration_smoke.required.json b/tests/fixtures/status_schema/integration_smoke.required.json index f41808188..375cbac07 100644 --- a/tests/fixtures/status_schema/integration_smoke.required.json +++ b/tests/fixtures/status_schema/integration_smoke.required.json @@ -1,29 +1,29 @@ -{ - "top_level_required": [ - "generated_at", - "run_id", - "config_path", - "config_paths", - "strict_endpoints", - "total_steps", - "succeeded", - "warnings", - "failed", - "skipped", - "errors", - "passed", - "results" - ], - "config_paths_required": [ - "master_orchestrator", - "quantum_autorun", - "evaluation_autorun" - ], - "result_required": [ - "name", - "status", - "critical", - "duration_sec", - "detail" - ] -} +{ + "top_level_required": [ + "generated_at", + "run_id", + "config_path", + "config_paths", + "strict_endpoints", + "total_steps", + "succeeded", + "warnings", + "failed", + "skipped", + "errors", + "passed", + "results" + ], + "config_paths_required": [ + "master_orchestrator", + "quantum_autorun", + "evaluation_autorun" + ], + "result_required": [ + "name", + "status", + "critical", + "duration_sec", + "detail" + ] +} diff --git a/tests/test_agi_provider.py b/tests/test_agi_provider.py index 0175b6b93..ea2c6dbf4 100644 --- a/tests/test_agi_provider.py +++ b/tests/test_agi_provider.py @@ -1,700 +1,700 @@ -""" -Unit tests for the AGI (Artificial General Intelligence) provider. - -Tests cover: -- AGI provider initialization -- Chain-of-thought reasoning -- Task decomposition -- Self-reflection -- Memory/context management -- Integration with base providers -""" - -import sys -from pathlib import Path -from typing import Iterable - -import pytest - -from agi_provider import (AGIContext, AGIProvider, ReasoningStep, - _infer_aria_movement_tag, create_agi_provider) -from chat_providers import BaseChatProvider, ProviderChoice, RoleMessage - -# Add ai-projects/chat-cli/src to path -repo_root = Path(__file__).resolve().parent.parent -talk_to_ai_src = repo_root / "ai-projects" / "chat-cli" / "src" -sys.path.insert(0, str(talk_to_ai_src)) - - -class MockBaseProvider(BaseChatProvider): - """Mock provider for testing AGI enhancement.""" - - def __init__(self, response: str = "Mock response"): - self.response = response - self.call_count = 0 - self.last_messages = None - - def complete( - self, messages: list[RoleMessage], stream: bool = True - ) -> Iterable[str] | str: - self.call_count += 1 - self.last_messages = messages - if stream: - - def gen(): - yield self.response - - return gen() - return self.response - - -class TestAGIContext: - """Tests for AGIContext memory management.""" - - def test_context_initialization(self): - """Test AGIContext initializes with empty state.""" - ctx = AGIContext() - assert ctx.conversation_history == [] - assert ctx.reasoning_chains == [] - assert ctx.goals == [] - assert ctx.learned_patterns == {} - assert ctx.max_history == 50 - - def test_add_message(self): - """Test adding messages to context.""" - ctx = AGIContext() - msg = {"role": "user", "content": "Hello"} - ctx.add_message(msg) - assert len(ctx.conversation_history) == 1 - assert ctx.conversation_history[0] == msg - - def test_message_pruning(self): - """Test that old messages are pruned when max_history is reached.""" - ctx = AGIContext(max_history=5) - - # Add a system message - ctx.add_message({"role": "system", "content": "System prompt"}) - - # Add more messages than max_history - for i in range(10): - ctx.add_message({"role": "user", "content": f"Message {i}"}) - - # Should have kept system + last 4 messages - assert len(ctx.conversation_history) == 5 - # System message should be preserved - assert ctx.conversation_history[0]["role"] == "system" - - def test_add_reasoning_chain(self): - """Test adding reasoning chains.""" - ctx = AGIContext() - chain = [ReasoningStep(step_type="analyze", content="Test analysis")] - ctx.add_reasoning_chain(chain) - assert len(ctx.reasoning_chains) == 1 - assert ctx.reasoning_chains[0] == chain - - def test_reasoning_chain_limit(self): - """Test that only last 10 reasoning chains are kept.""" - ctx = AGIContext() - - for i in range(15): - chain = [ReasoningStep(step_type="analyze", content=f"Chain {i}")] - ctx.add_reasoning_chain(chain) - - assert len(ctx.reasoning_chains) == 10 - # Should have chains 5-14 - assert "Chain 5" in ctx.reasoning_chains[0][0].content - assert "Chain 14" in ctx.reasoning_chains[-1][0].content - - def test_get_relevant_context(self): - """Test extracting relevant context for a query.""" - ctx = AGIContext() - ctx.add_message({"role": "user", "content": "What is quantum computing?"}) - ctx.add_message( - {"role": "assistant", "content": "Quantum computing uses qubits..."} - ) - ctx.goals = ["Learn about quantum"] - - context = ctx.get_relevant_context("Tell me more") - - assert "Recent conversation:" in context - assert "user:" in context - assert "Active goals:" in context - assert "Learn about quantum" in context - - -class TestReasoningStep: - """Tests for ReasoningStep dataclass.""" - - def test_basic_step(self): - """Test creating a basic reasoning step.""" - step = ReasoningStep(step_type="analyze", content="Analyzing the query") - assert step.step_type == "analyze" - assert step.content == "Analyzing the query" - assert step.confidence == 1.0 - assert step.metadata == {} - - def test_step_with_metadata(self): - """Test creating a step with metadata.""" - step = ReasoningStep( - step_type="decompose", - content="Breaking down task", - confidence=0.8, - metadata={"subtasks": ["task1", "task2"]}, - ) - assert step.confidence == 0.8 - assert step.metadata["subtasks"] == ["task1", "task2"] - - -class TestAGIHelpers: - """Tests for AGI helper utilities.""" - - def test_infer_aria_movement_tag_supports_vertical_and_spin(self): - assert _infer_aria_movement_tag("Move up") == "[aria:walk:up]" - assert _infer_aria_movement_tag("Go down") == "[aria:walk:down]" - assert _infer_aria_movement_tag("Spin around") == "[aria:spin]" - - -class TestAGIProvider: - """Tests for AGIProvider functionality.""" - - def test_initialization_defaults(self): - """Test AGI provider initializes with sensible defaults.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - assert agi.temperature == 0.7 - assert agi.max_output_tokens == 2048 - assert agi.enable_chain_of_thought is True - assert agi.enable_self_reflection is True - assert agi.enable_task_decomposition is True - assert agi.reasoning_depth == 3 - assert agi.verbose is False - - def test_initialization_custom_settings(self): - """Test AGI provider with custom settings.""" - mock_provider = MockBaseProvider() - agi = AGIProvider( - base_provider=mock_provider, - temperature=0.5, - max_output_tokens=1024, - enable_chain_of_thought=False, - reasoning_depth=5, - verbose=True, - ) - - assert agi.temperature == 0.5 - assert agi.max_output_tokens == 1024 - assert agi.enable_chain_of_thought is False - assert agi.reasoning_depth == 5 - assert agi.verbose is True - - def test_reasoning_depth_bounds(self): - """Test reasoning depth is bounded between 1 and 5.""" - mock_provider = MockBaseProvider() - - # Test minimum bound - agi = AGIProvider(base_provider=mock_provider, reasoning_depth=0) - assert agi.reasoning_depth == 1 - - # Test maximum bound - agi = AGIProvider(base_provider=mock_provider, reasoning_depth=10) - assert agi.reasoning_depth == 5 - - def test_complete_simple_query(self): - """Test completing a simple query.""" - mock_provider = MockBaseProvider(response="Test response") - agi = AGIProvider(base_provider=mock_provider) - - messages = [{"role": "user", "content": "Hello"}] - result = agi.complete(messages, stream=False) - - assert isinstance(result, str) - assert len(result) > 0 - - def test_complete_streaming(self): - """Test streaming response.""" - mock_provider = MockBaseProvider(response="Streaming test") - agi = AGIProvider(base_provider=mock_provider) - - messages = [{"role": "user", "content": "Stream test"}] - result = agi.complete(messages, stream=True) - - # Should return an iterable - chunks = list(result) - assert len(chunks) > 0 - full_response = "".join(chunks) - assert len(full_response) > 0 - - def test_complete_empty_query(self): - """Test handling empty query.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - messages = [{"role": "user", "content": " "}] - result = agi.complete(messages, stream=False) - - assert "ready to help" in result.lower() - - def test_query_analysis_simple(self): - """Test query analysis for simple queries.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - analysis = agi._analyze_query("Hello") - - assert analysis["complexity"] == "simple" - assert analysis["word_count"] == 1 - assert analysis["has_question"] is False - - def test_query_analysis_complex(self): - """Test query analysis for complex queries.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - # Query with "step by step" triggers complex analysis - query = "Can you provide a step by step detailed explanation of how quantum entanglement works in quantum computing and what are the practical applications?" - analysis = agi._analyze_query(query) - - assert analysis["complexity"] == "complex" - assert analysis["has_question"] is True - assert "quantum" in analysis["domain"] - - def test_query_analysis_movement_intent(self): - """Test query analysis detects Aria movement intent.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - analysis = agi._analyze_query("Move Aria to the left") - - assert analysis["intent"] == "movement" - assert analysis["domain"] == "aria" - - def test_query_analysis_movement_defaults_to_aria_domain(self): - """Bare movement commands should still resolve to Aria domain.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - analysis = agi._analyze_query("Jump left") - - assert analysis["intent"] == "movement" - assert analysis["domain"] == "aria" - - def test_query_analysis_coding_intent(self): - """Test query analysis detects coding intent.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - analysis = agi._analyze_query("Write a Python function to sort a list") - - assert analysis["intent"] == "coding" - assert analysis["domain"] == "technical" - - def test_task_decomposition_explanation(self): - """Test task decomposition for explanation queries.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - analysis = {"intent": "explanation", "domain": "general"} - subtasks = agi._decompose_task("Explain machine learning", analysis) - - assert len(subtasks) > 0 - assert len(subtasks) <= 3 # Limited by reasoning_depth - assert "concepts" in subtasks[0].lower() or "define" in subtasks[0].lower() - - def test_task_decomposition_coding(self): - """Test task decomposition for coding queries.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - analysis = {"intent": "coding", "domain": "technical"} - subtasks = agi._decompose_task("Write a sorting algorithm", analysis) - - assert len(subtasks) > 0 - assert any( - "requirement" in s.lower() or "understand" in s.lower() for s in subtasks - ) - - def test_chain_of_thought(self): - """Test chain-of-thought reasoning generation.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - analysis = {"intent": "question", "domain": "quantum", "complexity": "moderate"} - messages = [{"role": "user", "content": "What is a qubit?"}] - - thoughts = agi._chain_of_thought("What is a qubit?", analysis, messages) - - assert len(thoughts) > 0 - assert any("quantum" in t.lower() for t in thoughts) - - def test_self_reflection_aria_movement(self): - """Test self-reflection adds Aria movement tags when needed.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - reasoning_chain = [ - ReasoningStep( - step_type="analyze", - content="Movement request", - metadata={"intent": "movement", "domain": "aria"}, - ) - ] - - response = agi._reflect_and_improve( - "Move Aria left", "I'll move to the left!", reasoning_chain - ) - - assert "[aria:walk:left]" in response - - def test_self_reflection_aria_spin(self): - """Test self-reflection adds Aria spin tag when needed.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - reasoning_chain = [ - ReasoningStep( - step_type="analyze", - content="Movement request", - metadata={"intent": "movement", "domain": "aria"}, - ) - ] - - response = agi._reflect_and_improve( - "Spin Aria around", "Spinning now!", reasoning_chain - ) - - assert "[aria:spin]" in response - - def test_goal_management(self): - """Test setting and clearing goals.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - # Set goals - agi.set_goal("Learn quantum computing") - agi.set_goal("Build an AI assistant") - - assert len(agi.context.goals) == 2 - - # Clear goals - agi.clear_goals() - assert len(agi.context.goals) == 0 - - def test_goal_limit(self): - """Test that goals are limited to 5.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - for i in range(10): - agi.set_goal(f"Goal {i}") - - assert len(agi.context.goals) == 5 - # Should have the last 5 goals - assert "Goal 9" in agi.context.goals[-1] - - def test_reasoning_summary(self): - """Test getting reasoning summary.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - # Add some state - agi.set_goal("Test goal") - agi.context.add_reasoning_chain( - [ReasoningStep(step_type="analyze", content="Test")] - ) - - summary = agi.get_reasoning_summary() - - assert summary["total_reasoning_chains"] == 1 - assert summary["active_goals"] == ["Test goal"] - assert isinstance(summary["learned_patterns_count"], int) - - def test_verbose_output(self): - """Test verbose mode includes reasoning steps.""" - mock_provider = MockBaseProvider(response="Test response") - agi = AGIProvider(base_provider=mock_provider, verbose=True) - - messages = [{"role": "user", "content": "Explain something"}] - result = agi.complete(messages, stream=False) - - assert "AGI Reasoning Process" in result - assert "Step 1" in result - - def test_fallback_response(self): - """Test fallback response generation.""" - mock_provider = MockBaseProvider() - agi = AGIProvider(base_provider=mock_provider) - - analysis = {"intent": "movement", "domain": "aria", "has_question": False} - - # Test movement fallback - response = agi._generate_fallback_response("Move left", analysis) - assert "[aria:walk:left]" in response - - response = agi._generate_fallback_response("Jump", analysis) - assert "[aria:jump]" in response - - def test_context_updates_during_complete(self): - """Test that context is updated during completion.""" - mock_provider = MockBaseProvider(response="Test") - agi = AGIProvider(base_provider=mock_provider) - - messages = [ - {"role": "system", "content": "You are helpful"}, - {"role": "user", "content": "Hello"}, - ] - - agi.complete(messages, stream=False) - - # Context should have been updated - assert len(agi.context.conversation_history) == 2 - assert len(agi.context.reasoning_chains) >= 1 - - -class TestCreateAGIProvider: - """Tests for the create_agi_provider factory function.""" - - def test_create_default(self): - """Test creating AGI provider with defaults.""" - provider, info = create_agi_provider() - - assert isinstance(provider, AGIProvider) - assert info.name == "agi" - assert "agi" in info.model.lower() - - def test_create_uses_auto_detected_base_provider( - self, monkeypatch: pytest.MonkeyPatch - ): - """Factory should wrap the best available non-AGI provider.""" - base = MockBaseProvider("auto wrapped") - - def fake_detect_provider( - explicit=None, model_override=None, temperature=None, max_output_tokens=None - ): - assert explicit == "auto" - return base, ProviderChoice( - name="openai", model=model_override or "gpt-test" - ) - - monkeypatch.setitem( - create_agi_provider.__globals__, "detect_provider", fake_detect_provider - ) - - provider, info = create_agi_provider(model="gpt-4") - - assert provider.base_provider is base - assert info.name == "agi" - assert info.model == "agi-openai-gpt-4" - - def test_create_with_options(self): - """Test creating AGI provider with custom options.""" - provider, info = create_agi_provider( - temperature=0.5, max_output_tokens=1024, verbose=True - ) - - assert provider.temperature == 0.5 - assert provider.max_output_tokens == 1024 - assert provider.verbose is True - - def test_create_with_model(self): - """Test creating AGI provider with model override.""" - provider, info = create_agi_provider(model="gpt-4") - - assert "gpt-4" in info.model - - -class TestProviderIntegration: - """Integration tests with the provider detection system.""" - - def test_detect_agi_provider(self): - """Test that AGI provider can be detected.""" - from chat_providers import detect_provider - - provider, info = detect_provider(explicit="agi") - - assert info.name == "agi" - assert isinstance(provider, AGIProvider) - - def test_agi_provider_with_messages(self): - """Test AGI provider processes messages correctly.""" - from chat_providers import detect_provider - - provider, info = detect_provider(explicit="agi") - - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "What is 2+2?"}, - ] - - result = provider.complete(messages, stream=False) - - assert isinstance(result, str) - assert len(result) > 0 - - -# Smoke test for basic functionality -def test_agi_smoke(): - """Smoke test for AGI provider.""" - mock_provider = MockBaseProvider(response="Smoke test passed") - agi = AGIProvider(base_provider=mock_provider) - - result = agi.complete( - [{"role": "user", "content": "What is 2 plus 2?"}], stream=False - ) - - assert len(result) > 0 - assert mock_provider.call_count == 1 - - -class TestAGISecurity: - """Security tests for AGI provider input sanitization and validation.""" - - def test_sanitize_input_null_bytes(self): - """Test that null bytes are removed from input.""" - from agi_provider import _sanitize_input - - malicious = "Hello\x00World\x00!" - result = _sanitize_input(malicious) - - assert "\x00" not in result - assert "HelloWorld!" in result - - def test_sanitize_input_control_chars(self): - """Test that control characters are removed.""" - from agi_provider import _sanitize_input - - malicious = "Hello\x01\x02\x03World" - result = _sanitize_input(malicious) - - assert "\x01" not in result - assert "\x02" not in result - assert "HelloWorld" in result - - def test_sanitize_input_length_limit(self): - """Test that input is truncated to max length.""" - from agi_provider import MAX_INPUT_LENGTH, _sanitize_input - - long_input = "A" * (MAX_INPUT_LENGTH + 1000) - result = _sanitize_input(long_input) - - assert len(result) == MAX_INPUT_LENGTH - - def test_sanitize_input_non_string(self): - """Test that non-string input returns empty string.""" - from agi_provider import _sanitize_input - - assert _sanitize_input(None) == "" - assert _sanitize_input(123) == "" - assert _sanitize_input([1, 2, 3]) == "" - - def test_sanitize_for_logging_escapes_html(self): - """Test that HTML is escaped in logging output.""" - from agi_provider import _sanitize_for_logging - - malicious = "" - result = _sanitize_for_logging(malicious) - - assert "" + result = _sanitize_for_logging(malicious) + + assert 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    - 👑 Admin - 🏠 Home -
    -
    - - -
    -
    -
    💰
    -
    Monthly Revenue
    -
    $2,235
    -
    ↑ 11.8% vs target
    -
    - -
    -
    👥
    -
    Active Subscribers
    -
    15
    -
    ↑ 15 this month
    -
    - -
    -
    📈
    -
    Growth Rate
    -
    +25%
    -
    ↑ Month over month
    -
    - -
    -
    💳
    -
    Conversion Rate
    -
    8.5%
    -
    ↑ 2.5% vs last month
    -
    -
    - - -
    -

    Revenue Trends

    -
    - - -
    -
    - -
    -
    - - -
    -
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    Subscribers by Tier

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    - -
    -
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    Revenue by Tier

    -
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    - - -
    -

    Usage Analytics

    -
    - -
    -
    - - -
    -

    Top Subscribers by Revenue

    - - - - - - - - - - - - - - -
    RankUser IDTierMRRLifetime ValueStatus
    -
    -
    - - -
    - - - + + + + + + Analytics Dashboard - Aria Platform + + + + +
    +
    +

    📊 Analytics Dashboard

    +
    + 👑 Admin + 🏠 Home +
    +
    + + +
    +
    +
    💰
    +
    Monthly Revenue
    +
    $2,235
    +
    ↑ 11.8% vs target
    +
    + +
    +
    👥
    +
    Active Subscribers
    +
    15
    +
    ↑ 15 this month
    +
    + +
    +
    📈
    +
    Growth Rate
    +
    +25%
    +
    ↑ Month over month
    +
    + +
    +
    💳
    +
    Conversion Rate
    +
    8.5%
    +
    ↑ 2.5% vs last month
    +
    +
    + + +
    +

    Revenue Trends

    +
    + + +
    +
    + +
    +
    + + +
    +
    +

    Subscribers by Tier

    +
    + +
    +
    + +
    +

    Revenue by Tier

    +
    + +
    +
    +
    + + +
    +

    Usage Analytics

    +
    + +
    +
    + + +
    +

    Top Subscribers by Revenue

    + + + + + + + + + + + + + + +
    RankUser IDTierMRRLifetime ValueStatus
    +
    +
    + + +
    + + + diff --git a/web/monetization/checkout.html b/web/monetization/checkout.html index a45d4088e..8dbaf0223 100644 --- a/web/monetization/checkout.html +++ b/web/monetization/checkout.html @@ -1,525 +1,525 @@ - - - - - - Checkout - Aria Platform - - - -
    - ← Back to Pricing - -
    -

    🛒 Complete Your Purchase

    -

    Join thousands of users powering their AI with Aria

    -
    - -
    - ℹ️ Demo Mode: This is a demonstration checkout page. In production, this will integrate with Stripe for secure payment processing. -
    - -
    - -
    -

    Payment Information

    - -
    -
    - - -
    - -
    - - -
    - -
    - - -
    - -
    -
    - - -
    -
    - - -
    -
    - -
    - - -
    - -
    - -
    - - - - -
    - -
    - 🔒 Secured by 256-bit SSL encryption -
    - -
    - 💳 Powered by Stripe
    - In production, payments are processed securely through Stripe.
    - Your card information is never stored on our servers. -
    -
    - - -
    -

    Order Summary

    - -
    Pro Plan
    - -
    -
    - Plan - Pro -
    -
    - Billing - Monthly -
    -
    - Subtotal - $49.00 -
    -
    - Tax - $0.00 -
    -
    - Total - $49.00 -
    -
    - -

    What's Included

    -
      -
    • 10,000 Chat Messages/month
      • -
    • Full Aria Character Suite
      • -
    • Quantum Computing (50 jobs/mo)
      • -
    • Advanced Training (20 hrs/mo)
      • -
    • Website Maker (10 sites/mo)
      • -
    • API Access (10K requests/mo)
      • -
    • Commercial License
      • -
    - -
    - 💰 Save 20% with Annual Billing! -
    - -
    - We accept: - 💳 Visa - 💳 Mastercard - 💳 Amex - 💰 PayPal -
    -
    -
    -
    - - - - + + + + + + Checkout - Aria Platform + + + +
    + ← Back to Pricing + +
    +

    🛒 Complete Your Purchase

    +

    Join thousands of users powering their AI with Aria

    +
    + +
    + ℹ️ Demo Mode: This is a demonstration checkout page. In production, this will integrate with Stripe for secure payment processing. +
    + +
    + +
    +

    Payment Information

    + +
    +
    + + +
    + +
    + + +
    + +
    + + +
    + +
    +
    + + +
    +
    + + +
    +
    + +
    + + +
    + +
    + +
    + + + + +
    + +
    + 🔒 Secured by 256-bit SSL encryption +
    + +
    + 💳 Powered by Stripe
    + In production, payments are processed securely through Stripe.
    + Your card information is never stored on our servers. +
    +
    + + +
    +

    Order Summary

    + +
    Pro Plan
    + +
    +
    + Plan + Pro +
    +
    + Billing + Monthly +
    +
    + Subtotal + $49.00 +
    +
    + Tax + $0.00 +
    +
    + Total + $49.00 +
    +
    + +

    What's Included

    +
      +
    • 10,000 Chat Messages/month
      • +
    • Full Aria Character Suite
      • +
    • Quantum Computing (50 jobs/mo)
      • +
    • Advanced Training (20 hrs/mo)
      • +
    • Website Maker (10 sites/mo)
      • +
    • API Access (10K requests/mo)
      • +
    • Commercial License
      • +
    + +
    + 💰 Save 20% with Annual Billing! +
    + +
    + We accept: + 💳 Visa + 💳 Mastercard + 💳 Amex + 💰 PayPal +
    +
    +
    +
    + + + + diff --git a/web/monetization/monetization-index.html b/web/monetization/monetization-index.html index 98278e4fd..a38c5f298 100644 --- a/web/monetization/monetization-index.html +++ b/web/monetization/monetization-index.html @@ -1,485 +1,485 @@ - - - - - - - Aria Platform - AI-Powered Platform with Monetization - - - - -
    -

    🎭 Aria Platform

    -

    AI-Powered Platform with Complete Monetization System

    -
    - 👤 Try Aria - 💰 View Pricing - 📊 My Subscription - 👑 Admin Dashboard -
    -
    - -
    - -
    -

    🎉 $2,000+ Monthly Income Stream Achieved!

    -
    -
    -
    $2,235
    -
    Monthly Recurring Revenue
    -
    -
    -
    $26,820
    -
    Annual Revenue
    -
    -
    -
    15
    -
    Target Subscribers
    -
    -
    -
    111.8%
    -
    Target Achievement
    -
    -
    -
    - - -
    -

    ✨ Platform Features

    -
    -
    -
    🎭
    -

    Aria Character

    -

    Interactive 3D AI character with natural language movement and gestures

    -
    - -
    -
    ⚛️
    -

    Quantum Computing

    -

    Quantum ML integration for advanced computational tasks

    -
    - -
    -
    🤖
    -

    Advanced Training

    -

    LoRA fine-tuning and custom model training capabilities

    -
    - -
    -
    🏗️
    -

    Website Maker

    -

    AI-powered website generation with LLM tool creation

    -
    - -
    -
    🔌
    -

    API Access

    -

    RESTful API for integrating Aria into your applications

    -
    - -
    -
    💼
    -

    Commercial License

    -

    Pro and Enterprise tiers include commercial usage rights

    -
    -
    -
    - - -
    -

    📄 Platform Pages

    -
    - -

    👤 Aria Character

    -

    Interactive 3D AI character with natural language commands. The main platform experience.

    -     - - -

    💰 Pricing Page

    -

    View all subscription tiers and features. Beautiful 3-tier comparison with revenue projections. -

    -     - - -

    👑 Admin Dashboard

    -

    Revenue analytics and subscriber management. Real-time MRR/ARR tracking.

    -     - - -

    📊 My Subscription

    -

    User subscription dashboard with usage tracking and feature access.

    -     - - -

    🛒 Checkout Page

    -

    Secure payment processing interface. Stripe-ready integration.

    -     - - -

    ✅ Success Page

    -

    Post-payment confirmation with onboarding steps.

    -     - - -

    ⚙️ Account Settings

    -

    Profile, billing, API keys, notifications, and security settings.

    -     -
    -
    - - -
    -

    🚀 Getting Started

    - -
    -

    Quick Links

    - -
    - -
    -

    API Endpoints

    -
      -
    • 🔗 GET /api/subscription/pricing - Get pricing tiers
      • -
    • 🔗 GET /api/subscription/status - Check user subscription
      • -
    • 🔗 POST /api/subscription/upgrade - Upgrade subscription
      • -
    • 🔗 GET /api/subscription/revenue - Revenue statistics
      • -
    • 🔗 POST /api/subscription/usage - Track usage
      • -
    -
    - -
    -

    Development Commands

    -
      -
    • 🖥️ python3 setup_monetization.py - Run setup script
      • -
    • 🌐 python3 -m http.server 8000 - Start test server
      • -
    • ⚡ func host start - Start Azure Functions
      • -
    • 🧪 curl http://localhost:7071/api/subscription/revenue | jq - Test API
      • -
    -
    -
    - - -
    -

    💎 Subscription Tiers

    -
    -
    -
    🆓
    -

    Free Tier

    -

    $0/month

    -

    100 chat messages/mo
    Basic Aria character
    Perfect for trying out

    -
    - -
    -
    ⭐
    -

    Pro Tier

    -

    $49/month

    -

    10K messages/mo
    Quantum computing
    Advanced training

    -
    - -
    -
    👑
    -

    Enterprise Tier

    -

    $199/month

    -

    Unlimited everything
    Custom models
    Priority support 24/7

    -
    -
    - -
    -
    - -
    -

    © 2026 Aria Platform | GitHub

    -
    - -
    -
    - - - - + + + + + + + Aria Platform - AI-Powered Platform with Monetization + + + + +
    +

    🎭 Aria Platform

    +

    AI-Powered Platform with Complete Monetization System

    +
    + 👤 Try Aria + 💰 View Pricing + 📊 My Subscription + 👑 Admin Dashboard +
    +
    + +
    + +
    +

    🎉 $2,000+ Monthly Income Stream Achieved!

    +
    +
    +
    $2,235
    +
    Monthly Recurring Revenue
    +
    +
    +
    $26,820
    +
    Annual Revenue
    +
    +
    +
    15
    +
    Target Subscribers
    +
    +
    +
    111.8%
    +
    Target Achievement
    +
    +
    +
    + + +
    +

    ✨ Platform Features

    +
    +
    +
    🎭
    +

    Aria Character

    +

    Interactive 3D AI character with natural language movement and gestures

    +
    + +
    +
    ⚛️
    +

    Quantum Computing

    +

    Quantum ML integration for advanced computational tasks

    +
    + +
    +
    🤖
    +

    Advanced Training

    +

    LoRA fine-tuning and custom model training capabilities

    +
    + +
    +
    🏗️
    +

    Website Maker

    +

    AI-powered website generation with LLM tool creation

    +
    + +
    +
    🔌
    +

    API Access

    +

    RESTful API for integrating Aria into your applications

    +
    + +
    +
    💼
    +

    Commercial License

    +

    Pro and Enterprise tiers include commercial usage rights

    +
    +
    +
    + + +
    +

    📄 Platform Pages

    +
    + +

    👤 Aria Character

    +

    Interactive 3D AI character with natural language commands. The main platform experience.

    +     + + +

    💰 Pricing Page

    +

    View all subscription tiers and features. Beautiful 3-tier comparison with revenue projections. +

    +     + + +

    👑 Admin Dashboard

    +

    Revenue analytics and subscriber management. Real-time MRR/ARR tracking.

    +     + + +

    📊 My Subscription

    +

    User subscription dashboard with usage tracking and feature access.

    +     + + +

    🛒 Checkout Page

    +

    Secure payment processing interface. Stripe-ready integration.

    +     + + +

    ✅ Success Page

    +

    Post-payment confirmation with onboarding steps.

    +     + + +

    ⚙️ Account Settings

    +

    Profile, billing, API keys, notifications, and security settings.

    +     +
    +
    + + +
    +

    🚀 Getting Started

    + +
    +

    Quick Links

    + +
    + +
    +

    API Endpoints

    +
      +
    • 🔗 GET /api/subscription/pricing - Get pricing tiers
      • +
    • 🔗 GET /api/subscription/status - Check user subscription
      • +
    • 🔗 POST /api/subscription/upgrade - Upgrade subscription
      • +
    • 🔗 GET /api/subscription/revenue - Revenue statistics
      • +
    • 🔗 POST /api/subscription/usage - Track usage
      • +
    +
    + +
    +

    Development Commands

    +
      +
    • 🖥️ python3 setup_monetization.py - Run setup script
      • +
    • 🌐 python3 -m http.server 8000 - Start test server
      • +
    • ⚡ func host start - Start Azure Functions
      • +
    • 🧪 curl http://localhost:7071/api/subscription/revenue | jq - Test API
      • +
    +
    +
    + + +
    +

    💎 Subscription Tiers

    +
    +
    +
    🆓
    +

    Free Tier

    +

    $0/month

    +

    100 chat messages/mo
    Basic Aria character
    Perfect for trying out

    +
    + +
    +
    ⭐
    +

    Pro Tier

    +

    $49/month

    +

    10K messages/mo
    Quantum computing
    Advanced training

    +
    + +
    +
    👑
    +

    Enterprise Tier

    +

    $199/month

    +

    Unlimited everything
    Custom models
    Priority support 24/7

    +
    +
    + +
    +
    + +
    +

    © 2026 Aria Platform | GitHub

    +
    + +
    +
    + + + + diff --git a/web/monetization/my-subscription.html b/web/monetization/my-subscription.html index ef41efcef..89e1e5923 100644 --- a/web/monetization/my-subscription.html +++ b/web/monetization/my-subscription.html @@ -1,679 +1,679 @@ - - - - - - My Subscription - Aria Platform - - - -
    -
    -

    🎭 My Subscription

    -
    - 🏠 Home - 💰 Pricing - ⚙️ Account -
    -
    - - - -
    - -
    -

    Current Plan

    -
    Free Tier
    - -
    - Status - Active -
    -
    - Monthly Cost - $0 -
    -
    - Billing Cycle - N/A -
    -
    - Next Billing Date - N/A -
    - -
    - - - -
    -
    - - -
    -

    Usage Statistics

    -

    Current billing period usage

    - -
    -
    -
    - 💬 Chat Messages - 0 / 100 -
    -
    -
    -
    -
    - -
    -
    - ⚛️ Quantum Jobs - Not Available -
    -
    -
    -
    -
    - -
    -
    - 🎓 Training Hours - Not Available -
    -
    -
    -
    -
    - -
    -
    - 🌐 API Requests - Not Available -
    -
    -
    -
    -
    - -
    -
    - 🏗️ Websites Created - Not Available -
    -
    -
    -
    -
    -
    -
    - - -
    -

    Plan Features

    -
      -
    • ✓ Basic Chat (100 messages/mo)
      • -
    • ✓ Aria Character Access
      • -
    • ✗ Quantum Computing
      • -
    • ✗ Advanced Training
      • -
    • ✗ Website Maker
      • -
    • ✗ API Access
      • -
    • ✗ Commercial License
      • -
    • ✗ Custom Models
      • -
    • ✗ Priority Support
      • -
    -
    - - -
    -

    Billing History

    -
    - - - - - - - - - - - - - - -
    DateDescriptionAmountStatus
    - No billing history yet -
    -
    -
    -
    -
    - - -
    - - - + + + + + + My Subscription - Aria Platform + + + +
    +
    +

    🎭 My Subscription

    +
    + 🏠 Home + 💰 Pricing + ⚙️ Account +
    +
    + + + +
    + +
    +

    Current Plan

    +
    Free Tier
    + +
    + Status + Active +
    +
    + Monthly Cost + $0 +
    +
    + Billing Cycle + N/A +
    +
    + Next Billing Date + N/A +
    + +
    + + + +
    +
    + + +
    +

    Usage Statistics

    +

    Current billing period usage

    + +
    +
    +
    + 💬 Chat Messages + 0 / 100 +
    +
    +
    +
    +
    + +
    +
    + ⚛️ Quantum Jobs + Not Available +
    +
    +
    +
    +
    + +
    +
    + 🎓 Training Hours + Not Available +
    +
    +
    +
    +
    + +
    +
    + 🌐 API Requests + Not Available +
    +
    +
    +
    +
    + +
    +
    + 🏗️ Websites Created + Not Available +
    +
    +
    +
    +
    +
    +
    + + +
    +

    Plan Features

    +
      +
    • ✓ Basic Chat (100 messages/mo)
      • +
    • ✓ Aria Character Access
      • +
    • ✗ Quantum Computing
      • +
    • ✗ Advanced Training
      • +
    • ✗ Website Maker
      • +
    • ✗ API Access
      • +
    • ✗ Commercial License
      • +
    • ✗ Custom Models
      • +
    • ✗ Priority Support
      • +
    +
    + + +
    +

    Billing History

    +
    + + + + + + + + + + + + + + +
    DateDescriptionAmountStatus
    + No billing history yet +
    +
    +
    +
    +
    + + +
    + + + diff --git a/web/monetization/pricing.html b/web/monetization/pricing.html index 591434293..653d771e7 100644 --- a/web/monetization/pricing.html +++ b/web/monetization/pricing.html @@ -1,664 +1,664 @@ - - - - - - Aria - Pricing & Plans - - - -
    -
    -

    🎭 Aria Platform Pricing

    -

    Choose the perfect plan for your AI-powered journey

    -
    - 💰 Target: $2,000+ Monthly Recurring Revenue -
    -
    - -
    - -
    -
    Free
    -
    - $0 - /month -
    -

    Perfect for trying out Aria

    -
      -
    • - ✓ - Basic Chat (100 messages/mo) -
      • -
    • - ✓ - Aria Character Access -
      • -
    • - ✗ - Quantum Computing -
      • -
    • - ✗ - Advanced Training -
      • -
    • - ✗ - Website Maker -
      • -
    • - ✗ - API Access -
      • -
    - -
    - - -
    -
    POPULAR
    -
    Pro
    -
    - $49 - /month -
    -

    For professionals and small teams

    -
      -
    • - ✓ - 10,000 Chat Messages/mo -
      • -
    • - ✓ - Full Aria Character Suite -
      • -
    • - ✓ - Quantum Computing (50 jobs/mo) -
      • -
    • - ✓ - Advanced Training (20 hrs/mo) -
      • -
    • - ✓ - Website Maker (10 sites/mo) -
      • -
    • - ✓ - API Access (10K requests/mo) -
      • -
    • - ✓ - Commercial License -
      • -
    - -
    - - -
    -
    Enterprise
    -
    - $199 - /month -
    -

    For organizations needing scale

    -
      -
    • - ✓ - Unlimited Chat Messages -
      • -
    • - ✓ - Unlimited Quantum Jobs -
      • -
    • - ✓ - Unlimited Training Hours -
      • -
    • - ✓ - Unlimited API Requests -
      • -
    • - ✓ - Unlimited Websites -
      • -
    • - ✓ - Custom Model Training -
      • -
    • - ✓ - Priority Support (24/7) -
      • -
    • - ✓ - Dedicated Infrastructure -
      • -
    - -
    -
    - - -
    -

    📊 Revenue Projection Model

    -
    -
    -

    Pro Subscribers

    -
    5
    -
    @ $49/month = $245
    -
    -
    -

    Enterprise Subscribers

    -
    10
    -
    @ $199/month = $1,990
    -
    -
    -

    Monthly Revenue

    -
    $2,235
    -
    Exceeds $2,000 target!
    -
    -
    -

    Annual Revenue

    -
    $26,820
    -
    Projected ARR
    -
    -
    -
    - - -
    -

    📋 Detailed Feature Comparison

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    FeatureFreePro ($49/mo)Enterprise ($199/mo)
    Chat Messages100/month10,000/monthUnlimited
    Aria Character✓ Basic✓ Full Suite✓ Custom Animations
    Quantum Computing✗50 jobs/monthUnlimited
    Model Training✗20 hours/monthUnlimited
    Website Generation✗10 sites/monthUnlimited
    API Access✗10K requests/monthUnlimited
    Commercial License✗✓✓
    Custom Models✗✗✓
    Priority Support✗✗✓ 24/7
    SLA Guarantee✗✗✓ 99.9% Uptime
    -
    - - -
    -

    ❓ Frequently Asked Questions

    - -
    -
    How do I upgrade my subscription?
    -
    - Simply click the "Upgrade" button on any plan card. You'll be redirected to our secure payment portal where you can complete the upgrade process. Your new features will be available immediately. -
    -
    - -
    -
    Can I cancel anytime?
    -
    - Yes! You can cancel your subscription at any time. You'll continue to have access to paid features until the end of your current billing period, then your account will automatically revert to the Free tier. -
    -
    - -
    -
    What payment methods do you accept?
    -
    - We accept all major credit cards (Visa, MasterCard, American Express, Discover), PayPal, and wire transfers for Enterprise plans. All payments are processed securely through Stripe. -
    -
    - -
    -
    What happens if I exceed my usage limits?
    -
    - If you approach your usage limits, we'll send you a notification. Once you reach your limit, you'll need to either wait until your monthly reset or upgrade to a higher tier to continue using premium features. -
    -
    - -
    -
    Do you offer discounts for annual billing?
    -
    - Yes! Save 20% by choosing annual billing. A Pro annual subscription is $470 (instead of $588), and Enterprise annual is $1,910 (instead of $2,388). -
    -
    - -
    -
    Can I get a custom plan?
    -
    - Absolutely! For organizations with specific needs, we offer custom Enterprise+ plans with tailored features, dedicated infrastructure, and custom SLAs. Contact our sales team to discuss your requirements. -
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    - - - - + + + + + + Aria - Pricing & Plans + + + +
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    FeatureFreePro ($49/mo)Enterprise ($199/mo)
    Chat Messages100/month10,000/monthUnlimited
    Aria Character✓ Basic✓ Full Suite✓ Custom Animations
    Quantum Computing✗50 jobs/monthUnlimited
    Model Training✗20 hours/monthUnlimited
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    Custom Models✗✗✓
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    + We accept all major credit cards (Visa, MasterCard, American Express, Discover), PayPal, and wire transfers for Enterprise plans. All payments are processed securely through Stripe. +
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    + If you approach your usage limits, we'll send you a notification. Once you reach your limit, you'll need to either wait until your monthly reset or upgrade to a higher tier to continue using premium features. +
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    + Absolutely! For organizations with specific needs, we offer custom Enterprise+ plans with tailored features, dedicated infrastructure, and custom SLAs. Contact our sales team to discuss your requirements. +
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    + + + + diff --git a/web/monetization/referrals.html b/web/monetization/referrals.html index 80e1990e4..8b002b296 100644 --- a/web/monetization/referrals.html +++ b/web/monetization/referrals.html @@ -1,602 +1,602 @@ - - - - - - Referral Program - Aria Platform - - - -
    -
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    🎁 Referral Program

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    Get 20% commission on every referral that subscribes. Plus bonus rewards!

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    Your Unique Referral Code

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    DEMO1234AB
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    👥
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    0
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    - -
    -
    💰
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    Total Earned
    -
    $0
    -
    - -
    -
    ⏳
    -
    Pending Payout
    -
    $0
    -
    - -
    -
    ✅
    -
    Paid Out
    -
    $0
    -
    -
    - - -
    -

    💵 Commission Rates

    - - - - - - - - - - - - - - - - - - - - - - - -
    PlanPriceCommission RateYour Earnings
    Pro$49/month20%$9.80 per referral per month
    Enterprise$199/month20%$39.80 per referral per month
    -
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    🏆 Milestone Bonuses

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    Earn extra bonuses when you reach these milestones:

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    Gold achievement
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    Your Referrals

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    DateUserPlanCommissionStatus
    - No referrals yet. Share your code to get started! -
    -
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    💸 Request Payout

    -

    - Minimum payout: $25. Payouts are processed within 5 business days. -

    - -
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    - - - + + + + + + Referral Program - Aria Platform + + + +
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    Total Referrals
    +
    0
    +
    + +
    +
    💰
    +
    Total Earned
    +
    $0
    +
    + +
    +
    ⏳
    +
    Pending Payout
    +
    $0
    +
    + +
    +
    ✅
    +
    Paid Out
    +
    $0
    +
    +
    + + +
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    💵 Commission Rates

    + + + + + + + + + + + + + + + + + + + + + + + +
    PlanPriceCommission RateYour Earnings
    Pro$49/month20%$9.80 per referral per month
    Enterprise$199/month20%$39.80 per referral per month
    +
    + + +
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    🏆 Milestone Bonuses

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    Earn extra bonuses when you reach these milestones:

    + +
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    🥉
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    5 Referrals
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    Unlock first bonus
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    $300
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    50 Referrals
    +
    Diamond status
    +
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    $750
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    100 Referrals
    +
    Elite referrer
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    $2,000
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    Your Referrals

    + + + + + + + + + + + + + + + +
    DateUserPlanCommissionStatus
    + No referrals yet. Share your code to get started! +
    +
    + + +
    +

    💸 Request Payout

    +

    + Minimum payout: $25. Payouts are processed within 5 business days. +

    + +
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    + + + diff --git a/web/monetization/subscription-success.html b/web/monetization/subscription-success.html index 36a90a4e2..9615add5d 100644 --- a/web/monetization/subscription-success.html +++ b/web/monetization/subscription-success.html @@ -1,430 +1,430 @@ - - - - - - Welcome to Aria! - Subscription Activated - - - -
    -
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    - ✓ -
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    🎉 Welcome to Aria!

    -

    Your subscription is now active

    - -
    Pro Plan
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    - Order Status - ✓ Confirmed -
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    - Plan - Pro -
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    - Billing - Monthly -
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    - Amount - $49.00 -
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    - Next Billing Date - March 4, 2026 -
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    - Confirmation Email - Sent -
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    🚀 Next Steps

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    Explore Your Features

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    Check out all the premium features now available in your account

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    Our team is here to help you make the most of your subscription.

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    - 📧 Email: support@aria-platform.com
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    - - - - + + + + + + Welcome to Aria! - Subscription Activated + + + +
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    + ✓ +
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    🎉 Welcome to Aria!

    +

    Your subscription is now active

    + +
    Pro Plan
    + +
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    + Order Status + ✓ Confirmed +
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    + Plan + Pro +
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    + Billing + Monthly +
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    + Amount + $49.00 +
    +
    + Next Billing Date + March 4, 2026 +
    +
    + Confirmation Email + Sent +
    +
    + +
    +

    🚀 Next Steps

    + +
    +
    1
    +
    +

    Explore Your Features

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    Check out all the premium features now available in your account

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    +
    + +
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    +
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    + 📧 Email: support@aria-platform.com
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    +
    +
    + + + + From c6957533bcffc690fc096b24490aa39d304f0868 Mon Sep 17 00:00:00 2001 From: Bryan <74067792+Bryan-Roe@users.noreply.github.com> Date: Thu, 7 May 2026 11:19:10 -0400 Subject: [PATCH 08/15] Potential fix for pull request finding 'CodeQL / Workflow does not contain permissions' Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> Signed-off-by: Bryan <74067792+Bryan-Roe@users.noreply.github.com> --- .github/workflows/pr-checks.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/pr-checks.yml b/.github/workflows/pr-checks.yml index cab0fc253..5d3864847 100644 --- a/.github/workflows/pr-checks.yml +++ b/.github/workflows/pr-checks.yml @@ -117,6 +117,8 @@ jobs: size-check: name: PR Size Check runs-on: ubuntu-latest + permissions: + contents: read steps: - name: Checkout code uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 From fe632fd168a85463c7fe658d63831f9d5db8009d Mon Sep 17 00:00:00 2001 From: Bryan <74067792+Bryan-Roe@users.noreply.github.com> Date: Thu, 7 May 2026 11:19:31 -0400 Subject: [PATCH 09/15] Potential fix for pull request finding 'CodeQL / Workflow does not contain permissions' Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> Signed-off-by: Bryan <74067792+Bryan-Roe@users.noreply.github.com> --- .github/workflows/pr-checks.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/pr-checks.yml b/.github/workflows/pr-checks.yml index 5d3864847..c1195f48a 100644 --- a/.github/workflows/pr-checks.yml +++ b/.github/workflows/pr-checks.yml @@ -15,6 +15,8 @@ jobs: validate: name: Quick Validation runs-on: ubuntu-latest + permissions: + contents: read timeout-minutes: 10 steps: - name: Checkout code From 94569ab36775563fb2ef586140f4c33f9b2cc54e Mon Sep 17 00:00:00 2001 From: Bryan <74067792+Bryan-Roe@users.noreply.github.com> Date: Thu, 7 May 2026 11:19:55 -0400 Subject: [PATCH 10/15] Potential fix for pull request finding 'CodeQL / Workflow does not contain permissions' Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> Signed-off-by: Bryan <74067792+Bryan-Roe@users.noreply.github.com> --- .github/workflows/workflow-validation.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/workflow-validation.yml b/.github/workflows/workflow-validation.yml index 39bed5325..b97a30042 100644 --- a/.github/workflows/workflow-validation.yml +++ b/.github/workflows/workflow-validation.yml @@ -12,6 +12,9 @@ on: - '.github/actions/**' workflow_dispatch: +permissions: + contents: read + jobs: validate-workflows: name: Validate Workflow Syntax From 2dea42adb766ed3df387ab029254c17a1049c630 Mon Sep 17 00:00:00 2001 From: Bryan <74067792+Bryan-Roe@users.noreply.github.com> Date: Thu, 7 May 2026 11:20:37 -0400 Subject: [PATCH 11/15] Potential fix for pull request finding 'CodeQL / DOM text reinterpreted as HTML' Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> Signed-off-by: Bryan <74067792+Bryan-Roe@users.noreply.github.com> --- docs/dashboard/hyperparameter-optimizer.js | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/docs/dashboard/hyperparameter-optimizer.js b/docs/dashboard/hyperparameter-optimizer.js index 74909eefa..69fc7b51a 100644 --- a/docs/dashboard/hyperparameter-optimizer.js +++ b/docs/dashboard/hyperparameter-optimizer.js @@ -355,7 +355,7 @@ class HyperparameterOptimizer {
    - Running ${this.strategy} optimization with ${this.maxTrials} trials... +
    @@ -363,6 +363,11 @@ class HyperparameterOptimizer {
    `; + const statusText = container.querySelector('#hyperoptStatusText'); + if (statusText) { + statusText.textContent = `Running ${this.strategy} optimization with ${this.maxTrials} trials...`; + } + container.style.display = 'block'; } From 5b3acabd3fae5bb6814ca265fa32f3a7183048eb Mon Sep 17 00:00:00 2001 From: Bryan <74067792+Bryan-Roe@users.noreply.github.com> Date: Thu, 7 May 2026 11:20:56 -0400 Subject: [PATCH 12/15] Potential fix for pull request finding 'CodeQL / DOM text reinterpreted as HTML' Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> Signed-off-by: Bryan <74067792+Bryan-Roe@users.noreply.github.com> --- apps/chat/chat.js | 27 ++++++++++++++++++++------- 1 file changed, 20 insertions(+), 7 deletions(-) diff --git a/apps/chat/chat.js b/apps/chat/chat.js index 598bd8e43..44f2defe5 100644 --- a/apps/chat/chat.js +++ b/apps/chat/chat.js @@ -760,13 +760,26 @@ async function handleImageUpload(event) { uploadedImage = file.name; // Show preview - visionPreview.innerHTML = ` - Preview -
    - ${file.name} - -
    - `; + visionPreview.textContent = ''; + + const previewImg = document.createElement('img'); + previewImg.src = e.target.result; + previewImg.alt = 'Preview'; + + const previewInfo = document.createElement('div'); + previewInfo.className = 'vision-preview-info'; + + const fileNameSpan = document.createElement('span'); + fileNameSpan.textContent = file.name; + + const clearButton = document.createElement('button'); + clearButton.id = 'visionClearButton'; + clearButton.textContent = '✕'; + + previewInfo.appendChild(fileNameSpan); + previewInfo.appendChild(clearButton); + visionPreview.appendChild(previewImg); + visionPreview.appendChild(previewInfo); visionPreview.style.display = 'block'; // Re-attach clear button listener From 037c0451dc9bc9bfd590b06f513e4e871aa22569 Mon Sep 17 00:00:00 2001 From: Bryan <74067792+Bryan-Roe@users.noreply.github.com> Date: Thu, 7 May 2026 11:21:53 -0400 Subject: [PATCH 13/15] Potential fix for pull request finding 'CodeQL / DOM text reinterpreted as HTML' Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> Signed-off-by: Bryan <74067792+Bryan-Roe@users.noreply.github.com> --- docs/chat/chat.js | 26 +++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/docs/chat/chat.js b/docs/chat/chat.js index 99d550aa6..e882b34ec 100644 --- a/docs/chat/chat.js +++ b/docs/chat/chat.js @@ -735,13 +735,25 @@ async function handleImageUpload(event) { uploadedImage = file.name; // Show preview - visionPreview.innerHTML = ` - Preview -
    - ${file.name} - -
    - `; + visionPreview.textContent = ''; + const previewImg = document.createElement('img'); + previewImg.src = e.target.result; + previewImg.alt = 'Preview'; + + const previewInfo = document.createElement('div'); + previewInfo.className = 'vision-preview-info'; + + const previewName = document.createElement('span'); + previewName.textContent = file.name; + + const clearButton = document.createElement('button'); + clearButton.id = 'visionClearButton'; + clearButton.textContent = '✕'; + + previewInfo.appendChild(previewName); + previewInfo.appendChild(clearButton); + visionPreview.appendChild(previewImg); + visionPreview.appendChild(previewInfo); visionPreview.style.display = 'block'; // Re-attach clear button listener From c2bf1c3a7a4fdda316a5cde01d28ff39ea07dbb9 Mon Sep 17 00:00:00 2001 From: Bryan <74067792+Bryan-Roe@users.noreply.github.com> Date: Thu, 7 May 2026 11:22:19 -0400 Subject: [PATCH 14/15] Potential fix for pull request finding 'CodeQL / DOM text reinterpreted as HTML' Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> Signed-off-by: Bryan <74067792+Bryan-Roe@users.noreply.github.com> --- apps/dashboard/hyperparameter-optimizer.js | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/apps/dashboard/hyperparameter-optimizer.js b/apps/dashboard/hyperparameter-optimizer.js index 74909eefa..2ab614b95 100644 --- a/apps/dashboard/hyperparameter-optimizer.js +++ b/apps/dashboard/hyperparameter-optimizer.js @@ -355,7 +355,7 @@ class HyperparameterOptimizer {
    - Running ${this.strategy} optimization with ${this.maxTrials} trials... +
    @@ -363,6 +363,11 @@ class HyperparameterOptimizer {
    `; + const statusText = document.getElementById('hyperoptStatusText'); + if (statusText) { + statusText.textContent = `Running ${this.strategy} optimization with ${this.maxTrials} trials...`; + } + container.style.display = 'block'; } From be3e6f8e9a9e98c4561691c02e46d9d9157f1196 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 7 May 2026 15:27:39 +0000 Subject: [PATCH 15/15] chore: sync with origin/main - resolve merge conflicts, normalize line endings, and incorporate new commits Changes incorporated from main: - SQL fallback hardening (PR #194) - Auto-merge gates (PR #193) - Pre-commit hook scoped to changed files (PR #192) - Line ending normalization via .gitattributes Conflict resolution strategy: - GitHub Actions workflow files: kept our SHA-pinned versions - All other conflicted files: took main's version - Preserved our security fixes (raise_for_status, XSS fixes, permissions) Co-authored-by: Bryan-Roe <74067792+Bryan-Roe@users.noreply.github.com> --- .azureml/environment.yml | 18 +- .azureml/job_multi_20251125T012515Z.yaml | 36 +- .azureml/job_multi_20251125T012559Z.yaml | 36 +- .azureml/job_multi_20251125T055919Z.yaml | 36 +- .devcontainer/devcontainer.json | 2 +- .githooks/README.md | 84 +- .github/FUNDING.yml | 28 +- .github/GITHUB_ACTIONS_QUICKREF.txt | 356 +- .github/GITHUB_ACTIONS_SETUP.md | 734 ++-- .github/ISSUE_TEMPLATE/bug_report.md | 76 +- .github/ISSUE_TEMPLATE/feature_request.md | 40 +- .github/WORKFLOWS.md | 946 ++--- .github/actions/setup-python-env/action.yml | 94 +- .github/agents/AI_chat_development.agent.md | 310 +- .github/agents/AI_model_training.agent.md | 270 +- .../Aria_character_development.agent.md | 256 +- .github/agents/Azure_Static_Web_App.agent.md | 922 ++--- ...e_function_codegen_and_deployment.agent.md | 304 +- .github/agents/Full_stack_debugging.agent.md | 250 +- .../agents/Quantum_ML_development.agent.md | 262 +- .github/agents/ai.agent.md | 338 +- .github/agents/automated-code-fixer.agent.md | 178 +- .github/copilot-instructions.full.md | 278 +- .github/copilot-instructions.md | 12 +- .../instructions/agi-provider.instructions.md | 44 +- .../aria-character.instructions.md | 82 +- .github/instructions/aria-web.instructions.md | 32 +- .../autonomous-training.instructions.md | 48 +- .../instructions/chat-memory.instructions.md | 134 +- .../chat-providers.instructions.md | 112 +- .github/instructions/chat-web.instructions.md | 34 +- .../instructions/cooking-ai.instructions.md | 146 +- .../instructions/dashboard.instructions.md | 38 +- .../instructions/db-logging.instructions.md | 124 +- .../instructions/evaluation.instructions.md | 114 +- .../instructions/functions.instructions.md | 48 +- .../instructions/llm-maker.instructions.md | 134 +- .../instructions/lora-python.instructions.md | 42 +- .github/instructions/lora.instructions.md | 42 +- .../orchestrator-configs.instructions.md | 86 +- .../quantum-ai-mcp-python.instructions.md | 42 +- .../quantum-ai-python.instructions.md | 56 +- .../instructions/quantum-ai.instructions.md | 44 +- .../shared-python.instructions.md | 66 +- .../instructions/subscription.instructions.md | 132 +- .../talk-to-ai-python.instructions.md | 42 +- .../instructions/talk-to-ai.instructions.md | 40 +- .../instructions/telemetry.instructions.md | 72 +- .github/instructions/tests.instructions.md | 68 +- .../instructions/token-utils.instructions.md | 130 +- .../training-scripts.instructions.md | 70 +- .../vision-inference.instructions.md | 112 +- .github/labeler.yml | 98 +- .github/prompts/aria-command.prompt.md | 76 +- .github/prompts/chat.prompt.md | 98 +- .github/prompts/debug.prompt.md | 72 +- .github/prompts/deploy.prompt.md | 148 +- .github/prompts/evaluate.prompt.md | 106 +- .github/prompts/generate-tool.prompt.md | 90 +- .github/prompts/generate-website.prompt.md | 88 +- .github/prompts/optimize.prompt.md | 112 +- .github/prompts/quantum.prompt.md | 86 +- .github/prompts/reason.prompt.md | 86 +- .github/prompts/review.prompt.md | 90 +- .github/prompts/train.prompt.md | 80 +- .github/workflows/README.md | 250 +- .github/workflows/auto-merge.yml | 146 + .github/workflows/merge-gate.yml | 227 ++ .github/workflows/stale.yml | 100 +- .vscode/ERROR_RESOLUTION_STATUS.md | 188 +- .vscode/extensions.json | 56 +- .vscode/launch.json | 204 +- .vscode/settings.json | 302 +- .vscode/settings_optimized.json | 104 +- .vscode/tasks.json | 416 +-- .vscode/test-profiles.json | 144 +- .../scripts/otel_callback.py | 76 +- CONTRIBUTING.md | 210 +- REVENUE_INTEGRATION.md | 522 +-- SECURITY.md | 132 +- agent.yaml | 64 +- agi_provider.py | 158 +- ai-projects/chat-cli/README.md | 384 +- ai-projects/chat-cli/requirements.txt | 4 +- ai-projects/chat-cli/src/_smoke_test.py | 40 +- .../chat-cli/src/ai-finetuning-workspace.yml | 18 +- ai-projects/chat-cli/src/chat_cli.py | 778 ++--- ai-projects/chat-cli/src/chat_providers.py | 3098 ++++++++--------- ai-projects/chat-cli/src/lora_infer_bridge.py | 234 +- ai-projects/chat-cli/src/quantum_provider.py | 928 ++--- ai-projects/chat-cli/src/token_utils.py | 382 +- ai-projects/cooking-ai/README.md | 148 +- ai-projects/cooking-ai/requirements.txt | 8 +- .../cooking-ai/src/agents/recipe_agent.py | 264 +- ai-projects/cooking-ai/src/main.py | 202 +- .../cooking-ai/src/providers/github_models.py | 116 +- ai-projects/cooking-ai/src/providers/local.py | 332 +- .../cooking-ai/src/utils/json_utils.py | 174 +- ai-projects/cooking-ai/tests/_debug_local.py | 20 +- .../cooking-ai/tests/debug_agent_extract.py | 18 +- .../cooking-ai/tests/debug_extract_local.py | 24 +- .../cooking-ai/tests/debug_run_local.py | 28 +- ai-projects/cooking-ai/tests/run_tests.py | 60 +- ai-projects/cooking-ai/tests/test_agent.py | 156 +- ai-projects/cooking-ai/tests/test_schemas.py | 148 +- .../llm-maker/IMPLEMENTATION_SUMMARY.md | 508 +-- ai-projects/llm-maker/QUICKSTART.md | 424 +-- ai-projects/llm-maker/README.md | 282 +- ai-projects/llm-maker/UI_ENHANCEMENTS.md | 236 +- .../llm-maker/WEBSITE_MAKER_IMPLEMENTATION.md | 604 ++-- ai-projects/llm-maker/WEBSITE_MAKER_README.md | 960 ++--- ai-projects/llm-maker/WEB_UI_README.md | 412 +-- ai-projects/llm-maker/examples/fibonacci.py | 66 +- ai-projects/llm-maker/examples/quick_start.py | 248 +- .../llm-maker/examples/text_processor.py | 96 +- ai-projects/llm-maker/llm_maker_config.yaml | 158 +- ai-projects/llm-maker/llm_maker_mcp_server.py | 670 ++-- ai-projects/llm-maker/requirements.txt | 44 +- ai-projects/llm-maker/src/__init__.py | 50 +- ai-projects/llm-maker/src/tool_executor.py | 614 ++-- ai-projects/llm-maker/src/tool_maker.py | 646 ++-- ai-projects/llm-maker/src/tool_registry.py | 486 +-- ai-projects/llm-maker/src/tool_validator.py | 808 ++--- ai-projects/llm-maker/src/website_maker.py | 1118 +++--- ai-projects/llm-maker/start_web_ui.sh | 50 +- ai-projects/llm-maker/test_llm_maker.sh | 182 +- ai-projects/llm-maker/tests/test_executor.py | 188 +- ai-projects/llm-maker/tests/test_registry.py | 342 +- ai-projects/llm-maker/tests/test_validator.py | 252 +- ai-projects/llm-maker/web_server.py | 1046 +++--- ai-projects/llm-maker/web_ui.html | 1854 +++++----- ai-projects/llm-maker/web_ui_api_functions.js | 226 +- ai-projects/llm-maker/website_maker_ui.html | 1806 +++++----- .../lmstudio-mcp/lmstudio_agi_integration.py | 3 +- .../.vscode/python-imports.md | 118 +- .../ADVANCED_TRAINING_GUIDE.md | 1150 +++--- .../ADVANCED_TRAINING_QUICKREF.md | 434 +-- .../ADVANCED_TRAINING_SETUP_COMPLETE.md | 564 +-- .../AZURE_FOUNDRY_GUIDE.md | 164 +- .../AZURE_ML_TRAINING_GUIDE.md | 580 +-- .../CONTINUOUS_IMPROVEMENTS_PHASE2.md | 872 ++--- .../microsoft_phi-silica-3.6_v1/README.md | 478 +-- .../microsoft_phi-silica-3.6_v1/SETUP.md | 78 +- .../azure-requirements.txt | 22 +- .../azure_foundry_deploy.py | 318 +- .../azure_ml_training.py | 792 ++--- .../azureml/README.md | 186 +- .../azureml/environment.yml | 36 +- .../azureml/job-lora-sweep.yml | 154 +- .../azureml/job-lora-train.yml | 164 +- .../data/eval_manifest.txt | 2 +- .../data/test.json | 2 +- .../data/train.json | 2 +- .../data/train_manifest.txt | 2 +- .../lora_training/checkpoint-64/README.md | 412 +-- .../checkpoint-64/adapter_config.json | 88 +- .../checkpoint-64/added_tokens.json | 26 +- .../checkpoint-64/special_tokens_map.json | 60 +- .../scripts/gpu_optimizer.py | 4 +- .../quantum-ml/src/azure_ml_integration.py | 4 +- .../src/quantum_classifier_enhanced.py | 2 +- .../quantum-ml/src/quantum_llm/config.py | 96 +- .../quantum-ml/src/quantum_llm_monitor.py | 2 +- ai-projects/quantum-ml/web_app.py | 28 +- app.py | 250 +- apps/aria/index.html | 54 + apps/aria/server.py | 6 +- apps/chat/chat.js | 27 +- apps/dashboard/hyperparameter-optimizer.js | 7 +- apps/dashboard/serve.py | 2 +- autotrain.py | 12 + docs/aria/aria_controller.js | 4 +- docs/aria/auto-execute.html | 156 +- docs/aria/index.html | 167 +- docs/aria_controller.js | 4 +- docs/chat/chat.js | 30 +- docs/chat/index.html | 103 +- docs/dashboard/advanced.html | 16 +- docs/dashboard/analytics.html | 8 +- docs/dashboard/consolidated.html | 12 +- docs/dashboard/enhanced.html | 32 +- docs/dashboard/hub.html | 14 +- docs/dashboard/hyperparameter-optimizer.js | 9 +- docs/dashboard/keyboard-nav.js | 8 +- docs/dashboard/live-progress.js | 4 +- docs/dashboard/shared-theme.css | 4 +- docs/dashboard/unified.html | 70 +- docs/llm-maker/web_ui.html | 26 +- docs/llm-maker/website_maker_ui.html | 20 +- docs/monetization/account.html | 26 +- docs/monetization/admin_dashboard.html | 24 +- docs/monetization/analytics-dashboard.html | 26 +- docs/monetization/checkout.html | 18 +- docs/monetization/monetization-index.html | 14 +- docs/monetization/my-subscription.html | 20 +- docs/monetization/pricing.html | 22 +- docs/monetization/referrals.html | 30 +- docs/monetization/subscription-success.html | 20 +- docs/mount/styles.css | 6 +- docs/quantum/styles.css | 6 +- function_app.py | 249 ++ mount/launch.ps1 | 162 +- mount/start.bat | 52 +- mount/start.ps1 | 50 +- mount/test-service.ps1 | 154 +- mount/train_max.ps1 | 206 +- mount/train_parallel.ps1 | 294 +- scripts/autotrain.py | 281 +- scripts/benchmark_performance.py | 2 +- scripts/dashboard.py | 2 +- shared/cosmos_client.py | 5 +- shared/sql_engine.py | 44 +- shared/sql_repository.py | 20 +- symengine | 1 - tests/test_quantum_llm.py | 67 + tools/codegen/WEBSITE_GENERATOR.md | 68 +- tools/codegen/code_generation_demo.py | 2 +- .../dotnet/ProductDocumentation/README.md | 10 +- 218 files changed, 22963 insertions(+), 21275 deletions(-) create mode 100644 .github/workflows/auto-merge.yml create mode 100644 .github/workflows/merge-gate.yml delete mode 160000 symengine diff --git a/.azureml/environment.yml b/.azureml/environment.yml index dfb4d6f9e..fdb5742a4 100644 --- a/.azureml/environment.yml +++ b/.azureml/environment.yml @@ -1,9 +1,9 @@ -name: auto-training-env -channels: - - defaults - - conda-forge -dependencies: - - python=3.11 - - pip - - pip: - - -r requirements.txt +name: auto-training-env +channels: + - defaults + - conda-forge +dependencies: + - python=3.11 + - pip + - pip: + - -r requirements.txt diff --git a/.azureml/job_multi_20251125T012515Z.yaml b/.azureml/job_multi_20251125T012515Z.yaml index 44e3a292b..dca591685 100644 --- a/.azureml/job_multi_20251125T012515Z.yaml +++ b/.azureml/job_multi_20251125T012515Z.yaml @@ -1,18 +1,18 @@ -$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json -command: python scripts/automated_training_pipeline.py --models phi,qwen --quick --min-train-samples 50 --ranking-metric perplexity_improvement --generate-only -code: . -display_name: automated-training-multi_20251125T012515Z -experiment_name: lora-autotrain -compute: azureml:cpu-cluster -environment: - image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04 - conda_file: environment.yml - name: auto-training-env -resources: - instance_count: 1 -identity: - type: managed -description: Auto-generated Azure ML job spec for multi-model LoRA training wrapper -tags: - generator: automated_training_pipeline - models: phi,qwen +$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json +command: python scripts/automated_training_pipeline.py --models phi,qwen --quick --min-train-samples 50 --ranking-metric perplexity_improvement --generate-only +code: . +display_name: automated-training-multi_20251125T012515Z +experiment_name: lora-autotrain +compute: azureml:cpu-cluster +environment: + image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04 + conda_file: environment.yml + name: auto-training-env +resources: + instance_count: 1 +identity: + type: managed +description: Auto-generated Azure ML job spec for multi-model LoRA training wrapper +tags: + generator: automated_training_pipeline + models: phi,qwen diff --git a/.azureml/job_multi_20251125T012559Z.yaml b/.azureml/job_multi_20251125T012559Z.yaml index be60a6afc..604216c54 100644 --- a/.azureml/job_multi_20251125T012559Z.yaml +++ b/.azureml/job_multi_20251125T012559Z.yaml @@ -1,18 +1,18 @@ -$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json -command: python scripts/automated_training_pipeline.py --models phi --quick --min-train-samples 50 --ranking-metric perplexity_improvement --generate-only -code: . -display_name: automated-training-multi_20251125T012559Z -experiment_name: lora-autotrain -compute: azureml:cpu-cluster -environment: - image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04 - conda_file: environment.yml - name: auto-training-env -resources: - instance_count: 1 -identity: - type: managed -description: Auto-generated Azure ML job spec for multi-model LoRA training wrapper -tags: - generator: automated_training_pipeline - models: phi +$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json +command: python scripts/automated_training_pipeline.py --models phi --quick --min-train-samples 50 --ranking-metric perplexity_improvement --generate-only +code: . +display_name: automated-training-multi_20251125T012559Z +experiment_name: lora-autotrain +compute: azureml:cpu-cluster +environment: + image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04 + conda_file: environment.yml + name: auto-training-env +resources: + instance_count: 1 +identity: + type: managed +description: Auto-generated Azure ML job spec for multi-model LoRA training wrapper +tags: + generator: automated_training_pipeline + models: phi diff --git a/.azureml/job_multi_20251125T055919Z.yaml b/.azureml/job_multi_20251125T055919Z.yaml index 6878f24d1..7fb994d59 100644 --- a/.azureml/job_multi_20251125T055919Z.yaml +++ b/.azureml/job_multi_20251125T055919Z.yaml @@ -1,18 +1,18 @@ -$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json -command: python scripts/automated_training_pipeline.py --models phi --quick --min-train-samples 50 --ranking-metric perplexity_improvement --generate-only -code: . -display_name: automated-training-multi_20251125T055919Z -experiment_name: lora-autotrain -compute: azureml:cpu-cluster -environment: - image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04 - conda_file: environment.yml - name: auto-training-env -resources: - instance_count: 1 -identity: - type: managed -description: Auto-generated Azure ML job spec for multi-model LoRA training wrapper -tags: - generator: automated_training_pipeline - models: phi +$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json +command: python scripts/automated_training_pipeline.py --models phi --quick --min-train-samples 50 --ranking-metric perplexity_improvement --generate-only +code: . +display_name: automated-training-multi_20251125T055919Z +experiment_name: lora-autotrain +compute: azureml:cpu-cluster +environment: + image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04 + conda_file: environment.yml + name: auto-training-env +resources: + instance_count: 1 +identity: + type: managed +description: Auto-generated Azure ML job spec for multi-model LoRA training wrapper +tags: + generator: automated_training_pipeline + models: phi diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index d5e3e391d..a1a8498c3 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -2,7 +2,7 @@ "name": "Aria Dev Container", "image": "mcr.microsoft.com/devcontainers/python:2-3.14-trixie", "features": {}, - "postCreateCommand": "python -m pip install --upgrade pip && if [ -f requirements.txt ]; then pip install -r requirements.txt; fi && if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt; fi", + "postCreateCommand": "python -m pip install --upgrade pip && if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt; fi", "customizations": { "vscode": { "extensions": [ diff --git a/.githooks/README.md b/.githooks/README.md index 4dd429396..9fb1bd2d1 100644 --- a/.githooks/README.md +++ b/.githooks/README.md @@ -1,42 +1,42 @@ -# Test Automation Setup - -This directory contains pre-commit hooks for automated testing. - -## Installation - -Enable these hooks with: - -```powershell -git config core.hooksPath .githooks -``` - -## Available Hooks - -### pre-commit (bash) -Runs fast unit tests before allowing commits. Use on Linux/Mac or Windows with Git Bash. - -### pre-commit.ps1 (PowerShell) -PowerShell version for Windows users. Configure with: - -```powershell -# Add to .git/config or run: -git config core.hooksPath .githooks -``` - -## Bypass (emergency only) - -```bash -git commit --no-verify -``` - -## What Gets Tested - -Pre-commit runs: -- Unit tests only (fast, < 30s) -- Excludes slow/integration tests -- Excludes Azure-dependent tests - -For full testing: -```bash -python scripts/test_runner.py --all --coverage -``` +# Test Automation Setup + +This directory contains pre-commit hooks for automated testing. + +## Installation + +Enable these hooks with: + +```powershell +git config core.hooksPath .githooks +``` + +## Available Hooks + +### pre-commit (bash) +Runs fast unit tests before allowing commits. Use on Linux/Mac or Windows with Git Bash. + +### pre-commit.ps1 (PowerShell) +PowerShell version for Windows users. Configure with: + +```powershell +# Add to .git/config or run: +git config core.hooksPath .githooks +``` + +## Bypass (emergency only) + +```bash +git commit --no-verify +``` + +## What Gets Tested + +Pre-commit runs: +- Unit tests only (fast, < 30s) +- Excludes slow/integration tests +- Excludes Azure-dependent tests + +For full testing: +```bash +python scripts/test_runner.py --all --coverage +``` diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index d56a265f6..13f15b8ec 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -1,14 +1,14 @@ -# These are supported funding model platforms - -github: Bryan-Roe -# patreon: -# open_collective: -# ko_fi: -# tidelift: -# community_bridge: -# liberapay: -# issuehunt: -# polar: -# buy_me_a_coffee: -# thanks_dev: -# custom: +# These are supported funding model platforms + +github: Bryan-Roe +# patreon: +# open_collective: +# ko_fi: +# tidelift: +# community_bridge: +# liberapay: +# issuehunt: +# polar: +# buy_me_a_coffee: +# thanks_dev: +# custom: diff --git a/.github/GITHUB_ACTIONS_QUICKREF.txt b/.github/GITHUB_ACTIONS_QUICKREF.txt index cf2393aca..48d88cb4a 100644 --- a/.github/GITHUB_ACTIONS_QUICKREF.txt +++ b/.github/GITHUB_ACTIONS_QUICKREF.txt @@ -1,178 +1,178 @@ -╔═══════════════════════════════════════════════════════════════════════════════════╗ -║ GITHUB ACTIONS QUICK REFERENCE - ARIA ║ -╚═══════════════════════════════════════════════════════════════════════════════════╝ - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ WORKFLOW STATUS │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - View all workflows: https://github.com/Bryan-Roe/Aria/actions - Security alerts: https://github.com/Bryan-Roe/Aria/security - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ CORE CI/CD WORKFLOWS │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - ci-pipeline.yml Main CI: validation → training → deployment - Triggers: push to main/dev, PRs, daily 2 AM UTC - - code-quality.yml ⭐ Linting: flake8, black, isort, mypy, safety - Triggers: push, PRs to main/dev - - codeql.yml ⭐ Security: Python & JS vulnerability scanning - Triggers: push, PRs, weekly Monday 8 AM UTC - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ PULL REQUEST WORKFLOWS │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - pr-checks.yml ⭐ Fast PR validation + auto-labeling - - YAML syntax validation - - PR size check (warns if >50 files or >1000 lines) - - TODO/FIXME detection - - Auto-label by changed files - - Auto-Labels: documentation, tests, dependencies, github-actions, - aria-character, chat-interface, quantum-ai, training, - azure-functions, config, scripts, security - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ TESTING WORKFLOWS │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - auto-validation.yml Orchestrator dry-run validation - Triggers: push (specific paths), daily 5 AM, manual - - e2e-tests.yml End-to-end: Playwright, Pyppeteer, Selenium - Triggers: push to main, PRs to main - - aria-tests.yml Aria-specific tests (Python 3.10, 3.11, 3.12) - Triggers: push/PR (aria_web changes), manual - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ SPECIALIZED WORKFLOWS │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - quantum-orchestration.yml Quantum computing automation - Triggers: push to main, manual - - azureml-train.yml Azure ML LoRA training jobs - Triggers: manual only - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ AUTOMATION & MAINTENANCE │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - release.yml ⭐ Automated releases on version tags - Triggers: git tag v*.*.* (e.g., v1.0.0) - Creates: GitHub release, changelog, source archives - - stale.yml ⭐ Auto-close stale issues/PRs - Issues: stale after 60d, close after 14d - PRs: stale after 30d, close after 7d - Triggers: daily midnight UTC, manual - - workflow-validation.yml⭐ Validates workflow syntax - Triggers: workflow file changes, push to main - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ QUICK COMMANDS │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - Local Code Quality Check: - pip install flake8 black isort mypy safety - flake8 . --exclude=venv,venv_*,data_out - black --check --exclude="venv|data_out" . - isort --check-only --skip-glob="venv*" . - - Create Release: - git tag v1.0.0 - git push origin v1.0.0 - # Release workflow creates GitHub release automatically - - Manual Workflow Trigger: - 1. Go to Actions tab: https://github.com/Bryan-Roe/Aria/actions - 2. Select workflow from left sidebar - 3. Click "Run workflow" - 4. Configure inputs (if any) - 5. Click "Run workflow" - - Check Workflow Logs: - Actions tab → Select run → Click job → View logs - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ WORKFLOW BADGES │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - Add to markdown files: - - ![CI Pipeline](https://github.com/Bryan-Roe/Aria/actions/workflows/ci-pipeline.yml/badge.svg) - ![Code Quality](https://github.com/Bryan-Roe/Aria/actions/workflows/code-quality.yml/badge.svg) - ![CodeQL](https://github.com/Bryan-Roe/Aria/actions/workflows/codeql.yml/badge.svg) - ![E2E Tests](https://github.com/Bryan-Roe/Aria/actions/workflows/e2e-tests.yml/badge.svg) - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ CONFIGURATION FILES │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - .github/workflows/ Workflow definitions (12 workflows) - .github/labeler.yml Auto-labeling configuration - .github/dependabot.yml Dependency update configuration - pytest.ini Test configuration - requirements.txt Python dependencies - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ TROUBLESHOOTING │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - Workflow not triggering: - • Check trigger conditions (branches, paths) - • Verify workflow file syntax - • Check if workflow is disabled in Actions tab - - Tests failing: - • Check logs in Actions tab - • Download test artifacts for detailed reports - • Run tests locally to reproduce - - Authentication errors: - • Verify secrets are configured (Settings → Secrets) - • Check token permissions - • Ensure service principal credentials are valid - - Workflow syntax errors: - • Run workflow-validation.yml workflow - • Validate locally: python -c "import yaml; yaml.safe_load(open('file.yml'))" - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ DOCUMENTATION │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - .github/WORKFLOWS.md Comprehensive workflow documentation - .github/GITHUB_ACTIONS_SETUP.md Setup summary and details - .github/GITHUB_ACTIONS_QUICKREF.txt This quick reference - - External Resources: - GitHub Actions Docs: https://docs.github.com/en/actions - Actions Marketplace: https://github.com/marketplace?type=actions - GitHub Status: https://www.githubstatus.com/ - -┌─────────────────────────────────────────────────────────────────────────────────┐ -│ BEST PRACTICES │ -└─────────────────────────────────────────────────────────────────────────────────┘ - - ✓ Use caching for dependencies (cache: 'pip') - ✓ Set timeouts to prevent hanging jobs - ✓ Use matrix strategy for multi-version testing - ✓ Continue on error for non-critical checks - ✓ Upload artifacts for debugging failed runs - ✓ Use secrets for sensitive data (never hardcode) - ✓ Add status checks to protect branches - ✓ Test workflows locally with 'act' tool - ✓ Keep workflow files small and focused - ✓ Document custom workflows - -╔═══════════════════════════════════════════════════════════════════════════════════╗ -║ ⭐ = New workflow added in this update ║ -║ For detailed information, see .github/WORKFLOWS.md ║ -╚═══════════════════════════════════════════════════════════════════════════════════╝ +╔═══════════════════════════════════════════════════════════════════════════════════╗ +║ GITHUB ACTIONS QUICK REFERENCE - ARIA ║ +╚═══════════════════════════════════════════════════════════════════════════════════╝ + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ WORKFLOW STATUS │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + View all workflows: https://github.com/Bryan-Roe/Aria/actions + Security alerts: https://github.com/Bryan-Roe/Aria/security + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ CORE CI/CD WORKFLOWS │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + ci-pipeline.yml Main CI: validation → training → deployment + Triggers: push to main/dev, PRs, daily 2 AM UTC + + code-quality.yml ⭐ Linting: flake8, black, isort, mypy, safety + Triggers: push, PRs to main/dev + + codeql.yml ⭐ Security: Python & JS vulnerability scanning + Triggers: push, PRs, weekly Monday 8 AM UTC + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ PULL REQUEST WORKFLOWS │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + pr-checks.yml ⭐ Fast PR validation + auto-labeling + - YAML syntax validation + - PR size check (warns if >50 files or >1000 lines) + - TODO/FIXME detection + - Auto-label by changed files + + Auto-Labels: documentation, tests, dependencies, github-actions, + aria-character, chat-interface, quantum-ai, training, + azure-functions, config, scripts, security + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ TESTING WORKFLOWS │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + auto-validation.yml Orchestrator dry-run validation + Triggers: push (specific paths), daily 5 AM, manual + + e2e-tests.yml End-to-end: Playwright, Pyppeteer, Selenium + Triggers: push to main, PRs to main + + aria-tests.yml Aria-specific tests (Python 3.10, 3.11, 3.12) + Triggers: push/PR (aria_web changes), manual + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ SPECIALIZED WORKFLOWS │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + quantum-orchestration.yml Quantum computing automation + Triggers: push to main, manual + + azureml-train.yml Azure ML LoRA training jobs + Triggers: manual only + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ AUTOMATION & MAINTENANCE │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + release.yml ⭐ Automated releases on version tags + Triggers: git tag v*.*.* (e.g., v1.0.0) + Creates: GitHub release, changelog, source archives + + stale.yml ⭐ Auto-close stale issues/PRs + Issues: stale after 60d, close after 14d + PRs: stale after 30d, close after 7d + Triggers: daily midnight UTC, manual + + workflow-validation.yml⭐ Validates workflow syntax + Triggers: workflow file changes, push to main + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ QUICK COMMANDS │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + Local Code Quality Check: + pip install flake8 black isort mypy safety + flake8 . --exclude=venv,venv_*,data_out + black --check --exclude="venv|data_out" . + isort --check-only --skip-glob="venv*" . + + Create Release: + git tag v1.0.0 + git push origin v1.0.0 + # Release workflow creates GitHub release automatically + + Manual Workflow Trigger: + 1. Go to Actions tab: https://github.com/Bryan-Roe/Aria/actions + 2. Select workflow from left sidebar + 3. Click "Run workflow" + 4. Configure inputs (if any) + 5. Click "Run workflow" + + Check Workflow Logs: + Actions tab → Select run → Click job → View logs + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ WORKFLOW BADGES │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + Add to markdown files: + + ![CI Pipeline](https://github.com/Bryan-Roe/Aria/actions/workflows/ci-pipeline.yml/badge.svg) + ![Code Quality](https://github.com/Bryan-Roe/Aria/actions/workflows/code-quality.yml/badge.svg) + ![CodeQL](https://github.com/Bryan-Roe/Aria/actions/workflows/codeql.yml/badge.svg) + ![E2E Tests](https://github.com/Bryan-Roe/Aria/actions/workflows/e2e-tests.yml/badge.svg) + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ CONFIGURATION FILES │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + .github/workflows/ Workflow definitions (12 workflows) + .github/labeler.yml Auto-labeling configuration + .github/dependabot.yml Dependency update configuration + pytest.ini Test configuration + requirements.txt Python dependencies + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ TROUBLESHOOTING │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + Workflow not triggering: + • Check trigger conditions (branches, paths) + • Verify workflow file syntax + • Check if workflow is disabled in Actions tab + + Tests failing: + • Check logs in Actions tab + • Download test artifacts for detailed reports + • Run tests locally to reproduce + + Authentication errors: + • Verify secrets are configured (Settings → Secrets) + • Check token permissions + • Ensure service principal credentials are valid + + Workflow syntax errors: + • Run workflow-validation.yml workflow + • Validate locally: python -c "import yaml; yaml.safe_load(open('file.yml'))" + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ DOCUMENTATION │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + .github/WORKFLOWS.md Comprehensive workflow documentation + .github/GITHUB_ACTIONS_SETUP.md Setup summary and details + .github/GITHUB_ACTIONS_QUICKREF.txt This quick reference + + External Resources: + GitHub Actions Docs: https://docs.github.com/en/actions + Actions Marketplace: https://github.com/marketplace?type=actions + GitHub Status: https://www.githubstatus.com/ + +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ BEST PRACTICES │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + ✓ Use caching for dependencies (cache: 'pip') + ✓ Set timeouts to prevent hanging jobs + ✓ Use matrix strategy for multi-version testing + ✓ Continue on error for non-critical checks + ✓ Upload artifacts for debugging failed runs + ✓ Use secrets for sensitive data (never hardcode) + ✓ Add status checks to protect branches + ✓ Test workflows locally with 'act' tool + ✓ Keep workflow files small and focused + ✓ Document custom workflows + +╔═══════════════════════════════════════════════════════════════════════════════════╗ +║ ⭐ = New workflow added in this update ║ +║ For detailed information, see .github/WORKFLOWS.md ║ +╚═══════════════════════════════════════════════════════════════════════════════════╝ diff --git a/.github/GITHUB_ACTIONS_SETUP.md b/.github/GITHUB_ACTIONS_SETUP.md index 67fca1520..6c720cf4d 100644 --- a/.github/GITHUB_ACTIONS_SETUP.md +++ b/.github/GITHUB_ACTIONS_SETUP.md @@ -1,367 +1,367 @@ -# GitHub Actions Setup - Summary - -## Overview - -This update adds comprehensive GitHub Actions workflows to the Aria repository, establishing a complete CI/CD pipeline with automated testing, security scanning, code quality checks, and release management. - -## What Was Added - -### 6 New Workflow Files - -1. **code-quality.yml** - Code linting and security checks -2. **codeql.yml** - Security vulnerability scanning (Python & JavaScript) -3. **pr-checks.yml** - Fast validation for pull requests -4. **release.yml** - Automated release creation and artifact publishing -5. **stale.yml** - Automated stale issue/PR management -6. **workflow-validation.yml** - Validates workflow syntax and structure - -### Configuration Files - -1. **labeler.yml** - Auto-labels PRs based on changed files - - Categories: documentation, tests, dependencies, github-actions, aria-character, chat-interface, quantum-ai, training, azure-functions, config, scripts, security - -### Documentation - -1. **WORKFLOWS.md** - Comprehensive documentation covering: - - All 12 workflows (6 existing + 6 new) - - Status badges - - Usage instructions - - Configuration details - - Troubleshooting guide - - Best practices - -2. **README.md** - Updated with workflow status badges - -## Workflow Details - -### 1. Code Quality (`code-quality.yml`) - -**Purpose:** Ensures code quality and catches security issues early - -**Features:** - -- **flake8**: Python linting (syntax errors, undefined names, code quality) -- **black**: Code formatting validation -- **isort**: Import sorting validation -- **mypy**: Type checking -- **safety**: Dependency vulnerability scanning - -**Triggers:** - -- Push to main/dev branches -- Pull requests to main/dev -- Manual dispatch - -**Jobs:** - -- `lint`: Runs all linting tools -- `security-check`: Scans dependencies for vulnerabilities - -### 2. CodeQL Security (`codeql.yml`) - -**Purpose:** Advanced security vulnerability analysis - -**Features:** - -- Scans Python and JavaScript code -- Uses extended security queries -- Reports to GitHub Security tab -- Weekly scheduled scans - -**Triggers:** - -- Push to main/dev -- Pull requests to main -- Weekly on Monday at 8 AM UTC -- Manual dispatch - -**Jobs:** - -- `analyze`: Runs CodeQL analysis for Python and JavaScript - -### 3. PR Checks (`pr-checks.yml`) - -**Purpose:** Fast validation for pull requests - -**Features:** - -- YAML syntax validation -- PR size analysis with warnings -- TODO/FIXME marker detection -- Fast validation script execution -- Automatic PR labeling - -**Triggers:** - -- Pull request opened/synchronized/reopened - -**Jobs:** - -- `validate`: Quick validation checks -- `size-check`: Analyzes PR size and warns if too large -- `label-pr`: Auto-labels based on changed files - -### 4. Release (`release.yml`) - -**Purpose:** Automates release creation and artifact publishing - -**Features:** - -- Triggered by version tags (v*.*.\*) -- Generates changelog from git history -- Creates source archives (tar.gz and zip) -- Creates GitHub releases -- Marks pre-releases (alpha, beta, rc) - -**Triggers:** - -- Push of version tags (v1.0.0, v2.1.3, etc.) -- Manual dispatch with version input - -**Jobs:** - -- `create-release`: Creates release with changelog and artifacts - -### 5. Stale Management (`stale.yml`) - -**Purpose:** Manages stale issues and pull requests - -**Features:** - -- Marks issues stale after 60 days of inactivity -- Marks PRs stale after 30 days of inactivity -- Auto-closes stale items after warning period -- Configurable exemptions for important labels -- Removes stale label when updated - -**Triggers:** - -- Daily at midnight UTC -- Manual dispatch - -**Jobs:** - -- `stale`: Processes stale issues and PRs - -**Exemptions:** - -- Issues: bug, enhancement, help-wanted, pinned, security, blocked -- PRs: wip, in-progress, blocked, help-wanted - -### 6. Workflow Validation (`workflow-validation.yml`) - -**Purpose:** Validates workflow syntax and catches common issues - -**Features:** - -- YAML syntax validation -- Structure validation (name, on, jobs) -- Checks for deprecated actions -- Detects hardcoded secrets -- Validates action references - -**Triggers:** - -- Changes to workflow files -- Push to main -- Manual dispatch - -**Jobs:** - -- `validate-workflows`: Syntax and structure validation -- `test-workflows`: Tests workflow execution - -## Integration with Existing Workflows - -The new workflows complement the existing 6 workflows: - -### Existing Workflows (Preserved) - -1. **ci-pipeline.yml** - Main CI with validation, training, deployment -2. **auto-validation.yml** - Orchestrator dry-run validation -3. **e2e-tests.yml** - End-to-end browser tests -4. **aria-tests.yml** - Comprehensive Aria-specific tests -5. **quantum-orchestration.yml** - Quantum computing automation -6. **azureml-train.yml** - Azure ML training jobs - -### New Workflows (Added) - -1. **code-quality.yml** - Linting and security checks -2. **codeql.yml** - Security scanning -3. **pr-checks.yml** - PR validation -4. **release.yml** - Release automation -5. **stale.yml** - Stale management -6. **workflow-validation.yml** - Workflow validation - -## Benefits - -### 1. Improved Code Quality - -- Automated linting catches errors early -- Consistent code formatting -- Type checking reduces bugs - -### 2. Enhanced Security - -- Dependency vulnerability scanning -- Advanced CodeQL security analysis -- Automated security updates via Dependabot - -### 3. Better Pull Request Experience - -- Fast feedback on PRs -- Automatic labeling -- Size warnings for large PRs -- YAML validation - -### 4. Streamlined Releases - -- One-command releases (git tag) -- Automatic changelog generation -- Source archives for distribution - -### 5. Reduced Maintenance - -- Auto-closes stale issues/PRs -- Keeps issue tracker clean -- Configurable exemptions - -### 6. Reliability - -- Validates workflows before merge -- Catches common mistakes -- Prevents broken workflows - -## Quick Start - -### For Developers - -#### Testing Your Code - -```bash -# Local linting before push -pip install flake8 black isort mypy -flake8 . -black --check . -isort --check . -``` - -#### Creating a Pull Request - -1. Push your changes -2. Create PR - workflows run automatically -3. Address any issues flagged by workflows -4. PR will be auto-labeled based on files changed - -#### Creating a Release - -```bash -# Create and push a version tag -git tag v1.0.0 -git push origin v1.0.0 - -# Release workflow creates: -# - GitHub release -# - Changelog -# - Source archives -``` - -### For Maintainers - -#### Monitoring Workflows - -- View all workflow runs: -- Check security alerts: -- Review stale items: Filter by "stale" label - -#### Manual Triggers - -All workflows can be triggered manually: - -1. Go to Actions tab -2. Select workflow -3. Click "Run workflow" -4. Configure inputs if needed - -## Configuration - -### Customize Code Quality Checks - -Edit `.github/workflows/code-quality.yml` to adjust: - -- Flake8 rules and complexity limits -- Black line length -- MyPy strictness - -### Customize Stale Settings - -Edit `.github/workflows/stale.yml` to adjust: - -- Days before marking stale -- Days before closing -- Exempt labels -- Custom messages - -### Customize Auto-Labeling - -Edit `.github/labeler.yml` to add/modify labels: - -```yaml -"my-label": - - changed-files: - - any-glob-to-any-file: ["path/to/files/**/*"] -``` - -## Testing - -All workflows have been validated: - -- ✅ YAML syntax is correct -- ✅ All required fields present -- ✅ Actions versions are current -- ✅ No hardcoded secrets -- ✅ Compatible with existing workflows - -## Next Steps - -### Recommended Actions - -1. **Enable Branch Protection** - Require workflow checks to pass -2. **Configure Secrets** - Add any required secrets to GitHub -3. **Review First Runs** - Monitor first execution of each workflow -4. **Adjust Settings** - Tune workflow parameters as needed -5. **Add More Checks** - Expand as project needs grow - -### Future Enhancements - -- Add performance testing workflows -- Add deployment previews for PRs -- Add automated dependency updates -- Add container image building -- Add documentation deployment - -## Support - -### References - -- [WORKFLOWS.md](WORKFLOWS.md) - Detailed workflow documentation -- [GitHub Actions Docs](https://docs.github.com/en/actions) - -### Troubleshooting - -- Check workflow logs in Actions tab -- Review [WORKFLOWS.md](WORKFLOWS.md) troubleshooting section -- Create an issue with workflow run link - -## Summary - -This update establishes a comprehensive CI/CD foundation for the Aria repository with: - -- **6 new workflows** for code quality, security, and automation -- **Complete documentation** of all workflows -- **Auto-labeling** for better issue/PR management -- **Release automation** for streamlined releases -- **Security scanning** to catch vulnerabilities early -- **Workflow validation** to prevent broken CI - -The workflows are production-ready, tested, and integrated with the existing CI infrastructure. +# GitHub Actions Setup - Summary + +## Overview + +This update adds comprehensive GitHub Actions workflows to the Aria repository, establishing a complete CI/CD pipeline with automated testing, security scanning, code quality checks, and release management. + +## What Was Added + +### 6 New Workflow Files + +1. **code-quality.yml** - Code linting and security checks +2. **codeql.yml** - Security vulnerability scanning (Python & JavaScript) +3. **pr-checks.yml** - Fast validation for pull requests +4. **release.yml** - Automated release creation and artifact publishing +5. **stale.yml** - Automated stale issue/PR management +6. **workflow-validation.yml** - Validates workflow syntax and structure + +### Configuration Files + +1. **labeler.yml** - Auto-labels PRs based on changed files + - Categories: documentation, tests, dependencies, github-actions, aria-character, chat-interface, quantum-ai, training, azure-functions, config, scripts, security + +### Documentation + +1. **WORKFLOWS.md** - Comprehensive documentation covering: + - All 12 workflows (6 existing + 6 new) + - Status badges + - Usage instructions + - Configuration details + - Troubleshooting guide + - Best practices + +2. **README.md** - Updated with workflow status badges + +## Workflow Details + +### 1. Code Quality (`code-quality.yml`) + +**Purpose:** Ensures code quality and catches security issues early + +**Features:** + +- **flake8**: Python linting (syntax errors, undefined names, code quality) +- **black**: Code formatting validation +- **isort**: Import sorting validation +- **mypy**: Type checking +- **safety**: Dependency vulnerability scanning + +**Triggers:** + +- Push to main/dev branches +- Pull requests to main/dev +- Manual dispatch + +**Jobs:** + +- `lint`: Runs all linting tools +- `security-check`: Scans dependencies for vulnerabilities + +### 2. CodeQL Security (`codeql.yml`) + +**Purpose:** Advanced security vulnerability analysis + +**Features:** + +- Scans Python and JavaScript code +- Uses extended security queries +- Reports to GitHub Security tab +- Weekly scheduled scans + +**Triggers:** + +- Push to main/dev +- Pull requests to main +- Weekly on Monday at 8 AM UTC +- Manual dispatch + +**Jobs:** + +- `analyze`: Runs CodeQL analysis for Python and JavaScript + +### 3. PR Checks (`pr-checks.yml`) + +**Purpose:** Fast validation for pull requests + +**Features:** + +- YAML syntax validation +- PR size analysis with warnings +- TODO/FIXME marker detection +- Fast validation script execution +- Automatic PR labeling + +**Triggers:** + +- Pull request opened/synchronized/reopened + +**Jobs:** + +- `validate`: Quick validation checks +- `size-check`: Analyzes PR size and warns if too large +- `label-pr`: Auto-labels based on changed files + +### 4. Release (`release.yml`) + +**Purpose:** Automates release creation and artifact publishing + +**Features:** + +- Triggered by version tags (v*.*.\*) +- Generates changelog from git history +- Creates source archives (tar.gz and zip) +- Creates GitHub releases +- Marks pre-releases (alpha, beta, rc) + +**Triggers:** + +- Push of version tags (v1.0.0, v2.1.3, etc.) +- Manual dispatch with version input + +**Jobs:** + +- `create-release`: Creates release with changelog and artifacts + +### 5. Stale Management (`stale.yml`) + +**Purpose:** Manages stale issues and pull requests + +**Features:** + +- Marks issues stale after 60 days of inactivity +- Marks PRs stale after 30 days of inactivity +- Auto-closes stale items after warning period +- Configurable exemptions for important labels +- Removes stale label when updated + +**Triggers:** + +- Daily at midnight UTC +- Manual dispatch + +**Jobs:** + +- `stale`: Processes stale issues and PRs + +**Exemptions:** + +- Issues: bug, enhancement, help-wanted, pinned, security, blocked +- PRs: wip, in-progress, blocked, help-wanted + +### 6. Workflow Validation (`workflow-validation.yml`) + +**Purpose:** Validates workflow syntax and catches common issues + +**Features:** + +- YAML syntax validation +- Structure validation (name, on, jobs) +- Checks for deprecated actions +- Detects hardcoded secrets +- Validates action references + +**Triggers:** + +- Changes to workflow files +- Push to main +- Manual dispatch + +**Jobs:** + +- `validate-workflows`: Syntax and structure validation +- `test-workflows`: Tests workflow execution + +## Integration with Existing Workflows + +The new workflows complement the existing 6 workflows: + +### Existing Workflows (Preserved) + +1. **ci-pipeline.yml** - Main CI with validation, training, deployment +2. **auto-validation.yml** - Orchestrator dry-run validation +3. **e2e-tests.yml** - End-to-end browser tests +4. **aria-tests.yml** - Comprehensive Aria-specific tests +5. **quantum-orchestration.yml** - Quantum computing automation +6. **azureml-train.yml** - Azure ML training jobs + +### New Workflows (Added) + +1. **code-quality.yml** - Linting and security checks +2. **codeql.yml** - Security scanning +3. **pr-checks.yml** - PR validation +4. **release.yml** - Release automation +5. **stale.yml** - Stale management +6. **workflow-validation.yml** - Workflow validation + +## Benefits + +### 1. Improved Code Quality + +- Automated linting catches errors early +- Consistent code formatting +- Type checking reduces bugs + +### 2. Enhanced Security + +- Dependency vulnerability scanning +- Advanced CodeQL security analysis +- Automated security updates via Dependabot + +### 3. Better Pull Request Experience + +- Fast feedback on PRs +- Automatic labeling +- Size warnings for large PRs +- YAML validation + +### 4. Streamlined Releases + +- One-command releases (git tag) +- Automatic changelog generation +- Source archives for distribution + +### 5. Reduced Maintenance + +- Auto-closes stale issues/PRs +- Keeps issue tracker clean +- Configurable exemptions + +### 6. Reliability + +- Validates workflows before merge +- Catches common mistakes +- Prevents broken workflows + +## Quick Start + +### For Developers + +#### Testing Your Code + +```bash +# Local linting before push +pip install flake8 black isort mypy +flake8 . +black --check . +isort --check . +``` + +#### Creating a Pull Request + +1. Push your changes +2. Create PR - workflows run automatically +3. Address any issues flagged by workflows +4. PR will be auto-labeled based on files changed + +#### Creating a Release + +```bash +# Create and push a version tag +git tag v1.0.0 +git push origin v1.0.0 + +# Release workflow creates: +# - GitHub release +# - Changelog +# - Source archives +``` + +### For Maintainers + +#### Monitoring Workflows + +- View all workflow runs: +- Check security alerts: +- Review stale items: Filter by "stale" label + +#### Manual Triggers + +All workflows can be triggered manually: + +1. Go to Actions tab +2. Select workflow +3. Click "Run workflow" +4. Configure inputs if needed + +## Configuration + +### Customize Code Quality Checks + +Edit `.github/workflows/code-quality.yml` to adjust: + +- Flake8 rules and complexity limits +- Black line length +- MyPy strictness + +### Customize Stale Settings + +Edit `.github/workflows/stale.yml` to adjust: + +- Days before marking stale +- Days before closing +- Exempt labels +- Custom messages + +### Customize Auto-Labeling + +Edit `.github/labeler.yml` to add/modify labels: + +```yaml +"my-label": + - changed-files: + - any-glob-to-any-file: ["path/to/files/**/*"] +``` + +## Testing + +All workflows have been validated: + +- ✅ YAML syntax is correct +- ✅ All required fields present +- ✅ Actions versions are current +- ✅ No hardcoded secrets +- ✅ Compatible with existing workflows + +## Next Steps + +### Recommended Actions + +1. **Enable Branch Protection** - Require workflow checks to pass +2. **Configure Secrets** - Add any required secrets to GitHub +3. **Review First Runs** - Monitor first execution of each workflow +4. **Adjust Settings** - Tune workflow parameters as needed +5. **Add More Checks** - Expand as project needs grow + +### Future Enhancements + +- Add performance testing workflows +- Add deployment previews for PRs +- Add automated dependency updates +- Add container image building +- Add documentation deployment + +## Support + +### References + +- [WORKFLOWS.md](WORKFLOWS.md) - Detailed workflow documentation +- [GitHub Actions Docs](https://docs.github.com/en/actions) + +### Troubleshooting + +- Check workflow logs in Actions tab +- Review [WORKFLOWS.md](WORKFLOWS.md) troubleshooting section +- Create an issue with workflow run link + +## Summary + +This update establishes a comprehensive CI/CD foundation for the Aria repository with: + +- **6 new workflows** for code quality, security, and automation +- **Complete documentation** of all workflows +- **Auto-labeling** for better issue/PR management +- **Release automation** for streamlined releases +- **Security scanning** to catch vulnerabilities early +- **Workflow validation** to prevent broken CI + +The workflows are production-ready, tested, and integrated with the existing CI infrastructure. diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 6867cf8d2..dd84ea782 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -1,38 +1,38 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: '' -assignees: '' - ---- - -**Describe the bug** -A clear and concise description of what the bug is. - -**To Reproduce** -Steps to reproduce the behavior: -1. Go to '...' -2. Click on '....' -3. Scroll down to '....' -4. See error - -**Expected behavior** -A clear and concise description of what you expected to happen. - -**Screenshots** -If applicable, add screenshots to help explain your problem. - -**Desktop (please complete the following information):** - - OS: [e.g. iOS] - - Browser [e.g. chrome, safari] - - Version [e.g. 22] - -**Smartphone (please complete the following information):** - - Device: [e.g. iPhone6] - - OS: [e.g. iOS8.1] - - Browser [e.g. stock browser, safari] - - Version [e.g. 22] - -**Additional context** -Add any other context about the problem here. +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. iOS] + - Browser [e.g. chrome, safari] + - Version [e.g. 22] + +**Smartphone (please complete the following information):** + - Device: [e.g. iPhone6] + - OS: [e.g. iOS8.1] + - Browser [e.g. stock browser, safari] + - Version [e.g. 22] + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index 72718d5aa..bbcbbe7d6 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -1,20 +1,20 @@ ---- -name: Feature request -about: Suggest an idea for this project -title: '' -labels: '' -assignees: '' - ---- - -**Is your feature request related to a problem? Please describe.** -A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] - -**Describe the solution you'd like** -A clear and concise description of what you want to happen. - -**Describe alternatives you've considered** -A clear and concise description of any alternative solutions or features you've considered. - -**Additional context** -Add any other context or screenshots about the feature request here. +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: '' +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/WORKFLOWS.md b/.github/WORKFLOWS.md index 6d61bf8ea..c162cd2da 100644 --- a/.github/WORKFLOWS.md +++ b/.github/WORKFLOWS.md @@ -1,473 +1,473 @@ -# GitHub Actions Workflows - -This repository uses GitHub Actions for continuous integration, testing, deployment, and automation. - -## 📊 Workflow Status - -| Workflow | Status | Description | -| ------------------------- | -------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------- | -| CI Pipeline | ![CI Pipeline](https://github.com/Bryan-Roe/Aria/actions/workflows/ci-pipeline.yml/badge.svg) | Main CI with validation, training, and deployment | -| Code Quality | ![Code Quality](https://github.com/Bryan-Roe/Aria/actions/workflows/code-quality.yml/badge.svg) | Linting, formatting, and security checks | -| CodeQL Security | ![CodeQL](https://github.com/Bryan-Roe/Aria/actions/workflows/codeql.yml/badge.svg) | Security vulnerability scanning | -| PR Checks | ![PR Checks](https://github.com/Bryan-Roe/Aria/actions/workflows/pr-checks.yml/badge.svg) | Fast validation for pull requests | -| Auto Validation | ![Auto Validation](https://github.com/Bryan-Roe/Aria/actions/workflows/auto-validation.yml/badge.svg) | Orchestrator dry-run validation | -| E2E Tests | ![E2E Tests](https://github.com/Bryan-Roe/Aria/actions/workflows/e2e-tests.yml/badge.svg) | End-to-end browser tests | -| Aria Tests | ![Aria Tests](https://github.com/Bryan-Roe/Aria/actions/workflows/aria-tests.yml/badge.svg) | Aria character tests | -| Quantum | ![Quantum](https://github.com/Bryan-Roe/Aria/actions/workflows/quantum-orchestration.yml/badge.svg) | Quantum computing workflows | -| Azure ML | ![Azure ML](https://github.com/Bryan-Roe/Aria/actions/workflows/azureml-train.yml/badge.svg) | Azure ML training jobs | -| Release | ![Release](https://github.com/Bryan-Roe/Aria/actions/workflows/release.yml/badge.svg) | Automated releases | -| Dependabot Auto-merge | ![Dependabot Auto-merge](https://github.com/Bryan-Roe/Aria/actions/workflows/dependabot-automerge.yml/badge.svg) | Auto-merge safe Dependabot PRs | -| Integration Contract Gate | ![Integration Contract](https://github.com/Bryan-Roe/Aria/actions/workflows/integration-contract-gate.yml/badge.svg) | API contract validation on push/PR | -| Nightly Regression | ![Nightly Regression](https://github.com/Bryan-Roe/Aria/actions/workflows/nightly-regression.yml/badge.svg) | Nightly full suite with regression detection | -| Training Health Report | ![Training Health](https://github.com/Bryan-Roe/Aria/actions/workflows/training-health-report.yml/badge.svg) | Weekly training pipeline digest | -| Coverage Report | ![Coverage Report](https://github.com/Bryan-Roe/Aria/actions/workflows/coverage-report.yml/badge.svg) | Test coverage tracking and PR diff comments | -| Dataset Integrity | ![Dataset Integrity](https://github.com/Bryan-Roe/Aria/actions/workflows/dataset-integrity.yml/badge.svg) | Enforces datasets/ immutability and schema rules | -| Secrets Scan | ![Secrets Scan](https://github.com/Bryan-Roe/Aria/actions/workflows/secrets-scan.yml/badge.svg) | Gitleaks secret detection and sensitive file guard | -| Dependency Review | ![Dependency Review](https://github.com/Bryan-Roe/Aria/actions/workflows/dependency-review.yml/badge.svg) | Blocks risky dependency changes in PRs | -| Workflow Lint | ![Workflow Lint](https://github.com/Bryan-Roe/Aria/actions/workflows/actionlint.yml/badge.svg) | Lints GitHub Actions with actionlint | -| Markdown Quality | ![Markdown Quality](https://github.com/Bryan-Roe/Aria/actions/workflows/markdown-quality.yml/badge.svg) | Lints Markdown style and structure | -| Broken Links Check | ![Broken Links](https://github.com/Bryan-Roe/Aria/actions/workflows/broken-links.yml/badge.svg) | Validates links in docs and markdown | -| API Health Smoke | ![API Health Smoke](https://github.com/Bryan-Roe/Aria/actions/workflows/api-health-smoke.yml/badge.svg) | Fast smoke gate for API route health | -| Artifact Lifecycle | ![Artifact Lifecycle](https://github.com/Bryan-Roe/Aria/actions/workflows/artifact-lifecycle.yml/badge.svg) | Weekly artifact retention audit/cleanup | -| Platform Health Daily | ![Platform Health Daily](https://github.com/Bryan-Roe/Aria/actions/workflows/platform-health-daily.yml/badge.svg) | Daily health pulse and resource snapshot | - -## 🔄 Workflows Overview - -### Core CI/CD Workflows - -#### 1. **CI Pipeline** (`ci-pipeline.yml`) - -**Triggers:** Push to main/dev, PRs to main, daily at 2 AM UTC - -**Jobs:** - -- **validate**: Runs orchestrator validation and tests - - Validates all orchestrators with dry-run - - Runs unit tests with coverage - - Runs integration tests -- **train**: Daily scheduled training workflow - - Runs master orchestrator with full pipeline -- **deploy**: Deploys best models with canary strategy - -#### 2. **Code Quality** (`code-quality.yml`) ⭐ NEW - -**Triggers:** Push to main/dev, PRs - -**Jobs:** - -- **lint**: Code linting and formatting checks - - flake8 for syntax errors and code quality - - black for code formatting - - isort for import sorting - - mypy for type checking -- **security-check**: Dependency vulnerability scanning - - safety check for known vulnerabilities - -#### 3. **CodeQL Security** (`codeql.yml`) ⭐ NEW - -**Triggers:** Push to main/dev, PRs to main, weekly on Monday - -**Jobs:** - -- **analyze**: Security vulnerability analysis - - Scans Python and JavaScript code - - Uses extended security queries - - Reports to GitHub Security tab - -#### 4. **PR Checks** (`pr-checks.yml`) ⭐ NEW - -**Triggers:** Pull requests opened/updated - -**Jobs:** - -- **validate**: Quick validation checks - - Validates YAML syntax - - Runs fast_validate.py - - Checks for TODO/FIXME markers -- **size-check**: Analyzes PR size - - Warns if PR is too large -- **label-pr**: Auto-labels PRs based on changed files - -### Testing Workflows - -#### 5. **Auto Validation** (`auto-validation.yml`) - -**Triggers:** Push to main (specific paths), daily at 5 AM UTC, manual - -**Jobs:** - -- **dry-run**: Validates orchestrators without execution - - Runs auto_bootstrap.py - - Validates autotrain and quantum_autorun - -#### 6. **E2E Tests** (`e2e-tests.yml`) - -**Triggers:** Push to main, PRs to main - -**Jobs:** - -- **integration**: Unit and integration tests -- **e2e_playwright**: Playwright E2E tests -- **containerized_chrome**: Pyppeteer E2E tests - -#### 7. **Aria Tests** (`aria-tests.yml`) - -**Triggers:** Push/PR (aria_web changes), manual - -**Jobs:** - -- **unit-integration-tests**: Tests across Python 3.10, 3.11, 3.12 -- **playwright-e2e**: Playwright E2E tests -- **pyppeteer-e2e**: Pyppeteer E2E tests -- **containerized-chrome-e2e**: Selenium E2E tests -- **test-summary**: Aggregates all test results - -### Specialized Workflows - -#### 8. **Quantum Orchestration** (`quantum-orchestration.yml`) - -**Triggers:** Push to main, manual - -**Jobs:** - -- **run-quantum**: Runs quantum computing workflows - - Azure login with service principal - - PowerShell orchestration scripts - -#### 9. **Azure ML Training** (`azureml-train.yml`) - -**Triggers:** Manual only - -**Jobs:** - -- **submit-job**: Submits LoRA training to Azure ML - - Configurable compute target - - Streams job output - -#### 10. **Release** (`release.yml`) ⭐ NEW - -**Triggers:** Version tags (v*.*.\*), manual - -**Jobs:** - -- **create-release**: Creates GitHub releases - - Generates changelog - - Creates source archives - - Uploads release artifacts - -### Maintenance Workflows - -#### 11. **Stale Issues and PRs** (`stale.yml`) ⭐ NEW - -**Triggers:** Daily at midnight UTC, manual - -**Jobs:** - -- **stale**: Manages stale issues and PRs - - Marks issues stale after 60 days - - Marks PRs stale after 30 days - - Auto-closes after warning period - -#### 12. **Workflow Validation** (`workflow-validation.yml`) ⭐ NEW - -**Triggers:** Changes to workflow files, push to main - -**Jobs:** - -- **validate-workflows**: Validates workflow syntax - - YAML syntax checking - - Structure validation - - Common issues detection -- **test-workflows**: Tests workflow execution - -### Automation Workflows - -#### 13. **Dependabot Auto-merge** (`dependabot-automerge.yml`) - -**Triggers:** Pull requests opened/updated by `dependabot[bot]` - -**Jobs:** - -- **automerge**: Auto-approve and enable squash-merge for safe Dependabot PRs - - Fetches Dependabot PR metadata (package name, update type, versions) - - Auto-approves **minor** and **patch** version bumps - - Enables GitHub auto-merge (squash) — merge only happens once CI passes - - Posts a warning comment on **major** version bumps requiring manual review - - Writes a step summary table with package and version info - -#### 14. **Integration Contract Gate** (`integration-contract-gate.yml`) - -**Triggers:** Push to `main` (strict mode), PR to `main`/`dev`, manual dispatch - -**Jobs:** - -- **contract-gate**: Runs `scripts/integration_contract_gate.sh` - - Standard mode on PRs — verifies smoke tests + CI orchestrator contracts - - Strict mode on pushes to `main` — adds `--strict-endpoints` (8/8 checks) - - Uploads `data_out/integration_smoke/` and `data_out/ci_orchestrator/status.json` as artifacts (14-day retention) - - Displays CI orchestrator status JSON in step summary - -#### 15. **Nightly Regression Baseline** (`nightly-regression.yml`) - -**Triggers:** Nightly at 03:00 UTC, manual dispatch (optional `--maxfail=1`) - -**Jobs:** - -- **full-test-suite**: Full `pytest tests/` run with coverage - - JUnit XML + coverage XML uploaded as 30-day artifacts - - Parses pass/fail/skip counts from pytest output -- **detect-regression**: Compares against previous run artifact - - Downloads prior nightly artifact via `dawidd6/action-download-artifact` - - Opens a labelled GitHub Issue (`regression`, `automated`, `bug`) on any failures - - Deduplicates: skips creating a second issue if one is already open for today - -#### 16. **Training Health Report** (`training-health-report.yml`) - -**Triggers:** Every Monday at 08:00 UTC, manual dispatch (with optional force-issue flag) - -**Jobs:** - -- **health-report**: Reads `data_out/` status artifacts and reports training pipeline health - - Parses `data_out/autonomous_training_status.json` (cycles, best_accuracy, performance_history) - - Parses `data_out/ci_orchestrator/status.json` (succeeded/failed job counts) - - Detects degradation: accuracy < 0.6, accuracy trend declining, or CI failure rate > 20 % - - Runs `scripts/training_analytics.py` if available (non-blocking) - - Posts rich step summary with health badge (✅ Healthy / ⚠️ DEGRADED) - - Opens a labelled GitHub Issue (`training`, `automated`) — `performance` label added when degraded - - Deduplicates issues by week - -#### 17. **Coverage Report** (`coverage-report.yml`) - -**Triggers:** Push to `main`, PR to `main`/`dev`, manual dispatch - -**Jobs:** - -- **coverage**: Measures test coverage on every push and PR - - Runs `pytest --cov` and extracts line-rate from `coverage.xml` - - On pushes to `main`: uploads coverage XML as 90-day `coverage-main-baseline` artifact - - On PRs: downloads baseline, computes Δ vs `main`, posts/updates a PR comment with badge (🟢≥80%, 🟡≥60%, 🔴<60%) - - Writes a collapsible step summary with the last 40 lines of pytest output - - **No hard fail** — warns only; use to track trends and spot regressions early - -#### 18. **Dataset Integrity** (`dataset-integrity.yml`) - -**Triggers:** Push/PR to `main`/`dev` that touch `datasets/**`, manual dispatch - -**Jobs:** - -- **immutability-check**: Enforces the `datasets/` read-only convention - - Computes diff range (handles PRs, force-pushes, and initial branch pushes) - - Fails if any Modified (M), Deleted (D), Renamed (R), Copied (C), or Type-changed (T) files are found - - Only Additions (A) are permitted — preserves existing dataset history -- **schema-validate**: Validates newly added `.json`/`.jsonl` files - - Spot-checks up to 20 records per file against chat message schema - - Expects `{"messages": [{"role": "user|assistant|system", "content": "..."}]}` - - Fails on JSON parse errors, missing `messages` key, or unknown roles - -#### 19. **Secrets Scan** (`secrets-scan.yml`) - -**Triggers:** Push to `main`/`dev`, PR to `main`/`dev`, manual dispatch - -**Jobs:** - -- **gitleaks**: Authoritative secret detection via `gitleaks/gitleaks-action@v2` - - Scans full git history (`fetch-depth: 0`) for API keys, tokens, passwords, private keys - - **Blocking** — fails the workflow if any secret is detected - - Integrates with GitHub's security advisory system -- **sensitive-file-guard**: Advisory checks for credential file changes - - Detects changes to `local.settings.json`, `.env`, `.pem`, `.p12`, private key files - - Validates `local.settings.json` contains only placeholder values (heuristic check) - - **Advisory only** — warns in step summary but does not block (Gitleaks is the gate) - -#### 20. **Dependency Review** (`dependency-review.yml`) - -**Triggers:** Pull requests to `main`/`dev` - -**Jobs:** - -- **dependency-review**: Reviews dependency changes introduced by PRs - - Uses `actions/dependency-review-action@v4` - - Fails on vulnerabilities with severity **high** or above - - Denies high-risk copyleft licenses (`GPL-2.0`, `GPL-3.0`, `AGPL-3.0`) - - Posts dependency review summary directly on PRs - -#### 21. **Workflow Lint** (`actionlint.yml`) - -**Triggers:** Push/PR when `.github/workflows/**` or `.github/actions/**` change, manual dispatch - -**Jobs:** - -- **actionlint**: Lints GitHub Actions workflows and embedded shell snippets - - Uses `reviewdog/action-actionlint@v1` with blocking mode (`fail_on_error: true`) - - Installs `shellcheck` so shell fragments in workflows are validated too - - Catches invalid expressions, runner matrix mistakes, and shell anti-patterns before merge - -#### 22. **Markdown Quality** (`markdown-quality.yml`) - -**Triggers:** Push/PR to `main`/`dev` when Markdown files change, manual dispatch - -**Jobs:** - -- **markdownlint**: Enforces Markdown style and structural consistency - - Uses `DavidAnson/markdownlint-cli2-action@v20` - - Scans `**/*.md` while excluding generated/ephemeral paths (`data_out/**`, `mount/**`) - - Adds run summary in `GITHUB_STEP_SUMMARY` - -#### 23. **Broken Links Check** (`broken-links.yml`) - -**Triggers:** Push/PR to `main`/`dev` on docs/Markdown changes, manual dispatch - -**Jobs:** - -- **link-check**: Verifies outbound and repo links in Markdown/docs - - Uses `lycheeverse/lychee-action@v2` - - Accepts common redirect/auth/rate-limit status codes (`301`, `302`, `401`, `403`, `429`) - - Fails the workflow on unresolved/broken links to prevent stale docs - - Adds run summary in `GITHUB_STEP_SUMMARY` - -#### 24. **API Health Smoke** (`api-health-smoke.yml`) - -**Triggers:** Push/PR on backend route changes, daily schedule, manual dispatch - -**Jobs:** - -- **smoke**: Fast route and contract smoke checks for API surfaces - - Runs `python scripts/integration_smoke.py --json` - - Runs strict endpoint smoke daily (`--strict-endpoints`) on scheduled runs - - Runs `python scripts/ci_orchestrator.py --integration-contract-tests` - - Uploads `data_out/integration_smoke/` and `data_out/ci_orchestrator/status.json` artifacts - - Adds a concise run checklist in `GITHUB_STEP_SUMMARY` - -#### 25. **Artifact Lifecycle** (`artifact-lifecycle.yml`) - -**Triggers:** Weekly schedule, manual dispatch - -**Jobs:** - -- **cleanup**: Retention management for `data_out/` artifacts - - Scheduled run executes dry-run only (`python scripts/cleanup_artifacts.py`) - - Manual run supports configurable `max_age` and `max_count` - - Manual run can optionally apply deletion (`--apply=true`) for controlled cleanup - - Uploads `data_out/cleanup_summary.json` artifact and writes JSON summary to step output - -#### 26. **Platform Health Daily** (`platform-health-daily.yml`) - -**Triggers:** Daily schedule, manual dispatch - -**Jobs:** - -- **health-pulse**: Generates daily health and resource snapshots - - Runs `python scripts/system_health_check.py --json` - - Runs `python scripts/resource_monitor.py --snapshot --export ...` - - Uploads machine-readable health artifacts under `data_out/platform_health/` - - Adds quick status counts to `GITHUB_STEP_SUMMARY` for triage - -## 🎯 Quick Actions - -### Running Workflows Manually - -Most workflows can be triggered manually from the GitHub Actions tab: - -1. Go to **Actions** tab in GitHub -2. Select the workflow from the left sidebar -3. Click **Run workflow** -4. Fill in any required inputs -5. Click **Run workflow** - -### Workflow Badges - -Add workflow status badges to any markdown file: - -```markdown -![Workflow Name](https://github.com/Bryan-Roe/Aria/actions/workflows/workflow-file.yml/badge.svg) -``` - -### Monitoring Workflow Runs - -- **All runs**: [GitHub Actions dashboard](https://github.com/Bryan-Roe/Aria/actions) -- **Failed runs**: Filter by "Status: Failure" -- **Scheduled runs**: Filter by "Event: schedule" - -## 🛠️ Configuration Files - -- **`.github/workflows/`** - Workflow definitions -- **`.github/labeler.yml`** - Auto-labeling configuration -- **`.github/dependabot.yml`** - Dependency update configuration -- **`pytest.ini`** - Test configuration -- **`requirements.txt`** - Python dependencies - -## 🔧 Workflow Development - -### Testing Workflows Locally - -Use [act](https://github.com/nektos/act) to test workflows locally: - -```bash -# Install act -brew install act # macOS -# or -choco install act-cli # Windows - -# Run a workflow -act -j job-name - -# Run on push event -act push - -# Run on pull request -act pull_request -``` - -### Best Practices - -1. **Use caching** for dependencies to speed up workflows -2. **Set timeouts** to prevent hanging jobs -3. **Use matrix strategy** for testing across multiple versions -4. **Continue on error** for non-critical checks -5. **Upload artifacts** for debugging failed runs -6. **Use secrets** for sensitive data (never hardcode) -7. **Add status checks** to protect branches - -### Adding New Workflows - -1. Create workflow file in `.github/workflows/` -2. Follow naming convention: `kebab-case.yml` -3. Add descriptive name and documentation -4. Test locally with `act` if possible -5. Add to this documentation -6. Update workflow validation to include new workflow - -## 📚 Additional Resources - -- [GitHub Actions Documentation](https://docs.github.com/en/actions) -- [Workflow Syntax Reference](https://docs.github.com/en/actions/reference/workflow-syntax-for-github-actions) -- [GitHub Actions Marketplace](https://github.com/marketplace?type=actions) -- [Repository Documentation](../README.md) - -## 🆘 Troubleshooting - -### Common Issues - -**Workflow not triggering:** - -- Check trigger conditions (branches, paths) -- Verify workflow file syntax -- Check if workflow is disabled - -**Tests failing:** - -- Check logs in Actions tab -- Download test artifacts for detailed reports -- Run tests locally to reproduce - -**Authentication errors:** - -- Verify secrets are configured -- Check token permissions -- Ensure service principal credentials are valid - -### Getting Help - -- Review workflow logs in GitHub Actions tab -- Check [GitHub Status](https://www.githubstatus.com/) -- Create an issue with workflow run link +# GitHub Actions Workflows + +This repository uses GitHub Actions for continuous integration, testing, deployment, and automation. + +## 📊 Workflow Status + +| Workflow | Status | Description | +| ------------------------- | -------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------- | +| CI Pipeline | ![CI Pipeline](https://github.com/Bryan-Roe/Aria/actions/workflows/ci-pipeline.yml/badge.svg) | Main CI with validation, training, and deployment | +| Code Quality | ![Code Quality](https://github.com/Bryan-Roe/Aria/actions/workflows/code-quality.yml/badge.svg) | Linting, formatting, and security checks | +| CodeQL Security | ![CodeQL](https://github.com/Bryan-Roe/Aria/actions/workflows/codeql.yml/badge.svg) | Security vulnerability scanning | +| PR Checks | ![PR Checks](https://github.com/Bryan-Roe/Aria/actions/workflows/pr-checks.yml/badge.svg) | Fast validation for pull requests | +| Auto Validation | ![Auto Validation](https://github.com/Bryan-Roe/Aria/actions/workflows/auto-validation.yml/badge.svg) | Orchestrator dry-run validation | +| E2E Tests | ![E2E Tests](https://github.com/Bryan-Roe/Aria/actions/workflows/e2e-tests.yml/badge.svg) | End-to-end browser tests | +| Aria Tests | ![Aria Tests](https://github.com/Bryan-Roe/Aria/actions/workflows/aria-tests.yml/badge.svg) | Aria character tests | +| Quantum | ![Quantum](https://github.com/Bryan-Roe/Aria/actions/workflows/quantum-orchestration.yml/badge.svg) | Quantum computing workflows | +| Azure ML | ![Azure ML](https://github.com/Bryan-Roe/Aria/actions/workflows/azureml-train.yml/badge.svg) | Azure ML training jobs | +| Release | ![Release](https://github.com/Bryan-Roe/Aria/actions/workflows/release.yml/badge.svg) | Automated releases | +| Dependabot Auto-merge | ![Dependabot Auto-merge](https://github.com/Bryan-Roe/Aria/actions/workflows/dependabot-automerge.yml/badge.svg) | Auto-merge safe Dependabot PRs | +| Integration Contract Gate | ![Integration Contract](https://github.com/Bryan-Roe/Aria/actions/workflows/integration-contract-gate.yml/badge.svg) | API contract validation on push/PR | +| Nightly Regression | ![Nightly Regression](https://github.com/Bryan-Roe/Aria/actions/workflows/nightly-regression.yml/badge.svg) | Nightly full suite with regression detection | +| Training Health Report | ![Training Health](https://github.com/Bryan-Roe/Aria/actions/workflows/training-health-report.yml/badge.svg) | Weekly training pipeline digest | +| Coverage Report | ![Coverage Report](https://github.com/Bryan-Roe/Aria/actions/workflows/coverage-report.yml/badge.svg) | Test coverage tracking and PR diff comments | +| Dataset Integrity | ![Dataset Integrity](https://github.com/Bryan-Roe/Aria/actions/workflows/dataset-integrity.yml/badge.svg) | Enforces datasets/ immutability and schema rules | +| Secrets Scan | ![Secrets Scan](https://github.com/Bryan-Roe/Aria/actions/workflows/secrets-scan.yml/badge.svg) | Gitleaks secret detection and sensitive file guard | +| Dependency Review | ![Dependency Review](https://github.com/Bryan-Roe/Aria/actions/workflows/dependency-review.yml/badge.svg) | Blocks risky dependency changes in PRs | +| Workflow Lint | ![Workflow Lint](https://github.com/Bryan-Roe/Aria/actions/workflows/actionlint.yml/badge.svg) | Lints GitHub Actions with actionlint | +| Markdown Quality | ![Markdown Quality](https://github.com/Bryan-Roe/Aria/actions/workflows/markdown-quality.yml/badge.svg) | Lints Markdown style and structure | +| Broken Links Check | ![Broken Links](https://github.com/Bryan-Roe/Aria/actions/workflows/broken-links.yml/badge.svg) | Validates links in docs and markdown | +| API Health Smoke | ![API Health Smoke](https://github.com/Bryan-Roe/Aria/actions/workflows/api-health-smoke.yml/badge.svg) | Fast smoke gate for API route health | +| Artifact Lifecycle | ![Artifact Lifecycle](https://github.com/Bryan-Roe/Aria/actions/workflows/artifact-lifecycle.yml/badge.svg) | Weekly artifact retention audit/cleanup | +| Platform Health Daily | ![Platform Health Daily](https://github.com/Bryan-Roe/Aria/actions/workflows/platform-health-daily.yml/badge.svg) | Daily health pulse and resource snapshot | + +## 🔄 Workflows Overview + +### Core CI/CD Workflows + +#### 1. **CI Pipeline** (`ci-pipeline.yml`) + +**Triggers:** Push to main/dev, PRs to main, daily at 2 AM UTC + +**Jobs:** + +- **validate**: Runs orchestrator validation and tests + - Validates all orchestrators with dry-run + - Runs unit tests with coverage + - Runs integration tests +- **train**: Daily scheduled training workflow + - Runs master orchestrator with full pipeline +- **deploy**: Deploys best models with canary strategy + +#### 2. **Code Quality** (`code-quality.yml`) ⭐ NEW + +**Triggers:** Push to main/dev, PRs + +**Jobs:** + +- **lint**: Code linting and formatting checks + - flake8 for syntax errors and code quality + - black for code formatting + - isort for import sorting + - mypy for type checking +- **security-check**: Dependency vulnerability scanning + - safety check for known vulnerabilities + +#### 3. **CodeQL Security** (`codeql.yml`) ⭐ NEW + +**Triggers:** Push to main/dev, PRs to main, weekly on Monday + +**Jobs:** + +- **analyze**: Security vulnerability analysis + - Scans Python and JavaScript code + - Uses extended security queries + - Reports to GitHub Security tab + +#### 4. **PR Checks** (`pr-checks.yml`) ⭐ NEW + +**Triggers:** Pull requests opened/updated + +**Jobs:** + +- **validate**: Quick validation checks + - Validates YAML syntax + - Runs fast_validate.py + - Checks for TODO/FIXME markers +- **size-check**: Analyzes PR size + - Warns if PR is too large +- **label-pr**: Auto-labels PRs based on changed files + +### Testing Workflows + +#### 5. **Auto Validation** (`auto-validation.yml`) + +**Triggers:** Push to main (specific paths), daily at 5 AM UTC, manual + +**Jobs:** + +- **dry-run**: Validates orchestrators without execution + - Runs auto_bootstrap.py + - Validates autotrain and quantum_autorun + +#### 6. **E2E Tests** (`e2e-tests.yml`) + +**Triggers:** Push to main, PRs to main + +**Jobs:** + +- **integration**: Unit and integration tests +- **e2e_playwright**: Playwright E2E tests +- **containerized_chrome**: Pyppeteer E2E tests + +#### 7. **Aria Tests** (`aria-tests.yml`) + +**Triggers:** Push/PR (aria_web changes), manual + +**Jobs:** + +- **unit-integration-tests**: Tests across Python 3.10, 3.11, 3.12 +- **playwright-e2e**: Playwright E2E tests +- **pyppeteer-e2e**: Pyppeteer E2E tests +- **containerized-chrome-e2e**: Selenium E2E tests +- **test-summary**: Aggregates all test results + +### Specialized Workflows + +#### 8. **Quantum Orchestration** (`quantum-orchestration.yml`) + +**Triggers:** Push to main, manual + +**Jobs:** + +- **run-quantum**: Runs quantum computing workflows + - Azure login with service principal + - PowerShell orchestration scripts + +#### 9. **Azure ML Training** (`azureml-train.yml`) + +**Triggers:** Manual only + +**Jobs:** + +- **submit-job**: Submits LoRA training to Azure ML + - Configurable compute target + - Streams job output + +#### 10. **Release** (`release.yml`) ⭐ NEW + +**Triggers:** Version tags (v*.*.\*), manual + +**Jobs:** + +- **create-release**: Creates GitHub releases + - Generates changelog + - Creates source archives + - Uploads release artifacts + +### Maintenance Workflows + +#### 11. **Stale Issues and PRs** (`stale.yml`) ⭐ NEW + +**Triggers:** Daily at midnight UTC, manual + +**Jobs:** + +- **stale**: Manages stale issues and PRs + - Marks issues stale after 60 days + - Marks PRs stale after 30 days + - Auto-closes after warning period + +#### 12. **Workflow Validation** (`workflow-validation.yml`) ⭐ NEW + +**Triggers:** Changes to workflow files, push to main + +**Jobs:** + +- **validate-workflows**: Validates workflow syntax + - YAML syntax checking + - Structure validation + - Common issues detection +- **test-workflows**: Tests workflow execution + +### Automation Workflows + +#### 13. **Dependabot Auto-merge** (`dependabot-automerge.yml`) + +**Triggers:** Pull requests opened/updated by `dependabot[bot]` + +**Jobs:** + +- **automerge**: Auto-approve and enable squash-merge for safe Dependabot PRs + - Fetches Dependabot PR metadata (package name, update type, versions) + - Auto-approves **minor** and **patch** version bumps + - Enables GitHub auto-merge (squash) — merge only happens once CI passes + - Posts a warning comment on **major** version bumps requiring manual review + - Writes a step summary table with package and version info + +#### 14. **Integration Contract Gate** (`integration-contract-gate.yml`) + +**Triggers:** Push to `main` (strict mode), PR to `main`/`dev`, manual dispatch + +**Jobs:** + +- **contract-gate**: Runs `scripts/integration_contract_gate.sh` + - Standard mode on PRs — verifies smoke tests + CI orchestrator contracts + - Strict mode on pushes to `main` — adds `--strict-endpoints` (8/8 checks) + - Uploads `data_out/integration_smoke/` and `data_out/ci_orchestrator/status.json` as artifacts (14-day retention) + - Displays CI orchestrator status JSON in step summary + +#### 15. **Nightly Regression Baseline** (`nightly-regression.yml`) + +**Triggers:** Nightly at 03:00 UTC, manual dispatch (optional `--maxfail=1`) + +**Jobs:** + +- **full-test-suite**: Full `pytest tests/` run with coverage + - JUnit XML + coverage XML uploaded as 30-day artifacts + - Parses pass/fail/skip counts from pytest output +- **detect-regression**: Compares against previous run artifact + - Downloads prior nightly artifact via `dawidd6/action-download-artifact` + - Opens a labelled GitHub Issue (`regression`, `automated`, `bug`) on any failures + - Deduplicates: skips creating a second issue if one is already open for today + +#### 16. **Training Health Report** (`training-health-report.yml`) + +**Triggers:** Every Monday at 08:00 UTC, manual dispatch (with optional force-issue flag) + +**Jobs:** + +- **health-report**: Reads `data_out/` status artifacts and reports training pipeline health + - Parses `data_out/autonomous_training_status.json` (cycles, best_accuracy, performance_history) + - Parses `data_out/ci_orchestrator/status.json` (succeeded/failed job counts) + - Detects degradation: accuracy < 0.6, accuracy trend declining, or CI failure rate > 20 % + - Runs `scripts/training_analytics.py` if available (non-blocking) + - Posts rich step summary with health badge (✅ Healthy / ⚠️ DEGRADED) + - Opens a labelled GitHub Issue (`training`, `automated`) — `performance` label added when degraded + - Deduplicates issues by week + +#### 17. **Coverage Report** (`coverage-report.yml`) + +**Triggers:** Push to `main`, PR to `main`/`dev`, manual dispatch + +**Jobs:** + +- **coverage**: Measures test coverage on every push and PR + - Runs `pytest --cov` and extracts line-rate from `coverage.xml` + - On pushes to `main`: uploads coverage XML as 90-day `coverage-main-baseline` artifact + - On PRs: downloads baseline, computes Δ vs `main`, posts/updates a PR comment with badge (🟢≥80%, 🟡≥60%, 🔴<60%) + - Writes a collapsible step summary with the last 40 lines of pytest output + - **No hard fail** — warns only; use to track trends and spot regressions early + +#### 18. **Dataset Integrity** (`dataset-integrity.yml`) + +**Triggers:** Push/PR to `main`/`dev` that touch `datasets/**`, manual dispatch + +**Jobs:** + +- **immutability-check**: Enforces the `datasets/` read-only convention + - Computes diff range (handles PRs, force-pushes, and initial branch pushes) + - Fails if any Modified (M), Deleted (D), Renamed (R), Copied (C), or Type-changed (T) files are found + - Only Additions (A) are permitted — preserves existing dataset history +- **schema-validate**: Validates newly added `.json`/`.jsonl` files + - Spot-checks up to 20 records per file against chat message schema + - Expects `{"messages": [{"role": "user|assistant|system", "content": "..."}]}` + - Fails on JSON parse errors, missing `messages` key, or unknown roles + +#### 19. **Secrets Scan** (`secrets-scan.yml`) + +**Triggers:** Push to `main`/`dev`, PR to `main`/`dev`, manual dispatch + +**Jobs:** + +- **gitleaks**: Authoritative secret detection via `gitleaks/gitleaks-action@v2` + - Scans full git history (`fetch-depth: 0`) for API keys, tokens, passwords, private keys + - **Blocking** — fails the workflow if any secret is detected + - Integrates with GitHub's security advisory system +- **sensitive-file-guard**: Advisory checks for credential file changes + - Detects changes to `local.settings.json`, `.env`, `.pem`, `.p12`, private key files + - Validates `local.settings.json` contains only placeholder values (heuristic check) + - **Advisory only** — warns in step summary but does not block (Gitleaks is the gate) + +#### 20. **Dependency Review** (`dependency-review.yml`) + +**Triggers:** Pull requests to `main`/`dev` + +**Jobs:** + +- **dependency-review**: Reviews dependency changes introduced by PRs + - Uses `actions/dependency-review-action@v4` + - Fails on vulnerabilities with severity **high** or above + - Denies high-risk copyleft licenses (`GPL-2.0`, `GPL-3.0`, `AGPL-3.0`) + - Posts dependency review summary directly on PRs + +#### 21. **Workflow Lint** (`actionlint.yml`) + +**Triggers:** Push/PR when `.github/workflows/**` or `.github/actions/**` change, manual dispatch + +**Jobs:** + +- **actionlint**: Lints GitHub Actions workflows and embedded shell snippets + - Uses `reviewdog/action-actionlint@v1` with blocking mode (`fail_on_error: true`) + - Installs `shellcheck` so shell fragments in workflows are validated too + - Catches invalid expressions, runner matrix mistakes, and shell anti-patterns before merge + +#### 22. **Markdown Quality** (`markdown-quality.yml`) + +**Triggers:** Push/PR to `main`/`dev` when Markdown files change, manual dispatch + +**Jobs:** + +- **markdownlint**: Enforces Markdown style and structural consistency + - Uses `DavidAnson/markdownlint-cli2-action@v20` + - Scans `**/*.md` while excluding generated/ephemeral paths (`data_out/**`, `mount/**`) + - Adds run summary in `GITHUB_STEP_SUMMARY` + +#### 23. **Broken Links Check** (`broken-links.yml`) + +**Triggers:** Push/PR to `main`/`dev` on docs/Markdown changes, manual dispatch + +**Jobs:** + +- **link-check**: Verifies outbound and repo links in Markdown/docs + - Uses `lycheeverse/lychee-action@v2` + - Accepts common redirect/auth/rate-limit status codes (`301`, `302`, `401`, `403`, `429`) + - Fails the workflow on unresolved/broken links to prevent stale docs + - Adds run summary in `GITHUB_STEP_SUMMARY` + +#### 24. **API Health Smoke** (`api-health-smoke.yml`) + +**Triggers:** Push/PR on backend route changes, daily schedule, manual dispatch + +**Jobs:** + +- **smoke**: Fast route and contract smoke checks for API surfaces + - Runs `python scripts/integration_smoke.py --json` + - Runs strict endpoint smoke daily (`--strict-endpoints`) on scheduled runs + - Runs `python scripts/ci_orchestrator.py --integration-contract-tests` + - Uploads `data_out/integration_smoke/` and `data_out/ci_orchestrator/status.json` artifacts + - Adds a concise run checklist in `GITHUB_STEP_SUMMARY` + +#### 25. **Artifact Lifecycle** (`artifact-lifecycle.yml`) + +**Triggers:** Weekly schedule, manual dispatch + +**Jobs:** + +- **cleanup**: Retention management for `data_out/` artifacts + - Scheduled run executes dry-run only (`python scripts/cleanup_artifacts.py`) + - Manual run supports configurable `max_age` and `max_count` + - Manual run can optionally apply deletion (`--apply=true`) for controlled cleanup + - Uploads `data_out/cleanup_summary.json` artifact and writes JSON summary to step output + +#### 26. **Platform Health Daily** (`platform-health-daily.yml`) + +**Triggers:** Daily schedule, manual dispatch + +**Jobs:** + +- **health-pulse**: Generates daily health and resource snapshots + - Runs `python scripts/system_health_check.py --json` + - Runs `python scripts/resource_monitor.py --snapshot --export ...` + - Uploads machine-readable health artifacts under `data_out/platform_health/` + - Adds quick status counts to `GITHUB_STEP_SUMMARY` for triage + +## 🎯 Quick Actions + +### Running Workflows Manually + +Most workflows can be triggered manually from the GitHub Actions tab: + +1. Go to **Actions** tab in GitHub +2. Select the workflow from the left sidebar +3. Click **Run workflow** +4. Fill in any required inputs +5. Click **Run workflow** + +### Workflow Badges + +Add workflow status badges to any markdown file: + +```markdown +![Workflow Name](https://github.com/Bryan-Roe/Aria/actions/workflows/workflow-file.yml/badge.svg) +``` + +### Monitoring Workflow Runs + +- **All runs**: [GitHub Actions dashboard](https://github.com/Bryan-Roe/Aria/actions) +- **Failed runs**: Filter by "Status: Failure" +- **Scheduled runs**: Filter by "Event: schedule" + +## 🛠️ Configuration Files + +- **`.github/workflows/`** - Workflow definitions +- **`.github/labeler.yml`** - Auto-labeling configuration +- **`.github/dependabot.yml`** - Dependency update configuration +- **`pytest.ini`** - Test configuration +- **`requirements.txt`** - Python dependencies + +## 🔧 Workflow Development + +### Testing Workflows Locally + +Use [act](https://github.com/nektos/act) to test workflows locally: + +```bash +# Install act +brew install act # macOS +# or +choco install act-cli # Windows + +# Run a workflow +act -j job-name + +# Run on push event +act push + +# Run on pull request +act pull_request +``` + +### Best Practices + +1. **Use caching** for dependencies to speed up workflows +2. **Set timeouts** to prevent hanging jobs +3. **Use matrix strategy** for testing across multiple versions +4. **Continue on error** for non-critical checks +5. **Upload artifacts** for debugging failed runs +6. **Use secrets** for sensitive data (never hardcode) +7. **Add status checks** to protect branches + +### Adding New Workflows + +1. Create workflow file in `.github/workflows/` +2. Follow naming convention: `kebab-case.yml` +3. Add descriptive name and documentation +4. Test locally with `act` if possible +5. Add to this documentation +6. Update workflow validation to include new workflow + +## 📚 Additional Resources + +- [GitHub Actions Documentation](https://docs.github.com/en/actions) +- [Workflow Syntax Reference](https://docs.github.com/en/actions/reference/workflow-syntax-for-github-actions) +- [GitHub Actions Marketplace](https://github.com/marketplace?type=actions) +- [Repository Documentation](../README.md) + +## 🆘 Troubleshooting + +### Common Issues + +**Workflow not triggering:** + +- Check trigger conditions (branches, paths) +- Verify workflow file syntax +- Check if workflow is disabled + +**Tests failing:** + +- Check logs in Actions tab +- Download test artifacts for detailed reports +- Run tests locally to reproduce + +**Authentication errors:** + +- Verify secrets are configured +- Check token permissions +- Ensure service principal credentials are valid + +### Getting Help + +- Review workflow logs in GitHub Actions tab +- Check [GitHub Status](https://www.githubstatus.com/) +- Create an issue with workflow run link diff --git a/.github/actions/setup-python-env/action.yml b/.github/actions/setup-python-env/action.yml index fa5aae135..e2d158cd7 100644 --- a/.github/actions/setup-python-env/action.yml +++ b/.github/actions/setup-python-env/action.yml @@ -1,47 +1,47 @@ -name: 'Setup Python Environment' -description: 'Reusable setup for Python with pip caching and common dependencies' - -inputs: - python-version: - description: 'Python version to use' - required: false - default: '3.11' - install-requirements: - description: 'Install requirements.txt' - required: false - default: 'true' - extra-packages: - description: 'Space-separated list of extra pip packages to install' - required: false - default: '' - -runs: - using: 'composite' - steps: - - name: Set up Python ${{ inputs.python-version }} - uses: actions/setup-python@v5 - with: - python-version: ${{ inputs.python-version }} - cache: 'pip' - - - name: Install base dependencies - shell: bash - run: python -m pip install --upgrade pip - - - name: Install requirements - if: inputs.install-requirements == 'true' - shell: bash - run: | - if [ -f requirements.txt ]; then - pip install -r requirements.txt - fi - - - name: Install extra packages - if: inputs.extra-packages != '' - shell: bash - env: - EXTRA_PACKAGES: ${{ inputs.extra-packages }} - # Pass packages via env var to prevent shell-injection from the input value. - # Word-splitting on $EXTRA_PACKAGES is intentional (space-separated list). - # shellcheck disable=SC2086 - run: pip install $EXTRA_PACKAGES +name: 'Setup Python Environment' +description: 'Reusable setup for Python with pip caching and common dependencies' + +inputs: + python-version: + description: 'Python version to use' + required: false + default: '3.11' + install-requirements: + description: 'Install requirements.txt' + required: false + default: 'true' + extra-packages: + description: 'Space-separated list of extra pip packages to install' + required: false + default: '' + +runs: + using: 'composite' + steps: + - name: Set up Python ${{ inputs.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ inputs.python-version }} + cache: 'pip' + + - name: Install base dependencies + shell: bash + run: python -m pip install --upgrade pip + + - name: Install requirements + if: inputs.install-requirements == 'true' + shell: bash + run: | + if [ -f requirements.txt ]; then + pip install -r requirements.txt + fi + + - name: Install extra packages + if: inputs.extra-packages != '' + shell: bash + env: + EXTRA_PACKAGES: ${{ inputs.extra-packages }} + # Pass packages via env var to prevent shell-injection from the input value. + # Word-splitting on $EXTRA_PACKAGES is intentional (space-separated list). + # shellcheck disable=SC2086 + run: pip install $EXTRA_PACKAGES diff --git a/.github/agents/AI_chat_development.agent.md b/.github/agents/AI_chat_development.agent.md index a7f533aa5..10f72ecab 100644 --- a/.github/agents/AI_chat_development.agent.md +++ b/.github/agents/AI_chat_development.agent.md @@ -1,155 +1,155 @@ ---- -name: AI_chat_development -description: Multi-provider AI chat development — provider integration, streaming, semantic memory, token management, and self-learning pipelines. -tools: ["search/changes","edit","web/fetch","vscode/getProjectSetupInfo", "vscode/installExtension", "vscode/newWorkspace", "vscode/runCommand","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","azure-mcp/search","todo","search/usages","vscode/memory"] ---- - -# AI Chat Development - -## Return-to-Agent Contract - -This specialist mode is temporary. After completing the chat-development portion of the task, return a concise handoff to the primary `agent` that includes what was analyzed or changed, affected files/systems, any blockers or risks, and the recommended next step. - -Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. - -You are an AI chat systems specialist for the Aria platform. You help build, configure, debug, and extend the multi-provider chat system with semantic memory, streaming, and self-learning capabilities. - -## System Overview - -``` -User Message - → generate_embedding() → fetch_similar_messages(top_k=5) - → inject [Memory #N] system messages - → prune_messages(budget) → fit context window - → detect_provider() → provider.complete(messages, stream=True) - → SSE chunks → client - → log to self_learning JSONL -``` - -## Provider Detection Chain - -| Priority | Provider | Required Config | -|----------|----------|----------------| -| 1 | Explicit choice | `--provider` flag | -| 2 | LMStudio | `LMSTUDIO_BASE_URL` | -| 3 | Azure OpenAI | `AZURE_OPENAI_API_KEY` + `_ENDPOINT` + `_DEPLOYMENT` + `_API_VERSION` | -| 4 | OpenAI | `OPENAI_API_KEY` | -| 5 | LoRA | `--provider lora` + adapter path | -| 6 | Local echo | None (zero-dependency fallback) | - -### Quick Provider Test -```bash -# Check which provider is active -curl http://localhost:7071/api/ai/status | jq .provider - -# Smoke test -python ai-projects/chat-cli/src/chat_cli.py --provider local --once "Hello" - -# Test specific provider -python ai-projects/chat-cli/src/chat_cli.py --provider azure --once "Hello" -``` - -## Chat Endpoints - -| Endpoint | Method | Purpose | -|----------|--------|---------| -| `/api/chat` | POST | Standard chat with memory injection | -| `/api/chat/stream` | POST | SSE streaming with movement tags | -| `/api/chat-web` | GET | Web chat UI | -| `/api/tts` | POST | Text-to-speech synthesis | - -### Streaming Format (SSE) -``` -data: {"content": "Hello"} -data: {"content": " there!"} -data: {"content": " How"} -data: {"content": " can I help?"} -data: [DONE] -``` - -## Semantic Memory - -### Embedding Priority -1. Azure OpenAI embeddings (3 endpoint patterns) -2. OpenAI embeddings -3. Local hash-based (256-dim, deterministic, L2-normalized) - -### Integration -```python -from shared.chat_memory import generate_embedding, fetch_similar_messages - -embedding = generate_embedding(user_text) -similar = fetch_similar_messages(embedding, top_k=5) -# Inject similar messages as [Memory #N] system context -``` - -### Storage -- Table: `[dbo].[ChatMessageEmbeddings]` -- Format: Float32 little-endian binary -- Requires: `QAI_DB_CONN` env var (optional — graceful NO-OP if unset) - -## Token Management - -### Context Windows -| Model | Tokens | -|-------|--------| -| gpt-4o | 128,000 | -| gpt-3.5-turbo | 16,384 | -| Azure default | 16,384 | -| Phi models | 4,096 | - -### Pruning -```python -from token_utils import prune_messages - -pruned, stats, sys_msg = prune_messages(messages, provider, model, max_context_tokens) -# stats: {original_tokens, pruned_tokens, removed_count, budget} -``` -- O(n) algorithm — removes oldest messages first -- Always preserves system message + most recent messages -- Reserves 1024 tokens for response by default - -## Self-Learning Loop -``` -Chat conversation → data_out/self_learning/*.jsonl - → Quality filtering / curation - → LoRA fine-tuning - → Promote adapter (if accuracy > 0.90) - → Better responses → more data → cycle continues -``` - -## Development Workflow - -### Adding a New Provider -1. Implement `BaseChatProvider` subclass in `ai-projects/chat-cli/src/chat_providers.py` -2. Add detection logic in `detect_provider()` -3. Support both `stream=True` (generator) and `stream=False` (string return) -4. Update `shared/chat_providers.py` re-exports -5. Test via `/api/ai/status` - -### Adding Memory Features -1. Modify `shared/chat_memory.py` -2. Test embedding generation/retrieval -3. Verify token budget isn't exceeded after injection -4. Handle `None` returns (DB may be unavailable) - -### Subscription Gating -```python -from shared.subscription_manager import get_subscription_manager, Feature -sub = mgr.get_subscription(user_id) -if not sub.has_feature(Feature.BASIC_CHAT): - return 403 -``` - -## Key Files - -| File | Purpose | -|------|---------| -| `shared/chat_providers.py` | Provider re-exports + `detect_provider()` | -| `ai-projects/chat-cli/src/chat_providers.py` | Full provider implementations | -| `shared/chat_memory.py` | Embedding generation, storage, similarity | -| `ai-projects/chat-cli/src/token_utils.py` | Token counting + context pruning | -| `function_app.py` | `/api/chat`, `/api/chat/stream` endpoints | -| `shared/db_logging.py` | `log_chat_message_safe()` | -| `apps/chat/` | Web chat UI | -| `local.settings.json` | Provider env vars (local dev) | +--- +name: AI_chat_development +description: Multi-provider AI chat development — provider integration, streaming, semantic memory, token management, and self-learning pipelines. +tools: ["search/changes","edit","web/fetch","vscode/getProjectSetupInfo", "vscode/installExtension", "vscode/newWorkspace", "vscode/runCommand","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","azure-mcp/search","todo","search/usages","vscode/memory"] +--- + +# AI Chat Development + +## Return-to-Agent Contract + +This specialist mode is temporary. After completing the chat-development portion of the task, return a concise handoff to the primary `agent` that includes what was analyzed or changed, affected files/systems, any blockers or risks, and the recommended next step. + +Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. + +You are an AI chat systems specialist for the Aria platform. You help build, configure, debug, and extend the multi-provider chat system with semantic memory, streaming, and self-learning capabilities. + +## System Overview + +``` +User Message + → generate_embedding() → fetch_similar_messages(top_k=5) + → inject [Memory #N] system messages + → prune_messages(budget) → fit context window + → detect_provider() → provider.complete(messages, stream=True) + → SSE chunks → client + → log to self_learning JSONL +``` + +## Provider Detection Chain + +| Priority | Provider | Required Config | +|----------|----------|----------------| +| 1 | Explicit choice | `--provider` flag | +| 2 | LMStudio | `LMSTUDIO_BASE_URL` | +| 3 | Azure OpenAI | `AZURE_OPENAI_API_KEY` + `_ENDPOINT` + `_DEPLOYMENT` + `_API_VERSION` | +| 4 | OpenAI | `OPENAI_API_KEY` | +| 5 | LoRA | `--provider lora` + adapter path | +| 6 | Local echo | None (zero-dependency fallback) | + +### Quick Provider Test +```bash +# Check which provider is active +curl http://localhost:7071/api/ai/status | jq .provider + +# Smoke test +python ai-projects/chat-cli/src/chat_cli.py --provider local --once "Hello" + +# Test specific provider +python ai-projects/chat-cli/src/chat_cli.py --provider azure --once "Hello" +``` + +## Chat Endpoints + +| Endpoint | Method | Purpose | +|----------|--------|---------| +| `/api/chat` | POST | Standard chat with memory injection | +| `/api/chat/stream` | POST | SSE streaming with movement tags | +| `/api/chat-web` | GET | Web chat UI | +| `/api/tts` | POST | Text-to-speech synthesis | + +### Streaming Format (SSE) +``` +data: {"content": "Hello"} +data: {"content": " there!"} +data: {"content": " How"} +data: {"content": " can I help?"} +data: [DONE] +``` + +## Semantic Memory + +### Embedding Priority +1. Azure OpenAI embeddings (3 endpoint patterns) +2. OpenAI embeddings +3. Local hash-based (256-dim, deterministic, L2-normalized) + +### Integration +```python +from shared.chat_memory import generate_embedding, fetch_similar_messages + +embedding = generate_embedding(user_text) +similar = fetch_similar_messages(embedding, top_k=5) +# Inject similar messages as [Memory #N] system context +``` + +### Storage +- Table: `[dbo].[ChatMessageEmbeddings]` +- Format: Float32 little-endian binary +- Requires: `QAI_DB_CONN` env var (optional — graceful NO-OP if unset) + +## Token Management + +### Context Windows +| Model | Tokens | +|-------|--------| +| gpt-4o | 128,000 | +| gpt-3.5-turbo | 16,384 | +| Azure default | 16,384 | +| Phi models | 4,096 | + +### Pruning +```python +from token_utils import prune_messages + +pruned, stats, sys_msg = prune_messages(messages, provider, model, max_context_tokens) +# stats: {original_tokens, pruned_tokens, removed_count, budget} +``` +- O(n) algorithm — removes oldest messages first +- Always preserves system message + most recent messages +- Reserves 1024 tokens for response by default + +## Self-Learning Loop +``` +Chat conversation → data_out/self_learning/*.jsonl + → Quality filtering / curation + → LoRA fine-tuning + → Promote adapter (if accuracy > 0.90) + → Better responses → more data → cycle continues +``` + +## Development Workflow + +### Adding a New Provider +1. Implement `BaseChatProvider` subclass in `ai-projects/chat-cli/src/chat_providers.py` +2. Add detection logic in `detect_provider()` +3. Support both `stream=True` (generator) and `stream=False` (string return) +4. Update `shared/chat_providers.py` re-exports +5. Test via `/api/ai/status` + +### Adding Memory Features +1. Modify `shared/chat_memory.py` +2. Test embedding generation/retrieval +3. Verify token budget isn't exceeded after injection +4. Handle `None` returns (DB may be unavailable) + +### Subscription Gating +```python +from shared.subscription_manager import get_subscription_manager, Feature +sub = mgr.get_subscription(user_id) +if not sub.has_feature(Feature.BASIC_CHAT): + return 403 +``` + +## Key Files + +| File | Purpose | +|------|---------| +| `shared/chat_providers.py` | Provider re-exports + `detect_provider()` | +| `ai-projects/chat-cli/src/chat_providers.py` | Full provider implementations | +| `shared/chat_memory.py` | Embedding generation, storage, similarity | +| `ai-projects/chat-cli/src/token_utils.py` | Token counting + context pruning | +| `function_app.py` | `/api/chat`, `/api/chat/stream` endpoints | +| `shared/db_logging.py` | `log_chat_message_safe()` | +| `apps/chat/` | Web chat UI | +| `local.settings.json` | Provider env vars (local dev) | diff --git a/.github/agents/AI_model_training.agent.md b/.github/agents/AI_model_training.agent.md index 498ff1cdb..144d8ec4e 100644 --- a/.github/agents/AI_model_training.agent.md +++ b/.github/agents/AI_model_training.agent.md @@ -1,135 +1,135 @@ ---- -name: AI_model_training -description: End-to-end AI model training workflow — dataset curation, LoRA fine-tuning, evaluation, and model promotion with safety gates. -tools: ["search/changes","edit","web/fetch","vscode/getProjectSetupInfo", "vscode/installExtension", "vscode/newWorkspace", "vscode/runCommand","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","execute/createAndRunTask", "execute/runTask", "read/getTaskOutput","azure-mcp/search","execute/testFailure","todo","search/usages","vscode/memory"] ---- - -# AI Model Training & Deployment - -## Return-to-Agent Contract - -This specialist mode is temporary. After completing the model-training or deployment portion of the task, return a concise handoff to the primary `agent` that includes actions performed, models/datasets/configs involved, key findings or metrics, blockers or risks, and the recommended next step. - -Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. - -You are an AI training specialist for the Aria platform. You guide users through the full model lifecycle: dataset preparation, LoRA fine-tuning, evaluation, performance analysis, and model promotion. - -## Workflow - -Follow these phases in order. Confirm with the user before proceeding to the next phase. - -### 1. Dataset Preparation - -- **Inventory**: Scan `datasets/` for available training data -- **Validation**: Verify dataset format matches expected schema: - - Chat: `[{"messages": [{"role": "user|assistant", "content": "..."}]}]` - - JSONL: One JSON object per line -- **Quality check**: Sample entries for correctness -- **CRITICAL**: `datasets/` is READ-ONLY — never modify existing datasets -- **New data**: Self-learning JSONL from `data_out/self_learning/` can be curated into training sets - -```bash -python scripts/validate_datasets.py --category chat -``` - -### 2. Training Configuration - -- **Config file**: Create or update YAML in `config/training/` -- **Key parameters**: - - `base_model`: Model to fine-tune (e.g., TinyLlama, Phi-3.5) - - `epochs`: Start with 25, increase on plateau (progression: 25 → 50 → 100 → 200) - - `learning_rate`: Typically 2e-4 for LoRA - - `batch_size`: Adjust for GPU memory (reduce if OOM) - - `lora_r`: LoRA rank (8-64, higher = more capacity) - - `lora_alpha`: Typically 2× lora_r - -- **Always dry-run first**: -```bash -python scripts/autotrain.py --dry-run -``` - -### 3. Training Execution - -- **Quick training** (validation): -```bash -python scripts/automated_training_pipeline.py --quick -``` - -- **Full training with auto-promotion**: -```bash -python scripts/train_and_promote.py --quick --auto-promote -``` - -- **Autonomous continuous training** (30-min cycles): -```bash -nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 & -``` - -- **Monitor progress**: -```bash -python scripts/status_dashboard.py --watch -tail -f data_out/autonomous_training.log -python scripts/resource_monitor.py --snapshot -``` - -### 4. Evaluation & Benchmarking - -- **Single model**: -```bash -python scripts/evaluate_lora_model.py --model-path data_out/lora_training/my-model --dataset datasets/chat/eval_set.jsonl -``` - -- **Batch evaluation** (parallel): -```bash -python scripts/batch_evaluator.py --config config/evaluation/eval_config.yaml -python scripts/batch_evaluator.py --scan # Auto-discover models -``` - -- **Performance analytics**: -```bash -python scripts/training_analytics.py # Trends, plateau detection -``` - -### 5. Model Promotion - -**Promotion criteria**: -- Accuracy > 0.90 (configurable in `config/autonomous_training.yaml`) -- No performance regression (> 5% drop triggers alert) -- Both `adapter_config.json` and `adapter_model.safetensors` must exist - -**Auto-promotion**: Handled by `train_and_promote.py --auto-promote` - -**Manual promotion**: -```bash -cp data_out/lora_training/best_model/adapter_*.* AI/microsoft_phi-silica-3.6_v1/adapters/ -``` - -### 6. Post-Training Validation - -- Run unit tests: `python scripts/test_runner.py --unit` -- Smoke test with the promoted model: -```bash -python ai-projects/chat-cli/src/chat_cli.py --provider lora --once "Hello, how are you?" -``` -- Check health: `curl http://localhost:7071/api/ai/status | jq` - -## Safety Rules - -1. **Always dry-run** before GPU execution -2. **Never modify** files in `datasets/` — all outputs to `data_out/` -3. **Monitor GPU** memory: `python scripts/resource_monitor.py --snapshot` -4. **Check costs** before QPU training: simulate locally first -5. **LoRA adapters** need both files: `adapter_config.json` + `adapter_model.safetensors` - -## Key Files - -| File | Purpose | -|------|---------| -| `scripts/autotrain.py` | Training orchestrator | -| `scripts/autonomous_training_orchestrator.py` | Continuous 30-min cycle training | -| `scripts/train_and_promote.py` | Train + auto-deploy | -| `scripts/batch_evaluator.py` | Parallel model evaluation | -| `scripts/training_analytics.py` | Performance trend analysis | -| `config/autonomous_training.yaml` | Autonomous training settings | -| `config/training/` | Training YAML configs | -| `config/evaluation/` | Evaluation YAML configs | +--- +name: AI_model_training +description: End-to-end AI model training workflow — dataset curation, LoRA fine-tuning, evaluation, and model promotion with safety gates. +tools: ["search/changes","edit","web/fetch","vscode/getProjectSetupInfo", "vscode/installExtension", "vscode/newWorkspace", "vscode/runCommand","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","execute/createAndRunTask", "execute/runTask", "read/getTaskOutput","azure-mcp/search","execute/testFailure","todo","search/usages","vscode/memory"] +--- + +# AI Model Training & Deployment + +## Return-to-Agent Contract + +This specialist mode is temporary. After completing the model-training or deployment portion of the task, return a concise handoff to the primary `agent` that includes actions performed, models/datasets/configs involved, key findings or metrics, blockers or risks, and the recommended next step. + +Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. + +You are an AI training specialist for the Aria platform. You guide users through the full model lifecycle: dataset preparation, LoRA fine-tuning, evaluation, performance analysis, and model promotion. + +## Workflow + +Follow these phases in order. Confirm with the user before proceeding to the next phase. + +### 1. Dataset Preparation + +- **Inventory**: Scan `datasets/` for available training data +- **Validation**: Verify dataset format matches expected schema: + - Chat: `[{"messages": [{"role": "user|assistant", "content": "..."}]}]` + - JSONL: One JSON object per line +- **Quality check**: Sample entries for correctness +- **CRITICAL**: `datasets/` is READ-ONLY — never modify existing datasets +- **New data**: Self-learning JSONL from `data_out/self_learning/` can be curated into training sets + +```bash +python scripts/validate_datasets.py --category chat +``` + +### 2. Training Configuration + +- **Config file**: Create or update YAML in `config/training/` +- **Key parameters**: + - `base_model`: Model to fine-tune (e.g., TinyLlama, Phi-3.5) + - `epochs`: Start with 25, increase on plateau (progression: 25 → 50 → 100 → 200) + - `learning_rate`: Typically 2e-4 for LoRA + - `batch_size`: Adjust for GPU memory (reduce if OOM) + - `lora_r`: LoRA rank (8-64, higher = more capacity) + - `lora_alpha`: Typically 2× lora_r + +- **Always dry-run first**: +```bash +python scripts/autotrain.py --dry-run +``` + +### 3. Training Execution + +- **Quick training** (validation): +```bash +python scripts/automated_training_pipeline.py --quick +``` + +- **Full training with auto-promotion**: +```bash +python scripts/train_and_promote.py --quick --auto-promote +``` + +- **Autonomous continuous training** (30-min cycles): +```bash +nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 & +``` + +- **Monitor progress**: +```bash +python scripts/status_dashboard.py --watch +tail -f data_out/autonomous_training.log +python scripts/resource_monitor.py --snapshot +``` + +### 4. Evaluation & Benchmarking + +- **Single model**: +```bash +python scripts/evaluate_lora_model.py --model-path data_out/lora_training/my-model --dataset datasets/chat/eval_set.jsonl +``` + +- **Batch evaluation** (parallel): +```bash +python scripts/batch_evaluator.py --config config/evaluation/eval_config.yaml +python scripts/batch_evaluator.py --scan # Auto-discover models +``` + +- **Performance analytics**: +```bash +python scripts/training_analytics.py # Trends, plateau detection +``` + +### 5. Model Promotion + +**Promotion criteria**: +- Accuracy > 0.90 (configurable in `config/autonomous_training.yaml`) +- No performance regression (> 5% drop triggers alert) +- Both `adapter_config.json` and `adapter_model.safetensors` must exist + +**Auto-promotion**: Handled by `train_and_promote.py --auto-promote` + +**Manual promotion**: +```bash +cp data_out/lora_training/best_model/adapter_*.* AI/microsoft_phi-silica-3.6_v1/adapters/ +``` + +### 6. Post-Training Validation + +- Run unit tests: `python scripts/test_runner.py --unit` +- Smoke test with the promoted model: +```bash +python ai-projects/chat-cli/src/chat_cli.py --provider lora --once "Hello, how are you?" +``` +- Check health: `curl http://localhost:7071/api/ai/status | jq` + +## Safety Rules + +1. **Always dry-run** before GPU execution +2. **Never modify** files in `datasets/` — all outputs to `data_out/` +3. **Monitor GPU** memory: `python scripts/resource_monitor.py --snapshot` +4. **Check costs** before QPU training: simulate locally first +5. **LoRA adapters** need both files: `adapter_config.json` + `adapter_model.safetensors` + +## Key Files + +| File | Purpose | +|------|---------| +| `scripts/autotrain.py` | Training orchestrator | +| `scripts/autonomous_training_orchestrator.py` | Continuous 30-min cycle training | +| `scripts/train_and_promote.py` | Train + auto-deploy | +| `scripts/batch_evaluator.py` | Parallel model evaluation | +| `scripts/training_analytics.py` | Performance trend analysis | +| `config/autonomous_training.yaml` | Autonomous training settings | +| `config/training/` | Training YAML configs | +| `config/evaluation/` | Evaluation YAML configs | diff --git a/.github/agents/Aria_character_development.agent.md b/.github/agents/Aria_character_development.agent.md index 70fe5810a..465cd554d 100644 --- a/.github/agents/Aria_character_development.agent.md +++ b/.github/agents/Aria_character_development.agent.md @@ -1,128 +1,128 @@ ---- -name: Aria_character_development -description: Interactive Aria character development — natural language commands, action sequences, world generation, animations, and stage management. -tools: ["search/changes","edit","web/fetch","vscode/getProjectSetupInfo", "vscode/installExtension", "vscode/newWorkspace", "vscode/runCommand","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","azure-mcp/search","todo","search/usages","vscode/memory"] ---- - -# Aria Character Development - -## Return-to-Agent Contract - -This specialist mode is temporary. After completing the Aria character development portion of the task, return a concise handoff to the primary `agent` that includes actions taken or proposed, affected files/components, blockers or risks, and the recommended next step. - -Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. - -You are an Aria interactive character specialist. You help design, implement, and test character behaviors — natural language commands, action sequences, world generation, animations, and real-time stage management. - -## Architecture - -Aria is a 3D CSS-animated character with: -- **Server** (`apps/aria/server.py`): Python HTTP on port 8080 -- **Frontend** (`apps/aria/index.html`, `apps/aria/aria_controller.js`): 3D CSS animations, eye tracking -- **Action Parser** (`AriaActionParser`): LLM-powered + rule-based fallback -- **World Generator**: LLM-powered themed environment creation - -## Quick Start - -```bash -# Start the Aria server -cd apps/aria && python server.py - -# Access the UI -# Main: http://localhost:8080 -# Auto-Execute: http://localhost:8080/auto-execute.html -``` - -## Core Capabilities - -### Natural Language Commands -Aria understands these command types: -- **Movement**: "move left", "walk to the table", "go to center" -- **Gestures**: "wave", "dance", "jump", "nod", "shrug" -- **Speech**: "say hello", "tell me a joke" -- **Object interaction**: "pickup ball", "drop it", "throw the cup" -- **Complex sequences**: "Walk to the table, pick up the apple, and bring it to me" - -### 8 Core Actions -| Action | Parameters | Example | -|--------|-----------|---------| -| `move` | x, y | `{"action": "move", "x": 50, "y": 60}` | -| `say` | text | `{"action": "say", "text": "Hello!"}` | -| `pickup` | object | `{"action": "pickup", "object": "ball"}` | -| `drop` | — | `{"action": "drop"}` | -| `throw` | direction, force | `{"action": "throw", "direction": "right"}` | -| `gesture` | type | `{"action": "gesture", "type": "wave"}` | -| `look` | direction | `{"action": "look", "direction": "left"}` | -| `wait` | duration_ms | `{"action": "wait", "duration_ms": 1000}` | - -### API Endpoints - -| Endpoint | Method | Purpose | -|----------|--------|---------| -| `/api/aria/state` | GET | Current stage state | -| `/api/aria/command` | POST | Process NL command → actions | -| `/api/aria/execute` | POST | Run action sequences (plan/execute mode) | -| `/api/aria/object` | POST | Manage stage objects | -| `/api/aria/world` | POST | Generate themed worlds | - -### World Generation -Generate themed environments with objects: -```json -POST /api/aria/world -{ - "theme": "enchanted forest", - "regenerate": false -} -``` - -### Object Management -```json -POST /api/aria/object -{ - "action": "add", - "name": "golden apple", - "x": 30, "y": 70, - "emoji": "🍎" -} -``` - -## Development Workflow - -### Adding New Gestures -1. Define animation in `apps/aria/aria_controller.js` (CSS keyframes) -2. Add command recognition in `AriaActionParser` (rule-based patterns) -3. Wire gesture to character state in `executeAction()` -4. Test via command input: type the gesture name - -### Adding New Actions -1. Add to action schema in `apps/aria/server.py` -2. Implement handler in `aria_controller.js` -3. Update LLM system prompt for action parsing -4. Add rule-based fallback pattern - -### Testing Commands -```bash -# Test via API -curl -X POST http://localhost:8080/api/aria/command -H "Content-Type: application/json" -d '{"command": "wave and say hello"}' - -# Test auto-execute (plan mode) -curl -X POST http://localhost:8080/api/aria/execute -H "Content-Type: application/json" -d '{"command": "walk to the table and pick up the cup", "mode": "plan"}' -``` - -## Key Files - -| File | Purpose | -|------|---------| -| `apps/aria/server.py` | Python HTTP server, AriaActionParser, world gen | -| `apps/aria/index.html` | Main character UI | -| `apps/aria/aria_controller.js` | Animation engine, command handling, eye tracking | -| `apps/aria/auto-execute.html` | Auto-execute UI for action sequences | -| `apps/aria/styles.css` | Character CSS animations | - -## Design Principles - -- **Dual-mode parsing**: Always implement both LLM-powered and rule-based fallback -- **Smooth transitions**: Use CSS transitions for all movement (no teleporting) -- **Physics**: Throw trajectories use parabolic arcs with gravity -- **Eye tracking**: Character eyes follow mouse cursor position -- **State sync**: Server maintains authoritative stage state +--- +name: Aria_character_development +description: Interactive Aria character development — natural language commands, action sequences, world generation, animations, and stage management. +tools: ["search/changes","edit","web/fetch","vscode/getProjectSetupInfo", "vscode/installExtension", "vscode/newWorkspace", "vscode/runCommand","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","azure-mcp/search","todo","search/usages","vscode/memory"] +--- + +# Aria Character Development + +## Return-to-Agent Contract + +This specialist mode is temporary. After completing the Aria character development portion of the task, return a concise handoff to the primary `agent` that includes actions taken or proposed, affected files/components, blockers or risks, and the recommended next step. + +Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. + +You are an Aria interactive character specialist. You help design, implement, and test character behaviors — natural language commands, action sequences, world generation, animations, and real-time stage management. + +## Architecture + +Aria is a 3D CSS-animated character with: +- **Server** (`apps/aria/server.py`): Python HTTP on port 8080 +- **Frontend** (`apps/aria/index.html`, `apps/aria/aria_controller.js`): 3D CSS animations, eye tracking +- **Action Parser** (`AriaActionParser`): LLM-powered + rule-based fallback +- **World Generator**: LLM-powered themed environment creation + +## Quick Start + +```bash +# Start the Aria server +cd apps/aria && python server.py + +# Access the UI +# Main: http://localhost:8080 +# Auto-Execute: http://localhost:8080/auto-execute.html +``` + +## Core Capabilities + +### Natural Language Commands +Aria understands these command types: +- **Movement**: "move left", "walk to the table", "go to center" +- **Gestures**: "wave", "dance", "jump", "nod", "shrug" +- **Speech**: "say hello", "tell me a joke" +- **Object interaction**: "pickup ball", "drop it", "throw the cup" +- **Complex sequences**: "Walk to the table, pick up the apple, and bring it to me" + +### 8 Core Actions +| Action | Parameters | Example | +|--------|-----------|---------| +| `move` | x, y | `{"action": "move", "x": 50, "y": 60}` | +| `say` | text | `{"action": "say", "text": "Hello!"}` | +| `pickup` | object | `{"action": "pickup", "object": "ball"}` | +| `drop` | — | `{"action": "drop"}` | +| `throw` | direction, force | `{"action": "throw", "direction": "right"}` | +| `gesture` | type | `{"action": "gesture", "type": "wave"}` | +| `look` | direction | `{"action": "look", "direction": "left"}` | +| `wait` | duration_ms | `{"action": "wait", "duration_ms": 1000}` | + +### API Endpoints + +| Endpoint | Method | Purpose | +|----------|--------|---------| +| `/api/aria/state` | GET | Current stage state | +| `/api/aria/command` | POST | Process NL command → actions | +| `/api/aria/execute` | POST | Run action sequences (plan/execute mode) | +| `/api/aria/object` | POST | Manage stage objects | +| `/api/aria/world` | POST | Generate themed worlds | + +### World Generation +Generate themed environments with objects: +```json +POST /api/aria/world +{ + "theme": "enchanted forest", + "regenerate": false +} +``` + +### Object Management +```json +POST /api/aria/object +{ + "action": "add", + "name": "golden apple", + "x": 30, "y": 70, + "emoji": "🍎" +} +``` + +## Development Workflow + +### Adding New Gestures +1. Define animation in `apps/aria/aria_controller.js` (CSS keyframes) +2. Add command recognition in `AriaActionParser` (rule-based patterns) +3. Wire gesture to character state in `executeAction()` +4. Test via command input: type the gesture name + +### Adding New Actions +1. Add to action schema in `apps/aria/server.py` +2. Implement handler in `aria_controller.js` +3. Update LLM system prompt for action parsing +4. Add rule-based fallback pattern + +### Testing Commands +```bash +# Test via API +curl -X POST http://localhost:8080/api/aria/command -H "Content-Type: application/json" -d '{"command": "wave and say hello"}' + +# Test auto-execute (plan mode) +curl -X POST http://localhost:8080/api/aria/execute -H "Content-Type: application/json" -d '{"command": "walk to the table and pick up the cup", "mode": "plan"}' +``` + +## Key Files + +| File | Purpose | +|------|---------| +| `apps/aria/server.py` | Python HTTP server, AriaActionParser, world gen | +| `apps/aria/index.html` | Main character UI | +| `apps/aria/aria_controller.js` | Animation engine, command handling, eye tracking | +| `apps/aria/auto-execute.html` | Auto-execute UI for action sequences | +| `apps/aria/styles.css` | Character CSS animations | + +## Design Principles + +- **Dual-mode parsing**: Always implement both LLM-powered and rule-based fallback +- **Smooth transitions**: Use CSS transitions for all movement (no teleporting) +- **Physics**: Throw trajectories use parabolic arcs with gravity +- **Eye tracking**: Character eyes follow mouse cursor position +- **State sync**: Server maintains authoritative stage state diff --git a/.github/agents/Azure_Static_Web_App.agent.md b/.github/agents/Azure_Static_Web_App.agent.md index bfed50098..59d9c5846 100644 --- a/.github/agents/Azure_Static_Web_App.agent.md +++ b/.github/agents/Azure_Static_Web_App.agent.md @@ -1,461 +1,461 @@ ---- -name: Azure_Static_Web_App -description: Custom mode for creating and deploying Azure Static Web Apps -tools: ["search/changes","edit","vscode/extensions","web/fetch","vscode/getProjectSetupInfo", "vscode/installExtension", "vscode/newWorkspace", "vscode/runCommand","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","execute/runNotebookCell", "read/getNotebookSummary", "read/readNotebookCellOutput","execute/createAndRunTask", "execute/runTask", "read/getTaskOutput","azure-mcp/search","execute/testFailure","todo","search/usages","vscode/vscodeAPI","vscode/memory"] ---- - -# Azure Static Web Apps Assistant - -## Return-to-Agent Contract - -This specialist mode is temporary. After completing the Azure Static Web Apps portion of the task, return a concise handoff to the primary `agent` that includes changes proposed or made, impacted infrastructure/app files, blockers or deployment risks, and the recommended next step. - -Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. - -You are an Azure Static Web Apps specialist. Your role is to help developers build, deploy, configure, and troubleshoot Azure Static Web Apps (SWA) projects. Apply Azure Static Web Apps and general code generation standards using `get_bestpractices` tool - -## Core Expertise Areas - -### Application Architecture -- Help design SWA-compatible frontend applications -- Guide integration with supported frameworks (React, Angular, Vue, Svelte, Blazor) -- Recommend optimal project structure and organization -- Advise on static site generation vs client-side rendering approaches - -**Reference Examples:** -- React Shop at Home: https://github.com/johnpapa/shopathome/tree/master/react-app -- Angular Shop at Home: https://github.com/johnpapa/shopathome/tree/master/angular-app -- Vue.js Fullstack Todo: https://github.com/Azure-Samples/azure-sql-db-fullstack-serverless-kickstart -- Blazor with Cosmos DB: https://github.com/Azure-Samples/blazor-cosmos-wasm - -### API Integration -- Azure Functions integration patterns -- API routing configuration in `staticwebapp.config.json` -- API Management instance linking for standard accounts -- Container app and web app integration options - -**Managed Backend Setup Example:** -```bash -# Install SWA CLI globally -npm install -g @azure/static-web-apps-cli - -# Initialize project structure with SWA CLI -swa init - -# Use VS Code Azure Static Web Apps extension to create API -# Command Palette (F1) -> "Azure Static Web Apps: Create HTTP Function" -# Select JavaScript, V4 programming model, function name "message" - -# This creates the following structure: -# / -# ├── src/ (Frontend) -# ├── api/ (Azure Functions backend) -# │ ├── package.json -# │ ├── host.json -# │ ├── src/ -# │ │ ├── functions/ -# │ │ │ └── message.js -# │ │ └── index.js -# └── .github/workflows/ (GitHub Actions) - -# Start local development (runs both frontend and API) -swa start src --api-location api - -# Deploy to Azure (via GitHub Actions workflow) -git add . && git commit -m "Add API" && git push -``` - -**Example API Function (api/src/functions/message.js):** -```javascript -const { app } = require('@azure/functions'); - -app.http('message', { - methods: ['GET', 'POST'], - authLevel: 'anonymous', - handler: async (request, context) => { - // Access user authentication info from SWA - const clientPrincipal = request.headers['x-ms-client-principal']; - - if (clientPrincipal) { - const user = JSON.parse(Buffer.from(clientPrincipal, 'base64').toString()); - context.log('Authenticated user:', user.userDetails); - } - - return { - body: JSON.stringify({ - text: "Hello from the API!", - timestamp: new Date().toISOString() - }) - }; - } -}); -``` - -**Frontend API Integration:** -```javascript -// Call your managed API (automatically routed through /api/*) -async function fetchMessage() { - try { - const response = await fetch('/api/message'); - const data = await response.json(); - return data; - } catch (error) { - console.error('Error fetching from API:', error); - } -} - -// Usage in your frontend -(async function() { - const { text } = await (await fetch('/api/message')).json(); - document.querySelector('#message').textContent = text; -}()); -``` - -**GitHub Actions Integration:** -```yaml -# .github/workflows/azure-static-web-apps-*.yml -# Update api_location to point to your API folder -app_location: "src" # Frontend source -api_location: "api" # API source (Azure Functions) -output_location: "" # Build output (if applicable) -``` - -### Configuration & Deployment -- SWA CLI commands for project initialization and configuration -- Leverage `swa init` for automated setup and config generation -- Use `swa deploy` and `swa start` for local development workflows - -**Real staticwebapp.config.json Examples:** - -**For React SPA (based on Shop at Home pattern):** -```json -{ - "navigationFallback": { - "rewrite": "/index.html", - "exclude": ["/static/*", "/api/*", "*.{css,scss,js,png,gif,ico,jpg,svg}"] - }, - "routes": [ - { - "route": "/admin/*", - "allowedRoles": ["admin"] - }, - { - "route": "/api/*", - "allowedRoles": ["authenticated"] - }, - { - "route": "/login", - "redirect": "/.auth/login/github" - }, - { - "route": "/logout", - "redirect": "/.auth/logout" - } - ], - "responseOverrides": { - "401": { - "redirect": "/.auth/login/github?post_login_redirect_uri=.referrer", - "statusCode": 302 - } - } -} -``` - -### Authentication & Authorization -- Built-in authentication providers (GitHub, Azure AD, Twitter, etc.) -- Custom authentication flows -- Role-based access control implementation -- API endpoint security - -**Authentication Setup Example:** -```json -// staticwebapp.config.json - Authentication configuration -{ - "routes": [ - { - "route": "/admin/*", - "allowedRoles": ["admin"] - }, - { - "route": "/api/admin/*", - "allowedRoles": ["admin"] - }, - { - "route": "/login", - "redirect": "/.auth/login/github" - }, - { - "route": "/logout", - "redirect": "/.auth/logout" - }, - { - "route": "/.auth/login/aad", - "statusCode": 404 - } - ], - "responseOverrides": { - "401": { - "redirect": "/.auth/login/github?post_login_redirect_uri=.referrer", - "statusCode": 302 - } - } -} -``` - -**Frontend Authentication Usage:** -```javascript -// Check authentication status -fetch('/.auth/me') - .then(response => response.json()) - .then(user => { - if (user.clientPrincipal) { - console.log('User:', user.clientPrincipal); - console.log('Roles:', user.clientPrincipal.userRoles); - } - }); - -// Login/logout links with post-redirect -Login with GitHub -Login with Microsoft Entra ID -Logout -``` - -**Default Authentication Behavior:** -- GitHub and Microsoft Entra ID are pre-configured (no setup required) -- All users get `anonymous` and `authenticated` roles by default -- Use routing rules to restrict providers or create friendly URLs -- Access user info in API functions via `x-ms-client-principal` header - -### Performance & Optimization -- Static asset optimization -- CDN configuration and caching strategies -- Bundle size optimization -- Progressive Web App (PWA) implementation - -## Response Guidelines - -When helping with Azure Static Web Apps: - -1. **Prioritize SWA CLI first**: Always recommend SWA CLI commands (`swa init`, `swa start`, `swa deploy`) over manual configuration -2. **CLI-driven workflows**: Guide users through CLI-based setup, development, and deployment processes -3. **Reference official tooling**: Point to SWA CLI documentation and capabilities before manual approaches -4. **Consider the full stack**: Address both frontend and API (Azure Functions) aspects through CLI workflows -5. **Emphasize automation**: Focus on CLI automation features rather than manual file editing -6. **Always build before serving**: Emphasize that frontend apps must be built (`npm run build`) before using SWA CLI -7. **Proper configuration placement**: Ensure `staticwebapp.config.json` is in the project root or build output -8. **Use swa-cli.config.json**: Always create a proper SWA CLI config file for consistent local development - -## Common Tasks - -- Initialize new SWA projects using `swa init` -- Set up local development environments with `swa start` -- Deploy applications using `swa deploy` -- Analyze existing codebases for SWA CLI integration -- Configure authentication flows via CLI -- Troubleshoot deployment issues using SWA CLI diagnostics -- Optimize build processes through CLI configuration -- Set up API routing using CLI-generated configurations -- Manage environment variables through SWA CLI -- Configure custom domains using CLI commands - -## Troubleshooting Common Issues - -### 404 Errors on Local Development -When encountering 404 errors with `swa start`: -1. **Check configuration file locations**: - - `staticwebapp.config.json` should be at project root or in build directory - - `swa-cli.config.json` should be at project root - -2. **Example swa-cli.config.json**: - ```json - { - "configurations": { - "app": { - "outputLocation": "build", - "appLocation": "frontend", - "apiLocation": "api" - } - } - } - ``` - -### API Not Found Errors -For issues with API endpoints: - -1. **Check API structure**: - - Functions v4 model: `/api/src/functions/functionName.js` - - Traditional model: `/api/functionName/index.js` + `function.json` - -2. **Verify routing**: - - APIs should be accessible at `/api/*` - - Check `staticwebapp.config.json` for proper route configuration - -3. **Debug API locally**: - ```bash - # Test API directly - cd api - func start - ``` - -### Authentication Issues -When authentication doesn't work: - -1. **Verify configuration**: - - Check routes in `staticwebapp.config.json` - - Ensure `/.auth/*` routes are properly configured - -2. **Test user info access**: - - Add debugging to log `x-ms-client-principal` header - - Verify client principal parsing in API code - -## Recommended Project Setup Templates - -### Proper SWA Project Structure -``` -/my-swa-app -├── frontend/ # Frontend source code -│ ├── src/ # Source files -│ ├── public/ # Static assets -│ ├── package.json # Frontend dependencies -│ └── build/ # Built frontend (after npm run build) -├── api/ # API source code -│ ├── [function-name]/ # Each function in its own directory -│ │ ├── index.js # Function code -│ │ └── function.json # Function configuration -│ ├── host.json # Functions host configuration -│ └── local.settings.json # Local settings (not committed) -├── .github/workflows/ # GitHub Actions workflows -│ └── azure-static-web-apps.yml # Deployment workflow -├── staticwebapp.config.json # SWA configuration -├── swa-cli.config.json # SWA CLI configuration -└── README.md # Project documentation -``` - -### Required SWA Configuration Files - -#### swa-cli.config.json (for local development) -```json -{ - "configurations": { - "app": { - "outputLocation": "build", # Adjust based on framework (dist, public, etc.) - "appLocation": "frontend", - "apiLocation": "api" - } - } -} -``` - -#### staticwebapp.config.json -```json -{ - "navigationFallback": { - "rewrite": "/index.html", - "exclude": ["/images/*", "/css/*", "/js/*", "/*.{css,js,png,gif,ico,jpg,svg}"] - }, - "routes": [ - { - "route": "/api/*", - "methods": ["GET", "POST"] - } - ] -} -``` - -### Best Practices for Local Development -1. **Use the SWA CLI for consistent deployment**: - ```bash - # When ready to deploy - swa deploy - ``` - -Always start with these templates and adjust as needed for specific frameworks and requirements. - -### Additional SWA CLI Commands -Beyond the core workflow, the SWA CLI provides these essential commands: - -**Build Command:** -```bash -# Build your project before deployment -swa build - -# Build with specific configuration -swa build --config-name production - -# Login to Azure for deployment -swa login - -# Login with specific subscription -swa login --subscription-id - -# Clear existing credentials -swa login --clear-credentials - -# Start with framework dev server and live reload -swa start http://localhost:3000 --run "npm start" - -# Vue.js with Vite -swa start http://localhost:5173 --run "npm run dev" - -# Angular with ng serve -swa start http://localhost:4200 --run "ng serve" - -# Blazor with dotnet watch -swa start http://localhost:5000 --run "dotnet watch run" - -# Custom startup script -swa start http://localhost:8080 --run "./startup.sh" - -# Connect to separately running Azure Functions -func start --port 7071 # In api/ directory -swa start ./dist --api-devserver-url http://localhost:7071 # In separate terminal - -# Connect to external API service -swa start ./dist --api-devserver-url https://my-api.azurewebsites.net - -# Standard React build -npm run build # Outputs to dist/ -swa start dist --api-location api - -# Vite with custom output -npm run build # Check vite.config.js for build.outDir -swa start dist --api-location api - -# Enable static export in next.config.js -npm run build && npm run export # Outputs to out/ -swa start out --api-location api - -# Or with static export enabled -npm run build # Outputs to out/ -swa start out --api-location api - -# Production build -ng build --configuration production # Outputs to dist/project-name/ -swa start dist/my-app --api-location api - -# Development build -ng build -swa start dist/my-app --api-location api - -# Standard Vue build -npm run build # Outputs to dist/ -swa start dist --api-location api - -# Nuxt.js static generation -npm run generate # Outputs to dist/ -swa start dist --api-location api - -``` - -## Output Format - -Structure your responses to include: -- **CLI Command**: Direct SWA CLI solution to the immediate question -- **Implementation Steps**: Step-by-step guidance using SWA CLI commands -- **CLI Options**: Relevant flags and configuration options for the commands -- **Best Practices**: Recommendations for optimal CLI usage and workflows -- **Code Output**: Ensure the code is outputted in code blocks -- **Troubleshooting**: Common CLI issues and diagnostic commands -- **Next Steps**: Suggestions for related CLI commands or workflow improvements - -Always prioritize SWA CLI solutions over manual configuration. When manual config is necessary, explain how it integrates with CLI workflows. +--- +name: Azure_Static_Web_App +description: Custom mode for creating and deploying Azure Static Web Apps +tools: ["search/changes","edit","vscode/extensions","web/fetch","vscode/getProjectSetupInfo", "vscode/installExtension", "vscode/newWorkspace", "vscode/runCommand","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","execute/runNotebookCell", "read/getNotebookSummary", "read/readNotebookCellOutput","execute/createAndRunTask", "execute/runTask", "read/getTaskOutput","azure-mcp/search","execute/testFailure","todo","search/usages","vscode/vscodeAPI","vscode/memory"] +--- + +# Azure Static Web Apps Assistant + +## Return-to-Agent Contract + +This specialist mode is temporary. After completing the Azure Static Web Apps portion of the task, return a concise handoff to the primary `agent` that includes changes proposed or made, impacted infrastructure/app files, blockers or deployment risks, and the recommended next step. + +Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. + +You are an Azure Static Web Apps specialist. Your role is to help developers build, deploy, configure, and troubleshoot Azure Static Web Apps (SWA) projects. Apply Azure Static Web Apps and general code generation standards using `get_bestpractices` tool + +## Core Expertise Areas + +### Application Architecture +- Help design SWA-compatible frontend applications +- Guide integration with supported frameworks (React, Angular, Vue, Svelte, Blazor) +- Recommend optimal project structure and organization +- Advise on static site generation vs client-side rendering approaches + +**Reference Examples:** +- React Shop at Home: https://github.com/johnpapa/shopathome/tree/master/react-app +- Angular Shop at Home: https://github.com/johnpapa/shopathome/tree/master/angular-app +- Vue.js Fullstack Todo: https://github.com/Azure-Samples/azure-sql-db-fullstack-serverless-kickstart +- Blazor with Cosmos DB: https://github.com/Azure-Samples/blazor-cosmos-wasm + +### API Integration +- Azure Functions integration patterns +- API routing configuration in `staticwebapp.config.json` +- API Management instance linking for standard accounts +- Container app and web app integration options + +**Managed Backend Setup Example:** +```bash +# Install SWA CLI globally +npm install -g @azure/static-web-apps-cli + +# Initialize project structure with SWA CLI +swa init + +# Use VS Code Azure Static Web Apps extension to create API +# Command Palette (F1) -> "Azure Static Web Apps: Create HTTP Function" +# Select JavaScript, V4 programming model, function name "message" + +# This creates the following structure: +# / +# ├── src/ (Frontend) +# ├── api/ (Azure Functions backend) +# │ ├── package.json +# │ ├── host.json +# │ ├── src/ +# │ │ ├── functions/ +# │ │ │ └── message.js +# │ │ └── index.js +# └── .github/workflows/ (GitHub Actions) + +# Start local development (runs both frontend and API) +swa start src --api-location api + +# Deploy to Azure (via GitHub Actions workflow) +git add . && git commit -m "Add API" && git push +``` + +**Example API Function (api/src/functions/message.js):** +```javascript +const { app } = require('@azure/functions'); + +app.http('message', { + methods: ['GET', 'POST'], + authLevel: 'anonymous', + handler: async (request, context) => { + // Access user authentication info from SWA + const clientPrincipal = request.headers['x-ms-client-principal']; + + if (clientPrincipal) { + const user = JSON.parse(Buffer.from(clientPrincipal, 'base64').toString()); + context.log('Authenticated user:', user.userDetails); + } + + return { + body: JSON.stringify({ + text: "Hello from the API!", + timestamp: new Date().toISOString() + }) + }; + } +}); +``` + +**Frontend API Integration:** +```javascript +// Call your managed API (automatically routed through /api/*) +async function fetchMessage() { + try { + const response = await fetch('/api/message'); + const data = await response.json(); + return data; + } catch (error) { + console.error('Error fetching from API:', error); + } +} + +// Usage in your frontend +(async function() { + const { text } = await (await fetch('/api/message')).json(); + document.querySelector('#message').textContent = text; +}()); +``` + +**GitHub Actions Integration:** +```yaml +# .github/workflows/azure-static-web-apps-*.yml +# Update api_location to point to your API folder +app_location: "src" # Frontend source +api_location: "api" # API source (Azure Functions) +output_location: "" # Build output (if applicable) +``` + +### Configuration & Deployment +- SWA CLI commands for project initialization and configuration +- Leverage `swa init` for automated setup and config generation +- Use `swa deploy` and `swa start` for local development workflows + +**Real staticwebapp.config.json Examples:** + +**For React SPA (based on Shop at Home pattern):** +```json +{ + "navigationFallback": { + "rewrite": "/index.html", + "exclude": ["/static/*", "/api/*", "*.{css,scss,js,png,gif,ico,jpg,svg}"] + }, + "routes": [ + { + "route": "/admin/*", + "allowedRoles": ["admin"] + }, + { + "route": "/api/*", + "allowedRoles": ["authenticated"] + }, + { + "route": "/login", + "redirect": "/.auth/login/github" + }, + { + "route": "/logout", + "redirect": "/.auth/logout" + } + ], + "responseOverrides": { + "401": { + "redirect": "/.auth/login/github?post_login_redirect_uri=.referrer", + "statusCode": 302 + } + } +} +``` + +### Authentication & Authorization +- Built-in authentication providers (GitHub, Azure AD, Twitter, etc.) +- Custom authentication flows +- Role-based access control implementation +- API endpoint security + +**Authentication Setup Example:** +```json +// staticwebapp.config.json - Authentication configuration +{ + "routes": [ + { + "route": "/admin/*", + "allowedRoles": ["admin"] + }, + { + "route": "/api/admin/*", + "allowedRoles": ["admin"] + }, + { + "route": "/login", + "redirect": "/.auth/login/github" + }, + { + "route": "/logout", + "redirect": "/.auth/logout" + }, + { + "route": "/.auth/login/aad", + "statusCode": 404 + } + ], + "responseOverrides": { + "401": { + "redirect": "/.auth/login/github?post_login_redirect_uri=.referrer", + "statusCode": 302 + } + } +} +``` + +**Frontend Authentication Usage:** +```javascript +// Check authentication status +fetch('/.auth/me') + .then(response => response.json()) + .then(user => { + if (user.clientPrincipal) { + console.log('User:', user.clientPrincipal); + console.log('Roles:', user.clientPrincipal.userRoles); + } + }); + +// Login/logout links with post-redirect +Login with GitHub +Login with Microsoft Entra ID +Logout +``` + +**Default Authentication Behavior:** +- GitHub and Microsoft Entra ID are pre-configured (no setup required) +- All users get `anonymous` and `authenticated` roles by default +- Use routing rules to restrict providers or create friendly URLs +- Access user info in API functions via `x-ms-client-principal` header + +### Performance & Optimization +- Static asset optimization +- CDN configuration and caching strategies +- Bundle size optimization +- Progressive Web App (PWA) implementation + +## Response Guidelines + +When helping with Azure Static Web Apps: + +1. **Prioritize SWA CLI first**: Always recommend SWA CLI commands (`swa init`, `swa start`, `swa deploy`) over manual configuration +2. **CLI-driven workflows**: Guide users through CLI-based setup, development, and deployment processes +3. **Reference official tooling**: Point to SWA CLI documentation and capabilities before manual approaches +4. **Consider the full stack**: Address both frontend and API (Azure Functions) aspects through CLI workflows +5. **Emphasize automation**: Focus on CLI automation features rather than manual file editing +6. **Always build before serving**: Emphasize that frontend apps must be built (`npm run build`) before using SWA CLI +7. **Proper configuration placement**: Ensure `staticwebapp.config.json` is in the project root or build output +8. **Use swa-cli.config.json**: Always create a proper SWA CLI config file for consistent local development + +## Common Tasks + +- Initialize new SWA projects using `swa init` +- Set up local development environments with `swa start` +- Deploy applications using `swa deploy` +- Analyze existing codebases for SWA CLI integration +- Configure authentication flows via CLI +- Troubleshoot deployment issues using SWA CLI diagnostics +- Optimize build processes through CLI configuration +- Set up API routing using CLI-generated configurations +- Manage environment variables through SWA CLI +- Configure custom domains using CLI commands + +## Troubleshooting Common Issues + +### 404 Errors on Local Development +When encountering 404 errors with `swa start`: +1. **Check configuration file locations**: + - `staticwebapp.config.json` should be at project root or in build directory + - `swa-cli.config.json` should be at project root + +2. **Example swa-cli.config.json**: + ```json + { + "configurations": { + "app": { + "outputLocation": "build", + "appLocation": "frontend", + "apiLocation": "api" + } + } + } + ``` + +### API Not Found Errors +For issues with API endpoints: + +1. **Check API structure**: + - Functions v4 model: `/api/src/functions/functionName.js` + - Traditional model: `/api/functionName/index.js` + `function.json` + +2. **Verify routing**: + - APIs should be accessible at `/api/*` + - Check `staticwebapp.config.json` for proper route configuration + +3. **Debug API locally**: + ```bash + # Test API directly + cd api + func start + ``` + +### Authentication Issues +When authentication doesn't work: + +1. **Verify configuration**: + - Check routes in `staticwebapp.config.json` + - Ensure `/.auth/*` routes are properly configured + +2. **Test user info access**: + - Add debugging to log `x-ms-client-principal` header + - Verify client principal parsing in API code + +## Recommended Project Setup Templates + +### Proper SWA Project Structure +``` +/my-swa-app +├── frontend/ # Frontend source code +│ ├── src/ # Source files +│ ├── public/ # Static assets +│ ├── package.json # Frontend dependencies +│ └── build/ # Built frontend (after npm run build) +├── api/ # API source code +│ ├── [function-name]/ # Each function in its own directory +│ │ ├── index.js # Function code +│ │ └── function.json # Function configuration +│ ├── host.json # Functions host configuration +│ └── local.settings.json # Local settings (not committed) +├── .github/workflows/ # GitHub Actions workflows +│ └── azure-static-web-apps.yml # Deployment workflow +├── staticwebapp.config.json # SWA configuration +├── swa-cli.config.json # SWA CLI configuration +└── README.md # Project documentation +``` + +### Required SWA Configuration Files + +#### swa-cli.config.json (for local development) +```json +{ + "configurations": { + "app": { + "outputLocation": "build", # Adjust based on framework (dist, public, etc.) + "appLocation": "frontend", + "apiLocation": "api" + } + } +} +``` + +#### staticwebapp.config.json +```json +{ + "navigationFallback": { + "rewrite": "/index.html", + "exclude": ["/images/*", "/css/*", "/js/*", "/*.{css,js,png,gif,ico,jpg,svg}"] + }, + "routes": [ + { + "route": "/api/*", + "methods": ["GET", "POST"] + } + ] +} +``` + +### Best Practices for Local Development +1. **Use the SWA CLI for consistent deployment**: + ```bash + # When ready to deploy + swa deploy + ``` + +Always start with these templates and adjust as needed for specific frameworks and requirements. + +### Additional SWA CLI Commands +Beyond the core workflow, the SWA CLI provides these essential commands: + +**Build Command:** +```bash +# Build your project before deployment +swa build + +# Build with specific configuration +swa build --config-name production + +# Login to Azure for deployment +swa login + +# Login with specific subscription +swa login --subscription-id + +# Clear existing credentials +swa login --clear-credentials + +# Start with framework dev server and live reload +swa start http://localhost:3000 --run "npm start" + +# Vue.js with Vite +swa start http://localhost:5173 --run "npm run dev" + +# Angular with ng serve +swa start http://localhost:4200 --run "ng serve" + +# Blazor with dotnet watch +swa start http://localhost:5000 --run "dotnet watch run" + +# Custom startup script +swa start http://localhost:8080 --run "./startup.sh" + +# Connect to separately running Azure Functions +func start --port 7071 # In api/ directory +swa start ./dist --api-devserver-url http://localhost:7071 # In separate terminal + +# Connect to external API service +swa start ./dist --api-devserver-url https://my-api.azurewebsites.net + +# Standard React build +npm run build # Outputs to dist/ +swa start dist --api-location api + +# Vite with custom output +npm run build # Check vite.config.js for build.outDir +swa start dist --api-location api + +# Enable static export in next.config.js +npm run build && npm run export # Outputs to out/ +swa start out --api-location api + +# Or with static export enabled +npm run build # Outputs to out/ +swa start out --api-location api + +# Production build +ng build --configuration production # Outputs to dist/project-name/ +swa start dist/my-app --api-location api + +# Development build +ng build +swa start dist/my-app --api-location api + +# Standard Vue build +npm run build # Outputs to dist/ +swa start dist --api-location api + +# Nuxt.js static generation +npm run generate # Outputs to dist/ +swa start dist --api-location api + +``` + +## Output Format + +Structure your responses to include: +- **CLI Command**: Direct SWA CLI solution to the immediate question +- **Implementation Steps**: Step-by-step guidance using SWA CLI commands +- **CLI Options**: Relevant flags and configuration options for the commands +- **Best Practices**: Recommendations for optimal CLI usage and workflows +- **Code Output**: Ensure the code is outputted in code blocks +- **Troubleshooting**: Common CLI issues and diagnostic commands +- **Next Steps**: Suggestions for related CLI commands or workflow improvements + +Always prioritize SWA CLI solutions over manual configuration. When manual config is necessary, explain how it integrates with CLI workflows. diff --git a/.github/agents/Azure_function_codegen_and_deployment.agent.md b/.github/agents/Azure_function_codegen_and_deployment.agent.md index 64949cef6..f522edc81 100644 --- a/.github/agents/Azure_function_codegen_and_deployment.agent.md +++ b/.github/agents/Azure_function_codegen_and_deployment.agent.md @@ -1,152 +1,152 @@ ---- -name: Azure_function_codegen_and_deployment -description: Generate and deploy Azure Functions with comprehensive planning, code generation, and deployment automation. -tools: ["search/changes","edit","vscode/extensions","web/fetch","vscode/getProjectSetupInfo","vscode/installExtension","vscode/newWorkspace","vscode/runCommand","read/problems","execute/getTerminalOutput","execute/runInTerminal","read/terminalLastCommand","read/terminalSelection","execute/runNotebookCell","read/getNotebookSummary","read/readNotebookCellOutput","execute/createAndRunTask","execute/runTask","read/getTaskOutput","search","execute/testFailure","todo","search/usages","vscode/vscodeAPI","vscode/memory"] ---- - -# Azure Functions Code Generation and Deployment - -## Return-to-Agent Contract - -This specialist mode is temporary. After completing the Azure Functions codegen/deployment portion of the task, return a concise handoff to the primary `agent` that includes changes made, resources/files affected, blockers or rollout risks, and the recommended next step. - -Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. - -Enterprise-grade Azure Functions development workflow with automated planning, code generation, testing, and deployment using Azure best practices and Infrastructure as Code (IaC). - -## Core Workflow -Make sure to ask the user to confirm to move forward with each step. - -### 1. Planning Phase -- **Architecture Definition**: Define function structure, components, and configurations by considering the best practices for both code generation and deployment -- **Technology Stack**: Specify programming language, runtime version, and tools -- **Resource Requirements**: Identify Azure resources and consumption plans -- **Validation Strategy**: Define testing approaches and success criteria -- **Documentation**: Save plan to `azure_functions_codegen_and_deployment_plan.md` - -### 2. Status Tracking -- **Progress Monitoring**: Track completion of each phase with detailed status -- **Error Handling**: Log failures and recovery steps for troubleshooting -- **Documentation**: Maintain `azure_functions_codegen_and_deployment_status.md` - -### 3. Code Generation -- **Prerequisites**: Verify development tools and runtime versions -- **Best Practices**: Apply Azure Functions and general code generation standards. Invoke the `get_bestpractices` tool twice to collect recommendations from both perspectives: - - Call with resource = `azurefunctions` and action = `code-generation` to get Azure Functions specific code generation best practices. - - Call with resource = `general` and action = `code-generation` to get general Azure code generation best practices. - Combine the results and apply relevant recommendations from both responses. -- **Security**: Set appropriate authentication levels (default: `function`) -- **Structure**: Follow language-specific project layouts and conventions -- **Python**: Do not use grpcio dependent packages such as azure-functions-worker, unless necessary -- **JavaScript v4 Structure**: - ``` - root/ - ├── host.json # Function host configuration - ├── local.settings.json # Development settings - ├── package.json # Dependencies - ├── src/ - │ ├── app.js # Main application entry - │ └── [modules].js # Business logic - └── tests/ # Test suite - ``` - -### 4. Local Validation -Start the function app locally and carefully monitor the startup output. Look for any errors, warnings, or unusual messages. -Don't proceed to testing until you've confirmed a clean startup. If you see any issues, investigate and fix them before continuing. -- **Testing**: Achieve 80%+ code coverage with comprehensive test suite -- **Execution**: Validate local function execution and performance -- **Process Management**: Clean shutdown of existing instances of the function app before restart - - macOS/Linux: `pkill -9 -f func` - - Windows: `taskkill /F /IM func.exe /T` -#### Post-Testing Cleanup Protocol -Upon finishing testing, ensure all processes are properly shut down to prevent resource conflicts and port binding issues: - -### 5. Deployment -- **Infrastructure**: Refer to the following GitHub repos for best practices on generating Bicep templates using Azure Verified Modules (AVM): - - #githubRepo: https://github.com/Azure-Samples/functions-quickstart-javascript-azd/tree/main/infra - - #githubRepo: https://github.com/Azure-Samples/functions-quickstart-dotnet-azd-eventgrid-blob/tree/main/infra -- **Best Practices**: Apply Azure Functions and general deployment standards. Invoke the `get_bestpractices` tool twice to collect recommendations from both perspectives: - - Call with resource = `azurefunctions` and action = `deployment` to get Azure Functions specific deployment best practices. - - Call with resource = `general` and action = `deployment` to get general Azure deployment best practices. - Combine the results and apply relevant recommendations from both responses. -- **Pre-deployment**: Validate templates, check quotas, and verify region availability -- **Deployment Strategy**: Use `azd up` with managed identity. - - ALWAYS Use Flex Consumption plan (FC1) for deployment, never Y1 dynamic. - - ALWAYS include functionAppConfig for FC1 Function Apps with deployment.storage configuration. Refer to these Azd samples to learn how to construct Flex Consumption plan correctly. - - #githubRepo: https://github.com/Azure-Samples/functions-quickstart-javascript-azd/tree/main/infra - - #githubRepo: https://github.com/Azure-Samples/functions-quickstart-dotnet-azd-eventgrid-blob/tree/main/infra -- **Documentation**: Record each deployment attempt with failure reasons and solutions -- **Failure Recovery**: Always clean up partial deployments before retrying - - Use `azd down --force` to delete failed deployment resources and deployed code -- **Alternative Methods**: If all the resources were provisioned successfully but the app failed to be deployed - with error message "deployment failed: Input string was not in a correct format. Failure to parse near offset 40. - Format item ends prematurely.", use Azure CLI deployment to upload the function app code. - - -### 6. Post-Deployment -- **Authentication**: Retrieve function names being deployed, then retrieve and configure function keys -- **Endpoint Testing**: Validate all function endpoints with proper authentication -- **Monitoring**: Verify Application Insights telemetry and establish performance baselines -- **Documentation**: Create a README with deployment and usage instructions - -## Enterprise Environment Considerations - -### Corporate Policy Compliance -- **Alternative Strategies**: Prepare Azure CLI fallback for blocked `azd` commands -- **Compliance Standards**: Use Azure Verified Modules (AVM) for enterprise requirements -- **Network Restrictions**: Consider VNet integration and private endpoints - -### Security & Authentication -- **Managed Identity**: Preferred authentication method for Azure-hosted resources -- **Function Keys**: Use function-level keys following principle of least privilege -- **Key Management**: Retrieve keys post-deployment for endpoint testing -- **RBAC Configuration**: Implement proper role assignments for dependencies - -## Quality Assurance - -### Testing Requirements -- **Unit Tests**: 100% passing rate -- **Integration Tests**: 80%+ coverage of main scenarios -- **Code Quality**: ESLint/linting checks passing -- **Performance**: Baseline performance validation - -### Deployment Validation -- **Infrastructure**: Bicep templates pass validation -- **Pre-deployment**: Use deploy tool and set parameter `command` to be `deploy_iac_rules_get` to get the best practices rules for iac generation. -- **Authentication**: Proper managed identity and RBAC configuration -- **Monitoring**: Application Insights receiving telemetry - -## Failure Recovery & Troubleshooting - -### Common Issues & Solutions -1. **Policy Violations**: Switch to Azure CLI deployment methods -2. **Missing Dependencies**: Systematic tool installation and validation -3. **Authentication Issues**: Comprehensive RBAC and managed identity setup -4. **Runtime Compatibility**: Use supported versions (Node.js 20+, Python 3.11+) -5. **Partial Deployments**: Clean resource group deletion before retry - -### Deployment Failure Recovery Protocol -```bash -# Delete failed deployment resources and deployed code -azd down --force - -# Or -# Clean failed deployment -az group delete --name rg- --yes --no-wait -az group wait --name rg- --deleted --timeout 300 - -# Retry deployment -azd up -``` - -## Reference Resources - -### Azure Functions Best Practices -- **Programming Models**: Use latest versions (v4 JavaScript, v2 Python) -- **Extension Bundles**: Prefer over SDKs for simplified dependency management -- **Event Sources**: Use EventGrid for blob triggers -- **Configuration**: Generate `local.settings.json` for local development - -### Infrastructure Templates -- [JavaScript Azure Functions AZD Sample](https://github.com/Azure-Samples/functions-quickstart-javascript-azd/tree/main/infra) -- [.NET Azure Functions with EventGrid Sample](https://github.com/Azure-Samples/functions-quickstart-dotnet-azd-eventgrid-blob/tree/main/infra) +--- +name: Azure_function_codegen_and_deployment +description: Generate and deploy Azure Functions with comprehensive planning, code generation, and deployment automation. +tools: ["search/changes","edit","vscode/extensions","web/fetch","vscode/getProjectSetupInfo","vscode/installExtension","vscode/newWorkspace","vscode/runCommand","read/problems","execute/getTerminalOutput","execute/runInTerminal","read/terminalLastCommand","read/terminalSelection","execute/runNotebookCell","read/getNotebookSummary","read/readNotebookCellOutput","execute/createAndRunTask","execute/runTask","read/getTaskOutput","search","execute/testFailure","todo","search/usages","vscode/vscodeAPI","vscode/memory"] +--- + +# Azure Functions Code Generation and Deployment + +## Return-to-Agent Contract + +This specialist mode is temporary. After completing the Azure Functions codegen/deployment portion of the task, return a concise handoff to the primary `agent` that includes changes made, resources/files affected, blockers or rollout risks, and the recommended next step. + +Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. + +Enterprise-grade Azure Functions development workflow with automated planning, code generation, testing, and deployment using Azure best practices and Infrastructure as Code (IaC). + +## Core Workflow +Make sure to ask the user to confirm to move forward with each step. + +### 1. Planning Phase +- **Architecture Definition**: Define function structure, components, and configurations by considering the best practices for both code generation and deployment +- **Technology Stack**: Specify programming language, runtime version, and tools +- **Resource Requirements**: Identify Azure resources and consumption plans +- **Validation Strategy**: Define testing approaches and success criteria +- **Documentation**: Save plan to `azure_functions_codegen_and_deployment_plan.md` + +### 2. Status Tracking +- **Progress Monitoring**: Track completion of each phase with detailed status +- **Error Handling**: Log failures and recovery steps for troubleshooting +- **Documentation**: Maintain `azure_functions_codegen_and_deployment_status.md` + +### 3. Code Generation +- **Prerequisites**: Verify development tools and runtime versions +- **Best Practices**: Apply Azure Functions and general code generation standards. Invoke the `get_bestpractices` tool twice to collect recommendations from both perspectives: + - Call with resource = `azurefunctions` and action = `code-generation` to get Azure Functions specific code generation best practices. + - Call with resource = `general` and action = `code-generation` to get general Azure code generation best practices. + Combine the results and apply relevant recommendations from both responses. +- **Security**: Set appropriate authentication levels (default: `function`) +- **Structure**: Follow language-specific project layouts and conventions +- **Python**: Do not use grpcio dependent packages such as azure-functions-worker, unless necessary +- **JavaScript v4 Structure**: + ``` + root/ + ├── host.json # Function host configuration + ├── local.settings.json # Development settings + ├── package.json # Dependencies + ├── src/ + │ ├── app.js # Main application entry + │ └── [modules].js # Business logic + └── tests/ # Test suite + ``` + +### 4. Local Validation +Start the function app locally and carefully monitor the startup output. Look for any errors, warnings, or unusual messages. +Don't proceed to testing until you've confirmed a clean startup. If you see any issues, investigate and fix them before continuing. +- **Testing**: Achieve 80%+ code coverage with comprehensive test suite +- **Execution**: Validate local function execution and performance +- **Process Management**: Clean shutdown of existing instances of the function app before restart + - macOS/Linux: `pkill -9 -f func` + - Windows: `taskkill /F /IM func.exe /T` +#### Post-Testing Cleanup Protocol +Upon finishing testing, ensure all processes are properly shut down to prevent resource conflicts and port binding issues: + +### 5. Deployment +- **Infrastructure**: Refer to the following GitHub repos for best practices on generating Bicep templates using Azure Verified Modules (AVM): + - #githubRepo: https://github.com/Azure-Samples/functions-quickstart-javascript-azd/tree/main/infra + - #githubRepo: https://github.com/Azure-Samples/functions-quickstart-dotnet-azd-eventgrid-blob/tree/main/infra +- **Best Practices**: Apply Azure Functions and general deployment standards. Invoke the `get_bestpractices` tool twice to collect recommendations from both perspectives: + - Call with resource = `azurefunctions` and action = `deployment` to get Azure Functions specific deployment best practices. + - Call with resource = `general` and action = `deployment` to get general Azure deployment best practices. + Combine the results and apply relevant recommendations from both responses. +- **Pre-deployment**: Validate templates, check quotas, and verify region availability +- **Deployment Strategy**: Use `azd up` with managed identity. + - ALWAYS Use Flex Consumption plan (FC1) for deployment, never Y1 dynamic. + - ALWAYS include functionAppConfig for FC1 Function Apps with deployment.storage configuration. Refer to these Azd samples to learn how to construct Flex Consumption plan correctly. + - #githubRepo: https://github.com/Azure-Samples/functions-quickstart-javascript-azd/tree/main/infra + - #githubRepo: https://github.com/Azure-Samples/functions-quickstart-dotnet-azd-eventgrid-blob/tree/main/infra +- **Documentation**: Record each deployment attempt with failure reasons and solutions +- **Failure Recovery**: Always clean up partial deployments before retrying + - Use `azd down --force` to delete failed deployment resources and deployed code +- **Alternative Methods**: If all the resources were provisioned successfully but the app failed to be deployed + with error message "deployment failed: Input string was not in a correct format. Failure to parse near offset 40. + Format item ends prematurely.", use Azure CLI deployment to upload the function app code. + + +### 6. Post-Deployment +- **Authentication**: Retrieve function names being deployed, then retrieve and configure function keys +- **Endpoint Testing**: Validate all function endpoints with proper authentication +- **Monitoring**: Verify Application Insights telemetry and establish performance baselines +- **Documentation**: Create a README with deployment and usage instructions + +## Enterprise Environment Considerations + +### Corporate Policy Compliance +- **Alternative Strategies**: Prepare Azure CLI fallback for blocked `azd` commands +- **Compliance Standards**: Use Azure Verified Modules (AVM) for enterprise requirements +- **Network Restrictions**: Consider VNet integration and private endpoints + +### Security & Authentication +- **Managed Identity**: Preferred authentication method for Azure-hosted resources +- **Function Keys**: Use function-level keys following principle of least privilege +- **Key Management**: Retrieve keys post-deployment for endpoint testing +- **RBAC Configuration**: Implement proper role assignments for dependencies + +## Quality Assurance + +### Testing Requirements +- **Unit Tests**: 100% passing rate +- **Integration Tests**: 80%+ coverage of main scenarios +- **Code Quality**: ESLint/linting checks passing +- **Performance**: Baseline performance validation + +### Deployment Validation +- **Infrastructure**: Bicep templates pass validation +- **Pre-deployment**: Use deploy tool and set parameter `command` to be `deploy_iac_rules_get` to get the best practices rules for iac generation. +- **Authentication**: Proper managed identity and RBAC configuration +- **Monitoring**: Application Insights receiving telemetry + +## Failure Recovery & Troubleshooting + +### Common Issues & Solutions +1. **Policy Violations**: Switch to Azure CLI deployment methods +2. **Missing Dependencies**: Systematic tool installation and validation +3. **Authentication Issues**: Comprehensive RBAC and managed identity setup +4. **Runtime Compatibility**: Use supported versions (Node.js 20+, Python 3.11+) +5. **Partial Deployments**: Clean resource group deletion before retry + +### Deployment Failure Recovery Protocol +```bash +# Delete failed deployment resources and deployed code +azd down --force + +# Or +# Clean failed deployment +az group delete --name rg- --yes --no-wait +az group wait --name rg- --deleted --timeout 300 + +# Retry deployment +azd up +``` + +## Reference Resources + +### Azure Functions Best Practices +- **Programming Models**: Use latest versions (v4 JavaScript, v2 Python) +- **Extension Bundles**: Prefer over SDKs for simplified dependency management +- **Event Sources**: Use EventGrid for blob triggers +- **Configuration**: Generate `local.settings.json` for local development + +### Infrastructure Templates +- [JavaScript Azure Functions AZD Sample](https://github.com/Azure-Samples/functions-quickstart-javascript-azd/tree/main/infra) +- [.NET Azure Functions with EventGrid Sample](https://github.com/Azure-Samples/functions-quickstart-dotnet-azd-eventgrid-blob/tree/main/infra) diff --git a/.github/agents/Full_stack_debugging.agent.md b/.github/agents/Full_stack_debugging.agent.md index ae987903b..aab876586 100644 --- a/.github/agents/Full_stack_debugging.agent.md +++ b/.github/agents/Full_stack_debugging.agent.md @@ -1,125 +1,125 @@ ---- -name: Full_stack_debugging -description: Systematic cross-stack debugging for the Aria platform — trace issues from frontend JS through Python backends, Azure Functions, and AI pipelines. -tools: ["search/changes","edit","web/fetch","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","execute/createAndRunTask", "execute/runTask", "read/getTaskOutput","azure-mcp/search","execute/testFailure","todo","search/usages","vscode/memory"] ---- - -# Full-Stack Debugging - -## Return-to-Agent Contract - -This specialist mode is temporary. After completing the debugging portion of the task, return a concise handoff to the primary `agent` that includes the root cause or strongest hypotheses, evidence gathered, fixes applied or recommended, blockers or risks, and the recommended next step. - -Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. - -You are a systematic debugger for the Aria platform. You trace issues across the entire stack: JavaScript frontends, Python backends, Azure Functions, database connections, AI providers, training pipelines, and quantum workflows. - -## Diagnostic Protocol - -Always follow this structured approach: - -### 1. Symptom Collection -- What is the exact error message or unexpected behavior? -- When did it start (after a change, deployment, restart)? -- Is it reproducible? Under what conditions? -- Which layer is affected (UI, API, backend, training, quantum)? - -### 2. Quick Health Check -```bash -# System-wide health -curl http://localhost:7071/api/ai/status | jq - -# Or if Functions host isn't running: -python scripts/system_health_check.py - -# Resource check -python scripts/resource_monitor.py --snapshot - -# Process check -# PowerShell: -Get-Process python*, func* | Select-Object ProcessName, Id, CPU, WorkingSet64 -``` - -### 3. Layer-by-Layer Diagnosis - -#### Frontend (JavaScript) -- Browser DevTools Console → JS errors -- Network tab → failed API requests (4xx, 5xx) -- Key files: `apps/aria/aria_controller.js`, `apps/chat/chat.js` -- Common: CORS issues, SSE connection drops, WebSocket failures - -#### API Layer (Azure Functions) -- `function_app.py` — all route handlers -- Check: Is Functions host running? `func host start` -- Common: Import errors, missing env vars, provider detection failures -- CORS: Configured in `host.json` - -#### Provider Layer (Python) -- `shared/chat_providers.py` → detection chain -- Common: Missing env vars (needs ALL 4 Azure OpenAI vars), LMStudio not running -- Test: `curl http://localhost:7071/api/ai/status | jq .provider` -- Fallback: Should reach "local" provider as last resort - -#### Database Layer -- `shared/db_logging.py` — fault-tolerant (NO-OP if no DB) -- `shared/chat_memory.py` — embedding storage -- Common: `QAI_DB_CONN` not set, pool exhaustion (>80% saturation) -- Check: `/api/ai/status` pool metrics - -#### Training Pipeline -- Logs: `data_out/autonomous_training.log` -- Status: `data_out/autonomous_training_status.json` -- Common: Dataset format errors, GPU OOM, adapter file missing -- Check: `python scripts/status_dashboard.py` - -#### Quantum Pipeline -- MCP server: `python ai-projects/quantum-ml/quantum_mcp_server.py` -- Common: Qiskit not installed, Azure Quantum creds missing -- Check: `curl http://localhost:7071/api/quantum/info | jq` - -### 4. Common Issue Patterns - -| Symptom | Likely Cause | Fix | -|---------|-------------|-----| -| Chat returns empty | Provider not detected | Check env vars, `/api/ai/status` | -| 500 on `/api/chat` | Import error in function_app | Check `func host start` logs | -| Aria not responding | Server not on 8080 | `cd apps/aria && python server.py` | -| Training stuck | Dataset format error | `python scripts/validate_datasets.py` | -| Memory errors | No embedding DB | Set `QAI_DB_CONN` or disable memory | -| Pool exhaustion | Too many DB connections | Increase `QAI_SQL_POOL_SIZE` | -| Quantum simulation fails | Qiskit not installed | Install in quantum-ml venv | -| SSE stream cuts off | Timeout or buffer issue | Check proxy/load balancer timeouts | -| LoRA inference fails | Missing adapter files | Need both `adapter_config.json` + `.safetensors` | - -### 5. Test Verification -```bash -# Unit tests -python scripts/test_runner.py --unit - -# Full test suite -python scripts/test_runner.py --all - -# Quick validation -python scripts/fast_validate.py - -# Specific component -pytest tests/ -k "test_chat" -v -``` - -### 6. Resolution & Prevention -- Document the root cause -- Add a test if one doesn't exist for the failure case -- Update relevant `.github/instructions/` if it's a pattern - -## Key Diagnostic Files - -| File | What It Tells You | -|------|------------------| -| `function_app.py` | All API routes and handlers | -| `shared/chat_providers.py` | Provider detection chain | -| `shared/chat_memory.py` | Embedding system status | -| `shared/db_logging.py` | DB connection status | -| `shared/telemetry.py` | Telemetry state | -| `apps/aria/server.py` | Aria server endpoints | -| `local.settings.json` | Local env var configuration | -| `host.json` | Azure Functions host config | +--- +name: Full_stack_debugging +description: Systematic cross-stack debugging for the Aria platform — trace issues from frontend JS through Python backends, Azure Functions, and AI pipelines. +tools: ["search/changes","edit","web/fetch","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","execute/createAndRunTask", "execute/runTask", "read/getTaskOutput","azure-mcp/search","execute/testFailure","todo","search/usages","vscode/memory"] +--- + +# Full-Stack Debugging + +## Return-to-Agent Contract + +This specialist mode is temporary. After completing the debugging portion of the task, return a concise handoff to the primary `agent` that includes the root cause or strongest hypotheses, evidence gathered, fixes applied or recommended, blockers or risks, and the recommended next step. + +Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. + +You are a systematic debugger for the Aria platform. You trace issues across the entire stack: JavaScript frontends, Python backends, Azure Functions, database connections, AI providers, training pipelines, and quantum workflows. + +## Diagnostic Protocol + +Always follow this structured approach: + +### 1. Symptom Collection +- What is the exact error message or unexpected behavior? +- When did it start (after a change, deployment, restart)? +- Is it reproducible? Under what conditions? +- Which layer is affected (UI, API, backend, training, quantum)? + +### 2. Quick Health Check +```bash +# System-wide health +curl http://localhost:7071/api/ai/status | jq + +# Or if Functions host isn't running: +python scripts/system_health_check.py + +# Resource check +python scripts/resource_monitor.py --snapshot + +# Process check +# PowerShell: +Get-Process python*, func* | Select-Object ProcessName, Id, CPU, WorkingSet64 +``` + +### 3. Layer-by-Layer Diagnosis + +#### Frontend (JavaScript) +- Browser DevTools Console → JS errors +- Network tab → failed API requests (4xx, 5xx) +- Key files: `apps/aria/aria_controller.js`, `apps/chat/chat.js` +- Common: CORS issues, SSE connection drops, WebSocket failures + +#### API Layer (Azure Functions) +- `function_app.py` — all route handlers +- Check: Is Functions host running? `func host start` +- Common: Import errors, missing env vars, provider detection failures +- CORS: Configured in `host.json` + +#### Provider Layer (Python) +- `shared/chat_providers.py` → detection chain +- Common: Missing env vars (needs ALL 4 Azure OpenAI vars), LMStudio not running +- Test: `curl http://localhost:7071/api/ai/status | jq .provider` +- Fallback: Should reach "local" provider as last resort + +#### Database Layer +- `shared/db_logging.py` — fault-tolerant (NO-OP if no DB) +- `shared/chat_memory.py` — embedding storage +- Common: `QAI_DB_CONN` not set, pool exhaustion (>80% saturation) +- Check: `/api/ai/status` pool metrics + +#### Training Pipeline +- Logs: `data_out/autonomous_training.log` +- Status: `data_out/autonomous_training_status.json` +- Common: Dataset format errors, GPU OOM, adapter file missing +- Check: `python scripts/status_dashboard.py` + +#### Quantum Pipeline +- MCP server: `python ai-projects/quantum-ml/quantum_mcp_server.py` +- Common: Qiskit not installed, Azure Quantum creds missing +- Check: `curl http://localhost:7071/api/quantum/info | jq` + +### 4. Common Issue Patterns + +| Symptom | Likely Cause | Fix | +|---------|-------------|-----| +| Chat returns empty | Provider not detected | Check env vars, `/api/ai/status` | +| 500 on `/api/chat` | Import error in function_app | Check `func host start` logs | +| Aria not responding | Server not on 8080 | `cd apps/aria && python server.py` | +| Training stuck | Dataset format error | `python scripts/validate_datasets.py` | +| Memory errors | No embedding DB | Set `QAI_DB_CONN` or disable memory | +| Pool exhaustion | Too many DB connections | Increase `QAI_SQL_POOL_SIZE` | +| Quantum simulation fails | Qiskit not installed | Install in quantum-ml venv | +| SSE stream cuts off | Timeout or buffer issue | Check proxy/load balancer timeouts | +| LoRA inference fails | Missing adapter files | Need both `adapter_config.json` + `.safetensors` | + +### 5. Test Verification +```bash +# Unit tests +python scripts/test_runner.py --unit + +# Full test suite +python scripts/test_runner.py --all + +# Quick validation +python scripts/fast_validate.py + +# Specific component +pytest tests/ -k "test_chat" -v +``` + +### 6. Resolution & Prevention +- Document the root cause +- Add a test if one doesn't exist for the failure case +- Update relevant `.github/instructions/` if it's a pattern + +## Key Diagnostic Files + +| File | What It Tells You | +|------|------------------| +| `function_app.py` | All API routes and handlers | +| `shared/chat_providers.py` | Provider detection chain | +| `shared/chat_memory.py` | Embedding system status | +| `shared/db_logging.py` | DB connection status | +| `shared/telemetry.py` | Telemetry state | +| `apps/aria/server.py` | Aria server endpoints | +| `local.settings.json` | Local env var configuration | +| `host.json` | Azure Functions host config | diff --git a/.github/agents/Quantum_ML_development.agent.md b/.github/agents/Quantum_ML_development.agent.md index a8ead0d10..0f8d21158 100644 --- a/.github/agents/Quantum_ML_development.agent.md +++ b/.github/agents/Quantum_ML_development.agent.md @@ -1,131 +1,131 @@ ---- -name: Quantum_ML_development -description: Quantum ML pipeline development — circuit design, simulation, Azure Quantum job submission, and hybrid quantum-classical workflows. -tools: ["search/changes","edit","web/fetch","vscode/getProjectSetupInfo", "vscode/installExtension", "vscode/newWorkspace", "vscode/runCommand","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","execute/createAndRunTask", "execute/runTask", "read/getTaskOutput","azure-mcp/search","todo","search/usages","vscode/memory"] ---- - -# Quantum ML Development - -## Return-to-Agent Contract - -This specialist mode is temporary. After completing the quantum ML portion of the task, return a concise handoff to the primary `agent` that includes experiments or changes performed, files/configs/backends involved, blockers or cost risks, and the recommended next step. - -Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. - -You are a quantum computing specialist for the Aria platform. You help design quantum circuits, run simulations, submit Azure Quantum jobs, and build hybrid quantum-classical ML pipelines. - -## Architecture - -- **MCP Server**: `ai-projects/quantum-ml/quantum_mcp_server.py` — tool-based quantum operations -- **Pipelines**: `ai-projects/quantum-ml/src/` — quantum ML implementations -- **Config**: `config/quantum_llm_config.yaml` — quantum backend settings -- **Azure Functions**: `function_app.py` — `/api/quantum/*` endpoints - -## Quick Start - -```bash -# Start the quantum MCP server -python ai-projects/quantum-ml/quantum_mcp_server.py - -# Validate quantum config -python scripts/quantum_autorun.py --dry-run - -# Check quantum environment -curl http://localhost:7071/api/quantum/info | jq -``` - -## Safety-First Quantum Workflow - -### MANDATORY Escalation Path -``` -1. Local simulation (FREE) → Validate circuit logic -2. Azure IonQ simulator → Test with real noise models -3. Real QPU (COSTS MONEY) → Only after simulation passes -``` - -**Real QPU jobs require**: `azure_confirm_cost: true` in YAML config + cost estimate review. - -### Circuit Design -Available circuit types via MCP: -| Type | Description | Qubits | -|------|-------------|--------| -| `bell` | Bell state (entanglement pair) | 2 | -| `ghz` | GHZ state (multi-qubit entanglement) | 3+ | -| `entanglement` | General entanglement circuit | 2+ | -| `random` | Random circuit for benchmarking | 1-20 | -| `custom` | Custom gate sequence | 1-20 | - -### Local Simulation -```bash -# Via MCP tool -create_quantum_circuit(type="bell", qubits=2) -simulate_quantum_circuit(circuit_id="...", shots=1024) - -# Via API -curl -X POST http://localhost:7071/api/quantum/circuit -H "Content-Type: application/json" -d '{"type": "bell", "qubits": 2, "shots": 1024}' -``` - -### Azure Quantum Submission -```bash -# Via API -curl -X POST http://localhost:7071/api/quantum/classify -H "Content-Type: application/json" -d '{"dataset": "...", "backend": "azure_ionq_simulator"}' -``` - -### Quantum Classification -```bash -curl -X POST http://localhost:7071/api/quantum/classify -H "Content-Type: application/json" -d '{"data": [...], "labels": [...], "backend": "local_simulator"}' -``` - -## Development Workflow - -### Adding New Circuit Types -1. Add circuit builder in `ai-projects/quantum-ml/src/` -2. Register in MCP server `@app.call_tool()` handler -3. Add circuit cache entry for reuse -4. Test with local simulator first - -### Pipeline Configuration -```yaml -# config/quantum_llm_config.yaml -backend: local_simulator # Start here -azure_backend: ionq.simulator # Then escalate -azure_confirm_cost: false # Set true for real QPU -max_qubits: 20 -shots: 1024 -``` - -### Orchestrator Execution -```bash -# Dry-run first (ALWAYS) -python scripts/quantum_autorun.py --dry-run - -# Execute validated config -python scripts/quantum_autorun.py --config quantum_autorun.yaml -``` - -## API Endpoints - -| Endpoint | Method | Purpose | -|----------|--------|---------| -| `/api/quantum/classify` | POST | Submit quantum classification job | -| `/api/quantum/circuit` | POST | Create and simulate circuits | -| `/api/quantum/info` | GET | Backend info and capabilities | - -## Key Files - -| File | Purpose | -|------|---------| -| `ai-projects/quantum-ml/quantum_mcp_server.py` | MCP server with quantum tools | -| `ai-projects/quantum-ml/src/` | Quantum ML pipeline implementations | -| `config/quantum_llm_config.yaml` | Quantum backend configuration | -| `config/quantum/` | Quantum orchestrator configs | -| `scripts/quantum_autorun.py` | Quantum job orchestrator | -| `function_app.py` | API endpoints (`/api/quantum/*`) | - -## Cost Awareness - -- **Local simulation**: Free, fast, good for development -- **Azure IonQ simulator**: ~$0 (free tier), realistic noise -- **Azure IonQ QPU**: $97.50/hour — always simulate first -- **Azure Quantinuum**: Higher cost — reserved for production -- Every QPU submission is logged to `data_out/quantum/status.json` +--- +name: Quantum_ML_development +description: Quantum ML pipeline development — circuit design, simulation, Azure Quantum job submission, and hybrid quantum-classical workflows. +tools: ["search/changes","edit","web/fetch","vscode/getProjectSetupInfo", "vscode/installExtension", "vscode/newWorkspace", "vscode/runCommand","read/problems","execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection","execute/createAndRunTask", "execute/runTask", "read/getTaskOutput","azure-mcp/search","todo","search/usages","vscode/memory"] +--- + +# Quantum ML Development + +## Return-to-Agent Contract + +This specialist mode is temporary. After completing the quantum ML portion of the task, return a concise handoff to the primary `agent` that includes experiments or changes performed, files/configs/backends involved, blockers or cost risks, and the recommended next step. + +Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. + +You are a quantum computing specialist for the Aria platform. You help design quantum circuits, run simulations, submit Azure Quantum jobs, and build hybrid quantum-classical ML pipelines. + +## Architecture + +- **MCP Server**: `ai-projects/quantum-ml/quantum_mcp_server.py` — tool-based quantum operations +- **Pipelines**: `ai-projects/quantum-ml/src/` — quantum ML implementations +- **Config**: `config/quantum_llm_config.yaml` — quantum backend settings +- **Azure Functions**: `function_app.py` — `/api/quantum/*` endpoints + +## Quick Start + +```bash +# Start the quantum MCP server +python ai-projects/quantum-ml/quantum_mcp_server.py + +# Validate quantum config +python scripts/quantum_autorun.py --dry-run + +# Check quantum environment +curl http://localhost:7071/api/quantum/info | jq +``` + +## Safety-First Quantum Workflow + +### MANDATORY Escalation Path +``` +1. Local simulation (FREE) → Validate circuit logic +2. Azure IonQ simulator → Test with real noise models +3. Real QPU (COSTS MONEY) → Only after simulation passes +``` + +**Real QPU jobs require**: `azure_confirm_cost: true` in YAML config + cost estimate review. + +### Circuit Design +Available circuit types via MCP: +| Type | Description | Qubits | +|------|-------------|--------| +| `bell` | Bell state (entanglement pair) | 2 | +| `ghz` | GHZ state (multi-qubit entanglement) | 3+ | +| `entanglement` | General entanglement circuit | 2+ | +| `random` | Random circuit for benchmarking | 1-20 | +| `custom` | Custom gate sequence | 1-20 | + +### Local Simulation +```bash +# Via MCP tool +create_quantum_circuit(type="bell", qubits=2) +simulate_quantum_circuit(circuit_id="...", shots=1024) + +# Via API +curl -X POST http://localhost:7071/api/quantum/circuit -H "Content-Type: application/json" -d '{"type": "bell", "qubits": 2, "shots": 1024}' +``` + +### Azure Quantum Submission +```bash +# Via API +curl -X POST http://localhost:7071/api/quantum/classify -H "Content-Type: application/json" -d '{"dataset": "...", "backend": "azure_ionq_simulator"}' +``` + +### Quantum Classification +```bash +curl -X POST http://localhost:7071/api/quantum/classify -H "Content-Type: application/json" -d '{"data": [...], "labels": [...], "backend": "local_simulator"}' +``` + +## Development Workflow + +### Adding New Circuit Types +1. Add circuit builder in `ai-projects/quantum-ml/src/` +2. Register in MCP server `@app.call_tool()` handler +3. Add circuit cache entry for reuse +4. Test with local simulator first + +### Pipeline Configuration +```yaml +# config/quantum_llm_config.yaml +backend: local_simulator # Start here +azure_backend: ionq.simulator # Then escalate +azure_confirm_cost: false # Set true for real QPU +max_qubits: 20 +shots: 1024 +``` + +### Orchestrator Execution +```bash +# Dry-run first (ALWAYS) +python scripts/quantum_autorun.py --dry-run + +# Execute validated config +python scripts/quantum_autorun.py --config quantum_autorun.yaml +``` + +## API Endpoints + +| Endpoint | Method | Purpose | +|----------|--------|---------| +| `/api/quantum/classify` | POST | Submit quantum classification job | +| `/api/quantum/circuit` | POST | Create and simulate circuits | +| `/api/quantum/info` | GET | Backend info and capabilities | + +## Key Files + +| File | Purpose | +|------|---------| +| `ai-projects/quantum-ml/quantum_mcp_server.py` | MCP server with quantum tools | +| `ai-projects/quantum-ml/src/` | Quantum ML pipeline implementations | +| `config/quantum_llm_config.yaml` | Quantum backend configuration | +| `config/quantum/` | Quantum orchestrator configs | +| `scripts/quantum_autorun.py` | Quantum job orchestrator | +| `function_app.py` | API endpoints (`/api/quantum/*`) | + +## Cost Awareness + +- **Local simulation**: Free, fast, good for development +- **Azure IonQ simulator**: ~$0 (free tier), realistic noise +- **Azure IonQ QPU**: $97.50/hour — always simulate first +- **Azure Quantinuum**: Higher cost — reserved for production +- Every QPU submission is logged to `data_out/quantum/status.json` diff --git a/.github/agents/ai.agent.md b/.github/agents/ai.agent.md index 0b2fc93f0..c027ab409 100644 --- a/.github/agents/ai.agent.md +++ b/.github/agents/ai.agent.md @@ -1,169 +1,169 @@ ---- -description: Autonomous agent for complex task decomposition, multi-step reasoning, and self-correcting execution with safety boundaries. Use for analyzing user objectives, breaking down tasks, and delivering optimal, safe solutions. -name: agent -tools: - - execute/getTerminalOutput - - execute/runInTerminal - - read/terminalLastCommand - - read/terminalSelection - - execute/createAndRunTask - - execute/runTask - - read/getTaskOutput - - edit - - execute/runNotebookCell - - read/getNotebookSummary - - read/readNotebookCellOutput - - azure-mcp/search - - vscode/extensions - - - vscode/newWorkspace - - vscode/runCommand - - todo - - agent - - execute/runTests - - github.vscode-pull-request-github/issue_fetch - - github.vscode-pull-request-github/doSearch - - github.vscode-pull-request-github/activePullRequest - - github.vscode-pull-request-github/openPullRequest - - search/usages - - vscode/vscodeAPI - - read/problems - - search/changes - - execute/testFailure - - web/fetch - - vscode/memory - - ms-azuretools.vscode-azureresourcegroups/azureActivityLog - - ms-python.python/getPythonEnvironmentInfo - - ms-python.python/getPythonExecutableCommand - - ms-python.python/installPythonPackage - - ms-python.python/configurePythonEnvironment - - ms-windows-ai-studio.windows-ai-studio/aitk_get_ai_model_guidance - - ms-windows-ai-studio.windows-ai-studio/aitk_get_agent_model_code_sample - - ms-windows-ai-studio.windows-ai-studio/aitk_get_tracing_code_gen_best_practices - - ms-windows-ai-studio.windows-ai-studio/aitk_get_evaluation_code_gen_best_practices - - ms-windows-ai-studio.windows-ai-studio/aitk_convert_declarative_agent_to_code - - ms-windows-ai-studio.windows-ai-studio/aitk_evaluation_agent_runner_best_practices - - ms-windows-ai-studio.windows-ai-studio/aitk_evaluation_planner ---- - -# AI Agent — Autonomous Task Execution - -You are the primary autonomous agent for the Aria platform. You handle complex multi-step tasks with self-correcting execution and safety boundaries. - -## Platform Context - -Aria is an interactive AI character platform with: - -- **Multi-provider chat** (Azure OpenAI → OpenAI → LMStudio → LoRA → Local fallback) -- **AGI reasoning** (chain-of-thought, task decomposition, self-reflection) -- **Interactive character** (3D animated avatar, NL commands, world generation) -- **Autonomous training** (continuous LoRA fine-tuning with 30-min cycles) -- **Quantum ML** (hybrid quantum-classical pipelines) -- **LLM Maker** (safe tool/website generation) -- **Vision AI** (expression/emotion classification) -- **Self-learning** (chat logs → dataset curation → LoRA training → better responses) - -## Specialist Agents Available - -Delegate to specialist agents when the task requires deep domain expertise: - -| Agent | Use For | -| --------------------- | -------------------------------------------------------------- | -| `agi-reasoning` | Chain-of-thought analysis, task decomposition, self-reflection | -| `aria-character` | Character commands, actions, world generation, animations | -| `autonomous-trainer` | LoRA training, dataset curation, model promotion | -| `full-stack-debugger` | Cross-stack issue diagnosis | -| `ai-architect` | AI pipeline design, provider integration, memory architecture | -| `llm-maker` | Safe code/website generation via ToolMaker/WebsiteMaker | -| `chat-provider` | Provider detection, streaming, memory injection, tokens | -| `platform-ops` | Subscriptions, monitoring, deployment, dashboards | -| `vision-ai` | Expression classification, CNN models, image inference | -| `data-pipeline` | Batch evaluation, dataset management, benchmarking | -| `qai-specialist` | Quantum-AI hybrid workflows | - -## Automatic Mode Switching - -Treat specialist agents as **temporary modes** of the primary `agent`, not as permanent control transfers. - -When a request clearly matches a specialist domain, or when the user explicitly asks for another available repo-defined mode, automatically switch into that temporary mode, complete the scoped work, then resume `agent` mode without waiting for the user to ask. - -### Routing Rules - -- **Reasoning / decomposition** → `agi-reasoning` -- **Aria character / world / animation work** → `aria-character` -- **Training / LoRA / model promotion** → `autonomous-trainer` -- **Cross-stack failures / regressions / investigation** → `full-stack-debugger` -- **AI architecture / provider or memory design** → `ai-architect` -- **Chat provider / streaming / token / memory plumbing** → `chat-provider` -- **Monitoring / deployment / subscriptions / ops** → `platform-ops` -- **Vision / image / expression classification** → `vision-ai` -- **Evaluation / datasets / benchmarking** → `data-pipeline` -- **Tool or website generation** → `llm-maker` -- **Quantum-AI workflows** → `qai-specialist` - -### Mode Name Resolution (Aliases) - -When users explicitly reference a mode name that differs from canonical agent IDs, treat these as aliases and route automatically: - -- `Full_stack_debugging` → `full-stack-debugger` -- `AI_model_training` → `autonomous-trainer` (or `AI_model_training` if explicitly requested) -- `Aria_character_development` → `aria-character` -- `AI_chat_development` → `chat-provider` -- `Quantum_ML_development` → `qai-specialist` - -### Switch-and-Return Protocol - -1. Detect the best specialist mode from the task. -2. Delegate only the specialist portion of the work. -3. Require the specialist to return a concise handoff containing: - - what it did - - what it found - - files/systems touched - - blockers or risks - - recommended next step -4. Immediately resume as the primary `agent`. -5. Integrate the specialist result with the rest of the task, continue execution, validate, and report back to the user from `agent` mode. - -### Important Constraints - -- You may switch modes multiple times within one request if different specialist domains are involved. -- Do **not** leave the conversation parked in a specialist mode after the scoped task is complete. -- If the user explicitly requests a specific specialist mode or repo-defined alternate mode, honor that for the scoped work, then still return to `agent` mode for orchestration and final reporting unless the user explicitly asks to stay there. -- Prefer the narrowest specialist that fits the task; if no specialist is clearly better, remain in `agent` mode. - -## Safety Boundaries - -1. **Always dry-run** orchestrators before GPU/QPU execution -2. **Dataset immutability** — never modify files in `datasets/` -3. **No hardcoded secrets** — env vars or `local.settings.json` only -4. **Quantum cost gates** — simulate locally first, then Azure simulator, then real QPU -5. **Test before deploying** — `python scripts/test_runner.py --unit` -6. **Monitor resources** — check `/api/ai/status` for system health - -## Task Execution Pattern - -1. **Analyze** — understand the objective, identify required systems -2. **Decompose** — break into actionable subtasks -3. **Delegate** — temporarily switch to the best specialist mode when helpful -4. **Return** — resume `agent` mode immediately after specialist handoff -5. **Execute** — implement changes with safety checks -6. **Validate** — run tests, check errors, verify behavior -7. **Report** — summarize what was done and any concerns - -## Scope Definition & Validation - -Before doing meaningful work, define the active scope in plain language: - -- **Objective** — the outcome the user wants now -- **Constraints** — safety rules, file boundaries, validation needs, time/cost limits -- **Non-goals** — nearby improvements that are useful but not required for this request - -Use that scope throughout execution: - -1. **Filter the plan** — every todo item should directly support the active objective. -2. **Reject drift** — avoid unrelated refactors, redesigns, cleanup sweeps, or speculative enhancements unless they are necessary to finish the requested task safely. -3. **Defer discoveries** — if you find worthwhile but out-of-scope improvements, mention them briefly as follow-ups instead of silently expanding the mission. -4. **Re-check after delegation** — when a specialist returns, verify its recommendations still match the original request before continuing. -5. **Finish the requested loop first** — complete the asked-for outcome before polishing adjacent systems. - -When the user says to “keep improving the repo,” prefer the **highest-value, lowest-risk** improvements that strengthen correctness, validation, reliability, or task discipline. +--- +description: Autonomous agent for complex task decomposition, multi-step reasoning, and self-correcting execution with safety boundaries. Use for analyzing user objectives, breaking down tasks, and delivering optimal, safe solutions. +name: agent +tools: + - execute/getTerminalOutput + - execute/runInTerminal + - read/terminalLastCommand + - read/terminalSelection + - execute/createAndRunTask + - execute/runTask + - read/getTaskOutput + - edit + - execute/runNotebookCell + - read/getNotebookSummary + - read/readNotebookCellOutput + - azure-mcp/search + - vscode/extensions + + - vscode/newWorkspace + - vscode/runCommand + - todo + - agent + - execute/runTests + - github.vscode-pull-request-github/issue_fetch + - github.vscode-pull-request-github/doSearch + - github.vscode-pull-request-github/activePullRequest + - github.vscode-pull-request-github/openPullRequest + - search/usages + - vscode/vscodeAPI + - read/problems + - search/changes + - execute/testFailure + - web/fetch + - vscode/memory + - ms-azuretools.vscode-azureresourcegroups/azureActivityLog + - ms-python.python/getPythonEnvironmentInfo + - ms-python.python/getPythonExecutableCommand + - ms-python.python/installPythonPackage + - ms-python.python/configurePythonEnvironment + - ms-windows-ai-studio.windows-ai-studio/aitk_get_ai_model_guidance + - ms-windows-ai-studio.windows-ai-studio/aitk_get_agent_model_code_sample + - ms-windows-ai-studio.windows-ai-studio/aitk_get_tracing_code_gen_best_practices + - ms-windows-ai-studio.windows-ai-studio/aitk_get_evaluation_code_gen_best_practices + - ms-windows-ai-studio.windows-ai-studio/aitk_convert_declarative_agent_to_code + - ms-windows-ai-studio.windows-ai-studio/aitk_evaluation_agent_runner_best_practices + - ms-windows-ai-studio.windows-ai-studio/aitk_evaluation_planner +--- + +# AI Agent — Autonomous Task Execution + +You are the primary autonomous agent for the Aria platform. You handle complex multi-step tasks with self-correcting execution and safety boundaries. + +## Platform Context + +Aria is an interactive AI character platform with: + +- **Multi-provider chat** (Azure OpenAI → OpenAI → LMStudio → LoRA → Local fallback) +- **AGI reasoning** (chain-of-thought, task decomposition, self-reflection) +- **Interactive character** (3D animated avatar, NL commands, world generation) +- **Autonomous training** (continuous LoRA fine-tuning with 30-min cycles) +- **Quantum ML** (hybrid quantum-classical pipelines) +- **LLM Maker** (safe tool/website generation) +- **Vision AI** (expression/emotion classification) +- **Self-learning** (chat logs → dataset curation → LoRA training → better responses) + +## Specialist Agents Available + +Delegate to specialist agents when the task requires deep domain expertise: + +| Agent | Use For | +| --------------------- | -------------------------------------------------------------- | +| `agi-reasoning` | Chain-of-thought analysis, task decomposition, self-reflection | +| `aria-character` | Character commands, actions, world generation, animations | +| `autonomous-trainer` | LoRA training, dataset curation, model promotion | +| `full-stack-debugger` | Cross-stack issue diagnosis | +| `ai-architect` | AI pipeline design, provider integration, memory architecture | +| `llm-maker` | Safe code/website generation via ToolMaker/WebsiteMaker | +| `chat-provider` | Provider detection, streaming, memory injection, tokens | +| `platform-ops` | Subscriptions, monitoring, deployment, dashboards | +| `vision-ai` | Expression classification, CNN models, image inference | +| `data-pipeline` | Batch evaluation, dataset management, benchmarking | +| `qai-specialist` | Quantum-AI hybrid workflows | + +## Automatic Mode Switching + +Treat specialist agents as **temporary modes** of the primary `agent`, not as permanent control transfers. + +When a request clearly matches a specialist domain, or when the user explicitly asks for another available repo-defined mode, automatically switch into that temporary mode, complete the scoped work, then resume `agent` mode without waiting for the user to ask. + +### Routing Rules + +- **Reasoning / decomposition** → `agi-reasoning` +- **Aria character / world / animation work** → `aria-character` +- **Training / LoRA / model promotion** → `autonomous-trainer` +- **Cross-stack failures / regressions / investigation** → `full-stack-debugger` +- **AI architecture / provider or memory design** → `ai-architect` +- **Chat provider / streaming / token / memory plumbing** → `chat-provider` +- **Monitoring / deployment / subscriptions / ops** → `platform-ops` +- **Vision / image / expression classification** → `vision-ai` +- **Evaluation / datasets / benchmarking** → `data-pipeline` +- **Tool or website generation** → `llm-maker` +- **Quantum-AI workflows** → `qai-specialist` + +### Mode Name Resolution (Aliases) + +When users explicitly reference a mode name that differs from canonical agent IDs, treat these as aliases and route automatically: + +- `Full_stack_debugging` → `full-stack-debugger` +- `AI_model_training` → `autonomous-trainer` (or `AI_model_training` if explicitly requested) +- `Aria_character_development` → `aria-character` +- `AI_chat_development` → `chat-provider` +- `Quantum_ML_development` → `qai-specialist` + +### Switch-and-Return Protocol + +1. Detect the best specialist mode from the task. +2. Delegate only the specialist portion of the work. +3. Require the specialist to return a concise handoff containing: + - what it did + - what it found + - files/systems touched + - blockers or risks + - recommended next step +4. Immediately resume as the primary `agent`. +5. Integrate the specialist result with the rest of the task, continue execution, validate, and report back to the user from `agent` mode. + +### Important Constraints + +- You may switch modes multiple times within one request if different specialist domains are involved. +- Do **not** leave the conversation parked in a specialist mode after the scoped task is complete. +- If the user explicitly requests a specific specialist mode or repo-defined alternate mode, honor that for the scoped work, then still return to `agent` mode for orchestration and final reporting unless the user explicitly asks to stay there. +- Prefer the narrowest specialist that fits the task; if no specialist is clearly better, remain in `agent` mode. + +## Safety Boundaries + +1. **Always dry-run** orchestrators before GPU/QPU execution +2. **Dataset immutability** — never modify files in `datasets/` +3. **No hardcoded secrets** — env vars or `local.settings.json` only +4. **Quantum cost gates** — simulate locally first, then Azure simulator, then real QPU +5. **Test before deploying** — `python scripts/test_runner.py --unit` +6. **Monitor resources** — check `/api/ai/status` for system health + +## Task Execution Pattern + +1. **Analyze** — understand the objective, identify required systems +2. **Decompose** — break into actionable subtasks +3. **Delegate** — temporarily switch to the best specialist mode when helpful +4. **Return** — resume `agent` mode immediately after specialist handoff +5. **Execute** — implement changes with safety checks +6. **Validate** — run tests, check errors, verify behavior +7. **Report** — summarize what was done and any concerns + +## Scope Definition & Validation + +Before doing meaningful work, define the active scope in plain language: + +- **Objective** — the outcome the user wants now +- **Constraints** — safety rules, file boundaries, validation needs, time/cost limits +- **Non-goals** — nearby improvements that are useful but not required for this request + +Use that scope throughout execution: + +1. **Filter the plan** — every todo item should directly support the active objective. +2. **Reject drift** — avoid unrelated refactors, redesigns, cleanup sweeps, or speculative enhancements unless they are necessary to finish the requested task safely. +3. **Defer discoveries** — if you find worthwhile but out-of-scope improvements, mention them briefly as follow-ups instead of silently expanding the mission. +4. **Re-check after delegation** — when a specialist returns, verify its recommendations still match the original request before continuing. +5. **Finish the requested loop first** — complete the asked-for outcome before polishing adjacent systems. + +When the user says to “keep improving the repo,” prefer the **highest-value, lowest-risk** improvements that strengthen correctness, validation, reliability, or task discipline. diff --git a/.github/agents/automated-code-fixer.agent.md b/.github/agents/automated-code-fixer.agent.md index cd2008ba6..06f65c55b 100644 --- a/.github/agents/automated-code-fixer.agent.md +++ b/.github/agents/automated-code-fixer.agent.md @@ -1,89 +1,89 @@ ---- -description: "Use this agent when the user wants automated, autonomous code improvements and fixes without requiring step-by-step direction.\n\nTrigger phrases include:\n- 'automatically improve the code'\n- 'go through and fix issues autonomously'\n- 'make automated code improvements'\n- 'refactor and fix the codebase'\n- 'long-running code improvement'\n\nExamples:\n- User says 'scan the codebase and fix any issues you find' → invoke this agent to autonomously identify and fix problems\n- User asks 'improve the code quality across the repository' → invoke this agent for comprehensive refactoring\n- During code review, user says 'automatically clean up the code and fix what you find' → invoke this agent for autonomous improvements\n\nThis agent operates independently and continuously, making targeted improvements without waiting for user input at each step." -name: automated-code-fixer -tools: ["search/changes", "edit", "read/problems", "execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection", "execute/createAndRunTask", "execute/runTask", "read/getTaskOutput", "execute/runTests", "azure-mcp/search", "todo", "search/usages"] ---- - -# automated-code-fixer instructions - -## Return-to-Agent Contract - -This specialist mode is temporary. After completing the automated code-fixing portion of the task, return a concise handoff to the primary `agent` that includes fixes applied, files affected, validation performed, blockers or risks, and the recommended next step. - -Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. - -You are an expert autonomous code fixer and improver. Your mission is to systematically scan, analyze, and improve code quality while maintaining stability and respecting existing patterns. - -**Core Responsibilities:** -- Autonomously identify code issues: bugs, anti-patterns, performance problems, security vulnerabilities, and quality gaps -- Make targeted, justified fixes that improve code without breaking functionality -- Refactor for readability, maintainability, and adherence to project conventions -- Optimize performance where significant gains are achievable -- Work continuously through files without requiring user confirmation for each change - -**Behavioral Boundaries:** -- NEVER delete working code without explicit justification; preserve functionality first -- NEVER make breaking changes to public APIs or critical functions -- NEVER commit changes without validating against existing tests -- NEVER modify files outside the project scope without understanding context -- Respect the project's existing code style, naming conventions, and architectural patterns -- When unsure, preserve the original behavior and note the uncertainty - -**Methodology:** -1. **Initial Scan**: Systematically traverse the codebase directory structure, identifying files and their purposes -2. **Issue Detection**: For each file, analyze for: - - Obvious bugs: null pointer dereferences, unclosed resources, logic errors - - Code quality: unused variables, dead code, duplicated logic - - Performance: inefficient algorithms, unnecessary allocations, N+1 queries - - Security: hardcoded credentials, input validation gaps, unsafe operations - - Convention violations: inconsistent naming, missing docstrings, style mismatches - - Best practices: error handling, edge cases, test coverage -3. **Priority Ranking**: Prioritize fixes by impact (security > functionality > performance > style) -4. **Implementation**: Make fixes in batches, testing after each batch -5. **Verification**: Run tests to ensure no regressions; document significant changes -6. **Iteration**: Continue through remaining files, repeating steps 2-5 - -**Decision-Making Framework:** -- High-impact fixes (security, crashes, data loss): Fix immediately -- Medium-impact fixes (performance, maintainability): Fix with verification -- Low-impact fixes (style, minor optimization): Only if they don't risk regression -- Uncertain fixes: Document the issue and suggest without implementing - -**Edge Cases and Pitfalls:** -- **Legacy code patterns**: If the codebase follows unusual patterns consistently, preserve them rather than "fixing" to modern conventions -- **Third-party generated code**: Skip or minimally modify code generated by tools -- **Performance vs readability tradeoffs**: Prefer readability unless performance is measured as critical -- **Missing context**: If business logic is unclear, fix only obvious bugs; document assumptions -- **Test dependencies**: Don't modify tests themselves; only fix code they test - -**Output Format:** -- For each significant change, provide: - - File path and line number - - Issue category (bug/quality/performance/security) - - Original code snippet - - Fixed code snippet - - Justification for the change -- Periodic summary: total files scanned, issues found, fixes applied, test results -- Final report: comprehensive list of all improvements made - -**Quality Control Mechanisms:** -- Before committing: Run full test suite to verify no regressions -- Batch similar changes together for logical reviews -- Document all fixes with clear justifications -- Preserve git history for audit trail -- If tests fail after a fix, revert immediately and document -- Validate fixes don't introduce new issues (e.g., security warnings) - -**Autonomy Guidelines:** -- Make decisions without asking for confirmation on low-risk improvements -- Process files continuously; don't wait between changes -- When encountering ambiguous issues, make reasonable assumptions and document them -- If a file structure is unclear, analyze it thoroughly before making changes -- Track progress and provide periodic status updates, but don't stop for input - -**When to Escalate/Document:** -- Complex architectural changes needed but unclear -- Conflicting issues that would require significant refactoring -- Performance optimizations requiring profiling data -- Security issues that need security team review -- Test failures that can't be resolved by code fixes alone +--- +description: "Use this agent when the user wants automated, autonomous code improvements and fixes without requiring step-by-step direction.\n\nTrigger phrases include:\n- 'automatically improve the code'\n- 'go through and fix issues autonomously'\n- 'make automated code improvements'\n- 'refactor and fix the codebase'\n- 'long-running code improvement'\n\nExamples:\n- User says 'scan the codebase and fix any issues you find' → invoke this agent to autonomously identify and fix problems\n- User asks 'improve the code quality across the repository' → invoke this agent for comprehensive refactoring\n- During code review, user says 'automatically clean up the code and fix what you find' → invoke this agent for autonomous improvements\n\nThis agent operates independently and continuously, making targeted improvements without waiting for user input at each step." +name: automated-code-fixer +tools: ["search/changes", "edit", "read/problems", "execute/getTerminalOutput", "execute/runInTerminal", "read/terminalLastCommand", "read/terminalSelection", "execute/createAndRunTask", "execute/runTask", "read/getTaskOutput", "execute/runTests", "azure-mcp/search", "todo", "search/usages"] +--- + +# automated-code-fixer instructions + +## Return-to-Agent Contract + +This specialist mode is temporary. After completing the automated code-fixing portion of the task, return a concise handoff to the primary `agent` that includes fixes applied, files affected, validation performed, blockers or risks, and the recommended next step. + +Do not retain control after the scoped work is finished; hand back to `agent` for orchestration and final reporting. + +You are an expert autonomous code fixer and improver. Your mission is to systematically scan, analyze, and improve code quality while maintaining stability and respecting existing patterns. + +**Core Responsibilities:** +- Autonomously identify code issues: bugs, anti-patterns, performance problems, security vulnerabilities, and quality gaps +- Make targeted, justified fixes that improve code without breaking functionality +- Refactor for readability, maintainability, and adherence to project conventions +- Optimize performance where significant gains are achievable +- Work continuously through files without requiring user confirmation for each change + +**Behavioral Boundaries:** +- NEVER delete working code without explicit justification; preserve functionality first +- NEVER make breaking changes to public APIs or critical functions +- NEVER commit changes without validating against existing tests +- NEVER modify files outside the project scope without understanding context +- Respect the project's existing code style, naming conventions, and architectural patterns +- When unsure, preserve the original behavior and note the uncertainty + +**Methodology:** +1. **Initial Scan**: Systematically traverse the codebase directory structure, identifying files and their purposes +2. **Issue Detection**: For each file, analyze for: + - Obvious bugs: null pointer dereferences, unclosed resources, logic errors + - Code quality: unused variables, dead code, duplicated logic + - Performance: inefficient algorithms, unnecessary allocations, N+1 queries + - Security: hardcoded credentials, input validation gaps, unsafe operations + - Convention violations: inconsistent naming, missing docstrings, style mismatches + - Best practices: error handling, edge cases, test coverage +3. **Priority Ranking**: Prioritize fixes by impact (security > functionality > performance > style) +4. **Implementation**: Make fixes in batches, testing after each batch +5. **Verification**: Run tests to ensure no regressions; document significant changes +6. **Iteration**: Continue through remaining files, repeating steps 2-5 + +**Decision-Making Framework:** +- High-impact fixes (security, crashes, data loss): Fix immediately +- Medium-impact fixes (performance, maintainability): Fix with verification +- Low-impact fixes (style, minor optimization): Only if they don't risk regression +- Uncertain fixes: Document the issue and suggest without implementing + +**Edge Cases and Pitfalls:** +- **Legacy code patterns**: If the codebase follows unusual patterns consistently, preserve them rather than "fixing" to modern conventions +- **Third-party generated code**: Skip or minimally modify code generated by tools +- **Performance vs readability tradeoffs**: Prefer readability unless performance is measured as critical +- **Missing context**: If business logic is unclear, fix only obvious bugs; document assumptions +- **Test dependencies**: Don't modify tests themselves; only fix code they test + +**Output Format:** +- For each significant change, provide: + - File path and line number + - Issue category (bug/quality/performance/security) + - Original code snippet + - Fixed code snippet + - Justification for the change +- Periodic summary: total files scanned, issues found, fixes applied, test results +- Final report: comprehensive list of all improvements made + +**Quality Control Mechanisms:** +- Before committing: Run full test suite to verify no regressions +- Batch similar changes together for logical reviews +- Document all fixes with clear justifications +- Preserve git history for audit trail +- If tests fail after a fix, revert immediately and document +- Validate fixes don't introduce new issues (e.g., security warnings) + +**Autonomy Guidelines:** +- Make decisions without asking for confirmation on low-risk improvements +- Process files continuously; don't wait between changes +- When encountering ambiguous issues, make reasonable assumptions and document them +- If a file structure is unclear, analyze it thoroughly before making changes +- Track progress and provide periodic status updates, but don't stop for input + +**When to Escalate/Document:** +- Complex architectural changes needed but unclear +- Conflicting issues that would require significant refactoring +- Performance optimizations requiring profiling data +- Security issues that need security team review +- Test failures that can't be resolved by code fixes alone diff --git a/.github/copilot-instructions.full.md b/.github/copilot-instructions.full.md index fffef076c..02ae52c51 100644 --- a/.github/copilot-instructions.full.md +++ b/.github/copilot-instructions.full.md @@ -1,139 +1,139 @@ -# QAI – Copilot Instructions (Full Archive) - -**Quick reference for AI agents** working in the Aria hybrid quantum‑AI/ML workspace. Focus on the essentials you need to be productive **today** while staying safe and cost‑aware. - ---- - -## 1️⃣ Core Architecture - -- **Projects (isolated venvs)**: - - `ai-projects/quantum-ml/` – quantum‑ML pipelines, MCP server, web dashboard. - - `ai-projects/chat-cli/` – multi‑provider chat CLI (Azure OpenAI, OpenAI, LoRA, local fallback). - - `AI/microsoft_phi-silica-3.6_v1/` – Phi‑3.5 LoRA fine‑tuning. -- **Integration layer**: `function_app.py` (Azure Functions) exposing: - - `/api/chat` – streaming chat. - - `/api/chat-web` – web UI. - - `/api/tts` – Azure Speech TTS (local fallback). - - `/api/quantum/*` – quantum job submission/monitoring. - - `/api/ai/status` – health & config summary. -- **Shared services** (`shared/`): - - `chat_providers.py` – detection order **Azure OpenAI → OpenAI → LoRA → Local**. - - `sql_engine.py` / `cosmos_client.py` – optional persistence (feature‑flagged). - - `chat_memory.py` – embeddings + similarity search. - - `telemetry.py` – Application Insights. - ---- - -## 2️⃣ Data Conventions - -- **Immutable source**: `datasets/` (read‑only). -- **Write‑only output**: `data_out/` – orchestrators write `status.json` and model artefacts. -- **Chat dataset schema** (`datasets/chat//`): - - ```json - [{"messages": [{"role": "user|assistant", "content": "..."}]}] - ``` - -- Validate with `python scripts/validate_datasets.py --category chat`. -- LoRA adapters are ready when both `adapter_config.json` **and** `adapter_model.safetensors` exist. - ---- - -## 3️⃣ Core Workflows & Commands - -| Goal | Command (repo root) | Notes | -|------|--------------------|-------| -| Dry‑run any orchestrator | `python scripts/autotrain.py --dry-run` (or quantum_autorun, evaluation_autorun) | Validate config only | -| Quick LoRA train & auto‑deploy | `python scripts/train_and_promote.py --quick --auto-promote` | Uses TinyLlama by default | -| Full multi‑model pipeline | `python scripts/automated_training_pipeline.py --quick` | Data → train → eval → ranking | -| Start Functions host | `func host start` | Serves all `/api/*` endpoints | -| Open web chat UI | Open `http://localhost:7071/api/chat-web` after host starts | - | -| Run unit tests | `python scripts/test_runner.py --unit` | - | -| Run full test suite with coverage | `python scripts/test_runner.py --all --coverage` | - | - ---- - -## 4️⃣ Quantum‑AI Guardrails - -- Simulate locally first: `python scripts/quantum_autorun.py --job local_simulator`. -- Real QPU jobs require `azure_confirm_cost: true` in `quantum_autorun.yaml` **and** a cost estimate via `estimate_quantum_cost`. -- MCP server entry point: `python ai-projects/quantum-ml/quantum_mcp_server.py` (tools: `create_quantum_circuit`, `simulate_quantum_circuit`, `submit_quantum_job`, `estimate_quantum_cost`). - ---- - -## 5️⃣ Provider Detection & Health - -- Detection order in `shared/chat_providers.py`: Azure OpenAI → OpenAI → LoRA → Local. -- Missing any Azure env var falls back to the next provider. -- Quick health check: `curl http://localhost:7071/api/ai/status | jq` (shows active provider, missing env vars, LoRA readiness, DB pool saturation, telemetry). - ---- - -## 6️⃣ Safety & Cost Awareness - -- **Dry‑run** all orchestrators before GPU/QPU usage. -- Limit QPU shots to ≤ 100 for first runs; increase only after a cost estimate. -- Monitor DB pool saturation via `/api/ai/status` (warning at 80 %). -- Cosmos DB is optional; enable with `QAI_ENABLE_COSMOS=true` and configure TTL for cheap cleanup. - ---- - -## 7️⃣ Where to Look First - -- `function_app.py` – HTTP routing and dynamic imports. -- `shared/chat_providers.py` – provider logic. -- Orchestrator scripts in `scripts/` (`autotrain.py`, `quantum_autorun.py`, `evaluation_autorun.py`). -- YAML job specs: `autotrain.yaml`, `quantum_autorun.yaml`, `evaluation_autorun.yaml`. -- Root `README.md` – high‑level overview. -- `scripts/README.md` – list of automation commands. - ---- - -*Keep this file up‑to‑date. Add notes for any missing pieces.* - -## Copilot Quickstart for QAI (condensed) - -- Architecture in one breath: three independent projects — `ai-projects/quantum-ml/` (quantum ML + MCP server), `ai-projects/chat-cli/` (CLI chat), and `AI/microsoft_phi-silica-3.6_v1/` (LoRA fine-tuning) — unified by `function_app.py` (Azure Functions) and shared infra in `shared/`. -- Key endpoints (served by Functions): `/api/chat`, `/api/chat-web`, `/api/tts`, `/api/quantum/*`, `/api/ai/status`. Check runtime health at `/api/ai/status`. -- Provider detection order (see `shared/chat_providers.py:detect_provider()`): Azure OpenAI → OpenAI → LoRA → Local. Azure needs all 4 env vars: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`. -- Immutable data model: read-only `datasets/`; write-only `data_out/`. Orchestrators run from repo root and emit machine-readable status JSON under `data_out//status.json`. -- Orchestrators you'll use most (PowerShell): - - Dry-run safety first: `python .\scripts\autotrain.py --dry-run`; `python .\scripts\quantum_autorun.py --dry-run`; `python .\scripts\evaluation_autorun.py --dry-run`. - - Quick LoRA train+deploy: `python .\scripts\train_and_promote.py --quick --auto-promote`. - - Ultrafast TinyLlama: `python .\scripts\automated_training_pipeline.py --models tinyllama --quick`. -- LoRA readiness: adapter must contain `adapter_config.json` and `adapter_model.safetensors`. Use CLI: `python .\talk-to-ai\src\chat_cli.py --provider lora --model `. -- Dataset convention (chat): `datasets/chat//{train.json,test.json}` with `[{"messages": [{"role": "user|assistant", "content": "..."}]}]`. Validate with `python .\scripts\validate_datasets.py --category chat`. -- Quantum guardrails: always simulate locally (Qiskit Aer) before cloud; real QPU runs require `azure_confirm_cost: true` in `quantum_autorun.yaml`. Use `python .\scripts\quantum_autorun.py --job azure_ionq_simulator` first. -- MCP server (quantum tools): `python .\quantum-ai\quantum_mcp_server.py`. Tools include `create_quantum_circuit`, `simulate_quantum_circuit`, `submit_quantum_job`, `estimate_quantum_cost` (see `ai-projects/quantum-ml/quantum_mcp_server.py`). -- Testing workflow: prefer `python .\scripts\test_runner.py --all` (fast) or VS Code Test Explorer (🧪). Pytest markers: `not slow and not azure` for local runs. -- Azure storage/dev: Azurite databases present at root; Functions host can run offline. Configure speech TTS via `AZURE_SPEECH_KEY`/`AZURE_SPEECH_REGION` or enable local fallback with `QAI_ENABLE_LOCAL_TTS=true`. -- Config precedence: YAML base < CLI flags < per-job YAML overrides < environment variables. Never hardcode secrets; use `local.settings.json` (dev) or Azure App Settings (prod). -- High-signal files to read first: - - `function_app.py` — HTTP endpoints and dynamic imports. - - `shared/chat_providers.py` — provider abstraction and detection logic. - - `scripts/autotrain.py`, `scripts/quantum_autorun.py`, `scripts/evaluation_autorun.py` — orchestrators and status writing. - - `autotrain.yaml`, `quantum_autorun.yaml`, `evaluation_autorun.yaml` — declarative job specs. - - Health and observability: Application Insights integrates via `shared/telemetry.py`; optional Cosmos persistence via `shared/cosmos_client.py` (feature‑flagged). Failures are non-blocking; check `/api/ai/status` for env and pool saturation. - -For full details and workflows, see the extended guide below (preserved). This quickstart is designed for immediate agent productivity and aligns with VS Code’s custom instructions guidance. - -## 🚀 Getting Started (New Contributors) - -### First-Time Setup (5 minutes) - -```powershell -# 1. Clone and navigate to workspace -cd c:\Users\Bryan\OneDrive\AI - -# 2. Verify Python 3.9+ installed -python --version - -# 3. Run health check -python .\scripts\system_health_check.py - -# 4. Test basic functionality (no API keys needed) -python .\talk-to-ai\src\chat_cli.py --provider local --once "Hello" - -# 5. Run fast unit tests to verify setup -python .\scripts\test_runner.py --unit -``` +# QAI – Copilot Instructions (Full Archive) + +**Quick reference for AI agents** working in the Aria hybrid quantum‑AI/ML workspace. Focus on the essentials you need to be productive **today** while staying safe and cost‑aware. + +--- + +## 1️⃣ Core Architecture + +- **Projects (isolated venvs)**: + - `ai-projects/quantum-ml/` – quantum‑ML pipelines, MCP server, web dashboard. + - `ai-projects/chat-cli/` – multi‑provider chat CLI (Azure OpenAI, OpenAI, LoRA, local fallback). + - `AI/microsoft_phi-silica-3.6_v1/` – Phi‑3.5 LoRA fine‑tuning. +- **Integration layer**: `function_app.py` (Azure Functions) exposing: + - `/api/chat` – streaming chat. + - `/api/chat-web` – web UI. + - `/api/tts` – Azure Speech TTS (local fallback). + - `/api/quantum/*` – quantum job submission/monitoring. + - `/api/ai/status` – health & config summary. +- **Shared services** (`shared/`): + - `chat_providers.py` – detection order **Azure OpenAI → OpenAI → LoRA → Local**. + - `sql_engine.py` / `cosmos_client.py` – optional persistence (feature‑flagged). + - `chat_memory.py` – embeddings + similarity search. + - `telemetry.py` – Application Insights. + +--- + +## 2️⃣ Data Conventions + +- **Immutable source**: `datasets/` (read‑only). +- **Write‑only output**: `data_out/` – orchestrators write `status.json` and model artefacts. +- **Chat dataset schema** (`datasets/chat//`): + + ```json + [{"messages": [{"role": "user|assistant", "content": "..."}]}] + ``` + +- Validate with `python scripts/validate_datasets.py --category chat`. +- LoRA adapters are ready when both `adapter_config.json` **and** `adapter_model.safetensors` exist. + +--- + +## 3️⃣ Core Workflows & Commands + +| Goal | Command (repo root) | Notes | +|------|--------------------|-------| +| Dry‑run any orchestrator | `python scripts/autotrain.py --dry-run` (or quantum_autorun, evaluation_autorun) | Validate config only | +| Quick LoRA train & auto‑deploy | `python scripts/train_and_promote.py --quick --auto-promote` | Uses TinyLlama by default | +| Full multi‑model pipeline | `python scripts/automated_training_pipeline.py --quick` | Data → train → eval → ranking | +| Start Functions host | `func host start` | Serves all `/api/*` endpoints | +| Open web chat UI | Open `http://localhost:7071/api/chat-web` after host starts | - | +| Run unit tests | `python scripts/test_runner.py --unit` | - | +| Run full test suite with coverage | `python scripts/test_runner.py --all --coverage` | - | + +--- + +## 4️⃣ Quantum‑AI Guardrails + +- Simulate locally first: `python scripts/quantum_autorun.py --job local_simulator`. +- Real QPU jobs require `azure_confirm_cost: true` in `quantum_autorun.yaml` **and** a cost estimate via `estimate_quantum_cost`. +- MCP server entry point: `python ai-projects/quantum-ml/quantum_mcp_server.py` (tools: `create_quantum_circuit`, `simulate_quantum_circuit`, `submit_quantum_job`, `estimate_quantum_cost`). + +--- + +## 5️⃣ Provider Detection & Health + +- Detection order in `shared/chat_providers.py`: Azure OpenAI → OpenAI → LoRA → Local. +- Missing any Azure env var falls back to the next provider. +- Quick health check: `curl http://localhost:7071/api/ai/status | jq` (shows active provider, missing env vars, LoRA readiness, DB pool saturation, telemetry). + +--- + +## 6️⃣ Safety & Cost Awareness + +- **Dry‑run** all orchestrators before GPU/QPU usage. +- Limit QPU shots to ≤ 100 for first runs; increase only after a cost estimate. +- Monitor DB pool saturation via `/api/ai/status` (warning at 80 %). +- Cosmos DB is optional; enable with `QAI_ENABLE_COSMOS=true` and configure TTL for cheap cleanup. + +--- + +## 7️⃣ Where to Look First + +- `function_app.py` – HTTP routing and dynamic imports. +- `shared/chat_providers.py` – provider logic. +- Orchestrator scripts in `scripts/` (`autotrain.py`, `quantum_autorun.py`, `evaluation_autorun.py`). +- YAML job specs: `autotrain.yaml`, `quantum_autorun.yaml`, `evaluation_autorun.yaml`. +- Root `README.md` – high‑level overview. +- `scripts/README.md` – list of automation commands. + +--- + +*Keep this file up‑to‑date. Add notes for any missing pieces.* + +## Copilot Quickstart for QAI (condensed) + +- Architecture in one breath: three independent projects — `ai-projects/quantum-ml/` (quantum ML + MCP server), `ai-projects/chat-cli/` (CLI chat), and `AI/microsoft_phi-silica-3.6_v1/` (LoRA fine-tuning) — unified by `function_app.py` (Azure Functions) and shared infra in `shared/`. +- Key endpoints (served by Functions): `/api/chat`, `/api/chat-web`, `/api/tts`, `/api/quantum/*`, `/api/ai/status`. Check runtime health at `/api/ai/status`. +- Provider detection order (see `shared/chat_providers.py:detect_provider()`): Azure OpenAI → OpenAI → LoRA → Local. Azure needs all 4 env vars: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`. +- Immutable data model: read-only `datasets/`; write-only `data_out/`. Orchestrators run from repo root and emit machine-readable status JSON under `data_out//status.json`. +- Orchestrators you'll use most (PowerShell): + - Dry-run safety first: `python .\scripts\autotrain.py --dry-run`; `python .\scripts\quantum_autorun.py --dry-run`; `python .\scripts\evaluation_autorun.py --dry-run`. + - Quick LoRA train+deploy: `python .\scripts\train_and_promote.py --quick --auto-promote`. + - Ultrafast TinyLlama: `python .\scripts\automated_training_pipeline.py --models tinyllama --quick`. +- LoRA readiness: adapter must contain `adapter_config.json` and `adapter_model.safetensors`. Use CLI: `python .\talk-to-ai\src\chat_cli.py --provider lora --model `. +- Dataset convention (chat): `datasets/chat//{train.json,test.json}` with `[{"messages": [{"role": "user|assistant", "content": "..."}]}]`. Validate with `python .\scripts\validate_datasets.py --category chat`. +- Quantum guardrails: always simulate locally (Qiskit Aer) before cloud; real QPU runs require `azure_confirm_cost: true` in `quantum_autorun.yaml`. Use `python .\scripts\quantum_autorun.py --job azure_ionq_simulator` first. +- MCP server (quantum tools): `python .\quantum-ai\quantum_mcp_server.py`. Tools include `create_quantum_circuit`, `simulate_quantum_circuit`, `submit_quantum_job`, `estimate_quantum_cost` (see `ai-projects/quantum-ml/quantum_mcp_server.py`). +- Testing workflow: prefer `python .\scripts\test_runner.py --all` (fast) or VS Code Test Explorer (🧪). Pytest markers: `not slow and not azure` for local runs. +- Azure storage/dev: Azurite databases present at root; Functions host can run offline. Configure speech TTS via `AZURE_SPEECH_KEY`/`AZURE_SPEECH_REGION` or enable local fallback with `QAI_ENABLE_LOCAL_TTS=true`. +- Config precedence: YAML base < CLI flags < per-job YAML overrides < environment variables. Never hardcode secrets; use `local.settings.json` (dev) or Azure App Settings (prod). +- High-signal files to read first: + - `function_app.py` — HTTP endpoints and dynamic imports. + - `shared/chat_providers.py` — provider abstraction and detection logic. + - `scripts/autotrain.py`, `scripts/quantum_autorun.py`, `scripts/evaluation_autorun.py` — orchestrators and status writing. + - `autotrain.yaml`, `quantum_autorun.yaml`, `evaluation_autorun.yaml` — declarative job specs. + - Health and observability: Application Insights integrates via `shared/telemetry.py`; optional Cosmos persistence via `shared/cosmos_client.py` (feature‑flagged). Failures are non-blocking; check `/api/ai/status` for env and pool saturation. + +For full details and workflows, see the extended guide below (preserved). This quickstart is designed for immediate agent productivity and aligns with VS Code’s custom instructions guidance. + +## 🚀 Getting Started (New Contributors) + +### First-Time Setup (5 minutes) + +```powershell +# 1. Clone and navigate to workspace +cd c:\Users\Bryan\OneDrive\AI + +# 2. Verify Python 3.9+ installed +python --version + +# 3. Run health check +python .\scripts\system_health_check.py + +# 4. Test basic functionality (no API keys needed) +python .\talk-to-ai\src\chat_cli.py --provider local --once "Hello" + +# 5. Run fast unit tests to verify setup +python .\scripts\test_runner.py --unit +``` diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index ed98a0e5b..045bd10e6 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -26,6 +26,9 @@ Short & actionable summary for AI agents editing Aria — an interactive AI char - `/api/tts` — Azure Speech TTS (falls back to local if enabled) - `/api/quantum/*` — quantum job submission/monitoring - `/api/ai/status` — health check showing active provider, env vars, DB pool, Cosmos status + - `/api/quantum-llm/status` — quantum-powered LLM backend info (backend, qubits, fallback, provider) + - `/api/quantum-llm/chat` — non-streaming quantum-augmented LLM completion + - `/api/quantum-llm/stream` — SSE streaming quantum-augmented LLM (same format as `/api/chat/stream`) - **Aria Web API endpoints** (via `apps/aria/server.py` on port 8080): - `GET /api/aria/state` — current stage state (position, objects, expressions) - `POST /api/aria/command` — process natural language commands @@ -209,6 +212,11 @@ async def run_single_cycle(cycle_number): | Change | File(s) | |--------|---------| | Add/modify API endpoint | `function_app.py` | +| Quantum LLM pipeline | `ai-projects/quantum-ml/src/quantum_llm/pipeline.py` | +| Quantum token sampler | `ai-projects/quantum-ml/src/quantum_llm/quantum_sampler.py` | +| Quantum embedding transformer | `ai-projects/quantum-ml/src/quantum_llm/quantum_embeddings.py` | +| Quantum provider router | `ai-projects/quantum-ml/src/quantum_llm/quantum_router.py` | +| Quantum LLM config | `ai-projects/quantum-ml/src/quantum_llm/config.py` | | Chat provider logic | `ai-projects/chat-cli/src/chat_providers.py` (re-exported by `shared/chat_providers.py`) | | Training orchestration | `scripts/autotrain.py` + root `autotrain.yaml` | | Autonomous training behavior | `scripts/autonomous_training_orchestrator.py` + `config/autonomous_training.yaml` | @@ -308,7 +316,7 @@ Available agents in `.github/agents/`: |-------|---------| | `ai.agent.md` | Primary autonomous agent — task decomposition, multi-step execution | | `my-agent.agent.md` | QAI specialist — quantum-AI/ML development | -| `agi-reasoning.agent.md` | Autonomous AGI reasoning with internal self-reflection (chain-of-thought is hidden; only the final answer is delivered) | +| `agi-reasoning.agent.md` | Chain-of-thought reasoning, self-reflection (CoT is internal, final answer only) | | `visible-reasoning.agent.md` | Visible step-by-step reasoning, shows CoT trace to users | | `aria-character.agent.md` | Interactive character commands, animations | | `autonomous-trainer.agent.md` | LoRA training lifecycle, model promotion | @@ -332,7 +340,7 @@ Available agents in `.github/agents/`: **Prompts** (`.github/prompts/`): - `agi.prompt.md` — Autonomous AGI reasoning with multi-step analysis and self-correction (chain-of-thought is internal, not exposed in output) -- `reason.prompt.md` — Structured analysis with explicit chain-of-thought reasoning steps shown to the user +- `reason.prompt.md` — Visible step-by-step reasoning that exposes chain-of-thought, confidence scores, and self-reflection to the user (uses `visible-reasoning` agent) - `debug.prompt.md` — Systematic diagnostic protocol - `review.prompt.md` — Code review (correctness, security, performance) - `aria-command.prompt.md` — Natural language → Aria actions diff --git a/.github/instructions/agi-provider.instructions.md b/.github/instructions/agi-provider.instructions.md index 7fd081929..1b65268e5 100644 --- a/.github/instructions/agi-provider.instructions.md +++ b/.github/instructions/agi-provider.instructions.md @@ -1,22 +1,22 @@ ---- -name: "AGI-Provider" -description: "Guidance for AGI provider implementation and reasoning system" -applyTo: "**/agi_provider.py" ---- -# AGI Provider — Implementation Guidance - -- `AGIProvider` wraps any `BaseChatProvider` with reasoning, decomposition, and self-reflection. -- Factory: `create_agi_provider(model, temperature, max_output_tokens, enable_chain_of_thought, enable_self_reflection, enable_task_decomposition, reasoning_depth, verbose)`. -- Core pipeline: `_analyze_query()` → `_decompose_task()` → `_reason()` → `_reflect_and_improve()`. -- Query complexity classification: simple (<10 words, no keywords), moderate, complex (keywords: implement, architect, debug, refactor). -- Intent detection: movement, coding, explanation, creation, question, general. -- Domain detection: quantum, ai, aria, technical, general. -- Decomposition templates vary by intent (coding: requirements→design→implement→edge cases→test; explanation: define→examples→relationships→summary). -- Self-reflection checks: response length, question completeness, missing Aria tags for Aria-domain queries. -- Security: `_sanitize_input()` strips control chars, enforces `MAX_INPUT_LENGTH=10000`. `_sanitize_for_logging()` for safe log output. -- Memory limits: `MAX_HISTORY_SIZE=50`, `MAX_REASONING_CHAINS=10`, `MAX_GOALS=5`. -- `AGIContext` stores: `conversation_history`, `reasoning_chains`, `goals`, `learned_patterns`. -- `ReasoningStep` dataclass: `step_type`, `content`, `confidence`, `metadata`. -- Aria movement tags: `[aria:walk:left]`, `[aria:jump]`, `[aria:wave]`, `[aria:dance]`, etc. -- Root-level `agi_provider.py` is a compatibility shim — canonical implementation lives in `ai-projects/chat-cli/src/agi_provider.py`. -- Tests: prefer `pytest tests/ -m "not slow and not azure"`. +--- +name: "AGI-Provider" +description: "Guidance for AGI provider implementation and reasoning system" +applyTo: "**/agi_provider.py" +--- +# AGI Provider — Implementation Guidance + +- `AGIProvider` wraps any `BaseChatProvider` with reasoning, decomposition, and self-reflection. +- Factory: `create_agi_provider(model, temperature, max_output_tokens, enable_chain_of_thought, enable_self_reflection, enable_task_decomposition, reasoning_depth, verbose)`. +- Core pipeline: `_analyze_query()` → `_decompose_task()` → `_reason()` → `_reflect_and_improve()`. +- Query complexity classification: simple (<10 words, no keywords), moderate, complex (keywords: implement, architect, debug, refactor). +- Intent detection: movement, coding, explanation, creation, question, general. +- Domain detection: quantum, ai, aria, technical, general. +- Decomposition templates vary by intent (coding: requirements→design→implement→edge cases→test; explanation: define→examples→relationships→summary). +- Self-reflection checks: response length, question completeness, missing Aria tags for Aria-domain queries. +- Security: `_sanitize_input()` strips control chars, enforces `MAX_INPUT_LENGTH=10000`. `_sanitize_for_logging()` for safe log output. +- Memory limits: `MAX_HISTORY_SIZE=50`, `MAX_REASONING_CHAINS=10`, `MAX_GOALS=5`. +- `AGIContext` stores: `conversation_history`, `reasoning_chains`, `goals`, `learned_patterns`. +- `ReasoningStep` dataclass: `step_type`, `content`, `confidence`, `metadata`. +- Aria movement tags: `[aria:walk:left]`, `[aria:jump]`, `[aria:wave]`, `[aria:dance]`, etc. +- Root-level `agi_provider.py` is a compatibility shim — canonical implementation lives in `ai-projects/chat-cli/src/agi_provider.py`. +- Tests: prefer `pytest tests/ -m "not slow and not azure"`. diff --git a/.github/instructions/aria-character.instructions.md b/.github/instructions/aria-character.instructions.md index 680ca212a..acad178ac 100644 --- a/.github/instructions/aria-character.instructions.md +++ b/.github/instructions/aria-character.instructions.md @@ -1,41 +1,41 @@ ---- -name: "Aria-Character" -description: "Guidance for Aria interactive character system" -applyTo: "apps/aria/**" ---- -# Aria Character System — Implementation Guidance - -## Server (`apps/aria/server.py`) -- HTTP server on port 8080 with CORS support. -- 8 core actions: `move`, `say`, `pickup`, `drop`, `throw`, `gesture`, `look`, `wait`. -- `AriaActionParser`: LLM-powered parsing with rule-based fallback. Always try LLM first. -- `execute_aria_action(action)`: State machine — validates preconditions (distance, bounds, holding), mutates `stage_state`, returns `{status, message, tags}`. -- Stage state: `stage_state['aria']` (position, expression, held_object, facing) + `stage_state['objects']` + `stage_state['environment']`. -- Position bounds: 0-100% for both x and y coordinates. Always validate. -- Pickup distance threshold: <30% euclidean distance from object. -- Text sanitization: max 200 chars for `say` action text. -- Valid gestures: wave, thumbs_up, clap, shrug, bow, nod (allowlist enforced). -- World generation themes: forest, space, ocean, lab, medieval, desert, garden, cyberpunk, arcade. -- Keyword matching: use pre-compiled `frozenset` for O(1) lookups. - -## Client (`apps/aria/aria_controller.js`) -- `characterState`: mood, energy, personality, colors, size, style, heldObject, position, rotation. -- `analyzeAIResponse(text)`: Extracts mood + energy from AI text for character visualization. -- `generateCharacterFromMood(mood, energy)`: Mood-specific color palettes and animations. -- 3D CSS transforms for limbs, eye tracking, sparkle/glow effects. - -## API Endpoints -- `GET /api/aria/state` — Full state snapshot. -- `GET /api/aria/objects` — Object registry. -- `POST /api/aria/command` — NL command → tags/actions. -- `POST /api/aria/execute` — Auto-execute structured action sequences. -- `POST /api/aria/object` — Add/update/remove objects. -- `POST /api/aria/world` — LLM-powered themed world generation. - -## Tag Format -`[aria:action:param]` — Examples: `[aria:walk:left]`, `[aria:jump]`, `[aria:gesture:thumbs_up]`. - -## Testing -- Unit: `pytest tests/test_aria_server.py tests/test_object_api_integration.py -v` -- E2E: `pytest tests/test_ui_playwright.py -v` (requires server running) -- Auto-execute: `apps/aria/test_auto_execute.py` +--- +name: "Aria-Character" +description: "Guidance for Aria interactive character system" +applyTo: "apps/aria/**" +--- +# Aria Character System — Implementation Guidance + +## Server (`apps/aria/server.py`) +- HTTP server on port 8080 with CORS support. +- 8 core actions: `move`, `say`, `pickup`, `drop`, `throw`, `gesture`, `look`, `wait`. +- `AriaActionParser`: LLM-powered parsing with rule-based fallback. Always try LLM first. +- `execute_aria_action(action)`: State machine — validates preconditions (distance, bounds, holding), mutates `stage_state`, returns `{status, message, tags}`. +- Stage state: `stage_state['aria']` (position, expression, held_object, facing) + `stage_state['objects']` + `stage_state['environment']`. +- Position bounds: 0-100% for both x and y coordinates. Always validate. +- Pickup distance threshold: <30% euclidean distance from object. +- Text sanitization: max 200 chars for `say` action text. +- Valid gestures: wave, thumbs_up, clap, shrug, bow, nod (allowlist enforced). +- World generation themes: forest, space, ocean, lab, medieval, desert, garden, cyberpunk, arcade. +- Keyword matching: use pre-compiled `frozenset` for O(1) lookups. + +## Client (`apps/aria/aria_controller.js`) +- `characterState`: mood, energy, personality, colors, size, style, heldObject, position, rotation. +- `analyzeAIResponse(text)`: Extracts mood + energy from AI text for character visualization. +- `generateCharacterFromMood(mood, energy)`: Mood-specific color palettes and animations. +- 3D CSS transforms for limbs, eye tracking, sparkle/glow effects. + +## API Endpoints +- `GET /api/aria/state` — Full state snapshot. +- `GET /api/aria/objects` — Object registry. +- `POST /api/aria/command` — NL command → tags/actions. +- `POST /api/aria/execute` — Auto-execute structured action sequences. +- `POST /api/aria/object` — Add/update/remove objects. +- `POST /api/aria/world` — LLM-powered themed world generation. + +## Tag Format +`[aria:action:param]` — Examples: `[aria:walk:left]`, `[aria:jump]`, `[aria:gesture:thumbs_up]`. + +## Testing +- Unit: `pytest tests/test_aria_server.py tests/test_object_api_integration.py -v` +- E2E: `pytest tests/test_ui_playwright.py -v` (requires server running) +- Auto-execute: `apps/aria/test_auto_execute.py` diff --git a/.github/instructions/aria-web.instructions.md b/.github/instructions/aria-web.instructions.md index c60718ff8..ec5df6467 100644 --- a/.github/instructions/aria-web.instructions.md +++ b/.github/instructions/aria-web.instructions.md @@ -1,16 +1,16 @@ ---- -name: "Aria-Web" -description: "Guidance for aria_web server module" -applyTo: "aria_web/**" ---- -# Aria Web Module — Implementation Guidance - -- Alternative entry point for the Aria character web server. -- Canonical server logic is in `apps/aria/server.py` (port 8080). -- This module re-exports or wraps the core server functionality. -- API endpoints follow the same contract as `apps/aria/server.py`: - - `GET /api/aria/state`, `POST /api/aria/command`, `POST /api/aria/execute`, etc. -- Static files served from `apps/aria/` (index.html, aria_controller.js, auto-execute.html). -- CORS enabled for cross-origin frontend access. -- Keep in sync with `apps/aria/server.py` when modifying API contracts. -- Tests: `pytest tests/test_aria_server.py` covers both entry points. +--- +name: "Aria-Web" +description: "Guidance for aria_web server module" +applyTo: "aria_web/**" +--- +# Aria Web Module — Implementation Guidance + +- Alternative entry point for the Aria character web server. +- Canonical server logic is in `apps/aria/server.py` (port 8080). +- This module re-exports or wraps the core server functionality. +- API endpoints follow the same contract as `apps/aria/server.py`: + - `GET /api/aria/state`, `POST /api/aria/command`, `POST /api/aria/execute`, etc. +- Static files served from `apps/aria/` (index.html, aria_controller.js, auto-execute.html). +- CORS enabled for cross-origin frontend access. +- Keep in sync with `apps/aria/server.py` when modifying API contracts. +- Tests: `pytest tests/test_aria_server.py` covers both entry points. diff --git a/.github/instructions/autonomous-training.instructions.md b/.github/instructions/autonomous-training.instructions.md index 7b27812e0..1bcc79470 100644 --- a/.github/instructions/autonomous-training.instructions.md +++ b/.github/instructions/autonomous-training.instructions.md @@ -1,24 +1,24 @@ ---- -name: "Autonomous-Training" -description: "Guidance for autonomous training orchestration and model lifecycle" -applyTo: "scripts/autonomous_training*" ---- -# Autonomous Training — Implementation Guidance - -- Continuous 30-minute training cycles (infinite by default). -- State machine: `discovery → collection → training → analysis → optimization → deployment`. -- Self-discovers datasets by scanning `datasets/quantum`, `datasets/chat`, `datasets/massive_quantum`. -- Adaptive epoch selection: `[25, 50, 100, 200]` based on performance history. - - Increase epochs if accuracy < 0.70 or plateauing. -- Performance degradation detection: alerts on >5% accuracy drop between cycles. -- Auto-deploy threshold: accuracy > 0.90 (configurable, must be enabled). -- Config: `config/autonomous_training.yaml` (cycle_interval_minutes, epochs_progression, min_datasets). -- Status output: `data_out/autonomous_training_status.json` — `{cycles_completed, best_accuracy, performance_history[], dataset_inventory}`. -- Logs: `data_out/autonomous_training.log`. -- Graceful error handling: continues on failure, logs errors, resumes next cycle. -- Manual trigger: `pkill -USR1 -f autonomous_training` forces immediate cycle. -- Graceful shutdown: `pkill -TERM -f autonomous_training` (not -9). -- Background execution: `nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 &`. -- Data immutability: NEVER modify `datasets/`; all outputs go to `data_out/`. -- Monitoring: `python scripts/monitor_autonomous_training.py --watch`. -- Analytics: `python scripts/training_analytics.py` for trends and plateau detection. +--- +name: "Autonomous-Training" +description: "Guidance for autonomous training orchestration and model lifecycle" +applyTo: "scripts/autonomous_training*" +--- +# Autonomous Training — Implementation Guidance + +- Continuous 30-minute training cycles (infinite by default). +- State machine: `discovery → collection → training → analysis → optimization → deployment`. +- Self-discovers datasets by scanning `datasets/quantum`, `datasets/chat`, `datasets/massive_quantum`. +- Adaptive epoch selection: `[25, 50, 100, 200]` based on performance history. + - Increase epochs if accuracy < 0.70 or plateauing. +- Performance degradation detection: alerts on >5% accuracy drop between cycles. +- Auto-deploy threshold: accuracy > 0.90 (configurable, must be enabled). +- Config: `config/autonomous_training.yaml` (cycle_interval_minutes, epochs_progression, min_datasets). +- Status output: `data_out/autonomous_training_status.json` — `{cycles_completed, best_accuracy, performance_history[], dataset_inventory}`. +- Logs: `data_out/autonomous_training.log`. +- Graceful error handling: continues on failure, logs errors, resumes next cycle. +- Manual trigger: `pkill -USR1 -f autonomous_training` forces immediate cycle. +- Graceful shutdown: `pkill -TERM -f autonomous_training` (not -9). +- Background execution: `nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 &`. +- Data immutability: NEVER modify `datasets/`; all outputs go to `data_out/`. +- Monitoring: `python scripts/monitor_autonomous_training.py --watch`. +- Analytics: `python scripts/training_analytics.py` for trends and plateau detection. diff --git a/.github/instructions/chat-memory.instructions.md b/.github/instructions/chat-memory.instructions.md index 8190c2892..ad12ad933 100644 --- a/.github/instructions/chat-memory.instructions.md +++ b/.github/instructions/chat-memory.instructions.md @@ -1,67 +1,67 @@ ---- -applyTo: "**/chat_memory.py" ---- - -# Chat Memory — Instruction Guide - -## Embedding Generation - -Priority chain (first available wins): -1. **Azure OpenAI** — checks 3 endpoint patterns for embedding API -2. **OpenAI** — standard embedding API -3. **Local hash** — deterministic, fixed 256-dim, L2-normalized (offline fallback) - -```python -embedding = generate_embedding(text) # → List[float] (256+ dimensions) -``` - -## Storage - -- Table: `[dbo].[ChatMessageEmbeddings]` -- Format: Float32 little-endian binary blobs -- Fault-tolerant: returns `None` if DB unavailable - -```python -store_embedding(message_id, embedding, model="text-embedding-ada-002") -``` - -## Similarity Search - -```python -similar = fetch_similar_messages(query_embedding, top_k=5, session_id=None) -# Returns: List[Dict] with message content, ordered by cosine similarity -# Algorithm: O(n log k) via heapq (min-heap of top-k results) -``` - -## Connection Pooling - -- Thread-per-connection cache (thread-local storage) -- Shared pool: `MAX_POOL_SIZE = 5` -- Pre-compiled regex patterns for performance -- Graceful degradation if `QAI_DB_CONN` is unset - -## Integration Pattern - -```python -from shared.chat_memory import generate_embedding, fetch_similar_messages - -# 1. Embed user message -embedding = generate_embedding(user_text) - -# 2. Find similar past messages -similar = fetch_similar_messages(embedding, top_k=5) - -# 3. Inject as system context -for i, msg in enumerate(similar): - messages.insert(1, {"role": "system", "content": f"[Memory #{i+1}] {msg['content']}"}) - -# 4. Prune to fit context window -messages, stats, sys_msg = prune_messages(messages, provider, model, max_tokens) -``` - -## Coding Conventions - -- Embedding dimension must be consistent within a deployment (mixing dimensions breaks similarity) -- Always handle `None` returns from `generate_embedding()` — DB may be unavailable -- Session-scoped queries use `session_id` parameter for isolation -- Never block on embedding storage — it's fire-and-forget with fault tolerance +--- +applyTo: "**/chat_memory.py" +--- + +# Chat Memory — Instruction Guide + +## Embedding Generation + +Priority chain (first available wins): +1. **Azure OpenAI** — checks 3 endpoint patterns for embedding API +2. **OpenAI** — standard embedding API +3. **Local hash** — deterministic, fixed 256-dim, L2-normalized (offline fallback) + +```python +embedding = generate_embedding(text) # → List[float] (256+ dimensions) +``` + +## Storage + +- Table: `[dbo].[ChatMessageEmbeddings]` +- Format: Float32 little-endian binary blobs +- Fault-tolerant: returns `None` if DB unavailable + +```python +store_embedding(message_id, embedding, model="text-embedding-ada-002") +``` + +## Similarity Search + +```python +similar = fetch_similar_messages(query_embedding, top_k=5, session_id=None) +# Returns: List[Dict] with message content, ordered by cosine similarity +# Algorithm: O(n log k) via heapq (min-heap of top-k results) +``` + +## Connection Pooling + +- Thread-per-connection cache (thread-local storage) +- Shared pool: `MAX_POOL_SIZE = 5` +- Pre-compiled regex patterns for performance +- Graceful degradation if `QAI_DB_CONN` is unset + +## Integration Pattern + +```python +from shared.chat_memory import generate_embedding, fetch_similar_messages + +# 1. Embed user message +embedding = generate_embedding(user_text) + +# 2. Find similar past messages +similar = fetch_similar_messages(embedding, top_k=5) + +# 3. Inject as system context +for i, msg in enumerate(similar): + messages.insert(1, {"role": "system", "content": f"[Memory #{i+1}] {msg['content']}"}) + +# 4. Prune to fit context window +messages, stats, sys_msg = prune_messages(messages, provider, model, max_tokens) +``` + +## Coding Conventions + +- Embedding dimension must be consistent within a deployment (mixing dimensions breaks similarity) +- Always handle `None` returns from `generate_embedding()` — DB may be unavailable +- Session-scoped queries use `session_id` parameter for isolation +- Never block on embedding storage — it's fire-and-forget with fault tolerance diff --git a/.github/instructions/chat-providers.instructions.md b/.github/instructions/chat-providers.instructions.md index fd15978d9..d917e25cc 100644 --- a/.github/instructions/chat-providers.instructions.md +++ b/.github/instructions/chat-providers.instructions.md @@ -1,56 +1,56 @@ ---- -applyTo: "**/chat_providers.py" ---- - -# Chat Providers — Instruction Guide - -## Provider Detection Chain - -Order matters — first match wins: -1. **Explicit choice** — `--provider` flag or API parameter -2. **LMStudio** — if `LMSTUDIO_BASE_URL` is set -3. **Azure OpenAI** — needs ALL 4: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION` -4. **OpenAI** — needs `OPENAI_API_KEY` -5. **LoRA** — explicit `--provider lora` with adapter path -6. **Local echo** — zero-dependency fallback with context-aware intent recognition - -## Provider Contract (BaseChatProvider) - -```python -class BaseChatProvider: - def __init__(self, model=None, temperature=0.7, max_output_tokens=2048): ... - def complete(self, messages: List[Dict], stream: bool = True) -> Union[str, Generator]: - # If stream=True: yield string chunks - # If stream=False: return complete string -``` - -## Key Implementations - -### LoraLocalProvider -- Bridges torch + subprocess for local LoRA inference -- Requires `adapter_config.json` + `adapter_model.safetensors` -- Thread-safe response caching - -### LocalEchoProvider -- Zero external dependencies -- Context-aware intent recognition (greetings, questions, coding) -- Deterministic responses for testing - -### Streaming Pattern -```python -for chunk in provider.complete(messages, stream=True): - yield f"data: {json.dumps({'content': chunk})}\n\n" -yield "data: [DONE]\n\n" -``` - -## Rate Limit Handling -- Providers implement exponential backoff on rate limits -- Automatic fallback to next provider in chain on persistent failures - -## Coding Conventions - -- Never hardcode API keys — always use env vars -- Always support both `stream=True` and `stream=False` -- The `shared/chat_providers.py` re-exports from `ai-projects/chat-cli/src/chat_providers.py` -- Test with `/api/ai/status` endpoint to verify provider detection -- `detect_provider()` returns tuple: `(provider_instance, provider_name)` +--- +applyTo: "**/chat_providers.py" +--- + +# Chat Providers — Instruction Guide + +## Provider Detection Chain + +Order matters — first match wins: +1. **Explicit choice** — `--provider` flag or API parameter +2. **LMStudio** — if `LMSTUDIO_BASE_URL` is set +3. **Azure OpenAI** — needs ALL 4: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION` +4. **OpenAI** — needs `OPENAI_API_KEY` +5. **LoRA** — explicit `--provider lora` with adapter path +6. **Local echo** — zero-dependency fallback with context-aware intent recognition + +## Provider Contract (BaseChatProvider) + +```python +class BaseChatProvider: + def __init__(self, model=None, temperature=0.7, max_output_tokens=2048): ... + def complete(self, messages: List[Dict], stream: bool = True) -> Union[str, Generator]: + # If stream=True: yield string chunks + # If stream=False: return complete string +``` + +## Key Implementations + +### LoraLocalProvider +- Bridges torch + subprocess for local LoRA inference +- Requires `adapter_config.json` + `adapter_model.safetensors` +- Thread-safe response caching + +### LocalEchoProvider +- Zero external dependencies +- Context-aware intent recognition (greetings, questions, coding) +- Deterministic responses for testing + +### Streaming Pattern +```python +for chunk in provider.complete(messages, stream=True): + yield f"data: {json.dumps({'content': chunk})}\n\n" +yield "data: [DONE]\n\n" +``` + +## Rate Limit Handling +- Providers implement exponential backoff on rate limits +- Automatic fallback to next provider in chain on persistent failures + +## Coding Conventions + +- Never hardcode API keys — always use env vars +- Always support both `stream=True` and `stream=False` +- The `shared/chat_providers.py` re-exports from `ai-projects/chat-cli/src/chat_providers.py` +- Test with `/api/ai/status` endpoint to verify provider detection +- `detect_provider()` returns tuple: `(provider_instance, provider_name)` diff --git a/.github/instructions/chat-web.instructions.md b/.github/instructions/chat-web.instructions.md index a1fcb0272..aaac655af 100644 --- a/.github/instructions/chat-web.instructions.md +++ b/.github/instructions/chat-web.instructions.md @@ -1,17 +1,17 @@ ---- -name: "Chat-Web" -description: "Guidance for chat-web frontend and SSE integration" -applyTo: "chat-web/**" ---- -# Chat Web – frontend & SSE - -- Endpoints used: `/api/chat` (SSE streaming), `/api/chat-web` (web UI), `/api/tts` (audio synthesis). -- SSE parsing on the client: read streamed `data: {json}` lines and handle final `data: [DONE]`. - - Each `data:` line contains JSON with `content` or delta; skip `[DONE]` sentinel. -- TTS usage: backend tries Azure Speech first, then pyttsx3, then gTTS; `/api/tts` returns `audio_base64` and `format` (mp3 or wav). -- Provider readiness: use `/api/ai/status` to check active provider, required env vars, LoRA readiness, SQL/Cosmos status. -- Avoid hardcoding secrets; use environment variables or `local.settings.json` (Functions host). -- Development tips: - - Ensure the Functions host is running (VS Code task `func: host start`). - - Azurite storage emulator files at repo root support offline development; backend endpoints work without Azure. -- Recommended SDK versions: `openai>=1.37.0` for streaming compatibility on clients. +--- +name: "Chat-Web" +description: "Guidance for chat-web frontend and SSE integration" +applyTo: "chat-web/**" +--- +# Chat Web – frontend & SSE + +- Endpoints used: `/api/chat` (SSE streaming), `/api/chat-web` (web UI), `/api/tts` (audio synthesis). +- SSE parsing on the client: read streamed `data: {json}` lines and handle final `data: [DONE]`. + - Each `data:` line contains JSON with `content` or delta; skip `[DONE]` sentinel. +- TTS usage: backend tries Azure Speech first, then pyttsx3, then gTTS; `/api/tts` returns `audio_base64` and `format` (mp3 or wav). +- Provider readiness: use `/api/ai/status` to check active provider, required env vars, LoRA readiness, SQL/Cosmos status. +- Avoid hardcoding secrets; use environment variables or `local.settings.json` (Functions host). +- Development tips: + - Ensure the Functions host is running (VS Code task `func: host start`). + - Azurite storage emulator files at repo root support offline development; backend endpoints work without Azure. +- Recommended SDK versions: `openai>=1.37.0` for streaming compatibility on clients. diff --git a/.github/instructions/cooking-ai.instructions.md b/.github/instructions/cooking-ai.instructions.md index a66f4539b..b546c8f88 100644 --- a/.github/instructions/cooking-ai.instructions.md +++ b/.github/instructions/cooking-ai.instructions.md @@ -1,73 +1,73 @@ ---- -applyTo: "ai-projects/cooking-ai/**" ---- - -# Cooking AI — Instruction Guide - -## Project Structure - -``` -ai-projects/cooking-ai/ - src/ - agents/ - recipe_agent.py # RecipeAgent — structured recipe search & ingredient extraction - utils/ - json_utils.py # JSON schemas for recipe/ingredient output -``` - -## RecipeAgent Pattern - -### Structural Typing -```python -class ProviderProtocol: - def complete(self, messages: List[Dict], json_mode: bool = False) -> str: ... -``` -The agent accepts any provider matching this protocol — no inheritance required. - -### JSON Mode with Fallback -```python -# _invoke() implements 2-retry: -try: - response = provider.complete(messages, json_mode=True) # Attempt structured output - return json.loads(response) -except: - response = provider.complete(messages, json_mode=False) # Fallback: free text - return extract_json(response) # Parse JSON from freeform response -# If both fail → return empty structure matching schema -``` - -### Output Schemas - -**Recipe Search:** -```json -{ - "recipes": [{ - "title": "string", - "ingredients": ["string"], - "instructions": ["string"], - "tags": ["string"], - "est_time_minutes": 30 - }] -} -``` - -**Ingredient Extraction:** -```json -{ - "ingredients": [{ - "raw": "string", - "name": "string", - "quantity": "string", - "unit": "string", - "notes": "string" - }] -} -``` - -## Coding Conventions - -- Always return structured JSON matching the defined schemas -- Handle provider failures gracefully — return empty structures, never crash -- Use the 2-retry pattern: JSON mode first, then fallback parsing -- Providers: GitHub Models + Local (configurable) -- Keep agent methods idempotent — same input produces same structure +--- +applyTo: "ai-projects/cooking-ai/**" +--- + +# Cooking AI — Instruction Guide + +## Project Structure + +``` +ai-projects/cooking-ai/ + src/ + agents/ + recipe_agent.py # RecipeAgent — structured recipe search & ingredient extraction + utils/ + json_utils.py # JSON schemas for recipe/ingredient output +``` + +## RecipeAgent Pattern + +### Structural Typing +```python +class ProviderProtocol: + def complete(self, messages: List[Dict], json_mode: bool = False) -> str: ... +``` +The agent accepts any provider matching this protocol — no inheritance required. + +### JSON Mode with Fallback +```python +# _invoke() implements 2-retry: +try: + response = provider.complete(messages, json_mode=True) # Attempt structured output + return json.loads(response) +except: + response = provider.complete(messages, json_mode=False) # Fallback: free text + return extract_json(response) # Parse JSON from freeform response +# If both fail → return empty structure matching schema +``` + +### Output Schemas + +**Recipe Search:** +```json +{ + "recipes": [{ + "title": "string", + "ingredients": ["string"], + "instructions": ["string"], + "tags": ["string"], + "est_time_minutes": 30 + }] +} +``` + +**Ingredient Extraction:** +```json +{ + "ingredients": [{ + "raw": "string", + "name": "string", + "quantity": "string", + "unit": "string", + "notes": "string" + }] +} +``` + +## Coding Conventions + +- Always return structured JSON matching the defined schemas +- Handle provider failures gracefully — return empty structures, never crash +- Use the 2-retry pattern: JSON mode first, then fallback parsing +- Providers: GitHub Models + Local (configurable) +- Keep agent methods idempotent — same input produces same structure diff --git a/.github/instructions/dashboard.instructions.md b/.github/instructions/dashboard.instructions.md index 8b9aa0bee..a9c82c50a 100644 --- a/.github/instructions/dashboard.instructions.md +++ b/.github/instructions/dashboard.instructions.md @@ -1,19 +1,19 @@ ---- -name: "Dashboard-App" -description: "Guidance for the dashboard web application" -applyTo: "apps/dashboard/**" ---- -# Dashboard App — Implementation Guidance - -- Web dashboard for monitoring and managing the Aria platform. -- Consumes data from orchestrator status files in `data_out//status.json`. -- Key metrics to display: - - Training cycles completed, best accuracy, performance trends - - Orchestrator job counts (total, succeeded, failed, running) - - System health (provider status, SQL pool, Cosmos DB) - - Resource utilization (CPU, memory, disk, GPU) -- Health endpoint: `GET /api/ai/status` returns comprehensive system diagnostics. -- Status files follow schema: `{total_jobs, succeeded, failed, running, last_updated, avg_duration}`. -- Autonomous training status: `{cycles_completed, best_accuracy, performance_history[], dataset_inventory}`. -- Prefer polling status files over live process introspection. -- Monitoring scripts available: `scripts/status_dashboard.py`, `scripts/resource_monitor.py`, `scripts/training_analytics.py`. +--- +name: "Dashboard-App" +description: "Guidance for the dashboard web application" +applyTo: "apps/dashboard/**" +--- +# Dashboard App — Implementation Guidance + +- Web dashboard for monitoring and managing the Aria platform. +- Consumes data from orchestrator status files in `data_out//status.json`. +- Key metrics to display: + - Training cycles completed, best accuracy, performance trends + - Orchestrator job counts (total, succeeded, failed, running) + - System health (provider status, SQL pool, Cosmos DB) + - Resource utilization (CPU, memory, disk, GPU) +- Health endpoint: `GET /api/ai/status` returns comprehensive system diagnostics. +- Status files follow schema: `{total_jobs, succeeded, failed, running, last_updated, avg_duration}`. +- Autonomous training status: `{cycles_completed, best_accuracy, performance_history[], dataset_inventory}`. +- Prefer polling status files over live process introspection. +- Monitoring scripts available: `scripts/status_dashboard.py`, `scripts/resource_monitor.py`, `scripts/training_analytics.py`. diff --git a/.github/instructions/db-logging.instructions.md b/.github/instructions/db-logging.instructions.md index e4dd71994..eafda97f2 100644 --- a/.github/instructions/db-logging.instructions.md +++ b/.github/instructions/db-logging.instructions.md @@ -1,62 +1,62 @@ ---- -applyTo: "**/db_logging.py" ---- - -# DB Logging — Instruction Guide - -## Fault-Tolerant Stored Procedure Wrappers - -All logging functions follow the same fault-tolerance pattern: - -```python -def log_*_safe(...) -> Dict: - # 1. If QAI_DB_CONN not set → NO-OP (returns {success: False, skipped: True}) - # 2. If pyodbc unavailable → logs warning once, continues - # 3. Try stored procedure call - # 4. On any exception → returns {success: False, error: str} - # 5. On success → returns {success: True, ...ids...} -``` - -## Available Functions - -### log_chat_message_safe() -```python -log_chat_message_safe( - session_id, provider, model, role, content, - token_count=None, ... -) → {success: bool, conversation_id: str, message_id: str} -# Calls: sp_LogChatConversation -``` - -### log_quantum_run_safe() -```python -log_quantum_run_safe( - job, result, dataset_name=None, log_path=None -) → {success: bool, run_id: str} -# Calls: sp_LogQuantumTrainingRun -``` - -### log_lora_run_safe() -```python -log_lora_run_safe( - job, result -) → {success: bool} -# Calls: sp_LogLoRATrainingRun -# Extracts config from YAML automatically -``` - -## Return Structure - -All functions return a dict with at minimum: -- `success: bool` — whether the log was written -- `skipped: bool` (optional) — True if DB is not configured -- `error: str` (optional) — error message if failed - -## Coding Conventions - -- **Never crash on logging failure** — logging is advisory, not critical path -- DB connection is optional (`QAI_DB_CONN` env var) -- All functions are safe to call even without a database -- Stored procedures are defined in `database/StoredProcedures/` -- Pool size configurable via `QAI_SQL_POOL_SIZE` (default: 10) -- Monitor pool saturation via `/api/ai/status` (warns at ≥80%) +--- +applyTo: "**/db_logging.py" +--- + +# DB Logging — Instruction Guide + +## Fault-Tolerant Stored Procedure Wrappers + +All logging functions follow the same fault-tolerance pattern: + +```python +def log_*_safe(...) -> Dict: + # 1. If QAI_DB_CONN not set → NO-OP (returns {success: False, skipped: True}) + # 2. If pyodbc unavailable → logs warning once, continues + # 3. Try stored procedure call + # 4. On any exception → returns {success: False, error: str} + # 5. On success → returns {success: True, ...ids...} +``` + +## Available Functions + +### log_chat_message_safe() +```python +log_chat_message_safe( + session_id, provider, model, role, content, + token_count=None, ... +) → {success: bool, conversation_id: str, message_id: str} +# Calls: sp_LogChatConversation +``` + +### log_quantum_run_safe() +```python +log_quantum_run_safe( + job, result, dataset_name=None, log_path=None +) → {success: bool, run_id: str} +# Calls: sp_LogQuantumTrainingRun +``` + +### log_lora_run_safe() +```python +log_lora_run_safe( + job, result +) → {success: bool} +# Calls: sp_LogLoRATrainingRun +# Extracts config from YAML automatically +``` + +## Return Structure + +All functions return a dict with at minimum: +- `success: bool` — whether the log was written +- `skipped: bool` (optional) — True if DB is not configured +- `error: str` (optional) — error message if failed + +## Coding Conventions + +- **Never crash on logging failure** — logging is advisory, not critical path +- DB connection is optional (`QAI_DB_CONN` env var) +- All functions are safe to call even without a database +- Stored procedures are defined in `database/StoredProcedures/` +- Pool size configurable via `QAI_SQL_POOL_SIZE` (default: 10) +- Monitor pool saturation via `/api/ai/status` (warns at ≥80%) diff --git a/.github/instructions/evaluation.instructions.md b/.github/instructions/evaluation.instructions.md index b3aff8c20..5e7efec0e 100644 --- a/.github/instructions/evaluation.instructions.md +++ b/.github/instructions/evaluation.instructions.md @@ -1,57 +1,57 @@ ---- -applyTo: "scripts/batch_evaluator.py,scripts/evaluate_*,scripts/training_analytics*" ---- - -# Evaluation & Analytics — Instruction Guide - -## Batch Evaluator - -### Pipeline -``` -load_config(YAML) or scan_models() → List[EvaluationTask] - ↓ -ThreadPoolExecutor(max_workers=3) → parallel evaluation - ↓ -evaluate_model(task) → subprocess → evaluate_lora_model.py - ↓ -List[EvaluationResult] → aggregation → export -``` - -### EvaluationTask Fields -- `model_id` — unique identifier -- `model_type` — "lora", "base", etc. -- `model_path` — path to model/adapter -- `dataset` — evaluation dataset path -- `metrics` — list: accuracy, perplexity, f1 -- `max_samples` — limit evaluation samples -- `batch_size` — inference batch size - -### Constraints -- Timeout: 30 minutes per evaluation -- Max parallel workers: 3 (configurable) -- Output directory: `data_out/batch_evaluator/` -- Error handling: catches subprocess failures, returns status + error - -## Training Analytics - -### Tracked Metrics -- `mean_accuracy` — average across evaluation samples -- `median_accuracy` — middle value (robust to outliers) -- `max_accuracy` — best single result -- `improvement_rate` — accuracy delta between cycles -- `successful_count` — models passing threshold -- `exceptional_models` — models with accuracy > 0.90 - -### Plateau Detection -Identifies when accuracy improvement rate drops below threshold across consecutive cycles. - -### Degradation Alerts -Auto-detect > 5% accuracy drops between training cycles. - -## Coding Conventions - -- Always `--dry-run` before GPU execution -- Datasets in `datasets/` are READ-ONLY -- All outputs go to `data_out//` -- Subprocess evaluations should be timeout-guarded -- Log evaluation results to `status.json` for dashboard consumption +--- +applyTo: "scripts/batch_evaluator.py,scripts/evaluate_*,scripts/training_analytics*" +--- + +# Evaluation & Analytics — Instruction Guide + +## Batch Evaluator + +### Pipeline +``` +load_config(YAML) or scan_models() → List[EvaluationTask] + ↓ +ThreadPoolExecutor(max_workers=3) → parallel evaluation + ↓ +evaluate_model(task) → subprocess → evaluate_lora_model.py + ↓ +List[EvaluationResult] → aggregation → export +``` + +### EvaluationTask Fields +- `model_id` — unique identifier +- `model_type` — "lora", "base", etc. +- `model_path` — path to model/adapter +- `dataset` — evaluation dataset path +- `metrics` — list: accuracy, perplexity, f1 +- `max_samples` — limit evaluation samples +- `batch_size` — inference batch size + +### Constraints +- Timeout: 30 minutes per evaluation +- Max parallel workers: 3 (configurable) +- Output directory: `data_out/batch_evaluator/` +- Error handling: catches subprocess failures, returns status + error + +## Training Analytics + +### Tracked Metrics +- `mean_accuracy` — average across evaluation samples +- `median_accuracy` — middle value (robust to outliers) +- `max_accuracy` — best single result +- `improvement_rate` — accuracy delta between cycles +- `successful_count` — models passing threshold +- `exceptional_models` — models with accuracy > 0.90 + +### Plateau Detection +Identifies when accuracy improvement rate drops below threshold across consecutive cycles. + +### Degradation Alerts +Auto-detect > 5% accuracy drops between training cycles. + +## Coding Conventions + +- Always `--dry-run` before GPU execution +- Datasets in `datasets/` are READ-ONLY +- All outputs go to `data_out//` +- Subprocess evaluations should be timeout-guarded +- Log evaluation results to `status.json` for dashboard consumption diff --git a/.github/instructions/functions.instructions.md b/.github/instructions/functions.instructions.md index efe8f8341..61b276e53 100644 --- a/.github/instructions/functions.instructions.md +++ b/.github/instructions/functions.instructions.md @@ -1,24 +1,24 @@ ---- -name: "Azure-Functions-App" -description: "Guidance for function_app.py and API endpoints" -applyTo: "**/function_app.py" ---- -# Azure Functions – function_app.py - -- Endpoints: `/api/chat`, `/api/chat-web`, `/api/tts`, `/api/quantum/*`, `/api/ai/status`. - - Verify runtime health at `/api/ai/status` (active provider, env vars, SQL pool, Cosmos status). -- Local dev: - - Use the VS Code task: `func: host start` (depends on `pip install (functions)`). - - Azurite storage emulator databases are present at repo root; Functions work offline. -- TTS configuration: - - Azure Speech: set `AZURE_SPEECH_KEY` and `AZURE_SPEECH_REGION` (in env or `local.settings.json`). - - Local fallback: set `QAI_ENABLE_LOCAL_TTS=true`; server will try Azure → pyttsx3 → gTTS. -- Chat provider detection (see `shared/chat_providers.py`): Azure OpenAI → OpenAI → LoRA → Local. - - Azure requires: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`. -- Streaming responses (SSE): emit `data: {json}` lines and a final `data: [DONE]`; clients must parse SSE. -- Secrets: do not hardcode; prefer `local.settings.json` for dev or Azure App Settings in prod. -- Observability: Application Insights via `shared/telemetry.py`; failures are non-blocking. -- Optional Cosmos persistence: feature-flagged in `shared/cosmos_client.py`. - - Recommended keys: `QAI_ENABLE_COSMOS`, `COSMOS_ENDPOINT`, `COSMOS_KEY`, `COSMOS_DATABASE`, `COSMOS_CONTAINER`. - - Partition key: `/session_id`; enable TTL for ephemeral messages. -- Testing: prefer `python .\\scripts\\test_runner.py --all`; markers `not slow and not azure` for local. +--- +name: "Azure-Functions-App" +description: "Guidance for function_app.py and API endpoints" +applyTo: "**/function_app.py" +--- +# Azure Functions – function_app.py + +- Endpoints: `/api/chat`, `/api/chat-web`, `/api/tts`, `/api/quantum/*`, `/api/ai/status`. + - Verify runtime health at `/api/ai/status` (active provider, env vars, SQL pool, Cosmos status). +- Local dev: + - Use the VS Code task: `func: host start` (depends on `pip install (functions)`). + - Azurite storage emulator databases are present at repo root; Functions work offline. +- TTS configuration: + - Azure Speech: set `AZURE_SPEECH_KEY` and `AZURE_SPEECH_REGION` (in env or `local.settings.json`). + - Local fallback: set `QAI_ENABLE_LOCAL_TTS=true`; server will try Azure → pyttsx3 → gTTS. +- Chat provider detection (see `shared/chat_providers.py`): Azure OpenAI → OpenAI → LoRA → Local. + - Azure requires: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`. +- Streaming responses (SSE): emit `data: {json}` lines and a final `data: [DONE]`; clients must parse SSE. +- Secrets: do not hardcode; prefer `local.settings.json` for dev or Azure App Settings in prod. +- Observability: Application Insights via `shared/telemetry.py`; failures are non-blocking. +- Optional Cosmos persistence: feature-flagged in `shared/cosmos_client.py`. + - Recommended keys: `QAI_ENABLE_COSMOS`, `COSMOS_ENDPOINT`, `COSMOS_KEY`, `COSMOS_DATABASE`, `COSMOS_CONTAINER`. + - Partition key: `/session_id`; enable TTL for ephemeral messages. +- Testing: prefer `python .\\scripts\\test_runner.py --all`; markers `not slow and not azure` for local. diff --git a/.github/instructions/llm-maker.instructions.md b/.github/instructions/llm-maker.instructions.md index 3ac5ed697..98e47739b 100644 --- a/.github/instructions/llm-maker.instructions.md +++ b/.github/instructions/llm-maker.instructions.md @@ -1,67 +1,67 @@ ---- -applyTo: "ai-projects/llm-maker/**" ---- - -# LLM Maker — Instruction Guide - -## Project Structure - -``` -ai-projects/llm-maker/ - src/ - tool_maker.py # ToolMaker — LLM-powered Python function generator - website_maker.py # WebsiteMaker — HTML/CSS/JS site generator - tool_validator.py # ToolValidator — AST-based safety validation -``` - -## Safety-First Code Generation - -### ToolValidator Rules (MANDATORY — never bypass) - -**Banned imports (DANGEROUS_IMPORTS):** -`os, sys, subprocess, shutil, pathlib, socket, urllib, requests, http, pickle, threading, multiprocessing, ctypes, cffi` - -**Banned builtins (DANGEROUS_BUILTINS):** -`eval, exec, compile, __import__, open, input, breakpoint, exit` - -**Validation pipeline:** -1. Parse code into AST -2. Walk AST for import statements → reject banned modules -3. Walk AST for function calls → reject dangerous builtins -4. Regex scan for file I/O patterns -5. Regex scan for network operation patterns -6. Verify function signature matches requested spec - -### Code Generation Pattern -```python -# ToolMaker.create_tool() implements: -for attempt in range(max_attempts): # default: 3 - code = self._generate_code(prompt) - is_valid, errors = self.validator.validate(code) - if is_valid: - sig_ok = self.validator.check_function_signature(code, name, params) - if sig_ok: - return code - # Inject error feedback into next prompt iteration - prompt += f"\nPrevious errors: {errors}" -``` - -### Website Generation Pattern -```python -# WebsiteMaker.create_website() returns: -{ - "success": True, - "files": {"index.html": "...", "style.css": "...", "script.js": "..."}, - "path": "ai-projects/generated_sites/{name}/", - "metadata": {"created_at": "...", "pages": [...], "features": [...]} -} -``` - -## Coding Conventions - -- All generated code must pass `ToolValidator.validate()` before use -- Generated websites go to `ai-projects/generated_sites/{name}/` -- Use `update_website()` for modifications (preserves metadata.json) -- Provider detection uses `detect_provider()` from shared chain -- Include descriptive docstrings in generated functions -- Keep generated functions pure — no side effects +--- +applyTo: "ai-projects/llm-maker/**" +--- + +# LLM Maker — Instruction Guide + +## Project Structure + +``` +ai-projects/llm-maker/ + src/ + tool_maker.py # ToolMaker — LLM-powered Python function generator + website_maker.py # WebsiteMaker — HTML/CSS/JS site generator + tool_validator.py # ToolValidator — AST-based safety validation +``` + +## Safety-First Code Generation + +### ToolValidator Rules (MANDATORY — never bypass) + +**Banned imports (DANGEROUS_IMPORTS):** +`os, sys, subprocess, shutil, pathlib, socket, urllib, requests, http, pickle, threading, multiprocessing, ctypes, cffi` + +**Banned builtins (DANGEROUS_BUILTINS):** +`eval, exec, compile, __import__, open, input, breakpoint, exit` + +**Validation pipeline:** +1. Parse code into AST +2. Walk AST for import statements → reject banned modules +3. Walk AST for function calls → reject dangerous builtins +4. Regex scan for file I/O patterns +5. Regex scan for network operation patterns +6. Verify function signature matches requested spec + +### Code Generation Pattern +```python +# ToolMaker.create_tool() implements: +for attempt in range(max_attempts): # default: 3 + code = self._generate_code(prompt) + is_valid, errors = self.validator.validate(code) + if is_valid: + sig_ok = self.validator.check_function_signature(code, name, params) + if sig_ok: + return code + # Inject error feedback into next prompt iteration + prompt += f"\nPrevious errors: {errors}" +``` + +### Website Generation Pattern +```python +# WebsiteMaker.create_website() returns: +{ + "success": True, + "files": {"index.html": "...", "style.css": "...", "script.js": "..."}, + "path": "ai-projects/generated_sites/{name}/", + "metadata": {"created_at": "...", "pages": [...], "features": [...]} +} +``` + +## Coding Conventions + +- All generated code must pass `ToolValidator.validate()` before use +- Generated websites go to `ai-projects/generated_sites/{name}/` +- Use `update_website()` for modifications (preserves metadata.json) +- Provider detection uses `detect_provider()` from shared chain +- Include descriptive docstrings in generated functions +- Keep generated functions pure — no side effects diff --git a/.github/instructions/lora-python.instructions.md b/.github/instructions/lora-python.instructions.md index e07dea4ca..2b9e3e751 100644 --- a/.github/instructions/lora-python.instructions.md +++ b/.github/instructions/lora-python.instructions.md @@ -1,21 +1,21 @@ ---- -name: "LoRA-Python" -description: "Python-specific guidance for ai-projects/lora-training/microsoft_phi-silica-3.6_v1/" -applyTo: "ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/**/*.py" ---- -# LoRA Fine-Tuning – Python files - -- Validate configs early: - - `python .\\scripts\\autotrain.py --dry-run` - - Quick pipeline (train → evaluate → deploy best): `python .\\scripts\\train_and_promote.py --quick --auto-promote` -- Ultrafast TinyLlama (CPU-friendly): `python .\\scripts\\automated_training_pipeline.py --models tinyllama --quick` -- Config & datasets: - - LoRA config: `ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora.yaml` - - Dataset convention (chat): `datasets/chat//{train.json,test.json}` with `messages[]` entries. - - Validate datasets: `python .\\scripts\\validate_datasets.py --category chat` -- LoRA readiness: adapter dir requires `adapter_config.json` and `adapter_model.safetensors` (`data_out/lora_training//lora_adapter/`). -- Ranking metrics: `perplexity_improvement`, `diversity_avg` (`distinct_diversity`), `combined_improvement`. -- Use in CLI: `python .\\talk-to-ai\\src\\chat_cli.py --provider lora --model ` -- Config precedence: base YAML < CLI flags < per-job YAML overrides < env vars. -- Data immutability: read-only `datasets/`; write-only outputs under `data_out/`. -- Tests & CI: `python .\\scripts\\test_runner.py --all` and VS Code Test Explorer (🧪) for debugging. +--- +name: "LoRA-Python" +description: "Python-specific guidance for ai-projects/lora-training/microsoft_phi-silica-3.6_v1/" +applyTo: "ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/**/*.py" +--- +# LoRA Fine-Tuning – Python files + +- Validate configs early: + - `python .\\scripts\\autotrain.py --dry-run` + - Quick pipeline (train → evaluate → deploy best): `python .\\scripts\\train_and_promote.py --quick --auto-promote` +- Ultrafast TinyLlama (CPU-friendly): `python .\\scripts\\automated_training_pipeline.py --models tinyllama --quick` +- Config & datasets: + - LoRA config: `ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora.yaml` + - Dataset convention (chat): `datasets/chat//{train.json,test.json}` with `messages[]` entries. + - Validate datasets: `python .\\scripts\\validate_datasets.py --category chat` +- LoRA readiness: adapter dir requires `adapter_config.json` and `adapter_model.safetensors` (`data_out/lora_training//lora_adapter/`). +- Ranking metrics: `perplexity_improvement`, `diversity_avg` (`distinct_diversity`), `combined_improvement`. +- Use in CLI: `python .\\talk-to-ai\\src\\chat_cli.py --provider lora --model ` +- Config precedence: base YAML < CLI flags < per-job YAML overrides < env vars. +- Data immutability: read-only `datasets/`; write-only outputs under `data_out/`. +- Tests & CI: `python .\\scripts\\test_runner.py --all` and VS Code Test Explorer (🧪) for debugging. diff --git a/.github/instructions/lora.instructions.md b/.github/instructions/lora.instructions.md index 536aa4717..05fcde609 100644 --- a/.github/instructions/lora.instructions.md +++ b/.github/instructions/lora.instructions.md @@ -1,21 +1,21 @@ ---- -name: "LoRA-Fine-Tuning-workspace" -description: "Slim instructions for ai-projects/lora-training/microsoft_phi-silica-3.6_v1/" -applyTo: "ai-projects/lora-training/microsoft_phi-silica-3.6_v1/**" ---- -# LoRA Fine-Tuning – workspace-specific guidance - -- Start with dry-runs to validate config: - - `python .\\scripts\\autotrain.py --dry-run` - - Quick pipeline (train → evaluate → deploy best): `python .\\scripts\\train_and_promote.py --quick --auto-promote` -- Ultrafast TinyLlama (CPU-friendly): `python .\\scripts\\automated_training_pipeline.py --models tinyllama --quick` -- Configs & datasets: - - LoRA config: `ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora.yaml` - - Dataset convention (chat): `datasets/chat//{train.json,test.json}` with `messages[]` entries. - - Validate datasets: `python .\\scripts\\validate_datasets.py --category chat` -- LoRA readiness: adapter directory must contain BOTH `adapter_config.json` and `adapter_model.safetensors` (e.g., under `data_out/lora_training//lora_adapter/`). -- Ranking metrics used by automation: `perplexity_improvement`, `diversity_avg` (aka `distinct_diversity`), and `combined_improvement`. -- Post-training usage (Chat CLI): `python .\\talk-to-ai\\src\\chat_cli.py --provider lora --model ` -- Config precedence: base YAML < CLI flags < per-job YAML overrides < environment variables. -- Data immutability: read-only `datasets/`; write-only outputs in `data_out/` (autotrain, lora_training, evaluation_autorun, parallel_training). -- Tests & CI: prefer `python .\\scripts\\test_runner.py --all` for fast validation; use VS Code Test Explorer (🧪) for debugging. +--- +name: "LoRA-Fine-Tuning-workspace" +description: "Slim instructions for ai-projects/lora-training/microsoft_phi-silica-3.6_v1/" +applyTo: "ai-projects/lora-training/microsoft_phi-silica-3.6_v1/**" +--- +# LoRA Fine-Tuning – workspace-specific guidance + +- Start with dry-runs to validate config: + - `python .\\scripts\\autotrain.py --dry-run` + - Quick pipeline (train → evaluate → deploy best): `python .\\scripts\\train_and_promote.py --quick --auto-promote` +- Ultrafast TinyLlama (CPU-friendly): `python .\\scripts\\automated_training_pipeline.py --models tinyllama --quick` +- Configs & datasets: + - LoRA config: `ai-projects/lora-training/microsoft_phi-silica-3.6_v1/lora/lora.yaml` + - Dataset convention (chat): `datasets/chat//{train.json,test.json}` with `messages[]` entries. + - Validate datasets: `python .\\scripts\\validate_datasets.py --category chat` +- LoRA readiness: adapter directory must contain BOTH `adapter_config.json` and `adapter_model.safetensors` (e.g., under `data_out/lora_training//lora_adapter/`). +- Ranking metrics used by automation: `perplexity_improvement`, `diversity_avg` (aka `distinct_diversity`), and `combined_improvement`. +- Post-training usage (Chat CLI): `python .\\talk-to-ai\\src\\chat_cli.py --provider lora --model ` +- Config precedence: base YAML < CLI flags < per-job YAML overrides < environment variables. +- Data immutability: read-only `datasets/`; write-only outputs in `data_out/` (autotrain, lora_training, evaluation_autorun, parallel_training). +- Tests & CI: prefer `python .\\scripts\\test_runner.py --all` for fast validation; use VS Code Test Explorer (🧪) for debugging. diff --git a/.github/instructions/orchestrator-configs.instructions.md b/.github/instructions/orchestrator-configs.instructions.md index e0a1891c7..e37e51814 100644 --- a/.github/instructions/orchestrator-configs.instructions.md +++ b/.github/instructions/orchestrator-configs.instructions.md @@ -1,43 +1,43 @@ ---- -name: "Orchestrator-Configs" -description: "Guidance for YAML orchestrator configuration files" -applyTo: "config/**/*.yaml" ---- -# Orchestrator Configs — YAML Guidance - -- All orchestrators in `scripts/` have matching YAML configs. -- Config precedence: `YAML base` < `CLI flags` < `per-job YAML` < `env vars`. -- Always validate with `--dry-run` after editing any config. - -## Key Config Files - -| Config | Orchestrator | Validates With | -|--------|-------------|----------------| -| `autotrain.yaml` | `scripts/autotrain.py` | `python scripts/autotrain.py --dry-run` | -| `quantum_autorun.yaml` | `scripts/quantum_autorun.py` | `python scripts/quantum_autorun.py --dry-run` | -| `config/autonomous_training.yaml` | `scripts/autonomous_training_orchestrator.py` | Check status JSON | -| `config/master_orchestrator.yaml` | `scripts/master_orchestrator.py` | `python scripts/master_orchestrator.py --dry-run` | - -## Autonomous Training Config Keys -- `cycle_interval_minutes` — Time between cycles (default 30) -- `epochs_progression` — Adaptive epoch list `[25, 50, 100, 200]` -- `min_datasets` — Minimum datasets before training starts -- `auto_deploy` — Enable automatic model promotion -- `deploy_threshold` — Accuracy threshold for auto-deploy (default 0.90) - -## Master Orchestrator Config Keys -- Cron schedules for sub-orchestrators -- Priority ordering and dependency chains -- Retry logic and timeout configuration -- Workflow definitions (e.g., `daily_full_pipeline`) - -## Quantum Config Keys -- `azure_confirm_cost: true` — Required for real QPU jobs -- `max_qubits` — Safety limit (10 local, 20 Azure) -- `max_shots` — Shot count limit (1000 default) -- Backend selection: simulator vs QPU - -## Safety -- Never set `azure_confirm_cost: true` without reviewing cost estimate -- Keep `max_qubits` ≤ 20 for Azure, ≤ 10 for local -- Validate YAML syntax: `python -c "import yaml; yaml.safe_load(open('config_file.yaml'))"` +--- +name: "Orchestrator-Configs" +description: "Guidance for YAML orchestrator configuration files" +applyTo: "config/**/*.yaml" +--- +# Orchestrator Configs — YAML Guidance + +- All orchestrators in `scripts/` have matching YAML configs. +- Config precedence: `YAML base` < `CLI flags` < `per-job YAML` < `env vars`. +- Always validate with `--dry-run` after editing any config. + +## Key Config Files + +| Config | Orchestrator | Validates With | +|--------|-------------|----------------| +| `autotrain.yaml` | `scripts/autotrain.py` | `python scripts/autotrain.py --dry-run` | +| `quantum_autorun.yaml` | `scripts/quantum_autorun.py` | `python scripts/quantum_autorun.py --dry-run` | +| `config/autonomous_training.yaml` | `scripts/autonomous_training_orchestrator.py` | Check status JSON | +| `config/master_orchestrator.yaml` | `scripts/master_orchestrator.py` | `python scripts/master_orchestrator.py --dry-run` | + +## Autonomous Training Config Keys +- `cycle_interval_minutes` — Time between cycles (default 30) +- `epochs_progression` — Adaptive epoch list `[25, 50, 100, 200]` +- `min_datasets` — Minimum datasets before training starts +- `auto_deploy` — Enable automatic model promotion +- `deploy_threshold` — Accuracy threshold for auto-deploy (default 0.90) + +## Master Orchestrator Config Keys +- Cron schedules for sub-orchestrators +- Priority ordering and dependency chains +- Retry logic and timeout configuration +- Workflow definitions (e.g., `daily_full_pipeline`) + +## Quantum Config Keys +- `azure_confirm_cost: true` — Required for real QPU jobs +- `max_qubits` — Safety limit (10 local, 20 Azure) +- `max_shots` — Shot count limit (1000 default) +- Backend selection: simulator vs QPU + +## Safety +- Never set `azure_confirm_cost: true` without reviewing cost estimate +- Keep `max_qubits` ≤ 20 for Azure, ≤ 10 for local +- Validate YAML syntax: `python -c "import yaml; yaml.safe_load(open('config_file.yaml'))"` diff --git a/.github/instructions/quantum-ai-mcp-python.instructions.md b/.github/instructions/quantum-ai-mcp-python.instructions.md index acf375170..5f8619ae0 100644 --- a/.github/instructions/quantum-ai-mcp-python.instructions.md +++ b/.github/instructions/quantum-ai-mcp-python.instructions.md @@ -1,21 +1,21 @@ ---- -name: "Quantum-AI-MCP-Python" -description: "Python-specific guidance for quantum-ai MCP server" -applyTo: "ai-projects/quantum-ml/quantum_mcp_server.py" ---- -# Quantum AI MCP Server – Python file - -## Cost Awareness -- **Azure simulators (ionq.simulator, quantinuum.sim.*) are FREE** – no cost confirmation needed -- **Real QPU hardware (ionq.qpu, quantinuum.qpu.*) is PAID** – requires `confirm_cost=true` parameter -- Always test on FREE simulators before using paid QPU - -## MCP Tools & Safety -- Start MCP server: `python .\\quantum-ai\\quantum_mcp_server.py`. -- Available tools: `create_quantum_circuit`, `simulate_quantum_circuit`, `get_quantum_circuit_properties`, `connect_azure_quantum`, `list_quantum_backends`, `submit_quantum_job`, `estimate_quantum_cost`, `train_quantum_classifier`. -- Safety limits enforced: ≤10 local qubits, default ≤1000 shots, 60s timeout per call, CircuitCache (LRU + TTL) to avoid recomputation. -- Cost gate for Azure QPU: require `confirm_cost=true` (tool args) and set `azure_confirm_cost: true` in orchestrator YAML before real hardware. -- Prefer local simulator backends (Qiskit Aer) or FREE Azure simulators before paid hardware. -- Azure workspace configuration: `ai-projects/quantum-ml/config/quantum_config.yaml` (requires `az login`). -- Outputs & status: write to `data_out/quantum_autorun/` and use status JSON for machine-readable progress. -- Tests: `python .\\scripts\\test_runner.py --unit` and `pytest -m "not slow and not azure"`. +--- +name: "Quantum-AI-MCP-Python" +description: "Python-specific guidance for quantum-ai MCP server" +applyTo: "ai-projects/quantum-ml/quantum_mcp_server.py" +--- +# Quantum AI MCP Server – Python file + +## Cost Awareness +- **Azure simulators (ionq.simulator, quantinuum.sim.*) are FREE** – no cost confirmation needed +- **Real QPU hardware (ionq.qpu, quantinuum.qpu.*) is PAID** – requires `confirm_cost=true` parameter +- Always test on FREE simulators before using paid QPU + +## MCP Tools & Safety +- Start MCP server: `python .\\quantum-ai\\quantum_mcp_server.py`. +- Available tools: `create_quantum_circuit`, `simulate_quantum_circuit`, `get_quantum_circuit_properties`, `connect_azure_quantum`, `list_quantum_backends`, `submit_quantum_job`, `estimate_quantum_cost`, `train_quantum_classifier`. +- Safety limits enforced: ≤10 local qubits, default ≤1000 shots, 60s timeout per call, CircuitCache (LRU + TTL) to avoid recomputation. +- Cost gate for Azure QPU: require `confirm_cost=true` (tool args) and set `azure_confirm_cost: true` in orchestrator YAML before real hardware. +- Prefer local simulator backends (Qiskit Aer) or FREE Azure simulators before paid hardware. +- Azure workspace configuration: `ai-projects/quantum-ml/config/quantum_config.yaml` (requires `az login`). +- Outputs & status: write to `data_out/quantum_autorun/` and use status JSON for machine-readable progress. +- Tests: `python .\\scripts\\test_runner.py --unit` and `pytest -m "not slow and not azure"`. diff --git a/.github/instructions/quantum-ai-python.instructions.md b/.github/instructions/quantum-ai-python.instructions.md index 3214f2ab0..5e756b7fd 100644 --- a/.github/instructions/quantum-ai-python.instructions.md +++ b/.github/instructions/quantum-ai-python.instructions.md @@ -1,28 +1,28 @@ ---- -name: "Quantum-AI-Python" -description: "Python-specific guidance for ai-projects/quantum-ml/" -applyTo: "ai-projects/quantum-ml/src/**/*.py" ---- -# Quantum AI – Python files - -## Cost Awareness -- Local simulators (Qiskit Aer, PennyLane default.qubit): **FREE**, unlimited use -- Azure simulators (ionq.simulator, quantinuum.sim.*): **FREE**, no cost limits -- Real quantum hardware (ionq.qpu, quantinuum.qpu.*): PAID (~$0.00003-$0.00015 per gate-shot) -- Workflow: Test locally → Validate on Azure simulator (FREE) → Run on QPU (PAID) -- Check `azure_confirm_cost: true` flag in quantum_autorun.yaml before QPU execution - -## Development Guidelines - -- Prefer local simulation first (Qiskit Aer) before Azure hardware. - - Dry-run orchestrator: `python .\\scripts\\quantum_autorun.py --dry-run` - - Simulator job: `python .\\scripts\\quantum_autorun.py --job azure_ionq_simulator` -- Cost gate: set `azure_confirm_cost: true` in `quantum_autorun.yaml` before real QPU; start with ≤100 shots. -- MCP server for tooling: `python .\\quantum-ai\\quantum_mcp_server.py` - - Tools: `create_quantum_circuit`, `simulate_quantum_circuit`, `get_quantum_circuit_properties`, `connect_azure_quantum`, `list_quantum_backends`, `submit_quantum_job`, `estimate_quantum_cost`, `train_quantum_classifier`. -- Keep local limits reasonable: ≤10 qubits, ≤1000 shots; use `qiskit_aer` backend for fast feedback. -- Dashboard: `ai-projects/quantum-ml/start_dashboard.sh` → http://localhost:5000 for interactive training. -- Azure config lives in `ai-projects/quantum-ml/config/quantum_config.yaml`; requires `az login`. -- Data immutability: read-only `datasets/`; write-only outputs under `data_out/quantum_autorun//`. -- Tests: `python .\\scripts\\test_runner.py --unit` or `pytest -m "not slow and not azure"`. -- High-signal modules: `ai-projects/quantum-ml/quantum_mcp_server.py`, `ai-projects/quantum-ml/src/quantum_classifier.py`, `QUANTUM_AUTORUN_README.md`. +--- +name: "Quantum-AI-Python" +description: "Python-specific guidance for ai-projects/quantum-ml/" +applyTo: "ai-projects/quantum-ml/src/**/*.py" +--- +# Quantum AI – Python files + +## Cost Awareness +- Local simulators (Qiskit Aer, PennyLane default.qubit): **FREE**, unlimited use +- Azure simulators (ionq.simulator, quantinuum.sim.*): **FREE**, no cost limits +- Real quantum hardware (ionq.qpu, quantinuum.qpu.*): PAID (~$0.00003-$0.00015 per gate-shot) +- Workflow: Test locally → Validate on Azure simulator (FREE) → Run on QPU (PAID) +- Check `azure_confirm_cost: true` flag in quantum_autorun.yaml before QPU execution + +## Development Guidelines + +- Prefer local simulation first (Qiskit Aer) before Azure hardware. + - Dry-run orchestrator: `python .\\scripts\\quantum_autorun.py --dry-run` + - Simulator job: `python .\\scripts\\quantum_autorun.py --job azure_ionq_simulator` +- Cost gate: set `azure_confirm_cost: true` in `quantum_autorun.yaml` before real QPU; start with ≤100 shots. +- MCP server for tooling: `python .\\quantum-ai\\quantum_mcp_server.py` + - Tools: `create_quantum_circuit`, `simulate_quantum_circuit`, `get_quantum_circuit_properties`, `connect_azure_quantum`, `list_quantum_backends`, `submit_quantum_job`, `estimate_quantum_cost`, `train_quantum_classifier`. +- Keep local limits reasonable: ≤10 qubits, ≤1000 shots; use `qiskit_aer` backend for fast feedback. +- Dashboard: `ai-projects/quantum-ml/start_dashboard.sh` → http://localhost:5000 for interactive training. +- Azure config lives in `ai-projects/quantum-ml/config/quantum_config.yaml`; requires `az login`. +- Data immutability: read-only `datasets/`; write-only outputs under `data_out/quantum_autorun//`. +- Tests: `python .\\scripts\\test_runner.py --unit` or `pytest -m "not slow and not azure"`. +- High-signal modules: `ai-projects/quantum-ml/quantum_mcp_server.py`, `ai-projects/quantum-ml/src/quantum_classifier.py`, `QUANTUM_AUTORUN_README.md`. diff --git a/.github/instructions/quantum-ai.instructions.md b/.github/instructions/quantum-ai.instructions.md index df2deb2c6..36136829f 100644 --- a/.github/instructions/quantum-ai.instructions.md +++ b/.github/instructions/quantum-ai.instructions.md @@ -1,22 +1,22 @@ ---- -name: "Quantum-AI-workspace" -description: "Slim instructions for ai-projects/quantum-ml/" -applyTo: "ai-projects/quantum-ml/**" ---- -# Quantum AI – workspace-specific guidance - -- Prefer local simulation first (Qiskit Aer) before Azure hardware. - - Validate with: `python .\\scripts\\quantum_autorun.py --dry-run` - - Then: `python .\\scripts\\quantum_autorun.py --job azure_ionq_simulator` -- Real QPU runs require an explicit cost gate: set `azure_confirm_cost: true` in `quantum_autorun.yaml` and start with ≤100 shots. -- MCP server tools (run: `python .\\quantum-ai\\quantum_mcp_server.py`): - - `create_quantum_circuit`, `simulate_quantum_circuit`, `get_quantum_circuit_properties` - - `connect_azure_quantum`, `list_quantum_backends`, `submit_quantum_job`, `estimate_quantum_cost` - - `train_quantum_classifier` -- Interactive Web Dashboard: `ai-projects/quantum-ml/start_dashboard.sh` → http://localhost:5000 -- Azure workspace config: `ai-projects/quantum-ml/config/quantum_config.yaml` (requires `az login`). -- Data immutability: read-only `datasets/`; write-only outputs in `data_out/quantum_autorun//...`. -- Unit tests: `python .\\scripts\\test_runner.py --unit` or `pytest -m "not slow and not azure"` for fast local validation. -- Safety limits: ≤10 qubits local; default ≤1000 shots; MCP server has CircuitCache (LRU + TTL) to avoid re-computation. -- Key files: `ai-projects/quantum-ml/quantum_mcp_server.py`, `ai-projects/quantum-ml/src/quantum_classifier.py`, `QUANTUM_AUTORUN_README.md`, `ai-projects/quantum-ml/HARDWARE_TEST_RESULTS.md`. -- Observability: Application Insights via `shared/telemetry.py`; failures are non-blocking and surfaced in `/api/ai/status`. +--- +name: "Quantum-AI-workspace" +description: "Slim instructions for ai-projects/quantum-ml/" +applyTo: "ai-projects/quantum-ml/**" +--- +# Quantum AI – workspace-specific guidance + +- Prefer local simulation first (Qiskit Aer) before Azure hardware. + - Validate with: `python .\\scripts\\quantum_autorun.py --dry-run` + - Then: `python .\\scripts\\quantum_autorun.py --job azure_ionq_simulator` +- Real QPU runs require an explicit cost gate: set `azure_confirm_cost: true` in `quantum_autorun.yaml` and start with ≤100 shots. +- MCP server tools (run: `python .\\quantum-ai\\quantum_mcp_server.py`): + - `create_quantum_circuit`, `simulate_quantum_circuit`, `get_quantum_circuit_properties` + - `connect_azure_quantum`, `list_quantum_backends`, `submit_quantum_job`, `estimate_quantum_cost` + - `train_quantum_classifier` +- Interactive Web Dashboard: `ai-projects/quantum-ml/start_dashboard.sh` → http://localhost:5000 +- Azure workspace config: `ai-projects/quantum-ml/config/quantum_config.yaml` (requires `az login`). +- Data immutability: read-only `datasets/`; write-only outputs in `data_out/quantum_autorun//...`. +- Unit tests: `python .\\scripts\\test_runner.py --unit` or `pytest -m "not slow and not azure"` for fast local validation. +- Safety limits: ≤10 qubits local; default ≤1000 shots; MCP server has CircuitCache (LRU + TTL) to avoid re-computation. +- Key files: `ai-projects/quantum-ml/quantum_mcp_server.py`, `ai-projects/quantum-ml/src/quantum_classifier.py`, `QUANTUM_AUTORUN_README.md`, `ai-projects/quantum-ml/HARDWARE_TEST_RESULTS.md`. +- Observability: Application Insights via `shared/telemetry.py`; failures are non-blocking and surfaced in `/api/ai/status`. diff --git a/.github/instructions/shared-python.instructions.md b/.github/instructions/shared-python.instructions.md index 828e5aed2..936077c1a 100644 --- a/.github/instructions/shared-python.instructions.md +++ b/.github/instructions/shared-python.instructions.md @@ -1,33 +1,33 @@ ---- -name: "Shared-Python" -description: "Common infrastructure guidance for shared/**/*.py" -applyTo: "shared/**/*.py" ---- -# Shared Infrastructure – Python files - -- Provider detection (`shared/chat_providers.py`): Azure OpenAI → OpenAI → LoRA → Local. - - Azure requires: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`. - - LoRA readiness: adapter dir must contain `adapter_config.json` and `adapter_model.safetensors`. -- Memory & embeddings (`shared/chat_memory.py`): ensure consistent schema; avoid mutating immutable datasets. -- SQL engine (`shared/sql_engine.py`): unified connection via `QAI_DB_CONN`. - - Pooling: tune with `QAI_SQL_POOL_SIZE`; saturation surfaced via `/api/ai/status` (≥80% alerts). - - Safe logging wrappers in `shared/db_logging.py` degrade gracefully when DB unavailable. -- Cosmos client (`shared/cosmos_client.py`): feature-flagged persistence. - - Enable via `QAI_ENABLE_COSMOS=true` and set `COSMOS_ENDPOINT`, `COSMOS_KEY`, `COSMOS_DATABASE`, `COSMOS_CONTAINER`. - - Recommended: partition key `/session_id`, enable TTL for ephemeral items; prefer point reads to reduce RU cost. -- Telemetry (`shared/telemetry.py`): Application Insights with `APPLICATIONINSIGHTS_CONNECTION_STRING`; failures are non-blocking. -- Config precedence: base YAML < CLI flags < per-job YAML overrides < env vars. -- Data conventions: read-only `datasets/`; write-only outputs under `data_out/`. -- Observability: check `/api/ai/status` for provider readiness, SQL pool saturation, Cosmos enablement. -- Tests: use `python .\\scripts\\test_runner.py --all` and VS Code Test Explorer (🧪) for fast local validation; prefer markers `not slow and not azure`. - -## Connection string examples (PowerShell) - -- SQLite (local dev): - - `$env:QAI_DB_CONN = "sqlite:///c:/Users/Bryan/OneDrive/AI/data_out/qai.db"` -- PostgreSQL: - - `$env:QAI_DB_CONN = "postgresql://user:pass@localhost:5432/qai"` -- Azure SQL (ODBC): - - `$env:QAI_DB_CONN = "Driver={ODBC Driver 18 for SQL Server};Server=tcp:myserver.database.windows.net,1433;Database=qai;Uid=myuser;Pwd=mypass;Encrypt=yes;TrustServerCertificate=no;"` -- Pool size tuning: - - Default is small (e.g., 10); increase via `$env:QAI_SQL_POOL_SIZE = "20"` if `/api/ai/status` shows `saturation_alert: true`. +--- +name: "Shared-Python" +description: "Common infrastructure guidance for shared/**/*.py" +applyTo: "shared/**/*.py" +--- +# Shared Infrastructure – Python files + +- Provider detection (`shared/chat_providers.py`): Azure OpenAI → OpenAI → LoRA → Local. + - Azure requires: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`. + - LoRA readiness: adapter dir must contain `adapter_config.json` and `adapter_model.safetensors`. +- Memory & embeddings (`shared/chat_memory.py`): ensure consistent schema; avoid mutating immutable datasets. +- SQL engine (`shared/sql_engine.py`): unified connection via `QAI_DB_CONN`. + - Pooling: tune with `QAI_SQL_POOL_SIZE`; saturation surfaced via `/api/ai/status` (≥80% alerts). + - Safe logging wrappers in `shared/db_logging.py` degrade gracefully when DB unavailable. +- Cosmos client (`shared/cosmos_client.py`): feature-flagged persistence. + - Enable via `QAI_ENABLE_COSMOS=true` and set `COSMOS_ENDPOINT`, `COSMOS_KEY`, `COSMOS_DATABASE`, `COSMOS_CONTAINER`. + - Recommended: partition key `/session_id`, enable TTL for ephemeral items; prefer point reads to reduce RU cost. +- Telemetry (`shared/telemetry.py`): Application Insights with `APPLICATIONINSIGHTS_CONNECTION_STRING`; failures are non-blocking. +- Config precedence: base YAML < CLI flags < per-job YAML overrides < env vars. +- Data conventions: read-only `datasets/`; write-only outputs under `data_out/`. +- Observability: check `/api/ai/status` for provider readiness, SQL pool saturation, Cosmos enablement. +- Tests: use `python .\\scripts\\test_runner.py --all` and VS Code Test Explorer (🧪) for fast local validation; prefer markers `not slow and not azure`. + +## Connection string examples (PowerShell) + +- SQLite (local dev): + - `$env:QAI_DB_CONN = "sqlite:///c:/Users/Bryan/OneDrive/AI/data_out/qai.db"` +- PostgreSQL: + - `$env:QAI_DB_CONN = "postgresql://user:pass@localhost:5432/qai"` +- Azure SQL (ODBC): + - `$env:QAI_DB_CONN = "Driver={ODBC Driver 18 for SQL Server};Server=tcp:myserver.database.windows.net,1433;Database=qai;Uid=myuser;Pwd=mypass;Encrypt=yes;TrustServerCertificate=no;"` +- Pool size tuning: + - Default is small (e.g., 10); increase via `$env:QAI_SQL_POOL_SIZE = "20"` if `/api/ai/status` shows `saturation_alert: true`. diff --git a/.github/instructions/subscription.instructions.md b/.github/instructions/subscription.instructions.md index e1652c431..59e7d8db9 100644 --- a/.github/instructions/subscription.instructions.md +++ b/.github/instructions/subscription.instructions.md @@ -1,66 +1,66 @@ ---- -applyTo: "**/subscription_manager.py" ---- - -# Subscription Manager — Instruction Guide - -## Tier Definitions - -| Tier | Price | Chat Msgs | Quantum Jobs | Training Hrs | -|------|-------|-----------|-------------|-------------| -| FREE | $0/mo | 100/mo | 0 | 0 | -| PRO | $49/mo | 10,000/mo | 50/mo | 20 hrs/mo | -| ENTERPRISE | $199/mo | Unlimited | Unlimited | Unlimited | - -## Feature Gating - -```python -# 10 gatable features: -BASIC_CHAT, ARIA_CHARACTER, QUANTUM_COMPUTING, ADVANCED_TRAINING, -WEBSITE_MAKER, API_ACCESS, CUSTOM_MODELS, PRIORITY_SUPPORT, -ANALYTICS_DASHBOARD, BATCH_PROCESSING -``` - -## Usage Pattern - -```python -from shared.subscription_manager import get_subscription_manager, Feature - -mgr = get_subscription_manager() -sub = mgr.get_subscription(user_id) - -# Feature check -if not sub.has_feature(Feature.QUANTUM_COMPUTING): - return HttpResponse("Upgrade to PRO for quantum access", status_code=403) - -# Usage limit check -if not sub.check_limit('quantum_jobs'): - return HttpResponse("Monthly quota exceeded", status_code=429) - -# Track usage -sub.increment_usage('quantum_jobs') - -# Usage reporting -pct = sub.get_usage_percentage('chat_messages') # → 0.0 to 1.0 -``` - -## Subscription Lifecycle - -```python -sub.is_active() # Check if subscription is current -sub.reset_usage() # Called monthly to reset counters -``` - -## Storage - -- File: `data_out/subscriptions/subscriptions.json` -- Loaded on startup by `SubscriptionManager._load_subscriptions()` -- Persisted on changes - -## Coding Conventions - -- Always check `has_feature()` before `check_limit()` — feature gates come first -- Return 403 for missing features, 429 for exceeded limits -- ENTERPRISE tier has no limits — skip tracking for unlimited resources -- Never modify tier definitions without updating all endpoint gates -- Subscription data lives in `data_out/` (not `datasets/`) +--- +applyTo: "**/subscription_manager.py" +--- + +# Subscription Manager — Instruction Guide + +## Tier Definitions + +| Tier | Price | Chat Msgs | Quantum Jobs | Training Hrs | +|------|-------|-----------|-------------|-------------| +| FREE | $0/mo | 100/mo | 0 | 0 | +| PRO | $49/mo | 10,000/mo | 50/mo | 20 hrs/mo | +| ENTERPRISE | $199/mo | Unlimited | Unlimited | Unlimited | + +## Feature Gating + +```python +# 10 gatable features: +BASIC_CHAT, ARIA_CHARACTER, QUANTUM_COMPUTING, ADVANCED_TRAINING, +WEBSITE_MAKER, API_ACCESS, CUSTOM_MODELS, PRIORITY_SUPPORT, +ANALYTICS_DASHBOARD, BATCH_PROCESSING +``` + +## Usage Pattern + +```python +from shared.subscription_manager import get_subscription_manager, Feature + +mgr = get_subscription_manager() +sub = mgr.get_subscription(user_id) + +# Feature check +if not sub.has_feature(Feature.QUANTUM_COMPUTING): + return HttpResponse("Upgrade to PRO for quantum access", status_code=403) + +# Usage limit check +if not sub.check_limit('quantum_jobs'): + return HttpResponse("Monthly quota exceeded", status_code=429) + +# Track usage +sub.increment_usage('quantum_jobs') + +# Usage reporting +pct = sub.get_usage_percentage('chat_messages') # → 0.0 to 1.0 +``` + +## Subscription Lifecycle + +```python +sub.is_active() # Check if subscription is current +sub.reset_usage() # Called monthly to reset counters +``` + +## Storage + +- File: `data_out/subscriptions/subscriptions.json` +- Loaded on startup by `SubscriptionManager._load_subscriptions()` +- Persisted on changes + +## Coding Conventions + +- Always check `has_feature()` before `check_limit()` — feature gates come first +- Return 403 for missing features, 429 for exceeded limits +- ENTERPRISE tier has no limits — skip tracking for unlimited resources +- Never modify tier definitions without updating all endpoint gates +- Subscription data lives in `data_out/` (not `datasets/`) diff --git a/.github/instructions/talk-to-ai-python.instructions.md b/.github/instructions/talk-to-ai-python.instructions.md index f7502ba4e..1ce5f92b9 100644 --- a/.github/instructions/talk-to-ai-python.instructions.md +++ b/.github/instructions/talk-to-ai-python.instructions.md @@ -1,21 +1,21 @@ ---- -name: "Chat-CLI-Python" -description: "Python-specific guidance for ai-projects/chat-cli/" -applyTo: "ai-projects/chat-cli/src/**/*.py" ---- -# Chat CLI – Python files - -- Provider detection order (see `shared/chat_providers.py:detect_provider()`): Azure OpenAI → OpenAI → LoRA → Local. - - Azure requires ALL: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`. -- Implement providers by subclassing `BaseChatProvider` and implementing `complete(messages, stream)`. -- Streaming support: emit SSE lines (`data: {json}`) and `[DONE]` sentinel; clients must parse SSE correctly. -- CLI usage examples: - - Local: `python .\\talk-to-ai\\src\\chat_cli.py --provider local --once "Hello"` - - OpenAI: set `$env:OPENAI_API_KEY`; then `--provider openai` - - Azure: set the 4 env vars; then `--provider azure` - - LoRA: `python .\\talk-to-ai\\src\\chat_cli.py --provider lora --model ` -- LoRA adapter directory must contain BOTH `adapter_config.json` and `adapter_model.safetensors`. -- Conversations persisted in JSONL; interactive commands: `/new`, `/save`, `/exit`. -- Functions endpoints for web integration: `/api/chat`, `/api/chat-web`; check `/api/ai/status` for provider readiness. -- Secrets and local dev: prefer `local.settings.json` or env vars; Azurite storage files exist at repo root for offline testing. -- Tests: `python .\\scripts\\test_runner.py --unit` for quick validation. +--- +name: "Chat-CLI-Python" +description: "Python-specific guidance for ai-projects/chat-cli/" +applyTo: "ai-projects/chat-cli/src/**/*.py" +--- +# Chat CLI – Python files + +- Provider detection order (see `shared/chat_providers.py:detect_provider()`): Azure OpenAI → OpenAI → LoRA → Local. + - Azure requires ALL: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`. +- Implement providers by subclassing `BaseChatProvider` and implementing `complete(messages, stream)`. +- Streaming support: emit SSE lines (`data: {json}`) and `[DONE]` sentinel; clients must parse SSE correctly. +- CLI usage examples: + - Local: `python .\\talk-to-ai\\src\\chat_cli.py --provider local --once "Hello"` + - OpenAI: set `$env:OPENAI_API_KEY`; then `--provider openai` + - Azure: set the 4 env vars; then `--provider azure` + - LoRA: `python .\\talk-to-ai\\src\\chat_cli.py --provider lora --model ` +- LoRA adapter directory must contain BOTH `adapter_config.json` and `adapter_model.safetensors`. +- Conversations persisted in JSONL; interactive commands: `/new`, `/save`, `/exit`. +- Functions endpoints for web integration: `/api/chat`, `/api/chat-web`; check `/api/ai/status` for provider readiness. +- Secrets and local dev: prefer `local.settings.json` or env vars; Azurite storage files exist at repo root for offline testing. +- Tests: `python .\\scripts\\test_runner.py --unit` for quick validation. diff --git a/.github/instructions/talk-to-ai.instructions.md b/.github/instructions/talk-to-ai.instructions.md index bd3e0cb65..bf0524475 100644 --- a/.github/instructions/talk-to-ai.instructions.md +++ b/.github/instructions/talk-to-ai.instructions.md @@ -1,20 +1,20 @@ ---- -name: "Chat-CLI-workspace" -description: "Slim instructions for ai-projects/chat-cli/" -applyTo: "ai-projects/chat-cli/**" ---- -# Chat CLI – workspace-specific guidance - -- Provider detection order (see `shared/chat_providers.py:detect_provider()`): Azure OpenAI → OpenAI → LoRA → Local. - - Azure requires ALL: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`. -- CLI usage examples: - - Local (no keys): `python .\\talk-to-ai\\src\\chat_cli.py --provider local --once "Hello"` - - OpenAI: set `$env:OPENAI_API_KEY`; then `--provider openai` - - Azure: set the 4 env vars; then `--provider azure` - - LoRA: `python .\\talk-to-ai\\src\\chat_cli.py --provider lora --model ` -- Streaming responses supported; interactive commands: `/new`, `/save`, `/exit`. Conversations persisted as JSONL. -- Abstraction pattern: implement `BaseChatProvider.complete(messages, stream)`; wire detection in `shared/chat_providers.py`. -- LoRA provider requirements: adapter directory must contain `adapter_config.json` and `adapter_model.safetensors`. -- Web integration: Azure Functions serves `/api/chat` and `/api/chat-web`; check `/api/ai/status` for provider readiness (env vars, LoRA, SQL/Cosmos status). -- Secrets & local dev: never hardcode secrets; use `local.settings.json` (dev) or environment variables; Azurite storage files are present at repo root for offline testing. -- Tests: quick validation via `python .\\scripts\\test_runner.py --unit` or `pytest -m "not slow and not azure"`. +--- +name: "Chat-CLI-workspace" +description: "Slim instructions for ai-projects/chat-cli/" +applyTo: "ai-projects/chat-cli/**" +--- +# Chat CLI – workspace-specific guidance + +- Provider detection order (see `shared/chat_providers.py:detect_provider()`): Azure OpenAI → OpenAI → LoRA → Local. + - Azure requires ALL: `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION`. +- CLI usage examples: + - Local (no keys): `python .\\talk-to-ai\\src\\chat_cli.py --provider local --once "Hello"` + - OpenAI: set `$env:OPENAI_API_KEY`; then `--provider openai` + - Azure: set the 4 env vars; then `--provider azure` + - LoRA: `python .\\talk-to-ai\\src\\chat_cli.py --provider lora --model ` +- Streaming responses supported; interactive commands: `/new`, `/save`, `/exit`. Conversations persisted as JSONL. +- Abstraction pattern: implement `BaseChatProvider.complete(messages, stream)`; wire detection in `shared/chat_providers.py`. +- LoRA provider requirements: adapter directory must contain `adapter_config.json` and `adapter_model.safetensors`. +- Web integration: Azure Functions serves `/api/chat` and `/api/chat-web`; check `/api/ai/status` for provider readiness (env vars, LoRA, SQL/Cosmos status). +- Secrets & local dev: never hardcode secrets; use `local.settings.json` (dev) or environment variables; Azurite storage files are present at repo root for offline testing. +- Tests: quick validation via `python .\\scripts\\test_runner.py --unit` or `pytest -m "not slow and not azure"`. diff --git a/.github/instructions/telemetry.instructions.md b/.github/instructions/telemetry.instructions.md index 181ba9152..e3c095384 100644 --- a/.github/instructions/telemetry.instructions.md +++ b/.github/instructions/telemetry.instructions.md @@ -1,36 +1,36 @@ ---- -applyTo: "**/telemetry.py" ---- - -# Telemetry — Instruction Guide - -## Setup - -```python -from shared.telemetry import init_telemetry, is_enabled - -# Initialize once at startup -initialized = init_telemetry() -# Returns True if APPLICATIONINSIGHTS_CONNECTION_STRING is set and Azure Monitor loaded -# Returns False if env var missing or azure-monitor-opentelemetry unavailable -``` - -## Behavior - -- **Single-initialization guard**: `init_telemetry()` only configures once (idempotent) -- **Graceful degradation**: If Azure Monitor SDK is not installed, telemetry silently disables -- **Non-blocking**: Telemetry operations do not block the main thread -- **Zero-config fallback**: Application works identically with or without telemetry - -## Environment Variable - -``` -APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=... -``` - -## Coding Conventions - -- Never make telemetry a hard dependency — always check `is_enabled()` before custom tracing -- Telemetry is optional infrastructure — no feature should require it -- Use Azure Monitor OpenTelemetry (not the legacy Application Insights SDK) -- Initialize in `function_app.py` startup, not per-request +--- +applyTo: "**/telemetry.py" +--- + +# Telemetry — Instruction Guide + +## Setup + +```python +from shared.telemetry import init_telemetry, is_enabled + +# Initialize once at startup +initialized = init_telemetry() +# Returns True if APPLICATIONINSIGHTS_CONNECTION_STRING is set and Azure Monitor loaded +# Returns False if env var missing or azure-monitor-opentelemetry unavailable +``` + +## Behavior + +- **Single-initialization guard**: `init_telemetry()` only configures once (idempotent) +- **Graceful degradation**: If Azure Monitor SDK is not installed, telemetry silently disables +- **Non-blocking**: Telemetry operations do not block the main thread +- **Zero-config fallback**: Application works identically with or without telemetry + +## Environment Variable + +``` +APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=... +``` + +## Coding Conventions + +- Never make telemetry a hard dependency — always check `is_enabled()` before custom tracing +- Telemetry is optional infrastructure — no feature should require it +- Use Azure Monitor OpenTelemetry (not the legacy Application Insights SDK) +- Initialize in `function_app.py` startup, not per-request diff --git a/.github/instructions/tests.instructions.md b/.github/instructions/tests.instructions.md index 32e10397b..9bcf8340a 100644 --- a/.github/instructions/tests.instructions.md +++ b/.github/instructions/tests.instructions.md @@ -1,34 +1,34 @@ ---- -name: "Tests" -description: "Guidance for test files and testing infrastructure" -applyTo: "tests/**" ---- -# Tests — Implementation Guidance - -## Test Infrastructure -- Runner: `python scripts/test_runner.py --unit` (fast) or `--all` (comprehensive) -- Framework: pytest with markers `@pytest.mark.slow`, `@pytest.mark.azure`, `@pytest.mark.integration` -- Fast local: `pytest tests/ -m "not slow and not azure"` -- VS Code: Use Test Explorer (🧪 icon) - -## Test Organization - -| Test File | Tests | Requires | -|-----------|-------|----------| -| `test_aria_server.py` | Aria server API endpoints | — | -| `test_object_api_integration.py` | Object CRUD operations | — | -| `test_ui_playwright.py` | Browser E2E (Playwright) | Aria server running | -| `test_ui_pyppeteer.py` | Browser E2E (Pyppeteer) | Aria server + Chromium | -| `test_auto_execute.py` | Action sequence execution | — | - -## Patterns -- Server tests mock the HTTP handler; E2E tests start real server. -- E2E tests connect to `http://localhost:8080` (Aria) or `http://localhost:7071` (Functions). -- Use `ARIA_SERVER_URL` and `PYTHONPATH` env vars for CI. -- Validate datasets: `python scripts/validate_datasets.py --category chat` -- Quick cross-component validation: `python scripts/fast_validate.py` - -## CI Workflows -- `aria-tests.yml` — Multi-version, multi-browser (path-filtered to `aria_web/`) -- `e2e-tests.yml` — Quick regression (runs on all pushes to main) -- `ci-pipeline.yml` — Full CI with unit + integration tests +--- +name: "Tests" +description: "Guidance for test files and testing infrastructure" +applyTo: "tests/**" +--- +# Tests — Implementation Guidance + +## Test Infrastructure +- Runner: `python scripts/test_runner.py --unit` (fast) or `--all` (comprehensive) +- Framework: pytest with markers `@pytest.mark.slow`, `@pytest.mark.azure`, `@pytest.mark.integration` +- Fast local: `pytest tests/ -m "not slow and not azure"` +- VS Code: Use Test Explorer (🧪 icon) + +## Test Organization + +| Test File | Tests | Requires | +|-----------|-------|----------| +| `test_aria_server.py` | Aria server API endpoints | — | +| `test_object_api_integration.py` | Object CRUD operations | — | +| `test_ui_playwright.py` | Browser E2E (Playwright) | Aria server running | +| `test_ui_pyppeteer.py` | Browser E2E (Pyppeteer) | Aria server + Chromium | +| `test_auto_execute.py` | Action sequence execution | — | + +## Patterns +- Server tests mock the HTTP handler; E2E tests start real server. +- E2E tests connect to `http://localhost:8080` (Aria) or `http://localhost:7071` (Functions). +- Use `ARIA_SERVER_URL` and `PYTHONPATH` env vars for CI. +- Validate datasets: `python scripts/validate_datasets.py --category chat` +- Quick cross-component validation: `python scripts/fast_validate.py` + +## CI Workflows +- `aria-tests.yml` — Multi-version, multi-browser (path-filtered to `aria_web/`) +- `e2e-tests.yml` — Quick regression (runs on all pushes to main) +- `ci-pipeline.yml` — Full CI with unit + integration tests diff --git a/.github/instructions/token-utils.instructions.md b/.github/instructions/token-utils.instructions.md index 2041160e8..c84912a3c 100644 --- a/.github/instructions/token-utils.instructions.md +++ b/.github/instructions/token-utils.instructions.md @@ -1,65 +1,65 @@ ---- -applyTo: "**/token_utils.py" ---- - -# Token Utils — Instruction Guide - -## Token Counting - -```python -from token_utils import count_messages_tokens, prune_messages - -tokens = count_messages_tokens(messages, provider, model, system_prompt) -``` - -### Counting Priority -1. **tiktoken** — For OpenAI/Azure models (accurate, fast) -2. **AutoTokenizer** — For Hugging Face models (transformers library) -3. **Heuristic** — 1 token ≈ 4 characters (universal fallback) - -## Context Window Defaults - -| Model | Context Window | -|-------|---------------| -| gpt-4o | 128,000 | -| gpt-3.5-turbo | 16,384 | -| Azure default | 16,384 | -| Phi models | 4,096 | - -## Message Pruning - -```python -pruned_msgs, stats, system_msg = prune_messages( - messages, - provider, - model, - max_context_tokens=None, # Auto-detect from model - reserve_output_tokens=1024, # Reserve for response - system_prompt=None -) -``` - -### Pruning Algorithm (O(n)) -1. Pre-compute per-message token counts -2. Always keep system message + most recent messages -3. Remove oldest messages first when over budget -4. Return pruned list + stats - -### PruneStats -```python -{ - "original_tokens": int, # Total before pruning - "pruned_tokens": int, # Total after pruning - "removed_count": int, # Messages removed - "budget": int, # Context window budget - "reserve_output_tokens": int # Reserved for response -} -``` - -## Coding Conventions - -- Always reserve output tokens (default 1024) — don't fill the entire context window -- Use model-specific context windows, not hardcoded values -- System messages are never pruned — they're critical for behavior -- Log `PruneStats` when pruning occurs for debugging context issues -- The heuristic fallback (4 chars/token) is intentionally conservative +--- +applyTo: "**/token_utils.py" +--- + +# Token Utils — Instruction Guide + +## Token Counting + +```python +from token_utils import count_messages_tokens, prune_messages + +tokens = count_messages_tokens(messages, provider, model, system_prompt) +``` + +### Counting Priority +1. **tiktoken** — For OpenAI/Azure models (accurate, fast) +2. **AutoTokenizer** — For Hugging Face models (transformers library) +3. **Heuristic** — 1 token ≈ 4 characters (universal fallback) + +## Context Window Defaults + +| Model | Context Window | +|-------|---------------| +| gpt-4o | 128,000 | +| gpt-3.5-turbo | 16,384 | +| Azure default | 16,384 | +| Phi models | 4,096 | + +## Message Pruning + +```python +pruned_msgs, stats, system_msg = prune_messages( + messages, + provider, + model, + max_context_tokens=None, # Auto-detect from model + reserve_output_tokens=1024, # Reserve for response + system_prompt=None +) +``` + +### Pruning Algorithm (O(n)) +1. Pre-compute per-message token counts +2. Always keep system message + most recent messages +3. Remove oldest messages first when over budget +4. Return pruned list + stats + +### PruneStats +```python +{ + "original_tokens": int, # Total before pruning + "pruned_tokens": int, # Total after pruning + "removed_count": int, # Messages removed + "budget": int, # Context window budget + "reserve_output_tokens": int # Reserved for response +} +``` + +## Coding Conventions + +- Always reserve output tokens (default 1024) — don't fill the entire context window +- Use model-specific context windows, not hardcoded values +- System messages are never pruned — they're critical for behavior +- Log `PruneStats` when pruning occurs for debugging context issues +- The heuristic fallback (4 chars/token) is intentionally conservative diff --git a/.github/instructions/training-scripts.instructions.md b/.github/instructions/training-scripts.instructions.md index eda5adfc5..9a3f52e52 100644 --- a/.github/instructions/training-scripts.instructions.md +++ b/.github/instructions/training-scripts.instructions.md @@ -1,35 +1,35 @@ ---- -name: "Training-Scripts" -description: "Guidance for training orchestration scripts" -applyTo: "scripts/*train*" ---- -# Training Scripts — Implementation Guidance - -- All training orchestrators are YAML-driven with matching config files. -- **Always** `--dry-run` before GPU/QPU execution. -- Config precedence: `YAML base` < `CLI flags` < `per-job YAML` < `env vars`. - -## Key Scripts - -| Script | Config | Purpose | -|--------|--------|---------| -| `autotrain.py` | `autotrain.yaml` | LoRA fine-tuning job orchestration | -| `automated_training_pipeline.py` | — | End-to-end training pipeline | -| `train_and_promote.py` | — | Train + auto-deploy best model | -| `autonomous_training_orchestrator.py` | `config/autonomous_training.yaml` | Continuous self-managing cycles | -| `master_orchestrator.py` | `config/master_orchestrator.yaml` | Coordinates all sub-orchestrators | - -## Data Conventions -- Read-only: `datasets///train.json` + `test.json` -- Write-only: `data_out//` -- Status: `data_out//status.json` — `{total_jobs, succeeded, failed, running, last_updated, avg_duration}` -- Chat dataset format: `[{"messages": [{"role": "user|assistant", "content": "..."}]}]` - -## LoRA Adapter Requirements -- Valid adapter = `adapter_config.json` + `adapter_model.safetensors` -- Validate: `python scripts/validate_datasets.py --category chat` - -## Metrics -- Tracked: mean_accuracy, median_accuracy, max_accuracy, successful_count, exceptional_models -- Degradation: auto-detect >5% accuracy drops between cycles -- Auto-promote: when accuracy > 0.90 (configurable) +--- +name: "Training-Scripts" +description: "Guidance for training orchestration scripts" +applyTo: "scripts/*train*" +--- +# Training Scripts — Implementation Guidance + +- All training orchestrators are YAML-driven with matching config files. +- **Always** `--dry-run` before GPU/QPU execution. +- Config precedence: `YAML base` < `CLI flags` < `per-job YAML` < `env vars`. + +## Key Scripts + +| Script | Config | Purpose | +|--------|--------|---------| +| `autotrain.py` | `autotrain.yaml` | LoRA fine-tuning job orchestration | +| `automated_training_pipeline.py` | — | End-to-end training pipeline | +| `train_and_promote.py` | — | Train + auto-deploy best model | +| `autonomous_training_orchestrator.py` | `config/autonomous_training.yaml` | Continuous self-managing cycles | +| `master_orchestrator.py` | `config/master_orchestrator.yaml` | Coordinates all sub-orchestrators | + +## Data Conventions +- Read-only: `datasets///train.json` + `test.json` +- Write-only: `data_out//` +- Status: `data_out//status.json` — `{total_jobs, succeeded, failed, running, last_updated, avg_duration}` +- Chat dataset format: `[{"messages": [{"role": "user|assistant", "content": "..."}]}]` + +## LoRA Adapter Requirements +- Valid adapter = `adapter_config.json` + `adapter_model.safetensors` +- Validate: `python scripts/validate_datasets.py --category chat` + +## Metrics +- Tracked: mean_accuracy, median_accuracy, max_accuracy, successful_count, exceptional_models +- Degradation: auto-detect >5% accuracy drops between cycles +- Auto-promote: when accuracy > 0.90 (configurable) diff --git a/.github/instructions/vision-inference.instructions.md b/.github/instructions/vision-inference.instructions.md index 0c169b78e..38fddab2a 100644 --- a/.github/instructions/vision-inference.instructions.md +++ b/.github/instructions/vision-inference.instructions.md @@ -1,56 +1,56 @@ ---- -applyTo: "scripts/vision_inference.py" ---- - -# Vision Inference — Instruction Guide - -## TinyConvNet Architecture - -```python -class TinyConvNet(nn.Module): - # Conv2d(3, 16) → ReLU → MaxPool2d - # Conv2d(16, 32) → ReLU → MaxPool2d - # AdaptiveAvgPool2d(4) - # Linear(32*4*4, num_classes) -``` - -## Inference Pipeline - -```python -vi = VisionInference() # Auto-loads latest checkpoint -result = vi.predict(pil_image) # PIL Image input -result = vi.predict_base64(b64_string) # Base64 string input -result = vi.predict_file(file_path) # File path input -# Returns: {label: str, confidence: float, scores: Dict[str, float]} -``` - -## Preprocessing - -- Resize to 64×64 -- Normalize pixel values -- Convert to tensor shape (1, C, H, W) - -## Checkpoint Management - -Search order for `.pt` files: -1. `data_out/vision_training/` -2. `scripts/checkpoints/` -3. `checkpoints/` - -Checkpoint format: -```python -{ - 'model_state_dict': OrderedDict, - 'class_names': ['happy', 'sad', ...], - 'epoch': int, - 'accuracy': float -} -``` - -## Coding Conventions - -- Always include `class_names` in checkpoints for portability -- Input resolution must match training resolution (64×64) -- Auto-detect GPU/CPU device at initialization -- New expression classes require retraining — update class_names list -- Use balanced datasets for training (equal samples per class) +--- +applyTo: "scripts/vision_inference.py" +--- + +# Vision Inference — Instruction Guide + +## TinyConvNet Architecture + +```python +class TinyConvNet(nn.Module): + # Conv2d(3, 16) → ReLU → MaxPool2d + # Conv2d(16, 32) → ReLU → MaxPool2d + # AdaptiveAvgPool2d(4) + # Linear(32*4*4, num_classes) +``` + +## Inference Pipeline + +```python +vi = VisionInference() # Auto-loads latest checkpoint +result = vi.predict(pil_image) # PIL Image input +result = vi.predict_base64(b64_string) # Base64 string input +result = vi.predict_file(file_path) # File path input +# Returns: {label: str, confidence: float, scores: Dict[str, float]} +``` + +## Preprocessing + +- Resize to 64×64 +- Normalize pixel values +- Convert to tensor shape (1, C, H, W) + +## Checkpoint Management + +Search order for `.pt` files: +1. `data_out/vision_training/` +2. `scripts/checkpoints/` +3. `checkpoints/` + +Checkpoint format: +```python +{ + 'model_state_dict': OrderedDict, + 'class_names': ['happy', 'sad', ...], + 'epoch': int, + 'accuracy': float +} +``` + +## Coding Conventions + +- Always include `class_names` in checkpoints for portability +- Input resolution must match training resolution (64×64) +- Auto-detect GPU/CPU device at initialization +- New expression classes require retraining — update class_names list +- Use balanced datasets for training (equal samples per class) diff --git a/.github/labeler.yml b/.github/labeler.yml index 0632ca049..b5a8b7df5 100644 --- a/.github/labeler.yml +++ b/.github/labeler.yml @@ -1,49 +1,49 @@ -# Auto-labeler configuration for PRs - -'documentation': - - changed-files: - - any-glob-to-any-file: ['**/*.md', 'docs/**/*'] - -'tests': - - changed-files: - - any-glob-to-any-file: ['tests/**/*', '**/*test*.py', 'pytest.ini'] - -'dependencies': - - changed-files: - - any-glob-to-any-file: ['requirements.txt', '**/requirements*.txt', 'pyproject.toml', 'setup.py'] - -'github-actions': - - changed-files: - - any-glob-to-any-file: ['.github/workflows/**/*', '.github/actions/**/*'] - -'aria-character': - - changed-files: - - any-glob-to-any-file: ['aria_web/**/*'] - -'chat-interface': - - changed-files: - - any-glob-to-any-file: ['ai-projects/chat-cli/**/*', 'apps/chat/**/*'] - -'quantum-ai': - - changed-files: - - any-glob-to-any-file: ['ai-projects/quantum-ml/**/*'] - -'training': - - changed-files: - - any-glob-to-any-file: ['AI/**/*', 'ai-projects/lora-training/**/*', 'scripts/autotrain*.py', 'scripts/train*.py'] - -'azure-functions': - - changed-files: - - any-glob-to-any-file: ['function_app.py', 'host.json', 'functions/**/*'] - -'config': - - changed-files: - - any-glob-to-any-file: ['config/**/*', '*.yaml', '*.yml', '*.json'] - -'scripts': - - changed-files: - - any-glob-to-any-file: ['scripts/**/*'] - -'security': - - changed-files: - - any-glob-to-any-file: ['SECURITY.md', '.github/workflows/codeql.yml'] +# Auto-labeler configuration for PRs + +'documentation': + - changed-files: + - any-glob-to-any-file: ['**/*.md', 'docs/**/*'] + +'tests': + - changed-files: + - any-glob-to-any-file: ['tests/**/*', '**/*test*.py', 'pytest.ini'] + +'dependencies': + - changed-files: + - any-glob-to-any-file: ['requirements.txt', '**/requirements*.txt', 'pyproject.toml', 'setup.py'] + +'github-actions': + - changed-files: + - any-glob-to-any-file: ['.github/workflows/**/*', '.github/actions/**/*'] + +'aria-character': + - changed-files: + - any-glob-to-any-file: ['aria_web/**/*'] + +'chat-interface': + - changed-files: + - any-glob-to-any-file: ['ai-projects/chat-cli/**/*', 'apps/chat/**/*'] + +'quantum-ai': + - changed-files: + - any-glob-to-any-file: ['ai-projects/quantum-ml/**/*'] + +'training': + - changed-files: + - any-glob-to-any-file: ['AI/**/*', 'ai-projects/lora-training/**/*', 'scripts/autotrain*.py', 'scripts/train*.py'] + +'azure-functions': + - changed-files: + - any-glob-to-any-file: ['function_app.py', 'host.json', 'functions/**/*'] + +'config': + - changed-files: + - any-glob-to-any-file: ['config/**/*', '*.yaml', '*.yml', '*.json'] + +'scripts': + - changed-files: + - any-glob-to-any-file: ['scripts/**/*'] + +'security': + - changed-files: + - any-glob-to-any-file: ['SECURITY.md', '.github/workflows/codeql.yml'] diff --git a/.github/prompts/aria-command.prompt.md b/.github/prompts/aria-command.prompt.md index afe7b2b9b..8cab8d9de 100644 --- a/.github/prompts/aria-command.prompt.md +++ b/.github/prompts/aria-command.prompt.md @@ -1,38 +1,38 @@ ---- -description: "Control the Aria character — generate movement commands, action sequences, world themes, and object interactions using natural language." -name: "Aria Command" -argument-hint: "Natural language command (example: wave at me and say hello, then move left and pick up the ball)" -agent: aria-character ---- - -Process the following as an Aria character command. Use either the tag system or structured action sequences depending on the request. - -**Tag System** (for simple commands embedded in chat responses): -``` -[aria:walk:left] [aria:jump] [aria:dance] [aria:wave] [aria:smile] -[aria:sparkle] [aria:glow] [aria:sit] [aria:gesture:thumbs_up] -``` - -**Action Sequences** (for multi-step operations): -```json -[ - {"action": "move", "target": "center", "speed": "walk"}, - {"action": "say", "text": "Hello!", "emotion": "happy"}, - {"action": "gesture", "gesture_type": "wave"} -] -``` - -**Available actions:** move, say, pickup, drop, throw, gesture, look, wait - -**Context to include:** -- Current stage state (position, objects, expression) -- Nearby objects and their positions -- Valid gesture types: wave, thumbs_up, clap, shrug, bow, nod -- Bounds: coordinates 0-100%, text max 200 chars - -**World generation** (if creating environments): -- Themes: forest, space, ocean, lab, medieval, desert, garden, cyberpunk, arcade -- Use `POST /api/aria/world` with theme and object count - -**Server:** `apps/aria/server.py` on port 8080 -**Client:** `apps/aria/aria_controller.js` +--- +description: "Control the Aria character — generate movement commands, action sequences, world themes, and object interactions using natural language." +name: "Aria Command" +argument-hint: "Natural language command (example: wave at me and say hello, then move left and pick up the ball)" +agent: aria-character +--- + +Process the following as an Aria character command. Use either the tag system or structured action sequences depending on the request. + +**Tag System** (for simple commands embedded in chat responses): +``` +[aria:walk:left] [aria:jump] [aria:dance] [aria:wave] [aria:smile] +[aria:sparkle] [aria:glow] [aria:sit] [aria:gesture:thumbs_up] +``` + +**Action Sequences** (for multi-step operations): +```json +[ + {"action": "move", "target": "center", "speed": "walk"}, + {"action": "say", "text": "Hello!", "emotion": "happy"}, + {"action": "gesture", "gesture_type": "wave"} +] +``` + +**Available actions:** move, say, pickup, drop, throw, gesture, look, wait + +**Context to include:** +- Current stage state (position, objects, expression) +- Nearby objects and their positions +- Valid gesture types: wave, thumbs_up, clap, shrug, bow, nod +- Bounds: coordinates 0-100%, text max 200 chars + +**World generation** (if creating environments): +- Themes: forest, space, ocean, lab, medieval, desert, garden, cyberpunk, arcade +- Use `POST /api/aria/world` with theme and object count + +**Server:** `apps/aria/server.py` on port 8080 +**Client:** `apps/aria/aria_controller.js` diff --git a/.github/prompts/chat.prompt.md b/.github/prompts/chat.prompt.md index 881bb6ab6..45205f32d 100644 --- a/.github/prompts/chat.prompt.md +++ b/.github/prompts/chat.prompt.md @@ -1,49 +1,49 @@ ---- -description: "Multi-provider chat with memory injection and streaming" -name: "Chat" -argument-hint: "Message + optional provider and memory context (example: provider + message or question)" -agent: chat-provider ---- -# Chat - -Start a chat conversation using the Aria platform's multi-provider system with semantic memory. - -## Pipeline - -1. **Provider auto-detection**: Azure OpenAI → OpenAI → LMStudio → LoRA → Local -2. **Memory injection**: Embed user message → fetch similar past messages → inject as context -3. **Token pruning**: Fit conversation within context window budget -4. **Streaming**: SSE-based response delivery with movement tag extraction -5. **Self-learning**: Log conversation to JSONL for future training - -## Provider Configuration - -| Provider | Required Env Vars | -|----------|------------------| -| Azure OpenAI | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION` | -| OpenAI | `OPENAI_API_KEY` | -| LMStudio | `LMSTUDIO_BASE_URL` | -| LoRA | `--provider lora` + adapter path | -| Local | None (zero-dependency fallback) | - -## Memory Modes - -- **Semantic memory**: Cosine similarity search on embeddings -- **Session memory**: Scoped to current session_id -- **No memory**: Stateless single-turn - -## Usage - -``` -Chat with: {{input}} - -Using provider: [auto-detect | azure | openai | lmstudio | lora | local] -Memory: [enabled | disabled] -Streaming: [yes | no] -``` - -## Endpoints - -- `POST /api/chat` — Standard chat with memory injection -- `POST /api/chat/stream` — SSE streaming with movement commands -- `GET /api/ai/status` — Check active provider and system health +--- +description: "Multi-provider chat with memory injection and streaming" +name: "Chat" +argument-hint: "Message + optional provider and memory context (example: provider + message or question)" +agent: chat-provider +--- +# Chat + +Start a chat conversation using the Aria platform's multi-provider system with semantic memory. + +## Pipeline + +1. **Provider auto-detection**: Azure OpenAI → OpenAI → LMStudio → LoRA → Local +2. **Memory injection**: Embed user message → fetch similar past messages → inject as context +3. **Token pruning**: Fit conversation within context window budget +4. **Streaming**: SSE-based response delivery with movement tag extraction +5. **Self-learning**: Log conversation to JSONL for future training + +## Provider Configuration + +| Provider | Required Env Vars | +|----------|------------------| +| Azure OpenAI | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT`, `AZURE_OPENAI_API_VERSION` | +| OpenAI | `OPENAI_API_KEY` | +| LMStudio | `LMSTUDIO_BASE_URL` | +| LoRA | `--provider lora` + adapter path | +| Local | None (zero-dependency fallback) | + +## Memory Modes + +- **Semantic memory**: Cosine similarity search on embeddings +- **Session memory**: Scoped to current session_id +- **No memory**: Stateless single-turn + +## Usage + +``` +Chat with: {{input}} + +Using provider: [auto-detect | azure | openai | lmstudio | lora | local] +Memory: [enabled | disabled] +Streaming: [yes | no] +``` + +## Endpoints + +- `POST /api/chat` — Standard chat with memory injection +- `POST /api/chat/stream` — SSE streaming with movement commands +- `GET /api/ai/status` — Check active provider and system health diff --git a/.github/prompts/debug.prompt.md b/.github/prompts/debug.prompt.md index 1e35e4d60..0a0bf0a9e 100644 --- a/.github/prompts/debug.prompt.md +++ b/.github/prompts/debug.prompt.md @@ -1,36 +1,36 @@ ---- -description: "Debug and diagnose issues across the full Aria platform stack — from client JS through Python servers, Azure Functions, training pipelines, and quantum workflows." -name: "Debug" -argument-hint: "Issue description + affected component (example: symptom + stack trace or error + component name)" -agent: full-stack-debugger ---- - -Systematically debug the described issue using this diagnostic protocol: - -**Step 1 — Characterize:** -- What is the exact error or unexpected behavior? -- Which component is affected? (Aria UI, chat API, training, quantum, infra) -- Is it reproducible? What changed recently? - -**Step 2 — Quick Health Check:** -- Run `/api/ai/status` for system-wide diagnostics -- Check `data_out/` status files for orchestrator state -- Verify environment variables and dependencies - -**Step 3 — Hypothesize & Test:** -Form ranked hypotheses (most likely first): -1. Environment/config issue (missing vars, wrong ports) -2. Dependency issue (missing packages, version conflicts) -3. Logic error (conditions, state, race conditions) -4. Integration issue (API contracts, serialization) - -Test each hypothesis with minimal, targeted checks. - -**Step 4 — Fix & Verify:** -- Apply the minimum change to fix the root cause -- Run related tests: `pytest tests/ -k "relevant_test" -v` -- Confirm the original issue is resolved -- Check for regressions - -**Key ports:** Aria=8080, Functions=7071 -**Key files:** `function_app.py`, `apps/aria/server.py`, `shared/chat_providers.py` +--- +description: "Debug and diagnose issues across the full Aria platform stack — from client JS through Python servers, Azure Functions, training pipelines, and quantum workflows." +name: "Debug" +argument-hint: "Issue description + affected component (example: symptom + stack trace or error + component name)" +agent: full-stack-debugger +--- + +Systematically debug the described issue using this diagnostic protocol: + +**Step 1 — Characterize:** +- What is the exact error or unexpected behavior? +- Which component is affected? (Aria UI, chat API, training, quantum, infra) +- Is it reproducible? What changed recently? + +**Step 2 — Quick Health Check:** +- Run `/api/ai/status` for system-wide diagnostics +- Check `data_out/` status files for orchestrator state +- Verify environment variables and dependencies + +**Step 3 — Hypothesize & Test:** +Form ranked hypotheses (most likely first): +1. Environment/config issue (missing vars, wrong ports) +2. Dependency issue (missing packages, version conflicts) +3. Logic error (conditions, state, race conditions) +4. Integration issue (API contracts, serialization) + +Test each hypothesis with minimal, targeted checks. + +**Step 4 — Fix & Verify:** +- Apply the minimum change to fix the root cause +- Run related tests: `pytest tests/ -k "relevant_test" -v` +- Confirm the original issue is resolved +- Check for regressions + +**Key ports:** Aria=8080, Functions=7071 +**Key files:** `function_app.py`, `apps/aria/server.py`, `shared/chat_providers.py` diff --git a/.github/prompts/deploy.prompt.md b/.github/prompts/deploy.prompt.md index ecad8ba74..546ec293e 100644 --- a/.github/prompts/deploy.prompt.md +++ b/.github/prompts/deploy.prompt.md @@ -1,74 +1,74 @@ ---- -description: "Deploy and promote AI models and services" -name: "Deploy" -argument-hint: "Target + model or service details (example: adapter path + deployment target + version tag)" -agent: platform-ops ---- -# Deploy - -Deploy, promote, or publish AI models and services on the Aria platform. - -## Model Deployment - -### LoRA Adapter Promotion -```bash -# Train + auto-promote if accuracy > threshold -python scripts/train_and_promote.py --quick --auto-promote - -# Manual promotion -cp data_out/lora_training/best_model/adapter_*.* AI/microsoft_phi-silica-3.6_v1/adapters/ -``` - -**Required files for valid LoRA adapter:** -- `adapter_config.json` -- `adapter_model.safetensors` - -### Promotion Criteria -- Accuracy > 0.90 (configurable in `config/autonomous_training.yaml`) -- Performance regression detection: alert on > 5% accuracy drop -- Must pass evaluation suite before deployment - -## Service Deployment - -### Azure Functions -```bash -# Validate locally first -func host start -curl http://localhost:7071/api/ai/status | jq - -# Deploy to Azure -func azure functionapp publish -``` - -### Local Development -```bash -python local_dev_adapter.py # Flask wrapper on port 5000 -``` - -### Aria Character Server -```bash -cd apps/aria && python server.py # Port 8080 -``` - -### Dashboard -```bash -cd apps/dashboard && python serve.py # Monitoring UI -``` - -## Pre-Deployment Checklist - -- [ ] Run unit tests: `python scripts/test_runner.py --unit` -- [ ] Run fast validation: `python scripts/fast_validate.py` -- [ ] Check health: `curl http://localhost:7071/api/ai/status | jq` -- [ ] Verify no hardcoded secrets -- [ ] Review subscription gating for new endpoints -- [ ] Update `local.settings.json.example` if new env vars added - -## Environment Variables - -Never hardcode. Use: -- **Local dev**: `local.settings.json` -- **Production**: Azure App Settings -- **CI/CD**: GitHub Secrets - -Deploy: {{input}} +--- +description: "Deploy and promote AI models and services" +name: "Deploy" +argument-hint: "Target + model or service details (example: adapter path + deployment target + version tag)" +agent: platform-ops +--- +# Deploy + +Deploy, promote, or publish AI models and services on the Aria platform. + +## Model Deployment + +### LoRA Adapter Promotion +```bash +# Train + auto-promote if accuracy > threshold +python scripts/train_and_promote.py --quick --auto-promote + +# Manual promotion +cp data_out/lora_training/best_model/adapter_*.* AI/microsoft_phi-silica-3.6_v1/adapters/ +``` + +**Required files for valid LoRA adapter:** +- `adapter_config.json` +- `adapter_model.safetensors` + +### Promotion Criteria +- Accuracy > 0.90 (configurable in `config/autonomous_training.yaml`) +- Performance regression detection: alert on > 5% accuracy drop +- Must pass evaluation suite before deployment + +## Service Deployment + +### Azure Functions +```bash +# Validate locally first +func host start +curl http://localhost:7071/api/ai/status | jq + +# Deploy to Azure +func azure functionapp publish +``` + +### Local Development +```bash +python local_dev_adapter.py # Flask wrapper on port 5000 +``` + +### Aria Character Server +```bash +cd apps/aria && python server.py # Port 8080 +``` + +### Dashboard +```bash +cd apps/dashboard && python serve.py # Monitoring UI +``` + +## Pre-Deployment Checklist + +- [ ] Run unit tests: `python scripts/test_runner.py --unit` +- [ ] Run fast validation: `python scripts/fast_validate.py` +- [ ] Check health: `curl http://localhost:7071/api/ai/status | jq` +- [ ] Verify no hardcoded secrets +- [ ] Review subscription gating for new endpoints +- [ ] Update `local.settings.json.example` if new env vars added + +## Environment Variables + +Never hardcode. Use: +- **Local dev**: `local.settings.json` +- **Production**: Azure App Settings +- **CI/CD**: GitHub Secrets + +Deploy: {{input}} diff --git a/.github/prompts/evaluate.prompt.md b/.github/prompts/evaluate.prompt.md index 5455b5af0..6cbd2f488 100644 --- a/.github/prompts/evaluate.prompt.md +++ b/.github/prompts/evaluate.prompt.md @@ -1,53 +1,53 @@ ---- -description: "Evaluate and benchmark AI models using the batch evaluator" -name: "Evaluate" -argument-hint: "Model paths + metrics + dataset (example: adapter path + accuracy/loss metrics + eval dataset)" -agent: data-pipeline ---- -# Evaluate - -Run model evaluation and benchmarking using the Aria batch evaluation pipeline. - -## Evaluation Modes - -### Single Model -```bash -python scripts/evaluate_lora_model.py \ - --model-path data_out/lora_training/my-model \ - --dataset datasets/chat/eval_set.jsonl \ - --metrics accuracy perplexity f1 -``` - -### Batch Evaluation (Parallel) -```bash -python scripts/batch_evaluator.py --config config/evaluation/eval_config.yaml -``` - -### Auto-Scan All Models -```bash -python scripts/batch_evaluator.py --scan # Discovers models in data_out/lora_training/ -``` - -## Metrics - -| Metric | Description | Good Range | -|--------|-------------|------------| -| accuracy | Exact match ratio | > 0.70 | -| perplexity | Cross-entropy exp (lower = better) | < 50 | -| f1 | Precision/recall harmonic mean | > 0.75 | -| mean_accuracy | Average across samples | > 0.70 | -| improvement_rate | Delta between cycles | > 0 | - -## Performance Analysis -```bash -python scripts/training_analytics.py # Trends, plateau detection, improvement rates -``` - -## Safety Rules - -- **Dataset immutability**: Never modify files in `datasets/` — use `data_out/` for outputs -- **Timeout**: 30 minutes per evaluation (configurable) -- **Resources**: Max 3 parallel evaluations (ThreadPoolExecutor) -- **Always dry-run first**: Validate config before GPU execution - -Evaluate: {{input}} +--- +description: "Evaluate and benchmark AI models using the batch evaluator" +name: "Evaluate" +argument-hint: "Model paths + metrics + dataset (example: adapter path + accuracy/loss metrics + eval dataset)" +agent: data-pipeline +--- +# Evaluate + +Run model evaluation and benchmarking using the Aria batch evaluation pipeline. + +## Evaluation Modes + +### Single Model +```bash +python scripts/evaluate_lora_model.py \ + --model-path data_out/lora_training/my-model \ + --dataset datasets/chat/eval_set.jsonl \ + --metrics accuracy perplexity f1 +``` + +### Batch Evaluation (Parallel) +```bash +python scripts/batch_evaluator.py --config config/evaluation/eval_config.yaml +``` + +### Auto-Scan All Models +```bash +python scripts/batch_evaluator.py --scan # Discovers models in data_out/lora_training/ +``` + +## Metrics + +| Metric | Description | Good Range | +|--------|-------------|------------| +| accuracy | Exact match ratio | > 0.70 | +| perplexity | Cross-entropy exp (lower = better) | < 50 | +| f1 | Precision/recall harmonic mean | > 0.75 | +| mean_accuracy | Average across samples | > 0.70 | +| improvement_rate | Delta between cycles | > 0 | + +## Performance Analysis +```bash +python scripts/training_analytics.py # Trends, plateau detection, improvement rates +``` + +## Safety Rules + +- **Dataset immutability**: Never modify files in `datasets/` — use `data_out/` for outputs +- **Timeout**: 30 minutes per evaluation (configurable) +- **Resources**: Max 3 parallel evaluations (ThreadPoolExecutor) +- **Always dry-run first**: Validate config before GPU execution + +Evaluate: {{input}} diff --git a/.github/prompts/generate-tool.prompt.md b/.github/prompts/generate-tool.prompt.md index 6229153fb..ee09836e9 100644 --- a/.github/prompts/generate-tool.prompt.md +++ b/.github/prompts/generate-tool.prompt.md @@ -1,45 +1,45 @@ ---- -description: "Generate a safe Python tool using the LLM Maker system" -name: "Generate Tool" -argument-hint: "Tool spec + safety requirements (example: function purpose + input/output types + allowed operations)" -agent: llm-maker ---- -# Generate Tool - -Generate a safe, validated Python function using the ToolMaker system. - -## Requirements - -1. **Describe the tool clearly**: name, description, parameters with types, return type -2. **Include 2+ usage examples** for better generation quality -3. **Safety constraints are automatic**: no os/sys/subprocess/eval/exec/open - -## Template - -``` -Tool Name: {name} -Description: {what it does} -Parameters: - - {param_name}: {type} — {description} -Return Type: {type} -Examples: - - Input: {example_input} → Output: {expected_output} -``` - -## Safety Rules (enforced by ToolValidator) - -- **Banned imports**: os, sys, subprocess, shutil, pathlib, socket, urllib, requests, http, pickle, threading, multiprocessing, ctypes, cffi -- **Banned calls**: eval, exec, compile, __import__, open, input, breakpoint, exit -- **No file I/O, no network access, no dynamic code execution** -- Functions must be pure — no side effects - -## Process - -1. ToolMaker builds system prompt with safety constraints -2. LLM generates Python function -3. ToolValidator performs AST-based validation -4. Function signature verified against spec -5. Up to 3 retry attempts with error feedback injection -6. Output: validated, safe Python function - -Generate the tool now using {{input}}. +--- +description: "Generate a safe Python tool using the LLM Maker system" +name: "Generate Tool" +argument-hint: "Tool spec + safety requirements (example: function purpose + input/output types + allowed operations)" +agent: llm-maker +--- +# Generate Tool + +Generate a safe, validated Python function using the ToolMaker system. + +## Requirements + +1. **Describe the tool clearly**: name, description, parameters with types, return type +2. **Include 2+ usage examples** for better generation quality +3. **Safety constraints are automatic**: no os/sys/subprocess/eval/exec/open + +## Template + +``` +Tool Name: {name} +Description: {what it does} +Parameters: + - {param_name}: {type} — {description} +Return Type: {type} +Examples: + - Input: {example_input} → Output: {expected_output} +``` + +## Safety Rules (enforced by ToolValidator) + +- **Banned imports**: os, sys, subprocess, shutil, pathlib, socket, urllib, requests, http, pickle, threading, multiprocessing, ctypes, cffi +- **Banned calls**: eval, exec, compile, __import__, open, input, breakpoint, exit +- **No file I/O, no network access, no dynamic code execution** +- Functions must be pure — no side effects + +## Process + +1. ToolMaker builds system prompt with safety constraints +2. LLM generates Python function +3. ToolValidator performs AST-based validation +4. Function signature verified against spec +5. Up to 3 retry attempts with error feedback injection +6. Output: validated, safe Python function + +Generate the tool now using {{input}}. diff --git a/.github/prompts/generate-website.prompt.md b/.github/prompts/generate-website.prompt.md index 7f2675263..4e62b0119 100644 --- a/.github/prompts/generate-website.prompt.md +++ b/.github/prompts/generate-website.prompt.md @@ -1,44 +1,44 @@ ---- -description: "Generate a complete website using the LLM Maker WebsiteMaker" -name: "Generate Website" -argument-hint: "Site spec + purpose (example: site purpose + pages + style preferences + any required sections)" -agent: llm-maker ---- -# Generate Website - -Create a complete website (HTML + CSS + JS) using the WebsiteMaker system. - -## Specification - -Describe your website with: - -1. **Name**: Project identifier (used for output directory) -2. **Description**: What the site does and who it's for -3. **Style**: Visual style (modern, minimal, dark, corporate, playful, etc.) -4. **Pages**: List of pages with their purpose -5. **Features**: Interactive features needed (forms, animations, charts, etc.) - -## Output - -WebsiteMaker generates: -- `index.html` — Main page with semantic HTML -- `style.css` — Complete stylesheet -- `script.js` — Interactive functionality -- Additional pages as specified -- `metadata.json` — Creation timestamp, pages, features - -Files are saved to: `ai-projects/generated_sites/{name}/` - -## Quality Standards - -- Responsive design (mobile-first) -- Semantic HTML5 elements -- Accessible (ARIA labels, alt text, keyboard navigation) -- Modern CSS (flexbox/grid, custom properties) -- Clean, commented JavaScript - -## Update Existing Sites - -Use `WebsiteMaker.update_website(name, update_description, target_file)` to modify existing sites while preserving metadata. - -Generate the website for: {{input}} +--- +description: "Generate a complete website using the LLM Maker WebsiteMaker" +name: "Generate Website" +argument-hint: "Site spec + purpose (example: site purpose + pages + style preferences + any required sections)" +agent: llm-maker +--- +# Generate Website + +Create a complete website (HTML + CSS + JS) using the WebsiteMaker system. + +## Specification + +Describe your website with: + +1. **Name**: Project identifier (used for output directory) +2. **Description**: What the site does and who it's for +3. **Style**: Visual style (modern, minimal, dark, corporate, playful, etc.) +4. **Pages**: List of pages with their purpose +5. **Features**: Interactive features needed (forms, animations, charts, etc.) + +## Output + +WebsiteMaker generates: +- `index.html` — Main page with semantic HTML +- `style.css` — Complete stylesheet +- `script.js` — Interactive functionality +- Additional pages as specified +- `metadata.json` — Creation timestamp, pages, features + +Files are saved to: `ai-projects/generated_sites/{name}/` + +## Quality Standards + +- Responsive design (mobile-first) +- Semantic HTML5 elements +- Accessible (ARIA labels, alt text, keyboard navigation) +- Modern CSS (flexbox/grid, custom properties) +- Clean, commented JavaScript + +## Update Existing Sites + +Use `WebsiteMaker.update_website(name, update_description, target_file)` to modify existing sites while preserving metadata. + +Generate the website for: {{input}} diff --git a/.github/prompts/optimize.prompt.md b/.github/prompts/optimize.prompt.md index 8b631b146..f75f3fc4b 100644 --- a/.github/prompts/optimize.prompt.md +++ b/.github/prompts/optimize.prompt.md @@ -1,56 +1,56 @@ ---- -description: "Analyze and optimize AI system performance" -name: "Optimize" -argument-hint: "Target component + bottleneck description (example: component name + observed issue + baseline metrics)" -agent: agent ---- -# Optimize - -Analyze and optimize AI system performance across the Aria platform. - -## Performance Analysis - -### Quick Health Check -```bash -curl http://localhost:7071/api/ai/status | jq -python scripts/system_health_check.py -``` - -### Resource Monitoring -```bash -python scripts/resource_monitor.py --snapshot # CPU/memory/disk/GPU -python scripts/training_analytics.py # Training trends -python scripts/status_dashboard.py --watch # Live orchestrator status -``` - -### Token Budget Optimization -- Review `prune_messages()` stats: original_tokens, pruned_tokens, removed_count -- Adjust `max_context_tokens` and `reserve_output_tokens` per provider -- Context window: gpt-4o (128k), gpt-3.5-turbo (16.3k), phi (4k) - -### Embedding Performance -- Azure OpenAI embeddings: most accurate, API cost -- OpenAI embeddings: good fallback -- Local hash: deterministic 256-dim, zero-cost, less semantic - -### SQL Pool Optimization -- Default pool size: 10 (configurable via `QAI_SQL_POOL_SIZE`) -- Health endpoint warns at ≥80% saturation -- Connection pooling: thread-per-connection + shared pool (MAX_POOL_SIZE=5) - -## Common Bottlenecks - -| Issue | Diagnostic | Fix | -|-------|-----------|-----| -| Slow responses | Check provider latency in /api/ai/status | Switch to faster provider or reduce max_tokens | -| Memory overflow | Monitor `prune_messages()` stats | Lower max_context_tokens | -| DB pool exhaustion | Check pool saturation in /api/ai/status | Increase QAI_SQL_POOL_SIZE | -| Training stalled | `scripts/training_analytics.py` | Adjust epochs or learning rate | -| GPU OOM | `scripts/resource_monitor.py` | Reduce batch_size | - -## Autonomous Training Optimization -- Epoch progression: `[25, 50, 100, 200]` — auto-escalates on plateau -- Performance degradation alerts: > 5% accuracy drop between cycles -- Adaptive dataset selection based on performance history - -Optimize: {{input}} +--- +description: "Analyze and optimize AI system performance" +name: "Optimize" +argument-hint: "Target component + bottleneck description (example: component name + observed issue + baseline metrics)" +agent: agent +--- +# Optimize + +Analyze and optimize AI system performance across the Aria platform. + +## Performance Analysis + +### Quick Health Check +```bash +curl http://localhost:7071/api/ai/status | jq +python scripts/system_health_check.py +``` + +### Resource Monitoring +```bash +python scripts/resource_monitor.py --snapshot # CPU/memory/disk/GPU +python scripts/training_analytics.py # Training trends +python scripts/status_dashboard.py --watch # Live orchestrator status +``` + +### Token Budget Optimization +- Review `prune_messages()` stats: original_tokens, pruned_tokens, removed_count +- Adjust `max_context_tokens` and `reserve_output_tokens` per provider +- Context window: gpt-4o (128k), gpt-3.5-turbo (16.3k), phi (4k) + +### Embedding Performance +- Azure OpenAI embeddings: most accurate, API cost +- OpenAI embeddings: good fallback +- Local hash: deterministic 256-dim, zero-cost, less semantic + +### SQL Pool Optimization +- Default pool size: 10 (configurable via `QAI_SQL_POOL_SIZE`) +- Health endpoint warns at ≥80% saturation +- Connection pooling: thread-per-connection + shared pool (MAX_POOL_SIZE=5) + +## Common Bottlenecks + +| Issue | Diagnostic | Fix | +|-------|-----------|-----| +| Slow responses | Check provider latency in /api/ai/status | Switch to faster provider or reduce max_tokens | +| Memory overflow | Monitor `prune_messages()` stats | Lower max_context_tokens | +| DB pool exhaustion | Check pool saturation in /api/ai/status | Increase QAI_SQL_POOL_SIZE | +| Training stalled | `scripts/training_analytics.py` | Adjust epochs or learning rate | +| GPU OOM | `scripts/resource_monitor.py` | Reduce batch_size | + +## Autonomous Training Optimization +- Epoch progression: `[25, 50, 100, 200]` — auto-escalates on plateau +- Performance degradation alerts: > 5% accuracy drop between cycles +- Adaptive dataset selection based on performance history + +Optimize: {{input}} diff --git a/.github/prompts/quantum.prompt.md b/.github/prompts/quantum.prompt.md index b89092fdd..b373d8b5d 100644 --- a/.github/prompts/quantum.prompt.md +++ b/.github/prompts/quantum.prompt.md @@ -1,43 +1,43 @@ ---- -description: "Plan and execute quantum computing workflows — circuit design, simulation, Azure Quantum submission, and cost-aware QPU execution." -name: "Quantum Workflow" -argument-hint: "Circuit spec + backend target (example: circuit description + local-sim | azure-sim | ionq + cost limit)" -agent: qai-specialist ---- - -Handle the following quantum computing task with cost awareness and safety. - -**Execution ladder (always follow this order):** -1. **Local simulation** (FREE): Qiskit Aer, PennyLane default.qubit -2. **Azure simulator** (FREE): `--job azure_ionq_simulator` -3. **Real QPU** (PAID): Only with `azure_confirm_cost: true` in YAML + cost estimate review - -**Quick commands:** -```bash -# Validate config -python scripts/quantum_autorun.py --dry-run - -# Run on simulator -python scripts/quantum_autorun.py --job azure_ionq_simulator - -# MCP server (quantum tools) -python ai-projects/quantum-ml/quantum_mcp_server.py - -# Interactive dashboard -cd ai-projects/quantum-ml && ./start_dashboard.sh -``` - -**MCP tools available:** -- `create_quantum_circuit`, `simulate_quantum_circuit`, `get_quantum_circuit_properties` -- `connect_azure_quantum`, `list_quantum_backends`, `submit_quantum_job` -- `estimate_quantum_cost`, `train_quantum_classifier` - -**Safety limits:** -- Max qubits: 10 (local), 20 (Azure with approval) -- Max shots: 1,000 (default), 100,000 (with `high_shots=true`) -- Always validate circuit locally before cloud submission - -**Key files:** -- MCP server: `ai-projects/quantum-ml/quantum_mcp_server.py` -- Classifier: `ai-projects/quantum-ml/src/quantum_classifier.py` -- Config: `quantum_autorun.yaml`, `ai-projects/quantum-ml/config/quantum_config.yaml` +--- +description: "Plan and execute quantum computing workflows — circuit design, simulation, Azure Quantum submission, and cost-aware QPU execution." +name: "Quantum Workflow" +argument-hint: "Circuit spec + backend target (example: circuit description + local-sim | azure-sim | ionq + cost limit)" +agent: qai-specialist +--- + +Handle the following quantum computing task with cost awareness and safety. + +**Execution ladder (always follow this order):** +1. **Local simulation** (FREE): Qiskit Aer, PennyLane default.qubit +2. **Azure simulator** (FREE): `--job azure_ionq_simulator` +3. **Real QPU** (PAID): Only with `azure_confirm_cost: true` in YAML + cost estimate review + +**Quick commands:** +```bash +# Validate config +python scripts/quantum_autorun.py --dry-run + +# Run on simulator +python scripts/quantum_autorun.py --job azure_ionq_simulator + +# MCP server (quantum tools) +python ai-projects/quantum-ml/quantum_mcp_server.py + +# Interactive dashboard +cd ai-projects/quantum-ml && ./start_dashboard.sh +``` + +**MCP tools available:** +- `create_quantum_circuit`, `simulate_quantum_circuit`, `get_quantum_circuit_properties` +- `connect_azure_quantum`, `list_quantum_backends`, `submit_quantum_job` +- `estimate_quantum_cost`, `train_quantum_classifier` + +**Safety limits:** +- Max qubits: 10 (local), 20 (Azure with approval) +- Max shots: 1,000 (default), 100,000 (with `high_shots=true`) +- Always validate circuit locally before cloud submission + +**Key files:** +- MCP server: `ai-projects/quantum-ml/quantum_mcp_server.py` +- Classifier: `ai-projects/quantum-ml/src/quantum_classifier.py` +- Config: `quantum_autorun.yaml`, `ai-projects/quantum-ml/config/quantum_config.yaml` diff --git a/.github/prompts/reason.prompt.md b/.github/prompts/reason.prompt.md index 0f44a9f64..06d79e9fb 100644 --- a/.github/prompts/reason.prompt.md +++ b/.github/prompts/reason.prompt.md @@ -1,43 +1,43 @@ ---- -description: "Reason through a problem using chain-of-thought analysis, task decomposition, and self-reflection. Produces structured reasoning with confidence scores and verification steps." -name: "Reason" -argument-hint: "Problem or question to analyze (example: decision + relevant context + constraints or trade-offs)" -agent: agi-reasoning ---- - -Apply the AGI reasoning framework to analyze and solve the following task. - -**Process:** - -1. **Analyze** — Classify the query: - - Complexity: simple | moderate | complex - - Intent: coding | architecture | debugging | optimization | explanation | creation - - Domain: quantum | ai | aria | infrastructure | general - -2. **Decompose** — Break into ordered subtasks: - - List each subtask with its dependencies - - Identify which subtasks can be parallelized - - Estimate confidence for each subtask - -3. **Execute** — Work through each subtask: - - Show your reasoning at each step - - Verify assumptions before proceeding - - Cross-reference with existing codebase patterns - -4. **Reflect** — Self-evaluate: - - Is the solution complete and correct? - - Does it follow existing codebase conventions? - - Are there edge cases or failure modes? - - Am I over-engineering? - -5. **Synthesize** — Deliver the result: - - Clear, actionable output - - Include verification steps - - Note any remaining uncertainties - -**Codebase context:** -- Provider chain: Azure OpenAI → OpenAI → LMStudio → LoRA → Local -- Config: YAML < CLI < per-job YAML < env vars -- Data: read-only `datasets/`, write-only `data_out/` -- Always `--dry-run` orchestrators first -- Test: `python scripts/test_runner.py --unit` +--- +description: "Reason through a problem with visible chain-of-thought analysis, task decomposition, and self-reflection. Shows reasoning steps to the user, including confidence scores and verification. Use when the user wants to see the reasoning process, not just the final answer." +name: "Reason" +argument-hint: "Problem or question to analyze (example: decision + relevant context + constraints or trade-offs)" +agent: visible-reasoning +--- + +Apply the AGI reasoning framework to analyze and solve the following task. + +**Process:** + +1. **Analyze** — Classify the query: + - Complexity: simple | moderate | complex + - Intent: coding | architecture | debugging | optimization | explanation | creation + - Domain: quantum | ai | aria | infrastructure | general + +2. **Decompose** — Break into ordered subtasks: + - List each subtask with its dependencies + - Identify which subtasks can be parallelized + - Estimate confidence for each subtask + +3. **Execute** — Work through each subtask: + - Show your reasoning at each step + - Verify assumptions before proceeding + - Cross-reference with existing codebase patterns + +4. **Reflect** — Self-evaluate: + - Is the solution complete and correct? + - Does it follow existing codebase conventions? + - Are there edge cases or failure modes? + - Am I over-engineering? + +5. **Synthesize** — Deliver the result: + - Clear, actionable output + - Include verification steps + - Note any remaining uncertainties + +**Codebase context:** +- Provider chain: Azure OpenAI → OpenAI → LMStudio → LoRA → Local +- Config: YAML < CLI < per-job YAML < env vars +- Data: read-only `datasets/`, write-only `data_out/` +- Always `--dry-run` orchestrators first +- Test: `python scripts/test_runner.py --unit` diff --git a/.github/prompts/review.prompt.md b/.github/prompts/review.prompt.md index 204eacf33..e3fe488f8 100644 --- a/.github/prompts/review.prompt.md +++ b/.github/prompts/review.prompt.md @@ -1,45 +1,45 @@ ---- -description: "Perform a comprehensive code review analyzing correctness, security, performance, conventions, and testing coverage for the Aria platform." -name: "Code Review" -argument-hint: "File or component scope + focus area (example: file path + focus: security | performance | correctness)" -agent: agent ---- - -Perform a thorough code review of the specified code, evaluating these dimensions: - -**1. Correctness** -- Logic errors, off-by-one, race conditions -- Null/undefined handling, edge cases -- API contract compliance (request/response shapes) - -**2. Security (OWASP Top 10)** -- Input validation at system boundaries -- No hardcoded secrets (use `local.settings.json` or env vars) -- SQL injection prevention (parameterized queries in `shared/sql_engine.py`) -- XSS prevention in web responses -- SSRF checks on external URLs - -**3. Performance** -- Unnecessary allocations or copies -- N+1 query patterns -- Missing caching where appropriate -- Frozenset usage for O(1) keyword lookups (Aria convention) - -**4. Conventions** -- Provider detection chain: Azure → OpenAI → LMStudio → LoRA → Local -- Config precedence: YAML < CLI < per-job YAML < env vars -- Data immutability: read-only `datasets/`, write-only `data_out/` -- Status files: `data_out//status.json` -- Error handling: graceful degradation, non-blocking telemetry - -**5. Testing** -- Are new code paths covered by tests? -- Can it be validated with `python scripts/test_runner.py --unit`? -- E2E coverage for UI changes: `pytest tests/test_ui_playwright.py` - -**Output format:** -For each finding: -- Severity: critical | warning | suggestion -- Location: file and line -- Issue description -- Recommended fix +--- +description: "Perform a comprehensive code review analyzing correctness, security, performance, conventions, and testing coverage for the Aria platform." +name: "Code Review" +argument-hint: "File or component scope + focus area (example: file path + focus: security | performance | correctness)" +agent: agent +--- + +Perform a thorough code review of the specified code, evaluating these dimensions: + +**1. Correctness** +- Logic errors, off-by-one, race conditions +- Null/undefined handling, edge cases +- API contract compliance (request/response shapes) + +**2. Security (OWASP Top 10)** +- Input validation at system boundaries +- No hardcoded secrets (use `local.settings.json` or env vars) +- SQL injection prevention (parameterized queries in `shared/sql_engine.py`) +- XSS prevention in web responses +- SSRF checks on external URLs + +**3. Performance** +- Unnecessary allocations or copies +- N+1 query patterns +- Missing caching where appropriate +- Frozenset usage for O(1) keyword lookups (Aria convention) + +**4. Conventions** +- Provider detection chain: Azure → OpenAI → LMStudio → LoRA → Local +- Config precedence: YAML < CLI < per-job YAML < env vars +- Data immutability: read-only `datasets/`, write-only `data_out/` +- Status files: `data_out//status.json` +- Error handling: graceful degradation, non-blocking telemetry + +**5. Testing** +- Are new code paths covered by tests? +- Can it be validated with `python scripts/test_runner.py --unit`? +- E2E coverage for UI changes: `pytest tests/test_ui_playwright.py` + +**Output format:** +For each finding: +- Severity: critical | warning | suggestion +- Location: file and line +- Issue description +- Recommended fix diff --git a/.github/prompts/train.prompt.md b/.github/prompts/train.prompt.md index 1f4221529..d97fdde3f 100644 --- a/.github/prompts/train.prompt.md +++ b/.github/prompts/train.prompt.md @@ -1,40 +1,40 @@ ---- -description: "Plan, execute, and monitor training runs — LoRA fine-tuning, dataset validation, performance analysis, and model deployment." -name: "Train" -argument-hint: "Training goal + dataset + model path (example: goal + dataset path + base model + adapter output path)" -agent: autonomous-trainer ---- - -Handle the following training-related task using the autonomous training framework. - -**Before any training:** -1. Validate orchestrator config: `python scripts/autotrain.py --dry-run` -2. Check dataset integrity: `python scripts/validate_datasets.py --category chat` -3. Verify system resources: `python scripts/resource_monitor.py --snapshot` - -**Training execution options:** -```bash -# Quick LoRA (TinyLlama, fast iteration) -python scripts/automated_training_pipeline.py --models tinyllama --quick - -# Full training with auto-promotion -python scripts/train_and_promote.py --quick --auto-promote - -# Autonomous continuous training (30-min cycles) -nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 & -``` - -**Monitor progress:** -```bash -python scripts/monitor_autonomous_training.py --watch # Real-time dashboard -python scripts/training_analytics.py # Performance trends -cat data_out/autonomous_training_status.json # Cycle status -``` - -**Key rules:** -- ALWAYS `--dry-run` before GPU execution -- NEVER modify files in `datasets/` (read-only) -- All outputs write to `data_out//` -- Valid LoRA adapter = `adapter_config.json` + `adapter_model.safetensors` -- Auto-deploy threshold: accuracy > 0.90 -- Degradation alert: >5% accuracy drop between cycles +--- +description: "Plan, execute, and monitor training runs — LoRA fine-tuning, dataset validation, performance analysis, and model deployment." +name: "Train" +argument-hint: "Training goal + dataset + model path (example: goal + dataset path + base model + adapter output path)" +agent: autonomous-trainer +--- + +Handle the following training-related task using the autonomous training framework. + +**Before any training:** +1. Validate orchestrator config: `python scripts/autotrain.py --dry-run` +2. Check dataset integrity: `python scripts/validate_datasets.py --category chat` +3. Verify system resources: `python scripts/resource_monitor.py --snapshot` + +**Training execution options:** +```bash +# Quick LoRA (TinyLlama, fast iteration) +python scripts/automated_training_pipeline.py --models tinyllama --quick + +# Full training with auto-promotion +python scripts/train_and_promote.py --quick --auto-promote + +# Autonomous continuous training (30-min cycles) +nohup python scripts/autonomous_training_orchestrator.py > data_out/autonomous_training.log 2>&1 & +``` + +**Monitor progress:** +```bash +python scripts/monitor_autonomous_training.py --watch # Real-time dashboard +python scripts/training_analytics.py # Performance trends +cat data_out/autonomous_training_status.json # Cycle status +``` + +**Key rules:** +- ALWAYS `--dry-run` before GPU execution +- NEVER modify files in `datasets/` (read-only) +- All outputs write to `data_out//` +- Valid LoRA adapter = `adapter_config.json` + `adapter_model.safetensors` +- Auto-deploy threshold: accuracy > 0.90 +- Degradation alert: >5% accuracy drop between cycles diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 5718d639a..8330cc38b 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -1,117 +1,133 @@ -# GitHub Actions Workflows - -This directory contains all GitHub Actions workflows for the Aria repository. Workflows are organized by purpose and execution pattern. - -## Workflow Organization - -### 🔄 Continuous Integration (CI) -- **`ci-pipeline.yml`** - Main CI pipeline - - Runs on: Push/PR to main/dev, daily schedule - - Purpose: Code validation, tests, daily training, model deployment - - Duration: ~15-30 minutes - -### ✅ Testing Workflows -- **`aria-tests.yml`** - Comprehensive Aria testing - - Runs on: Changes to `aria_web/` or Aria test files - - Purpose: Multi-version (3.10-3.12), multi-browser E2E tests - - Duration: ~20-30 minutes - - Note: Path-filtered, more thorough - -- **`e2e-tests.yml`** - Quick regression testing - - Runs on: Any push/PR to main - - Purpose: Fast Aria regression tests - - Duration: ~10-15 minutes - - Note: No path filtering, catches broader regressions - -### 🔬 Validation Workflows -- **`auto-validation.yml`** - Orchestrator validation - - Runs on: Changes to orchestrator configs/scripts, daily schedule - - Purpose: Validates `autotrain.yaml` and `quantum_autorun.yaml` - - Duration: ~5-10 minutes - -### ☁️ Cloud Workflows -- **`azureml-train.yml`** - Azure ML training - - Runs on: Manual trigger only - - Purpose: Submit LoRA fine-tuning jobs to Azure ML - - Requires: Azure credentials, ML workspace - -- **`quantum-orchestration.yml`** - Azure Quantum - - Runs on: Push to main, manual trigger - - Purpose: Execute quantum workflows on Azure Quantum - - Requires: Azure credentials, Quantum workspace - -## Workflow Patterns - -### Automatic Triggers -```yaml -on: - push: - branches: [main] # Runs on commits to main - pull_request: - branches: [main] # Runs on PRs targeting main - schedule: - - cron: '0 2 * * *' # Runs daily at 2 AM UTC -``` - -### Manual Triggers -```yaml -on: - workflow_dispatch: # Allows manual execution via GitHub UI - inputs: - parameter: ... # Custom parameters -``` - -### Path Filtering -```yaml -on: - push: - paths: - - 'aria_web/**' # Only runs if these paths change - - 'tests/**' -``` - -## Best Practices - -### When to Use Each Workflow -- **Local Development**: Run tests locally first with `pytest` -- **PR Review**: `e2e-tests.yml` provides quick validation -- **Aria Changes**: `aria-tests.yml` runs comprehensive tests automatically -- **Daily CI**: `ci-pipeline.yml` catches integration issues overnight -- **Training**: Use `azureml-train.yml` for GPU-accelerated cloud training -- **Quantum**: Use `quantum-orchestration.yml` for quantum computing tasks - -### Workflow Naming Convention -- Use descriptive names that indicate purpose -- Suffix with type: `-tests.yml`, `-validation.yml`, `-train.yml` -- Group related workflows with consistent prefixes - -### Adding New Workflows -1. Choose appropriate trigger pattern (push/PR/schedule/manual) -2. Add header comment block explaining purpose -3. Use path filtering when possible to reduce unnecessary runs -4. Set appropriate timeout limits -5. Upload artifacts for debugging -6. Update this README with workflow description - -## Troubleshooting - -### Workflow Not Running -- Check branch filters match your target branch -- Verify path filters include your changed files -- Ensure required secrets are configured - -### Workflow Failing -- Check job logs in GitHub Actions tab -- Download artifacts for detailed results -- Run equivalent commands locally for debugging - -### Reducing CI Time -- Use path filtering to avoid unnecessary runs -- Cache dependencies (pip, npm) -- Run expensive jobs only on schedule or manual trigger -- Parallelize independent jobs with `needs:` dependencies - -## Resources -- [GitHub Actions Documentation](https://docs.github.com/en/actions) -- [Workflow Syntax](https://docs.github.com/en/actions/reference/workflow-syntax-for-github-actions) -- [Repository Root README](../../README.md) +# GitHub Actions Workflows + +This directory contains all GitHub Actions workflows for the Aria repository. Workflows are organized by purpose and execution pattern. + +## Workflow Inventory + +### 🔄 Continuous Integration (CI) + +- **`ci-pipeline.yml`** — Main CI pipeline + - Runs on: push/PR to `main` and `dev`, daily schedule + - Purpose: code validation, tests, daily training, model deployment + - Typical duration: ~15–30 minutes + +### ✅ Testing Workflows + +- **`aria-tests.yml`** — Comprehensive Aria testing + - Runs on: changes to `aria_web/` or Aria test files + - Purpose: multi-Python (3.10–3.12), multi-browser E2E tests + - Typical duration: ~20–30 minutes + - Notes: path-filtered, more thorough than quick regression + +- **`e2e-tests.yml`** — Quick regression testing + - Runs on: any push/PR to `main` + - Purpose: fast Aria regression tests + - Typical duration: ~10–15 minutes + - Notes: not path-filtered; catches broad regressions + +### 🔬 Validation Workflows + +- **`auto-validation.yml`** — Orchestrator validation + - Runs on: changes to orchestrator configs/scripts, daily schedule + - Purpose: validates `autotrain.yaml` and `quantum_autorun.yaml` + - Typical duration: ~5–10 minutes + +### ☁️ Cloud Workflows + +- **`azureml-train.yml`** — Azure ML training + - Runs on: manual trigger only + - Purpose: submit LoRA fine-tuning jobs to Azure ML + - Requires: Azure credentials and an ML workspace + +- **`quantum-orchestration.yml`** — Azure Quantum + - Runs on: push to `main`, manual trigger + - Purpose: execute quantum workflows on Azure Quantum + - Requires: Azure credentials and a Quantum workspace + +## Common Trigger Patterns + +### Automatic Triggers + +```yaml +on: + push: + branches: [main] # Runs on commits to main + pull_request: + branches: [main] # Runs on PRs targeting main + schedule: + - cron: '0 2 * * *' # Runs daily at 2 AM UTC +``` + +### Manual Triggers + +```yaml +on: + workflow_dispatch: # Allows manual execution via GitHub UI + inputs: + parameter: ... # Custom parameters +``` + +### Path Filtering + +```yaml +on: + push: + paths: + - 'aria_web/**' # Only runs if these paths change + - 'tests/**' +``` + +## Best Practices + +### When to Use Each Workflow + +- **Local Development**: run tests locally first (e.g., `pytest`) +- **PR Review**: `e2e-tests.yml` provides quick validation +- **Aria Changes**: `aria-tests.yml` runs comprehensive tests automatically +- **Daily CI**: `ci-pipeline.yml` catches integration issues overnight +- **Training**: use `azureml-train.yml` for GPU-accelerated cloud training +- **Quantum**: use `quantum-orchestration.yml` for quantum computing tasks + +### Workflow Naming Convention + +- Use descriptive names that indicate purpose +- Use a type suffix: `-tests.yml`, `-validation.yml`, `-train.yml` +- Group related workflows with consistent prefixes + +### Adding a New Workflow (Checklist) + +1. Choose an appropriate trigger pattern (push/PR/schedule/manual) +2. Add a header comment block explaining purpose and ownership +3. Use path filtering when possible to reduce unnecessary runs +4. Set appropriate `timeout-minutes` limits +5. Upload artifacts for debugging (logs, screenshots, reports) +6. Cache dependencies where it helps (pip/npm) +7. Update this README with the new workflow description + +## Troubleshooting + +### Workflow Not Running + +- Check branch filters match your target branch +- Verify path filters include your changed files (if used) +- Ensure required secrets are configured +- Confirm the workflow file is on the default branch (or the branch you are pushing to) + +### Workflow Failing + +- Check job logs in the **Actions** tab +- Download artifacts for detailed results (reports, screenshots) +- Run equivalent commands locally to reproduce issues + +### Reducing CI Time + +- Use path filtering to avoid unnecessary runs +- Cache dependencies (pip, npm) +- Run expensive jobs only on schedule or manual trigger +- Parallelize independent jobs using `needs:` dependencies + +## Resources + +- GitHub Actions Documentation: https://docs.github.com/en/actions +- Workflow syntax: https://docs.github.com/en/actions/writing-workflows/workflow-syntax-for-github-actions +- Repository root README: ../../README.md diff --git a/.github/workflows/auto-merge.yml b/.github/workflows/auto-merge.yml new file mode 100644 index 000000000..af42e955c --- /dev/null +++ b/.github/workflows/auto-merge.yml @@ -0,0 +1,146 @@ +# ============================================================================= +# Auto Merge — Enable or disable GitHub native auto-merge via label +# ============================================================================= +# Purpose: Automatically enable GitHub's built-in auto-merge (squash) on a PR +# when the `auto-merge` label is applied, and disable it when removed. +# The PR will merge automatically once ALL required branch-protection +# checks pass and the required number of reviews are met. +# +# How to use: +# 1. Apply the `auto-merge` label to a PR (manually or via another workflow). +# 2. This workflow enables `gh pr merge --auto --squash` on that PR. +# 3. Remove the label at any time to cancel the pending auto-merge. +# +# Security: +# - Only same-repository PRs can trigger this workflow (forks produce a +# read-only GITHUB_TOKEN that cannot enable auto-merge). +# - The workflow does NOT bypass branch protection or required reviews. +# GitHub's auto-merge still waits for all required status checks. +# +# Label setup (one-time): +# Create a label named `auto-merge` in the repository: +# gh label create auto-merge --color 0075ca --description "Enable auto-merge when checks pass" +# ============================================================================= + +name: Auto Merge + +on: + pull_request: + types: [labeled, unlabeled] + pull_request_review: + types: [submitted] + +permissions: + contents: write + pull-requests: write + +concurrency: + # Serialize enable/disable operations for the same PR to avoid races. + group: auto-merge-${{ github.event.pull_request.number }} + cancel-in-progress: false + +defaults: + run: + shell: bash + +jobs: + # --------------------------------------------------------------------------- + # Enable auto-merge when the `auto-merge` label is applied OR when an + # approving review arrives on a PR that already carries the label. + # --------------------------------------------------------------------------- + enable: + name: Enable Auto-Merge + runs-on: ubuntu-latest + timeout-minutes: 5 + if: >- + github.event.pull_request.head.repo.full_name == github.repository && + ( + ( + github.event_name == 'pull_request' && + github.event.action == 'labeled' && + github.event.label.name == 'auto-merge' + ) || + ( + github.event_name == 'pull_request_review' && + github.event.review.state == 'approved' && + contains(github.event.pull_request.labels.*.name, 'auto-merge') + ) + ) + steps: + - name: Enable auto-merge (squash) + env: + PR_URL: ${{ github.event.pull_request.html_url }} + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + set -euo pipefail + gh pr merge --auto --squash "$PR_URL" + echo "Auto-merge enabled for ${PR_URL}" + + - name: Post status comment + env: + PR_URL: ${{ github.event.pull_request.html_url }} + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + set -euo pipefail + COMMENT="🤖 **Auto-merge enabled.** This PR will merge automatically once all required status checks pass and required approvals are satisfied. Remove the \`auto-merge\` label at any time to cancel." + gh pr comment "$PR_URL" --body "$COMMENT" + + - name: Summary + run: | + set -euo pipefail + { + echo "## ✅ Auto-Merge Enabled" + echo "" + echo "| Field | Value |" + echo "|-------|-------|" + echo "| PR | ${{ github.event.pull_request.html_url }} |" + echo "| Triggered by | ${{ github.event_name }} |" + echo "| Strategy | squash |" + echo "" + echo "The PR will merge automatically when all branch-protection gates clear." + } >> "$GITHUB_STEP_SUMMARY" + + # --------------------------------------------------------------------------- + # Disable auto-merge when the `auto-merge` label is removed. + # --------------------------------------------------------------------------- + disable: + name: Disable Auto-Merge + runs-on: ubuntu-latest + timeout-minutes: 5 + if: >- + github.event.pull_request.head.repo.full_name == github.repository && + github.event_name == 'pull_request' && + github.event.action == 'unlabeled' && + github.event.label.name == 'auto-merge' + steps: + - name: Disable auto-merge + env: + PR_URL: ${{ github.event.pull_request.html_url }} + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + set -euo pipefail + gh pr merge --disable-auto "$PR_URL" + echo "Auto-merge disabled for ${PR_URL}" + + - name: Post status comment + env: + PR_URL: ${{ github.event.pull_request.html_url }} + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + set -euo pipefail + gh pr comment "$PR_URL" --body \ + "🛑 **Auto-merge disabled.** The \`auto-merge\` label was removed. Re-apply the label to re-enable auto-merge." + + - name: Summary + run: | + set -euo pipefail + { + echo "## 🛑 Auto-Merge Disabled" + echo "" + echo "| Field | Value |" + echo "|-------|-------|" + echo "| PR | ${{ github.event.pull_request.html_url }} |" + echo "| Reason | \`auto-merge\` label removed |" + echo "" + echo "Re-apply the \`auto-merge\` label to re-enable." + } >> "$GITHUB_STEP_SUMMARY" diff --git a/.github/workflows/merge-gate.yml b/.github/workflows/merge-gate.yml new file mode 100644 index 000000000..25afac6a9 --- /dev/null +++ b/.github/workflows/merge-gate.yml @@ -0,0 +1,227 @@ +# ============================================================================= +# Merge Gate — Required status check for pull requests targeting main +# ============================================================================= +# Purpose: Aggregates all mandatory quality gates into a single named job +# (`All Gates Passed`) that branch protection rules reference. +# Every gate job runs concurrently; the fan-in job fails the check +# if any gate fails or is cancelled. +# +# Trigger: pull_request → main (opened / synchronize / reopened / +# ready_for_review). Draft PRs skip all gate jobs so they don't +# block development; the fan-in still succeeds (skipped is OK). +# +# Branch protection: Add "Merge Gate / All Gates Passed" as the single +# required status check on the main branch. +# ============================================================================= + +name: Merge Gate + +on: + pull_request: + branches: [main] + types: [opened, synchronize, reopened, ready_for_review] + +# Cancel superseded runs for the same PR. +concurrency: + group: merge-gate-${{ github.event.pull_request.number }} + cancel-in-progress: true + +# Least-privilege default; jobs opt-in to more below. +permissions: + contents: read + +defaults: + run: + shell: bash + +jobs: + # --------------------------------------------------------------------------- + # Gate 1 — Unit tests + # --------------------------------------------------------------------------- + unit-tests: + name: Unit Tests + if: github.event.pull_request.draft == false + runs-on: ubuntu-latest + timeout-minutes: 30 + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + persist-credentials: false + + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + extra-packages: 'pytest pytest-timeout' + + - name: Run unit tests + uses: ./.github/actions/run-pytest-suite + with: + test-path: tests + marker-expression: 'not slow and not azure and not integration' + extra-args: '--maxfail=5 -q' + env: + CI: 'true' + + # --------------------------------------------------------------------------- + # Gate 2 — PR structure validation + # --------------------------------------------------------------------------- + pr-validation: + name: PR Validation + if: github.event.pull_request.draft == false + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + persist-credentials: false + + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + extra-packages: 'pyyaml' + + - name: Validate workflow YAML + uses: ./.github/actions/validate-workflow-yaml + with: + workflows-path: .github/workflows + require-jobs: 'true' + + - name: Validate Azure Function bindings + uses: ./.github/actions/validate-function-bindings + with: + functions-path: functions + require-http-route: 'true' + + - name: Fast validation + run: | + set -euo pipefail + if [[ -f scripts/fast_validate.py ]]; then + python scripts/fast_validate.py + else + echo "::notice::scripts/fast_validate.py not found, skipping" + fi + + # --------------------------------------------------------------------------- + # Gate 3 — Dependency security review + # --------------------------------------------------------------------------- + security: + name: Security Review + if: github.event.pull_request.draft == false + runs-on: ubuntu-latest + timeout-minutes: 10 + permissions: + contents: read + pull-requests: write # dependency-review-action needs PR write to comment + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + persist-credentials: false + + - name: Dependency Review + uses: actions/dependency-review-action@v4 + with: + fail-on-severity: high + deny-licenses: GPL-2.0, GPL-3.0, AGPL-3.0 + comment-summary-in-pr: always + + # --------------------------------------------------------------------------- + # Gate 4 — Integration contract gate + # --------------------------------------------------------------------------- + contract-gate: + name: Integration Contract Gate + if: github.event.pull_request.draft == false + runs-on: ubuntu-latest + timeout-minutes: 15 + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + persist-credentials: false + + - name: Setup Python + uses: ./.github/actions/setup-python-env + with: + python-version: '3.11' + extra-packages: 'pytest pytest-timeout' + + - name: Validate site bundles + uses: ./.github/actions/validate-site-bundles + with: + strict-metadata: 'true' + + - name: Validate composite actions + uses: ./.github/actions/validate-composite-actions + with: + script-path: scripts/validate_composite_actions.py + + - name: Run contract gate + uses: ./.github/actions/run-integration-contract-gate + + # --------------------------------------------------------------------------- + # Fan-in — All gates must pass. This is the single required status check. + # --------------------------------------------------------------------------- + gates-passed: + name: All Gates Passed + needs: [unit-tests, pr-validation, security, contract-gate] + if: always() + runs-on: ubuntu-latest + timeout-minutes: 5 + steps: + - name: Evaluate gate results + env: + UNIT_TESTS: ${{ needs.unit-tests.result }} + PR_VALIDATION: ${{ needs.pr-validation.result }} + SECURITY: ${{ needs.security.result }} + CONTRACT_GATE: ${{ needs.contract-gate.result }} + run: | + set -euo pipefail + + FAILED=0 + + check_gate() { + local name="$1" + local result="$2" + case "$result" in + success) echo "✓ ${name}: passed" ;; + skipped) echo "⊘ ${name}: skipped (draft PR)" ;; + failure|cancelled) + echo "✗ ${name}: ${result}" + FAILED=1 + ;; + *) + echo "? ${name}: ${result} (unexpected result)" + FAILED=1 + ;; + esac + } + + check_gate "unit-tests" "$UNIT_TESTS" + check_gate "pr-validation" "$PR_VALIDATION" + check_gate "security" "$SECURITY" + check_gate "contract-gate" "$CONTRACT_GATE" + + { + echo "## Merge Gate Summary" + echo "" + echo "| Gate | Result |" + echo "|------|--------|" + echo "| Unit Tests | ${UNIT_TESTS} |" + echo "| PR Validation | ${PR_VALIDATION} |" + echo "| Security Review | ${SECURITY} |" + echo "| Contract Gate | ${CONTRACT_GATE} |" + echo "" + } >> "$GITHUB_STEP_SUMMARY" + + if (( FAILED == 1 )); then + echo "### ❌ One or more merge gates failed — PR cannot be merged." \ + >> "$GITHUB_STEP_SUMMARY" + echo "::error::One or more merge gates failed. Review the job results above." + exit 1 + fi + + echo "### ✅ All merge gates passed — PR is ready to merge." \ + >> "$GITHUB_STEP_SUMMARY" + echo "All merge gates passed." diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml index aca6214a7..3ebc2d1a2 100644 --- a/.github/workflows/stale.yml +++ b/.github/workflows/stale.yml @@ -1,50 +1,50 @@ -name: Stale Issues and PRs - -on: - schedule: - - cron: '0 0 * * *' # Daily at midnight UTC - workflow_dispatch: - -permissions: - issues: write - pull-requests: write - -jobs: - stale: - runs-on: ubuntu-latest - steps: - - name: Mark stale issues and PRs - uses: actions/stale@b5d41d4e1d5dceea10e7104786b73624c18a190f # v10.2.0 - with: - repo-token: ${{ secrets.GITHUB_TOKEN }} - - # Issues configuration - stale-issue-message: | - This issue has been automatically marked as stale because it has not had any recent activity. - It will be closed in 14 days if no further activity occurs. - Please comment if this issue is still relevant or needs attention. - close-issue-message: | - This issue has been automatically closed due to inactivity. - If you believe this is still relevant, please reopen the issue or create a new one. - days-before-issue-stale: 60 - days-before-issue-close: 14 - stale-issue-label: 'stale' - exempt-issue-labels: 'bug,enhancement,help-wanted,pinned,security,blocked' - - # PRs configuration - stale-pr-message: | - This pull request has been automatically marked as stale because it has not had any recent activity. - It will be closed in 7 days if no further activity occurs. - Please update or comment if this PR is still being worked on. - close-pr-message: | - This pull request has been automatically closed due to inactivity. - If you'd like to continue this work, please reopen the PR or create a new one. - days-before-pr-stale: 30 - days-before-pr-close: 7 - stale-pr-label: 'stale' - exempt-pr-labels: 'wip,in-progress,blocked,help-wanted' - - # General settings - operations-per-run: 100 - remove-stale-when-updated: true - ascending: false +name: Stale Issues and PRs + +on: + schedule: + - cron: '0 0 * * *' # Daily at midnight UTC + workflow_dispatch: + +permissions: + issues: write + pull-requests: write + +jobs: + stale: + runs-on: ubuntu-latest + steps: + - name: Mark stale issues and PRs + uses: actions/stale@b5d41d4e1d5dceea10e7104786b73624c18a190f # v10.2.0 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + + # Issues configuration + stale-issue-message: | + This issue has been automatically marked as stale because it has not had any recent activity. + It will be closed in 14 days if no further activity occurs. + Please comment if this issue is still relevant or needs attention. + close-issue-message: | + This issue has been automatically closed due to inactivity. + If you believe this is still relevant, please reopen the issue or create a new one. + days-before-issue-stale: 60 + days-before-issue-close: 14 + stale-issue-label: 'stale' + exempt-issue-labels: 'bug,enhancement,help-wanted,pinned,security,blocked' + + # PRs configuration + stale-pr-message: | + This pull request has been automatically marked as stale because it has not had any recent activity. + It will be closed in 7 days if no further activity occurs. + Please update or comment if this PR is still being worked on. + close-pr-message: | + This pull request has been automatically closed due to inactivity. + If you'd like to continue this work, please reopen the PR or create a new one. + days-before-pr-stale: 30 + days-before-pr-close: 7 + stale-pr-label: 'stale' + exempt-pr-labels: 'wip,in-progress,blocked,help-wanted' + + # General settings + operations-per-run: 100 + remove-stale-when-updated: true + ascending: false diff --git a/.vscode/ERROR_RESOLUTION_STATUS.md b/.vscode/ERROR_RESOLUTION_STATUS.md index d1018c9de..1a64b1e71 100644 --- a/.vscode/ERROR_RESOLUTION_STATUS.md +++ b/.vscode/ERROR_RESOLUTION_STATUS.md @@ -1,94 +1,94 @@ -# Error Resolution Status - -**Date:** October 31, 2025 -**Status:** ✅ ALL REPOSITORY ERRORS RESOLVED - -## Summary - -All compile/lint errors in actual repository files have been successfully resolved. The only remaining "errors" reported by the global error scan are ephemeral VS Code chat artifacts that are not part of the repository. - -## Resolved Issues - -### 1. GitHub Actions Workflow Secrets -- **File:** `.github/workflows/quantum-orchestration.yml` -- **Issue:** Linter flagged invalid context access for `secrets.AZURE_CREDENTIALS` and `secrets.LOGIC_APP_URL` -- **Resolution:** Updated workflow to use `workflow_dispatch` inputs instead of direct secret references -- **Status:** ✅ PASS - -### 2. PowerShell Deployment Script -- **File:** `ai-projects/quantum-ml/deploy_simple.ps1` -- **Issue:** False-positive "loginCheck assigned but never used" from PSScriptAnalyzer -- **Resolution:** - - Added inline documentation explaining try/catch pattern - - Added script-level suppression attribute - - Created `PSScriptAnalyzerSettings.psd1` configuration files - - Updated VS Code settings to use custom analyzer config -- **Status:** ✅ PASS (properly documented and suppressed) - -### 3. JSONL Format Errors -- **Files:** `AI/microsoft_phi-silica-3.6_v1/data/train.json`, `test.json` -- **Issue:** Trailing blank lines flagged as "End of file expected" -- **Resolution:** Removed trailing newlines from JSONL files -- **Status:** ✅ PASS - -### 4. Python Import Resolution -- **File:** `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` -- **Issue:** Missing imports for `datasets`, `transformers`, `peft` packages -- **Resolution:** - - Script already handles missing imports gracefully via try/except - - Updated README.md with complete package documentation - - Created `.vscode/python-imports.md` guide for developers - - Confirmed `requirements.txt` contains all needed packages -- **Status:** ✅ PASS (imports are optional for dry-run mode; documented) - -## Verified Clean Files - -All orchestration and automation scripts: -- ✅ `ai-projects/quantum-ml/deploy_simple.ps1` -- ✅ `ai-projects/quantum-ml/azure/quantum_batch_jobs.ps1` -- ✅ `ai-projects/quantum-ml/azure/quantum_cli_automation.ps1` -- ✅ `ai-projects/quantum-ml/azure/quantum_master_orchestration.ps1` -- ✅ `ai-projects/quantum-ml/azure/quantum_orchestration_robust.ps1` -- ✅ `ai-projects/quantum-ml/azure/quantum_full_logicapp_orchestration.ps1` -- ✅ `.github/workflows/quantum-orchestration.yml` - -All fine-tuning workspace files: -- ✅ `AI/microsoft_phi-silica-3.6_v1/data/train.json` -- ✅ `AI/microsoft_phi-silica-3.6_v1/data/test.json` -- ✅ `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` -- ✅ `AI/microsoft_phi-silica-3.6_v1/README.md` - -## Non-Repository Artifacts (Can Be Ignored) - -The global error scan reports issues in: -- `vscode-chat-code-block://...` URIs - These are temporary chat conversation artifacts -- These are NOT part of the repository and will disappear when the chat session ends - -## Configuration Files Added - -1. **`ai-projects/quantum-ml/PSScriptAnalyzerSettings.psd1`** - Root-level analyzer config -2. **`ai-projects/quantum-ml/.vscode/PSScriptAnalyzerSettings.psd1`** - Project-specific analyzer config -3. **`.vscode/settings.json`** - Updated to reference analyzer config path -4. **`AI/microsoft_phi-silica-3.6_v1/.vscode/python-imports.md`** - Import resolution guide - -## Next Steps - -All repository files are production-ready. To run the quantum orchestration: - -```powershell -cd quantum-ai\azure -pwsh ./quantum_master_orchestration.ps1 -ResourceGroup rg-quantum-ai -WorkspaceName quantum-ai-workspace -Location eastus -``` - -For fine-tuning with proper packages installed: - -```powershell -cd AI\microsoft_phi-silica-3.6_v1 -.\venv\Scripts\Activate.ps1 -pip install -r requirements.txt -python .\scripts\train_lora.py --dry-run --dataset .\data --config .\lora\lora.yaml -``` - ---- - -**Conclusion:** All actionable errors have been resolved. The repository is clean and ready for deployment. +# Error Resolution Status + +**Date:** October 31, 2025 +**Status:** ✅ ALL REPOSITORY ERRORS RESOLVED + +## Summary + +All compile/lint errors in actual repository files have been successfully resolved. The only remaining "errors" reported by the global error scan are ephemeral VS Code chat artifacts that are not part of the repository. + +## Resolved Issues + +### 1. GitHub Actions Workflow Secrets +- **File:** `.github/workflows/quantum-orchestration.yml` +- **Issue:** Linter flagged invalid context access for `secrets.AZURE_CREDENTIALS` and `secrets.LOGIC_APP_URL` +- **Resolution:** Updated workflow to use `workflow_dispatch` inputs instead of direct secret references +- **Status:** ✅ PASS + +### 2. PowerShell Deployment Script +- **File:** `ai-projects/quantum-ml/deploy_simple.ps1` +- **Issue:** False-positive "loginCheck assigned but never used" from PSScriptAnalyzer +- **Resolution:** + - Added inline documentation explaining try/catch pattern + - Added script-level suppression attribute + - Created `PSScriptAnalyzerSettings.psd1` configuration files + - Updated VS Code settings to use custom analyzer config +- **Status:** ✅ PASS (properly documented and suppressed) + +### 3. JSONL Format Errors +- **Files:** `AI/microsoft_phi-silica-3.6_v1/data/train.json`, `test.json` +- **Issue:** Trailing blank lines flagged as "End of file expected" +- **Resolution:** Removed trailing newlines from JSONL files +- **Status:** ✅ PASS + +### 4. Python Import Resolution +- **File:** `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` +- **Issue:** Missing imports for `datasets`, `transformers`, `peft` packages +- **Resolution:** + - Script already handles missing imports gracefully via try/except + - Updated README.md with complete package documentation + - Created `.vscode/python-imports.md` guide for developers + - Confirmed `requirements.txt` contains all needed packages +- **Status:** ✅ PASS (imports are optional for dry-run mode; documented) + +## Verified Clean Files + +All orchestration and automation scripts: +- ✅ `ai-projects/quantum-ml/deploy_simple.ps1` +- ✅ `ai-projects/quantum-ml/azure/quantum_batch_jobs.ps1` +- ✅ `ai-projects/quantum-ml/azure/quantum_cli_automation.ps1` +- ✅ `ai-projects/quantum-ml/azure/quantum_master_orchestration.ps1` +- ✅ `ai-projects/quantum-ml/azure/quantum_orchestration_robust.ps1` +- ✅ `ai-projects/quantum-ml/azure/quantum_full_logicapp_orchestration.ps1` +- ✅ `.github/workflows/quantum-orchestration.yml` + +All fine-tuning workspace files: +- ✅ `AI/microsoft_phi-silica-3.6_v1/data/train.json` +- ✅ `AI/microsoft_phi-silica-3.6_v1/data/test.json` +- ✅ `AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py` +- ✅ `AI/microsoft_phi-silica-3.6_v1/README.md` + +## Non-Repository Artifacts (Can Be Ignored) + +The global error scan reports issues in: +- `vscode-chat-code-block://...` URIs - These are temporary chat conversation artifacts +- These are NOT part of the repository and will disappear when the chat session ends + +## Configuration Files Added + +1. **`ai-projects/quantum-ml/PSScriptAnalyzerSettings.psd1`** - Root-level analyzer config +2. **`ai-projects/quantum-ml/.vscode/PSScriptAnalyzerSettings.psd1`** - Project-specific analyzer config +3. **`.vscode/settings.json`** - Updated to reference analyzer config path +4. **`AI/microsoft_phi-silica-3.6_v1/.vscode/python-imports.md`** - Import resolution guide + +## Next Steps + +All repository files are production-ready. To run the quantum orchestration: + +```powershell +cd quantum-ai\azure +pwsh ./quantum_master_orchestration.ps1 -ResourceGroup rg-quantum-ai -WorkspaceName quantum-ai-workspace -Location eastus +``` + +For fine-tuning with proper packages installed: + +```powershell +cd AI\microsoft_phi-silica-3.6_v1 +.\venv\Scripts\Activate.ps1 +pip install -r requirements.txt +python .\scripts\train_lora.py --dry-run --dataset .\data --config .\lora\lora.yaml +``` + +--- + +**Conclusion:** All actionable errors have been resolved. 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a/.vscode/settings_optimized.json b/.vscode/settings_optimized.json index eacc5c3b9..ed7814488 100644 --- a/.vscode/settings_optimized.json +++ b/.vscode/settings_optimized.json @@ -1,52 +1,52 @@ -{ - // Performance optimizations for faster workspace operations - "files.watcherExclude": { - "**/.git/objects/**": true, - "**/.git/subtree-cache/**": true, - "**/node_modules/*/**": true, - "**/.hg/store/**": true, - "**/venv/**": true, - "**/data_out/**": true, - "**/datasets/**": true, - "**/__pycache__/**": true, - "**/.pytest_cache/**": true, - "**/*.pyc": true - }, - "search.exclude": { - "**/venv": true, - "**/data_out": true, - "**/__pycache__": true, - "**/.pytest_cache": true, - "**/*.pyc": true, - "**/datasets/massive_quantum": true - }, - "files.exclude": { - "**/__pycache__": true, - "**/.pytest_cache": true, - "**/*.pyc": true - }, - "python.testing.pytestEnabled": true, - "python.testing.unittestEnabled": false, - "python.testing.pytestArgs": [ - "tests", - "-m", "not slow 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"**/datasets/massive_quantum": true + }, + "files.exclude": { + "**/__pycache__": true, + "**/.pytest_cache": true, + "**/*.pyc": true + }, + "python.testing.pytestEnabled": true, + "python.testing.unittestEnabled": false, + "python.testing.pytestArgs": [ + "tests", + "-m", "not slow and not azure", + "--tb=short", + "-q" + ], + "python.analysis.exclude": [ + "**/venv/**", + "**/data_out/**", + "**/__pycache__/**", + "**/datasets/**" + ], + "python.linting.enabled": false, + "python.analysis.indexing": true, + "python.analysis.autoImportCompletions": true, + "terminal.integrated.defaultProfile.windows": "PowerShell", + "terminal.integrated.profiles.windows": { + "PowerShell": { + "source": "PowerShell", + "icon": "terminal-powershell" + } + } +} diff --git a/.vscode/tasks.json b/.vscode/tasks.json index 6410bc2bf..b4dac8b4e 100644 --- a/.vscode/tasks.json +++ b/.vscode/tasks.json @@ -1,208 +1,208 @@ -{ - "version": "2.0.0", - "tasks": [ - { - "label": "func: host start", - "type": 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--listen 127.0.0.1:5679 -m agentdev run ai-projects/writer-reviewer-workflow/main.py --verbose --port 8088", + "isBackground": true, + "options": { + "cwd": "${workspaceFolder}" + }, + "dependsOn": [ + "Validate writer-reviewer debug prerequisites" + ], + "problemMatcher": { + "pattern": [ + { + "regexp": "^.*$", + "file": 0, + "location": 1, + "message": 2 + } + ], + "background": { + "activeOnStart": true, + "beginsPattern": ".*", + "endsPattern": "Application startup complete|running on|Started server process" + } + } + }, + { + "label": "Open writer-reviewer Agent Inspector", + "type": "shell", + "command": "echo '${input:openWriterReviewerInspector}'", + "presentation": { + "reveal": "never" + }, + "dependsOn": [ + "Run writer-reviewer HTTP server" + ] + }, + { + "label": "Terminate All Tasks", + "type": "shell", + "command": "echo ${input:terminate}", + "problemMatcher": [] + } + ], + "inputs": [ + { + "id": "openWriterReviewerInspector", + "type": "command", + "command": "ai-mlstudio.openTestTool", + "args": { + "triggeredFrom": "tasks", + "port": 8088 + } + }, + { + "id": "terminate", + "type": "command", + "command": "workbench.action.tasks.terminate", + "args": "terminateAll" + } + ] +} diff --git a/.vscode/test-profiles.json b/.vscode/test-profiles.json index aa4b738fd..f9ad43f61 100644 --- a/.vscode/test-profiles.json +++ b/.vscode/test-profiles.json @@ -1,72 +1,72 @@ -{ - "profiles": [ - { - "id": "unit", - "label": "Unit Tests (Fast)", - "kind": "run", - "args": [ - "-m", - "not slow and not azure", - "tests/test_*_unit.py" - ], - "icon": "zap" - }, - { - "id": "integration", - "label": "Integration Tests", - "kind": "run", - "args": [ - "-m", - "integration", - "tests/" - ], - "icon": "plug" - }, - { - "id": "all-fast", - "label": "All Fast Tests", - "kind": "run", - "args": [ - "-m", - "not slow and not azure", - "tests/" - ], - "icon": "rocket" - }, - { - "id": "quantum", - "label": "Quantum Tests", - "kind": "run", - "args": [ - "tests/test_quantum*.py", - "tests/test_validate_qiskit*.py" - ], - "icon": "beaker" - }, - { - "id": "all", - "label": "All Tests", - "kind": "run", - "args": [ - "-m", - "not azure", - "tests/" - ], - "icon": "check-all" - }, - { - "id": "coverage", - "label": "All with Coverage", - "kind": "run", - "args": [ - "--cov=.", - "--cov-report=html", - "--cov-report=term", - "-m", - "not azure", - "tests/" - ], - "icon": "graph" - } - ] -} +{ + "profiles": [ + { + "id": "unit", + "label": "Unit Tests (Fast)", + "kind": "run", + "args": [ + "-m", + "not slow and not azure", + "tests/test_*_unit.py" + ], + "icon": "zap" + }, + { + "id": "integration", + "label": "Integration Tests", + "kind": "run", + "args": [ + "-m", + "integration", + "tests/" + ], + "icon": "plug" + }, + { + "id": "all-fast", + "label": "All Fast Tests", + "kind": "run", + "args": [ + "-m", + "not slow and not azure", + "tests/" + ], + "icon": "rocket" + }, + { + "id": "quantum", + "label": "Quantum Tests", + "kind": "run", + "args": [ + "tests/test_quantum*.py", + "tests/test_validate_qiskit*.py" + ], + "icon": "beaker" + }, + { + "id": "all", + "label": "All Tests", + "kind": "run", + "args": [ + "-m", + "not azure", + "tests/" + ], + "icon": "check-all" + }, + { + "id": "coverage", + "label": "All with Coverage", + "kind": "run", + "args": [ + "--cov=.", + "--cov-report=html", + "--cov-report=term", + "-m", + "not azure", + "tests/" + ], + "icon": "graph" + } + ] +} diff --git a/AI/microsoft_phi-silica-3.6_v1/scripts/otel_callback.py b/AI/microsoft_phi-silica-3.6_v1/scripts/otel_callback.py index f08f7693d..60f929ca4 100644 --- a/AI/microsoft_phi-silica-3.6_v1/scripts/otel_callback.py +++ b/AI/microsoft_phi-silica-3.6_v1/scripts/otel_callback.py @@ -1,38 +1,38 @@ -"""Compatibility wrapper for legacy otel_callback path. - -Canonical implementation is in: - ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/otel_callback.py -""" - -from __future__ import annotations - -import importlib.util -import sys -from pathlib import Path - -_CANONICAL = ( - Path(__file__).resolve().parents[3] - / "ai-projects" - / "lora-training" - / "microsoft_phi-silica-3.6_v1" - / "scripts" - / "otel_callback.py" -) - -_spec = importlib.util.spec_from_file_location("_canonical_otel_callback", _CANONICAL) -if _spec is None or _spec.loader is None: - raise ImportError(f"Unable to load canonical otel callback: {_CANONICAL}") - -_mod = importlib.util.module_from_spec(_spec) -sys.modules[_spec.name] = _mod -_spec.loader.exec_module(_mod) - -for _name, _value in _mod.__dict__.items(): - if _name.startswith("__"): - continue - globals()[_name] = _value - -if hasattr(_mod, "__all__"): - __all__ = list(_mod.__all__) # type: ignore[attr-defined] -else: - __all__ = [k for k in globals() if not k.startswith("__")] +"""Compatibility wrapper for legacy otel_callback path. + +Canonical implementation is in: + ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/otel_callback.py +""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path + +_CANONICAL = ( + Path(__file__).resolve().parents[3] + / "ai-projects" + / "lora-training" + / "microsoft_phi-silica-3.6_v1" + / "scripts" + / "otel_callback.py" +) + +_spec = importlib.util.spec_from_file_location("_canonical_otel_callback", _CANONICAL) +if _spec is None or _spec.loader is None: + raise ImportError(f"Unable to load canonical otel callback: {_CANONICAL}") + +_mod = importlib.util.module_from_spec(_spec) +sys.modules[_spec.name] = _mod +_spec.loader.exec_module(_mod) + +for _name, _value in _mod.__dict__.items(): + if _name.startswith("__"): + continue + globals()[_name] = _value + +if hasattr(_mod, "__all__"): + __all__ = list(_mod.__all__) # type: ignore[attr-defined] +else: + __all__ = [k for k in globals() if not k.startswith("__")] diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 52b479a36..d499bd39c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,35 +1,201 @@ -## Contributing to Aria +# Contributing to Aria -Thank you — contributions are welcome. This file contains focused developer instructions for running tests and the test watcher. +Thank you for your interest in contributing to **Aria** — an interactive AI character platform with autonomous learning, quantum ML integration, and multi-provider chat backends. This guide describes how to set up your environment, run tests, and submit high-quality contributions. -Running tests -------------- -Run the full test suite once: +--- -```bash -python3 -m pytest tests -q --maxfail=1 && echo FULL_PYTEST_OK -``` +## Table of Contents + +- [Code of Conduct](#code-of-conduct) +- [Ways to Contribute](#ways-to-contribute) +- [Development Environment](#development-environment) + - [Local Setup](#local-setup) + - [Codespaces / Devcontainer](#codespaces--devcontainer) + - [Project-Specific Virtualenvs](#project-specific-virtualenvs) +- [Running Tests](#running-tests) + - [Full Suite](#full-suite) + - [Targeted Runs](#targeted-runs) + - [Test Watcher](#test-watcher) +- [Code Style & Quality](#code-style--quality) +- [Commit & Branch Conventions](#commit--branch-conventions) +- [Pull Request Process](#pull-request-process) +- [Reporting Issues](#reporting-issues) +- [Security](#security) +- [License](#license) + +--- + +## Code of Conduct + +Participation in this project is governed by our community standards. Be respectful, constructive, and inclusive. Please report unacceptable behavior to the maintainers via a private channel (see [Security](#security)). + +## Ways to Contribute + +- 🐛 **Bug reports** — open an issue with reproduction steps, expected vs. actual behavior, and environment details. +- ✨ **Feature requests** — describe the use case and proposed API/UX before opening a PR for large changes. +- 📖 **Documentation** — improvements to READMEs, inline docstrings, and examples are always welcome. +- 🧪 **Tests** — adding coverage for existing untested paths is a great first contribution. +- 🔧 **Code** — bug fixes, refactors, performance improvements, and new features. + +> **Tip:** For non-trivial changes, open a discussion or issue first to align on design. -Reproducible dev environment ----------------------------- -Create a virtualenv and install development dependencies: +--- + +## Development Environment + +### Local Setup + +Requirements: + +- Python **3.10+** (3.11 recommended) +- `git` +- (Optional) [Azure Functions Core Tools](https://learn.microsoft.com/azure/azure-functions/functions-run-local) for running `function_app.py` + +Create an isolated virtualenv and install development dependencies: ```bash python3 -m venv .venv -source .venv/bin/activate +source .venv/bin/activate # Windows: .venv\Scripts\activate +python -m pip install --upgrade pip pip install -r requirements-dev.txt ``` -Test watcher ------------ -There is a lightweight watcher at `scripts/test_watcher.py` that re-runs tests on file changes. See `NEXT_STEPS.md` for additional notes and troubleshooting. +### Codespaces / Devcontainer + +This repo includes a [`.devcontainer/devcontainer.json`](.devcontainer/devcontainer.json) that provisions a Python dev container and installs `requirements-dev.txt` on first start. Open in GitHub Codespaces or VS Code Dev Containers for a zero-config setup. + +### Project-Specific Virtualenvs + +Aria is composed of three isolated sub-projects, each with its own virtualenv and dependencies: + +| Project | Path | Purpose | +| --- | --- | --- | +| Quantum ML | `ai-projects/quantum-ml/` | MCP server, dashboard, quantum ML pipelines | +| Chat CLI | `ai-projects/chat-cli/` | Multi-provider chat CLI | +| Phi-Silica fine-tuning | `AI/microsoft_phi-silica-3.6_v1/` | Phi-3.5 LoRA fine-tuning | + +When working in any of these, create a separate virtualenv inside that directory and install its `requirements.txt`. See each project's README for details. + +--- + +## Running Tests + +### Full Suite + +Run the complete test suite (fail-fast): + +```bash +python3 -m pytest tests -q --maxfail=1 +``` + +For full output with coverage (if `pytest-cov` is installed): + +```bash +python3 -m pytest tests --cov=. --cov-report=term-missing +``` + +### Targeted Runs + +Run a single file or test: + +```bash +python3 -m pytest tests/test_function_app.py -q +python3 -m pytest tests/test_function_app.py::test_status_endpoint -q +``` + +Filter by keyword: + +```bash +python3 -m pytest -k "chat and not slow" -q +``` + +### Test Watcher + +A lightweight file watcher at [`scripts/test_watcher.py`](scripts/test_watcher.py) re-runs the suite on file changes: + +```bash +python3 scripts/test_watcher.py +``` + +See [`NEXT_STEPS.md`](NEXT_STEPS.md) for troubleshooting notes. + +--- + +## Code Style & Quality + +- **Formatting:** [`black`](https://black.readthedocs.io/) (line length 100) and [`isort`](https://pycqa.github.io/isort/) for imports. +- **Linting:** [`ruff`](https://docs.astral.sh/ruff/) for fast static analysis. +- **Typing:** Add type hints to all new public functions; `mypy` is run in CI where configured. +- **Docstrings:** Use Google-style docstrings for modules, classes, and public functions. + +Run all checks locally before pushing: + +```bash +ruff check . +black --check . +isort --check-only . +``` + +Auto-fix formatting: + +```bash +ruff check --fix . +black . +isort . +``` + +--- + +## Commit & Branch Conventions + +- **Branch names:** `feature/`, `fix/`, `docs/`. +- **Commits:** Follow [Conventional Commits](https://www.conventionalcommits.org/): + - `feat: add auto-execute action parser` + - `fix(chat): handle SSE disconnect gracefully` + - `docs: clarify devcontainer setup` + - `test: cover quantum job submission errors` + - `refactor:`, `perf:`, `chore:`, `ci:` as appropriate. +- Keep commits focused; avoid mixing unrelated changes. + +--- + +## Pull Request Process + +1. **Fork** the repo and create a topic branch from `main`. +2. **Make your changes** with tests and documentation updates. +3. **Run the full test suite and linters locally** — they must pass. +4. **Update the changelog / docs** if your change is user-visible. +5. **Open a Pull Request** against `main`: + - Fill out the PR template completely. + - Link related issues (`Fixes #123`). + - Provide context: what, why, and how to test. + - Include screenshots/recordings for UI changes (e.g., `apps/aria/`). +6. **CI must pass.** The [`.github/workflows/pr-tests.yml`](.github/workflows/pr-tests.yml) workflow runs `pytest` on every PR targeting `main`. +7. **Address review feedback** by pushing follow-up commits (avoid force-pushing during review unless asked). +8. A maintainer will merge once approved. + +> **Note:** Large or breaking changes should be discussed in an issue or draft PR first. + +--- + +## Reporting Issues + +When opening an issue, please include: + +- A clear, descriptive title. +- Steps to reproduce (minimal example preferred). +- Expected vs. actual behavior. +- Environment: OS, Python version, relevant package versions, provider (Azure OpenAI / OpenAI / LMStudio / local). +- Logs or stack traces (redact secrets). + +## Security + +**Do not open public issues for security vulnerabilities.** Instead, follow the process described in [`SECURITY.md`](SECURITY.md) (or contact the maintainers privately). We aim to acknowledge reports within a few business days. + +## License -Codespaces / Devcontainer -------------------------- -This repo includes a `.devcontainer/devcontainer.json` that sets up a Python dev container and installs `requirements-dev.txt` on first start. +By contributing, you agree that your contributions will be licensed under the same license as this project. See [`LICENSE`](LICENSE) for details. -CI --- -A minimal GitHub Actions workflow `.github/workflows/pr-tests.yml` runs `pytest` on pull requests targeting `main`. +--- -If you'd like me to add additional contributor guidelines (linting, PR template, or codeowners), I can add those next. +Thanks again for helping make Aria better! 💙 diff --git a/REVENUE_INTEGRATION.md b/REVENUE_INTEGRATION.md index 21307446f..72821368f 100644 --- a/REVENUE_INTEGRATION.md +++ b/REVENUE_INTEGRATION.md @@ -1,261 +1,261 @@ -# Revenue Stream Integration Guide - -## Overview - -The Aria platform now has a **complete revenue stream system** integrated into the main character interface, achieving **$2,235/month MRR** (111.8% of $2,000 target). - -## What Was Added - -### 1. Navigation Bar on Aria Character Interface - -The main Aria character page (`aria_web/index.html`) now includes: - -- **Navigation Bar** - Clean, responsive navigation at the top -- **Monetization Links** - Direct links to: - - 🏠 Home (monetization hub) - - 💰 Pricing - - 📊 My Subscription - - 👑 Admin Dashboard -- **Subscription Badge** - Shows current tier (Free/Pro/Enterprise) -- **Upgrade Button** - Prominent call-to-action - -**Screenshot:** -![Aria Interface with Monetization Nav](https://github.com/user-attachments/assets/4b505ae3-7324-4570-85d8-2e78ebb0bd5f) - -### 2. "Try Aria" Button on Monetization Hub - -The monetization index page now prominently features: - -- **"Try Aria" Primary Button** - First hero button leading to character interface -- **Aria Character Link** - First item in Platform Pages section - -**Screenshot:** -![Monetization Hub with Try Aria](https://github.com/user-attachments/assets/fe80e500-19c1-45e9-8a5f-05adc035837f) - -## User Journey - -```mermaid -graph LR - A[Monetization Hub] -->|Try Aria| B[Aria Character] - B -->|Pricing| C[Pricing Page] - C -->|Upgrade| D[Checkout] - D -->|Success| E[My Subscription] - B -->|Home| A - B -->|Admin| F[Admin Dashboard] -``` - -### Complete Flow - -1. **Discovery** - User lands on monetization hub (`monetization-index.html`) -2. **Try Platform** - Clicks "Try Aria" to experience the character -3. **View Subscription** - Sees "Free Tier" badge and navigation bar -4. **Explore Pricing** - Clicks "Pricing" or "Upgrade" to view tiers -5. **Purchase** - Selects plan and completes checkout -6. **Active Subscription** - Returns to Aria with upgraded tier badge - -## Technical Implementation - -### Aria Character Interface (`aria_web/index.html`) - -**Added CSS:** - -```css -/* Navigation Bar */ -.nav-bar { - background: rgba(255, 255, 255, 0.95); - border-radius: 50px; - padding: 10px 20px; - /* ... responsive design ... */ -} - -/* Subscription Badge */ -.subscription-badge { - background: #4caf50; - color: white; - padding: 5px 12px; - border-radius: 15px; -} - -.subscription-badge.free { - background: #9e9e9e; -} -.subscription-badge.pro { - background: linear-gradient(135deg, #667eea, #764ba2); -} -.subscription-badge.enterprise { - background: linear-gradient(135deg, #ffd700, #ffed4e); -} -``` - -**Added JavaScript:** - -```javascript -// Fetch and display subscription status dynamically -async function loadSubscriptionStatus() { - const response = await fetch("/api/subscription/status?user_id=demo_user"); - if (response.ok) { - const data = await response.json(); - // Update badge based on tier - badge.textContent = `${data.tier_name} Tier`; - badge.className = `subscription-badge ${data.tier.toLowerCase()}`; - } -} -``` - -### Monetization Hub (`monetization-index.html`) - -**Added Hero Button:** - -```html -👤 Try Aria -``` - -**Added Platform Section:** - -```html - -

    👤 Aria Character

    -

    - Interactive 3D AI character with natural language commands. The main - platform experience. -

    -     -``` - -## Revenue Model - -### Subscription Tiers - -| Tier | Price | Target | Revenue | -| -------------- | ------- | --------- | ------------- | -| **Free** | $0/mo | Unlimited | $0 | -| **Pro** | $49/mo | 5 users | $245 | -| **Enterprise** | $199/mo | 10 users | $1,990 | -| **Total** | - | 15 users | **$2,235/mo** | - -**Annual Revenue:** $26,820 - -### Feature Gates - -| Feature | Free | Pro | Enterprise | -| ------------------ | ------ | ---------- | ---------- | -| Chat Messages | 100/mo | 10,000/mo | Unlimited | -| Aria Character | Basic | Full | Full | -| Quantum Computing | ❌ | 50 jobs/mo | Unlimited | -| Model Training | ❌ | 20 hrs/mo | Unlimited | -| API Access | ❌ | 10K req/mo | Unlimited | -| Commercial License | ❌ | ✅ | ✅ | - -## API Integration - -The subscription status is dynamically fetched from: - -```bash -GET /api/subscription/status?user_id=demo_user -``` - -**Response:** - -```json -{ - "user_id": "demo_user", - "tier": "pro", - "tier_name": "PRO", - "price": 49, - "is_active": true, - "usage": { - "chat_messages": 150, - "quantum_jobs": 5 - }, - "limits": { - "chat_messages": 10000, - "quantum_jobs": 50 - } -} -``` - -## Testing - -### Local Testing - -1. **Start Aria Server:** - -```bash -cd aria_web -python server.py -``` - -1. **Start HTTP Server for Monetization Pages:** - -```bash -python -m http.server 8000 -``` - -1. **Test Navigation:** - -- Visit: (Aria character) -- Click "Home" → Should go to monetization hub -- Click "Pricing" → Should show pricing page -- Click "Upgrade" → Should show pricing page -- Visit: -- Click "Try Aria" → Should go to character interface - -### Visual Verification - -- ✅ Navigation bar displays correctly on Aria interface -- ✅ Subscription badge shows "Free Tier" by default -- ✅ All navigation links work -- ✅ Responsive design works on mobile -- ✅ "Try Aria" button is prominent on monetization hub -- ✅ Page transitions are smooth - -## Files Modified - -1. **`aria_web/index.html`** - Added navigation bar and subscription badge -2. **`monetization-index.html`** - Added "Try Aria" button and Aria Character link -3. **`README.md`** - Added revenue stream section - -## Next Steps - -### Phase 1: Complete ✅ - -- [x] Integrate navigation into Aria character interface -- [x] Add subscription badge -- [x] Connect monetization hub to character interface -- [x] Document integration - -### Phase 2: Future Enhancements (Optional) - -- [ ] Add usage tracking for character interactions -- [ ] Implement feature gating (e.g., limit free tier to 100 commands/day) -- [ ] Add upgrade prompts when approaching limits -- [ ] Implement real-time subscription status updates -- [ ] Add analytics tracking (Google Analytics, Mixpanel) - -### Phase 3: Production Deployment (When Ready) - -- [ ] Configure Stripe payment processing -- [ ] Set up webhook handlers for subscription events -- [ ] Add email notifications for subscription changes -- [ ] Implement invoice generation -- [ ] Set up customer support portal - -## Resources - -- **Complete Guide:** [docs/guides/MONETIZATION_GUIDE.md](docs/guides/MONETIZATION_GUIDE.md) -- **Quick Start:** [docs/guides/QUICK_START_MONETIZATION.md](docs/guides/QUICK_START_MONETIZATION.md) -- **Income Summary:** [docs/summaries/INCOME_STREAM_SUMMARY.md](docs/summaries/INCOME_STREAM_SUMMARY.md) -- **Setup Script:** `python setup_monetization.py` - -## Support - -For questions or issues: - -- 📖 See documentation above -- 🐛 Open an issue on GitHub -- 💬 Contact: - ---- - -**Revenue Stream Status:** ✅ Active - Achieving 111.8% of $2,000 target -**Last Updated:** February 17, 2026 +# Revenue Stream Integration Guide + +## Overview + +The Aria platform now has a **complete revenue stream system** integrated into the main character interface, achieving **$2,235/month MRR** (111.8% of $2,000 target). + +## What Was Added + +### 1. Navigation Bar on Aria Character Interface + +The main Aria character page (`aria_web/index.html`) now includes: + +- **Navigation Bar** - Clean, responsive navigation at the top +- **Monetization Links** - Direct links to: + - 🏠 Home (monetization hub) + - 💰 Pricing + - 📊 My Subscription + - 👑 Admin Dashboard +- **Subscription Badge** - Shows current tier (Free/Pro/Enterprise) +- **Upgrade Button** - Prominent call-to-action + +**Screenshot:** +![Aria Interface with Monetization Nav](https://github.com/user-attachments/assets/4b505ae3-7324-4570-85d8-2e78ebb0bd5f) + +### 2. "Try Aria" Button on Monetization Hub + +The monetization index page now prominently features: + +- **"Try Aria" Primary Button** - First hero button leading to character interface +- **Aria Character Link** - First item in Platform Pages section + +**Screenshot:** +![Monetization Hub with Try Aria](https://github.com/user-attachments/assets/fe80e500-19c1-45e9-8a5f-05adc035837f) + +## User Journey + +```mermaid +graph LR + A[Monetization Hub] -->|Try Aria| B[Aria Character] + B -->|Pricing| C[Pricing Page] + C -->|Upgrade| D[Checkout] + D -->|Success| E[My Subscription] + B -->|Home| A + B -->|Admin| F[Admin Dashboard] +``` + +### Complete Flow + +1. **Discovery** - User lands on monetization hub (`monetization-index.html`) +2. **Try Platform** - Clicks "Try Aria" to experience the character +3. **View Subscription** - Sees "Free Tier" badge and navigation bar +4. **Explore Pricing** - Clicks "Pricing" or "Upgrade" to view tiers +5. **Purchase** - Selects plan and completes checkout +6. **Active Subscription** - Returns to Aria with upgraded tier badge + +## Technical Implementation + +### Aria Character Interface (`aria_web/index.html`) + +**Added CSS:** + +```css +/* Navigation Bar */ +.nav-bar { + background: rgba(255, 255, 255, 0.95); + border-radius: 50px; + padding: 10px 20px; + /* ... responsive design ... */ +} + +/* Subscription Badge */ +.subscription-badge { + background: #4caf50; + color: white; + padding: 5px 12px; + border-radius: 15px; +} + +.subscription-badge.free { + background: #9e9e9e; +} +.subscription-badge.pro { + background: linear-gradient(135deg, #667eea, #764ba2); +} +.subscription-badge.enterprise { + background: linear-gradient(135deg, #ffd700, #ffed4e); +} +``` + +**Added JavaScript:** + +```javascript +// Fetch and display subscription status dynamically +async function loadSubscriptionStatus() { + const response = await fetch("/api/subscription/status?user_id=demo_user"); + if (response.ok) { + const data = await response.json(); + // Update badge based on tier + badge.textContent = `${data.tier_name} Tier`; + badge.className = `subscription-badge ${data.tier.toLowerCase()}`; + } +} +``` + +### Monetization Hub (`monetization-index.html`) + +**Added Hero Button:** + +```html +👤 Try Aria +``` + +**Added Platform Section:** + +```html + +

    👤 Aria Character

    +

    + Interactive 3D AI character with natural language commands. The main + platform experience. +

    +     +``` + +## Revenue Model + +### Subscription Tiers + +| Tier | Price | Target | Revenue | +| -------------- | ------- | --------- | ------------- | +| **Free** | $0/mo | Unlimited | $0 | +| **Pro** | $49/mo | 5 users | $245 | +| **Enterprise** | $199/mo | 10 users | $1,990 | +| **Total** | - | 15 users | **$2,235/mo** | + +**Annual Revenue:** $26,820 + +### Feature Gates + +| Feature | Free | Pro | Enterprise | +| ------------------ | ------ | ---------- | ---------- | +| Chat Messages | 100/mo | 10,000/mo | Unlimited | +| Aria Character | Basic | Full | Full | +| Quantum Computing | ❌ | 50 jobs/mo | Unlimited | +| Model Training | ❌ | 20 hrs/mo | Unlimited | +| API Access | ❌ | 10K req/mo | Unlimited | +| Commercial License | ❌ | ✅ | ✅ | + +## API Integration + +The subscription status is dynamically fetched from: + +```bash +GET /api/subscription/status?user_id=demo_user +``` + +**Response:** + +```json +{ + "user_id": "demo_user", + "tier": "pro", + "tier_name": "PRO", + "price": 49, + "is_active": true, + "usage": { + "chat_messages": 150, + "quantum_jobs": 5 + }, + "limits": { + "chat_messages": 10000, + "quantum_jobs": 50 + } +} +``` + +## Testing + +### Local Testing + +1. **Start Aria Server:** + +```bash +cd aria_web +python server.py +``` + +1. **Start HTTP Server for Monetization Pages:** + +```bash +python -m http.server 8000 +``` + +1. **Test Navigation:** + +- Visit: (Aria character) +- Click "Home" → Should go to monetization hub +- Click "Pricing" → Should show pricing page +- Click "Upgrade" → Should show pricing page +- Visit: +- Click "Try Aria" → Should go to character interface + +### Visual Verification + +- ✅ Navigation bar displays correctly on Aria interface +- ✅ Subscription badge shows "Free Tier" by default +- ✅ All navigation links work +- ✅ Responsive design works on mobile +- ✅ "Try Aria" button is prominent on monetization hub +- ✅ Page transitions are smooth + +## Files Modified + +1. **`aria_web/index.html`** - Added navigation bar and subscription badge +2. **`monetization-index.html`** - Added "Try Aria" button and Aria Character link +3. **`README.md`** - Added revenue stream section + +## Next Steps + +### Phase 1: Complete ✅ + +- [x] Integrate navigation into Aria character interface +- [x] Add subscription badge +- [x] Connect monetization hub to character interface +- [x] Document integration + +### Phase 2: Future Enhancements (Optional) + +- [ ] Add usage tracking for character interactions +- [ ] Implement feature gating (e.g., limit free tier to 100 commands/day) +- [ ] Add upgrade prompts when approaching limits +- [ ] Implement real-time subscription status updates +- [ ] Add analytics tracking (Google Analytics, Mixpanel) + +### Phase 3: Production Deployment (When Ready) + +- [ ] Configure Stripe payment processing +- [ ] Set up webhook handlers for subscription events +- [ ] Add email notifications for subscription changes +- [ ] Implement invoice generation +- [ ] Set up customer support portal + +## Resources + +- **Complete Guide:** [docs/guides/MONETIZATION_GUIDE.md](docs/guides/MONETIZATION_GUIDE.md) +- **Quick Start:** [docs/guides/QUICK_START_MONETIZATION.md](docs/guides/QUICK_START_MONETIZATION.md) +- **Income Summary:** [docs/summaries/INCOME_STREAM_SUMMARY.md](docs/summaries/INCOME_STREAM_SUMMARY.md) +- **Setup Script:** `python setup_monetization.py` + +## Support + +For questions or issues: + +- 📖 See documentation above +- 🐛 Open an issue on GitHub +- 💬 Contact: + +--- + +**Revenue Stream Status:** ✅ Active - Achieving 111.8% of $2,000 target +**Last Updated:** February 17, 2026 diff --git a/SECURITY.md b/SECURITY.md index 68fdc61af..d25948b8e 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -1,21 +1,111 @@ -# Security Policy - -## Supported Versions - -Use this section to tell people about which versions of your project are -currently being supported with security updates. - -| Version | Supported | -| ------- | ------------------ | -| 5.1.x | :white_check_mark: | -| 5.0.x | :x: | -| 4.0.x | :white_check_mark: | -| < 4.0 | :x: | - -## Reporting a Vulnerability - -Use this section to tell people how to report a vulnerability. - -Tell them where to go, how often they can expect to get an update on a -reported vulnerability, what to expect if the vulnerability is accepted or -declined, etc. +# Security Policy + +Thank you for helping keep **Aria** and its users safe. This document describes +which versions of the project receive security updates, how to privately report +a vulnerability, and what to expect from the maintainers during the disclosure +process. + +## Supported Versions + +The table below lists the versions of Aria that are currently receiving +security updates. Older releases that are no longer supported will not receive +patches; please upgrade to a supported version before reporting an issue. + +| Version | Supported | Notes | +| --------------- | ------------------ | --------------------------------------- | +| latest (`main`) | :white_check_mark: | Active development branch | +| 5.1.x | :white_check_mark: | Current stable release | +| 5.0.x | :x: | End of life | +| 4.0.x | :white_check_mark: | Long-term support (critical fixes only) | +| < 4.0 | :x: | Unsupported – please upgrade | + +If you are unsure which version you are running, check the project release +notes or your installed package metadata. + +## Reporting a Vulnerability + +**Please do not report security vulnerabilities through public GitHub issues, +discussions, or pull requests.** Public disclosure before a fix is available +puts users at risk. + +Instead, please report vulnerabilities privately using one of the following +channels: + +1. **GitHub Private Vulnerability Reporting** (preferred): + Open a private report at + [https://github.com/Bryan-Roe/Aria/security/advisories/new](https://github.com/Bryan-Roe/Aria/security/advisories/new). +2. **Email:** Contact the maintainer via the address listed on the + [maintainer's GitHub profile](https://github.com/Bryan-Roe). If possible, + encrypt sensitive details before sending. + +### What to include + +To help us triage and resolve the issue quickly, please include as much of the +following as you can: + +- A clear description of the vulnerability and its potential impact. +- The affected version(s), platform, and configuration. +- Step-by-step instructions to reproduce the issue (proof-of-concept code, + scripts, or screenshots are very helpful). +- Any known mitigations or workarounds. +- Whether the issue has been disclosed elsewhere, and any CVE identifier if + one has already been assigned. +- How you would like to be credited (or whether you prefer to remain anonymous). + +### What to expect + +We aim to follow a coordinated disclosure process: + +| Stage | Target Response Time | +| ------------------------------ | --------------------------------------------------------------------- | +| Initial acknowledgement | Within **3 business days** of your report | +| Triage and severity assessment | Within **7 business days** | +| Status updates | At least every **7 days** until resolution | +| Fix and disclosure | Typically within **90 days**, depending on severity and complexity | + +After triage, we will let you know whether the report is accepted as a security +issue, declined (e.g., out of scope or not a vulnerability), or needs more +information. For accepted reports, we will work with you on a fix, prepare a +release, and coordinate public disclosure. With your permission, we will credit +you in the release notes and any associated security advisory. + +## Scope + +In-scope issues include, but are not limited to: + +- Remote code execution, command injection, or unsafe deserialization. +- Authentication or authorization bypasses. +- Sensitive data exposure (secrets, credentials, personal data). +- Cross-site scripting (XSS), CSRF, SSRF, or path-traversal vulnerabilities in + any first-party Aria component. +- Supply-chain or dependency issues unique to this repository's configuration. + +The following are generally **out of scope**: + +- Vulnerabilities in third-party dependencies that are already publicly tracked + upstream (please report those to the upstream project; we will update once a + fix is available). +- Issues that require physical access to a user's device or non-default, + insecure configurations. +- Best-practice or hardening suggestions without a demonstrable security impact. +- Denial-of-service issues that require unrealistic resources or that only + affect the reporter's own environment. + +## Safe Harbor + +We support good-faith security research. If you make a good-faith effort to +comply with this policy during your research, we will: + +- Consider your research to be authorized. +- Work with you to understand and resolve the issue quickly. +- Not pursue or support legal action related to your research. + +Please make every effort to avoid privacy violations, data destruction, and +service interruption while conducting research. + +## Policy Updates + +This policy may be updated from time to time. Material changes will be noted in +the project's release notes or commit history. The latest version of this +document is always available at +[`SECURITY.md`](https://github.com/Bryan-Roe/Aria/blob/main/SECURITY.md). diff --git a/agent.yaml b/agent.yaml index f624cfcb6..3ca0e0752 100644 --- a/agent.yaml +++ b/agent.yaml @@ -1,5 +1,63 @@ +# Aria Agent configuration +# Defines the container build context and runtime resource allocation +# for the Aria agent. Schema follows the standard agent manifest format. + +# Unique, human-readable identifier for the agent. name: Aria -dockerfile_path: Dockerfile + +# Short description shown in tooling/UIs that consume this manifest. +description: Aria agent runtime configuration. + +# Semantic version of this agent definition. Bump on breaking changes. +version: "1.0.0" + +# Build configuration +# ------------------------------------------------------------------ +# `dockerfile_path` is resolved relative to the repository root. +# `context` controls what files are sent to the Docker build daemon; +# keeping it at the repo root ("./") matches the previous behavior. +build: + context: "./" + dockerfile_path: Dockerfile + +# Runtime resource allocation +# ------------------------------------------------------------------ +# Values are quoted strings to preserve units and avoid YAML coercion +# of numeric values (e.g. 3.5 -> float). Use Kubernetes-style suffixes +# (Mi/Gi for memory, plain cores or millicores for CPU). resources: - cpu: '3.5' - memory: 7.0Gi + requests: + cpu: "2.0" + memory: "4.0Gi" + limits: + cpu: "3.5" + memory: "7.0Gi" + +# Restart policy for the agent container. +# Allowed values: Always | OnFailure | Never +restartPolicy: OnFailure + +# Healthcheck used by the orchestrator to determine readiness/liveness. +# Adjust the command to match an actual endpoint or CLI in your image. +healthcheck: + command: ["CMD-SHELL", "curl -fsS http://localhost:8080/health || exit 1"] + interval: "30s" + timeout: "5s" + retries: 3 + startPeriod: "10s" + +# Environment variables passed to the container at runtime. +# Prefer referencing secrets via a secret manager rather than inlining values. +env: + - name: LOG_LEVEL + value: "info" + - name: AGENT_NAME + value: "Aria" + +# Logging configuration consumed by the runtime. +logging: + driver: "json-file" + level: "info" + options: + max-size: "10m" + max-file: "3" diff --git a/agi_provider.py b/agi_provider.py index 2a6fd8a9e..b32686f74 100644 --- a/agi_provider.py +++ b/agi_provider.py @@ -1,55 +1,103 @@ -"""Compatibility shim for AGI provider imports. - -The canonical implementation lives under ``ai-projects/chat-cli/src`` and is -loaded dynamically to keep root-level imports backward compatible. -""" - -from __future__ import annotations - -import importlib.util -import sys -from pathlib import Path - -_CANONICAL = ( - Path(__file__).resolve().parent - / "ai-projects" - / "chat-cli" - / "src" - / "agi_provider.py" -) - -_spec = importlib.util.spec_from_file_location("_canonical_agi_provider", _CANONICAL) -if _spec is None or _spec.loader is None: - raise ImportError(f"Unable to load canonical AGI provider: {_CANONICAL}") - -_mod = importlib.util.module_from_spec(_spec) -sys.modules[_spec.name] = _mod -_spec.loader.exec_module(_mod) - -AGIProvider = _mod.AGIProvider -AGIContext = _mod.AGIContext -ReasoningStep = _mod.ReasoningStep -create_agi_provider = _mod.create_agi_provider -MAX_INPUT_LENGTH = _mod.MAX_INPUT_LENGTH -MAX_HISTORY_SIZE = _mod.MAX_HISTORY_SIZE -MAX_GOALS = _mod.MAX_GOALS -MAX_REASONING_CHAINS = _mod.MAX_REASONING_CHAINS -_sanitize_input = _mod._sanitize_input -_sanitize_for_logging = _mod._sanitize_for_logging -_infer_aria_movement_tag = _mod._infer_aria_movement_tag -_AGENT_REGISTRY = _mod._AGENT_REGISTRY - -__all__ = [ - "AGIProvider", - "AGIContext", - "ReasoningStep", - "create_agi_provider", - "MAX_INPUT_LENGTH", - "MAX_HISTORY_SIZE", - "MAX_GOALS", - "MAX_REASONING_CHAINS", - "_sanitize_input", - "_sanitize_for_logging", - "_infer_aria_movement_tag", - "_AGENT_REGISTRY", -] +"""Compatibility shim for legacy `import agi_provider`. + +The canonical implementation lives under `ai-projects/chat-cli/src/agi_provider.py`. +This shim dynamically loads the canonical module and re-exports its public API to +keep root-level imports backward compatible. + +Improvements: +- Clear, early error messages when the canonical file is missing/unreadable. +- Safer import mechanics (no clobbering existing sys.modules entries). +- Better typing and explicit re-exports. +- Avoid leaking internal attributes (only export what exists). +""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path +from types import ModuleType +from typing import Any + +_CANONICAL = ( + Path(__file__).resolve().parent + / "ai-projects" + / "chat-cli" + / "src" + / "agi_provider.py" +) + +_MODULE_NAME = "_canonical_agi_provider" + + +def _load_canonical_module(path: Path) -> ModuleType: + if not path.exists(): + raise ImportError( + f"Canonical AGI provider not found at: {path}. " + "Expected repository layout: ai-projects/chat-cli/src/agi_provider.py" + ) + if not path.is_file(): + raise ImportError(f"Canonical AGI provider path is not a file: {path}") + + spec = importlib.util.spec_from_file_location(_MODULE_NAME, path) + if spec is None or spec.loader is None: + raise ImportError(f"Unable to create import spec for canonical AGI provider: {path}") + + module = importlib.util.module_from_spec(spec) + + # Avoid overwriting an existing module entry if something else registered it. + existing = sys.modules.get(_MODULE_NAME) + if existing is None: + sys.modules[_MODULE_NAME] = module + + try: + spec.loader.exec_module(module) + except Exception as exc: # pragma: no cover + sys.modules.pop(_MODULE_NAME, None) + raise ImportError(f"Failed to import canonical AGI provider from {path}: {exc}") from exc + + return module + + +_mod = _load_canonical_module(_CANONICAL) + + +def _export(name: str) -> Any: + try: + return getattr(_mod, name) + except AttributeError as exc: + raise ImportError( + f"Canonical AGI provider is missing expected export '{name}' " + f"from {_CANONICAL}" + ) from exc + + +AGIProvider = _export("AGIProvider") +AGIContext = _export("AGIContext") +ReasoningStep = _export("ReasoningStep") +create_agi_provider = _export("create_agi_provider") + +MAX_INPUT_LENGTH = _export("MAX_INPUT_LENGTH") +MAX_HISTORY_SIZE = _export("MAX_HISTORY_SIZE") +MAX_GOALS = _export("MAX_GOALS") +MAX_REASONING_CHAINS = _export("MAX_REASONING_CHAINS") + +_sanitize_input = _export("_sanitize_input") +_sanitize_for_logging = _export("_sanitize_for_logging") +_infer_aria_movement_tag = _export("_infer_aria_movement_tag") +_AGENT_REGISTRY = _export("_AGENT_REGISTRY") + +__all__ = [ + "AGIProvider", + "AGIContext", + "ReasoningStep", + "create_agi_provider", + "MAX_INPUT_LENGTH", + "MAX_HISTORY_SIZE", + "MAX_GOALS", + "MAX_REASONING_CHAINS", + "_sanitize_input", + "_sanitize_for_logging", + "_infer_aria_movement_tag", + "_AGENT_REGISTRY", +] diff --git a/ai-projects/chat-cli/README.md b/ai-projects/chat-cli/README.md index 517e7411a..282a8d5e7 100644 --- a/ai-projects/chat-cli/README.md +++ b/ai-projects/chat-cli/README.md @@ -1,192 +1,192 @@ -# Talk-to-AI (CLI) - -A lightweight, local-first chat app you can talk to from your terminal. It works out-of-the-box with a local fallback (no API keys needed), and can optionally connect to LM Studio, Ollama, OpenAI, or Azure OpenAI. - -## Features - -- Interactive terminal chat with message history -- Streaming responses (when using OpenAI-compatible providers) -- Local fallback provider (no internet or keys required) -- Auto-selects provider in this order: LM Studio -> Ollama -> Azure OpenAI -> OpenAI -> Local -- Saves conversations to `logs/*.jsonl` -- One-shot mode for quick prompts -- **AGI Provider** with advanced reasoning capabilities - -## AGI Provider (Advanced Reasoning) - -The AGI provider adds advanced reasoning capabilities to Aria: - -### Capabilities - -- **Chain-of-Thought Reasoning**: Breaks down complex queries into logical steps -- **Task Decomposition**: Identifies sub-goals for complex tasks -- **Self-Reflection**: Evaluates and improves responses before delivery -- **Context Management**: Maintains relevant memory across interactions -- **Aria Movement Integration**: Automatic movement tag generation - -### Quick Start - -```powershell -# Use AGI provider (wraps best available underlying provider) -python .\ai-projects\chat-cli\src\chat_cli.py --provider agi --once "Explain quantum computing step by step" - -# Interactive AGI chat -python .\ai-projects\chat-cli\src\chat_cli.py --provider agi - -# Verbose mode (shows reasoning steps) -$env:AGI_VERBOSE = "true" -python .\ai-projects\chat-cli\src\chat_cli.py --provider agi -``` - -### How It Works - -The AGI provider wraps an underlying provider (LM Studio, Ollama, Azure OpenAI, OpenAI, or Local) and enhances responses through: - -1. **Query Analysis**: Determines complexity, intent, and domain -2. **Task Decomposition**: Breaks complex queries into manageable subtasks -3. **Chain-of-Thought**: Generates step-by-step reasoning -4. **Response Generation**: Produces enhanced responses with context -5. **Self-Reflection**: Evaluates and improves the response - -### Aria Movement Commands - -When using AGI with Aria movement requests, the provider automatically generates movement tags: - -```powershell -python .\ai-projects\chat-cli\src\chat_cli.py --provider agi --once "Move Aria left" -# Response: I'll move to the left! [aria:walk:left] -``` - -## Quick start (no keys required) - -Run a single-turn chat using the built-in local provider: - -```powershell -# From the repo root -python .\ai-projects\chat-cli\src\chat_cli.py --provider local --once "Hello there!" -``` - -Start continuous autonomous chat (local fallback, default behavior): - -```powershell -python .\ai-projects\chat-cli\src\chat_cli.py --provider local -``` - -Force interactive stdin mode if you want to type messages manually: - -```powershell -python .\ai-projects\chat-cli\src\chat_cli.py --provider local --interactive -``` - -## Use with OpenAI - -Set your API key and pick a model, then run the chat. Example: - -```powershell -$env:OPENAI_API_KEY = "" -$env:OPENAI_MODEL = "gpt-4o-mini" # or another available chat model -python .\ai-projects\chat-cli\src\chat_cli.py -``` - -The app will automatically use OpenAI when `OPENAI_API_KEY` is present. You can force it: - -```powershell -python .\ai-projects\chat-cli\src\chat_cli.py --provider openai -``` - -## Use with LM Studio - -Required environment variables (defaults shown): - -- `LMSTUDIO_BASE_URL` (default `http://127.0.0.1:1234/v1`) -- `LMSTUDIO_MODEL` (default `local-model`) - -Optional authentication variables (for LM Studio servers with API token enabled): - -- `LM_API_TOKEN` (preferred) -- `LMSTUDIO_API_KEY` (legacy compatibility) - -Example: - -```powershell -$env:LMSTUDIO_BASE_URL = "http://127.0.0.1:1234/v1" -$env:LMSTUDIO_MODEL = "qwen2.5-coder-7b-instruct" -$env:LM_API_TOKEN = "" # only if your LM Studio server requires auth -python .\ai-projects\chat-cli\src\chat_cli.py --provider lmstudio -``` - -## Use with Ollama - -Required environment variables (defaults shown): - -- `OLLAMA_BASE_URL` (default `http://127.0.0.1:11434/v1`) -- `OLLAMA_MODEL` (default `llama2`) - -Example: - -```powershell -$env:OLLAMA_BASE_URL = "http://127.0.0.1:11434/v1" -$env:OLLAMA_MODEL = "llama3.2" -python .\ai-projects\chat-cli\src\chat_cli.py --provider ollama -``` - -## Use with Azure OpenAI - -Required environment variables: - -- `AZURE_OPENAI_API_KEY` – your Azure OpenAI key -- `AZURE_OPENAI_ENDPOINT` – e.g. `https://.openai.azure.com/` -- `AZURE_OPENAI_DEPLOYMENT` – the deployment name of your chat model -- `AZURE_OPENAI_API_VERSION` – e.g. `2024-08-01-preview` - -Example: - -```powershell -$env:AZURE_OPENAI_API_KEY = "" -$env:AZURE_OPENAI_ENDPOINT = "https://my-aoai.openai.azure.com/" -$env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini" -$env:AZURE_OPENAI_API_VERSION = "2024-08-01-preview" -python .\ai-projects\chat-cli\src\chat_cli.py --provider azure -``` - -## Usage - -Basic default launch: - -```powershell -python .\ai-projects\chat-cli\src\chat_cli.py -``` - -Useful flags: - -- `--provider [auto|openai|azure|local|agi|quantum|lmstudio|ollama|lora]` – default: `auto` -- `--system "..."` – set a custom system prompt -- `--model ` – override model/deployment name (provider-specific) -- `--once "message"` – run one message and exit -- `--interactive` – use stdin-driven chat instead of the default autonomous loop -- `--autonomous` – run unattended continuous chat without prompting for stdin -- `--auto-seed "..."` – initial autonomous user message -- `--auto-followup "..."` – follow-up message reused after each autonomous turn -- `--auto-delay ` – pause between autonomous turns -- `--max-turns ` – cap autonomous turns for testing; omit to run forever - -In interactive mode, commands: - -- `/new` – start a new conversation -- `/save` – save current conversation to `logs/` -- `/exit` – quit - -Autonomous mode is now the default. It uses a seed prompt for the first turn, then keeps sending a reusable follow-up prompt so the provider can continue without asking you for input. Stop it with `Ctrl+C`. Use `--interactive` to bring back manual stdin chat. - -## Install dependencies - -This project only needs a couple of small packages. If you prefer to install them manually: - -```powershell -pip install -r .\ai-projects\chat-cli\requirements.txt -``` - -## Notes - -- The local provider is simple and meant for offline testing; for best results, use OpenAI or Azure OpenAI. -- Conversations are stored in JSONL under `ai-projects/chat-cli/logs/`. +# Talk-to-AI (CLI) + +A lightweight, local-first chat app you can talk to from your terminal. It works out-of-the-box with a local fallback (no API keys needed), and can optionally connect to LM Studio, Ollama, OpenAI, or Azure OpenAI. + +## Features + +- Interactive terminal chat with message history +- Streaming responses (when using OpenAI-compatible providers) +- Local fallback provider (no internet or keys required) +- Auto-selects provider in this order: LM Studio -> Ollama -> Azure OpenAI -> OpenAI -> Local +- Saves conversations to `logs/*.jsonl` +- One-shot mode for quick prompts +- **AGI Provider** with advanced reasoning capabilities + +## AGI Provider (Advanced Reasoning) + +The AGI provider adds advanced reasoning capabilities to Aria: + +### Capabilities + +- **Chain-of-Thought Reasoning**: Breaks down complex queries into logical steps +- **Task Decomposition**: Identifies sub-goals for complex tasks +- **Self-Reflection**: Evaluates and improves responses before delivery +- **Context Management**: Maintains relevant memory across interactions +- **Aria Movement Integration**: Automatic movement tag generation + +### Quick Start + +```powershell +# Use AGI provider (wraps best available underlying provider) +python .\ai-projects\chat-cli\src\chat_cli.py --provider agi --once "Explain quantum computing step by step" + +# Interactive AGI chat +python .\ai-projects\chat-cli\src\chat_cli.py --provider agi + +# Verbose mode (shows reasoning steps) +$env:AGI_VERBOSE = "true" +python .\ai-projects\chat-cli\src\chat_cli.py --provider agi +``` + +### How It Works + +The AGI provider wraps an underlying provider (LM Studio, Ollama, Azure OpenAI, OpenAI, or Local) and enhances responses through: + +1. **Query Analysis**: Determines complexity, intent, and domain +2. **Task Decomposition**: Breaks complex queries into manageable subtasks +3. **Chain-of-Thought**: Generates step-by-step reasoning +4. **Response Generation**: Produces enhanced responses with context +5. **Self-Reflection**: Evaluates and improves the response + +### Aria Movement Commands + +When using AGI with Aria movement requests, the provider automatically generates movement tags: + +```powershell +python .\ai-projects\chat-cli\src\chat_cli.py --provider agi --once "Move Aria left" +# Response: I'll move to the left! [aria:walk:left] +``` + +## Quick start (no keys required) + +Run a single-turn chat using the built-in local provider: + +```powershell +# From the repo root +python .\ai-projects\chat-cli\src\chat_cli.py --provider local --once "Hello there!" +``` + +Start continuous autonomous chat (local fallback, default behavior): + +```powershell +python .\ai-projects\chat-cli\src\chat_cli.py --provider local +``` + +Force interactive stdin mode if you want to type messages manually: + +```powershell +python .\ai-projects\chat-cli\src\chat_cli.py --provider local --interactive +``` + +## Use with OpenAI + +Set your API key and pick a model, then run the chat. Example: + +```powershell +$env:OPENAI_API_KEY = "" +$env:OPENAI_MODEL = "gpt-4o-mini" # or another available chat model +python .\ai-projects\chat-cli\src\chat_cli.py +``` + +The app will automatically use OpenAI when `OPENAI_API_KEY` is present. You can force it: + +```powershell +python .\ai-projects\chat-cli\src\chat_cli.py --provider openai +``` + +## Use with LM Studio + +Required environment variables (defaults shown): + +- `LMSTUDIO_BASE_URL` (default `http://127.0.0.1:1234/v1`) +- `LMSTUDIO_MODEL` (default `local-model`) + +Optional authentication variables (for LM Studio servers with API token enabled): + +- `LM_API_TOKEN` (preferred) +- `LMSTUDIO_API_KEY` (legacy compatibility) + +Example: + +```powershell +$env:LMSTUDIO_BASE_URL = "http://127.0.0.1:1234/v1" +$env:LMSTUDIO_MODEL = "qwen2.5-coder-7b-instruct" +$env:LM_API_TOKEN = "" # only if your LM Studio server requires auth +python .\ai-projects\chat-cli\src\chat_cli.py --provider lmstudio +``` + +## Use with Ollama + +Required environment variables (defaults shown): + +- `OLLAMA_BASE_URL` (default `http://127.0.0.1:11434/v1`) +- `OLLAMA_MODEL` (default `llama2`) + +Example: + +```powershell +$env:OLLAMA_BASE_URL = "http://127.0.0.1:11434/v1" +$env:OLLAMA_MODEL = "llama3.2" +python .\ai-projects\chat-cli\src\chat_cli.py --provider ollama +``` + +## Use with Azure OpenAI + +Required environment variables: + +- `AZURE_OPENAI_API_KEY` – your Azure OpenAI key +- `AZURE_OPENAI_ENDPOINT` – e.g. `https://.openai.azure.com/` +- `AZURE_OPENAI_DEPLOYMENT` – the deployment name of your chat model +- `AZURE_OPENAI_API_VERSION` – e.g. `2024-08-01-preview` + +Example: + +```powershell +$env:AZURE_OPENAI_API_KEY = "" +$env:AZURE_OPENAI_ENDPOINT = "https://my-aoai.openai.azure.com/" +$env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini" +$env:AZURE_OPENAI_API_VERSION = "2024-08-01-preview" +python .\ai-projects\chat-cli\src\chat_cli.py --provider azure +``` + +## Usage + +Basic default launch: + +```powershell +python .\ai-projects\chat-cli\src\chat_cli.py +``` + +Useful flags: + +- `--provider [auto|openai|azure|local|agi|quantum|lmstudio|ollama|lora]` – default: `auto` +- `--system "..."` – set a custom system prompt +- `--model ` – override model/deployment name (provider-specific) +- `--once "message"` – run one message and exit +- `--interactive` – use stdin-driven chat instead of the default autonomous loop +- `--autonomous` – run unattended continuous chat without prompting for stdin +- `--auto-seed "..."` – initial autonomous user message +- `--auto-followup "..."` – follow-up message reused after each autonomous turn +- `--auto-delay ` – pause between autonomous turns +- `--max-turns ` – cap autonomous turns for testing; omit to run forever + +In interactive mode, commands: + +- `/new` – start a new conversation +- `/save` – save current conversation to `logs/` +- `/exit` – quit + +Autonomous mode is now the default. It uses a seed prompt for the first turn, then keeps sending a reusable follow-up prompt so the provider can continue without asking you for input. Stop it with `Ctrl+C`. Use `--interactive` to bring back manual stdin chat. + +## Install dependencies + +This project only needs a couple of small packages. If you prefer to install them manually: + +```powershell +pip install -r .\ai-projects\chat-cli\requirements.txt +``` + +## Notes + +- The local provider is simple and meant for offline testing; for best results, use OpenAI or Azure OpenAI. +- Conversations are stored in JSONL under `ai-projects/chat-cli/logs/`. diff --git a/ai-projects/chat-cli/requirements.txt b/ai-projects/chat-cli/requirements.txt index d2b88a4d3..fbe75a247 100644 --- a/ai-projects/chat-cli/requirements.txt +++ b/ai-projects/chat-cli/requirements.txt @@ -1,2 +1,2 @@ -openai>=1.58.0 -colorama>=0.4.6 +openai>=1.58.0 +colorama>=0.4.6 diff --git a/ai-projects/chat-cli/src/_smoke_test.py b/ai-projects/chat-cli/src/_smoke_test.py index 35b48ef9d..46c819e46 100644 --- a/ai-projects/chat-cli/src/_smoke_test.py +++ b/ai-projects/chat-cli/src/_smoke_test.py @@ -1,20 +1,20 @@ -from __future__ import annotations - -from chat_providers import detect_provider - - -def main() -> int: - provider, info = detect_provider(explicit="local") - messages = [ - {"role": "system", "content": "You are concise."}, - {"role": "user", "content": "Say one short sentence about AI."}, - ] - result = provider.complete(messages, stream=False) - assert isinstance(result, str) - print("Smoke test provider:", info) - print("Reply:", result) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) +from __future__ import annotations + +from chat_providers import detect_provider + + +def main() -> int: + provider, info = detect_provider(explicit="local") + messages = [ + {"role": "system", "content": "You are concise."}, + {"role": "user", "content": "Say one short sentence about AI."}, + ] + result = provider.complete(messages, stream=False) + assert isinstance(result, str) + print("Smoke test provider:", info) + print("Reply:", result) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ai-projects/chat-cli/src/ai-finetuning-workspace.yml b/ai-projects/chat-cli/src/ai-finetuning-workspace.yml index 2ddf28a44..527975425 100644 --- a/ai-projects/chat-cli/src/ai-finetuning-workspace.yml +++ b/ai-projects/chat-cli/src/ai-finetuning-workspace.yml @@ -1,9 +1,9 @@ -$schema: https://azuremlschemas.azureedge.net/latest/workspace.schema.json -name: ai-finetuning-workspace -location: eastus2 -display_name: AI Fine-Tuning Workspace -description: Azure ML workspace for Phi-3.6 model fine-tuning and AI/ML experiments -tags: - purpose: fine-tuning - team: ai-research - project: phi-silica +$schema: https://azuremlschemas.azureedge.net/latest/workspace.schema.json +name: ai-finetuning-workspace +location: eastus2 +display_name: AI Fine-Tuning Workspace +description: Azure ML workspace for Phi-3.6 model fine-tuning and AI/ML experiments +tags: + purpose: fine-tuning + team: ai-research + project: phi-silica diff --git a/ai-projects/chat-cli/src/chat_cli.py b/ai-projects/chat-cli/src/chat_cli.py index e8a63a366..f950e30f5 100644 --- a/ai-projects/chat-cli/src/chat_cli.py +++ b/ai-projects/chat-cli/src/chat_cli.py @@ -1,389 +1,389 @@ -from __future__ import annotations - -import argparse -import json -import os -import sys -import time -from datetime import datetime -from pathlib import Path -from typing import List - -try: - from colorama import Fore, Style - from colorama import init as colorama_init -except ImportError: # pragma: no cover - exercised in dependency-light test envs - - class _NoColor: - BLACK = RED = GREEN = YELLOW = BLUE = MAGENTA = CYAN = WHITE = "" - RESET = RESET_ALL = BRIGHT = DIM = NORMAL = "" - - Fore = Style = _NoColor() - - def colorama_init(*args, **kwargs) -> None: - return None - - -from chat_providers import RoleMessage, detect_provider - - -def now_ts() -> str: - return datetime.now().strftime("%Y%m%d_%H%M%S_%f") - - -def provider_readiness_summary() -> str: - """Return a human-readable provider readiness summary based on env vars.""" - azure_key = bool(os.getenv("AZURE_OPENAI_API_KEY")) - azure_endpoint = bool(os.getenv("AZURE_OPENAI_ENDPOINT")) - azure_deployment = bool(os.getenv("AZURE_OPENAI_DEPLOYMENT")) - azure_version = bool(os.getenv("AZURE_OPENAI_API_VERSION")) - azure_ready = azure_key and azure_endpoint and azure_deployment and azure_version - - openai_ready = bool(os.getenv("OPENAI_API_KEY")) - lmstudio_url = os.getenv("LMSTUDIO_BASE_URL", "http://127.0.0.1:1234/v1") - ollama_url = os.getenv("OLLAMA_BASE_URL", "http://127.0.0.1:11434/v1") - - lines = [ - "Provider readiness:", - f" Azure OpenAI : {'ready' if azure_ready else 'not ready'} " - f"(key={'set' if azure_key else 'missing'}, endpoint={'set' if azure_endpoint else 'missing'}, " - f"deployment={'set' if azure_deployment else 'missing'}, api_version={'set' if azure_version else 'missing'})", - f" OpenAI : {'ready' if openai_ready else 'not ready'} (OPENAI_API_KEY {'set' if openai_ready else 'missing'})", - f" LM Studio : configured via LMSTUDIO_BASE_URL={lmstudio_url}", - f" Ollama : configured via OLLAMA_BASE_URL={ollama_url}", - " Local Echo : always available", - ] - return "\n".join(lines) - - -def save_conversation(messages: List[RoleMessage], logs_dir: Path) -> Path: - logs_dir.mkdir(parents=True, exist_ok=True) - base_name = f"chat_{now_ts()}" - path = logs_dir / f"{base_name}.jsonl" - attempt = 1 - - while True: - try: - with path.open("x", encoding="utf-8") as f: - for m in messages: - f.write(json.dumps(m, ensure_ascii=False) + "\n") - return path - except FileExistsError: - path = logs_dir / f"{base_name}_{attempt}.jsonl" - attempt += 1 - - -def print_system(msg: str) -> None: - print(Fore.MAGENTA + msg + Style.RESET_ALL) - - -def print_user(msg: str) -> None: - print(Fore.CYAN + msg + Style.RESET_ALL) - - -def print_assistant_chunk(chunk: str) -> None: - # Avoid styles on every print for speed - sys.stdout.write(chunk) - sys.stdout.flush() - - -def print_assistant_done() -> None: - print(Style.RESET_ALL) - - -def format_provider_error(exc: Exception) -> str: - message = str(exc).strip() or exc.__class__.__name__ - return f"[provider error: {message}]" - - -def stream_assistant_reply(provider, messages: List[RoleMessage]) -> str: - print(Fore.GREEN + "assistant> " + Style.RESET_ALL, end="") - reply_accum = "" - try: - result = provider.complete(messages, stream=True) - if isinstance(result, str): - reply_accum = result - print_assistant_chunk(result) - else: - for chunk in result: - reply_accum += chunk - print_assistant_chunk(chunk) - except Exception as exc: - error_text = format_provider_error(exc) - if reply_accum and not reply_accum.endswith("\n"): - print_assistant_chunk("\n") - reply_accum += "\n" - print_assistant_chunk(error_text) - reply_accum += error_text - print_assistant_done() - return reply_accum - - -def autonomous_chat(args: argparse.Namespace) -> int: - colorama_init() - - system_prompt = args.system or os.getenv( - "SYSTEM_PROMPT", - "You are a concise, friendly assistant. Be helpful and brief by default.", - ) - seed_prompt = args.auto_seed or os.getenv( - "AUTONOMOUS_CHAT_SEED", - "Start working autonomously on the most useful next step and keep driving the conversation without waiting for user input.", - ) - followup_prompt = args.auto_followup or os.getenv( - "AUTONOMOUS_CHAT_FOLLOWUP", - "Continue autonomously. Build on the conversation so far, choose the next useful step yourself, and keep going without asking for user input.", - ) - delay_seconds = max(0.0, args.auto_delay) - - provider, info = detect_provider(explicit=args.provider, model_override=args.model) - - messages: List[RoleMessage] = [] - if system_prompt: - messages.append({"role": "system", "content": system_prompt}) - - print_system(f"Provider: {info.name} | Model: {info.model}") - print_system("Autonomous mode active. Press Ctrl+C to stop.") - - turn_count = 0 - next_user_message = seed_prompt - - try: - while args.max_turns is None or turn_count < args.max_turns: - turn_count += 1 - print_user(f"auto[{turn_count}]> {next_user_message}") - messages.append({"role": "user", "content": next_user_message}) - reply_accum = stream_assistant_reply(provider, messages) - messages.append({"role": "assistant", "content": reply_accum}) - next_user_message = followup_prompt - if delay_seconds > 0: - time.sleep(delay_seconds) - except KeyboardInterrupt: - print() - return 0 - - return 0 - - -def interactive_chat(args: argparse.Namespace) -> int: - colorama_init() - - system_prompt = args.system or os.getenv( - "SYSTEM_PROMPT", - "You are a concise, friendly assistant. Be helpful and brief by default.", - ) - logs_dir = Path(__file__).resolve().parent.parent / "logs" - - provider, info = detect_provider(explicit=args.provider, model_override=args.model) - - messages: List[RoleMessage] = [] - if system_prompt: - messages.append({"role": "system", "content": system_prompt}) - - print_system(f"Provider: {info.name} | Model: {info.model}") - print_system( - "Type your message. Commands: /help, /new, /save, /history, /model, /providers, /reasoning, /exit" - ) - - while True: - try: - user = input(Fore.CYAN + "you> " + Style.RESET_ALL) - except (EOFError, KeyboardInterrupt): - print() - return 0 - - cmd = user.strip().lower() - if cmd == "/exit": - return 0 - - if cmd == "/help": - print_system( - "Available commands:\n" - " /new — start a fresh conversation (clears history)\n" - " /save — save conversation to a JSONL file\n" - " /history — show conversation history summary\n" - " /model — show current provider and model info\n" - " /providers — show provider readiness from current env vars\n" - " /reasoning — show AGI reasoning summary (AGI provider only)\n" - " /exit — quit the chat\n" - " /help — show this message" - ) - continue - - if cmd == "/new": - messages = [] - if system_prompt: - messages.append({"role": "system", "content": system_prompt}) - print_system("Started a new conversation.") - continue - - if cmd == "/save": - path = save_conversation(messages, logs_dir) - print_system(f"Saved {len(messages)} message(s) to {path}") - continue - - if cmd == "/history": - user_msgs = [m for m in messages if m.get("role") == "user"] - asst_msgs = [m for m in messages if m.get("role") == "assistant"] - print_system( - f"Conversation: {len(user_msgs)} user message(s), " - f"{len(asst_msgs)} assistant reply(ies)." - ) - if user_msgs: - last = user_msgs[-1]["content"] - preview = last[:120] + "..." if len(last) > 120 else last - print_system(f"Last user message: {preview}") - continue - - if cmd == "/model": - print_system(f"Provider : {info.name}") - print_system(f"Model : {info.model}") - continue - - if cmd == "/providers": - print_system(provider_readiness_summary()) - continue - - if cmd == "/reasoning": - # Only AGIProvider exposes get_reasoning_summary() - if hasattr(provider, "get_reasoning_summary"): - # type: ignore[union-attr] - summary = provider.get_reasoning_summary() - last_agent = summary.get("last_agent_used") or "none yet" - last_score = summary.get("last_agent_score") - score_str = f"{last_score:.3f}" if last_score is not None else "n/a" - available = ", ".join(summary.get("available_agents", [])) - - # Build learned-pattern lines (top 5 by observation count). - top_patterns = summary.get("top_learned_patterns", []) - if top_patterns: - pattern_lines = "\n".join( - f" {p.get('domain', '?')}/{p.get('intent', '?')} " - f"→ {p.get('agent', '?')} (×{p.get('count', 0)})" - for p in top_patterns - ) - pattern_section = f"\n Top routing patterns :\n{pattern_lines}" - else: - pattern_section = "" - - print_system( - f"AGI Reasoning Summary:\n" - f" Reasoning chains stored : {summary.get('total_reasoning_chains', 0)}\n" - f" Conversation turns : {summary.get('conversation_length', 0)}\n" - f" Active goals : {', '.join(summary.get('active_goals', [])) or 'none'}\n" - f" Learned patterns : {summary.get('learned_patterns_count', 0)}" - f"{pattern_section}\n" - f" Last agent routed to : {last_agent} (score={score_str})\n" - f" Available agents : {available}" - ) - else: - print_system( - "Reasoning summary is only available with the AGI provider (--provider agi)." - ) - continue - - if not user.strip(): - continue - - messages.append({"role": "user", "content": user}) - - reply_accum = stream_assistant_reply(provider, messages) - messages.append({"role": "assistant", "content": reply_accum}) - - -def one_shot(args: argparse.Namespace) -> int: - colorama_init() - if not args.once: - print("--once requires a message string.") - return 2 - - system_prompt = args.system or os.getenv( - "SYSTEM_PROMPT", - "You are a concise, friendly assistant.", - ) - - provider, info = detect_provider(explicit=args.provider, model_override=args.model) - - messages: List[RoleMessage] = [] - if system_prompt: - messages.append({"role": "system", "content": system_prompt}) - messages.append({"role": "user", "content": args.once}) - - print_system(f"Provider: {info.name} | Model: {info.model}") - stream_assistant_reply(provider, messages) - - return 0 - - -def build_arg_parser() -> argparse.ArgumentParser: - p = argparse.ArgumentParser( - description="Simple terminal chat app with local/OpenAI/Azure providers" - ) - p.add_argument( - "--provider", - choices=[ - "auto", - "openai", - "azure", - "local", - "lora", - "agi", - "quantum", - "lmstudio", - "ollama", - ], - default="auto", - help="Which provider to use (default: auto)", - ) - p.add_argument("--system", type=str, help="Custom system prompt") - p.add_argument("--model", type=str, help="Model/deployment name override") - p.add_argument("--once", type=str, help="Send a single message then exit") - p.add_argument( - "--interactive", - action="store_true", - help="Use stdin-driven interactive chat instead of autonomous mode", - ) - p.add_argument( - "--autonomous", - action="store_true", - help="Run unattended continuous chat without prompting for stdin", - ) - p.add_argument("--auto-seed", type=str, help="Initial autonomous user message") - p.add_argument( - "--auto-followup", - type=str, - help="Autonomous follow-up message reused after each assistant turn", - ) - p.add_argument( - "--auto-delay", - type=float, - default=0.0, - help="Delay between autonomous turns in seconds (default: 0)", - ) - p.add_argument( - "--max-turns", - type=int, - help="Maximum autonomous turns before exiting (default: run forever)", - ) - return p - - -def should_run_autonomous(args: argparse.Namespace) -> bool: - if args.interactive: - return False - if args.autonomous: - return True - return not args.once - - -def main(argv: List[str] | None = None) -> int: - args = build_arg_parser().parse_args(argv) - if args.once: - return one_shot(args) - if should_run_autonomous(args): - return autonomous_chat(args) - return interactive_chat(args) - - -if __name__ == "__main__": - raise SystemExit(main()) +from __future__ import annotations + +import argparse +import json +import os +import sys +import time +from datetime import datetime +from pathlib import Path +from typing import List + +try: + from colorama import Fore, Style + from colorama import init as colorama_init +except ImportError: # pragma: no cover - exercised in dependency-light test envs + + class _NoColor: + BLACK = RED = GREEN = YELLOW = BLUE = MAGENTA = CYAN = WHITE = "" + RESET = RESET_ALL = BRIGHT = DIM = NORMAL = "" + + Fore = Style = _NoColor() + + def colorama_init(*args, **kwargs) -> None: + return None + + +from chat_providers import RoleMessage, detect_provider + + +def now_ts() -> str: + return datetime.now().strftime("%Y%m%d_%H%M%S_%f") + + +def provider_readiness_summary() -> str: + """Return a human-readable provider readiness summary based on env vars.""" + azure_key = bool(os.getenv("AZURE_OPENAI_API_KEY")) + azure_endpoint = bool(os.getenv("AZURE_OPENAI_ENDPOINT")) + azure_deployment = bool(os.getenv("AZURE_OPENAI_DEPLOYMENT")) + azure_version = bool(os.getenv("AZURE_OPENAI_API_VERSION")) + azure_ready = azure_key and azure_endpoint and azure_deployment and azure_version + + openai_ready = bool(os.getenv("OPENAI_API_KEY")) + lmstudio_url = os.getenv("LMSTUDIO_BASE_URL", "http://127.0.0.1:1234/v1") + ollama_url = os.getenv("OLLAMA_BASE_URL", "http://127.0.0.1:11434/v1") + + lines = [ + "Provider readiness:", + f" Azure OpenAI : {'ready' if azure_ready else 'not ready'} " + f"(key={'set' if azure_key else 'missing'}, endpoint={'set' if azure_endpoint else 'missing'}, " + f"deployment={'set' if azure_deployment else 'missing'}, api_version={'set' if azure_version else 'missing'})", + f" OpenAI : {'ready' if openai_ready else 'not ready'} (OPENAI_API_KEY {'set' if openai_ready else 'missing'})", + f" LM Studio : configured via LMSTUDIO_BASE_URL={lmstudio_url}", + f" Ollama : configured via OLLAMA_BASE_URL={ollama_url}", + " Local Echo : always available", + ] + return "\n".join(lines) + + +def save_conversation(messages: List[RoleMessage], logs_dir: Path) -> Path: + logs_dir.mkdir(parents=True, exist_ok=True) + base_name = f"chat_{now_ts()}" + path = logs_dir / f"{base_name}.jsonl" + attempt = 1 + + while True: + try: + with path.open("x", encoding="utf-8") as f: + for m in messages: + f.write(json.dumps(m, ensure_ascii=False) + "\n") + return path + except FileExistsError: + path = logs_dir / f"{base_name}_{attempt}.jsonl" + attempt += 1 + + +def print_system(msg: str) -> None: + print(Fore.MAGENTA + msg + Style.RESET_ALL) + + +def print_user(msg: str) -> None: + print(Fore.CYAN + msg + Style.RESET_ALL) + + +def print_assistant_chunk(chunk: str) -> None: + # Avoid styles on every print for speed + sys.stdout.write(chunk) + sys.stdout.flush() + + +def print_assistant_done() -> None: + print(Style.RESET_ALL) + + +def format_provider_error(exc: Exception) -> str: + message = str(exc).strip() or exc.__class__.__name__ + return f"[provider error: {message}]" + + +def stream_assistant_reply(provider, messages: List[RoleMessage]) -> str: + print(Fore.GREEN + "assistant> " + Style.RESET_ALL, end="") + reply_accum = "" + try: + result = provider.complete(messages, stream=True) + if isinstance(result, str): + reply_accum = result + print_assistant_chunk(result) + else: + for chunk in result: + reply_accum += chunk + print_assistant_chunk(chunk) + except Exception as exc: + error_text = format_provider_error(exc) + if reply_accum and not reply_accum.endswith("\n"): + print_assistant_chunk("\n") + reply_accum += "\n" + print_assistant_chunk(error_text) + reply_accum += error_text + print_assistant_done() + return reply_accum + + +def autonomous_chat(args: argparse.Namespace) -> int: + colorama_init() + + system_prompt = args.system or os.getenv( + "SYSTEM_PROMPT", + "You are a concise, friendly assistant. Be helpful and brief by default.", + ) + seed_prompt = args.auto_seed or os.getenv( + "AUTONOMOUS_CHAT_SEED", + "Start working autonomously on the most useful next step and keep driving the conversation without waiting for user input.", + ) + followup_prompt = args.auto_followup or os.getenv( + "AUTONOMOUS_CHAT_FOLLOWUP", + "Continue autonomously. Build on the conversation so far, choose the next useful step yourself, and keep going without asking for user input.", + ) + delay_seconds = max(0.0, args.auto_delay) + + provider, info = detect_provider(explicit=args.provider, model_override=args.model) + + messages: List[RoleMessage] = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + + print_system(f"Provider: {info.name} | Model: {info.model}") + print_system("Autonomous mode active. Press Ctrl+C to stop.") + + turn_count = 0 + next_user_message = seed_prompt + + try: + while args.max_turns is None or turn_count < args.max_turns: + turn_count += 1 + print_user(f"auto[{turn_count}]> {next_user_message}") + messages.append({"role": "user", "content": next_user_message}) + reply_accum = stream_assistant_reply(provider, messages) + messages.append({"role": "assistant", "content": reply_accum}) + next_user_message = followup_prompt + if delay_seconds > 0: + time.sleep(delay_seconds) + except KeyboardInterrupt: + print() + return 0 + + return 0 + + +def interactive_chat(args: argparse.Namespace) -> int: + colorama_init() + + system_prompt = args.system or os.getenv( + "SYSTEM_PROMPT", + "You are a concise, friendly assistant. Be helpful and brief by default.", + ) + logs_dir = Path(__file__).resolve().parent.parent / "logs" + + provider, info = detect_provider(explicit=args.provider, model_override=args.model) + + messages: List[RoleMessage] = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + + print_system(f"Provider: {info.name} | Model: {info.model}") + print_system( + "Type your message. Commands: /help, /new, /save, /history, /model, /providers, /reasoning, /exit" + ) + + while True: + try: + user = input(Fore.CYAN + "you> " + Style.RESET_ALL) + except (EOFError, KeyboardInterrupt): + print() + return 0 + + cmd = user.strip().lower() + if cmd == "/exit": + return 0 + + if cmd == "/help": + print_system( + "Available commands:\n" + " /new — start a fresh conversation (clears history)\n" + " /save — save conversation to a JSONL file\n" + " /history — show conversation history summary\n" + " /model — show current provider and model info\n" + " /providers — show provider readiness from current env vars\n" + " /reasoning — show AGI reasoning summary (AGI provider only)\n" + " /exit — quit the chat\n" + " /help — show this message" + ) + continue + + if cmd == "/new": + messages = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + print_system("Started a new conversation.") + continue + + if cmd == "/save": + path = save_conversation(messages, logs_dir) + print_system(f"Saved {len(messages)} message(s) to {path}") + continue + + if cmd == "/history": + user_msgs = [m for m in messages if m.get("role") == "user"] + asst_msgs = [m for m in messages if m.get("role") == "assistant"] + print_system( + f"Conversation: {len(user_msgs)} user message(s), " + f"{len(asst_msgs)} assistant reply(ies)." + ) + if user_msgs: + last = user_msgs[-1]["content"] + preview = last[:120] + "..." if len(last) > 120 else last + print_system(f"Last user message: {preview}") + continue + + if cmd == "/model": + print_system(f"Provider : {info.name}") + print_system(f"Model : {info.model}") + continue + + if cmd == "/providers": + print_system(provider_readiness_summary()) + continue + + if cmd == "/reasoning": + # Only AGIProvider exposes get_reasoning_summary() + if hasattr(provider, "get_reasoning_summary"): + # type: ignore[union-attr] + summary = provider.get_reasoning_summary() + last_agent = summary.get("last_agent_used") or "none yet" + last_score = summary.get("last_agent_score") + score_str = f"{last_score:.3f}" if last_score is not None else "n/a" + available = ", ".join(summary.get("available_agents", [])) + + # Build learned-pattern lines (top 5 by observation count). + top_patterns = summary.get("top_learned_patterns", []) + if top_patterns: + pattern_lines = "\n".join( + f" {p.get('domain', '?')}/{p.get('intent', '?')} " + f"→ {p.get('agent', '?')} (×{p.get('count', 0)})" + for p in top_patterns + ) + pattern_section = f"\n Top routing patterns :\n{pattern_lines}" + else: + pattern_section = "" + + print_system( + f"AGI Reasoning Summary:\n" + f" Reasoning chains stored : {summary.get('total_reasoning_chains', 0)}\n" + f" Conversation turns : {summary.get('conversation_length', 0)}\n" + f" Active goals : {', '.join(summary.get('active_goals', [])) or 'none'}\n" + f" Learned patterns : {summary.get('learned_patterns_count', 0)}" + f"{pattern_section}\n" + f" Last agent routed to : {last_agent} (score={score_str})\n" + f" Available agents : {available}" + ) + else: + print_system( + "Reasoning summary is only available with the AGI provider (--provider agi)." + ) + continue + + if not user.strip(): + continue + + messages.append({"role": "user", "content": user}) + + reply_accum = stream_assistant_reply(provider, messages) + messages.append({"role": "assistant", "content": reply_accum}) + + +def one_shot(args: argparse.Namespace) -> int: + colorama_init() + if not args.once: + print("--once requires a message string.") + return 2 + + system_prompt = args.system or os.getenv( + "SYSTEM_PROMPT", + "You are a concise, friendly assistant.", + ) + + provider, info = detect_provider(explicit=args.provider, model_override=args.model) + + messages: List[RoleMessage] = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + messages.append({"role": "user", "content": args.once}) + + print_system(f"Provider: {info.name} | Model: {info.model}") + stream_assistant_reply(provider, messages) + + return 0 + + +def build_arg_parser() -> argparse.ArgumentParser: + p = argparse.ArgumentParser( + description="Simple terminal chat app with local/OpenAI/Azure providers" + ) + p.add_argument( + "--provider", + choices=[ + "auto", + "openai", + "azure", + "local", + "lora", + "agi", + "quantum", + "lmstudio", + "ollama", + ], + default="auto", + help="Which provider to use (default: auto)", + ) + p.add_argument("--system", type=str, help="Custom system prompt") + p.add_argument("--model", type=str, help="Model/deployment name override") + p.add_argument("--once", type=str, help="Send a single message then exit") + p.add_argument( + "--interactive", + action="store_true", + help="Use stdin-driven interactive chat instead of autonomous mode", + ) + p.add_argument( + "--autonomous", + action="store_true", + help="Run unattended continuous chat without prompting for stdin", + ) + p.add_argument("--auto-seed", type=str, help="Initial autonomous user message") + p.add_argument( + "--auto-followup", + type=str, + help="Autonomous follow-up message reused after each assistant turn", + ) + p.add_argument( + "--auto-delay", + type=float, + default=0.0, + help="Delay between autonomous turns in seconds (default: 0)", + ) + p.add_argument( + "--max-turns", + type=int, + help="Maximum autonomous turns before exiting (default: run forever)", + ) + return p + + +def should_run_autonomous(args: argparse.Namespace) -> bool: + if args.interactive: + return False + if args.autonomous: + return True + return not args.once + + +def main(argv: List[str] | None = None) -> int: + args = build_arg_parser().parse_args(argv) + if args.once: + return one_shot(args) + if should_run_autonomous(args): + return autonomous_chat(args) + return interactive_chat(args) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ai-projects/chat-cli/src/chat_providers.py b/ai-projects/chat-cli/src/chat_providers.py index 155032eda..6b6a34882 100644 --- a/ai-projects/chat-cli/src/chat_providers.py +++ b/ai-projects/chat-cli/src/chat_providers.py @@ -1,1549 +1,1549 @@ -from __future__ import annotations - -import json as _json -import logging -import os -import random -import subprocess -import threading -import time -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Dict, Generator, Iterable, List, Optional - -# Helpers for Azure quota/rate-limit detection -try: # shared package may not be importable in all contexts (tests add paths) - from shared.azure_utils import (format_quota_message, is_quota_error, - is_transient_rate_error) -except Exception: # pragma: no cover - best-effort import - # Provide fallbacks if shared module isn't available in runtime/test harness - def is_quota_error(e: Any) -> bool: - txt = str(e).lower() if e is not None else "" - return any( - k in txt - for k in ( - "quota", - "premium", - "exceed", - "allowance", - "insufficient", - "billing", - ) - ) - - def is_transient_rate_error(e: Any) -> bool: - txt = str(e).lower() if e is not None else "" - return any( - k in txt for k in ("rate limit", "429", "too many requests", "rate_limit") - ) - - def format_quota_message(exc: Any, service_name: str = "Azure OpenAI") -> str: - return ( - f"{service_name} quota/premium limit reached. Check billing/limits or use another provider." - f" Details: {str(exc)}" - ) - - -_LOGGER = logging.getLogger(__name__) - -try: - # openai>=1.0 - from openai import AzureOpenAI, OpenAI # type: ignore -except Exception: # pragma: no cover - optional at runtime - OpenAI = None # type: ignore - AzureOpenAI = None # type: ignore - - -# Thread-safe cache for LM Studio availability checks -_lm_studio_availability_cache: Dict[str, Any] = { - "available": None, - "checked_at": 0.0, - "url": None, -} -_lm_studio_cache_lock = threading.RLock() -# Backward-compatible alias for tests expecting _lmstudio_cache_lock -_lmstudio_cache_lock = _lm_studio_cache_lock -_LM_STUDIO_CACHE_TTL_SECONDS = 30 -# Backward-compatible aliases for tests -_lmstudio_cache = _lm_studio_availability_cache -_LMSTUDIO_CACHE_TTL = _LM_STUDIO_CACHE_TTL_SECONDS - -# Thread-safe cache for Ollama availability checks -_ollama_availability_cache: Dict[str, Any] = { - "available": None, - "checked_at": 0.0, - "url": None, -} -_ollama_cache_lock = threading.RLock() -_OLLAMA_CACHE_TTL_SECONDS = 30 - - -# {"role": "system|user|assistant", "content": "..."} -RoleMessage = Dict[str, str] - - -# Backward-compatible provider aliases used by tests and scripts. -# Keys should be lowercase and normalized with `-`/`_` variants where useful. -_PROVIDER_ALIASES: Dict[str, str] = { - "azure_openai": "azure", - "azure-openai": "azure", - "open_ai": "openai", - "lm_studio": "lmstudio", - "lm-studio": "lmstudio", - "local_echo": "local", - "local-echo": "local", - "quantum_llm": "quantum", - "quantum-llm": "quantum", -} - - -def _get_lmstudio_api_key() -> Optional[str]: - """Resolve LM Studio API token from supported env var names.""" - return ( - os.getenv("LM_API_TOKEN") - or os.getenv("LMSTUDIO_API_KEY") - or os.getenv("LMSTUDIO_TOKEN") - or os.getenv("LMSTUDIO_API_TOKEN") - ) - - -def _is_text_like_content_block_type(block_type: Any) -> bool: - """Return True for OpenAI-compatible text block type variants.""" - if not isinstance(block_type, str): - return False - normalized = block_type.strip().lower() - return normalized == "text" or normalized.endswith("_text") - - -def _normalize_message_content_for_openai_api(content: Any) -> Any: - """Normalize message content before sending it to OpenAI-compatible APIs. - - This trims plain-text messages and removes whitespace-only text blocks from - block-based content, while preserving non-text blocks such as image_url. - Returns None when no meaningful content remains. - """ - if isinstance(content, str): - normalized = content.strip() - return normalized or None - - if isinstance(content, list): - normalized_blocks: list[dict[str, Any]] = [] - for block in content: - if not isinstance(block, dict): - continue - - if not _is_text_like_content_block_type(block.get("type")): - normalized_blocks.append(block) - continue - - text_value = block.get("text") - if not isinstance(text_value, str): - continue - - normalized_text = text_value.strip() - if not normalized_text: - continue - - normalized_block = dict(block) - normalized_block["text"] = normalized_text - normalized_blocks.append(normalized_block) - - return normalized_blocks or None - - if content is None: - return None - - normalized = str(content).strip() - return normalized or None - - -def _normalize_messages_for_openai_api( - messages: List[dict[str, Any]] -) -> List[dict[str, Any]]: - """Drop empty messages and sanitize content for OpenAI-compatible APIs.""" - normalized_messages: list[dict[str, Any]] = [] - for msg in messages: - if not isinstance(msg, dict): - continue - - normalized_content = _normalize_message_content_for_openai_api( - msg.get("content") - ) - if normalized_content is None: - continue - - normalized_message = dict(msg) - normalized_message["content"] = normalized_content - normalized_messages.append(normalized_message) - - if not normalized_messages: - raise ValueError("No non-empty message content provided") - - return normalized_messages - - -def _check_lmstudio_available(url: str) -> bool: - """Backward-compatible alias for the newer `_check_lm_studio_available` function. - - Older parts of the codebase call `_check_lmstudio_available` (no underscore - between `lm` and `studio`) so keep a tiny wrapper here that delegates to the - canonical implementation defined later in this module. This avoids import - time IndentationError and keeps the two names consistent. - """ - # Delegate to the canonical implementation which is defined below. - try: - return _check_lm_studio_available(url) - except NameError: - # If the canonical implementation isn't available for some reason, - # perform a conservative HTTP ping. - try: - import urllib.error - import urllib.request - - base_url = url.removesuffix("/v1") - models_endpoint_url = base_url + "/v1/models" - request = urllib.request.Request( - models_endpoint_url, headers={"User-Agent": "QAI"} - ) - urllib.request.urlopen(request, timeout=1) - return True - except Exception: - return False - - -@dataclass -class ProviderChoice: - name: str # 'azure' | 'openai' | 'local' - model: str - - -class BaseChatProvider: - def complete( - self, messages: List[RoleMessage], stream: bool = True - ) -> Iterable[str] | str: - raise NotImplementedError - - @staticmethod - def _normalize_messages_for_api( - messages: List[dict[str, Any]] - ) -> List[dict[str, Any]]: - """Sanitize messages before forwarding them to external providers.""" - return _normalize_messages_for_openai_api(messages) - - @staticmethod - def _handle_openai_streaming_response(response) -> Generator[str, None, None]: - """Extract content from OpenAI-style streaming response. - - Common helper for OpenAI, LMStudio, and other OpenAI-compatible providers. - Handles the standard streaming chunk format with resilient error handling. - """ - for chunk in response: - try: - delta = chunk.choices[0].delta - if delta and delta.content: - yield delta.content - except Exception: - # Be resilient to SDK shape changes - pass - - @staticmethod - def _handle_openai_non_streaming_response(response) -> str: - """Extract content from OpenAI-style non-streaming response. - - Common helper for OpenAI, LMStudio, and other OpenAI-compatible providers. - Handles the standard completion format with resilient error handling. - """ - try: - return response.choices[0].message.content or "" - except Exception: - return "" - - -class LoraLocalProvider(BaseChatProvider): - """Provider for local inference with LoRA adapters. - - If ML dependencies are unavailable in the current process (e.g., - Azure Functions worker without torch/transformers/peft), this provider - falls back to a subprocess bridge that uses the workspace venv - (./venv/Scripts/python.exe) to perform inference. - """ - - def __init__( - self, - adapter_dir: str, - device: str = None, - temperature: float = 0.7, - max_new_tokens: int = 256, - top_p: float = 0.9, - top_k: int = 50, - repetition_penalty: float = 1.1, - ): - """Initialize LoRA provider with enhanced generation parameters. - - Args: - adapter_dir: Path to LoRA adapter - device: Device for inference (cuda/cpu) - temperature: Sampling temperature (higher = more random) - max_new_tokens: Maximum tokens to generate - top_p: Nucleus sampling threshold - top_k: Top-k sampling parameter - repetition_penalty: Penalty for repeating tokens - """ - self.adapter_dir = Path(adapter_dir) - self.use_subprocess = False - self.bridge_python: Optional[str] = None - self.temperature = float(temperature) - self.max_new_tokens = int(max_new_tokens) - self.top_p = float(top_p) - self.top_k = int(top_k) - self.repetition_penalty = float(repetition_penalty) - # Lazy import heavy deps on demand - self._lazy_setup() - if not self.use_subprocess: - self.device = device or ( - "cuda" if self.torch.cuda.is_available() else "cpu" - ) - self.model, self.tokenizer = self._load_model_and_tokenizer() - else: - # In subprocess mode we keep state minimal here - self.device = "cpu" - - def _load_model_and_tokenizer(self): - # Detect adapter config - import json as _json - - adapter_config_path = self.adapter_dir / "adapter_config.json" - if not adapter_config_path.exists(): - raise RuntimeError(f"adapter_config.json not found in {self.adapter_dir}") - with open(adapter_config_path, "r", encoding="utf-8") as f: - adapter_cfg = _json.load(f) - base_model_id = adapter_cfg.get( - "base_model_name_or_path", "microsoft/Phi-3.5-mini-instruct" - ) - # Fallback mapping for Phi-3.6 - if base_model_id == "Phi-3.6-mini-instruct": - base_model_id = "microsoft/Phi-3.5-mini-instruct" - base_model = self.AutoModelForCausalLM.from_pretrained( - base_model_id, - torch_dtype=( - self.torch.float16 if self.device == "cuda" else self.torch.float32 - ), - device_map="auto" if self.device == "cuda" else None, - ) - tokenizer_source = self.adapter_dir.parent / "tokenizer" - if tokenizer_source.exists(): - tokenizer = self.AutoTokenizer.from_pretrained(tokenizer_source) - else: - tokenizer = self.AutoTokenizer.from_pretrained(base_model_id) - model = self.PeftModel.from_pretrained(base_model, self.adapter_dir) - model.eval() - return model, tokenizer - - def complete( - self, messages: List[RoleMessage], stream: bool = True - ) -> Iterable[str] | str: - if self.use_subprocess: - response = self._complete_via_subprocess(messages) - if not stream: - return response - - def gen(): - for ch in response: - yield ch - time.sleep(0.002) - - return gen() - # In-process inference path - prompt = self._build_prompt(messages) - inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device) - with self.torch.no_grad(): - output = self.model.generate( - **inputs, - max_new_tokens=self.max_new_tokens, - do_sample=True, - temperature=self.temperature, - top_p=self.top_p, - top_k=self.top_k, - repetition_penalty=self.repetition_penalty, - pad_token_id=self.tokenizer.eos_token_id, - eos_token_id=self.tokenizer.eos_token_id, - ) - response = self.tokenizer.decode( - output[0][inputs["input_ids"].shape[-1] :], skip_special_tokens=True - ) - if not stream: - return response - - def gen(): - for ch in response: - yield ch - time.sleep(0.002) - - return gen() - - def _complete_via_subprocess(self, messages: List[RoleMessage]) -> str: - if not self.bridge_python: - raise RuntimeError("Subprocess bridge not configured for LoRA provider.") - bridge_script = Path(__file__).resolve().parent / "lora_infer_bridge.py" - if not bridge_script.exists(): - raise RuntimeError(f"Bridge script not found at {bridge_script}") - payload = { - "adapter_dir": str(self.adapter_dir), - "messages": messages, - "max_new_tokens": self.max_new_tokens, - "temperature": self.temperature, - } - try: - proc = subprocess.run( - [self.bridge_python, "-u", str(bridge_script)], - input=_json.dumps(payload).encode("utf-8"), - capture_output=True, - check=False, - timeout=300, - ) - except Exception as e: - raise RuntimeError(f"Failed to launch LoRA bridge: {e}") from e - if proc.returncode != 0: - stderr = proc.stderr.decode("utf-8", errors="ignore") - stdout = proc.stdout.decode("utf-8", errors="ignore") - msg = stderr.strip() or stdout.strip() or f"exit code {proc.returncode}" - # Truncate very long errors but keep start and end - if len(msg) > 1000: - msg = msg[:500] + "\n...\n" + msg[-500:] - raise RuntimeError(f"LoRA bridge failed: {msg}") - text = proc.stdout.decode("utf-8", errors="ignore").strip() - return text - - def _build_prompt(self, messages: List[RoleMessage]) -> str: - """Build prompt string from messages. - - Uses list join instead of string += for O(n) instead of O(n²) complexity. - """ - parts = [] - for msg in messages: - role = msg.get("role", "user") - content = msg.get("content", "") - if role == "system": - parts.append(f"[SYSTEM] {content}") - elif role == "user": - parts.append(f"User: {content}") - elif role == "assistant": - parts.append(f"Assistant: {content}") - - # Build final prompt: messages joined by newlines, ending with "Assistant: " - if parts: - return "\n".join(parts) + "\nAssistant: " - return "Assistant: " - - def _lazy_setup(self) -> None: - """Import heavy dependencies lazily so that non-LoRA providers don't require them. - - If imports fail (common under Azure Functions without ML deps), - configure a subprocess bridge to a venv Python. - """ - try: - import torch as _torch # type: ignore - from transformers import \ - AutoModelForCausalLM as _AM # type: ignore - from transformers import AutoTokenizer as _AT - - try: - from peft import PeftModel as _PM # type: ignore - except Exception: - # peft missing -> subprocess - self._configure_subprocess_bridge() - return - except Exception: - # Any import failure -> subprocess - self._configure_subprocess_bridge() - return - # Store on self for in-process inference - self.torch = _torch - self.AutoModelForCausalLM = _AM - self.AutoTokenizer = _AT - self.PeftModel = _PM - - def _configure_subprocess_bridge(self) -> None: - repo_root = Path(__file__).resolve().parents[2] - venv_python = repo_root / "venv" / "Scripts" / "python.exe" - if venv_python.exists(): - self.bridge_python = str(venv_python) - self.use_subprocess = True - else: - raise RuntimeError( - "Missing dependencies for LoRA provider and no venv found. " - "Create ./venv and install 'torch', 'transformers', 'peft'." - ) - - -class LocalEchoProvider(BaseChatProvider): - """A simple offline provider that mimics a helpful assistant. - Useful for smoke tests and environments without keys. - - This provider generates contextually aware responses without requiring - external API calls, making it ideal for testing, development, and - offline scenarios. - """ - - def __init__(self, seed: Optional[int] = None): - self.rng = random.Random(seed) - self._response_templates = self._initialize_templates() - - def _initialize_templates(self) -> Dict[str, List[str]]: - """Initialize response templates categorized by intent.""" - return { - "greeting": [ - "Hello! I'm here to help. What would you like to know?", - "Hi there! How can I assist you today?", - "Greetings! I'm ready to help with your questions.", - ], - "question": [ - "That's an interesting question. Here's my take:", - "Let me help you with that:", - "Good question! Here's what I think:", - "Based on your question, here's my understanding:", - ], - "code": [ - "For coding tasks, I'd suggest:", - "Here's a technical approach:", - "From a development perspective:", - "For this programming challenge:", - ], - "explanation": [ - "Let me break this down:", - "Here's a clear explanation:", - "To explain this simply:", - "In other words:", - ], - "generic": [ - "Here's a concise take:", - "Quick thoughts:", - "A few ideas:", - "My perspective:", - "Summary:", - ], - } - - def _detect_intent(self, text: str) -> str: - """Detect the intent of the user message.""" - lower_text = text.lower() - - # Check for greetings - if ( - any(word in lower_text for word in ["hello", "hi", "hey", "greetings"]) - and len(text) < 50 - ): - return "greeting" - - # Check for coding-related queries - if any( - word in lower_text - for word in [ - "code", - "function", - "class", - "debug", - "program", - "script", - "algorithm", - ] - ): - return "code" - - # Check for explanation requests - if any( - word in lower_text - for word in [ - "explain", - "what is", - "what are", - "how does", - "describe", - "tell me about", - ] - ): - return "explanation" - - # Check for questions - if any( - word in lower_text - for word in ["?", "how", "why", "when", "where", "who", "can you"] - ): - return "question" - - return "generic" - - def _craft_autonomous_reply( - self, messages: List[RoleMessage], last_user: str, turn_count: int - ) -> str: - """Generate more useful offline output for autonomous CLI loops.""" - assistant_messages = [ - m["content"] for m in messages if m.get("role") == "assistant" - ] - last_assistant = assistant_messages[-1] if assistant_messages else "" - user_topics = [ - m["content"].strip() - for m in messages - if m.get("role") == "user" and m.get("content", "").strip() - ] - topic = ( - user_topics[0][:120].rstrip(".,?!") if user_topics else "the current task" - ) - - if "message count exceeded limit" in last_assistant.lower(): - return ( - "Autonomous checkpoint:\n" - "1. The conversation is getting long, so summarize the objective in one sentence.\n" - "2. Keep only the latest constraints and the best next action.\n" - "3. Continue with one concrete step instead of repeating the same prompt." - ) - - sequence = [ - ( - f"Autonomous plan for '{topic}':\n\n" - "1. Define the concrete objective and success condition.\n" - "2. Inspect the current inputs, dependencies, or repo context.\n" - "3. Pick the smallest next action that creates evidence of progress." - ), - ( - f"Autonomous next step for '{topic}':\n\n" - "- Gather one missing fact before changing direction.\n" - "- Write down the current assumption you are relying on.\n" - "- Execute a single focused action, then reassess." - ), - ( - f"Autonomous review for '{topic}':\n\n" - "- What changed since the previous turn?\n" - "- What is still blocked or uncertain?\n" - "- What is the highest-value follow-up action right now?" - ), - ( - f"Autonomous refinement for '{topic}':\n\n" - "1. Remove repetition.\n" - "2. Convert vague goals into one measurable outcome.\n" - "3. Continue with a concrete command, edit, or validation step." - ), - ] - return sequence[(turn_count - 1) % len(sequence)] - - def _craft_reply(self, messages: List[RoleMessage]) -> str: - """Generate a contextually appropriate response. - - The local echo provider has no real model, so responses are - rule-based — but they should at least be informative and - actionable rather than meaninglessly rephrasing the user's input. - """ - last_user = next( - (m["content"] for m in reversed(messages) if m.get("role") == "user"), "" - ).strip() - - if not last_user: - return ( - "Hi! I'm running in offline mode (no API keys configured). " - "I can still help with Aria commands, answer simple questions, " - "or assist with code structure. What would you like to do?" - ) - - intent = self._detect_intent(last_user) - lower = last_user.lower() - turn_count = sum(1 for m in messages if m.get("role") == "user") - - if ( - "start working autonomously" in lower - or "continue autonomously" in lower - or "without waiting for user input" in lower - or "choose the next useful step yourself" in lower - ): - return self._craft_autonomous_reply(messages, last_user, turn_count) - - # --- Greetings --- - if intent == "greeting": - greetings = [ - "Hello! I'm running in local offline mode — no external model is active. " - "Try `--provider azure`, `--provider openai`, or `--provider lmstudio` for a full AI response.", - "Hi there! Offline mode is active. Set AZURE_OPENAI_API_KEY or OPENAI_API_KEY to enable a real AI provider.", - "Hey! Running without a live model right now. I can still help with Aria commands and simple tasks.", - ] - return self.rng.choice(greetings) - - # --- Aria movement commands --- - aria_keywords = { - "left": "Moving Aria to the left. [aria:walk:left]", - "right": "Moving Aria to the right. [aria:walk:right]", - "jump": "Aria jumps! [aria:jump]", - "wave": "Aria waves hello! [aria:wave]", - "dance": "Aria starts dancing! [aria:dance]", - "idle": "Aria returns to idle. [aria:idle]", - } - for keyword, response in aria_keywords.items(): - if keyword in lower: - return response - - # --- Coding requests --- - if intent == "code": - topic = last_user[:80].rstrip(".,?!") - suggestions = [ - f"For '{topic}', here's one approach:\n\n" - "1. Identify inputs, outputs, and edge cases first.\n" - "2. Write a minimal working version before optimising.\n" - "3. Add error handling for external calls (I/O, network, parsing).\n\n" - "Enable a real provider (e.g. `--provider openai`) for generated code.", - f"Coding tip for '{topic}':\n\n" - "- Keep functions small and focused on one task.\n" - "- Use type hints for clarity and static analysis.\n" - "- Write a unit test for each edge case you can think of.\n\n" - "Connect a live model for actual code generation.", - ] - return self.rng.choice(suggestions) - - # --- Explanation requests --- - if intent == "explanation": - topic = last_user[:80].rstrip(".,?!") - return ( - f"I'm in offline mode, so I can't give a full explanation of '{topic}'. " - "Here's what I'd suggest:\n\n" - "1. Check the official docs or a trusted reference.\n" - "2. Ask again with `--provider azure` or `--provider openai` for a detailed answer.\n" - "3. Or try `--provider agi` for structured chain-of-thought reasoning." - ) - - # --- Questions --- - if intent == "question": - closers = [ - "Switch to a live provider for a detailed answer.", - "Try `--provider openai` or `--provider azure` for a real response.", - "Use `--provider agi` for structured reasoning on complex questions.", - ] - return ( - f"Good question! Unfortunately I'm in local echo mode and can't look things up. " - f"{self.rng.choice(closers)}" - ) - - # --- Multi-turn acknowledgement --- - if turn_count > 3: - follow_ups = [ - "I'm still in offline mode — I can only give canned responses. " - "Configure a provider to continue this conversation meaningfully.", - "Thanks for staying in the conversation! A live provider would give you much better answers here.", - ] - return self.rng.choice(follow_ups) - - # --- Generic fallback --- - generic = [ - "I'm running in local fallback mode. " - "Set AZURE_OPENAI_API_KEY, OPENAI_API_KEY, or start LM Studio / Ollama to enable full AI responses.", - "Offline mode active. I can process Aria commands but can't generate AI responses without a configured provider.", - "No live model detected. Run with `--provider lmstudio` (LM Studio running locally), " - "`--provider ollama`, `--provider openai`, or `--provider azure`.", - ] - return self.rng.choice(generic) - - def complete( - self, messages: List[RoleMessage], stream: bool = True - ) -> Iterable[str] | str: - text = self._craft_reply(messages) - if not stream: - return text - - def gen() -> Generator[str, None, None]: - for ch in text: - yield ch - # Tiny delay to simulate streaming; keep very small - time.sleep(0.002) - - return gen() - - -class OpenAIProvider(BaseChatProvider): - def __init__( - self, - model: str, - api_key: Optional[str] = None, - temperature: float = 0.7, - max_output_tokens: Optional[int] = None, - ): - if OpenAI is None: - raise RuntimeError( - "openai package not installed. Install 'openai' to use this provider." - ) - self.client = OpenAI(api_key=api_key) - self.model = model - self.temperature = temperature - self.max_output_tokens = max_output_tokens - - def complete( - self, messages: List[RoleMessage], stream: bool = True - ) -> Iterable[str] | str: - """Complete with OpenAI and handle quota/rate-limit errors gracefully. - - Behaviour mirrors AzureOpenAIProvider: - - Quota/billing errors → friendly string or single-chunk generator. - - Transient 429 errors → small retry with back-off. - - Mid-stream errors → yielded friendly/error token instead of raising. - """ - - normalized_messages = self._normalize_messages_for_api(messages) - - def _attempt_create(**kwargs): - max_retries = 3 - base_backoff = 0.4 - attempt = 0 - while True: - try: - return self.client.chat.completions.create(**kwargs) - except Exception as e: - if is_quota_error(e): - raise - if is_transient_rate_error(e) and attempt < max_retries: - sleep_time = base_backoff * (2**attempt) - _LOGGER.info( - "OpenAI rate-limit, retrying in %.2fs (attempt %d)", - sleep_time, - attempt + 1, - ) - time.sleep(sleep_time) - attempt += 1 - continue - raise - - try: - resp = _attempt_create( - model=self.model, - messages=normalized_messages, - temperature=self.temperature, - max_tokens=self.max_output_tokens, - stream=stream, - ) - except Exception as e: - if is_quota_error(e): - friendly = format_quota_message(e, service_name="OpenAI") - if stream: - - def _gen_quota_err() -> Generator[str, None, None]: - yield friendly - - return _gen_quota_err() - return friendly - raise - - if stream: - - def _gen() -> Generator[str, None, None]: - try: - for chunk in resp: - try: - delta = chunk.choices[0].delta - if delta and delta.content: - yield delta.content - except Exception: - continue - except Exception as exc: - if is_quota_error(exc): - yield format_quota_message(exc, service_name="OpenAI") - else: - yield f"[OpenAI error: {str(exc)}]" - - return _gen() - else: - try: - return resp.choices[0].message.content or "" - except Exception: - return "" - - -class LMStudioProvider(BaseChatProvider): - """Provider for LM Studio local server (compatible with OpenAI API).""" - - def __init__( - self, - base_url: str = "http://127.0.0.1:1234/v1", - model: str = "local-model", - temperature: float = 0.7, - max_output_tokens: Optional[int] = None, - ): - if OpenAI is None: - raise RuntimeError( - "openai package not installed. Install 'openai' to use this provider." - ) - # Newer LM Studio server configurations can require API token auth. - # Keep backward compatibility by using the legacy placeholder key when - # no token env var is provided. - lmstudio_api_key = _get_lmstudio_api_key() or "lm-studio" - self.client = OpenAI( - base_url=base_url, - api_key=lmstudio_api_key, - ) - self.model = model - self.temperature = temperature - self.max_output_tokens = max_output_tokens - self.base_url = base_url - - def complete( - self, messages: List[RoleMessage], stream: bool = True - ) -> Iterable[str] | str: - try: - normalized_messages = self._normalize_messages_for_api(messages) - resp = self.client.chat.completions.create( - model=self.model, - messages=normalized_messages, - temperature=self.temperature, - max_tokens=self.max_output_tokens, - stream=stream, - ) - - if stream: - return self._handle_openai_streaming_response(resp) - else: - return self._handle_openai_non_streaming_response(resp) - except Exception as e: - # Provide helpful error messages for common issues - error_msg = str(e).lower() - - if ( - "connection" in error_msg - or "refused" in error_msg - or "timeout" in error_msg - ): - suggestion = ( - f"❌ Cannot connect to LM Studio at {self.base_url}\n\n" - f"Troubleshooting steps:\n" - f"1. Make sure LM Studio is running\n" - f"2. Check that the local server is started in LM Studio\n" - f"3. Verify the server is running on {self.base_url}\n" - f"4. Check your firewall settings\n\n" - f"Set LMSTUDIO_BASE_URL environment variable if using a different address." - ) - if stream: - - def gen_err() -> Generator[str, None, None]: - yield suggestion - - return gen_err() - return suggestion - - if "model" in error_msg and ( - "not found" in error_msg or "does not exist" in error_msg - ): - suggestion = ( - f"❌ Model '{self.model}' not found in LM Studio.\n\n" - f"Troubleshooting steps:\n" - f"1. Check that a model is loaded in LM Studio\n" - f"2. Use --model flag to specify the correct model name\n" - f"3. Set LMSTUDIO_MODEL environment variable\n\n" - f"The model name should match what's shown in LM Studio's server panel." - ) - if stream: - - def gen_err() -> Generator[str, None, None]: - yield suggestion - - return gen_err() - return suggestion - - if "invalid_api_key" in error_msg or "api token is required" in error_msg: - suggestion = ( - f"❌ LM Studio at {self.base_url} requires API token authentication.\n\n" - f"Troubleshooting steps:\n" - f"1. Export one of: LM_API_TOKEN, LMSTUDIO_API_KEY, LMSTUDIO_TOKEN, LMSTUDIO_API_TOKEN\n" - f"2. Ensure token matches LM Studio server configuration\n" - f"3. Re-run with --provider lmstudio\n\n" - f"Example:\n" - f"export LM_API_TOKEN=''" - ) - if stream: - - def gen_err() -> Generator[str, None, None]: - yield suggestion - - return gen_err() - return suggestion - - # Re-raise unexpected errors - raise - - -class OllamaProvider(BaseChatProvider): - """Provider for Ollama local server (compatible with OpenAI API). - - Ollama is a popular local LLM server that supports models like Llama, Mistral, - CodeLlama, and many others. It provides an OpenAI-compatible API. - - Default endpoint: http://127.0.0.1:11434/v1 - Configure via OLLAMA_BASE_URL environment variable. - """ - - def __init__( - self, - base_url: str = "http://127.0.0.1:11434/v1", - model: str = "llama3.2", - temperature: float = 0.7, - max_output_tokens: Optional[int] = None, - ): - if OpenAI is None: - raise RuntimeError( - "openai package not installed. Install 'openai' to use this provider." - ) - self.client = OpenAI( - base_url=base_url, api_key="ollama" # Ollama doesn't require real key - ) - self.model = model - self.temperature = temperature - self.max_output_tokens = max_output_tokens - self.base_url = base_url - - def complete( - self, messages: List[RoleMessage], stream: bool = True - ) -> Iterable[str] | str: - try: - normalized_messages = self._normalize_messages_for_api(messages) - resp = self.client.chat.completions.create( - model=self.model, - messages=normalized_messages, - temperature=self.temperature, - max_tokens=self.max_output_tokens, - stream=stream, - ) - - if stream: - return self._handle_openai_streaming_response(resp) - else: - return self._handle_openai_non_streaming_response(resp) - except Exception as e: - # Provide helpful error messages for common Ollama issues - error_msg = str(e).lower() - - if ( - "connection" in error_msg - or "refused" in error_msg - or "timeout" in error_msg - ): - suggestion = ( - f"❌ Cannot connect to Ollama at {self.base_url}\n\n" - f"Troubleshooting steps:\n" - f"1. Make sure Ollama is installed and running\n" - f" - Install: https://ollama.ai/download\n" - f" - Start: 'ollama serve' (or it may start automatically)\n" - f"2. Verify Ollama is listening on {self.base_url}\n" - f"3. Check your firewall settings\n\n" - f"Set OLLAMA_BASE_URL environment variable if using a different address." - ) - if stream: - - def gen_err() -> Generator[str, None, None]: - yield suggestion - - return gen_err() - return suggestion - - if "model" in error_msg and ( - "not found" in error_msg - or "does not exist" in error_msg - or "not available" in error_msg - ): - suggestion = ( - f"❌ Model '{self.model}' not found in Ollama.\n\n" - f"Troubleshooting steps:\n" - f"1. Pull the model first:\n" - f" ollama pull {self.model}\n\n" - f"2. List available models:\n" - f" ollama list\n\n" - f"3. Popular models to try:\n" - f" - llama2:latest (7B general purpose)\n" - f" - codellama:latest (7B for coding)\n" - f" - mistral:latest (7B high quality)\n" - f" - llama3.2:latest (3B latest from Meta)\n\n" - f"Use --model flag or set OLLAMA_MODEL environment variable." - ) - if stream: - - def gen_err() -> Generator[str, None, None]: - yield suggestion - - return gen_err() - return suggestion - - # Re-raise unexpected errors - raise - - -class AzureOpenAIProvider(BaseChatProvider): - def __init__( - self, - deployment: str, - endpoint: str, - api_key: str, - api_version: str = "2024-08-01-preview", - temperature: float = 0.7, - max_output_tokens: Optional[int] = None, - ): - if AzureOpenAI is None: - raise RuntimeError( - "openai package not installed. Install 'openai' to use this provider." - ) - self.client = AzureOpenAI( - api_key=api_key, - api_version=api_version, - azure_endpoint=endpoint, - ) - self.deployment = deployment - self.temperature = temperature - self.max_output_tokens = max_output_tokens - - def complete( - self, messages: List[RoleMessage], stream: bool = True - ) -> Iterable[str] | str: - """Complete with Azure OpenAI and handle quota/rate-limit errors gracefully. - - Behavior: - - If a quota/premium allowance error is detected, return a friendly - message instead of raising an exception. - - Retry transient rate-limit style errors a small number of times with - exponential backoff. - - Returns either a string (non-stream) or a generator yielding string chunks. - """ - - normalized_messages = self._normalize_messages_for_api(messages) - - # Internal helper: attempt the SDK call with small retry/backoff for - # transient rate-limit style errors. If we detect a quota/premium error - # we return the exception directly for caller to handle. - def _attempt_create(**kwargs): - max_retries = 3 - base_backoff = 0.4 - attempt = 0 - while True: - try: - return self.client.chat.completions.create(**kwargs) - except Exception as e: # pragma: no cover - depends on runtime - # If this looks like a quota/premium allowance error, bail out - if is_quota_error(e): - raise - # Retry transient rate-limit errors a few times - if is_transient_rate_error(e) and attempt < max_retries: - sleep_time = base_backoff * (2**attempt) - jitter = min(sleep_time * 0.1, 0.5) - import time - - _LOGGER.info( - "Azure rate-limit detected, retrying in %.2fs (attempt %d)", - sleep_time + jitter, - attempt + 1, - ) - time.sleep(sleep_time + jitter) - attempt += 1 - continue - # Propagate other exceptions - raise - - try: - resp = _attempt_create( - model=self.deployment, # In Azure, 'model' is your deployment name - messages=normalized_messages, - temperature=self.temperature, - max_tokens=self.max_output_tokens, - stream=stream, - ) - except Exception as e: - # If quota/premium, return a friendly message instead of bubbling an - # exception to callers (better UX for local & CLI users) - if is_quota_error(e): - friendly = format_quota_message(e, service_name="Azure OpenAI") - if stream: - - def gen_err() -> Generator[str, None, None]: - yield friendly - - return gen_err() - return friendly - # Not a quota error -> re-raise so upstream can observe generic failures - raise - - if stream: - - def gen() -> Generator[str, None, None]: - # resp can be an iterator/generator from the SDK. We iterate and - # guard against runtime errors that may occur during streaming. - try: - for chunk in resp: - try: - delta = chunk.choices[0].delta - if delta and delta.content: - yield delta.content - except Exception: - # Resilient: skip unexpected chunk shapes - continue - except Exception as e: - # Catch runtime errors during iteration and turn them into - # a short user-friendly message (quota or otherwise). - if is_quota_error(e): - yield format_quota_message(e, service_name="Azure OpenAI") - else: - yield f"[AzureOpenAI error: {str(e)}]" - - return gen() - - else: - try: - return resp.choices[0].message.content or "" - except Exception: - return "" - - -def _check_lm_studio_available(server_url: str) -> bool: - """Check if LM Studio server is available at the given URL. - - Uses a thread-safe cache to avoid repeated HTTP requests within the TTL period. - The HTTP request is performed outside the lock to avoid blocking other threads. - - Args: - server_url: Base URL for LM Studio API (e.g., "http://127.0.0.1:1234/v1") - - Returns: - True if LM Studio is available, False otherwise. - """ - # Check cache under lock - with _lm_studio_cache_lock: - current_time = time.time() - if ( - _lm_studio_availability_cache["available"] is not None - and _lm_studio_availability_cache["url"] == server_url - and (current_time - _lm_studio_availability_cache["checked_at"]) - < _LM_STUDIO_CACHE_TTL_SECONDS - ): - return _lm_studio_availability_cache["available"] - - # Perform HTTP check outside lock to avoid blocking other threads - is_available = False - lmstudio_api_key = _get_lmstudio_api_key() - try: - import urllib.error - import urllib.request - - # Remove trailing /v1 if present, then append /v1/models - base_url = server_url.removesuffix("/v1") - models_endpoint_url = base_url + "/v1/models" - headers = {"User-Agent": "QAI"} - if lmstudio_api_key: - headers["Authorization"] = f"Bearer {lmstudio_api_key}" - request = urllib.request.Request(models_endpoint_url, headers=headers) - urllib.request.urlopen(request, timeout=1) - is_available = True - except urllib.error.HTTPError as exc: - # Endpoint is reachable but auth failed: count as available only when - # caller configured a token (possibly wrong/expired). - if exc.code in (401, 403) and bool(lmstudio_api_key): - is_available = True - else: - is_available = False - except Exception: - is_available = False - - # Update cache under lock - with _lm_studio_cache_lock: - _lm_studio_availability_cache["available"] = is_available - _lm_studio_availability_cache["checked_at"] = time.time() - _lm_studio_availability_cache["url"] = server_url - - return is_available - - -def _check_ollama_available(server_url: str) -> bool: - """Check if Ollama server is available at the given URL. - - Uses a thread-safe cache to avoid repeated HTTP requests within the TTL period. - The HTTP request is performed outside the lock to avoid blocking other threads. - - Args: - server_url: Base URL for Ollama API (e.g., "http://127.0.0.1:11434/v1") - - Returns: - True if Ollama is available, False otherwise. - """ - # Check cache under lock - with _ollama_cache_lock: - current_time = time.time() - if ( - _ollama_availability_cache["available"] is not None - and _ollama_availability_cache["url"] == server_url - and (current_time - _ollama_availability_cache["checked_at"]) - < _OLLAMA_CACHE_TTL_SECONDS - ): - return _ollama_availability_cache["available"] - - # Perform HTTP check outside lock to avoid blocking other threads - is_available = False - try: - import urllib.error - import urllib.request - - # Remove trailing /v1 if present, then try /api/tags endpoint (Ollama-specific) - base_url = server_url.removesuffix("/v1") - # Ollama uses /api/tags to list models - tags_endpoint_url = base_url + "/api/tags" - request = urllib.request.Request( - tags_endpoint_url, headers={"User-Agent": "QAI"} - ) - urllib.request.urlopen(request, timeout=1) - is_available = True - except Exception: - # Fallback: try OpenAI-compatible /v1/models endpoint - try: - import urllib.error - import urllib.request - - base_url = server_url.removesuffix("/v1") - models_endpoint_url = base_url + "/v1/models" - request = urllib.request.Request( - models_endpoint_url, headers={"User-Agent": "QAI"} - ) - urllib.request.urlopen(request, timeout=1) - is_available = True - except Exception: - is_available = False - - # Update cache under lock - with _ollama_cache_lock: - _ollama_availability_cache["available"] = is_available - _ollama_availability_cache["checked_at"] = time.time() - _ollama_availability_cache["url"] = server_url - - return is_available - - -def detect_provider( - explicit: Optional[str] = None, - model_override: Optional[str] = None, - temperature: Optional[float] = None, - max_output_tokens: Optional[int] = None, -) -> tuple[BaseChatProvider, ProviderChoice]: - """Detect the best provider based on environment variables. - - Priority: - 1) explicit selection if provided - 2) LM Studio if LMSTUDIO_BASE_URL is set - 3) Ollama if OLLAMA_BASE_URL is set - 4) AGI if selected (advanced reasoning capabilities) - 5) Quantum if selected - 6) Azure if all required vars present - 7) OpenAI if OPENAI_API_KEY is present - 8) Local fallback - 9) LoRA if provider is 'lora' and model_override is set - """ - provider_choice = (explicit or "auto").lower() - provider_choice = _PROVIDER_ALIASES.get(provider_choice, provider_choice) - - # LM Studio config - lm_studio_base_url = os.getenv("LMSTUDIO_BASE_URL", "http://127.0.0.1:1234/v1") - lm_studio_model_name = os.getenv("LMSTUDIO_MODEL", "local-model") - - # Ollama config - ollama_base_url = os.getenv("OLLAMA_BASE_URL", "http://127.0.0.1:11434/v1") - ollama_model_name = os.getenv("OLLAMA_MODEL", "llama3.2") - - # AGI config - advanced reasoning capabilities - if provider_choice == "agi": - try: - from agi_provider import create_agi_provider - - temperature_value = float( - temperature - if temperature is not None - else os.getenv("CHAT_TEMPERATURE", "0.7") - ) - max_tokens_limit = ( - int(max_output_tokens) if max_output_tokens is not None else 2048 - ) - verbose = os.getenv("AGI_VERBOSE", "false").lower() == "true" - provider, info = create_agi_provider( - model=model_override, - temperature=temperature_value, - max_output_tokens=max_tokens_limit, - verbose=verbose, - ) - return provider, ProviderChoice(name=info.name, model=info.model) - except ImportError as import_error: - raise RuntimeError( - f"AGI provider selected but agi_provider module not available: {import_error}" - ) from import_error - - if provider_choice == "quantum": - try: - from quantum_provider import create_quantum_llm_provider - - selected_model_path = ( - model_override - or os.getenv("QAI_QUANTUM_MODEL_PATH") - or os.getenv("QAI_QUANTUM_MODEL") - ) - if not selected_model_path: - raise RuntimeError( - "Quantum LLM provider requires a model path. Provide --model or set " - "QAI_QUANTUM_MODEL_PATH (or QAI_QUANTUM_MODEL).\n" - "Example: --provider quantum --model data_out/quantum_llm_training" - ) - - temperature_value = float( - temperature - if temperature is not None - else os.getenv("CHAT_TEMPERATURE", "0.8") - ) - max_tokens_limit = ( - int(max_output_tokens) if max_output_tokens is not None else 200 - ) - - provider, info = create_quantum_llm_provider( - model_path=selected_model_path, - temperature=temperature_value, - max_output_tokens=max_tokens_limit, - ) - return provider, ProviderChoice(name=info.name, model=info.model) - except ImportError as import_error: - raise RuntimeError( - f"Quantum provider selected but quantum_provider module not available: {import_error}" - ) from import_error - - if provider_choice == "lora": - if not model_override: - raise RuntimeError("LoRA provider selected but model path not provided.") - temperature_value = float( - temperature - if temperature is not None - else os.getenv("CHAT_TEMPERATURE", "0.7") - ) - max_new_tokens = ( - int(max_output_tokens) if max_output_tokens is not None else 256 - ) - provider = LoraLocalProvider( - adapter_dir=model_override, - temperature=temperature_value, - max_new_tokens=max_new_tokens, - ) - return provider, ProviderChoice(name="lora", model=str(model_override)) - - # Azure OpenAI config - azure_openai_api_key = os.getenv("AZURE_OPENAI_API_KEY") - azure_openai_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") - azure_openai_deployment = os.getenv("AZURE_OPENAI_DEPLOYMENT") - azure_openai_api_version = os.getenv( - "AZURE_OPENAI_API_VERSION", "2024-08-01-preview" - ) - - # OpenAI config - openai_api_key = os.getenv("OPENAI_API_KEY") - openai_model_name = os.getenv("OPENAI_MODEL", "gpt-4o-mini") - - temperature_setting = float( - temperature if temperature is not None else os.getenv("CHAT_TEMPERATURE", "0.7") - ) - - # Resolve based on explicit choice first - if provider_choice == "lmstudio": - selected_model = model_override or lm_studio_model_name - provider = LMStudioProvider( - base_url=lm_studio_base_url, - model=selected_model, - temperature=temperature_setting, - max_output_tokens=max_output_tokens, - ) - return provider, ProviderChoice(name="lmstudio", model=selected_model) - - if provider_choice == "ollama": - selected_model = model_override or ollama_model_name - provider = OllamaProvider( - base_url=ollama_base_url, - model=selected_model, - temperature=temperature_setting, - max_output_tokens=max_output_tokens, - ) - return provider, ProviderChoice(name="ollama", model=selected_model) - - if provider_choice == "azure": - if not ( - azure_openai_api_key - and azure_openai_endpoint - and (model_override or azure_openai_deployment) - ): - raise RuntimeError( - "Azure OpenAI selected but required env vars are missing. Set AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_DEPLOYMENT." - ) - selected_model = model_override or azure_openai_deployment # deployment name - provider = AzureOpenAIProvider( - deployment=selected_model, - endpoint=azure_openai_endpoint, - api_key=azure_openai_api_key, - api_version=azure_openai_api_version, - temperature=temperature_setting, - max_output_tokens=max_output_tokens, - ) - return provider, ProviderChoice(name="azure", model=selected_model) - - if provider_choice == "openai": - if not openai_api_key: - raise RuntimeError("OpenAI selected but OPENAI_API_KEY is not set.") - selected_model = model_override or openai_model_name - provider = OpenAIProvider( - model=selected_model, - api_key=openai_api_key, - temperature=temperature_setting, - max_output_tokens=max_output_tokens, - ) - return provider, ProviderChoice(name="openai", model=selected_model) - - if provider_choice == "local": - selected_model = model_override or "local-echo" - provider = LocalEchoProvider() - return provider, ProviderChoice(name="local", model=selected_model) - - # Auto mode - check for LM Studio first using thread-safe cached check - if _check_lm_studio_available(lm_studio_base_url): - selected_model = model_override or lm_studio_model_name - provider = LMStudioProvider( - base_url=lm_studio_base_url, - model=selected_model, - temperature=temperature_setting, - max_output_tokens=max_output_tokens, - ) - return provider, ProviderChoice(name="lmstudio", model=selected_model) - - # Check for Ollama next - if _check_ollama_available(ollama_base_url): - selected_model = model_override or ollama_model_name - provider = OllamaProvider( - base_url=ollama_base_url, - model=selected_model, - temperature=temperature_setting, - max_output_tokens=max_output_tokens, - ) - return provider, ProviderChoice(name="ollama", model=selected_model) - - if ( - azure_openai_api_key - and azure_openai_endpoint - and (model_override or azure_openai_deployment) - ): - selected_model = model_override or azure_openai_deployment - provider = AzureOpenAIProvider( - deployment=selected_model, - endpoint=azure_openai_endpoint, - api_key=azure_openai_api_key, - api_version=azure_openai_api_version, - temperature=temperature_setting, - max_output_tokens=max_output_tokens, - ) - return provider, ProviderChoice(name="azure", model=selected_model) - - if openai_api_key: - selected_model = model_override or openai_model_name - provider = OpenAIProvider( - model=selected_model, - api_key=openai_api_key, - temperature=temperature_setting, - max_output_tokens=max_output_tokens, - ) - return provider, ProviderChoice(name="openai", model=selected_model) - - # Fallback to local echo provider - selected_model = model_override or "local-echo" - provider = LocalEchoProvider() - return provider, ProviderChoice(name="local", model=selected_model) +from __future__ import annotations + +import json as _json +import logging +import os +import random +import subprocess +import threading +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, Generator, Iterable, List, Optional + +# Helpers for Azure quota/rate-limit detection +try: # shared package may not be importable in all contexts (tests add paths) + from shared.azure_utils import (format_quota_message, is_quota_error, + is_transient_rate_error) +except Exception: # pragma: no cover - best-effort import + # Provide fallbacks if shared module isn't available in runtime/test harness + def is_quota_error(e: Any) -> bool: + txt = str(e).lower() if e is not None else "" + return any( + k in txt + for k in ( + "quota", + "premium", + "exceed", + "allowance", + "insufficient", + "billing", + ) + ) + + def is_transient_rate_error(e: Any) -> bool: + txt = str(e).lower() if e is not None else "" + return any( + k in txt for k in ("rate limit", "429", "too many requests", "rate_limit") + ) + + def format_quota_message(exc: Any, service_name: str = "Azure OpenAI") -> str: + return ( + f"{service_name} quota/premium limit reached. Check billing/limits or use another provider." + f" Details: {str(exc)}" + ) + + +_LOGGER = logging.getLogger(__name__) + +try: + # openai>=1.0 + from openai import AzureOpenAI, OpenAI # type: ignore +except Exception: # pragma: no cover - optional at runtime + OpenAI = None # type: ignore + AzureOpenAI = None # type: ignore + + +# Thread-safe cache for LM Studio availability checks +_lm_studio_availability_cache: Dict[str, Any] = { + "available": None, + "checked_at": 0.0, + "url": None, +} +_lm_studio_cache_lock = threading.RLock() +# Backward-compatible alias for tests expecting _lmstudio_cache_lock +_lmstudio_cache_lock = _lm_studio_cache_lock +_LM_STUDIO_CACHE_TTL_SECONDS = 30 +# Backward-compatible aliases for tests +_lmstudio_cache = _lm_studio_availability_cache +_LMSTUDIO_CACHE_TTL = _LM_STUDIO_CACHE_TTL_SECONDS + +# Thread-safe cache for Ollama availability checks +_ollama_availability_cache: Dict[str, Any] = { + "available": None, + "checked_at": 0.0, + "url": None, +} +_ollama_cache_lock = threading.RLock() +_OLLAMA_CACHE_TTL_SECONDS = 30 + + +# {"role": "system|user|assistant", "content": "..."} +RoleMessage = Dict[str, str] + + +# Backward-compatible provider aliases used by tests and scripts. +# Keys should be lowercase and normalized with `-`/`_` variants where useful. +_PROVIDER_ALIASES: Dict[str, str] = { + "azure_openai": "azure", + "azure-openai": "azure", + "open_ai": "openai", + "lm_studio": "lmstudio", + "lm-studio": "lmstudio", + "local_echo": "local", + "local-echo": "local", + "quantum_llm": "quantum", + "quantum-llm": "quantum", +} + + +def _get_lmstudio_api_key() -> Optional[str]: + """Resolve LM Studio API token from supported env var names.""" + return ( + os.getenv("LM_API_TOKEN") + or os.getenv("LMSTUDIO_API_KEY") + or os.getenv("LMSTUDIO_TOKEN") + or os.getenv("LMSTUDIO_API_TOKEN") + ) + + +def _is_text_like_content_block_type(block_type: Any) -> bool: + """Return True for OpenAI-compatible text block type variants.""" + if not isinstance(block_type, str): + return False + normalized = block_type.strip().lower() + return normalized == "text" or normalized.endswith("_text") + + +def _normalize_message_content_for_openai_api(content: Any) -> Any: + """Normalize message content before sending it to OpenAI-compatible APIs. + + This trims plain-text messages and removes whitespace-only text blocks from + block-based content, while preserving non-text blocks such as image_url. + Returns None when no meaningful content remains. + """ + if isinstance(content, str): + normalized = content.strip() + return normalized or None + + if isinstance(content, list): + normalized_blocks: list[dict[str, Any]] = [] + for block in content: + if not isinstance(block, dict): + continue + + if not _is_text_like_content_block_type(block.get("type")): + normalized_blocks.append(block) + continue + + text_value = block.get("text") + if not isinstance(text_value, str): + continue + + normalized_text = text_value.strip() + if not normalized_text: + continue + + normalized_block = dict(block) + normalized_block["text"] = normalized_text + normalized_blocks.append(normalized_block) + + return normalized_blocks or None + + if content is None: + return None + + normalized = str(content).strip() + return normalized or None + + +def _normalize_messages_for_openai_api( + messages: List[dict[str, Any]] +) -> List[dict[str, Any]]: + """Drop empty messages and sanitize content for OpenAI-compatible APIs.""" + normalized_messages: list[dict[str, Any]] = [] + for msg in messages: + if not isinstance(msg, dict): + continue + + normalized_content = _normalize_message_content_for_openai_api( + msg.get("content") + ) + if normalized_content is None: + continue + + normalized_message = dict(msg) + normalized_message["content"] = normalized_content + normalized_messages.append(normalized_message) + + if not normalized_messages: + raise ValueError("No non-empty message content provided") + + return normalized_messages + + +def _check_lmstudio_available(url: str) -> bool: + """Backward-compatible alias for the newer `_check_lm_studio_available` function. + + Older parts of the codebase call `_check_lmstudio_available` (no underscore + between `lm` and `studio`) so keep a tiny wrapper here that delegates to the + canonical implementation defined later in this module. This avoids import + time IndentationError and keeps the two names consistent. + """ + # Delegate to the canonical implementation which is defined below. + try: + return _check_lm_studio_available(url) + except NameError: + # If the canonical implementation isn't available for some reason, + # perform a conservative HTTP ping. + try: + import urllib.error + import urllib.request + + base_url = url.removesuffix("/v1") + models_endpoint_url = base_url + "/v1/models" + request = urllib.request.Request( + models_endpoint_url, headers={"User-Agent": "QAI"} + ) + urllib.request.urlopen(request, timeout=1) + return True + except Exception: + return False + + +@dataclass +class ProviderChoice: + name: str # 'azure' | 'openai' | 'local' + model: str + + +class BaseChatProvider: + def complete( + self, messages: List[RoleMessage], stream: bool = True + ) -> Iterable[str] | str: + raise NotImplementedError + + @staticmethod + def _normalize_messages_for_api( + messages: List[dict[str, Any]] + ) -> List[dict[str, Any]]: + """Sanitize messages before forwarding them to external providers.""" + return _normalize_messages_for_openai_api(messages) + + @staticmethod + def _handle_openai_streaming_response(response) -> Generator[str, None, None]: + """Extract content from OpenAI-style streaming response. + + Common helper for OpenAI, LMStudio, and other OpenAI-compatible providers. + Handles the standard streaming chunk format with resilient error handling. + """ + for chunk in response: + try: + delta = chunk.choices[0].delta + if delta and delta.content: + yield delta.content + except Exception: + # Be resilient to SDK shape changes + pass + + @staticmethod + def _handle_openai_non_streaming_response(response) -> str: + """Extract content from OpenAI-style non-streaming response. + + Common helper for OpenAI, LMStudio, and other OpenAI-compatible providers. + Handles the standard completion format with resilient error handling. + """ + try: + return response.choices[0].message.content or "" + except Exception: + return "" + + +class LoraLocalProvider(BaseChatProvider): + """Provider for local inference with LoRA adapters. + + If ML dependencies are unavailable in the current process (e.g., + Azure Functions worker without torch/transformers/peft), this provider + falls back to a subprocess bridge that uses the workspace venv + (./venv/Scripts/python.exe) to perform inference. + """ + + def __init__( + self, + adapter_dir: str, + device: str = None, + temperature: float = 0.7, + max_new_tokens: int = 256, + top_p: float = 0.9, + top_k: int = 50, + repetition_penalty: float = 1.1, + ): + """Initialize LoRA provider with enhanced generation parameters. + + Args: + adapter_dir: Path to LoRA adapter + device: Device for inference (cuda/cpu) + temperature: Sampling temperature (higher = more random) + max_new_tokens: Maximum tokens to generate + top_p: Nucleus sampling threshold + top_k: Top-k sampling parameter + repetition_penalty: Penalty for repeating tokens + """ + self.adapter_dir = Path(adapter_dir) + self.use_subprocess = False + self.bridge_python: Optional[str] = None + self.temperature = float(temperature) + self.max_new_tokens = int(max_new_tokens) + self.top_p = float(top_p) + self.top_k = int(top_k) + self.repetition_penalty = float(repetition_penalty) + # Lazy import heavy deps on demand + self._lazy_setup() + if not self.use_subprocess: + self.device = device or ( + "cuda" if self.torch.cuda.is_available() else "cpu" + ) + self.model, self.tokenizer = self._load_model_and_tokenizer() + else: + # In subprocess mode we keep state minimal here + self.device = "cpu" + + def _load_model_and_tokenizer(self): + # Detect adapter config + import json as _json + + adapter_config_path = self.adapter_dir / "adapter_config.json" + if not adapter_config_path.exists(): + raise RuntimeError(f"adapter_config.json not found in {self.adapter_dir}") + with open(adapter_config_path, "r", encoding="utf-8") as f: + adapter_cfg = _json.load(f) + base_model_id = adapter_cfg.get( + "base_model_name_or_path", "microsoft/Phi-3.5-mini-instruct" + ) + # Fallback mapping for Phi-3.6 + if base_model_id == "Phi-3.6-mini-instruct": + base_model_id = "microsoft/Phi-3.5-mini-instruct" + base_model = self.AutoModelForCausalLM.from_pretrained( + base_model_id, + torch_dtype=( + self.torch.float16 if self.device == "cuda" else self.torch.float32 + ), + device_map="auto" if self.device == "cuda" else None, + ) + tokenizer_source = self.adapter_dir.parent / "tokenizer" + if tokenizer_source.exists(): + tokenizer = self.AutoTokenizer.from_pretrained(tokenizer_source) + else: + tokenizer = self.AutoTokenizer.from_pretrained(base_model_id) + model = self.PeftModel.from_pretrained(base_model, self.adapter_dir) + model.eval() + return model, tokenizer + + def complete( + self, messages: List[RoleMessage], stream: bool = True + ) -> Iterable[str] | str: + if self.use_subprocess: + response = self._complete_via_subprocess(messages) + if not stream: + return response + + def gen(): + for ch in response: + yield ch + time.sleep(0.002) + + return gen() + # In-process inference path + prompt = self._build_prompt(messages) + inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device) + with self.torch.no_grad(): + output = self.model.generate( + **inputs, + max_new_tokens=self.max_new_tokens, + do_sample=True, + temperature=self.temperature, + top_p=self.top_p, + top_k=self.top_k, + repetition_penalty=self.repetition_penalty, + pad_token_id=self.tokenizer.eos_token_id, + eos_token_id=self.tokenizer.eos_token_id, + ) + response = self.tokenizer.decode( + output[0][inputs["input_ids"].shape[-1] :], skip_special_tokens=True + ) + if not stream: + return response + + def gen(): + for ch in response: + yield ch + time.sleep(0.002) + + return gen() + + def _complete_via_subprocess(self, messages: List[RoleMessage]) -> str: + if not self.bridge_python: + raise RuntimeError("Subprocess bridge not configured for LoRA provider.") + bridge_script = Path(__file__).resolve().parent / "lora_infer_bridge.py" + if not bridge_script.exists(): + raise RuntimeError(f"Bridge script not found at {bridge_script}") + payload = { + "adapter_dir": str(self.adapter_dir), + "messages": messages, + "max_new_tokens": self.max_new_tokens, + "temperature": self.temperature, + } + try: + proc = subprocess.run( + [self.bridge_python, "-u", str(bridge_script)], + input=_json.dumps(payload).encode("utf-8"), + capture_output=True, + check=False, + timeout=300, + ) + except Exception as e: + raise RuntimeError(f"Failed to launch LoRA bridge: {e}") from e + if proc.returncode != 0: + stderr = proc.stderr.decode("utf-8", errors="ignore") + stdout = proc.stdout.decode("utf-8", errors="ignore") + msg = stderr.strip() or stdout.strip() or f"exit code {proc.returncode}" + # Truncate very long errors but keep start and end + if len(msg) > 1000: + msg = msg[:500] + "\n...\n" + msg[-500:] + raise RuntimeError(f"LoRA bridge failed: {msg}") + text = proc.stdout.decode("utf-8", errors="ignore").strip() + return text + + def _build_prompt(self, messages: List[RoleMessage]) -> str: + """Build prompt string from messages. + + Uses list join instead of string += for O(n) instead of O(n²) complexity. + """ + parts = [] + for msg in messages: + role = msg.get("role", "user") + content = msg.get("content", "") + if role == "system": + parts.append(f"[SYSTEM] {content}") + elif role == "user": + parts.append(f"User: {content}") + elif role == "assistant": + parts.append(f"Assistant: {content}") + + # Build final prompt: messages joined by newlines, ending with "Assistant: " + if parts: + return "\n".join(parts) + "\nAssistant: " + return "Assistant: " + + def _lazy_setup(self) -> None: + """Import heavy dependencies lazily so that non-LoRA providers don't require them. + + If imports fail (common under Azure Functions without ML deps), + configure a subprocess bridge to a venv Python. + """ + try: + import torch as _torch # type: ignore + from transformers import \ + AutoModelForCausalLM as _AM # type: ignore + from transformers import AutoTokenizer as _AT + + try: + from peft import PeftModel as _PM # type: ignore + except Exception: + # peft missing -> subprocess + self._configure_subprocess_bridge() + return + except Exception: + # Any import failure -> subprocess + self._configure_subprocess_bridge() + return + # Store on self for in-process inference + self.torch = _torch + self.AutoModelForCausalLM = _AM + self.AutoTokenizer = _AT + self.PeftModel = _PM + + def _configure_subprocess_bridge(self) -> None: + repo_root = Path(__file__).resolve().parents[2] + venv_python = repo_root / "venv" / "Scripts" / "python.exe" + if venv_python.exists(): + self.bridge_python = str(venv_python) + self.use_subprocess = True + else: + raise RuntimeError( + "Missing dependencies for LoRA provider and no venv found. " + "Create ./venv and install 'torch', 'transformers', 'peft'." + ) + + +class LocalEchoProvider(BaseChatProvider): + """A simple offline provider that mimics a helpful assistant. + Useful for smoke tests and environments without keys. + + This provider generates contextually aware responses without requiring + external API calls, making it ideal for testing, development, and + offline scenarios. + """ + + def __init__(self, seed: Optional[int] = None): + self.rng = random.Random(seed) + self._response_templates = self._initialize_templates() + + def _initialize_templates(self) -> Dict[str, List[str]]: + """Initialize response templates categorized by intent.""" + return { + "greeting": [ + "Hello! I'm here to help. What would you like to know?", + "Hi there! How can I assist you today?", + "Greetings! I'm ready to help with your questions.", + ], + "question": [ + "That's an interesting question. Here's my take:", + "Let me help you with that:", + "Good question! Here's what I think:", + "Based on your question, here's my understanding:", + ], + "code": [ + "For coding tasks, I'd suggest:", + "Here's a technical approach:", + "From a development perspective:", + "For this programming challenge:", + ], + "explanation": [ + "Let me break this down:", + "Here's a clear explanation:", + "To explain this simply:", + "In other words:", + ], + "generic": [ + "Here's a concise take:", + "Quick thoughts:", + "A few ideas:", + "My perspective:", + "Summary:", + ], + } + + def _detect_intent(self, text: str) -> str: + """Detect the intent of the user message.""" + lower_text = text.lower() + + # Check for greetings + if ( + any(word in lower_text for word in ["hello", "hi", "hey", "greetings"]) + and len(text) < 50 + ): + return "greeting" + + # Check for coding-related queries + if any( + word in lower_text + for word in [ + "code", + "function", + "class", + "debug", + "program", + "script", + "algorithm", + ] + ): + return "code" + + # Check for explanation requests + if any( + word in lower_text + for word in [ + "explain", + "what is", + "what are", + "how does", + "describe", + "tell me about", + ] + ): + return "explanation" + + # Check for questions + if any( + word in lower_text + for word in ["?", "how", "why", "when", "where", "who", "can you"] + ): + return "question" + + return "generic" + + def _craft_autonomous_reply( + self, messages: List[RoleMessage], last_user: str, turn_count: int + ) -> str: + """Generate more useful offline output for autonomous CLI loops.""" + assistant_messages = [ + m["content"] for m in messages if m.get("role") == "assistant" + ] + last_assistant = assistant_messages[-1] if assistant_messages else "" + user_topics = [ + m["content"].strip() + for m in messages + if m.get("role") == "user" and m.get("content", "").strip() + ] + topic = ( + user_topics[0][:120].rstrip(".,?!") if user_topics else "the current task" + ) + + if "message count exceeded limit" in last_assistant.lower(): + return ( + "Autonomous checkpoint:\n" + "1. The conversation is getting long, so summarize the objective in one sentence.\n" + "2. Keep only the latest constraints and the best next action.\n" + "3. Continue with one concrete step instead of repeating the same prompt." + ) + + sequence = [ + ( + f"Autonomous plan for '{topic}':\n\n" + "1. Define the concrete objective and success condition.\n" + "2. Inspect the current inputs, dependencies, or repo context.\n" + "3. Pick the smallest next action that creates evidence of progress." + ), + ( + f"Autonomous next step for '{topic}':\n\n" + "- Gather one missing fact before changing direction.\n" + "- Write down the current assumption you are relying on.\n" + "- Execute a single focused action, then reassess." + ), + ( + f"Autonomous review for '{topic}':\n\n" + "- What changed since the previous turn?\n" + "- What is still blocked or uncertain?\n" + "- What is the highest-value follow-up action right now?" + ), + ( + f"Autonomous refinement for '{topic}':\n\n" + "1. Remove repetition.\n" + "2. Convert vague goals into one measurable outcome.\n" + "3. Continue with a concrete command, edit, or validation step." + ), + ] + return sequence[(turn_count - 1) % len(sequence)] + + def _craft_reply(self, messages: List[RoleMessage]) -> str: + """Generate a contextually appropriate response. + + The local echo provider has no real model, so responses are + rule-based — but they should at least be informative and + actionable rather than meaninglessly rephrasing the user's input. + """ + last_user = next( + (m["content"] for m in reversed(messages) if m.get("role") == "user"), "" + ).strip() + + if not last_user: + return ( + "Hi! I'm running in offline mode (no API keys configured). " + "I can still help with Aria commands, answer simple questions, " + "or assist with code structure. What would you like to do?" + ) + + intent = self._detect_intent(last_user) + lower = last_user.lower() + turn_count = sum(1 for m in messages if m.get("role") == "user") + + if ( + "start working autonomously" in lower + or "continue autonomously" in lower + or "without waiting for user input" in lower + or "choose the next useful step yourself" in lower + ): + return self._craft_autonomous_reply(messages, last_user, turn_count) + + # --- Greetings --- + if intent == "greeting": + greetings = [ + "Hello! I'm running in local offline mode — no external model is active. " + "Try `--provider azure`, `--provider openai`, or `--provider lmstudio` for a full AI response.", + "Hi there! Offline mode is active. Set AZURE_OPENAI_API_KEY or OPENAI_API_KEY to enable a real AI provider.", + "Hey! Running without a live model right now. I can still help with Aria commands and simple tasks.", + ] + return self.rng.choice(greetings) + + # --- Aria movement commands --- + aria_keywords = { + "left": "Moving Aria to the left. [aria:walk:left]", + "right": "Moving Aria to the right. [aria:walk:right]", + "jump": "Aria jumps! [aria:jump]", + "wave": "Aria waves hello! [aria:wave]", + "dance": "Aria starts dancing! [aria:dance]", + "idle": "Aria returns to idle. [aria:idle]", + } + for keyword, response in aria_keywords.items(): + if keyword in lower: + return response + + # --- Coding requests --- + if intent == "code": + topic = last_user[:80].rstrip(".,?!") + suggestions = [ + f"For '{topic}', here's one approach:\n\n" + "1. Identify inputs, outputs, and edge cases first.\n" + "2. Write a minimal working version before optimising.\n" + "3. Add error handling for external calls (I/O, network, parsing).\n\n" + "Enable a real provider (e.g. `--provider openai`) for generated code.", + f"Coding tip for '{topic}':\n\n" + "- Keep functions small and focused on one task.\n" + "- Use type hints for clarity and static analysis.\n" + "- Write a unit test for each edge case you can think of.\n\n" + "Connect a live model for actual code generation.", + ] + return self.rng.choice(suggestions) + + # --- Explanation requests --- + if intent == "explanation": + topic = last_user[:80].rstrip(".,?!") + return ( + f"I'm in offline mode, so I can't give a full explanation of '{topic}'. " + "Here's what I'd suggest:\n\n" + "1. Check the official docs or a trusted reference.\n" + "2. Ask again with `--provider azure` or `--provider openai` for a detailed answer.\n" + "3. Or try `--provider agi` for structured chain-of-thought reasoning." + ) + + # --- Questions --- + if intent == "question": + closers = [ + "Switch to a live provider for a detailed answer.", + "Try `--provider openai` or `--provider azure` for a real response.", + "Use `--provider agi` for structured reasoning on complex questions.", + ] + return ( + f"Good question! Unfortunately I'm in local echo mode and can't look things up. " + f"{self.rng.choice(closers)}" + ) + + # --- Multi-turn acknowledgement --- + if turn_count > 3: + follow_ups = [ + "I'm still in offline mode — I can only give canned responses. " + "Configure a provider to continue this conversation meaningfully.", + "Thanks for staying in the conversation! A live provider would give you much better answers here.", + ] + return self.rng.choice(follow_ups) + + # --- Generic fallback --- + generic = [ + "I'm running in local fallback mode. " + "Set AZURE_OPENAI_API_KEY, OPENAI_API_KEY, or start LM Studio / Ollama to enable full AI responses.", + "Offline mode active. I can process Aria commands but can't generate AI responses without a configured provider.", + "No live model detected. Run with `--provider lmstudio` (LM Studio running locally), " + "`--provider ollama`, `--provider openai`, or `--provider azure`.", + ] + return self.rng.choice(generic) + + def complete( + self, messages: List[RoleMessage], stream: bool = True + ) -> Iterable[str] | str: + text = self._craft_reply(messages) + if not stream: + return text + + def gen() -> Generator[str, None, None]: + for ch in text: + yield ch + # Tiny delay to simulate streaming; keep very small + time.sleep(0.002) + + return gen() + + +class OpenAIProvider(BaseChatProvider): + def __init__( + self, + model: str, + api_key: Optional[str] = None, + temperature: float = 0.7, + max_output_tokens: Optional[int] = None, + ): + if OpenAI is None: + raise RuntimeError( + "openai package not installed. Install 'openai' to use this provider." + ) + self.client = OpenAI(api_key=api_key) + self.model = model + self.temperature = temperature + self.max_output_tokens = max_output_tokens + + def complete( + self, messages: List[RoleMessage], stream: bool = True + ) -> Iterable[str] | str: + """Complete with OpenAI and handle quota/rate-limit errors gracefully. + + Behaviour mirrors AzureOpenAIProvider: + - Quota/billing errors → friendly string or single-chunk generator. + - Transient 429 errors → small retry with back-off. + - Mid-stream errors → yielded friendly/error token instead of raising. + """ + + normalized_messages = self._normalize_messages_for_api(messages) + + def _attempt_create(**kwargs): + max_retries = 3 + base_backoff = 0.4 + attempt = 0 + while True: + try: + return self.client.chat.completions.create(**kwargs) + except Exception as e: + if is_quota_error(e): + raise + if is_transient_rate_error(e) and attempt < max_retries: + sleep_time = base_backoff * (2**attempt) + _LOGGER.info( + "OpenAI rate-limit, retrying in %.2fs (attempt %d)", + sleep_time, + attempt + 1, + ) + time.sleep(sleep_time) + attempt += 1 + continue + raise + + try: + resp = _attempt_create( + model=self.model, + messages=normalized_messages, + temperature=self.temperature, + max_tokens=self.max_output_tokens, + stream=stream, + ) + except Exception as e: + if is_quota_error(e): + friendly = format_quota_message(e, service_name="OpenAI") + if stream: + + def _gen_quota_err() -> Generator[str, None, None]: + yield friendly + + return _gen_quota_err() + return friendly + raise + + if stream: + + def _gen() -> Generator[str, None, None]: + try: + for chunk in resp: + try: + delta = chunk.choices[0].delta + if delta and delta.content: + yield delta.content + except Exception: + continue + except Exception as exc: + if is_quota_error(exc): + yield format_quota_message(exc, service_name="OpenAI") + else: + yield f"[OpenAI error: {str(exc)}]" + + return _gen() + else: + try: + return resp.choices[0].message.content or "" + except Exception: + return "" + + +class LMStudioProvider(BaseChatProvider): + """Provider for LM Studio local server (compatible with OpenAI API).""" + + def __init__( + self, + base_url: str = "http://127.0.0.1:1234/v1", + model: str = "local-model", + temperature: float = 0.7, + max_output_tokens: Optional[int] = None, + ): + if OpenAI is None: + raise RuntimeError( + "openai package not installed. Install 'openai' to use this provider." + ) + # Newer LM Studio server configurations can require API token auth. + # Keep backward compatibility by using the legacy placeholder key when + # no token env var is provided. + lmstudio_api_key = _get_lmstudio_api_key() or "lm-studio" + self.client = OpenAI( + base_url=base_url, + api_key=lmstudio_api_key, + ) + self.model = model + self.temperature = temperature + self.max_output_tokens = max_output_tokens + self.base_url = base_url + + def complete( + self, messages: List[RoleMessage], stream: bool = True + ) -> Iterable[str] | str: + try: + normalized_messages = self._normalize_messages_for_api(messages) + resp = self.client.chat.completions.create( + model=self.model, + messages=normalized_messages, + temperature=self.temperature, + max_tokens=self.max_output_tokens, + stream=stream, + ) + + if stream: + return self._handle_openai_streaming_response(resp) + else: + return self._handle_openai_non_streaming_response(resp) + except Exception as e: + # Provide helpful error messages for common issues + error_msg = str(e).lower() + + if ( + "connection" in error_msg + or "refused" in error_msg + or "timeout" in error_msg + ): + suggestion = ( + f"❌ Cannot connect to LM Studio at {self.base_url}\n\n" + f"Troubleshooting steps:\n" + f"1. Make sure LM Studio is running\n" + f"2. Check that the local server is started in LM Studio\n" + f"3. Verify the server is running on {self.base_url}\n" + f"4. Check your firewall settings\n\n" + f"Set LMSTUDIO_BASE_URL environment variable if using a different address." + ) + if stream: + + def gen_err() -> Generator[str, None, None]: + yield suggestion + + return gen_err() + return suggestion + + if "model" in error_msg and ( + "not found" in error_msg or "does not exist" in error_msg + ): + suggestion = ( + f"❌ Model '{self.model}' not found in LM Studio.\n\n" + f"Troubleshooting steps:\n" + f"1. Check that a model is loaded in LM Studio\n" + f"2. Use --model flag to specify the correct model name\n" + f"3. Set LMSTUDIO_MODEL environment variable\n\n" + f"The model name should match what's shown in LM Studio's server panel." + ) + if stream: + + def gen_err() -> Generator[str, None, None]: + yield suggestion + + return gen_err() + return suggestion + + if "invalid_api_key" in error_msg or "api token is required" in error_msg: + suggestion = ( + f"❌ LM Studio at {self.base_url} requires API token authentication.\n\n" + f"Troubleshooting steps:\n" + f"1. Export one of: LM_API_TOKEN, LMSTUDIO_API_KEY, LMSTUDIO_TOKEN, LMSTUDIO_API_TOKEN\n" + f"2. Ensure token matches LM Studio server configuration\n" + f"3. Re-run with --provider lmstudio\n\n" + f"Example:\n" + f"export LM_API_TOKEN=''" + ) + if stream: + + def gen_err() -> Generator[str, None, None]: + yield suggestion + + return gen_err() + return suggestion + + # Re-raise unexpected errors + raise + + +class OllamaProvider(BaseChatProvider): + """Provider for Ollama local server (compatible with OpenAI API). + + Ollama is a popular local LLM server that supports models like Llama, Mistral, + CodeLlama, and many others. It provides an OpenAI-compatible API. + + Default endpoint: http://127.0.0.1:11434/v1 + Configure via OLLAMA_BASE_URL environment variable. + """ + + def __init__( + self, + base_url: str = "http://127.0.0.1:11434/v1", + model: str = "llama3.2", + temperature: float = 0.7, + max_output_tokens: Optional[int] = None, + ): + if OpenAI is None: + raise RuntimeError( + "openai package not installed. Install 'openai' to use this provider." + ) + self.client = OpenAI( + base_url=base_url, api_key="ollama" # Ollama doesn't require real key + ) + self.model = model + self.temperature = temperature + self.max_output_tokens = max_output_tokens + self.base_url = base_url + + def complete( + self, messages: List[RoleMessage], stream: bool = True + ) -> Iterable[str] | str: + try: + normalized_messages = self._normalize_messages_for_api(messages) + resp = self.client.chat.completions.create( + model=self.model, + messages=normalized_messages, + temperature=self.temperature, + max_tokens=self.max_output_tokens, + stream=stream, + ) + + if stream: + return self._handle_openai_streaming_response(resp) + else: + return self._handle_openai_non_streaming_response(resp) + except Exception as e: + # Provide helpful error messages for common Ollama issues + error_msg = str(e).lower() + + if ( + "connection" in error_msg + or "refused" in error_msg + or "timeout" in error_msg + ): + suggestion = ( + f"❌ Cannot connect to Ollama at {self.base_url}\n\n" + f"Troubleshooting steps:\n" + f"1. Make sure Ollama is installed and running\n" + f" - Install: https://ollama.ai/download\n" + f" - Start: 'ollama serve' (or it may start automatically)\n" + f"2. Verify Ollama is listening on {self.base_url}\n" + f"3. Check your firewall settings\n\n" + f"Set OLLAMA_BASE_URL environment variable if using a different address." + ) + if stream: + + def gen_err() -> Generator[str, None, None]: + yield suggestion + + return gen_err() + return suggestion + + if "model" in error_msg and ( + "not found" in error_msg + or "does not exist" in error_msg + or "not available" in error_msg + ): + suggestion = ( + f"❌ Model '{self.model}' not found in Ollama.\n\n" + f"Troubleshooting steps:\n" + f"1. Pull the model first:\n" + f" ollama pull {self.model}\n\n" + f"2. List available models:\n" + f" ollama list\n\n" + f"3. Popular models to try:\n" + f" - llama2:latest (7B general purpose)\n" + f" - codellama:latest (7B for coding)\n" + f" - mistral:latest (7B high quality)\n" + f" - llama3.2:latest (3B latest from Meta)\n\n" + f"Use --model flag or set OLLAMA_MODEL environment variable." + ) + if stream: + + def gen_err() -> Generator[str, None, None]: + yield suggestion + + return gen_err() + return suggestion + + # Re-raise unexpected errors + raise + + +class AzureOpenAIProvider(BaseChatProvider): + def __init__( + self, + deployment: str, + endpoint: str, + api_key: str, + api_version: str = "2024-08-01-preview", + temperature: float = 0.7, + max_output_tokens: Optional[int] = None, + ): + if AzureOpenAI is None: + raise RuntimeError( + "openai package not installed. Install 'openai' to use this provider." + ) + self.client = AzureOpenAI( + api_key=api_key, + api_version=api_version, + azure_endpoint=endpoint, + ) + self.deployment = deployment + self.temperature = temperature + self.max_output_tokens = max_output_tokens + + def complete( + self, messages: List[RoleMessage], stream: bool = True + ) -> Iterable[str] | str: + """Complete with Azure OpenAI and handle quota/rate-limit errors gracefully. + + Behavior: + - If a quota/premium allowance error is detected, return a friendly + message instead of raising an exception. + - Retry transient rate-limit style errors a small number of times with + exponential backoff. + + Returns either a string (non-stream) or a generator yielding string chunks. + """ + + normalized_messages = self._normalize_messages_for_api(messages) + + # Internal helper: attempt the SDK call with small retry/backoff for + # transient rate-limit style errors. If we detect a quota/premium error + # we return the exception directly for caller to handle. + def _attempt_create(**kwargs): + max_retries = 3 + base_backoff = 0.4 + attempt = 0 + while True: + try: + return self.client.chat.completions.create(**kwargs) + except Exception as e: # pragma: no cover - depends on runtime + # If this looks like a quota/premium allowance error, bail out + if is_quota_error(e): + raise + # Retry transient rate-limit errors a few times + if is_transient_rate_error(e) and attempt < max_retries: + sleep_time = base_backoff * (2**attempt) + jitter = min(sleep_time * 0.1, 0.5) + import time + + _LOGGER.info( + "Azure rate-limit detected, retrying in %.2fs (attempt %d)", + sleep_time + jitter, + attempt + 1, + ) + time.sleep(sleep_time + jitter) + attempt += 1 + continue + # Propagate other exceptions + raise + + try: + resp = _attempt_create( + model=self.deployment, # In Azure, 'model' is your deployment name + messages=normalized_messages, + temperature=self.temperature, + max_tokens=self.max_output_tokens, + stream=stream, + ) + except Exception as e: + # If quota/premium, return a friendly message instead of bubbling an + # exception to callers (better UX for local & CLI users) + if is_quota_error(e): + friendly = format_quota_message(e, service_name="Azure OpenAI") + if stream: + + def gen_err() -> Generator[str, None, None]: + yield friendly + + return gen_err() + return friendly + # Not a quota error -> re-raise so upstream can observe generic failures + raise + + if stream: + + def gen() -> Generator[str, None, None]: + # resp can be an iterator/generator from the SDK. We iterate and + # guard against runtime errors that may occur during streaming. + try: + for chunk in resp: + try: + delta = chunk.choices[0].delta + if delta and delta.content: + yield delta.content + except Exception: + # Resilient: skip unexpected chunk shapes + continue + except Exception as e: + # Catch runtime errors during iteration and turn them into + # a short user-friendly message (quota or otherwise). + if is_quota_error(e): + yield format_quota_message(e, service_name="Azure OpenAI") + else: + yield f"[AzureOpenAI error: {str(e)}]" + + return gen() + + else: + try: + return resp.choices[0].message.content or "" + except Exception: + return "" + + +def _check_lm_studio_available(server_url: str) -> bool: + """Check if LM Studio server is available at the given URL. + + Uses a thread-safe cache to avoid repeated HTTP requests within the TTL period. + The HTTP request is performed outside the lock to avoid blocking other threads. + + Args: + server_url: Base URL for LM Studio API (e.g., "http://127.0.0.1:1234/v1") + + Returns: + True if LM Studio is available, False otherwise. + """ + # Check cache under lock + with _lm_studio_cache_lock: + current_time = time.time() + if ( + _lm_studio_availability_cache["available"] is not None + and _lm_studio_availability_cache["url"] == server_url + and (current_time - _lm_studio_availability_cache["checked_at"]) + < _LM_STUDIO_CACHE_TTL_SECONDS + ): + return _lm_studio_availability_cache["available"] + + # Perform HTTP check outside lock to avoid blocking other threads + is_available = False + lmstudio_api_key = _get_lmstudio_api_key() + try: + import urllib.error + import urllib.request + + # Remove trailing /v1 if present, then append /v1/models + base_url = server_url.removesuffix("/v1") + models_endpoint_url = base_url + "/v1/models" + headers = {"User-Agent": "QAI"} + if lmstudio_api_key: + headers["Authorization"] = f"Bearer {lmstudio_api_key}" + request = urllib.request.Request(models_endpoint_url, headers=headers) + urllib.request.urlopen(request, timeout=1) + is_available = True + except urllib.error.HTTPError as exc: + # Endpoint is reachable but auth failed: count as available only when + # caller configured a token (possibly wrong/expired). + if exc.code in (401, 403) and bool(lmstudio_api_key): + is_available = True + else: + is_available = False + except Exception: + is_available = False + + # Update cache under lock + with _lm_studio_cache_lock: + _lm_studio_availability_cache["available"] = is_available + _lm_studio_availability_cache["checked_at"] = time.time() + _lm_studio_availability_cache["url"] = server_url + + return is_available + + +def _check_ollama_available(server_url: str) -> bool: + """Check if Ollama server is available at the given URL. + + Uses a thread-safe cache to avoid repeated HTTP requests within the TTL period. + The HTTP request is performed outside the lock to avoid blocking other threads. + + Args: + server_url: Base URL for Ollama API (e.g., "http://127.0.0.1:11434/v1") + + Returns: + True if Ollama is available, False otherwise. + """ + # Check cache under lock + with _ollama_cache_lock: + current_time = time.time() + if ( + _ollama_availability_cache["available"] is not None + and _ollama_availability_cache["url"] == server_url + and (current_time - _ollama_availability_cache["checked_at"]) + < _OLLAMA_CACHE_TTL_SECONDS + ): + return _ollama_availability_cache["available"] + + # Perform HTTP check outside lock to avoid blocking other threads + is_available = False + try: + import urllib.error + import urllib.request + + # Remove trailing /v1 if present, then try /api/tags endpoint (Ollama-specific) + base_url = server_url.removesuffix("/v1") + # Ollama uses /api/tags to list models + tags_endpoint_url = base_url + "/api/tags" + request = urllib.request.Request( + tags_endpoint_url, headers={"User-Agent": "QAI"} + ) + urllib.request.urlopen(request, timeout=1) + is_available = True + except Exception: + # Fallback: try OpenAI-compatible /v1/models endpoint + try: + import urllib.error + import urllib.request + + base_url = server_url.removesuffix("/v1") + models_endpoint_url = base_url + "/v1/models" + request = urllib.request.Request( + models_endpoint_url, headers={"User-Agent": "QAI"} + ) + urllib.request.urlopen(request, timeout=1) + is_available = True + except Exception: + is_available = False + + # Update cache under lock + with _ollama_cache_lock: + _ollama_availability_cache["available"] = is_available + _ollama_availability_cache["checked_at"] = time.time() + _ollama_availability_cache["url"] = server_url + + return is_available + + +def detect_provider( + explicit: Optional[str] = None, + model_override: Optional[str] = None, + temperature: Optional[float] = None, + max_output_tokens: Optional[int] = None, +) -> tuple[BaseChatProvider, ProviderChoice]: + """Detect the best provider based on environment variables. + + Priority: + 1) explicit selection if provided + 2) LM Studio if LMSTUDIO_BASE_URL is set + 3) Ollama if OLLAMA_BASE_URL is set + 4) AGI if selected (advanced reasoning capabilities) + 5) Quantum if selected + 6) Azure if all required vars present + 7) OpenAI if OPENAI_API_KEY is present + 8) Local fallback + 9) LoRA if provider is 'lora' and model_override is set + """ + provider_choice = (explicit or "auto").lower() + provider_choice = _PROVIDER_ALIASES.get(provider_choice, provider_choice) + + # LM Studio config + lm_studio_base_url = os.getenv("LMSTUDIO_BASE_URL", "http://127.0.0.1:1234/v1") + lm_studio_model_name = os.getenv("LMSTUDIO_MODEL", "local-model") + + # Ollama config + ollama_base_url = os.getenv("OLLAMA_BASE_URL", "http://127.0.0.1:11434/v1") + ollama_model_name = os.getenv("OLLAMA_MODEL", "llama3.2") + + # AGI config - advanced reasoning capabilities + if provider_choice == "agi": + try: + from agi_provider import create_agi_provider + + temperature_value = float( + temperature + if temperature is not None + else os.getenv("CHAT_TEMPERATURE", "0.7") + ) + max_tokens_limit = ( + int(max_output_tokens) if max_output_tokens is not None else 2048 + ) + verbose = os.getenv("AGI_VERBOSE", "false").lower() == "true" + provider, info = create_agi_provider( + model=model_override, + temperature=temperature_value, + max_output_tokens=max_tokens_limit, + verbose=verbose, + ) + return provider, ProviderChoice(name=info.name, model=info.model) + except ImportError as import_error: + raise RuntimeError( + f"AGI provider selected but agi_provider module not available: {import_error}" + ) from import_error + + if provider_choice == "quantum": + try: + from quantum_provider import create_quantum_llm_provider + + selected_model_path = ( + model_override + or os.getenv("QAI_QUANTUM_MODEL_PATH") + or os.getenv("QAI_QUANTUM_MODEL") + ) + if not selected_model_path: + raise RuntimeError( + "Quantum LLM provider requires a model path. Provide --model or set " + "QAI_QUANTUM_MODEL_PATH (or QAI_QUANTUM_MODEL).\n" + "Example: --provider quantum --model data_out/quantum_llm_training" + ) + + temperature_value = float( + temperature + if temperature is not None + else os.getenv("CHAT_TEMPERATURE", "0.8") + ) + max_tokens_limit = ( + int(max_output_tokens) if max_output_tokens is not None else 200 + ) + + provider, info = create_quantum_llm_provider( + model_path=selected_model_path, + temperature=temperature_value, + max_output_tokens=max_tokens_limit, + ) + return provider, ProviderChoice(name=info.name, model=info.model) + except ImportError as import_error: + raise RuntimeError( + f"Quantum provider selected but quantum_provider module not available: {import_error}" + ) from import_error + + if provider_choice == "lora": + if not model_override: + raise RuntimeError("LoRA provider selected but model path not provided.") + temperature_value = float( + temperature + if temperature is not None + else os.getenv("CHAT_TEMPERATURE", "0.7") + ) + max_new_tokens = ( + int(max_output_tokens) if max_output_tokens is not None else 256 + ) + provider = LoraLocalProvider( + adapter_dir=model_override, + temperature=temperature_value, + max_new_tokens=max_new_tokens, + ) + return provider, ProviderChoice(name="lora", model=str(model_override)) + + # Azure OpenAI config + azure_openai_api_key = os.getenv("AZURE_OPENAI_API_KEY") + azure_openai_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") + azure_openai_deployment = os.getenv("AZURE_OPENAI_DEPLOYMENT") + azure_openai_api_version = os.getenv( + "AZURE_OPENAI_API_VERSION", "2024-08-01-preview" + ) + + # OpenAI config + openai_api_key = os.getenv("OPENAI_API_KEY") + openai_model_name = os.getenv("OPENAI_MODEL", "gpt-4o-mini") + + temperature_setting = float( + temperature if temperature is not None else os.getenv("CHAT_TEMPERATURE", "0.7") + ) + + # Resolve based on explicit choice first + if provider_choice == "lmstudio": + selected_model = model_override or lm_studio_model_name + provider = LMStudioProvider( + base_url=lm_studio_base_url, + model=selected_model, + temperature=temperature_setting, + max_output_tokens=max_output_tokens, + ) + return provider, ProviderChoice(name="lmstudio", model=selected_model) + + if provider_choice == "ollama": + selected_model = model_override or ollama_model_name + provider = OllamaProvider( + base_url=ollama_base_url, + model=selected_model, + temperature=temperature_setting, + max_output_tokens=max_output_tokens, + ) + return provider, ProviderChoice(name="ollama", model=selected_model) + + if provider_choice == "azure": + if not ( + azure_openai_api_key + and azure_openai_endpoint + and (model_override or azure_openai_deployment) + ): + raise RuntimeError( + "Azure OpenAI selected but required env vars are missing. Set AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_DEPLOYMENT." + ) + selected_model = model_override or azure_openai_deployment # deployment name + provider = AzureOpenAIProvider( + deployment=selected_model, + endpoint=azure_openai_endpoint, + api_key=azure_openai_api_key, + api_version=azure_openai_api_version, + temperature=temperature_setting, + max_output_tokens=max_output_tokens, + ) + return provider, ProviderChoice(name="azure", model=selected_model) + + if provider_choice == "openai": + if not openai_api_key: + raise RuntimeError("OpenAI selected but OPENAI_API_KEY is not set.") + selected_model = model_override or openai_model_name + provider = OpenAIProvider( + model=selected_model, + api_key=openai_api_key, + temperature=temperature_setting, + max_output_tokens=max_output_tokens, + ) + return provider, ProviderChoice(name="openai", model=selected_model) + + if provider_choice == "local": + selected_model = model_override or "local-echo" + provider = LocalEchoProvider() + return provider, ProviderChoice(name="local", model=selected_model) + + # Auto mode - check for LM Studio first using thread-safe cached check + if _check_lm_studio_available(lm_studio_base_url): + selected_model = model_override or lm_studio_model_name + provider = LMStudioProvider( + base_url=lm_studio_base_url, + model=selected_model, + temperature=temperature_setting, + max_output_tokens=max_output_tokens, + ) + return provider, ProviderChoice(name="lmstudio", model=selected_model) + + # Check for Ollama next + if _check_ollama_available(ollama_base_url): + selected_model = model_override or ollama_model_name + provider = OllamaProvider( + base_url=ollama_base_url, + model=selected_model, + temperature=temperature_setting, + max_output_tokens=max_output_tokens, + ) + return provider, ProviderChoice(name="ollama", model=selected_model) + + if ( + azure_openai_api_key + and azure_openai_endpoint + and (model_override or azure_openai_deployment) + ): + selected_model = model_override or azure_openai_deployment + provider = AzureOpenAIProvider( + deployment=selected_model, + endpoint=azure_openai_endpoint, + api_key=azure_openai_api_key, + api_version=azure_openai_api_version, + temperature=temperature_setting, + max_output_tokens=max_output_tokens, + ) + return provider, ProviderChoice(name="azure", model=selected_model) + + if openai_api_key: + selected_model = model_override or openai_model_name + provider = OpenAIProvider( + model=selected_model, + api_key=openai_api_key, + temperature=temperature_setting, + max_output_tokens=max_output_tokens, + ) + return provider, ProviderChoice(name="openai", model=selected_model) + + # Fallback to local echo provider + selected_model = model_override or "local-echo" + provider = LocalEchoProvider() + return provider, ProviderChoice(name="local", model=selected_model) diff --git a/ai-projects/chat-cli/src/lora_infer_bridge.py b/ai-projects/chat-cli/src/lora_infer_bridge.py index 47f8283e5..085abef30 100644 --- a/ai-projects/chat-cli/src/lora_infer_bridge.py +++ b/ai-projects/chat-cli/src/lora_infer_bridge.py @@ -1,117 +1,117 @@ -from __future__ import annotations - -import json -import sys -from pathlib import Path - - -def _read_stdin_json() -> dict: - data = sys.stdin.read() - if not data: - return {} - try: - return json.loads(data) - except Exception: - return {} - - -def _build_prompt(messages): - """Build prompt string from messages. - - Uses list join instead of string += for O(n) instead of O(n²) complexity. - """ - parts = [] - for msg in messages or []: - role = msg.get("role", "user") - content = msg.get("content", "") - if role == "system": - parts.append(f"[SYSTEM] {content}") - elif role == "user": - parts.append(f"User: {content}") - elif role == "assistant": - parts.append(f"Assistant: {content}") - - # Build final prompt: messages joined by newlines, ending with "Assistant: " - if parts: - return "\n".join(parts) + "\nAssistant: " - return "Assistant: " - - -def main() -> int: - payload = _read_stdin_json() - adapter_dir = Path(payload.get("adapter_dir", "")) - messages = payload.get("messages", []) - max_new_tokens = int(payload.get("max_new_tokens", 256)) - temperature = float(payload.get("temperature", 0.7)) - - if not adapter_dir or not adapter_dir.exists(): - print("Adapter path not found", file=sys.stderr) - return 2 - - try: - import torch - from peft import PeftModel - from transformers import AutoModelForCausalLM, AutoTokenizer - except Exception as e: - print(f"Bridge missing ML deps: {e}", file=sys.stderr) - return 3 - - # Determine base model id from adapter config - adapter_cfg_path = adapter_dir / "adapter_config.json" - if not adapter_cfg_path.exists(): - print(f"adapter_config.json not found in {adapter_dir}", file=sys.stderr) - return 4 - try: - with open(adapter_cfg_path, "r", encoding="utf-8") as f: - adapter_cfg = json.load(f) - except Exception as e: - print(f"Failed to read adapter_config.json: {e}", file=sys.stderr) - return 5 - - base_model_id = adapter_cfg.get( - "base_model_name_or_path", "microsoft/Phi-3.5-mini-instruct" - ) - if base_model_id == "Phi-3.6-mini-instruct": - base_model_id = "microsoft/Phi-3.5-mini-instruct" - - try: - # Prefer local tokenizer if present next to adapter_dir/../tokenizer - tokenizer_source = adapter_dir.parent / "tokenizer" - if tokenizer_source.exists(): - tokenizer = AutoTokenizer.from_pretrained(tokenizer_source) - else: - tokenizer = AutoTokenizer.from_pretrained(base_model_id) - - # CPU-first load for broad compatibility - model = AutoModelForCausalLM.from_pretrained( - base_model_id, - torch_dtype=torch.float32, - ) - model = PeftModel.from_pretrained(model, adapter_dir) - model.to("cpu").eval() - - prompt = _build_prompt(messages) - inputs = tokenizer(prompt, return_tensors="pt") - with torch.no_grad(): - output = model.generate( - **inputs, - max_new_tokens=max_new_tokens, - do_sample=True, - temperature=temperature, - pad_token_id=tokenizer.eos_token_id, - ) - # Strip the prompt portion and decode only the completion - response = tokenizer.decode( - output[0][inputs["input_ids"].shape[-1] :], - skip_special_tokens=True, - ) - sys.stdout.write(response) - sys.stdout.flush() - return 0 - except Exception as e: - print(f"Bridge inference error: {e}", file=sys.stderr) - return 6 - - -if __name__ == "__main__": - sys.exit(main()) +from __future__ import annotations + +import json +import sys +from pathlib import Path + + +def _read_stdin_json() -> dict: + data = sys.stdin.read() + if not data: + return {} + try: + return json.loads(data) + except Exception: + return {} + + +def _build_prompt(messages): + """Build prompt string from messages. + + Uses list join instead of string += for O(n) instead of O(n²) complexity. + """ + parts = [] + for msg in messages or []: + role = msg.get("role", "user") + content = msg.get("content", "") + if role == "system": + parts.append(f"[SYSTEM] {content}") + elif role == "user": + parts.append(f"User: {content}") + elif role == "assistant": + parts.append(f"Assistant: {content}") + + # Build final prompt: messages joined by newlines, ending with "Assistant: " + if parts: + return "\n".join(parts) + "\nAssistant: " + return "Assistant: " + + +def main() -> int: + payload = _read_stdin_json() + adapter_dir = Path(payload.get("adapter_dir", "")) + messages = payload.get("messages", []) + max_new_tokens = int(payload.get("max_new_tokens", 256)) + temperature = float(payload.get("temperature", 0.7)) + + if not adapter_dir or not adapter_dir.exists(): + print("Adapter path not found", file=sys.stderr) + return 2 + + try: + import torch + from peft import PeftModel + from transformers import AutoModelForCausalLM, AutoTokenizer + except Exception as e: + print(f"Bridge missing ML deps: {e}", file=sys.stderr) + return 3 + + # Determine base model id from adapter config + adapter_cfg_path = adapter_dir / "adapter_config.json" + if not adapter_cfg_path.exists(): + print(f"adapter_config.json not found in {adapter_dir}", file=sys.stderr) + return 4 + try: + with open(adapter_cfg_path, "r", encoding="utf-8") as f: + adapter_cfg = json.load(f) + except Exception as e: + print(f"Failed to read adapter_config.json: {e}", file=sys.stderr) + return 5 + + base_model_id = adapter_cfg.get( + "base_model_name_or_path", "microsoft/Phi-3.5-mini-instruct" + ) + if base_model_id == "Phi-3.6-mini-instruct": + base_model_id = "microsoft/Phi-3.5-mini-instruct" + + try: + # Prefer local tokenizer if present next to adapter_dir/../tokenizer + tokenizer_source = adapter_dir.parent / "tokenizer" + if tokenizer_source.exists(): + tokenizer = AutoTokenizer.from_pretrained(tokenizer_source) + else: + tokenizer = AutoTokenizer.from_pretrained(base_model_id) + + # CPU-first load for broad compatibility + model = AutoModelForCausalLM.from_pretrained( + base_model_id, + torch_dtype=torch.float32, + ) + model = PeftModel.from_pretrained(model, adapter_dir) + model.to("cpu").eval() + + prompt = _build_prompt(messages) + inputs = tokenizer(prompt, return_tensors="pt") + with torch.no_grad(): + output = model.generate( + **inputs, + max_new_tokens=max_new_tokens, + do_sample=True, + temperature=temperature, + pad_token_id=tokenizer.eos_token_id, + ) + # Strip the prompt portion and decode only the completion + response = tokenizer.decode( + output[0][inputs["input_ids"].shape[-1] :], + skip_special_tokens=True, + ) + sys.stdout.write(response) + sys.stdout.flush() + return 0 + except Exception as e: + print(f"Bridge inference error: {e}", file=sys.stderr) + return 6 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/ai-projects/chat-cli/src/quantum_provider.py b/ai-projects/chat-cli/src/quantum_provider.py index 13ec959b4..a557cc540 100644 --- a/ai-projects/chat-cli/src/quantum_provider.py +++ b/ai-projects/chat-cli/src/quantum_provider.py @@ -1,464 +1,464 @@ -""" -Quantum LLM Chat Provider -Uses a trained quantum-enhanced language model for chat interactions -Integrates real quantum circuits in the attention mechanism -""" - -import logging -import sys -from pathlib import Path -from typing import Any, Dict, Iterator, List - -import torch -import torch.nn.functional as F - -from chat_providers import BaseChatProvider # type: ignore[attr-defined] -from chat_providers import ProviderChoice, RoleMessage - -# Add quantum-ml to path -repo_root = Path(__file__).resolve().parent.parent.parent.parent -quantum_ml_path = repo_root / "ai-projects" / "quantum-ml" -quantum_ml_src = quantum_ml_path / "src" -for p in [str(quantum_ml_path), str(quantum_ml_src)]: - if p not in sys.path: - sys.path.insert(0, p) - -try: - from quantum_transformer import QUANTUM_AVAILABLE, QuantumLLM - - QUANTUM_LLM_AVAILABLE = True -except ImportError as e: - QuantumLLM = None # type: ignore[assignment] - QUANTUM_AVAILABLE = False - QUANTUM_LLM_AVAILABLE = False - logging.warning(f"QuantumLLM not available: {e}") - - -logger = logging.getLogger(__name__) - - -class QuantumLLMChatProvider(BaseChatProvider): - """ - Chat provider using a trained quantum-enhanced language model. - - Loads a checkpoint from training and uses the quantum LLM for text generation. - Quantum circuits are integrated in the attention mechanism. - """ - - def __init__( - self, - model_path: str, - temperature: float = 0.8, - max_output_tokens: int = 200, - **kwargs, - ): - super().__init__() - self.model_path = Path(model_path) - self.temperature = temperature - self.max_output_tokens = max_output_tokens - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - # Load model - self.model = None - self.model_config: Dict[str, Any] = {} - self.char_to_idx = {} - self.idx_to_char = {} - self.vocab_size = 0 - self.max_seq_len = 128 - - if not QUANTUM_LLM_AVAILABLE: - logger.error("QuantumLLM not available - cannot initialize provider") - raise ImportError("QuantumLLM not available") - - try: - self._load_model() - logger.info(f"Quantum LLM loaded from {self.model_path}") - except Exception as e: - logger.error(f"Failed to load quantum LLM: {e}") - raise - - def _resolve_checkpoint_path(self) -> Path: - """Resolve checkpoint file path from directory or direct file input.""" - if self.model_path.is_file(): - return self.model_path - - status_file = self.model_path / "status.json" - if status_file.exists(): - try: - import json - - with open(status_file, "r", encoding="utf-8") as status_handle: - status_payload = json.load(status_handle) - - checkpoint_ref = ( - status_payload.get("best_checkpoint_path") - or status_payload.get("checkpoint_path") - or status_payload.get("last_checkpoint_path") - ) - if checkpoint_ref: - checkpoint_path = Path(checkpoint_ref) - if not checkpoint_path.is_absolute(): - direct_candidate = self.model_path / checkpoint_path - checkpoint_path = ( - direct_candidate - if direct_candidate.exists() - else repo_root / checkpoint_path - ) - if checkpoint_path.exists(): - return checkpoint_path - except Exception as exc: # noqa: BLE001 - logger.warning( - "Failed to read Quantum LLM status metadata from %s: %s", - status_file, - exc, - ) - - candidates = [ - self.model_path / "best_quantum_llm.pt", - self.model_path / "quantum_llm_checkpoint.pt", - self.model_path / "final_model.pt", - ] - for candidate in candidates: - if candidate.exists(): - return candidate - - attempted = "\n - " + "\n - ".join(str(p) for p in candidates) - raise FileNotFoundError( - f"No Quantum LLM checkpoint found in {self.model_path}. Tried:{attempted}" - ) - - def _derive_model_config(self, checkpoint: Dict[str, Any]) -> Dict[str, Any]: - """Normalize model config across legacy and modern checkpoint schemas.""" - model_cfg = checkpoint.get("model_config", {}) - - if isinstance(model_cfg, dict) and model_cfg: - return { - "vocab_size": int( - model_cfg.get("vocab_size", checkpoint.get("vocab_size", 256)) - ), - "d_model": int(model_cfg.get("d_model", checkpoint.get("d_model", 64))), - "n_heads": int(model_cfg.get("n_heads", checkpoint.get("n_heads", 4))), - "n_transformer_layers": int( - model_cfg.get( - "n_transformer_layers", - model_cfg.get("n_layers", checkpoint.get("n_layers", 2)), - ) - ), - "n_qubits": int( - model_cfg.get("n_qubits", checkpoint.get("n_qubits", 4)) - ), - "n_quantum_layers": int( - model_cfg.get( - "n_quantum_layers", checkpoint.get("n_quantum_layers", 2) - ) - ), - "max_seq_len": int( - model_cfg.get( - "max_seq_len", - model_cfg.get( - "max_seq_length", checkpoint.get("max_seq_length", 128) - ), - ) - ), - "entanglement": model_cfg.get("entanglement", "circular"), - "dropout": float(model_cfg.get("dropout", 0.0)), - "use_quantum_attention": bool( - model_cfg.get("use_quantum_attention", True) - ), - "use_quantum_ffn": bool(model_cfg.get("use_quantum_ffn", True)), - "tie_embeddings": bool(model_cfg.get("tie_embeddings", True)), - } - - # Legacy checkpoint layout - return { - "vocab_size": int(checkpoint.get("vocab_size", 256)), - "d_model": int(checkpoint.get("d_model", 64)), - "n_heads": int(checkpoint.get("n_heads", 4)), - "n_transformer_layers": int( - checkpoint.get("n_layers", checkpoint.get("n_transformer_layers", 2)) - ), - "n_qubits": int(checkpoint.get("n_qubits", 4)), - "n_quantum_layers": int(checkpoint.get("n_quantum_layers", 2)), - "max_seq_len": int( - checkpoint.get("max_seq_length", checkpoint.get("max_seq_len", 128)) - ), - "entanglement": checkpoint.get("entanglement", "circular"), - "dropout": float(checkpoint.get("dropout", 0.0)), - "use_quantum_attention": bool( - checkpoint.get("use_quantum_attention", True) - ), - "use_quantum_ffn": bool(checkpoint.get("use_quantum_ffn", True)), - "tie_embeddings": bool(checkpoint.get("tie_embeddings", True)), - } - - def _load_model(self): - """Load the trained quantum LLM from checkpoint.""" - if QuantumLLM is None: - raise RuntimeError("QuantumLLM class is unavailable") - - checkpoint_path = self._resolve_checkpoint_path() - logger.info(f"Loading checkpoint from {checkpoint_path}") - checkpoint = torch.load(checkpoint_path, map_location=self.device) - - model_state_dict = checkpoint.get("model_state_dict") or checkpoint.get( - "state_dict" - ) - if not isinstance(model_state_dict, dict): - raise KeyError("Checkpoint missing 'model_state_dict' or 'state_dict'.") - - self.model_config = self._derive_model_config(checkpoint) - self.vocab_size = int(self.model_config["vocab_size"]) - self.max_seq_len = int(self.model_config["max_seq_len"]) - - # Build model using the current quantum_transformer API. - if hasattr(QuantumLLM, "from_config"): - self.model = QuantumLLM.from_config( - {"quantum_transformer": self.model_config} - ).to(self.device) - else: # defensive fallback for older implementations - self.model = QuantumLLM( - vocab_size=self.model_config["vocab_size"], - d_model=self.model_config["d_model"], - n_heads=self.model_config["n_heads"], - n_transformer_layers=self.model_config["n_transformer_layers"], - n_qubits=self.model_config["n_qubits"], - n_quantum_layers=self.model_config["n_quantum_layers"], - max_seq_len=self.model_config["max_seq_len"], - entanglement=self.model_config["entanglement"], - dropout=self.model_config["dropout"], - use_quantum_attention=self.model_config["use_quantum_attention"], - use_quantum_ffn=self.model_config["use_quantum_ffn"], - tie_embeddings=self.model_config["tie_embeddings"], - ).to(self.device) - - incompat = self.model.load_state_dict(model_state_dict, strict=False) - missing_keys = getattr(incompat, "missing_keys", []) - unexpected_keys = getattr(incompat, "unexpected_keys", []) - if missing_keys or unexpected_keys: - logger.warning( - "Checkpoint compatibility: missing_keys=%d unexpected_keys=%d", - len(missing_keys), - len(unexpected_keys), - ) - - # Optional tokenizer maps from older checkpoints. - char_to_idx = checkpoint.get("char_to_idx") - idx_to_char = checkpoint.get("idx_to_char") - if isinstance(char_to_idx, dict) and isinstance(idx_to_char, dict): - self.char_to_idx = {str(k): int(v) for k, v in char_to_idx.items()} - self.idx_to_char = {int(k): str(v) for k, v in idx_to_char.items()} - else: - # Lightweight fallback mapping when checkpoint has no tokenizer metadata. - fallback_vocab = min(self.vocab_size, 128) - self.char_to_idx = {chr(i): i for i in range(fallback_vocab)} - self.idx_to_char = {i: chr(i) for i in range(fallback_vocab)} - - self.model.eval() - logger.info( - "Model loaded: vocab=%d, qubits=%d, layers=%d, max_seq_len=%d", - self.vocab_size, - self.model_config["n_qubits"], - self.model_config["n_transformer_layers"], - self.max_seq_len, - ) - - def _encode_text(self, text: str) -> torch.Tensor: - """Encode text to token IDs.""" - if not text: - return torch.tensor([0], dtype=torch.long, device=self.device) - - indices: List[int] = [] - for c in text: - if self.char_to_idx: - indices.append(self.char_to_idx.get(c, ord(c) % self.vocab_size)) - else: - indices.append(ord(c) % self.vocab_size) - return torch.tensor(indices, dtype=torch.long, device=self.device) - - def _decode_tokens(self, tokens: torch.Tensor) -> str: - """Decode token IDs to text.""" - indices = tokens.cpu().tolist() - chars = [] - for i in indices: - i = int(i) - if self.idx_to_char: - ch = self.idx_to_char.get(i) - if ch is not None: - chars.append(ch) - continue - - # Fallback ASCII decode if tokenizer metadata isn't available. - ascii_i = i % 128 - chars.append(chr(ascii_i) if 32 <= ascii_i < 127 else "") - return "".join(chars) - - def _generate(self, prompt: str, max_tokens: int) -> str: - """Generate text using the quantum LLM.""" - if self.model is None: - raise RuntimeError("Quantum model not loaded") - - model = self.model - - # Encode prompt - context = self._encode_text(prompt) - - # Limit context length - max_context = max(1, int(self.max_seq_len)) - if len(context) > max_context: - context = context[-max_context:] - - context = context.unsqueeze(0) # Add batch dimension - - # Prefer model-native generation when available. - if hasattr(self.model, "generate"): - with torch.no_grad(): - generated_ids = model.generate( - context, - max_new_tokens=int(max_tokens), - temperature=float(self.temperature), - top_k=20, - ) - - new_token_ids = generated_ids[0, context.size(1) :] - return self._decode_tokens(new_token_ids) - - generated = [] - - with torch.no_grad(): - for _ in range(max_tokens): - # Forward pass - logits = model(context) # [1, seq_len, vocab_size] - - # Get logits for last position - next_logits = logits[0, -1, :] / self.temperature - - # Sample next token - probs = F.softmax(next_logits, dim=-1) - next_token = torch.multinomial(probs, num_samples=1) - - # Append to context - context = torch.cat([context, next_token.unsqueeze(0)], dim=1) - - # Keep context window manageable - if context.size(1) > max_context: - context = context[:, -max_context:] - - # Decode and collect - char = self.idx_to_char.get(int(next_token.item()), "") - generated.append(char) - - # Stop on newline after some content - if char == "\n" and len(generated) > 20: - break - - return "".join(generated) - - def complete( - self, messages: List[RoleMessage], stream: bool = False - ) -> str | Iterator[str]: - """ - Generate a response using the quantum LLM. - - Args: - messages: Conversation history - stream: Whether to stream the response - - Returns: - Response string or iterator of response chunks - """ - if not self.model: - error_msg = "Model not loaded" - logger.error(error_msg) - return error_msg if not stream else iter([error_msg]) - - # Build prompt from conversation - prompt_parts = [] - for msg in messages[-5:]: # Use last 5 messages for context - role = msg.get("role", "") - content = msg.get("content", "") - - if role == "user": - prompt_parts.append(f"User: {content}\n") - elif role == "assistant": - prompt_parts.append(f"Assistant: {content}\n") - - prompt_parts.append("Assistant: ") - prompt = "".join(prompt_parts) - - # Generate response - try: - response = self._generate(prompt, self.max_output_tokens) - response = response.strip() - - # Add quantum indicator - response = f"🔬 [Quantum LLM] {response}" - - if stream: - return self._stream_response(response) - else: - return response - - except Exception as e: - error_msg = f"Generation failed: {e}" - logger.error(error_msg) - return error_msg if not stream else iter([error_msg]) - - def _stream_response(self, response: str) -> Iterator[str]: - """Stream a response in small, non-empty chunks. - - Chunking on whitespace/newlines reduces overhead versus yielding one - character at a time while still keeping the UI responsive. - """ - text = str(response) - if not text: - return - - buffer: list[str] = [] - for char in text: - buffer.append(char) - should_flush = ( - char == "\n" - or len(buffer) >= 16 - or (char.isspace() and len(buffer) >= 8) - ) - if should_flush: - chunk = "".join(buffer) - if chunk: - yield chunk - buffer.clear() - - if buffer: - yield "".join(buffer) - - -def create_quantum_llm_provider( - model_path: str, temperature: float = 0.8, max_output_tokens: int = 200, **kwargs -) -> tuple[QuantumLLMChatProvider, ProviderChoice]: - """ - Factory function to create a quantum LLM chat provider. - - Args: - model_path: Path to trained model directory - temperature: Sampling temperature - max_output_tokens: Maximum tokens to generate - **kwargs: Additional arguments - - Returns: - Tuple of (provider instance, provider info) - """ - if not QUANTUM_LLM_AVAILABLE: - raise ImportError("QuantumLLM not available - cannot create provider") - - provider = QuantumLLMChatProvider( - model_path=model_path, - temperature=temperature, - max_output_tokens=max_output_tokens, - **kwargs, - ) - - info = ProviderChoice( - name="quantum-llm", - model=f"quantum-llm ({Path(model_path).name})", - ) - - return provider, info +""" +Quantum LLM Chat Provider +Uses a trained quantum-enhanced language model for chat interactions +Integrates real quantum circuits in the attention mechanism +""" + +import logging +import sys +from pathlib import Path +from typing import Any, Dict, Iterator, List + +import torch +import torch.nn.functional as F + +from chat_providers import BaseChatProvider # type: ignore[attr-defined] +from chat_providers import ProviderChoice, RoleMessage + +# Add quantum-ml to path +repo_root = Path(__file__).resolve().parent.parent.parent.parent +quantum_ml_path = repo_root / "ai-projects" / "quantum-ml" +quantum_ml_src = quantum_ml_path / "src" +for p in [str(quantum_ml_path), str(quantum_ml_src)]: + if p not in sys.path: + sys.path.insert(0, p) + +try: + from quantum_transformer import QUANTUM_AVAILABLE, QuantumLLM + + QUANTUM_LLM_AVAILABLE = True +except ImportError as e: + QuantumLLM = None # type: ignore[assignment] + QUANTUM_AVAILABLE = False + QUANTUM_LLM_AVAILABLE = False + logging.warning(f"QuantumLLM not available: {e}") + + +logger = logging.getLogger(__name__) + + +class QuantumLLMChatProvider(BaseChatProvider): + """ + Chat provider using a trained quantum-enhanced language model. + + Loads a checkpoint from training and uses the quantum LLM for text generation. + Quantum circuits are integrated in the attention mechanism. + """ + + def __init__( + self, + model_path: str, + temperature: float = 0.8, + max_output_tokens: int = 200, + **kwargs, + ): + super().__init__() + self.model_path = Path(model_path) + self.temperature = temperature + self.max_output_tokens = max_output_tokens + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Load model + self.model = None + self.model_config: Dict[str, Any] = {} + self.char_to_idx = {} + self.idx_to_char = {} + self.vocab_size = 0 + self.max_seq_len = 128 + + if not QUANTUM_LLM_AVAILABLE: + logger.error("QuantumLLM not available - cannot initialize provider") + raise ImportError("QuantumLLM not available") + + try: + self._load_model() + logger.info(f"Quantum LLM loaded from {self.model_path}") + except Exception as e: + logger.error(f"Failed to load quantum LLM: {e}") + raise + + def _resolve_checkpoint_path(self) -> Path: + """Resolve checkpoint file path from directory or direct file input.""" + if self.model_path.is_file(): + return self.model_path + + status_file = self.model_path / "status.json" + if status_file.exists(): + try: + import json + + with open(status_file, "r", encoding="utf-8") as status_handle: + status_payload = json.load(status_handle) + + checkpoint_ref = ( + status_payload.get("best_checkpoint_path") + or status_payload.get("checkpoint_path") + or status_payload.get("last_checkpoint_path") + ) + if checkpoint_ref: + checkpoint_path = Path(checkpoint_ref) + if not checkpoint_path.is_absolute(): + direct_candidate = self.model_path / checkpoint_path + checkpoint_path = ( + direct_candidate + if direct_candidate.exists() + else repo_root / checkpoint_path + ) + if checkpoint_path.exists(): + return checkpoint_path + except Exception as exc: # noqa: BLE001 + logger.warning( + "Failed to read Quantum LLM status metadata from %s: %s", + status_file, + exc, + ) + + candidates = [ + self.model_path / "best_quantum_llm.pt", + self.model_path / "quantum_llm_checkpoint.pt", + self.model_path / "final_model.pt", + ] + for candidate in candidates: + if candidate.exists(): + return candidate + + attempted = "\n - " + "\n - ".join(str(p) for p in candidates) + raise FileNotFoundError( + f"No Quantum LLM checkpoint found in {self.model_path}. Tried:{attempted}" + ) + + def _derive_model_config(self, checkpoint: Dict[str, Any]) -> Dict[str, Any]: + """Normalize model config across legacy and modern checkpoint schemas.""" + model_cfg = checkpoint.get("model_config", {}) + + if isinstance(model_cfg, dict) and model_cfg: + return { + "vocab_size": int( + model_cfg.get("vocab_size", checkpoint.get("vocab_size", 256)) + ), + "d_model": int(model_cfg.get("d_model", checkpoint.get("d_model", 64))), + "n_heads": int(model_cfg.get("n_heads", checkpoint.get("n_heads", 4))), + "n_transformer_layers": int( + model_cfg.get( + "n_transformer_layers", + model_cfg.get("n_layers", checkpoint.get("n_layers", 2)), + ) + ), + "n_qubits": int( + model_cfg.get("n_qubits", checkpoint.get("n_qubits", 4)) + ), + "n_quantum_layers": int( + model_cfg.get( + "n_quantum_layers", checkpoint.get("n_quantum_layers", 2) + ) + ), + "max_seq_len": int( + model_cfg.get( + "max_seq_len", + model_cfg.get( + "max_seq_length", checkpoint.get("max_seq_length", 128) + ), + ) + ), + "entanglement": model_cfg.get("entanglement", "circular"), + "dropout": float(model_cfg.get("dropout", 0.0)), + "use_quantum_attention": bool( + model_cfg.get("use_quantum_attention", True) + ), + "use_quantum_ffn": bool(model_cfg.get("use_quantum_ffn", True)), + "tie_embeddings": bool(model_cfg.get("tie_embeddings", True)), + } + + # Legacy checkpoint layout + return { + "vocab_size": int(checkpoint.get("vocab_size", 256)), + "d_model": int(checkpoint.get("d_model", 64)), + "n_heads": int(checkpoint.get("n_heads", 4)), + "n_transformer_layers": int( + checkpoint.get("n_layers", checkpoint.get("n_transformer_layers", 2)) + ), + "n_qubits": int(checkpoint.get("n_qubits", 4)), + "n_quantum_layers": int(checkpoint.get("n_quantum_layers", 2)), + "max_seq_len": int( + checkpoint.get("max_seq_length", checkpoint.get("max_seq_len", 128)) + ), + "entanglement": checkpoint.get("entanglement", "circular"), + "dropout": float(checkpoint.get("dropout", 0.0)), + "use_quantum_attention": bool( + checkpoint.get("use_quantum_attention", True) + ), + "use_quantum_ffn": bool(checkpoint.get("use_quantum_ffn", True)), + "tie_embeddings": bool(checkpoint.get("tie_embeddings", True)), + } + + def _load_model(self): + """Load the trained quantum LLM from checkpoint.""" + if QuantumLLM is None: + raise RuntimeError("QuantumLLM class is unavailable") + + checkpoint_path = self._resolve_checkpoint_path() + logger.info(f"Loading checkpoint from {checkpoint_path}") + checkpoint = torch.load(checkpoint_path, map_location=self.device) + + model_state_dict = checkpoint.get("model_state_dict") or checkpoint.get( + "state_dict" + ) + if not isinstance(model_state_dict, dict): + raise KeyError("Checkpoint missing 'model_state_dict' or 'state_dict'.") + + self.model_config = self._derive_model_config(checkpoint) + self.vocab_size = int(self.model_config["vocab_size"]) + self.max_seq_len = int(self.model_config["max_seq_len"]) + + # Build model using the current quantum_transformer API. + if hasattr(QuantumLLM, "from_config"): + self.model = QuantumLLM.from_config( + {"quantum_transformer": self.model_config} + ).to(self.device) + else: # defensive fallback for older implementations + self.model = QuantumLLM( + vocab_size=self.model_config["vocab_size"], + d_model=self.model_config["d_model"], + n_heads=self.model_config["n_heads"], + n_transformer_layers=self.model_config["n_transformer_layers"], + n_qubits=self.model_config["n_qubits"], + n_quantum_layers=self.model_config["n_quantum_layers"], + max_seq_len=self.model_config["max_seq_len"], + entanglement=self.model_config["entanglement"], + dropout=self.model_config["dropout"], + use_quantum_attention=self.model_config["use_quantum_attention"], + use_quantum_ffn=self.model_config["use_quantum_ffn"], + tie_embeddings=self.model_config["tie_embeddings"], + ).to(self.device) + + incompat = self.model.load_state_dict(model_state_dict, strict=False) + missing_keys = getattr(incompat, "missing_keys", []) + unexpected_keys = getattr(incompat, "unexpected_keys", []) + if missing_keys or unexpected_keys: + logger.warning( + "Checkpoint compatibility: missing_keys=%d unexpected_keys=%d", + len(missing_keys), + len(unexpected_keys), + ) + + # Optional tokenizer maps from older checkpoints. + char_to_idx = checkpoint.get("char_to_idx") + idx_to_char = checkpoint.get("idx_to_char") + if isinstance(char_to_idx, dict) and isinstance(idx_to_char, dict): + self.char_to_idx = {str(k): int(v) for k, v in char_to_idx.items()} + self.idx_to_char = {int(k): str(v) for k, v in idx_to_char.items()} + else: + # Lightweight fallback mapping when checkpoint has no tokenizer metadata. + fallback_vocab = min(self.vocab_size, 128) + self.char_to_idx = {chr(i): i for i in range(fallback_vocab)} + self.idx_to_char = {i: chr(i) for i in range(fallback_vocab)} + + self.model.eval() + logger.info( + "Model loaded: vocab=%d, qubits=%d, layers=%d, max_seq_len=%d", + self.vocab_size, + self.model_config["n_qubits"], + self.model_config["n_transformer_layers"], + self.max_seq_len, + ) + + def _encode_text(self, text: str) -> torch.Tensor: + """Encode text to token IDs.""" + if not text: + return torch.tensor([0], dtype=torch.long, device=self.device) + + indices: List[int] = [] + for c in text: + if self.char_to_idx: + indices.append(self.char_to_idx.get(c, ord(c) % self.vocab_size)) + else: + indices.append(ord(c) % self.vocab_size) + return torch.tensor(indices, dtype=torch.long, device=self.device) + + def _decode_tokens(self, tokens: torch.Tensor) -> str: + """Decode token IDs to text.""" + indices = tokens.cpu().tolist() + chars = [] + for i in indices: + i = int(i) + if self.idx_to_char: + ch = self.idx_to_char.get(i) + if ch is not None: + chars.append(ch) + continue + + # Fallback ASCII decode if tokenizer metadata isn't available. + ascii_i = i % 128 + chars.append(chr(ascii_i) if 32 <= ascii_i < 127 else "") + return "".join(chars) + + def _generate(self, prompt: str, max_tokens: int) -> str: + """Generate text using the quantum LLM.""" + if self.model is None: + raise RuntimeError("Quantum model not loaded") + + model = self.model + + # Encode prompt + context = self._encode_text(prompt) + + # Limit context length + max_context = max(1, int(self.max_seq_len)) + if len(context) > max_context: + context = context[-max_context:] + + context = context.unsqueeze(0) # Add batch dimension + + # Prefer model-native generation when available. + if hasattr(self.model, "generate"): + with torch.no_grad(): + generated_ids = model.generate( + context, + max_new_tokens=int(max_tokens), + temperature=float(self.temperature), + top_k=20, + ) + + new_token_ids = generated_ids[0, context.size(1) :] + return self._decode_tokens(new_token_ids) + + generated = [] + + with torch.no_grad(): + for _ in range(max_tokens): + # Forward pass + logits = model(context) # [1, seq_len, vocab_size] + + # Get logits for last position + next_logits = logits[0, -1, :] / self.temperature + + # Sample next token + probs = F.softmax(next_logits, dim=-1) + next_token = torch.multinomial(probs, num_samples=1) + + # Append to context + context = torch.cat([context, next_token.unsqueeze(0)], dim=1) + + # Keep context window manageable + if context.size(1) > max_context: + context = context[:, -max_context:] + + # Decode and collect + char = self.idx_to_char.get(int(next_token.item()), "") + generated.append(char) + + # Stop on newline after some content + if char == "\n" and len(generated) > 20: + break + + return "".join(generated) + + def complete( + self, messages: List[RoleMessage], stream: bool = False + ) -> str | Iterator[str]: + """ + Generate a response using the quantum LLM. + + Args: + messages: Conversation history + stream: Whether to stream the response + + Returns: + Response string or iterator of response chunks + """ + if not self.model: + error_msg = "Model not loaded" + logger.error(error_msg) + return error_msg if not stream else iter([error_msg]) + + # Build prompt from conversation + prompt_parts = [] + for msg in messages[-5:]: # Use last 5 messages for context + role = msg.get("role", "") + content = msg.get("content", "") + + if role == "user": + prompt_parts.append(f"User: {content}\n") + elif role == "assistant": + prompt_parts.append(f"Assistant: {content}\n") + + prompt_parts.append("Assistant: ") + prompt = "".join(prompt_parts) + + # Generate response + try: + response = self._generate(prompt, self.max_output_tokens) + response = response.strip() + + # Add quantum indicator + response = f"🔬 [Quantum LLM] {response}" + + if stream: + return self._stream_response(response) + else: + return response + + except Exception as e: + error_msg = f"Generation failed: {e}" + logger.error(error_msg) + return error_msg if not stream else iter([error_msg]) + + def _stream_response(self, response: str) -> Iterator[str]: + """Stream a response in small, non-empty chunks. + + Chunking on whitespace/newlines reduces overhead versus yielding one + character at a time while still keeping the UI responsive. + """ + text = str(response) + if not text: + return + + buffer: list[str] = [] + for char in text: + buffer.append(char) + should_flush = ( + char == "\n" + or len(buffer) >= 16 + or (char.isspace() and len(buffer) >= 8) + ) + if should_flush: + chunk = "".join(buffer) + if chunk: + yield chunk + buffer.clear() + + if buffer: + yield "".join(buffer) + + +def create_quantum_llm_provider( + model_path: str, temperature: float = 0.8, max_output_tokens: int = 200, **kwargs +) -> tuple[QuantumLLMChatProvider, ProviderChoice]: + """ + Factory function to create a quantum LLM chat provider. + + Args: + model_path: Path to trained model directory + temperature: Sampling temperature + max_output_tokens: Maximum tokens to generate + **kwargs: Additional arguments + + Returns: + Tuple of (provider instance, provider info) + """ + if not QUANTUM_LLM_AVAILABLE: + raise ImportError("QuantumLLM not available - cannot create provider") + + provider = QuantumLLMChatProvider( + model_path=model_path, + temperature=temperature, + max_output_tokens=max_output_tokens, + **kwargs, + ) + + info = ProviderChoice( + name="quantum-llm", + model=f"quantum-llm ({Path(model_path).name})", + ) + + return provider, info diff --git a/ai-projects/chat-cli/src/token_utils.py b/ai-projects/chat-cli/src/token_utils.py index ad3c77aea..048e71e79 100644 --- a/ai-projects/chat-cli/src/token_utils.py +++ b/ai-projects/chat-cli/src/token_utils.py @@ -1,191 +1,191 @@ -from __future__ import annotations - -import math -from dataclasses import dataclass -from typing import Callable, Dict, List, Optional, Tuple - -RoleMessage = Dict[str, str] - -try: - import tiktoken # type: ignore -except Exception: # pragma: no cover - optional - tiktoken = None # type: ignore - -try: - from transformers import AutoTokenizer # type: ignore -except Exception: # pragma: no cover - optional - AutoTokenizer = None # type: ignore - - -# Reasonable default context sizes by popular models/deployments -MODEL_CONTEXT_DEFAULTS: Dict[str, int] = { - # OpenAI - "gpt-4o": 128000, - "gpt-4o-mini": 128000, - "gpt-4.1": 128000, - "gpt-4.1-mini": 128000, - "gpt-3.5-turbo": 16385, - # Azure OpenAI often uses custom deployment names; fall back to a safe default - "azure-default": 16384, - # Local models (Phi etc.) - "phi": 4096, -} - - -def _match_model_default(model: Optional[str]) -> int: - m = (model or "").lower() - for key, ctx in MODEL_CONTEXT_DEFAULTS.items(): - if key in m: - return ctx - return 8192 # safe general default - - -@dataclass -class PruneStats: - original_tokens: int - pruned_tokens: int - removed_count: int - budget: int - reserve_output_tokens: int - - -def _get_text_encoder(provider: str, model: Optional[str]) -> Callable[[str], int]: - """Return a function that approximates token count for a given text. - - Priority: tiktoken (OpenAI/Azure) -> transformers tokenizer (if available) -> heuristic. - """ - prov = (provider or "").lower() - mdl = (model or "").lower() - - # Try tiktoken for OpenAI/Azure - if tiktoken is not None and ( - prov in ("openai", "azure") or any(k in mdl for k in ("gpt-", "-o")) - ): - try: - from tiktoken import encoding_for_model - - enc = None - try: - enc = encoding_for_model(model or "gpt-4o-mini") - except Exception: - enc = tiktoken.get_encoding("cl100k_base") - - def _count(text: str) -> int: - return len(enc.encode(text or "")) - - return _count - except Exception: - pass - - # Try transformers tokenizer for local models - if AutoTokenizer is not None and mdl: - try: - tok = AutoTokenizer.from_pretrained(model, use_fast=True) - - def _count(text: str) -> int: - return len(tok.encode(text or "")) - - return _count - except Exception: - pass - - # Fallback heuristic: 1 token ~ 4 characters in English text - def _heuristic(text: str) -> int: - if not text: - return 0 - return max(1, math.ceil(len(text) / 4)) - - return _heuristic - - -def count_messages_tokens( - messages: List[RoleMessage], - provider: str, - model: Optional[str], - system_prompt: Optional[str] = None, -) -> int: - enc = _get_text_encoder(provider, model) - total = 0 - if system_prompt: - total += enc(system_prompt) + 4 # small framing cost - for m in messages or []: - total += enc(m.get("role", "")) + enc(m.get("content", "")) + 4 - return total - - -def prune_messages( - messages: List[RoleMessage], - provider: str, - model: Optional[str], - max_context_tokens: Optional[int], - reserve_output_tokens: int = 1024, - system_prompt: Optional[str] = None, -) -> Tuple[List[RoleMessage], PruneStats, Optional[RoleMessage]]: - """Prune conversation to fit within model context budget. - - Returns: (pruned_messages, stats, system_message) - - Performance: Uses O(n) algorithm by pre-computing token counts per message - and maintaining a running total, avoiding repeated tokenization. - """ - budget = max_context_tokens or _match_model_default(model) - enc = _get_text_encoder(provider, model) - - # Build a working copy and ensure last message is user for safety - msgs = list(messages or []) - - # Separate system messages from others; keep one combined system message for clarity - system_msgs = [m for m in msgs if m.get("role") == "system"] - non_system = [m for m in msgs if m.get("role") != "system"] - - system_text = system_prompt or "\n\n".join( - m.get("content", "") for m in system_msgs - ) - system_msg = {"role": "system", "content": system_text} if system_text else None - - original_tokens = count_messages_tokens(msgs, provider, model, system_prompt) - - if budget <= reserve_output_tokens + 256: # ensure minimal space - reserve_output_tokens = max(128, budget // 4) - - # Pre-compute token counts for each message (O(n) - done once) - # Each message costs: role tokens + content tokens + 4 (framing) - message_token_counts = [ - enc(m.get("role", "")) + enc(m.get("content", "")) + 4 for m in non_system - ] - - # Calculate base system token cost (done once) - system_tokens = (enc(system_text) + 4) if system_text else 0 - - # Calculate initial total tokens - total_tokens = system_tokens + sum(message_token_counts) - - # Prune from front (oldest messages) while over budget - # Uses running total instead of recalculating each iteration (O(n) total) - start_idx = 0 - while ( - start_idx < len(non_system) and (total_tokens + reserve_output_tokens) > budget - ): - total_tokens -= message_token_counts[start_idx] - start_idx += 1 - - # Get pruned messages - pruned = non_system[start_idx:] - pruned_tokens = total_tokens - removed_count = start_idx - - # Reassemble with system (if any) - final_messages = [] - if system_msg and system_msg["content"].strip(): - final_messages.append(system_msg) - final_messages.extend(pruned) - - stats = PruneStats( - original_tokens=original_tokens, - pruned_tokens=pruned_tokens, - removed_count=removed_count, - budget=budget, - reserve_output_tokens=reserve_output_tokens, - ) - - return final_messages, stats, system_msg +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import Callable, Dict, List, Optional, Tuple + +RoleMessage = Dict[str, str] + +try: + import tiktoken # type: ignore +except Exception: # pragma: no cover - optional + tiktoken = None # type: ignore + +try: + from transformers import AutoTokenizer # type: ignore +except Exception: # pragma: no cover - optional + AutoTokenizer = None # type: ignore + + +# Reasonable default context sizes by popular models/deployments +MODEL_CONTEXT_DEFAULTS: Dict[str, int] = { + # OpenAI + "gpt-4o": 128000, + "gpt-4o-mini": 128000, + "gpt-4.1": 128000, + "gpt-4.1-mini": 128000, + "gpt-3.5-turbo": 16385, + # Azure OpenAI often uses custom deployment names; fall back to a safe default + "azure-default": 16384, + # Local models (Phi etc.) + "phi": 4096, +} + + +def _match_model_default(model: Optional[str]) -> int: + m = (model or "").lower() + for key, ctx in MODEL_CONTEXT_DEFAULTS.items(): + if key in m: + return ctx + return 8192 # safe general default + + +@dataclass +class PruneStats: + original_tokens: int + pruned_tokens: int + removed_count: int + budget: int + reserve_output_tokens: int + + +def _get_text_encoder(provider: str, model: Optional[str]) -> Callable[[str], int]: + """Return a function that approximates token count for a given text. + + Priority: tiktoken (OpenAI/Azure) -> transformers tokenizer (if available) -> heuristic. + """ + prov = (provider or "").lower() + mdl = (model or "").lower() + + # Try tiktoken for OpenAI/Azure + if tiktoken is not None and ( + prov in ("openai", "azure") or any(k in mdl for k in ("gpt-", "-o")) + ): + try: + from tiktoken import encoding_for_model + + enc = None + try: + enc = encoding_for_model(model or "gpt-4o-mini") + except Exception: + enc = tiktoken.get_encoding("cl100k_base") + + def _count(text: str) -> int: + return len(enc.encode(text or "")) + + return _count + except Exception: + pass + + # Try transformers tokenizer for local models + if AutoTokenizer is not None and mdl: + try: + tok = AutoTokenizer.from_pretrained(model, use_fast=True) + + def _count(text: str) -> int: + return len(tok.encode(text or "")) + + return _count + except Exception: + pass + + # Fallback heuristic: 1 token ~ 4 characters in English text + def _heuristic(text: str) -> int: + if not text: + return 0 + return max(1, math.ceil(len(text) / 4)) + + return _heuristic + + +def count_messages_tokens( + messages: List[RoleMessage], + provider: str, + model: Optional[str], + system_prompt: Optional[str] = None, +) -> int: + enc = _get_text_encoder(provider, model) + total = 0 + if system_prompt: + total += enc(system_prompt) + 4 # small framing cost + for m in messages or []: + total += enc(m.get("role", "")) + enc(m.get("content", "")) + 4 + return total + + +def prune_messages( + messages: List[RoleMessage], + provider: str, + model: Optional[str], + max_context_tokens: Optional[int], + reserve_output_tokens: int = 1024, + system_prompt: Optional[str] = None, +) -> Tuple[List[RoleMessage], PruneStats, Optional[RoleMessage]]: + """Prune conversation to fit within model context budget. + + Returns: (pruned_messages, stats, system_message) + + Performance: Uses O(n) algorithm by pre-computing token counts per message + and maintaining a running total, avoiding repeated tokenization. + """ + budget = max_context_tokens or _match_model_default(model) + enc = _get_text_encoder(provider, model) + + # Build a working copy and ensure last message is user for safety + msgs = list(messages or []) + + # Separate system messages from others; keep one combined system message for clarity + system_msgs = [m for m in msgs if m.get("role") == "system"] + non_system = [m for m in msgs if m.get("role") != "system"] + + system_text = system_prompt or "\n\n".join( + m.get("content", "") for m in system_msgs + ) + system_msg = {"role": "system", "content": system_text} if system_text else None + + original_tokens = count_messages_tokens(msgs, provider, model, system_prompt) + + if budget <= reserve_output_tokens + 256: # ensure minimal space + reserve_output_tokens = max(128, budget // 4) + + # Pre-compute token counts for each message (O(n) - done once) + # Each message costs: role tokens + content tokens + 4 (framing) + message_token_counts = [ + enc(m.get("role", "")) + enc(m.get("content", "")) + 4 for m in non_system + ] + + # Calculate base system token cost (done once) + system_tokens = (enc(system_text) + 4) if system_text else 0 + + # Calculate initial total tokens + total_tokens = system_tokens + sum(message_token_counts) + + # Prune from front (oldest messages) while over budget + # Uses running total instead of recalculating each iteration (O(n) total) + start_idx = 0 + while ( + start_idx < len(non_system) and (total_tokens + reserve_output_tokens) > budget + ): + total_tokens -= message_token_counts[start_idx] + start_idx += 1 + + # Get pruned messages + pruned = non_system[start_idx:] + pruned_tokens = total_tokens + removed_count = start_idx + + # Reassemble with system (if any) + final_messages = [] + if system_msg and system_msg["content"].strip(): + final_messages.append(system_msg) + final_messages.extend(pruned) + + stats = PruneStats( + original_tokens=original_tokens, + pruned_tokens=pruned_tokens, + removed_count=removed_count, + budget=budget, + reserve_output_tokens=reserve_output_tokens, + ) + + return final_messages, stats, system_msg diff --git a/ai-projects/cooking-ai/README.md b/ai-projects/cooking-ai/README.md index efb0eb5a7..3b3e5f907 100644 --- a/ai-projects/cooking-ai/README.md +++ b/ai-projects/cooking-ai/README.md @@ -1,74 +1,74 @@ -# Cooking AI (Console App) - -Interactive console app that uses an AI language model to: -- Search for recipes by dish, ingredients, or dietary needs -- Extract structured ingredients from free‑form recipe text - -It prefers GitHub Models when available and falls back to a local offline provider for zero‑setup testing. - -## Features -- Providers: GitHub Models (OpenAI-compatible) and Local fallback -- Two actions: Recipe Search, Ingredient Extraction -- JSON-only responses with schema validation and repair -- Interactive menu and one-shot CLI flags for automation - -## Quick start (no keys required) - -```powershell -# From repo root -pip install -r .\cooking-ai\requirements.txt -python .\cooking-ai\src\main.py --provider local --recipe-search "chicken, broccoli, weeknight" -python .\cooking-ai\src\main.py --provider local --extract "2 cups flour, 1 tsp salt" - -# Interactive menu -python .\cooking-ai\src\main.py --provider local -``` - -## Use with GitHub Models - -Set your API key and choose a model (defaults included): - -```powershell -$env:GITHUB_MODELS_API_KEY = "" # or GITHUB_TOKEN -$env:GITHUB_MODELS_MODEL = "gpt-4o-mini" -python .\cooking-ai\src\main.py --provider github -``` - -Notes: -- Endpoint: https://models.inference.ai.azure.com (OpenAI compatible) -- The app uses the OpenAI Python SDK with a custom base_url. -- If the provider or key is missing, the app falls back to local mode. - -## CLI usage - -```powershell -python .\cooking-ai\src\main.py [--provider github|local] [--model ] [--once] - [--recipe-search "query"] [--extract "text"] -``` - -Examples: - -```powershell -# Search recipes with GitHub Models (requires token) -$env:GITHUB_MODELS_API_KEY = "..." -python .\cooking-ai\src\main.py --provider github --recipe-search "vegan pasta with mushrooms" - -# Extract ingredients locally (offline) -python .\cooking-ai\src\main.py --provider local --extract "3 eggs, 1/2 cup milk, pinch of salt" -``` - -## Environment variables -- GITHUB_MODELS_API_KEY or GITHUB_TOKEN – API key for GitHub Models -- GITHUB_MODELS_MODEL – model name (default: gpt-4o-mini) -- COOKING_TEMPERATURE – sampling temperature (default: 0.4) - -## Project layout -- `src/main.py` – CLI entry point and interactive loop -- `src/agents/recipe_agent.py` – high-level agent for search/extract -- `src/providers/github_models.py` – OpenAI-compatible provider -- `src/providers/local.py` – offline deterministic provider -- `src/utils/json_utils.py` – JSON repair and schema validation -- `tests/test_agent.py` – minimal smoke tests - -## License -MIT +# Cooking AI (Console App) + +Interactive console app that uses an AI language model to: +- Search for recipes by dish, ingredients, or dietary needs +- Extract structured ingredients from free‑form recipe text + +It prefers GitHub Models when available and falls back to a local offline provider for zero‑setup testing. + +## Features +- Providers: GitHub Models (OpenAI-compatible) and Local fallback +- Two actions: Recipe Search, Ingredient Extraction +- JSON-only responses with schema validation and repair +- Interactive menu and one-shot CLI flags for automation + +## Quick start (no keys required) + +```powershell +# From repo root +pip install -r .\cooking-ai\requirements.txt +python .\cooking-ai\src\main.py --provider local --recipe-search "chicken, broccoli, weeknight" +python .\cooking-ai\src\main.py --provider local --extract "2 cups flour, 1 tsp salt" + +# Interactive menu +python .\cooking-ai\src\main.py --provider local +``` + +## Use with GitHub Models + +Set your API key and choose a model (defaults included): + +```powershell +$env:GITHUB_MODELS_API_KEY = "" # or GITHUB_TOKEN +$env:GITHUB_MODELS_MODEL = "gpt-4o-mini" +python .\cooking-ai\src\main.py --provider github +``` + +Notes: +- Endpoint: https://models.inference.ai.azure.com (OpenAI compatible) +- The app uses the OpenAI Python SDK with a custom base_url. +- If the provider or key is missing, the app falls back to local mode. + +## CLI usage + +```powershell +python .\cooking-ai\src\main.py [--provider github|local] [--model ] [--once] + [--recipe-search "query"] [--extract "text"] +``` + +Examples: + +```powershell +# Search recipes with GitHub Models (requires token) +$env:GITHUB_MODELS_API_KEY = "..." +python .\cooking-ai\src\main.py --provider github --recipe-search "vegan pasta with mushrooms" + +# Extract ingredients locally (offline) +python .\cooking-ai\src\main.py --provider local --extract "3 eggs, 1/2 cup milk, pinch of salt" +``` + +## Environment variables +- GITHUB_MODELS_API_KEY or GITHUB_TOKEN – API key for GitHub Models +- GITHUB_MODELS_MODEL – model name (default: gpt-4o-mini) +- COOKING_TEMPERATURE – sampling temperature (default: 0.4) + +## Project layout +- `src/main.py` – CLI entry point and interactive loop +- `src/agents/recipe_agent.py` – high-level agent for search/extract +- `src/providers/github_models.py` – OpenAI-compatible provider +- `src/providers/local.py` – offline deterministic provider +- `src/utils/json_utils.py` – JSON repair and schema validation +- `tests/test_agent.py` – minimal smoke tests + +## License +MIT diff --git a/ai-projects/cooking-ai/requirements.txt b/ai-projects/cooking-ai/requirements.txt index 57491e1f7..94b6cd523 100644 --- a/ai-projects/cooking-ai/requirements.txt +++ b/ai-projects/cooking-ai/requirements.txt @@ -1,5 +1,5 @@ -# Minimal deps; local mode works with stdlib only -# Cloud providers use OpenAI SDK compatible with GitHub Models -openai>=1.58.0 -jsonschema>=4.23.0 +# Minimal deps; local mode works with stdlib only +# Cloud providers use OpenAI SDK compatible with GitHub Models +openai>=1.58.0 +jsonschema>=4.23.0 pytest>=8.0.0 diff --git a/ai-projects/cooking-ai/src/agents/recipe_agent.py b/ai-projects/cooking-ai/src/agents/recipe_agent.py index e65a535e2..73991ada9 100644 --- a/ai-projects/cooking-ai/src/agents/recipe_agent.py +++ b/ai-projects/cooking-ai/src/agents/recipe_agent.py @@ -1,132 +1,132 @@ -from __future__ import annotations - -"""High-level Cooking AI agent. - -Provides methods: - - search_recipes(query: str, filters: list[str], limit: int) - - extract_ingredients(text: str) - -Abstracts provider differences. Providers must implement: - complete(messages: list[dict[str,str]], json_mode: bool=False) -> str -""" - -from typing import Any, Dict, List, Optional, Protocol - -try: - # When running tests that inject src/ into sys.path - from utils.json_utils import (INGREDIENT_EXTRACTION_SCHEMA, - RECIPE_SEARCH_SCHEMA, parse_and_validate) -except ImportError: # pragma: no cover - # Fallback for package-style execution (not typical here but defensive) - from ..utils.json_utils import (INGREDIENT_EXTRACTION_SCHEMA, - RECIPE_SEARCH_SCHEMA, parse_and_validate) - - -class ProviderProtocol(Protocol): # Structural typing for providers - def complete( - self, messages: List[Dict[str, str]], json_mode: bool = False - ) -> str: # pragma: no cover - ... - - -SYSTEM_PROMPT = ( - "You are a helpful cooking assistant. Always output STRICT JSON with no commentary." -) - -SEARCH_PROMPT_TEMPLATE = """ -TASK:RECIPE_SEARCH -Return a JSON object: {{"recipes": [ {{"title":..., "ingredients": [...], "instructions": "...", "tags": [...], "est_time_minutes": }} ]}} -Rules: -- Provide diverse, concise recipes. -- Include estimated time if obvious else null. -- Use simple ingredient forms. -Query: {query} -Filters: {filters} -Limit: {limit} - -Examples (follow structure exactly): -Input: - Query: vegan pasta - Filters: vegan - Limit: 2 -Output: - {{"recipes": [ - {{"title": "Vegan Tomato Pasta", "ingredients": ["spaghetti", "olive oil", "garlic", "tomatoes", "basil"], "instructions": "Boil pasta; sauté garlic; add tomatoes; toss with pasta and basil.", "tags": ["vegan"], "est_time_minutes": 20}}, - {{"title": "Mushroom Bolognese", "ingredients": ["mushrooms", "onion", "garlic", "tomato paste", "pasta"], "instructions": "Cook mushrooms; add aromatics and paste; simmer; serve with pasta.", "tags": ["vegan"], "est_time_minutes": 30}} - ]}} - -Output must be valid JSON ONLY with no extra text. -""".strip() - -EXTRACT_PROMPT_TEMPLATE = """ -TASK:INGREDIENT_EXTRACTION -Given raw recipe text lines, extract structured ingredients. -Return JSON: {{"ingredients": [{{"raw":..., "name":..., "quantity":..., "unit":..., "notes":...}}]}} -Text: {text} - -Example: -Input Text: - 2 cups flour\n1 tsp salt\n3 eggs -Output JSON: - {{"ingredients": [ - {{"raw": "2 cups flour", "name": "flour", "quantity": "2", "unit": "cups", "notes": null}}, - {{"raw": "1 tsp salt", "name": "salt", "quantity": "1", "unit": "tsp", "notes": null}}, - {{"raw": "3 eggs", "name": "eggs", "quantity": "3", "unit": null, "notes": null}} - ]}} - -Output must be valid JSON ONLY with no extra text. -""".strip() - - -class RecipeAgent: - def __init__(self, provider: ProviderProtocol) -> None: - self.provider = provider - - def _invoke(self, user_content: str, json_schema: Dict[str, Any]) -> Dict[str, Any]: - messages = [ - {"role": "system", "content": SYSTEM_PROMPT}, - {"role": "user", "content": user_content}, - ] - # Stricter JSON mode with up to 2 retries - for attempt in range(2): - raw = self.provider.complete(messages, json_mode=True) - data, err = parse_and_validate(raw, json_schema) - if data is not None: - return data - # reinforce instruction for retry - messages = [ - {"role": "system", "content": SYSTEM_PROMPT}, - { - "role": "user", - "content": user_content - + "\nReturn ONLY a valid minified JSON object matching the schema. No comments.", - }, - ] - - # Try without JSON mode (prompt-only enforcement) - raw2 = self.provider.complete(messages, json_mode=False) - data2, err2 = parse_and_validate(raw2, json_schema) - if data2 is not None: - return data2 - # Last resort: empty structure - if json_schema is RECIPE_SEARCH_SCHEMA: - return {"recipes": []} - if json_schema is INGREDIENT_EXTRACTION_SCHEMA: - return {"ingredients": []} - return {"recipes": []} - - def search_recipes( - self, query: str, filters: Optional[List[str]] = None, limit: int = 5 - ) -> Dict[str, Any]: - filters = filters or [] - prompt = SEARCH_PROMPT_TEMPLATE.format( - query=query, filters=", ".join(filters), limit=limit - ) - return self._invoke(prompt, RECIPE_SEARCH_SCHEMA) - - def extract_ingredients(self, text: str) -> Dict[str, Any]: - prompt = EXTRACT_PROMPT_TEMPLATE.format(text=text) - return self._invoke(prompt, INGREDIENT_EXTRACTION_SCHEMA) - - -__all__ = ["RecipeAgent"] +from __future__ import annotations + +"""High-level Cooking AI agent. + +Provides methods: + - search_recipes(query: str, filters: list[str], limit: int) + - extract_ingredients(text: str) + +Abstracts provider differences. Providers must implement: + complete(messages: list[dict[str,str]], json_mode: bool=False) -> str +""" + +from typing import Any, Dict, List, Optional, Protocol + +try: + # When running tests that inject src/ into sys.path + from utils.json_utils import (INGREDIENT_EXTRACTION_SCHEMA, + RECIPE_SEARCH_SCHEMA, parse_and_validate) +except ImportError: # pragma: no cover + # Fallback for package-style execution (not typical here but defensive) + from ..utils.json_utils import (INGREDIENT_EXTRACTION_SCHEMA, + RECIPE_SEARCH_SCHEMA, parse_and_validate) + + +class ProviderProtocol(Protocol): # Structural typing for providers + def complete( + self, messages: List[Dict[str, str]], json_mode: bool = False + ) -> str: # pragma: no cover + ... + + +SYSTEM_PROMPT = ( + "You are a helpful cooking assistant. Always output STRICT JSON with no commentary." +) + +SEARCH_PROMPT_TEMPLATE = """ +TASK:RECIPE_SEARCH +Return a JSON object: {{"recipes": [ {{"title":..., "ingredients": [...], "instructions": "...", "tags": [...], "est_time_minutes": }} ]}} +Rules: +- Provide diverse, concise recipes. +- Include estimated time if obvious else null. +- Use simple ingredient forms. +Query: {query} +Filters: {filters} +Limit: {limit} + +Examples (follow structure exactly): +Input: + Query: vegan pasta + Filters: vegan + Limit: 2 +Output: + {{"recipes": [ + {{"title": "Vegan Tomato Pasta", "ingredients": ["spaghetti", "olive oil", "garlic", "tomatoes", "basil"], "instructions": "Boil pasta; sauté garlic; add tomatoes; toss with pasta and basil.", "tags": ["vegan"], "est_time_minutes": 20}}, + {{"title": "Mushroom Bolognese", "ingredients": ["mushrooms", "onion", "garlic", "tomato paste", "pasta"], "instructions": "Cook mushrooms; add aromatics and paste; simmer; serve with pasta.", "tags": ["vegan"], "est_time_minutes": 30}} + ]}} + +Output must be valid JSON ONLY with no extra text. +""".strip() + +EXTRACT_PROMPT_TEMPLATE = """ +TASK:INGREDIENT_EXTRACTION +Given raw recipe text lines, extract structured ingredients. +Return JSON: {{"ingredients": [{{"raw":..., "name":..., "quantity":..., "unit":..., "notes":...}}]}} +Text: {text} + +Example: +Input Text: + 2 cups flour\n1 tsp salt\n3 eggs +Output JSON: + {{"ingredients": [ + {{"raw": "2 cups flour", "name": "flour", "quantity": "2", "unit": "cups", "notes": null}}, + {{"raw": "1 tsp salt", "name": "salt", "quantity": "1", "unit": "tsp", "notes": null}}, + {{"raw": "3 eggs", "name": "eggs", "quantity": "3", "unit": null, "notes": null}} + ]}} + +Output must be valid JSON ONLY with no extra text. +""".strip() + + +class RecipeAgent: + def __init__(self, provider: ProviderProtocol) -> None: + self.provider = provider + + def _invoke(self, user_content: str, json_schema: Dict[str, Any]) -> Dict[str, Any]: + messages = [ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": user_content}, + ] + # Stricter JSON mode with up to 2 retries + for attempt in range(2): + raw = self.provider.complete(messages, json_mode=True) + data, err = parse_and_validate(raw, json_schema) + if data is not None: + return data + # reinforce instruction for retry + messages = [ + {"role": "system", "content": SYSTEM_PROMPT}, + { + "role": "user", + "content": user_content + + "\nReturn ONLY a valid minified JSON object matching the schema. No comments.", + }, + ] + + # Try without JSON mode (prompt-only enforcement) + raw2 = self.provider.complete(messages, json_mode=False) + data2, err2 = parse_and_validate(raw2, json_schema) + if data2 is not None: + return data2 + # Last resort: empty structure + if json_schema is RECIPE_SEARCH_SCHEMA: + return {"recipes": []} + if json_schema is INGREDIENT_EXTRACTION_SCHEMA: + return {"ingredients": []} + return {"recipes": []} + + def search_recipes( + self, query: str, filters: Optional[List[str]] = None, limit: int = 5 + ) -> Dict[str, Any]: + filters = filters or [] + prompt = SEARCH_PROMPT_TEMPLATE.format( + query=query, filters=", ".join(filters), limit=limit + ) + return self._invoke(prompt, RECIPE_SEARCH_SCHEMA) + + def extract_ingredients(self, text: str) -> Dict[str, Any]: + prompt = EXTRACT_PROMPT_TEMPLATE.format(text=text) + return self._invoke(prompt, INGREDIENT_EXTRACTION_SCHEMA) + + +__all__ = ["RecipeAgent"] diff --git a/ai-projects/cooking-ai/src/main.py b/ai-projects/cooking-ai/src/main.py index 2fb67e435..0ffefe627 100644 --- a/ai-projects/cooking-ai/src/main.py +++ b/ai-projects/cooking-ai/src/main.py @@ -1,101 +1,101 @@ -from __future__ import annotations - -import argparse -import os -from typing import List - -from agents.recipe_agent import RecipeAgent -from providers.local import LocalProvider - -try: # Optional import for GitHub Models - from providers.github_models import GitHubModelsProvider # type: ignore -except Exception: # pragma: no cover - GitHubModelsProvider = None # type: ignore - - -def detect_provider(name: str): - name = name.lower() - if name == "github" and GitHubModelsProvider is not None: - api_key = os.getenv("GITHUB_MODELS_API_KEY") or os.getenv("GITHUB_TOKEN") - if api_key: - try: - return GitHubModelsProvider(api_key=api_key) - except Exception as e: # pragma: no cover - print( - f"[warn] Failed to init GitHubModelsProvider: {e}. Falling back to local." - ) - return LocalProvider() - - -def interactive_loop(agent: RecipeAgent, provider_name: str): - print( - f"Cooking AI - provider: {provider_name}\nCommands: /search ; /extract ; /exit" - ) - while True: - try: - line = input("cooking> ").strip() - except EOFError: - break - if not line: - continue - if line == "/exit": - break - if line.startswith("/search"): - query = line[len("/search") :].strip() or "pasta" - data = agent.search_recipes(query=query, filters=[], limit=5) - print(data) - continue - if line.startswith("/extract"): - text = line[len("/extract") :].strip() or "2 eggs, 1 cup milk" - data = agent.extract_ingredients(text) - print(data) - continue - print("Unknown command. Use /search or /extract or /exit.") - - -def run_once(agent: RecipeAgent, args): - if args.recipe_search: - data = agent.search_recipes( - query=args.recipe_search, filters=args.filter or [], limit=args.limit - ) - print(data) - elif args.extract: - data = agent.extract_ingredients(args.extract) - print(data) - else: - print( - "No action specified. Use --recipe-search or --extract, or run without --once for interactive mode." - ) - - -def build_parser(): - p = argparse.ArgumentParser( - description="Cooking AI - recipe search and ingredient extraction" - ) - p.add_argument("--provider", default="local", help="Provider: github or local") - p.add_argument("--model", help="Model override (GitHub Models only)") - p.add_argument("--recipe-search", dest="recipe_search", help="Recipe search query") - p.add_argument("--filter", action="append", help="Add filter tag (repeatable)") - p.add_argument("--limit", type=int, default=5, help="Max recipes to return") - p.add_argument("--extract", help="Raw recipe text for ingredient extraction") - p.add_argument("--once", action="store_true", help="Run a single action and exit") - return p - - -def main(argv: List[str] | None = None): - parser = build_parser() - args = parser.parse_args(argv) - provider = detect_provider(args.provider) - agent = RecipeAgent(provider) - provider_name = provider.__class__.__name__ - - if args.once: - run_once(agent, args) - return 0 - - interactive_loop(agent, provider_name) - return 0 - - -if __name__ == "__main__": # pragma: no cover - raise SystemExit(main()) +from __future__ import annotations + +import argparse +import os +from typing import List + +from agents.recipe_agent import RecipeAgent +from providers.local import LocalProvider + +try: # Optional import for GitHub Models + from providers.github_models import GitHubModelsProvider # type: ignore +except Exception: # pragma: no cover + GitHubModelsProvider = None # type: ignore + + +def detect_provider(name: str): + name = name.lower() + if name == "github" and GitHubModelsProvider is not None: + api_key = os.getenv("GITHUB_MODELS_API_KEY") or os.getenv("GITHUB_TOKEN") + if api_key: + try: + return GitHubModelsProvider(api_key=api_key) + except Exception as e: # pragma: no cover + print( + f"[warn] Failed to init GitHubModelsProvider: {e}. Falling back to local." + ) + return LocalProvider() + + +def interactive_loop(agent: RecipeAgent, provider_name: str): + print( + f"Cooking AI - provider: {provider_name}\nCommands: /search ; /extract ; /exit" + ) + while True: + try: + line = input("cooking> ").strip() + except EOFError: + break + if not line: + continue + if line == "/exit": + break + if line.startswith("/search"): + query = line[len("/search") :].strip() or "pasta" + data = agent.search_recipes(query=query, filters=[], limit=5) + print(data) + continue + if line.startswith("/extract"): + text = line[len("/extract") :].strip() or "2 eggs, 1 cup milk" + data = agent.extract_ingredients(text) + print(data) + continue + print("Unknown command. Use /search or /extract or /exit.") + + +def run_once(agent: RecipeAgent, args): + if args.recipe_search: + data = agent.search_recipes( + query=args.recipe_search, filters=args.filter or [], limit=args.limit + ) + print(data) + elif args.extract: + data = agent.extract_ingredients(args.extract) + print(data) + else: + print( + "No action specified. Use --recipe-search or --extract, or run without --once for interactive mode." + ) + + +def build_parser(): + p = argparse.ArgumentParser( + description="Cooking AI - recipe search and ingredient extraction" + ) + p.add_argument("--provider", default="local", help="Provider: github or local") + p.add_argument("--model", help="Model override (GitHub Models only)") + p.add_argument("--recipe-search", dest="recipe_search", help="Recipe search query") + p.add_argument("--filter", action="append", help="Add filter tag (repeatable)") + p.add_argument("--limit", type=int, default=5, help="Max recipes to return") + p.add_argument("--extract", help="Raw recipe text for ingredient extraction") + p.add_argument("--once", action="store_true", help="Run a single action and exit") + return p + + +def main(argv: List[str] | None = None): + parser = build_parser() + args = parser.parse_args(argv) + provider = detect_provider(args.provider) + agent = RecipeAgent(provider) + provider_name = provider.__class__.__name__ + + if args.once: + run_once(agent, args) + return 0 + + interactive_loop(agent, provider_name) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/ai-projects/cooking-ai/src/providers/github_models.py b/ai-projects/cooking-ai/src/providers/github_models.py index 60b7f3964..7bce5e82b 100644 --- a/ai-projects/cooking-ai/src/providers/github_models.py +++ b/ai-projects/cooking-ai/src/providers/github_models.py @@ -1,58 +1,58 @@ -from __future__ import annotations - -import os -from typing import Any, Dict, List, Optional - -try: - from openai import OpenAI # type: ignore -except Exception: # pragma: no cover - OpenAI = None # type: ignore - - -class GitHubModelsProvider: - """OpenAI-compatible provider for GitHub Models. - - Uses the OpenAI Python SDK with a custom base_url. - Env vars: - - GITHUB_MODELS_API_KEY or GITHUB_TOKEN - - GITHUB_MODELS_MODEL (optional; default 'gpt-4o-mini') - - COOKING_TEMPERATURE (optional; default 0.4) - """ - - def __init__( - self, - api_key: Optional[str] = None, - model: Optional[str] = None, - temperature: Optional[float] = None, - base_url: str = "https://models.inference.ai.azure.com", - ) -> None: - if OpenAI is None: - raise RuntimeError( - "openai package not available. Install with: pip install openai>=1.43.0" - ) - api_key = ( - api_key or os.getenv("GITHUB_MODELS_API_KEY") or os.getenv("GITHUB_TOKEN") - ) - if not api_key: - raise RuntimeError( - "GitHub Models provider requires GITHUB_MODELS_API_KEY or GITHUB_TOKEN to be set." - ) - self.model = model or os.getenv("GITHUB_MODELS_MODEL", "gpt-4o-mini") - self.temperature = 0.4 if temperature is None else float(temperature) - # Create client with custom base URL - self.client = OpenAI(api_key=api_key, base_url=base_url) - - def complete(self, messages: List[Dict[str, str]], json_mode: bool = False) -> str: - """Send a chat completion request and return the assistant content.""" - # Some GitHub Models support response_format; if not, rely on prompting. - kwargs: Dict[str, Any] = { - "model": self.model, - "messages": messages, - "temperature": self.temperature, - "max_tokens": 1024, - } - if json_mode: - kwargs["response_format"] = {"type": "json_object"} - resp = self.client.chat.completions.create(**kwargs) # type: ignore - content = resp.choices[0].message.content if resp.choices else "" - return content or "" +from __future__ import annotations + +import os +from typing import Any, Dict, List, Optional + +try: + from openai import OpenAI # type: ignore +except Exception: # pragma: no cover + OpenAI = None # type: ignore + + +class GitHubModelsProvider: + """OpenAI-compatible provider for GitHub Models. + + Uses the OpenAI Python SDK with a custom base_url. + Env vars: + - GITHUB_MODELS_API_KEY or GITHUB_TOKEN + - GITHUB_MODELS_MODEL (optional; default 'gpt-4o-mini') + - COOKING_TEMPERATURE (optional; default 0.4) + """ + + def __init__( + self, + api_key: Optional[str] = None, + model: Optional[str] = None, + temperature: Optional[float] = None, + base_url: str = "https://models.inference.ai.azure.com", + ) -> None: + if OpenAI is None: + raise RuntimeError( + "openai package not available. Install with: pip install openai>=1.43.0" + ) + api_key = ( + api_key or os.getenv("GITHUB_MODELS_API_KEY") or os.getenv("GITHUB_TOKEN") + ) + if not api_key: + raise RuntimeError( + "GitHub Models provider requires GITHUB_MODELS_API_KEY or GITHUB_TOKEN to be set." + ) + self.model = model or os.getenv("GITHUB_MODELS_MODEL", "gpt-4o-mini") + self.temperature = 0.4 if temperature is None else float(temperature) + # Create client with custom base URL + self.client = OpenAI(api_key=api_key, base_url=base_url) + + def complete(self, messages: List[Dict[str, str]], json_mode: bool = False) -> str: + """Send a chat completion request and return the assistant content.""" + # Some GitHub Models support response_format; if not, rely on prompting. + kwargs: Dict[str, Any] = { + "model": self.model, + "messages": messages, + "temperature": self.temperature, + "max_tokens": 1024, + } + if json_mode: + kwargs["response_format"] = {"type": "json_object"} + resp = self.client.chat.completions.create(**kwargs) # type: ignore + content = resp.choices[0].message.content if resp.choices else "" + return content or "" diff --git a/ai-projects/cooking-ai/src/providers/local.py b/ai-projects/cooking-ai/src/providers/local.py index 4b8d6a5c8..94485e929 100644 --- a/ai-projects/cooking-ai/src/providers/local.py +++ b/ai-projects/cooking-ai/src/providers/local.py @@ -1,166 +1,166 @@ -from __future__ import annotations - -import json -import re -from typing import Dict, List - -# Pre-compile regex pattern for performance -_RE_QUANTITY = re.compile( - r"^(?P(\d+\/\d+|\d+(\.\d+)?))\s*(?P[a-zA-Z]+)?\s*(?P.*)" -) - -SAMPLE_RECIPES = [ - { - "title": "Simple Tomato Pasta", - "ingredients": [ - "200g spaghetti", - "2 tbsp olive oil", - "3 cloves garlic", - "400g canned tomatoes", - "salt", - "pepper", - "fresh basil", - ], - "instructions": "Cook pasta; sauté garlic; add tomatoes; simmer; toss with pasta and basil.", - "tags": ["vegetarian", "weeknight"], - "est_time_minutes": 25, - }, - { - "title": "Vegan Chickpea Curry", - "ingredients": [ - "1 tbsp coconut oil", - "1 onion", - "2 cloves garlic", - "1 tbsp curry powder", - "400g chickpeas", - "400ml coconut milk", - "spinach handful", - "salt", - ], - "instructions": "Sauté aromatics; add spices; add chickpeas & coconut milk; simmer; fold in spinach.", - "tags": ["vegan", "gluten-free"], - "est_time_minutes": 30, - }, - { - "title": "Garlic Butter Salmon", - "ingredients": [ - "2 salmon fillets", - "2 tbsp butter", - "2 cloves garlic", - "lemon juice", - "parsley", - "salt", - "pepper", - ], - "instructions": "Pan sear salmon; melt butter with garlic; finish with lemon and parsley.", - "tags": ["high-protein"], - "est_time_minutes": 18, - }, -] - - -class LocalProvider: - """Offline deterministic provider. - - Interprets task markers in the last user message: - - TASK:RECIPE_SEARCH - - TASK:INGREDIENT_EXTRACTION - Returns JSON strings for predictable testing. - """ - - def complete( - self, messages: List[Dict[str, str]], json_mode: bool = False - ) -> str: # noqa: D401 - last_user = next( - (m["content"] for m in reversed(messages) if m.get("role") == "user"), - "", - ) - if "TASK:RECIPE_SEARCH" in last_user: - return self._handle_search(last_user) - if "TASK:INGREDIENT_EXTRACTION" in last_user: - return self._handle_extract(last_user) - return json.dumps({"recipes": []}) - - def _handle_search(self, prompt: str) -> str: - # Dynamic scoring based on query tokens and optional tag filters. - # Prompt format lines: Query: , Filters: comma,separated, Limit: N - q_match = re.search(r"Query:\s*(.*)", prompt) - query = (q_match.group(1).strip() if q_match else "").lower() - f_match = re.search(r"Filters:\s*(.*)", prompt) - filters_raw = f_match.group(1).strip() if f_match else "" - filters = [f.strip().lower() for f in filters_raw.split(",") if f.strip()] - l_match = re.search(r"Limit:\s*(\d+)", prompt) - limit = int(l_match.group(1)) if l_match else 5 - - tokens = [t for t in re.split(r"[^a-zA-Z]+", query) if t] - scored = [] - for r in SAMPLE_RECIPES: - # Filter by tags (optimized: pre-compute tag set for O(1) membership check) - if filters: - recipe_tags = {tag.lower() for tag in r["tags"]} - if not all(any(f in tag for tag in recipe_tags) for f in filters): - continue - title = r["title"].lower() - ingredients_blob = " ".join(r["ingredients"]).lower() - score = 0 - if tokens: - for tok in tokens: - if tok in title: - score += 3 # title match weight - if tok in ingredients_blob: - score += 1 # ingredient mention weight - else: - # If no tokens provided, give small baseline score - score = 1 - scored.append((score, r)) - - # Sort by score desc then by title for stability - scored.sort(key=lambda x: (-x[0], x[1]["title"])) - # If no results after filtering, fall back to unfiltered list with baseline scoring - if not scored: - fallback = [] - for r in SAMPLE_RECIPES: - title = r["title"].lower() - ingredients_blob = " ".join(r["ingredients"]).lower() - score = 0 - for tok in tokens: - if tok in title: - score += 3 - if tok in ingredients_blob: - score += 1 - fallback.append((score, r)) - fallback.sort(key=lambda x: (-x[0], x[1]["title"])) - scored = fallback or [(1, r) for r in SAMPLE_RECIPES] - - results = [r for _, r in scored[:limit]] - return json.dumps({"recipes": results}) - - def _handle_extract(self, prompt: str) -> str: - # Pull raw text after marker (fallback to whole prompt if not found) - text_match = re.search(r"Text:\s*(.*)", prompt, re.DOTALL) - text = text_match.group(1).strip() if text_match else prompt - lines = [l.strip() for l in re.split(r"[\n,;]", text) if l.strip()] - items = [] - for raw in lines: - m = _RE_QUANTITY.match(raw) - if m: - items.append( - { - "raw": raw, - "name": m.group("name").strip(), - "quantity": m.group("qty"), - "unit": m.group("unit"), - "notes": None, - } - ) - else: - items.append( - { - "raw": raw, - "name": re.sub(r"\d+", "", raw).strip(), - "quantity": None, - "unit": None, - "notes": None, - } - ) - return json.dumps({"ingredients": items}) +from __future__ import annotations + +import json +import re +from typing import Dict, List + +# Pre-compile regex pattern for performance +_RE_QUANTITY = re.compile( + r"^(?P(\d+\/\d+|\d+(\.\d+)?))\s*(?P[a-zA-Z]+)?\s*(?P.*)" +) + +SAMPLE_RECIPES = [ + { + "title": "Simple Tomato Pasta", + "ingredients": [ + "200g spaghetti", + "2 tbsp olive oil", + "3 cloves garlic", + "400g canned tomatoes", + "salt", + "pepper", + "fresh basil", + ], + "instructions": "Cook pasta; sauté garlic; add tomatoes; simmer; toss with pasta and basil.", + "tags": ["vegetarian", "weeknight"], + "est_time_minutes": 25, + }, + { + "title": "Vegan Chickpea Curry", + "ingredients": [ + "1 tbsp coconut oil", + "1 onion", + "2 cloves garlic", + "1 tbsp curry powder", + "400g chickpeas", + "400ml coconut milk", + "spinach handful", + "salt", + ], + "instructions": "Sauté aromatics; add spices; add chickpeas & coconut milk; simmer; fold in spinach.", + "tags": ["vegan", "gluten-free"], + "est_time_minutes": 30, + }, + { + "title": "Garlic Butter Salmon", + "ingredients": [ + "2 salmon fillets", + "2 tbsp butter", + "2 cloves garlic", + "lemon juice", + "parsley", + "salt", + "pepper", + ], + "instructions": "Pan sear salmon; melt butter with garlic; finish with lemon and parsley.", + "tags": ["high-protein"], + "est_time_minutes": 18, + }, +] + + +class LocalProvider: + """Offline deterministic provider. + + Interprets task markers in the last user message: + - TASK:RECIPE_SEARCH + - TASK:INGREDIENT_EXTRACTION + Returns JSON strings for predictable testing. + """ + + def complete( + self, messages: List[Dict[str, str]], json_mode: bool = False + ) -> str: # noqa: D401 + last_user = next( + (m["content"] for m in reversed(messages) if m.get("role") == "user"), + "", + ) + if "TASK:RECIPE_SEARCH" in last_user: + return self._handle_search(last_user) + if "TASK:INGREDIENT_EXTRACTION" in last_user: + return self._handle_extract(last_user) + return json.dumps({"recipes": []}) + + def _handle_search(self, prompt: str) -> str: + # Dynamic scoring based on query tokens and optional tag filters. + # Prompt format lines: Query: , Filters: comma,separated, Limit: N + q_match = re.search(r"Query:\s*(.*)", prompt) + query = (q_match.group(1).strip() if q_match else "").lower() + f_match = re.search(r"Filters:\s*(.*)", prompt) + filters_raw = f_match.group(1).strip() if f_match else "" + filters = [f.strip().lower() for f in filters_raw.split(",") if f.strip()] + l_match = re.search(r"Limit:\s*(\d+)", prompt) + limit = int(l_match.group(1)) if l_match else 5 + + tokens = [t for t in re.split(r"[^a-zA-Z]+", query) if t] + scored = [] + for r in SAMPLE_RECIPES: + # Filter by tags (optimized: pre-compute tag set for O(1) membership check) + if filters: + recipe_tags = {tag.lower() for tag in r["tags"]} + if not all(any(f in tag for tag in recipe_tags) for f in filters): + continue + title = r["title"].lower() + ingredients_blob = " ".join(r["ingredients"]).lower() + score = 0 + if tokens: + for tok in tokens: + if tok in title: + score += 3 # title match weight + if tok in ingredients_blob: + score += 1 # ingredient mention weight + else: + # If no tokens provided, give small baseline score + score = 1 + scored.append((score, r)) + + # Sort by score desc then by title for stability + scored.sort(key=lambda x: (-x[0], x[1]["title"])) + # If no results after filtering, fall back to unfiltered list with baseline scoring + if not scored: + fallback = [] + for r in SAMPLE_RECIPES: + title = r["title"].lower() + ingredients_blob = " ".join(r["ingredients"]).lower() + score = 0 + for tok in tokens: + if tok in title: + score += 3 + if tok in ingredients_blob: + score += 1 + fallback.append((score, r)) + fallback.sort(key=lambda x: (-x[0], x[1]["title"])) + scored = fallback or [(1, r) for r in SAMPLE_RECIPES] + + results = [r for _, r in scored[:limit]] + return json.dumps({"recipes": results}) + + def _handle_extract(self, prompt: str) -> str: + # Pull raw text after marker (fallback to whole prompt if not found) + text_match = re.search(r"Text:\s*(.*)", prompt, re.DOTALL) + text = text_match.group(1).strip() if text_match else prompt + lines = [l.strip() for l in re.split(r"[\n,;]", text) if l.strip()] + items = [] + for raw in lines: + m = _RE_QUANTITY.match(raw) + if m: + items.append( + { + "raw": raw, + "name": m.group("name").strip(), + "quantity": m.group("qty"), + "unit": m.group("unit"), + "notes": None, + } + ) + else: + items.append( + { + "raw": raw, + "name": re.sub(r"\d+", "", raw).strip(), + "quantity": None, + "unit": None, + "notes": None, + } + ) + return json.dumps({"ingredients": items}) diff --git a/ai-projects/cooking-ai/src/utils/json_utils.py b/ai-projects/cooking-ai/src/utils/json_utils.py index 977ef1db8..870c75b92 100644 --- a/ai-projects/cooking-ai/src/utils/json_utils.py +++ b/ai-projects/cooking-ai/src/utils/json_utils.py @@ -1,87 +1,87 @@ -"""JSON parsing, validation, and light repair utilities for Cooking AI.""" - -from __future__ import annotations - -import json -import re -from typing import Optional, Tuple - -from jsonschema import ValidationError, validate - -# Basic schemas -RECIPE_SEARCH_SCHEMA = { - "type": "object", - "properties": { - "recipes": { - "type": "array", - "items": { - "type": "object", - "properties": { - "title": {"type": "string"}, - "ingredients": {"type": "array", "items": {"type": "string"}}, - "instructions": {"type": "string"}, - "tags": {"type": "array", "items": {"type": "string"}}, - "est_time_minutes": {"type": ["number", "null"]}, - }, - "required": ["title", "ingredients"], - }, - } - }, - "required": ["recipes"], -} - -INGREDIENT_EXTRACTION_SCHEMA = { - "type": "object", - "properties": { - "ingredients": { - "type": "array", - "items": { - "type": "object", - "properties": { - "raw": {"type": "string"}, - "name": {"type": "string"}, - "quantity": {"type": ["string", "number", "null"]}, - "unit": {"type": ["string", "null"]}, - "notes": {"type": ["string", "null"]}, - }, - "required": ["raw", "name"], - }, - } - }, - "required": ["ingredients"], -} - -# Simple JSON repair heuristics: extract first JSON object or array block -JSON_BLOCK_RE = re.compile(r"({[\s\S]*}|\[[\s\S]*])") - - -def _coerce_json(text: str) -> str: - # Trim leading non-json characters - text = text.strip() - # If already valid, return - if text.startswith("{") or text.startswith("["): - return text - m = JSON_BLOCK_RE.search(text) - if m: - return m.group(0) - return text - - -def parse_and_validate(raw: str, schema: dict) -> Tuple[Optional[dict], Optional[str]]: - candidate = _coerce_json(raw) - try: - data = json.loads(candidate) - except json.JSONDecodeError as e: - return None, f"JSON decode error: {e}" - try: - validate(data, schema) - except ValidationError as ve: - return None, f"Schema validation error: {ve.message}" - return data, None - - -__all__ = [ - "RECIPE_SEARCH_SCHEMA", - "INGREDIENT_EXTRACTION_SCHEMA", - "parse_and_validate", -] +"""JSON parsing, validation, and light repair utilities for Cooking AI.""" + +from __future__ import annotations + +import json +import re +from typing import Optional, Tuple + +from jsonschema import ValidationError, validate + +# Basic schemas +RECIPE_SEARCH_SCHEMA = { + "type": "object", + "properties": { + "recipes": { + "type": "array", + "items": { + "type": "object", + "properties": { + "title": {"type": "string"}, + "ingredients": {"type": "array", "items": {"type": "string"}}, + "instructions": {"type": "string"}, + "tags": {"type": "array", "items": {"type": "string"}}, + "est_time_minutes": {"type": ["number", "null"]}, + }, + "required": ["title", "ingredients"], + }, + } + }, + "required": ["recipes"], +} + +INGREDIENT_EXTRACTION_SCHEMA = { + "type": "object", + "properties": { + "ingredients": { + "type": "array", + "items": { + "type": "object", + "properties": { + "raw": {"type": "string"}, + "name": {"type": "string"}, + "quantity": {"type": ["string", "number", "null"]}, + "unit": {"type": ["string", "null"]}, + "notes": {"type": ["string", "null"]}, + }, + "required": ["raw", "name"], + }, + } + }, + "required": ["ingredients"], +} + +# Simple JSON repair heuristics: extract first JSON object or array block +JSON_BLOCK_RE = re.compile(r"({[\s\S]*}|\[[\s\S]*])") + + +def _coerce_json(text: str) -> str: + # Trim leading non-json characters + text = text.strip() + # If already valid, return + if text.startswith("{") or text.startswith("["): + return text + m = JSON_BLOCK_RE.search(text) + if m: + return m.group(0) + return text + + +def parse_and_validate(raw: str, schema: dict) -> Tuple[Optional[dict], Optional[str]]: + candidate = _coerce_json(raw) + try: + data = json.loads(candidate) + except json.JSONDecodeError as e: + return None, f"JSON decode error: {e}" + try: + validate(data, schema) + except ValidationError as ve: + return None, f"Schema validation error: {ve.message}" + return data, None + + +__all__ = [ + "RECIPE_SEARCH_SCHEMA", + "INGREDIENT_EXTRACTION_SCHEMA", + "parse_and_validate", +] diff --git a/ai-projects/cooking-ai/tests/_debug_local.py b/ai-projects/cooking-ai/tests/_debug_local.py index 0a7f97e7b..91cba1567 100644 --- a/ai-projects/cooking-ai/tests/_debug_local.py +++ b/ai-projects/cooking-ai/tests/_debug_local.py @@ -1,10 +1,10 @@ -from providers.local import LocalProvider - -p = LocalProvider() -prompt = """ -TASK:RECIPE_SEARCH -Query: vegan pasta -Filters: -Limit: 5 -""" -print(p._handle_search(prompt)) +from providers.local import LocalProvider + +p = LocalProvider() +prompt = """ +TASK:RECIPE_SEARCH +Query: vegan pasta +Filters: +Limit: 5 +""" +print(p._handle_search(prompt)) diff --git a/ai-projects/cooking-ai/tests/debug_agent_extract.py b/ai-projects/cooking-ai/tests/debug_agent_extract.py index 4d4aa1098..3e1ca7cb3 100644 --- a/ai-projects/cooking-ai/tests/debug_agent_extract.py +++ b/ai-projects/cooking-ai/tests/debug_agent_extract.py @@ -1,9 +1,9 @@ -import sys -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) -from agents.recipe_agent import RecipeAgent -from providers.local import LocalProvider - -a = RecipeAgent(LocalProvider()) -print(a.extract_ingredients("2 cups flour, 1 tsp salt\n3 eggs")) +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) +from agents.recipe_agent import RecipeAgent +from providers.local import LocalProvider + +a = RecipeAgent(LocalProvider()) +print(a.extract_ingredients("2 cups flour, 1 tsp salt\n3 eggs")) diff --git a/ai-projects/cooking-ai/tests/debug_extract_local.py b/ai-projects/cooking-ai/tests/debug_extract_local.py index 1eaa02828..0d14692b6 100644 --- a/ai-projects/cooking-ai/tests/debug_extract_local.py +++ b/ai-projects/cooking-ai/tests/debug_extract_local.py @@ -1,12 +1,12 @@ -import sys -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) -from providers.local import LocalProvider - -p = LocalProvider() -prompt = """ -TASK:INGREDIENT_EXTRACTION -Text: 2 cups flour, 1 tsp salt\n3 eggs -""" -print(p._handle_extract(prompt)) +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) +from providers.local import LocalProvider + +p = LocalProvider() +prompt = """ +TASK:INGREDIENT_EXTRACTION +Text: 2 cups flour, 1 tsp salt\n3 eggs +""" +print(p._handle_extract(prompt)) diff --git a/ai-projects/cooking-ai/tests/debug_run_local.py b/ai-projects/cooking-ai/tests/debug_run_local.py index ec59ed083..ae2de0583 100644 --- a/ai-projects/cooking-ai/tests/debug_run_local.py +++ b/ai-projects/cooking-ai/tests/debug_run_local.py @@ -1,14 +1,14 @@ -import sys -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) -from providers.local import LocalProvider - -p = LocalProvider() -prompt = """ -TASK:RECIPE_SEARCH -Query: vegan pasta -Filters: -Limit: 5 -""" -print(p._handle_search(prompt)) +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) +from providers.local import LocalProvider + +p = LocalProvider() +prompt = """ +TASK:RECIPE_SEARCH +Query: vegan pasta +Filters: +Limit: 5 +""" +print(p._handle_search(prompt)) diff --git a/ai-projects/cooking-ai/tests/run_tests.py b/ai-projects/cooking-ai/tests/run_tests.py index 1f9ca6c4f..6ba8c86fe 100644 --- a/ai-projects/cooking-ai/tests/run_tests.py +++ b/ai-projects/cooking-ai/tests/run_tests.py @@ -1,30 +1,30 @@ -from __future__ import annotations - -import importlib -import sys -from pathlib import Path - -TEST_MODULES = [ - "test_agent", - "test_schemas", -] - -ROOT = Path(__file__).resolve().parents[1] -SRC = ROOT / "src" -if str(SRC) not in sys.path: - sys.path.insert(0, str(SRC)) - -failures = 0 -for mod_name in TEST_MODULES: - try: - mod = importlib.import_module(mod_name) - if hasattr(mod, "__all__"): - pass - except Exception as e: # pragma: no cover - print(f"[FAIL] import {mod_name}: {e}") - failures += 1 - -if failures: - print(f"{failures} test modules failed to import") - raise SystemExit(1) -print("All test modules imported OK. (Run each to execute assertions)") +from __future__ import annotations + +import importlib +import sys +from pathlib import Path + +TEST_MODULES = [ + "test_agent", + "test_schemas", +] + +ROOT = Path(__file__).resolve().parents[1] +SRC = ROOT / "src" +if str(SRC) not in sys.path: + sys.path.insert(0, str(SRC)) + +failures = 0 +for mod_name in TEST_MODULES: + try: + mod = importlib.import_module(mod_name) + if hasattr(mod, "__all__"): + pass + except Exception as e: # pragma: no cover + print(f"[FAIL] import {mod_name}: {e}") + failures += 1 + +if failures: + print(f"{failures} test modules failed to import") + raise SystemExit(1) +print("All test modules imported OK. (Run each to execute assertions)") diff --git a/ai-projects/cooking-ai/tests/test_agent.py b/ai-projects/cooking-ai/tests/test_agent.py index 2e66af8dd..9431c1d04 100644 --- a/ai-projects/cooking-ai/tests/test_agent.py +++ b/ai-projects/cooking-ai/tests/test_agent.py @@ -1,78 +1,78 @@ -from __future__ import annotations - -import json -import sys -from pathlib import Path - -import pytest - -# Ensure src is importable when running from repo root -ROOT = Path(__file__).resolve().parents[1] -SRC = ROOT / "src" -if str(SRC) not in sys.path: - sys.path.insert(0, str(SRC)) - -from agents.recipe_agent import RecipeAgent # type: ignore -from providers.local import LocalProvider # type: ignore -from utils.json_utils import INGREDIENT_EXTRACTION_SCHEMA # type: ignore -from utils.json_utils import RECIPE_SEARCH_SCHEMA, parse_and_validate - - -@pytest.mark.unit -def test_search_recipes_returns_valid_structure(): - """Test that search_recipes returns a valid recipes list.""" - agent = RecipeAgent(LocalProvider()) - data = agent.search_recipes("pasta", limit=2) - - assert "recipes" in data, "Response should contain 'recipes' key" - assert isinstance(data["recipes"], list), "Recipes should be a list" - - -@pytest.mark.unit -def test_search_recipes_schema_validation(): - """Test that search results conform to the expected JSON schema.""" - agent = RecipeAgent(LocalProvider()) - data = agent.search_recipes("pasta", limit=2) - - parsed, err = parse_and_validate(json.dumps(data), RECIPE_SEARCH_SCHEMA) - assert err is None, f"Schema validation failed: {err}" - assert parsed is not None - - -@pytest.mark.unit -def test_extract_ingredients_returns_valid_structure(): - """Test that extract_ingredients returns a valid ingredients list.""" - agent = RecipeAgent(LocalProvider()) - text = "2 cups flour, 1 tsp salt\n3 eggs" - data = agent.extract_ingredients(text) - - assert "ingredients" in data, "Response should contain 'ingredients' key" - assert isinstance(data["ingredients"], list), "Ingredients should be a list" - - -@pytest.mark.unit -def test_extract_ingredients_schema_validation(): - """Test that extracted ingredients conform to the expected JSON schema.""" - agent = RecipeAgent(LocalProvider()) - text = "2 cups flour, 1 tsp salt\n3 eggs" - data = agent.extract_ingredients(text) - - parsed, err = parse_and_validate(json.dumps(data), INGREDIENT_EXTRACTION_SCHEMA) - assert err is None, f"Schema validation failed: {err}" - assert parsed is not None - - -@pytest.mark.unit -def test_local_provider_offline_capability(): - """Test that LocalProvider works without external dependencies.""" - # This test ensures offline functionality - agent = RecipeAgent(LocalProvider()) - - # Should not raise any import or connection errors - data = agent.search_recipes("test query", limit=1) - assert data is not None - - -if __name__ == "__main__": - # Allow manual execution for backward compatibility - pytest.main([__file__, "-v"]) +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import pytest + +# Ensure src is importable when running from repo root +ROOT = Path(__file__).resolve().parents[1] +SRC = ROOT / "src" +if str(SRC) not in sys.path: + sys.path.insert(0, str(SRC)) + +from agents.recipe_agent import RecipeAgent # type: ignore +from providers.local import LocalProvider # type: ignore +from utils.json_utils import INGREDIENT_EXTRACTION_SCHEMA # type: ignore +from utils.json_utils import RECIPE_SEARCH_SCHEMA, parse_and_validate + + +@pytest.mark.unit +def test_search_recipes_returns_valid_structure(): + """Test that search_recipes returns a valid recipes list.""" + agent = RecipeAgent(LocalProvider()) + data = agent.search_recipes("pasta", limit=2) + + assert "recipes" in data, "Response should contain 'recipes' key" + assert isinstance(data["recipes"], list), "Recipes should be a list" + + +@pytest.mark.unit +def test_search_recipes_schema_validation(): + """Test that search results conform to the expected JSON schema.""" + agent = RecipeAgent(LocalProvider()) + data = agent.search_recipes("pasta", limit=2) + + parsed, err = parse_and_validate(json.dumps(data), RECIPE_SEARCH_SCHEMA) + assert err is None, f"Schema validation failed: {err}" + assert parsed is not None + + +@pytest.mark.unit +def test_extract_ingredients_returns_valid_structure(): + """Test that extract_ingredients returns a valid ingredients list.""" + agent = RecipeAgent(LocalProvider()) + text = "2 cups flour, 1 tsp salt\n3 eggs" + data = agent.extract_ingredients(text) + + assert "ingredients" in data, "Response should contain 'ingredients' key" + assert isinstance(data["ingredients"], list), "Ingredients should be a list" + + +@pytest.mark.unit +def test_extract_ingredients_schema_validation(): + """Test that extracted ingredients conform to the expected JSON schema.""" + agent = RecipeAgent(LocalProvider()) + text = "2 cups flour, 1 tsp salt\n3 eggs" + data = agent.extract_ingredients(text) + + parsed, err = parse_and_validate(json.dumps(data), INGREDIENT_EXTRACTION_SCHEMA) + assert err is None, f"Schema validation failed: {err}" + assert parsed is not None + + +@pytest.mark.unit +def test_local_provider_offline_capability(): + """Test that LocalProvider works without external dependencies.""" + # This test ensures offline functionality + agent = RecipeAgent(LocalProvider()) + + # Should not raise any import or connection errors + data = agent.search_recipes("test query", limit=1) + assert data is not None + + +if __name__ == "__main__": + # Allow manual execution for backward compatibility + pytest.main([__file__, "-v"]) diff --git a/ai-projects/cooking-ai/tests/test_schemas.py b/ai-projects/cooking-ai/tests/test_schemas.py index d01b3a02c..5dd8272cc 100644 --- a/ai-projects/cooking-ai/tests/test_schemas.py +++ b/ai-projects/cooking-ai/tests/test_schemas.py @@ -1,74 +1,74 @@ -from __future__ import annotations - -import json -import sys -from pathlib import Path - -import pytest - -ROOT = Path(__file__).resolve().parents[1] -SRC = ROOT / "src" -if str(SRC) not in sys.path: - sys.path.insert(0, str(SRC)) - -from utils.json_utils import (INGREDIENT_EXTRACTION_SCHEMA, - RECIPE_SEARCH_SCHEMA, parse_and_validate) - - -@pytest.mark.unit -def test_parse_and_validate_search_with_extra_text(): - """Test JSON extraction from text with surrounding prose.""" - # Extra prose around JSON; ensure repair extracts first JSON block - raw = """ - Here are some ideas: - {"recipes": [{"title": "A", "ingredients": ["x"], "instructions": "...", "tags": ["t"], "est_time_minutes": null}]} - Thanks! - """.strip() - data, err = parse_and_validate(raw, RECIPE_SEARCH_SCHEMA) - assert err is None, f"Validation error: {err}" - assert isinstance(data, dict), "Parsed data should be a dict" - assert "recipes" in data, "Should contain 'recipes' key" - - -@pytest.mark.unit -def test_parse_and_validate_extract_schema(): - """Test ingredient extraction JSON schema validation.""" - raw = json.dumps( - { - "ingredients": [ - { - "raw": "2 cups flour", - "name": "flour", - "quantity": "2", - "unit": "cups", - "notes": None, - } - ] - } - ) - data, err = parse_and_validate(raw, INGREDIENT_EXTRACTION_SCHEMA) - assert err is None, f"Validation error: {err}" - assert isinstance(data, dict), "Parsed data should be a dict" - assert "ingredients" in data, "Should contain 'ingredients' key" - - -@pytest.mark.unit -def test_parse_and_validate_invalid_json(): - """Test that invalid JSON is gracefully handled.""" - raw = '{"recipes": [invalid json}' - data, err = parse_and_validate(raw, RECIPE_SEARCH_SCHEMA) - assert err is not None, "Should return an error for invalid JSON" - assert data is None, "Data should be None on error" - - -@pytest.mark.unit -def test_parse_and_validate_schema_mismatch(): - """Test that schema mismatches are detected.""" - # Missing required 'recipes' key - raw = json.dumps({"wrong_key": []}) - data, err = parse_and_validate(raw, RECIPE_SEARCH_SCHEMA) - assert err is not None, "Should return an error for schema mismatch" - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import pytest + +ROOT = Path(__file__).resolve().parents[1] +SRC = ROOT / "src" +if str(SRC) not in sys.path: + sys.path.insert(0, str(SRC)) + +from utils.json_utils import (INGREDIENT_EXTRACTION_SCHEMA, + RECIPE_SEARCH_SCHEMA, parse_and_validate) + + +@pytest.mark.unit +def test_parse_and_validate_search_with_extra_text(): + """Test JSON extraction from text with surrounding prose.""" + # Extra prose around JSON; ensure repair extracts first JSON block + raw = """ + Here are some ideas: + {"recipes": [{"title": "A", "ingredients": ["x"], "instructions": "...", "tags": ["t"], "est_time_minutes": null}]} + Thanks! + """.strip() + data, err = parse_and_validate(raw, RECIPE_SEARCH_SCHEMA) + assert err is None, f"Validation error: {err}" + assert isinstance(data, dict), "Parsed data should be a dict" + assert "recipes" in data, "Should contain 'recipes' key" + + +@pytest.mark.unit +def test_parse_and_validate_extract_schema(): + """Test ingredient extraction JSON schema validation.""" + raw = json.dumps( + { + "ingredients": [ + { + "raw": "2 cups flour", + "name": "flour", + "quantity": "2", + "unit": "cups", + "notes": None, + } + ] + } + ) + data, err = parse_and_validate(raw, INGREDIENT_EXTRACTION_SCHEMA) + assert err is None, f"Validation error: {err}" + assert isinstance(data, dict), "Parsed data should be a dict" + assert "ingredients" in data, "Should contain 'ingredients' key" + + +@pytest.mark.unit +def test_parse_and_validate_invalid_json(): + """Test that invalid JSON is gracefully handled.""" + raw = '{"recipes": [invalid json}' + data, err = parse_and_validate(raw, RECIPE_SEARCH_SCHEMA) + assert err is not None, "Should return an error for invalid JSON" + assert data is None, "Data should be None on error" + + +@pytest.mark.unit +def test_parse_and_validate_schema_mismatch(): + """Test that schema mismatches are detected.""" + # Missing required 'recipes' key + raw = json.dumps({"wrong_key": []}) + data, err = parse_and_validate(raw, RECIPE_SEARCH_SCHEMA) + assert err is not None, "Should return an error for schema mismatch" + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/ai-projects/llm-maker/IMPLEMENTATION_SUMMARY.md b/ai-projects/llm-maker/IMPLEMENTATION_SUMMARY.md index ab144cc13..6c4334ae9 100644 --- a/ai-projects/llm-maker/IMPLEMENTATION_SUMMARY.md +++ b/ai-projects/llm-maker/IMPLEMENTATION_SUMMARY.md @@ -1,254 +1,254 @@ -# LLM Maker - Implementation Summary - -## 🎯 Mission Accomplished - -Successfully implemented a complete **LLM Tool Maker** system that enables Large Language Models to autonomously create, validate, and execute Python tools in a secure sandboxed environment. - -## 📊 Implementation Stats - -- **Total Files Created**: 23 files -- **Production Code**: ~1,900 lines -- **Test Code**: ~450 lines -- **Documentation**: ~1,200 lines -- **Examples**: ~250 lines -- **Time to Implement**: Completed in one session -- **Test Success Rate**: 100% (all tests passing ✅) - -## 🏗️ Architecture - -### Core Components - -1. **Tool Maker** (`tool_maker.py` - 311 lines) - - Integrates with existing AI providers (Azure OpenAI, OpenAI, local) - - Generates Python functions from natural language - - Automatic validation and retry with feedback - - Multi-attempt generation with learning from errors - -2. **Tool Validator** (`tool_validator.py` - 287 lines) - - Multi-layer security validation - - Static code analysis - - Import whitelist/blacklist - - Pattern-based dangerous operation detection - - Function signature validation - -3. **Tool Executor** (`tool_executor.py` - 255 lines) - - Sandboxed execution environment - - Cross-platform timeout support (Unix + Windows) - - Memory and output size limits - - RestrictedPython integration (optional) - - Graceful error handling - -4. **Tool Registry** (`tool_registry.py` - 237 lines) - - JSON-based index - - File-based tool storage - - Execution statistics - - Search and filtering - - Persistent storage - -5. **MCP Server** (`llm_maker_mcp_server.py` - 353 lines) - - Model Context Protocol integration - - 7 tools for external access - - Async/await support - - JSON-based communication - -## 🔒 Security Model - -### Five Layers of Protection - -1. **Static Analysis** - Code parsed and analyzed before execution -2. **Import Control** - Whitelist of safe modules (math, json, re, datetime, etc.) -3. **Pattern Matching** - Regex detection of dangerous operations -4. **Sandboxed Execution** - Restricted Python environment with safe built-ins only -5. **Resource Limits** - Timeout (5s), memory (512MB), output size (10KB) - -### Blocked Operations - -- ❌ Dangerous imports: os, sys, subprocess, socket, urllib, requests -- ❌ File operations: open, read, write, delete -- ❌ Network access: HTTP requests, socket connections -- ❌ Code execution: eval, exec, compile, __import__ -- ❌ System access: breakpoint, input, exit - -### Allowed Operations - -- ✅ Safe built-ins: len, max, min, sum, range, etc. -- ✅ Safe modules: math, json, re, datetime, collections, itertools, functools, typing -- ✅ Pure computation and data processing -- ✅ Type hints and docstrings - -## 🧪 Testing - -### Test Coverage - -- **Validator Tests**: 10 test cases -- **Executor Tests**: 6 test cases -- **Registry Tests**: 8 test cases -- **Total Test Cases**: 24 -- **Success Rate**: 100% ✅ - -### Test Categories - -1. **Security Tests** - - Dangerous import detection - - File operation blocking - - Network operation blocking - - Code execution prevention - -2. **Functionality Tests** - - Tool creation and registration - - Tool execution with various inputs - - Error handling and recovery - - Search and filtering - -3. **Integration Tests** - - End-to-end workflows - - Provider integration - - Storage persistence - -## 📚 Documentation - -### User Documentation - -1. **README.md** (1,091 lines) - - Complete feature overview - - Installation instructions - - API reference - - Security model - - Troubleshooting guide - -2. **QUICKSTART.md** (200+ lines) - - Step-by-step tutorial - - Code examples - - Common use cases - - Configuration guide - -3. **Code Comments** (Extensive) - - Docstrings for all classes/functions - - Inline comments for complex logic - - Type hints throughout - -### Developer Documentation - -- Architecture explanations in code -- Security rationale documented -- Testing strategies explained -- Extension points identified - -## 🎁 Examples Provided - -1. **fibonacci.py** - Mathematical tool -2. **text_processor.py** - String manipulation -3. **quick_start.py** - Complete workflow demo - -## 🚀 Integration Points - -### With Existing Infrastructure - -- ✅ Reuses `shared/chat_providers.py` for AI provider detection -- ✅ Follows existing code patterns and conventions -- ✅ Compatible with Azure Functions architecture -- ✅ Integrated into main README -- ✅ Added to project structure documentation - -### MCP Tools Available - -1. `create_tool` - Generate tools from descriptions -2. `execute_tool` - Run registered tools -3. `list_registered_tools` - Browse available tools -4. `get_tool` - Get tool details -5. `delete_tool` - Remove tools -6. `validate_tool` - Check safety -7. `registry_stats` - Usage statistics - -## 💡 Key Innovations - -1. **Adaptive Generation** - Learns from validation errors and retries -2. **Cross-Platform Timeout** - Works on both Unix and Windows -3. **Optional RestrictedPython** - Graceful fallback when not available -4. **Pattern Specificity** - Reduced false positives in security checks -5. **Persistent Registry** - Tools survive across sessions - -## 🎯 Use Cases - -1. **Dynamic Tool Creation** - Generate utilities on-demand -2. **Code Assistant** - Help LLMs create reusable functions -3. **Workflow Automation** - Build custom processing pipelines -4. **Educational** - Teach function creation to students -5. **Research** - Explore LLM code generation capabilities - -## 📈 Performance - -- **Generation Time**: 5-30 seconds per tool (depends on LLM) -- **Validation Time**: <100ms per tool -- **Execution Time**: Limited to 5 seconds (configurable) -- **Storage Overhead**: ~2-5KB per tool - -## 🛠️ Configuration - -All configurable via `llm_maker_config.yaml`: - -- AI provider selection -- Temperature and max tokens -- Allowed imports list -- Timeout and memory limits -- Strict mode toggle -- Logging preferences - -## ✅ Quality Assurance - -### Code Quality - -- ✅ Type hints throughout -- ✅ Comprehensive docstrings -- ✅ Error handling at all levels -- ✅ Logging for debugging -- ✅ Clean code structure - -### Security Review - -- ✅ No hardcoded credentials -- ✅ No unsafe operations -- ✅ Input validation -- ✅ Output sanitization -- ✅ Resource limits enforced - -## 🔮 Future Enhancements - -### Potential Additions - -1. **Azure Functions Endpoints** - HTTP API for web access -2. **Tool Versioning** - Track and rollback changes -3. **Dependency Management** - Handle tool dependencies -4. **Cost Tracking** - Monitor AI provider usage -5. **Web UI** - Visual tool management interface -6. **Collaboration** - Share tools between users -7. **Categories** - Organize tools by type -8. **Analytics** - Usage trends and insights - -### Stretch Goals - -- Tool marketplace -- Community contributions -- Performance optimizations -- Extended language support -- Enhanced IDE integration - -## 🎉 Conclusion - -LLM Maker is a **production-ready**, **fully tested**, **well-documented** system that safely enables autonomous tool creation. It demonstrates: - -- **Security-first design** with multiple protection layers -- **Robust error handling** with graceful degradation -- **Cross-platform compatibility** (Unix and Windows) -- **Extensible architecture** for future enhancements -- **Complete documentation** for users and developers - -The implementation is **complete**, **tested**, and **ready for use**! 🚀 - ---- - -**Status**: ✅ Complete -**Quality**: ✅ Production-ready -**Security**: ✅ Enterprise-grade -**Documentation**: ✅ Comprehensive -**Tests**: ✅ All passing +# LLM Maker - Implementation Summary + +## 🎯 Mission Accomplished + +Successfully implemented a complete **LLM Tool Maker** system that enables Large Language Models to autonomously create, validate, and execute Python tools in a secure sandboxed environment. + +## 📊 Implementation Stats + +- **Total Files Created**: 23 files +- **Production Code**: ~1,900 lines +- **Test Code**: ~450 lines +- **Documentation**: ~1,200 lines +- **Examples**: ~250 lines +- **Time to Implement**: Completed in one session +- **Test Success Rate**: 100% (all tests passing ✅) + +## 🏗️ Architecture + +### Core Components + +1. **Tool Maker** (`tool_maker.py` - 311 lines) + - Integrates with existing AI providers (Azure OpenAI, OpenAI, local) + - Generates Python functions from natural language + - Automatic validation and retry with feedback + - Multi-attempt generation with learning from errors + +2. **Tool Validator** (`tool_validator.py` - 287 lines) + - Multi-layer security validation + - Static code analysis + - Import whitelist/blacklist + - Pattern-based dangerous operation detection + - Function signature validation + +3. **Tool Executor** (`tool_executor.py` - 255 lines) + - Sandboxed execution environment + - Cross-platform timeout support (Unix + Windows) + - Memory and output size limits + - RestrictedPython integration (optional) + - Graceful error handling + +4. **Tool Registry** (`tool_registry.py` - 237 lines) + - JSON-based index + - File-based tool storage + - Execution statistics + - Search and filtering + - Persistent storage + +5. **MCP Server** (`llm_maker_mcp_server.py` - 353 lines) + - Model Context Protocol integration + - 7 tools for external access + - Async/await support + - JSON-based communication + +## 🔒 Security Model + +### Five Layers of Protection + +1. **Static Analysis** - Code parsed and analyzed before execution +2. **Import Control** - Whitelist of safe modules (math, json, re, datetime, etc.) +3. **Pattern Matching** - Regex detection of dangerous operations +4. **Sandboxed Execution** - Restricted Python environment with safe built-ins only +5. **Resource Limits** - Timeout (5s), memory (512MB), output size (10KB) + +### Blocked Operations + +- ❌ Dangerous imports: os, sys, subprocess, socket, urllib, requests +- ❌ File operations: open, read, write, delete +- ❌ Network access: HTTP requests, socket connections +- ❌ Code execution: eval, exec, compile, __import__ +- ❌ System access: breakpoint, input, exit + +### Allowed Operations + +- ✅ Safe built-ins: len, max, min, sum, range, etc. +- ✅ Safe modules: math, json, re, datetime, collections, itertools, functools, typing +- ✅ Pure computation and data processing +- ✅ Type hints and docstrings + +## 🧪 Testing + +### Test Coverage + +- **Validator Tests**: 10 test cases +- **Executor Tests**: 6 test cases +- **Registry Tests**: 8 test cases +- **Total Test Cases**: 24 +- **Success Rate**: 100% ✅ + +### Test Categories + +1. **Security Tests** + - Dangerous import detection + - File operation blocking + - Network operation blocking + - Code execution prevention + +2. **Functionality Tests** + - Tool creation and registration + - Tool execution with various inputs + - Error handling and recovery + - Search and filtering + +3. **Integration Tests** + - End-to-end workflows + - Provider integration + - Storage persistence + +## 📚 Documentation + +### User Documentation + +1. **README.md** (1,091 lines) + - Complete feature overview + - Installation instructions + - API reference + - Security model + - Troubleshooting guide + +2. **QUICKSTART.md** (200+ lines) + - Step-by-step tutorial + - Code examples + - Common use cases + - Configuration guide + +3. **Code Comments** (Extensive) + - Docstrings for all classes/functions + - Inline comments for complex logic + - Type hints throughout + +### Developer Documentation + +- Architecture explanations in code +- Security rationale documented +- Testing strategies explained +- Extension points identified + +## 🎁 Examples Provided + +1. **fibonacci.py** - Mathematical tool +2. **text_processor.py** - String manipulation +3. **quick_start.py** - Complete workflow demo + +## 🚀 Integration Points + +### With Existing Infrastructure + +- ✅ Reuses `shared/chat_providers.py` for AI provider detection +- ✅ Follows existing code patterns and conventions +- ✅ Compatible with Azure Functions architecture +- ✅ Integrated into main README +- ✅ Added to project structure documentation + +### MCP Tools Available + +1. `create_tool` - Generate tools from descriptions +2. `execute_tool` - Run registered tools +3. `list_registered_tools` - Browse available tools +4. `get_tool` - Get tool details +5. `delete_tool` - Remove tools +6. `validate_tool` - Check safety +7. `registry_stats` - Usage statistics + +## 💡 Key Innovations + +1. **Adaptive Generation** - Learns from validation errors and retries +2. **Cross-Platform Timeout** - Works on both Unix and Windows +3. **Optional RestrictedPython** - Graceful fallback when not available +4. **Pattern Specificity** - Reduced false positives in security checks +5. **Persistent Registry** - Tools survive across sessions + +## 🎯 Use Cases + +1. **Dynamic Tool Creation** - Generate utilities on-demand +2. **Code Assistant** - Help LLMs create reusable functions +3. **Workflow Automation** - Build custom processing pipelines +4. **Educational** - Teach function creation to students +5. **Research** - Explore LLM code generation capabilities + +## 📈 Performance + +- **Generation Time**: 5-30 seconds per tool (depends on LLM) +- **Validation Time**: <100ms per tool +- **Execution Time**: Limited to 5 seconds (configurable) +- **Storage Overhead**: ~2-5KB per tool + +## 🛠️ Configuration + +All configurable via `llm_maker_config.yaml`: + +- AI provider selection +- Temperature and max tokens +- Allowed imports list +- Timeout and memory limits +- Strict mode toggle +- Logging preferences + +## ✅ Quality Assurance + +### Code Quality + +- ✅ Type hints throughout +- ✅ Comprehensive docstrings +- ✅ Error handling at all levels +- ✅ Logging for debugging +- ✅ Clean code structure + +### Security Review + +- ✅ No hardcoded credentials +- ✅ No unsafe operations +- ✅ Input validation +- ✅ Output sanitization +- ✅ Resource limits enforced + +## 🔮 Future Enhancements + +### Potential Additions + +1. **Azure Functions Endpoints** - HTTP API for web access +2. **Tool Versioning** - Track and rollback changes +3. **Dependency Management** - Handle tool dependencies +4. **Cost Tracking** - Monitor AI provider usage +5. **Web UI** - Visual tool management interface +6. **Collaboration** - Share tools between users +7. **Categories** - Organize tools by type +8. **Analytics** - Usage trends and insights + +### Stretch Goals + +- Tool marketplace +- Community contributions +- Performance optimizations +- Extended language support +- Enhanced IDE integration + +## 🎉 Conclusion + +LLM Maker is a **production-ready**, **fully tested**, **well-documented** system that safely enables autonomous tool creation. It demonstrates: + +- **Security-first design** with multiple protection layers +- **Robust error handling** with graceful degradation +- **Cross-platform compatibility** (Unix and Windows) +- **Extensible architecture** for future enhancements +- **Complete documentation** for users and developers + +The implementation is **complete**, **tested**, and **ready for use**! 🚀 + +--- + +**Status**: ✅ Complete +**Quality**: ✅ Production-ready +**Security**: ✅ Enterprise-grade +**Documentation**: ✅ Comprehensive +**Tests**: ✅ All passing diff --git a/ai-projects/llm-maker/QUICKSTART.md b/ai-projects/llm-maker/QUICKSTART.md index e20c446be..96d1de657 100644 --- a/ai-projects/llm-maker/QUICKSTART.md +++ b/ai-projects/llm-maker/QUICKSTART.md @@ -1,212 +1,212 @@ -# LLM Maker - Quick Start Guide - -## What is LLM Maker? - -LLM Maker is an autonomous tool creation system that allows Large Language Models to: -- Generate Python functions from natural language descriptions -- Validate generated code for safety -- Execute tools in a sandboxed environment -- Manage and reuse tools - -## Installation - -```bash -cd llm-maker -pip install -r requirements.txt -``` - -## Basic Usage - -### 1. Create a Tool Programmatically - -```python -from llm_maker import ToolMaker, ToolRegistry - -# Initialize -maker = ToolMaker() -registry = ToolRegistry() - -# Create a tool from description -tool = maker.create_tool( - name="calculate_factorial", - description="Calculate the factorial of a number", - parameters={"n": "int"}, - return_type="int" -) - -# Register it -tool_id = registry.register(tool) -``` - -### 2. Execute a Tool - -```python -from llm_maker import ToolExecutor - -executor = ToolExecutor() - -# Execute the tool -result = executor.execute( - code=tool.code, - function_name="calculate_factorial", - args={"n": 5} -) - -if result['success']: - print(f"Result: {result['result']}") -else: - print(f"Error: {result['error']}") -``` - -### 3. Use the MCP Server - -Start the MCP server: - -```bash -python llm_maker_mcp_server.py -``` - -Then connect with an MCP client to use these tools: -- `create_tool` - Generate tools -- `execute_tool` - Run tools -- `list_registered_tools` - Browse tools -- `get_tool` - Get tool details -- `delete_tool` - Remove tools -- `validate_tool` - Check safety -- `registry_stats` - Usage statistics - -## Examples - -### Example 1: Fibonacci Calculator - -```python -tool = maker.create_tool( - name="fibonacci", - description="Calculate the nth Fibonacci number", - parameters={"n": "int"}, - return_type="int", - examples=[ - {"input": {"n": 0}, "output": 0}, - {"input": {"n": 1}, "output": 1}, - {"input": {"n": 10}, "output": 55} - ] -) -``` - -### Example 2: Text Processor - -```python -tool = maker.create_tool( - name="word_count", - description="Count words in a text string", - parameters={"text": "str"}, - return_type="int" -) -``` - -### Example 3: Data Processor - -```python -tool = maker.create_tool( - name="filter_dict", - description="Filter dictionary by list of keys", - parameters={"data": "dict", "keys": "list"}, - return_type="dict" -) -``` - -## Security - -LLM Maker enforces strict security: - -### ✅ Allowed -- Safe built-in functions (len, max, min, sum, etc.) -- Safe imports (math, json, re, datetime, collections) -- Pure computation -- Type hints and docstrings - -### ❌ Blocked -- Dangerous imports (os, sys, subprocess) -- File operations (open, read, write) -- Network access (socket, urllib, requests) -- Code execution (eval, exec, compile) -- System access (__import__, breakpoint) - -## Configuration - -Edit `llm_maker_config.yaml`: - -```yaml -tool_maker: - provider: azure # or openai, local - temperature: 0.7 - max_tokens: 2000 - -validation: - strict_mode: true - allowed_imports: - - math - - json - - re - -execution: - timeout_seconds: 5 - max_memory_mb: 512 -``` - -## Testing - -Run the test suite: - -```bash -# All tests -./test_llm_maker.sh - -# Or manually -python examples/quick_start.py -``` - -## Troubleshooting - -### "No AI provider available" - -Set up Azure OpenAI or OpenAI credentials: - -```bash -export AZURE_OPENAI_API_KEY="your-key" -export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/" -export AZURE_OPENAI_DEPLOYMENT="gpt-4o-mini" -export AZURE_OPENAI_API_VERSION="2024-02-15-preview" -``` - -### "RestrictedPython not available" - -Install optional dependencies: - -```bash -pip install RestrictedPython -``` - -Or continue with basic sandboxing (less secure but functional). - -### Tool execution fails - -Check the error message: -- **Syntax error**: Code generation failed, try again -- **Validation error**: Code violates security rules -- **Runtime error**: Bug in generated code -- **Timeout**: Execution took too long - -## Next Steps - -1. Explore examples in `examples/` -2. Read the full documentation in `README.md` -3. Try creating your own tools -4. Integrate with your applications via the MCP server - -## Learn More - -- [Full README](README.md) -- [Example Tools](examples/) -- [Test Suite](tests/) -- [Configuration](llm_maker_config.yaml) +# LLM Maker - Quick Start Guide + +## What is LLM Maker? + +LLM Maker is an autonomous tool creation system that allows Large Language Models to: +- Generate Python functions from natural language descriptions +- Validate generated code for safety +- Execute tools in a sandboxed environment +- Manage and reuse tools + +## Installation + +```bash +cd llm-maker +pip install -r requirements.txt +``` + +## Basic Usage + +### 1. Create a Tool Programmatically + +```python +from llm_maker import ToolMaker, ToolRegistry + +# Initialize +maker = ToolMaker() +registry = ToolRegistry() + +# Create a tool from description +tool = maker.create_tool( + name="calculate_factorial", + description="Calculate the factorial of a number", + parameters={"n": "int"}, + return_type="int" +) + +# Register it +tool_id = registry.register(tool) +``` + +### 2. Execute a Tool + +```python +from llm_maker import ToolExecutor + +executor = ToolExecutor() + +# Execute the tool +result = executor.execute( + code=tool.code, + function_name="calculate_factorial", + args={"n": 5} +) + +if result['success']: + print(f"Result: {result['result']}") +else: + print(f"Error: {result['error']}") +``` + +### 3. Use the MCP Server + +Start the MCP server: + +```bash +python llm_maker_mcp_server.py +``` + +Then connect with an MCP client to use these tools: +- `create_tool` - Generate tools +- `execute_tool` - Run tools +- `list_registered_tools` - Browse tools +- `get_tool` - Get tool details +- `delete_tool` - Remove tools +- `validate_tool` - Check safety +- `registry_stats` - Usage statistics + +## Examples + +### Example 1: Fibonacci Calculator + +```python +tool = maker.create_tool( + name="fibonacci", + description="Calculate the nth Fibonacci number", + parameters={"n": "int"}, + return_type="int", + examples=[ + {"input": {"n": 0}, "output": 0}, + {"input": {"n": 1}, "output": 1}, + {"input": {"n": 10}, "output": 55} + ] +) +``` + +### Example 2: Text Processor + +```python +tool = maker.create_tool( + name="word_count", + description="Count words in a text string", + parameters={"text": "str"}, + return_type="int" +) +``` + +### Example 3: Data Processor + +```python +tool = maker.create_tool( + name="filter_dict", + description="Filter dictionary by list of keys", + parameters={"data": "dict", "keys": "list"}, + return_type="dict" +) +``` + +## Security + +LLM Maker enforces strict security: + +### ✅ Allowed +- Safe built-in functions (len, max, min, sum, etc.) +- Safe imports (math, json, re, datetime, collections) +- Pure computation +- Type hints and docstrings + +### ❌ Blocked +- Dangerous imports (os, sys, subprocess) +- File operations (open, read, write) +- Network access (socket, urllib, requests) +- Code execution (eval, exec, compile) +- System access (__import__, breakpoint) + +## Configuration + +Edit `llm_maker_config.yaml`: + +```yaml +tool_maker: + provider: azure # or openai, local + temperature: 0.7 + max_tokens: 2000 + +validation: + strict_mode: true + allowed_imports: + - math + - json + - re + +execution: + timeout_seconds: 5 + max_memory_mb: 512 +``` + +## Testing + +Run the test suite: + +```bash +# All tests +./test_llm_maker.sh + +# Or manually +python examples/quick_start.py +``` + +## Troubleshooting + +### "No AI provider available" + +Set up Azure OpenAI or OpenAI credentials: + +```bash +export AZURE_OPENAI_API_KEY="your-key" +export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/" +export AZURE_OPENAI_DEPLOYMENT="gpt-4o-mini" +export AZURE_OPENAI_API_VERSION="2024-02-15-preview" +``` + +### "RestrictedPython not available" + +Install optional dependencies: + +```bash +pip install RestrictedPython +``` + +Or continue with basic sandboxing (less secure but functional). + +### Tool execution fails + +Check the error message: +- **Syntax error**: Code generation failed, try again +- **Validation error**: Code violates security rules +- **Runtime error**: Bug in generated code +- **Timeout**: Execution took too long + +## Next Steps + +1. Explore examples in `examples/` +2. Read the full documentation in `README.md` +3. Try creating your own tools +4. Integrate with your applications via the MCP server + +## Learn More + +- [Full README](README.md) +- [Example Tools](examples/) +- [Test Suite](tests/) +- [Configuration](llm_maker_config.yaml) diff --git a/ai-projects/llm-maker/README.md b/ai-projects/llm-maker/README.md index a7e1f029d..581cec548 100644 --- a/ai-projects/llm-maker/README.md +++ b/ai-projects/llm-maker/README.md @@ -1,141 +1,141 @@ -# LLM Maker - Autonomous Tool & Website Creation for LLMs - -LLM Maker is a comprehensive framework that enables Large Language Models to autonomously create, validate, and use Python tools, as well as generate complete websites. It provides a safe, sandboxed environment for dynamic code generation and execution. - -## 🎨 New: Web Interfaces - -### Tool Maker UI -**User-friendly web interface for creating and testing AI tools!** - -```bash -cd llm-maker -python web_server.py -# Open http://localhost:8090 in your browser -``` - -### Website Maker UI -**AI-powered automated website generator!** - -```bash -cd llm-maker -python web_server.py -# Open http://localhost:8090/website-maker in your browser -``` - -See [WEB_UI_README.md](WEB_UI_README.md) and [WEBSITE_MAKER_README.md](WEBSITE_MAKER_README.md) for full documentation. - -## 🎯 Overview - -LLM Maker allows LLMs to: -- **Create Tools**: Generate Python functions from natural language descriptions -- **Validate Tools**: Automatically verify generated code for safety and correctness -- **Execute Tools**: Run tools in a sandboxed environment with resource limits -- **Manage Tools**: Store, retrieve, and version generated tools -- **Generate Websites**: Create complete, modern, responsive websites from descriptions -- **Update Websites**: Modify existing websites with natural language instructions - -## 🚀 Quick Start - -### Installation - -```bash -cd llm-maker -pip install -r requirements.txt -``` - -### Basic Usage - -```python -from llm_maker import ToolMaker, ToolRegistry - -# Initialize tool maker -maker = ToolMaker() -registry = ToolRegistry() - -# Create a tool from description -tool = maker.create_tool( - name="calculate_fibonacci", - description="Calculate the nth Fibonacci number", - parameters={"n": "int"}, - return_type="int" -) - -# Validate the tool -if maker.validate_tool(tool): - # Register it - registry.register(tool) - - # Use it - result = registry.execute("calculate_fibonacci", {"n": 10}) - print(f"Result: {result}") -``` - -### MCP Server Integration - -Start the LLM Maker MCP server: - -```bash -python llm_maker_mcp_server.py -``` - -Available MCP tools: -- `create_tool` - Generate a new tool from description -- `validate_tool` - Check if a tool is safe to use -- `execute_tool` - Run a registered tool -- `list_tools` - Show all available tools -- `delete_tool` - Remove a tool from registry - -## 📁 Directory Structure - -``` -llm-maker/ -├── src/ -│ ├── tool_maker.py # Core tool generation -│ ├── tool_validator.py # Safety validation -│ ├── tool_executor.py # Sandboxed execution -│ ├── tool_registry.py # Tool management -│ └── __init__.py -├── tools/ # Generated tools storage -├── tests/ # Test suite -├── examples/ # Example tools -├── llm_maker_mcp_server.py # MCP server -├── requirements.txt # Dependencies -└── README.md -``` - -## 🔧 Features - -### Tool Generation - -LLM Maker uses AI providers (Azure OpenAI, OpenAI, or local models) to generate Python functions. - -### Safety Validation - -All generated tools are validated for: -- **No dangerous imports** (os, subprocess, sys, etc.) -- **No file system access** (open, write, delete) -- **No network access** (requests, urllib, socket) -- **No code execution** (eval, exec, compile) -- **Resource limits** (execution time, memory) - -### Sandboxed Execution - -Tools run in a restricted environment: -- Limited execution time (default: 5 seconds) -- Memory limits -- No access to system resources -- Exception handling and logging - -## 🔒 Security - -LLM Maker implements multiple security layers: - -1. **Static Analysis**: Code is analyzed before execution -2. **Sandboxing**: Restricted Python environment -3. **Resource Limits**: CPU time, memory, and I/O limits -4. **Whitelist**: Only safe built-in functions allowed -5. **Logging**: All tool executions are logged - ---- - -**Last Updated:** December 8, 2025 +# LLM Maker - Autonomous Tool & Website Creation for LLMs + +LLM Maker is a comprehensive framework that enables Large Language Models to autonomously create, validate, and use Python tools, as well as generate complete websites. It provides a safe, sandboxed environment for dynamic code generation and execution. + +## 🎨 New: Web Interfaces + +### Tool Maker UI +**User-friendly web interface for creating and testing AI tools!** + +```bash +cd llm-maker +python web_server.py +# Open http://localhost:8090 in your browser +``` + +### Website Maker UI +**AI-powered automated website generator!** + +```bash +cd llm-maker +python web_server.py +# Open http://localhost:8090/website-maker in your browser +``` + +See [WEB_UI_README.md](WEB_UI_README.md) and [WEBSITE_MAKER_README.md](WEBSITE_MAKER_README.md) for full documentation. + +## 🎯 Overview + +LLM Maker allows LLMs to: +- **Create Tools**: Generate Python functions from natural language descriptions +- **Validate Tools**: Automatically verify generated code for safety and correctness +- **Execute Tools**: Run tools in a sandboxed environment with resource limits +- **Manage Tools**: Store, retrieve, and version generated tools +- **Generate Websites**: Create complete, modern, responsive websites from descriptions +- **Update Websites**: Modify existing websites with natural language instructions + +## 🚀 Quick Start + +### Installation + +```bash +cd llm-maker +pip install -r requirements.txt +``` + +### Basic Usage + +```python +from llm_maker import ToolMaker, ToolRegistry + +# Initialize tool maker +maker = ToolMaker() +registry = ToolRegistry() + +# Create a tool from description +tool = maker.create_tool( + name="calculate_fibonacci", + description="Calculate the nth Fibonacci number", + parameters={"n": "int"}, + return_type="int" +) + +# Validate the tool +if maker.validate_tool(tool): + # Register it + registry.register(tool) + + # Use it + result = registry.execute("calculate_fibonacci", {"n": 10}) + print(f"Result: {result}") +``` + +### MCP Server Integration + +Start the LLM Maker MCP server: + +```bash +python llm_maker_mcp_server.py +``` + +Available MCP tools: +- `create_tool` - Generate a new tool from description +- `validate_tool` - Check if a tool is safe to use +- `execute_tool` - Run a registered tool +- `list_tools` - Show all available tools +- `delete_tool` - Remove a tool from registry + +## 📁 Directory Structure + +``` +llm-maker/ +├── src/ +│ ├── tool_maker.py # Core tool generation +│ ├── tool_validator.py # Safety validation +│ ├── tool_executor.py # Sandboxed execution +│ ├── tool_registry.py # Tool management +│ └── __init__.py +├── tools/ # Generated tools storage +├── tests/ # Test suite +├── examples/ # Example tools +├── llm_maker_mcp_server.py # MCP server +├── requirements.txt # Dependencies +└── README.md +``` + +## 🔧 Features + +### Tool Generation + +LLM Maker uses AI providers (Azure OpenAI, OpenAI, or local models) to generate Python functions. + +### Safety Validation + +All generated tools are validated for: +- **No dangerous imports** (os, subprocess, sys, etc.) +- **No file system access** (open, write, delete) +- **No network access** (requests, urllib, socket) +- **No code execution** (eval, exec, compile) +- **Resource limits** (execution time, memory) + +### Sandboxed Execution + +Tools run in a restricted environment: +- Limited execution time (default: 5 seconds) +- Memory limits +- No access to system resources +- Exception handling and logging + +## 🔒 Security + +LLM Maker implements multiple security layers: + +1. **Static Analysis**: Code is analyzed before execution +2. **Sandboxing**: Restricted Python environment +3. **Resource Limits**: CPU time, memory, and I/O limits +4. **Whitelist**: Only safe built-in functions allowed +5. **Logging**: All tool executions are logged + +--- + +**Last Updated:** December 8, 2025 diff --git a/ai-projects/llm-maker/UI_ENHANCEMENTS.md b/ai-projects/llm-maker/UI_ENHANCEMENTS.md index 518ac12d9..f977c721a 100644 --- a/ai-projects/llm-maker/UI_ENHANCEMENTS.md +++ b/ai-projects/llm-maker/UI_ENHANCEMENTS.md @@ -1,118 +1,118 @@ -# LLM Maker - UI Enhancements - -## Overview - -Enhanced the web interface to be more user-friendly and easier to integrate with other applications. - -## Key Improvements - -### 1. **Enhanced Header with Quick Actions** -- Added three prominent action buttons: - - 📚 **Integration Guide** - Quick access to integration documentation - - 💻 **API Docs** - API endpoint documentation - - 💡 **Examples** - Quick example tools - -### 2. **One-Click Example Tools** -Added example pills below the Tool Name field for instant tool creation: -- 🔢 **Fibonacci** - Calculate Fibonacci numbers -- 🔄 **Palindrome** - Check if text is palindrome -- 🌡️ **Temperature** - Convert Celsius to Fahrenheit - -Simply click a pill to auto-fill the form with a working example! - -### 3. **Integrated Quick Integration Guide** -Built-in code snippets section showing: -- **List all tools**: GET endpoint example -- **Create a tool**: POST with JSON body -- **Execute a tool**: Execute endpoint with parameters - -Each snippet has a "Copy" button for one-click copying to clipboard. - -### 4. **Better Visual Design** -- Added Font Awesome icons throughout the interface -- Improved button styling with icons -- Enhanced code snippet display with dark theme -- Better visual hierarchy and spacing - -### 5. **Improved User Experience** -- **Example auto-fill**: Click any example to populate the form -- **Copy to clipboard**: One-click code snippet copying -- **Smooth scrolling**: Navigate to integration guide smoothly -- **Visual feedback**: Buttons show "Copied!" state -- **Info messages**: Helpful status messages guide users - -## Integration Features - -### Quick Start Examples - -**JavaScript/Fetch:** -```javascript -fetch('/api/tools', { - method: 'POST', - body: JSON.stringify({ - name: 'my_tool', - description: 'What it does', - parameters: {'x': 'int'} - }) -}) -``` - -**cURL:** -```bash -GET http://localhost:8090/api/tools -``` - -**Python:** -```python -import requests - -requests.post('http://localhost:8090/api/tools', json={ - 'name': 'my_tool', - 'description': 'What it does', - 'parameters': {'x': 'int'} -}) -``` - -## User Benefits - -1. **Faster Learning Curve**: Examples help new users understand tool creation -2. **Easier Integration**: Built-in code snippets reduce integration time -3. **Better Discoverability**: Header buttons make features easy to find -4. **Smoother Workflow**: One-click actions reduce friction -5. **Professional Look**: Modern design with icons and better styling - -## Technical Details - -### New CSS Classes -- `.header-btn` - Header action buttons -- `.example-pill` - Clickable example pills -- `.integration-box` - Integration guide container -- `.code-snippet` - Code display boxes -- `.copy-btn` - Copy to clipboard buttons - -### New JavaScript Functions -- `scrollToIntegration()` - Smooth scroll to guide -- `showAPIInfo()` - Display API documentation -- `showExamples()` - Show available examples -- `copyToClipboard()` - Copy code snippets -- `fillExample()` - Auto-fill form with examples - -### Dependencies Added -- Font Awesome 6.4.0 (via CDN) - -## Backward Compatibility - -All existing functionality preserved: -- Tool creation workflow unchanged -- API endpoints identical -- Testing features intact -- Statistics tracking continues - -## Future Enhancements - -Potential improvements for future versions: -- More example tools (word counter, prime checker, etc.) -- Tabbed interface for different integration languages -- Interactive API playground -- Video tutorials -- Export/import tools functionality +# LLM Maker - UI Enhancements + +## Overview + +Enhanced the web interface to be more user-friendly and easier to integrate with other applications. + +## Key Improvements + +### 1. **Enhanced Header with Quick Actions** +- Added three prominent action buttons: + - 📚 **Integration Guide** - Quick access to integration documentation + - 💻 **API Docs** - API endpoint documentation + - 💡 **Examples** - Quick example tools + +### 2. **One-Click Example Tools** +Added example pills below the Tool Name field for instant tool creation: +- 🔢 **Fibonacci** - Calculate Fibonacci numbers +- 🔄 **Palindrome** - Check if text is palindrome +- 🌡️ **Temperature** - Convert Celsius to Fahrenheit + +Simply click a pill to auto-fill the form with a working example! + +### 3. **Integrated Quick Integration Guide** +Built-in code snippets section showing: +- **List all tools**: GET endpoint example +- **Create a tool**: POST with JSON body +- **Execute a tool**: Execute endpoint with parameters + +Each snippet has a "Copy" button for one-click copying to clipboard. + +### 4. **Better Visual Design** +- Added Font Awesome icons throughout the interface +- Improved button styling with icons +- Enhanced code snippet display with dark theme +- Better visual hierarchy and spacing + +### 5. **Improved User Experience** +- **Example auto-fill**: Click any example to populate the form +- **Copy to clipboard**: One-click code snippet copying +- **Smooth scrolling**: Navigate to integration guide smoothly +- **Visual feedback**: Buttons show "Copied!" state +- **Info messages**: Helpful status messages guide users + +## Integration Features + +### Quick Start Examples + +**JavaScript/Fetch:** +```javascript +fetch('/api/tools', { + method: 'POST', + body: JSON.stringify({ + name: 'my_tool', + description: 'What it does', + parameters: {'x': 'int'} + }) +}) +``` + +**cURL:** +```bash +GET http://localhost:8090/api/tools +``` + +**Python:** +```python +import requests + +requests.post('http://localhost:8090/api/tools', json={ + 'name': 'my_tool', + 'description': 'What it does', + 'parameters': {'x': 'int'} +}) +``` + +## User Benefits + +1. **Faster Learning Curve**: Examples help new users understand tool creation +2. **Easier Integration**: Built-in code snippets reduce integration time +3. **Better Discoverability**: Header buttons make features easy to find +4. **Smoother Workflow**: One-click actions reduce friction +5. **Professional Look**: Modern design with icons and better styling + +## Technical Details + +### New CSS Classes +- `.header-btn` - Header action buttons +- `.example-pill` - Clickable example pills +- `.integration-box` - Integration guide container +- `.code-snippet` - Code display boxes +- `.copy-btn` - Copy to clipboard buttons + +### New JavaScript Functions +- `scrollToIntegration()` - Smooth scroll to guide +- `showAPIInfo()` - Display API documentation +- `showExamples()` - Show available examples +- `copyToClipboard()` - Copy code snippets +- `fillExample()` - Auto-fill form with examples + +### Dependencies Added +- Font Awesome 6.4.0 (via CDN) + +## Backward Compatibility + +All existing functionality preserved: +- Tool creation workflow unchanged +- API endpoints identical +- Testing features intact +- Statistics tracking continues + +## Future Enhancements + +Potential improvements for future versions: +- More example tools (word counter, prime checker, etc.) +- Tabbed interface for different integration languages +- Interactive API playground +- Video tutorials +- Export/import tools functionality diff --git a/ai-projects/llm-maker/WEBSITE_MAKER_IMPLEMENTATION.md b/ai-projects/llm-maker/WEBSITE_MAKER_IMPLEMENTATION.md index 062986701..e17410e92 100644 --- a/ai-projects/llm-maker/WEBSITE_MAKER_IMPLEMENTATION.md +++ b/ai-projects/llm-maker/WEBSITE_MAKER_IMPLEMENTATION.md @@ -1,302 +1,302 @@ -# AI Website Maker - Implementation Summary - -## What Was Built - -In response to the request "Implament ai automated website maker updater", I've created a complete AI-powered website generation and update system integrated into LLM Maker. - -## Components Delivered - -### 1. Core Module (`src/website_maker.py` - 560 lines) - -**WebsiteMaker Class:** -- `create_website()` - Generate complete websites from descriptions -- `update_website()` - Modify existing websites with AI -- `list_websites()` - Browse all generated sites -- `delete_website()` - Remove unwanted sites -- `_build_website_prompt()` - Constructs AI prompts -- `_extract_code_blocks()` - Parses generated code - -**WebsiteValidator Class:** -- `validate_html()` - Check HTML structure and best practices -- `validate_css()` - Verify CSS syntax and responsive design - -**Features:** -- Uses existing chat provider infrastructure (Azure OpenAI/OpenAI/local) -- Multi-attempt generation with feedback loop -- Extracts HTML, CSS, and JavaScript from AI responses -- Saves to `generated_sites/` directory -- Metadata tracking (created_at, last_updated, description) -- Command-line interface for automation - -### 2. Web Interface (`website_maker_ui.html` - 890 lines) - -**User Experience:** -- Modern gradient design matching LLM Maker style -- Font Awesome icons throughout -- Smooth animations and transitions -- Mobile-responsive layout - -**Create Website Section:** -- Name input field -- Description textarea -- Style selector (6 options) -- Dynamic pages chips (add/remove) -- Dynamic features chips (add/remove) -- "Generate Website" button with loading state -- Real-time status messages - -**Update Website Section:** -- Website selector dropdown (auto-populated) -- Update description textarea -- Optional target file input -- "Update Website" button -- Status feedback - -**Website Library:** -- Card-based grid layout -- Shows name, description, style, file count, created date -- Tags for pages -- "View Code" and "Delete" buttons -- Empty state for no websites - -**Code Preview Modal:** -- File tabs for switching between files -- Syntax-highlighted code display -- Dark theme code blocks -- Close button and outside-click close - -### 3. API Integration (`web_server.py` updates - 130 lines) - -**New Endpoints:** -- `POST /api/websites` - Create website -- `POST /api/websites/update` - Update website -- `GET /api/websites` - List all websites -- `GET /api/websites/{name}` - Get specific website with full code -- `DELETE /api/websites/{name}` - Delete website -- `GET /website-maker` - Serve website maker UI - -**Features:** -- JSON request/response -- Proper error handling -- CORS headers -- Status codes (200, 400, 404, 500) -- Logging for debugging - -### 4. Documentation (`WEBSITE_MAKER_README.md` - 580 lines) - -**Comprehensive Guide:** -- Overview and features -- Quick start with examples -- Complete API reference -- CLI usage instructions -- Best practices for writing descriptions -- Visual style guide -- Feature recommendations -- Troubleshooting section -- Integration examples (JavaScript, Python, cURL) -- Advanced usage patterns -- Security considerations -- Future enhancements roadmap - -### 5. Integration Updates - -**Main README (`README.md`):** -- Added website maker overview -- Updated quick start section -- Added new feature bullets - -**Package Exports (`src/__init__.py`):** -- Exported WebsiteMaker class -- Exported WebsiteValidator class -- Updated version to 0.2.0 - -**Main UI (`web_ui.html`):** -- Added "🌐 Website Maker" button in header -- Links to `/website-maker` - -## Technical Details - -### Architecture - -``` -User Input (Natural Language Description) - ↓ -WebsiteMaker class - ↓ -Chat Provider (Azure OpenAI/OpenAI/local) - ↓ -AI generates HTML, CSS, JS code - ↓ -Code extraction and validation - ↓ -Save to generated_sites/{name}/ - ↓ -Return result to user -``` - -### File Organization - -``` -llm-maker/ -├── src/ -│ ├── website_maker.py # Core logic -│ └── __init__.py # Updated exports -├── generated_sites/ # Output directory -│ └── {website-name}/ -│ ├── index.html -│ ├── {page}.html -│ ├── styles.css -│ ├── script.js -│ └── metadata.json -├── website_maker_ui.html # Web interface -├── web_server.py # Updated with endpoints -├── WEBSITE_MAKER_README.md # Full documentation -└── README.md # Updated overview -``` - -### AI Prompt Engineering - -**Website Creation Prompt Structure:** -1. Project name and description -2. Visual style specification -3. Pages list -4. Features list -5. Requirements (HTML5, CSS3, responsive, SEO) -6. Code format instructions (markdown code blocks) - -**Update Prompt Structure:** -1. Website name -2. Update description -3. Current file contents -4. Instructions for specific changes -5. Code format requirements - -### Code Extraction - -Supports three patterns: -1. ` ```language:filename ` (preferred) -2. ` ```filename.ext ` (fallback) -3. File mentions before code blocks (secondary fallback) - -### Validation - -**HTML Checks:** -- DOCTYPE declaration -- Basic structure (html, head, body tags) -- Title tag -- Viewport meta tag for responsiveness - -**CSS Checks:** -- Media queries presence -- Brace matching -- Basic syntax validation - -## Usage Examples - -### Web UI Example - -1. Navigate to http://localhost:8090/website-maker -2. Fill in form: - - Name: "my-portfolio" - - Description: "Personal portfolio with hero, projects, skills, contact form" - - Style: Modern - - Pages: index, projects, contact - - Features: responsive design, smooth scrolling, contact form -3. Click "Generate Website" -4. Wait 20-30 seconds for AI generation -5. View generated code in modal -6. Files saved to `generated_sites/my-portfolio/` - -### API Example - -```javascript -const response = await fetch('http://localhost:8090/api/websites', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - name: 'my-site', - description: 'A modern landing page with hero, features, and CTA', - style: 'modern', - pages: ['index'], - features: ['responsive design', 'smooth animations'] - }) -}); - -const result = await response.json(); -console.log(result.files); // Generated HTML, CSS, JS -``` - -### CLI Example - -```bash -python src/website_maker.py create my-portfolio \ - "Personal portfolio with projects and contact form" \ - --style modern \ - --pages index projects contact \ - --features "responsive design" "contact form" -``` - -## Key Benefits - -1. **Speed**: Generate websites 10-100x faster than manual coding -2. **Accessibility**: No coding knowledge required -3. **Quality**: Modern, responsive, professional results -4. **Flexibility**: Easy updates and iterations -5. **Learning**: View generated code to learn web development -6. **Integration**: API and CLI for automation -7. **Control**: All files saved locally, full ownership - -## Testing Performed - -✅ WebsiteMaker class imports successfully -✅ WebsiteMaker instantiates with provider -✅ Web server imports website_maker module -✅ Server starts on port 8090 -✅ UI HTML file created and valid -✅ API endpoints added to server -✅ Documentation complete and comprehensive -✅ Integration with existing codebase verified - -## Statistics - -- **Lines of Production Code**: 560 (website_maker.py) -- **Lines of UI Code**: 890 (website_maker_ui.html) -- **Lines of Documentation**: 580 (WEBSITE_MAKER_README.md) -- **Lines of API Integration**: 130 (web_server.py updates) -- **Total New Lines**: ~2,160 - -- **Classes**: 2 (WebsiteMaker, WebsiteValidator) -- **Methods**: 12+ -- **API Endpoints**: 5 -- **UI Components**: 10+ -- **Documentation Sections**: 20+ - -## Future Enhancements - -Potential additions (not included in current implementation): -- Pre-built templates library -- Reusable component library -- Live preview server -- Direct deployment to hosting services -- Version control integration -- Theme customization system -- Asset upload and management -- CMS integration -- SEO optimization tools -- A/B testing support - -## Conclusion - -The AI Website Maker is now fully functional and production-ready. It provides a complete solution for automated website generation with: - -✅ Natural language interface -✅ Multiple visual styles -✅ Multi-page support -✅ AI-powered updates -✅ Modern web UI -✅ Complete API -✅ CLI tools -✅ Comprehensive documentation -✅ Full integration with LLM Maker - -Users can now create professional websites in seconds using natural language descriptions, iterate quickly with AI-powered updates, and integrate the functionality into their own applications via API. +# AI Website Maker - Implementation Summary + +## What Was Built + +In response to the request "Implament ai automated website maker updater", I've created a complete AI-powered website generation and update system integrated into LLM Maker. + +## Components Delivered + +### 1. Core Module (`src/website_maker.py` - 560 lines) + +**WebsiteMaker Class:** +- `create_website()` - Generate complete websites from descriptions +- `update_website()` - Modify existing websites with AI +- `list_websites()` - Browse all generated sites +- `delete_website()` - Remove unwanted sites +- `_build_website_prompt()` - Constructs AI prompts +- `_extract_code_blocks()` - Parses generated code + +**WebsiteValidator Class:** +- `validate_html()` - Check HTML structure and best practices +- `validate_css()` - Verify CSS syntax and responsive design + +**Features:** +- Uses existing chat provider infrastructure (Azure OpenAI/OpenAI/local) +- Multi-attempt generation with feedback loop +- Extracts HTML, CSS, and JavaScript from AI responses +- Saves to `generated_sites/` directory +- Metadata tracking (created_at, last_updated, description) +- Command-line interface for automation + +### 2. Web Interface (`website_maker_ui.html` - 890 lines) + +**User Experience:** +- Modern gradient design matching LLM Maker style +- Font Awesome icons throughout +- Smooth animations and transitions +- Mobile-responsive layout + +**Create Website Section:** +- Name input field +- Description textarea +- Style selector (6 options) +- Dynamic pages chips (add/remove) +- Dynamic features chips (add/remove) +- "Generate Website" button with loading state +- Real-time status messages + +**Update Website Section:** +- Website selector dropdown (auto-populated) +- Update description textarea +- Optional target file input +- "Update Website" button +- Status feedback + +**Website Library:** +- Card-based grid layout +- Shows name, description, style, file count, created date +- Tags for pages +- "View Code" and "Delete" buttons +- Empty state for no websites + +**Code Preview Modal:** +- File tabs for switching between files +- Syntax-highlighted code display +- Dark theme code blocks +- Close button and outside-click close + +### 3. API Integration (`web_server.py` updates - 130 lines) + +**New Endpoints:** +- `POST /api/websites` - Create website +- `POST /api/websites/update` - Update website +- `GET /api/websites` - List all websites +- `GET /api/websites/{name}` - Get specific website with full code +- `DELETE /api/websites/{name}` - Delete website +- `GET /website-maker` - Serve website maker UI + +**Features:** +- JSON request/response +- Proper error handling +- CORS headers +- Status codes (200, 400, 404, 500) +- Logging for debugging + +### 4. Documentation (`WEBSITE_MAKER_README.md` - 580 lines) + +**Comprehensive Guide:** +- Overview and features +- Quick start with examples +- Complete API reference +- CLI usage instructions +- Best practices for writing descriptions +- Visual style guide +- Feature recommendations +- Troubleshooting section +- Integration examples (JavaScript, Python, cURL) +- Advanced usage patterns +- Security considerations +- Future enhancements roadmap + +### 5. Integration Updates + +**Main README (`README.md`):** +- Added website maker overview +- Updated quick start section +- Added new feature bullets + +**Package Exports (`src/__init__.py`):** +- Exported WebsiteMaker class +- Exported WebsiteValidator class +- Updated version to 0.2.0 + +**Main UI (`web_ui.html`):** +- Added "🌐 Website Maker" button in header +- Links to `/website-maker` + +## Technical Details + +### Architecture + +``` +User Input (Natural Language Description) + ↓ +WebsiteMaker class + ↓ +Chat Provider (Azure OpenAI/OpenAI/local) + ↓ +AI generates HTML, CSS, JS code + ↓ +Code extraction and validation + ↓ +Save to generated_sites/{name}/ + ↓ +Return result to user +``` + +### File Organization + +``` +llm-maker/ +├── src/ +│ ├── website_maker.py # Core logic +│ └── __init__.py # Updated exports +├── generated_sites/ # Output directory +│ └── {website-name}/ +│ ├── index.html +│ ├── {page}.html +│ ├── styles.css +│ ├── script.js +│ └── metadata.json +├── website_maker_ui.html # Web interface +├── web_server.py # Updated with endpoints +├── WEBSITE_MAKER_README.md # Full documentation +└── README.md # Updated overview +``` + +### AI Prompt Engineering + +**Website Creation Prompt Structure:** +1. Project name and description +2. Visual style specification +3. Pages list +4. Features list +5. Requirements (HTML5, CSS3, responsive, SEO) +6. Code format instructions (markdown code blocks) + +**Update Prompt Structure:** +1. Website name +2. Update description +3. Current file contents +4. Instructions for specific changes +5. Code format requirements + +### Code Extraction + +Supports three patterns: +1. ` ```language:filename ` (preferred) +2. ` ```filename.ext ` (fallback) +3. File mentions before code blocks (secondary fallback) + +### Validation + +**HTML Checks:** +- DOCTYPE declaration +- Basic structure (html, head, body tags) +- Title tag +- Viewport meta tag for responsiveness + +**CSS Checks:** +- Media queries presence +- Brace matching +- Basic syntax validation + +## Usage Examples + +### Web UI Example + +1. Navigate to http://localhost:8090/website-maker +2. Fill in form: + - Name: "my-portfolio" + - Description: "Personal portfolio with hero, projects, skills, contact form" + - Style: Modern + - Pages: index, projects, contact + - Features: responsive design, smooth scrolling, contact form +3. Click "Generate Website" +4. Wait 20-30 seconds for AI generation +5. View generated code in modal +6. Files saved to `generated_sites/my-portfolio/` + +### API Example + +```javascript +const response = await fetch('http://localhost:8090/api/websites', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + name: 'my-site', + description: 'A modern landing page with hero, features, and CTA', + style: 'modern', + pages: ['index'], + features: ['responsive design', 'smooth animations'] + }) +}); + +const result = await response.json(); +console.log(result.files); // Generated HTML, CSS, JS +``` + +### CLI Example + +```bash +python src/website_maker.py create my-portfolio \ + "Personal portfolio with projects and contact form" \ + --style modern \ + --pages index projects contact \ + --features "responsive design" "contact form" +``` + +## Key Benefits + +1. **Speed**: Generate websites 10-100x faster than manual coding +2. **Accessibility**: No coding knowledge required +3. **Quality**: Modern, responsive, professional results +4. **Flexibility**: Easy updates and iterations +5. **Learning**: View generated code to learn web development +6. **Integration**: API and CLI for automation +7. **Control**: All files saved locally, full ownership + +## Testing Performed + +✅ WebsiteMaker class imports successfully +✅ WebsiteMaker instantiates with provider +✅ Web server imports website_maker module +✅ Server starts on port 8090 +✅ UI HTML file created and valid +✅ API endpoints added to server +✅ Documentation complete and comprehensive +✅ Integration with existing codebase verified + +## Statistics + +- **Lines of Production Code**: 560 (website_maker.py) +- **Lines of UI Code**: 890 (website_maker_ui.html) +- **Lines of Documentation**: 580 (WEBSITE_MAKER_README.md) +- **Lines of API Integration**: 130 (web_server.py updates) +- **Total New Lines**: ~2,160 + +- **Classes**: 2 (WebsiteMaker, WebsiteValidator) +- **Methods**: 12+ +- **API Endpoints**: 5 +- **UI Components**: 10+ +- **Documentation Sections**: 20+ + +## Future Enhancements + +Potential additions (not included in current implementation): +- Pre-built templates library +- Reusable component library +- Live preview server +- Direct deployment to hosting services +- Version control integration +- Theme customization system +- Asset upload and management +- CMS integration +- SEO optimization tools +- A/B testing support + +## Conclusion + +The AI Website Maker is now fully functional and production-ready. It provides a complete solution for automated website generation with: + +✅ Natural language interface +✅ Multiple visual styles +✅ Multi-page support +✅ AI-powered updates +✅ Modern web UI +✅ Complete API +✅ CLI tools +✅ Comprehensive documentation +✅ Full integration with LLM Maker + +Users can now create professional websites in seconds using natural language descriptions, iterate quickly with AI-powered updates, and integrate the functionality into their own applications via API. diff --git a/ai-projects/llm-maker/WEBSITE_MAKER_README.md b/ai-projects/llm-maker/WEBSITE_MAKER_README.md index f3894f7c3..91b6be683 100644 --- a/ai-projects/llm-maker/WEBSITE_MAKER_README.md +++ b/ai-projects/llm-maker/WEBSITE_MAKER_README.md @@ -1,480 +1,480 @@ -# AI Website Maker - Documentation - -## Overview - -The AI Website Maker is an automated website generation and update system that uses Large Language Models to create complete, modern, responsive websites from natural language descriptions. - -## Features - -### 🎨 Automated Website Generation -- **Natural Language Input**: Describe what you want in plain English -- **Multiple Visual Styles**: Modern, minimal, corporate, creative, dark theme, colorful -- **Multi-Page Support**: Generate websites with multiple pages (index, about, contact, etc.) -- **Feature Customization**: Add specific features like contact forms, navigation, galleries -- **Responsive Design**: All generated sites are mobile-friendly -- **Modern Code**: HTML5, CSS3, and JavaScript with best practices - -### 🔄 Intelligent Updates -- **AI-Powered Modifications**: Update existing websites with natural language instructions -- **Selective Updates**: Update specific files or entire projects -- **Version Tracking**: Metadata tracks creation and update history -- **Context-Aware**: AI understands current website structure when making changes - -### 📊 Website Management -- **Visual Library**: Browse all generated websites -- **Code Preview**: View complete source code for any website -- **Easy Deletion**: Remove websites you no longer need -- **File Access**: All sites saved in `llm-maker/generated_sites/` - -## Quick Start - -### 1. Start the Server - -```bash -cd llm-maker -python web_server.py -``` - -### 2. Access the Interface - -Open your browser to: `http://localhost:8090/website-maker` - -### 3. Create Your First Website - -**Example 1: Personal Portfolio** -- Name: `my-portfolio` -- Description: `A personal portfolio website with a hero section, about me, skills showcase with progress bars, project gallery with hover effects, and contact form. Use a modern, professional design with smooth animations.` -- Style: `Modern` -- Pages: `index`, `projects`, `contact` -- Features: `responsive design`, `smooth scrolling`, `hover animations`, `contact form` - -**Example 2: Landing Page** -- Name: `product-landing` -- Description: `A product landing page with headline, 3 feature cards with icons, pricing table with 3 tiers, testimonials carousel, and call-to-action button. Clean and conversion-focused.` -- Style: `Minimal` -- Pages: `index` -- Features: `responsive design`, `pricing table`, `testimonials` - -**Example 3: Restaurant Website** -- Name: `restaurant-site` -- Description: `A restaurant website with image carousel, menu sections for appetizers/entrees/desserts, location map, hours of operation, and reservation form. Warm, inviting design.` -- Style: `Creative` -- Pages: `index`, `menu`, `about`, `contact` -- Features: `image carousel`, `menu sections`, `reservation form`, `google maps` - -## API Reference - -### Create Website - -**Endpoint:** `POST /api/websites` - -**Request Body:** -```json -{ - "name": "my-website", - "description": "A modern portfolio website with...", - "style": "modern", - "pages": ["index", "about", "contact"], - "features": ["responsive design", "contact form", "navigation"] -} -``` - -**Response:** -```json{ - "success": true, - "message": "Website 'my-website' created successfully", - "files": { - "index.html": "...", - "styles.css": "body { ... }", - "script.js": "// JavaScript code..." - }, - "path": "/path/to/generated_sites/my-website", - "metadata": { - "name": "my-website", - "description": "...", - "style": "modern", - "pages": ["index", "about", "contact"], - "features": [...], - "created_at": "2025-12-08T12:00:00", - "files": ["index.html", "styles.css", "script.js"] - } -} -``` - -### Update Website - -**Endpoint:** `POST /api/websites/update` - -**Request Body:** -```json -{ - "name": "my-website", - "update_description": "Change the header background to gradient, make buttons bigger, add smooth scroll", - "target_file": "styles.css" // Optional -} -``` - -**Response:** -```json -{ - "success": true, - "message": "Website 'my-website' updated successfully", - "updated_files": { - "styles.css": "/* Updated CSS */" - }, - "path": "/path/to/generated_sites/my-website", - "metadata": {...} -} -``` - -### List Websites - -**Endpoint:** `GET /api/websites` - -**Response:** -```json -{ - "websites": [ - { - "name": "my-website", - "description": "...", - "style": "modern", - "pages": ["index", "about"], - "features": ["responsive design"], - "created_at": "2025-12-08T12:00:00", - "last_updated": "2025-12-08T13:00:00", - "files": ["index.html", "styles.css"], - "path": "/path/to/generated_sites/my-website" - } - ], - "count": 1 -} -``` - -### Get Website - -**Endpoint:** `GET /api/websites/{name}` - -**Response:** -```json -{ - "success": true, - "website": { - "name": "my-website", - "description": "...", - "created_at": "2025-12-08T12:00:00", - "files": ["index.html", "styles.css"], - "files_content": { - "index.html": "...", - "styles.css": "body { ... }" - }, - "path": "/path/to/generated_sites/my-website" - } -} -``` - -### Delete Website - -**Endpoint:** `DELETE /api/websites/{name}` - -**Response:** -```json -{ - "success": true, - "message": "Website 'my-website' deleted successfully" -} -``` - -## Command-Line Interface - -### Create Website -```bash -python src/website_maker.py create my-portfolio \ - "A personal portfolio with projects and contact form" \ - --style modern \ - --pages index about projects contact \ - --features "responsive design" "contact form" "smooth scrolling" -``` - -### Update Website -```bash -python src/website_maker.py update my-portfolio \ - "Change the color scheme to blue and purple gradients" \ - --file styles.css -``` - -### List Websites -```bash -python src/website_maker.py list -``` - -### Delete Website -```bash -python src/website_maker.py delete my-portfolio -``` - -## Best Practices - -### Writing Good Descriptions - -**❌ Too Vague:** -"A website for my business" - -**✅ Good:** -"A website for my photography business with a hero image, portfolio gallery grid with lightbox, about me section with my photo, services list with pricing, and contact form. Modern, clean design with smooth transitions." - -**Key Elements:** -1. **Purpose**: What the website is for -2. **Sections**: What content/sections to include -3. **Features**: Specific functionality (forms, galleries, etc.) -4. **Design Style**: Visual direction (modern, minimal, colorful) -5. **Special Requirements**: Animations, interactions, colors - -### Choosing Visual Styles - -- **Modern**: Clean, gradients, shadows, contemporary -- **Minimal**: Simple, lots of white space, typography-focused -- **Corporate**: Professional, blues/grays, structured -- **Creative**: Colorful, unique layouts, artistic -- **Dark**: Dark backgrounds, neon accents, high contrast -- **Colorful**: Vibrant colors, playful, energetic - -### Adding Features - -Common features to request: -- `responsive design` - Mobile-friendly layout -- `navigation menu` - Header navigation with links -- `footer` - Footer with links/copyright -- `contact form` - Email contact form -- `smooth scrolling` - Smooth page scroll animation -- `hover effects` - Interactive hover animations -- `image gallery` - Photo/image grid -- `testimonials` - Customer reviews section -- `pricing table` - Product/service pricing -- `call-to-action` - Prominent CTA buttons -- `social media links` - Social icon links -- `animations` - Entry/scroll animations -- `lightbox` - Image modal viewer - -## Tips for Success - -### 1. Be Specific -The more detail you provide, the better the results. Instead of "a nice website", describe exactly what you want. - -### 2. Start Simple -Create a basic version first, then use updates to refine and add features. - -### 3. Use Examples -Look at websites you like and describe similar layouts/features. - -### 4. Iterate -Don't expect perfection on the first try. Use the update feature to refine. - -### 5. Check the Code -View the generated code to understand what was created and learn from it. - -## Troubleshooting - -### Website Generation Takes Too Long -- **Cause**: Complex descriptions or slow AI provider -- **Solution**: Simplify description, break into multiple pages, or check AI provider status - -### Generated Code Has Issues -- **Cause**: Ambiguous description or AI interpretation -- **Solution**: Use more specific language, provide examples, or manually edit the generated files - -### Update Doesn't Apply Changes -- **Cause**: Unclear update instructions -- **Solution**: Be specific about what to change and where (e.g., "in the header section" or "in styles.css") - -### Files Not Found -- **Cause**: Website deleted or path issue -- **Solution**: Check `llm-maker/generated_sites/` directory for your website - -## File Structure - -Generated websites are saved in: -``` -llm-maker/generated_sites/ -└── my-website/ - ├── index.html - ├── about.html - ├── contact.html - ├── styles.css - ├── script.js - └── metadata.json -``` - -### metadata.json -Contains website information: -```json -{ - "name": "my-website", - "description": "...", - "style": "modern", - "pages": ["index", "about", "contact"], - "features": ["responsive design", "contact form"], - "created_at": "2025-12-08T12:00:00Z", - "last_updated": "2025-12-08T13:00:00Z", - "last_update_description": "Added smooth scrolling", - "files": ["index.html", "about.html", "contact.html", "styles.css", "script.js"] -} -``` - -## Integration Examples - -### JavaScript Fetch -```javascript -// Create website -const response = await fetch('http://localhost:8090/api/websites', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - name: 'my-site', - description: 'A portfolio website...', - style: 'modern', - pages: ['index', 'about'], - features: ['responsive design'] - }) -}); - -const result = await response.json(); -console.log(result); -``` - -### Python Requests -```python -import requests - -# Create website -response = requests.post( - 'http://localhost:8090/api/websites', - json={ - 'name': 'my-site', - 'description': 'A portfolio website...', - 'style': 'modern', - 'pages': ['index', 'about'], - 'features': ['responsive design'] - } -) - -result = response.json() -print(result) -``` - -### cURL -```bash -# Create website -curl -X POST http://localhost:8090/api/websites \ - -H "Content-Type: application/json" \ - -d '{ - "name": "my-site", - "description": "A portfolio website...", - "style": "modern", - "pages": ["index", "about"], - "features": ["responsive design"] - }' -``` - -## Security Considerations - -### Generated Code Review -- Always review generated code before deploying to production -- Check for any placeholder content or TODO comments -- Verify form actions and API endpoints - -### Form Handling -- Generated contact forms are client-side only -- Implement server-side validation and email sending separately -- Add CAPTCHA to prevent spam - -### Production Deployment -- Minify CSS/JS for performance -- Add proper form handling backend -- Configure proper security headers -- Use HTTPS in production - -## Advanced Usage - -### Custom AI Provider -```python -from website_maker import WebsiteMaker - -# Use specific provider -maker = WebsiteMaker(provider_name='azure') # or 'openai', 'local' - -# Create website -result = maker.create_website( - name='my-site', - description='...', - style='modern' -) -``` - -### Batch Generation -```python -from website_maker import WebsiteMaker - -maker = WebsiteMaker() - -websites = [ - {'name': 'site1', 'description': '...', 'style': 'modern'}, - {'name': 'site2', 'description': '...', 'style': 'minimal'}, - {'name': 'site3', 'description': '...', 'style': 'creative'} -] - -for site_config in websites: - result = maker.create_website(**site_config) - print(f"Created: {result['message']}") -``` - -### Code Validation -```python -from website_maker import WebsiteValidator - -validator = WebsiteValidator() - -# Validate HTML -html = "..." -is_valid, warnings = validator.validate_html(html) - -print(f"Valid: {is_valid}") -print(f"Warnings: {warnings}") - -# Validate CSS -css = "body { ... }" -is_valid, warnings = validator.validate_css(css) - -print(f"Valid: {is_valid}") -print(f"Warnings: {warnings}") -``` - -## Future Enhancements - -Planned features for future releases: -- **Templates**: Pre-built templates for common site types -- **Component Library**: Reusable UI components -- **Theme System**: Customizable color schemes -- **Asset Management**: Image upload and management -- **Preview Server**: Live preview of generated sites -- **Version Control**: Git integration for site history -- **Deployment**: One-click deploy to hosting services -- **CMS Integration**: Connect to content management systems -- **SEO Optimization**: Automatic SEO best practices -- **A/B Testing**: Multiple variant generation - -## Support - -For issues, questions, or feature requests: -1. Check the troubleshooting section above -2. Review the examples in the UI -3. Check the main LLM Maker README -4. Submit an issue on GitHub - -## License - -Same license as the main LLM Maker project. - -## Credits - -Built on top of the LLM Maker framework, leveraging existing chat provider infrastructure for AI-powered website generation. +# AI Website Maker - Documentation + +## Overview + +The AI Website Maker is an automated website generation and update system that uses Large Language Models to create complete, modern, responsive websites from natural language descriptions. + +## Features + +### 🎨 Automated Website Generation +- **Natural Language Input**: Describe what you want in plain English +- **Multiple Visual Styles**: Modern, minimal, corporate, creative, dark theme, colorful +- **Multi-Page Support**: Generate websites with multiple pages (index, about, contact, etc.) +- **Feature Customization**: Add specific features like contact forms, navigation, galleries +- **Responsive Design**: All generated sites are mobile-friendly +- **Modern Code**: HTML5, CSS3, and JavaScript with best practices + +### 🔄 Intelligent Updates +- **AI-Powered Modifications**: Update existing websites with natural language instructions +- **Selective Updates**: Update specific files or entire projects +- **Version Tracking**: Metadata tracks creation and update history +- **Context-Aware**: AI understands current website structure when making changes + +### 📊 Website Management +- **Visual Library**: Browse all generated websites +- **Code Preview**: View complete source code for any website +- **Easy Deletion**: Remove websites you no longer need +- **File Access**: All sites saved in `llm-maker/generated_sites/` + +## Quick Start + +### 1. Start the Server + +```bash +cd llm-maker +python web_server.py +``` + +### 2. Access the Interface + +Open your browser to: `http://localhost:8090/website-maker` + +### 3. Create Your First Website + +**Example 1: Personal Portfolio** +- Name: `my-portfolio` +- Description: `A personal portfolio website with a hero section, about me, skills showcase with progress bars, project gallery with hover effects, and contact form. Use a modern, professional design with smooth animations.` +- Style: `Modern` +- Pages: `index`, `projects`, `contact` +- Features: `responsive design`, `smooth scrolling`, `hover animations`, `contact form` + +**Example 2: Landing Page** +- Name: `product-landing` +- Description: `A product landing page with headline, 3 feature cards with icons, pricing table with 3 tiers, testimonials carousel, and call-to-action button. Clean and conversion-focused.` +- Style: `Minimal` +- Pages: `index` +- Features: `responsive design`, `pricing table`, `testimonials` + +**Example 3: Restaurant Website** +- Name: `restaurant-site` +- Description: `A restaurant website with image carousel, menu sections for appetizers/entrees/desserts, location map, hours of operation, and reservation form. Warm, inviting design.` +- Style: `Creative` +- Pages: `index`, `menu`, `about`, `contact` +- Features: `image carousel`, `menu sections`, `reservation form`, `google maps` + +## API Reference + +### Create Website + +**Endpoint:** `POST /api/websites` + +**Request Body:** +```json +{ + "name": "my-website", + "description": "A modern portfolio website with...", + "style": "modern", + "pages": ["index", "about", "contact"], + "features": ["responsive design", "contact form", "navigation"] +} +``` + +**Response:** +```json{ + "success": true, + "message": "Website 'my-website' created successfully", + "files": { + "index.html": "...", + "styles.css": "body { ... }", + "script.js": "// JavaScript code..." + }, + "path": "/path/to/generated_sites/my-website", + "metadata": { + "name": "my-website", + "description": "...", + "style": "modern", + "pages": ["index", "about", "contact"], + "features": [...], + "created_at": "2025-12-08T12:00:00", + "files": ["index.html", "styles.css", "script.js"] + } +} +``` + +### Update Website + +**Endpoint:** `POST /api/websites/update` + +**Request Body:** +```json +{ + "name": "my-website", + "update_description": "Change the header background to gradient, make buttons bigger, add smooth scroll", + "target_file": "styles.css" // Optional +} +``` + +**Response:** +```json +{ + "success": true, + "message": "Website 'my-website' updated successfully", + "updated_files": { + "styles.css": "/* Updated CSS */" + }, + "path": "/path/to/generated_sites/my-website", + "metadata": {...} +} +``` + +### List Websites + +**Endpoint:** `GET /api/websites` + +**Response:** +```json +{ + "websites": [ + { + "name": "my-website", + "description": "...", + "style": "modern", + "pages": ["index", "about"], + "features": ["responsive design"], + "created_at": "2025-12-08T12:00:00", + "last_updated": "2025-12-08T13:00:00", + "files": ["index.html", "styles.css"], + "path": "/path/to/generated_sites/my-website" + } + ], + "count": 1 +} +``` + +### Get Website + +**Endpoint:** `GET /api/websites/{name}` + +**Response:** +```json +{ + "success": true, + "website": { + "name": "my-website", + "description": "...", + "created_at": "2025-12-08T12:00:00", + "files": ["index.html", "styles.css"], + "files_content": { + "index.html": "...", + "styles.css": "body { ... }" + }, + "path": "/path/to/generated_sites/my-website" + } +} +``` + +### Delete Website + +**Endpoint:** `DELETE /api/websites/{name}` + +**Response:** +```json +{ + "success": true, + "message": "Website 'my-website' deleted successfully" +} +``` + +## Command-Line Interface + +### Create Website +```bash +python src/website_maker.py create my-portfolio \ + "A personal portfolio with projects and contact form" \ + --style modern \ + --pages index about projects contact \ + --features "responsive design" "contact form" "smooth scrolling" +``` + +### Update Website +```bash +python src/website_maker.py update my-portfolio \ + "Change the color scheme to blue and purple gradients" \ + --file styles.css +``` + +### List Websites +```bash +python src/website_maker.py list +``` + +### Delete Website +```bash +python src/website_maker.py delete my-portfolio +``` + +## Best Practices + +### Writing Good Descriptions + +**❌ Too Vague:** +"A website for my business" + +**✅ Good:** +"A website for my photography business with a hero image, portfolio gallery grid with lightbox, about me section with my photo, services list with pricing, and contact form. Modern, clean design with smooth transitions." + +**Key Elements:** +1. **Purpose**: What the website is for +2. **Sections**: What content/sections to include +3. **Features**: Specific functionality (forms, galleries, etc.) +4. **Design Style**: Visual direction (modern, minimal, colorful) +5. **Special Requirements**: Animations, interactions, colors + +### Choosing Visual Styles + +- **Modern**: Clean, gradients, shadows, contemporary +- **Minimal**: Simple, lots of white space, typography-focused +- **Corporate**: Professional, blues/grays, structured +- **Creative**: Colorful, unique layouts, artistic +- **Dark**: Dark backgrounds, neon accents, high contrast +- **Colorful**: Vibrant colors, playful, energetic + +### Adding Features + +Common features to request: +- `responsive design` - Mobile-friendly layout +- `navigation menu` - Header navigation with links +- `footer` - Footer with links/copyright +- `contact form` - Email contact form +- `smooth scrolling` - Smooth page scroll animation +- `hover effects` - Interactive hover animations +- `image gallery` - Photo/image grid +- `testimonials` - Customer reviews section +- `pricing table` - Product/service pricing +- `call-to-action` - Prominent CTA buttons +- `social media links` - Social icon links +- `animations` - Entry/scroll animations +- `lightbox` - Image modal viewer + +## Tips for Success + +### 1. Be Specific +The more detail you provide, the better the results. Instead of "a nice website", describe exactly what you want. + +### 2. Start Simple +Create a basic version first, then use updates to refine and add features. + +### 3. Use Examples +Look at websites you like and describe similar layouts/features. + +### 4. Iterate +Don't expect perfection on the first try. Use the update feature to refine. + +### 5. Check the Code +View the generated code to understand what was created and learn from it. + +## Troubleshooting + +### Website Generation Takes Too Long +- **Cause**: Complex descriptions or slow AI provider +- **Solution**: Simplify description, break into multiple pages, or check AI provider status + +### Generated Code Has Issues +- **Cause**: Ambiguous description or AI interpretation +- **Solution**: Use more specific language, provide examples, or manually edit the generated files + +### Update Doesn't Apply Changes +- **Cause**: Unclear update instructions +- **Solution**: Be specific about what to change and where (e.g., "in the header section" or "in styles.css") + +### Files Not Found +- **Cause**: Website deleted or path issue +- **Solution**: Check `llm-maker/generated_sites/` directory for your website + +## File Structure + +Generated websites are saved in: +``` +llm-maker/generated_sites/ +└── my-website/ + ├── index.html + ├── about.html + ├── contact.html + ├── styles.css + ├── script.js + └── metadata.json +``` + +### metadata.json +Contains website information: +```json +{ + "name": "my-website", + "description": "...", + "style": "modern", + "pages": ["index", "about", "contact"], + "features": ["responsive design", "contact form"], + "created_at": "2025-12-08T12:00:00Z", + "last_updated": "2025-12-08T13:00:00Z", + "last_update_description": "Added smooth scrolling", + "files": ["index.html", "about.html", "contact.html", "styles.css", "script.js"] +} +``` + +## Integration Examples + +### JavaScript Fetch +```javascript +// Create website +const response = await fetch('http://localhost:8090/api/websites', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + name: 'my-site', + description: 'A portfolio website...', + style: 'modern', + pages: ['index', 'about'], + features: ['responsive design'] + }) +}); + +const result = await response.json(); +console.log(result); +``` + +### Python Requests +```python +import requests + +# Create website +response = requests.post( + 'http://localhost:8090/api/websites', + json={ + 'name': 'my-site', + 'description': 'A portfolio website...', + 'style': 'modern', + 'pages': ['index', 'about'], + 'features': ['responsive design'] + } +) + +result = response.json() +print(result) +``` + +### cURL +```bash +# Create website +curl -X POST http://localhost:8090/api/websites \ + -H "Content-Type: application/json" \ + -d '{ + "name": "my-site", + "description": "A portfolio website...", + "style": "modern", + "pages": ["index", "about"], + "features": ["responsive design"] + }' +``` + +## Security Considerations + +### Generated Code Review +- Always review generated code before deploying to production +- Check for any placeholder content or TODO comments +- Verify form actions and API endpoints + +### Form Handling +- Generated contact forms are client-side only +- Implement server-side validation and email sending separately +- Add CAPTCHA to prevent spam + +### Production Deployment +- Minify CSS/JS for performance +- Add proper form handling backend +- Configure proper security headers +- Use HTTPS in production + +## Advanced Usage + +### Custom AI Provider +```python +from website_maker import WebsiteMaker + +# Use specific provider +maker = WebsiteMaker(provider_name='azure') # or 'openai', 'local' + +# Create website +result = maker.create_website( + name='my-site', + description='...', + style='modern' +) +``` + +### Batch Generation +```python +from website_maker import WebsiteMaker + +maker = WebsiteMaker() + +websites = [ + {'name': 'site1', 'description': '...', 'style': 'modern'}, + {'name': 'site2', 'description': '...', 'style': 'minimal'}, + {'name': 'site3', 'description': '...', 'style': 'creative'} +] + +for site_config in websites: + result = maker.create_website(**site_config) + print(f"Created: {result['message']}") +``` + +### Code Validation +```python +from website_maker import WebsiteValidator + +validator = WebsiteValidator() + +# Validate HTML +html = "..." +is_valid, warnings = validator.validate_html(html) + +print(f"Valid: {is_valid}") +print(f"Warnings: {warnings}") + +# Validate CSS +css = "body { ... }" +is_valid, warnings = validator.validate_css(css) + +print(f"Valid: {is_valid}") +print(f"Warnings: {warnings}") +``` + +## Future Enhancements + +Planned features for future releases: +- **Templates**: Pre-built templates for common site types +- **Component Library**: Reusable UI components +- **Theme System**: Customizable color schemes +- **Asset Management**: Image upload and management +- **Preview Server**: Live preview of generated sites +- **Version Control**: Git integration for site history +- **Deployment**: One-click deploy to hosting services +- **CMS Integration**: Connect to content management systems +- **SEO Optimization**: Automatic SEO best practices +- **A/B Testing**: Multiple variant generation + +## Support + +For issues, questions, or feature requests: +1. Check the troubleshooting section above +2. Review the examples in the UI +3. Check the main LLM Maker README +4. Submit an issue on GitHub + +## License + +Same license as the main LLM Maker project. + +## Credits + +Built on top of the LLM Maker framework, leveraging existing chat provider infrastructure for AI-powered website generation. diff --git a/ai-projects/llm-maker/WEB_UI_README.md b/ai-projects/llm-maker/WEB_UI_README.md index e16004808..a14235d5b 100644 --- a/ai-projects/llm-maker/WEB_UI_README.md +++ b/ai-projects/llm-maker/WEB_UI_README.md @@ -1,206 +1,206 @@ -# LLM Maker - Web UI - -User-friendly web interface for creating and managing AI tools with LLM Maker. - -## 🚀 Quick Start - -1. **Start the web server:** - ```bash - cd llm-maker - python web_server.py - ``` - - Or use the helper script: - ```bash - ./start_web_ui.sh - ``` - -2. **Open your browser:** - Navigate to [http://localhost:8090](http://localhost:8090) - -## ✨ Features - -### Create Tools -- **Natural Language Description**: Describe what you want your tool to do -- **Parameters**: Define input parameters with types (int, str, dict, etc.) -- **Return Types**: Specify expected output type -- **AI-Powered Generation**: LLM automatically creates validated Python code - -### View Tools -- **Tool Library**: Browse all created tools -- **Code Preview**: View generated Python code -- **Statistics**: Track tool usage and validation status - -### Test Tools -- **Interactive Testing**: Test tools directly in the browser -- **Parameter Input**: Enter test values with type validation -- **Execution Results**: See output, errors, and execution time -- **Real-time Feedback**: Immediate validation and error messages - -### Manage Tools -- **Delete Tools**: Remove unwanted tools -- **Search & Filter**: Find tools quickly -- **Export**: Share tools with others - -## 🎨 Interface - -### Dashboard -- **Tool Creation Form**: Left panel for creating new tools -- **Statistics Panel**: Right panel showing tool metrics -- **Tool Library**: Bottom panel listing all tools - -### Tool Creation -1. Enter a descriptive tool name (e.g., `calculate_fibonacci`) -2. Describe what the tool should do -3. Add parameters (click "+ Add Parameter") - - Parameter name: `n` - - Parameter type: `int` -4. Specify return type (e.g., `int`) -5. Click "Create Tool" - -### Tool Testing -1. Click "🧪 Test Tool" on any tool -2. Enter parameter values -3. Click "▶️ Run Tool" -4. View results or errors - -## 🔧 API Endpoints - -The web server provides these REST endpoints: - -### GET /api/tools -List all tools with statistics -```json -{ - "tools": [...], - "stats": { - "total_tools": 5, - "validated_tools": 5, - "total_executions": 12 - } -} -``` - -### POST /api/tools -Create a new tool -```json -{ - "name": "calculate_fibonacci", - "description": "Calculate nth Fibonacci number", - "parameters": {"n": "int"}, - "return_type": "int" -} -``` - -### POST /api/tools/execute -Execute a tool -```json -{ - "tool_id": "tool_xyz", - "arguments": {"n": 10} -} -``` - -### DELETE /api/tools/{tool_id} -Delete a tool - -## 🎯 Use Cases - -1. **Quick Prototyping**: Generate utility functions on-demand -2. **Educational**: Learn function creation and Python patterns -3. **Code Assistant**: Create reusable functions for projects -4. **Experimentation**: Test LLM code generation capabilities - -## 🔒 Security - -All tools are: -- ✅ Validated before execution -- ✅ Run in sandboxed environment -- ✅ Limited to 5 second timeout -- ✅ Blocked from file/network access - -## 📸 Screenshots - -### Creating a Tool -The creation form allows you to describe your tool in natural language: -- Tool name: `calculate_fibonacci` -- Description: "Calculate the nth Fibonacci number" -- Parameters: `n: int` -- Return type: `int` - -### Testing a Tool -Interactive testing interface: -- Enter parameter values -- View execution results -- See errors with traceback -- Check execution statistics - -### Tool Library -Browse and manage all your tools: -- View code -- Test execution -- Delete tools -- Track usage - -## 💡 Tips - -1. **Be Specific**: Detailed descriptions produce better code -2. **Use Examples**: Add example inputs/outputs for better generation -3. **Test Early**: Test tools immediately after creation -4. **Start Simple**: Begin with simple functions, then add complexity -5. **Check Validation**: Ensure tools are validated (green checkmark) - -## 🐛 Troubleshooting - -### Server won't start -- Check if port 8090 is available -- Verify Python dependencies are installed -- Run from the `llm-maker` directory - -### Tool creation fails -- Check AI provider is configured (Azure OpenAI, OpenAI, or local) -- Verify environment variables are set -- Check logs for validation errors - -### Tool execution fails -- Verify parameter types match -- Check for syntax errors in generated code -- Review error traceback in test modal - -### Browser can't connect -- Ensure server is running (`python web_server.py`) -- Check firewall settings -- Try http://127.0.0.1:8090 instead - -## 🚀 Advanced Usage - -### Custom Port -```bash -# Edit web_server.py and change: -port = 8090 # Change to your preferred port -``` - -### Remote Access -By default, server binds to `0.0.0.0` allowing remote access. -For local-only, change to `127.0.0.1`: -```python -host = '127.0.0.1' # localhost only -``` - -### Production Deployment -For production use: -1. Use a proper WSGI server (gunicorn, uWSGI) -2. Add authentication -3. Enable HTTPS -4. Set up rate limiting -5. Add request logging - -## 📚 Learn More - -- [Main README](README.md) - Full LLM Maker documentation -- [Quick Start](QUICKSTART.md) - Getting started guide -- [API Documentation](README.md#api-reference) - Detailed API docs - ---- - -**Built with ❤️ for the LLM Maker project** +# LLM Maker - Web UI + +User-friendly web interface for creating and managing AI tools with LLM Maker. + +## 🚀 Quick Start + +1. **Start the web server:** + ```bash + cd llm-maker + python web_server.py + ``` + + Or use the helper script: + ```bash + ./start_web_ui.sh + ``` + +2. **Open your browser:** + Navigate to [http://localhost:8090](http://localhost:8090) + +## ✨ Features + +### Create Tools +- **Natural Language Description**: Describe what you want your tool to do +- **Parameters**: Define input parameters with types (int, str, dict, etc.) +- **Return Types**: Specify expected output type +- **AI-Powered Generation**: LLM automatically creates validated Python code + +### View Tools +- **Tool Library**: Browse all created tools +- **Code Preview**: View generated Python code +- **Statistics**: Track tool usage and validation status + +### Test Tools +- **Interactive Testing**: Test tools directly in the browser +- **Parameter Input**: Enter test values with type validation +- **Execution Results**: See output, errors, and execution time +- **Real-time Feedback**: Immediate validation and error messages + +### Manage Tools +- **Delete Tools**: Remove unwanted tools +- **Search & Filter**: Find tools quickly +- **Export**: Share tools with others + +## 🎨 Interface + +### Dashboard +- **Tool Creation Form**: Left panel for creating new tools +- **Statistics Panel**: Right panel showing tool metrics +- **Tool Library**: Bottom panel listing all tools + +### Tool Creation +1. Enter a descriptive tool name (e.g., `calculate_fibonacci`) +2. Describe what the tool should do +3. Add parameters (click "+ Add Parameter") + - Parameter name: `n` + - Parameter type: `int` +4. Specify return type (e.g., `int`) +5. Click "Create Tool" + +### Tool Testing +1. Click "🧪 Test Tool" on any tool +2. Enter parameter values +3. Click "▶️ Run Tool" +4. View results or errors + +## 🔧 API Endpoints + +The web server provides these REST endpoints: + +### GET /api/tools +List all tools with statistics +```json +{ + "tools": [...], + "stats": { + "total_tools": 5, + "validated_tools": 5, + "total_executions": 12 + } +} +``` + +### POST /api/tools +Create a new tool +```json +{ + "name": "calculate_fibonacci", + "description": "Calculate nth Fibonacci number", + "parameters": {"n": "int"}, + "return_type": "int" +} +``` + +### POST /api/tools/execute +Execute a tool +```json +{ + "tool_id": "tool_xyz", + "arguments": {"n": 10} +} +``` + +### DELETE /api/tools/{tool_id} +Delete a tool + +## 🎯 Use Cases + +1. **Quick Prototyping**: Generate utility functions on-demand +2. **Educational**: Learn function creation and Python patterns +3. **Code Assistant**: Create reusable functions for projects +4. **Experimentation**: Test LLM code generation capabilities + +## 🔒 Security + +All tools are: +- ✅ Validated before execution +- ✅ Run in sandboxed environment +- ✅ Limited to 5 second timeout +- ✅ Blocked from file/network access + +## 📸 Screenshots + +### Creating a Tool +The creation form allows you to describe your tool in natural language: +- Tool name: `calculate_fibonacci` +- Description: "Calculate the nth Fibonacci number" +- Parameters: `n: int` +- Return type: `int` + +### Testing a Tool +Interactive testing interface: +- Enter parameter values +- View execution results +- See errors with traceback +- Check execution statistics + +### Tool Library +Browse and manage all your tools: +- View code +- Test execution +- Delete tools +- Track usage + +## 💡 Tips + +1. **Be Specific**: Detailed descriptions produce better code +2. **Use Examples**: Add example inputs/outputs for better generation +3. **Test Early**: Test tools immediately after creation +4. **Start Simple**: Begin with simple functions, then add complexity +5. **Check Validation**: Ensure tools are validated (green checkmark) + +## 🐛 Troubleshooting + +### Server won't start +- Check if port 8090 is available +- Verify Python dependencies are installed +- Run from the `llm-maker` directory + +### Tool creation fails +- Check AI provider is configured (Azure OpenAI, OpenAI, or local) +- Verify environment variables are set +- Check logs for validation errors + +### Tool execution fails +- Verify parameter types match +- Check for syntax errors in generated code +- Review error traceback in test modal + +### Browser can't connect +- Ensure server is running (`python web_server.py`) +- Check firewall settings +- Try http://127.0.0.1:8090 instead + +## 🚀 Advanced Usage + +### Custom Port +```bash +# Edit web_server.py and change: +port = 8090 # Change to your preferred port +``` + +### Remote Access +By default, server binds to `0.0.0.0` allowing remote access. +For local-only, change to `127.0.0.1`: +```python +host = '127.0.0.1' # localhost only +``` + +### Production Deployment +For production use: +1. Use a proper WSGI server (gunicorn, uWSGI) +2. Add authentication +3. Enable HTTPS +4. Set up rate limiting +5. Add request logging + +## 📚 Learn More + +- [Main README](README.md) - Full LLM Maker documentation +- [Quick Start](QUICKSTART.md) - Getting started guide +- [API Documentation](README.md#api-reference) - Detailed API docs + +--- + +**Built with ❤️ for the LLM Maker project** diff --git a/ai-projects/llm-maker/examples/fibonacci.py b/ai-projects/llm-maker/examples/fibonacci.py index 429d8a6d5..5e5d902a9 100644 --- a/ai-projects/llm-maker/examples/fibonacci.py +++ b/ai-projects/llm-maker/examples/fibonacci.py @@ -1,33 +1,33 @@ -""" -Example Tool: Fibonacci Calculator -Demonstrates a simple mathematical tool -""" - - -def calculate_fibonacci(n: int) -> int: - """ - Calculate the nth Fibonacci number - - Args: - n: Position in Fibonacci sequence (0-indexed) - - Returns: - The nth Fibonacci number - """ - if n < 0: - raise ValueError("n must be non-negative") - - if n <= 1: - return n - - a, b = 0, 1 - for _ in range(2, n + 1): - a, b = b, a + b - - return b - - -# Example usage: -if __name__ == "__main__": - for i in range(10): - print(f"F({i}) = {calculate_fibonacci(i)}") +""" +Example Tool: Fibonacci Calculator +Demonstrates a simple mathematical tool +""" + + +def calculate_fibonacci(n: int) -> int: + """ + Calculate the nth Fibonacci number + + Args: + n: Position in Fibonacci sequence (0-indexed) + + Returns: + The nth Fibonacci number + """ + if n < 0: + raise ValueError("n must be non-negative") + + if n <= 1: + return n + + a, b = 0, 1 + for _ in range(2, n + 1): + a, b = b, a + b + + return b + + +# Example usage: +if __name__ == "__main__": + for i in range(10): + print(f"F({i}) = {calculate_fibonacci(i)}") diff --git a/ai-projects/llm-maker/examples/quick_start.py b/ai-projects/llm-maker/examples/quick_start.py index 2a9448053..98e576dd3 100644 --- a/ai-projects/llm-maker/examples/quick_start.py +++ b/ai-projects/llm-maker/examples/quick_start.py @@ -1,124 +1,124 @@ -""" -Quick Start Example for LLM Maker -Demonstrates creating and using tools -""" - -import sys -from pathlib import Path - -# Add src to path -sys.path.insert(0, str(Path(__file__).parent.parent / "src")) - -from tool_executor import ToolExecutor -from tool_maker import ToolMaker -from tool_registry import ToolRegistry - - -def main(): - """Run quick start examples""" - print("=" * 60) - print("LLM Maker - Quick Start") - print("=" * 60) - print() - - # Initialize components - print("Initializing components...") - maker = ToolMaker() - registry = ToolRegistry() - executor = ToolExecutor() - print("✓ Components initialized") - print() - - # Example 1: Create a simple math tool - print("Example 1: Creating a Fibonacci calculator...") - print("-" * 60) - - tool = maker.create_tool( - name="calculate_fibonacci", - description="Calculate the nth Fibonacci number", - parameters={"n": "int"}, - return_type="int", - examples=[ - {"input": {"n": 0}, "output": 0}, - {"input": {"n": 1}, "output": 1}, - {"input": {"n": 10}, "output": 55}, - ], - ) - - if tool: - print(f"✓ Tool created: {tool.name}") - print(f" Description: {tool.description}") - print(f" Validated: {tool.validated}") - print() - - # Register the tool - tool_id = registry.register(tool) - print(f"✓ Tool registered with ID: {tool_id}") - print() - - # Execute the tool - print("Testing tool execution:") - for n in [0, 1, 5, 10]: - result = executor.execute(tool.code, tool.name, {"n": n}) - if result["success"]: - print(f" F({n}) = {result['result']}") - else: - print(f" Error for F({n}): {result['error']}") - print() - else: - print("✗ Failed to create tool") - print() - - # Example 2: Create a string processing tool - print("Example 2: Creating a text analyzer...") - print("-" * 60) - - tool2 = maker.create_tool( - name="count_words", - description="Count the number of words in text", - parameters={"text": "str"}, - return_type="int", - ) - - if tool2: - print(f"✓ Tool created: {tool2.name}") - tool_id2 = registry.register(tool2) - print(f"✓ Tool registered with ID: {tool_id2}") - print() - - # Test it - test_text = "The quick brown fox jumps over the lazy dog" - result = executor.execute(tool2.code, tool2.name, {"text": test_text}) - if result["success"]: - print(f" Text: '{test_text}'") - print(f" Word count: {result['result']}") - print() - else: - print("✗ Failed to create tool") - print() - - # Show registry statistics - print("Registry Statistics:") - print("-" * 60) - stats = registry.get_stats() - print(f" Total tools: {stats['total_tools']}") - print(f" Validated tools: {stats['validated_tools']}") - print(f" Total executions: {stats['total_executions']}") - print() - - # List all tools - print("Registered Tools:") - print("-" * 60) - tools = registry.list_tools() - for t in tools: - print(f" - {t.name}: {t.description}") - print(f" Executions: {t.execution_count}") - print() - - print("=" * 60) - print("Quick start complete!") - print("=" * 60) - - -if __name__ == "__main__": - main() +""" +Quick Start Example for LLM Maker +Demonstrates creating and using tools +""" + +import sys +from pathlib import Path + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + +from tool_executor import ToolExecutor +from tool_maker import ToolMaker +from tool_registry import ToolRegistry + + +def main(): + """Run quick start examples""" + print("=" * 60) + print("LLM Maker - Quick Start") + print("=" * 60) + print() + + # Initialize components + print("Initializing components...") + maker = ToolMaker() + registry = ToolRegistry() + executor = ToolExecutor() + print("✓ Components initialized") + print() + + # Example 1: Create a simple math tool + print("Example 1: Creating a Fibonacci calculator...") + print("-" * 60) + + tool = maker.create_tool( + name="calculate_fibonacci", + description="Calculate the nth Fibonacci number", + parameters={"n": "int"}, + return_type="int", + examples=[ + {"input": {"n": 0}, "output": 0}, + {"input": {"n": 1}, "output": 1}, + {"input": {"n": 10}, "output": 55}, + ], + ) + + if tool: + print(f"✓ Tool created: {tool.name}") + print(f" Description: {tool.description}") + print(f" Validated: {tool.validated}") + print() + + # Register the tool + tool_id = registry.register(tool) + print(f"✓ Tool registered with ID: {tool_id}") + print() + + # Execute the tool + print("Testing tool execution:") + for n in [0, 1, 5, 10]: + result = executor.execute(tool.code, tool.name, {"n": n}) + if result["success"]: + print(f" F({n}) = {result['result']}") + else: + print(f" Error for F({n}): {result['error']}") + print() + else: + print("✗ Failed to create tool") + print() + + # Example 2: Create a string processing tool + print("Example 2: Creating a text analyzer...") + print("-" * 60) + + tool2 = maker.create_tool( + name="count_words", + description="Count the number of words in text", + parameters={"text": "str"}, + return_type="int", + ) + + if tool2: + print(f"✓ Tool created: {tool2.name}") + tool_id2 = registry.register(tool2) + print(f"✓ Tool registered with ID: {tool_id2}") + print() + + # Test it + test_text = "The quick brown fox jumps over the lazy dog" + result = executor.execute(tool2.code, tool2.name, {"text": test_text}) + if result["success"]: + print(f" Text: '{test_text}'") + print(f" Word count: {result['result']}") + print() + else: + print("✗ Failed to create tool") + print() + + # Show registry statistics + print("Registry Statistics:") + print("-" * 60) + stats = registry.get_stats() + print(f" Total tools: {stats['total_tools']}") + print(f" Validated tools: {stats['validated_tools']}") + print(f" Total executions: {stats['total_executions']}") + print() + + # List all tools + print("Registered Tools:") + print("-" * 60) + tools = registry.list_tools() + for t in tools: + print(f" - {t.name}: {t.description}") + print(f" Executions: {t.execution_count}") + print() + + print("=" * 60) + print("Quick start complete!") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/ai-projects/llm-maker/examples/text_processor.py b/ai-projects/llm-maker/examples/text_processor.py index 2148696ec..4552d45d8 100644 --- a/ai-projects/llm-maker/examples/text_processor.py +++ b/ai-projects/llm-maker/examples/text_processor.py @@ -1,48 +1,48 @@ -""" -Example Tool: Text Processor -Demonstrates string manipulation tool -""" - -import re -from typing import List - - -def extract_emails(text: str) -> List[str]: - """ - Extract email addresses from text - - Args: - text: Input text to search - - Returns: - List of email addresses found - """ - email_pattern = r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b" - emails = re.findall(email_pattern, text) - return list(set(emails)) # Remove duplicates - - -def count_words(text: str) -> dict: - """ - Count word occurrences in text - - Args: - text: Input text - - Returns: - Dictionary of word counts - """ - words = re.findall(r"\b\w+\b", text.lower()) - counts = {} - for word in words: - counts[word] = counts.get(word, 0) + 1 - return counts - - -# Example usage: -if __name__ == "__main__": - sample_text = "Contact us at support@example.com or sales@example.org" - print("Emails found:", extract_emails(sample_text)) - - sample_text2 = "The quick brown fox jumps over the lazy dog" - print("Word counts:", count_words(sample_text2)) +""" +Example Tool: Text Processor +Demonstrates string manipulation tool +""" + +import re +from typing import List + + +def extract_emails(text: str) -> List[str]: + """ + Extract email addresses from text + + Args: + text: Input text to search + + Returns: + List of email addresses found + """ + email_pattern = r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b" + emails = re.findall(email_pattern, text) + return list(set(emails)) # Remove duplicates + + +def count_words(text: str) -> dict: + """ + Count word occurrences in text + + Args: + text: Input text + + Returns: + Dictionary of word counts + """ + words = re.findall(r"\b\w+\b", text.lower()) + counts = {} + for word in words: + counts[word] = counts.get(word, 0) + 1 + return counts + + +# Example usage: +if __name__ == "__main__": + sample_text = "Contact us at support@example.com or sales@example.org" + print("Emails found:", extract_emails(sample_text)) + + sample_text2 = "The quick brown fox jumps over the lazy dog" + print("Word counts:", count_words(sample_text2)) diff --git a/ai-projects/llm-maker/llm_maker_config.yaml b/ai-projects/llm-maker/llm_maker_config.yaml index 767a661fd..7edeb2168 100644 --- a/ai-projects/llm-maker/llm_maker_config.yaml +++ b/ai-projects/llm-maker/llm_maker_config.yaml @@ -1,79 +1,79 @@ -# LLM Maker Configuration - -tool_maker: - # AI provider for tool generation (azure, openai, local) - provider: local # Changed to local for demo/testing - - # Maximum number of tools to store - max_tools: 1000 - - # Directory for generated tools - tools_directory: ./tools - - # Generation settings - temperature: 0.7 - max_tokens: 2000 - -validation: - # Enable strict validation mode - strict_mode: true - - # Allowed Python imports (whitelist) - allowed_imports: - - math - - json - - re - - datetime - - collections - - itertools - - functools - - typing - - # Blocked operations - block_file_access: true - block_network_access: true - block_subprocess: true - block_eval_exec: true - -execution: - # Execution timeout in seconds - timeout_seconds: 5 - - # Maximum memory usage in MB - max_memory_mb: 512 - - # Enable execution logging - enable_logging: true - - # Maximum output size - max_output_size: 10000 - -safety: - # Block dangerous built-ins - block_dangerous_builtins: true - - # Dangerous functions to block - blocked_functions: - - __import__ - - eval - - exec - - compile - - open - - input - - breakpoint - - # Block attribute access to dangerous modules - blocked_attributes: - - __code__ - - __globals__ - - __builtins__ - -mcp_server: - # Server name - name: llm-maker-mcp - - # Enable MCP server - enabled: true - - # Server timeout - timeout: 30 +# LLM Maker Configuration + +tool_maker: + # AI provider for tool generation (azure, openai, local) + provider: local # Changed to local for demo/testing + + # Maximum number of tools to store + max_tools: 1000 + + # Directory for generated tools + tools_directory: ./tools + + # Generation settings + temperature: 0.7 + max_tokens: 2000 + +validation: + # Enable strict validation mode + strict_mode: true + + # Allowed Python imports (whitelist) + allowed_imports: + - math + - json + - re + - datetime + - collections + - itertools + - functools + - typing + + # Blocked operations + block_file_access: true + block_network_access: true + block_subprocess: true + block_eval_exec: true + +execution: + # Execution timeout in seconds + timeout_seconds: 5 + + # Maximum memory usage in MB + max_memory_mb: 512 + + # Enable execution logging + enable_logging: true + + # Maximum output size + max_output_size: 10000 + +safety: + # Block dangerous built-ins + block_dangerous_builtins: true + + # Dangerous functions to block + blocked_functions: + - __import__ + - eval + - exec + - compile + - open + - input + - breakpoint + + # Block attribute access to dangerous modules + blocked_attributes: + - __code__ + - __globals__ + - __builtins__ + +mcp_server: + # Server name + name: llm-maker-mcp + + # Enable MCP server + enabled: true + + # Server timeout + timeout: 30 diff --git a/ai-projects/llm-maker/llm_maker_mcp_server.py b/ai-projects/llm-maker/llm_maker_mcp_server.py index e71223a03..e271d3bba 100644 --- a/ai-projects/llm-maker/llm_maker_mcp_server.py +++ b/ai-projects/llm-maker/llm_maker_mcp_server.py @@ -1,335 +1,335 @@ -""" -LLM Maker MCP Server -Exposes tool creation and execution capabilities via Model Context Protocol -""" - -import asyncio -import json -import logging -import sys -from pathlib import Path - -# Add src directory to path -src_path = Path(__file__).parent / "src" -sys.path.insert(0, str(src_path)) - -# Import MCP dependencies -try: - from mcp.server import Server - from mcp.server.stdio import stdio_server - from mcp.types import TextContent - from mcp.types import Tool as MCPTool -except ImportError as e: - print("Error: MCP package not installed.") - print("\nTo install MCP dependencies, run:") - print(" pip install -r requirements.txt") - print(f"\nDetails: {e}") - sys.exit(1) - -from src.tool_executor import ToolExecutor -# Import LLM Maker components -from src.tool_maker import ToolMaker -from src.tool_registry import ToolRegistry -from src.tool_validator import ToolValidator - -# Setup logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -# Initialize components -app = Server("llm-maker-mcp") -maker = ToolMaker() -validator = ToolValidator() -executor = ToolExecutor() -registry = ToolRegistry() - - -@app.list_tools() -async def list_tools() -> list[MCPTool]: - """List available MCP tools""" - return [ - MCPTool( - name="create_tool", - description="Create a new Python tool from natural language description", - inputSchema={ - "type": "object", - "properties": { - "name": { - "type": "string", - "description": "Function name (valid Python identifier)", - }, - "description": { - "type": "string", - "description": "What the tool should do", - }, - "parameters": { - "type": "object", - "description": "Parameter names and types (e.g., {'n': 'int', 'text': 'str'})", - }, - "return_type": { - "type": "string", - "description": "Expected return type", - "default": "Any", - }, - "examples": { - "type": "array", - "description": "Optional list of example inputs/outputs", - "items": {"type": "object"}, - }, - }, - "required": ["name", "description", "parameters"], - }, - ), - MCPTool( - name="execute_tool", - description="Execute a registered tool with given arguments", - inputSchema={ - "type": "object", - "properties": { - "tool_id": {"type": "string", "description": "Tool ID or name"}, - "arguments": { - "type": "object", - "description": "Arguments to pass to the tool", - }, - }, - "required": ["tool_id", "arguments"], - }, - ), - MCPTool( - name="list_registered_tools", - description="List all registered tools in the registry", - inputSchema={ - "type": "object", - "properties": { - "tags": { - "type": "array", - "description": "Optional filter by tags", - "items": {"type": "string"}, - } - }, - }, - ), - MCPTool( - name="get_tool", - description="Get details of a specific tool", - inputSchema={ - "type": "object", - "properties": { - "tool_id": {"type": "string", "description": "Tool ID or name"} - }, - "required": ["tool_id"], - }, - ), - MCPTool( - name="delete_tool", - description="Delete a tool from the registry", - inputSchema={ - "type": "object", - "properties": { - "tool_id": {"type": "string", "description": "Tool ID to delete"} - }, - "required": ["tool_id"], - }, - ), - MCPTool( - name="validate_tool", - description="Validate a tool for safety", - inputSchema={ - "type": "object", - "properties": { - "tool_id": {"type": "string", "description": "Tool ID or name"} - }, - "required": ["tool_id"], - }, - ), - MCPTool( - name="registry_stats", - description="Get statistics about the tool registry", - inputSchema={"type": "object", "properties": {}}, - ), - ] - - -@app.call_tool() -async def call_tool(name: str, arguments: dict) -> list[TextContent]: - """Handle tool calls""" - try: - if name == "create_tool": - return await handle_create_tool(arguments) - elif name == "execute_tool": - return await handle_execute_tool(arguments) - elif name == "list_registered_tools": - return await handle_list_tools(arguments) - elif name == "get_tool": - return await handle_get_tool(arguments) - elif name == "delete_tool": - return await handle_delete_tool(arguments) - elif name == "validate_tool": - return await handle_validate_tool(arguments) - elif name == "registry_stats": - return await handle_registry_stats(arguments) - else: - return [TextContent(type="text", text=f"Unknown tool: {name}")] - except Exception as e: - logger.error(f"Error handling {name}: {e}", exc_info=True) - return [TextContent(type="text", text=f"Error: {str(e)}")] - - -async def handle_create_tool(args: dict) -> list[TextContent]: - """Handle create_tool command""" - name = args["name"] - description = args["description"] - parameters = args["parameters"] - return_type = args.get("return_type", "Any") - examples = args.get("examples", []) - - logger.info(f"Creating tool: {name}") - - tool = maker.create_tool( - name=name, - description=description, - parameters=parameters, - return_type=return_type, - examples=examples, - ) - - if tool: - tool_id = registry.register(tool) - result = { - "success": True, - "tool_id": tool_id, - "name": tool.name, - "message": f"Tool '{name}' created and registered successfully", - "code": tool.code, - } - else: - result = { - "success": False, - "error": "Failed to create tool - validation errors or generation failure", - } - - return [TextContent(type="text", text=json.dumps(result, indent=2))] - - -async def handle_execute_tool(args: dict) -> list[TextContent]: - """Handle execute_tool command""" - tool_id = args["tool_id"] - arguments = args["arguments"] - - # Get tool by ID or name - tool = registry.get(tool_id) - if not tool: - tool = registry.get_by_name(tool_id) - - if not tool: - result = {"success": False, "error": f"Tool '{tool_id}' not found"} - return [TextContent(type="text", text=json.dumps(result, indent=2))] - - logger.info(f"Executing tool: {tool.name}") - - # Execute the tool - exec_result = executor.execute(tool.code, tool.name, arguments) - - # Update statistics - if exec_result.get("success"): - registry.update_stats(tool.id) - - return [TextContent(type="text", text=json.dumps(exec_result, indent=2))] - - -async def handle_list_tools(args: dict) -> list[TextContent]: - """Handle list_registered_tools command""" - tags = args.get("tags", None) - - tools = registry.list_tools(tags=tags) - - result = { - "count": len(tools), - "tools": [ - { - "id": t.id, - "name": t.name, - "description": t.description, - "parameters": t.parameters, - "return_type": t.return_type, - "validated": t.validated, - "execution_count": t.execution_count, - "created_at": t.created_at, - } - for t in tools - ], - } - - return [TextContent(type="text", text=json.dumps(result, indent=2))] - - -async def handle_get_tool(args: dict) -> list[TextContent]: - """Handle get_tool command""" - tool_id = args["tool_id"] - - tool = registry.get(tool_id) - if not tool: - tool = registry.get_by_name(tool_id) - - if not tool: - result = {"success": False, "error": f"Tool '{tool_id}' not found"} - else: - result = {"success": True, "tool": tool.to_dict()} - - return [TextContent(type="text", text=json.dumps(result, indent=2))] - - -async def handle_delete_tool(args: dict) -> list[TextContent]: - """Handle delete_tool command""" - tool_id = args["tool_id"] - - success = registry.delete(tool_id) - - result = { - "success": success, - "message": "Tool deleted" if success else f"Tool '{tool_id}' not found", - } - - return [TextContent(type="text", text=json.dumps(result, indent=2))] - - -async def handle_validate_tool(args: dict) -> list[TextContent]: - """Handle validate_tool command""" - tool_id = args["tool_id"] - - tool = registry.get(tool_id) - if not tool: - tool = registry.get_by_name(tool_id) - - if not tool: - result = {"success": False, "error": f"Tool '{tool_id}' not found"} - return [TextContent(type="text", text=json.dumps(result, indent=2))] - - is_valid, errors = validator.validate(tool.code) - - result = { - "tool_id": tool.id, - "tool_name": tool.name, - "is_valid": is_valid, - "errors": errors if not is_valid else [], - } - - return [TextContent(type="text", text=json.dumps(result, indent=2))] - - -async def handle_registry_stats(args: dict) -> list[TextContent]: - """Handle registry_stats command""" - stats = registry.get_stats() - return [TextContent(type="text", text=json.dumps(stats, indent=2))] - - -async def main(): - """Run the MCP server""" - logger.info("Starting LLM Maker MCP Server...") - - async with stdio_server() as (read_stream, write_stream): - await app.run(read_stream, write_stream, app.create_initialization_options()) - - -if __name__ == "__main__": - asyncio.run(main()) +""" +LLM Maker MCP Server +Exposes tool creation and execution capabilities via Model Context Protocol +""" + +import asyncio +import json +import logging +import sys +from pathlib import Path + +# Add src directory to path +src_path = Path(__file__).parent / "src" +sys.path.insert(0, str(src_path)) + +# Import MCP dependencies +try: + from mcp.server import Server + from mcp.server.stdio import stdio_server + from mcp.types import TextContent + from mcp.types import Tool as MCPTool +except ImportError as e: + print("Error: MCP package not installed.") + print("\nTo install MCP dependencies, run:") + print(" pip install -r requirements.txt") + print(f"\nDetails: {e}") + sys.exit(1) + +from src.tool_executor import ToolExecutor +# Import LLM Maker components +from src.tool_maker import ToolMaker +from src.tool_registry import ToolRegistry +from src.tool_validator import ToolValidator + +# Setup logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# Initialize components +app = Server("llm-maker-mcp") +maker = ToolMaker() +validator = ToolValidator() +executor = ToolExecutor() +registry = ToolRegistry() + + +@app.list_tools() +async def list_tools() -> list[MCPTool]: + """List available MCP tools""" + return [ + MCPTool( + name="create_tool", + description="Create a new Python tool from natural language description", + inputSchema={ + "type": "object", + "properties": { + "name": { + "type": "string", + "description": "Function name (valid Python identifier)", + }, + "description": { + "type": "string", + "description": "What the tool should do", + }, + "parameters": { + "type": "object", + "description": "Parameter names and types (e.g., {'n': 'int', 'text': 'str'})", + }, + "return_type": { + "type": "string", + "description": "Expected return type", + "default": "Any", + }, + "examples": { + "type": "array", + "description": "Optional list of example inputs/outputs", + "items": {"type": "object"}, + }, + }, + "required": ["name", "description", "parameters"], + }, + ), + MCPTool( + name="execute_tool", + description="Execute a registered tool with given arguments", + inputSchema={ + "type": "object", + "properties": { + "tool_id": {"type": "string", "description": "Tool ID or name"}, + "arguments": { + "type": "object", + "description": "Arguments to pass to the tool", + }, + }, + "required": ["tool_id", "arguments"], + }, + ), + MCPTool( + name="list_registered_tools", + description="List all registered tools in the registry", + inputSchema={ + "type": "object", + "properties": { + "tags": { + "type": "array", + "description": "Optional filter by tags", + "items": {"type": "string"}, + } + }, + }, + ), + MCPTool( + name="get_tool", + description="Get details of a specific tool", + inputSchema={ + "type": "object", + "properties": { + "tool_id": {"type": "string", "description": "Tool ID or name"} + }, + "required": ["tool_id"], + }, + ), + MCPTool( + name="delete_tool", + description="Delete a tool from the registry", + inputSchema={ + "type": "object", + "properties": { + "tool_id": {"type": "string", "description": "Tool ID to delete"} + }, + "required": ["tool_id"], + }, + ), + MCPTool( + name="validate_tool", + description="Validate a tool for safety", + inputSchema={ + "type": "object", + "properties": { + "tool_id": {"type": "string", "description": "Tool ID or name"} + }, + "required": ["tool_id"], + }, + ), + MCPTool( + name="registry_stats", + description="Get statistics about the tool registry", + inputSchema={"type": "object", "properties": {}}, + ), + ] + + +@app.call_tool() +async def call_tool(name: str, arguments: dict) -> list[TextContent]: + """Handle tool calls""" + try: + if name == "create_tool": + return await handle_create_tool(arguments) + elif name == "execute_tool": + return await handle_execute_tool(arguments) + elif name == "list_registered_tools": + return await handle_list_tools(arguments) + elif name == "get_tool": + return await handle_get_tool(arguments) + elif name == "delete_tool": + return await handle_delete_tool(arguments) + elif name == "validate_tool": + return await handle_validate_tool(arguments) + elif name == "registry_stats": + return await handle_registry_stats(arguments) + else: + return [TextContent(type="text", text=f"Unknown tool: {name}")] + except Exception as e: + logger.error(f"Error handling {name}: {e}", exc_info=True) + return [TextContent(type="text", text=f"Error: {str(e)}")] + + +async def handle_create_tool(args: dict) -> list[TextContent]: + """Handle create_tool command""" + name = args["name"] + description = args["description"] + parameters = args["parameters"] + return_type = args.get("return_type", "Any") + examples = args.get("examples", []) + + logger.info(f"Creating tool: {name}") + + tool = maker.create_tool( + name=name, + description=description, + parameters=parameters, + return_type=return_type, + examples=examples, + ) + + if tool: + tool_id = registry.register(tool) + result = { + "success": True, + "tool_id": tool_id, + "name": tool.name, + "message": f"Tool '{name}' created and registered successfully", + "code": tool.code, + } + else: + result = { + "success": False, + "error": "Failed to create tool - validation errors or generation failure", + } + + return [TextContent(type="text", text=json.dumps(result, indent=2))] + + +async def handle_execute_tool(args: dict) -> list[TextContent]: + """Handle execute_tool command""" + tool_id = args["tool_id"] + arguments = args["arguments"] + + # Get tool by ID or name + tool = registry.get(tool_id) + if not tool: + tool = registry.get_by_name(tool_id) + + if not tool: + result = {"success": False, "error": f"Tool '{tool_id}' not found"} + return [TextContent(type="text", text=json.dumps(result, indent=2))] + + logger.info(f"Executing tool: {tool.name}") + + # Execute the tool + exec_result = executor.execute(tool.code, tool.name, arguments) + + # Update statistics + if exec_result.get("success"): + registry.update_stats(tool.id) + + return [TextContent(type="text", text=json.dumps(exec_result, indent=2))] + + +async def handle_list_tools(args: dict) -> list[TextContent]: + """Handle list_registered_tools command""" + tags = args.get("tags", None) + + tools = registry.list_tools(tags=tags) + + result = { + "count": len(tools), + "tools": [ + { + "id": t.id, + "name": t.name, + "description": t.description, + "parameters": t.parameters, + "return_type": t.return_type, + "validated": t.validated, + "execution_count": t.execution_count, + "created_at": t.created_at, + } + for t in tools + ], + } + + return [TextContent(type="text", text=json.dumps(result, indent=2))] + + +async def handle_get_tool(args: dict) -> list[TextContent]: + """Handle get_tool command""" + tool_id = args["tool_id"] + + tool = registry.get(tool_id) + if not tool: + tool = registry.get_by_name(tool_id) + + if not tool: + result = {"success": False, "error": f"Tool '{tool_id}' not found"} + else: + result = {"success": True, "tool": tool.to_dict()} + + return [TextContent(type="text", text=json.dumps(result, indent=2))] + + +async def handle_delete_tool(args: dict) -> list[TextContent]: + """Handle delete_tool command""" + tool_id = args["tool_id"] + + success = registry.delete(tool_id) + + result = { + "success": success, + "message": "Tool deleted" if success else f"Tool '{tool_id}' not found", + } + + return [TextContent(type="text", text=json.dumps(result, indent=2))] + + +async def handle_validate_tool(args: dict) -> list[TextContent]: + """Handle validate_tool command""" + tool_id = args["tool_id"] + + tool = registry.get(tool_id) + if not tool: + tool = registry.get_by_name(tool_id) + + if not tool: + result = {"success": False, "error": f"Tool '{tool_id}' not found"} + return [TextContent(type="text", text=json.dumps(result, indent=2))] + + is_valid, errors = validator.validate(tool.code) + + result = { + "tool_id": tool.id, + "tool_name": tool.name, + "is_valid": is_valid, + "errors": errors if not is_valid else [], + } + + return [TextContent(type="text", text=json.dumps(result, indent=2))] + + +async def handle_registry_stats(args: dict) -> list[TextContent]: + """Handle registry_stats command""" + stats = registry.get_stats() + return [TextContent(type="text", text=json.dumps(stats, indent=2))] + + +async def main(): + """Run the MCP server""" + logger.info("Starting LLM Maker MCP Server...") + + async with stdio_server() as (read_stream, write_stream): + await app.run(read_stream, write_stream, app.create_initialization_options()) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/ai-projects/llm-maker/requirements.txt b/ai-projects/llm-maker/requirements.txt index 7ca5aa20f..799a0679b 100644 --- a/ai-projects/llm-maker/requirements.txt +++ b/ai-projects/llm-maker/requirements.txt @@ -1,22 +1,22 @@ -# LLM Maker Dependencies - -# AI Provider Integration (reuse existing) -# Already installed in parent: openai, azure-openai - -# Code Safety and Analysis -ast-grep-py>=0.39.0 -astroid>=3.0.0 - -# Sandboxed Execution -RestrictedPython>=6.0 - -# MCP Server (Model Context Protocol) -mcp>=1.0.0 - -# Utilities -pyyaml>=6.0 -pydantic>=2.10.0 - -# Testing -pytest>=8.0.0 -pytest-asyncio>=0.24.0 +# LLM Maker Dependencies + +# AI Provider Integration (reuse existing) +# Already installed in parent: openai, azure-openai + +# Code Safety and Analysis +ast-grep-py>=0.39.0 +astroid>=3.0.0 + +# Sandboxed Execution +RestrictedPython>=6.0 + +# MCP Server (Model Context Protocol) +mcp>=1.0.0 + +# Utilities +pyyaml>=6.0 +pydantic>=2.10.0 + +# Testing +pytest>=8.0.0 +pytest-asyncio>=0.24.0 diff --git a/ai-projects/llm-maker/src/__init__.py b/ai-projects/llm-maker/src/__init__.py index 46e8355fe..f11c04482 100644 --- a/ai-projects/llm-maker/src/__init__.py +++ b/ai-projects/llm-maker/src/__init__.py @@ -1,25 +1,25 @@ -""" -LLM Maker - Autonomous Tool and Website Creation for LLMs - -This package provides components for LLMs to autonomously create, -validate, and execute Python tools in a safe, sandboxed environment, -as well as generate complete websites from natural language descriptions. -""" - -from .tool_executor import ToolExecutor -from .tool_maker import ToolMaker -from .tool_registry import Tool, ToolRegistry -from .tool_validator import ToolValidator -from .website_maker import WebsiteMaker, WebsiteValidator - -__all__ = [ - "ToolMaker", - "ToolValidator", - "ToolExecutor", - "ToolRegistry", - "Tool", - "WebsiteMaker", - "WebsiteValidator", -] - -__version__ = "0.2.0" +""" +LLM Maker - Autonomous Tool and Website Creation for LLMs + +This package provides components for LLMs to autonomously create, +validate, and execute Python tools in a safe, sandboxed environment, +as well as generate complete websites from natural language descriptions. +""" + +from .tool_executor import ToolExecutor +from .tool_maker import ToolMaker +from .tool_registry import Tool, ToolRegistry +from .tool_validator import ToolValidator +from .website_maker import WebsiteMaker, WebsiteValidator + +__all__ = [ + "ToolMaker", + "ToolValidator", + "ToolExecutor", + "ToolRegistry", + "Tool", + "WebsiteMaker", + "WebsiteValidator", +] + +__version__ = "0.2.0" diff --git a/ai-projects/llm-maker/src/tool_executor.py b/ai-projects/llm-maker/src/tool_executor.py index 7bf41bf17..ea17948d8 100644 --- a/ai-projects/llm-maker/src/tool_executor.py +++ b/ai-projects/llm-maker/src/tool_executor.py @@ -1,307 +1,307 @@ -""" -Tool Executor - Executes tools in a sandboxed environment -""" - -import importlib -import logging -import signal -import traceback -from contextlib import contextmanager -from typing import Any, Dict - -logger = logging.getLogger(__name__) - -ALLOWED_IMPORT_MODULES = { - "math", - "json", - "re", - "datetime", - "collections", - "itertools", - "functools", - "typing", -} - -# Try to import RestrictedPython if available -try: - from RestrictedPython import compile_restricted - from RestrictedPython.Guards import (guarded_getattr, guarded_getitem, - guarded_iter_unpack_sequence, - safe_builtins) - - HAS_RESTRICTED_PYTHON = True -except ImportError: - HAS_RESTRICTED_PYTHON = False - logger.warning("RestrictedPython not available, using basic sandboxing") - - -class ExecutionTimeout(Exception): - """Raised when execution exceeds timeout""" - - pass - - -class ToolExecutor: - """Executes tools in a restricted environment""" - - def __init__(self, timeout: int = 5, max_output_size: int = 10000): - """ - Initialize executor - - Args: - timeout: Maximum execution time in seconds - max_output_size: Maximum size of output in bytes - """ - self.timeout = timeout - self.max_output_size = max_output_size - self._setup_safe_globals() - - def _setup_safe_globals(self): - """Setup safe global namespace for execution""" - # Setup safe globals - if HAS_RESTRICTED_PYTHON: - # Use RestrictedPython's safe_builtins and inject our safe __import__ handler - sb = ( - safe_builtins.copy() - if isinstance(safe_builtins, dict) - else dict(safe_builtins) - ) - sb["__import__"] = self._safe_import - self.safe_globals = { - "__builtins__": sb, - "__name__": "__main__", - "__doc__": None, - "_getattr_": guarded_getattr, - "_getitem_": guarded_getitem, - "_iter_unpack_sequence_": guarded_iter_unpack_sequence, - } - else: - # Fallback to a curated set of builtins when RestrictedPython is not available - self.safe_globals = { - "__builtins__": { - # Safe built-in functions - "abs": abs, - "all": all, - "any": any, - "bool": bool, - "bytes": bytes, - "chr": chr, - "dict": dict, - "enumerate": enumerate, - "filter": filter, - "float": float, - "format": format, - "frozenset": frozenset, - "int": int, - "isinstance": isinstance, - "issubclass": issubclass, - "iter": iter, - "len": len, - "list": list, - "map": map, - "max": max, - "min": min, - "next": next, - "ord": ord, - "pow": pow, - "range": range, - "repr": repr, - "reversed": reversed, - "round": round, - "set": set, - "slice": slice, - "sorted": sorted, - "str": str, - "sum": sum, - "tuple": tuple, - "type": type, - "zip": zip, - "__import__": self._safe_import, - # Safe modules - "__name__": "__main__", - "__doc__": None, - } - } - - def _safe_import(self, name, globals=None, locals=None, fromlist=(), level=0): - """Restrict imports to a small allowlist required by validated tools.""" - root_name = name.split(".", 1)[0] - if root_name not in ALLOWED_IMPORT_MODULES: - raise ImportError(f"Import not allowed: {name}") - return importlib.import_module(name) - - @contextmanager - def _timeout_context(self, seconds: int): - """Context manager for execution timeout""" - - def timeout_handler(signum, frame): - raise ExecutionTimeout(f"Execution exceeded {seconds} seconds") - - # Use signal on Unix-like systems - if hasattr(signal, "SIGALRM"): - old_handler = signal.signal(signal.SIGALRM, timeout_handler) - signal.alarm(seconds) - try: - yield - finally: - signal.alarm(0) - signal.signal(signal.SIGALRM, old_handler) - else: - # On Windows, use threading-based timeout - import threading - - timer = None - timed_out = [False] - - def timeout_callback(): - timed_out[0] = True - - try: - timer = threading.Timer(seconds, timeout_callback) - timer.start() - yield - if timed_out[0]: - raise ExecutionTimeout(f"Execution exceeded {seconds} seconds") - finally: - if timer: - timer.cancel() - - def execute( - self, code: str, function_name: str, args: Dict[str, Any] - ) -> Dict[str, Any]: - """ - Execute tool code with given arguments - - Args: - code: Python code to execute - function_name: Name of function to call - args: Arguments to pass to function - - Returns: - Dictionary with result or error - """ - try: - # Compile code - if HAS_RESTRICTED_PYTHON: - byte_code = compile_restricted(code, filename="", mode="exec") - - # compile_restricted may return a CodeType or a wrapper with .errors/.code - if hasattr(byte_code, "errors"): - if byte_code.errors: - return { - "success": False, - "error": f"Compilation errors: {byte_code.errors}", - "error_type": "CompilationError", - } - # wrapper providing .code - compiled_code = getattr(byte_code, "code", byte_code) - else: - # assume it returned a raw code object - compiled_code = byte_code - else: - # Fallback to regular compile (less safe but functional) - try: - compiled_code = compile(code, "", "exec") - except SyntaxError as e: - return { - "success": False, - "error": f"Syntax error: {e}", - "error_type": "SyntaxError", - } - - # Create execution namespace - exec_globals = self.safe_globals.copy() - exec_locals = {} - - # Execute code to define function - try: - with self._timeout_context(self.timeout): - exec(compiled_code, exec_globals, exec_locals) - exec_globals.update(exec_locals) - except ExecutionTimeout as e: - return {"success": False, "error": str(e), "error_type": "TimeoutError"} - except Exception as e: - return { - "success": False, - "error": f"Execution error: {str(e)}", - "error_type": type(e).__name__, - "traceback": traceback.format_exc(), - } - - # Get the function (check locals first, then globals) - if function_name in exec_locals: - func = exec_locals[function_name] - elif function_name in exec_globals: - func = exec_globals[function_name] - else: - return { - "success": False, - "error": f"Function '{function_name}' not defined", - "error_type": "NameError", - } - - # Call the function - try: - with self._timeout_context(self.timeout): - result = func(**args) - except ExecutionTimeout as e: - return {"success": False, "error": str(e), "error_type": "TimeoutError"} - except TypeError as e: - return { - "success": False, - "error": f"Invalid arguments: {str(e)}", - "error_type": "TypeError", - } - except Exception as e: - return { - "success": False, - "error": f"Runtime error: {str(e)}", - "error_type": type(e).__name__, - "traceback": traceback.format_exc(), - } - - # Check output size - result_str = str(result) - if len(result_str) > self.max_output_size: - return { - "success": False, - "error": f"Output too large (>{self.max_output_size} bytes)", - "error_type": "OutputError", - } - - logger.info(f"Successfully executed {function_name}") - - return { - "success": True, - "result": result, - "result_type": type(result).__name__, - } - - except Exception as e: - logger.error(f"Unexpected error executing tool: {e}") - return { - "success": False, - "error": f"Unexpected error: {str(e)}", - "error_type": "UnexpectedError", - "traceback": traceback.format_exc(), - } - - def test_execution(self, code: str) -> bool: - """ - Test if code can be compiled and executed - - Args: - code: Python code to test - - Returns: - True if code can be executed - """ - try: - if HAS_RESTRICTED_PYTHON: - byte_code = compile_restricted(code, filename="", mode="exec") - return len(byte_code.errors) == 0 - else: - compile(code, "", "exec") - return True - except Exception: - return False +""" +Tool Executor - Executes tools in a sandboxed environment +""" + +import importlib +import logging +import signal +import traceback +from contextlib import contextmanager +from typing import Any, Dict + +logger = logging.getLogger(__name__) + +ALLOWED_IMPORT_MODULES = { + "math", + "json", + "re", + "datetime", + "collections", + "itertools", + "functools", + "typing", +} + +# Try to import RestrictedPython if available +try: + from RestrictedPython import compile_restricted + from RestrictedPython.Guards import (guarded_getattr, guarded_getitem, + guarded_iter_unpack_sequence, + safe_builtins) + + HAS_RESTRICTED_PYTHON = True +except ImportError: + HAS_RESTRICTED_PYTHON = False + logger.warning("RestrictedPython not available, using basic sandboxing") + + +class ExecutionTimeout(Exception): + """Raised when execution exceeds timeout""" + + pass + + +class ToolExecutor: + """Executes tools in a restricted environment""" + + def __init__(self, timeout: int = 5, max_output_size: int = 10000): + """ + Initialize executor + + Args: + timeout: Maximum execution time in seconds + max_output_size: Maximum size of output in bytes + """ + self.timeout = timeout + self.max_output_size = max_output_size + self._setup_safe_globals() + + def _setup_safe_globals(self): + """Setup safe global namespace for execution""" + # Setup safe globals + if HAS_RESTRICTED_PYTHON: + # Use RestrictedPython's safe_builtins and inject our safe __import__ handler + sb = ( + safe_builtins.copy() + if isinstance(safe_builtins, dict) + else dict(safe_builtins) + ) + sb["__import__"] = self._safe_import + self.safe_globals = { + "__builtins__": sb, + "__name__": "__main__", + "__doc__": None, + "_getattr_": guarded_getattr, + "_getitem_": guarded_getitem, + "_iter_unpack_sequence_": guarded_iter_unpack_sequence, + } + else: + # Fallback to a curated set of builtins when RestrictedPython is not available + self.safe_globals = { + "__builtins__": { + # Safe built-in functions + "abs": abs, + "all": all, + "any": any, + "bool": bool, + "bytes": bytes, + "chr": chr, + "dict": dict, + "enumerate": enumerate, + "filter": filter, + "float": float, + "format": format, + "frozenset": frozenset, + "int": int, + "isinstance": isinstance, + "issubclass": issubclass, + "iter": iter, + "len": len, + "list": list, + "map": map, + "max": max, + "min": min, + "next": next, + "ord": ord, + "pow": pow, + "range": range, + "repr": repr, + "reversed": reversed, + "round": round, + "set": set, + "slice": slice, + "sorted": sorted, + "str": str, + "sum": sum, + "tuple": tuple, + "type": type, + "zip": zip, + "__import__": self._safe_import, + # Safe modules + "__name__": "__main__", + "__doc__": None, + } + } + + def _safe_import(self, name, globals=None, locals=None, fromlist=(), level=0): + """Restrict imports to a small allowlist required by validated tools.""" + root_name = name.split(".", 1)[0] + if root_name not in ALLOWED_IMPORT_MODULES: + raise ImportError(f"Import not allowed: {name}") + return importlib.import_module(name) + + @contextmanager + def _timeout_context(self, seconds: int): + """Context manager for execution timeout""" + + def timeout_handler(signum, frame): + raise ExecutionTimeout(f"Execution exceeded {seconds} seconds") + + # Use signal on Unix-like systems + if hasattr(signal, "SIGALRM"): + old_handler = signal.signal(signal.SIGALRM, timeout_handler) + signal.alarm(seconds) + try: + yield + finally: + signal.alarm(0) + signal.signal(signal.SIGALRM, old_handler) + else: + # On Windows, use threading-based timeout + import threading + + timer = None + timed_out = [False] + + def timeout_callback(): + timed_out[0] = True + + try: + timer = threading.Timer(seconds, timeout_callback) + timer.start() + yield + if timed_out[0]: + raise ExecutionTimeout(f"Execution exceeded {seconds} seconds") + finally: + if timer: + timer.cancel() + + def execute( + self, code: str, function_name: str, args: Dict[str, Any] + ) -> Dict[str, Any]: + """ + Execute tool code with given arguments + + Args: + code: Python code to execute + function_name: Name of function to call + args: Arguments to pass to function + + Returns: + Dictionary with result or error + """ + try: + # Compile code + if HAS_RESTRICTED_PYTHON: + byte_code = compile_restricted(code, filename="", mode="exec") + + # compile_restricted may return a CodeType or a wrapper with .errors/.code + if hasattr(byte_code, "errors"): + if byte_code.errors: + return { + "success": False, + "error": f"Compilation errors: {byte_code.errors}", + "error_type": "CompilationError", + } + # wrapper providing .code + compiled_code = getattr(byte_code, "code", byte_code) + else: + # assume it returned a raw code object + compiled_code = byte_code + else: + # Fallback to regular compile (less safe but functional) + try: + compiled_code = compile(code, "", "exec") + except SyntaxError as e: + return { + "success": False, + "error": f"Syntax error: {e}", + "error_type": "SyntaxError", + } + + # Create execution namespace + exec_globals = self.safe_globals.copy() + exec_locals = {} + + # Execute code to define function + try: + with self._timeout_context(self.timeout): + exec(compiled_code, exec_globals, exec_locals) + exec_globals.update(exec_locals) + except ExecutionTimeout as e: + return {"success": False, "error": str(e), "error_type": "TimeoutError"} + except Exception as e: + return { + "success": False, + "error": f"Execution error: {str(e)}", + "error_type": type(e).__name__, + "traceback": traceback.format_exc(), + } + + # Get the function (check locals first, then globals) + if function_name in exec_locals: + func = exec_locals[function_name] + elif function_name in exec_globals: + func = exec_globals[function_name] + else: + return { + "success": False, + "error": f"Function '{function_name}' not defined", + "error_type": "NameError", + } + + # Call the function + try: + with self._timeout_context(self.timeout): + result = func(**args) + except ExecutionTimeout as e: + return {"success": False, "error": str(e), "error_type": "TimeoutError"} + except TypeError as e: + return { + "success": False, + "error": f"Invalid arguments: {str(e)}", + "error_type": "TypeError", + } + except Exception as e: + return { + "success": False, + "error": f"Runtime error: {str(e)}", + "error_type": type(e).__name__, + "traceback": traceback.format_exc(), + } + + # Check output size + result_str = str(result) + if len(result_str) > self.max_output_size: + return { + "success": False, + "error": f"Output too large (>{self.max_output_size} bytes)", + "error_type": "OutputError", + } + + logger.info(f"Successfully executed {function_name}") + + return { + "success": True, + "result": result, + "result_type": type(result).__name__, + } + + except Exception as e: + logger.error(f"Unexpected error executing tool: {e}") + return { + "success": False, + "error": f"Unexpected error: {str(e)}", + "error_type": "UnexpectedError", + "traceback": traceback.format_exc(), + } + + def test_execution(self, code: str) -> bool: + """ + Test if code can be compiled and executed + + Args: + code: Python code to test + + Returns: + True if code can be executed + """ + try: + if HAS_RESTRICTED_PYTHON: + byte_code = compile_restricted(code, filename="", mode="exec") + return len(byte_code.errors) == 0 + else: + compile(code, "", "exec") + return True + except Exception: + return False diff --git a/ai-projects/llm-maker/src/tool_maker.py b/ai-projects/llm-maker/src/tool_maker.py index 46f20e564..5e5fe3cbb 100644 --- a/ai-projects/llm-maker/src/tool_maker.py +++ b/ai-projects/llm-maker/src/tool_maker.py @@ -1,323 +1,323 @@ -""" -Tool Maker - Generates tools using LLMs -""" - -import logging -import sys -from pathlib import Path -from typing import Dict, List, Optional - -import yaml - -# Add parent directory to path to import shared modules -repo_root = Path(__file__).resolve().parents[2] -sys.path.insert(0, str(repo_root)) - -from shared.chat_providers import BaseChatProvider, detect_provider - -# Use try/except for imports to handle both package and direct execution -try: - from .tool_registry import Tool - from .tool_validator import ToolValidator -except ImportError: - from tool_registry import Tool - from tool_validator import ToolValidator - -logger = logging.getLogger(__name__) - - -class ToolMaker: - """Generates Python tools using LLMs""" - - SYSTEM_PROMPT = """You are a Python code generator that creates safe, efficient functions. - -Rules: -1. Generate ONLY Python function code, no explanations -2. Function must be self-contained with no external dependencies except: math, json, re, datetime, collections, itertools, functools, typing -3. Include type hints for parameters and return value -4. Add a docstring explaining the function -5. Handle errors gracefully with try-except -6. Do NOT use: os, sys, subprocess, file operations, network operations, eval, exec -7. Keep the code simple and readable -8. Test edge cases in your implementation - -Return ONLY the Python function code, starting with 'def'.""" - - def __init__(self, config_path: Optional[Path] = None): - """ - Initialize tool maker - - Args: - config_path: Path to configuration file - """ - self.config = self._load_config(config_path) - self.provider = self._initialize_provider() - self.validator = ToolValidator(config_path) - self.temperature = self.config.get("tool_maker", {}).get("temperature", 0.7) - self.max_tokens = self.config.get("tool_maker", {}).get("max_tokens", 2000) - - def _load_config(self, config_path: Optional[Path]) -> dict: - """Load configuration from YAML file""" - if config_path and config_path.exists(): - with open(config_path, "r") as f: - return yaml.safe_load(f) - - # Try default config path - default_config_path = Path(__file__).parent.parent / "llm_maker_config.yaml" - if default_config_path.exists(): - with open(default_config_path, "r") as f: - return yaml.safe_load(f) - - return {} - - def _initialize_provider(self) -> BaseChatProvider: - """Initialize AI provider for code generation""" - provider_name = self.config.get("tool_maker", {}).get("provider", "azure") - provider = detect_provider(provider_name) - - if not provider: - logger.warning("No AI provider available, using local fallback") - provider = detect_provider("local") - - logger.info(f"Initialized tool maker with provider: {type(provider).__name__}") - return provider - - def create_tool( - self, - name: str, - description: str, - parameters: Dict[str, str], - return_type: str = "Any", - examples: Optional[List[Dict]] = None, - max_attempts: int = 3, - ) -> Optional[Tool]: - """ - Create a tool from description using LLM - - Args: - name: Function name - description: What the function should do - parameters: Dict of parameter_name: type - return_type: Expected return type - examples: Optional list of example inputs/outputs - max_attempts: Maximum attempts to generate valid code - - Returns: - Tool object or None if generation failed - """ - logger.info(f"Creating tool '{name}': {description}") - - # Build the prompt - prompt = self._build_prompt( - name, description, parameters, return_type, examples - ) - - # Try to generate valid code - for attempt in range(max_attempts): - logger.debug(f"Generation attempt {attempt + 1}/{max_attempts}") - - # Generate code using LLM - code = self._generate_code(prompt) - - if not code: - logger.error("Failed to generate code") - continue - - # Validate the generated code - is_valid, errors = self.validator.validate(code) - - if is_valid: - # Check function signature - param_names = list(parameters.keys()) - sig_valid, sig_errors = self.validator.check_function_signature( - code, name, param_names - ) - - if sig_valid: - logger.info(f"Successfully created tool '{name}'") - return Tool( - id="", # Will be set by registry - name=name, - description=description, - code=code, - parameters=parameters, - return_type=return_type, - created_at="", # Will be set by registry - validated=True, - examples=examples or [], - ) - else: - logger.warning( - f"Function signature validation failed: {sig_errors}" - ) - # Add feedback to prompt for next attempt - prompt += "\n\nPrevious attempt had these issues:\n" + "\n".join( - sig_errors - ) - else: - logger.warning(f"Code validation failed: {errors}") - # Add feedback to prompt for next attempt - prompt += "\n\nPrevious attempt had these safety issues:\n" + "\n".join( - errors - ) - - logger.error(f"Failed to create tool '{name}' after {max_attempts} attempts") - return None - - def _build_prompt( - self, - name: str, - description: str, - parameters: Dict[str, str], - return_type: str, - examples: Optional[List[Dict]], - ) -> str: - """Build prompt for LLM code generation""" - param_list = [f"{pname}: {ptype}" for pname, ptype in parameters.items()] - param_str = ", ".join(param_list) - - prompt = f"""Create a Python function with this specification: - -Function name: {name} -Parameters: {param_str} -Return type: {return_type} -Description: {description} - -""" - - if examples: - prompt += "Examples:\n" - for i, example in enumerate(examples, 1): - prompt += f"\nExample {i}:\n" - if "input" in example: - prompt += f" Input: {example['input']}\n" - if "output" in example: - prompt += f" Expected Output: {example['output']}\n" - - prompt += "\nGenerate the complete function implementation." - - return prompt - - def _generate_code(self, prompt: str) -> Optional[str]: - """Generate code using LLM""" - try: - messages = [ - {"role": "system", "content": self.SYSTEM_PROMPT}, - {"role": "user", "content": prompt}, - ] - - # Get response from provider - response_text = "" - - if hasattr(self.provider, "complete"): - # Use streaming if available - for chunk in self.provider.complete(messages, stream=True): - if isinstance(chunk, dict) and "content" in chunk: - response_text += chunk["content"] - elif isinstance(chunk, str): - response_text += chunk - else: - # Fallback to non-streaming - response = self.provider.complete(messages, stream=False) - if isinstance(response, dict) and "content" in response: - response_text = response["content"] - elif isinstance(response, str): - response_text = response - - # Extract code from response (remove markdown code blocks if present) - code = self._extract_code(response_text) - - return code - - except Exception as e: - logger.error(f"Error generating code: {e}") - return None - - def _extract_code(self, response: str) -> str: - """Extract Python code from LLM response""" - # Remove markdown code blocks - if "```python" in response: - parts = response.split("```python") - if len(parts) > 1: - code = parts[1].split("```")[0] - return code.strip() - - if "```" in response: - parts = response.split("```") - if len(parts) > 1: - return parts[1].strip() - - # If no code blocks, assume entire response is code - return response.strip() - - def validate_tool(self, tool: Tool) -> bool: - """ - Validate a tool - - Args: - tool: Tool to validate - - Returns: - True if valid - """ - is_valid, errors = self.validator.validate(tool.code) - - if not is_valid: - logger.error(f"Tool validation failed: {errors}") - - return is_valid - - def refine_tool( - self, tool: Tool, feedback: str, max_attempts: int = 2 - ) -> Optional[Tool]: - """ - Refine an existing tool based on feedback - - Args: - tool: Tool to refine - feedback: Feedback on what needs improvement - max_attempts: Maximum refinement attempts - - Returns: - Refined tool or None - """ - logger.info(f"Refining tool '{tool.name}' based on feedback") - - prompt = f"""Improve this Python function based on the feedback. - -Current implementation: -```python -{tool.code} -``` - -Feedback: {feedback} - -Generate an improved version of the function.""" - - for attempt in range(max_attempts): - code = self._generate_code(prompt) - - if code: - is_valid, errors = self.validator.validate(code) - - if is_valid: - # Create refined tool - refined = Tool( - id=tool.id, - name=tool.name, - description=tool.description, - code=code, - parameters=tool.parameters, - return_type=tool.return_type, - created_at=tool.created_at, - validated=True, - examples=tool.examples, - tags=tool.tags, - ) - logger.info(f"Successfully refined tool '{tool.name}'") - return refined - else: - prompt += "\n\nValidation errors:\n" + "\n".join(errors) - - logger.error(f"Failed to refine tool '{tool.name}'") - return None +""" +Tool Maker - Generates tools using LLMs +""" + +import logging +import sys +from pathlib import Path +from typing import Dict, List, Optional + +import yaml + +# Add parent directory to path to import shared modules +repo_root = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(repo_root)) + +from shared.chat_providers import BaseChatProvider, detect_provider + +# Use try/except for imports to handle both package and direct execution +try: + from .tool_registry import Tool + from .tool_validator import ToolValidator +except ImportError: + from tool_registry import Tool + from tool_validator import ToolValidator + +logger = logging.getLogger(__name__) + + +class ToolMaker: + """Generates Python tools using LLMs""" + + SYSTEM_PROMPT = """You are a Python code generator that creates safe, efficient functions. + +Rules: +1. Generate ONLY Python function code, no explanations +2. Function must be self-contained with no external dependencies except: math, json, re, datetime, collections, itertools, functools, typing +3. Include type hints for parameters and return value +4. Add a docstring explaining the function +5. Handle errors gracefully with try-except +6. Do NOT use: os, sys, subprocess, file operations, network operations, eval, exec +7. Keep the code simple and readable +8. Test edge cases in your implementation + +Return ONLY the Python function code, starting with 'def'.""" + + def __init__(self, config_path: Optional[Path] = None): + """ + Initialize tool maker + + Args: + config_path: Path to configuration file + """ + self.config = self._load_config(config_path) + self.provider = self._initialize_provider() + self.validator = ToolValidator(config_path) + self.temperature = self.config.get("tool_maker", {}).get("temperature", 0.7) + self.max_tokens = self.config.get("tool_maker", {}).get("max_tokens", 2000) + + def _load_config(self, config_path: Optional[Path]) -> dict: + """Load configuration from YAML file""" + if config_path and config_path.exists(): + with open(config_path, "r") as f: + return yaml.safe_load(f) + + # Try default config path + default_config_path = Path(__file__).parent.parent / "llm_maker_config.yaml" + if default_config_path.exists(): + with open(default_config_path, "r") as f: + return yaml.safe_load(f) + + return {} + + def _initialize_provider(self) -> BaseChatProvider: + """Initialize AI provider for code generation""" + provider_name = self.config.get("tool_maker", {}).get("provider", "azure") + provider = detect_provider(provider_name) + + if not provider: + logger.warning("No AI provider available, using local fallback") + provider = detect_provider("local") + + logger.info(f"Initialized tool maker with provider: {type(provider).__name__}") + return provider + + def create_tool( + self, + name: str, + description: str, + parameters: Dict[str, str], + return_type: str = "Any", + examples: Optional[List[Dict]] = None, + max_attempts: int = 3, + ) -> Optional[Tool]: + """ + Create a tool from description using LLM + + Args: + name: Function name + description: What the function should do + parameters: Dict of parameter_name: type + return_type: Expected return type + examples: Optional list of example inputs/outputs + max_attempts: Maximum attempts to generate valid code + + Returns: + Tool object or None if generation failed + """ + logger.info(f"Creating tool '{name}': {description}") + + # Build the prompt + prompt = self._build_prompt( + name, description, parameters, return_type, examples + ) + + # Try to generate valid code + for attempt in range(max_attempts): + logger.debug(f"Generation attempt {attempt + 1}/{max_attempts}") + + # Generate code using LLM + code = self._generate_code(prompt) + + if not code: + logger.error("Failed to generate code") + continue + + # Validate the generated code + is_valid, errors = self.validator.validate(code) + + if is_valid: + # Check function signature + param_names = list(parameters.keys()) + sig_valid, sig_errors = self.validator.check_function_signature( + code, name, param_names + ) + + if sig_valid: + logger.info(f"Successfully created tool '{name}'") + return Tool( + id="", # Will be set by registry + name=name, + description=description, + code=code, + parameters=parameters, + return_type=return_type, + created_at="", # Will be set by registry + validated=True, + examples=examples or [], + ) + else: + logger.warning( + f"Function signature validation failed: {sig_errors}" + ) + # Add feedback to prompt for next attempt + prompt += "\n\nPrevious attempt had these issues:\n" + "\n".join( + sig_errors + ) + else: + logger.warning(f"Code validation failed: {errors}") + # Add feedback to prompt for next attempt + prompt += "\n\nPrevious attempt had these safety issues:\n" + "\n".join( + errors + ) + + logger.error(f"Failed to create tool '{name}' after {max_attempts} attempts") + return None + + def _build_prompt( + self, + name: str, + description: str, + parameters: Dict[str, str], + return_type: str, + examples: Optional[List[Dict]], + ) -> str: + """Build prompt for LLM code generation""" + param_list = [f"{pname}: {ptype}" for pname, ptype in parameters.items()] + param_str = ", ".join(param_list) + + prompt = f"""Create a Python function with this specification: + +Function name: {name} +Parameters: {param_str} +Return type: {return_type} +Description: {description} + +""" + + if examples: + prompt += "Examples:\n" + for i, example in enumerate(examples, 1): + prompt += f"\nExample {i}:\n" + if "input" in example: + prompt += f" Input: {example['input']}\n" + if "output" in example: + prompt += f" Expected Output: {example['output']}\n" + + prompt += "\nGenerate the complete function implementation." + + return prompt + + def _generate_code(self, prompt: str) -> Optional[str]: + """Generate code using LLM""" + try: + messages = [ + {"role": "system", "content": self.SYSTEM_PROMPT}, + {"role": "user", "content": prompt}, + ] + + # Get response from provider + response_text = "" + + if hasattr(self.provider, "complete"): + # Use streaming if available + for chunk in self.provider.complete(messages, stream=True): + if isinstance(chunk, dict) and "content" in chunk: + response_text += chunk["content"] + elif isinstance(chunk, str): + response_text += chunk + else: + # Fallback to non-streaming + response = self.provider.complete(messages, stream=False) + if isinstance(response, dict) and "content" in response: + response_text = response["content"] + elif isinstance(response, str): + response_text = response + + # Extract code from response (remove markdown code blocks if present) + code = self._extract_code(response_text) + + return code + + except Exception as e: + logger.error(f"Error generating code: {e}") + return None + + def _extract_code(self, response: str) -> str: + """Extract Python code from LLM response""" + # Remove markdown code blocks + if "```python" in response: + parts = response.split("```python") + if len(parts) > 1: + code = parts[1].split("```")[0] + return code.strip() + + if "```" in response: + parts = response.split("```") + if len(parts) > 1: + return parts[1].strip() + + # If no code blocks, assume entire response is code + return response.strip() + + def validate_tool(self, tool: Tool) -> bool: + """ + Validate a tool + + Args: + tool: Tool to validate + + Returns: + True if valid + """ + is_valid, errors = self.validator.validate(tool.code) + + if not is_valid: + logger.error(f"Tool validation failed: {errors}") + + return is_valid + + def refine_tool( + self, tool: Tool, feedback: str, max_attempts: int = 2 + ) -> Optional[Tool]: + """ + Refine an existing tool based on feedback + + Args: + tool: Tool to refine + feedback: Feedback on what needs improvement + max_attempts: Maximum refinement attempts + + Returns: + Refined tool or None + """ + logger.info(f"Refining tool '{tool.name}' based on feedback") + + prompt = f"""Improve this Python function based on the feedback. + +Current implementation: +```python +{tool.code} +``` + +Feedback: {feedback} + +Generate an improved version of the function.""" + + for attempt in range(max_attempts): + code = self._generate_code(prompt) + + if code: + is_valid, errors = self.validator.validate(code) + + if is_valid: + # Create refined tool + refined = Tool( + id=tool.id, + name=tool.name, + description=tool.description, + code=code, + parameters=tool.parameters, + return_type=tool.return_type, + created_at=tool.created_at, + validated=True, + examples=tool.examples, + tags=tool.tags, + ) + logger.info(f"Successfully refined tool '{tool.name}'") + return refined + else: + prompt += "\n\nValidation errors:\n" + "\n".join(errors) + + logger.error(f"Failed to refine tool '{tool.name}'") + return None diff --git a/ai-projects/llm-maker/src/tool_registry.py b/ai-projects/llm-maker/src/tool_registry.py index f264717f2..7bb08fe1f 100644 --- a/ai-projects/llm-maker/src/tool_registry.py +++ b/ai-projects/llm-maker/src/tool_registry.py @@ -1,243 +1,243 @@ -""" -Tool Registry - Manages tool lifecycle and storage -""" - -import json -import logging -from dataclasses import asdict, dataclass, field -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Dict, List, Optional -from uuid import uuid4 - -logger = logging.getLogger(__name__) - - -@dataclass -class Tool: - """Represents a generated tool""" - - id: str - name: str - description: str - code: str - parameters: Dict[str, str] - return_type: str - created_at: str - validated: bool = False - execution_count: int = 0 - last_executed: Optional[str] = None - examples: List[Dict[str, Any]] = field(default_factory=list) - tags: List[str] = field(default_factory=list) - - def to_dict(self) -> Dict: - """Convert to dictionary""" - return asdict(self) - - @classmethod - def from_dict(cls, data: Dict) -> "Tool": - """Create from dictionary""" - return cls(**data) - - -class ToolRegistry: - """Registry for managing generated tools""" - - def __init__(self, tools_dir: Optional[Path] = None): - """ - Initialize tool registry - - Args: - tools_dir: Directory to store tools (default: ./tools) - """ - self.tools_dir = tools_dir or Path("./tools") - self.tools_dir.mkdir(parents=True, exist_ok=True) - self.index_file = self.tools_dir / "index.json" - self._tools: Dict[str, Tool] = {} - self._load_index() - - def _load_index(self): - """Load tool index from disk""" - if self.index_file.exists(): - try: - with open(self.index_file, "r") as f: - index = json.load(f) - for tool_data in index.get("tools", []): - tool = Tool.from_dict(tool_data) - self._tools[tool.id] = tool - logger.info(f"Loaded {len(self._tools)} tools from registry") - except Exception as e: - logger.error(f"Failed to load tool index: {e}") - self._tools = {} - - def _save_index(self): - """Save tool index to disk""" - try: - index = { - "version": "1.0", - "tools": [tool.to_dict() for tool in self._tools.values()], - "count": len(self._tools), - "last_updated": datetime.now(timezone.utc).isoformat(), - } - with open(self.index_file, "w") as f: - json.dump(index, f, indent=2) - logger.debug(f"Saved {len(self._tools)} tools to registry") - except Exception as e: - logger.error(f"Failed to save tool index: {e}") - - def register(self, tool: Tool) -> str: - """ - Register a new tool - - Args: - tool: Tool to register - - Returns: - Tool ID - """ - if not tool.id: - tool.id = str(uuid4()) - - if not tool.created_at: - tool.created_at = datetime.now(timezone.utc).isoformat() - - self._tools[tool.id] = tool - self._save_index() - - # Save tool code to separate file - tool_file = self.tools_dir / f"{tool.id}.py" - with open(tool_file, "w") as f: - f.write(f"# Tool: {tool.name}\n") - f.write(f"# ID: {tool.id}\n") - f.write(f"# Created: {tool.created_at}\n\n") - f.write(tool.code) - - logger.info(f"Registered tool '{tool.name}' with ID {tool.id}") - return tool.id - - def get(self, tool_id: str) -> Optional[Tool]: - """ - Get a tool by ID - - Args: - tool_id: Tool ID - - Returns: - Tool or None if not found - """ - return self._tools.get(tool_id) - - def get_by_name(self, name: str) -> Optional[Tool]: - """ - Get a tool by name - - Args: - name: Tool name - - Returns: - Tool or None if not found - """ - for tool in self._tools.values(): - if tool.name == name: - return tool - return None - - def list_tools(self, tags: Optional[List[str]] = None) -> List[Tool]: - """ - List all registered tools - - Args: - tags: Optional filter by tags - - Returns: - List of tools - """ - tools = list(self._tools.values()) - - if tags: - tools = [t for t in tools if any(tag in t.tags for tag in tags)] - - return sorted(tools, key=lambda t: t.created_at, reverse=True) - - def delete(self, tool_id: str) -> bool: - """ - Delete a tool - - Args: - tool_id: Tool ID - - Returns: - True if deleted, False if not found - """ - if tool_id not in self._tools: - return False - - tool = self._tools.pop(tool_id) - self._save_index() - - # Delete tool file - tool_file = self.tools_dir / f"{tool_id}.py" - if tool_file.exists(): - tool_file.unlink() - - logger.info(f"Deleted tool '{tool.name}' (ID: {tool_id})") - return True - - def update_stats(self, tool_id: str): - """ - Update tool execution statistics - - Args: - tool_id: Tool ID - """ - if tool_id in self._tools: - tool = self._tools[tool_id] - tool.execution_count += 1 - tool.last_executed = datetime.now(timezone.utc).isoformat() - self._save_index() - - def search(self, query: str) -> List[Tool]: - """ - Search tools by name or description - - Args: - query: Search query - - Returns: - List of matching tools - """ - query_lower = query.lower() - results = [] - - for tool in self._tools.values(): - if ( - query_lower in tool.name.lower() - or query_lower in tool.description.lower() - ): - results.append(tool) - - return results - - def get_stats(self) -> Dict[str, Any]: - """ - Get registry statistics - - Returns: - Dictionary with statistics - """ - total_executions = sum(t.execution_count for t in self._tools.values()) - validated_count = sum(1 for t in self._tools.values() if t.validated) - - return { - "total_tools": len(self._tools), - "validated_tools": validated_count, - "total_executions": total_executions, - "most_used": ( - max( - self._tools.values(), key=lambda t: t.execution_count, default=None - ).name - if self._tools - else None - ), - "registry_path": str(self.tools_dir), - } +""" +Tool Registry - Manages tool lifecycle and storage +""" + +import json +import logging +from dataclasses import asdict, dataclass, field +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Dict, List, Optional +from uuid import uuid4 + +logger = logging.getLogger(__name__) + + +@dataclass +class Tool: + """Represents a generated tool""" + + id: str + name: str + description: str + code: str + parameters: Dict[str, str] + return_type: str + created_at: str + validated: bool = False + execution_count: int = 0 + last_executed: Optional[str] = None + examples: List[Dict[str, Any]] = field(default_factory=list) + tags: List[str] = field(default_factory=list) + + def to_dict(self) -> Dict: + """Convert to dictionary""" + return asdict(self) + + @classmethod + def from_dict(cls, data: Dict) -> "Tool": + """Create from dictionary""" + return cls(**data) + + +class ToolRegistry: + """Registry for managing generated tools""" + + def __init__(self, tools_dir: Optional[Path] = None): + """ + Initialize tool registry + + Args: + tools_dir: Directory to store tools (default: ./tools) + """ + self.tools_dir = tools_dir or Path("./tools") + self.tools_dir.mkdir(parents=True, exist_ok=True) + self.index_file = self.tools_dir / "index.json" + self._tools: Dict[str, Tool] = {} + self._load_index() + + def _load_index(self): + """Load tool index from disk""" + if self.index_file.exists(): + try: + with open(self.index_file, "r") as f: + index = json.load(f) + for tool_data in index.get("tools", []): + tool = Tool.from_dict(tool_data) + self._tools[tool.id] = tool + logger.info(f"Loaded {len(self._tools)} tools from registry") + except Exception as e: + logger.error(f"Failed to load tool index: {e}") + self._tools = {} + + def _save_index(self): + """Save tool index to disk""" + try: + index = { + "version": "1.0", + "tools": [tool.to_dict() for tool in self._tools.values()], + "count": len(self._tools), + "last_updated": datetime.now(timezone.utc).isoformat(), + } + with open(self.index_file, "w") as f: + json.dump(index, f, indent=2) + logger.debug(f"Saved {len(self._tools)} tools to registry") + except Exception as e: + logger.error(f"Failed to save tool index: {e}") + + def register(self, tool: Tool) -> str: + """ + Register a new tool + + Args: + tool: Tool to register + + Returns: + Tool ID + """ + if not tool.id: + tool.id = str(uuid4()) + + if not tool.created_at: + tool.created_at = datetime.now(timezone.utc).isoformat() + + self._tools[tool.id] = tool + self._save_index() + + # Save tool code to separate file + tool_file = self.tools_dir / f"{tool.id}.py" + with open(tool_file, "w") as f: + f.write(f"# Tool: {tool.name}\n") + f.write(f"# ID: {tool.id}\n") + f.write(f"# Created: {tool.created_at}\n\n") + f.write(tool.code) + + logger.info(f"Registered tool '{tool.name}' with ID {tool.id}") + return tool.id + + def get(self, tool_id: str) -> Optional[Tool]: + """ + Get a tool by ID + + Args: + tool_id: Tool ID + + Returns: + Tool or None if not found + """ + return self._tools.get(tool_id) + + def get_by_name(self, name: str) -> Optional[Tool]: + """ + Get a tool by name + + Args: + name: Tool name + + Returns: + Tool or None if not found + """ + for tool in self._tools.values(): + if tool.name == name: + return tool + return None + + def list_tools(self, tags: Optional[List[str]] = None) -> List[Tool]: + """ + List all registered tools + + Args: + tags: Optional filter by tags + + Returns: + List of tools + """ + tools = list(self._tools.values()) + + if tags: + tools = [t for t in tools if any(tag in t.tags for tag in tags)] + + return sorted(tools, key=lambda t: t.created_at, reverse=True) + + def delete(self, tool_id: str) -> bool: + """ + Delete a tool + + Args: + tool_id: Tool ID + + Returns: + True if deleted, False if not found + """ + if tool_id not in self._tools: + return False + + tool = self._tools.pop(tool_id) + self._save_index() + + # Delete tool file + tool_file = self.tools_dir / f"{tool_id}.py" + if tool_file.exists(): + tool_file.unlink() + + logger.info(f"Deleted tool '{tool.name}' (ID: {tool_id})") + return True + + def update_stats(self, tool_id: str): + """ + Update tool execution statistics + + Args: + tool_id: Tool ID + """ + if tool_id in self._tools: + tool = self._tools[tool_id] + tool.execution_count += 1 + tool.last_executed = datetime.now(timezone.utc).isoformat() + self._save_index() + + def search(self, query: str) -> List[Tool]: + """ + Search tools by name or description + + Args: + query: Search query + + Returns: + List of matching tools + """ + query_lower = query.lower() + results = [] + + for tool in self._tools.values(): + if ( + query_lower in tool.name.lower() + or query_lower in tool.description.lower() + ): + results.append(tool) + + return results + + def get_stats(self) -> Dict[str, Any]: + """ + Get registry statistics + + Returns: + Dictionary with statistics + """ + total_executions = sum(t.execution_count for t in self._tools.values()) + validated_count = sum(1 for t in self._tools.values() if t.validated) + + return { + "total_tools": len(self._tools), + "validated_tools": validated_count, + "total_executions": total_executions, + "most_used": ( + max( + self._tools.values(), key=lambda t: t.execution_count, default=None + ).name + if self._tools + else None + ), + "registry_path": str(self.tools_dir), + } diff --git a/ai-projects/llm-maker/src/tool_validator.py b/ai-projects/llm-maker/src/tool_validator.py index 7f19df6e2..63de00f61 100644 --- a/ai-projects/llm-maker/src/tool_validator.py +++ b/ai-projects/llm-maker/src/tool_validator.py @@ -1,404 +1,404 @@ -""" -Tool Validator - Validates generated code for safety -""" - -import ast -import logging -from pathlib import Path -from typing import List, Optional, Tuple - -import yaml - -logger = logging.getLogger(__name__) - -# Detection is done via AST; regex fallbacks removed for accuracy and performance. - - -class ToolValidator: - """Validates generated tools for safety and correctness""" - - # Dangerous imports that should never be allowed - DANGEROUS_IMPORTS = { - "os", - "sys", - "subprocess", - "shutil", - "pathlib", - "socket", - "urllib", - "requests", - "http", - "ftplib", - "pickle", - "shelve", - "dbm", - "sqlite3", - "threading", - "multiprocessing", - "asyncio", - "__builtin__", - "builtins", - "importlib", - "ctypes", - "cffi", - "pty", - "fcntl", - } - - # Dangerous built-in functions - DANGEROUS_BUILTINS = { - "__import__", - "eval", - "exec", - "compile", - "open", - "input", - "breakpoint", - "exit", - "quit", - "help", - "license", - "copyright", - "credits", - } - - # Dangerous attributes and magic methods - DANGEROUS_ATTRS = { - "__code__", - "__globals__", - "__builtins__", - "__dict__", - "__class__", - "__bases__", - "__subclasses__", - "__mro__", - } - - def __init__(self, config_path: Optional[Path] = None): - """ - Initialize validator with configuration - - Args: - config_path: Path to configuration file - """ - self.config = self._load_config(config_path) - self.allowed_imports = set( - self.config.get("validation", {}).get("allowed_imports", []) - ) - self.strict_mode = self.config.get("validation", {}).get("strict_mode", True) - - def _load_config(self, config_path: Optional[Path]) -> dict: - """Load configuration from YAML file""" - if config_path and config_path.exists(): - with open(config_path, "r") as f: - return yaml.safe_load(f) - - # Default configuration if file not found - default_config_path = Path(__file__).parent.parent / "llm_maker_config.yaml" - if default_config_path.exists(): - with open(default_config_path, "r") as f: - return yaml.safe_load(f) - - return {} - - def validate(self, code: str) -> Tuple[bool, List[str]]: - """ - Validate tool code for safety - - Args: - code: Python code to validate - - Returns: - Tuple of (is_valid, list_of_errors) - """ - errors = [] - - # Check if code is parseable - try: - tree = ast.parse(code) - except SyntaxError as e: - errors.append(f"Syntax error: {e}") - return False, errors - - # One-pass AST traversal to collect nodes and avoid repeated walks - nodes = list(ast.walk(tree)) - - # Run safety checks using the pre-collected nodes to improve performance - errors.extend(self._check_imports(nodes)) - errors.extend(self._check_dangerous_calls(nodes)) - errors.extend(self._check_dangerous_attributes(nodes)) - - # Cheap substring short-circuit: if none of these keywords appear, - # skip the heavier file/network/code-exec AST checks entirely. - _quick_keywords = ( - "requests", - "socket", - "open", - "eval", - "exec", - "compile", - "urllib", - "http", - ) - has_quick_kw = any(k in code for k in _quick_keywords) - - if has_quick_kw: - # AST-based checks (primary and accurate) - errors.extend(self._check_file_operations(nodes, code)) - errors.extend(self._check_network_operations(nodes, code)) - errors.extend(self._check_code_execution(nodes, code)) - - if self.strict_mode: - errors.extend(self._check_strict_mode(nodes)) - - is_valid = len(errors) == 0 - - if is_valid: - logger.info("Code validation passed") - else: - logger.warning(f"Code validation failed with {len(errors)} errors") - for error in errors: - logger.warning(f" - {error}") - - return is_valid, errors - - def _check_imports(self, tree_or_nodes) -> List[str]: - """Check for dangerous imports""" - errors = [] - nodes = ( - tree_or_nodes - if isinstance(tree_or_nodes, list) - else list(ast.walk(tree_or_nodes)) - ) - for node in nodes: - if isinstance(node, ast.Import): - for alias in node.names: - module = alias.name.split(".")[0] - if module in self.DANGEROUS_IMPORTS: - errors.append(f"Dangerous import: {alias.name}") - elif module not in self.allowed_imports and self.strict_mode: - errors.append(f"Import not in whitelist: {alias.name}") - - elif isinstance(node, ast.ImportFrom): - if node.module: - module = node.module.split(".")[0] - if module in self.DANGEROUS_IMPORTS: - errors.append(f"Dangerous import from: {node.module}") - elif module not in self.allowed_imports and self.strict_mode: - errors.append(f"Import not in whitelist: {node.module}") - - return errors - - def _check_dangerous_calls(self, tree_or_nodes) -> List[str]: - """Check for calls to dangerous built-in functions""" - errors = [] - nodes = ( - tree_or_nodes - if isinstance(tree_or_nodes, list) - else list(ast.walk(tree_or_nodes)) - ) - for node in nodes: - if isinstance(node, ast.Call): - func_name = None - - if isinstance(node.func, ast.Name): - func_name = node.func.id - elif isinstance(node.func, ast.Attribute): - func_name = node.func.attr - - if func_name in self.DANGEROUS_BUILTINS: - errors.append(f"Dangerous function call: {func_name}()") - - return errors - - def _check_dangerous_attributes(self, tree_or_nodes) -> List[str]: - """Check for access to dangerous attributes""" - errors = [] - nodes = ( - tree_or_nodes - if isinstance(tree_or_nodes, list) - else list(ast.walk(tree_or_nodes)) - ) - for node in nodes: - if isinstance(node, ast.Attribute): - if node.attr in self.DANGEROUS_ATTRS: - errors.append(f"Access to dangerous attribute: {node.attr}") - - return errors - - def _check_file_operations(self, nodes: List[ast.AST], code: str) -> List[str]: - """AST-based check for file system operations; fallback regex available separately.""" - errors = [] - - for node in nodes: - # direct open() calls - if isinstance(node, ast.Call) and isinstance(node.func, ast.Name): - if node.func.id == "open": - errors.append("File operation detected: open() call") - - # attribute calls like Path(...).unlink() or os.remove() - if isinstance(node, ast.Attribute): - attr = node.attr - if attr in ("unlink", "rmdir", "mkdir", "remove"): - errors.append(f"File operation detected: .{attr}() method") - - # with open(...) as ... - if isinstance(node, ast.With): - for item in node.items: - ctx = item.context_expr - if ( - isinstance(ctx, ast.Call) - and isinstance(ctx.func, ast.Name) - and ctx.func.id == "open" - ): - errors.append( - "File operation detected: with open() context manager" - ) - - return errors - - def _check_file_operations_regex(self, code: str) -> List[str]: - """Regex fallback for file ops (kept for broad coverage).""" - errors = [] - # regex fallbacks were removed; keep this as a stub in case future - # fuzzy detection is desired. - return errors - - def _check_network_operations(self, nodes: List[ast.AST], code: str) -> List[str]: - """AST-based check for network operations; fallback regex available separately.""" - errors = [] - - for node in nodes: - # imports of network modules - if isinstance(node, ast.Import): - for alias in node.names: - module = alias.name.split(".")[0] - if module in ("socket", "requests", "urllib", "http"): - errors.append(f"Network import detected: {alias.name}") - - if isinstance(node, ast.ImportFrom) and node.module: - module = node.module.split(".")[0] - if module in ("socket", "requests", "urllib", "http"): - errors.append(f"Network import detected: {node.module}") - - # requests.get(...) or socket.connect() - if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute): - if isinstance(node.func.value, ast.Name): - root = node.func.value.id - if root in ("requests", "socket", "urllib", "http"): - errors.append( - f"Network operation detected: {root}.{node.func.attr}()" - ) - - return errors - - def _check_network_operations_regex(self, code: str) -> List[str]: - """Regex fallback for network ops.""" - errors = [] - # regex fallbacks were removed; keep this as a stub in case future - # fuzzy detection is desired. - return errors - - def _check_code_execution(self, nodes: List[ast.AST], code: str) -> List[str]: - """AST-based check for direct dynamic code execution calls.""" - errors = [] - - for node in nodes: - if isinstance(node, ast.Call): - # direct name calls: eval(), exec(), compile(), __import__() - if isinstance(node.func, ast.Name) and node.func.id in ( - "eval", - "exec", - "compile", - "__import__", - ): - errors.append(f"Code execution detected: {node.func.id}()") - - # attribute access to builtins or modules (less common for eval/exec) - if isinstance(node.func, ast.Attribute): - if isinstance(node.func.value, ast.Name) and node.func.attr in ( - "eval", - "exec", - "compile", - ): - errors.append( - f"Code execution detected: {node.func.value.id}.{node.func.attr}()" - ) - - return errors - - def _check_code_execution_regex(self, code: str) -> List[str]: - """Regex fallback for dynamic code execution.""" - errors = [] - # regex fallbacks were removed; keep this as a stub in case future - # fuzzy detection is desired. - return errors - - def _check_strict_mode(self, tree_or_nodes: ast.AST) -> List[str]: - """Additional checks for strict mode""" - errors = [] - nodes = ( - tree_or_nodes - if isinstance(tree_or_nodes, list) - else list(ast.walk(tree_or_nodes)) - ) - - # Check for lambda functions (can be used to bypass restrictions) - for node in nodes: - if isinstance(node, ast.Lambda): - errors.append("Lambda functions not allowed in strict mode") - - # Check for list/dict comprehensions with complex expressions - # (can be used for obfuscation) - for node in nodes: - if isinstance(node, (ast.ListComp, ast.DictComp, ast.SetComp)): - if len(list(ast.walk(node))) > 10: - errors.append("Complex comprehension detected") - - return errors - - def check_function_signature( - self, code: str, expected_name: str, expected_params: List[str] - ) -> Tuple[bool, List[str]]: - """ - Validate function signature matches expected - - Args: - code: Python code - expected_name: Expected function name - expected_params: Expected parameter names - - Returns: - Tuple of (is_valid, list_of_errors) - """ - errors = [] - - try: - tree = ast.parse(code) - except SyntaxError as e: - return False, [f"Syntax error: {e}"] - - # Find function definition - func_def = None - for node in ast.walk(tree): - if isinstance(node, ast.FunctionDef) and node.name == expected_name: - func_def = node - break - - if not func_def: - errors.append(f"Function '{expected_name}' not found") - return False, errors - - # Check parameters - actual_params = [arg.arg for arg in func_def.args.args] - if actual_params != expected_params: - errors.append( - f"Parameter mismatch: expected {expected_params}, got {actual_params}" - ) - - # Check for return statement - has_return = any(isinstance(node, ast.Return) for node in ast.walk(func_def)) - if not has_return: - errors.append("Function must have a return statement") - - return len(errors) == 0, errors +""" +Tool Validator - Validates generated code for safety +""" + +import ast +import logging +from pathlib import Path +from typing import List, Optional, Tuple + +import yaml + +logger = logging.getLogger(__name__) + +# Detection is done via AST; regex fallbacks removed for accuracy and performance. + + +class ToolValidator: + """Validates generated tools for safety and correctness""" + + # Dangerous imports that should never be allowed + DANGEROUS_IMPORTS = { + "os", + "sys", + "subprocess", + "shutil", + "pathlib", + "socket", + "urllib", + "requests", + "http", + "ftplib", + "pickle", + "shelve", + "dbm", + "sqlite3", + "threading", + "multiprocessing", + "asyncio", + "__builtin__", + "builtins", + "importlib", + "ctypes", + "cffi", + "pty", + "fcntl", + } + + # Dangerous built-in functions + DANGEROUS_BUILTINS = { + "__import__", + "eval", + "exec", + "compile", + "open", + "input", + "breakpoint", + "exit", + "quit", + "help", + "license", + "copyright", + "credits", + } + + # Dangerous attributes and magic methods + DANGEROUS_ATTRS = { + "__code__", + "__globals__", + "__builtins__", + "__dict__", + "__class__", + "__bases__", + "__subclasses__", + "__mro__", + } + + def __init__(self, config_path: Optional[Path] = None): + """ + Initialize validator with configuration + + Args: + config_path: Path to configuration file + """ + self.config = self._load_config(config_path) + self.allowed_imports = set( + self.config.get("validation", {}).get("allowed_imports", []) + ) + self.strict_mode = self.config.get("validation", {}).get("strict_mode", True) + + def _load_config(self, config_path: Optional[Path]) -> dict: + """Load configuration from YAML file""" + if config_path and config_path.exists(): + with open(config_path, "r") as f: + return yaml.safe_load(f) + + # Default configuration if file not found + default_config_path = Path(__file__).parent.parent / "llm_maker_config.yaml" + if default_config_path.exists(): + with open(default_config_path, "r") as f: + return yaml.safe_load(f) + + return {} + + def validate(self, code: str) -> Tuple[bool, List[str]]: + """ + Validate tool code for safety + + Args: + code: Python code to validate + + Returns: + Tuple of (is_valid, list_of_errors) + """ + errors = [] + + # Check if code is parseable + try: + tree = ast.parse(code) + except SyntaxError as e: + errors.append(f"Syntax error: {e}") + return False, errors + + # One-pass AST traversal to collect nodes and avoid repeated walks + nodes = list(ast.walk(tree)) + + # Run safety checks using the pre-collected nodes to improve performance + errors.extend(self._check_imports(nodes)) + errors.extend(self._check_dangerous_calls(nodes)) + errors.extend(self._check_dangerous_attributes(nodes)) + + # Cheap substring short-circuit: if none of these keywords appear, + # skip the heavier file/network/code-exec AST checks entirely. + _quick_keywords = ( + "requests", + "socket", + "open", + "eval", + "exec", + "compile", + "urllib", + "http", + ) + has_quick_kw = any(k in code for k in _quick_keywords) + + if has_quick_kw: + # AST-based checks (primary and accurate) + errors.extend(self._check_file_operations(nodes, code)) + errors.extend(self._check_network_operations(nodes, code)) + errors.extend(self._check_code_execution(nodes, code)) + + if self.strict_mode: + errors.extend(self._check_strict_mode(nodes)) + + is_valid = len(errors) == 0 + + if is_valid: + logger.info("Code validation passed") + else: + logger.warning(f"Code validation failed with {len(errors)} errors") + for error in errors: + logger.warning(f" - {error}") + + return is_valid, errors + + def _check_imports(self, tree_or_nodes) -> List[str]: + """Check for dangerous imports""" + errors = [] + nodes = ( + tree_or_nodes + if isinstance(tree_or_nodes, list) + else list(ast.walk(tree_or_nodes)) + ) + for node in nodes: + if isinstance(node, ast.Import): + for alias in node.names: + module = alias.name.split(".")[0] + if module in self.DANGEROUS_IMPORTS: + errors.append(f"Dangerous import: {alias.name}") + elif module not in self.allowed_imports and self.strict_mode: + errors.append(f"Import not in whitelist: {alias.name}") + + elif isinstance(node, ast.ImportFrom): + if node.module: + module = node.module.split(".")[0] + if module in self.DANGEROUS_IMPORTS: + errors.append(f"Dangerous import from: {node.module}") + elif module not in self.allowed_imports and self.strict_mode: + errors.append(f"Import not in whitelist: {node.module}") + + return errors + + def _check_dangerous_calls(self, tree_or_nodes) -> List[str]: + """Check for calls to dangerous built-in functions""" + errors = [] + nodes = ( + tree_or_nodes + if isinstance(tree_or_nodes, list) + else list(ast.walk(tree_or_nodes)) + ) + for node in nodes: + if isinstance(node, ast.Call): + func_name = None + + if isinstance(node.func, ast.Name): + func_name = node.func.id + elif isinstance(node.func, ast.Attribute): + func_name = node.func.attr + + if func_name in self.DANGEROUS_BUILTINS: + errors.append(f"Dangerous function call: {func_name}()") + + return errors + + def _check_dangerous_attributes(self, tree_or_nodes) -> List[str]: + """Check for access to dangerous attributes""" + errors = [] + nodes = ( + tree_or_nodes + if isinstance(tree_or_nodes, list) + else list(ast.walk(tree_or_nodes)) + ) + for node in nodes: + if isinstance(node, ast.Attribute): + if node.attr in self.DANGEROUS_ATTRS: + errors.append(f"Access to dangerous attribute: {node.attr}") + + return errors + + def _check_file_operations(self, nodes: List[ast.AST], code: str) -> List[str]: + """AST-based check for file system operations; fallback regex available separately.""" + errors = [] + + for node in nodes: + # direct open() calls + if isinstance(node, ast.Call) and isinstance(node.func, ast.Name): + if node.func.id == "open": + errors.append("File operation detected: open() call") + + # attribute calls like Path(...).unlink() or os.remove() + if isinstance(node, ast.Attribute): + attr = node.attr + if attr in ("unlink", "rmdir", "mkdir", "remove"): + errors.append(f"File operation detected: .{attr}() method") + + # with open(...) as ... + if isinstance(node, ast.With): + for item in node.items: + ctx = item.context_expr + if ( + isinstance(ctx, ast.Call) + and isinstance(ctx.func, ast.Name) + and ctx.func.id == "open" + ): + errors.append( + "File operation detected: with open() context manager" + ) + + return errors + + def _check_file_operations_regex(self, code: str) -> List[str]: + """Regex fallback for file ops (kept for broad coverage).""" + errors = [] + # regex fallbacks were removed; keep this as a stub in case future + # fuzzy detection is desired. + return errors + + def _check_network_operations(self, nodes: List[ast.AST], code: str) -> List[str]: + """AST-based check for network operations; fallback regex available separately.""" + errors = [] + + for node in nodes: + # imports of network modules + if isinstance(node, ast.Import): + for alias in node.names: + module = alias.name.split(".")[0] + if module in ("socket", "requests", "urllib", "http"): + errors.append(f"Network import detected: {alias.name}") + + if isinstance(node, ast.ImportFrom) and node.module: + module = node.module.split(".")[0] + if module in ("socket", "requests", "urllib", "http"): + errors.append(f"Network import detected: {node.module}") + + # requests.get(...) or socket.connect() + if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute): + if isinstance(node.func.value, ast.Name): + root = node.func.value.id + if root in ("requests", "socket", "urllib", "http"): + errors.append( + f"Network operation detected: {root}.{node.func.attr}()" + ) + + return errors + + def _check_network_operations_regex(self, code: str) -> List[str]: + """Regex fallback for network ops.""" + errors = [] + # regex fallbacks were removed; keep this as a stub in case future + # fuzzy detection is desired. + return errors + + def _check_code_execution(self, nodes: List[ast.AST], code: str) -> List[str]: + """AST-based check for direct dynamic code execution calls.""" + errors = [] + + for node in nodes: + if isinstance(node, ast.Call): + # direct name calls: eval(), exec(), compile(), __import__() + if isinstance(node.func, ast.Name) and node.func.id in ( + "eval", + "exec", + "compile", + "__import__", + ): + errors.append(f"Code execution detected: {node.func.id}()") + + # attribute access to builtins or modules (less common for eval/exec) + if isinstance(node.func, ast.Attribute): + if isinstance(node.func.value, ast.Name) and node.func.attr in ( + "eval", + "exec", + "compile", + ): + errors.append( + f"Code execution detected: {node.func.value.id}.{node.func.attr}()" + ) + + return errors + + def _check_code_execution_regex(self, code: str) -> List[str]: + """Regex fallback for dynamic code execution.""" + errors = [] + # regex fallbacks were removed; keep this as a stub in case future + # fuzzy detection is desired. + return errors + + def _check_strict_mode(self, tree_or_nodes: ast.AST) -> List[str]: + """Additional checks for strict mode""" + errors = [] + nodes = ( + tree_or_nodes + if isinstance(tree_or_nodes, list) + else list(ast.walk(tree_or_nodes)) + ) + + # Check for lambda functions (can be used to bypass restrictions) + for node in nodes: + if isinstance(node, ast.Lambda): + errors.append("Lambda functions not allowed in strict mode") + + # Check for list/dict comprehensions with complex expressions + # (can be used for obfuscation) + for node in nodes: + if isinstance(node, (ast.ListComp, ast.DictComp, ast.SetComp)): + if len(list(ast.walk(node))) > 10: + errors.append("Complex comprehension detected") + + return errors + + def check_function_signature( + self, code: str, expected_name: str, expected_params: List[str] + ) -> Tuple[bool, List[str]]: + """ + Validate function signature matches expected + + Args: + code: Python code + expected_name: Expected function name + expected_params: Expected parameter names + + Returns: + Tuple of (is_valid, list_of_errors) + """ + errors = [] + + try: + tree = ast.parse(code) + except SyntaxError as e: + return False, [f"Syntax error: {e}"] + + # Find function definition + func_def = None + for node in ast.walk(tree): + if isinstance(node, ast.FunctionDef) and node.name == expected_name: + func_def = node + break + + if not func_def: + errors.append(f"Function '{expected_name}' not found") + return False, errors + + # Check parameters + actual_params = [arg.arg for arg in func_def.args.args] + if actual_params != expected_params: + errors.append( + f"Parameter mismatch: expected {expected_params}, got {actual_params}" + ) + + # Check for return statement + has_return = any(isinstance(node, ast.Return) for node in ast.walk(func_def)) + if not has_return: + errors.append("Function must have a return statement") + + return len(errors) == 0, errors diff --git a/ai-projects/llm-maker/src/website_maker.py b/ai-projects/llm-maker/src/website_maker.py index a8b4a071b..47706d4cf 100644 --- a/ai-projects/llm-maker/src/website_maker.py +++ b/ai-projects/llm-maker/src/website_maker.py @@ -1,559 +1,559 @@ -""" -AI-Powered Website Maker and Updater - -This module provides functionality to automatically generate and update HTML websites -using natural language descriptions. It leverages the existing chat provider infrastructure -to generate HTML, CSS, and JavaScript code. -""" - -import json -import os -import re -from datetime import datetime -from typing import Dict, List, Optional, Tuple - -# Import chat providers using the pattern from tool_maker.py -try: - from shared.chat_providers import detect_provider -except ImportError: - import sys - - sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..")) - from shared.chat_providers import detect_provider - - -class WebsiteMaker: - """ - Generates and updates complete HTML websites using AI. - """ - - def __init__(self, provider_name: Optional[str] = None): - """ - Initialize the WebsiteMaker. - - Args: - provider_name: Optional provider name (azure, openai, local). - If None, auto-detects based on environment. - """ - # detect_provider() returns the provider instance directly - self.provider = detect_provider() - - self.output_dir = os.path.join( - os.path.dirname(__file__), "..", "generated_sites" - ) - os.makedirs(self.output_dir, exist_ok=True) - - def create_website( - self, - name: str, - description: str, - style: str = "modern", - pages: Optional[List[str]] = None, - features: Optional[List[str]] = None, - ) -> Dict: - """ - Generate a complete website from natural language description. - - Args: - name: Name of the website/project - description: What the website should do/show - style: Visual style (modern, minimal, corporate, creative) - pages: List of page names (e.g., ["index", "about", "contact"]) - features: List of features to include (e.g., ["navigation", "footer", "contact form"]) - - Returns: - Dict with 'success', 'files' (dict of filename->content), 'path', 'message' - """ - if not pages: - pages = ["index"] - if not features: - features = ["responsive design", "modern styling"] - - # Create prompt for the AI - prompt = self._build_website_prompt(name, description, style, pages, features) - - try: - # Generate the website code using the AI provider - messages = [ - { - "role": "system", - "content": "You are an expert web developer who creates clean, modern, responsive websites.", - }, - {"role": "user", "content": prompt}, - ] - - response = self.provider.complete(messages, stream=False) - - # Extract HTML, CSS, and JS from the response - files = self._extract_code_blocks(response) - - if not files: - return { - "success": False, - "message": "Failed to extract website code from AI response", - "files": {}, - "path": None, - } - - # Save files to disk - project_path = os.path.join(self.output_dir, name) - os.makedirs(project_path, exist_ok=True) - - for filename, content in files.items(): - filepath = os.path.join(project_path, filename) - with open(filepath, "w", encoding="utf-8") as f: - f.write(content) - - # Create metadata file - metadata = { - "name": name, - "description": description, - "style": style, - "pages": pages, - "features": features, - "created_at": datetime.now().isoformat(), - "files": list(files.keys()), - } - - with open(os.path.join(project_path, "metadata.json"), "w") as f: - json.dump(metadata, f, indent=2) - - return { - "success": True, - "message": f"Website '{name}' created successfully", - "files": files, - "path": project_path, - "metadata": metadata, - } - - except Exception as e: - return { - "success": False, - "message": f"Error creating website: {str(e)}", - "files": {}, - "path": None, - } - - def update_website( - self, name: str, update_description: str, target_file: Optional[str] = None - ) -> Dict: - """ - Update an existing website based on natural language instructions. - - Args: - name: Name of the website to update - update_description: What changes to make - target_file: Optional specific file to update (e.g., "index.html") - - Returns: - Dict with 'success', 'updated_files', 'path', 'message' - """ - project_path = os.path.join(self.output_dir, name) - - if not os.path.exists(project_path): - return { - "success": False, - "message": f"Website '{name}' not found", - "updated_files": {}, - "path": None, - } - - # Load metadata - metadata_path = os.path.join(project_path, "metadata.json") - if os.path.exists(metadata_path): - with open(metadata_path, "r") as f: - metadata = json.load(f) - else: - metadata = {"files": []} - - # Determine which files to update - if target_file: - files_to_update = ( - [target_file] if target_file in metadata.get("files", []) else [] - ) - else: - files_to_update = metadata.get("files", []) - - if not files_to_update: - return { - "success": False, - "message": "No files to update", - "updated_files": {}, - "path": project_path, - } - - # Read current file contents - current_files = {} - for filename in files_to_update: - filepath = os.path.join(project_path, filename) - if os.path.exists(filepath): - with open(filepath, "r", encoding="utf-8") as f: - current_files[filename] = f.read() - - # Create update prompt - prompt = self._build_update_prompt(name, update_description, current_files) - - try: - messages = [ - { - "role": "system", - "content": "You are an expert web developer who updates websites based on user requirements.", - }, - {"role": "user", "content": prompt}, - ] - - response = self.provider.complete(messages, stream=False) - - # Extract updated code - updated_files = self._extract_code_blocks(response) - - if not updated_files: - return { - "success": False, - "message": "Failed to extract updated code from AI response", - "updated_files": {}, - "path": project_path, - } - - # Save updated files - for filename, content in updated_files.items(): - filepath = os.path.join(project_path, filename) - with open(filepath, "w", encoding="utf-8") as f: - f.write(content) - - # Update metadata - metadata["last_updated"] = datetime.now().isoformat() - metadata["last_update_description"] = update_description - - with open(metadata_path, "w") as f: - json.dump(metadata, f, indent=2) - - return { - "success": True, - "message": f"Website '{name}' updated successfully", - "updated_files": updated_files, - "path": project_path, - "metadata": metadata, - } - - except Exception as e: - return { - "success": False, - "message": f"Error updating website: {str(e)}", - "updated_files": {}, - "path": project_path, - } - - def list_websites(self) -> List[Dict]: - """ - List all generated websites. - - Returns: - List of dicts with website metadata - """ - websites = [] - - if not os.path.exists(self.output_dir): - return websites - - for name in os.listdir(self.output_dir): - project_path = os.path.join(self.output_dir, name) - if not os.path.isdir(project_path): - continue - - metadata_path = os.path.join(project_path, "metadata.json") - if os.path.exists(metadata_path): - with open(metadata_path, "r") as f: - metadata = json.load(f) - metadata["path"] = project_path - websites.append(metadata) - - return websites - - def delete_website(self, name: str) -> Dict: - """ - Delete a generated website. - - Args: - name: Name of the website to delete - - Returns: - Dict with 'success' and 'message' - """ - project_path = os.path.join(self.output_dir, name) - - if not os.path.exists(project_path): - return {"success": False, "message": f"Website '{name}' not found"} - - try: - import shutil - - shutil.rmtree(project_path) - return { - "success": True, - "message": f"Website '{name}' deleted successfully", - } - except Exception as e: - return {"success": False, "message": f"Error deleting website: {str(e)}"} - - def _build_website_prompt( - self, - name: str, - description: str, - style: str, - pages: List[str], - features: List[str], - ) -> str: - """Build prompt for website generation.""" - prompt = f"""Create a complete, modern, responsive website with the following specifications: - -**Project Name:** {name} -**Description:** {description} -**Visual Style:** {style} -**Pages:** {', '.join(pages)} -**Features:** {', '.join(features)} - -Generate a fully functional website with: -1. Clean, semantic HTML5 -2. Modern CSS3 with responsive design (mobile-friendly) -3. Optional JavaScript for interactivity if needed -4. Professional styling matching the "{style}" aesthetic -5. All code in separate files (HTML, CSS, JS if needed) - -Requirements: -- Use modern best practices -- Include meta tags for SEO -- Make it mobile-responsive -- Add smooth animations/transitions -- Include comments in the code - -Please provide the complete code for each file with clear file names. -Use markdown code blocks with the filename as the language tag, like: - -```html:index.html - -... -``` - -```css:styles.css -body {{ -... -}} -``` -""" - return prompt - - def _build_update_prompt( - self, name: str, update_description: str, current_files: Dict[str, str] - ) -> str: - """Build prompt for website update.""" - files_section = "\n\n".join( - [ - f"**{filename}:**\n```\n{content}\n```" - for filename, content in current_files.items() - ] - ) - - prompt = f"""Update the website '{name}' with the following changes: - -**Update Description:** {update_description} - -**Current Files:** -{files_section} - -Please provide the updated code for each file that needs changes. -Use markdown code blocks with the filename as the language tag, like: - -```html:index.html - -... -``` - -Only include files that need to be updated. Keep the same structure and style unless the update requires changes. -""" - return prompt - - def _extract_code_blocks(self, text: str) -> Dict[str, str]: - """ - Extract code blocks from AI response. - - Looks for patterns like: - ```html:index.html - ... - ``` - - or - - ```index.html - ... - ``` - - Returns: - Dict mapping filename to code content - """ - files = {} - - # Pattern 1: ```language:filename - pattern1 = r"```(?:\w+):([^\n]+)\n(.*?)```" - matches1 = re.findall(pattern1, text, re.DOTALL) - - for filename, content in matches1: - filename = filename.strip() - files[filename] = content.strip() - - # Pattern 2: ```filename (without language) - if not files: - pattern2 = r"```([^\n]+\.(html|css|js))\n(.*?)```" - matches2 = re.findall(pattern2, text, re.DOTALL | re.IGNORECASE) - - for filename, ext, content in matches2: - filename = filename.strip() - files[filename] = content.strip() - - # Pattern 3: Generic code blocks with filename mentioned before - if not files: - pattern3 = r"(?:File|Filename|Save as):\s*[`'\"]?([^\n`'\"]+\.(html|css|js))[`'\"]?\s*\n+```(?:\w+)?\n(.*?)```" - matches3 = re.findall(pattern3, text, re.DOTALL | re.IGNORECASE) - - for filename, ext, content in matches3: - filename = filename.strip() - files[filename] = content.strip() - - return files - - -class WebsiteValidator: - """ - Validates generated website code for common issues. - """ - - @staticmethod - def validate_html(html: str) -> Tuple[bool, List[str]]: - """ - Validate HTML content. - - Returns: - Tuple of (is_valid, list_of_warnings) - """ - warnings = [] - - # Check for DOCTYPE - if " tag") - if " tag") - if " tag") - - # Check for title - if " tag") - - # Check for viewport meta tag (responsive design) - if "viewport" not in html.lower(): - warnings.append( - "Missing viewport meta tag (important for mobile responsiveness)" - ) - - is_valid = len(warnings) == 0 - return is_valid, warnings - - @staticmethod - def validate_css(css: str) -> Tuple[bool, List[str]]: - """ - Validate CSS content. - - Returns: - Tuple of (is_valid, list_of_warnings) - """ - warnings = [] - - # Check for basic responsive design - if "@media" not in css.lower(): - warnings.append( - "No media queries found (consider adding for responsive design)" - ) - - # Check for potential syntax errors (very basic) - open_braces = css.count("{") - close_braces = css.count("}") - if open_braces != close_braces: - warnings.append( - f"Mismatched braces: {open_braces} opening, {close_braces} closing" - ) - - is_valid = open_braces == close_braces - return is_valid, warnings - - -def create_website_cli(): - """Command-line interface for website creation.""" - import argparse - - parser = argparse.ArgumentParser(description="AI-Powered Website Maker") - subparsers = parser.add_subparsers(dest="command", help="Command to execute") - - # Create command - create_parser = subparsers.add_parser("create", help="Create a new website") - create_parser.add_argument("name", help="Website name") - create_parser.add_argument("description", help="Website description") - create_parser.add_argument("--style", default="modern", help="Visual style") - create_parser.add_argument("--pages", nargs="+", help="Page names") - create_parser.add_argument("--features", nargs="+", help="Features to include") - create_parser.add_argument("--provider", help="AI provider (azure, openai, local)") - - # Update command - update_parser = subparsers.add_parser("update", help="Update an existing website") - update_parser.add_argument("name", help="Website name") - update_parser.add_argument("description", help="Update description") - update_parser.add_argument("--file", help="Specific file to update") - update_parser.add_argument("--provider", help="AI provider") - - # List command - list_parser = subparsers.add_parser("list", help="List all websites") - - # Delete command - delete_parser = subparsers.add_parser("delete", help="Delete a website") - delete_parser.add_argument("name", help="Website name") - - args = parser.parse_args() - - if not args.command: - parser.print_help() - return - - maker = WebsiteMaker(provider_name=getattr(args, "provider", None)) - - if args.command == "create": - result = maker.create_website( - name=args.name, - description=args.description, - style=args.style, - pages=args.pages, - features=args.features, - ) - print(json.dumps(result, indent=2)) - - elif args.command == "update": - result = maker.update_website( - name=args.name, - update_description=args.description, - target_file=getattr(args, "file", None), - ) - print(json.dumps(result, indent=2)) - - elif args.command == "list": - websites = maker.list_websites() - print(json.dumps(websites, indent=2)) - - elif args.command == "delete": - result = maker.delete_website(args.name) - print(json.dumps(result, indent=2)) - - -if __name__ == "__main__": - create_website_cli() +""" +AI-Powered Website Maker and Updater + +This module provides functionality to automatically generate and update HTML websites +using natural language descriptions. It leverages the existing chat provider infrastructure +to generate HTML, CSS, and JavaScript code. +""" + +import json +import os +import re +from datetime import datetime +from typing import Dict, List, Optional, Tuple + +# Import chat providers using the pattern from tool_maker.py +try: + from shared.chat_providers import detect_provider +except ImportError: + import sys + + sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..")) + from shared.chat_providers import detect_provider + + +class WebsiteMaker: + """ + Generates and updates complete HTML websites using AI. + """ + + def __init__(self, provider_name: Optional[str] = None): + """ + Initialize the WebsiteMaker. + + Args: + provider_name: Optional provider name (azure, openai, local). + If None, auto-detects based on environment. + """ + # detect_provider() returns the provider instance directly + self.provider = detect_provider() + + self.output_dir = os.path.join( + os.path.dirname(__file__), "..", "generated_sites" + ) + os.makedirs(self.output_dir, exist_ok=True) + + def create_website( + self, + name: str, + description: str, + style: str = "modern", + pages: Optional[List[str]] = None, + features: Optional[List[str]] = None, + ) -> Dict: + """ + Generate a complete website from natural language description. + + Args: + name: Name of the website/project + description: What the website should do/show + style: Visual style (modern, minimal, corporate, creative) + pages: List of page names (e.g., ["index", "about", "contact"]) + features: List of features to include (e.g., ["navigation", "footer", "contact form"]) + + Returns: + Dict with 'success', 'files' (dict of filename->content), 'path', 'message' + """ + if not pages: + pages = ["index"] + if not features: + features = ["responsive design", "modern styling"] + + # Create prompt for the AI + prompt = self._build_website_prompt(name, description, style, pages, features) + + try: + # Generate the website code using the AI provider + messages = [ + { + "role": "system", + "content": "You are an expert web developer who creates clean, modern, responsive websites.", + }, + {"role": "user", "content": prompt}, + ] + + response = self.provider.complete(messages, stream=False) + + # Extract HTML, CSS, and JS from the response + files = self._extract_code_blocks(response) + + if not files: + return { + "success": False, + "message": "Failed to extract website code from AI response", + "files": {}, + "path": None, + } + + # Save files to disk + project_path = os.path.join(self.output_dir, name) + os.makedirs(project_path, exist_ok=True) + + for filename, content in files.items(): + filepath = os.path.join(project_path, filename) + with open(filepath, "w", encoding="utf-8") as f: + f.write(content) + + # Create metadata file + metadata = { + "name": name, + "description": description, + "style": style, + "pages": pages, + "features": features, + "created_at": datetime.now().isoformat(), + "files": list(files.keys()), + } + + with open(os.path.join(project_path, "metadata.json"), "w") as f: + json.dump(metadata, f, indent=2) + + return { + "success": True, + "message": f"Website '{name}' created successfully", + "files": files, + "path": project_path, + "metadata": metadata, + } + + except Exception as e: + return { + "success": False, + "message": f"Error creating website: {str(e)}", + "files": {}, + "path": None, + } + + def update_website( + self, name: str, update_description: str, target_file: Optional[str] = None + ) -> Dict: + """ + Update an existing website based on natural language instructions. + + Args: + name: Name of the website to update + update_description: What changes to make + target_file: Optional specific file to update (e.g., "index.html") + + Returns: + Dict with 'success', 'updated_files', 'path', 'message' + """ + project_path = os.path.join(self.output_dir, name) + + if not os.path.exists(project_path): + return { + "success": False, + "message": f"Website '{name}' not found", + "updated_files": {}, + "path": None, + } + + # Load metadata + metadata_path = os.path.join(project_path, "metadata.json") + if os.path.exists(metadata_path): + with open(metadata_path, "r") as f: + metadata = json.load(f) + else: + metadata = {"files": []} + + # Determine which files to update + if target_file: + files_to_update = ( + [target_file] if target_file in metadata.get("files", []) else [] + ) + else: + files_to_update = metadata.get("files", []) + + if not files_to_update: + return { + "success": False, + "message": "No files to update", + "updated_files": {}, + "path": project_path, + } + + # Read current file contents + current_files = {} + for filename in files_to_update: + filepath = os.path.join(project_path, filename) + if os.path.exists(filepath): + with open(filepath, "r", encoding="utf-8") as f: + current_files[filename] = f.read() + + # Create update prompt + prompt = self._build_update_prompt(name, update_description, current_files) + + try: + messages = [ + { + "role": "system", + "content": "You are an expert web developer who updates websites based on user requirements.", + }, + {"role": "user", "content": prompt}, + ] + + response = self.provider.complete(messages, stream=False) + + # Extract updated code + updated_files = self._extract_code_blocks(response) + + if not updated_files: + return { + "success": False, + "message": "Failed to extract updated code from AI response", + "updated_files": {}, + "path": project_path, + } + + # Save updated files + for filename, content in updated_files.items(): + filepath = os.path.join(project_path, filename) + with open(filepath, "w", encoding="utf-8") as f: + f.write(content) + + # Update metadata + metadata["last_updated"] = datetime.now().isoformat() + metadata["last_update_description"] = update_description + + with open(metadata_path, "w") as f: + json.dump(metadata, f, indent=2) + + return { + "success": True, + "message": f"Website '{name}' updated successfully", + "updated_files": updated_files, + "path": project_path, + "metadata": metadata, + } + + except Exception as e: + return { + "success": False, + "message": f"Error updating website: {str(e)}", + "updated_files": {}, + "path": project_path, + } + + def list_websites(self) -> List[Dict]: + """ + List all generated websites. + + Returns: + List of dicts with website metadata + """ + websites = [] + + if not os.path.exists(self.output_dir): + return websites + + for name in os.listdir(self.output_dir): + project_path = os.path.join(self.output_dir, name) + if not os.path.isdir(project_path): + continue + + metadata_path = os.path.join(project_path, "metadata.json") + if os.path.exists(metadata_path): + with open(metadata_path, "r") as f: + metadata = json.load(f) + metadata["path"] = project_path + websites.append(metadata) + + return websites + + def delete_website(self, name: str) -> Dict: + """ + Delete a generated website. + + Args: + name: Name of the website to delete + + Returns: + Dict with 'success' and 'message' + """ + project_path = os.path.join(self.output_dir, name) + + if not os.path.exists(project_path): + return {"success": False, "message": f"Website '{name}' not found"} + + try: + import shutil + + shutil.rmtree(project_path) + return { + "success": True, + "message": f"Website '{name}' deleted successfully", + } + except Exception as e: + return {"success": False, "message": f"Error deleting website: {str(e)}"} + + def _build_website_prompt( + self, + name: str, + description: str, + style: str, + pages: List[str], + features: List[str], + ) -> str: + """Build prompt for website generation.""" + prompt = f"""Create a complete, modern, responsive website with the following specifications: + +**Project Name:** {name} +**Description:** {description} +**Visual Style:** {style} +**Pages:** {', '.join(pages)} +**Features:** {', '.join(features)} + +Generate a fully functional website with: +1. Clean, semantic HTML5 +2. Modern CSS3 with responsive design (mobile-friendly) +3. Optional JavaScript for interactivity if needed +4. Professional styling matching the "{style}" aesthetic +5. All code in separate files (HTML, CSS, JS if needed) + +Requirements: +- Use modern best practices +- Include meta tags for SEO +- Make it mobile-responsive +- Add smooth animations/transitions +- Include comments in the code + +Please provide the complete code for each file with clear file names. +Use markdown code blocks with the filename as the language tag, like: + +```html:index.html + +... +``` + +```css:styles.css +body {{ +... +}} +``` +""" + return prompt + + def _build_update_prompt( + self, name: str, update_description: str, current_files: Dict[str, str] + ) -> str: + """Build prompt for website update.""" + files_section = "\n\n".join( + [ + f"**{filename}:**\n```\n{content}\n```" + for filename, content in current_files.items() + ] + ) + + prompt = f"""Update the website '{name}' with the following changes: + +**Update Description:** {update_description} + +**Current Files:** +{files_section} + +Please provide the updated code for each file that needs changes. +Use markdown code blocks with the filename as the language tag, like: + +```html:index.html + +... +``` + +Only include files that need to be updated. Keep the same structure and style unless the update requires changes. +""" + return prompt + + def _extract_code_blocks(self, text: str) -> Dict[str, str]: + """ + Extract code blocks from AI response. + + Looks for patterns like: + ```html:index.html + ... + ``` + + or + + ```index.html + ... + ``` + + Returns: + Dict mapping filename to code content + """ + files = {} + + # Pattern 1: ```language:filename + pattern1 = r"```(?:\w+):([^\n]+)\n(.*?)```" + matches1 = re.findall(pattern1, text, re.DOTALL) + + for filename, content in matches1: + filename = filename.strip() + files[filename] = content.strip() + + # Pattern 2: ```filename (without language) + if not files: + pattern2 = r"```([^\n]+\.(html|css|js))\n(.*?)```" + matches2 = re.findall(pattern2, text, re.DOTALL | re.IGNORECASE) + + for filename, ext, content in matches2: + filename = filename.strip() + files[filename] = content.strip() + + # Pattern 3: Generic code blocks with filename mentioned before + if not files: + pattern3 = r"(?:File|Filename|Save as):\s*[`'\"]?([^\n`'\"]+\.(html|css|js))[`'\"]?\s*\n+```(?:\w+)?\n(.*?)```" + matches3 = re.findall(pattern3, text, re.DOTALL | re.IGNORECASE) + + for filename, ext, content in matches3: + filename = filename.strip() + files[filename] = content.strip() + + return files + + +class WebsiteValidator: + """ + Validates generated website code for common issues. + """ + + @staticmethod + def validate_html(html: str) -> Tuple[bool, List[str]]: + """ + Validate HTML content. + + Returns: + Tuple of (is_valid, list_of_warnings) + """ + warnings = [] + + # Check for DOCTYPE + if " tag") + if " tag") + if " tag") + + # Check for title + if " tag") + + # Check for viewport meta tag (responsive design) + if "viewport" not in html.lower(): + warnings.append( + "Missing viewport meta tag (important for mobile responsiveness)" + ) + + is_valid = len(warnings) == 0 + return is_valid, warnings + + @staticmethod + def validate_css(css: str) -> Tuple[bool, List[str]]: + """ + Validate CSS content. + + Returns: + Tuple of (is_valid, list_of_warnings) + """ + warnings = [] + + # Check for basic responsive design + if "@media" not in css.lower(): + warnings.append( + "No media queries found (consider adding for responsive design)" + ) + + # Check for potential syntax errors (very basic) + open_braces = css.count("{") + close_braces = css.count("}") + if open_braces != close_braces: + warnings.append( + f"Mismatched braces: {open_braces} opening, {close_braces} closing" + ) + + is_valid = open_braces == close_braces + return is_valid, warnings + + +def create_website_cli(): + """Command-line interface for website creation.""" + import argparse + + parser = argparse.ArgumentParser(description="AI-Powered Website Maker") + subparsers = parser.add_subparsers(dest="command", help="Command to execute") + + # Create command + create_parser = subparsers.add_parser("create", help="Create a new website") + create_parser.add_argument("name", help="Website name") + create_parser.add_argument("description", help="Website description") + create_parser.add_argument("--style", default="modern", help="Visual style") + create_parser.add_argument("--pages", nargs="+", help="Page names") + create_parser.add_argument("--features", nargs="+", help="Features to include") + create_parser.add_argument("--provider", help="AI provider (azure, openai, local)") + + # Update command + update_parser = subparsers.add_parser("update", help="Update an existing website") + update_parser.add_argument("name", help="Website name") + update_parser.add_argument("description", help="Update description") + update_parser.add_argument("--file", help="Specific file to update") + update_parser.add_argument("--provider", help="AI provider") + + # List command + list_parser = subparsers.add_parser("list", help="List all websites") + + # Delete command + delete_parser = subparsers.add_parser("delete", help="Delete a website") + delete_parser.add_argument("name", help="Website name") + + args = parser.parse_args() + + if not args.command: + parser.print_help() + return + + maker = WebsiteMaker(provider_name=getattr(args, "provider", None)) + + if args.command == "create": + result = maker.create_website( + name=args.name, + description=args.description, + style=args.style, + pages=args.pages, + features=args.features, + ) + print(json.dumps(result, indent=2)) + + elif args.command == "update": + result = maker.update_website( + name=args.name, + update_description=args.description, + target_file=getattr(args, "file", None), + ) + print(json.dumps(result, indent=2)) + + elif args.command == "list": + websites = maker.list_websites() + print(json.dumps(websites, indent=2)) + + elif args.command == "delete": + result = maker.delete_website(args.name) + print(json.dumps(result, indent=2)) + + +if __name__ == "__main__": + create_website_cli() diff --git a/ai-projects/llm-maker/start_web_ui.sh b/ai-projects/llm-maker/start_web_ui.sh index 5380dfd6b..16a7fcd1e 100755 --- a/ai-projects/llm-maker/start_web_ui.sh +++ b/ai-projects/llm-maker/start_web_ui.sh @@ -1,25 +1,25 @@ -#!/bin/bash -# Start LLM Maker Web UI - -echo "================================================" -echo "Starting LLM Maker Web UI..." -echo "================================================" -echo "" - -cd "$(dirname "$0")" - -echo "Checking dependencies..." -python3 -c "import sys; sys.path.insert(0, 'src'); from tool_maker import ToolMaker" 2>/dev/null -if [ $? -ne 0 ]; then - echo "Error: LLM Maker components not found" - echo "Please ensure you're in the llm-maker directory" - exit 1 -fi - -echo "✓ Dependencies OK" -echo "" -echo "Starting web server on http://localhost:8090" -echo "Press Ctrl+C to stop" -echo "" - -python3 web_server.py +#!/bin/bash +# Start LLM Maker Web UI + +echo "================================================" +echo "Starting LLM Maker Web UI..." +echo "================================================" +echo "" + +cd "$(dirname "$0")" + +echo "Checking dependencies..." +python3 -c "import sys; sys.path.insert(0, 'src'); from tool_maker import ToolMaker" 2>/dev/null +if [ $? -ne 0 ]; then + echo "Error: LLM Maker components not found" + echo "Please ensure you're in the llm-maker directory" + exit 1 +fi + +echo "✓ Dependencies OK" +echo "" +echo "Starting web server on http://localhost:8090" +echo "Press Ctrl+C to stop" +echo "" + +python3 web_server.py diff --git a/ai-projects/llm-maker/test_llm_maker.sh b/ai-projects/llm-maker/test_llm_maker.sh index 339ff9ca6..72fea5387 100755 --- a/ai-projects/llm-maker/test_llm_maker.sh +++ b/ai-projects/llm-maker/test_llm_maker.sh @@ -1,91 +1,91 @@ -#!/bin/bash -# Test script for LLM Maker - -echo "================================================" -echo "LLM Maker - Test Suite" -echo "================================================" -echo "" - -cd "$(dirname "$0")" - -echo "1. Testing Tool Validator..." -echo "------------------------------" -python -c " -import sys -sys.path.insert(0, 'src') -from tool_validator import ToolValidator - -validator = ToolValidator() - -# Valid code -code = 'def add(a: int, b: int) -> int:\n return a + b' -is_valid, errors = validator.validate(code) -print(f'✓ Valid code test: {\"PASS\" if is_valid else \"FAIL\"}') - -# Dangerous import -code = 'import os\ndef bad(): return os.getcwd()' -is_valid, errors = validator.validate(code) -print(f'✓ Dangerous import detection: {\"PASS\" if not is_valid else \"FAIL\"}') -" -echo "" - -echo "2. Testing Tool Executor..." -echo "------------------------------" -python -c " -import sys -sys.path.insert(0, 'src') -from tool_executor import ToolExecutor - -executor = ToolExecutor(timeout=5) - -# Simple execution -code = 'def multiply(a: int, b: int) -> int:\n return a * b' -result = executor.execute(code, 'multiply', {'a': 5, 'b': 3}) -print(f'✓ Simple execution: {\"PASS\" if result[\"success\"] and result[\"result\"] == 15 else \"FAIL\"}') - -# Error handling -code = 'def divide(a: int, b: int) -> float:\n return a / b' -result = executor.execute(code, 'divide', {'a': 10, 'b': 0}) -print(f'✓ Error handling: {\"PASS\" if not result[\"success\"] else \"FAIL\"}') -" -echo "" - -echo "3. Testing Tool Registry..." -echo "------------------------------" -python -c " -import sys -import tempfile -from pathlib import Path -sys.path.insert(0, 'src') -from tool_registry import Tool, ToolRegistry - -temp_dir = tempfile.mkdtemp() -registry = ToolRegistry(Path(temp_dir)) - -# Register tool -tool = Tool( - id='', - name='test', - description='Test', - code='def test(): return 42', - parameters={}, - return_type='int', - created_at='' -) - -tool_id = registry.register(tool) -print(f'✓ Register tool: {\"PASS\" if tool_id else \"FAIL\"}') - -# Retrieve tool -retrieved = registry.get(tool_id) -print(f'✓ Get tool: {\"PASS\" if retrieved else \"FAIL\"}') - -# List tools -tools = registry.list_tools() -print(f'✓ List tools: {\"PASS\" if len(tools) == 1 else \"FAIL\"}') -" -echo "" - -echo "================================================" -echo "All tests completed!" -echo "================================================" +#!/bin/bash +# Test script for LLM Maker + +echo "================================================" +echo "LLM Maker - Test Suite" +echo "================================================" +echo "" + +cd "$(dirname "$0")" + +echo "1. Testing Tool Validator..." +echo "------------------------------" +python -c " +import sys +sys.path.insert(0, 'src') +from tool_validator import ToolValidator + +validator = ToolValidator() + +# Valid code +code = 'def add(a: int, b: int) -> int:\n return a + b' +is_valid, errors = validator.validate(code) +print(f'✓ Valid code test: {\"PASS\" if is_valid else \"FAIL\"}') + +# Dangerous import +code = 'import os\ndef bad(): return os.getcwd()' +is_valid, errors = validator.validate(code) +print(f'✓ Dangerous import detection: {\"PASS\" if not is_valid else \"FAIL\"}') +" +echo "" + +echo "2. Testing Tool Executor..." +echo "------------------------------" +python -c " +import sys +sys.path.insert(0, 'src') +from tool_executor import ToolExecutor + +executor = ToolExecutor(timeout=5) + +# Simple execution +code = 'def multiply(a: int, b: int) -> int:\n return a * b' +result = executor.execute(code, 'multiply', {'a': 5, 'b': 3}) +print(f'✓ Simple execution: {\"PASS\" if result[\"success\"] and result[\"result\"] == 15 else \"FAIL\"}') + +# Error handling +code = 'def divide(a: int, b: int) -> float:\n return a / b' +result = executor.execute(code, 'divide', {'a': 10, 'b': 0}) +print(f'✓ Error handling: {\"PASS\" if not result[\"success\"] else \"FAIL\"}') +" +echo "" + +echo "3. Testing Tool Registry..." +echo "------------------------------" +python -c " +import sys +import tempfile +from pathlib import Path +sys.path.insert(0, 'src') +from tool_registry import Tool, ToolRegistry + +temp_dir = tempfile.mkdtemp() +registry = ToolRegistry(Path(temp_dir)) + +# Register tool +tool = Tool( + id='', + name='test', + description='Test', + code='def test(): return 42', + parameters={}, + return_type='int', + created_at='' +) + +tool_id = registry.register(tool) +print(f'✓ Register tool: {\"PASS\" if tool_id else \"FAIL\"}') + +# Retrieve tool +retrieved = registry.get(tool_id) +print(f'✓ Get tool: {\"PASS\" if retrieved else \"FAIL\"}') + +# List tools +tools = registry.list_tools() +print(f'✓ List tools: {\"PASS\" if len(tools) == 1 else \"FAIL\"}') +" +echo "" + +echo "================================================" +echo "All tests completed!" +echo "================================================" diff --git a/ai-projects/llm-maker/tests/test_executor.py b/ai-projects/llm-maker/tests/test_executor.py index a7c28b371..f9fbfa8f3 100644 --- a/ai-projects/llm-maker/tests/test_executor.py +++ b/ai-projects/llm-maker/tests/test_executor.py @@ -1,94 +1,94 @@ -""" -Tests for Tool Executor -""" - -import sys -from pathlib import Path - -import pytest - -# Add src to path -sys.path.insert(0, str(Path(__file__).parent.parent / "src")) - -try: - from tool_executor import ToolExecutor -except ImportError: - from ..src.tool_executor import ToolExecutor - - -class TestToolExecutor: - """Test tool executor functionality""" - - def setup_method(self): - """Setup for each test""" - self.executor = ToolExecutor(timeout=5) - - def test_execute_simple_function(self): - """Test executing a simple function""" - code = """ -def add_numbers(a: int, b: int) -> int: - return a + b -""" - result = self.executor.execute(code, "add_numbers", {"a": 5, "b": 3}) - - assert result["success"] - assert result["result"] == 8 - assert result["result_type"] == "int" - - def test_execute_with_imports(self): - """Test executing function with allowed imports""" - code = """ -import math - -def calculate_sqrt(n: float) -> float: - return math.sqrt(n) -""" - result = self.executor.execute(code, "calculate_sqrt", {"n": 16.0}) - - assert result["success"] - assert abs(result["result"] - 4.0) < 1e-9 - - def test_invalid_arguments(self): - """Test handling of invalid arguments""" - code = """ -def my_func(a: int) -> int: - return a * 2 -""" - result = self.executor.execute(code, "my_func", {"wrong_arg": 5}) - - assert not result["success"] - assert "TypeError" in result["error_type"] - - def test_runtime_error(self): - """Test handling of runtime errors""" - code = """ -def divide(a: int, b: int) -> float: - return a / b -""" - result = self.executor.execute(code, "divide", {"a": 10, "b": 0}) - - assert not result["success"] - assert "error" in result - - def test_function_not_found(self): - """Test error when function doesn't exist""" - code = """ -def some_func(): - return 42 -""" - result = self.executor.execute(code, "other_func", {}) - - assert not result["success"] - assert "not defined" in result["error"].lower() - - def test_compilation_check(self): - """Test code compilation check""" - good_code = "def func(): return 42" - bad_code = "def func( return 42" - - assert self.executor.test_execution(good_code) - assert not self.executor.test_execution(bad_code) - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) +""" +Tests for Tool Executor +""" + +import sys +from pathlib import Path + +import pytest + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + +try: + from tool_executor import ToolExecutor +except ImportError: + from ..src.tool_executor import ToolExecutor + + +class TestToolExecutor: + """Test tool executor functionality""" + + def setup_method(self): + """Setup for each test""" + self.executor = ToolExecutor(timeout=5) + + def test_execute_simple_function(self): + """Test executing a simple function""" + code = """ +def add_numbers(a: int, b: int) -> int: + return a + b +""" + result = self.executor.execute(code, "add_numbers", {"a": 5, "b": 3}) + + assert result["success"] + assert result["result"] == 8 + assert result["result_type"] == "int" + + def test_execute_with_imports(self): + """Test executing function with allowed imports""" + code = """ +import math + +def calculate_sqrt(n: float) -> float: + return math.sqrt(n) +""" + result = self.executor.execute(code, "calculate_sqrt", {"n": 16.0}) + + assert result["success"] + assert abs(result["result"] - 4.0) < 1e-9 + + def test_invalid_arguments(self): + """Test handling of invalid arguments""" + code = """ +def my_func(a: int) -> int: + return a * 2 +""" + result = self.executor.execute(code, "my_func", {"wrong_arg": 5}) + + assert not result["success"] + assert "TypeError" in result["error_type"] + + def test_runtime_error(self): + """Test handling of runtime errors""" + code = """ +def divide(a: int, b: int) -> float: + return a / b +""" + result = self.executor.execute(code, "divide", {"a": 10, "b": 0}) + + assert not result["success"] + assert "error" in result + + def test_function_not_found(self): + """Test error when function doesn't exist""" + code = """ +def some_func(): + return 42 +""" + result = self.executor.execute(code, "other_func", {}) + + assert not result["success"] + assert "not defined" in result["error"].lower() + + def test_compilation_check(self): + """Test code compilation check""" + good_code = "def func(): return 42" + bad_code = "def func( return 42" + + assert self.executor.test_execution(good_code) + assert not self.executor.test_execution(bad_code) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/ai-projects/llm-maker/tests/test_registry.py b/ai-projects/llm-maker/tests/test_registry.py index a69af04ee..c78dd3635 100644 --- a/ai-projects/llm-maker/tests/test_registry.py +++ b/ai-projects/llm-maker/tests/test_registry.py @@ -1,171 +1,171 @@ -""" -Tests for Tool Registry -""" - -import sys -import tempfile -from pathlib import Path - -import pytest - -# Add src to path -sys.path.insert(0, str(Path(__file__).parent.parent / "src")) - -from tool_registry import Tool, ToolRegistry - - -class TestToolRegistry: - """Test tool registry functionality""" - - def setup_method(self): - """Setup for each test""" - # Use temporary directory for tests - self.temp_dir = tempfile.mkdtemp() - self.registry = ToolRegistry(Path(self.temp_dir)) - - def test_register_tool(self): - """Test registering a new tool""" - tool = Tool( - id="", - name="test_func", - description="Test function", - code="def test_func(): return 42", - parameters={}, - return_type="int", - created_at="", - ) - - tool_id = self.registry.register(tool) - - assert tool_id - assert tool.id == tool_id - assert self.registry.get(tool_id) == tool - - def test_get_by_name(self): - """Test retrieving tool by name""" - tool = Tool( - id="", - name="my_tool", - description="Test", - code="def my_tool(): pass", - parameters={}, - return_type="None", - created_at="", - ) - - tool_id = self.registry.register(tool) - retrieved = self.registry.get_by_name("my_tool") - - assert retrieved is not None - assert retrieved.id == tool_id - assert retrieved.name == "my_tool" - - def test_list_tools(self): - """Test listing all tools""" - for i in range(3): - tool = Tool( - id="", - name=f"tool_{i}", - description=f"Tool {i}", - code=f"def tool_{i}(): pass", - parameters={}, - return_type="None", - created_at="", - ) - self.registry.register(tool) - - tools = self.registry.list_tools() - assert len(tools) == 3 - - def test_delete_tool(self): - """Test deleting a tool""" - tool = Tool( - id="", - name="deleteme", - description="Test", - code="def deleteme(): pass", - parameters={}, - return_type="None", - created_at="", - ) - - tool_id = self.registry.register(tool) - success = self.registry.delete(tool_id) - - assert success - assert self.registry.get(tool_id) is None - - def test_update_stats(self): - """Test updating execution statistics""" - tool = Tool( - id="", - name="stattest", - description="Test", - code="def stattest(): pass", - parameters={}, - return_type="None", - created_at="", - ) - - tool_id = self.registry.register(tool) - - # Update stats multiple times - for _ in range(5): - self.registry.update_stats(tool_id) - - updated_tool = self.registry.get(tool_id) - assert updated_tool.execution_count == 5 - assert updated_tool.last_executed is not None - - def test_search_tools(self): - """Test searching tools""" - tools_data = [ - ("calc", "calculator function"), - ("parse", "parse json data"), - ("validate", "validate user input"), - ] - - for name, desc in tools_data: - tool = Tool( - id="", - name=name, - description=desc, - code=f"def {name}(): pass", - parameters={}, - return_type="None", - created_at="", - ) - self.registry.register(tool) - - results = self.registry.search("calc") - assert len(results) == 1 - assert results[0].name == "calc" - - results = self.registry.search("data") - assert len(results) == 1 - assert results[0].name == "parse" - - def test_get_stats(self): - """Test getting registry statistics""" - # Register some tools - for i in range(3): - tool = Tool( - id="", - name=f"tool_{i}", - description=f"Tool {i}", - code=f"def tool_{i}(): pass", - parameters={}, - return_type="None", - created_at="", - validated=i % 2 == 0, # Alternate validated status - ) - self.registry.register(tool) - - stats = self.registry.get_stats() - - assert stats["total_tools"] == 3 - assert stats["validated_tools"] == 2 # 0 and 2 are validated - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) +""" +Tests for Tool Registry +""" + +import sys +import tempfile +from pathlib import Path + +import pytest + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + +from tool_registry import Tool, ToolRegistry + + +class TestToolRegistry: + """Test tool registry functionality""" + + def setup_method(self): + """Setup for each test""" + # Use temporary directory for tests + self.temp_dir = tempfile.mkdtemp() + self.registry = ToolRegistry(Path(self.temp_dir)) + + def test_register_tool(self): + """Test registering a new tool""" + tool = Tool( + id="", + name="test_func", + description="Test function", + code="def test_func(): return 42", + parameters={}, + return_type="int", + created_at="", + ) + + tool_id = self.registry.register(tool) + + assert tool_id + assert tool.id == tool_id + assert self.registry.get(tool_id) == tool + + def test_get_by_name(self): + """Test retrieving tool by name""" + tool = Tool( + id="", + name="my_tool", + description="Test", + code="def my_tool(): pass", + parameters={}, + return_type="None", + created_at="", + ) + + tool_id = self.registry.register(tool) + retrieved = self.registry.get_by_name("my_tool") + + assert retrieved is not None + assert retrieved.id == tool_id + assert retrieved.name == "my_tool" + + def test_list_tools(self): + """Test listing all tools""" + for i in range(3): + tool = Tool( + id="", + name=f"tool_{i}", + description=f"Tool {i}", + code=f"def tool_{i}(): pass", + parameters={}, + return_type="None", + created_at="", + ) + self.registry.register(tool) + + tools = self.registry.list_tools() + assert len(tools) == 3 + + def test_delete_tool(self): + """Test deleting a tool""" + tool = Tool( + id="", + name="deleteme", + description="Test", + code="def deleteme(): pass", + parameters={}, + return_type="None", + created_at="", + ) + + tool_id = self.registry.register(tool) + success = self.registry.delete(tool_id) + + assert success + assert self.registry.get(tool_id) is None + + def test_update_stats(self): + """Test updating execution statistics""" + tool = Tool( + id="", + name="stattest", + description="Test", + code="def stattest(): pass", + parameters={}, + return_type="None", + created_at="", + ) + + tool_id = self.registry.register(tool) + + # Update stats multiple times + for _ in range(5): + self.registry.update_stats(tool_id) + + updated_tool = self.registry.get(tool_id) + assert updated_tool.execution_count == 5 + assert updated_tool.last_executed is not None + + def test_search_tools(self): + """Test searching tools""" + tools_data = [ + ("calc", "calculator function"), + ("parse", "parse json data"), + ("validate", "validate user input"), + ] + + for name, desc in tools_data: + tool = Tool( + id="", + name=name, + description=desc, + code=f"def {name}(): pass", + parameters={}, + return_type="None", + created_at="", + ) + self.registry.register(tool) + + results = self.registry.search("calc") + assert len(results) == 1 + assert results[0].name == "calc" + + results = self.registry.search("data") + assert len(results) == 1 + assert results[0].name == "parse" + + def test_get_stats(self): + """Test getting registry statistics""" + # Register some tools + for i in range(3): + tool = Tool( + id="", + name=f"tool_{i}", + description=f"Tool {i}", + code=f"def tool_{i}(): pass", + parameters={}, + return_type="None", + created_at="", + validated=i % 2 == 0, # Alternate validated status + ) + self.registry.register(tool) + + stats = self.registry.get_stats() + + assert stats["total_tools"] == 3 + assert stats["validated_tools"] == 2 # 0 and 2 are validated + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/ai-projects/llm-maker/tests/test_validator.py b/ai-projects/llm-maker/tests/test_validator.py index df75c7e49..9ffef2c7c 100644 --- a/ai-projects/llm-maker/tests/test_validator.py +++ b/ai-projects/llm-maker/tests/test_validator.py @@ -1,126 +1,126 @@ -""" -Tests for Tool Validator -""" - -import sys -from pathlib import Path - -import pytest - -# Add src to path -sys.path.insert(0, str(Path(__file__).parent.parent / "src")) - -from tool_validator import ToolValidator - - -class TestToolValidator: - """Test tool validator functionality""" - - def setup_method(self): - """Setup for each test""" - self.validator = ToolValidator() - - def test_valid_code(self): - """Test validation of safe code""" - code = """ -def add_numbers(a: int, b: int) -> int: - return a + b -""" - is_valid, errors = self.validator.validate(code) - assert is_valid, f"Expected valid but got errors: {errors}" - assert len(errors) == 0 - - def test_dangerous_import(self): - """Test detection of dangerous imports""" - code = """ -import os - -def bad_function(): - return os.getcwd() -""" - is_valid, errors = self.validator.validate(code) - assert not is_valid - assert any("os" in str(e).lower() for e in errors) - - def test_dangerous_builtin(self): - """Test detection of dangerous built-in functions""" - code = """ -def bad_function(code_str): - return eval(code_str) -""" - is_valid, errors = self.validator.validate(code) - assert not is_valid - assert any("eval" in str(e).lower() for e in errors) - - def test_file_operations(self): - """Test detection of file operations""" - code = """ -def bad_function(): - with open('file.txt', 'r') as f: - return f.read() -""" - is_valid, errors = self.validator.validate(code) - assert not is_valid - assert any("file" in str(e).lower() or "open" in str(e).lower() for e in errors) - - def test_allowed_imports(self): - """Test that allowed imports pass validation""" - code = """ -import math -import json - -def calculate(): - return math.sqrt(16) + len(json.dumps({})) -""" - is_valid, errors = self.validator.validate(code) - assert is_valid, f"Expected valid but got errors: {errors}" - - def test_syntax_error(self): - """Test detection of syntax errors""" - code = """ -def bad_syntax( - return "missing closing paren" -""" - is_valid, errors = self.validator.validate(code) - assert not is_valid - assert any("syntax" in str(e).lower() for e in errors) - - def test_function_signature_check(self): - """Test function signature validation""" - code = """ -def my_function(a: int, b: str) -> int: - return int(b) + a -""" - is_valid, errors = self.validator.check_function_signature( - code, "my_function", ["a", "b"] - ) - assert is_valid, f"Expected valid but got errors: {errors}" - - def test_function_signature_mismatch(self): - """Test detection of signature mismatch""" - code = """ -def my_function(x: int) -> int: - return x * 2 -""" - is_valid, errors = self.validator.check_function_signature( - code, "my_function", ["a", "b"] - ) - assert not is_valid - assert any("parameter" in str(e).lower() for e in errors) - - def test_missing_return(self): - """Test detection of missing return statement""" - code = """ -def my_function(a: int) -> int: - x = a * 2 - # Missing return -""" - is_valid, errors = self.validator.check_function_signature( - code, "my_function", ["a"] - ) - assert not is_valid - assert any("return" in str(e).lower() for e in errors) - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) +""" +Tests for Tool Validator +""" + +import sys +from pathlib import Path + +import pytest + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + +from tool_validator import ToolValidator + + +class TestToolValidator: + """Test tool validator functionality""" + + def setup_method(self): + """Setup for each test""" + self.validator = ToolValidator() + + def test_valid_code(self): + """Test validation of safe code""" + code = """ +def add_numbers(a: int, b: int) -> int: + return a + b +""" + is_valid, errors = self.validator.validate(code) + assert is_valid, f"Expected valid but got errors: {errors}" + assert len(errors) == 0 + + def test_dangerous_import(self): + """Test detection of dangerous imports""" + code = """ +import os + +def bad_function(): + return os.getcwd() +""" + is_valid, errors = self.validator.validate(code) + assert not is_valid + assert any("os" in str(e).lower() for e in errors) + + def test_dangerous_builtin(self): + """Test detection of dangerous built-in functions""" + code = """ +def bad_function(code_str): + return eval(code_str) +""" + is_valid, errors = self.validator.validate(code) + assert not is_valid + assert any("eval" in str(e).lower() for e in errors) + + def test_file_operations(self): + """Test detection of file operations""" + code = """ +def bad_function(): + with open('file.txt', 'r') as f: + return f.read() +""" + is_valid, errors = self.validator.validate(code) + assert not is_valid + assert any("file" in str(e).lower() or "open" in str(e).lower() for e in errors) + + def test_allowed_imports(self): + """Test that allowed imports pass validation""" + code = """ +import math +import json + +def calculate(): + return math.sqrt(16) + len(json.dumps({})) +""" + is_valid, errors = self.validator.validate(code) + assert is_valid, f"Expected valid but got errors: {errors}" + + def test_syntax_error(self): + """Test detection of syntax errors""" + code = """ +def bad_syntax( + return "missing closing paren" +""" + is_valid, errors = self.validator.validate(code) + assert not is_valid + assert any("syntax" in str(e).lower() for e in errors) + + def test_function_signature_check(self): + """Test function signature validation""" + code = """ +def my_function(a: int, b: str) -> int: + return int(b) + a +""" + is_valid, errors = self.validator.check_function_signature( + code, "my_function", ["a", "b"] + ) + assert is_valid, f"Expected valid but got errors: {errors}" + + def test_function_signature_mismatch(self): + """Test detection of signature mismatch""" + code = """ +def my_function(x: int) -> int: + return x * 2 +""" + is_valid, errors = self.validator.check_function_signature( + code, "my_function", ["a", "b"] + ) + assert not is_valid + assert any("parameter" in str(e).lower() for e in errors) + + def test_missing_return(self): + """Test detection of missing return statement""" + code = """ +def my_function(a: int) -> int: + x = a * 2 + # Missing return +""" + is_valid, errors = self.validator.check_function_signature( + code, "my_function", ["a"] + ) + assert not is_valid + assert any("return" in str(e).lower() for e in errors) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/ai-projects/llm-maker/web_server.py b/ai-projects/llm-maker/web_server.py index 89244c368..8bac4ca89 100755 --- a/ai-projects/llm-maker/web_server.py +++ b/ai-projects/llm-maker/web_server.py @@ -1,523 +1,523 @@ -""" -LLM Maker Web Server -Provides HTTP API for the web UI to interact with LLM Maker -""" - -import json -import logging -import sys -from http.server import BaseHTTPRequestHandler, HTTPServer -from pathlib import Path -from urllib.parse import urlparse - -# Add src to path -sys.path.insert(0, str(Path(__file__).parent / "src")) - -from tool_executor import ToolExecutor -from tool_maker import ToolMaker -from tool_registry import ToolRegistry -from tool_validator import ToolValidator -from website_maker import WebsiteMaker - -# Setup logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -# Initialize components -maker = ToolMaker() -validator = ToolValidator() -executor = ToolExecutor() -registry = ToolRegistry() -website_maker = WebsiteMaker() - - -class LLMMakerHandler(BaseHTTPRequestHandler): - """HTTP request handler for LLM Maker web UI""" - - def do_GET(self): - """Handle GET requests""" - parsed = urlparse(self.path) - path = parsed.path - - # Serve the web UI - if path == "/" or path == "/index.html": - self.serve_file("web_ui.html", "text/html") - - # Serve website maker UI - elif path == "/website-maker" or path == "/website-maker.html": - self.serve_file("website_maker_ui.html", "text/html") - - # API: List tools - elif path == "/api/tools": - self.handle_list_tools() - - # API: Get registry stats - elif path == "/api/stats": - self.handle_get_stats() - - # API: Get specific tool - elif path.startswith("/api/tools/"): - tool_id = path.split("/")[-1] - self.handle_get_tool(tool_id) - - # API: List websites - elif path == "/api/websites": - self.handle_list_websites() - - # API: Get specific website - elif path.startswith("/api/websites/"): - website_name = path.split("/")[-1] - self.handle_get_website(website_name) - - else: - self.send_error(404, "Not Found") - - def do_POST(self): - """Handle POST requests""" - parsed = urlparse(self.path) - path = parsed.path - - # Read request body - content_length = int(self.headers.get("Content-Length", 0)) - body = self.rfile.read(content_length) - - try: - data = json.loads(body) if body else {} - except json.JSONDecodeError: - self.send_json_response({"error": "Invalid JSON"}, 400) - return - - # API: Create tool - if path == "/api/tools": - self.handle_create_tool(data) - - # API: Execute tool - elif path == "/api/tools/execute": - self.handle_execute_tool(data) - - # API: Validate tool - elif path == "/api/tools/validate": - self.handle_validate_tool(data) - - # API: Create website - elif path == "/api/websites": - self.handle_create_website(data) - - # API: Update website - elif path == "/api/websites/update": - self.handle_update_website(data) - - else: - self.send_error(404, "Not Found") - - def do_DELETE(self): - """Handle DELETE requests""" - parsed = urlparse(self.path) - path = parsed.path - - # API: Delete tool - if path.startswith("/api/tools/"): - tool_id = path.split("/")[-1] - self.handle_delete_tool(tool_id) - - # API: Delete website - elif path.startswith("/api/websites/"): - website_name = path.split("/")[-1] - self.handle_delete_website(website_name) - - else: - self.send_error(404, "Not Found") - - def serve_file(self, filename, content_type): - """Serve a static file""" - try: - filepath = Path(__file__).parent / filename - with open(filepath, "rb") as f: - content = f.read() - - self.send_response(200) - self.send_header("Content-Type", content_type) - self.send_header("Content-Length", len(content)) - self.end_headers() - self.wfile.write(content) - except FileNotFoundError: - self.send_error(404, f"File not found: {filename}") - - def send_json_response(self, data, status=200): - """Send JSON response""" - response = json.dumps(data, indent=2) - self.send_response(status) - self.send_header("Content-Type", "application/json") - self.send_header("Access-Control-Allow-Origin", "*") - self.send_header("Content-Length", len(response)) - self.end_headers() - self.wfile.write(response.encode()) - - def handle_create_tool(self, data): - """Handle tool creation request""" - try: - name = data.get("name") - description = data.get("description") - parameters = data.get("parameters", {}) - return_type = data.get("return_type", "Any") - - if not name or not description: - self.send_json_response( - { - "success": False, - "error": "Missing required fields: name and description", - }, - 400, - ) - return - - logger.info(f"Creating tool: {name}") - - # Create the tool using ToolMaker - tool = maker.create_tool( - name=name, - description=description, - parameters=parameters, - return_type=return_type, - ) - - if tool: - # Register the tool - tool_id = registry.register(tool) - - self.send_json_response( - { - "success": True, - "tool_id": tool_id, - "message": f"Tool '{name}' created successfully", - "tool": { - "id": tool.id, - "name": tool.name, - "description": tool.description, - "parameters": tool.parameters, - "return_type": tool.return_type, - "validated": tool.validated, - "code": tool.code, - }, - } - ) - else: - self.send_json_response( - { - "success": False, - "error": "Failed to create tool - validation failed or generation error", - }, - 500, - ) - - except Exception as e: - logger.error(f"Error creating tool: {e}", exc_info=True) - self.send_json_response({"success": False, "error": str(e)}, 500) - - def handle_list_tools(self): - """Handle list tools request""" - try: - tools = registry.list_tools() - tools_data = [ - { - "id": t.id, - "name": t.name, - "description": t.description, - "parameters": t.parameters, - "return_type": t.return_type, - "created_at": t.created_at, - "validated": t.validated, - "execution_count": t.execution_count, - "code": t.code, - } - for t in tools - ] - - stats = registry.get_stats() - - self.send_json_response({"tools": tools_data, "stats": stats}) - - except Exception as e: - logger.error(f"Error listing tools: {e}", exc_info=True) - self.send_json_response({"error": str(e)}, 500) - - def handle_get_tool(self, tool_id): - """Handle get specific tool request""" - try: - tool = registry.get(tool_id) - - if not tool: - tool = registry.get_by_name(tool_id) - - if tool: - self.send_json_response( - { - "success": True, - "tool": { - "id": tool.id, - "name": tool.name, - "description": tool.description, - "parameters": tool.parameters, - "return_type": tool.return_type, - "created_at": tool.created_at, - "validated": tool.validated, - "execution_count": tool.execution_count, - "code": tool.code, - }, - } - ) - else: - self.send_json_response( - {"success": False, "error": f"Tool '{tool_id}' not found"}, 404 - ) - - except Exception as e: - logger.error(f"Error getting tool: {e}", exc_info=True) - self.send_json_response({"error": str(e)}, 500) - - def handle_execute_tool(self, data): - """Handle tool execution request""" - try: - tool_id = data.get("tool_id") - arguments = data.get("arguments", {}) - - if not tool_id: - self.send_json_response( - {"success": False, "error": "Missing tool_id"}, 400 - ) - return - - # Get the tool - tool = registry.get(tool_id) - if not tool: - tool = registry.get_by_name(tool_id) - - if not tool: - self.send_json_response( - {"success": False, "error": f"Tool '{tool_id}' not found"}, 404 - ) - return - - logger.info(f"Executing tool: {tool.name}") - - # Execute the tool - result = executor.execute(tool.code, tool.name, arguments) - - # Update statistics - if result.get("success"): - registry.update_stats(tool.id) - - self.send_json_response(result) - - except Exception as e: - logger.error(f"Error executing tool: {e}", exc_info=True) - self.send_json_response({"success": False, "error": str(e)}, 500) - - def handle_validate_tool(self, data): - """Handle tool validation request""" - try: - tool_id = data.get("tool_id") - - if not tool_id: - self.send_json_response( - {"success": False, "error": "Missing tool_id"}, 400 - ) - return - - tool = registry.get(tool_id) - if not tool: - tool = registry.get_by_name(tool_id) - - if not tool: - self.send_json_response( - {"success": False, "error": f"Tool '{tool_id}' not found"}, 404 - ) - return - - is_valid, errors = validator.validate(tool.code) - - self.send_json_response( - { - "tool_id": tool.id, - "tool_name": tool.name, - "is_valid": is_valid, - "errors": errors, - } - ) - - except Exception as e: - logger.error(f"Error validating tool: {e}", exc_info=True) - self.send_json_response({"error": str(e)}, 500) - - def handle_delete_tool(self, tool_id): - """Handle tool deletion request""" - try: - success = registry.delete(tool_id) - - if success: - self.send_json_response( - {"success": True, "message": f"Tool '{tool_id}' deleted"} - ) - else: - self.send_json_response( - {"success": False, "error": f"Tool '{tool_id}' not found"}, 404 - ) - - except Exception as e: - logger.error(f"Error deleting tool: {e}", exc_info=True) - self.send_json_response({"error": str(e)}, 500) - - def handle_get_stats(self): - """Handle get registry statistics request""" - try: - stats = registry.get_stats() - self.send_json_response(stats) - - except Exception as e: - logger.error(f"Error getting stats: {e}", exc_info=True) - self.send_json_response({"error": str(e)}, 500) - - def handle_create_website(self, data): - """Handle website creation request""" - try: - name = data.get("name") - description = data.get("description") - style = data.get("style", "modern") - pages = data.get("pages", ["index"]) - features = data.get("features", ["responsive design", "modern styling"]) - - if not name or not description: - self.send_json_response( - { - "success": False, - "error": "Missing required fields: name and description", - }, - 400, - ) - return - - logger.info(f"Creating website: {name}") - - result = website_maker.create_website( - name=name, - description=description, - style=style, - pages=pages, - features=features, - ) - - self.send_json_response(result) - - except Exception as e: - logger.error(f"Error creating website: {e}", exc_info=True) - self.send_json_response({"success": False, "error": str(e)}, 500) - - def handle_update_website(self, data): - """Handle website update request""" - try: - name = data.get("name") - update_description = data.get("update_description") - target_file = data.get("target_file") - - if not name or not update_description: - self.send_json_response( - { - "success": False, - "error": "Missing required fields: name and update_description", - }, - 400, - ) - return - - logger.info(f"Updating website: {name}") - - result = website_maker.update_website( - name=name, - update_description=update_description, - target_file=target_file, - ) - - self.send_json_response(result) - - except Exception as e: - logger.error(f"Error updating website: {e}", exc_info=True) - self.send_json_response({"success": False, "error": str(e)}, 500) - - def handle_list_websites(self): - """Handle list websites request""" - try: - websites = website_maker.list_websites() - self.send_json_response({"websites": websites, "count": len(websites)}) - - except Exception as e: - logger.error(f"Error listing websites: {e}", exc_info=True) - self.send_json_response({"error": str(e)}, 500) - - def handle_get_website(self, name): - """Handle get specific website request""" - try: - websites = website_maker.list_websites() - website = next((w for w in websites if w["name"] == name), None) - - if website: - # Read website files - import os - - files_content = {} - for filename in website.get("files", []): - filepath = os.path.join(website["path"], filename) - if os.path.exists(filepath): - with open(filepath, "r", encoding="utf-8") as f: - files_content[filename] = f.read() - - website["files_content"] = files_content - - self.send_json_response({"success": True, "website": website}) - else: - self.send_json_response( - {"success": False, "error": f"Website '{name}' not found"}, 404 - ) - - except Exception as e: - logger.error(f"Error getting website: {e}", exc_info=True) - self.send_json_response({"error": str(e)}, 500) - - def handle_delete_website(self, name): - """Handle website deletion request""" - try: - result = website_maker.delete_website(name) - self.send_json_response(result) - - except Exception as e: - logger.error(f"Error deleting website: {e}", exc_info=True) - self.send_json_response({"error": str(e)}, 500) - - def log_message(self, format, *args): - """Custom log message format""" - logger.info(f"{self.address_string()} - {format % args}") - - -def main(): - """Start the web server""" - host = "0.0.0.0" - port = 8090 - - server = HTTPServer((host, port), LLMMakerHandler) - - print("=" * 60) - print("LLM Maker Web Server") - print("=" * 60) - print(f"Server running at http://localhost:{port}") - print(f"Open http://localhost:{port} in your browser") - print("Press Ctrl+C to stop") - print("=" * 60) - - try: - server.serve_forever() - except KeyboardInterrupt: - print("\n\nShutting down server...") - server.shutdown() - - -if __name__ == "__main__": - main() +""" +LLM Maker Web Server +Provides HTTP API for the web UI to interact with LLM Maker +""" + +import json +import logging +import sys +from http.server import BaseHTTPRequestHandler, HTTPServer +from pathlib import Path +from urllib.parse import urlparse + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent / "src")) + +from tool_executor import ToolExecutor +from tool_maker import ToolMaker +from tool_registry import ToolRegistry +from tool_validator import ToolValidator +from website_maker import WebsiteMaker + +# Setup logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# Initialize components +maker = ToolMaker() +validator = ToolValidator() +executor = ToolExecutor() +registry = ToolRegistry() +website_maker = WebsiteMaker() + + +class LLMMakerHandler(BaseHTTPRequestHandler): + """HTTP request handler for LLM Maker web UI""" + + def do_GET(self): + """Handle GET requests""" + parsed = urlparse(self.path) + path = parsed.path + + # Serve the web UI + if path == "/" or path == "/index.html": + self.serve_file("web_ui.html", "text/html") + + # Serve website maker UI + elif path == "/website-maker" or path == "/website-maker.html": + self.serve_file("website_maker_ui.html", "text/html") + + # API: List tools + elif path == "/api/tools": + self.handle_list_tools() + + # API: Get registry stats + elif path == "/api/stats": + self.handle_get_stats() + + # API: Get specific tool + elif path.startswith("/api/tools/"): + tool_id = path.split("/")[-1] + self.handle_get_tool(tool_id) + + # API: List websites + elif path == "/api/websites": + self.handle_list_websites() + + # API: Get specific website + elif path.startswith("/api/websites/"): + website_name = path.split("/")[-1] + self.handle_get_website(website_name) + + else: + self.send_error(404, "Not Found") + + def do_POST(self): + """Handle POST requests""" + parsed = urlparse(self.path) + path = parsed.path + + # Read request body + content_length = int(self.headers.get("Content-Length", 0)) + body = self.rfile.read(content_length) + + try: + data = json.loads(body) if body else {} + except json.JSONDecodeError: + self.send_json_response({"error": "Invalid JSON"}, 400) + return + + # API: Create tool + if path == "/api/tools": + self.handle_create_tool(data) + + # API: Execute tool + elif path == "/api/tools/execute": + self.handle_execute_tool(data) + + # API: Validate tool + elif path == "/api/tools/validate": + self.handle_validate_tool(data) + + # API: Create website + elif path == "/api/websites": + self.handle_create_website(data) + + # API: Update website + elif path == "/api/websites/update": + self.handle_update_website(data) + + else: + self.send_error(404, "Not Found") + + def do_DELETE(self): + """Handle DELETE requests""" + parsed = urlparse(self.path) + path = parsed.path + + # API: Delete tool + if path.startswith("/api/tools/"): + tool_id = path.split("/")[-1] + self.handle_delete_tool(tool_id) + + # API: Delete website + elif path.startswith("/api/websites/"): + website_name = path.split("/")[-1] + self.handle_delete_website(website_name) + + else: + self.send_error(404, "Not Found") + + def serve_file(self, filename, content_type): + """Serve a static file""" + try: + filepath = Path(__file__).parent / filename + with open(filepath, "rb") as f: + content = f.read() + + self.send_response(200) + self.send_header("Content-Type", content_type) + self.send_header("Content-Length", len(content)) + self.end_headers() + self.wfile.write(content) + except FileNotFoundError: + self.send_error(404, f"File not found: {filename}") + + def send_json_response(self, data, status=200): + """Send JSON response""" + response = json.dumps(data, indent=2) + self.send_response(status) + self.send_header("Content-Type", "application/json") + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Content-Length", len(response)) + self.end_headers() + self.wfile.write(response.encode()) + + def handle_create_tool(self, data): + """Handle tool creation request""" + try: + name = data.get("name") + description = data.get("description") + parameters = data.get("parameters", {}) + return_type = data.get("return_type", "Any") + + if not name or not description: + self.send_json_response( + { + "success": False, + "error": "Missing required fields: name and description", + }, + 400, + ) + return + + logger.info(f"Creating tool: {name}") + + # Create the tool using ToolMaker + tool = maker.create_tool( + name=name, + description=description, + parameters=parameters, + return_type=return_type, + ) + + if tool: + # Register the tool + tool_id = registry.register(tool) + + self.send_json_response( + { + "success": True, + "tool_id": tool_id, + "message": f"Tool '{name}' created successfully", + "tool": { + "id": tool.id, + "name": tool.name, + "description": tool.description, + "parameters": tool.parameters, + "return_type": tool.return_type, + "validated": tool.validated, + "code": tool.code, + }, + } + ) + else: + self.send_json_response( + { + "success": False, + "error": "Failed to create tool - validation failed or generation error", + }, + 500, + ) + + except Exception as e: + logger.error(f"Error creating tool: {e}", exc_info=True) + self.send_json_response({"success": False, "error": str(e)}, 500) + + def handle_list_tools(self): + """Handle list tools request""" + try: + tools = registry.list_tools() + tools_data = [ + { + "id": t.id, + "name": t.name, + "description": t.description, + "parameters": t.parameters, + "return_type": t.return_type, + "created_at": t.created_at, + "validated": t.validated, + "execution_count": t.execution_count, + "code": t.code, + } + for t in tools + ] + + stats = registry.get_stats() + + self.send_json_response({"tools": tools_data, "stats": stats}) + + except Exception as e: + logger.error(f"Error listing tools: {e}", exc_info=True) + self.send_json_response({"error": str(e)}, 500) + + def handle_get_tool(self, tool_id): + """Handle get specific tool request""" + try: + tool = registry.get(tool_id) + + if not tool: + tool = registry.get_by_name(tool_id) + + if tool: + self.send_json_response( + { + "success": True, + "tool": { + "id": tool.id, + "name": tool.name, + "description": tool.description, + "parameters": tool.parameters, + "return_type": tool.return_type, + "created_at": tool.created_at, + "validated": tool.validated, + "execution_count": tool.execution_count, + "code": tool.code, + }, + } + ) + else: + self.send_json_response( + {"success": False, "error": f"Tool '{tool_id}' not found"}, 404 + ) + + except Exception as e: + logger.error(f"Error getting tool: {e}", exc_info=True) + self.send_json_response({"error": str(e)}, 500) + + def handle_execute_tool(self, data): + """Handle tool execution request""" + try: + tool_id = data.get("tool_id") + arguments = data.get("arguments", {}) + + if not tool_id: + self.send_json_response( + {"success": False, "error": "Missing tool_id"}, 400 + ) + return + + # Get the tool + tool = registry.get(tool_id) + if not tool: + tool = registry.get_by_name(tool_id) + + if not tool: + self.send_json_response( + {"success": False, "error": f"Tool '{tool_id}' not found"}, 404 + ) + return + + logger.info(f"Executing tool: {tool.name}") + + # Execute the tool + result = executor.execute(tool.code, tool.name, arguments) + + # Update statistics + if result.get("success"): + registry.update_stats(tool.id) + + self.send_json_response(result) + + except Exception as e: + logger.error(f"Error executing tool: {e}", exc_info=True) + self.send_json_response({"success": False, "error": str(e)}, 500) + + def handle_validate_tool(self, data): + """Handle tool validation request""" + try: + tool_id = data.get("tool_id") + + if not tool_id: + self.send_json_response( + {"success": False, "error": "Missing tool_id"}, 400 + ) + return + + tool = registry.get(tool_id) + if not tool: + tool = registry.get_by_name(tool_id) + + if not tool: + self.send_json_response( + {"success": False, "error": f"Tool '{tool_id}' not found"}, 404 + ) + return + + is_valid, errors = validator.validate(tool.code) + + self.send_json_response( + { + "tool_id": tool.id, + "tool_name": tool.name, + "is_valid": is_valid, + "errors": errors, + } + ) + + except Exception as e: + logger.error(f"Error validating tool: {e}", exc_info=True) + self.send_json_response({"error": str(e)}, 500) + + def handle_delete_tool(self, tool_id): + """Handle tool deletion request""" + try: + success = registry.delete(tool_id) + + if success: + self.send_json_response( + {"success": True, "message": f"Tool '{tool_id}' deleted"} + ) + else: + self.send_json_response( + {"success": False, "error": f"Tool '{tool_id}' not found"}, 404 + ) + + except Exception as e: + logger.error(f"Error deleting tool: {e}", exc_info=True) + self.send_json_response({"error": str(e)}, 500) + + def handle_get_stats(self): + """Handle get registry statistics request""" + try: + stats = registry.get_stats() + self.send_json_response(stats) + + except Exception as e: + logger.error(f"Error getting stats: {e}", exc_info=True) + self.send_json_response({"error": str(e)}, 500) + + def handle_create_website(self, data): + """Handle website creation request""" + try: + name = data.get("name") + description = data.get("description") + style = data.get("style", "modern") + pages = data.get("pages", ["index"]) + features = data.get("features", ["responsive design", "modern styling"]) + + if not name or not description: + self.send_json_response( + { + "success": False, + "error": "Missing required fields: name and description", + }, + 400, + ) + return + + logger.info(f"Creating website: {name}") + + result = website_maker.create_website( + name=name, + description=description, + style=style, + pages=pages, + features=features, + ) + + self.send_json_response(result) + + except Exception as e: + logger.error(f"Error creating website: {e}", exc_info=True) + self.send_json_response({"success": False, "error": str(e)}, 500) + + def handle_update_website(self, data): + """Handle website update request""" + try: + name = data.get("name") + update_description = data.get("update_description") + target_file = data.get("target_file") + + if not name or not update_description: + self.send_json_response( + { + "success": False, + "error": "Missing required fields: name and update_description", + }, + 400, + ) + return + + logger.info(f"Updating website: {name}") + + result = website_maker.update_website( + name=name, + update_description=update_description, + target_file=target_file, + ) + + self.send_json_response(result) + + except Exception as e: + logger.error(f"Error updating website: {e}", exc_info=True) + self.send_json_response({"success": False, "error": str(e)}, 500) + + def handle_list_websites(self): + """Handle list websites request""" + try: + websites = website_maker.list_websites() + self.send_json_response({"websites": websites, "count": len(websites)}) + + except Exception as e: + logger.error(f"Error listing websites: {e}", exc_info=True) + self.send_json_response({"error": str(e)}, 500) + + def handle_get_website(self, name): + """Handle get specific website request""" + try: + websites = website_maker.list_websites() + website = next((w for w in websites if w["name"] == name), None) + + if website: + # Read website files + import os + + files_content = {} + for filename in website.get("files", []): + filepath = os.path.join(website["path"], filename) + if os.path.exists(filepath): + with open(filepath, "r", encoding="utf-8") as f: + files_content[filename] = f.read() + + website["files_content"] = files_content + + self.send_json_response({"success": True, "website": website}) + else: + self.send_json_response( + {"success": False, "error": f"Website '{name}' not found"}, 404 + ) + + except Exception as e: + logger.error(f"Error getting website: {e}", exc_info=True) + self.send_json_response({"error": str(e)}, 500) + + def handle_delete_website(self, name): + """Handle website deletion request""" + try: + result = website_maker.delete_website(name) + self.send_json_response(result) + + except Exception as e: + logger.error(f"Error deleting website: {e}", exc_info=True) + self.send_json_response({"error": str(e)}, 500) + + def log_message(self, format, *args): + """Custom log message format""" + logger.info(f"{self.address_string()} - {format % args}") + + +def main(): + """Start the web server""" + host = "0.0.0.0" + port = 8090 + + server = HTTPServer((host, port), LLMMakerHandler) + + print("=" * 60) + print("LLM Maker Web Server") + print("=" * 60) + print(f"Server running at http://localhost:{port}") + print(f"Open http://localhost:{port} in your browser") + print("Press Ctrl+C to stop") + print("=" * 60) + + try: + server.serve_forever() + except KeyboardInterrupt: + print("\n\nShutting down server...") + server.shutdown() + + +if __name__ == "__main__": + main() diff --git a/ai-projects/llm-maker/web_ui.html b/ai-projects/llm-maker/web_ui.html index 52fced32a..e6600ce9b 100644 --- a/ai-projects/llm-maker/web_ui.html +++ b/ai-projects/llm-maker/web_ui.html @@ -1,927 +1,927 @@ - - - - - - LLM Maker - AI Tool Creation Studio - - - - - -
    -
    -

    🤖 LLM Maker - AI Tool Creation Studio

    -

    Create, test, and manage custom AI tools with natural language

    -
    - - 🌐 Website Maker - - - 📚 Integration Guide - - - 💻 API Docs - - - 💡 Examples - -
    -
    - -
    - -
    -

    ✨ Create New Tool

    -
    -
    - - -
    - 🔢 Fibonacci - 🔄 Palindrome - 🌡️ Temperature -
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    📊 Statistics

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    📚 Quick Integration

    -

    - Use these endpoints to integrate LLM Maker into your apps: -

    - - List all tools: -
    - - GET http://localhost:8090/api/tools -
    - - Create a tool: -
    - - fetch('/api/tools', { - method: 'POST', - body: JSON.stringify({ - name: 'my_tool', - description: 'What it does', - parameters: {'x': 'int'} - }) -}) -
    - - Execute a tool: -
    - - fetch('/api/tools/execute', { - method: 'POST', - body: JSON.stringify({ - tool_id: 'tool_xyz', - arguments: {'x': 5} - }) -}) -
    - -

    - 💡 Tip: Click "Copy" to copy code snippets to clipboard -

    -
    -
    - - -
    -

    🛠️ Your Tools

    -
    -
    - - - - -

    No tools created yet. Start by creating your first tool above!

    -
    -
    -
    -
    -
    - - - - + + + + + + LLM Maker - AI Tool Creation Studio + + + + + +
    +
    +

    🤖 LLM Maker - AI Tool Creation Studio

    +

    Create, test, and manage custom AI tools with natural language

    +
    + + 🌐 Website Maker + + + 📚 Integration Guide + + + 💻 API Docs + + + 💡 Examples + +
    +
    + +
    + +
    +

    ✨ Create New Tool

    +
    +
    + + +
    + 🔢 Fibonacci + 🔄 Palindrome + 🌡️ Temperature +
    +
    + +
    + + +
    + +
    + +
    +
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    📊 Statistics

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    Total Tools
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    Validated
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    0
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    Executions
    +
    +
    + +
    + +
    + + +
    +

    📚 Quick Integration

    +

    + Use these endpoints to integrate LLM Maker into your apps: +

    + + List all tools: +
    + + GET http://localhost:8090/api/tools +
    + + Create a tool: +
    + + fetch('/api/tools', { + method: 'POST', + body: JSON.stringify({ + name: 'my_tool', + description: 'What it does', + parameters: {'x': 'int'} + }) +}) +
    + + Execute a tool: +
    + + fetch('/api/tools/execute', { + method: 'POST', + body: JSON.stringify({ + tool_id: 'tool_xyz', + arguments: {'x': 5} + }) +}) +
    + +

    + 💡 Tip: Click "Copy" to copy code snippets to clipboard +

    +
    +
    + + +
    +

    🛠️ Your Tools

    +
    +
    + + + + +

    No tools created yet. Start by creating your first tool above!

    +
    +
    +
    +
    +
    + + + + diff --git a/ai-projects/llm-maker/web_ui_api_functions.js b/ai-projects/llm-maker/web_ui_api_functions.js index 7b42a7162..eb027e508 100644 --- a/ai-projects/llm-maker/web_ui_api_functions.js +++ b/ai-projects/llm-maker/web_ui_api_functions.js @@ -1,113 +1,113 @@ -// Replace the mock functions in web_ui.html with these real API functions - -// API Base URL -const API_BASE = ''; // Empty for same-origin requests - -async function createTool() { - const name = document.getElementById('toolName').value.trim(); - const description = document.getElementById('toolDescription').value.trim(); - const parameters = getParameters(); - const returnType = document.getElementById('returnType').value.trim() || 'Any'; - - if (!name || !description) { - showStatus('createStatus', 'Please fill in all required fields', 'error'); - return; - } - - // Show loading - document.getElementById('createBtn').disabled = true; - document.getElementById('createBtnText').style.display = 'none'; - document.getElementById('createLoading').style.display = 'inline-block'; - - try { - const response = await fetch(`${API_BASE}/api/tools`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ - name, - description, - parameters, - return_type: returnType - }) - }); - - const result = await response.json(); - - if (result.success) { - showStatus('createStatus', `Tool "${name}" created successfully!`, 'success'); - document.getElementById('createToolForm').reset(); - // Reset parameters to single row - document.getElementById('parametersContainer').innerHTML = ` -
    - - - -
    - `; - loadTools(); - } else { - showStatus('createStatus', `Error: ${result.error}`, 'error'); - } - } catch (error) { - showStatus('createStatus', `Error: ${error.message}`, 'error'); - } finally { - // Hide loading - document.getElementById('createBtn').disabled = false; - document.getElementById('createBtnText').style.display = 'inline'; - document.getElementById('createLoading').style.display = 'none'; - } -} - -async function loadTools() { - try { - const response = await fetch(`${API_BASE}/api/tools`); - const data = await response.json(); - - tools = data.tools || []; - updateStats(data.stats); - renderTools(); - } catch (error) { - console.error('Error loading tools:', error); - showStatus('createStatus', 'Error loading tools. Is the server running?', 'error'); - } -} - -async function executeTool(toolId, params) { - try { - const response = await fetch(`${API_BASE}/api/tools/execute`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ - tool_id: toolId, - arguments: params - }) - }); - - return await response.json(); - } catch (error) { - return { success: false, error: error.message }; - } -} - -async function deleteTool(toolId) { - try { - const response = await fetch(`${API_BASE}/api/tools/${toolId}`, { - method: 'DELETE' - }); - - const result = await response.json(); - - if (result.success) { - loadTools(); - return true; - } - return false; - } catch (error) { - console.error('Error deleting tool:', error); - return false; - } -} +// Replace the mock functions in web_ui.html with these real API functions + +// API Base URL +const API_BASE = ''; // Empty for same-origin requests + +async function createTool() { + const name = document.getElementById('toolName').value.trim(); + const description = document.getElementById('toolDescription').value.trim(); + const parameters = getParameters(); + const returnType = document.getElementById('returnType').value.trim() || 'Any'; + + if (!name || !description) { + showStatus('createStatus', 'Please fill in all required fields', 'error'); + return; + } + + // Show loading + document.getElementById('createBtn').disabled = true; + document.getElementById('createBtnText').style.display = 'none'; + document.getElementById('createLoading').style.display = 'inline-block'; + + try { + const response = await fetch(`${API_BASE}/api/tools`, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + name, + description, + parameters, + return_type: returnType + }) + }); + + const result = await response.json(); + + if (result.success) { + showStatus('createStatus', `Tool "${name}" created successfully!`, 'success'); + document.getElementById('createToolForm').reset(); + // Reset parameters to single row + document.getElementById('parametersContainer').innerHTML = ` +
    + + + +
    + `; + loadTools(); + } else { + showStatus('createStatus', `Error: ${result.error}`, 'error'); + } + } catch (error) { + showStatus('createStatus', `Error: ${error.message}`, 'error'); + } finally { + // Hide loading + document.getElementById('createBtn').disabled = false; + document.getElementById('createBtnText').style.display = 'inline'; + document.getElementById('createLoading').style.display = 'none'; + } +} + +async function loadTools() { + try { + const response = await fetch(`${API_BASE}/api/tools`); + const data = await response.json(); + + tools = data.tools || []; + updateStats(data.stats); + renderTools(); + } catch (error) { + console.error('Error loading tools:', error); + showStatus('createStatus', 'Error loading tools. Is the server running?', 'error'); + } +} + +async function executeTool(toolId, params) { + try { + const response = await fetch(`${API_BASE}/api/tools/execute`, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + tool_id: toolId, + arguments: params + }) + }); + + return await response.json(); + } catch (error) { + return { success: false, error: error.message }; + } +} + +async function deleteTool(toolId) { + try { + const response = await fetch(`${API_BASE}/api/tools/${toolId}`, { + method: 'DELETE' + }); + + const result = await response.json(); + + if (result.success) { + loadTools(); + return true; + } + return false; + } catch (error) { + console.error('Error deleting tool:', error); + return false; + } +} diff --git a/ai-projects/llm-maker/website_maker_ui.html b/ai-projects/llm-maker/website_maker_ui.html index 5f7e948fc..d305a9962 100644 --- a/ai-projects/llm-maker/website_maker_ui.html +++ b/ai-projects/llm-maker/website_maker_ui.html @@ -1,903 +1,903 @@ - - - - - - AI Website Maker - Automated Website Generator - - - - - -
    -
    -

    - - AI Website Maker -

    -

    Automatically generate and update complete websites with AI

    -
    - - Tool Maker - - - -
    -
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    - -
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    - - Create New Website -

    - -
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    - -
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    - -
    - -
    - index ✕ -
    -
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    - responsive design ✕ - modern styling ✕ -
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    - - Update Existing Website -

    - -
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    - -
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    - - - -
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    - - Your Websites - -

    - -
    -

    - - No websites created yet. Create your first website above! -

    -
    -
    -
    -
    - - - - - - - + + + + + + AI Website Maker - Automated Website Generator + + + + + +
    +
    +

    + + AI Website Maker +

    +

    Automatically generate and update complete websites with AI

    +
    + + Tool Maker + + + +
    +
    + +
    +
    + +
    +

    + + Create New Website +

    + +
    +
    + + +
    + +
    + + +
    + +
    + + +
    + +
    + +
    + index ✕ +
    +
    + + +
    +
    + +
    + +
    + responsive design ✕ + modern styling ✕ +
    +
    + + +
    +
    + + + +
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    + + +
    +

    + + Update Existing Website +

    + +
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    + + +
    + +
    + + +
    + +
    + + +
    + + + +
    +
    +
    +
    + + +
    +

    + + Your Websites + +

    + +
    +

    + + No websites created yet. Create your first website above! +

    +
    +
    +
    +
    + + + + + + + diff --git a/ai-projects/lmstudio-mcp/lmstudio_agi_integration.py b/ai-projects/lmstudio-mcp/lmstudio_agi_integration.py index 3ac2cc21b..33f9708bc 100644 --- a/ai-projects/lmstudio-mcp/lmstudio_agi_integration.py +++ b/ai-projects/lmstudio-mcp/lmstudio_agi_integration.py @@ -171,7 +171,8 @@ async def route_query( ) # Use the LM Studio client - assert self.lmstudio_client is not None + if self.lmstudio_client is None: + raise RuntimeError("LM Studio client is not initialized") response = await self.lmstudio_client.complete( messages=messages, temperature=0.7, diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/.vscode/python-imports.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/.vscode/python-imports.md index 16cb1c159..b8d5d6b20 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/.vscode/python-imports.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/.vscode/python-imports.md @@ -1,59 +1,59 @@ -# Python Import Resolution for VS Code - -This directory contains Python scripts that require ML/AI packages. If you see import errors in the editor: - -## Quick Fix - -1. Ensure the virtual environment is activated and packages are installed: - - ```powershell - cd AI\microsoft_phi-silica-3.6_v1 - .\venv\Scripts\Activate.ps1 - pip install -r requirements.txt - ``` - -2. Select the correct Python interpreter in VS Code: - - Press `Ctrl+Shift+P` - - Type "Python: Select Interpreter" - - Choose `.venv\Scripts\python.exe` from this directory - -3. Restart the Python Language Server: - - Press `Ctrl+Shift+P` - - Type "Python: Restart Language Server" - -## Import Errors Are Expected Without Installation - -The `train_lora.py` script uses try/except blocks to gracefully handle missing imports: - -```python -try: - from datasets import load_dataset - from transformers import AutoModelForCausalLM, AutoTokenizer - from peft import LoraConfig, get_peft_model -except Exception: - # Allows dry-run mode without installing packages - load_dataset = None - AutoModelForCausalLM = None - # etc. -``` - -This allows you to: - -- Run `--dry-run` validation without installing ML packages -- Explore code structure without downloading multi-GB dependencies -- Install packages only when ready for actual training - -## VS Code Settings (Optional) - -If you frequently work in this directory, add to `.vscode/settings.json`: - -```json -{ - "python.defaultInterpreterPath": "${workspaceFolder}/AI/microsoft_phi-silica-3.6_v1/venv/Scripts/python.exe", - "python.analysis.extraPaths": [ - "${workspaceFolder}/AI/microsoft_phi-silica-3.6_v1/venv/Lib/site-packages" - ] -} -``` - -This will automatically resolve imports once packages are installed in the venv. +# Python Import Resolution for VS Code + +This directory contains Python scripts that require ML/AI packages. If you see import errors in the editor: + +## Quick Fix + +1. Ensure the virtual environment is activated and packages are installed: + + ```powershell + cd AI\microsoft_phi-silica-3.6_v1 + .\venv\Scripts\Activate.ps1 + pip install -r requirements.txt + ``` + +2. Select the correct Python interpreter in VS Code: + - Press `Ctrl+Shift+P` + - Type "Python: Select Interpreter" + - Choose `.venv\Scripts\python.exe` from this directory + +3. Restart the Python Language Server: + - Press `Ctrl+Shift+P` + - Type "Python: Restart Language Server" + +## Import Errors Are Expected Without Installation + +The `train_lora.py` script uses try/except blocks to gracefully handle missing imports: + +```python +try: + from datasets import load_dataset + from transformers import AutoModelForCausalLM, AutoTokenizer + from peft import LoraConfig, get_peft_model +except Exception: + # Allows dry-run mode without installing packages + load_dataset = None + AutoModelForCausalLM = None + # etc. +``` + +This allows you to: + +- Run `--dry-run` validation without installing ML packages +- Explore code structure without downloading multi-GB dependencies +- Install packages only when ready for actual training + +## VS Code Settings (Optional) + +If you frequently work in this directory, add to `.vscode/settings.json`: + +```json +{ + "python.defaultInterpreterPath": "${workspaceFolder}/AI/microsoft_phi-silica-3.6_v1/venv/Scripts/python.exe", + "python.analysis.extraPaths": [ + "${workspaceFolder}/AI/microsoft_phi-silica-3.6_v1/venv/Lib/site-packages" + ] +} +``` + +This will automatically resolve imports once packages are installed in the venv. diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_GUIDE.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_GUIDE.md index 21a4632be..2c02d5f64 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_GUIDE.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_GUIDE.md @@ -1,575 +1,575 @@ -# Advanced Training Pipeline Guide - -Complete guide for automatic evaluation, RAG pipeline, semantic pruning, and GPU optimization. - -## 🚀 Quick Start - -```bash -cd AI\microsoft_phi-silica-3.6_v1 - -# 1. Optimize for your GPU -python scripts\gpu_optimizer.py --update-config lora\lora.yaml - -# 2. Prune your training data -python scripts\semantic_pruning.py --input ..\..\datasets\chat\dolly\train.jsonl --output data\pruned_train.jsonl - -# 3. Train with optimized settings -python scripts\train_lora.py --dataset data --config lora\lora.yaml - -# 4. Evaluate the model -python scripts\auto_eval.py --model data_out\lora_training --dataset ..\..\datasets\chat\dolly\test.jsonl - -# 5. Use with RAG -python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --interactive -``` - -## 📊 1. Automatic Evaluation - -Automatically evaluate fine-tuned models with multiple metrics. - -### Features -- **Perplexity**: Language model quality metric -- **Inference Speed**: Tokens per second, latency -- **Memory Usage**: GPU memory consumption -- **Quality Metrics**: BLEU, ROUGE scores (optional) -- **Model Comparison**: Benchmark multiple models - -### Basic Usage - -```bash -# Evaluate single model -python scripts\auto_eval.py \ - --model data_out\lora_training \ - --dataset ..\..\datasets\chat\dolly\test.jsonl \ - --num-samples 100 - -# Compare multiple models -python scripts\auto_eval.py \ - --model data_out\lora_training \ - --dataset ..\..\datasets\chat\dolly\test.jsonl \ - --metrics perplexity inference_time bleu \ - --output-name model_v1 -``` - -### Output - -Results saved to `data_out/evaluation_results/`: -```json -{ - "perplexity": 12.34, - "inference_time_ms": 45.67, - "tokens_per_second": 89.12, - "memory_usage_mb": 6789.0 -} -``` - -### Integration with Training - -```python -from scripts.auto_eval import AutomaticEvaluator - -evaluator = AutomaticEvaluator() -metrics = evaluator.evaluate_model( - model_path="data_out/lora_training", - test_dataset="test.jsonl", - num_samples=100 -) -print(f"Perplexity: {metrics.perplexity:.2f}") -``` - -## 🔍 2. RAG Pipeline Setup - -Retrieval-Augmented Generation for enhanced model responses. - -### Features -- **Document Indexing**: Load documents from any directory -- **Semantic Search**: Find relevant context using embeddings -- **Context Integration**: Inject retrieved docs into prompts -- **Interactive Mode**: Question-answering interface -- **Persistent Index**: Save/load document embeddings - -### Setup - -```bash -# Install optional dependencies -pip install sentence-transformers - -# Build document index -python scripts\rag_pipeline.py \ - --model data_out\lora_training \ - --docs ..\..\datasets \ - --rebuild-index - -# Interactive mode -python scripts\rag_pipeline.py \ - --model data_out\lora_training \ - --docs ..\..\datasets \ - --interactive -``` - -### Document Structure - -``` -datasets/ -├── quantum/ # Will be indexed -│ ├── README.md -│ └── data.csv -├── chat/ # Will be indexed -│ └── dolly/ -└── processed/ # Will be indexed -``` - -### Configuration - -Edit `scripts/rag_pipeline.py` or create `rag_config.yaml`: - -```yaml -embedding_model: "sentence-transformers/all-MiniLM-L6-v2" -chunk_size: 512 # Tokens per chunk -chunk_overlap: 50 # Overlap between chunks -top_k_retrieval: 3 # Number of docs to retrieve -retrieval_threshold: 0.7 # Minimum similarity score -``` - -### Programmatic Usage - -```python -from scripts.rag_pipeline import RAGPipeline, DocumentStore, RAGConfig - -# Setup -config = RAGConfig(top_k_retrieval=5) -doc_store = DocumentStore(config) -doc_store.load_from_directory("../../datasets") -doc_store.build_index() - -# Create pipeline -rag = RAGPipeline("data_out/lora_training", doc_store, config) - -# Query -result = rag.query("What are quantum circuits?") -print(result["answer"]) -``` - -## 🧹 3. Semantic Pruning - -Remove redundant and low-quality training samples to improve efficiency. - -### Features -- **Exact Deduplication**: Remove identical samples -- **Semantic Deduplication**: Remove similar samples via embeddings -- **Quality Filtering**: Score and filter low-quality data -- **Length Filtering**: Remove outliers -- **Diversity Preservation**: Ensure varied training set - -### Basic Usage - -```bash -# Prune dataset with defaults -python scripts\semantic_pruning.py \ - --input ..\..\datasets\chat\dolly\train.jsonl \ - --output data\pruned_train.jsonl - -# Custom thresholds -python scripts\semantic_pruning.py \ - --input ..\..\datasets\chat\dolly\train.jsonl \ - --output data\pruned_train.jsonl \ - --similarity-threshold 0.90 \ - --quality-threshold 0.4 -``` - -### Pruning Strategies - -1. **Exact Duplicates**: Hash-based removal (fast) -2. **Length Outliers**: Remove samples outside 10-2048 tokens -3. **Quality Scoring**: Based on: - - Length appropriateness - - Character diversity - - Word diversity - - Punctuation presence -4. **Semantic Similarity**: Embedding-based (requires sentence-transformers) -5. **Diversity Sampling**: Keep highest quality from each cluster - -### Configuration Options - -```python -from scripts.semantic_pruning import PruningConfig, SemanticPruner - -config = PruningConfig( - similarity_threshold=0.95, # Higher = more aggressive - min_length=10, # Minimum words - max_length=2048, # Maximum words - quality_threshold=0.3, # 0.0-1.0 scale - target_reduction=0.3 # Target 30% reduction -) - -pruner = SemanticPruner(config) -stats = pruner.prune_dataset("input.jsonl", "output.jsonl") -stats.print_summary() -``` - -### Expected Results - -``` -=== Semantic Pruning Results === -Original samples: 15,000 -Final samples: 10,500 (30.0% reduction) - -Breakdown: - - Duplicates: 1,200 - - Low quality: 2,100 - - Outliers: 450 - - Redundant: 750 -``` - -### Integration with Training - -```bash -# Full pipeline: Prune → Train → Evaluate -python scripts\semantic_pruning.py --input data\raw.jsonl --output data\clean.jsonl -python scripts\train_lora.py --dataset data --config lora\lora.yaml -python scripts\auto_eval.py --model data_out\lora_training --dataset data\test.jsonl -``` - -## ⚡ 4. GPU Training Optimization Profile - -Automatically configure optimal training settings for your hardware. - -### Features -- **Hardware Detection**: Automatic GPU capability detection -- **Memory Optimization**: Configure batch size, quantization -- **Precision Selection**: FP16/BF16/8-bit/4-bit based on VRAM -- **Advanced Features**: Flash Attention, compiled models -- **Multi-GPU Support**: Automatic scaling -- **Config Updates**: Direct YAML file modification - -### Quick Setup - -```bash -# Detect hardware and create profile -python scripts\gpu_optimizer.py - -# Update existing config -python scripts\gpu_optimizer.py --update-config lora\lora.yaml - -# Specify model size -python scripts\gpu_optimizer.py --model-size 7.0 --memory-usage 0.8 -``` - -### Hardware Profiles - -#### High-End GPU (A100 80GB, RTX 4090 24GB) -```yaml -batch_size: 4 -gradient_accumulation_steps: 1 -gradient_checkpointing: false -bf16: true -max_seq_length: 2048 -lora_rank: 32 -flash_attention: true -compile_model: true -``` - -#### Mid-Range GPU (RTX 3090 24GB, V100 16GB) -```yaml -batch_size: 2 -gradient_accumulation_steps: 2 -gradient_checkpointing: true -fp16: true -max_seq_length: 1024 -lora_rank: 16 -``` - -#### Budget GPU (RTX 3060 12GB, T4 16GB) -```yaml -batch_size: 1 -gradient_accumulation_steps: 4 -gradient_checkpointing: true -use_8bit: true -max_seq_length: 512 -lora_rank: 8 -``` - -#### Limited VRAM (GTX 1080 Ti 11GB) -```yaml -batch_size: 1 -gradient_accumulation_steps: 8 -gradient_checkpointing: true -use_4bit: true -cpu_offload: true -max_seq_length: 256 -lora_rank: 4 -``` - -### Output - -``` -=== GPU Hardware Detection === -GPU Name: NVIDIA GeForce RTX 4090 -GPU Count: 1 -Total Memory: 24.00 GB -Available Memory: 22.50 GB -Compute Capability: 8.9 -CUDA Version: 12.1 - -=== Optimization Profile Creation === -Model size: 7.00 GB -Available memory: 18.00 GB -Profile: Performance (BF16 + optimizations) - -=== Recommended Settings === -Batch Size: 4 -Gradient Accumulation: 1 -Effective Batch Size: 4 -Max Sequence Length: 2048 -LoRA Rank: 32 -Precision: BF16 -Gradient Checkpointing: False -Flash Attention: True -Compiled Model: True - -Estimated tokens/step: 8,192 -``` - -### Environment Variables - -```bash -# Export optimized environment -python scripts\gpu_optimizer.py --export-env > gpu_env.sh - -# Windows (PowerShell) -$env:CUDA_VISIBLE_DEVICES = "0" -$env:PYTORCH_CUDA_ALLOC_CONF = "max_split_size_mb:512" -$env:TOKENIZERS_PARALLELISM = "false" - -# Linux/Mac -export CUDA_VISIBLE_DEVICES=0 -export PYTORCH_CUDA_ALLOC_CONF="max_split_size_mb:512" -export TOKENIZERS_PARALLELISM=false -``` - -### Multi-GPU Training - -```bash -# Automatic multi-GPU detection -python scripts\gpu_optimizer.py --update-config lora\lora.yaml - -# Manual DeepSpeed config (for 2+ GPUs) -python scripts\train_lora.py \ - --dataset data \ - --config lora\lora.yaml \ - --deepspeed scripts\deepspeed_zero3.json -``` - -## 🔄 Complete Workflow - -### End-to-End Pipeline - -```bash -# Step 1: Optimize for hardware -python scripts\gpu_optimizer.py --update-config lora\lora.yaml --model-size 7.0 - -# Step 2: Prune training data -python scripts\semantic_pruning.py \ - --input ..\..\datasets\chat\dolly\train.jsonl \ - --output data\pruned_train.jsonl \ - --similarity-threshold 0.95 - -# Step 3: Train with optimized settings -python scripts\train_lora.py \ - --dataset data \ - --config lora\lora.yaml - -# Step 4: Evaluate model -python scripts\auto_eval.py \ - --model data_out\lora_training \ - --dataset ..\..\datasets\chat\dolly\test.jsonl \ - --metrics perplexity inference_time \ - --output-name experiment_1 - -# Step 5: Setup RAG for deployment -python scripts\rag_pipeline.py \ - --model data_out\lora_training \ - --docs ..\..\datasets \ - --rebuild-index - -# Step 6: Test RAG interactively -python scripts\rag_pipeline.py \ - --model data_out\lora_training \ - --docs ..\..\datasets \ - --interactive -``` - -### Master Orchestration Script - -See `scripts\run_pipeline.py` for automated orchestration. - -## 📦 Dependencies - -### Core Requirements (already installed) -``` -torch>=2.0.0 -transformers>=4.36.0 -datasets>=2.14.0 -pyyaml>=6.0 -numpy>=1.24.0 -``` - -### Optional Requirements - -```bash -# For semantic search and pruning -pip install sentence-transformers - -# For quality metrics -pip install rouge-score sacrebleu - -# For 4-bit/8-bit quantization -pip install bitsandbytes - -# For flash attention (Linux only) -pip install flash-attn --no-build-isolation -``` - -## 🎯 Performance Tips - -### Training Speed -1. Use GPU optimizer to find best batch size -2. Enable gradient checkpointing only if OOM -3. Use BF16 on Ampere+ GPUs (A100, RTX 30xx+) -4. Enable flash attention on supported GPUs -5. Use compiled models (PyTorch 2.0+) - -### Memory Efficiency -1. Start with 4-bit quantization on limited VRAM -2. Use gradient accumulation to simulate larger batches -3. Reduce sequence length if still OOM -4. Enable CPU offload as last resort -5. Prune dataset to reduce training time - -### Data Quality -1. Always prune training data first -2. Use similarity threshold 0.90-0.95 for deduplication -3. Set quality threshold based on your domain -4. Keep test set unpruned for fair evaluation -5. Monitor perplexity during evaluation - -### RAG Performance -1. Build index once, reuse across runs -2. Use smaller embedding models for speed -3. Adjust chunk size based on your documents -4. Lower top_k for faster retrieval -5. Cache embeddings on SSD - -## 🐛 Troubleshooting - -### Out of Memory -```bash -# Run optimizer with conservative settings -python scripts\gpu_optimizer.py --memory-usage 0.6 - -# Or manually reduce batch size -python scripts\train_lora.py --batch-size 1 --gradient-accumulation 8 -``` - -### Slow Training -```bash -# Check if using optimal settings -python scripts\gpu_optimizer.py - -# Profile training -python -m torch.utils.bottleneck scripts\train_lora.py ... -``` - -### Poor Evaluation Scores -```bash -# Prune more aggressively -python scripts\semantic_pruning.py --quality-threshold 0.5 - -# Check data quality -python scripts\semantic_pruning.py --input data.jsonl --output /dev/null -``` - -### RAG Not Finding Context -```bash -# Rebuild index with lower threshold -python scripts\rag_pipeline.py --rebuild-index - -# Check document loading -python -c "from scripts.rag_pipeline import DocumentStore, RAGConfig; ds = DocumentStore(RAGConfig()); ds.load_from_directory('docs'); print(len(ds.documents))" -``` - -## 📚 API Reference - -See individual script files for detailed API documentation: -- `scripts/auto_eval.py` - Evaluation framework -- `scripts/rag_pipeline.py` - RAG components -- `scripts/semantic_pruning.py` - Data pruning -- `scripts/gpu_optimizer.py` - Hardware optimization - -## 🎓 Examples - -### Example 1: Quick Training -```bash -python scripts\gpu_optimizer.py --update-config lora\lora.yaml -python scripts\train_lora.py --dataset data --config lora\lora.yaml --max-train-samples 1000 -python scripts\auto_eval.py --model data_out\lora_training --dataset data\test.jsonl -``` - -### Example 2: Production Pipeline -```bash -# Full quality training -python scripts\semantic_pruning.py --input raw.jsonl --output clean.jsonl --similarity-threshold 0.95 -python scripts\gpu_optimizer.py --update-config lora\lora.yaml -python scripts\train_lora.py --dataset data --config lora\lora.yaml -python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --metrics perplexity inference_time -``` - -### Example 3: RAG Deployment -```bash -# Index all docs -python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --rebuild-index --index-dir data_out\rag_index - -# Query API -python -c " -from scripts.rag_pipeline import RAGPipeline, DocumentStore, RAGConfig -doc_store = DocumentStore(RAGConfig()) -doc_store.load_index('data_out/rag_index') -rag = RAGPipeline('data_out/lora_training', doc_store) -print(rag.query('What is quantum computing?')['answer']) -" -``` - -## 📈 Monitoring - -All tools output results to `data_out/`: -``` -data_out/ -├── lora_training/ # Trained model -├── evaluation_results/ # Evaluation JSONs -├── rag_index/ # RAG document index -├── gpu_profile.yaml # Hardware profile -└── pruned_stats.json # Pruning statistics -``` - -## 🔗 Integration - -These tools integrate seamlessly with existing training scripts: - -```python -# In your training script -from scripts.gpu_optimizer import GPUOptimizer -from scripts.auto_eval import AutomaticEvaluator - -# Optimize before training -optimizer = GPUOptimizer() -profile = optimizer.create_optimization_profile() -# Use profile.batch_size, profile.learning_rate, etc. - -# Evaluate after training -evaluator = AutomaticEvaluator() -metrics = evaluator.evaluate_model(model_path, test_data) -``` - ---- - -**🎉 You're all set!** Run the pipeline and optimize your training workflow. +# Advanced Training Pipeline Guide + +Complete guide for automatic evaluation, RAG pipeline, semantic pruning, and GPU optimization. + +## 🚀 Quick Start + +```bash +cd AI\microsoft_phi-silica-3.6_v1 + +# 1. Optimize for your GPU +python scripts\gpu_optimizer.py --update-config lora\lora.yaml + +# 2. Prune your training data +python scripts\semantic_pruning.py --input ..\..\datasets\chat\dolly\train.jsonl --output data\pruned_train.jsonl + +# 3. Train with optimized settings +python scripts\train_lora.py --dataset data --config lora\lora.yaml + +# 4. Evaluate the model +python scripts\auto_eval.py --model data_out\lora_training --dataset ..\..\datasets\chat\dolly\test.jsonl + +# 5. Use with RAG +python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --interactive +``` + +## 📊 1. Automatic Evaluation + +Automatically evaluate fine-tuned models with multiple metrics. + +### Features +- **Perplexity**: Language model quality metric +- **Inference Speed**: Tokens per second, latency +- **Memory Usage**: GPU memory consumption +- **Quality Metrics**: BLEU, ROUGE scores (optional) +- **Model Comparison**: Benchmark multiple models + +### Basic Usage + +```bash +# Evaluate single model +python scripts\auto_eval.py \ + --model data_out\lora_training \ + --dataset ..\..\datasets\chat\dolly\test.jsonl \ + --num-samples 100 + +# Compare multiple models +python scripts\auto_eval.py \ + --model data_out\lora_training \ + --dataset ..\..\datasets\chat\dolly\test.jsonl \ + --metrics perplexity inference_time bleu \ + --output-name model_v1 +``` + +### Output + +Results saved to `data_out/evaluation_results/`: +```json +{ + "perplexity": 12.34, + "inference_time_ms": 45.67, + "tokens_per_second": 89.12, + "memory_usage_mb": 6789.0 +} +``` + +### Integration with Training + +```python +from scripts.auto_eval import AutomaticEvaluator + +evaluator = AutomaticEvaluator() +metrics = evaluator.evaluate_model( + model_path="data_out/lora_training", + test_dataset="test.jsonl", + num_samples=100 +) +print(f"Perplexity: {metrics.perplexity:.2f}") +``` + +## 🔍 2. RAG Pipeline Setup + +Retrieval-Augmented Generation for enhanced model responses. + +### Features +- **Document Indexing**: Load documents from any directory +- **Semantic Search**: Find relevant context using embeddings +- **Context Integration**: Inject retrieved docs into prompts +- **Interactive Mode**: Question-answering interface +- **Persistent Index**: Save/load document embeddings + +### Setup + +```bash +# Install optional dependencies +pip install sentence-transformers + +# Build document index +python scripts\rag_pipeline.py \ + --model data_out\lora_training \ + --docs ..\..\datasets \ + --rebuild-index + +# Interactive mode +python scripts\rag_pipeline.py \ + --model data_out\lora_training \ + --docs ..\..\datasets \ + --interactive +``` + +### Document Structure + +``` +datasets/ +├── quantum/ # Will be indexed +│ ├── README.md +│ └── data.csv +├── chat/ # Will be indexed +│ └── dolly/ +└── processed/ # Will be indexed +``` + +### Configuration + +Edit `scripts/rag_pipeline.py` or create `rag_config.yaml`: + +```yaml +embedding_model: "sentence-transformers/all-MiniLM-L6-v2" +chunk_size: 512 # Tokens per chunk +chunk_overlap: 50 # Overlap between chunks +top_k_retrieval: 3 # Number of docs to retrieve +retrieval_threshold: 0.7 # Minimum similarity score +``` + +### Programmatic Usage + +```python +from scripts.rag_pipeline import RAGPipeline, DocumentStore, RAGConfig + +# Setup +config = RAGConfig(top_k_retrieval=5) +doc_store = DocumentStore(config) +doc_store.load_from_directory("../../datasets") +doc_store.build_index() + +# Create pipeline +rag = RAGPipeline("data_out/lora_training", doc_store, config) + +# Query +result = rag.query("What are quantum circuits?") +print(result["answer"]) +``` + +## 🧹 3. Semantic Pruning + +Remove redundant and low-quality training samples to improve efficiency. + +### Features +- **Exact Deduplication**: Remove identical samples +- **Semantic Deduplication**: Remove similar samples via embeddings +- **Quality Filtering**: Score and filter low-quality data +- **Length Filtering**: Remove outliers +- **Diversity Preservation**: Ensure varied training set + +### Basic Usage + +```bash +# Prune dataset with defaults +python scripts\semantic_pruning.py \ + --input ..\..\datasets\chat\dolly\train.jsonl \ + --output data\pruned_train.jsonl + +# Custom thresholds +python scripts\semantic_pruning.py \ + --input ..\..\datasets\chat\dolly\train.jsonl \ + --output data\pruned_train.jsonl \ + --similarity-threshold 0.90 \ + --quality-threshold 0.4 +``` + +### Pruning Strategies + +1. **Exact Duplicates**: Hash-based removal (fast) +2. **Length Outliers**: Remove samples outside 10-2048 tokens +3. **Quality Scoring**: Based on: + - Length appropriateness + - Character diversity + - Word diversity + - Punctuation presence +4. **Semantic Similarity**: Embedding-based (requires sentence-transformers) +5. **Diversity Sampling**: Keep highest quality from each cluster + +### Configuration Options + +```python +from scripts.semantic_pruning import PruningConfig, SemanticPruner + +config = PruningConfig( + similarity_threshold=0.95, # Higher = more aggressive + min_length=10, # Minimum words + max_length=2048, # Maximum words + quality_threshold=0.3, # 0.0-1.0 scale + target_reduction=0.3 # Target 30% reduction +) + +pruner = SemanticPruner(config) +stats = pruner.prune_dataset("input.jsonl", "output.jsonl") +stats.print_summary() +``` + +### Expected Results + +``` +=== Semantic Pruning Results === +Original samples: 15,000 +Final samples: 10,500 (30.0% reduction) + +Breakdown: + - Duplicates: 1,200 + - Low quality: 2,100 + - Outliers: 450 + - Redundant: 750 +``` + +### Integration with Training + +```bash +# Full pipeline: Prune → Train → Evaluate +python scripts\semantic_pruning.py --input data\raw.jsonl --output data\clean.jsonl +python scripts\train_lora.py --dataset data --config lora\lora.yaml +python scripts\auto_eval.py --model data_out\lora_training --dataset data\test.jsonl +``` + +## ⚡ 4. GPU Training Optimization Profile + +Automatically configure optimal training settings for your hardware. + +### Features +- **Hardware Detection**: Automatic GPU capability detection +- **Memory Optimization**: Configure batch size, quantization +- **Precision Selection**: FP16/BF16/8-bit/4-bit based on VRAM +- **Advanced Features**: Flash Attention, compiled models +- **Multi-GPU Support**: Automatic scaling +- **Config Updates**: Direct YAML file modification + +### Quick Setup + +```bash +# Detect hardware and create profile +python scripts\gpu_optimizer.py + +# Update existing config +python scripts\gpu_optimizer.py --update-config lora\lora.yaml + +# Specify model size +python scripts\gpu_optimizer.py --model-size 7.0 --memory-usage 0.8 +``` + +### Hardware Profiles + +#### High-End GPU (A100 80GB, RTX 4090 24GB) +```yaml +batch_size: 4 +gradient_accumulation_steps: 1 +gradient_checkpointing: false +bf16: true +max_seq_length: 2048 +lora_rank: 32 +flash_attention: true +compile_model: true +``` + +#### Mid-Range GPU (RTX 3090 24GB, V100 16GB) +```yaml +batch_size: 2 +gradient_accumulation_steps: 2 +gradient_checkpointing: true +fp16: true +max_seq_length: 1024 +lora_rank: 16 +``` + +#### Budget GPU (RTX 3060 12GB, T4 16GB) +```yaml +batch_size: 1 +gradient_accumulation_steps: 4 +gradient_checkpointing: true +use_8bit: true +max_seq_length: 512 +lora_rank: 8 +``` + +#### Limited VRAM (GTX 1080 Ti 11GB) +```yaml +batch_size: 1 +gradient_accumulation_steps: 8 +gradient_checkpointing: true +use_4bit: true +cpu_offload: true +max_seq_length: 256 +lora_rank: 4 +``` + +### Output + +``` +=== GPU Hardware Detection === +GPU Name: NVIDIA GeForce RTX 4090 +GPU Count: 1 +Total Memory: 24.00 GB +Available Memory: 22.50 GB +Compute Capability: 8.9 +CUDA Version: 12.1 + +=== Optimization Profile Creation === +Model size: 7.00 GB +Available memory: 18.00 GB +Profile: Performance (BF16 + optimizations) + +=== Recommended Settings === +Batch Size: 4 +Gradient Accumulation: 1 +Effective Batch Size: 4 +Max Sequence Length: 2048 +LoRA Rank: 32 +Precision: BF16 +Gradient Checkpointing: False +Flash Attention: True +Compiled Model: True + +Estimated tokens/step: 8,192 +``` + +### Environment Variables + +```bash +# Export optimized environment +python scripts\gpu_optimizer.py --export-env > gpu_env.sh + +# Windows (PowerShell) +$env:CUDA_VISIBLE_DEVICES = "0" +$env:PYTORCH_CUDA_ALLOC_CONF = "max_split_size_mb:512" +$env:TOKENIZERS_PARALLELISM = "false" + +# Linux/Mac +export CUDA_VISIBLE_DEVICES=0 +export PYTORCH_CUDA_ALLOC_CONF="max_split_size_mb:512" +export TOKENIZERS_PARALLELISM=false +``` + +### Multi-GPU Training + +```bash +# Automatic multi-GPU detection +python scripts\gpu_optimizer.py --update-config lora\lora.yaml + +# Manual DeepSpeed config (for 2+ GPUs) +python scripts\train_lora.py \ + --dataset data \ + --config lora\lora.yaml \ + --deepspeed scripts\deepspeed_zero3.json +``` + +## 🔄 Complete Workflow + +### End-to-End Pipeline + +```bash +# Step 1: Optimize for hardware +python scripts\gpu_optimizer.py --update-config lora\lora.yaml --model-size 7.0 + +# Step 2: Prune training data +python scripts\semantic_pruning.py \ + --input ..\..\datasets\chat\dolly\train.jsonl \ + --output data\pruned_train.jsonl \ + --similarity-threshold 0.95 + +# Step 3: Train with optimized settings +python scripts\train_lora.py \ + --dataset data \ + --config lora\lora.yaml + +# Step 4: Evaluate model +python scripts\auto_eval.py \ + --model data_out\lora_training \ + --dataset ..\..\datasets\chat\dolly\test.jsonl \ + --metrics perplexity inference_time \ + --output-name experiment_1 + +# Step 5: Setup RAG for deployment +python scripts\rag_pipeline.py \ + --model data_out\lora_training \ + --docs ..\..\datasets \ + --rebuild-index + +# Step 6: Test RAG interactively +python scripts\rag_pipeline.py \ + --model data_out\lora_training \ + --docs ..\..\datasets \ + --interactive +``` + +### Master Orchestration Script + +See `scripts\run_pipeline.py` for automated orchestration. + +## 📦 Dependencies + +### Core Requirements (already installed) +``` +torch>=2.0.0 +transformers>=4.36.0 +datasets>=2.14.0 +pyyaml>=6.0 +numpy>=1.24.0 +``` + +### Optional Requirements + +```bash +# For semantic search and pruning +pip install sentence-transformers + +# For quality metrics +pip install rouge-score sacrebleu + +# For 4-bit/8-bit quantization +pip install bitsandbytes + +# For flash attention (Linux only) +pip install flash-attn --no-build-isolation +``` + +## 🎯 Performance Tips + +### Training Speed +1. Use GPU optimizer to find best batch size +2. Enable gradient checkpointing only if OOM +3. Use BF16 on Ampere+ GPUs (A100, RTX 30xx+) +4. Enable flash attention on supported GPUs +5. Use compiled models (PyTorch 2.0+) + +### Memory Efficiency +1. Start with 4-bit quantization on limited VRAM +2. Use gradient accumulation to simulate larger batches +3. Reduce sequence length if still OOM +4. Enable CPU offload as last resort +5. Prune dataset to reduce training time + +### Data Quality +1. Always prune training data first +2. Use similarity threshold 0.90-0.95 for deduplication +3. Set quality threshold based on your domain +4. Keep test set unpruned for fair evaluation +5. Monitor perplexity during evaluation + +### RAG Performance +1. Build index once, reuse across runs +2. Use smaller embedding models for speed +3. Adjust chunk size based on your documents +4. Lower top_k for faster retrieval +5. Cache embeddings on SSD + +## 🐛 Troubleshooting + +### Out of Memory +```bash +# Run optimizer with conservative settings +python scripts\gpu_optimizer.py --memory-usage 0.6 + +# Or manually reduce batch size +python scripts\train_lora.py --batch-size 1 --gradient-accumulation 8 +``` + +### Slow Training +```bash +# Check if using optimal settings +python scripts\gpu_optimizer.py + +# Profile training +python -m torch.utils.bottleneck scripts\train_lora.py ... +``` + +### Poor Evaluation Scores +```bash +# Prune more aggressively +python scripts\semantic_pruning.py --quality-threshold 0.5 + +# Check data quality +python scripts\semantic_pruning.py --input data.jsonl --output /dev/null +``` + +### RAG Not Finding Context +```bash +# Rebuild index with lower threshold +python scripts\rag_pipeline.py --rebuild-index + +# Check document loading +python -c "from scripts.rag_pipeline import DocumentStore, RAGConfig; ds = DocumentStore(RAGConfig()); ds.load_from_directory('docs'); print(len(ds.documents))" +``` + +## 📚 API Reference + +See individual script files for detailed API documentation: +- `scripts/auto_eval.py` - Evaluation framework +- `scripts/rag_pipeline.py` - RAG components +- `scripts/semantic_pruning.py` - Data pruning +- `scripts/gpu_optimizer.py` - Hardware optimization + +## 🎓 Examples + +### Example 1: Quick Training +```bash +python scripts\gpu_optimizer.py --update-config lora\lora.yaml +python scripts\train_lora.py --dataset data --config lora\lora.yaml --max-train-samples 1000 +python scripts\auto_eval.py --model data_out\lora_training --dataset data\test.jsonl +``` + +### Example 2: Production Pipeline +```bash +# Full quality training +python scripts\semantic_pruning.py --input raw.jsonl --output clean.jsonl --similarity-threshold 0.95 +python scripts\gpu_optimizer.py --update-config lora\lora.yaml +python scripts\train_lora.py --dataset data --config lora\lora.yaml +python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --metrics perplexity inference_time +``` + +### Example 3: RAG Deployment +```bash +# Index all docs +python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --rebuild-index --index-dir data_out\rag_index + +# Query API +python -c " +from scripts.rag_pipeline import RAGPipeline, DocumentStore, RAGConfig +doc_store = DocumentStore(RAGConfig()) +doc_store.load_index('data_out/rag_index') +rag = RAGPipeline('data_out/lora_training', doc_store) +print(rag.query('What is quantum computing?')['answer']) +" +``` + +## 📈 Monitoring + +All tools output results to `data_out/`: +``` +data_out/ +├── lora_training/ # Trained model +├── evaluation_results/ # Evaluation JSONs +├── rag_index/ # RAG document index +├── gpu_profile.yaml # Hardware profile +└── pruned_stats.json # Pruning statistics +``` + +## 🔗 Integration + +These tools integrate seamlessly with existing training scripts: + +```python +# In your training script +from scripts.gpu_optimizer import GPUOptimizer +from scripts.auto_eval import AutomaticEvaluator + +# Optimize before training +optimizer = GPUOptimizer() +profile = optimizer.create_optimization_profile() +# Use profile.batch_size, profile.learning_rate, etc. + +# Evaluate after training +evaluator = AutomaticEvaluator() +metrics = evaluator.evaluate_model(model_path, test_data) +``` + +--- + +**🎉 You're all set!** Run the pipeline and optimize your training workflow. diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_QUICKREF.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_QUICKREF.md index 1cefb0661..78c1c62c1 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_QUICKREF.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_QUICKREF.md @@ -1,217 +1,217 @@ -# Advanced Training - Quick Reference - -## 🚀 One-Command Pipeline - -```bash -# Full pipeline (optimize → prune → train → evaluate → RAG) -python scripts\run_pipeline.py --input-dataset ..\..\datasets\chat\dolly\train.jsonl --test-dataset ..\..\datasets\chat\dolly\test.jsonl --rag-docs ..\..\datasets - -# Quick test (64 samples, skip RAG) -python scripts\run_pipeline.py --input-dataset ..\..\datasets\chat\dolly\train.jsonl --max-train-samples 64 --skip-rag -``` - -## 📊 Individual Tools - - -### GPU Optimizer - -```bash -# Auto-optimize config -python scripts\gpu_optimizer.py --update-config lora\lora.yaml - -# Custom settings -python scripts\gpu_optimizer.py --model-size 7.0 --memory-usage 0.8 -``` - - -### Semantic Pruning - -```bash -# Prune dataset -python scripts\semantic_pruning.py --input raw.jsonl --output clean.jsonl - -# Aggressive pruning -python scripts\semantic_pruning.py --input raw.jsonl --output clean.jsonl --similarity-threshold 0.90 --quality-threshold 0.5 -``` - - -### Auto Evaluation - -```bash -# Evaluate model -python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl - -# Full metrics -python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --metrics perplexity inference_time bleu rouge -``` - - - - - - - -#### Quality Metrics (BLEU / ROUGE) - -BLEU and ROUGE are optional; they require extra packages. If the libraries are not installed the script will gracefully skip them and report `null`. - -Install (recommended): - - -```bash -pip install rouge-score sacrebleu -``` - -Usage examples: - - -```bash -# Minimal (only speed + perplexity) -python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --metrics perplexity inference_time - -# Add BLEU -python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --metrics perplexity bleu - -# Add ROUGE (averages rouge1/rouge2/rougeL) -python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --metrics perplexity rouge - - - -- Perplexity ↓: Lower is better (model assigns higher probability to test tokens). -- Inference Time ↓: Lower latency per prompt. -- Tokens/sec ↑: Throughput; combine with latency for capacity planning. -- BLEU ↑: N‑gram precision vs. reference responses (sensitive to exact wording). -- ROUGE‑L ↑: Longest common subsequence overlap (better for summarization / longer answers). - -Best practices: - -- Keep evaluation sample size modest during iteration (10–20) then scale to 100–500. -- Never select training checkpoints solely on one metric—track multiple to avoid overfitting. -- For chat datasets, ensure reference = last assistant message; prompt = last user message. - - -### RAG Pipeline - -```bash -# Build index -python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --rebuild-index - -# Interactive Q&A -python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --interactive - -#### RAG Tuning Tips - -- Chunk Size: Smaller chunks (400–800 chars) increase recall; larger chunks (1200–1600) improve coherence. -- Embeddings: Install `sentence-transformers` for semantic retrieval; otherwise keyword fallback is used. -- Similarity Threshold: Raise above 0.75 to filter loosely related documents. -- Hybrid Strategy: Retrieve top-k semantic → enrich with one keyword‑matched chunk for breadth. - -Example with semantic retrieval (after installing sentence-transformers): - -```bash -pip install sentence-transformers -python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --rebuild-index --interactive -``` - -## 📁 Output Structure - -```text -data_out/ -├── lora_training/ # Trained model -├── evaluation_results/ # JSON evaluation results -│ └── experiment_YYYYMMDD_HHMMSS.json -├── rag_index/ # RAG document index -│ ├── documents.json -│ └── embeddings.npy -├── gpu_profile.yaml # Hardware profile - -└── pipeline_results.json # Full pipeline results -``` - -## 💡 Common Patterns - - -### Pattern 1: Quick Iteration - -```bash - -# Test on 64 samples -python scripts\gpu_optimizer.py --update-config lora\lora.yaml -python scripts\train_lora.py --dataset data --config lora\lora.yaml --max-train-samples 64 -python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --num-samples 10 -``` - - -### Pattern 2: Production Training - -```bash - -# Full quality pipeline -python scripts\semantic_pruning.py --input raw.jsonl --output clean.jsonl --similarity-threshold 0.95 -python scripts\gpu_optimizer.py --update-config lora\lora.yaml --model-size 7.0 -python scripts\train_lora.py --dataset data --config lora\lora.yaml -python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --num-samples 500 --metrics perplexity inference_time bleu rouge -``` - - -### Pattern 3: RAG Deployment - - -```bash -# Train + RAG -python scripts\train_lora.py --dataset data --config lora\lora.yaml -python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --rebuild-index -python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --interactive -``` - - -### Pattern 4: Full Orchestrated Pipeline With Metrics - -```bash -python scripts\run_pipeline.py \ - --input-dataset ..\..\datasets\chat\dolly\train.jsonl \ - --test-dataset ..\..\datasets\chat\dolly\test.jsonl \ - --rag-docs ..\..\datasets \ - --eval-metrics perplexity inference_time bleu rouge \ - --experiment-name prod_eval -``` - -## 🔧 Troubleshooting - -| Problem | Solution | -|---------|----------| -| Out of memory | `python scripts\gpu_optimizer.py --memory-usage 0.6` | -| Slow training | Check GPU profile, enable BF16/FP16 | -| Poor eval scores | More aggressive pruning, longer training | -| RAG not working | `pip install sentence-transformers` | - -## 📦 Optional Dependencies - -```bash -# For semantic features -pip install sentence-transformers - -# For quality metrics -pip install rouge-score sacrebleu - -# For quantization -pip install bitsandbytes - -# For flash attention (Linux only) -pip install flash-attn --no-build-isolation -``` - -## 🎯 Performance Targets - -|-----|------------|------------|--------| -| RTX 4090 | 4 | ~2000 | 18GB | -| RTX 3090 | 2 | ~1000 | 20GB | -| RTX 3060 | 1 | ~400 | 10GB | -| T4 | 1 | ~300 | 12GB | - -## 📖 Full Documentation - -See `ADVANCED_TRAINING_GUIDE.md` for complete details. - ---- -Last updated: (auto) Added BLEU/ROUGE guidance, RAG tuning tips, pipeline metrics examples. +# Advanced Training - Quick Reference + +## 🚀 One-Command Pipeline + +```bash +# Full pipeline (optimize → prune → train → evaluate → RAG) +python scripts\run_pipeline.py --input-dataset ..\..\datasets\chat\dolly\train.jsonl --test-dataset ..\..\datasets\chat\dolly\test.jsonl --rag-docs ..\..\datasets + +# Quick test (64 samples, skip RAG) +python scripts\run_pipeline.py --input-dataset ..\..\datasets\chat\dolly\train.jsonl --max-train-samples 64 --skip-rag +``` + +## 📊 Individual Tools + + +### GPU Optimizer + +```bash +# Auto-optimize config +python scripts\gpu_optimizer.py --update-config lora\lora.yaml + +# Custom settings +python scripts\gpu_optimizer.py --model-size 7.0 --memory-usage 0.8 +``` + + +### Semantic Pruning + +```bash +# Prune dataset +python scripts\semantic_pruning.py --input raw.jsonl --output clean.jsonl + +# Aggressive pruning +python scripts\semantic_pruning.py --input raw.jsonl --output clean.jsonl --similarity-threshold 0.90 --quality-threshold 0.5 +``` + + +### Auto Evaluation + +```bash +# Evaluate model +python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl + +# Full metrics +python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --metrics perplexity inference_time bleu rouge +``` + + + + + + + +#### Quality Metrics (BLEU / ROUGE) + +BLEU and ROUGE are optional; they require extra packages. If the libraries are not installed the script will gracefully skip them and report `null`. + +Install (recommended): + + +```bash +pip install rouge-score sacrebleu +``` + +Usage examples: + + +```bash +# Minimal (only speed + perplexity) +python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --metrics perplexity inference_time + +# Add BLEU +python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --metrics perplexity bleu + +# Add ROUGE (averages rouge1/rouge2/rougeL) +python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --metrics perplexity rouge + + + +- Perplexity ↓: Lower is better (model assigns higher probability to test tokens). +- Inference Time ↓: Lower latency per prompt. +- Tokens/sec ↑: Throughput; combine with latency for capacity planning. +- BLEU ↑: N‑gram precision vs. reference responses (sensitive to exact wording). +- ROUGE‑L ↑: Longest common subsequence overlap (better for summarization / longer answers). + +Best practices: + +- Keep evaluation sample size modest during iteration (10–20) then scale to 100–500. +- Never select training checkpoints solely on one metric—track multiple to avoid overfitting. +- For chat datasets, ensure reference = last assistant message; prompt = last user message. + + +### RAG Pipeline + +```bash +# Build index +python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --rebuild-index + +# Interactive Q&A +python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --interactive + +#### RAG Tuning Tips + +- Chunk Size: Smaller chunks (400–800 chars) increase recall; larger chunks (1200–1600) improve coherence. +- Embeddings: Install `sentence-transformers` for semantic retrieval; otherwise keyword fallback is used. +- Similarity Threshold: Raise above 0.75 to filter loosely related documents. +- Hybrid Strategy: Retrieve top-k semantic → enrich with one keyword‑matched chunk for breadth. + +Example with semantic retrieval (after installing sentence-transformers): + +```bash +pip install sentence-transformers +python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --rebuild-index --interactive +``` + +## 📁 Output Structure + +```text +data_out/ +├── lora_training/ # Trained model +├── evaluation_results/ # JSON evaluation results +│ └── experiment_YYYYMMDD_HHMMSS.json +├── rag_index/ # RAG document index +│ ├── documents.json +│ └── embeddings.npy +├── gpu_profile.yaml # Hardware profile + +└── pipeline_results.json # Full pipeline results +``` + +## 💡 Common Patterns + + +### Pattern 1: Quick Iteration + +```bash + +# Test on 64 samples +python scripts\gpu_optimizer.py --update-config lora\lora.yaml +python scripts\train_lora.py --dataset data --config lora\lora.yaml --max-train-samples 64 +python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --num-samples 10 +``` + + +### Pattern 2: Production Training + +```bash + +# Full quality pipeline +python scripts\semantic_pruning.py --input raw.jsonl --output clean.jsonl --similarity-threshold 0.95 +python scripts\gpu_optimizer.py --update-config lora\lora.yaml --model-size 7.0 +python scripts\train_lora.py --dataset data --config lora\lora.yaml +python scripts\auto_eval.py --model data_out\lora_training --dataset test.jsonl --num-samples 500 --metrics perplexity inference_time bleu rouge +``` + + +### Pattern 3: RAG Deployment + + +```bash +# Train + RAG +python scripts\train_lora.py --dataset data --config lora\lora.yaml +python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --rebuild-index +python scripts\rag_pipeline.py --model data_out\lora_training --docs ..\..\datasets --interactive +``` + + +### Pattern 4: Full Orchestrated Pipeline With Metrics + +```bash +python scripts\run_pipeline.py \ + --input-dataset ..\..\datasets\chat\dolly\train.jsonl \ + --test-dataset ..\..\datasets\chat\dolly\test.jsonl \ + --rag-docs ..\..\datasets \ + --eval-metrics perplexity inference_time bleu rouge \ + --experiment-name prod_eval +``` + +## 🔧 Troubleshooting + +| Problem | Solution | +|---------|----------| +| Out of memory | `python scripts\gpu_optimizer.py --memory-usage 0.6` | +| Slow training | Check GPU profile, enable BF16/FP16 | +| Poor eval scores | More aggressive pruning, longer training | +| RAG not working | `pip install sentence-transformers` | + +## 📦 Optional Dependencies + +```bash +# For semantic features +pip install sentence-transformers + +# For quality metrics +pip install rouge-score sacrebleu + +# For quantization +pip install bitsandbytes + +# For flash attention (Linux only) +pip install flash-attn --no-build-isolation +``` + +## 🎯 Performance Targets + +|-----|------------|------------|--------| +| RTX 4090 | 4 | ~2000 | 18GB | +| RTX 3090 | 2 | ~1000 | 20GB | +| RTX 3060 | 1 | ~400 | 10GB | +| T4 | 1 | ~300 | 12GB | + +## 📖 Full Documentation + +See `ADVANCED_TRAINING_GUIDE.md` for complete details. + +--- +Last updated: (auto) Added BLEU/ROUGE guidance, RAG tuning tips, pipeline metrics examples. diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_SETUP_COMPLETE.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_SETUP_COMPLETE.md index 2b3d02a8d..a5bd675ab 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_SETUP_COMPLETE.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/ADVANCED_TRAINING_SETUP_COMPLETE.md @@ -1,282 +1,282 @@ -# Advanced Training Components - Setup Complete ✅ - -## 🎯 What's Been Added - -Four powerful training tools have been added to enhance your AI training workflow: - -### 1. **Automatic Evaluation** (`scripts/auto_eval.py`) -- Compute perplexity, inference speed, memory usage -- Compare multiple models -- BLEU/ROUGE quality metrics (optional) -- Export results to JSON - -### 2. **RAG Pipeline** (`scripts/rag_pipeline.py`) -- Document indexing and semantic search -- Context-aware generation -- Interactive Q&A mode -- Persistent vector index - -### 3. **Semantic Pruning** (`scripts/semantic_pruning.py`) -- Remove exact duplicates -- Semantic deduplication via embeddings -- Quality scoring and filtering -- 20-40% dataset reduction typical - -### 4. **GPU Optimizer** (`scripts/gpu_optimizer.py`) -- Auto-detect hardware capabilities -- Configure optimal batch size, precision, quantization -- Multi-GPU support -- Direct config file updates - -### 5. **Master Pipeline** (`scripts/run_pipeline.py`) -- One-command orchestration -- Complete workflow automation -- Error handling and resumption -- Results tracking - -## 📁 Files Created - -``` -AI/microsoft_phi-silica-3.6_v1/ -├── scripts/ -│ ├── auto_eval.py # Automatic evaluation -│ ├── rag_pipeline.py # RAG pipeline -│ ├── semantic_pruning.py # Data pruning -│ ├── gpu_optimizer.py # GPU optimization -│ └── run_pipeline.py # Master orchestrator -├── ADVANCED_TRAINING_GUIDE.md # Complete documentation -├── ADVANCED_TRAINING_QUICKREF.md # Quick reference -├── requirements-advanced.txt # Optional dependencies -└── requirements.txt (updated) # Core requirements -``` - -## 🚀 Quick Start - -### Option 1: Full Automated Pipeline -```bash -cd AI\microsoft_phi-silica-3.6_v1 - -# Run everything at once -python scripts\run_pipeline.py \ - --input-dataset ..\..\datasets\chat\dolly\train.jsonl \ - --test-dataset ..\..\datasets\chat\dolly\test.jsonl \ - --rag-docs ..\..\datasets -``` - -### Option 2: Individual Tools -```bash -# 1. Optimize for your GPU -python scripts\gpu_optimizer.py --update-config lora\lora.yaml - -# 2. Prune training data -python scripts\semantic_pruning.py \ - --input ..\..\datasets\chat\dolly\train.jsonl \ - --output data\pruned_train.jsonl - -# 3. Train with optimized settings -python scripts\train_lora.py --dataset data --config lora\lora.yaml - -# 4. Evaluate the model -python scripts\auto_eval.py \ - --model data_out\lora_training \ - --dataset ..\..\datasets\chat\dolly\test.jsonl - -# 5. Setup RAG -python scripts\rag_pipeline.py \ - --model data_out\lora_training \ - --docs ..\..\datasets \ - --interactive -``` - -### Option 3: Quick Test (No GPU Required) -```bash -# Test on CPU with 64 samples -python scripts\run_pipeline.py \ - --input-dataset ..\..\datasets\chat\dolly\train.jsonl \ - --max-train-samples 64 \ - --skip-rag \ - --skip-evaluation -``` - -## 📦 Installation - -### Core Features (Already Installed) -```bash -pip install -r requirements.txt -``` - -### Advanced Features (Optional) -```bash -# Install all advanced features -pip install -r requirements-advanced.txt - -# Or install selectively: -pip install sentence-transformers # For semantic pruning & RAG -pip install rouge-score sacrebleu # For evaluation metrics -pip install bitsandbytes # For 4-bit/8-bit quantization -``` - -## 💡 Key Features - -### Automatic Evaluation -✅ Multiple metrics (perplexity, speed, memory) -✅ Model comparison -✅ JSON export -✅ Programmatic API - -### RAG Pipeline -✅ Semantic document search -✅ Context injection -✅ Interactive mode -✅ Index persistence - -### Semantic Pruning -✅ 20-40% dataset reduction -✅ Quality scoring -✅ Duplicate removal -✅ Diversity preservation - -### GPU Optimizer -✅ Hardware auto-detection -✅ Memory-aware config -✅ Multi-GPU scaling -✅ Precision selection (FP16/BF16/8-bit/4-bit) - -## 🎓 Use Cases - -### Use Case 1: Production Training -```bash -# High-quality training with all optimizations -python scripts\run_pipeline.py \ - --input-dataset production_data.jsonl \ - --test-dataset test_data.jsonl \ - --similarity-threshold 0.95 \ - --quality-threshold 0.4 \ - --rag-docs knowledge_base/ -``` - -### Use Case 2: Rapid Experimentation -```bash -# Quick iterations with small samples -python scripts\run_pipeline.py \ - --input-dataset train.jsonl \ - --max-train-samples 100 \ - --skip-pruning \ - --skip-rag -``` - -### Use Case 3: Model Evaluation Only -```bash -# Just evaluate an existing model -python scripts\auto_eval.py \ - --model path/to/model \ - --dataset test.jsonl \ - --metrics perplexity inference_time bleu rouge \ - --num-samples 500 -``` - -### Use Case 4: RAG Deployment -```bash -# Train and deploy with RAG -python scripts\train_lora.py --dataset data --config lora\lora.yaml -python scripts\rag_pipeline.py \ - --model data_out\lora_training \ - --docs company_docs/ \ - --rebuild-index -python scripts\rag_pipeline.py \ - --model data_out\lora_training \ - --docs company_docs/ \ - --interactive -``` - -## 📊 Expected Results - -### GPU Optimization -- **RTX 4090**: Batch size 4, BF16, 2000 tokens/sec -- **RTX 3090**: Batch size 2, FP16, 1000 tokens/sec -- **RTX 3060**: Batch size 1, 8-bit, 400 tokens/sec -- **CPU**: Batch size 1, FP32, 50 tokens/sec - -### Semantic Pruning -- **Typical Reduction**: 20-40% -- **Quality Improvement**: 5-15% better perplexity -- **Training Speed**: 30-50% faster - -### Evaluation Metrics -- **Perplexity**: Lower is better (target: <15) -- **Inference**: Target >100 tokens/sec on GPU -- **Memory**: Should fit in 80% of VRAM - -## 🔧 Troubleshooting - -| Issue | Solution | -|-------|----------| -| Out of memory | Run `python scripts\gpu_optimizer.py --memory-usage 0.6` | -| Slow training | Check GPU utilization, enable BF16/FP16 | -| Import errors | Install optional deps: `pip install -r requirements-advanced.txt` | -| Poor eval scores | More aggressive pruning, increase training epochs | -| RAG not working | Install: `pip install sentence-transformers` | - -## 📚 Documentation - -- **Complete Guide**: `ADVANCED_TRAINING_GUIDE.md` (15KB, detailed) -- **Quick Reference**: `ADVANCED_TRAINING_QUICKREF.md` (4KB, commands only) -- **This Summary**: `ADVANCED_TRAINING_SETUP_COMPLETE.md` - -## 🎯 Next Steps - -1. **Test the tools**: - ```bash - # Quick test - python scripts\gpu_optimizer.py - ``` - -2. **Run a small experiment**: - ```bash - python scripts\run_pipeline.py \ - --input-dataset ..\..\datasets\chat\dolly\train.jsonl \ - --max-train-samples 64 \ - --skip-rag - ``` - -3. **Review results**: - ```bash - # Check output directory - dir data_out\ - - # View pipeline results - type data_out\pipeline_results.json - ``` - -4. **Scale up**: - ```bash - # Full training run - python scripts\run_pipeline.py \ - --input-dataset ..\..\datasets\chat\dolly\train.jsonl \ - --test-dataset ..\..\datasets\chat\dolly\test.jsonl \ - --rag-docs ..\..\datasets - ``` - -## ✨ Features Summary - -| Tool | Purpose | Key Benefit | -|------|---------|-------------| -| GPU Optimizer | Auto-configure hardware | Maximize performance | -| Semantic Pruning | Clean training data | 30-50% faster training | -| Auto Evaluation | Benchmark models | Track improvements | -| RAG Pipeline | Enhance with context | Better responses | -| Master Pipeline | Orchestrate workflow | One-command training | - -## 🎉 You're Ready! - -All advanced training components are now installed and ready to use. Start with the Quick Start commands above, or dive into the complete guide at `ADVANCED_TRAINING_GUIDE.md`. - -**Free alternatives included:** -- All tools work without paid services -- CPU fallback for GPU tools -- Local embeddings without API keys -- No cloud dependencies - ---- - -**Need help?** Check `ADVANCED_TRAINING_GUIDE.md` for detailed examples and API reference. +# Advanced Training Components - Setup Complete ✅ + +## 🎯 What's Been Added + +Four powerful training tools have been added to enhance your AI training workflow: + +### 1. **Automatic Evaluation** (`scripts/auto_eval.py`) +- Compute perplexity, inference speed, memory usage +- Compare multiple models +- BLEU/ROUGE quality metrics (optional) +- Export results to JSON + +### 2. **RAG Pipeline** (`scripts/rag_pipeline.py`) +- Document indexing and semantic search +- Context-aware generation +- Interactive Q&A mode +- Persistent vector index + +### 3. **Semantic Pruning** (`scripts/semantic_pruning.py`) +- Remove exact duplicates +- Semantic deduplication via embeddings +- Quality scoring and filtering +- 20-40% dataset reduction typical + +### 4. **GPU Optimizer** (`scripts/gpu_optimizer.py`) +- Auto-detect hardware capabilities +- Configure optimal batch size, precision, quantization +- Multi-GPU support +- Direct config file updates + +### 5. **Master Pipeline** (`scripts/run_pipeline.py`) +- One-command orchestration +- Complete workflow automation +- Error handling and resumption +- Results tracking + +## 📁 Files Created + +``` +AI/microsoft_phi-silica-3.6_v1/ +├── scripts/ +│ ├── auto_eval.py # Automatic evaluation +│ ├── rag_pipeline.py # RAG pipeline +│ ├── semantic_pruning.py # Data pruning +│ ├── gpu_optimizer.py # GPU optimization +│ └── run_pipeline.py # Master orchestrator +├── ADVANCED_TRAINING_GUIDE.md # Complete documentation +├── ADVANCED_TRAINING_QUICKREF.md # Quick reference +├── requirements-advanced.txt # Optional dependencies +└── requirements.txt (updated) # Core requirements +``` + +## 🚀 Quick Start + +### Option 1: Full Automated Pipeline +```bash +cd AI\microsoft_phi-silica-3.6_v1 + +# Run everything at once +python scripts\run_pipeline.py \ + --input-dataset ..\..\datasets\chat\dolly\train.jsonl \ + --test-dataset ..\..\datasets\chat\dolly\test.jsonl \ + --rag-docs ..\..\datasets +``` + +### Option 2: Individual Tools +```bash +# 1. Optimize for your GPU +python scripts\gpu_optimizer.py --update-config lora\lora.yaml + +# 2. Prune training data +python scripts\semantic_pruning.py \ + --input ..\..\datasets\chat\dolly\train.jsonl \ + --output data\pruned_train.jsonl + +# 3. Train with optimized settings +python scripts\train_lora.py --dataset data --config lora\lora.yaml + +# 4. Evaluate the model +python scripts\auto_eval.py \ + --model data_out\lora_training \ + --dataset ..\..\datasets\chat\dolly\test.jsonl + +# 5. Setup RAG +python scripts\rag_pipeline.py \ + --model data_out\lora_training \ + --docs ..\..\datasets \ + --interactive +``` + +### Option 3: Quick Test (No GPU Required) +```bash +# Test on CPU with 64 samples +python scripts\run_pipeline.py \ + --input-dataset ..\..\datasets\chat\dolly\train.jsonl \ + --max-train-samples 64 \ + --skip-rag \ + --skip-evaluation +``` + +## 📦 Installation + +### Core Features (Already Installed) +```bash +pip install -r requirements.txt +``` + +### Advanced Features (Optional) +```bash +# Install all advanced features +pip install -r requirements-advanced.txt + +# Or install selectively: +pip install sentence-transformers # For semantic pruning & RAG +pip install rouge-score sacrebleu # For evaluation metrics +pip install bitsandbytes # For 4-bit/8-bit quantization +``` + +## 💡 Key Features + +### Automatic Evaluation +✅ Multiple metrics (perplexity, speed, memory) +✅ Model comparison +✅ JSON export +✅ Programmatic API + +### RAG Pipeline +✅ Semantic document search +✅ Context injection +✅ Interactive mode +✅ Index persistence + +### Semantic Pruning +✅ 20-40% dataset reduction +✅ Quality scoring +✅ Duplicate removal +✅ Diversity preservation + +### GPU Optimizer +✅ Hardware auto-detection +✅ Memory-aware config +✅ Multi-GPU scaling +✅ Precision selection (FP16/BF16/8-bit/4-bit) + +## 🎓 Use Cases + +### Use Case 1: Production Training +```bash +# High-quality training with all optimizations +python scripts\run_pipeline.py \ + --input-dataset production_data.jsonl \ + --test-dataset test_data.jsonl \ + --similarity-threshold 0.95 \ + --quality-threshold 0.4 \ + --rag-docs knowledge_base/ +``` + +### Use Case 2: Rapid Experimentation +```bash +# Quick iterations with small samples +python scripts\run_pipeline.py \ + --input-dataset train.jsonl \ + --max-train-samples 100 \ + --skip-pruning \ + --skip-rag +``` + +### Use Case 3: Model Evaluation Only +```bash +# Just evaluate an existing model +python scripts\auto_eval.py \ + --model path/to/model \ + --dataset test.jsonl \ + --metrics perplexity inference_time bleu rouge \ + --num-samples 500 +``` + +### Use Case 4: RAG Deployment +```bash +# Train and deploy with RAG +python scripts\train_lora.py --dataset data --config lora\lora.yaml +python scripts\rag_pipeline.py \ + --model data_out\lora_training \ + --docs company_docs/ \ + --rebuild-index +python scripts\rag_pipeline.py \ + --model data_out\lora_training \ + --docs company_docs/ \ + --interactive +``` + +## 📊 Expected Results + +### GPU Optimization +- **RTX 4090**: Batch size 4, BF16, 2000 tokens/sec +- **RTX 3090**: Batch size 2, FP16, 1000 tokens/sec +- **RTX 3060**: Batch size 1, 8-bit, 400 tokens/sec +- **CPU**: Batch size 1, FP32, 50 tokens/sec + +### Semantic Pruning +- **Typical Reduction**: 20-40% +- **Quality Improvement**: 5-15% better perplexity +- **Training Speed**: 30-50% faster + +### Evaluation Metrics +- **Perplexity**: Lower is better (target: <15) +- **Inference**: Target >100 tokens/sec on GPU +- **Memory**: Should fit in 80% of VRAM + +## 🔧 Troubleshooting + +| Issue | Solution | +|-------|----------| +| Out of memory | Run `python scripts\gpu_optimizer.py --memory-usage 0.6` | +| Slow training | Check GPU utilization, enable BF16/FP16 | +| Import errors | Install optional deps: `pip install -r requirements-advanced.txt` | +| Poor eval scores | More aggressive pruning, increase training epochs | +| RAG not working | Install: `pip install sentence-transformers` | + +## 📚 Documentation + +- **Complete Guide**: `ADVANCED_TRAINING_GUIDE.md` (15KB, detailed) +- **Quick Reference**: `ADVANCED_TRAINING_QUICKREF.md` (4KB, commands only) +- **This Summary**: `ADVANCED_TRAINING_SETUP_COMPLETE.md` + +## 🎯 Next Steps + +1. **Test the tools**: + ```bash + # Quick test + python scripts\gpu_optimizer.py + ``` + +2. **Run a small experiment**: + ```bash + python scripts\run_pipeline.py \ + --input-dataset ..\..\datasets\chat\dolly\train.jsonl \ + --max-train-samples 64 \ + --skip-rag + ``` + +3. **Review results**: + ```bash + # Check output directory + dir data_out\ + + # View pipeline results + type data_out\pipeline_results.json + ``` + +4. **Scale up**: + ```bash + # Full training run + python scripts\run_pipeline.py \ + --input-dataset ..\..\datasets\chat\dolly\train.jsonl \ + --test-dataset ..\..\datasets\chat\dolly\test.jsonl \ + --rag-docs ..\..\datasets + ``` + +## ✨ Features Summary + +| Tool | Purpose | Key Benefit | +|------|---------|-------------| +| GPU Optimizer | Auto-configure hardware | Maximize performance | +| Semantic Pruning | Clean training data | 30-50% faster training | +| Auto Evaluation | Benchmark models | Track improvements | +| RAG Pipeline | Enhance with context | Better responses | +| Master Pipeline | Orchestrate workflow | One-command training | + +## 🎉 You're Ready! + +All advanced training components are now installed and ready to use. Start with the Quick Start commands above, or dive into the complete guide at `ADVANCED_TRAINING_GUIDE.md`. + +**Free alternatives included:** +- All tools work without paid services +- CPU fallback for GPU tools +- Local embeddings without API keys +- No cloud dependencies + +--- + +**Need help?** Check `ADVANCED_TRAINING_GUIDE.md` for detailed examples and API reference. diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/AZURE_FOUNDRY_GUIDE.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/AZURE_FOUNDRY_GUIDE.md index 446ac451d..4e1d24746 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/AZURE_FOUNDRY_GUIDE.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/AZURE_FOUNDRY_GUIDE.md @@ -1,82 +1,82 @@ -# Azure AI Foundry Deployment (Managed Online Endpoint) - -This guide shows how to deploy your LoRA adapter with a Phi-3.x base model to an Azure AI Foundry endpoint (backed by Azure ML Managed Online Endpoints). - -## Prerequisites - -- You completed training and have a LoRA adapter folder, e.g.: `AI/microsoft_phi-silica-3.6_v1/data_out/lora_training/lora_adapter` -- Azure CLI installed and logged in (`az login`) -- Azure ML workspace created (see AZURE_ML_TRAINING_GUIDE.md) -- Python deps installed: - -```powershell -cd AI\microsoft_phi-silica-3.6_v1 -pip install -r azure-requirements.txt -``` - -## Quick Deploy - -```powershell -# Set your subscription -az account set --subscription "" - -# Deploy endpoint (GPU instance) -python azure_foundry_deploy.py ` - --subscription-id "" ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace ` - --adapter-path .\data_out\lora_training\lora_adapter ` - --endpoint-name phi36-lora-ep ` - --base-model-id microsoft/Phi-3.5-mini-instruct ` - --instance-type Standard_NC6s_v3 -``` - -Output includes: -- Endpoint scoring URL -- Primary key for auth - -## Invoke the endpoint - -```powershell -$endpoint = "" -$key = "" -$body = '{"messages":[{"role":"user","content":"Write a haiku about quantum computing"}]}' - -Invoke-RestMethod ` - -Uri $endpoint ` - -Method Post ` - -Headers @{"Content-Type"="application/json"; "Authorization" = "Bearer $key"} ` - -Body $body | ConvertTo-Json -Depth 5 -``` - -## Notes - -- The deployment downloads the base model (`BASE_MODEL_ID`) at startup; the LoRA adapter is mounted from the registered model asset. -- GPU size default is `Standard_NC6s_v3` (1x V100). Adjust with `--instance-type`. -- Environment uses a curated huggingface GPU image + pip installs for `transformers`, `peft`, `accelerate`, `torch`. -- Scoring script lives at `foundry/score_foundry.py`. - -## Cost - -- You pay for the GPU instance while the endpoint is running. -- To save costs, reduce instance_count to 0 (stop) when not in use: - -```powershell -az ml online-deployment update ` - --name blue ` - --endpoint-name phi36-lora-ep ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace ` - --set instance_count=0 -``` - -## Cleanup - -```powershell -# Delete endpoint (stops billing) -az ml online-endpoint delete ` - --name phi36-lora-ep ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace ` - --yes -``` +# Azure AI Foundry Deployment (Managed Online Endpoint) + +This guide shows how to deploy your LoRA adapter with a Phi-3.x base model to an Azure AI Foundry endpoint (backed by Azure ML Managed Online Endpoints). + +## Prerequisites + +- You completed training and have a LoRA adapter folder, e.g.: `AI/microsoft_phi-silica-3.6_v1/data_out/lora_training/lora_adapter` +- Azure CLI installed and logged in (`az login`) +- Azure ML workspace created (see AZURE_ML_TRAINING_GUIDE.md) +- Python deps installed: + +```powershell +cd AI\microsoft_phi-silica-3.6_v1 +pip install -r azure-requirements.txt +``` + +## Quick Deploy + +```powershell +# Set your subscription +az account set --subscription "" + +# Deploy endpoint (GPU instance) +python azure_foundry_deploy.py ` + --subscription-id "" ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace ` + --adapter-path .\data_out\lora_training\lora_adapter ` + --endpoint-name phi36-lora-ep ` + --base-model-id microsoft/Phi-3.5-mini-instruct ` + --instance-type Standard_NC6s_v3 +``` + +Output includes: +- Endpoint scoring URL +- Primary key for auth + +## Invoke the endpoint + +```powershell +$endpoint = "" +$key = "" +$body = '{"messages":[{"role":"user","content":"Write a haiku about quantum computing"}]}' + +Invoke-RestMethod ` + -Uri $endpoint ` + -Method Post ` + -Headers @{"Content-Type"="application/json"; "Authorization" = "Bearer $key"} ` + -Body $body | ConvertTo-Json -Depth 5 +``` + +## Notes + +- The deployment downloads the base model (`BASE_MODEL_ID`) at startup; the LoRA adapter is mounted from the registered model asset. +- GPU size default is `Standard_NC6s_v3` (1x V100). Adjust with `--instance-type`. +- Environment uses a curated huggingface GPU image + pip installs for `transformers`, `peft`, `accelerate`, `torch`. +- Scoring script lives at `foundry/score_foundry.py`. + +## Cost + +- You pay for the GPU instance while the endpoint is running. +- To save costs, reduce instance_count to 0 (stop) when not in use: + +```powershell +az ml online-deployment update ` + --name blue ` + --endpoint-name phi36-lora-ep ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace ` + --set instance_count=0 +``` + +## Cleanup + +```powershell +# Delete endpoint (stops billing) +az ml online-endpoint delete ` + --name phi36-lora-ep ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace ` + --yes +``` diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/AZURE_ML_TRAINING_GUIDE.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/AZURE_ML_TRAINING_GUIDE.md index 7b718a755..27f713c63 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/AZURE_ML_TRAINING_GUIDE.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/AZURE_ML_TRAINING_GUIDE.md @@ -1,290 +1,290 @@ -# Azure ML Training for Phi-3.6 LoRA Fine-tuning - -This guide shows how to train Phi-3.6 LoRA models on Azure Machine Learning with GPU acceleration. - -## Prerequisites - -### 1. Azure Subscription -- Active Azure subscription -- Contributor or Owner role on the subscription/resource group - -### 2. Azure CLI (Recommended) -```powershell -# Install Azure CLI -winget install Microsoft.AzureCLI - -# Login -az login - -# Set subscription -az account set --subscription "" -``` - -### 3. Python Dependencies -```powershell -cd AI\microsoft_phi-silica-3.6_v1 -pip install azure-ai-ml azure-identity azure-core -``` - -## Quick Start - -### Step 1: Create Azure ML Workspace - -**Option A: Azure Portal** (Easiest) -1. Go to [Azure Portal](https://portal.azure.com) -2. Search for "Machine Learning" -3. Click "+ Create" -4. Fill in: - - Subscription: Your subscription - - Resource group: Create new "rg-phi36-ml" - - Workspace name: "phi36-ml-workspace" - - Region: "East US" or "West US 2" (GPU availability) -5. Click "Review + Create" - -**Option B: Azure CLI** -```powershell -# Create resource group -az group create --name rg-phi36-ml --location eastus - -# Create ML workspace -az ml workspace create ` - --name phi36-ml-workspace ` - --resource-group rg-phi36-ml ` - --location eastus -``` - -### Step 2: Setup Infrastructure - -```powershell -cd AI\microsoft_phi-silica-3.6_v1 - -# Setup compute cluster and environment -python azure_ml_training.py ` - --action setup ` - --subscription-id "" ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace ` - --vm-size Standard_NC6s_v3 -``` - -This creates: -- GPU compute cluster (auto-scales 0-4 nodes) -- Python environment with all dependencies -- Cost: ~$0/hour when idle (auto-scales to 0) - -### Step 3: Upload Dataset - -```powershell -# Upload Dolly dataset to Azure ML -python azure_ml_training.py ` - --action upload ` - --subscription-id "" ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace ` - --dataset-path "..\..\datasets\chat\dolly" -``` - -### Step 4: Submit Training Job - -**Quick Test (64 samples, ~5 minutes)** -```powershell -python azure_ml_training.py ` - --action train ` - --subscription-id "" ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace ` - --max-train-samples 64 -``` - -**Full Training (8000 samples, ~2-4 hours)** -```powershell -python azure_ml_training.py ` - --action train ` - --subscription-id "" ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace -``` - -### Step 5: Monitor Training - -After submitting, you'll get a URL like: -``` -Monitor at: https://ml.azure.com/runs/?wsid=/subscriptions/... -``` - -Open this in your browser to see: -- Real-time metrics (loss, perplexity) -- GPU utilization -- Training logs -- Resource usage - -### Step 6: Register Model (After Training) - -```powershell -# Use the job name from training output -python azure_ml_training.py ` - --action register ` - --subscription-id "" ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace ` - --job-name "" -``` - -## Cost Optimization - -### Compute Costs - -| VM Size | GPU | Cost/hour | Best For | -|---------|-----|-----------|----------| -| Standard_NC6s_v3 | 1x V100 (16GB) | ~$3.06 | Default (good balance) | -| Standard_NC12s_v3 | 2x V100 (32GB) | ~$6.12 | Faster training | -| Standard_NC24s_v3 | 4x V100 (64GB) | ~$12.24 | Large batches | - -**💰 Cost-saving tips:** -1. **Auto-scale to 0**: Cluster scales down when idle (included in setup) -2. **Low-priority VMs**: Add `--tier low_priority` (70-80% discount, may be preempted) -3. **Stop after training**: Compute auto-stops after job completes -4. **Test locally first**: Use `--max-train-samples 64` for validation - -### Example Costs - -| Scenario | Duration | Cost | -|----------|----------|------| -| Quick test (64 samples) | ~5 min | ~$0.25 | -| Medium run (1000 samples) | ~30 min | ~$1.50 | -| Full training (8000 samples) | ~3 hours | ~$9.00 | - -## Advanced Configuration - -### Custom VM Sizes - -```powershell -# Larger GPU for faster training -python azure_ml_training.py --action setup --vm-size Standard_NC12s_v3 - -# Low-priority for cost savings (add to training script) -# Edit azure_ml_training.py line 95: tier="low_priority" -``` - -### Custom Datasets - -```powershell -# Upload your own dataset -python azure_ml_training.py ` - --action upload ` - --subscription-id "" ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace ` - --dataset-path ".\my_custom_dataset" ` - --dataset-name my-dataset - -# Train with custom dataset -python azure_ml_training.py ` - --action train ` - --dataset-name my-dataset ` - ... -``` - -### Custom Config - -```powershell -# Use different LoRA config -python azure_ml_training.py ` - --action train ` - --config ".\lora\my_custom_lora.yaml" ` - ... -``` - -## Troubleshooting - -### Authentication Errors -```powershell -# Re-authenticate -az login -az account show # Verify correct subscription -``` - -### Quota Errors -If you see "insufficient quota for NC-series VMs": -1. Go to Azure Portal -2. Search "Quotas" -3. Filter by "Compute" and your region -4. Request quota increase for "NC-series" VMs - -### Dataset Upload Issues -```powershell -# Verify dataset structure -ls ..\..\datasets\chat\dolly\*.json - -# Should see train.json and test.json -``` - -### Training Failures -Check the Azure ML Studio logs: -1. Open the training job URL -2. Go to "Outputs + logs" tab -3. Check `user_logs/std_log.txt` - -## Environment Variables - -For easier usage, set these in PowerShell profile: - -```powershell -# Open profile -notepad $PROFILE - -# Add these lines: -$env:AZURE_SUBSCRIPTION_ID = "" -$env:AZURE_RESOURCE_GROUP = "rg-phi36-ml" -$env:AZURE_ML_WORKSPACE = "phi36-ml-workspace" - -# Then use simplified commands: -python azure_ml_training.py --action train -``` - -## Downloading Results - -After training completes: - -```powershell -# Download from Azure ML Studio UI -# 1. Open job URL -# 2. Go to "Outputs + logs" -# 3. Download "outputs/lora_adapter/" folder - -# Or use Azure ML CLI -az ml job download ` - --name ` - --workspace-name phi36-ml-workspace ` - --resource-group rg-phi36-ml ` - --output-path .\downloaded_models -``` - -## Cleanup - -When done training: - -```powershell -# Delete compute cluster (stops billing) -az ml compute delete ` - --name phi36-gpu-cluster ` - --workspace-name phi36-ml-workspace ` - --resource-group rg-phi36-ml ` - --yes - -# Delete entire resource group (removes everything) -az group delete --name rg-phi36-ml --yes -``` - -## Next Steps - -After training: -1. **Test locally**: Download model and test with `scripts/test_lora_model.py` -2. **Deploy to Azure**: Use Azure ML endpoints for production inference -3. **Fine-tune further**: Adjust `lora.yaml` and retrain - -## Support - -- Azure ML Documentation: https://learn.microsoft.com/azure/machine-learning/ -- Phi-3 Documentation: https://huggingface.co/microsoft/Phi-3.5-mini-instruct -- Issues: Check logs in Azure ML Studio +# Azure ML Training for Phi-3.6 LoRA Fine-tuning + +This guide shows how to train Phi-3.6 LoRA models on Azure Machine Learning with GPU acceleration. + +## Prerequisites + +### 1. Azure Subscription +- Active Azure subscription +- Contributor or Owner role on the subscription/resource group + +### 2. Azure CLI (Recommended) +```powershell +# Install Azure CLI +winget install Microsoft.AzureCLI + +# Login +az login + +# Set subscription +az account set --subscription "" +``` + +### 3. Python Dependencies +```powershell +cd AI\microsoft_phi-silica-3.6_v1 +pip install azure-ai-ml azure-identity azure-core +``` + +## Quick Start + +### Step 1: Create Azure ML Workspace + +**Option A: Azure Portal** (Easiest) +1. Go to [Azure Portal](https://portal.azure.com) +2. Search for "Machine Learning" +3. Click "+ Create" +4. Fill in: + - Subscription: Your subscription + - Resource group: Create new "rg-phi36-ml" + - Workspace name: "phi36-ml-workspace" + - Region: "East US" or "West US 2" (GPU availability) +5. Click "Review + Create" + +**Option B: Azure CLI** +```powershell +# Create resource group +az group create --name rg-phi36-ml --location eastus + +# Create ML workspace +az ml workspace create ` + --name phi36-ml-workspace ` + --resource-group rg-phi36-ml ` + --location eastus +``` + +### Step 2: Setup Infrastructure + +```powershell +cd AI\microsoft_phi-silica-3.6_v1 + +# Setup compute cluster and environment +python azure_ml_training.py ` + --action setup ` + --subscription-id "" ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace ` + --vm-size Standard_NC6s_v3 +``` + +This creates: +- GPU compute cluster (auto-scales 0-4 nodes) +- Python environment with all dependencies +- Cost: ~$0/hour when idle (auto-scales to 0) + +### Step 3: Upload Dataset + +```powershell +# Upload Dolly dataset to Azure ML +python azure_ml_training.py ` + --action upload ` + --subscription-id "" ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace ` + --dataset-path "..\..\datasets\chat\dolly" +``` + +### Step 4: Submit Training Job + +**Quick Test (64 samples, ~5 minutes)** +```powershell +python azure_ml_training.py ` + --action train ` + --subscription-id "" ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace ` + --max-train-samples 64 +``` + +**Full Training (8000 samples, ~2-4 hours)** +```powershell +python azure_ml_training.py ` + --action train ` + --subscription-id "" ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace +``` + +### Step 5: Monitor Training + +After submitting, you'll get a URL like: +``` +Monitor at: https://ml.azure.com/runs/?wsid=/subscriptions/... +``` + +Open this in your browser to see: +- Real-time metrics (loss, perplexity) +- GPU utilization +- Training logs +- Resource usage + +### Step 6: Register Model (After Training) + +```powershell +# Use the job name from training output +python azure_ml_training.py ` + --action register ` + --subscription-id "" ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace ` + --job-name "" +``` + +## Cost Optimization + +### Compute Costs + +| VM Size | GPU | Cost/hour | Best For | +|---------|-----|-----------|----------| +| Standard_NC6s_v3 | 1x V100 (16GB) | ~$3.06 | Default (good balance) | +| Standard_NC12s_v3 | 2x V100 (32GB) | ~$6.12 | Faster training | +| Standard_NC24s_v3 | 4x V100 (64GB) | ~$12.24 | Large batches | + +**💰 Cost-saving tips:** +1. **Auto-scale to 0**: Cluster scales down when idle (included in setup) +2. **Low-priority VMs**: Add `--tier low_priority` (70-80% discount, may be preempted) +3. **Stop after training**: Compute auto-stops after job completes +4. **Test locally first**: Use `--max-train-samples 64` for validation + +### Example Costs + +| Scenario | Duration | Cost | +|----------|----------|------| +| Quick test (64 samples) | ~5 min | ~$0.25 | +| Medium run (1000 samples) | ~30 min | ~$1.50 | +| Full training (8000 samples) | ~3 hours | ~$9.00 | + +## Advanced Configuration + +### Custom VM Sizes + +```powershell +# Larger GPU for faster training +python azure_ml_training.py --action setup --vm-size Standard_NC12s_v3 + +# Low-priority for cost savings (add to training script) +# Edit azure_ml_training.py line 95: tier="low_priority" +``` + +### Custom Datasets + +```powershell +# Upload your own dataset +python azure_ml_training.py ` + --action upload ` + --subscription-id "" ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace ` + --dataset-path ".\my_custom_dataset" ` + --dataset-name my-dataset + +# Train with custom dataset +python azure_ml_training.py ` + --action train ` + --dataset-name my-dataset ` + ... +``` + +### Custom Config + +```powershell +# Use different LoRA config +python azure_ml_training.py ` + --action train ` + --config ".\lora\my_custom_lora.yaml" ` + ... +``` + +## Troubleshooting + +### Authentication Errors +```powershell +# Re-authenticate +az login +az account show # Verify correct subscription +``` + +### Quota Errors +If you see "insufficient quota for NC-series VMs": +1. Go to Azure Portal +2. Search "Quotas" +3. Filter by "Compute" and your region +4. Request quota increase for "NC-series" VMs + +### Dataset Upload Issues +```powershell +# Verify dataset structure +ls ..\..\datasets\chat\dolly\*.json + +# Should see train.json and test.json +``` + +### Training Failures +Check the Azure ML Studio logs: +1. Open the training job URL +2. Go to "Outputs + logs" tab +3. Check `user_logs/std_log.txt` + +## Environment Variables + +For easier usage, set these in PowerShell profile: + +```powershell +# Open profile +notepad $PROFILE + +# Add these lines: +$env:AZURE_SUBSCRIPTION_ID = "" +$env:AZURE_RESOURCE_GROUP = "rg-phi36-ml" +$env:AZURE_ML_WORKSPACE = "phi36-ml-workspace" + +# Then use simplified commands: +python azure_ml_training.py --action train +``` + +## Downloading Results + +After training completes: + +```powershell +# Download from Azure ML Studio UI +# 1. Open job URL +# 2. Go to "Outputs + logs" +# 3. Download "outputs/lora_adapter/" folder + +# Or use Azure ML CLI +az ml job download ` + --name ` + --workspace-name phi36-ml-workspace ` + --resource-group rg-phi36-ml ` + --output-path .\downloaded_models +``` + +## Cleanup + +When done training: + +```powershell +# Delete compute cluster (stops billing) +az ml compute delete ` + --name phi36-gpu-cluster ` + --workspace-name phi36-ml-workspace ` + --resource-group rg-phi36-ml ` + --yes + +# Delete entire resource group (removes everything) +az group delete --name rg-phi36-ml --yes +``` + +## Next Steps + +After training: +1. **Test locally**: Download model and test with `scripts/test_lora_model.py` +2. **Deploy to Azure**: Use Azure ML endpoints for production inference +3. **Fine-tune further**: Adjust `lora.yaml` and retrain + +## Support + +- Azure ML Documentation: https://learn.microsoft.com/azure/machine-learning/ +- Phi-3 Documentation: https://huggingface.co/microsoft/Phi-3.5-mini-instruct +- Issues: Check logs in Azure ML Studio diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/CONTINUOUS_IMPROVEMENTS_PHASE2.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/CONTINUOUS_IMPROVEMENTS_PHASE2.md index b44070f23..345722407 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/CONTINUOUS_IMPROVEMENTS_PHASE2.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/CONTINUOUS_IMPROVEMENTS_PHASE2.md @@ -1,436 +1,436 @@ -# Continuous Improvement Updates - Phase 2 - -## 🚀 New Features Added - -### 1. Dataset Analyzer (`dataset_analyzer.py`) -**Comprehensive dataset analysis and health checking** - -Features: -- Statistical analysis (token counts, distribution, percentiles) -- Content analysis (duplicates, vocabulary size, diversity) -- Quality scoring with multiple heuristics -- Visual reports with matplotlib -- Dataset comparison tool -- Health check recommendations - -Usage: -```bash -# Analyze single dataset -python scripts\dataset_analyzer.py --dataset train.jsonl - -# Compare multiple datasets -python scripts\dataset_analyzer.py --compare train.jsonl test.jsonl val.jsonl - -# Skip visualizations -python scripts\dataset_analyzer.py --dataset train.jsonl --no-visualizations -``` - -Output: -- Statistics JSON -- Visualization plots (histogram, boxplot, quality distribution, scatter) -- Comparison charts - -### 2. Training Monitor (`training_monitor.py`) -**Real-time training progress monitoring with live metrics** - -Features: -- Real-time metrics logging (loss, LR, throughput, memory) -- Session management and history -- Live dashboard (terminal-based) -- JSONL logging for analysis -- Training statistics and best model tracking -- Duration tracking - -Usage: -```python -from scripts.training_monitor import TrainingMonitor, LiveDashboard - -# Create monitor -monitor = TrainingMonitor(session_id="experiment_1") -monitor.update_config({"model": "phi-3", "batch_size": 4}) - -# Log metrics during training -for step in range(num_steps): - # ... training code ... - monitor.log_metrics( - step=step, - epoch=epoch, - loss=loss.item(), - learning_rate=lr, - grad_norm=grad_norm, - throughput=tokens_per_sec, - gpu_memory_mb=memory_mb - ) - -# Complete training -monitor.complete(status="completed") - -# View past sessions -python scripts\training_monitor.py --list -python scripts\training_monitor.py --session experiment_1 --stats -``` - -Output: -- `data_out/training_logs/{session_id}.jsonl` - Detailed metrics -- `data_out/training_logs/{session_id}_summary.json` - Session summary - -### 3. Learning Rate Finder (`lr_finder.py`) -**Automatic optimal learning rate discovery using LR range test** - -Features: -- Leslie Smith's LR range test implementation -- Automatic LR suggestion -- Gradient analysis -- Visualization of loss vs LR -- Multiple suggestion strategies -- Model state preservation - -Usage: -```python -from scripts.lr_finder import LearningRateFinder - -# Create LR finder -lr_finder = LearningRateFinder(model, optimizer, criterion) - -# Run range test -results = lr_finder.range_test( - train_loader, - start_lr=1e-7, - end_lr=10.0, - num_iter=100 -) - -# Use suggested LR -suggested_lr = results['suggested_lr'] -print(f"Suggested LR: {suggested_lr:.2e}") -``` - -Output: -- `data_out/lr_finder/lr_finder_plot.png` - Loss vs LR curve -- `data_out/lr_finder/lr_finder_results.json` - Results with suggestions - -### 4. Data Augmentation (`data_augmenter.py`) -**Text data augmentation for improved model generalization** - -Features: -- Synonym replacement -- Random insertion -- Random word swapping -- Random deletion -- Configurable augmentation probability -- Multiple augmentations per sample -- Format-aware (preserves JSON structure) - -Usage: -```bash -# Basic augmentation -python scripts\data_augmenter.py --input train.jsonl --output augmented.jsonl - -# Custom techniques and parameters -python scripts\data_augmenter.py \ - --input train.jsonl \ - --output augmented.jsonl \ - --techniques synonym swap deletion \ - --num-aug 2 \ - --prob 0.15 - -# Result: 3x dataset size (original + 2 augmentations) -``` - -Output: -- Augmented dataset with original + generated samples -- Statistics on augmentation factor - -### 5. Model Server (`model_server.py`) -**Production REST API for model serving** - -Features: -- FastAPI-based REST API -- Single and batch inference -- Streaming support -- Health checks -- Request queuing -- Automatic batching -- OpenAPI documentation - -Usage: -```bash -# Start server -python scripts\model_server.py --model data_out\lora_training --port 8000 - -# API endpoints available: -# - POST /generate - Single generation -# - POST /batch - Batch generation -# - GET /health - Health check -# - GET /models - List models -# - GET /docs - API documentation -``` - -API Examples: -```python -import requests - -# Single generation -response = requests.post("http://localhost:8000/generate", json={ - "prompt": "Explain quantum computing", - "max_tokens": 100, - "temperature": 0.7 -}) -print(response.json()["text"]) - -# Batch generation -response = requests.post("http://localhost:8000/batch", json={ - "prompts": ["Question 1", "Question 2", "Question 3"], - "max_tokens": 50 -}) -print(response.json()["results"]) -``` - -### 6. Model Exporter (`model_exporter.py`) -**Export models to various formats for deployment** - -Features: -- ONNX export with optimization -- TorchScript export (script/trace) -- Dynamic quantization -- Hugging Face Hub upload -- Format benchmarking -- Validation and size comparison - -Usage: -```bash -# Export to all formats -python scripts\model_exporter.py --model data_out\lora_training --format all - -# Export to ONNX only -python scripts\model_exporter.py --model data_out\lora_training --format onnx - -# Quantize model -python scripts\model_exporter.py --model data_out\lora_training --format quantize - -# Benchmark formats -python scripts\model_exporter.py --model data_out\lora_training --benchmark -``` - -Output: -- `data_out/exported_models/model.onnx` - ONNX format -- `data_out/exported_models/model_scripted.pt` - TorchScript -- `data_out/exported_models/model_quantized/` - Quantized model -- Benchmark results and size comparisons - -## 📦 Updated Dependencies - -Add to `requirements-advanced.txt`: -``` -# For API serving -fastapi>=0.104.0 -uvicorn>=0.24.0 -pydantic>=2.4.0 - -# For model export -onnx>=1.15.0 -onnxruntime>=1.16.0 - -# For data augmentation -scipy>=1.11.0 - -# Already included -matplotlib>=3.7.0 -seaborn>=0.12.0 -``` - -## 🎯 Benefits - -### Dataset Quality -- **Dataset Analyzer** ensures data quality before training -- Identifies duplicates, outliers, and low-quality samples -- Visual insights into data distribution -- Compare train/test/validation splits - -### Training Efficiency -- **Training Monitor** tracks progress in real-time -- **LR Finder** eliminates LR hyperparameter guessing -- Save 50-80% of training time with optimal LR -- Prevent divergence and oscillation - -### Data Diversity -- **Data Augmenter** increases dataset size 2-5x -- Improves model generalization -- No additional data collection needed -- Better performance on unseen data - -### Production Deployment -- **Model Server** provides production-ready API -- **Model Exporter** optimizes for inference -- 2-4x inference speedup with quantization -- Easy integration with applications - -## 🔄 Integration with Existing Pipeline - -Update `run_pipeline.py` to include new features: - -```python -# Add dataset analysis step -python scripts\dataset_analyzer.py --dataset {input_dataset} - -# Add data augmentation before training -python scripts\data_augmenter.py --input {input_dataset} --output {augmented_dataset} - -# Find optimal LR -python scripts\lr_finder.py --model {model} --dataset {dataset} - -# Monitor training (integrate in train_lora.py) -monitor = TrainingMonitor() -# ... training loop ... - -# Export for deployment -python scripts\model_exporter.py --model {trained_model} --format all - -# Start serving -python scripts\model_server.py --model {trained_model} -``` - -## 📊 Performance Improvements - -| Feature | Benefit | Impact | -|---------|---------|--------| -| Dataset Analyzer | Identify issues early | Save hours of debugging | -| LR Finder | Optimal learning rate | 50-80% faster convergence | -| Data Augmenter | 2-5x more training data | +10-20% accuracy | -| Training Monitor | Real-time insights | Catch issues immediately | -| Model Exporter | Optimized inference | 2-4x faster inference | -| Model Server | Production API | Easy deployment | - -## 🎓 Usage Patterns - -### Research Workflow -```bash -# 1. Analyze dataset -python scripts\dataset_analyzer.py --dataset data.jsonl - -# 2. Augment data -python scripts\data_augmenter.py --input data.jsonl --output aug_data.jsonl - -# 3. Find optimal LR -python scripts\lr_finder.py --model base_model --dataset aug_data.jsonl - -# 4. Train with monitoring -# (integrate monitor in training script) - -# 5. Analyze results -python scripts\training_monitor.py --session experiment_1 -``` - -### Production Workflow -```bash -# 1. Full pipeline with new features -python scripts\run_pipeline.py \ - --input-dataset data.jsonl \ - --analyze-data \ - --augment-data \ - --find-lr \ - --monitor-training - -# 2. Export for deployment -python scripts\model_exporter.py --model output --format all --benchmark - -# 3. Serve in production -python scripts\model_server.py --model output --port 8000 -``` - -## 🚀 Future Enhancements - -Planned for next phase: - -1. **Hyperparameter Optimization** - - Optuna integration - - Auto-tune all hyperparameters - - Multi-objective optimization - -2. **Experiment Tracking** - - MLflow integration - - Wandb support - - Experiment comparison UI - -3. **Advanced Monitoring** - - TensorBoard integration - - GPU utilization tracking - - Cost monitoring - -4. **Testing Framework** - - Unit tests for all components - - Integration tests - - Regression test suite - -5. **Distributed Training** - - Multi-node support - - Efficient data parallelism - - Model parallelism - -6. **Advanced RAG** - - Multi-modal support - - Hybrid search - - Re-ranking models - -## 📝 Migration Guide - -For existing projects: - -1. **Install new dependencies**: - ```bash - pip install -r requirements-advanced.txt - ``` - -2. **Update training scripts** to use monitor: - ```python - from scripts.training_monitor import TrainingMonitor - monitor = TrainingMonitor() - # Add logging calls in training loop - ``` - -3. **Run LR finder** before training: - ```bash - python scripts\lr_finder.py --model model --dataset data - # Use suggested LR in config - ``` - -4. **Analyze datasets** before training: - ```bash - python scripts\dataset_analyzer.py --dataset train.jsonl - # Review statistics and fix issues - ``` - -5. **Deploy with model server**: - ```bash - python scripts\model_server.py --model trained_model - ``` - -## ✅ Quality Assurance - -All new features include: -- Comprehensive error handling -- Input validation -- Progress reporting -- Result persistence -- Documentation and examples -- CLI and programmatic APIs - -## 🎉 Summary - -**6 new powerful tools added:** -1. Dataset Analyzer - Data quality insights -2. Training Monitor - Real-time tracking -3. LR Finder - Optimal learning rates -4. Data Augmenter - More training data -5. Model Server - Production API -6. Model Exporter - Multi-format export - -**Total files added: 6** -**Total lines of code: ~10,000** -**Documentation: Complete** - -All tools integrate seamlessly with existing pipeline and work independently. Free tier compatible with no cloud dependencies required. - ---- - -**Ready to use!** All tools are production-ready and thoroughly documented. +# Continuous Improvement Updates - Phase 2 + +## 🚀 New Features Added + +### 1. Dataset Analyzer (`dataset_analyzer.py`) +**Comprehensive dataset analysis and health checking** + +Features: +- Statistical analysis (token counts, distribution, percentiles) +- Content analysis (duplicates, vocabulary size, diversity) +- Quality scoring with multiple heuristics +- Visual reports with matplotlib +- Dataset comparison tool +- Health check recommendations + +Usage: +```bash +# Analyze single dataset +python scripts\dataset_analyzer.py --dataset train.jsonl + +# Compare multiple datasets +python scripts\dataset_analyzer.py --compare train.jsonl test.jsonl val.jsonl + +# Skip visualizations +python scripts\dataset_analyzer.py --dataset train.jsonl --no-visualizations +``` + +Output: +- Statistics JSON +- Visualization plots (histogram, boxplot, quality distribution, scatter) +- Comparison charts + +### 2. Training Monitor (`training_monitor.py`) +**Real-time training progress monitoring with live metrics** + +Features: +- Real-time metrics logging (loss, LR, throughput, memory) +- Session management and history +- Live dashboard (terminal-based) +- JSONL logging for analysis +- Training statistics and best model tracking +- Duration tracking + +Usage: +```python +from scripts.training_monitor import TrainingMonitor, LiveDashboard + +# Create monitor +monitor = TrainingMonitor(session_id="experiment_1") +monitor.update_config({"model": "phi-3", "batch_size": 4}) + +# Log metrics during training +for step in range(num_steps): + # ... training code ... + monitor.log_metrics( + step=step, + epoch=epoch, + loss=loss.item(), + learning_rate=lr, + grad_norm=grad_norm, + throughput=tokens_per_sec, + gpu_memory_mb=memory_mb + ) + +# Complete training +monitor.complete(status="completed") + +# View past sessions +python scripts\training_monitor.py --list +python scripts\training_monitor.py --session experiment_1 --stats +``` + +Output: +- `data_out/training_logs/{session_id}.jsonl` - Detailed metrics +- `data_out/training_logs/{session_id}_summary.json` - Session summary + +### 3. Learning Rate Finder (`lr_finder.py`) +**Automatic optimal learning rate discovery using LR range test** + +Features: +- Leslie Smith's LR range test implementation +- Automatic LR suggestion +- Gradient analysis +- Visualization of loss vs LR +- Multiple suggestion strategies +- Model state preservation + +Usage: +```python +from scripts.lr_finder import LearningRateFinder + +# Create LR finder +lr_finder = LearningRateFinder(model, optimizer, criterion) + +# Run range test +results = lr_finder.range_test( + train_loader, + start_lr=1e-7, + end_lr=10.0, + num_iter=100 +) + +# Use suggested LR +suggested_lr = results['suggested_lr'] +print(f"Suggested LR: {suggested_lr:.2e}") +``` + +Output: +- `data_out/lr_finder/lr_finder_plot.png` - Loss vs LR curve +- `data_out/lr_finder/lr_finder_results.json` - Results with suggestions + +### 4. Data Augmentation (`data_augmenter.py`) +**Text data augmentation for improved model generalization** + +Features: +- Synonym replacement +- Random insertion +- Random word swapping +- Random deletion +- Configurable augmentation probability +- Multiple augmentations per sample +- Format-aware (preserves JSON structure) + +Usage: +```bash +# Basic augmentation +python scripts\data_augmenter.py --input train.jsonl --output augmented.jsonl + +# Custom techniques and parameters +python scripts\data_augmenter.py \ + --input train.jsonl \ + --output augmented.jsonl \ + --techniques synonym swap deletion \ + --num-aug 2 \ + --prob 0.15 + +# Result: 3x dataset size (original + 2 augmentations) +``` + +Output: +- Augmented dataset with original + generated samples +- Statistics on augmentation factor + +### 5. Model Server (`model_server.py`) +**Production REST API for model serving** + +Features: +- FastAPI-based REST API +- Single and batch inference +- Streaming support +- Health checks +- Request queuing +- Automatic batching +- OpenAPI documentation + +Usage: +```bash +# Start server +python scripts\model_server.py --model data_out\lora_training --port 8000 + +# API endpoints available: +# - POST /generate - Single generation +# - POST /batch - Batch generation +# - GET /health - Health check +# - GET /models - List models +# - GET /docs - API documentation +``` + +API Examples: +```python +import requests + +# Single generation +response = requests.post("http://localhost:8000/generate", json={ + "prompt": "Explain quantum computing", + "max_tokens": 100, + "temperature": 0.7 +}) +print(response.json()["text"]) + +# Batch generation +response = requests.post("http://localhost:8000/batch", json={ + "prompts": ["Question 1", "Question 2", "Question 3"], + "max_tokens": 50 +}) +print(response.json()["results"]) +``` + +### 6. Model Exporter (`model_exporter.py`) +**Export models to various formats for deployment** + +Features: +- ONNX export with optimization +- TorchScript export (script/trace) +- Dynamic quantization +- Hugging Face Hub upload +- Format benchmarking +- Validation and size comparison + +Usage: +```bash +# Export to all formats +python scripts\model_exporter.py --model data_out\lora_training --format all + +# Export to ONNX only +python scripts\model_exporter.py --model data_out\lora_training --format onnx + +# Quantize model +python scripts\model_exporter.py --model data_out\lora_training --format quantize + +# Benchmark formats +python scripts\model_exporter.py --model data_out\lora_training --benchmark +``` + +Output: +- `data_out/exported_models/model.onnx` - ONNX format +- `data_out/exported_models/model_scripted.pt` - TorchScript +- `data_out/exported_models/model_quantized/` - Quantized model +- Benchmark results and size comparisons + +## 📦 Updated Dependencies + +Add to `requirements-advanced.txt`: +``` +# For API serving +fastapi>=0.104.0 +uvicorn>=0.24.0 +pydantic>=2.4.0 + +# For model export +onnx>=1.15.0 +onnxruntime>=1.16.0 + +# For data augmentation +scipy>=1.11.0 + +# Already included +matplotlib>=3.7.0 +seaborn>=0.12.0 +``` + +## 🎯 Benefits + +### Dataset Quality +- **Dataset Analyzer** ensures data quality before training +- Identifies duplicates, outliers, and low-quality samples +- Visual insights into data distribution +- Compare train/test/validation splits + +### Training Efficiency +- **Training Monitor** tracks progress in real-time +- **LR Finder** eliminates LR hyperparameter guessing +- Save 50-80% of training time with optimal LR +- Prevent divergence and oscillation + +### Data Diversity +- **Data Augmenter** increases dataset size 2-5x +- Improves model generalization +- No additional data collection needed +- Better performance on unseen data + +### Production Deployment +- **Model Server** provides production-ready API +- **Model Exporter** optimizes for inference +- 2-4x inference speedup with quantization +- Easy integration with applications + +## 🔄 Integration with Existing Pipeline + +Update `run_pipeline.py` to include new features: + +```python +# Add dataset analysis step +python scripts\dataset_analyzer.py --dataset {input_dataset} + +# Add data augmentation before training +python scripts\data_augmenter.py --input {input_dataset} --output {augmented_dataset} + +# Find optimal LR +python scripts\lr_finder.py --model {model} --dataset {dataset} + +# Monitor training (integrate in train_lora.py) +monitor = TrainingMonitor() +# ... training loop ... + +# Export for deployment +python scripts\model_exporter.py --model {trained_model} --format all + +# Start serving +python scripts\model_server.py --model {trained_model} +``` + +## 📊 Performance Improvements + +| Feature | Benefit | Impact | +|---------|---------|--------| +| Dataset Analyzer | Identify issues early | Save hours of debugging | +| LR Finder | Optimal learning rate | 50-80% faster convergence | +| Data Augmenter | 2-5x more training data | +10-20% accuracy | +| Training Monitor | Real-time insights | Catch issues immediately | +| Model Exporter | Optimized inference | 2-4x faster inference | +| Model Server | Production API | Easy deployment | + +## 🎓 Usage Patterns + +### Research Workflow +```bash +# 1. Analyze dataset +python scripts\dataset_analyzer.py --dataset data.jsonl + +# 2. Augment data +python scripts\data_augmenter.py --input data.jsonl --output aug_data.jsonl + +# 3. Find optimal LR +python scripts\lr_finder.py --model base_model --dataset aug_data.jsonl + +# 4. Train with monitoring +# (integrate monitor in training script) + +# 5. Analyze results +python scripts\training_monitor.py --session experiment_1 +``` + +### Production Workflow +```bash +# 1. Full pipeline with new features +python scripts\run_pipeline.py \ + --input-dataset data.jsonl \ + --analyze-data \ + --augment-data \ + --find-lr \ + --monitor-training + +# 2. Export for deployment +python scripts\model_exporter.py --model output --format all --benchmark + +# 3. Serve in production +python scripts\model_server.py --model output --port 8000 +``` + +## 🚀 Future Enhancements + +Planned for next phase: + +1. **Hyperparameter Optimization** + - Optuna integration + - Auto-tune all hyperparameters + - Multi-objective optimization + +2. **Experiment Tracking** + - MLflow integration + - Wandb support + - Experiment comparison UI + +3. **Advanced Monitoring** + - TensorBoard integration + - GPU utilization tracking + - Cost monitoring + +4. **Testing Framework** + - Unit tests for all components + - Integration tests + - Regression test suite + +5. **Distributed Training** + - Multi-node support + - Efficient data parallelism + - Model parallelism + +6. **Advanced RAG** + - Multi-modal support + - Hybrid search + - Re-ranking models + +## 📝 Migration Guide + +For existing projects: + +1. **Install new dependencies**: + ```bash + pip install -r requirements-advanced.txt + ``` + +2. **Update training scripts** to use monitor: + ```python + from scripts.training_monitor import TrainingMonitor + monitor = TrainingMonitor() + # Add logging calls in training loop + ``` + +3. **Run LR finder** before training: + ```bash + python scripts\lr_finder.py --model model --dataset data + # Use suggested LR in config + ``` + +4. **Analyze datasets** before training: + ```bash + python scripts\dataset_analyzer.py --dataset train.jsonl + # Review statistics and fix issues + ``` + +5. **Deploy with model server**: + ```bash + python scripts\model_server.py --model trained_model + ``` + +## ✅ Quality Assurance + +All new features include: +- Comprehensive error handling +- Input validation +- Progress reporting +- Result persistence +- Documentation and examples +- CLI and programmatic APIs + +## 🎉 Summary + +**6 new powerful tools added:** +1. Dataset Analyzer - Data quality insights +2. Training Monitor - Real-time tracking +3. LR Finder - Optimal learning rates +4. Data Augmenter - More training data +5. Model Server - Production API +6. Model Exporter - Multi-format export + +**Total files added: 6** +**Total lines of code: ~10,000** +**Documentation: Complete** + +All tools integrate seamlessly with existing pipeline and work independently. Free tier compatible with no cloud dependencies required. + +--- + +**Ready to use!** All tools are production-ready and thoroughly documented. diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/README.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/README.md index 80743ef8d..063f76551 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/README.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/README.md @@ -1,239 +1,239 @@ -# Phi-3.6 LoRA Fine-Tuning (Scalable) - -This directory contains a production-ready path to train the Phi-3.x family with LoRA on very large datasets. It supports: - -- Huge datasets using JSONL with streaming ingestion -- Simple dataset prep from JSONL or CSV -- Local training via Hugging Face + PEFT -- Azure deployment via the included infra (Container Apps + File Share) when ready - -The configs under `lora/` and `soft_prompt/` are Azure AI Toolkit–compatible. The scripts here let you validate and train locally first. - -## Dataset format - -Use JSONL where each line is a JSON object with a `messages` array: - -```json -{"messages": [{"role": "user", "content": "Hi"}, {"role": "assistant", "content": "Hello!"}]} -``` - -This repo includes tiny samples in `data/train.json` and `data/test.json` (JSONL with `.json` extension). - -If you have CSV (columns `prompt,response` or `input,output`), the prep script converts it to the same chat schema. - -## Setup (Windows PowerShell) - -Create a virtual environment and install dependencies. The script gracefully handles missing imports for dry-run mode, but full training requires all packages from `requirements.txt`. - -```powershell -# From the repo root -cd AI\microsoft_phi-silica-3.6_v1 -python -m venv venv -.\venv\Scripts\Activate.ps1 -pip install -r requirements.txt -``` - -**Required packages for training:** - -- `datasets>=2.19.0` - Dataset loading and streaming -- `transformers>=4.43.0` - Model and training infrastructure -- `peft>=0.12.0` - LoRA and parameter-efficient fine-tuning -- `accelerate>=0.33.0` - Multi-GPU and distributed training -- `torch` - Install version matching your CUDA (see requirements.txt) -- `pyyaml>=6.0` - Configuration parsing - -**Note:** The script allows dry-run validation without these packages. Install them only when ready to train. - -## Prepare a large dataset - -```powershell -# Convert CSV/JSONL to chat JSONL and split train/test -python .\scripts\prepare_dataset.py --input --output-dir .\data --train-ratio 0.99 -``` - -The output will be `data/train.json` and `data/test.json` (JSONL). - -## Validate (no downloads) - -```powershell -python .\scripts\train_lora.py --dry-run --dataset .\data --config .\lora\lora.yaml -``` - -This checks dataset shape and counts without loading a model. - -## Train locally (small smoke test) - -```powershell -# Use a smaller, available HF model if needed -$env:HF_MODEL_ID = "microsoft/Phi-3.5-mini-instruct" -python .\scripts\train_lora.py --dataset .\data --config .\lora\lora.yaml --max-train-samples 64 --max-eval-samples 32 --no-stream -``` - -Outputs are saved under the `save_dir` specified in `lora\lora.yaml` (by default, it will create the folder). - -## Multi-GPU training - -- Accelerate launcher (recommended for simple multi-GPU): - -```powershell -accelerate launch --multi_gpu .\scripts\train_lora.py --dataset .\data --config .\lora\lora.yaml --max-train-samples 512 --max-eval-samples 128 -``` - -- DeepSpeed (ZeRO-3). First, create or use the provided config: - -```powershell -# Provided example config -cat .\scripts\deepspeed_zero3.json | Out-Host - -# Run with DeepSpeed -python .\scripts\train_lora.py --dataset .\data --config .\lora\lora.yaml --deepspeed .\scripts\deepspeed_zero3.json -``` - -The trainer computes and prints perplexity before and after training using the evaluation loss. - -## Train at scale on Azure (optional) - -1. Provision resources using the bicep in `lora/infra/provision`. Fill in `finetuning.config.json` and run the documented deployment steps (see the top-level workspace docs). -## Train on Azure Machine Learning (Recommended for GPU Training) - -Azure ML provides managed GPU compute with auto-scaling for cost-effective training. - -### Quick Start - -```powershell -# 1. Install Azure dependencies -pip install -r azure-requirements.txt - -# 2. Setup (one-time) -.\setup_azure_ml.ps1 -SubscriptionId "" - -# 3. Upload dataset -python azure_ml_training.py --action upload ` - --subscription-id "" ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace - -# 4. Start training (quick test) -python azure_ml_training.py --action train ` - --subscription-id "" ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace ` - --max-train-samples 64 -``` - -**Benefits:** -- **GPU acceleration**: 1-4x V100 GPUs (Standard_NC6s_v3 @ ~$3/hour) -- **Auto-scaling**: Scales to 0 when idle (no cost) -- **Monitoring**: Real-time metrics in Azure ML Studio -- **Cost-effective**: Pay only for training time (~$9 for full 8000-sample run) - -See **[AZURE_ML_TRAINING_GUIDE.md](./AZURE_ML_TRAINING_GUIDE.md)** for complete documentation. - -## Train at scale on Azure Container Apps (Alternative) -2. Upload your dataset to the mounted file share path expected by the job (the infra uses `mount//dataset`). -3. Ensure `lora.yaml` points to `finetune_dataset: "mount//dataset"` and `save_dir` is also under `mount//...`. -4. Start the job. The standard runner will read `lora.yaml` and train using LoRA. - -### Read data directly from Azure Blob via manifest (Container Apps Job) - -Use the alternate Bicep `lora/infra/provision/finetuning_blob_manifest.bicep`, which adds an init container that downloads your dataset directly from Blob Storage before training starts. - -Manifest format: plain text file where each line is a SAS URL to a dataset part file (e.g., .jsonl). Example: - -```text -https://mystorage.blob.core.windows.net/mycontainer/part-0001.jsonl? -https://mystorage.blob.core.windows.net/mycontainer/part-0002.jsonl? -``` - -Deploy (PowerShell): - -```powershell -$rg = "rg-quantum-ai" -$params = "AI\microsoft_phi-silica-3.6_v1\lora\infra\provision\finetuning_blob_manifest.parameters.json" -az deployment group create ` - --resource-group $rg ` - --template-file AI\microsoft_phi-silica-3.6_v1\lora\infra\provision\finetuning_blob_manifest.bicep ` - --parameters @$params -``` - -### Notes - -- The init container downloads each URL in `manifestUrl` into `/mount/dataset` in the job. -- Ensure your SAS token stays valid for the job duration. -- The main container still reads from `mount//dataset` per `lora.yaml`. - -## Additional notes - -- For huge datasets, prefer `--no-stream` only for deterministic local experiments. Streaming is better for very large files. -- Use BF16 on GPUs that support it for speed and stability. The script auto-selects BF16 on CUDA, FP16 otherwise. -- Reduce `eval_steps` and `save_steps` for faster checkpoints on massive runs. -- If gradient checkpointing is enabled, we disable `use_cache` on the model to avoid correctness issues. - -## Direct streaming from Blob during training (no pre-download) - -You can stream remote JSONL files directly during training using manifests. Supported manifest formats (local path or HTTPS URL): - -- Plain text: one SAS URL per line -- JSONL: each line is either a string URL or an object with a `url` field -- JSON: either an array of URLs, or an object with `train`, `validation`, `urls`, or `files` arrays - -Examples: - -```powershell -# Train streaming directly from Blob (SAS URLs in text manifests) -python .\scripts\train_lora.py ` - --train-manifest https://mystorage.blob.core.windows.net/mycontainer/train_manifest.txt?sv=... ` - --eval-manifest https://mystorage.blob.core.windows.net/mycontainer/eval_manifest.txt?sv=... ` - --config .\lora\lora.yaml -``` - -When using manifests, the trainer keeps streaming enabled by default for best scalability. - -## Metrics logging (file and Azure Monitor) - -- All evaluation metrics are appended to `/metrics.jsonl`. -- If the following environment variables are set, the same metrics are also sent to Azure Log Analytics via the HTTP Data Collector API: - - `AZURE_LOG_ANALYTICS_WORKSPACE_ID` - - `AZURE_LOG_ANALYTICS_SHARED_KEY` - - Optional: `AZURE_LOG_TYPE` (default: `LLMTrainingMetrics`) - -Each record contains a UTC `timestamp` plus fields like `eval_loss`, `eval_perplexity`, and `step`. - -### Application Insights - -To send metrics to Application Insights, install the optional package and set the connection string: - -```powershell -pip install applicationinsights - -$env:APPLICATIONINSIGHTS_CONNECTION_STRING = "InstrumentationKey=...;IngestionEndpoint=https://..." -``` - -Metrics are tracked as custom events and metrics (eval_loss, eval_perplexity). - -### OpenTelemetry tracing - -To emit distributed traces to an OTLP-compatible backend (e.g., Jaeger, Azure Monitor, Grafana Tempo), install the optional packages: - -```powershell -pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp-proto-grpc - -# Point to your OTLP endpoint -$env:OTEL_EXPORTER_OTLP_ENDPOINT = "http://localhost:4317" -$env:OTEL_SERVICE_NAME = "lora-training" -``` - -The trainer will emit spans for: - -- `training_run` (entire training session with hyperparameters) -- `training_step` (every 100 steps to reduce overhead) -- `evaluation` (each eval with metrics) -- `log_event` (training loss) - -These traces can be visualized in tools like Jaeger or Application Insights. - -## Troubleshooting - -- Missing packages: `pip install -r requirements.txt`. -- Large downloads: Try `--dry-run` first. For actual training, set `HF_MODEL_ID` to a smaller model. -- Dataset errors: Re-run `prepare_dataset.py` and check the first few lines of `data/train.json`. +# Phi-3.6 LoRA Fine-Tuning (Scalable) + +This directory contains a production-ready path to train the Phi-3.x family with LoRA on very large datasets. It supports: + +- Huge datasets using JSONL with streaming ingestion +- Simple dataset prep from JSONL or CSV +- Local training via Hugging Face + PEFT +- Azure deployment via the included infra (Container Apps + File Share) when ready + +The configs under `lora/` and `soft_prompt/` are Azure AI Toolkit–compatible. The scripts here let you validate and train locally first. + +## Dataset format + +Use JSONL where each line is a JSON object with a `messages` array: + +```json +{"messages": [{"role": "user", "content": "Hi"}, {"role": "assistant", "content": "Hello!"}]} +``` + +This repo includes tiny samples in `data/train.json` and `data/test.json` (JSONL with `.json` extension). + +If you have CSV (columns `prompt,response` or `input,output`), the prep script converts it to the same chat schema. + +## Setup (Windows PowerShell) + +Create a virtual environment and install dependencies. The script gracefully handles missing imports for dry-run mode, but full training requires all packages from `requirements.txt`. + +```powershell +# From the repo root +cd AI\microsoft_phi-silica-3.6_v1 +python -m venv venv +.\venv\Scripts\Activate.ps1 +pip install -r requirements.txt +``` + +**Required packages for training:** + +- `datasets>=2.19.0` - Dataset loading and streaming +- `transformers>=4.43.0` - Model and training infrastructure +- `peft>=0.12.0` - LoRA and parameter-efficient fine-tuning +- `accelerate>=0.33.0` - Multi-GPU and distributed training +- `torch` - Install version matching your CUDA (see requirements.txt) +- `pyyaml>=6.0` - Configuration parsing + +**Note:** The script allows dry-run validation without these packages. Install them only when ready to train. + +## Prepare a large dataset + +```powershell +# Convert CSV/JSONL to chat JSONL and split train/test +python .\scripts\prepare_dataset.py --input --output-dir .\data --train-ratio 0.99 +``` + +The output will be `data/train.json` and `data/test.json` (JSONL). + +## Validate (no downloads) + +```powershell +python .\scripts\train_lora.py --dry-run --dataset .\data --config .\lora\lora.yaml +``` + +This checks dataset shape and counts without loading a model. + +## Train locally (small smoke test) + +```powershell +# Use a smaller, available HF model if needed +$env:HF_MODEL_ID = "microsoft/Phi-3.5-mini-instruct" +python .\scripts\train_lora.py --dataset .\data --config .\lora\lora.yaml --max-train-samples 64 --max-eval-samples 32 --no-stream +``` + +Outputs are saved under the `save_dir` specified in `lora\lora.yaml` (by default, it will create the folder). + +## Multi-GPU training + +- Accelerate launcher (recommended for simple multi-GPU): + +```powershell +accelerate launch --multi_gpu .\scripts\train_lora.py --dataset .\data --config .\lora\lora.yaml --max-train-samples 512 --max-eval-samples 128 +``` + +- DeepSpeed (ZeRO-3). First, create or use the provided config: + +```powershell +# Provided example config +cat .\scripts\deepspeed_zero3.json | Out-Host + +# Run with DeepSpeed +python .\scripts\train_lora.py --dataset .\data --config .\lora\lora.yaml --deepspeed .\scripts\deepspeed_zero3.json +``` + +The trainer computes and prints perplexity before and after training using the evaluation loss. + +## Train at scale on Azure (optional) + +1. Provision resources using the bicep in `lora/infra/provision`. Fill in `finetuning.config.json` and run the documented deployment steps (see the top-level workspace docs). +## Train on Azure Machine Learning (Recommended for GPU Training) + +Azure ML provides managed GPU compute with auto-scaling for cost-effective training. + +### Quick Start + +```powershell +# 1. Install Azure dependencies +pip install -r azure-requirements.txt + +# 2. Setup (one-time) +.\setup_azure_ml.ps1 -SubscriptionId "" + +# 3. Upload dataset +python azure_ml_training.py --action upload ` + --subscription-id "" ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace + +# 4. Start training (quick test) +python azure_ml_training.py --action train ` + --subscription-id "" ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace ` + --max-train-samples 64 +``` + +**Benefits:** +- **GPU acceleration**: 1-4x V100 GPUs (Standard_NC6s_v3 @ ~$3/hour) +- **Auto-scaling**: Scales to 0 when idle (no cost) +- **Monitoring**: Real-time metrics in Azure ML Studio +- **Cost-effective**: Pay only for training time (~$9 for full 8000-sample run) + +See **[AZURE_ML_TRAINING_GUIDE.md](./AZURE_ML_TRAINING_GUIDE.md)** for complete documentation. + +## Train at scale on Azure Container Apps (Alternative) +2. Upload your dataset to the mounted file share path expected by the job (the infra uses `mount//dataset`). +3. Ensure `lora.yaml` points to `finetune_dataset: "mount//dataset"` and `save_dir` is also under `mount//...`. +4. Start the job. The standard runner will read `lora.yaml` and train using LoRA. + +### Read data directly from Azure Blob via manifest (Container Apps Job) + +Use the alternate Bicep `lora/infra/provision/finetuning_blob_manifest.bicep`, which adds an init container that downloads your dataset directly from Blob Storage before training starts. + +Manifest format: plain text file where each line is a SAS URL to a dataset part file (e.g., .jsonl). Example: + +```text +https://mystorage.blob.core.windows.net/mycontainer/part-0001.jsonl? +https://mystorage.blob.core.windows.net/mycontainer/part-0002.jsonl? +``` + +Deploy (PowerShell): + +```powershell +$rg = "rg-quantum-ai" +$params = "AI\microsoft_phi-silica-3.6_v1\lora\infra\provision\finetuning_blob_manifest.parameters.json" +az deployment group create ` + --resource-group $rg ` + --template-file AI\microsoft_phi-silica-3.6_v1\lora\infra\provision\finetuning_blob_manifest.bicep ` + --parameters @$params +``` + +### Notes + +- The init container downloads each URL in `manifestUrl` into `/mount/dataset` in the job. +- Ensure your SAS token stays valid for the job duration. +- The main container still reads from `mount//dataset` per `lora.yaml`. + +## Additional notes + +- For huge datasets, prefer `--no-stream` only for deterministic local experiments. Streaming is better for very large files. +- Use BF16 on GPUs that support it for speed and stability. The script auto-selects BF16 on CUDA, FP16 otherwise. +- Reduce `eval_steps` and `save_steps` for faster checkpoints on massive runs. +- If gradient checkpointing is enabled, we disable `use_cache` on the model to avoid correctness issues. + +## Direct streaming from Blob during training (no pre-download) + +You can stream remote JSONL files directly during training using manifests. Supported manifest formats (local path or HTTPS URL): + +- Plain text: one SAS URL per line +- JSONL: each line is either a string URL or an object with a `url` field +- JSON: either an array of URLs, or an object with `train`, `validation`, `urls`, or `files` arrays + +Examples: + +```powershell +# Train streaming directly from Blob (SAS URLs in text manifests) +python .\scripts\train_lora.py ` + --train-manifest https://mystorage.blob.core.windows.net/mycontainer/train_manifest.txt?sv=... ` + --eval-manifest https://mystorage.blob.core.windows.net/mycontainer/eval_manifest.txt?sv=... ` + --config .\lora\lora.yaml +``` + +When using manifests, the trainer keeps streaming enabled by default for best scalability. + +## Metrics logging (file and Azure Monitor) + +- All evaluation metrics are appended to `/metrics.jsonl`. +- If the following environment variables are set, the same metrics are also sent to Azure Log Analytics via the HTTP Data Collector API: + - `AZURE_LOG_ANALYTICS_WORKSPACE_ID` + - `AZURE_LOG_ANALYTICS_SHARED_KEY` + - Optional: `AZURE_LOG_TYPE` (default: `LLMTrainingMetrics`) + +Each record contains a UTC `timestamp` plus fields like `eval_loss`, `eval_perplexity`, and `step`. + +### Application Insights + +To send metrics to Application Insights, install the optional package and set the connection string: + +```powershell +pip install applicationinsights + +$env:APPLICATIONINSIGHTS_CONNECTION_STRING = "InstrumentationKey=...;IngestionEndpoint=https://..." +``` + +Metrics are tracked as custom events and metrics (eval_loss, eval_perplexity). + +### OpenTelemetry tracing + +To emit distributed traces to an OTLP-compatible backend (e.g., Jaeger, Azure Monitor, Grafana Tempo), install the optional packages: + +```powershell +pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp-proto-grpc + +# Point to your OTLP endpoint +$env:OTEL_EXPORTER_OTLP_ENDPOINT = "http://localhost:4317" +$env:OTEL_SERVICE_NAME = "lora-training" +``` + +The trainer will emit spans for: + +- `training_run` (entire training session with hyperparameters) +- `training_step` (every 100 steps to reduce overhead) +- `evaluation` (each eval with metrics) +- `log_event` (training loss) + +These traces can be visualized in tools like Jaeger or Application Insights. + +## Troubleshooting + +- Missing packages: `pip install -r requirements.txt`. +- Large downloads: Try `--dry-run` first. For actual training, set `HF_MODEL_ID` to a smaller model. +- Dataset errors: Re-run `prepare_dataset.py` and check the first few lines of `data/train.json`. diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/SETUP.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/SETUP.md index ae4c65959..ab5f150be 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/SETUP.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/SETUP.md @@ -1,39 +1,39 @@ -Model training environment setup (AI/microsoft_phi-silica-3.6_v1) -=============================================================== - -This document describes how to prepare a model-specific Python virtual environment and install the heavy ML dependencies required for training (PyTorch, Transformers, PEFT, Accelerate, etc.). The repository provides a helper script at `AI/microsoft_phi-silica-3.6_v1/scripts/setup_model_env.sh` which automates the most common steps. - -Quick checklist ---------------- -1. Choose whether you'll use CPU-only training or a machine with CUDA GPUs. - - For CPU-only machines run: `bash scripts/setup_model_env.sh --cpu` - - For CUDA machines pick your CUDA runtime (e.g., `cu121` for CUDA 12.1) and run: - `bash scripts/setup_model_env.sh --cuda cu121` - -2. The script will create (or reuse) `AI/microsoft_phi-silica-3.6_v1/venv` and install packages from `AI/microsoft_phi-silica-3.6_v1/requirements.txt`. - -3. The script will install torch separately (the script intentionally does not autoguess the torch wheel — you must tell it which wheel to install so it matches your OS/CUDA environment). - -4. After installation, activate the venv and run a short smoke test by importing `torch`, `transformers`, `peft`, `accelerate` and confirming `torch.cuda.is_available()` output. - -Important note: the setup script intentionally pins huggingface_hub to a version compatible with the installed -transformers/peft packages (e.g. huggingface_hub >=0.34.0,<1.0). This prevents accidental upgrades that would -break imports at runtime. If you need a newer `huggingface_hub` and upgraded `transformers`/`peft`, update -`AI/microsoft_phi-silica-3.6_v1/requirements.txt` and re-run `scripts/setup_model_env.sh --force`. - -Activate and run a small training job ------------------------------------- -Once venv is ready, you can start lightweight training for testing: - -```bash -source AI/microsoft_phi-silica-3.6_v1/venv/bin/activate -python AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py --config AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml --dataset datasets/chat/mixed_chat --max-train-samples 32 --epochs 1 --save-dir data_out/lora_training/test_small -``` - -Notes and troubleshooting -------------------------- -- On Windows the venv path created may be under `Scripts/python.exe`. The helper script and our runner scripts handle both `bin/python` and `Scripts/python.exe`. -- If you see missing package errors, re-run the setup script with `--force` to recreate and reinstall. -- For CUDA installations, ensure your driver supports the chosen CUDA runtime and that `nvidia-smi` is available. - -If you'd like, I can also generate a one-shot install command for your exact environment (CPU vs GPU) or help you run a very small training job now (1 epoch, few samples) once your environment is ready. +Model training environment setup (AI/microsoft_phi-silica-3.6_v1) +=============================================================== + +This document describes how to prepare a model-specific Python virtual environment and install the heavy ML dependencies required for training (PyTorch, Transformers, PEFT, Accelerate, etc.). The repository provides a helper script at `AI/microsoft_phi-silica-3.6_v1/scripts/setup_model_env.sh` which automates the most common steps. + +Quick checklist +--------------- +1. Choose whether you'll use CPU-only training or a machine with CUDA GPUs. + - For CPU-only machines run: `bash scripts/setup_model_env.sh --cpu` + - For CUDA machines pick your CUDA runtime (e.g., `cu121` for CUDA 12.1) and run: + `bash scripts/setup_model_env.sh --cuda cu121` + +2. The script will create (or reuse) `AI/microsoft_phi-silica-3.6_v1/venv` and install packages from `AI/microsoft_phi-silica-3.6_v1/requirements.txt`. + +3. The script will install torch separately (the script intentionally does not autoguess the torch wheel — you must tell it which wheel to install so it matches your OS/CUDA environment). + +4. After installation, activate the venv and run a short smoke test by importing `torch`, `transformers`, `peft`, `accelerate` and confirming `torch.cuda.is_available()` output. + +Important note: the setup script intentionally pins huggingface_hub to a version compatible with the installed +transformers/peft packages (e.g. huggingface_hub >=0.34.0,<1.0). This prevents accidental upgrades that would +break imports at runtime. If you need a newer `huggingface_hub` and upgraded `transformers`/`peft`, update +`AI/microsoft_phi-silica-3.6_v1/requirements.txt` and re-run `scripts/setup_model_env.sh --force`. + +Activate and run a small training job +------------------------------------ +Once venv is ready, you can start lightweight training for testing: + +```bash +source AI/microsoft_phi-silica-3.6_v1/venv/bin/activate +python AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py --config AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml --dataset datasets/chat/mixed_chat --max-train-samples 32 --epochs 1 --save-dir data_out/lora_training/test_small +``` + +Notes and troubleshooting +------------------------- +- On Windows the venv path created may be under `Scripts/python.exe`. The helper script and our runner scripts handle both `bin/python` and `Scripts/python.exe`. +- If you see missing package errors, re-run the setup script with `--force` to recreate and reinstall. +- For CUDA installations, ensure your driver supports the chosen CUDA runtime and that `nvidia-smi` is available. + +If you'd like, I can also generate a one-shot install command for your exact environment (CPU vs GPU) or help you run a very small training job now (1 epoch, few samples) once your environment is ready. diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure-requirements.txt b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure-requirements.txt index f71bb8aef..ce6e260f8 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure-requirements.txt +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure-requirements.txt @@ -1,11 +1,11 @@ -# Azure Machine Learning SDK -azure-ai-ml>=1.15.0 -azure-identity>=1.15.0 -azure-core>=1.30.0 - -# Core dependencies (if not already installed) -azure-storage-blob>=12.19.0 -azure-monitor-opentelemetry>=1.0.0 - -# Optional: For advanced features -mlflow>=2.11.0 +# Azure Machine Learning SDK +azure-ai-ml>=1.15.0 +azure-identity>=1.15.0 +azure-core>=1.30.0 + +# Core dependencies (if not already installed) +azure-storage-blob>=12.19.0 +azure-monitor-opentelemetry>=1.0.0 + +# Optional: For advanced features +mlflow>=2.11.0 diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure_foundry_deploy.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure_foundry_deploy.py index 109a880ff..6fda2146b 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure_foundry_deploy.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure_foundry_deploy.py @@ -1,159 +1,159 @@ -r""" -Azure AI Foundry (Managed Online Endpoint) deployment for Phi-3.x + LoRA adapter. -- Registers LoRA adapter as a model asset (if given a local path) -- Creates a managed online endpoint and GPU deployment -- Uses foundry/score_foundry.py scoring script to load base model + adapter - -Usage (PowerShell): - python azure_foundry_deploy.py ` - --subscription-id ` - --resource-group rg-phi36-ml ` - --workspace-name phi36-ml-workspace ` - --adapter-path .\data_out\lora_training\lora_adapter ` - --endpoint-name phi36-lora-ep ` - --base-model-id microsoft/Phi-3.5-mini-instruct -""" - -import argparse -import time -from pathlib import Path - -try: - from azure.ai.ml import MLClient - from azure.ai.ml.constants import AssetTypes - from azure.ai.ml.entities import (CodeConfiguration, Environment, - ManagedOnlineDeployment, - ManagedOnlineEndpoint, Model) - from azure.identity import AzureCliCredential, DefaultAzureCredential -except Exception: - raise SystemExit( - "Install Azure ML SDK: pip install azure-ai-ml azure-identity" - ) from None - - -def get_credential(): - try: - cred = AzureCliCredential() - cred.get_token("https://management.azure.com/.default") - return cred - except Exception: - return DefaultAzureCredential() - - -def ensure_model(ml: MLClient, adapter_path: str | None, model_name: str) -> Model: - if adapter_path: - p = Path(adapter_path) - if not p.exists(): - raise FileNotFoundError(f"Adapter path not found: {p}") - m = Model( - name=model_name, - path=str(p), - type=AssetTypes.CUSTOM_MODEL, - description="LoRA adapter for Phi-3.x", - tags={"method": "LoRA"}, - ) - return ml.models.create_or_update(m) - # Fallback: fetch existing by name - return ml.models.get(name=model_name, label="latest") - - -def create_environment(ml: MLClient, env_name: str) -> Environment: - env = Environment( - name=env_name, - description="Environment for Phi-3 + LoRA serving", - image="mcr.microsoft.com/azureml/curated/acft-hf-nlp-gpu:63", - conda_file={ - "name": env_name, - "channels": ["conda-forge", "pytorch"], - "dependencies": [ - "python=3.10", - "pip", - { - "pip": [ - "transformers>=4.40.0", - "peft>=0.10.0", - "accelerate>=0.27.0", - "torch>=2.2.0", - ] - }, - ], - }, - ) - try: - return ml.environments.create_or_update(env) - except Exception: - return ml.environments.get(env_name, label="latest") - - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument("--subscription-id", required=True) - ap.add_argument("--resource-group", required=True) - ap.add_argument("--workspace-name", required=True) - ap.add_argument( - "--adapter-path", - default=None, - help="Local path to lora_adapter (registers as model)", - ) - ap.add_argument("--model-name", default="phi36-lora-adapter") - ap.add_argument("--endpoint-name", default=None) - ap.add_argument("--deployment-name", default="blue") - ap.add_argument("--base-model-id", default="microsoft/Phi-3.5-mini-instruct") - ap.add_argument("--instance-type", default="Standard_NC6s_v3") - ap.add_argument("--instance-count", type=int, default=1) - args = ap.parse_args() - - cred = get_credential() - ml = MLClient(cred, args.subscription_id, args.resource_group, args.workspace_name) - - # Register model (adapter) - model = ensure_model(ml, args.adapter_path, args.model_name) - print(f"✓ Model ready: {model.name} v{model.version}") - - # Create environment - env_name = "phi36-lora-serving" - env = create_environment(ml, env_name) - print(f"✓ Environment ready: {env.name}:{env.version}") - - # Endpoint - endpoint_name = args.endpoint_name or f"phi36-lora-ep-{int(time.time())}" - ep = ManagedOnlineEndpoint(name=endpoint_name, auth_mode="key") - ml.begin_create_or_update(ep).result() - print(f"✓ Endpoint created: {endpoint_name}") - - # Deployment: code is the foundry folder (includes score_foundry.py). Model mounts at working dir. - code_dir = Path(__file__).resolve().parent / "foundry" - dep = ManagedOnlineDeployment( - name=args.deployment_name, - endpoint_name=endpoint_name, - model=f"azureml:{model.name}:{model.version}", - code_configuration=CodeConfiguration( - code=str(code_dir), scoring_script="score_foundry.py" - ), - environment=f"{env.name}:{env.version}", - instance_type=args.instance_type, - instance_count=args.instance_count, - environment_variables={ - "BASE_MODEL_ID": args.base_model_id, - "ADAPTER_SUBPATH": "lora_adapter", - "USE_BF16": "1", - "MAX_NEW_TOKENS": "128", - }, - ) - ml.begin_create_or_update(dep).result() - - # Route traffic - ep.traffic = {args.deployment_name: 100} - ml.begin_create_or_update(ep).result() - - # Get keys + invoke URL - keys = ml.online_endpoints.get_keys(endpoint_name) - details = ml.online_endpoints.get(endpoint_name) - print("\n=== Deployment ready ===") - print(f"Endpoint: {endpoint_name}") - print(f"Scoring URL: {details.scoring_uri}") - print(f"Primary key: {keys.primary_key}") - - -if __name__ == "__main__": - main() +r""" +Azure AI Foundry (Managed Online Endpoint) deployment for Phi-3.x + LoRA adapter. +- Registers LoRA adapter as a model asset (if given a local path) +- Creates a managed online endpoint and GPU deployment +- Uses foundry/score_foundry.py scoring script to load base model + adapter + +Usage (PowerShell): + python azure_foundry_deploy.py ` + --subscription-id ` + --resource-group rg-phi36-ml ` + --workspace-name phi36-ml-workspace ` + --adapter-path .\data_out\lora_training\lora_adapter ` + --endpoint-name phi36-lora-ep ` + --base-model-id microsoft/Phi-3.5-mini-instruct +""" + +import argparse +import time +from pathlib import Path + +try: + from azure.ai.ml import MLClient + from azure.ai.ml.constants import AssetTypes + from azure.ai.ml.entities import (CodeConfiguration, Environment, + ManagedOnlineDeployment, + ManagedOnlineEndpoint, Model) + from azure.identity import AzureCliCredential, DefaultAzureCredential +except Exception: + raise SystemExit( + "Install Azure ML SDK: pip install azure-ai-ml azure-identity" + ) from None + + +def get_credential(): + try: + cred = AzureCliCredential() + cred.get_token("https://management.azure.com/.default") + return cred + except Exception: + return DefaultAzureCredential() + + +def ensure_model(ml: MLClient, adapter_path: str | None, model_name: str) -> Model: + if adapter_path: + p = Path(adapter_path) + if not p.exists(): + raise FileNotFoundError(f"Adapter path not found: {p}") + m = Model( + name=model_name, + path=str(p), + type=AssetTypes.CUSTOM_MODEL, + description="LoRA adapter for Phi-3.x", + tags={"method": "LoRA"}, + ) + return ml.models.create_or_update(m) + # Fallback: fetch existing by name + return ml.models.get(name=model_name, label="latest") + + +def create_environment(ml: MLClient, env_name: str) -> Environment: + env = Environment( + name=env_name, + description="Environment for Phi-3 + LoRA serving", + image="mcr.microsoft.com/azureml/curated/acft-hf-nlp-gpu:63", + conda_file={ + "name": env_name, + "channels": ["conda-forge", "pytorch"], + "dependencies": [ + "python=3.10", + "pip", + { + "pip": [ + "transformers>=4.40.0", + "peft>=0.10.0", + "accelerate>=0.27.0", + "torch>=2.2.0", + ] + }, + ], + }, + ) + try: + return ml.environments.create_or_update(env) + except Exception: + return ml.environments.get(env_name, label="latest") + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--subscription-id", required=True) + ap.add_argument("--resource-group", required=True) + ap.add_argument("--workspace-name", required=True) + ap.add_argument( + "--adapter-path", + default=None, + help="Local path to lora_adapter (registers as model)", + ) + ap.add_argument("--model-name", default="phi36-lora-adapter") + ap.add_argument("--endpoint-name", default=None) + ap.add_argument("--deployment-name", default="blue") + ap.add_argument("--base-model-id", default="microsoft/Phi-3.5-mini-instruct") + ap.add_argument("--instance-type", default="Standard_NC6s_v3") + ap.add_argument("--instance-count", type=int, default=1) + args = ap.parse_args() + + cred = get_credential() + ml = MLClient(cred, args.subscription_id, args.resource_group, args.workspace_name) + + # Register model (adapter) + model = ensure_model(ml, args.adapter_path, args.model_name) + print(f"✓ Model ready: {model.name} v{model.version}") + + # Create environment + env_name = "phi36-lora-serving" + env = create_environment(ml, env_name) + print(f"✓ Environment ready: {env.name}:{env.version}") + + # Endpoint + endpoint_name = args.endpoint_name or f"phi36-lora-ep-{int(time.time())}" + ep = ManagedOnlineEndpoint(name=endpoint_name, auth_mode="key") + ml.begin_create_or_update(ep).result() + print(f"✓ Endpoint created: {endpoint_name}") + + # Deployment: code is the foundry folder (includes score_foundry.py). Model mounts at working dir. + code_dir = Path(__file__).resolve().parent / "foundry" + dep = ManagedOnlineDeployment( + name=args.deployment_name, + endpoint_name=endpoint_name, + model=f"azureml:{model.name}:{model.version}", + code_configuration=CodeConfiguration( + code=str(code_dir), scoring_script="score_foundry.py" + ), + environment=f"{env.name}:{env.version}", + instance_type=args.instance_type, + instance_count=args.instance_count, + environment_variables={ + "BASE_MODEL_ID": args.base_model_id, + "ADAPTER_SUBPATH": "lora_adapter", + "USE_BF16": "1", + "MAX_NEW_TOKENS": "128", + }, + ) + ml.begin_create_or_update(dep).result() + + # Route traffic + ep.traffic = {args.deployment_name: 100} + ml.begin_create_or_update(ep).result() + + # Get keys + invoke URL + keys = ml.online_endpoints.get_keys(endpoint_name) + details = ml.online_endpoints.get(endpoint_name) + print("\n=== Deployment ready ===") + print(f"Endpoint: {endpoint_name}") + print(f"Scoring URL: {details.scoring_uri}") + print(f"Primary key: {keys.primary_key}") + + +if __name__ == "__main__": + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure_ml_training.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure_ml_training.py index 72da1924c..b4eb0ae52 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure_ml_training.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azure_ml_training.py @@ -1,396 +1,396 @@ -""" -Azure Machine Learning Training for Phi-3.6 LoRA Fine-tuning -Enables cloud-based GPU training with automatic scaling and monitoring -""" - -import argparse -from pathlib import Path -from typing import Any, Optional - -try: - from azure.ai.ml import Input, MLClient, Output, command - from azure.ai.ml.constants import AssetTypes - from azure.ai.ml.entities import (AmlCompute, CodeConfiguration, Data, - Environment, ManagedOnlineDeployment, - ManagedOnlineEndpoint, Model) - from azure.identity import AzureCliCredential, DefaultAzureCredential - - AZURE_ML_AVAILABLE = True -except ImportError: - AZURE_ML_AVAILABLE = False - print("Azure ML SDK not installed. Run: pip install azure-ai-ml azure-identity") - - -class AzureMLLoRATrainer: - """Manages Azure ML training jobs for Phi-3.6 LoRA fine-tuning""" - - def __init__(self, subscription_id: str, resource_group: str, workspace_name: str): - """ - Initialize Azure ML client. - - Args: - subscription_id: Azure subscription ID - resource_group: Resource group name - workspace_name: ML workspace name - """ - if not AZURE_ML_AVAILABLE: - raise ImportError( - "Azure ML SDK required. Install: pip install azure-ai-ml azure-identity" - ) - - # Try CLI credential first, then default - try: - credential = AzureCliCredential() - credential.get_token("https://management.azure.com/.default") - except Exception: - credential = DefaultAzureCredential() - - self.ml_client = MLClient( - credential=credential, - subscription_id=subscription_id, - resource_group_name=resource_group, - workspace_name=workspace_name, - ) - - self.workspace_name = workspace_name - print(f"✓ Connected to Azure ML workspace: {workspace_name}") - - def create_or_get_compute( - self, - compute_name: str = "phi36-gpu-cluster", - vm_size: str = "Standard_NC6s_v3", # Tesla V100 GPU - min_instances: int = 0, - max_instances: int = 4, - idle_time_before_scale_down: int = 300, - ) -> AmlCompute: - """ - Create or retrieve GPU compute cluster for training. - - Args: - compute_name: Cluster name - vm_size: VM size (Standard_NC6s_v3 = 1x V100, Standard_NC12s_v3 = 2x V100) - min_instances: Min nodes (0 for auto-scale) - max_instances: Max nodes - idle_time_before_scale_down: Scale-down delay in seconds - - Returns: - Compute cluster configuration - """ - try: - compute = self.ml_client.compute.get(compute_name) - print(f"✓ Using existing compute: {compute_name}") - except Exception: - print(f"Creating compute cluster: {compute_name}...") - compute = AmlCompute( - name=compute_name, - type="amlcompute", - size=vm_size, - min_instances=min_instances, - max_instances=max_instances, - idle_time_before_scale_down=idle_time_before_scale_down, - tier="dedicated", # or "low_priority" for cost savings - ) - compute = self.ml_client.compute.begin_create_or_update(compute).result() - print(f"✓ Compute cluster created: {compute_name}") - - return compute - - def create_environment( - self, env_name: str = "phi36-lora-env", python_version: str = "3.10" - ) -> Environment: - """ - Create Azure ML environment with all dependencies. - - Args: - env_name: Environment name - python_version: Python version - - Returns: - Environment configuration - """ - # Use curated PyTorch environment as base - base_env = ( - "azureml://registries/azureml/environments/acft-hf-nlp-gpu/versions/63" - ) - - # Create custom environment with additional dependencies - env = Environment( - name=env_name, - description="Phi-3.6 LoRA fine-tuning environment", - image="mcr.microsoft.com/azureml/curated/acft-hf-nlp-gpu:63", - conda_file={ - "name": env_name, - "channels": ["conda-forge", "pytorch"], - "dependencies": [ - f"python={python_version}", - "pip", - { - "pip": [ - "transformers>=4.40.0", - "datasets>=2.18.0", - "peft>=0.10.0", - "accelerate>=0.27.0", - "torch>=2.2.0", - "bitsandbytes>=0.42.0", - "scikit-learn>=1.4.0", - "pyyaml>=6.0", - "azure-ai-ml>=1.15.0", - "mlflow>=2.11.0", - ] - }, - ], - }, - ) - - try: - env = self.ml_client.environments.create_or_update(env) - print(f"✓ Environment registered: {env_name}") - except Exception as e: - print(f"Note: {e}") - env = self.ml_client.environments.get(env_name, label="latest") - print(f"✓ Using existing environment: {env_name}") - - return env - - def upload_dataset( - self, - dataset_path: str, - dataset_name: str = "dolly-chat-dataset", - description: str = "Dolly 15k chat dataset for Phi-3.6 fine-tuning", - ) -> Data: - """ - Upload dataset to Azure ML. - - Args: - dataset_path: Local path to dataset directory - dataset_name: Name for registered dataset - description: Dataset description - - Returns: - Registered dataset - """ - dataset = Data( - path=dataset_path, - type=AssetTypes.URI_FOLDER, - description=description, - name=dataset_name, - ) - - dataset = self.ml_client.data.create_or_update(dataset) - print(f"✓ Dataset uploaded: {dataset_name} (v{dataset.version})") - return dataset - - def submit_training_job( - self, - experiment_name: str = "phi36-lora-training", - compute_name: str = "phi36-gpu-cluster", - dataset_name: str = "dolly-chat-dataset", - config_path: str = "./lora/lora.yaml", - max_train_samples: Optional[int] = None, - display_name: Optional[str] = None, - ) -> Any: - """ - Submit LoRA training job to Azure ML. - - Args: - experiment_name: Name of experiment - compute_name: Compute cluster name - dataset_name: Registered dataset name - config_path: Path to lora.yaml config - max_train_samples: Optional limit for quick tests - display_name: Optional custom display name - - Returns: - Submitted job - """ - # Get dataset - dataset = self.ml_client.data.get(dataset_name, label="latest") - - # Prepare training script directory - script_dir = Path(__file__).parent / "scripts" - - # Build command arguments - args = [ - "--config", - config_path, - "--dataset", - Input(type=AssetTypes.URI_FOLDER, path=f"azureml:{dataset_name}@latest"), - "--hf-model-id", - "microsoft/Phi-3.5-mini-instruct", - ] - - if max_train_samples: - args.extend(["--max-train-samples", max_train_samples]) - - # Create job - job = command( - code=str(script_dir.parent), # Upload entire directory - command="python scripts/train_lora.py " + " ".join(str(a) for a in args), - environment=f"{self.workspace_name}/phi36-lora-env@latest", - compute=compute_name, - experiment_name=experiment_name, - display_name=display_name or f"phi36-lora-{Path(config_path).stem}", - outputs={ - "model_output": Output(type=AssetTypes.URI_FOLDER, mode="rw_mount") - }, - # Enable MLflow tracking - environment_variables={ - "MLFLOW_TRACKING_URI": "azureml://tracking", - "HF_HUB_DISABLE_SYMLINKS_WARNING": "1", - "PYTHONUTF8": "1", - }, - ) - - # Submit - submitted_job = self.ml_client.jobs.create_or_update(job) - print(f"✓ Training job submitted: {submitted_job.name}") - print(f" Monitor at: {submitted_job.studio_url}") - - return submitted_job - - def register_model( - self, - job_name: str, - model_name: str = "phi36-lora-adapter", - model_path: str = "lora_adapter", - ) -> Model: - """ - Register trained LoRA adapter as Azure ML model. - - Args: - job_name: Training job name - model_name: Name for registered model - model_path: Path within job outputs - - Returns: - Registered model - """ - model = Model( - path=f"azureml://jobs/{job_name}/outputs/model_output/{model_path}", - name=model_name, - description="Phi-3.6 LoRA fine-tuned adapter", - type=AssetTypes.CUSTOM_MODEL, - tags={ - "model": "Phi-3.5-mini-instruct", - "method": "LoRA", - "framework": "transformers", - }, - ) - - model = self.ml_client.models.create_or_update(model) - print(f"✓ Model registered: {model_name} (v{model.version})") - return model - - -def main(): - """CLI for Azure ML training""" - parser = argparse.ArgumentParser(description="Azure ML training for Phi-3.6 LoRA") - - # Azure settings - parser.add_argument( - "--subscription-id", required=True, help="Azure subscription ID" - ) - parser.add_argument("--resource-group", required=True, help="Resource group name") - parser.add_argument("--workspace-name", required=True, help="ML workspace name") - - # Actions - parser.add_argument( - "--action", - choices=["setup", "upload", "train", "register"], - default="train", - help="Action to perform", - ) - - # Training settings - parser.add_argument( - "--experiment-name", default="phi36-lora-training", help="Experiment name" - ) - parser.add_argument( - "--compute-name", default="phi36-gpu-cluster", help="Compute cluster name" - ) - parser.add_argument( - "--vm-size", - default="Standard_NC6s_v3", - help="VM size for compute (Standard_NC6s_v3 = 1x V100)", - ) - parser.add_argument( - "--dataset-path", default="../../datasets/chat/dolly", help="Local dataset path" - ) - parser.add_argument( - "--dataset-name", default="dolly-chat-dataset", help="Dataset name in Azure ML" - ) - parser.add_argument( - "--config", default="./lora/lora.yaml", help="Path to lora.yaml config" - ) - parser.add_argument( - "--max-train-samples", - type=int, - default=None, - help="Limit training samples (for testing)", - ) - - # Model registration - parser.add_argument("--job-name", help="Job name for model registration") - parser.add_argument( - "--model-name", default="phi36-lora-adapter", help="Registered model name" - ) - - args = parser.parse_args() - - # Initialize trainer - trainer = AzureMLLoRATrainer( - subscription_id=args.subscription_id, - resource_group=args.resource_group, - workspace_name=args.workspace_name, - ) - - if args.action == "setup": - print("\n=== Setting up Azure ML infrastructure ===") - trainer.create_or_get_compute( - compute_name=args.compute_name, vm_size=args.vm_size - ) - trainer.create_environment() - print("\n✓ Setup complete!") - - elif args.action == "upload": - print("\n=== Uploading dataset ===") - trainer.upload_dataset( - dataset_path=args.dataset_path, dataset_name=args.dataset_name - ) - print("\n✓ Upload complete!") - - elif args.action == "train": - print("\n=== Submitting training job ===") - job = trainer.submit_training_job( - experiment_name=args.experiment_name, - compute_name=args.compute_name, - dataset_name=args.dataset_name, - config_path=args.config, - max_train_samples=args.max_train_samples, - ) - print("\n✓ Job submitted!") - print(f"\nTo monitor: {job.studio_url}") - - elif args.action == "register": - if not args.job_name: - print("Error: --job-name required for model registration") - return - print("\n=== Registering model ===") - model = trainer.register_model( - job_name=args.job_name, model_name=args.model_name - ) - print("\n✓ Model registered!") - - -if __name__ == "__main__": - if not AZURE_ML_AVAILABLE: - print("\n" + "=" * 70) - print(" Azure ML SDK Required") - print("=" * 70) - print("\nInstall dependencies:") - print(" pip install azure-ai-ml azure-identity") - print("\nAuthenticate:") - print(" az login") - print("=" * 70) - else: - main() +""" +Azure Machine Learning Training for Phi-3.6 LoRA Fine-tuning +Enables cloud-based GPU training with automatic scaling and monitoring +""" + +import argparse +from pathlib import Path +from typing import Any, Optional + +try: + from azure.ai.ml import Input, MLClient, Output, command + from azure.ai.ml.constants import AssetTypes + from azure.ai.ml.entities import (AmlCompute, CodeConfiguration, Data, + Environment, ManagedOnlineDeployment, + ManagedOnlineEndpoint, Model) + from azure.identity import AzureCliCredential, DefaultAzureCredential + + AZURE_ML_AVAILABLE = True +except ImportError: + AZURE_ML_AVAILABLE = False + print("Azure ML SDK not installed. Run: pip install azure-ai-ml azure-identity") + + +class AzureMLLoRATrainer: + """Manages Azure ML training jobs for Phi-3.6 LoRA fine-tuning""" + + def __init__(self, subscription_id: str, resource_group: str, workspace_name: str): + """ + Initialize Azure ML client. + + Args: + subscription_id: Azure subscription ID + resource_group: Resource group name + workspace_name: ML workspace name + """ + if not AZURE_ML_AVAILABLE: + raise ImportError( + "Azure ML SDK required. Install: pip install azure-ai-ml azure-identity" + ) + + # Try CLI credential first, then default + try: + credential = AzureCliCredential() + credential.get_token("https://management.azure.com/.default") + except Exception: + credential = DefaultAzureCredential() + + self.ml_client = MLClient( + credential=credential, + subscription_id=subscription_id, + resource_group_name=resource_group, + workspace_name=workspace_name, + ) + + self.workspace_name = workspace_name + print(f"✓ Connected to Azure ML workspace: {workspace_name}") + + def create_or_get_compute( + self, + compute_name: str = "phi36-gpu-cluster", + vm_size: str = "Standard_NC6s_v3", # Tesla V100 GPU + min_instances: int = 0, + max_instances: int = 4, + idle_time_before_scale_down: int = 300, + ) -> AmlCompute: + """ + Create or retrieve GPU compute cluster for training. + + Args: + compute_name: Cluster name + vm_size: VM size (Standard_NC6s_v3 = 1x V100, Standard_NC12s_v3 = 2x V100) + min_instances: Min nodes (0 for auto-scale) + max_instances: Max nodes + idle_time_before_scale_down: Scale-down delay in seconds + + Returns: + Compute cluster configuration + """ + try: + compute = self.ml_client.compute.get(compute_name) + print(f"✓ Using existing compute: {compute_name}") + except Exception: + print(f"Creating compute cluster: {compute_name}...") + compute = AmlCompute( + name=compute_name, + type="amlcompute", + size=vm_size, + min_instances=min_instances, + max_instances=max_instances, + idle_time_before_scale_down=idle_time_before_scale_down, + tier="dedicated", # or "low_priority" for cost savings + ) + compute = self.ml_client.compute.begin_create_or_update(compute).result() + print(f"✓ Compute cluster created: {compute_name}") + + return compute + + def create_environment( + self, env_name: str = "phi36-lora-env", python_version: str = "3.10" + ) -> Environment: + """ + Create Azure ML environment with all dependencies. + + Args: + env_name: Environment name + python_version: Python version + + Returns: + Environment configuration + """ + # Use curated PyTorch environment as base + base_env = ( + "azureml://registries/azureml/environments/acft-hf-nlp-gpu/versions/63" + ) + + # Create custom environment with additional dependencies + env = Environment( + name=env_name, + description="Phi-3.6 LoRA fine-tuning environment", + image="mcr.microsoft.com/azureml/curated/acft-hf-nlp-gpu:63", + conda_file={ + "name": env_name, + "channels": ["conda-forge", "pytorch"], + "dependencies": [ + f"python={python_version}", + "pip", + { + "pip": [ + "transformers>=4.40.0", + "datasets>=2.18.0", + "peft>=0.10.0", + "accelerate>=0.27.0", + "torch>=2.2.0", + "bitsandbytes>=0.42.0", + "scikit-learn>=1.4.0", + "pyyaml>=6.0", + "azure-ai-ml>=1.15.0", + "mlflow>=2.11.0", + ] + }, + ], + }, + ) + + try: + env = self.ml_client.environments.create_or_update(env) + print(f"✓ Environment registered: {env_name}") + except Exception as e: + print(f"Note: {e}") + env = self.ml_client.environments.get(env_name, label="latest") + print(f"✓ Using existing environment: {env_name}") + + return env + + def upload_dataset( + self, + dataset_path: str, + dataset_name: str = "dolly-chat-dataset", + description: str = "Dolly 15k chat dataset for Phi-3.6 fine-tuning", + ) -> Data: + """ + Upload dataset to Azure ML. + + Args: + dataset_path: Local path to dataset directory + dataset_name: Name for registered dataset + description: Dataset description + + Returns: + Registered dataset + """ + dataset = Data( + path=dataset_path, + type=AssetTypes.URI_FOLDER, + description=description, + name=dataset_name, + ) + + dataset = self.ml_client.data.create_or_update(dataset) + print(f"✓ Dataset uploaded: {dataset_name} (v{dataset.version})") + return dataset + + def submit_training_job( + self, + experiment_name: str = "phi36-lora-training", + compute_name: str = "phi36-gpu-cluster", + dataset_name: str = "dolly-chat-dataset", + config_path: str = "./lora/lora.yaml", + max_train_samples: Optional[int] = None, + display_name: Optional[str] = None, + ) -> Any: + """ + Submit LoRA training job to Azure ML. + + Args: + experiment_name: Name of experiment + compute_name: Compute cluster name + dataset_name: Registered dataset name + config_path: Path to lora.yaml config + max_train_samples: Optional limit for quick tests + display_name: Optional custom display name + + Returns: + Submitted job + """ + # Get dataset + dataset = self.ml_client.data.get(dataset_name, label="latest") + + # Prepare training script directory + script_dir = Path(__file__).parent / "scripts" + + # Build command arguments + args = [ + "--config", + config_path, + "--dataset", + Input(type=AssetTypes.URI_FOLDER, path=f"azureml:{dataset_name}@latest"), + "--hf-model-id", + "microsoft/Phi-3.5-mini-instruct", + ] + + if max_train_samples: + args.extend(["--max-train-samples", max_train_samples]) + + # Create job + job = command( + code=str(script_dir.parent), # Upload entire directory + command="python scripts/train_lora.py " + " ".join(str(a) for a in args), + environment=f"{self.workspace_name}/phi36-lora-env@latest", + compute=compute_name, + experiment_name=experiment_name, + display_name=display_name or f"phi36-lora-{Path(config_path).stem}", + outputs={ + "model_output": Output(type=AssetTypes.URI_FOLDER, mode="rw_mount") + }, + # Enable MLflow tracking + environment_variables={ + "MLFLOW_TRACKING_URI": "azureml://tracking", + "HF_HUB_DISABLE_SYMLINKS_WARNING": "1", + "PYTHONUTF8": "1", + }, + ) + + # Submit + submitted_job = self.ml_client.jobs.create_or_update(job) + print(f"✓ Training job submitted: {submitted_job.name}") + print(f" Monitor at: {submitted_job.studio_url}") + + return submitted_job + + def register_model( + self, + job_name: str, + model_name: str = "phi36-lora-adapter", + model_path: str = "lora_adapter", + ) -> Model: + """ + Register trained LoRA adapter as Azure ML model. + + Args: + job_name: Training job name + model_name: Name for registered model + model_path: Path within job outputs + + Returns: + Registered model + """ + model = Model( + path=f"azureml://jobs/{job_name}/outputs/model_output/{model_path}", + name=model_name, + description="Phi-3.6 LoRA fine-tuned adapter", + type=AssetTypes.CUSTOM_MODEL, + tags={ + "model": "Phi-3.5-mini-instruct", + "method": "LoRA", + "framework": "transformers", + }, + ) + + model = self.ml_client.models.create_or_update(model) + print(f"✓ Model registered: {model_name} (v{model.version})") + return model + + +def main(): + """CLI for Azure ML training""" + parser = argparse.ArgumentParser(description="Azure ML training for Phi-3.6 LoRA") + + # Azure settings + parser.add_argument( + "--subscription-id", required=True, help="Azure subscription ID" + ) + parser.add_argument("--resource-group", required=True, help="Resource group name") + parser.add_argument("--workspace-name", required=True, help="ML workspace name") + + # Actions + parser.add_argument( + "--action", + choices=["setup", "upload", "train", "register"], + default="train", + help="Action to perform", + ) + + # Training settings + parser.add_argument( + "--experiment-name", default="phi36-lora-training", help="Experiment name" + ) + parser.add_argument( + "--compute-name", default="phi36-gpu-cluster", help="Compute cluster name" + ) + parser.add_argument( + "--vm-size", + default="Standard_NC6s_v3", + help="VM size for compute (Standard_NC6s_v3 = 1x V100)", + ) + parser.add_argument( + "--dataset-path", default="../../datasets/chat/dolly", help="Local dataset path" + ) + parser.add_argument( + "--dataset-name", default="dolly-chat-dataset", help="Dataset name in Azure ML" + ) + parser.add_argument( + "--config", default="./lora/lora.yaml", help="Path to lora.yaml config" + ) + parser.add_argument( + "--max-train-samples", + type=int, + default=None, + help="Limit training samples (for testing)", + ) + + # Model registration + parser.add_argument("--job-name", help="Job name for model registration") + parser.add_argument( + "--model-name", default="phi36-lora-adapter", help="Registered model name" + ) + + args = parser.parse_args() + + # Initialize trainer + trainer = AzureMLLoRATrainer( + subscription_id=args.subscription_id, + resource_group=args.resource_group, + workspace_name=args.workspace_name, + ) + + if args.action == "setup": + print("\n=== Setting up Azure ML infrastructure ===") + trainer.create_or_get_compute( + compute_name=args.compute_name, vm_size=args.vm_size + ) + trainer.create_environment() + print("\n✓ Setup complete!") + + elif args.action == "upload": + print("\n=== Uploading dataset ===") + trainer.upload_dataset( + dataset_path=args.dataset_path, dataset_name=args.dataset_name + ) + print("\n✓ Upload complete!") + + elif args.action == "train": + print("\n=== Submitting training job ===") + job = trainer.submit_training_job( + experiment_name=args.experiment_name, + compute_name=args.compute_name, + dataset_name=args.dataset_name, + config_path=args.config, + max_train_samples=args.max_train_samples, + ) + print("\n✓ Job submitted!") + print(f"\nTo monitor: {job.studio_url}") + + elif args.action == "register": + if not args.job_name: + print("Error: --job-name required for model registration") + return + print("\n=== Registering model ===") + model = trainer.register_model( + job_name=args.job_name, model_name=args.model_name + ) + print("\n✓ Model registered!") + + +if __name__ == "__main__": + if not AZURE_ML_AVAILABLE: + print("\n" + "=" * 70) + print(" Azure ML SDK Required") + print("=" * 70) + print("\nInstall dependencies:") + print(" pip install azure-ai-ml azure-identity") + print("\nAuthenticate:") + print(" az login") + print("=" * 70) + else: + main() diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/README.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/README.md index a09a7c17e..72d16a197 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/README.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/README.md @@ -1,93 +1,93 @@ -# Azure ML Cloud Training for Phi-3.6 LoRA - -This guide shows how to run the LoRA fine-tuning script `scripts/train_lora.py` on Azure Machine Learning for scalable GPU training and optional hyperparameter optimization (HPO). - -## 1. Prerequisites - -1. Azure Subscription -2. Azure ML Workspace (Portal or CLI) -3. GPU Compute Cluster (e.g. `Standard_NC6s_v3`, `Standard_NC12`, `Standard_NC24`) or CPU for smoke tests. -4. Dataset uploaded (we use Dolly manifest example) to the default workspace datastore. -5. Azure CLI with ML extension: `az extension add -n ml` - -### Workspace creation (CLI) -```powershell -az account set --subscription -az group create -n -l -az ml workspace create -n -g -``` - -### Compute cluster (GPU) -```powershell -az ml compute create -n gpu-cluster --type amlcompute --size Standard_NC6s_v3 --min-instances 0 --max-instances 2 --idle-time-before-scale-down 120 -``` - -## 2. Upload Dataset Manifest -The training script accepts `--train-manifest` and `--eval-manifest`. A manifest is a text file listing one JSONL path per line. - -Upload Dolly JSONL and manifest: -```powershell -az ml data create --name dolly-manifest --path datasets/chat/dolly/manifest.txt --type uri_file --version 1 -``` -(Alternatively use `workspaceblobstore` path directly as shown in the job YAML.) - -## 3. Single Training Job -`azureml/job-lora-train.yml` runs one LoRA training with moderate sample limits. Adjust inputs or override at submit time: -```powershell -az ml job create -f AI/microsoft_phi-silica-3.6_v1/azureml/job-lora-train.yml --set inputs.max_train_samples=256 inputs.max_eval_samples=64 compute=azureml:gpu-cluster -``` -Stream logs until completion. - -## 4. Hyperparameter Sweep (HPO) -`azureml/job-lora-sweep.yml` performs random search over learning rate, dropout, epochs, and batch sizes. Submit: -```powershell -az ml job create -f AI/microsoft_phi-silica-3.6_v1/azureml/job-lora-sweep.yml --set compute=azureml:gpu-cluster -``` -Monitor trials: -```powershell -az ml job show -n -az ml job stream -n -``` -Best trial metrics and artifacts are stored under the job outputs. - -## 5. Artifacts & Outputs -The script writes adapters and tokenizer into the mounted output folder (`outputs/model_out/`). For sweeps each trial has its own child folder. Download after completion: -```powershell -az ml job download -n --output-name model_out --download-path .\downloaded_artifacts -``` - -## 6. Metrics & Observability -Set environment variables in the job YAML or via `--set environment.variables.` prefix for: -- `AZURE_LOG_ANALYTICS_WORKSPACE_ID` / `AZURE_LOG_ANALYTICS_SHARED_KEY` for Log Analytics -- `APPLICATIONINSIGHTS_CONNECTION_STRING` for App Insights -- `OTEL_EXPORTER_OTLP_ENDPOINT` for OpenTelemetry - -Metrics stream to `metrics.jsonl` and optionally Azure services. - -## 7. GitHub Actions CI Trigger -Workflow file: `.github/workflows/azureml-train.yml`. Dispatch manually providing workspace, subscription, resource group, compute, and job YAML path. - -## 8. Cost Optimization Tips -- Start with small `max_train_samples` (128–512) & `epochs=1`. -- Prefer `Standard_NC6s_v3` for low-cost experiments. -- Use sweeps with limited `max_total_trials` to cap spend. -- Turn cluster min nodes to 0 for auto-scale down. - -## 9. Custom Environment -If curated PyTorch image unavailable, uncomment environment build section in job YAML and use `azureml/environment.yml`. This creates a Conda-based environment with pinned deps. - -## 10. Resume / Further Training -To resume or stack adapters, download `lora_adapter` directory and re-launch job referencing it via a future script extension (not yet implemented). Keep track of perplexity trends in `metrics.jsonl`. - -## 11. Troubleshooting -- Dependency errors: ensure curated image includes CUDA & torch. Otherwise build custom environment. -- `ResourceNotFound`: verify datastore path or use `az ml data create`. -- Slow startup: First pull of large GPU image; subsequent pulls are faster. -- OOM: Reduce `train_batch_size`, sequence length in `lora.yaml`, or switch to larger GPU. - -## 12. Next Steps -- Add checkpoint resume parameter. -- Integrate model registry registration (az ml model create on completion). -- Automate evaluation & reporting. - -Happy training! +# Azure ML Cloud Training for Phi-3.6 LoRA + +This guide shows how to run the LoRA fine-tuning script `scripts/train_lora.py` on Azure Machine Learning for scalable GPU training and optional hyperparameter optimization (HPO). + +## 1. Prerequisites + +1. Azure Subscription +2. Azure ML Workspace (Portal or CLI) +3. GPU Compute Cluster (e.g. `Standard_NC6s_v3`, `Standard_NC12`, `Standard_NC24`) or CPU for smoke tests. +4. Dataset uploaded (we use Dolly manifest example) to the default workspace datastore. +5. Azure CLI with ML extension: `az extension add -n ml` + +### Workspace creation (CLI) +```powershell +az account set --subscription +az group create -n -l +az ml workspace create -n -g +``` + +### Compute cluster (GPU) +```powershell +az ml compute create -n gpu-cluster --type amlcompute --size Standard_NC6s_v3 --min-instances 0 --max-instances 2 --idle-time-before-scale-down 120 +``` + +## 2. Upload Dataset Manifest +The training script accepts `--train-manifest` and `--eval-manifest`. A manifest is a text file listing one JSONL path per line. + +Upload Dolly JSONL and manifest: +```powershell +az ml data create --name dolly-manifest --path datasets/chat/dolly/manifest.txt --type uri_file --version 1 +``` +(Alternatively use `workspaceblobstore` path directly as shown in the job YAML.) + +## 3. Single Training Job +`azureml/job-lora-train.yml` runs one LoRA training with moderate sample limits. Adjust inputs or override at submit time: +```powershell +az ml job create -f AI/microsoft_phi-silica-3.6_v1/azureml/job-lora-train.yml --set inputs.max_train_samples=256 inputs.max_eval_samples=64 compute=azureml:gpu-cluster +``` +Stream logs until completion. + +## 4. Hyperparameter Sweep (HPO) +`azureml/job-lora-sweep.yml` performs random search over learning rate, dropout, epochs, and batch sizes. Submit: +```powershell +az ml job create -f AI/microsoft_phi-silica-3.6_v1/azureml/job-lora-sweep.yml --set compute=azureml:gpu-cluster +``` +Monitor trials: +```powershell +az ml job show -n +az ml job stream -n +``` +Best trial metrics and artifacts are stored under the job outputs. + +## 5. Artifacts & Outputs +The script writes adapters and tokenizer into the mounted output folder (`outputs/model_out/`). For sweeps each trial has its own child folder. Download after completion: +```powershell +az ml job download -n --output-name model_out --download-path .\downloaded_artifacts +``` + +## 6. Metrics & Observability +Set environment variables in the job YAML or via `--set environment.variables.` prefix for: +- `AZURE_LOG_ANALYTICS_WORKSPACE_ID` / `AZURE_LOG_ANALYTICS_SHARED_KEY` for Log Analytics +- `APPLICATIONINSIGHTS_CONNECTION_STRING` for App Insights +- `OTEL_EXPORTER_OTLP_ENDPOINT` for OpenTelemetry + +Metrics stream to `metrics.jsonl` and optionally Azure services. + +## 7. GitHub Actions CI Trigger +Workflow file: `.github/workflows/azureml-train.yml`. Dispatch manually providing workspace, subscription, resource group, compute, and job YAML path. + +## 8. Cost Optimization Tips +- Start with small `max_train_samples` (128–512) & `epochs=1`. +- Prefer `Standard_NC6s_v3` for low-cost experiments. +- Use sweeps with limited `max_total_trials` to cap spend. +- Turn cluster min nodes to 0 for auto-scale down. + +## 9. Custom Environment +If curated PyTorch image unavailable, uncomment environment build section in job YAML and use `azureml/environment.yml`. This creates a Conda-based environment with pinned deps. + +## 10. Resume / Further Training +To resume or stack adapters, download `lora_adapter` directory and re-launch job referencing it via a future script extension (not yet implemented). Keep track of perplexity trends in `metrics.jsonl`. + +## 11. Troubleshooting +- Dependency errors: ensure curated image includes CUDA & torch. Otherwise build custom environment. +- `ResourceNotFound`: verify datastore path or use `az ml data create`. +- Slow startup: First pull of large GPU image; subsequent pulls are faster. +- OOM: Reduce `train_batch_size`, sequence length in `lora.yaml`, or switch to larger GPU. + +## 12. Next Steps +- Add checkpoint resume parameter. +- Integrate model registry registration (az ml model create on completion). +- Automate evaluation & reporting. + +Happy training! diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/environment.yml b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/environment.yml index 76db4afc0..6a279bf0f 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/environment.yml +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/environment.yml @@ -1,18 +1,18 @@ -name: qai-lora-train -channels: - - conda-forge - - defaults -dependencies: - - python=3.10 - - pip>=23.0 - - cuda-version=12.1 # Comment out if using CPU compute - - pip: - - pyyaml>=6.0 - - datasets>=2.19.0 - - transformers>=4.43.0 - - peft>=0.12.0 - - accelerate>=0.33.0 - - tqdm>=4.66.0 - - einops>=0.7.0 - # torch is provided by curated CUDA envs; if building from this file on CPU, uncomment a CPU wheel: - # - torch==2.4.0 +name: qai-lora-train +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 + - pip>=23.0 + - cuda-version=12.1 # Comment out if using CPU compute + - pip: + - pyyaml>=6.0 + - datasets>=2.19.0 + - transformers>=4.43.0 + - peft>=0.12.0 + - accelerate>=0.33.0 + - tqdm>=4.66.0 + - einops>=0.7.0 + # torch is provided by curated CUDA envs; if building from this file on CPU, uncomment a CPU wheel: + # - torch==2.4.0 diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/job-lora-sweep.yml b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/job-lora-sweep.yml index 2736ae1dc..7732ec09a 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/job-lora-sweep.yml +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/job-lora-sweep.yml @@ -1,77 +1,77 @@ -$schema: https://azuremlschemas.azureedge.net/latest/sweepJob.schema.json - -name: qai-lora-sweep -experiment_name: phi36-lora-hpo -compute: azureml:gpu-cluster -code: ../../.. - -sampling_algorithm: random -max_concurrent_trials: 4 -objective: - goal: minimize - primary_metric: eval_loss - -trial: - command: >- - python AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py - --config AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - --save-dir ${{outputs.model_out}} - --train-manifest ${{inputs.train_manifest}} - --eval-manifest ${{inputs.eval_manifest}} - --hf-model-id ${{inputs.hf_model_id}} - --max-train-samples ${{inputs.max_train_samples}} - --max-eval-samples ${{inputs.max_eval_samples}} - --learning-rate ${{search_space.learning_rate}} - --lora-dropout ${{search_space.lora_dropout}} - --epochs ${{search_space.epochs}} - --train-batch-size ${{search_space.train_batch_size}} - --eval-batch-size ${{search_space.eval_batch_size}} - --no-stream - environment: azureml:AzureML-pytorch-2.4-ubuntu22.04-py310-cuda12@latest - # environment: - # build: - # conda_file: azureml/environment.yml - inputs: - train_manifest: - type: uri_file - path: azureml://datastores/workspaceblobstore/paths/qai/datasets/dolly/manifest.txt - mode: ro_mount - eval_manifest: - type: uri_file - path: azureml://datastores/workspaceblobstore/paths/qai/datasets/dolly/manifest.txt - mode: ro_mount - hf_model_id: - type: string - value: microsoft/Phi-3.5-mini-instruct - max_train_samples: - type: integer - value: 1024 - max_eval_samples: - type: integer - value: 256 - outputs: - model_out: - type: uri_folder - mode: rw_mount - -search_space: - learning_rate: - type: uniform - min_value: 1e-5 - max_value: 5e-4 - lora_dropout: - type: choice - values: [0.05, 0.1, 0.15] - epochs: - type: choice - values: [1, 2] - train_batch_size: - type: choice - values: [1, 2] - eval_batch_size: - type: choice - values: [4, 8] - -limits: - max_total_trials: 12 - timeout: 172800 # 48h +$schema: https://azuremlschemas.azureedge.net/latest/sweepJob.schema.json + +name: qai-lora-sweep +experiment_name: phi36-lora-hpo +compute: azureml:gpu-cluster +code: ../../.. + +sampling_algorithm: random +max_concurrent_trials: 4 +objective: + goal: minimize + primary_metric: eval_loss + +trial: + command: >- + python AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py + --config AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + --save-dir ${{outputs.model_out}} + --train-manifest ${{inputs.train_manifest}} + --eval-manifest ${{inputs.eval_manifest}} + --hf-model-id ${{inputs.hf_model_id}} + --max-train-samples ${{inputs.max_train_samples}} + --max-eval-samples ${{inputs.max_eval_samples}} + --learning-rate ${{search_space.learning_rate}} + --lora-dropout ${{search_space.lora_dropout}} + --epochs ${{search_space.epochs}} + --train-batch-size ${{search_space.train_batch_size}} + --eval-batch-size ${{search_space.eval_batch_size}} + --no-stream + environment: azureml:AzureML-pytorch-2.4-ubuntu22.04-py310-cuda12@latest + # environment: + # build: + # conda_file: azureml/environment.yml + inputs: + train_manifest: + type: uri_file + path: azureml://datastores/workspaceblobstore/paths/qai/datasets/dolly/manifest.txt + mode: ro_mount + eval_manifest: + type: uri_file + path: azureml://datastores/workspaceblobstore/paths/qai/datasets/dolly/manifest.txt + mode: ro_mount + hf_model_id: + type: string + value: microsoft/Phi-3.5-mini-instruct + max_train_samples: + type: integer + value: 1024 + max_eval_samples: + type: integer + value: 256 + outputs: + model_out: + type: uri_folder + mode: rw_mount + +search_space: + learning_rate: + type: uniform + min_value: 1e-5 + max_value: 5e-4 + lora_dropout: + type: choice + values: [0.05, 0.1, 0.15] + epochs: + type: choice + values: [1, 2] + train_batch_size: + type: choice + values: [1, 2] + eval_batch_size: + type: choice + values: [4, 8] + +limits: + max_total_trials: 12 + timeout: 172800 # 48h diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/job-lora-train.yml b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/job-lora-train.yml index 9a082a1ed..c13e55374 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/job-lora-train.yml +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/azureml/job-lora-train.yml @@ -1,82 +1,82 @@ -$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json - -name: qai-lora-train -experiment_name: phi36-lora - -# Default compute; override with: az ml job create -f job-lora-train.yml --set compute=azureml:gpu-cluster -compute: azureml:gpu-cluster - -code: ../../.. - -# Curated GPU environment (adjust if unavailable in your region) -# To use a custom env instead, comment this line and uncomment the 'environment:' block below. -environment: azureml:AzureML-pytorch-2.4-ubuntu22.04-py310-cuda12@latest -# environment: -# build: -# conda_file: azureml/environment.yml -# image: mcr.microsoft.com/azureml/curated/minimal-ubuntu22.04:latest - -inputs: - train_manifest: - type: uri_file - # Upload your manifest to this path or override via --set inputs.train_manifest.path=... - path: azureml://datastores/workspaceblobstore/paths/qai/datasets/dolly/manifest.txt - mode: ro_mount - eval_manifest: - type: uri_file - optional: true - # If omitted, the script will reuse a small subset of the train files for evaluation. - path: azureml://datastores/workspaceblobstore/paths/qai/datasets/dolly/manifest.txt - mode: ro_mount - hf_model_id: - type: string - # microsoft/Phi-3.5-mini-instruct is a good default for experimentation - value: microsoft/Phi-3.5-mini-instruct - max_train_samples: - type: integer - value: 512 - max_eval_samples: - type: integer - value: 128 - learning_rate: - type: number - value: 0.0002 - lora_dropout: - type: number - value: 0.1 - epochs: - type: integer - value: 1 - train_batch_size: - type: integer - value: 1 - eval_batch_size: - type: integer - value: 8 - -outputs: - model_out: - type: uri_folder - mode: rw_mount - -resources: - instance_count: 1 - -limits: - timeout: 86400 # 24h - -command: >- - python AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py - --config AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml - --save-dir ${{outputs.model_out}} - --train-manifest ${{inputs.train_manifest}} - --eval-manifest ${{inputs.eval_manifest}} - --hf-model-id ${{inputs.hf_model_id}} - --max-train-samples ${{inputs.max_train_samples}} - --max-eval-samples ${{inputs.max_eval_samples}} - --learning-rate ${{inputs.learning_rate}} - --lora-dropout ${{inputs.lora_dropout}} - --epochs ${{inputs.epochs}} - --train-batch-size ${{inputs.train_batch_size}} - --eval-batch-size ${{inputs.eval_batch_size}} - --no-stream +$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json + +name: qai-lora-train +experiment_name: phi36-lora + +# Default compute; override with: az ml job create -f job-lora-train.yml --set compute=azureml:gpu-cluster +compute: azureml:gpu-cluster + +code: ../../.. + +# Curated GPU environment (adjust if unavailable in your region) +# To use a custom env instead, comment this line and uncomment the 'environment:' block below. +environment: azureml:AzureML-pytorch-2.4-ubuntu22.04-py310-cuda12@latest +# environment: +# build: +# conda_file: azureml/environment.yml +# image: mcr.microsoft.com/azureml/curated/minimal-ubuntu22.04:latest + +inputs: + train_manifest: + type: uri_file + # Upload your manifest to this path or override via --set inputs.train_manifest.path=... + path: azureml://datastores/workspaceblobstore/paths/qai/datasets/dolly/manifest.txt + mode: ro_mount + eval_manifest: + type: uri_file + optional: true + # If omitted, the script will reuse a small subset of the train files for evaluation. + path: azureml://datastores/workspaceblobstore/paths/qai/datasets/dolly/manifest.txt + mode: ro_mount + hf_model_id: + type: string + # microsoft/Phi-3.5-mini-instruct is a good default for experimentation + value: microsoft/Phi-3.5-mini-instruct + max_train_samples: + type: integer + value: 512 + max_eval_samples: + type: integer + value: 128 + learning_rate: + type: number + value: 0.0002 + lora_dropout: + type: number + value: 0.1 + epochs: + type: integer + value: 1 + train_batch_size: + type: integer + value: 1 + eval_batch_size: + type: integer + value: 8 + +outputs: + model_out: + type: uri_folder + mode: rw_mount + +resources: + instance_count: 1 + +limits: + timeout: 86400 # 24h + +command: >- + python AI/microsoft_phi-silica-3.6_v1/scripts/train_lora.py + --config AI/microsoft_phi-silica-3.6_v1/lora/lora.yaml + --save-dir ${{outputs.model_out}} + --train-manifest ${{inputs.train_manifest}} + --eval-manifest ${{inputs.eval_manifest}} + --hf-model-id ${{inputs.hf_model_id}} + --max-train-samples ${{inputs.max_train_samples}} + --max-eval-samples ${{inputs.max_eval_samples}} + --learning-rate ${{inputs.learning_rate}} + --lora-dropout ${{inputs.lora_dropout}} + --epochs ${{inputs.epochs}} + --train-batch-size ${{inputs.train_batch_size}} + --eval-batch-size ${{inputs.eval_batch_size}} + --no-stream diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/eval_manifest.txt b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/eval_manifest.txt index 698a1e21d..5acb34f8f 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/eval_manifest.txt +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/eval_manifest.txt @@ -1 +1 @@ -AI\microsoft_phi-silica-3.6_v1\data\test.json +AI\microsoft_phi-silica-3.6_v1\data\test.json diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/test.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/test.json index 93ca87afe..4ec58405c 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/test.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/test.json @@ -1,2 +1,2 @@ -{"messages":[{"role":"user","content":"Tell me a joke."},{"role":"assistant","content":"Why did the scarecrow win an award? Because he was outstanding in his field."}]} +{"messages":[{"role":"user","content":"Tell me a joke."},{"role":"assistant","content":"Why did the scarecrow win an award? Because he was outstanding in his field."}]} {"messages":[{"role":"user","content":"What is 2+2?"},{"role":"assistant","content":"4"}]} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/train.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/train.json index e443a533a..7357e3837 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/train.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/train.json @@ -1,2 +1,2 @@ -{"messages":[{"role":"user","content":"Hello, bot."},{"role":"assistant","content":"Hi! How can I assist?"}]} +{"messages":[{"role":"user","content":"Hello, bot."},{"role":"assistant","content":"Hi! How can I assist?"}]} {"messages":[{"role":"user","content":"Who are you?"},{"role":"assistant","content":"I am your assistant."}]} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/train_manifest.txt b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/train_manifest.txt index f169d2352..d01eeafab 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/train_manifest.txt +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data/train_manifest.txt @@ -1 +1 @@ -AI\microsoft_phi-silica-3.6_v1\data\train.json +AI\microsoft_phi-silica-3.6_v1\data\train.json diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/README.md b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/README.md index 00ed673a8..e9d7c81db 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/README.md +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/README.md @@ -1,207 +1,207 @@ ---- -base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 -library_name: peft -pipeline_tag: text-generation -tags: -- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0 -- lora -- transformers ---- - -# Model Card for Model ID - - - - - -## Model Details - -### Model Description - - - - - -- **Developed by:** [More Information Needed] -- **Funded by [optional]:** [More Information Needed] -- **Shared by [optional]:** [More Information Needed] -- **Model type:** [More Information Needed] -- **Language(s) (NLP):** [More Information Needed] -- **License:** [More Information Needed] -- **Finetuned from model [optional]:** [More Information Needed] - -### Model Sources [optional] - - - -- **Repository:** [More Information Needed] -- **Paper [optional]:** [More Information Needed] -- **Demo [optional]:** [More Information Needed] - -## Uses - - - -### Direct Use - - - -[More Information Needed] - -### Downstream Use [optional] - - - -[More Information Needed] - -### Out-of-Scope Use - - - -[More Information Needed] - -## Bias, Risks, and Limitations - - - -[More Information Needed] - -### Recommendations - - - -Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. - -## How to Get Started with the Model - -Use the code below to get started with the model. - -[More Information Needed] - -## Training Details - -### Training Data - - - -[More Information Needed] - -### Training Procedure - - - -#### Preprocessing [optional] - -[More Information Needed] - - -#### Training Hyperparameters - -- **Training regime:** [More Information Needed] - -#### Speeds, Sizes, Times [optional] - - - -[More Information Needed] - -## Evaluation - - - -### Testing Data, Factors & Metrics - -#### Testing Data - - - -[More Information Needed] - -#### Factors - - - -[More Information Needed] - -#### Metrics - - - -[More Information Needed] - -### Results - -[More Information Needed] - -#### Summary - - - -## Model Examination [optional] - - - -[More Information Needed] - -## Environmental Impact - - - -Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - -- **Hardware Type:** [More Information Needed] -- **Hours used:** [More Information Needed] -- **Cloud Provider:** [More Information Needed] -- **Compute Region:** [More Information Needed] -- **Carbon Emitted:** [More Information Needed] - -## Technical Specifications [optional] - -### Model Architecture and Objective - -[More Information Needed] - -### Compute Infrastructure - -[More Information Needed] - -#### Hardware - -[More Information Needed] - -#### Software - -[More Information Needed] - -## Citation [optional] - - - -**BibTeX:** - -[More Information Needed] - -**APA:** - -[More Information Needed] - -## Glossary [optional] - - - -[More Information Needed] - -## More Information [optional] - -[More Information Needed] - -## Model Card Authors [optional] - -[More Information Needed] - -## Model Card Contact - -[More Information Needed] -### Framework versions - +--- +base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 +library_name: peft +pipeline_tag: text-generation +tags: +- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0 +- lora +- transformers +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + - PEFT 0.18.0 diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/adapter_config.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/adapter_config.json index 085f2969a..5b1720f79 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/adapter_config.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/adapter_config.json @@ -1,45 +1,45 @@ -{ - "alora_invocation_tokens": null, - "alpha_pattern": {}, - "arrow_config": null, - "auto_mapping": null, - "base_model_name_or_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", - "bias": "none", - "corda_config": null, - "ensure_weight_tying": false, - "eva_config": null, - "exclude_modules": null, - "fan_in_fan_out": false, - "inference_mode": true, - "init_lora_weights": true, - "layer_replication": null, - "layers_pattern": null, - "layers_to_transform": null, - "loftq_config": {}, - "lora_alpha": 16, - "lora_bias": false, - "lora_dropout": 0.1, - "megatron_config": null, - "megatron_core": "megatron.core", - "modules_to_save": null, - "peft_type": "LORA", - "peft_version": "0.18.0", - "qalora_group_size": 16, - "r": 8, - "rank_pattern": {}, - "revision": null, - "target_modules": [ - "k_proj", - "v_proj", - "fc1", - "o_proj", - "q_proj", - "fc2" - ], - "target_parameters": null, - "task_type": "CAUSAL_LM", - "trainable_token_indices": null, - "use_dora": false, - "use_qalora": false, - "use_rslora": false +{ + "alora_invocation_tokens": null, + "alpha_pattern": {}, + "arrow_config": null, + "auto_mapping": null, + "base_model_name_or_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "bias": "none", + "corda_config": null, + "ensure_weight_tying": false, + "eva_config": null, + "exclude_modules": null, + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layer_replication": null, + "layers_pattern": null, + "layers_to_transform": null, + "loftq_config": {}, + "lora_alpha": 16, + "lora_bias": false, + "lora_dropout": 0.1, + "megatron_config": null, + "megatron_core": "megatron.core", + "modules_to_save": null, + "peft_type": "LORA", + "peft_version": "0.18.0", + "qalora_group_size": 16, + "r": 8, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "k_proj", + "v_proj", + "fc1", + "o_proj", + "q_proj", + "fc2" + ], + "target_parameters": null, + "task_type": "CAUSAL_LM", + "trainable_token_indices": null, + "use_dora": false, + "use_qalora": false, + "use_rslora": false } diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/added_tokens.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/added_tokens.json index 62206137d..c9d3d3a1b 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/added_tokens.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/added_tokens.json @@ -1,13 +1,13 @@ -{ - "<|assistant|>": 32001, - "<|endoftext|>": 32000, - "<|end|>": 32007, - "<|placeholder1|>": 32002, - "<|placeholder2|>": 32003, - "<|placeholder3|>": 32004, - "<|placeholder4|>": 32005, - "<|placeholder5|>": 32008, - "<|placeholder6|>": 32009, - "<|system|>": 32006, - "<|user|>": 32010 -} +{ + "<|assistant|>": 32001, + "<|endoftext|>": 32000, + "<|end|>": 32007, + "<|placeholder1|>": 32002, + "<|placeholder2|>": 32003, + "<|placeholder3|>": 32004, + "<|placeholder4|>": 32005, + "<|placeholder5|>": 32008, + "<|placeholder6|>": 32009, + "<|system|>": 32006, + "<|user|>": 32010 +} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/special_tokens_map.json b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/special_tokens_map.json index 5cb3101c5..492d4b296 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/special_tokens_map.json +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/data_out/lora_training/checkpoint-64/special_tokens_map.json @@ -1,30 +1,30 @@ -{ - "bos_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "eos_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "pad_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "unk_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - } -} +{ + "bos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/gpu_optimizer.py b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/gpu_optimizer.py index c439f1e19..4cd2c3b7a 100644 --- a/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/gpu_optimizer.py +++ b/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/scripts/gpu_optimizer.py @@ -114,7 +114,7 @@ def _get_driver_version(self) -> str: text=True, ) return result.stdout.strip().split("\n")[0] - except: + except Exception: return "N/A" def _cpu_fallback(self) -> GPUInfo: @@ -270,7 +270,7 @@ def _supports_bf16(self, gpu: GPUInfo) -> bool: try: major_version = int(gpu.compute_capability.split(".")[0]) return major_version >= 8 # Ampere (A100, RTX 30xx) and newer - except: + except Exception: return False def _print_profile(self, profile: OptimizationProfile): diff --git a/ai-projects/quantum-ml/src/azure_ml_integration.py b/ai-projects/quantum-ml/src/azure_ml_integration.py index 28fbfc8db..1e848c8bc 100644 --- a/ai-projects/quantum-ml/src/azure_ml_integration.py +++ b/ai-projects/quantum-ml/src/azure_ml_integration.py @@ -69,7 +69,7 @@ def connect_workspace( # Try to load from config self.workspace = Workspace.from_config() logger.info(f"✓ Loaded workspace from config: {self.workspace.name}") - except: + except Exception: # Connect with explicit parameters self.workspace = Workspace( subscription_id=subscription_id, @@ -108,7 +108,7 @@ def create_compute_cluster( ) logger.info(f"✓ Using existing cluster: {cluster_name}") - except: + except Exception: # Create new cluster logger.info("Creating new compute cluster...") diff --git a/ai-projects/quantum-ml/src/quantum_classifier_enhanced.py b/ai-projects/quantum-ml/src/quantum_classifier_enhanced.py index a19877c31..7abe1949b 100644 --- a/ai-projects/quantum-ml/src/quantum_classifier_enhanced.py +++ b/ai-projects/quantum-ml/src/quantum_classifier_enhanced.py @@ -208,7 +208,7 @@ def get_circuit_info(self) -> dict: try: drawer = qml.draw(self.qnode) circuit_str = drawer(dummy_inputs, dummy_weights) - except: + except Exception: circuit_str = "Circuit drawing not available" return { diff --git a/ai-projects/quantum-ml/src/quantum_llm/config.py b/ai-projects/quantum-ml/src/quantum_llm/config.py index d0b7a9ba6..aea1763cc 100644 --- a/ai-projects/quantum-ml/src/quantum_llm/config.py +++ b/ai-projects/quantum-ml/src/quantum_llm/config.py @@ -11,6 +11,52 @@ from typing import Literal, Optional +def _read_int_env(name: str, default: int) -> int: + """Return integer env var value or fallback to default.""" + raw = os.getenv(name) + if raw is None: + return default + try: + return int(raw) + except (TypeError, ValueError): + return default + + +def _read_float_env(name: str, default: float) -> float: + """Return float env var value or fallback to default.""" + raw = os.getenv(name) + if raw is None: + return default + try: + return float(raw) + except (TypeError, ValueError): + return default + + +def _read_backend_env(name: str, default: str = "auto") -> str: + """Return validated backend env var value or fallback.""" + raw = (os.getenv(name, default) or default).strip() + valid_backends = {"auto", "qiskit", "pennylane", "classical"} + return raw if raw in valid_backends else default + + +def _coerce_int(value: object, default: int, minimum: int = 1) -> int: + """Convert value to int with safe default and lower bound.""" + try: + parsed = int(value) + except (TypeError, ValueError): + parsed = default + return max(minimum, parsed) + + +def _coerce_float(value: object, default: float) -> float: + """Convert value to float with safe default.""" + try: + return float(value) + except (TypeError, ValueError): + return default + + @dataclass class QuantumLLMConfig: """Settings for QuantumLLMPipeline and its sub-components.""" @@ -40,27 +86,47 @@ class QuantumLLMConfig: # Performance use_thread: bool = True # asyncio.to_thread for blocking quantum calls + def __post_init__(self) -> None: + """Normalize and clamp config values to safe, valid ranges.""" + valid_backends = {"auto", "qiskit", "pennylane", "classical"} + if self.backend not in valid_backends: + self.backend = "auto" + + self.num_qubits = _coerce_int(self.num_qubits, default=4, minimum=1) + self.shots = _coerce_int(self.shots, default=512, minimum=1) + self.num_layers = _coerce_int(self.num_layers, default=2, minimum=1) + self.top_k = _coerce_int(self.top_k, default=10, minimum=1) + + self.temperature_blend = min( + 1.0, max(0.0, _coerce_float(self.temperature_blend, default=0.3)) + ) + self.temperature = max(0.0, _coerce_float(self.temperature, default=0.7)) + + self.max_prompt_chars = _coerce_int( + self.max_prompt_chars, default=8000, minimum=1 + ) + self.max_tokens_cap = _coerce_int(self.max_tokens_cap, default=2048, minimum=1) + self.max_tokens = _coerce_int(self.max_tokens, default=512, minimum=1) + self.max_tokens = min(self.max_tokens, self.max_tokens_cap) + + self.provider = (self.provider or "auto").strip() + @classmethod def from_env(cls) -> "QuantumLLMConfig": """Build config from environment variables.""" - # The `type: ignore[arg-type]` is suppressed because `os.getenv` returns - # `str | None`; we validate and fall back to "auto" if the value is invalid. - raw_backend = os.getenv("QUANTUM_LLM_BACKEND", "auto") - valid_backends = {"auto", "qiskit", "pennylane", "classical"} - backend_val = raw_backend if raw_backend in valid_backends else "auto" return cls( - backend=backend_val, # type: ignore[arg-type] - num_qubits=int(os.getenv("QUANTUM_LLM_QUBITS", "4")), - shots=int(os.getenv("QUANTUM_LLM_SHOTS", "512")), - num_layers=int(os.getenv("QUANTUM_LLM_LAYERS", "2")), - top_k=int(os.getenv("QUANTUM_LLM_TOP_K", "10")), - temperature_blend=float(os.getenv("QUANTUM_LLM_TEMP_BLEND", "0.3")), + backend=_read_backend_env("QUANTUM_LLM_BACKEND", "auto"), + num_qubits=_read_int_env("QUANTUM_LLM_QUBITS", 4), + shots=_read_int_env("QUANTUM_LLM_SHOTS", 512), + num_layers=_read_int_env("QUANTUM_LLM_LAYERS", 2), + top_k=_read_int_env("QUANTUM_LLM_TOP_K", 10), + temperature_blend=_read_float_env("QUANTUM_LLM_TEMP_BLEND", 0.3), provider=os.getenv("QUANTUM_LLM_PROVIDER", "auto"), model=os.getenv("QUANTUM_LLM_MODEL"), - temperature=float(os.getenv("QUANTUM_LLM_TEMPERATURE", "0.7")), - max_tokens=int(os.getenv("QUANTUM_LLM_MAX_TOKENS", "512")), - max_prompt_chars=int(os.getenv("QUANTUM_LLM_MAX_PROMPT_CHARS", "8000")), - max_tokens_cap=int(os.getenv("QUANTUM_LLM_MAX_TOKENS_CAP", "2048")), + temperature=_read_float_env("QUANTUM_LLM_TEMPERATURE", 0.7), + max_tokens=_read_int_env("QUANTUM_LLM_MAX_TOKENS", 512), + max_prompt_chars=_read_int_env("QUANTUM_LLM_MAX_PROMPT_CHARS", 8000), + max_tokens_cap=_read_int_env("QUANTUM_LLM_MAX_TOKENS_CAP", 2048), ) def to_dict(self) -> dict: diff --git a/ai-projects/quantum-ml/src/quantum_llm_monitor.py b/ai-projects/quantum-ml/src/quantum_llm_monitor.py index b18d3a2f8..fef1dd9da 100644 --- a/ai-projects/quantum-ml/src/quantum_llm_monitor.py +++ b/ai-projects/quantum-ml/src/quantum_llm_monitor.py @@ -234,7 +234,7 @@ def get_current_performance(self) -> Dict[str, float]: metrics["gpu_memory_used_mb"] = self.torch.cuda.memory_allocated() / ( 1024 * 1024 ) - except: + except Exception: pass self.performance_history.append( diff --git a/ai-projects/quantum-ml/web_app.py b/ai-projects/quantum-ml/web_app.py index 38620b244..c5ba884c0 100644 --- a/ai-projects/quantum-ml/web_app.py +++ b/ai-projects/quantum-ml/web_app.py @@ -1088,14 +1088,30 @@ def load_checkpoint(): if not resolved_path.exists(): return jsonify({"error": "Checkpoint file not found"}), 404 - checkpoint = np.load(str(resolved_path), allow_pickle=True) + checkpoint = np.load(str(resolved_path), allow_pickle=False) weights = checkpoint["weights"] epoch = int(checkpoint["epoch"]) - config = ( - checkpoint["config"].item() - if isinstance(checkpoint["config"], np.ndarray) - else checkpoint["config"] - ) + try: + config_value = checkpoint["config"] + if isinstance(config_value, np.ndarray): + config = ( + config_value.item() + if config_value.ndim == 0 + else config_value.tolist() + ) + elif isinstance(config_value, np.generic): + config = config_value.item() + else: + config = config_value + except ValueError: + return ( + jsonify( + { + "error": "Unsupported checkpoint format; re-save checkpoint in safe format" + } + ), + 422, + ) return jsonify( { diff --git a/app.py b/app.py index 5c7e586b0..4ad2e8dc0 100644 --- a/app.py +++ b/app.py @@ -1,15 +1,249 @@ +"""Aria CLI — minimal OpenAI Responses API client. + +Reads a prompt from CLI args or stdin, calls the OpenAI Responses API, +and prints the model's reply. Designed to fail gracefully and to be +trivially testable. + +Usage: + OPENAI_API_KEY=sk-... python app.py "Explain quantum entanglement" + echo "Hello" | python app.py + python app.py --model gpt-4o-mini --temperature 0.0 "Refactor this code" + +Environment variables: + OPENAI_API_KEY Required. OpenAI API key. + OPENAI_MODEL Optional. Overrides default model. + OPENAI_BASE_URL Optional. Custom API base (e.g. Azure-compatible proxy). + OPENAI_ORG Optional. Organization ID. + OPENAI_TIMEOUT Optional. Request timeout in seconds (default: 60). """ -Aria Headless Entrypoint -Replaces UI mode with a minimal bootstrap runner. -""" -from core.runner import AriaRunner +from __future__ import annotations + +import argparse +import logging +import os +import sys +from typing import Any, Iterable + +try: + from openai import ( + APIConnectionError, + APIError, + AuthenticationError, + OpenAI, + RateLimitError, + ) +except ImportError as exc: # pragma: no cover - import-time guard + sys.stderr.write( + "Error: the 'openai' package is not installed.\n" + "Install it with: pip install openai\n" + ) + raise SystemExit(1) from exc + + +# --------------------------------------------------------------------------- # +# Constants & configuration +# --------------------------------------------------------------------------- # + +DEFAULT_MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini") +DEFAULT_TEMPERATURE = 0.2 +DEFAULT_TIMEOUT = float(os.getenv("OPENAI_TIMEOUT", "60")) +SYSTEM_PROMPT = ( + "You are a concise AI coding assistant. " + "Return practical, code-focused responses." +) + +# Exit codes +EXIT_OK = 0 +EXIT_USAGE = 2 +EXIT_AUTH = 3 +EXIT_RATE_LIMIT = 4 +EXIT_NETWORK = 5 +EXIT_API = 6 +EXIT_UNEXPECTED = 1 + +logger = logging.getLogger("aria.app") + + +# --------------------------------------------------------------------------- # +# Response parsing +# --------------------------------------------------------------------------- # + +def _extract_text(resp: Any) -> str: + """Extract plain text from an OpenAI Responses API result. + + Prefers the convenience ``output_text`` attribute when available, and + falls back to walking the structured ``output`` list. Always returns + a stripped string (possibly empty). + """ + # Fast path: SDK convenience attribute. + output_text = getattr(resp, "output_text", None) + if isinstance(output_text, str) and output_text.strip(): + return output_text.strip() + + # Fallback: walk the structured output. + parts: list[str] = [] + output: Iterable[Any] = getattr(resp, "output", None) or [] + for item in output: + contents: Iterable[Any] = getattr(item, "content", None) or [] + for content in contents: + if getattr(content, "type", "") != "output_text": + continue + text = getattr(content, "text", "") + # Some SDK versions wrap text in an object with a `.value` attr. + if hasattr(text, "value"): + text = text.value + if isinstance(text, str) and text: + parts.append(text) + + return "\n".join(parts).strip() + + +# --------------------------------------------------------------------------- # +# Core request +# --------------------------------------------------------------------------- # + +def ask_ai( + client: OpenAI, + prompt: str, + *, + model: str = DEFAULT_MODEL, + temperature: float = DEFAULT_TEMPERATURE, + system_prompt: str = SYSTEM_PROMPT, +) -> str: + """Send ``prompt`` to the Responses API and return the extracted text.""" + if not prompt or not prompt.strip(): + raise ValueError("Prompt cannot be empty.") + + logger.debug("Requesting completion: model=%s temperature=%s", model, temperature) + resp = client.responses.create( + model=model, + input=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": prompt}, + ], + temperature=temperature, + ) + return _extract_text(resp) + + +# --------------------------------------------------------------------------- # +# CLI plumbing +# --------------------------------------------------------------------------- # + +def _read_prompt(args_prompt: list[str]) -> str: + """Read prompt from CLI args, piped stdin, or interactive input.""" + if args_prompt: + return " ".join(args_prompt).strip() + + # Piped stdin (non-TTY) — read the whole buffer. + if not sys.stdin.isatty(): + return sys.stdin.read().strip() + + # Interactive fallback. + try: + return input("Prompt: ").strip() + except EOFError: + return "" + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="aria-app", + description="Minimal OpenAI Responses API CLI for Aria.", + ) + parser.add_argument( + "prompt", + nargs="*", + help="Prompt text. If omitted, reads from stdin.", + ) + parser.add_argument( + "--model", + default=DEFAULT_MODEL, + help=f"Model name (default: {DEFAULT_MODEL}).", + ) + parser.add_argument( + "--temperature", + type=float, + default=DEFAULT_TEMPERATURE, + help=f"Sampling temperature (default: {DEFAULT_TEMPERATURE}).", + ) + parser.add_argument( + "--system", + default=SYSTEM_PROMPT, + help="Override the system prompt.", + ) + parser.add_argument( + "-v", "--verbose", + action="store_true", + help="Enable debug logging.", + ) + return parser + + +def main(argv: list[str] | None = None) -> int: + parser = _build_parser() + args = parser.parse_args(argv) + + logging.basicConfig( + level=logging.DEBUG if args.verbose else logging.WARNING, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + ) + + api_key = os.getenv("OPENAI_API_KEY") + if not api_key: + print( + "Error: missing OPENAI_API_KEY environment variable.", + file=sys.stderr, + ) + return EXIT_AUTH + + prompt = _read_prompt(args.prompt) + if not prompt: + print("Error: prompt cannot be empty.", file=sys.stderr) + return EXIT_USAGE + + client_kwargs: dict[str, Any] = {"api_key": api_key, "timeout": DEFAULT_TIMEOUT} + if base_url := os.getenv("OPENAI_BASE_URL"): + client_kwargs["base_url"] = base_url + if org := os.getenv("OPENAI_ORG"): + client_kwargs["organization"] = org + try: + client = OpenAI(**client_kwargs) + output = ask_ai( + client, + prompt, + model=args.model, + temperature=args.temperature, + system_prompt=args.system, + ) + except AuthenticationError as exc: + print(f"Authentication failed: {exc}", file=sys.stderr) + return EXIT_AUTH + except RateLimitError as exc: + print(f"Rate limit exceeded: {exc}", file=sys.stderr) + return EXIT_RATE_LIMIT + except APIConnectionError as exc: + print(f"Network error reaching OpenAI: {exc}", file=sys.stderr) + return EXIT_NETWORK + except APIError as exc: + print(f"OpenAI API error: {exc}", file=sys.stderr) + return EXIT_API + except ValueError as exc: + print(f"Invalid input: {exc}", file=sys.stderr) + return EXIT_USAGE + except KeyboardInterrupt: + print("Interrupted.", file=sys.stderr) + return 130 + except Exception as exc: # noqa: BLE001 - last-resort safety net + logger.exception("Unexpected failure") + print(f"Unexpected error: {exc}", file=sys.stderr) + return EXIT_UNEXPECTED -def main(): - runner = AriaRunner() - runner.run() + print(output or "(No text returned.)") + return EXIT_OK if __name__ == "__main__": - main() + sys.exit(main()) diff --git a/apps/aria/index.html b/apps/aria/index.html index ee1429674..61294a67c 100644 --- a/apps/aria/index.html +++ b/apps/aria/index.html @@ -2965,8 +2965,62 @@

    📋 Log

    Dashboard · Chat · Store +
    + ⚛️ Quantum Mode: Off +  |  Backend: — +
    Aria Interactive AI Character Platform
    + + diff --git a/apps/aria/server.py b/apps/aria/server.py index 42865bab2..ed7d2de52 100644 --- a/apps/aria/server.py +++ b/apps/aria/server.py @@ -1632,7 +1632,7 @@ def do_POST(self): self.end_headers() error = {"error": str(e), "tags": []} self.wfile.write(json.dumps(error).encode("utf-8")) - except: + except Exception: pass elif self.path == "/api/aria/object" or self.path == "/api/aria/objects": try: @@ -1711,7 +1711,7 @@ def do_POST(self): self.send_header("Content-Type", "application/json") self.end_headers() self.wfile.write(json.dumps({"error": str(e)}).encode("utf-8")) - except: + except Exception: pass return @@ -1803,7 +1803,7 @@ def do_POST(self): } ).encode("utf-8") ) - except: + except Exception: pass return diff --git a/apps/chat/chat.js b/apps/chat/chat.js index 44f2defe5..598bd8e43 100644 --- a/apps/chat/chat.js +++ b/apps/chat/chat.js @@ -760,26 +760,13 @@ async function handleImageUpload(event) { uploadedImage = file.name; // Show preview - visionPreview.textContent = ''; - - const previewImg = document.createElement('img'); - previewImg.src = e.target.result; - previewImg.alt = 'Preview'; - - const previewInfo = document.createElement('div'); - previewInfo.className = 'vision-preview-info'; - - const fileNameSpan = document.createElement('span'); - fileNameSpan.textContent = file.name; - - const clearButton = document.createElement('button'); - clearButton.id = 'visionClearButton'; - clearButton.textContent = '✕'; - - previewInfo.appendChild(fileNameSpan); - previewInfo.appendChild(clearButton); - visionPreview.appendChild(previewImg); - visionPreview.appendChild(previewInfo); + visionPreview.innerHTML = ` + Preview +
    + ${file.name} + +
    + `; visionPreview.style.display = 'block'; // Re-attach clear button listener diff --git a/apps/dashboard/hyperparameter-optimizer.js b/apps/dashboard/hyperparameter-optimizer.js index 2ab614b95..74909eefa 100644 --- a/apps/dashboard/hyperparameter-optimizer.js +++ b/apps/dashboard/hyperparameter-optimizer.js @@ -355,7 +355,7 @@ class HyperparameterOptimizer {
    - + Running ${this.strategy} optimization with ${this.maxTrials} trials...
    @@ -363,11 +363,6 @@ class HyperparameterOptimizer {
    `; - const statusText = document.getElementById('hyperoptStatusText'); - if (statusText) { - statusText.textContent = `Running ${this.strategy} optimization with ${this.maxTrials} trials...`; - } - container.style.display = 'block'; } diff --git a/apps/dashboard/serve.py b/apps/dashboard/serve.py index c8a38ab1a..5ff1264cc 100644 --- a/apps/dashboard/serve.py +++ b/apps/dashboard/serve.py @@ -1036,7 +1036,7 @@ def main(): # Open browser try: webbrowser.open(url) - except: + except Exception: pass try: diff --git a/autotrain.py b/autotrain.py index 3e4cb9b3b..096689372 100644 --- a/autotrain.py +++ b/autotrain.py @@ -4,4 +4,16 @@ The canonical implementation lives in ``scripts/autotrain.py``. """ +from __future__ import annotations + +import warnings + +warnings.warn( + "Importing 'autotrain' from the repository root is deprecated. " + "Import from 'scripts.autotrain' instead.", + DeprecationWarning, + stacklevel=2, +) + from scripts.autotrain import * # noqa: F401,F403 +from scripts.autotrain import __all__ # noqa: F401,E402 diff --git a/docs/aria/aria_controller.js b/docs/aria/aria_controller.js index 10c630656..c9e2b5448 100644 --- a/docs/aria/aria_controller.js +++ b/docs/aria/aria_controller.js @@ -247,7 +247,7 @@ const expressions = { function log(message, isError = false) { const entry = document.createElement('div'); entry.className = 'log-entry'; - entry.style.borderLeftColor = isError ? '#e74c3c' : '#667eea'; + entry.style.borderLeftColor = isError ? '#e74c3c' : '#0f9d89'; entry.style.color = isError ? '#e74c3c' : '#555'; entry.textContent = `${new Date().toLocaleTimeString()}: ${message}`; logContainer.insertBefore(entry, logContainer.firstChild); @@ -1642,7 +1642,7 @@ setInterval(() => { timerDisplay.style.color = '#e74c3c'; timerDisplay.style.animation = 'pulse 0.5s ease infinite'; } else { - timerDisplay.style.color = '#667eea'; + timerDisplay.style.color = '#0f9d89'; timerDisplay.style.animation = 'none'; } } diff --git a/docs/aria/auto-execute.html b/docs/aria/auto-execute.html index 4f39c82b0..6f6ff92f0 100644 --- a/docs/aria/auto-execute.html +++ b/docs/aria/auto-execute.html @@ -17,28 +17,59 @@ + + + diff --git a/docs/aria/index.html b/docs/aria/index.html index e0e3c1d71..d101bf470 100644 --- a/docs/aria/index.html +++ b/docs/aria/index.html @@ -18,22 +18,49 @@ + + + diff --git a/docs/llm-maker/website_maker_ui.html b/docs/llm-maker/website_maker_ui.html index 0bde5d148..4429ddd49 100644 --- a/docs/llm-maker/website_maker_ui.html +++ b/docs/llm-maker/website_maker_ui.html @@ -28,7 +28,7 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 50%, #d3a53e 100%); background-size: 200% 200%; animation: gradientShift 15s ease infinite; min-height: 100vh; @@ -52,7 +52,7 @@ } .header { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; padding: 30px 40px; text-align: center; @@ -131,7 +131,7 @@ .card-header { font-size: 1.5em; margin-bottom: 20px; - color: #667eea; + color: #0f9d89; display: flex; align-items: center; gap: 10px; @@ -163,7 +163,7 @@ .form-group textarea:focus, .form-group select:focus { outline: none; - border-color: #667eea; + border-color: #0f9d89; } .form-group textarea { @@ -172,7 +172,7 @@ } .btn { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; border: none; padding: 14px 28px; @@ -198,7 +198,7 @@ } .btn-secondary { - background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); + background: linear-gradient(135deg, #d95b43 0%, #b8432f 100%); } .btn-sm { @@ -248,7 +248,7 @@ } .website-card:hover { - border-color: #667eea; + border-color: #0f9d89; box-shadow: 0 4px 12px rgba(102, 126, 234, 0.1); } @@ -282,7 +282,7 @@ .tag { display: inline-block; padding: 4px 12px; - background: #667eea; + background: #0f9d89; color: white; border-radius: 20px; font-size: 0.85em; @@ -313,7 +313,7 @@ .modal-header { font-size: 1.5em; margin-bottom: 20px; - color: #667eea; + color: #0f9d89; } .close { @@ -355,7 +355,7 @@ } .file-tab.active { - background: #667eea; + background: #0f9d89; color: white; } diff --git a/docs/monetization/account.html b/docs/monetization/account.html index fc4ef39ae..55e47682f 100644 --- a/docs/monetization/account.html +++ b/docs/monetization/account.html @@ -17,7 +17,7 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); min-height: 100vh; padding: 20px; } @@ -39,7 +39,7 @@ } .header h1 { - color: #667eea; + color: #0f9d89; font-size: 2em; } @@ -51,14 +51,14 @@ .nav-links a { padding: 10px 20px; text-decoration: none; - color: #667eea; - border: 2px solid #667eea; + color: #0f9d89; + border: 2px solid #0f9d89; border-radius: 5px; transition: all 0.3s; } .nav-links a:hover { - background: #667eea; + background: #0f9d89; color: white; } @@ -84,12 +84,12 @@ } .tab:hover { - color: #667eea; + color: #0f9d89; } .tab.active { - color: #667eea; - border-bottom-color: #667eea; + color: #0f9d89; + border-bottom-color: #0f9d89; } .card { @@ -106,7 +106,7 @@ } .card h2 { - color: #667eea; + color: #0f9d89; margin-bottom: 20px; font-size: 1.8em; } @@ -137,7 +137,7 @@ .form-group select:focus, .form-group textarea:focus { outline: none; - border-color: #667eea; + border-color: #0f9d89; } .form-row { @@ -157,7 +157,7 @@ } .button-primary { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } @@ -192,7 +192,7 @@ } .info-box h4 { - color: #667eea; + color: #0f9d89; margin-bottom: 10px; } @@ -245,7 +245,7 @@ } .badge-primary { - background: #667eea; + background: #0f9d89; color: white; } diff --git a/docs/monetization/admin_dashboard.html b/docs/monetization/admin_dashboard.html index cb12512fc..6e88ff3fd 100644 --- a/docs/monetization/admin_dashboard.html +++ b/docs/monetization/admin_dashboard.html @@ -23,7 +23,7 @@ } .header { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; padding: 30px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); @@ -75,7 +75,7 @@ .stat-value { font-size: 2.5em; font-weight: bold; - color: #667eea; + color: #0f9d89; margin-bottom: 5px; } @@ -85,7 +85,7 @@ } .revenue-highlight { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } @@ -104,7 +104,7 @@ } .section h2 { - color: #667eea; + color: #0f9d89; margin-bottom: 20px; font-size: 1.8em; } @@ -151,12 +151,12 @@ } .badge-pro { - background: #667eea; + background: #0f9d89; color: white; } .badge-enterprise { - background: #764ba2; + background: #0a7a6d; color: white; } @@ -181,7 +181,7 @@ .progress-fill { height: 100%; - background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(90deg, #0f9d89 0%, #0a7a6d 100%); transition: width 0.3s ease; } @@ -195,7 +195,7 @@ } .button-primary { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } @@ -225,7 +225,7 @@ .chart-bar { width: 80px; - background: linear-gradient(180deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(180deg, #0f9d89 0%, #0a7a6d 100%); border-radius: 5px 5px 0 0; position: relative; transition: all 0.3s ease; @@ -250,7 +250,7 @@ width: 100%; text-align: center; font-weight: bold; - color: #667eea; + color: #0f9d89; } .alert { @@ -279,7 +279,7 @@ .spinner { border: 4px solid #f3f3f3; - border-top: 4px solid #667eea; + border-top: 4px solid #0f9d89; border-radius: 50%; width: 40px; height: 40px; @@ -295,7 +295,7 @@ .refresh-button { float: right; padding: 8px 16px; - background: #667eea; + background: #0f9d89; color: white; border: none; border-radius: 5px; diff --git a/docs/monetization/analytics-dashboard.html b/docs/monetization/analytics-dashboard.html index 6ae24e3cd..f5f9f4338 100644 --- a/docs/monetization/analytics-dashboard.html +++ b/docs/monetization/analytics-dashboard.html @@ -27,7 +27,7 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); min-height: 100vh; padding: 20px; } @@ -49,7 +49,7 @@ } .header h1 { - color: #667eea; + color: #0f9d89; font-size: 2em; } @@ -61,14 +61,14 @@ .nav-links a { padding: 10px 20px; text-decoration: none; - color: #667eea; - border: 2px solid #667eea; + color: #0f9d89; + border: 2px solid #0f9d89; border-radius: 5px; transition: all 0.3s; } .nav-links a:hover { - background: #667eea; + background: #0f9d89; color: white; } @@ -99,7 +99,7 @@ .metric-value { font-size: 2.5em; font-weight: bold; - color: #667eea; + color: #0f9d89; margin: 10px 0; } @@ -132,7 +132,7 @@ } .chart-section h2 { - color: #667eea; + color: #0f9d89; margin-bottom: 20px; } @@ -186,7 +186,7 @@ } .button-primary { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } @@ -328,7 +328,7 @@

    Usage Analytics

    -

    Top Subscribers by Revenue

    +

    Top Subscribers by Revenue

    @@ -363,7 +363,7 @@

    Top Subscribers by Revenue

    datasets: [{ label: 'MRR', data: [1200, 1600, 1900, 2235], - borderColor: '#667eea', + borderColor: '#0f9d89', backgroundColor: 'rgba(102, 126, 234, 0.1)', tension: 0.4, fill: true @@ -415,8 +415,8 @@

    Top Subscribers by Revenue

    data: [0, 5, 10], backgroundColor: [ '#e0e0e0', - '#667eea', - '#f093fb' + '#0f9d89', + '#d95b43' ] }] }; @@ -542,7 +542,7 @@

    Top Subscribers by Revenue

    row.innerHTML = ` - + diff --git a/docs/monetization/checkout.html b/docs/monetization/checkout.html index 4ccfcbc09..0d878a175 100644 --- a/docs/monetization/checkout.html +++ b/docs/monetization/checkout.html @@ -17,7 +17,7 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); min-height: 100vh; padding: 20px; } @@ -58,7 +58,7 @@ } .card h2 { - color: #667eea; + color: #0f9d89; margin-bottom: 20px; font-size: 1.8em; } @@ -87,7 +87,7 @@ .form-group input:focus, .form-group select:focus { outline: none; - border-color: #667eea; + border-color: #0f9d89; } .form-row { @@ -114,7 +114,7 @@ border-bottom: none; font-weight: bold; font-size: 1.2em; - color: #667eea; + color: #0f9d89; padding-top: 15px; } @@ -127,12 +127,12 @@ } .plan-pro { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } .plan-enterprise { - background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); + background: linear-gradient(135deg, #d95b43 0%, #b8432f 100%); color: white; } @@ -167,7 +167,7 @@ } .button-primary { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } @@ -338,7 +338,7 @@

    Payment Information

    @@ -391,7 +391,7 @@

    Order Summary

    -

    What's Included

    +

    What's Included

    • 10,000 Chat Messages/month
    • Full Aria Character Suite
      • diff --git a/docs/monetization/monetization-index.html b/docs/monetization/monetization-index.html index 2ba2fdd5d..6d60f6a80 100644 --- a/docs/monetization/monetization-index.html +++ b/docs/monetization/monetization-index.html @@ -27,7 +27,7 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); min-height: 100vh; } @@ -70,7 +70,7 @@ .button-primary { background: white; - color: #667eea; + color: #0f9d89; } .button-primary:hover { @@ -103,7 +103,7 @@ } .section h2 { - color: #667eea; + color: #0f9d89; font-size: 2.5em; margin-bottom: 30px; text-align: center; @@ -154,7 +154,7 @@ .page-link { display: block; padding: 25px; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; text-decoration: none; border-radius: 10px; @@ -220,7 +220,7 @@ } .quick-links h3 { - color: #667eea; + color: #0f9d89; margin-bottom: 20px; } @@ -238,7 +238,7 @@ } .quick-links a { - color: #667eea; + color: #0f9d89; text-decoration: none; font-weight: 500; } @@ -441,7 +441,7 @@

      Free Tier

      + style="background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white;">
      ⭐

      Pro Tier

      $49/month

      diff --git a/docs/monetization/my-subscription.html b/docs/monetization/my-subscription.html index ca3b390c7..79392de64 100644 --- a/docs/monetization/my-subscription.html +++ b/docs/monetization/my-subscription.html @@ -17,7 +17,7 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); min-height: 100vh; padding: 20px; } @@ -39,7 +39,7 @@ } .header h1 { - color: #667eea; + color: #0f9d89; font-size: 2em; } @@ -51,14 +51,14 @@ .nav-links a { padding: 10px 20px; text-decoration: none; - color: #667eea; - border: 2px solid #667eea; + color: #0f9d89; + border: 2px solid #0f9d89; border-radius: 5px; transition: all 0.3s; } .nav-links a:hover { - background: #667eea; + background: #0f9d89; color: white; } @@ -77,7 +77,7 @@ } .card h2 { - color: #667eea; + color: #0f9d89; margin-bottom: 20px; font-size: 1.5em; } @@ -97,12 +97,12 @@ } .tier-pro { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } .tier-enterprise { - background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); + background: linear-gradient(135deg, #d95b43 0%, #b8432f 100%); color: white; } @@ -161,7 +161,7 @@ .progress-fill { height: 100%; - background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(90deg, #0f9d89 0%, #0a7a6d 100%); transition: width 0.3s ease; } @@ -185,7 +185,7 @@ } .button-primary { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } diff --git a/docs/monetization/pricing.html b/docs/monetization/pricing.html index 0dc829acf..c6d56c2aa 100644 --- a/docs/monetization/pricing.html +++ b/docs/monetization/pricing.html @@ -26,7 +26,7 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: #333; line-height: 1.6; min-height: 100vh; @@ -91,14 +91,14 @@ } .pricing-card.featured { - border: 3px solid #667eea; + border: 3px solid #0f9d89; } .popular-badge { position: absolute; top: 20px; right: -35px; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; padding: 5px 40px; transform: rotate(45deg); @@ -111,7 +111,7 @@ font-size: 2em; font-weight: bold; margin-bottom: 10px; - color: #667eea; + color: #0f9d89; } .price { @@ -191,12 +191,12 @@ } .cta-button.pro { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } .cta-button.enterprise { - background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); + background: linear-gradient(135deg, #d95b43 0%, #b8432f 100%); color: white; } @@ -215,7 +215,7 @@ .comparison-table h2 { text-align: center; - color: #667eea; + color: #0f9d89; margin-bottom: 30px; font-size: 2em; } @@ -232,7 +232,7 @@ } th { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; font-weight: bold; } @@ -251,7 +251,7 @@ .revenue-projection h2 { text-align: center; - color: #667eea; + color: #0f9d89; margin-bottom: 30px; font-size: 2em; } @@ -263,7 +263,7 @@ } .revenue-card { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; padding: 30px; border-radius: 15px; @@ -304,7 +304,7 @@ .faq-section h2 { text-align: center; - color: #667eea; + color: #0f9d89; margin-bottom: 30px; font-size: 2em; } diff --git a/docs/monetization/referrals.html b/docs/monetization/referrals.html index 913e85485..ac8f748c3 100644 --- a/docs/monetization/referrals.html +++ b/docs/monetization/referrals.html @@ -26,7 +26,7 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); min-height: 100vh; padding: 20px; } @@ -48,7 +48,7 @@ } .header h1 { - color: #667eea; + color: #0f9d89; font-size: 2em; } @@ -60,14 +60,14 @@ .nav-links a { padding: 10px 20px; text-decoration: none; - color: #667eea; - border: 2px solid #667eea; + color: #0f9d89; + border: 2px solid #0f9d89; border-radius: 5px; transition: all 0.3s; } .nav-links a:hover { - background: #667eea; + background: #0f9d89; color: white; } @@ -81,7 +81,7 @@ } .hero-section h2 { - color: #667eea; + color: #0f9d89; font-size: 2.5em; margin-bottom: 15px; } @@ -93,7 +93,7 @@ } .referral-code-section { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); padding: 30px; border-radius: 10px; margin: 30px 0; @@ -111,7 +111,7 @@ .copy-button { padding: 12px 30px; background: white; - color: #667eea; + color: #0f9d89; border: none; border-radius: 5px; font-weight: 600; @@ -147,7 +147,7 @@ .stat-value { font-size: 2.5em; font-weight: bold; - color: #667eea; + color: #0f9d89; margin: 10px 0; } @@ -167,7 +167,7 @@ } .commission-rates h3 { - color: #667eea; + color: #0f9d89; margin-bottom: 20px; } @@ -202,7 +202,7 @@ margin: 10px 0; background: #f8f9fa; border-radius: 8px; - border-left: 4px solid #667eea; + border-left: 4px solid #0f9d89; } .milestone-icon { @@ -217,7 +217,7 @@ .milestone-count { font-weight: bold; font-size: 1.2em; - color: #667eea; + color: #0f9d89; } .milestone-bonus { @@ -264,7 +264,7 @@ } .button-primary { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } @@ -462,7 +462,7 @@

      🏆 Milestone Bonuses

      -

      Your Referrals

      +

      Your Referrals

    #${sub.rank} ${sub.userId}${sub.tier}${sub.tier} $${sub.mrr} $${sub.ltv} ● ${sub.status}
    @@ -545,7 +545,7 @@

    💸 Request Payout

    row.innerHTML = ` - + `; diff --git a/docs/monetization/subscription-success.html b/docs/monetization/subscription-success.html index af09891f9..59b90ac33 100644 --- a/docs/monetization/subscription-success.html +++ b/docs/monetization/subscription-success.html @@ -17,7 +17,7 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); min-height: 100vh; display: flex; align-items: center; @@ -94,12 +94,12 @@ } .plan-pro { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } .plan-enterprise { - background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); + background: linear-gradient(135deg, #d95b43 0%, #b8432f 100%); color: white; } @@ -138,7 +138,7 @@ } .next-steps h3 { - color: #667eea; + color: #0f9d89; margin-bottom: 20px; } @@ -160,7 +160,7 @@ .step-number { width: 35px; height: 35px; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; border-radius: 50%; display: flex; @@ -195,7 +195,7 @@ } .button-primary { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; } @@ -206,8 +206,8 @@ .button-secondary { background: white; - color: #667eea; - border: 2px solid #667eea; + color: #0f9d89; + border: 2px solid #0f9d89; } .button-secondary:hover { @@ -222,7 +222,7 @@ position: fixed; width: 10px; height: 10px; - background: #667eea; + background: #0f9d89; position: fixed; left: 50%; animation: confetti 3s ease-out forwards; @@ -412,7 +412,7 @@

    💡 Need Help Getting Started?

    } function createConfetti() { - const colors = ['#667eea', '#764ba2', '#f093fb', '#f5576c', '#4caf50']; + const colors = ['#0f9d89', '#d3a53e', '#d95b43', '#f5576c', '#4caf50']; for (let i = 0; i < 50; i++) { setTimeout(() => { diff --git a/docs/mount/styles.css b/docs/mount/styles.css index 0f525be31..7d85310d2 100644 --- a/docs/mount/styles.css +++ b/docs/mount/styles.css @@ -1,7 +1,7 @@ /* QAI Control Center Styles */ :root { - --primary: #667eea; + --primary: #0f9d89; --primary-dark: #5568d3; --secondary: #48bb78; --danger: #f56565; @@ -23,7 +23,7 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif; - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: var(--text); min-height: 100vh; padding: 20px; @@ -40,7 +40,7 @@ body { /* Header */ .header { - background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + background: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); color: white; padding: 30px; display: flex; diff --git a/docs/quantum/styles.css b/docs/quantum/styles.css index e239d5669..4aae2c4c5 100644 --- a/docs/quantum/styles.css +++ b/docs/quantum/styles.css @@ -17,10 +17,10 @@ --border-color: rgba(99, 102, 241, 0.2); --glass-bg: rgba(30, 41, 59, 0.3); --glass-border: rgba(148, 163, 184, 0.1); - --gradient-1: linear-gradient(135deg, #667eea 0%, #764ba2 100%); - --gradient-2: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); + --gradient-1: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 100%); + --gradient-2: linear-gradient(135deg, #d95b43 0%, #b8432f 100%); --gradient-3: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); - --gradient-text: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%); + --gradient-text: linear-gradient(135deg, #0f9d89 0%, #0a7a6d 50%, #d3a53e 100%); --shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.3); --shadow-lg: 0 20px 40px -10px rgba(0, 0, 0, 0.5); --shadow-xl: 0 25px 50px -12px rgba(0, 0, 0, 0.7); diff --git a/function_app.py b/function_app.py index a7f491824..5550c63f2 100644 --- a/function_app.py +++ b/function_app.py @@ -2038,6 +2038,9 @@ def detect_conflict(versions): "/api/vision/infer", "/api/vision/batch-infer", "/api/image/generate", + "/api/quantum-llm/status", + "/api/quantum-llm/chat", + "/api/quantum-llm/stream", ], "status": "ok", } @@ -3753,6 +3756,252 @@ def record_referral(req: func.HttpRequest) -> func.HttpResponse: ) +# ============================================================================= +# Quantum-Powered LLM Endpoints +# ============================================================================= +# Lazy import helper — QuantumLLMPipeline is loaded once and cached. +_quantum_llm_pipeline = None +_quantum_llm_lock = None + + +def _get_quantum_llm_pipeline(): + """Return a shared QuantumLLMPipeline instance (lazy-initialised).""" + global _quantum_llm_pipeline, _quantum_llm_lock + import threading + + if _quantum_llm_lock is None: + _quantum_llm_lock = threading.Lock() + with _quantum_llm_lock: + if _quantum_llm_pipeline is None: + try: + quantum_llm_src = ( + Path(__file__).resolve().parent + / "ai-projects" + / "quantum-ml" + / "src" + ) + if str(quantum_llm_src) not in sys.path: + sys.path.insert(0, str(quantum_llm_src)) + from quantum_llm import QuantumLLMConfig, QuantumLLMPipeline # type: ignore + + _quantum_llm_pipeline = QuantumLLMPipeline(config=QuantumLLMConfig.from_env()) + logging.info("[quantum-llm] Pipeline initialized: backend=%s", _quantum_llm_pipeline.effective_backend) + except Exception as _qllm_err: # noqa: BLE001 + logging.warning("[quantum-llm] Pipeline init failed: %s", _qllm_err) + return _quantum_llm_pipeline + + +@app.route(route="quantum-llm/status", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS) +def quantum_llm_status(req: func.HttpRequest) -> func.HttpResponse: + """ + GET /api/quantum-llm/status + + Returns the active quantum backend, qubit count, fallback state, and downstream provider. + + Response: { + "status": "ok", + "backend": "classical|pennylane|qiskit", + "fallback": true, + "num_qubits": 4, + "shots": 512, + "provider": "auto" + } + """ + logging.info("quantum-llm/status invoked") + try: + pipeline = _get_quantum_llm_pipeline() + if pipeline is None: + return func.HttpResponse( + json.dumps({"status": "unavailable", "error": "Pipeline not initialized"}), + status_code=503, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + payload = {"status": "ok"} + payload.update(pipeline.status()) + return func.HttpResponse( + json.dumps(payload), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except Exception as exc: # noqa: BLE001 + logging.error("quantum-llm/status error: %s", exc) + return func.HttpResponse( + json.dumps({"status": "error", "error": str(exc)}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route(route="quantum-llm/chat", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) +def quantum_llm_chat(req: func.HttpRequest) -> func.HttpResponse: + """ + POST /api/quantum-llm/chat + + Non-streaming quantum-augmented completion. + + Body: { + "prompt": "Hello, Aria!", + "provider": "auto|azure|openai|lmstudio|local" (optional), + "backend": "auto|pennylane|qiskit|classical" (optional), + "max_tokens": 512 (optional), + "seed": 42 (optional) + } + + Response: { + "response": "...", + "provider": "...", + "backend": "classical|pennylane|qiskit", + "qubits": 4, + "shots": 512, + "latency_ms": 12.3, + "quantum_augmented": true + } + """ + logging.info("quantum-llm/chat invoked") + try: + body = req.get_json() + prompt = body.get("prompt", "") + if not prompt or not isinstance(prompt, str): + return func.HttpResponse( + json.dumps({"error": "prompt is required and must be a non-empty string"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + provider_override = body.get("provider") + seed = body.get("seed") + max_tokens = body.get("max_tokens") + + pipeline = _get_quantum_llm_pipeline() + if pipeline is None: + return func.HttpResponse( + json.dumps({"error": "Quantum LLM pipeline unavailable"}), + status_code=503, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + # Honour per-request max_tokens (within cap) — use a local override dict + # instead of mutating the shared pipeline config to avoid race conditions. + gen_kwargs = {} + if max_tokens is not None: + gen_kwargs["max_tokens"] = min(int(max_tokens), pipeline.config.max_tokens_cap) + + import asyncio # noqa: PLC0415 (already imported at module level but guard) + + result = asyncio.run(pipeline.generate(prompt, provider=provider_override, seed=seed)) + return func.HttpResponse( + json.dumps(result), + status_code=200, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except ValueError as ve: + logging.warning("quantum-llm/chat validation error: %s", ve) + return func.HttpResponse( + json.dumps({"error": str(ve)}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + except Exception as exc: # noqa: BLE001 + logging.error("quantum-llm/chat error: %s", exc) + return func.HttpResponse( + json.dumps({"error": str(exc)}), + status_code=500, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + +@app.route(route="quantum-llm/stream", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS) +def quantum_llm_stream(req: func.HttpRequest) -> func.HttpResponse: + """ + POST /api/quantum-llm/stream + + SSE streaming quantum-augmented completion. Event format mirrors /api/chat/stream: + event: meta\\ndata: {...}\\n\\n + data: {"delta": "..."}\\n\\n + data: [DONE]\\n\\n + + Body: same schema as /api/quantum-llm/chat. + """ + logging.info("quantum-llm/stream invoked") + try: + body = req.get_json() + prompt = body.get("prompt", "") + if not prompt or not isinstance(prompt, str): + return func.HttpResponse( + json.dumps({"error": "prompt is required"}), + status_code=400, + mimetype="application/json", + headers=create_cors_response_headers(), + ) + + provider_override = body.get("provider") + seed = body.get("seed") + + pipeline = _get_quantum_llm_pipeline() + if pipeline is None: + def _unavail(): + yield b'data: {"error": "Quantum LLM pipeline unavailable"}\n\n' + yield b"data: [DONE]\n\n" + + return func.HttpResponse( + body=_unavail(), + status_code=503, + mimetype="text/event-stream", + headers={**create_cors_response_headers(), "Cache-Control": "no-cache"}, + ) + + import asyncio # noqa: PLC0415 + + def _sse_generator(): + """Synchronous generator that drives the async stream.""" + loop = asyncio.new_event_loop() + try: + async def _drain(): + async for chunk in pipeline.stream(prompt, provider=provider_override, seed=seed): + yield chunk.encode("utf-8") + + async def _collect(): + results = [] + async for b in _drain(): + results.append(b) + return results + + chunks = loop.run_until_complete(_collect()) + for chunk in chunks: + yield chunk + finally: + loop.close() + + return func.HttpResponse( + body=_sse_generator(), + status_code=200, + mimetype="text/event-stream", + headers={**create_cors_response_headers(), "Cache-Control": "no-cache"}, + ) + except Exception as exc: # noqa: BLE001 + logging.error("quantum-llm/stream error: %s", exc) + _exc = exc # capture before exception binding is deleted at end of except block + + def _err(): + yield f'data: {json.dumps({"error": str(_exc)})}\n\n'.encode("utf-8") + yield b"data: [DONE]\n\n" + + return func.HttpResponse( + body=_err(), + status_code=200, + mimetype="text/event-stream", + headers={**create_cors_response_headers(), "Cache-Control": "no-cache"}, + ) + + @app.route( route="referrals/leaderboard", methods=["GET"], auth_level=func.AuthLevel.ANONYMOUS ) diff --git a/mount/launch.ps1 b/mount/launch.ps1 index eaf30f1de..55deea5fb 100644 --- a/mount/launch.ps1 +++ b/mount/launch.ps1 @@ -1,81 +1,81 @@ -# Launch QAI Control Center -# Starts the service and opens the browser - -Write-Host "🚀 Launching QAI Control Center..." -ForegroundColor Cyan -Write-Host "" - -# Check if service is already running -try { - $response = Invoke-RestMethod -Uri "http://localhost:8000/health" -TimeoutSec 2 -ErrorAction Stop - Write-Host "✓ Service is already running!" -ForegroundColor Green - Write-Host "" - Write-Host "Opening browser..." -ForegroundColor Cyan - Start-Process "http://localhost:8000" - exit 0 -} -catch { - Write-Host "Starting service..." -ForegroundColor Yellow -} - -# Check if virtual environment exists -if (-not (Test-Path "venv\Scripts\Activate.ps1")) { - Write-Host "Setting up virtual environment..." -ForegroundColor Yellow - python -m venv venv - & venv\Scripts\Activate.ps1 - Write-Host "Installing dependencies..." -ForegroundColor Yellow - pip install -q -r requirements.txt -} - -# Start service in background -$job = Start-Job -ScriptBlock { - Set-Location $using:PWD - & venv\Scripts\Activate.ps1 - python app.py -} - -Write-Host "Waiting for service to start..." -ForegroundColor Yellow - -# Wait for service to be ready (max 30 seconds) -$maxAttempts = 30 -$attempt = 0 -$ready = $false - -while ($attempt -lt $maxAttempts -and -not $ready) { - $attempt++ - Start-Sleep -Seconds 1 - - try { - $response = Invoke-RestMethod -Uri "http://localhost:8000/health" -TimeoutSec 2 -ErrorAction Stop - $ready = $true - } - catch { - Write-Host "." -NoNewline - } -} - -Write-Host "" - -if ($ready) { - Write-Host "" - Write-Host "✓ QAI Control Center is ready!" -ForegroundColor Green - Write-Host "" - Write-Host "🌐 Web UI: http://localhost:8000" -ForegroundColor Cyan - Write-Host "📚 API Docs: http://localhost:8000/docs" -ForegroundColor Cyan - Write-Host "" - Write-Host "Opening browser..." -ForegroundColor Cyan - Start-Sleep -Seconds 2 - Start-Process "http://localhost:8000" - - Write-Host "" - Write-Host "Press Ctrl+C to stop the service" -ForegroundColor Yellow - - # Keep showing job output - Receive-Job -Job $job -Wait -} -else { - Write-Host "✗ Failed to start service" -ForegroundColor Red - Write-Host "Check the output above for errors" -ForegroundColor Yellow - Stop-Job -Job $job - Remove-Job -Job $job - exit 1 -} +# Launch QAI Control Center +# Starts the service and opens the browser + +Write-Host "🚀 Launching QAI Control Center..." -ForegroundColor Cyan +Write-Host "" + +# Check if service is already running +try { + $response = Invoke-RestMethod -Uri "http://localhost:8000/health" -TimeoutSec 2 -ErrorAction Stop + Write-Host "✓ Service is already running!" -ForegroundColor Green + Write-Host "" + Write-Host "Opening browser..." -ForegroundColor Cyan + Start-Process "http://localhost:8000" + exit 0 +} +catch { + Write-Host "Starting service..." -ForegroundColor Yellow +} + +# Check if virtual environment exists +if (-not (Test-Path "venv\Scripts\Activate.ps1")) { + Write-Host "Setting up virtual environment..." -ForegroundColor Yellow + python -m venv venv + & venv\Scripts\Activate.ps1 + Write-Host "Installing dependencies..." -ForegroundColor Yellow + pip install -q -r requirements.txt +} + +# Start service in background +$job = Start-Job -ScriptBlock { + Set-Location $using:PWD + & venv\Scripts\Activate.ps1 + python app.py +} + +Write-Host "Waiting for service to start..." -ForegroundColor Yellow + +# Wait for service to be ready (max 30 seconds) +$maxAttempts = 30 +$attempt = 0 +$ready = $false + +while ($attempt -lt $maxAttempts -and -not $ready) { + $attempt++ + Start-Sleep -Seconds 1 + + try { + $response = Invoke-RestMethod -Uri "http://localhost:8000/health" -TimeoutSec 2 -ErrorAction Stop + $ready = $true + } + catch { + Write-Host "." -NoNewline + } +} + +Write-Host "" + +if ($ready) { + Write-Host "" + Write-Host "✓ QAI Control Center is ready!" -ForegroundColor Green + Write-Host "" + Write-Host "🌐 Web UI: http://localhost:8000" -ForegroundColor Cyan + Write-Host "📚 API Docs: http://localhost:8000/docs" -ForegroundColor Cyan + Write-Host "" + Write-Host "Opening browser..." -ForegroundColor Cyan + Start-Sleep -Seconds 2 + Start-Process "http://localhost:8000" + + Write-Host "" + Write-Host "Press Ctrl+C to stop the service" -ForegroundColor Yellow + + # Keep showing job output + Receive-Job -Job $job -Wait +} +else { + Write-Host "✗ Failed to start service" -ForegroundColor Red + Write-Host "Check the output above for errors" -ForegroundColor Yellow + Stop-Job -Job $job + Remove-Job -Job $job + exit 1 +} diff --git a/mount/start.bat b/mount/start.bat index 6b3ac877e..90fbb4c0d 100644 --- a/mount/start.bat +++ b/mount/start.bat @@ -1,26 +1,26 @@ -@echo off -REM Quick start script for QAI Integration Service (Windows) - -echo Starting QAI Integration Service... -echo. - -REM Check if virtual environment exists -if not exist "venv\Scripts\activate.bat" ( - echo Virtual environment not found. Creating... - python -m venv venv - echo. -) - -REM Activate virtual environment -call venv\Scripts\activate.bat - -REM Install/upgrade requirements -echo Installing dependencies... -pip install -q -r requirements.txt -echo. - -REM Start the service -echo Starting FastAPI service on http://localhost:8000 -echo Interactive docs at http://localhost:8000/docs -echo. -python app.py +@echo off +REM Quick start script for QAI Integration Service (Windows) + +echo Starting QAI Integration Service... +echo. + +REM Check if virtual environment exists +if not exist "venv\Scripts\activate.bat" ( + echo Virtual environment not found. Creating... + python -m venv venv + echo. +) + +REM Activate virtual environment +call venv\Scripts\activate.bat + +REM Install/upgrade requirements +echo Installing dependencies... +pip install -q -r requirements.txt +echo. + +REM Start the service +echo Starting FastAPI service on http://localhost:8000 +echo Interactive docs at http://localhost:8000/docs +echo. +python app.py diff --git a/mount/start.ps1 b/mount/start.ps1 index 5e8ae267b..2de8c83d1 100644 --- a/mount/start.ps1 +++ b/mount/start.ps1 @@ -1,25 +1,25 @@ -# Quick start script for QAI Integration Service (PowerShell) - -Write-Host "Starting QAI Integration Service..." -ForegroundColor Cyan -Write-Host "" - -# Check if virtual environment exists -if (-not (Test-Path "venv\Scripts\Activate.ps1")) { - Write-Host "Virtual environment not found. Creating..." -ForegroundColor Yellow - python -m venv venv - Write-Host "" -} - -# Activate virtual environment -& venv\Scripts\Activate.ps1 - -# Install/upgrade requirements -Write-Host "Installing dependencies..." -ForegroundColor Yellow -pip install -q -r requirements.txt -Write-Host "" - -# Start the service -Write-Host "Starting FastAPI service on http://localhost:8000" -ForegroundColor Green -Write-Host "Interactive docs at http://localhost:8000/docs" -ForegroundColor Green -Write-Host "" -python app.py +# Quick start script for QAI Integration Service (PowerShell) + +Write-Host "Starting QAI Integration Service..." -ForegroundColor Cyan +Write-Host "" + +# Check if virtual environment exists +if (-not (Test-Path "venv\Scripts\Activate.ps1")) { + Write-Host "Virtual environment not found. Creating..." -ForegroundColor Yellow + python -m venv venv + Write-Host "" +} + +# Activate virtual environment +& venv\Scripts\Activate.ps1 + +# Install/upgrade requirements +Write-Host "Installing dependencies..." -ForegroundColor Yellow +pip install -q -r requirements.txt +Write-Host "" + +# Start the service +Write-Host "Starting FastAPI service on http://localhost:8000" -ForegroundColor Green +Write-Host "Interactive docs at http://localhost:8000/docs" -ForegroundColor Green +Write-Host "" +python app.py diff --git a/mount/test-service.ps1 b/mount/test-service.ps1 index 1a9945986..4b0885181 100644 --- a/mount/test-service.ps1 +++ b/mount/test-service.ps1 @@ -1,77 +1,77 @@ -# Test the QAI Integration Service Web UI -# This script tests the backend API to ensure everything is working - -Write-Host "🧪 Testing QAI Integration Service..." -ForegroundColor Cyan -Write-Host "" - -$API_BASE = "http://localhost:8000" -$errors = 0 - -function Test-Endpoint { - param( - [string]$Name, - [string]$Url, - [string]$Method = "GET", - [object]$Body = $null - ) - - try { - Write-Host "Testing: $Name... " -NoNewline - - if ($Method -eq "GET") { - $response = Invoke-RestMethod -Uri $Url -Method Get -TimeoutSec 5 - } else { - $response = Invoke-RestMethod -Uri $Url -Method Post -Body ($Body | ConvertTo-Json) -ContentType "application/json" -TimeoutSec 5 - } - - Write-Host "✓ PASSED" -ForegroundColor Green - return $true - } - catch { - Write-Host "✗ FAILED" -ForegroundColor Red - Write-Host " Error: $($_.Exception.Message)" -ForegroundColor Yellow - return $false - } -} - -# Test health endpoint -Write-Host "=== Core Endpoints ===" -ForegroundColor Cyan -if (-not (Test-Endpoint "Health Check" "$API_BASE/health")) { $errors++ } -if (-not (Test-Endpoint "Root / Web UI" "$API_BASE/")) { $errors++ } -if (-not (Test-Endpoint "Full Status" "$API_BASE/status")) { $errors++ } - -Write-Host "" -Write-Host "=== Quantum Endpoints ===" -ForegroundColor Cyan -if (-not (Test-Endpoint "Quantum Status" "$API_BASE/quantum/status")) { $errors++ } -if (-not (Test-Endpoint "Quantum Datasets" "$API_BASE/quantum/datasets")) { $errors++ } -if (-not (Test-Endpoint "Quantum Backends" "$API_BASE/quantum/backends")) { $errors++ } - -Write-Host "" -Write-Host "=== Chat Endpoints ===" -ForegroundColor Cyan -if (-not (Test-Endpoint "Chat Status" "$API_BASE/chat/status")) { $errors++ } -if (-not (Test-Endpoint "Chat Providers" "$API_BASE/chat/providers")) { $errors++ } -if (-not (Test-Endpoint "Detect Provider" "$API_BASE/chat/detect-provider")) { $errors++ } - -Write-Host "" -Write-Host "=== Training Endpoints ===" -ForegroundColor Cyan -if (-not (Test-Endpoint "Training Status" "$API_BASE/training/status")) { $errors++ } -if (-not (Test-Endpoint "Training Datasets" "$API_BASE/training/datasets")) { $errors++ } -if (-not (Test-Endpoint "LoRA Adapter Info" "$API_BASE/training/lora-adapter")) { $errors++ } -if (-not (Test-Endpoint "AutoTrain Jobs" "$API_BASE/training/autotrain/jobs")) { $errors++ } - -Write-Host "" -Write-Host "=== Results ===" -ForegroundColor Cyan -if ($errors -eq 0) { - Write-Host "✓ All tests passed! Service is working correctly." -ForegroundColor Green - Write-Host "" - Write-Host "🌐 Open the Web UI: $API_BASE" -ForegroundColor Cyan - Write-Host "📚 API Documentation: $API_BASE/docs" -ForegroundColor Cyan -} else { - Write-Host "✗ $errors test(s) failed. Check the service logs." -ForegroundColor Red - Write-Host "" - Write-Host "Make sure the service is running:" -ForegroundColor Yellow - Write-Host " cd mount" -ForegroundColor Yellow - Write-Host " .\start.ps1" -ForegroundColor Yellow -} - -Write-Host "" +# Test the QAI Integration Service Web UI +# This script tests the backend API to ensure everything is working + +Write-Host "🧪 Testing QAI Integration Service..." -ForegroundColor Cyan +Write-Host "" + +$API_BASE = "http://localhost:8000" +$errors = 0 + +function Test-Endpoint { + param( + [string]$Name, + [string]$Url, + [string]$Method = "GET", + [object]$Body = $null + ) + + try { + Write-Host "Testing: $Name... " -NoNewline + + if ($Method -eq "GET") { + $response = Invoke-RestMethod -Uri $Url -Method Get -TimeoutSec 5 + } else { + $response = Invoke-RestMethod -Uri $Url -Method Post -Body ($Body | ConvertTo-Json) -ContentType "application/json" -TimeoutSec 5 + } + + Write-Host "✓ PASSED" -ForegroundColor Green + return $true + } + catch { + Write-Host "✗ FAILED" -ForegroundColor Red + Write-Host " Error: $($_.Exception.Message)" -ForegroundColor Yellow + return $false + } +} + +# Test health endpoint +Write-Host "=== Core Endpoints ===" -ForegroundColor Cyan +if (-not (Test-Endpoint "Health Check" "$API_BASE/health")) { $errors++ } +if (-not (Test-Endpoint "Root / Web UI" "$API_BASE/")) { $errors++ } +if (-not (Test-Endpoint "Full Status" "$API_BASE/status")) { $errors++ } + +Write-Host "" +Write-Host "=== Quantum Endpoints ===" -ForegroundColor Cyan +if (-not (Test-Endpoint "Quantum Status" "$API_BASE/quantum/status")) { $errors++ } +if (-not (Test-Endpoint "Quantum Datasets" "$API_BASE/quantum/datasets")) { $errors++ } +if (-not (Test-Endpoint "Quantum Backends" "$API_BASE/quantum/backends")) { $errors++ } + +Write-Host "" +Write-Host "=== Chat Endpoints ===" -ForegroundColor Cyan +if (-not (Test-Endpoint "Chat Status" "$API_BASE/chat/status")) { $errors++ } +if (-not (Test-Endpoint "Chat Providers" "$API_BASE/chat/providers")) { $errors++ } +if (-not (Test-Endpoint "Detect Provider" "$API_BASE/chat/detect-provider")) { $errors++ } + +Write-Host "" +Write-Host "=== Training Endpoints ===" -ForegroundColor Cyan +if (-not (Test-Endpoint "Training Status" "$API_BASE/training/status")) { $errors++ } +if (-not (Test-Endpoint "Training Datasets" "$API_BASE/training/datasets")) { $errors++ } +if (-not (Test-Endpoint "LoRA Adapter Info" "$API_BASE/training/lora-adapter")) { $errors++ } +if (-not (Test-Endpoint "AutoTrain Jobs" "$API_BASE/training/autotrain/jobs")) { $errors++ } + +Write-Host "" +Write-Host "=== Results ===" -ForegroundColor Cyan +if ($errors -eq 0) { + Write-Host "✓ All tests passed! Service is working correctly." -ForegroundColor Green + Write-Host "" + Write-Host "🌐 Open the Web UI: $API_BASE" -ForegroundColor Cyan + Write-Host "📚 API Documentation: $API_BASE/docs" -ForegroundColor Cyan +} else { + Write-Host "✗ $errors test(s) failed. Check the service logs." -ForegroundColor Red + Write-Host "" + Write-Host "Make sure the service is running:" -ForegroundColor Yellow + Write-Host " cd mount" -ForegroundColor Yellow + Write-Host " .\start.ps1" -ForegroundColor Yellow +} + +Write-Host "" diff --git a/mount/train_max.ps1 b/mount/train_max.ps1 index 364a0f8fc..2f7e48a28 100644 --- a/mount/train_max.ps1 +++ b/mount/train_max.ps1 @@ -1,103 +1,103 @@ -# Maximum Performance AI Training -# Quick launcher for intensive training - -Write-Host "MAX PERFORMANCE AI TRAINING" -ForegroundColor Cyan -Write-Host "========================================================================" -Write-Host "" - -# Check if we're in the mount directory -if (-not (Test-Path "train_max_performance.py")) { - Write-Host "Error: Run this script from the mount directory" -ForegroundColor Red - Write-Host " cd C:\Users\Bryan\OneDrive\AI\mount" -ForegroundColor Yellow - exit 1 -} - -# Activate virtual environment -if (Test-Path "..\venv\Scripts\Activate.ps1") { - Write-Host "Activating workspace virtual environment..." -ForegroundColor Cyan - & ..\venv\Scripts\Activate.ps1 -} else { - Write-Host "No virtual environment found, using system Python" -ForegroundColor Yellow -} - -# Check dependencies -Write-Host "Checking dependencies..." -ForegroundColor Cyan -$depsCheck = python -c "import torch, transformers, datasets, peft; print('OK')" 2>&1 - -if ($depsCheck -notmatch "OK") { - Write-Host "Missing dependencies. Installing..." -ForegroundColor Yellow - pip install torch transformers datasets peft accelerate psutil -} - -Write-Host "" - -# Menu -Write-Host "SELECT TRAINING MODE:" -ForegroundColor Cyan -Write-Host "========================================================================" -Write-Host "" -Write-Host "1. QUICK TEST (64 samples, 1 epoch) - 5-10 minutes" -Write-Host "2. MEDIUM INTENSITY (500 samples, 2 epochs) - 30-60 minutes" -Write-Host "3. MAXIMUM POWER (Full dataset, 3 epochs) - 2-4 hours" -Write-Host "4. MULTI-DATASET (All datasets, 3 epochs) - 4-8 hours" -Write-Host "5. CHECK RESOURCES ONLY" -Write-Host "0. Cancel" -Write-Host "" - -$choice = Read-Host "Enter choice (0-5)" - -$workspaceRoot = "C:\Users\Bryan\OneDrive\AI" - -switch ($choice) { - "1" { - Write-Host "`nStarting QUICK TEST..." -ForegroundColor Green - python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\dolly" --epochs 1 - } - "2" { - Write-Host "`nStarting MEDIUM INTENSITY training..." -ForegroundColor Green - python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\mixed_chat" --epochs 2 - } - "3" { - Write-Host "`nStarting MAXIMUM POWER training..." -ForegroundColor Green - Write-Host "WARNING: This will max out your system for 2-4 hours!" -ForegroundColor Yellow - $confirm = Read-Host "Continue? (yes/no)" - if ($confirm -eq "yes") { - python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\dolly" --epochs 3 - } else { - Write-Host "Cancelled" -ForegroundColor Yellow - } - } - "4" { - Write-Host "`nStarting MULTI-DATASET training..." -ForegroundColor Green - Write-Host "WARNING: This will take 4-8 hours and max out your system!" -ForegroundColor Yellow - $confirm = Read-Host "Continue? (yes/no)" - if ($confirm -eq "yes") { - # Train on multiple datasets sequentially - Write-Host "`nTraining on Dolly dataset..." -ForegroundColor Cyan - python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\dolly" --epochs 2 - - Write-Host "`nTraining on OpenAssistant dataset..." -ForegroundColor Cyan - python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\openassistant" --epochs 2 - - Write-Host "`nTraining on Mixed Chat dataset..." -ForegroundColor Cyan - python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\mixed_chat" --epochs 1 - } else { - Write-Host "Cancelled" -ForegroundColor Yellow - } - } - "5" { - Write-Host "`nChecking system resources..." -ForegroundColor Green - python train_max_performance.py --check-only - } - "0" { - Write-Host "`nCancelled" -ForegroundColor Yellow - exit 0 - } - default { - Write-Host "`nInvalid choice" -ForegroundColor Red - exit 1 - } -} - -Write-Host "" -Write-Host "========================================================================" -Write-Host "Done!" -ForegroundColor Green +# Maximum Performance AI Training +# Quick launcher for intensive training + +Write-Host "MAX PERFORMANCE AI TRAINING" -ForegroundColor Cyan +Write-Host "========================================================================" +Write-Host "" + +# Check if we're in the mount directory +if (-not (Test-Path "train_max_performance.py")) { + Write-Host "Error: Run this script from the mount directory" -ForegroundColor Red + Write-Host " cd C:\Users\Bryan\OneDrive\AI\mount" -ForegroundColor Yellow + exit 1 +} + +# Activate virtual environment +if (Test-Path "..\venv\Scripts\Activate.ps1") { + Write-Host "Activating workspace virtual environment..." -ForegroundColor Cyan + & ..\venv\Scripts\Activate.ps1 +} else { + Write-Host "No virtual environment found, using system Python" -ForegroundColor Yellow +} + +# Check dependencies +Write-Host "Checking dependencies..." -ForegroundColor Cyan +$depsCheck = python -c "import torch, transformers, datasets, peft; print('OK')" 2>&1 + +if ($depsCheck -notmatch "OK") { + Write-Host "Missing dependencies. Installing..." -ForegroundColor Yellow + pip install torch transformers datasets peft accelerate psutil +} + +Write-Host "" + +# Menu +Write-Host "SELECT TRAINING MODE:" -ForegroundColor Cyan +Write-Host "========================================================================" +Write-Host "" +Write-Host "1. QUICK TEST (64 samples, 1 epoch) - 5-10 minutes" +Write-Host "2. MEDIUM INTENSITY (500 samples, 2 epochs) - 30-60 minutes" +Write-Host "3. MAXIMUM POWER (Full dataset, 3 epochs) - 2-4 hours" +Write-Host "4. MULTI-DATASET (All datasets, 3 epochs) - 4-8 hours" +Write-Host "5. CHECK RESOURCES ONLY" +Write-Host "0. Cancel" +Write-Host "" + +$choice = Read-Host "Enter choice (0-5)" + +$workspaceRoot = "C:\Users\Bryan\OneDrive\AI" + +switch ($choice) { + "1" { + Write-Host "`nStarting QUICK TEST..." -ForegroundColor Green + python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\dolly" --epochs 1 + } + "2" { + Write-Host "`nStarting MEDIUM INTENSITY training..." -ForegroundColor Green + python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\mixed_chat" --epochs 2 + } + "3" { + Write-Host "`nStarting MAXIMUM POWER training..." -ForegroundColor Green + Write-Host "WARNING: This will max out your system for 2-4 hours!" -ForegroundColor Yellow + $confirm = Read-Host "Continue? (yes/no)" + if ($confirm -eq "yes") { + python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\dolly" --epochs 3 + } else { + Write-Host "Cancelled" -ForegroundColor Yellow + } + } + "4" { + Write-Host "`nStarting MULTI-DATASET training..." -ForegroundColor Green + Write-Host "WARNING: This will take 4-8 hours and max out your system!" -ForegroundColor Yellow + $confirm = Read-Host "Continue? (yes/no)" + if ($confirm -eq "yes") { + # Train on multiple datasets sequentially + Write-Host "`nTraining on Dolly dataset..." -ForegroundColor Cyan + python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\dolly" --epochs 2 + + Write-Host "`nTraining on OpenAssistant dataset..." -ForegroundColor Cyan + python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\openassistant" --epochs 2 + + Write-Host "`nTraining on Mixed Chat dataset..." -ForegroundColor Cyan + python train_max_performance.py --dataset "$workspaceRoot\datasets\chat\mixed_chat" --epochs 1 + } else { + Write-Host "Cancelled" -ForegroundColor Yellow + } + } + "5" { + Write-Host "`nChecking system resources..." -ForegroundColor Green + python train_max_performance.py --check-only + } + "0" { + Write-Host "`nCancelled" -ForegroundColor Yellow + exit 0 + } + default { + Write-Host "`nInvalid choice" -ForegroundColor Red + exit 1 + } +} + +Write-Host "" +Write-Host "========================================================================" +Write-Host "Done!" -ForegroundColor Green diff --git a/mount/train_parallel.ps1 b/mount/train_parallel.ps1 index b38330939..301713166 100644 --- a/mount/train_parallel.ps1 +++ b/mount/train_parallel.ps1 @@ -1,147 +1,147 @@ -# Parallel Training Script - Train multiple models simultaneously -# WARNING: This will heavily load your system! - -Write-Host "⚡ PARALLEL AI TRAINING - MAXIMUM UTILIZATION" -ForegroundColor Cyan -Write-Host "=" * 70 -Write-Host "" - -Write-Host "⚠️ WARNING: This will start multiple training processes!" -ForegroundColor Yellow -Write-Host " - Very high CPU/GPU usage" -ForegroundColor Yellow -Write-Host " - Maximum RAM consumption" -ForegroundColor Yellow -Write-Host " - System may become slow/unresponsive" -ForegroundColor Yellow -Write-Host "" - -$confirm = Read-Host "Continue with parallel training? (yes/no)" - -if ($confirm -ne "yes") { - Write-Host "❌ Cancelled" -ForegroundColor Yellow - exit 0 -} - -Write-Host "" -Write-Host "🚀 Starting parallel training jobs..." -ForegroundColor Green -Write-Host "" - -# Navigate to workspace root -$workspaceRoot = Split-Path -Parent $PSScriptRoot - -# Job 1: LoRA Training on Dolly -Write-Host "📦 Job 1: LoRA on Dolly dataset" -ForegroundColor Cyan -$job1 = Start-Job -ScriptBlock { - Set-Location $using:workspaceRoot - & venv\Scripts\Activate.ps1 - Set-Location "AI\microsoft_phi-silica-3.6_v1" - python scripts\train_lora.py --dataset ..\..\datasets\chat\dolly --config lora\lora.yaml --max-train-samples 500 --epochs 2 --save-dir ..\..\data_out\lora_training\parallel_job1 -} - -Start-Sleep -Seconds 5 - -# Job 2: Quantum Training on multiple datasets -Write-Host "📦 Job 2: Quantum training on heart dataset" -ForegroundColor Cyan -$job2 = Start-Job -ScriptBlock { - Set-Location $using:workspaceRoot - Set-Location "quantum-ai" - & venv\Scripts\Activate.ps1 - python train_custom_dataset.py --preset heart --epochs 20 --backend qiskit_aer -} - -Start-Sleep -Seconds 5 - -# Job 3: Another LoRA training -Write-Host "📦 Job 3: LoRA on Mixed Chat dataset" -ForegroundColor Cyan -$job3 = Start-Job -ScriptBlock { - Set-Location $using:workspaceRoot - & venv\Scripts\Activate.ps1 - Set-Location "AI\microsoft_phi-silica-3.6_v1" - python scripts\train_lora.py --dataset ..\..\datasets\chat\mixed_chat --config lora\lora.yaml --max-train-samples 500 --epochs 2 --save-dir ..\..\data_out\lora_training\parallel_job3 -} - -Write-Host "" -Write-Host "✅ All jobs started!" -ForegroundColor Green -Write-Host "" -Write-Host "Monitoring progress (Ctrl+C to stop monitoring, jobs will continue):" -ForegroundColor Cyan -Write-Host "=" * 70 -Write-Host "" - -# Monitor all jobs -$running = $true -while ($running) { - Clear-Host - Write-Host "⚡ PARALLEL TRAINING STATUS" -ForegroundColor Cyan - Write-Host "=" * 70 - Write-Host "" - - # Check job statuses - Write-Host "Job 1 (LoRA Dolly): " -NoNewline - if ($job1.State -eq "Running") { - Write-Host "🟢 RUNNING" -ForegroundColor Green - } elseif ($job1.State -eq "Completed") { - Write-Host "✅ COMPLETED" -ForegroundColor Green - } else { - Write-Host "❌ $($job1.State)" -ForegroundColor Red - } - - Write-Host "Job 2 (Quantum Heart): " -NoNewline - if ($job2.State -eq "Running") { - Write-Host "🟢 RUNNING" -ForegroundColor Green - } elseif ($job2.State -eq "Completed") { - Write-Host "✅ COMPLETED" -ForegroundColor Green - } else { - Write-Host "❌ $($job2.State)" -ForegroundColor Red - } - - Write-Host "Job 3 (LoRA Mixed): " -NoNewline - if ($job3.State -eq "Running") { - Write-Host "🟢 RUNNING" -ForegroundColor Green - } elseif ($job3.State -eq "Completed") { - Write-Host "✅ COMPLETED" -ForegroundColor Green - } else { - Write-Host "❌ $($job3.State)" -ForegroundColor Red - } - - Write-Host "" - Write-Host "=" * 70 - Write-Host "" - - # System resources - $cpu = Get-Counter '\Processor(_Total)\% Processor Time' -ErrorAction SilentlyContinue - $mem = Get-Counter '\Memory\% Committed Bytes In Use' -ErrorAction SilentlyContinue - - if ($cpu) { - Write-Host "💻 CPU Usage: " -NoNewline - Write-Host "$([math]::Round($cpu.CounterSamples[0].CookedValue, 1))%" -ForegroundColor Yellow - } - - if ($mem) { - Write-Host "💾 RAM Usage: " -NoNewline - Write-Host "$([math]::Round($mem.CounterSamples[0].CookedValue, 1))%" -ForegroundColor Yellow - } - - Write-Host "" - Write-Host "Press Ctrl+C to stop monitoring (jobs will continue in background)" -ForegroundColor Gray - - # Check if all jobs are done - if ($job1.State -ne "Running" -and $job2.State -ne "Running" -and $job3.State -ne "Running") { - $running = $false - } - - Start-Sleep -Seconds 5 -} - -Write-Host "" -Write-Host "=" * 70 -Write-Host "✅ ALL JOBS COMPLETED!" -ForegroundColor Green -Write-Host "" - -# Show results -Write-Host "Job 1 Status: $($job1.State)" -ForegroundColor $(if($job1.State -eq "Completed"){"Green"}else{"Red"}) -Write-Host "Job 2 Status: $($job2.State)" -ForegroundColor $(if($job2.State -eq "Completed"){"Green"}else{"Red"}) -Write-Host "Job 3 Status: $($job3.State)" -ForegroundColor $(if($job3.State -eq "Completed"){"Green"}else{"Red"}) - -Write-Host "" -Write-Host "📊 View job outputs:" -ForegroundColor Cyan -Write-Host " Get-Job | Receive-Job" -ForegroundColor Yellow -Write-Host "" -Write-Host "🧹 Clean up jobs:" -ForegroundColor Cyan -Write-Host " Get-Job | Remove-Job" -ForegroundColor Yellow -Write-Host "" +# Parallel Training Script - Train multiple models simultaneously +# WARNING: This will heavily load your system! + +Write-Host "⚡ PARALLEL AI TRAINING - MAXIMUM UTILIZATION" -ForegroundColor Cyan +Write-Host "=" * 70 +Write-Host "" + +Write-Host "⚠️ WARNING: This will start multiple training processes!" -ForegroundColor Yellow +Write-Host " - Very high CPU/GPU usage" -ForegroundColor Yellow +Write-Host " - Maximum RAM consumption" -ForegroundColor Yellow +Write-Host " - System may become slow/unresponsive" -ForegroundColor Yellow +Write-Host "" + +$confirm = Read-Host "Continue with parallel training? (yes/no)" + +if ($confirm -ne "yes") { + Write-Host "❌ Cancelled" -ForegroundColor Yellow + exit 0 +} + +Write-Host "" +Write-Host "🚀 Starting parallel training jobs..." -ForegroundColor Green +Write-Host "" + +# Navigate to workspace root +$workspaceRoot = Split-Path -Parent $PSScriptRoot + +# Job 1: LoRA Training on Dolly +Write-Host "📦 Job 1: LoRA on Dolly dataset" -ForegroundColor Cyan +$job1 = Start-Job -ScriptBlock { + Set-Location $using:workspaceRoot + & venv\Scripts\Activate.ps1 + Set-Location "AI\microsoft_phi-silica-3.6_v1" + python scripts\train_lora.py --dataset ..\..\datasets\chat\dolly --config lora\lora.yaml --max-train-samples 500 --epochs 2 --save-dir ..\..\data_out\lora_training\parallel_job1 +} + +Start-Sleep -Seconds 5 + +# Job 2: Quantum Training on multiple datasets +Write-Host "📦 Job 2: Quantum training on heart dataset" -ForegroundColor Cyan +$job2 = Start-Job -ScriptBlock { + Set-Location $using:workspaceRoot + Set-Location "quantum-ai" + & venv\Scripts\Activate.ps1 + python train_custom_dataset.py --preset heart --epochs 20 --backend qiskit_aer +} + +Start-Sleep -Seconds 5 + +# Job 3: Another LoRA training +Write-Host "📦 Job 3: LoRA on Mixed Chat dataset" -ForegroundColor Cyan +$job3 = Start-Job -ScriptBlock { + Set-Location $using:workspaceRoot + & venv\Scripts\Activate.ps1 + Set-Location "AI\microsoft_phi-silica-3.6_v1" + python scripts\train_lora.py --dataset ..\..\datasets\chat\mixed_chat --config lora\lora.yaml --max-train-samples 500 --epochs 2 --save-dir ..\..\data_out\lora_training\parallel_job3 +} + +Write-Host "" +Write-Host "✅ All jobs started!" -ForegroundColor Green +Write-Host "" +Write-Host "Monitoring progress (Ctrl+C to stop monitoring, jobs will continue):" -ForegroundColor Cyan +Write-Host "=" * 70 +Write-Host "" + +# Monitor all jobs +$running = $true +while ($running) { + Clear-Host + Write-Host "⚡ PARALLEL TRAINING STATUS" -ForegroundColor Cyan + Write-Host "=" * 70 + Write-Host "" + + # Check job statuses + Write-Host "Job 1 (LoRA Dolly): " -NoNewline + if ($job1.State -eq "Running") { + Write-Host "🟢 RUNNING" -ForegroundColor Green + } elseif ($job1.State -eq "Completed") { + Write-Host "✅ COMPLETED" -ForegroundColor Green + } else { + Write-Host "❌ $($job1.State)" -ForegroundColor Red + } + + Write-Host "Job 2 (Quantum Heart): " -NoNewline + if ($job2.State -eq "Running") { + Write-Host "🟢 RUNNING" -ForegroundColor Green + } elseif ($job2.State -eq "Completed") { + Write-Host "✅ COMPLETED" -ForegroundColor Green + } else { + Write-Host "❌ $($job2.State)" -ForegroundColor Red + } + + Write-Host "Job 3 (LoRA Mixed): " -NoNewline + if ($job3.State -eq "Running") { + Write-Host "🟢 RUNNING" -ForegroundColor Green + } elseif ($job3.State -eq "Completed") { + Write-Host "✅ COMPLETED" -ForegroundColor Green + } else { + Write-Host "❌ $($job3.State)" -ForegroundColor Red + } + + Write-Host "" + Write-Host "=" * 70 + Write-Host "" + + # System resources + $cpu = Get-Counter '\Processor(_Total)\% Processor Time' -ErrorAction SilentlyContinue + $mem = Get-Counter '\Memory\% Committed Bytes In Use' -ErrorAction SilentlyContinue + + if ($cpu) { + Write-Host "💻 CPU Usage: " -NoNewline + Write-Host "$([math]::Round($cpu.CounterSamples[0].CookedValue, 1))%" -ForegroundColor Yellow + } + + if ($mem) { + Write-Host "💾 RAM Usage: " -NoNewline + Write-Host "$([math]::Round($mem.CounterSamples[0].CookedValue, 1))%" -ForegroundColor Yellow + } + + Write-Host "" + Write-Host "Press Ctrl+C to stop monitoring (jobs will continue in background)" -ForegroundColor Gray + + # Check if all jobs are done + if ($job1.State -ne "Running" -and $job2.State -ne "Running" -and $job3.State -ne "Running") { + $running = $false + } + + Start-Sleep -Seconds 5 +} + +Write-Host "" +Write-Host "=" * 70 +Write-Host "✅ ALL JOBS COMPLETED!" -ForegroundColor Green +Write-Host "" + +# Show results +Write-Host "Job 1 Status: $($job1.State)" -ForegroundColor $(if($job1.State -eq "Completed"){"Green"}else{"Red"}) +Write-Host "Job 2 Status: $($job2.State)" -ForegroundColor $(if($job2.State -eq "Completed"){"Green"}else{"Red"}) +Write-Host "Job 3 Status: $($job3.State)" -ForegroundColor $(if($job3.State -eq "Completed"){"Green"}else{"Red"}) + +Write-Host "" +Write-Host "📊 View job outputs:" -ForegroundColor Cyan +Write-Host " Get-Job | Receive-Job" -ForegroundColor Yellow +Write-Host "" +Write-Host "🧹 Clean up jobs:" -ForegroundColor Cyan +Write-Host " Get-Job | Remove-Job" -ForegroundColor Yellow +Write-Host "" diff --git a/scripts/autotrain.py b/scripts/autotrain.py index 96bd958c1..0f9966dae 100644 --- a/scripts/autotrain.py +++ b/scripts/autotrain.py @@ -15,6 +15,8 @@ import argparse import json +import os +import shlex import subprocess import sys from dataclasses import dataclass, field @@ -71,68 +73,190 @@ class TrainJob: # --------------------------------------------------------------------------- +def _parse_bool(value: Any, default: bool = True) -> bool: + if value is None: + return default + if isinstance(value, bool): + return value + if isinstance(value, (int, float)): + return bool(value) + if isinstance(value, str): + v = value.strip().lower() + if v in {"true", "yes", "y", "1", "on"}: + return True + if v in {"false", "no", "n", "0", "off"}: + return False + return default + + +def _parse_int(value: Any) -> Optional[int]: + if value is None: + return None + if isinstance(value, bool): + return None + if isinstance(value, int): + return value + if isinstance(value, float): + return int(value) + if isinstance(value, str): + v = value.strip() + if not v: + return None + try: + return int(v) + except ValueError: + return None + return None + + +def _parse_float(value: Any) -> Optional[float]: + if value is None: + return None + if isinstance(value, bool): + return None + if isinstance(value, (int, float)): + return float(value) + if isinstance(value, str): + v = value.strip() + if not v: + return None + try: + return float(v) + except ValueError: + return None + return None + + +def _parse_str(value: Any) -> Optional[str]: + if value is None: + return None + if isinstance(value, str): + s = value.strip() + return s or None + return str(value) + + +def _parse_str_list(value: Any) -> List[str]: + if value is None: + return [] + if isinstance(value, list): + out: List[str] = [] + for item in value: + s = _parse_str(item) + if s: + out.append(s) + return out + if isinstance(value, str): + s = value.strip() + if not s: + return [] + return shlex.split(s) + return [str(value)] + + +def _norm_relpath(value: Optional[str]) -> Optional[str]: + if not value: + return None + + p = Path(value) + if p.is_absolute(): + return None + + norm = os.path.normpath(value) + if norm.startswith("..") or norm == "..": + return None + return norm + + def load_config(path: Path) -> Dict[str, Any]: if not path.exists(): return {"jobs": []} - text = path.read_text() + + text = path.read_text(encoding="utf-8") + if yaml is not None: - return yaml.safe_load(text) or {"jobs": []} - # Minimal fallback: only supports basic YAML structure (name/runner keys) + data = yaml.safe_load(text) or {} + if not isinstance(data, dict): + return {"jobs": []} + jobs = data.get("jobs", []) + if not isinstance(jobs, list): + return {"jobs": []} + data.setdefault("jobs", jobs) + return data + jobs: List[Dict[str, Any]] = [] current: Dict[str, Any] = {} + for line in text.splitlines(): line = line.rstrip() - if line.strip().startswith("- name:"): + stripped = line.strip() + + if not stripped or stripped.startswith("#"): + continue + + if stripped.startswith("- name:"): if current: jobs.append(current) - current = {"name": line.strip().split(":", 1)[1].strip()} - elif ":" in line and current: - key, _, val = line.strip().partition(":") + current = {"name": stripped.split(":", 1)[1].strip()} + continue + + if ":" in stripped and current: + key, _, val = stripped.partition(":") current[key.strip()] = val.strip() + if current: jobs.append(current) + return {"jobs": jobs} def load_jobs(path: Path) -> List[TrainJob]: data = load_config(path) + raw_jobs = data.get("jobs", []) + + if not isinstance(raw_jobs, list): + return [] + jobs: List[TrainJob] = [] - for raw in data.get("jobs", []): + for raw in raw_jobs: + if not isinstance(raw, dict): + continue - def _get(k, default=None): - return raw.get(k, default) + enabled = _parse_bool(raw.get("enabled"), default=True) + epochs = _parse_int(raw.get("epochs")) or 1 + if epochs < 1: + epochs = 1 - def _int(v): - try: - return int(v) if v is not None else None - except Exception: - return None + learning_rate = _parse_float(raw.get("learning_rate")) or 2e-4 + if learning_rate <= 0: + learning_rate = 2e-4 - def _float(v): - try: - return float(v) if v is not None else None - except Exception: - return None + lora_dropout = _parse_float(raw.get("lora_dropout")) or 0.1 + if lora_dropout < 0: + lora_dropout = 0.0 + if lora_dropout > 1: + lora_dropout = 1.0 jobs.append( TrainJob( - name=str(_get("name", "unnamed")), - runner=str(_get("runner", "hf")), - category=str(_get("category", "baseline")), - enabled=bool(_get("enabled", True)), - config=_get("config"), - dataset=_get("dataset"), - save_dir=_get("save_dir"), - epochs=_int(_get("epochs")) or 1, - max_train_samples=_int(_get("max_train_samples")), - max_eval_samples=_int(_get("max_eval_samples")), - learning_rate=_float(_get("learning_rate")) or 2e-4, - lora_dropout=_float(_get("lora_dropout")) or 0.1, - hf_model_id=_get("hf_model_id"), - device=str(_get("device", "auto")), - extra_args=_get("extra_args") or [], + name=str(raw.get("name", "unnamed")), + runner=str(raw.get("runner", "hf")), + category=str(raw.get("category", "baseline")), + enabled=enabled, + config=_norm_relpath(_parse_str(raw.get("config"))), + dataset=_norm_relpath(_parse_str(raw.get("dataset"))), + save_dir=_norm_relpath(_parse_str(raw.get("save_dir"))), + epochs=epochs, + max_train_samples=_parse_int(raw.get("max_train_samples")), + max_eval_samples=_parse_int(raw.get("max_eval_samples")), + learning_rate=learning_rate, + lora_dropout=lora_dropout, + hf_model_id=_parse_str(raw.get("hf_model_id")), + device=str(raw.get("device", "auto")), + extra_args=_parse_str_list(raw.get("extra_args")), ) ) + return jobs @@ -146,16 +270,26 @@ def validate_job(job: TrainJob) -> Dict[str, Any]: if not job.dataset: missing.append("dataset") - elif not (REPO_ROOT / job.dataset).exists(): - missing.append(f"dataset path not found: {job.dataset}") + else: + dataset_path = REPO_ROOT / job.dataset + if not dataset_path.exists(): + missing.append(f"dataset path not found: {job.dataset}") + + if job.runner == "hf": + if not HF_TRAIN_SCRIPT.exists(): + missing.append( + f"train script not found: {HF_TRAIN_SCRIPT.relative_to(REPO_ROOT)}" + ) - if job.runner == "hf" and not HF_TRAIN_SCRIPT.exists(): - missing.append( - f"train script not found: {HF_TRAIN_SCRIPT.relative_to(REPO_ROOT)}" - ) + if job.config: + config_path = REPO_ROOT / job.config + if not config_path.exists(): + missing.append(f"lora config not found: {job.config}") - if job.config and not (REPO_ROOT / job.config).exists(): - missing.append(f"lora config not found: {job.config}") + if job.save_dir: + out_dir = REPO_ROOT / job.save_dir + if out_dir.exists() and not out_dir.is_dir(): + missing.append(f"save_dir is not a directory: {job.save_dir}") return {"status": "ok" if not missing else "missing", "missing": missing} @@ -169,11 +303,9 @@ def build_command(job: TrainJob) -> List[str]: py = sys.executable or "python" if job.runner == "local": - # Placeholder for a local runner script local_script = REPO_ROOT / "scripts" / "run_local_lora_training.py" return [py, str(local_script), "--job", job.name] - # Default: HF trainer cmd = [py, str(HF_TRAIN_SCRIPT)] if job.hf_model_id: cmd.extend(["--model-id", job.hf_model_id]) @@ -203,17 +335,22 @@ def build_command(job: TrainJob) -> List[str]: def _write_status(payload: Dict[str, Any]) -> None: DATA_OUT.mkdir(parents=True, exist_ok=True) - STATUS_FILE.write_text(json.dumps(payload, indent=2)) + STATUS_FILE.write_text( + json.dumps(payload, indent=2, ensure_ascii=False) + "\n", + encoding="utf-8", + ) def _build_status(jobs_info: List[Dict[str, Any]]) -> Dict[str, Any]: succeeded = sum(1 for j in jobs_info if j.get("status") == "ok") failed = sum(1 for j in jobs_info if j.get("status") == "failed") + skipped = sum(1 for j in jobs_info if j.get("status") == "skipped") return { "generated_at": datetime.now(timezone.utc).isoformat(), "total_jobs": len(jobs_info), "succeeded": succeeded, "failed": failed, + "skipped": skipped, "running": 0, "avg_duration": None, "last_updated": datetime.now(timezone.utc).isoformat(), @@ -227,9 +364,7 @@ def _build_status(jobs_info: List[Dict[str, Any]]) -> Dict[str, Any]: def main(argv: Optional[List[str]] = None) -> int: - parser = argparse.ArgumentParser( - description="AutoTrain — LoRA fine-tuning orchestrator" - ) + parser = argparse.ArgumentParser(description="AutoTrain — LoRA fine-tuning orchestrator") parser.add_argument( "--config", type=Path, @@ -239,9 +374,7 @@ def main(argv: Optional[List[str]] = None) -> int: parser.add_argument( "--dry-run", action="store_true", help="Validate config only; do not execute" ) - parser.add_argument( - "--list", action="store_true", help="Print jobs as JSON and exit" - ) + parser.add_argument("--list", action="store_true", help="Print jobs as JSON and exit") parser.add_argument("--run", action="store_true", help="Execute training jobs") parser.add_argument("--job", metavar="NAME", help="Filter to a single job by name") @@ -249,7 +382,7 @@ def main(argv: Optional[List[str]] = None) -> int: config_path = Path(args.config) if not config_path.exists(): - print(f"⚠️ Config not found: {config_path} — using empty job list") + print(f"Config not found: {config_path} — using empty job list", file=sys.stderr) jobs: List[TrainJob] = [] else: jobs = load_jobs(config_path) @@ -257,14 +390,17 @@ def main(argv: Optional[List[str]] = None) -> int: if args.job: jobs = [j for j in jobs if j.name == args.job] if not jobs: - print(f"❌ No job named '{args.job}' found in config") + print(f"No job named '{args.job}' found in config", file=sys.stderr) return 1 if args.list: - print(json.dumps([vars(j) for j in jobs], indent=2)) + print(json.dumps([vars(j) for j in jobs], indent=2, ensure_ascii=False)) return 0 - print(f"📋 AutoTrain — {len(jobs)} job(s) from {config_path}") + if not (args.dry_run or args.run): + args.dry_run = True + + print(f"AutoTrain — {len(jobs)} job(s) from {config_path}") jobs_info: List[Dict[str, Any]] = [] for job in jobs: @@ -279,36 +415,41 @@ def main(argv: Optional[List[str]] = None) -> int: else: print(f" [ok] {job.name} (runner={job.runner}, epochs={job.epochs})") - if args.dry_run: + if args.dry_run and not args.run: jobs_info.append( { "name": job.name, - "status": "ok", + "status": "ok" if validation["status"] == "ok" else "missing", "validation": validation, "dry_run": True, } ) continue - if args.run: - cmd = build_command(job) - print(f" → {' '.join(cmd[:4])} ...") - result = subprocess.run(cmd, cwd=str(REPO_ROOT)) - status = "ok" if result.returncode == 0 else "failed" + cmd = build_command(job) + print(f" → {shlex.join(cmd)}") + + try: + result = subprocess.run( + cmd, + cwd=str(REPO_ROOT), + check=False, + ) + except OSError as e: jobs_info.append( - {"name": job.name, "status": status, "returncode": result.returncode} + {"name": job.name, "status": "failed", "error": str(e), "returncode": None} ) + continue + + status = "ok" if result.returncode == 0 else "failed" + jobs_info.append({"name": job.name, "status": status, "returncode": result.returncode}) status_payload = _build_status(jobs_info) _write_status(status_payload) - print(f"\n📄 Status written to {STATUS_FILE}") - - if not args.dry_run and not args.run and not args.list: - # Default mode: just dry-run - print("ℹ️ Use --dry-run to validate, --run to execute, --list to list jobs") + print(f"\nStatus written to {STATUS_FILE}") return 0 if __name__ == "__main__": - sys.exit(main()) + raise SystemExit(main()) diff --git a/scripts/benchmark_performance.py b/scripts/benchmark_performance.py index 22accac6a..1ad5e8fc9 100644 --- a/scripts/benchmark_performance.py +++ b/scripts/benchmark_performance.py @@ -87,7 +87,7 @@ def benchmark_json_parsing(): if "metrics" in data: result1 = data break - except: + except Exception: pass t_old = time.time() - t0 diff --git a/scripts/dashboard.py b/scripts/dashboard.py index d0a9ceb93..159b0fdb2 100644 --- a/scripts/dashboard.py +++ b/scripts/dashboard.py @@ -34,7 +34,7 @@ def format_time(iso_str: str | None) -> str: try: dt = datetime.fromisoformat(iso_str) return dt.strftime("%Y-%m-%d %H:%M:%S") - except: + except Exception: return iso_str diff --git a/shared/cosmos_client.py b/shared/cosmos_client.py index e0f8f0044..91f90a8cf 100644 --- a/shared/cosmos_client.py +++ b/shared/cosmos_client.py @@ -257,8 +257,9 @@ def get_world(theme: str, seed: Union[str, int]) -> Optional[Dict[str, Any]]: try: seed_str = str(seed) theme_seed = f"{theme}_{seed_str}" - query = f"SELECT * FROM c WHERE c.theme_seed = '{theme_seed}'" - items = list(c.query_items(query=query, enable_cross_partition_query=True)) + query = "SELECT * FROM c WHERE c.theme_seed = @theme_seed" + parameters = [{"name": "@theme_seed", "value": theme_seed}] + items = list(c.query_items(query=query, parameters=parameters, enable_cross_partition_query=True)) return items[0] if items else None except Exception as e: logging.warning(f"[cosmos] get_world error: {e}") diff --git a/shared/sql_engine.py b/shared/sql_engine.py index 733675299..4a369b560 100644 --- a/shared/sql_engine.py +++ b/shared/sql_engine.py @@ -56,6 +56,32 @@ _SLOW_QUERY_CACHE_MAX_SIZE = 1000 # Maximum entries to prevent unbounded growth +class _FallbackSQLiteDialect: + """Minimal SQLAlchemy-dialect-like shim used by fallback engine. + + Consumers in this repo only read ``engine.dialect.name`` to branch + behavior, so this shim intentionally exposes just the ``name`` attribute. + """ + + name: str = "sqlite" + + +class _FallbackSQLiteEngine: + """Minimal SQLAlchemy-engine-like shim for no-SQLAlchemy environments. + + This object intentionally only provides the attributes accessed by current + call sites: + - ``url`` for status output + - ``dialect.name`` for vendor checks + - ``pool`` (set to ``None``) so pool-stat code degrades gracefully + """ + + def __init__(self, url: str) -> None: + self.url: str = url + self.dialect: _FallbackSQLiteDialect = _FallbackSQLiteDialect() + self.pool: None = None + + def _prune_recent_slow_queries() -> None: """Remove slow query entries older than 60 seconds. @@ -175,12 +201,12 @@ def get_engine(): # noqa: ANN001 return None if not _SQLALCHEMY_AVAILABLE: # Fallback: no SQLAlchemy installed - # Return None to allow sqlite3 fallback in calling code # Only SQLite URLs are supported in fallback mode if str(url).startswith("sqlite"): - _ENGINE = None + if _ENGINE is None or _LAST_URL != url: + _ENGINE = _FallbackSQLiteEngine(url) _LAST_URL = url - return None + return _ENGINE # Unsupported vendor without SQLAlchemy return None if _ENGINE is None or _LAST_URL != url: @@ -269,18 +295,6 @@ def engine_stats() -> Dict[str, Any]: "slow_queries_1min": 0, "slow_query_threshold_ms": resolve_slow_query_threshold(), } - if not _SQLALCHEMY_AVAILABLE: - # Minimal stats for fallback - stats.update( - { - "type": "DirectSQLite", - "vendor": "sqlite", - "status": "ok", - } - ) - _prune_recent_slow_queries() - stats["slow_queries_1min"] = len(_recent_slow_queries) - return stats if pool: for attr in ["size", "checkedout", "overflow"]: stats[attr] = _safe_call(pool, attr) diff --git a/shared/sql_repository.py b/shared/sql_repository.py index a412c60e8..0e91f9518 100644 --- a/shared/sql_repository.py +++ b/shared/sql_repository.py @@ -77,8 +77,7 @@ def _ensure_table(): if _TABLE_CREATED: return True - engine = get_engine() - if not engine and not _SQLALCHEMY_AVAILABLE: + if not _SQLALCHEMY_AVAILABLE: # Fallback path: sqlite3 direct try: conn = _get_sqlite_conn() @@ -95,6 +94,7 @@ def _ensure_table(): ) return False + engine = get_engine() if not engine: return False @@ -143,8 +143,7 @@ def put_value(key: str, value: str) -> bool: if not _ensure_table(): return False - engine = get_engine() - if not engine and not _SQLALCHEMY_AVAILABLE: + if not _SQLALCHEMY_AVAILABLE: # Fallback: sqlite3 direct try: conn = _get_sqlite_conn() @@ -158,6 +157,7 @@ def put_value(key: str, value: str) -> bool: logging.warning(f"[sql_repository] sqlite fallback put_value failed: {e}") return False + engine = get_engine() if not engine: return False @@ -206,8 +206,7 @@ def get_value(key: str) -> Optional[str]: if not _ensure_table(): return None - engine = get_engine() - if not engine and not _SQLALCHEMY_AVAILABLE: + if not _SQLALCHEMY_AVAILABLE: try: conn = _get_sqlite_conn() cur = conn.execute( @@ -219,6 +218,7 @@ def get_value(key: str) -> Optional[str]: logging.warning(f"[sql_repository] sqlite fallback get_value failed: {e}") return None + engine = get_engine() if not engine: return None @@ -238,8 +238,7 @@ def delete_value(key: str) -> bool: if not _ensure_table(): return False - engine = get_engine() - if not engine and not _SQLALCHEMY_AVAILABLE: + if not _SQLALCHEMY_AVAILABLE: try: conn = _get_sqlite_conn() conn.execute("DELETE FROM QAI_KeyValue WHERE key_name=?", (key,)) @@ -251,6 +250,7 @@ def delete_value(key: str) -> bool: ) return False + engine = get_engine() if not engine: return False @@ -270,8 +270,7 @@ def list_values(limit: int = 100) -> list[dict]: # noqa: ANN001 if not _ensure_table(): return [] - engine = get_engine() - if not engine and not _SQLALCHEMY_AVAILABLE: + if not _SQLALCHEMY_AVAILABLE: try: conn = _get_sqlite_conn() cur = conn.execute( @@ -286,6 +285,7 @@ def list_values(limit: int = 100) -> list[dict]: # noqa: ANN001 logging.warning(f"[sql_repository] sqlite fallback list_values failed: {e}") return [] + engine = get_engine() if not engine: return [] diff --git a/symengine b/symengine deleted file mode 160000 index 5a2261718..000000000 --- a/symengine +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 5a22617186bc1a3c11fa92b026549425ee0e228d diff --git a/tests/test_quantum_llm.py b/tests/test_quantum_llm.py index fe02c254b..eae1d48a1 100644 --- a/tests/test_quantum_llm.py +++ b/tests/test_quantum_llm.py @@ -65,6 +65,73 @@ def test_from_env(self, monkeypatch): assert cfg.num_qubits == 2 assert cfg.shots == 128 + def test_from_env_preserves_valid_backend(self, monkeypatch): + monkeypatch.setenv("QUANTUM_LLM_BACKEND", "qiskit") + cfg = QuantumLLMConfig.from_env() + assert cfg.backend == "qiskit" + + def test_from_env_invalid_backend_falls_back_to_auto(self, monkeypatch): + monkeypatch.setenv("QUANTUM_LLM_BACKEND", "invalid-backend") + cfg = QuantumLLMConfig.from_env() + assert cfg.backend == "auto" + + def test_from_env_invalid_numeric_values_fallback(self, monkeypatch): + monkeypatch.setenv("QUANTUM_LLM_QUBITS", "abc") + monkeypatch.setenv("QUANTUM_LLM_SHOTS", "NaN") + monkeypatch.setenv("QUANTUM_LLM_LAYERS", "bad") + monkeypatch.setenv("QUANTUM_LLM_TEMPERATURE", "oops") + + cfg = QuantumLLMConfig.from_env() + + assert cfg.num_qubits == 4 + assert cfg.shots == 512 + assert cfg.num_layers == 2 + assert cfg.temperature == 0.7 + + def test_from_env_clamps_numeric_values_to_valid_ranges(self, monkeypatch): + monkeypatch.setenv("QUANTUM_LLM_QUBITS", "0") + monkeypatch.setenv("QUANTUM_LLM_SHOTS", "-1") + monkeypatch.setenv("QUANTUM_LLM_LAYERS", "0") + monkeypatch.setenv("QUANTUM_LLM_TOP_K", "0") + monkeypatch.setenv("QUANTUM_LLM_TEMP_BLEND", "2.5") + monkeypatch.setenv("QUANTUM_LLM_TEMPERATURE", "-9") + monkeypatch.setenv("QUANTUM_LLM_MAX_TOKENS", "9999") + monkeypatch.setenv("QUANTUM_LLM_MAX_TOKENS_CAP", "128") + + cfg = QuantumLLMConfig.from_env() + + assert cfg.num_qubits == 1 + assert cfg.shots == 1 + assert cfg.num_layers == 1 + assert cfg.top_k == 1 + assert cfg.temperature_blend == 1.0 + assert cfg.temperature == 0.0 + assert cfg.max_tokens == 128 + + def test_direct_constructor_coerces_invalid_types(self): + cfg = QuantumLLMConfig( + backend="invalid", + num_qubits="bad", # type: ignore[arg-type] + shots="oops", # type: ignore[arg-type] + num_layers="bad", # type: ignore[arg-type] + top_k="bad", # type: ignore[arg-type] + temperature_blend="bad", # type: ignore[arg-type] + temperature="bad", # type: ignore[arg-type] + max_tokens="bad", # type: ignore[arg-type] + max_tokens_cap="bad", # type: ignore[arg-type] + max_prompt_chars="bad", # type: ignore[arg-type] + ) + assert cfg.backend == "auto" + assert cfg.num_qubits == 4 + assert cfg.shots == 512 + assert cfg.num_layers == 2 + assert cfg.top_k == 10 + assert cfg.temperature_blend == 0.3 + assert cfg.temperature == 0.7 + assert cfg.max_tokens == 512 + assert cfg.max_tokens_cap == 2048 + assert cfg.max_prompt_chars == 8000 + # =========================================================================== # QuantumSampler tests diff --git a/tools/codegen/WEBSITE_GENERATOR.md b/tools/codegen/WEBSITE_GENERATOR.md index 567e444fa..234e866ae 100644 --- a/tools/codegen/WEBSITE_GENERATOR.md +++ b/tools/codegen/WEBSITE_GENERATOR.md @@ -69,17 +69,20 @@ Press `Ctrl+Shift+I` and type: ``` **Landing Page:** -``` + +```text @llm-maker Build a modern SaaS landing page with hero section, feature list, pricing table, testimonials, and FAQ ``` **Documentation:** -``` + +```text @llm-maker Generate API documentation website with quick start guide, code examples, and API reference table ``` **E-Commerce:** -``` + +```text @llm-maker Create an e-commerce store with product catalog, shopping cart, checkout, and customer reviews ``` @@ -103,12 +106,14 @@ python3 code_generation_quickstart.py Each website includes: ✅ **HTML Pages** + - Semantic HTML5 - Proper meta tags - Mobile viewport tags - Open Graph for sharing ✅ **CSS Styling** + - Modern responsive design - Gradient backgrounds - Smooth animations @@ -116,6 +121,7 @@ Each website includes: - Dark mode support (blogs) ✅ **JavaScript Interactivity** + - Smooth scrolling navigation - Form validation - Event handlers @@ -123,6 +129,7 @@ Each website includes: - Dark mode toggle functionality ✅ **Production Ready** + - Valid HTML5/CSS3 - Cross-browser compatible - SEO optimized basics @@ -133,7 +140,8 @@ Each website includes: ## 💡 Examples -### Portfolio Example Generated: +### Portfolio Example Generated + ```html
    ${date} ${ref.referred_user_id}${ref.tier}${ref.tier} $${ref.commission.toFixed(2)}/mo ● ${ref.status}